diff --git "a/1185.jsonl" "b/1185.jsonl" new file mode 100644--- /dev/null +++ "b/1185.jsonl" @@ -0,0 +1,2053 @@ +{"seq_id":"25241546316","text":"def string_match(a, b):\r\n\tcount = 0\r\n\tif len(a) > len(b):\r\n\t\tlength = len(a)\r\n\telse:\r\n\t\tlength = len(b)\r\n\tfor i in range(length-2):\r\n\t\tif a[i] == b[i] and a[i+1] == b[i+1]:\r\n\t\t\tcount += 1\r\n\treturn count\r\n\r\nprint (string_match('xxcaazz', 'xxbaaz'))\r\n","repo_name":"ismk/Python-Examples","sub_path":"codingbat/string_match.py","file_name":"string_match.py","file_ext":"py","file_size_in_byte":249,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"6330418533","text":"import demistomock as demisto # noqa: F401\nfrom CommonServerPython import * # noqa: F401\nimport io\n\n\ndef main():\n args = demisto.args()\n testName = args.get(\"testName\", \"\")\n listName = args.get(\"listName\", \"\")\n try:\n if listName != \"\":\n listlines = demisto.executeCommand(\"getList\", {'listName': listName})[0]['Contents']\n buf = io.StringIO(listlines)\n else:\n raise DemistoException(\"No test case list provided\")\n\n line = buf.readline()\n while line != \"\":\n words = line.split(\"|\", 1)\n testType = words[0].strip()\n datalist = words[1].strip().replace(\"\\n\", \"\")\n # Process commands to load fields or context\n if testType == \"LoadFields\":\n demisto.executeCommand(\"UnitTestLoadFieldsList\", {'list': datalist})\n elif testType == \"LoadContext\":\n demisto.executeCommand(\"UnitTestLoadContextList\", {'list': datalist})\n line = buf.readline()\n\n except Exception as ex:\n demisto.error(traceback.format_exc())\n return_error(f\"UnitTestCasePrep: {testName} Exception failed to execute. Error: {str(ex)}\")\n\n\nif __name__ in ('__main__', '__builtin__', 'builtins'):\n main()\n","repo_name":"demisto/content","sub_path":"Packs/ContentTesting/Scripts/UnitTestCasePrep/UnitTestCasePrep.py","file_name":"UnitTestCasePrep.py","file_ext":"py","file_size_in_byte":1259,"program_lang":"python","lang":"en","doc_type":"code","stars":1023,"dataset":"github-code","pt":"50"} +{"seq_id":"17952345720","text":"import glob\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport os\nimport tensorflow as tf\nimport pickle\n\nfrom scipy import misc\n\n\nnp.random.seed(0)\n\n\nSAMPLE_QTY = 30000\nTRAINING_STEP = 0.001\nTRAINING_EPOCHS = 1000\nCLASS_QTY = None\nTEST_SET_PROPORTION = 0.25\nPICKLE_FILENAME = 'notMNIST.pickle'\n\n\ndef load_samples(filenames):\n matrix = None\n for filename in filenames:\n try:\n arr = misc.imread(filename)\n except:\n continue\n length = arr.shape[0] * arr.shape[1]\n sample = arr.reshape(1, length)\n if matrix is not None:\n matrix = np.vstack((matrix, sample))\n else:\n matrix = np.matrix(sample)\n return matrix\n\n\ndef load_dataset():\n global CLASS_QTY\n classes = glob.glob('../datasets/notMNIST_large/*')\n sample_files = {}\n for class_filepath in classes:\n name = class_filepath[-1]\n sample_files[name] = np.asarray(glob.glob('%s/*.png' % class_filepath))\n\n keys = tuple(sample_files.keys())\n CLASS_QTY = len(keys)\n class_sample_qty = SAMPLE_QTY // CLASS_QTY\n\n X = None\n y = None\n for key, val in sample_files.items():\n cls_value_qty = len(val)\n limit = min(cls_value_qty, class_sample_qty)\n perm = np.random.permutation(cls_value_qty)[:limit]\n samples = val[perm]\n mat = load_samples(samples)\n cls_id = ord(key) - ord('A')\n targets = np.zeros((mat.shape[0], CLASS_QTY))\n targets[:, cls_id] = 1\n if X is not None:\n X = np.vstack((X, mat))\n y = np.vstack((y, targets))\n else:\n X = mat\n y = targets\n perm = np.random.permutation(X.shape[0])\n X = X[perm, :]\n y = y[perm]\n return X, y\n\n\ndef divide_dataset(samples, targets):\n assert samples.shape[0] == targets.shape[0]\n sample_qty = samples.shape[0]\n train_size = int(sample_qty * (1 - TEST_SET_PROPORTION))\n return samples[:train_size], targets[:train_size],\\\n samples[train_size:], targets[train_size:]\n\n\ndef manual_testing(samples, targets, predictions):\n sample_qty = samples.shape[0]\n for i in range(20):\n index = np.random.randint(0, sample_qty)\n arr = samples[index, :].reshape(28, 28)\n tar = targets[index]\n pred = predictions[index]\n print(\"%d) Sample %d: is %s, but %s was predicted\" % (i, index, chr(tar + ord('A')), chr(pred + ord('A'))))\n plt.imshow(arr, cmap='Greys_r')\n plt.show()\n\n\nif __name__ == '__main__':\n if os.path.exists(PICKLE_FILENAME):\n with open(PICKLE_FILENAME, 'rb') as f:\n obj = pickle.load(f)\n samples, targets = obj['samples'], obj['targets']\n print(\"Dataset loaded from file...\")\n else:\n samples, targets = load_dataset()\n with open(PICKLE_FILENAME, 'wb') as f:\n obj = {'samples': samples, 'targets': targets}\n pickle.dump(obj, f)\n print(\"File saved....\")\n\n X_train, y_train, X_test, y_test = divide_dataset(samples, targets)\n\n train_mean = np.mean(X_train, 0)\n train_std = np.std(X_train, 0)\n preprocess = lambda X : np.divide(X - train_mean, train_std)\n\n X_proc = preprocess(X_train)\n X_test = preprocess(X_test)\n\n\n X = tf.placeholder(tf.float32, shape=[None, 784])\n y = tf.placeholder(tf.float32, shape=[None, 10])\n\n W = tf.Variable(tf.random_normal([784, 10], stddev=1))\n b = tf.Variable(tf.zeros([10]))\n\n with tf.Session() as sess:\n sess.run(tf.initialize_all_variables())\n\n yhat = tf.nn.softmax(tf.matmul(X, W) + b)\n\n #neg_log = -tf.reduce_sum(y * tf.log(yhat), reduction_indices=[1])\n neg_log = -tf.reduce_sum(y * tf.log(tf.clip_by_value(yhat, 1e-10, 1.0)))\n ce_loss = tf.reduce_mean(neg_log)\n\n train_step = tf.train.GradientDescentOptimizer(TRAINING_STEP).minimize(ce_loss)\n\n for iter in range(TRAINING_EPOCHS):\n train_step.run(feed_dict={X: X_proc, y: y_train})\n if iter % 100 == 0:\n print(\"Iter %d: %f\" % (iter, ce_loss.eval({X: X_proc, y: y_train})))\n\n correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(yhat, 1))\n accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n print(accuracy.eval({X: X_train, y: y_train}))\n real = tf.argmax(y, 1).eval({X: X_train, y: y_train})\n pred = tf.argmax(yhat, 1).eval({X: X_train, y: y_train})\n manual_testing(X_train, real, pred)\n\n","repo_name":"ealtamir/udacity_deeplearning","sub_path":"tf_mnist_logit.py","file_name":"tf_mnist_logit.py","file_ext":"py","file_size_in_byte":4474,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"40668618346","text":"from werkzeug.http import HTTP_STATUS_CODES\n\n\ndef is_informational(code):\n return code >= 100 and code <= 199\n\n\ndef is_success(code):\n return code >= 200 and code <= 299\n\n\ndef is_redirect(code):\n return code >= 300 and code <= 399\n\n\ndef is_client_error(code):\n return code >= 400 and code <= 499\n\n\ndef is_server_error(code):\n return code >= 500 and code <= 599\n\ndef get_status(code):\n if is_success(code):\n return 'success'\n elif is_client_error(code):\n return 'client_error'\n elif is_server_error(code):\n return 'server_error'\n elif is_redirect(code):\n return 'redirect'\n elif is_informational(code):\n return 'informational'\n\ndef get_message(code):\n ''' Maps an HTTP status code to the textual status\n '''\n return HTTP_STATUS_CODES.get(code, '')\n\n\nCONTINUE = 100\nSWITCHING_PROTOCOLS = 101\nOK = 200\nCREATED = 201\nACCEPTED = 202\nNON_AUTHORITATIVE_INFORMATION = 203\nNO_CONTENT = 204\nRESET_CONTENT = 205\nPARTIAL_CONTENT = 206\nMULTI_STATUS = 207\nMULTIPLE_CHOICES = 300\nMOVED_PERMANENTLY = 301\nFOUND = 302\nSEE_OTHER = 303\nNOT_MODIFIED = 304\nUSE_PROXY = 305\nRESERVED = 306\nTEMPORARY_REDIRECT = 307\nPERMANENT_REDIRECT = 308\nBAD_REQUEST = 400\nUNAUTHORIZED = 401\nPAYMENT_REQUIRED = 402\nFORBIDDEN = 403\nNOT_FOUND = 404\nMETHOD_NOT_ALLOWED = 405\nNOT_ACCEPTABLE = 406\nPROXY_AUTHENTICATION_REQUIRED = 407\nREQUEST_TIMEOUT = 408\nCONFLICT = 409\nGONE = 410\nLENGTH_REQUIRED = 411\nPRECONDITION_FAILED = 412\nREQUEST_ENTITY_TOO_LARGE = 413\nREQUEST_URI_TOO_LONG = 414\nUNSUPPORTED_MEDIA_TYPE = 415\nREQUESTED_RANGE_NOT_SATISFIABLE = 416\nEXPECTATION_FAILED = 417\nUNPROCESSABLE_ENTITY = 422\nPRECONDITION_REQUIRED = 428\nTOO_MANY_REQUESTS = 429\nREQUEST_HEADER_FIELDS_TOO_LARGE = 431\nCONNECTION_CLOSED_WITHOUT_RESPONSE = 444\nINTERNAL_SERVER_ERROR = 500\nNOT_IMPLEMENTED = 501\nBAD_GATEWAY = 502\nSERVICE_UNAVAILABLE = 503\nGATEWAY_TIMEOUT = 504\nHTTP_VERSION_NOT_SUPPORTED = 505\nLOOP_DETECTED = 508\nNOT_EXTENDED = 510\nNETWORK_AUTHENTICATION_REQUIRED = 511\n","repo_name":"NikitinAP/ingenix","sub_path":"api/restful/status.py","file_name":"status.py","file_ext":"py","file_size_in_byte":1949,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"33936920249","text":"import numpy as np\nimport cv2\nimport pandas as pd\nfrom pathlib import Path\nimport math\n\ndef get_angle(line1, line2):\n d1 = (line1[1][0] - line1[0][0], line1[1][1] - line1[0][1])\n d2 = (line2[1][0] - line2[0][0], line2[1][1] - line2[0][1])\n p = d1[0] * d2[0] + d1[1] * d2[1]\n n1 = math.sqrt(d1[0] * d1[0] + d1[1] * d1[1])\n n2 = math.sqrt(d2[0] * d2[0] + d2[1] * d2[1])\n ang = math.acos(p / (n1 * n2))\n ang = math.degrees(ang)\n return ang\ndef get_intersections(line1, line2):\n A = np.array(line1)\n B = np.array(line2)\n t, s = np.linalg.solve(np.array([A[1]-A[0], B[0]-B[1]]).T, B[0]-A[0]) \n \n return (1-t)*A[0] + t*A[1]\ndef getPerspectiveTransformMatrix(vid_path , show_frame = False):\n\n cap = cv2.VideoCapture(vid_path)\n # width , height = 1080 , 720\n border = np.zeros((720,1280,3), np.uint8)\n print(\"Start\")\n ret, frame = cap.read()\n\n test_frame =np.copy(frame)\n\n h, w = frame.shape[:2]\n mask = np.zeros([h+2, w+2], np.uint8) \n # bgr\n diff_value = (3,1,3)\n black = (0,0,0)\n frame = cv2.GaussianBlur(frame , (7,7) , 0)\n\n fillpoints = [(100,0) , (100,700) , (300,350) , (1000,350) , (1200,700)]\n \n for point in fillpoints:\n cv2.floodFill(frame, mask, point, black, diff_value, diff_value)\n for point in fillpoints:\n cv2.circle(frame , point , 5 ,(0,0,255) , -1)\n\n # 灰階\n imgray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)\n # 二值化\n ret,thresh = cv2.threshold(imgray,100,255,0)\n # 找輪廓\n contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n\n contours = list(contours)\n # 根據輪廓面積大小進行sort\n contours.sort(key = cv2.contourArea , reverse=True)\n # 畫出最大的輪廓\n cv2.drawContours(border, contours[0:1], -1, (0,0,255), 10)\n\n\n\n # 灰階\n imgray = cv2.cvtColor(border,cv2.COLOR_BGR2GRAY)\n\n canny = cv2.Canny(imgray, 30, 100)\n lines = cv2.HoughLinesP(canny , 1.0 , np.pi/180 , 100 , np.array([]) , 200 , 100)\n\n cv2.line(frame,(100,100),(100,500),(255,255,255),5)\n\n horizon_line = []\n left_vertical_line = None\n right_vertical_line = None\n\n try:\n # 畫球場的垂直線 和 找到水平線座標\n for line in lines:\n for x1,y1,x2,y2 in line:\n line_angle = get_angle([(x1,y1),(x2,y2)] , [(0,0),(0,100)])\n line_angle_90 = 180 - line_angle if line_angle > 90 else line_angle\n vectorx = x2 - x1\n vectory = y2 - y1\n \n if (line_angle_90 < 40 and int(line_angle)): \n\n if left_vertical_line == None and line_angle > 90:\n # left_vertical_line = [(x1 - 20 , y1) , (x2 -20, y2)]\n left_vertical_line = [(x1 , y1) , (x2, y2)]\n cv2.line(frame,(x1 - vectorx * 100, y1 - vectory * 100),(x1 + vectorx * 100,y1 + vectory * 100),(255,0,0),5)\n elif right_vertical_line == None and line_angle < 90:\n right_vertical_line = [(x1, y1) , (x2, y2)]\n cv2.line(frame,(x1 - vectorx * 100, y1 - vectory * 100),(x1 + vectorx * 100,y1 + vectory * 100),(255,0,0),5)\n\n elif line_angle_90 > 85:\n\n horizon_line.append([[x1 ,y1] , [x2,y2] ])\n\n # 畫上下兩條水平線\n top_line = min(horizon_line , key = lambda x : x[0][1] + x[1][1])\n # top_line[0][1] -= 20\n # top_line[1][1] -= 20\n x1 , y1 = top_line[0]\n x2 , y2 = top_line[1]\n cv2.line(frame,(x1 - (x2-x1) * 100, y1 - (y2-y1) * 100),(x1 + (x2-x1) * 100,y1 + (y2-y1) * 100),(255,0,0),5) \n bottom_line = max(horizon_line , key = lambda x : x[0][1] + x[1][1])\n print(bottom_line)\n x1 , y1 = bottom_line[0]\n x2 , y2 = bottom_line[1]\n cv2.line(frame,(x1 - (x2-x1) * 100, y1 - (y2-y1) * 100),(x1 + (x2-x1) * 100,y1 + (y2-y1) * 100),(255,0,0),5) \n\n # print(get_intersections(top_line , vertical_line[0]).astype(int))\n corner = []\n corner.append(get_intersections(top_line , left_vertical_line).astype(int))\n corner.append(get_intersections(bottom_line , left_vertical_line).astype(int))\n corner.append(get_intersections(bottom_line , right_vertical_line).astype(int))\n corner.append(get_intersections(top_line , right_vertical_line).astype(int))\n cv2.circle(frame , get_intersections(top_line , left_vertical_line).astype(int) , 5 , (0,255,0),-1)\n cv2.circle(frame , get_intersections(top_line , right_vertical_line).astype(int) , 5 , (0,255,0),-1)\n cv2.circle(frame , get_intersections(bottom_line , left_vertical_line).astype(int) , 5 , (0,255,0),-1)\n cv2.circle(frame , get_intersections(bottom_line , right_vertical_line).astype(int) , 5 , (0,255,0),-1)\n \n except:\n return -1 , -1 , -1\n \n\n # cv2.imshow('board' , border) \n \n # 進行透視變換\n old = np.float32(corner)\n new = np.float32([[0,0], [0,h-1], [w-1,h-1] , [w-1,0] ])\n matrix = cv2.getPerspectiveTransform(old , new)\n imgOutput = cv2.warpPerspective(test_frame, matrix, (w , h), cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT, borderValue=(0,0,0))\n\n \n if show_frame:\n cv2.imshow('thresh' , thresh) \n cv2.imshow('board' , border) \n cv2.imshow('frame' , frame)\n cv2.imshow('Perspective', imgOutput)\n\n while cv2.waitKey(1) == -1:\n pass\n # cv2.destroyAllWindows()\n \n\n\n return matrix , corner , 1\n\n\n","repo_name":"Yui-Arthur/AI_badminton","sub_path":"utils/homography_transformation.py","file_name":"homography_transformation.py","file_ext":"py","file_size_in_byte":5576,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"16146516364","text":"bl_info = {\r\n \"name\": \"BMAX Connector\",\r\n \"author\": \"Titus Lavrov / Email: Titus.mailbox@gmail.com\",\r\n \"version\": (0, 1, 6),\r\n \"blender\": (2, 80, 0),\r\n \"location\": \"View3D > Toolbar and View3D\",\r\n \"warning\": \"\",\r\n \"description\": \"Bridge between 3dmax and Blender\",\r\n \"wiki_url\": \"\"\r\n \"\",\r\n \"category\": \"Import-Export\",\r\n}\r\n\r\nimport bpy\r\n\r\nfrom .operators.operators import (BMAX_OT_Export, \r\n BMAX_OT_Import,\r\n BMAX_OT_Export_USD,\r\n BMAX_OT_Import_USD)\r\n\r\nfrom .ui.panel import VIEW3D_PT_BMAX\r\nfrom .preferences.preferences import BMAX_AddonPreferences\r\n\r\n#Classes for register and unregister\r\nclasses = (\r\n BMAX_OT_Export,\r\n BMAX_OT_Import,\r\n BMAX_OT_Export_USD,\r\n BMAX_OT_Import_USD,\r\n VIEW3D_PT_BMAX,\r\n BMAX_AddonPreferences\r\n )\r\n \r\ndef register():\r\n for cls in classes:\r\n bpy.utils.register_class(cls)\r\n print (\"BMAX Connector - Registred!\")\r\n\r\ndef unregister(): \r\n for cls in reversed(classes):\r\n bpy.utils.unregister_class(cls)\r\n print (\"BMAX Connector - UnRegistred!\")\r\n\r\nif __name__ == \"__main__\":\r\n register()","repo_name":"TitusLVR/BMAX_Connector","sub_path":"__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1263,"program_lang":"python","lang":"en","doc_type":"code","stars":111,"dataset":"github-code","pt":"50"} +{"seq_id":"16637172478","text":"\"\"\"Code for generating simulations using phase correction models\"\"\"\n\nimport numpy as np\nimport pandas as pd\nimport numba as nb\nfrom joblib import Parallel, delayed\nimport dill as pickle\nfrom datetime import timedelta\nimport statsmodels.formula.api as smf\n\nimport src.analyse.analysis_utils as autils\nfrom src.analyse.phase_correction_models import PhaseCorrectionModel\n\n# Define the objects we can import from this file into others\n__all__ = [\n 'generate_phase_correction_simulations_for_coupling_parameters',\n 'generate_phase_correction_simulations_for_individual_conditions',\n 'Simulation'\n]\n\n\nclass Simulation:\n \"\"\"\n Creates X number (default 500) of simulated performances from a given phase correction model.\n\n Number of simulations defaults to 500, the same number in Jacoby et al. (2021).\n \"\"\"\n def __init__(\n self, pcm: PhaseCorrectionModel, num_simulations: int = 500, **kwargs\n ):\n # Default parameters\n self._resample_interval: timedelta = timedelta(seconds=1) # Get mean of IOIs within this window\n # Check and generate our input data\n self.total_beats: int = 1000\n self.num_simulations: int = num_simulations\n # Rolling window parameters -- should be the same as for the phase correction models\n self._rolling_window_size: str = kwargs.get('rolling_window_size', '2s') # 2 seconds = 2 beats at 120BPM\n self._rolling_min_periods: int = kwargs.get('rolling_min_periods', 2)\n # Get the raw data from our phase correction model\n if isinstance(pcm, PhaseCorrectionModel):\n self.keys_pcm = pd.DataFrame([pcm.keys_dic])\n self.drms_pcm = pd.DataFrame([pcm.drms_dic])\n elif isinstance(pcm, pd.DataFrame):\n self.keys_pcm = pcm[pcm['instrument'] == 'Keys']\n self.drms_pcm = pcm[pcm['instrument'] != 'Keys']\n # Get the zoom array used in the performance\n self.latency: np.ndarray = self._append_timestamps_to_latency_array(self.keys_pcm['zoom_arr'].iloc[0])\n # Raw simulation parameters, which will be used to create the dictionaries used in the simulations.\n self.parameter: str = kwargs.get('parameter', 'original')\n # Noise parameters for the simulation\n self.noise = kwargs.get('noise', autils.CONSTANT_RESID_NOISE) # Default noise term\n self.use_original_noise: bool = kwargs.get('use_original_noise', False) # Whether to use noise from model\n # Musician parameters\n self.keys_params_raw: dict = self._get_raw_musician_parameters(init=self.keys_pcm)\n self.drms_params_raw: dict = self._get_raw_musician_parameters(init=self.drms_pcm)\n self.keys_params: nb.typed.Dict = self._convert_musician_parameters_dict_to_numba(\n self._modify_musician_parameters_by_simulation_type(self.keys_params_raw)\n )\n self.drms_params: nb.typed.Dict = self._convert_musician_parameters_dict_to_numba(\n self._modify_musician_parameters_by_simulation_type(self.drms_params_raw)\n )\n # Empty lists to store our keys and drums simulations in\n self.keys_simulations: list[pd.DataFrame] = []\n self.drms_simulations: list[pd.DataFrame] = []\n # Empty attribute that we will fill with our results dictionary after creating simulations\n self.results_dic: dict = None\n\n @staticmethod\n def _get_number_of_beats_for_simulation(\n kp, dp\n ) -> int:\n \"\"\"\n Averages the total number of beats across both keys and drums, then gets the upper ceiling.\n \"\"\"\n return int(np.ceil(np.mean([kp['total_beats'].iloc[0], dp['total_beats'].iloc[0]])))\n\n @staticmethod\n def _append_timestamps_to_latency_array(\n latency_array, offset: int = 8, resample_rate: float = 0.75\n ) -> np.array:\n \"\"\"\n Appends timestamps showing the onset time for each value in the latency array applied to a performance\n \"\"\"\n # Define the endpoint for the linear space\n end = offset + (len(latency_array) * resample_rate)\n # Create the linear space\n lin = np.linspace(offset, end, num=len(latency_array), endpoint=False)\n # Append the two arrays together\n return np.c_[latency_array / 1000, lin]\n\n @staticmethod\n def _get_raw_musician_parameters(\n init: pd.DataFrame\n ) -> dict:\n \"\"\"\n Gets necessary simulation parameters from pandas dataframe and converts to a dictionary\n \"\"\"\n # Need to reset our index so we can use 0 indexing\n init = init.reset_index(drop=True)\n # Variables we need from our input\n cols = [\n 'correction_self', 'correction_partner', 'intercept', 'resid_std',\n ]\n # Create the dictionary and return\n dic = {s: init[s].iloc[0].astype(np.float64) for s in cols}\n dic.update({'instrument': init['instrument'].iloc[0]})\n return dic\n\n def _modify_musician_parameters_by_simulation_type(\n self, input_data\n ):\n \"\"\"\n Modifies a simulated musician's parameters according to the given simulation type\n \"\"\"\n # Used to get the mean of a particular coefficient across both musicians\n return {\n 'correction_self': input_data['correction_self'],\n 'correction_partner': input_data['correction_partner'],\n 'intercept': input_data['intercept'],\n 'resid_std': input_data['resid_std'] if self.use_original_noise else self.noise\n }\n\n @staticmethod\n def _convert_musician_parameters_dict_to_numba(\n python_dict: dict\n ) -> nb.typed.Dict:\n \"\"\"\n Converts a Python dictionary into a type that can be utilised by Numba\n \"\"\"\n # Create the empty dictionary\n nb_dict = nb.typed.Dict.empty(key_type=nb.types.unicode_type, value_type=nb.types.float64,)\n # Iterate through our dictionary\n for k, v in python_dict.items():\n # If the type is compatible with numba floats, set it in the numba dictionary\n if type(v) != str:\n nb_dict[k] = v\n return nb_dict\n\n def _initialise_empty_data(\n self, iois: tuple[float] = (0.5, 0.5), onset: float = 8\n ) -> nb.typed.Dict:\n \"\"\"\n Initialise an empty numba dictionary of string-array pairs, for storing data from one simulation in.\n \"\"\"\n # Make dictionary with strings as keys and arrays as values\n nb_dict = nb.typed.Dict.empty(\n key_type=nb.types.unicode_type,\n value_type=nb.types.float64[:],\n )\n # Fill the dictionary with arrays (pre-allocated in order to make running the simulation easier)\n for s in [\n 'my_onset', 'asynchrony', 'asynchrony_third_person',\n 'my_next_ioi', 'my_prev_ioi', 'my_next_ioi_diff', 'my_prev_ioi_diff'\n ]:\n nb_dict[s] = np.zeros(shape=self.total_beats)\n # Fill the dictionary arrays with our starting values\n # My onset\n nb_dict['my_onset'][0] = onset\n nb_dict['my_onset'][1] = onset + iois[0]\n # My next ioi\n nb_dict['my_next_ioi'][0] = iois[0]\n nb_dict['my_next_ioi'][1] = iois[1]\n # My previous ioi\n nb_dict['my_prev_ioi'][0] = np.nan\n nb_dict['my_prev_ioi'][1] = iois[0]\n # My next ioi diff\n nb_dict['my_next_ioi_diff'][0] = np.nan\n nb_dict['my_next_ioi_diff'][1] = iois[1] - iois[0] # This will always be 0\n # My previous ioi diff\n nb_dict['my_prev_ioi_diff'][0] = np.nan\n nb_dict['my_prev_ioi_diff'][1] = np.nan\n return nb_dict\n\n def create_all_simulations(\n self\n ) -> None:\n \"\"\"\n Run the simulations and create a list of dataframes for each individual performer\n \"\"\"\n # Create the simulations with joblib and numba function\n all_sims = Parallel(n_jobs=-1, prefer='threads')(delayed(autils.create_one_simulation)(\n self._initialise_empty_data(),\n self._initialise_empty_data(),\n self.keys_params,\n self.drms_params,\n np.random.normal(0, self.keys_params['resid_std'], 10000),\n np.random.normal(0, self.drms_params['resid_std'], 10000),\n self.latency,\n self.total_beats\n ) for _ in range(self.num_simulations))\n # Format simulated data\n self.keys_simulations = [self._format_simulated_data(d[0]) for d in all_sims]\n self.drms_simulations = [self._format_simulated_data(d[1]) for d in all_sims]\n # After running simulations, clean up by converting simulation parameters from numba back to python dictionary\n # This will allow us to pickle instances of the Simulation class without errors, as numba dictionaries are\n # currently not supported by the pickle package.\n self.keys_params = dict(self.keys_params)\n self.drms_params = dict(self.drms_params)\n # Save our results dictionary to an attribute in the class, so we don't have to do it later (or multiple times!)\n self.results_dic = self._create_summary_dictionary()\n\n def _format_simulated_data(\n self, data: nb.typed.Dict\n ) -> pd.DataFrame:\n \"\"\"\n Formats data from one simulation by creating a dataframe, adding in the timedelta column, and resampling\n to get the mean IOI (defaults to every second)\n \"\"\"\n # Create dataframe from the numba dictionary by first converting it to a python dictionary then to a dataframe\n df = pd.DataFrame(dict(data))\n # Drop rows with all zeros in\n df = df.loc[~(df == 0).all(axis=1)]\n # Convert my onset column to a timedelta\n idx = pd.to_timedelta([timedelta(seconds=val) for val in df['my_onset']])\n offset = timedelta(seconds=8 - df.iloc[0]['my_onset'])\n # Set the index to our timedelta column\n df = df.set_index(idx)\n # Get our rolling IOI standard deviation values\n df = self._get_rolling_standard_deviation_values(df)\n # Resample to the desired frequency with offset, get the mean values, and interpolate to fill NaNs\n return df.resample('1s', offset=offset).apply(np.nanmean).interpolate(limit_direction='backward')\n\n def _get_rolling_standard_deviation_values(\n self, df: pd.DataFrame, cols: tuple[str] = ('my_prev_ioi',)\n ) -> pd.DataFrame:\n # Create the rolling window with the desired window size\n roll = df.rolling(\n window=self._rolling_window_size, min_periods=self._rolling_min_periods, closed='both', on=df.index\n )\n # Iterate through the required columns\n for col in cols:\n # Extract the standard deviation and convert into milliseconds\n df[f'{col}_std'] = roll[col].std() * 1000\n return df\n\n @staticmethod\n def _get_average_var_for_one_simulation(\n all_perf: list[pd.DataFrame], var: str = 'my_next_ioi'\n ) -> pd.DataFrame:\n \"\"\"\n Concatenate all simulations together and get the row-wise average (i.e. avg IOI every second)\n \"\"\"\n # We use absolute values here, as this makes most sense when getting mean async across performers\n # For example, if keys-drums async = -0.5 and drums-keys async = 0.5, mean without absolute values == 0\n # Instead, this should be 0.5.\n return pd.DataFrame(\n pd.concat([df_[var] for df_ in all_perf], axis=1).abs().mean(axis=1), columns=[var]\n )\n\n def get_average_tempo_slope(\n self, func=np.nanmean, **kwargs\n ) -> float:\n \"\"\"\n Returns the average tempo slope for all simulations.\n\n Method:\n ---\n - For every simulation, zip the corresponding keys and drums performance together.\n - Then, get the average IOI for every second across both keys and drums.\n - This is straightforward, because we resampled to average IOI per second in _format_simulated_data\n - Convert average IOI to average BPM by dividing by 60, then regress against elapsed seconds\n - Extract the slope coefficient, take the median across all simulations, and return\n \"\"\"\n coeffs = []\n # Iterate through every simulation individually\n for keys, drms in zip(self.keys_simulations, self.drms_simulations):\n # Concatenate keyboard and drums performance and get average IOI every second\n avg = self._get_average_var_for_one_simulation([keys, drms])\n # Get elapsed number of seconds\n avg['elapsed_seconds'] = avg.index.seconds\n # Convert IOIs to BPM for tempo slope regression\n avg['my_next_bpm'] = 60 / avg['my_next_ioi']\n avg = avg.dropna()\n # Conduct and fit the regression model\n md = smf.ols('my_next_bpm~elapsed_seconds', data=avg.dropna()).fit()\n # Extract the tempo slope coefficient and append to list\n coeffs.append(md.params[1])\n # Calculate the median tempo slope coefficient (robust to outliers!) from all simulations and return\n return func(pd.Series(coeffs).replace(-np.Inf, np.nan), **kwargs)\n\n def get_average_ioi_variability(\n self, func=np.nanmean, **kwargs\n ) -> float:\n \"\"\"\n Returns the average tempo slope for all simulations.\n\n Method:\n ---\n - For every simulation, get the median IOI standard deviation value over the window size\n - Calculate the mean of all of these values.\n \"\"\"\n return func([\n [s['my_prev_ioi_std'].median() for s in self.keys_simulations],\n [s['my_prev_ioi_std'].median() for s in self.drms_simulations]\n ], **kwargs)\n\n def get_average_pairwise_asynchrony(\n self, func=np.nanmean, async_col: str = 'asynchrony', **kwargs\n ) -> float:\n \"\"\"\n Gets the average pairwise asynchrony (in milliseconds!) across all simulated performances\n \"\"\"\n def pw_async(keys, drms):\n \"\"\"\n Function used to calculate the pairwise asynchrony for a single simulation, in milliseconds\n \"\"\"\n # Concatenate the two asynchrony columns together\n conc = np.concatenate((keys[async_col].to_numpy(), drms[async_col].to_numpy()))\n # Square the values, take the mean, then the square root, then convert to miliseconds and return\n return np.sqrt(np.nanmean(np.square(conc))) * 1000\n\n # Calculate the mean pairwise async across all performances\n return func([pw_async(k_, d_) for k_, d_ in zip(self.keys_simulations, self.drms_simulations)], **kwargs)\n\n def get_simulation_data_for_plotting(\n self, plot_individual: bool = True, plot_average: bool = True, var: str = 'my_next_ioi',\n timespan: tuple = (7, 101),\n ) -> tuple:\n \"\"\"\n Wrangles simulation data into a format that can be plotted and returns.\n \"\"\"\n # Create simulations if we haven't already done so\n if len(self.keys_simulations) < 1 or len(self.drms_simulations) < 1:\n self.create_all_simulations()\n # If we're plotting individual simulations\n individual_sims = []\n grand_avg = None\n if plot_individual:\n # Iterate through individual keys and drums simulations\n for k_, d_ in zip(self.keys_simulations, self.drms_simulations):\n # Average individual simulation\n avg = self._get_average_var_for_one_simulation([k_, d_], var=var)\n # Subset for required timespan\n avg = avg[(avg.index.seconds >= timespan[0]) & (avg.index.seconds <= timespan[1])]\n individual_sims.append(avg)\n # If we're plotting our average simulation\n if plot_average:\n # Get grand average simulation by averaging our average simulations\n zi = zip(self.keys_simulations, self.drms_simulations)\n grand_avg = self._get_average_var_for_one_simulation(\n [self._get_average_var_for_one_simulation([k_, d_], var=var) for k_, d_ in zi], var=var\n )\n # Subset for required timespan\n grand_avg = grand_avg[(grand_avg.index.seconds >= timespan[0]) & (grand_avg.index.seconds <= timespan[1])]\n return individual_sims, grand_avg\n\n def _create_summary_dictionary(\n self,\n ) -> dict:\n \"\"\"\n Creates a summary dictionary with important simulation parameters\n \"\"\"\n return {\n # Condition metadata\n 'trial': self.keys_pcm[\"trial\"].iloc[0],\n 'block': self.keys_pcm[\"block\"].iloc[0],\n 'latency': self.keys_pcm[\"latency\"].iloc[0],\n 'jitter': self.keys_pcm[\"jitter\"].iloc[0],\n # Simulation metadata\n 'parameter': self.parameter,\n 'original_noise': self.use_original_noise,\n 'keys_parameters': self.keys_params_raw,\n 'drums_parameters': self.drms_params_raw,\n # Metrics from the actual original performance on which this simulation was based\n 'tempo_slope_original': np.mean(\n [self.keys_pcm['tempo_slope'].iloc[0], self.drms_pcm['tempo_slope'].iloc[0]]\n ),\n 'ioi_variability_original': np.mean(\n [self.keys_pcm['ioi_std'].iloc[0], self.drms_pcm['ioi_std'].iloc[0]]\n ),\n 'asynchrony_original': np.mean(\n [self.keys_pcm['pw_asym'].iloc[0], self.drms_pcm['pw_asym'].iloc[0]]\n ),\n # Summary metrics from all the simulated performances\n 'tempo_slope_simulated': self.get_average_tempo_slope(func=np.nanmedian), # average\n 'tempo_slope_simulated_std': self.get_average_tempo_slope(func=np.nanstd), # standard deviation\n 'tempo_slope_simulated_ci': self.get_average_tempo_slope(func=np.nanpercentile, q=[2.5, 97.5]), # 95% ci\n 'ioi_variability_simulated': self.get_average_ioi_variability(func=np.nanmedian),\n 'ioi_variability_simulated_std': self.get_average_ioi_variability(func=np.nanstd),\n 'ioi_variability_simulated_ci': self.get_average_ioi_variability(func=np.nanpercentile, q=[2.5, 97.5]),\n 'asynchrony_simulated': self.get_average_pairwise_asynchrony(func=np.nanmedian,\n async_col='asynchrony_third_person'),\n 'asynchrony_simulated_indiv': self.get_average_pairwise_asynchrony(func=np.nanmedian),\n 'asynchrony_simulated_std': self.get_average_pairwise_asynchrony(func=np.nanstd),\n 'asynchrony_simulated_ci': self.get_average_pairwise_asynchrony(func=np.nanpercentile, q=[2.5, 97.5]),\n }\n\n\ndef generate_phase_correction_simulations_for_individual_conditions(\n mds: list[PhaseCorrectionModel], output_dir: str, logger=None, force_rebuild: bool = False,\n num_simulations: int = autils.NUM_SIMULATIONS\n) -> list[Simulation]:\n \"\"\"\n Create simulated performances using the coupling within every individual performance.\n \"\"\"\n # Try and load the models from the disk to save time, unless we're forcing them to rebuild anyway\n if not force_rebuild:\n all_sims = autils.load_from_disc(output_dir, filename='phase_correction_sims_orig.p')\n # If we've successfully loaded models, return these straight away\n if all_sims is not None:\n return all_sims\n # Create an empty list to hold simulations in\n all_sims = []\n for pcm in mds:\n # Initialise the simulation class\n sim = Simulation(\n pcm=pcm, num_simulations=num_simulations, parameter='original', leader=None, use_original_noise=True\n )\n autils.log_simulation(sim, logger)\n # Create all simulations for this parameter/leader combination\n sim.create_all_simulations()\n # Append the simulation to our list\n all_sims.append(sim)\n # Pickle the result -- this will be quite large, depending on the number of simulations!\n pickle.dump(all_sims, open(f\"{output_dir}\\\\phase_correction_sims_orig.p\", \"wb\"))\n return all_sims\n\n\ndef generate_phase_correction_simulations_for_coupling_parameters(\n mds: list[PhaseCorrectionModel], output_dir: str, logger=None, force_rebuild: bool = False,\n num_simulations: int = autils.NUM_SIMULATIONS\n) -> tuple[list[Simulation], str]:\n \"\"\"\n Create simulated performances across a range of artificial coupling parameters for every phase correction model\n \"\"\"\n def grouper(gr):\n return gr.groupby('instrument', as_index=False).agg(\n {\n 'trial': 'mean', 'block': 'mean', 'latency': 'mean', 'jitter': 'mean', 'tempo_slope': 'mean',\n 'ioi_std': 'mean', 'pw_asym': 'mean', 'zoom_arr': 'first', 'intercept': 'mean',\n 'correction_partner': 'mean', 'correction_self': 'mean', 'resid_std': 'mean'\n }\n )\n\n # Try and load the models from the disk to save time, unless we're forcing them to rebuild anyway\n if not force_rebuild:\n all_sims = autils.load_from_disc(output_dir, filename='phase_correction_sims.p')\n # If we've successfully loaded models, return these straight away\n if all_sims is not None and isinstance(all_sims, list):\n if len(all_sims) != 0:\n return (\n all_sims,\n f'... skipping, simulations loaded from {output_dir}\\\\phase_correction_sims.p'\n )\n # Create the dataframe\n df = pd.concat(\n [pd.concat([pd.DataFrame([pcm.keys_dic]), pd.DataFrame([pcm.drms_dic])]) for pcm in mds]\n ).reset_index(drop=True)\n # Create an empty list to hold our simulations\n all_sims = []\n avg_noise = 0.005\n # Iterate through each condition\n for idx, grp in df.groupby(by=['latency', 'jitter']):\n # Iterate through each duo\n for i, g in grp.groupby('trial'):\n # Create the grouped model, averaging performance of each duo for one condition over both sessions\n pcm_o = grouper(g)\n # Create the simulation object\n sim = Simulation(\n pcm=pcm_o, num_simulations=num_simulations, parameter='original', leader=None, use_original_noise=False,\n noise=avg_noise\n )\n # Log the current duo and condition in our GUI, if we've passed a logger\n autils.log_simulation(sim, logger)\n # Create all simulations and append the simulation object to our list\n sim.create_all_simulations()\n all_sims.append(sim)\n # Create the grouped phase correction model, across all trials\n pcm_a = grouper(grp)\n # Set our trial metadata to 0 (helpful when logging)\n pcm_a['trial'] = 0\n # Create our anarchy model: both coupling coefficients set to 0\n anarchy_md = pcm_a.copy()\n anarchy_md['correction_partner'] = 0\n anarchy_md['intercept'] = 0\n # Create our democracy model: both coupling coefficients set to their means\n democracy_md = pcm_a.copy()\n democracy_md['correction_partner'] = democracy_md['correction_partner'].mean()\n democracy_md['intercept'] = 0\n # Create our leadership model: drums coupling set to 0, keys coupling set to mean\n leadership_md = pcm_a.copy()\n leadership_md['correction_partner'] = np.where(\n leadership_md['instrument'] == 'Drums', 0,\n leadership_md[leadership_md['instrument'] == 'Keys']['correction_partner']\n )\n leadership_md['intercept'] = 0\n # Iterate over all of our paradigm models\n for md, param in zip([anarchy_md, democracy_md, leadership_md],\n ['anarchy', 'democracy', 'leadership']):\n # Create our simulation\n sim = Simulation(\n pcm=md, num_simulations=num_simulations, parameter=param, leader=None, use_original_noise=False,\n noise=avg_noise\n )\n # Log the current simulation in our GUI\n autils.log_simulation(sim, logger)\n # Create the simulation and append to our list\n sim.create_all_simulations()\n all_sims.append(sim)\n # Pickle the result -- this can be quite large, if we're creating lots of simulations!\n pickle.dump(all_sims, open(f\"{output_dir}\\\\phase_correction_sims.p\", \"wb\"))\n return all_sims, f'...simulations saved in {output_dir}\\\\phase_correction_sims.p'\n\n\nif __name__ == '__main__':\n import logging\n import os\n\n # Configure logger\n log_fmt = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'\n logging.basicConfig(level=logging.INFO, format=log_fmt)\n logger = logging.getLogger(__name__)\n # Default location for phase correction models\n logger.info(f\"Making simulations from models in {os.path.abspath(r'../../models')}\")\n\n # Default location for phase correction models\n raw = autils.load_from_disc(r\"..\\..\\models\", filename=\"phase_correction_mds.p\")\n # Default location to save output simulations\n output = r\"..\\..\\models\"\n # Generate simulations using coupling parameters and pickle\n generate_phase_correction_simulations_for_coupling_parameters(\n mds=raw, output_dir=output, force_rebuild=True, logger=logger\n )\n","repo_name":"HuwCheston/Jazz-Jitter-Analysis","sub_path":"src/analyse/simulations.py","file_name":"simulations.py","file_ext":"py","file_size_in_byte":25454,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"12793296864","text":"from flake8 import engine\nimport pep8\n\nimport hacking.tests\n\n\ndef check(physical_line):\n \"\"\"Test check to make sure local-checks are working.\"\"\"\n if physical_line.strip() == \"#this-is-the-test-phrase\":\n return (0, \"L100: Found local-check test case\")\n\n\nclass HackingTestCase(hacking.tests.TestCase):\n def test_local_check(self):\n flake8_style = engine.get_style_guide(parse_argv=False, ignore='F')\n report = pep8.BaseReport(flake8_style.options)\n line = [\"#this-is-the-test-phrase\"]\n checker = pep8.Checker(lines=line, options=flake8_style.options,\n report=report)\n checker.check_all()\n self.assertIn(\"L100\", report.counters)\n","repo_name":"michaelhenkel/contrail-tripleo-docu","sub_path":".tox/docs/lib/python2.7/site-packages/hacking/tests/test_local.py","file_name":"test_local.py","file_ext":"py","file_size_in_byte":714,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"50"} +{"seq_id":"74249101914","text":"\"\"\" ************************************************\n* fileName: frames_folder_demo.py\n* desc: The demo of inference only \"a\" video splited into consecutive framess\n* author: mingdeng_cao\n* date: 2021/07/09 11:31\n* last revised: None\n************************************************ \"\"\"\n\nimport os\nimport sys\nimport argparse\n\nimport torch\nimport torch.nn as nn \nimport torch.nn.functional as F\nfrom easydict import EasyDict as edict\n\nfrom simdeblur.config import build_config\nfrom simdeblur.model import build_backbone\nfrom simdeblur.dataset.base import DatasetBase\nfrom simdeblur.dataset.frames_folder import FramesFolder\n\ndef parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"config\", type=str, help=\"The config .yaml file of deblurring model. \")\n parser.add_argument(\"ckpt\", type=str, help=\"The trained checkpoint of the selected deblurring model. \")\n parser.add_argument(\"--frames_folder_path\", type=str, help=\"The video frames folder path. \")\n parser.add_argument(\"--save_path\", type=str, help=\"The output deblurred path\")\n\n args = parser.parse_args()\n\n return args\n\n\ndef frames_foler_demo():\n args = parse_args()\n config = build_config(args.config)\n config.args = args\n model = build_backbone(config.model).cuda()\n\n ckpt = torch.load(args.ckpt, map_location=\"cuda:0\")\n\n model_ckpt = ckpt[\"model\"]\n model_ckpt = {k[7:]: v for k, v in model_ckpt.items()}\n model.load_state_dict(model_ckpt)\n\n data_config = edict({\n \"root_input\": \"/home/cmd/projects/simdeblur/datasets/DVD/qualitative_datasets/alley/input\",\n \"num_frames\": 5,\n \"overlapping\": True,\n \"sampling\": \"n_c\"\n })\n frames_data = FramesFolder(data_config)\n frames_dataloader = torch.utils.data.DataLoader(frames_data, 1)\n\n model.eval()\n with torch.no_grad():\n for i, batch_data in enumerate(frames_dataloader):\n out = model(batch_data[\"input_frames\"].cuda())\n print(batch_data[\"gt_names\"], out.shape)\n\n\nif __name__ == \"__main__\":\n frames_foler_demo()\n","repo_name":"ljzycmd/SimDeblur","sub_path":"demo/frames_folder_demo.py","file_name":"frames_folder_demo.py","file_ext":"py","file_size_in_byte":2055,"program_lang":"python","lang":"en","doc_type":"code","stars":262,"dataset":"github-code","pt":"50"} +{"seq_id":"5647586322","text":"from django.shortcuts import render\nfrom .forms import StudentForm\nfrom .models import Student\n# Create your views here.\ndef student_form_view(request):\n form=StudentForm()\n if request.method=='POST':\n form=StudentForm(request.POST)\n if form.is_valid():\n form.save(commit=True)\n\n return render(request,'testapp/registration.html',{'form':form})\n","repo_name":"sandipdeshmukh77/django-practice-projects","sub_path":"modelformprojectdemo_1/testapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":380,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"13464451866","text":"def isEven(n):\n return n % 2 == 0\n\ndef fibonacci(n):\n if n == 1 or n == 2:\n return n\n else:\n return fibonacci(n - 1) + fibonacci(n - 2)\n\ndef fibSequence(n):\n counter = 1\n totalSum = 0\n fibSeq = 0\n while(fibSeq < n):\n fibSeq = fibonacci(counter)\n if(isEven(fibSeq)):\n totalSum += fibSeq\n counter += 1\n return totalSum\n\nif __name__ == '__main__':\n print(isEven(4))\n print(isEven(7))\n print(fibonacci(10))\n print(fibSequence(4000000))\n \n","repo_name":"sbedell/CodingChallenges","sub_path":"ProjectEuler/euler2.py","file_name":"euler2.py","file_ext":"py","file_size_in_byte":519,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"30780740438","text":"from __future__ import division\r\n\r\n'''Simulation of Chemotaxis, a modified randon walk used by some bacteria to\r\nfind food sources. This uses a run and tumble process'''\r\n\r\nimport numpy\r\nimport matplotlib.pyplot as pyplot\r\nimport time\r\nimport matplotlib.cm\r\nimport pylab\r\nimport random\r\n\r\n# Parameters\r\nx0, y0 = 20, 40 # microns\r\nk = 0.2\r\nV = 2 # microns per second\r\ndt = 0.1\r\ntimebase = numpy.arange(0, 100, dt)\r\nr = numpy.array((x0, y0))\r\n\r\ndef energy_density(r):\r\n '''This function simulates the sugar energy source specified by the function'''\r\n x, y = r\r\n f = 4000 - (x**2 + y**2)\r\n return f\r\n\r\ndef velocity(a):\r\n '''This function is for the velocities in the x and y plane'''\r\n Vx = V * numpy.cos(a)\r\n Vy = V * numpy.sin(a)\r\n \r\n return numpy.array((Vx, Vy))\r\n\r\ndef position(r):\r\n '''This function returns the positions of the bacteria''' \r\n x, y = r\r\n a = random.random() * 2 * numpy.pi\r\n \r\n position = numpy.zeros((len(timebase), 2))\r\n shift = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\r\n \r\n for i in range(len(timebase)):\r\n\r\n position[i] = r\r\n \r\n eNew = energy_density(r)\r\n shift.append(eNew)\r\n shift = shift[-10:]\r\n df = shift[-1] - shift[0]\r\n\r\n t_half = 1 + k * df\r\n if t_half < 0.1: t_half = 0.1\r\n\r\n tau = t_half/ numpy.log(2)\r\n \r\n p = numpy.exp(-dt/ tau)\r\n\r\n if random.random() < p:\r\n '''move at speed * dt'''\r\n r = r + velocity(a) * dt\r\n \r\n else:\r\n a = random.random() * 2 * numpy.pi\r\n\r\n return position\r\n\r\ndef MSD(r):\r\n '''This function calculates the mean square displacement of bacteria\r\nagainst time from the origin and location of max energy'''\r\n x, y = r\r\n \r\n s_d_f_initial = numpy.zeros((20,1000))\r\n # Squared displacement from initial\r\n s_d_f_origin = numpy.zeros((20,1000))\r\n # Squared displacement form origin\r\n \r\n\r\n for i in range(20):\r\n\r\n x_data = position([20, 40])[:,0]\r\n y_data = position([20, 40])[:,1]\r\n s_d_f_initial[i] = ((20-x_data)**2+(40-y_data)**2)\r\n s_d_f_origin[i] = (x_data**2+y_data**2)\r\n\r\n MSD_i = numpy.average(s_d_f_initial, axis=0)\r\n MSD_f = numpy.average(s_d_f_origin, axis=0) \r\n\r\n \r\n return MSD_i, MSD_f\r\n \r\ny_axis = numpy.arange(0, 50, 0.1)\r\nx_axis = numpy.arange(0, 40, 0.1)\r\n\r\ndat = numpy.zeros((len(y_axis), len(x_axis)))\r\n\r\nfor iy, y in enumerate(y_axis):\r\n for ix, x in enumerate(x_axis):\r\n dat[iy, ix] = energy_density((x, y))\r\n\r\npyplot.subplot(221) \r\nfor i in range(20):\r\n\r\n position_list = position([20, 40])\r\n\r\n x = position_list[: ,0]\r\n y = position_list[: ,1]\r\n \r\n pyplot.subplot(221)\r\n pyplot.plot(x,y)\r\n \r\n x0, y0 = 20, 40\r\n x_f, y_f = x[-1], y[-1]\r\n x_TR = x0, x_f\r\n y_TR = y0, y_f\r\n pyplot.subplot(222)\r\n pyplot.plot(x_TR, y_TR, marker='o')\r\n \r\n \r\n\r\nMSD_i, MSD_f = MSD([20, 40])\r\n\r\npyplot.xlabel('x /microns')\r\npyplot.ylabel('y /microns')\r\npyplot.title('Inital and final bacteria position')\r\npyplot.tight_layout()\r\n\r\npyplot.subplot(221)\r\nim = pyplot.imshow(dat, extent = (-20, 40, -20, 50),\r\n origin = 'lower', cmap = matplotlib.cm.gray,\r\n aspect = 'auto')\r\npyplot.colorbar(im, orientation = 'vertical', label = 'energy density')\r\npyplot.xlabel('x /microns')\r\npyplot.ylabel('y /microns')\r\npyplot.title('Bacteria trajectory')\r\npyplot.tight_layout()\r\n\r\npyplot.subplot(212)\r\npyplot.plot(timebase, MSD_i, label = 'from origin', color = 'blue')\r\npyplot.plot(timebase, MSD_f, label = 'from Max energy', color = 'green')\r\npyplot.title('Mean Squared diplacement')\r\npyplot.xlabel('Time')\r\npyplot.ylabel('MSD')\r\npyplot.legend(loc= 'lower right') \r\npyplot.tight_layout()\r\npyplot.show()\r\n \r\n\r\n","repo_name":"sochiho/Chemotaxis","sub_path":"Chemotaxis.py","file_name":"Chemotaxis.py","file_ext":"py","file_size_in_byte":3817,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"148357369","text":"import json\nfrom django.core.management.base import BaseCommand\nfrom classification.models import Node\n\nclass Command(BaseCommand):\n help = 'Populates rds with hierachy'\n\n def handle(self, *args, **kwargs):\n \n with open(\"fixtures/hierachy.json\", \"r\") as hierachy:\n f = json.load(hierachy)\n for node in f:\n self.append_children(node)\n\n\n def append_children(self, node, parent=None):\n\n print(f\"Adding {node['title']}\")\n parent = Node.objects.create(title=node[\"title\"], parent=parent)\n if node[\"children\"]:\n for child in node[\"children\"]:\n self.append_children(child, parent)\n\n return 0\n \n","repo_name":"IamMiracleAlex/admintools","sub_path":"classification/management/commands/json_to_sql.py","file_name":"json_to_sql.py","file_ext":"py","file_size_in_byte":716,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"35522820470","text":"import time\n\nimport matplotlib as mpl \nfrom mpl_toolkits.mplot3d import axes3d\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\nfrom matplotlib import animation\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport curses\nimport logging\nimport math\nimport cflib.crtp\nfrom cflib.crazyflie import Crazyflie\nfrom cflib.crazyflie.syncCrazyflie import SyncCrazyflie\nfrom cflib.crazyflie.log import LogConfig\nfrom cflib.crazyflie.syncLogger import SyncLogger\n\nimport csv\nfilename = \"datas\"\n\nURI = 'radio://0/88/2M/E7E7E7E7EB'\n# Only output errors from the logging framework\nlogging.basicConfig(level=logging.ERROR)\nlogging.basicConfig(filename='test.log',level=logging.DEBUG)\n\nx_list = list()\ny_list = list()\nz_list = list()\n\n\ndef init():\n line.set_data(x[:2],y[:2])\n return line,\n#The following functions are created for easy use of them\ndef Backward(t,vf):\n print(\"Backward \")\n for _ in range(t):\n cf.commander.send_hover_setpoint(vf, 0, 0, 0.2)\n time.sleep(0.1)\n real_time()\n return\ndef Forward(t,vf):\n print(\"Forward \")\n for y in range(t):\n cf.commander.send_hover_setpoint(vf, 0, 0, 0.2)\n time.sleep(0.1)\n real_time()\n return\ndef Right(t,vs):\n print(\"Right \")\n for _ in range(t):\n cf.commander.send_hover_setpoint(0, vs, 0, 0.2)\n time.sleep(0.1)\n real_time()\n return\ndef Left(t,vs):\n print(\"Left \")\n for _ in range(t):\n cf.commander.send_hover_setpoint(0, vs, 0, 0.2)\n time.sleep(0.1)\n real_time()\n return\ndef Hovering(t):\n print(\"Hovering \")\n for _ in range(t):\n cf.commander.send_hover_setpoint(0, 0, 0, 0.2)\n time.sleep(0.1)\n real_time()\n return\ndef Takeoff(t):\n print(\"Takeoff \")\n for y in range(t):\n cf.commander.send_hover_setpoint(0, 0, 0, y/25)\n time.sleep(0.1)\n real_time()\n return\ndef landing(t):\n print(\"Landing \")\n for y in range(t):\n cf.commander.send_hover_setpoint(0, 0, 0, (t-y) / 25)\n time.sleep(0.1)\n cf.commander.send_stop_setpoint()\n real_time()\n return\n\ndef main(stdscr):\n curses.curs_set(0)\n stdscr.clear()\n while 1:\n key=stdscr.getch()\n #print(key)\n #TAKING OFF\n if key == 10:#curses.KEY_ENTER:#take off move\n print(\"Takeoff \\r\")\n for y in range(5):\n if key==curses.KEY_UP:\n Forward(30,0.2)\n elif key==curses.KEY_DOWN:\n Backward(30,-0.2)\n\n elif key==curses.KEY_RIGHT:\n Right(30,-0.2)\n\n elif key==curses.KEY_LEFT:\n Left(30,0.2) \n elif key==curses.KEY_BACKSPACE:\n landing(5)\n\n elif key==27:\n break\n elif key==curses.KEY_HOME:\n Hovering(30)\n cf.commander.send_hover_setpoint(0, 0, 0, y / 25)\n time.sleep(0.1)\n logging.debug('take off'.format(0,0,0,y/25))\n real_time()\n \n #LANDING\n elif key== curses.KEY_BACKSPACE:#landing \n print(\"Landing \\r\")\n for y in range(5):\n if key==curses.KEY_UP:\n Forward(30,0.2)\n \n elif key==curses.KEY_DOWN:\n Backward(30,-0.2) \n\n elif key==curses.KEY_RIGHT:\n Right(30,-0.2) \n elif key==curses.KEY_LEFT:\n Left(30,0.2) \n elif key==curses.KEY_ENTER:\n Takeoff(10) \n elif key==27:\n break\n elif key==curses.KEY_HOME:\n Hovering(30)\n \n cf.commander.send_hover_setpoint(0, 0, 0, (5 - y) / 25)\n time.sleep(0.1)\n cf.commander.send_stop_setpoint()\n real_time()\n #ESCAPING\n elif key == 27:#Escape tab\n break\n #FORWARD_DIRECTION\n elif key==curses.KEY_UP:\n \n print(\"Forward \\r\")\n for _ in range(10):\n if key==curses.KEY_ENTER:\n\n Takeoff(10) \n elif key==curses.KEY_DOWN:\n Backward(30,-0.2) \n elif key==curses.KEY_RIGHT:\n Right(30,-0.2)\n \n elif key==curses.KEY_LEFT:\n Left(30,0.2)\n \n elif key==curses.KEY_BACKSPACE:\n landing(5) \n elif key==27:\n break\n elif key==curses.KEY_HOME:\n Hovering(30)\n cf.commander.send_hover_setpoint(0.2, 0, 0, 0.2)\n time.sleep(0.1)\n real_time()\n \n #BACKWARD_DIRECTION\n elif key==curses.KEY_DOWN:\n print(\"Backward \\r\")\n for _ in range(30):\n if key==curses.KEY_UP:\n Forward(30,0.2)\n \n elif key==curses.KEY_ENTER:\n Takeoff(10) \n elif key==curses.KEY_RIGHT:\n Right(30,-0.2) \n elif key==curses.KEY_LEFT:\n Left(30,0.2) \n elif key==curses.KEY_BACKSPACE:\n landing(5)\n \n elif key==27:\n break\n elif key==curses.KEY_HOME:\n Hovering(30) \n cf.commander.send_hover_setpoint(-0.2, 0, 0, 0.2)\n time.sleep(0.1)\n real_time()\n #RIGHT_DIRECTION \n elif key==curses.KEY_RIGHT:\n print(\"Right \\r\")\n for _ in range(30):\n if key==curses.KEY_UP:\n Forward(30,0.2) \n elif key==curses.KEY_DOWN:\n Backward(30,-0.2) \n elif key==curses.KEY_ENTER:\n Takeoff(10)\n \n elif key==curses.KEY_LEFT:\n Left(30,0.2)\n \n elif key==curses.KEY_BACKSPACE:\n landing(5)\n \n elif key==27:\n break\n elif key==curses.KEY_HOME:\n Hovering(30)\n \n cf.commander.send_hover_setpoint(0, -0.2, 0, 0.2)\n time.sleep(0.1)\n real_time()\n #LEFT_DIRECTION \n elif key==curses.KEY_LEFT:\n print(\"Left \\r\")\n for _ in range(30):\n if key==curses.KEY_UP:\n Forward(30,0.2) \n elif key==curses.KEY_DOWN:\n Backward(30,-0.2)\n \n elif key==curses.KEY_ENTER:\n Takeoff(10)\n \n elif key==curses.KEY_RIGHT:\n Right(30,-0.2)\n \n elif key==curses.KEY_BACKSPACE:\n landing(5)\n \n elif key==27:\n break\n elif key==curses.KEY_HOME:\n Hovering(30)\n cf.commander.send_hover_setpoint(0, 0.2, 0, 0.2)\n time.sleep(0.1)\n real_time()\n #HOVERING \n elif key==curses.KEY_HOME:#This is Hovering==Fn+Left arrow\n print(\"Hovering \\r\")\n for _ in range(30):\n if key==curses.KEY_UP:\n Forward(30,0.2)\n \n elif key==curses.KEY_DOWN:\n Backward(30,-0.2)\n \n elif key==curses.KEY_ENTER:\n Takeoff(10)\n \n elif key==curses.KEY_LEFT:\n Left(30,0.2) \n elif key==curses.KEY_BACKSPACE:\n landing(5) \n elif key==27:\n break\n elif key==curses.KEY_RIGHT:\n Right(30,-0.2) \n cf.commander.send_hover_setpoint(0, 0, 0, 0.2)\n time.sleep(0.1) \n real_time()\n \n \n stdscr.refresh()\n\ndef wait_for_position_estimator(scf):\n print('Waiting for estimator to find position...')\n\n log_config = LogConfig(name='Kalman Variance', period_in_ms=500)\n log_config.add_variable('kalman.varPX', 'float')\n log_config.add_variable('kalman.varPY', 'float')\n log_config.add_variable('kalman.varPZ', 'float')\n\n var_y_history = [1000] * 10\n var_x_history = [1000] * 10\n var_z_history = [1000] * 10\n\n threshold = 0.001\n\n with SyncLogger(scf, log_config) as logger:\n for log_entry in logger:\n data = log_entry[1]\n\n var_x_history.append(data['kalman.varPX'])\n var_x_history.pop(0)\n var_y_history.append(data['kalman.varPY'])\n var_y_history.pop(0)\n var_z_history.append(data['kalman.varPZ'])\n var_z_history.pop(0)\n\n min_x = min(var_x_history)\n max_x = max(var_x_history)\n min_y = min(var_y_history)\n max_y = max(var_y_history)\n min_z = min(var_z_history)\n max_z = max(var_z_history)\n\n #print(\"{} {} {}\".format(max_x - min_x, max_y - min_y, max_z - min_z))\n\n if (max_x - min_x) < threshold and (\n max_y - min_y) < threshold and (\n max_z - min_z) < threshold:\n break\ndef reset_estimator(scf):\n cf = scf.cf\n cf.param.set_value('kalman.resetEstimation', '1')\n time.sleep(0.1)\n cf.param.set_value('kalman.resetEstimation', '0')\n\n wait_for_position_estimator(cf)\n\ndef start_position_printing(scf):\n log_conf = LogConfig(name='Position', period_in_ms=500)\n log_conf.add_variable('kalman.stateX', 'float')\n log_conf.add_variable('kalman.stateY', 'float')\n log_conf.add_variable('kalman.stateZ', 'float')\n\n scf.cf.log.add_config(log_conf)\n log_conf.data_received_cb.add_callback(position_callback)\n log_conf.start()\n\ndef position_callback(timestamp, data, logconf):\n x = data['kalman.stateX']\n y = data['kalman.stateY']\n z = data['kalman.stateZ']\n # print('pos: ({}, {}, {})'.format(x, y, z))\n x_list.append(x)\n y_list.append(y)\n z_list.append(z)\n#real time plot in 2d \ny = y_list\nx = x_list\n\nfig, ax = plt.subplots(1,1)\nline, = ax.plot([], [], '-')\nplt.ion()\nplt.show()\nax.margins(0.05)\n\ndef animate(i):\n xdata = x[:i]\n ydata = y[:i]\n \n line.set_data(xdata, ydata)\n plt.plot(x,y)\n plt.gca().line[0].set_xdata(x)\n plt.gca().line[0].set_ydata(y)\n plt.gcf().canvas.flush_events()\n plt.gca().relim()\n plt.gca().autoscale_view()\n plt.pause(0.05)\n \n return line,\ndef real_time():#creating function which can plot trajectories in 2D in real time\n anim = animation.FuncAnimation(fig, animate, init_func=init, interval=5)\n plt.ion()\n plt.draw()\n \n plt.pause(0.00001) \n return\n\n# this is the last line of code for real time plotting in 2d\nif __name__ == '__main__':\n # Initialize the low-level drivers (don't list the debug drivers)\n cflib.crtp.init_drivers(enable_debug_driver=False)\n \n with SyncCrazyflie(URI, cf=Crazyflie(rw_cache='./cache')) as scf:\n cf = scf.cf\n time.sleep(2)\n logging.basicConfig(filename='app.log', filemode='w', format='%(name)s - %(levelname)s - %(message)s')\n \n reset_estimator(scf)\n start_position_printing(scf)\n curses.wrapper(main)\n f = open(filename + '.csv', 'w')\n\n\n with f:\n\n writer = csv.writer(f)\n writer.writerows([x_list,y_list,z_list])\n #3d plot after the flight\n fig=plt.figure()\n ax=fig.gca(projection='3d')\n\n ax.plot(x_list,y_list,z_list,'b-')\n ax.set_xlabel('x')\n ax.set_ylabel('y')\n ax.set_zlabel('z')\n ax.plot([x_list[0]],[y_list[0]],[z_list[0]],'o',markerfacecolor='none', markeredgecolor='red',markersize=12.5,label='start')\n ax.plot([x_list[-1]],[y_list[-1]],[z_list[-1]],'o',markerfacecolor='red', markeredgecolor='red',markersize=12.5,label='end')\n plt.title('Trajectory of CF')\n \n ax.legend(numpoints=1)\n plt.show(block=True)\n plt.show()\n \n\n\n\n\n\n\n\n","repo_name":"ozay-group/drone-testbed","sub_path":"trajectory-planning/keyboard.py","file_name":"keyboard.py","file_ext":"py","file_size_in_byte":13275,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"42688332175","text":"\"\"\"``tamago`` lives on\nhttps://github.com/hhollenstain/tamago-web\n\"\"\"\nfrom setuptools import setup, find_packages\nimport tamago_web\n\nINSTALL_REQUIREMENTS = [\n 'coloredlogs',\n 'flask',\n 'waitress',\n]\n\nTEST_REQUIREMENTS = {\n 'test':[\n 'pytest',\n 'pylint',\n 'sure',\n ]\n }\n\nsetup(\n name='Tamago Web',\n version=tamago_web.VERSION,\n description='Tamago web for Tamago Discord Bot',\n url='https://github.com/hhollenstain/tamago-web',\n packages=find_packages(),\n include_package_data=True,\n install_requires=INSTALL_REQUIREMENTS,\n extras_require=TEST_REQUIREMENTS,\n entry_points={\n 'console_scripts': [\n 'tamago_web = tamago_web.tamago_web:main',\n ],\n },\n )\n","repo_name":"hhollenstain/tamago-web","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":752,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"14290470161","text":"import os\nimport sys\n\nimport pandas as pd\nimport numpy as np\nimport dill\n\n\nfrom pathlib import Path\n\nfrom src.exception import CustomException\nfrom src.logger import logger\n\ndef save_object(file_path: Path, obj: object) -> None:\n \"\"\"Guarda un objeto en un archivo .pkl.\n\n Parámetros:\n - file_path: Ruta del archivo.\n - obj: Objeto a guardar.\n\n \"\"\"\n\n try:\n dir_path = os.path.dirname(file_path)\n\n os.makedirs(dir_path, exist_ok=True)\n\n with open(file_path, 'wb') as f:\n dill.dump(obj, f)\n logger.info(f'Objeto guardado exitosamente en {file_path}')\n \n except Exception as e:\n logger.error(f'Ocurrió un error al guardar el objeto: {e}', exc_info=True)\n raise CustomException(f'Error al guardar el objeto en {file_path}', e, sys.exc_info()[2])\n \ndef load_object(file_path: Path) -> object:\n \"\"\"Carga un objeto desde un archivo .pkl.\n\n Parámetros:\n - file_path: Ruta del archivo.\n\n Retorna:\n - Objeto cargado desde el archivo.\n \"\"\"\n try:\n with open(file_path, 'rb') as f:\n obj = dill.load(f)\n logger.info(f'Objeto cargado exitosamente desde {file_path}')\n return obj\n except Exception as e:\n logger.error(f'Ocurrió un error al cargar el objeto: {e}', exc_info=True)\n raise CustomException(f'Error al cargar el objeto desde {file_path}', e, sys.exc_info()[2])\n\n\ndef get_random_params(model_name: str) -> list:\n \"\"\"Obtiene una lista de parámetros aleatorios para el pipeline dado.\n\n Parámetros:\n - model_name: Nombre del modelo.\n \n Retorna:\n - Lista de parámetros aleatorios.\n \"\"\"\n if model_name == 'DecisionTree':\n return [\n {\n \"smote__sampling_strategy\": [float(x) for x in np.arange(0.4, 1.0, 0.1)],\n \"smote__k_neighbors\": [2, 3, 4, 5, 6],\n \"selectfrommodel__max_features\": [2, 3, 4, 5, 6],\n \"decisiontreeclassifier__criterion\": ['gini', 'entropy', 'log_loss'],\n \"decisiontreeclassifier__max_depth\": [int(x) for x in np.arange(1, 7)],\n \"decisiontreeclassifier__min_samples_split\": [float(x) for x in np.arange(0.05, 0.3, 0.05)],\n \"decisiontreeclassifier__splitter\": ['best', 'random'],\n \"decisiontreeclassifier__min_samples_leaf\": [1, 5, 10, 15, 20],\n \"decisiontreeclassifier__class_weight\": [None, 'balanced']\n\n }\n ]\n elif model_name == 'RandomForest':\n return [\n {\n \"smote__sampling_strategy\": [float(x) for x in np.arange(0.5, 1.1, 0.1)],\n \"smote__k_neighbors\": [3, 4, 5, 6],\n \"randomforestclassifier__n_estimators\": [int(x) for x in np.linspace(100, 500, 10)],\n \"randomforestclassifier__criterion\": ['gini', 'entropy', 'log_loss'],\n \"randomforestclassifier__min_samples_split\": [float(x) for x in np.arange(0.05, 0.3, 0.05)],\n \"randomforestclassifier__min_samples_leaf\": [1, 5, 10, 15, 20],\n \"randomforestclassifier__max_features\": ['sqrt', 'log2'],\n \"randomforestclassifier__class_weight\": [None, 'balanced', 'balanced_subsample'],\n \"randomforestclassifier__bootstrap\": [True],\n \"randomforestclassifier__oob_score\": [True],\n \"randomforestclassifier__warm_start\": [True, False],\n \"randomforestclassifier__max_samples\": [0.5, 0.8, 1.0],\n \"randomforestclassifier__max_depth\": [int(x) for x in np.arange(1, 7)]\n }\n ]\n else:\n raise ValueError(f\"{model_name} : Modelo no sujeto a optimización.\")\n\n\ndef get_params_grid(puntuaciones, model_name: str) -> list:\n \"\"\"Obtiene una lista de parámetros para el pipeline dado.\n\n Parámetros:\n - model_name: Nombre del modelo.\n\n Retorna:\n - Lista de parámetros.\n \"\"\"\n \n\n if model_name == 'DecisionTree':\n return [\n {\n \"smote__sampling_strategy\": [puntuaciones.best_params_[\"smote__sampling_strategy\"] - 0.1, \n puntuaciones.best_params_[\"smote__sampling_strategy\"],\n puntuaciones.best_params_[\"smote__sampling_strategy\"] + 0.1],\n \"smote__k_neighbors\": [puntuaciones.best_params_[\"smote__k_neighbors\"]],\n \"selectfrommodel__max_features\": [puntuaciones.best_params_['selectfrommodel__max_features']],\n \"decisiontreeclassifier__criterion\": ['entropy', 'gini', 'log_loss'],\n \"decisiontreeclassifier__max_depth\": [puntuaciones.best_params_[\"decisiontreeclassifier__max_depth\"] - 1,\n puntuaciones.best_params_[\"decisiontreeclassifier__max_depth\"],\n puntuaciones.best_params_[\"decisiontreeclassifier__max_depth\"] + 1],\n \"decisiontreeclassifier__min_samples_split\": [puntuaciones.best_params_[\"decisiontreeclassifier__min_samples_split\"] - 0.05,\n puntuaciones.best_params_[\"decisiontreeclassifier__min_samples_split\"],\n puntuaciones.best_params_[\"decisiontreeclassifier__min_samples_split\"] + 0.05],\n \"decisiontreeclassifier__splitter\": [puntuaciones.best_params_[\"decisiontreeclassifier__splitter\"]],\n \"decisiontreeclassifier__min_samples_leaf\": [puntuaciones.best_params_[\"decisiontreeclassifier__min_samples_leaf\"]],\n \"decisiontreeclassifier__class_weight\": [puntuaciones.best_params_[\"decisiontreeclassifier__class_weight\"]]\n }\n ]\n elif model_name == 'RandomForest':\n return [\n {\n \"smote__sampling_strategy\" : [puntuaciones.best_params_[\"smote__sampling_strategy\"] - 0.1 , puntuaciones.best_params_[\"smote__sampling_strategy\"] , puntuaciones.best_params_[\"smote__sampling_strategy\"] +0.1],\n \"smote__k_neighbors\" : [puntuaciones.best_params_[\"smote__k_neighbors\"]],\n \"randomforestclassifier__n_estimators\": [puntuaciones.best_params_[\"randomforestclassifier__n_estimators\"]],\n \"randomforestclassifier__criterion\": ['gini', 'entropy', 'log_loss'],\n \"randomforestclassifier__min_samples_split\" : [puntuaciones.best_params_['randomforestclassifier__min_samples_split']],\n \"randomforestclassifier__min_samples_leaf\" : [puntuaciones.best_params_['randomforestclassifier__min_samples_leaf']-5,puntuaciones.best_params_['randomforestclassifier__min_samples_leaf'],puntuaciones.best_params_['randomforestclassifier__min_samples_leaf']+5 ],\n \"randomforestclassifier__max_features\" : [puntuaciones.best_params_['randomforestclassifier__max_features']],\n \"randomforestclassifier__class_weight\": [puntuaciones.best_params_['randomforestclassifier__class_weight']],\n \"randomforestclassifier__bootstrap\": [True],\n \"randomforestclassifier__oob_score\": [True],\n \"randomforestclassifier__warm_start\": [puntuaciones.best_params_['randomforestclassifier__warm_start']],\n \"randomforestclassifier__max_samples\": [puntuaciones.best_params_['randomforestclassifier__max_samples']-0.1,puntuaciones.best_params_['randomforestclassifier__max_samples'],puntuaciones.best_params_['randomforestclassifier__max_samples']+0.1],\n \"randomforestclassifier__max_depth\": [puntuaciones.best_params_['randomforestclassifier__max_depth']-1,puntuaciones.best_params_['randomforestclassifier__max_depth'],puntuaciones.best_params_['randomforestclassifier__max_depth']+1]\n }\n ]\n else:\n raise ValueError(f\"{model_name} : Modelo no sujeto a optimización.\")","repo_name":"DavidPalazon/Implementacion-en-python-de-metodos-de-ayuda-al-diagnostico-precoz-de-glaucoma","sub_path":"src/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":7847,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"72238644635","text":"#\n# ABOUT\n# Artisan Profile Transposer\n\n# LICENSE\n# This program or module is free software: you can redistribute it and/or\n# modify it under the terms of the GNU General Public License as published\n# by the Free Software Foundation, either version 2 of the License, or\n# version 3 of the License, or (at your option) any later version. It is\n# provided for educational purposes and is distributed in the hope that\n# it will be useful, but WITHOUT ANY WARRANTY; without even the implied\n# warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See\n# the GNU General Public License for more details.\n\n# AUTHOR\n# Marko Luther, 2023\n\nimport time as libtime\nimport warnings\nimport copy\nimport numpy\nfrom typing import List, Tuple, Callable, Optional, TYPE_CHECKING\n\nif TYPE_CHECKING:\n from artisanlib.main import ApplicationWindow # noqa: F401 # pylint: disable=unused-import\n from PyQt6.QtWidgets import QWidget # noqa: F401 # pylint: disable=unused-import\n import numpy.typing as npt # pylint: disable=unused-import\n\nfrom artisanlib.dialogs import ArtisanDialog\nfrom artisanlib.util import stringfromseconds, stringtoseconds\n\n\ntry:\n from PyQt6.QtCore import Qt, pyqtSlot, QSettings, QRegularExpression, QDateTime # @UnusedImport @Reimport @UnresolvedImport\n from PyQt6.QtGui import QRegularExpressionValidator # @UnusedImport @Reimport @UnresolvedImport\n from PyQt6.QtWidgets import (QApplication, QHeaderView, QAbstractItemView, QWidget, QLabel, QLineEdit, QComboBox, QDialogButtonBox, # @UnusedImport @Reimport @UnresolvedImport\n QTableWidget, QTableWidgetItem, QGroupBox, QLayout, QHBoxLayout, QVBoxLayout, QFrame) # @UnusedImport @Reimport @UnresolvedImport\nexcept ImportError:\n from PyQt5.QtCore import Qt, pyqtSlot, QSettings, QRegularExpression, QDateTime # type: ignore # @UnusedImport @Reimport @UnresolvedImport\n from PyQt5.QtGui import QRegularExpressionValidator # type: ignore # @UnusedImport @Reimport @UnresolvedImport\n from PyQt5.QtWidgets import (QApplication, QHeaderView, QAbstractItemView, QWidget, QLabel, QLineEdit, QComboBox, QDialogButtonBox, # type: ignore # @UnusedImport @Reimport @UnresolvedImport\n QTableWidget, QTableWidgetItem, QGroupBox, QLayout, QHBoxLayout, QVBoxLayout, QFrame) # type: ignore # @UnusedImport @Reimport @UnresolvedImport\n\n\nclass MyQRegularExpressionValidator(QRegularExpressionValidator): # pyright: ignore [reportGeneralTypeIssues] # Argument to class must be a base class\n # we fix partial time input like '12' => '12:00', '12:' => '12:00' and '12:0' => '12:00'\n\n @staticmethod\n def fixup(value):\n if ':' not in value:\n return value + ':00'\n if value.endswith(':'):\n return value + '00'\n if len(value[value.index(':')+1:]) == 1:\n return value + '0'\n return value\n\nclass profileTransformatorDlg(ArtisanDialog):\n def __init__(self, parent:'QWidget', aw:'ApplicationWindow') -> None:\n super().__init__(parent, aw)\n self.setModal(True)\n self.setWindowTitle(QApplication.translate('Form Caption','Profile Transposer'))\n\n self.helpdialog = None\n\n self.regexpercent = QRegularExpression(r'^$|[0-9]?[0-9]?(\\.[0-9])?')\n self.regextime = QRegularExpression(r'^$|[0-9]?[0-9]:[0-5][0-9]')\n self.regextemp = QRegularExpression(r'^$|[0-9]?[0-9]?[0-9]?(\\.[0-9])?')\n\n # original data\n self.org_transMappingMode = self.aw.qmc.transMappingMode\n self.org_timex = self.aw.qmc.timex[:]\n self.org_temp2 = self.aw.qmc.temp2[:]\n self.org_extratimex = copy.deepcopy(self.aw.qmc.extratimex)\n self.org_curFile = self.aw.curFile\n self.org_UUID = self.aw.qmc.roastUUID\n self.org_roastdate = self.aw.qmc.roastdate\n self.org_roastepoch = self.aw.qmc.roastepoch\n self.org_roasttzoffset = self.aw.qmc.roasttzoffset\n self.org_roastbatchnr = self.aw.qmc.roastbatchnr\n self.org_safesaveflag = self.aw.qmc.safesaveflag\n self.org_l_event_flags_dict = self.aw.qmc.l_event_flags_dict\n self.org_l_annotations_dict = self.aw.qmc.l_annotations_dict\n\n self.phasestable = QTableWidget()\n self.timetable = QTableWidget()\n self.temptable = QTableWidget()\n\n # time table widgets initialized by createTimeTable() to a list (target/result) with 4 widgets each\n # DRY, FCs, SCs, DROP\n # if an event is not set in the profile, None is set instead of a widget\n #\n self.phases_target_widgets_time:Optional[List[Optional[QLineEdit]]] = None\n self.phases_target_widgets_percent:Optional[List[Optional[QLineEdit]]] = None\n self.phases_result_widgets:Optional[List[Optional[QTableWidgetItem]]] = None\n #\n self.time_target_widgets:Optional[List[Optional[QLineEdit]]] = None\n self.time_result_widgets:Optional[List[Optional[QTableWidgetItem]]] = None\n\n # profileTimes: list of DRY, FCs, SCs and DROP times in seconds if event is set, otherwise None\n self.profileTimes:List[Optional[float]] = self.getProfileTimes()\n # list of DRY, FCs, SCs, and DROP target times in seconds as specified by the user, or None if not set\n self.targetTimes:List[Optional[float]] = self.getTargetTimes()\n\n # temp table widgets initialized by createTempTable() to a list (target/result) with 5 widgets each\n # CHARGE, DRY, FCs, SCs, DROP\n # if an event is not set in the profile, None is set instead of a widget\n self.temp_target_widgets:Optional[List[Optional[QLineEdit]]] = None\n self.temp_result_widgets:Optional[List[Optional[QTableWidgetItem]]] = None\n\n # list of CHARGE, DRY, FCs, SCs and DROP BT temperatures\n self.profileTemps = self.getProfileTemps()\n # list of DRY, FCs, SCs, and DROP target temperatures as specified by the user, or None if not set\n self.targetTemps = self.getTargetTemps()\n\n self.createPhasesTable()\n self.createTimeTable()\n self.createTempTable()\n\n # connect the ArtisanDialog standard OK/Cancel buttons\n self.dialogbuttons.accepted.connect(self.applyTransformations)\n self.dialogbuttons.rejected.connect(self.restoreState)\n self.applyButton = self.dialogbuttons.addButton(QDialogButtonBox.StandardButton.Apply)\n self.resetButton = self.dialogbuttons.addButton(QDialogButtonBox.StandardButton.Reset)\n self.helpButton = self.dialogbuttons.addButton(QDialogButtonBox.StandardButton.Help)\n if self.applyButton is not None:\n self.applyButton.clicked.connect(self.apply)\n self.setButtonTranslations(self.applyButton,'Apply',QApplication.translate('Button','Apply'))\n if self.resetButton is not None:\n self.resetButton.clicked.connect(self.restore)\n self.setButtonTranslations(self.resetButton,'Reset',QApplication.translate('Button','Reset'))\n if self.helpButton is not None:\n self.helpButton.clicked.connect(self.openHelp)\n self.setButtonTranslations(self.helpButton,'Help',QApplication.translate('Button','Help'))\n\n #buttons\n buttonsLayout = QHBoxLayout()\n buttonsLayout.addWidget(self.dialogbuttons)\n\n mappingLabel = QLabel(QApplication.translate('Label','Mapping'))\n self.mappingModeComboBox = QComboBox()\n self.mappingModeComboBox.addItems([QApplication.translate('ComboBox','discrete'),\n QApplication.translate('ComboBox','linear'),\n QApplication.translate('ComboBox','quadratic')])\n self.mappingModeComboBox.setCurrentIndex(self.aw.qmc.transMappingMode)\n self.mappingModeComboBox.currentIndexChanged.connect(self.changeMappingMode)\n\n self.temp_formula:QLabel = QLabel()\n self.temp_formula.setTextInteractionFlags(Qt.TextInteractionFlag.TextSelectableByMouse)\n\n settingsHLayout = QHBoxLayout()\n settingsHLayout.addStretch()\n settingsHLayout.addWidget(mappingLabel)\n settingsHLayout.addWidget(self.mappingModeComboBox)\n settingsHLayout.addStretch()\n\n phasesHLayout = QHBoxLayout()\n phasesHLayout.addStretch()\n phasesHLayout.addWidget(self.phasestable)\n phasesHLayout.addStretch()\n phasesLayout = QVBoxLayout()\n phasesLayout.addLayout(phasesHLayout)\n\n timeHLayout = QHBoxLayout()\n timeHLayout.addStretch()\n timeHLayout.addWidget(self.timetable)\n timeHLayout.addStretch()\n timeLayout = QVBoxLayout()\n timeLayout.addLayout(timeHLayout)\n timeLayout.addStretch()\n\n tempHLayout = QHBoxLayout()\n tempHLayout.addWidget(self.temptable)\n tempHLayout.addStretch()\n formulaHLayout = QHBoxLayout()\n formulaHLayout.addStretch()\n formulaHLayout.addWidget(self.temp_formula)\n formulaHLayout.addStretch()\n tempLayout = QVBoxLayout()\n tempLayout.addLayout(tempHLayout)\n tempLayout.addLayout(formulaHLayout)\n tempLayout.addStretch()\n\n phasesGroupLayout = QGroupBox(QApplication.translate('Table','Phases'))\n phasesGroupLayout.setLayout(phasesLayout)\n timeGroupLayout = QGroupBox(QApplication.translate('Table','Time'))\n timeGroupLayout.setLayout(timeLayout)\n tempGroupLayout = QGroupBox(QApplication.translate('Table','BT'))\n tempGroupLayout.setLayout(tempLayout)\n\n #main\n mainlayout = QVBoxLayout()\n mainlayout.addLayout(settingsHLayout)\n mainlayout.addWidget(phasesGroupLayout)\n mainlayout.addWidget(timeGroupLayout)\n mainlayout.addWidget(tempGroupLayout)\n mainlayout.addStretch()\n mainlayout.addLayout(buttonsLayout)\n\n self.setLayout(mainlayout)\n ok_button = self.dialogbuttons.button(QDialogButtonBox.StandardButton.Ok)\n if ok_button is not None:\n ok_button.setFocus()\n\n settings = QSettings()\n if settings.contains('TransformatorPosition'):\n self.move(settings.value('TransformatorPosition'))\n\n mainlayout.setSizeConstraint(QLayout.SizeConstraint.SetFixedSize)\n\n\n # utility functions\n\n def forgroundOffset(self) -> float:\n if self.aw.qmc.timeindex[0] == -1:\n return 0\n return self.org_timex[self.aw.qmc.timeindex[0]]\n\n def backgroundOffset(self):\n if self.aw.qmc.timeindexB[0] != -1 and len(self.aw.qmc.timeB) > self.aw.qmc.timeindexB[0]:\n return self.aw.qmc.timeB[self.aw.qmc.timeindexB[0]]\n return 0\n\n def clearPhasesTargetTimes(self):\n if self.phases_target_widgets_time is not None and len(self.phases_target_widgets_time)>2:\n for i in range(3):\n phases_target_widgets_time = self.phases_target_widgets_time[i]\n if phases_target_widgets_time is not None:\n phases_target_widgets_time.setText('')\n\n def clearPhasesTargetPercent(self):\n if self.phases_target_widgets_percent is not None and len(self.phases_target_widgets_percent)>2:\n for i in range(3):\n phases_target_widgets_percent = self.phases_target_widgets_percent[i]\n if phases_target_widgets_percent is not None:\n phases_target_widgets_percent.setText('')\n\n def clearPhasesResults(self):\n if self.phases_result_widgets is not None and len(self.phases_result_widgets)>2:\n for i in range(3):\n phases_result_widgets = self.phases_result_widgets[i]\n if phases_result_widgets is not None:\n phases_result_widgets.setText('')\n\n def clearTimeTargets(self):\n if self.time_target_widgets is not None and len(self.time_target_widgets)>3:\n for i in range(4):\n time_target_widgets = self.time_target_widgets[i]\n if time_target_widgets is not None:\n time_target_widgets.setText('')\n\n def clearTimeResults(self):\n if self.time_result_widgets is not None and len(self.time_result_widgets)>3:\n for i in range(4):\n time_result_widgets = self.time_result_widgets[i]\n if time_result_widgets is not None:\n time_result_widgets.setText('')\n\n def clearTempTargets(self):\n if self.temp_target_widgets is not None and len(self.temp_target_widgets)>4:\n for i in range(5):\n temp_target_widget:Optional[QLineEdit] = self.temp_target_widgets[i]\n if temp_target_widget is not None:\n temp_target_widget.setText('')\n\n def clearTempResults(self):\n if self.temp_result_widgets is not None and len(self.temp_result_widgets)>4:\n for i in range(5):\n temp_result_widget:Optional[QTableWidgetItem] = self.temp_result_widgets[i]\n if temp_result_widget is not None:\n temp_result_widget.setText('')\n self.temp_formula.setText('')\n self.temp_formula.repaint()\n\n # returns list of DRY, FCs, SCs and DROP profile times in seconds if event is set, otherwise None\n def getProfileTimes(self) -> List[Optional[float]]:\n offset = self.forgroundOffset()\n res:List[Optional[float]] = []\n for i in [1,2,4,6]:\n idx = self.aw.qmc.timeindex[i]\n if idx == 0 or len(self.aw.qmc.timex) < idx:\n res.append(None)\n else:\n res.append(self.aw.qmc.timex[idx] - offset)\n return res\n\n # returns list of CHARGE, DRY, FCs, SCs and DROP BT temperatures if event is set, otherwise None\n def getProfileTemps(self) -> List[Optional[float]]:\n res:List[Optional[float]] = []\n for i in [0,1,2,4,6]:\n idx = self.aw.qmc.timeindex[i]\n if (i == 0 and idx == -1) or (i != 0 and idx == 0) or len(self.aw.qmc.timex) < idx:\n res.append(None)\n elif len(self.aw.qmc.temp2) > idx:\n res.append(self.aw.qmc.temp2[idx])\n else:\n res.append(None)\n return res\n\n # returns list of DRYING, MAILARD, FINISHING target phases times in seconds as first result and phases percentages (float) as second result\n # if a phase is set not set None is returned instead of a value\n def getTargetPhases(self) -> Tuple[List[Optional[int]], List[Optional[float]]]:\n res_times:List[Optional[int]] = []\n res_phases:List[Optional[float]] = []\n if self.phases_target_widgets_time is not None:\n for w in self.phases_target_widgets_time:\n ri:Optional[int] = None\n if w is not None:\n txt = w.text()\n if txt is not None and txt != '':\n ri = stringtoseconds(txt)\n res_times.append(ri)\n if self.phases_target_widgets_percent is not None:\n for w in self.phases_target_widgets_percent:\n rf:Optional[float] = None\n if w is not None:\n txt = w.text()\n if txt is not None and txt != '':\n rf = float(txt)\n res_phases.append(rf)\n return res_times, res_phases\n\n # returns list of DRY, FCs, SCs and DROP target times in seconds if event is set, otherwise None\n def getTargetTimes(self) -> List[Optional[float]]:\n res:List[Optional[float]] = []\n if self.time_target_widgets is not None:\n for w in self.time_target_widgets:\n r = None\n if w is not None:\n txt = w.text()\n if txt is not None and txt != '':\n r = stringtoseconds(txt)\n res.append(r)\n return res\n\n # returns list of CHARGE, DRY, FCs, SCs and DROP BT temperatures if event is set, otherwise None\n def getTargetTemps(self):\n res = []\n if self.temp_target_widgets is not None:\n for w in self.temp_target_widgets:\n r = None\n if w is not None:\n txt = w.text()\n if txt is not None and txt != '':\n r = float(txt)\n res.append(r)\n return res\n\n\n # message slots\n\n @pyqtSlot(int)\n def changeMappingMode(self,i):\n self.aw.qmc.transMappingMode = i\n self.updateTimeResults()\n self.updateTempResults()\n\n @pyqtSlot(int)\n def phasesTableColumnHeaderClicked(self,i):\n if (self.phases_target_widgets_time is not None and\n self.phases_target_widgets_time[i] is not None and\n self.phases_target_widgets_percent is not None and\n self.phases_target_widgets_percent[i] is not None):\n # clear target value i\n if self.phases_target_widgets_time[i].text() != '' or self.phases_target_widgets_percent[i].text() != '':\n self.phases_target_widgets_time[i].setText('')\n self.phases_target_widgets_percent[i].setText('')\n elif self.aw.qmc.backgroundprofile is not None and self.aw.qmc.timeindexB[1]>0 and self.aw.qmc.timeindexB[2]>0 and self.aw.qmc.timeindexB[6]>0 and \\\n self.aw.qmc.timeindex[1]>0 and self.aw.qmc.timeindex[2]>0 and self.aw.qmc.timeindex[6]>0:\n back_offset = self.backgroundOffset()\n back_dry = self.aw.qmc.timeB[self.aw.qmc.timeindexB[1]]\n back_fcs = self.aw.qmc.timeB[self.aw.qmc.timeindexB[2]]\n back_drop = self.aw.qmc.timeB[self.aw.qmc.timeindexB[6]]\n s:str = ''\n if i == 0:\n # DRYING\n s = stringfromseconds(back_dry - back_offset)\n elif i == 1:\n # MAILARD\n s = stringfromseconds(back_fcs - back_dry)\n elif i == 2:\n s = stringfromseconds(back_drop - back_fcs)\n self.phases_target_widgets_time[i].setText(s)\n self.updateTimeResults()\n\n @pyqtSlot(int)\n def phasesTableRowHeaderClicked(self,i):\n if i == 1: # row targets\n # clear all targets and results\n # clear all targets\n self.clearPhasesTargetTimes()\n self.clearPhasesTargetPercent()\n self.clearPhasesResults()\n\n @pyqtSlot(int)\n def timeTableColumnHeaderClicked(self,i):\n if self.time_target_widgets is not None and self.time_target_widgets[i] is not None:\n # clear target value i\n if self.time_target_widgets[i].text() != '':\n self.time_target_widgets[i].setText('')\n self.updateTimeResults()\n elif self.aw.qmc.backgroundprofile is not None:\n timeidx = [1,2,4,6][i]\n if self.aw.qmc.timeindex[timeidx] and self.aw.qmc.timeindexB[timeidx]:\n s = stringfromseconds(self.aw.qmc.timeB[self.aw.qmc.timeindexB[timeidx]]-self.backgroundOffset(),False)\n self.time_target_widgets[i].setText(s)\n self.updateTimeResults()\n\n @pyqtSlot(int)\n def timeTableRowHeaderClicked(self,i):\n if i == 1: # row targets\n self.clearTimeTargets()\n self.clearTimeResults()\n\n @pyqtSlot(int)\n def tempTableColumnHeaderClicked(self,i):\n if self.temp_target_widgets is not None and self.temp_target_widgets[i] is not None:\n # clear target value i\n if self.temp_target_widgets[i].text() != '':\n self.temp_target_widgets[i].setText('')\n self.updateTempResults()\n elif self.aw.qmc.backgroundprofile is not None:\n timeidx = [0,1,2,4,6][i]\n if self.aw.qmc.timeindexB[timeidx] > 0:\n self.temp_target_widgets[i].setText(str(self.aw.float2float(self.aw.qmc.temp2B[self.aw.qmc.timeindexB[timeidx]])))\n self.updateTempResults()\n\n @pyqtSlot(int)\n def tempTableRowHeaderClicked(self,i):\n if i == 1: # row targets\n self.clearTempTargets()\n self.clearTempResults()\n\n @pyqtSlot()\n def updatePhasesWidget(self):\n self.clearTimeTargets()\n if self.phases_target_widgets_time is not None and self.phases_target_widgets_percent is not None:\n sender = self.sender()\n assert isinstance(sender, QLineEdit)\n # clear corresponding time target if percentage target is set, or the otherway around\n if sender.text() != '':\n try:\n time_idx = self.phases_target_widgets_time.index(sender)\n phases_target_widgets_percent = self.phases_target_widgets_percent[time_idx]\n if phases_target_widgets_percent is not None:\n phases_target_widgets_percent.setText('')\n except Exception: # pylint: disable=broad-except\n pass\n try:\n percent_idx = self.phases_target_widgets_percent.index(sender)\n phases_target_widgets_time = self.phases_target_widgets_time[percent_idx]\n if phases_target_widgets_time is not None:\n phases_target_widgets_time.setText('')\n except Exception: # pylint: disable=broad-except\n pass\n self.updateTimeResults()\n\n @pyqtSlot()\n def updateTimesWidget(self):\n self.clearPhasesTargetTimes()\n self.clearPhasesTargetPercent()\n self.updateTimeResults()\n\n # updates time and phases result widgets\n def updateTimeResults(self):\n self.targetTimes = self.getTargetTimes()\n time_targets_clear = all(v is None for v in self.targetTimes)\n target_times, target_phases = self.getTargetPhases()\n phases_targets_clear = all(v is None for v in target_times + target_phases)\n self.clearPhasesResults()\n self.clearTimeResults()\n if not (phases_targets_clear and time_targets_clear):\n # phases targets are set, first clear the time targets\n if not phases_targets_clear:\n self.targetTimes = self.getTargetPhasesTimes()\n else:\n self.targetTimes = self.getTargetTimes()\n # set new time results\n result_times = self.calcTimeResults()\n if self.time_result_widgets is not None:\n for i in range(4):\n time_result_widget = self.time_result_widgets[i]\n if time_result_widget is not None:\n if result_times[i] is None:\n s = ''\n else:\n s = stringfromseconds(result_times[i],leadingzero=False)\n time_result_widget.setText(s)\n # set new phases results\n if self.phases_result_widgets is not None:\n result_times = self.calcTimeResults()\n if all(result_times[r] is not None for r in [0,1,3]):\n # DRYING\n drying_period = result_times[0]\n drying_percentage = 100 * drying_period / result_times[3]\n drying_str = \\\n f'{stringfromseconds(drying_period,leadingzero=False)} {self.aw.float2float(drying_percentage)}%'\n phases_result_widgets = self.phases_result_widgets[0]\n if phases_result_widgets is not None:\n phases_result_widgets.setText(drying_str)\n # MAILARD\n mailard_period = result_times[1] - result_times[0]\n mailard_percentage = 100 * mailard_period / result_times[3]\n mailard_str = \\\n f'{stringfromseconds(mailard_period,leadingzero=False)} {self.aw.float2float(mailard_percentage)}%'\n phases_result_widgets= self.phases_result_widgets[1]\n if phases_result_widgets is not None:\n phases_result_widgets.setText(mailard_str)\n # FINISHING\n finishing_period = result_times[3] - result_times[1]\n finishing_percentage = 100 * finishing_period / result_times[3]\n finishing_str = \\\n f'{stringfromseconds(finishing_period,leadingzero=False)} {self.aw.float2float(finishing_percentage)}%'\n phases_result_widgets = self.phases_result_widgets[2]\n if phases_result_widgets is not None:\n phases_result_widgets.setText(finishing_str)\n else:\n for w in self.phases_result_widgets:\n if w is not None:\n w.setText('')\n\n @pyqtSlot()\n def updateTempResults(self):\n self.targetTemps = self.getTargetTemps()\n if all(v is None for v in self.targetTemps):\n # clear all results if no targets are set\n self.clearTempResults()\n # set new results\n elif self.temp_result_widgets is not None and len(self.temp_result_widgets)>4:\n result_temps,fit = self.calcTempResults()\n for i in range(5):\n temp_result_widget:Optional[QTableWidgetItem] = self.temp_result_widgets[i]\n result_temp = result_temps[i]\n if temp_result_widget is not None and result_temp is not None:\n temp_result_widget.setText(str(self.aw.float2float(result_temp)) + self.aw.qmc.mode)\n s = ''\n if fit is not None:\n s = fit\n self.temp_formula.setText(s)\n self.temp_formula.repaint()\n\n #called from Apply button\n @pyqtSlot(bool)\n def apply(self,_=False):\n applied_time = self.applyTimeTransformation()\n applied_temp = self.applyTempTransformation()\n if applied_time or applied_temp:\n self.aw.qmc.roastUUID = None\n self.aw.qmc.roastdate = QDateTime.currentDateTime()\n self.aw.qmc.roastepoch = self.aw.qmc.roastdate.toSecsSinceEpoch()\n self.aw.qmc.roasttzoffset = libtime.timezone\n self.aw.qmc.roastbatchnr = 0\n self.aw.setCurrentFile(None,addToRecent=False)\n self.aw.qmc.l_event_flags_dict = {}\n self.aw.qmc.l_annotations_dict = {}\n self.aw.qmc.fileDirty()\n self.aw.qmc.timealign()\n self.aw.autoAdjustAxis()\n self.aw.qmc.redraw()\n else:\n self.restore()\n\n #called from Restore button\n @pyqtSlot(bool)\n def restore(self,_=False):\n self.aw.setCurrentFile(self.org_curFile,addToRecent=False)\n self.aw.qmc.roastUUID = self.org_UUID\n self.aw.qmc.roastdate = self.org_roastdate\n self.aw.qmc.roastepoch = self.org_roastepoch\n self.aw.qmc.roasttzoffset = self.org_roasttzoffset\n self.aw.qmc.roastbatchnr = self.org_roastbatchnr\n if self.org_safesaveflag:\n self.aw.qmc.fileDirty()\n else:\n self.aw.qmc.fileClean()\n self.aw.qmc.l_event_flags_dict = self.org_l_event_flags_dict\n self.aw.qmc.l_annotations_dict = self.org_l_annotations_dict\n self.aw.qmc.timex = self.org_timex[:]\n self.aw.qmc.extratimex = copy.deepcopy(self.org_extratimex)\n self.aw.qmc.temp2 = self.org_temp2[:]\n self.aw.autoAdjustAxis()\n self.aw.qmc.redraw()\n\n #called from OK button\n @pyqtSlot()\n def applyTransformations(self):\n self.apply()\n #save window position (only; not size!)\n settings = QSettings()\n settings.setValue('TransformatorPosition',self.frameGeometry().topLeft())\n self.accept()\n\n #called from Cancel button\n @pyqtSlot()\n def restoreState(self):\n self.restore()\n self.aw.qmc.transMappingMode = self.org_transMappingMode\n #save window position (only; not size!)\n settings = QSettings()\n settings.setValue('TransformatorPosition',self.geometry().topLeft())\n self.closeHelp()\n self.reject()\n\n @pyqtSlot(bool)\n def openHelp(self,_=False):\n from help import transposer_help # type: ignore [attr-defined] # pylint: disable=no-name-in-module\n self.helpdialog = self.aw.showHelpDialog(\n self, # this dialog as parent\n self.helpdialog, # the existing help dialog\n QApplication.translate('Form Caption','Profile Transposer Help'),\n transposer_help.content())\n\n def closeHelp(self):\n self.aw.closeHelpDialog(self.helpdialog)\n\n def closeEvent(self, _):\n self.restoreState()\n\n\n # Calculations\n\n # returns the list of results times in seconds\n def calcTimeResults(self):\n res = []\n profileTime:Optional[float]\n if self.aw.qmc.transMappingMode == 0:\n # discrete mapping\n # adding CHARGE\n fits:List[Optional['npt.NDArray[numpy.float64]']] = self.calcDiscretefits([0] + self.profileTimes,[0] + self.targetTimes)\n if len(fits)>4 and len(self.profileTimes)>3:\n for i in range(4):\n fit:Optional['npt.NDArray[numpy.float64]'] = fits[i+1]\n profileTime = self.profileTimes[i]\n if fit is not None and profileTime is not None:\n res.append(numpy.poly1d(fit)(profileTime))\n else:\n res.append(None)\n else:\n with warnings.catch_warnings():\n warnings.filterwarnings('error')\n try:\n fit_fuc = self.calcTimePolyfit() # note that this fit is already applied to numpy.polyfit !!\n for i in range(4):\n profileTime = self.profileTimes[i]\n if fit_fuc is not None and profileTime is not None:\n res.append(fit_fuc(profileTime))\n else:\n res.append(None)\n except numpy.RankWarning:\n pass\n except Exception: # pylint: disable=broad-except\n pass\n return res\n\n # returns the list of results temperatures and the polyfit or None as second result\n def calcTempResults(self) -> Tuple[List[Optional[float]], Optional[str]]:\n res:List[Optional[float]] = []\n fit:Optional['npt.NDArray[numpy.float64]'] = None\n fit_str:Optional[str] = None\n profileTemp:Optional[float]\n if self.aw.qmc.transMappingMode == 0:\n # discrete mapping\n fits = self.calcDiscretefits(self.profileTemps,self.targetTemps)\n for i in range(5):\n fit = fits[i]\n profileTemp = self.profileTemps[i]\n if profileTemp is not None and fit is not None:\n res.append(numpy.poly1d(fit)(profileTemp))\n else:\n res.append(None)\n active_fits = list(filter(lambda x: x[1][1] is not None,zip(fits,zip(self.profileTemps,self.targetTemps))))\n if len(active_fits) > 0 and len(active_fits) < 3:\n fit_str = self.aw.fit2str(fits[0])\n else:\n formula = ''\n last_target:Optional[Tuple] = None\n for f,tpl in reversed(active_fits[:-1]):\n if last_target is None:\n formula = self.aw.fit2str(f)\n else:\n formula = f'({self.aw.fit2str(f)} if x<{last_target} else {formula})'\n last_target = tpl[1]\n fit_str = formula\n else:\n with warnings.catch_warnings():\n warnings.filterwarnings('error')\n try:\n fit_func = self.calcTempPolyfit() # numpy.poly1d not yet applied to this fit\n if fit_func is not None:\n p = numpy.poly1d(fit_func)\n for i in range(5):\n profileTemp = self.profileTemps[i]\n if profileTemp is not None:\n res.append(p(profileTemp))\n else:\n res.append(None)\n fit_str = self.aw.fit2str(fit_func)\n else:\n res = [None]*5\n except numpy.RankWarning:\n pass\n except Exception: # pylint: disable=broad-except\n pass\n return res,fit_str\n\n # returns target times based on the phases target\n def getTargetPhasesTimes(self):\n # get the offset\n offset:float = self.forgroundOffset()\n # get profile phases events time\n dry:float = self.aw.qmc.timex[self.aw.qmc.timeindex[1]] - offset\n fcs:float = self.aw.qmc.timex[self.aw.qmc.timeindex[2]] - offset\n drop:float = self.aw.qmc.timex[self.aw.qmc.timeindex[6]] - offset\n # flags for targets set\n dry_set:bool = False\n drop_set:bool = False\n fcs_set:bool = False\n\n if self.phases_target_widgets_time is None or self.phases_target_widgets_percent is None:\n return []\n\n # first determine the target DROP time (relative to the profile drop) if any\n drop_phases_target_widget_time = self.phases_target_widgets_time[2]\n drop_phases_target_widget_percent = self.phases_target_widgets_percent[2]\n if drop_phases_target_widget_time is not None and drop_phases_target_widget_time.text() != '':\n drop = fcs + stringtoseconds(drop_phases_target_widget_time.text())\n drop_set = True\n elif drop_phases_target_widget_percent is not None and drop_phases_target_widget_percent.text() != '':\n drop = fcs + (float(drop_phases_target_widget_percent.text()) * drop / 100)\n drop_set = True\n\n # determine the target DRY time (relative to the target drop of above) if any\n dry_phases_target_widgets_time = self.phases_target_widgets_time[0]\n dry_phases_target_widgets_percent = self.phases_target_widgets_percent[0]\n if dry_phases_target_widgets_time is not None and dry_phases_target_widgets_time.text() != '':\n dry = stringtoseconds(dry_phases_target_widgets_time.text())\n dry_set = True\n elif dry_phases_target_widgets_percent is not None and dry_phases_target_widgets_percent.text() != '':\n dry = float(dry_phases_target_widgets_percent.text()) * drop / 100\n dry_set = True\n\n # determine the target FCs time (relative to the target drop of above) if any\n fcs_phases_target_widgets_time = self.phases_target_widgets_time[1]\n fcs_phases_target_widgets_percent = self.phases_target_widgets_percent[1]\n if fcs_phases_target_widgets_time is not None and fcs_phases_target_widgets_time.text() != '':\n fcs = dry + stringtoseconds(fcs_phases_target_widgets_time.text())\n fcs_set = True\n elif fcs_phases_target_widgets_percent is not None and fcs_phases_target_widgets_percent.text() != '':\n fcs = dry + (float(fcs_phases_target_widgets_percent.text()) * drop / 100)\n fcs_set = True\n\n# return [(dry if dry_set else None),(fcs if fcs_set else None), None, (drop if drop_set else None)]\n # set all unset target times to the profile times\n return [\n (dry if dry_set else (self.aw.qmc.timex[self.aw.qmc.timeindex[1]] - offset)),\n (fcs if fcs_set else (self.aw.qmc.timex[self.aw.qmc.timeindex[2]] - offset)),\n None,\n (drop if drop_set else (self.aw.qmc.timex[self.aw.qmc.timeindex[6]] - offset))]\n\n # calculates the linear (self.aw.qmc.transMappingMode = 1) or quadratic (self.aw.qmc.transMappingMode = 2) mapping\n # between the profileTimes and the targetTimes\n def calcTimePolyfit(self) -> Optional[Callable[[float], float]]:\n # initialized by CHARGE time 00:00\n xa:List[float] = [0]\n ya:List[float] = [0]\n for i in range(4):\n profileTime:Optional[float] = self.profileTimes[i]\n targetTime:Optional[float] = self.targetTimes[i]\n if profileTime is not None and targetTime is not None:\n xa.append(profileTime)\n ya.append(targetTime)\n deg = self.aw.qmc.transMappingMode\n if len(xa) > 1:\n try:\n deg = min(len(xa) - 1,deg)\n z = numpy.polyfit(xa, ya, deg)\n return numpy.poly1d(z)\n except Exception: # pylint: disable=broad-except\n return None\n else:\n return None\n\n # calculates the linear (self.aw.qmc.transMappingMode = 1) or quadratic (self.aw.qmc.transMappingMode = 2) mapping\n # between the profileTemps and the targetTemps\n def calcTempPolyfit(self):\n xa:List[float] = []\n ya:List[float] = []\n for i in range(5):\n profileTemp:Optional[float] = self.profileTemps[i]\n targetTemp:Optional[float] = self.targetTemps[i]\n if profileTemp is not None and targetTemp is not None:\n xa.append(profileTemp)\n ya.append(targetTemp)\n deg = self.aw.qmc.transMappingMode\n if len(xa) > 0:\n try:\n deg = min(len(xa) - 1,deg)\n if deg == 0:\n z = numpy.array([1, ya[0] - xa[0]])\n else:\n z = numpy.polyfit(xa, ya, deg)\n return z\n except Exception: # pylint: disable=broad-except\n return None\n else:\n return None\n\n # returns a list of segment-wise fits between sources and targets\n # each fit is a numpy.array as returned by numpy.polyfit\n # a source element of None generates None as fit\n # a target element of None is skipped and previous and next segments are joined\n # the lists of sources and targets are expected to be of the same length\n # the length of the result list is the same as that of the sources and targets\n @staticmethod\n def calcDiscretefits(sources:List[Optional[float]], targets:List[Optional[float]]) -> List[Optional['npt.NDArray[numpy.float64]']]:\n if len(sources) != len(targets):\n return [None]*len(sources)\n fits:List[Optional['npt.NDArray[numpy.float64]']] = [None]*len(sources)\n last_fit:Optional['npt.NDArray[numpy.float64]'] = None\n for i, _ in enumerate(sources):\n if sources[i] is not None:\n if targets[i] is None:\n # we take the last fit\n fits[i] = last_fit\n else:\n next_idx = None # the index of the next non-empty source/target pair\n for j in range(i+1,len(sources)):\n if sources[j] is not None and targets[j] is not None:\n next_idx = j\n break\n if next_idx is None:\n sources_i = sources[i]\n targets_i = targets[i]\n if last_fit is not None:\n fits[i] = last_fit # copy previous fit\n elif sources_i is not None and targets_i is not None:\n # set a simple offset only as there is no previous nor next fit\n fits[i] = numpy.array([1,targets_i - sources_i])\n else:\n fits[i] = numpy.array([1,0])\n else:\n sources_next = sources[next_idx]\n targets_next = targets[next_idx]\n if sources_i is None or targets_i is None or sources_next is None or targets_next is None:\n fits[i] = numpy.array([1,0])\n else:\n fits[i] = numpy.polyfit([sources_i, sources_next], [targets_i, targets_next] ,1)\n # if this is the first fit, we copy it to all previous positions\n if last_fit is None:\n for k in range(i):\n if sources[k] is not None:\n fits[k] = fits[i]\n # register this fit\n last_fit = fits[i]\n return fits\n\n # fits of length 5\n def applyDiscreteTimeMapping(self,timex,fits):\n offset = self.forgroundOffset()\n res_timex = []\n if offset == 0 or fits[0] is None:\n new_offset = 0\n else:\n new_offset = numpy.poly1d(fits[0])(offset)\n for i, _ in enumerate(timex):\n # first fit is to be applied for all readings before DRY\n j = 0\n if self.aw.qmc.timeindex[6] > 0 and i >= self.aw.qmc.timeindex[6]:\n # last fit counts after DROP\n j = 4\n elif self.aw.qmc.timeindex[4] > 0 and i >= self.aw.qmc.timeindex[4]:\n j = 3 # after SCs\n elif self.aw.qmc.timeindex[2] > 0 and i >= self.aw.qmc.timeindex[2]:\n j = 2 # after FCs\n elif self.aw.qmc.timeindex[1] > 0 and i >= self.aw.qmc.timeindex[1]:\n j = 1 # after DRY\n if fits[j] is None:\n res_timex.append(timex[i] - offset + new_offset)\n else:\n fit = numpy.poly1d(fits[j]) # fit to be applied\n res_timex.append(fit(timex[i] - offset)+new_offset)\n return res_timex\n\n # returns False if no transformation was applied\n def applyTimeTransformation(self):\n # first update the targets\n self.targetTimes = self.getTargetTimes()\n if all(v is None for v in self.targetTimes):\n target_times, target_phases = self.getTargetPhases()\n if all(v is None for v in target_times + target_phases):\n self.aw.qmc.timex = self.org_timex[:]\n self.aw.qmc.extratimex = copy.deepcopy(self.org_extratimex)\n return False\n self.targetTimes = self.getTargetPhasesTimes()\n # calculate the offset of 00:00\n offset = self.forgroundOffset()\n # apply either the discrete or the polyfit mappings\n if self.aw.qmc.transMappingMode == 0:\n # discrete mapping\n fits = self.calcDiscretefits([0] + self.profileTimes,[0] + self.targetTimes)\n self.aw.qmc.timex = self.applyDiscreteTimeMapping(self.org_timex,fits)\n # apply to the extra timex\n self.aw.qmc.extratimex = []\n for timex in self.org_extratimex:\n try:\n timex_trans = self.applyDiscreteTimeMapping(timex,fits)\n except Exception: # pylint: disable=broad-except\n timex_trans = timex\n self.aw.qmc.extratimex.append(timex_trans)\n else:\n # polyfit mappings\n with warnings.catch_warnings():\n warnings.filterwarnings('error')\n try:\n fit = self.calcTimePolyfit() # the fit returned here is already applied to numpy.poly1d\n if fit is not None:\n self.aw.qmc.timex = [fit(tx-offset) for tx in self.org_timex]\n if len(self.aw.qmc.timex) > 0 and self.aw.qmc.timeindex[0] != -1:\n foffset = self.aw.qmc.timex[0]\n self.aw.qmc.timex = [tx+foffset for tx in self.aw.qmc.timex]\n extratimex = []\n for timex in self.org_extratimex:\n offset = 0\n if len(timex) > 0 and self.aw.qmc.timeindex[0] != -1:\n offset = timex[self.aw.qmc.timeindex[0]]\n new_timex = [fit(tx-offset) for tx in timex]\n if len(new_timex) > 0 and self.aw.qmc.timeindex[0] != -1:\n foffset = new_timex[0]\n new_timex = [tx+foffset for tx in new_timex]\n extratimex.append(new_timex)\n self.aw.qmc.extratimex = extratimex\n except numpy.RankWarning:\n pass\n return True\n\n # returns False if no transformation was applied\n def applyTempTransformation(self):\n # first update the targets\n self.targetTemps = self.getTargetTemps()\n if all(v is None for v in self.targetTemps):\n self.aw.qmc.temp2 = self.org_temp2[:]\n return False\n # apply either the discrete or the polyfit mappings\n if self.aw.qmc.transMappingMode == 0:\n # discrete mappings, length 5\n fits = self.calcDiscretefits(self.profileTemps,self.targetTemps)\n self.aw.qmc.temp2 = []\n for i, _ in enumerate(self.org_temp2):\n # first fit is to be applied for all readings before DRY\n j = 0\n if self.aw.qmc.timeindex[6] > 0 and i >= self.aw.qmc.timeindex[6]:\n # last fit counts after DROP\n j = 4\n elif self.aw.qmc.timeindex[4] > 0 and i >= self.aw.qmc.timeindex[4]:\n j = 3 # after SCs\n elif self.aw.qmc.timeindex[2] > 0 and i >= self.aw.qmc.timeindex[2]:\n j = 2 # after FCs\n elif self.aw.qmc.timeindex[1] > 0 and i >= self.aw.qmc.timeindex[1]:\n j = 1 # after DRY\n\n tp = self.org_temp2[i]\n fitj = fits[j]\n if tp is None or tp == -1 or fitj is None:\n self.aw.qmc.temp2.append(tp)\n else:\n fit = numpy.poly1d(fitj) # fit to be applied\n self.aw.qmc.temp2.append(fit(tp))\n return True\n # polyfit mappings\n with warnings.catch_warnings():\n warnings.filterwarnings('error')\n try:\n p = self.calcTempPolyfit()\n if p is not None:\n fit = numpy.poly1d(p)\n if fit is not None:\n self.aw.qmc.temp2 = [(-1 if (temp is None) or (temp == -1) else fit(temp)) for temp in self.org_temp2]\n except numpy.RankWarning:\n pass\n return True\n\n # tables\n\n def createPhasesTable(self):\n vheader = self.phasestable.verticalHeader()\n hheader = self.phasestable.horizontalHeader()\n\n self.phasestable.setStyleSheet('QTableView { background-color: red); }')\n\n self.phasestable.setRowCount(3)\n self.phasestable.setColumnCount(3)\n if hheader is not None:\n hheader.setStretchLastSection(False)\n hheader.setSectionResizeMode(QHeaderView.ResizeMode.Fixed)\n hheader.setHighlightSections(False)\n if vheader is not None:\n vheader.setSectionResizeMode(QHeaderView.ResizeMode.Fixed)\n\n self.phasestable.setHorizontalHeaderLabels([QApplication.translate('Label','Drying'),\n QApplication.translate('Label','Maillard'),\n QApplication.translate('Label','Finishing')])\n self.phasestable.setVerticalHeaderLabels([QApplication.translate('Table','Profile'),\n QApplication.translate('Table','Target'),\n QApplication.translate('Table','Result')])\n self.phasestable.setShowGrid(True)\n self.phasestable.setAlternatingRowColors(True)\n self.phasestable.setVerticalScrollBarPolicy(Qt.ScrollBarPolicy.ScrollBarAlwaysOff)\n self.phasestable.setHorizontalScrollBarPolicy(Qt.ScrollBarPolicy.ScrollBarAlwaysOff)\n if hheader is not None and vheader is not None:\n self.phasestable.setFixedSize(\n hheader.length() +\n vheader.sizeHint().width(),\n vheader.length() +\n hheader.height())\n self.phasestable.setEditTriggers(QAbstractItemView.EditTrigger.NoEditTriggers)\n self.phasestable.setFocusPolicy(Qt.FocusPolicy.NoFocus)\n self.phasestable.setSelectionMode(QAbstractItemView.SelectionMode.NoSelection)\n self.phasestable.setAutoScroll(False)\n if vheader is not None:\n vheader.sectionClicked.connect(self.phasesTableRowHeaderClicked)\n if hheader is not None:\n hheader.sectionClicked.connect(self.phasesTableColumnHeaderClicked)\n\n self.phases_target_widgets_time = []\n self.phases_target_widgets_percent = []\n self.phases_result_widgets = []\n\n profilePhasesTimes:List[Optional[float]] = [None]*3 # DRYING, MAILARD, FINISHING\n profilePhasesPercentages:List[Optional[float]] = [None] * 3\n #\n # the phases transformation are only enabled if at least DRY, FCs and DROP events are set\n phases_enabled = self.aw.qmc.timeindex[1] and self.aw.qmc.timeindex[2] and self.aw.qmc.timeindex[6]\n #\n if phases_enabled:\n profilePhasesTimes[0] = self.profileTimes[0] # DRYING == DRY\n if self.profileTimes[0] is not None and self.profileTimes[1] is not None:\n profilePhasesTimes[1] = self.profileTimes[1] - self.profileTimes[0]\n if self.profileTimes[1] is not None and self.profileTimes[3] is not None:\n profilePhasesTimes[2] = self.profileTimes[3] - self.profileTimes[1]\n if self.profileTimes[3] is not None:\n profilePhasesPercentages = [(ppt/self.profileTimes[3])*100 for ppt in profilePhasesTimes if ppt is not None]\n\n for i in range(3):\n profilePhasesTime = profilePhasesTimes[i]\n profilePhasesPercentage = profilePhasesPercentages[i]\n if len(profilePhasesTimes) > i and profilePhasesTime is not None and profilePhasesPercentage is not None:\n profile_phases_time_str = \\\n f'{stringfromseconds(int(round(profilePhasesTime)),leadingzero=False)} {self.aw.float2float(profilePhasesPercentage)}%'\n profile_phases_widget = QTableWidgetItem(profile_phases_time_str)\n profile_phases_widget.setTextAlignment(Qt.AlignmentFlag.AlignCenter|Qt.AlignmentFlag.AlignVCenter)\n self.phasestable.setItem(0,i,profile_phases_widget)\n #\n target_widget_time = QLineEdit('')\n target_widget_time.setValidator(MyQRegularExpressionValidator(self.regextime))\n target_widget_time.setAlignment(Qt.AlignmentFlag.AlignCenter|Qt.AlignmentFlag.AlignVCenter)\n if phases_enabled:\n target_widget_time.editingFinished.connect(self.updatePhasesWidget)\n else:\n target_widget_time.setEnabled(False)\n target_widget_percent = QLineEdit('')\n target_widget_percent.setValidator(QRegularExpressionValidator(self.regexpercent))\n target_widget_percent.setAlignment(Qt.AlignmentFlag.AlignCenter|Qt.AlignmentFlag.AlignVCenter)\n if phases_enabled:\n target_widget_percent.editingFinished.connect(self.updatePhasesWidget)\n else:\n target_widget_percent.setEnabled(False)\n target_cell_widget = QWidget()\n target_cell_layout = QHBoxLayout(target_cell_widget)\n target_cell_layout.setAlignment(Qt.AlignmentFlag.AlignCenter|Qt.AlignmentFlag.AlignVCenter)\n target_cell_layout.setContentsMargins(4,4,4,4)\n target_cell_layout.addWidget(target_widget_time)\n target_cell_layout.addWidget(target_widget_percent)\n target_cell_widget.setLayout(target_cell_layout)\n self.phasestable.setCellWidget(1,i,target_cell_widget)\n #\n result_widget = QTableWidgetItem('')\n result_widget.setTextAlignment(Qt.AlignmentFlag.AlignCenter|Qt.AlignmentFlag.AlignVCenter)\n self.phasestable.setItem(2,i,result_widget)\n else:\n target_widget_time = None\n target_widget_percent = None\n result_widget = None\n self.phases_target_widgets_time.append(target_widget_time)\n self.phases_target_widgets_percent.append(target_widget_percent)\n self.phases_result_widgets.append(result_widget)\n\n def createTimeTable(self):\n hheader = self.timetable.horizontalHeader()\n vheader = self.timetable.verticalHeader()\n self.timetable.clear()\n self.timetable.setRowCount(3)\n self.timetable.setColumnCount(4)\n if hheader is not None:\n hheader.setStretchLastSection(False)\n hheader.setSectionResizeMode(QHeaderView.ResizeMode.Fixed)\n hheader.setHighlightSections(False)\n if vheader is not None:\n vheader.setSectionResizeMode(QHeaderView.ResizeMode.Fixed)\n self.timetable.setHorizontalHeaderLabels([QApplication.translate('Label','DRY END'),\n QApplication.translate('Label','FC START'),\n QApplication.translate('Label','SC START'),\n QApplication.translate('Label','DROP')])\n self.timetable.setVerticalHeaderLabels([QApplication.translate('Table','Profile'),\n QApplication.translate('Table','Target'),\n QApplication.translate('Table','Result')])\n self.timetable.setShowGrid(True)\n self.timetable.setAlternatingRowColors(False)\n self.timetable.setVerticalScrollBarPolicy(Qt.ScrollBarPolicy.ScrollBarAlwaysOff)\n self.timetable.setHorizontalScrollBarPolicy(Qt.ScrollBarPolicy.ScrollBarAlwaysOff)\n self.timetable.setFrameStyle(QFrame.Shape.NoFrame)\n if hheader is not None and vheader is not None:\n self.timetable.setFixedSize(\n hheader.length() +\n vheader.sizeHint().width(),\n vheader.length() +\n hheader.height())\n self.timetable.setEditTriggers(QAbstractItemView.EditTrigger.NoEditTriggers)\n self.timetable.setFocusPolicy(Qt.FocusPolicy.NoFocus)\n self.timetable.setSelectionMode(QAbstractItemView.SelectionMode.NoSelection)\n self.timetable.setAutoScroll(False)\n if vheader is not None:\n vheader.sectionClicked.connect(self.timeTableRowHeaderClicked)\n if hheader is not None:\n hheader.sectionClicked.connect(self.timeTableColumnHeaderClicked)\n\n self.time_target_widgets = []\n self.time_result_widgets = []\n\n for i in range(4):\n profileTime = self.profileTimes[i]\n if len(self.profileTimes) > i and profileTime is not None:\n profile_time_str = stringfromseconds(int(round(profileTime)),leadingzero=False)\n profile_widget = QTableWidgetItem(profile_time_str)\n profile_widget.setTextAlignment(Qt.AlignmentFlag.AlignCenter|Qt.AlignmentFlag.AlignVCenter)\n self.timetable.setItem(0,i,profile_widget)\n #\n target_widget = QLineEdit('')\n target_widget.setValidator(MyQRegularExpressionValidator(self.regextime))\n target_widget.setAlignment(Qt.AlignmentFlag.AlignCenter|Qt.AlignmentFlag.AlignVCenter)\n target_widget.editingFinished.connect(self.updateTimesWidget)\n target_cell_widget = QWidget()\n target_cell_layout = QHBoxLayout(target_cell_widget)\n target_cell_layout.setAlignment(Qt.AlignmentFlag.AlignCenter|Qt.AlignmentFlag.AlignVCenter)\n target_cell_layout.setContentsMargins(4,4,4,4)\n target_cell_layout.addWidget(target_widget)\n target_cell_widget.setLayout(target_cell_layout)\n self.timetable.setCellWidget(1,i,target_cell_widget)\n #\n result_widget = QTableWidgetItem('') #profile_time_str)\n result_widget.setTextAlignment(Qt.AlignmentFlag.AlignCenter|Qt.AlignmentFlag.AlignVCenter)\n self.timetable.setItem(2,i,result_widget)\n else:\n target_widget = None\n result_widget = None\n self.time_target_widgets.append(target_widget)\n self.time_result_widgets.append(result_widget)\n\n def createTempTable(self):\n vheader = self.temptable.verticalHeader()\n hheader = self.temptable.horizontalHeader()\n self.temptable.clear()\n self.temptable.setRowCount(3)\n self.temptable.setColumnCount(5)\n if hheader is not None:\n hheader.setStretchLastSection(False)\n hheader.setSectionResizeMode(QHeaderView.ResizeMode.Fixed)\n if vheader is not None:\n vheader.setSectionResizeMode(QHeaderView.ResizeMode.Fixed)\n self.temptable.setHorizontalHeaderLabels([QApplication.translate('Label','CHARGE'),\n QApplication.translate('Label','DRY END'),\n QApplication.translate('Label','FC START'),\n QApplication.translate('Label','SC START'),\n QApplication.translate('Label','DROP')])\n self.temptable.setVerticalHeaderLabels([QApplication.translate('Table','Profile'),\n QApplication.translate('Table','Target'),\n QApplication.translate('Table','Result')])\n self.temptable.setShowGrid(True)\n self.temptable.setAlternatingRowColors(False)\n self.temptable.setVerticalScrollBarPolicy(Qt.ScrollBarPolicy.ScrollBarAlwaysOff)\n self.temptable.setHorizontalScrollBarPolicy(Qt.ScrollBarPolicy.ScrollBarAlwaysOff)\n hheader = self.temptable.horizontalHeader()\n vheader = self.temptable.verticalHeader()\n if hheader is not None and vheader is not None:\n self.temptable.setFixedSize(\n hheader.length() +\n vheader.sizeHint().width(),\n vheader.length() +\n hheader.height())\n self.temptable.setEditTriggers(QAbstractItemView.EditTrigger.NoEditTriggers)\n self.temptable.setFocusPolicy(Qt.FocusPolicy.NoFocus)\n self.temptable.setSelectionMode(QAbstractItemView.SelectionMode.NoSelection)\n self.temptable.setAutoScroll(False)\n if vheader is not None:\n vheader.sectionClicked.connect(self.tempTableRowHeaderClicked)\n if hheader is not None:\n hheader.sectionClicked.connect(self.tempTableColumnHeaderClicked)\n\n self.temp_target_widgets = []\n self.temp_result_widgets = []\n\n for i in range(5):\n profileTemp = self.profileTemps[i]\n if len(self.profileTemps) > i and profileTemp is not None:\n profile_temp_str = str(self.aw.float2float(profileTemp)) + self.aw.qmc.mode\n profile_widget = QTableWidgetItem(profile_temp_str)\n profile_widget.setTextAlignment(Qt.AlignmentFlag.AlignCenter|Qt.AlignmentFlag.AlignVCenter)\n self.temptable.setItem(0,i,profile_widget)\n #\n target_widget = QLineEdit('')\n target_widget.setValidator(QRegularExpressionValidator(self.regextemp))\n target_widget.editingFinished.connect(self.updateTempResults)\n target_widget.setAlignment(Qt.AlignmentFlag.AlignCenter|Qt.AlignmentFlag.AlignVCenter)\n\n target_cell_widget = QWidget()\n target_cell_layout = QHBoxLayout(target_cell_widget)\n target_cell_layout.setAlignment(Qt.AlignmentFlag.AlignCenter|Qt.AlignmentFlag.AlignVCenter)\n target_cell_layout.setContentsMargins(4,4,4,4)\n target_cell_layout.addWidget(target_widget)\n# target_cell_layout.addWidget(QLabel(self.aw.qmc.mode))\n target_cell_widget.setLayout(target_cell_layout)\n self.temptable.setCellWidget(1,i,target_cell_widget)\n #\n result_widget = QTableWidgetItem('')\n result_widget.setTextAlignment(Qt.AlignmentFlag.AlignCenter|Qt.AlignmentFlag.AlignVCenter)\n self.temptable.setItem(2,i,result_widget)\n else:\n target_widget = None\n result_widget = None\n self.temp_target_widgets.append(target_widget)\n self.temp_result_widgets.append(result_widget)\n","repo_name":"artisan-roaster-scope/artisan","sub_path":"src/artisanlib/transposer.py","file_name":"transposer.py","file_ext":"py","file_size_in_byte":61260,"program_lang":"python","lang":"en","doc_type":"code","stars":809,"dataset":"github-code","pt":"50"} +{"seq_id":"39815188910","text":"import os\nimport torch\nimport numpy as np\nimport torch.utils.data as data\nfrom utils.logger import *\nfrom .build import DATASETS\nfrom numpy.random import default_rng\nfrom .group_utils import Group\nfrom torchvision import transforms\nfrom datasets import data_transforms\n\n\nbiomass_transforms = transforms.Compose(\n [\n data_transforms.PointcloudRotate(),\n #data_transforms.PointcloudJitter(std=0.01, clip=0.05),\n data_transforms.RandomHorizontalFlip(),\n data_transforms.PointcloudTranslate(translate_range=0.2)\n ])\n\ntrain_transforms = transforms.Compose(\n [\n data_transforms.PointcloudRotate(),\n data_transforms.PointcloudScaleAndTranslate(scale_low=0.9, scale_high=1.1, translate_range=0.1),\n data_transforms.RandomHorizontalFlip(),\n ])\n\ntoken_transforms = transforms.Compose(\n [\n data_transforms.PatchDropout(max_ratio=0.95)\n ])\n\ntest_transforms = transforms.Compose([])\n\nVAR = \"transfer_treeset\"\n@DATASETS.register_module()\nclass Cls_Treeset(data.Dataset):\n def __init__(self, args, config):\n self.plot_folders = config.plot_folders\n self.npoints = config.npoints\n self.normalization = config.normalization\n self.normalization_pars = np.array(config.normalization_pars)\n self.fewshot = config.few_shot if hasattr(config, \"few_shot\") else None\n self.target_label = config.target_type == \"label\"\n self.biomass = config.target_type == \"biomass\"\n assert config.target_type == \"biomass\" or config.target_type == \"label\"\n\n self.samples_list = []\n for folder in self.plot_folders:\n files = os.listdir(folder)\n files = [os.path.join(folder, file) for file in files]\n self.samples_list += files\n self.samples_list = np.array(self.samples_list)\n # grouping config\n num_group, group_size, sampling_method = config.model.num_group, config.model.group_size, config.sampling_method\n self.grouper = Group(num_group, group_size, sampling_method)\n\n if self.fewshot is None:\n rng = default_rng(seed=config.seed)\n self.samples_list = rng.permutation(self.samples_list)\n splitidx = np.floor(config.train_ratio * len(self.samples_list)).astype(int)\n print_log(\"SHUFFLED THE DATA\", logger = VAR+ config.subset)\n \n if config.subset == \"train\":\n self.samples_list = self.samples_list[0:splitidx]\n else:\n self.samples_list = self.samples_list[splitidx:]\n else: # if self.fewshot is smth\n if config.subset == \"train\":\n idx = np.load(config.few_shot_train_path)[self.fewshot]\n else:\n idx = np.load(config.few_shot_eval_path)[self.fewshot]\n self.samples_list = self.samples_list[idx.reshape(-1)]\n print_log(f'[DATASET]use fewshot {self.fewshot}', logger = VAR + config.subset)\n self.samples_path = self.samples_list.copy()\n self.samples_list = [np.load(sample, allow_pickle=True) for sample in self.samples_list]\n if config.validate_samples:\n for idx, (mass, sample) in enumerate(self.samples_list):\n if len(sample) < self.npoints:\n print(idx, \"has length\", len(sample))\n \n self.token_transforms =transforms.Compose([data_transforms.PatchDropout(max_ratio=args.patch_dropout)])\n\n if self.target_label:\n self.transforms = train_transforms\n\n elif self.biomass:\n print(\"use biomass transforms\")\n self.transforms = biomass_transforms\n else:\n raise NotImplementedError\n if config.subset != \"train\":\n self.transforms, self.token_transforms = test_transforms, test_transforms\n self.center = True\n \n def normalize(self, pc):\n pc = pc - pc.mean(axis=0)\n pc = pc / self.normalization_pars\n return pc\n\n def random_sample(self, pc, num):\n if len(pc) < num:\n choice = np.random.choice(len(pc), num, replace=True)\n else:\n choice = np.random.choice(len(pc), num, replace=False)\n return pc[choice]\n\n def __getitem__(self, idx):\n target, points = self.samples_list[idx]\n points = self.random_sample(points, self.npoints)\n if self.normalization:\n points = self.normalize(points)\n points = self.transforms(points)\n neighborhood, center, idx = self.grouper.group(points)\n if self.center:\n neighborhood = neighborhood - center.reshape(-1, 1, 3)\n neighborhood, center = self.token_transforms((neighborhood, center))\n #neighborhood = np.zeros(neighborhood.shape)\n neighborhood = torch.from_numpy(neighborhood).float()\n center = torch.from_numpy(center).float()\n if self.target_label:\n target = torch.from_numpy(np.array(target)).int()\n elif self.biomass: \n target = torch.from_numpy(np.array(target)).float()\n return neighborhood, center, target\n\n def __len__(self):\n return len(self.samples_list)\n\nif __name__ == '__main__':\n pass\n","repo_name":"JanvDelden/Forest-Point-MAE","sub_path":"datasets/cls_treeset.py","file_name":"cls_treeset.py","file_ext":"py","file_size_in_byte":5190,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"50"} +{"seq_id":"28704663076","text":"import ast\nimport re\n\nfrom setuptools import setup, find_packages\n\n_version_re = re.compile(r'__version__\\s+=\\s+(.*)')\n\nwith open('__init__.py', 'rb') as f:\n version = str(ast.literal_eval(_version_re.search(\n f.read().decode('utf-8')).group(1)))\n\nsetup(\n name = 'docker_rest_service',\n version = version,\n url = 'https://github.com/alsbi/docker_rest_service',\n license = 'MIT',\n author = 'alsbi',\n author_email = 'feano4ik@gmail.com',\n description = 'Docker control service',\n long_description = open('README.md').read(),\n packages = find_packages(),\n include_package_data = True,\n install_requires = [\n 'flask',\n 'requests_unixsocket',\n ],\n entry_points = {\n 'console_scripts': [\n 'docker_rest_service=docker_rest_service:main'\n ],\n }\n)\n","repo_name":"alsbi/docker_rest_service","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":830,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"1717803019","text":"# отдельные импорты\n\n\n# импорты aiogram\nfrom aiogram.types import KeyboardButton\nfrom aiogram.utils.keyboard import ReplyKeyboardMarkup\n\n\n\nasync def main_menu_keyboard():\n '''\n Клавиатура главного меню\n :return: markup\n '''\n markup = ReplyKeyboardMarkup(\n keyboard=[\n [KeyboardButton(text='Прогуляться по городу'),\n KeyboardButton(text='О проекте')]\n ],\n resize_keyboard=True)\n\n return markup","repo_name":"RevelRies/BotMayakovka_2","sub_path":"Bot/keyboards/main_menu_keyboard.py","file_name":"main_menu_keyboard.py","file_ext":"py","file_size_in_byte":525,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"73281367194","text":"# -*- coding: utf-8 -*-\nimport xbmc\nimport xbmcaddon\nimport xbmcgui\nimport xbmcvfs\nimport json\nimport os\nimport sys\nimport requests.cookies\nimport requests.adapters\nimport requests\nfrom datetime import datetime\nfrom datetime import timedelta\nfrom resources.lib import xml_structure\nfrom resources.lib import channel_selector\nfrom resources.lib import mapper\nfrom resources.lib import filesplit\n\nprovider = 'MAGENTA TV (DE)'\nlang = 'de'\n\nADDON = xbmcaddon.Addon(id=\"service.takealug.epg-grabber\")\naddon_name = ADDON.getAddonInfo('name')\naddon_version = ADDON.getAddonInfo('version')\nloc = ADDON.getLocalizedString\ndatapath = xbmcvfs.translatePath(ADDON.getAddonInfo('profile'))\ntemppath = os.path.join(datapath, \"temp\")\nprovider_temppath = os.path.join(temppath, \"magentaDE\")\n\n## Enable Multithread\nenable_multithread = True if ADDON.getSetting('enable_multithread').upper() == 'TRUE' else False\nif enable_multithread:\n try:\n from multiprocessing import Process\n except:\n pass\n\n## MAPPING Variables Thx @ sunsettrack4\ntkm_genres_url = 'https://raw.githubusercontent.com/sunsettrack4/config_files/master/tkm_genres.json'\ntkm_genres_json = os.path.join(provider_temppath, 'tkm_genres.json')\ntkm_channels_url = 'https://raw.githubusercontent.com/sunsettrack4/config_files/master/tkm_channels.json'\ntkm_channels_json = os.path.join(provider_temppath, 'tkm_channels.json')\n\n## Log Files\nmagentaDE_genres_warnings_tmp = os.path.join(provider_temppath, 'magentaDE_genres_warnings.txt')\nmagentaDE_genres_warnings = os.path.join(temppath, 'magentaDE_genres_warnings.txt')\nmagentaDE_channels_warnings_tmp = os.path.join(provider_temppath, 'magentaDE_channels_warnings.txt')\nmagentaDE_channels_warnings = os.path.join(temppath, 'magentaDE_channels_warnings.txt')\n\n## Read Magenta DE Settings\ndays_to_grab = int(ADDON.getSetting('magentaDE_days_to_grab'))\nepisode_format = ADDON.getSetting('magentaDE_episode_format')\nchannel_format = ADDON.getSetting('magentaDE_channel_format')\ngenre_format = ADDON.getSetting('magentaDE_genre_format')\n\n\n# Make a debug logger\ndef log(message, loglevel=xbmc.LOGDEBUG):\n xbmc.log('[{} {}] {}'.format(addon_name, addon_version, message), loglevel)\n\n\n# Make OSD Notify Messages\nOSD = xbmcgui.Dialog()\n\n\ndef notify(title, message, icon=xbmcgui.NOTIFICATION_INFO):\n OSD.notification(title, message, icon)\n\ndef get_epgLength(days_to_grab):\n # Calculate Date and Time\n today = datetime.today()\n calc_today = datetime(today.year, today.month, today.day, hour=00, minute=00, second=1)\n\n calc_then = datetime(today.year, today.month, today.day, hour=23, minute=59, second=59)\n calc_then += timedelta(days=days_to_grab)\n\n starttime = calc_today.strftime(\"%Y%m%d%H%M%S\")\n endtime = calc_then.strftime(\"%Y%m%d%H%M%S\")\n\n return starttime, endtime\n\n## Channel Files\nmagentaDE_chlist_provider_tmp = os.path.join(provider_temppath, 'chlist_magentaDE_provider_tmp.json')\nmagentaDE_chlist_provider = os.path.join(provider_temppath, 'chlist_magentaDE_provider.json')\nmagentaDE_chlist_selected = os.path.join(datapath, 'chlist_magentaDE_selected.json')\n\nmagentaDE_login_url = 'https://api.prod.sngtv.magentatv.de/EPG/JSON/Login?&T=PC_firefox_75'\nmagentaDE_authenticate_url = 'https://api.prod.sngtv.magentatv.de/EPG/JSON/Authenticate?SID=firstup&T=PC_firefox_75'\nmagentaDE_channellist_url = 'https://api.prod.sngtv.magentatv.de/EPG/JSON/AllChannel?SID=first&T=PC_firefox_75'\nmagentaDE_data_url = 'https://api.prod.sngtv.magentatv.de/EPG/JSON/PlayBillList?userContentFilter=241221015&sessionArea=1&SID=ottall&T=PC_firefox_75'\n\nmagentaDE_login = {'userId': 'Guest', 'mac': '00:00:00:00:00:00'}\nmagentaDE_authenticate = {'terminalid': '00:00:00:00:00:00', 'mac': '00:00:00:00:00:00', 'terminaltype': 'WEBTV','utcEnable': '1', 'timezone': 'UTC', 'userType': '3', 'terminalvendor': 'Unknown','preSharedKeyID': 'PC01P00002', 'cnonce': '5c6ff0b9e4e5efb1498e7eaa8f54d9fb'}\nmagentaDE_get_chlist = {'properties': [{'name': 'logicalChannel','include': '/channellist/logicalChannel/contentId,/channellist/logicalChannel/name,/channellist/logicalChannel/pictures/picture/imageType,/channellist/logicalChannel/pictures/picture/href'}],'metaDataVer': 'Channel/1.1', 'channelNamespace': '2','filterlist': [{'key': 'IsHide', 'value': '-1'}], 'returnSatChannel': '0'}\nmagentaDE_header = {'Host': 'api.prod.sngtv.magentatv.de',\n 'User-Agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:75.0) Gecko/20100101 Firefox/75.0',\n 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',\n 'Accept-Language': 'de,en-US;q=0.7,en;q=0.3',\n 'Accept-Encoding': 'gzip, deflate, br',\n 'Connection': 'keep-alive',\n 'Upgrade-Insecure-Requests': '1'}\nmagentaDE_session_cookie = os.path.join(provider_temppath, 'cookies.json')\n\n\n## Login and Authenticate to web.magenta.tv\ndef magentaDE_session():\n session = requests.Session()\n session.post(magentaDE_login_url, data=json.dumps(magentaDE_login), headers=magentaDE_header)\n session.post(magentaDE_authenticate_url, data=json.dumps(magentaDE_authenticate), headers=magentaDE_header)\n ## Save Cookies to Disk\n with open(magentaDE_session_cookie, 'w', encoding='utf-8') as f:\n json.dump(requests.utils.dict_from_cookiejar(session.cookies), f)\n\n\n## Get channel list(url)\ndef get_channellist():\n magentaDE_session()\n session = requests.Session()\n ## Load Cookies from Disk\n with open(magentaDE_session_cookie, 'r', encoding='utf-8') as f:\n session.cookies = requests.utils.cookiejar_from_dict(json.load(f))\n\n magenta_CSRFToken = session.cookies[\"CSRFSESSION\"]\n session.headers.update({'X_CSRFToken': magenta_CSRFToken})\n magenta_chlist_url = session.post(magentaDE_channellist_url, data=json.dumps(magentaDE_get_chlist),headers=magentaDE_header)\n magenta_chlist_url.raise_for_status()\n response = magenta_chlist_url.json()\n\n with open(magentaDE_chlist_provider_tmp, 'w', encoding='utf-8') as provider_list_tmp:\n json.dump(response, provider_list_tmp)\n\n #### Transform magentaDE_chlist_provider_tmp to Standard chlist Format as magentaDE_chlist_provider\n\n # Load Channellist from Provider\n with open(magentaDE_chlist_provider_tmp, 'r', encoding='utf-8') as provider_list_tmp:\n magentaDE_channels = json.load(provider_list_tmp)\n\n # Create empty new hznDE_chlist_provider\n with open(magentaDE_chlist_provider, 'w', encoding='utf-8') as provider_list:\n provider_list.write(json.dumps({\"channellist\": []}))\n\n ch_title = ''\n\n # Load New Channellist from Provider\n with open(magentaDE_chlist_provider, encoding='utf-8') as provider_list:\n data = json.load(provider_list)\n\n temp = data['channellist']\n\n for channels in magentaDE_channels['channellist']:\n ch_id = channels['contentId']\n ch_title = channels['name']\n for image in channels['pictures']:\n if image['imageType'] == '15':\n hdimage = image['href']\n # channel to be appended\n y = {\"contentId\": ch_id,\n \"name\": ch_title,\n \"pictures\": [{\"href\": hdimage}]}\n\n # appending channels to data['channellist']\n temp.append(y)\n\n #Save New Channellist from Provider\n with open(magentaDE_chlist_provider, 'w', encoding='utf-8') as provider_list:\n json.dump(data, provider_list, indent=4)\n\ndef select_channels():\n ## Create Provider Temppath if not exist\n if not os.path.exists(provider_temppath):\n os.makedirs(provider_temppath)\n\n ## Create empty (Selected) Channel List if not exist\n if not os.path.isfile(magentaDE_chlist_selected):\n with open((magentaDE_chlist_selected), 'w', encoding='utf-8') as selected_list:\n selected_list.write(json.dumps({\"channellist\": []}))\n\n ## Download chlist_magenta_provider.json\n get_channellist()\n dialog = xbmcgui.Dialog()\n\n with open(magentaDE_chlist_provider, 'r', encoding='utf-8') as o:\n provider_list = json.load(o)\n\n with open(magentaDE_chlist_selected, 'r', encoding='utf-8') as s:\n selected_list = json.load(s)\n\n ## Start Channel Selector\n user_select = channel_selector.select_channels(provider, provider_list, selected_list)\n\n if user_select is not None:\n with open(magentaDE_chlist_selected, 'w', encoding='utf-8') as f:\n json.dump(user_select, f, indent=4)\n if os.path.isfile(magentaDE_chlist_selected):\n valid = check_selected_list()\n if valid is True:\n ok = dialog.ok(provider, loc(32402))\n if ok:\n log(loc(32402), xbmc.LOGINFO)\n elif valid is False:\n log(loc(32403), xbmc.LOGINFO)\n yn = OSD.yesno(provider, loc(32403))\n if yn:\n select_channels()\n else:\n xbmcvfs.delete(magentaDE_chlist_selected)\n exit()\n else:\n valid = check_selected_list()\n if valid is True:\n ok = dialog.ok(provider, loc(32404))\n if ok:\n log(loc(32404), xbmc.LOGINFO)\n elif valid is False:\n log(loc(32403), xbmc.LOGINFO)\n yn = OSD.yesno(provider, loc(32403))\n if yn:\n select_channels()\n else:\n xbmcvfs.delete(magentaDE_chlist_selected)\n exit()\n\ndef check_selected_list():\n check = 'invalid'\n with open(magentaDE_chlist_selected, 'r', encoding='utf-8') as c:\n selected_list = json.load(c)\n for user_list in selected_list['channellist']:\n if 'contentId' in user_list:\n check = 'valid'\n if check == 'valid':\n return True\n else:\n return False\n\ndef download_multithread(thread_temppath, download_threads):\n # delete old broadcast files if exist\n for f in os.listdir(provider_temppath):\n if f.endswith('_broadcast.json'):\n xbmcvfs.delete(os.path.join(provider_temppath, f))\n\n magentaDE_session()\n list = os.path.join(provider_temppath, 'list.txt')\n splitname = os.path.join(thread_temppath, 'chlist_magentaDE_selected')\n starttime, endtime = get_epgLength(days_to_grab)\n\n with open(magentaDE_chlist_selected, 'r', encoding='utf-8') as s:\n selected_list = json.load(s)\n if filesplit.split_chlist_selected(thread_temppath, magentaDE_chlist_selected, splitname, download_threads, enable_multithread):\n multi = True\n needed_threads = sum([len(files) for r, d, files in os.walk(thread_temppath)])\n items_to_download = str(len(selected_list['channellist']))\n log('{} {} {} '.format(provider, items_to_download, loc(32361)), xbmc.LOGINFO)\n pDialog = xbmcgui.DialogProgressBG()\n log('{} Multithread({}) Mode'.format(provider, needed_threads), xbmc.LOGINFO)\n pDialog.create('{} {} '.format(loc(32500), provider), '{} {}'.format('100', loc(32501)))\n\n jobs = []\n for thread in range(0, int(needed_threads)):\n p = Process(target=download_thread, args=('{}_{}.json'.format(splitname, int(thread)), multi, list, starttime, endtime, ))\n jobs.append(p)\n p.start()\n for j in jobs:\n while j.is_alive():\n xbmc.sleep(100)\n try:\n last_line = ''\n with open(list, 'r', encoding='utf-8') as f:\n last_line = f.readlines()[-1]\n except:\n pass\n items = sum(1 for f in os.listdir(provider_temppath) if f.endswith('_broadcast.json'))\n percent_remain = int(100) - int(items) * int(100) / int(items_to_download)\n percent_completed = int(100) * int(items) / int(items_to_download)\n pDialog.update(int(percent_completed), '{} {} '.format(loc(32500), last_line), '{} {} {}'.format(int(percent_remain), loc(32501), provider))\n if int(items) == int(items_to_download):\n log('{} {}'.format(provider, loc(32363)), xbmc.LOGINFO)\n break\n j.join()\n pDialog.close()\n for file in os.listdir(thread_temppath): xbmcvfs.delete(os.path.join(thread_temppath, file))\n\n else:\n multi = False\n log('{} {} '.format(provider, 'Can`t download in Multithreading mode, loading single...'), xbmc.LOGINFO)\n download_thread(magentaDE_chlist_selected, multi, list, starttime, endtime)\n\ndef download_thread(magentaDE_chlist_selected, multi, list, starttime, endtime):\n requests.adapters.DEFAULT_RETRIES = 5\n session = requests.Session()\n\n ## Load Cookies from Disk\n with open(magentaDE_session_cookie, 'r', encoding='utf-8') as f:\n session.cookies = requests.utils.cookiejar_from_dict(json.load(f))\n magenta_CSRFToken = session.cookies[\"CSRFSESSION\"]\n session.headers.update({'X_CSRFToken': magenta_CSRFToken})\n\n with open(magentaDE_chlist_selected, 'r', encoding='utf-8') as s:\n selected_list = json.load(s)\n\n if not multi:\n items_to_download = str(len(selected_list['channellist']))\n log('{} {} {} '.format(provider, items_to_download, loc(32361)), xbmc.LOGINFO)\n pDialog = xbmcgui.DialogProgressBG()\n pDialog.create('{} {} '.format(loc(32500), provider), '{} {}'.format('100', loc(32501)))\n\n for user_item in selected_list['channellist']:\n contentID = user_item['contentId']\n channel_name = user_item['name']\n magentaDE_data = {'channelid': contentID, 'type': '2', 'offset': '0', 'count': '-1', 'isFillProgram': '1','properties': '[{\"name\":\"playbill\",\"include\":\"ratingForeignsn,id,channelid,name,subName,starttime,endtime,cast,casts,country,producedate,ratingid,pictures,type,introduce,foreignsn,seriesID,genres,subNum,seasonNum\"}]','endtime': endtime, 'begintime': starttime}\n response = session.post(magentaDE_data_url, data=json.dumps(magentaDE_data), headers=magentaDE_header)\n response.raise_for_status()\n tkm_data = response.json()\n broadcast_files = os.path.join(provider_temppath, '{}_broadcast.json'.format(contentID))\n with open(broadcast_files, 'w', encoding='utf-8') as playbill:\n json.dump(tkm_data, playbill)\n\n ## Create a List with downloaded channels\n last_channel_name = '{}\\n'.format(channel_name)\n with open(list, 'a', encoding='utf-8') as f:\n f.write(last_channel_name)\n\n if not multi:\n items = sum(1 for f in os.listdir(provider_temppath) if f.endswith('_broadcast.json'))\n percent_remain = int(100) - int(items) * int(100) / int(items_to_download)\n percent_completed = int(100) * int(items) / int(items_to_download)\n pDialog.update(int(percent_completed), '{} {} '.format(loc(32500), channel_name), '{} {} {}'.format(int(percent_remain), loc(32501), provider))\n if int(items) == int(items_to_download):\n log('{} {}'.format(provider, loc(32363)), xbmc.LOGINFO)\n break\n if not multi:\n pDialog.close()\n\n\ndef create_xml_channels():\n log('{} {}'.format(provider,loc(32362)), xbmc.LOGINFO)\n if channel_format == 'rytec':\n ## Save tkm_channels.json to Disk\n tkm_channels_response = requests.get(tkm_channels_url).json()\n with open(tkm_channels_json, 'w', encoding='utf-8') as tkm_channels:\n json.dump(tkm_channels_response, tkm_channels)\n\n with open(magentaDE_chlist_selected, 'r', encoding='utf-8') as c:\n selected_list = json.load(c)\n\n items_to_download = str(len(selected_list['channellist']))\n items = 0\n pDialog = xbmcgui.DialogProgressBG()\n pDialog.create('{} {} '.format(loc(32502),provider), '{} {}'.format('100',loc(32501)))\n\n ## Create XML Channels Provider information\n xml_structure.xml_channels_start(provider)\n\n for user_item in selected_list['channellist']:\n items += 1\n percent_remain = int(100) - int(items) * int(100) / int(items_to_download)\n percent_completed = int(100) * int(items) / int(items_to_download)\n channel_name = user_item['name']\n channel_icon = user_item['pictures'][0]['href']\n channel_id = channel_name\n pDialog.update(int(percent_completed), '{} {} '.format(loc(32502),channel_name),'{} {} {}'.format(int(percent_remain),loc(32501),provider))\n if str(percent_completed) == str(100):\n log('{} {}'.format(provider,loc(32364)), xbmc.LOGINFO)\n\n ## Map Channels\n if not channel_id == '':\n channel_id = mapper.map_channels(channel_id, channel_format, tkm_channels_json, magentaDE_channels_warnings_tmp, lang)\n\n ## Create XML Channel Information with provided Variables\n xml_structure.xml_channels(channel_name, channel_id, channel_icon, lang)\n pDialog.close()\n\n\ndef create_xml_broadcast(enable_rating_mapper, thread_temppath, download_threads):\n\n download_multithread(thread_temppath, download_threads)\n log('{} {}'.format(provider, loc(32365)), xbmc.LOGINFO)\n\n if genre_format == 'eit':\n ## Save tkm_genres.json to Disk\n tkm_genres_response = requests.get(tkm_genres_url).json()\n with open(tkm_genres_json, 'w', encoding='utf-8') as tkm_genres:\n json.dump(tkm_genres_response, tkm_genres)\n\n with open(magentaDE_chlist_selected, 'r', encoding='utf-8') as c:\n selected_list = json.load(c)\n\n items_to_download = str(len(selected_list['channellist']))\n items = 0\n pDialog = xbmcgui.DialogProgressBG()\n pDialog.create('{} {} '.format(loc(32503), provider), '{} Prozent verbleibend'.format('100'))\n\n ## Create XML Broadcast Provider information\n xml_structure.xml_broadcast_start(provider)\n\n for user_item in selected_list['channellist']:\n items += 1\n percent_remain = int(100) - int(items) * int(100) / int(items_to_download)\n percent_completed = int(100) * int(items) / int(items_to_download)\n contentID = user_item['contentId']\n channel_name = user_item['name']\n channel_id = channel_name\n pDialog.update(int(percent_completed), '{} {} '.format(loc(32503), channel_name), '{} {} {}'.format(int(percent_remain), loc(32501), provider))\n if str(percent_completed) == str(100):\n log('{} {}'.format(provider, loc(32366)), xbmc.LOGINFO)\n\n broadcast_files = os.path.join(provider_temppath, '{}_broadcast.json'.format(contentID))\n with open(broadcast_files, 'r', encoding='utf-8') as b:\n broadcastfiles = json.load(b)\n\n ### Map Channels\n if not channel_id == '':\n channel_id = mapper.map_channels(channel_id, channel_format, tkm_channels_json, magentaDE_channels_warnings_tmp, lang)\n\n try:\n for playbilllist in broadcastfiles['playbilllist']:\n try:\n item_title = playbilllist['name']\n except (KeyError, IndexError):\n item_title = ''\n try:\n item_starttime = playbilllist['starttime']\n except (KeyError, IndexError):\n item_starttime = ''\n try:\n item_endtime = playbilllist['endtime']\n except (KeyError, IndexError):\n item_endtime = ''\n try:\n item_description = playbilllist['introduce']\n except (KeyError, IndexError):\n item_description = ''\n try:\n item_country = playbilllist['country']\n except (KeyError, IndexError):\n item_country = ''\n try:\n item_picture = playbilllist['pictures'][1]['href']\n except (KeyError, IndexError):\n item_picture = ''\n try:\n item_subtitle = playbilllist['subName']\n except (KeyError, IndexError):\n item_subtitle = ''\n try:\n items_genre = playbilllist['genres']\n except (KeyError, IndexError):\n items_genre = ''\n try:\n item_date = playbilllist['producedate']\n except (KeyError, IndexError):\n item_date = ''\n try:\n item_season = playbilllist['seasonNum']\n except (KeyError, IndexError):\n item_season = ''\n try:\n item_episode = playbilllist['subNum']\n except (KeyError, IndexError):\n item_episode = ''\n try:\n item_agerating = playbilllist['ratingid']\n except (KeyError, IndexError):\n item_agerating = ''\n try:\n items_director = playbilllist['cast']['director']\n except (KeyError, IndexError):\n items_director = ''\n try:\n items_producer = playbilllist['cast']['producer']\n except (KeyError, IndexError):\n items_producer = ''\n try:\n items_actor = playbilllist['cast']['actor']\n except (KeyError, IndexError):\n items_actor = ''\n\n # Transform items to Readable XML Format\n item_starrating = ''\n if not item_date == '':\n item_date = item_date.split('-')\n item_date = item_date[0]\n if (not item_starttime == '' and not item_endtime == ''):\n start = item_starttime.split(' UTC')\n item_starttime = start[0].replace(' ', '').replace('-', '').replace(':', '')\n stop = item_endtime.split(' UTC')\n item_endtime = stop[0].replace(' ', '').replace('-', '').replace(':', '')\n if not item_country == '':\n item_country = item_country.upper()\n if item_agerating == '-1':\n item_agerating = ''\n\n # Map Genres\n if not items_genre == '':\n items_genre = mapper.map_genres(items_genre, genre_format, tkm_genres_json, magentaDE_genres_warnings_tmp, lang)\n\n ## Create XML Broadcast Information with provided Variables\n xml_structure.xml_broadcast(episode_format, channel_id, item_title, item_starttime, item_endtime,\n item_description, item_country, item_picture, item_subtitle, items_genre,\n item_date, item_season, item_episode, item_agerating, item_starrating, items_director,\n items_producer, items_actor, enable_rating_mapper, lang)\n\n except (KeyError, IndexError):\n log('{} {} {} {} {} {}'.format(provider,loc(32367),channel_name,loc(32368),contentID,loc(32369)))\n pDialog.close()\n\n ## Create Channel Warnings Textile\n channel_pull = '\\nPlease Create an Pull Request for Missing Rytec Id´s to https://github.com/sunsettrack4/config_files/blob/master/tkm_channels.json\\n'\n mapper.create_channel_warnings(magentaDE_channels_warnings_tmp, magentaDE_channels_warnings, provider, channel_pull)\n\n ## Create Genre Warnings Textfile\n genre_pull = '\\nPlease Create an Pull Request for Missing EIT Genres to https://github.com/sunsettrack4/config_files/blob/master/tkm_genres.json\\n'\n mapper.create_genre_warnings(magentaDE_genres_warnings_tmp, magentaDE_genres_warnings, provider, genre_pull)\n\n notify(addon_name, '{} {} {}'.format(loc(32370),provider,loc(32371)), icon=xbmcgui.NOTIFICATION_INFO)\n log('{} {} {}'.format(loc(32370),provider,loc(32371), xbmc.LOGINFO))\n xbmc.sleep(4000)\n\n if (os.path.isfile(magentaDE_channels_warnings) or os.path.isfile(magentaDE_genres_warnings)):\n notify(provider, '{}'.format(loc(32372)), icon=xbmcgui.NOTIFICATION_WARNING)\n xbmc.sleep(3000)\n\n ## Delete old Tempfiles, not needed any more\n for file in os.listdir(provider_temppath): xbmcvfs.delete(os.path.join(provider_temppath, file))\n\n\ndef check_provider():\n ## Create Provider Temppath if not exist\n if not os.path.exists(provider_temppath):\n os.makedirs(provider_temppath)\n\n ## Create empty (Selected) Channel List if not exist\n if not os.path.isfile(magentaDE_chlist_selected):\n with open((magentaDE_chlist_selected), 'w', encoding='utf-8') as selected_list:\n selected_list.write(json.dumps({\"channellist\": []}))\n\n ## If no Channellist exist, ask to create one\n yn = OSD.yesno(provider, loc(32405))\n if yn:\n select_channels()\n else:\n xbmcvfs.delete(magentaDE_chlist_selected)\n return False\n\n ## If a Selected list exist, check valid\n valid = check_selected_list()\n if valid is False:\n yn = OSD.yesno(provider, loc(32405))\n if yn:\n select_channels()\n else:\n xbmcvfs.delete(magentaDE_chlist_selected)\n return False\n return True\n\ndef startup():\n if check_provider():\n get_channellist()\n return True\n else:\n return False\n\n# Channel Selector\ntry:\n if sys.argv[1] == 'select_channels_magentaDE':\n select_channels()\nexcept IndexError:\n pass","repo_name":"DeBaschdi/service.takealug.epg-grabber","sub_path":"resources/providers/magenta_DE.py","file_name":"magenta_DE.py","file_ext":"py","file_size_in_byte":25575,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"50"} +{"seq_id":"40886594389","text":"lista = [1,0,2,4,6]\r\nordem = list()\r\n\r\nfor item in lista:\r\n\tindexMenor = 0\r\n\tfor item_2 in lista:\r\n\t\tif item_2 < item and item != item_2:\r\n\t\t\tindexMenor += 1\r\n\r\n\tordem.insert(indexMenor, item)\t\r\n\tindexMenor = 0\r\n\r\nprint (ordem)","repo_name":"lucasclemerson/course-python","sub_path":"exercicios/exe080.py","file_name":"exe080.py","file_ext":"py","file_size_in_byte":227,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"40568811159","text":"#lv sorting list of tuples\nt = {'p':10,'y':1,'t':22}\nprint(t.items())\nat = sorted(t.items())\nprint(at)\n\n#using sorted()\nfor k,v in sorted(t.items()):\n print(k,v)\n\n#lv1 sort by values instead of key\nl = {'k':10, 'i':1, 'a':22}\ntmp = list()\nfor k,v in l.items():\n tmp.append((v,k))\nprint(tmp)\ntmp = sorted(tmp, reverse=True)\nprint(tmp)\n\n#lv2 most common word find fro disc\nfhand = open('romeo.txt')\ncounts = dict()\nfor line in fhand:\n words = line.split()\n for word in words:\n counts[word] = counts.get(word,0) + 1\n\nlst = list()\nfor key, val in counts.items():\n newtup = (val, key)\n lst.append(newtup)\nlst = sorted(lst, reverse=True)\nfor val, key in lst[:10]:\n print(key, val)\n\n#lv3 even shorter version\nc = {'a': 10, 'b':1, 'c':22}\nprint(sorted([(v,k) for k,v in c.items()]))\n\n#test QA\nlst = []\nfor key, val in counts.items():\n newtup = (val, key)\n lst.append(newtup)\nlst = sorted(lst, reverse=True)\nprint(lst)\n#short version\nprint(sorted([(v,k) for k,v in counts.items()], reverse=True))","repo_name":"Rafalc13/python13","sub_path":"day_7/Tuples_compare_and_sort.py","file_name":"Tuples_compare_and_sort.py","file_ext":"py","file_size_in_byte":1021,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"37666637527","text":"\"\"\"Spectogram input/output.\n\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport argparse\nimport sys\n\nimport tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib import cm\n\nfrom tensorflow.python.ops import io_ops\nfrom tensorflow.contrib.framework.python.ops import audio_ops as contrib_audio\n\nimport scipy.io.wavfile\nimport librosa\n\ndef main(_):\n # enable logging\n tf.logging.set_verbosity(tf.logging.INFO)\n # start tensorflow session\n sess = tf.Session()\n \n ''' model settings '''\n desired_samples = int(FLAGS.sample_rate * FLAGS.clip_duration_ms / 1000)\n window_size_samples = int(FLAGS.sample_rate * FLAGS.window_size_ms / 1000)\n window_stride_samples = int(FLAGS.sample_rate * FLAGS.window_stride_ms / 1000)\n length_minus_window = (desired_samples - window_size_samples)\n if length_minus_window < 0:\n spectrogram_length = 0\n else:\n spectrogram_length = 1 + int(length_minus_window / window_stride_samples)\n fingerprint_size = FLAGS.dct_coefficient_count * spectrogram_length\n ''' ------------- '''\n \n ''' loads and reads audio file '''\n wav_loader = io_ops.read_file(FLAGS.input_wav)\n wav_decoder = contrib_audio.decode_wav(\n wav_loader, desired_channels=1, desired_samples=desired_samples) \n sample_rate, audio = sess.run(\n [\n wav_decoder.sample_rate,\n wav_decoder.audio\n ])\n\n print('original:', audio.shape)\n #print(audio)\n #plt.figure(1)\n #plt.plot(np.concatenate(audio,axis=0))\n #plt.show()\n \n ''' scale shift and padd '''\n scaled_foreground = sess.run(tf.multiply(audio, FLAGS.scale_factor))\n print('scaled:', scaled_foreground.shape)\n \n if FLAGS.time_shift == 0:\n time_shift_amount = 0\n else:\n time_shift_amount = np.random.randint(-FLAGS.time_shift, FLAGS.time_shift)\n if time_shift_amount > 0:\n time_shift_padding = [[time_shift_amount, 0], [0, 0]]\n time_shift_offset = [0, 0]\n else:\n time_shift_padding = [[0, -time_shift_amount], [0, 0]]\n time_shift_offset = [-time_shift_amount, 0]\n \n print('padding :', time_shift_offset, 'ms')\n print('shifting :', time_shift_padding)\n \n # padding\n padded_foreground = tf.pad(\n scaled_foreground,\n time_shift_padding,\n mode='CONSTANT') \n padded_foreground = sess.run(padded_foreground)\n \n #plt.figure(2)\n #plt.plot(padded_foreground)\n #plt.show()\n \n # slicing \n sliced_foreground = tf.slice(padded_foreground,\n time_shift_offset,\n [FLAGS.sample_rate, -1])\n sliced_foreground = sess.run(sliced_foreground) \n \n #plt.figure(3)\n #plt.plot(sliced_foreground)\n #plt.show()\n\n test_diff = scaled_foreground - sliced_foreground\n print('diff between padded and non-padded:', np.linalg.norm(test_diff))\n\n current_wav = sliced_foreground\n noise_add = current_wav\n\n if bool(FLAGS.add_noise):\n ''' loads and reads noise audio file '''\n wav_loader = io_ops.read_file(FLAGS.noise_input_wav)\n wav_decoder = contrib_audio.decode_wav(\n wav_loader, desired_channels=1, desired_samples=desired_samples) \n noise_sample_rate, noise_audio = sess.run(\n [\n wav_decoder.sample_rate,\n wav_decoder.audio\n ])\n noise_audio = sess.run(tf.multiply(noise_audio, FLAGS.noise_scale_factor))\n\n #plt.figure(4)\n #plt.plot(np.concatenate(noise_audio,axis=0))\n #plt.show()\n\n ''' add noise to audio '''\n noise_add = sess.run(tf.add(noise_audio, sliced_foreground))\n #print('add:', noise_add)\n #plt.figure(4)\n #plt.plot(np.concatenate(noise_add,axis=0))\n #plt.show()\n\n current_wav = noise_add\n\n #output_data = np.concatenate(current_wav, axis=0)\n #print('blurred shape:', output_data.shape)\n #print(output_data)\n #scipy.io.wavfile.write('yes_noised.wav', sample_rate, output_data)\n #librosa.output.write_wav('yes_noised1.wav',output_data,sample_rate)\n\n \n ''' clip all tensor values to segment [-1.0,1.0] '''\n clipped_wav = sess.run(tf.clip_by_value(current_wav, -1.0, 1.0))\n #print('clamp:',noise_clamp.shape)\n #plt.figure(3)\n #plt.plot(np.concatenate(clipped_wav,axis=0))\n #plt.show()\n\n \n ''' create spectrogram '''\n spectrogram = contrib_audio.audio_spectrogram(\n clipped_wav,\n window_size=window_size_samples,\n stride=window_stride_samples,\n magnitude_squared=True) \n spectrogram = sess.run(spectrogram)\n print('spectrogram shape :', spectrogram.shape)\n\n print(spectrogram) \n spectrogram_length = desired_samples / window_size_samples\n \n print('spectrogram length:', spectrogram_length)\n print('fingerprint_size :', fingerprint_size)\n \n ''' create mfcc '''\n mfcc = contrib_audio.mfcc(\n spectrogram,\n wav_decoder.sample_rate,\n dct_coefficient_count=FLAGS.dct_coefficient_count) \n mfcc = sess.run(mfcc)#.flatten()\n print('mfcc shape :', mfcc.shape)\n #print(mfcc)\n \n\n ''' plotting process '''\n f, ((ax1,ax2),(ax3,ax4),(ax5,ax6)) = plt.subplots(3,2,sharey='row')\n \n ax1.set_title('original audio file') \n ax1.plot(audio)\n\n ax2.set_title('scaled, padded, sliced')\n ax2.plot(sliced_foreground)\n\n ax3.set_title('noised') \n ax3.plot(noise_add)\n\n ax4.set_title('clipped')\n ax4.plot(clipped_wav)\n\n ax5.set_title('spectrogram')\n ax5.matshow(spectrogram[0], \n interpolation='nearest', \n aspect='auto', \n cmap=plt.get_cmap('Greys_r'),\n origin='lower')\n ax5.xaxis.set_ticks_position('bottom')\n\n ax6.set_title('MFCC')\n ax6.matshow(mfcc[0], \n interpolation='nearest', \n aspect='auto', \n cmap=plt.get_cmap('Greys_r'), \n origin='lower')\n ax6.xaxis.set_ticks_position('bottom')\n\n plt.tight_layout()\n plt.show()\n\n \n ''' plot audio and mfcc only '''\n f, (ax1,ax2) = plt.subplots(2,1)\n\n ax1.set_title('noised, shifted audio file; word YES') \n ax1.set_xlim([0,current_wav.shape[0]])\n ax1.set_ylim([1.1*min(current_wav),1.1*max(current_wav)])\n ax1.plot(current_wav)\n\n ax2.set_title('MFCC') \n ax2.set_xlim([0,mfcc.shape[2]])\n ax2.set_ylim([0,mfcc.shape[1]])\n ax2.matshow(mfcc[0], \n interpolation='nearest', \n aspect='auto', \n cmap=plt.get_cmap('Greys_r'),\n origin='lower')\n ax2.xaxis.set_ticks_position('bottom')\n \n plt.tight_layout()\n plt.show()\n\n\n\nif __name__=='__main__':\n parser = argparse.ArgumentParser()\n \n parser.add_argument(\n '--input_wav',\n type=str,\n default='',\n help='.wav file to create spectrogram from.')\n \n parser.add_argument(\n '--clip_duration_ms',\n type=int,\n default=1000,\n help='.wav duration in ms.')\n \n parser.add_argument(\n '--sample_rate',\n type=int,\n default=16000,\n help='expected sample rate of wav files.')\n\n parser.add_argument(\n '--add_noise',\n type=int,\n default=1,\n help='whether to add noise.')\n\n parser.add_argument(\n '--noise_input_wav',\n type=str,\n default='data/_background_noise_/white_noise.wav',\n help='noise .wav files.')\n\n parser.add_argument(\n '--noise_scale_factor',\n type=float,\n default=0.1,\n help='coefficient to scale noise volume by.')\n \n parser.add_argument(\n '--time_shift',\n type=float,\n default=1000.0,\n help='range to randomly shift audio in time (ms).')\n \n parser.add_argument(\n '--scale_factor',\n type=float,\n default=1.0,\n help='coefficient to scale volume by.')\n \n parser.add_argument(\n '--window_size_ms',\n type=float,\n default=30.0,\n help=' --- ')\n \n parser.add_argument(\n '--window_stride_ms',\n type=float,\n default=10.0,\n help=' --- ')\n \n parser.add_argument(\n '--dct_coefficient_count',\n type=int,\n default=30,\n help='How many bins to use for the MFCC fingerprint')\n\n FLAGS, unparsed = parser.parse_known_args()\n tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)\n\n","repo_name":"Qkvad/SpeechRecognition","sub_path":"tf/src/preprocess.py","file_name":"preprocess.py","file_ext":"py","file_size_in_byte":7931,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"22502042249","text":"#! /usr/bin/env python\nimport sys\nimport re\nfrom subprocess import Popen,PIPE\nimport re\n\n# Slurm states\nSLURM_TO_STATE = {\n 'COMPLETED': 'success',\n 'FAILED': 'failed',\n 'CANCELLED': 'failed',\n 'TIMEOUT': 'failed',\n 'PREEMPTED': 'failed',\n 'NODE_FAIL': 'failed',\n \n 'PENDING': 'running',\n 'RUNNING': 'running',\n 'SUSPENDED': 'running',\n 'COMPLETING': 'running',\n 'CONFIGURING': 'running',\n 'REQUEUED': 'running',\n\n 'REVOKED': 'failed',\n 'SPECIAL_EXIT': 'failed',\n}\nDEFAULT_STATE = 'running'\n\ndef jobstatus(jobid):\n try:\n o,e = Popen('sacct -u $(whoami) -nXPo state -j ' + sys.argv[1],\n shell=True, stdout=PIPE, stderr=PIPE).communicate()\n ret = '{}'.format(str(o, 'utf-8').split()[0])\n if ret in SLURM_TO_STATE:\n return (SLURM_TO_STATE[ret], ret)\n else:\n print('Unknown slurm state: {}'.format(ret), file=sys.stderr)\n return (DEFAULT_STATE, ret)\n except Exception as e:\n print(e, file=sys.stderr)\n return (DEFAULT_STATE, None)\n\nif __name__ == '__main__':\n jobid = sys.argv[1].strip()\n if not re.match('\\d+', jobid):\n print(\"Argument is not job ID: {}\".format(jobid), file=sys.stderr)\n print(DEFAULT_STATE)\n else:\n state, ret = jobstatus(jobid) \n print(state)\n","repo_name":"mlbendall/TelescopeEncode","sub_path":"scripts/slurm_status.py","file_name":"slurm_status.py","file_ext":"py","file_size_in_byte":1345,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"50"} +{"seq_id":"258160480","text":"# Contains all magic numbers and strings, as well as useful generic functions\n# Constant values and functions are defined for easy reference\n\nimport sys\nimport os\n\n\napp_name = 'Cheese Whiz'\nverbose = True\n\n\ndef warning(text, exit=False, status=1):\n \"\"\"\n prints a custom error message to console\n :param text: text to print\n :param exit: if True, system throws FATAL ERROR and exits\n :param status: status code to return on system exit\n \"\"\"\n if exit:\n sys.stderr.write(\"FATAL ERROR: \"+text+\"\\n\")\n sys.exit(status)\n else:\n sys.stderr.write(\"WARNING: \"+text+\"\\n\")\n\n\ndef close(message=None):\n \"\"\"\n for debugging, exits program and outputs some variable\n :param message: text to print on exit\n \"\"\"\n if message:\n print(message)\n sys.exit(0)\n\n\ndef vprint(text):\n \"\"\"\n prints a custom message to console if in verbose mode\n :param text: text to print\n \"\"\"\n if verbose:\n print(text)\n\n\ndef relative_path(path):\n \"\"\"\n Get file path relative to calling script's directory\n :param path: filename or file path\n :return: full path name, relative to script location\n \"\"\"\n return os.path.join(os.path.join(os.getcwd(), os.path.dirname(__file__)), path)\n\n\ndef fraction_to_decimal(string):\n \"\"\"\n Converts string to float\n :param string: Fraction string of the form \"a/b\"\n :return: Float\n \"\"\"\n parts = string.split()\n numerical_parts = []\n for part in parts:\n try:\n if '/' in part:\n num_dom = part.split(\"/\")\n part_val = float(num_dom[0])/float(num_dom[1])\n elif '.' in string:\n part_val = float(part)\n else:\n part_val = int(part)\n except ValueError:\n warning('Could not convert quantity to decimal: '+part+', assuming 1')\n if len(numerical_parts):\n part_val = 0\n else:\n part_val = 1\n numerical_parts.append(part_val)\n return sum(numerical_parts)","repo_name":"armsnyder/cheese-whiz","sub_path":"app/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":2040,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"13528237258","text":"import numpy as np\n\nfrom itertools import count\n\n\n\ndef load(filename: str= \"input.txt\"):\n with open(filename, mode=\"r\", encoding=\"UTF-8\") as cin:\n cin = map(str.strip, cin)\n data = map(list, cin)\n data = list(data)\n data = np.asarray(data).astype(int)\n\n return data\n\n\n\ndef perform_step(data):\n \"\"\"\n \"\"\"\n # First, the energy level of each octopus increases by 1.\n data += 1\n\n # This process continues as long as new octopuses keep having their energy\n # level increased beyond 9.\n while np.count_nonzero(data > 9) > 0:\n flashed = np.argwhere(data > 9)\n for x, y in flashed:\n # This increases the energy level of all adjacent octopuses by 1,\n # including octopuses that are diagonally adjacent.\n data[\n max(0, x-1):min(9, x+1)+1,\n max(0, y-1):min(9, y+1)+1\n ] += 1\n # An octopus can only flash at most once per step.\n data[x,y] = -100000\n\n # Finally, any octopus that flashed during this step has its energy level\n # set to 0, as it used all of its energy to flash.\n data[data < 0] = 0\n\n return data\n\n\n\ndef part1(data, num_steps=100) -> int:\n \"\"\"\n How many total flashes are there after 100 steps?\n \"\"\"\n total = 0\n for step in range(num_steps):\n data = perform_step(data)\n total += np.count_nonzero(data == 0)\n #print(f\"Step {step+1}:\", data, sep=\"\\n\")\n\n return total\n\n\n\ndef part2(data) -> int:\n \"\"\"\n What is the first step during which all octopuses flash?\n \"\"\"\n for step in count(1):\n data = perform_step(data)\n if np.all(data == 0):\n return step\n","repo_name":"SamuelLarkin/AdventOfCode2021","sub_path":"day11/tools.py","file_name":"tools.py","file_ext":"py","file_size_in_byte":1716,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"10191033920","text":"def search(maxinos, index, cores, N):\n if len(cores)-1 < index:\n return 0\n \n if cores[index][0] == 0 or cores[index][0] == N-1 or cores[index][1] == 0 or cores[index][1] == N-1:\n return search(maxinos, index+1, cores, N)\n\n result = N\n maxinos_ = maxinos\n x = cores[index][0]\n y = cores[index][1]\n\n print(x, y)\n\n #top\n top = 0\n temp = [maxinos[i][y] for i in range(0,x)]\n if (1 or 2) not in temp:\n for i in range(0, x):\n maxinos_[i][y] = 2\n top += x + search(maxinos_, index+1, cores, N)\n\n if 0 < top:\n result = top\n maxinos_ = maxinos\n\n #bottom\n bottom = 0\n temp = [maxinos[i][y] for i in range(x+1,N)]\n if (1 or 2) not in temp:\n for i in range(x+1, N):\n maxinos_[i][y] = 2\n bottom += (N-x-1) + search(maxinos_, index+1, cores, N)\n\n if 0 < bottom and bottom < result:\n result = bottom\n maxinos_ = maxinos\n\n\n #left\n left = 0\n temp = [maxinos[x][i] for i in range(0,y)]\n if (1 or 2) not in temp:\n for i in range(0, y):\n maxinos_[x][i] = 2\n left += y + search(maxinos_, index+1, cores, N)\n\n if 0 < left and left < result:\n result = left\n maxinos_ = maxinos\n\n #right\n right = 0\n temp = [maxinos[x][i] for i in range(y+1,N)]\n if (1 or 2) not in temp:\n for i in range(y+1, N):\n maxinos_[x][i] = 2\n right += (N-y-1) + search(maxinos_, index+1, cores, N)\n\n if 0 < right and right < result:\n result = right\n maxinos_ = maxinos\n\n print(result)\n\n return result\n\nT = int(input())\nfor t in range(T):\n result = 0\n N = int(input())\n cores = list()\n maxinos = [[0 for _ in range(N)] for _ in range(N)]\n for i in range(N):\n temp = input().split()\n for j in range(N):\n maxinos[i][j] = int(temp[j])\n if maxinos[i][j] == 1:\n cores.append((i, j))\n\n print(cores)\n\n if len(cores) == 0:\n print('#'+str(t+1)+' 0')\n continue\n\n result = search(maxinos, 0, cores, N)\n\n print('#'+str(t+1), str(result))\n","repo_name":"rmk1075/SW-Expert-Academy-Java","sub_path":"SW-Expert-Academy-Python/1767.py","file_name":"1767.py","file_ext":"py","file_size_in_byte":2127,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"12488296611","text":"\"\"\" numbers=[1,2,3,4]\nnumbers_v2=[]\n\nfor i in numbers:\n numbers_v2.append (i*2)\nprint(numbers)\nprint(numbers_v2) \"\"\"\n #podemos hacer esto en una sola línea haciendo uso del MAP\n\nfrom unittest import result\n\n\nnumbers=[1,2,3,4]\nnumbers_v2= list(map(lambda i: i*2, numbers))\nprint(numbers)\nprint(numbers_v2)\n \n\"\"\"Hemos usado list() de forma que el objeto map se devuelve como una lista,\n en vez de como un objeto menos legible por el ser humano como .\n El objeto map es un iterador sobre nuestros resultados, \nde forma que podríamos hacer un bucle con for o podemos usar list()\n para convertirlo en una lista. Estamos haciendo esto aquí porque es una buena forma de revisar los resultados.\"\"\"\n\nprint(\" \")\nnumbers_v3=[1,2,3,4,5]\nnumbers_v4=[1,1,1,1,1]\n\nresult=list(map(lambda x,y :x+y, numbers_v3,numbers_v4))\nprint(result)","repo_name":"deManrrique/Python","sub_path":"map.py","file_name":"map.py","file_ext":"py","file_size_in_byte":864,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"8702958196","text":"from distutils.core import setup, Extension\n\nfizzmodule = Extension(\n 'fizzmodule', \n sources=['fizzmodule.c'],\n extra_objects=['../fizzy.o'],\n extra_compile_args=['-m32'],\n extra_link_args=['-m32 -read_only_relocs suppress'])\n\nsetup(\n name='fizzmodule',\n version='1.0',\n description='FizzBuzz test',\n ext_modules=[fizzmodule],\n)\n","repo_name":"Flushot/sandbox","sub_path":"python/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":357,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"28665719978","text":"import sys\nfrom PIL import Image\n#by Yusdel Lorenzo 2019\ndef AStar(start, goal, neighborNodes, distance, cost):\n def makePath(parentNode, currNode):\n path = []\n while currNode is not None:\n path.append(currNode)\n currNode = parentNode[currNode]\n return list(reversed(path))\n\n gScore = {start: 0}\n fScore = {start: gScore[start] + cost(start, goal)}\n openSet = {start}\n closedSet = set()\n parentNode = {start: None}\n\n while openSet:\n current = min(openSet, key=lambda x: fScore[x])\n if current == goal:\n return makePath(parentNode, goal)\n openSet.remove(current)\n closedSet.add(current)\n for neighbor in neighborNodes(current):\n if neighbor in closedSet:\n continue\n if neighbor not in openSet:\n openSet.add(neighbor)\n newScore = gScore[current] + distance(current, neighbor)\n if newScore >= gScore.get(neighbor, float('inf')):\n continue\n parentNode[neighbor] = current\n gScore[neighbor] = newScore\n fScore[neighbor] = newScore + cost(neighbor, goal)\n return []\n\ndef blocked(p):\n x,y = p\n pixel = path_pixels[x,y]\n if any(c < 225 for c in pixel):\n return True\n\ndef getNeighbors(Pixel):\n x, y = Pixel\n neighbors = [(x-1, y), (x, y-1), (x+1, y), (x, y+1)]\n return [Pixel for Pixel in neighbors if not blocked(Pixel)]\n\ndef manhattan(pixel1, pixel2):\n return abs(pixel1[0]-pixel2[0]) + abs(pixel1[1]-pixel2[1])\n\ndef crowFlies(pixel1, pixel2):\n return (pixel1[0]-pixel2[0])**2 + (pixel1[1]-pixel2[1])**2\n\nstart = (400, 984) #middle at the top\ngoal = (398, 25) #middle at the bottom\npath_img = Image.open(sys.argv[1])\npath_pixels = path_img.load()\ndistance = manhattan\nheuristic = manhattan\npath = AStar(start, goal, getNeighbors, distance, heuristic)\nfor position in path:\n x,y = position\n path_pixels[x,y] = (255,0,0) # red\npath_img.save(sys.argv[2])\n","repo_name":"lima-yusdel/Python","sub_path":"Procedurally generated maze solving/Solver.py","file_name":"Solver.py","file_ext":"py","file_size_in_byte":2017,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"38532309201","text":"# def name(): // Creating Your Own Function Just like built-in funcitons\r\n\r\n# Ex1\r\nprint(\"Example 1\")\r\ndef greet():\r\n print(\"Hi there\")\r\n print(\"Welcome aboard\")\r\ngreet()\r\n\r\n# Ex2\r\nprint(\"\\nExample 2\") # greet('Parameter')\r\ndef greet(First_Name, Last_name):\r\n print(f\"Hi {First_Name} {Last_name}\")\r\n print(\"Welcome aboard\\n\")\r\n #greet('Arguments')\r\ngreet(\"Mohammed\", \"Ahmed\")\r\ngreet(\"Shan\", \"Ahmed\")\r\n\r\n\r\n# Ex3\r\nprint(\"\\nExample 3\\n\"\r\n \"Done! Written Hi, Mohammed in Content.txt... \\n\")\r\n# In Function 2 Types:-\r\n # 1- Perform a task\r\n # 2- Return a value\r\ndef round(Name):\r\n return f\"Hi {Name}\"\r\n\r\nMsg = round(\"Mohammed\")\r\nfile = open(\"content.txt\", \"w\") # W = Write\r\nfile.write(Msg)\r\n\r\n# Ex4\r\nprint(\"Ex 4\")\r\ndef increment(num, by):\r\n return num + by\r\n # result = increment(2,1) = 3\r\n # print(result)\r\n # by=1 is kwarg helps you read code easier!\r\nprint(increment(2,by=1))\r\n\r\n# Ex5\r\n\"\"\"\r\n# NB: def incrment(Required Parameter, Optional Parameter):\r\ndef increment(number, by=1):\r\n return number + by\r\nprint(increment(2,5))\r\n\"\"\"\r\n\r\n# Ex6\r\n\"\"\"\r\ndef multiply(*numbers): #Number(S) // [] = List | () = Tuple, E.g. Immutable\r\n total = 1\r\n for number in numbers:\r\n total *= number # Ttl = Ttl * Number\r\n return total\r\nprint(multiply(2,3,4,5))\r\n\"\"\"\r\n\r\n# Ex7\r\n\"\"\"\r\ndef save_user(**user):\r\n print(user[\"Name\"])\r\n# {Dictonary w Key : Values}\r\nsave_user(ID=1, Name=\"Mo\", Age=18)\r\n\"\"\"\r\n\r\n# Ex8 Variable Scope\r\n# Name Error Msg Not Defined unless parameter is added (name)\r\n# IF NOT CALLED, while later.py release memory allocated for LOCAL-Var\r\n# Output is 'a' | Cuz it's Global Variable & Outside UDF\r\n\"\"\"Msg = \"a\" | # Global Var // Perma-Memory // Outisde UDF\r\ndef greet(name):\r\n Msg = \"b\"\r\ngreet(\"Mo\")\r\nprint(Msg)\r\n\"\"\"\r\n\r\n# Excercise Interview Tough Question\r\ndef fizz_buzz(input):\r\n if (input % 3== 0) and (input % 5 == 0):\r\n return \"FizzBuzz\"\r\n elif input % 3 == 0:\r\n return \"Fizz\"\r\n elif input % 5 == 0:\r\n return \"Buzz\"\r\n return input\r\nprint(fizz_buzz(5))","repo_name":"MohammedA-04/SDD_Python","sub_path":"Home Projects/03-10_LearnFunctions.py","file_name":"03-10_LearnFunctions.py","file_ext":"py","file_size_in_byte":2048,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"24586875577","text":"from importlib.resources import path\nfrom controller import *\nimport os.path\n\ndef criarArquivos(*args):\n for i in args:\n if not os.path.exists(i):\n with open(i , 'w') as arq:\n arq.writelines('')\n\nprint('\\n Bem-Vindo a Mercearia!\\n')\n\ncriarArquivos('Categoria.txt', 'Cliente.txt', 'Estoque.txt',\n 'Fornecedor.txt', 'Funcionario.txt', 'Vendas.txt')\n\nprint('Iniciando sistema...')\n\nif __name__ == '__main__':\n \n while True:\n \n print('\\nDigite 1 para acessar ( Categorias )\\n'\n 'Digite 2 para acessar ( Estoque )\\n'\n 'Digite 3 para acessar ( Vendas )\\n'\n 'Digite 4 para acessar ( Fornecedores )\\n'\n 'Digite 5 para acessar ( Clientes )\\n'\n 'Digite 6 para acessar ( Funcionarios )\\n'\n 'Digite 7 para acessar ( Produtos mais vendidos )\\n'\n 'Digite 0 para Encerrar o sistema.\\n')\n \n opcao = int(input('Opção selecionada: '))\n\n if opcao == 1:\n cat = ControllerCategoria()\n\n while True:\n \n print('\\nDigite 1 para cadastrar Categoria\\n'\n 'Digite 2 para alterar Categoria\\n'\n 'Digite 3 para remover Categoria\\n'\n 'Digite 4 para exibir Categorias\\n'\n 'Digite 0 para sair\\n')\n \n decisao = int(input('Opção selecionada: '))\n\n if decisao == 1:\n categoria = input('Digite a categoria que deseja cadastrar: ')\n print('')\n cat.cadastrarCategoria(categoria)\n \n elif decisao == 2:\n alteraCategoria = input('Digite a categoria que deseja alterar: ')\n novaCategoria = input('Digite para qual categoria será alterada: ')\n print('')\n cat.alterarCategoria(alteraCategoria, novaCategoria)\n \n elif decisao == 3: \n removeCategoria = input('Digite a categoria que deseja remover:')\n print('')\n cat.removerCategoria(removeCategoria)\n \n elif decisao == 4:\n print('')\n cat.exibirCategoria()\n\n elif decisao == 0:\n print('Voltando para o menu principal...')\n break\n\n else:\n print('Opção inválida! Tente novamente') \n\n elif opcao == 2:\n est = ControllerEstoque()\n\n while True:\n \n print('\\nDigite 1 para cadastrar Produto\\n'\n 'Digite 2 para alterar Produto\\n'\n 'Digite 3 para remover Produto\\n'\n 'Digite 4 para exibir Estoque\\n'\n 'Digite 0 para sair\\n')\n \n decisao = int(input('\\nOpção selecionada: '))\n\n if decisao == 1:\n print('\\nCadastrar produto: ') \n nome = input('Digite o nome: ')\n valor = input('Digite o valor: ')\n cat = input('Digite a categoria: ')\n qnt = input('Digite a quantidade: ') \n print('')\n est.cadastrarProduto(nome, valor, cat, qnt)\n \n elif decisao == 2:\n alteraProduto = input('\\nDigite o nome do produto que deseja alterar: ')\n novoNome = input('Digite o novo nome: ')\n novoValor = input('Digite o valor: ')\n novaCat = input('Digite a categoria: ')\n novaQnt = input('Digite a quantidade: ')\n print('')\n est.alterarProduto(alteraProduto, novoNome, novoValor, novaCat, novaQnt)\n \n elif decisao == 3: \n removeNome = input('\\nDigite o nome do produto que deseja remover:')\n print('')\n est.removerProduto(removeNome)\n \n elif decisao == 4:\n print('')\n est.exibirEstoque()\n\n elif decisao == 0:\n print('\\nVoltando para o menu principal...')\n break\n\n else:\n print('\\nOpção inválida! Tente novamente')\n\n elif opcao == 3:\n vend = ControllerVenda()\n\n while True:\n \n print('Digite 1 para cadastrar vendas\\n'\n 'Digite 2 para exibir vendas\\n'\n 'Digite 0 para sair')\n decisao = int(input('\\nOpção selecionada: '))\n\n if decisao == 1:\n print('\\nCadastrar venda')\n produto = input('Digite o nome do produto: ')\n quantidade = input('Digite a quantidade vendida: ')\n comprador = input('Digite o nome do cliente: ')\n vendedor = input('Digite o vendedor que realizou a venda: ')\n print('')\n vend.cadastrarVenda(produto, vendedor, comprador, quantidade)\n\n elif decisao == 2:\n inicio = input('\\nDigite a data de inicio (dd/mm/aaaa): ')\n fim = input('Digite a data limite: ')\n print('')\n vend.exibirVenda(inicio, fim)\n \n elif decisao == 0:\n print('\\nVoltando para o menu principal...')\n break\n\n else:\n print('\\nOpção invalida!')\n \n elif opcao == 4:\n fornecedores = ControllerFornecedor()\n\n while True:\n \n print('\\nDigite 1 para cadastrar Fornecedor\\n'\n 'Digite 2 para alterar Fornecedor\\n'\n 'Digite 3 para remover Fornecedor\\n'\n 'Digite 4 para exibir Fornecedores\\n'\n 'Digite 0 para sair\\n')\n decisao = int(input('Opção selecionada: '))\n\n if decisao == 1:\n print('\\nCadastrar Fornecedor')\n nome = input('Digite o nome: ')\n cnpj = input('Digite o cnpj: ')\n telefone = input('Digite o telefone: ')\n categoria = input('Digite a categoria: ')\n print('')\n fornecedores.cadastrarFornecedor(Fornecedor(nome, cnpj, telefone, categoria))\n\n elif decisao == 2:\n print('\\nAlterar Fornecedor')\n alterarNome: input('Digite o nome do fornecedor que deseja alterar')\n nome = input('Digite o nome: ')\n cnpj = input('Digite o cnpj: ')\n telefone = input('Digite o telefone: ')\n categoria = input('Digite a categoria: ')\n print('')\n fornecedores.alterarFornecedor(alterarNome, Fornecedor(nome, cnpj, telefone, categoria))\n\n elif decisao == 3:\n print('\\nRemover Fornecedor')\n removecnpj = input('Digite o cnpj do fornecedor que deseja remover: ')\n print('')\n fornecedores.removerFornecedor(removecnpj)\n\n elif decisao == 4:\n print('')\n fornecedores.exibirFornecedor()\n\n elif decisao == 0:\n print('\\nVoltando para o menu principal...')\n break\n\n else:\n print('\\nOpção invalida!')\n\n elif opcao == 5:\n cliente = ControllerCliente()\n\n while True: \n \n print('\\nDigite 1 para cadastrar Cliente\\n'\n 'Digite 2 para alterar Cliente\\n'\n 'Digite 3 para remover Cliente\\n'\n 'Digite 4 para exibir Clientes\\n'\n 'Digite 0 para sair\\n')\n decisao = int(input('Opção selecionada: '))\n\n if decisao == 1:\n print('\\nCadastrar Cliente')\n nome = input('Digite o nome: ')\n cpf = input('Digite o cpf: ')\n telefone = input('Digite o telefone: ')\n email = input('Digite o email: ')\n endereco = input('Digite o endereco: ')\n print('')\n cliente.cadastrarCliente(Cliente(nome, telefone, cpf, email, endereco))\n\n elif decisao == 2:\n print('\\nAlterar Cliente')\n alterarcpf: input('Digite o cpf do cliente que deseja alterar')\n nome = input('Digite o nome: ')\n cpf = input('Digite o cpf: ')\n telefone = input('Digite o telefone: ')\n email = input('Digite o email: ')\n endereco = input('Digite o endereco: ')\n print('')\n cliente.alterarCliente(alterarcpf, Cliente(nome, telefone, cpf, email, endereco))\n\n elif decisao == 3:\n print('\\nRemover Cliente')\n removecpf = input('Digite o cpf do cliente que deseja remover: ')\n print('')\n cliente.removerCliente(removecpf)\n\n elif decisao == 4:\n print('')\n cliente.exibirCliente()\n\n elif decisao == 0:\n print('\\nVoltando para o menu principal...')\n break\n\n else:\n print('\\nOpção invalida!')\n\n elif opcao == 6:\n func = ControllerFuncionario()\n\n while True: \n \n print('\\nDigite 1 para cadastrar Funcionario\\n'\n 'Digite 2 para alterar Funcionario\\n'\n 'Digite 3 para remover Funcionario\\n'\n 'Digite 4 para exibir Funcionarios\\n'\n 'Digite 0 para sair\\n')\n decisao = int(input('Opção selecionada: '))\n\n if decisao == 1:\n print('\\nCadastrar Funcionario')\n nome = input('Digite o nome: ')\n cpf = input('Digite o cpf: ')\n clt = input('Digite a clt: ')\n telefone = input('Digite o telefone: ')\n email = input('Digite o email: ')\n endereco = input('Digite o endereco: ')\n print('')\n func.cadastrarFuncionario(Funcionario(clt, nome, telefone, cpf, email, endereco))\n\n elif decisao == 2:\n print('\\nAlterar Funcionario')\n alterarcpf: input('Digite o cpf do funcionario que deseja alterar')\n nome = input('Digite o nome: ')\n cpf = input('Digite o cpf: ')\n clt = input('Digite a clt: ')\n telefone = input('Digite o telefone: ')\n email = input('Digite o email: ')\n endereco = input('Digite o endereco: ')\n print('')\n func.alterarFuncionario(alterarcpf, Funcionario(clt, nome, telefone, cpf, email, endereco))\n\n elif decisao == 3:\n print('\\nRemover Funcionario')\n removecpf = input('Digite o cpf do funcionario que deseja remover: ')\n print('')\n func.removerFuncionario(removecpf)\n\n elif decisao == 4:\n print('')\n func.exibirFuncionario()\n\n elif decisao == 0:\n print('\\nVoltando para o menu principal...')\n break\n\n else:\n print('\\nOpção invalida!')\n\n elif opcao == 7:\n venda = ControllerVenda()\n venda.relatorioProdutos()\n print('')\n continua = input('Deseja continuar?')\n\n elif opcao == 0:\n print('\\nEncerrando sistema...')\n break\n\n else: \n print('\\nOpção inválida!')\n","repo_name":"FernandohsSantos1/MVC_Mercearia","sub_path":"view.py","file_name":"view.py","file_ext":"py","file_size_in_byte":12448,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"23169170262","text":"from fastapi import APIRouter, Depends, HTTPException\nfrom sqlalchemy import or_\nfrom sqlalchemy.orm import Session, joinedload\n\nimport schemas\nfrom auth import user_dependency\nfrom database import get_db\nfrom models import Author, Book\n\nrouter = APIRouter()\n\n# current_user: user_dependency,\n\n\ndef verify_author_id(db, author_id):\n db_author = db.query(Author).filter(Author.id == author_id).first()\n if not db_author:\n raise HTTPException(status_code=404, detail=\"Author not found\")\n\n\n@router.post(\"/\", response_model=schemas.BookWithID)\ndef add_book(current_user: user_dependency, book: schemas.BookUpsert, db: Session = Depends(get_db)):\n # Verify that the author exists\n verify_author_id(db, book.author_id)\n\n db_book = Book(name=book.name, author_id=book.author_id, num_pages=book.num_pages)\n db.add(db_book)\n db.commit()\n return db_book\n\n\n@router.delete(\"/{book_id}\", response_model=schemas.BookWithID)\ndef delete_book(current_user: user_dependency, book_id: int, db: Session = Depends(get_db)):\n db_book = db.query(Book).options(joinedload(Book.author)).filter(Book.id == book_id).first()\n if db_book is None:\n raise HTTPException(status_code=404, detail=\"Book not found\")\n\n db.delete(db_book)\n db.commit()\n\n return db_book\n\n\n@router.get(\"/\", response_model=schemas.PaginationResponse[schemas.Book])\ndef get_books(current_user: user_dependency, skip: int = 0, limit: int = 10, db: Session = Depends(get_db)):\n total_count = db.query(Book).count()\n books = db.query(Book, Author).join(Author).order_by(Book.id.desc()).offset(skip).limit(limit).all()\n books_with_authors = [\n {\"id\": book.id, \"name\": book.name, \"author\": author, \"num_pages\": book.num_pages} for book, author in books\n ]\n\n current_page = (skip // limit) + 1\n total_pages = (total_count - 1) // limit + 1\n has_next_page = skip + limit < total_count\n has_prev_page = skip > 0\n\n return schemas.PaginationResponse[schemas.Book](\n total_count=total_count,\n current_page=current_page,\n total_pages=total_pages,\n has_next_page=has_next_page,\n has_prev_page=has_prev_page,\n data=books_with_authors,\n )\n\n\n@router.get(\"/{book_id}\", response_model=schemas.Book)\ndef get_one_book(current_user: user_dependency, book_id: int, db: Session = Depends(get_db)):\n result = db.query(Book, Author).join(Author).filter(Book.id == book_id).first()\n\n if not result:\n raise HTTPException(status_code=404, detail=\"Book not found\")\n\n book, author = result\n\n return {\n \"id\": book.id,\n \"name\": book.name,\n \"author\": author,\n \"num_pages\": book.num_pages,\n }\n\n\n@router.patch(\"/{book_id}\", response_model=schemas.BookWithID)\ndef update_book(current_user: user_dependency, book_id: int, book: schemas.BookUpsert, db: Session = Depends(get_db)):\n db_book = db.query(Book).filter(Book.id == book_id).first()\n if not db_book:\n raise HTTPException(status_code=404, detail=\"Book not found\")\n\n # Verify that the author exists\n # if \"author_id\" in book.dict():\n # verify_author_id(db, book.author_id)\n\n book_data = book.dict(exclude_unset=True)\n for key, value in book_data.items():\n setattr(db_book, key, value)\n\n db.commit()\n db.refresh(db_book)\n return db_book\n\n\n@router.get(\"/search/\", response_model=list[schemas.Book])\ndef search_books(current_user: user_dependency, query: str, db: Session = Depends(get_db)):\n books = (\n db.query(Book, Author)\n .filter(\n or_(Book.name.ilike(f\"%{query}%\"), Author.name.ilike(f\"%{query}%\")),\n )\n .join(Book.author)\n .all()\n )\n\n results = []\n for book, author in books:\n result = {\n \"id\": book.id,\n \"name\": book.name,\n \"author\": {\n \"id\": author.id,\n \"name\": author.name,\n },\n \"num_pages\": book.num_pages,\n }\n results.append(result)\n\n return results\n\n\n@router.get(\"/{author_id}\", response_model=schemas.PaginationResponse[schemas.Book])\ndef get_books_by_author(\n current_user: user_dependency,\n author_id: int,\n skip: int = 0,\n limit: int = 10,\n db: Session = Depends(get_db),\n):\n total_count = db.query(Book).count()\n books = db.query(Book, Author).join(Author).offset(skip).limit(limit).filter(Author.id == author_id)\n books_with_authors = [{\"id\": book.id, \"name\": book.name, \"author\": author} for book, author in books]\n\n current_page = (skip // limit) + 1\n total_pages = (total_count - 1) // limit + 1\n has_next_page = skip + limit < total_count\n has_prev_page = skip > 0\n\n return schemas.PaginationResponse[schemas.Book](\n total_count=total_count,\n current_page=current_page,\n total_pages=total_pages,\n has_next_page=has_next_page,\n has_prev_page=has_prev_page,\n data=books_with_authors,\n )\n","repo_name":"mrhassaan012/developer-full-stack-challenge","sub_path":"src/api/routers/books.py","file_name":"books.py","file_ext":"py","file_size_in_byte":4948,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"15619801019","text":"from flask import Blueprint, request, render_template, redirect , url_for\nimport json\nfrom models.store import Store\nfrom models.user import requires_admin, requires_login\n\nstore_blueprint = Blueprint(\"stores\", __name__)\n\n\n@store_blueprint.route('/')\n@requires_login\ndef index():\n stores = Store.all()\n return render_template(\"stores/index.html\", stores=stores)\n\n\n@store_blueprint.route('/new', methods=['GET', 'POST'])\n@requires_admin\ndef new_store():\n if request.method == 'POST':\n name = request.form['name']\n url_prefix = request.form['url_prefix']\n tag_name = request.form['tag_name']\n query_json = request.form['query']\n query = json.loads(query_json)\n Store(name, url_prefix, tag_name, query).save_to_mongo()\n stores = Store.all()\n return render_template(\"stores/index.html\", stores=stores)\n return render_template(\"stores/new_store.html\")\n\n@store_blueprint.route('/edit/' , methods=['GET','POST'])\n@requires_admin\ndef edit_store(store_id):\n store = Store.get_by_id(store_id)\n\n if request.method == \"POST\":\n store.url_prefix = request.form['url_prefix']\n store.tag_name = request.form['tag_name']\n query_json = request.form['query']\n store.query = json.loads(query_json.replace('\\'','\"'))\n store.save_to_mongo()\n return redirect(url_for('.index'))\n return render_template(\"stores/edit_store.html\" , store = store)\n\n@store_blueprint.route('/delete/' , methods=['GET'])\n@requires_admin\ndef delete_store(store_id):\n store = Store.get_by_id(store_id)\n store.remove_from_mongo()\n stores = Store.all()\n return render_template(\"stores/index.html\", stores=stores)","repo_name":"amalmajeed/Pricing_Service","sub_path":"views/stores.py","file_name":"stores.py","file_ext":"py","file_size_in_byte":1727,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"8136000528","text":"import os\r\nfrom werkzeug.utils import secure_filename\r\nfrom flask import Flask , render_template ,request,Response\r\nimport vehicle_counting\r\nimport cv2\r\n\r\nUPLOAD_FOLDER = r'C:\\Users\\AQEEL\\Desktop\\Aqeel\\sem-5\\MP\\flaskkkkk\\test\\flaskk\\fol'\r\n\r\nvidpath=''\r\napp=Flask(__name__)\r\napp.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER\r\n\r\n@app.route('/', methods=['GET','POST'])\r\ndef home():\r\n return render_template('index1.html')\r\n@app.route('/v', methods=['GET','POST'])\r\ndef hello_world():\r\n return render_template('vidcap.html')\r\n\r\n@app.route('/vid',methods=['GET','POST'])\r\ndef path():\r\n global vidpath\r\n vidfile=request.files['vidfile']\r\n filename = secure_filename(vidfile.filename)\r\n vidpath = os.path.join(app.config['UPLOAD_FOLDER'], filename)\r\n vidfile.save(vidpath)\r\n return render_template('display2.html')\r\n@app.route('/display', methods=['GET'])\r\ndef hello():\r\n return render_template('display.html')\r\n\r\n@app.route('/p', methods=['POST','GET'])\r\ndef frames(type,address):\r\n if type==0:\r\n cap=cv2.VideoCapture(address)\r\n if type==1:\r\n cap = cv2.VideoCapture(0,cv2.CAP_DSHOW)\r\n i=0\r\n j=0\r\n while(cap.isOpened()):\r\n flag,frame=cap.read()\r\n if flag==False:\r\n break \r\n if j%30==0: \r\n add='luci'+str(i)+'.jpg'\r\n cv2.imwrite(add,frame)\r\n img_addr=rf\"C:/Users/AQEEL/Desktop/Aqeel/sem-5/MP/flaskkkkk/test/{add}\" \r\n vehicle_counting.count(img_addr)\r\n i+=1\r\n img = cv2.imread(img_addr)\r\n ret, buffer = cv2.imencode('.jpg', img)\r\n frame = buffer.tobytes()\r\n yield (b'--frame\\r\\n'b'Content-Type: image/jpeg\\r\\n\\r\\n' + frame + b'\\r\\n')\r\n \r\n j+=1\r\n cap.release()\r\n@app.route('/live_feed')\r\ndef live_feed():\r\n return Response(frames(1,0), mimetype='multipart/x-mixed-replace; boundary=frame')\r\n@app.route('/video_feed')\r\ndef video_feed():\r\n print(vidpath)\r\n return Response(frames(0,vidpath), mimetype='multipart/x-mixed-replace; boundary=frame')\r\n\r\nif __name__==\"__main__\":\r\n app.run(debug='true')","repo_name":"aftabahmed-09/ATST","sub_path":"flaskk/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2166,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"13198362458","text":"import argparse\nimport os\nimport h5py\nimport numpy as np\nimport torch\nimport torchtext.data as data\nimport torch.utils.data\nimport sys\nfrom tqdm import tqdm\nimport json\n\ndef get_CMU_words(CMU_path):\n words = []\n with open(CMU_path) as f:\n lines = f.readlines()\n for wcnt, line in enumerate(lines):\n grapheme, phoneme = line.split(\" \",1)\n words.append(grapheme)\n return words\n\ndef get_LRW_split(args, split, CMUwords):\n lst_path = args.LRW_words_path\n word_indices = []\n with open(lst_path) as f:\n lines = f.readlines() #list of words\n for word in tqdm(lines):\n word = word.strip()\n widx_array = []\n widx = CMUwords.index(word) \n widx_array.append(widx)\n for filename in os.listdir(os.path.join(args.LRW_path, word.upper(), split)):\n Fwidx = {}\n L = filename.strip()\n path = os.path.join(word.upper(), split, L).replace(\".mp4.npy\", \"\")\n Fwidx['widx']=widx_array\n Fwidx['fn']=path\n word_indices.append(Fwidx)\n return word_indices\n\ndef get_LRW_splits():\n parser = argparse.ArgumentParser(description='Script for creating main splits of LRW.')\n parser.add_argument('--CMUdict_path', default='../data/vocab/cmudict.dict')\n parser.add_argument('--LRW_path', default='../data/lrw/features/main/') \n parser.add_argument('--LRW_words_path', default='../data/lrw/LRWwords.lst')\n args = parser.parse_args()\n CMUwords = get_CMU_words(args.CMUdict_path) \n S = ['train', 'val', 'test']\n Dsplits = {}\n for i,s in enumerate(S):\n Dsplits[s] = get_LRW_split(args, s, CMUwords)\n with open(\"../data/lrw/DsplitsLRW.json\", \"w\") as fp:\n json.dump(Dsplits, fp)\n\nif __name__=='__main__':\n get_LRW_splits()\n","repo_name":"lilianemomeni/KWS-Net","sub_path":"misc/data_splits_lrw.py","file_name":"data_splits_lrw.py","file_ext":"py","file_size_in_byte":1764,"program_lang":"python","lang":"en","doc_type":"code","stars":57,"dataset":"github-code","pt":"52"} +{"seq_id":"12962777643","text":"import os\nfrom typing import Dict, Optional\n\nfrom ..datapoint.annotation import ImageAnnotation\nfrom ..datapoint.box import BoundingBox\nfrom ..datapoint.image import Image\nfrom ..utils.detection_types import JsonDict\nfrom ..utils.fs import load_image_from_file\nfrom ..utils.settings import get_type\nfrom .maputils import MappingContextManager, curry, maybe_get_fake_score\n\n\n@curry\ndef pascal_voc_dict_to_image(\n dp: JsonDict,\n categories_name_as_key: Dict[str, str],\n load_image: bool,\n filter_empty_image: bool,\n fake_score: bool,\n category_name_mapping: Optional[Dict[str, str]] = None,\n) -> Optional[Image]:\n \"\"\"\n Map a dataset in a structure equivalent to iiitar13k annotation style to image format\n\n :param dp: a datapoint in serialized iiitar13k format. Note that another conversion from xml to\n a dict structure is required.\n :param categories_name_as_key: A dict of categories, e.g. DatasetCategories.get_categories(name_as_key=True)\n :param load_image: If 'True' it will load image to attr: Image.image\n :param filter_empty_image: Will return None, if datapoint has no annotations\n :param fake_score: If dp does not contain a score, a fake score with uniform random variables in (0,1)\n will be added.\n :param category_name_mapping: Map incoming category names, e.g. {\"source_name\":\"target_name\"}\n :return: Image\n \"\"\"\n\n anns = dp.get(\"objects\", [])\n if not anns and filter_empty_image:\n return None\n\n with MappingContextManager(dp.get(\"filename\")) as mapping_context:\n image = Image(\n file_name=os.path.split(dp[\"filename\"])[1].replace(\".xml\", \".jpg\"),\n location=dp[\"filename\"].replace(\".xml\", \".jpg\").replace(\"xml\", \"images\"),\n )\n\n if load_image:\n image.image = load_image_from_file(image.location)\n image.set_width_height(float(dp.get(\"width\", 0)), float(dp.get(\"height\", 0)))\n\n for ann in anns:\n x_1 = min(max(ann[\"xmin\"], 0), image.width if image.width else float(dp.get(\"width\", 0)))\n x_2 = min(max(ann[\"xmax\"], 0), image.width if image.width else float(dp.get(\"width\", 0)))\n y_1 = min(max(ann[\"ymin\"], 0), image.height if image.height else float(dp.get(\"height\", 0)))\n y_2 = min(max(ann[\"ymax\"], 0), image.height if image.height else float(dp.get(\"height\", 0)))\n\n bbox = BoundingBox(absolute_coords=True, ulx=x_1, uly=y_1, lrx=x_2, lry=y_2)\n\n if category_name_mapping is not None:\n label = category_name_mapping.get(ann[\"name\"])\n if not label:\n label = ann[\"name\"]\n else:\n label = ann[\"label\"]\n assert isinstance(label, str)\n\n annotation = ImageAnnotation(\n category_name=get_type(label),\n bounding_box=bbox,\n category_id=categories_name_as_key[label],\n score=maybe_get_fake_score(fake_score),\n )\n image.dump(annotation)\n\n if mapping_context.context_error:\n return None\n return image\n","repo_name":"deepdoctection/deepdoctection","sub_path":"deepdoctection/mapper/pascalstruct.py","file_name":"pascalstruct.py","file_ext":"py","file_size_in_byte":3124,"program_lang":"python","lang":"en","doc_type":"code","stars":1814,"dataset":"github-code","pt":"52"} +{"seq_id":"36557397280","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\n\nfrom oslo_db.sqlalchemy import utils\nfrom sqlalchemy import Column\nfrom sqlalchemy import MetaData\nfrom sqlalchemy import String\n\nfrom nova.db.sqlalchemy import api\n\n\ndef upgrade(migrate_engine):\n meta = MetaData()\n meta.bind = migrate_engine\n\n shadow_prefix = api._SHADOW_TABLE_PREFIX\n uuid_col = Column('uuid', String(36))\n\n pci_devices = utils.get_table(migrate_engine, 'pci_devices')\n if not hasattr(pci_devices.c, 'uuid'):\n pci_devices.create_column(uuid_col.copy())\n\n shadow_pci_devices = utils.get_table(migrate_engine,\n shadow_prefix + 'pci_devices')\n if not hasattr(shadow_pci_devices.c, 'uuid'):\n shadow_pci_devices.create_column(uuid_col.copy())\n","repo_name":"starlingx-staging/stx-nova","sub_path":"nova/db/sqlalchemy/migrate_repo/versions/362_add_pci_devices_uuid.py","file_name":"362_add_pci_devices_uuid.py","file_ext":"py","file_size_in_byte":1309,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"52"} +{"seq_id":"28508486790","text":"import os\nfrom app import create_app, db\nfrom app.models import User, Role\n\n\nfrom flask_script import Manager, Shell\nfrom flask_migrate import Migrate, MigrateCommand\nfrom flask_login import login_required\n\n# from flask_mail import Mail, Message\n# from threading import Thread\n# from flask_bootstrap import Bootstrap\n# from flask_wtf import Form\n# from wtforms import StringField, SubmitField\n# from wtforms.validators import Required\n# from flask_sqlalchemy import SQLAlchemy\n# from flask_moment import Moment\n# from datetime import datetime\n# from flask import Flask, request, render_template, redirect, session, url_for, flash\n\n\n# basedir = os.path.abspath(os.path.dirname(__file__))\n# print('basedir ' + basedir)\n# app = Flask(__name__)\n#\n# app.config['SQLALCHEMY_DATABASE_URI'] =\\\n# 'sqlite:///' + os.path.join(basedir, 'data.sqlite')\n# app.config['SQLALCHEMY_COMMIT_ON_TEARDOWN'] = True\n# db = SQLAlchemy(app)\n#\n# app.config['SECRET_KEY'] = '123456789'\n# app.config.update(dict(\n# DEBUG=True,\n# MAIL_SERVER='smtp.163.com',\n# MAIL_PORT=25,\n# MAIL_USE_TLS=True,\n# MAIL_USE_SSL=False,\n# MAIL_USERNAME=os.environ.get('MAIL_USERNAME'),\n# MAIL_PASSWORD=os.environ.get('MAIL_PASSWORD'),\n# ))\n# app.config['FLASKY_MAIL_SUBJECT_PREFIX'] = '[Flasky]'\n# app.config['FLASKY_MAIL_SENDER'] = os.environ.get('MAIL_USERNAME')\n# app.config['FLASKY_ADMIN'] = os.environ.get('MAIL_USERNAME')\n#\n#\n# mail = Mail(app)\n# manager = Manager(app)\n# bootstrap = Bootstrap(app)\n# moment = Moment(app)\n#\n# migrate = Migrate(app, db)\n\n#\n# def send_async_email(app, msg):\n# with app.app_context():\n# mail.send(msg)\n#\n#\n# def send_email(to, subject, template, **kwarqs):\n#\n# # print(os.environ.get('MAIL_USERNAME'))\n# msg = Message(app.config['FLASKY_MAIL_SUBJECT_PREFIX']+subject, sender=app.config['FLASKY_ADMIN'], recipients=[to])\n# msg.body = render_template(template + '.txt', **kwarqs)\n# msg.html = render_template(template + '.html', **kwarqs)\n# thr = Thread(target=send_async_email, args=[app, msg])\n# thr.start()\n# return thr\n# # mail.send(msg)\n\n\n# class NameForm(Form):\n# name = StringField('what is your name?', validators=[Required()])\n# submit = SubmitField('submit')\n#\n#\n# class Role(db.Model):\n# __tablename__ = 'roles'\n# id = db.Column(db.Integer, primary_key=True)\n# name = db.Column(db.String(64), unique=True)\n# users = db.relationship('User', backref='role')\n#\n# def __repr__(self):\n# return '' % self.name\n#\n#\n# class User(db.Model):\n# __tablename__ = 'user'\n# id = db.Column(db.Integer, primary_key=True)\n# name = db.Column(db.String(64), unique=True, index=True)\n# role_id = db.Column(db.Integer, db.ForeignKey('roles.id'))\n#\n# def __repr__(self):\n# return '' % self.name\n\n\n# @app.route('/', methods=[\"GET\", \"POST\"])\n# def index():\n# # user_agent = request.headers.get('User-Agent')\n# # name = None\n# # env_dist = os.environ\n# # for key in env_dist:\n# # print(key + ' : ' + env_dist[key])\n#\n# print(os.environ.get('MAIL_USERNAME'))\n#\n# form = NameForm()\n# if form.validate_on_submit():\n# # old_name = session.get('name')\n# # if old_name != None and old_name != form.name.data:\n# # flash('looks like you have chenged your name')\n# # session['name'] = form.name.data\n# # form.name.data = \"\"\n# user = User.query.filter_by(name=form.name.data).first()\n# if user is None:\n# user = User(name=form.name.data)\n# db.session.add(user)\n# session['known'] = False\n# # if app.config['FlASY_ADMIN']:\n# send_email('zhaowh3613@outlook.com', 'new user '+user.name, 'mail/new_user', user=user)\n# else:\n# session['known'] = True\n# session['name'] = form.name.data\n# form.name.data = ''\n# return redirect(url_for('index'))\n# return render_template('index.html', form=form, name=session.get('name'), known=session.get('known'), current_time=datetime.utcnow())\n#\n#\n# @app.errorhandler(404)\n# def page_not_found(e):\n# return render_template('404.html'), 404\n#\n#\n# @app.errorhandler(500)\n# def service_not_available(e):\n# return render_template('500.html'), 500\n\napp = create_app('default')\nmanager = Manager(app)\nmigrate = Migrate(app, db)\n\n\ndef make_shell_context():\n return dict(app=app, db=db, User=User, Role=Role)\n\n\nmanager.add_command(\"shell\", Shell(make_context=make_shell_context))\nmanager.add_command('db', MigrateCommand)\n\n\n\n\n@app.route('/secret')\n@login_required\ndef secret():\n return 'Only authenticated users are allowed'\n\n\n@manager.command\ndef test():\n import unittest\n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)\n\n\nif __name__ == '__main__':\n manager.run()\n","repo_name":"zhaowh3613/flaskblog","sub_path":"manage.py","file_name":"manage.py","file_ext":"py","file_size_in_byte":4887,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"19682427235","text":"from fastapi.testclient import TestClient\n\nfrom main import app\n\nclient = TestClient(app)\n\n\ndef test_show_cat_facts():\n response = client.get(\"/cat-facts/50\")\n assert response.status_code == 200\n\ndef test_cat_facts_bigger_amount():\n response = client.get(\"/cat-facts/501\")\n assert response.status_code == 422\n assert response.json() == {\n \"detail\": [\n {\n \"loc\": [\n \"path\",\n \"fact_amount\"\n ],\n \"msg\": \"ensure this value is less than or equal to 500\",\n \"type\": \"value_error.number.not_le\",\n \"ctx\": {\n \"limit_value\": 500\n }\n }\n ]\n }\n\ndef test_cat_facts_lower_amount():\n response = client.get(\"/cat-facts/0\")\n assert response.status_code == 422\n assert response.json() == {\n \"detail\": [\n {\n \"loc\": [\n \"path\",\n \"fact_amount\"\n ],\n \"msg\": \"ensure this value is greater than 0\",\n \"type\": \"value_error.number.not_gt\",\n \"ctx\": {\n \"limit_value\": 0\n }\n }\n ]\n }\n","repo_name":"jbsg97/Cat-API","sub_path":"src/test_main.py","file_name":"test_main.py","file_ext":"py","file_size_in_byte":1245,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"9650306436","text":"import json\nimport requests\n\nfrom http import HTTPStatus\n\nclass RewardsServiceClient:\n\n def __init__(self):\n self.rewards_url = \"http://rewardsservice:7050/rewards\"\n self.customer_rewards_url = \"http://rewardsservice:7050/customerrewards\"\n\n def get_rewards(self):\n response = requests.get(self.rewards_url)\n if response.status_code == HTTPStatus.NO_CONTENT.value:\n return []\n else:\n return response.json()\n\n # Code to get the list of customer rewards\n def get_all_customer_rewards(self):\n response = requests.get(self.customer_rewards_url)\n if response.status_code == HTTPStatus.NO_CONTENT.value:\n return []\n else:\n return response.json()\n\n # Code to get the list of customer rewards\n def get_customer_rewards(self, email):\n query_params = {'email':email}\n response = requests.get(self.customer_rewards_url, params=query_params)\n if response.status_code == HTTPStatus.NO_CONTENT.value:\n return []\n else:\n return response.json()\n\n # Code to get the list of customer rewards\n def add_customer_rewards(self, email, orderTotal):\n data = {'email':email, 'orderTotal':orderTotal}\n headers = {'content-type': 'application/json'}\n response = requests.post(self.customer_rewards_url, data=json.dumps(data), headers=headers)\n if response.status_code == HTTPStatus.NO_CONTENT.value:\n return []\n else:\n return response.json()\n","repo_name":"getvijayji/platform-services-python-test","sub_path":"source/RewardsUI/rewards/clients/rewards_service_client.py","file_name":"rewards_service_client.py","file_ext":"py","file_size_in_byte":1545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"52"} +{"seq_id":"43227806326","text":"from .animal import Animal\nimport random\nclass Cat(Animal):\n def __init__(self, name: str, age: int, say_word='Мяуууу!'):\n \"\"\"\n Класс отвечает за симуляцию животного кошки\n \"\"\"\n super().__init__(\n name=name,\n age=age,\n say_word=say_word,\n preferred_food={'рыба', 'мясо', 'молоко'}\n )\n self.animal_type = 'Кошка'\n self.vet_care_needed = None # Змінна для зберігання результату перевірки\n\n def treat(self, hours: int) -> str:\n \"\"\"\n Ухаживая за кошкой должное количество времени, мы получаем пойманых мышей\n :param hours: время ухаживания\n :return: мышь или ничего\n \"\"\"\n if hours > 1:\n print(f'Вы ухаживаете за {self} {hours} часов и получаете пойманых мышей')\n return 'Пойманых мышей'\n print(f'Вы ухаживаете за {self} {hours} часов.')\n return ''\n\n def needs_vet_care(self) -> bool:\n \"\"\"\n Метод перевіряє, чи потрібно до ветеринара\n :return: True, якщо потрібно до ветеринара, False - якщо ні\n \"\"\"\n if self.vet_care_needed is None: # Перевіряємо, чи була вже виконана перевірка\n self.vet_care_needed = random.choice([True, False]) # Рандомно встановлюємо результат перевірки\n return self.vet_care_needed","repo_name":"Denysius/Learn-Python-hillel-05-05","sub_path":"Homework 12/animals/cat.py","file_name":"cat.py","file_ext":"py","file_size_in_byte":1743,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"34983160680","text":"playliststart = 1\nplaylistend = 24\ndownload_link = ''\n\ndef guess_name():\n artist = ''\n title = ''\n if file[:-16].find(' - ') != -1:\n #if splittable\n artist = file[:-16].split(' - ')[0]\n title = file[:-16].split(' - ')[1]\n print(f'Artist: {artist} \\nTitle: {title}')\n answer = input('Is this correct? (y/n)')\n else:\n title = file[:-16]\n print(f'Title: {title}')\n answer = input('Is this correct? (y/n)')\n return eval_answer(answer, artist, title)\n\ndef eval_answer(answer, artist, title):\n print(f'answer: {answer}')\n if answer == 'n':\n print(f'Filename: {file}')\n artist = input('Artist: ')\n title = input('Title: ')\n elif answer == 'y' and artist == '':\n print(f'Filename: {file}')\n artist = input('Artist: ')\n elif answer == 'y' and artist != '':\n print('You said title and artist are correct')\n else:\n print('answer must be y/n')\n guess_name()\n\n return artist, title\n\n\nif __name__ == '__main__':\n import os\n if not os.path.exists('music'):\n os.mkdir('music')\n os.chdir('music')\n # download mp3\n import youtube_dl\n\n def my_hook(d):\n if d['status'] == 'finished':\n print('Done downloading, now converting ...')\n\n ydl_opts = {\n 'format': 'bestaudio/best',\n 'postprocessors': [{\n 'key': 'FFmpegExtractAudio',\n 'preferredcodec': 'mp3',\n 'preferredquality': '192',\n }],\n 'playliststart': playliststart,\n 'playlistend': playlistend,\n 'skip_download': False,\n 'progress_hooks': [my_hook],\n 'writethumbnail': True,\n 'ignoreerrors': True\n }\n with youtube_dl.YoutubeDL(ydl_opts) as ydl:\n ydl.download([''])\n\n # add the thumbnails\n import eyed3\n import glob\n\n print('\\n\\n\\nmp3 editing')\n\n # for every file\n for file in glob.glob(\"*.mp3\"):\n print(file)\n # load mp3\n audiofile = eyed3.load(file)\n if (audiofile.tag == None):\n audiofile.initTag()\n\n print('thumbnail')\n\n # if .webp format exists, must convert\n if os.path.exists(file[:-4] + '.webp'):\n image_name = file[:-4] + '.webp'\n #convert thumbnails to jpeg\n from PIL import Image\n im = Image.open(image_name).convert('RGB')\n im.save(image_name[:-5] + '.jpg', 'jpeg')\n # save jpg to mp3\n if os.path.exists(file[:-4] + '.jpg'):\n image_file = open(file[:-4] + '.jpg', 'rb').read()\n audiofile.tag.images.set(3, image_file, 'image/jpeg')\n else:\n print(f'\\n\\n\\n\\n\\n\\n\\nNO THUMBNAIL for {file}')\n\n #generate artist title\n artist, title = guess_name()\n audiofile.tag.title = title\n audiofile.tag.artist = artist\n\n audiofile.tag.save()\n print('\\n\\n\\n')\n\n print('\\n\\n\\n\\n\\nStarting Cleanup')\n\n for item in os.listdir('.'):\n if item.endswith(\".jpg\") or item.endswith(\".webp\"):\n print(f'Deleting: {item}')\n os.remove(item)\n os.chdir('..')\n","repo_name":"kjelpw/yt-music-downloader","sub_path":"download.py","file_name":"download.py","file_ext":"py","file_size_in_byte":3137,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"70058283044","text":"from companyClass import Person, Employee\r\nimport pickle\r\n\r\nivan = Employee(\"Ivan Doberman\", 24, \"Boss\", 12000)\r\nstepan = Employee(\"Stepan\", 23, \"Underboss\", 6000)\r\nbabavalya = Employee(\"Baba Valya\", 84, \"Big Boss\", 120000)\r\n\r\ncompany = [ivan, stepan, babavalya]\r\n\r\nbabavalya.salary = 160000\r\n\r\nprint(\"Before Export\\n\")\r\nfor i in company:\r\n print(i)\r\n\r\nprint(\"Exporting...\\n\") \r\ntry:\r\n dumpfile = open(\"comp.db\", \"wb\")\r\n res = pickle.dump(company, dumpfile) \r\nexcept:\r\n print(\"ERROR!\\n\")\r\nelse:\r\n print(\"DONE!\\n\")\r\nfinally:\r\n dumpfile.close()\r\n\r\nprint(\"After Export\\n\")\r\nfor i in company:\r\n print(i)\r\n\r\nprint(Person.__str__(babavalya))\r\nprint(Employee.__str__(babavalya))\r\n\r\n\r\n ","repo_name":"zberidze/Python_Pro","sub_path":"UrFU/companyApp.py","file_name":"companyApp.py","file_ext":"py","file_size_in_byte":708,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"17498402782","text":"import re\nimport itertools\nimport asciitable.core as core\nfrom asciitable.core import io, next, izip, any\n\nclass FixedWidthSplitter(core.BaseSplitter):\n \"\"\"Split line based on fixed start and end positions for each ``col`` in\n ``self.cols``.\n\n This class requires that the Header class will have defined ``col.start``\n and ``col.end`` for each column. The reference to the ``header.cols`` gets\n put in the splitter object by the base Reader.read() function just in time\n for splitting data lines by a ``data`` object. \n\n Note that the ``start`` and ``end`` positions are defined in the pythonic\n style so line[start:end] is the desired substring for a column. This splitter\n class does not have a hook for ``process_lines`` since that is generally not\n useful for fixed-width input.\n \"\"\"\n delimiter_pad = ''\n bookend = False\n\n def __call__(self, lines):\n for line in lines:\n vals = [line[x.start:x.end] for x in self.cols]\n if self.process_val:\n yield [self.process_val(x) for x in vals]\n else:\n yield vals\n\n def join(self, vals, widths):\n pad = self.delimiter_pad or ''\n delimiter = self.delimiter or ''\n padded_delim = pad + delimiter + pad\n if self.bookend:\n bookend_left = delimiter + pad\n bookend_right = pad + delimiter\n else:\n bookend_left = ''\n bookend_right = ''\n vals = [' ' * (width - len(val)) + val for val, width in zip(vals, widths)]\n return bookend_left + padded_delim.join(vals) + bookend_right\n\n\nclass FixedWidthHeader(core.BaseHeader):\n \"\"\"Fixed width table header reader.\n\n The key settable class attributes are:\n\n :param auto_format: format string for auto-generating column names\n :param start_line: None, int, or a function of ``lines`` that returns None or int\n :param comment: regular expression for comment lines\n :param splitter_class: Splitter class for splitting data lines into columns\n :param names: list of names corresponding to each data column\n :param include_names: list of names to include in output (default=None selects all names)\n :param exclude_names: list of names to exlude from output (applied after ``include_names``)\n :param position_line: row index of line that specifies position (default = 1)\n :param position_char: character used to write the position line (default = \"-\")\n :param col_starts: list of start positions for each column (0-based counting)\n :param col_ends: list of end positions (inclusive) for each column\n :param delimiter_pad: padding around delimiter when writing (default = None)\n :param bookend: put the delimiter at start and end of line when writing (default = False)\n \"\"\"\n\n position_line = None # secondary header line position\n\n def get_line(self, lines, index):\n for i, line in enumerate(self.process_lines(lines)):\n if i == index:\n break\n else: # No header line matching\n raise InconsistentTableError('No header line found in table')\n return line\n\n def get_cols(self, lines):\n \"\"\"Initialize the header Column objects from the table ``lines``.\n\n Based on the previously set Header attributes find or create the column names.\n Sets ``self.cols`` with the list of Columns. This list only includes the actual\n requested columns after filtering by the include_names and exclude_names\n attributes. See ``self.names`` for the full list.\n\n :param lines: list of table lines\n :returns: None\n \"\"\"\n\n # See \"else\" clause below for explanation of start_line and position_line\n start_line = core._get_line_index(self.start_line, self.process_lines(lines))\n position_line = core._get_line_index(self.position_line, self.process_lines(lines))\n\n # If start_line is none then there is no header line. Column positions are\n # determined from first data line and column names are either supplied by user\n # or auto-generated.\n if start_line is None:\n if position_line is not None:\n raise ValueError(\"Cannot set position_line without also setting header_start\")\n data_lines = self.data.process_lines(lines)\n if not data_lines:\n raise InconsistentTableError('No data lines found so cannot autogenerate column names')\n vals, starts, ends = self.get_fixedwidth_params(data_lines[0])\n\n if self.names is None:\n self.names = [self.auto_format % i for i in range(1, len(vals) + 1)]\n\n else:\n # This bit of code handles two cases:\n # start_line = and position_line = None\n # Single header line where that line is used to determine both the\n # column positions and names.\n # start_line = and position_line = \n # Two header lines where the first line defines the column names and\n # the second line defines the column positions\n\n if position_line is not None:\n # Define self.col_starts and self.col_ends so that the call to\n # get_fixedwidth_params below will use those to find the header\n # column names. Note that get_fixedwidth_params returns Python\n # slice col_ends but expects inclusive col_ends on input (for\n # more intuitive user interface).\n line = self.get_line(lines, position_line)\n vals, self.col_starts, col_ends = self.get_fixedwidth_params(line)\n self.col_ends = [x - 1 for x in col_ends]\n\n # Get the header column names and column positions\n line = self.get_line(lines, start_line)\n vals, starts, ends = self.get_fixedwidth_params(line)\n\n # Possibly override the column names with user-supplied values\n if self.names is None:\n self.names = vals\n \n # Filter self.names using include_names and exclude_names, then create\n # the actual Column objects.\n self._set_cols_from_names()\n self.n_data_cols = len(self.cols)\n \n # Set column start and end positions. Also re-index the cols because\n # the FixedWidthSplitter does NOT return the ignored cols (as is the\n # case for typical delimiter-based splitters)\n for i, col in enumerate(self.cols):\n col.start = starts[col.index]\n col.end = ends[col.index]\n col.index = i\n\n def get_fixedwidth_params(self, line):\n \"\"\"Split ``line`` on the delimiter and determine column values and column\n start and end positions. This might include null columns with zero length\n (e.g. for header row = \"| col1 || col2 | col3 |\" or\n header2_row = \"----- ------- -----\"). The null columns are\n stripped out. Returns the values between delimiters and the corresponding\n start and end positions.\n\n :param line: input line\n :returns: (vals, starts, ends)\n \"\"\"\n\n # If column positions are already specified then just use those, otherwise\n # figure out positions between delimiters.\n if self.col_starts is not None and self.col_ends is not None:\n starts = list(self.col_starts) # could be any iterable, e.g. np.array\n ends = [x + 1 for x in self.col_ends] # user supplies inclusive endpoint\n if len(starts) != len(ends):\n raise ValueError('Fixed width col_starts and col_ends must have the same length')\n vals = [line[start:end].strip() for start, end in zip(starts, ends)]\n else:\n # There might be a cleaner way to do this but it works...\n vals = line.split(self.splitter.delimiter)\n starts = [0]\n ends = []\n for val in vals:\n if val:\n ends.append(starts[-1] + len(val))\n starts.append(ends[-1] + 1)\n else:\n starts[-1] += 1\n starts = starts[:-1]\n vals = [x.strip() for x in vals if x]\n if len(vals) != len(starts) or len(vals) != len(ends):\n raise InconsistentTableError('Error parsing fixed width header')\n\n return vals, starts, ends\n\n def write(self, lines):\n # Header line not written until data are formatted. Until then it is\n # not known how wide each column will be for fixed width.\n pass\n\n\nclass FixedWidthData(core.BaseData):\n \"\"\"Base table data reader.\n\n :param start_line: None, int, or a function of ``lines`` that returns None or int\n :param end_line: None, int, or a function of ``lines`` that returns None or int\n :param comment: Regular expression for comment lines\n :param splitter_class: Splitter class for splitting data lines into columns\n \"\"\"\n\n splitter_class = FixedWidthSplitter\n\n def write(self, lines):\n formatters = []\n for col in self.cols:\n formatter = self.formats.get(col.name, self.default_formatter)\n if not hasattr(formatter, '__call__'):\n formatter = core._format_func(formatter)\n col.formatter = formatter\n \n vals_list = []\n # Col iterator does the formatting defined above so each val is a string\n # and vals is a tuple of strings for all columns of each row\n for vals in izip(*self.cols):\n vals_list.append(vals)\n \n for i, col in enumerate(self.cols):\n col.width = max([len(vals[i]) for vals in vals_list])\n if self.header.start_line is not None:\n col.width = max(col.width, len(col.name))\n\n widths = [col.width for col in self.cols]\n\n if self.header.start_line is not None:\n lines.append(self.splitter.join([col.name for col in self.cols], widths))\n\n if self.header.position_line is not None:\n char = self.header.position_char\n if len(char) != 1:\n raise ValueError('Position_char=\"%s\" must be a single character' % char)\n vals = [char * col.width for col in self.cols]\n lines.append(self.splitter.join(vals, widths))\n\n for vals in vals_list:\n lines.append(self.splitter.join(vals, widths))\n\n return lines\n\n\nclass FixedWidth(core.BaseReader):\n \"\"\"Read or write a fixed width table with a single header line that defines column\n names and positions. Examples::\n\n # Bar delimiter in header and data\n \n | Col1 | Col2 | Col3 |\n | 1.2 | hello there | 3 |\n | 2.4 | many words | 7 |\n \n # Bar delimiter in header only\n \n Col1 | Col2 | Col3 \n 1.2 hello there 3 \n 2.4 many words 7 \n \n # No delimiter with column positions specified as input\n \n Col1 Col2Col3 \n 1.2hello there 3 \n 2.4many words 7 \n\n See the :ref:`fixed_width_gallery` for specific usage examples.\n\n :param col_starts: list of start positions for each column (0-based counting)\n :param col_ends: list of end positions (inclusive) for each column\n :param delimiter_pad: padding around delimiter when writing (default = None)\n :param bookend: put the delimiter at start and end of line when writing (default = False)\n \"\"\"\n def __init__(self, col_starts=None, col_ends=None, delimiter_pad=' ', bookend=True):\n core.BaseReader.__init__(self)\n\n self.header = FixedWidthHeader()\n self.data = FixedWidthData()\n self.data.header = self.header\n self.header.data = self.data\n\n self.header.splitter.delimiter = '|'\n self.data.splitter.delimiter = '|'\n self.data.splitter.delimiter_pad = delimiter_pad\n self.data.splitter.bookend = bookend\n self.header.start_line = 0\n self.data.start_line = 1\n self.header.comment = r'\\s*#'\n self.header.write_comment = '# '\n self.data.comment = r'\\s*#'\n self.data.write_comment = '# '\n self.header.col_starts = col_starts\n self.header.col_ends = col_ends\n\n\nclass FixedWidthNoHeader(FixedWidth):\n \"\"\"Read or write a fixed width table which has no header line. Column\n names are either input (``names`` keyword) or auto-generated. Column\n positions are determined either by input (``col_starts`` and ``col_stops``\n keywords) or by splitting the first data line. In the latter case a\n ``delimiter`` is required to split the data line.\n\n Examples::\n\n # Bar delimiter in header and data\n \n | 1.2 | hello there | 3 |\n | 2.4 | many words | 7 |\n \n # Compact table having no delimiter and column positions specified as input\n\n 1.2hello there3 \n 2.4many words 7 \n\n This class is just a convenience wrapper around :class:`~asciitable.FixedWidth`\n but with ``header.start_line = None`` and ``data.start_line = 0``.\n\n See the :ref:`fixed_width_gallery` for specific usage examples.\n\n :param col_starts: list of start positions for each column (0-based counting)\n :param col_ends: list of end positions (inclusive) for each column\n :param delimiter_pad: padding around delimiter when writing (default = None)\n :param bookend: put the delimiter at start and end of line when writing (default = False)\n \"\"\"\n def __init__(self, col_starts=None, col_ends=None, delimiter_pad=' ', bookend=True):\n FixedWidth.__init__(self, col_starts, col_ends,\n delimiter_pad=delimiter_pad, bookend=bookend)\n self.header.start_line = None\n self.data.start_line = 0\n\n \nclass FixedWidthTwoLine(FixedWidth):\n \"\"\"Read or write a fixed width table which has two header lines. The first\n header line defines the column names and the second implicitly defines the\n column positions. Examples::\n\n # Typical case with column extent defined by ---- under column names.\n\n col1 col2 <== header_start = 0\n ----- ------------ <== position_line = 1, position_char = \"-\"\n 1 bee flies <== data_start = 2\n 2 fish swims\n\n # Pretty-printed table \n\n +------+------------+\n | Col1 | Col2 |\n +------+------------+\n | 1.2 | \"hello\" |\n | 2.4 | there world|\n +------+------------+\n\n See the :ref:`fixed_width_gallery` for specific usage examples.\n\n :param position_line: row index of line that specifies position (default = 1)\n :param position_char: character used to write the position line (default = \"-\")\n :param delimiter_pad: padding around delimiter when writing (default = None)\n :param bookend: put the delimiter at start and end of line when writing (default = False)\n \"\"\"\n def __init__(self, position_line=1, position_char='-', delimiter_pad=None, bookend=False):\n FixedWidth.__init__(self, delimiter_pad=delimiter_pad, bookend=bookend)\n self.header.position_line = position_line\n self.header.position_char = position_char\n self.data.start_line = position_line + 1\n self.header.splitter.delimiter = ' '\n self.data.splitter.delimiter = ' '\n\n \n","repo_name":"taldcroft/asciitable","sub_path":"asciitable/fixedwidth.py","file_name":"fixedwidth.py","file_ext":"py","file_size_in_byte":15363,"program_lang":"python","lang":"en","doc_type":"code","stars":36,"dataset":"github-code","pt":"52"} +{"seq_id":"72792818725","text":"from collections import Counter\n\n\ndef high_card(hand):\n highest = 0\n for i in hand:\n if i == 'A':\n return 14\n elif i == 'K':\n if 13 >= highest:\n highest = 13\n elif i == 'Q':\n if 12 >= highest:\n highest = 12\n elif i == 'J':\n if 11 >= highest:\n highest = 11\n elif i == 'T':\n if 10 >= highest:\n highest = 10\n elif int(i) > highest:\n highest = int(i)\n return highest\n\n\ndef convert_to_lists(s):\n cards = s.split(' ')\n card_value = []\n card_type = []\n for i in cards:\n card_value.append(i[0])\n card_type.append(i[1])\n return [card_value, card_type]\n\n\ndef number_of_pairs(l):\n count = Counter(l) - Counter(set(l))\n pairs = 0\n for i in count:\n if count[i] == 1:\n pairs += 1\n if pairs:\n return pairs + 1\n return 0\n\n\ndef three_of_a_kind(l):\n count = Counter(l) - Counter(set(l))\n for i in count:\n if count[i] == 2:\n return 4\n return 0\n\n\ndef straight(l):\n rating = {14: 'A', 13: 'K', 12: 'Q', 11: 'J', 10: 'T', 9: '9',\n 8: '8', 7: '7', 6: '6', 5: '5', 4: '4', 3: '3', 2: '2', 1: '1'}\n hv = high_card(l)\n if rating[hv-1] in l:\n if rating[hv-2] in l:\n if rating[hv-3] in l:\n if rating[hv-4] in l:\n return 5\n return 0\n\n\ndef flush(l):\n if len(set(l)) == 1:\n return 6\n return 0\n\n\ndef full_house(l):\n if three_of_a_kind(l):\n if number_of_pairs(l) == 2:\n return 7\n return 0\n\n\ndef four_of_a_kind(l):\n dup = Counter(l) - Counter(set(l))\n for i in dup:\n if dup[i] == 3:\n return 8\n return 0\n\n\ndef straight_flush(l, v):\n if straight(l) and flush(v):\n return 9\n return 0\n\n\ndef royal_flush(l, v):\n if set(['T', 'J', 'Q', 'K', 'A']) == set(l):\n if len(set(v)) == 1:\n return 10\n return 0\n\n\ndef paired_number(l):\n repeated = (Counter(l) - Counter(set(l))).keys()\n rating = {'A': 14, 'K': 13, 'Q': 12, 'J': 11, 'T': 10, '9': 9,\n '8': 8, '7': 7, '6': 6, '5': 5, '4': 4, '3': 3, '2': 2, '1': 1}\n highest = 0\n for i in repeated:\n if rating[i] > highest:\n highest = rating[i]\n return highest\n\nf = open('poker.txt')\n\ngames = f.read()\nf.close()\ntwo_hands = games.strip().split('\\n')\nhands = []\nfor i in two_hands:\n fh = convert_to_lists(i[:14])\n sh = convert_to_lists(i[15:])\n hands.append([fh, sh])\n\nplayer1 = 0\n\nfor i in hands:\n p1v = i[0][0]\n p2v = i[1][0]\n p1s = i[0][1]\n p2s = i[1][1]\n p1 = 0\n p2 = 0\n flag = False\n if number_of_pairs(p1v):\n p1 = number_of_pairs(p1v)\n flag = True\n if three_of_a_kind(p1v):\n p1 = three_of_a_kind(p1v)\n flag = True\n if straight(p1v):\n p1 = straight(p1v)\n if flush(p1s):\n p1 = flush(p1s)\n if full_house(p1v):\n p1 = full_house(p1v)\n flag = True\n if four_of_a_kind(p1v):\n p1 = four_of_a_kind(p1v)\n flag = True\n if straight_flush(p1v, p1s):\n p1 = straight_flush(p1v, p1s)\n if royal_flush(p1v, p1s):\n p1 = royal_flush(p1v, p1s)\n\n if number_of_pairs(p2v):\n p2 = number_of_pairs(p2v)\n flag = True\n if three_of_a_kind(p2v):\n p2 = three_of_a_kind(p2v)\n flag = True\n if straight(p2v):\n p2 = straight(p2v)\n if flush(p2s):\n p2 = flush(p2s)\n if full_house(p2v):\n p2 = full_house(p2v)\n flag = True\n if four_of_a_kind(p2v):\n p2 = four_of_a_kind(p2v)\n flg = True\n if straight_flush(p2v, p2s):\n p2 = straight_flush(p2v, p2s)\n if royal_flush(p2v, p2s):\n p2 = royal_flush(p2v, p2s)\n\n if p1 > p2:\n player1 += 1\n elif p1 == p2:\n if flag:\n if paired_number(i[0][0]) > paired_number(i[1][0]):\n player1 += 1\n elif paired_number(i[0][0]) == paired_number(i[1][0]):\n if high_card(i[0][0]) > high_card(i[1][0]):\n player1 += 1\n else:\n if high_card(i[0][0]) > high_card(i[1][0]):\n player1 += 1\n\nprint( player1)","repo_name":"Krish-bhardwaj/PROJECT_EULER","sub_path":"P54.py","file_name":"P54.py","file_ext":"py","file_size_in_byte":4250,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"29476017828","text":"from argparse import Namespace\nfrom django.urls import path\nfrom django.urls.resolvers import URLPattern\nfrom .views import home,panelAdmin,panelMedico,panelBodeguero,panelFarmaceutico,caducarMedicamentos,eliminarCuentas,generarInformes,modificarCuentas,ConsultarMedicamentos,registrarConsulta,registrarCuentas,registrarMedicamentos, reservarMedicamentos,retiroMedicamentos,verCuentas,carrito,caducar\n\nfrom django.conf import settings\nfrom django.conf.urls.static import static\nfrom .views import Agregar_productos, Eliminar_productos, Restar_productos,Limpiar\n\n\nurlpatterns = [\n\n path('',home,name='home'),\n path('panel_medico/',panelMedico,name='panelMedico'),\n path('panel_bodeguero/',panelBodeguero,name='panelBodeguero'),\n path('panel_farmaceutico/',panelFarmaceutico,name='panelFarmaceutico'),\n path('panel_admin/',panelAdmin,name='panelAdmin'),\n path('registrar_medicamentos/',registrarMedicamentos,name='registrarMedicamentos'),\n path('registrar_consulta/',registrarConsulta,name='registrarConsulta'),\n path('ver_medicamentos/',ConsultarMedicamentos,name='ConsultarMedicamentos'),\n path('caducar_medicamentos/',caducarMedicamentos,name='caducarMedicamentos'),\n path('eliminar_cuentas/',eliminarCuentas,name='eliminarCuentas'),\n path('generar_informes/',generarInformes,name='generarInformes'),\n path('modificar_cuentas/',modificarCuentas,name='modificarCuentas'),\n path('ver_cuentas/',verCuentas,name='verCuentas'),\n path('registrar_cuentas/',registrarCuentas,name='registrarCuentas'),\n path('retirar_medicamentos/',retiroMedicamentos,name='retiroMedicamentos'),\n path('reservar_medicamentos/',reservarMedicamentos,name='reservarMedicamentos'),\n path('carrito/',carrito,name='carrito'),\n path('caducar/',caducar,name='caducar'),\n \n \n]\n\nif settings.DEBUG:\n urlpatterns+= static(settings.MEDIA_URL,document_root=settings.MEDIA_ROOT)\n","repo_name":"RkzJacob/APPWEB","sub_path":"ProyectoWeb/AppWeb/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1914,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"18198911237","text":"#Nhập một dãy số nguyên từ bàn phím, và cho biết số lần xuất hiện của từng số trong dãy số\r\nimport array as arr\r\na=[]\r\nfor i in range(1,16):\r\n a.append(int(input(\"Nhập phần tử cho dãy số: \")))\r\narr2=arr.array(\"i\",a)\r\nprint(\"Dãy số ban đầu: \",a)\r\nn=dict()\r\n\r\nfor i in arr2:\r\n if i in n:\r\n n[i]+=1\r\n else:\r\n n[i]=1\r\nprint(\"Phần tử tương ứng với số lần xuất hiện\",n)\r\n\r\n\r\n\r\n","repo_name":"hoangnuthuphuong/CTDL-GT","sub_path":"Ex2.py","file_name":"Ex2.py","file_ext":"py","file_size_in_byte":459,"program_lang":"python","lang":"vi","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"25500020374","text":"from sqlalchemy.orm import Session\nfrom db import models\nfrom app import schemas\n\nfrom typing import List\n\ndef get_count_ambassadors_without_link_repo(db: Session) -> int:\n count = db.query(models.Ambassadors.id).filter(models.Ambassadors.link.is_(None)).count()\n return count\n\ndef get_ambassador_by_link(db: Session, link: str) -> models.Ambassadors:\n return (\n db.query(models.Ambassadors)\n .filter(models.Ambassadors.link == link)\n .first()\n )\n\ndef insert_urls(db: Session, links:List[str]):\n ambassadors = db.query(models.Ambassadors).filter(models.Ambassadors.link.is_(None)).all()\n\n for index, row in enumerate(ambassadors):\n row.link = links[index]\n\n db.add_all(ambassadors)\n db.commit()\n\ndef insert_ambassadors_repo(db: Session, ambassadors_input: List):\n ambassadors = []\n\n for row in ambassadors_input:\n ambassadors.append(\n models.Ambassadors(\n name=row[0],\n email=row[1],\n link=None,\n points=0,\n is_valid=True,\n )\n )\n \n db.add_all(ambassadors)\n db.commit()","repo_name":"ramasbeinaty/keygen","sub_path":"db/repo.py","file_name":"repo.py","file_ext":"py","file_size_in_byte":1146,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"36565494180","text":"#!/usr/bin/env python\n# coding: utf-8\nimport unittest\nimport pybullet\nfrom qibullet import SimulationManager\nfrom qibullet import NaoVirtual, PepperVirtual, RomeoVirtual\n\n\nclass LinkTest(unittest.TestCase):\n \"\"\"\n Unittests for the virtual links (virtual class don't use directly)\n \"\"\"\n\n def test_links_characteristics(self):\n \"\"\"\n Test the behaviour of the Joint class with the robot's characteristics\n \"\"\"\n for value in LinkTest.robot.link_dict.values():\n index = value.index\n name = value.name\n parent_index = value.parent_index\n\n self.assertEqual(index, value.getIndex())\n self.assertEqual(name, value.getName())\n self.assertEqual(parent_index, value.getParentIndex())\n\n # Test Link equality\n self.assertEqual(\n list(LinkTest.robot.link_dict.values())[0],\n list(LinkTest.robot.link_dict.values())[0])\n\n self.assertNotEqual(\n list(LinkTest.robot.link_dict.values())[0],\n list(LinkTest.robot.link_dict.values())[1])\n\n def test_get_link(self):\n \"\"\"\n Test the @getLink method\n \"\"\"\n for key, value in LinkTest.robot.link_dict.items():\n self.assertEqual(LinkTest.robot.getLink(key), value)\n\n with self.assertRaises(KeyError):\n LinkTest.robot.getLink(\"non_existent_link\")\n\n def test_get_link_position(self):\n \"\"\"\n Test the @getLinkPosition method\n \"\"\"\n # Only test that the values returned by getLinkPosition are indeed a\n # 3D translation and a 4D orientation (quaternion)\n translation, orientation = LinkTest.robot.getLinkPosition(\"torso\")\n self.assertEqual(len(translation), 3)\n self.assertEqual(len(orientation), 4)\n\n with self.assertRaises(KeyError):\n LinkTest.robot.getLinkPosition(\"non_existent_link\")\n\n\nclass PepperLinkTest(LinkTest):\n \"\"\"\n Unittests for the control of Pepper virtual's joints\n \"\"\"\n\n @classmethod\n def setUpClass(cls):\n \"\"\"\n Launches a simulation and spawns the Pepper virtual robot\n \"\"\"\n LinkTest.simulation = SimulationManager()\n LinkTest.client = LinkTest.simulation.launchSimulation(\n gui=False)\n\n LinkTest.robot = LinkTest.simulation.spawnPepper(\n LinkTest.client,\n spawn_ground_plane=True)\n\n @classmethod\n def tearDownClass(cls):\n \"\"\"\n Stops the simulation\n \"\"\"\n LinkTest.simulation.stopSimulation(\n LinkTest.client)\n\n\nclass NaoLinkTest(LinkTest):\n \"\"\"\n Unittests for the control of Nao virtual's joints\n \"\"\"\n\n @classmethod\n def setUpClass(cls):\n \"\"\"\n Launches a simulation and spawns the NAO virtual robot\n \"\"\"\n LinkTest.simulation = SimulationManager()\n LinkTest.client = LinkTest.simulation.launchSimulation(\n gui=False)\n\n LinkTest.robot = LinkTest.simulation.spawnNao(\n LinkTest.client,\n spawn_ground_plane=True)\n\n @classmethod\n def tearDownClass(cls):\n \"\"\"\n Stops the simulation\n \"\"\"\n LinkTest.simulation.stopSimulation(\n LinkTest.client)\n\n\nclass RomeoLinkTest(LinkTest):\n \"\"\"\n Unittests for the control of Nao virtual's joints\n \"\"\"\n\n @classmethod\n def setUpClass(cls):\n \"\"\"\n Launches a simulation and spawns the NAO virtual robot\n \"\"\"\n LinkTest.simulation = SimulationManager()\n LinkTest.client = LinkTest.simulation.launchSimulation(\n gui=False)\n\n LinkTest.robot = LinkTest.simulation.spawnRomeo(\n LinkTest.client,\n spawn_ground_plane=True)\n\n @classmethod\n def tearDownClass(cls):\n \"\"\"\n Stops the simulation\n \"\"\"\n LinkTest.simulation.stopSimulation(\n LinkTest.client)\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","repo_name":"softbankrobotics-research/qibullet","sub_path":"tests/link_test.py","file_name":"link_test.py","file_ext":"py","file_size_in_byte":3964,"program_lang":"python","lang":"en","doc_type":"code","stars":130,"dataset":"github-code","pt":"52"} +{"seq_id":"13404833410","text":"from django.conf import settings\nfrom rest_framework.routers import DefaultRouter, SimpleRouter\n\nfrom hades_star_backend.members.views import MemberViewSet\n\napp_name = \"corporations\"\nif settings.DEBUG:\n router = DefaultRouter()\nelse:\n router = SimpleRouter()\n\nrouter.register(\"\", MemberViewSet)\n\nurlpatterns = router.urls\n","repo_name":"michal-stachura/hades-star-backend","sub_path":"hades_star_backend/members/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":328,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"11241123932","text":"# 2020-05-09 Bokyong Seo\nimport sentry_sdk\nfrom db.query import get_query\nfrom db.connector import MssqlConnector\nimport messages\n\n\ndef update_item_parameter(df):\n \"\"\"\n 문항 난이도, 변별도 DB 저장\n\n Parameters\n ----------\n df: pandas.DataFrame\n 문항 난이도, 변별도 결과 데이터프레임\n\n Returns\n -------\n None\n \"\"\"\n param_list = []\n for _, row in df.iterrows():\n param_list.append((\n row['item_id'],\n row['item_difficulty'],\n row['item_difficulty_code'],\n row['item_discrimination'],\n row['item_discrimination_code'],\n ))\n\n num_of_items = len(param_list)\n sentry_sdk.capture_message(\n messages.MSG_NUM_OF_ITEM.format(str(num_of_items)))\n\n if num_of_items > 0:\n _set_list('item', param_list)\n\n\ndef update_student_parameter(df):\n \"\"\"\n 학생 능력 DB 저장\n\n Parameters\n ----------\n df: pandas.DataFrame\n 학생 능력 결과 데이터프레임\n\n Returns\n -------\n None\n \"\"\"\n param_list = []\n for _, row in df.iterrows():\n param_list.append((\n row['student_id'],\n row['student_ability'],\n ))\n\n num_of_students = len(param_list)\n sentry_sdk.capture_message(\n messages.MSG_NUM_OF_STUDENT.format(str(num_of_students)))\n\n if num_of_students > 0:\n _set_list('student', param_list)\n\n\ndef _set_list(query_name, data_list):\n \"\"\"\n 데이터 리스트를 DB에 저장\n\n Parameters\n ----------\n query_name: str\n 학생 능력 결과 데이터프레임\n data_list: list\n 저장할 데이터 튜플 리스트\n\n Returns\n -------\n None\n \"\"\"\n query = get_query(query_name, 'write')\n\n with MssqlConnector().connect() as conn:\n cursor = conn.cursor()\n cursor.executemany(query, data_list)\n conn.commit()\n","repo_name":"kendricklee91/portfolio","sub_path":"dbdk/ai_based_learner_knowledge_state_prediction_model/irt/irt-batch/model/irt/dataupdater.py","file_name":"dataupdater.py","file_ext":"py","file_size_in_byte":1933,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"70900801444","text":"import os\nimport time\nimport re\nfrom tabulate import tabulate\nfrom .common import writeCSV, readCSV\n\n\nclass ThinkData:\n def __init__(\n self, title: str, samples: list, features: list, weights: list, scores: list\n ):\n self.title = title\n self.samples = samples\n self.features = features\n self.weights = weights\n self.scores = scores\n assert len(self.scores) == len(self.features)\n for col in self.scores:\n assert len(col) == len(self.samples)\n\n @property\n def nrow(self):\n return len(self.samples)\n\n @property\n def ncol(self):\n nSampleCol = 1\n nFinalScoreCol = 1\n return len(self.features) + nSampleCol + nFinalScoreCol\n\n @property\n def headers(self):\n featureHeaders = []\n for i in range(len(self.features)):\n header = \"{feature}({weight})\".format(\n feature=self.features[i], weight=self.weights[i]\n )\n featureHeaders.append(header)\n return [\"Options\"] + featureHeaders + [\"Final score(Sorted)\"]\n\n @property\n def rows(self):\n rows = []\n for i in range(self.nrow):\n sample = self.samples[i]\n values = [col[i] for col in self.scores]\n finalScore = weightSum(values, self.weights)\n row = [sample] + values + [finalScore]\n rows.append(row)\n # sortedRows = sorted(rows, key=lambda x: x[-1], reverse=True)\n return rows\n\n @property\n def table(self):\n rows = sorted(self.rows, key=lambda x: x[-1], reverse=True)\n return tabulate(rows, headers=self.headers)\n\n def __str__(self):\n return self.table\n\n def save(self, saveDir):\n \"\"\"save data as csv file\"\"\"\n filename = \"\".join([self.title, \".csv\"])\n filepath = os.path.join(saveDir, filename)\n writeCSV(filepath, rows=self.rows, headers=self.headers)\n return filepath\n\n\ndef readExists(filepath, title):\n rows, headers = readCSV(filepath)\n samples = []\n weights = []\n features = []\n scores = []\n for header in headers[1:-1]:\n # search = re.search(r\"(.+)\\((-?\\d+)\\)\", header)\n feature, weight = parseHeader(header)\n features.append(feature)\n weights.append(weight)\n for row in rows:\n row.pop()\n samples.append(row.pop(0))\n for i in range(len(features)):\n colSocre = [row[i] for row in rows]\n scores.append([int(x) for x in colSocre])\n tkdata = ThinkData(title, samples, features, weights, scores)\n return tkdata\n\n\ndef parseHeader(header):\n search = re.search(r\"(.+)\\((-?\\d+)\\)\", header)\n feature, weight = search.groups()\n weight = int(weight)\n return feature, weight\n\n\ndef uniqueId():\n uid = time.strftime(\"%Y%m%d%H%m%S\", time.localtime())\n return uid\n\n\ndef weightSum(values, weights):\n s = 0\n for i in range(len(values)):\n s += values[i] * weights[i]\n return s\n","repo_name":"brendonlin/yunyun","sub_path":"yunyun/thinkdata.py","file_name":"thinkdata.py","file_ext":"py","file_size_in_byte":2964,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"42046958193","text":"import cv2\nimport torch\nimport RPi.GPIO as GPIO\nfrom yolov5.models.experimental import attempt_load\nfrom yolov5.utils.general import non_max_suppression, scale_coords\nfrom yolov5.utils.torch_utils import select_device\n\n# Set up YOLOv5\ndevice = select_device('')\nmodel = attempt_load('yolov5s.pt', map_location=device)\nstride = int(model.stride.max())\n\n# Set up GPIO for controlling LEDs on the Raspberry Pi\nGPIO.setmode(GPIO.BCM)\nled_pin = 18 # Change this pin number to the GPIO pin connected to the LED\nGPIO.setup(led_pin, GPIO.OUT)\n\n# Load the video\nvideo_path = 'path_to_your_video.mp4' # Replace this with the path to your video\ncap = cv2.VideoCapture(video_path)\n\nwhile cap.isOpened():\n ret, frame = cap.read()\n\n if not ret:\n break\n\n # Convert frame to tensor\n img = torch.from_numpy(frame).to(device)\n img = img.float() / 255.0\n if img.ndimension() == 3:\n img = img.unsqueeze(0)\n\n # Detect objects in the frame using YOLOv5\n pred = model(img, augment=False)[0]\n pred = non_max_suppression(pred, 0.4, 0.5)\n\n # Check for person detection\n if pred[0] is not None:\n det = scale_coords(img.shape[2:], pred[0][:, :4], frame.shape).round()\n for *xyxy, conf, cls in reversed(pred[0]):\n if int(cls) == 0: # Person class index\n # If person detected, turn on the LED\n GPIO.output(led_pin, GPIO.HIGH)\n break\n else:\n # If no person detected, turn off the LED\n GPIO.output(led_pin, GPIO.LOW)\n\n cv2.imshow('Video', frame)\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n\ncap.release()\ncv2.destroyAllWindows()\nGPIO.cleanup()\n","repo_name":"Sravanji/project","sub_path":"yolov5.py","file_name":"yolov5.py","file_ext":"py","file_size_in_byte":1676,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"27871327919","text":"import hashlib\nimport hmac\nfrom Cryptodome.Cipher import AES\nfrom Cryptodome.Util.Padding import pad, unpad\nfrom time import time\nfrom typing import Optional, Union\n\n\nclass Clock:\n\n def now(self) -> float:\n return time()\n\n def nonce(self):\n return round(self.now() * 1000)\n\n\nclass RequestVerifier:\n\n def __init__(self, signing_secret: str, access_token: Optional[str] = None, clock: Clock = Clock()):\n self.signing_secret = signing_secret\n self.access_token = access_token\n self.clock = clock\n\n def request_id(self, timestamp: Optional[int] = None):\n if timestamp is not None:\n return self.md5_string(str(self.md5_string(timestamp)))\n\n return self.md5_string(self.md5_string(str(self.clock.nonce())))\n\n def md5_string(self, body: Union[str, bytes] = \"\") -> str:\n if isinstance(body, str):\n body = str.encode(body)\n return hashlib.md5(body).hexdigest()\n\n def generate_signature(\n self, *, timestamp: str, body: Union[str, bytes]\n ) -> Optional[str]:\n \"\"\"Generates a standard signature\"\"\"\n if timestamp is None:\n return None\n if body is None:\n body = \"\"\n if isinstance(body, bytes):\n body = body.decode(\"utf-8\")\n\n format_req = str.encode(f\"{body}\")\n encoded_secret = str.encode(self.signing_secret)\n request_hash = hmac.new(encoded_secret, format_req, hashlib.md5).hexdigest()\n calculated_signature = f\"{request_hash}\"\n return calculated_signature\n\n def generate_dynamic_signature(\n self, *, timestamp: str, body: Union[str, bytes]\n ) -> Optional[str]:\n \"\"\"Generates a dynamic signature\"\"\"\n if timestamp is None:\n return None\n if body is None:\n body = \"\"\n if isinstance(body, bytes):\n body = body.decode(\"utf-8\")\n\n format_req = str.encode(f\"{body}\")\n encoded_secret = str.encode(self.md5_string(f\"{self.access_token}{self.signing_secret}\"))\n request_hash = hmac.new(encoded_secret, format_req, hashlib.md5).hexdigest()\n calculated_signature = f\"{request_hash}\"\n return calculated_signature\n\n\nclass MD5Hasher:\n\n def hash(self, data: Union[str, bytes] = \"\") -> bytes:\n if isinstance(data, str):\n data = data.encode()\n return hashlib.md5(data).digest()\n\n def hex(self, data: Union[str, bytes] = \"\") -> str:\n if isinstance(data, str):\n data = data.encode()\n return hashlib.md5(data).hexdigest()\n\n\nclass CBCEncryptor:\n\n def __init__(self, iv: Union[str, bytes]):\n if isinstance(iv, str):\n iv = iv.encode()\n self.iv = iv\n\n def encrypt(self, key: Union[str, bytes], data: Union[str, bytes]) -> bytes:\n if isinstance(key, str):\n key = key.encode()\n if isinstance(data, str):\n data = data.encode()\n cipher = AES.new(key, AES.MODE_CBC, self.iv)\n return cipher.encrypt(pad(data, AES.block_size))\n\n def decrypt(self, key: Union[str, bytes], data: Union[str, bytes]) -> bytes:\n if isinstance(key, str):\n key = key.encode()\n if isinstance(data, str):\n data = bytes.fromhex(data)\n cipher = AES.new(key, AES.MODE_CBC, self.iv)\n return unpad(cipher.decrypt(data), AES.block_size)\n","repo_name":"shauntarves/wyze-sdk","sub_path":"wyze_sdk/signature/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3369,"program_lang":"python","lang":"en","doc_type":"code","stars":264,"dataset":"github-code","pt":"52"} +{"seq_id":"39401682142","text":"# coding: utf-8\n\n\"\"\"\n Marketing API v.1.0\n\n IMPORTANT: This swagger links to Criteo production environment. Any test applied here will thus impact real campaigns. # noqa: E501\n\n The version of the OpenAPI document: v.1.0\n Generated by: https://openapi-generator.tech\n\"\"\"\n\n\nimport pprint\nimport re # noqa: F401\n\nimport six\n\n\nclass ProductShippingV3(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 'price': 'Price',\n 'country': 'str',\n 'region': 'str',\n 'service': 'str',\n 'location_id': 'object',\n 'location_group_name': 'str',\n 'postal_code': 'str'\n }\n\n attribute_map = {\n 'price': 'price',\n 'country': 'country',\n 'region': 'region',\n 'service': 'service',\n 'location_id': 'locationId',\n 'location_group_name': 'locationGroupName',\n 'postal_code': 'postalCode'\n }\n\n def __init__(self, price=None, country=None, region=None, service=None, location_id=None, location_group_name=None, postal_code=None): # noqa: E501\n \"\"\"ProductShippingV3 - a model defined in OpenAPI\"\"\" # noqa: E501\n\n self._price = None\n self._country = None\n self._region = None\n self._service = None\n self._location_id = None\n self._location_group_name = None\n self._postal_code = None\n self.discriminator = None\n\n if price is not None:\n self.price = price\n if country is not None:\n self.country = country\n if region is not None:\n self.region = region\n if service is not None:\n self.service = service\n if location_id is not None:\n self.location_id = location_id\n if location_group_name is not None:\n self.location_group_name = location_group_name\n if postal_code is not None:\n self.postal_code = postal_code\n\n @property\n def price(self):\n \"\"\"Gets the price of this ProductShippingV3. # noqa: E501\n\n\n :return: The price of this ProductShippingV3. # noqa: E501\n :rtype: Price\n \"\"\"\n return self._price\n\n @price.setter\n def price(self, price):\n \"\"\"Sets the price of this ProductShippingV3.\n\n\n :param price: The price of this ProductShippingV3. # noqa: E501\n :type: Price\n \"\"\"\n\n self._price = price\n\n @property\n def country(self):\n \"\"\"Gets the country of this ProductShippingV3. # noqa: E501\n\n The CLDR territory code of the country to which an item will ship. # noqa: E501\n\n :return: The country of this ProductShippingV3. # noqa: E501\n :rtype: str\n \"\"\"\n return self._country\n\n @country.setter\n def country(self, country):\n \"\"\"Sets the country of this ProductShippingV3.\n\n The CLDR territory code of the country to which an item will ship. # noqa: E501\n\n :param country: The country of this ProductShippingV3. # noqa: E501\n :type: str\n \"\"\"\n\n self._country = country\n\n @property\n def region(self):\n \"\"\"Gets the region of this ProductShippingV3. # noqa: E501\n\n The geographic region to which a shipping rate applies. # noqa: E501\n\n :return: The region of this ProductShippingV3. # noqa: E501\n :rtype: str\n \"\"\"\n return self._region\n\n @region.setter\n def region(self, region):\n \"\"\"Sets the region of this ProductShippingV3.\n\n The geographic region to which a shipping rate applies. # noqa: E501\n\n :param region: The region of this ProductShippingV3. # noqa: E501\n :type: str\n \"\"\"\n\n self._region = region\n\n @property\n def service(self):\n \"\"\"Gets the service of this ProductShippingV3. # noqa: E501\n\n A free-form description of the service class or delivery speed. # noqa: E501\n\n :return: The service of this ProductShippingV3. # noqa: E501\n :rtype: str\n \"\"\"\n return self._service\n\n @service.setter\n def service(self, service):\n \"\"\"Sets the service of this ProductShippingV3.\n\n A free-form description of the service class or delivery speed. # noqa: E501\n\n :param service: The service of this ProductShippingV3. # noqa: E501\n :type: str\n \"\"\"\n\n self._service = service\n\n @property\n def location_id(self):\n \"\"\"Gets the location_id of this ProductShippingV3. # noqa: E501\n\n The numeric ID of a location that the shipping rate applies to as defined in the AdWords API. # noqa: E501\n\n :return: The location_id of this ProductShippingV3. # noqa: E501\n :rtype: object\n \"\"\"\n return self._location_id\n\n @location_id.setter\n def location_id(self, location_id):\n \"\"\"Sets the location_id of this ProductShippingV3.\n\n The numeric ID of a location that the shipping rate applies to as defined in the AdWords API. # noqa: E501\n\n :param location_id: The location_id of this ProductShippingV3. # noqa: E501\n :type: object\n \"\"\"\n\n self._location_id = location_id\n\n @property\n def location_group_name(self):\n \"\"\"Gets the location_group_name of this ProductShippingV3. # noqa: E501\n\n The location where the shipping is applicable, represented by a location group name. # noqa: E501\n\n :return: The location_group_name of this ProductShippingV3. # noqa: E501\n :rtype: str\n \"\"\"\n return self._location_group_name\n\n @location_group_name.setter\n def location_group_name(self, location_group_name):\n \"\"\"Sets the location_group_name of this ProductShippingV3.\n\n The location where the shipping is applicable, represented by a location group name. # noqa: E501\n\n :param location_group_name: The location_group_name of this ProductShippingV3. # noqa: E501\n :type: str\n \"\"\"\n\n self._location_group_name = location_group_name\n\n @property\n def postal_code(self):\n \"\"\"Gets the postal_code of this ProductShippingV3. # noqa: E501\n\n The postal code range that the shipping rate applies to, represented by a postal code, a postal code prefix followed by a * wildcard, a range between two postal codes or two postal code prefixes of equal length. # noqa: E501\n\n :return: The postal_code of this ProductShippingV3. # noqa: E501\n :rtype: str\n \"\"\"\n return self._postal_code\n\n @postal_code.setter\n def postal_code(self, postal_code):\n \"\"\"Sets the postal_code of this ProductShippingV3.\n\n The postal code range that the shipping rate applies to, represented by a postal code, a postal code prefix followed by a * wildcard, a range between two postal codes or two postal code prefixes of equal length. # noqa: E501\n\n :param postal_code: The postal_code of this ProductShippingV3. # noqa: E501\n :type: str\n \"\"\"\n\n self._postal_code = postal_code\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, ProductShippingV3):\n return False\n\n return self.__dict__ == other.__dict__\n\n def __ne__(self, other):\n \"\"\"Returns true if both objects are not equal\"\"\"\n return not self == other\n","repo_name":"criteo/criteo-python-marketing-sdk","sub_path":"criteo_marketing/models/product_shipping_v3.py","file_name":"product_shipping_v3.py","file_ext":"py","file_size_in_byte":8744,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"52"} +{"seq_id":"31627565906","text":"import sqlite3\nimport os\n\nDATABASE_FILE = os.path.abspath(os.path.join(__file__, \"../llama.db\"))\nconn = sqlite3.connect(DATABASE_FILE)\nconn.execute(\"\"\"CREATE TABLE IF NOT EXISTS users\n (username VARCHAR(50) PRIMARY KEY,\n points INTEGER);\"\"\")\nconn.commit()\n\ndef newConnection():\n # Needed for multiple threads working in the db.\n # cron thread calls and uses this for its changes.\n return Connection()\n\nclass Connection:\n def __init__(self):\n self.conn = sqlite3.connect(DATABASE_FILE)\n\n def setPoints(self, user, points):\n cmd = \"INSERT OR REPLACE INTO users VALUES (?,?)\"\n print(\"Set points\", user, points)\n self.conn.execute(cmd, (user, points))\n self.conn.commit()\n\n def addPoints(self, user, delta):\n points = self.getPoints(user)\n self.setPoints(user, delta + points)\n\n def getPoints(self, user):\n print(\"Get points:\", user)\n cmd = \"SELECT points FROM users WHERE username = ?\"\n cursor = self.conn.execute(cmd, (user,))\n self.conn.commit()\n row = cursor.fetchone()\n if row is None:\n return 0\n return row[0]\n\n def hasUser(self, user):\n print(\"---- has user\", user)\n cmd = \"SELECT points FROM users WHERE username = ?\"\n row = self.conn.execute(cmd, (user,)).fetchone()\n return row is not None\n\n def getTopUsers(self):\n cmd = \"SELECT username, points FROM users ORDER BY points*1 DESC LIMIT 13\"\n cursor = self.conn.execute(cmd)\n rows = cursor.fetchall()\n out = []\n for user, points in rows:\n out.append(\"{} {}\".format(user, points))\n return \" | \".join(out)\n\n","repo_name":"tehNinth/lorenzotherobot","sub_path":"src/lib/llama.py","file_name":"llama.py","file_ext":"py","file_size_in_byte":1585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"52"} +{"seq_id":"73267395684","text":"#https://codeforces.com/gym/403917/problem/C\n\nt = int(input())\nfor i in range(t):\n a, r, b = [int(i) for i in input().split()]\n s = input()\n valid = True\n\n for i in range(a):\n if (b==0 or r==0):\n print(\"Invalid\")\n valid = False\n break\n if s[i] == \"R\" and r!=0:\n b-=1\n elif s[i] == \"B\" and b!=0:\n r-=1\n if (valid):\n if(b>r):print(\"Bheem\")\n elif(r>b):print(\"Ram\")\n elif(r==b):print(\"Equal\")","repo_name":"AshDaGrat/Competitive-Programming-Problems","sub_path":"Shastraa_22/3.py","file_name":"3.py","file_ext":"py","file_size_in_byte":500,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"7000946777","text":"import unittest\nfrom unittest.mock import mock_open, patch\nfrom main import task_4, task_3, task_2, task_1\nimport builtins\n\n\nclass TestTasks(unittest.TestCase):\n def test_task_1(self):\n result = task_1(5)\n\n self.assertEqual(result, 'Hello, world!\\nAndhiagain!\\n' + '!!!!!')\n\n @patch('builtins.input', side_effect=['John', 'Doe', '25', 'y'])\n def test_task_2(self, mock_input):\n result = task_2()\n\n expected_result = {\n 'list_of_people': [{'name': 'John', 'surname': 'Doe', 'age': 25}],\n 'min_age': 25,\n 'max_age': 25,\n 'av_age': 25.0\n }\n\n self.assertEqual(result, expected_result)\n\n\n @patch('builtins.input', side_effect=['20.43', '30.01'])\n def test_task_3(self, mock_input):\n result = task_3()\n\n self.assertEqual(round(result, 4), 613.1043)\n\n\n def test_task_4(self):\n # Вызываем функцию и сохраняем результат\n html_code = task_4()\n\n # Проверяем, что функция вернула не пустую строку\n self.assertNotEqual(html_code, \"\")\n\n # Проверяем, что сгенерированный HTML-код содержит необходимые элементы\n self.assertIn(\"\", html_code)\n self.assertIn(\"
\", html_code)\n self.assertIn(\"Row 1\", html_code)\n self.assertIn(\"Data 1\", html_code)\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"kosmp/BSUIR_Labs","sub_path":"5-term(Software verification)/Lab1-2/tests_for_tasks.py","file_name":"tests_for_tasks.py","file_ext":"py","file_size_in_byte":1502,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"27425667552","text":"from rest_framework import status\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\nfrom django.contrib.auth.models import User\nfrom apps.characters.models import Character\nfrom apps.characters.serializers import CharacterSerializer\n\n\n@api_view(['GET'])\ndef characterList(request):\n characters = Character.objects.all()\n serializer = CharacterSerializer(characters, many=True)\n return Response(serializer.data)\n\n@api_view(['POST'])\ndef characterCreate(request):\n serializer = CharacterSerializer(data=request.data, many=True, partial=True)\n\n if serializer.is_valid():\n serializer.save()\n\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n \n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n@api_view(['POST', 'GET'])\ndef characterUpdate(request, id):\n characters = Character.objects.get(id=id)\n serializer = CharacterSerializer(instance=characters, data=request.data, partial=True)\n\n if serializer.is_valid():\n serializer.save()\n\n return Response(serializer.data)\n\n@api_view(['DELETE'])\ndef characterDelete(request, id):\n characters = Character.objects.get(id=id)\n characters.delete()\n\n return Response('Character successfully deleted!')\n","repo_name":"lucasjaroszewski/Anaden-Tracker","sub_path":"apps/characters/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1286,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"74587649125","text":"n, m = map(int, input().split())\n\na = [list(map(int, input().split())) for _ in range(m)]\n\ninf = 1000000000\ndist = [inf]*(n+1)\ndist[1] = 0\n\ntime_stone = False\n\nfor i in range(1, n+1):\n \n for x, y, z in a:\n if dist[x]!=inf and dist[y] > dist[x] + z:\n dist[y] = dist[x] + z\n if i == n:\n time_stone = True\n\nif time_stone:\n print(-1)\nelse:\n for i in range(2, len(dist)):\n if dist[i] == inf:\n print(-1)\n else:\n print(dist[i])","repo_name":"jngcii/TIL","sub_path":"Algorithm/Advanced/Graph/Bellman-Ford/11657.타임머신-re.py","file_name":"11657.타임머신-re.py","file_ext":"py","file_size_in_byte":512,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"17488167326","text":"from typing import List\nclass Solution:\n\n#DETECT CYCLE IN DIRECTED GRAPH. KET POINT : UNMARK A NODE AS VISITED EVERYTIME YOU BACKTRACK. \n def cycle(self,node,visited,adj,d) :\n if node in d :\n return d[node]\n \n if node in visited : \n d[node] = True\n return d[node]\n else :\n visited.add(node) #MARK VISITED\n for i in adj[node] :\n c = self.cycle(i,visited,adj,d)\n if c == True :\n d[node] = True\n return d[node]\n visited.remove(node) #UNMARK VISITED\n d[node] = False\n return d[node]\n \n def canFinish(self, numCourses: int, prerequisites: List[List[int]]) -> bool:\n adj = [[] for i in range((numCourses))]\n for i in prerequisites :\n adj[i[1]].append(i[0])\n #print(adj)\n visited = set()\n d = {}\n c = False\n for i in range(len(adj)) :\n \n c = self.cycle(i,visited,adj,d)\n if c == True :\n return False\n visited = set()\n return True\n ","repo_name":"shauryasoni/Leetcode","sub_path":"graphs_trees/courseScheduleI.py","file_name":"courseScheduleI.py","file_ext":"py","file_size_in_byte":1163,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"7801329554","text":"from Question import Question\nimport requests\nfrom bs4 import BeautifulSoup\n\n\nclass FragGabiEntry:\n \"\"\"\n Scraper for the Frag Gabi Section on maedchen.de\n \"\"\"\n answer = str()\n question = Question()\n\n def __init__(self, url):\n \"\"\"\n On object creation directly initiate scraping of given site\n :param url: URL to a question on maedchen.de (Format like https://www.maedchen.de/love/frag-gabi/)\n \"\"\"\n self.scrape_site(url)\n\n def scrape_site(self, url):\n \"\"\"\n Request site and extract contents\n :param url: URL to a question on maedchen.de (Format like https://www.maedchen.de/love/frag-gabi/)\n :return: True on success\n \"\"\"\n r = requests.get(url)\n soup = BeautifulSoup(r.text, 'html.parser')\n question_content_raw = soup.find_all(class_='question-detail')[0].get_text().strip()\n self.question.content = question_content_raw.split('\\n\\n\\n')[0].strip()\n self.question.title = soup.find_all(class_='question-header')[0].get_text().strip()\n author_date = question_content_raw.split('\\n\\n\\n')[1].strip()[4:].split(' / ')\n self.question.author = author_date[0]\n self.question.set_date(author_date[1])\n self.answer = soup.find_all(class_='question-answers__content--expert')[0].get_text(separator='\\n')\n return True\n","repo_name":"cmantsch/FragGabiScraper","sub_path":"FragGabiEntry.py","file_name":"FragGabiEntry.py","file_ext":"py","file_size_in_byte":1388,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"11603071410","text":"#%%\r\nimport pandas as pd\r\nimport numpy as np\r\nimport ast\r\nimport ipdb\r\nimport json\r\n\r\n#the goal of this script is to help fix the issues with arrays in Firebase\r\n#Issue: Artists and genres are stored in arrays in the data, but Firebase doesn't support arrays\r\n# Solution:\r\n# * Explode the arrays in the cells as new rows with each value in the array as a single value in the new row\r\n# * Combine Genres and Artists together\r\n\r\n\r\n#import data\r\ngenreData = pd.read_csv(\"c:/code/MusicMe/data/data_w_genres.csv\",\r\nheader = 0,\r\nsep = \",\",\r\nindex_col = False)\r\n\r\ntrackData = pd.read_csv(\"C:/code/MusicMe/data/data.csv\")\r\ntrackData = trackData\r\ngenreData = genreData\r\n\r\n#added after the first two but the same function will be done. \r\ntrackData_genres = pd.read_csv(\"C:/code/MusicMe/data/trackData_genres.csv\")\r\nprint(trackData_genres.columns)\r\n\r\n#convert the data from having '[]' to be []. json.dumps is not my favorite. I was not right in its use\r\ngenreData[\"genres\"] = genreData[\"genres\"].apply(ast.literal_eval)\r\n\r\ntrackData[\"artists\"] = trackData[\"artists\"].apply(ast.literal_eval)\r\n\r\ntrackData_genres[\"genres\"] = trackData_genres[\"genres\"].apply(ast.literal_eval)\r\n\r\n#this function opens dataframe columns that have embedded lists and makes each value in the list assigned to the new_column as one value\r\n#substantially expands the quantity of rows in a dataframe though \r\ndef expand_list(df, list_column, new_column): #be careful that the values in the columns are actually lists not strings that look like lists\r\n lens_of_lists = df[list_column].apply(len)\r\n origin_rows = range(df.shape[0])\r\n destination_rows = np.repeat(origin_rows, lens_of_lists)\r\n non_list_cols = (\r\n [idx for idx, col in enumerate(df.columns)\r\n if col != list_column]\r\n )\r\n expanded_df = df.iloc[destination_rows, non_list_cols].copy()\r\n expanded_df[new_column] = (\r\n [item for items in df[list_column] for item in items]\r\n )\r\n expanded_df.reset_index(inplace=True, drop=True)\r\n return expanded_df\r\n\r\n#usage\r\ngenreData_expanded = expand_list(genreData,\"genres\", \"genre\")\r\ntrackData_expanded = expand_list(trackData, \"artists\", \"artists\")\r\ntrackData_genres_expanded = expand_list(trackData_genres, \"genres\", \"genres\")\r\n\r\n#verify that genres and artists are being printed, NOT letters\r\nprint(genreData_expanded[\"genre\"])\r\nprint(trackData_expanded[\"artists\"])\r\n#save data to local directory\r\n#genreData_expanded.to_csv(\"c:/code/musicme/data/genreData_expanded.csv\")\r\n#trackData_expanded.to_csv(\"c:/code/musicme/data/trackData_expanded.csv\")\r\ntrackData_genres_expanded.to_csv(\"c:/code/musicme/data/trackData_genres_expanded.csv\")\r\n\r\nprint(trackData_genres_expanded.columns)\r\n","repo_name":"alexstjean808/MusicMe2021","sub_path":"python/explodingArrays.py","file_name":"explodingArrays.py","file_ext":"py","file_size_in_byte":2694,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"15490533072","text":"\"\"\"\nEspecificar os tipos de objetos a serem criados\nusando uma instância-protótipo e criar novos objetos\npela cópia desse protótipo\n\"\"\"\n#import for correction anotations future in order creation \nfrom __future__ import annotations\n\nfrom typing import List\nfrom copy import deepcopy\n\nname1 = 'Luiz'\nname2 = name1 \n\nname1 = 'Outra Coisa'\nprint(name1)\nprint(name2)\n\nclass StringReprMixin:\n def __str__(self):\n params = ','.join([f'{k}={v}' for k,v in self.__dict__.items()])\n return f'{self.__class__.__name__}({params})'\n\n def __repr__(self):\n return self.__str__()\n\nclass Tv(StringReprMixin):\n def __init__(self,name: str,polegadas: int)->None:\n self.name = name\n self.polegadas = polegadas\n self.especifications: List[Specification] = []\n\n def add_especifications(self,specification:Specification)-> None:\n self.especifications.append(specification)\n\n def clone(self) -> Tv:\n return deepcopy(self)\n\nclass Specification(StringReprMixin):\n def __init__(self,modelo,resolucao):\n self.modelo = modelo\n self.resolucao = resolucao\n\nif __name__ == \"__main__\":\n tv_samsung = Tv('Tv Qled',60)\n specification_tu7000 = Specification('TU8000','4k')\n tv_samsung.add_especifications(specification_tu7000)\n\n tv_samsung_55 = tv_samsung.clone()\n tv_samsung_55.polegadas = 55\n\n print(tv_samsung)\n print(tv_samsung_55)\n\n\n","repo_name":"Diego07101985/pytreino","sub_path":"design_patterns/criacionais/prototype/prototype.py","file_name":"prototype.py","file_ext":"py","file_size_in_byte":1415,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"35254944913","text":"import serial\nimport sys\nimport os\n\ncurrent = os.path.dirname(os.path.realpath(__file__))\nparent_directory = os.path.dirname(current)\nsys.path.append(parent_directory)\nfrom pynmeagps import NMEAReader\nfrom SocketUtils.SocketUtils import SITQueuedUDPSender\nfrom CoTUtils.CoTUtility import CoTUtility\n\nclass bcolors:\n HEADER = '\\033[95m'\n OKBLUE = '\\033[94m'\n OKCYAN = '\\033[96m'\n OKGREEN = '\\033[92m'\n WARNING = '\\033[93m'\n FAIL = '\\033[91m'\n ENDC = '\\033[0m'\n BOLD = '\\033[1m'\n UNDERLINE = '\\033[4m'\n\n\ndef start_formatter(com, baud, host_ip, host_port):\n ser = serial.Serial(com, baud, timeout=1)\n reader = NMEAReader(ser)\n\n with SITQueuedUDPSender(host_ip, default_destination=(host_ip, host_port)) as out:\n out.Debug = False\n\n while True:\n # read data from serial port using NMEA parser\n (raw_data, parsed_data) = reader.read()\n \n # only ingest GPRMC messages\n if parsed_data.msgID == 'RMC':\n # check if message is valid\n if parsed_data.status == 'A':\n # parse out lat, lon\n lat = parsed_data.lat\n lon = parsed_data.lon\n\n # convert to CoT and send over UTPSender\n cot_data = { 'uid': 'GPRMC', 'identity': 'friend', 'dimension': 'land-unit', 'type': 'C', 'lat': lat, 'lon': lon, 'detail': { 'track': { 'course': 0, 'speed': parsed_data.spd }}}\n print(cot_data)\n cot_xml = CoTUtility.toCoT(cot_data)\n out.putitem(cot_xml)\n\n\n# starter code\nif __name__ == '__main__':\n\n #check command line args\n if len(sys.argv) != 5 or not sys.argv[2].isdigit() or not sys.argv[3].isdigit():\n print(f'{bcolors.FAIL}Usage: python NMEAIngest.py ')\n\n # get command line arguments\n host_ip = sys.argv[1] #\"127.0.0.1\"\n host_port = int(sys.argv[2]) # 1870\n baud = int(sys.argv[3]) # 9600\n com = sys.argv[4].upper() # COM5\n\n print(f'{bcolors.OKBLUE}Starting NMEA Formatter on... \\nIP: {host_ip} PORT: {host_port} COM: {com} BAUD: {baud}')\n start_formatter(com, baud, host_ip, host_port)\n \n\n\n \n \n \n \n \n","repo_name":"Senior-Design-2022/Real-Time-GIS","sub_path":"NMEAUtils/NMEAIngest.py","file_name":"NMEAIngest.py","file_ext":"py","file_size_in_byte":2280,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"27815698806","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nfrom django.conf import settings\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Contact',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('name', models.CharField(max_length=50)),\n ('number', models.CharField(max_length=15)),\n ('age', models.IntegerField()),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.CreateModel(\n name='ContactList',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('name', models.CharField(max_length=30)),\n ('owner', models.ForeignKey(to=settings.AUTH_USER_MODEL)),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.AddField(\n model_name='contact',\n name='contact_list',\n field=models.ForeignKey(to='myapp.ContactList'),\n preserve_default=True,\n ),\n ]\n","repo_name":"aaj/form-wizardry","sub_path":"myapp/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":1398,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"13586169003","text":"import dbus\nimport dbus.glib\n\n# core interface stuff\nimport gtk\nimport gtk.glade\n\n# timer\nimport gobject\n\n# file loading\nimport os\n\nglobal win_position # store the window position on the screen\n\nglobal playing\nplaying = False\n\nglobal shuffle # playlist will play randomly\nglobal repeat # repeat the playlist\nglobal loop # loop the current element\n\n# mpris doesn't support getting the status of these (at the moment)\nshuffle = False\nrepeat = False\nloop = False\n\n# these are defined on the mpris detected unique name\nglobal root # / org.freedesktop.MediaPlayer\nglobal player # /Player org.freedesktop.MediaPlayer\nglobal tracklist # /Tracklist org.freedesktop.MediaPlayer\n\nglobal bus # Connection to the session bus\nglobal identity # MediaPlayer Identity\n\n\n# If a Media Player connects to the bus, we'll use it\n# Note that we forget the previous Media Player we were connected to\ndef NameOwnerChanged(name, new, old):\n if old != \"\" and \"org.mpris.\" in name:\n Connect(name)\n\n# Callback for when \"TrackChange\" signal is emitted\ndef TrackChange(Track):\n # the only mandatory metadata is \"location\"\n try:\n a = Track[\"artist\"]\n except:\n a = \"\"\n try:\n t = Track[\"title\"]\n except:\n t = Track[\"location\"]\n try:\n length = Track[\"length\"]\n except:\n length = 0\n if length > 0:\n time_s.set_range(0,Track[\"length\"])\n time_s.set_sensitive(True)\n else:\n # disable the position scale if length isn't available\n time_s.set_sensitive(False)\n # update the labels\n l_artist.set_text(a)\n l_title.set_text(t)\n\n# Connects to the Media Player we detected\ndef Connect(name):\n global root, player, tracklist\n global playing, identity\n\n # first we connect to the objects\n root_o = bus.get_object(name, \"/\")\n player_o = bus.get_object(name, \"/Player\")\n tracklist_o = bus.get_object(name, \"/TrackList\")\n\n # there is only 1 interface per object\n root = dbus.Interface(root_o, \"org.freedesktop.MediaPlayer\")\n tracklist = dbus.Interface(tracklist_o, \"org.freedesktop.MediaPlayer\")\n player = dbus.Interface(player_o, \"org.freedesktop.MediaPlayer\")\n\n # connect to the TrackChange signal\n player_o.connect_to_signal(\"TrackChange\", TrackChange, dbus_interface=\"org.freedesktop.MediaPlayer\")\n\n # determine if the Media Player is playing something\n if player.GetStatus() == 0:\n playing = True\n TrackChange(player.GetMetadata())\n\n # gets its identity (name and version)\n identity = root.Identity()\n window.set_title(identity)\n\n#plays an element\ndef AddTrack(widget):\n mrl = e_mrl.get_text()\n if mrl != None and mrl != \"\":\n tracklist.AddTrack(mrl, True)\n e_mrl.set_text('')\n else:\n mrl = bt_file.get_filename()\n if mrl != None and mrl != \"\":\n tracklist.AddTrack(\"directory://\" + mrl, True)\n update(0)\n\n# basic control\n\ndef Next(widget):\n player.Next(reply_handler=(lambda *args: None), error_handler=(lambda *args: None))\n update(0)\n\ndef Prev(widget):\n player.Prev(reply_handler=(lambda *args: None), error_handler=(lambda *args: None))\n update(0)\n\ndef Stop(widget):\n player.Stop(reply_handler=(lambda *args: None), error_handler=(lambda *args: None))\n update(0)\n\ndef Quit(widget):\n root.Quit(reply_handler=(lambda *args: None), error_handler=(lambda *args: None))\n l_title.set_text(\"\")\n\ndef Pause(widget):\n player.Pause()\n status = player.GetStatus()\n if status == 0:\n img_bt_toggle.set_from_stock(gtk.STOCK_MEDIA_PAUSE, gtk.ICON_SIZE_SMALL_TOOLBAR)\n else:\n img_bt_toggle.set_from_stock(gtk.STOCK_MEDIA_PLAY, gtk.ICON_SIZE_SMALL_TOOLBAR)\n update(0)\n\ndef Repeat(widget):\n global repeat\n repeat = not repeat\n player.Repeat(repeat)\n\ndef Shuffle(widget):\n global shuffle\n shuffle = not shuffle\n tracklist.SetRandom(shuffle)\n\ndef Loop(widget):\n global loop\n loop = not loop\n tracklist.SetLoop(loop)\n\n# update status display\ndef update(widget):\n Track = player.GetMetadata()\n vol.set_value(player.VolumeGet())\n try: \n a = Track[\"artist\"]\n except:\n a = \"\"\n try:\n t = Track[\"title\"]\n except: \n t = \"\"\n if t == \"\":\n try:\n t = Track[\"location\"]\n except:\n t = \"\"\n l_artist.set_text(a)\n l_title.set_text(t)\n try:\n length = Track[\"length\"]\n except:\n length = 0\n if length > 0:\n time_s.set_range(0,Track[\"length\"])\n time_s.set_sensitive(True)\n else:\n # disable the position scale if length isn't available\n time_s.set_sensitive(False)\n GetPlayStatus(0)\n\n# callback for volume change\ndef volchange(widget):\n player.VolumeSet(vol.get_value_as_int(), reply_handler=(lambda *args: None), error_handler=(lambda *args: None))\n\n# callback for position change\ndef timechange(widget, x=None, y=None):\n player.PositionSet(int(time_s.get_value()), reply_handler=(lambda *args: None), error_handler=(lambda *args: None))\n\n# refresh position change\ndef timeset():\n global playing\n if playing == True:\n try:\n time_s.set_value(player.PositionGet())\n except:\n playing = False\n return True\n\n# toggle simple/full display\ndef expander(widget):\n if exp.get_expanded() == False:\n exp.set_label(\"Less\")\n else:\n exp.set_label(\"More\")\n\n# close event : hide in the systray\ndef delete_event(self, widget):\n self.hide()\n return True\n\n# shouldn't happen\ndef destroy(widget):\n gtk.main_quit()\n\n# hide the controller when 'Esc' is pressed\ndef key_release(widget, event):\n if event.keyval == gtk.keysyms.Escape:\n global win_position\n win_position = window.get_position()\n widget.hide()\n\n# callback for click on the tray icon\ndef tray_button(widget):\n global win_position\n if window.get_property('visible'):\n # store position\n win_position = window.get_position()\n window.hide()\n else:\n # restore position\n window.move(win_position[0], win_position[1])\n window.show()\n\n# hack: update position, volume, and metadata\ndef icon_clicked(widget, event):\n update(0)\n\n# get playing status, modify the Play/Pause button accordingly\ndef GetPlayStatus(widget):\n global playing\n global shuffle\n global loop\n global repeat\n status = player.GetStatus()\n\n playing = status[0] == 0\n if playing:\n img_bt_toggle.set_from_stock(\"gtk-media-pause\", gtk.ICON_SIZE_SMALL_TOOLBAR)\n else:\n img_bt_toggle.set_from_stock(\"gtk-media-play\", gtk.ICON_SIZE_SMALL_TOOLBAR)\n shuffle = status[1] == 1\n bt_shuffle.set_active( shuffle )\n loop = status[2] == 1\n bt_loop.set_active( loop )\n repeat = status[3] == 1\n bt_repeat.set_active( repeat )\n# loads glade file from the directory where the script is,\n# so we can use /path/to/mpris.py to execute it.\nimport sys\nxml = gtk.glade.XML(os.path.join(os.path.dirname(sys.argv[0]) , 'mpris.glade'))\n\n# ui setup\nbt_close = xml.get_widget('close')\nbt_quit = xml.get_widget('quit')\nbt_file = xml.get_widget('ChooseFile')\nbt_next = xml.get_widget('next')\nbt_prev = xml.get_widget('prev')\nbt_stop = xml.get_widget('stop')\nbt_toggle = xml.get_widget('toggle')\nbt_mrl = xml.get_widget('AddMRL')\nbt_shuffle = xml.get_widget('shuffle')\nbt_repeat = xml.get_widget('repeat')\nbt_loop = xml.get_widget('loop')\nl_artist = xml.get_widget('l_artist')\nl_title = xml.get_widget('l_title')\ne_mrl = xml.get_widget('mrl')\nwindow = xml.get_widget('window1')\nimg_bt_toggle=xml.get_widget('image6')\nexp = xml.get_widget('expander2')\nexpvbox = xml.get_widget('expandvbox')\naudioicon = xml.get_widget('eventicon')\nvol = xml.get_widget('vol')\ntime_s = xml.get_widget('time_s')\ntime_l = xml.get_widget('time_l')\n\n# connect to the different callbacks\n\nwindow.connect('delete_event', delete_event)\nwindow.connect('destroy', destroy)\nwindow.connect('key_release_event', key_release)\n\ntray = gtk.status_icon_new_from_icon_name(\"audio-x-generic\")\ntray.connect('activate', tray_button)\n\nbt_close.connect('clicked', destroy)\nbt_quit.connect('clicked', Quit)\nbt_mrl.connect('clicked', AddTrack)\nbt_toggle.connect('clicked', Pause)\nbt_next.connect('clicked', Next)\nbt_prev.connect('clicked', Prev)\nbt_stop.connect('clicked', Stop)\nbt_loop.connect('clicked', Loop)\nbt_repeat.connect('clicked', Repeat)\nbt_shuffle.connect('clicked', Shuffle)\nexp.connect('activate', expander)\nvol.connect('changed', volchange)\ntime_s.connect('adjust-bounds', timechange)\naudioicon.set_events(gtk.gdk.BUTTON_PRESS_MASK) # hack for the bottom right icon\naudioicon.connect('button_press_event', icon_clicked) \ntime_s.set_update_policy(gtk.UPDATE_DISCONTINUOUS)\n\nlibrary = \"/media/mp3\" # editme\n\n# set the Directory chooser to a default location\ntry:\n os.chdir(library)\n bt_file.set_current_folder(library)\nexcept:\n bt_file.set_current_folder(os.path.expanduser(\"~\"))\n\n# connect to the bus\nbus = dbus.SessionBus()\ndbus_names = bus.get_object( \"org.freedesktop.DBus\", \"/org/freedesktop/DBus\" )\ndbus_names.connect_to_signal(\"NameOwnerChanged\", NameOwnerChanged, dbus_interface=\"org.freedesktop.DBus\") # to detect new Media Players\n\ndbus_o = bus.get_object(\"org.freedesktop.DBus\", \"/\")\ndbus_intf = dbus.Interface(dbus_o, \"org.freedesktop.DBus\")\nname_list = dbus_intf.ListNames()\n\n# connect to the first Media Player found\nfor n in name_list:\n if \"org.mpris.\" in n:\n Connect(n)\n window.set_title(identity)\n vol.set_value(player.VolumeGet())\n update(0)\n break\n\n# run a timer to update position\ngobject.timeout_add( 1000, timeset)\n\nwindow.set_icon_name('audio-x-generic')\nwindow.show()\n\nicon_theme = gtk.icon_theme_get_default()\ntry:\n pix = icon_theme.load_icon(\"audio-x-generic\",24,0)\n window.set_icon(pix)\nexcept:\n True\n\nwin_position = window.get_position()\n\ngtk.main() # execute the main loop\n","repo_name":"shaobin0604/faplayer","sub_path":"jni/vlc/extras/misc/mpris.py","file_name":"mpris.py","file_ext":"py","file_size_in_byte":10092,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"52"} +{"seq_id":"35701533430","text":"\n##################################################################################\n# isFireUnwind - takes wind direction and fire bearing, and works out whether\n# the fire is within a +/- 45 degrees cone upwind\ndef isFireUpwind(fWindDirection, fFireBearing):\n fDiff = fWindDirection - fFireBearing\n\n if fDiff < 0:\n fDiff = fDiff + 360\n elif fDiff > 360:\n fDiff = fDiff - 360\n\n if fDiff > 315 or fDiff < 45:\n return True\n\n return False\n\ndef TestisFireUpwind():\n if isFireUpwind(45, 44) != True:\n return False\n\n if isFireUpwind(0, -44) != True:\n return False\n\n if isFireUpwind(180, 190) != True:\n return False \n\n if isFireUpwind(180, 170) != True:\n return False \n \n\n##################################################################################\n# bases on distance, wind speed and magnitude, tries to work out whether\n# this fire is affecting us\ndef isFireAffectingUs(fFireMagnitude, fFireDistance, fWindSpeed):\n\n if fFireDistance < 1000000:\n return True\n\n return False\n\n","repo_name":"ao00/giscs4_fires","sub_path":"FireHelpers.py","file_name":"FireHelpers.py","file_ext":"py","file_size_in_byte":1085,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"38584043102","text":"import numpy as np\nimport pandas as pd\nfrom sklearn.metrics import confusion_matrix\n\ndef back_rule(rule, df_tree,i):\n parent_index = df_tree.loc[i,'parent_feat_index']\n parent_th = df_tree.loc[i,'parent_th']\n\n index_parent = df_tree.loc[np.logical_and(df_tree.feat_index==parent_index,df_tree.th==parent_th)].index[0]\n\n if df_tree.loc[index_parent,'side']=='root':\n rule.append([parent_index,parent_th,'root'])\n return rule\n else:\n rule.append([parent_index,parent_th,df_tree.loc[index_parent,'side']])\n rule = back_rule(rule,df_tree,index_parent)\n return rule\n\ndef test_rules(X,y,rule,value):\n df_data = pd.DataFrame(X)\n y[y!=value]=-1\n df_data['y_true'] = y\n df_data['y_pred'] = -1\n df_rule = pd.DataFrame(columns=['feat_'+str(i)+'_<' for i in range(X.shape[1])]+['feat_'+str(i)+'_>=' for i in range(X.shape[1])],index=[0])\n rule.reverse()\n sub_X = df_data.copy()\n for i,r in enumerate(rule[:-1]):\n\n index = r[0]\n th = r[1]\n side = rule[i+1][2]\n if side=='left':\n df_rule.loc[0,'feat_'+str(index)+'_<'] = th\n\n sub_X = sub_X.loc[sub_X[index]='] = th\n\n sub_X = sub_X.loc[sub_X[index]>=th]\n\n df_data.loc[sub_X.index,'y_pred'] = 0\n tn, fp, fn, tp = confusion_matrix(df_data.y_true,df_data.y_pred,labels=[value,-1]).ravel()\n df_rule['precision'] = tp/(tp+fp)\n df_rule['recall'] = tp/(fn+tp)\n df_rule['accuracy'] = (tp + tn)/ (tp + fn + tn + fp)\n return df_rule\n\ndef get_rules(value,df_forest,X_test,y_test):\n rules = []\n\n for tree in df_forest.tree.unique():\n df_tree = df_forest.loc[df_forest.tree==tree]\n leaf_interest = df_tree.loc[df_tree.value==value]\n\n for i in leaf_interest.index:\n rule = [[df_tree.loc[i,'feat_index'],df_tree.loc[i,'th'],df_tree.loc[i,'side']]]\n rule = back_rule(rule,df_tree,i)\n rules.append(rule)\n\n for i,r in enumerate(rules):\n df_rule = test_rules(X_test,y_test,r,value)\n if i==0:\n df_rules = df_rule\n else:\n df_rules = pd.concat([df_rules,df_rule])\n\n return df_rules","repo_name":"vlt-ro/random_forest_rules_extraction","sub_path":"src/extract_rules.py","file_name":"extract_rules.py","file_ext":"py","file_size_in_byte":2239,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"16372538253","text":"from django import forms\nfrom owner_admin.models import Offers\n\n\nclass AddOrEditOfferForm(forms.ModelForm):\n class Meta:\n model = Offers\n fields = '__all__'\n widgets = {'date_added': forms.DateInput(attrs={'hidden': True, 'readonly': True}),\n 'date': forms.DateInput(attrs={'type': 'date'}),\n 'price': forms.NumberInput(attrs={'step': '0.01', 'min': '0.00', 'max': '9999.99'})}\n","repo_name":"AbdulAhadKhan/FlyBoredom","sub_path":"owner_admin/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":438,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"70127055205","text":"import numpy as np\n\nimport os\nimport deepdish as dd\n\n\nclass posterior(object):\n \"\"\" Provides access to the outputs from fitting models to data and\n calculating posterior predictions for derived parameters (e.g. for\n star-formation histories, rest-frane magnitudes etc).\n\n Parameters\n ----------\n run : string - optional\n The subfolder into which outputs will be saved, useful e.g. for\n fitting more than one model configuration to the same data.\n\n n_samples : float - optional\n The number of posterior samples to generate for each quantity.\n \"\"\"\n\n def __init__(self, ID, run=\".\", n_samples=500):\n\n self.ID = ID\n self.run = run\n self.n_samples = n_samples\n\n fname = \"bff/\" + self.run + \"/\" + self.ID + \".h5\"\n\n # Check to see whether the object has been fitted.\n if not os.path.exists(fname):\n raise IOError(\"Fit results not found for \" + self.ID + \".\")\n\n # Reconstruct the fitted model.\n self.fit_instructions = dd.io.load(fname, group=\"/fit_instructions\")\n\n # 2D array of samples for the fitted parameters only.\n self.samples2d = dd.io.load(fname, group=\"/samples2d\")\n\n # If fewer than n_samples exist in posterior, reduce n_samples\n if self.samples2d.shape[0] < self.n_samples:\n self.n_samples = self.samples2d.shape[0]\n\n # Randomly choose points to generate posterior quantities\n self.indices = np.random.choice(self.samples2d.shape[0],\n size=self.n_samples, replace=False)\n\n self.samples = {} # Store all posterior samples\n\n # Add 1D posteriors for fitted params to the samples dictionary\n for i in range(self.fitted_model.ndim):\n param_name = self.fitted_model.params[i]\n\n self.samples[param_name] = self.samples2d[self.indices, i]\n","repo_name":"ACCarnall/bayesianflexifit","sub_path":"bayesianflexifit/posterior.py","file_name":"posterior.py","file_ext":"py","file_size_in_byte":1887,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"4561665715","text":"import tensorflow as tf\nfrom model_helpers import Embedding, build_bidirectional_rnn, xavier\nimport os\nfrom os import makedirs\nfrom os.path import join, dirname\nfrom time import strftime, gmtime\nfrom helpers import build_vocab, build_dis_data, generate_dis_batches, print_out\nimport numpy as np\nimport json\n\nclass TweetDiscriminator(object):\n\n def __init__(self, num_unit, batch_size, vocab_size, embed_size,\n cell_type=tf.nn.rnn_cell.BasicLSTMCell,\n num_gpu=2,\n lr=0.001):\n\n self.label = tf.placeholder(tf.int32, shape=[batch_size], name=\"label\")\n self.text = tf.placeholder(tf.int32, shape=[None, batch_size], name=\"embed-tweet\") # [max_len, batch_size]\n self.len = tf.placeholder(tf.int32, shape=[batch_size], name=\"tweet_length\")\n\n with tf.variable_scope(\"embeddings\"):\n embedding = Embedding(vocab_size, embed_size)\n text_embed = embedding(self.text)\n\n with tf.variable_scope(\"text-encoder\"):\n _, encoder_state = build_bidirectional_rnn(\n num_unit, text_embed, self.len, cell_type, num_gpu)\n text_vec = tf.concat([encoder_state[0], encoder_state[1]], axis=1)\n\n with tf.variable_scope(\"turing-result\"):\n logits = tf.layers.dense(text_vec, 2, activation=None, kernel_initializer=xavier)\n self.prob = tf.nn.softmax(logits)\n\n with tf.variable_scope(\"loss\"):\n self.loss = tf.reduce_mean(\n tf.nn.sparse_softmax_cross_entropy_with_logits(labels=self.label, logits=logits))\n\n with tf.variable_scope(\"accuracy\"):\n accuracy = tf.nn.in_top_k(logits, self.label, k=1)\n self.accuracy = tf.reduce_mean(tf.cast(accuracy, tf.float32))\n\n with tf.variable_scope(\"optimization\"):\n optimizer = tf.train.AdamOptimizer(lr)\n self.update_step = optimizer.minimize(self.loss)\n\n def train_update(self, batch, sess):\n sess = sess or sess.get_default_session()\n text = batch[0]\n length = batch[1]\n label = batch[2]\n\n _, loss, accuracy = sess.run(\n [self.update_step, self.loss, self.accuracy],\n feed_dict={self.text: text, self.label: label, self.len: length})\n return loss, accuracy\n\n def eval(self, batches, sess):\n sess = sess or sess.get_default_session()\n loss_l = []\n accuracy_l = []\n\n for batch in batches:\n text = batch[0]\n length = batch[1]\n label = batch[2]\n\n loss, accuracy = sess.run(\n [self.loss, self.accuracy],\n feed_dict={self.text: text, self.label: label, self.len: length})\n\n loss_l.append(loss)\n accuracy_l.append(accuracy)\n return float(np.mean(loss_l)), float(np.mean(accuracy_l))\n\nif __name__ == '__main__':\n from params.full import *\n num_epoch = 6\n test_step = 50\n\n # for machine samples\n from collections import Counter\n\n c = Counter()\n os.chdir(\"../data/full_64_input/dis_pretrain\")\n output_dir = strftime(\"%m-%d_%H-%M-%S\", gmtime())\n\n # build vocab\n word2index, index2word = build_vocab(\"vocab.ori\")\n start_i, end_i = word2index[''], word2index['']\n vocab_size = len(word2index)\n\n discriminator = TweetDiscriminator(num_unit, batch_size, vocab_size, embed_size,\n cell_type=tf.nn.rnn_cell.GRUCell, num_gpu=2, lr=0.001)\n \n train_data = build_dis_data(\"human_train.txt\", \"machine_train.txt\", word2index)\n test_data = build_dis_data(\"human_test.txt\", \"machine_test.txt\", word2index)\n test_batches = generate_dis_batches(test_data, batch_size, False)\n\n print_out(\"*** DATA READY ***\")\n\n makedirs(dirname(join(output_dir, \"breakpoints/\")), exist_ok=True)\n log_f = open(join(output_dir, \"log.log\"), \"w\")\n\n saver = tf.train.Saver()\n with tf.Session() as sess:\n best_f = join(output_dir, \"best_accuracy.txt\")\n\n global_step = best_step = 1\n start_epoch = best_epoch = 1\n best_loss = 1000.\n sess.run(tf.global_variables_initializer())\n\n for epoch in range(start_epoch, num_epoch + 1):\n train_batches = generate_dis_batches(train_data, batch_size, True)\n\n loss_l = []\n accuracy_l = []\n \n for batch in train_batches:\n loss, accuracy = discriminator.train_update(batch, sess)\n loss_l.append(loss)\n accuracy_l.append(accuracy)\n\n if global_step % test_step == 0:\n time_now = strftime(\"%m-%d %H:%M:%S\", gmtime())\n print_out('epoch:\\t%d\\tstep:\\t%d\\tbatch-loss/accuracy:\\t%.3f\\t%.1f\\t\\t%s' %\n (epoch, global_step,\n np.mean(loss_l), np.mean(accuracy_l) * 100, time_now),\n f=log_f)\n if global_step % (test_step * 10) == 0:\n loss, accuracy = discriminator.eval(test_batches, sess)\n print_out('EPOCH-\\t%d\\tSTEP-\\t%d\\tTEST-loss/accuracy/accuracy5-\\t%.3f\\t%.1f' %\n (epoch, global_step,\n loss, accuracy * 100),\n f=log_f)\n\n if best_loss >= loss:\n best_loss = loss\n\n best_epoch = epoch\n best_step = global_step\n\n # save breakpoint\n path = join(output_dir, \"breakpoints/best_test_loss.ckpt\")\n save_path = saver.save(sess, path)\n\n # save best epoch/step\n best_dict = {\n \"loss\": best_loss, \"epoch\": best_epoch, \"step\": best_step, \"accuracy\": accuracy}\n with open(path, \"w\") as f:\n f.write(json.dumps(best_dict, indent=2))\n global_step += 1\n\n loss, accuracy = discriminator.eval(train_batches, sess)\n print_out('EPOCH!\\t%d\\tTRAIN!\\t%d\\tTRAIN-loss/accuracy-\\t%.3f\\t%.1f' %\n (epoch, global_step,\n np.mean(loss_l), np.mean(accuracy_l) * 100),\n f=log_f)\n\n log_f.close()\n","repo_name":"claude-zhou/emo","sub_path":"discriminator.py","file_name":"discriminator.py","file_ext":"py","file_size_in_byte":6325,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"30019384436","text":"from util import (Config, JsonHandler, DynamicDataclass, TerminalQuiz as tq, \n ApplicationFlow as af)\nimport typing as type \nfrom dataclasses import dataclass\n\n@dataclass(init=False)\nclass ManageDatabaseArgs(DynamicDataclass):\n choice: type.Literal[\"Add database\", \"Remove database\"]\n filepath_to_add: str | None\n filepaths_to_remove: list\n \n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n\nclass ManageDatabase:\n def __init__(self):\n raise NotImplementedError(\"This class cannot be instantiated\")\n \n @staticmethod\n def run(config: Config):\n try:\n arguments = ManageDatabase.__show_terminal_quiz(config)\n if arguments.choice == \"Add database\":\n config.db_filepath_list.append(arguments.filepath_to_add)\n\n elif arguments.choice == \"Remove database\":\n for filepath in arguments.filepaths_to_remove:\n config.db_filepath_list.remove(filepath)\n \n JsonHandler.save_json_file(config.config_filepath, config.dict())\n \n except KeyboardInterrupt:\n af.print_returning_to_menu()\n \n\n\n @staticmethod\n def __show_terminal_quiz(config: Config):\n choice = tq.ask_for_list(\"Choose an option\", [\"Add database\", \"Remove database\"])\n\n filepath_to_add = None\n filepaths_to_remove = []\n\n if choice == \"Add database\":\n filepath_to_add = tq.ask_for_string(\"Enter the filepath of the database you want to add: \")\n elif choice == \"Remove database\":\n filepaths_to_remove = tq.ask_for_checkbox(\"Choose the database you want to remove\", config.db_filepath_list)\n return ManageDatabaseArgs(choice = choice, filepath_to_add = filepath_to_add, filepaths_to_remove = filepaths_to_remove)","repo_name":"Guisilcol/milho-sqlite3","sub_path":"src/commands/manage_databases.py","file_name":"manage_databases.py","file_ext":"py","file_size_in_byte":1845,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"1937721243","text":"from flask import Flask, render_template, request\nfrom SimilarityAnalyzer import analyseSimilarity\nfrom SimilarityAnalyzer import analyseNodes\nimport json\n\napp = Flask(__name__)\n\ncodeOne = ''\ncodeTwo = ''\n\n@app.route(\"/\")\ndef index():\n return render_template(\"index.html\")\n\n@app.route(\"/api/fileOne\", methods=['POST'])\ndef submitFileOne():\n global codeOne\n codeOne = request.get_data().decode(\"utf-8\") \n return \"Success\"\n\n@app.route(\"/api/fileTwo\", methods=['POST'])\ndef submitFileTwo():\n global codeTwo\n codeTwo = request.get_data().decode(\"utf-8\") \n return \"Success\"\n\n@app.route(\"/api/astOne\")\ndef resultsOfASTOne():\n global codeOne\n global codeTwo\n jsonObject = analyseSimilarity(codeOne, codeTwo) \n return json.loads(jsonObject)\n\n@app.route(\"/api/astTwo\")\ndef resultsOfASTTwo():\n global codeOne\n global codeTwo\n jsonObject = analyseSimilarity(codeTwo, codeOne) \n return json.loads(jsonObject)\n\n@app.route(\"/api/compareNodes\")\ndef compareNodes():\n global codeOne\n global codeTwo\n jsonObject = analyseNodes(codeOne, codeTwo) \n return json.loads(jsonObject) ","repo_name":"hamzahussyn/SimilarityAnalyser","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1103,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"31603460799","text":"#!/usr/bin/python3\n\"\"\"A script to create the State “California” with the City \"San Francisco\"\n from the database hbtn_0e_100_usa\n\"\"\"\n\nfrom relationship_city import City\nfrom relationship_state import Base, State\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom sys import argv\n\n\nif __name__ == '__main__':\n # Creating and connecting session modal to local database\n DATABASE_URL = 'mysql+mysqldb://{}:{}@localhost:3306/{}'\n engine = create_engine(\n DATABASE_URL.format(argv[1], argv[2], argv[3]),\n pool_pre_ping=True\n )\n Base.metadata.create_all(engine)\n Session = sessionmaker(bind=engine)\n session = Session()\n\n # Creating new state called California\n new_state = State(name='California')\n\n # Creating new city called San Francisco\n new_city = City(name='San Francisco')\n\n # Making relationship between new_state to new_city\n new_state.cities.append(new_city)\n\n # Adding created City and State to session model\n session.add(new_state)\n session.add(new_city)\n\n # Committing all changes to database\n session.commit()\n # Closing session model\n session.close()\n","repo_name":"njdam/alx-higher_level_programming","sub_path":"0x0F-python-object_relational_mapping/100-relationship_states_cities.py","file_name":"100-relationship_states_cities.py","file_ext":"py","file_size_in_byte":1188,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"18604213343","text":"from maya import cmds\n\ndef tween(percentage, obj=None, attrs=None, selection=True):\n if not obj and not selection:\n raise ValueError(\"No object given to tween\")\n if not obj:\n obj = cmds.ls(selection=True)[0]\n\n if not attrs:\n attrs = cmds.listAttr(obj, keyable=True)\n\n currentTime = cmds.currentTime(query=True)\n\n for attr in attrs:\n attr_full = '%s.%s' % (obj, attr)\n key_frames = cmds.keyframe(attr_full, query=True)\n if not key_frames:\n continue\n previous_key_frames = []\n for frame in key_frames:\n if frame < currentTime:\n previous_key_frames.append(frame)\n later_key_frames = [frame for frame in key_frames if frame > currentTime]\n\n if not previous_key_frames and not later_key_frames:\n continue\n\n if previous_key_frames:\n previous_frame = max(previous_key_frames)\n else:\n previous_frame = None\n\n next_frame = min(later_key_frames) if later_key_frames else None\n\n if not previous_frame or not next_frame:\n continue\n\n previous_value = cmds.getAttr(attr_full, time=previous_frame)\n next_value = cmds.getAttr(attr_full, time=next_frame)\n\n diff = next_value - previous_value\n\n weight_diff = (diff * percentage) / 100.0\n\n current_val = previous_value + weight_diff\n\n cmds.setKeyframe(attr_full, time=currentTime, value=current_val)\n\nclass TweenWindow(object):\n\n window_name = \"TweenerWindow\"\n\n def show(self):\n\n if cmds.window(self.window_name, query=True, exists=True):\n cmds.deleteUI(self.window_name)\n\n cmds.window(self.window_name)\n\n self.buildUI()\n\n cmds.showWindow()\n\n def buildUI(self):\n column = cmds.columnLayout()\n\n cmds.text(label=\"Use this slider to set the tween amount\")\n row = cmds.rowLayout(numberOfColumns=2)\n self.slider = cmds.floatSlider(min=0, max=100, value=50, step=1, changeCommand=tween)\n\n cmds.button(label=\"Reset\", command=self.reset)\n\n cmds.setParent(column)\n\n cmds.button(label=\"Close\", command=self.close)\n\n def reset(self, *args):\n cmds.floatSlider(self.slider, edit=True, value=50)\n\n def close(self, *args):\n cmds.deleteUI(self.window_name)\n\nTweenWindow().show()","repo_name":"Olsonrowan/maya-scripts","sub_path":"tweenerUI.py","file_name":"tweenerUI.py","file_ext":"py","file_size_in_byte":2346,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"549032953","text":"\"\"\"\n Author: Mauro Mendez.\n Date: 02/11/2020.\n\n File to run testing metrics once the model has trained.\n\"\"\"\n\n\nimport glob\nimport pathlib\n\nimport torch\nimport segmentation_models_pytorch as smp\n\nfrom hyperparameters import parameters as params\nfrom models import create_model\nfrom dataset import get_dataloader\nfrom testing import test_report\n\n\ndef main():\n \"\"\"\n main Main function, flow of program.\n \"\"\"\n\n # Model\n model = create_model(model=params['model'], encoder=params['encoder'],\\\n encoder_weights=params['encoder_weights'])\n\n # Running architecture (GPU or CPU)\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n print('Using GPU?: ', torch.cuda.is_available())\n\n # Image loader\n proc_fn = smp.encoders.get_preprocessing_fn(params['encoder'], params['encoder_weights'])\n test_loader = get_dataloader(data_file=params['test_file'], img_size=params['img_size'],\\\n batch_size=1, proc_fn=proc_fn, data_split='Testing')\n\n # Creates the criterion (loss function)\n criterion = smp.utils.losses.DiceLoss()\n\n # Weights Load Up\n weights_file = glob.glob(params['weights_path']+'/'+params['save_name']+'*.pth')[0]\n\n checkpoint = torch.load(weights_file)\n model.load_state_dict(checkpoint['model_state_dict'])\n\n print('Model Loaded!\\nDice Loss: {:.4}\\nIoU: {:.4}\\nFscore: {:.4}\\nAccuracy: {:.4}'\\\n .format(checkpoint['dice_loss'], checkpoint['iou_score'],\\\n checkpoint['fscore'], checkpoint['accuracy']))\n\n\n # Create folder for weights\n pathlib.Path(params['report_path']).mkdir(parents=True, exist_ok=True)\n\n # Metrics\n metrics = [\\\n smp.utils.metrics.IoU(threshold=0.5),\\\n smp.utils.metrics.Fscore(threshold=0.5),\\\n smp.utils.metrics.Accuracy(threshold=0.5),\\\n smp.utils.metrics.Recall(threshold=0.5),\\\n smp.utils.metrics.Precision(threshold=0.5)]\n\n # Run test metrics and creates a report\n test_report(model=model, dataloader=test_loader, criterion=criterion,\\\n metrics=metrics, device=device, report_path=params['report_path'],\\\n save_name=params['save_name'])\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"mamemo/CNN-Semantic-Segmentation","sub_path":"code/run_test.py","file_name":"run_test.py","file_ext":"py","file_size_in_byte":2235,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"12676359947","text":"import socket,time,json\nimport socketserver,select,struct\n\nclass ThreadingTCPServer(socketserver.ThreadingMixIn,socketserver.TCPServer):\n pass\n\nclass Socks5Server(socketserver.StreamRequestHandler):\n def handle(self):\n try:\n print('new connection :', self.request)\n sock = self.connection\n data=sock.recv(8192)\n print('recieving data:',data)\n print('length:',len(data))\n self.wfile.write(b'success')\n except socket.error:\n print('socket error')\n raise\n\ndef clean_linux_port(port):\n import os\n try:\n os.system('cleanp '+str(port))\n except:\n print('clean port failed.')\nif __name__=='__main__':\n addr,port ='0.0.0.0',8888\n import platform\n sysstr=platform.system()\n print('system type: %s'%(sysstr))\n if str=='Linux':\n clean_linux_port(port)\n S=ThreadingTCPServer((addr,port),Socks5Server)\n print('waiting for connect...')\n S.serve_forever()\n\n","repo_name":"Peiiii/ss","sub_path":"trash/test_server.py","file_name":"test_server.py","file_ext":"py","file_size_in_byte":1003,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"542192201","text":"import time\n\nfrom pytube import YouTube,Playlist\nfrom utils import make_dir\n\nFILE_EXT='mp4'\n\ndef playlist(arg):\n \n playlist_url =Playlist(arg.url)\n\n save_dir = make_dir(arg.dir)\n\n for arg.url in playlist_url:\n start = time.time()\n \n yt = YouTube(arg.url)\n print('now :',yt.title)\n yt.streams.filter(adaptive=True, file_extension=FILE_EXT).order_by('resolution').desc().first().download(save_dir)\n \n end = time.time()\n\n print('done',f\"{end-start:.3f} sec\")\n return\n \ndef not_playlist(arg):\n\n save_dir = make_dir(arg.dir)\n \n start = time.time()\n \n yt = YouTube(arg.url)\n print('now :',yt.title)\n yt.streams.filter(adaptive=True, file_extension=FILE_EXT).order_by('resolution').desc().first().download(save_dir)\n \n end = time.time()\n return print('done',f\"{end-start:.3f} sec\")\n\n","repo_name":"horcrux22/youtubedownloader","sub_path":"ytdown.py","file_name":"ytdown.py","file_ext":"py","file_size_in_byte":880,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"30362846253","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[2]:\n\n\nimport os\nimport numpy as np\nimport torch\n\n\n# In[5]:\n\n\nfrom torch.utils.data import DataLoader\n\nfrom utils.functions import load_model\nfrom vrp.problem_vrp import CVRPTW\n\n\n# In[1]:\n\n\n# get_ipython().run_line_magic('matplotlib', 'inline')\nfrom matplotlib import pyplot as plt\n\nfrom matplotlib.collections import PatchCollection\nfrom matplotlib.patches import Rectangle\nfrom matplotlib.lines import Line2D\n\n# Code inspired by Google OR Tools plot:\n# https://github.com/google/or-tools/blob/fb12c5ded7423d524fc6c95656a9bdc290a81d4d/examples/python/cvrptw_plot.py\n\ndef discrete_cmap(N, base_cmap=None):\n \"\"\"\n Create an N-bin discrete colormap from the specified input map\n \"\"\"\n # Note that if base_cmap is a string or None, you can simply do\n # return plt.cm.get_cmap(base_cmap, N)\n # The following works for string, None, or a colormap instance:\n\n base = plt.cm.get_cmap(base_cmap)\n color_list = base(np.linspace(0, 1, N))\n cmap_name = base.name + str(N)\n return base.from_list(cmap_name, color_list, N)\n\ndef plot_vehicle_routes(data, route, t, ax1, markersize=5, visualize_demands=False, demand_scale=1, round_demand=False):\n \"\"\"\n Plot the vehicle routes on matplotlib axis ax1.\n \"\"\"\n print(route)\n print(data['time_window'])\n print(t)\n # route is one sequence, separating different routes with 0 (depot)\n routes = [r[r!=0] for r in np.split(route.cpu().numpy(), np.where(route==0)[0]) if (r != 0).any()]\n depot = data['depot'].cpu().numpy()\n locs = data['loc'].cpu().numpy()\n demands = data['demand'].cpu().numpy() * demand_scale\n capacity = demand_scale # Capacity is always 1\n \n x_dep, y_dep = depot\n ax1.plot(x_dep, y_dep, 'sk', markersize=markersize*4)\n# ax1.set_xlim(10, 60)\n# ax1.set_ylim(40, 90)\n ax1.set_xlim(0, 1)\n ax1.set_ylim(0, 1)\n \n legend = ax1.legend(loc='upper center')\n \n cmap = discrete_cmap(len(routes) + 2, 'nipy_spectral')\n dem_rects = []\n used_rects = []\n cap_rects = []\n qvs = []\n total_dist = 0\n for veh_number, r in enumerate(routes):\n color = cmap(len(routes) - veh_number) # Invert to have in rainbow order\n \n route_demands = demands[r - 1]\n coords = locs[r - 1, :]\n xs, ys = coords.transpose()\n\n total_route_demand = sum(route_demands)\n print(total_route_demand,capacity)\n assert total_route_demand <= capacity+1\n if not visualize_demands:\n ax1.plot(xs, ys, 'o', mfc=color, markersize=markersize, markeredgewidth=0.0)\n \n dist = 0\n x_prev, y_prev = x_dep, y_dep\n cum_demand = 0\n for (x, y), d in zip(coords, route_demands):\n dist += np.sqrt((x - x_prev) ** 2 + (y - y_prev) ** 2)\n \n cap_rects.append(Rectangle((x, y), 0.01, 0.1))\n used_rects.append(Rectangle((x, y), 0.01, 0.1 * total_route_demand / capacity))\n dem_rects.append(Rectangle((x, y + 0.1 * cum_demand / capacity), 0.01, 0.1 * d / capacity))\n \n x_prev, y_prev = x, y\n cum_demand += d\n \n dist += np.sqrt((x_dep - x_prev) ** 2 + (y_dep - y_prev) ** 2)\n total_dist += dist\n qv = ax1.quiver(\n xs[:-1],\n ys[:-1],\n xs[1:] - xs[:-1],\n ys[1:] - ys[:-1],\n scale_units='xy',\n angles='xy',\n scale=1,\n color=color,\n label='R{}, # {}, c {} / {}, d {:.2f}'.format(\n veh_number, \n len(r), \n int(total_route_demand) if round_demand else total_route_demand, \n int(capacity) if round_demand else capacity,\n dist\n )\n )\n \n qvs.append(qv)\n \n ax1.set_title('{} routes, total distance {:.2f}'.format(len(routes), total_dist))\n ax1.legend(handles=qvs)\n \n pc_cap = PatchCollection(cap_rects, facecolor='whitesmoke', alpha=1.0, edgecolor='lightgray')\n pc_used = PatchCollection(used_rects, facecolor='lightgray', alpha=1.0, edgecolor='lightgray')\n pc_dem = PatchCollection(dem_rects, facecolor='black', alpha=1.0, edgecolor='black')\n \n if visualize_demands:\n ax1.add_collection(pc_cap)\n ax1.add_collection(pc_used)\n ax1.add_collection(pc_dem)\n\n\n# In[10]:\n\nif __name__ == \"__main__\":\n model, _ = load_model('outputs\\\\25\\\\cvrptw25_rollout_20211115T021240\\\\epoch-104.pt')\n torch.manual_seed(4321)\n # datapath = 'data/cvrptw/cvrptw25_solomon.pkl'\n # dataset = CVRPTW.make_dataset(filename=datapath, size=25, num_samples=5)\n dataset = CVRPTW.make_dataset(size=25, num_samples=1)\n # Need a dataloader to batch instances\n dataloader = DataLoader(dataset, batch_size=1)\n\n # Make var works for dicts\n batch = next(iter(dataloader))\n\n # Run the model\n model.eval()\n model.set_decode_type('greedy')\n with torch.no_grad():\n length, log_p, pi, time = model(batch, return_pi=True)\n tours = pi\n times = time\n for i in dataset:\n print(np.array(i['loc']))\n print(np.array(i['demand']))\n print(np.array(tours))\n print(np.array(times))\n\n\n\n\n\n","repo_name":"bepolygonum/AttentionVRP","sub_path":"plot_cvrptw_25.py","file_name":"plot_cvrptw_25.py","file_ext":"py","file_size_in_byte":5202,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"72128251995","text":"__author__ = 'yantianyu'\n\nfrom urllib import request\nimport json\nimport time\n\nurl = 'http://ip.taobao.com/service/getIpInfo.php?ip='\n\n\ndef checkTaobaoIP(ip):\n try:\n response = request.urlopen(url + ip, timeout=5)\n result = response.readlines()\n data = json.loads(result[0])\n return \"%15s: %s-%s-%s\" % (ip, data['data']['isp'], data['data']['region'], data['data']['city'])\n except:\n return \"%15s: timeout\" % ip\n\n\nif __name__ == \"__main__\":\n f = open('ip.txt')\n ips = f.readlines()\n f.close()\n\n f = open('ip-check.txt', 'w')\n for ip in ips:\n line = checkTaobaoIP(ip.strip())\n if line:\n print(line.encode('utf-8'))\n f.write(line.encode('utf-8') + '\\n')\n else:\n print(line)\n f.write(line + '\\n')\n f.close()\n print(\"Done!\")\n","repo_name":"EvanHongYousan/toycode","sub_path":"python-learning/crash/ipAnalysis.py","file_name":"ipAnalysis.py","file_ext":"py","file_size_in_byte":847,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"26234904065","text":"#coding:utf-8\n__author__ = \"ila\"\n\ndef pyFileCheck(filename)->bool:\n '''\n check current file is or not py file\n :param filename:\n :return:\n '''\n if type(filename)!=type(\"\"):\n return False\n if filename[-3:]==\".py\":\n return True\n else:\n return False","repo_name":"royal-dargon/ccnu_SE","sub_path":"blog manage system/utils/py_file_check.py","file_name":"py_file_check.py","file_ext":"py","file_size_in_byte":291,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"40209212080","text":"import FWCore.ParameterSet.Config as cms\n\nstringResolution = cms.ESProducer(\"StringResolutionProviderESProducer\",\n ## specify parametrization (see\n ## SWGuidePATKinematicResolutions for more details)\n parametrization = cms.string ('EtEtaPhi'),\n functions = cms.VPSet(\n cms.PSet(\n ## set the eta bin as selection string.(optional)\n ## See SWGuidePhysicsCutParser for more details\n bin = cms.string(\"\"),\n ## define resolution functions of each parameter\n et = cms.string(\"et * (sqrt(0.08^2 + (1./sqrt(et))^2 + (5./et)^2))\"),\n eta = cms.string(\"sqrt(0.008^2 + (1.5/et)^2)\"),\n phi = cms.string(\"sqrt(0.008^2 + (2.6/et)^2)\"),\n ),\n ),\n ## add constraints (depending on the choice of para-\n ## metrization); for et/eta/phi this has to be set\n ## to 0 (have a look at SWGuidePATKinematicResolutions\n ## for more details)\n constraints = cms.vdouble(0)\n)\n","repo_name":"cms-sw/cmssw","sub_path":"PhysicsTools/PatAlgos/python/recoLayer0/stringResolutionProvider_cfi.py","file_name":"stringResolutionProvider_cfi.py","file_ext":"py","file_size_in_byte":897,"program_lang":"python","lang":"en","doc_type":"code","stars":985,"dataset":"github-code","pt":"50"} +{"seq_id":"26048793717","text":"\"\"\"\nPrograma la funció matriu2d, que li passes 2 nombres enters n1, n2 i retorna una matriu m de 2 dimensions n1 x n2 plena de nombres aleatoris entre l’1 i el 100.\n\nPrograma la funció printMatriu, que imprimeix la matriu que li passes com a paràmetre de manera similar a l’exemple:\n\nPrograma la funció llistaMultiples que li passes la matriu m de 2 dimensions com a argument, i un enter e entre l’1 i el 10 i retorna una llista ll d’1 \ndimensió formada pels múltiples d’e existents a la matriu.\n\nPrograma la funció llistaUnica que li passes la matriu m i retorna una llista única (sense elements repetits) d’1 dimensió.\n\nPrograma la funció minMax que retorna el mínim i el màxim de la matriu m que li passes com a paràmetre.\n\nPrograma la funció acabaEn que li passes la matriu m com a argument, i un enter e entre l’0 i el 9 i retorna una llista d’1 dimensió formada pels nombres que \nla seva última xifra és e.\n\nPrograma la funció quadrat que donat un sencer s entre 10 i 20 retorna una matriu quadrada de 2 dimensions de mida s x s inicialitzada al caràcter que li passes \ncom a segon paràmetre.\n\nPrograma la funció inicialitza que inicialitza tots els elements de la matriu al caràcter que li passes per argument. (arguments, la matriu q i el caràcter c).\n\nPrograma la funció diagonal que posa en les diagonals de la matriu quadrada q, el caràcter c. Es passen com a paràmetres de la funció q i c.\n\n\n\"\"\"\nimport random\nacabat=False\n\ndef matriu2d(n1,n2):\n if n1>0 and n2>0:\n m=[]\n for i in range(n1):\n fila=[]\n for j in range(n2):\n fila.append(random.randint(1,100))\n m.append(fila)\n else:\n m=-1\n return m \ndef printMatriu():\n imprimir=\"\"\n for el in matriu2d(num1,num2):\n imprimir+=\" \".join(str(el2) for el2 in el)+\"\\n\"\n return imprimir\n\ndef llistaMultiples(e,m):\n Multiples=[]\n for el in m:\n Multiples+=[el2 for el2 in el if el2%e==0]\n return Multiples\n\ndef llistaUnitaria(m):\n ll=[el2 for el in m for el2 in el]\n set(ll)\n return list(ll)\n \ndef minMax(m):\n ll=[int(el2) for el in m for el2 in el]\n return min(ll),max(ll)\ndef acabaEn(e,m):\n ll=[el2 for el in m for el2 in el if el2-e==0 or (el2-e)%10==0] #Comproba si a el2 li restem e i dona 0 o es múltiple de 10, significa que el numero \n return ll #enmagatzemat en el2 acaba amb el numero enmagatzemat a e\ndef quadrat(s,c):\n imprimir=\"\"\n for i in range (s):\n imprimir+=s*c+\"\\n\"\n\n return imprimir\n\ndef inicialitza(c,q):\n imprimir=\"\"\n for el in q:\n imprimir+= len(el)*c+\"\\n\"\n return imprimir \ndef diagonal(c,q):\n imprimir=\"\"\n j=0\n k=-1\n for el in q:\n fila=[]\n for i in range(len(el)):\n fila.append(\"-\")\n fila[j]=c\n fila[k]=c\n j+=1\n k-=1\n imprimir+=\"\".join(map(str,fila))+\"\\n\" \n \n return imprimir\n\ndef creu (n,c):\n imprimir=\"\"\n j=0\n k=-1\n x=0\n for el in n:\n fila=[]\n x+=1\n for i in range(len(el)):\n if i==(len(el)//2):\n fila.append(c)\n else: \n if x==(len(n)//2)+1:\n fila.append(c)\n else:\n fila.append(\"-\")\n\n fila[j]=c\n fila[k]=c\n j+=1\n k-=1\n imprimir+=\"\".join(map(str,fila))+\"\\n\" \n \n return imprimir\n\n\nwhile not acabat:\n\n imprimir=\" Exercicis \\n\"+\"==================\\n\"+\"1. \\n\"+\"2. \\n\"+\"3. \\n\"+\"4.\\n\"+\"5. \\n\"+\"6.\\n\"+\"7. \\n\"+\"8. \\n\"+\"9. \\n\"+\"10. \\n\"+\"0.Per sortir \\n\"\n print(imprimir)\n User_input=int(input(\"Quin exercici vols? \"))\n\n if User_input==1:\n num1=int(input(\"Posa un número: \"))\n num2=int(input(\"Posa un altre número: \"))\n print(matriu2d(num1,num2))\n m=matriu2d(num1,num2)\n if User_input==2:\n print(printMatriu())\n printMatriu()\n if User_input==3:\n e=1\n while e<10 and e>0:\n e=input(\"Posa un número entre 1-10: \")\n \n print(llistaMultiples(e,m))\n if User_input==4:\n print(llistaUnitaria(m))\n if User_input==5:\n print(minMax(m))\n if User_input==6:\n num=int(input(\"Posa un número entre l'1 i el 9: \"))\n print(acabaEn(num,m))\n if User_input==7:\n sencer=int(input(\"Posa un número entre el 10 i el 20: \"))\n caracter=input(\"Posa un caracter: \")\n print(quadrat(sencer,caracter))\n if User_input==8:\n caracter=input(\"Posa un carácter:\")\n print(inicialitza(caracter,m))\n if User_input==9:\n caracter=input(\"Posa un carácter:\")\n print(diagonal(caracter,m))\n if User_input==10:\n caracter=input(\"Posa un carácter:\")\n print(creu(m,caracter))\n if User_input<=0:\n acabat=True","repo_name":"Terminator-True/python","sub_path":"1er/PR-UF2-03-Funcions i Matrius.py","file_name":"PR-UF2-03-Funcions i Matrius.py","file_ext":"py","file_size_in_byte":4943,"program_lang":"python","lang":"ca","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"72965466075","text":"from enum import Enum\n\n#Definition of Errors for the WrongDict in value_verifier to use\n#Key is Enum, Value is more info regarding error as described in comments below\nclass ErrorType(Enum):\n MAX_OPEN_PORTS = \"MAX_OPEN_PORTS\", # Number of ports over limit\n BANNED_PORTS = \"BANNED_PORTS\", # List of ports used that are banned\n NO_ROOT = \"NO_ROOT\", # None, key existing is enough\n BANNED_IMAGES = \"BANNED_IMAGES\", # List of images used that are not allowed\n BANNED_USERS = \"BANNED_USERS\", # List of users who are banned\n BANNED_APIS = \"BANNED_APIS\", # List of banned APIs\n BANNED_SERVICES = \"BANNED SERVICES\", # List of banned services\n PORT_NUMBER = \"PORT_NUMBER\", # If user only wants one port, then this is specified\n RBAC = \"RBAC\", # Checks to see if Role Based Access Control is enabled or not\n TLS = \"TLS\", # Checks to see if transport layer security is used\n REPLICA_COUNT = \"REPLICA_COUNT\", # Number of replica counts\n PULL_POLICY = \"PULL_POLICY\" # If the pull policy is present or not\n ALLOWED_IMAGES = \"ALLOWED_IMAGES\"\n ALLOWED_REGISTRY_REPO = \"ALLOWED_REGISTRY_REPO\"\n","repo_name":"cyberatlas/K8sCheckmate","sub_path":"Project/Models/error_type.py","file_name":"error_type.py","file_ext":"py","file_size_in_byte":1145,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"50"} +{"seq_id":"3980300030","text":"import pandas as pd\nimport string\nimport argparse\nimport os\n\nstr2exact = {}\n\ndef main():\n print('\\n#######################')\n print('Preprocess Part 1')\n print('#######################')\n\n data_dir = [(os.path.join(args.data_dir,'atomic2020.tsv'),'atomic2020'),\n (os.path.join(args.data_dir,'conceptnet.tsv'),'conceptnet'),\n (os.path.join(args.data_dir,'transomcs.tsv'),'transomcs'),\n (os.path.join(args.data_dir,'atomic.tsv'),'atomic')\n ]\n\n conceptnet_label_whitelist = {\n 'AtLocation':None,#\n 'CapableOf':None,#\n 'Causes':None,#\n 'CausesDesire':None,#\n 'Desires':None,#\n 'HasA':None,#\n 'HasFirstSubevent':None,#\n 'HasLastSubevent':None,#\n 'HasPrerequisite':None,#\n 'HasProperty':None,#\n 'HasSubevent':None,#\n 'MadeOf':None,#\n 'MotivatedByGoal':None,#\n 'NotDesires':None,#\n 'PartOf':None,#\n 'ReceivesAction':None,#\n 'UsedFor':None,#\n 'ObstructedBy':None\n }\n\n for f,kb in data_dir:\n print('\\n{}: reading file'.format(kb))\n df_all = pd.read_csv(f, sep='\\t')\n before_size = len(df_all)\n df_all.drop_duplicates(inplace=True)\n before_uniq = len(df_all)\n\n if kb.startswith('atomic'):\n df = df_all.copy()\n else:\n df = df_all[df_all['relation'].isin(conceptnet_label_whitelist)].copy()\n\n print('{}: processing head'.format(kb))\n df['head_exact'] = df[['head','relation']].apply(lambda x: str2exact[x['head']] if x['head'] in str2exact else clean_str(x['head'], kb, x['relation']), axis=1)\n\n print('{}: processing tail'.format(kb))\n df['tail_exact'] = df[['tail','relation']].apply(lambda x: str2exact[x['tail']] if x['tail'] in str2exact else clean_str(x['tail'], kb, x['relation']), axis=1)\n\n print('{}: writing processed file'.format(kb))\n\n d = '/'.join(f.split('/')[:-1]) + '/'\n df[['head', 'relation', 'tail', 'head_exact', 'tail_exact']].to_csv(d + kb + '_exact.tsv', index=False, sep='\\t')\n\n\n\ndef clean_str(s_raw,kb, relation):\n if pd.isnull(s_raw):\n s_raw = ''\n\n s = s_raw.lower()\n if kb[:6] == 'atomic' and 'person' in s:\n s = s.replace('personx','person')\n s = s.replace('persony','person')\n s = s.replace('personz','person')\n\n s = s.strip().translate(str.maketrans('', '', string.punctuation))\n l = s.split()\n\n if not l:\n rv = ''\n elif kb[:6] == 'atomic' and (relation[0] in [\"o\",\"x\"] or relation in ['isFilledBy', 'HinderedBy', 'isBefore', 'isAfter']) and l[0][:6]=='person':\n rv = ' '.join(l[1:])\n else:\n rv = ' '.join(l)\n\n str2exact[s_raw] = rv\n return rv\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument('data_dir', help=\"directory with KBs in tab separated data files. Required headers and columns: [head, relation, tail]\")\n\n args = parser.parse_args()\n\n main()\n","repo_name":"allenai/comet-atomic-2020","sub_path":"human_eval/coverage/preprocess_kb_triples_part1.py","file_name":"preprocess_kb_triples_part1.py","file_ext":"py","file_size_in_byte":3014,"program_lang":"python","lang":"en","doc_type":"code","stars":199,"dataset":"github-code","pt":"50"} +{"seq_id":"27726057220","text":"# Ejercicio 28 - MCM\r\n\r\ndef min_comun_multiplo(n1, n2):\r\n if n1 == n2:\r\n resultado = f\"El minimo común multiplo es : {n1}\"\r\n return resultado\r\n else:\r\n mayor = 0\r\n if n1 > n2:\r\n mayor = n1\r\n else:\r\n mayor = n2\r\n\r\n\r\n while (True):\r\n if ((mayor % n1 == 0) and (mayor % n2 == 0)):\r\n mcm = mayor\r\n break\r\n mayor += 1\r\n return mcm\r\n\r\nprint(min_comun_multiplo(10, 111))\r\n\r\n\r\n# Ejercicio 28 - MCD\r\n\r\ndef min_comun_divisor(n1,n2):\r\n if n1 == n2:\r\n return f\"El minimo común divisor es {n1}\"\r\n else:\r\n minimo = min(n1,n2)\r\n\r\n while minimo > 0:\r\n if n1 % minimo == 0 and n2 % minimo == 0:\r\n return minimo\r\n break\r\n minimo -= 1\r\n\r\nprint(min_comun_divisor(60,12))\r\n","repo_name":"Ismaelbase/sistemas-de-gestion-empresarial","sub_path":"Tema 3 - Inicio Python/Ejercicios/Ejercicios28-31.py","file_name":"Ejercicios28-31.py","file_ext":"py","file_size_in_byte":838,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"32527389798","text":"import os\nimport sys\nimport subprocess\nimport time\nimport ruamel.yaml\nimport matplotlib.pyplot as plt\nimport csv\n\nyaml = ruamel.yaml.YAML()\nyaml.preserve_quotes = True\n\ndef get_pod_yaml(name: str, file = None):\n return subprocess.run(\n [\"kubectl\", \"get\", \"pod\", name, \"--output\", \"yaml\"],\n stdout=(subprocess.PIPE if file is None else file)\n ).stdout\n\n\ndef get_pod_resources(name: str):\n resources = subprocess.run(\n [\"grep\", \"limits:\", \"-A\", \"5\"],\n input=get_pod_yaml(name),\n stdout=subprocess.PIPE,\n )\n resources_obj = resources.stdout.decode(\"utf-8\").split()\n return {\n \"limits\": {\n \"cpu\": int(resources_obj[2].replace(\"m\", \"\")),\n },\n \"requests\": {\n \"cpu\": int(resources_obj[7].replace(\"m\", \"\")),\n }\n }\n\n\ndef get_pod_usage(name: str):\n usage_output = subprocess.run(\n [\"kubectl\", \"top\", \"pod\", name, \"--use-protocol-buffers\"],\n stdout=subprocess.PIPE\n ).stdout\n\n usage = subprocess.run(\n [\"grep\", name],\n input=usage_output,\n stdout=subprocess.PIPE,\n )\n usage_obj = usage.stdout.decode(\"utf-8\").split()\n return {\n \"cpu\": int(usage_obj[1].replace(\"m\", \"\")),\n }\n\n\ndef get_vpa_pod_name():\n name_output = subprocess.run(\n [\"kubectl\", \"get\", \"pods\", \"-l\", \"app=vpa-container\"],\n stdout=subprocess.PIPE\n ).stdout\n\n name_output_split = subprocess.run(\n [\"grep\", \"vpa-deployment\"],\n input=name_output,\n stdout=subprocess.PIPE,\n )\n\n name_split = name_output_split.stdout.decode(\"utf-8\").split()\n return name_split[0]\n\n\ndef get_vpa_recommendations(name: str):\n usage_output = subprocess.run(\n [\"kubectl\", \"get\", \"vpa\", \"vpa\", \"--output\", \"yaml\"],\n stdout=subprocess.PIPE\n ).stdout\n data = yaml.load(usage_output.decode(\"utf-8\"))\n\n upper_cpu = 0\n lower_cpu = 0\n target_cpu = 0\n recommendation = data[\"status\"][\"recommendation\"][\"containerRecommendations\"]\n for pod in recommendation:\n if pod[\"containerName\"] == \"vpa-container\":\n upper_cpu = pod[\"upperBound\"][\"cpu\"][:-1]\n lower_cpu = pod[\"lowerBound\"][\"cpu\"][:-1]\n target_cpu = pod[\"target\"][\"cpu\"][:-1]\n\n return {\n \"upperBound\": {\n \"cpu\": upper_cpu\n },\n \"lowerBound\": {\n \"cpu\": lower_cpu\n },\n \"target\": {\n \"cpu\": target_cpu\n },\n }\n\n\ndef uniquify(path):\n filename, extension = os.path.splitext(path)\n counter = 1\n\n while os.path.exists(path):\n path = filename + \" (\" + str(counter) + \")\" + extension\n counter += 1\n\n return path\n\n\nname = sys.argv[1]\nvpa_type = sys.argv[2]\nif vpa_type == \"kube\":\n name = get_vpa_pod_name()\nusages = []\nlimits = []\nrequests = []\nvpa_upper_bound = []\nvpa_lower_bound = []\nvpa_target = []\ntime_list = []\ncurrent_time = 0\n\nstart = time.time()\n\nwhile True:\n try:\n resources = get_pod_resources(name)\n usage = get_pod_usage(name)\n if vpa_type == \"kube\":\n recommendation = get_vpa_recommendations(name)\n vpa_upper_bound.append(int(recommendation[\"upperBound\"][\"cpu\"]))\n vpa_lower_bound.append(int(recommendation[\"lowerBound\"][\"cpu\"]))\n vpa_target.append(int(recommendation[\"target\"][\"cpu\"]))\n usages.append(usage[\"cpu\"])\n time_list.append(current_time)\n requests.append(resources[\"requests\"][\"cpu\"])\n limits.append(resources[\"limits\"][\"cpu\"])\n time.sleep(5)\n current_time += 5\n except KeyboardInterrupt:\n print(\"Stopped Data collector\")\n break\n except:\n while True:\n is_created = subprocess.run(\n [\"kubectl\", \"get\", \"pod\", name],\n stdout=subprocess.PIPE\n ).stdout.decode(\"utf-8\")\n if vpa_type == \"kube\":\n name = get_vpa_pod_name()\n if \"NotFound\" not in is_created:\n break\n usages.append(0)\n vpa_upper_bound.append(0)\n vpa_lower_bound.append(0)\n vpa_target.append(0)\n time_list.append(current_time)\n requests.append(0)\n limits.append(0)\n time.sleep(5)\n current_time += 5\n\nwith open(uniquify(f\"results/{name}{vpa_type}graph.csv\"), \"w\") as f:\n writer = csv.writer(f)\n writer.writerows([\n limits, requests, usages, time_list, vpa_upper_bound, vpa_lower_bound, vpa_target\n ])\n\nplt.plot(time_list, limits, label = \"Limits\")\nplt.plot(time_list, requests, label = \"Requests\")\nif vpa_type == \"kube\":\n plt.plot(time_list, vpa_lower_bound, label = \"Lower Bound\")\n plt.plot(time_list, vpa_target, label = \"Target\")\nplt.plot(time_list, usages, label = \"Usage\")\nplt.xlabel(\"Time (s)\")\nplt.ylabel(\"CPU (m)\")\nplt.title(\"Pod - Usage, limits and requests\")\nplt.legend()\nplt.savefig(uniquify(f\"results/{name}{vpa_type}graph.png\"))\n\nplt.show()\n\n# Used to re-plot data if something went wrong\n# \n# with open(\"results/vpa-deployment-5b8fb9c68b-hpcjrkubegraph.csv\", \"r\") as f:\n# # reader = csv.reader(f, delimiter=',')\n# # rows = list(reader)\n# # for i in range(0, 7):\n# # print(len(rows[i]))\n# # sys.exit()\n# reader = csv.reader(f, delimiter=',')\n# rows = list(reader)\n# for i in range(0, 7):\n# rows[i] = [int(x) for x in rows[i][:500]]\n# plt.plot(rows[3], rows[0], label = \"Limits\", color='#1f77b4')\n# plt.plot(rows[3], rows[1], label = \"Requests\", color='#ff7f0e')\n# plt.plot(rows[3], rows[2], label = \"Usage\", color='#2ca02c')\n# if vpa_type == \"kube\":\n# plt.plot(rows[3], rows[5], label = \"Lower Bound\", color='#d62728')\n# plt.plot(rows[3], rows[6], label = \"Target\", color='#9467bd')\n# plt.xlabel(\"Time (s)\")\n# plt.ylabel(\"CPU (m)\")\n# plt.title(\"Pod - Usage, limits and requests\")\n# plt.legend()\n# plt.savefig(\"results/vpa-deployment-5b8fb9c68b-hpcjrkubegraph (1).png\")\n# \n# plt.show()\n","repo_name":"SanderBuK/Kubernetes-VPA","sub_path":"collect-vpa-data.py","file_name":"collect-vpa-data.py","file_ext":"py","file_size_in_byte":6009,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"50"} +{"seq_id":"35397224293","text":"from datetime import datetime\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flaskblog import db, login_manager, app\nfrom flask_login import UserMixin\nfrom itsdangerous import TimedJSONWebSignatureSerializer as Serializer\n\n\n@login_manager.user_loader\ndef load_user(user_id):\n return User.query.get(int(user_id))\n\nclass User(db.Model, UserMixin):\n id = db.Column(db.Integer, primary_key = True)\n username = db.Column(db.String(20), nullable=False, unique=True)\n email = db.Column(db.String(120), nullable=False, unique=True)\n password = db.Column(db.String(), nullable=False)\n image_file = db.Column(db.String(60), nullable=False, default='default.jpg')\n posts = db.relationship('Post', backref='author', lazy=True)\n\n def __repr__(self):\n return f\"User('{self.username}', '{self.email}', '{self.image_file}')\"\n\n def get_reset_token(self, expired_sec = 1800):\n s = Serializer(app.config['SECRET_KEY'], expired_sec)\n token = s.dumps({'user_id':self.id}).decode('utf-8')\n return token\n\n @staticmethod\n def verify_reset_token(token):\n s = Serializer(app.config['SECRET_KEY'])\n try:\n user_id = s.loads(token)['user_id']\n except:\n return None\n return User.query.get(user_id)\n\nclass Post(db.Model):\n id = db.Column(db.Integer, primary_key = True)\n title = db.Column(db.String(100), nullable=False)\n content = db.Column(db.Text, nullable=False)\n posted_date = db.Column(db.DateTime, nullable=False, default=datetime.utcnow)\n user_id = db.Column(db.Integer, db.ForeignKey('user.id'),nullable=False)\n comments = db.relationship('Comment', backref='post', lazy=True)\n\n def __repr__(self):\n return f\"Post('{self.title}', '{self.posted_date}')\"\n\nclass Comment(db.Model):\n id = db.Column(db.Integer, primary_key = True)\n content = db.Column(db.String(255), nullable=False)\n created_date = db.Column(db.DateTime, nullable=False, default=datetime.utcnow)\n user = db.Column(db.Boolean(), default=False)\n post_id = db.Column(db.Integer, db.ForeignKey('post.id'),nullable=False)\n\n def __repr__(self):\n return f\"Comment('{self.user}')\"","repo_name":"adeoti-ade/FlaskBlog","sub_path":"flaskblog/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2177,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"41030958655","text":"\"\"\"import base64\nfrom Crypto.Cipher import AES\n\nx = base64.b64decode(open('7.txt', 'r').read())\n\nkey = b'YELLOW SUBMARINE'\ncipher = AES.new(key, AES.MODE_ECB)\ny = cipher.decrypt(x)\nprint(y)\n\"\"\"\nfrom Crypto.Cipher import AES\nimport base64\nfrom Crypto.Util.strxor import strxor\n\ndef cbc_encrypt(plaintext,key):\n #split the plaintext into blocks of size 16 bytes\n blocks = [plaintext[i: i + 16] for i in range(0, len(plaintext), 16)]\n c = AES.new(key, AES.MODE_ECB)\n IV = b\"\\x00\"*16\n ciphertext = b\"\"\n prev = IV\n for plaintext_block in blocks:\n cipherblock = c.encrypt(strxor(plaintext_block, prev))\n ciphertext += cipherblock\n prev = cipherblock\n return ciphertext\ndef cbc_decrypt(ciphertext, key):\n blocks = [ciphertext[i: i + 16] for i in range(0, len(ciphertext), 16)]\n plaintext = b\"\"\n c = AES.new(key, AES.MODE_ECB)\n prev = b\"\\x00\"*16\n for cipherblock in blocks:\n plaintext_block = strxor(c.decrypt(cipherblock), prev)\n plaintext += plaintext_block\n prev = cipherblock\n return plaintext\n\n\nx = base64.b64decode(open('file').read())\ndecrypted = cbc_decrypt(x, b'YELLOW SUBMARINE')\nprint(decrypted)\nprint(base64.b64encode(cbc_encrypt(decrypted, b'YELLOW SUBMARINE')))\n","repo_name":"josephxu1234/cryptopals","sub_path":"set2/chal10/cbc.py","file_name":"cbc.py","file_ext":"py","file_size_in_byte":1252,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"41417159228","text":"import init_functions as init\nimport full_conditionals as cond\nimport full_conditionals_opt as cond_opt\nimport mcem_sampler as mcem_samp\nimport ordinate as ordinate\nimport numpy as np\nimport scipy.stats as st\n\ndef sampler(Yn, model, m, cond_module, max_iter=6000, burn_iter=1000):\n '''\n Detect the location of change points\n\n Args\n Yn: array, time-series data\n model: string, e.g. \"binary\", \"poisson\"\n m: int, number of change points\n cond: module, e.g. cond, cond_opt\n Whether to use the regular or optimized version\n max_iter: int, number of iterations\n burn_iter: int, number of burn-in iterations\n '''\n # Initialize Sn and P\n n = len(Yn) ; m = m\n Sn = init.S(n, m)\n P = init.P(m + 1)\n\n # Initializing Theta\n if model == \"binary\":\n Theta = np.repeat(0.5, m + 1)\n elif model == \"poisson\":\n Theta = np.repeat(2, m + 1) # From paper\n \n # Prior on P\n if m == 1:\n a = 8 ; b = 0.1\n elif m == 2:\n a = 5 ; b = 0.1\n\n Sn_mcmc = np.empty((n, max_iter))\n F1_mcmc = np.empty((n, m + 1, max_iter))\n F_mcmc = np.empty((n, m + 1, max_iter))\n Theta_mcmc = np.empty((m + 1, max_iter))\n P_mcmc = np.empty((m + 1, m + 1, max_iter))\n\n i = 0\n while (i < max_iter):\n\n Sn, F, F1 = cond_module.S_sampling(Yn, Theta, P, model=model)\n Theta = cond_module.Theta_sampling(Yn, Sn, model=model)\n P = cond_module.P_sampling(Sn, a, b)\n\n Sn_mcmc[:, i] = Sn\n F1_mcmc[:, :, i] = F1\n F_mcmc[:, :, i] = F\n Theta_mcmc[:, i] = Theta\n P_mcmc[:, :, i] = P\n i += 1\n\n return Sn_mcmc, F1_mcmc, F_mcmc, Theta_mcmc, P_mcmc\n\ndef sampler_with_mcem(Yn, model, m, cond_module, mcem_module, ordinate_module, max_iter=6000, burn_iter=1000):\n '''\n Detect the location of change points and calculate marginal likelihood\n \n Using marginal likelihood, we can compare different models\n with different number of change points\n\n Args\n Yn: array, time-series data\n model: string, e.g. \"binary\", \"poisson\"\n m: int, number of change points\n cond: string, e.g. \"cond\", \"cond_opt\"\n Whether to use the regular or optimized version of sub-functions\n max_iter: int, number of iterations\n burn_iter: int, number of burn-in iterations\n\n Returns\n marg_lik: marginal likelihood of the input model\n '''\n \n # MCEM to get the MLE estimate\n Thetas, Ps = mcem_samp.mcem_sampler(Yn, model, m, mcem_module)\n Theta_mle = Thetas[:, -1] ; P_mle = Ps[:, :, -1]\n\n # Initialize\n n = len(Yn) ; m = m\n Sn = init.S(n, m)\n P = init.P(m + 1)\n\n # Initializing Theta\n if model == \"binary\":\n Theta = np.repeat(0.5, m + 1)\n elif model == \"poisson\":\n Theta = np.repeat(2, m + 1) # From paper\n \n # Prior on P\n if m == 1:\n a = 8 ; b = 0.1\n elif m == 2:\n a = 5 ; b = 0.1\n\n # Pre-populate result arrays\n F1_mcmc = np.empty((n, m + 1, max_iter))\n F_mcmc = np.empty((n, m + 1, max_iter))\n Theta_mcmc = np.empty((m + 1, max_iter))\n Sn_mcmc = np.empty((n, max_iter))\n # Extra result arrays for marginal likelihood calculation\n Sn_extra_mcmc = np.empty((n, max_iter))\n pii_extra_mcmc = np.empty((m + 1, max_iter))\n\n # Start MCMC\n i = 0\n while (i < max_iter):\n # print \"Iteration\" + str(i)\n # print P, Theta\n\n Sn, F, F1 = cond_module.S_sampling(Yn, Theta, P, model=model)\n Theta = cond_module.Theta_sampling(Yn, Sn, model=model)\n P = cond_module.P_sampling(Sn, a, b)\n\n # Additional sampling of Sn for marginal likelihood\n Sn_extra, F, F1 = cond.S_sampling(Yn, Theta_mle, P, model=model)\n P_extra = cond.P_sampling(Sn_extra, a, b)\n\n F1_mcmc[:, :, i] = F1\n F_mcmc[:, :, i] = F\n Theta_mcmc[:, i] = Theta\n Sn_mcmc[:, i] = Sn\n Sn_extra_mcmc[:, i] = Sn_extra\n pii_extra_mcmc[:, i] = np.diag(P_extra)\n i += 1\n\n # Calculate marginal likelihood\n log_likelihood = ordinate_module.log_likelihood(Yn, Theta_mle, P_mle, model=model)\n log_prior_Theta = ordinate_module.log_prior_Theta(Theta_mle, model=model)\n log_prior_P = ordinate_module.log_prior_P(P_mle, a, b)\n log_posterior_Theta = ordinate_module.log_posterior_Theta(Theta_mle, Yn, Sn_mcmc, model=model)\n log_posterior_P = ordinate_module.log_posterior_P(P_mle, Sn_extra_mcmc, a, b)\n\n marg_lik = log_likelihood + log_prior_Theta + log_prior_P - log_posterior_Theta - log_posterior_P\n\n return Sn_mcmc, F1_mcmc, F_mcmc, Theta_mcmc, Theta_mle, P_mle, log_likelihood, marg_lik","repo_name":"anhqle/changepoint_model","sub_path":"changepoint/mcmc_sampler.py","file_name":"mcmc_sampler.py","file_ext":"py","file_size_in_byte":4645,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"607553898","text":"import sys\n\nfrom django.apps import AppConfig\nfrom django.conf import settings\n\nfrom .statsutils import get_logger\n\nclass Fail2banNgStatsAppConfig(AppConfig):\n name = 'Fail2banNgStatsApp'\n\n def ready(self):\n if settings.REFRESH_ON or not self.__server_starting():\n return\n\n self.logger = get_logger('Fail2banNgStatsAppConfig')\n lock = settings.REFRESH_LOCK\n if lock.acquire(blocking=False):\n settings.REFRESH_ON = True\n try:\n self.logger.info('Run scheduled refresh')\n self.__run_scheduled_refresh()\n except Exception as e:\n self.logger.warning('Failed to run shceduled refresh')\n self.logger.debug(str(e))\n lock.release()\n\n def __server_starting(self):\n if len(sys.argv) >= 2:\n return sys.argv[1] == 'runserver'\n return False\n\n def __refresh_thread_job(self, exit_event):\n from .statsreader import read_config, run_scheduled_refresh\n from .statsutils import ArgsMock\n args = ArgsMock(config_path=settings.REFRESH_CONFIG, database=True)\n _config = dict(read_config(args.config))\n _config['event'] = exit_event\n self.logger.debug(str(_config))\n run_scheduled_refresh(args, _config)\n\n def __run_scheduled_refresh(self):\n from threading import Event, Thread\n import atexit\n\n exit_event = Event()\n thread = Thread(target=self.__refresh_thread_job, args=(exit_event,), daemon=True)\n thread.start()\n\n atexit.register(self.__join_refresh_thread, thread, exit_event)\n\n def __join_refresh_thread(self, thread, exit_event):\n exit_event.set()\n self.logger.debug('Join refresh thread')\n thread.join(timeout=float(15))\n if thread.is_alive():\n self.logger.warning('Refreash thread join timed out.')\n else:\n self.logger.info('Refresh thread terminated')\n","repo_name":"Mini2Inz/fail2ban-stats","sub_path":"Fail2banNgStatsApp/apps.py","file_name":"apps.py","file_ext":"py","file_size_in_byte":1973,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"11626942451","text":"from typing import Callable, Dict, Type, TypeVar, List\n\nfrom brt.runtime import log\n\nlogger = log.get_logger(__file__)\n\nT = TypeVar(\"T\")\n\n\nclass Registry:\n sub_cls_registries: Dict[T, Dict[str, T]] = {}\n func_registries: Dict[str, Callable] = {}\n cls_registries: Dict[str, List[T]] = {}\n\n @classmethod\n def register_sub_cls(cls, sub_cls_type: str, base_cls: T) -> Callable:\n def register_func(sub_cls) -> Type[T]:\n if base_cls not in cls.sub_cls_registries:\n cls.sub_cls_registries[base_cls] = {}\n\n if sub_cls_type in cls.sub_cls_registries[base_cls]:\n logger.warning(f\"{sub_cls_type} is already registered, overwrite it.\")\n\n if not issubclass(sub_cls, base_cls):\n raise ValueError(f\"{sub_cls} is not a subclass of {base_cls}.\")\n sub_cls.brt_abs_type = sub_cls_type\n cls.sub_cls_registries[base_cls][sub_cls_type] = sub_cls\n return sub_cls\n\n return register_func\n\n @classmethod\n def get_sub_cls(cls, sub_cls_type, base_cls) -> Type[T]:\n if base_cls not in cls.sub_cls_registries:\n raise ValueError(f\"{base_cls} is not registered.\")\n\n if sub_cls_type not in cls.sub_cls_registries[base_cls]:\n return None\n\n sub_cls = cls.sub_cls_registries[base_cls][sub_cls_type]\n\n return sub_cls\n\n @classmethod\n def sub_cls_exists_and_registered(cls, sub_cls, base_cls) -> bool:\n if base_cls not in cls.sub_cls_registries:\n raise ValueError(\n f\"No base class of {base_cls} exists in the registry, register the base class first.\"\n )\n if (\n issubclass(sub_cls, base_cls)\n and sub_cls in cls.sub_cls_registries[base_cls].values()\n ):\n return True\n\n return False\n\n @classmethod\n def register_cls(cls, cls_type: str) -> Callable:\n def register_func(registered_cls) -> Type[T]:\n if cls_type not in cls.cls_registries:\n cls.cls_registries[cls_type] = []\n\n if registered_cls in cls.cls_registries[cls_type]:\n logger.warning(f\"{cls_type} is already registered, overwrite it.\")\n\n cls.cls_registries[cls_type].append(registered_cls)\n\n return registered_cls\n\n return register_func\n\n @classmethod\n def register_cls_type(cls, cls_type: str):\n if cls_type not in cls.cls_registries:\n cls.cls_registries[cls_type] = []\n\n @classmethod\n def cls_exists_and_registered(cls, registered_cls, cls_type: str) -> bool:\n\n if cls_type not in cls.cls_registries:\n cls.register_cls_type(cls_type)\n\n if registered_cls in cls.cls_registries[cls_type]:\n return True\n\n return False\n","repo_name":"Raphael-Hao/brainstorm","sub_path":"python/brt/runtime/registry.py","file_name":"registry.py","file_ext":"py","file_size_in_byte":2800,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"50"} +{"seq_id":"2511436372","text":"#!/usr/bin/env python\n\n############################################\n# Module to prepare the folder structure for processing with gamma\n############################################\n\nimport os\nimport re\nimport argparse\nfrom functions import *\nfrom datetime import date\n\n##################\n# parse option to work locally or on server\n##################\n\n# parse some arguments\nparser = argparse.ArgumentParser(description=\"Decide whether you want to print the folder structur or create it\")\n# get positional arguments\nparser.add_argument(\"-p\", \"--print\", dest=\"print\", help=\"only print cmd call\", action=\"store_const\", const=True)\nargs =parser.parse_args()\n\n\n# parse the arguments in a list\nargs = parser.parse_args()\n\n\ndef get_directories():\n pass\n\ndef make_folder_struc(data_dir = \"../data\",\n slc_dir = \"../data/SLC\",\n dem_dir = \"../data/DEM\",\n dir_tuples = \"../data/tuples\"):\n\n \"\"\"\n Function to create the Folder Structure\n\n :param data_dir: Directory with all the Zip-files\n :param slc_dir: Directory where all \"single-date\" data will be stored\n :param dem_dir: Directory where all DEM-related data will be stored\n :param tuples_dir: Directory where all \"dual-date\"-data will be stored\n :return:\n \"\"\"\n\n zips = [x for x in os.listdir(data_dir) if x.endswith(\".zip\")]\n # get all the dates\n dates = []\n for d in zips:\n # match the first date in the zip files\n m = re.match(\".*__1SDV_(\\d{4})(\\d{2})(\\d{2})\", d)\n da = m.groups()\n d_str = ''.join(da)\n dt = date(year=int(d_str[0:4]), month=int(d_str[4:6]), day=int(d_str[6:8]))\n dates.append(dt)\n\n\n date_combs = []\n for i in dates:\n for j in dates:\n if i != j:\n if abs((i-j).days) < 13:\n if i < j:\n\n main = \"\".join(str(i).split(\"-\"))\n second = \"\".join(str(j).split(\"-\"))\n tuple = main + \"_\" + second\n date_combs.append(tuple)\n\n # this modifies the actual element date_combs\n date_combs.sort()\n\n # # make a list for summer and one for winter ??\n # summer = []\n # winter = []\n # for d in dates:\n # if int(d[4:6]) in [12,1,2]:\n # winter.append(d)\n # else:\n # summer.append(d)\n #\n # # for every date in the summer\n # date_combs_summer = []\n # for d in summer:\n # # get the year\n # year = int(d[0:4])\n # for o in summer:\n # # for every other date check if the year is the same\n # if o != d:\n # year_o = int(o[0:4])\n # if year_o == year:\n # mas = min(int(d), int(o))\n # sl = max(int(d), int(o))\n # dir = str(mas) + \"_\" + str(sl)\n #\n # # will produce the double amount\n # if not dir in date_combs_summer:\n # date_combs_summer.append(dir)\n #\n # # print(\"Summer Combinations\")\n # sorted_date_combs_summer = sorted(date_combs_summer)\n # # print(sorted_date_combs_summer)\n #\n #\n # # for every date in the winter\n # date_combs_winter = []\n # for d in winter:\n # # get the year\n # year = int(d[0:4])\n # for o in winter:\n # # for every other date check if the year is the same\n # if o != d:\n # year_o = int(o[0:4])\n # if year_o == year:\n # mas = min(int(d), int(o))\n # sl = max(int(d), int(o))\n # dir = str(mas) + \"_\" + str(sl)\n #\n # # will produce the double amount\n # if not dir in date_combs_winter:\n # date_combs_winter.append(dir)\n #\n #\n # # print(\"Winter Combinations\")\n # sorted_date_combs_winter = sorted(date_combs_winter)\n # # print(sorted_date_combs_winter)\n #\n # # add them both to one list\n # comb_dates_all = sorted_date_combs_summer + sorted_date_combs_winter\n # print(comb_dates_all)\n # print()\n # print(\"There are {} pairs\".format(len(comb_dates_all)))\n # print()\n\n\n modes = [\"intensity\", \"fringe\"]\n\n # create subdirectory for all files that come in tuples\n tuple_dir = dir_tuples; os.makedirs(tuple_dir) if not os.path.isdir(tuple_dir) else print(\".....\")\n\n # create both if not existent\n for d in date_combs:\n for m in modes:\n dirs = os.path.join(tuple_dir,d, m)\n if args.print:\n print(\"-----------------------------\")\n print(\"Creating Tuple Directory: {}\".format(dirs),end=\"\")\n print(\" \" + TRED + \"<-- Already Exists\" + ENDC) if os.path.isdir(dirs) else print()\n print(\"-----------------------------\")\n else:\n print(\"-----------------------------\")\n print(\"Creating Tuple Directory: {}\".format(dirs))\n print(\" \" + TRED + \"<-- Already Exists\" + ENDC) if os.path.isdir(dirs) else print()\n print(\"-----------------------------\")\n os.makedirs(dirs) if not os.path.isdir(dirs) else print()\n\n ###########################\n # Create directories for DEM and SLCs\n ###########################\n aux_dirs = [dem_dir, slc_dir]\n for dir in aux_dirs:\n if args.print:\n print(\"==========\")\n print(\"Creating Directory \\n {}\".format(dir), end=\"\")\n print(\" \" + TRED + \"<-- Already exists\" + ENDC) if os.path.isdir(dir) else print()\n print(\"==========\")\n else:\n print(\"==========\")\n print(\"Creating Directory \\n {}\".format(dir))\n print(\" \" + TRED + \"<-- Already exists (not creating)\" + ENDC) if os.path.isdir(dir) else print()\n print(\"==========\")\n os.makedirs(dir) if not os.path.isdir(dir) else print()\n\ndef main():\n #data_dir, slc_dir, dem_dir, tuples_dir = get_directories()\n make_folder_struc()\n\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"RobinKohrs/gammaGlacierOffset","sub_path":"gamma_scripts/00_makeFolderStructure.py","file_name":"00_makeFolderStructure.py","file_ext":"py","file_size_in_byte":6111,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"50"} +{"seq_id":"41070286457","text":"import mock\n\nfrom .base import BaseTest\nfrom ..runner import Runner\n\n\nclass TestRunner(BaseTest):\n\n @mock.patch('smedley.core.config.DriverConfig.get_task_executor')\n def test__execute(self, get_task_executor, tasks):\n runner = Runner()\n run = mock.Mock(return_value=None)\n executor = mock.Mock(run=run)\n get_task_executor.return_value = executor\n runner._execute(task=tasks[0])\n assert run.called\n\n @mock.patch('smedley.runner.logger.info')\n def test__show_summary(self, info, tasks):\n runner = Runner()\n runner._show_summary(tasks=tasks)\n assert info.called\n\n @mock.patch.object(Runner, '_execute')\n @mock.patch.object(Runner, '_show_summary')\n def test__start(self, _show_summary, _execute, tasks):\n runner = Runner()\n runner._start(tasks=tasks)\n assert _show_summary.called\n assert _execute.call_count == len(tasks)\n\n @mock.patch('smedley.core.loaders.BaseTaskLoader.get_loader')\n @mock.patch.object(Runner, '_start')\n def test_run(self, _start, get_loader, tasks):\n runner = Runner()\n _start.return_value = None\n load_tasks = mock.Mock(return_value=tasks)\n get_loader.return_value = mock.Mock(load_tasks=load_tasks)\n runner.run()\n assert _start.called\n","repo_name":"luizrabachini/smedley","sub_path":"smedley/tests/test_runner.py","file_name":"test_runner.py","file_ext":"py","file_size_in_byte":1320,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"16484062939","text":"from . import basetask\nfrom data.ai.task import SuccessTask\n\n\nclass FindResourceTaskParser(basetask.BaseTaskParser):\n \"\"\" Parses find resource tasks.\n\n Member:\n resource -- The resource type to find (string).\n successful -- Indicates if task is successful (bool).\n \"\"\"\n\n def __init__(self, base_task_parameter=None, variance_min=None,\n variance_max=None, task=None, resource=None):\n super().__init__(base_task_parameter, variance_min, variance_max, task)\n self.resource = resource\n if self.input in self.pipeline:\n self.resource = self.pipeline[self.input]\n self.successful = task.successful if task else False\n\n def create_new(self, data):\n entity_pos = data.game.region.get_pos(self.entity)\n resource_entity = data.game.region.find_resource(self.resource, entity_pos)\n if resource_entity:\n self.successful = True\n if self.output:\n self.pipeline[self.output] = resource_entity\n else:\n raise basetask.PipelineParameterException()\n \n self.task = SuccessTask(self.base_task_parameter(), self.successful)\n return self.task\n\n def is_success(self):\n return self.successful","repo_name":"tea2code/fantasy-rts","sub_path":"python-prototype/ai/task/findresourcetask.py","file_name":"findresourcetask.py","file_ext":"py","file_size_in_byte":1258,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"3495704600","text":"import pyglet\nimport mathlib\nfrom mathlib import Vector\nimport physicalobject, resources\nimport math, time, inspect, sys\n\ngroup2 = pyglet.graphics.OrderedGroup(2)\n\nclass Component(object):\n\tcategory = \"\" \t\t#Broad, used by shop tab, like \"weapon\", \"engine\", \"sensor\"\n\ttype = \"\" \t\t\t#Narrow, describes how the component works, like \"lasercannon\", \"laserturret\", \"ionthruster\"\n\tsubType = \"\" \t\t#Unique to a particular combination of attributes, like \"lasercannonsmall\", \"gravgun100\"\n\tname = \"\"\t\t\t#Player viewable pretty name, like \"Pulse Laser Turret - 10W\", \"Singularity Launcher\"\n\timg = \"items/base.png\" #Used by the shop\n\tlicenseCost = 0\t\t#In credits\n\tgoodsCost = {}\t\t#Keys are materials, values are # of tons of that material required for crafting\n\tdef __init__(self, ship=None):\n\t\tself.ship = ship\n\t\t\n\tdef addToShip(self, ship):\t\t#add a component to a ship\n\t\tself.ship = ship\n\t\tslot = ship.slots[self.category]\n\t\tfor i in range(len(slot)):\n\t\t\tif slot[i] == 0:\n\t\t\t\tslot[i] = self\n\t\t\t\tbreak\n\nclass Gun(Component):\n\tcategory = \"mainGuns\"\n\t\n\tdelay = 0.25\n\t\n\tdef __init__(self, *args, **kwargs):\n\t\tsuper(Gun, self).__init__(*args, **kwargs)\n\t\tself.window = pyglet.window.get_platform().get_default_display().get_windows()[0]\t\n\t\tself.shootTime = time.time()\n\nclass Cannon(Gun):\n\ttype = \"cannon\"\n\tsubType = \"laserGun1\"\n\tname = \"Pulse Laser Cannon - 10W\"\n\timg = \"items/laserGun1.png\"\n\tlicenseCost = 3000\n\tgoodsCost = {\"steel\": 5, \"lithium\": 5}\n\t\n\tdef fire(self):\t\n\t\tif time.time() > self.shootTime:\t\t\n\t\t\tbulletImg = resources.loadImage(\"bullet.png\", center=True)\n\t\t\tbullet = physicalobject.Bullet(ship=self.ship, x=self.ship.x, y=self.ship.y, img=bulletImg, batch=self.window.currentSystem.batch, group=group2)\n\t\t\tbullet.rotation = self.ship.rotation\n\t\t\tangleRadians = -math.radians(self.ship.rotation)\n\t\t\tbullet.vel.x = (self.ship.vel.x + math.cos(angleRadians) * bullet.maxSpeed)\n\t\t\tbullet.vel.y = (self.ship.vel.y + math.sin(angleRadians) * bullet.maxSpeed)\t\n\t\t\tself.window.currentSystem.tempObjs.append(bullet)\n\t\t\tself.shootTime = time.time() + self.delay\n\t\t\nclass Turret(Gun):\n\ttype = \"turret\"\n\tsubType = \"laserTurret1\"\n\tname = \"Pulse Laser Turret - 10W\"\n\timg = \"items/laserGun1.png\"\t\n\tlicenseCost = 5000\n\tgoodsCost = {\"steel\": 7, \"lithium\": 5}\n\t\n\tdef fire(self):\t\n\t\tif time.time() > self.shootTime:\t\t\n\t\t\t#direction = tar.normalized()\n\t\t\t#direction.x *= 100\n\t\t\t#direction.y *= 100\n\t\t\t#target = Vector(direction.x - self.ship.x, direction.y - self.ship.y)\t\n\t\t\tbulletImg = resources.loadImage(\"bullet.png\", center=True)\n\t\t\tbullet = physicalobject.Bullet(ship=self.ship, x=self.ship.x, y=self.ship.y, img=bulletImg, batch=self.window.currentSystem.batch, group=group2)\n\t\t\tbullet.vel.x = ((self.ship.vel.x/2) + tar.x - self.ship.x) * bullet.turretSpeed\n\t\t\tbullet.vel.y = ((self.ship.vel.y/2) + tar.y - self.ship.y) * bullet.turretSpeed\n\t\t\tself.window.currentSystem.tempObjs.append(bullet)\n\t\t\tself.shootTime = time.time() + self.delay\t\n\t\t\t\t\nclass GravGun(Gun):\n\tcategory = \"secondaryGuns\"\n\ttype = \"gravgun\"\n\tsubType = \"gravgun\"\n\timg = \"items/gravGun.png\"\n\tname = \"Singularity Launcher\"\n\tlicenseCost = 7000 #TODO: This should be balanced to be way more expensive\n\tgoodsCost = {\"steel\": 1, \"lithium\": 1, \"medicine\": 1}\n\t\n\tdelay = 2\n\t\n\tdef fire(self):\t\n\t\tif time.time() > self.shootTime:\t\t\n\t\t\tbulletImg = resources.loadImage(\"bullet.png\", center=True)\n\t\t\tbullet = physicalobject.GravBullet(ship=self.ship, x=self.ship.x, y=self.ship.y, img=bulletImg, batch=self.window.mainBatch, group=group2, deathTime=0.5)\n\t\t\tbullet.rotation = self.ship.rotation\n\t\t\tangleRadians = -math.radians(self.ship.rotation)\n\t\t\tbullet.vel.x = (self.ship.vel.x + math.cos(angleRadians) * bullet.maxSpeed)\n\t\t\tbullet.vel.y = (self.ship.vel.y + math.sin(angleRadians) * bullet.maxSpeed)\n\t\t\tself.window.currentSystem.tempObjs.append(bullet)\n\t\t\tself.shootTime = time.time() + self.delay\t\n\t\t\t\nclass MissileGun(Gun):\n\tcategory = \"secondaryGuns\"\n\ttype = \"missilegun\"\n\tsupType = \"missilegun\"\n\timg = \"items/laserGun1.png\"\n\tname = \"Missile Launcher\"\n\tlicenseCost = 4500\n\tgoodsCost = {\"steel\": 3, \"lithium\": 1}\n\t\n\tdelay = 2\n\tmissileImg = resources.loadImage(\"missile.png\", center=True)\n\t\n\tdef fire(self):\n\t\tif time.time() > self.shootTime:\t\t\n\t\t\tbullet = physicalobject.Missile(ship=self.ship, x=self.ship.x, y=self.ship.y, img=self.missileImg, batch=self.window.mainBatch, group=group2, deathTime=5)\n\t\t\tbullet.scale = 0.15\n\t\t\tbullet.rotation = self.ship.rotation\n\t\t\tangleRadians = -math.radians(self.ship.rotation)\n\t\t\tbullet.vel.x = (self.ship.vel.x + math.cos(angleRadians) * bullet.maxSpeed)/2\n\t\t\tbullet.vel.y = (self.ship.vel.y + math.sin(angleRadians) * bullet.maxSpeed)/2\n\t\t\tself.window.currentSystem.tempObjs.append(bullet)\n\t\t\tself.shootTime = time.time() + self.delay\t\n\t\t\t\nclass Engine(Component):\n\tcategory = \"engines\"\n\ttype = \"engine\"\n\tsubType = \"engine\"\n\tname = \"Engine\"\n\t\n\tdef __init__(self, *args, **kwargs):\n\t\tsuper(Engine, self).__init__(*args, **kwargs)\n\t\tself.strength = 200\nclass Engine2(Component):\n\tcategory = \"engines\"\n\ttype = \"engine2\"\n\tsubType = \"engine2\"\n\tname = \"Engine2\"\n\t\n\tdef __init__(self, *args, **kwargs):\n\t\tsuper(Engine2, self).__init__(*args, **kwargs)\n\t\tself.strength = 300\n\nclass Battery(Component):\n\tcategory = \"batteries\"\n\timg = \"items/battery.png\"\n\ttype = \"battery\"\n\tsubType = \"battery\"\n\tname = \"Battery\"\n\tdef __init__(self, *args, **kwargs):\n\t\tsuper(Battery, self).__init__(*args, **kwargs)\n\t\tself.capacity = 100\n\nComponents = [] #To populate the shop\ndef init():\n\tfor name, Cls in inspect.getmembers(sys.modules[__name__], inspect.isclass):\n\t\tif issubclass(Cls, Component):\n\t\t\t#Create a new instance of each component\n\t\t\tinstance = Cls()\n\t\t\tif instance.name: \n\t\t\t\t#Don't put it in the shop unless it has a name (to block parent classes)\n\t\t\t\tComponents.append(instance)\n","repo_name":"Nebual/spad","sub_path":"components.py","file_name":"components.py","file_ext":"py","file_size_in_byte":5760,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"43578630642","text":"import csv\nimport datetime\nimport random\n\nfrom typing import Any, Iterable\n\ncountries = ['RU', 'US', 'UK', 'ES']\ntotal_uids = 1000\ndays = 90\ntotal_days = datetime.timedelta(days=days)\nnow = datetime.datetime.today()\nfrom_date = now - total_days - datetime.timedelta(minutes=now.minute, hours=now.hour, seconds=now.second, microseconds=now.microsecond)\n\ndef gen_regs():\n yield [\"user_id\",\"email\",\"country\",\"ref\",\"created_at\"]\n reg_date = from_date\n total = 0\n print(\"Generating registrations...\")\n for uid in range(1, total_uids):\n ref = \"\"\n if uid%30 == 0:\n ref = \"http://bro.bro?bro=%d\" % (uid-15)\n if uid%200 == 0:\n reg_date += datetime.timedelta(days=1)\n total +=1\n yield [uid, 'foo%d@bar.com' % uid, countries[uid%len(countries)], ref, reg_date + datetime.timedelta(seconds=random.randint(0, total_days.total_seconds()))]\n print(\"Generated %d registrations events\" % total)\n\ndef gen_logins():\n yield [\"user_id\",\"created_at\"]\n total = 0\n print(\"Generating logins...\")\n for dt in [from_date - datetime.timedelta(days=x) for x in range(days)]:\n for _ in range(50):\n total +=1\n yield [random.randint(1, total_uids), dt + datetime.timedelta(seconds=random.randint(0, total_days.total_seconds()))]\n print(\"Generated %d logins events\" % total)\n\ndef gen_bro():\n yield [\"from_bro\",\"to_bro\",\"created_at\"]\n total = 0\n print(\"Generating bros...\")\n for dt in [from_date - datetime.timedelta(days=x) for x in range(days)]:\n for _ in range(250):\n total +=1\n yield [random.randint(1, total_uids), random.randint(1, total_uids), dt + datetime.timedelta(seconds=random.randint(0, total_days.total_seconds()))]\n print(\"Generated %d bros events\" % total)\n\ndef write_csv(filename: str, rows: Iterable[Iterable[Any]]):\n with open(filename, 'w+', newline='') as f:\n w = csv.writer(f, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n w.writerows(rows)\n\n\nif __name__ == \"__main__\":\n write_csv(\"./data/events_registration.csv\", gen_regs())\n write_csv(\"./data/events_login.csv\", gen_logins())\n write_csv(\"./data/events_bro.csv\", gen_bro())\n","repo_name":"chapsuk/dbt-vertica-example","sub_path":"scripts/generate_datasets.py","file_name":"generate_datasets.py","file_ext":"py","file_size_in_byte":2215,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"24243814594","text":"import requests\r\nimport os\r\nimport re\r\nimport pprint\r\n\r\n# appid = '0fb87e9c7207bb8b3d51cb647269f5c9'\r\n#s_city = \"Kiev, UA\" # name of city and country\r\n# city_id = 703448 # city id of Kiev\r\n\r\nappid = os.getenv('TOKEN')\r\n\r\n#---------------------------------------------------------------------------------------------------\r\n\r\ndef help():\r\n print(\r\n '----------------------------------------------------------------\\n'\r\n 'Лузер, то есть Юзер!Следуй по инструкции, программа простая.\\n'\r\n 'Эта прога ищет для тебя прогноз погоды по городу \\n'\r\n 'и по дате с временем которую ты потом выберешь из\\n'\r\n 'предлагаемого списка.\\n'\r\n '----------------------------------------------------------------\\n'\r\n 'Мини пошаговая инструкция:\\n'\r\n '----------------------------------------------------------------\\n'\r\n '1. Введи название города в спец формате(Kiev, UA)\\n'\r\n 'или цифру из выпадающего списка предложеных городов.\\n'\r\n '2. Выбери и введи дату цифру-ответ из выпадающего списка\\n'\r\n 'предложенных дат.\\n'\r\n '3. Выбери и введи цифру ответ с предложенным временем.\\n'\r\n '4. Получи ответ, дальше или удовлетворись либо пробуй по-новой\\n'\r\n '5. Для выхода напиши в ответе exit'\r\n '----------------------------------------------------------------\\n '\r\n\r\n ' Если что то не понятно, то мне очень жаль.\\n '\r\n '!!Вдохни поглубже и давай еще разок!!\\n'\r\n )\r\n\r\n\r\n#---------------------------------------------------------------------------------------------------\r\n\r\ndef returned_geolocation_id(s_city):\r\n '''\r\n '''\r\n try:\r\n\r\n res = requests.get(\"http://api.openweathermap.org/data/2.5/find\",\r\n params={'q': s_city, 'type': 'like', 'units': 'metric', 'APPID': appid})\r\n data = res.json()\r\n cities = [\"{} ({})\".format(d['name'], d['sys']['country'])\r\n for d in data['list']]\r\n city_id = data['list'][0]['id']\r\n\r\n return city_id\r\n\r\n except Exception as e:\r\n print(\"Exception (find):\", e)\r\n\r\n\r\n\r\n#---------------------------------------------------------------------------------------------------\r\n\r\ndef question_location():\r\n '''Начало программы, приветсвие с выбором города для погоды\r\n '''\r\n print('Привет, я могу узнать прогноз погоды для тебя.\\n'\r\n 'Напиши ЦИФРУ-ОТВЕТ или ГОРОД СО СТРАНОЙ в формате: Kiev, UA\\n'\r\n 'P.S.Если ты будешь писать название города то не забудь то что\\n'\r\n 'оно должно быть написано англ. буквами как в оригинале\\n'\r\n 'Если это украинский город, то не Lvov а Lviv.\\n\\n'\r\n '1.) Kiev, UA\\n'\r\n '2.) Lviv, UA\\n'\r\n '3.) Odessa, UA\\n'\r\n '4.) Kharkov, UA\\n'\r\n '5.) Yalta, UA\\n'\r\n '6.) Help\\n'\r\n '7.) Да ну его все!'\r\n )\r\n\r\n while True:\r\n answer = input('-------------------------input answer ----------------------------\\n')\r\n if not answer.isalpha() and not answer.isdigit():\r\n re_answer = re.findall(r'[A-Z][a-z]+,.[A-Z]{2}', answer)\r\n if re_answer:\r\n if str(returned_geolocation_id(re_answer)).isdigit():\r\n\r\n return re_answer[0]\r\n break\r\n else:\r\n print('Wrong name of city or format0!You need: Lviv, UA ')\r\n continue\r\n\r\n else:\r\n print('Wrong name of city or format#!You need: Lviv, UA ')\r\n continue\r\n\r\n elif answer.isdigit():\r\n if answer == '1':\r\n return 'Kiev, UA'\r\n break\r\n elif answer == '2':\r\n return 'Lviv, UA'\r\n break\r\n elif answer == '3':\r\n return 'Odessa, UA'\r\n break\r\n elif answer == '4':\r\n return 'Kharkiv, UA'\r\n break\r\n elif answer == '5':\r\n return 'Yalta, UA'\r\n break\r\n elif answer == '6':\r\n help()\r\n continue\r\n elif answer == '7':\r\n print('Ну и вали!!!')\r\n exit()\r\n else:\r\n print('Wrong name of city or format!!You need: Lviv, UA ')\r\n continue\r\n\r\n elif answer == 'exit':\r\n print('Ну и вали!!!')\r\n exit()\r\n else:\r\n print('input corrected name of city!?You need: Lviv, UA')\r\n continue\r\n\r\ns_city = question_location()\r\n#---------------------------------------------------------------------------------------------------\r\n\r\ndef get_request():\r\n '''\r\n The function sends a request to API openweathermap\r\n '''\r\n\r\n try:\r\n # res = requests.get(\"http://api.openweathermap.org/data/2.5/weather\",\r\n # params={'id': question_location(), 'units': 'metric',\r\n # 'lang': 'ru', 'APPID': appid})\r\n res = requests.get(\"https://api.openweathermap.org/data/2.5/forecast?q={}\\\r\n &appid={}\".format(s_city, appid))\r\n data = {}\r\n data = res.json()\r\n\r\n\r\n # print(\"conditions:\", data['weather'][0]['description'])\r\n # print(\"temp:\", data['main']['temp'])\r\n # print(\"temp_min:\", data['main']['temp_min'])\r\n # print(\"temp_max:\", data['main']['temp_max'])\r\n # print(\"Weather:\", data['weather'][0]['description'])\r\n # five_days_weather = [pprint.pprint(data)]\r\n\r\n # pprint.pprint(data)\r\n\r\n\r\n\r\n\r\n except Exception as e:\r\n print(\"Exception (weather):\", e)\r\n pass\r\n return data\r\n\r\n\r\ndata = get_request()\r\n#---------------------------------------------------------------------------------------------------\r\n\r\ndef new_dict():\r\n DT_txt = {}\r\n for index, value in enumerate(data['list']):\r\n for key, val in value.items():\r\n if key == 'dt_txt':\r\n DT_txt[val] = {}\r\n DT_txt[val].update({'pozition': index})\r\n\r\n for index, value in DT_txt.items():\r\n DT_txt[index].update({'clouds': data['list'][DT_txt.get(index).get('pozition'\r\n )].get('clouds').get('all')})\r\n DT_txt[index].update({'humidity': data['list'][DT_txt.get(index).get('pozition'\r\n )].get('main').get('humidity')})\r\n DT_txt[index].update({'temp': data['list'][DT_txt.get(index).get('pozition'\r\n )].get('main').get('temp')})\r\n\r\n DT_txt[index].update({'weather': data['list'][DT_txt.get(index).get('pozition'\r\n )].get('weather')[0].get('description')})\r\n DT_txt[index].update({'wind': data['list'][DT_txt.get(index).get('pozition'\r\n )].get('wind').get('speed')})\r\n DT_txt[index].update({'dt': index})\r\n\r\n return DT_txt\r\n\r\nnew_dict = new_dict()\r\n#----------------------------------------------------------------------------------------------------------------------\r\n\r\ndef questions_time():\r\n '''\r\n '''\r\n print('Я тебя понял, есть такой город как {0}.\\n\\n'\r\n 'Теперь давай выбери дату и время с прогнозом\\n'\r\n 'из этого огромного списка.\\n\\n'\r\n 'Если ты готов его увидить,\\n'\r\n 'то жми ENTER.\\n'.format(re.findall('([A-Za-z]+),',s_city)[0]))\r\n\r\n answer_to_contin = input('-------------------------input answer ----------------------------\\n')\r\n if answer_to_contin or not answer_to_contin:\r\n\r\n for index, value in enumerate(new_dict.items()):\r\n print(str(index + 1) + ').',value[0])\r\n print('------------------------------------------------------------------\\n\\n')\r\n while True:\r\n print('И снова я ожидаю от тебя ЦИФРУ-ОТВЕТ:\\n\\n'\r\n 'Которая в диапазоне данного списка.\\n')\r\n answer_the_time = input('-------------------------input answer ----------------------------\\n')\r\n time_dict = {}\r\n if answer_the_time.isdigit():\r\n for index, value in enumerate(new_dict.items()):\r\n time_dict.update({index + 1: value[0]})\r\n\r\n if time_dict.get(int(answer_the_time)):\r\n td = time_dict.get(int(answer_the_time))\r\n break\r\n elif answer_the_time == 'exit':\r\n exit()\r\n else:\r\n continue\r\n elif answer_to_contin == 'exit':\r\n exit()\r\n\r\n return td\r\n\r\nquestions_time = questions_time()\r\n\r\n\r\n#-----------------------------------------------------------------------------------------------------------\r\ndef weather_to_screen(arg):\r\n print('\\nИ так прогноз погоды на {0}!\\n'\r\n 'Небо будет на {1} % покрыто облаками.\\n'\r\n 'И как сказал бы англичанин:\\n'\r\n '\"And in general, at this time the weather will be {2}\"\\n'\r\n 'Влажность воздуха {3} %\\n'\r\n 'А на термометрах обесчают {4} C*\\n'\r\n 'Скорость ветра будет {5} м/с\\n'\r\n 'На этом все!\\n'.format(\r\n arg.get('dt'),\r\n arg.get('clouds'),\r\n arg.get('weather'),\r\n arg.get('humidity'),\r\n int(arg.get('temp')) - 273,\r\n arg.get('wind')\r\n ))\r\n exit()\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\nTOKEN=0fb87e9c7207bb8b3d51cb647269f5c9\r\n\"\"\"","repo_name":"AleksanderYa/weather-forecast","sub_path":"work.py","file_name":"work.py","file_ext":"py","file_size_in_byte":10749,"program_lang":"python","lang":"ru","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"42092674638","text":"from umit.pm.core.logger import log\nfrom umit.pm.gui.plugins.engine import Plugin\nfrom umit.pm.manager.auditmanager import *\nfrom umit.pm.manager.sessionmanager import SessionManager\n\nWAIT_AUTH = 1\nWAIT_CHALLENGE = 2\nWAIT_RESPONSE = 3\nWAIT_RESULT = 4\nNO_AUTH = 5\nLOGIN_OK = 6\nLOGIN_FAILED = 7\nLOGIN_TOOMANY = 8\n\nRFB_3 = 0\nRFB_7 = 1\nRFB_8 = 2\n\nclass VNCStatus(object):\n def __init__(self, version):\n self.status = WAIT_AUTH\n self.challenge = ''\n self.response = ''\n self.version = version\n\n if version == RFB_7 or version == RFB_8:\n self.status = WAIT_CHALLENGE\n\ndef vnc_dissector():\n VNC_NAME = 'dissector.vnc'\n VNC_PORTS = (5900, 5901, 5902, 5903)\n\n manager = AuditManager()\n sessions = SessionManager()\n\n def vnc(mpkt):\n if mpkt.l4_src in VNC_PORTS:\n # Packets from the server\n sess = sessions.lookup_session(mpkt, VNC_PORTS, VNC_NAME)\n\n if not sess:\n if mpkt.data and mpkt.data.startswith(\"RFB \"):\n version = mpkt.data.strip()\n\n if version.endswith('003.003'):\n ver = RFB_3\n elif version.endswith('003.007'):\n ver = RFB_7\n elif version.endswith('003.008'):\n ver = RFB_8\n else:\n log.debug('No supported RFB protocol version %s' % version)\n return\n\n mpkt.set_cfield('banner', version)\n\n newsess = sessions.lookup_session(mpkt, VNC_PORTS, VNC_NAME, True)\n newsess.data = VNCStatus(ver)\n else:\n conn_status = sess.data\n\n if conn_status.status == WAIT_AUTH and mpkt.data:\n if mpkt.data.startswith(\"\\x00\\x00\\x00\\x01\"):\n conn_status.status = NO_AUTH\n elif mpkt.data.startswith(\"\\x00\\x00\\x00\\x00\"):\n sessions.delete_session(sess)\n elif mpkt.data.startswith(\"\\x00\\x00\\x00\\x02\"):\n conn_status.status = WAIT_CHALLENGE\n\n\n if conn_status.status == WAIT_CHALLENGE and len(mpkt.data) == 16:\n conn_status.status = WAIT_RESPONSE\n conn_status.challenge = mpkt.data\n elif conn_status.status == WAIT_RESULT and mpkt.data:\n if mpkt.data.startswith(\"\\x00\\x00\\x00\\x00\"):\n conn_status.status = LOGIN_OK\n elif mpkt.data.startswith(\"\\x00\\x00\\x00\\x01\"):\n conn_status.status = LOGIN_FAILED\n elif mpkt.data.startswith(\"\\x00\\x00\\x00\\x02\"):\n conn_stauts.status = LOGIN_TOOMANY\n else:\n # Packets from the client\n sess = sessions.lookup_session(mpkt, VNC_PORTS, VNC_NAME)\n\n if sess:\n conn_status = sess.data\n\n if conn_status.status == NO_AUTH:\n mpkt.set_cfield('username', 'vnc')\n mpkt.set_cfield('password', 'No password')\n\n manager.user_msg('VNC : %s:%d -> No authentication' % \\\n (mpkt.l3_dst, mpkt.l4_dst),\n 6, VNC_NAME)\n sessions.delete_session(sess)\n\n elif conn_status.status >= LOGIN_OK:\n convert = lambda x:\"\".join([hex(ord(c))[2:].zfill(2) for c in x])\n\n password = convert(conn_status.challenge) + ':' + \\\n convert(conn_status.response)\n\n if conn_status.status > LOGIN_OK:\n manager.user_msg('VNC : %s:%d -> %s (failed)' % \\\n (mpkt.l3_dst, mpkt.l4_dst, password),\n 6, VNC_NAME)\n else:\n manager.user_msg('VNC : %s:%d -> %s' % \\\n (mpkt.l3_dst, mpkt.l4_dst, password),\n 6, VNC_NAME)\n\n mpkt.set_cfield('username', 'vnc')\n mpkt.set_cfield('password', password)\n\n sessions.delete_session(sess)\n elif conn_status.status == WAIT_RESPONSE and len(mpkt.data) >= 16:\n conn_status.status = WAIT_RESULT\n conn_status.response = mpkt.data[:16]\n\n return vnc\n\nclass VNCDissector(Plugin, PassiveAudit):\n def start(self, reader):\n self.dissector = vnc_dissector()\n\n def register_decoders(self):\n for port in xrange(5900, 5904):\n AuditManager().add_dissector(APP_LAYER_TCP, port, self.dissector)\n\n def stop(self):\n for port in xrange(5900, 5904):\n AuditManager().remove_dissector(APP_LAYER_TCP, port, self.dissector)\n\n__plugins__ = [VNCDissector]\n__plugins_deps__ = [('VNCDissector', ['TCPDecoder'], ['VNCDissector-1.0'], []),]\n\n__audit_type__ = 0\n__protocols__ = (('tcp', 5900), ('tcp', 5901),\n ('tcp', 5902), ('tcp', 5903), ('vnc', None))\n__vulnerabilities__ = (('VNC dissector', {\n 'description' : 'Virtual Network Computing (VNC) is a graphical desktop '\n 'sharing system that uses the RFB protocol to remotely '\n 'control another computer. It transmits the keyboard and '\n 'mouse events from one computer to another, relaying the '\n 'graphical screen updates back in the other direction, '\n 'over a network.',\n 'references' : ((None, 'http://en.wikipedia.org/wiki/'\n 'Virtual_Network_Computing'), )\n }),\n)\n","repo_name":"umitproject/packet-manipulator","sub_path":"audits/passive/vnc/sources/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5807,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"50"} +{"seq_id":"13130127905","text":"import torch\nimport torch.nn as nn\nfrom model.net.base import Base\nfrom model.net.convbnrelu import ConvBNRelu\nfrom model.net.gatedresidualblock import GatedResidualBlock\nclass SoftGatedSkipConnection(nn.Module):\n def __init__(self,stack,feature,num_ch,num_key):\n super(SoftGatedSkipConnection, self).__init__()\n self.out_ch= 2 * num_key + 1\n self.stack=stack\n self.prelayer=nn.Sequential(ConvBNRelu(num_ch,feature,7,2,3),nn.MaxPool2d(2),GatedResidualBlock(feature))\n self.bases=nn.ModuleList([Base(depth=4, feature=feature) for i in range(stack)])\n self.out=nn.ModuleList([nn.Conv2d(feature,num_key*2+1,1) for _ in range(stack)])\n def forward(self,x):\n x=self.prelayer(x)\n ret=[]\n for s in range(self.stack):\n x=self.bases[s](x)\n out=self.out[s](x)\n center,kps=torch.split(out, [1, self.out_ch - 1], 1)\n ret+=[(torch.sigmoid(center),torch.tanh(kps))]\n return ret\n\nif __name__=='__main__':\n from torchviz import make_dot\n model=SoftGatedSkipConnection(stack=4,feature=256,num_ch=3,num_key=12)\n optim=torch.optim.Adam(model.parameters())\n input=torch.randn(2,3,128,128)\n output=model(input)\n dot=make_dot(output[0][0],params=dict(model.named_parameters()))\n dot.format='png'\n dot.render('graph_image')\n [print(f'{input.shape} -> {output[i][0].shape},{output[i][1].shape}') for i in range(len(output))]\n loss=torch.stack([o[0] for o in output]).mean()\n optim.step()\n optim.zero_grad()","repo_name":"sikidaten/single_stage_pose_estimation","sub_path":"model/softgatedskipconnection.py","file_name":"softgatedskipconnection.py","file_ext":"py","file_size_in_byte":1538,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"50"} +{"seq_id":"11234780801","text":"\"\"\"circinus URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/2.0/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 . import views\n\nurlpatterns = [\n path('', views.Home.as_view(), name='index'),\n # product\n path('product/create/', views.ProductCreate.as_view(), name='product-create'),\n path('product/list/', views.ProductList.as_view(), name='product-list'),\n path('product/detail//', views.ProductDetail.as_view(), name='product-detail'),\n path('product/update//', views.ProductUpdate.as_view(), name='product-update'),\n path('product/delete//', views.ProductDelete.as_view(), name='product-delete'),\n # provider\n path('provider/create/', views.ProviderCreate.as_view(), name='provider-create'),\n path('provider/list/', views.ProviderList.as_view(), name='provider-list'),\n path('provider/detail//', views.ProviderDetail.as_view(), name='provider-detail'),\n path('provider/update//', views.ProviderUpdate.as_view(), name='provider-update'),\n path('provider/delete//', views.ProviderDelete.as_view(), name='provider-delete'),\n # entry\n path('entry/create/', views.EntryCreate.as_view(), name='entry-create'),\n path('entry/list/', views.EntryList.as_view(), name='entry-list'),\n path('entry/detail//', views.EntryDetail.as_view(), name='entry-detail'),\n # path('entry/update//', views.EntryUpdate.as_view(), name='entry-update'),\n path('entry/delete//', views.EntryDelete.as_view(), name='entry-delete'),\n\n path('entry/csv//', views.some_view, name='entry-csv')\n]\n","repo_name":"dementedshaman/cepheus","sub_path":"warehouse/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2147,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"10238679295","text":"from utils.aesutils import *\nfrom utils.aes import AES\nfrom utils.binutils import FileHelper\nfrom vulnexamples.aesvulns import SimpleInputECB, ExampleECBCBCOracle, ECBOrCBC, CBCBitFlip\n\n\nclass TestAES:\n def test_encrypt(self):\n assert True\n\n def test_decrypt(self):\n with open('../challenge_files/challenge7_decrypted.txt', 'rb') as f:\n plaintext = f.read()\n ciphertext = FileHelper.readb64filetohex('../challenge_files/challenge7.txt')\n cipher = AES(b'YELLOW SUBMARINE', 'ECB')\n assert cipher.decrypt(ciphertext) == plaintext\n\n def test_cbcdecrypt(self):\n with open('../challenge_files/challenge10_decrypted.txt', 'rb') as f:\n plaintext = f.read()\n ciphertext = FileHelper.readb64filetohex('../challenge_files/challenge10.txt')\n cipher = AES(b'YELLOW SUBMARINE', 'CBC')\n assert cipher.decrypt('00' * 16 + ciphertext) == plaintext\n\n\nclass Test_ECBOrCBC:\n def test_test(self):\n oracle = ExampleECBCBCOracle()\n isECBORCBC = ECBOrCBC(oracle.oracle)\n assert isECBORCBC.test() == oracle.isECB\n\n\nclass TestByteAtATimeDecryptECB:\n\n # def __init__(self):\n # ecboracle = SimpleInputECB(FileHelper.readb64filetobytes('../challenge_files/challenge12.txt'))\n # self.baatecb = ByteAtATimeDecryptionECB(ecboracle.oracle)\n def test_detect_blocklength(self):\n ecboracle = SimpleInputECB(FileHelper.readb64filetobytes('../challenge_files/challenge12.txt'))\n baatecb = ByteAtATimeDecryptionECB(ecboracle.oracle)\n assert baatecb.detect_blocklength() == 16\n\n def test_detect_ecb(self):\n ecboracle = SimpleInputECB(FileHelper.readb64filetobytes('../challenge_files/challenge12.txt'))\n baatecb = ByteAtATimeDecryptionECB(ecboracle.oracle)\n baatecb.detect_blocklength()\n assert baatecb.detect_ECB()\n\n def test_crack(self):\n assert True\n\n\nclass TestCBCbitflipblocks:\n def test_cbcbitflipblocks(self):\n cbcbitflip = CBCBitFlip()\n ciphertext = cbcbitflip.create_ciphertext('A'*16)\n newctext = CBCbitflipblocks(ciphertext,3,b'A'*16,b'A;admin=true;A=A')\n assert cbcbitflip.parse_ciphertext(newctext)\n","repo_name":"nullpsifer/cryptopals_python","sub_path":"tests/test_aesutils.py","file_name":"test_aesutils.py","file_ext":"py","file_size_in_byte":2212,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"30422241455","text":"from kmeans import assign_data_to_closest_centroid\nfrom utils import load_centroids, read_data\n\n\ndef update_assignment(data, labels, centroids):\n \"\"\"Assign all data points to the closest centroids and keep track of their\n labels. The i-th point in \"data\" corresponds to the i-th label in \"label\".\n\n Arguments:\n data: a list of lists representing all data points\n labels: a list of ints representing all data labels\n centroids: the centroid dictionary\n\n Returns: a new dictionary whose keys are the centroids' key names and\n values are a list of labels of the data points that are assigned\n to that centroid.\n \"\"\"\n point_assignment = {}\n for index in range(len(data)):\n closest_centroid = \\\n assign_data_to_closest_centroid(data[index], centroids)\n if closest_centroid not in point_assignment.keys():\n point_assignment[closest_centroid] = []\n point_assignment[closest_centroid].append(labels[index])\n return point_assignment\n\n\ndef majority_count(labels):\n \"\"\"Return the count of the majority labels in the label list\n\n Arguments:\n labels: a list of labels\n\n Returns: the count of the majority labels in the list\n \"\"\"\n counter = 0\n for num in labels:\n frequency = labels.count(num)\n if frequency > counter:\n counter = frequency\n return counter\n\n\ndef accuracy(data, labels, centroids):\n \"\"\"Calculate the accuracy of the algorithm. You should use\n update_assignment and majority_count (that you previously implemented)\n\n Arguments:\n data: a list of lists representing all data points\n labels: a list of ints representing all data labels\n centroids: the centroid dictionary\n\n Returns: a float representing the accuracy of the algorithm\n \"\"\"\n label_dict = update_assignment(data, labels, centroids)\n sum = 0\n for label_list in label_dict.values():\n sum += majority_count(label_list)\n accuracy = sum / len(labels)\n return accuracy\n\n\nif __name__ == '__main__':\n centroids = load_centroids(\"mnist_final_centroids.csv\")\n # print(len(centroids))\n data, label = read_data(\"data/mnist.csv\")\n print(accuracy(data, label, centroids))\n","repo_name":"micozmei/Python-Data-Analysis","sub_path":"analysis.py","file_name":"analysis.py","file_ext":"py","file_size_in_byte":2258,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"31575907208","text":"# -*- coding:utf-8 -*-\n\"\"\"\n @Time: 2021/10/12 下午3:10\n @Author: LiuHe\n @File: Write.py\n @Describe: the write module for test\n\"\"\"\nimport RPi.GPIP as GPIO\nfrom mfrc522 import SimpleMFRC522\n\nreader = SimpleMFRC522.SimpleMFRC522()\n\ntry:\n uid, text = reader.read()\n print(\"uid: \", uid)\n print(\"text: \", text)\nfinally:\n GPIO.cleanup()","repo_name":"Okiii-lh/MFRC522","sub_path":"Write.py","file_name":"Write.py","file_ext":"py","file_size_in_byte":343,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"22867233999","text":"import argparse, sys\n\nif __name__ == '__main__':\n\n parser = argparse.ArgumentParser(description=\n 'Print a vector of line numbers containing each line of reference file that occurs in input files')\n parser.add_argument('input_files', nargs='*', default=None)\n parser.add_argument('reference_file')\n parser.add_argument('-p', '--python', action='store_true', help='format output as python list')\n parser.add_argument('-l', '--lisp', action='store_true', help='format output as lisp list')\n args = parser.parse_args()\n\n input_lines = []\n if len(args.input_files) > 0:\n for f in args.input_files:\n input_lines += [line for line in open(f)]\n else:\n input_lines = [line for line in sys.stdin]\n\n indices = [i for i, line in enumerate(open(args.reference_file)) if line in input_lines]\n\n if args.python:\n print('[%s]'.join(', '.join(map(str, indices))))\n if args.lisp:\n print('(%s)'.join(' '.join(map(str, indices))))\n if not (args.python and args.lisp):\n print('\\n'.join(map(str, indices)))\n\n\n","repo_name":"bjvanderweij/rhythm","sub_path":"rhythm/findl.py","file_name":"findl.py","file_ext":"py","file_size_in_byte":1083,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"71895155676","text":"import pandas as pd\r\nimport numpy as np\r\n\r\noriginalData = pd.read_csv('C:/Users/kexic/Documents/AI/anime-recommendations-database/anime_new.csv')\r\ngenre = set()\r\n\r\n#get the set\r\ngenreVal = originalData['genre'].values\r\nfor row in genreVal:\r\n if isinstance(row,str):\r\n rowList = row.split(',')\r\n for ele in rowList:\r\n genre.add(ele.strip())\r\n\r\nrows = len(originalData)\r\n\r\nfor types in list(genre):\r\n originalData[types] = np.zeros(rows)\r\n\r\nfor i in xrange(rows):\r\n tmp = originalData.ix[i]['genre']\r\n if isinstance(tmp,str):\r\n for types in tmp.split(','):\r\n originalData.ix[i][types.strip()] = 1\r\n","repo_name":"Jiayi-Yang/Anime-Recommendation-System","sub_path":"genre.py","file_name":"genre.py","file_ext":"py","file_size_in_byte":651,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"41154478043","text":"import tensorflow as tf\n\n# model paramaters\nW = tf.Variable([.3], tf.float32, name='weight')\nb = tf.Variable([-.3], tf.float32, name='bias')\n\n# model input and output\nx = tf.placeholder(tf.float32)\nlinear_model = W * x + b\ny = tf.placeholder(tf.float32)\n\n# loss\nloss = tf.reduce_sum(tf.square(linear_model - y))\n\n# optimizer\noptimizer = tf.train.GradientDescentOptimizer(0.01)\ntrain = optimizer.minimize(loss)\n\ninit = tf.global_variables_initializer()\n\n# training data\nx_train = [1, 2, 3, 4]\ny_train = [0, -1, -2, -3]\n\nwith tf.Session() as sess:\n sess.run(init)\n\n # train loop\n for i in range(1000):\n sess.run(train, {x: x_train, y: y_train})\n\n curr_W, curr_b, curr_loss = sess.run([W, b, loss], {x: x_train, y: y_train})\n print('Weight: {weight}, bias: {bias}, loss: {loss}'.format(weight=curr_W,\n bias=curr_b,\n loss=curr_loss))\n","repo_name":"shirakiya/practice-tensorflow","sub_path":"get_started/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":978,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"8699028168","text":"################## TP1 : REVERSE COMPLEMENT le 17/09/2021 ##################\n\nimport random\nimport time\n\n# Question 1)\n\ndef reverse_complement_naive(seq):\n \"\"\"consigne : \n renverser la sequence \n et retourner sa sequence renversée complémente\n \"\"\"\n # on crée la séquence initialement vide\n result = \"\"\n # puis, on parcourt la séquence, et on itère sur chaque nucléotide\n # Si il n'y a pas de A T C G, on retourne une erreur\n for nucleo in seq :\n if nucleo != \"A\" and nucleo != \"T\" and nucleo != \"C\" and nucleo != \"G\":\n print(\"ERREUR : la séquence initiale est incorrecte\")\n return\n else :\n if nucleo == \"A\":\n result = \"T\" + result\n if nucleo == \"T\":\n result = \"A\" + result\n if nucleo == \"C\":\n result = \"G\" + result\n if nucleo == \"G\":\n result = \"C\" + result\n # enfin, on retourne la séquence complémentaire inversée\n return result\n\ndef reverse_complement_naive2(seq):\n \"\"\"consigne : \n renverser la sequence \n et retourner sa sequence renversée complémente\n \"\"\"\n ### VERSION OPTIMISEE ###\n # on crée la séquence initialement vide\n result = \"\"\n # puis, on parcourt la séquence, et on itère sur chaque nucléotide\n # Si il n'y a pas de A T C G, on retourne une erreur\n for nucleo in seq[::-1] :\n if nucleo != \"A\" and nucleo != \"T\" and nucleo != \"C\" and nucleo != \"G\":\n print(\"ERREUR : la séquence initiale est incorrecte\")\n return\n else :\n if nucleo == \"A\":\n result = \"T\" + result\n if nucleo == \"T\":\n result = \"A\" + result\n if nucleo == \"C\":\n result = \"G\" + result\n if nucleo == \"G\":\n result = \"C\" + result\n # enfin, on retourne la séquence complémentaire inversée\n return result\n\n\n# Question 2)\n\ndef reverse_complement_less_naive(seq):\n \"\"\"consigne : \n renverser la sequence \n et retourner sa sequence renversée complémente\n \"\"\" \n # on code la fonction de hashage :\n hash_funct = {\"A\" : \"T\", \"T\" : \"A\", \"C\" : \"G\", \"G\" : \"C\"}\n # puis on recommence le raisonnement (seq vide, on itère, on complète)\n result = \"\"\n for nucleo in seq :\n result = hash_funct[nucleo] + result\n return result\n\ndef reverse_complement_less_naive2(seq):\n \"\"\"consigne : \n renverser la sequence \n et retourner sa sequence renversée complémente\n \"\"\" \n ### VERSION OPTIMISEE ###\n # on code la fonction de hashage :\n hash_funct = {\"A\" : \"T\", \"T\" : \"A\", \"C\" : \"G\", \"G\" : \"C\"}\n # puis on recommence le raisonnement (seq vide, on itère, on complète)\n result = \"\"\n for nucleo in seq[::-1] :\n result = hash_funct[nucleo] + result\n return result\n\n# Question 3)\n\ndef generator_of_sequence(size):\n \"\"\"consigne :\n on cree une sequence de nucleotide aléatoirement choisie entre\n A T C G, de taille size\n \"\"\"\n # initialisation :\n result = \"\"\n # on va la remplir aléatoirement\n for i in range(size):\n list_nucleo = [\"A\", \"T\", \"C\", \"G\"]\n result += random.choice(list_nucleo)\n return result\n\n# Question 4)\n\ndef main():\n # Test Question 1)\n seq = \"AATCGT\"\n result = reverse_complement_naive(seq)\n print(result)\n # Test Question 2)\n seq2 = \"AATCGT\"\n result2 = reverse_complement_less_naive(seq2)\n print(result2)\n # Test Question 3)\n size = 14\n print(generator_of_sequence(size))\n # Question 4)\n \"\"\"consigne : on va tester la rapidité de \n toutes nos fonctions\n \"\"\"\n # on importe la bibli time\n # je cree une sequence assez longue :\n seq_test = generator_of_sequence(500000)\n print(\"#### Question 4) ####\")\n start = time.time()\n func1 = reverse_complement_naive(seq_test)\n end = time.time()\n print(\"func1 (reverse_complement_naive) : {:.12f}s\".format(end-start))\n start = time.time()\n func2 = reverse_complement_naive2(seq_test)\n end = time.time()\n print(\"func2 (reverse_complement_naive2) : {:.12f}s\".format(end-start))\n start = time.time()\n func3 = reverse_complement_less_naive(seq_test)\n end = time.time()\n print(\"func3 (reverse_complement_less_naive) : {:.12f}s\".format(end-start))\n start = time.time()\n func4 = reverse_complement_less_naive2(seq_test)\n end = time.time()\n print(\"func4 (reverse_complement_less_naive2) : {:.12f}s\".format(end-start))\nmain()\n\n\n\n\n\n","repo_name":"ecortial/python","sub_path":"EIDD 2A/TP ALGO 2A/TP reverse complement/exercice1.py","file_name":"exercice1.py","file_ext":"py","file_size_in_byte":4553,"program_lang":"python","lang":"fr","doc_type":"code","dataset":"github-code","pt":"50"} +{"seq_id":"37589385993","text":"#!/usr/bin/env python\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\ndef label_histograms(Y, use_log_scale=False):\n\tH = plt.hist(Y, bins=20)\n\tplt.ylabel('Probability')\n\tplt.xlabel('value')\n\tplt.title('Label Histogram')\n\tif use_log_scale: plt.xscale('log')\n\n\tplt.savefig('label_histo.png')\n\tplt.show()\n\tplt.clf()\n\n\ndata_name = '../data/car'\nY = np.loadtxt(data_name + '_label.csv', delimiter=',', dtype=np.int32)\t\t\t\nlabel_histograms(Y)\n","repo_name":"endsley/sklearn_example","sub_path":"DataStats/label_functions.py","file_name":"label_functions.py","file_ext":"py","file_size_in_byte":441,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"74722826075","text":"\"\"\"\nFetching supplimentary model input from the Planetary Computer for STAC project\nThis notebook produces additional input layers for the training data used in the\nsentinel 1 flood detection competition run by DrivenData.\n\nIf fetches JRC Global Surface Water and NASADEM elevation data from the Planetary\nComputer (PC) STAC API and creates pixel-aligned chips that match what will be\nused in the evaluation process for the competition.\n\nThe notebook will iterate through chip paths and query the PC STAC API for the\nnasadem and jrc-gsw Collections. It then creates a set of GeoTiffs by\n\"coregistering\" the raster data with the chip GeoTIFF, so that all of the\nadditional input layers have the same CRS, bounds, and resolution as the chip.\nThese additional layers are then saved alongside the training chip.\nhttps://github.com/wonderdong11/PlanetaryComputerExamples/blob/main/competitions/s1floods/generate_auxiliary_input.ipynb\n\n\"\"\"\n\nfrom dataclasses import dataclass\nimport os\nfrom tempfile import TemporaryDirectory\nfrom typing import List, Any, Dict\n\nfrom shapely.geometry import box, mapping\nimport rasterio\nfrom rasterio.warp import reproject, Resampling\nimport pyproj\nfrom osgeo import gdal\n\nfrom pystac_client import Client\nimport planetary_computer as pc\n\n\n# Define functions and classes\n# Define a ChipInfo dataclass to encapsulate the required data for the target chip.\n# This includes geospatial information that will be used to coregister the\n# incoming jrc-gsw and nasadem data.\n@dataclass\nclass ChipInfo:\n \"\"\"\n Holds information about a training chip, including geospatial info for coregistration\n \"\"\"\n\n path: str\n prefix: str\n crs: Any\n shape: List[int]\n transform: List[float]\n bounds: rasterio.coords.BoundingBox\n footprint: Dict[str, Any]\n\n\ndef get_footprint(bounds, crs):\n \"\"\"Gets a GeoJSON footprint (in epsg:4326) from rasterio bounds and CRS\"\"\"\n transformer = pyproj.Transformer.from_crs(crs, \"epsg:4326\", always_xy=True)\n minx, miny = transformer.transform(bounds.left, bounds.bottom)\n maxx, maxy = transformer.transform(bounds.right, bounds.top)\n return mapping(box(minx, miny, maxx, maxy))\n\ndef get_chip_info(chip_path):\n \"\"\"Gets chip info from a GeoTIFF file\"\"\"\n with rasterio.open(chip_path) as ds:\n chip_crs = ds.crs\n chip_shape = ds.shape\n chip_transform = ds.transform\n chip_bounds = ds.bounds\n\n # Use the first part of the chip filename as a prefix\n prefix = os.path.basename(chip_path).split(\"_\")[0]\n\n return ChipInfo(\n path=chip_path,\n prefix=prefix,\n crs=chip_crs,\n shape=chip_shape,\n transform=chip_transform,\n bounds=chip_bounds,\n footprint=get_footprint(chip_bounds, chip_crs),\n )\n\n\ndef reproject_to_chip(\n chip_info, input_path, output_path, resampling=Resampling.nearest\n):\n \"\"\"\n reprojects coregisters raster data to the bounds, CRS and resolution\n described by the ChipInfo.\n\n Reproject a raster at input_path to chip_info, saving to output_path.\n\n Use Resampling.nearest for classification rasters. Otherwise use something\n like Resampling.bilinear for continuous data.\n \"\"\"\n with rasterio.open(input_path) as src:\n kwargs = src.meta.copy()\n kwargs.update(\n {\n \"crs\": chip_info.crs,\n \"transform\": chip_info.transform,\n \"width\": chip_info.shape[1],\n \"height\": chip_info.shape[0],\n \"driver\": \"GTiff\",\n }\n )\n\n with rasterio.open(output_path, \"w\", **kwargs) as dst:\n for i in range(1, src.count + 1):\n reproject(\n source=rasterio.band(src, i),\n destination=rasterio.band(dst, i),\n src_transform=src.transform,\n src_crs=src.crs,\n dst_transform=chip_info.transform,\n dst_crs=chip_info.crs,\n resampling=Resampling.nearest,\n )\n\n# This method will take in a set of items and a asset key and write a VRT using\n# signed HREFs. This is useful when there's multiple results from the query, so we\n# can treat the resulting rasters as a single set of raster data. It uses the\n# planetary_computer.sign method to sign the HREFs with a SAS token generated by\n# the PC Data Auth API.\ndef write_vrt(items, asset_key, dest_path):\n \"\"\"Write a VRT with hrefs extracted from a list of items for a specific asset.\"\"\"\n hrefs = [pc.sign(item.assets[asset_key].href) for item in items]\n vsi_hrefs = [f\"/vsicurl/{href}\" for href in hrefs]\n gdal.BuildVRT(dest_path, vsi_hrefs).FlushCache()\n\n# This method ties it all together - for a given ChipInfo, Collection, and Asset,\n# write an auxiliary input chip with the given file name.\ndef create_chip_aux_file(\n chip_info, collection_id, asset_key, file_name, resampling=Resampling.nearest\n):\n \"\"\"\n Write an auxiliary chip file.\n\n The auxiliary chip file includes chip_info for the Collection and Asset, and is\n saved in the same directory as the original chip with the given file_name.\n \"\"\"\n\n # Create the STAC API client¶\n # This will be used in the methods below to query the PC STAC API.\n STAC_API = \"https://planetarycomputer.microsoft.com/api/stac/v1\"\n catalog = Client.open(STAC_API)\n\n output_path = os.path.join(\n os.path.dirname(chip_info.path), f\"{chip_info.prefix}_{file_name}\"\n )\n search = catalog.search(collections=[collection_id], intersects=chip_info.footprint)\n items = list(search.get_items())\n with TemporaryDirectory() as tmp_dir:\n vrt_path = os.path.join(tmp_dir, \"source.vrt\")\n write_vrt(items, asset_key, vrt_path)\n reproject_to_chip(chip_info, vrt_path, output_path, resampling=resampling)\n return output_path\n\n\n# Download the flood-train-images.tgz file from competition Data Download page and\n# upload it to the Hub in the same directory as this notebook.\n\n# Then run the following code to uncompress this. Afterwards you should see an\n# train_features directory containing all of the training chips ending in .tif.\n# !tar -xvf flood-train-images.tgz\n\n# Use this directory to define the location of the chips, or if you have\n# already uncompressed the chips elsewhere set the location here:\nTRAINING_DATA_DIR = \"training_data/train_features\"\n\n# Gather chip paths\n# These chip paths will be used later in the notebook to process the chips.\n# These paths should be to only one GeoTIFF per chip; for example, if both\n# VV.tif and VH.tif are available for a chip, use only one of these paths.\n# The GeoTIFFs at these paths will be read to get the bounds, CRS and resolution\n# that will be used to fetch auxiliary input data. These can be relative paths.\n# The auxiliary input data will be saved in the same directory as the GeoTIFF\n# files at these paths.\nchip_paths = []\nfor file_name in os.listdir(TRAINING_DATA_DIR):\n if file_name.endswith(\"_vv.tif\"):\n chip_paths.append(os.path.join(TRAINING_DATA_DIR, file_name))\nprint(f\"{len(chip_paths)} chips found.\")\n\n# Configurate the auxiliary input files that we will generate.\n# Define a set of parameters to pass into create_chip_aux_file\naux_file_params = [\n (\"nasadem\", \"elevation\", \"nasadem.tif\", Resampling.bilinear),\n (\"jrc-gsw\", \"extent\", \"jrc-gsw-extent.tif\", Resampling.nearest),\n (\"jrc-gsw\", \"occurrence\", \"jrc-gsw-occurrence.tif\", Resampling.nearest),\n (\"jrc-gsw\", \"recurrence\", \"jrc-gsw-recurrence.tif\", Resampling.nearest),\n (\"jrc-gsw\", \"seasonality\", \"jrc-gsw-seasonality.tif\", Resampling.nearest),\n (\"jrc-gsw\", \"transitions\", \"jrc-gsw-transitions.tif\", Resampling.nearest),\n (\"jrc-gsw\", \"change\", \"jrc-gsw-change.tif\", Resampling.nearest),\n]\n\n# Generate auxiliary input chips for NASADEM and JRC\n# Iterate over the chips and generate all aux input files.\ncount = len(chip_paths)\nfor i, chip_path in enumerate(chip_paths):\n if i < 446:\n continue\n print(f\"({i+1} of {count}) {chip_path}\")\n chip_info = get_chip_info(chip_path)\n for collection_id, asset_key, file_name, resampling_method in aux_file_params:\n print(f\" ... Creating chip data for {collection_id} {asset_key}\")\n create_chip_aux_file(\n chip_info, collection_id, asset_key, file_name, resampling=resampling_method )\n","repo_name":"dahaorendrm/STAC_overflow","sub_path":"fetch_additional_data.py","file_name":"fetch_additional_data.py","file_ext":"py","file_size_in_byte":8356,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"50"} +{"seq_id":"4113328681","text":"import string\n\nfrom aiogram.types import InlineKeyboardMarkup, InlineKeyboardButton, ReplyKeyboardMarkup, KeyboardButton\nfrom emoji import emojize\n\nfrom config import available_groups\nfrom defs import transform_into_user_groups\n\n\nasync def groups_markup(self_id):\n markup = InlineKeyboardMarkup()\n user_groups = await transform_into_user_groups()\n for button in await available_groups():\n if button in user_groups[self_id]:\n display_button = button + f' {emojize(\":check_mark_button:\")}'\n else:\n display_button = button\n markup.row(InlineKeyboardButton(string.capwords(display_button), callback_data=button))\n return markup\n\n\nasync def menu_markup():\n markup = ReplyKeyboardMarkup(resize_keyboard=True)\n group_button = KeyboardButton(emojize('Выбор группы:family_man_man_girl_boy:'))\n ping_button = KeyboardButton(emojize('Пинг | Созыв:telephone:'))\n nickname_button = KeyboardButton(emojize('Изменить ник:radioactive:'))\n markup.row(ping_button).row(group_button).row(nickname_button)\n return markup","repo_name":"DarkWood312/ping_group_of_players_bot","sub_path":"keyboards.py","file_name":"keyboards.py","file_ext":"py","file_size_in_byte":1104,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"32300036304","text":"'''\nHangman\n'''\nimport time\nimport os\nimport json\nclear_screen = lambda: os.system('cls')\ndef menu():\n '''\n Hangman game in command line with python !!\n How to play\n 1. Choose category to play\n 2. Play it !!\n\n score calculator just your Life x 25\n have fun have fun\n '''\n #Setup variable\n directory_category = []\n category_list = []\n chosen_category = 0\n\n #Setup menu\n for x in os.listdir('category'):\n directory_category.append(\"category/\" + x)\n file = open(\"category/\" + x).read()\n json_file = json.loads(file)\n print(json_file[\"name\"])\n category_list.append(json_file[\"name\"])\n\n #Choose category\n category_list_len = len(category_list)\n print(\"Select Category:\")\n for i in range(category_list_len):\n print(str(i+1) + \".\", category_list[i])\n\n #Take input and heck selection\n while chosen_category == 0 and (chosen_category <= category_list_len):\n try:\n chosen_category = int(input(\"Choose : \"))\n except ValueError:\n print(\"Error!! Please enter number from 1 - \" + str(category_list_len))\n if chosen_category > category_list_len:\n print(\"It not have this menu please try again\")\n chosen_category = 0\n print(\"You choose\", category_list[chosen_category-1])\n print(\"Have fun with hangman, please wait\", end=\"\", flush=True)\n for _ in range(0,3):\n print(\".\", end=\"\", flush=True)\n time.sleep(1)\n print(\"\")\n clear_screen()\n #Play game\n play(directory_category[chosen_category-1])\n\ndef play(category):\n '''\n this is function to play this game \n '''\n #Setup game\n life = 10\n score = 0\n file = open(category).read()\n json_file = json.loads(file)\n item = 1\n questions = json_file[\"questions\"]\n\n #Start game\n for question in questions:\n print(\"Number\", item)\n print(\"Hint: \" '\"' + questions[question] + '\"')\n user_answer = []\n user_wrong = []\n\n #Setup word\n correct_answer = question\n for i in correct_answer:\n if i == \" \":\n user_answer += \" \"\n else:\n user_answer += \"_\"\n for i in user_answer:\n print(i, end=\" \")\n print(\"score %d, remaining wrong guess %d\" % (score, life), end=\"\")\n if len(user_wrong) != 0:\n print(\", wrong guessed: \", end=\"\")\n for wrong_character in user_wrong: print(wrong_character, end=\" \")\n print(\"\")\n\n #Play and Check answer\n while True:\n #Recieve user Input\n user_char = input(\"Enter your Character : \")\n while(len(user_char) != 1 or user_char == \" \"):\n print(\"Please enter only one Character or not have spacebar\")\n user_char = input(\"Enter your Character : \")\n if user_char.lower() in correct_answer.lower():\n char_count = 0\n for char in correct_answer:\n if(user_char.lower() == char.lower()):\n #get score protect repeat\n if(user_answer[char_count] == \"_\"):\n score += (life*25)\n user_answer[char_count] = correct_answer[char_count]\n char_count += 1\n else:\n user_wrong.append(user_char)\n life -= 1\n for i in user_answer:\n print(i, end=\" \")\n print(\"score %d, remaining wrong guess %d\" % (score, life), end=\"\")\n if len(user_wrong) != 0:\n print(\", wrong guessed: \", end=\"\")\n for wrong_character in user_wrong: print(wrong_character, end=\" \")\n print(\"\")\n\n #Check state\n if life <= 0:\n return print(\"Your lose with %d score\" %(score))\n try:\n user_answer.index(\"_\")\n except:\n print(\"Your pass !! please wait to next level\", end=\"\", flush=True)\n for _ in range(0,3):\n print(\".\", end=\"\", flush=True)\n time.sleep(1)\n item += 1\n print(\"\")\n clear_screen()\n break\n\n print(\"You win %d score in %s category. Thank you for play this I hope you will play another category.\" % (score, json_file[\"name\"]))\nmenu()\n","repo_name":"startmt/theinternship-developer","sub_path":"hangman/hangman.py","file_name":"hangman.py","file_ext":"py","file_size_in_byte":4396,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"36490074533","text":"#-----------------------------------------------------------------------------\n# Main process for topography downscale for meteorological forcing \n#\n# Author: Lu Li, Sisi Chen, Zhongwang Wei\n#-----------------------------------------------------------------------------\nimport os\nimport argparse\nimport datetime\nfrom multiprocessing import Process\n\nimport numpy as np\nimport netCDF4 as nc\nimport matplotlib.pyplot as plt\n\nfrom topo import (downscale_air_pressure, \n downscale_air_temperature,\n downscale_dew_temperature,\n downscale_in_longwave_radiation,\n downscale_in_shortwave_radiation,\n downscale_precipitation, \n downscale_specific_humidity,\n downscale_wind_speed, \n downscale_precipitation)\nfrom utils import save2nc\n\n\n\ndef main(\n year, \n month, \n day_of_month, # Start from 0\n blat, ulat,\n llon, rlon,\n region_name,\n DATA_PATH='/tera06/lilu/ForDs/data/GD/'):\n \n print('Downscaling forcing in {year:04}-{month:02}-{day:02}'.format(\n year=year, month=month, day=day_of_month))\n print('The region is {region_name}'.format(region_name=region_name))\n print('The lat range is {blat}-{ulat}'.format(blat=blat,ulat=ulat))\n print('The lon range is {llon}-{rlon}'.format(llon=llon,rlon=rlon))\n print('The authors are Lu Li, Sisi Chen, Zhongwang Wei, SYSU')\n print('\\033[1;31m%s\\033[0m' % ' Read and Processing input data')\n print(\"read data begin at: \", datetime.datetime.today())\n \n # load coare DEM data （20x20)\n f = nc.Dataset(DATA_PATH+'DEM/SRTM/ERA5Land_height.nc', 'r')\n lat_coarse, lon_coarse = f['latitude'][:], f['longitude'][:]\n lat_coarse_index = np.where((lat_coarse>blat) & (lat_coarsellon) & (lon_coarselat_coarse[-1]) & (lat_finelon_coarse[0]) & (lon_fine rad\n f = nc.Dataset(DATA_PATH+'DEM/MERITDEM/MERITDEM_GD_curvature.nc', 'r')\n curvature_fine = f['slp'][lat_fine_index][:,lon_fine_index]\n f = nc.Dataset(DATA_PATH+'DEM/MERITDEM/MERITDEM_GD_SkyViewFactor.nc', 'r')\n svf_fine = f['svf'][lat_fine_index][:,lon_fine_index]\n shadow_mask_LUT = np.load(DATA_PATH+'DEM/MERITDEM/shadow_mask_LUT.npy')[lat_fine_index][:,lon_fine_index]\n \n\n # get bilinear interpolate weight by cdo\n os.system(\"cdo griddes DEM_coarse.nc > grid_coarse.txt\")\n os.system(\"sed -i 's/generic/lonlat/g' grid_coarse.txt\")\n os.system(\"cdo griddes DEM_fine.nc > grid_fine.txt\")\n os.system(\"sed -i 's/generic/lonlat/g' grid_fine.txt\")\n os.system(\"cdo setgrid,grid_coarse.txt DEM_coarse.nc DEM_coarse_1.nc\")\n os.system(\"cdo genbil,grid_fine.txt DEM_coarse_1.nc weight.nc\")\n os.system(\"rm -rf DEM_coarse.nc DEM_coarse_1.nc\")\n \n\n print('\\033[1;31m%s\\033[0m' % ' Downscaling')\n print('We downscale {shape_coarse} to {shape_fine}'.format(\n shape_coarse=elev_coarse.shape, shape_fine=elev_fine.shape))\n print(\"downscaling begin at: \",datetime.datetime.today())\n\n # downscale air temperature\n print('processing t2m')\n f = nc.Dataset(DATA_PATH+'forcing/ERA5LAND_GD_{year:04}_{month:02}_t2m_interp.nc'.format(year=year, month=month), 'r')\n t2m_fine_interp = f['t2m'][day_of_month*24:day_of_month*24+24][:,lat_fine_index][:,:,lon_fine_index]\n f = nc.Dataset(DATA_PATH+'forcing/ERA5LAND_GD_{year:04}_{month:02}_t2m.nc'.format(year=year, month=month), 'r')\n t2m_coarse = f['t2m'][day_of_month*24:day_of_month*24+24][:,lat_coarse_index][:,:,lon_coarse_index]\n t2m_fine = downscale_air_temperature(t2m_coarse,\n t2m_fine_interp,\n elev_coarse,\n elev_fine_interp,\n elev_fine,\n lat_coarse,\n lon_coarse,\n lat_fine,\n lon_fine,\n year,\n month,\n day_of_month)\n save2nc('t2m', year, month, day_of_month, t2m_fine, lat_fine, lon_fine)\n\n \n # downscale dew temperature\n print('processing d2m')\n f = nc.Dataset(DATA_PATH+'forcing/ERA5LAND_GD_{year:04}_{month:02}_d2m_interp.nc'.format(year=year, month=month), 'r')\n d2m_fine_interp = f['d2m'][day_of_month*24:day_of_month*24+24][:,lat_fine_index][:,:,lon_fine_index]\n f = nc.Dataset(DATA_PATH+'forcing/ERA5LAND_GD_{year:04}_{month:02}_d2m.nc'.format(year=year, month=month), 'r')\n d2m_coarse = f['d2m'][day_of_month*24:day_of_month*24+24][:,lat_coarse_index][:,:,lon_coarse_index]\n d2m_fine = downscale_dew_temperature(d2m_coarse,\n d2m_fine_interp,\n elev_coarse,\n elev_fine_interp,\n elev_fine,\n lat_coarse,\n lon_coarse,\n lat_fine,\n lon_fine,\n year,\n month,\n day_of_month)\n save2nc('d2m', year, month, day_of_month, d2m_fine, lat_fine, lon_fine)\n\n # downscale air pressure\n print('processing sp')\n f = nc.Dataset(DATA_PATH+'forcing/ERA5LAND_GD_{year:04}_{month:02}_sp_interp.nc'.format(year=year, month=month), 'r')\n sp_fine_interp = f['sp'][day_of_month*24:day_of_month*24+24][:,lat_fine_index][:,:,lon_fine_index]\n sp_fine = downscale_air_pressure(sp_fine_interp,\n t2m_fine_interp,\n t2m_fine,\n elev_fine_interp,\n elev_fine)\n save2nc('sp', year, month, day_of_month, sp_fine, lat_fine, lon_fine)\n\n # downscale specific humidity\n print('processing Q')\n Q_fine = downscale_specific_humidity(sp_fine, d2m_fine)\n save2nc('Q', year, month, day_of_month, Q_fine, lat_fine, lon_fine)\n\n # downscale longwave radiation\n print('processing longwave radiation')\n f = nc.Dataset(DATA_PATH+'forcing/ERA5LAND_GD_{year:04}_{month:02}_strd_interp.nc'.format(year=year, month=month), 'r') \n strd_fine_interp = f['strd'][day_of_month*24:day_of_month*24+24][:,lat_fine_index][:,:,lon_fine_index] \n f = nc.Dataset(DATA_PATH+'forcing/ERA5LAND_GD_{year:04}_{month:02}_strd.nc'.format(year=year, month=month), 'r')\n strd_coarse = f['strd'][day_of_month*24:day_of_month*24+24][:,lat_coarse_index][:,:,lon_coarse_index] \n strd_fine = downscale_in_longwave_radiation(strd_coarse,\n strd_fine_interp,\n t2m_coarse,\n d2m_coarse,\n t2m_fine,\n d2m_fine,\n t2m_fine_interp,\n lat_coarse,\n lon_coarse, \n year, \n month, \n day_of_month)\n save2nc('strd', year, month, day_of_month, strd_fine, lat_fine, lon_fine)\n \n # downscale wind\n print('processing wind speed')\n f = nc.Dataset(DATA_PATH+'forcing/ERA5LAND_GD_{year:04}_{month:02}_u10_interp.nc'.format(year=year, month=month), 'r')\n u10_fine_interp = f['u10'][day_of_month*24:day_of_month*24+24][:,lat_fine_index][:,:,lon_fine_index] \n f = nc.Dataset(DATA_PATH+'forcing/ERA5LAND_GD_{year:04}_{month:02}_v10_interp.nc'.format(year=year, month=month), 'r')\n v10_fine_interp = f['v10'][day_of_month*24:day_of_month*24+24][:,lat_fine_index][:,:,lon_fine_index] \n ws_fine = downscale_wind_speed(u10_fine_interp,\n v10_fine_interp,\n slope_fine,\n aspect_fine,\n curvature_fine) \n save2nc('ws', year, month, day_of_month, ws_fine, lat_fine, lon_fine)\n \n \n # downscale short radiation\n print('processing short radiation')\n f = nc.Dataset(DATA_PATH+'Albedo/{year:04}_{month:02}_GD_bsa_interp.nc'.format(year=year, month=month), 'r')\n black_sky_albedo_interp = np.array(f['bsa'][day_of_month,:,:][lat_fine_index][:,lon_fine_index])\n black_sky_albedo_interp[black_sky_albedo_interp<0] = 0\n black_sky_albedo_interp[black_sky_albedo_interp>1] = 0\n black_sky_albedo_interp = np.tile(black_sky_albedo_interp[np.newaxis], (24, 1, 1))\n f = nc.Dataset(DATA_PATH+'Albedo/{year:04}_{month:02}_GD_wsa_interp.nc'.format(year=year, month=month), 'r')\n white_sky_albedo_interp = np.array(f['wsa'][day_of_month,:,:][lat_fine_index][:,lon_fine_index])\n white_sky_albedo_interp[white_sky_albedo_interp<0] = 0\n white_sky_albedo_interp[white_sky_albedo_interp>1] = 0\n white_sky_albedo_interp = np.tile(white_sky_albedo_interp[np.newaxis], (24, 1, 1))\n\n f = nc.Dataset(DATA_PATH+'forcing/ERA5LAND_GD_{year:04}_{month:02}_ssrd.nc'.format(year=year, month=month), 'r')\n ssrd_coarse = f['ssrd'][day_of_month*24:day_of_month*24+24,:,:][:,lat_coarse_index][:,:,lon_coarse_index]\n ssrd_coarse[ssrd_coarse<0] = 0\n f = nc.Dataset(DATA_PATH+'forcing/ERA5LAND_GD_{year:04}_{month:02}_sp.nc'.format(year=year, month=month), 'r')\n sp_coarse = f['sp'][day_of_month*24:day_of_month*24+24,:,:][:,lat_coarse_index][:,:,lon_coarse_index]\n ssrd_fine = downscale_in_shortwave_radiation(\n ssrd_coarse,\n sp_coarse,\n sp_fine,\n sp_fine_interp,\n black_sky_albedo_interp,\n white_sky_albedo_interp,\n slope_fine,\n aspect_fine,\n svf_fine,\n year,\n month,\n day_of_month,\n lat_coarse,\n lon_coarse,\n lat_fine,\n lon_fine,\n shadow_mask_LUT)\n save2nc('ssrd', year, month, day_of_month, ssrd_fine, lat_fine, lon_fine)\n \n # downscale precipitation\n \"\"\"\n # standardization\n norm_const = np.load('norm_params.npy')\n t2m_min, t2m_max = norm_const[0,0], norm_const[0,1]\n d2m_min, d2m_max = norm_const[1,0], norm_const[1,1]\n Q_min, Q_max = norm_const[2,0], norm_const[2,1]\n strd_min, strd_max = norm_const[3,0], norm_const[3,1]\n ssrd_min, ssrd_max = norm_const[4,0], norm_const[4,1]\n sp_min, sp_max = norm_const[5,0], norm_const[5,1]\n ws_min, ws_max = norm_const[6,0], norm_const[6,1]\n elev_max = norm_const[7,0]\n \n t2m_fine = (t2m_fine-t2m_min)/(t2m_max-t2m_min)\n d2m_fine = (d2m_fine-d2m_min)/(d2m_max-d2m_min)\n Q_fine = (Q_fine-Q_min)/(Q_max-Q_min)\n strd_fine = (strd_fine-strd_min)/(strd_max-strd_min)\n ssrd_fine = (ssrd_fine-ssrd_min)/(ssrd_max-ssrd_min)\n sp_fine = (sp_fine-sp_min)/(sp_max-sp_min)\n ws_fine = (ws_fine-ws_min)/(ws_max-ws_min)\n elev_fine = elev_fine/elev_max\n lat_fine = lat_fine/360\n lon_fine = lon_fine/360\n \"\"\"\n \n downscale_precipitation(\n t2m_fine,\n d2m_fine,\n sp_fine,\n Q_fine,\n strd_fine,\n ssrd_fine,\n ws_fine,\n lat_fine,\n lon_fine,\n elev_fine,\n year, \n month, \n day_of_month)\n print(\"Please use predict_tp.py to downscale precipitation, \\\n this function only save test data\")\n print(\"downscaling end at: \",datetime.datetime.today())\n \n\ndef par_main(args):\n # generate hour length according to year and month\n if ((args.year%4==0) and (args.year%100!=0)) or args.year%400==0:\n month_day = [31,29,31,30,31,30,31,31,30,31,30,31]\n else:\n month_day = [31,28,31,30,31,30,31,31,30,31,30,31]\n \n # generate multiprocessing for each 24 hour interval\n for i in range(args.begin_day, args.end_day):\n job = Process(target=main, args=( \n args.year,\n args.month,\n i,\n args.blat,\n args.ulat,\n args.llon,\n args.rlon,\n args.region_name))\n job.start()\n job.join()\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('--year', type=int, default=2018)\n parser.add_argument('--month', type=int, default=1)\n parser.add_argument('--begin_day', type=int, default=1)\n parser.add_argument('--end_day', type=int, default=2)\n parser.add_argument('--blat', type=float, default=23.5)\n parser.add_argument('--ulat', type=float, default=24.5)\n parser.add_argument('--llon', type=float, default=112.5)\n parser.add_argument('--rlon', type=float, default=113.5)\n parser.add_argument('--region_name', type=str, default='random')\n args = parser.parse_args()\n \n par_main(args)\n\n","repo_name":"leelew/ForDs","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":16399,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"3808543646","text":"import cv2\r\nimport numpy as np\r\n\r\n\r\ndef find_color(frame, color):\r\n hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\r\n lower_color = np.array([color[0] - 10, 100, 100])\r\n upper_color = np.array([color[0] + 10, 255, 255])\r\n\r\n mask = cv2.inRange(hsv, lower_color, upper_color)\r\n contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\r\n\r\n for contour in contours:\r\n area = cv2.contourArea(contour)\r\n if area > 1000:\r\n cv2.drawContours(frame, contour, -1, (0, 255, 0), 2)\r\n\r\n\r\ndef main():\r\n color = (0, 255, 0) # Зеленый цвет\r\n cap = cv2.VideoCapture(0)\r\n\r\n while True:\r\n ret, frame = cap.read()\r\n if not ret:\r\n break\r\n\r\n find_color(frame, color)\r\n\r\n cv2.imshow('frame', frame)\r\n\r\n key = cv2.waitKey(1) & 0xFF\r\n\r\n if key == ord('q'):\r\n break\r\n elif key == ord('r'):\r\n color = (0, 0, 255) # Красный\r\n elif key == ord('g'):\r\n color = (60, 255, 0) # Зеленый\r\n elif key == ord('b'):\r\n color = (120, 255, 255) # Синий\r\n\r\n cap.release()\r\n cv2.destroyAllWindows()\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n main()","repo_name":"GavriilFursov/RGBFindButton","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1230,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"18749599886","text":"import os.path as op\n# from collections import Counter\nfrom bcbio.utils import splitext_plus, file_exists, safe_makedir, chdir\nfrom bcbio.provenance import do\nfrom bcbio.distributed.transaction import file_transaction, tx_tmpdir\nfrom bcbio.bam import index\nfrom bcbio.variation import annotation\n\nfrom asm.select import _correct_vcf\n\n\nfrom ichwrapper import log\n\n\ndef _count_covars(in_bam, sample, workdir, snp, reference, config):\n \"\"\"\n countcovars from BisSNP tool\n \"\"\"\n bissnp = do.find_cmd(\"bissnp\")\n basename = sample\n num_cores = config['algorithm'].get('cores', 1)\n memory = config['algorithm'].get('memory', 4)\n jvm_opts = \"-Xms750m -Xmx%sg\" % memory\n cmd = (\"{bissnp} {jvm_opts} -R {reference} -I {in_bam} \"\n \"-T BisulfiteCountCovariates \"\n \"-knownSites {snp} \"\n \"-cov ReadGroupCovariate \"\n \"-cov QualityScoreCovariate \"\n \"-cov CycleCovariate \"\n \"-recalFile {tx_out} \"\n \"-nt {num_cores} \")\n with chdir(workdir):\n out_count = op.join(workdir, sample + \"_recal1.csv\")\n if not file_exists(out_count):\n with file_transaction(out_count) as tx_out:\n log.logger.debug(cmd.format(**locals()))\n do.run(cmd.format(**locals()), \"BisSNP countcovarts in %s\" % in_bam)\n return out_count\n\n\ndef _recal_BQ_score(in_bam, sample, workdir, counts_file, reference, config):\n \"\"\"\n recalibration from BisSNP tool\n \"\"\"\n bissnp = do.find_cmd(\"bissnp\")\n basename = sample\n num_cores = config['algorithm'].get('cores', 1)\n memory = config['algorithm'].get('memory', 4)\n jvm_opts = \"-Xms750m -Xmx%sg\" % memory\n cmd = (\"{bissnp} {jvm_opts} -R {reference} -I {in_bam} \"\n \"-T BisulfiteTableRecalibration \"\n \"-recalFile {counts_file} \"\n \"-o {tx_out} \"\n \"-maxQ 60 \")\n with chdir(workdir):\n out_recal = op.join(workdir, sample + \"_recal1.bam\")\n if not file_exists(out_recal):\n with file_transaction(out_recal) as tx_out:\n log.logger.debug(cmd.format(**locals()))\n do.run(cmd.format(**locals()), \"BisSNP writerecal in %s\" % in_bam)\n index(out_recal, config)\n return out_recal\n\n\ndef _call_vcf(in_bam, sample, workdir, reference, config):\n \"\"\"\n recalibration from BisSNP tool\n \"\"\"\n bissnp = do.find_cmd(\"bissnp\")\n basename = sample\n num_cores = config['algorithm'].get('cores', 1)\n memory = config['algorithm'].get('memory', 4)\n jvm_opts = \"-Xms750m -Xmx%sg\" % memory\n cmd = (\"{bissnp} {jvm_opts} -R {reference} -I {in_bam} \"\n \"-T BisulfiteGenotyper \"\n \"-vfn1 {tx_out} \"\n \"-vfn2 {out_vfn2} \"\n \"-stand_call_conf 20 \"\n \"-stand_emit_conf 0 \"\n \"-mmq 0 \"\n \"-mbq 0 \"\n \"-nt {num_cores}\")\n with chdir(workdir):\n out_vfn1 = op.join(workdir, sample + \".rawcpg.vcf\")\n out_vfn2 = op.join(workdir, sample + \".rawsnp.vcf\")\n if not file_exists(out_vfn1):\n with file_transaction(out_vfn1) as tx_out:\n log.logger.debug(cmd.format(**locals()))\n do.run(cmd.format(**locals()), \"BisSNP writerecal in %s\" % in_bam)\n return out_vfn1, out_vfn2\n\n\ndef call_variations(data, args):\n \"\"\"\n Run BisSNP tool\n \"\"\"\n safe_makedir(\"bissnp\")\n sample = data['name']\n workdir = op.abspath(safe_makedir(op.join(\"bissnp\", sample)))\n counts_file = _count_covars(data['final_bam'], sample, workdir, args.snp, args.reference, data['config'])\n recal_bam = _recal_BQ_score(data['final_bam'], sample, workdir, counts_file, args.reference, data['config'])\n cpg, snp = _call_vcf(recal_bam, sample, workdir, args.reference, data['config'])\n sort_snp = _correct_vcf(snp)\n snp = annotation.annotate_nongatk_vcf(sort_snp, [data['final_bam']],\n args.snp,\n args.reference, data['config'])\n return data\n","repo_name":"lpantano/ASMfinder","sub_path":"asm/bissnp.py","file_name":"bissnp.py","file_ext":"py","file_size_in_byte":4007,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"50"} +{"seq_id":"35797442570","text":"from google.appengine.ext import db\nfrom sns.api.facade import iapi\nfrom sns.api import consts as api_const\nfrom django import forms\nfrom sns.deal import consts as deal_const\nfrom sns.deal import api as deal_api\nfrom sns.dm import consts as dm_const\nfrom sns.view import consts as view_const\nfrom sns.view.baseform import BaseForm, NameMultipleChoiceField\nfrom common.utils import string as str_util\n\n\nclass DMCampaignForm(BaseForm):\n \n def __init__( self, *args, **kwargs ):\n super(BaseForm, self ).__init__( *args, **kwargs )\n if self.__class__.__name__.find('CreateForm')!=-1:\n channelChoice=[]\n channels = iapi(api_const.API_M_CHANNEL).query_base().fetch(limit=1000)\n for channel in channels:\n channelChoice.append((channel.key(),channel.name))\n self.fields['sourceChannel'] = forms.ChoiceField(choices=channelChoice,widget=forms.Select(\n attrs={\"name\":\"channel\",\"id\":\"channel\"}),label=\"Define DM Source Twitter account\", required=False)\n \n articleChoice=[]\n articles=iapi(api_const.API_M_ARTICLE).query_base().fetch(limit=100) \n for article in articles:\n articleChoice.append((article.id,article.msgShort80))\n self.fields['contents'] = NameMultipleChoiceField(choices=articleChoice, label_attr='msg', required=False)\n \n choices = []\n cities = deal_api.GrouponApi.get_city_2_division_map().keys()\n cities.sort()\n for location in cities:\n choices.append((location,location))\n self.fields['locations'] = forms.MultipleChoiceField(choices=choices,required=False)\n \n choices = []\n cats = deal_const.TOPIC_2_GROUPON_CATEGORY_MAP.keys()\n cats.sort()\n for cat in cats:\n choices.append((str_util.name_2_key(cat),cat))\n self.fields['nationalTopics'] = forms.MultipleChoiceField(choices=choices,required=False)\n \n choices = []\n cats = deal_const.TOPIC_2_GROUPON_CATEGORY_MAP.keys()\n cats.sort()\n for cat in cats:\n choices.append((str_util.name_2_key(cat),cat))\n self.fields['topics'] = forms.MultipleChoiceField(choices=choices,required=False)\n \n sendOrder = forms.ChoiceField(choices=[(dm_const.DM_LATEST_TO_OLDEST,'Latest to Oldest'),(dm_const.DM_OLDEST_TO_LATEST,'Oldest to Latest')],required=True)\n interval_candidates=[]\n for i in dm_const.DM_INTERVALS:\n interval_candidates.append((i,dm_const.INTERVAL_MAP[i])) \n scheduleInterval=forms.ChoiceField(choices=interval_candidates,widget=forms.Select(\n attrs={\"id\":\"schedule_interval\"}),required=False)\n dailyTarget = forms.IntegerField(required=False)\n totalTarget = forms.IntegerField(required=False)\n gaOn=forms.BooleanField(widget=forms.CheckboxInput(attrs={'class':'retro analyticsCheck','onclick':'toggleAnalytics(this)'}),\n required=False)\n gaUseCampaignName=forms.BooleanField(widget=forms.CheckboxInput(attrs={'class':'retro campaignCheck','onclick':'toggleCampaign(this)'}),\n required=False)\n gaCampaign=forms.CharField(widget=forms.TextInput(attrs={'name':\"utm_campaign\",\n 'size':'25'}),required=False)\n gaSource=forms.CharField(widget=forms.TextInput(attrs={'name':\"utm_source\",\n 'size':'25'}),required=False)\n gaMedium=forms.CharField(widget=forms.TextInput(attrs={'name':\"utm_medium\",\n 'size':'25'}),required=False)\n gaTerm=forms.CharField(widget=forms.TextInput(attrs={'name':\"utm_term\",\n 'size':'25'}),required=False)\n gaContent=forms.CharField(widget=forms.TextInput(attrs={'name':\"utm_content\",\n 'size':'25'}),required=False)\n \n \n def api_module(self):\n return api_const.API_M_DM_RULE\n \n\nclass DMCampaignCreateForm(DMCampaignForm): \n pass\n\nclass DMCampaignUpdateForm(DMCampaignForm):\n id=forms.CharField(widget=forms.HiddenInput)\n current_page=forms.CharField(widget=forms.HiddenInput,required=False)\n sChannel = forms.CharField(widget=forms.HiddenInput(attrs={'size':'50'}),label=\"DM source account\")\n \nclass AdvancedDMCampaignForm(DMCampaignForm):\n promoteType=forms.ChoiceField(choices=[(dm_const.PROMOTE_TYPE_ACCOUNT,'Promote Accounts'),(dm_const.PROMOTE_TYPE_DEAL,'Promote Deals')],widget=forms.Select(\n attrs={'class':'retro promoteCheck','onchange':'togglePromote(this)'}),required=False)\n accountPromoteType=forms.ChoiceField(choices=[(dm_const.ACCOUNT_PROMOTE_TYPE_CITY,'City Accounts'),\n (dm_const.ACCOUNT_PROMOTE_TYPE_CITY_CATEGORY,'City Category Accounts')],widget=forms.Select(\n attrs={'class':'retro promoteCityCat','onchange':'toggleCityCat(this)'}),required=False)\n categoryType = forms.ChoiceField(choices=[(dm_const.PROMOTE_CATEGORY_TYPE_NATION,'National Category'),(dm_const.PROMOTE_CATEGORY_TYPE_CITY,'City Category')], widget=forms.Select(\n attrs={'class':'retro promoteCategory','onchange':'toggleCategory(this)'}),required=False) \n \n def api_module(self):\n return api_const.API_M_ADVANCED_DM_RULE\n \n def clean_topics(self):\n locations = self.cleaned_data['locations']\n topics = self.cleaned_data['topics']\n nationalTopics = self.cleaned_data['nationalTopics']\n catType = self.cleaned_data['categoryType']\n if catType == dm_const.PROMOTE_CATEGORY_TYPE_NATION:\n if len(nationalTopics) == 0:\n raise forms.ValidationError('National category is required!')\n elif catType == dm_const.PROMOTE_CATEGORY_TYPE_CITY:\n if len(locations) == 0 and len(topics) == 0 :\n raise forms.ValidationError('Location or category is required!')\n return topics\n \n \nclass AdvancedDMCampaignCreateForm(AdvancedDMCampaignForm): \n pass\n\nclass AdvancedDMCampaignUpdateForm(AdvancedDMCampaignForm):\n id=forms.CharField(widget=forms.HiddenInput)\n current_page=forms.CharField(widget=forms.HiddenInput,required=False)\n\n \nclass DMCampaignChartForm(BaseForm):\n def __init__( self,id, *args, **kwargs ):\n super(BaseForm, self ).__init__( *args, **kwargs )\n choices = []\n choices.append((999,\"send count\"))\n rule = db.get(id)\n for t in range(0,len(rule.sendTurn)):\n choices.append((t,\"click count - \"+str(t)))\n self.fields['type'] = forms.ChoiceField(choices=choices,\n widget=forms.Select(\n attrs={\"id\":\"chartType\",\"onchange\":\"chooseDMChartType(this)\",\"class\":\"retro loadDMChart\"}),required=True)\n\nclass AdvancedDMCampaignChartForm(BaseForm):\n def __init__( self, *args, **kwargs ):\n super(BaseForm, self ).__init__( *args, **kwargs )\n choices = []\n choices.append((0,\"click count\"))\n self.fields['type'] = forms.ChoiceField(choices=choices,\n widget=forms.Select(\n attrs={\"id\":\"chartType\",\"onchange\":\"chooseDMChartType(this)\",\"class\":\"retro loadDMChart\"}),required=True)\n\n\nclass DMCampaignSortByForm(forms.Form): \n type = forms.ChoiceField(choices=[('nameLower','Name'),('state','Status')],\n widget=forms.Select(\n attrs={\"id\":\"id_sortBy_type\",\"name\":\"id_sortBy_type\",\"onchange\":\"sortByKeyWord()\"}),required=True)\n order = forms.ChoiceField(choices=view_const.LIST_ORDER,\n widget=forms.Select(\n attrs={\"id\":\"id_direct_type\",\"name\":\"id_direct_type\",\"onchange\":\"sortByKeyWord()\"}),required=True)\n paginate = forms.ChoiceField(choices=view_const.LIST_PAGINATE,\n widget=forms.Select(\n attrs={\"id\":\"id_paginate_num\",\"name\":\"id_paginate_num\",\"onchange\":\"sortByKeyWord()\"}),required=True)\n\n\nclass SystemSettingsForm(forms.Form): \n monitor = forms.BooleanField(widget=forms.CheckboxInput(attrs={'onclick':\"changeFollowMonitor('followmonitor')\"}),\n required=False)\n weekStop = forms.BooleanField(widget=forms.CheckboxInput(attrs={'onclick':\"changeFollowMonitor('followweekstop')\"}),\n required=False)\n ","repo_name":"fantascy/snsanalytics","sub_path":"src/sns/dm/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":8666,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"31564299858","text":"import pandas as pd\nimport re\nimport pickle\nimport os\nimport json\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom wordcloud import WordCloud\nfrom sklearn.decomposition import LatentDirichletAllocation, NMF\nfrom sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\nfrom sklearn.svm import LinearSVC\nfrom sklearn.cross_validation import train_test_split\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.multiclass import OneVsRestClassifier, OneVsOneClassifier\n\n\ndef print_topic(model, feature_names, n_top_word):\n topics = []\n for topic_idx, topic in enumerate(model.components_):\n topic_dict = [{\"keyword\": feature_names[i], \"score\": topic[i]}\n for i in topic.argsort()[:-n_top_word - 1:-1]]\n topic_dict.sort(key=lambda x: x.get(\"score\"), reverse=True)\n topics.append(topic_dict)\n\n file_name = 'Topic.json'\n json_output_dir = os.path.join(os.getcwd(), file_name)\n with open(json_output_dir, 'w') as f:\n json.dump(topics, f)\n return topics\n\n\ndef print_top_words(data, n_top_words=20):\n for topic_idx, topic in enumerate(data):\n plt.figure()\n plt.imshow(WordCloud().fit_words(\n [(i[\"keyword\"], i[\"score\"])\n for i in topic[:-n_top_words - 1:-1]]\n ))\n plt.axis(\"off\")\n plt.title(\"Topic #%d\" % (topic_idx + 1))\n plt.savefig(\"Topic_\" + str(topic_idx + 1) + \".png\")\n print()\n\nstops = stop_words = [line.strip() for line in open(\"indonesia.txt\")]\ntrain_anies = pd.read_csv(\n \"DATA_ANIES_FIX.csv\"\n)\n\ntrain_anies = train_anies.replace(\"1\", \"anies+\")\ntrain_anies = train_anies.replace(\"0\", \"anies-\")\n\ntrain_ahok = pd.read_csv(\n \"data_training_ahok.csv\"\n)\n\ntrain_ahok = train_ahok.replace(\"1\", \"ahok+\")\ntrain_ahok = train_ahok.replace(\"0\", \"ahok-\")\n\ntrain_ahy = pd.read_csv(\n \"data_training_AHY.csv\"\n)\n\ntrain_ahy = train_ahy.replace(\"1\", \"ahy+\")\ntrain_ahy = train_ahy.replace(\"0\", \"ahy-\")\n\nframes = [train_anies, train_ahok, train_ahy]\n\ndata_total = pd.concat(frames)\n\ndata_train = data_total[(data_total[\"Sentimen\"] == \"ahok+\") | (data_total[\"Sentimen\"] == \"ahok-\") | (data_total[\"Sentimen\"] == \"ahy+\") | (data_total[\"Sentimen\"] == \"ahy-\") | (data_total[\"Sentimen\"] == \"anies+\") | (data_total[\"Sentimen\"] == \"anies-\")]\ndata_train = data_train.reset_index(drop=True)\n\nclean_train_tweet = []\nsentiment_all = []\n\nfor i in range(len(data_train[\"_id\"])):\n clean_train_tweet.append(' '.join(\n re.sub(\"(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)|RT\", \" \",\n data_train[\"_id\"][i]).split()))\n\nfor i in range(len(data_train[\"Sentimen\"])):\n sentiment_all.append(data_train[\"Sentimen\"][i])\n\nvectorizer = TfidfVectorizer(analyzer=\"word\",\n stop_words=stops, min_df=0.001, max_df=0.85)\n\ntrain_data_features = vectorizer.fit_transform(clean_train_tweet)\nx_train, x_test, y_train, y_test = train_test_split(\n train_data_features, sentiment_all, test_size=0.3, random_state=42\n)\ntrain_data_features = x_train.toarray()\n\nmodel = OneVsRestClassifier(LinearSVC(random_state=0))\nmodel_fit = model.fit(train_data_features, y_train)\nresult = model.predict(x_test)\nprint(accuracy_score(result, y_test))\n\nfilename = 'model.sav'\npickle.dump(model, open(filename, 'wb'))\n\nfilename = 'vectorizer.sav'\npickle.dump(vectorizer, open(filename, 'wb'))\n\n#load\n#loaded_model = pickle.load(open(filename, 'rb'))\n\nprint(\"Fitting LDA models with tf \")\nlda_array = nmf = NMF(n_components=10)\nlda_array.fit(train_data_features)\n\ntfidf_feature_names = vectorizer.get_feature_names()\ntopics = print_topic(lda_array, tfidf_feature_names, 200)\nprint_top_words(topics, 200)\n\ntrain_unique = pd.read_csv(\n \"data_unique_users.csv\"\n)\ntrain_unique = train_unique.replace(np.nan, \" \")\nclean_train_tweet_unique = []\n\nfor i in range(len(train_unique[\"_id\"])):\n clean_train_tweet_unique.append(' '.join(\n re.sub(\"(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)|RT\", \" \",\n train_unique[\"text\"][i]).split()))\n\ntest_data_features = vectorizer.transform(clean_train_tweet_unique)\ntest_data_features = test_data_features.toarray()\n\nresult_unique = model.predict(test_data_features)\n\nres_unique = result_unique.tolist()\npro_ahok = res_unique.count(\"ahok+\")\npro_anies = res_unique.count(\"anies+\")\npro_ahy = res_unique.count(\"ahy+\")\n\nadd_ahok = (res_unique.count(\"ahy-\")*res_unique.count(\"ahok+\")/(res_unique.count(\"ahok+\") + res_unique.count(\"anies+\"))) + (res_unique.count(\"anies-\")*res_unique.count(\"ahok+\")/(res_unique.count(\"ahok+\") + res_unique.count(\"ahy+\")))\nadd_anies = (res_unique.count(\"ahy-\")*res_unique.count(\"anies+\")/(res_unique.count(\"anies+\") + res_unique.count(\"ahok+\"))) + (res_unique.count(\"ahok-\")*res_unique.count(\"anies+\")/(res_unique.count(\"anies+\") + res_unique.count(\"ahy+\")))\nadd_ahy = (res_unique.count(\"anies-\")*res_unique.count(\"ahy+\")/(res_unique.count(\"ahy+\") + res_unique.count(\"ahok+\"))) + (res_unique.count(\"ahok-\")*res_unique.count(\"ahy+\")/(res_unique.count(\"ahy+\") + res_unique.count(\"anies+\")))\n\ntotal = (pro_ahok + add_ahok) + (pro_anies + add_anies) + (pro_ahy + add_ahy)\n\npres_ahok = (pro_ahok + add_ahok)/total*100\npres_anies = (pro_anies + add_anies)/total*100\npres_ahy = (pro_ahy + add_ahy)/total*100\n\nprint(\"elektabilitas ahy-sylvi \" + str(pres_ahy))\nprint(\"elektabilitas ahok-djarot \" + str(pres_ahok))\nprint(\"elektabilitas anies-sandi \" + str(pres_anies))\n","repo_name":"okids/tweetserpent","sub_path":"classify.py","file_name":"classify.py","file_ext":"py","file_size_in_byte":5399,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"1081897736","text":"import streamlit as st\nfrom PIL import Image\nimport stylize_image\n\nst.title(\"Fast Neural Style Transfer Web-app.\")\nst.write(\"Choose an existing image from the sidebar or upload your own....\")\n\nimg = st.sidebar.selectbox(\n 'Select Image',\n ('Captain America',\n 'Batman',\n 'Deadpool',\n 'Dragon',\n 'Godzilla',\n 'Iron Man',\n 'One Punch Man',\n 'Spider Man',\n 'Superman',\n 'Thor',\n 'Wonder Woman')\n)\nimg = img+'.jpg'\n\nstyle_name = st.sidebar.selectbox(\n 'Select Style',\n ('Candy', 'Mosaic', 'Rain Princess', 'Udnie')\n)\n\nuploaded_file = st.file_uploader(\"Choose an image...\", type=\"jpg\")\n\nif uploaded_file:\n input_image = uploaded_file\nelse:\n input_image = \"content_images/\" + img\n\nmodel = \"models/\" + style_name + \".pth\"\noutput_image = \"output_images/\" + style_name + \"-\" + img\n\nst.write(\"### Source image:\")\nimage = Image.open(input_image)\nst.image(image, width=400)\n\nclicked = st.button('Stylize')\n\nif clicked:\n model = stylize_image.load_model(model)\n stylize_image.stylize(model, input_image, output_image)\n\n st.write('#### Output image:')\n image = Image.open(output_image)\n st.image(image, width=400)\n","repo_name":"mrinmoy2000/End-to-End-Neural-Style-Transferring-Web-App","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1173,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"18707251220","text":"'''Module for parsing directory structures.'''\n\nimport os, re, warnings\nfrom operator import attrgetter\nfrom os.path import normpath\nfrom collections import namedtuple\nimport scandir\n\n\ntry:\n basestring\nexcept NameError:\n basestring = str # Python3\n\n\nclass Singleton(type):\n _instances = {}\n def __call__(cls, *args, **kwargs):\n if cls not in cls._instances:\n cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs)\n return cls._instances[cls]\n\n\nclass NoMatchType(object):\n '''Singleton that can be returned by rules to indicate they did not match\n the given input.'''\n __metaclass__ = Singleton\n\n def __nonzero__(self):\n return False\n\n __bool__ = __nonzero__\n\n\nNoMatch = NoMatchType()\n\n\ndef make_regex_rule(regex_str):\n '''Takes a string containing a regex pattern and returns a function\n that can be used as a PathMap rule.\n\n The returned 'match_info' will be a list containing the full regex match\n plus any matched subgroups.\n '''\n regex = re.compile(regex_str)\n def rule(path, dir_entry):\n result = regex.search(path)\n if result:\n return [result.group()] + list(result.groups())\n return NoMatch\n\n return rule\n\n\ndef default_match_rule(path, dir_entry):\n '''Matches anything and returns None as the 'match_info'.'''\n return None\n\n\ndef warn_on_error(oserror):\n '''The default callback function for scandir errors. Raises a\n warning.\n\n Can be overridden with any function that takes a single argument\n (the OSError exception).'''\n warnings.warn('Error on listdir: ' + str(oserror))\n\n\nMatchResult = namedtuple('MatchResult', 'path dir_entry match_info')\n'''The return type for `PathMap.matches`. Contains the path (relative or \nabsolute depending on the `root_path` supplied to `PathMap.matches`), the\n`scandir.DirEntry` object, and the return value from the match rule.'''\n\n\nclass PathMap(object):\n '''Object which contains a number of 'rules' that define how it will\n traverse a directory structure and what paths it will yield. The PathMap \n object can then be used to generate matches starting from one or more \n root paths.\n\n Each 'rule' is a callable that takes two arguments, the path and the\n corresponding scandir.DirEntry object. The path may be relative or \n absolute depending on the supplied root_path. Any rule can also be \n provided as a string, in which case it will be converted to a callable \n using `make_regex_rule`.\n\n Parameters\n ----------\n match_rule : callable\n Returns the 'match_info' result or `NoMatch` if the path should be\n ignored. If None the `default_match_rule` will be used.\n\n ignore_rules : list of callable\n If any of these callables return a value that evaluates to True the\n path will be ignored. The first rule that returns True will cause all\n subsequent `ignore_rules` and the `match_rule` to be skipped.\n\n prune_rules : list of callable\n If a path is a directory and any of these callables return a value\n that evaluates to True the directory will not be descended into. The\n directory path itself may still be matched.\n\n depth : tuple or int\n The minimum and maximum depth for recursion. If an single int is\n given only paths at that depth will be generated.\n\n sort : bool\n If true the paths in each directory will be processed and generated\n in sorted order.\n\n on_error : callable\n Callback for errors from scandir. The errors are typically due to a\n directory being deleted between being found and being recursed into.\n\n follow_symlinks : bool\n Follow symbolic links. If set to True it is possible to get stuck in\n an infinite loop.\n\n '''\n def __init__(self, match_rule=None, ignore_rules=None, prune_rules=None,\n depth=(0,None), sort=False, on_error=None,\n follow_symlinks=False):\n\n if match_rule is None:\n match_rule = default_match_rule\n self.match_rule = match_rule\n if ignore_rules:\n self.ignore_rules = ignore_rules[:]\n else:\n self.ignore_rules = []\n if prune_rules:\n self.prune_rules = prune_rules[:]\n else:\n self.prune_rules = []\n if not isinstance(depth, tuple):\n depth = (depth, depth)\n if depth[0] < 0:\n raise ValueError(\"The minimum depth must be positive\")\n if not depth[1] is None and depth[1] < depth[0]:\n raise ValueError(\"The maximum depth must be None or greater than \"\n \"the minimum\")\n self.depth = depth\n self.sort = sort\n self.on_error = on_error\n self.follow_symlinks = follow_symlinks\n\n def _convert_regex_rules(self):\n if isinstance(self.match_rule, basestring):\n self.match_rule = make_regex_rule(self.match_rule)\n for index, rule in enumerate(self.ignore_rules):\n if isinstance(rule, basestring):\n self.ignore_rules[index] = make_regex_rule(rule)\n for index, rule in enumerate(self.prune_rules):\n if isinstance(rule, basestring):\n self.prune_rules[index] = make_regex_rule(rule)\n\n def _test_target_path(self, path, dir_entry):\n for rule in self.ignore_rules:\n if bool(rule(path, dir_entry)) == True:\n return NoMatch\n result = self.match_rule(path, dir_entry)\n return result\n\n def matches(self, root_paths, dir_entries=None):\n '''Generate matches by recursively walking from the 'root_paths' down\n into the directory structure(s).\n\n The object's rules define which paths cause a result to be generated, \n and the `match_rule` provides the `match_info` attribute in the \n generated `MatchResult` object.\n\n Parameters\n ----------\n root_paths : iter\n Provides the paths to start our walk from. If you want these to\n be processed into sorted order you must sort them yourself.\n\n dir_entries : list or None\n If given, must provide a scandir.DirEntry for each root path. If\n not provided we must call stat for each root path.\n\n Returns\n -------\n result : MatchResult\n A `MatchResult` object is generated for each matched path.\n '''\n # Allow a single path or an iterable to be passed\n if isinstance(root_paths, basestring):\n root_paths = [root_paths]\n if dir_entries is not None:\n dir_entries = [dir_entries]\n\n # Make sure any regex rules have been converted to a callable\n self._convert_regex_rules()\n\n # Crawl through each root path\n for root_idx, root_path in enumerate(root_paths):\n # Get rid of any extra path seperators\n root_path = normpath(root_path)\n\n # Get the corresponding DirEntry\n if dir_entries is None:\n p, name = os.path.split(root_path)\n if p == '':\n p = '.'\n root_entry = scandir.GenericDirEntry(p, name)\n else:\n root_entry = dir_entries[root_idx]\n\n # Check if the root path itself is matched\n if self.depth[0] == 0:\n match_info = self._test_target_path(root_path, root_entry)\n if not match_info is NoMatch:\n yield MatchResult(root_path, root_entry, match_info)\n if not root_entry.is_dir():\n continue\n\n # Check if the root_path is pruned\n prune_root = False\n for rule in self.prune_rules:\n if rule(root_path, root_entry):\n prune_root = True\n break\n if prune_root:\n continue\n\n # Walk through directory structure checking paths against\n # rules\n curr_dir = (root_path, root_entry)\n next_dirs = []\n while True:\n # Determine the current depth from the root_path\n curr_depth = (curr_dir[0].count(os.sep) -\n root_path.count(os.sep)) + 1\n\n #Build a list of entries for this level so we can sort if\n #requested\n curr_entries = []\n\n # Try getting the contents of the current directory\n try:\n for e in scandir.scandir(curr_dir[0]):\n # Keep directories under the depth limit so we can\n # resurse into them\n if e.is_dir():\n if (self.depth[1] is not None and\n curr_depth > self.depth[1]\n ):\n continue\n else:\n # Plain files can be ignored if they violate\n # either depth limit\n if (curr_depth < self.depth[0] or\n (self.depth[1] is not None and\n curr_depth > self.depth[1])\n ):\n continue\n\n #Add to the list of entries for the curr_dir\n curr_entries.append(e)\n\n except OSError as error:\n #Handle errors from the scandir call\n if self.on_error is not None:\n self.on_error(error)\n else:\n raise\n else:\n # Sort the entries if requested\n if self.sort:\n curr_entries.sort(key=attrgetter('name'))\n\n # Iterate through the entries, yielding them if they are a\n # match\n for e in curr_entries:\n p = os.path.join(curr_dir[0], e.name)\n\n if e.is_dir(follow_symlinks=self.follow_symlinks):\n # If it is not pruned, add it to next_dirs. \n for rule in self.prune_rules:\n if rule(p, e):\n break\n else:\n next_dirs.append((p, e))\n\n # If we are below min depth we don't try matching\n # the dir\n if curr_depth < self.depth[0]:\n continue\n\n # Test the path against the match/ignore rules\n match_info = self._test_target_path(p, e)\n if not match_info is NoMatch:\n yield MatchResult(p, e, match_info)\n\n # Update curr_dir or break if we are done\n try:\n curr_dir = next_dirs.pop(0)\n except IndexError:\n break\n\n","repo_name":"moloney/pathmap","sub_path":"pathmap.py","file_name":"pathmap.py","file_ext":"py","file_size_in_byte":11160,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"50"} +{"seq_id":"72422193755","text":"#!/usr/bin/env python3\n# my_states.py\n\nfrom state import State\n\n# Start of our states\nclass LockedState(State):\n \"\"\"\n The state which indicates that there are limited device capabilities.\n \"\"\"\n\n def on_event(self, event):\n if event == 'pin_entered':\n return UnlockedState()\n\n if event == 'device_locked':\n print(\"Device already locked\")\n\n if event == 'make_call' or event == 'start_game' or event == 'use_camera':\n print(\"Device is locked\")\n\n if event == 'hang_up' or event == 'end_game' or event == 'close_camera':\n print(\"That program is currently not in use\")\n\n return self\n\n\nclass UnlockedState(State):\n \"\"\"\n The state which indicates that there are no limitations on device\n capabilities.\n \"\"\"\n\n def on_event(self, event):\n if event == 'device_locked':\n return LockedState()\n\n if event == 'pin_entered':\n print(\"Device already unlocked\")\n\n if event == 'make_call':\n return CallingState()\n\n if event == 'start_game':\n return GamingState()\n\n if event == 'use_camera':\n return CameraState()\n\n return self\n\nclass CallingState(State):\n \"\"\"\n The state which indicates that you are making a call.\n \"\"\"\n\n def on_event(self, event):\n if event == 'device_locked':\n print(\"That's rude, you should hang up first\")\n\n if event == 'pin_entered':\n print(\"Device already unlocked\")\n\n if event == 'hang_up' or event == 'main_menu':\n return UnlockedState()\n\n return self\n\nclass GamingState(State):\n \"\"\"\n The state which indicates that you are playing a game.\n \"\"\"\n\n def on_event(self, event):\n if event == 'device_locked':\n return LockedState()\n\n if event == 'pin_entered':\n print(\"Device already unlocked\")\n\n if event == 'end_game' or event == 'main_menu':\n return UnlockedState()\n\n return self\n\nclass CameraState(State):\n \"\"\"\n The state which indicates that you are taking a photo/video.\n \"\"\"\n\n def on_event(self, event):\n if event == 'device_locked':\n return LockedState()\n\n if event == 'pin_entered':\n print(\"Device already unlocked\")\n\n if event == 'close_camera' or event == 'main_menu':\n return UnlockedState()\n\n return self\n# End of our states.\n","repo_name":"gjcampbell777/python-state-machine","sub_path":"my_states.py","file_name":"my_states.py","file_ext":"py","file_size_in_byte":2460,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"39710499493","text":"\"\"\"\r\nBernoulli Likelihood with Hierarchical Prior!\r\n\"\"\"\r\nimport numpy as np\r\nimport pymc3 as pm\r\nimport sys\r\nfrom scipy.stats import beta, binom\r\nimport matplotlib.pyplot as plt\r\nplt.style.use('seaborn-darkgrid')\r\n\r\n\r\n# Data for figure 9.11\r\nN = [10, 10, 10] # Number of flips per coin\r\nz = [5, 5, 5] # Number of heads per coin\r\n## Data for figure 9.12\r\n#N = [10, 10, 10] # Number of flips per coin\r\n#z = [1, 5, 9] # Number of heads per coin\r\n\r\n## Data for exercise 9.1\r\n#ncoins = 50\r\n#nflipspercoin = 5\r\n#mu_act = .7\r\n#kappa_act = 20\r\n#theta_act = beta.rvs(mu_act*kappa_act+1, (1-mu_act)*kappa_act+1, size=ncoins)\r\n#z = binom.rvs(n=nflipspercoin, p=theta_act, size=ncoins)\r\n#N = [nflipspercoin] * ncoins\r\n\r\n\r\n# Arrange the data into a more convenient way to feed the PyMC model.\r\ncoin = [] # list/vector index for each coins (from 0 to number of coins)\r\ny = [] # list/vector with head (1) or tails (0) for each flip.\r\nfor i, flips in enumerate(N):\r\n heads = z[i]\r\n if heads > flips:\r\n sys.exit(\"The number of heads can't be greater than the number of flips\")\r\n else:\r\n y = y + [1] * heads + [0] * (flips-heads)\r\n coin = coin + [i] * flips\r\n\r\n\r\n# Specify the model in PyMC\r\nwith pm.Model() as model:\r\n# define the hyperparameters\r\n mu = pm.Beta('mu', 2, 2)\r\n kappa = pm.Gamma('kappa', 1, 0.1)\r\n # define the prior\r\n theta = pm.Beta('theta', mu * kappa, (1 - mu) * kappa, shape=len(N))\r\n # define the likelihood\r\n y = pm.Bernoulli('y', p=theta[coin], observed=y)\r\n\r\n# Generate a MCMC chain\r\n\r\n trace = pm.sample(1000, progressbar=False)\r\n\r\n\r\n## Check the results.\r\n\r\n## Print summary for each trace\r\n#pm.df_summary(trace)\r\n#pm.df_summary(trace)\r\n\r\n## Check for mixing and autocorrelation\r\npm.autocorrplot(trace, varnames=['mu', 'kappa'])\r\n#pm.autocorrplot(trace, varnames =[mu, kappa])\r\n\r\n## Plot KDE and sampled values for each parameter.\r\npm.traceplot(trace)\r\n#pm.traceplot(trace)\r\n\r\n# Create arrays with the posterior sample\r\ntheta1_sample = trace['theta'][:,0]\r\ntheta2_sample = trace['theta'][:,1]\r\ntheta3_sample = trace['theta'][:,2]\r\nmu_sample = trace['mu']\r\nkappa_sample = trace['kappa']\r\n\r\n\r\n# Scatter plot hyper-parameters\r\nfig, ax = plt.subplots(4, 3, figsize=(12,12))\r\nax[0, 0].scatter(mu_sample, kappa_sample, marker='o', color='skyblue')\r\nax[0, 0].set_xlim(0,1)\r\nax[0, 0].set_xlabel(r'$\\mu$')\r\nax[0, 0].set_ylabel(r'$\\kappa$')\r\n\r\n# Plot mu histogram\r\n#plot_post(mu_sample, xlab=r'$\\mu$', show_mode=False, labelsize=9, framealpha=0.5)\r\n\r\npm.plot_posterior(mu_sample, ax=ax[0, 1], color='skyblue')\r\nax[0, 1].set_xlabel(r'$\\mu$')\r\nax[0, 1].set_xlim(0,1)\r\n\r\n# Plot kappa histogram\r\n#plot_post(kappa_sample, xlab=r'$\\kappa$', show_mode=False, labelsize=9, framealpha=0.5)\r\npm.plot_posterior(kappa_sample, ax=ax[0, 2], color='skyblue')\r\nax[0, 2].set_xlabel(r'$\\kappa$')\r\n\r\n# Plot theta 1\r\n\r\n#plot_post(theta1_sample, xlab=r'$\\theta1$', show_mode=False, labelsize=9, framealpha=0.5)\r\npm.plot_posterior(theta1_sample, ax=ax[1, 0], color='skyblue')\r\nax[1, 0].set_xlabel(r'$\\theta1$')\r\nax[1, 0].set_xlim(0,1)\r\n\r\n# Scatter theta 1 vs mu\r\nax[1, 1].scatter(theta1_sample, mu_sample, marker='o', color='skyblue')\r\nax[1, 1].set_xlim(0,1)\r\nax[1, 1].set_ylim(0,1)\r\nax[1, 1].set_xlabel(r'$\\theta1$')\r\nax[1, 1].set_ylabel(r'$\\mu$')\r\n\r\n# Scatter theta 1 vs kappa\r\nax[1, 2].scatter(theta1_sample, kappa_sample, marker='o', color='skyblue')\r\nax[1, 2].set_xlim(0,1)\r\nax[1, 2].set_xlabel(r'$\\theta1$')\r\nax[1, 2].set_ylabel(r'$\\kappa$')\r\n\r\n# Plot theta 2\r\n#plot_post(theta2_sample, xlab=r'$\\theta2$', show_mode=False, labelsize=9, framealpha=0.5)\r\npm.plot_posterior(theta2_sample, ax=ax[2, 0], color='skyblue')\r\nax[2, 0].set_xlabel(r'$\\theta2$')\r\nax[2, 0].set_xlim(0,1)\r\n\r\n# Scatter theta 2 vs mu\r\nax[2, 1].scatter(theta2_sample, mu_sample, marker='o', color='skyblue')\r\nax[2, 1].set_xlim(0,1)\r\nax[2, 1].set_ylim(0,1)\r\nax[2, 1].set_xlabel(r'$\\theta2$')\r\nax[2, 1].set_ylabel(r'$\\mu$')\r\n\r\n# Scatter theta 2 vs kappa\r\nax[2, 2].scatter(theta2_sample, kappa_sample, marker='o', color='skyblue')\r\nax[2, 2].set_xlim(0,1)\r\nax[2, 2].set_xlabel(r'$\\theta2$')\r\nax[2, 2].set_ylabel(r'$\\kappa$')\r\n\r\n# Plot theta 3\r\n\r\n#plot_post(theta3_sample, xlab=r'$\\theta3$', show_mode=False, labelsize=9, framealpha=0.5)\r\npm.plot_posterior(theta3_sample, ax=ax[3, 0], color='skyblue')\r\nax[3, 0].set_xlabel(r'$\\theta3$')\r\nax[3, 0].set_xlim(0,1)\r\n\r\n# Scatter theta 3 vs mu\r\nax[3, 1].scatter(theta3_sample, mu_sample, marker='o', color='skyblue')\r\nax[3, 1].set_xlim(0,1)\r\nax[3, 1].set_ylim(0,1)\r\nax[3, 1].set_xlabel(r'$\\theta3$')\r\nax[3, 1].set_ylabel(r'$\\mu$')\r\n\r\n# Scatter theta 3 vs kappa\r\nax[3, 2].scatter(theta3_sample, kappa_sample, marker='o', color='skyblue')\r\nax[3, 2].set_xlim(0,1)\r\nax[3, 2].set_xlabel(r'$\\theta3$')\r\nax[3, 2].set_ylabel(r'$\\kappa$')\r\n\r\nplt.tight_layout()\r\nplt.savefig('Figure_9.11.png')\r\nplt.show()\r\n\r\n","repo_name":"aloctavodia/Doing_bayesian_data_analysis","sub_path":"09_BernBetaMuKappaPyMC.py","file_name":"09_BernBetaMuKappaPyMC.py","file_ext":"py","file_size_in_byte":4851,"program_lang":"python","lang":"en","doc_type":"code","stars":878,"dataset":"github-code","pt":"50"} +{"seq_id":"18526071606","text":"import requests\nfrom bs4 import BeautifulSoup\nimport openpyxl\n\nheaders = {\n 'User-Agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:39.0) Gecko/20100101 Firefox/39.0',\n 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',\n 'Accept-Language': 'en-US,en;q=0.5',\n 'Accept-Encoding': 'gzip, deflate',\n 'Connection': 'keep-alive'}\n\ndef parser_city():\n html=open('./city.html','r').read()\n table=BeautifulSoup(html,'lxml').find_all('a')\n f=open('city.txt','w')\n for item in table:\n city=item.get_text()\n url=item.get('href')\n f.write(city+'--'+url+'\\n')\n f.close()\n\ndef parser(city,url):\n html=requests.get(url,headers=headers).text\n table=BeautifulSoup(html,'lxml').find('div',{'class':'api_month_list'}).find('table').find_all('tr')\n result=[]\n for item in table:\n line=[city]\n for td in item.find_all('td'):\n try:\n line.append(td.get_text().replace('\\r\\n','').replace(' ',''))\n except:\n line.append('')\n result.append(line)\n return result\n\ndef get_air_report(city,city_url):\n year=2013\n month=11\n result=[]\n while(year<2018):\n while(month%13!=0):\n url='http://www.tianqihoubao.com'+city_url.replace('.html','-%s%02d.html'%(year,month))\n try:\n result+=parser(city,url)\n except:\n print(city,year,month,'failed')\n continue\n print(city,year,month,'ok')\n if year==2017 and month==3:\n break\n month+=1\n month=1\n year+=1\n return result\n\ndef main():\n for line in open('city.txt','r'):\n line=line.replace('\\n','').split('--')\n result=get_air_report(line[0],line[1])\n f=open('result.txt','a')\n for line in result:\n f.write(str(line)+'\\n')\n f.close()\n\nmain()\n","repo_name":"19js/Nyspider","sub_path":"www.tianqihoubao.com/air_report_history.py","file_name":"air_report_history.py","file_ext":"py","file_size_in_byte":1940,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"50"} +{"seq_id":"34081725625","text":"# downloads stock data for specified date range\n# mind that this script outputs nothing for bank holidays\n\nfrom datetime import date, timedelta\nimport csv\nimport os\nimport pandas_datareader.data as web\nfrom time import sleep\n#############\n### SETUP ###\n\n# start and end date of required stock data (YYYY, M, D)\nstart = date(2017, 9, 27) # including\nend = date(2017, 9, 29) # including\n\n# input file (relative path)\ninput_file = 'companies.csv'\n\n# output file (relative path)\noutput_dir = 'stock_data'\noutput_file = output_dir + '/' + 'stock_' + str(start) + '_' + str(end) + '.csv'\n\n##############\n#### CODE ####\n\n# create output dir\nos.makedirs(output_dir, exist_ok=True)\n\n# create output file with header\nwith open(output_file, 'w') as f:\n f.write('Date,Open,High,Low,Close,Adj Close,Volume,Company\\n')\n\n# open input file\nwith open(input_file, 'r') as comp:\n reader = csv.reader(comp)\n\n # for each company\n for row in reader:\n name = str(row[0])\n ticker = str(row[1]).strip()\n print('\\n' + name)\n\n try:\n # download stock data\n df = web.DataReader(ticker, 'yahoo', start, end)\n sleep(5)\n # add company column\n df['Company'] = name\n # append to output file\n with open(output_file, 'a') as f:\n df.to_csv(f, header=False)\n except Exception as e:\n print(e)\n","repo_name":"bromjiri/StockLoad","sub_path":"download_stock_yahoo/download_stock_range.py","file_name":"download_stock_range.py","file_ext":"py","file_size_in_byte":1418,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"21686291625","text":"import PlBase\nimport EnvMon\nfrom PyQt4 import QtGui, QtCore\n\nclass EnvMonPlugin(PlBase.Plugin):\n \"\"\" EnvMon \"\"\"\n aliases = ['TSCL.WINDD', 'STATS.AZ_ADJ', 'TSCL.WINDS_O', 'TSCL.WINDS_I', \\\n 'TSCL.TEMP_O', 'TSCL.TEMP_I', 'TSCL.HUMI_O', 'TSCL.HUMI_I', \\\n \"STATL.DEW_POINT_O\", 'TSCL.M1_TEMP', \\\n 'TSCL.TOPRING_WINDS_F', 'TSCL.TOPRING_WINDS_R']\n\n def build_gui(self, container):\n self.root = container\n\n qtwidget = QtGui.QWidget()\n\n self.em = EnvMon.EnvMon(qtwidget, logger=self.logger)\n \n layout = QtGui.QVBoxLayout()\n layout.setContentsMargins(0, 0, 0, 0)\n layout.setSpacing(0)\n layout.addWidget(self.em, stretch=1)\n container.setLayout(layout)\n \n def start(self):\n self.controller.register_select('envmon', self.update, EnvMonPlugin.aliases)\n self.em.start()\n\n def update(self, statusDict):\n self.logger.debug('status=%s' %str(statusDict))\n self.em.update_envmon(status_dict=statusDict)\n\n","repo_name":"scexao-org/Instrument-Control-Main","sub_path":"src/lib/python/gen2/Gen2/statmon/plugins/EnvMonPlugin.py","file_name":"EnvMonPlugin.py","file_ext":"py","file_size_in_byte":1035,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"12560281101","text":"import cv2\nimport numpy as np\n\ndef draw_circle(event,x,y,flags, params):\n if event == cv2.EVENT_RBUTTONDOWN:\n cv2.circle(img, (x,y), 70, (0,0,255), 3)\n\ncv2.namedWindow(winname='my_dog')\ncv2.setMouseCallback('my_dog', draw_circle)\n\nimg = cv2.imread('data/dog_backpack.jpg')\n\nwhile True:\n cv2.imshow('my_dog', img)\n \n if cv2.waitKey(1) & 0xFF == 27:\n break\n\ncv2.destroyAllWindows() \n \n ","repo_name":"Manjeete/ImageProccessing","sub_path":".ipynb_checkpoints/Assessmentpart-checkpoint.py","file_name":"Assessmentpart-checkpoint.py","file_ext":"py","file_size_in_byte":445,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"26871756379","text":"import requests\nfrom typing import Optional, List, Type, Union\nimport json\nfrom fastapi.testclient import TestClient\n\nfrom src.main import API_VERSION_PREFIX\n\n\ndef header(uid, role=None):\n headers = {\"uid\": uid, \"api_key\": \"key\"}\n if role is not None:\n headers[\"role\"] = role\n return headers\n\n\ndef post_user(\n client,\n uid,\n user_name=\"generic_user_name\",\n location=\"location\",\n interests=\"interests\",\n include_pfp=False,\n):\n data = {\n \"name\": user_name,\n \"location\": location,\n \"interests\": interests,\n }\n if include_pfp:\n with open(\"./pfp.img\", \"wb\") as f:\n f.write(b\"test\")\n with open(\"./pfp.img\", \"rb\") as f:\n files = {\"img\": (\"pfp.img\", f, \"plain/text\")}\n response_post = client.post(\n API_VERSION_PREFIX + \"/users/\",\n headers={\"api_key\": \"key\", \"uid\": uid},\n data=data,\n files=files,\n )\n assert response_post.status_code == 200\n else:\n response_post = client.post(\n API_VERSION_PREFIX + \"/users/\",\n headers={\"api_key\": \"key\", \"uid\": uid},\n data=data,\n )\n\n return response_post\n\n\ndef post_song(\n client,\n uid: Optional[str] = None,\n name: Optional[str] = \"song_name\",\n description: Optional[str] = \"song_desc\",\n artists: Optional[List[str]] = None,\n genre: Optional[str] = \"song_genre\",\n sub_level: Optional[int] = 0,\n file: Optional[str] = \"./tests/test.song\",\n blocked: Optional[bool] = False,\n headers: Optional[dict] = None,\n role: Optional[str] = \"artist\",\n album_id: Optional[int] = None,\n unwrap_id: bool = True,\n):\n if uid is None:\n uid = get_uid_or_create(client)\n\n if headers is None:\n headers = header(uid, role=role)\n if artists is None:\n artists = [\"song_artist_name\"]\n\n with open(file, \"wb\") as f:\n f.write(b\"test\")\n with open(file, \"rb\") as f:\n response_post = client.post(\n API_VERSION_PREFIX + \"/songs/\",\n data={\n \"name\": name,\n \"description\": description,\n \"artists\": json.dumps(artists),\n \"genre\": genre,\n \"sub_level\": sub_level,\n \"album_id\": album_id,\n },\n files={\"file\": (\"song.txt\", f, \"plain/text\")},\n headers=headers,\n )\n\n if blocked:\n client.put(\n f\"{API_VERSION_PREFIX}/songs/{response_post.json()['id']}\",\n data={\"blocked\": True},\n headers={\"api_key\": \"key\", \"uid\": uid, \"role\": \"admin\"},\n )\n\n if unwrap_id:\n return response_post.json()[\"id\"]\n return response_post\n\n\ndef post_album(\n client,\n uid: Optional[str] = None,\n name: Optional[str] = \"album_name\",\n description: Optional[str] = \"album_desc\",\n genre: Optional[str] = \"album_genre\",\n songs_ids: Optional[List[str]] = None,\n cover: Optional[str] = \"./tests/test.cover\",\n blocked: bool = False,\n headers: Optional[dict] = None,\n role: Optional[str] = \"artist\",\n unwrap_id: bool = True,\n):\n if uid is None:\n uid = get_uid_or_create(client)\n\n if headers is None:\n headers = header(uid, role=role)\n if songs_ids is None:\n songs_ids = []\n\n with open(cover, \"wb\") as f:\n f.write(b\"test\")\n with open(cover, \"rb\") as f:\n response_post = client.post(\n API_VERSION_PREFIX + \"/albums/\",\n data={\n \"name\": name,\n \"description\": description,\n \"songs_ids\": json.dumps(songs_ids),\n \"genre\": genre,\n },\n files={\"cover\": (\"cover.txt\", f, \"plain/text\")},\n headers=headers,\n )\n\n if blocked:\n response_put = client.put(\n f\"{API_VERSION_PREFIX}/albums/{response_post.json()['id']}\",\n data={\"blocked\": True},\n headers={\"api_key\": \"key\", \"uid\": uid, \"role\": \"admin\"},\n )\n assert response_put.status_code == 200\n if unwrap_id:\n return response_post.json()[\"id\"]\n return response_post\n\n\ndef post_album_with_song(\n client,\n uid: Optional[str] = None,\n album_name=\"album_name\",\n album_genre=\"album_genre\",\n song_name=\"song_name\",\n song_genre=\"song_genre\",\n song_sub_level=0,\n):\n song_id = post_song(\n client, uid=uid, name=song_name, genre=song_genre, sub_level=song_sub_level\n )\n return post_album(\n client,\n uid=uid,\n name=album_name,\n genre=album_genre,\n songs_ids=[song_id],\n )\n\n\ndef post_playlist(\n client,\n uid: Optional[str] = None,\n playlist_name: Optional[str] = \"playlist_name\",\n description: Optional[str] = \"playlist_desc\",\n songs_ids: Optional[List[str]] = None,\n colabs_ids: Optional[List[str]] = None,\n blocked: Optional[bool] = False,\n headers: Optional[dict] = None,\n unwrap_id: bool = True,\n):\n if uid is None:\n uid = get_uid_or_create(client)\n if headers is None:\n headers = {\n \"api_key\": \"key\",\n \"uid\": uid,\n }\n if songs_ids is None:\n songs_ids = []\n if colabs_ids is None:\n colabs_ids = []\n\n response_post = client.post(\n f\"{API_VERSION_PREFIX}/playlists/\",\n data={\n \"name\": playlist_name,\n \"description\": description,\n \"songs_ids\": json.dumps(songs_ids),\n \"colabs_ids\": json.dumps(colabs_ids),\n },\n headers=headers,\n )\n\n if blocked:\n response_put = client.put(\n f\"{API_VERSION_PREFIX}/playlists/{response_post.json()['id']}\",\n data={\"blocked\": True},\n headers={\"api_key\": \"key\", \"uid\": uid, \"role\": \"admin\"},\n )\n assert response_put.status_code == 200\n\n if unwrap_id:\n return response_post.json()[\"id\"]\n return response_post\n\n\ndef wrap_post_playlist(client, unwrap_id: bool = True):\n post_user(client, \"user_playlist_owner\", user_name=\"Ricardito\")\n post_user(client, \"user_playlist_colab\", user_name=\"Fernandito\")\n song_id_1 = post_song(client, uid=\"user_playlist_owner\", name=\"song_for_playlist1\")\n song_id_2 = post_song(client, uid=\"user_playlist_owner\", name=\"song_for_playlist2\")\n colabs_id = [\"user_playlist_colab\"]\n songs_id = [song_id_1, song_id_2]\n return post_playlist(\n client,\n uid=\"user_playlist_owner\",\n playlist_name=\"playlist_name\",\n description=\"playlist_description\",\n colabs_ids=colabs_id,\n songs_ids=songs_id,\n unwrap_id=unwrap_id,\n )\n\n\ndef block_song(client, song_id: int):\n post_user(client, \"__blocker__id__\", user_name=\"__blocker__name__\")\n response_put = client.put(\n f\"{API_VERSION_PREFIX}/songs/{song_id}\",\n data={\"blocked\": True},\n headers={\"api_key\": \"key\", \"uid\": \"__blocker__id__\", \"role\": \"admin\"},\n )\n assert response_put.status_code == 200\n return response_put\n\n\ndef post_review(\n client,\n album_id: int,\n uid: Optional[str] = None,\n text: Optional[str] = \"review text\",\n score: Optional[int] = 5,\n role: Optional[str] = \"listener\",\n):\n return post_json(\n client,\n f\"/albums/{album_id}/reviews/\",\n json={\"text\": text, \"score\": score},\n uid=uid,\n role=role,\n )\n\n\ndef add_song_to_favorites(client, uid, song_id):\n return post(\n client, f\"/users/{uid}/favorites/songs/?song_id={song_id}\", uid=uid, data={}\n )\n\n\ndef get_favorite_songs(client, uid, role=\"listener\"):\n return get(client, f\"/users/{uid}/favorites/songs/\", uid=uid, role=role)\n\n\ndef delete_song_from_favorites(client, uid, song_id):\n response_delete = client.delete(\n f\"{API_VERSION_PREFIX}/users/{uid}/favorites/songs/?song_id={song_id}\",\n headers={\"api_key\": \"key\", \"uid\": uid},\n )\n return response_delete\n\n\ndef get_favorite_albums(client, uid, role: Optional[str] = \"listener\"):\n return get(client, f\"/users/{uid}/favorites/albums/\", uid=uid, role=role)\n\n\ndef add_album_to_favorites(client, uid, album_id):\n response_post = client.post(\n f\"{API_VERSION_PREFIX}/users/{uid}/favorites/albums/?album_id={album_id}\",\n headers={\"api_key\": \"key\", \"uid\": uid},\n )\n return response_post\n\n\ndef add_song_to_album(client, uid: str, song_id: int, album_id: int):\n response_get = client.get(\n f\"{API_VERSION_PREFIX}/albums/{album_id}/songs/\",\n headers={\"api_key\": \"key\", \"uid\": uid},\n )\n songs_ids = [song[\"id\"] for song in response_get.json()]\n songs_ids.append(song_id)\n response_put = client.put(\n f\"{API_VERSION_PREFIX}/albums/{album_id}/songs/\",\n data={\"songs_ids\": json.dumps(songs_ids)},\n headers={\"api_key\": \"key\", \"uid\": uid},\n )\n return response_put\n\n\ndef block_album(client, id: int):\n post_user(client, \"__blocker__id__\", user_name=\"__blocker__name__\")\n return put(\n client,\n f\"/albums/{id}\",\n data={\"blocked\": True},\n uid=\"__blocker__id__\",\n role=\"admin\",\n )\n\n\ndef remove_album_from_favorites(client, uid, album_id):\n return delete(\n client, f\"/users/{uid}/favorites/albums/?album_id={album_id}\", uid=uid\n )\n\n\ndef get_favorite_playlists(client, uid, role=\"listener\"):\n return get(client, f\"/users/{uid}/favorites/playlists/\", uid=uid, role=role)\n\n\ndef add_playlist_to_favorites(client, uid, playlist_id, role=\"listener\"):\n return post(\n client,\n f\"/users/{uid}/favorites/playlists/?playlist_id={playlist_id}\",\n uid=uid,\n role=role,\n data={},\n )\n\n\ndef block_playlist(client, playlist_id: int):\n post_user(client, \"__blocker__id__\", user_name=\"__blocker__name__\")\n response_put = client.put(\n f\"{API_VERSION_PREFIX}/playlists/{playlist_id}\",\n data={\"blocked\": True},\n headers={\"api_key\": \"key\", \"uid\": \"__blocker__id__\", \"role\": \"admin\"},\n )\n assert response_put.status_code == 200\n return response_put\n\n\ndef remove_playlist_from_favorites(client, uid, playlist_id):\n response_delete = client.delete(\n f\"{API_VERSION_PREFIX}/users/{uid}/favorites/playlists/?playlist_id={playlist_id}\",\n headers={\"api_key\": \"key\", \"uid\": uid},\n )\n return response_delete\n\n\ndef post_streaming(client, uid: str, name=\"streaming_name\", include_img=False):\n data = {\"name\": name}\n\n if include_img:\n with open(\"./streaming.img\", \"wb\") as f:\n f.write(b\"test\")\n with open(\"./streaming.img\", \"rb\") as f:\n files = {\"img\": (\"streaming.img\", f, \"plain/text\")}\n response_post = client.post(\n API_VERSION_PREFIX + \"/streamings/\",\n headers={\"api_key\": \"key\", \"uid\": uid, \"role\": \"artist\"},\n data=data,\n files=files,\n )\n else:\n response_post = client.post(\n API_VERSION_PREFIX + \"/streamings/\",\n headers={\"api_key\": \"key\", \"uid\": uid, \"role\": \"artist\"},\n data=data,\n )\n\n return response_post\n\n\ndef post_user_with_sub_level(client, user_id: str, user_name: str, sub_level: int):\n response = post_user(client, user_id, user_name)\n assert response.status_code == 200\n\n response = client.post(\n f\"{API_VERSION_PREFIX}/subscriptions/\",\n headers={\"api_key\": \"key\", \"uid\": user_id},\n json={\"sub_level\": sub_level},\n )\n assert response.status_code == 200\n\n response = client.post(\n f\"{API_VERSION_PREFIX}/subscriptions/\",\n headers={\"api_key\": \"key\", \"uid\": user_id},\n json={\"sub_level\": sub_level},\n )\n return response\n\n\ndef delete_user(client, user_id: str):\n return delete(client, f\"/users/{user_id}\", uid=user_id)\n\n\ndef get(\n client,\n endpoint: str,\n uid: Optional[str] = None,\n role: str = \"listener\",\n unwrap=False,\n offset: Optional[Union[int, str]] = None,\n limit: Optional[Union[int, str]] = None,\n):\n if uid is None:\n uid = get_uid_or_create(client)\n headers = {\n \"api_key\": \"key\",\n \"uid\": uid,\n \"role\": role,\n }\n params = {}\n if limit is not None:\n params[\"offset\"] = offset\n params[\"limit\"] = limit\n\n response = client.get(\n f\"{API_VERSION_PREFIX}{endpoint}\",\n headers=headers,\n params=params,\n )\n if limit is None:\n if response.status_code < 299 and \"items\" in response.json():\n items = response.json()[\"items\"]\n resp = requests.models.Response()\n resp.status_code = response.status_code\n resp._content = json.dumps(items, indent=2).encode(\"utf-8\")\n response = resp\n if unwrap:\n return response.json()\n return response\n\n\ndef post(\n client,\n endpoint: str,\n data: dict,\n uid: Optional[str] = None,\n role: str = \"listener\",\n files: Optional[dict] = None,\n unwrap: bool = False,\n):\n if uid is None:\n uid = get_uid_or_create(client)\n\n response = client.post(\n f\"{API_VERSION_PREFIX}{endpoint}\",\n headers={\"api_key\": \"key\", \"uid\": uid, \"role\": role},\n data=data,\n files=files,\n )\n if unwrap:\n return response.json()\n return response\n\n\ndef post_json(\n client,\n endpoint: str,\n json: dict,\n uid: Optional[str] = None,\n role: str = \"listener\",\n files: Optional[dict] = None,\n unwrap: bool = False,\n):\n if uid is None:\n uid = get_uid_or_create(client)\n\n response = client.post(\n f\"{API_VERSION_PREFIX}{endpoint}\",\n headers={\"api_key\": \"key\", \"uid\": uid, \"role\": role},\n json=json,\n files=files,\n )\n if unwrap:\n return response.json()\n return response\n\n\ndef delete(\n client,\n endpoint: str,\n uid: Optional[str] = None,\n role: str = \"listener\",\n unwrap=False,\n):\n if uid is None:\n uid = get_uid_or_create(client)\n\n response = client.delete(\n f\"{API_VERSION_PREFIX}{endpoint}\",\n headers={\"api_key\": \"key\", \"uid\": uid, \"role\": role},\n )\n if unwrap:\n return response.json()\n return response\n\n\ndef put(\n client,\n endpoint: str,\n data: dict,\n uid: Optional[str] = None,\n role: str = \"listener\",\n unwrap=False,\n):\n if uid is None:\n uid = get_uid_or_create(client)\n\n response = client.put(\n f\"{API_VERSION_PREFIX}{endpoint}\",\n data=data,\n headers={\"api_key\": \"key\", \"uid\": uid, \"role\": role},\n )\n if unwrap:\n return response.json()\n return response\n\n\ndef put_json(\n client,\n endpoint: str,\n json: dict,\n uid: Optional[str] = None,\n role: str = \"listener\",\n unwrap=False,\n):\n if uid is None:\n uid = get_uid_or_create(client)\n\n response = client.put(\n f\"{API_VERSION_PREFIX}{endpoint}\",\n json=json,\n headers={\"api_key\": \"key\", \"uid\": uid, \"role\": role},\n )\n if unwrap:\n return response.json()\n return response\n\n\ndef get_album(\n client,\n album_id: int,\n uid: Optional[str] = None,\n role: str = \"listener\",\n unwrap=False,\n):\n return get(client, f\"/albums/{album_id}\", uid, role, unwrap)\n\n\ndef add_query(endpoint: str, query: str):\n if \"?\" in endpoint:\n return f\"{endpoint}&{query}\"\n return f\"{endpoint}?{query}\"\n\n\ndef search_albums(\n client,\n artist: Optional[str] = None,\n genre: Optional[str] = None,\n name: Optional[str] = None,\n creator: Optional[str] = None,\n uid: Optional[str] = None,\n role: str = \"listener\",\n unwrap=False,\n offset: Optional[int] = None,\n limit: Optional[int] = None,\n):\n endpoint = \"/albums/\"\n for search_term, value in (\n (\"artist\", artist),\n (\"genre\", genre),\n (\"name\", name),\n (\"creator\", creator),\n ):\n if value is not None:\n endpoint = add_query(endpoint, f\"{search_term}={value}\")\n\n return get(\n client, endpoint, uid=uid, role=role, unwrap=unwrap, offset=offset, limit=limit\n )\n\n\ndef get_my_albums(client, uid: str, role: str = \"listener\", unwrap=False):\n return get(client, \"/my_albums/\", uid, role, unwrap)\n\n\ndef put_album(\n client,\n album_id: int,\n data: dict,\n uid: Optional[str] = None,\n role: str = \"listener\",\n unwrap=False,\n):\n return put(client, f\"/albums/{album_id}\", data, uid, role, unwrap)\n\n\ndef delete_album(\n client,\n album_id: int,\n uid: Optional[str] = None,\n role: str = \"listener\",\n unwrap=False,\n):\n return delete(client, f\"/albums/{album_id}\", uid, role, unwrap)\n\n\ndef get_song(\n client,\n song_id: int,\n uid: Optional[str] = None,\n role: Optional[str] = \"listener\",\n unwrap=False,\n):\n return get(client, f\"/songs/{song_id}\", uid, role, unwrap)\n\n\ndef get_my_songs(\n client,\n uid: Optional[str] = None,\n role: str = \"listener\",\n unwrap=False,\n offset: Optional[int] = None,\n limit: Optional[int] = None,\n):\n return get(client, \"/my_songs/\", uid, role, unwrap, offset=offset, limit=limit)\n\n\ndef search_songs(\n client,\n artist: Optional[str] = None,\n genre: Optional[str] = None,\n name: Optional[str] = None,\n creator: Optional[str] = None,\n sub_level: Optional[int] = None,\n uid: Optional[str] = None,\n role: str = \"listener\",\n unwrap=False,\n offset: Optional[int] = None,\n limit: Optional[int] = None,\n):\n endpoint = \"/songs/\"\n for search_term, value in (\n (\"artist\", artist),\n (\"genre\", genre),\n (\"name\", name),\n (\"creator\", creator),\n (\"sub_level\", sub_level),\n ):\n if value is not None:\n endpoint = add_query(endpoint, f\"{search_term}={value}\")\n\n return get(\n client, endpoint, uid=uid, role=role, unwrap=unwrap, offset=offset, limit=limit\n )\n\n\ndef put_song(\n client,\n song_id: int,\n data: dict,\n uid: Optional[str] = None,\n role: str = \"listener\",\n unwrap=False,\n):\n return put(client, f\"/songs/{song_id}\", data, uid, role, unwrap)\n\n\ndef delete_song(\n client,\n song_id: int,\n uid: Optional[str] = None,\n role: str = \"listener\",\n unwrap=False,\n):\n return delete(client, f\"/songs/{song_id}\", uid, role, unwrap)\n\n\ndef get_songs_by_creator(\n client,\n creator_id: str,\n uid: Optional[str] = None,\n role: str = \"listener\",\n unwrap=False,\n):\n return get(client, f\"/songs/?creator={creator_id}\", uid, role, unwrap)\n\n\ndef get_playlist(\n client,\n playlist_id: int,\n uid: Optional[str] = None,\n role: str = \"listener\",\n unwrap=False,\n):\n return get(client, f\"/playlists/{playlist_id}\", uid, role, unwrap)\n\n\ndef get_my_playlists(\n client,\n uid: Optional[str] = None,\n role: str = \"listener\",\n unwrap=False,\n):\n return get(client, \"/my_playlists/\", uid, role, unwrap)\n\n\ndef search_playlists(\n client,\n uid: Optional[str] = None,\n colab: Optional[str] = None,\n creator: Optional[str] = None,\n role: str = \"listener\",\n unwrap=False,\n offset: Optional[int] = None,\n limit: Optional[int] = None,\n):\n endpoint = \"/playlists/\"\n for search_term, value in (\n (\"colab\", colab),\n (\"creator\", creator),\n ):\n if value is not None:\n endpoint = add_query(endpoint, f\"{search_term}={value}\")\n return get(\n client, endpoint, uid=uid, role=role, unwrap=unwrap, offset=offset, limit=limit\n )\n\n\ndef put_playlist(\n client,\n playlist_id: int,\n data: dict,\n uid: Optional[str] = None,\n role: str = \"listener\",\n unwrap=False,\n):\n return put(client, f\"/playlists/{playlist_id}\", data, uid, role, unwrap)\n\n\ndef delete_playlist(\n client,\n playlist_id: int,\n uid: Optional[str] = None,\n role: str = \"listener\",\n unwrap=False,\n):\n return delete(client, f\"/playlists/{playlist_id}\", uid, role, unwrap)\n\n\ndef add_playlist_song(\n client,\n playlist_id: int,\n song_id: int,\n uid: Optional[str] = None,\n role: str = \"listener\",\n unwrap=False,\n):\n return post(\n client,\n f\"/playlists/{playlist_id}/songs/\",\n {\"song_id\": song_id},\n uid,\n role,\n unwrap=unwrap,\n )\n\n\ndef remove_playlist_song(\n client,\n playlist_id: int,\n song_id: int,\n uid: Optional[str] = None,\n role: str = \"listener\",\n unwrap=False,\n):\n return delete(\n client,\n f\"/playlists/{playlist_id}/songs/{song_id}/\",\n uid,\n role,\n unwrap,\n )\n\n\ndef add_playlist_colab(\n client,\n playlist_id: int,\n colab_id: str,\n uid: Optional[str] = None,\n role: str = \"listener\",\n unwrap=False,\n):\n return post(\n client,\n f\"/playlists/{playlist_id}/colabs/\",\n {\"colab_id\": colab_id},\n uid,\n role,\n unwrap=unwrap,\n )\n\n\ndef post_users(client: Type[TestClient], *users_ids):\n for user_id in users_ids:\n post_user(client, user_id)\n\n\ndef get_uid_or_create(client):\n users = client.get(\n f\"{API_VERSION_PREFIX}/users\", headers={\"api_key\": \"key\"}\n ).json()[\"items\"]\n if len(users) == 0:\n uid = \"__user_id__\"\n post_user(client, uid)\n else:\n uid = users[0][\"id\"]\n return uid\n\n\ndef get_album_comments(\n client,\n album_id: int,\n uid: Optional[str] = None,\n role: str = \"listener\",\n unwrap=False,\n offset: Optional[int] = None,\n limit: Optional[int] = None,\n):\n return get(\n client, f\"/albums/{album_id}/comments/\", uid, role, unwrap, offset, limit\n )\n\n\ndef post_comment(\n client,\n album_id: int,\n message: str = \"comment_text\",\n parent_id: Optional[int] = None,\n uid: Optional[str] = None,\n role: str = \"listener\",\n unwrap_id=True,\n):\n response = post_json(\n client,\n f\"/albums/{album_id}/comments/\",\n {\"text\": message, \"parent_id\": parent_id},\n uid,\n role,\n unwrap=unwrap_id,\n )\n if unwrap_id:\n return response[\"id\"]\n return response\n\n\ndef get_user_comments(\n client,\n uid: Optional[str] = None,\n role: str = \"listener\",\n unwrap=False,\n):\n return get(client, \"/users/comments/\", uid, role, unwrap)\n\n\ndef put_comment(\n client,\n comment_id: int,\n message: str,\n uid: Optional[str] = None,\n role: str = \"listener\",\n unwrap=False,\n):\n return put_json(\n client,\n f\"/albums/comments/{comment_id}/\",\n {\"text\": message},\n uid,\n role,\n unwrap,\n )\n\n\ndef delete_comment(\n client,\n comment_id: int,\n uid: Optional[str] = None,\n role: str = \"listener\",\n unwrap=False,\n):\n return delete(client, f\"/albums/comments/{comment_id}/\", uid, role, unwrap)\n\n\ndef get_reviews_of_album(\n client,\n album_id: int,\n uid: Optional[str] = None,\n role: str = \"listener\",\n unwrap=False,\n offset: Optional[str] = None,\n limit: Optional[int] = None,\n):\n return get(client, f\"/albums/{album_id}/reviews/\", uid, role, unwrap, offset, limit)\n\n\ndef delete_review_of_album(\n client,\n album_id: int,\n uid: Optional[str] = None,\n role: str = \"listener\",\n unwrap=False,\n):\n return delete(client, f\"/albums/{album_id}/reviews/\", uid, role, unwrap)\n\n\ndef put_review_of_album(\n client,\n album_id: int,\n text: Optional[str],\n score: Optional[int],\n uid: Optional[str] = None,\n role: str = \"listener\",\n unwrap=False,\n):\n return put_json(\n client,\n f\"/albums/{album_id}/reviews/\",\n {\"text\": text, \"score\": score},\n uid,\n role,\n unwrap,\n )\n\n\ndef get_users(\n client,\n uid: Optional[str] = None,\n role: str = \"listener\",\n unwrap=False,\n offset: Optional[int] = None,\n limit: Optional[int] = None,\n):\n return get(client, \"/users/\", uid, role, unwrap, offset, limit)\n\n\ndef get_user_reviews(\n client,\n uid: Optional[str] = None,\n role: str = \"listener\",\n unwrap=False,\n offset: Optional[int] = None,\n limit: Optional[int] = None,\n):\n return get(client, f\"/users/{uid}/reviews/\", uid, role, unwrap, offset, limit)\n\n\ndef get_user(\n client,\n uid: Optional[str] = None,\n role: str = \"listener\",\n unwrap=False,\n):\n return get(client, f\"/users/{uid}/\", uid, role, unwrap)\n\n\ndef get_streamings(\n client,\n uid: Optional[str] = None,\n role: str = \"listener\",\n unwrap=False,\n offset: Optional[int] = None,\n limit: Optional[int] = None,\n):\n return get(client, \"/streamings/\", uid, role, unwrap, offset, limit)\n","repo_name":"taller2-grupo5-rostov-1c2022/songs-server","sub_path":"tests/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":24458,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"14405725921","text":"import json\nimport os\nimport queue\nimport time\n\nfrom loguru import logger\nimport paho.mqtt.client as mqtt\nimport boto3\nimport threading\n\nfrom fastapi import FastAPI, HTTPException\nfrom starlette.middleware.cors import CORSMiddleware\nfrom starlette.status import (\n HTTP_202_ACCEPTED,\n HTTP_404_NOT_FOUND, HTTP_409_CONFLICT)\n\n\nfrom hobs.__init__ import __version__\nfrom hobs.csv import csvManager\nfrom hobs.sub import Subscriber\n\n\nlogger.info({'version': __version__})\n\napp = FastAPI()\napp.add_middleware(\n CORSMiddleware,\n allow_origins=['*'],\n allow_credentials=True,\n allow_methods=['*'],\n allow_headers=['*'],\n)\n\nMQTT_KEEP_ALIVE = 5\nmqtt_port = os.getenv('HOBS_MQTT_PORT')\nmqtt_host = os.getenv('HOBS_MQTT_HOST')\nca_certs = os.getenv('HOBS_CA_CERTS')\ncertifile = os.getenv('HOBS_CERTFILE')\nkeyfile = os.getenv('HOBS_KEYFILE')\n\nsub = threading.Thread(target=Subscriber)\nsub.start()\n\n\ndef device_get():\n dyn_resource = boto3.resource('dynamodb')\n table_exp = dyn_resource.Table('lab_rpi')\n response = table_exp.scan()\n rpidev = []\n # logger.info(response['Items'])\n for i in range(len(response['Items'])):\n rpidev.append({\n 'name': response['Items'][i]['name'],\n 'rpi_id': response['Items'][i]['rpi_id'],\n })\n return rpidev\n\n\nresponses_post = {\n HTTP_404_NOT_FOUND: {\n \"description\": \"Device not found\"\n },\n # HTTP_409_CONFLICT: {\n # \"description\": \"Already in use\",\n # \"content\": {\n # \"application/json\": {\n # }\n # }\n # },\n}\n\nresponses_get = {\n HTTP_404_NOT_FOUND: {\n \"description\": \"Device not found\"\n },\n HTTP_409_CONFLICT: {\n \"description\": \"The status of container was abnormal\",\n \"content\": {\n \"application/json\": {\n }\n }\n },\n}\n\nresponses_put = responses_get\n\nresponses_delete = {\n HTTP_404_NOT_FOUND: {\n \"description\": \"Device not found\"\n },\n}\n\n\nclass MqttRequester(mqtt.Client):\n def __init__(\n self,\n url=mqtt_host,\n port=int(mqtt_port),\n ca_certs=ca_certs,\n certfile=certifile,\n keyfile=keyfile,\n ):\n mqtt.Client.__init__(self)\n self.tls_set(\n ca_certs=ca_certs,\n certfile=certfile,\n keyfile=keyfile\n )\n self.tls_insecure_set(True)\n self._queue = queue.Queue(1)\n self.connect(url, port, MQTT_KEEP_ALIVE)\n self.loop_start()\n\n # def on_connect(self, mqttc, obj, flags, rc):\n # logger.info({\n # 'event': 'on_connect',\n # 'rc': str(rc),\n # })\n\n # def on_disconnect(self, client, userdata, rc):\n # logger.info({\n # 'event': 'on_disconnect',\n # 'userdata': userdata,\n # 'rc': str(rc),\n # })\n\n def on_message(self, mqttc, obj, msg):\n # logger.info({\n # 'event': 'on_message',\n # 'topic': msg.topic,\n # 'qos': str(msg.qos),\n # 'payload': msg.payload.decode(),\n # })\n self._queue.put(msg.payload)\n\n # def on_publish(self, mqttc, obj, mid):\n # logger.debug({\n # 'event': 'on_publish',\n # 'mid': str(mid),\n # })\n\n # def on_subscribe(self, mqttc, obj, mid, granted_qos):\n # logger.debug({\n # 'event': 'on_subscribe',\n # 'mid': str(mid),\n # 'qos': str(granted_qos),\n # })\n\n def on_log(self, mqttc, obj, level, string):\n if level is mqtt.MQTT_LOG_ERR:\n logger.error({\n 'event': 'on_log',\n 'level': level,\n 'string': string,\n })\n\n def run(self, pub, dev_id):\n msg_id = str(time.time())\n\n self.subscribe('/dev_id/' + dev_id + '/srw/#')\n\n try:\n if pub == 'scan':\n self.publish('/dev_id/' + dev_id + '/wrs/', 'scan')\n resmsg = self._queue.get(block=True, timeout=15.0).decode()\n\n elif pub == 'get_status':\n self.publish('/dev_id/' + dev_id + '/wrs/', 'get_status')\n resmsg = self._queue.get(block=True, timeout=0.9).decode()\n\n elif pub == 'update':\n self.publish('/dev_id/' + dev_id + '/wrs/', 'update')\n resmsg = self._queue.get(block=True, timeout=10.0).decode()\n\n else:\n resmsg = \"invalid pub\"\n\n # logger.debug({\n # 'msg_id': msg_id,\n # 'resmsg': resmsg,\n # })\n\n return json.loads(resmsg)\n\n except queue.Empty as e:\n logger.warning({\n 'title': 'mqtt',\n 'dev-id': dev_id,\n 'action': 'timeout',\n 'except': e,\n })\n raise HTTPException(\n status_code=HTTP_404_NOT_FOUND,\n detail=\"Item not found\"\n )\n\n except Exception as e:\n logger.exception(e)\n raise e\n\n finally:\n self.disconnect()\n\n\n@app.get(\n '/ble',\n status_code=HTTP_202_ACCEPTED,\n responses={**responses_get},\n tags=['device'],)\ndef get_ble(dev_id: str):\n try:\n logger.info({\n 'dev-id': dev_id,\n })\n\n return MqttRequester().run('scan', dev_id)\n\n except Exception as e:\n logger.exception(e)\n raise HTTPException(\n status_code=HTTP_404_NOT_FOUND,\n detail=\"Item not found\")\n\n\n@app.get(\n '/status',\n tags=['device'])\ndef get_status(dev_id: str):\n try:\n # logger.info({\n # 'method': 'GET',\n # 'dev-id': dev_id,\n # })\n\n return MqttRequester().run('get_status', dev_id)\n\n except Exception as e:\n logger.exception(e)\n raise HTTPException(status_code=404, detail=\"Item not found\")\n\n\n@app.put(\n '/',\n tags=['device'],)\ndef update(dev_id: str):\n try:\n logger.info({\n 'method': 'GET',\n 'dev-id': dev_id,\n })\n\n return MqttRequester().run('update', dev_id)\n\n except Exception as e:\n logger.exception(e)\n raise HTTPException(status_code=404, detail=\"Item not found\")\n\n\n@app.get(\n '/db/device',\n tags=['db'],\n )\ndef get_device():\n rpi = device_get()\n\n rpid = {\n 'devices': rpi\n }\n return rpid\n\n\n# XXX status code 를 넣고 , CODE 넘버 바꾸고, Details 에다가 detail 만들것,\n@app.post(\n '/db/csv',\n tags=['db'],\n)\ndef get_csv(experiment_id: str):\n csvm = csvManager(experiment_id).execute()\n if csvm != 0:\n return {\n 'code': 1,\n 'detail': 'success',\n }\n else:\n return {\n 'code': 0,\n 'detail': 'Invalied exp_name',\n }","repo_name":"nbbeom/ble_serv","sub_path":"hobs/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":6821,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"8475341334","text":"\nimport bpy\nimport socket\nimport os\nimport sys\nimport json\nfrom bpy.app.handlers import persistent\n\n\nprint('\\n\\n\\n\\n\\n======================== INIT BLENDER SESSION ============================')\n\nhome = os.path.expanduser('~') \nincludepath = os.path.join(home, 'Documents', 'lab', 'm2b')\n\n\n# /// SETTINGS\nOBJ_FILE = ''\n\n\n# disable splash\nbpy.context.user_preferences.view.show_splash = False\n\n\ndef get_sceneinfo():\n \"\"\"Check if last argument is a file\n \"\"\"\n arg = sys.argv[-1]\n print('> CMD LINE ARG:', arg)\n if 'blend' in arg:\n basepath = os.path.dirname(arg)\n uuid = os.path.basename(arg)\n uuid = os.path.splitext(uuid)[0]\n jsonfile = os.path.join(basepath, uuid + '.json')\n with open(jsonfile) as conffile:\n conf_data = json.load(conffile)\n print(conf_data)\n return conf_data\n \n elif 'json' in arg:\n with open(arg) as conffile:\n conf_data = json.load(conffile)\n return conf_data\n\n\n\n\ndef import_obj(fp):\n old_state = list(bpy.context.scene.objects)\n bpy.ops.import_scene.obj(filepath=fp)\n new_state = list(bpy.context.scene.objects)\n return set(new_state) - set(old_state)\n\n\ndef setup_scene():\n print('> SETTING UP SCENE')\n\n print('> texture:', TEXTURE)\n for object in bpy.data.objects:\n if object.name == 'Cube':\n object.select = True\n bpy.ops.object.delete()\n\n new_items = import_obj(OBJ_FILE)\n for object in bpy.context.scene.objects:\n object.select = False\n\n scn = bpy.context.scene\n scn.render.engine = 'CYCLES'\n \n mat = bpy.data.materials.new('MayaTexture')\n mat.use_nodes = True\n texnode = mat.node_tree.nodes.new(type=\"ShaderNodeTexImage\")\n mat.node_tree.links.new(texnode.outputs['Color'], mat.node_tree.nodes['Diffuse BSDF'].inputs['Color'])\n if os.path.isfile(str(TEXTURE)):\n texnode.image = bpy.data.images.load(TEXTURE)\n\n for item in new_items:\n if item.type == 'MESH':\n ob = item\n mesh = ob.data\n mesh.materials.append(mat)\n item.select = True\n bpy.context.scene.objects.active = item\n\n \n preview_texture(TEXTURE)\n\n print('SAVING AS', BLEND)\n bpy.ops.wm.save_as_mainfile(filepath=BLEND, check_existing=False)\n\n\ndef update_maya():\n print('MSG > MAYA', PORT)\n host = '127.0.0.1'\n port = PORT\n #message = 'import sys;sys.path.append(\"' + includepath + '\");import m2b;m2b.update(\"' + UUID + '\")'\n message = 'import m2b;m2b.edit_mesh.update(\"' + UUID + '\")'\n \n maya = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n maya.connect((host, port))\n msg = bytes(message, 'UTF-8')\n try:\n print('sending')\n maya.send(msg)\n except:\n print('failed')\n print(msg)\n finally:\n print('closed')\n maya.close()\n\ndef deselect():\n for object in bpy.context.scene.objects:\n object.select = False\n\ndef preview_texture(image):\n print('> PREVIEW TEXTURE GENERATION', TEXTURE)\n\n for area in bpy.data.screens['Default'].areas:\n if area.type == 'VIEW_3D':\n for space in area.spaces:\n if space.type == 'VIEW_3D':\n space.viewport_shade = 'TEXTURED'\n space.show_textured_solid = True\n deselect()\n #mat = bpy.data.materials.new('TexMat')\n\n\ndef export():\n selection = bpy.context.selected_objects\n\n deselect()\n\n for object in bpy.context.scene.objects:\n if not object.hide_render:\n object.select = True\n bpy.ops.export_scene.obj(filepath=OBJ_FILE,\n use_materials=False,\n use_blen_objects=False,\n use_selection=True)\n\n # restore selection\n for object in bpy.context.scene.objects:\n object.select = False\n if object in selection:\n object.select = True\n\n update_maya()\n\n@persistent\ndef save_handler(dummy):\n export()\n\n\nclass BlenderBridge(object):\n def __init__(self):\n self.handler = save_handler\n\n\ninfo = get_sceneinfo()\nif info is not None:\n OBJ_FILE = info['obj']\n TEXTURE = info['tex']\n BLEND = info['blend']\n UUID = info['uuid']\n PORT = info['port']\n\nif os.path.isfile(BLEND):\n print('> matching blend file found. using that.')\nelse:\n setup_scene()\n\n\n\n\n\nif len(bpy.app.handlers.save_post) < 1:\n bpy.app.handlers.save_post.append(save_handler)\n","repo_name":"woelper/maya_edit_outside","sub_path":"b2m.py","file_name":"b2m.py","file_ext":"py","file_size_in_byte":4461,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"73613833446","text":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nsns.set_style(\"whitegrid\")\n\nimport sklearn\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import datasets\nfrom sklearn import svm\n\nboston = datasets.load_boston()\n\n# Cross Validation (CV)\n'''\n- Hold out Cross Validation\n- k-fold Cross Validation\n\nA test set should still be held out for final evaluation, but the validation set is no longer needed when doing CV.\n\nIn the basic approach, called k-fold CV, the training set is split into k smaller sets. \nThe following procedure is followed for each of the k “folds”:\n- A model is trained using k-1 of the folds as training data;\n- the resulting model is validated on the remaining part of the data (i.e., it is used as a test set to compute a \nperformance measure such as accuracy).\n\nThe performance measure reported by k-fold cross-validation is then the average of the values computed in the loop.\n'''\n\n# Holdout Method\n'''\n- Split initial dataset into a separate training and test dataset\n- Training dataset - model training\n- Test dataset - estimate its generalisation performance\n\nA variation is to split the training set to two :- training set and validation set\n\nTraining set:- For fitting different models\nValidation set :- For tuning and comparing different parameter settings to further improve the \nperformance for making predictions on unseen data. And finally for model selection.\n\nThis process is called model selection. We want to select the optimal values of tuning parameters (also called hyperparameters).\n'''\n\n# K-fold Cross-validation\n'''\n- Randomly split the training dataset into k folds without replacement.\n- k — 1 folds are used for the model training.\n- The one fold is used for performance evaluation.\n\nThis procedure is repeated k times.\n\nFinal outcomes:- k models and performance estimates.\n\n- calculate the average performance of the models based on the different, \nindependent folds to obtain a performance estimate that is less sensitive to the sub-partitioning of the training data \ncompared to the holdout method.\n\n- k-fold cross-validation is used for model tuning. Finding the optimal hyperparameter values that yields a satisfying \ngeneralization performance.\n\n- Once we have found satisfactory hyperparameter values, we can retrain the model on the complete training set and \nobtain a final performance estimate using the independent test set. The rationale behind fitting a model to the whole \ntraining dataset after k-fold cross-validation is that providing more training samples to a learning algorithm usually \nresults in a more accurate and robust model.\n\n- Common k is 10\n\n- For relatively small training sets, increase the number of folds.\n'''\n\n# Stratified k-fold cross-validation\n'''\n- variation of k-fold\n- Can yield better bias and variance estimates, especially in cases of unequal class proportions\n'''\n\n\n# Cross-validation: evaluating estimator performance\nX_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target, test_size=0.4, random_state=0)\n\nprint(X_train.shape, y_train.shape)\n\nprint(X_test.shape, y_test.shape)\n\nregression = svm.SVR(kernel='linear', C=1).fit(X_train, y_train)\nregression.score(X_test, y_test)\n\n'''\nWhen evaluating different settings (“hyperparameters”) for estimators, such as the C setting that must be manually set \nfor an SVM, there is still a risk of overfitting on the test set because the parameters can be tweaked until \nthe estimator performs optimally.\n\nThis way, knowledge about the test set can “leak” into the model and evaluation metrics no longer report on generalization performance.\n\nTo solve this problem, yet another part of the dataset can be held out as a so-called “validation set”: \ntraining proceeds on the training set, after which evaluation is done on the validation set, \nand when the experiment seems to be successful, final evaluation can be done on the test set.\n\nHowever, by partitioning the available data into three sets, we drastically reduce the number of samples which can be \nused for learning the model, and the results can depend on a particular random choice for the pair of (train, validation) sets.\n\nA solution to this problem, as discussed earlier, is a procedure called cross-validation (CV for short). \nA test set should still be held out for final evaluation, but the validation set is no longer needed when doing CV. \nIn the basic approach, called k-fold CV, the training set is split into k smaller sets \n(other approaches are described below, but generally follow the same principles). \nThe following procedure is followed for each of the k “folds”:\n\n- A model is trained using k-1 of the folds as training data;\n- the resulting model is validated on the remaining part of the data \n (i.e., it is used as a test set to compute a performance measure such as accuracy).\n\nThe performance measure reported by k-fold cross-validation is then the average of the values computed in the loop. \nThis approach can be computationally expensive, but does not waste too much data \n(as it is the case when fixing an arbitrary test set), \nwhich is a major advantage in problem such as inverse inference where the number of samples is very small.\n'''\n\n# Computing cross-validated metrics\nfrom sklearn.model_selection import cross_val_score\nregression = svm.SVR(kernel='linear', C=1)\nscores = cross_val_score(regression, boston.data, boston.target, cv=5)\nscores\n\n# The mean score and the 95% confidence interval of the score estimate are hence given by:\nprint(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std()))\n\n# By default, the score computed at each CV iteration is the score method of the estimator.\n# It is possible to change this by using the scoring parameter:\n\nscores = cross_val_score(regression, boston.data, boston.target, cv=5, scoring='neg_mean_squared_error')\nscores\n\n\n# K-fold\n'''\nKFold divides all the samples in k groups of samples, called folds \n(if k = n, this is equivalent to the Leave One Out strategy), of equal sizes (if possible). \nThe prediction function is learned using k - 1 folds, and the fold left out is used for test.\n\nExample of 2-fold cross-validation on a dataset with 4 samples:\n'''\n\nfrom sklearn.model_selection import KFold\n\nX = [\"a\", \"b\", \"c\", \"d\"]\nkf = KFold(n_splits=2)\nfor train, test in kf.split(X):\n print(\"%s %s\" % (train, test))\n\n\n# Stratified k-fold\n'''\nStratifiedKFold is a variation of k-fold which returns stratified folds: each set contains approximately \nthe same percentage of samples of each target class as the complete set.\n\nExample of stratified 3-fold cross-validation on a dataset with 10 samples from two slightly unbalanced classes\n'''\nfrom sklearn.model_selection import StratifiedKFold\n\nX = np.ones(10)\ny = [0, 0, 0, 0, 1, 1, 1, 1, 1, 1]\nskf = StratifiedKFold(n_splits=3)\nfor train, test in skf.split(X, y):\n print(\"%s %s\" % (train, test))\n\nprint(X)\nprint(y)\n\n\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.decomposition import PCA\nfrom sklearn.pipeline import make_pipeline\npipe_svm = make_pipeline(StandardScaler(),\n PCA(n_components=2),\n svm.SVR(kernel='linear', C=1)) # LogisticRegression(random_state=1)\npipe_svm.fit(X_train, y_train)\ny_pred = pipe_svm.predict(X_test)\nprint('Test Accuracy: %.3f' % pipe_svm.score(X_test, y_test))\n\n\nscores = cross_val_score(estimator=pipe_svm,\n X=X_train,\n y=y_train,\n cv=10,\n n_jobs=1)\nprint('CV accuracy scores: %s' % scores)\n\nprint('CV accuracy: %.3f +/- %.3f' % (np.mean(scores), np.std(scores)))\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"FISHWONGY/udemy_repo","sub_path":"The_Complete_Machine_Learning_Course_with_Python/3_Regression/08_Cross Validation.py","file_name":"08_Cross Validation.py","file_ext":"py","file_size_in_byte":7706,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"37018506141","text":"#Embedded file name: e:\\jenkins\\workspace\\client_SERENITY\\branches\\release\\SERENITY\\eve\\client\\script\\ui\\inflight\\shipHud\\__init__.py\r\nfrom eve.client.script.ui.inflight.shipHud.activeShipController import ActiveShipController\r\n\r\ndef GetSlotOrder():\r\n defaultOrder = []\r\n for r in xrange(3):\r\n for i in xrange(8):\r\n slotFlag = getattr(const, 'flag%sSlot%s' % (['Hi', 'Med', 'Lo'][r], i), None)\r\n if slotFlag is not None:\r\n defaultOrder.append(slotFlag)\r\n\r\n try:\r\n return settings.user.ui.Get('slotOrder', {}).get(session.shipid, defaultOrder)\r\n except:\r\n import log\r\n LogException()\r\n sys.exc_clear()\r\n return defaultOrder\r\n","repo_name":"connoryang/dec-eve-serenity","sub_path":"client/eve/client/script/ui/inflight/shipHud/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":715,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"42007807936","text":"from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration\nimport torch\nfrom datasets import load_from_disk\ndevice = torch.device(\"cuda:0\")\n\nds = load_from_disk(\"/workspace/data/filtered_small_embeddings\")[\"train\"]\ndef get_title_examples(examples):\n split_title = [ i.split(\"\\n\")[0] for i in examples[\"sentence_split\"]]\n examples[\"title\"] = split_title\n return examples\nds = ds.map(get_title_examples, batched=True, num_proc=32)\n\nprint(\"Loaded the Ds\")\nprint(ds)\n\ndf = ds.to_pandas()\n\nfrom collections import Counter\ndf[\"sentence_split_list\"] = [ [i for i in i.split(\"\\n\")[1:] if len(i)>0] for i in df[\"non_split\"]]\n\ndf_filterd = df[[\"date\",\"hash\",\"title\",\"sentence_split_list\"]]\nfiltered = []\nfor i in df_filterd.values:\n for j in i[3]:\n filtered.append([i[0],i[1],i[2],j])\nlen(filtered)\n\nimport pandas as pd\nfiltered_df= pd.DataFrame(filtered, columns=[\"date\",\"hash\",\"title\",\"sentence\"])\n\nimport matplotlib.pyplot as plt \nall_sizes = [len(i.split()) for i in filtered_df[\"sentence\"]]\nplt.hist(all_sizes, bins=100)\n\nfrom datasets import Dataset \ndataset = Dataset.from_pandas(filtered_df)\n\n\nfrom transformers import DPRContextEncoder, DPRContextEncoderTokenizer\nimport torch\ntorch.set_grad_enabled(False)\nctx_encoder = DPRContextEncoder.from_pretrained(\"facebook/dpr-ctx_encoder-single-nq-base\")\nctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained(\"facebook/dpr-ctx_encoder-single-nq-base\")\n\nctx_encoder.cuda()\nprint(ctx_encoder.device)\ndevice = torch.device(\"cuda\")\nctx_encoder.eval()\nwith torch.no_grad():\n ds_with_embeddings = dataset.map(lambda example: {'embeddings': ctx_encoder(**ctx_tokenizer(example[\"sentence\"], return_tensors=\"pt\",padding=True ,truncation=True).to(device))[\"pooler_output\"].detach().cpu().numpy()},num_proc=1)\n print(ds_with_embeddings)\n ds_with_embeddings.save_to_disk(\"/workspace/data/filtered_split_dprc_sentences_embeddings\")","repo_name":"saisurbehera/dller_NLG","sub_path":"notebooks/convert_rag.py","file_name":"convert_rag.py","file_ext":"py","file_size_in_byte":1912,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"17563724231","text":"import jieba\n\nwith open('145.txt', 'r', encoding='utf-8') as f:\n gov = f.read()\njieba.load_userdict(\"AIDict.txt\")\nseg_list = jieba.cut(gov, cut_all=False)\n#print(list(seg_list))\n\ntf = {}\nfor seg in seg_list:\n if seg in tf:\n tf[seg] += 1\n else:\n tf[seg] = 1\nprint(len(tf))\n\nci = list(tf.keys())\n# print(ci)\nwith open('stopword.txt', 'r',encoding='utf-8') as ft:\n stopword = ft.read()\n\nfor seg in ci:\n if tf[seg] < 10 or len(seg) < 2 or seg in stopword or '一' in seg:\n tf.pop(seg)\n\nprint(len(tf))\nci = list(tf.keys())\nnum = list(tf.values())\ndata = []\n\nfor i in range(len(tf)):\n data.append((num[i], ci[i]))\n\ndata.sort()\ndata.reverse()\n# print(data)\nf = open(\"result.txt\", \"w\",encoding='utf-8')\nfor i in range(len(data)):\n f.write(data[i][1] + \",\" + str(data[i][0]) + \"\\r\\n\")\nf.close()\n\nsortdata = {}\nfor d in data:\n sortdata[d[1]] = d[0]\nprint(sortdata)","repo_name":"5ycrow/2021-yuqingfenxi-code","sub_path":"Jieba145/jieba145.py","file_name":"jieba145.py","file_ext":"py","file_size_in_byte":899,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"41167866114","text":"from pygame import image, PixelArray\nimport sys\n\nimg = sys.argv[1]\n\narr = PixelArray(image.load(img))\nmax_size = int(str(arr[0][0])[-1])\ndigits = int(str(arr[0][1])[-1])\n\nprint(digits)\n\nlength = ''\nfor i in range(2,digits+2):\n length += str(arr[0][i])[-1:]\n\nlength = int(length)\nprint('Length:', length)\n\nheight = len(arr[0])\n\nword = ''\nx = 0\ny = digits+2\nchar_buffer = ''\nfor i in range(length*max_size+1):\n if len(char_buffer) == max_size:\n print('Decoding character:', char_buffer)\n word += chr(\n int(\n char_buffer\n ))\n char_buffer = ''\n\n char_buffer += str(arr[x][y])[-1:]\n\n if y < height - 1:\n y += 1\n else:\n x += 1\n y = 0\n\nprint(word)\n","repo_name":"JellyWX/ImageConceal","sub_path":"reveal.py","file_name":"reveal.py","file_ext":"py","file_size_in_byte":681,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"11310786856","text":"import json\nimport censys.ipv4\nfrom google.cloud import bigquery\n\nfrom censys_ml import utils\n\n\ndef data_from_json_file(path):\n with open(path, 'r') as in_file:\n return json.load(in_file)\n\n\ndef data_to_json_file(path, data):\n with open(path, 'w') as out_file:\n json.dump(data, out_file, indent=2, sort_keys=True)\n\n\ndef data_from_txt_file(path):\n with open(path, 'r') as in_file:\n return [p.replace('\\n', '')\n for p in in_file.readlines()]\n\n\ndef parse_html(raw_html):\n rows = raw_html.replace('&', 'and').split('')\n res = set()\n for row in rows:\n row_entry = row.split('')[0]\n name = row_entry.split('table-field-prefix\">')[-1].replace('', '')\n res.add(name.strip())\n return list(res)\n\n\ndef filter_leaf_fields(fields):\n res = []\n for field in fields:\n parent = '.'.join(field.split('.')[:-1])\n if (parent != '') and (parent in res):\n del res[res.index(parent)]\n res.append(field)\n return res\n\n\ndef filter_parent_fields(fields):\n return list({f.split('.')[0] for f in fields})\n\n\ndef parse_for_domain_names(fields):\n res = []\n leaves = filter_leaf_fields(fields)\n for leaf in leaves:\n if leaf[0] == 'p' and ('name' in leaf or 'dn' in leaf):\n res.append(leaf)\n return res\n\n\ndef bigquery_dry_run(client, field, table):\n project = ''\n dataset_id = ''\n query = (\"SELECT {} FROM `{}.{}.{}` WHERE ip='8.8.8.8';\".format(\n field, project, dataset_id, table\n ))\n job_config = bigquery.QueryJobConfig()\n job_config.dry_run = True\n job_config.use_query_cache = False\n query_job = client.query(query, job_config=job_config)\n\n # A dry run query completes immediately.\n assert query_job.state == 'DONE'\n assert query_job.dry_run\n return query_job.total_bytes_processed/(2.0 ** 20) # in MiB\n\n\ndef parse_schema(fields):\n result = {}\n for field in fields:\n j_field = field\n\n if 'fields' in j_field and len(j_field['fields']) != 0:\n parsed = parse_schema(j_field['fields'])\n for k in parsed:\n result[j_field['name'] + '.' + k] = parsed[k]\n else:\n result[j_field['name']] = j_field\n\n return result\n\n# def type_precedence(item1, item2):\n# # If the type changed over time, return the type with highest number\n# precedence = {\n# 'STRING': 0,\n# 'INTEGER': 1\n# }\n# return item1\n\n\n# def handle_time_changes(model_definition, json_schema, k):\n# TODO : handle model changes over time (str in 2018 -> int in 2019)\n# if k in model_definition:\n# model_definition[k] = type_precedence(item1=json_schema[k],\n# item2=model_definition[k])\n# else:\n# pass\n\nreport_cache = {}\ncensys_api = None\nREPORT_WHITELIST = {\n 'country_code'\n}\n\ndef setup_censys_api():\n if censys_api == None:\n CENSYS_API_KEY_FILEPATH = utils.get_config()['censys']['report_key']\n CENSYS_API_KEY = utils.get_json_data(filepath=CENSYS_API_KEY_FILEPATH)\n censys_api = censys.ipv4.CensysIPv4(**CENSYS_API_KEY)\n\ndef grab_top_string_values(field, n_values=20):\n setup_censys_api()\n report_args = {\n \"query\": \"\",\n \"field\": field,\n \"buckets\": n_values\n }\n\n if field not in report_cache:\n try:\n report = censys_api.report(**report_args)\n report_cache[field] = report\n except Exception as e:\n print(f\"[!] Ran into an error '{str(e)}' when \"\n f\"generating a report for field '{field}'...\")\n report_cache[field] = dict()\n\n return report_cache[field]\n\n\ndef include_top_occurrences(json_schema, k):\n if json_schema[k]['type'] == 'STRING' and any([v in k for v in REPORT_WHITELIST]):\n json_schema[k]['top_values'] = grab_top_string_values(field=k)\n\n\ndef update_model_definition(json_schema, model_definition):\n for k in json_schema:\n include_top_occurrences(json_schema, k)\n model_definition[k] = json_schema[k]\n # handle_time_changes(model_definition, json_schema, k)\n\n\ndef get_model_definition(client, dataset_ref):\n model_definition = {}\n total = len([0 for _ in client.list_tables(dataset_ref)])\n\n utils.printProgressBar(iteration=0, total=total,\n prefix='Table : ', suffix='Complete',\n length=50)\n for i, table in enumerate(client.list_tables(dataset_ref)):\n utils.printProgressBar(iteration=i, total=total,\n prefix='Table : {}'.format(table.table_id),\n suffix='Complete', length=50)\n\n table_ref = dataset_ref.table(table.table_id)\n raw_schema = client.get_table(table_ref).schema\n json_schema = parse_schema([r.to_api_repr() for r in raw_schema])\n update_model_definition(json_schema, model_definition)\n return model_definition\n\n\ndef save_model_definition(model_definition):\n outfile = utils.get_config()['censys']['model_outfile']\n with open(outfile, 'w') as _file:\n json.dump(model_definition, _file, indent=2)\n\n\ndef main():\n print('[x] Gathering censys data model')\n config = utils.get_config()\n service_file = config['censys']['service_file']\n project = config['censys']['project']\n dataset_id = config['censys']['dataset_id']\n\n client = bigquery.Client.from_service_account_json(service_file)\n dataset_ref = client.dataset(dataset_id, project=project)\n model_def = get_model_definition(client, dataset_ref)\n save_model_definition(model_definition=model_def)\n\n# OLD\n# def main():\n# fields = data_from_json('../../raw/aggregated_columns.json')\n# tables = ['20170106', '20180530', '20190211']\n# costs = {}\n# for t, table in enumerate(tables):\n# print \"\\n[X] Table {}\".format(table)\n#\n# columns = only_parents()\n# for i, field in enumerate(only_parents(fields)):\n# print \"[X] Field {}.{} ) {}\".format(t, i, field)\n# if field in costs:\n# costs[field].append(get_dry_run(field, table) if field in columns else 0)\n# else:\n# costs[field] = [get_dry_run(field, table) if field in columns else 0]\n# avg_costs = {k: max(costs[k]) for k in costs}\n# data_to_json('../../raw/aggregated_costs.json', avg_costs)\n# print(\"{} query will process {} bytes.\".format(f, avg_proc))\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"censys-ml/censys-ml","sub_path":"censys_ml/update_censys_model.py","file_name":"update_censys_model.py","file_ext":"py","file_size_in_byte":6518,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"33456378552","text":"#!/usr/bin/python\n\nfrom mininet.topo import Topo\nfrom mininet.net import Mininet\nfrom mininet.link import TCLink\nfrom mininet.cli import CLI\nimport threading as thd\nimport os\nimport time\nimport random\nimport argparse\n\ndef parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument(\n '--test-time', '-t', \n type=int, \n default=60, \n dest='test_time',\n help='Test time in seconds.')\n args = parser.parse_args()\n return args\nargs = parse_args()\n\nenter_time = str(int(time.time()))\n\nclass Single(Topo):\n def build(self):\n h1 = self.addHost(\"h1\")\n h2 = self.addHost(\"h2\")\n self.addLink(h1, h2, bw=100, delay=\"100ms\", loss=1)\n\n\ndef change_link_state():\n h2 = net.get(\"h2\")\n intf = h2.intf()\n\n # disconnect simulation\n disconnect_prob = 0.05 # randomly disconnect\n disconnect = random.random() < disconnect_prob\n if disconnect:\n # disc_time = random.uniform(0.1, 1)\n disc_time = random.choice([i/10. for i in range(1, 11)])\n timestamp = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime(time.time()))\n intf.ifconfig(\"down\")\n with open(\"./log/link_state_log_\" + enter_time, 'a') as f:\n conn_state = \"DISCONN\" if disconnect else \"CONN\"\n line = \"{}\\t{}\\t{}\\t{}%\\t{}\\n\".format(timestamp, \"0\", \"INFms\", \"100\", conn_state)\n f.write(line)\n time.sleep(disc_time)\n intf.ifconfig(\"up\")\n return\n\n # set new link arguments\n # new_loss = random.choice([i/10. for i in range(1, 11)])\n new_loss = random.choice([i for i in range(1, 6)])\n new_bw = random.randint(100, 200) # new bandwidth\n new_delay = \"{}ms\".format(random.randint(20, 100)) # new delay\n\n # change link state\n timestamp = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime(time.time()))\n intf.config(\n bw=new_bw, \n delay=new_delay, \n loss=new_loss, \n # max_queue_size=500\n )\n \n # record link state change into log\n with open(\"./log/link_state_log_\" + enter_time, 'a') as f:\n conn_state = \"DISCONN\" if disconnect else \"CONN\"\n line = \"{}\\t{}\\t{}\\t{}%\\t{}\\n\".format(timestamp, new_bw, new_delay, new_loss, conn_state)\n f.write(line)\n\ndef change_link_state_thread():\n start_time = time.time()\n while time.time() - start_time < args.test_time:\n print(\"Link changed!\")\n change_link_state()\n stable_time = random.randint(1, 10)\n time.sleep(stable_time)\n\n\nif __name__ == \"__main__\":\n if \"log\" not in os.listdir(\".\"):\n os.mkdir(\"./log\")\n\n single_topo = Single()\n net = Mininet(topo=single_topo, link=TCLink, controller=None)\n\n h1_addr, h2_addr = \"10.0.0.1\", \"10.0.0.2\"\n h1, h2 = net.get(\"h1\", \"h2\")\n h1.cmd(\"ifconfig h1-eth0 \" + h1_addr + \"/8\")\n h2.cmd(\"ifconfig h2-eth0 \" + h2_addr + \"/8\")\n h1.cmd(\"sysctl net.ipv4.ip_forward=1\")\n h2.cmd(\"sysctl net.ipv4.ip_forward=1\")\n\n net.start()\n # h1.popen(\"python server.py -i \" + h1_addr)\n # h2.popen(\"python client.py -i \" + h1_addr + \" -t \" + str(args.test_time - 10))\n CLI(net)\n t = thd.Thread(target=change_link_state_thread) # change link state\n print(\"Link state is changable!\")\n t.start()\n t.join()\n net.stop()","repo_name":"ponedo/satellite-link-simulation","sub_path":"single.py","file_name":"single.py","file_ext":"py","file_size_in_byte":3266,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"39453079964","text":"from save import _save_ckpt\n\ndef train(self):\n\n loss_mva = None\n loss_ph = self.placeholders\n profile = list ()\n loss_op = self.loss\n\n self.ann = self.parse_annotation(self.hyperparameters.ann_directory, self.classes)\n\n input_packs = self.read_data()\n\n for i,(x_batch, feed_batch) in enumerate(input_packs):\n\n feed_dict = {\n loss_ph[key]: feed_batch[key]\n for key in loss_ph }\n feed_dict[self.layers['input']] = x_batch\n # feed_dict.update(self.feed)\n\n fetches = [self.train_op, loss_op]\n fetched = self.sess.run(fetches, feed_dict)\n loss = fetched[1]\n\n if loss_mva is None: loss_mva = loss\n loss_mva = .9 * loss_mva + .1 * loss\n step_now = self.hyperparameters.load + i + 1\n\n # form = 'step {} - loss {} - moving ave loss {}'\n # self.say(form.format(step_now, loss, loss_mva))\n profile += [(loss, loss_mva)]\n\n ckpt = (i+1) % (self.hyperparameters.checkpoint // self.hyperparameters.batch_size)\n args = [step_now, profile]\n if not ckpt: _save_ckpt(self, *args)\n\n if ckpt: _save_ckpt(self, *args)\n","repo_name":"iRiisH/yolo","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":1151,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"37282088478","text":"#\n# @lc app=leetcode id=1288 lang=python3\n#\n# [1288] Remove Covered Intervals\n#\n# https://leetcode.com/problems/remove-covered-intervals/description/\n#\n# algorithms\n# Medium (61.17%)\n# Likes: 93\n# Dislikes: 7\n# Total Accepted: 5.4K\n# Total Submissions: 8.9K\n# Testcase Example: '[[1,4],[3,6],[2,8]]'\n#\n# Given a list of intervals, remove all intervals that are covered by another\n# interval in the list. Interval [a,b) is covered by interval [c,d) if and only\n# if c <= a and b <= d.\n# \n# After doing so, return the number of remaining intervals.\n# \n# \n# Example 1:\n# \n# \n# Input: intervals = [[1,4],[3,6],[2,8]]\n# Output: 2\n# Explanation: Interval [3,6] is covered by [2,8], therefore it is removed.\n# \n# \n# \n# Constraints:\n# \n# \n# 1 <= intervals.length <= 1000\n# 0 <= intervals[i][0] < intervals[i][1] <= 10^5\n# intervals[i] != intervals[j] for all i != j\n# \n# \n#\n\n# @lc code=start\nclass Solution:\n def removeCoveredIntervals(self, intervals: List[List[int]]) -> int:\n # Greedy\n # O(NlogN)\n res = right = 0\n for i, j in sorted(intervals, key=lambda a: [a[0], -a[1]]):\n res += j > right\n right = max(right, j)\n return res\n \n# @lc code=end\n\n","repo_name":"chenxu0602/LeetCode","sub_path":"1288.remove-covered-intervals.py","file_name":"1288.remove-covered-intervals.py","file_ext":"py","file_size_in_byte":1220,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"33735310032","text":"from django.test import TestCase\nfrom django.db.models import Count\nfrom tests.models import Book\nfrom django_tabulate import tabulate_qs\n\nclass TabulateTests(TestCase):\n\n def setUp(self):\n Book.objects.create(name='Python')\n Book.objects.create(name='Django')\n Book.objects.create(name='Tabulate')\n \n def test_method_execution(self):\n qs = Book.objects.all()\n self.assertEqual(qs.tabulate(), '\\n'.join([\n ' id name',\n '---- --------',\n ' 1 Python',\n ' 2 Django',\n ' 3 Tabulate',\n ]))\n\n self.assertEqual(qs.tabulate(), tabulate_qs(qs))\n\n def test_kwargs_execution(self):\n qs = Book.objects.all()\n self.assertEqual(qs.tabulate(tablefmt='grid'), '\\n'.join([\n '+------+----------+',\n '| id | name |',\n '+======+==========+',\n '| 1 | Python |',\n '+------+----------+',\n '| 2 | Django |',\n '+------+----------+',\n '| 3 | Tabulate |',\n '+------+----------+',\n ]))\n\n def test_values_with_annotation(self):\n qs = Book.objects.values('name').annotate(Count('pk'))\n self.assertEqual(qs.tabulate(tablefmt='psql'), '\\n'.join([\n '+----------+-------------+',\n '| name | pk__count |',\n '|----------+-------------|',\n '| Django | 1 |',\n '| Python | 1 |',\n '| Tabulate | 1 |',\n '+----------+-------------+',\n ]))","repo_name":"todorvelichkov/django-tabulate","sub_path":"tests/test_mixin.py","file_name":"test_mixin.py","file_ext":"py","file_size_in_byte":1601,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"52"} +{"seq_id":"8597795338","text":"#greatest common factor from 2 positive digits\n\ndef GCF(arr):\n # code goes here\n count = 0\n\n if max(arr) % min(arr) == 0:\n return min(arr)\n\n for i in range(1, max(arr) + 1):\n if (max(arr) % i == 0) and (min(arr) % i == 0):\n count = i\n\n return count\n\n\n# keep this function call here\nprint(GCF([45, 12]))","repo_name":"Diiego300years/Python-tasks-which-I-did","sub_path":"from coderbyte/greatest common factor.py","file_name":"greatest common factor.py","file_ext":"py","file_size_in_byte":342,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"332108686","text":"import csv\nimport re\n\n\ndef count_words(file_paths):\n result_dict = {}\n for file_path in file_paths:\n with open(file_path) as fp:\n for line in fp:\n splits = re.split(',', line)\n if len(splits) == 4:\n streamer = splits[1]\n commenter = splits[2]\n message = splits[3]\n if commenter != streamer:\n tokens = message.split()\n for i in range(0, len(tokens)):\n token = tokens[i]\n try:\n result_dict[token] += 1\n except KeyError:\n result_dict[token] = 1\n return result_dict\n\n\ndef count_bigrams(file_paths):\n result_dict = {}\n for file_path in file_paths:\n with open(file_path, 'r') as fp:\n for line in fp:\n splits = re.split(',', line)\n if len(splits) == 4:\n streamer = splits[1]\n commenter = splits[2]\n message = splits[3]\n if commenter != streamer:\n tokens = message.split()\n for itr in range(0, len(tokens) - 1):\n token1 = tokens[itr]\n token2 = tokens[itr + 1]\n token = token1 + \" \" + token2\n try:\n result_dict[token] += 1\n except KeyError:\n result_dict[token] = 1\n return result_dict\n\n\ndef generate_token_counts():\n gender_category_types = ['female', 'male']\n\n for gender_category in gender_category_types:\n\n all_names = [line.strip().split(',')[0] for line in open(\"../\" + gender_category + \"_channels.csv\", 'r')]\n\n divided_names = {'top_' + gender_category : all_names[0:100], 'bottom_' + gender_category : all_names[100:200]}\n count_types = ['bigram', 'word']\n\n for n in divided_names:\n file_names = divided_names[n]\n for count_type in count_types:\n final_result = {}\n for file_name in file_names:\n if count_type == 'bigram':\n result = count_bigrams([\"../../data/channel_chat_logs/cleaned/\" + file_name + \"_chat_log.csv\"])\n else:\n result = count_words([\"../../data/channel_chat_logs/cleaned/\" + file_name + \"_chat_log.csv\"])\n sorted_dic = ((k, result[k]) for k in sorted(result, key=result.get, reverse=True))\n for key, value in sorted_dic:\n if int(value) < 5:\n break\n try:\n final_result[key] += int(value)\n except KeyError:\n final_result[key] = int(value)\n sorted_dic = ((k, result[k]) for k in sorted(result, key=result.get, reverse=True))\n with open(\"../../data/word_counts/channel_\"+count_type+\"_counts/\"+ file_name + \"_\"+count_type+\"_counts.csv\", 'w') as fp:\n writer = csv.writer(fp, delimiter=',')\n writer.writerows(sorted_dic)\n sorted_dic = ((k, final_result[k]) for k in sorted(final_result, key=final_result.get, reverse=True))\n with open(\"../../data/word_counts/\" + n + \"_\"+count_type+\"_counts.csv\", 'w') as fp:\n writer = csv.writer(fp, delimiter=',')\n writer.writerows(sorted_dic)\n\n\nif __name__ == '__main__':\n generate_token_counts()","repo_name":"scnakandala/twitch-gender","sub_path":"exps/20160603_top_words_analysis/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3751,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"52"} +{"seq_id":"41702662499","text":"\"\"\"\nCreated on June 24, 2022\n@author: Lance A. Endres\n\"\"\"\nimport numpy as np\nfrom matplotlib import pyplot as plt\nimport math\nimport cv2\n\nfrom lendres.plotting.PlotHelper import PlotHelper\n\n\nclass ImageHelper():\n arrayImageSize = 2.5\n\n\n @classmethod\n def DefaultSettings(cls):\n \"\"\"\n Gets the default image plotting settings parameter file.\n\n Recommended usage:\n PlotHelper.PushSettings(parameterFile=ImageHelper.DefaultSettings())\n # Display images.\n ...\n PlotHelper.PopSettings()\n\n Returns\n -------\n : str\n The name of the parameters file..\n \"\"\"\n return \"imagedefault\"\n\n\n @classmethod\n def PlotImage(cls, image, title=None, size=6, colorConversion=None):\n \"\"\"\n Plot example image.\n\n Parameters\n ----------\n image : image\n Image to plot.\n title : string\n Title of the figure.\n size : float\n Size (width and height) of figure.\n colorConversion : OpenCV color conversion enumeration.\n Color conversion to perform before plotting. Images are plotted in RGB. For example, if the\n image is in BGR (as is used in OpenCV) then cv2.COLOR_BGR2RGB should be passed.\n\n Returns\n -------\n figure : matplotlib.figure.Figure\n The newly created figure.\n \"\"\"\n # Defining the figure size. Automatically adjust for the number of images to be displayed.\n PlotHelper.Format()\n\n # Adding subplots with 3 rows and 4 columns.\n figure = plt.gcf()\n figure.set_figwidth(size)\n figure.set_figheight(size)\n\n axis = plt.gca()\n\n # Plotting the image.\n if colorConversion != None:\n image = cv2.cvtColor(image, colorConversion)\n axis.imshow(image)\n\n if title != None:\n axis.set_title(title)\n\n # Turn off the grid lines.\n axis.grid(False)\n\n plt.show()\n return figure\n\n\n @classmethod\n def CreateImageArrayPlot(cls, images, labels, columns=4, colorConversion=None):\n \"\"\"\n Plots the images in an array.\n\n Parameters\n ----------\n images : array like\n Set of images to plot.\n labels : array like\n Set of labels to use for the individual images.\n columns : integer\n The number of columns to plot.\n colorConversion : OpenCV color conversion enumeration.\n Color conversion to perform before plotting. Images are plotted in RGB. For example, if the\n image is in BGR cv2.COLOR_BGR2RGB should be passed.\n\n Returns\n -------\n None.\n \"\"\"\n # Calculate required values.\n numberOfImages = len(images)\n rows = math.ceil(numberOfImages / columns)\n\n # Defining the figure size. Automatically adjust for the number of images to be displayed.\n PlotHelper.Format()\n figure = plt.figure()\n figure.set_figwidth(columns*ImageHelper.arrayImageSize+2)\n figure.set_figheight(rows*ImageHelper.arrayImageSize+2)\n\n # Position in the index array/range.\n k = -1\n\n for i in range(columns):\n for j in range(rows):\n # Adding subplots with 3 rows and 4 columns.\n axis = figure.add_subplot(rows, columns, i*rows+j+1)\n\n # Plot the image. Convert colors if required.\n k +=1\n image = images[k]\n if colorConversion != None:\n image = cv2.cvtColor(image, colorConversion)\n axis.imshow(image)\n\n # Turn off white grid lines.\n axis.grid(False)\n\n axis.set_title(labels[k], y=0.9)\n\n # Adjust spacing so titles don't run together.\n plt.subplots_adjust(left=0.1, bottom=0.1, right=0.9, top=0.9, wspace=0.4, hspace=0.4)\n\n plt.show()\n\n\n @classmethod\n def DisplayColorChannels(cls, image, colorConversion=None):\n \"\"\"\n Displays an image along with the individual color channels.\n\n Parameters\n ----------\n image : image\n Image in an array.\n colorConversion : OpenCV color conversion enumeration.\n Color conversion to perform before plotting. Images are plotted in RGB. For example, if the\n image is in BGR cv2.COLOR_BGR2RGB should be passed.\n\n Returns\n -------\n None.\n \"\"\"\n imageArray = cv2.split(image)\n imageArray.insert(0, image)\n titles = [\"Original\", \"Blue\", \"Green\", \"Red\"]\n ImageHelper.CreateImageArrayPlot(imageArray, titles, columns=4, colorConversion=colorConversion)\n\n\n @classmethod\n def DisplayChromaKey(cls, image, lowerBounds, upperBounds, maskBlurSize=3, colorConversion=None, inputBoundsFormat=\"hsv\"):\n \"\"\"\n Displays an image along with the image separated into two components based on chroma keying.\n\n Parameters\n ----------\n image : image\n Image in an array.\n lowerBounds : numpy array of 3 values.\n Lower bounds of mask.\n maskBlurSize : int\n Size of the blur to apply to the mask. Must be an odd number.\n upperBounds : numpy array of 3 values.\n Upper bounds of mask.\n colorConversion : OpenCV color conversion enumeration.\n Color conversion to perform before plotting. Images are plotted in RGB. For example, if the\n image is in BGR cv2.COLOR_BGR2RGB should be passed.\n inputBoundsFormat : string\n Format of lowerBounds and upperBounds.\n\n Returns\n -------\n None.\n \"\"\"\n imageArray = ImageHelper.ChromaKey(image, lowerBounds, upperBounds, maskBlurSize, inputBoundsFormat)\n\n imageArray.insert(0, image)\n titles = [\"Original\", \"Masked Image\", \"Image Remainder\", \"Mask\"]\n ImageHelper.CreateImageArrayPlot(imageArray, titles, columns=4, colorConversion=colorConversion)\n\n\n @classmethod\n def ApplyColorConversion(cls, images, colorConversion):\n \"\"\"\n Applies a color conversion to the images.\n\n Parameters\n ----------\n images : array like set of images\n Images in an array.\n colorConversion : OpenCV color conversion enumeration.\n Color conversion to perform before plotting. Images are plotted in RGB. For example, if the\n image is in BGR cv2.COLOR_BGR2RGB should be passed.\n\n Returns\n -------\n newImages : array like set of images\n The new images with the conversion applied.\n \"\"\"\n newImages = np.empty(images.shape, dtype=images.dtype)\n\n if len(images.shape) < 4:\n # Only one image provided. Shape of input is similar to (width, height, color_depth).\n newImages = cv2.cvtColor(images, colorConversion)\n else:\n # More than one image provided.\n for i in range(len(images)):\n newImages[i] = cv2.cvtColor(images[i], colorConversion)\n\n return newImages\n\n\n @classmethod\n def ApplyGaussianBlur(cls, images, **kwargs):\n \"\"\"\n Applies a gaussian blur to the images.\n\n Parameters\n ----------\n images : array like set of images\n Images in an array.\n **kwargs : keyword arguments\n Arguments passed to the Gaussian filter. For example, \"ksize=(5,5), sigmaX=0\"\n\n Returns\n -------\n newImages : array like set of images\n The new images with the blur applied.\n \"\"\"\n newImages = np.empty(images.shape, dtype=images.dtype)\n\n if len(images.shape) < 4:\n # Only one image provided. Shape of input is similar to (width, height, color_depth).\n newImages = cv2.GaussianBlur(images, **kwargs)\n else:\n # More than one image provided.\n for i in range(len(images)):\n newImages[i] = cv2.GaussianBlur(images[i], **kwargs)\n\n return newImages\n\n\n @classmethod\n def ApplyHighPassFilter(cls, images, convertToGrey=True, **kwargs):\n \"\"\"\n Applies a high pass filter to images(s).\n\n Parameters\n ----------\n images : array like set of images\n Images in an array.\n convertToGrey : TYPE, optional\n DESCRIPTION. The default is True.\n **kwargs : keyword arguments\n Arguments passed to the Gaussian filter. For example, \"ksize=(21, 21), sigmaX=3\"\n\n Returns\n -------\n highPass : array like set of images\n The high passed images.\n \"\"\"\n # The high pass filter is created by subtracting a low pass filter from the original image(s).\n lowPass = cls.ApplyGaussianBlur(images, **kwargs)\n highPass = images - lowPass\n\n # If specified, the images are converted to a greyish color. This is the expected result of a high pass.\n if convertToGrey:\n highPass -= 127\n\n return highPass\n\n\n @classmethod\n def ChromaKey(cls, image, lowerBounds, upperBounds, maskBlurSize=3, inputBoundsFormat=\"hsv\"):\n \"\"\"\n Splits the image into two components based on chroma keying.\n\n Parameters\n ----------\n image : image\n Image in an array.\n lowerBounds : numpy array of 3 values.\n Lower bounds of mask.\n upperBounds : numpy array of 3 values.\n Upper bounds of mask.\n maskBlurSize : int\n Size of the blur to apply to the mask. Must be an odd number.\n inputBoundsFormat : string\n Format of lowerBounds and upperBounds.\n\n Returns\n -------\n maskedImage : image\n Part of the image that passes the mask.\n imageRemainder : image\n Part of the image that did not pass the mask.\n mask : image\n Mask used on the image.\n \"\"\"\n imageArray = []\n if inputBoundsFormat == \"bgr\":\n imageArray = ImageHelper.ChromaKeyWithBGR(image, lowerBounds, upperBounds, maskBlurSize)\n elif inputBoundsFormat == \"hsv\":\n imageArray = ImageHelper.ChromaKeyWithHSV(image, lowerBounds, upperBounds, maskBlurSize)\n else:\n raise Exception(\"Input bounds format argument not valid.\")\n\n return imageArray\n\n\n @classmethod\n def GetChromaKeyPart(cls, images, lowerBounds, upperBounds, maskBlurSize=3, inputBoundsFormat=\"hsv\", keep=\"bounded\"):\n \"\"\"\n Applies a chroma key filter to the images and returns the portion of interest.\n\n The ChromaKey functions splits an image into 3 parts, the bounded part, the remained, and the mask. This function\n goes through an array of images and returns just one of those parts for all images.\n\n Parameters\n ----------\n images : array like set of images\n Images in an array.\n lowerBounds : numpy array of 3 values.\n Lower bounds of mask.\n upperBounds : numpy array of 3 values.\n Upper bounds of mask.\n maskBlurSize : int\n Size of the blur to apply to the mask. Must be an odd number.\n inputBoundsFormat : string\n Format of lowerBounds and upperBounds.\n keep : string\n Part of the split image to keep.\n bounded : The original image that is bounded by the input.\n remainder : The original image that is outside of the input bounds.\n mask : The mask used to separate the image.\n\n Returns\n -------\n newImages : array like set of images\n An array of images that contains the specified part of the split image.\n \"\"\"\n\n \"\"\"\n Should be updated to use np arrays like ApplyGaussianBlur.\n \"\"\"\n\n keepIndex = 0\n if keep == \"bounded\":\n keepIndex = 0\n elif keep == \"remainder\":\n keepIndex = 1\n elif keep == \"mask\":\n keepIndex = 2\n else:\n raise Exception(\"The input argument \\\"keep\\\" contains an invalid value.\")\n\n newImages = []\n\n for i in range(len(images)):\n imageArray = ImageHelper.ChromaKey(images[i], lowerBounds, upperBounds, maskBlurSize, inputBoundsFormat)\n newImages.append(imageArray[keepIndex])\n\n return newImages\n\n\n @classmethod\n def ChromaKeyWithBGR(cls, image, lowerBounds, upperBounds, maskBlurSize=3):\n \"\"\"\n Splits the image into two components based on chroma keying.\n\n Parameters\n ----------\n image : image\n Image in an array.\n lowerBounds : numpy array of 3 values.\n Lower bounds of mask.\n upperBounds : numpy array of 3 values.\n Upper bounds of mask.\n maskBlurSize : int\n Size of the blur to apply to the mask. Must be an odd number.\n\n Returns\n -------\n maskedImage : image\n Part of the image that passes the mask.\n imageRemainder : image\n Part of the image that did not pass the mask.\n mask : image\n Mask used on the image.\n \"\"\"\n mask = cv2.inRange(image, lowerBounds, upperBounds)\n mask = cv2.medianBlur(mask, maskBlurSize)\n maskedImage = cv2.bitwise_and(image, image, mask=mask)\n imageRemainder = image - maskedImage\n\n return [maskedImage, imageRemainder, mask]\n\n\n @classmethod\n def ChromaKeyWithHSV(cls, image, lowerBounds, upperBounds, maskBlurSize=3):\n \"\"\"\n Splits the image into two components based on chroma keying.\n\n Parameters\n ----------\n image : image\n Image in an array.\n lowerBounds : numpy array of 3 values.\n Lower bounds of mask.\n upperBounds : numpy array of 3 values.\n Upper bounds of mask.\n maskBlurSize : int\n Size of the blur to apply to the mask. Must be an odd number.\n\n Returns\n -------\n maskedImage : image\n Part of the image that passes the mask.\n imageRemainder : image\n Part of the image that did not pass the mask.\n mask : image\n Mask used on the image.\n \"\"\"\n hsvImage = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n mask = cv2.inRange(hsvImage, lowerBounds, upperBounds)\n mask = cv2.medianBlur(mask, maskBlurSize)\n maskedImage = cv2.bitwise_and(image, image, mask=mask)\n imageRemainder = image - maskedImage\n\n return [maskedImage, imageRemainder, mask]","repo_name":"lendres/Python","sub_path":"lendres/ImageHelper.py","file_name":"ImageHelper.py","file_ext":"py","file_size_in_byte":14904,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"748827434","text":"from gensim.models import KeyedVectors\nfrom gensim.scripts.glove2word2vec import glove2word2vec \nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom bert_serving.client import BertClient\n\n\nfilename = \"MTURK-771.csv\"\n\nfor i in range(1):\n if i == 0:\n # Google word2vec\n model = KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True)\n elif i == 1:\n # Glove\n #glove_input_file = 'glove.6B.200d.txt'\n word2vec_output_file = 'glove.6B.50d.txt.word2vec'\n #glove2word2vec(glove_input_file, word2vec_output_file)\n model = KeyedVectors.load_word2vec_format(word2vec_output_file, binary=False)\n\n elif i== 2:\n word2vec_output_file = 'glove.6B.100d.txt.word2vec'\n model = KeyedVectors.load_word2vec_format(word2vec_output_file, binary=False)\n elif i == 3:\n word2vec_output_file = 'glove.6B.200d.txt.word2vec'\n model = KeyedVectors.load_word2vec_format(word2vec_output_file, binary=False)\n elif i == 4:\n word2vec_output_file = 'glove.6B.300d.txt.word2vec'\n model = KeyedVectors.load_word2vec_format(word2vec_output_file, binary=False)\n elif i == 5:\n # FastText\n model = KeyedVectors.load_word2vec_format('wiki-news-300d-1M.vec') \n elif i == 6:\n model = KeyedVectors.load_word2vec_format('wiki-news-300d-1M-subword.vec')\n\n dev = 0\n length = 0\n final = []\n summ = 0\n nf = 0\n with open(filename) as f:\n lis = [line.split() for line in f] \n # create a list of lists\n length = len(lis)\n for i, x in enumerate(lis): #print the list items \n if (i == 0):\n continue\n t = x[0].split(',')\n #t = x # For space separated files\n a = t[0]\n b = t[1]\n c = t[2]\n c = float(c)*2\n summ += float(c)\n try:\n res = model.similarity(a,b)*10\n except KeyError:\n res = float(c)\n nf = nf + 1\n dev = dev + abs(res - float(c))\n dev = dev/length \n final.append(dev)\n summ = summ/length\n percentage = (dev/summ)*100\n print (dev)\n print(percentage)\n print(nf)\n \n \n \n# BERT \nbc = BertClient() \ndev = 0\nlength = 0\nfinal = []\nsumm = 0\nnf = 0\nwith open(filename) as f:\n lis = [line.split() for line in f] \n # create a list of lists\n length = len(lis)\n for i, x in enumerate(lis): #print the list items \n if (i == 0):\n continue\n t = x[0].split(',')\n #t = x\n a = t[0]\n b = t[1]\n c = t[2]\n c = float(c)*2\n summ += float(c)\n try:\n #res = model.similarity(a,b)*10\n x = bc.encode([a, b])\n t1 = x[0]\n t2 = x[1]\n t1 = t1.reshape(1,768)\n t2 = t2.reshape(1,768)\n res = cosine_similarity(t1,t2)\n res = res[0][0]*10\n except KeyError:\n res = float(c)\n nf = nf + 1\n dev = dev + abs(res - float(c))\ndev = dev/length \nfinal.append(dev)\nsumm = summ/length\npercentage = (dev/summ)*100\nprint(dev)\nprint(percentage)\nprint(nf) ","repo_name":"YatinGupta777/Comparing-different-Word-Embeddings","sub_path":"comparison.py","file_name":"comparison.py","file_ext":"py","file_size_in_byte":3249,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"10296710744","text":"from django.conf import settings\nfrom django.contrib.auth.decorators import login_required\nfrom django.core.exceptions import ObjectDoesNotExist, PermissionDenied, ValidationError\nfrom django.http import FileResponse, Http404, JsonResponse\nfrom django.shortcuts import get_object_or_404, redirect\nfrom django.urls import reverse\nfrom django.utils.decorators import method_decorator\nfrom django.utils.translation import gettext\nfrom django.views.decorators.cache import never_cache\nfrom django.views.decorators.http import require_POST\nfrom django.views.generic import TemplateView, View\n\nfrom weblate.trans.forms import (\n AddCategoryForm,\n AnnouncementForm,\n BaseDeleteForm,\n CategoryRenameForm,\n ComponentRenameForm,\n ComponentSettingsForm,\n ProjectRenameForm,\n ProjectSettingsForm,\n WorkflowSettingForm,\n)\nfrom weblate.trans.models import (\n Announcement,\n Category,\n Component,\n Project,\n Translation,\n WorkflowSetting,\n)\nfrom weblate.trans.tasks import (\n category_removal,\n component_removal,\n create_project_backup,\n project_removal,\n)\nfrom weblate.trans.util import redirect_param, render\nfrom weblate.utils import messages\nfrom weblate.utils.stats import CategoryLanguage, ProjectLanguage\nfrom weblate.utils.views import parse_path, show_form_errors\n\n\n@never_cache\n@login_required\ndef change(request, path):\n obj = parse_path(request, path, (Component, Project, ProjectLanguage))\n if not request.user.has_perm(obj.settings_permission, obj):\n raise Http404\n\n if isinstance(obj, Component):\n return change_component(request, obj)\n if isinstance(obj, ProjectLanguage):\n return change_project_language(request, obj)\n return change_project(request, obj)\n\n\ndef change_project(request, obj):\n if request.method == \"POST\":\n settings_form = ProjectSettingsForm(request, request.POST, instance=obj)\n if settings_form.is_valid():\n settings_form.save()\n messages.success(request, gettext(\"Settings saved\"))\n return redirect(\"settings\", path=obj.get_url_path())\n messages.error(\n request, gettext(\"Invalid settings. Please check the form for errors.\")\n )\n else:\n settings_form = ProjectSettingsForm(request, instance=obj)\n\n return render(\n request,\n \"project-settings.html\",\n {\"object\": obj, \"form\": settings_form},\n )\n\n\ndef change_project_language(request, obj):\n try:\n instance = obj.project.workflowsetting_set.get(language=obj.language)\n except WorkflowSetting.DoesNotExist:\n instance = None\n\n if request.method == \"POST\":\n settings_form = WorkflowSettingForm(request.POST, instance=instance)\n if settings_form.is_valid():\n settings_form.instance.project = obj.project\n settings_form.instance.language = obj.language\n settings_form.save()\n messages.success(request, gettext(\"Settings saved\"))\n return redirect(\"settings\", path=obj.get_url_path())\n messages.error(\n request, gettext(\"Invalid settings. Please check the form for errors.\")\n )\n else:\n settings_form = WorkflowSettingForm(instance=instance)\n\n return render(\n request,\n \"project-language-settings.html\",\n {\"object\": obj, \"form\": settings_form},\n )\n\n\ndef change_component(request, obj):\n if not request.user.has_perm(\"component.edit\", obj):\n raise Http404\n\n if request.method == \"POST\":\n form = ComponentSettingsForm(request, request.POST, instance=obj)\n if form.is_valid():\n form.save()\n messages.success(request, gettext(\"Settings saved\"))\n return redirect(\"settings\", path=obj.get_url_path())\n messages.error(\n request, gettext(\"Invalid settings. Please check the form for errors.\")\n )\n # Get a fresh copy of object, otherwise it will use unsaved changes\n # from the failed form\n obj = Component.objects.get(pk=obj.pk)\n else:\n form = ComponentSettingsForm(request, instance=obj)\n\n if obj.repo_needs_merge():\n messages.warning(\n request,\n gettext(\n \"The repository is outdated. You might not get \"\n \"expected results until you update it.\"\n ),\n )\n\n return render(\n request,\n \"component-settings.html\",\n {\"project\": obj.project, \"object\": obj, \"form\": form},\n )\n\n\n@never_cache\n@login_required\n@require_POST\ndef dismiss_alert(request, path):\n obj = parse_path(request, path, (Component,))\n\n if not request.user.has_perm(\"component.edit\", obj):\n raise Http404\n\n try:\n alert = obj.alert_set.get(name=request.POST[\"dismiss\"])\n if alert.obj.dismissable:\n alert.dismissed = True\n alert.save(update_fields=[\"dismissed\"])\n except ObjectDoesNotExist:\n pass\n\n return redirect_param(obj, \"#alerts\")\n\n\n@login_required\n@require_POST\ndef remove(request, path):\n obj = parse_path(\n request,\n path,\n (Translation, Component, Project, ProjectLanguage, CategoryLanguage, Category),\n )\n\n if not request.user.has_perm(obj.remove_permission, obj):\n raise PermissionDenied\n\n form = BaseDeleteForm(obj, request.POST)\n if not form.is_valid():\n show_form_errors(request, form)\n return redirect_param(obj, \"#organize\")\n\n if isinstance(obj, Translation):\n parent = obj.component\n obj.remove(request.user)\n messages.success(request, gettext(\"The translation has been removed.\"))\n elif isinstance(obj, Component):\n parent = obj.category or obj.project\n component_removal.delay(obj.pk, request.user.pk)\n messages.success(\n request, gettext(\"The translation component was scheduled for removal.\")\n )\n elif isinstance(obj, Category):\n parent = obj.category or obj.project\n category_removal.delay(obj.pk, request.user.pk)\n messages.success(request, gettext(\"The category was scheduled for removal.\"))\n elif isinstance(obj, Project):\n parent = reverse(\"home\")\n project_removal.delay(obj.pk, request.user.pk)\n messages.success(request, gettext(\"The project was scheduled for removal.\"))\n elif isinstance(obj, ProjectLanguage):\n parent = obj.project\n for translation in obj.translation_set:\n translation.remove(request.user)\n\n messages.success(request, gettext(\"A language in the project was removed.\"))\n elif isinstance(obj, CategoryLanguage):\n parent = obj.category\n for translation in obj.translation_set:\n translation.remove(request.user)\n\n messages.success(request, gettext(\"A language in the category was removed.\"))\n\n return redirect(parent)\n\n\ndef perform_rename(form_cls, request, obj, perm: str):\n if not request.user.has_perm(perm, obj):\n raise PermissionDenied\n\n # Make sure any non-rename related issues are resolved first\n try:\n obj.full_clean()\n except ValidationError as err:\n messages.error(\n request,\n gettext(\"Could not change %s due to outstanding issue in its settings: %s\")\n % (obj, err),\n )\n return redirect_param(obj, \"#organize\")\n\n form = form_cls(request, request.POST, instance=obj)\n if not form.is_valid():\n show_form_errors(request, form)\n # Reload the object from DB to revert possible rejected change\n obj.refresh_from_db()\n return redirect_param(obj, \"#organize\")\n\n # Invalidate old stats\n old_stats = list(obj.stats.get_update_objects())\n\n obj = form.save()\n\n # Invalidate new stats\n obj.stats.update_parents(extra_objects=old_stats)\n\n return redirect(obj)\n\n\n@login_required\n@require_POST\ndef rename(request, path):\n obj = parse_path(request, path, (Component, Project, Category))\n if isinstance(obj, Component):\n return perform_rename(ComponentRenameForm, request, obj, \"component.edit\")\n if isinstance(obj, Category):\n return perform_rename(CategoryRenameForm, request, obj, \"project.edit\")\n return perform_rename(ProjectRenameForm, request, obj, \"project.edit\")\n\n\n@login_required\n@require_POST\ndef add_category(request, path):\n obj = parse_path(request, path, (Project, Category))\n if not request.user.has_perm(\"project.edit\", obj) or not obj.can_add_category:\n raise PermissionDenied\n form = AddCategoryForm(request, obj, request.POST)\n if not form.is_valid():\n show_form_errors(request, form)\n return redirect_param(obj, \"#organize\")\n form.save()\n return redirect(form.instance)\n\n\n@login_required\n@require_POST\ndef announcement(request, path):\n obj = parse_path(request, path, (Translation, Component, Project))\n\n if not request.user.has_perm(\"component.edit\", obj):\n raise PermissionDenied\n\n form = AnnouncementForm(request.POST)\n if not form.is_valid():\n show_form_errors(request, form)\n return redirect_param(obj, \"#announcement\")\n\n # Scope specific attributes\n scope = {}\n if isinstance(obj, Translation):\n scope[\"project\"] = obj.component.project\n scope[\"component\"] = obj.component\n scope[\"language\"] = obj.language\n elif isinstance(obj, Component):\n scope[\"project\"] = obj.project\n scope[\"component\"] = obj\n elif isinstance(obj, Project):\n scope[\"project\"] = obj\n\n Announcement.objects.create(\n user=request.user,\n **scope,\n **form.cleaned_data,\n )\n\n return redirect(obj)\n\n\n@login_required\n@require_POST\ndef announcement_delete(request, pk):\n announcement = get_object_or_404(Announcement, pk=pk)\n\n if request.user.has_perm(\"announcement.delete\", announcement):\n announcement.delete()\n\n return JsonResponse({\"responseStatus\": 200})\n\n\n@login_required\ndef component_progress(request, path):\n obj = parse_path(request, path, (Component,))\n return_url = \"show\" if \"info\" in request.GET else \"guide\"\n if not obj.in_progress():\n return redirect(return_url, path=obj.get_url_path())\n\n progress, log = obj.get_progress()\n\n return render(\n request,\n \"component-progress.html\",\n {\n \"object\": obj,\n \"progress\": progress,\n \"log\": \"\\n\".join(log),\n \"return_url\": return_url,\n },\n )\n\n\nclass BackupsMixin:\n def setup(self, request, *args, **kwargs):\n super().setup(request, *args, **kwargs)\n self.obj = parse_path(request, [kwargs[\"project\"]], (Project,))\n if not request.user.has_perm(\"project.edit\", self.obj):\n raise PermissionDenied\n\n\n@method_decorator(login_required, name=\"dispatch\")\nclass BackupsView(BackupsMixin, TemplateView):\n template_name = \"trans/backups.html\"\n\n def post(self, request, *args, **kwargs):\n create_project_backup.delay(self.obj.pk)\n messages.success(\n request, gettext(\"Backup scheduled. It will be available soon.\")\n )\n return redirect(\"backups\", project=self.obj.slug)\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n context[\"keep_count\"] = settings.PROJECT_BACKUP_KEEP_COUNT\n context[\"keep_days\"] = settings.PROJECT_BACKUP_KEEP_DAYS\n context[\"object\"] = self.obj\n context[\"backups\"] = self.obj.list_backups()\n return context\n\n\n@method_decorator(login_required, name=\"dispatch\")\nclass BackupsDownloadView(BackupsMixin, View):\n def get(self, request, *args, **kwargs):\n for backup in self.obj.list_backups():\n if backup[\"name\"] == kwargs[\"backup\"]:\n return FileResponse(\n open(backup[\"path\"], \"rb\"), # noqa: SIM115\n as_attachment=True,\n filename=backup[\"name\"],\n )\n raise Http404\n","repo_name":"WeblateOrg/weblate","sub_path":"weblate/trans/views/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":11963,"program_lang":"python","lang":"en","doc_type":"code","stars":3905,"dataset":"github-code","pt":"52"} +{"seq_id":"12950739893","text":"# 11/04/2022\n# Philip Boesch\n\nimport os\nimport pandas as pd\nimport plotly.graph_objects as go \nfrom compare_maic import df_copy, df_copy2\n\ndf = df_copy2\n\n# fpath = r'maic_raw/real_raw/third_test_inhuang'\n\ntop = list(df['2021-03-01.txt'].nlargest(n=10).index)\ndf = df[df.index.isin(top)]\ndrop_list = [\n '2021-05-01.txt', \n '2021-05-01.txt', \n '2021-07-01.txt',\n '2021-09-01.txt',\n '2021-11-01.txt',\n '2022-01-01.txt']\ndf = df.drop(columns = drop_list)\n\n\nlabels = top\nsources = []\ntargets = []\nvalues = []\n\n# source:\nsourcedic = { top[i] : i for i in range(0, len(top) ) }\n\n# def sourcing(interval):\n# \tfor row in interval:\n# \t\tif row.isnull() == True:\n# \t\t\tpass\n# \t\telse:\n# \t\t\tsources.append(row)\n\n# sourcing(df['2020-07-01.txt'])\n\n\n\nprint(sourcedic)","repo_name":"PhilipBoesch/ACCPMProject","sub_path":"sankeynocat.py","file_name":"sankeynocat.py","file_ext":"py","file_size_in_byte":767,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"40956227757","text":"# dictionaries in python are collections of key value pairs.\nstudent1 = {'name': 'Eric', 'gender': 'M', 'age': 21}\nstudent2 = {'name': 'Lisa', 'gender': 'F', 'age': 22}\nstudents = [student1, student2]\n\n# deleting a key-value\ndel student2['age']\n\ndef add_blood_group(student):\n if student['name'].lower() == 'eric':\n updated_student = {**student, 'blood_group': 'A+'}\n elif student['name'].lower() == 'lisa':\n updated_student = {**student, 'blood_group': 'B+'}\n else:\n updated_student = student\n return updated_student\n\ncount = 0\nfor student in students:\n students[count] = add_blood_group(student)\n # to remove a key-value pair\n # del students[count]['blood_group']\n count =+ 1\nprint(students)\n\n\n# looping a dictionary\nfav_languages = {\n 'eric': 'js',\n 'lisa': 'java',\n 'miguel': 'python'\n}\n# on looping, python doesnt care about the order\nfor key, value in fav_languages.items():\n print(key.title() + ' codes in ' + value.upper())\n\n# looping keys of dictionary:\nparents = ['eric', 'lisa']\nfor name in fav_languages.keys():\n if name in parents:\n print('Hello ' + name.title() + ', its awesome that you code in ' + fav_languages[name].upper())\n\n# looping through values of dictionary\nfor language in fav_languages.values():\n print(language.title())\n\n# list in a dictionary:\npizza = {\n 'name': 'local',\n 'size': 'small',\n 'toppings': ['extra cheese', 'papperoni']\n}\n\nprint(pizza['size'].title() + ' ' + pizza['name'] + ' pizza has the following toppings:')\nfor topping in pizza['toppings']:\n print('\\t' + topping)\n\n# dictionary in a dictionary:\nusers = {\n 'chingaloEric': {\n 'firstname': 'eric',\n 'lastname': 'chingalo'\n },\n 'tibs': {\n 'firstname': 'lisa',\n 'lastname': 'tibenda'\n }\n}\n\nfor user_name, user_info in users.items():\n full_name = user_info['firstname'] + ' ' + user_info['lastname']\n print('Hola! ' + user_name.title())\n print('Welcome ' + full_name.title()) \n ","repo_name":"ericchingalo/python-tutorials","sub_path":"dictionaries.py","file_name":"dictionaries.py","file_ext":"py","file_size_in_byte":2005,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"11027290535","text":"import datetime\nfrom dateutil.relativedelta import *\nstart = datetime.datetime(1901, 1, 1)\nend = datetime.datetime(2000, 12, 1)\n# print(start)\n\n# print(start.weekday())\ncount = 0\nwhile start < end:\n\tif start.weekday() == 6:\n\t\tcount += 1\n\tstart += relativedelta(months=+1)\nprint(count)\n\n\n\n\n\n\n","repo_name":"umairkarel/Project-Euler","sub_path":"#19.py","file_name":"#19.py","file_ext":"py","file_size_in_byte":291,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"35207950613","text":"#Author Kelvin Keller\r\nimport turtle\r\nturtle.bgcolor(\"skyblue\")\r\nturtle.setup(1000,1000)\r\npen = turtle.Turtle()\r\npen.speed(0)\r\npen.color(\"black\")\r\n\r\npen.up()\r\npen.setpos(100,100)\r\npen.down()\r\nlength = 100\r\n\r\n#square \r\npen.fillcolor(\"magenta\")\r\npen.begin_fill()\r\n\r\nfor i in range(4):\r\n pen.forward(length)\r\n pen.left(90)\r\n\r\npen.end_fill()\r\n\r\npen.up()\r\npen.setpos(-200,100)\r\npen.down()\r\n\r\n#draw triangle\r\nfor i in range(3):\r\n pen.forward(length)\r\n pen.left(120)\r\npen.end_fill()\r\n\r\npen.up()\r\npen.setpos(-200,-100)\r\npen.down()\r\n\r\n#draw upside down triangle\r\npen.begin_fill()\r\n\r\n\r\nturtle.done()","repo_name":"Kelvvv/CSCE204SPRING","sub_path":"CSCE204SPRING/CSCE204SPRING/Excercises/wkoffeb8/for.loops_shapes.py","file_name":"for.loops_shapes.py","file_ext":"py","file_size_in_byte":601,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"31744917162","text":"def left(sub, num):\n for i in sub:\n if i >= num:\n return False\n return True\n\ndef right(sub, num):\n for i in sub:\n if i <= num:\n return False\n return True\n\n\nn = int(input(\"Enter no. of elements in unsorted list: \"))\n\nList = []\nprint(\"Enter elements of list one by one: \")\nfor i in range(n):\n ele = int(input())\n List.append(ele)\n\nc = 0\nfor i in range(1,n):\n sub1 = List[:i]\n sub2 = List[i+1:]\n\n if left(sub1, List[i]) and right(sub2, List[i]):\n c = -1\n print(\"Required smallest element: \", List[i])\n break\n\nif c != -1:\n print(\"No such element found\")\n","repo_name":"deepsjuneja/tathastu_week_of_code","sub_path":"day6/program15.py","file_name":"program15.py","file_ext":"py","file_size_in_byte":637,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"5815270277","text":"# Definition for singly-linked list.\n# class ListNode(object):\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\nclass Solution(object):\n def swapPairs(self, head):\n \"\"\"\n :type head: ListNode\n :rtype: ListNode\n \"\"\"\n if not head or not head.next:\n return head\n dummy = ListNode('-inf')\n dummy.next = head\n cur = dummy\n while cur.next and cur.next.next:\n next_one, next_two, next_three = cur.next, cur.next.next, cur.next.next.next\n cur.next = next_two\n next_two.next = next_one\n next_one.next = next_three\n cur = next_one\n return dummy.next\n","repo_name":"shaniavina/Leetcode_Python","sub_path":"swap_nodes_in_pairs.py","file_name":"swap_nodes_in_pairs.py","file_ext":"py","file_size_in_byte":709,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"72432043364","text":"from sentence_transformers import SentenceTransformer\nfrom chunker import DocumentChunker\nfrom actions import (\n index_chroma_document,\n create_chroma_collection,\n index_elastic_document,\n create_elastic_index,\n)\nfrom utils import anonymize\nimport torch\n\n\nclass ChromaIndexer:\n def __init__(self, embedding_model: str, chunk_size: int, chunk_overlap: int):\n self.embedding_model = SentenceTransformer(embedding_model)\n self.embedding_model.to(\"cuda\")\n self.embedding_model.eval()\n\n self.chunker = DocumentChunker(\n chunk_size=chunk_size, chunk_overlap=chunk_overlap\n )\n\n def __embed(self, text: str):\n chunks = self.chunker.chunk(text)\n embeddings = []\n with torch.no_grad():\n embeddings = self.embedding_model.encode(chunks)\n\n embeddings = embeddings.tolist()\n return chunks, embeddings\n\n def create_index(self, name: str):\n return create_chroma_collection(name)\n\n def index(self, collection: str, doc: dict, metadata):\n chunks, embeddings = self.__embed(doc[\"text\"])\n\n metadatas = [metadata for _ in chunks]\n\n res = index_chroma_document(\n collection,\n {\n \"documents\": chunks,\n \"embeddings\": embeddings,\n \"metadatas\": metadatas,\n },\n )\n del embeddings\n del chunks\n\n return res\n\n\nclass ElasticsearchIndexer:\n def __init__(self, anonymize_type=[]):\n self.anonymize_type = anonymize_type\n\n def create_index(self, name: str):\n return create_elastic_index(name)\n\n def index(self, index: str, doc: dict):\n annotations = [\n {\n \"id\": ann[\"_id\"],\n # this will be a real ER id when it exists\n \"id_ER\": ann[\"_id\"],\n \"start\": ann[\"start\"],\n \"end\": ann[\"end\"],\n \"type\": ann[\"type\"],\n \"mention\": ann[\"features\"][\"mention\"],\n \"is_linked\": ann[\"features\"][\"url\"] != None\n and (not ann[\"features\"][\"linking\"][\"is_nil\"]),\n # this is temporary, there will be a display name directly in the annotaion object\n \"display_name\": anonymize(ann[\"features\"][\"mention\"])\n if ann[\"type\"] in self.anonymize_type\n else ann[\"features\"][\"mention\"],\n }\n for ann in doc[\"annotation_sets\"][\"entities_merged\"][\"annotations\"]\n ]\n\n metadata = [\n # for now let's make them static\n {\"type\": \"anno sentenza\", \"value\": doc[\"features\"].get(\"annosentenza\", \"\")},\n {\"type\": \"anno ruolo\", \"value\": doc[\"features\"].get(\"annoruolo\", \"\")},\n ]\n\n elastic_doc = {\n \"mongo_id\": doc[\"id\"],\n \"name\": doc[\"name\"],\n \"text\": doc[\"text\"],\n \"metadata\": metadata,\n \"annotations\": annotations,\n }\n\n return index_elastic_document(index, elastic_doc)\n","repo_name":"marcoripa96/dave-search","sub_path":"packages/indexer/src/indexer.py","file_name":"indexer.py","file_ext":"py","file_size_in_byte":3026,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"35651225046","text":"import pytest\nfrom modules import MainPage\nfrom modules import SortPages\nfrom modules import Menu\nfrom modules import Genres\nfrom helper import page_helper\nfrom locators.Genres import *\nfrom locators.Sort import *\nfrom locators.User import *\n\n\nclass TestGenres:\n @pytest.mark.smoke\n def test_genres_name(self, browser):\n Menu.go_to_menu_genres(browser)\n name1 = page_helper.get_text_element(browser, *GenresPageLocators.GENRE_NAME)\n Genres.open_random_genre(browser)\n h1 = page_helper.get_h1_text(browser)\n assert name1 in h1\n\n @pytest.mark.regression\n def test_genres_sorting(self, browser):\n Menu.go_to_menu_genres(browser)\n SortPages.press_second_sort(browser)\n name1 = page_helper.get_class_element(browser, *SortLocators.SECOND_SORT)\n name2 = SortLocators.ACTIVE_SORT\n assert name1 in name2\n\n @pytest.mark.smoke\n def test_genres_like_track_button(self, browser):\n Menu.go_to_menu_genres(browser)\n Genres.open_random_genre(browser)\n MainPage.like_track(browser)\n assert page_helper.is_element_clickable(browser, *UserLocators.VK_AUTH)\n","repo_name":"kocteban/Zvonko-test","sub_path":"Mp3ha/Mp3ha/tests/test_genres.py","file_name":"test_genres.py","file_ext":"py","file_size_in_byte":1155,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"73626917604","text":"import time\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\n\nurl = 'https://www.avito.ma/fr/maroc/voitures-à_vendre'\n\ndriver = webdriver.Chrome()\ndriver.get(url)\n\n# Create an empty list to store phone numbers\nphone_numbers = []\n\n# Loop indefinitely to check for new phone numbers\nwhile True:\n # Extract links to all vehicle posts on the first page\n links = []\n posts = driver.find_elements(\"xpath\", '//*[@id=\"__next\"]/div/main/div/div[6]/div[1]/div/div[2]')\n for post in posts:\n link = post.find_element(\"xpath\", './/a').get_attribute('href')\n links.append(link)\n\n # Extract phone numbers from each vehicle post\n for link in links:\n driver.get(link)\n try:\n # Click the button to reveal the phone number\n button = WebDriverWait(driver, 10).until(EC.element_to_be_clickable((By.XPATH, '//*[@id=\"__next\"]/div/main/div/div[3]/div[1]/div[2]/div[1]/div[1]/div[2]/div[2]/div/div/div/div[4]/button[2]')))\n button.click()\n\n # Get the phone number\n phone_number = WebDriverWait(driver, 10).until(EC.visibility_of_element_located((By.XPATH, '//*[@id=\"__next\"]/div/main/div/div[2]/div/div/div/div/a/span/span')))\n phone_number = phone_number.text.strip()\n\n # Check if the phone number is new\n if phone_number not in phone_numbers:\n # Save the phone number to a file\n with open('phone_number.txt', 'a') as f:\n f.write(phone_number + '\\n')\n phone_numbers.append(phone_number)\n\n print(phone_number)\n\n except:\n print(\"Phone number not found\")\n\n # Refresh the main page to check for new posts\n driver.quit()\n driver = webdriver.Chrome()\n time.sleep(6)\n driver.get(url)\n time.sleep(5)\n\ndriver.quit()","repo_name":"abdelilahBouaiss/marketing-tools","sub_path":"hamza.py","file_name":"hamza.py","file_ext":"py","file_size_in_byte":1979,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"29469064176","text":"import pandas as pd\nimport re\nimport matplotlib.pyplot as plt\n\nfile_name = 'vacancies_with_skills.csv'\nvacancy = ['Web-разработчик','web develop', 'веб разработчик', 'web разработчик', 'web programmer', 'web программист', 'веб программист', 'битрикс разработчик', 'bitrix разработчик', 'drupal разработчик', 'cms разработчик', 'wordpress разработчик', 'wp разработчик', 'joomla разработчик', 'drupal developer', 'cms developer', 'wordpress developer', 'wp developer', 'joomla developer']\n\ndf = pd.read_csv(file_name, low_memory=False)\ndf = df.dropna(subset='key_skills')\ndf['Год'] = df['published_at'].str.partition('-')[0].astype(int)\n\ndf_this_job = df.loc[df['name'].str.contains(vacancy[0], flags=re.IGNORECASE, regex=True)]\nfor i in range(len(vacancy) - 1):\n df_t = df.loc[df['name'].str.contains(vacancy[i + 1], flags=re.IGNORECASE, regex=True)]\n df_this_job = pd.concat([df_this_job, df_t])\n\nkey_skills = df_this_job['key_skills'].tolist()\nfor i in range(len(key_skills)):\n key_skills[i] = key_skills[i].split('\\n')\n\nskills_dict = dict()\n\nfor skills in key_skills:\n for skill in skills:\n if skill in skills_dict:\n skills_dict[skill] = skills_dict[skill] + 1\n else:\n skills_dict[skill] = 1\n\nskills_df = pd.DataFrame.from_dict(skills_dict, orient='index', columns=['Кол-во'])\nskills_df.index.rename('Навык', inplace=True)\nskills_df = skills_df.sort_values(by=['Кол-во'], ascending=False).head(10)\n# print(skills_df)\ntop10 = skills_df.index.values.tolist()\n# print(top10)\nkey_skillses = []\nfor i in range(2015, 2023):\n df = df_this_job.loc[df_this_job['Год'] == i]\n key_skills = df['key_skills'].tolist()\n key_skillses.append(key_skills)\n\ntable = pd.DataFrame()\nfor key_skills in key_skillses:\n for i in range(len(key_skills)):\n key_skills[i] = key_skills[i].split('\\n')\n\n skills_dict = dict()\n\n for skills in key_skills:\n for skill in skills:\n if skill in skills_dict and skill in top10:\n skills_dict[skill] = skills_dict[skill] + 1\n else:\n skills_dict[skill] = 1\n\n df = pd.DataFrame.from_dict(skills_dict, orient='index', columns=['Кол-во'])\n df.index.rename('Навык', inplace=True)\n df = df.reset_index()\n df = df.sort_values(by=['Кол-во'], ascending=False).head(10)\n res = (df\n .assign(n=df.groupby(\"Навык\").cumcount())\n .pivot_table(index=\"n\", columns=\"Навык\", values=\"Кол-во\")\n .rename_axis(None))\n table = pd.concat([table, res])\n\ntable.reset_index()\ntable['Год'] = ['2015', '2016', '2017', '2018', '2019', '2020', '2021', '2022']\n\nax = table.plot(x='Год', y=top10)\nfig = ax.get_figure()\nfig.savefig('skills_graph.png', dpi=300)\nfor skill in top10:\n text_file = open(f\"{skill}.html\", \"w\", encoding=\"utf-8\")\n df = pd.concat([table['Год'], table[skill]], axis=1)\n text_file.write(df.to_html())\n text_file.close()\n","repo_name":"Parcurcik/web_vacancies","sub_path":"scripts/skills.py","file_name":"skills.py","file_ext":"py","file_size_in_byte":3117,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"7189192983","text":"import os\nfrom os.path import dirname\n\nfrom gi.repository import Adw\nfrom gi.repository import Gtk\n\nfrom .accounts_list import DharmaAccountsList\nfrom .filepath_settings import DharmaFilePathSettings\nfrom .machines_window import DharmaMachinesWindow\nfrom .guest.main import Dharma\n\n\n@Gtk.Template.from_file(dirname(__file__) + \"/gtk/window.ui\")\nclass DharmaWindow(Adw.ApplicationWindow):\n __gtype_name__ = \"DharmaWindow\"\n\n accounts_list = Gtk.Template.Child()\n qcow_file_button = Gtk.Template.Child()\n game_files_button = Gtk.Template.Child()\n run_button = Gtk.Template.Child()\n\n filepath_settings = None\n accounts_list_object = None\n\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n\n self.accounts_list_object = DharmaAccountsList(self.accounts_list, self)\n self.filepath_settings = DharmaFilePathSettings(\n self.qcow_file_button, self.game_files_button, self\n )\n self.run_button.connect(\"clicked\", self.run)\n\n def run(self, button):\n dharma_main = Dharma(\n self.accounts_list_object.accounts,\n self.filepath_settings.qcow_image_path,\n self.filepath_settings.game_folder_path,\n )\n dharma_main.run_loop(2)\n DharmaMachinesWindow(self, dharma_main.manager)","repo_name":"wineTGH/Dharma","sub_path":"Dharma/window.py","file_name":"window.py","file_ext":"py","file_size_in_byte":1299,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"40557206606","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport time\n\nfrom matplotlib import animation\nfrom scipy.sparse import csr_matrix\n\nfolder = r'C:\\Users\\thoma\\Documents\\MEGAsync\\Cours\\Maths\\10_1_ALN\\Projet\\gif'\n\nR, tmax = 0.065, 60. # en mètres, en secondes\nNx = 1_000 # 35, 1000 stable | 35, 978 instable\nD = 98.8e-6 # Diffusivité thermique de l'aluminium\nTmax = 80 # °C\nTamb = 20 # °C\n\ndx = R/(Nx+1)\ndt = dx**2/(2*D)\nNt = int(tmax/dt)\n\nbeta = D*dt/dx**2\nwhile beta >= 1/2:\n Nt += 1\n dt = tmax/(Nt + 1)\n beta = D * dt / dx ** 2\n\nNt = 2_811_756 # Nt -= 13\ndt = tmax/(Nt + 1)\nbeta = D * dt / dx ** 2\nprint(beta, Nx, Nt)\n\n# Matrice Euler explicite\nM = np.array([[0. for _ in range(Nx+2)] for _ in range(Nx+2)])\n\nM[0][0], M[Nx+1][Nx+1] = 1, 1 # Condition aux bornes\nfor i in range(1, Nx+1):\n M[i][i] = 1-2.*beta\n\nfor i in range(1, Nx+1):\n M[i][i+1] = beta\n\nfor i in range(Nx):\n M[i+1][i] = beta\n\nsparceM = csr_matrix(M)\n# print(M)\n\n# On pose les valeurs de la fonction f en les x_i.\nB = np.transpose(np.array([Tamb for _ in range(Nx+2)]))\nB[0] = Tmax\n\n# print(B)\n\n# Préparation de l'affichage graphique\nx_i = [i * dx * 100 for i in range(Nx + 2)] # Positions des points à l'intérieur de l'intervalle\n# print(x_i)\nit = np.linspace(start=0., stop=1., num=10_000 + 1, endpoint=True)\n\n# Itérations\n\nfig = plt.figure()\nline, = plt.plot([], [])\nplt.xlim(0, R*100)\nplt.ylim(Tamb, Tmax)\n#plt.xlim(2.0, 2.1)\n#plt.ylim(58.5, 63.5)\nplt.xlabel('Position (cm)')\nplt.ylabel('Temperature (°C)')\n\nfor i in range(Nt):\n X = sparceM @ B\n B = X.copy()\n #if i % 50_000 == 0:\n # plt.plot(x_i, X, 'b')\nplt.plot(x_i, X, 'b,', label=\"Solution approchée\")\nplt.plot(x_i, [Tmax-(Tmax-Tamb)*x/(R*100) for x in x_i], \"k\", label=\"Solution exacte\")\nplt.legend()\nplt.show()\n\n'''\ndef animate(i):\n global M, B\n X = sparceM @ B # X = np.linalg.solve(M, B)\n # print(X)\n\n if i % 1_000 == 999:\n plt.plot(x_i, X, 'b')\n # line.set_data(x_i, X)\n B = X.copy()\n\n # filename = folder+'/{:0>9}.png'.format(i)\n\n # save frame\n # plt.savefig(filename)\n\n return line,\n\n\nani = animation.FuncAnimation(fig, animate, frames=Nt, blit=True, interval=.5, repeat=False)\n\n# time.sleep(5)\nplt.show()\n'''","repo_name":"UnderscorePoY/L3_Python","sub_path":"AlgebreLineaireNumerique/Projet_ALN/main_anim.py","file_name":"main_anim.py","file_ext":"py","file_size_in_byte":2231,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"12095233990","text":"import numpy as np\nfrom dgCore import computeRHS\ndef advanceSolRK4(U,grid,eqns,dt):\n U0 = np.zeros(np.shape(U))\n U0[:] = U[:]\n rk4const = np.array([1./4,1./3,1./2,1.])\n for i in range(0,4):\n RHS = computeRHS(U,grid.tri,eqns)\n RHSsolve = np.einsum('ijk,njk->nik',grid.Minv,RHS)\n U[:] = U0[:] + dt*rk4const[i]*RHSsolve[:]\n return U\n\n","repo_name":"eparish1/PyDG_unstructured","sub_path":"src/timeSchemes.py","file_name":"timeSchemes.py","file_ext":"py","file_size_in_byte":346,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"35788436391","text":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nraw_us = pd.read_csv('data/youtube-new/USvideos.csv')\nraw_us.columns\n\ndf_us = raw_us[['video_id', 'trending_date', 'title', 'channel_title', 'category_id','publish_time', 'tags', 'views', 'likes', 'dislikes']]\ndf_us.info\n\ndf_us.sort_values(['video_id','views'])\ndf_counts = df_us.groupby(['video_id','views'])\n\ndf_counts = df_us.set_index(['video_id']).groupby('video_id').sum()\ndf_counts.reset_index(inplace = True)\n\ndf_id_views = pd.DataFrame([df_counts['video_id'],df_counts['views']]).T\ndf_id_views.columns = ['video_id','views']\n\ndf_id_counts = pd.DataFrame([df_counts['video_id'],\n pd.to_numeric(df_counts['views']),\n pd.to_numeric(df_id_views['views'])\n ]).T\ndf_id_counts.columns = ['video_id','days_trending','views']\n# df_id_counts = pd.DataFrame([df_id_counts['video_id'],\n# pd.to_numeric(df_id_counts['days_trending']),\n# pd.to_numeric(df_id_counts['views'])\n# ])\ndf_id_counts['views'] = pd.to_numeric(df_id_counts['views'])\ndf_id_counts['days_trending'] = pd.to_numeric(df_id_counts['days_trending'])\n\nif __name__ == '__main__':\n print(df_id_counts.head())","repo_name":"Laszarus/dsi-pandas-eda-case-study","sub_path":"YouTubePlot.py","file_name":"YouTubePlot.py","file_ext":"py","file_size_in_byte":1312,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"32515572569","text":"\nclass CurrentGoalListener(object):\n def __init__(self, handlers, observers, costmaps, **kwargs):\n self._update_goal_observers = [observers[\"force_recovery\"]]\n self._costmaps = costmaps\n self._handlers = handlers\n\n def __call__(self, msg):\n msg = self._handlers[\"goal\"].get_current_goal()\n for observer in self._update_goal_observers:\n observer.update_goal(msg)\n if \"goal_costmap\" in self._costmaps:\n origin = np.array((msg.pose.position.x, msg.pose.position.y))\n costmap = self._costmaps[\"goal_costmap\"]\n costmap.update_origin(costmap.world_to_map_abs(origin))\n","repo_name":"ymd-stella/stella_nav","sub_path":"stella_nav_listener/src/stella_nav_listener/current_goal_listener.py","file_name":"current_goal_listener.py","file_ext":"py","file_size_in_byte":654,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"9169775417","text":"from typing import Union\n\nfrom django.conf import settings\nfrom django.db import models, transaction\nfrom django.urls import reverse\nfrom django.utils import timezone\n\nfrom hasker.users.models import User\n\n\nclass Tag(models.Model):\n\n name = models.CharField(max_length=100)\n\n def __str__(self) -> str:\n return self.name\n\n\nclass SimpleAction(models.IntegerChoices):\n\n LIKE = 1\n DISLIKE = -1\n\n\nclass AbstractQuestionAnswer(models.Model):\n\n class Meta:\n abstract = True\n\n text = models.TextField()\n creation_date = models.DateTimeField(default=timezone.now)\n\n @transaction.atomic\n def make_user_action(self, user: User, action: SimpleAction) -> None:\n make_user_action_(action_object=self, user=user, action=action)\n\n @property\n def rating(self):\n rating_aggregation_result = self.actions.aggregate(rating=models.Sum(\"action\"))[\"rating\"]\n rating = rating_aggregation_result if rating_aggregation_result is not None else 0\n return rating\n\n\nclass Question(AbstractQuestionAnswer):\n\n author = models.ForeignKey(\n to=settings.AUTH_USER_MODEL,\n related_name=\"questions\",\n on_delete=models.CASCADE\n )\n title = models.CharField(max_length=300)\n\n tags = models.ManyToManyField(\n to=Tag,\n blank=True\n )\n correct_answer = models.OneToOneField(\n to=\"Answer\",\n null=True,\n blank=True,\n related_name=\"correct_answer_for\",\n on_delete=models.CASCADE\n )\n\n def get_absolute_url(self):\n return reverse(\"questions:question\", kwargs={'question_id': self.pk})\n\n @property\n def url(self):\n return self.get_absolute_url()\n\n\nclass Answer(AbstractQuestionAnswer):\n\n author = models.ForeignKey(\n to=settings.AUTH_USER_MODEL,\n related_name=\"answers\",\n on_delete=models.CASCADE\n )\n question = models.ForeignKey(\n to=Question,\n related_name=\"answers\",\n on_delete=models.CASCADE\n )\n\n\nclass AbstractUserAction(models.Model):\n\n action = models.IntegerField(choices=SimpleAction.choices)\n action_date = models.DateTimeField(default=timezone.now)\n\n class Meta:\n abstract = True\n\n\nclass QuestionAction(AbstractUserAction):\n\n user = models.ForeignKey(\n to=settings.AUTH_USER_MODEL,\n related_name=\"question_actions\",\n on_delete=models.CASCADE\n )\n question = models.ForeignKey(\n to=Question,\n related_name=\"actions\",\n on_delete=models.CASCADE\n )\n\n\nclass AnswerAction(AbstractUserAction):\n\n user = models.ForeignKey(\n to=settings.AUTH_USER_MODEL,\n related_name=\"answer_actions\",\n on_delete=models.CASCADE\n )\n answer = models.ForeignKey(\n to=Answer,\n related_name=\"actions\",\n on_delete=models.CASCADE\n )\n\n\ndef make_user_action_(action_object: Union[Question, Answer],\n user: User,\n action: SimpleAction) -> None:\n\n \"\"\"\n Makes action (like or dislike) on object (question or answer\n :param action_object: object to maker action on (concrete question or answer)\n :param user: user who makes action\n :param action: action that should be made (like or dislike)\n \"\"\"\n\n opposite_action = SimpleAction.DISLIKE if action == SimpleAction.LIKE else SimpleAction.LIKE\n action_object.actions.filter(\n models.Q(user_id=user.id) &\n models.Q(action=opposite_action)\n ).delete()\n\n user_action_is_already_done = action_object.actions.filter(\n models.Q(user_id=user.id) &\n models.Q(action=action)\n )\n\n if user_action_is_already_done.exists():\n user_action_is_already_done.delete()\n return\n\n action_object.actions.create(\n user_id=user.id,\n action=action\n )\n","repo_name":"DalerBakhriev/otus_homeworks","sub_path":"hw_week_7/hasker/hasker/questions/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3803,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"36612240906","text":"#Binary Search Using For loop\n\npos = -1\ndef Srch(a,b):\n\n l=0\n u=len(lst)\n\n\n for i in range(u):\n mid = (l+u) // 2\n\n if lst[mid] == y:\n globals() ['pos'] = mid\n return True \n else:\n if lst[mid] < y:\n l = mid\n else:\n u = mid\n\n\n return False\n\nlst = [2,4,6,7,8,10,11,23,30,35,40]\ny = int(input(\"Enter searchine element: \"))\n\nif Srch(lst,y):\n print(\"item founded at\",pos)\n\nelse:\n print(\"item not founded!\")\n","repo_name":"SarfrazAHd/BaseRepo","sub_path":"Python/Python_basic/BinarySearch.py","file_name":"BinarySearch.py","file_ext":"py","file_size_in_byte":515,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"28160212399","text":"# -*- coding: utf-8 -*-\nfrom collections import (defaultdict, deque)\n\ndef dfs_step(graph, start, end):\n \"\"\"\n DFS最小步数\n\n Parameters\n ----------\n graph : defaultdict\n 图.\n start : str\n 起点.\n end : str\n 终点.\n\n Returns\n -------\n int\n 最小步数.\n\n \"\"\"\n stack = [(start, 0)]\n visited = {start}\n \n while stack:\n node_curt, step_curt = stack.pop()\n \n for node_next in graph[node_curt]:\n if node_next == end:\n return step_curt + 1\n else:\n if node_next not in visited:\n visited.add(node_next)\n stack.append((node_next, step_curt+1))\n \n return 0\n\n\nif __name__ == \"__main__\":\n graph = defaultdict(list)\n graph[\"A\"] = [\"B\", \"C\"]\n graph[\"B\"] = [\"A\", \"D\", \"C\"]\n graph[\"C\"] = [\"A\", \"B\", \"D\", \"E\"]\n graph[\"D\"] = [\"B\", \"C\", \"F\"]\n graph[\"E\"] = [\"C\", \"D\"]\n graph[\"F\"] = [\"D\"]\n print(\"graph \\n\", graph)\n \n start = \"A\"\n end = \"E\"\n \n minimum_step = dfs_step(graph, start, end)\n print(minimum_step)\n","repo_name":"Lukaschen1986/LeetCodeProgress","sub_path":"graph/func_dfs_step.py","file_name":"func_dfs_step.py","file_ext":"py","file_size_in_byte":1124,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"11505494440","text":"\"\"\"\n Given a string containing digits from 2-9 inclusive, return all possible letter combinations that the number could represent.\n \n A mapping of digit to letters (just like on the telephone buttons) is given below. Note that 1 does not map to any letters.\n \n \n Example:\n \n Input: \"23\"\n Output: [\"ad\", \"ae\", \"af\", \"bd\", \"be\", \"bf\", \"cd\", \"ce\", \"cf\"].\n Note:\n \n Although the above answer is in lexicographical order, your answer could be in any order you want.\n \n\"\"\"\ndef letterCombinations(digits):\n \"\"\"\n :type digits: str\n :rtype: List[str]\n \"\"\"\n if len(digits) == 0: return []\n self.map = {\"1\":\"\", \"2\":\"abc\", \"3\":\"def\", \"4\":\"ghi\", \"5\":\"jkl\",\"6\":\"mno\", \"7\":\"pqrs\",\"8\":\"tuv\",\"9\":\"wxyz\"}\n \n self.res = []\n self.dfs(digits, \"\",0)\n return self.res\n\ndef dfs(digits,path,idx):\n \n if len(path) > len(digits):\n return\n if len(path) == len(digits):\n self.res.append(path)\n for i in range(idx,len(digits)):\n for char in self.map[digits[i]]:\n self.dfs(digits,path+char,i+1)\n","repo_name":"NiuNiu-jupiter/Leetcode","sub_path":"17. Letter Combinations of a Phone Number .py","file_name":"17. Letter Combinations of a Phone Number .py","file_ext":"py","file_size_in_byte":1101,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"31254567909","text":"import wx\n\n# 自定义窗口类\nclass MyFrame(wx.Frame):\n def __init__(self):\n super().__init__(None, title=\"The first UI program of python\", size=(768, 576), pos=(100,100))\n #\n \n def OnInit(self):\n return true\n\n\n# 创建应用程序对象 \napp = wx.App()\n\n# 创建窗口对象\nfrm = MyFrame()\n\nfrm.Show()\n# 主循环\napp.MainLoop()\n\n\n","repo_name":"jeremyjia/Games","sub_path":"python/sample/wxPythonUI.py","file_name":"wxPythonUI.py","file_ext":"py","file_size_in_byte":372,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"4567906518","text":"from django.shortcuts import render\nfrom django.contrib.auth.models import User\nfrom django.utils.text import slugify\n\nfrom rest_framework.response import Response\nfrom rest_framework.decorators import api_view, authentication_classes, permission_classes\n\nfrom .models import Course, Lesson, Comment, Category\nfrom .serializers import CourseListSerializer, CourseDetailSerializer, LessonListSerializer, CommentSerializer, CategorySerializer, QuizSerializer, UserSerializer\n\n@api_view(['GET'])\ndef get_quiz(request, course_slug, lesson_slug):\n lesson = Lesson.objects.get(slug=lesson_slug)\n quiz = lesson.quizzes.first()\n serializer = QuizSerializer(quiz)\n return Response(serializer.data)\n\n@api_view(['GET'])\n@authentication_classes([])\n@permission_classes([])\ndef get_categories(request):\n categories = Category.objects.all()\n serializer = CategorySerializer(categories, many=True)\n return Response(serializer.data)\n\n@api_view(['GET'])\n@authentication_classes([])\n@permission_classes([])\ndef get_courses(request):\n category_id = request.GET.get('category_id', '')\n # courses = Course.objects.all()\n courses = Course.objects.filter(status=Course.PUBLISHED)\n \n if category_id:\n courses = courses.filter(categories__in=[int(category_id)])\n\n serializer = CourseListSerializer(courses, many=True)\n return Response(serializer.data)\n\n@api_view(['GET'])\n@authentication_classes([])\n@permission_classes([])\ndef get_frontpage_courses(request):\n courses = Course.objects.filter(status=Course.PUBLISHED)[0:4]\n serializer = CourseListSerializer(courses, many=True)\n return Response(serializer.data)\n\n@api_view(['GET'])\n@permission_classes([])\ndef get_course(request, slug):\n course = Course.objects.filter(status=Course.PUBLISHED).get(slug=slug)\n course_serializer = CourseDetailSerializer(course)\n lesson_serializer = LessonListSerializer(course.lessons.all(), many=True)\n print(\"COURSE SERIALIZER\")\n print(course_serializer.data)\n print(request.user.is_authenticated)\n\n if request.user.is_authenticated:\n print('user login')\n course_data = course_serializer.data\n else:\n course_data = {}\n\n data = {\n 'course': course_data,\n 'lessons': lesson_serializer.data\n }\n return Response(data)\n\n@api_view(['GET'])\ndef get_comments(request, course_slug, lesson_slug):\n lesson = Lesson.objects.get(slug=lesson_slug)\n serializer = CommentSerializer(lesson.comments.all(), many=True)\n return Response(serializer.data)\n\n\n@api_view(['POST'])\ndef add_comment(request, course_slug, lesson_slug):\n data = request.data\n name = data.get('name')\n content = data.get('content')\n\n course = Course.objects.get(slug=course_slug)\n lesson = Lesson.objects.get(slug=lesson_slug)\n\n comment = Comment.objects.create(course=course, lesson=lesson, name=name, content=content, created_by=request.user)\n serializer = CommentSerializer(comment)\n\n return Response(serializer.data)\n\n@api_view(['POST'])\ndef create_course(request):\n data = request.data.get('_rawValue')\n\n course = Course.objects.create(\n title = data['title'],\n slug = slugify(data['title']),\n short_description = data['short_description'],\n long_description = data['long_description'],\n created_by = request.user\n )\n\n for id in data['categories']:\n course.categories.add(id)\n\n course.save()\n\n print(course)\n\n return Response({'Yo': 'yo'})\n\n\n@api_view(['GET'])\ndef get_author_courses(request, user_id):\n user = User.objects.get(pk=user_id)\n courses = user.courses.filter(status=Course.PUBLISHED)\n\n user_serializer = UserSerializer(user, many=False)\n courses_serializer = CourseListSerializer(courses, many=True)\n\n return Response({\n 'courses': courses_serializer.data,\n 'created_by': user_serializer.data\n })\n","repo_name":"jcromerohdz/learning-management-system","sub_path":"studynet_django/course/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3883,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"70864324325","text":"# coding: utf-8\n\nimport re\nimport download\nimport urlparse\n\ndef link_crawler(seed_url, link_regex, max_depth=2):\n \"\"\"Crawl from the given seed URL following links matched by link_regex\"\"\"\n # 增加深度\n max_depth = 2\n seen = {}\n crawl_queue = [seed_url]\n\n\n\n while crawl_queue:\n url = crawl_queue.pop()\n html = download.download(url)\n\n depth = seen[url]\n if depth != max_depth:\n for link in get_links(html):\n if re.match(link_regex, link):\n link = urlparse.urljoin(seed_url, link)\n if link not in seen:\n seen[link] = depth + 1\n crawl_queue.append(link)\n\ndef get_links(html):\n \"\"\"Return a list of links from html\"\"\"\n webpage_regex = re.compile(']+href=[\"\\'](.*?)[\"\\']', re.IGNORECASE)\n return webpage_regex.findall(html)\n\nlink_crawler('http://example.webscraping.com', '/(index|view)')","repo_name":"BoyuZhou/learn_python_crawler","sub_path":"link_crwaler.py","file_name":"link_crwaler.py","file_ext":"py","file_size_in_byte":955,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"1762879336","text":"# 가로수\nfrom math import gcd\nn = int(input())\ntree = [int(input()) for _ in range(n)]\n\n''' 유클리드 호제법(euclidean algorithm)\n1. 최대공약수를 구하는 함수를 gcd(x, y)라고 가정\n2. x % y == 0 이라면 gcd(x, y) == y가 성립\n3. x % y != 0 이라면 gcd(x, y) == gcd(y, x%y)가 성립\n4. 2번이 될 때까지 2~3번을 반복\n'''\n\n# gcd = lambda a, b: gcd(a, a % b) if a % b else b\ng = tree[1] - tree[0] # 인접한 가로수 사이의 간격\n\nfor i in range(2, n):\n g = gcd(g, tree[i] - tree[i - 1])\n\nresult = (tree[n-1]-tree[0]) // g - n + 1\nprint(result)","repo_name":"Sangmyung-ICPC-Team/Jiwoo","sub_path":"BOJ/baekjoon2485_RoadsideTrees.py","file_name":"baekjoon2485_RoadsideTrees.py","file_ext":"py","file_size_in_byte":588,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"3127325258","text":"from __future__ import print_function, division\n\nfrom math import *\nfrom numpy import *\n\n## From http://stackoverflow.com/questions/1237379/how-do-i-set-sys-excepthook-to-invoke-pdb-globally-in-python\nimport pdb, sys, traceback\ndef info(type, value, tb):\n\ttraceback.print_exception(type, value, tb)\n\tpdb.pm()\nsys.excepthook = info\n\n## To match pydb.debugger() function:\ndebugger = pdb.set_trace\n\n\n# a small set of helper functions to\n# call common array creation functions\n# these are useful to ensure that\n# all arrays are created as double-precision\n# floats, no matter what data are provided\n# as argument. For example array([1,3,4]) normally returns\n# an array with data of type int, but arrayf([1,3,4])\n# always creates an array of floats \n\nkFloatType = float64\n\ndef arrayf( arg ):\n\treturn array( arg, kFloatType )\ndef asarrayf( arg ):\n\treturn asarray( arg, kFloatType )\ndef zerosf( arg ):\n\treturn zeros( arg, kFloatType )\ndef onesf( arg ):\n\treturn ones( arg, kFloatType )\ndef identityf( arg ):\n\treturn identity( arg, kFloatType )\ndef emptyf( arg ):\n\treturn empty( arg, kFloatType )\n\ndef mag2( vec ):\n\tvec = asarray(vec)\n\treturn dot(vec,vec)\ndef mag( vec ):\n\treturn sqrt(mag2(vec))\ndef dir( vec ):\n\tassert mag(vec) != 0\n\tvec = asarray(vec)\n\treturn vec * 1./mag(vec)\n\nkEps = 1e-7\n\ndef expect( value ):\n if not value:\n print( 'Expect failed!' )\n import traceback\n traceback.print_stack()\n \n #import pydb\n #pydb.debugger()\n \n #if not value:\n # import sys\n # print >> sys.stderr, 'Expect failed!'\n\ndef rotate( vec, axis, radians ):\n '''\n Returns 3-vector 'vec' rotated about the given 'axis' by 'radians'.\n \n tested\n '''\n \n vec = asarrayf( vec )\n axis = asarrayf( axis )\n \n ## This code comes from adt/cvec3t.h\n \n c1 = asarray( vec )\n c2 = asarray( axis )\n s = radians\n \n c2l = mag(c2)\n assert c2l != 0.\n unitc2 = c2/c2l\n c1par = dot(c1,unitc2)*unitc2\n c1perp = c1-c1par\n return c1par + cos(s)*c1perp + sin(s)*cross(unitc2,c1perp)\n\ndef orthogonal( vec ):\n '''\n Returns an arbitrary (deterministic) unit vector orthogonal to 'vec'.\n \n tested\n '''\n \n vec = asarrayf( vec )\n \n ## This code comes from scene2problem.cpp\n \n assert( mag2( vec ) > kEps )\n \n ## Find the smallest component of 'vec'.\n mini = 0\n if( fabs( vec[1] ) < fabs( vec[ mini ] ) ): mini = 1\n if( fabs( vec[2] ) < fabs( vec[ mini ] ) ): mini = 2\n \n ## Add one to it.\n orth = vec.copy()\n orth[ mini ] += 1.\n \n ## Return the normalized cross product of 'vec' and 'orth',\n ## since it must be perpendicular to 'vec'.\n orth = dir( cross( vec, orth ) )\n assert( mag2( orth ) > kEps )\n return orth\n\ndef lerp( a, b, t ):\n '''\n Returns the vector 't' along the line from 'a' to 'b'.\n In effect, when 't' is 0, 'a' is returned, and when 't' is 1, 'b' is returned.\n \n tested\n '''\n \n a = asarrayf( a )\n b = asarrayf( b )\n \n return a + t * ( b - a )\n\ndef lerps( a, b, ts ):\n '''\n Vectorized lerp.\n Returns [ lerp( a, b, t ) for t in ts ].\n \n tested\n '''\n \n a = asarrayf( a )\n b = asarrayf( b )\n ts = asarrayf( ts )\n \n return a[newaxis,:] + ts[:,newaxis] * ( b - a )[newaxis,:]\n\ndef get_axis_angle_rotation( start, end ):\n '''\n Returns the tuple (axis, radians) about which to rotate the vector 'start' so that it becomes\n parallel to, and pointing in the same direction as, the vector 'end'.\n \n tested\n '''\n \n start = asarrayf( start )\n end = asarrayf( end )\n \n ## This code comes from scene2problem.cpp\n \n startn = dir( start )\n endn = dir( end )\n \n ## Now twist (rotate around the eye vector) until 'cur_problems_eye_refl_line_dir'\n ## matches 'problem_eye_refl_line_dir'.\n axis = cross( startn, endn )\n radians = dot( startn, endn )\n \n ## If the rotate to and from vectors have 0 cross product, they're either\n ## the same or opposite.\n if( mag2( axis ) < kEps ):\n ## In this case, the axis is underspecified. Any orthogonal vector will do.\n axis = orthogonal( startn );\n \n ## The dot product is either 1 or -1\n assert( fabs( radians ) - 1. < kEps )\n \n ## YOTAM: Even though the input vectors to the cross product are\n ## normalized, without a call to dir(), the magnitude of the\n ## cross product was off by ~1e-5 on my machine.\n axis = dir( axis )\n \n ## Finally, take the arc cosine of the dot product to get the radians.\n radians = acos( min( 1., max( -1., radians ) ) )\n \n return axis, radians\n\ndef slerp( start, end, t ):\n '''\n Returns the vector 't' along the rotation parameterized between [0,1] that rotates\n vector 'start' to vector 'end'. If 'start' and 'end' differ in length, the length\n is interpolated linearly.\n \n tested\n '''\n \n start = asarrayf( start )\n end = asarrayf( end )\n \n ## This code comes from scene2problem.cpp\n \n axis, radians = get_axis_angle_rotation( start, end )\n \n result = rotate( dir( start ), axis, t * radians ) * lerp( mag( start ), mag( end ), t )\n return result\n\ndef reflect( n, r ):\n '''\n Returns the vector 'n' reflected about 'r'.\n In other words, negate the portion of n that is perpendicular to r\n and keep the part of n that is parallel to r.\n \n tested\n '''\n \n n = asarrayf( n )\n r = asarrayf( r )\n \n ## This code comes from scene2problem.cpp\n \n r = dir( r )\n \n ## To reflect a vector n about a unit vector r, we want to negate the portion of n\n ## that is perpendicular to r and keep the part of n that is parallel to r.\n ## The part that is parallel to r is: dot( n, r ) * r\n ## The part that is perpendicular to r is: n - dot( n, r ) * r\n ## Adding the parallel part and negative the perpendicular part, we get:\n ## dot( n, r ) * r - ( n - dot( n, r ) * r )\n ## <=>\n ## 2 * dot( n, r ) * r - n\n return ( 2 * dot( n, r ) ) * r - n\n\n\ndef get_rotation_matrix( axis, radians ):\n '''\n Given the axis, angle rotation defined by 'axis' and 'radians', returns a\n 3x3 numpy.matrix M suitable for multiplying a 3-vector v: M*v.\n \n NOTE: the angle is in radians, not degrees as glRotate*() takes.\n \n EXAMPLE:\n ## A CCW rotation of pi/2 radians about the z axis in the xy plane. Maps <1,0,0> to <0,1,0>.\n dot( asarray( get_rotation_matrix( (0,0,1), pi/2 ) ), (1,0,0) )\n \n ## The matrix transforming a plane with arbitrary normal to the xy plane:\n asarray( get_rotation_matrix( *get_axis_angle_rotation( plane_normal, (0,0,1) ) ) )\n \n tested\n '''\n \n axis = asarrayf( axis )\n \n ## This code comes from hmatrix.h / adt/mat3t.h\n \n u = dir( axis )\n \n u1 = u[0]\n u2 = u[1]\n u3 = u[2]\n \n U = matrix([\n [u1*u1, u1*u2, u1*u3],\n [u2*u1, u2*u2, u2*u3],\n [u3*u1, u3*u2, u3*u3]\n ])\n \n S = matrix([\n [0, -u3, u2],\n [u3, 0, -u1],\n [-u2, u1, 0]\n ])\n \n result = U + (asmatrix(identity(3)) - U) * cos(radians) + S * sin(radians)\n return result\n\ndef deg2rad( degrees ):\n '''\n Given degrees, returns equivalent radians.\n \n tested\n '''\n return degrees * (pi/180.)\n\ndef rad2deg( radians ):\n '''\n Given radians, returns equivalent degrees.\n \n tested\n '''\n return radians * (180./pi)\n\ndef sphere_contains_point( sphere, point ):\n '''\n Given a sphere ( 2/3d center, radius ) and a 2/3d point,\n returns whether or not the point is inside the sphere.\n \n tested\n '''\n \n return sphere_contains_points( sphere, (point,) )[0]\n\ndef sphere_contains_points( sphere, points ):\n '''\n Given a sphere ( 2/3d center, radius ) and a sequence of 2/3d points,\n returns a sequence of booleans, whether or not each point is inside the sphere.\n \n tested\n '''\n \n sphere_pt = asarrayf( sphere[0] ).ravel()\n radius = sphere[1]\n assert len( sphere_pt ) in (2,3)\n \n if len( points ) == 0: return []\n points = asarrayf( points )\n assert points.shape[1] in (2,3)\n \n assert points.shape[1] == len( sphere_pt )\n \n return (( points - sphere_pt )**2).sum(1) < radius**2\n\ndef sphere_normal_at_point( sphere, point ):\n '''\n Given a sphere ( 2/3d center, radius ) and a 2/3d point,\n returns the normal for the point.\n Note that the point need not be on the surface of the sphere.\n \n untested\n '''\n \n return sphere_normal_at_points( sphere, (point,) )[0]\n\ndef sphere_normal_at_points( sphere, points ):\n '''\n Given a sphere ( 2/3d center, radius ) and a sequence of 2/3d points,\n returns a sequence of normals for each point.\n Note that the points need not be on the surface of the sphere.\n \n untested\n '''\n \n sphere_pt = asarrayf( sphere[0] ).ravel()\n radius = sphere[1]\n assert len( sphere_pt ) in (2,3)\n \n if len( points ) == 0: return []\n points = asarrayf( points )\n assert points.shape[1] in (2,3)\n \n assert points.shape[1] == len( sphere_pt )\n \n result = points - sphere_pt\n result *= 1 / sqrt( ( result**2 ).sum(1) )\n return result\n\ndef ray_sphere_intersection_ts( sphere, ray, ray_is_line = False ):\n '''\n Given a ray ( 2/3d point, 2/3d direction ) and sphere ( 2/3d center, radius ),\n returns a list of 't' values such that ( ray[0] + t * ray[1] ) lies on the sphere.\n If 'ray_is_line' is True, then returned 't' values may be negative.\n \n tested\n '''\n \n ray_pt = asarrayf( ray[0] ).ravel()\n ## Don't normalize ray_dir, because the result is dependent on the scale of ray_dir.\n ray_dir = asarrayf( ray[1] ).ravel()\n assert len( ray_pt ) in (2,3)\n assert len( ray_dir ) in (2,3)\n assert len( ray_pt ) == len( ray_dir )\n \n sphere_pt = asarrayf( sphere[0] ).ravel()\n radius = sphere[1]\n assert len( sphere_pt ) in (2,3)\n \n assert len( sphere_pt ) == len( ray_pt )\n \n return __ray_sphere_intersection_ts( sphere_pt, radius, ray_pt, ray_dir, ray_is_line )\ndef __ray_sphere_intersection_ts( sphere_pt, radius, ray_pt, ray_dir, ray_is_line ):\n '''\n Parameters already unpacked and type-checked version of ray_sphere_intersection_ts(),\n used internally by this module.\n '''\n ## We want 't' such that | ( ray_pt + t * ray_dir ) - sphere_pt |^2 = radius^2\n ## Let d = ray_dir, c = sphere_pt - ray_pt, and r = radius:\n d = ray_dir\n c = sphere_pt - ray_pt\n r = radius\n ## | td - c |^2 = r^2\n ## <=>\n ## (t d.x - c.x )^2 + (t d.y - c.y)^2 + (t d.z - c.z)^2 = r^2\n ## <=>\n ## t^2 dot( d,d ) - 2t dot( d,c ) + dot( c,c ) = r^2\n ## <=> quadratic equation x = ( -b +- sqrt( b^2 - 4ac ) )/( 2a )\n qa = dot( d,d )\n qb = -2 * dot( d,c )\n qc = dot( c,c ) - r**2\n qrad = qb**2 - 4*qa*qc\n if qrad < 0.: return []\n qrad = sqrt( max( 0., qrad ) )\n ts = [ ( -qb + qrad )/( 2*qa ), ( -qb - qrad )/( 2*qa ) ]\n if not ray_is_line: ts = [ t for t in ts if t >= 0. ]\n #assert len( ts ) > 0\n return ts\n\ndef closest_point_to_sphere_on_ray( sphere, ray, ray_is_line = False ):\n '''\n Given a ray ( 2/3d point, 2/3d direction ) and sphere ( 2/3d center, radius ),\n returns a tuple containing the closest point to the sphere along ray ---\n which is the intersection point if they intersect --- and whether or not\n the ray intersected the sphere.\n If 'ray_is_line' is True, then the closest point may lie along the negative ray direction.\n \n tested\n '''\n \n ray_pt = asarrayf( ray[0] ).ravel()\n ray_dir = dir( asarrayf( ray[1] ).ravel() )\n assert len( ray_pt ) in (2,3)\n assert len( ray_dir ) in (2,3)\n assert len( ray_pt ) == len( ray_dir )\n \n sphere_pt = asarrayf( sphere[0] ).ravel()\n radius = sphere[1]\n assert len( sphere_pt ) in (2,3)\n \n assert len( sphere_pt ) == len( ray_pt )\n \n ## Does the ray intersect the sphere?\n ### Adapted from distance_to_line_squared()\n sphere_to_ray = ray_pt - sphere_pt\n ## subtract the component of the ray's direction from the vector towards the ray point\n sphere_to_ray -= dot( sphere_to_ray, ray_dir ) * ray_dir\n \n ## If the magnitude of sphere_to_ray is greater than radius, then the ray doesn't intersect\n ## the sphere; return the point.\n if mag2( sphere_to_ray ) > radius*radius: return sphere_to_ray + sphere_pt, False\n \n #from pydb import debugger\n #debugger()\n \n ts = __ray_sphere_intersection_ts( sphere_pt, radius, ray_pt, ray_dir, ray_is_line )\n assert len( ts ) > 0\n ts = [ ( fabs(t), t ) for t in ts ]\n t = min( ts )[1]\n \n #print 't:', t, 'd:', d, 'ray_pt:', ray_pt\n result = t*ray_dir + ray_pt\n return result, True\n\ndef closest_point_on_sphere_to_ray( sphere, ray, ray_is_line = False ):\n '''\n Similar to closest_point_to_sphere_on_ray().\n Differs only in the case where the ray does not intersect the sphere, in which case\n the closest point on the sphere, instead of on the ray, is returned.\n \n tested\n '''\n \n result = closest_point_to_sphere_on_ray( sphere, ray, ray_is_line )\n if result[1]: return result\n else:\n ## The closest point on the sphere is the intersection of the closest point on the ray\n ## with the surface of the sphere.\n ## To get this point, simply return the point radius along the line between the\n ## sphere center and the point.\n sphere_center = asarray( sphere[0] ).ravel()\n sphere_to_pt = asarray( result[0] ).ravel() - sphere_center\n return sphere_center + sphere[1] * dir( sphere_to_pt ), False\n\ndef closest_point_on_sphere_to_point( sphere, pt ):\n '''\n Given a point 'pt' and a sphere ( 2/3d center, radius ),\n returns the closest point on the surface of the sphere to 'pt'.\n \n tested\n '''\n \n sphere_center = asarray( sphere[0] ).ravel()\n sphere_to_pt = asarray( pt ).ravel() - sphere_center\n return sphere_center + sphere[1] * dir( sphere_to_pt )\n\ndef axes_inverse( axes ):\n '''\n Given the axes of an ellipse ( list of orthogonal but not necessarily unit length 2/3d vectors ),\n returns the inverse transform matrix which transforms points into a space where\n the ellipse is a unit circle/sphere.\n '''\n \n axes = asarray( axes )\n \n ## axes.T is the sphere to ellipse rotation and scale matrix.\n ## We want the inverse.\n \n ## We could do this:\n return linalg.inv( axes.T )\n ## But I think it's more efficient to do this:\n ## UPDATE: It's probably more efficient, but only works if the axes are orthonormal.\n rot_scale_mat = axes / ( axes**2 ).sum(1).reshape( ( axes.shape[0], 1 ) )\n #print 'inverse?:', mag( rot_scale_mat.ravel() - linalg.inv( axes.T ).ravel() )\n #return rot_scale_mat\n\ndef ray_ellipse_intersection_ts( ellipse, ray, ray_is_line = False ):\n '''\n Given a ray ( 2/3d point, 2/3d direction ) and ellipse ( 2/3d center, list of 2/3d axes ),\n returns a list of 't' values such that ( ray[0] + t * ray[1] ) lies on the sphere.\n If 'ray_is_line' is True, then returned 't' values may be negative.\n \n untested\n '''\n \n center = asarrayf( ellipse[0] ).ravel()\n axes = asarrayf( ellipse[1] )\n assert len( center ) in (2,3)\n assert axes.shape[0] == axes.shape[1]\n assert axes.shape[1] in (2,3)\n assert len( center ) == axes.shape[1]\n \n ## Copy ray[0], since we will use -=.\n ray_pt = arrayf( ray[0] ).ravel()\n ## Don't normalize ray_dir, because the result is dependent on the scale of ray_dir.\n ray_dir = asarrayf( ray[1] ).ravel()\n assert len( ray_pt ) in (2,3)\n assert len( ray_dir ) in (2,3)\n assert len( ray_pt ) == len( ray_dir )\n assert len( ray_pt ) == len( center )\n \n ## Translate so that the ellipsoid's center is at the origin.\n ray_pt -= center\n \n ## self.axes.T is the sphere to ellipse rotation and scale matrix.\n ## We want the inverse.\n rot_scale_mat = axes_inverse( axes )\n \n ## Transform the ray.\n ray_pt = dot( rot_scale_mat, ray_pt )\n ray_dir = dot( rot_scale_mat, ray_dir )\n \n ## Intersect with a unit sphere.\n ts = ray_sphere_intersection_ts(\n ( zeros(ray_pt.shape), 1 ),\n ( ray_pt, ray_dir ),\n ray_is_line\n )\n return ts\n\ndef closest_point_to_ellipse_on_ray( ellipse, ray, ray_is_line = False ):\n '''\n Given a ray ( 2/3d point, 2/3d direction ) and ellipse ( 2/3d center, list of 2/3d axes ),\n returns a tuple containing the closest point to the ellipsoid along ray ---\n which is the intersection point if they intersect --- and whether or not\n the ray intersected the ellipsoid.\n If 'ray_is_line' is True, then the closest point may lie along the negative ray direction.\n \n tested\n '''\n \n center = asarrayf( ellipse[0] ).ravel()\n axes = asarrayf( ellipse[1] )\n assert len( center ) in (2,3)\n assert axes.shape[0] == axes.shape[1]\n assert axes.shape[1] in (2,3)\n assert len( center ) == axes.shape[1]\n \n ## Copy ray[0], since we will use -=.\n ray_pt = arrayf( ray[0] ).ravel()\n ray_dir = dir( asarrayf( ray[1] ) ).ravel()\n assert len( ray_pt ) in (2,3)\n assert len( ray_dir ) in (2,3)\n assert len( ray_pt ) == len( ray_dir )\n assert len( ray_pt ) == len( center )\n \n ## Translate so that the ellipsoid's center is at the origin.\n ray_pt -= center\n \n ## self.axes.T is the sphere to ellipse rotation and scale matrix.\n ## We want the inverse.\n rot_scale_mat = axes_inverse( axes )\n \n ## Transform the ray.\n ray_pt = dot( rot_scale_mat, ray_pt )\n ray_dir = dot( rot_scale_mat, ray_dir )\n \n ## Intersect with a unit sphere.\n closest_pt, intersected = closest_point_to_sphere_on_ray(\n ( zeros(ray_pt.shape), 1 ),\n ( ray_pt, ray_dir ),\n ray_is_line\n )\n \n ## Transform the closest point back to world (ellipse) space.\n closest_pt = dot( axes.T, closest_pt ) + center\n \n return closest_pt, intersected\n\ndef closest_point_on_ellipse_to_ray( ellipse, ray, ray_is_line = False ):\n '''\n Similar to closest_point_to_ellipse_on_ray().\n Differs only in the case where the ray does not intersect the ellipse, in which case\n the closest point on the ellipse, instead of on the ray, is returned.\n \n untested\n '''\n \n center = asarrayf( ellipse[0] ).ravel()\n axes = asarrayf( ellipse[1] )\n assert len( center ) in (2,3)\n assert axes.shape[0] == axes.shape[1]\n assert axes.shape[1] in (2,3)\n assert len( center ) == axes.shape[1]\n \n ## Copy ray[0], since we will use -=.\n ray_pt = arrayf( ray[0] ).ravel()\n ray_dir = dir( asarrayf( ray[1] ) ).ravel()\n assert len( ray_pt ) in (2,3)\n assert len( ray_dir ) in (2,3)\n assert len( ray_pt ) == len( ray_dir )\n assert len( ray_pt ) == len( center )\n \n ## Translate so that the ellipsoid's center is at the origin.\n ray_pt -= center\n \n ## self.axes.T is the sphere to ellipse rotation and scale matrix.\n ## We want the inverse.\n rot_scale_mat = axes_inverse( axes )\n \n ## Transform the ray.\n ray_pt = dot( rot_scale_mat, ray_pt )\n ray_dir = dot( rot_scale_mat, ray_dir )\n \n ## Intersect with a unit sphere.\n closest_pt, intersected = closest_point_on_sphere_to_ray(\n ( zeros(ray_pt.shape), 1 ),\n ( ray_pt, ray_dir ),\n ray_is_line\n )\n \n ## Transform the closest point back to world (ellipse) space.\n closest_pt = dot( axes.T, closest_pt ) + center\n \n return closest_pt, intersected\n\ndef closest_point_on_ellipse_to_point( ellipse, pt ):\n '''\n Given a point 'pt' and an ellipse ( 2/3d center, list of 2/3d axes ),\n returns the closest point on the surface of the ellipse to 'pt'.\n \n lightly tested\n '''\n \n center = asarrayf( ellipse[0] ).ravel()\n axes = asarrayf( ellipse[1] )\n assert len( center ) in (2,3)\n assert axes.shape[0] == axes.shape[1]\n assert axes.shape[1] in (2,3)\n assert len( center ) == axes.shape[1]\n \n ## Copy pt, since we will use -=.\n pt = arrayf( pt ).ravel()\n assert len( pt ) in (2,3)\n assert len( pt ) == len( center )\n \n ## Translate so that the ellipsoid's center is at the origin.\n pt -= center\n \n ## self.axes.T is the sphere to ellipse rotation and scale matrix.\n ## We want the inverse.\n rot_scale_mat = axes_inverse( axes )\n \n ## Transform the point.\n pt = dot( rot_scale_mat, pt )\n \n ## Find the closest point to a unit sphere.\n closest_pt = closest_point_on_sphere_to_point(\n ( zeros(pt.shape), 1 ),\n pt\n )\n \n ## Transform the closest point back to world (ellipse) space.\n closest_pt = dot( axes.T, closest_pt ) + center\n \n return closest_pt\n\ndef ellipse_contains_point( ellipse, point ):\n '''\n Given an ellipse ( 2/3d center, list of 2/3 axes ) and a 2/3d point,\n returns whether or not the point is inside the ellipse.\n \n tested\n '''\n \n return ellipse_contains_points( ellipse, (point,) )[0]\n\ndef ellipse_contains_points( ellipse, points ):\n '''\n Given an ellipse ( 2/3d center, list of 2/3 axes ) and a sequence of 2/3d points,\n returns a sequence of booleans, whether or not each point is inside the ellipse.\n \n tested\n '''\n \n center = asarrayf( ellipse[0] ).ravel()\n axes = asarrayf( ellipse[1] )\n ## 2d or 3d\n assert len( center ) in (2,3)\n ## Same number of axes as dimensions.\n assert axes.shape[0] == axes.shape[1]\n ## 2d or 3d\n assert axes.shape[1] in (2,3)\n ## The center and the axes have the same number of dimensions.\n assert len( center ) == axes.shape[1]\n \n if len( points ) == 0: return []\n ## Copy points, since we will use -=.\n points = arrayf( points )\n ## 2d or 3d\n assert points.shape[1] in (2,3)\n ## The point and the ellipse must have the same number of dimensions.\n assert points.shape[1] == len( center )\n \n ## Translate so that the ellipsoid's center is at the origin.\n points -= center\n \n ## axes.T is the sphere to ellipse rotation and scale matrix.\n ## We want the inverse.\n rot_scale_mat = axes_inverse( axes )\n \n ## Transform the points.\n points = dot( rot_scale_mat, points.T ).T\n \n ## Test against a unit sphere.\n return ( points**2 ).sum(1) < 1\n\n\ndef ellipse_normal_at_point( ellipse, point ):\n '''\n Given an ellipse ( 2/3d center, list of 2/3d axes ) and a 2/3d point,\n returns the normal for the point.\n Note that the point need not be on the surface of the ellipse.\n \n untested\n '''\n \n return ellipse_normal_at_points( ellipse, (point,) )[0]\n\ndef ellipse_normal_at_points( ellipse, points ):\n '''\n Given an ellipse ( 2/3d center, list of 2/3d axes ) and a sequence of 2/3d points,\n returns a sequence of normals for each point.\n Note that the points need not be on the surface of the ellipse.\n \n untested\n '''\n \n center = asarrayf( ellipse[0] ).ravel()\n axes = asarrayf( ellipse[1] )\n ## 2d or 3d\n assert len( center ) in (2,3)\n ## Same number of axes as dimensions.\n assert axes.shape[0] == axes.shape[1]\n ## 2d or 3d\n assert axes.shape[1] in (2,3)\n ## The center and the axes have the same number of dimensions.\n assert len( center ) == axes.shape[1]\n \n if len( points ) == 0: return []\n ## Copy points, since we will use -=.\n points = arrayf( points )\n ## 2d or 3d\n assert points.shape[1] in (2,3)\n ## The point and the ellipse must have the same number of dimensions.\n assert points.shape[1] == len( center )\n \n ## Translate so that the ellipsoid's center is at the origin.\n points -= center\n \n ## self.axes.T is the sphere to ellipse rotation and scale matrix.\n ## We want the inverse.\n rot_scale_mat = axes_inverse( axes )\n \n ## Transform the points into object-space.\n points = dot( rot_scale_mat, points.T ).T\n \n ## Get normals for the unit sphere.\n sphere_normals = points / sqrt( ( points**2 ).sum(1) )\n assert sphere_normals.shape == points.shape\n \n ## Normals must be transformed using the transpose of the object-to-world matrix.\n result = dot( axes, sphere_normals.T ).T\n assert result.shape == points.shape\n return result\n\ndef ellipse_points_for_thetas( ellipse, thetas ):\n '''\n Given an ellipse ( 2d center, list of 2d axes ) and a sequence of theta values,\n returns a sequence of 2d points:\n ellipse[0] + cos( thetas ) * ellipse[1][0] + sin( thetas ) * ellipse[1][1]\n \n The inverse routine is ellipse_thetas_for_points().\n \n tested\n '''\n \n d = 2\n N = len(thetas)\n \n center = asarray( ellipse[0] ).reshape((1,d))\n axis0 = asarray( ellipse[1][0] ).reshape((1,d))\n axis1 = asarray( ellipse[1][1] ).reshape((1,d))\n \n return center + cos( thetas ).reshape((N,1)) * axis0 + sin( thetas ).reshape((N,1)) * axis1\n\ndef ellipse_thetas_for_points( ellipse, points ):\n '''\n Given an ellipse ( 2d center, list of 2d axes ) and a sequence of 2d points on the ellipse,\n returns a sequence of theta values 'thetas' such that\n ellipse_points_for_thetas( ellipse, thetas ) returns 'points'.\n \n tested\n '''\n \n center = asarrayf( ellipse[0] ).ravel()\n axes = asarrayf( ellipse[1] )\n ## 2d\n assert len( center ) in (2,)\n ## Same number of axes as dimensions.\n assert axes.shape[0] == axes.shape[1]\n ## 2d\n assert axes.shape[1] in (2,)\n ## The center and the axes have the same number of dimensions.\n assert len( center ) == axes.shape[1]\n \n if len( points ) == 0: return []\n ## Copy points, since we will use -=.\n points = arrayf( points )\n ## 2d\n assert points.shape[1] in (2,)\n ## The point and the ellipse must have the same number of dimensions.\n assert points.shape[1] == len( center )\n \n ## Translate so that the ellipsoid's center is at the origin.\n points -= center\n \n ## axes.T is the sphere to ellipse rotation and scale matrix.\n ## We want the inverse.\n rot_scale_mat = axes_inverse( axes )\n \n ## Transform the points.\n points = dot( rot_scale_mat, points.T ).T\n \n ## Returns atan2 thetas for the unit circle.\n return [ atan2( y,x ) for x,y in points ]\n\ndef ellipse_volume( axes ):\n '''\n Given a sequence of ellipse axes ( orthogonal 2/3d vectors ), returns its area/volume.\n \n tested\n '''\n \n axes = asarrayf( axes )\n assert axes.shape[0] == axes.shape[1]\n abc = sqrt( ( axes**2 ).sum(1) ).prod()\n \n if axes.shape[0] == 2:\n return pi * abc\n elif axes.shape[0] == 3:\n return 4./3. * pi * abc\n\ndef sphere_volume( sphere ):\n '''\n Given a sphere ( 2/3d center, radius ), returns its area/volume.\n \n untested\n '''\n \n radius = sphere[1]\n \n dim = len( sphere[0] )\n if dim == 2:\n return pi * radius**3\n elif dim == 3:\n return 4./3. * pi * radius**3\n\ndef distance_to_line_squared( pt, line ):\n '''\n Given a point 'pt' and a tuple of two points on a line 'line',\n returns the distance from the point to the line, squared.\n \n tested\n '''\n \n ## This code is adapted from navi/.../yigYigCode/Geometry.cpp\n \n pt = asarrayf( pt )\n linePt = asarrayf( line[0] )\n lineVec = dir( asarrayf( line[1] ) - linePt )\n \n ## find the vector towards the point\n toPt = pt - linePt\n \n ## subtract the component of the line's direction from the vector towards the point\n toPt -= dot( toPt, lineVec ) * lineVec\n \n ## the result is the vector from the closest point on the line towards the pt\n return mag2( toPt )\n\ndef distance_to_ray_squared( pt, ray ):\n '''\n Given a point 'pt'\n and a tuple of a point and a direction vector 'ray',\n returns the distance from the point to the ray, squared.\n \n tested\n '''\n \n return mag2( asarray(pt) - closest_point_on_ray_to_point( ray, pt ) )\n\ndef closest_point_on_ray_to_point( ray, pt ):\n '''\n Given a tuple of a point and a direction vector 'ray'\n and a point 'pt',\n returns the closest point on the ray to the point.\n \n tested\n '''\n \n ray_pt, ray_dir = ray\n ray_pt = asarray( ray_pt )\n ray_dir = asarray( ray_dir )\n pt = asarray( pt )\n \n ## Find the closest point on the ray as a line.\n ## NOTE: This could be more efficient if we had a way to tell\n ## closest_point_on_line_to_point() to only perform 'max( t, 0 )'.\n closest_pt = closest_point_on_line_to_point( ( ray_pt, ray_pt + ray_dir ), pt )\n ## If that closest point is *behind* the ray,\n ## the ray point itself should be the closest point.\n if dot( closest_pt - ray_pt, ray_dir ) < 0:\n closest_pt = array( ray_pt )\n \n return closest_pt\n\ndef closest_point_on_line_to_point( line, pt, line_is_segment = False ):\n '''\n Given a tuple of two points on a line 'line',\n and a point 'pt',\n returns the closest point on the line to the point.\n If 'line_is_segment' is True, then the line is interpreted as a line segment.\n \n tested\n '''\n \n assert len( line ) == 2\n line = [ asarrayf( linept ).ravel() for linept in line ]\n pt = asarrayf( pt )\n ## All points on the line must have the same dimension.\n assert len( set( [ len(linept) for linept in line ] ) ) == 1\n ## The point and the line endpoints must have the same dimension.\n assert len( pt ) == len( line[0] )\n \n ## Get the normalized line direction.\n linevec = line[1] - line[0]\n linelen2 = mag2( linevec )\n \n ## Project the point onto the line.\n t = dot( pt - line[0], linevec )\n \n ## Clip t to [0,1] if 'line_is_segment'\n if line_is_segment: t = min( linelen2, max( 0., t ) )\n \n result = line[0] + (t/linelen2) * linevec\n return result\n\ndef closest_distsqr_and_point_and_edge_index_on_line_strip_to_point( line_strip, pt ):\n '''\n Given a line strip (sequence of n-dimensional points) 'linestrip' and n-dimensional point 'pt',\n returns the tuple (\n distance to the closest point on 'line_strip' to 'pt', squared,\n closest point on 'line_strip' to 'pt',\n index of point in 'line_strip' where the edge (index, index+1) contains the closest point )\n ).\n \n used\n '''\n \n assert len( line_strip ) > 0\n pt = asarrayf( pt )\n \n ## Degenerate case, one point in the line strip.\n if len( line_strip ) == 1: return ( mag2( pt - line_strip[0] ), line_strip[0], 0 )\n \n def gen_distsqr_pt_edge( i ):\n closest_pt = closest_point_on_line_to_point( line_strip[i:i+2], pt, line_is_segment = True )\n return ( mag2( pt - closest_pt ), closest_pt, i )\n \n return min( [ gen_distsqr_pt_edge( i ) for i in range( len( line_strip )-1 ) ], key = lambda el: ( el[0], el[2] ) )\n\ndef closest_distsqr_and_point_and_edge_index_on_line_loop_to_point( line_loop, pt ):\n '''\n Same as closest_distsqr_and_point_and_edge_index_on_line_strip_to_point(), but\n takes a line loop (closed path) instead of a line strip (open path).\n \n NOTE: The index of the closing edge is len( line_loop )-1.\n '''\n ## Append the first point to the end to turn the line loop into a line strip.\n return closest_distsqr_and_point_and_edge_index_on_line_strip_to_point(\n list( line_loop ) + [line_loop[0]], pt\n )\n\ndef closest_normal_with_cosangle_to_vector_on_plane( plane_normal, with_vector, I, closest_to, closest_not_farthest = True ):\n '''\n NOTE: Adapted from LaplacianEditingHelper.cpp in the my private git-devel meshopt branch.\n \n Returns a normal (unit vector) such that the cosine of the angle with the vector 'with_vector'\n is 'I', but constrained to lie on the plane whose normal is 'plane_normal'.\n There may be two solutions, in which case we choose the one closer to 'closest_to'.\n \n Q: How to compute this?\n A: Find the intersection of the cone and the circle on the plane.\n If there is no intersection, take the closest point to the cone, which will\n be the normalized projection of the light direction on the 'plane_normal' plane.\n Let normal = orthogonal( plane_normal ),\n Let right = dir( normal x plane_normal ),\n Let l = dir( with_vector ) . normal,\n Let m = dir( with_vector ) . right.\n Then (l,m) is the projection of the light direction onto the normal,right axes,\n and we want to find x,y such that x^2 + y^2 = 1 (x,y are on a unit circle)\n and (x,y) . (l,m) = x*l + y*m = I.\n There may be no solution, in which case the best we can do is the point on the circle\n closest to the cone, which is the unit vector in the direction of (l,m).\n (This assumes I >= 0.)\n There are ordinarily two solutions intersecting the circle, and Maxima gave them to me.\n We choose the solution closest to the 'closest_to' vector.\n \n (%i3) solve( [ x^2 + y^2 = 1, x*l + y*m = I], [x,y] );\n \n 2 2 2 2 2 2\n m sqrt(- I + m + l ) - l I l sqrt(- I + m + l ) + m I\n (%o3) [[x = - ----------------------------, y = ----------------------------], \n 2 2 2 2\n m + l m + l\n \n 2 2 2 2 2 2\n m sqrt(- I + m + l ) + l I l sqrt(- I + m + l ) - m I\n [x = ----------------------------, y = - ----------------------------]]\n 2 2 2 2\n m + l m + l\n \n tested\n '''\n \n plane_normal = asarrayf( plane_normal )\n with_vector = asarrayf( with_vector )\n closest_to = asarrayf( closest_to )\n \n ## I must be positive. Actually, we can handle negative by negating both I and with_vector.\n expect( I >= 0. )\n if( I < 0 ):\n with_vector = -with_vector\n I = -I\n \n ## I expect I to be less than 1, but if it's greater than one we will choose the \"closest\"\n ## normal, which will simply be the normal in the same direction as the projection of with_vector.\n expect( I <= 1. )\n \n with_vector = dir( with_vector )\n normal = orthogonal( plane_normal )\n right = dir( cross( plane_normal, normal ) )\n l = dot( with_vector, normal )\n m = dot( with_vector, right )\n \n denom = l*l + m*m\n ## Map the no solution case onto the 1 solution case.\n I = min( I, sqrt( denom ) )\n radical = sqrt( max( denom - I*I, 0. ) )\n \n x1 = -( m*radical - l*I )/denom\n y1 = ( l*radical + m*I )/denom\n \n x2 = ( m*radical + l*I )/denom\n y2 = -( l*radical - m*I )/denom\n \n ## We want the solution closest to the parameter 'closest_to'.\n xc = dot( closest_to, normal )\n yc = dot( closest_to, right )\n if( ( (x1-xc)*(x1-xc) + (y1-yc)*(y1-yc) < (x2-xc)*(x2-xc) + (y2-yc)*(y2-yc) ) == closest_not_farthest ):\n return x1 * normal + y1 * right\n else:\n return x2 * normal + y2 * right\n\ndef closest_point_along_ray_with_cosangle_to_vector( ray, I, with_vector, ray_is_line = False ):\n '''\n Given a ( point, vector ) tuple 'ray', returns a point along 'ray' that, when\n viewed as a vector (from the origin), has the given cosine angle 'I' with\n the vector 'with_vector'.\n If 'ray_is_line' is True, then the closest point may lie along the negative ray direction.\n \n tested\n '''\n \n ## We want 't' such that dot( ( ray_point + t * ray_dir ) / | ray_point + t * ray_dir |, with_vector ) = I\n ## Let d = ray_dir, c = ray_point, and v = with_vector.\n d = asarrayf( ray[1] )\n c = asarrayf( ray[0] )\n v = dir( asarrayf( with_vector ) )\n ## dot( c + td / |c + td|, v ) = I\n ## <=>\n ## dot( c + td, v ) / |c + td| = I\n ## <=>\n ## ( dot( c + td, v ) / |c + td| )^2 = I^2\n ## <=>\n ## dot( c + td, v )^2 / dot( c + td, c + td ) = I^2\n ## <=>\n ## ( dot( c, v ) + t dot( d, v ) )^2 = I^2 dot( c + td, c + td )\n ## <=>\n ## ( dot( c, v ) + t dot( d, v ) )^2 = I^2 ( dot( c, c + td ) + dot( td, c + td ) )\n ## <=>\n ## ( dot( c, v ) + t dot( d, v ) )^2 = I^2 ( dot( c, c ) + dot( c, td ) + dot( td, c ) + dot( td, td ) )\n ## <=>\n ## ( dot( c, v ) + t dot( d, v ) )^2 = I^2 ( dot( c, c ) + 2 dot( c, td ) + dot( td, td ) )\n ## <=>\n ## dot( c, v )^2 + 2 t dot( c, v ) dot( d, v ) + t^2 dot( d, v )^2 = I^2 ( dot( c, c ) + 2 dot( c, td ) + dot( td, td ) )\n ## <=>\n ## dot( c, v )^2 + 2 t dot( c, v ) dot( d, v ) + t^2 dot( d, v )^2 = I^2 ( dot( c, c ) + 2 t dot( c, d ) + t^2 dot( d, d ) )\n ## Let cv = dot( c, v ), dv = dot( d, v ), cc = dot( c, c ), cd = dot( c, d ), dd = dot( d, d )\n cv = dot( c, v )\n dv = dot( d, v )\n cc = dot( c, c )\n cd = dot( c, d )\n dd = dot( d, d )\n ## <=>\n ## cv^2 + 2 t cv dv + t^2 dv^2 = I^2 ( cc + 2 t cd + t^2 dd )\n ## <=>\n ## t^2 ( dv^2 - I^2 dd ) + t 2 ( cv dv - I^2 cd ) + ( cv^2 - I^2 cc ) = 0\n ## <=> quadratic equation x = ( -b +- sqrt( b^2 - 4ac ) )/( 2a )\n qa = dv**2 - I**2 * dd\n qb = 2 * ( cv * dv - I**2 * cd )\n qc = cv**2 - I**2 * cc\n qrad = qb**2 - 4*qa*qc\n ## This should be ~zero, or else we may have a degenerate have used the \"closest\n ## point outside the sphere\".\n assert qrad >= -1e-5\n qrad = sqrt( max( 0., qrad ) )\n ts = ( -qb + qrad )/( 2*qa ), ( -qb - qrad )/( 2*qa )\n if not ray_is_line: ts = [ t for t in ts if t >= 0. ]\n assert len( ts ) > 0\n ts = [ ( fabs(t), t ) for t in ts ]\n t = min( ts )[1]\n \n #print 't:', t, 'd:', d, 'c:', c\n result = c + t * d\n return result\n \n ## If we were interested in the dot product and not the cosine angle (that is, un-normalized),\n ## then we could do the much simpler:\n ## NOTE: This code is untested.\n '''\n ## We want 't' such that dot( ( ray_point + t * ray_dir ), with_vector ) = I\n ## Let d = ray_dir, c = ray_point, and v = with_vector.\n d = asarrayf( ray[1] )\n c = asarrayf( ray[0] )\n v = asarrayf( with_vector )\n ## dot( c + td, v ) = I\n ## <=>\n ## dot( c, v ) + t dot( d, v ) = I\n ## <=>\n ## t dot( d, v ) = I - dot( c, v )\n ## <=>\n ## t = ( I - dot( c, v ) ) / dot( d, v )\n t = ( I - dot( c, v ) ) / dot( d, v )\n \n return c + t * d\n '''\n\ndef project_point_to_plane( point, plane ):\n '''\n Given a point 'point' and a tuple 'plane' consisting of ( point on plane, normal to plane ),\n returns the closest point in the plane to 'point'.\n \n tested\n '''\n \n point = asarrayf( point )\n plane_p = asarrayf( plane[0] )\n plane_n = asarrayf( plane[1] )\n \n assert len( point ) == len( plane_p )\n assert len( plane_p ) == len( plane_n )\n \n ## Make sure the plane normal is normalized.\n plane_n = dir( plane_n )\n \n plane_to_point = point - plane_p\n result = plane_p + ( plane_to_point - dot( plane_to_point, plane_n ) * plane_n )\n return result\n\ndef reflect_point_across_plane( point, plane ):\n '''\n Given a point 'point' and a tuple 'plane' consisting of ( point on plane, normal to plane ),\n returns 'point' reflected across 'plane'.\n \n tested\n '''\n \n point = asarrayf( point ).ravel()\n plane_point = project_point_to_plane( point, plane )\n ## Reflection of 'point' across 'plane' is adding to 'point' twice the vector from\n ## 'point' to the closest point on the plane, 'plane_point'.\n ## point + 2 * ( plane_point - point )\n ## <=>\n ## 2 * plane_point - point\n return 2 * plane_point - point\n\ndef ray_plane_intersection_t( ray, plane ):\n '''\n Given a ( point, vector ) tuple 'ray'\n and a tuple 'plane' consisting of ( point on plane, normal to plane ),\n returns the t value such that ray[0] + t*ray[1] intersects the plane\n or None if they do not intersect.\n \n tested\n '''\n \n ## A ray o + t*d intersects a plane passing through point p with normal N when\n ## dot( o + t*d - p, N ) = 0 = dot( o - p, N ) + t*dot( d, N )\n ## <=>\n ## t*dot( d, N ) = dot( p - o, N )\n ## <=>\n ## t = dot( p - o, N ) / dot( d, N )\n \n o = ray[0]\n d = ray[1]\n p = plane[0]\n N = plane[1]\n \n assert len( o ) == len( d )\n assert len( p ) == len( N )\n assert len( o ) == len( p )\n \n denom = dot( d, N )\n if abs( denom ) < kEps:\n #print \"Ray and plane don't intersect!\"\n return None\n \n t = dot( asarray(p) - o, N ) / float(denom)\n return t\n\ndef ray_plane_intersection( ray, plane, ray_is_line = False ):\n '''\n Given a ( point, vector ) tuple 'ray'\n and a tuple 'plane' consisting of ( point on plane, normal to plane ),\n returns the closest point along ray that intersects the plane\n or None if they do not intersect.\n If 'ray_is_line' is True, then the closest point may lie along the negative ray direction.\n \n tested\n '''\n \n t = ray_plane_intersection_t( ray, plane )\n if t is None or t < 0. and not ray_is_line: return None\n \n return asarray( ray[0] ) + float(t) * asarray( ray[1] )\n\ndef print_matlab( arr ):\n '''\n Given an array or matrix, prints it out in such a way that it can be copied and pasted into\n Matlab.\n \n tested\n '''\n \n arr = asarray( arr )\n N = 1\n if len( arr.shape ) > 1: N = arr.shape[1]\n \n print( '[ ', end='' )\n count = 0\n for v in arr.ravel():\n if count == N:\n print( '; ', end='' )\n count = 0\n print( '%s ' % (v,), end='' )\n count += 1\n print( ']' )\n\ndef lsq_ellipse( pts ):\n '''\n Given a list of points 'pts', returns a least squares fit of an ellipse to the points as a\n tuple ( ellipse_center, [ ellipse_axis1, ellipse_axis2 ] ).\n The long axis is ellipse_axis1.\n \n tested\n '''\n \n try:\n from fitellipse import fitellipse\n #z, a, b, alpha = fitellipse( asarray( pts ), 'nonlinear', constraint = 'trace' )\n #print_matlab( pts )\n #z, a, b, alpha = fitellipse( asarray( pts ), 'linear', constraint = 'trace' )\n #z, a, b, alpha = fitellipse( asarray( pts ), 'linear', constraint = 'bookstein' )\n z, a, b, alpha = fitellipse( asarray( pts ), 'linear' )\n #z, a, b, alpha = fitellipse( asarray( pts ) )\n #print z, a, b, alpha\n \n center = asarray( z ).reshape((2,))\n axis1 = a * cos( alpha ), a * sin( alpha )\n axis2 = b * -sin( alpha ), b * cos( alpha )\n \n if b > a: axis1, axis2 = axis2, axis1\n \n return center, asarray( [ asarray( axis1 ), asarray( axis2 ) ] )\n \n except linalg.linalg.LinAlgError:\n return None\n except RuntimeError as e:\n return None\n \n #print e\n \n pt0 = asarray( pts[0] )\n pt1 = asarray( pts[-1] )\n r = .5 * mag( pt1 - pt0 )\n return ( .5 * (pt0 + pt1), asarray( [ r * array((1,0)), r * array((0,1)) ] ) )\n\ndef ellipse_bbox( ellipse ):\n '''\n Returns the bounding box of an ellipse.\n Each ellipse is a tuple ( 2/3d center, list of 2/3d axes ),\n and the bounding box result is the tuple ( minimum coordinates, maximum coordinates ).\n \n used\n '''\n \n ellipse = asarray( ellipse[0] ), asarray( ellipse[1] )\n ## center +- ( take the absolute value of the axes, and then take the max coordinate among all axes )\n return ellipse[0] + abs( ellipse[1] ).max(0), ellipse[0] - abs( ellipse[1] ).max(0)\n\ndef bbox_union( bbox1, bbox2 ):\n '''\n Returns the union of two bounding boxes 'bbox1', 'bbox2', where each bounding box\n parameter is the tuple ( maximum coordinates, minimum coordinates ),\n where the coordinates are, for example, xyz.\n \n used\n '''\n \n return (\n asarray( [ bbox1[0], bbox2[0] ] ).max(0),\n asarray( [ bbox1[1], bbox2[1] ] ).min(0)\n )\n\ndef bbox_intersection( bbox1, bbox2 ):\n '''\n Returns the intersection of two bounding boxes 'bbox1', 'bbox2', where each bounding box\n parameter is the tuple ( maximum coordinates, minimum coordinates ),\n where the coordinates are, for example, xyz.\n \n used\n '''\n \n return (\n asarray( [ bbox1[0], bbox2[0] ] ).min(0),\n asarray( [ bbox1[1], bbox2[1] ] ).max(0)\n )\n\ndef ellipses_intersect( ellipse1, ellipse2 ):\n '''\n Returns whether or not 'ellipse1' and 'ellipse2' intersect.\n Each ellipse is a tuple ( 2/3d center, list of 2/3d axes ).\n Currently only 2d ellipses are implemented, so center must be 2d and there must be two 2d axes.\n \n tested\n '''\n \n ellipse1 = ( asarray( ellipse1[0] ).ravel(), asarray( ellipse1[1] ) )\n ellipse2 = ( asarray( ellipse2[0] ).ravel(), asarray( ellipse2[1] ) )\n \n assert len( ellipse1[0] ) == 2\n assert ellipse1[1].shape[1] == 2\n assert len( ellipse2[0] ) == 2\n assert ellipse2[1].shape[1] == 2\n \n ellipse1_bbox = ellipse_bbox( ellipse1 )\n ellipse2_bbox = ellipse_bbox( ellipse2 )\n both_bbox = bbox_intersection( ellipse1_bbox, ellipse2_bbox )\n \n maxx = both_bbox[0][0]\n maxy = both_bbox[0][1]\n minx = both_bbox[1][0]\n miny = both_bbox[1][1]\n \n ## Sample a uniform grid inside the bounding box\n N = 1000\n ## We want to sample space uniformly.\n ## This means N samples should cover the area (maxx-minx) * (maxy-miny) uniformly.\n ## Let NX be the number of samples in x.\n ## Then we want NX * (maxy-miny)/(maxx-minx) samples in y\n ## and N = NX * NX * (maxy-miny)/(maxx-minx).\n ## So\n NX = max( 3, sqrt( N / (maxy-miny) * (maxx-minx) ) )\n NY = max( 3, NX * (maxy-miny)/(maxx-minx) )\n #print 'NX, NY:', NX, NY\n xs = linspace( minx, maxx, int(round( NX )) )\n ys = linspace( miny, maxy, int(round( NY )) )\n ## This line performs the cartesian product of 'xs' and 'ys', giving us a list of 2d points.\n pts = asarray( map( lambda a: a.ravel(), meshgrid( xs, ys ) ) ).T\n ellipse1_contains = ellipse_contains_points( ellipse1, pts )\n ellipse2_contains = ellipse_contains_points( ellipse2, pts )\n return logical_and( ellipse1_contains, ellipse2_contains ).any()\n\ndef ellipses_overlap_area( ellipse1, ellipse2, accuracy = 1e-3 ):\n '''\n Returns the area of overlap between 'ellipse1' and 'ellipse2'.\n Each ellipse is a tuple ( 2/3d center, list of 2/3d axes ).\n Currently only 2d ellipses are implemented, so center must be 2d and there must be two 2d axes.\n \n Optional parameters:\n accuracy, which determines the floating point accuracy to achieve (default 1e-3)\n \n used\n '''\n \n assert accuracy > 0.\n oo_accuracy = 1. / accuracy\n \n ellipse1 = ( asarray( ellipse1[0] ).ravel(), asarray( ellipse1[1] ) )\n ellipse2 = ( asarray( ellipse2[0] ).ravel(), asarray( ellipse2[1] ) )\n \n assert len( ellipse1[0] ) == 2\n assert ellipse1[1].shape[1] == 2\n assert len( ellipse2[0] ) == 2\n assert ellipse2[1].shape[1] == 2\n \n ellipse1_bbox = ellipse_bbox( ellipse1 )\n ellipse2_bbox = ellipse_bbox( ellipse2 )\n both_bbox = bbox_intersection( ellipse1_bbox, ellipse2_bbox )\n \n maxx = both_bbox[0][0]\n maxy = both_bbox[0][1]\n minx = both_bbox[1][0]\n miny = both_bbox[1][1]\n \n ## Sample a uniform grid inside the bounding box\n N = max( 1, int( round( oo_accuracy ) ) )\n ## We want to sample space uniformly.\n ## This means N samples should cover the area (maxx-minx) * (maxy-miny) uniformly.\n ## Let NX be the number of samples in x.\n ## Then we want NX * (maxy-miny)/(maxx-minx) samples in y\n ## and N = NX * NX * (maxy-miny)/(maxx-minx).\n ## So\n NX = max( 3, sqrt( N / (maxy-miny) * (maxx-minx) ) )\n NY = max( 3, NX * (maxy-miny)/(maxx-minx) )\n ## Round to the nearest integer.\n NX = int( round( NX ) )\n NY = int( round( NY ) )\n #print 'NX, NY:', NX, NY\n xs = linspace( minx, maxx, NX )\n ys = linspace( miny, maxy, NY )\n ## This line performs the cartesian product of 'xs' and 'ys', giving us a list of 2d points.\n pts = asarray( map( lambda a: a.ravel(), meshgrid( xs, ys ) ) ).T\n ellipse1_contains = ellipse_contains_points( ellipse1, pts )\n ellipse2_contains = ellipse_contains_points( ellipse2, pts )\n both_contain_samples = sum( logical_and( ellipse1_contains, ellipse2_contains ) )\n ## Each sample represents a little square with area ( (maxx-minx)/NX ) * ( (maxy-miny)/NY )\n return both_contain_samples * ( (maxx-minx)/NX ) * ( (maxy-miny)/NY )\n\ndef center_of_mass_of_ellipses_intersection( ellipse1, ellipse2, accuracy = 1e-3, timeout = 1e20 ):\n '''\n Returns the center of mass of the intersection of 'ellipse1' and 'ellipse2' as a 2/3d point.\n Each ellipse is a tuple ( 2/3d center, list of 2/3d axes ).\n NOTE: This routine will not terminate if the ellipses don't overlap.\n \n Optional parameters:\n accuracy, which determines the floating point accuracy to achieve (default 1e-3)\n timeout, which determines the number of seconds after which to terminate via exception with two args, number of hits and current center-of-mass\n \n used\n '''\n \n assert accuracy > 0.\n oo_accuracy = 1. / accuracy\n \n if timeout != 1e20: raise NotImplementedError\n \n ellipse1 = ( asarray( ellipse1[0] ).ravel(), asarray( ellipse1[1] ) )\n ellipse2 = ( asarray( ellipse2[0] ).ravel(), asarray( ellipse2[1] ) )\n \n ellipse1_bbox = ellipse_bbox( ellipse1 )\n ellipse2_bbox = ellipse_bbox( ellipse2 )\n both_bbox = bbox_intersection( ellipse1_bbox, ellipse2_bbox )\n \n #from pydb import debugger\n #debugger()\n \n ## Random sample many times\n \n ## The number of points to test at once\n M = max( 1, int( round( oo_accuracy ) ) )\n \n tries = 0\n hits = 0\n com = zeros( ellipse1[0].shape )\n while hits < oo_accuracy and (tries == 0 or float(hits)/tries > accuracy):\n \n ## Test M points at once using ellipse_contains_points()\n #pt = [ random.uniform( minval, maxval ) for minval, maxval in zip( both_bbox[1], both_bbox[0] ) ]\n pts = random.random_sample( ( M, len( ellipse1[0] ) ) )\n for i, (minval, maxval) in enumerate( zip( both_bbox[1], both_bbox[0] ) ):\n pts[:,i] *= (maxval - minval)\n pts[:,i] += minval\n \n new_hits = logical_and(\n ellipse_contains_points( ( ellipse1[0], ellipse1[1] ), pts ),\n ellipse_contains_points( ( ellipse2[0], ellipse2[1] ), pts )\n )\n \n hits += sum( new_hits )\n com += pts[ new_hits ].sum(0)\n tries += M\n \n #print 'hits:', hits\n \n if hits == 0: raise RuntimeError( \"Ellipses don't intersect\" )\n com *= 1./hits\n return com\n\ndef closest_point_on_tilted_circle2d_to_line( tilted_circle, line ):\n '''\n Given a tilted 2d circle in space (3d center, radius, 3d normal)\n and a 3d line defined by a pair of 3d points,\n returns the closest 3d point on the circle to the line.\n \n untested\n '''\n \n center, radius, normal = tilted_circle\n \n assert len( center ) == 3\n assert float( radius ) == radius\n assert len( normal ) == 3\n \n normal = asarray( normal ).ravel()\n \n axis0 = radius * orthogonal( normal )\n axis1 = radius * dir( cross( normal, axis0 ) )\n \n tilted_ellipse = ( center, ( axis0, axis1 ) )\n return closest_point_on_tilted_ellipse2d_to_line( tilted_ellipse, line )\n\ndef closest_point_on_tilted_ellipse2d_to_line( tilted_ellipse, line ):\n '''\n Given a tilted 2d ellipse in space (3d center, list of two 3d axes)\n and a 3d line defined by a pair of 3d points,\n returns the closest 3d point on the ellipse to the line.\n \n untested\n '''\n \n tilted_ellipse = ( asarray( tilted_ellipse[0] ).ravel(), asarray( tilted_ellipse[1] ) )\n cos_theta, sin_theta = closest_point_coeffs_on_tilted_ellipse2d_to_line( tilted_ellipse, line )\n return tilted_ellipse[0] + cos_theta * tilted_ellipse[1][0] + sin_theta * tilted_ellipse[1][1]\n\ndef closest_point_coeffs_on_tilted_ellipse2d_to_line( tilted_ellipse, line ):\n '''\n Given a tilted 2d ellipse in space (3d center, list of two 3d axes)\n and a 3d line defined by a pair of 3d points,\n returns the ellipse axis coefficients of the closest point on the ellipse to the line,\n where the ellipse axis coefficients are a pair of values (a,b) such that\n tilted_ellipse[0] + a * tilted_ellipse[1][0] + b * tilted_ellipse[1][1]\n is the closest point.\n \n NOTE: If the ellipse is parameterized in radians from 0 to 2*pi, where\n 0 is the ellipse center + first axis,\n pi/2 is the ellipse center + second axis,\n pi is the ellipse center - first axis, and\n 3*pi/2 is the ellipse center 0 second axis,\n then the parameter in radians is atan2( b, a ).\n \n tested\n '''\n \n tilted_ellipse = ( asarray( tilted_ellipse[0] ).ravel(), asarray( tilted_ellipse[1] ) )\n line = ( asarray( line[0] ).ravel(), asarray( line[1] ).ravel() )\n \n assert len( tilted_ellipse[0] ) == 3\n assert tilted_ellipse[1].shape == (2,3)\n assert len( line[0] ) == 3\n assert len( line[0] ) == len( line[1] )\n \n ## 1 Transform (by rotation) into the space where the line is a point in the\n ## xy plane (its direction is parallel to the z axis).\n ## 2 Find the closest point on the transformed ellipse projected onto the xy plane.\n \n ## 1\n ray = ( line[0], line[1] - line[0] )\n ray2point_rot_mat = array( get_rotation_matrix( *get_axis_angle_rotation( ray[1], (0,0,-1) ) ) )\n ray_point = dot( ray2point_rot_mat, ray[0] )\n \n tilted_ellipse_rot = (\n dot( ray2point_rot_mat, tilted_ellipse[0] ),\n dot( ray2point_rot_mat, tilted_ellipse[1].T ).T\n )\n tilted_ellipse2d = (\n tilted_ellipse_rot[0][:2],\n tilted_ellipse_rot[1][:,:2]\n )\n \n ## 2\n closest_pt_ellipse2d = closest_point_on_ellipse_to_point( tilted_ellipse2d, ray_point[:2] )\n ## NOTE: The desired 3d point is *not* obtained by inverse rotation of the 2d point,\n ## dot( ray2point_rot_mat.T, append( tilted_ellipse2d_pt, 0. ) ),\n ## because we have stripped away the z component.\n ## Instead, we have to find the coefficients for each axis, which is the same in both\n ## 2d and 3d. The coefficients can then be used to find the 3d point.\n ## NOTE: We have to invert the ellipse axes --- we can't just take the transpose ---\n ## because they aren't orthonormal.\n cos_theta, sin_theta = linalg.solve(\n asarray( tilted_ellipse2d[1] ).T,\n closest_pt_ellipse2d - tilted_ellipse2d[0]\n )\n return cos_theta, sin_theta\n\ndef line_loop_is_CCW( line_loop ):\n '''\n Given a 2d line loop (closed sequence of 2d points) 'line_loop',\n returns true if the line loop is oriented in a counter clock-wise (CCW) fashion.\n \n used\n '''\n \n line_loop = asarray( line_loop )\n N = line_loop.shape[0]\n \n ## A 2d line loop is an N-by-2 array, where N is at least three.\n assert len( line_loop.shape ) == 2\n assert N >= 3\n assert line_loop.shape[1] == 2\n \n ## Based on comp.graphics.algorithms FAQ 2.07:\n ## http://www.faqs.org/faqs/graphics/algorithms-faq/\n ## http://maven.smith.edu/~orourke/Code/polyorient.C\n \n ## First find the bottom vertex (and right-most, in case of a tie).\n ## Find the smallest y coordinate.\n miny = min( line_loop[:,1] )\n ## Find all points with y coordinate equal to the smallest y coordinate.\n candidates = [\n ( pt, index )\n for index, pt in enumerate( line_loop )\n if pt[1] == miny\n ]\n ## Get the index of the point with the largest x coordinate, among the candidates.\n br = max( candidates, key = lambda el: el[0][0] )[1]\n \n a = line_loop[ br - 1 ]\n b = line_loop[ br ]\n c = line_loop[ ( br + 1 ) % N ]\n \n two_area = (\n a[0] * b[1] - a[1] * b[0] +\n a[1] * c[0] - a[0] * c[1] +\n b[0] * c[1] - c[0] * b[1]\n )\n return two_area >= 0.\n\ndef angle2D( one, two ):\n '''\n Returns the angle in radians [-pi,pi] turned counter-clockwise between vector `one`\n and vector `two`.\n In other words, if `one` were to turn counter-clockwise by the returned angle,\n it would have the same direction as `two`:\n dir( two ) == rotate2D( dir( one ), angle2D( one, two ) )\n \n tested:\n >>> one = (1,0)\n >>> two = (0,1)\n >>> allclose( two, rotate2D( one, angle2D( one, two ) ) )\n '''\n \n one = dir( asfarray( one ) )\n two = dir( asfarray( two ) )\n \n angle = acos( max( -1., min( 1., dot( one,two ) ) ) )\n if cross_z( one,two ) < 0.:\n angle = -angle\n return angle\n\ndef cross_z( a,b ):\n '''\n Returns the z component of the cross product of vectors a,b.\n \n NOTE: tested\n '''\n return a[0]*b[1] - a[1]*b[0]\n\ndef rotate2D( vec, radians ):\n '''\n Returns the 2D vector `vec` rotated by `radians`.\n '''\n \n assert len(vec) == 2\n assert radians == float(radians)\n \n rotated = zeros( 2 )\n c = cos( radians )\n s = sin( radians )\n rotated[0] = c*vec[0] - s*vec[1]\n rotated[1] = s*vec[0] + c*vec[1]\n return rotated\n\ndef solve_for_fixed_points( points, src0, src1, dst0, dst1 ):\n '''\n Given 2d points `src0`, `src1` and `dst0`, `dst1`,\n finds the 2D transform (uniform scale, rotate, translate)\n that maps src0 to dst0 and src1 to dst1.\n Returns 'points', a list of 2d points, with this transform applied.\n \n If any parameters have more than two coordinates, only the first two coordinates are used,\n and 2d coordinates are returned.\n '''\n \n coefs = array((\n ( src0[0], -src0[1], 1, 0 ),\n ( src1 [0], -src1 [1], 1, 0 ),\n ( src0[1], src0[0], 0, 1 ),\n ( src1 [1], src1 [0], 0, 1 )\n ))\n rhs = array( (\n dst0[0],\n dst1[0],\n dst0[1],\n dst1[1]\n ) )\n \n sol = linalg.solve( coefs, rhs )\n \n xformed = [\n (\n sol[2] + sol[0] * pt[0] - sol[1] * pt[1],\n sol[3] + sol[1] * pt[0] + sol[0] * pt[1]\n )\n for pt in points\n ]\n \n return xformed\n","repo_name":"yig/vecutil","sub_path":"vecutil.py","file_name":"vecutil.py","file_ext":"py","file_size_in_byte":58588,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"20516061007","text":"from IPython.core.magic import Magics, magics_class, line_cell_magic\nfrom mlnotify.mlnotify import plugin_manager\n\n# Jupyter line and cell magic\n@magics_class\nclass MLNotifyMagic(Magics):\n @line_cell_magic\n def notify(self, line, cell=None):\n plugin_manager.run_before()\n self.shell.run_cell(line)\n if cell is not None:\n self.shell.run_cell(cell)\n plugin_manager.run_after()\n\n\ndef register_jupyter_magic():\n ipython = get_ipython()\n ipython.register_magics(MLNotifyMagic)\n\n","repo_name":"aporia-ai/mlnotify","sub_path":"sdk/src/mlnotify/jupyter_magic.py","file_name":"jupyter_magic.py","file_ext":"py","file_size_in_byte":524,"program_lang":"python","lang":"en","doc_type":"code","stars":339,"dataset":"github-code","pt":"52"} +{"seq_id":"18779924162","text":"from enum import Enum\nfrom abc import ABC, abstractmethod\n\nclass Member:\n def __init__(\n self,\n hit_point: int,\n magic_point: int,\n max_hit_point: int\n ):\n self.hit_point = hit_point\n self.magic_point = magic_point\n self.max_hit_point = max_hit_point\n \n def can_act(self):\n return True\n \n def consume_magic_point(self, cost_magic_point: int):\n print(\"consume!\")\n\n def chant(self, magic):\n print(\"발동!\")\n \n def add_state(self, state: \"HealthCondition\"):\n ...\n\nclass HealthCondition(Enum):\n dead = 1\n danger = 2\n caution = 3\n fine = 4\n\n\nclass DamageType(Enum):\n hit_point = 1\n magic_point = 2\n\n\nclass Damage(ABC):\n @abstractmethod\n def execute(self, damage_amount: int) -> Member:\n ...\n\nclass HitPointDamage(Damage):\n def execute(self, member: Member, damage_amount: int) -> Member:\n member.hit_point -= damage_amount\n if 0 < member.hit_point: \n return member\n\n member.hit_point = 0\n member.add_state(HealthCondition.dead)\n\n return member\n\n\nclass MagicPointDamage(Damage):\n def execute(self, member: Member, damage_amount: int) -> Member:\n member.magic_point -= damage_amount\n if 0 < member.magic_point: \n return member\n\n member.magic_point = 0\n\n return member\n\ndamages = {\n DamageType.hit_point: HitPointDamage(), \n DamageType.magic_point: MagicPointDamage()\n}\n\ndef apply_damage(member: Member, damage_type: DamageType, damage_amount: int):\n damage = damages.get(damage_type)\n return damage.execute(member, damage_amount)\n\n\nif __name__ == \"__main__\":\n member = Member(\n hit_point=50,\n magic_point=60,\n max_hit_point=100\n )\n member = apply_damage(member, DamageType.hit_point, 30)\n print(member.hit_point)\n\n member = apply_damage(member, DamageType.hit_point, 50)\n print(member.hit_point)\n\n member = apply_damage(member, DamageType.magic_point, 50)\n print(member.magic_point)\n\n member = apply_damage(member, DamageType.magic_point, 50)\n print(member.magic_point)\n","repo_name":"salmon131/ood","sub_path":"ch_06/06.py","file_name":"06.py","file_ext":"py","file_size_in_byte":2173,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"16099904057","text":"#----------------------------------------------------------------------------\n# PROGRAM NAME: MC_pi.py\n# PURPOSE: Playing with random numbers. \n# CREATED: Craig Group 2022\n#\n# Class 25\n# UVA Honor Pledge\n# \n# To run me: python MC_pi.py 100000 1 \n# To run me: python MC_pi.py N_loop seed \n#____________________________________________________________________________\n\n#------------------------------------------------\n# IMPORTS\n#------------------------------------------------\nimport sys\nimport random\nfrom datetime import datetime\nimport time\nimport math\n\n\n#------------------------------------------------\n# GLOBAL CONSTANTS\n#------------------------------------------------\n\n\n\nPROGRAM_NAME=sys.argv[0]\nSTART = time.time()\n\nYMIN=-1\nYMAX=1\nXMIN=-1\nXMAX=1\n\nn_int=0\nseed=1\n\n#Input validation\nif (len(sys.argv)==3):\n\n print(f\"\\n-->User input: N:{sys.argv[1]} seed:{sys.argv[2]}\\n\")\n try:\n n_int=int(sys.argv[1])\n seed=int(sys.argv[2])\n except:\n print(\"ALERT: Prameters should be integers!\\n\")\n n_int=0 #This makes sure the program won't try to run\n\nelse:\n print(\"ALERT: Please re-run the program with 2 parameters: N and seed.\\n\")\n \n#------------------------------------------------\n# METHODS\n#------------------------------------------------\n#None\n\ndef in_fnc(x,y):\n if (math.sqrt(x*x+y*y)<1):\n return 1\n else:\n return 0\n\n\n#------------------------------------------------\n# MAIN FUNCTION\n#------------------------------------------------\ndef main(n_int,seed):\n\n\n n_in=0\n for i in range(0,int(n_int)):\n x=random.uniform(XMIN,XMAX)\n y=random.uniform(YMIN,YMAX)\n #print(x,\" \",y)\n if in_fnc(x,y):\n n_in+=1\n \n area=(XMAX-XMIN)*(YMAX-YMIN)*n_in/float(n_int)\n print(area)\n print(f\"\\n\\npi={area:15.14f} with n_int={n_int} \\n \")\n\n\n\n#Only run the main function is n_int is not zero\nif n_int:\n START = time.time()\n random.seed(int(seed))\n print(\"-->You are running the program: \"+PROGRAM_NAME+\"\\n\\n\")\n #print(PROGRAM_NAME) \n main(int(n_int),seed)\n #print info about how long it takes... \n print(f\" --- {(time.time() - START):6.2f} seconds ---\" )\n\n","repo_name":"PHYS1655F22/class25","sub_path":"MC_pi.py","file_name":"MC_pi.py","file_ext":"py","file_size_in_byte":2279,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"4657367763","text":"import numpy as np\nimport pytest\nfrom bamot.core.base_types import (CameraParameters, Landmark, ObjectTrack,\n Observation, StereoCamera)\nfrom bamot.core.optimization import object_bundle_adjustment\nfrom bamot.util.cv import from_homogeneous, project, to_homogeneous\n\nRNG = np.random.default_rng()\n\n\n@pytest.fixture\ndef camera_params():\n return CameraParameters(fx=500, fy=500, cx=320, cy=240)\n\n\n@pytest.fixture\ndef T_left_right():\n T = np.identity(4)\n T[3, 3] = 0.03 # baseline of 3 cms\n\n\n@pytest.fixture\ndef stereo_camera(camera_params, T_left_right):\n return StereoCamera(\n left=camera_params, right=camera_params, T_left_right=T_left_right\n )\n\n\n@pytest.fixture\ndef steps():\n return 10\n\n\n@pytest.fixture(scope=\"module\")\ndef object_points():\n # landmarks are w.r.t. object coordinate system (which is identical to first camera seeing object)\n # sample 500 points uniformly around (x, y, z) = (1, 1, 3) with max dimensions of 3m around center\n # add landmarks\n pts_3d = RNG.uniform(-1.5, 1.5, (500, 3)) + np.array([1, 1, 3])\n return pts_3d\n\n\n@pytest.fixture\ndef object_poses(steps):\n poses = {}\n pose = np.identity(4)\n poses[0] = pose\n motion_transform = np.identity(4)\n motion_translation = np.array([0.05, 0.01, 0.1])\n motion_transform[:3, 3] = motion_translation\n for i in range(1, steps - 1):\n pose = motion_transform @ poses[i - 1]\n poses[i] = pose\n return poses\n\n\n@pytest.fixture\ndef landmarks(object_points, object_poses, camera_params):\n # go through all object points\n lms = {}\n for i, pt_obj in enumerate(object_points):\n # for every object pose, decide whether point was seen\n # transform point to current pose\n # project onto camera + add some noise\n observations = []\n for pose_id, pose in object_poses.items():\n if RNG.normal(0.8, 0.5) <= 0.5:\n # point isn't observed at this pose\n continue\n pt_3d = from_homogeneous(pose @ to_homogeneous(pt_obj))\n pt_2d = (project(camera_params, pt_3d) + RNG.random() * 2).reshape(\n (2,)\n ) # up to two pixel error\n observations.append(\n Observation(descriptor=None, pt_2d=pt_2d, timecam_id=(pose_id, 0))\n )\n landmark = Landmark(pt_obj, observations)\n lms[i] = landmark\n return lms\n\n\n@pytest.fixture\ndef object_track(landmarks, object_poses):\n return ObjectTrack(\n landmarks=landmarks,\n current_pose=list(object_poses.values())[-1],\n poses=object_poses,\n velocity=np.array([0.0, 0.0, 0.0]),\n active=True,\n )\n\n\ndef test_object_bundle_adjustment(object_track, object_poses, stereo_camera):\n updated_object_track = object_bundle_adjustment(\n object_track=object_track, all_poses=object_poses, stereo_cam=stereo_camera\n )\n assert False\n\n\n# TODO:\n# Create same test but with example optimization and see if it converges\n","repo_name":"AnselmC/bamot","sub_path":"bamot/core/test_optimization.py","file_name":"test_optimization.py","file_ext":"py","file_size_in_byte":3022,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"52"} +{"seq_id":"519588236","text":"import asyncio\nfrom bleak import BleakClient\n\nclass BleClient:\n address = ''\n write_characteristic_id = ''\n read_characteristic_id = ''\n loop = None\n\n def __init__(self, address, write_characteristic_id, read_characteristic_id):\n self.address = address\n self.write_characteristic_id = write_characteristic_id\n self.read_characteristic_id = read_characteristic_id\n self.loop = asyncio.get_event_loop()\n\n def read(self, commands):\n return self.loop.run_until_complete(self.__read(commands))\n\n def write(self, commands, values):\n return self.loop.run_until_complete(self.__write(commands, values))\n\n def __create_read_command(self, c):\n r_command = bytearray([c])\n r_command.append(0x00) # 長さ0\n return r_command\n\n def __create_write_command(self, c, v):\n wc = c + 0x80 # write command\n w_command = bytearray([wc])\n w_command.extend(bytearray([len(v)]))\n w_command.extend(v)\n return w_command\n\n async def __read(self, commands):\n async with BleakClient(self.address) as client:\n if not client.is_connected:\n return None\n ret = []\n for c in commands:\n r_command = self.__create_read_command(c)\n await client.write_gatt_char(self.write_characteristic_id, r_command)\n value = await client.read_gatt_char(self.read_characteristic_id)\n ret.append(value[2:])\n return ret\n\n async def __write(self, commands, values):\n async with BleakClient(self.address) as client:\n if not client.is_connected:\n return None\n ret = []\n compare = []\n for i, c in enumerate(commands):\n # write\n v = values[i]\n w_command = self.__create_write_command(c, v)\n await client.write_gatt_char(self.write_characteristic_id, w_command)\n\n # read\n r_command = self.__create_read_command(c)\n await client.write_gatt_char(self.write_characteristic_id, r_command)\n value = await client.read_gatt_char(self.read_characteristic_id)\n v2 = value[2:]\n ret.append(v2)\n if v == v2:\n compare.append(True)\n else:\n compare.append(False)\n return ret, compare\n\n","repo_name":"yasunori/bleclient","sub_path":"bleclient.py","file_name":"bleclient.py","file_ext":"py","file_size_in_byte":2463,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"75204677284","text":"import os\nimport pandas as pd\nimport streamlit as st\nimport altair as alt\nimport sqlite3\nfrom utils import find_file_id, download_file, load_css\nst.set_page_config(layout=\"wide\")\nload_css()\n\n@st.cache_data(ttl=60*60*12)\ndef get_anki_db():\n file_id = find_file_id(\"name contains 'collection.anki2'\")[0]['id']\n file = download_file(file_id=file_id)\n return file\n\ndef load_anki():\n anki_db = get_anki_db()\n with open(os.path.join('data', 'anki.db'), 'wb') as anki_file:\n anki_file.write(anki_db.getbuffer())\n\n con = sqlite3.connect(os.path.join('data', 'anki.db'))\n return con\n\nanki_con = load_anki()\n\nst.title('Anki')\n\n# Loading cards\ncards = pd.read_sql('SELECT * FROM cards', anki_con)\n# Loading notes\nnotes = pd.read_sql('SELECT * FROM notes', anki_con)\n# Loading revlog\nrevlog = pd.read_sql('SELECT * FROM revlog', anki_con)\nrevlog['id'] = pd.to_datetime(revlog.id, unit='ms', utc=True).dt.tz_convert('Europe/Paris')\n# Loading decks\ndecks = pd.read_json(pd.read_sql('SELECT * FROM col', anki_con).decks[0]).T\n# Get HSK data\nhsk_deck_id = decks[decks.name.str.contains('HSK')].id.iloc[0]\nhsk_cards = cards[cards.did == hsk_deck_id].rename(columns={'id': 'cid'})\nhsk_notes = notes[notes.id.isin(hsk_cards.nid)].rename(columns={'id': 'nid'})\nhsk_notes = hsk_notes.assign(HSK=hsk_notes.tags.str.extract('(HSK\\d)'))\nhsk_notes = hsk_notes.drop(columns=['tags'])\nhsk_revlog = revlog[revlog.cid.isin(hsk_cards.cid)]\n\nhsk_df = pd.merge(left=hsk_cards, right=hsk_notes, on='nid')\nhsk_detailed_revlog = pd.merge(left=hsk_df, right=hsk_revlog, on='cid')\n\n# Info about Anki\nst.write(\"Anki is an app I use every day to learn chinese vocabulary.\")\nst.header('Overall metrics')\noverall_metrics = st.columns(3)\noverall_metrics[0].metric(\n label='Cards reviewed',\n value=len(hsk_detailed_revlog)\n)\noverall_metrics[1].metric(\n label='Current streak',\n value=f\"{hsk_detailed_revlog['id'].dt.date.nunique():.0f} days\"\n)\n\ntotal_time_spent = hsk_detailed_revlog['time'].astype(int).sum() // 1000\noverall_metrics[2].metric(\n label='Total time spent',\n value=f\"{total_time_spent // 3600:.0f}h {(total_time_spent // 60) % 60:.0f}min\"\n)\n\nst.header('HSK progress')\nhsk_metrics = st.columns(6)\nfor i, hsk_level in enumerate(hsk_df.HSK.dropna().unique()):\n hsk_df_ = hsk_df[hsk_df.HSK == hsk_level]\n hsk_metrics[i].metric(\n label=hsk_level,\n value=f\"{len(hsk_df_[hsk_df_.ivl == 200]) / len(hsk_df_):.0%}\"\n )\n\n# hsk_chart_levels = alt.Chart(\n# hsk_df.dropna(subset=['HSK'])\n# ).transform_density(\n# 'ivl',\n# groupby=['HSK'],\n# as_=['ivl', 'density'],\n# extent=[1, 200],\n# ).mark_area().encode(\n# x=alt.X('ivl:Q'),\n# y=alt.Y('density:Q')\n# ).facet(\n# # row=alt.Row('HSK')\n# \"HSK\",\n# columns=6\n# )\n\n# st.altair_chart(hsk_chart_levels, theme='streamlit')\n\n# Display metrics\n# Day changes at 3am\nhistory_plot = alt.Chart(\n hsk_detailed_revlog[['id']] - pd.Timedelta('3H')\n).mark_bar(\n color=st.secrets[\"theme\"]['primaryColor']\n).encode(\n x=alt.X('yearmonthdate(id):T', title='', axis=alt.Axis(format='%Y', tickCount='year', grid=True)),\n y=alt.Y('count():Q', title='Cards Reviewed')\n)\nst.altair_chart(history_plot, use_container_width=True, theme='streamlit')\n\ncumulative_hsk = hsk_detailed_revlog.drop_duplicates(subset=['cid'])\ncumulative_hsk = cumulative_hsk.dropna(subset=['HSK'])\ncumulative_hsk = cumulative_hsk.assign(\n cumcount=(cumulative_hsk\n .sort_values(by='id')\n .groupby('HSK')\n .cumcount()\n )\n)\n\nhsk_plot = alt.Chart(cumulative_hsk).mark_line().encode(\n x='id:T',\n y='cumcount:Q',\n color='HSK'\n)\nst.altair_chart(hsk_plot, use_container_width=True, theme='streamlit')","repo_name":"hugolmn/quantified-self","sub_path":"pages/Anki.py","file_name":"Anki.py","file_ext":"py","file_size_in_byte":3704,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"52"} +{"seq_id":"16296688405","text":"from msrest.service_client import ServiceClient\nfrom msrest import Serializer, Deserializer\nfrom msrestazure import AzureConfiguration\nfrom .version import VERSION\n\n\nclass StorageManagementClientConfiguration(AzureConfiguration):\n \"\"\"Configuration for StorageManagementClient\n Note that all parameters used to create this instance are saved as instance\n attributes.\n\n :param credentials: Credentials needed for the client to connect to Azure.\n :type credentials: :mod:`A msrestazure Credentials\n object`\n :param subscription_id: Gets subscription credentials which uniquely\n identify the Microsoft Azure subscription. The subscription ID forms part\n of the URI for every service call.\n :type subscription_id: str\n :param str base_url: Service URL\n \"\"\"\n\n def __init__(\n self, credentials, subscription_id, base_url=None):\n\n if credentials is None:\n raise ValueError(\"Parameter 'credentials' must not be None.\")\n if subscription_id is None:\n raise ValueError(\"Parameter 'subscription_id' must not be None.\")\n if not isinstance(subscription_id, str):\n raise TypeError(\"Parameter 'subscription_id' must be str.\")\n if not base_url:\n base_url = 'https://management.azure.com'\n\n super(StorageManagementClientConfiguration, self).__init__(base_url)\n\n self.add_user_agent('storagemanagementclient/{}'.format(VERSION))\n self.add_user_agent('Azure-SDK-For-Python')\n\n self.credentials = credentials\n self.subscription_id = subscription_id\n\n\nclass StorageManagementClient(object):\n \"\"\"The Azure Storage Management API.\n\n :ivar config: Configuration for client.\n :vartype config: StorageManagementClientConfiguration\n\n :param credentials: Credentials needed for the client to connect to Azure.\n :type credentials: :mod:`A msrestazure Credentials\n object`\n :param subscription_id: Gets subscription credentials which uniquely\n identify the Microsoft Azure subscription. The subscription ID forms part\n of the URI for every service call.\n :type subscription_id: str\n :param str base_url: Service URL\n \"\"\"\n\n DEFAULT_API_VERSION = '2017-06-01'\n\n def __init__(\n self, credentials, subscription_id, api_version=DEFAULT_API_VERSION, base_url=None):\n\n self.config = StorageManagementClientConfiguration(credentials, subscription_id, base_url)\n self._client = ServiceClient(self.config.credentials, self.config)\n\n client_models = {k: v for k, v in self.models(api_version).__dict__.items() if isinstance(v, type)}\n self.api_version = api_version\n self._serialize = Serializer(client_models)\n self._deserialize = Deserializer(client_models)\n\n @classmethod\n def models(cls, api_version=DEFAULT_API_VERSION):\n \"\"\"Module depends on the API version:\n\n * 2015-06-15: :mod:`v2015_06_15.models`\n * 2016-12-01: :mod:`v2016_12_01.models`\n * 2017-06-01: :mod:`v2017_06_01.models`\n \"\"\"\n if api_version == '2015-06-15':\n from .v2015_06_15 import models\n return models\n elif api_version == '2016-12-01':\n from .v2016_12_01 import models\n return models\n elif api_version == '2017-06-01':\n from .v2017_06_01 import models\n return models\n raise NotImplementedError(\"APIVersion {} is not available\".format(api_version))\n\n @property\n def operations(self):\n \"\"\"Instance depends on the API version:\n\n * 2017-06-01: :class:`Operations`\n \"\"\"\n if self.api_version == '2017-06-01':\n from .v2017_06_01.operations import Operations as OperationClass\n else:\n raise NotImplementedError(\"APIVersion {} is not available\".format(self.api_version))\n return OperationClass(self._client, self.config, self._serialize, self._deserialize)\n\n @property\n def storage_accounts(self):\n \"\"\"Instance depends on the API version:\n\n * 2015-06-15: :class:`StorageAccountsOperations`\n * 2016-12-01: :class:`StorageAccountsOperations`\n * 2017-06-01: :class:`StorageAccountsOperations`\n \"\"\"\n if self.api_version == '2015-06-15':\n from .v2015_06_15.operations import StorageAccountsOperations as OperationClass\n elif self.api_version == '2016-12-01':\n from .v2016_12_01.operations import StorageAccountsOperations as OperationClass\n elif self.api_version == '2017-06-01':\n from .v2017_06_01.operations import StorageAccountsOperations as OperationClass\n else:\n raise NotImplementedError(\"APIVersion {} is not available\".format(self.api_version))\n return OperationClass(self._client, self.config, self._serialize, self._deserialize)\n\n @property\n def usage(self):\n \"\"\"Instance depends on the API version:\n\n * 2015-06-15: :class:`UsageOperations`\n * 2016-12-01: :class:`UsageOperations`\n * 2017-06-01: :class:`UsageOperations`\n \"\"\"\n if self.api_version == '2015-06-15':\n from .v2015_06_15.operations import UsageOperations as OperationClass\n elif self.api_version == '2016-12-01':\n from .v2016_12_01.operations import UsageOperations as OperationClass\n elif self.api_version == '2017-06-01':\n from .v2017_06_01.operations import UsageOperations as OperationClass\n else:\n raise NotImplementedError(\"APIVersion {} is not available\".format(self.api_version))\n return OperationClass(self._client, self.config, self._serialize, self._deserialize)\n","repo_name":"EnjoyLifeFund/macHighSierra-cellars","sub_path":"azure-cli/2.0.18/libexec/lib/python3.6/site-packages/azure/mgmt/storage/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":6324,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"52"} +{"seq_id":"13454439880","text":"#!/usr/bin/env python \n# _*_ coding:utf-8 _*_ \n# \n# @Version : 1.0 \n# @Time : 2020/3/18\n# @Author : 圈圈烃\n# @File : parserBodyData\n# @Description:\n#\n#\nfrom bs4 import BeautifulSoup\nimport json\n\n\ndef parserBody():\n \"\"\"解析页面数据\"\"\"\n bodyData = {\"name\": \"e-human-adult-male-body\"}\n bodyData[\"groups\"] = []\n with open(\"../templates/MaleBodyApp/female.html\", \"r\", encoding=\"utf-8\") as f:\n html = f.read()\n soup = BeautifulSoup(html, \"html.parser\")\n svg = soup.find('svg')\n # 未分组\n ngpaths = svg.find_all(\"path\", recursive=False)\n for path in ngpaths:\n group = {\"elements\": []}\n ele = {}\n ele[\"type\"] = \"path\"\n try:\n ele[\"d\"] = path[\"d\"]\n except:\n ele[\"d\"] = \"\"\n ele[\"id\"] = path[\"id\"]\n ele[\"fill\"] = path[\"fill\"]\n ele[\"style\"] = path[\"style\"]\n ele[\"stroke\"] = path[\"stroke\"]\n group[\"resource\"] = \"useless\"\n try:\n ele[\"datagroup\"] = path[\"data-group\"]\n except:\n ele[\"datagroup\"] = \"\"\n try:\n ele[\"strokewidth\"] = path[\"stroke-width\"]\n except:\n ele[\"strokewidth\"] = \"\"\n group[\"elements\"].append(ele)\n bodyData[\"groups\"].append(group)\n # 有分组\n gs = svg.find_all('g')\n for g in gs:\n group = {\"elements\": []}\n try:\n group[\"data-group\"] = g[\"data-group\"]\n except:\n group[\"data-group\"] = \"\"\n try:\n group[\"resource\"] = g[\"resource\"]\n except:\n group[\"resource\"] = \"\"\n paths = g.find_all()\n for path in paths:\n ele = {}\n if path.name == \"g\":\n pass\n else:\n if path.name == \"path\":\n ele[\"type\"] = \"path\"\n try:\n ele[\"d\"] = path[\"d\"]\n except:\n ele[\"d\"] = \"\"\n elif path.name == \"circle\":\n ele[\"type\"] = \"circle\"\n ele[\"cx\"] = path[\"cx\"]\n ele[\"cy\"] = path[\"cy\"]\n ele[\"r\"] = path[\"r\"]\n elif path.name == \"ellipse\":\n ele[\"type\"] = \"ellipse\"\n ele[\"cx\"] = path[\"cx\"]\n ele[\"cy\"] = path[\"cy\"]\n ele[\"rx\"] = path[\"rx\"]\n ele[\"ry\"] = path[\"ry\"]\n else:\n pass\n # 公共属性\n ele[\"id\"] = path[\"id\"]\n ele[\"fill\"] = path[\"fill\"]\n ele[\"style\"] = path[\"style\"]\n ele[\"stroke\"] = path[\"stroke\"]\n try:\n ele[\"datagroup\"] = path[\"data-group\"]\n except:\n ele[\"datagroup\"] = \"\"\n try:\n ele[\"strokewidth\"] = path[\"stroke-width\"]\n except:\n ele[\"strokewidth\"] = \"\"\n group[\"elements\"].append(ele)\n bodyData[\"groups\"].append(group)\n # print(svg)\n # print(bodyData)\n with open(\"../static/MaleBodyApp/assets/illustrations/\" + bodyData[\"name\"] + \".json\", 'w', encoding=\"utf-8\") as fw:\n json.dump(bodyData, fw, ensure_ascii=\"utf-8\")\n\n\nif __name__ == '__main__':\n parserBody()\n","repo_name":"StuPeter/HumanBodyWiki","sub_path":"MaleBodyApp/libs/parserBodyData.py","file_name":"parserBodyData.py","file_ext":"py","file_size_in_byte":3317,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"9016682138","text":"import numpy as np\r\nnp.set_printoptions(precision = 4, linewidth = 150)\r\nimport matplotlib.pyplot as plt\r\n\r\nd = lambda x,T,L,A,E: - T*(x**3 - L**3)/(6*A*E)\r\nf = lambda x,T,A: - T*(x**2)/(2*A)\r\n\r\nLargo = 1.50\r\nT = -200000\r\nelementos = 10\r\nA = 0.001\r\nE = 210e9\r\n\r\nDesplazamientos = np.zeros(elementos + 1)\r\nFuerzas = np.zeros(elementos + 1)\r\n\r\nVectLong = np.linspace(0, Largo, elementos + 1)\r\n\r\nKGlob = np.zeros((elementos + 1, elementos + 1))\r\nKloc = np.eye(2)\r\nKloc[0,1] = -1\r\nKloc[1,0] = -1\r\nTensión = []\r\nFtot = T*(VectLong[1]**2 - VectLong[0]**2)/2\r\nFvec = np.zeros(2)\r\nCentroElem = []\r\n\r\nfor i in range(elementos):\r\n\r\n\tFuerzas[i] += Ftot/3 + (VectLong[i + 1] - VectLong[i])*T*VectLong[i]/2\r\n\tFuerzas[i+1] += Ftot*2/3 + (VectLong[i + 1] - VectLong[i])*T*VectLong[i]/2\r\n\r\n\tK = E*A/(VectLong[i + 1] - VectLong[i])\r\n\tKGlob[i : i + 2, i : i + 2] += Kloc*K\r\n\r\nDesplazamientos[0:elementos] = np.linalg.solve(KGlob[np.ix_(np.arange(elementos),\\\r\n\tnp.arange(elementos))], Fuerzas[np.arange(elementos)])\r\nRespuesta = np.dot(KGlob[elementos,np.arange(elementos + 1)], Desplazamientos) - Fuerzas[elementos]\r\n\r\nfor i in range(elementos):\r\n\tTensión.append(E*np.array(\r\n\t\t[-1/(VectLong[i+1] - VectLong[i]), 1/(VectLong[i+1] - VectLong[i])]).dot(Desplazamientos[i:i+2]\r\n\t\t\t\t\t\t\t\t))\r\n\tCentroElem.append((VectLong[i + 1] + VectLong[i])/2)\r\n\r\nplt.figure(figsize = (12,8))\r\nplt.plot(np.linspace(0, Largo, 100), d(np.linspace(0, Largo, 100),T,Largo,A,E),'r', label = 'Ecuación')\r\nplt.plot(np.linspace(0, Largo, elementos + 1), Desplazamientos, label = 'Aproximación')\r\nplt.legend()\r\nplt.show()\r\n\r\nplt.figure(figsize = (12,8))\r\nplt.plot(np.linspace(0, Largo, 100), f(np.linspace(0, Largo, 100),T,A),'r', label = 'Ecuación')\r\nplt.plot(CentroElem, Tensión, 's', label = 'Aproximación')\r\nplt.step(CentroElem, Tensión, 'k', where = 'mid', label = 'Step')\r\nplt.legend()\r\nplt.show()\r\n\r\n#Cada elemento tiene tensiones constantes Sigma = E * [-1/L 1/L]*[d1, d2],son constantes porque \r\n# uso una aproximación tal que la tensión dentro de cada elemento es constante\r\n#\r\n#TENY = np.vstack( ([0.0], self.Sig) )\r\n# plt.step(self.MN[:, 0], TENY, 'r',\r\n# where='pre', label='Solución Numérica')","repo_name":"Modelizacion-de-Materiales/Modelizacion-2021-Ingenieria","sub_path":"Consultas/ConsultasManuel/Guía2/Ejercicio3.py","file_name":"Ejercicio3.py","file_ext":"py","file_size_in_byte":2195,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"17760624155","text":"import csv\nimport json\nimport os\nimport sys\nimport uuid\nfrom inspect import getfullargspec\nfrom math import ceil\n\nimport sqlalchemy\nfrom sqlalchemy import func, inspect, select\nfrom sqlalchemy.sql.expression import text\n\nfrom paths import MIGRATIONS_DIRECTORY, PEPYS_IMPORT_DIRECTORY\nfrom pepys_import.resolvers.command_line_input import create_menu\nfrom pepys_import.utils.sqlalchemy_utils import get_primary_key_for_table\nfrom pepys_import.utils.table_name_utils import table_name_to_class_name\nfrom pepys_import.utils.text_formatting_utils import (\n custom_print_formatted_text,\n format_error_message,\n)\n\nSTORED_PROC_PATH = os.path.join(PEPYS_IMPORT_DIRECTORY, \"database\", \"postgres_stored_procedures\")\n\n\ndef import_from_csv(data_store, path, files, change_id):\n for file in sorted(files):\n # split file into filename and extension\n table_name, _ = os.path.splitext(file)\n if table_name.lower() == \"synonyms\":\n import_synonyms(data_store, os.path.join(path, file), change_id)\n continue\n possible_method = \"add_to_\" + table_name.lower().replace(\" \", \"_\")\n method_to_call = getattr(data_store, possible_method, None)\n if method_to_call:\n # Get all arguments of the method, except the first argument which is 'self'\n arguments = getfullargspec(method_to_call).args[1:]\n possible_arguments = \",\".join(arguments)\n with open(os.path.join(path, file), \"r\") as file_object:\n reader = csv.reader(file_object)\n # extract header\n header = next(reader)\n if not set(header).issubset(set(arguments)):\n custom_print_formatted_text(\n format_error_message(\n f\"Headers and the arguments of DataStore.{possible_method}() don't match!\"\n f\"\\nPossible arguments: {possible_arguments}\"\n f\"\\nPlease check your CSV file.\"\n )\n )\n return\n for row_number, row in enumerate(reader):\n row_as_string = \"\".join(row).strip()\n if row_as_string == \"\":\n continue\n keyword_arguments = dict(zip(header, row))\n try:\n method_to_call(**keyword_arguments, change_id=change_id)\n except Exception as e:\n custom_print_formatted_text(\n format_error_message(f\"Error importing row {row} from {file}\")\n )\n custom_print_formatted_text(format_error_message(f\" Error was '{str(e)}'\"))\n else:\n custom_print_formatted_text(\n format_error_message(f\"Method({possible_method}) not found!\")\n )\n\n\ndef import_synonyms(data_store, filepath, change_id):\n with open(filepath, \"r\") as file_object:\n reader = csv.reader(file_object)\n # extract header\n header = next(reader)\n if not set(header).issubset({\"synonym\", \"table\", \"target_name\"}):\n custom_print_formatted_text(\n format_error_message(\n \"Headers of the Synonyms.csv file are wrong or missing!\"\n \"\\nNecessary arguments: synonym,table,target_name\"\n \"\\nPlease check your CSV file.\"\n )\n )\n return\n # For every row in the CSV\n for row in reader:\n row_as_string = \"\".join(row).strip()\n if row_as_string == \"\":\n continue\n\n values = dict(zip(header, row))\n\n # Search in the given table for the name\n class_name = table_name_to_class_name(values[\"table\"])\n\n try:\n db_class = getattr(data_store.db_classes, class_name)\n pri_key_column_name = db_class.__table__.primary_key.columns.values()[0].name\n except AttributeError:\n custom_print_formatted_text(format_error_message(f\"Error on row {row}\"))\n custom_print_formatted_text(\n format_error_message(f\" Invalid table name {values['table']}\")\n )\n continue\n\n # Try and find a name column to use\n possibilities = [\"name\", \"reference\"]\n\n name_col = None\n for poss in possibilities:\n try:\n name_col = getattr(db_class, poss)\n except AttributeError:\n continue\n\n if name_col is None:\n custom_print_formatted_text(format_error_message(f\"Error on row {row}\"))\n custom_print_formatted_text(\n format_error_message(f\" Cannot find name column for table {values['table']}\")\n )\n continue\n\n results = (\n data_store.session.query(db_class).filter(name_col == values[\"target_name\"]).all()\n )\n\n if len(results) == 0:\n # Nothing to link synonym to so give error\n custom_print_formatted_text(format_error_message(f\"Error on row {row}\"))\n custom_print_formatted_text(\n format_error_message(\n f\" Name '{values['target_name']}' is not found in table {values['table']}\"\n )\n )\n continue\n elif len(results) == 1:\n guid = getattr(results[0], pri_key_column_name)\n # Found one entry, so can create synonym\n data_store.add_to_synonyms(values[\"table\"], values[\"synonym\"], guid, change_id)\n elif len(results) > 1:\n if values[\"table\"] != \"Platforms\":\n custom_print_formatted_text(format_error_message(f\"Error on row {row}\"))\n custom_print_formatted_text(\n format_error_message(\n f\" Name '{values['target_name']}' occurs multiple times in table {values['table']}.\"\n f\" Asking user to resolve is only supported for Platforms table.\"\n )\n )\n continue\n\n results = sorted(results, key=lambda x: x.identifier)\n chosen_item = ask_user_for_synonym_link(data_store, results, values)\n\n if chosen_item is None:\n print(\"Skipping row\")\n continue\n else:\n guid = getattr(chosen_item, pri_key_column_name)\n data_store.add_to_synonyms(values[\"table\"], values[\"synonym\"], guid, change_id)\n\n\ndef ask_user_for_synonym_link(data_store, results, values):\n options = [\n f\"{result.name} / {result.identifier} / {result.nationality_name}\" for result in results\n ]\n\n options += [\"Skip this row\"]\n\n def is_valid(option): # pragma: no cover\n return option.lower() in [str(i) for i in range(1, len(options) + 1)] or option == \".\"\n\n choice = create_menu(\n f\"Choose which Platform to link synonym '{values['target_name']}'' to:\",\n options,\n validate_method=is_valid,\n )\n\n if choice == \".\":\n print(\"Quitting\")\n sys.exit(1)\n elif choice == str(len(options)):\n return None\n elif choice in [str(i) for i in range(1, len(options) + 1)]:\n return results[int(choice) - 1]\n\n\ndef is_schema_created(engine, db_type):\n \"\"\"Returns True if Pepys Tables are created, False otherwise.\"\"\"\n inspector = inspect(engine)\n if db_type == \"sqlite\":\n table_names = inspector.get_table_names()\n # SQLite table numbers vary by mod_spatialite. The version of mod_spatialiate\n # that is installed can vary by platform - so both numbers should be acceptable.\n if len(table_names) >= 77 and len(table_names) <= 79:\n return True\n else:\n table_names = inspector.get_table_names(schema=\"pepys\")\n if len(table_names) == 41:\n return True\n\n if len(table_names) == 0:\n message = \"Database tables are not found! (Hint: Did you initialise the DataStore?)\"\n else:\n message = \"Please run database migration to bring tables up to date.\"\n custom_print_formatted_text(format_error_message(message))\n return False\n\n\ndef create_spatial_tables_for_sqlite(engine):\n \"\"\"Create geometry_columns and spatial_ref_sys metadata table\"\"\"\n\n if not inspect(engine).has_table(\"spatial_ref_sys\"):\n with engine.begin() as connection:\n connection.execute(select(func.InitSpatialMetaData(1)))\n\n\ndef create_spatial_tables_for_postgres(engine):\n \"\"\"Create schema pepys and extension for PostGIS\"\"\"\n query = \"\"\"\n CREATE SCHEMA IF NOT EXISTS pepys;\n CREATE EXTENSION IF NOT EXISTS postgis;\n SET search_path = pepys,public;\n \"\"\"\n with engine.begin() as connection:\n connection.execute(text(query))\n\n\ndef create_stored_procedures_for_postgres(engine):\n stored_procedure_files = [\n os.path.join(STORED_PROC_PATH, \"dashboard_metadata.sql\"),\n os.path.join(STORED_PROC_PATH, \"dashboard_stats.sql\"),\n os.path.join(STORED_PROC_PATH, \"Comments_for.sql\"),\n os.path.join(STORED_PROC_PATH, \"Contacts_for.sql\"),\n os.path.join(STORED_PROC_PATH, \"Datafiles_for.sql\"),\n os.path.join(STORED_PROC_PATH, \"States_for.sql\"),\n ]\n\n with engine.begin() as connection:\n for filename in stored_procedure_files:\n with open(filename) as f:\n procedure_definition = f.read()\n connection.execute(text(procedure_definition))\n\n\ndef create_alembic_version_table(engine, db_type):\n with open(os.path.join(MIGRATIONS_DIRECTORY, \"latest_revisions.json\"), \"r\") as file:\n versions = json.load(file)\n if \"LATEST_POSTGRES_VERSION\" not in versions or \"LATEST_SQLITE_VERSION\" not in versions:\n custom_print_formatted_text(format_error_message(\"Latest revision IDs couldn't found!\"))\n return\n\n if db_type == \"sqlite\":\n # Try and get all entries from alembic_version table\n try:\n with engine.begin() as connection:\n table_contents = connection.execute(\n text(\"SELECT * from alembic_version;\")\n ).fetchall()\n\n if len(table_contents) == 0:\n # Table exists but no version number row, so stamp it:\n sql = \"INSERT INTO alembic_version (version_num) VALUES (:id)\"\n connection.execute(text(sql), {\"id\": versions[\"LATEST_SQLITE_VERSION\"]})\n if len(table_contents) == 1:\n if table_contents[0][0] == versions[\"LATEST_SQLITE_VERSION\"]:\n # Current version already stamped in table - so just continue\n print(\n \"Initialising database - alembic version in database matches latest version.\"\n )\n else:\n # The version in the database doesn't match the current version - so raise an error\n raise ValueError(\n f\"Database revision in alembic_version table ({table_contents[0][0]}) does not match latest revision ({versions['LATEST_SQLITE_VERSION']}).\"\n \"Please run database migration.\"\n )\n if len(table_contents) > 1:\n raise ValueError(\n \"Multiple rows detected in alembic_version table. Database potentially in inconsistent state.\"\n \"Migration functionality will not work. Please contact support.\"\n )\n except sqlalchemy.exc.OperationalError:\n with engine.begin() as connection:\n # Error running select, so table doesn't exist - create it and stamp the current version\n connection.execute(\n text(\n \"\"\"\n CREATE TABLE IF NOT EXISTS alembic_version\n (\n version_num VARCHAR(32) NOT NULL,\n CONSTRAINT alembic_version_pkc PRIMARY KEY (version_num)\n );\n \"\"\"\n )\n )\n sql = \"INSERT INTO alembic_version (version_num) VALUES (:id)\"\n connection.execute(text(sql), {\"id\": versions[\"LATEST_SQLITE_VERSION\"]})\n else:\n # Try and get all entries from alembic_version table\n try:\n with engine.begin() as connection:\n table_contents = connection.execute(\n text(\"SELECT * from pepys.alembic_version;\")\n ).fetchall()\n\n if len(table_contents) == 0:\n # Table exists but no version number row, so stamp it:\n sql = \"INSERT INTO pepys.alembic_version (version_num) VALUES (:id)\"\n connection.execute(text(sql), {\"id\": versions[\"LATEST_POSTGRES_VERSION\"]})\n if len(table_contents) == 1:\n if table_contents[0][0] == versions[\"LATEST_POSTGRES_VERSION\"]:\n # Current version already stamped in table - so just continue\n print(\n \"Initialising database - alembic version in database matches latest version.\"\n )\n else:\n # The version in the database doesn't match the current version - so raise an error\n raise ValueError(\n f\"Database revision in alembic_version table ({table_contents[0][0]}) does not match latest revision ({versions['LATEST_POSTGRES_VERSION']}).\"\n \"Please run database migration.\"\n )\n if len(table_contents) > 1:\n raise ValueError(\n \"Multiple rows detected in alembic_version table. Database potentially in inconsistent state.\"\n \"Migration functionality will not work. Please contact support.\"\n )\n except (sqlalchemy.exc.OperationalError, sqlalchemy.exc.ProgrammingError):\n # Error running select, so table doesn't exist - create it and stamp the current version\n with engine.begin() as connection:\n connection.execute(\n text(\n \"\"\"\n CREATE TABLE IF NOT EXISTS pepys.alembic_version\n (\n version_num VARCHAR(32) NOT NULL,\n CONSTRAINT alembic_version_pkc PRIMARY KEY (version_num)\n );\n \"\"\"\n )\n )\n sql = \"INSERT INTO pepys.alembic_version (version_num) VALUES (:id)\"\n connection.execute(text(sql), {\"id\": versions[\"LATEST_POSTGRES_VERSION\"]})\n\n\ndef cache_results_if_not_none(cache_attribute):\n def real_decorator(f):\n def helper(self, name):\n cache = eval(\"self.\" + cache_attribute)\n if name not in cache:\n result = f(self, name)\n if result:\n self.session.expunge(result)\n cache[name] = result\n return result\n else:\n return cache[name]\n\n return helper\n\n return real_decorator\n\n\ndef shorten_uuid(id): # pragma: no cover\n return str(id)[-6:]\n\n\nclass MissingDataException(Exception):\n pass\n\n\ndef lowercase_or_none(obj):\n if obj is None:\n return None\n else:\n return obj.lower()\n\n\ndef chunked_list(lst, size):\n \"\"\"Split a list into multiple chunks of length size.\n Returns a list containing sublists of length size.\n\n If the list doesn't divide by size exactly, then the\n last sublist will have a length < size.\n \"\"\"\n # Quick 'short-circuit' for a list less than size\n if len(lst) < size:\n return [lst]\n\n n_chunks = ceil(len(lst) / size)\n\n # We're returning a list containing lots of sublists\n # rather than yielding items as a generator\n # This is because we use a tqdm progress bar around this\n # function, and that needs to know the number of sublists\n # to be able to show a proper progress bar\n result = []\n\n for i in range(n_chunks):\n result.append(lst[i * size : (i + 1) * size])\n\n return result\n\n\ndef convert_edit_dict_columns(edit_dict, table_object):\n update_dict = {}\n # Convert the edit_dict we get from the GUI into a dict suitable for use in the update function\n # This involves converting any relationship columns into their ID column\n for col_name, new_value in edit_dict.items():\n attr_from_db_class = getattr(table_object, col_name)\n try:\n if isinstance(\n attr_from_db_class.prop, sqlalchemy.orm.relationships.RelationshipProperty\n ):\n local_column = list(attr_from_db_class.prop.local_columns)[0].key\n update_dict[local_column] = new_value\n else:\n update_dict[col_name] = new_value\n except Exception:\n update_dict[col_name] = new_value\n\n return update_dict\n\n\ndef convert_objects_to_ids(items, table_obj):\n if isinstance(items, list):\n new_id_list = []\n for value in items:\n if not isinstance(value, uuid.UUID):\n value = getattr(value, get_primary_key_for_table(table_obj))\n new_id_list.append(value)\n\n return new_id_list\n else:\n if not isinstance(items, uuid.UUID):\n value = getattr(items, get_primary_key_for_table(table_obj))\n else:\n value = items\n return value\n\n\ndef read_version_from_pepys_install(path):\n init_path = os.path.join(path, \"pepys_import\", \"__init__.py\")\n\n try:\n with open(init_path, \"r\") as f:\n for line in f:\n if \"__version__\" in line:\n splitted = line.split(\"=\")\n # Remove whitespace, double-quotes and single-quotes from either end\n version = splitted[1].strip().strip('\"').strip(\"'\")\n return version\n return None\n except Exception:\n print(f\"WARNING: Cannot read Pepys version from network master install at {path}\")\n return None\n","repo_name":"debrief/pepys-import","sub_path":"pepys_import/utils/data_store_utils.py","file_name":"data_store_utils.py","file_ext":"py","file_size_in_byte":18573,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"52"} +{"seq_id":"41735975366","text":"from models import models_list\nimport sys\nsys.path.append(\"..\")\n\n\nclass BadConnector(Exception):\n pass\n\n\nclass SQLTables:\n def __init__(self, connector) -> None:\n self.connector = connector\n connector = dir(self.connector)\n for i in ['open_connection', 'execute_sql', 'close_connectoin']:\n if i not in connector:\n raise BadConnector(f'Connector need to has function {i}')\n\n @staticmethod\n def check_query(query: str) -> bool:\n # return Flase if sql injection in query\n query = query.lower()\n for operation in ['select', 'update', 'insert', 'delete', 'drop', 'or']:\n if operation in query:\n return False\n return True\n\n def get_tables(self) -> list:\n if not models_list:\n raise BadConnector('Models list can not be empty')\n\n for model in models_list:\n columns = list(dict(model.__dict__)['__annotations__'].keys())\n if len(columns) == 0:\n raise BadConnector('Table needs to have 1 or more columns')\n if columns[0] != 'id':\n raise BadConnector('All tables needs to have an id column')\n return models_list\n\n def get_data_from_table(self, table_name: str, limit: int = None, offset: int = 0) -> list:\n self.connector.open_connection()\n if not SQLTables.check_query(table_name):\n return []\n if limit != None:\n data = self.connector.execute_sql(\n f'SELECT * FROM {table_name} LIMIT ? OFFSET ?',\n (limit, offset)\n )\n else:\n data = self.connector.execute_sql(\n f'SELECT * FROM {table_name} OFFSET {offset}')\n self.connector.close_connectoin()\n return data\n","repo_name":"badger-py/flask-panel","sub_path":"data_views/sql.py","file_name":"sql.py","file_ext":"py","file_size_in_byte":1792,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"52"} +{"seq_id":"36664650267","text":"import psycopg2\nimport traceback\nfrom backend.DBManager import DBManager\nfrom backend.PedidoProduto import PedidoProduto\n\nclass PedidoProdutoDAO:\n\n def listar_todos(self) -> list:\n \"retorna todos os pedido_produtos\"\n\n pedido_produto_list = []\n try:\n connection = DBManager.connect_with_database()\n\n cursor = connection.cursor()\n cursor.execute(\"SELECT pedido_id, produto_id, quantidade FROM pedido_produto\")\n\n rows_in_table = cursor.fetchall()\n for row in rows_in_table:\n pp = PedidoProduto()\n pp.pedido_id = row[0]\n pp.produto_id = row[1]\n pp.quantidade = row[2]\n\n pedido_produto_list.append(pp)\n\n except (Exception, psycopg2.Error) as error:\n traceback.print_exc()\n finally:\n if connection:\n cursor.close()\n connection.close()\n return pedido_produto_list\n\n\n def listar(self, _pedido_id, _produto_id) -> PedidoProduto:\n \"retorna uma linha de pedido_produto. Param: pedido_id e produto_id\"\n\n pedido_produto = None\n\n try:\n connection = DBManager.connect_with_database()\n\n cursor = connection.cursor()\n cursor.execute(f\"SELECT pedido_id, produto_id, quantidade FROM pedido_produto WHERE pedido_id = {_pedido_id } AND produto_id = {_produto_id}\")\n\n row = cursor.fetchone()\n\n if row is not None and len(row) > 0:\n pedido_produto = PedidoProduto()\n pedido_produto.pedido_id = row[0]\n pedido_produto.produto_id = row[1]\n pedido_produto.quantidade = row[2]\n\n\n except (Exception, psycopg2.Error) as error:\n traceback.print_exc()\n finally:\n if connection:\n cursor.close()\n connection.close()\n return pedido_produto\n \n\n def adicionar(self, _pedido_id, _produto_id, _quantidade) -> bool:\n \"Adiciona um novo pedido_produto no banco de dados. params: pedido_id, produto_id e quantidade\"\n\n success = False\n try:\n connection = DBManager.connect_with_database()\n\n cursor = connection.cursor()\n cursor.execute(f\"INSERT INTO pedido_produto ( pedido_id, produto_id, quantidade) VALUES ({_pedido_id}, {_produto_id}, {_quantidade})\")\n \n connection.commit()\n\n if cursor.rowcount == 1:\n success = True\n\n except (Exception, psycopg2.Error) as error:\n traceback.print_exc()\n finally:\n if connection:\n cursor.close()\n connection.close()\n return success\n \n\n def atualizar(self, _pedido_id, _produto_id, _quantidade) -> bool:\n \"Atualiza a quantidade de produtos de um estoque no banco de dados. params: estoque.id, produto.id e quantidade\"\n\n success = False\n try:\n connection = DBManager.connect_with_database()\n\n cursor = connection.cursor()\n cursor.execute(f\"UPDATE pedido_produto SET quantidade = {_quantidade} WHERE pedido_id = {_pedido_id } AND produto_id = {_produto_id}\")\n connection.commit()\n if cursor.rowcount == 1:\n success = True\n except (Exception, psycopg2.Error) as error:\n traceback.print_exc()\n finally:\n if connection:\n cursor.close()\n connection.close()\n return success\n\n\n def remover(self, _pedido_id, _produto_id) -> bool:\n \"Remove um produto em um pedido. params: pedido.id e produto.id\"\n\n success = False\n try:\n connection = DBManager.connect_with_database()\n\n cursor = connection.cursor()\n cursor.execute(f\"DELETE FROM pedido_produto WHERE pedido_id = {_pedido_id} AND produto_id = {_produto_id}\")\n connection.commit()\n if cursor.rowcount == 1:\n success = True\n except (Exception, psycopg2.Error) as error:\n traceback.print_exc()\n finally:\n if connection:\n cursor.close()\n connection.close()\n return success","repo_name":"paulohgs/wofi","sub_path":"backend/PedidoProdutoDAO.py","file_name":"PedidoProdutoDAO.py","file_ext":"py","file_size_in_byte":4263,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"13468492347","text":"'''\nSupport code for HW5 RNN part\n'''\nimport os.path, time\nimport unicodedata, string\nimport glob, random \n\nimport torch\n\n'''\n-------- Helper Function ------\n'''\ncharspace = string.ascii_letters + \".,;'\"\ndef letter_index(letter): \n return charspace.find(letter)\n\n# find all files in path\ndef find(path): \n return glob.glob(path)\n\n# turn unicode string to ascii\ndef convert_to_ascii(unicode_strings):\n return ''.join(c for c in unicodedata.normalize('NFD', unicode_strings)\n if unicodedata.category(c) != 'Mn' and c in charspace)\n\n# convert string to a one-hot tensor\ndef string_to_tensor(inputstring, pad_to=0):\n # pad_to: pad result to a certain length\n tensor = torch.zeros(max(len(inputstring), pad_to), \n len(charspace))\n for i, letter in enumerate(inputstring):\n tensor[i][letter_index(letter)] = 1\n return tensor\n\n'''\n-------- Dataset ---------\n'''\nfrom torch.utils.data import Dataset, DataLoader\n# make it a dataset class\nclass InternationalNameDataset(Dataset):\n def __init__(self, root_path, ext='.txt', shuffle=True, padded=True):\n self.meta = {\"root_path\": root_path,\n \"ext\": ext,\n \"shuffle\": shuffle,\n \"padded\": padded}\n if root_path is None:\n self.name_list = None\n self.categories = None\n if padded: self.meta[\"pad_maxsize\"] = 0\n else:\n categories, name_list = self.get_namelist()\n self.name_list = name_list\n self.categories = categories\n if shuffle: self.shuffle()\n if padded: self.meta[\"pad_maxsize\"] = self.get_pad_maxsize()\n\n def get_namelist(self):\n path = os.path.join(self.meta['root_path'], \"*\" + self.meta['ext'])\n categories = []\n name_list = [] # list of names in a language i.e. {'lang':'name'}\n for filename in find(path): # iterate through file names\n category = os.path.split(filename)[-1].split('.')[0]\n categories.append(category)\n with open(filename, 'r', encoding='utf-8') as infile:\n # read the file content to a list\n lines = infile.read().strip().split('\\n')\n lines = [convert_to_ascii(line) for line in lines]\n # add the list of line-language pair to name_list\n name_list += [(line, category) for line in lines]\n return categories, name_list\n\n def shuffle(self):\n random.shuffle(self.name_list)\n\n def get_pad_maxsize(self):\n maxx = 0\n for line, lang in self.name_list:\n if maxx < len(line): maxx = len(line)\n return maxx\n\n def split_train_test(self, train_fraction=0.7):\n '''\n Creates 2 new InternationalNameDataset and fill them\n with 70/30 of own data.\n '''\n midline = int(len(self.name_list) * train_fraction)\n train = InternationalNameDataset(None)\n train.meta = self.meta\n train.categories = self.categories\n train.name_list = self.name_list[:midline]\n\n test = InternationalNameDataset(None)\n test.meta = self.meta\n test.categories = self.categories\n test.name_list = self.name_list[midline:]\n return train, test\n\n def __len__(self):\n return len(self.name_list)\n\n def __getitem__(self, index):\n '''\n Take the name-language pair and convert them to tensors\n '''\n line, category = self.name_list[index]\n category_index = self.categories.index(category)\n line_tensor = string_to_tensor(line, pad_to=self.meta[\"pad_maxsize\"])\n return {'label': category_index,\n 'input': line_tensor,\n 'seq_len': len(line)}\n\n'''\n-------- RNN/LSTM ---------\n'''\nimport torch.nn as nn\nimport torch.nn.utils.rnn as rnn_utils\nclass CoveredLSTM(nn.LSTM):\n '''\n A stack of LSTM that is covered by a fully-connected layer\n as the last layer. The fully-connected layer is fed the hidden\n state of the last LSTM stack (if any). CrossEntropyLoss should be \n used as the output is a tensor of length num_class.\n '''\n def __init__(self, input_size, hidden_size, num_layers, num_classes):\n super(CoveredLSTM, self).__init__(input_size, hidden_size, num_layers)\n self.num_layers = num_layers\n self.cover_fc = nn.Linear(hidden_size, num_classes)\n \n def forward(self, inputs, cache):\n '''\n cache = (hidden0, cell0) in a tuple\n '''\n output, (hn, cn) = super(CoveredLSTM, self).forward(inputs, cache)\n covered_output = self.cover_fc.forward(hn[-1]) # taking the last lstm's hn\n return covered_output, (output, hn, cn) \n\n def init_cache(self, batch=1, use_gpu = True):\n '''\n \"batch\" parameter is added in case a\n stacked multiple hidden matrix is needed\n (e.g. for multibatch forward pass)\n '''\n h0 = torch.zeros(self.num_layers, batch, self.hidden_size)\n c0 = torch.zeros(self.num_layers, batch, self.hidden_size)\n if use_gpu:\n h0, c0 = h0.cuda(), c0.cuda()\n return (h0, c0)\n\nclass RNNSampler(object):\n '''\n Sampler for RNN/LSTM training\n It will generate a batch list (of index)\n and sort them by the sequence length\n '''\n def __init__(self, dataset, batch_size):\n self.dataset = dataset\n self.batch_size = batch_size\n\n def __len__(self):\n return len(self.dataset)\n\n def __iter__(self):\n batch = []\n for d in range(len(self.dataset)):\n batch.append(d)\n if len(batch) >= self.batch_size or d+1 == len(self.dataset):\n # sort to decreasing length\n sorted_batch = sorted(batch, reverse=True,\n key=lambda x: self.dataset[x]['seq_len'])\n yield sorted_batch\n batch = []\n\n'''\nTraining \n'''\nfrom torch.autograd import Variable\nimport torch.optim as optim\n\ndef train(dataset, model, batch_size=8, use_gpu=True, mode='train', lr=5e-2,\n epoch=1, print_every=100):\n loader = DataLoader(dataset, batch_sampler=RNNSampler(dataset, batch_size), num_workers=4)\n optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9)\n criterion = nn.CrossEntropyLoss() \n model.train(mode == 'train')\n model.zero_grad()\n total_iter_count = (len(dataset) // batch_size) + 1\n\n for e in range(epoch):\n running_loss = 0.0\n running_corrects = 0\n epoch_start = time.clock()\n\n iterr = 0\n for data in loader:\n iterr += 1\n optimizer.zero_grad()\n inputs = data['input'].transpose(0,1) \n # transposing to match LSTM formatting\n labels = data['label']\n if use_gpu:\n inputs, labels = Variable(inputs.cuda()), Variable(labels.cuda())\n else:\n inputs, labels = Variable(inputs), Variable(labels)\n\n # packing padded sequence\n padded_inputs = rnn_utils.pack_padded_sequence(inputs, data['seq_len'])\n cache = model.init_cache(batch=inputs.size()[1], use_gpu=use_gpu)\n model.zero_grad()\n\n output, cache = model(padded_inputs, cache)\n _, pred = output.max(dim=1)\n \n loss = criterion(output, labels.long())\n if mode == 'train':\n loss.backward()\n optimizer.step()\n\n running_loss += loss.item() / float(len(dataset))\n running_corrects += (pred == labels.long()).sum().item()/float(len(dataset))\n\n if (iterr % print_every) == 0:\n print(' ...iteration {}/{}'.format(iterr, total_iter_count), end='\\r')\n \n epoch_time = time.clock() - epoch_start\n print(\" >> Epoch loss {:.5f} accuracy {:.3f} \\\n in {:.4f}s\".format(running_loss, running_corrects, epoch_time))\n return model, running_loss, running_corrects\n\n'''\n----- Plotting\n'''\nimport matplotlib.pyplot as plt \n\ndef plot_varying_epochs(loss_acc_dict):\n plt.figure()\n train_losses = [loss_acc_dict[i][0] for i in sorted(loss_acc_dict.keys())]\n test_losses = [loss_acc_dict[i][1] for i in sorted(loss_acc_dict.keys())]\n accuracy = [loss_acc_dict[i][2] for i in sorted(loss_acc_dict.keys())]\n\n plt.subplot(131)\n plt.plot(train_losses, color='purple')\n plt.xticks(range(4), [1,2,5,10])\n plt.xlabel('Epoch')\n plt.title(\"train_loss\")\n\n plt.subplot(132)\n plt.plot(test_losses, color='gray')\n plt.xticks(range(4), [1,2,5,10])\n plt.xlabel('Epoch')\n plt.title(\"test_loss\")\n\n plt.subplot(133)\n plt.plot(accuracy, color='blue')\n plt.xticks(range(4), [1,2,5,10])\n plt.xlabel('Epoch')\n plt.title(\"accuracy\")\n\ndef main():\n dataset = InternationalNameDataset('../datasets/simplelanguage/data/names/')\n train_data, test_data = dataset.split_train_test()\n\n train_sampler = RNNSampler(train_data, 8)\n test_sampler = RNNSampler(test_data, 8)\n lstm_mod = CoveredLSTM(len(charspace), 128, 2, len(dataset.categories)).cuda()\n print(\"training\")\n trained_model = train(train_data, lstm_mod, lr=5e-2, batch_size=8, mode='train')\n print(\"On testing data\")\n final_model = train(test_data, lstm_mod, lr=5e-2, batch_size=8, mode='test')\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"ghosalya/arti-intel","sub_path":"week5/hw5_rnn.py","file_name":"hw5_rnn.py","file_ext":"py","file_size_in_byte":9411,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"42839962128","text":"'''The implementation of the diffusion method.'''\nfrom __future__ import annotations\nfrom typing import Tuple, Any, Union, TypeVar, Callable\nfrom chex import Array\nimport jax\nimport jax.numpy as jnp\nfrom einops import rearrange\nimport haiku as hk\n\nfrom .nn import ForwardFn, Model\nfrom .utils import Params, get_logger\n\n\nlogger = get_logger('kigo.diffusion')\nNumT = TypeVar('NumT', bound=Union[float, Array])\n\n\ndef expand(x: Union[float, Array], ref: Array) -> Array:\n if isinstance(x, float):\n x = jnp.array([x] * ref.shape[0])\n if isinstance(x, Array) and len(x.shape) == 1:\n x = rearrange(x, 'b -> b 1 1 1')\n assert isinstance(x, Array)\n return x\n\n\ndef gt0(x: NumT, eps: float = 1e-8) -> NumT:\n '''Ensures that x is greater than zero, i.e. can be safely used as a\n divisor or for sqrts.'''\n return jnp.clip(x, eps)\n\n\ndef cosine_snr(t: Union[float, Array], s: float = 0.008) -> Array:\n '''Signal-to-noise ratio according to a cosine schedule.'''\n t = jnp.array(t)\n t = jnp.clip(t, 0., 1.)\n return jnp.cos((t + s) / (1. + s) * jnp.pi / 2) ** 2\n\n\ndef sample_q(x0: Array, noise: Array, snr: Array) -> Array:\n snr = rearrange(snr, 'b -> b 1 1 1')\n # Eq. 4 in DDIM\n return gt0(snr) ** 0.5 * x0 + gt0(1. - snr) ** 0.5 * noise\n\n\ndef sample_p_step(xt: Array,\n noise_pred: Array,\n snr: Union[float, Array],\n snr_next: Union[float, Array],\n eta: Union[float, Array],\n noise: Array,\n clip_percentile: Union[float, Array] = 0.995,\n ) -> Tuple[Array, Array]:\n snr = expand(snr, xt)\n snr_next = expand(snr_next, xt)\n eta = expand(eta, xt)\n # Eq. 16 in DDIM, we can interpolate between DDPM (when eta = 1) and DDIM\n # (when eta = 0).\n sigma = (eta\n * gt0((1 - snr_next) / gt0(1 - snr)) ** 0.5\n * gt0((1 - snr) / gt0(snr_next)) ** 0.5)\n # Eq. 9 in DDIM\n x0_hat = (xt - gt0(1. - snr) ** 0.5 * noise_pred) / gt0(snr) ** 0.5\n # Dynamic thresholding from Imagen by the Google Brain Team.\n s = jnp.quantile(jnp.abs(x0_hat), clip_percentile, axis=(1, 2, 3),\n keepdims=True)\n x0_hat = jnp.where(s > 1.,\n jnp.clip(x0_hat, -s, s) / gt0(s),\n x0_hat)\n # Eq. 12 in DDIM\n xt = (x0_hat * gt0(snr_next) ** 0.5\n + noise_pred * gt0(1. - snr_next - sigma ** 2) ** 0.5\n + noise * sigma)\n return xt, x0_hat\n\n\ndef sample_p(xT: Array,\n forward_fn: ForwardFn,\n steps: int,\n rng: Any,\n eta: Union[float, Array] = 0.,\n clip_percentile: float = 0.995,\n ) -> Array:\n\n def body_fn(index: int, state: Tuple[Array, Any]) -> Tuple[Array, Any]:\n xt, rng = state\n rng, rng_split = jax.random.split(rng)\n snr = jnp.repeat(cosine_snr(1. - index / steps), len(xt))\n snr_next = jnp.repeat(cosine_snr(1. - (index + 1) / steps), len(xt))\n noise_pred = forward_fn(xt, snr, False)\n noise = jax.random.normal(rng_split, shape=xt.shape)\n xt_next, _ = sample_p_step(xt, noise_pred, snr, snr_next, eta, noise,\n clip_percentile)\n return xt_next, rng\n\n initial_state = xT, rng\n x0, _ = jax.lax.fori_loop(0, steps, body_fn, initial_state)\n return x0\n\n\nclass Sampler:\n\n def __init__(self, params: Params, model_fn: Callable[[], Model]) -> None:\n self.params = params\n\n def forward_fn(xt: Array, snr: Array) -> Array:\n return model_fn()(xt, snr, False)\n\n forward = hk.transform(forward_fn)\n forward = hk.without_apply_rng(forward)\n\n def body_fn(index: int,\n state: Tuple[Array, Params, Any, int, float, float],\n ) -> Tuple[Array, Params, Any, int, float, float]:\n xt, params, rng, steps, eta, clip_percentile = state\n rng, rng_split = jax.random.split(rng)\n snr = jnp.repeat(cosine_snr(1. - index / steps), len(xt))\n snr_next = jnp.repeat(cosine_snr(1. - (index + 1) / steps),\n len(xt))\n noise_pred = forward.apply(params, xt, snr)\n noise = jax.random.normal(rng_split, shape=xt.shape)\n xt_next, _ = sample_p_step(xt, noise_pred, snr, snr_next, eta,\n noise, clip_percentile)\n return xt_next, params, rng, steps, eta, clip_percentile\n\n self.body_fn = jax.jit(body_fn)\n\n def set_params(self, params: Params) -> Sampler:\n self._params = params\n return self\n\n def sample_p(self,\n xT: Array,\n steps: int,\n rng: Any,\n eta: float = 0.,\n clip_percentile: float = 0.995,\n ) -> Array:\n initial_state = xT, self.params, rng, steps, eta, clip_percentile\n x0, *_ = jax.lax.fori_loop(0, steps, self.body_fn, initial_state)\n logger.info(f'min={x0.min()}, max={x0.max()}, mean={x0.mean()}, '\n f'std={x0.std()}')\n x0 = x0.clip(-1., 1.)\n return x0\n","repo_name":"nlsfnr/Kigo","sub_path":"kigo/diffusion.py","file_name":"diffusion.py","file_ext":"py","file_size_in_byte":5186,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"27785371752","text":"# -*- coding: utf-8 -*-\n\"\"\"\n Figshare\n\n robots.txt as of 02/15/2020\n https://figshare.com/robots.txt\n\n User-agent: *\n Disallow: /search\n Sitemap: https://figshare.com/sitemap/siteindex.xml\n\n Required to configure es:\n\n PUT figshare_api\n {\n \"mappings\": {\n \"enabled\": true,\n \"dynamic_templates\": [\n {\n \"published_date_fild\": {\n \"match_mapping_type\": \"date\",\n \"mapping\": {\n \"type\": \"date\"\n }\n }\n },\n {\n \"object_fields\": {\n \"match_mapping_type\": \"object\",\n \"mapping\": {\n \"enabled\": false\n }\n }\n },\n {\n \"all_other_fields\": {\n \"match_mapping_type\": \"*\",\n \"mapping\": {\n \"index\": false\n }\n }\n }\n ]\n }\n }\n\n\"\"\"\nimport json\nimport logging\nimport os\n\nimport scrapy\nfrom elasticsearch import Elasticsearch\n\nfrom ..helper import JsonLdMixin\n\n\nclass FigshareAPISpider(scrapy.Spider):\n \"\"\"\n\n May need to run multiple times\n\n Example field:\n \"published_date\": \"1996-01-10T00:00:00Z\"\n \"\"\"\n\n name = 'figshare_api'\n base_url = 'https://api.figshare.com/v2/articles?'\n client = Elasticsearch(os.getenv('ES_HOST', 'localhost:9200'))\n query_params = {\n \"page_size\": \"1000\",\n \"order\": \"published_date\",\n \"order_direction\": \"asc\",\n \"item_type\": \"3\" # dataset type\n }\n\n def form_url(self, **kwargs):\n\n params = ['='.join(item_pair) for item_pair in self.query_params.items()]\n url = self.base_url + '&'.join(params)\n for key, value in kwargs.items():\n if value:\n url += f'&{key}={value}'\n return url\n\n def start_requests(self):\n\n res = self.client.indices.get_mapping(index=self.name)\n mapping = res[self.name]['mappings']\n published_date = mapping.get('_meta', {}).get('published_date', '')\n url = self.form_url(published_since=published_date)\n return [scrapy.FormRequest(url, callback=self.parse, cb_kwargs={'published_since': published_date})]\n\n def parse(self, response, published_since='', page=1):\n\n api_res = json.loads(response.body)\n published_date = published_since\n\n if isinstance(api_res, list):\n\n for item in api_res:\n published_date = item['published_date'][:10]\n # skip already scrapped\n if self.client.exists(index=self.name, id=item['id']):\n logging.info('Skipping %s.', item['id'])\n continue\n item.update(_id=item['id'])\n yield item\n\n self.client.indices.put_mapping(index=self.name, body={\"_meta\": {\"published_date\": published_date}})\n\n if page + 1 < 10:\n logging.info('Requesting page %s since %s.', page + 1, published_since)\n yield scrapy.Request(\n url=self.form_url(page=page + 1, published_since=published_since),\n cb_kwargs={\n 'page': page + 1,\n 'published_since': published_since\n }\n )\n else:\n logging.info('Requesting page %s since %s.', 1, published_date)\n yield scrapy.Request(\n url=self.form_url(page= 1, published_since=published_date),\n cb_kwargs={\n 'page': 1,\n 'published_since': published_date\n }\n ) \n\n","repo_name":"biothings/biothings.crawler","sub_path":"crawler/spiders/focusedscrape/figshare.py","file_name":"figshare.py","file_ext":"py","file_size_in_byte":3937,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"31913833016","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jul 31 20:57:58 2017\n\n@author: Rachana\n\"\"\"\n\nimport tweepy\nfrom textblob import TextBlob\nfrom accesscodes import *\n\nauthorization = tweepy.OAuthHandler(consuer_key,consumer_secrect)\nauthorization.set_access_token(access_token,access_token_screct)\n\napi = tweepy.API(authorization)\n\ndef userinput():\n topic = input('Enter a topic: ')\n public_tweet = api.search(topic)\n return public_tweet\n \ndef analysis():\n pub = userinput()\n for tweets in pub:\n print(tweets.text)\n ana = TextBlob(tweets.text)\n print(ana.sentiment)\n\n\n\nanalysis()\n","repo_name":"Prachana/twitter_sentiment","sub_path":"twitter_analysis.py","file_name":"twitter_analysis.py","file_ext":"py","file_size_in_byte":632,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"22001349977","text":"\"\"\" Unit tests for classes in the optimisers.batch module, including\nFullTrainingSet, ConstantBatchSize, and DynamicBatchSize \"\"\"\nimport os\nimport numpy as np\nimport pytest\nimport optimisers, data, models\nfrom .util import (\n get_output_dir,\n set_random_seed_from_args,\n get_random_dataset,\n get_random_network,\n)\n\n# Get name of output directory, and create it if it doesn't exist\noutput_dir = get_output_dir(\"Batch sizes\")\n\n@pytest.mark.parametrize(\"repeat\", range(3))\ndef test_full_training_set_batch(repeat):\n \"\"\" Test using the full training set as a batch with the\n optimisers.batch.FullTrainingSet class \"\"\"\n set_random_seed_from_args(\"test_full_training_set_batch\", repeat)\n dataset = get_random_dataset()\n batch_getter = optimisers.batch.FullTrainingSet()\n x_batch, y_batch = batch_getter.get_batch(dataset.train)\n assert x_batch.shape == dataset.train.x.shape\n assert y_batch.shape == dataset.train.y.shape\n\n@pytest.mark.parametrize(\"repeat\", range(3))\ndef test_constant_batch_size(repeat):\n \"\"\" Test using a constant batch size with the\n optimisers.batch.ConstantBatchSize class \"\"\"\n set_random_seed_from_args(\"test_constant_batch_size\", repeat)\n dataset = get_random_dataset()\n batch_size = np.random.randint(10, 20)\n batch_getter = optimisers.batch.ConstantBatchSize(batch_size)\n x_batch, y_batch = batch_getter.get_batch(dataset.train)\n assert x_batch.shape == (dataset.train.x.shape[0], batch_size)\n assert y_batch.shape == (dataset.train.y.shape[0], batch_size)\n\ndef test_constant_batch_size_non_integer_fail():\n \"\"\" Test that using a non-integer batch size raises an exception when using\n the optimisers.batch.ConstantBatchSize class \"\"\"\n batch_size = 3.7\n with pytest.raises(TypeError):\n batch_getter = optimisers.batch.ConstantBatchSize(batch_size)\n","repo_name":"jakelevi1996/backprop2","sub_path":"Tests/test_batch_sizes.py","file_name":"test_batch_sizes.py","file_ext":"py","file_size_in_byte":1849,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"11348089179","text":"from __future__ import print_function\nfrom collections import defaultdict\nimport os.path as osp\nimport numpy as np\nimport torch\nfrom sklearn.metrics import average_precision_score\nfrom torch.utils.data import DataLoader\nimport os\nfrom torch import nn\nfrom tqdm import tqdm\nfrom time import time\n#os.environ['CUDA_VISIBLE_DEVICES'] = '2'\n#DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nDEVICE = torch.device(\"cuda\")\nprint(DEVICE)\n\n\ndef may_make_dir(path):\n if path in [None, '']:\n return\n if not osp.exists(path):\n os.makedirs(path)\n\ndef save_ckpt(modules_optims, ep, scores, ckpt_file):\n state_dicts = [m.state_dict() for m in modules_optims]\n ckpt = dict(state_dicts=state_dicts, ep=ep, scores=scores)\n may_make_dir(osp.dirname(osp.abspath(ckpt_file)))\n torch.save(ckpt, ckpt_file)\n\n\ndef load_ckpt(modules_optims, ckpt_file, load_to_cpu=True, verbose=True,strict=True):\n map_location = (lambda storage, loc: storage) if load_to_cpu else None\n ckpt = torch.load(ckpt_file, map_location=map_location)\n for m, sd in zip(modules_optims, ckpt['state_dicts']):\n m.load_state_dict(sd,strict=strict)\n if verbose:\n print('Resume from ckpt {}, \\nepoch {}, \\nscores {}'.format(\n ckpt_file, ckpt['ep'], ckpt['scores']))\n return ckpt['ep'], ckpt['scores']\n\n\ndef _unique_sample(ids_dict, num):\n mask = np.zeros(num, dtype=np.bool)\n for _, indices in ids_dict.items():\n i = np.random.choice(indices)\n mask[i] = True\n return mask\n\n\ndef cmc(distmat, query_ids=None, gallery_ids=None,\n query_cams=None, gallery_cams=None, topk=100,\n separate_camera_set=False,\n single_gallery_shot=False,\n first_match_break=False):\n m, n = distmat.shape\n # Fill up default values\n if query_ids is None:\n query_ids = np.arange(m)\n if gallery_ids is None:\n gallery_ids = np.arange(n)\n if query_cams is None:\n query_cams = np.zeros(m).astype(np.int32)\n if gallery_cams is None:\n gallery_cams = np.ones(n).astype(np.int32)\n # Ensure numpy array\n query_ids = np.asarray(query_ids)\n gallery_ids = np.asarray(gallery_ids)\n query_cams = np.asarray(query_cams)\n gallery_cams = np.asarray(gallery_cams)\n # Sort and find correct matches\n indices = np.argsort(distmat, axis=1)\n\n # print('gallery_ids', gallery_ids.shape )\n # print('query_ids[:, np.newaxis]', query_ids[:, np.newaxis].shape )\n # print('indices',indices.shape)\n\n matches = (gallery_ids[indices] == query_ids[:, np.newaxis])\n # Compute CMC for each query\n ret = np.zeros(topk)\n num_valid_queries = 0\n for i in range(m):\n # Filter out the same id and same camera\n valid = ((gallery_ids[indices[i]] != query_ids[i]) |\n (gallery_cams[indices[i]] != query_cams[i]))\n if separate_camera_set:\n # Filter out samples from same camera\n valid &= (gallery_cams[indices[i]] != query_cams[i])\n if not np.any(matches[i, valid]):\n continue\n if single_gallery_shot:\n repeat = 10\n gids = gallery_ids[indices[i][valid]]\n inds = np.where(valid)[0]\n ids_dict = defaultdict(list)\n for j, x in zip(inds, gids):\n ids_dict[x].append(j)\n else:\n repeat = 1\n for _ in range(repeat):\n if single_gallery_shot:\n # Randomly choose one instance for each id\n sampled = (valid & _unique_sample(ids_dict, len(valid)))\n index = np.nonzero(matches[i, sampled])[0]\n else:\n index = np.nonzero(matches[i, valid])[0]\n delta = 1. / (len(index) * repeat)\n for j, k in enumerate(index):\n if k - j >= topk:\n break\n if first_match_break:\n ret[k - j] += 1\n break\n ret[k - j] += delta\n num_valid_queries += 1\n if num_valid_queries == 0:\n raise RuntimeError(\"No valid query\")\n return ret.cumsum() / num_valid_queries\n\n\ndef mean_ap(distmat, query_ids=None, gallery_ids=None,\n query_cams=None, gallery_cams=None):\n m, n = distmat.shape\n # Fill up default values\n if query_ids is None:\n query_ids = np.arange(m)\n if gallery_ids is None:\n gallery_ids = np.arange(n)\n if query_cams is None:\n query_cams = np.zeros(m).astype(np.int32)\n if gallery_cams is None:\n gallery_cams = np.ones(n).astype(np.int32)\n # Ensure numpy array\n query_ids = np.asarray(query_ids)\n gallery_ids = np.asarray(gallery_ids)\n query_cams = np.asarray(query_cams)\n gallery_cams = np.asarray(gallery_cams)\n # Sort and find correct matches\n indices = np.argsort(distmat, axis=1)\n matches = (gallery_ids[indices] == query_ids[:, np.newaxis])\n # Compute AP for each query\n aps = []\n for i in range(m):\n # Filter out the same id and same camera\n valid = ((gallery_ids[indices[i]] != query_ids[i]) |\n (gallery_cams[indices[i]] != query_cams[i]))\n y_true = matches[i, valid]\n y_score = -distmat[i][indices[i]][valid]\n if not np.any(y_true):\n continue\n aps.append(average_precision_score(y_true, y_score))\n if len(aps) == 0:\n raise RuntimeError(\"No valid query\")\n return np.mean(aps)\n\ndef creat_test_data_set_loader(test_path,data_set_class,test_transform ,batch_test):\n\n test_dataset = data_set_class(test_path, transform=test_transform)\n test_loader = DataLoader(test_dataset, batch_size=batch_test, shuffle=False)\n\n return test_dataset, test_loader\n\ndef creat_train_data_set_loader(train_path,data_set_class,RandomIdSampler,train_transform ,batch_train, batch_id,batch_image):\n train_dataset = data_set_class(train_path, transform=train_transform)\n train_loader_tri = DataLoader(train_dataset,\n sampler=RandomIdSampler(train_dataset, batch_image=batch_image),\n batch_size=batch_id * batch_image)\n\n train_loader_all = DataLoader(train_dataset, batch_size=batch_train, shuffle=True, drop_last=True)\n\n return train_dataset, train_loader_tri, train_loader_all\n\n\n","repo_name":"Klitter/Pyramidal_Person_ReID","sub_path":"__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":6297,"program_lang":"python","lang":"en","doc_type":"code","stars":34,"dataset":"github-code","pt":"52"} +{"seq_id":"23284401965","text":"import json\n\nfrom json import JSONDecodeError\n\n\nfrom constants import JSON_FILE, COMMENT_FILE\n\n\nclass PostHandler:\n @staticmethod\n def load_json(file_name):\n \"\"\"\n Функция загружает данные из json файла\n :param file_name: json файл\n :return: возвращает список словарей\n \"\"\"\n try:\n with open(file_name, 'r', encoding='utf-8') as f:\n posts = json.load(f)\n except FileNotFoundError:\n print('Ошибка файл не найден')\n return posts\n except JSONDecodeError:\n print('Ошибка получения данных из Json')\n return posts\n return posts\n\n\n def get_posts_all(self):\n \"\"\"\n Функция загружает возвращает все посты\n :return: все посты из json формата в формате python.\n \"\"\"\n return self.load_json(JSON_FILE)\n\n\n def get_posts_by_user(self, user_name):\n \"\"\"\n Метод возвращает посты определенного пользователя.\n :param user_name: атрибут метода для поиска поста по имени.\n :return: посты определенного пользователя.\n \"\"\"\n posts = []\n posts_data = self.get_posts_all()\n try:\n for poster in posts_data:\n if user_name.lower() == poster['poster_name'].lower():\n posts.append(poster)\n except ValueError:\n print('Такого пользователя нет')\n return posts\n return posts\n\n # post_handler = PostHandler()\n # print(post_handler.get_posts_by_user(\"leo\"))\n\n def get_comments_by_post_id(self, post_id):\n \"\"\"\n Метод возвращает комментарии определенного поста.\n :param post_id:id для поиска комментарий определенного поста\n :return:комментарии определенного пользователя\n \"\"\"\n found_comments = []\n found_posts = self.get_post_by_pk(post_id)\n if not found_posts:\n raise ValueError\n\n comments_data = self.load_json(COMMENT_FILE)\n try:\n for comment in comments_data:\n if post_id == comment['post_id']:\n found_comments.append(comment)\n except KeyError:\n print('Нет такого ключа')\n return []\n return found_comments\n\n def search_for_posts(self, query):\n \"\"\"\n Функция возвращает список постов по ключевому слову\n :param query:атрибут метода для поиска поста по ключевому слову\n :return: список постов по ключевому слову\n \"\"\"\n list_query = []\n posts = self.get_posts_all()\n for post in posts:\n if query.lower() in post['content'].lower():\n list_query.append(post)\n return list_query\n\n def get_post_by_pk(self, pk):\n \"\"\"\n Функция возвращает один пост по его идентификатору.\n :param pk:атрибут метода для поиска поста по идентификатору\n :return:возвращаает один пост по его идентификатору\n \"\"\"\n\n posts_data = self.get_posts_all()\n for post in posts_data:\n if post[\"pk\"] == pk:\n return post\n\n# post_handler = PostHandler()\n# print(post_handler.get_post_by_pk(3))\n#post_handler = PostHandler()\n#print(post_handler.search_for_posts(\"еда\"))\n#post_handler = PostHandler()\n#print(post_handler.search_for_posts(\"еда\"))\n\n\n\n\n\n\n","repo_name":"nika1987/course_paper3","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3992,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"40234694350","text":"### TODO ###\n # playoff games on the game table should be a different color\n # add more color encoding to the statistical report\n # ptpr should be able to track multiple statlines\n # have \"performance\" part of PTPR handle equations. And perhaps relations to other players!\n # Add \"huck\" index, where a player's FGA can get too high where it yields a losing % > career losing %\n # - minimum # of games per FGA# must be 10% of player's total career games\n # Add \"rivalry\" heuristic, measures competitiveness of matchups\n # Ask if the user wants to AMEND their search or start a new one\n # >>>>>>> redisplay gameList after first analysis\n # SPLIT UP DISPLAY FUNC\n # PROVE implications: e.g., Marcus Smart:FGA>14 => L (calculate percent chance)\n # 'prove' category\n # change year to season\n # get below 800 lines (hahahaha)\n # speed up winReason (use sets)\n # display team's record for that season\n # be able to look for games that contain more than one player (differentiate teammates vs. matchups)\n # calculate player or team avg. of a statistic, either all-time or season-based\n # look for games with triple-double scorers\n # CONDENSE EVERYTHING!!!!\n\n ### IDEAS FOR USE ###\n # tell a team where its strengths and weaknesses are (winReason)\n # tell a team for what reason (statistically) they lose to a specific opponent (also winReason)\n # tell a team when a player helps/harms them (performance)\n\nimport csv\nimport sys\nimport math\nimport random\n# import scipy.stats\n\n# font colors\nYELLOW = '\\033[33m' \nGREEN = '\\033[32m'\nBGREEN = '\\033[1;32m'\nCYAN = '\\033[1;36m'\nRED = '\\033[31m'\nBRED = '\\033[1;31m'\nBOLDW = '\\033[1;37m'\nRESET = '\\033[m'\n\n# Reads the CSV files for train and test sets, returns list of examples\ndef read_csv(filename):\n rows = 0\n new_table = []\n with open(filename, newline='') as csvfile:\n spamreader = csv.reader(csvfile, delimiter=',', quotechar='|')\n table = list(spamreader)\n # table.pop(0) #remove first row of CSV\n return table #list of lists\n\n# format blurb for a game based on its box score\ndef analyze(displayed, home, away, game, homePlayers, awayPlayers, playerInfo, homeColor, awayColor, specs):\n stats = [\"Date\",\"GameID\",\"Status\",\"HomeID\",\"AwayID\",\"Season\",\"IDHome\",\"PTS\",\"FG_PCT\",\"FG3_PCT\",\"FT_PCT\",\"FTA\",\"AST\",\"REB\",\"STL\",\"BLK\",\"TO\"]\n statVerb = [\"scored\",\"shot\",\"shot\",\"made\",\"got to the line\",\"dished out\",\"grabbed\",\"stole the ball\",\"had\",\"had\"]\n statNoun = [\"points\",\"percent from the field\",\"percent from 3\",\"percent of his free throws\",\"times\",\"assists\",\"rebounds\",\"times\",\"blocks\",\"turnovers\"]\n statData = [0] * 17 \n wLData = []\n results = []\n homeMVP = [''] * 20 # contains name, pts, FGM, FGA, reb, asts, ... , home/away \n awayMVP = [''] * 20 # ^\n reqMVP = [''] * 20 # ^\n maxHomeScore = 0 \n maxAwayScore = 0 \n # contains relevant i's of player data\n rel = [7,8,10,9,1,11,12]\n # contains relevant i's of MVP data\n MVPrel = [5,27,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,28] # added 1 after 5\n # contains i's for accessing/storing stat data\n statIndices = [16,22,23,24]\n statStoreI = [11,14,15,16]\n homeData = [0] * 17\n awayData = [0] * 17\n\n # void: set vars to either home or away based on who won\n def setWinner(lst, tw, tl, MVPw, maxScore, wpt, lpt):\n\n lst.append(tw)\n lst.append(tl)\n lst.append(wpt)\n lst.append(lpt)\n lst.append(MVPw)\n lst.append(maxScore)\n\n # gather home and away stat data as well as find MVP\n def storeStats(players, maxScore, MVP, data, reqMVP): \n for x in players:\n #print(homeMVP)\n #print(awayMVP)\n if x[5] == reqMVP[0]:\n for i in range(0, len(reqMVP)-1):\n reqMVP[i] = x[MVPrel[i]]\n if players == homePlayers:\n reqMVP[-1] = \"home\"\n else:\n reqMVP[-1] = \"away\"\n # check if player has the maxScore\n if x[27] == \"\": \n maxScore += 0 \n elif int(float(x[27])) > maxScore: \n maxScore = int(float(x[27])) \n for i in range(0, len(MVP)):\n MVP[i] = x[MVPrel[i]]\n for j in range(0, len(statIndices)):\n if x[statIndices[j]] != \"\":\n data[statStoreI[j]] += int(float(x[statIndices[j]])) \n\n # returns a sorted stat list by notability, used for both good and bad stats\n def sortStats(oldStatList, b, maxDiff, bold, unbold, reset, results):\n\n def last(n):\n return n[1]\n\n newStatList = []\n if len(oldStatList) != 0:\n newStatList = sorted(oldStatList, key=last, reverse=b)\n if abs(maxDiff) > 0:\n l = \"\"\n for i in range(0, len(newStatList)):\n if i <= 2:\n if i == 0 and len(newStatList) == 1:\n l += bold + newStatList[i][0] + reset\n elif i == 0 and len(newStatList) == 2:\n l += bold + newStatList[i][0] + reset + \" and \"\n elif i == 0 and len(newStatList) > 2:\n l += bold + newStatList[i][0] + reset + \", \"\n elif i == 1 and len(newStatList) > 2:\n l += unbold + newStatList[i][0] + reset + \", and \"\n else:\n l += unbold + newStatList[i][0] + reset\n results.append(l) \n else:\n results.append(\"N/A\")\n\n return newStatList\n\n # returns a array of data on notable statistic\n def evalNotable(players, playersI, madeI, attI, minAtt):\n maxNotableStat = 0\n made = 0\n attempted = 0\n notableMVP = \"\"\n\n notableList = []\n for x in players:\n if x[playersI] != \"\":\n if int(float(x[attI])) > minAtt:\n if float(x[playersI]) > maxNotableStat: \n maxNotableStat = float(x[playersI])\n made = int(float(x[madeI]))\n attempted = int(float(x[attI]))\n notableMVP = x[5]\n\n # append all the locals\n notableList.append(maxNotableStat)\n notableList.append(made)\n notableList.append(attempted)\n notableList.append(notableMVP)\n\n return notableList\n\n # appends MVP data to the main results list\n def sendMVPData(req, MVP, results, lb, reqStat, alreadyMVP, reqStatNews):\n #if(req):\n #print(\"MVP: \" + str(MVP))\n #print(\"results: \" + str(results))\n #print(\"lb: \" + str(lb))\n msg = \"\"\n lastMsg = \"\"\n term = [\"\",\" points (\",\"\",\"\",\"FG_PCT\",\"FG3M\",\"FG3A\",\"FG3_PCT\",\"FTM\",\"FTA\",\"FT_PCT\",\"OREB\",\"DREB\",\"rebounds\",\"assists\",\n \"steals\",\"blocks\",\"turnovers\",\"personal fouls\",\"plus-minus\"]\n termTrans = [\"Name\",\"PTS\",\"FGM\",\"FGA\",\"FG_PCT\",\"FG3M\",\"FG3A\",\"FG3_PCT\",\"FTM\",\"FTA\",\"FT_PCT\",\"OREB\",\"DREB\",\"REB\",\"AST\",\n \"STL\",\"BLK\",\"TO\",\"PF\",\"+/-\"]\n reqStatV = [\"was\",\"scored\",\"made\",\"attempted\",\"shot\",\"made\",\"attempted\",\"shot\",\"made\",\"attempted\",\"shot\",\"grabbed\",\"grabbed\",\n \"grabbed\",\"dished out\",\"had\",\"had\",\"had\",\"had\",\"had a\"]\n reqStatN = [\"himself\",\"points\",\"shots from the field\",\"shots from the field\",\"percent from the field\",\"threes\",\"threes\",\"percent from three\",\n \"free throws\",\"free throws\",\"percent from the line\",\"offensive rebounds\",\"defensive rebounds\",\"rebounds\",\"assists\",\"steals\",\n \"blocks\",\"turnovers\",\"personal fouls\",\"plus/minus\"]\n MVPdata = []\n reqOnlyData = []\n req1 = True\n req2 = True\n intFloat = 0\n #print(MVP)\n if req:\n if reqStat != \"\":\n if reqStat == \"FG_PCT\" or reqStat == \"FG3_PCT\" or reqStat == \"FT_PCT\":\n intFloat = str(float(MVP[termTrans.index(reqStat)])*100)\n else:\n intFloat = str(int(float(MVP[termTrans.index(reqStat)])))\n results.append(\"This game fit the criteria as \" + str(MVP[0]) + \" \" \n + reqStatV[termTrans.index(reqStat)] + \" \" + BOLDW\n + intFloat + RESET + \" \" + reqStatN[termTrans.index(reqStat)])\n if not alreadyMVP:\n results.append(\".\\n\\nHis main statline \")\n MVP[0] = \"was \"\n elif not alreadyMVP:\n results.append(\"Requested player \")\n msg = \" scored \"\n else:\n msg += \"\"\n req1 = False\n if not alreadyMVP:\n for i in range(0, len(MVP)): \n if not req and i == 0:\n msg += \" scored a team-high \"\n if i != 0:\n if MVP[i] != '':\n if \"in\" in MVP[i]:\n lastMsg = MVP[i]\n elif MVP[i] != \"home\" and MVP[i] != \"away\":\n s = float(MVP[i])\n if s > lb or i == 4 or i == 7 or i == 10 or i == 19:\n if i == 1:\n results.append(str(int(s)) + term[i])\n elif i == 2:\n results.append(str(int(s)) + \"-\")\n elif i == 3:\n results.append(str(int(s)) + \") \")\n elif i == 4 or i == 7 or i == 10:\n reqOnlyData.append((100*round(s,2), term[i]))\n elif i == 5:\n MVP.append((int(s), term[i]))\n elif i > 12 and i < 18:\n MVPdata.append((int(s), term[i]))\n else:\n reqOnlyData.append((s, term[i]))\n else:\n results.append(\"N/A\")\n else:\n results.append(str(MVP[i]))\n results.append(msg)\n else:\n req2 = False\n\n def first(n):\n return n[0] \n\n MVPdata = sorted(MVPdata, reverse=True, key=first)\n # print(\"MVPdata: \" + str(MVPdata))\n if len(MVPdata) > 0:\n results.append(\"to go along with \" + str(int(float(MVPdata[0][0]))) + \" \" + str(MVPdata[0][1]))\n if len(MVPdata) > 1:\n results.append(\" and \" + str(int(float(MVPdata[1][0]))) + \" \" + str(MVPdata[1][1]))\n if req1 or req2:\n results.append(lastMsg + \".\")\n else:\n lastMsg = \"\"\n # print(results)\n\n # formats row of table\n def printRow(i, s, wI, lI, color, reset, results):\n # print(f'{s[i][0]:10}' + f'{str(s[i][wI]):7}' + \"| \" + f'{str(s[i][lI]):7}' + \" ==> \" + color + str(s[i][3]), reset)\n results.append(f'{s[i][0]:10}' + f'{str(s[i][wI]):7}' + \"| \" + f'{str(s[i][lI]):7}' + \" ==> \" + color + str(s[i][3]) + reset + \"\\n\")\n\n # color stat green if good (bold green if best), red if bad (bold red if worst), white if insignificant\n def printTable(g, b, wI, lI, dData, results):\n # print(\"Team Statistics: (W | L ==> Difference) \\n\")\n results.append(\"Team Statistics: (W | L ==> Difference) \\n\\n\")\n for i in range(0, len(dData)):\n if dData[i][0] in g[0:3]:\n if dData[i][0] == g[0]:\n printRow(i, dData, wI, lI, BGREEN, RESET, results)\n else:\n printRow(i, dData, wI, lI, GREEN, RESET, results)\n elif dData[i][0] in b[0:3]:\n if dData[i][0] == b[0]:\n printRow(i, dData, wI, lI, BRED, RESET, results)\n else:\n printRow(i, dData, wI, lI, RED, RESET, results)\n else:\n printRow(i, dData, wI, lI, RESET, RESET, results)\n\n reqMVPStat = \"\"\n\n # grab stats\n for x in specs:\n if 'ptpr' in x:\n reqMVP[0] = x[1].split(\":\")[0]\n perf = x[1].split(\":\")[2]\n if \">\" in perf: \n reqMVPStat = x[1].split(\":\")[2].split(\">\")[0]\n elif \"<\" in perf: \n reqMVPStat = x[1].split(\":\")[2].split(\"<\")[0]\n # print(reqMVP)\n # print(reqMVPStat)\n storeStats(homePlayers, maxHomeScore, homeMVP, homeData, reqMVP)\n storeStats(awayPlayers, maxAwayScore, awayMVP, awayData, reqMVP)\n\n # calculate stat differences\n for i in range(7,14):\n # FG_PCT, FG3_PCT, or FT_PCT\n if i == 8 or i == 9 or i == 10:\n if game[rel[i-7]] != '':\n homeData[i] = round(float(game[rel[i-7]]), 2)\n if game[rel[i-7]+7] != '':\n awayData[i] = round(float(game[rel[i-7]+7]), 2)\n # skip FTA\n elif i == 11:\n statData[i] = None\n else:\n if game[rel[i-7]] != '':\n homeData[i] = int(float(game[rel[i-7]]))\n if game[rel[i-7]+7] != '':\n awayData[i] = int(float(game[rel[i-7]+7]))\n\n for i in range(7, 17):\n # round percentage stats\n if i == 8 or i == 9 or i == 10:\n statData[i] = round(homeData[i] - awayData[i], 2)\n else:\n statData[i] = homeData[i] - awayData[i]\n \n # print(statData)\n \n if statData[7] > 0:\n setWinner(wLData, home, away, homeMVP, maxHomeScore, game[7], game[14])\n \n else:\n setWinner(wLData, away, home, awayMVP, maxAwayScore, game[14], game[7])\n \n winner = wLData[0]\n loser = wLData[1]\n winScore = wLData[2]\n loseScore = wLData[3]\n MVP = wLData[4]\n MVPscore = wLData[5]\n\n ptDiff = abs(statData[7])\n if ptDiff >= 20:\n winMsg = \"blew out\"\n elif ptDiff >= 10:\n winMsg = \"defeated\"\n elif ptDiff >= 2:\n winMsg = \"beat\"\n else:\n winMsg = \"just barely survived\"\n \n if winner == away: # invert every stat difference if away team won\n for i in range(7,17):\n if statData[i] != 0:\n statData[i] *= -1\n \n goodStats = [] # list of tuples: (\"Stat Category\", Value)\n badStats = [] # ^\n maxDiff = 0\n maxDiffStat = \"\"\n minDiff = 0\n minDiffStat = \"\"\n tempStatData = [0] * 17\n\n # copy statData to make calculations\n for i in range(0, len(statData)):\n tempStatData[i] = statData[i] \n\n # find maxDiff and minDiff statistics\n statWeights = [0,0,0,0,0,0,0,1,100,100,100,1,1,1,1,1,-1]\n for i in range(8,17): \n tempStatData[i] *= statWeights[i]\n if tempStatData[i] > 4:\n goodStats.append(((stats[i]), tempStatData[i]))\n if tempStatData[i] > maxDiff:\n maxDiff = tempStatData[i]\n maxDiffStat = stats[i]\n if tempStatData[i] < -5:\n badStats.append(((stats[i]), tempStatData[i]))\n if statData[i] < minDiff:\n minDiff = tempStatData[i]\n minDiffStat = stats[i]\n\n goodStatsReported = []\n badStatsReported = []\n revGoodStats = sortStats(goodStats, True, maxDiff, BGREEN, GREEN, RESET, goodStatsReported)\n revBadStats = sortStats(badStats, False, minDiff, BRED, RED, RESET, badStatsReported)\n\n if displayed:\n\n winnerColor = \"\"\n loserColor = \"\"\n\n if winner == home:\n winnerColor = homeColor\n loserColor = awayColor\n else:\n winnerColor = awayColor\n loserColor = homeColor\n\n # create parallel arrays with just the names\n goodStatNames = []\n badStatNames = []\n for i in range(0,len(revGoodStats)): \n goodStatNames.append(revGoodStats[i][0])\n for i in range(0,len(revBadStats)): \n badStatNames.append(revBadStats[i][0])\n\n # determine index of most notable stat in stats[]\n statI = 0\n if len(goodStatNames) != 0:\n for i in range(0, len(stats)):\n if goodStatNames[0] == stats[i]:\n statI = i-7\n break\n\n # determine index of most notable stat in playerInfo[]\n playersI = 0\n if len(goodStatNames) != 0:\n for i in range(0, len(playerInfo)):\n if goodStatNames[0] == playerInfo[i]:\n playersI = i\n break\n\n # find index of notable statistic\n madeI = 0\n attI = 0\n minAtt = 0\n floatStats = [11,12,14,15,16,17,18]\n # FGM,FG_PCT,FGA,FG3_PCT,FTA,FG3A,FT_PCT\n if playersI in floatStats:\n if playersI == 16:\n attI = playersI\n minAtt = 5 # FTA\n elif playersI == 17:\n attI = playersI - 1\n minAtt = 4 # FG3A\n else:\n attI = playersI - 1\n minAtt = 5 # FGM,FG_PCT,FGA,FG3_PCT,FT_PCT\n madeI = attI - 1\n\n notableData = []\n if home == winner:\n if reqMVP[-1] == \"home\":\n reqMVP[-1] = \" in the \" + GREEN + \"win\" + RESET\n else:\n reqMVP[-1] = \" in the \" + RED + \"loss\" + RESET\n if statI != 9:\n notableData = evalNotable(homePlayers, playersI, madeI, attI, minAtt)\n else:\n notableData = evalNotable(awayPlayers, playersI, madeI, attI, minAtt)\n else:\n if reqMVP[-1] == \"away\":\n reqMVP[-1] = \" in the \" + GREEN + \"win\" + RESET\n else:\n reqMVP[-1] = \" in the \" + RED + \"loss\" + RESET\n if statI != 9:\n notableData = evalNotable(awayPlayers, playersI, madeI, attI, minAtt)\n else:\n notableData = evalNotable(homePlayers, playersI, madeI, attI, minAtt)\n\n maxNotableStat = notableData[0]\n made = notableData[1]\n attempted = notableData[2]\n notableMVP = notableData[3]\n keyPlayer = []\n\n if maxNotableStat == 0 or maxNotableStat > 100:\n keyPlayer.append(\"N/A\")\n elif playersI == 12 or playersI == 15 or playersI == 16 or playersI == 18:\n if playersI != 16:\n maxNotableStat = round(maxNotableStat*100,2)\n else:\n maxNotableStat = int(maxNotableStat)\n keyPlayer.append(notableMVP + \" \" + statVerb[statI] + \" \" + str(maxNotableStat) + \" \" + \n str(statNoun[statI]) + f' ({made}-{attempted}), which heavily impacted the game.\\n')\n else:\n maxNotableStat = int(maxNotableStat)\n keyPlayer.append(notableMVP + \" \" + statVerb[statI] + \" \" + str(maxNotableStat) + \" \" + \n str(statNoun[statI]) + \", which heavily impacted the game.\\n\") \n \n # send winner MVP first, then loser MVP\n winMVP = []\n loseMVP = []\n alreadyMVP = False\n reqStatNews = True\n\n MVP.append(\" in the \" + GREEN + \"win\" + RESET)\n sendMVPData(False, MVP, winMVP, 1, reqMVPStat, False, True)\n if MVP == homeMVP:\n awayMVP.append(\" in the \" + RED + \"loss\" + RESET)\n sendMVPData(False, awayMVP, loseMVP, 1, reqMVPStat, False, True)\n else:\n homeMVP.append(\" in the \" + RED + \"loss\" + RESET)\n sendMVPData(False, homeMVP, loseMVP, 1, reqMVPStat, False, True)\n\n # if requested player, send statline\n reqMVPDis = []\n reqUsed = False\n if reqMVP[1] != '':\n if reqMVP[0] == homeMVP[0]:\n alreadyMVP = True\n elif reqMVP[0] == awayMVP[0]:\n alreadyMVP = True\n sendMVPData(True, reqMVP, reqMVPDis, -1, reqMVPStat, alreadyMVP, reqStatNews)\n else:\n reqMVPDis.append('N/A')\n \n # print(results)\n\n # multiply percentages by 100 for readability\n for i in range(8, 11):\n homeData[i] = round(homeData[i]*100, 2)\n awayData[i] = round(awayData[i]*100, 2)\n statData[i] = round(statData[i]*100, 2)\n\n # make this into a function\n if len(goodStatNames) < 3:\n if len(goodStatNames) < 1:\n goodStatNames.append(\"\")\n if len(goodStatNames) < 2:\n goodStatNames.append(\"\")\n goodStatNames.append(\"\")\n\n if len(badStatNames) < 3:\n if len(badStatNames) < 1:\n badStatNames.append(\"\")\n if len(badStatNames) < 2:\n badStatNames.append(\"\")\n badStatNames.append(\"\")\n\n def last(n):\n return n[3]\n\n # invert turnover number\n statData[16] *= -1\n\n fullStatData = []\n for i in range(8, len(stats)):\n fullStatData.append([stats[i], homeData[i], awayData[i], statData[i]])\n ordStatData = sorted(fullStatData, key=last, reverse=True)\n\n results.append(\"\\n\" + game[0] + \" W: \" + \"\\033[1;38;5;\" + winnerColor + winner + RESET + \", \" + \"L: \" + \n \"\\033[1;38;5;\" + loserColor + loser + RESET + \"\\n\\n\")\n\n # print out score before printing table\n if winner == home:\n results.append(\"Score: \" + str(homeData[7]) + \" - \" + str(awayData[7]) + RESET + \"\\n\\n\")\n printTable(goodStatNames, badStatNames, 1, 2, ordStatData, results)\n else:\n results.append(\"Score: \" + str(awayData[7]) + \" - \" + str(homeData[7]) + RESET + \"\\n\\n\")\n printTable(goodStatNames, badStatNames, 2, 1, ordStatData, results)\n\n # sum of stat differences correlates with competitiveness\n compScore = int(sum(statData[7:]))\n compSpec = ['*'] * 10\n results.append(\"\\nSum of Stat Differences: ==> \" + CYAN + str(compScore) + RESET + \"\\n\\n\")\n\n # if compScore is negative, set to 0\n if compScore < 0:\n compScore = 0\n\n # if compScore is 60 or above, set to 60\n if compScore > 59:\n compScore = 60\n\n # rescale sum of stat differences to fit on spectrum\n compScore = int((round(compScore*1.5/10)*10)/10)\n\n # print out spectrum, color compScore index cyan\n results.append(\"Very Competitive | \")\n for i in range(0, len(compSpec)):\n if i == compScore:\n compSpec[i] = CYAN + compSpec[i] + RESET\n results.append(compSpec[i] + \" \")\n results.append(\"| Not Very Competitive\\n\\n\")\n\n # NON-DIS CARES ABOUT\n results.append(winner + \" \") \n results.append(winMsg + \" the \")\n results.append(loser + \" \") \n if winScore != '':\n results.append(str(int(float(winScore))) + \" to \") \n if loseScore != '':\n results.append(str(int(float(loseScore)))) \n if \"N/A\" not in goodStatsReported:\n results.append(\" thanks to better \" + goodStatsReported[0])\n if \"N/A\" not in badStatsReported:\n results.append(\" despite worse \" + badStatsReported[0] + \".\\n\\n\")\n else:\n results.append(\".\\n\\n\")\n\n if displayed:\n if keyPlayer[0] != \"N/A\":\n results.append(keyPlayer[0] + \"\\n\")\n for x in winMVP:\n if x != \"N/A\":\n results.append(x)\n results.append(\"\\n\\n\")\n for y in loseMVP:\n if y != \"N/A\":\n results.append(y)\n results.append(\"\\n\\n\")\n pickedPlayer = \"\"\n for z in reqMVPDis:\n if z != \"N/A\":\n results.append(z)\n pickedPlayer = \"\\n\\n\"\n results.append(pickedPlayer)\n results.append(game[len(game)-1])\n else:\n results.append(homeColor)\n results.append(awayColor)\n\n # print(results)\n return(results)\n\n # END OF ANALYZE--------------------------------------------------------------------------------------\n\n# prints a separator line\ndef printSeparator():\n # print separator line\n separator = ['-'] * 100\n for j in range(0, len(separator)):\n print(separator[j], end=\"\")\n print()\n \n# main UI and search filter - contains display() which utilizes analyze()\ndef welcome(gameData, attributes, teamData, gameDetails, playerInfo, playerData):\n\n #### DISPLAY #### -- display analysis of game ----------------------------------------------------\n def display(displayed, gameList, gameI, gameData, teamData, gameDetails, playerInfo, stat, name, specs, q):\n\n # replay prompt to analyze a new game ---------------------------------------------------------\n def newSearch(msg, s, n, specs):\n replay = \"\"\n prompt = msg + YELLOW + \"Would you like to analyze another \"\n for x in specs:\n prompt += str(x[0]) + \"=\" + str(x[1])\n if x != specs[len(specs)-1]:\n prompt += \", \"\n if prompt == msg + YELLOW + \"Would you like to analyze another \":\n prompt += \"random\"\n if s == \"random\":\n replay = input(prompt + \" game? (y or n or quit)\\n\\n\" + RESET)\n if replay.lower() == 'y': \n newGameI = random.randint(0, len(gameData)-1)\n print()\n display(True, gameList, newGameI, gameData, teamData, gameDetails, playerInfo, stat, name, specs, \"\")\n elif replay.lower() == 'n':\n print()\n welcome(gameData, attributes, teamData, gameDetails, playerInfo, playerData)\n elif replay.lower() == 'quit':\n print(\"\\nQuitting...\\n\")\n quit()\n else:\n print()\n newSearch(YELLOW + \"I'm sorry, I didn't quite get that. \" + RESET, s ,n, specs)\n else:\n replay = input(prompt + \" game? (y or n or quit)\\n\\n\" + RESET)\n if replay.lower() == 'y': \n if len(gameList) > 1:\n for i in range(len(gameList)):\n if gameList[i] == pickedGame:\n if i == len(gameList)-1:\n ind = 0\n else:\n ind = i + 1\n # ind = random.randint(0, len(gameList)-1)\n newGameI = gameList[ind]\n elif len(gameList) == 1:\n newGameI = gameList[0]\n else:\n newGameI = random.randint(0, len(gameData)-1)\n print()\n display(True, gameList, newGameI, gameData, teamData, gameDetails, playerInfo, stat, name, specs, \"y\")\n elif replay.lower() == 'n': \n print()\n welcome(gameData, attributes, teamData, gameDetails, playerInfo, playerData)\n elif replay.lower() == \"quit\":\n print(\"\\nQuitting...\\n\")\n quit()\n else:\n print()\n newSearch(YELLOW + \"I'm sorry, I didn't quite get that. \" + RESET, s ,n, specs)\n \n pickedGame = gameI\n game = gameData[pickedGame]\n homePlayers = []\n awayPlayers = []\n teams = []\n teamColors = ['45m','106m','160m','88m','34m','203m','86m','9m','198m','87m','148m','98m','202m','129m','33m',\n '27m','153m','220m','226m','216m','198m','1m','196m','245m','214m','166m','46m','124m','221m','69m']\n home = \"\"\n away = \"\"\n\n # classify the home and away team\n for i in range(len(teamData)): # -4 ????\n teams.append(teamData[i][5])\n if teamData[i][1] == game[3]:\n home = teamData[i][5]\n elif teamData[i][1] == game[4]:\n away = teamData[i][5]\n \n # store home and away players in their respective list\n for x in gameDetails:\n if x[0] == game[1]:\n if x[1] == game[3]:\n homePlayers.append(x)\n elif x[1] == game[4]:\n awayPlayers.append(x)\n\n # default home and away color\n homeColor = '\\033[1;38;5;202m'\n awayColor = '\\033[1;38;5;198m'\n\n sortedTeams = sorted(teams)\n homeColor = teamColors[sortedTeams.index(home)]\n awayColor = teamColors[sortedTeams.index(away)]\n\n # check if playoff or finals matchup\n playoff = \" \"\n playoffMonths = [\"04\",\"05\"]\n bubblePlayoffMonths = [\"08\",\"09\"]\n years = [\"2003\",\"2004\",\"2005\",\"2006\",\"2007\",\"2008\",\"2009\",\"2010\",\n \"2011\",\"2012\",\"2013\",\"2014\",\"2015\",\"2016\",\"2017\",\"2018\",\"2019\",\"2020\",\"2021\"]\n playoffStartDates = [18, 16, 22, 22, 20, 19, 17, 16, 15, 27, 19, 18, 17, 15, 14, 13, 12, 16, 21]\n finalStartDates = [3, 5, 8, 7, 6, 4, 3, 2, 0, 11, 5, 4, 3, 1, 0, 0, 0, 0, 0]\n gameYear = gameData[pickedGame][0].split(\"-\")[0]\n gameMonth = gameData[pickedGame][0].split(\"-\")[1]\n gameDay = gameData[pickedGame][0].split(\"-\")[2]\n\n # CONDENSE THIS\n if gameYear == \"2020\":\n if gameMonth in bubblePlayoffMonths:\n if gameMonth == \"09\" and gameDay == \"30\":\n playoff = \" finals \"\n elif gameMonth == \"08\":\n if int(gameDay) > playoffStartDates[years.index(gameYear)]:\n playoff = \" playoff \"\n else:\n playoff = \" playoff \"\n elif gameMonth == \"10\":\n playoff = \" finals \"\n\n if gameMonth in playoffMonths:\n if gameMonth == \"04\":\n if int(gameDay) > playoffStartDates[years.index(gameYear)]:\n playoff = \" playoff \"\n else:\n playoff = \" playoff \"\n elif gameMonth == \"06\":\n if int(gameDay) > finalStartDates[years.index(gameYear)]:\n playoff = \" finals \"\n else:\n playoff = \" playoff \"\n\n results = []\n if displayed:\n # recursive loop where user can decide whether or not to analyze\n def studyGame(ques):\n inp = ques\n if inp == \"y\":\n print() \n print(\"********************* Analyzing ********************* \")\n results = analyze(displayed, home, away, game, homePlayers, awayPlayers, playerInfo, homeColor, awayColor, specs)\n for x in results:\n if x != results[len(results)-1]:\n print(str(x), end=\"\")\n newSearch(\"\", stat, name, specs)\n elif inp == \"n\":\n print()\n welcome(gameData, attributes, teamData, gameDetails, playerInfo, playerData)\n elif inp == \"quit\":\n print(\"\\nQuitting... \\n\")\n else:\n print(\"\\n\" + YELLOW + \"I'm sorry, I didn't quite get that.\\n\" + RESET)\n studyGame(ques)\n\n if q == \"y\" or q == \"n\":\n studyGame(q)\n else:\n print(\"Let's look specifically at \" + BOLDW + game[0] + RESET + \"'s \" + '\\033[1;38;5;' + homeColor + home + RESET + \" vs. \" + '\\033[1;38;5;' + awayColor + away + RESET + BOLDW + playoff + RESET + \"matchup.\\n\", RESET) \n a = input(\"Do you want to analyze? (y or n or quit) \\n\\n\") \n studyGame(a)\n else:\n results = analyze(displayed, home, away, game, homePlayers, awayPlayers, playerInfo, homeColor, awayColor, specs)\n results.append(home)\n results.append(away)\n results.append(game[0])\n return results\n\n # recursive function that prompts possible search parameters until 'go' is entered\n def completeSearch(ans):\n inpString = \"Select a category to narrow your search. When finished, enter \\'\" + BGREEN + \"go\" + RESET + \"'.\\n\\n\"\n for i in range(0, len(searchOptions)-1):\n if catList[i] != \"?\":\n inpString += str(searchOptions[i]) + \"=\" + YELLOW + str(catList[i]) + RESET + \", \"\n else:\n inpString += str(searchOptions[i]) + \"=\" + str(catList[i]) + \", \"\n inpString += str(searchOptions[len(searchOptions)-1]) + \"\\n\\n\"\n if ans != 'go' or (ans == 'go' and not specs and wReason[0] == False): \n if ans != 'go':\n ans3 = input(inpString).lower()\n else:\n ans3 = \"random game\"\n if ans3 in searchOptions: \n if ans3 == \"random game\":\n print(\"\\n\" + YELLOW + \"A random game will be chosen.\" + RESET + \"\\n\")\n ans2 = \"random\"\n pickedGame = random.randint(0, len(gameData)-1)\n print(\"Searching...\\n\")\n return \n # completeSearch(\"go\")\n else: \n ans2 = input(YELLOW + \"\\nWhich \" + ans3 + \" would you like to select?\\n\\n\" + RESET)\n print()\n if ans3 == 'month': \n if ans2 in monthNames:\n catList[searchOptions.index(ans3)] = \"*\" + ans2 + \"*\"\n print(YELLOW + \"You have selected \" + ans2 + \" as a month.\" + RESET + \"\\n\")\n ans2 = monthNums[monthNames.index(ans2)]\n #print(\"ans3: \" + ans3)\n #print(\"ans : \" + ans)\n #print(catList)\n if specs and ans3 == ans and ans3 not in catList:\n specs.remove(specs[len(specs)-1])\n specs.append((ans3, ans2)) \n else:\n print(YELLOW + \"That's not a valid month.\" + RESET + \"\\n\")\n completeSearch(ans3) \n elif ans3 == 'year':\n if ans2 in validYears:\n catList[searchOptions.index(ans3)] = \"*\" + ans2 + \"*\"\n if specs and ans3 == ans and ans3 not in catList:\n specs.remove(specs[len(specs)-1])\n specs.append((ans3, ans2)) \n print(YELLOW + \"You have chosen the year \" + ans2 + \".\" + RESET + \"\\n\")\n else:\n print(YELLOW + \"That's not a valid year.\" + RESET + \"\\n\")\n completeSearch(ans3)\n elif ans3 == 'day':\n try:\n if int(ans2) > 0 and int(ans2) < 32:\n catList[searchOptions.index(ans3)] = \"*\" + ans2 + \"*\"\n if specs and ans3 == ans and ans3 not in catList:\n specs.remove(specs[len(specs)-1])\n specs.append((ans3, ans2)) \n print(YELLOW + \"You have chosen \" + ans2 + \" as a day.\" + RESET + \"\\n\")\n else:\n print(YELLOW + \"That's not a valid day.\" + RESET + \"\\n\")\n \n except:\n print(YELLOW + \"That's not a valid day.\" + RESET + \"\\n\")\n completeSearch(ans3)\n elif ans3 == 'home_team' or ans3 == 'away_team':\n if ans2 in {x[5] for x in teamData}:\n catList[searchOptions.index(ans3)] = \"*\" + ans2 + \"*\"\n if specs and ans3 == ans and ans3 not in catList:\n specs.remove(specs[len(specs)-1])\n specs.append((ans3, ans2))\n else:\n print(YELLOW + \"That's not a valid team.\" + RESET + \"\\n\")\n completeSearch(ans3)\n else:\n # if newly entered searchOption is already lingering in specs, update it\n if specs and ans3 == ans and ans3 not in catList:\n specs.remove(specs[-1])\n catList[searchOptions.index(ans3)] = \"*\" + ans2 + \"*\"\n # don't append special categories\n if ans3 != \"win_reason\" and ans3 != \"ptpr\":\n specs.append((ans3, ans2))\n catList[searchOptions.index(ans3)] = \"*\" + ans2 + \"*\" \n elif ans3 == \"ptpr\":\n # perf = [True, ans2.split(\":\")[0], ans2.split(\":\")[1]]\n allPlayers = {x[0] for x in playerData}\n anyPMode = False\n anyTMode = False\n careerMode = False\n if ans2.count(\":\") > 0:\n wlTrans = {\"W\": \" and won\", \"L\": \" and lost\", \"any\": \"\"}\n a2Parts = ans2.split(\":\")\n tTrans = {True: \"\", False: \" for the \" + a2Parts[1]}\n if ans2.count(\":\") == 1 and a2Parts[1] == \"career\":\n careerMode = True\n specs.append((ans3, ans2))\n catList[searchOptions.index(ans3)] = \"*\" + ans2 + \"*\"\n print(YELLOW + \"You want to look all games that feature \" + a2Parts[0] + \".\\n\" + RESET)\n if a2Parts[0] == \"any\":\n anyPMode = True \n if a2Parts[1] == \"any\":\n anyTMode = True \n if a2Parts[0] in allPlayers or anyPMode:\n if a2Parts[1] in {x[5] for x in teamData} or anyTMode:\n if \"<\" in a2Parts[2] or anyTMode:\n if a2Parts[2].split(\"<\")[0] in legalPStats or anyTMode:\n specs.append((ans3, ans2))\n catList[searchOptions.index(ans3)] = \"*\" + ans2 + \"*\"\n print(YELLOW + \"You want to look at games\" + tTrans[anyTMode] + \" where \"\n + a2Parts[0] + \" performed \" + a2Parts[2] + wlTrans[a2Parts[3]] + \".\\n\" + RESET)\n else:\n print(YELLOW + \"That's not a valid stat. Legal stats are shown below:\\n\" + str(legalStats) + RESET + \"\\n\")\n elif \">\" in a2Parts[2] or anyTMode:\n if a2Parts[2].split(\">\")[0] in legalPStats or anyTMode:\n specs.append((ans3, ans2))\n catList[searchOptions.index(ans3)] = \"*\" + ans2 + \"*\"\n print(YELLOW + \"You want to look at games\" + tTrans[anyTMode] + \" where \"\n + a2Parts[0] + \" performed \" + a2Parts[2] + wlTrans[a2Parts[3]] + \".\\n\" + RESET)\n else:\n print(YELLOW + \"That's not a valid stat. Legal stats are shown below:\\n\" + str(legalStats) + RESET + \"\\n\")\n elif not careerMode:\n print(YELLOW + \"That's not a valid team.\" + RESET + \"\\n\")\n else:\n print(YELLOW + \"That's not a valid player.\" + RESET + \"\\n\")\n else:\n print(YELLOW + \"Please answer in the format 'Player Name:Team:Performance:Result'.\" + RESET + \"\\n\")\n print(YELLOW + \"You can also use the format 'Player Name:career' for all games played by the player.\" + RESET + \"\\n\")\n elif ans3 == \"win_reason\":\n if \":\" in ans2:\n if ans2.split(\":\")[1] in legalStats:\n wReason[0] = (True, ans2.split(\":\")[0], ans2.split(\":\")[1])\n catList[searchOptions.index(ans3)] = \"*\" + ans2 + \"*\"\n print(YELLOW + \"You have chosen \" + ans2.split(\":\")[0] + \" games that were \"\n + \"won because of \" + ans2.split(\":\")[1] + \".\" + RESET + \"\\n\")\n elif ans2 in legalStats:\n wReason[0] = (True, \"any\", ans2)\n catList[searchOptions.index(ans3)] = \"*\" + ans2 + \"*\"\n print(YELLOW + \"You have chosen games that were \"\n + \"won because of \" + ans2 + \".\" + RESET + \"\\n\")\n else:\n print(YELLOW + \"Please answer using the format \\\"home/away:stat\\\".\" + RESET + \"\\n\")\n completeSearch(ans3) \n elif ans3 != 'go':\n if ans3 == \"random\":\n print(\"\\n\" + YELLOW + \"A random game will be chosen.\" + RESET + \"\\n\")\n ans2 = \"random\"\n pickedGame = random.randint(0, len(gameData)-1)\n completeSearch(\"go\")\n elif ans3 == \"quit\":\n print(\"\\nQuitting... \\n\")\n quit()\n else:\n print(\"\\n\" + YELLOW + \"I'm sorry, I didn't quite get that.\\n\" + RESET) \n completeSearch(ans3)\n else:\n print()\n completeSearch(ans3)\n else:\n print(\"Searching...\\n\")\n return \n \n # create set of all player performances, keep those that meet criteria\n def createPlayerSet(on):\n # perfData = [\"Ben Simmons\", \"76ers\", \"TO>5\", \"L\"]\n teamTrans = {x[4] : x[5] for x in teamData}\n gameDetSet = {\"1\"}\n if playerParam:\n for i in range(0, len(gameDetails)):\n addString = \"\"\n if gameDetails[i][8] != \"\":\n addString += gameDetails[i][0]\n if perfData[0] == \"any\":\n addString += \":any:\"\n else:\n addString += \":\" + gameDetails[i][5] + \":\"\n if perfData[1] == \"any\":\n addString += \"any:\"\n else:\n addString += teamTrans[gameDetails[i][2]] + \":\"\n\n if \">\" in perfData[2]:\n pStat = perfData[2].split(\">\")[0]\n statVal = perfData[2].split(\">\")[1]\n if gameDetails[i][playerInfo.index(pStat)] != \"\":\n if float(gameDetails[i][playerInfo.index(pStat)]) > float(statVal):\n # print(float(gameDetails[i][playerInfo.index(pStat)]))\n # perfData[0] = gameDetails[i][5]\n addString += perfData[2] + \":\"\n elif \"<\" in perfData[2]:\n pStat = perfData[2].split(\"<\")[0]\n statVal = perfData[2].split(\"<\")[1]\n if gameDetails[i][playerInfo.index(pStat)] != \"\":\n if float(gameDetails[i][playerInfo.index(pStat)]) < float(statVal):\n # print(float(gameDetails[i][playerInfo.index(pStat)]))\n # perfData[0] = gameDetails[i][5]\n addString += perfData[2] + \":\"\n else:\n addString += \"any:\"\n addString += perfData[3]\n\n gameDetSet.add(addString + \":\")\n\n gameDetSet.remove(\"1\")\n return gameDetSet\n\n # if reason, keep home/away games won by reason\n def checkReason(reasonData, color, i):\n reasonUsed = reasonData[0][0]\n side = reasonData[0][1]\n stat = reasonData[0][2]\n coll = []\n preSort = []\n removedGames = []\n if reasonUsed: \n for x in gameList:\n keep = False\n winner = \"\"\n loser = \"\"\n if len(gameList) == 0:\n break\n coll.append(display(False, gameList, x, gameData, teamData, gameDetails, playerInfo, ans1, ans2, specs, \"\")) \n if stat != \"any\":\n statReason = color + stat \n else:\n statReason = \",\"\n # keep = true if home selected and won\n if str(coll[-1][-3]) + \" \" == coll[-1][0] and side == \"home\": \n keep = True\n # keep = true if away selected and won\n if str(coll[-1][-2]) + \" \" == coll[-1][0] and side == \"away\": \n keep = True\n if side == \"any\":\n keep = True\n if statReason not in coll[-1][i] or not keep:\n removedGames.append(x)\n\n for y in removedGames:\n gameList.remove(y)\n\n ##### WELCOME #####\n\n # data for search and welcome screen\n searchOptions = [\"year\",\"month\",\"day\",\"home_team\",\"away_team\",\"win_reason\",\"ptpr\",\"random game\"]\n # lowercase version of searchOptions\n #searchOpLower = [x.lower() for x in searchOptions]\n # display version of searchOptions\n searchOpDis = [\"year\",\"month\",\"day\",\"home_team\",\"away_team\",\"win_reason\",\"ptpr\",\"random_game\"]\n catList = [\"?\",\"?\",\"?\",\"?\",\"?\",\"?\",\"?\",\"?\",\"?\"]\n searchI = [0,0,0,3,4,1,1]\n legalStats = [\"FG_PCT\",\"FG3_PCT\",\"FT_PCT\",\"FTA\",\"AST\",\"REB\",\"STL\",\"BLK\",\"TO\"]\n legalPStats = [\"MIN\",\"FGM\",\"FGA\",\"FG_PCT\",\"FG3M\",\"FG3A\",\"FG3_PCT\",\"FTM\",\"FTA\",\n \"FT_PCT\",\"OREB\",\"DREB\",\"REB\",\"AST\",\"STL\",\"BLK\",\"TO\",\"PF\",\"PTS\",\"PLUS_MINUS\"]\n validYears = [str(2002 + i) for i in range(1, 22)]\n monthNames = [\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"]\n monthNums = [\"01\",\"02\",\"03\",\"04\",\"05\",\"06\",\"07\",\"08\",\"09\",\"10\",\"11\",\"12\"]\n specs = [] # holds tuple responses to searchOptions, (category, response)\n gameList = []\n pickedGame = 0\n reqPlayer = \"N/A\"\n wReason = [(False,\"\",\"\")]\n perf = [False,\"\",\"\"]\n\n print(YELLOW + \"Welcome to NBA Researcher!\\n\", RESET)\n completeSearch(\"\") # fills specs[]\n ans1 = \"\"\n ans2 = \"\"\n\n # if ptpr in specs, parse, also translate \"career\" if present\n perfData = []\n playerParam = False\n for x in specs:\n pCareer = x\n if \"ptpr\" in x:\n if \"career\" in x[1]:\n specs.remove(x)\n pCareer = (x[0], x[1][:x[1].index(\":\")] + \":any:any:any\")\n specs.append(pCareer)\n perfData = pCareer[1].split(\":\")\n playerParam = True\n\n # specs are fully ready\n\n print(\"Finding games that meet the parameters...\\n\")\n \n # create set of all player performances\n pSet = createPlayerSet(playerParam)\n\n # does game(i) contain all of the specifications?\n for i in range(0, len(gameData)):\n # holds specified aspect of game(i)\n x = \"\"\n checkList = []\n for j in range(0, len(specs)): \n x = gameData[i][searchI[searchOptions.index(specs[j][0])]]\n if specs[j][0] == \"year\" or specs[j][0] == \"month\" or specs[j][0] == \"day\":\n x = x.split(\"-\")[searchOptions.index(specs[j][0])]\n elif specs[j][0] == \"home_team\" or specs[j][0] == \"away_team\":\n for k in range(0, len(teamData)):\n if teamData[k][1] == x:\n x = teamData[k][5]\n else:\n x = str(x) + \":\" + specs[j][1] + \":\"\n #print(x)\n #if specs[j][1].split(\":\")[0] == \"any\":\n #if x.split(\":\")[2] in pSet:\n #specs[j][1] = str()\n if x in pSet:\n x = specs[j][1]\n\n if x == specs[j][1]:\n checkList.append(specs[j]) \n if checkList == specs:\n gameList.append(i) # index of gameData is appended\n\n checkReason(wReason, BGREEN, 5)\n validGame = []\n resultList = []\n winCol = \"\"\n loseCol = \"\"\n ws = 0\n ls = 0\n\n rand = False\n plperf = False\n tf = \"\"\n finalTf = \"\"\n finalPerf = \"\"\n finalAction = \"\"\n\n for x in specs:\n if \"year\" in x:\n tf = x[1]\n if \"ptpr\" in x:\n plperf = True\n playerName = x[1].split(\":\")[0]\n team = x[1].split(\":\")[1]\n performance = x[1].split(\":\")[2]\n result = x[1].split(\":\")[3]\n\n print(\"Displaying results...\\n\")\n \n if not specs:\n gameList.clear()\n rand = True\n \n allTeams = []\n for i in range(len(teamData)): # -4 ???\n allTeams.append(teamData[i][5])\n sortTeams = sorted(allTeams)\n\n # ['76ers-45m', 'Bucks-106m', 'Bulls-160m', 'Cavaliers-88m', 'Celtics-34m', 'Clippers-203m', 'Grizzlies-86m', \n # 'Hawks-9m', 'Heat-198m', 'Hornets-87m', 'Jazz-148m', 'Kings-98m', 'Knicks-202m', 'Lakers-129m', 'Magic-33m', \n # 'Mavericks-27m', 'Nets-153m', 'Nuggets-220m', 'Pacers-226m', 'Pelicans-216m', 'Pistons-198m', 'Raptors-1m', \n # 'Rockets-196m', 'Spurs-245m', 'Suns-214m', 'Thunder-166m', 'Timberwolves-46m', 'Trail Blazers-124m', \n # 'Warriors-221m', 'Wizards-69m']\n\n teamColors = ['45m','106m','160m','88m','34m','203m','86m','9m','198m','87m','148m','98m','202m','129m','33m',\n '27m','153m','220m','226m','216m','198m','1m','196m','245m','214m','166m','46m','124m','221m','69m']\n\n playerTeams = set()\n\n # get wNick, lNick, wColor, lColor, and you're free from extra display!!!\n for x in gameList: \n homePts = gameData[x][7]\n homeTeam = gameData[x][3]\n awayPts = gameData[x][14]\n awayTeam = gameData[x][4]\n wTeam = \"\"\n wPts = \"\"\n lTeam = \"\"\n lPts = \"\"\n lNick = \"\" # validGame[2]\n wNick = \"\" # validGame[0]\n date = gameData[x][0] # validGame[-1]\n season = gameData[x][5]\n awayNick = \"\" # validGame[-2]\n homeNick = \"\" # validGame[-3\n wColor = \"\" # validGame[-4]\n lColor = \"\" # validGame[-5]\n # teamSeasons = []\n if float(homePts) > float(awayPts):\n wTeam = homeTeam\n wPts = str(int(float(homePts)))\n lTeam = awayTeam\n lPts = str(int(float(awayPts)))\n else:\n wTeam = awayTeam\n wPts = str(int(float(awayPts)))\n lTeam = homeTeam\n lPts = str(int(float(homePts)))\n for y in teamData:\n if y[1] == wTeam:\n wNick = y[5]\n if y[1] == lTeam:\n lNick = y[5]\n if y[1] == homeTeam:\n homeNick = y[5]\n if y[1] == awayTeam:\n awayNick = y[5]\n wColor = teamColors[sortTeams.index(wNick)]\n lColor = teamColors[sortTeams.index(lNick)]\n homeColor = teamColors[sortTeams.index(homeNick)]\n awayColor = teamColors[sortTeams.index(awayNick)]\n\n if plperf:\n teamSeasons = []\n if team != \"any\":\n for i in range(2003, 2022):\n teamSeasons.append(([team], str(i)))\n if wNick == team: # for validGame, + \" \"\n ws += 1\n elif lNick == team:\n ls += 1\n else:\n maxSeason = (\"\", 0)\n minSeason = (\"\", 1000000)\n for p in playerData:\n if playerName in p:\n teamSeasons.append(([y[5] for y in teamData if y[1] == p[1]], p[3]))\n for t in teamSeasons:\n if int(list(t[1])[0]) > int(maxSeason[1]):\n maxSeason = (t[0], t[1])\n # print(\"Max: \" + str(maxSeason))\n if int(list(t[1])[0]) < int(minSeason[1]):\n minSeason = (t[0], t[1])\n # print(\"Min: \" + str(minSeason))\n \n i = int(maxSeason[1])+1\n #print(i)\n while i < 2021:\n teamSeasons.insert(0, (maxSeason[0], str(i)))\n i += 1\n j = int(minSeason[1])-1\n #print(j)\n while j > 2002:\n teamSeasons.append((minSeason[0], str(j)))\n j -= 1\n\n for y in teamSeasons:\n if playerTeams:\n playerTeams.add(y[0][0])\n\n if ([wNick], list({y[5] for y in gameData if y[0] == date})[0]) in teamSeasons:\n ws += 1\n elif ([lNick], list({y[5] for y in gameData if y[0] == date})[0]) in teamSeasons:\n ls += 1\n\n homeColOG = \"\\033[1;38;5;\" + homeColor\n awayColOG = \"\\033[1;38;5;\" + awayColor\n wColOG= \"\\033[1;38;5;\" + wColor\n lColOG = \"\\033[1;38;5;\" + lColor\n playerWin = \"\"\n playerLoss = \"\"\n unNecG = 0\n unNecW = 0\n unNecL = 0\n # print(([wNick], str(season)))\n # print()\n # print(teamSeasons)\n if plperf:\n unNecL -= 7\n if ([wNick], str(season)) in teamSeasons:\n playerWin = BGREEN\n unNecW -= 7\n unNecL += 9\n else:\n playerWin = \"\"\n if ([lNick], str(season)) in teamSeasons:\n playerLoss = BRED\n unNecL += 2\n else:\n playerLoss = \"\"\n if len(homeColor) == 2:\n unNecG += 2\n elif len(homeColor) == 3:\n unNecG += 1\n if len(awayColor) == 2:\n unNecG += 2\n elif len(awayColor) == 3:\n unNecG += 1\n if len(wColor) == 2:\n unNecW += 2\n elif len(wColor) == 3:\n unNecW += 1\n if len(lColor) == 2:\n unNecL += 2\n elif len(lColor) == 3:\n unNecL += 1\n gameInfo = str(date + \" (\" + homeColOG + homeNick + RESET + \" @ \" + awayColOG + awayNick + RESET + \")\").ljust(70-(unNecG), \" \")\n winInfo = str(playerWin + \"W\" + RESET + \": \" + wColOG + wNick + RESET).ljust(40-(unNecW), \" \")\n lossInfo = str(playerLoss + \"L\" + RESET + \": \" + lColOG + lNick + RESET).ljust(40-(unNecL), \" \")\n winPtInfo = \" (\" + wPts + \"-\"\n lossPtInfo = lPts + \")\"\n finalGameHeader = gameInfo + winInfo + lossInfo + winPtInfo + lossPtInfo\n resultList.append(finalGameHeader)\n resultList = list(set(resultList))\n tempList = []\n for i in range(len(resultList)):\n for j in range(len(gameList)):\n if resultList[i][:10] == gameData[gameList[j]][0]:\n tempList.append((resultList[i], gameList[j]))\n break\n\n tempList = sorted(tempList, key=lambda tup:tup[0])\n finalResultList = [x[0] for x in tempList]\n gameList = [x[1] for x in tempList]\n amend = False\n goBack = False\n for i in range(len(finalResultList)):\n if i < 9:\n print(str(i+1) + \": \" + finalResultList[i]) \n elif i < 99:\n print(str(i+1) + \": \" + finalResultList[i])\n elif i < 999:\n print(str(i+1) + \": \" + finalResultList[i])\n else:\n print(str(i+1) + \": \" + finalResultList[i])\n if len(gameList) != 0:\n print(\"\\n\" + str(len(finalResultList)) + \" games found!\\n\")\n if plperf:\n finalTeam = \" as a member of the \" + team\n finalTf = \"\"\n winPerc = round(float(ws)/len(gameList), 2)\n if winPerc == 0.0:\n finalResult = \" won \" + str(winPerc)[0] + \"% of games\"\n elif winPerc == 1.0:\n finalResult = \" won \" + str(winPerc)[0] + \"00\" + \"% of games\"\n elif (winPerc*100) % 10 == 0:\n finalResult = \" won \" + str(winPerc)[2:] + \"0\" + \"% of games\"\n else:\n finalResult = \" won \" + str(winPerc)[2:] + \"% of games\"\n finalPerf = \" when performing \" + performance\n if team == \"any\":\n finalTeam = \"\"\n if tf != \"\":\n finalTf = \" in \" + tf\n if result == \"L\":\n finalResult = \" lost \" + str(round(float(ls)/len(gameList), 2))[2:] + \"% of games\"\n if result == \"any\":\n finalResult = \" won \" + str(ws) + \" out of \" + str(len(gameList)) + \" games (\" + str(round(float(ws)/len(gameList), 2)*100) + \"%)\"\n if performance == \"any\":\n finalPerf = \"\"\n print(playerName + finalTeam + finalTf + finalResult + finalPerf + \".\\n\")\n # Report player's career winning/losing percentage and compare!\n oneOrMany = \"a game from the list\"\n if len(gameList) == 1:\n oneOrMany = \"the game\"\n while True:\n gamePicked = False\n repAns = input(YELLOW + \"Do you want to analyze \" + oneOrMany + \" (y or n or quit)?\\n\\n\" + RESET)\n if repAns.lower() == \"y\":\n if len(gameList) != 1:\n while not gamePicked:\n gameNo = input(YELLOW + \"\\nWhich game from the list would you like to analyze?\\n\\n\" + RESET)\n try: \n if int(gameNo) > 0 and int(gameNo) < len(gameList)+1:\n pickedGame = gameList[int(gameNo)-1]\n gamePicked = True\n else:\n print(YELLOW + \"\\nPlease enter a number in the range 1-\" + str(len(gameList)) + \".\" + RESET)\n except:\n if gameNo.lower() == \"any\":\n randGame = random.randrange(len(gameList)-1)\n pickedGame = gameList[randGame]\n print(\"\\n\" + YELLOW + \"Game \" + str(randGame+1) + \" chosen!\\n\" + RESET)\n gamePicked = True\n else:\n print(YELLOW + \"\\nPlease enter a number in the range 1-\" + str(len(gameList)) + \".\" + RESET)\n else:\n pickedGame = gameList[0]\n break\n elif repAns.lower() == \"n\":\n goBack = True\n break\n elif repAns.lower() == \"quit\":\n print(\"\\nQuitting...\\n\")\n quit()\n else:\n print(\"\\n\" + YELLOW + \"I'm sorry, I didn't quite get that.\\n\" + RESET)\n else:\n if not rand:\n while True:\n amendAns = input(\"Sorry, no games found. Would you like to amend your search? (y or n or quit)\\n\\n\")\n if amendAns.lower() == \"y\":\n specs.clear()\n amend = True\n print()\n break\n elif amendAns.lower() == \"n\":\n specs.clear()\n goBack = True\n print()\n break\n elif amendAns.lower() == \"quit\":\n print(\"\\nQuitting...\\n\")\n break\n quit()\n else:\n print(\"\\n\" + YELLOW + \"I'm sorry, I didn't quite get that.\\n\" + RESET)\n else:\n ans2 = \"random\"\n specs.clear()\n pickedGame = random.randint(0, len(gameData)-1)\n\n # display analysis of picked game\n q = \"\"\n if wReason[0][0]:\n specs.append((\"winReason\", wReason[0][1] + \":\" + wReason[0][2]))\n if not amend and not goBack and not rand:\n q = \"y\"\n display(True, gameList, pickedGame, gameData, teamData, gameDetails, playerInfo, ans1, ans2, specs, q)\n elif not rand:\n q = \"n\"\n display(True, gameList, pickedGame, gameData, teamData, gameDetails, playerInfo, ans1, ans2, specs, q)\n else:\n display(True, gameList, pickedGame, gameData, teamData, gameDetails, playerInfo, ans1, ans2, specs, q)\n\n# gets general database info stored\ndef main():\n print(RED + \"Reading in game database...\\n\", RESET)\n folder = sys.argv[1]\n gameData = read_csv(folder + \"/games.csv\")\n attributes = gameData[0]\n teamData = read_csv(folder + \"/teams.csv\")\n gameDetails = read_csv(folder + \"/games_details.csv\")\n playerData = read_csv(folder + \"/players.csv\")\n gameData.pop(0)\n teamData.pop(0)\n playerInfo = gameDetails[0]\n gameDetails.pop(0)\n playerData.pop(0)\n welcome(gameData, attributes, teamData, gameDetails, playerInfo, playerData)\n \nif __name__ == '__main__':\n main()\n","repo_name":"BLSHEE22/NBA-Researcher","sub_path":"boxscores.py","file_name":"boxscores.py","file_ext":"py","file_size_in_byte":61214,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"443324045","text":"from unittest.mock import patch\n\nimport pytest\n\nfrom rubicon_ml import domain\nfrom rubicon_ml.client.experiment import Experiment\nfrom rubicon_ml.sklearn.estimator_logger import EstimatorLogger\n\n\ndef test_log_parameters_triggers_experiment_log_parameter(project_client, fake_estimator_cls):\n project = project_client\n experiment = Experiment(domain.Experiment(project_name=project.name), project)\n estimator = fake_estimator_cls()\n\n base_logger = EstimatorLogger(estimator=estimator, experiment=experiment, step_name=\"vect\")\n\n with patch.object(Experiment, \"log_parameter\", return_value=None) as mock_log_parameter:\n base_logger.log_parameters()\n\n assert mock_log_parameter.call_count == 3\n\n # the step name gets prepended to each param\n mock_log_parameter.assert_called_with(name=\"vect__ngram_range\", value=(1, 2))\n\n\ndef test_log_unserializable_param_triggers_exception(project_client, fake_estimator_cls):\n project = project_client\n experiment = Experiment(domain.Experiment(project_name=project.name), project)\n estimator = fake_estimator_cls(params={\"unserializable\": b\"not serializable\"})\n\n base_logger = EstimatorLogger(estimator=estimator, experiment=experiment, step_name=\"vect\")\n\n with patch.object(Experiment, \"log_parameter\", side_effect=Exception(\"test\")):\n with pytest.warns(Warning):\n base_logger.log_parameters()\n","repo_name":"capitalone/rubicon-ml","sub_path":"tests/unit/sklearn/test_estimator_logger.py","file_name":"test_estimator_logger.py","file_ext":"py","file_size_in_byte":1392,"program_lang":"python","lang":"en","doc_type":"code","stars":105,"dataset":"github-code","pt":"52"} +{"seq_id":"20514325868","text":"#!/usr/bin/python3\nimport os, fcntl\ncredentials_file = os.path.join(os.path.dirname(__file__), \"credentials.oracle\")\nif os.path.isfile(credentials_file):\n lock_file = \"/var/lock/oracle.lock\"\n f = open(lock_file, 'w')\n try:\n fcntl.lockf(f, fcntl.LOCK_EX | fcntl.LOCK_NB)\n print(\"No other uploads in progress, proceeding...\")\n import database # requires MySQLdb python 2 library which is not ported to python 3 yet\n db = database.weather_database()\n db.upload()\n except IOError:\n print(\"Another upload is running exiting now\")\n finally:\n f.close()\nelse:\n print(\"Credentials file not found\")\n","repo_name":"RaspberryPiFoundation/weather-station","sub_path":"upload_to_oracle.py","file_name":"upload_to_oracle.py","file_ext":"py","file_size_in_byte":656,"program_lang":"python","lang":"en","doc_type":"code","stars":175,"dataset":"github-code","pt":"52"} +{"seq_id":"21391951825","text":"import logging\nfrom src.connection import ConnectionHandler\n\nclass Actions:\n\n def __init__(self, organization, config_file = None):\n self.orgs_endpoint = f'/orgs/{organization}/actions/permissions'\n self.repos_endpoint = f'/repos/{organization}/[REPO]/actions/permissions'\n self.conn = ConnectionHandler(config_file=config_file)\n\n async def enable_selected_repositories_in_org(self):\n data = {\n 'enabled_repositories': 'selected'\n }\n resp = await self.conn.put(self.orgs_endpoint, json_data=data)\n return resp\n \n # Make sure the repo identifiers are used as an input and not the repo names.\n async def enable_selected_repository_ids_in_org(self, repo_ids = []):\n data = {\n 'selected_repository_ids': repo_ids\n }\n resp = await self.conn.put(self.orgs_endpoint + f'/repositories', json_data=data)\n return resp\n\n # The following allowed_actions are available: 'all', 'local_only', or 'selected'\n async def enable_actions_in_repo(self, repo, allowed_actions = 'selected'):\n data = {\n 'enabled': True,\n 'allowed_actions': allowed_actions\n }\n resp = await self.conn.put(self.repos_endpoint.replace('[REPO]', repo), json_data=data)\n return resp\n \n\n async def enable_selected_actions_in_repo(self, repo, verified_allowed = True):\n data = {\n 'github_owned_allowed': True,\n 'verified_allowed': verified_allowed\n }\n resp = await self.conn.put(self.repos_endpoint.replace('[REPO]', repo) + f'/selected-actions', json_data=data)\n return resp\n\n ","repo_name":"arnica-ext/GitGoat","sub_path":"src/actions.py","file_name":"actions.py","file_ext":"py","file_size_in_byte":1662,"program_lang":"python","lang":"en","doc_type":"code","stars":161,"dataset":"github-code","pt":"52"} +{"seq_id":"27077304657","text":"import numpy as np\nimport sys\nimport json\nfrom tflite_runtime.interpreter import Interpreter\n\nx = sys.argv[1]\ndict_file = json.loads(x)\ndata = dict_file[\"feeds\"][0]\ndata[\"field5\"] = data[\"field5\"][:-4]\ninput_data = [float(data[\"field1\"]), float(data[\"field2\"]),\n float(data[\"field3\"]), float(data[\"field4\"]), float(data[\"field5\"])]\ninput_array = np.array(input_data, dtype = np.float32)\ninput_array = np.resize(input_array, (1,5))\ntflite_interpreter = Interpreter(model_path='converted_model.tflite')\n\ninput_details = tflite_interpreter.get_input_details()\noutput_details = tflite_interpreter.get_output_details()\ntflite_interpreter.allocate_tensors()\n\ntflite_interpreter.set_tensor(input_details[0]['index'], input_array)\ntflite_interpreter.invoke()\noutput_array = tflite_interpreter.get_tensor(output_details[0]['index'])\nresult = np.squeeze(output_array)\nprint(int(result > 0.6))\n","repo_name":"ariawahyuw/raspi_tflite","sub_path":"predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":884,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"12354197421","text":"# -*- coding: utf-8 -*-\nimport scrapy\n\ncomune = \"Milano\"\n\nclass AddressesSpider(scrapy.Spider):\n name = \"addresses\"\n start_urls = [\n 'http://serviziweb.tabaccai.it/voucherinps/VistaAdesioni.aspx',\n ]\n\n def parse(self, response):\n formdata = {\n \"ctl00$ContentPlaceHolder1$tbCap\": \"\",\n \"ctl00$ContentPlaceHolder1$tbComune\": comune,\n }\n form_request = scrapy.FormRequest.from_response(response, formdata=formdata, callback=self.after_query)\n self.logger.info(\"form request: %s\", form_request.body)\n return form_request\n\n def after_query(self, response):\n requests = []\n requests.append(scrapy.FormRequest.from_response(response, callback=self.parse_tabaccai))\n pages = response.xpath(\"//table[@id='ctl00_ContentPlaceHolder1_GridView1']/tr[@class='pager']//a/text()\")\n for page in pages:\n formdata = {\n \"__EVENTTARGET\": \"ctl00$ContentPlaceHolder1$GridView1\",\n \"__EVENTARGUMENT\": \"Page$\" + page.extract()\n }\n self.logger.info(\"request page: %s\", page)\n requests.append(scrapy.FormRequest.from_response(response, formdata=formdata, callback=self.parse_tabaccai))\n self.logger.debug(\"requests: \" + str(map( (lambda form: form.body), requests )))\n return requests\n\n def parse_tabaccai(self, response):\n trs = response.xpath(\"//table[@id='ctl00_ContentPlaceHolder1_GridView1']/tr\")\n addresses = trs[1:-2]\n items = []\n for line in addresses:\n from tabaccai.items import Address\n address = Address()\n address['indirizzo'] = line.xpath(\"td[1]/text()\").extract()\n address['cap'] = line.xpath(\"td[2]/text()\").extract()\n address['comune'] = line.xpath(\"td[3]/text()\").extract()\n address['provincia'] = line.xpath(\"td[4]/text()\").extract()\n self.logger.info(\"indirizzo: %s\", address)\n items.append(address)\n return items\n","repo_name":"antonkojin/voucher","sub_path":"tabaccai/tabaccai/spiders/addresses.py","file_name":"addresses.py","file_ext":"py","file_size_in_byte":2027,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"40835473748","text":"import unittest\nimport os\nfrom os import path\nimport subprocess\n\n\nclass TestIntegration(unittest.TestCase):\n\n def testIntegration(self):\n \"\"\" Integration test. \"\"\"\n base = path.dirname(__file__)\n test_base = path.join(base, 'data')\n\n barcodes = path.join(test_base, 'barcodes.fas')\n input_fasta = path.join(test_base, 'ref.fq')\n output_fasta = path.join(test_base, 'test_output.fq')\n expected_output = path.join(test_base, 'expected_output.fas')\n\n subprocess.call(\"{} {} {} {} {}\".format('pychopper', \"-Y 0 -B 3 -q 0.5 -m edlib -b\", barcodes, input_fasta, output_fasta), shell=True)\n retval = subprocess.call(['cmp', output_fasta, expected_output])\n self.assertEqual(retval, 0)\n os.remove(output_fasta)\n","repo_name":"nanoporetech/pychopper","sub_path":"pychopper/tests/test_regression_simple.py","file_name":"test_regression_simple.py","file_ext":"py","file_size_in_byte":783,"program_lang":"python","lang":"en","doc_type":"code","stars":77,"dataset":"github-code","pt":"52"} +{"seq_id":"26044403660","text":"import torch\nimport numpy as np\nimport torch.nn as nn\nimport torch.optim as optim\nfrom collections import OrderedDict\nimport torch.nn.functional as F\nfrom sklearn.model_selection import train_test_split\nfrom torch.autograd import Variable\nfrom torch.utils.data import Dataset, DataLoader, Subset\nfrom sklearn.metrics import roc_auc_score\nimport pandas as pd\nimport sys, os\nimport random\nimport argparse\n\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=globals()[\"__doc__\"])\n parser.add_argument(\"--seed\", type=int, default=100, help=\"Random seed\")\n parser.add_argument(\n \"--perm\", type=int, default = 1000, help=\"The number of permutations\"\n )\n parser.add_argument(\n \"--gpu\", type=int, default = \"0\", help=\"The number of GPU\"\n )\n parser.add_argument(\n \"--dir\", type = str, help = \"the base working directory\"\n )\n parser.add_argument(\n \"--activation\", type =str, default = \"leakyrelu\", help = \"the used activation function (tanh, relu, leakyrelu, identity)\"\n )\n parser.add_argument(\n \"--loss\", type = str, default = \"BCELoss\", help = \"the used loss function (BCELoss or MSELoss)\" \n )\n parser.add_argument(\n \"--reg_type\", type = str, default = \"l1\", help = \"the used regularization function type(l1 or l2)\"\n )\n parser.add_argument(\n \"--reg_const_pathway_disease\", type = float, default = 0, help = \"regularization constant between pathways and phenotype\")\n parser.add_argument(\n \"--reg_const_bio_pathway\", type = float, default = 0, help = \"regularization constant between biological factors and pathway\")\n parser.add_argument(\n \"--leakyrelu_const\", type = float, default = 0.2, help = \"the constants for leakyrelu\"\n )\n parser.add_argument(\n \"--dropout_rate\", type = float, default = 0.5, help = \"the constants for dropout_rate\"\n )\n parser.add_argument(\n \"--experiment_name\", type=str, default=\"exp\", help=\"A folder name (relative path) for saving results\"\n )\n parser.add_argument(\n \"--batch_size\", type=int, default=\"0\", help=\"Batch size for training. If batch_size ==0, then input all data for training at once\"\n )\n parser.add_argument(\n \"--learning_rate\", type = float, default = 0.001, help = \"the constants for dropout_rate\"\n )\n parser.add_argument(\n \"--stop_type\", type = int, default = 0, help = \"stoping type\"\n )\n parser.add_argument(\n \"--divide_rate\", type = float, default = 0.2, help = \"divide rate\"\n )\n parser.add_argument(\n \"--count_lim\", type = int, default = 5, help = \"specify we stop\"\n )\n parser.add_argument(\n \"--cov\", type = int, default = 0, help = \"if cov exists, then set cov as 1\"\n )\n\n args = parser.parse_args()\n return args\n\nact_fn_by_name = {\"tanh\": nn.Tanh, \"relu\": nn.ReLU, \"identity\": nn.Identity, \"leakyrelu\": nn.LeakyReLU}\n\nargs = parse_args()\ndirectory = args.dir\nseed, permtime, GPU_NUM, batch_size, learning_rate = args.seed, args.perm, args.gpu, args.batch_size, args.learning_rate\nactivation, losstype, reg_type = args.activation, args.loss, args.reg_type\npenalty_path_disease,penalty_bio_path, leakyrelu_const, dropout_rate = args.reg_const_pathway_disease, args.reg_const_bio_pathway, args.leakyrelu_const, args.dropout_rate\nexperiment_name = args.experiment_name\nstop_type = args.stop_type\ndivide_rate = args.divide_rate\ncount_lim = args.count_lim\ncov_exist = args.cov\n\n\n#stop_type1 = loss with all\n#stop_type2 = AUC with all\n#stop_type3 = loss with train test divide\n#stop_type4 = AUC with train test divide\n#stop_type5 = epoch fix with all\n\nos.chdir(directory)\nlayerinfo = pd.read_csv(\"layerinfo.csv\")\nnode_num = layerinfo[\"node_num\"].tolist()\nlayer_num = layerinfo[\"layer_num\"].tolist()\n\ndef createFolder(directory):\n try:\n if not os.path.exists(directory):\n os.makedirs(directory)\n except OSError:\n print ('Error: Creating directory. ' + directory)\n \ncreateFolder(directory + '/' + experiment_name)\ncreateFolder(directory + '/' + experiment_name+ '/tmp')\n\n\n\n\nif activation == \"leakyrelu\":\n act_fn = act_fn_by_name[activation](leakyrelu_const)\nelif activation in act_fn_by_name:\n act_fn = act_fn_by_name[activation]()\nelse:\n raise NotImplementedError(\"Not predefined activation function\")\n\ndropout_fn = nn.Dropout(dropout_rate)\n\ndef init_weights(m):\n if isinstance(m, nn.Linear):\n m.weight.data.fill_(0.01)\n\n\nclass CustomDataset(Dataset): \n def __init__(self, DATA, inputlength):\n self.x_data = DATA[:,0:(inputlength-1)]\n self.y_data = DATA[:,(inputlength-1)].reshape(-1,1) \n\n def __len__(self): \n return len(self.x_data)\n\n def __getitem__(self, idx): \n x = self.x_data[idx]\n y = self.y_data[idx]\n return x, y\n\nclass pathwayblock(nn.Module):\n def __init__(self, input_dim, hidden_dim, layer_num):\n super().__init__()\n self.input_dim = input_dim\n self.hidden_dim = hidden_dim\n self.layer_num = layer_num\n if hidden_dim == 0:\n self.block = nn.Sequential(\n nn.Linear(input_dim, 1, bias = False),\n act_fn,\n dropout_fn\n )\n else: #layer_num = btw pathway and biological factor number. if layer_num ==0, then hidden num should be zero for setting. we do not allow =0\n modules = []\n modules.append(nn.Linear(input_dim, hidden_dim, bias = False))\n modules.append(act_fn)\n modules.append(dropout_fn)\n for i in range(layer_num-1):\n modules.append(nn.Linear(hidden_dim, hidden_dim, bias = False))\n modules.append(act_fn)\n modules.append(dropout_fn)\n modules.append(nn.Linear(hidden_dim, 1, bias = False))\n modules.append(act_fn)\n modules.append(dropout_fn)\n self.block = nn.Sequential(*modules)\n self.block.apply(init_weights)\n\n def forward(self, x):\n return self.block(x)\n\n\n\nclass DeepHisCoM(nn.Module):\n def __init__(self, nvar, width, layer, covariate, device):\n super(DeepHisCoM, self).__init__()\n self.nvar = nvar\n self.width = width\n self.layer = layer \n self.pathway_nn = nn.ModuleList([pathwayblock(nvar[i], width[i], layer[i]) for i in range(len(self.nvar))])\n self.bn_path = nn.BatchNorm1d(len(nvar))\n self.dropout_path = dropout_fn\n self.covariate = covariate\n self.fc_path_disease=nn.Linear(len(nvar) +covariate ,1)\n self.fc_path_disease.weight.data.fill_(0)\n self.fc_path_disease.bias.data.fill_(0.001)\n self.device = device\n\n def forward(self, x):\n kk=0\n nvarlist = list()\n nvarlist.append(kk)\n for i in range(len(self.nvar)):\n k=kk\n kk=kk+self.nvar[i]\n nvarlist.append(kk)\n nvarlist.append(kk + self.covariate)\n pathway_layer = torch.cat([self.pathway_nn[i](x[:,nvarlist[i]:nvarlist[i+1]]) for i in range(len(self.nvar))],1)\n pathway_layer = self.bn_path(pathway_layer)\n pathway_layer = pathway_layer/(torch.norm(pathway_layer,2))\n x = torch.cat([pathway_layer, x[:, nvarlist[len(self.nvar)]:nvarlist[len(self.nvar) + 1]]], 1)\n x = self.dropout_path(x)\n x = self.fc_path_disease(x)\n x = torch.sigmoid(x)\n return(x)\n\n\nis_cuda = torch.cuda.is_available() \ndevice = torch.device(f'cuda:{GPU_NUM}' if torch.cuda.is_available() else 'cpu')\nif is_cuda:\n torch.cuda.set_device(device) # change allocation of current GPU\ndtype = torch.FloatTensor\n\n'''\nclass_weight = torch.FloatTensor([1,1]) # if unbalanced data, we can change it for BCELoss\n'''\n\ntrain_base = pd.read_csv(\"train_data.csv\")\n\nfor permutation in range(0, permtime):\n save = []\n print(\"start \", permutation )\n torch.manual_seed(permutation); random.seed(permutation); np.random.seed(permutation) # for permutation random seed\n train = train_base\n if permutation != 0:\n train['phenotype'] = np.random.permutation(train['phenotype']) # permute y-data\n phenotype = train['phenotype']\n train = (train-train.mean())/train.std()\n train['phenotype'] = phenotype\n\n\n\n train = torch.from_numpy(train.values).type(dtype)\n train_varlen = train.shape[1]; train_datanum = train.shape[0]\n torch.manual_seed(seed); random.seed(seed); np.random.seed(seed)\n data_train = CustomDataset(train, train_varlen)\n\n annot = pd.read_csv(\"annot.csv\") # annotation data\n groupunique = list(OrderedDict.fromkeys(annot[\"group\"])) # nvar setting \n grouplen = len(groupunique)\n nvar = []\n if cov_exist == 1:\n cov = pd.read_csv(\"cov.csv\") # cov data\n cov_num = len(cov[\"x\"])\n else:\n cov_num = 0\n\n\n\n for i in range(0,grouplen): \n nvar.append(sum(annot[\"group\"]==groupunique[i]))\n \n if 3<=stop_type<=4:\n TEST_SIZE = divide_rate\n train_indices, test_indices, _, _ = train_test_split(range(len(data_train)), data_train.y_data, stratify = data_train.y_data, \n test_size = TEST_SIZE)\n\n train_split = Subset(data_train, train_indices)\n test_split = Subset(data_train, test_indices)\n if batch_size == 0:\n data_train = DataLoader(train_split, batch_size = len(train_split), shuffle = True)\n data_test = DataLoader(test_split, batch_size = len(test_split))\n else:\n data_train = DataLoader(train_split, batch_size = batch_size, shuffle = True)\n data_test = DataLoader(test_split, batch_size = batch_size)\n else:\n if batch_size == 0:\n data_train = DataLoader(data_train, batch_size = len(data_train), shuffle = True)\n else:\n data_train = DataLoader(data_train, batch_size = batch_size, shuffle = True)\n\n model_DeepHisCoM = DeepHisCoM(nvar, node_num, layer_num, cov_num, device)\n if is_cuda: \n model_DeepHisCoM.to('cuda')\n optimizer_DeepHisCoM = torch.optim.Adam([{'params': model_DeepHisCoM.parameters()}]\n , lr=learning_rate) # can change optimizer\n\n if losstype == 'bceloss':\n criterion = nn.BCELoss()\n elif losstype == 'mseloss':\n criterion = nn.MSELoss()\n else:\n raise NotImplementedError(\"Not predefined loss function\")\n \n count = 0\n\n for epoch in range(0,10000):\n save_tmp = []\n for batch_idx, samples in enumerate(data_train):\n model_DeepHisCoM.train()\n optimizer_DeepHisCoM.zero_grad()\n x_train, y_train = samples \n if is_cuda: # If we use GPU.\n x_train = x_train.cuda()\n y_train = y_train.cuda()\n output_DeepHisCoM = torch.squeeze(model_DeepHisCoM(x_train))\n y_train = torch.squeeze(y_train)\n loss_DeepHisCoM = (criterion(output_DeepHisCoM, y_train)).type(dtype)\n if penalty_path_disease != 0 :\n for param in model_DeepHisCoM.fc_path_disease.parameters():\n if reg_type == 'l1':\n loss_DeepHisCoM = loss_DeepHisCoM + penalty_path_disease * torch.norm(param,1)\n elif reg_type == 'l2':\n loss_DeepHisCoM = loss_DeepHisCoM + penalty_path_disease * torch.norm(param,2) * torch.norm(param,2)\n else:\n raise NotImplementedError(\"Not predefined reg function\")\n if penalty_bio_path != 0: \n for param in model_DeepHisCoM.pathway_nn.parameters():\n if reg_type == 'l1':\n loss_DeepHisCoM = loss_DeepHisCoM + penalty_bio_path * torch.norm(param,1)\n elif reg_type == 'l2':\n loss_DeepHisCoM = loss_DeepHisCoM + penalty_bio_path * torch.norm(param,2) * torch.norm(param,2) \n else:\n raise NotImplementedError(\"Not predefined reg function\")\n \n loss_DeepHisCoM.backward()\n optimizer_DeepHisCoM.step()\n if stop_type == 1:\n save_tmp.append(-loss_DeepHisCoM.item())\n if stop_type == 2:\n if torch.sum(torch.isnan(output_DeepHisCoM))==0:\n AUC_train = roc_auc_score(y_train.cpu().detach().numpy(), output_DeepHisCoM.cpu().detach().numpy())\n else:\n AUC_train = 0 \n save_tmp.append(AUC_train)\n if stop_type == 5:\n param_save = np.array(list(model_DeepHisCoM.fc_path_disease.parameters())[0].tolist()[0])\n\n if 3 <= stop_type <= 4:\n for batch_idx, samples in enumerate(data_test):\n model_DeepHisCoM.eval()\n x_test, y_test = samples\n if is_cuda: # If we use GPU.\n x_test = x_test.cuda()\n y_test = y_test.cuda()\n output_DeepHisCoM = torch.squeeze(model_DeepHisCoM(x_test))\n y_test = torch.squeeze(y_test)\n\n if stop_type == 3:\n loss_DeepHisCoM_test = (criterion(output_DeepHisCoM, y_test)).type(dtype).item()\n save_tmp.append(-loss_DeepHisCoM_test)\n\n if stop_type == 4:\n if torch.sum(torch.isnan(output_DeepHisCoM))==0:\n AUC_test = roc_auc_score(y_test.cpu().detach().numpy(), output_DeepHisCoM.cpu().detach().numpy())\n else:\n AUC_test = 0 \n save_tmp.append(AUC_test)\n\n if stop_type in [1,2,3,4]:\n save_avg = sum(save_tmp)/len(save_tmp)\n save.append(save_avg)\n if max(save) == save_avg:\n count = 0\n param_save = np.array(list(model_DeepHisCoM.fc_path_disease.parameters())[0].tolist()[0])\n else:\n count = count + 1\n \n\n if(count > count_lim):\n break\n elif stop_type == 5:\n if epoch > count_lim:\n break\n \n param = param_save\n np.savetxt(experiment_name + \"/tmp/param\"+ str(permutation) + \".txt\" ,param)\n","repo_name":"chanwoo-park-official/DeepHisCoM","sub_path":"deephiscom.py","file_name":"deephiscom.py","file_ext":"py","file_size_in_byte":14720,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"52"} +{"seq_id":"42573528478","text":"from copy import deepcopy\n\nfrom ordered_set import OrderedSet\n\nfrom ..mixin import IdentifiedMixIn, PropertyContainer\nfrom .tree import Tree\n\n\nclass Label(PropertyContainer, IdentifiedMixIn):\n \"\"\"A |Label| class which acts as a |Tree| node and stores a name.\n\n The :attr:`name` is assumed to be the |Label| true content and its :func:`hash` value will reflect that.\n\n The |Label| acts as the core body of any |Tree| creation and modification. In fact, |Label| are what defines\n **actual** trees of which the |Tree| class acts as **views**.\n\n To define a tree, simply links two |Label| together:\n\n .. code-block:: python\n\n >>> label_a = Label('a')\n >>> label_b = Label('b')\n >>> label_a.add(label_b) # We have a tree !\n >>> print(Tree(label_a))\n a\n ╰── b\n >>> label_c = Label('c')\n >>> label_c.attach(label_a)\n >>> print(Tree(label_a))\n a\n ├── b\n ╰── c\n\n .. hint::\n\n Note that a single |Label| is a valid |Tree| in itself with a single element. More generally, any |Label|\n defines its own |Tree|, which is called the |Label|'s `clade`_.\n\n Because |Tree| are dynamic in nature, any attach between two |Label| can be undone with the :meth:`detach` method,\n *e.g.*:\n\n .. code-block:: python\n\n >>> label_a.detach(label_c)\n >>> print(Tree(label_a))\n a\n ╰── b\n >>> label_b.detach(label_a)\n >>> print(Tree(label_a))\n a\n\n Args:\n name (str): The |Label| name. This is the actual value that is \"tagged\" on |Record|.\n id (str): Optional. Default to a random *UUID4*. An id to store along the instance.\n parent (|Label|): Optional. Default to ``None``. A |Label| to be registered as self's parent.\n children (Iterable[|Label|]): Optional. Default to ``{}``. An iterable of |Label| to be registered as\n self's children.\n **properties (Any): Additional properties to store alongside the |Label|.\n\n .. _clade: https://en.wikipedia.org/wiki/Clade\n\n Attributes:\n properties (dict): Properties provided as kwargs in the constructor.\n\n \"\"\"\n\n __slots__ = '_parent', '_children', '_name', '_hash', '_depth', '_ancestors', '_descendants'\n\n def __new__(cls, name, *args, **kwargs):\n \"\"\"Create a new hashable |Label| instance.\"\"\"\n label = super(Label, cls).__new__(cls)\n label._name = name\n label._hash = None\n return label\n\n def __init__(self, name, id=None, parent=None, children=None, **properties):\n super(Label, self).__init__(id=id, **properties)\n\n self._children = OrderedSet()\n self._parent = None\n self._depth = 0\n self._ancestors = ()\n self._descendants = {}\n\n # Setup attributes\n self._name = name\n\n # Setup hash\n self._hash = None\n\n # Setup Label position in Label tree\n self.parent = parent\n self.children = children\n\n @property\n def name(self):\n \"\"\"str: The |Label| name. This is the actual value that is \"tagged\" on |Record|.\n\n Note:\n It is a *read-only* property because a |Label| is assumed to be immutable.\n\n \"\"\"\n return self._name\n\n @property\n def id(self):\n \"\"\"str: An identifier unique for all |Label| instance *independent* on content.\"\"\"\n return self._id\n\n @property\n def parent(self):\n \"\"\"|Label|: A |Label| registered as self's parent or ``None`` if self is a tree root.\n\n Using the property's *setter* is equivalent to a call of :meth:`attach`.\n\n \"\"\"\n return self._parent\n\n @parent.setter\n def parent(self, label):\n if label is None:\n return\n if self._parent is not None:\n self.detach(self._parent)\n self.attach(label)\n\n @property\n def children(self):\n \"\"\"list: A list of |Label| which are registered as self's children.\n\n Using the property's *setter* is equivalent to a joint call of :meth:`detach(self.children) `\n and :meth:`add`.\n\n \"\"\"\n return self._children\n\n @children.setter\n def children(self, labels):\n if labels is None or not labels:\n return\n\n self.detach(*self._children)\n self.add(*labels)\n\n @property\n def ancestors(self):\n \"\"\"(|Label|, ): All of self's ancestors up to the root, going upward from :attr:`parent`.\"\"\"\n return self._ancestors\n\n @property\n def descendants(self): # noqa: D401\n \"\"\"dict: All of self's descendants as a flattened dictionary of the form :code:`{name: label}`.\"\"\"\n return self._descendants\n\n @property\n def depth(self):\n \"\"\"int: The |label| absolute depth in the global implicit label tree.\"\"\"\n return self._depth\n\n def __getnewargs__(self):\n \"\"\"Pass name and identifier as :meth:`~object.__new__` arguments to pickle.\"\"\"\n return self._name,\n\n def __setstate__(self, state):\n \"\"\"Set attributes values from a dictionary of all slotted and in dictionary attributes.\"\"\"\n for key, value in state.items():\n if key == '_hash':\n value = None\n setattr(self, key, value)\n\n def __deepcopy__(self, memo=None):\n \"\"\"Construct a deep copy of a :class:`SlottedDict`.\"\"\"\n memo = {} if memo is None else memo\n\n # Make semi-empty Label\n cls = self.__class__\n result = cls.__new__(cls, deepcopy(self._name, memo))\n result._properties = deepcopy(self._properties, memo)\n result._id = deepcopy(self._id, memo)\n result._name = deepcopy(self._name, memo)\n result._hash = None\n result._depth = deepcopy(self._depth, memo)\n result._children = OrderedSet()\n result._parent = None\n result._ancestors = ()\n result._descendants = {}\n\n # Update memo with semi-empty Label\n memo[id(self)] = result\n\n # Recursively copy Label tree\n result._children = deepcopy(self._children, memo)\n result._descendants = deepcopy(self._descendants, memo)\n result._parent = deepcopy(self._parent, memo)\n result._ancestors = deepcopy(self._ancestors, memo)\n\n return result\n\n def attach(self, label):\n \"\"\"Attach a parent |Label| to self.\n\n Args:\n label (|Label|): A |Label| to register as self's parent.\n\n \"\"\"\n if label is None or label == self.parent:\n return\n\n label.add(self)\n\n # If for some reason (most probably name collision) nothing happened in label realm we'd better back off too...\n if self.id not in {_label.id for _label in label.children}: # True id match to avoid name collision here\n return\n\n self._parent = label\n self.update_ancestry()\n\n def add(self, *labels):\n \"\"\"Attach a child or a group of children |Label| to self.\n\n Args:\n *labels (|Label|): A |Label| to register as self's child.\n\n \"\"\"\n for label in labels:\n if label in self._children:\n continue\n\n if label == self:\n raise ValueError('Invalid tree: adding {} to {}\\'s tree is impossible.'.format(label, self))\n\n self_root = self._ancestors[-1] if self._ancestors else self\n label_root = label.ancestors[-1] if label.ancestors else label\n overlap = dict({self_root.name: self_root}, **self_root.descendants).keys() \\\n & dict({label_root.name: label_root}, **label_root.descendants).keys()\n\n if overlap:\n raise ValueError('Invalid tree: Overlapping tree {} found in:\\n'\n '{}\\n{}.'.format(overlap,\n Tree(self_root),\n Tree(label_root)))\n\n self._children.add(label)\n self.update_descent()\n\n label.attach(self)\n\n def detach(self, *labels):\n \"\"\"Detach one or a group of |Label| as self's parent or child.\n\n If a |Label| was never attached to self to begin with, it is silently ignored.\n\n Args:\n *labels (|Label|): A |Label| to be detached as either self's parent or self's child.\n\n \"\"\"\n for label in labels:\n if label == self._parent:\n # If it is the parent we force remove it (by-passing the property which would lead to recursion)\n self._parent = None\n self.update_ancestry()\n label.detach(self)\n elif label in self._children:\n # If it is a child we force remove it (by-passing the property which would lead to recursion)\n self._children.remove(label)\n self.update_descent()\n label.detach(self)\n # If it is nothing we silently ignore it (thus ending the recursion)\n\n def __repr__(self):\n \"\"\"Pythonic representation of the object.\"\"\"\n return '{}(name={})'.format(self.__class__.__name__, self.name)\n\n def __str__(self):\n \"\"\"Return the |Label| :attr:`name`.\"\"\"\n return self.name\n\n def __hash__(self):\n \"\"\"Return the python hash of the |Label| :attr:`name`.\"\"\"\n if self._hash is None:\n self._hash = hash(self._name)\n return self._hash\n\n def __eq__(self, other):\n \"\"\"Return whether two |Label| have the same name and id.\n\n Args:\n other (|Label|): Another |Label| to compare itself to.\n\n Returns:\n bool: ``True`` if ``other`` is a |Label| and have the same name as self, or is a :class:`str` which equals\n :attr:`name`.\n\n \"\"\"\n try:\n return self.name == other.name\n except AttributeError:\n return self.name == other\n\n def __ne__(self, other):\n \"\"\"Return whether two |Label| do not have the same name and id.\n\n Args:\n other (|Label|): Another |Label| to compare itself to.\n\n Returns:\n bool: ``True`` if ``other`` is not a |Label| or do not have the same name as self, or is a :class:`str`\n which does not equal :attr:`name`.\n\n \"\"\"\n return not self == other\n\n def last_common_ancestor(self, other):\n \"\"\"Return the *most downward* |Label| encountered in self and other :attr:`ancestors`.\n\n Args:\n other (|Label|): Another |Label| to compare ancestors with.\n\n Returns:\n |Label|: The `last common parent`_, or ``None`` if both |Label| share no family.\n\n\n .. _last common parent: https://en.wikipedia.org/wiki/Most_recent_common_ancestor\n\n \"\"\"\n family = OrderedSet((self, ) + self._ancestors).intersection(OrderedSet((other, ) + other.ancestors))\n if family:\n family = sorted(family,\n key=lambda x: ((self, ) + self._ancestors).index(x))\n return family[0]\n return None\n\n def clade(self, other):\n \"\"\"Return the common `clade`_ enclosing both self and other.\n\n Args:\n other (|Label|): Another |Label| to compare ancestors with.\n\n Returns:\n |Tree|: A |Tree| rooted in self and other :meth:`last_common_ancestor`, effectively representing\n their common `clade`_, or ``None`` if both |Label| share no family.\n\n .. _clade: https://en.wikipedia.org/wiki/Clade\n\n \"\"\"\n last_common_ancestor = self.last_common_ancestor(other)\n if last_common_ancestor is None:\n return None\n\n return Tree(last_common_ancestor, type='clade', enclosed=(self, other))\n\n def update_ancestry(self):\n \"\"\"Update :attr:`ancestors` attribute alongside the label's `clade`_.\n\n .. _clade: https://en.wikipedia.org/wiki/Clade\n\n \"\"\"\n if self.parent is None:\n self._ancestors = ()\n else:\n self._ancestors = (self.parent,) + self.parent.ancestors\n self._depth = len(self._ancestors)\n for child in self._children:\n child.update_ancestry()\n\n def update_descent(self):\n \"\"\"Update :attr:`descendants` attribute alongside the label's ancestry.\"\"\"\n self._descendants = {}\n for child in self._children:\n self._descendants[child.name] = child\n self._descendants.update(child.descendants)\n if self._parent is not None:\n self._parent.update_descent()\n","repo_name":"airbusgeo/playground-plums","sub_path":"plums/commons/data/taxonomy/label.py","file_name":"label.py","file_ext":"py","file_size_in_byte":12532,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"22251840085","text":"import logging\nimport sys\nimport configparser\nimport multiprocessing as mp\nimport database_utils\nfrom evaluation import evaluate_scenario\nfrom approaches.single_best_solver import SingleBestSolver\nfrom approaches.oracle import Oracle\nfrom approaches.survival_forests.surrogate import SurrogateSurvivalForest\nfrom approaches.survival_forests.auto_surrogate import SurrogateAutoSurvivalForest\nfrom baselines.per_algorithm_regressor import PerAlgorithmRegressor\nfrom baselines.multiclass_algorithm_selector import MultiClassAlgorithmSelector\nfrom baselines.sunny import SUNNY\nfrom baselines.snnap import SNNAP\nfrom baselines.isac import ISAC\nfrom baselines.satzilla11 import SATzilla11\nfrom baselines.satzilla07 import SATzilla07\nfrom sklearn.linear_model import Ridge\nfrom par_10_metric import Par10Metric\nfrom number_unsolved_instances import NumberUnsolvedInstances\n\n\nlogger = logging.getLogger(\"run\")\nlogger.addHandler(logging.StreamHandler())\n\n\ndef initialize_logging():\n logging.basicConfig(filename='logs/log_file.log', filemode='w',\n format='%(asctime)s %(message)s', datefmt='%m/%d/%Y %I:%M:%S %p', level=logging.DEBUG)\n\n\ndef load_configuration():\n config = configparser.ConfigParser()\n config.read_file(open('conf/experiment_configuration.cfg'))\n return config\n\n\ndef print_config(config: configparser.ConfigParser):\n for section in config.sections():\n logger.info(str(section) + \": \" + str(dict(config[section])))\n\n\ndef log_result(result):\n logger.info(\"Finished experiements for scenario: \" + result)\n\n\ndef create_approach(approach_names):\n approaches = list()\n for approach_name in approach_names:\n if approach_name == 'sbs':\n approaches.append(SingleBestSolver())\n if approach_name == 'oracle':\n approaches.append(Oracle())\n if approach_name == 'ExpectationSurvivalForest':\n approaches.append(SurrogateSurvivalForest(criterion='Expectation'))\n if approach_name == 'PolynomialSurvivalForest':\n approaches.append(SurrogateSurvivalForest(criterion='Polynomial'))\n if approach_name == 'GridSearchSurvivalForest':\n approaches.append(SurrogateSurvivalForest(criterion='GridSearch'))\n if approach_name == 'ExponentialSurvivalForest':\n approaches.append(SurrogateSurvivalForest(criterion='Exponential'))\n if approach_name == 'SurrogateAutoSurvivalForest':\n approaches.append(SurrogateAutoSurvivalForest())\n if approach_name == 'PAR10SurvivalForest':\n approaches.append(SurrogateSurvivalForest(criterion='PAR10'))\n if approach_name == 'per_algorithm_regressor':\n approaches.append(PerAlgorithmRegressor())\n if approach_name == 'imputed_per_algorithm_rf_regressor':\n approaches.append(PerAlgorithmRegressor(impute_censored=True))\n if approach_name == 'imputed_per_algorithm_ridge_regressor':\n approaches.append(PerAlgorithmRegressor(\n scikit_regressor=Ridge(alpha=1.0), impute_censored=True))\n if approach_name == 'multiclass_algorithm_selector':\n approaches.append(MultiClassAlgorithmSelector())\n if approach_name == 'sunny':\n approaches.append(SUNNY())\n if approach_name == 'snnap':\n approaches.append(SNNAP())\n if approach_name == 'satzilla-11':\n approaches.append(SATzilla11())\n if approach_name == 'satzilla-07':\n approaches.append(SATzilla07())\n if approach_name == 'isac':\n approaches.append(ISAC())\n return approaches\n\n\n#######################\n# MAIN #\n#######################\n\ninitialize_logging()\nconfig = load_configuration()\nlogger.info(\"Running experiments with config:\")\nprint_config(config)\n\n#fold = int(sys.argv[1])\n#logger.info(\"Running experiments for fold \" + str(fold))\n\ndb_handle, table_name = database_utils.initialize_mysql_db_and_table_name_from_config(\n config)\ndatabase_utils.create_table_if_not_exists(db_handle, table_name)\n\namount_of_cpus_to_use = int(config['EXPERIMENTS']['amount_of_cpus'])\npool = mp.Pool(amount_of_cpus_to_use)\n\n\nscenarios = config[\"EXPERIMENTS\"][\"scenarios\"].split(\",\")\napproach_names = config[\"EXPERIMENTS\"][\"approaches\"].split(\",\")\namount_of_scenario_training_instances = int(\n config[\"EXPERIMENTS\"][\"amount_of_training_scenario_instances\"])\ntune_hyperparameters = bool(int(config[\"EXPERIMENTS\"][\"tune_hyperparameters\"]))\n\nfor fold in range(1, 11):\n\n for scenario in scenarios:\n approaches = create_approach(approach_names)\n\n if len(approaches) < 1:\n logger.error(\"No approaches recognized!\")\n for approach in approaches:\n metrics = list()\n metrics.append(Par10Metric())\n if approach.get_name() != 'oracle':\n metrics.append(NumberUnsolvedInstances(False))\n metrics.append(NumberUnsolvedInstances(True))\n logger.info(\"Submitted pool task for approach \\\"\" +\n str(approach.get_name()) + \"\\\" on scenario: \" + scenario)\n pool.apply_async(evaluate_scenario, args=(scenario, approach, metrics,\n amount_of_scenario_training_instances, fold, config, tune_hyperparameters), callback=log_result)\n\n #evaluate_scenario(scenario, approach, metrics,\n # amount_of_scenario_training_instances, fold, config, tune_hyperparameters)\n print('Finished evaluation of fold')\n\npool.close()\npool.join()\nlogger.info(\"Finished all experiments.\")\n","repo_name":"alexandertornede/algorithm_survival_analysis","sub_path":"survival_tests/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":5614,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"52"} +{"seq_id":"14730548980","text":"\nfrom tkinter import Frame\nimport cv2 ,random,socket,base64,imutils,time\n\n\nclass UDP_Streamer():\n def __init__(self) -> None:\n self.BUFF_SIZE = 65535\n self.server_socket = socket.socket(socket.AF_INET,socket.SOCK_DGRAM)\n self.server_socket.setsockopt(socket.SOL_SOCKET,socket.SO_RCVBUF,self.BUFF_SIZE)\n self.host_name = socket.gethostname()\n self.host_ip = socket.gethostbyname(self.host_name) #192.168.1.102# \n self.client_add=[]\n self.client_add.append( (\"127.0.0.1\",5051) )\n print(\"print Host IP\",self.host_ip)\n self.port = 9999\n self.img_size= (400,600)\n self.socket_address = (self.host_ip,self.port)\n\n def set_frame_size(self,size):\n self.img_size=size\n\n def set_client_add(self,client_add):\n self.client_add.append( client_add )\n\n def send_frame(self,frame):\n try:\n self.encode_frame(frame)\n self.create_packet()\n self.send_buffer()\n except Exception as e:\n print(\"send frame\",e)\n \n def encode_frame(self,frame):\n self.alpha = 1.2 # Contrast control (1.0-3.0)\n self.beta = 0 # Brightness control (0-100)\n self.full_frame=frame\n self.small_frame=cv2.resize(self.full_frame,self.img_size)\n self.frame = cv2.convertScaleAbs(self.small_frame, alpha=self.alpha, beta=self.beta)\n #self.frame = imutils.resize(self.frame,width=self.width)\n encoded,self.buffer = cv2.imencode('.jpg',self.frame,[cv2.IMWRITE_JPEG_QUALITY,80])\n self.buffer = base64.b64encode(self.buffer)\n \n def create_packet(self):\n header=format(0x010203)\n tail=format(0x0405060)\n header=header.encode('utf-8')\n tail=tail.encode('utf-8')\n self.packet= header+self.buffer+tail\n \n def send_buffer(self):\n print(\"packet len \",len(self.packet)) \n total=0\n for y in range(0,len(self.packet),60000):\n chunk=self.packet[y:y+60000]\n #print(udp_buffer)\n total=total+len(chunk)\n for cl in self.client_add:\n try:\n self.server_socket.sendto(chunk,cl)\n except Exception as e:\n print(\"Error in send frame to \",cl,e)\n\n time.sleep(0.01)\n\nif __name__==\"__main__\":\n\n img=cv2.imread(\"test.jpg\")\n \n streamer=UDP_Streamer()\n streamer.set_frame_size((600,400))\n #streamer.encode_frame(img)\n #streamer.create_packet()\n \n vid_file=\"traffic.mp4\"\n vid=cv2.VideoCapture(vid_file)\n\n while True:\n ret,frame=vid.read()\n if ret:\n streamer.send_frame(frame)\n cv2.imshow(\"streamer\",frame)\n cv2.waitKey(1)\n \n #streamer.send_buffer()\n #streamer.send_frame(img)\n #time.sleep(1)\n \n","repo_name":"Alkasabi/Video-Streamer","sub_path":"UDP_Server.py","file_name":"UDP_Server.py","file_ext":"py","file_size_in_byte":2853,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"2459729307","text":"import numpy as np\nimport pandas as pd\nfrom functools import reduce\nimport seaborn as sns\nfrom matplotlib import pyplot\n\ndef hap_load_and_process(url_or_path_to_csv_file, rename_dict,final_list):\n\n # Method Chain 1 (Load data and deal with missing data)\n\n df1 = (\n pd.read_csv(url_or_path_to_csv_file)\n .rename(columns=rename_dict)\n #.dropna()\n # etc...\n )\n\n # Method Chain 2 (Create new columns, drop others, and do processing)\n\n df2 = (\n df1\n #.assign(status=lambda x: np.where((x.period > 2014), 1, 0))\n .sort_values(\"country\", ascending=True)\n .reset_index(drop=True)\n .loc[:, final_list]\n )\n\n # Make sure to return the latest dataframe\n\n return df2 \n\ndef ind_load_and_process(url_or_path_to_csv_file, ind):\n\n # Method Chain 1 (Load data and deal with missing data)\n\n df1 = (\n pd.read_csv(url_or_path_to_csv_file)\n .rename(columns={\"Location\":\"country\",\"Period\":\"period\",\"Value\":ind})\n #.dropna()\n # etc...\n )\n\n # Method Chain 2 (Create new columns, drop others, and do processing)\n\n df2 = (\n df1\n .assign(status=lambda x: np.where((x.period > 2014), 1, 0))\n .sort_values(['country','period'],ascending=[True, True], ignore_index=True)\n .reset_index(drop=True)\n .loc[:, [\"country\", \"period\", ind,'status']]\n )\n\n # Make sure to return the latest dataframe\n\n return df2 \n\ndef merge_hap(data_frames):\n \n merged= reduce(lambda left,right: pd.merge(left,right,on=['country'],\n how='inner'), data_frames)\n return merged\n \ndef filter_years(df):\n #df['status'] = df.apply(keep_or_discard,axis='columns')\n df=df.loc[df['status'] == 1]\n df=df.drop(columns=['status'])\n return df\n\ndef edit_data(dataFrame):\n \n numbs=list(dataFrame[list(dataFrame.columns)[2]])\n for string in numbs:\n loc = string.find(' ')\n numbs[numbs.index(string)]=string[0:loc].replace(',','')\n\n return numbs\n\ndef clean_ind(df):\n \n df=filter_years(df)\n df[[list(df.columns)[2]]]=edit_data(df)\n \n return df\n\n\nH21RawData=pd.read_csv(\"../data/raw/world-happiness-report-2021.csv\")\nH19RawData=pd.read_csv('../data/raw/2019.csv')\nH18RawData=pd.read_csv('../data/raw/2018.csv')\nH17RawData=pd.read_csv('../data/raw/2017.csv')\nH16RawData=pd.read_csv('../data/raw/2016.csv')\nH15RawData=pd.read_csv('../data/raw/2015.csv')\nLivCostRawData=pd.read_csv('../data/raw/cost_of_living 2020.csv')\nGDPRawData=pd.read_csv('../data/raw/gdp.csv')\nH20RawData=pd.read_csv('../data/raw/world-happiness-report.csv')\n\n\ndef load_process(path_to_csv):\n# Method Chain 1\n df1=(\n pd.read_csv(path_to_csv)\n .drop(['Family','Freedom'],axis=1)\n .dropna(subset=['Happiness Score'])\n )\n# Method Chain 2 \n df2=(\n df1\n .sort_values('Country')\n .rename(columns={'Country':'Country_Sorted'})\n .reset_index\n \n )\n \n return df2\n \n\ndef process_df(DataFrame):\n df=(DataFrame\n .drop(['Family','Freedom'],axis=1)\n .dropna(subset=['Happiness Score'])\n .reset_index(drop=True)\n .groupby('Region')['Happiness Rank']\n .max()\n .sort_values()\n .to_frame()\n )\n return df\n\n\ndef select_col (df):\n df=df[['Country','Happiness Rank','Happiness Score','Economy (GDP per Capita)','Freedom','Trust (Government Corruption)','Generosity']]\n return (df)\n\n\n\n","repo_name":"ubco-W2020ST2-data301/project-group16-data-301-project","sub_path":"analysis/scripts/project_functions.py","file_name":"project_functions.py","file_ext":"py","file_size_in_byte":3537,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"11845445787","text":"def exercise_one():\n nome = input(\"Digite seu nome: \")\n\n print(f\"Seu nome é {nome}\")\n\n\ndef exercise_two():\n nome = input(\"Digite seu nome: \")\n idade = int(input(\"Digite sua idade: \"))\n\n print(f\"Seu nome é {nome} e sua idade é {idade}\")\n\n\ndef exercise_three():\n nome = input(\"Digite seu nome: \")\n\n print(f\"Seu nome é {nome}\")\n\n altura = float(input(\"Digite sua altura: \"))\n\n print(f\"Sua altura é {altura}\")\n print(\"Obrigado pelas informações!\")\n\n\ndef exercise_four():\n pais = input(\"Digite o nome do seu país: \")\n unidade_federativa = input(\"Digite a sua UF (Unidade Federativa): \")\n cep = input(\"Digite seu CEP: \")\n cidade = input(\"Digite sua cidade: \")\n bairro = input(\"Digite o nome do seu bairro: \")\n rua = input(\"Digite o nome da sua rua: \")\n numero_da_casa = int(input(\"Digite o número da sua casa: \"))\n\n print(\n f\"Este é seu endereço: {rua} {numero_da_casa}, {bairro},\"\n f\" {cidade}-{unidade_federativa}, {pais} {cep}\"\n )\n\n\ndef exercise_five():\n soma = 0\n produto = 1\n\n for index in range(5):\n numero_digitado = int(input(f\"Digite o {index + 1}° número: \"))\n soma += numero_digitado\n produto *= numero_digitado\n\n print(f\"A soma dos valores digitados é: {soma}\")\n print(f\"O produto dos valores digitados é: {produto}\")\n\n\ndef exercise_six():\n a = int(input(\"Digite o 1° número: \"))\n b = int(input(\"Digite o 2° número: \"))\n c = int(input(\"Digite o 3° número: \"))\n d = int(input(\"Digite o 4° número: \"))\n e = int(input(\"Digite o 5° número: \"))\n\n # sabendo que B e C são respectivamente a base e a altura de\n # um triângulo, imprima a área deste triângulo\n area_triangulo = (b * c) / 2\n print(f\"A área do trinagulo de base {b} e altura {c} é {area_triangulo}\")\n\n # sabendo que A, B, C e D formam um retângulo, imprima o\n # perímetro deste retângulo\n area_retangulo = a + b + c + d\n print(\n f\"O perímetro de um retângulo com os lados sendo\"\n f\"({a}, {b}, {c}, {d}) é {area_retangulo}\"\n )\n\n # sabendo que E é o valor do raio de um determinado círculo,\n # imprima a área deste círculo\n area_circulo = e**2 * 3.14\n print(f\"A área do circulo de raio {e} é: {area_circulo}\")\n\n\ndef exercise_seven():\n # Uma determinada disciplina possui apenas 3 avaliações: o\n # trabalho (que vale 10% da nota), a prova (que vale 60% da\n # nota) e o teste (que vale 30% da nota).\n\n nota_trabalho = float(input(\"Digite a nota do trabalho: \"))\n nota_trabalho *= 0.1\n\n nota_prova = float(input(\"Digite a nota da prova: \"))\n nota_prova *= 0.6\n\n nota_teste = float(input(\"Digite a nota do teste: \"))\n nota_teste *= 0.3\n\n nota_final = round(nota_trabalho + nota_prova + nota_teste, 2)\n print(f\"Sua nota final é: {nota_final}\")\n\n\ndef exercise_eight():\n # Uma disciplina possui Grau A e Grau B. A nota do Grau A vale\n # 33% da nota final, enquanto a nota do Grau B vale 67% da\n # nota final. O Grau A possui as seguintes avaliações:\n # – Atividade prática: 45% da nota do Grau A\n # – Atividade teórica: 55% da nota do Grau A\n # Já o Grau B possui as seguintes avaliações:\n # – Prova em laboratório: 60% da nota do Grau B\n # – Teste teórico: 20% da nota do Grau B\n # – Trabalho extraclasse: 20% da nota do Grau B\n\n print(\"------ Notas do Grau A ------\")\n atividade_pratica = float(input(\"Digite a nota da atividade prática: \"))\n atividade_pratica *= 0.45\n atividade_teorica = float(input(\"Digite a nota da atividade teórica: \"))\n atividade_teorica *= 0.55\n\n final_grade_a = round(atividade_teorica + atividade_pratica, 2)\n print(f\"A nota do grau A é {final_grade_a}\")\n\n final_grade_a *= 0.33\n\n print(\"------ Notas do Grau B ------\")\n prova_laboratorio = float(input(\"Digite a nota da prova em laboratório: \"))\n prova_laboratorio *= 0.60\n\n teste_teorico = float(input(\"Digite a nota do teste teórico: \"))\n teste_teorico *= 0.20\n\n trabalho_extraclasse = float(input(\"Digite a nota do trabalho teórico: \"))\n trabalho_extraclasse *= 0.20\n\n final_grade_b = round(trabalho_extraclasse + teste_teorico + prova_laboratorio, 2)\n print(f\"A nota do grau B é {final_grade_b}\")\n\n final_grade_b *= 0.67\n\n nota_final = round(final_grade_b + final_grade_a, 2)\n print(f\"A nota final do curso é {nota_final}\")\n","repo_name":"UdyWeber/university_studies","sub_path":"python_exercises/first_class_exercises.py","file_name":"first_class_exercises.py","file_ext":"py","file_size_in_byte":4450,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"26299068735","text":"from bs4 import BeautifulSoup\nimport requests\nfrom newspaper import Article\n\ndef get_summary_of_article(\n url:str,\n): \n try:\n article = Article(url)\n article.download()\n article.parse()\n article.nlp()\n return article.summary\n except Exception:\n return \"\"\n\ndef get_keyword_of_article(\n url:str,\n): \n try:\n article = Article(url)\n article.download()\n article.parse()\n article.nlp()\n return article.keywords\n except Exception:\n return []\n\ndef return_information(\n url: str,\n tag_article: str,\n tag_title: str,\n tag_link: str,\n tag_description: str,\n tag_image: str,\n tag_image_h: str,\n tag_image_w: str,\n tag_image_link: str,\n):\n website = requests.get(url)\n data = {}\n content = website.content\n soup = BeautifulSoup(content, \"xml\")\n items = soup.find_all(tag_article)\n for count, item in enumerate(items):\n\n try:\n title = item.find(tag_title).text\n except AttributeError:\n title = None\n\n try:\n link = item.find(tag_link).text\n except AttributeError:\n link = None\n\n try:\n description = item.find(tag_description).text\n except AttributeError:\n description = None\n\n images = {}\n for count_image, image in enumerate(item.find_all(tag_image)):\n try:\n h = image.get(tag_image_h)\n w = image.get(tag_image_w)\n if h is None or w is None:\n key = count_image\n else:\n key = f\"{h}x{w}\"\n link_image = image.get(tag_image_link)\n images[key] = link_image\n except Exception:\n pass\n\n data[f\"{count}\"] = {\n \"link\": link,\n \"images\": images,\n \"title\": title,\n \"description\": description,\n \"summary\":get_summary_of_article(link),\n \"keyword\":get_keyword_of_article(link)\n }\n\n return data\n","repo_name":"SamirPS/News-Scrapper","sub_path":"newsscrapper/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2083,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"35036172514","text":"import numpy as np\nimport pandas as pd\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.svm import SVC\n\ndata = pd.read_csv('dataset/dataset.csv').sample(frac = 0.01)\nresult = pd.DataFrame()\nresult['WEATHER_DELAY'] = data.iloc[:,6]\ndata = data.iloc[:,0:6] \n\nx_train, x_test, y_train, y_test = train_test_split(data.values, result.values, test_size = 0.2) \n\nsvc = SVC()\nsvc.fit(x_train, y_train.ravel())\nprediction = svc.predict(x_test)\n\n\ndata_df = {\n 'test' : y_test.flatten(),\n 'pred' : prediction.flatten(),\n}\n\ndf = pd.DataFrame(data_df)\ndf.to_csv(\"result.csv\")\n\n\n\n\ncm = confusion_matrix(y_test, prediction)\nsum = 0\nfor i in range(cm.shape[0]):\n sum += cm[i][i]\n \naccuracy = sum/x_test.shape[0] \nprint(accuracy)\n","repo_name":"nsssayom/flight_delay_predictor","sub_path":"svc.py","file_name":"svc.py","file_ext":"py","file_size_in_byte":865,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"52"} +{"seq_id":"2924190001","text":"#!/data/salomonis2/LabFiles/Frank-Li/neoantigen/revision/ts/bayesian/pytorch_pyro_mamba_env/bin/python3.7\n\nimport os,sys\nimport datetime\nfrom functools import partial\nimport numpy as np\nimport torch\nimport pyro\nimport pyro.distributions as dist\nfrom pyro.infer import SVI, Trace_ELBO\nfrom pyro.infer.autoguide import AutoNormal\nfrom pyro.optim import ClippedAdam\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\n\n\nif torch.cuda.is_available():\n print(\"Using GPU\")\n torch.set_default_tensor_type(\"torch.cuda.FloatTensor\")\nelse:\n print(\"Using CPU\")\n\ndataset = torch.load('nextstrain.data.pt',map_location=torch.tensor(0.0).device)\ncounts = dataset['counts']\n\ndef get_lineage_id(s):\n \"\"\"Get lineage id from string name\"\"\"\n return np.argmax(np.array([s]) == dataset['lineages'])\n\ndef get_location_id(s):\n \"\"\"Get location id from string name\"\"\"\n return np.argmax(np.array([s]) == dataset['locations'])\n\ndef get_aggregated_counts_from_locations(locations):\n \"\"\"Get aggregated counts from a list of locations\"\"\"\n return sum([dataset['counts'][:, get_location_id(loc)] for loc in locations])\n\nstart = dataset[\"start_date\"]\nstep = datetime.timedelta(days=dataset[\"time_step_days\"])\ndate_range = np.array([start + step * t for t in range(len(dataset[\"counts\"]))])\n\nnortheast_states = ['USA / Massachusetts',\n 'USA / New York',\n 'USA / Connecticut',\n 'USA / New Hampshire',\n 'USA / Vermont',\n 'USA / New Jersey',\n 'USA / Maine',\n 'USA / Rhode Island',\n 'USA / Pennsylvania']\n\nnortheast_counts = get_aggregated_counts_from_locations(northeast_states) # 27*1316\n\n\n# The Alpha and Delta variants include many PANGO lineages, which we need to aggregate.\nQ_lineages = [lin for lin in dataset['lineages'] if lin[:2] == 'Q.']\nAY_lineages = [lin for lin in dataset['lineages'] if lin[:3] == 'AY.']\n\nalpha_lineages = ['B.1.1.7'] + Q_lineages\ndelta_lineages = ['B.1.617.2'] + AY_lineages\n\nalpha_ids = [get_lineage_id(lin) for lin in alpha_lineages]\ndelta_ids = [get_lineage_id(lin) for lin in delta_lineages]\n\nalpha_counts = northeast_counts[:, alpha_ids].sum(-1)\ndelta_counts = northeast_counts[:, delta_ids].sum(-1)\nalpha_delta_counts = torch.stack([alpha_counts, delta_counts]).T # 27*2\n\ndef basic_model(counts):\n T, L = counts.shape\n\n # Define plates over lineage and time\n lineage_plate = pyro.plate(\"lineages\", L, dim=-1)\n time_plate = pyro.plate(\"time\", T, dim=-2)\n\n # Define a growth rate (i.e. slope) and an init (i.e. intercept) for each lineage\n with lineage_plate:\n rate = pyro.sample(\"rate\", dist.Normal(0, 1))\n init = pyro.sample(\"init\", dist.Normal(0, 1))\n\n # We measure time in units of the SARS-CoV-2 generation time of 5.5 days\n time = torch.arange(float(T)) * dataset[\"time_step_days\"] / 5.5\n\n # Assume lineages grow linearly in logit space\n logits = init + rate * time[:, None]\n\n # We use the softmax function (the multivariate generalization of the\n # sigmoid function) to define normalized probabilities from the logits\n probs = torch.softmax(logits, dim=-1)\n assert probs.shape == (T, L)\n\n # Observe counts via a multinomial likelihood.\n with time_plate:\n pyro.sample(\n \"obs\",\n dist.Multinomial(probs=probs.unsqueeze(-2), validate_args=False),\n obs=counts.unsqueeze(-2),\n )\n\ndef fit_svi(model, lr=0.1, num_steps=1001, log_every=250):\n pyro.clear_param_store() # clear parameters from previous runs\n pyro.set_rng_seed(20211214)\n if False:\n num_steps = 2\n\n # Define a mean field guide (i.e. variational distribution)\n guide = AutoNormal(model, init_scale=0.01)\n optim = ClippedAdam({\"lr\": lr, \"lrd\": 0.1 ** (1 / num_steps)})\n svi = SVI(model, guide, optim, Trace_ELBO())\n\n # Train (i.e. do ELBO optimization) for num_steps iterations\n losses = []\n for step in range(num_steps):\n loss = svi.step()\n losses.append(loss)\n if step % log_every == 0:\n print(f\"step {step: >4d} loss = {loss:0.6g}\")\n\n # Plot to assess convergence.\n plt.figure(figsize=(6, 3))\n plt.plot(losses)\n plt.xlabel(\"SVI step\", fontsize=18)\n plt.ylabel(\"ELBO loss\", fontsize=18)\n plt.tight_layout()\n\n return guide\n\nguide = fit_svi(partial(basic_model, alpha_delta_counts[13:]), num_steps=1501)\n\nfor k, v in guide.median().items():\n print(k, v.data.cpu().numpy())\n\n\n# Model lineage proportions in each region as multivariate logistic growth\ndef regional_model(counts):\n T, R, L = counts.shape\n\n # Now we also define a region plate in addition to the time/lineage plates\n lineage_plate = pyro.plate(\"lineages\", L, dim=-1)\n region_plate = pyro.plate(\"region\", R, dim=-2)\n time_plate = pyro.plate(\"time\", T, dim=-3)\n\n # We use the same growth rate (i.e. slope) for each region\n with lineage_plate:\n rate = pyro.sample(\"rate\", dist.Normal(0, 1))\n\n # We allow the init to vary from region to region\n init_scale = pyro.sample(\"init_scale\", dist.LogNormal(0, 2))\n with region_plate, lineage_plate:\n init = pyro.sample(\"init\", dist.Normal(0, init_scale))\n\n # We measure time in units of the SARS-CoV-2 generation time of 5.5 days\n time = torch.arange(float(T)) * dataset[\"time_step_days\"] / 5.5\n\n # Instead of using the softmax function we directly use the\n # logits parameterization of the Multinomial distribution\n logits = init + rate * time[:, None, None]\n\n # Observe sequences via a multinomial likelihood.\n with time_plate, region_plate:\n pyro.sample(\n \"obs\",\n dist.Multinomial(logits=logits.unsqueeze(-2), validate_args=False),\n obs=counts.unsqueeze(-2),\n )\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"frankligy/SNAF","sub_path":"images/bayesian/pyro/workflow.py","file_name":"workflow.py","file_ext":"py","file_size_in_byte":5838,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"52"} +{"seq_id":"171870685","text":"from __future__ import absolute_import, print_function\n\nimport copy\nimport os\nimport logging\nimport random\nimport sys\nimport subprocess\nimport six\nimport string\nfrom . import util\n\nfrom . import util\n\ndef add_entrypoint_if_needed(cmd, host_name, container_host_name):\n from . import myhosts\n script = myhosts.get_startup_script(container_host_name)\n\n if not script:\n return\n \n # Write the commands in a temporary file\n random_string = util.get_random_string()\n tmp_dir = \"/tmp\"\n path = \"{}/gae123_com_{}_{}\".format(tmp_dir, container_host_name, random_string)\n with open(path, \"w\") as file_desc:\n file_desc.write(\"#!/usr/bin/env bash\\n\")\n file_desc.write(script)\n\n os.system(\"chmod +x {path} && \\\n scp {path} {host}:{path} && \\\n rm -f {path}\".format(path=path, host=host_name))\n\n # Mound the temp file in the container and make it they entrypoint\n dest_path = path\n cmd.append(\"--mount type=bind,source={},destination={}\".format(path, dest_path))\n cmd.append(\"--entrypoint={}\". format(dest_path))\n\n\ndef create_start_docker(host_name, container_host_name, dryrun=False):\n from . import myhosts, gen_interfaces\n if not dryrun:\n gen_interfaces.gen_interfaces(host_name)\n cmd = []\n ns_host_auth = myhosts.get_run_before(container_host_name)\n if ns_host_auth:\n cmd.extend([\n ns_host_auth,\n \";\"\n ])\n \n ns_container_ip = myhosts.get_container_ip(container_host_name)\n ns_container_host_ip = myhosts.get_container_host_ip(container_host_name)\n sshport = 22 if util.is_localhost(host_name) else 2222\n ports = copy.copy(myhosts.get_container_ports(container_host_name))\n ports.append(\"{}:22\".format(sshport))\n ns_host_ports = ' '.join(['{}:{}'.format(ns_container_ip,x) for x in ports])\n ns_extra_args = myhosts.get_container_extra_flags(container_host_name)\n ns_aliases = myhosts.get_container_aliases(container_host_name)\n ns_hostAliases = myhosts.get_container_hostAliases(container_host_name)\n\n cmd.extend([\n 'sudo',\n 'docker',\n 'run',\n '--detach',\n '--privileged',\n \"--name={sd2ContainerName}\",\n \"--hostname={sd2ContainerName}\",\n ])\n for alias in ns_aliases:\n cmd.append(\"--add-host='{alias}:{ip}'\".format(alias=alias, ip=ns_container_ip))\n for alias in ns_hostAliases:\n cmd.append(\"--add-host='{alias}:{ip}'\".format(alias=alias, ip=ns_container_host_ip))\n\n if myhosts.get_container_mount_home_dir(container_host_name):\n cmd.extend([\"--volume\", \"$HOME:/home/$USER\"])\n if util.remote_path_exists(host_name, '/mnt'):\n cmd.extend(['--volume', '/mnt:/mnt'])\n\n for ports in ns_host_ports.split():\n cmd.append('--publish')\n cmd.append(ports)\n \n env = {\n \"SD2IP\": ns_container_ip\n }\n env.update()\n for var in myhosts.get_container_env(container_host_name):\n env[var['name']] = var['value']\n for kk,vv in six.iteritems(env):\n cmd.append(\"--env\")\n cmd.append(\"{}={}\".format(kk,vv))\n\n cmd.extend(['--workdir', \"/home/$USER\"])\n cmd.append('--tty')\n\n add_entrypoint_if_needed(cmd, host_name, container_host_name)\n if isinstance(ns_extra_args, six.string_types):\n ns_extra_args.split()\n for arg in ns_extra_args.split():\n cmd.append(arg)\n cmd.append(myhosts.get_container_docker_image(container_host_name))\n cmd.append(myhosts.get_container_command(container_host_name))\n\n command = ' '.join(cmd)\n command = command.format(sd2ContainerName=container_host_name)\n if (not dryrun):\n logging.info(\"EXEC %s\", command)\n util.remote_system(host_name, command)\n from .events import events\n events.emit({\"hostname\": container_host_name, \"action\": \"start\"})\n else:\n print(command)\n\ndef find_id_of_container(host_name, container_name):\n containers = util.remote_subprocess_check_output(host_name,\n [\"sudo\", \"docker\", \"ps\", \"-qa\"])\n cmd = [\n \"sudo\", \"docker\", \"inspect\", \"-f\",\n '{{.Id}},{{.Config.Hostname}}',\n ] + containers.split()\n inspout = util.remote_subprocess_check_output(host_name, cmd)\n ids = [line.split(',')[0] for line in inspout.split() if\n line.split(',')[1].startswith(container_name)]\n return None if not ids else ids[0]\n\ndef remove_container_by_hostname(host_name, container_host_name, dryrun):\n contid = find_id_of_container(host_name, container_host_name)\n cmd = \"sudo docker rm -f \" + contid\n if not dryrun:\n logging.debug(\"EXEC: {}\".format(cmd))\n util.remote_system(host_name, cmd)\n else:\n print(cmd)\n\ndef create_start_docker_if_needed(host_name, container_host_name, dryrun):\n rr = False\n from . import myhosts\n running = None\n containers = util.remote_subprocess_check_output(host_name,\n [\"sudo\", \"docker\", \"ps\", \"-qa\"])\n if containers:\n cmd = [\n \"sudo\", \"docker\", \"inspect\", \"-f\",\n '{{.State.Running}},{{.Config.Hostname}},{{.Config.Image}}',\n ] + containers.split()\n inspout = util.remote_subprocess_check_output(host_name, cmd)\n running = [line.split(',')[0] for line in inspout.split() if\n line.split(',')[1] == container_host_name]\n image = [line.split(',')[2] for line in inspout.split() if\n line.split(',')[1] == container_host_name]\n\n create_new_one = False\n if running == ['true']:\n remove = False\n if (image[0] == myhosts.get_container_docker_image(container_host_name) and\n myhosts.is_container_enabled(container_host_name)):\n print((container_host_name + ': Found running...'))\n else:\n if (not myhosts.is_container_enabled(container_host_name)):\n print((container_host_name + ': Found running when it should not.'))\n reason = \" because it should not be running\"\n remove = myhosts.get_container_remove_flag(container_host_name)\n else:\n print((container_host_name + ': Found running a different image {}.'.format(image)))\n reason = 'to start one with the right image {}'.format(\n myhosts.get_container_docker_image(container_host_name))\n remove = myhosts.get_container_upgrade_flag(container_host_name)\n if remove:\n print(('{}: Removing container {}'.format(container_host_name, reason)))\n remove_container_by_hostname(host_name, container_host_name, dryrun)\n create_new_one = myhosts.is_container_enabled(container_host_name)\n elif running == ['false']:\n create_new_one = myhosts.is_container_enabled(container_host_name)\n print((container_host_name + \n ': Found stopped and removing. ({})'.format(create_new_one)))\n remove_container_by_hostname(host_name, container_host_name, dryrun)\n else:\n create_new_one = myhosts.is_container_enabled(container_host_name)\n print((container_host_name + \n ': Not Found. ({})'.format('NOT OK' if create_new_one else 'OK')))\n if create_new_one:\n print((container_host_name + ' Creating a new one...'))\n create_start_docker(host_name, container_host_name, dryrun)\n rr = True\n cmd = 'sudo docker exec -i -t {} su - {}'.format(\n container_host_name, os.getenv('USER'))\n print((\"Attach by running: '{}'\".format(cmd)))\n return rr\n \n\ndef do_containers(host, containers, force, dryrun):\n if force:\n for cont in containers:\n cmd = \"sudo docker rm -f \" + cont\n if not dryrun:\n logging.info(\"EXEC: \" + cmd)\n util.remote_system(host, cmd)\n else:\n print(cmd)\n ret = False\n for cont in containers:\n ret0 = create_start_docker_if_needed(host, cont, dryrun)\n ret = ret or ret0\n return ret\n\ndef check_for_prereqs(args):\n tools_found = True\n tools = [\n ['ssh', 'Please install ssh and restart..'],\n ['rsync', 'Please install rsync and restart..'],\n ]\n if args.fswatch:\n tools.append(['fswatch', 'Please install fswatch and restart..'])\n for tool in tools:\n try:\n rr = subprocess.check_output(\"type {}\".format(tool[0]), shell=True)\n except Exception as ex:\n sys.stdout.write(\"{}\\n\".format(tool[1]))\n tools_found = False\n if not tools_found:\n sys.exit(1)\n \n \ndef main(args):\n from . import gen_all, myhosts\n if not args.noinit:\n gen_all.gen_all()\n \n containers = []\n our_hostname = util.get_our_hostname()\n if not args.hostname:\n args.hostname = our_hostname\n if not args.containers and args.all:\n containers = myhosts.get_container_names(args.hostname)\n else:\n containers = args.containers\n \n # if the user just passed 0 convert it too hostname-0\n containers = [(x if x.startswith(args.hostname)\n else (args.hostname + '-' + x)) for x in containers]\n ret = do_containers(args.hostname, containers, args.force, args.dryrun)\n \n logging.debug(\"sd2cont: Considered %s on %s\", containers, args.hostname)\n \n # 0 means it did start one or more\n sys.exit(0 if ret else 1)\n \n \ndef add_argument_parsing(subparsers):\n parser_cont = subparsers.add_parser(\n 'cont',\n description='Start containers in hosts'\n )\n parser_cont.add_argument('--noinit', '-i', action=\"store_true\",\n default=False,\n help=\"do not attempty to initialize /etc/hosts etc\")\n parser_cont.add_argument('--force', '-f', action=\"store_true\",\n default=False,\n help=\"first remove existing container if exists\")\n \n parser_cont.add_argument('--all', '-a', action=\"store_true\", default=False,\n help=\"Used to restart all containers in this host\")\n parser_cont.add_argument(\"hostname\", nargs=\"?\",\n help=\"hostname where to start the images\",\n default='')\n parser_cont.add_argument(\n \"containers\", nargs=\"*\",\n help=\"containers to start, leave it empty and pass --all to restart all of them\",\n default='')\n parser_cont.set_defaults(func=main, logging=True)\n\n\n\n\n\n\n\n\n","repo_name":"gae123/sd2","sub_path":"src/lib/sd2/sd2cont.py","file_name":"sd2cont.py","file_ext":"py","file_size_in_byte":10468,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"52"} +{"seq_id":"19256156437","text":"# nltk data available at C:\\Users\\marcs\\AppData\\Roaming\\nltk_data\n\n# Sioux Valley Self Government Agreement ATRIS Parser\n# Marc St. Pierre 1/3/2023\n\nimport string\nimport csv\nimport requests\nfrom bs4 import BeautifulSoup\n\nurl = requests.get('https://www.rcaanc-cirnac.gc.ca/eng/1385741084467/1551118616967')\nsoup = BeautifulSoup(url.content, 'html.parser')\ndbtitle = 'SVprovisionsdb.csv'\nagreement = 'Sioux Valley Dakota Nation Governance Agreement and Tripartite Governance Agreement'\nyear = '2013'\npart = ''\nsection = ''\nroman = ['i', 'ii', 'iii', 'iv', 'v', 'vi', 'vii', 'viii', 'ix', 'x']\nalpha = list(string.ascii_lowercase)\nprevEnumType = 'lst-spcd' \nintIndex = 0\nalphaIndex = 0\nromanIndex = 0\nprovNum = 0\ndata = []\n\nfor child in soup.h1.next_elements:\n if child.name == 'h2':\n part = child.get_text()\n section = ''\n if child.name == 'h3':\n section = child.get_text()\n if child.name in ['h4', 'h5', 'h6']:\n alphaIndex = 0\n romanIndex = 0\n intIndex = 0\n prevEnumType = 'lst-spcd'\n try:\n int(child.get_text()[0])\n provNum = (child.get_text().split(' ', 1)[0]).strip('\\n')\n text = \" \".join(child.get_text().split(' ')[1:])\n data.append([provNum, part, section, text])\n except ValueError:\n data.append([provNum, part, section, child.get_text()])\n except IndexError:\n data.append([provNum, part, section, child.get_text()])\n if child.name == 'p':\n alphaIndex = 0\n romanIndex = 0\n intIndex = 0\n prevEnumType = 'lst-spcd'\n data.append([provNum, part, section, child.get_text()])\n if child.name == 'li':\n if 'class' in child.parent.attrs:\n enumType = child.parent.attrs['class'][0]\n if enumType == 'lst-spcd' and prevEnumType == 'lst-spcd':\n intIndex += 1\n data.append([str(provNum) + '(' + str(intIndex) + ')', part, section, child.get_text()])\n elif enumType == 'lst-lwr-alph' and prevEnumType == 'lst-spcd':\n alphaIndex = 0\n data.append([str(provNum) + '(' + alpha[alphaIndex] + ')', part, section, child.get_text()])\n elif enumType == 'lst-lwr-alph' and prevEnumType == 'lst-lwr-alph':\n alphaIndex += 1\n data.append([str(provNum) + '(' + alpha[alphaIndex] + ')', part, section, child.get_text()])\n elif enumType == 'lst-lwr-rmn' and prevEnumType == 'lst-lwr-alph':\n romanIndex = 0\n data.append([str(provNum) + '(' + alpha[alphaIndex] + ')' + '(' + roman[romanIndex] + ')', part, section, child.get_text()])\n elif enumType == 'lst-lwr-rmn' and prevEnumType == 'lst-lwr-rmn':\n romanIndex += 1\n data.append([str(provNum) + '(' + alpha[alphaIndex] + ')' + '(' + roman[romanIndex] + ')', part, section, child.get_text()])\n elif enumType == 'lst-lwr-alph' and prevEnumType == 'lst-lwr-rmn':\n alphaIndex += 1\n data.append([str(provNum) + '(' + alpha[alphaIndex], part, section, child.get_text()])\n elif enumType == 'lst-spcd' and prevEnumType == 'lst-lwr-alph':\n intIndex += 1\n data.append([str(provNum), part, section, child.get_text()])\n elif enumType == 'lst-spcd' and prevEnumType == 'lst-lwr-rmn':\n intIndex += 1\n data.append([str(provNum), part, section, child.get_text()])\n else:\n data.append([provNum, part, section, child.get_text()])\n prevEnumType = enumType\n\nwith open(dbtitle, 'w', newline='') as csvfile:\n provisionswriter = csv.writer(csvfile)\n provisionswriter.writerow(['agreement', 'year', 'provisionNum', 'part', 'section', 'provisionText'])\n for i in data:\n row = [agreement, year] + i\n provisionswriter.writerow(row)\n csvfile.close()\n\n\n# for i in range(100):\n# print(data[i])\n\n# for i in data:\n# print(i)\n\n\n\n","repo_name":"mkstp/comparative-provisions-generator","sub_path":"parsers/Old_Parsers/siouxValleyParser.py","file_name":"siouxValleyParser.py","file_ext":"py","file_size_in_byte":3926,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"70941275355","text":"import requests\n\nfrom tqdm import tqdm\n\nimport numpy as np\n\nfrom bs4 import BeautifulSoup\nfrom urllib.request import urlretrieve\nimport re\n\nimport os\nfrom io import StringIO\n\nimport numpy as np\n\nfrom astroquery.mast import Observations\nfrom astroquery.simbad import Simbad\n\nfrom . import calculations as c\n\nclass Target:\n \"\"\"\n Performance of web reconnaisanse on an interesting target.\n \n Attributes:\n :aliases: (list of strings) names by which the target is known in other catalogs.\n :input_name: (str) name of target you're interested in.\n :webb_approved: (bool) whether or not the target has been included in approved webb program.\n :hst_approved: (bool) whether or not the target has been included in a public HST program.\n :webb_proposal_link: (list of strings) if there are associated JWST proposals, these\n are the associated URLs.\n :webb_proposal_names: (list of strings) if there are associated JWST proposals, these\n are the associated proposal names.\n :hst_data: (dict) keys are HST proposals, vals are links to associated data producs.\n :exoplanet_archive_data: (dict)\n :arxiv_links: (list) list to PDFs of arxiv papers that have self.input_name or self.aliases in \n their abstracts\n \n Methods\n --------\n :__init__: initializes.\n :scrape_all: master run method.\n :find_aliases: finds aliases.\n :search_webb_site: manual scraping, not preferred.\n \n \n \"\"\"\n \n def __init__(self, input_name):\n # instantiates object\n self.input_name = input_name\n self.aliases = []\n self.webb_approved = None\n self.hst_approved = None\n self.webb_proposal_link = []\n self.webb_proposal_names = []\n self.hst_data = {}\n self.exoplanet_archive_data = {}\n self.arxiv_links = []\n self.planet_properties = None\n \n def scrape_all(self):\n \"\"\"\n The preferred scraping method.\n This calls all other main scraping methods.\n \"\"\"\n self.find_aliases()\n self.scrape_arxiv()\n self.scrape_webb_MAST()\n self.scrape_HST()\n self.scrape_planet_properties()\n \n def scrape_planet_properties(self):\n \"\"\"\n Uses exo_MAST to get planet properties for this target.\n\n \"\"\"\n # First, we need to find the \"canonical name\" of the planet — this is how the parameters\n # can be subsequently searched.\n planet_name = self.input_name\n planet_request = requests.get(\n f'https://exo.mast.stsci.edu/api/v0.1/exoplanets/identifiers/?name={planet_name}')\n \n no_nulls = planet_request.text.replace('null', 'np.nan')\n correct_trues = no_nulls.replace('true', 'True')\n correct_bools = correct_trues.replace('false', 'False')\n planet_names = eval(correct_bools)\n canonical_name = planet_names['canonicalName']\n\n properties_request = requests.get(\n f'https://exo.mast.stsci.edu/api/v0.1/exoplanets/{str.lower(canonical_name)}/properties')\n \n no_nulls = properties_request.text.replace('null', 'np.nan')\n correct_trues = no_nulls.replace('true', 'True')\n correct_bools = correct_trues.replace('false', 'False')\n planet_properties = eval(correct_bools)[0]\n self.planet_properties = planet_properties\n \n \n def run_all_calculations(self, verbose=False):\n \"\"\"\n Calculates the TSM and ESM (Kempton+ 18) for this target, using known planet\n properties.\n \"\"\"\n if not self.planet_properties:\n self.scrape_planet_properties()\n TSM = c.TSM(self.planet_properties, verbose=verbose)\n ESM = c.ESM(self.planet_properties, verbose=verbose)\n self.TSM, self.ESM = TSM, ESM\n \n \n def search_webb_site(self, URL):\n \"\"\"\n Checks whether self has been approved via GTO or ERS. Needs debugging as missing above targets still.\n Adds any links to webb_proposal_links. changed webb_approved. not validated to ERS.\n \"\"\"\n if not self.aliases:\n print('Not checking aliases.')\n page = requests.get(URL)\n\n soup = BeautifulSoup(page.content, 'html.parser')\n\n all_targets = []\n\n gto_pages = []\n for link in soup.find_all('a'):\n if link.has_attr('href'):\n str_begin = '/jwst/observing-programs/program-information?id='\n if link.attrs['href'][:48] == str_begin:\n gto_page = 'http://www.stsci.edu/' + link.attrs['href'] # give better name\n gto_pages.append(gto_page)\n\n for gto_page in tqdm(gto_pages, position=0, leave=True):\n ID = gto_page[-4:]\n pdf_link = f'http://www.stsci.edu/jwst/phase2-public/{ID}.pdf'\n urlretrieve(pdf_link, \"tmp.pdf\")\n text = convert_pdf_to_txt(\"tmp.pdf\")\n start = text.find(\"Science Target\") + len(\"Science Target\")\n end = text.find(\"ABSTRACT\")\n target_table = text[start:end]\n targets = list(set(re.findall(r\"\"\"\\(\\d\\)\\ (\\w+)\"\"\", target_table)))\n # targets += list(set(re.findall(r\"\"\"\\(\\d\\)(\\w+)\"\"\", target_table)))\n targets += list(set(re.findall(r\"\"\"\\(\\d\\)\\ (\\w+-\\w+)\"\"\", target_table)))\n targets += list(set(re.findall(r\"\"\"\\(\\d\\)\\ (\\w+-\\w+-\\w+)\"\"\", target_table))) # for HAT-P-35, for example\n targets += list(set(re.findall(r\"\"\"\\(\\d\\)\\ (\\w+ \\w+)\"\"\", target_table)))\n in_targets = [a for a in self.aliases if a in targets]\n if len(in_targets) > 0 and self.input_name in _targets:\n self.webb_approved = True\n self.webb_proposal_links.append(pdf_link)\n os.remove('tmp.pdf')\n if self.webb_approved is None: # has not been changed to True\n self.webb_approved = False\n return\n \n def search_GTO(self):\n \"\"\"\n Manually scrapes the JWST GTO page.\n \"\"\"\n URL = 'http://www.stsci.edu/jwst/observing-programs/approved-gto-programs'\n self.search_webb_site(URL)\n \n def search_ERS(self):\n \"\"\"\n Manually scrapes the JWST ERS page.\n \"\"\"\n URL = 'http://www.stsci.edu/jwst/observing-programs/approved-ers-programs'\n self.search_webb_site(URL)\n \n def search_webb(self):\n \"\"\"\n Manually scrapes both the JWST ERS and GTO pages.\n \"\"\"\n self.search_GTO()\n self.search_ERS()\n \n def find_aliases(self):\n \"\"\"\n Uses astroquery and Simbad to find any aliases of input_name; these are then \n put into the self.aliases list.\n \"\"\"\n try:\n self.aliases = list(Simbad.query_objectids(self.input_name)['ID'])\n except TypeError as e:\n if str(e) == \"\"\"'NoneType' object is not subscriptable\"\"\":\n print(f'SIMBAD could not resolve {self.input_name}. Attempting to scrape ExoFOP.')\n if self.input_name[:3] != 'TIC':\n print(f'Could not scrape {self.input_name}; please try again after changing input_name to a TICID.')\n else:\n self.scrape_exoFOP_aliases(self.input_name[5:])\n \n \n def scrape_HST(self):\n \"\"\"\n Checks MAST for the target's relevant HST proposals/data.\n Modifies hst_approved: if there are observations, sets it to True; otherwise False.\n Appends links to relevant HST data to hst_data.\n \"\"\"\n obs = Observations.query_object(self.input_name, radius=\".02 deg\") # should work. waliases\n HST_obs = obs[obs['obs_collection']=='HST']\n if len(HST_obs) > 0:\n self.hst_approved = True\n for ob in HST_obs:\n self.hst_data[ob['obs_title']] = ob['dataURL']\n if self.hst_approved is None:\n self.hst_approved = False\n \n def scrape_webb_MAST(self):\n \"\"\"\n Checks MAST for the target's relevant JWST proposals/data.\n Modifies webb_approved: if there are relevant proposals, sets it to True; otherwise False.\n Appends the names of these proposals to webb_proposal_names.\n \"\"\"\n obs = Observations.query_object(self.input_name, radius=\".02 deg\") # should work. waliases\n JWST_obs = obs[obs['obs_collection']=='JWST']\n if len(JWST_obs) > 0:\n self.webb_approved = True\n for ob in JWST_obs:\n self.webb_proposal_names.append(ob['obs_title'])\n if self.webb_approved is None:\n self.webb_approved = False\n return\n \n \n def scrape_arxiv(self, progress=False):\n \"\"\"\n Searches through arXiv abstracts for the target.\n Appends links of relevant arXiv pdfs to arxiv_links.\n If progress=True, outputs a tqdm progress bar.\n \"\"\"\n if self.aliases:\n for alias in tqdm(self.aliases, position=0, leave=True, desc='Scraping arXiv'):\n query_URL = f'https://arxiv.org/search/astro-ph?query={alias}&searchtype=abstract&abstracts=show&order=-announced_date_first&size=50'\n page = requests.get(query_URL)\n soup = BeautifulSoup(page.content, 'html.parser')\n for link in soup.find_all('a'):\n try:\n paper = link.get('href')\n if 'pdf'in paper and paper not in self.arxiv_links:\n self.arxiv_links.append(paper)\n except TypeError:\n continue\n else: # I'm sure there's a more elegant way to do this!\n query_URL = f'https://arxiv.org/search/astro-ph?query={self.input_name}&searchtype=abstract&abstracts=show&order=-announced_date_first&size=50'\n page = requests.get(query_URL)\n soup = BeautifulSoup(page.content, 'html.parser')\n for link in soup.find_all('a'):\n try:\n paper = link.get('href')\n if 'pdf'in paper and paper not in self.arxiv_links:\n self.arxiv_links.append(paper)\n except TypeError:\n continue\n \n \n def scrape_exoplanet_archive(self):\n if not self.aliases:\n print('Not checking aliases.')\n raise NotImplementedError\n \n \n def scrape_exoFOP_aliases(self, ticid):\n \"\"\"\n This manually scrapes exoFOP for aliases, given a TICID.\n \"\"\"\n URL = f'https://exofop.ipac.caltech.edu/tess/target.php?id={ticid}'\n page = requests.get(URL)\n soup = BeautifulSoup(page.content, 'html.parser')\n aliases = soup.find_all('table')[7].td.string.split(', ')\n aliases_formatted = [a if a[0] != ' ' else a[1:] for a in aliases]\n aliases_to_add = [a for a in \\\n tqdm(aliases_formatted, leave=True, position=0, desc='Checking aliases to add') \\\n if a not in self.aliases]\n self.aliases += aliases_to_add\n \n \n ","repo_name":"arjunsavel/webb_scraping","sub_path":"webb_scraping/target.py","file_name":"target.py","file_ext":"py","file_size_in_byte":11233,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"29775638197","text":"import numpy as np\n\nfrom cpp_wrapper.face_detection import FaceDetector\nfrom cpp_wrapper.alignment import LBFLandmarkDetector\n\n\n\nclass FaceDetectorAndTracker:\n \n def __init__(self):\n self.detector = FaceDetector()\n self.alignment_with_face_detector = LBFLandmarkDetector(detector=\"opencv\", landmarks=68)\n self.alignment_for_tracking = LBFLandmarkDetector(detector=\"perfect\", landmarks=68)\n \n \n def detectFaces(self, image):\n faces = self.detector.detectFaces(image)\n return faces\n \n def detectLandmarks(self, faces, image):\n shapes = []\n \n for face in faces:\n shapes.append(self.alignment_with_face_detector.detectLandmarks(image, face))\n \n return shapes\n\n def detect(self, image):\n faces = self.detector.detectFaces(image)\n shapes = []\n \n for face in faces:\n shapes.append(self.alignment_with_face_detector.detectLandmarks(image, face))\n \n return shapes\n \n \n def track(self, image, shapes):\n new_shapes = []\n \n for shape in shapes:\n old_shape = shape.astype(np.int)\n face = (np.min(old_shape[:,0]), np.min(old_shape[:,1]), np.max(old_shape[:,0]), np.max(old_shape[:,1]))\n new_shapes.append(self.alignment_for_tracking.detectLandmarks(image, face))\n \n return new_shapes\n\n","repo_name":"ina-foss/ID-Fits","sub_path":"lib/face_detector.py","file_name":"face_detector.py","file_ext":"py","file_size_in_byte":1406,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"50"} +{"seq_id":"23640891446","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreate motion plots similar to the ones created by BrainVoyager, using the *3DMC.sdm files\r\n\"\"\"\r\n__date__ = \"25-01-2023\"\r\n\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib as mpl\r\nimport numpy as np\r\nimport bvbabel\r\nimport os.path\r\nimport sys\r\nfrom PyQt5.QtWidgets import QFileDialog, QApplication\r\n\r\napp = QApplication(sys.argv)\r\n\r\n# change default plotting properties\r\nmpl.rcParams['axes.labelsize'] = 'small'\r\nmpl.rcParams['xtick.labelsize'] = 'small'\r\nmpl.rcParams['ytick.labelsize'] = 'small'\r\nmpl.rcParams['axes.titlesize'] = 'medium'\r\n\r\n# set plotdisp to True if you would like to see the plots while running the script\r\nplotdisp = False\r\n\r\nmotionfiles, _ = QFileDialog.getOpenFileNames(None, 'Select *3DMC.sdm files', 'H:/AppleGame/derivatives/workflow_id-3_type-1_name-func-preprocessing/', 'Motion Files (*3DMC.sdm)')\r\n\r\n\r\nfor f in range(len(motionfiles)):\r\n sdm_header, sdm_data = bvbabel.sdm.read_sdm(motionfiles[f])\r\n\r\n motion = np.empty((0, sdm_header['NrOfDataPoints']))\r\n for pred in range(len(sdm_data)):\r\n motion = np.vstack([motion, sdm_data[pred]['ValuesOfPredictor']])\r\n\r\n # Plot the Motion\r\n plt.style.use('dark_background')\r\n fig_motion = plt.figure('Motion', figsize=(10, 8))\r\n ax_motion = fig_motion.add_subplot(111)\r\n plt.subplots_adjust(right=0.75)\r\n ax_motion.set_ylim(np.floor(np.amin(motion)).astype(int), np.ceil(np.amax(motion)).astype(int))\r\n ax_motion.set_yticks(range(np.floor(np.amin(motion)).astype(int), np.ceil(np.amax(motion)).astype(int) + 1))\r\n ax_motion.grid(axis='y')\r\n ax_motion.spines['right'].set_visible(False)\r\n ax_motion.spines['top'].set_visible(False)\r\n\r\n ax_motion.set_title('Rigid Body Motion Parameters - 3 Translations, 3 Rotations')\r\n\r\n # plot the motion\r\n plot_transx, = ax_motion.plot(motion[0, :], linewidth=1, color=np.array(sdm_data[0]['ColorOfPredictor']) / 255,\r\n label=sdm_data[0]['NameOfPredictor'])\r\n plot_transy, = ax_motion.plot(motion[1, :], linewidth=1, color=np.array(sdm_data[1]['ColorOfPredictor']) / 255,\r\n label=sdm_data[1]['NameOfPredictor'])\r\n plot_transz, = ax_motion.plot(motion[2, :], linewidth=1, color=np.array(sdm_data[2]['ColorOfPredictor']) / 255,\r\n label=sdm_data[2]['NameOfPredictor'])\r\n plot_transx, = ax_motion.plot(motion[3, :], linewidth=1, color=np.array(sdm_data[3]['ColorOfPredictor']) / 255,\r\n label=sdm_data[3]['NameOfPredictor'])\r\n plot_transy, = ax_motion.plot(motion[4, :], linewidth=1, color=np.array(sdm_data[4]['ColorOfPredictor']) / 255,\r\n label=sdm_data[4]['NameOfPredictor'])\r\n plot_transz, = ax_motion.plot(motion[5, :], linewidth=1, color=np.array(sdm_data[5]['ColorOfPredictor']) / 255,\r\n label=sdm_data[5]['NameOfPredictor'])\r\n\r\n handles, labels = ax_motion.get_legend_handles_labels()\r\n\r\n fig_motion.legend(handles=handles, loc=\"center right\", frameon=False, framealpha=1, fontsize='small')\r\n\r\n fig_motion.savefig((motionfiles[f].split('.')[0] + '_MotionPlot.png'), dpi=600, format='png')\r\n if plotdisp == True:\r\n plt.show()\r\n plt.clf()\r\n\r\n # subtract first timepoint from all 6 timeseries, so that they start at 0\r\n motion_run = motion[:, 0:] - motion[:, 0, None]\r\n\r\n print('')\r\n print('Maximum movement per run:')\r\n print('Filename:', motionfiles[f].split('/')[-1])\r\n print('Maximum Motion with Respect to Reference Run:', np.max(np.absolute(motion)), ',',\r\n sdm_data[np.argmax(np.amax(np.absolute(motion), 1))]['NameOfPredictor'])\r\n print('Maximum Motion within the Run:', np.max(np.absolute(motion_run)), ',',\r\n sdm_data[np.argmax(np.amax(np.absolute(motion_run), 1))]['NameOfPredictor'])\r\n print('Maximum Motion Range within the Run:', np.max(np.amax(motion, 1) - np.amin(motion, 1)), ',',\r\n sdm_data[np.argmax(np.amax(motion, 1) - np.amin(motion, 1))]['NameOfPredictor'])\r\n\r\n# Restore the default plotting parameters after script is finished\r\nmpl.rcParams.update(mpl.rcParamsDefault)","repo_name":"petitro/Grid-Cells","sub_path":"4.2_GetPlots.py","file_name":"4.2_GetPlots.py","file_ext":"py","file_size_in_byte":4174,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"30201223060","text":"import os\nimport ctypes\nimport flet as ft\nfrom typing import Type\nfrom utils.card import Card\nfrom flet_mvc import FletController\nclass HomeController(FletController):\n def get_name_user(self) -> str:\n size = ctypes.pointer(ctypes.c_ulong(0))\n ctypes.windll.secur32.GetUserNameExW(3, None, size)\n buffer = ctypes.create_unicode_buffer(size.contents.value)\n ctypes.windll.secur32.GetUserNameExW(3, buffer, size)\n full_name = buffer.value\n return full_name\n\n def responsive_cards(self,responsive_row:ft.ResponsiveRow) -> None:\n cards : list = self.model.home_card_data()\n if len(cards) == 0:\n cards : list = self.model.home_card_default()\n for card in cards:\n #-------------------------------------------------------------------------|\n # NO TOMAR EN CUENTA ESTE CODIGO\n # if hasattr(controller,card.get('card_route')): \n # try:\n # card_route = getattr(controller,card.get('card_route'))\n # except Exception as e:\n # card_route = self.error_method\n # else:\n # card_route = self.error_method\n #-----------------------------------------------------------------------|\n responsive_row.controls.append(\n Card(\n page=self.page,\n title=card.get('title'),\n description=card.get('description'),\n icon=card.get('icon'),\n icon_color=card.get('color_icon'),\n route=card.get('route'),\n disabled=card.get('disabled'),\n ).build(self.route_card)\n )\n\n def route_card(self,e: ft.ControlEvent) -> None:\n if os.path.exists(os.environ.get('userprofile')+\"\\Desktop\\Configs\"):\n self.page.go(f'/{e.control.data}')\n self._update\n else:\n self.error_directory\n\n def error_directory(self) -> None:\n error_directory = ft.AlertDialog(\n title=ft.Text(\"Error de directorio\"),\n content=ft.Text(\"No se encuentra la carpeta 'Configs' en el escritorio\"),\n )\n self.page.dialog = error_directory\n error_directory.open = True\n self._update()\n\n def _update(self) -> None:\n self.page.update()\n","repo_name":"TryNeo/DeskNeo","sub_path":"controllers/home.py","file_name":"home.py","file_ext":"py","file_size_in_byte":2386,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"34655429974","text":"_author_ = 'jake'\n_project_ = 'leetcode'\n\n# https://leetcode.com/problems/search-a-2d-matrix/\n# Write an efficient algorithm that searches for a value in an m x n matrix. This matrix has the following properties:\n# Integers in each row are sorted from left to right.\n# The first integer of each row is greater than the last integer of the previous row.\n\n# Treat the 2-d matrix as a 1-d list of length rows * cols. Binary search indices from 0 to rows * cols - 1.\n# Time - O(log m + log n)\n# Space - O(1)\n\nclass Solution(object):\n def searchMatrix(self, matrix, target):\n \"\"\"\n :type matrix: List[List[int]]\n :type target: int\n :rtype: bool\n \"\"\"\n if not matrix or not matrix[0]:\n return False\n\n rows, cols = len(matrix), len(matrix[0])\n low, high = 0, rows * cols - 1\n\n while high >= low:\n\n mid = (high + low) // 2\n value = matrix[mid // cols][mid % cols] # convert mid to a row and column\n\n if target == value:\n return True\n if target > value:\n low = mid + 1\n else:\n high = mid - 1\n\n return False\n\n\n","repo_name":"jakehoare/leetcode","sub_path":"python_1_to_1000/074_Search_a_2D_Matrix.py","file_name":"074_Search_a_2D_Matrix.py","file_ext":"py","file_size_in_byte":1186,"program_lang":"python","lang":"en","doc_type":"code","stars":49,"dataset":"github-code","pt":"50"} +{"seq_id":"73372290394","text":"\nfrom INTERACTION import Interaction, pygame, sys\n\ndef mul(x,y): # Multiply 2 minterms\n res = []\n for i in x:\n if i+\"'\" in y or (len(i)==2 and i[0] in y):\n return []\n else:\n res.append(i)\n for i in y:\n if i not in res:\n res.append(i)\n return res\n\ndef multiply(x,y): # Multiply 2 expressions\n res = []\n for i in x:\n for j in y:\n tmp = mul(i,j)\n res.append(tmp) if len(tmp) != 0 else None\n return res\n\ndef refine(my_list,dc_list): # Removes don't care terms from a given list and returns refined list\n res = []\n for i in my_list:\n if int(i) not in dc_list:\n res.append(i)\n return res\n\ndef findEPI(x): # Function to find essential prime implicants from prime implicants chart\n res = []\n for i in x:\n if len(x[i]) == 1:\n res.append(x[i][0]) if x[i][0] not in res else None\n return res\n\ndef findVariables(x): # Function to find variables in a meanterm. For example, the minterm --01 has C' and D as variables\n var_list = []\n for i in range(len(x)):\n if x[i] == '0':\n var_list.append(chr(i+65)+\"'\")\n elif x[i] == '1':\n var_list.append(chr(i+65))\n return var_list\n\ndef flatten(x): # Flattens a list\n flattened_items = []\n for i in x:\n flattened_items.extend(x[i])\n return flattened_items\n\ndef findminterms(a): #Function for finding out which minterms are merged. For example, 10-1 is obtained by merging 9(1001) and 11(1011)\n gaps = a.count('-')\n if gaps == 0:\n return [str(int(a,2))]\n x = [bin(i)[2:].zfill(gaps) for i in range(pow(2,gaps))]\n temp = []\n for i in range(pow(2,gaps)):\n temp2,ind = a[:],-1\n for j in x[0]:\n if ind != -1:\n ind = ind+temp2[ind+1:].find('-')+1\n else:\n ind = temp2[ind+1:].find('-')\n temp2 = temp2[:ind]+j+temp2[ind+1:]\n temp.append(str(int(temp2,2)))\n x.pop(0)\n return temp\n\ndef compare(a,b): # Function for checking if 2 minterms differ by 1 bit only\n c = 0\n for i in range(len(a)):\n if a[i] != b[i]:\n mismatch_index = i\n c += 1\n if c>1:\n return (False,None)\n return (True,mismatch_index)\n\ndef removeTerms(_chart,terms): # Removes minterms which are already covered from chart\n for i in terms:\n for j in findminterms(i):\n try:\n del _chart[j]\n except KeyError:\n pass\n\n\n#######################################\n\n\nt = [\n [0, 0, 0, 0],\n [0, 0, 0, 0],\n [0, 0, 0, 0],\n [0, 0, 0, 0]\n]\na = Interaction((800, 800), t, (4, 4), 130, 3)\nscreen_type = 'decide'\n\nwhile True:\n el = pygame.event.get()\n\n for end_event in el:\n if end_event.type == pygame.QUIT:\n print('program ended')\n sys.exit()\n a.ShowScreen('white')\n\n decision = 0\n if screen_type == 'decide':\n a.ShowDecisionPage('black', (a.screen_size[0] / 2, a.screen_size[1] / 2))\n decision = a.IsEvent('number', (a.screen_size[0] / 2, a.screen_size[1] / 2), el)\n if decision is not None:\n print(decision)\n if decision != -1:\n if decision == 2:\n a.table = [\n [0, 0],\n [0, 0]\n ]\n a.table_size = (2, 2)\n if decision == 3:\n a.table = [\n [0, 0],\n [0, 0],\n [0, 0],\n [0, 0]\n ]\n a.table_size = (4, 2)\n if decision == 4:\n a.table = [\n [0, 0, 0, 0],\n [0, 0, 0, 0],\n [0, 0, 0, 0],\n [0, 0, 0, 0]\n ]\n a.table_size = (4, 4)\n if decision == 5:\n a.table = [\n [\n [0, 0, 0, 0],\n [0, 0, 0, 0],\n [0, 0, 0, 0],\n [0, 0, 0, 0]\n ],\n [\n [0, 0, 0, 0],\n [0, 0, 0, 0],\n [0, 0, 0, 0],\n [0, 0, 0, 0]\n ]\n ]\n a.table_size = (4, 4)\n screen_type = 'done'\n elif screen_type == 'done':\n a.BackButton()\n input_done = a.AnswerButton(el)\n if input_done:\n screen_type = 'answer'\n continue\n\n stp = (a.screen_size[0] / 2 - a.table_size[1] * a.cell_size / 2.0,\n a.screen_size[1] / 2 - a.table_size[0] * a.cell_size / 2.0 - a.cell_size / 2)\n\n for back_event in el:\n if back_event.type == pygame.MOUSEBUTTONDOWN:\n (mx, my) = pygame.mouse.get_pos()\n if 30 < mx < 30 + 150 / 2 and 30 < my < 30 + 150 / 2:\n screen_type = 'decide'\n decision = None\n\n a.IsEvent('table', stp, el)\n a.ShowTable('black')\n\n elif screen_type == 'answer':\n a.BackButton()\n\n for back_event in el:\n if back_event.type == pygame.MOUSEBUTTONDOWN:\n (mx, my) = pygame.mouse.get_pos()\n if 30 < mx < 30 + 150 / 2 and 30 < my < 30 + 150 / 2:\n screen_type = 'done'\n\n if a.table_size == (2, 2):\n from sukyoung import get_2by2\n ans = get_2by2(a.table)\n qwer = 0\n for asdf in ans:\n a.ShowText(asdf, 50, 'black', (100, 100 + qwer * 40))\n qwer += 1\n print(ans)\n continue\n\n mt = []\n\n for i in range(a.table_size[0]):\n for j in range(a.table_size[1]):\n if a.table[i][j] == 1:\n if i == 0 and j == 0:\n mt.append(0)\n if i == 0 and j == 1:\n mt.append(4)\n if i == 0 and j == 2:\n mt.append(12)\n if i == 0 and j == 3:\n mt.append(8)\n if i == 1 and j == 0:\n mt.append(1)\n if i == 1 and j == 1:\n mt.append(5)\n if i == 1 and j == 2:\n mt.append(13)\n if i == 1 and j == 3:\n mt.append(9)\n if i == 2 and j == 0:\n mt.append(3)\n if i == 2 and j == 1:\n mt.append(7)\n if i == 2 and j == 2:\n mt.append(15)\n if i == 2 and j == 3:\n mt.append(11)\n if i == 3 and j == 0:\n mt.append(2)\n if i == 3 and j == 1:\n mt.append(6)\n if i == 3 and j == 2:\n mt.append(14)\n if i == 3 and j == 3:\n mt.append(10)\n\n dc = []\n mt.sort()\n minterms = mt + dc\n minterms.sort()\n size = len(bin(minterms[-1])) - 2\n groups, all_pi = {}, set()\n\n # Primary grouping starts\n for minterm in minterms:\n try:\n groups[bin(minterm).count('1')].append(bin(minterm)[2:].zfill(size))\n except KeyError:\n groups[bin(minterm).count('1')] = [bin(minterm)[2:].zfill(size)]\n # Primary grouping ends\n\n # Primary group printing starts\n # print(\"\\n\\n\\n\\nGroup No.\\tMinterms\\tBinary of Minterms\\n%s\" % ('=' * 50))\n # for i in sorted(groups.keys()):\n # print(\"%5d:\" % i) # Prints group number\n # for j in groups[i]:\n # print(\"\\t\\t %-20d%s\" % (int(j, 2), j)) # Prints minterm and its binary representation\n # print('-' * 50)\n # Primary group printing ends\n\n # Process for creating tables and finding prime implicants starts\n while True:\n tmp = groups.copy()\n groups, m, marked, should_stop = {}, 0, set(), True\n l = sorted(list(tmp.keys()))\n for i in range(len(l) - 1):\n for j in tmp[l[i]]: # Loop which iterates through current group elements\n for k in tmp[l[i + 1]]: # Loop which iterates through next group elements\n res = compare(j, k) # Compare the minterms\n if res[0]: # If the minterms differ by 1 bit only\n try:\n groups[m].append(j[:res[1]] + '-' + j[res[1] + 1:]) if j[:res[1]] + '-' + j[res[\n 1] + 1:] not in \\\n groups[\n m] else None # Put a '-' in the changing bit and add it to corresponding group\n except KeyError:\n groups[m] = [j[:res[1]] + '-' + j[res[\n 1] + 1:]] # If the group doesn't exist, create the group at first and then put a '-' in the changing bit and add it to the newly created group\n should_stop = False\n marked.add(j) # Mark element j\n marked.add(k) # Mark element k\n m += 1\n local_unmarked = set(flatten(tmp)).difference(marked) # Unmarked elements of each table\n all_pi = all_pi.union(local_unmarked) # Adding Prime Implicants to global list\n # print(\"Unmarked elements(Prime Implicants) of this table:\", None if len(local_unmarked) == 0 else ', '.join(\n # local_unmarked)) # Printing Prime Implicants of current table\n if should_stop: # If the minterms cannot be combined further\n # print('--==-=-=-=-=-=123123123123123123123')\n print(all_pi)\n\n # i = 0\n # for answers in all_pi:\n # a.ShowText(answers, 50, 'black', (100, 150 + 50 * i))\n # i += 1\n\n # print('329875904238720398534523452849357092435')\n\n # print(\"\\n\\nAll Prime Implicants: \",\n # None if len(all_pi) == 0 else ', '.join(all_pi)) # Print all prime implicants\n break\n # Printing of all the next groups starts\n # print(\"\\n\\n\\n\\nGroup No.\\tMinterms\\tBinary of Minterms\\n%s\" % ('=' * 50))\n # for i in sorted(groups.keys()):\n # print(\"%5d:\" % i) # Prints group number\n # for j in groups[i]:\n # print(\n # \"\\t\\t%-24s%s\" % (','.join(findminterms(j)), j)) # Prints minterms and its binary representation\n # print('-' * 50)\n # Printing of all the next groups ends\n # Process for creating tables and finding prime implicants ends\n\n sz = len(str(mt[-1])) # The number of digits of the largest minterm\n chart = {}\n # print('\\n\\n\\nPrime Implicants chart:\\n\\n Minterms |%s\\n%s' % (\n # ' '.join((' ' * (sz - len(str(i)))) + str(i) for i in mt), '=' * (len(mt) * (sz + 1) + 16)))\n for i in all_pi:\n merged_minterms, y = findminterms(i), 0\n # print(\"%-16s|\" % ','.join(merged_minterms), end='')\n for j in refine(merged_minterms, dc):\n x = mt.index(int(j)) * (sz + 1) # The position where we should put 'X'\n # print(' ' * abs(x - y) + ' ' * (sz - 1) + 'X', end='')\n y = x + sz\n try:\n chart[j].append(i) if i not in chart[j] else None # Add minterm in chart\n except KeyError:\n chart[j] = [i]\n # print('\\n' + '-' * (len(mt) * (sz + 1) + 16))\n # Printing and processing of Prime Implicant chart ends\n\n EPI = findEPI(chart) # Finding essential prime implicants\n print(\"\\nEssential Prime Implicants: \" + ', '.join(str(i) for i in EPI))\n removeTerms(chart, EPI) # Remove EPI related columns from chart\n\n if (len(chart) == 0): # If no minterms remain after removing EPI related columns\n final_result = [findVariables(i) for i in EPI] # Final result with only EPIs\n else: # Else follow Petrick's method for further simplification\n P = [[findVariables(j) for j in chart[i]] for i in chart]\n while len(P) > 1: # Keep multiplying until we get the SOP form of P\n P[1] = multiply(P[0], P[1])\n P.pop(0)\n final_result = [min(P[0], key=len)] # Choosing the term with minimum variables from P\n final_result.extend(findVariables(i) for i in EPI) # Adding the EPIs to final solution\n print('\\n\\nSolution: F = ' + ' + '.join(''.join(i) for i in final_result))\n final_answer = 'Solution:' + ' + '.join(''.join(i) for i in final_result)\n a.ShowText(final_answer, 50, 'black', (100, 100))\n\n pygame.display.flip()\n\n\n\n###############################\n\n\n\n","repo_name":"DeokyongKim/APTP2022","sub_path":"LAST_RESORT.py","file_name":"LAST_RESORT.py","file_ext":"py","file_size_in_byte":13608,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"4727097831","text":"import requests\r\nimport re\r\nfrom bs4 import BeautifulSoup\r\n\r\nheaders = {'User-Agent' : 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/62.0.3202.94 Safari/537.36'}\r\n\r\nbase_url = 'https://www.qiushibaike.com/8hr/page/'\r\n\r\nfor num in range(1,5):\r\n\tprint('第{}页'.format(num))\r\n\r\n\tr = requests.get(base_url + str(num), headers = headers)\r\n\tcontent = r.text\r\n\tsoup = BeautifulSoup(content, 'lxml') \r\n\r\n\tdivs = soup.find_all(name = 'div' ,attrs={\"class\" : re.compile(r\"article block untagged mb15 typs_[A-z]{0,4}\")});\r\n\r\n\tfor div in divs:\r\n\t\tif div.find_all(class_ = 'thumb'):\r\n\t\t\tcontinue\r\n\t\tjoke = div.span.get_text()\r\n\t\tprint(joke)\r\n\t\tprint('--------------------')\r\n\t\t","repo_name":"JieWHuang/Python_Crawler_Learning-documents","sub_path":"01.py","file_name":"01.py","file_ext":"py","file_size_in_byte":704,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"8652579865","text":"from sys import stdin\nstdin = open(\"input5.txt\", \"r\")\n\ninput = stdin.readline\nd = [(1, 0), (-1, 0), (0, 1), (0, -1)]\nd2 = [(-1, -1), (1, 1), (-1, 1), (1, -1), (-1, 0), (1, 0), (0, -1), (0, 1)]\n\n\ndef solve():\n N, M = map(int, input().split())\n board = []\n virus = []\n safeArea = []\n\n # 바이러스(2) 이면 바이러스에, 0이면 saftArea에 추가\n for i in range(N):\n board.append(list(map(int, input().split())))\n for j in range(len(board[i])):\n if board[i][j] == 2:\n virus.append((i, j))\n elif board[i][j] == 0:\n safeArea.append((i, j))\n \n # 주변에 벽에 있는지 탐색\n def nearWall(_x, _y, _board):\n l, r, u, d = False, False, False, False\n # 입력된 _x, _y에 대해 8방향 탐색\n for dx, dy in d2:\n x, y = _x + dx, _y + dy\n # up True\n if x < 0 or (x < _x and 0 <= y < M and _board[x][y] == 1):\n u = True\n # down True\n elif x >= N or (x > _x and 0 <= y < M and _board[x][y] == 1):\n d = True\n # left True\n if y < 0 or (y < _y and 0 <= x < N and _board[x][y] == 1):\n l = True\n # right True\n elif y >= M or (y > _y and 0 <= x < N and _board[x][y] == 1):\n r = True\n # 만약 4면이 다 True면 True반환\n if (l and r) or (u and d):\n return True\n # 그게아니면 False반환\n return False\n\n \n def countSafeCell(a, b, c):\n # board 카피 & 3곳 벽세우기\n _board = [[*board[i]] for i in range(len(board))]\n _board[a[0]][a[1]] = 1\n _board[b[0]][b[1]] = 1\n _board[c[0]][c[1]] = 1\n\n countSafeArea = len(safeArea) - 3\n if (nearWall(*a, _board) and nearWall(*b, _board) and nearWall(*c, _board)):\n \n # 바이러스 확산\n q = [*virus]\n idx = 0\n while idx != len(q):\n _x, _y = q[idx]\n idx += 1\n for dx, dy in d:\n x, y = _x + dx, _y + dy\n if 0 <= x < N and 0 <= y < M and _board[x][y] == 0:\n q.append((x, y))\n _board[x][y] = 2\n countSafeArea -= 1\n return countSafeArea\n\n return 0\n\n # 벽을 세웠을 때 안전 영역의 최댓값 반환\n def buildWall():\n _ans = 0\n # 안전한 곳 저장해둔 리스트에서 3개 뽑기\n for i in range(len(safeArea)):\n for j in range(i + 1, len(safeArea)):\n for k in range(j + 1, len(safeArea)):\n _ans = max(_ans, countSafeCell(safeArea[i], safeArea[j], safeArea[k]))\n return _ans\n\n return buildWall()\n\n\nprint(solve())","repo_name":"TValgoStudy/algo_study","sub_path":"쌔피맨조/다은/BOJ14502/BOJ14502_ranker.py","file_name":"BOJ14502_ranker.py","file_ext":"py","file_size_in_byte":2858,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"50"} +{"seq_id":"72148476314","text":"# 1. Write a Python function to find the maximum of three numbers. \n\n\n# positional argument\n\n\n# def max_of_3(a,b,c) :\n\n# if(a>b and a>c) :\n# return a\n# elif(b>a and b>c):\n# return b\n# else:\n# return c\n \n# ans=max_of_3(10,30,20)\n# print(ans)\n\n\n# Default argument\n\n# def max_of_3(a=0,b=0,c=0) :\n\n# if(a>b and a>c) :\n# return a\n# elif(b>a and b>c):\n# return b\n# else:\n# return c\n \n# ans=max_of_3(10,20)\n# print(ans)\n\n# keyword argument\n\ndef max_of_3(a=0,b=0,c=0) :\n\n if(a>b and a>c) :\n return a\n elif(b>a and b>c):\n return b3\n else:\n return c\n \nans=max_of_3(c=10,a=20,b=6)\nprint(ans)\n\n\n","repo_name":"mayuri0610/python","sub_path":"PYTHON/Python Code/1-3-2023/pro1.py","file_name":"pro1.py","file_ext":"py","file_size_in_byte":694,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"19988121580","text":"\nimport subprocess\nimport json\nimport time\n\nduration = 100 # seconds\nsleep_time = 0.4 # seconds\n\nmeas_duration = 0\nmeas_list = []\nt0 = time.time()\nwhile meas_duration < duration:\n t = time.time_ns()\n res = subprocess.check_output([\"curl\",\"-s\", \"http://10.42.3.1:50000\"])\n res = res.decode('utf-8').strip()\n print(res)\n res = json.loads(res)\n res['timestamp'] = t\n meas_list.append(res)\n time.sleep(sleep_time)\n meas_duration = time.time()-t0\n\nprint(meas_duration)\n\nfor elem in meas_list:\n elem['RSRP'] = elem.pop('Signal Strength')\n elem['RSRQ'] = elem.pop('Signal Quality')\n new_RSRP = float(elem['RSRP'].split()[0])\n elem['RSRP'] = new_RSRP\n new_RSRQ = float(elem['RSRQ'].split()[0])\n elem['RSRQ'] = new_RSRQ\n\nwith open(\"/tmp/results/ntw_meas_data.json\", \"w\") as outfile:\n json.dump(meas_list, outfile)\n\n\n\n \n\n","repo_name":"nflores99/measurements-service","sub_path":"ntw-meas/ntw-meas.py","file_name":"ntw-meas.py","file_ext":"py","file_size_in_byte":862,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"9735407078","text":"from weaviate import Client\n\nimport weaviate\nimport json\nimport sys\n\ndef initializeClient(weaviateUrl: str) -> Client:\n return Client(weaviateUrl)\n\n\ndef createSchemas(client: Client, schemaPath: str):\n with open(schemaPath, \"r\") as schemaFile:\n schemaClasses = json.load(schemaFile)\n schemaClassNames = (schemaClass[\"class\"] for schemaClass in schemaClasses[\"classes\"])\n schemas = client.schema.get()\n existingSchemaClassNames = (schemaClass[\"class\"] for schemaClass in schemas[\"classes\"])\n\n duplicateExists = False\n\n for existingClass in schemas[\"classes\"]:\n for classToCreate in schemaClasses[\"classes\"]:\n if existingClass[\"class\"] == classToCreate[\"class\"]:\n duplicateExists = True\n break\n\n if schemas[\"classes\"] is not None and not duplicateExists:\n print(\"Creating\", \",\".join(schemaClassNames), \"schema(s)\")\n client.schema.create(schemaPath)\n else:\n print(\"Trying to create classes:\", \",\".join(schemaClassNames))\n print(\"These classes already exist:\", \",\".join(existingSchemaClassNames))\n print(\"Skipped creating any new schema\")\n\n\ntry:\n weaviateServerAddress = sys.argv[1].strip()\nexcept IndexError:\n weaviateServerAddress = None\n\ntry:\n weaviateSchema = sys.argv[2].strip()\nexcept IndexError:\n weaviateSchema = None\n\n\n\nif weaviateServerAddress is None or weaviateServerAddress == \"\" or weaviateSchema is None or weaviateSchema == \"\":\n print(\"Missing argument. Usage:\")\n print(\"python3 weaviate-setup.py \")\n exit(0)\n\n\nprint(f\"Using weaviate server: {weaviateServerAddress}\")\nprint(f\"Using weaviate schema from: {weaviateSchema}\")\n\nclient = initializeClient(weaviateUrl = weaviateServerAddress)\n\ncreateSchemas(client, schemaPath = weaviateSchema)\n\n","repo_name":"arconsis/vector-search","sub_path":"ImageSearchCoreService/assets/weaviate-setup.py","file_name":"weaviate-setup.py","file_ext":"py","file_size_in_byte":1864,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"36520003393","text":"from flask import Flask, request\nimport sqlite3\nfrom dotenv import load_dotenv\nimport requests\nfrom twilio.twiml.messaging_response import MessagingResponse\n\napp = Flask(__name__)\n\n\ndef get_db_connection():\n conn = sqlite3.connect('database.db')\n conn.row_factory = sqlite3.Row\n return conn\n\n@app.route('/bot', methods=['POST'])\ndef bot():\n incoming_msg = request.values.get('Body', '').lower()\n phone = request.values.get('WaId')\n name = request.values.get('ProfileName')\n resp = MessagingResponse()\n msg = resp.message()\n responded = False\n if 'menu' in incoming_msg:\n conn = get_db_connection()\n menu_items = conn.execute('select id, item_name, image_url from menu').fetchall()\n message = \"Hi \"+name+\", todays menu \\n\"\n for item in menu_items:\n message = message +str(item['id'])+\" \"+ item['item_name']+\" \\n\"\n message = message + \" Please select your choice\" \n msg.body(message)\n msg.media(\"https://i.imgur.com/jG0KQx5.png\")\n conn.close()\n responded = True\n if 'selection' in incoming_msg:\n conn = get_db_connection()\n menu_items = conn.execute('select menu.item_name, menu.image_url, user.id,user_selection.menu_id from user, user_selection, menu where user.phone='+phone+' and user_selection.user_id=user.id and menu.id=user_selection.menu_id').fetchall()\n if menu_items:\n message = \"Your selection is \"+menu_items[0]['item_name']\n menu_image = menu_items[0]['image_url']\n msg.body(message)\n if menu_image:\n msg.media(menu_image)\n else:\n msg.body(\"Please choose a dish from the menu.\")\n msg.media(\"https://i.imgur.com/jG0KQx5.png\")\n conn.close()\n responded = True\n\n if 'choose' in incoming_msg:\n conn = get_db_connection()\n item_id = incoming_msg.split(' ')[1]\n conn.execute('INSERT OR REPLACE INTO user(phone) values (?)', [int(phone)])\n conn.execute('INSERT OR REPLACE INTO user_selection (user_id, menu_id) select id as user_id, '+item_id+' as menu_id from user where phone='+phone+'')\n menu_items = conn.execute('select menu.item_name, menu.image_url, user.id,user_selection.menu_id from user, user_selection, menu where phone='+phone+' and user_selection.user_id=user.id and menu.id=user_selection.menu_id').fetchall()\n message = 'Congrats '+name+\"!!\\n You have selected \\n\"+menu_items[0]['item_name']+\" for your meal.\"\n menu_image = menu_items[0]['image_url']\n msg.body(message)\n if menu_image:\n msg.media(menu_image)\n conn.commit()\n conn.close()\n responded = True\n\n if not responded:\n msg.body('Hi '+name+' please choose \\n 1. menu to see menu \\n 2. selection to see your selection \\n 3. choose to select item.')\n return str(resp)\n\n\nif __name__ == '__main__':\n app.run(host='0.0.0.0')\n","repo_name":"vmuneeb/whatsapp-bot-demo","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2941,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"4649332739","text":"from __future__ import absolute_import\n\nimport decimal\n\nfrom django.forms.fields import DecimalField\nfrom django.utils.translation import gettext_lazy as _\n\nfrom fobi.base import FormFieldPlugin, get_theme\nfrom fobi.widgets import NumberInput\n\nfrom . import UID\nfrom .forms import DecimalInputForm\n\n__title__ = \"fobi.contrib.plugins.form_elements.fields.decimal.base\"\n__author__ = \"Artur Barseghyan \"\n__copyright__ = \"2014-2019 Artur Barseghyan\"\n__license__ = \"GPL 2.0/LGPL 2.1\"\n__all__ = (\"DecimalInputPlugin\",)\n\ntheme = get_theme(request=None, as_instance=True)\n\n\nclass DecimalInputPlugin(FormFieldPlugin):\n \"\"\"Decimal input plugin.\"\"\"\n\n uid = UID\n name = _(\"Decimal\")\n group = _(\"Fields\")\n form = DecimalInputForm\n\n def get_form_field_instances(\n self, request=None, form_entry=None, form_element_entries=None, **kwargs\n ):\n \"\"\"Get form field instances.\"\"\"\n widget_attrs = {\n \"class\": theme.form_element_html_class,\n \"type\": \"number\",\n \"placeholder\": self.data.placeholder,\n }\n field_kwargs = {\n \"label\": self.data.label,\n \"help_text\": self.data.help_text,\n \"required\": self.data.required,\n }\n\n if self.data.initial is not None:\n data_initial = decimal.Decimal(str(self.data.initial))\n field_kwargs.update({\"initial\": data_initial})\n\n if self.data.max_value is not None:\n data_max_value = decimal.Decimal(str(self.data.max_value))\n field_kwargs[\"max_value\"] = data_max_value\n widget_attrs[\"max\"] = data_max_value\n\n if self.data.min_value is not None:\n data_min_value = decimal.Decimal(str(self.data.min_value))\n field_kwargs[\"min_value\"] = data_min_value\n widget_attrs[\"min\"] = data_min_value\n\n if self.data.max_digits is not None:\n data_max_digits = int(self.data.max_digits)\n field_kwargs[\"max_digits\"] = data_max_digits\n\n if self.data.decimal_places is not None:\n data_decimal_places = int(self.data.decimal_places)\n field_kwargs[\"decimal_places\"] = data_decimal_places\n\n field_kwargs[\"widget\"] = NumberInput(attrs=widget_attrs)\n\n return [(self.data.name, DecimalField, field_kwargs)]\n","repo_name":"barseghyanartur/django-fobi","sub_path":"src/fobi/contrib/plugins/form_elements/fields/decimal/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":2329,"program_lang":"python","lang":"en","doc_type":"code","stars":474,"dataset":"github-code","pt":"50"} +{"seq_id":"7345866709","text":"with open('day1_part1_input.txt') as file:\n lines = [line for line in file]\n\n total = 0\n for line in lines:\n\n first_number = None\n last_number = None\n for character in line:\n\n if character.isnumeric():\n if first_number == None:\n first_number = character\n last_number = character\n else:\n last_number = character\n \n number_for_line = int(first_number + last_number)\n total += number_for_line\n\n print(total)","repo_name":"AE-nv/aedvent-code-2023","sub_path":"day 01/Matthias - Python/day1_part1.py","file_name":"day1_part1.py","file_ext":"py","file_size_in_byte":568,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"23276958539","text":"import numpy as np\nimport pandas as pd\n\n\ndef cleanNANs(data):\n # substitute nans at the bottom of data with last known value\n def cleanNANcolumn(column, index):\n assert(index < len(column))\n value = column.iloc[index]\n\n # print(column.iloc[index])\n if np.isnan(column.iloc[index]):\n if index == 0:\n column.iloc[index] = 0\n return 0\n value = cleanNANcolumn(column, index - 1)\n column.iloc[index] = value\n\n return value\n\n dataLength = len(data)\n for col in data:\n print(col)\n cleanNANcolumn(data[col], dataLength - 1)\n\n return data\n\n\ndef pairsToWeights(pairs, weights):\n pairsWeights = {}\n for i in range(len(pairs)):\n pairsWeights[pairs[i]] = weights[i]\n return pairsWeights\n\n\ndef staggeredRatios(data, chartPeriod):\n # returns ratios based on last chart period\n # so for instance if the latest date is 140000 and\n # chart_period is 14400,\n # all data from 140000 to 125600 will be ratioed by the data\n # in 125600\n latestDate = data[\"date\"].iloc[-1]\n earliestDate = data[\"date\"].iloc[0]\n ratios = pd.DataFrame()\n while (latestDate - earliestDate) >= chartPeriod:\n currentDate = latestDate - chartPeriod\n dataSubset = data.loc[(currentDate < data[\"date\"]) & (data[\"date\"] <= latestDate)]\n dates = dataSubset.ix[:, [\"date\"]]\n dataSub = dataSubset.ix[:, dataSubset.columns != \"date\"]\n dataBase = data.loc[data[\"date\"] == currentDate].ix[:, dataSubset.columns != \"date\"].iloc[0,:]\n # print(dataSub)\n # print(type(dataBase))\n ratiosSubset = dataSub.div(dataBase)\n ratios = ratios.append(pd.concat([dates, ratiosSubset], axis=1), ignore_index=True)\n latestDate = currentDate\n ratios = ratios.sort_values(\"date\")\n return ratios\n\n\n\nif __name__ == \"__main__\":\n data = pd.read_csv(\"./poloTestData.csv\")\n LATEST_DATE = data[\"date\"].iloc[-1]\n\n # removes index and data column and removes NANs\n data = cleanNANs(data)\n print(staggeredRatios(data, 14400))\n data = data.drop(data.columns[[0, 1]], 1)\n\n","repo_name":"ongteckwu/pamr-polo-bot","sub_path":"utilities.py","file_name":"utilities.py","file_ext":"py","file_size_in_byte":2150,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"4736300320","text":"from collections import namedtuple\n\nimport bitstruct\n\nfrom .errors import Error\n\nFrameId = namedtuple('FrameId',\n [\n 'priority',\n 'reserved',\n 'data_page',\n 'pdu_format',\n 'pdu_specific',\n 'source_address',\n ])\n\n\nPGN = namedtuple('PGN',\n [\n 'reserved',\n 'data_page',\n 'pdu_format',\n 'pdu_specific'\n ])\n\n\ndef is_pdu_format_1(pdu_format):\n return (pdu_format < 240)\n\n\ndef frame_id_pack(priority,\n reserved,\n data_page,\n pdu_format,\n pdu_specific,\n source_address):\n \"\"\"Pack given values as a frame id and return it as an integer.\n\n \"\"\"\n\n try:\n packed = bitstruct.pack('u3u1u1u8u8u8',\n priority,\n reserved,\n data_page,\n pdu_format,\n pdu_specific,\n source_address)\n except bitstruct.Error:\n if priority > 7:\n raise Error(f'Expected priority 0..7, but got {priority}.') from None\n elif reserved > 1:\n raise Error(f'Expected reserved 0..1, but got {reserved}.') from None\n elif data_page > 1:\n raise Error(f'Expected data page 0..1, but got {data_page}.') from None\n elif pdu_format > 255:\n raise Error(f'Expected PDU format 0..255, but got {pdu_format}.') from None\n elif pdu_specific > 255:\n raise Error(f'Expected PDU specific 0..255, but got {pdu_specific}.') from None\n elif source_address > 255:\n raise Error(f'Expected source address 0..255, but got {source_address}.') from None\n else:\n raise Error('Internal error.') from None\n\n return bitstruct.unpack('u29', packed)[0]\n\n\ndef frame_id_unpack(frame_id):\n \"\"\"Unpack given frame id and return a tuple of priority, reserved,\n data page, PDU format, PDU specific and source address.\n\n \"\"\"\n\n try:\n packed = bitstruct.pack('u29', frame_id)\n except bitstruct.Error:\n raise Error(f'Expected a frame id 0..0x1fffffff, but got {hex(frame_id)}.') from None\n\n return FrameId(*bitstruct.unpack('u3u1u1u8u8u8', packed))\n\n\ndef pgn_pack(reserved, data_page, pdu_format, pdu_specific=0):\n \"\"\"Pack given values as a parameter group number (PGN) and return it\n as an integer.\n\n \"\"\"\n\n if pdu_format < 240 and pdu_specific != 0:\n raise Error(\n 'Expected PDU specific 0 when PDU format is 0..239, but got {}.'.format(\n pdu_specific))\n\n try:\n packed = bitstruct.pack('u1u1u8u8',\n reserved,\n data_page,\n pdu_format,\n pdu_specific)\n except bitstruct.Error:\n if reserved > 1:\n raise Error(f'Expected reserved 0..1, but got {reserved}.') from None\n elif data_page > 1:\n raise Error(f'Expected data page 0..1, but got {data_page}.') from None\n elif pdu_format > 255:\n raise Error(f'Expected PDU format 0..255, but got {pdu_format}.') from None\n elif pdu_specific > 255:\n raise Error(f'Expected PDU specific 0..255, but got {pdu_specific}.') from None\n else:\n raise Error('Internal error.') from None\n\n return bitstruct.unpack('u18', packed)[0]\n\n\ndef pgn_unpack(pgn):\n \"\"\"Unpack given parameter group number (PGN) and return a tuple of\n Reserved, Data Page, PDU Format and PDU Specific.\n\n \"\"\"\n\n try:\n packed = bitstruct.pack('u18', pgn)\n except bitstruct.Error:\n raise Error(f'Expected a parameter group number 0..0x3ffff, but got {hex(pgn)}.') from None\n\n return PGN(*bitstruct.unpack('u1u1u8u8', packed))\n\n\ndef pgn_from_frame_id(frame_id):\n \"\"\"Get the parameter group number (PGN) from given frame id.\n\n \"\"\"\n\n unpacked = frame_id_unpack(frame_id)\n\n if unpacked.pdu_format < 240:\n pdu_specific = 0\n else:\n pdu_specific = unpacked.pdu_specific\n\n return pgn_pack(unpacked.reserved,\n unpacked.data_page,\n unpacked.pdu_format,\n pdu_specific)\n","repo_name":"cantools/cantools","sub_path":"src/cantools/j1939.py","file_name":"j1939.py","file_ext":"py","file_size_in_byte":4509,"program_lang":"python","lang":"en","doc_type":"code","stars":1613,"dataset":"github-code","pt":"50"} +{"seq_id":"34656519424","text":"_author_ = 'jake'\n_project_ = 'leetcode'\n\n# https://leetcode.com/problems/teemo-attacking/\n# In LOL world, there is a hero called Teemo and his attacking can make his enemy Ashe be in poisoned condition.\n# Now, given the Teemo's attacking ascending time series towards Ashe and the poisoning time duration per Teemo's\n# attacking, you need to output the total time that Ashe is in poisoned condition.\n# You may assume that Teemo attacks at the very beginning of a specific time point, and makes Ashe be in poisoned condition immediately.\n\n# For each time step, add min of the step and duration.\n# Time - O(n)\n# Space - O(1)\n\nclass Solution(object):\n def findPoisonedDuration(self, timeSeries, duration):\n \"\"\"\n :type timeSeries: List[int]\n :type duration: int\n :rtype: int\n \"\"\"\n poisoned = 0\n timeSeries.append(float(\"inf\"))\n for i in range(1, len(timeSeries)):\n poisoned += min(duration, timeSeries[i] - timeSeries[i- 1])\n return poisoned","repo_name":"jakehoare/leetcode","sub_path":"python_1_to_1000/495_Teemo_Attacking.py","file_name":"495_Teemo_Attacking.py","file_ext":"py","file_size_in_byte":1015,"program_lang":"python","lang":"en","doc_type":"code","stars":49,"dataset":"github-code","pt":"50"} +{"seq_id":"9606521225","text":"from tic_tac_toe.models.board import Board\n\nclass TicTacToe:\n\n def __init__(self, board):\n self.board = board\n self.current_piece = Board.Piece.X\n\n def start_game(self):\n while not self.board.has_end_state():\n self.board.add_piece(self.get_position(), self.current_piece)\n self.print_board()\n self.swap_piece()\n\n if self.board.has_winning_state():\n self.swap_piece()\n print(f'Player with piece {self.current_piece} won')\n else:\n print('Tie game')\n\n def print_board(self):\n for row in self.board.state:\n print(f'{row!r}')\n\n def get_position(self):\n available_positions = self.board.get_available_positions()\n str_position = input(f'Available positions {available_positions!r}\\nPlease enter position: ')\n position = tuple(map(int, str_position.split(\", \")))\n\n while position not in available_positions:\n str_position = input(f'Available positions {available_positions!r}\\nPlease enter position: ')\n position = tuple(map(int, str_position.split(\", \")))\n\n return position\n\n def swap_piece(self):\n if self.current_piece is Board.Piece.X:\n self.current_piece = Board.Piece.O\n else:\n self.current_piece = Board.Piece.X\n","repo_name":"thomasjinlo/TDD","sub_path":"tic-tac-toe/tic_tac_toe/tic_tac_toe.py","file_name":"tic_tac_toe.py","file_ext":"py","file_size_in_byte":1339,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"11806682951","text":"from flask import Flask, render_template, request, session, redirect, url_for, flash\n\napp = Flask(__name__)\napp.config.from_object(\"config\")\n\n\nfrom controllers import user as userController\nfrom controllers import post as postController\n\n@app.route(\"/login\")\ndef login():\n return render_template(\"login.html\")\n\n\n@app.route(\"/loginSubmit\", methods=[\"POST\"])\ndef loginSubmit():\n isLoginSuccessful, message = userController.login(request.form[\"username\"], request.form[\"password\"])\n flash(message)\n if isLoginSuccessful:\n return redirect(url_for(\"home\"))\n else:\n return redirect(url_for(\"login\"))\n\n\n@app.route(\"/register\")\ndef register():\n return render_template(\"register.html\")\n\n\n@app.route(\"/registrationSubmit\", methods=[\"POST\"])\ndef registrationSubmit():\n isRegistrationSuccesful, message = userController.register(\n request.form[\"username\"],\n request.form[\"password\"],\n request.form[\"fullName\"]\n )\n flash(message)\n if isRegistrationSuccesful:\n return redirect(url_for(\"home\"))\n else:\n return redirect(url_for(\"register\"))\n\n\n@app.route(\"/logout\")\ndef logout():\n userController.logout()\n flash(\"Succesfully logged out.. !\")\n return redirect(url_for(\"login\"))\n\n\n@app.route(\"/home\")\ndef home():\n allPosts = postController.findAll()\n loggedInUserInfo = userController.getLoggedInUserInfo()\n\n return render_template(\"home.html\", posts=allPosts, loggedInUserInfo=loggedInUserInfo)\n\n@app.route(\"/newPostSubmit\", methods=[\"POST\"])\ndef newPostSubmit():\n isNewPostCreated, message = postController.add(request.form[\"content\"])\n flash(message)\n return redirect(url_for(\"home\"))\n\n\nif __name__ == \"__main__\":\n app.run()\n","repo_name":"nitish-kr/twitterClone","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1721,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"23080451356","text":"import sys\nimport os\nimport yaml\nimport argparse\nimport subprocess\n\nfrom benchmark_utils import PaddleInferBenchmark\n\n\"\"\"\n{'CPU_UTILIZATION': 0.8, 'MAX_GPU_MEMORY': 0, 'GPU_UTILIZATION': '0 %', 'DAG': {'50': 670.256, '60': 670.256, '70': 670.765, '80': 671.23, '90': 687.546, '95': 687.546, '99': 687.546, 'avg': 670.755625, 'qps': 0.8, 'query_count': 8, 'succ': 1.0}, 'demo': {'midp': 669.484375, 'postp': 0.184875, 'prep': 1.001875}}\n\"\"\"\n\n\nclass LogHandler(object):\n def __init__(self):\n self.fstr = \"\"\n\n def print(self):\n print(self.fstr)\n\n def dump(self, filename):\n with open(filename, 'w') as fout:\n fout.write(self.fstr)\n\n def append(self, new_str):\n self.fstr += new_str + \"\\n\"\n\n\ndef handle_benchmark(benchmark_config, benchmark_raw, indentifier):\n model_info = {\n 'model_name': benchmark_config[\"model_name\"],\n 'precision': benchmark_config[\"precision\"],\n }\n data_info = {\n 'batch_size': benchmark_config[\"batch_size\"],\n 'shape': benchmark_config[\"input_shape\"],\n 'data_num': int(benchmark_raw[\"Total count\"]),\n }\n perf_info = {\n 'client_mode' : benchmark_raw[\"client_mode\"],\n 'server_mode' : benchmark_raw[\"server_mode\"],\n 'thread_num' : benchmark_raw[\"thread_num\"],\n 'preprocess_time_s': \"\",\n 'inference_time_ms': float(benchmark_raw[\"mean\"][0:-2]), # *** ms\n 'median(ms)': float(benchmark_raw[\"median\"][0:-2]),\n 'postprocess_time_s': \"\",\n 'total_time_s': float(benchmark_raw[\"Total cost\"][0:-1]),\n 'each_time_s': float(benchmark_raw[\"Each thread cost\"][0:-2]),\n 'inference_time_ms_80': float(benchmark_raw[\"80_percent\"][0:-2]), # *** ms\n 'inference_time_ms_90': float(benchmark_raw[\"90_percent\"][0:-2]), # *** ms\n 'inference_time_ms_99': float(benchmark_raw[\"99_percent\"][0:-2]), # *** ms\n 'qps': benchmark_raw[\"AVG_QPS\"]\n }\n resource_info = {\n 'cpu_rss_mb': \"\",\n 'cpu_vms_mb': \"\",\n 'cpu_shared_mb': \"\",\n 'cpu_dirty_mb': \"\",\n 'cpu_util': benchmark_raw[\"CPU_UTILIZATION\"],\n 'gpu_rss_mb': \"\",\n 'gpu_util': f'{float(benchmark_raw[\"GPU_UTILIZATION\"].split(\" \")[0]):.2f}',\n 'gpu_mem': benchmark_raw[\"MAX_GPU_MEMORY\"],\n 'gpu_mem_util': int(benchmark_raw[\"MAX_GPU_MEMORY\"].split(\" \")[0]) / total_memory * 100\n }\n\n server_log = PaddleInferBenchmark(\n benchmark_config, model_info, data_info, perf_info, resource_info, indentifier)\n server_log(indentifier)\n\n\ndef parse_args(): # pylint: disable=doc-string-missing\n parser = argparse.ArgumentParser(\"serve\")\n parser.add_argument(\n \"--benchmark_cfg\",\n type=str,\n required=True,\n help=\"benchmark config yaml. including general info, model info, data info, conf info\"\n )\n parser.add_argument(\n \"--benchmark_log\",\n type=str,\n required=True,\n help=\"benchmark log, generated by a web service or pipeline.\")\n parser.add_argument(\n \"--output\",\n type=str,\n default=\"std_benchmark.log\",\n help=\"the output filename, default std_benchmark.log\")\n return parser.parse_args()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n benchmark_cfg_filename = args.benchmark_cfg\n sub = subprocess.Popen(\"nvidia-smi --id=0 --query-gpu=memory.total --format=csv,noheader\", encoding=\"utf-8\",\n shell=True, stdout=subprocess.PIPE)\n memory_str = sub.stdout.read() or \"16160 Mib\"\n total_memory = int(memory_str.split(' ')[0])\n f = open(benchmark_cfg_filename, 'r')\n benchmark_config = yaml.load(f, yaml.FullLoader)\n f.close()\n benchmark_log_filename = args.benchmark_log\n f = open(benchmark_log_filename, 'r')\n lines = f.readlines()\n lines.append(\"\\n\")\n line_no = 0\n while line_no < len(lines):\n if len(lines[line_no]) > 5 and lines[line_no].startswith(\"#---\"):\n iden = lines[line_no][5: -5]\n line_no += 1\n line_count = lines[line_no:].index(\"\\n\")\n sub_log = lines[line_no: line_no + line_count]\n sub_dict = yaml.safe_load(\"\".join(sub_log))\n client_mode = iden.split(\" \")[8]\n server_mode = iden.split(\" \")[10]\n sub_dict[\"client_mode\"] = client_mode\n sub_dict[\"server_mode\"] = server_mode\n handle_benchmark(benchmark_config, sub_dict, iden)\n line_no += line_count\n else:\n line_no += 1\n","repo_name":"ZhangYulongg/ServingAPI","sub_path":"benchmark/parse_profile.py","file_name":"parse_profile.py","file_ext":"py","file_size_in_byte":4504,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"19926108935","text":"import time\n\nimport requests\nfrom bs4 import BeautifulSoup\n\n\n## Constants.\n\n\nLAST_YEAR = 2022\nURL_ROOT = \"https://www.baseball-reference.com\"\nBOLD_START = \"\\033[1m\"\nBOLD_END = \"\\033[0m\"\n\n\n## Main.\n\n\ndef main():\n matchups = get_todays_matchups()\n print(f\"\\n\\t{len(matchups)} matchups\\n\")\n\n team_batting_1st_inning_runs_per_inning = (\n get_1st_inning_runs_per_inning_for_batting_teams(LAST_YEAR)\n )\n\n for matchup_idx, matchup in enumerate(matchups):\n team_abbrev = matchup[\"team_abbrev\"]\n pitcher_player_id = matchup[\"pitcher_player_id\"]\n pitcher_name = matchup[\"pitcher_name\"]\n\n team_batting_runs_per_inning = team_batting_1st_inning_runs_per_inning[\n team_abbrev\n ]\n pitcher_runs_per_inning = get_1st_inning_runs_per_inning_for_pitcher(\n pitcher_player_id, LAST_YEAR\n )\n\n print(\n f\"\\t{matchup_idx + 1}.\"\n f\" {team_abbrev} batting\"\n f\" ({BOLD_START}{display_number(team_batting_runs_per_inning)}{BOLD_END} runs per inning)\"\n f\" vs.\"\n f\" {pitcher_name} pitching\"\n f\" ({BOLD_START}{display_number(pitcher_runs_per_inning)}{BOLD_END} runs per inning)\"\n )\n\n print(\"\\nPress Enter to exit...\")\n\n input()\n\n\n## Helpers.\n\n\nlast_get = -1\n\n\ndef rate_limited_get(*args, **kwargs):\n \"\"\"\n This function is for abiding by the website's rules and avoiding getting blocked\n (https://www.sports-reference.com/bot-traffic.html).\n \"\"\"\n global last_get\n while time.time() - last_get < 3.1:\n time.sleep(0.1)\n\n results = requests.get(*args, **kwargs)\n\n last_get = time.time()\n\n return results\n\n\ndef display_number(x):\n if x is None:\n return \"N/A\"\n else:\n return \"{:.2f}\".format(x)\n\n\ndef get_todays_matchups():\n game_previews_url = f\"{URL_ROOT}/previews\"\n fetch_res = rate_limited_get(game_previews_url)\n if fetch_res.status_code != 200:\n print(f\"Unable to get today's matchups at {game_previews_url}\")\n return\n\n raw_html = fetch_res.content.decode().replace(\"4 bits, flags-->4 bits)\r\n # if dup = 0 then qos = 0\r\n # calculate remaining length\r\n packet_type = {\r\n # \"Reserved\": '0000', # we use only 4 bits, so the rest 4\r\n # are the length of the packet... cannot be null\r\n \"CONNECT\": '00010000',\r\n \"CONNACK\": '00100000',\r\n \"PUBLISH_DUP0_Q0_R0\": '00110000',\r\n \"PUBLISH_DUP0_Q3_R1\": '00110111',\r\n \"PUBLISH_DUP0_Q3_R0\": '00110110',\r\n \"PUBLISH_DUP0_Q2_R1\": '00110101',\r\n \"PUBLISH_DUP0_Q2_R0\": '00110100',\r\n \"PUBLISH_DUP0_Q1_R1\": '00110011',\r\n \"PUBLISH_DUP0_Q1_R0\": '00110010',\r\n \"PUBLISH_DUP0_Q0_R1\": '00110001',\r\n # \"PUBLISH_DUP1_Q0_R0\": '00111000',\r\n \"PUBLISH_DUP1_Q1_R0\": '00111010',\r\n \"PUBLISH_DUP1_Q2_R0\": '00111100',\r\n \"PUBLISH_DUP1_Q3_R0\": '00111110',\r\n # \"PUBLISH_DUP1_Q0_R1\": '00111001',\r\n \"PUBLISH_DUP1_Q1_R1\": '00111011',\r\n \"PUBLISH_DUP1_Q2_R1\": '00111101',\r\n \"PUBLISH_DUP1_Q3_R1\": '00111111',\r\n \"PUBACK\": '01000000',\r\n \"PUBREC\": '01010000',\r\n \"PUBREL\": '01100010',\r\n \"PUBCOMP\": '01110000',\r\n \"SUBSCRIBE\": '10000010',\r\n \"SUBACK\": '10010000',\r\n \"UNSUBSCRIBE\": '10100010',\r\n \"UNSUBACK\": '10110000',\r\n \"PINGREQ\": '11000000',\r\n \"PINGRESP\": '11010000',\r\n \"DISCONNECT\": '11100000'\r\n }\r\n bits_to_hex = [create_hex_from_bits(value) for value in packet_type.values()]\r\n return bits_to_hex\r\n\r\n\r\n# REFERENCES: https://www.rabbitmq.com/resources/specs/amqp0-9-1.pdf\r\ndef is_amqp(payload, current_packet, type_protocol): # type_protocol = TCP/UDP\r\n \"\"\"Decides whether the packet is amqp or not\"\"\"\r\n answer = \"no\"\r\n if 'AMQP' in str(current_packet.layers):\r\n answer = \"yes\"\r\n return answer\r\n if \"AMQP\" in payload: # initiates the connection (first amqp packet)\r\n answer = \"yes\"\r\n return answer\r\n else:\r\n if len(payload) >= 8: # Header, having a fixed size (8 byte);\r\n # frame end is always %xce\r\n end_code = \"\\\\xce\"\r\n index = payload.find(end_code)\r\n right_index = len(payload) - 5\r\n if index == right_index:\r\n answer = \"yes\"\r\n return answer\r\n # also packets from given ports\r\n src_port = current_packet[type_protocol].srcport\r\n dst_port = current_packet[type_protocol].dstport\r\n if (src_port == 5671) or (dst_port == 5671) or (src_port == 5672) or (dst_port == 5672):\r\n answer = \"yes\"\r\n return answer\r\n\r\n return answer\r\n\r\n\r\ndef create_coap_first_byte():\r\n # version is always 01 = coap version 1\r\n ver = '01'\r\n my_types = {\"confirmable\": '00', \"Non-cofirmable\": '01', \"Acknowledgement\": '10', \"Reset\": '11'}\r\n token_length = {0: '0000', 1: '0001', 2: '0010', 3: '0011', 4: '0100', 5: '0101', 6: '0110', 7: '0111',\r\n 8: '1000'}\r\n first_byte = []\r\n for protocol_type, token in product(my_types.values(), token_length.values()): # removed nested loop\r\n combination = ver + protocol_type + token\r\n hex_combination = create_hex_from_bits(combination)\r\n if hex_combination not in first_byte:\r\n # create possible combinations\r\n first_byte.append(hex_combination)\r\n return first_byte\r\n\r\n\r\ndef is_coap(payload, current_packet, type_protocol, first_byte):\r\n # first byte is the list with the possible combinations for a coap byte\r\n answer = \"no\"\r\n if 'COAP' in str(current_packet.layers):\r\n answer = \"yes\"\r\n return answer\r\n if len(payload) >= 4: # fixed header 4 bytes\r\n for byte in first_byte:\r\n if byte in payload[:7]:\r\n answer = \"yes\"\r\n return answer\r\n\r\n src_port = current_packet[type_protocol].srcport\r\n dst_port = current_packet[type_protocol].dstport\r\n if (src_port == 5683) or (dst_port == 5683):\r\n answer = \"yes\"\r\n return answer\r\n\r\n return answer\r\n\r\n\r\ndef is_mqtt(payload, list_with_codes, current_packet, type_protocol):\r\n \"\"\"decide if the packet uses mqtt or not\"\"\"\r\n answer = \"no\"\r\n if 'MQTT' in str(current_packet.layers):\r\n answer = \"yes\"\r\n return answer\r\n if \"MQTT\" in payload:\r\n answer = \"yes\"\r\n return answer\r\n\r\n for mqtt_code in list_with_codes: # e.g xe0\r\n exact_code = \"\\\\\" + mqtt_code + \"\\\\\" # e.g \\xe0\\\r\n if exact_code in payload[:7]:\r\n answer = \"yes\"\r\n return answer\r\n\r\n # also packets from given ports\r\n src_port = current_packet[type_protocol].srcport\r\n dst_port = current_packet[type_protocol].dstport\r\n if (src_port == 1883) or (dst_port == 1883) or (src_port == 8883) or (dst_port == 8883):\r\n answer = \"yes\"\r\n return answer\r\n\r\n return answer\r\n\r\n\r\ndef if_file_exists_delete_it():\r\n \"\"\"because write_all method appends on file, we need to delete it if was previously existed\r\n in order to avoid a wrong output\"\"\"\r\n if os.path.exists(\"all.csv\"):\r\n print(\"file deleted\")\r\n os.remove(\"all.csv\")\r\n\r\n\r\ndef is_malicious_dataset():\r\n \"\"\"check if the packets come from honeypot or not\"\"\"\r\n value = input(\"Are the packets from the honeypot? (yes/no answer)\")\r\n if value.lower() == \"yes\":\r\n print(\"malicious dataset\")\r\n return True\r\n else:\r\n print(\"the dataset is not malicious\")\r\n return False\r\n\r\n\r\ndef write_headers():\r\n \"\"\"will write only the headers\"\"\"\r\n if os.path.exists(\"all.csv\"):\r\n print(\"output already exists\")\r\n else:\r\n fileName = 'all.csv'\r\n with open(fileName, 'w', newline='') as csv_file:\r\n csv_file = csv.writer(csv_file, delimiter=',')\r\n csv_file.writerow(\r\n ['app_protocol', 'transport_protocol', 'layer', 'mac_src', 'mac_dst', 'src_ip', 'dst_ip',\r\n 'src_port', 'dst_port', 'pkt_size', 'is_encrypted', 'payload size', 'payload_ratio', 'previous_ratio',\r\n 'packet_time_diff', 'payload', 'p_date', 'p_time', 'flag'])\r\n\r\n\r\ndef write_all(app_protocol, type_protocol, layer, mac_src, mac_dst, src_ip, dst_ip,\r\n src_port, dst_port, pkt_size, is_encrypted, payload_size, payload_ratio, previous_ratio,\r\n packet_time_diff, payload, p_date, p_hour,flag):\r\n \"\"\"creates a file with all packets\"\"\"\r\n fileName = 'all.csv'\r\n # append mode\r\n with open(fileName, 'a', newline='') as csv_file: # automatically close the file\r\n csv_file = csv.writer(csv_file, delimiter=',')\r\n\r\n csv_file.writerow([app_protocol, type_protocol, layer, mac_src, mac_dst, src_ip, dst_ip,\r\n src_port, dst_port, pkt_size, is_encrypted, payload_size, payload_ratio, previous_ratio,\r\n packet_time_diff, payload, p_date, p_hour, flag])\r\n\r\n # References: https://stackoverflow.com/questions/4959741/python-print-mac-address-out-of-6-byte-string\r\n\r\n\r\ndef prettify(mac_string):\r\n \"\"\"convert byte format to hex format, about mac\"\"\"\r\n return ':'.join('%02x' % ord(b) for b in mac_string)\r\n\r\n\r\ndef extract_characteristics_from_packet(pkt, previous_packet_time, previous_packet_size, payload):\r\n \"\"\" this method extracts the most important characteristics of the packets, probably will be used in Ml\"\"\"\r\n # 1st vital characteristic is packet length\r\n pkt_size = len(pkt)\r\n # 2nd vital characteristic is whether the packet is encrypted\r\n is_encrypted = 0\r\n layer_level = 0\r\n searching_layers = True # e.g. Ethernet, Ip, Icmp, Raw\r\n while searching_layers:\r\n layer = pkt.layers\r\n if layer is not None:\r\n if 'SSL' in layer: # encryption protocols\r\n is_encrypted = 1 # encrypted packet\r\n else:\r\n searching_layers = False\r\n layer_level += 1 # check next layer\r\n # 3rd vital characteristic is the payload size\r\n # 4rth vital characteristic is the payload ratio\r\n payload_size = len(payload)\r\n payload_ratio = (payload_size / pkt_size) * 100\r\n # 5th vital characteristic is previous packet ratio\r\n # defaults to 0 for the first packet of the session\r\n if previous_packet_size != 0:\r\n previous_ratio = (pkt_size / previous_packet_size) * 100\r\n else:\r\n previous_ratio = 1\r\n previous_packet_size = pkt_size\r\n # 6th vital characteristic is time difference\r\n if previous_packet_time != 0:\r\n packet_time_diff = pkt.sniff_time - previous_packet_time\r\n else:\r\n packet_time_diff = 0\r\n previous_packet_time = pkt.sniff_time\r\n\r\n # 7th vital characteristic is payload content\r\n # convert to hex type\r\n payload_fix_format_type = binascii.hexlify(bytes(payload))\r\n\r\n return pkt_size, is_encrypted, payload_size, payload_ratio, previous_packet_time, \\\r\n previous_packet_size, previous_ratio, packet_time_diff, payload_fix_format_type\r\n\r\n\r\ndef find_first_layer_protocol(pkt):\r\n \"\"\"get mac address src and dst and first layer\"\"\"\r\n try:\r\n mac_src_in_bytes = str(pkt.eth.src)\r\n mac_src = prettify(mac_src_in_bytes)\r\n mac_dst_in_bytes = str(pkt.eth.dst)\r\n mac_dst = prettify(mac_dst_in_bytes)\r\n layer = \"Ethernet\"\r\n except AttributeError:\r\n mac_src = \"\"\r\n mac_dst = \"\"\r\n layer = \"CookedLinux\"\r\n\r\n return mac_src, mac_dst, layer\r\n\r\n\r\ndef get_date_and_time(pkt):\r\n \"\"\"get the date and the time of the packet\"\"\"\r\n pkt_time = int(float(pkt.sniff_timestamp))\r\n p_date = datetime.fromtimestamp(pkt_time).strftime('%Y-%m-%d ') # format: 2020-10-08\r\n p_hour = datetime.fromtimestamp(pkt_time).strftime('%H:%M:%S') # format: 22:40:41\r\n return p_date, p_hour\r\n\r\n\r\ndef get_ip_addresses(pkt):\r\n \"\"\"get src and dst mac\"\"\"\r\n # get ip addresses\r\n src_ip = pkt[\"IP\"].src\r\n dst_ip = pkt[\"IP\"].dst\r\n return src_ip, dst_ip\r\n\r\n\r\ndef get_ports(pkt, type_protocol):\r\n \"\"\"get ports: dst and src\"\"\"\r\n src_port = pkt[type_protocol].srcport\r\n dst_port = pkt[type_protocol].dstport\r\n print(\"Port: \" + src_port)\r\n return src_port, dst_port\r\n\r\n\r\ndef store_data(app_protocol, pkt, type_protocol, layer, mac_src, mac_dst, src_ip, dst_ip, src_port, dst_port, p_date,\r\n p_hour, payload, flag):\r\n global previous_packet_size\r\n global previous_packet_time\r\n\r\n pkt_size, is_encrypted, payload_size, payload_ratio, previous_packet_time, \\\r\n previous_packet_size, previous_ratio, packet_time_diff, payload = extract_characteristics_from_packet(\r\n pkt, previous_packet_time, previous_packet_size, payload)\r\n\r\n write_all(app_protocol, type_protocol, layer, mac_src, mac_dst, src_ip, dst_ip,\r\n src_port, dst_port, pkt_size, is_encrypted, payload_size, payload_ratio, previous_ratio,\r\n packet_time_diff, payload, p_date, p_hour, flag)\r\n\r\n\r\ndef pcap_pkt_reader():\r\n \"\"\"extracts the basic information of the packets, only for the 3 basic IoT protocols\"\"\"\r\n file_name = 'save.pcap'\r\n # if this file is not in the system...\r\n if not os.path.isfile(file_name):\r\n print('\"{}\" does not exist'.format(file_name), file=sys.stderr)\r\n sys.exit(-1)\r\n # if the file is in the system...\r\n else:\r\n print('file found!')\r\n # packets_list = rdpcap(file_name)\r\n\r\n tcp_counter = 0\r\n udp_counter = 0\r\n mqtt_counter = 0\r\n coap_counter = 0\r\n amqp_counter = 0\r\n\r\n # initialise list with mqtt codes\r\n list_with_mqtt_codes = create_mqtt_first_byte()\r\n list_with_coap_codes = create_coap_first_byte()\r\n\r\n # counter = 0\r\n pkt_id = 0\r\n # if_file_exists_delete_it() # delete the previous output file\r\n write_headers() # initialise the file\r\n flag = is_malicious_dataset()\r\n try:\r\n packets = pyshark.FileCapture(file_name)\r\n print(\"read packets\")\r\n for pkt in packets:\r\n pkt_id += 1\r\n print(pkt_id)\r\n # date and time of the packet\r\n p_date, p_hour = get_date_and_time(pkt)\r\n\r\n if ('HTTP') not in str(pkt.layers):\r\n # TCP CASE\r\n if \"TCP\" in pkt.transport_layer:\r\n try:\r\n type_protocol = \"TCP\"\r\n payload = pkt.tcp.payload\r\n payload = bytes.fromhex(payload.replace(\":\", \"\"))\r\n except AttributeError:\r\n payload = \"\"\r\n continue\r\n if \"UDP\" in pkt.transport_layer:\r\n try:\r\n type_protocol = \"UDP\"\r\n print(type_protocol)\r\n if 'COAP' in str(pkt.layers):\r\n print(\"COAP\")\r\n payload = pkt.data.data\r\n payload = bytes.fromhex(payload.replace(\":\", \"\"))\r\n except AttributeError:\r\n payload = \"\"\r\n continue\r\n\r\n # get mac addresses and first layer\r\n mac_src, mac_dst, layer = find_first_layer_protocol(pkt)\r\n\r\n # get ip addresses\r\n src_ip, dst_ip = get_ip_addresses(pkt)\r\n\r\n # get port numbers\r\n src_port, dst_port = get_ports(pkt, type_protocol)\r\n\r\n # check for IoΤ protocols\r\n # search tcp payload in order to find the application layer level\r\n\r\n str_payload = str(payload)\r\n # print(\"Payload: \"+str_payload)\r\n if str_payload: # not b'' TCP payload is zero so there is no header fro the application layer\r\n\r\n # capture AMQP protocol:\r\n amqp_answer = is_amqp(str_payload, pkt, type_protocol)\r\n if amqp_answer == \"yes\":\r\n amqp_counter += 1\r\n app_protocol = \"AMQP\"\r\n # store the information about amqp protocol\r\n store_data(app_protocol, pkt, type_protocol, layer, mac_src, mac_dst, src_ip, dst_ip, src_port,\r\n dst_port, p_date, p_hour, payload,flag)\r\n else:\r\n # capture mqtt protocol\r\n mqtt_answer = is_mqtt(str_payload, list_with_mqtt_codes, pkt, type_protocol)\r\n if mqtt_answer == \"yes\":\r\n mqtt_counter += 1\r\n app_protocol = \"MQTT\"\r\n # store the information about mqtt protocol\r\n store_data(app_protocol, pkt, type_protocol, layer, mac_src, mac_dst, src_ip, dst_ip,\r\n src_port, dst_port, p_date, p_hour, payload,flag)\r\n else:\r\n # capture Coap protocol:\r\n coap_answer = is_coap(str_payload, pkt, type_protocol, list_with_coap_codes)\r\n if coap_answer == \"yes\":\r\n coap_counter += 1\r\n app_protocol = \"COAP\"\r\n store_data(app_protocol, pkt, type_protocol, layer, mac_src, mac_dst, src_ip, dst_ip,\r\n src_port, dst_port, p_date, p_hour, payload, flag)\r\n finally:\r\n packets.close()\r\n\r\n print(\"end\")\r\n # print(f\"we have {udp_counter} udp packets.\")\r\n # print(f\"we have {mqtt_counter} MQTT packets.\")\r\n # print(f\"we have {coap_counter} CoAp packets.\")\r\n # print(f\"we have {amqp_counter} AMQP packets.\")\r\n\r\n\r\npcap_pkt_reader()\r\n","repo_name":"MarilenaKokkini/IoT-Packets-Classification-Thesis-Aueb","sub_path":"final_packet_reader.py","file_name":"final_packet_reader.py","file_ext":"py","file_size_in_byte":16451,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"52"} +{"seq_id":"9907405880","text":"#!/usr/bin/python\n# -*- coding: UTF-8 -*-\n\nimport psutil\nimport time\nfrom operator import itemgetter\n\n\n# 取出指定进程占用的进程名，进程ID，进程实际内存, 虚拟内存,CPU使用率\ndef getProcessInfo(p):\n try:\n cpu = int(p.cpu_percent(interval=0))\n memory = p.memory_info()\n rss = memory.rss / 1024\n vms = memory.vms / 1024\n name = p.name()\n pid = p.pid\n except psutil.Error:\n name = \"Closed_Process\"\n pid = 0\n rss = 0\n vms = 0\n cpu = 0\n # return [name.upper(), pid, rss, vms]\n return [name, pid, vms, rss, cpu]\n\n\ndef getAllProcessInfo():\n \"\"\"取出全部进程的进程名，进程ID，进程实际内存, 虚拟��存,CPU使用率\n \"\"\"\n instances = []\n all_processes = list(psutil.process_iter())\n for proc in all_processes:\n proc.cpu_percent(interval=0)\n # 此处sleep1秒是取正确取出CPU使用率的重点\n time.sleep(1)\n for proc in all_processes:\n instances.append(getProcessInfo(proc))\n return instances\n\n\nif __name__ == '__main__':\n processInfoList = getAllProcessInfo()\n processInfoList.sort(key=itemgetter(2), reverse=True)\n for p in processInfoList:\n print(p)\n","repo_name":"zhigang0529/PythonLearn","sub_path":"src/main/operation/ProcessInfo.py","file_name":"ProcessInfo.py","file_ext":"py","file_size_in_byte":1246,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"32420908864","text":"# Definir una función que detecte si una palabra es un palíndromo y devuelve True o False.\n# Ejemplos:\n# palindromo( \"python\" ) => False\n# palindromo( \"reconocer\" ) => True\n# palindromo( \"Neuquén\" ) => False\n\ndef esPalindromo(cadena):\n # retorna true si es capicua\n cadena1 = cadena[::-1].lower()\n if(cadena1 == str(cadena).lower()):\n return True\n return False\n\nx = esPalindromo(\"Neuquen\")\nprint(x)","repo_name":"caidevOficial/Python_IEEE_Team14293","sub_path":"Ejercicios_integradores/C2_Integradores/C2_easy_Palindromo.py","file_name":"C2_easy_Palindromo.py","file_ext":"py","file_size_in_byte":420,"program_lang":"python","lang":"es","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"26563207818","text":"from flask import Flask, request, jsonify\n\nfrom basic_calculator_function import basic_calculator\n\napp = Flask(__name__)\n\n\n@app.route('/', methods=['POST', \"GET\"])\ndef handle_post():\n if request.method == 'POST':\n content = request.get_json()\n print(content)\n # {\"operator1: \"\", \"operator2\":\"\", \"operation\":\"\"}\n result = basic_calculator(content[\"operator1\"], content[\"operator2\"], content[\"operation\"])\n return jsonify({\"result\": result})\n\n elif request.method == 'GET':\n return \"A sample calculator app - backend\"\n\n else:\n return \"Not a valid method\"\n\n\nif __name__ == '__main__':\n app.run(host=\"0.0.0.0\", port=8000)\n","repo_name":"Praneethvvs/calculator-flask-microservice-backend","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":679,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"19360977694","text":"class Student:\n # 생성자\n def __init__(self, name):\n self.name = name\n # 메소드\n def sayHello(self):\n print(\"안녕 내 이름은 \"+self.name)\n\nstudent_1 = Student(\"이미란\")\nprint(student_1.name)\n\nstudent_2 = Student(\"김미란\")\nprint(student_2.name)\nstudent_2.sayHello()\n","repo_name":"MiranLeeeee/Python-Study","sub_path":"class/class_basic.py","file_name":"class_basic.py","file_ext":"py","file_size_in_byte":308,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"74384031205","text":"\"\"\"\nDiaspora client.\n\"\"\"\nimport logging\n\nimport diaspy.connection\nimport diaspy.models\nimport diaspy.streams\n\nfrom feedspora.generic_client import GenericClient\n\n\nclass DiaspyClient(GenericClient):\n ''' The DiaspyClient handles the connection to Diaspora. '''\n stream = None\n connection = None\n\n def __init__(self, config, testing):\n '''\n Initialize\n :param config:\n :param testing:\n '''\n self._config = config\n\n if not testing:\n self.connection = diaspy.connection.Connection(\n pod=config['pod'],\n username=config['username'],\n password=config['password'])\n self.connection.login()\n try:\n self.stream = diaspy.streams.Stream(self.connection,\n 'stream.json')\n except diaspy.errors.PostError as exception:\n logging.error(\"Cannot get diaspy stream: %s\", str(exception))\n self.stream = None\n self.set_common_opts(config)\n\n def get_dict_output(self, **kwargs):\n '''\n Return dict output for testing purposes\n :param kwargs:\n '''\n\n return {\n \"client\": self._config['name'],\n \"content\": kwargs['text'],\n \"media\": kwargs['photo']\n }\n\n def post(self, feed, entry):\n '''\n Post entry to Diaspora.\n :param feed:\n :param entry:\n '''\n\n text = self.resolve_option(feed, 'post_prefix') + \\\n '['+entry.title +']('+self.shorten_url(feed, entry.link)+')'\n stripped_html = self.strip_html(feed, entry.content) \\\n if entry.content else None\n if self.resolve_option(feed, 'post_include_content') and stripped_html:\n text += \": \" + stripped_html\n text += self.resolve_option(feed, 'post_suffix')\n post_tags = ''.join([\" #{}\".format(k)\n for k in self.filter_tags(feed, entry)])\n if post_tags:\n text += ' |'+post_tags\n\n media_path = None\n if self.resolve_option(feed, 'post_include_media') and entry.media_url:\n # Need to download image from that URL in order to post it!\n media_path = self.download_media(entry.media_url)\n\n post_params = {'text': text,\n 'photo': media_path,\n 'aspect_ids': 'public',\n 'provider_display_name': 'FeedSpora'\n }\n\n to_return = False\n if self.stream:\n to_return = self.stream.post(**post_params)\n elif self.is_testing():\n self.accumulate_testing_output(self.get_dict_output(**post_params))\n else:\n logging.info(\"Diaspy stream is None, not posting anything\")\n\n return to_return\n","repo_name":"aurelg/feedspora","sub_path":"src/feedspora/diaspora_client.py","file_name":"diaspora_client.py","file_ext":"py","file_size_in_byte":2877,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"52"} +{"seq_id":"739988012","text":"\"\"\"\nMaximize z=x1+10x2\nSubject to\n.2x1+4>=x2\n-.2x1+6>=x2\n10x1+1>=x2\nx1,x2>=0\n\"\"\"\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.optimize import linprog\nimport matplotlib.pyplot as plt\n\n# Define the coefficients of the objective function\nc = [-1, -10]\n\n# Define the coefficients of the constraints\nA = [[0.2, -1], [-0.2, -1], [-10, -1]]\nb = [-4, -6, -1]\n\n# Define the bounds of the decision variables\nx0_bounds = (-10, 20)\nx1_bounds = (-10, 20)\n\n# Solve the linear programming problem\nres = linprog(c, A_ub=A, b_ub=b, bounds=[x0_bounds, x1_bounds])\n\n# Print the solution\nprint(res)\n\n# Plot the feasible region and the optimal solution\nx0 = np.linspace(0, 20, 100)\nx1_1 = 0.2*x0 + 4\nx1_2 = -0.2*x0 + 6\nx1_3 = 10*x0 + 1\nplt.plot(x0, x1_1, label='0.2x1 - x2 + 4 <= 0')\nplt.plot(x0, x1_2, label='-0.2x1 - x2 + 6 <= 0')\nplt.plot(x0, x1_3, label='10x1 - x2 + 1 <= 0')\nplt.fill_between(x0, 0, x1_1, where=(x1_1 <= x1_2) &\n (x1_1 <= x1_3), color='grey', alpha=0.5)\nplt.fill_between(x0, 0, x1_2, where=(x1_2 <= x1_1) &\n (x1_2 <= x1_3), color='grey', alpha=0.5)\nplt.fill_between(x0, 0, x1_3, where=(x1_3 <= x1_1) &\n (x1_3 <= x1_2), color='grey', alpha=0.5)\nplt.xlim(0, 22)\nplt.ylim(0, 22)\nplt.xlabel('x1')\nplt.ylabel('x2')\nplt.plot(res.x[0], res.x[1], 'o', label=\"Upper Bound\")\nplt.plot(0, 0, 'mo', label=\"Lower Bound\", zorder=10)\nplt.legend()\nplt.show()\n","repo_name":"prats3992/CVRPTW","sub_path":"MILP/milp_solving.py","file_name":"milp_solving.py","file_ext":"py","file_size_in_byte":1407,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"38726306531","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Aug 23 11:39:46 2020\r\n\r\n@author: camil\r\n\"\"\"\r\n\r\nfrom tkinter import *\r\n\r\nclass Parametrage(Frame):\r\n \r\n \"\"\"Fenetre de parametrage du dessin\"\"\"\r\n \r\n def __init__(self, fenetre, skribbl, **kwargs):\r\n Frame.__init__(self, fenetre, width=100, height=100, **kwargs)\r\n self.pack(fill=BOTH)\r\n \r\n self.skribbl = skribbl\r\n \r\n # Création de nos widgets \r\n self.bouton_quitter = Button(self, text=\"Quitter\", command=self.quit)\r\n self.bouton_quitter.pack(side=\"right\")\r\n \r\n self.bouton_cliquer = Button(self, text=\"help\", fg=\"red\", command=self.skribbl.draw)\r\n self.bouton_cliquer.pack(side=\"top\")\r\n \r\n self.crayon = StringVar(value = 3)\r\n self.petit_crayon = Radiobutton(self, text=\"petit\", variable=self.crayon, value=\"3\")\r\n self.moyen_crayon = Radiobutton(self, text=\"moyen\", variable=self.crayon, value=\"7\")\r\n self.grand_crayon = Radiobutton(self, text=\"grand\", variable=self.crayon, value=\"19\")\r\n self.tres_grand_crayon = Radiobutton(self, text=\"tres grand\", variable=self.crayon, value=\"40\")\r\n self.petit_crayon.pack()\r\n self.moyen_crayon.pack()\r\n self.grand_crayon.pack()\r\n self.tres_grand_crayon.pack()","repo_name":"CamilleGAR/skribbl.io","sub_path":"parametrage.py","file_name":"parametrage.py","file_ext":"py","file_size_in_byte":1305,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"16651324650","text":"import os\r\nfrom collections import Counter\r\n\r\nimport torch\r\nfrom keras_preprocessing.text import tokenizer_from_json\r\n\r\nimport sys\r\nsys.path.append('.')\r\n\r\nfrom sklearn import metrics\r\nimport sklearn.datasets\r\nfrom sklearn.feature_extraction.text import TfidfVectorizer\r\nfrom sklearn.datasets import fetch_20newsgroups\r\nimport numpy as np\r\n\r\nimport tensorflow as tf\r\n# import tensorflow.keras as keras\r\n# from tensorflow.keras import backend\r\n# from tensorflow.keras.layers import Dropout, Dense, GRU, Embedding, Input, Conv1D, MaxPooling1D, Concatenate, Flatten\r\n# from tensorflow.keras.models import Model, Sequential\r\n# from tensorflow.keras.preprocessing.text import Tokenizer\r\n# from tensorflow.keras.preprocessing.sequence import pad_sequences\r\n\r\nfrom keras import backend\r\nfrom keras.layers import Dropout, Dense, GRU, Embedding, Input, Conv1D, MaxPooling1D, Concatenate, Flatten\r\nfrom keras.models import Model, Sequential\r\nfrom keras.preprocessing.text import Tokenizer\r\nfrom keras.preprocessing.sequence import pad_sequences\r\n\r\nfrom util import *\r\nfrom config import *\r\n\r\nnum_cores = 3\r\n\r\ndef get_session():\r\n _config = tf.ConfigProto(intra_op_parallelism_threads=num_cores,\r\n inter_op_parallelism_threads=num_cores,\r\n device_count={'CPU': 1, 'GPU': 0})\r\n _session = tf.Session(config=_config)\r\n return _session\r\n\r\nbackend.set_session(get_session())\r\n\r\ndef load_sents(dataset_name):\r\n d = load_custom(os.path.join(annotation_path, 'dataset_{}.json'.format(dataset_name)))['caption_item']\r\n sents = []\r\n for item in d:\r\n for i in item.sentences:\r\n i.split = item.split\r\n sents.extend(item.sentences)\r\n return sents\r\n\r\ndef get_data(sents, style_tag, balance=True):\r\n all_sents = sents\r\n\r\n X = [' '.join(sent.words) for sent in all_sents]\r\n Y = [1 if hasattr(sent, 'tag') and sent.tag == style_tag else 0 for sent in all_sents]\r\n splits = [sent.split for sent in all_sents]\r\n\r\n train_pos, train_neg = [], []\r\n test_pos, test_neg = [], []\r\n\r\n for i, split in enumerate(splits):\r\n # if split == 'test':\r\n if random.random() < 0.15:\r\n if Y[i] == 1:\r\n test_pos.append((X[i], Y[i]))\r\n else:\r\n test_neg.append((X[i], Y[i]))\r\n else:\r\n if Y[i] == 1:\r\n train_pos.append((X[i], Y[i]))\r\n else:\r\n train_neg.append((X[i], Y[i]))\r\n\r\n if balance:\r\n if len(train_pos) > len(train_neg):\r\n train_pos = random.sample(train_pos, k=len(train_neg))\r\n if len(train_neg) > len(train_pos):\r\n train_neg = random.sample(train_neg, k=len(train_pos))\r\n if len(test_neg) > len(test_pos):\r\n test_neg = random.sample(test_neg, k=len(test_pos))\r\n if len(test_pos) > len(test_neg):\r\n test_pos = random.sample(test_pos, k=len(test_neg))\r\n\r\n X_train, Y_train = [[i[j] for i in train_pos + train_neg] for j in (0, 1)]\r\n X_test, Y_test = [[i[j] for i in test_pos + test_neg] for j in (0, 1)]\r\n\r\n print('train_pos:', len(train_pos), 'train_neg:', len(train_neg))\r\n print('test_pos:', len(test_pos), 'test_neg:', len(test_neg))\r\n\r\n return X_train, Y_train, X_test, Y_test\r\n\r\n# TODO: max length -> 25 (or other values)\r\ndef loadData_Tokenizer(X_train, X_test,MAX_NB_WORDS=75000, MAX_SEQUENCE_LENGTH=500):\r\n np.random.seed(7)\r\n text = np.concatenate((X_train, X_test), axis=0)\r\n text = np.array(text)\r\n tokenizer = Tokenizer(num_words=MAX_NB_WORDS)\r\n tokenizer.fit_on_texts(text)\r\n sequences = tokenizer.texts_to_sequences(text)\r\n word_index = tokenizer.word_index\r\n text = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)\r\n print('Found %s unique tokens.' % len(word_index))\r\n indices = np.arange(text.shape[0])\r\n # np.random.shuffle(indices)\r\n text = text[indices]\r\n print(text.shape)\r\n X_train = text[0:len(X_train), ]\r\n X_test = text[len(X_train):, ]\r\n embeddings_index = {}\r\n f = open(\"/media/wentian/sdb2/work/caption_dataset/Chinese Word Vectors/sgns.wiki.word\", encoding=\"utf8\")\r\n for line in f:\r\n values = line.split()\r\n word = values[0]\r\n if word not in word_index:\r\n continue\r\n try:\r\n coefs = np.asarray(values[1:], dtype='float32')\r\n except:\r\n pass\r\n embeddings_index[word] = coefs\r\n f.close()\r\n print('Total %s word vectors.' % len(embeddings_index))\r\n\r\n return (X_train, X_test, word_index, embeddings_index, tokenizer)\r\n\r\ndef Build_Model_RNN_Text(word_index, embeddings_index, nclasses, MAX_SEQUENCE_LENGTH=500, EMBEDDING_DIM=50, dropout=0.5):\r\n \"\"\"\r\n def buildModel_RNN(word_index, embeddings_index, nclasses, MAX_SEQUENCE_LENGTH=500, EMBEDDING_DIM=50, dropout=0.5):\r\n word_index in word index ,\r\n embeddings_index is embeddings index, look at data_helper.py\r\n nClasses is number of classes,\r\n MAX_SEQUENCE_LENGTH is maximum lenght of text sequences\r\n \"\"\"\r\n model = Sequential()\r\n hidden_layer = 3\r\n gru_node = 32\r\n\r\n embedding_matrix = np.random.random((len(word_index) + 1, EMBEDDING_DIM))\r\n for word, i in word_index.items():\r\n embedding_vector = embeddings_index.get(word)\r\n if embedding_vector is not None:\r\n # words not found in embedding index will be all-zeros.\r\n if len(embedding_matrix[i]) != len(embedding_vector):\r\n print(\"could not broadcast input array from shape\", str(len(embedding_matrix[i])),\r\n \"into shape\", str(len(embedding_vector)), \" Please make sure your\"\r\n \" EMBEDDING_DIM is equal to embedding_vector file ,GloVe,\")\r\n exit(1)\r\n embedding_matrix[i] = embedding_vector\r\n model.add(Embedding(len(word_index) + 1,\r\n EMBEDDING_DIM,\r\n weights=[embedding_matrix],\r\n input_length=MAX_SEQUENCE_LENGTH,\r\n trainable=True))\r\n\r\n for i in range(0,hidden_layer):\r\n model.add(GRU(gru_node,return_sequences=True, recurrent_dropout=0.2))\r\n model.add(Dropout(dropout))\r\n model.add(GRU(gru_node, recurrent_dropout=0.2))\r\n model.add(Dropout(dropout))\r\n model.add(Dense(256, activation='relu'))\r\n model.add(Dense(nclasses, activation='softmax'))\r\n\r\n\r\n model.compile(loss='sparse_categorical_crossentropy',\r\n optimizer='adam',\r\n metrics=['accuracy'])\r\n return model\r\n\r\ndef Build_Model_CNN_Text(word_index, embeddings_index, nclasses, MAX_SEQUENCE_LENGTH=500, EMBEDDING_DIM=50, dropout=0.5):\r\n\r\n \"\"\"\r\n def buildModel_CNN(word_index, embeddings_index, nclasses, MAX_SEQUENCE_LENGTH=500, EMBEDDING_DIM=50, dropout=0.5):\r\n word_index in word index ,\r\n embeddings_index is embeddings index, look at data_helper.py\r\n nClasses is number of classes,\r\n MAX_SEQUENCE_LENGTH is maximum lenght of text sequences,\r\n EMBEDDING_DIM is an int value for dimention of word embedding look at data_helper.py\r\n \"\"\"\r\n\r\n model = Sequential()\r\n embedding_matrix = np.random.random((len(word_index) + 1, EMBEDDING_DIM))\r\n for word, i in word_index.items():\r\n embedding_vector = embeddings_index.get(word)\r\n if embedding_vector is not None:\r\n # words not found in embedding index will be all-zeros.\r\n if len(embedding_matrix[i]) !=len(embedding_vector):\r\n print(\"could not broadcast input array from shape\",str(len(embedding_matrix[i])),\r\n \"into shape\",str(len(embedding_vector)),\" Please make sure your\"\r\n \" EMBEDDING_DIM is equal to embedding_vector file ,GloVe,\")\r\n exit(1)\r\n\r\n embedding_matrix[i] = embedding_vector\r\n\r\n embedding_layer = Embedding(len(word_index) + 1,\r\n EMBEDDING_DIM,\r\n weights=[embedding_matrix],\r\n input_length=MAX_SEQUENCE_LENGTH,\r\n trainable=True)\r\n\r\n # applying a more complex convolutional approach\r\n convs = []\r\n filter_sizes = []\r\n layer = 5\r\n print(\"Filter \",layer)\r\n for fl in range(0,layer):\r\n filter_sizes.append((fl+2))\r\n\r\n node = 128\r\n sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')\r\n embedded_sequences = embedding_layer(sequence_input)\r\n\r\n for fsz in filter_sizes:\r\n l_conv = Conv1D(node, kernel_size=fsz, activation='relu')(embedded_sequences)\r\n l_pool = MaxPooling1D(5)(l_conv)\r\n #l_pool = Dropout(0.25)(l_pool)\r\n convs.append(l_pool)\r\n\r\n l_merge = Concatenate(axis=1)(convs)\r\n l_cov1 = Conv1D(node, 5, activation='relu')(l_merge)\r\n l_cov1 = Dropout(dropout)(l_cov1)\r\n l_pool1 = MaxPooling1D(5)(l_cov1)\r\n l_cov2 = Conv1D(node, 5, activation='relu')(l_pool1)\r\n l_cov2 = Dropout(dropout)(l_cov2)\r\n l_pool2 = MaxPooling1D(30)(l_cov2)\r\n l_flat = Flatten()(l_pool2)\r\n l_dense = Dense(1024, activation='relu')(l_flat)\r\n l_dense = Dropout(dropout)(l_dense)\r\n l_dense = Dense(512, activation='relu')(l_dense)\r\n l_dense = Dropout(dropout)(l_dense)\r\n preds = Dense(nclasses, activation='softmax')(l_dense)\r\n model = Model(sequence_input, preds)\r\n\r\n model.compile(loss='sparse_categorical_crossentropy',\r\n optimizer='adam',\r\n metrics=['accuracy'])\r\n\r\n\r\n\r\n return model\r\n\r\ndef train(style_dataset, target_style, all_datasets=('coco', 'senticap', 'flickrstyle')):\r\n MAX_SEQUENCE_LENGTH = 25\r\n nclasses = 2\r\n\r\n all_sent = []\r\n for d in all_datasets:\r\n all_sent.extend(load_sents(d))\r\n\r\n c = Counter()\r\n c.update([i.tag if hasattr(i, 'tag') else None for i in all_sent])\r\n\r\n X_train, Y_train, X_test, Y_test = get_data(all_sent, target_style)\r\n print('get_data done')\r\n X_train_Glove, X_test_Glove, word_index, embeddings_index, tokenizer = loadData_Tokenizer(X_train, X_test,\r\n MAX_SEQUENCE_LENGTH=MAX_SEQUENCE_LENGTH)\r\n print('loadData_Tokenizer done')\r\n\r\n model_RNN = Build_Model_RNN_Text(word_index, embeddings_index, nclasses=nclasses, MAX_SEQUENCE_LENGTH=MAX_SEQUENCE_LENGTH,\r\n EMBEDDING_DIM=300)\r\n _ = time.time()\r\n model_RNN.fit(X_train_Glove, Y_train,\r\n validation_data=(X_test_Glove, Y_test),\r\n epochs=15,\r\n batch_size=128,\r\n verbose=2)\r\n print('fit used {}'.format(time.time() - _))\r\n predicted = model_RNN.predict_classes(X_test_Glove)\r\n print(metrics.classification_report(Y_test, predicted))\r\n\r\n model_save_dir = '../data/clf_nn'\r\n if not os.path.exists(model_save_dir):\r\n os.makedirs(model_save_dir)\r\n with open(os.path.join(model_save_dir, 'model_rnn_info_{}_{}.pkl'.format(style_dataset, target_style)), 'wb') as f:\r\n pickle.dump(obj={'word_index': word_index, 'embeddings_index': embeddings_index,\r\n 'nclasses': nclasses, 'MAX_SEQUENCE_LENGTH': MAX_SEQUENCE_LENGTH,\r\n 'EMBEDDING_DIM': 300,\r\n 'tokenizer_config': tokenizer.to_json(),\r\n '_classification_report': metrics.classification_report(Y_test, predicted)},\r\n file=f)\r\n model_RNN.save_weights(filepath=os.path.join(model_save_dir, 'model_rnn_{}_{}.h5'.format(style_dataset, target_style)))\r\n\r\n# def train_multi():\r\n# MAX_SEQUENCE_LENGTH = 25\r\n# nclasses = 5\r\n#\r\n# sents_coco = load_sents('coco')\r\n# sents_senticap = load_sents('senticap')\r\n# sents_flickrstyle = load_sents('flickrstyle')\r\n#\r\n# all_sent = sents_coco + sents_senticap + sents_flickrstyle\r\n#\r\n# c = Counter()\r\n# c.update([i.tag if hasattr(i, 'tag') else None for i in all_sent])\r\n#\r\n# X_train, Y_train, X_test, Y_test = get_data(all_sent, target_style)\r\n# print('get_data done')\r\n# X_train_Glove, X_test_Glove, word_index, embeddings_index, tokenizer = loadData_Tokenizer(X_train, X_test,\r\n# MAX_SEQUENCE_LENGTH=MAX_SEQUENCE_LENGTH)\r\n# print('loadData_Tokenizer done')\r\n#\r\n# model_RNN = Build_Model_RNN_Text(word_index, embeddings_index, nclasses=nclasses, MAX_SEQUENCE_LENGTH=MAX_SEQUENCE_LENGTH)\r\n# _ = time.time()\r\n# model_RNN.fit(X_train_Glove, Y_train,\r\n# validation_data=(X_test_Glove, Y_test),\r\n# epochs=15,\r\n# batch_size=128,\r\n# verbose=2)\r\n# print('fit used {}'.format(time.time() - _))\r\n# predicted = model_RNN.predict_classes(X_test_Glove)\r\n# print(metrics.classification_report(Y_test, predicted))\r\n#\r\n# model_save_dir = '../data/clf_nn'\r\n# if not os.path.exists(model_save_dir):\r\n# os.makedirs(model_save_dir)\r\n# with open(os.path.join(model_save_dir, 'model_rnn_info_{}_{}.pkl'.format(style_dataset, target_style)), 'wb') as f:\r\n# pickle.dump(obj={'word_index': word_index, 'embeddings_index': embeddings_index,\r\n# 'nclasses': nclasses, 'MAX_SEQUENCE_LENGTH': MAX_SEQUENCE_LENGTH,\r\n# 'tokenizer_config': tokenizer.to_json(),\r\n# '_classification_report': metrics.classification_report(Y_test, predicted)},\r\n# file=f)\r\n# model_RNN.save_weights(filepath=os.path.join(model_save_dir, 'model_rnn_{}_{}.h5'.format(style_dataset, target_style)))\r\n\r\ndef test_tokenize(tokenizer, sents, MAX_SEQUENCE_LENGTH=500):\r\n sequences = tokenizer.texts_to_sequences(sents)\r\n text = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)\r\n return text\r\n\r\ndef test(dataset, style, test_file):\r\n obj = json.load(open(test_file, 'r'))\r\n # obj = json.load(open('/media/wentian/sdb2/work/caption_ma/save/2019-10-04_21-09-37_2agent_neg/annotation.json', 'r'))['annotations']\r\n sents = [i['caption'] for i in obj]\r\n\r\n d = pickle.load(open(r'../data/clf_nn/model_rnn_info_{}_{}.pkl'.format(dataset, style), 'rb'))\r\n w, e, tokenizer_config = d['word_index'], d['embeddings_index'], d['tokenizer_config']\r\n MAX_SEQUENCE_LENGTH, nclasses, EMBEDDING_DIM = d['MAX_SEQUENCE_LENGTH'], d['nclasses'], d['EMBEDDING_DIM']\r\n tokenizer = tokenizer_from_json(tokenizer_config)\r\n model_RNN = Build_Model_RNN_Text(w, e, nclasses=nclasses, MAX_SEQUENCE_LENGTH=MAX_SEQUENCE_LENGTH, EMBEDDING_DIM=EMBEDDING_DIM)\r\n model_RNN.load_weights('../data/clf_nn/model_rnn_{}_{}.h5'.format(dataset, style))\r\n\r\n X_train_Glove = test_tokenize(tokenizer, sents, MAX_SEQUENCE_LENGTH)\r\n predicted = model_RNN.predict_classes(X_train_Glove, verbose=0)\r\n\r\n for i in range(len(predicted)):\r\n if predicted[i] == 0:\r\n print(sents[i], predicted[i])\r\n print(sum(predicted) / len(predicted))\r\n\r\n\r\nif __name__ == '__main__':\r\n # train('senticap', 'positive')\r\n # train('senticap', 'negative')\r\n # train('flickrstyle', 'humor')\r\n # train('flickrstyle', 'romantic')\r\n\r\n # train('chn_styled_word', 'positive', all_datasets=['youku_chn_word', 'chn_styled_word'])\r\n # train('chn_styled_word', 'negative', all_datasets=['youku_chn_word', 'chn_styled_word'])\r\n # train('chn_styled_word', 'humor', all_datasets=['youku_chn_word', 'chn_styled_word'])\r\n # train('chn_styled_word', 'romantic', all_datasets=['youku_chn_word', 'chn_styled_word'])\r\n\r\n # test('youku_chn_word', 'positive', '/media/wentian/sdb2/work/styled_caption/save/2019-08-22_21-17-19_chn_positive_word/result_youku_chn_word_positive_10.json')\r\n # test('youku_chn_word', 'negative', '/media/wentian/sdb2/work/styled_caption/save/2019-08-22_23-50-59_chn_negative_word/result_youku_chn_word_negative_20.json')\r\n # test('youku_chn_word', 'humor', '/media/wentian/sdb2/work/styled_caption/save/2019-08-22_21-08-48_chn_humor_word/result_youku_chn_word_humor_7.json')\r\n test('youku_chn_word', 'romantic', '/media/wentian/sdb2/work/styled_caption/save/2019-08-22_21-13-01_chn_romantic_word/result_youku_chn_word_romantic_8.json')","repo_name":"entalent/MemCap","sub_path":"src/styled_eval/train_clf_nn_chn.py","file_name":"train_clf_nn_chn.py","file_ext":"py","file_size_in_byte":16377,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"52"} +{"seq_id":"20567749361","text":"from django.shortcuts import render, redirect\nfrom django.http import HttpResponse\nfrom libros.models import Libros\nfrom libros.forms import FormLibros\n\n\n# Create your views here.\n\ndef index(request):\n return render(request, 'libros/index.html')\n\ndef listadoLibros(request):\n libros = Libros.objects.all()\n data = {'libros' : libros}\n return render(request,'libros/libros.html', data)\n\n\ndef agregarLibros(request):\n form = FormLibros()\n if request.method == 'POST' :\n form = FormLibros(request.POST)\n if form.is_valid() :\n form.save()\n return index(request)\n data = {'form' : form}\n return render(request,'libros/agregarlibros.html', data)\n\ndef actualizarLibros(request, id):\n libros = Libros.objects.get(id = id)\n form = FormLibros(instance=libros)\n if request.method == 'POST' :\n form = FormLibros(request.POST, instance=libros)\n if form.is_valid():\n form.save()\n return index(request)\n data = {'form' : form}\n return render(request, 'libros/actualizarlibros.html', data)\n\ndef eliminarLibros(request, id):\n try:\n libros = Libros.objects.get(id=id)\n libros.delete()\n return redirect('libros') \n except Libros.DoesNotExist:\n pass\n\n","repo_name":"Panxho-12/elgranpoeta","sub_path":"pruebadjango/libros/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1269,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"16059473540","text":"from django import template\n\nregister = template.Library()\n\n\n''' fixing value to 5 \n\ndef truncate5(value) :\n\tresult = value[0:5]\n\treturn result\n'''\n\ndef truncate_n(value, n) :\n\tresult = value[0:n]\n\treturn result\n\n\n\n\n# register.filter('tcate5', truncate5)\nregister.filter('tcate', truncate_n)\n\n\n\"\"\" another way of registering filters :\n\n@register.filter(name='tcate_n')\ndef truncate_n(value, n) :\n\tresult = value[0:n]\n\treturn result\n\n\"\"\"","repo_name":"kapil-malviya/learning-Django","sub_path":"filtertemplate/testapp/templatetags/custom_filters.py","file_name":"custom_filters.py","file_ext":"py","file_size_in_byte":439,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"10951201109","text":"import torch\nimport numpy as np\n\n\ndef symmetric(A):\n return 0.5 * (A + A.t())\n\ndef is_nan_or_inf(A):\n C1 = torch.nonzero(A == float('inf'))\n C2 = torch.nonzero(A != A)\n if len(C1.size()) > 0 or len(C2.size()) > 0:\n return True\n return False\n\ndef is_pos_def(x):\n x = x.cpu().numpy()\n return np.all(np.linalg.eigvals(x) > 0)\n\ndef matrix_operator(A, operator):\n u, s, v = A.svd()\n if operator == 'sqrtm':\n s.sqrt_()\n elif operator == 'rsqrtm':\n s.rsqrt_()\n elif operator == 'logm':\n s.log_()\n elif operator == 'expm':\n s.exp_()\n else:\n raise('operator %s is not implemented' % operator)\n \n output = u.mm(s.diag().mm(u.t()))\n \n return output\n\ndef tangent_space(A, ref, inverse_transform=False):\n ref_sqrt = matrix_operator(ref, 'sqrtm')\n ref_sqrt_inv = matrix_operator(ref, 'rsqrtm')\n middle = ref_sqrt_inv.mm(A.mm(ref_sqrt_inv))\n if inverse_transform:\n middle = matrix_operator(middle, 'logm')\n else:\n middle = matrix_operator(middle, 'expm')\n out = ref_sqrt.mm(middle.mm(ref_sqrt))\n return out\n\ndef untangent_space(A, ref):\n return tangent_space(A, ref, True)\n\ndef parallel_transform(A, ref1, ref2):\n print(A.size(), ref1.size(), ref2.size())\n out = untangent_space(A, ref1)\n out = tangent_space(out, ref2)\n return out\n\ndef orthogonal_projection(A, B):\n out = A - B.mm(symmetric(B.transpose(0,1).mm(A)))\n return out\n\ndef retraction(A, ref):\n data = A + ref\n Q, R = data.qr()\n # To avoid (any possible) negative values in the output matrix, we multiply the negative values by -1\n sign = (R.diag().sign() + 0.5).sign().diag()\n out = Q.mm(sign)\n return out\n","repo_name":"adavoudi/spdnet","sub_path":"spdnet/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1719,"program_lang":"python","lang":"en","doc_type":"code","stars":51,"dataset":"github-code","pt":"52"} +{"seq_id":"21208665983","text":"import math\nimport numpy as np\nfrom robosimian_GM_simulation import robosimianSimulator\nfrom copy import deepcopy\nimport copy\nimport configs\n#global robot\n# q_2D = np.array([0.0,1.02,0.0] + [0.6- 1.5708,0.0,-0.6]+[0.6+1.5708,0.0,-0.6]+[0.6-1.5708,0.0,-0.6] \\\n# \t+[0.6+1.5708,0.0,-0.6])[np.newaxis].T\n# q_dot_2D = np.array([0.0]*15)[np.newaxis].T\n# robot = robosimianSimulator(q = q_2D,q_dot= q_dot_2D,dt = 0.005,solver = 'cvxpy',print_level = 1)\n# def jac_dyn(x, u, p=None):\n\n# \t#calculate accleration and dynamics jacobian\n# \ta,J_SA = robot.getDynJac(x,u)\n# \t# print(x,np.ravel(a))\n# \ta = np.concatenate([x[15:30],np.ravel(a)])\t\t\n\n# \t#calculate jacobian with finite-difference\n# \teps = 1e-6\n# \tJ = np.zeros((30,30+12))\n# \tfor i in [0,1,2,3]:\n# \t\tFD_vector = np.zeros(30)\n# \t\tFD_vector[i] = eps\n# \t\ttmp_x = np.add(x,FD_vector)\n# \t\ttmp_a,_,_,_= robot.getDyn(tmp_x,u)\n# \t\tJ[:,i] = np.multiply(np.subtract(np.concatenate([tmp_x[15:30],np.ravel(tmp_a)]),a),1.0/eps)\n\n# \tprint('FD')\n# \tprint(J[15:30,0:4])\n# \t#print(J[15:30,4:8])\n# \tprint('SA')\n# \tprint(J_SA[15:30,0:4])\n# \t#print(J_SA[15:30,4:8])\n\n# \treturn\n# x0 = np.array(configs.q_staggered_limbs+[0.0]*15) #0.936 -- -0.08 ankle depth\n# x0[1] = 1.0\n# u0 = np.array([6.08309021,0.81523653, 2.53641154 ,5.83534863 ,0.72158568, 2.59685143,\\\n# \t5.50487329, 0.54710471,2.57836468, 5.75260704, 0.64075017, 2.51792186])\n\n# jac_dyn(x0,u0)\n\n####\nclass checker:\n\tdef __init__(self,N = 1):\n\t\tq_2D = np.array([0.0,1.02,0.0] + [0.6- 1.5708,0.0,-0.6]+[0.6+1.5708,0.0,-0.6]+[0.6-1.5708,0.0,-0.6] \\\n\t\t\t+[0.6+1.5708,0.0,-0.6])[np.newaxis].T\n\t\tq_dot_2D = np.array([0.0]*15)[np.newaxis].T\n\t\tself.dt = 0.01\n\t\tself.robot = robosimianSimulator(q = q_2D,q_dot= q_dot_2D,dt = self.dt,solver = 'cvxpy',print_level = 0,extrapolation=True)\n\t\tself.N = N\n\n\tdef jac_dyn(self,x, u, p=None):\n\n\t\tcurrent_x = copy.deepcopy(x)\n\t\tu0 = copy.deepcopy(u) #this remains the same\n\t\t#simualte 10 times at 100Hz for an interval of 0.1s, using the same control\n\t\ttotal_f = np.zeros(30)\n\t\ttotal_J = np.zeros((30,42))\n\t\tK = np.eye(42) #this is dXidX0\n\t\tfor i in range(self.N):\n\t\t\told_x = copy.deepcopy(current_x)\n\t\t\ta,J_tmp,current_x = self.robot.getDynJac(current_x,u0,True) #This is a numpy column 2D vector N*1\n\t\t\ttotal_f = total_f + np.concatenate([old_x[15:30],a.ravel()])\n\t\t\tif i < self.N - 1:\t\t\t\n\t\t\t\ttotal_J += J_tmp@K\n\t\t\t\tdxidxi_1 = np.zeros((42,42))\n\t\t\t\tdxidxi_1[0:15,0:15] = np.eye(15)+self.dt*J_tmp[0:15,0:15]+(self.dt**2)*J_tmp[15:30,0:15]\n\t\t\t\tdxidxi_1[0:15,15:30] = self.dt*np.eye(15)+(self.dt**2)*J_tmp[15:30,15:30]\n\t\t\t\tdxidxi_1[0:15,30:42] = (self.dt**2)*J_tmp[15:30,30:42]\n\t\t\t\tdxidxi_1[15:30,0:15] = J_tmp[0:15,0:15] + self.dt*J_tmp[15:30,0:15]\n\t\t\t\tdxidxi_1[15:30,15:30] = np.eye(15) + self.dt*J_tmp[15:30,15:30]\n\t\t\t\tdxidxi_1[15:30,30:42] = self.dt*J_tmp[15:30,30:42]\n\t\t\t\tdxidxi_1[30:42,30:42] = np.eye(12)\n\t\t\t\tK = dxidxi_1@K\n\t\t\telse:\n\t\t\t\ttotal_J += J_tmp@K\n\t\tJ = np.zeros((30,30+12))\n\t\tJ[:,0:42] = total_J/self.N\n\n\t\treturn total_f/self.N,J\n\n\tdef dyn(self,x,u,p=None):\n\t\tcurrent_x = copy.deepcopy(x)\n\t\tu0 = copy.deepcopy(u) #this remains the same\n\n\t\t#simualte 10 times at 100Hz for an interval of 0.1s, using the same control\n\t\ttotal_f = np.zeros(30)\n\t\tfor i in range(self.N):\n\t\t\told_x = copy.deepcopy(current_x)\n\t\t\ta,current_x = self.robot.getDyn(current_x,u0,True) #This is a numpy column 2D vector N*1\n\t\t\ttotal_f = total_f + np.concatenate([old_x[15:30],a.ravel()])\n\n\t\t#print(total_f/10.0)\n\t\treturn total_f/self.N #1D numpy array\n\nx = np.array(configs.q_staggered_limbs+[0.0]*15) #0.936 -- -0.08 ankle depth\nx[1] = 1.01\nu = np.array([6.08309021,0.81523653, 2.53641154 ,5.83534863 ,0.72158568, 2.59685143,\\\n\t5.50487329, 0.54710471,2.57836468, 5.75260704, 0.64075017, 2.51792186])\n\nchecker = checker(1)\na,J_SA = checker.jac_dyn(x, u)\n#print(a)\n#calculate jacobian with finite-difference\neps = 1e-5\nJ = np.zeros((30,30+12))\nfor i in [0,1,2,3]:\n\tFD_vector = np.zeros(30)\n\tFD_vector[i] = eps\n\ttmp_x = np.add(x,FD_vector)\n\ttmp_a = checker.dyn(tmp_x,u)\n\tJ[:,i] = np.multiply(np.subtract(tmp_a,a),1.0/eps)\n\nprint('FD')\nprint(J[15:30,0:4])\n#print(J[15:30,4:8])\nprint('SA')\nprint(J_SA[15:30,0:4])\n#print(J_SA[15:30,4:8])\n\n","repo_name":"yifanzhu95/TOGM","sub_path":"derivative_check.py","file_name":"derivative_check.py","file_ext":"py","file_size_in_byte":4134,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"70995314084","text":"from pathlib import Path\n\nimport pytest\n\nfrom mutalyzer_retriever.sources.ensembl import fetch\nfrom mutalyzer_retriever.configuration import settings\n\nAPI_BASE = settings[\"ENSEMBL_API\"]\nAPI_BASE_GRCH37 = settings[\"ENSEMBL_API_GRCH37\"]\n\nAPI_BASE_MAP = {\n \"ENSG00000147889\": {\"version\": 18, \"species\": \"homo_sapiens\"},\n \"ENSMUSG00000022346\": {\"version\": 18, \"species\": \"mus_musculus\"},\n}\nAPI_BASE_GRCH37_MAP = {\"ENSG00000147889\": {\"version\": 12, \"species\": \"homo_sapiens\"}}\n\n\n@pytest.fixture(autouse=True)\ndef patch_retriever(monkeypatch):\n monkeypatch.setattr(\"mutalyzer_retriever.sources.ensembl.fetch_gff3\", _fetch_gff3)\n monkeypatch.setattr(\n \"mutalyzer_retriever.sources.ensembl._get_reference_information\",\n _get_reference_information,\n )\n\n\ndef _get_content(relative_location):\n data_file = Path(__file__).parent.joinpath(relative_location)\n with open(str(data_file), \"r\") as file:\n content = file.read()\n return content\n\n\ndef _fetch_gff3(feature_id, api_base, timeout=1):\n if api_base == API_BASE_GRCH37:\n return _get_content(\n f\"data/{feature_id}.{API_BASE_GRCH37_MAP[feature_id]['version']}.gff3\"\n )\n return _get_content(f\"data/{feature_id}.gff3\")\n\n\ndef _get_reference_information(reference_id, api_base, timeout=1):\n if api_base == API_BASE and reference_id in API_BASE_MAP.keys():\n return API_BASE_MAP[reference_id]\n if api_base == API_BASE_GRCH37 and reference_id in API_BASE_GRCH37_MAP.keys():\n return API_BASE_GRCH37_MAP[reference_id]\n\n\n@pytest.mark.parametrize(\"reference_id\", [(\"ENSG00000147889\")])\ndef test_ensembl_fetch_no_version(reference_id):\n assert fetch(reference_id)[0] == _get_content(f\"data/{reference_id}.gff3\")\n\n\n@pytest.mark.parametrize(\"reference_id\", [(\"ENSG00000147889.18\")])\ndef test_ensembl_fetch_version_newest(reference_id):\n assert fetch(reference_id)[0] == _get_content(f\"data/{reference_id}.gff3\")\n\n\n@pytest.mark.parametrize(\"reference_id\", [(\"ENSG00000147889.12\")])\ndef test_ensembl_fetch_version_grch37(reference_id):\n assert fetch(reference_id)[0] == _get_content(f\"data/{reference_id}.gff3\")\n\n\n@pytest.mark.parametrize(\"reference_id\", [(\"ENSG00000147889.15\")])\ndef test_ensembl_fetch_other_version(reference_id):\n with pytest.raises(NameError):\n fetch(reference_id)[0]\n\n\n@pytest.mark.parametrize(\"reference_id\", [(\"ENSMUSG00000022346.18\")])\ndef test_ensembl_fetch_no_version_mouse(reference_id):\n assert fetch(reference_id)[0] == _get_content(f\"data/{reference_id}.gff3\")\n\n\n@pytest.mark.parametrize(\"reference_id\", [(\"ENSMUSG00000022346\")])\ndef test_ensembl_fetch_version_newest_mouse(reference_id):\n assert fetch(reference_id)[0] == _get_content(f\"data/{reference_id}.gff3\")\n","repo_name":"mutalyzer/retriever","sub_path":"tests/test_fetch.py","file_name":"test_fetch.py","file_ext":"py","file_size_in_byte":2739,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"26302689390","text":"import base64\nimport json\nimport math\nimport os\nimport random\nimport re\nimport ssl\nfrom io import BytesIO\nfrom json.decoder import JSONDecodeError\nfrom traceback import format_exc\n\ntry:\n import certifi\nexcept ImportError:\n certifi = None\n\nfrom PIL import Image, ImageDraw, ImageFont\n \nfrom requests.exceptions import MissingSchema\nfrom telethon import Button\nfrom telethon.tl.types import DocumentAttributeAudio, DocumentAttributeVideo\n\ntry:\n import numpy as np\nexcept ImportError:\n np = None\n\n\nasync def async_searcher(\n url: str,\n post: bool = None,\n headers: dict = None,\n params: dict = None,\n json: dict = None,\n data: dict = None,\n ssl=None,\n re_json: bool = False,\n re_content: bool = False,\n real: bool = False,\n *args,\n **kwargs,\n):\n try:\n import aiohttp\n except ImportError:\n raise DependencyMissingError(\n \"'aiohttp' is not installed!\\nthis function requires aiohttp to be installed.\"\n )\n async with aiohttp.ClientSession(headers=headers) as client:\n if post:\n data = await client.post(\n url, json=json, data=data, ssl=ssl, *args, **kwargs\n )\n else:\n data = await client.get(url, params=params, ssl=ssl, *args, **kwargs)\n if re_json:\n return await data.json()\n if re_content:\n return await data.read()\n if real:\n return data\n return await data.text()\n\n\ndef _unquote_text(text):\n return text.replace(\"'\", \"'\").replace('\"', '\"')\n\n\ndef json_parser(data, indent=None, ascii=False):\n parsed = {}\n try:\n if isinstance(data, str):\n parsed = json.loads(str(data))\n if indent:\n parsed = json.dumps(\n json.loads(str(data)), indent=indent, ensure_ascii=ascii\n )\n elif isinstance(data, dict):\n parsed = data\n if indent:\n parsed = json.dumps(data, indent=indent, ensure_ascii=ascii)\n except JSONDecodeError:\n parsed = eval(data)\n return parsed\n\n\ndef check_filename(filroid):\n if os.path.exists(filroid):\n no = 1\n while True:\n ult = \"{0}_{2}{1}\".format(*os.path.splitext(filroid) + (no,))\n if os.path.exists(ult):\n no += 1\n else:\n return ult\n return filroid\n","repo_name":"Lover-Music/Lover-Managment-Bot","sub_path":"Loverbot/quotstuff/tools.py","file_name":"tools.py","file_ext":"py","file_size_in_byte":2398,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"52"} +{"seq_id":"42237229882","text":"# encoding: utf-8\n\n\"\"\"\nFile: __init__.py.py\nAuthor: Rock Johnson\n\"\"\"\nimport os\nimport sys\nimport shlex\nimport functools\nfrom pkgutil import iter_modules\nfrom argparse import ArgumentParser\nfrom importlib import import_module\nfrom difflib import get_close_matches\n\nfrom commander.base import BaseCommand\n\nPATH = None\n\n__version__ = '1.0.0'\n\n\ndef find_commands(app_dir):\n \"\"\"\n 给定应用程序的目录路径,返回所有可用命令名称的列表.\n \"\"\"\n commands_dir = os.path.join(app_dir, 'commands')\n\n if app_dir not in sys.path:\n sys.path.append(app_dir)\n return [name for _, name, is_pkg in iter_modules([commands_dir])\n if not is_pkg and not name.startswith('_')]\n\n\ndef load_command_class(name):\n \"\"\"\n 给定命令名称和应用程序名称,返回Command类实例.\n \"\"\"\n module = import_module(f'commands.{name}')\n return module.Command()\n\n\n@functools.lru_cache(maxsize=None)\ndef get_commands():\n \"\"\"\n 返回一个列表.\n\n 在应用程序中查找commands软件包,如果存在命令软件包,\n 则在该软件包中注册所有的命令.\n\n 始终包含本应用程序的命令.如果指定了用户应用程序,则还包括用户定义的命令.\n\n 该列表在第一次调用中缓存,并在后续调用中重用.\n \"\"\"\n commands = find_commands(__path__[0])\n\n if PATH:\n commands.extend(find_commands(PATH))\n return commands\n\n\nclass CommanderUtility:\n \"\"\"\n 仿照django命令行解析应用程序开发的命令行解析框架\n \"\"\"\n\n def __init__(self, argv=None, version=None):\n self.argv = argv or sys.argv\n self.version = version or __version__\n self.prog_name = os.path.basename(self.argv[0])\n\n def main_help_text(self, commands_only=False):\n \"\"\"返回脚本的主要帮助信息,字符串格式\"\"\"\n usage = []\n if not commands_only:\n usage.extend([\n f'输入\"{self.prog_name} help \"以获取子命令的帮助信息.',\n '',\n '可用子命令:',\n ''\n ])\n usage.extend(sorted(get_commands()))\n return '\\n'.join(usage)\n\n def fetch_command(self, subcommand):\n \"\"\"\n 尝试获取给定子命令,如果获取不到,则从命令行调用的相应命令打印一条信息.\n \"\"\"\n commands = get_commands()\n if subcommand not in commands:\n possible_matches = get_close_matches(subcommand, commands)\n sys.stderr.write(f'未知命令: \"{subcommand}\"')\n if possible_matches:\n sys.stderr.write(f'. 你是要使用\"{possible_matches[0]}\"吗?')\n sys.stderr.write(f'\\n输入\"{self.prog_name} help\"以获取用法.\\n')\n sys.exit(1)\n\n if isinstance(subcommand, BaseCommand):\n klass = subcommand\n else:\n klass = load_command_class(subcommand)\n return klass\n\n def execute(self):\n \"\"\"\n 运行已创建的子命令\n \"\"\"\n try:\n subcommand = self.argv[1]\n except:\n subcommand = 'help'\n\n parser = ArgumentParser(add_help=False, allow_abbrev=False)\n parser.add_argument('args', nargs='*')\n options, args = parser.parse_known_args(self.argv[2:])\n\n if subcommand == 'help':\n if options.args:\n self.fetch_command(options.args[0]).print_help(self.prog_name, options.args[0])\n else:\n sys.stdout.write(f'{self.main_help_text(commands_only=\"--commands\" in args)}\\n')\n elif subcommand == 'version' or self.argv[1:] in (['-v'], ['--version']):\n sys.stdout.write(f'{self.version}\\n')\n elif self.argv[1:] in (['-h'], ['--help']):\n sys.stdout.write(f'{self.main_help_text()}\\n')\n else:\n self.fetch_command(subcommand).run_from_argv(self.argv)\n\n\ndef execute_from_command_line(path, argv=None, version=None):\n \"\"\"运行CommanderUtility\"\"\"\n if not isinstance(path, str):\n raise TypeError('path必须是字符串')\n\n # 将字符串处理成命令行参数\n if isinstance(argv, str):\n argv = shlex.split(argv)\n\n global PATH\n PATH = path\n utility = CommanderUtility(argv, version)\n utility.execute()\n","repo_name":"RockJohnson503/commander","sub_path":"commander/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4305,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"13218317060","text":"\"\"\"Модуль с формой для задания параметров игры.\"\"\"\n\nimport tkinter as tk\n\nimport config as conf\nfrom scripts.gui.base_form import BaseForm\nfrom scripts.game.game_screen import GameManager\n\n\nclass Frame(tk.Frame):\n \"\"\"Создает участок формы для ввода параметров генетического алгоритма.\"\"\"\n\n def __init__(self, master):\n super().__init__(master)\n\n # Метки со значениями полей ввода\n labels = [\n \"Число повторений\", \"Вероятность мутации, %\",\n \"Множитель, x (размер популяции - 10*x)\", \"Количество поворотов\"\n ]\n for i, label in enumerate(labels):\n tk.Label(self, text=label).grid(row=i, column=0, sticky=\"W\")\n\n # Поля для ввода\n self.repeat = tk.Entry(self)\n self.mutation = tk.Entry(self)\n self.bird_multiplier = tk.Entry(self)\n self.rotations = tk.Entry(self)\n\n # Позиционирование полей для ввода\n self.repeat.grid(row=0, column=1, sticky=\"E\", pady=10)\n self.mutation.grid(row=1, column=1, sticky=\"E\", pady=10)\n self.bird_multiplier.grid(row=2, column=1, sticky=\"E\", pady=10)\n self.rotations.grid(row=3, column=1, sticky=\"E\", pady=10)\n\n self.set_default_values()\n\n def set_default_values(self):\n \"\"\"Устанавливает генетические параметры по умолчанию.\"\"\"\n self.repeat.insert(0, \"1\")\n self.mutation.insert(0, \"40\")\n self.bird_multiplier.insert(0, \"1\")\n self.rotations.insert(0, \"10\")\n\n\nclass Form(BaseForm):\n \"\"\"Генерирует форму для передачи данных и запуска игры.\"\"\"\n\n def __init__(self):\n super().__init__()\n self.create_elements()\n self.set_window_params(\"Параметры\", conf.ICONS[\"settings\"], False)\n\n def create_elements(self):\n self.frame = Frame(self)\n self.frame.pack(side=\"top\", padx=10)\n # Кнопка запуска и ее позиционирование\n tk.Button(self, text=\"Запустить\", command=self.launch_game).pack(pady=10)\n\n def transform_data_to_dict(self):\n \"\"\"Возвращает словарь с данными, введенными в форму.\"\"\"\n return {\n \"repeat\": int(self.frame.repeat.get()),\n \"mutation\": int(self.frame.mutation.get()) / 100,\n \"multiplier\": int(self.frame.bird_multiplier.get()),\n \"rotations\": int(self.frame.rotations.get())\n }\n\n def launch_game(self):\n \"\"\"Запускает игру с передачей необходимых параметров.\"\"\"\n manager = GameManager(self.transform_data_to_dict())\n self.destroy()\n manager.set_game_data()\n manager.create_games()\n manager.call_save_form()\n","repo_name":"neuro-rsu/python-bird","sub_path":"scripts/gui/game_form.py","file_name":"game_form.py","file_ext":"py","file_size_in_byte":3056,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"709316721","text":"\nimport os\nimport time\nfrom doit.action import CmdAction\nimport shutil\nimport pylab as pl\nimport pandas as pd\nimport numpy as np\nimport opensim as osm\n\nimport task\nimport utilities as util\nimport postprocessing as pp\n\n\nclass TaskCopyGenericModelFilesToResults(task.StudyTask):\n REGISTRY = []\n def __init__(self, study):\n super(TaskCopyGenericModelFilesToResults, self).__init__(study)\n self.name = '%s_copy_generic_model_files' % study.name\n self.doc = 'Copy generic model to the results directory.'\n self.add_action(\n [study.source_generic_model_fpath],\n [study.generic_model_fpath],\n self.copy_file)\n\n self.add_action(\n [study.source_reserve_actuators_fpath],\n [study.reserve_actuators_fpath],\n self.copy_file)\n\n if study.source_rra_actuators_fpath:\n self.add_action(\n [study.source_rra_actuators_fpath],\n [study.rra_actuators_fpath],\n self.copy_file)\n\n if study.source_cmc_actuators_fpath:\n self.add_action(\n [study.source_cmc_actuators_fpath],\n [study.cmc_actuators_fpath],\n self.copy_file)\n\n\nclass TaskCopyMotionCaptureData(task.StudyTask):\n \"\"\"This a very generic task for copying motion capture data (marker\n trajectories, ground reaction, electromyography) and putting it in\n place for creating simulations.\n\n You may want to create your own custom task(s) that is tailored to the\n organization of your experimental data.\n\n The other tasks expect an `expdata` folder in the condition folder (for\n treadmill trials) that contains `marker_trajectories.trc` and\n `ground_reaction.mot`.\n \n Task name: `_copy_data`\n \"\"\"\n REGISTRY = [] # TODO Find a way to make this unnecessary.\n def __init__(self, study, regex_replacements):\n \"\"\"Do not use this constructor directly; use `study.add_task()`.\n\n Parameters\n ----------\n study : \n This argument is provided internally by `study.add_task()`.\n regex_replacements : list of tuples\n Each tuple should have two elements: (a) the pattern to match with\n the path (relative to the motion capture data path) of any file\n within the `motion_capture_data_path`, and (b) the replacement that\n provides the path to where the file should be copied (relative to\n the `results_path`). The list contains as many of these tuples as\n you'd like. The regular expression replacements are performed with\n Python's `re.sub()`.\n\n Examples\n --------\n ```\n study.add_task(TaskCopyMotionCaptureData, [\n ('subject01/Data/Walk_100 02.trc',\n 'subject01/walk1/expdata/marker_trajectories.trc')])\n ```\n \"\"\"\n super(TaskCopyMotionCaptureData, self).__init__(study)\n self.name = '_'.join([study.name, 'copy_data'])\n self.doc = 'Copy and organize motion capture data.'\n self.regex_replacements = regex_replacements\n self.register_files()\n\n # self.add_action(self.registry.keys(), self.registry.values(),\n # self.copy_files)\n # May want to copy over files repeatedly during data processing,\n # so get rid of dependencies for now.\n self.actions += [self.copy_files]\n\n def register_files(self):\n # Keys are source file paths (file_dep), values are destination paths\n # (targets).\n self.registry = dict()\n mocap_dir = self.study.config['motion_capture_data_path']\n results_dir = self.study.config['results_path']\n # Use regular expressions to copy/rename files.\n import re\n\n # Check if each file in the mocap_dir matches any of the regular\n # expressions given to this task.\n for dirpath, dirnames, filenames in os.walk(mocap_dir):\n for fname in filenames:\n fpath = os.path.join(dirpath, fname)\n # Form path relative to the mocap directory.\n fpath_rel_to_mocap = os.path.relpath(fpath, mocap_dir)\n for pattern, replacement in self.regex_replacements:\n match = re.search(pattern, fpath_rel_to_mocap)\n if match != None:\n # Found at least one match.\n destination = os.path.join(results_dir, re.sub(pattern,\n replacement, fpath_rel_to_mocap))\n self.registry[fpath] = destination\n\n def copy_files(self):\n for source, destination in self.registry.items():\n fname = os.path.split(source)[1]\n to_dir = os.path.split(destination)[0]\n if not os.path.exists(to_dir): os.makedirs(to_dir)\n\n if os.path.exists(destination):\n overwriting = '(overwriting)'\n else:\n overwriting = ''\n\n print('%-30s -> %s %s' % (fname, to_dir, overwriting))\n\n import shutil\n shutil.copyfile(source, destination)\n\nclass TaskScaleSetup(task.SubjectTask):\n \"\"\"Create a setup file for the OpenSim Scale tool. You must place a\n template model markerset located at\n `templates/scale/prescale_markerset.xml`. This task creates a copy of this\n file for each subject, since you may need to tweak the markerset for\n individual subjects. The fields @STUDYNAME@ and @SUBJECTNAME@ in the\n template will be replaced by the correct values. You can find an example\n template in osimpipeline's templates directory.\n \n Task name: `subject_scale_setup`\n \"\"\"\n REGISTRY = []\n def __init__(self, subject, init_time, final_time,\n mocap_trial, edit_setup_function, addtl_file_dep=[]):\n \"\"\"\n Parameters\n ----------\n init_time : float\n The initial time from the motion capture trial at which to start\n averaging the marker frames.\n final_time : float\n The final time from the motion capture trial at which to stop\n averaging the marker frames.\n mocap_trial : study.Trial\n The Trial whose marker trajectories to use for both the model\n scaling and marker placer steps. This is ususally a static trial\n but it does not need to be.\n addtl_file_dep : list of str\n Any other files that should be added as file dependencies, changes\n to which would make this task out of date. Usually, you might\n specify the dodo file and whichever file contains the\n `edit_setup_function`.\n\n \"\"\"\n super(TaskScaleSetup, self).__init__(subject)\n self.subj_mass = self.subject.mass\n self.init_time = init_time\n self.final_time = final_time\n self.mocap_trial = mocap_trial\n self.name = '%s_scale_setup' % (self.subject.name)\n self.doc = \"Create a setup file for OpenSim's Scale Tool.\"\n self.edit_setup_function = edit_setup_function\n self.results_scale_path = os.path.join(\n self.study.config['results_path'], 'experiments',\n self.subject.rel_path, 'scale')\n self.output_model_fpath = os.path.join(\n self.study.config['results_path'], 'experiments',\n self.subject.rel_path, '%s.osim' % self.subject.name)\n self.output_motion_fpath = os.path.join(self.results_scale_path,\n '%s_%s_ik_solution.mot' % (self.study.name, self.subject.name))\n self.output_markerset_fpath = os.path.join(self.results_scale_path,\n '%s_%s_markerset.xml' % (self.study.name, self.subject.name))\n\n # setup.xml\n # ---------\n self.setup_fpath = os.path.join(self.results_scale_path, 'setup.xml')\n self.add_action(\n {'marker_traj':\n self.mocap_trial.marker_trajectories_fpath,\n 'generic_model': self.study.generic_model_fpath,\n },\n {'setup': self.setup_fpath\n },\n self.create_scale_setup)\n\n # MarkerSet for the Scale Tool.\n # -----------------------------\n self.source_scale_path = os.path.join(self.subject.rel_path, 'scale')\n self.prescale_template_fpath = 'templates/scale/prescale_markerset.xml'\n self.prescale_markerset_fname = '%s_prescale_markerset.xml' % (\n self.subject.name)\n self.source_prescale_markerset_fpath = os.path.join(\n self.source_scale_path, self.prescale_markerset_fname)\n self.results_prescale_markerset_fpath = os.path.join(\n self.results_scale_path, self.prescale_markerset_fname)\n if not os.path.exists(self.source_prescale_markerset_fpath):\n # The user does not yet have a markerset in place; fill out the\n # template.\n self.add_action(\n {'template': self.prescale_template_fpath},\n {'subjspecific': self.source_prescale_markerset_fpath},\n self.fill_prescale_markerset_template)\n self.actions.append((self.copy_file,\n [[self.source_prescale_markerset_fpath],\n [self.results_prescale_markerset_fpath]]))\n else:\n # We have already filled out the template prescale markerset,\n # and the user might have made changes to it.\n self.file_dep.append(self.source_prescale_markerset_fpath)\n self.add_action(\n [self.source_prescale_markerset_fpath],\n [self.results_prescale_markerset_fpath],\n self.copy_file)\n\n self.file_dep += addtl_file_dep\n\n def fill_prescale_markerset_template(self, file_dep, target):\n if not os.path.exists(target['subjspecific']):\n ft = open(file_dep['template'])\n content = ft.read()\n content = content.replace('@STUDYNAME@', self.study.name)\n content = content.replace('@SUBJECTNAME@', self.subject.name)\n ft.close()\n if not os.path.exists(self.source_scale_path):\n os.makedirs(self.source_scale_path)\n f = open(target['subjspecific'], 'w')\n f.write(content)\n f.close()\n\n def create_scale_setup(self, file_dep, target):\n\n # EDIT THESE FIELDS IN PARTICULAR.\n # --------------------------------\n time_range = osm.ArrayDouble()\n time_range.append(self.init_time)\n time_range.append(self.final_time)\n\n tool = osm.ScaleTool()\n tool.setName('%s_%s' % (self.study.name, self.subject.name))\n tool.setSubjectMass(self.subject.mass)\n\n # GenericModelMaker\n # =================\n gmm = tool.getGenericModelMaker()\n gmm.setModelFileName(os.path.relpath(file_dep['generic_model'],\n self.results_scale_path))\n gmm.setMarkerSetFileName(os.path.relpath(self.prescale_markerset_fname))\n\n # ModelScaler\n # ===========\n scaler = tool.getModelScaler()\n scaler.setPreserveMassDist(True)\n marker_traj_rel_fpath = os.path.relpath(file_dep['marker_traj'],\n self.results_scale_path)\n scaler.setMarkerFileName(marker_traj_rel_fpath)\n\n scale_order_str = osm.ArrayStr()\n scale_order_str.append('manualScale')\n scale_order_str.append('measurements')\n scaler.setScalingOrder(scale_order_str)\n\n scaler.setTimeRange(time_range)\n\n mset = scaler.getMeasurementSet()\n\n # Manual scalings\n # ---------------\n sset = scaler.getScaleSet()\n\n # MarkerPlacer\n # ============\n placer = tool.getMarkerPlacer()\n placer.setStaticPoseFileName(marker_traj_rel_fpath)\n placer.setTimeRange(time_range)\n placer.setOutputModelFileName(os.path.relpath(\n self.output_model_fpath, self.results_scale_path))\n placer.setOutputMotionFileName(os.path.relpath(\n self.output_motion_fpath, self.results_scale_path))\n placer.setOutputMarkerFileName(os.path.relpath(\n self.output_markerset_fpath, self.results_scale_path))\n ikts = util.IKTaskSet(placer.getIKTaskSet())\n\n self.edit_setup_function(util, mset, sset, ikts)\n\n # Validate Scales\n # ===============\n model = osm.Model(file_dep['generic_model'])\n bset = model.getBodySet()\n for iscale in range(sset.getSize()):\n segment_name = sset.get(iscale).getSegmentName()\n if not bset.contains(segment_name):\n raise Exception(\"You specified a Scale for \"\n \"body %s but it's not in the model.\" % segment_name)\n\n if not os.path.exists(self.results_scale_path):\n os.makedirs(self.results_scale_path)\n tool.printToXML(target['setup'])\n \n\nclass TaskScale(task.SubjectTask):\n REGISTRY = []\n residual_actuators_template = 'templates/residual_actuators.xml'\n def __init__(self, subject, scale_setup_task, \n ignore_nonexistant_data=False,\n ):\n super(TaskScale, self).__init__(subject)\n self.name = '%s_scale' % (self.subject.name)\n self.doc = \"Run OpenSim's Scale Tool.\"\n self.ignore_nonexistant_data = ignore_nonexistant_data\n\n # file_dep\n # --------\n setup_fname = 'setup.xml'\n self.setup_fpath = scale_setup_task.setup_fpath\n self.generic_model_fpath = self.study.generic_model_fpath\n self.marker_trajectories_fpath = \\\n scale_setup_task.mocap_trial.marker_trajectories_fpath\n self.prescale_markerset_fpath = \\\n scale_setup_task.results_prescale_markerset_fpath\n self.file_dep = [\n self.setup_fpath,\n self.generic_model_fpath,\n self.prescale_markerset_fpath,\n self.marker_trajectories_fpath,\n self.residual_actuators_template,\n ]\n\n # actions\n # -------\n self.actions += [\n self.check_tasks,\n CmdAction(\n '\"' + os.path.join(self.study.config['opensim_home'],\n 'bin','scale') + '\" -S %s' % (setup_fname),\n cwd=scale_setup_task.results_scale_path),\n self.create_residual_actuators,\n ]\n\n # targets\n # -------\n self.output_model_fpath = scale_setup_task.output_model_fpath\n self.residual_actuators_fpath = os.path.join(\n self.study.config['results_path'], 'experiments',\n self.subject.rel_path, '%s_residual_actuators.xml' %\n self.subject.name)\n self.targets += [\n self.output_model_fpath,\n scale_setup_task.output_motion_fpath,\n scale_setup_task.output_markerset_fpath,\n self.residual_actuators_fpath,\n ]\n\n def check_tasks(self):\n \"\"\"Lists tasks that are 'd for markers that either\n don't exist in the model or are not in the TRC file.\n\n Also lists tasks for which there is data, but that are either not in\n the model or do not have an IK task.\n\n \"\"\"\n scale = osm.ScaleTool(self.setup_fpath)\n tasks = scale.getMarkerPlacer().getIKTaskSet()\n trc = util.TRCFile(self.marker_trajectories_fpath)\n trc_names = trc.marker_names\n model = osm.Model(self.generic_model_fpath)\n markerset = osm.MarkerSet(self.prescale_markerset_fpath)\n\n # Markers with IK tasks but without data.\n # ---------------------------------------\n markers_without_data = []\n for i in range(tasks.getSize()):\n task = tasks.get(i)\n name = task.getName()\n applied = task.getApply()\n\n if applied:\n if (not name in trc_names) or (not markerset.contains(name)):\n if task.getConcreteClassName() != 'IKCoordinateTask':\n markers_without_data.append(name)\n\n if markers_without_data != [] and not self.ignore_nonexistant_data:\n raise Exception('There are IK tasks for the following markers, '\n 'yet data does not exist for them: {}'.format(\n markers_without_data))\n del name\n\n # Markers for which there is data but they're not specified elsewhere.\n # --------------------------------------------------------------------\n unused_markers = []\n for name in trc.marker_names:\n if (not markerset.contains(name)) or (not tasks.contains(name)):\n unused_markers.append(name)\n if unused_markers != []:\n raise Exception(\"You have data for the following markers, but \"\n \"you are not using them in Scale's IK: {}\".format(\n unused_markers))\n\n # No data for these markers in the model or prescale markerset.\n # -------------------------------------------------------------\n excess_model_markers = []\n for im in range(markerset.getSize()):\n name = markerset.get(im).getName()\n if not tasks.contains(name):\n excess_model_markers.append(name)\n if excess_model_markers != []:\n raise Exception(\"The following model markers do not have tasks or \"\n \"experimental data: {}\".format(excess_model_markers))\n\n def create_residual_actuators(self):\n ft = open(self.residual_actuators_template)\n content = ft.read()\n content = content.replace('@STUDYNAME@', self.study.name)\n content = content.replace('@SUBJECTNAME@', self.subject.name)\n\n def com_in_pelvis():\n import opensim\n m = opensim.Model(self.output_model_fpath)\n init_state = m.initSystem()\n com_in_ground = m.calcMassCenterPosition(init_state)\n com_in_pelvis = opensim.Vec3()\n simbody_engine = m.getSimbodyEngine()\n simbody_engine.transformPosition(init_state,\n m.getBodySet().get('ground'), com_in_ground,\n m.getBodySet().get('pelvis'), com_in_pelvis)\n com_xmeas = str(com_in_pelvis.get(0))\n com_ymeas = str(com_in_pelvis.get(1))\n com_zmeas = str(com_in_pelvis.get(2))\n return com_xmeas, com_ymeas, com_zmeas\n\n com_xmeas, com_ymeas, com_zmeas = com_in_pelvis()\n\n content = content.replace('@SYSTEM_COM_GLOBAL_X_MEAS@', com_xmeas)\n content = content.replace('@SYSTEM_COM_GLOBAL_Y_MEAS@', com_ymeas)\n content = content.replace('@SYSTEM_COM_GLOBAL_Z_MEAS@', com_zmeas)\n\n ft.close()\n\n f = open(self.residual_actuators_fpath, 'w')\n f.write(content)\n f.close()\n\n\nclass TaskGRFGaitLandmarks(task.TrialTask):\n # TODO not actually a trial task if for treadmill...\n REGISTRY = []\n def __init__(self, trial,\n right_grfy_column_name='ground_force_r_vy',\n left_grfy_column_name='ground_force_l_vy',\n threshold=5,\n **kwargs):\n super(TaskGRFGaitLandmarks, self).__init__(trial)\n self.name = '%s_gait_landmarks' % trial.id\n self.doc = 'Plot vertical ground reaction force.'\n self.right_grfy_column_name = right_grfy_column_name\n self.left_grfy_column_name = left_grfy_column_name\n self.kwargs = kwargs\n self.threshold = threshold\n self.add_action(\n [trial.ground_reaction_fpath],\n [os.path.join(trial.expdata_path, '..', '%s.pdf' % self.name)],\n self.save_gait_landmarks_fig)\n\n def save_gait_landmarks_fig(self, file_dep, target):\n util.gait_landmarks_from_grf(file_dep[0],\n right_grfy_column_name=self.right_grfy_column_name,\n left_grfy_column_name=self.left_grfy_column_name,\n threshold=self.threshold,\n do_plot=True,\n **self.kwargs)\n pl.gcf().savefig(target[0])\n\n\nclass TaskIKSetup(task.SetupTask):\n REGISTRY = []\n def __init__(self, trial, **kwargs):\n super(TaskIKSetup, self).__init__('ik', trial, **kwargs)\n self.doc = 'Create a setup file for Inverse Kinematics.'\n self.solution_fpath = os.path.join(self.path, \n '%s_%s_ik_solution.mot' % (self.study.name, self.tricycle.id))\n self.model_markers_fpath = os.path.join(self.path, \n 'ik_model_marker_locations.sto')\n\n # Fill out tasks.xml template and copy over to results directory\n self.create_tasks_action()\n\n # Fill out setup.xml template and write to results directory\n self.create_setup_action()\n\n def fill_setup_template(self, file_dep, target,\n init_time=None, final_time=None):\n with open(file_dep[0]) as ft:\n content = ft.read()\n content = content.replace('@STUDYNAME@', self.study.name)\n content = content.replace('@NAME@', self.tricycle.id)\n content = content.replace('@MODEL@', \n os.path.relpath(self.subject.scaled_model_fpath, self.path))\n content = content.replace('@MARKER_FILE@',\n os.path.relpath(self.trial.marker_trajectories_fpath, \n self.path))\n content = content.replace('@TASKS@', os.path.relpath(\n self.results_tasks_fpath, self.path))\n content = content.replace('@INIT_TIME@', '%.4f' % init_time)\n content = content.replace('@FINAL_TIME@', '%.4f' % final_time)\n \n with open(target[0], 'w') as f:\n f.write(content)\n\n\nclass TaskIK(task.ToolTask):\n REGISTRY = []\n def __init__(self, trial, ik_setup_task, **kwargs):\n super(TaskIK, self).__init__(ik_setup_task, trial, **kwargs)\n self.doc = \"Run OpenSim's Inverse Kinematics tool.\"\n \n self.file_dep += [\n self.subject.scaled_model_fpath,\n ik_setup_task.results_tasks_fpath,\n ik_setup_task.results_setup_fpath\n ]\n self.targets += [\n ik_setup_task.solution_fpath,\n ik_setup_task.model_markers_fpath\n ]\n\n\nclass TaskIKPost(task.PostTask):\n REGISTRY=[]\n def __init__(self, trial, ik_setup_task, error_markers=None, side=None,\n **kwargs):\n super(TaskIKPost, self).__init__(ik_setup_task, trial, **kwargs)\n self.doc = 'Create plots from the results of Inverse Kinematics.'\n self.joint_angles_plotpath = '%s/joint_angles.pdf' % self.path\n self.marker_errors_plotpath = '%s/marker_error.pdf' % self.path\n self.error_markers = error_markers\n self.side = side\n\n self.add_action([ik_setup_task.solution_fpath],\n [self.joint_angles_plotpath],\n self.joint_angle_plots)\n\n if self.error_markers:\n self.add_action([self.subject.scaled_model_fpath, \n ik_setup_task.model_markers_fpath,\n self.trial.marker_trajectories_fpath],\n [self.marker_errors_plotpath],\n self.marker_error_plots)\n\n def joint_angle_plots(self, file_dep, target):\n # if os.path.exists(self.fig_fpath):\n # os.rename(self.fig_fpath,\n # self.fig_fpath.replace('.pdf', '_backup.pdf'))\n fig = pp.plot_lower_limb_kinematics(file_dep[0], self.gl, \n side=self.side)\n fig.savefig(target[0])\n pl.close(fig)\n\n def marker_error_plots(self, file_dep, target):\n # if os.path.exists(self.errorplot_fpath):\n # os.rename(self.errorplot_fpath,\n # self.errorplot_fpath.replace('.pdf', '_backup.pdf'))\n pp.plot_marker_error(target[0], self.error_markers, \n 10, self.gl, file_dep[0], file_dep[1], file_dep[2])\n\n \nclass TaskIDSetup(task.SetupTask):\n REGISTRY = []\n def __init__(self, trial, ik_setup_task, **kwargs):\n super(TaskIDSetup, self).__init__('id', trial, **kwargs)\n self.doc = 'Create a setup file for Inverse Dynamics.'\n self.ik_setup_task = ik_setup_task\n self.rel_kinematics_fpath = os.path.relpath(\n ik_setup_task.solution_fpath, self.path)\n self.solution_fpath = os.path.join(\n self.path, 'results','%s_%s_id_solution.sto' % (\n self.study.name, trial.id))\n\n # Fill out external_loads.xml template and copy over to results \n # directory\n self.create_external_loads_action(self.rel_kinematics_fpath)\n\n # Fill out setup.xml template and write to results directory\n self.create_setup_action()\n\n def fill_setup_template(self, file_dep, target,\n init_time=None, final_time=None):\n with open(file_dep[0]) as ft:\n content = ft.read()\n content = content.replace('@STUDYNAME@', self.study.name)\n content = content.replace('@NAME@', self.tricycle.id)\n content = content.replace('@MODEL@', \n os.path.relpath(self.subject.scaled_model_fpath, self.path))\n content = content.replace('@COORDINATES_FILE@',\n self.rel_kinematics_fpath)\n content = content.replace('@INIT_TIME@', '%.4f' % init_time)\n content = content.replace('@FINAL_TIME@', '%.4f' % final_time)\n \n with open(target[0], 'w') as f:\n f.write(content)\n\nclass TaskID(task.ToolTask):\n REGISTRY = []\n def __init__(self, trial, id_setup_task, **kwargs):\n super(TaskID, self).__init__(id_setup_task, trial, **kwargs)\n self.doc = \"Run OpenSim's Inverse Dynamics tool.\"\n self.ik_setup_task = id_setup_task.ik_setup_task\n self.file_dep += [\n self.subject.scaled_model_fpath,\n id_setup_task.results_extloads_fpath,\n id_setup_task.results_setup_fpath,\n self.ik_setup_task.solution_fpath\n ]\n self.targets += [\n id_setup_task.solution_fpath\n ]\n\nclass TaskIDPost(task.PostTask):\n REGISTRY = []\n def __init__(self, trial, id_setup_task, **kwargs):\n super(TaskIDPost, self).__init__(id_setup_task, trial, **kwargs)\n self.doc = 'Create plots from the results of Inverse Dynamics.'\n self.trial = trial\n self.ik_setup_task = id_setup_task.ik_setup_task\n self.id_solution_fpath = id_setup_task.solution_fpath\n self.file_dep += [\n self.id_solution_fpath\n ]\n self.actions += [\n self.cycle_joint_torque_plots\n ]\n\n def cycle_joint_torque_plots(self):\n\n id_array = util.storage2numpy(self.id_solution_fpath)\n\n for cycle in self.trial.cycles:\n fname = 'joint_torques_cycle%02d.pdf' % cycle.num\n output_filepath = os.path.join(self.path, fname)\n\n pp.plot_gait_torques(output_filepath, id_array, \n self.trial.primary_leg, cycle.start, cycle.end,\n cycle.gl.right_strike, cycle.gl.left_strike, \n toeoff_time=cycle.gl.right_toeoff)\n\nclass TaskSOSetup(task.SetupTask):\n REGISTRY = []\n def __init__(self, trial, ik_setup_task, **kwargs):\n super(TaskSOSetup, self).__init__('so', trial, **kwargs)\n self.doc = 'Create a setup file for Static Optimization.'\n self.ik_setup_task = ik_setup_task\n self.kinematics_fpath = ik_setup_task.solution_fpath\n self.rel_kinematics_fpath = os.path.relpath(\n self.kinematics_fpath, self.path)\n self.solution_fpath = os.path.join(\n self.path, 'results','%s_%s_so_StaticOptimization_activation.sto' % (\n self.study.name, self.tricycle.id))\n\n # Fill out external_loads.xml template and copy over to results \n # directory\n self.create_external_loads_action(self.rel_kinematics_fpath)\n\n # Fill out setup.xml template and write to results directory\n self.create_setup_action()\n\n def fill_setup_template(self, file_dep, target,\n init_time=None, final_time=None):\n with open(file_dep[0]) as ft:\n content = ft.read()\n content = content.replace('@STUDYNAME@', self.study.name)\n content = content.replace('@NAME@', self.tricycle.id)\n content = content.replace('@MODEL@', \n os.path.relpath(self.subject.scaled_model_fpath, self.path))\n content = content.replace('@COORDINATES_FILE@',\n self.rel_kinematics_fpath)\n content = content.replace('@INIT_TIME@', '%.4f' % init_time)\n content = content.replace('@FINAL_TIME@', '%.4f' % final_time)\n force_set_files = [\n os.path.relpath(\n os.path.join(self.study.config['results_path'],\n 'experiments',\n self.subject.rel_path, \n '%s_residual_actuators.xml' % self.subject.name),\n self.path),\n os.path.relpath(\n self.study.reserve_actuators_fpath, self.path),\n ]\n force_set_str = ' '.join(force_set_files)\n content = content.replace('@FORCE_SET_FILES@', force_set_str)\n \n with open(target[0], 'w') as f:\n f.write(content)\n\nclass TaskSO(task.ToolTask):\n REGISTRY = []\n def __init__(self, trial, so_setup_task, **kwargs):\n super(TaskSO, self).__init__(so_setup_task, trial, exec_name='analyze',\n **kwargs)\n self.doc = \"Run OpenSim's Static Optimization tool.\"\n\n self.file_dep += [\n self.subject.scaled_model_fpath,\n self.study.reserve_actuators_fpath,\n so_setup_task.kinematics_fpath,\n so_setup_task.results_extloads_fpath,\n ]\n self.targets += [\n so_setup_task.solution_fpath,\n ]\n\nclass TaskSOPost(task.PostTask):\n REGISTRY = []\n def __init__(self, trial, so_setup_task, **kwargs):\n super(TaskSOPost, self).__init__(so_setup_task, trial, **kwargs)\n self.doc = \"Create plots from the results of Static Optimization.\"\n self.cycle = so_setup_task.cycle\n\n # Generate muscle activations plots from SO\n self.add_action([so_setup_task.solution_fpath],\n [os.path.join(self.path, 'activations.pdf')],\n self.plot_activations)\n\n def plot_activations(self, file_dep, target):\n import matplotlib.pyplot as plt\n import matplotlib.gridspec as gridspec\n from copy import copy\n\n residuals = ['FX', 'FY', 'FZ', 'MX', 'MY', 'MZ']\n\n act = util.storage2numpy(file_dep[0])\n names = copy(residuals)\n # To help with plotting left and right actuators on same axes.\n for name in act.dtype.names:\n if name.endswith('_r'):\n names.append(name[:-2])\n n_subplots = len(names)\n n_cols = 6\n n_rows = n_subplots / n_cols + 1\n\n fig = plt.figure(figsize=(4.5 * n_cols, 4 * n_rows))\n grid = gridspec.GridSpec(n_rows, n_cols)\n i_row = 0\n i_col = 0\n\n for name in names:\n ax = plt.Subplot(fig, grid[i_row, i_col])\n fig.add_subplot(ax)\n\n if name in residuals:\n pp.plot_pgc(act['time'], act[name], self.cycle.gl,\n side=self.cycle.gl.primary_leg,\n axes=ax)\n else:\n for s in ['left', 'right']:\n pp.plot_pgc(act['time'], act['%s_%s' % (name, s[0])],\n self.cycle.gl, side=s, axes=ax)\n\n ax.set_title(name)\n ax.set_xlim(0, 100)\n\n if not (name in residuals or name.startswith('reserve_')):\n # This is a muscle; set the ylims to [0, 1].\n ax.set_ylim(0, 1)\n\n if i_col < (n_cols - 1):\n i_col += 1\n else:\n i_col = 0\n i_row += 1\n plt.tight_layout()\n fig.savefig(target[0])\n plt.close(fig)\n\nclass TaskRRAModelSetup(task.SetupTask):\n REGISTRY = []\n def __init__(self, trial, adjust_body='torso', **kwargs):\n super(TaskRRAModelSetup, self).__init__('rramodel', trial, **kwargs)\n self.doc = \"Create a setup file for the Residual Reduction Algorithm, to create an adjusted model.\"\n self.adjust_body = adjust_body\n\n # Fill out external_loads.xml template and copy over to results \n # directory\n self.create_external_loads_action()\n\n # Fill out tasks.xml template and copy over to results directory\n self.create_tasks_action()\n\n # Fill out setup.xml template and write to results directory\n self.create_setup_action()\n\n def fill_setup_template(self, file_dep, target,\n init_time=None, final_time=None):\n with open(file_dep[0]) as ft:\n content = ft.read()\n content = content.replace('@STUDYNAME@', self.study.name)\n content = content.replace('@NAME@', self.tricycle.id)\n content = content.replace('@MODEL@', \n os.path.relpath(self.trial.model_to_adjust_fpath, self.path))\n content = content.replace('@INIT_TIME@', '%.4f' % init_time)\n content = content.replace('@FINAL_TIME@', '%.4f' % final_time)\n force_set_files = '%s %s' % (\n os.path.relpath(self.study.rra_actuators_fpath, self.path), \n os.path.relpath(self.subject.residual_actuators_fpath, \n self.path))\n content = content.replace('@FORCESETFILES@', force_set_files)\n content = content.replace('@ADJUSTCOMBODY@', self.adjust_body)\n # We always compute the mass change, but we just don't always USE\n # the resulting model.\n content = content.replace('@MODELADJUSTED@',\n self.adjusted_model)\n \n with open(target[0], 'w') as f:\n f.write(content)\n\nclass TaskRRAKinSetup(task.SetupTask):\n REGISTRY = []\n def __init__(self, trial, **kwargs):\n super(TaskRRAKinSetup, self).__init__('rrakin', trial, **kwargs)\n self.doc = \"Create a setup file for the Residual Reduction Algorithm tool to adjust kinematics.\"\n\n # Fill out external_loads.xml template and copy over to results \n # directory\n self.create_external_loads_action()\n\n # Fill out tasks.xml template and copy over to results directory\n self.create_tasks_action()\n\n # Fill out setup.xml template and write to results directory\n self.create_setup_action()\n\n def fill_setup_template(self, file_dep, target,\n init_time=None, final_time=None):\n with open(file_dep[0]) as ft:\n content = ft.read()\n content = content.replace('@STUDYNAME@', self.study.name)\n content = content.replace('@NAME@', self.tricycle.id)\n content = content.replace('@MODEL@', \n os.join.relpath(self.adjusted_model_fpath, self.path))\n content = content.replace('@INIT_TIME@', '%.4f' % init_time)\n content = content.replace('@FINAL_TIME@', '%.4f' % final_time)\n force_set_files = '%s %s' % (\n os.path.relpath(self.study.rra_actuators_fpath, self.path), \n os.path.relpath(self.subject.residual_actuators_fpath, \n self.path))\n content = content.replace('@FORCESETFILES@', force_set_files)\n\n with open(target[0], 'w') as f:\n f.write(content)\n\nclass TaskRRA(task.ToolTask):\n REGISTRY = []\n def __init__(self, setup_task, trial, **kwargs):\n kwargs['exec_name'] = 'rra'\n super(TaskRRA, self).__init__(setup_task, trial, **kwargs)\n self.doc = \"Abstract class for OpenSim's RRA tool.\"\n self.des_kinematics_fpath = '%s/ik/%s_%s_ik_solution.mot' % (\n trial.results_exp_path, self.study.name, setup_task.tricycle.id)\n self.des_kinetics_fpath = \\\n '%s/expdata/ground_reaction_orig.mot' % trial.results_exp_path\n\n # Set file dependencies\n self.file_dep += [\n self.des_kinematics_fpath,\n self.des_kinetics_fpath,\n self.subject.residual_actuators_fpath,\n self.study.rra_actuators_fpath\n ]\n\n # Set targets for all RRA outputs\n for rra_output in ['Actuation_force.sto',\n 'Actuation_power.sto', 'Actuation_speed.sto',\n 'avgResiduals.txt', 'controls.sto', 'controls.xml',\n 'Kinematics_dudt.sto', 'Kinematics_q.sto', 'Kinematics_u.sto',\n 'pErr.sto', 'states.sto']:\n self.targets += ['%s/results/%s_%s_%s_%s' % (\n self.path, self.study.name, setup_task.tricycle.id,\n setup_task.tool, rra_output)]\n\nclass TaskRRAModel(TaskRRA):\n REGISTRY = []\n def __init__(self, trial, rramodel_setup_task, reenable_probes=False, \n **kwargs):\n super(TaskRRAModel, self).__init__(rramodel_setup_task, trial, \n **kwargs)\n self.doc = \"Run OpenSim's RRA tool to create an adjusted model.\"\n\n # Set common file dependencies\n self.file_dep += [\n trial.model_to_adjust_fpath,\n rramodel_setup_task.results_setup_fpath,\n rramodel_setup_task.results_tasks_fpath,\n rramodel_setup_task.results_extloads_fpath, \n ]\n\n self.targets += [rramodel_setup_task.adjusted_model_fpath]\n\n # Deal with the fact that this operation would otherwise overwrite\n # the valuable RRA log.\n cur_log = '%s/out.log' % self.path\n rra_log = '%s/out_rra.log' % self.path\n\n def copylog():\n if os.path.exists(cur_log): shutil.copyfile(cur_log, rra_log)\n def removelog():\n if os.path.exists(cur_log): os.remove(cur_log)\n def renamelog():\n if os.path.exists(rra_log): os.rename(rra_log, cur_log)\n self.actions += [\n copylog,\n removelog,\n renamelog,\n ]\n\n if reenable_probes:\n self.actions += [self.reenable_probes]\n\n def reenable_probes(self, rramodel_setup_task):\n # RRA disables the probes; let's re-enable them.\n import opensim\n m = opensim.Model(rramodel_setup_task.adjusted_model_fpath)\n util.enable_probes(rramodel_setup_task.adjusted_model_fpath)\n\nclass TaskRRAKin(TaskRRA):\n REGISTRY = []\n def __init__(self, trial, rrakin_setup_task, **kwargs):\n super(TaskRRAKin, self).__init__(rrakin_setup_task, trial, **kwargs)\n self.doc = \"Run OpenSim's RRA tool to adjust model kinematics\"\n \n # Set file dependencies\n self.file_dep += [\n rrakin_setup_task.adjusted_model_fpath,\n rrakin_setup_task.results_setup_fpath,\n rrakin_setup_task.results_tasks_fpath,\n rrakin_setup_task.results_extloads_fpath\n ]\n\nclass TaskCMCSetup(task.SetupTask):\n REGISTRY = []\n def __init__(self, trial, des_kinematics='rrakin', \n control_constraints=None, **kwargs):\n super(TaskCMCSetup, self).__init__('cmc', trial, **kwargs)\n self.doc = \"Create a setup file for Computed Muscle Control.\"\n self.des_kinematics = des_kinematics\n self.control_constraints = control_constraints\n\n # Set desired kinematics path\n if self.des_kinematics=='rrakin':\n self.des_kinematics_fpath = os.path.join(trial.results_exp_path, \n 'rrakin', 'results',\n '%s_%s_rrakin_Kinematics_q.sto' % (self.study.name, \n self.tricycle.id))\n elif self.des_kinematics=='ik':\n self.des_kinematics_fpath = os.path.join(trial.results_exp_path,\n 'ik', '%s_%s_ik_solution.mot' % (self.study.name, \n self.tricycle.id))\n else:\n raise Exception(\"TaskCMCSetup: %s is not a valid kinematics task \"\n \"source, please choose 'rrakin' or 'ik'.\" % des_kinematics)\n \n # Set control constraints path (if any)\n if self.control_constraints:\n raise Exception(\"TaskCMCSetup: control constraints have yet to be \"\n \"implemented.\")\n else:\n self.control_constraints_fpath = ''\n\n # TODO\n # self.coord_act_fpath\n\n # Fill out external_loads.xml template and copy over to results \n # directory\n self.create_external_loads_action()\n\n # Fill out tasks.xml template and copy over to results directory\n self.create_tasks_action()\n\n # Fill out setup.xml template and write to results directory\n self.create_setup_action()\n\n # Override derived action method since different desired kinematics\n # may be specified \n def fill_external_loads_template(self, file_dep, target):\n with open(file_dep[0]) as ft:\n content = ft.read()\n content = content.replace('@STUDYNAME@', self.study.name)\n content = content.replace('@NAME@', self.tricycle.id)\n content = content.replace('@DESKINEMATICS@', \n os.join.relpath(self.des_kinematics_fpath, self.path))\n\n with open(target[0], 'w') as f:\n f.write(content)\n\n def fill_setup_template(self, file_dep, target, \n init_time=None, final_time=None):\n with open(file_dep[0]) as ft:\n content = ft.read()\n content = content.replace('@STUDYNAME@', self.study.name)\n content = content.replace('@NAME@', self.tricycle.id)\n content = content.replace('@MODEL@', \n os.path.relpath(self.adjusted_model_fpath, self.path))\n content = content.replace('@INIT_TIME@', '%.4f' % init_time)\n content = content.replace('@FINAL_TIME@', '%.4f' % final_time)\n force_set_files = '%s %s' % (\n os.path.relpath(self.study.cmc_actuators_fpath, self.path), \n os.path.relpath(self.subject.residual_actuators_fpath, \n self.path))\n content = content.replace('@FORCESETFILES@', force_set_files)\n content = content.replace('@DESKINEMATICS@',\n os.path.relpath(self.des_kinematics_fpath, self.path))\n content = content.replace('@CONTROLCONSTRAINTS@', \n os.path.relpath(self.control_constraints_fpath, self.path))\n \n with open(target[0], 'w') as f:\n f.write(content)\n\nclass TaskCMC(task.ToolTask):\n REGISTRY = []\n def __init__(self, trial, cmc_setup_task, **kwargs):\n super(TaskCMC, self).__init__(cmc_setup_task, trial, **kwargs)\n self.doc = \"Run OpenSim's Computed Muscle Control tool.\"\n self.des_kinetics_fpath = \\\n '%s/expdata/ground_reaction_orig.mot' % trial.results_exp_path\n\n self.file_dep += [\n cmc_setup_task.adjusted_model_fpath,\n cmc_setup_task.results_setup_fpath,\n cmc_setup_task.results_extloads_fpath,\n cmc_setup_task.results_tasks_fpath,\n cmc_setup_task.des_kinematics_fpath,\n self.des_kinetics_fpath,\n self.subject.residual_actuators_fpath,\n self.study.cmc_actuators_fpath\n ]\n\n if cmc_setup_task.control_constraints:\n self.file_dep += [self.control_contraints_fpath]\n\n # Set targets for all CMC outputs\n for cmc_output in ['Actuation_force.sto',\n 'Actuation_power.sto', 'Actuation_speed.sto',\n 'avgResiduals.txt', 'controls.sto', 'controls.xml',\n 'Kinematics_dudt.sto', 'Kinematics_q.sto', 'Kinematics_u.sto',\n 'pErr.sto', 'states.sto']:\n\n self.targets += ['%s/results/%s_%s_%s_%s' % (\n self.path, self.study.name, cmc_setup_task.tricycle.id, \n 'cmc', cmc_output)]","repo_name":"stanfordnmbl/osimpipeline","sub_path":"osimpipeline/vital_tasks.py","file_name":"vital_tasks.py","file_ext":"py","file_size_in_byte":44511,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"52"} +{"seq_id":"13208918016","text":"from Qt.QtWidgets import *\nfrom Qt import QtCore, QtGui\n\nfrom PyFlow.Input import InputActionType\nfrom PyFlow.UI.Widgets.KeyboardModifiersCapture import KeyboardModifiersCaptureWidget\nfrom PyFlow.UI.Widgets.KeyCapture import KeyCaptureWidget\nfrom PyFlow.UI.Widgets.MouseButtonCapture import MouseButtonCaptureWidget\n\n\nclass InputActionWidget(QWidget):\n \"\"\"docstring for InputActionWidget.\"\"\"\n def __init__(self, parent=None, inputActionRef=None):\n super(InputActionWidget, self).__init__(parent)\n self.currentActionRef = inputActionRef\n self.layout = QHBoxLayout(self)\n self.layout.setContentsMargins(0, 0, 0, 0)\n\n modifiersLabel = QLabel()\n modifiersLabel.setSizePolicy(QSizePolicy.Maximum, QSizePolicy.Preferred)\n modifiersLabel.setPixmap(QtGui.QPixmap(\":/shift-32.png\"))\n self.modifiersWidget = KeyboardModifiersCaptureWidget()\n self.modifiersWidget.captured.connect(self.updateActionModifiers)\n self.layout.addWidget(modifiersLabel)\n self.layout.addWidget(self.modifiersWidget)\n\n if self.actionType == InputActionType.Keyboard:\n keyLabel = QLabel()\n keyLabel.setSizePolicy(QSizePolicy.Maximum, QSizePolicy.Preferred)\n keyLabel.setPixmap(QtGui.QPixmap(\":/keyboard-32.png\"))\n self.keyCapture = KeyCaptureWidget()\n self.keyCapture.captured.connect(self.updateActionKey)\n self.layout.addWidget(keyLabel)\n self.layout.addWidget(self.keyCapture)\n\n if self.actionType == InputActionType.Mouse:\n mouseLabel = QLabel(\"Mouse:\")\n mouseLabel.setSizePolicy(QSizePolicy.Maximum, QSizePolicy.Preferred)\n mouseLabel.setPixmap(QtGui.QPixmap(\":/mouse-32.png\"))\n self.mouseCapture = MouseButtonCaptureWidget()\n self.mouseCapture.captured.connect(self.updateActionMouse)\n self.layout.addWidget(mouseLabel)\n self.layout.addWidget(self.mouseCapture)\n\n def updateActionMouse(self, value):\n if self.currentActionRef is not None:\n self.currentActionRef.setMouseButton(value)\n\n def updateActionKey(self, value):\n if self.currentActionRef is not None:\n self.currentActionRef.setKey(value)\n\n def updateActionModifiers(self, value):\n if self.currentActionRef is not None:\n self.currentActionRef.setModifiers(value)\n\n def setAction(self, inputAction):\n self.modifiersWidget.currentModifiers = inputAction.getModifiers()\n try:\n self.keyCapture.currentKey = inputAction.getKey()\n except:\n pass\n\n try:\n self.mouseCapture.currentButton = inputAction.getMouseButton()\n except:\n pass\n\n def getModifiers(self):\n return self.modifiersWidget.currentModifiers\n\n def getKey(self):\n try:\n return self.keyCapture.currentKey\n except:\n return None\n\n def getMouseButton(self):\n try:\n return self.mouseCapture.currentButton\n except:\n return None\n\n @property\n def actionType(self):\n return self.currentActionRef.actionType\n","repo_name":"wonderworks-software/PyFlow","sub_path":"PyFlow/UI/Widgets/InputActionWidget.py","file_name":"InputActionWidget.py","file_ext":"py","file_size_in_byte":3176,"program_lang":"python","lang":"en","doc_type":"code","stars":2099,"dataset":"github-code","pt":"52"} +{"seq_id":"6873244494","text":"\"\"\"Test case for chess.py.\"\"\"\n\nimport unittest\nfrom chess import Chessercise\n\n\nclass ChessTestCase(unittest.TestCase):\n \"\"\"ChessTestCase class for unit testing Chessercise class.\"\"\"\n def setUp(self):\n \"\"\"Setup() method.\"\"\"\n self.obj = Chessercise()\n\n def test_find_coordinates(self):\n \"\"\"Test case for find coordinates function.\"\"\"\n # \"D2\" is algebraic notation of cooridnate (3,1)\n input1 = 'd2'\n res = self.obj.find_coordinates(input1)\n self.assertEqual((3, 1), res)\n\n input2 = 'd9'\n res = self.obj.find_coordinates(input2)\n self.assertNotEqual(res, (3, 1))\n\n def test_get_algebraic_coordinate(self):\n \"\"\"Test case for get algebraic coordinate function.\"\"\"\n # (3,1) are the coordinates for algebraic notation d2\n input1 = [3, 1]\n res = self.obj.get_algebraic_coordinate(input1[0], input1[1])\n self.assertEqual('d2', res)\n\n input2 = [9, 9]\n res = self.obj.get_algebraic_coordinate(input2[0], input2[1])\n self.assertNotEqual(res, (9, 9))\n\n def test_find_queen_possible_moves(self):\n \"\"\"Test case for find possible queen moves function.\"\"\"\n input1 = 'g5'\n res = self.obj.find_queen_possible_moves(input1)\n self.assertEqual(res, 'h6, h4, f6, e7, d8, f4, e3, d2, c1, a5, b5, '\n 'c5, d5, e5, f5, h5, g1, g2, g3, g4, g6, g7, g8')\n\n def test_find_knight_possible_moves(self):\n \"\"\"Test case for find possible knight moves function.\"\"\"\n input1 = 'g4'\n res = self.obj.find_knight_possible_moves(input1)\n self.assertEqual(res, 'h6, h2, f6, f2, e5, e3')\n input2 = 'a1'\n res = self.obj.find_knight_possible_moves(input2)\n self.assertEqual(res, 'b3, c2')\n input3 = 'a8'\n res = self.obj.find_knight_possible_moves(input3)\n self.assertEqual(res, 'b6, c7')\n input4 = 'h1'\n res = self.obj.find_knight_possible_moves(input4)\n self.assertEqual(res, 'g3, f2')\n input5 = 'h8'\n res = self.obj.find_knight_possible_moves(input5)\n self.assertEqual(res, 'g6, f7')\n\n def test_find_rook_possible_moves(self):\n \"\"\"Test case for find rook possible moves function.\"\"\"\n input1 = 'e4'\n res = self.obj.find_rook_possible_moves(input1)\n self.assertEqual(\n res, 'a4, b4, c4, d4, f4, g4, h4, e1, e2, e3, e5, e6, e7, e8')\n\n###################### Test Coverage ####################################\n# nosetests test_chess.py - -with-coverage\n# .....\n# Name Stmts Miss Cover Missing\n# ----------------------------------------\n# chess.py 103 18 83 % 162-186\n# ----------------------------------------------------------------------\n# Ran 5 tests in 0.010s\n","repo_name":"inovizz/chess-moves","sub_path":"test_chess.py","file_name":"test_chess.py","file_ext":"py","file_size_in_byte":2791,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"16698138540","text":"from math import floor\n\nimport torch\n\n\ndef sequential_output_shape(self, shape):\n \"\"\"Computes the output shape of a torch.nn.Sequential.\n\n Optimistically assumes any layer without method does not change shape.\n \"\"\"\n for element in self:\n for cls, method in output_shape_methods.items():\n if isinstance(element, cls):\n shape = method(element, shape)\n break\n\n return shape\n\n\ndef sequential_feature_dim(self):\n \"\"\"Computes the feature dimension of a torch.nn.Sequential.\n\n Returns None if feature dimension cannot be determined.\n \"\"\"\n feature_dim = None\n for element in reversed(self):\n for cls, method in feature_dim_methods.items():\n if isinstance(element, cls):\n feature_dim = method(element)\n if feature_dim is not None:\n return feature_dim\n\n\ndef conv2d_output_shape(module, h_w):\n \"\"\"Computes the output shape of 2d convolutional operators.\"\"\"\n # grab operator properties\n props = module.kernel_size, module.stride, module.padding, module.dilation\n # diagonalize into tuples as needed\n props = [tuple((p, p)) if not isinstance(p, tuple) else p for p in props]\n # \"transpose\" operator properties -- list indices are height/width rather than property id\n props = list(zip(*props))\n\n h = conv1d_output_shape(h_w[0], *props[0]) # calculate h from height parameters of props\n w = conv1d_output_shape(h_w[1], *props[1]) # calculate w from width parameters of props\n\n assert (h > 0) & (w > 0), \"Invalid parameters\"\n\n return h, w\n\n\ndef conv1d_output_shape(lngth, kernel_size, stride, padding, dilation):\n \"\"\"Computes the change in dimensions for a 1d convolutional operator.\"\"\"\n return floor( ((lngth + (2 * padding) - (dilation * (kernel_size - 1)) - 1) / stride) + 1) # noqa\n\n\ndef convtranspose2d_output_shape(*args, **kwargs):\n raise NotImplementedError\n\n\noutput_shape_methods = { # order is important here; torch.nn.Module must be last\n torch.nn.Sequential: sequential_output_shape,\n torch.nn.Conv2d: conv2d_output_shape,\n torch.nn.MaxPool2d: conv2d_output_shape,\n torch.nn.Linear: lambda module, shape: module.out_features,\n torch.nn.AdaptiveAvgPool2d: lambda module, shape: module.output_size,\n torch.nn.Module: lambda module, shape: shape,\n }\n\nfeature_dim_methods = {\n torch.nn.Sequential: sequential_feature_dim,\n torch.nn.Conv2d: lambda module: module.out_channels,\n torch.nn.ConvTranspose2d: lambda module: module.out_channels,\n torch.nn.Linear: lambda module: module.out_features,\n }\n","repo_name":"wandb/lit_utils","sub_path":"nn/shapes.py","file_name":"shapes.py","file_ext":"py","file_size_in_byte":2616,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"52"} +{"seq_id":"32759214121","text":"from math import atan2, sqrt\nimport pygame as pg\n\nfrom src.util.settings import HEIGHT, WIDTH\n\nclass PlayerController():\n def __init__(self, index):\n self.index = index\n self.queued_ability = -1\n \n def movement_input(self):\n\n keys = pg.key.get_pressed()\n horizontal_input = 0\n vertical_input = 0\n\n if keys[pg.K_w]:\n vertical_input = -1\n\n if keys[pg.K_s]:\n vertical_input = 1\n\n if keys[pg.K_a]:\n horizontal_input = -1\n\n if keys[pg.K_d]:\n horizontal_input = 1\n \n\n return {\n 'i': self.index,\n 'x': horizontal_input,\n 'y': vertical_input\n }\n \n def ability_input(self, entity):\n keys = pg.key.get_pressed()\n # checks which number key is pressed\n for i in range(pg.K_1, pg.K_1+len(entity.abilities[self.index])):\n if keys[i]:\n self.queued_ability = i-pg.K_1\n \n # checks if the input is validated with a left click\n if pg.mouse.get_pressed()[0] and self.queued_ability!=-1:\n angle = atan2(pg.mouse.get_pos()[1]-HEIGHT//2, pg.mouse.get_pos()[0]-WIDTH//2)\n ability = {\n 'i': self.index,\n 'ability': entity.abilities[self.index][self.queued_ability],\n 'angle': angle,\n }\n self.queued_ability = -1\n return ability\n \n # cancel the ability if it is invalidated with the right click\n if pg.mouse.get_pressed()[2] and self.queued_ability!=-1:\n self.queued_ability = -1\n \n return {\n 'i': self.index,\n 'ability': -1,\n 'angle': 0,\n }","repo_name":"HuMangoPP/hello_universe","sub_path":"src/game_state/player_controller.py","file_name":"player_controller.py","file_ext":"py","file_size_in_byte":1745,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"37217552871","text":"#\n# @lc app=leetcode id=105 lang=python3\n#\n# [105] Construct Binary Tree from Preorder and Inorder Traversal\n#\n# https://leetcode.com/problems/construct-binary-tree-from-preorder-and-inorder-traversal/description/\n#\n# algorithms\n# Medium (39.61%)\n# Total Accepted: 216.7K\n# Total Submissions: 533.2K\n# Testcase Example: '[3,9,20,15,7]\\n[9,3,15,20,7]'\n#\n# Given preorder and inorder traversal of a tree, construct the binary tree.\n# \n# Note:\n# You may assume that duplicates do not exist in the tree.\n# \n# For example, given\n# \n# \n# preorder = [3,9,20,15,7]\n# inorder = [9,3,15,20,7]\n# \n# Return the following binary tree:\n# \n# \n# ⁠ 3\n# ⁠ / \\\n# ⁠ 9 20\n# ⁠ / \\\n# ⁠ 15 7\n# \n#\n# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\n# method 1\n# class Solution:\n# def buildTree(self, preorder: List[int], inorder: List[int]) -> TreeNode:\n# if len(preorder)!=len(inorder):\n# return None\n# if not preorder:\n# return None\n# root = TreeNode(preorder[0])\n# # index = inorder.index(preorder[0])\n# for index, val in enumerate(inorder):\n# if val==root.val:\n# break\n# root.left = self.buildTree(preorder[1:index+1], inorder[:index])\n# root.right = self.buildTree(preorder[index+1:], inorder[index+1:])\n# return root\n\n# method 2 相当于对 method 1 的升级版，用了preorder.pop，避免了preorder的偏移量\n# 这里对preorder的处理很微妙，因为当处理完左子树后，preorder中是左子树的元素都会被pop掉，不再需要偏移量\n# 或者换个思路想，其实是依次弹出preorder的元素，只是不知道弹出的元素是位于上一个结点的左子树还是右子树，\n# 需要通过inorder来判断元素位于左子树还是右子树\n# 这个思路也还是有问题，只能理解成对mothod1的遍历过程中发现preorder在处理right时，left已经全部处理结束\n# 这里 preorder 相当于一个全局变量\nclass Solution:\n def buildTree(self, preorder: List[int], inorder: List[int]) -> TreeNode:\n if not inorder:\n return \n ind = inorder.index(preorder.pop(0))\n root = TreeNode(inorder[ind])\n root.left = self.buildTree(preorder, inorder[:ind])\n root.right = self.buildTree(preorder, inorder[ind+1:])\n return root\n\n","repo_name":"TJJTJJTJJ/leetcode","sub_path":"105.construct-binary-tree-from-preorder-and-inorder-traversal.py","file_name":"105.construct-binary-tree-from-preorder-and-inorder-traversal.py","file_ext":"py","file_size_in_byte":2471,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"6857190582","text":"# -*- coding: utf-8 -*-\n\nfrom odoo import api, fields, models, _\nfrom odoo.exceptions import UserError\nimport datetime\nimport calendar \nimport logging\n_logger = logging.getLogger(__name__)\n\nclass FleetVehicleReport(models.TransientModel):\n _name = 'fleet.vehicle.report'\n\n @api.model\n def _default_tags(self):\n vehicle_tags = self.env['fleet.vehicle.tag'].sudo().search([ ] )\n return vehicle_tags.ids \n\n @api.model\n def _default_states(self):\n vehicle_states = self.env['fleet.vehicle.state'].sudo().search([ ] )\n return vehicle_states.ids \n\n # start_date = fields.Date('Start Date', required=True)\n # end_date = fields.Date(string=\"End Date\", required=True)\n tag_ids = fields.Many2many('fleet.vehicle.tag', 'vehicle_report_vehicle_tag_rel', 'vehicle_report_id', 'tag_id', 'Tags', store=True, default=_default_tags )\n state_ids = fields.Many2many('fleet.vehicle.state', 'vehicle_report_vehicle_state_rel', 'vehicle_report_id', 'state_id', 'States', store=True, default=_default_states )\n \n @api.multi\n def action_print(self):\n # vehicles = self.env['fleet.vehicle'].search([ ( 'tag_ids', 'in', self.tag_ids.ids ) ])\n # tag_names = [ tag_id.name for tag_id in self.tag_ids ]\n # # tag_names += [ \"-\" ]\n # final_dict = {}\n # tag_state_dict = {}\n # for tag_name in tag_names:\n # tag_state_dict[ tag_name ] = {}\n # for state_id in self.state_ids :\n # tag_state_dict[ tag_name ][ state_id.name ] = 0\n # for vehicle in vehicles:\n # if vehicle.tag_ids and vehicle.state_id :\n # tag_state_dict[ vehicle.tag_ids[0].name ][ vehicle.state_id.name ] += 1\n\n vehicles = self.env['fleet.vehicle'].search([ ( 'tag_ids', 'in', self.tag_ids.ids ) ])\n state_names = [ state_id.name for state_id in self.state_ids ]\n tag_names = [ tag_id.name for tag_id in self.tag_ids ]\n final_dict = {}\n tag_state_dict = {}\n tag_total_dict = {}\n for tag_name in tag_names:\n tag_state_dict[ tag_name ] = []\n tag_total_dict[ tag_name ] = {\n \"name\" : \"Total\"\n }\n for state_id in self.state_ids :\n tag_total_dict[ tag_name ][ state_id.name ] = 0\n\n for vehicle in vehicles:\n if vehicle.tag_ids and vehicle.state_id :\n row = {}\n row[ \"name\" ] = vehicle.name\n for state_id in self.state_ids :\n row[ state_id.name ] = 1 if vehicle.state_id.name == state_id.name else 0\n tag_state_dict[ vehicle.tag_ids[0].name ] += [ row ]\n tag_total_dict[ vehicle.tag_ids[0].name ][ vehicle.state_id.name ] += 1\n \n final_dict = tag_state_dict\n datas = {\n 'ids': self.ids,\n 'model': 'fleet.vehicle.report',\n 'form': final_dict,\n 'tag_total_dict': tag_total_dict,\n 'state_names': state_names,\n 'date': datetime.datetime.now().strftime(\"%d/%m/%Y\"),\n }\n return self.env['report'].get_action(self,'fleet_report.fleet_vehicle_temp', data=datas)","repo_name":"madukubah/fleet_report","sub_path":"wizard/fleet_vehicle_report.py","file_name":"fleet_vehicle_report.py","file_ext":"py","file_size_in_byte":3191,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"14805957568","text":"from sortedcontainers import SortedList\nimport itertools\n\n\n# represents a filtered chain complex over a field\nclass FCC:\n # vertices - a dictionary {v : filtration level}\n # edges - a set [FCC.Edge]\n def __init__(self, vertices, edges):\n self.vertices = vertices\n self.inv = {v: {} for v in vertices}\n self.outv = {v: {} for v in vertices}\n self.edges = {} # a dictionary {filtration level change : {FCC.Edge}}\n self.num_edges = 0 # keeps track of the number of edges in self.edges\n\n self.edge_priority = {}\n for i, e in enumerate(edges):\n print ('FCC: adding edge '+str(i)+'/'+str(len(edges)))\n self.add_edge(e)\n\n # set up the proper priorities before any reduction\n self.edges = {}\n for i, e in enumerate(edges):\n print ('FCC: updating edge priority '+str(i)+'/'+str(len(edges)))\n self.edge_priority[e] = len(self.inv[e.target]) * len(self.outv[e.source])\n if self.delta_f(e) in self.edges:\n self.edges[self.delta_f(e)].add((e, self.edge_priority[e]))\n else:\n self.edges[self.delta_f(e)] = SortedList([(e, self.edge_priority[e])], key = lambda x: x[1])\n\n # reduces this chain complex until there are no edges remaining\n # if a page is specified, then stop reducing at that page of the spectral sequence\n def reduce(self, page=None):\n for i in itertools.count():\n if (page is not None and i == page) or self.num_edges == 0:\n return\n\n while i in self.edges and len(self.edges[i]) > 0:\n u = self.edges[i].pop(index = 0)[0]\n self.num_edges -= 1\n\n x, y, c = u.source, u.target, u.coefficient\n\n del self.inv[y][x]\n del self.outv[x][y]\n\n new_edges = []\n print ('Reduction at '+str(i)+': '+str(len(self.vertices))+' vertices, '+\n str(len(self.edges[i]))+' edges')\n for w in self.inv[y]:\n t = -self.inv[y][w].coefficient * 1 / c\n for z in self.outv[x]:\n e = self.get_edge(w, z)\n e.coefficient += t * self.outv[x][z].coefficient\n new_edges.append(e)\n\n for e in new_edges:\n self.add_edge(e)\n\n self.remove_vertex(x)\n self.remove_vertex(y)\n\n # remove all vertices with polynomial degree > k\n def truncate(self, k):\n for v in self.vertices.keys():\n if v[1].lift().degree() > k:\n self.remove_vertex(v)\n\n def get_edge(self, source, target):\n if target in self.outv[source]:\n return self.outv[source][target]\n else:\n return FCC.Edge(source, target, 0)\n\n def add_edge(self, e):\n if e.coefficient == 0:\n self.remove_edge(e)\n return\n\n self.inv[e.target][e.source] = e\n self.outv[e.source][e.target] = e\n\n t = self.edge_priority[e] if e in self.edge_priority else None\n self.edge_priority[e] = len(self.inv[e.target]) * len(self.outv[e.source])\n\n if self.delta_f(e) in self.edges:\n if (e, t) in self.edges[self.delta_f(e)]:\n self.edges[self.delta_f(e)].remove((e, t))\n self.num_edges -= 1\n self.edges[self.delta_f(e)].add((e, self.edge_priority[e]))\n else:\n self.edges[self.delta_f(e)] = SortedList([(e, self.edge_priority[e])], key = lambda x: x[1])\n self.num_edges += 1\n\n def remove_edge(self, e):\n if e in self.edge_priority:\n if (e, self.edge_priority[e]) in self.edges[self.delta_f(e)]:\n self.edges[self.delta_f(e)].remove((e, self.edge_priority[e]))\n self.num_edges -= 1\n del self.edge_priority[e]\n if e.source in self.inv[e.target]:\n del self.inv[e.target][e.source]\n if e.target in self.outv[e.source]:\n del self.outv[e.source][e.target]\n\n def remove_vertex(self, v):\n for e in self.inv[v].values():\n self.remove_edge(e)\n for e in self.outv[v].values():\n self.remove_edge(e)\n\n del self.inv[v]\n del self.outv[v]\n\n del self.vertices[v]\n\n # returns how much this edge increases the filtration level\n def delta_f(self, e):\n return self.vertices[e.target] - self.vertices[e.source]\n\n class Edge:\n def __init__(self, source, target, coefficient):\n self.source = source\n self.target = target\n self.coefficient = coefficient\n\n def __repr__(self):\n return str(self.source) + '--- ' + str(self.coefficient) + ' -->' + str(self.target)\n\n def __eq__(self, other):\n return self.source == other.source and self.target == other.target\n\n def __hash__(self):\n return hash((self.source, self.target))\n","repo_name":"samueltripp/khovanov","sub_path":"FCC.py","file_name":"FCC.py","file_ext":"py","file_size_in_byte":5006,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"5324813246","text":"####################################################################################\n# Based on the Faster-LIME package (https://github.com/seansaito/Faster-LIME) \n# by author seansaito (https://github.com/seansaito)\n# Slight modifications have been made to make it compatiable with scikit-explain.\n####################################################################################\n\nimport numpy as np\nfrom scipy.spatial.distance import cdist\nfrom sklearn.preprocessing import OneHotEncoder\n\nfrom abc import ABC, abstractmethod\nfrom sklearn.preprocessing import StandardScaler\n\nfrom ..common.utils import ridge_solve, kernel_fn, discretize, dict_disc_to_bin\n\nclass BaseTabularExplainer(ABC):\n\n def __init__(self, training_data, feature_names=None,\n categorical_names=None, \n discretizer='quartile', **kwargs):\n \"\"\"\n Args:\n training_data (np.ndarray): Training data to measure training data statistics\n feature_names (list): List of feature names\n categorical_feature_idxes (list): List of idxes of features that are categorical\n discretizer (str): Discretization resolution\n\n Assumptions:\n * Data only contains categorical and/or numerical data\n * Categorical data is already converted to ordinal labels (e.g. via scikit-learn's\n OrdinalEncoder)\n\n \"\"\"\n self.training_data = training_data\n self.num_features = self.training_data.shape[1]\n\n # Parse columns\n if feature_names is not None:\n # TODO input validation\n self.feature_names = list(feature_names)\n else:\n self.feature_names = list(range(self.num_features))\n \n categorical_feature_idxes = [feature_names.index(f) for f in categorical_names]\n \n self.categorical_feature_idxes = categorical_feature_idxes\n if self.categorical_feature_idxes:\n self.categorical_features = [self.feature_names[i] for i in\n self.categorical_feature_idxes]\n self.numerical_features = [f for f in self.feature_names if\n f not in self.categorical_features]\n self.numerical_feature_idxes = [idx for idx in range(self.num_features) if\n idx not in self.categorical_feature_idxes]\n else:\n self.categorical_features = []\n self.numerical_features = self.feature_names\n self.numerical_feature_idxes = list(range(self.num_features))\n\n # Some book-keeping: keep track of the original indices of each feature\n self.dict_num_feature_to_idx = {feature: idx for (idx, feature) in\n enumerate(self.numerical_features)}\n self.dict_feature_to_idx = {feature: idx for (idx, feature) in\n enumerate(self.feature_names)}\n self.list_reorder = [self.dict_feature_to_idx[feature] for feature in\n self.numerical_features + self.categorical_features]\n\n # Get training data statistics\n # Numerical feature statistics\n if self.numerical_features:\n training_data_num = self.training_data[:, self.numerical_feature_idxes]\n self.sc = StandardScaler(with_mean=False)\n self.sc.fit(training_data_num)\n self.percentiles = dict_disc_to_bin[discretizer]\n self.all_bins_num = np.percentile(training_data_num, self.percentiles, axis=0).T\n\n # Categorical feature statistics\n if self.categorical_features:\n training_data_cat = self.training_data[:, self.categorical_feature_idxes]\n training_data_cat = training_data_cat.astype(int)\n \n self.dict_categorical_hist = {\n feature: np.bincount(training_data_cat[:, idx]) / self.training_data.shape[0] for\n (idx, feature) in enumerate(self.categorical_features)\n }\n\n # Another mapping from feature to type\n self.dict_feature_to_type = {\n feature: 'categorical' if feature in self.categorical_features else 'numerical' for\n feature in self.feature_names}\n\n @abstractmethod\n def explain_instance(self, **kwargs):\n raise NotImplementedError\n\n\n\nclass FastLimeTabularExplainer(BaseTabularExplainer):\n \"\"\"\n A basic tabular explainer\n \"\"\"\n def explain_instance(self, data_row, predict_fn, label=0, num_samples=5000, num_features=10,\n kernel_width=None, **kwargs):\n \"\"\"\n Explain a prediction on a given instance\n\n Args:\n data_row (np.ndarray): Data instance to explain\n predict_fn (func): A function which provides predictions from the target model\n label (int): The class to explain\n num_samples (int): Number of synthetic samples to generate\n num_features (int): Number of top features to return\n kernel_width (Optional[float]): Width of the Gaussian kernel when weighting synthetic samples\n\n Returns:\n (list) Tuples of feature and score, sorted by the score\n \"\"\"\n # Scale the data\n data_row = data_row.reshape((1, -1))\n\n # Split data into numerical and categorical data and process\n list_orig = []\n list_disc = []\n if self.numerical_features:\n data_num = data_row[:, self.numerical_feature_idxes]\n data_num = self.sc.transform(data_num)\n data_synthetic_num = np.tile(data_num, (num_samples, 1))\n # Add noise\n data_synthetic_num = data_synthetic_num + np.random.normal(\n size=(num_samples, data_num.shape[1]))\n data_synthetic_num[0] = data_num.ravel()\n # Convert back to original domain\n data_synthetic_num_original = self.sc.inverse_transform(data_synthetic_num)\n # Discretize\n data_synthetic_num_disc, _ = discretize(data_synthetic_num_original, self.percentiles,\n self.all_bins_num)\n list_disc.append(data_synthetic_num_disc)\n list_orig.append(data_synthetic_num_original)\n\n if self.categorical_features:\n # Sample from training distribution for each categorical feature\n data_cat = data_row[:, self.categorical_feature_idxes]\n list_buf = []\n for feature in self.categorical_features:\n list_buf.append(np.random.choice(a=len(self.dict_categorical_hist[feature]),\n size=(1, num_samples),\n p=self.dict_categorical_hist[feature]))\n data_cat_original = data_cat_disc = np.concatenate(list_buf).T\n data_cat_original[0] = data_cat.ravel()\n data_cat_disc[0] = data_cat.ravel()\n list_disc.append(data_cat_disc)\n list_orig.append(data_cat_original)\n\n # Concatenate the data and reorder the columns\n data_synthetic_original = np.concatenate(list_orig, axis=1)\n data_synthetic_disc = np.concatenate(list_disc, axis=1)\n data_synthetic_original = data_synthetic_original[:, self.list_reorder]\n data_synthetic_disc = data_synthetic_disc[:, self.list_reorder]\n\n # Get model predictions (i.e. groundtruth)\n model_pred = predict_fn(data_synthetic_original)\n \n # For classification tasks.\n if np.ndim(model_pred)==2:\n model_pred=model_pred[:,label]\n\n\n # Get distances between original sample and neighbors\n if self.numerical_features:\n distances = cdist(data_synthetic_num[:1], data_synthetic_num).reshape(-1, 1)\n else:\n distances = cdist(data_synthetic_disc[:1], data_synthetic_disc).reshape(-1, 1)\n\n # Weight distances according to some kernel (e.g. Gaussian)\n if kernel_width is None:\n kernel_width = np.sqrt(data_row.shape[1]) * 0.75\n weights = kernel_fn(distances, kernel_width=kernel_width).ravel()\n\n # Turn discretized data into onehot\n data_synthetic_onehot = OneHotEncoder().fit_transform(data_synthetic_disc)\n\n # Solve\n tup = (data_synthetic_onehot, model_pred, weights)\n importances, bias = ridge_solve(tup)\n #print(importances.shape, bias.shape)\n \n #explanations = sorted(list(zip(self.feature_names, importances)),\n # key=lambda x: x[1], reverse=True)[:num_features]\n\n return importances, bias ","repo_name":"monte-flora/scikit-explain","sub_path":"skexplain/main/lime_fast.py","file_name":"lime_fast.py","file_ext":"py","file_size_in_byte":8674,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"52"} +{"seq_id":"27793839777","text":"#!/usr/bin/env python\n#-*- coding: utf-8 -*-\n#pylint: disable=\n\n\"\"\"\nFile : preds2dataset.py\nAuthor : Valentin Kuznetsov \nDescription: Script which convert prediction file (dataset_id,prediction)\ninto dataset,prediction using datasets and prediction files\n\"\"\"\n\n# system modules\nimport optparse\n\n# local modules\nfrom DCAF.utils.utils import fopen\n\n# pandas\nimport pandas as pd\n\nclass OptionParser():\n \"User based option parser\"\n def __init__(self):\n usage = 'Usage: %prog [options]\\n'\n usage += 'Description: convert prediction file into human readable form\\n'\n usage += 'Example: %prog --fin=pred.txt --datasets=datasets.csv.gz --fout=pred.txt.out'\n self.parser = optparse.OptionParser(usage=usage)\n self.parser.add_option(\"--fin\", action=\"store\", type=\"string\", \\\n dest=\"fin\", default=\"\", help=\"Input file\")\n self.parser.add_option(\"--fout\", action=\"store\", type=\"string\", \\\n dest=\"fout\", default=\"\", help=\"Output file\")\n self.parser.add_option(\"--datasets\", action=\"store\", type=\"string\", \\\n dest=\"datasets\", default=\"\", help=\"DBS datasets input file\")\n self.parser.add_option(\"--sep\", action=\"store\", type=\"string\", \\\n dest=\"sep\", default=\",\", help=\"Output file separator, default comma\")\n def get_opt(self):\n \"Return list of options\"\n return self.parser.parse_args()\n\ndef convert(fin, fout, datasets, sep=','):\n \"\"\"\n Convert input prediction file (id,prediction) into (dataset,prediction)\n by using datasets file\n \"\"\"\n headers = None\n df = pd.read_csv(datasets)\n with fopen(fin, 'r') as istream, fopen(fout, 'w') as ostream:\n for line in istream.readlines():\n did, dbs, pred = line.replace('\\n', '').split(sep)\n if not headers:\n headers = '%s,%s,%s' % (did, dbs, pred)\n continue\n res = df[(df.hash==int(did)) & (df.dbsinst==int(dbs))]\n if not res.empty:\n dataset = res.get_value(res.index[0], 'dataset')\n ostream.write(\"%5.3f%s%s\\n\" % (float(pred), sep, dataset))\n\ndef main():\n \"Main function\"\n optmgr = OptionParser()\n opts, _ = optmgr.get_opt()\n convert(opts.fin, opts.fout, opts.datasets, opts.sep)\n\nif __name__ == '__main__':\n main()\n","repo_name":"dmwm/DMWMAnalytics","sub_path":"Popularity/DCAFPilot/src/python/DCAF/tools/preds2dataset.py","file_name":"preds2dataset.py","file_ext":"py","file_size_in_byte":2350,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"52"} +{"seq_id":"18867844125","text":"from argparse import ArgumentParser\nfrom port_scanner import PortScannerError, scan\nfrom typing import Any, Dict\n\n\ndef parse_args() -> Dict[str, Any]:\n parser = ArgumentParser(description='TCP and UDP port scanner')\n parser.add_argument('-t', '--tcp_only', help='Scan only TCP',\n action='store_true')\n parser.add_argument('-u', '--udp_only', help='Scan only UDP',\n action='store_true')\n parser.add_argument('-p', '--ports', nargs=2, default=['1', '65535'],\n metavar='PORT', help='Port range')\n parser.add_argument('host', help='Remote host')\n return parser.parse_args().__dict__\n\n\nif __name__ == '__main__':\n try:\n args = parse_args()\n scan(**args)\n except PortScannerError as e:\n print(e.message)\n exit(1)\n except KeyboardInterrupt:\n print('\\nTerminated.')\n exit()\n","repo_name":"AleksandrChirkin/Portscan","sub_path":"port_scanner/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":904,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"4732886816","text":"# -*- coding: utf-8 -*-\nimport re\nimport scrapy\nfrom article.items import ArticleItem\nimport html2text\nfrom article.tools import remove_csv_noise\nimport json\nfrom article.settings import UPDATE_DEEPTH as page_deepth\n\nimport coloredlogs,logging,sys\nlg = logging.getLogger(__name__)\ncoloredlogs.install(level='critical')\n\n# file_handler = logging.FileHandler(\"page_json.log\")\n# lg.addHandler(file_handler)\n\nclass GuandianSpider(scrapy.Spider):\n name = 'guandian'\n data_destination = 'ES'\n counter=0\n \n def start_requests(self):\n # 指定url\n for page in range(0,page_deepth):\n yield scrapy.Request(url='http://www.guandian.cn/api.php?op=getindex2016_content&modelid=1&type=query-scroll&a='+str(page), callback=self.parse)\n\n def parse(self, response):\n # 获取一个page的所有文章链接\n\n data = response.body_as_unicode()\n # 去除首尾的()\n Data = data[1:-1]\n lg.critical(Data)\n\n if 'html>' in Data:\n return\n try:\n Data = json.loads(Data)\n except Exception as e:\n lg.critical(str(data))\n pass\n \n article_urls = []\n summarys = []\n date = []\n title = []\n for i in Data:\n article_urls.append('http://www.guandian.cn'+i['mainURL'])\n summarys.append(i['abstract'])\n date.append(i['timer'])\n title.append(i['titleText'])\n print(self.counter,'-'*40,len(article_urls))\n for u in article_urls:\n self.counter += 1\n # lg.critical(str(self.counter)+'----'+u)\n meta_data = {\n 'summary':summarys[article_urls.index(u)],\n 'date':date[article_urls.index(u)],\n 'title':title[article_urls.index(u)],\n }\n yield scrapy.Request(url=u, meta=meta_data, callback=self.parse_article)\n\n def parse_article(self, response):\n if 'html>' in response.body_as_unicode():\n return\n\n item = ArticleItem()\n\n # item[\"the_id\"] it is a counter will be asigned in pipelines\n item[\"website\"] = '观点房产'\n item[\"title\"] = remove_csv_noise(response.meta['title'])\n item[\"link\"] = response.url\n item[\"summary\"] = remove_csv_noise(response.meta['summary'])\n item[\"date\"] = remove_csv_noise(response.meta['date'])\n item[\"category\"] = '资讯'\n item[\"author\"] = remove_csv_noise(response.xpath('//div[@class=\"con_l_info_l\"]/a/text()').extract()[-1])\n # 根据xpath语法选取正文部分的HTML传递给html2text\n item[\"text\"] = remove_csv_noise(html2text.html2text(response.xpath('//div[@class=\"con_l_inner\"]').extract()[0]))\n # item[\"crwaler_time\"] = \n item[\"other\"] = '观点房产 资讯'\n\n yield item","repo_name":"wonderfulsuccess/pyScript_dev","sub_path":"spider/article/article/spiders/guandian.py","file_name":"guandian.py","file_ext":"py","file_size_in_byte":2903,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"71027597284","text":"###############################################################################\n# General Information\n###############################################################################\n# Sparse Symplectically Integrated Neural Networks (2020)\n# Paper: https://arxiv.org/abs/2006.12972\n# Daniel DiPietro, Shiying Xiong, Bo Zhu\n\nimport torch\nfrom torch import nn\n\n# Used to wrap each function space so that the forward pass obtains the gradient\nclass gradient_wrapper(nn.Module):\n def __init__(self, space):\n super(gradient_wrapper, self).__init__()\n self.space = space\n\n def forward(self, x):\n return torch.autograd.grad(self.space(x), x, create_graph=True)[0]\n\n###############################################################################\n# Used for Henon-Heiles/Coupled Oscillator\n###############################################################################\n\n# Bivariate polynomial function space. For use with a single point in 2D space.\nclass bivariate_poly(nn.Module):\n def __init__(self, degree=3):\n super(bivariate_poly, self).__init__()\n self.degree = degree\n self.fc1 = nn.Linear(degree**2 + 2*degree, 1, bias=False)\n\n def forward(self, x):\n xc = torch.ones([self.degree])*(x[0][0])\n yc = torch.ones([self.degree])*(x[0][1])\n xc_pow = torch.pow(xc, torch.Tensor([i for i in range(1, self.degree+1)]))\n yc_pow = torch.pow(yc, torch.Tensor([i for i in range(1, self.degree+1)]))\n combos = torch.ger(xc_pow, yc_pow).flatten()\n input = torch.cat((xc_pow, yc_pow, combos))\n out = self.fc1(input)\n return out\n\n###############################################################################\n# Used for Mass-Spring\n###############################################################################\n\n# Polynomial space with 5 input variables. Only supports degree 2, 3, 4, 6, or 10.\nclass fivevariate_poly(nn.Module):\n def __init__(self, degree):\n super(fivevariate_poly, self).__init__()\n self.degree = degree\n if (degree == 2):\n self.fc1 = nn.Linear(50, 1, bias=False)\n elif (degree == 3):\n self.fc1 = nn.Linear(105, 1, bias=False)\n elif (degree == 4):\n self.fc1 = nn.Linear(180, 1, bias=False)\n elif (degree == 6):\n self.fc1 = nn.Linear(390, 1, bias=False)\n elif (degree == 10):\n self.fc1 = nn.Linear(1050, 1, bias=False)\n\n def forward(self, x):\n x1 = torch.ones([self.degree])*(x[0][0])\n x2 = torch.ones([self.degree])*(x[0][1])\n x3 = torch.ones([self.degree])*(x[0][2])\n x4 = torch.ones([self.degree])*(x[0][3])\n x5 = torch.ones([self.degree])*(x[0][4])\n x1_pow = torch.pow(x1, torch.Tensor([i for i in range(1, self.degree+1)]))\n x2_pow = torch.pow(x2, torch.Tensor([i for i in range(1, self.degree+1)]))\n x3_pow = torch.pow(x3, torch.Tensor([i for i in range(1, self.degree+1)]))\n x4_pow = torch.pow(x4, torch.Tensor([i for i in range(1, self.degree+1)]))\n x5_pow = torch.pow(x5, torch.Tensor([i for i in range(1, self.degree+1)]))\n x1x2 = torch.ger(x1_pow, x2_pow).flatten()\n x1x3 = torch.ger(x1_pow, x3_pow).flatten()\n x1x4 = torch.ger(x1_pow, x4_pow).flatten()\n x1x5 = torch.ger(x1_pow, x5_pow).flatten()\n x2x3 = torch.ger(x2_pow, x3_pow).flatten()\n x2x4 = torch.ger(x2_pow, x4_pow).flatten()\n x2x5 = torch.ger(x2_pow, x5_pow).flatten()\n x3x4 = torch.ger(x3_pow, x4_pow).flatten()\n x3x5 = torch.ger(x3_pow, x5_pow).flatten()\n x4x5 = torch.ger(x4_pow, x5_pow).flatten()\n input = torch.cat((x1_pow, x2_pow, x3_pow, x4_pow, x5_pow,\n x1x2, x1x3, x1x4, x1x5,\n x2x3, x2x4, x2x5,\n x3x4, x3x5,\n x4x5))\n out = self.fc1(input)\n return out\n\n###############################################################################\n# Used for Pendulum\n###############################################################################\n\n# Univariate trinomial. For use with a single point in 1D space (angular data).\nclass univariate_trinomial(nn.Module):\n def __init__(self):\n super(univariate_trinomial, self).__init__()\n self.fc1 = nn.Linear(3, 1, bias=False)\n\n def forward(self, x):\n x_power = torch.cat(3*[x]).squeeze(1)\n x_power = torch.pow(x_power, torch.Tensor([i for i in range(1, 4)]))\n out = self.fc1(x_power)\n return out\n\nclass univariate_fivenomial(nn.Module):\n def __init__(self):\n super(univariate_fivenomial, self).__init__()\n self.fc1 = nn.Linear(5, 1, bias=False)\n\n def forward(self, x):\n x_power = torch.cat(5*[x]).squeeze(1)\n x_power = torch.pow(x_power, torch.Tensor([i for i in range(1, 6)]))\n out = self.fc1(x_power)\n return out\n\n# Univariate trigonometric space. For use with a single point in 1D space (angular data).\nclass univariate_trigonometric_term(nn.Module):\n def __init__(self):\n super(univariate_trigonometric_term, self).__init__()\n self.fc1 = nn.Linear(1, 1, bias=False)\n self.trig = nn.Linear(1, 1, bias=True)\n\n def forward(self, x):\n x_sin = torch.sin(self.trig(x.squeeze(1)) + x.squeeze(1))\n out = self.fc1(x_sin)\n return out\n\n# Univariate trigonometric combined with third degree polynomial space.\n# For use with a single point in 1D space (angular data).\nclass univariate_trig_tri_mixed(nn.Module):\n def __init__(self):\n super(univariate_trig_tri_mixed, self).__init__()\n self.fc1 = nn.Linear(4, 1, bias=False)\n self.trig = nn.Linear(1, 1, bias=True)\n\n def forward(self, x):\n x_power = torch.cat(3*[x]).squeeze(1)\n x_power = torch.pow(x_power, torch.Tensor([i for i in range(1, 4)]))\n x_sin = torch.sin(self.trig(x.squeeze(1)) + x.squeeze(1))\n input = torch.cat((x_power, x_sin))\n out = self.fc1(input)\n return out\n","repo_name":"dandip/ssinn","sub_path":"function_spaces.py","file_name":"function_spaces.py","file_ext":"py","file_size_in_byte":6036,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"52"} +{"seq_id":"74825380964","text":"\"\"\"\nJuly Zhou\nSection AF\nCSE 163\nFinal Project\nThis file includes functions to clean and organize the data\nand plot graphs for research problem 1 and 2.\n\"\"\"\n\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport json\n\n\ndef dataclean(file):\n \"\"\"\n It cleans the txt data file which is in json format and return\n a dirable dataframe without data that is not needed or missing.\n It also select the appropriate data to present the result of\n research problem 1.\n \"\"\"\n with open(file) as json_file:\n data = json.load(json_file)\n df = pd.DataFrame.from_records(data)\n df1 = df.filter(items=['name', 'geography', 'data'])\n df2 = df1.dropna()\n df3 = df2[df2['name'] ==\n 'Transportation carbon dioxide emissions, '\n 'all fuels, Washington']\n df4 = df2[df2['name'] ==\n 'Industrial carbon dioxide emissions, '\n 'all fuels, Washington']\n data3 = df3['data']\n data4 = df4['data']\n wa3 = data3.to_numpy()[0]\n wa4 = data4.to_numpy()[0]\n year = []\n ems = []\n ems1 = []\n for i in wa3:\n year.append(i[0])\n ems.append(i[1])\n for i in wa4:\n ems1.append(i[1])\n tra = {'year': year, 'tra_emission': ems, 'ind_emission': ems1}\n dfwa = pd.DataFrame(tra, columns=['year', 'tra_emission',\n 'ind_emission'])\n dfwa = dfwa.sort_values(by=['year'], ascending=True)\n return dfwa\n\n\ndef prob1(dfwa):\n \"\"\"\n It takes a dataframe from the dataclean function that was well\n constructed for this function and plots a 1 by 2 line graph to\n present the comparison of transportation and industry CO2\n emission change over 40 years in WA.\n \"\"\"\n fig, (ax1, ax2) = plt.subplots(2, sharex=True, figsize=(10, 8))\n fig.suptitle('Transportation and industry CO2 emission over '\n '40 years in WA state')\n ax1.plot(dfwa['year'], dfwa['tra_emission'])\n ax1.set_title('Transportation CO2 emission')\n ax1.set_ylabel('CO2 emission, metric tons')\n ax1.grid(True)\n ax2.plot(dfwa['year'], dfwa['ind_emission'])\n ax2.set_title('Industry CO2 emission')\n ax2.set_ylabel('CO2 emission, metric tons')\n ax2.tick_params(axis='x', labelrotation=45)\n ax2.grid(True)\n plt.savefig('WA_trans_ind.png')\n\n\ndef prob2(file):\n \"\"\"\n It takes a the txt data file which is in json format and cleans\n up the data followed by plotting a 1 by 2 pie chart to show the\n CO2 emission from different fuels in 2007 and 2017 in the US.It\n also print out the % CO2 emission from different feuls.\n \"\"\"\n with open(file) as json_file:\n data = json.load(json_file)\n # data type is a list of dictionaries, 5119 items, 15 keys per item\n df = pd.DataFrame.from_records(data)\n df1 = df.filter(items=['name', 'geography', 'data'])\n df2 = df1.dropna()\n df2 = df1[df1['name'] ==\n 'Total carbon dioxide emissions from all sectors, '\n 'coal, United States']\n df3 = df1[df1['name'] ==\n 'Total carbon dioxide emissions from all sectors, '\n 'natural gas, United States']\n df4 = df1[df1['name'] ==\n 'Total carbon dioxide emissions from all sectors, '\n 'petroleum, United States']\n coal = df2['data'].to_numpy()[0]\n ngas = df3['data'].to_numpy()[0]\n petro = df4['data'].to_numpy()[0]\n ems = []\n ems1 = []\n ems2 = []\n for i in coal:\n ems.append(i[1])\n for i in ngas:\n ems1.append(i[1])\n for i in petro:\n ems2.append(i[1])\n emssum17 = [ems[0], ems1[0], ems2[0]]\n emssum07 = [ems[10], ems1[10], ems2[10]]\n total17 = np.sum(emssum17)\n total07 = np.sum(emssum07)\n df5 = pd.DataFrame({'CO2 emission in 2017, metric tons': emssum17,\n 'CO2 emission in 2007, metric tons': emssum07},\n index=['coal', 'natural gas', 'petroleum'])\n df5.plot.pie(subplots=True, figsize=(16, 8))\n plt.title('CO2 emission from different fuels in 2017 and 2007 in the US')\n print('total CO2 emission in 2017: ', round(total17, 2), 'metric tons')\n print('coal 2017: ', round(ems[0]/total17*100, 2), '%')\n print('natural gas 2017: ', round(ems1[0]/total17*100, 2), '%')\n print('petroleum 2017: ', round(ems2[0]/total17*100, 2), '%')\n print('total CO2 emission in 2007: ', round(total07, 2), 'metric tons')\n print('coal 2007: ', round(ems[10]/total07*100, 2), '%')\n print('natural gas 2007: ', round(ems1[10]/total07*100, 2), '%')\n print('petroleum 2007: ', round(ems2[10]/total07*100, 2), '%')\n plt.savefig('US_fuels.png')\n\n\ndef main():\n file = 'EMISS.json'\n df = dataclean(file)\n prob1(df)\n prob2(file)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"Julyzzzzzz/classprojectCO2","sub_path":"classprojectCO2/project.py","file_name":"project.py","file_ext":"py","file_size_in_byte":4773,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"25534581590","text":"import datetime\nimport os\nimport platform\nfrom typing import Optional\n\nimport PIL\nimport pytz\nfrom PIL import ExifTags, Image\n\n\ndef get_output_filepath_misc(base_input_path: str, file_path: str, output_path: str):\n _, extension = os.path.splitext(file_path)\n new_filename = os.path.basename(file_path)\n\n entry_base_path = os.path.dirname(file_path)\n new_subdirectory = entry_base_path.replace(base_input_path.rstrip(os.sep), '', 1).lstrip(os.sep)\n return os.path.join(output_path, new_subdirectory, new_filename)\n\n\ndef get_output_filepath(base_input_path: str, image_path: str, creation_time: datetime, output_path: str) -> str:\n _, extension = os.path.splitext(image_path)\n new_filename = f'{creation_time.year}_{creation_time.month}_{creation_time.day}_' \\\n f'{creation_time.hour}_{creation_time.minute}_{creation_time.second}' \\\n f'{extension}'\n entry_base_path = os.path.dirname(image_path)\n new_subdirectory = entry_base_path.replace(base_input_path.rstrip(os.sep), '', 1).lstrip(os.sep)\n new_filepath = os.path.join(output_path, new_subdirectory, new_filename)\n if os.path.exists(new_filepath):\n new_filepath = get_extra_filename(new_filepath)\n return new_filepath\n\n\ndef get_extra_filename(path: str) -> str:\n i = 1\n directory = os.path.dirname(path)\n filename = os.path.basename(path)\n name, extension = os.path.splitext(filename)\n while True:\n new_filename = name + f\"_{i}{extension}\"\n new_path = os.path.join(directory, new_filename)\n i += 1\n if not os.path.exists(new_path):\n return new_path\n\n\ndef is_processable(path: str, should_match_extension: str):\n \"\"\"\n Process scandir entries, copying the file if necessary\n \"\"\"\n if not os.path.isfile(path):\n return False\n\n filename = os.path.basename(path)\n _, extension = os.path.splitext(filename)\n if extension.lower() != should_match_extension.lower():\n return False\n\n return True\n\n\n# Print iterations progress\ndef print_progress_bar(iteration,\n total,\n prefix='',\n suffix='',\n decimals=1,\n length=100,\n fill='█',\n print_end=\"\\r\",\n percentage=None):\n \"\"\"\n Call in a loop to create terminal progress bar\n @params:\n iteration - Required : current iteration (Int)\n total - Required : total iterations (Int)\n prefix - Optional : prefix string (Str)\n suffix - Optional : suffix string (Str)\n decimals - Optional : positive number of decimals in percent complete (Int)\n length - Optional : character length of bar (Int)\n fill - Optional : bar fill character (Str)\n printEnd - Optional : end character (e.g. \"\\r\", \"\\r\\n\") (Str)\n \"\"\"\n percentage = iteration / float(total) if percentage is None else percentage\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * percentage)\n filledLength = int(length * percentage)\n bar = fill * filledLength + '-' * (length - filledLength)\n print(f'\\r{prefix} |{bar}| {percent}% {suffix}', end=print_end)\n # Print New Line on Complete\n if percentage == 1:\n print()\n\n\nclass ProgressStat:\n\n def __init__(self):\n self.processed_count = 0\n self.to_process_count = 0\n self._last_progress = 0\n\n def progress(self) -> float:\n p = min(1.0, self.processed_count / max(self.to_process_count, 1))\n self._last_progress = p\n return p\n\n\ndef localize_to_os_timezone(timestamp) -> datetime:\n dt = datetime.datetime.fromtimestamp(timestamp, tz=datetime.timezone.utc)\n return dt.astimezone()\n\n\ndef get_earliest_creation(path, timezone_map: dict) -> int:\n modified_datetime = modified_timestamp(path)\n created_datetime = creation_timestamp(path)\n exif_shutter_datetime = get_image_shot_on_timestamp(path, timezone_map)\n\n min_fs_datetime = min(modified_datetime, created_datetime)\n min_overall_datetime = min_fs_datetime\n if exif_shutter_datetime != 0:\n min_overall_datetime = min(min_fs_datetime, exif_shutter_datetime)\n\n return int(min_overall_datetime)\n\n\ndef get_image_shot_on_timestamp(path: str, timezone_map: dict) -> int:\n \"\"\"\n Gets the UTC timestamp of when a picture was shot.\n If a device model can't be mapped to a timezone, the current timezone is used.\n\n :param str path: The path to the image\n :param dict timezone_map: A map of device model: timezone. For example { 'nikon d5600': 'Asia/Tokyo' }\n :return: timestamp\n :rtype: int\n \"\"\"\n exif_data = get_exif_data_from_image(path)\n if exif_data is None or exif_data.date_time_original is None:\n return 0\n # Localize to source timezone and convert to UTC\n dt = exif_data.date_time_original\n src_timezone = timezone_map[exif_data.model] if exif_data.model in timezone_map else None\n if src_timezone is not None:\n dt_with_tz = pytz.timezone(src_timezone).localize(dt)\n else:\n dt_with_tz = dt.astimezone()\n\n exif_shutter_datetime = dt_with_tz.astimezone(datetime.timezone.utc)\n return int(exif_shutter_datetime.timestamp())\n\n\nclass ExifData:\n\n def __init__(self):\n self.date_time_original = None\n self.model = None\n\n\ndef get_exif_data_from_image(path: str) -> Optional[ExifData]:\n try:\n img = Image.open(path)\n img_exif = img.getexif()\n if img_exif is None:\n return None\n\n output = ExifData()\n date_format = \"%Y:%m:%d %H:%M:%S\"\n\n for key, val in img_exif.items():\n if key not in ExifTags.TAGS:\n continue\n exif_key = ExifTags.TAGS[key]\n\n if exif_key == \"DateTimeOriginal\":\n output.date_time_original = datetime.datetime.strptime(val, date_format)\n elif exif_key == \"Model\":\n output.model = str(val).lower()\n\n img.close()\n\n return output\n except PIL.UnidentifiedImageError:\n return None\n\n\ndef modified_timestamp(path_to_file: str) -> float:\n \"\"\"\n Try to get the date that a file was created, falling back to when it was\n last modified if that isn't possible.\n See http://stackoverflow.com/a/39501288/1709587 for explanation.\n \"\"\"\n if platform.system() == 'Windows':\n return os.path.getmtime(path_to_file)\n else:\n stat = os.stat(path_to_file)\n return stat.st_mtime\n\n\ndef creation_timestamp(path_to_file: str) -> int:\n \"\"\"\n Try to get the date that a file was created, falling back to when it was\n last modified if that isn't possible.\n See http://stackoverflow.com/a/39501288/1709587 for explanation.\n \"\"\"\n if platform.system() == 'Windows':\n return int(os.path.getctime(path_to_file))\n else:\n stat = os.stat(path_to_file)\n try:\n return stat.st_birthtime\n except AttributeError:\n # We're probably on Linux. No easy way to get creation dates here,\n # so we'll settle for when its content was last modified.\n return int(stat.st_mtime)\n","repo_name":"sp0x/reimage","sub_path":"src/reimage/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":7220,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"73831668966","text":"import random\nimport sys\n\nfrom app import Mass, Spring\nfrom preprocess import read_file\n\nimport networkx as nx\n\nimport matplotlib.pyplot as plt\n\nnetwork = read_file()\n\nnodes = set()\nedges = dict()\n\nmasses = dict()\nlog = dict()\nsprings = list()\n\nfor entry in network:\n nodes.update([entry[0]])\n edges[frozenset(entry[:2])] = entry[2]\n\nfor index in nodes:\n masses[index] = Mass()\n masses[index].position = complex(random.random(), random.random())\n\nfor key in masses.keys():\n masses[key].fixed = True\n masses[key].position = 1 + 1j\n break\n\nfor key in masses.keys():\n log[key] = []\n\nfor item in edges:\n x, y = item\n\n springs.append(Spring(masses[x], masses[y], relaxed_length=1/edges[item]))\n\n# Simulate\nfor gen in range(4000):\n # print(f\"Generation: {gen}\")\n\n for key in masses.keys():\n masses[key].force = complex(0)\n\n for s in springs:\n s.update()\n\n for key in masses.keys():\n m = masses[key]\n\n m.update(dt=0.1)\n\n log[key].append(m.position)\n\nlast_state = dict()\n\nfor key in log.keys():\n x = []\n y = []\n\n for item in log[key]:\n x.append(item.real)\n y.append(item.imag)\n\n last_state[key] = [x[-1], y[-1]]\n\n plt.plot(x, y)\n\nplt.savefig(\"vis/trajectory.png\")\nplt.show()\n\nfor connection in edges.keys():\n m1, m2 = connection\n\n x1, y1 = last_state[m1]\n x2, y2 = last_state[m2]\n\n plt.plot([x1, x2], [y1, y2])\n\nfor key in last_state.keys():\n plt.plot(*last_state[key], \"ro\")\n\nplt.savefig(\"vis/sim_network.png\")\nplt.show()\n\nplotting = False\n\nif plotting:\n g = nx.Graph()\n\n for item in edges:\n x, y = item\n\n g.add_edge(x, y)\n\n nx.draw_spring(g)\n\n plt.savefig(\"vis/network.png\")\n","repo_name":"sebastianjkern/spring-clustering","sub_path":"cluster_examples_sandbox.py","file_name":"cluster_examples_sandbox.py","file_ext":"py","file_size_in_byte":1716,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"3081296760","text":"import datetime\nimport json\nimport time\nfrom asyncio import CancelledError\n\nfrom loguru import logger\nfrom spade.agent import Agent\nfrom spade.behaviour import TimeoutBehaviour\nfrom spade.message import Message\nfrom spade.template import Template\n\nfrom .helpers import random_position\nfrom .protocol import (\n REQUEST_PROTOCOL,\n REGISTER_PROTOCOL,\n ACCEPT_PERFORMATIVE,\n REFUSE_PERFORMATIVE,\n REQUEST_PERFORMATIVE,\n TRAVEL_PROTOCOL,\n CANCEL_PERFORMATIVE,\n INFORM_PERFORMATIVE,\n)\nfrom .utils import (\n StrategyBehaviour,\n CyclicBehaviour,\n FREE_STATION,\n BUSY_STATION,\n TRANSPORT_MOVING_TO_STATION,\n TRANSPORT_IN_STATION_PLACE,\n TRANSPORT_CHARGED,\n)\n\n\nclass StationAgent(Agent):\n def __init__(self, agentjid, password):\n super().__init__(jid=agentjid, password=password)\n self.agent_id = None\n self.icon = None\n self.strategy = None\n self.running_strategy = False\n self.directory_id = None\n self.registration = False\n self.station_name = None\n self.station_type = None\n self.current_pos = None\n self.available_places = None\n self.status = None\n self.power = None\n self.stopped = False\n self.ready = False\n\n # waiting waiting_list\n self.waiting_list = list()\n\n # statistics\n self.charged_transports = 0\n self.queue_length = 0\n self.max_queue_length = 0\n\n self.transports_in_queue_time = None\n self.empty_queue_time = None\n self.total_busy_time = None # total time with some transport waiting in queue\n\n def is_ready(self):\n return self.ready\n\n async def setup(self):\n self.total_busy_time = 0.0\n logger.info(\"Station agent {} running\".format(self.name))\n self.set_type(\"station\")\n self.set_status()\n try:\n template = Template()\n template.set_metadata(\"protocol\", REGISTER_PROTOCOL)\n register_behaviour = RegistrationBehaviour()\n self.add_behaviour(register_behaviour, template)\n while not self.has_behaviour(register_behaviour):\n logger.warning(\n \"Station {} could not create RegisterBehaviour. Retrying...\".format(\n self.agent_id\n )\n )\n self.add_behaviour(register_behaviour, template)\n except Exception as e:\n logger.error(\n \"EXCEPTION creating RegisterBehaviour in Station {}: {}\".format(\n self.agent_id, e\n )\n )\n try:\n template = Template()\n template.set_metadata(\"protocol\", TRAVEL_PROTOCOL)\n travel_behaviour = TravelBehaviour()\n self.add_behaviour(travel_behaviour, template)\n while not self.has_behaviour(travel_behaviour):\n logger.warning(\n \"Customer {} could not create TravelBehaviour. Retrying...\".format(\n self.agent_id\n )\n )\n self.add_behaviour(travel_behaviour, template)\n except Exception as e:\n logger.error(\n \"EXCEPTION creating TravelBehaviour in Station {}: {}\".format(\n self.agent_id, e\n )\n )\n self.ready = True\n\n async def send(self, msg):\n if not msg.sender:\n msg.sender = str(self.jid)\n logger.debug(f\"Adding agent's jid as sender to message: {msg}\")\n aioxmpp_msg = msg.prepare()\n await self.client.send(aioxmpp_msg)\n msg.sent = True\n self.traces.append(msg, category=str(self))\n\n def set_id(self, agent_id):\n \"\"\"\n Sets the agent identifier\n\n Args:\n agent_id (str): The new Agent Id\n \"\"\"\n self.agent_id = agent_id\n\n def set_icon(self, icon):\n self.icon = icon\n\n def run_strategy(self):\n \"\"\"\n Sets the strategy for the transport agent.\n \"\"\"\n if not self.running_strategy:\n template = Template()\n template.set_metadata(\"protocol\", REQUEST_PROTOCOL)\n self.add_behaviour(self.strategy(), template)\n self.running_strategy = True\n\n def set_registration(self, status):\n \"\"\"\n Sets the status of registration\n Args:\n status (boolean): True if the transport agent has registered or False if not\n \"\"\"\n self.registration = status\n\n def set_directory(self, directory_id):\n \"\"\"\n Sets the directory JID address\n Args:\n directory_id (str): the DirectoryAgent jid\n \"\"\"\n self.directory_id = directory_id\n\n def set_type(self, station_type):\n self.station_type = station_type\n\n def set_position(self, coords=None):\n \"\"\"\n Sets the position of the station. If no position is provided it is located in a random position.\n\n Args:\n coords (list): a list coordinates (longitude and latitude)\n \"\"\"\n if coords:\n self.current_pos = coords\n else:\n self.current_pos = random_position()\n\n logger.debug(\n \"Station {} position is {}\".format(self.agent_id, self.current_pos)\n )\n\n def get_position(self):\n \"\"\"\n Returns the current position of the station.\n\n Returns:\n list: the coordinates of the current position of the customer (lon, lat)\n \"\"\"\n return self.current_pos\n\n def set_status(self, state=FREE_STATION):\n self.status = state\n\n def get_status(self):\n return self.status\n\n def set_available_places(self, places):\n self.available_places = places\n\n def get_available_places(self):\n return self.available_places\n\n def set_power(self, charge):\n self.power = charge\n\n def get_power(self):\n return self.power\n\n def to_json(self):\n \"\"\"\n Serializes the main information of a station agent to a JSON format.\n It includes the id of the agent, its current position, the destination coordinates of the agent,\n the current status, the transport that it has assigned (if any) and its waiting time.\n\n Returns:\n dict: a JSON doc with the main information of the station.\n\n Example::\n\n {\n \"id\": \"cphillips\",\n \"position\": [ 39.461327, -0.361839 ],\n \"status\": True,\n \"places\": 10,\n \"power\": 10\n }\n \"\"\"\n return {\n \"id\": self.agent_id,\n \"position\": self.current_pos,\n \"status\": self.status,\n \"places\": self.available_places,\n \"power\": self.power,\n \"icon\": self.icon,\n }\n\n async def assigning_place(self):\n \"\"\"\n Set a space in the charging station for the transport that has been accepted, when the available spaces are zero,\n the status will change to BUSY_STATION\n \"\"\"\n p = self.get_available_places()\n if p - 1 <= 0:\n self.set_status(BUSY_STATION)\n self.set_available_places(p - 1)\n logger.info(\n \"Station {} assigned place. Available places are now {}.\".format(\n self.name, self.get_available_places()\n )\n )\n\n async def deassigning_place(self):\n \"\"\"\n Leave a space of the charging station, when the station has free spaces, the status will change to FREE_STATION\n \"\"\"\n if self.waiting_list:\n transport_id = self.waiting_list.pop(0)\n # time statistics update\n if len(self.waiting_list) == 0:\n self.empty_queue_time = time.time()\n self.total_busy_time += (\n self.empty_queue_time - self.transports_in_queue_time\n )\n\n logger.debug(\n \"Station {} has a place to charge transport {}\".format(\n self.agent_id, transport_id\n )\n )\n # confirm EXPLICITLY to transport it can start charging\n reply = Message()\n reply.to = str(transport_id)\n reply.set_metadata(\"protocol\", REQUEST_PROTOCOL)\n reply.set_metadata(\"performative\", ACCEPT_PERFORMATIVE)\n content = {\"station_id\": self.agent_id}\n reply.body = json.dumps(content)\n await self.send(reply)\n # await send_confirmation_to_transport(transport_id)\n\n else:\n p = self.get_available_places()\n if p + 1:\n self.set_status(FREE_STATION)\n self.set_available_places(p + 1)\n\n async def charging_transport(self, need, transport_id):\n total_time = need / self.get_power()\n now = datetime.datetime.now()\n start_at = now + datetime.timedelta(seconds=total_time)\n logger.info(\n \"Station {} started charging transport {} for {} seconds. From {} to {}.\".format(\n self.name, transport_id, total_time, now, start_at\n )\n )\n # charged transports update\n self.charged_transports += 1\n\n charge_behaviour = ChargeBehaviour(start_at=start_at, transport_id=transport_id)\n self.add_behaviour(charge_behaviour)\n\n\nclass ChargeBehaviour(TimeoutBehaviour):\n def __init__(self, start_at, transport_id):\n self.transport_id = transport_id\n super().__init__(start_at)\n\n async def charging_complete(self):\n \"\"\"\n Send a message to the transport agent that the vehicle load has been completed\n \"\"\"\n reply = Message()\n reply.to = str(self.transport_id)\n reply.set_metadata(\"protocol\", REQUEST_PROTOCOL)\n reply.set_metadata(\"performative\", INFORM_PERFORMATIVE)\n content = {\"status\": TRANSPORT_CHARGED}\n reply.body = json.dumps(content)\n await self.send(reply)\n\n async def run(self):\n logger.debug(\"Station {} finished charging.\".format(self.agent.name))\n self.set(\"current_station\", None)\n await self.agent.deassigning_place()\n await self.charging_complete()\n\n\nclass RegistrationBehaviour(CyclicBehaviour):\n async def on_start(self):\n logger.debug(\"Strategy {} started in directory\".format(type(self).__name__))\n\n def set_registration(self, decision):\n self.agent.registration = decision\n\n async def send_registration(self):\n \"\"\"\n Send a ``spade.message.Message`` with a proposal to directory to register.\n \"\"\"\n logger.info(\n \"Station {} sent proposal to register to directory {}\".format(\n self.agent.name, self.agent.directory_id\n )\n )\n\n content = {\n \"jid\": str(self.agent.jid),\n \"type\": self.agent.station_type,\n \"status\": self.agent.status,\n \"position\": self.agent.get_position(),\n \"charge\": self.agent.power,\n }\n msg = Message()\n msg.to = str(self.agent.directory_id)\n msg.set_metadata(\"protocol\", REGISTER_PROTOCOL)\n msg.set_metadata(\"performative\", REQUEST_PERFORMATIVE)\n msg.body = json.dumps(content)\n await self.send(msg)\n\n async def run(self):\n try:\n if not self.agent.registration:\n await self.send_registration()\n msg = await self.receive(timeout=10)\n if msg:\n performative = msg.get_metadata(\"performative\")\n if performative == ACCEPT_PERFORMATIVE:\n self.set_registration(True)\n logger.debug(\"Registration in the directory\")\n except CancelledError:\n logger.debug(\"Cancelling async tasks...\")\n except Exception as e:\n logger.error(\n \"EXCEPTION in RegisterBehaviour of Station {}: {}\".format(\n self.agent.name, e\n )\n )\n\n\nclass TravelBehaviour(CyclicBehaviour):\n \"\"\"\n This is the internal behaviour that manages the inform of the station.\n It is triggered when the transport informs the station that it is going to the\n customer's position until the customer is droppped in its destination.\n \"\"\"\n\n async def on_start(self):\n logger.debug(\"Station {} started TravelBehavior.\".format(self.agent.name))\n\n async def run(self):\n try:\n msg = await self.receive(timeout=5)\n if not msg:\n return\n content = json.loads(msg.body)\n transport_id = msg.sender\n logger.debug(\"Station {} informed of: {}\".format(self.agent.name, content))\n if \"status\" in content:\n status = content[\"status\"]\n if status == TRANSPORT_MOVING_TO_STATION:\n logger.info(\n \"Transport {} coming to station {}.\".format(\n transport_id, self.agent.name\n )\n )\n elif status == TRANSPORT_IN_STATION_PLACE:\n logger.info(\n \"Station {} is going to start charging transport {}\".format(\n self.agent.name, transport_id\n )\n )\n\n await self.agent.charging_transport(content[\"need\"], transport_id)\n except CancelledError:\n logger.debug(\"Cancelling async tasks...\")\n except Exception as e:\n logger.error(\n \"EXCEPTION in Travel Behaviour of Station {}: {}\".format(\n self.agent.name, e\n )\n )\n\n\nclass StationStrategyBehaviour(StrategyBehaviour):\n \"\"\"\n Class from which to inherit to create a station strategy.\n You must overload the :func:`run` method\n\n Helper functions:\n * :func:`get_transport_agents`\n \"\"\"\n\n async def on_start(self):\n logger.debug(\"Strategy {} started in station\".format(type(self).__name__))\n\n async def accept_transport(self, transport_id):\n \"\"\"\n Sends a ``spade.message.Message`` to a transport to accept a travel proposal for charge.\n It uses the REQUEST_PROTOCOL and the ACCEPT_PERFORMATIVE.\n\n Args:\n transport_id (str): The Agent JID of the transport\n \"\"\"\n reply = Message()\n reply.to = str(transport_id)\n reply.set_metadata(\"protocol\", REQUEST_PROTOCOL)\n reply.set_metadata(\"performative\", INFORM_PERFORMATIVE)\n content = {\"station_id\": str(self.agent.jid), \"dest\": self.agent.current_pos}\n reply.body = json.dumps(content)\n await self.send(reply)\n logger.debug(\n \"Station {} accepted proposal for charge from transport {}\".format(\n self.agent.name, transport_id\n )\n )\n\n async def refuse_transport(self, transport_id):\n \"\"\"\n Sends an ``spade.message.Message`` to a transport to refuse a travel proposal for charge.\n It uses the REQUEST_PROTOCOL and the REFUSE_PERFORMATIVE.\n\n Args:\n transport_id (str): The Agent JID of the transport\n \"\"\"\n reply = Message()\n reply.to = str(transport_id)\n reply.set_metadata(\"protocol\", REQUEST_PROTOCOL)\n reply.set_metadata(\"performative\", REFUSE_PERFORMATIVE)\n content = {}\n reply.body = json.dumps(content)\n\n await self.send(reply)\n logger.debug(\n \"Station {} refused proposal for charge from transport {}\".format(\n self.agent.name, transport_id\n )\n )\n\n async def run(self):\n msg = await self.receive(timeout=5)\n\n if msg:\n performative = msg.get_metadata(\"performative\")\n transport_id = msg.sender\n if performative == CANCEL_PERFORMATIVE:\n logger.warning(\n \"Station {} received a CANCEL from Transport {}.\".format(\n self.agent.name, transport_id\n )\n )\n await self.agent.deassigning_place()\n elif (\n performative == ACCEPT_PERFORMATIVE\n ): # comes from send_confirmation_travel\n if self.agent.get_status() == FREE_STATION:\n logger.info(\n \"Station {} has a place to charge transport {}\".format(\n self.agent.name, transport_id\n )\n )\n # confirm EXPLICITLY to transport it can start charging\n reply = Message()\n reply.to = str(transport_id)\n reply.set_metadata(\"protocol\", REQUEST_PROTOCOL)\n reply.set_metadata(\"performative\", ACCEPT_PERFORMATIVE)\n content = {\"station_id\": self.agent.name}\n reply.body = json.dumps(content)\n await self.send(reply)\n await self.agent.assigning_place()\n # self.agent.assigning_place()\n\n else: # self.agent.get_status() == BUSY_STATION\n # time statistics update\n if len(self.agent.waiting_list) == 0:\n self.agent.transports_in_queue_time = time.time()\n # transport waits in a waiting_list until it is available to charge\n self.agent.waiting_list.append(str(transport_id))\n # list length statistics update\n self.agent.queue_length = len(self.agent.waiting_list)\n if self.agent.queue_length > self.agent.max_queue_length:\n self.agent.max_queue_length = self.agent.queue_length\n logger.info(\n \"{} is waiting at {}, whose waiting list is {}\".format(\n transport_id, self.agent.name, self.agent.waiting_list\n )\n )\n","repo_name":"0x1F602/simfleet","sub_path":"simfleet/station.py","file_name":"station.py","file_ext":"py","file_size_in_byte":18130,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"52"} +{"seq_id":"28180842099","text":"import os\n\nimport youtube_dl\n\n\ndef get_options(path):\n complete_path = os.path.join(path, '%(title)s.%(ext)s')\n return {\n 'outtmpl': complete_path,\n 'format': 'bestaudio/best',\n 'postprocessors': [{\n 'key': 'FFmpegExtractAudio',\n 'preferredcodec': 'mp3',\n 'preferredquality': '192',\n }],\n }\n\n\nclass DownloadHandler(object):\n\n def __init__(self, path_: str, max_length_: int):\n self.path = os.path.join(path_, 'downloads')\n self.inc = 0\n self.max_length = max_length_\n\n def get_dir_name(self):\n res = os.path.join(self.path, 'download_' + str(self.inc))\n while os.path.exists(res):\n self.inc += 1\n res = os.path.join(self.path, 'download_' + str(self.inc))\n return res\n\n def download_videos(self, video_list):\n # make directory with self.inc\n directory = self.get_dir_name()\n os.mkdir(directory)\n # download videos in video_list\n with youtube_dl.YoutubeDL(get_options(directory)) as downloader:\n for link in video_list:\n dict_meta = downloader.extract_info(link, download=False)\n if dict_meta['duration'] > self.max_length * 60:\n title = dict_meta['title'] + '.toolarge'\n f = open(os.path.join(directory, title), \"x\")\n f.close()\n else:\n downloader.download([link])\n # return directory name\n return directory\n","repo_name":"trapwired/EmailYoutubeDownload","sub_path":"src/DownloadHandler.py","file_name":"DownloadHandler.py","file_ext":"py","file_size_in_byte":1531,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"52"} +{"seq_id":"23321865401","text":"import os\n\n\ndef test_compile_tools_python_run():\n from util.compile_tools import Python\n is_ok = Python.run(os.path.join(os.getcwd(), 'testfile', 'fake_exam.py'))\n assert is_ok\n\n\ndef test_compile_tools_python_check():\n from util.compile_tools import Python\n result = Python.check(os.path.join(os.getcwd(), 'testfile', 'fake_exam.py'),\n os.path.join(os.getcwd(), 'testfile', 'fake_exam.input'))\n if isinstance(result, bool):\n assert result\n else:\n print(result)\n","repo_name":"StrayDragon/OJ-Solutions","sub_path":"tools/assistant/test/test_util.py","file_name":"test_util.py","file_ext":"py","file_size_in_byte":521,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"50"} +{"seq_id":"38474231900","text":"\"\"\"Chagne point detection using ruptures library.\"\"\"\nimport numpy as np\nfrom ruptures import Binseg\n\n\ndef binseg(arr: np.ndarray, nseg=3) -> list[int]:\n \"\"\"Use Binary Segmentation algorithm to detect change points.\n\n Args:\n arr (np.ndarray): 2d array of [x,features]\n nseg (int, optional): number of change points to return. Defaults to 3.\n\n Returns:\n list[int]: index of change points\n \"\"\"\n algo = Binseg(model=\"l2\", min_size=2).fit(arr)\n result = algo.predict(n_bkps=nseg)\n\n # remove element equal to len of first dimension of arr if exists\n if result[-1] == arr.shape[0]:\n result = result[:-1]\n return result # type: ignore\n\n\ndef find_shock_index_from_gradnd(gradnd: np.ndarray) -> int:\n \"\"\"Find the index of the shock from gradient of the number density.\n\n - If gradnd is 2d then find the peak of the mean.\n - Assume the shock is located at the peak of grad nd\n\n Args:\n gradnd (np.ndarray): gradient of the number density.\n\n Returns:\n int: index of the shock.\n \"\"\"\n if gradnd.ndim == 2:\n gradnd = gradnd.mean(axis=1)\n\n return int(np.argmax(gradnd))\n","repo_name":"jmsplank/hybrid-jp","sub_path":"src/hybrid_jp/change_points.py","file_name":"change_points.py","file_ext":"py","file_size_in_byte":1154,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"14594163203","text":"import sys\n\nfrom prettytable import PrettyTable\n\n\nclass NodeLim:\n def __init__(self, node_lim_entry):\n self.index = node_lim_entry[0]\n self.active = node_lim_entry[1].split(':')[2]\n self.standby = node_lim_entry[2].split(':')[2]\n self.primary = node_lim_entry[3].split(':')[2]\n self.secondary = node_lim_entry[4].split(':')[2]\n self.state = node_lim_entry[9]\n\n\nclass TsmTsft:\n def __init__(self, tsm_tsft_entry):\n self.tsid = tsm_tsft_entry[0]\n self.valid_prim = tsm_tsft_entry[1]\n self.valid_sec = tsm_tsft_entry[2]\n self.papid_prim = tsm_tsft_entry[3]\n self.papid_sec = tsm_tsft_entry[4]\n self.board_prim = (int(self.papid_prim.split('/')[0]) + 1) if (\n self.valid_prim == '1') else 0\n self.board_sec = (int(self.papid_sec.split('/')[0]) + 1) if (\n self.valid_sec == '1') else 0\n\n\nclass MergeNodeLim:\n def __init__(self, active, standby, primary, secondary, state,\n index_range=''):\n self.active = active\n self.standby = standby\n self.primary = primary\n self.secondary = secondary\n self.state = state\n self.index_range = index_range\n\n\nclass MergeTsmTsft:\n def __init__(self, valid_prim, valid_sec, board_prim, board_sec,\n tsid_range=''):\n self.valid_prim = valid_prim\n self.valid_sec = valid_sec\n self.board_prim = board_prim\n self.board_sec = board_sec\n self.tsid_range = tsid_range\n\n\ndef build_node_lim_list(filename):\n with open(filename) as file_object:\n\n node_lim_list = []\n\n while True:\n line = file_object.readline()\n if line.find('UP Index') != -1:\n break\n\n while True:\n line = file_object.readline()\n node_lim_entry = line.split()\n if len(node_lim_entry) > 0:\n node_lim = NodeLim(node_lim_entry)\n node_lim_list.append(node_lim)\n if int(node_lim_entry[0]) == 2047:\n break\n\n return node_lim_list\n\n\ndef build_tsm_tsft_list(filename):\n with open(filename) as file_object:\n\n tsm_tsft_list = []\n\n while True:\n line = file_object.readline()\n if line.find('TSID Valid Primary') != -1:\n break\n\n while True:\n line = file_object.readline()\n tsm_tsft_entry_prim = line.split()\n if len(tsm_tsft_entry_prim) > 0:\n line = file_object.readline()\n tsm_tsft_entry_sec = line.split()\n tsm_tsft_entry = [tsm_tsft_entry_prim[0],\n tsm_tsft_entry_prim[1],\n tsm_tsft_entry_sec[1],\n tsm_tsft_entry_prim[5],\n tsm_tsft_entry_sec[5]]\n tsm_tsft = TsmTsft(tsm_tsft_entry)\n tsm_tsft_list.append(tsm_tsft)\n if int(tsm_tsft_entry_prim[0]) == 2047:\n break\n\n return tsm_tsft_list\n\n\ndef analyze_node_lim_list(node_lim_list):\n merge_node_lim_list = []\n\n active = ''\n standby = ''\n primary = ''\n secondary = ''\n state = ''\n index = ''\n\n for node_lim_entry in node_lim_list:\n if (node_lim_entry.active != active) or (\n node_lim_entry.standby != standby) or (\n node_lim_entry.primary != primary) or (\n node_lim_entry.secondary != secondary) or (\n node_lim_entry.state != state):\n active = node_lim_entry.active\n standby = node_lim_entry.standby\n primary = node_lim_entry.primary\n secondary = node_lim_entry.secondary\n state = node_lim_entry.state\n index = node_lim_entry.index\n merge_node_lim = MergeNodeLim(active, standby, primary,\n secondary, state)\n\n merge_node_lim_list.append(merge_node_lim)\n else:\n merge_node_lim_list[-1].index_range = index + ' - ' + \\\n node_lim_entry.index\n\n format_node_lim_list(merge_node_lim_list)\n\n\ndef analyze_tsm_tsft_list(tsm_tsft_list):\n merge_tsm_tsft_list = []\n\n valid_prim = ''\n valid_sec = ''\n board_prim = ''\n board_sec = ''\n tsid = ''\n\n for tsm_tsft_entry in tsm_tsft_list:\n if (tsm_tsft_entry.valid_prim != valid_prim) or (\n tsm_tsft_entry.valid_sec != valid_sec) or (\n tsm_tsft_entry.board_prim != board_prim) or (\n tsm_tsft_entry.board_sec != board_sec):\n valid_prim = tsm_tsft_entry.valid_prim\n valid_sec = tsm_tsft_entry.valid_sec\n board_prim = tsm_tsft_entry.board_prim\n board_sec = tsm_tsft_entry.board_sec\n tsid = tsm_tsft_entry.tsid\n merge_tsm_tsft = MergeTsmTsft(valid_prim, valid_sec, board_prim,\n board_sec)\n\n merge_tsm_tsft_list.append(merge_tsm_tsft)\n else:\n merge_tsm_tsft_list[-1].tsid_range = tsid + ' - ' + \\\n tsm_tsft_entry.tsid\n\n format_tsm_tsft__list(merge_tsm_tsft_list)\n\n\ndef format_node_lim_list(merge_node_lim_list):\n with open('node_lim_and_tsm_tsft_analyze_result.txt', 'a') as file_object:\n\n headers = ['Index Range', 'Primary', 'Secondary', 'Active', 'Standby',\n 'State']\n table = PrettyTable(headers)\n for merge_node_lim in merge_node_lim_list:\n row = [merge_node_lim.index_range, merge_node_lim.primary,\n merge_node_lim.secondary, merge_node_lim.active,\n merge_node_lim.standby, merge_node_lim.state]\n table.add_row(row)\n\n file_object.write(str(table) + '\\n')\n\n\ndef format_tsm_tsft__list(merge_tsm_tsft_list):\n with open('node_lim_and_tsm_tsft_analyze_result.txt', 'a') as file_object:\n\n headers = ['Tsid Range', 'Primary', 'Secondary', 'Primary Valid',\n 'Secondary Valid']\n table = PrettyTable(headers)\n for merge_tsm_tsft in merge_tsm_tsft_list:\n row = [merge_tsm_tsft.tsid_range, merge_tsm_tsft.board_prim,\n merge_tsm_tsft.board_sec, merge_tsm_tsft.valid_prim,\n merge_tsm_tsft.valid_sec]\n table.add_row(row)\n\n file_object.write(str(table) + '\\n')\n\n\nif len(sys.argv) > 2:\n node_lim_master_table_filename = sys.argv[1]\n card_fabl_tsm_tsft_table_filename = sys.argv[2]\n\n node_lim_list_built = build_node_lim_list(node_lim_master_table_filename)\n tsm_tsft_list_built = build_tsm_tsft_list(\n card_fabl_tsm_tsft_table_filename)\n\n analyze_node_lim_list(node_lim_list_built)\n analyze_tsm_tsft_list(tsm_tsft_list_built)\n","repo_name":"crystalDf/EPGAnalyze","sub_path":"node_lim_and_tsm_tsft_analyze/node_lim_and_tsm_tsft_analyze.py","file_name":"node_lim_and_tsm_tsft_analyze.py","file_ext":"py","file_size_in_byte":6915,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"40137483230","text":"import FWCore.ParameterSet.Config as cms\n\nprocess = cms.Process(\"DQMOnlineSimData\")\n\nprocess.MessageLogger = cms.Service(\n \"MessageLogger\",\n debug_txt = cms.untracked.PSet( threshold = cms.untracked.string('DEBUG') ),\n destinations = cms.untracked.vstring('debug_txt'),\n #debugModules = cms.untracked.vstring(\"SiStripMonitorTrack\",\"SiStripClusterInfo\")\n debugModules = cms.untracked.vstring(\"SiStripMonitorTrack\")\n )\n\n#-------------------------------------------------\n## MAGNETIC FIELD\n#-------------------------------------------------\nprocess.load(\"Configuration.StandardSequences.MagneticField_0T_cff\")\nprocess.prefer(\"VolumeBasedMagneticFieldESProducer\")\n\n#-------------------------------------------------\n## GEOMETRY\n#-------------------------------------------------\n\n# CMS Geometry\nprocess.load(\"Configuration.StandardSequences.GeometryRecoDB_cff\")\n\n#-------------------------------------------------\n## CALIBRATION\n#-------------------------------------------------\nprocess.load(\"CalibTracker.Configuration.Tracker_FakeConditions_cff\")\n\n#--------------------------\n#### DQM\n#--------------------------\n# DQM services\nprocess.load(\"DQMServices.Core.DQM_cfg\")\n\n#-------------------------------------\n#### SiStripMonitorTrack + Scheduling\n#-------------------------------------\n# Standard Monitoring\nprocess.load(\"DQM.SiStripMonitorTrack.SiStripMonitorTrack_StandAlone_cff\")\nprocess.p = cms.Path(process.DQMSiStripMonitorTrack_Sim)\n# Monitoring with Eta Function from SiStripRawDigi\n#process.load(\"DQM.SiStripMonitorTrack.SiStripMonitorTrack_RawStandAlone_cff\")\n#process.p = cms.Path(process.DQMSiStripMonitorTrack_RawSim)\nprocess.TrackRefitter.TrajectoryInEvent = True\n\nprocess.printout = cms.OutputModule(\"AsciiOutputModule\")\nprocess.ep = cms.EndPath(process.printout)\n\nprocess.AdaptorConfig = cms.Service(\"AdaptorConfig\")\n\nprocess.source = cms.Source(\n \"PoolSource\",\n fileNames = cms.untracked.vstring('/store/relval/2008/6/6/RelVal-RelValSingleMuPt1-1212531852-IDEAL_V1-2nd-02/0000/74A33D36-E933-DD11-BC9E-001617E30F56.root')\n #firstRun = cms.untracked.uint32(6)\n #firstEvent = cms.untracked.uint32(15)\n )\n\nprocess.maxEvents = cms.untracked.PSet(\n input = cms.untracked.int32(100)\n )\n","repo_name":"cms-sw/cmssw","sub_path":"DQM/SiStripMonitorTrack/test/SiStripMonitorTrack_SimData_cfg.py","file_name":"SiStripMonitorTrack_SimData_cfg.py","file_ext":"py","file_size_in_byte":2236,"program_lang":"python","lang":"en","doc_type":"code","stars":985,"dataset":"github-code","pt":"50"} +{"seq_id":"16023478452","text":"\"\"\"Functions for downloading and reading MNIST data.\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport gzip\nimport os\nimport tempfile\n\nimport numpy\nfrom six.moves import urllib\nfrom six.moves import xrange # pylint: disable=redefined-builtin\nimport tensorflow as tf\nfrom tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets\n\n#10 classes\n#784 (28x28) pixels in each image\n#None (55000) images\n\n####################################################\n#mnist = read_data_sets(\"MNIST_data/\", one_hot=True)\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport itertools\nimport tensorflow as tf\n\n\nloaded = np.load('type_age_atRedshiftZero.npz')\ntrainImages = loaded['trainImages']\ntrainLabels = loaded['trainLabels']\n#trainFilenames = loaded['trainFilenames']\n#trainTypeNames = loaded['trainTypeNames']\ntestImages = loaded['testImages']\ntestLabels = loaded['testLabels']\n#testFilenames = loaded['testFilenames']\ntestTypeNames = loaded['testTypeNames']\ntypeNamesList = loaded['typeNamesList']\n#validateImages = sortData[2][0]\n#validateLabels = sortData[2][1]\n\n\nprint(\"Completed creatingArrays\")\n\nN = 1024\nntypes = len(testLabels[0])\nimWidth = 32 #Image size and width\nimWidthReduc = 8\n\na = []\n\nsess = tf.InteractiveSession()\n\n#WEIGHT INITIALISATION\ndef weight_variable(shape):\n initial = tf.truncated_normal(shape, stddev=0.1)\n return tf.Variable(initial)\n\ndef bias_variable(shape):\n initial = tf.constant(0.1, shape=shape)\n return tf.Variable(initial)\n\n#CONVOLUTION AND POOLING\ndef conv2d(x, W):\n return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')\n\ndef max_pool_2x2(x):\n return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n\nx = tf.placeholder(tf.float32, shape=[None, N])\ny_ = tf.placeholder(tf.float32, shape=[None, ntypes])\n\n#FIRST CONVOLUTIONAL LAYER\nW_conv1 = weight_variable([5, 5, 1, 32])\nb_conv1 = bias_variable([32])\nx_image = tf.reshape(x, [-1,imWidth,imWidth,1])\nh_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)\nh_pool1 = max_pool_2x2(h_conv1)\n\n#SECOND CONVOLUTIONAL LAYER\nW_conv2 = weight_variable([5, 5, 32, 64])\nb_conv2 = bias_variable([64])\nh_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)\nh_pool2 = max_pool_2x2(h_conv2)\n\n#DENSELY CONNECTED LAYER\nW_fc1 = weight_variable([imWidthReduc * imWidthReduc * 64, N])\nb_fc1 = bias_variable([N])\nh_pool2_flat = tf.reshape(h_pool2, [-1, imWidthReduc*imWidthReduc*64])\nh_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)\n\n#DROPOUT\nkeep_prob = tf.placeholder(tf.float32)\nh_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)\n\n#READOUT LAYER\nW_fc2 = weight_variable([N, ntypes])\nb_fc2 = bias_variable([ntypes])\n\ny_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)\n\n########\n##batch_xs1 = trainImages\n##batch_ys1 = trainLabels\n##print(sess.run(y_conv, feed_dict={x: batch_xs1, y_: batch_ys1}))\n########\n\n#TRAIN AND EVALUATE MODEL\ncross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv), reduction_indices=[1]))\ntrain_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)\ncorrect_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))\naccuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n\nsess.run(tf.initialize_all_variables())\n\ntrainImagesCycle = itertools.cycle(trainImages)\ntrainLabelsCycle = itertools.cycle(trainLabels)\nfor i in range(100000):\n batch_xs = np.array(list(itertools.islice(trainImagesCycle, 50*i, 50*i+50)))\n batch_ys = np.array(list(itertools.islice(trainLabelsCycle, 50*i, 50*i+50)))\n train_step.run(feed_dict={x: batch_xs, y_: batch_ys, keep_prob: 0.5})\n if (i % 100 == 0):\n train_accuracy = accuracy.eval(feed_dict={x:batch_xs, y_: batch_ys, keep_prob: 1.0})\n print(\"step %d, training accuracy %g\"%(i, train_accuracy))\n testacc = accuracy.eval(feed_dict={x: testImages, y_: testLabels, keep_prob: 1.0})\n print(\"test accuracy %g\"%(testacc))\n a.append(testacc)\nprint(\"test accuracy %g\"%accuracy.eval(feed_dict={x: testImages, y_: testLabels, keep_prob: 1.0}))\n\nyy = y_conv.eval(feed_dict={x: testImages, y_: testLabels, keep_prob: 1.0})\ncp = correct_prediction.eval(feed_dict={x: testImages, y_: testLabels, keep_prob: 1.0})\nprint(cp)\nfor i in range(len(cp)):\n if (cp[i] == False):\n predictedIndex = np.argmax(yy[i])\n print(i, testTypeNames[i], typeNamesList[predictedIndex])\n\n\n#ACTUAL ACCURACY, SUBTYPE ACCURACY, AGE ACCURACY\ntypeAndAgeCorrect = 0\ntypeCorrect = 0\nsubTypeCorrect = 0\nsubTypeAndAgeCorrect = 0\ntypeAndNearAgeCorrect = 0\nsubTypeAndNearAgeCorrect = 0\nfor i in range(len(testTypeNames)):\n predictedIndex = np.argmax(yy[i])\n testSubType = testTypeNames[i][0:2]\n actualSubType = typeNamesList[predictedIndex][0:2]\n if testTypeNames[i][0:3] == 'IIb':\n testSubType = 'Ib'\n if typeNamesList[predictedIndex][0:3] == 'IIb':\n actualSubType = 'Ib'\n testType = testTypeNames[i].split(': ')[0]\n actualType = typeNamesList[predictedIndex].split(': ')[0]\n testAge = testTypeNames[i].split(': ')[1]\n actualAge = typeNamesList[predictedIndex].split(': ')[1]\n nearTestAge = testAge.split(' to ')\n \n if (testTypeNames[i] == typeNamesList[predictedIndex]):\n typeAndAgeCorrect += 1\n if (testType == actualType): #correct type\n typeCorrect += 1\n if ((nearTestAge[0] in actualAge) or (nearTestAge[1] in actualAge)): #check if the age is in the neigbouring bin\n typeAndNearAgeCorrect += 1 #all correct except nearby bin\n if (testSubType == actualSubType): #correct subtype\n subTypeCorrect += 1\n if testAge == actualAge:\n subTypeAndAgeCorrect += 1\n if ((nearTestAge[0] in actualAge) or (nearTestAge[1] in actualAge)): #check if the age is in the neigbouring bin\n subTypeAndNearAgeCorrect += 1 #subtype and nearby bin\n\ntypeAndAgeAccuracy = float(typeAndAgeCorrect)/len(testTypeNames)\ntypeAccuracy = float(typeCorrect)/len(testTypeNames)\nsubTypeAccuracy = float(subTypeCorrect)/len(testTypeNames)\nsubTypeAndAgeAccuracy = float(subTypeAndAgeCorrect)/len(testTypeNames)\ntypeAndNearAgeAccuracy = float(typeAndNearAgeCorrect)/len(testTypeNames)\nsubTypeAndNearAgeAccuracy = float(subTypeAndNearAgeCorrect)/len(testTypeNames)\n\nprint(\"typeAndAgeAccuracy : \" + str(typeAndAgeAccuracy))\nprint(\"typeAccuracy : \" + str(typeAccuracy))\nprint(\"subTypeAccuracy : \" + str(subTypeAccuracy))\nprint(\"subTypeAndAgeAccuracy: \" + str(subTypeAndAgeAccuracy))\nprint(\"typeAndNearAgeAccuracy : \" + str(typeAndNearAgeAccuracy))\nprint(\"subTypeAndNearAgeAccuracy : \" + str(subTypeAndNearAgeAccuracy))\n \n\n#SAVE THE MODEL\nsaver = tf.train.Saver()\nsave_path = saver.save(sess, \"model_trainedAtZeroZ.ckpt\")\nprint(\"Model saved in file: %s\" % save_path)\n","repo_name":"daniel-muthukrishna/SNClassifying_Pre-alpha","sub_path":"tf_improved_model.py","file_name":"tf_improved_model.py","file_ext":"py","file_size_in_byte":6786,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"3517337578","text":"from tkinter import *\r\nimport pandas as pd\r\nfrom csv import DictReader\r\nimport numpy as np\r\nimport datetime\r\nfrom datetime import date, timedelta\r\nfrom datetime import datetime\r\nimport decimal\r\nfrom datetime import *\r\nimport shutil\r\nimport csv\r\nimport os\r\nimport pdfkit\r\nimport seaborn as sns\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport os\r\nimport openpyxl\r\nimport openpyxl.worksheet.worksheet\r\nfrom win32com.client import Dispatch\r\n\r\n\r\nop=True\r\nlarge_font = ('Verdana',30)\r\nx = True\r\nList=[]\r\ns=0\r\ny=0.0\r\nz=0\r\ndf=''\r\nmaster = Tk()\r\nf1 = 2.5\r\nf2 = 2.5\r\nf3 = 2.5\r\nf4 = 2.5\r\nf5 = 1.7\r\nf6 = 2\r\nf7 = 2.5\r\nf8 = 1.7\r\nf9 = 1.7\r\nf10 = 2.5\r\nf11 = 5.0\r\nmaster.geometry('5000x5000')\r\ndef paaji():\r\n file_name = \"C:/Users/DELL/Downloads/S/print.xlsx\"\r\n df1000 = pd.read_excel(file_name) #Read Excel file as a DataFrame\r\n df1000.set_index(\"दिनांक\", inplace = True)\r\n wb_obj = openpyxl.load_workbook('print.xlsx')\r\n \r\n sheet_obj = wb_obj.active\r\n ab = df1000['कुल'].sum()\r\n print(ab)\r\n ac = df1000['एडवांस'].sum()\r\n c3 = sheet_obj['Z2']\r\n c3.value = ab\r\n c4 = sheet_obj['Z3']\r\n c4.value = ac\r\n c4 = sheet_obj['AA3']\r\n c4.value = ab-ac\r\n c5 = sheet_obj['AB3']\r\n c5.value = sum2+TVP-ad2\r\n wb_obj.save('print.xlsx')\r\n df9000 = pd.read_excel('C:/Users/DELL/Downloads/S/print.xlsx')\r\n df9001 = df9000.T\r\n df9001.to_excel('C:/Users/DELL/Downloads/S/print.xlsx')\r\n excel = Dispatch('Excel.Application')\r\n wb = excel.Workbooks.Open(\"C:/Users/DELL/Downloads/S/print.xlsx\")\r\n\r\n #Activate second sheet\r\n excel.Worksheets(1).Activate()\r\n\r\n #Autofit column in active sheet\r\n excel.ActiveSheet.Columns.AutoFit()\r\n wb.Save()\r\n\r\n wb.Close()\r\n os.startfile(\"C:/Users/DELL/Downloads/S/print.xlsx\",'print')\r\ndef Sum():\r\n Label(master, text='आरंभ दिनांक').grid(row=1,column=7)\r\n Label(master, text='समाप्ति दिनांक').grid(row=3,column=7)\r\n global bd\r\n global ed\r\n global bb\r\n bb = 'jan-27-21'\r\n bd = Entry(master)\r\n ed = Entry(master)\r\n bd.grid(row=1, column=8)\r\n ed.grid(row=3, column=8)\r\n pp = 0\r\n EN = ''\r\n D = ''\r\n X = 0.0\r\n def search():\r\n data2 = pd.read_csv(\"Records.csv\")\r\n data = pd.read_csv(\"Records.csv\")\r\n data.set_index(\"Date\", inplace = True)\r\n df82 = data.loc[bd.get():ed.get(),['Haudi Katai Paddy(40Kg,30Kg)','Rate','Haudi Katai Rice(60Kg,50Kg)','Rate2','Paddy Stack(40Kg,30Kg)','Rate3','Doubling','Rate4','Rice Loading(60kg,50kg,40kg)','Rate5','Rice Loading(25kg)','Rate6','Polish Loading','Rate7','Rice Dhala','Rate8','Bundle Stack/Loading','Rate9','Rice Stack(25Kg)','Rate10','Rice Stack(50Kg,60Kg)','Rate11','Adv']]\r\n df2 = df82['Adv']\r\n ok =df82['Haudi Katai Paddy(40Kg,30Kg)']\r\n ok2 = df82['Rate']\r\n pro1 = ok*ok2\r\n ok3 = df82['Haudi Katai Rice(60Kg,50Kg)']\r\n ok4 = df82['Rate2']\r\n pro2 = ok3*ok4\r\n ok5 = df82['Paddy Stack(40Kg,30Kg)']\r\n ok6 = df82['Rate3']\r\n pro3 = ok5*ok6\r\n ok7 = df82['Rice Loading(60kg,50kg,40kg)']\r\n ok8 = df82['Rate5']\r\n pro4 = ok7*ok8\r\n ok9 = df82['Rice Loading(25kg)']\r\n ok10 = df82['Rate6']\r\n pro5 = ok9*ok10\r\n ok11 = df82['Polish Loading']\r\n ok12 = df82['Rate7']\r\n pro6 = ok11*ok12\r\n ok13 = df82['Rice Dhala']\r\n ok14 = df82['Rate8']\r\n pro7 = ok13*ok14\r\n ok15 = df82['Bundle Stack/Loading']\r\n ok16 = df82['Rate9']\r\n pro8 = ok15*ok16\r\n ok17 = df82['Rice Stack(25Kg)']\r\n ok18 = df82['Rate10']\r\n pro9 = ok17*ok18\r\n ok19 = df82['Rice Stack(50Kg,60Kg)']\r\n ok20 = df82['Rate11']\r\n ok21 = df82['Doubling']\r\n ok22 = df82['Rate4']\r\n pro10 = ok19*ok20\r\n pro11 = ok21*ok22\r\n result = pro1+pro2+pro3+pro4+pro5+pro6+pro7+pro8+pro9+pro10+pro11\r\n df82.to_excel('print.xlsx')\r\n def Excel3():\r\n col_names = ['दिनांक','हौदी कटाई धान(40Kg,30Kg)','Rate1','हौदी कटाई चावल(60Kg,50Kg)','Rate2','धान स्टैक(40Kg,30Kg)','Rate3','डब्लिंग','Rate4','चावल लोड(60kg,50kg,40kg)','Rate5','चावल लोड(25kg)','Rate6','पोलिश लोड','Rate7','चावल ढला','Rate8','बंडल स्टैक/लोड','Rate9','चावल स्टैक(25Kg)','Rate10','चावल स्टैक(50Kg,60Kg)','Rate11','एडवांस']\r\n file_name = \"C:/Users/DELL/Downloads/S/print.xlsx\"\r\n df1000 = pd.read_excel(file_name) #Read Excel file as a DataFrame\r\n df1000.columns=col_names\r\n df1000.set_index(\"दिनांक\", inplace = True)\r\n df1000['कुल'] = df1000['हौदी कटाई धान(40Kg,30Kg)']*df1000['Rate1']+df1000['हौदी कटाई चावल(60Kg,50Kg)']*df1000['Rate2']+df1000['धान स्टैक(40Kg,30Kg)']*df1000['Rate3']+df1000['डब्लिंग']*df1000['Rate4']+df1000['चावल लोड(60kg,50kg,40kg)']*df1000['Rate5']+df1000['चावल लोड(25kg)']*df1000['Rate6']+df1000['पोलिश लोड']*df1000['Rate7']+df1000['चावल ढला']*df1000['Rate8']+df1000['बंडल स्टैक/लोड']*df1000['Rate9']+df1000['चावल स्टैक(25Kg)']*df1000['Rate10']+df1000['चावल स्टैक(50Kg,60Kg)']*df1000['Rate11']\r\n df1000.to_excel(\"C:/Users/DELL/Downloads/S/print.xlsx\") \r\n global button5\r\n button5 = Button(master, text='Print', width=25, command=lambda:[Excel3(),paaji()])\r\n button5.grid(row=20,column=12)\r\n data2 = pd.read_csv(\"VRecords.csv\")\r\n data2.set_index(\"Date\", inplace = True)\r\n df6 = data2.loc[bd.get():ed.get(),['Haudi Katai Paddy(40Kg,30Kg)','Rate','Haudi Katai Rice(60Kg,50Kg)','Rate2','Paddy Stack(40Kg,30Kg)','Rate3','Doubling','Rate4','Rice Loading(60kg,50kg,40kg)','Rate5','Rice Loading(25kg)','Rate6','Polish Loading','Rate7','Rice Dhala','Rate8','Bundle Stack/Loading','Rate9','Rice Stack(25Kg)','Rate10','Rice Stack(50Kg,60Kg)','Rate11']]\r\n okkk =df6['Haudi Katai Paddy(40Kg,30Kg)']\r\n okkk2 = df6['Rate']\r\n prooo1 = okkk*okkk2\r\n okkk3 = df6['Haudi Katai Rice(60Kg,50Kg)']\r\n okkk4 = df6['Rate2']\r\n prooo2 = okkk3*okkk4\r\n okkk5 = df6['Paddy Stack(40Kg,30Kg)']\r\n okkk6 = df6['Rate3']\r\n prooo3 = okkk5*okkk6\r\n okkk7 = df6['Rice Loading(60kg,50kg,40kg)']\r\n okkk8 = df6['Rate5']\r\n prooo4 = okkk7*okkk8\r\n okkk9 = df6['Rice Loading(25kg)']\r\n okkk10 = df6['Rate6']\r\n prooo5 = okkk9*okkk10\r\n okkk11 = df6['Polish Loading']\r\n okkk12 = df6['Rate7']\r\n prooo6 = okkk11*okkk12\r\n okkk13 = df6['Rice Dhala']\r\n okkk14 = df6['Rate8']\r\n prooo7 = okkk13*okkk14\r\n okkk15 = df6['Bundle Stack/Loading']\r\n okkk16 = df6['Rate9']\r\n prooo8 = okkk15*okkk16\r\n okkk17 = df6['Rice Stack(25Kg)']\r\n okkk18 = df6['Rate10']\r\n prooo9 = okkk17*okkk18\r\n okkk19 = df6['Rice Stack(50Kg,60Kg)']\r\n okkk20 = df6['Rate11']\r\n okkk21 = df6['Doubling']\r\n okkk22 = df6['Rate4']\r\n prooo10 = okkk19*okkk20\r\n prooo11 = okkk21*okkk22\r\n global result5\r\n result5 = prooo1+prooo2+prooo3+prooo4+prooo5+prooo6+prooo7+prooo8+prooo9+prooo10+prooo11\r\n global L5\r\n global L6\r\n global L7\r\n global canvas\r\n global scroll_y\r\n canvas = Canvas(master, width=400, height=200)\r\n scroll_y = Scrollbar(master,orient=\"vertical\", command=canvas.yview)\r\n\r\n frame = Frame(canvas)\r\n # group of widgets\r\n for i in range(20):\r\n with pd.option_context('display.max_rows', None, 'display.max_columns', None): \r\n global L\r\n global L2\r\n global L3\r\n global L4\r\n global L8\r\n global L9\r\n L=Label(master, text=\"Advance\")\r\n L.grid(row=1,column=13)\r\n L2=Label(master, text=\"Sum\")\r\n L2.grid(row=1,column=11)\r\n L3=Label(frame,text=df2)\r\n L3.grid(row=3,column=13)\r\n L4=Label(frame, text=result)\r\n L4.grid(row=3,column=11)\r\n L8=Label(master, text=\"Vardhman Payment\")\r\n L8.grid(row=1,column=12)\r\n L9=Label(frame, text=result5)\r\n L9.grid(row=3,column=12)\r\n canvas.create_window(1000,1000, anchor='nw', window=frame)\r\n canvas.update_idletasks()\r\n\r\n canvas.configure(scrollregion=canvas.bbox('all'), \r\n yscrollcommand=scroll_y.set)\r\n \r\n canvas.grid(row=2,column=12)\r\n scroll_y.grid(row=2,column=14)\r\n sum = result.sum()\r\n ad = df2.sum()\r\n va = result5.sum()\r\n global L10\r\n L5=Label(master, text=float(sum))\r\n L5.grid(row=5,column=11)\r\n L6=Label(master,text=float(ad))\r\n L6.grid(row=5,column=13)\r\n L10=Label(master, text=float(va))\r\n L10.grid(row=5,column=12)\r\n L7=Label(master, text=float(sum+va-ad))\r\n L7.grid(row=7,column=12)\r\n data6 = pd.read_csv(\"VRecords.csv\")\r\n data6.set_index(\"Date\", inplace = True)\r\n df8 = data6.loc[bb:ed.get(),['Haudi Katai Paddy(40Kg,30Kg)','Rate','Haudi Katai Rice(60Kg,50Kg)','Rate2','Paddy Stack(40Kg,30Kg)','Rate3','Doubling','Rate4','Rice Loading(60kg,50kg,40kg)','Rate5','Rice Loading(25kg)','Rate6','Polish Loading','Rate7','Rice Dhala','Rate8','Bundle Stack/Loading','Rate9','Rice Stack(25Kg)','Rate10','Rice Stack(50Kg,60Kg)','Rate11']]\r\n okkkk =df8['Haudi Katai Paddy(40Kg,30Kg)']\r\n okkkk2 = df8['Rate']\r\n proooo1 = okkkk*okkkk2\r\n okkkk3 = df8['Haudi Katai Rice(60Kg,50Kg)']\r\n okkkk4 = df8['Rate2']\r\n proooo2 = okkkk3*okkkk4\r\n okkkk5 = df8['Paddy Stack(40Kg,30Kg)']\r\n okkkk6 = df8['Rate3']\r\n proooo3 = okkkk5*okkkk6\r\n okkkk7 = df8['Rice Loading(60kg,50kg,40kg)']\r\n okkkk8 = df8['Rate5']\r\n proooo4 = okkkk7*okkkk8\r\n okkkk9 = df8['Rice Loading(25kg)']\r\n okkkk10 = df8['Rate6']\r\n proooo5 = okkkk9*okkkk10\r\n okkkk11 = df8['Polish Loading']\r\n okkkk12 = df8['Rate7']\r\n proooo6 = okkkk11*okkkk12\r\n okkkk13 = df8['Rice Dhala']\r\n okkkk14 = df8['Rate8']\r\n proooo7 = okkkk13*okkkk14\r\n okkkk15 = df8['Bundle Stack/Loading']\r\n okkkk16 = df8['Rate9']\r\n proooo8 = okkkk15*okkkk16\r\n okkkk17 = df8['Rice Stack(25Kg)']\r\n okkkk18 = df8['Rate10']\r\n proooo9 = okkkk17*okkkk18\r\n okkkk19 = df8['Rice Stack(50Kg,60Kg)']\r\n okkkk20 = df8['Rate11']\r\n okkkk21 = df8['Doubling']\r\n okkkk22 = df8['Rate4']\r\n proooo10 = okkkk19*okkkk20\r\n proooo11 = okkkk21*okkkk22\r\n global result8\r\n result8 = proooo1+proooo2+proooo3+proooo4+proooo5+proooo6+proooo7+proooo8+proooo9+proooo10+proooo11\r\n global TVP\r\n TVP = result8.sum()\r\n data3 = pd.read_csv(\"Records.csv\")\r\n data3.set_index(\"Date\", inplace = True)\r\n df5 = data3.loc[bb:ed.get(),['Haudi Katai Paddy(40Kg,30Kg)','Rate','Haudi Katai Rice(60Kg,50Kg)','Rate2','Paddy Stack(40Kg,30Kg)','Rate3','Doubling','Rate4','Rice Loading(60kg,50kg,40kg)','Rate5','Rice Loading(25kg)','Rate6','Polish Loading','Rate7','Rice Dhala','Rate8','Bundle Stack/Loading','Rate9','Rice Stack(25Kg)','Rate10','Rice Stack(50Kg,60Kg)','Rate11','Adv']]\r\n df6 = df5['Adv']\r\n okk =df5['Haudi Katai Paddy(40Kg,30Kg)']\r\n okk2 = df5['Rate']\r\n proo1 = okk*okk2\r\n okk3 = df5['Haudi Katai Rice(60Kg,50Kg)']\r\n okk4 = df5['Rate2']\r\n proo2 = okk3*okk4\r\n okk5 = df5['Paddy Stack(40Kg,30Kg)']\r\n okk6 = df5['Rate3']\r\n proo3 = okk5*okk6\r\n okk7 = df5['Rice Loading(60kg,50kg,40kg)']\r\n okk8 = df5['Rate5']\r\n proo4 = okk7*okk8\r\n okk9 = df5['Rice Loading(25kg)']\r\n okk10 = df5['Rate6']\r\n proo5 = okk9*okk10\r\n okk11 = df5['Polish Loading']\r\n okk12 = df5['Rate7']\r\n proo6 = okk11*okk12\r\n okk13 = df5['Rice Dhala']\r\n okk14 = df5['Rate8']\r\n proo7 = okk13*okk14\r\n okk15 = df5['Bundle Stack/Loading']\r\n okk16 = df5['Rate9']\r\n proo8 = okk15*okk16\r\n okk17 = df5['Rice Stack(25Kg)']\r\n okk18 = df5['Rate10']\r\n proo9 = okk17*okk18\r\n okk19 = df5['Rice Stack(50Kg,60Kg)']\r\n okk20 = df5['Rate11']\r\n okk21 = df5['Doubling']\r\n okk22 = df5['Rate4']\r\n proo10 = okk19*okk20\r\n proo11 = okk21*okk22\r\n result2 = proo1+proo2+proo3+proo4+proo5+proo6+proo7+proo8+proo9+proo10+proo11\r\n global sum2\r\n sum2 = result2.sum()\r\n global ad2\r\n ad2 = df6.sum()\r\n global L90\r\n L90=Label(master, text=float(\"{:.2f}\".format(sum2+TVP-ad2)))\r\n L90.grid(row=8,column=12)\r\n def delete_label():\r\n L.destroy()\r\n L2.destroy()\r\n L3.destroy()\r\n L4.destroy()\r\n L5.destroy()\r\n L6.destroy()\r\n L7.destroy()\r\n L8.destroy()\r\n L9.destroy()\r\n L10.destroy()\r\n L90.destroy()\r\n canvas.destroy()\r\n scroll_y.destroy()\r\n bd.delete(0,END)\r\n ed.delete(0,END)\r\n button5.destroy()\r\n button = Button(master, text='Submit', width=25, command=search)\r\n button.grid(row=5,column=8)\r\n button2 = Button(master, text='Clear', width=25, command=delete_label)\r\n button2.grid(row=6,column=8)\r\ndef Vardhman():\r\n def some_callbackk(event):\r\n event.widget.delete(0, \"end\")\r\n return None\r\n a1 = Label(master, text='दिनांक')\r\n a1.grid(row=20)\r\n a2 = Label(master, text='हौदी कटाई धान(40Kg,30Kg)')\r\n a2.grid(row=21)\r\n ee1 = Entry(master)\r\n ee2 = Entry(master)\r\n ee2.insert(0,0)\r\n ee2.bind(\"\", some_callbackk)\r\n ee1.grid(row=20, column=1)\r\n ee2.grid(row=21, column=1)\r\n a3 = Label(master, text='हौदी कटाई चावल(60Kg,50Kg)')\r\n a3.grid(row=22)\r\n a4 = Label(master, text='धान स्टैक(40Kg,30Kg)')\r\n a4.grid(row=23)\r\n ee3 = Entry(master)\r\n ee4 = Entry(master)\r\n ee3.grid(row=22, column=1)\r\n ee4.grid(row=23, column=1)\r\n ee3.bind(\"\", some_callbackk)\r\n ee4.bind(\"\", some_callbackk)\r\n ee3.insert(0,0)\r\n ee4.insert(0,0)\r\n a5 = Label(master, text='डब्लिंग')\r\n a5.grid(row=24)\r\n ee13 = Entry(master)\r\n ee13.bind(\"\", some_callbackk)\r\n ee13.grid(row=24, column=1)\r\n ee13.insert(0,0)\r\n a6 = Label(master, text='चावल लोड(60kg,50kg,40kg)')\r\n a6.grid(row=25)\r\n a7 = Label(master, text='चावल लोड(25kg)')\r\n a7.grid(row=26)\r\n ee5 = Entry(master)\r\n ee6 = Entry(master)\r\n ee5.bind(\"\", some_callbackk)\r\n ee6.bind(\"\", some_callbackk)\r\n ee5.grid(row=25, column=1)\r\n ee6.grid(row=26, column=1)\r\n ee5.insert(0,0)\r\n ee6.insert(0,0)\r\n a8 = Label(master, text='पोलिश लोड')\r\n a8.grid(row=27)\r\n a9 = Label(master, text='चावल ढला')\r\n a9.grid(row=28)\r\n ee7 = Entry(master)\r\n ee8 = Entry(master)\r\n ee7.bind(\"\", some_callbackk)\r\n ee8.bind(\"\", some_callbackk)\r\n ee7.grid(row=27, column=1)\r\n ee8.grid(row=28, column=1)\r\n ee7.insert(0,0)\r\n ee8.insert(0,0)\r\n a10 = Label(master, text='बंडल स्टैक/लोड')\r\n a10.grid(row=29)\r\n a11 = Label(master, text='चावल स्टैक(25Kg)')\r\n a11.grid(row=30)\r\n ee9 = Entry(master)\r\n ee10 = Entry(master)\r\n ee9.bind(\"\", some_callbackk)\r\n ee10.bind(\"\", some_callbackk)\r\n ee9.grid(row=29, column=1)\r\n ee10.grid(row=30, column=1)\r\n ee9.insert(0,0)\r\n ee10.insert(0,0)\r\n a12 = Label(master, text='चावल स्टैक(50Kg,60Kg)')\r\n a12.grid(row=31)\r\n ee11 = Entry(master)\r\n ee11.bind(\"\", some_callbackk)\r\n ee11.grid(row=31, column=1)\r\n ee11.insert(0,0)\r\n def export2():\r\n \r\n List2=[ee1.get(),ee2.get(),f1,ee3.get(),f2,ee4.get(),f3,ee13.get(),f11,ee5.get(),f4,ee6.get(),f5,ee7.get(),f6,ee8.get(),f7,ee9.get(),f8,ee10.get(),f9,ee11.get(),f10]\r\n with open('VRecords.csv', 'a',newline='') as f_object:\r\n df99 = pd.read_csv('VRecords.csv')\r\n\r\n new_df = df99.dropna()\r\n\r\n writer_object =csv.writer(f_object)\r\n \r\n writer_object.writerow(List2)\r\n ee1.delete(0,END)\r\n ee2.delete(0, END)\r\n ee3.delete(0,END)\r\n ee4.delete(0,END)\r\n ee5.delete(0,END)\r\n ee6.delete(0,END)\r\n ee7.delete(0,END)\r\n ee8.delete(0,END)\r\n ee9.delete(0,END)\r\n ee10.delete(0,END)\r\n ee11.delete(0,END)\r\n ee13.delete(0,END)\r\n ee2.insert(0,0)\r\n ee3.insert(0,0)\r\n ee4.insert(0,0) \r\n ee5.insert(0,0)\r\n ee6.insert(0,0)\r\n ee7.insert(0,0)\r\n ee8.insert(0,0)\r\n ee9.insert(0,0)\r\n ee10.insert(0,0)\r\n ee11.insert(0,0)\r\n ee13.insert(0,0)\r\n button3 = Button(master, text='Submit', width=25, command=export2)\r\n button3.grid(row=33,column=1)\r\n def Excel2():\r\n os.startfile(\"C:/Users/DELL/Downloads/S/VRecords.csv\")\r\n button4 = Button(master, text='Excel', width=25, command=Excel2)\r\n button4.grid(row=33,column=2)\r\n def clear2():\r\n ee1.destroy()\r\n ee2.destroy()\r\n ee3.destroy()\r\n ee4.destroy()\r\n ee5.destroy()\r\n ee6.destroy()\r\n ee7.destroy()\r\n ee8.destroy()\r\n ee9.destroy()\r\n ee10.destroy()\r\n ee11.destroy()\r\n ee13.destroy()\r\n a1.destroy()\r\n a2.destroy()\r\n a3.destroy()\r\n a4.destroy()\r\n a5.destroy()\r\n a6.destroy()\r\n a7.destroy()\r\n a8.destroy()\r\n a9.destroy()\r\n a10.destroy()\r\n a11.destroy()\r\n a12.destroy()\r\n button3.destroy()\r\n button4.destroy()\r\n button5.destroy()\r\n button5 = Button(master, text='Clear', width=25, command=clear2)\r\n button5.grid(row=33,column=3)\r\n\r\ndef input():\r\n def some_callback(event):\r\n event.widget.delete(0, \"end\")\r\n return None\r\n Label(master, text='दिनांक').grid(row=0)\r\n Label(master, text='हौदी कटाई धान(40Kg,30Kg)').grid(row=1)\r\n e1 = Entry(master)\r\n e2 = Entry(master)\r\n e2.insert(0,0)\r\n e2.bind(\"\", some_callback)\r\n e1.grid(row=0, column=1)\r\n e2.grid(row=1, column=1)\r\n Label(master, text='हौदी कटाई चावल(60Kg,50Kg)').grid(row=2)\r\n Label(master, text='धान स्टैक(40Kg,30Kg)').grid(row=3)\r\n e3 = Entry(master)\r\n e4 = Entry(master)\r\n e3.grid(row=2, column=1)\r\n e4.grid(row=3, column=1)\r\n e3.bind(\"\", some_callback)\r\n e4.bind(\"\", some_callback)\r\n e3.insert(0,0)\r\n e4.insert(0,0)\r\n Label(master, text='डब्लिंग').grid(row=4)\r\n e13 = Entry(master)\r\n e13.bind(\"\", some_callback)\r\n e13.grid(row=4, column=1)\r\n e13.insert(0,0)\r\n Label(master, text='चावल लोड(60kg,50kg,40kg)').grid(row=5)\r\n Label(master, text='चावल लोड(25kg)').grid(row=6)\r\n e5 = Entry(master)\r\n e6 = Entry(master)\r\n e5.bind(\"\", some_callback)\r\n e6.bind(\"\", some_callback)\r\n e5.grid(row=5, column=1)\r\n e6.grid(row=6, column=1)\r\n e5.insert(0,0)\r\n e6.insert(0,0)\r\n Label(master, text='पोलिश लोड').grid(row=7)\r\n Label(master, text='चावल ढला').grid(row=8)\r\n e7 = Entry(master)\r\n e8 = Entry(master)\r\n e7.bind(\"\", some_callback)\r\n e8.bind(\"\", some_callback)\r\n e7.grid(row=7, column=1)\r\n e8.grid(row=8, column=1)\r\n e7.insert(0,0)\r\n e8.insert(0,0)\r\n Label(master, text='बंडल स्टैक/लोड').grid(row=9)\r\n Label(master, text='चावल स्टैक(25Kg)').grid(row=10)\r\n e9 = Entry(master)\r\n e10 = Entry(master)\r\n e9.bind(\"\", some_callback)\r\n e10.bind(\"\", some_callback)\r\n e9.grid(row=9, column=1)\r\n e10.grid(row=10, column=1)\r\n e9.insert(0,0)\r\n e10.insert(0,0)\r\n Label(master, text='चावल स्टैक(50Kg,60Kg)').grid(row=11)\r\n Label(master, text='एडवांस').grid(row=12)\r\n e11 = Entry(master)\r\n e12 = Entry(master)\r\n e11.bind(\"\", some_callback)\r\n e12.bind(\"\", some_callback)\r\n e11.grid(row=11, column=1)\r\n e12.grid(row=12, column=1)\r\n e11.insert(0,0)\r\n e12.insert(0,0)\r\n\r\n def export():\r\n \r\n List=[e1.get(),e2.get(),f1,e3.get(),f2,e4.get(),f3,e13.get(),f11,e5.get(),f4,e6.get(),f5,e7.get(),f6,e8.get(),f7,e9.get(),f8,e10.get(),f9,e11.get(),f10,e12.get()]\r\n if(e2.get()!=0 and e13.get()!=0 and e3.get()!=0 and e4.get()!=0 and e5.get()!=0 and e6.get()!=0 and e7.get()!=0 and e8.get()!=0 and e9.get()!=0 and e10.get()!=0 and e11.get()!=0 and e12!=0 ):\r\n with open('Records.csv', 'a',newline='') as f_object:\r\n df = pd.read_csv('Records.csv')\r\n\r\n new_df = df.dropna()\r\n\r\n writer_object =csv.writer(f_object)\r\n \r\n writer_object.writerow(List)\r\n e1.delete(0,END)\r\n e2.delete(0, END)\r\n e3.delete(0,END)\r\n e4.delete(0,END)\r\n e5.delete(0,END)\r\n e6.delete(0,END)\r\n e7.delete(0,END)\r\n e8.delete(0,END)\r\n e9.delete(0,END)\r\n e10.delete(0,END)\r\n e11.delete(0,END)\r\n e12.delete(0,END)\r\n e13.delete(0,END)\r\n e2.insert(0,0)\r\n e3.insert(0,0)\r\n e4.insert(0,0) \r\n e5.insert(0,0)\r\n e6.insert(0,0)\r\n e7.insert(0,0)\r\n e8.insert(0,0)\r\n e9.insert(0,0)\r\n e10.insert(0,0)\r\n e11.insert(0,0)\r\n e12.insert(0,0)\r\n e13.insert(0,0)\r\n button = Button(master, text='Submit', width=25, command=export)\r\n button.grid(row=14,column=1)\r\n\r\ndef Excel():\r\n os.startfile(\"C:/Users/DELL/Downloads/S/Records.csv\")\r\n\r\n\r\n \r\nwhile (x==True):\r\n input()\r\n button = Button(master, text='Excel', width=25, command=Excel)\r\n button.grid(row=18,column=1)\r\n button2 = Button(master, text='Vardhman', width=25, command=Vardhman)\r\n button2.grid(row=19,column=1)\r\n Sum()\r\n\r\n mainloop()","repo_name":"KUSHAGRAJAIN03/SanmatSoftware","sub_path":"sanmat.py","file_name":"sanmat.py","file_ext":"py","file_size_in_byte":22979,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"40159032820","text":"import FWCore.ParameterSet.Config as cms\n\nprocess = cms.Process(\"TEST\")\n\nprocess.load(\"FWCore.MessageLogger.MessageLogger_cfi\")\nprocess.MessageLogger.cerr.threshold = 'INFO'\nprocess.MessageLogger.cerr.INFO.limit = 200\n\n#process.options = cms.untracked.PSet(forceEventSetupCacheClearOnNewRun = cms.untracked.bool(True))\n\nprocess.maxEvents = cms.untracked.PSet(\n input = cms.untracked.int32(10)\n)\n\nprocess.source = cms.Source(\"EmptySource\",\n firstRun = cms.untracked.uint32(1),\n firstLuminosityBlock = cms.untracked.uint32(1),\n firstEvent = cms.untracked.uint32(1),\n numberEventsInLuminosityBlock = cms.untracked.uint32(1),\n numberEventsInRun = cms.untracked.uint32(1)\n)\n\nprocess.emptyESSourceB = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordB\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess.emptyESSourceC = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordC\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess.emptyESSourceD = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordD\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess.emptyESSourceE = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordE\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess.emptyESSourceF = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordF\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess.emptyESSourceG = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordG\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess.emptyESSourceH = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordH\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess.emptyESSourceI = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordI\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess.emptyESSourceJ = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordJ\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess.emptyESSourceK = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordK\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess.esTestProducerB = cms.ESProducer(\"ESTestProducerB\")\nprocess.esTestProducerC = cms.ESProducer(\"ESTestProducerC\")\nprocess.esTestProducerD = cms.ESProducer(\"ESTestProducerD\")\nprocess.esTestProducerE = cms.ESProducer(\"ESTestProducerE\")\nprocess.esTestProducerF = cms.ESProducer(\"ESTestProducerF\")\nprocess.esTestProducerG = cms.ESProducer(\"ESTestProducerG\")\nprocess.esTestProducerH = cms.ESProducer(\"ESTestProducerH\")\nprocess.esTestProducerI = cms.ESProducer(\"ESTestProducerI\")\nprocess.esTestProducerJ = cms.ESProducer(\"ESTestProducerJ\")\nprocess.esTestProducerK = cms.ESProducer(\"ESTestProducerK\")\nprocess.esTestProducerK1 = cms.ESProducer(\"ESTestProducerK\")\nprocess.aPrefer = cms.ESPrefer(\"ESTestProducerK\", \"esTestProducerK\")\n\nprocess.esTestProducerH1 = cms.ESProducer(\"ESTestProducerH\",\n appendToDataLabel = cms.string('abc')\n)\n\nprocess.esTestAnalyzerB = cms.EDAnalyzer(\"ESTestAnalyzerB\",\n runsToGetDataFor = cms.vint32(1,2,3,4,5,6,7,8,9,10)\n)\n\nprocess.esTestAnalyzerK = cms.EDAnalyzer(\"ESTestAnalyzerK\",\n runsToGetDataFor = cms.vint32(1,2,3,4,5,6,7,8,9,10)\n)\n\nprocess.path1 = cms.Path(process.esTestAnalyzerB*process.esTestAnalyzerK)\n\n# ---------------------------------------------------------\n# Test the sharing of ESTestProducerB as things associated\n# with dependent records change.\n\n# In this first case, nothing is different and sharing should\n# occur. Note this is visible in the output because DataB\n# is not gotten in event 2 and that counter in the producer\n# is only incremented after a get call causes the produce\n# function to be called.\n\nprocess1 = cms.Process(\"TEST1\")\nprocess.addSubProcess(cms.SubProcess(process1))\n\nprocess1.emptyESSourceB = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordB\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess1.emptyESSourceC = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordC\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess1.emptyESSourceD = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordD\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess1.emptyESSourceE = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordE\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess1.emptyESSourceF = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordF\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess1.emptyESSourceG = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordG\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess1.emptyESSourceH = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordH\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess1.emptyESSourceI = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordI\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess1.emptyESSourceJ = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordJ\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess1.emptyESSourceK = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordK\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess1.esTestProducerB = cms.ESProducer(\"ESTestProducerB\")\nprocess1.esTestProducerC = cms.ESProducer(\"ESTestProducerC\")\nprocess1.esTestProducerD = cms.ESProducer(\"ESTestProducerD\")\nprocess1.esTestProducerE = cms.ESProducer(\"ESTestProducerE\")\nprocess1.esTestProducerF = cms.ESProducer(\"ESTestProducerF\")\nprocess1.esTestProducerG = cms.ESProducer(\"ESTestProducerG\")\nprocess1.esTestProducerH = cms.ESProducer(\"ESTestProducerH\")\nprocess1.esTestProducerI = cms.ESProducer(\"ESTestProducerI\")\nprocess1.esTestProducerJ = cms.ESProducer(\"ESTestProducerJ\")\nprocess1.esTestProducerK = cms.ESProducer(\"ESTestProducerK\")\nprocess1.esTestProducerK1 = cms.ESProducer(\"ESTestProducerK\")\nprocess1.aPrefer = cms.ESPrefer(\"ESTestProducerK\", \"esTestProducerK\")\n\nprocess1.esTestProducerH1 = cms.ESProducer(\"ESTestProducerH\",\n appendToDataLabel = cms.string('abc')\n)\n\nprocess1.esTestAnalyzerB = cms.EDAnalyzer(\"ESTestAnalyzerB\",\n runsToGetDataFor = cms.vint32(1,3,4,5,6,7,8,9,10)\n)\n\nprocess1.esTestAnalyzerK = cms.EDAnalyzer(\"ESTestAnalyzerK\",\n runsToGetDataFor = cms.vint32(1,3,4,5,6,7,8,9,10)\n)\n\nprocess1.path1 = cms.Path(process1.esTestAnalyzerB*process1.esTestAnalyzerK)\n\n# ---------------------------------------------------------\n\n# Change the value of a tracked parameter in an ESProducer\n# associated with record H ( the module is labeled\n# esTestProducerH1). Do not get data for event 3.\n# Shows the esTestProducerB is not shared.\n\nprocess2 = cms.Process(\"TEST2\")\nprocess1.addSubProcess(cms.SubProcess(process2))\n\nprocess2.emptyESSourceB = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordB\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess2.emptyESSourceC = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordC\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess2.emptyESSourceD = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordD\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess2.emptyESSourceE = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordE\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess2.emptyESSourceF = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordF\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess2.emptyESSourceG = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordG\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess2.emptyESSourceH = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordH\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess2.emptyESSourceI = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordI\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess2.emptyESSourceJ = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordJ\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess2.emptyESSourceK = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordK\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess2.esTestProducerB = cms.ESProducer(\"ESTestProducerB\")\nprocess2.esTestProducerC = cms.ESProducer(\"ESTestProducerC\")\nprocess2.esTestProducerD = cms.ESProducer(\"ESTestProducerD\")\nprocess2.esTestProducerE = cms.ESProducer(\"ESTestProducerE\")\nprocess2.esTestProducerF = cms.ESProducer(\"ESTestProducerF\")\nprocess2.esTestProducerG = cms.ESProducer(\"ESTestProducerG\")\nprocess2.esTestProducerH = cms.ESProducer(\"ESTestProducerH\")\nprocess2.esTestProducerI = cms.ESProducer(\"ESTestProducerI\")\nprocess2.esTestProducerJ = cms.ESProducer(\"ESTestProducerJ\")\nprocess2.esTestProducerK = cms.ESProducer(\"ESTestProducerK\",\n x = cms.untracked.string(\"x\")\n)\n\nprocess2.esTestProducerH1 = cms.ESProducer(\"ESTestProducerH\",\n appendToDataLabel = cms.string('abcd')\n)\n\nprocess2.esTestAnalyzerB = cms.EDAnalyzer(\"ESTestAnalyzerB\",\n runsToGetDataFor = cms.vint32(1,2,4,5,6,7,8,9,10)\n)\n\nprocess2.esTestAnalyzerK = cms.EDAnalyzer(\"ESTestAnalyzerK\",\n runsToGetDataFor = cms.vint32(1,4,5,6,7,8,9,10)\n)\n\nprocess2.path1 = cms.Path(process2.esTestAnalyzerB*process2.esTestAnalyzerK)\n\n# ---------------------------------------------------------\n\n# Change the value of a untracked parameter in an ESProducer\n# associated with record H ( the module is labeled\n# esTestProducerH1). Do not get data for event 4.\n# Shows the esTestProducerB is not shared.\n\nprocess3 = cms.Process(\"TEST3\")\nprocess2.addSubProcess(cms.SubProcess(process3))\n\nprocess3.emptyESSourceB = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordB\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess3.emptyESSourceC = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordC\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess3.emptyESSourceD = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordD\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess3.emptyESSourceE = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordE\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess3.emptyESSourceF = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordF\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess3.emptyESSourceG = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordG\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess3.emptyESSourceH = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordH\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess3.esTestProducerB = cms.ESProducer(\"ESTestProducerB\")\nprocess3.esTestProducerC = cms.ESProducer(\"ESTestProducerC\")\nprocess3.esTestProducerD = cms.ESProducer(\"ESTestProducerD\")\nprocess3.esTestProducerE = cms.ESProducer(\"ESTestProducerE\")\nprocess3.esTestProducerF = cms.ESProducer(\"ESTestProducerF\")\nprocess3.esTestProducerG = cms.ESProducer(\"ESTestProducerG\")\nprocess3.esTestProducerH = cms.ESProducer(\"ESTestProducerH\")\n\nprocess3.esTestProducerH1 = cms.ESProducer(\"ESTestProducerH\",\n appendToDataLabel = cms.string('abc'),\n test = cms.untracked.string('xyz')\n)\n\nprocess3.esTestAnalyzerB = cms.EDAnalyzer(\"ESTestAnalyzerB\",\n runsToGetDataFor = cms.vint32(1,2,3,5,6,7,8,9,10)\n)\n\nprocess3.path1 = cms.Path(process3.esTestAnalyzerB)\n\n# ---------------------------------------------------------\n\n# This one should share with process3, not process1\n# Do not get data for events 4 and 5.\n\n\nprocess4 = cms.Process(\"TEST4\")\nprocess3.addSubProcess(cms.SubProcess(process4))\n\nprocess4.emptyESSourceB = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordB\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess4.emptyESSourceC = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordC\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess4.emptyESSourceD = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordD\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess4.emptyESSourceE = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordE\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess4.emptyESSourceF = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordF\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess4.emptyESSourceG = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordG\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess4.emptyESSourceH = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordH\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess4.esTestProducerB = cms.ESProducer(\"ESTestProducerB\")\nprocess4.esTestProducerC = cms.ESProducer(\"ESTestProducerC\")\nprocess4.esTestProducerD = cms.ESProducer(\"ESTestProducerD\")\nprocess4.esTestProducerE = cms.ESProducer(\"ESTestProducerE\")\nprocess4.esTestProducerF = cms.ESProducer(\"ESTestProducerF\")\nprocess4.esTestProducerG = cms.ESProducer(\"ESTestProducerG\")\nprocess4.esTestProducerH = cms.ESProducer(\"ESTestProducerH\")\n\nprocess4.esTestProducerH1 = cms.ESProducer(\"ESTestProducerH\",\n appendToDataLabel = cms.string('abc'),\n test = cms.untracked.string('xyz')\n)\n\nprocess4.esTestAnalyzerB = cms.EDAnalyzer(\"ESTestAnalyzerB\",\n runsToGetDataFor = cms.vint32(1,2,3,6,7,8,9,10)\n)\n\nprocess4.path1 = cms.Path(process4.esTestAnalyzerB)\n\n# ---------------------------------------------------------\n\n# This one is like the top level process except esTestProducerH1\n# has been removed completely. Does not do the get\n# for events 1 to 3.\n\n\nprocess5 = cms.Process(\"TEST5\")\nprocess4.addSubProcess(cms.SubProcess(process5))\n\nprocess5.emptyESSourceB = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordB\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess5.emptyESSourceC = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordC\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess5.emptyESSourceD = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordD\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess5.emptyESSourceE = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordE\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess5.emptyESSourceF = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordF\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess5.emptyESSourceG = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordG\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess5.emptyESSourceH = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordH\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess5.esTestProducerB = cms.ESProducer(\"ESTestProducerB\")\nprocess5.esTestProducerC = cms.ESProducer(\"ESTestProducerC\")\nprocess5.esTestProducerD = cms.ESProducer(\"ESTestProducerD\")\nprocess5.esTestProducerE = cms.ESProducer(\"ESTestProducerE\")\nprocess5.esTestProducerF = cms.ESProducer(\"ESTestProducerF\")\nprocess5.esTestProducerG = cms.ESProducer(\"ESTestProducerG\")\nprocess5.esTestProducerH = cms.ESProducer(\"ESTestProducerH\")\n\nprocess5.esTestAnalyzerB = cms.EDAnalyzer(\"ESTestAnalyzerB\",\n runsToGetDataFor = cms.vint32(4,5,6,7,8,9,10)\n)\n\nprocess5.path1 = cms.Path(process5.esTestAnalyzerB)\n\n# ---------------------------------------------------------\n\n# This one is like the top level process except esTestProducerH2\n# has been added. Does not do the get for events 1 to 4.\n\n\nprocess6 = cms.Process(\"TEST6\")\nprocess5.addSubProcess(cms.SubProcess(process6))\n\nprocess6.emptyESSourceB = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordB\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess6.emptyESSourceC = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordC\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess6.emptyESSourceD = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordD\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess6.emptyESSourceE = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordE\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess6.emptyESSourceF = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordF\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess6.emptyESSourceG = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordG\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess6.emptyESSourceH = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordH\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess6.esTestProducerB = cms.ESProducer(\"ESTestProducerB\")\nprocess6.esTestProducerC = cms.ESProducer(\"ESTestProducerC\")\nprocess6.esTestProducerD = cms.ESProducer(\"ESTestProducerD\")\nprocess6.esTestProducerE = cms.ESProducer(\"ESTestProducerE\")\nprocess6.esTestProducerF = cms.ESProducer(\"ESTestProducerF\")\nprocess6.esTestProducerG = cms.ESProducer(\"ESTestProducerG\")\nprocess6.esTestProducerH = cms.ESProducer(\"ESTestProducerH\")\n\nprocess6.esTestProducerH1 = cms.ESProducer(\"ESTestProducerH\",\n appendToDataLabel = cms.string('abc')\n)\n\nprocess6.esTestProducerH2 = cms.ESProducer(\"ESTestProducerH\",\n appendToDataLabel = cms.string('xyz')\n)\n\nprocess6.esTestAnalyzerB = cms.EDAnalyzer(\"ESTestAnalyzerB\",\n runsToGetDataFor = cms.vint32(5,6,7,8,9,10)\n)\n\nprocess6.path1 = cms.Path(process6.esTestAnalyzerB)\n\n# ---------------------------------------------------------\n\n# This one is like the top level process except an extra\n# ESSource has been added for record F. Does not do the\n# get for events 1 to 5.\n\n\nprocess7 = cms.Process(\"TEST7\")\nprocess6.addSubProcess(cms.SubProcess(process7))\n\nprocess7.emptyESSourceB = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordB\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess7.emptyESSourceC = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordC\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess7.emptyESSourceD = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordD\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess7.emptyESSourceE = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordE\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess7.emptyESSourceF = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordF\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess7.emptyESSourceF1 = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordF\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess7.emptyESSourceG = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordG\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess7.emptyESSourceH = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordH\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess7.esTestProducerB = cms.ESProducer(\"ESTestProducerB\")\nprocess7.esTestProducerC = cms.ESProducer(\"ESTestProducerC\")\nprocess7.esTestProducerD = cms.ESProducer(\"ESTestProducerD\")\nprocess7.esTestProducerE = cms.ESProducer(\"ESTestProducerE\")\nprocess7.esTestProducerF = cms.ESProducer(\"ESTestProducerF\")\nprocess7.esTestProducerG = cms.ESProducer(\"ESTestProducerG\")\nprocess7.esTestProducerH = cms.ESProducer(\"ESTestProducerH\")\n\nprocess7.esTestProducerH1 = cms.ESProducer(\"ESTestProducerH\",\n appendToDataLabel = cms.string('abc')\n)\n\nprocess7.esTestAnalyzerB = cms.EDAnalyzer(\"ESTestAnalyzerB\",\n runsToGetDataFor = cms.vint32(6,7,8,9,10)\n)\n\nprocess7.path1 = cms.Path(process7.esTestAnalyzerB)\n\n# ---------------------------------------------------------\n\n# This one is like the top level process except the\n# ESSource was removed for record D. Does not do the\n# get for events 1 to 6.\n\nprocess8 = cms.Process(\"TEST8\")\nprocess7.addSubProcess(cms.SubProcess(process8))\n\nprocess8.emptyESSourceB = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordB\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess8.emptyESSourceC = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordC\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess8.emptyESSourceE = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordE\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess8.emptyESSourceF = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordF\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess8.emptyESSourceG = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordG\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess8.emptyESSourceH = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordH\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess8.esTestProducerB = cms.ESProducer(\"ESTestProducerB\")\nprocess8.esTestProducerC = cms.ESProducer(\"ESTestProducerC\")\nprocess8.esTestProducerD = cms.ESProducer(\"ESTestProducerD\")\nprocess8.esTestProducerE = cms.ESProducer(\"ESTestProducerE\")\nprocess8.esTestProducerF = cms.ESProducer(\"ESTestProducerF\")\nprocess8.esTestProducerG = cms.ESProducer(\"ESTestProducerG\")\nprocess8.esTestProducerH = cms.ESProducer(\"ESTestProducerH\")\n\nprocess8.esTestProducerH1 = cms.ESProducer(\"ESTestProducerH\",\n appendToDataLabel = cms.string('abc')\n)\n\nprocess8.esTestAnalyzerB = cms.EDAnalyzer(\"ESTestAnalyzerB\",\n runsToGetDataFor = cms.vint32(7,8,9,10)\n)\n\nprocess8.path1 = cms.Path(process8.esTestAnalyzerB)\n\n# ---------------------------------------------------------\n\n# This one is like the top level process except the\n# configuration was modified for theESSource for\n# record G. Does not do the get for events 1 to 7.\n\nprocess9 = cms.Process(\"TEST9\")\nprocess8.addSubProcess(cms.SubProcess(process9))\n\nprocess9.emptyESSourceB = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordB\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess9.emptyESSourceC = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordC\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess9.emptyESSourceD = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordD\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess9.emptyESSourceE = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordE\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess9.emptyESSourceF = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordF\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess9.emptyESSourceG = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordG\"),\n firstValid = cms.vuint32(1),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess9.emptyESSourceH = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordH\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess9.esTestProducerB = cms.ESProducer(\"ESTestProducerB\")\nprocess9.esTestProducerC = cms.ESProducer(\"ESTestProducerC\")\nprocess9.esTestProducerD = cms.ESProducer(\"ESTestProducerD\")\nprocess9.esTestProducerE = cms.ESProducer(\"ESTestProducerE\")\nprocess9.esTestProducerF = cms.ESProducer(\"ESTestProducerF\")\nprocess9.esTestProducerG = cms.ESProducer(\"ESTestProducerG\")\nprocess9.esTestProducerH = cms.ESProducer(\"ESTestProducerH\")\n\nprocess9.esTestProducerH1 = cms.ESProducer(\"ESTestProducerH\",\n appendToDataLabel = cms.string('abc')\n)\n\nprocess9.esTestAnalyzerB = cms.EDAnalyzer(\"ESTestAnalyzerB\",\n runsToGetDataFor = cms.vint32(8,9,10)\n)\n\nprocess9.path1 = cms.Path(process9.esTestAnalyzerB)\n\n# ---------------------------------------------------------\n\n# Change the value of a tracked parameter in an ESProducer\n# associated with record H ( the module is labeled\n# esTestProducerH1). Do not get data for event 6.\n# Shows the esTestProducerB is not shared.\n\nprocess10 = cms.Process(\"TEST10\")\nprocess9.addSubProcess(cms.SubProcess(process10))\n\nprocess10.emptyESSourceB = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordB\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess10.emptyESSourceC = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordC\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess10.emptyESSourceD = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordD\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess10.emptyESSourceE = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordE\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess10.emptyESSourceF = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordF\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess10.emptyESSourceG = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordG\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess10.emptyESSourceH = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string(\"ESTestRecordH\"),\n firstValid = cms.vuint32(1,2,3,4,5,6,7,8,9),\n iovIsRunNotTime = cms.bool(True)\n)\n\nprocess10.esTestProducerB = cms.ESProducer(\"ESTestProducerB\")\nprocess10.esTestProducerC = cms.ESProducer(\"ESTestProducerC\")\nprocess10.esTestProducerD = cms.ESProducer(\"ESTestProducerD\")\nprocess10.esTestProducerE = cms.ESProducer(\"ESTestProducerE\")\nprocess10.esTestProducerF = cms.ESProducer(\"ESTestProducerF\")\nprocess10.esTestProducerG = cms.ESProducer(\"ESTestProducerG\")\nprocess10.esTestProducerH = cms.ESProducer(\"ESTestProducerH\")\n\nprocess10.esTestProducerH1 = cms.ESProducer(\"ESTestProducerH\",\n appendToDataLabel = cms.string('abc'),\n x = cms.string('abc') \n)\n\nprocess10.esTestAnalyzerB = cms.EDAnalyzer(\"ESTestAnalyzerB\",\n runsToGetDataFor = cms.vint32(1,2,3,4,5,7,8,9,10)\n)\n\nprocess10.path1 = cms.Path(process10.esTestAnalyzerB)\n\n","repo_name":"cms-sw/cmssw","sub_path":"FWCore/Integration/test/testSubProcessEventSetup1_cfg.py","file_name":"testSubProcessEventSetup1_cfg.py","file_ext":"py","file_size_in_byte":29426,"program_lang":"python","lang":"en","doc_type":"code","stars":985,"dataset":"github-code","pt":"50"} +{"seq_id":"11041025560","text":"import hashlib\nimport hmac\nimport json\nimport logging\nimport pprint\nfrom datetime import datetime\n\nfrom odoo import http\nfrom odoo.exceptions import ValidationError\nfrom odoo.http import request\nfrom odoo.tools import consteq\n\nfrom odoo.addons.payment_stripe import utils as stripe_utils\n\n\n_logger = logging.getLogger(__name__)\n\n\nclass StripeController(http.Controller):\n _checkout_return_url = '/payment/stripe/checkout_return'\n _validation_return_url = '/payment/stripe/validation_return'\n _webhook_url = '/payment/stripe/webhook'\n WEBHOOK_AGE_TOLERANCE = 10*60 # seconds\n\n @http.route(_checkout_return_url, type='http', auth='public', csrf=False)\n def stripe_return_from_checkout(self, **data):\n \"\"\" Process the data returned by Stripe after redirection for checkout.\n\n :param dict data: The GET params appended to the URL in `_stripe_create_checkout_session`\n \"\"\"\n # Retrieve the tx and acquirer based on the tx reference included in the return url\n tx_sudo = request.env['payment.transaction'].sudo()._get_tx_from_feedback_data(\n 'stripe', data\n )\n acquirer_sudo = tx_sudo.acquirer_id\n\n # Fetch the PaymentIntent, Charge and PaymentMethod objects from Stripe\n payment_intent = acquirer_sudo._stripe_make_request(\n f'payment_intents/{tx_sudo.stripe_payment_intent}', method='GET'\n )\n _logger.info(\"received payment_intents response:\\n%s\", pprint.pformat(payment_intent))\n self._include_payment_intent_in_feedback_data(payment_intent, data)\n\n # Handle the feedback data crafted with Stripe API objects\n request.env['payment.transaction'].sudo()._handle_feedback_data('stripe', data)\n\n # Redirect the user to the status page\n return request.redirect('/payment/status')\n\n @http.route(_validation_return_url, type='http', auth='public', csrf=False)\n def stripe_return_from_validation(self, **data):\n \"\"\" Process the data returned by Stripe after redirection for validation.\n\n :param dict data: The GET params appended to the URL in `_stripe_create_checkout_session`\n \"\"\"\n # Retrieve the acquirer based on the tx reference included in the return url\n acquirer_sudo = request.env['payment.transaction'].sudo()._get_tx_from_feedback_data(\n 'stripe', data\n ).acquirer_id\n\n # Fetch the Session, SetupIntent and PaymentMethod objects from Stripe\n checkout_session = acquirer_sudo._stripe_make_request(\n f'checkout/sessions/{data.get(\"checkout_session_id\")}',\n payload={'expand[]': 'setup_intent.payment_method'}, # Expand all required objects\n method='GET'\n )\n _logger.info(\"received checkout/session response:\\n%s\", pprint.pformat(checkout_session))\n self._include_setup_intent_in_feedback_data(checkout_session.get('setup_intent', {}), data)\n\n # Handle the feedback data crafted with Stripe API objects\n request.env['payment.transaction'].sudo()._handle_feedback_data('stripe', data)\n\n # Redirect the user to the status page\n return request.redirect('/payment/status')\n\n @http.route(_webhook_url, type='json', auth='public')\n def stripe_webhook(self):\n \"\"\" Process the `checkout.session.completed` event sent by Stripe to the webhook.\n\n :return: An empty string to acknowledge the notification with an HTTP 200 response\n :rtype: str\n \"\"\"\n event = json.loads(request.httprequest.data)\n _logger.info(\"event received:\\n%s\", pprint.pformat(event))\n try:\n if event['type'] == 'checkout.session.completed':\n checkout_session = event['data']['object']\n\n # Check the source and integrity of the event\n data = {'reference': checkout_session['client_reference_id']}\n tx_sudo = request.env['payment.transaction'].sudo()._get_tx_from_feedback_data(\n 'stripe', data\n )\n if self._verify_webhook_signature(\n stripe_utils.get_webhook_secret(tx_sudo.acquirer_id)\n ):\n # Fetch the PaymentIntent, Charge and PaymentMethod objects from Stripe\n if checkout_session.get('payment_intent'): # Can be None\n payment_intent = tx_sudo.acquirer_id._stripe_make_request(\n f'payment_intents/{tx_sudo.stripe_payment_intent}', method='GET'\n )\n _logger.info(\n \"received payment_intents response:\\n%s\", pprint.pformat(payment_intent)\n )\n self._include_payment_intent_in_feedback_data(payment_intent, data)\n # Fetch the SetupIntent and PaymentMethod objects from Stripe\n if checkout_session.get('setup_intent'): # Can be None\n setup_intent = tx_sudo.acquirer_id._stripe_make_request(\n f'setup_intents/{checkout_session.get(\"setup_intent\")}',\n payload={'expand[]': 'payment_method'},\n method='GET'\n )\n _logger.info(\n \"received setup_intents response:\\n%s\", pprint.pformat(setup_intent)\n )\n self._include_setup_intent_in_feedback_data(setup_intent, data)\n # Handle the feedback data crafted with Stripe API objects as a regular feedback\n request.env['payment.transaction'].sudo()._handle_feedback_data('stripe', data)\n except ValidationError: # Acknowledge the notification to avoid getting spammed\n _logger.exception(\"unable to handle the event data; skipping to acknowledge\")\n return ''\n\n @staticmethod\n def _include_payment_intent_in_feedback_data(payment_intent, data):\n data.update({'payment_intent': payment_intent})\n if payment_intent.get('charges', {}).get('total_count', 0) > 0:\n charge = payment_intent['charges']['data'][0] # Use the latest charge object\n data.update({\n 'charge': charge,\n 'payment_method': charge.get('payment_method_details'),\n })\n\n @staticmethod\n def _include_setup_intent_in_feedback_data(setup_intent, data):\n data.update({\n 'setup_intent': setup_intent,\n 'payment_method': setup_intent.get('payment_method')\n })\n\n def _verify_webhook_signature(self, webhook_secret):\n \"\"\" Check that the signature computed from the feedback matches the received one.\n\n See https://stripe.com/docs/webhooks/signatures#verify-manually.\n\n :param str webhook_secret: The secret webhook key of the acquirer handling the transaction\n :return: Whether the signatures match\n :rtype: str\n \"\"\"\n if not webhook_secret:\n _logger.warning(\"ignored webhook event due to undefined webhook secret\")\n return False\n\n notification_payload = request.httprequest.data.decode('utf-8')\n signature_entries = request.httprequest.headers.get('Stripe-Signature').split(',')\n signature_data = {k: v for k, v in [entry.split('=') for entry in signature_entries]}\n\n # Check the timestamp of the event\n event_timestamp = int(signature_data['t'])\n if datetime.utcnow().timestamp() - event_timestamp > self.WEBHOOK_AGE_TOLERANCE:\n _logger.warning(\"ignored webhook event due to age tolerance: %s\", event_timestamp)\n return False\n\n # Compare signatures\n received_signature = signature_data['v1']\n signed_payload = f'{event_timestamp}.{notification_payload}'\n expected_signature = hmac.new(\n webhook_secret.encode('utf-8'),\n signed_payload.encode('utf-8'),\n hashlib.sha256\n ).hexdigest()\n if not consteq(received_signature, expected_signature):\n _logger.warning(\"ignored event with invalid signature\")\n return False\n\n return True\n","repo_name":"anhjean/beanbakery_v15","sub_path":"addons/payment_stripe/controllers/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8188,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"50"} +{"seq_id":"12178193388","text":"import unreal\nimport importlib, pathlib, os, signal, venv, sys\nimport dependency_manager\n#import model_asset_tools\n\n\n# Get all python files in a folder\ndef get_py_files(src, ends_with):\n #cwd = os.getcwd() # Current Working directory\n py_files = [] \n for root, dirs, files in os.walk(src):\n for file in files:\n if file.endswith(ends_with):\n py_files.append(os.path.join(root, file))\n print(os.path.join(root, file))\n return py_files\n\n# Dynamically import a list of python files\ndef dynamic_import(module_name, py_path):\n module_spec = importlib.util.spec_from_file_location(module_name, py_path)\n module = importlib.util.module_from_spec(module_spec)\n module_spec.loader.exec_module(module)\n return module\n\n# Recursively load all python files in a directory\ndef dynamic_import_from_src(src, ends_with, star_import = False):\n my_py_files = get_py_files(src, ends_with)\n for py_file in my_py_files:\n module_name = os.path.split(py_file)[-1].strip(\".py\")\n imported_module = dynamic_import(module_name, py_file)\n if star_import:\n for obj in dir(imported_module):\n globals()[obj] = imported_module.__dict__[obj]\n else:\n globals()[module_name] = imported_module\n return\n\n# Load all manifest files that should be located next to each bridge file\ndef load_manifests(manifest_dir):\n # Get manifest objects\n manifests = {}\n manifest_files = get_py_files(manifest_dir, \"_dependencies.py\")\n for manifest in manifest_files:\n manifest_module_name = os.path.split(manifest)[-1][:-len(\"_dependencies.py\")]\n imported_module = dynamic_import(manifest_module_name, manifest)\n\n print(manifest_module_name)\n manifest = unreal.DependencyManifest()\n manifest_entries = imported_module.GetDependencies()\n manifest.set_editor_property(\"ManifestEntries\", manifest_entries)\n manifests[manifest_module_name] = manifest\n\n return manifests\n\n\n# Replace print() command to fix Unreal flagging every Python print call as an error (doesn't work)\nprint = unreal.log\n\n# Redirect missing SIGKILL signal on windows to SIGTERM\nsignal.SIGKILL = signal.SIGTERM\n\n# Get plugin startup options\ndependency_options = unreal.StableDiffusionBlueprintLibrary.get_dependency_options()\nplugin_options = unreal.StableDiffusionBlueprintLibrary.get_plugin_options()\n\n# Set up virtual environment\nlegacy_site_packages = pathlib.Path(unreal.Paths().engine_saved_dir()) / \"StableDiffusionToolsPyEnv\"\ndefault_site_packages = pathlib.Path(__file__).parent.parent.parent / \"FrozenPythonDependencies\"\nfrozen_python_deps_available = True if os.path.exists(default_site_packages) else False\n\nif not plugin_options.get_freeze_dependencies() and not os.path.exists(default_site_packages):\n default_site_packages = plugin_options.get_python_site_packages_override_path().path\n\nenv_dir = plugin_options.get_python_site_packages_override_path().path if plugin_options.get_use_override_python_site_packages_path() else default_site_packages\nenv_site_packages = pathlib.Path(env_dir) / \"Lib\" / \"site-packages\"\nprint(f\"Dependency installation dir: {env_site_packages}\")\n\n# Setup a new virtual environment to contain our downloaded python dependencies\nif not os.path.exists(env_site_packages):\n os.makedirs(env_site_packages)\nsys.path.append(str(env_site_packages))\n\n# Load dependency manager\ndep_manager = dependency_manager.PyDependencyManager()\ndep_manager.set_editor_property(\"PluginSitePackages\", str(env_site_packages))\ndep_manager.set_editor_property(\"FrozenDependenciesAvailable\", frozen_python_deps_available)\nsubsystem = unreal.get_editor_subsystem(unreal.StableDiffusionSubsystem)\nsubsystem.set_editor_property(\"DependencyManager\", dep_manager)\n\n# Nuke dependencies before loading them if we're trying to reset the editor dependencies\nreset_deps = dependency_options.get_editor_property(\"ClearDependenciesOnEditorRestart\")\nreset_system_deps = dependency_options.get_editor_property(\"ClearSystemDependenciesOnEditorRestart\")\n\nif reset_deps or reset_system_deps:\n print(f\"Clearing python dependendencies\")\n dep_manager.clear_all_dependencies(env_dir, reset_system_deps)\n\n # Clear out the old engine-level site-packages directory in case it doesn't match the new-style\n if os.path.exists(legacy_site_packages):\n print(f\"Removing old legacy site-packages from {legacy_site_packages}\")\n dep_manager.clear_all_dependencies(legacy_site_packages, False)\n \n # Flag dependencies as cleared so we don't keep clearing them every restart\n dep_manager.finished_clearing_dependencies()\n\n# Location of plugin bridge files\nbridge_dir = os.path.join(pathlib.Path(__file__).parent.resolve(), \"bridges\")\n\n# Loads a map of all bridge names and dependencies that need to be installed to import/run the bridge\ndep_manager.set_editor_property(\"DependencyManifests\", load_manifests(bridge_dir))\n\n# Import all bridges so we can pick which derived class we want to use in the plugin editor settings\nif dependency_options.get_editor_property(\"AutoLoadBridgeScripts\"):\n dynamic_import_from_src(os.path.join(pathlib.Path(__file__).parent.resolve(), \"bridges\"), \"Bridge.py\")\n\n# Let Unreal know we've finished loading our init script\nsubsystem.set_editor_property(\"PythonLoaded\", True)\nsubsystem.on_python_loaded_ex.broadcast()\n","repo_name":"Mystfit/Unreal-StableDiffusionTools","sub_path":"StableDiffusionTools/Content/Python/init_unreal.py","file_name":"init_unreal.py","file_ext":"py","file_size_in_byte":5389,"program_lang":"python","lang":"en","doc_type":"code","stars":157,"dataset":"github-code","pt":"50"} +{"seq_id":"14548158793","text":"import cv2\nimport helpers # helper functions\n\nimport random\nimport matplotlib \nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\n\n#%matplotlib inline\n\n# Image data directories\nIMAGE_DIR_TRAINING = \"traffic_light_images/training/\"\nIMAGE_DIR_TEST = \"traffic_light_images/test/\"\n\nIMAGE_LIST = helpers.load_dataset(IMAGE_DIR_TRAINING)\n\n## TODO: Write code to display an image in IMAGE_LIST (try finding a yellow traffic light!)\n## TODO: Print out 1. The shape of the image and 2. The image's label\n\n# ------------------- Global Definitions -------------------\n\n# Definition of the 3 possible traffic light states and theirs label\ntl_states = ['red', 'yellow', 'green']\ntl_state_red = 0\ntl_state_yellow = 1\ntl_state_green = 2\ntl_state_count = 3\ntl_state_red_string = tl_states[tl_state_red]\ntl_state_yellow_string = tl_states[tl_state_yellow]\ntl_state_green_string = tl_states[tl_state_green]\n\n# Index of image and label in image set\nimage_data_image_index = 0\nimage_data_label_index = 1\n\n# Normalized image size\ndefault_image_size = 32\n\n# ---------------- End of Global Definitions ---------------\n\nfig = plt.figure(figsize=(20,40))\n\nexample_count = 24\nif example_count>len(IMAGE_LIST):\n example_count = len(IMAGE_LIST)\n \nchosen = set()\n\n# print 24 random examples, prevent double choice\nfor example_index in range(example_count):\n tries = 0\n \n while tries<2:\n index = 0\n tries += 1\n if example_index==0: # first choice should be a yellow light\n for iterator in range(len(IMAGE_LIST)):\n if IMAGE_LIST[iterator][image_data_label_index]==tl_state_yellow_string:\n index = iterator\n break\n else: # all other choices are random\n index = random.randint(0, len(IMAGE_LIST)-1)\n \n if index in chosen: # try a second time if chosen already\n continue\n chosen.add(index)\n \n example_image = IMAGE_LIST[index][image_data_image_index]\n result = \"{}, shape: {}\".format(IMAGE_LIST[index][image_data_label_index],example_image.shape)\n ax = fig.add_subplot(example_count, 4, example_index+1, title=result)\n ax.imshow(example_image.squeeze())\n \nfig.tight_layout(pad=0.7)\n\ndef standardize(image_list):\n \n # Empty image data array\n standard_list = []\n\n # Iterate through all the image-label pairs\n for item in image_list:\n image = item[0]\n label = item[1]\n\n # Standardize the image\n standardized_im = standardize_input(image)\n\n # One-hot encode the label\n one_hot_label = one_hot_encode(label) \n\n # Append the image, and it's one hot encoded label to the full, processed list of image data \n standard_list.append((standardized_im, one_hot_label))\n \n return standard_list\n\n# Standardize all training images\nSTANDARDIZED_LIST = standardize(IMAGE_LIST)\n\n# TODO: Display a standardized image and its label\n\nfig = plt.figure(figsize=(20,40))\n\n# 12 example pairs\nexample_count = 12\nif example_count>len(IMAGE_LIST):\n example_count = len(IMAGE_LIST)\ntotal_count = example_count*2\n\nchosen = set() # use set to prevent double random selection\n\nfor example_index in range(example_count):\n\n tries = 0\n index = 0\n \n # select next image\n while tries<2:\n tries += 1\n index = random.randint(0, len(IMAGE_LIST)-1)\n \n if index in chosen:\n continue\n chosen.add(index)\n \n eff_index = example_index*2\n \n # print original\n example_image = IMAGE_LIST[index][image_data_image_index]\n result = \"{} {}\".format(IMAGE_LIST[index][image_data_label_index],example_image.shape)\n ax = fig.add_subplot(total_count, 4, eff_index+1, title=result)\n ax.imshow(example_image.squeeze())\n \n # print standardized counterpiece\n eff_index += 1\n example_image = STANDARDIZED_LIST[index][image_data_image_index]\n result = \"{} {}\".format(STANDARDIZED_LIST[index][image_data_label_index],example_image.shape)\n ax = fig.add_subplot(total_count, 4, eff_index+1, title=result)\n ax.imshow(example_image.squeeze())\n\nfig.tight_layout(pad=0.7)","repo_name":"xdoestech/PythonTutorials","sub_path":"TrafficLightClassifier_demo/Load_Visualize_data.py","file_name":"Load_Visualize_data.py","file_ext":"py","file_size_in_byte":4167,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"42959734268","text":"\"\"\"\nDaemonHandler: control daemon server\n\nHyungwon Yang\n23.08.07\n\"\"\"\n\n\nimport sys, os, time, psutil, signal\nimport argparse\nimport logging\nimport tornado\nimport yaml\n\nfrom src.handlers.ServerHandler import App\nfrom src.handlers.AlignHandler import AlignHandler\nfrom src.utils.LogUtils import initLog\n\n# config\nwith open(\"conf/server.yaml\") as f:\n server_config = yaml.load(f, Loader=yaml.FullLoader)\nparser = argparse.ArgumentParser()\nargs, unknwon = parser.parse_known_args(namespace=argparse.Namespace(**server_config))\n# args = parser.parse_args(namespace=argparse.Namespace(**server_config))\n_version = args.version\nCURRENT_PATH = \"/\".join([os.path.dirname(os.path.abspath(__file__)), \"../..\"])\n\n# variables\npauseRunLoop = 0 # 0 means none pause between the calling of run() method.\nrestartPause = 1 # 0 means without a pause between stop and start during the restart of the daemon\nwaitToHardKill = 3 # when terminate a process, wait until kill the process with SIGTERM signal\n\nisReloadSignal = False\ncanDaemonRun = True\nprocessName = os.path.basename(sys.argv[0])\nstdin = '/dev/null'\nstdout = '/dev/null'\nstderr = '/dev/null'\n\n# pid\npid_dir = args.server_pid_path\npid_path = os.path.join(CURRENT_PATH, pid_dir)\nserver_pidfile = args.server_pidfile\nif not os.path.exists(pid_path):\n os.makedirs(pid_path)\n# pid folder가 있을경우만 pid 파일 생성.\npid_file = os.path.join(pid_path, server_pidfile)\n\n# data : save uploaded and processed data(wav, txt, TextGrid)\ndata_dir = args.data_path\ndata_path = os.path.join(CURRENT_PATH, data_dir)\nif not os.path.exists(data_path):\n os.makedirs(data_path)\n\n# history log\nhistory_dir = args.history_path\nhistory_path = os.path.join(CURRENT_PATH, history_dir)\nif not os.path.exists(history_path):\n os.makedirs(history_path)\n\nprint(\"open server port: {}\".format(args.server_port))\ninitLog(args.log_path, args.log_in_date, args.log_file_name, args.log_format)\n\n# make pid file.\ndef savePidFile(pid, saveName: str):\n if \".pid\" in saveName:\n savePath = os.path.join(pid_path, saveName)\n else:\n savePath = os.path.join(pid_path, saveName + \".pid\")\n with open(savePath, 'w', encoding='utf-8') as wrt:\n wrt.write(str(pid)+\"\\n\")\n\n# remove pid file.\ndef removePidFile(fileName: str):\n if \".pid\" in fileName:\n filePath = os.path.join(pid_path, fileName)\n else:\n filePath = os.path.join(pid_path, fileName + \".pid\")\n if os.path.exists(filePath):\n os.remove(filePath)\n \ndef run():\n os.chdir(CURRENT_PATH)\n # open connection.\n app = App()\n app.listen(args.server_port)\n logging.info(\"open server port: {}\".format(args.server_port))\n server_pid = os.getpid()\n savePidFile(server_pid, server_pidfile)\n \n alignHandler = AlignHandler()\n alignHandler.getServerPort(args.server_port)\n \n # main job.\n main_fa_callback = tornado.ioloop.PeriodicCallback(alignHandler.process, args.running_time)\n main_fa_callback.start()\n\n tornado.ioloop.IOLoop.current().start()\n\n\ndef on_terminate(process):\n m = f\"The daemon process with PID {process.pid} has ended correctly.\"\n logging.info(m)\n print(m)\n\ndef _sigterm_handler(signum, frame):\n canDaemonRun = False\n\ndef _reload_handler(signum, frame):\n isReloadSignal = True\n\ndef _makeDaemon():\n \"\"\"\n Make a daemon, do double-fork magic.\n \"\"\"\n try:\n pid = os.fork()\n if pid > 0:\n # Exit first parent.\n sys.exit(0)\n except OSError as e:\n m = f\"Fork #1 failed: {e}\"\n logging.error(m)\n print(m)\n sys.exit(1)\n\n # Decouple from the parent environment.\n os.chdir(\"/\")\n os.setsid()\n os.umask(0)\n\n # Do second fork.\n try:\n pid = os.fork()\n if pid > 0:\n # Exit from second parent.\n sys.exit(0)\n except OSError as e:\n m = f\"Fork #2 failed: {e}\"\n logging.error(m)\n print(m)\n sys.exit(1)\n\n m = \"The daemon process is going to background.\"\n logging.info(m)\n print(m)\n\n # Redirect standard file descriptors.\n sys.stdout.flush()\n sys.stderr.flush()\n si = open(stdin, 'r')\n so = open(stdout, 'a+')\n se = open(stderr, 'a+')\n os.dup2(si.fileno(), sys.stdin.fileno())\n os.dup2(so.fileno(), sys.stdout.fileno())\n os.dup2(se.fileno(), sys.stderr.fileno())\n \ndef _infiniteLoop():\n try:\n if pauseRunLoop:\n time.sleep(pauseRunLoop)\n\n while canDaemonRun:\n run()\n time.sleep(pauseRunLoop)\n else:\n while canDaemonRun:\n run()\n\n except Exception as e:\n m = f\"Run method failed: {e}\"\n sys.stderr.write(m)\n sys.exit(1)\n\ndef getPidFiles():\n dir_list = os.listdir(pid_path)\n pid_file_list = [each_list for each_list in dir_list if \".pid\" in each_list]\n return pid_file_list\n\ndef getPids(pid_file_list):\n pid_list = dict()\n for each_file in pid_file_list:\n with open(os.path.join(pid_path, each_file), 'r', encoding='utf-8') as txt:\n pid_line = txt.read().strip()\n pid_list[each_file] = pid_line\n return pid_list\n\ndef start():\n # Handle signals\n signal.signal(signal.SIGINT, _sigterm_handler)\n signal.signal(signal.SIGTERM, _sigterm_handler)\n signal.signal(signal.SIGHUP, _reload_handler)\n\n # Check if the daemon is already running.\n pid_file_list = getPidFiles()\n\n if server_pidfile in pid_file_list:\n pid_list = getPids(pid_file_list)\n server_pid = pid_list[server_pidfile]\n print(f\"Find a previous daemon processes with PIDs {server_pid}. Is not already the daemon running?\")\n sys.exit(1)\n else:\n print(f\"Start the daemon version {_version}\")\n\n # Daemonize the main process\n _makeDaemon()\n # Start a infinitive loop that periodically runs run() method\n _infiniteLoop()\n\ndef stop():\n pid = 999999\n logging.info(\"stop daemon.\")\n print(\"stop daemon.\")\n try:\n # find pid file list.\n pid_file_list = getPidFiles()\n if pid_file_list == []:\n print(\"Cannot find any daemon process.\")\n return None\n \n # get process id\n logging.info(f\"Found process ids: {pid_file_list}\")\n print(f\"Found process ids: {pid_file_list}\")\n pid_list = getPids(pid_file_list)\n # kill process\n if pid_list:\n for pid_k in pid_list.keys():\n pid = int(pid_list[pid_k])\n if psutil.pid_exists(pid):\n p = psutil.Process(pid)\n p.terminate()\n gone, alive = psutil.wait_procs([p], timeout=3, callback=on_terminate)\n \n for p in alive:\n p.kill()\n print(f\"The daemon process with PID {p.pid} was killed with SIGTERM!\")\n else:\n m = f\"{pid} pid does not exist.\"\n logging.warning(m)\n print(m)\n removePidFile(pid_k)\n else:\n print(\"Cannot find any daemon process.\")\n except Exception as e:\n print(e)\n \ndef status():\n \"\"\"\n Get status of the daemon.\n \"\"\"\n\n pid_list = getPidFiles()\n\n if server_pidfile in pid_list:\n server_pids = getPids([server_pidfile])\n logging.info(f\"The daemon is running with PID {server_pids[server_pidfile]}.\")\n print(f\"The daemon is running with PID {server_pids[server_pidfile]}.\")\n else:\n logging.info(\"The daemon is not running!\")\n print(\"The daemon is not running!\")\n\ndef version():\n print(f\"The daemon version: {_version}\")\n","repo_name":"hyung8758/Korean_FA","sub_path":"src/handlers/DaemonHandler.py","file_name":"DaemonHandler.py","file_ext":"py","file_size_in_byte":7859,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"50"} +{"seq_id":"4663737570","text":"import lxml.html as lh\nfrom pathlib import Path\n\nfor f in Path(\"api-docs/docs-2.4/sphinx/html\").glob(\"**/*.html\"):\n print(f)\n doc = lh.parse(str(f))\n head = doc.find(\"head\")\n for l in head.findall(\"link\"):\n if \"jsdelivr\" not in l.get(\"href\", \"\"):\n continue\n l.set(\"href\", \"https://cdn.jsdelivr.net/npm/katex@0.11.1/dist/katex.min.css\")\n l.set(\n \"integrity\",\n \"sha384-zB1R0rpPzHqg7Kpt0Aljp8JPLqbXI3bhnPWROx27a9N0Ll6ZP/+DiW/UqRcLbRjq\",\n )\n l.set(\"crossorigin\", \"anonymous\")\n\n for l in head.findall(\"script\"):\n if \"jsdelivr\" not in l.get(\"src\", \"\"):\n continue\n if \"katex.min.js\" in l.get(\"src\"):\n l.set(\"src\", \"https://cdn.jsdelivr.net/npm/katex@0.11.1/dist/katex.min.js\")\n l.set(\n \"integrity\",\n \"sha384-y23I5Q6l+B6vatafAwxRu/0oK/79VlbSz7Q9aiSZUvyWYIYsd+qj+o24G5ZU2zJz\",\n )\n elif \"auto-render.min.js\" in l.get(\"src\"):\n l.set(\n \"src\",\n \"https://cdn.jsdelivr.net/npm/katex@0.11.1/dist/contrib/auto-render.min.js\",\n )\n l.set(\n \"integrity\",\n \"sha384-kWPLUVMOks5AQFrykwIup5lo0m3iMkkHrD0uJ4H5cjeGihAutqP0yW0J6dpFiVkI\",\n )\n\n l.set(\"defer\", None)\n l.set(\"crossorigin\", \"anonymous\")\n\n with open(f, \"w\") as file_obj:\n file_obj.write(lh.tostring(doc).decode(\"utf-8\"))\n","repo_name":"Cantera/api-docs","sub_path":"bump_katex.py","file_name":"bump_katex.py","file_ext":"py","file_size_in_byte":1460,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"38142783554","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jan 16 21:01:00 2023\n\n@author: dreardon\n\"\"\"\n\nimport bilby\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom correlations_utils import *\nfrom KDEpy import FFTKDE\nfrom scipy.signal import savgol_filter\n\n\nfrom matplotlib import rc\nimport matplotlib\nrc('text', usetex=True)\nrc('font', **{'family': 'serif', 'serif': ['Computer Modern']})\nmatplotlib.rcParams.update({'font.size': 16})\n\npsrnames = ['J0030+0451',\n 'J0125-2327',\n 'J0437-4715',\n 'J0613-0200',\n 'J0614-3329',\n 'J0711-6830',\n 'J0900-3144',\n 'J1017-7156',\n 'J1022+1001',\n 'J1024-0719',\n 'J1045-4509',\n 'J1125-6014',\n 'J1446-4701',\n 'J1545-4550',\n 'J1600-3053',\n 'J1603-7202',\n 'J1643-1224',\n 'J1713+0747',\n 'J1730-2304',\n 'J1744-1134',\n 'J1832-0836',\n 'J1857+0943',\n 'J1902-5105',\n 'J1909-3744',\n 'J1933-6211',\n 'J1939+2134',\n 'J2124-3358',\n 'J2129-5721',\n 'J2145-0750',\n 'J2241-5236']\n\ncorr_hd_all = {}\ndatadir = '/Users/dreardon/Desktop/ppta_correlations/corr_chains'\npairs = get_pairs(psrnames)\nfor i in range(1, 436): # 435 pulsar pairs\n pair = pairs[str(1 + (int(i) - 1) % 435)]\n corr_hd_all['_'.join(pair)] = np.load(\"corr_chains/{}_corr_hd.npy\".format(i))\n\nimport json, random\n\nts_scrambles = []\n\nbad_list = []\n\n\n#new_pos = {}\n#for i, k in enumerate(pos.keys()):\n# new_pos[k] = np.random.uniform(low=-1, high=1, size=3)\n\nfor _ in range(0, 1):\n\n with open('positions.json', 'r') as f:\n pos = json.load(f)\n\n npos = len(np.array(list(pos.values())))\n inds = np.linspace(0, npos-1, npos)\n random.shuffle(inds)\n new_pos = {}\n for i, k in enumerate(pos.keys()):\n #new_pos[k] = np.array(list(pos.values()))[int(inds[i])]\n new_pos[k] = np.random.uniform(low=-1, high=1, size=3)\n pos = new_pos\n\n\n # Look through pairs and make plots\n kde = {}\n pdf = {}\n ptot = {}\n\n orf_bins_total = {}\n numerator = 0\n denominator = 0\n numerator_68 = 0\n denominator_68 = 0\n donepair = {}\n likelihood_hd = {}\n likelihood_curn = {}\n likelihood_null = {}\n likelihood_hd_global = 0\n likelihood_mono_global = 0\n likelihood_dipole_global = 0\n likelihood_curn_global = 0\n likelihood_null_global = 0\n bf_hd = {}\n n_bins = {}\n n_tot = 0\n\n y_flat = np.linspace(-1, 1, 256)\n null_prob = np.ones(np.shape(y_flat))\n null_prob /= np.sum(null_prob)\n null_prob /= np.mean(np.diff(np.linspace(-1, 1, 256)))\n #null_prob = np.ones(np.shape(null_prob))\n\n nseps = 8\n vals = np.linspace(0, 180, nseps+1)\n halfdiff = np.mean(np.diff(vals))/2\n vals = (vals - halfdiff)[1:]\n seps = {}\n\n nseps2 = 7\n vals2 = np.array([1e-3, 30.0, 50.0, 80.0, 100.0, 120.0, 150.0, 180.0])\n halfdiff2 = np.mean(np.diff(vals2))/2\n vals2 = (vals2[1:] - np.diff(vals2)/2)\n seps2 = {}\n\n data_bins = np.load(\"bins_chain_total.npy\")\n\n\n for ibin in range(1, nseps+1):\n seps[\"bin_{}\".format(ibin)] = vals[ibin-1]\n for ibin in range(1, nseps2+1):\n seps2[\"bin_{}\".format(ibin)] = vals2[ibin-1]\n\n for _ in range(0,1):\n\n #for psri in psrnames:\n orf_bins = {}\n for i in range(1, 436): # 435 pulsar pairs\n\n pair = pairs[str(1 + (int(i) - 1) % 435)]\n print(i, '_'.join(pair))\n\n psr1 = pair[0]\n psr2 = pair[1]\n\n psri='dummy2'\n\n #if ('0437' not in psr1) and ('0437' not in psr2):\n # continue\n #if ('1909' not in psr1) and ('1909' not in psr2):\n # continue\n\n #if '1600' in psr1 or '1600' in psr2:\n # continue\n #if '2145' in psr1 or '2145' in psr2:\n # continue\n #if '1545' in psr1 or '1545' in psr2:\n # continue\n #if '0437' in psr1 or '0437' in psr2:\n # continue\n\n #if '0437' in psr1 or '0437' in psr2:\n # continue\n #if '1713' in psr1 or '1713' in psr2:\n # continue\n\n if not '_'.join(pair) in donepair:\n donepair['_'.join(pair)] = False\n\n # corr_hd = np.array(corr_hd_all['_'.join(pair)]) # bw=0.1, refecting correlations but not amplitude. Slice taken at -14.69\n # y_flat = np.linspace(-1, 1, 256)\n\n \"\"\"\n ind = -6 # corr coeff\n ind_amp = -7 # amplitude\n\n chain = np.load(\"corr_chains/{}.npy\".format(i))\n\n chain_2d = chain[:, [ind_amp, ind]].squeeze()\n\n\n amp = chain_2d[:,0]\n corr = chain_2d[:,1]\n\n reweight_factor = 10**(amp) / (10**-14 - 10**-18) # to uniform\n\n a2 = (10**(amp * reweight_factor) * 10**-14)**2 # uniform in linear\n\n square_reweight = abs(10**56 * a2 * corr)\n\n cov = a2 * square_reweight * corr\n print(len(cov))\n\n medcov = np.median(cov)\n q16 = np.percentile(cov, q=16)\n q84 = np.percentile(cov, q=84)\n stdcov = np.std(cov)\n cov68 = (q84-q16)/2\n\n print(medcov, q16, q84, stdcov, cov68)\n\n plt.hist(cov, bins=1000, density=True)\n plt.title(' '.join(pair))\n\n kde = gaussian_kde(cov, bw_method=0.1)\n\n grid_coords = np.linspace(-1, 1, 1000)\n cov_hd = kde(grid_coords)\n\n cov_hd /= np.sum(cov_hd)\n cov_hd /= np.mean(np.diff(grid_coords))\n\n plt.plot(grid_coords, cov_hd)\n plt.xlim([-1, 1])\n plt.show()\n\n np.save(\"corr_chains/{}_cov_hd.npy\".format(i), cov_hd)\n continue\n \"\"\"\n\n\n\n # Find index corresponding to the correlation corefficient\n ind = -6 # corr coeff\n ind_amp = -7 # amplitude\n\n chain_name = \"corr_chains/{}_more1713.npy\".format(i)\n chain = np.load(chain_name)\n\n #if len(chain[:, ind]) <= 3000:\n # print(i, pair, len(chain[:, ind]))\n # continue\n\n if not \"fixA\" in chain_name:\n # FOR TAKING ONLY SOME SAMPLES\n # chain_2d = chain[1::2, [ind_amp, ind]].squeeze()\n chain_2d = chain[:, [ind_amp, ind]].squeeze()\n\n \"\"\"\n COMPUTE OPTIMAL STATISTIC\n \"\"\"\n\n log10_amps = chain_2d[:,0]\n corrs = chain_2d[:,1]\n cov = (10**log10_amps) ** 2 * corrs\n\n # uniform in A**2 * Gam\n weights = abs(corrs * 2**(2*log10_amps + 1) * 5**(2*log10_amps) * np.log(10))\n\n # Uniform in A * Gam\n weights = abs(corrs * 10**log10_amps * np.log(10))\n\n\n vals = plt.hist(cov, bins=200, range=[-10e-30, 10e-30], weights=weights, density=True)\n plt.xlim([-10*10**-30, 10*10**-30])\n plt.title(' '.join(pair))\n\n densities = vals[0]\n edges = vals[1]\n centres = edges[:-1] + np.diff(edges)/2\n\n smooth_dense = savgol_filter(densities, 10, 1)\n plt.plot(centres, smooth_dense)\n plt.close()\n\n np.savez(\"corr_chains/{}_os.npz\".format(i), smooth_dense, centres)\n print(\"saved\", \"corr_chains/{}_os.npz\".format(i))\n\n continue\n\n print(\"Samples above -14.69: {}\".format(len(np.argwhere(chain_2d[:, 0] >= -14.69))))\n if len(np.argwhere(chain_2d[:, 0] >= -14.69)) < 1000:\n bad_list.append('_'.join(pair))\n print(\"*** WARNING! TOO FEW SAMPLES! ***\")\n\n #plt.subplots(1, 2, figsize=(12,6))\n #plt.subplot(1, 2, 1)\n #h, xedges, yedges, _ = plt.hist2d(chain_2d[:, 0], chain_2d[:, 1], bins=100, density=True, range=[[-18, -14],[-1, 1]])\n #plt.ylabel('Corr coeff')\n #plt.xlabel('log10 A')\n #plt.colorbar()\n #plt.title(' '.join(pair))\n #plt.tight_layout()\n\n\n chain_2d_reflect_corrs_pos = chain_2d.copy()\n chain_2d_reflect_corrs_pos[:, 1] = 2*(1) - chain_2d_reflect_corrs_pos[:, 1]\n chain_2d_reflect_corrs_neg = chain_2d.copy()\n chain_2d_reflect_corrs_neg[:, 1] = 2*(-1) - chain_2d_reflect_corrs_neg[:, 1]\n\n chain_mirror_corr = np.concatenate((chain_2d_reflect_corrs_neg, chain_2d, chain_2d_reflect_corrs_pos))\n\n chain_2d_reflect_amp_pos = chain_mirror_corr.copy()\n chain_2d_reflect_amp_pos[:, 0] = 2*(-14) - chain_2d_reflect_amp_pos[:, 0]\n chain_2d_reflect_amp_neg = chain_mirror_corr.copy()\n chain_2d_reflect_amp_neg[:, 0] = 2*(-18) - chain_2d_reflect_amp_neg[:, 0]\n\n chain_mirror = np.concatenate((chain_2d_reflect_amp_neg, chain_mirror_corr, chain_2d_reflect_amp_pos))\n #chain_mirror = np.concatenate((chain_2d_reflect_amp_neg, chain_mirror_corr))\n #chain_mirror = chain_mirror_corr\n\n #gkde_2d = gaussian_kde(np.transpose(chain_mirror))\n\n #p, bins, _ = plt.hist(chain[:, ind], range=(-1, 1), bins=100, density=True)\n #plt.xlim([-1, 1])\n #plt.close()\n #centres = bins[0:-1] + np.diff(bins)\n #gkde = gaussian_kde(chain[:, ind].squeeze(), bw_method=0.2)\n\n bw = 0.05\n\n data = chain_mirror.squeeze()\n grid_points = 1024 # Grid points in each dimension\n kde = FFTKDE(kernel='gaussian', bw=bw)\n grid, points = kde.fit(data).evaluate((grid_points, grid_points))\n # The grid is of shape (obs, dims), points are of shape (obs, 1)\n x, y = np.unique(grid[:, 0]), np.unique(grid[:, 1])\n # x is amplitudes\n # y is corr coeff\n z = points.reshape(grid_points, grid_points).T\n\n z[y<=-1, :] = 0 # Set the KDE to zero outside of the domain\n z[y>=1, :] = 0 # Set the KDE to zero outside of the domain\n z[:, x<=-18] = 0 # Set the KDE to zero outside of the domain\n z[:, x>=-14] = 0 # Set the KDE to zero outside of the domain\n z = z.squeeze() * 9 # multiply the kde to get integral of ~1\n\n indx = np.argwhere((x>=-18)*(x<=-14)).squeeze()\n indy = np.argwhere((y>=-1)*(y<=1)).squeeze()\n\n y = y[indy]\n x = x[indx]\n z = z[indy, :][:, indx]\n\n #plt.subplot(1, 2, 2)\n #plt.pcolormesh(x,y,z)\n #plt.colorbar()\n #plt.xlim([-18,-14])\n #plt.ylim([-1,1])\n\n #plt.ylabel('Corr coeff')\n #plt.xlabel('log10 A')\n\n np.savez(\"corr_chains/{}_pdf_importance_more1713.npz\".format(i), x, y, z)\n\n # sys.exit()\n\n # x_flat = np.linspace(-18, -14, 20)\n # # x_flat = np.array([-14.69])\n # y_flat = np.linspace(-1, 1, 20)\n # xcentre = xedges[1:] - np.mean(np.diff(xedges))/2\n # ycentre = yedges[1:] - np.mean(np.diff(yedges))/2\n # x, y = np.meshgrid(xcentre, ycentre)\n # grid_coords = np.append(x.reshape(-1,1),y.reshape(-1,1),axis=1)\n\n # z = kde(grid_coords.T)\n\n # z = z.reshape(20, 20)\n # z /= np.sum(z)\n # z /= np.mean(np.diff(xcentre))\n # z /= np.mean(np.diff(ycentre))\n\n # plt.subplot(3, 1, 2)\n # plt.title(\"KDE, bw={}\".format(bw))\n # plt.pcolormesh(xedges, yedges, z)\n # plt.colorbar()\n # plt.ylabel('Corr coeff')\n # plt.xlabel('log10 A')\n\n\n # plt.subplot(3, 1, 3)\n # plt.pcolormesh(xedges, yedges, z - h.T)\n # plt.colorbar()\n # plt.title(\"Residual, standard deviation = {}\".format(round(np.std(z - h.T), 2)))\n # plt.ylabel('Corr coeff')\n # plt.xlabel('log10 A')\n\n #plt.tight_layout()\n\n #plt.savefig('corr_plots/{}_2dhist.png'.format('_'.join(pair)))\n #plt.show()\n\n\n nsamp = 1000\n\n samples = np.array([])\n\n if nsamp > 1:\n for _ in range(0, nsamp):\n # draw an amplitude\n amp = np.random.normal(loc=-14.69, scale=0.05)\n indx = np.argmin(np.abs(x - amp)).squeeze()\n\n pdf = z[:, indx]\n pdf /= np.sum(pdf)\n\n samps = np.random.choice(y, size=1000, p=pdf)\n samples = np.concatenate((samples, samps))\n\n samples_pos = samples.copy()\n samples_pos = 2*(1) - samples_pos\n samples_neg = samples.copy()\n samples_neg = 2*(-1) - samples_neg\n\n samples = np.concatenate((samples_neg, samples, samples_pos))\n\n bw = 0.1\n\n data = samples.squeeze()\n grid_points = 4096 # Grid points in each dimension\n kde = FFTKDE(kernel='gaussian', bw=bw)\n grid, points = kde.fit(data).evaluate(grid_points)\n # The grid is of shape (obs, dims), points are of shape (obs, 1)\n y2 = np.unique(grid)\n\n z2 = points.reshape(grid_points).T\n\n z2[y2<=-1] = 0 # Set the KDE to zero outside of the domain\n z2[y2>=1] = 0 # Set the KDE to zero outside of the domain\n z2 = z2.squeeze() * 3 # multiply the kde to get integral of ~1\n\n indy = np.argwhere((y2>=-1)*(y2<=1)).squeeze()\n y2 = y2[indy]\n\n corr_hd2 = z2[indy] / np.sum(z2[indy])\n\n indx = np.argmin(np.abs(x + 14.69)).squeeze()\n corr_hd = z[:, indx]\n corr_hd /= np.sum(corr_hd)\n\n # if ('1713' in psr1 or '1713' in psr2) and not '1909' in psr2:\n # corr_hd = np.ones(np.shape(corr_hd))\n #corr_hd /= np.sum(corr_hd)\n\n corr_hd /= np.mean(np.diff(y))\n corr_hd2 /= np.mean(np.diff(y2))\n\n #plt.plot(y, corr_hd)\n #plt.plot(y2, corr_hd2)\n #yl = plt.ylim()\n #plt.ylim([0, yl[1]*1.1])\n #plt.xlim([-1, 1])\n #plt.savefig('corr_plots/{}_1dpdf.png'.format('_'.join(pair)))\n #plt.xlabel('Corr coeff')\n #plt.show()\n np.savez(\"corr_chains/{}_corr_hd_importance_more1713.npz\".format(i), corr_hd2, y2)\n print(\"Saved:\", \"corr_chains/{}_corr_hd_importance_more1713.npz\".format(i))\n print(\"\")\n continue\n\n else: # fixA, so only a 1d chain\n chain_1d = chain[:, ind].squeeze()\n\n chain_1d_reflect_corrs_pos = chain_1d.copy()\n chain_1d_reflect_corrs_pos = 2 - chain_1d_reflect_corrs_pos\n chain_1d_reflect_corrs_neg = chain_1d.copy()\n chain_1d_reflect_corrs_neg = -2 - chain_1d_reflect_corrs_neg\n\n chain_mirror = np.concatenate((chain_1d_reflect_corrs_neg, chain_1d, chain_1d_reflect_corrs_pos))\n\n bws = [0.075, 0.1, 0.125, 0.15]\n\n for bw in bws:\n\n data = chain_mirror.squeeze()\n grid_points = 1024 # Grid points in each dimension\n\n kde = FFTKDE(kernel='gaussian', bw=bw)\n grid, points = kde.fit(data).evaluate(grid_points)\n # The grid is of shape (obs, dims), points are of shape (obs, 1)\n y = np.unique(grid)\n # y is corr coeff\n z = points.reshape(grid_points)\n\n z[y<=-1] = 0 # Set the KDE to zero outside of the domain\n z[y>=1] = 0 # Set the KDE to zero outside of the domain\n z = z.squeeze() * 3 # multiply the kde to get integral of ~1\n\n indy = np.argwhere((y>=-1)*(y<=1)).squeeze()\n\n y = y[indy]\n z = z[indy]\n\n np.savez(\"corr_chains/{}_corr_hd_importance_fixA_{}.npz\".format(i, bw), z, y)\n print(\"Saved:\", \"corr_chains/{}_corr_hd_importance_fixA_{}.npz\".format(i, bw))\n print(\"\")\n continue\n\n\n\n sigma_k = np.std(np.random.choice(np.linspace(-1, 1, 256), p=corr_hd/np.sum(corr_hd), size=10000))\n q_16 = np.percentile(np.random.choice(np.linspace(-1, 1, 256), p=corr_hd/np.sum(corr_hd), size=10000), q=16)\n q_84 = np.percentile(np.random.choice(np.linspace(-1, 1, 256), p=corr_hd/np.sum(corr_hd), size=10000), q=84)\n sigma_68 = np.abs(q_84 - q_16)/2\n\n pos1 = pos[psr1]\n pos2 = pos[psr2]\n\n angsep = np.arccos(np.dot(pos1, pos2)) * 180/np.pi\n orf_val = hd_orf(np.array([angsep]))[0]\n dipole_val = dipole(np.array([angsep]))[0]\n\n try:\n likelihood_hd[psri] += np.log(corr_hd[np.argmin(np.abs(y_flat - orf_val))])\n likelihood_curn[psri] += np.log(corr_hd[np.argmin(np.abs(y_flat))])\n likelihood_null[psri] += np.log(null_prob[0])\n except KeyError:\n likelihood_hd[psri] = np.copy(np.log(corr_hd[np.argmin(np.abs(y_flat - orf_val))]))\n likelihood_curn[psri] = np.copy(np.log(corr_hd[np.argmin(np.abs(y_flat))]))\n likelihood_null[psri] = np.log(null_prob[0])\n\n ibin = np.argmin(np.abs(vals - angsep)) + 1\n try: orf_bins[\"bin_{}\".format(ibin)] *= corr_hd\n except KeyError as e: orf_bins[\"bin_{}\".format(ibin)] = corr_hd\n\n if not donepair['_'.join(pair)]:\n numerator += (sigma_k)**-2\n denominator += (sigma_k)**-4\n numerator_68 += (sigma_68)**-2\n denominator_68 += (sigma_68)**-4\n try:\n orf_bins_total[\"bin_{}\".format(ibin)] *= corr_hd\n n_bins[\"bin_{}\".format(ibin)] +=1\n except KeyError as e:\n orf_bins_total[\"bin_{}\".format(ibin)] = np.copy(corr_hd * null_prob)\n n_bins[\"bin_{}\".format(ibin)] = 1\n\n try:\n likelihood_hd_global += np.log(corr_hd[np.argmin(np.abs(y_flat - orf_val))])\n likelihood_curn_global += np.log(corr_hd[np.argmin(np.abs(y_flat))])\n likelihood_mono_global += np.log(corr_hd[np.argmin(np.abs(y_flat - 1))])\n likelihood_dipole_global += np.log(corr_hd[np.argmin(np.abs(y_flat - dipole_val))])\n likelihood_null_global += np.log(null_prob[0])\n except KeyError:\n likelihood_hd_global = corr_hd[np.argmin(np.abs(y_flat - orf_val))]\n likelihood_curn_global = corr_hd[np.argmin(np.abs(y_flat))]\n likelihood_mono_global = corr_hd[np.argmin(np.abs(y_flat - 1))]\n likelihood_dipole_global = corr_hd[np.argmin(np.abs(y_flat - dipole_val))]\n likelihood_null_global = np.log(null_prob[0])\n\n n_tot += 1\n donepair['_'.join(pair)] = True\n\n\n import sys\n sys.exit()\n\n # fig, ax = plt.subplots(1,1, figsize=(9,6))\n # for k in orf_bins.keys():\n # orf_bins[k] /= np.sum(orf_bins[k])\n # orf_bins[k] /= np.mean(np.diff(np.linspace(-1, 1, 256)))\n # draws = np.random.choice(np.linspace(-1, 1, 256), p=orf_bins[k]/np.sum(orf_bins[k]), size=100000)\n # ax = plot_violin(ax, seps[k], draws)\n\n\n # theta = np.linspace(0, 180, 1000)\n # orf = hd_orf(theta)\n # plt.plot(theta, orf, color='k', linewidth=3)\n # plt.ylim([-1, 1])\n # plt.savefig('corr_plots/{}.png'.format(psri))\n # plt.savefig('corr_plots/{}.pdf'.format(psri))\n # plt.close()\n\n #bf_hd[psri] = (likelihood_hd[psri] - likelihood_curn[psri])\n\n\n fig, ax1 = plt.subplots(1, 1, figsize=(9,6))\n ax2 = ax1.twinx()\n\n values = []\n for bini in range(1, nseps+1):\n #values.append(n_bins['bin_{}'.format(bini)])\n try:\n values.append(n_bins['bin_{}'.format(bini)])\n except KeyError:\n n_bins['bin_{}'.format(bini)] = 0\n values.append(n_bins['bin_{}'.format(bini)])\n edges = np.append(np.array(list(seps.values()) - halfdiff), 180)\n\n ax2.fill_between(np.insert(np.array(list(seps.values()) + halfdiff),0, 0),\n np.insert(np.array(values),0,values[0]),\n y2=-np.ones(len(values)+1),\n step=\"pre\", alpha=0.15, color='k')\n #plt.stairs(values, edges=edges, linewidth=1, color='k')\n\n for k in orf_bins_total.keys():\n orf_bins_total[k] /= np.sum(orf_bins_total[k])\n orf_bins_total[k] /= np.mean(np.diff(np.linspace(-1, 1, 256)))\n np.save('/Users/dreardon/Desktop/ppta_correlations/{}.npy'.format(k), orf_bins_total[k]/np.sum(orf_bins_total[k]))\n draws = np.random.choice(np.linspace(-1, 1, 256), p=orf_bins_total[k]/np.sum(orf_bins_total[k]), size=10000)\n ax = plot_violin(ax1, seps[k], draws, width=np.mean(np.diff(vals)), alpha=0.9)\n\n ibin = int(k.split('_')[-1])\n\n # for k in seps2.keys():\n # ibin = int(k.split('_')[-1])\n # ax = plot_violin(ax1, seps2[k], data_bins[:, -(14-ibin-1)], width=np.mean(np.diff(vals2)), colour='crimson', alpha=0.5)\n\n\n theta = np.linspace(0, 180, 1000)\n orf = hd_orf(theta)\n ax1.plot(theta, orf, color='k', linewidth=3)\n ax1.set_ylim([-1, 1])\n ax2.set_ylim([0, round(max(n_bins.values()), -1)])\n ax2.set_ylabel('Number of pulsar pairs')\n plt.xlim([0, 180])\n plt.tight_layout()\n ax1.set_zorder(ax2.get_zorder()+1)\n ax1.set_frame_on(False)\n plt.savefig('corr_plots/corr_total.png')\n plt.savefig('corr_plots/corr_total.pdf')\n plt.show()\n plt.close()\n\n neff = numerator**2 / denominator\n neff_68 = numerator_68**2 / denominator_68\n print(\"Number of effective pulsar pairs, using standard deviation = {}\".format(neff))\n print(\"Number of effective pulsar pairs, using 68% confidence = {}\".format(neff_68))\n print(len(donepair.keys()))\n\n # inds = np.argsort(list(bf_hd.values()))\n # for i in range(0, len(np.flip(np.array(list(bf_hd.keys()))[inds]))):\n # print(np.flip(np.array(list(bf_hd.keys()))[inds])[i],\n # np.flip(np.array(list(bf_hd.values()))[inds])[i])\n\n # print('')\n # bf_hd_psr = np.array(list(likelihood_hd.values())) - np.array(list(likelihood_null.values()))\n # inds = np.argsort(bf_hd_psr)\n # for i in range(0, len(np.flip(np.array(list(bf_hd.keys()))[inds]))):\n # print(np.flip(np.array(list(bf_hd.keys()))[inds])[i],\n # np.flip(bf_hd_psr[inds])[i])\n\n # print('')\n # bf_curn_psr = np.array(list(likelihood_curn.values())) - np.array(list(likelihood_null.values()))\n # inds = np.argsort(bf_curn_psr)\n # for i in range(0, len(np.flip(np.array(list(bf_hd.keys()))[inds]))):\n # print(np.flip(np.array(list(bf_hd.keys()))[inds])[i],\n # np.flip(bf_curn_psr[inds])[i])\n\n print(\" \")\n print('HD log likelihood difference')\n print(likelihood_hd_global - likelihood_null_global)\n print(\" \")\n print(\"HD S/N estimate\")\n print(np.sqrt(2*(likelihood_hd_global - likelihood_null_global)))\n\n\n\n ts = 2*(likelihood_hd_global - likelihood_null_global)\n\n ts_scrambles.append(ts)\n\n new_pos.clear()\n kde.clear()\n pdf.clear()\n ptot.clear()\n orf_bins_total.clear()\n donepair.clear()\n likelihood_hd.clear()\n likelihood_curn.clear()\n likelihood_null.clear()\n bf_hd.clear()\n n_bins.clear()\n seps.clear()\n seps2.clear()\n orf_bins.clear()\n\nts_scrambles = np.load(\"likelihood_ratios.npy\")\nts_scrambles = np.array(ts_scrambles)\n\nplt.hist(ts_scrambles, bins=20)\nyl=plt.ylim()\nplt.plot([1, 1], yl)\nplt.ylim(yl)\nplt.ylabel('N')\nplt.xlabel('Test statistic')\n\nlen(np.array(ts_scrambles)[(ts_scrambles > 1)]) / len(np.array(ts_scrambles)[(ts_scrambles < 1)]) * 100\n\n\n\n\n\n\n\n\n\n\n","repo_name":"MattTMiles/MPTAGW","sub_path":"process_correlations.py","file_name":"process_correlations.py","file_ext":"py","file_size_in_byte":24765,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"23777145294","text":"from django.conf.urls import url, include\n\n\nfrom rest_framework.urlpatterns import format_suffix_patterns\nfrom rest_framework.authtoken import views as drf_views\nfrom rest_framework.routers import DefaultRouter\n\nfrom .views import AbapUserViewSet, CategoryViewSet, ComponentViewSet, HostViewSet, LandscapeRoleViewSet, NoteViewSet, AbapUserList\n\n\n# Create a router and register our viewsets with it.\ncorerouter = DefaultRouter()\n\n\n\n\n# Register SAP Settings\nsaprouter = DefaultRouter()\nsaprouter.register(r'abapusers', AbapUserViewSet)\nsaprouter.register(r'categories', CategoryViewSet)\nsaprouter.register(r'components', ComponentViewSet)\nsaprouter.register(r'hosts', HostViewSet)\nsaprouter.register(r'landscaperoles', LandscapeRoleViewSet)\nsaprouter.register(r'notes', NoteViewSet)\n\n\nurlpatterns = [\n\n url(r'^access_token/', drf_views.obtain_auth_token),\n url(r'^api-auth/', include('rest_framework.urls', namespace='rest_framework')),\n url(r'^o/', include('oauth2_provider.urls', namespace='oauth2_provider')),\n url(r'^', include(corerouter.urls)),\n url(r'^sap/', include(saprouter.urls)),\n\n\n]\n","repo_name":"rafaelbasile/bratzsoft.com","sub_path":"bratzsoft/api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1110,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"26935797092","text":"import setuptools\n\nwith open(\"README.md\", \"r\") as fh:\n long_description = fh.read()\n\nsetuptools.setup(\n name=\"KronoPI_cybergenik\",\n version=\"0.0.1\",\n author=\"Luciano Remes\",\n author_email=\"cybergenik@gmail.com\",\n description=\"Generates random 4 digit number using user time and date from PI digits\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n url=\"https://github.com/Cybergenik/KronoPI\",\n packages=setuptools.find_packages(),\n classifiers=[\n \"Programming Language :: Python :: 3\",\n \"License :: OSI Approved :: MIT License\",\n \"Operating System :: OS Independent\",\n ],\n python_requires='>=3.6',\n)","repo_name":"Cybergenik/KronoPI","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":696,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"18706051439","text":"# 21611 마법사 상어와 비바라기\n\nN, M = map(int, input().split())\nboard = [list(map(int, input().split())) for _ in range(N)]\ninfo = [list(map(int, input().split())) for _ in range(M)]\n# info[i][0] -> di, info[i][1]-> si\ncloud = [(N - 1, 0), (N - 1, 1), (N - 2, 0), (N - 2, 1)]\n\n# 왼쪽부터 ~ 대각선왼쪽아래까지\ndx = [0, -1, -1, -1, 0, 1, 1, 1]\ndy = [-1, -1, 0, 1, 1, 1, 0, -1]\nnext_cloud = []\n\nbdx = [-1, -1, 1, 1]\nbdy = [-1, 1, 1, -1]\n\n\ndef waterMagic(x, y):\n cnt = 0\n for d in range(4):\n bnx = x + bdx[d]\n bny = y + bdy[d]\n if 0 <= bnx < N and 0 <= bny < N and board[bnx][bny] > 0:\n cnt += 1\n board[x][y] += cnt\n\n\nfor i in range(M):\n # 구름 이동\n for d in range(len(cloud)):\n dir = info[i][0] - 1\n nx = (N + cloud[d][0] + dx[dir] * info[i][1]) % N\n ny = (N + cloud[d][1] + dy[dir] * info[i][1]) % N\n next_cloud.append((nx, ny))\n\n # 구름 있는 칸에 비가 내림\n for cx, cy in next_cloud:\n board[cx][cy] += 1\n\n bug_dir = next_cloud\n cloud = []\n # 물이 증가한 칸 r,c에 물복사버그 마법 시전.\n for bx, by in bug_dir:\n waterMagic(bx, by)\n\n for j in range(N):\n for k in range(N):\n if board[j][k] >= 2 and (j, k) not in next_cloud:\n cloud.append((j, k))\n board[j][k] -= 2\n next_cloud = []\n\nans = 0\nfor i in range(N):\n for j in range(N):\n ans += board[i][j]\n\n\nprint(ans)\n","repo_name":"doodung/Algorithm","sub_path":"코딩테스트/삼성 sw 역량 테스트/21610_마법사상어와비바라기(2022).py","file_name":"21610_마법사상어와비바라기(2022).py","file_ext":"py","file_size_in_byte":1484,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"50"} +{"seq_id":"39177158566","text":"from django.db import models\nfrom wagtail.wagtailadmin.edit_handlers import FieldPanel\n\nfrom wagtailcomments.models import COMMENT_MODEL_SETTING, BaseComment\n\n\nclass Comment(BaseComment):\n body = models.TextField()\n\n form_class = 'wagtailcomments.basic.forms.CommentForm'\n\n panels = BaseComment.panels + [\n FieldPanel('body'),\n ]\n\n class Meta:\n swappable = COMMENT_MODEL_SETTING\n","repo_name":"neon-jungle/wagtailcomments","sub_path":"wagtailcomments/basic/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":408,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"50"} +{"seq_id":"41261617967","text":"# Import module\nimport tkinter as tk\nfrom PIL import ImageTk\n\n# Create object\nroot = tk.Tk()\n\n# Adjust size\nroot.geometry(\"1100x580\")\n\ndef make_draggable(widget):\n widget.bind(\"\", on_drag_start)\n widget.bind(\"\", on_drag_motion)\n\ndef on_drag_start(event):\n widget = event.widget\n widget._drag_start_x = event.x\n widget._drag_start_y = event.y\n\ndef on_drag_motion(event):\n widget = event.widget\n x = widget.winfo_x() - widget._drag_start_x + event.x\n y = widget.winfo_y() - widget._drag_start_y + event.y\n widget.place(x=x, y=y)\n\n\n\nframe = tk.Frame(root, bd=4, bg=\"grey\")\nframe.place(x)\nmake_draggable(frame)\n\nnotes = tk.Text(frame)\nnotes.pack()\n\n\n\n# Execute tkinter\nroot.mainloop()\n","repo_name":"roiatembo/watermarker","sub_path":"sandy.py","file_name":"sandy.py","file_ext":"py","file_size_in_byte":729,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"18768567073","text":"# 23 dars buyicha uyga vazifa!\n\n\nclass Avto:\n\tdef __init__(self,name,model,rang,karobka,narh,ish_davlat):\n\t\tself.name = name\n\t\tself.model = model\n\t\tself.rang = rang\n\t\tself.karobka = karobka\n\t\tself.narh = narh\n\t\tself.ish_davlat = ish_davlat\n\t\tself.ut_yuli = 10\n\t\tself.new_avto = []\n\n\tdef get_info(self):\n\t\t\"\"\"Avtomobil haqida to'liq ma'lumot\"\"\"\n\t\treturn f\" {self.name.title()} avtomobili {self.ish_davlat.title()}da ishlab chiqarilgan,\\n hozirda shu avtomobilning {self.model} modeli rangi {self.rang} va {self.karobka} karobka: {self.narh} $ , bosib otgan yoli {self.ut_yuli} km\"\n\n\tdef set_km(self,ut_yuli):\n\t\t\"\"\"Bosib otgan km yangilash\"\"\"\n\t\tself.ut_yuli = ut_yuli\n\n\tdef update_km(self):\n\t\t\"\"\"Bosib utgan yo'lini xisoblash\"\"\"\n\t\tself.ut_yuli +=10\n\n\tdef get_avto(self):\n\t\treturn [km.get_info() for km in self.new_avto]\n\navto_1 = Avto('mersedes_benz','sls','qora','avtomat',25000,'germaniya')\navto_2 = Avto('range rover','optima','qora','avtomat',45000,'angliya')\navto_3 = Avto('tayota','prado','oq','mexanik',35000,'yaponiya')\navto_4 = Avto('haval','x7','qizil','avtomat',14000,'xitoy')\n\navto_1.update_km()\navto_1.update_km()\navto_1.update_km()\navto_1.update_km()\navto_1.update_km()\n\nprint(avto_1.get_info())\n\nclass New_avto:\n\tdef __init__(self,salon):\n\t\tself.salon = salon\n\t\tself.yangi_avto = 0\n\n\tdef add_avto(self,saln_avto):\n\t\tself.new_avto.append(saln_avto)\t\n\t\tself.yangi_avto += 1\n\n\tdef get_sal_avto(self):\n\t\t\"\"\"Sotuvdagi avtomobillar\"\"\"\n\t\treturn [saln_avto.get_info() for saln_avto in self.new_avto]\n\tdef get_apd_avto(self):\n\t\treturn self.yangi_avto\n\nsl_avtolar = New_avto(\"Orion Avto Saloni\")\navto_1 = Avto('mersedes_benz','sls','qora','avtomat',25000,'germaniya')\navto_2 = Avto('range rover','optima','qora','avtomat',45000,'angliya')\navto_3 = Avto('tayota','prado','oq','mexanik',35000,'yaponiya')\navto_4 = Avto('haval','x7','qizil','avtomat',14000,'xitoy')\nsl_avtolar.add_avto(avto_1)\nsl_avtolar.add_avto(avto_2)\nsl_avtolar.add_avto(avto_3)\nsl_avtolar.add_avto(avto_4)\n\nprint(sl_avtolar.get_sal_avto())\n","repo_name":"obidjonqunduzov/python-darslari","sub_path":"dars_23_prac.py","file_name":"dars_23_prac.py","file_ext":"py","file_size_in_byte":2012,"program_lang":"python","lang":"uz","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"28075391376","text":"from django.http import HttpResponseRedirect\nfrom django.shortcuts import render\n\nfrom .forms import IngredientsForm, RecipesForm, WeekdayForm\nfrom .models import WeekDays\n\n\ndef home(request):\n context = {\n 'weekdays': WeekDays.objects.all(),\n }\n\n return render(request, 'madplan/home.html', context)\n\n\ndef add_weekday(request):\n if request.method == 'POST':\n form = WeekdayForm(request.POST)\n\n if form.is_valid():\n form.clean()\n form.save()\n return HttpResponseRedirect('/')\n else:\n form = WeekdayForm()\n\n return render(request, 'madplan/add_weekday.html', {'form': form})\n\n\ndef add_ingredients(request):\n if request.method == 'POST':\n form = IngredientsForm(request.POST)\n\n if form.is_valid():\n form.clean()\n form.save()\n return HttpResponseRedirect('/')\n else:\n form = IngredientsForm()\n\n return render(request, 'madplan/add_ingredients.html', {'form': form})\n\n\ndef add_recipes(request):\n if request.method == 'POST':\n form = RecipesForm(request.POST)\n\n if form.is_valid():\n form.clean()\n form.save()\n return HttpResponseRedirect('/')\n else:\n form = RecipesForm()\n\n return render(request, 'madplan/add_recipes.html', {'form': form})\n","repo_name":"razzervision/Mealplan","sub_path":"madplan/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1341,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"22245144481","text":"from __future__ import absolute_import, division, print_function\n\nimport os\nimport time\nimport csv\nimport glob\nimport argparse\n\nimport tifffile\nimport h5py\nimport numpy as np\nimport tensorflow as tf\nfrom tqdm import tqdm\nfrom keras.utils import multi_gpu_model\nfrom keras import optimizers\nfrom keras.backend.tensorflow_backend import set_session\nfrom keras import backend as K\nfrom keras.losses import get\nfrom keras.models import load_model, Model\n\nfrom unet.unet import unet_model\nfrom unet.loss import multi_loss\nfrom util.load_data import load_train_data\nfrom util.load_batch import LoadBatchGenGPU, polar_zoom\nfrom util.read_parameter_from_log_file import read_parameter_from_log_file\nfrom util.postprocessing import postprocessing\n\n\ndef main():\n \"\"\"Train or test a U-Net model to analyze OCT images.\n\n Notes:\n **All arguments are bash arguments**.\n\n Args:\n exp_def: experiment definition\n models_path: path for saving models\n lr: learning rate\n lr_decay: learning rate step for decay\n data_pat: data folder path\n nEpoch: number of epochs\n nBatch: batch size\n outCh: size of output channel\n inCh: size of input channel\n nZ: size of input depth\n w: size of input width (number of columns)\n l: size of input Length (number of rows)\n loss_w: loss wights\n isAug: Is data augmentation\n isCarts: whether images should be converted into Cartesian\n isTest: Is test run instead of train\n testEpoch: epoch of the saved model for testing\n saveEpoch: epoch interval to save the model\n epochSize: number of samples per epoch\n nFeature: number of features in the first layer\n nLayer: number of layers in the U-Nnet model\n gpu_id: ID of GPUs to be used\n optimizer: keras optimizer. see :meth:`keras.optimizers`\n\n See Also:\n * :meth:`unet.unet.unet_model`\n * :meth:`unet.loss.multi_loss_fun`\n * :meth:`util.load_data.load_train_data`\n * :meth:`util.load_batch.load_batch_parallel`\n * :meth:`keras.utils.multi_gpu_model`\n * :meth:`keras.optimizers`\n\n \"\"\"\n\n # parse arguments\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-exp_def\", type=str, default=\"test\", help=\"experiment definition\")\n parser.add_argument(\"-models_path\", type=str, default=\"model\", help=\"path for saving models\")\n parser.add_argument(\"-lr\", type=float, default=1e-4, help=\"learning rate\")\n parser.add_argument(\"-lr_decay\", type=float, default=0.0, help=\"learning rate decay\")\n parser.add_argument(\"-data_path\", type=str, default=\"D:\\\\MLIntravascularPolarimetry\\\\MLCardioPullbacks\\\\\",\n help=\"data folder path\")\n parser.add_argument(\"-nEpoch\", type=int, default=1000, help=\"number of epochs\")\n parser.add_argument(\"-nBatch\", type=int, default=30, help=\"batch size\")\n parser.add_argument(\"-outCh\", type=int, default=6, help=\"size of output channel\")\n parser.add_argument(\"-inCh\", type=int, default=3, help=\"size of input channel\")\n parser.add_argument(\"-nZ\", type=int, default=1, help=\"size of input depth\")\n parser.add_argument(\"-w\", type=int, default=512, help=\"size of input width (# of columns)\")\n parser.add_argument(\"-l\", type=int, default=512, help=\"size of input Length (# of rows)\")\n parser.add_argument(\"-loss_w\", type=str, default=\"1, 1, 1, 1, 1, 1, 1, 1, 1\", help=\"loss wights\")\n parser.add_argument(\"-isAug\", type=int, default=1, help=\"Is data augmentation\")\n parser.add_argument(\"-isCarts\", type=int, default=0, help=\"whether images should be converted into Cartesian\")\n parser.add_argument(\"-isTest\", type=int, default=0, help=\"Is test run instead of train. 1 when paramters are \"\n \"from arguments. 2 when paramters are from log file.\")\n parser.add_argument(\"-testEpoch\", type=int, default=10, help=\"epoch of the saved model for testing\")\n parser.add_argument(\"-testDir\", type=str, default=\"-\", help=\"test directory. Default is '-' for using the dataset.\")\n parser.add_argument(\"-saveEpoch\", type=int, default=100, help=\"epoch interval to save the model\")\n parser.add_argument(\"-epochSize\", type=int, default=10, help=\"number of samples per epoch as multiple of the \"\n \"training dataset size\")\n parser.add_argument(\"-nFeature\", type=int, default=8, help=\"number of features in the first layer\")\n parser.add_argument(\"-nLayer\", type=int, default=3, help=\"number of layers in the U-Net model\")\n parser.add_argument(\"-pool_scale\", type=int, default=2, help=\"max pooling scale factor.\")\n parser.add_argument(\"-gpu_id\", type=str, default=\"*\", help=\"ID of GPUs to be used. Use * for all and '' for none.\")\n parser.add_argument(\"-optimizer\", type=str, default=\"RMSprop\", help=\"optimizer\")\n parser.add_argument(\"-is_critique\", type=int, default=1, help=\"If critique model is used\")\n parser.add_argument(\"-critique_model\", type=str, default=\"critique-outCh6_v10\", help=\"critique definition\")\n parser.add_argument(\"-critiqueEpoch\", type=int, default=20000, help=\"epoch of the critique model\")\n parser.add_argument(\"-is_error_list\", type=int, default=0, help=\"use the error_list.txt file\")\n parser.add_argument(\"-error_case_ratio\", type=float, default=0.1, help=\"error case ratio in the batch\")\n parser.add_argument(\"--mode\", type=str)\n parser.add_argument(\"--port\", type=int)\n\n # assign the first part of args. The second part will ba assigned after reading parameter from log file\n args = parser.parse_args()\n experiment_def = args.exp_def\n isTest = args.isTest\n isTrain = 0 if args.isTest else 1\n models_path = args.models_path\n experiment_path = os.path.join(models_path, experiment_def)\n\n # prepare a folder for the saved models and log file\n if not os.path.exists(experiment_path):\n os.makedirs(experiment_path)\n save_file_name = os.path.join(experiment_path, 'model-epoch%06d.h5')\n log_file = os.path.join(experiment_path, 'log-' + experiment_def + '.csv')\n\n # read parameter from log file\n if args.isTest == 2:\n args = read_parameter_from_log_file(args, log_file)\n\n # assign the second part of args\n folder_path = args.data_path\n nEpoch = args.nEpoch\n nBatch = args.nBatch\n im_shape = (args.nZ, args.l, args.w, args.inCh)\n outCh = args.outCh\n loss_weight = np.array([float(i) for i in args.loss_w.split(',')], dtype='float32')\n loss_weight = loss_weight / np.sum(loss_weight)\n coord_sys = 'carts' if args.isCarts else 'polar'\n\n # initialize the log file or update the parameters\n if isTrain:\n if os.path.exists(log_file):\n with open(log_file, 'r') as f:\n L = f.readlines()\n L[0] = 'epoch, Time (hr), Train_Loss, Valid_Loss, ' + str(args) + '\\n'\n with open(log_file, 'w') as f:\n f.writelines(L)\n else:\n with open(log_file, 'w') as f:\n f.write('epoch, Time (hr), Train_Loss, Valid_Loss, ' + str(args) + '\\n')\n\n # GPU settings\n if '-' in args.gpu_id:\n numGPU = args.gpu_id.split('-')\n numGPU = int(numGPU[1]) - int(numGPU[0]) + 1\n elif '*' in args.gpu_id:\n numGPU = len(os.popen('nvidia-smi').read().split('+\\n')) - 5\n args.gpu_id = ','.join([str(i) for i in range(numGPU)])\n else:\n numGPU = len(args.gpu_id.split(','))\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = args.gpu_id\n gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=1.0)\n config = tf.ConfigProto(gpu_options=gpu_options)\n config.gpu_options.allow_growth = True\n config.allow_soft_placement = True\n set_session(tf.Session(config=config))\n\n # build the model\n model_template = unet_model(im_shape, nFeature=args.nFeature, outCh=outCh, nLayer=args.nLayer,\n pool_scale=args.pool_scale)\n\n # critique loss\n if args.is_critique:\n critique = load_model(os.path.join(models_path, args.critique_model,\n 'model-epoch%06d.h5' % args.critiqueEpoch),\n custom_objects={'loss': get(lambda y_, y: tf.reduce_mean(tf.abs(1 - y_ * y)))})\n critique.name = 'critique'\n for L in critique.layers:\n L.trainable = False\n\n model_template = Model(inputs=model_template.input,\n outputs=[model_template.output, critique([model_template.input, model_template.output])],\n name='with_critique')\n\n if isTrain:\n # load the last saved model if exists\n f = glob.glob(os.path.join(experiment_path, 'model-epoch*.h5'))\n f.sort()\n if len(f):\n iEpochStart = int(f[-1][-9:-3])\n model_template.load_weights(save_file_name % iEpochStart)\n with open(log_file, 'r') as f:\n reader = csv.DictReader(f, delimiter=',', skipinitialspace=True)\n for row in reader:\n if int(row['epoch']) == iEpochStart:\n last_time = float(row['Time (hr)']) * 3600\n print('model at epoch %d is loaded.' % iEpochStart)\n iEpochStart += 1\n iEpoch = nEpoch # in case training is done\n else:\n iEpochStart = 1\n last_time = 0\n print('Model is initialized.')\n elif isTest:\n iEpoch = args.testEpoch\n model_template = load_model(save_file_name % iEpoch)\n print('model at epoch %d is loaded.' % args.testEpoch)\n\n if numGPU > 1:\n model = multi_gpu_model(model_template, gpus=numGPU)\n else:\n model = model_template\n optimizer = getattr(optimizers, args.optimizer)\n if args.is_critique:\n model.compile(optimizer=optimizer(lr=args.lr, decay=args.lr_decay),\n loss=[get(multi_loss(loss_weight[:-1], outCh)), get(lambda y_, y: 0.5 - 0.5 * y)],\n loss_weights=[sum(loss_weight[:-1]), loss_weight[-1]])\n else:\n model.compile(optimizer=optimizer(lr=args.lr, decay=args.lr_decay), loss=get(multi_loss(loss_weight, outCh)))\n\n # load data\n if args.testDir == '-':\n data_file = os.path.join(folder_path, 'Dataset ' + coord_sys + ' Z%d-L%d-W%d-C%d.h5' % im_shape)\n if os.path.exists(data_file):\n with h5py.File(data_file, 'r') as f:\n im, label_9class, train_data_id, test_data_id, valid_data_id, sample_caseID, sample_sliceID = \\\n np.array(f.get('/im')), np.array(f.get('/label')), np.array(f.get('/train_data_id')), \\\n np.array(f.get('/test_data_id')), np.array(f.get('/valid_data_id')), \\\n np.array(f.get('/sample_caseID')), np.array(f.get('/sample_sliceID'))\n else:\n im, label_9class, train_data_id, test_data_id, valid_data_id, sample_caseID, sample_sliceID = \\\n load_train_data(folder_path, im_shape, coord_sys, saveOutput=True)\n if args.is_error_list:\n with open('error_list.txt', 'r') as f:\n error_list = f.readlines()\n error_list = [int(i) - 1 for i in error_list]\n error_list = np.union1d(train_data_id, valid_data_id)[error_list]\n error_list = np.intersect1d(train_data_id, error_list)\n else:\n error_list = []\n\n # labels and masks\n label = np.zeros(label_9class.shape[:-1] + (outCh,))\n\n if outCh == 4:\n # 4 channel: Ch1: Lumen , Ch2: visible intima , Ch3: visible media ,\n # Ch0: others , note visible is without GW and nonIEL\n nonIEL_GW_mask = np.logical_not(np.logical_or(label_9class[..., 0], label_9class[..., 1]))\n label[..., 1] = label_9class[..., 2]\n label[..., 2] = np.logical_and(label_9class[..., 3], nonIEL_GW_mask)\n label[..., 3] = np.logical_and(label_9class[..., 4], nonIEL_GW_mask)\n label[..., 0] = np.all(np.logical_not(label[..., 1:]), axis=-1)\n\n elif outCh == 6:\n # 6 channel: Ch1: Lumen , Ch2: visible intima , Ch3: visible media ,\n # Ch4 : GW outside Lumen , Ch5: nonIEL outside Lumen and GW,\n # Ch0: others , note visible is without GW and nonIEL\n nonIEL_GW_mask = np.logical_not(np.logical_or(label_9class[..., 0], label_9class[..., 1]))\n label[..., 1] = label_9class[..., 2]\n label[..., 2] = np.logical_and(label_9class[..., 3], nonIEL_GW_mask)\n label[..., 3] = np.logical_and(label_9class[..., 4], nonIEL_GW_mask)\n IEL_EEL = np.any(label_9class[..., 3:5], axis=-1)\n label[..., 4] = np.logical_and(label_9class[..., 0], IEL_EEL)\n nonIEL_withoutGW = np.logical_and(label_9class[..., 1], np.logical_not(label_9class[..., 0]))\n label[..., 5] = np.logical_and(nonIEL_withoutGW, IEL_EEL)\n label[..., 0] = np.all(np.logical_not(label[..., 1:]), axis=-1)\n\n # # an experimental 6 label (called 2x3)\n # elif outCh == 6:\n # # Ch0 : not(intima or medai) , Ch1: intima , Ch2 : media\n # # Ch3 : not(GW or nonIEL) , Ch4: GW , Ch5: nonIEL\n # label[..., 0] = np.logical_not(np.logical_or(label_9class[..., 3], label_9class[..., 4]))\n # label[..., 1] = label_9class[..., 3]\n # label[..., 2] = label_9class[..., 4]\n # label[..., 3] = np.logical_not(np.logical_or(label_9class[..., 0], label_9class[..., 1]))\n # label[..., 4] = label_9class[..., 0]\n # label[..., 5] = label_9class[..., 1]\n\n\n # training\n if isTrain:\n train_data_gen = LoadBatchGenGPU(im, train_data_id, nBatch, label, isAug=args.isAug, coord_sys=coord_sys,\n prob_lim=0.5, isCritique=args.is_critique,\n error_list=error_list, error_case_ratio=args.error_case_ratio)\n if args.epochSize == 0:\n args.epochSize = np.ceil(train_data_id.size / nBatch).astype('int')\n else:\n args.epochSize = np.ceil(args.epochSize * train_data_id.size / nBatch).astype('int')\n print('Data is loaded. Training: %d, validation: %d' % (len(np.unique(sample_caseID[train_data_id])),\n len(np.unique(sample_caseID[valid_data_id]))))\n\n start = time.time() - last_time\n for iEpoch in range(iEpochStart, nEpoch + 1):\n model.fit_generator(train_data_gen, steps_per_epoch=args.epochSize, verbose=1)\n # evaluation\n if args.is_critique:\n train_loss = model.evaluate(im[train_data_id, ...],\n [label[train_data_id, ...], np.zeros((train_data_id.size, 1))],\n batch_size=nBatch, verbose=0)[0]\n valid_loss = model.evaluate(im[valid_data_id, ...],\n [label[valid_data_id, ...], np.zeros((valid_data_id.size, 1))],\n batch_size=nBatch, verbose=0)[0]\n else:\n train_loss = model.evaluate(im[train_data_id, ...], label[train_data_id, ...],\n batch_size=nBatch, verbose=0)\n valid_loss = model.evaluate(im[valid_data_id, ...], label[valid_data_id, ...],\n batch_size=nBatch, verbose=0)\n\n rem_time = (nEpoch - iEpoch) / iEpoch * (time.time() - start) / 3600.0\n print(\"Epoch%d: %.2f hr to finish, Train Loss: %f, Valid Loss: %f\" % (iEpoch, rem_time,\n train_loss, valid_loss))\n with open(log_file, 'a') as f:\n f.write(\"%d, %.2f, %f, %f, \\n\" % (iEpoch, (time.time() - start) / 3600.0, train_loss, valid_loss))\n\n # save model\n if iEpoch % args.saveEpoch == 0:\n model_template.save(save_file_name % iEpoch)\n\n # feed forward\n if args.testDir == '-':\n train_valid_data_id = np.union1d(train_data_id, valid_data_id)\n out = model.predict(im, batch_size=nBatch, verbose=1)\n if args.is_critique:\n out = np.array(out[0])\n\n # see the loss for the first 20 slices\n LOSS = np.zeros((20, ) + label.shape[1:-1], dtype='float32')\n for i in tqdm(range(LOSS.shape[0])):\n if args.is_critique:\n LOSS[[i], ...] = K.eval(model.loss[0](\n tf.constant(label[[train_valid_data_id[i]], ...].astype('float32')),\n tf.constant((out[[train_valid_data_id[i]], ...]).astype('float32'))))\n else:\n LOSS[[i], ...] = K.eval(model.loss(\n tf.constant(label[[train_valid_data_id[i]], ...].astype('float32')),\n tf.constant((out[[train_valid_data_id[i]], ...]).astype('float32'))))\n\n out = np.argmax(out, -1)\n label = np.argmax(label, -1)\n if len(out.shape) > 3:\n i = int(out.shape[1] // 2)\n label, out, im = label[:, i, ...].squeeze(), out[:, i, ...].squeeze(), im[:, i, ...].squeeze()\n\n # set the label intensity of the training slices background to the number of classes, which is one more than\n # the last class value\n i = label[train_data_id, ...]\n i[i == 0] = outCh\n label[train_data_id, ...] = i\n\n # write files\n tifffile.imwrite(os.path.join(experiment_path, 'a-label.tif'), label[train_valid_data_id, ...].astype(\n np.uint8))\n tifffile.imwrite(os.path.join(experiment_path, 'a-out-epoch%06d.tif' % iEpoch),\n out[train_valid_data_id, ...].astype(np.uint8))\n tifffile.imwrite(os.path.join(experiment_path, 'a-im.tif'),\n (im[train_valid_data_id, ...] * 255).astype(np.uint8).squeeze())\n tifffile.imwrite(os.path.join(experiment_path, 'a-loss.tif'), LOSS.astype('float32'))\n else:\n files = glob.glob(os.path.join(args.testDir, '*.pstif'))\n for f in tqdm(files):\n im = tifffile.imread(f)\n im = im.astype(np.float32) / 255\n im = np.moveaxis(np.reshape(im, (-1, 3,) + im.shape[1:]), 1, -1)\n im = polar_zoom(im, scale=im_shape[1] / im.shape[1])\n shift = (0, 128, 256, 384)\n out_ = np.zeros(im.shape[:-1] + (len(shift),))\n for j in range(len(shift)):\n im_ = im[:, :, np.r_[shift[j]:im_shape[2], 0:shift[j]]]\n out = model.predict(im_, batch_size=nBatch, verbose=1)\n if args.is_critique:\n out = np.array(out[0])\n out = np.argmax(out, -1)\n out_[..., j] = out[:, :, np.r_[(512 - shift[j]):im_shape[2], 0:(512 - shift[j])]]\n out = np.median(out_, axis=3)\n tifffile.imwrite(f[:-6] + '-fwd.tif', out.astype(np.uint8))\n tifffile.imwrite(f[:-6] + '-fwd-post.tif', postprocessing(out.astype(np.uint8)))\n tifffile.imwrite(f[:-6] + '-im.tif', (im * 255).astype(np.uint8).squeeze())\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"mhaft/OCTseg","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":19359,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"50"} +{"seq_id":"72919618716","text":"# Q38. Your task is to create function is Divided By (or is_divide_by) to check if an integer\n# number is divisible by each out of two arguments.\n# A few cases:\n# (-12, 2, -6) -> true\n# (-12, 2, -5) -> false\n# (45, 1, 6) -> false\n# (45, 5, 15) -> true\n# (4, 1, 4) -> true\n# (15, -5, 3) -> true\n\ndef dividedby(a,b,c):\n if a%b==0 and a%c==0:\n print(True)\n elif b%a==0 and b%c==0:\n print(True)\n elif c%a==0 and c%b==0:\n print(True)\n else:\n print(False)\nx=int(input(\"enter the number :\"))\ny=int(input(\"enter the number :\"))\nz=int(input(\"enter the number :\"))\ndividedby(x,y,z)\n\n","repo_name":"anjalikri1/Python-Function","sub_path":"q38.py","file_name":"q38.py","file_ext":"py","file_size_in_byte":613,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"21560466932","text":"import resource\n\n\nDATABASE = {\n \"animals\":\n [{\n \"id\": 1,\n \"name\": \"Snickers\",\n \"species\": \"Dog\",\n \"locationId\": 1,\n \"customerId\": 4,\n \"status\": \"Admitted\"\n },\n {\n \"id\": 2,\n \"name\": \"Roman\",\n \"species\": \"Dog\",\n \"locationId\": 1,\n \"customerId\": 2,\n \"status\": \"Admitted\"\n },\n {\n \"id\": 3,\n \"name\": \"Blue\",\n \"species\": \"Cat\",\n \"locationId\": 2,\n \"customerId\": 1,\n \"status\": \"Admitted\"\n }],\n \"customers\": [\n {\n \"id\": 1,\n \"name\": \"Ryan Tanay\"\n }\n ],\n \"employees\": [\n {\n \"id\": 1,\n \"name\": \"Jenna Solis\"\n }\n ],\n \"locations\": [\n {\n \"id\": 1,\n \"name\": \"Nashville North\",\n \"address\": \"8422 Johnson Pike\"\n },\n {\n \"id\": 2,\n \"name\": \"Nashville South\",\n \"address\": \"209 Emory Drive\"\n }\n ]\n}\n\n\ndef expand(key, data):\n resource = key[:-2]+\"s\"\n matching_data = retrieve(resource, data[key])\n new_key = key[:-2]\n data[new_key] = matching_data\n del data[key]\n\n\ndef all(resource):\n return DATABASE[resource]\n\n\ndef retrieve(resource, id):\n requested_data = None\n\n for data in DATABASE[resource]:\n if data[\"id\"] == id:\n requested_data = data\n\n if resource == \"animals\":\n foreign_keys = [\"locationId\", \"customerId\"]\n\n for key in foreign_keys:\n expand(key, requested_data)\n\n return requested_data\n\n\ndef create(resource, data):\n max_id = DATABASE[resource][-1][\"id\"]\n\n # Add 1 to whatever that number is\n new_id = max_id + 1\n\n # Add an `id` property to the animal dictionary\n data[\"id\"] = new_id\n\n # Add the animal dictionary to the list\n DATABASE[resource].append(data)\n\n # Return the dictionary with `id` property added\n return data\n\n\ndef update(id, new_data, resource):\n \"\"\"For PUT requests to a single resource\"\"\"\n for index, data in enumerate(DATABASE[resource]):\n if data[\"id\"] == id:\n # Found the animal. Update the value.\n DATABASE[resource][index] = new_data\n break\n\n\ndef delete(resource, id):\n data_index = -1\n\n # Iterate the ANIMALS list, but use enumerate() so that you\n # can access the index value of each item\n for index, data in enumerate(DATABASE[resource]):\n if data[\"id\"] == id:\n # Found the animal. Store the current index.\n data_index = index\n\n # If the animal was found, use pop(int) to remove it from list\n if data_index >= 0:\n DATABASE[resource].pop(data_index)\n","repo_name":"TheOlDustyTrail/kennels-server","sub_path":"repository.py","file_name":"repository.py","file_ext":"py","file_size_in_byte":2739,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"25477819701","text":"#!/usr/bin/python\n# -*- coding: UTF-8 -*-\nimport time\nimport common.global_const as gc\n\n\ndef get_contract_by_item_no(item_no):\n sql = \"select * from contract where contract_asset_item_no = '%s'\" % item_no\n contract = gc.CONTRACT_DB.query(sql)\n return contract\n\n\ndef get_contract_by_item_no_opportunity(item_no, contract_sign_opportunity, status=None):\n table = get_contract_table(item_no)\n sql = \"select * from %s where contract_asset_item_no = '%s' and contract_sign_opportunity = '%s'\" \\\n % (table, item_no, contract_sign_opportunity)\n if status is not None:\n sql += \" and contract_status = '%s'\" % status\n sql += \" group by contract_type\"\n contract = gc.CONTRACT_DB.query(sql)\n return contract\n\n\ndef get_contract(item_no, contract_sign_opportunity, contract_type):\n table = get_contract_table(item_no)\n sql = \"select * from %s where contract_asset_item_no = '%s' and contract_sign_opportunity = '%s' \" \\\n \"and contract_type = '%s' order by contract_id asc\" % (table, item_no, contract_sign_opportunity, contract_type)\n contract = gc.CONTRACT_DB.query(sql)\n return contract\n\n\ndef get_open_task_by_item_no(item_no):\n sql = \"select * from task where task_order_no = '%s' and task_status = 'open'\" % item_no\n contract = gc.CONTRACT_DB.query(sql)\n return contract\n\n\ndef get_open_task_by_item_no_and_task_type(item_no, task_type):\n sql = \"select * from task where task_order_no = '%s' and task_status = 'open' and task_type = '%s'\" % (item_no, task_type)\n contract = gc.CONTRACT_DB.query(sql)\n return contract\n\n\ndef create_contract(item_no, contract_type, contract_type_text, url, opportunity=None):\n table = get_contract_table(item_no)\n sql = \"INSERT INTO %s (contract_create_at, contract_asset_item_no, contract_type, contract_type_text, \" \\\n \"contract_url, contract_status, contract_from_system, contract_code, contract_apply_id, contract_flow_key, \" \\\n \"contract_ref_item_no, contract_sign_at, contract_update_at, contract_sign_opportunity, contract_provider, \" \\\n \"contract_subject) \" \\\n \" VALUES (now(), '%s', '%s', '%s', \" \\\n \"'%s', 'SUCCESS', 'dsq', \" \\\n \"'BIZ111276200422034951', '20201587498579320992-20713-1587498591702', 'auth1_manman_flow', '%s_noloan', \" \\\n \"now(), now(), '%s', 'YUN', '苏州')\"\\\n % (table, item_no, contract_type, contract_type_text, url, item_no, opportunity)\n contract = gc.CONTRACT_DB.insert(sql)\n return contract\n\n\ndef create_change_channel_task(item_no, old_channel, new_channel):\n sql = \"INSERT INTO task (task_order_no, task_type, task_status, task_next_run_at, task_request_data, \" \\\n \"task_response_data, task_memo, task_create_at, task_update_at, task_version, task_priority, \" \\\n \"task_retrytimes) \" \\\n \"VALUES ('%s', 'ChangeChannel', 'open', '2020-07-01 02:42:57', \" \\\n \"'{\\\\\\\"apply_code\\\\\\\" : \\\\\\\"%s\\\\\\\",\\\\\\\"loan_channel\\\\\\\" : \\\\\\\"%s\\\\\\\",\\\\\\\"route_channel\\\\\\\" : \\\\\\\"%s\\\\\\\", \" \\\n \"\\\\\\\"old_channel\\\\\\\" : \\\\\\\"%s\\\\\\\",\\\\\\\"version\\\\\\\" : 1593542576551,\\\\\\\"need_register\\\\\\\" : 1}', \" \\\n \"'', '', '2020-07-01 02:42:57', '2020-07-01 02:42:57', 0, 1, 0)\"\\\n % (item_no, item_no, new_channel, new_channel, old_channel)\n result = gc.CONTRACT_DB.insert(sql)\n return result\n\n\ndef create_asset_import_task(item_no, channel):\n sql = \"INSERT INTO task (task_order_no, task_type, task_status, task_next_run_at, task_request_data, \" \\\n \"task_response_data, task_memo, task_create_at, task_update_at, task_version, task_priority, \" \\\n \"task_retrytimes) VALUES ('%s', 'AssetImport', 'open', NOW(), \" \\\n \"'{\\\\\\\"from_system\\\\\\\" : \\\\\\\"banana\\\\\\\",\\\\\\\"key\\\\\\\" : \\\\\\\"%s%s\\\\\\\",\" \\\n \"\\\\\\\"type\\\\\\\" : \\\\\\\"DSQAssetImport\\\\\\\",\\\\\\\"data\\\\\\\" : { \\\\\\\"configId\\\\\\\" : null, \\\\\\\"type\\\\\\\" : null, \" \\\n \"\\\\\\\"capital\\\\\\\" : \\\\\\\"%s\\\\\\\", \\\\\\\"scope\\\\\\\" : \\\\\\\"youxi_bill\\\\\\\", \\\\\\\"fromSystemName\\\\\\\" : null, \" \\\n \"\\\\\\\"periodCount\\\\\\\" : 6, \\\\\\\"signOpportunity\\\\\\\" : \\\\\\\"AssetImport\\\\\\\", \\\\\\\"signType\\\\\\\" : null, \" \\\n \"\\\\\\\"condition\\\\\\\" : {\\\\\\\"loan_channel\\\\\\\" : null,\\\\\\\"source_type\\\\\\\" : null,\" \\\n \"\\\\\\\"from_system_name\\\\\\\" : null,\\\\\\\"period_count\\\\\\\" : null,\\\\\\\"sub_type\\\\\\\" : null }, \" \\\n \"\\\\\\\"itemNo\\\\\\\" : \\\\\\\"%s\\\\\\\", \\\\\\\"refItemNo\\\\\\\" : \\\\\\\"%s_noloan\\\\\\\", \" \\\n \"\\\\\\\"cover\\\\\\\" : false, \\\\\\\"constMap\\\\\\\" : { }, \\\\\\\"sequenceList\\\\\\\" : null}\\\\\\n}', \" \\\n \"'', '', NOW(), NOW(), 0, 2, 0)\"\\\n % (item_no, item_no, channel, channel, item_no, item_no)\n result = gc.CONTRACT_DB.insert(sql)\n return result\n\n\ndef get_sendmsg(item_no, sendmsg_type):\n sql = \"select * from sendmsg where sendmsg_order_no = '%s' and sendmsg_type = '%s' \" \\\n \"order by sendmsg_id asc\" % (item_no, sendmsg_type)\n result = gc.CONTRACT_DB.query(sql)\n return result\n\n\ndef contract_create_attachment_by_item_no(item_no, channel, attachment_type, attachment_name, attachment_url):\n now_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n table = get_contract_table(item_no)\n sql = \"INSERT INTO %s\" \\\n \" ( `contract_create_at`, `contract_asset_item_no`, `contract_type`, `contract_type_text`, `contract_url`, \" \\\n \"`contract_status`, `contract_from_system`, `contract_code`, `contract_apply_id`, `contract_flow_key`, \" \\\n \"`contract_ref_item_no`, `contract_sign_at`, `contract_update_at`, `contract_sign_opportunity`, \" \\\n \"`contract_provider`, `contract_subject`, `contract_channel`, `contract_version`) VALUES( \" \\\n \"'%s', '%s', %s, '%s', \" \\\n \"'%s', 'SUCCESS', 'strawberry',\" \\\n \" FLOOR(RAND() * 10000), '%s-30300-1611906119811', 'tpl2007301443234707AE', \" \\\n \"'%s', '%s', '%s', 'AssetImport', 'YUN', '如皋智萃', \" \\\n \"'%s', 1);\" % \\\n (table, now_time, item_no, attachment_type, attachment_name, attachment_url, item_no, item_no, now_time, now_time, channel )\n result = gc.CONTRACT_DB.insert(sql)\n return result\n\n\ndef get_contract_table(item_no):\n if item_no[0:4] == 'enc_':\n table = 'contract_enc'\n else:\n if item_no[0:4].isupper():\n table = 'contract_' + str(item_no[1:5])\n else:\n table = 'contract_' + str(item_no[0:4])\n return table\n\n\nif __name__ == '__main__':\n print(get_contract_by_item_no(\"20201585883351257122\"))\n","repo_name":"xiujingyuan/framework-test","sub_path":"biztest/function/contract/contract_db_function.py","file_name":"contract_db_function.py","file_ext":"py","file_size_in_byte":6443,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"21533806266","text":"import numpy as np \nimport scipy.constants as sp\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\ntheta = np.radians(90)\nphi = np.radians(80)\nwavelength = sp.c/436000000\n\nd_x = 5.968620625e-01\nbeta = 2*np.pi/wavelength\n\narray_size = 50\nx_phase = np.degrees(beta*d_x*(np.sin(theta)*np.cos(phi)))\ny_phase = np.degrees(beta*d_x*(np.sin(theta)*np.sin(phi)))\n\nx_rows = np.tile([x_phase*x for x in range(0,array_size)], (array_size, 1))\n\n\ny_rows = np.array([y_phase*y for y in range(0,array_size)])\ny_rows2 = y_rows.transpose()\n\ny_rows2.shape = (array_size, 1)\ny_rows3 = np.tile(y_rows, (array_size, 1))\ny_rows4 = y_rows3.transpose()\n\n\noutput = np.add(x_rows,y_rows4)\n\nnp.savetxt(\"outputphasing.csv\",output.ravel(), fmt = '%.1f', delimiter=\",\")\n# print(output)\n# print(np.tile(y_rows2, (array_size, 1)))\n\n# output1 = np.reshape(np.outer(x_rows,y_rows).ravel(), (array_size, array_size))\n# output2 = output1.ravel()\n\n# np.savetxt(\"phasing.txt\",output2, fmt = '%.1f', delimiter=\",\")\n\n# nx, ny = 5, 5\n# x = range(nx)\n# y = range(ny)\n\n# hf1 = plt.figure()\n# ha = hf1.add_subplot(111, projection='3d')\n\n# X, Y = np.meshgrid(x, y) # `plot_surface` expects `x` and `y` data to be 2D\n# ha.plot_surface(X, Y, output1, cmap='jet')\n\n# plt.show()","repo_name":"BKVerkerk/Code-for-2018-lab-project","sub_path":"phasing.py","file_name":"phasing.py","file_ext":"py","file_size_in_byte":1253,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"31227732364","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport colorfield.fields\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('shop', '0001_initial'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='subcategory',\n name='color',\n field=colorfield.fields.ColorField(null=True, verbose_name=b'Subcategory Color', max_length=10, blank=True),\n preserve_default=True,\n ),\n ]\n","repo_name":"SparmedDev/sparmed01","sub_path":"shop/migrations/0002_subcategory_color.py","file_name":"0002_subcategory_color.py","file_ext":"py","file_size_in_byte":520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"11932631444","text":"import tensorflow as tf\nfrom tensorflow import keras\nimport tensorflow_datasets as tfds\nimport numpy as np\nfrom tensorflow.keras.preprocessing.text import Tokenizer\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences\nimport io\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport json\n\n\"\"\"\n!wget https://raw.githubusercontent.com/aliakbarbadri/nlp-tf/master/Sarcasm_Headlines_Dataset_v2.json \\\n -O /tmp/sarcasm.json\n\"\"\"\n\nsarcasm_data = [json.loads(line) for line in open('/tmp/sarcasm.json', 'r')]\n# len(sarcasm_data), sarcasm_data[0]\nsentences = []\nlabels = []\n\nfor item in sarcasm_data:\n sentences.append(item['headline'])\n labels.append(item['is_sarcastic'])\n\n# ------------------------------------- Prepare data\n\nvocab_size = 1000\nembedding_dim = 16\nmax_length = 120\ntrunc_type = 'post'\npadding_type = 'post'\noov_tok = \"\"\ntraining_size = 20000\n\ntraining_sentences = sentences[0:training_size]\ntesting_sentences = sentences[training_size:]\ntraining_labels = labels[0:training_size]\ntesting_labels = labels[training_size:]\n\ntokenizer = Tokenizer(num_words=vocab_size, oov_token=oov_tok)\ntokenizer.fit_on_texts(training_sentences)\n\nword_index = tokenizer.word_index\n\ntraining_sequences = tokenizer.texts_to_sequences(training_sentences)\ntraining_padded = pad_sequences(training_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)\n\ntesting_sequences = tokenizer.texts_to_sequences(testing_sentences)\ntesting_padded = pad_sequences(testing_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)\n\ntraining_padded = np.array(training_padded)\ntraining_labels = np.array(training_labels)\ntesting_padded = np.array(testing_padded)\ntesting_labels = np.array(testing_labels)\n\n# --------------------------------------------------\n\nmodel = tf.keras.Sequential([\n tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),\n tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32)),\n tf.keras.layers.Dense(24, activation='relu'),\n tf.keras.layers.Dense(1, activation='sigmoid')\n])\nmodel.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\nmodel.summary()\n\nhistory = model.fit(training_padded, training_labels, epochs=50, validation_data=(testing_padded, testing_labels),\n verbose=1)\n","repo_name":"salman-/small-codes-for-tensorflow-certificate-exam","sub_path":"tf-datasets/sarcasem.py","file_name":"sarcasem.py","file_ext":"py","file_size_in_byte":2322,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"42208783112","text":"import sys\nfrom PyQt5.QtWidgets import QApplication, QWidget, QVBoxLayout, QLabel, QPushButton, QFileDialog\nimport pandas as pd\n\nclass CSVViewer(QWidget):\n def __init__(self):\n super().__init__()\n\n self.init_ui()\n\n def init_ui(self):\n self.df = None\n self.current_index = 0\n\n self.layout = QVBoxLayout()\n\n self.result_label = QLabel()\n self.counter_label = QLabel()\n\n self.layout.addWidget(self.result_label)\n self.layout.addWidget(self.counter_label)\n\n self.prev_button = QPushButton('Previous')\n self.next_button = QPushButton('Next')\n\n self.prev_button.clicked.connect(self.prev_result)\n self.next_button.clicked.connect(self.next_result)\n\n self.layout.addWidget(self.prev_button)\n self.layout.addWidget(self.next_button)\n\n self.setLayout(self.layout)\n\n self.load_csv()\n\n self.show_result()\n\n def load_csv(self):\n options = QFileDialog.Options()\n file_name, _ = QFileDialog.getOpenFileName(self, \"Open CSV File\", \"\", \"CSV Files (*.csv);;All Files (*)\", options=options)\n\n if file_name:\n self.df = pd.read_csv(file_name)\n\n def show_result(self):\n if self.df is not None and not self.df.empty:\n current_row = self.df.iloc[self.current_index]\n result_text = f\"SMILES: {current_row['SMILES']}\\nName: {current_row['Name']}\\nMW: {current_row['MW']}\\nmp: {current_row['mp']}\\nProject: {current_row['Project']}\"\n self.result_label.setText(result_text)\n self.counter_label.setText(f\"Result {self.current_index + 1} of {len(self.df)}\")\n\n def prev_result(self):\n if self.current_index > 0:\n self.current_index -= 1\n self.show_result()\n\n def next_result(self):\n if self.df is not None and self.current_index < len(self.df) - 1:\n self.current_index += 1\n self.show_result()\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n viewer = CSVViewer()\n viewer.setGeometry(100, 100, 800, 600)\n viewer.setWindowTitle('CSV Viewer')\n viewer.show()\n sys.exit(app.exec_())\n","repo_name":"Tomisall/ProcessSafteyDB","sub_path":"csvSearch.py","file_name":"csvSearch.py","file_ext":"py","file_size_in_byte":2162,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"8900205239","text":"# Faça um programa que peça o tamanho de um arquivo para download (em MB) e a velocidade de um link de Internet (em Mbps), calcule e informe o tempo aproximado de download do arquivo usando este link (em minutos).\n\nmegabytes = float(input(\"quantidade de MB do arquivo: \"))\nmegabits_por_segundo = float(input(\"velocidade da internet em Mbps: \"))\n\nmegabits = megabytes * 8\nsegundos = megabits / megabits_por_segundo\nminutos = segundos / 60\n\nprint(f\"vali demorar {minutos:.2f} minuto(s).\")","repo_name":"natan-xav2019/Exercicios-Python","sub_path":"EstruturaSequencial/ex018.py","file_name":"ex018.py","file_ext":"py","file_size_in_byte":488,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"23760238558","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Nov 19 10:14:57 2018\n\nMonitor the residuals of flow variables during a run of COOLFluiD.\n\nOnce the log of residual has fallen enough, the CFL should be modified\naccording to CFL*=2 in the .inter file.\n\nMight-do list: \n * Play an alarm sound when the CFL should be changed. For this the necessary residual\n fall-off (for instance one order of magnitude) has to be specified\n * Automatically change the CFL parameter according to CFL *= 2 by overwriting\n the appropriate line .inter on the server\n\n@author: Peter Leitner\n\"\"\"\n\n\n#========================== SPECIFY RUNTIME PARAMETERS ==================================\n# Specify time to wait (in minutes) until the next check for an update:\nwaiting_time_for_updates = 0.2 # in minutes\n# Convergence file to be monitored on the server where the computation is running:\nmyPath = \"/users/cpa/peterl/COOLFluiD/OPENMPI/optim/apps/Solver\" \\\n \"/RESULTS_Corona_Restart/\"\nmyFn = \"convergence.plt-P0.FlowNamespace\"\n# SSH connection:\ncomputation = \"local\" # Choose either \"local\" or \"remote\"\nmyHost,myUser,myPassword,myPort = \"helium.esat.kuleuven.be\",\"?\",\"?\",22\n# Choose residual to monitor:\nres = \"T\" # \"rho\"\n# Save updated figure to eps and pdf format:\nsave_figs = False\n#========================================================================================\n\n\nimport matplotlib.pylab as plt\n# import StringManipulationTools as Tools # personal module of tools containing methods for string manipulation\n# ... No longer needed: function find_numbers_in_string included in order to make\n# the script portable\n\ndef find_numbers_in_string(a_string_containing_numbers):\n \"Find either integers, floats or numbers in scientific notation.\"\n import re\n numeric_const_pattern = r\"\"\"\n[-+]? # optional sign\n(?:\n(?: \\d* \\. \\d+ ) # .1 .12 .123 etc 9.1 etc 98.1 etc\n|\n(?: \\d+ \\.? ) # 1. 12. 123. etc 1 12 123 etc\n)\n# followed by optional exponent part if desired\n(?: [Ee] [+-]? \\d+ ) ?\n\"\"\"\n rx = re.compile(numeric_const_pattern, re.VERBOSE)\n listofnums = rx.findall(a_string_containing_numbers) # read the numbers out of the string\n return listofnums\n\n\n# Check whether directory for figure output exists. If not, the directory is created\nimport os\nif computation==\"local\":\n if not os.path.exists(myPath + \"Figures-convergence/\"):\n os.mkdir(myPath + \"Figures-convergence\")\nif computation==\"remote\":\n import paramiko\n transport = paramiko.Transport((myHost, 22))\n transport.connect(username=myUser,password=myPassword)\n sftp = paramiko.SFTPClient.from_transport(transport)\n try:\n sftp.chdir(myPath + \"Figures-convergence/\") # Test if remote_path exists\n except IOError:\n print(\"Path to save figures does not exist yet, I'm creating it for you.\")\n sftp.mkdir(myPath + \"Figures-convergence/\") # Create remote_path\n sftp.chdir(myPath + \"Figures-convergence/\")\n sftp.close()\n\n \nmyFile = myPath + myFn\n\n# Initialize the convergence parameter lists\niterations = []\n# Residuals\nBx_res = []; By_res = []; Bz_res = []\nEx_res = []; Ey_res = []; Ez_res = []\nrho_res = []\nvx_res = []; vy_res = []; vz_res = []\nT_res = []\nCFL = []\nPhysTime = []\nWallTime = []\n\nif computation == \"remote\":\n client = paramiko.SSHClient()\n client.load_system_host_keys()\n client.set_missing_host_key_policy(paramiko.AutoAddPolicy())\n client.connect(myHost,myPort,myUser,myPassword)\n \n sftp = client.open_sftp() \n fileObject = sftp.file(myFile,\"rb\").readlines()\n sftp.close()\nelif computation == \"local\":\n fileObject = open(myFile,\"rb\").readlines()\n \n\nlenfileObject = len(fileObject) # Number of iterations already computed\n\nfor i in range(2,len(fileObject)): # 2 header lines\n nums = find_numbers_in_string(str(fileObject[i]))\n # There are 19 numbers in the string:\n # 0...number of iterations\n # 9...rho\n # 13...T\n # 14...CFL\n # 15...PhysTime\n # 17...WallTime\n iterations.append(int(nums[0]))\n Bx_res.append(float(nums[1])); By_res.append(float(nums[2])); Bz_res.append(float(nums[3]))\n Ex_res.append(float(nums[4])); Ey_res.append(float(nums[5])); Ez_res.append(float(nums[6]))\n rho_res.append(float(nums[9]))\n vx_res.append(float(nums[10])); vy_res.append(float(nums[11])); vz_res.append(float(nums[12]))\n T_res.append(float(nums[13]))\n CFL.append(int(nums[14]))\n PhysTime.append(float(nums[15]))\n WallTime.append(float(nums[17]))\n \nWallTime = list(map(lambda x: x/60**2, WallTime)) # convert to hours\n\n\n# Convert into list of floats - NO LONGER NEEDED\n# iterations = [int(i) for i in iterations]\n# rho_res = [float(i) for i in rho_res]\n# T_res = [float(i) for i in T_res]\n# CFL = [int(i) for i in CFL]\n# PhysTime = [float(i) for i in PhysTime]\n# WallTime = [float(i) for i in WallTime]\n\n\n# Generate graph that is updated later periodically\nplt.rc(\"text\", usetex=True)\nplt.rcParams[\"text.latex.preamble\"]=[r\"\\usepackage{amsmath}\"]\nplt.close(0)\nfig0, ax0 = plt.subplots(num=0)\nplt.title(\"COOLFluiD Convergence Monitoring\")\nif res==\"T\":\n lh0, = ax0.plot(iterations,T_res,\"+\",color=\"#1f77b4\")\n ax0.set_ylabel(r\"$\\log\\, T$ Residual\",color=\"#1f77b4\")\nelif res==\"rho\":\n lh0, = ax0.plot(iterations,rho_res,\"+\",color=\"#1f77b4\")\n ax0.set_ylabel(r\"$\\log\\, \\varrho$ Residual\",color=\"#1f77b4\") \nax0.set_xlabel(r\"Number of iterations\")\nax0.tick_params(\"y\",colors=\"#1f77b4\")\nax0.grid(True)\nax1 = ax0.twinx()\nlh1, = ax1.plot(iterations,CFL,\".\",color=\"#d62728\")\nax1.set_ylabel(r\"CFL\",color=\"#d62728\")\nax1.tick_params(\"y\",colors=\"#d62728\")\nplt.pause(1) # flush the figure\n\nplt.close(1)\nfig1, ax2 = plt.subplots(num=1)\nplt.title(\"COOLFluiD Convergence Monitoring\")\nlh2, = ax2.plot(iterations,Bx_res,\"+\",color=\"#ff7f0e\",label=r\"$\\log\\,B$ residual\")\nlh3, = ax2.plot(iterations,Ex_res,\"+\",color=\"#2ca02c\",label=r\"$\\log\\,E$ residual\")\nlh4, = ax2.plot(iterations,vx_res,\"+\",color=\"#7f7f7f\",label=r\"$\\log\\,U$ residual\")\nif res==\"T\":\n # Plot the residual that is not already monitored in Fig. 1\n lh5, = ax2.plot(iterations,rho_res,\"+\",color=\"#9467bd\",label=r\"$\\log\\,\\varrho$ residual\")\nelif res==\"rho\":\n lh5, = ax2.plot(iterations,T_res,\"+\",color=\"#9467bd\",label=r\"$\\log\\,T$ residual\")\nplt.legend(loc=1)\nax2.set_xlabel(r\"Number of iterations\")\nax2.tick_params(\"y\",colors=\"k\")\nax2.grid(True)\nax3 = ax2.twinx()\nlh6, = ax3.plot(iterations,CFL,\".\",color=\"#d62728\")\nax3.set_ylabel(r\"CFL\",color=\"#d62728\")\nax3.tick_params(\"y\",colors=\"#d62728\")\nplt.pause(1) # flush the figure\n\nplt.close(2)\nfig2, ax4 = plt.subplots(num=2)\nplt.title(\"COOLFluiD Runtime\")\nlh7, = ax4.plot(iterations,WallTime,\"+\",color=\"#1f77b4\")\nax4.set_xlabel(r\"Number of iterations\")\nax4.set_ylabel(r\"Wall time (hrs)\",color=\"#1f77b4\")\nax4.tick_params(\"y\",colors=\"#1f77b4\")\nax4.grid(True)\nax5 = ax4.twinx()\nlh8, = ax5.plot(iterations,CFL,\".\",color=\"#d62728\")\nax5.set_ylabel(r\"CFL\",color=\"#d62728\")\nax5.tick_params(\"y\",colors=\"#d62728\")\nplt.pause(1) # flush the figure\n\n\n# Check every waiting_time_for_update minute whether the plot should be updated\nimport time\nwhile True:\n time.sleep(waiting_time_for_updates*60) # check for update on the server\n print(\"Looking for an update...\")\n \n if computation == \"remote\":\n sftp = client.open_sftp()\n fileObject = sftp.file(myFile).readlines()\n sftp.close()\n elif computation == \"local\":\n fileObject = open(myFile,\"rb\").readlines()\n \n if len(fileObject) != lenfileObject:\n print(\"Got an update, I replot.\")\n nums = find_numbers_in_string(str(fileObject[len(fileObject)-1]))\n iterations.append(int(nums[0]))\n Bx_res.append(float(nums[1])); By_res.append(float(nums[2])); Bz_res.append(float(nums[3]))\n Ex_res.append(float(nums[4])); Ey_res.append(float(nums[5])); Ez_res.append(float(nums[6]))\n rho_res.append(float(nums[9]))\n vx_res.append(float(nums[10])); vy_res.append(float(nums[11])); vz_res.append(float(nums[12]))\n T_res.append(float(nums[13]))\n CFL.append(int(nums[14]))\n PhysTime.append(float(nums[15]))\n WallTime.append(float(nums[17])/60**2)\n\n # Replot\n lh0.set_xdata(iterations)\n lh2.set_xdata(iterations)\n if res==\"T\":\n lh0.set_ydata(T_res)\n lh5.set_xdata(iterations); lh5.set_ydata(rho_res)\n elif res==\"rho\":\n lh0.set_ydata(rho_res)\n lh5.set_xdata(iterations); lh5.set_ydata(T_res)\n lh1.set_xdata(iterations); lh1.set_ydata(CFL)\n lh2.set_xdata(iterations); lh2.set_ydata(Bx_res)\n lh3.set_xdata(iterations); lh3.set_ydata(Ex_res)\n lh4.set_xdata(iterations); lh4.set_ydata(vx_res)\n lh6.set_xdata(iterations); lh6.set_ydata(CFL)\n lh7.set_xdata(iterations); lh7.set_ydata(WallTime)\n lh8.set_xdata(iterations); lh8.set_ydata(CFL)\n ax0.relim(); ax0.autoscale_view(True,True,True)\n ax1.relim(); ax1.autoscale_view(True,True,True)\n ax2.relim(); ax2.autoscale_view(True,True,True)\n ax3.relim(); ax3.autoscale_view(True,True,True)\n ax4.relim(); ax4.autoscale_view(True,True,True)\n ax5.relim(); ax5.autoscale_view(True,True,True)\n plt.draw()\n plt.pause(1.)\n if save_figs:\n plt.savefig(myPath + \"Figures-convergence/varCFL-\" + res + \"conv.eps\",bbox_inches=\"tight\")\n plt.savefig(myPath + \"Figures-convergence/varCFL-\" + res + \"conv.pdf\",bbox_inches=\"tight\")\n\n lenfileObject = len(fileObject)\n \n","repo_name":"andrealani/COOLFluiD","sub_path":"tools/tecplot/convergence-monitoring.py","file_name":"convergence-monitoring.py","file_ext":"py","file_size_in_byte":9513,"program_lang":"python","lang":"en","doc_type":"code","stars":203,"dataset":"github-code","pt":"50"} +{"seq_id":"492290210","text":"import pygame\nimport random\nfrom actor.baselife import BaseLife\nfrom actor.nourishment import Nourishment\nclass Plant(BaseLife):\n def __init__(self, x: int, y: int,color=None) -> None:\n if color==None:\n color=self.calc_first_color()\n self.splittime=int(pygame.math.lerp(30,5,color[0]/255))\n self.splitrange=int(pygame.math.lerp(400,900,color[2]/255))\n super().__init__(x, y,pygame.math.lerp(50,200,color[1]/255),1,color)\n def step(self,screen:pygame.surface,actors:list):\n pygame.draw.polygon(screen,self.color,((self.pos.x-20,self.pos.y+30),(self.pos.x,self.pos.y-40),(self.pos.x+20,self.pos.y+30)))\n self.splittime-=1\n if self.splittime==0:\n n=list(filter(lambda x:isinstance(x,Nourishment) and(self.pos.x-x.pos.x)**2+(self.pos.y-x.pos.y)**2<=self.splitrange**2 ,actors))\n if len(n)>0:\n item=n[random.randint(0,len(n)-1)]\n item.alive=False\n actors.append(Plant(item.pos.x,item.pos.y,self.calc_next_color(self.color)))\n self.splittime=int(pygame.math.lerp(30,5,self.color[0]/255))\n super().step(screen)\n","repo_name":"Fuses-Garage/LifeSimulator","sub_path":"actor/plant.py","file_name":"plant.py","file_ext":"py","file_size_in_byte":1155,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"29925118708","text":"import pygame\nfrom Constantes import *\nimport base64\nimport json\nimport csv\nimport gzip\nfrom Jugador import *\nfrom nudo import *\nfrom Escalera import *\nfrom Bloque import *\nfrom Gema import *\nimport sys\nfrom pygame.locals import*\nfrom Nivel1 import *\n\nwidth=100\nheight=720\nblack = (0,0,0)\nblue = (0, 0, 255)\nwhite = (255,255,255)\nred = (180,0,0)\ngreen = (0,180,0)\n\nbackgroundColor = (205,69,159)\n\nredSelected = (255,0,0)\ngreenSelected = (0,255,0)\n\nscreen = pygame.display.set_mode((widht, height))\n\ndef uploadMap(name):\n\n global mapWidth,mapHeight,tileHeight,tileWidth,matrizMap\n\n f = open(name+\".json\", \"r\")\n data = json.load(f)\n f.close()\n\n tileWidth=data[\"tilewidth\"]\n tileHeight=data[\"tileheight\"]\n\n mapWidth=data[\"width\"]\n mapHeight=data[\"height\"]\n\n #obtener mapa\n for item in data[\"layers\"]:\n mapa=item[\"data\"]\n\n #print (mapa)\n\n for i in range(0, len(mapa), mapWidth):\n matrizMap.append(mapa[i:i+mapWidth])\n #for i in range(mapHeight):\n # print (matrizMap[i])\n\ndef arrayTileset(img):\n x=0\n y=0\n\n hojaTiles=[]\n\n for i in range(8):\n for h in range(8):\n imagen=cut(img,(x,y,64,64))\n hojaTiles.append(imagen)\n x+=64\n x=0\n y+=64\n\n return hojaTiles\n\ndef cut (img, rectangle):\n rect = pygame.Rect(rectangle)\n image = pygame.Surface(rect.size).convert()\n image.blit(img,(0,0), rect)\n return image\n\ndef nivel2():\n #pygame.quit()\n screen = pygame.display.set_mode((1200, 1000))\n Cinematica2()\n pygame.display.set_caption(\"sprite\")\n reloj = pygame.time.Clock()\n game_over = False\n jugador = Jugador([600, 300])\n desplazamientoX = 0 #-100\n desplazamientoY = 0 #-1400\n jugador.gemas=0\n #inicializamos pygame\n pygame.init()\n #imagen = pygame.image.load(\"fondo.png\")\n #screen.blit(imagen ,(0,0))\n pygame.display.set_caption(\"Mapa\")\n clock = pygame.time.Clock()\n img = pygame.image.load(\"juego.png\")\n uploadMap(\"mapa2\")\n hoja = arrayTileset(img)\n\n #Cargar las imagenes de los Sprites\n Bloque1 = img.subsurface(0,0,64,64)\n Bloque2 = img.subsurface(64,0,64,64)\n Bloque3 = img.subsurface(128,0,64,64)\n #BloqueL = img.subsurface(384,448,64,64)\n Caja = img.subsurface(384,0,64,64)\n Escalera1 = img.subsurface(256,320,64,64)\n Escalera2 = img.subsurface(320,320,64,64)\n imgGema = img.subsurface(128,384,64,64)\n Bloque4 = img.subsurface(256,0,64,64)\n\n #Cargamos la musica\n pygame.mixer.music.load(\"Musica.mp3\")\n pygame.mixer.music.play(-1)\n\n #Cargamos los sonidos\n #sonidoGema = pygame.mixer.Sound(\"Gema.mp3\")\n #sonidoMuerte = pygame.mixer.Sound(\"Muerte.mp3\")\n\n #Creamos los grupos de sprites\n jugadores = pygame.sprite.Group()\n gemas = pygame.sprite.Group()\n escaleras = pygame.sprite.Group()\n bloques = pygame.sprite.Group()\n enemigos = pygame.sprite.Group()\n\n #Creamos los sprites\n jugadores.add(jugador)\n\n for i in range(mapHeight):\n for j in range(mapWidth):\n minum = matrizMap[i][j]\n if minum == 1 :\n b = Bloque([j*64,i*64],Bloque1)\n bloques.add(b)\n elif minum == 2 :\n b = Bloque([j*64,i*64],Bloque2)\n bloques.add(b)\n #elif minum == 63:\n # b = Bloque([j*64,i*64],BloqueL)\n # bloques.add(b)\n elif minum == 3 :\n b = Bloque([j*64,i*64],Bloque3)\n bloques.add(b)\n elif minum == 7 :\n b = Bloque([j*64,i*64],Caja)\n bloques.add(b)\n elif minum == 45 :\n e = Escalera([j*64,i*64],Escalera1,True)\n escaleras.add(e)\n elif minum == 46 :\n e = Escalera([j*64,i*64],Escalera2,True)\n escaleras.add(e)\n elif minum == 51 :\n g = Gema([j*64,i*64],imgGema)\n gemas.add(g)\n elif minum == 5 :\n b = Bloque([j*64,i*64],Bloque4)\n bloques.add(b)\n\n while not game_over:\n\n screen.fill((112, 145, 241))\n clock.tick(10)\n for event in pygame.event.get():\n if event.type == QUIT:\n game_over = True\n sys.exit()\n\n jugador.bajar = False\n jugador.escalar = False\n jugador.escalando = False\n jugador.bajando = False\n\n ls = pygame.sprite.spritecollide(jugador,escaleras,False)\n if len(ls) > 0 :\n jugador.escalar = True\n for e in ls:\n if e.bajar:\n jugador.bajar = True\n\n temp = jugador.evento(event)\n bloques.update(temp)\n escaleras.update(temp)\n gemas.update(temp)\n\n\n ls = pygame.sprite.spritecollide(jugador,escaleras,False)\n if len(ls) > 0 :\n jugador.escalar = True\n\n\n ls = pygame.sprite.spritecollide(jugador,bloques,False)\n for e in ls:\n if jugador.rect.top < e.rect.bottom and temp[1] < 0 and not jugador.escalando:\n temp = [0,(e.rect.bottom-jugador.rect.top)]\n bloques.update(temp)\n escaleras.update(temp)\n gemas.update(temp)\n jugador.contGravedad = 0\n break\n elif jugador.rect.bottom > e.rect.top and temp[1] > 0 and not jugador.bajando:\n temp = [0,(e.rect.top-jugador.rect.bottom)]\n jugador.contGravedad = 0\n bloques.update(temp)\n escaleras.update(temp)\n gemas.update(temp)\n break\n\n ls = pygame.sprite.spritecollide(jugador,bloques,False)\n for e in ls:\n if jugador.rect.right > e.rect.left and temp[0] > 0 :\n temp = [(e.rect.left-jugador.rect.right),0]\n bloques.update(temp)\n escaleras.update(temp)\n gemas.update(temp)\n break\n elif jugador.rect.left < e.rect.right and temp[0] < 0:\n temp = [(e.rect.right-jugador.rect.left),0]\n bloques.update(temp)\n escaleras.update(temp)\n gemas.update(temp)\n break\n\n ls = pygame.sprite.spritecollide(jugador,gemas,True)\n for e in ls:\n jugador.gemas += 1\n print(jugador.gemas)\n \n for b in bloques :\n screen.blit(b.image,b.rect)\n for e in escaleras :\n screen.blit(e.image,e.rect)\n for g in gemas :\n screen.blit(g.image,g.rect)\n screen.blit(jugador.imagen,jugador.rect)\n pygame.display.flip()","repo_name":"Cesar-2/Juego","sub_path":"Nivel2.py","file_name":"Nivel2.py","file_ext":"py","file_size_in_byte":6652,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"6624481457","text":"from django.urls import path\r\nfrom first import views\r\n\r\nurlpatterns = [\r\n path('', views.index,name='index'),\r\n path('detail//', views.detail, name='detail'),\r\n path('reply/', views.reply, name='reply'),\r\n path('issue/', views.issue, name='issue'),\r\n path('submit/', views.submit, name='submit'),\r\n path('choice_cate/', views.choice_cate, name='choice'),\r\n path('category//', views.category, name='category'),\r\n path('myquestion/',views.my_question, name='myquestion'),\r\n path('search/', views.search, name='search'),\r\n path('finish/', views.finish, name='finish'),\r\n path('hint/', views.hint, name='hint')\r\n]","repo_name":"KlausMichael0/The-Online-Answer-System","sub_path":"OAS/first/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":665,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"8426038930","text":"from facialRecog import calc_local\nfrom remoteCalc import calc_remote_single, calc_remote_multi\nimport time \nimport resource\nimport json\nimport os\n\ndef compare_single_picture(image_path):\n res, cost_1 = calc_local(image_path)\n print(\"单次本地计算时间: \", cost_1, \"ms\")\n print(\"单次本地人脸计数结果: \", res)\n start_time = int(round(time.time() * 1000))\n res = calc_remote_single(image_path)\n # print(res)\n for r in res:\n # print(r.get())\n tmp = json.loads(r.get()[-1])\n # print(tmp[\"result\"])\n print(\"单次远程人脸计数结果: \", tmp[\"result\"][0])\n cost_2 = tmp[\"result\"][1]\n end_time = int(round(time.time() * 1000))\n\n total_time_2 = end_time - start_time\n transfer_time_2 = total_time_2 - cost_2\n print(\"单次远程计算时间: \", cost_2, \"ms\")\n print(\"单次远程传输时间: \", transfer_time_2, \"ms\")\n\n\ndef compare_multi_picture(image_source): \n start_time = int(round(time.time() * 1000))\n for image_name in os.listdir(image_source):\n image_path = os.path.join(image_source, image_name)\n res1, cost_1 = calc_local(image_path)\n print(\"并行本地计算时间: \", cost_1, \"ms\")\n print(\"并行本地人脸计数结果: \", res1)\n # print(\"result_local: \", res1, cost_1)\n end_time = int(round(time.time() * 1000))\n total_time_1 = end_time - start_time\n print(\"并行本地计算总时间: \", total_time_1, \"ms\")\n \n start_time = int(round(time.time() * 1000))\n res = calc_remote_multi(image_source)\n cost_2 = 0\n for r in res:\n # print(r.get())\n tmp = json.loads(r.get()[-1])\n # print(tmp)\n cost_2 += int(tmp[\"result\"][1])\n end_time = int(round(time.time() * 1000))\n total_time_2 = end_time - start_time\n print(\"并行远程计算总时间: \", total_time_2, \"ms\")\n transfer_time_2 = total_time_2 - cost_2\n print(\"并行远程传输总时间: \", transfer_time_2, \"ms\")\n\n\nif __name__ == '__main__':\n image_source = \"./image\"\n for image_name in os.listdir(image_source):\n image_path = os.path.join(image_source, image_name)\n compare_single_picture(image_path)\n print('---单次测量结束，即将进行并行测量---')\n time.sleep(5)\n compare_multi_picture(image_source)\n print('---并行测量结束---')","repo_name":"skyerguo/django_transfer","sub_path":"client/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2344,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"23268135535","text":"'''\nKristina Kolibab\nProf. Tamon\nCS 456\nAssignment 2\n'''\n\ndef main():\n\n\t# cipher of big numbers\t\n\tf_cipher = open('a2.cipher', 'r')\n\tcipher_n = [line.strip() for line in f_cipher.readlines()]\n\tcipher_n = [int(i) for i in cipher_n]\n\n\t# output from .cpp file of PowerMod outputs\n\tf_ascii = open('ASCII_Chars.txt', 'r')\n\tascii_n = [line.strip() for line in f_ascii.readlines()]\t\n\tascii_n = [int(i) for i in ascii_n]\n\n\t# For loop for decryption\n\tfor (i, num) in enumerate(ascii_n):\n\t\tfor (j, bnum) in enumerate(cipher_n):\n\t\t\tif num == bnum:\n\t\t\t\tc = chr(i)\t\n\t\t\t\tcipher_n[j] = c\n\n\tf = open('decrypted_ans.txt', 'w')\n\tfor line in cipher_n:\n\t\tf.write(line)\n\n\tf_cipher.close()\n\tf_ascii.close()\t\n\tf.close()\n\n#\tprint(cipher_n)\n\nif __name__ == \"__main__\":\n\tmain()\n","repo_name":"kristinak228/RSA","sub_path":"rsa_decryption.py","file_name":"rsa_decryption.py","file_ext":"py","file_size_in_byte":752,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"14390209997","text":"#!/usr/bin/python3\n\"\"\" A function to multiply two matracies \"\"\"\n\n\ndef matrix_mul(m_a, m_b):\n \"\"\"\n matrix_mul - a function to multiple two equally sized matracies\n arg1: a matrix of integers or floats\n arg2: a matrix of integers or floats\n return - a new matrix of the products of m_a * m_b\n \"\"\"\n if not isinstance(m_a, list):\n raise TypeError('m_a must be a list')\n if m_a == [] or m_a == [[]]:\n raise ValueError(\"m_a can't be empty\")\n if not isinstance(m_b, list):\n raise TypeError('m_b must be a list')\n if m_b == [] or m_b == [[]]:\n raise ValueError(\"m_b can't be empty\")\n for alist in m_a:\n if not isinstance(alist, list):\n raise TypeError('m_a must be a list of lists')\n if len(alist) != len(m_a[0]):\n raise TypeError('each row of m_a must be of the same size')\n for num in alist:\n if not isinstance(num, (int, float)):\n raise TypeError('m_a should contain only integers or floats')\n for blist in m_b:\n if not isinstance(blist, list):\n raise TypeError('m_b must be a list of lists')\n if len(blist) != len(m_b[0]):\n raise TypeError('each row of m_b must be of the same size')\n for num in blist:\n if not isinstance(num, (int, float)):\n raise TypeError('m_b should contain only integers or floats')\n if len(m_a[0]) != len(m_b):\n raise ValueError(\"m_a and m_b can't be multiplied\")\n\n m_c = []\n\n for i in range(len(m_a)):\n res = []\n for j in range(len(m_b[0])):\n result = 0\n for k in range(len(m_b)):\n result += m_a[i][k] * m_b[k][j]\n res.append(result)\n m_c.append(res)\n\n return(m_c)\n","repo_name":"TMcMac/holbertonschool-higher_level_programming","sub_path":"0x07-python-test_driven_development/100-matrix_mul.py","file_name":"100-matrix_mul.py","file_ext":"py","file_size_in_byte":1776,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"31943490450","text":"# -*- coding: utf-8 -*-\nimport logging\nimport simplejson as json\nimport time\nimport requests\nfrom api.controllers.tracking_controllers import tracking_lab_order_controller\nfrom vendor.models import WxMetaProductRelationship, LensSpecmap\nfrom pg_oms.settings import WX_META_SERVICE, WX_META_PURCHASE\nfrom oms.models.order_models import PurchaseOrderRecords\n\nclass wx_meta_purchase_controller:\n '''\n WX Meta Purchase Controller class\n '''\n def __init__(self):\n self.orderType = 'meta'\n self.deliverType = '顺丰寄付' #快递方式指定圆通\n self.customerLinkman = '李莲英' #联系人\n self.customerTel = '15518639392' #联系电话\n self.customerProvince ='上海市' #省\n self.customerCity = '上海市' #市\n self.customerCounty = '奉贤区' #区\n self.customerAddress='大叶公路4601号伟星工业园' #地址\n\n self.name = 'A100311' # 智镜客户码 固定的 A100311\n self.pwd = 'zhijin123' # 获取令牌密码\n\n self.host = WX_META_SERVICE if isinstance(WX_META_SERVICE, str) else WX_META_SERVICE[0]#伟星系统地址，写入配置文件\n self.token_url = WX_META_PURCHASE.get('TOKEN_URL') #'/all/account/login'#获取令牌URL\n self.wx_meta_prd_url = WX_META_PURCHASE.get('WX_META_PRD_URL') #'/api/product/listCustomerProducts' #获取现片产品清单\n self.add_order_url = WX_META_PURCHASE.get('ADD_ORDER_URL') #'/api/order/addOrder'添加订单URL\n self.order_status_url= WX_META_PURCHASE.get('ORDER_STATUS_URL')#'/api/order/getOrderStatus' #获取订单状态URL\n\n #库存片产品对应关系，需写入对应关系表中\n self.meta_product_relation = {\n 'KD56L': '00000000000000008736',\n 'KD56': '00000000000000002750',\n 'KDB56-C': '00000000000000002443',\n 'KD61L': '00000000000000004648',\n 'KDB61-H-SHMC': '00000000000000004048',\n 'KD61': '00000000000000002549',\n\n }\n # 获取令牌\n '''\n ### 参数\n * `name`：名字\n * `pwd`：密码\n '''\n def get_headers(self,content_type='application/json'):\n headers = {\n 'Content-Type': 'application/x-www-form-urlencoded;charset=utf-8'\n }\n try:\n url = self.host + self.token_url + \"?name=%s&pwd=%s\"%(self.name,self.pwd)\n logging.debug(url)\n result = requests.post(url, headers=headers, timeout=60)\n account = json.loads(result.text)\n if account['code'] == '200' and account['map']['data']['token']:\n headers = {\n \"Content-Type\": content_type,\n \"X-Auth-Token\": account['map']['data']['token']\n }\n return {\"code\":0,\"headers\":headers,\"msg\":\"成功获取token\"}\n else:\n return {\"code\":-1,\"token\":\"\",\"msg\":\"伟星接口返回信息变化\"}\n except Exception as e:\n return {\"code\": -1, \"token\": \"\", \"msg\": \"get token failed%s\"%str(e)}\n\n #获取现片产品列表\n '''\n /api/product/listCustomerProducts\n ### 参数\n 无\n\n ### 响应\n ```json\n {\n \"success\": true,\n \"code\": \"200\",\n \"message\": null,\n \"map\": {\n \"data\": [{\n productId: `'xxx'`, 销售品名id\n brand: `'xxx'`, 品牌\n lenType: `'xxx'`, 光型\n sphStart: `0.00`, 球镜起始值\n sphEnd: `0.00`, 球镜结束值\n cylStart: `0.00`, 柱镜起始值\n cylEnd: `0.00`, 柱镜结束值\n addStart: `0.00`, 加光起始值\n addEnd: `0.00`, 加光结束值\n\n productName: `'xxx'`, 销售品名\n zsl: `'xxx'`, 折射率\n dl: `'xxx'`, 大类\n price: `100.0`, 单价\n rate: `1.0`, 折扣\n }]\n }\n }\n ```\n '''\n def list_wx_meta_products(self):\n content_type = 'application/x-www-form-urlencoded;'\n try:\n result = self.get_headers(content_type)\n logging.debug(result)\n if (result['code'] == -1): # 返回出错信息\n return result\n headers = result['headers']\n url = self.host + self.wx_meta_prd_url\n result = requests.post(url, headers=headers, timeout=60)\n response = json.loads(result.text)\n if response['code'] == \"200\":\n return {\"code\": 0, \"data\": response['map']['data'], \"msg\": \"success\"}\n else:\n return {\"code\": -1, \"msg\": response['message']}\n except Exception as e:\n return {\"code\": -1, \"msg\": \"获取车房列表失败，异常信息：%s\" % str(e)}\n\n\n\n\n #生成订单\n '''\n * `addOrderDTOStr`: json字符串，json格式：\n * orderType: 订单类型,`meta/house`分别表示现片/车房\n * apiOrderNo: 第三方订单号\n * deliveryDate: `'yyyy-MM-dd'`,要求发货日期\n * deliverType: `'圆通'`,快递方式\n * customerLinkman: `'张三'`,联系人\n * customerTel: `'152xxxx'`,联系电话\n * customerProvince: `'浙江省'`,省\n * customerCity: `'台州市'`,市\n * customerCounty: `'xxx'`,区\n * customerAddress: `'xxx'`,地址\n * note: `'xxx'`,备注\n\n * items: 订单明细列表\n * type: `'meta'`, 值为meta/house/frame,分别代表现片/车房/镜架\n * productId: `'J123'`,销售品名id\n * quantity: `1`,数量\n * brand: `'白包装'`,品牌\n * lr: `'l'`,左右眼\n * sphval: `'1.0'`,球镜\n * cylval: `'1.0'`,柱镜\n * addval: `'1.0'`,加光\n * lentype: `'近视'`,光型\n * axis: `1`,光轴\n * cbase: `'1'`,基弯\n * coloring：`'wf-01'`,染色内容\n * prismA：`'1'`,棱镜a\n * directionA：`'内'`,方向a\n * prismB：`'1'`,棱镜b\n * directionB：`'上'`,方向b\n * isAsse：`'Y'`,是否装配\n * isCut：`'N'`,是否割边\n * framePd：`'65'`,瞳距\n * frameIpd：`''`,近用瞳距\n * framePh：`''`,瞳高\n * glassDistance：`''`,眼距\n * frontAngle：`''`,前倾角\n * moveIn：`''`,移心-内移\n * moveOut：`''`,移心-外移\n * processes: `{'镀膜': '蓝光'}`,json格式,`工艺类型:工艺ID`的映射\n\n ### 响应\n ```json\n {\n \"success\": true,\n \"code\": \"200\",\n \"message\": null,\n \"map\": {\n \"data\": null\n }\n }\n ```\n '''\n def add_meta_order(self, dict_data, delveryDate=\"\", brand='白包装', isAsse=\"N\", isCut=\"N\"):\n result = self.get_headers()\n if(result['code'] == -1): #返回出错信息\n return result\n headers = result['headers']\n try:\n wx_meta_lens = LensSpecmap.objects.filter(inner_code=dict_data.get('rsku'), active='ACTIVE', vendor=dict_data.get('vendor'))\n #wx_meta_lens = WxMetaProductRelationship.objects.filter(sku=dict_data.get('rsku'))\n if len(wx_meta_lens) == 0 or len(wx_meta_lens) > 1:\n return {\"code\": -1, \"data\": '', \"msg\": \"未找到对应关系\"}\n wx_meta_len = wx_meta_lens[0]\n l_product_id = wx_meta_len.outer_code\n r_product_id = wx_meta_len.outer_code\n\n if(not delveryDate): #默认要求当天发货\n delveryDate = time.strftime(\"%Y-%m-%d\", time.localtime())\n\n order_dict = {\n \"orderType\": self.orderType,#默认现片\n \"apiOrderNo\": dict_data.get('order_number', ''),\n \"deliveryDate\": delveryDate,\n \"deliverType\": self.deliverType,\n \"customerLinkman\": self.customerLinkman,\n \"customerTel\": self.customerTel,\n \"customerProvince\": self.customerProvince,\n \"customerCity\": self.customerCity,\n \"customerCounty\":self.customerCounty,\n \"customerAddress\": self.customerAddress,\n \"note\": dict_data.get('comments', ''),\n \"items\": [\n {\n \"type\": self.orderType,\n \"productId\": l_product_id,\n \"quantity\": 1,\n \"brand\": brand,\n \"lr\": \"l\",\n \"sphval\": dict_data.get('lsph', '0'),\n \"cylval\": dict_data.get('lcyl', '0'),\n \"lentype\": dict_data.get('l_lens_type', ''),\n \"axis\": dict_data.get('laxis', '0'),\n \"addval\": \"0.00\",\n \"cbase\": \"\",\n \"coloring\": \"\",\n \"prismA\": 0,\n \"directionA\": \"\",\n \"prismB\": 0,\n \"directionB\": \"\",\n \"isAsse\": isAsse, #是否装配\n \"isCut\": isCut, #切边\n \"framePd\": 0,\n \"frameIpd\": \"\",\n \"framePh\": \"\",\n \"glassDistance\": \"\",\n \"frontAngle\": \"\", #前倾角 无\n \"moveIn\": \"\",\n \"moveOut\": \"\",\n \"processes\": {} #工艺，无\n },\n {\n \"type\": self.orderType,\n \"productId\": r_product_id,\n \"quantity\": 1,\n \"brand\": brand,\n \"lr\": \"r\",\n \"sphval\": dict_data.get('rsph', '0'),\n \"cylval\": dict_data.get('rcyl', '0'),\n \"lentype\": dict_data.get('r_lens_type', ''),\n \"axis\": dict_data.get('raxis', '0'),\n \"addval\": \"0.00\",\n \"cbase\": \"\",\n \"coloring\": \"\",\n \"prismA\": 0,\n \"directionA\": \"\",\n \"prismB\": 0,\n \"directionB\": \"\",\n \"isAsse\": isAsse, # 是否装配\n \"isCut\": isCut, # 切边\n \"framePd\": 0,\n \"frameIpd\": \"\",\n \"framePh\": \"\",\n \"glassDistance\": \"\",\n \"frontAngle\": \"\", # 前倾角 无\n \"moveIn\": \"\",\n \"moveOut\": \"\",\n \"processes\": {} # 工艺，无\n },\n ]\n }\n #{\"code\":\"500\",\"map\":{\"data\":null},\"message\":\"没有价格！产品ID：00000000000000008736 品牌：白包装 光型：近视 球：1 柱：2 加光：0\",\"success\":false}\n url = self.host + self.add_order_url\n result = requests.post(url, data=json.dumps(order_dict), headers=headers, timeout=60)\n response = json.loads(result.text)\n if response['code'] == \"200\":\n # 存储下单信息\n purchase_order_records = PurchaseOrderRecords.objects.filter(lab_number=dict_data.get('order_number', ''))\n if len(purchase_order_records) > 0:\n pur_order_records = purchase_order_records[0]\n pur_order_records.order_data = json.dumps(order_dict)\n pur_order_records.vendor = '10'\n pur_order_records.save()\n else:\n pur_order_records = PurchaseOrderRecords()\n pur_order_records.lab_number = dict_data.get('order_number', '')\n pur_order_records.order_data = json.dumps(order_dict)\n pur_order_records.vendor = '10'\n pur_order_records.save()\n return {\"code\": 0, \"data\": response['map']['data'], \"msg\": \"success!\"}\n else:\n return {\"code\": -1, \"data\": response['map']['data'], \"msg\": response['message']}\n except Exception as e:\n return {\"code\": -1, \"data\": \"\", \"msg\": \"生成订单失败，异常信息：%s\"%str(e)}\n\n #根据伟星订单号获取伟星订单生产状态\n '''\n ### 参数\n * `orderNo`: 订单号(不是订单ID)\n\n ### 响应\n ```json\n {\n \"success\": true,\n \"code\": \"200\",\n \"message\": null,\n \"map\": {\n \"data\": {\n \"status\": \"1\",\n \"deliverNo\": \"123\",\n \"deliverCompany\": \"圆通\"\n }\n }\n }\n ```\n\n #### 订单状态\n * \"-1\": 已删除\n * \"0\": 待引入\n * \"1\": 待审核\n * \"23\": 待确认\n * \"25\": 已终止\n * \"27\": 已取消\n * \"30\": 生产中\n * \"35\": 割边处理\n * \"37\": 单证打印\n * \"38\": 部分发货\n * \"40\": 完成\n '''\n def getOrderStatus(self,orderNo):\n if(orderNo == \"\"):\n return {\"code\": -1,\"msg\": \"订单号不能为空\"}\n content_type ='application/x-www-form-urlencoded;'\n try:\n result = self.get_headers(content_type)\n logging.debug(result)\n if (result['code'] == -1): # 返回出错信息\n return result\n headers = result['headers']\n url = self.host + self.order_status_url + \"?orderNo=%s\"%orderNo\n result = requests.post(url, headers=headers, timeout=60)\n response = json.loads(result.text)\n if response['code'] == \"200\":\n return {\"code\":0,\"data\":response['map']['data'],\"msg\":\"success\"}\n else:\n return {\"code\":-1,\"msg\":response['message']}\n except Exception as e:\n return {\"code\": -1, \"msg\": \"获取订单状态错误，异常信息：%s\"%str(e)}\n\n #封装数据\n def pack_request_value(self, lab):\n data_dict = {}\n try:\n if float(lab.od_sph) <= 0:\n r_lens_type = '近视'\n else:\n r_lens_type = '老花'\n\n if float(lab.os_sph) <= 0:\n l_lens_type = '近视'\n else:\n l_lens_type = '老花'\n\n if int(lab.vendor) > 9:\n act_lens_sku = lab.act_lens_sku[3:]\n else:\n act_lens_sku = lab.act_lens_sku[2:]\n\n data_dict['order_number'] = lab.lab_number\n data_dict['vendor'] = lab.vendor\n data_dict['rsku'] = act_lens_sku\n data_dict['rsph'] = lab.od_sph\n data_dict['rcyl'] = lab.od_cyl\n data_dict['raxis'] = lab.od_axis\n data_dict['r_lens_type'] = r_lens_type\n\n data_dict['lsku'] = act_lens_sku\n data_dict['lsph'] = lab.os_sph\n data_dict['lcyl'] = lab.os_cyl\n data_dict['laxis'] = lab.os_axis\n data_dict['l_lens_type'] = l_lens_type\n data_dict['comments'] = lab.comments\n return data_dict\n except Exception as e:\n return data_dict\n\n #处理返回结果\n def analysis_result(self, request, lbo, purchase_order, res):\n stat_dict = {}\n try:\n if res['code'] == 0:\n purchase_order.vendor_order_reference = res['data']['orderNo']\n purchase_order.save()\n lbo.vendor_order_reference = res['data']['orderNo']\n lbo.save()\n # 记录日志\n tloc = tracking_lab_order_controller()\n tloc.tracking(lbo, request.user, 'LENS_PURCHASE', '镜片采购',\n res['data']['orderNo'])\n stat_dict[lbo.lab_number] = {'Success': True, 'Message': '下单成功'}\n else:\n stat_dict[lbo.lab_number] = {'Success': False, 'Message': res['msg']}\n return stat_dict\n except Exception as e:\n stat_dict[lbo.lab_number] = {'Success': False, 'Message': e}\n return stat_dict\n\n\n\n","repo_name":"qiaozhizt/OMS","sub_path":"oms/controllers/wx_meta_purchase_controller.py","file_name":"wx_meta_purchase_controller.py","file_ext":"py","file_size_in_byte":17349,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"74718892965","text":"import nltk\r\nimport numpy\r\nimport random\r\n\r\n\r\n#Define the maximum iteration for K-Means Clustering\r\nmaxIte = 4\r\n\r\ntextFilePath = \"Research Areas.txt\"\r\nnewTextFilePath = \"Pre-Processed Research Areas.txt\"\r\n\r\n\r\n#Extracts key words to remove noise(like prepositions and conjunctions)\r\ndef extractImpWords():\r\n \r\n fp = open(textFilePath, \"r\")\r\n \r\n for line in fp:\r\n #String pre-processing\r\n text = line.strip()\r\n '''text = text.replace(',', '')\r\n text = text.replace('.', '')\r\n text = text.replace(';', ',')'''\r\n text = text.lower()\r\n\r\n '''#Split Sentence into each word\r\n textSplit = nltk.word_tokenize(text)\r\n #Tag each word with its type in the english language\r\n words = nltk.pos_tag(textSplit)\r\n\r\n newWords = \"\"\r\n count = 0\r\n\r\n #If word is co-ordinating conjuction(CC) or a preposition(IN), then dont account\r\n for i in range(0, len(textSplit)):\r\n if 'CC' == words[i][1] or 'IN' == words[i][1]:\r\n continue\r\n else:\r\n newWords+= words[i][0]+\" \"\r\n count+=1\r\n\r\n if count > maxSize:\r\n maxSize = count\r\n\r\n count = 0\r\n\r\n #Append new line to new file\r\n with open(newTextFilePath, \"a\") as fp2:\r\n fp2.write(newWords.strip()+\"\\n\")\r\n print(newWords.strip()+\"\\n\")'''\r\n\r\n with open(newTextFilePath, \"a\") as fp2:\r\n fp2.write(text+\"\\n\")\r\n\r\n \r\n\r\n fp.close()\r\n fp2.close()\r\n\r\n\r\n\r\ndef determineInitialCentroids(k):\r\n textFilePath = \"Research Areas.txt\"\r\n fp = open(textFilePath, \"r\")\r\n\r\n #All available topics are stored in this list\r\n topicList = []\r\n #The frequency of each topic is stored in this list\r\n freq = []\r\n #Used for checking\r\n flag = 0\r\n\r\n #Final chosen centroid(topics) list\r\n centroids = []\r\n\r\n #Read each line\r\n for line in fp:\r\n #Split each line into topics separated by comma\r\n words = line.strip().split(',')\r\n #Check if current topic already exists, then increase its count else append it to the list\r\n for i in range(0, len(words)):\r\n for j in range(0, len(topicList)):\r\n if topicList[j] == words[i].strip():\r\n freq[j]+=1\r\n flag = 1\r\n break\r\n if flag == 0:\r\n topicList.append(words[i].strip())\r\n freq.append(1)\r\n else:\r\n flag = 0\r\n\r\n #Sort the topics in decreasing order of frequency <--Requires optimal sorting\r\n for i in range(0, len(freq)):\r\n for j in range(0, len(freq)):\r\n if freq[i] > freq[j]:\r\n temp = freq[i]\r\n freq[i] = freq[j]\r\n freq[j] = temp\r\n tempStr = topicList[i]\r\n topicList[i] = topicList[j]\r\n topicList[j] = tempStr\r\n\r\n #Take the first k topics as centroid\r\n for i in range(0, k):\r\n centroids.append(topicList[i])\r\n\r\n #Remove unnecessary words from topics\r\n for i in range(0, len(centroids)):\r\n centroids[i] = centroids[i].replace('and ', '')\r\n centroids[i] = centroids[i].replace('for ', '')\r\n centroids[i] = centroids[i].lower()\r\n\r\n #Initial Centroids\r\n print(\"Initial Centroids : \\n\")\r\n for i in range(0, len(centroids)):\r\n print(centroids[i])\r\n\r\n return centroids\r\n\r\n\r\n\r\n\r\ndef distance(sent, cent):\r\n #Sent -> Sentence Cent -> Centroid\r\n words = sent.split()\r\n topics = cent.split()\r\n freq = 0\r\n\r\n #Finds the max occurence of a topic in a sentence. Returns the frequency\r\n for i in range(0, len(topics)):\r\n for j in range(0, len(words)):\r\n if topics[i].strip() == words[j].strip():\r\n freq+=1\r\n\r\n return freq\r\n\r\n\r\n\r\ndef newCentroid(lines):\r\n #All available topics are stored in this list\r\n topicList = []\r\n #The frequency of each topic is stored in this list\r\n freq = []\r\n #Used for checking\r\n flag = 0\r\n \r\n for line in range(0, len(lines)):\r\n #Split each line into topics separated by comma\r\n words = lines[line].strip().split(',')\r\n #Check if current topic already exists, then increase its count else append it to the list\r\n for i in range(0, len(words)):\r\n for j in range(0, len(topicList)):\r\n if topicList[j] == words[i].strip():\r\n freq[j]+=1\r\n flag = 1\r\n break\r\n if flag == 0:\r\n topicList.append(words[i].strip())\r\n freq.append(1)\r\n else:\r\n flag = 0\r\n\r\n #Sort the topics in decreasing order of frequency <--Requires optimal sorting\r\n for i in range(0, len(freq)):\r\n for j in range(0, len(freq)):\r\n if freq[i] > freq[j]:\r\n temp = freq[i]\r\n freq[i] = freq[j]\r\n freq[j] = temp\r\n tempStr = topicList[i]\r\n topicList[i] = topicList[j]\r\n topicList[j] = tempStr\r\n\r\n #Select the first topic which has occurred maximum number of times\r\n newCentroid = topicList[0]\r\n newCentroid = newCentroid.replace('and ', '')\r\n newCentroid = newCentroid.replace('for ', '')\r\n newCentroid = newCentroid.lower()\r\n\r\n return newCentroid\r\n \r\n \r\n\r\n\r\n\r\n\r\ndef kMeansClustering(k, centroids):\r\n #Counts the number of iterations\r\n ite = 1\r\n\r\n while(True):\r\n #2D list of clusters. Each row corressponds to a single cluster\r\n clusters = [[] for i in range(k)]\r\n #Open the pre-processed file\r\n fp = open(newTextFilePath, \"r\")\r\n\r\n #Pick each line and find its correct cluster by calculating the centroid which occurs max no. of times in that line\r\n for line in fp:\r\n dist = 0\r\n index = 0\r\n for i in range(0, k):\r\n if distance(line.strip(), centroids[i].strip()) > dist:\r\n dist = distance(line.strip(), centroids[i].strip())\r\n index = i\r\n clusters[index].append(line.strip())\r\n\r\n #Print the centroid and the corressponding cluster\r\n for i in range(0, k):\r\n print(\"\\n\"+centroids[i].strip()+\" :\")\r\n for j in range(0, len(clusters[i])):\r\n print(clusters[i][j])\r\n\r\n\r\n print(\"\\n\\n\\n\\nIteration \"+str(ite)+\" Done\\n\\n\\n\\n\")\r\n ite+=1\r\n\r\n fp.close()\r\n\r\n if ite > maxIte:\r\n break\r\n \r\n #Gets new centroid from each cluster\r\n for i in range(0, k):\r\n clus = clusters[i]\r\n if len(clus) > 0:\r\n centroids[i] = newCentroid(clus)\r\n\r\n\r\n #Final Centroids\r\n print(\"Final Centroids : \\n\")\r\n for i in range(0, k):\r\n print(centroids[i].strip())\r\n\r\n\r\n\r\n#extractImpWords()\r\n\r\ncentroids = determineInitialCentroids(10)\r\nprint(\"\\n\\n\\n\")\r\nkMeansClustering(10, centroids)\r\n","repo_name":"atif-hassan/Clustering-based-Topic-Recommendation","sub_path":"Research_Area_Clustering.py","file_name":"Research_Area_Clustering.py","file_ext":"py","file_size_in_byte":7015,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"52"} +{"seq_id":"10685071431","text":"import os\nimport sys\nfrom argparse import ArgumentParser\nfrom typing import List\nfrom collections import OrderedDict\n\nfrom flask import Flask\nfrom rick.base import Di, Container, MapLoader\nfrom rick.event import EventManager\nfrom rick.mixin import Injectable\nfrom rick.util.loader import load_class\nfrom rick.resource.console import ConsoleWriter\n\nfrom pokie.constants import (\n DI_CONFIG,\n DI_SERVICES,\n DI_FLASK,\n DI_APP,\n DI_EVENTS,\n DI_TTY,\n DI_SIGNAL, CFG_HTTP_ERROR_HANLDER, DI_HTTP_ERROR_HANDLER,\n)\nfrom .module import BaseModule\nfrom .command import CliCommand\nfrom pokie.util.cli_args import ArgParser\nfrom .signal import SignalManager\n\n\nclass FlaskApplication:\n CLI_CMD_SUCCESS = 0\n CLI_CMD_FAILED = 1\n CLI_CMD_NOT_FOUND = 2\n\n module_file_name = \"module\" # module class file name\n module_class_name = \"Module\" # default module class name\n\n system_modules = [\n \"pokie.contrib.base\",\n ] # system modules to always be included\n\n def __init__(self, cfg: Container):\n self.di = Di()\n self.app = None\n self.modules = {} # app module list\n\n self.di.add(DI_CONFIG, cfg)\n self.di.add(DI_APP, self)\n self.cfg = cfg\n\n def build(self, module_list: list, factories: List = None) -> Flask:\n \"\"\"\n Build the application\n\n Factories is a list of optional callables to assemble functionality on top of Di, eg. database connection,\n cache, logging, etc. Factories are called *before* modules are initialized, to ensure all required dependencies\n are available\n\n :param module_list: list of module names to initialize\n :param factories: optional list of callables to be initialized with the application\n :return:\n \"\"\"\n if not factories:\n factories = []\n\n self.app = Flask(type(self).__name__)\n self.app.di = self.di\n self.di.add(DI_FLASK, self.app)\n\n # initialize signal manager\n self.di.add(DI_SIGNAL, SignalManager(self.di))\n\n # initialize TTY\n self.di.add(DI_TTY, ConsoleWriter())\n\n # load modules\n self.modules = {}\n module_list = [*self.system_modules, *module_list]\n for name in module_list:\n cls = load_class(\n \"{}.{}.{}\".format(name, self.module_file_name, self.module_class_name),\n True\n )\n if cls is None:\n raise RuntimeError(\n \"build(): cannot load module '{}' - Module() class not found\".format(\n name\n )\n )\n if not issubclass(cls, BaseModule):\n raise RuntimeError(\n \"build(): Class Module on '{}' must extend BaseModule\".format(name)\n )\n if name in self.modules.keys():\n raise ValueError(\n \"build(): Module named '{}' already exists\".format(name)\n )\n self.modules[name] = cls(self.di)\n\n # build service map\n svc_map = {}\n for name, m in self.modules.items():\n services = getattr(m, \"services\", {})\n if type(services) is dict:\n svc_map.update(services)\n else:\n raise RuntimeError(\n \"build(): cannot load service map from module '{}'; attribute must be of type dict\".format(\n name\n )\n )\n # register service mapper\n self.di.add(DI_SERVICES, MapLoader(self.di, svc_map))\n\n # run factories\n for factory in factories:\n if type(factory) is str:\n # if factory is string, assume it is a path to a callable\n factory = load_class(factory, True)\n if not callable(factory):\n raise RuntimeError(\"build(): non-callable or non-existing factory\")\n else:\n factory(self.di)\n\n # parse events from modules\n evt_mgr = EventManager()\n for _, module in self.modules.items():\n module_events = getattr(module, \"events\", None)\n if isinstance(module_events, dict):\n for evt_name, evt_details in module_events.items():\n for priority, handlers in evt_details.items():\n for handler in handlers:\n evt_mgr.add_handler(evt_name, handler, int(priority))\n\n self.di.add(DI_EVENTS, evt_mgr)\n\n # register exception handler\n if self.cfg.has(CFG_HTTP_ERROR_HANLDER):\n handler = load_class(self.cfg.get(CFG_HTTP_ERROR_HANLDER), True)\n if not issubclass(handler, Injectable):\n raise RuntimeError(\"build(): HTTP_ERROR_HANDLER class does not extend Injectable\")\n # initialize & register handler\n handler = handler(self.di)\n self.di.add(DI_HTTP_ERROR_HANDLER, handler)\n\n # initialize modules\n for _, module in self.modules.items():\n module.build(self)\n\n return self.app\n\n def http(self, **kwargs):\n self.app.run(**kwargs)\n\n def cli_runner(self, command: str, args: list = None, **kwargs) -> int:\n # either console or inline commands\n if args is None:\n args = []\n\n # parameter parser\n parser = ArgParser(**kwargs)\n\n if \"writer\" in kwargs.keys():\n tty = kwargs[\"writer\"]\n else:\n tty = ConsoleWriter()\n\n # lookup handler\n for _, module in self.modules.items():\n if command in module.cmd.keys():\n handler = load_class(module.cmd[command])\n if not handler:\n raise RuntimeError(\n \"cli(): handler class '{}' not found\".format(\n module.cmd[command]\n )\n )\n if not issubclass(handler, CliCommand):\n raise RuntimeError(\n \"cli(): command handler does not extend CliCommand\"\n )\n handler = handler(self.di, writer=tty) # type: CliCommand\n if not handler.skipargs: # skipargs controls usage of argparser\n handler.arguments(parser)\n args = parser.parse_args(args)\n if parser.failed:\n # invalid/insufficient args\n tty.error(parser.error_message)\n parser.print_help(tty.stderr)\n return self.CLI_CMD_FAILED\n else:\n # skipargs is true, all argparsing is ignored\n # this allow for custom cli arg handling\n args = None\n\n if handler.run(args):\n return self.CLI_CMD_SUCCESS\n return self.CLI_CMD_FAILED\n\n # command not found\n tty.error(\"error executing '{}': command not found\".format(command))\n return self.CLI_CMD_NOT_FOUND\n\n def cli(self, **kwargs):\n \"\"\"\n Execute CLI commands\n :param kwargs: optional parameters for ArgumentParse\n :return:\n \"\"\"\n # default command when no args detected\n command = \"list\"\n # extract command if specified\n if len(sys.argv) > 1:\n command = str(sys.argv[1])\n\n if \"add_help\" not in kwargs.keys():\n kwargs[\"add_help\"] = False\n if \"usage\" not in kwargs.keys():\n kwargs[\"usage\"] = \"{} {} [OPTIONS...]\".format(\n os.path.basename(sys.argv[0]), command\n )\n\n # exit code directly maps return codes\n exit(self.cli_runner(command, sys.argv[2:], **kwargs))\n","repo_name":"oddbit-project/pokie","sub_path":"pokie/core/application.py","file_name":"application.py","file_ext":"py","file_size_in_byte":7792,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"11903385875","text":"__author__ = 'vikesh'\n\nimport os\nimport sys\n\n\ntry:\n from setuptools import setup\nexcept ImportError:\n from distutils.core import setup\n\n\nPYTHON3 = sys.version_info[0] > 2\n\nrequired = []\n\nif not PYTHON3:\n required += ['importlib>=1.0.3']\n\npackages = []\n\ntry:\n longdesc = open(\"README.rs\").read()\nexcept:\n longdesc = ''\n\n# Thanks to Kenneth Reitz, I stole the template for this : https://github.com/kennethreitz\nsetup(\n name='charmander',\n version='1.0.0',\n description='Simple and easy way to create bots for slack',\n long_description=longdesc,\n author='Vikesh Tiwari',\n author_email='vicky002@eulercoder.me',\n url='https://github.com/vicky002/Charmander',\n packages=packages,\n scripts= [''],\n package_data = {'':['LICENCE',],'':[]},\n include_package_data=True,\n license='MIT',\n classifiers=(\n 'Development Status :: 1 - Beta',\n 'Intended Audience :: Developers',\n 'Natural Language :: English',\n 'License :: OSI Approved :: MIT License',\n 'Programming Language :: Python',\n 'Programming Lanuguage :: Python :: 2.6',\n 'Programming Language :: Python :: 2.7',\n 'Programming Language :: Python :: 3.4',\n ),\n\n)","repo_name":"intfrr/Charmander","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1219,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"18489799676","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[ ]:\n\n\n#Задание 1\n#Импортируйте библиотеку Pandas и дайте ей псевдоним pd. Создайте датафрейм authors со столбцами author_id и author_name, \n#в которых соответственно содержатся данные: [1, 2, 3] и ['Тургенев', 'Чехов', 'Островский'].\n#Затем создайте датафрейм book cо столбцами author_id, book_title и price, в которых соответственно содержатся данные: \n#[1, 1, 1, 2, 2, 3, 3],\n#['Отцы и дети', 'Рудин', 'Дворянское гнездо', 'Толстый и тонкий', 'Дама с собачкой', 'Гроза', 'Таланты и поклонники'],\n#[450, 300, 350, 500, 450, 370, 290].\n\n\n# In[2]:\n\n\nimport pandas as pd\n\n\n# In[7]:\n\n\ndata1 ={'author_id':[1, 2, 3], 'author_name':['Тургенев', 'Чехов', 'Островский']}\nauthors = pd.DataFrame(data1)\n\n\n# In[10]:\n\n\ndata2 ={'author_id':[1, 1, 1, 2, 2, 3, 3], 'book_title':['Отцы и дети', 'Рудин', 'Дворянское гнездо', 'Толстый и тонкий', 'Дама с собачкой', 'Гроза', 'Таланты и поклонники'], 'price':[450, 300, 350, 500, 450, 370, 290]}\nauthor_name = pd.DataFrame(data2)\n\n\n# In[ ]:\n\n\n#Задание 2\n#Получите датафрейм authors_price, соединив датафреймы authors и books по полю author_id.\n\n\n# In[12]:\n\n\nauthors_price = pd.merge(authors, author_name, on='author_id', how='inner')\n\n\n# In[ ]:\n\n\n#Задание 3\n#Создайте датафрейм top5, в котором содержатся строки из authors_price с пятью самыми дорогими книгами.\n\n\n# In[14]:\n\n\ntop5=authors_price.nlargest(5, 'price')\n\n\n# In[ ]:\n\n\n#Задание 4\n#Создайте датафрейм authors_stat на основе информации из authors_price. В датафрейме authors_stat должны быть четыре столбца:\n#author_name, min_price, max_price и mean_price,\n#в которых должны содержаться соответственно имя автора, минимальн��я, максимальная и средняя цена на книги этого автора.\n\n\n# In[35]:\n\n\n#data3=\nauthors_stat=pd.DataFrame(data1)\n\nauthors_price['author_name'].unique()\nauthors_stat['max_price']=authors_price['price'].max()\nauthors_stat['min_price']=authors_price['price'].min()\nauthors_stat['mean_price']=authors_price['price'].mean()\nauthors_stat\n\n\n# In[23]:\n\n\nauthors_price.groupby('author_name')['price'].max\n\n\n# In[59]:\n\n\nmax_p=authors_price.groupby('author_name').agg({'price':max}) \nmin_p=authors_price.groupby('author_name').agg({'price':min})\nmean_p=authors_price.groupby('author_name').agg({'price':['mean']})\n\n\n# In[60]:\n\n\nmin_max = pd.merge(max_p, min_p, on='author_name')\nauthors_stat=pd.merge(min_max, mean_p, on='author_name')\nauthors_stat\n\n\n# In[61]:\n\n\nauthors_stat=authors_stat.rename(columns={'price_x':'max_price', 'price_y':'min_price', '(price, mean)':'mean_price'})\nauthors_stat\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"MikhailSivkov/Python-Numpy","sub_path":"lesson1 (pandas).py","file_name":"lesson1 (pandas).py","file_ext":"py","file_size_in_byte":3251,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"21578554428","text":"from django.urls import path\nfrom .views import ContactUsCreateView, ContactUsUpdateView, ContactUsDeleteView, contact_us_submission\n\napp_name = 'contact_us'\n\nurlpatterns = [\n path('contact_us/', ContactUsCreateView.as_view(), name='contact_us_submission'),\n path('contact_us//', ContactUsUpdateView.as_view(), name='update_contact'),\n path('contact_us//delete/', ContactUsDeleteView.as_view(), name='delete_contact'),\n]\n","repo_name":"Isaiah-Essien/Eden_Nasa","sub_path":"backend/contact_us/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":446,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"43647914384","text":"import logging\nimport operator\n\nimport sublime\n\nfrom . import cache, runners, settings, status, test_frameworks\nfrom .context import Context\nfrom .errors import EmptyHistory, Error, FrameworkNotFound, handle_errors\nfrom .history import History\nfrom .quick_panel_item import QuickPanelItem\nfrom .view_callbacks import ViewCallbacks\n\nSCOPE_LAST = \"last\"\n\nlogger = logging.getLogger(__name__)\n\n\ndef _build_runner_quick_panel_item(runner):\n trigger = (\n runner.cmd\n if runner.scope == test_frameworks.TestFramework.SCOPE_SUITE\n else runner.relpath\n )\n if runner.scope == test_frameworks.TestFramework.SCOPE_LINE:\n trigger += \":{}\".format(runner.line)\n\n scope = \"modified\" if runner.modified else runner.scope\n\n return QuickPanelItem(\n trigger,\n \"[{}] in '{}' with {}\".format(scope, runner.dir, runner.name),\n runner.framework,\n )\n\n\nclass Plugin:\n @classmethod\n @handle_errors\n def show_last_output(cls, focus=True):\n History.current().last().show_output(focus=focus)\n\n @classmethod\n @handle_errors\n def edit_last(cls):\n runner = History.current().last()\n sublime.active_window().open_file(\n \"{}:{}\".format(runner.file, runner.line), sublime.ENCODED_POSITION\n )\n\n @classmethod\n @handle_errors\n def clear_history(cls):\n History.current().clear()\n\n def __init__(self, view):\n self.view = view\n\n @handle_errors\n def select_test_framework(self, scope, edit=False):\n cache.clear()\n\n items = sorted(\n test_frameworks.items(), key=operator.attrgetter(\"language\", \"framework\")\n )\n\n if len(items) == 1 and not settings.get(\"always_show_test_framework_selection\"):\n self.run_test(scope, edit, items[0])\n else:\n self.view.window().show_quick_panel(\n [QuickPanelItem(item.framework, \"\", item.language) for item in items],\n lambda index: index != -1 and self.run_test(scope, edit, items[index]),\n )\n\n @handle_errors\n def run_test(self, scope, edit=False, test_framework=None):\n cache.clear()\n\n try:\n runner = self.build_runner(scope, test_framework=test_framework)\n\n if edit and runner.editable:\n self.view.window().show_input_panel(\n \"Command\",\n runner.cmd,\n lambda cmd: self.process_runner(runner, cmd),\n lambda _: None,\n lambda: None,\n )\n elif edit:\n raise Error(\"Runner '{}' is not editable\".format(runner.name))\n else:\n self.process_runner(runner)\n except FrameworkNotFound as exc:\n if (\n settings.get(\"select_test_framework_when_not_found\")\n and scope != SCOPE_LAST\n ):\n status.update(\"Couldn't find a test framework, please select one\")\n self.view.run_command(\n \"any_test_run\", {\"scope\": scope, \"edit\": edit, \"select\": True}\n )\n else:\n raise exc\n\n @handle_errors\n def show_history(self):\n runners = list(History.current().runners)\n\n if not bool(runners):\n raise EmptyHistory\n\n self.view.window().show_quick_panel(\n [_build_runner_quick_panel_item(runner) for runner in runners],\n lambda index: index > -1 and self.process_runner(runners[index]),\n )\n\n def build_runner(self, scope, test_framework=None):\n if scope == SCOPE_LAST:\n return History.current().last()\n\n context = Context(self.view)\n if test_framework is None:\n test_framework = test_frameworks.find(context.file)\n\n runner = runners.find(test_framework)\n\n return runner.build(test_framework(context), scope)\n\n @handle_errors\n def process_runner(self, runner, cmd=\"\"):\n if runner is None:\n raise Error(\"Runner is not set\")\n\n if bool(cmd) and runner.cmd != cmd:\n runner = runner._replace(cmd=cmd, modified=True)\n\n ViewCallbacks(self.view).run()\n\n logger.debug(\"Running '%s' from '%s'\", runner.cmd, runner.dir)\n runner.run()\n History.current().add(runner)\n","repo_name":"timfjord/AnyTest","sub_path":"plugin/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4331,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"52"} +{"seq_id":"23463208958","text":"import os\nimport json\nimport base64\nimport requests\nfrom bs4 import BeautifulSoup\nfrom PIL import Image\nfrom svglib.svglib import svg2rlg\nfrom reportlab.graphics import renderPM\n\n\ndef check_output_folder(folder):\n if os.path.exists(folder) is False:\n os.makedirs(folder)\n\n\ndef save_to_file(filename, data):\n with open(filename, \"w\") as f:\n f.write(json.dumps(data, indent=4))\n\n\ndef save_to_json(func):\n def wrapped(obj, *args, **kwargs):\n obj_name = obj.__class__.__name__\n func_name = func.__name__\n\n result = func(obj, *args, **kwargs)\n\n check_output_folder(f\"results/{obj_name}\")\n save_to_file(f\"results/{obj_name}/{func_name}.json\", result)\n\n return result\n return wrapped\n\n\ndef get_pokemon_sprite(sprite_name, shiny=False):\n check_output_folder(f\"sprites/pokemon/shiny\")\n\n is_shiny = \"-shiny\" if shiny else \"\"\n url = f\"https://dev.bframework.de/static/pokedex/sprites/front{is_shiny}/{sprite_name}.gif\"\n\n res = requests.get(url)\n content = res.content\n\n shiny_prefix = \"shiny/\" if shiny else \"\"\n file_path = f\"sprites/pokemon/{shiny_prefix}{sprite_name}.gif\"\n\n with open(file_path, \"wb\") as o:\n o.write(content)\n\n return open(file_path, \"rb\")\n\n\ndef get_sprite(sprite_type, sprite_name, shiny=False):\n try:\n if sprite_type == \"pokemon\":\n return get_pokemon_sprite(sprite_name, shiny)\n elif sprite_type == \"streamer\":\n return get_streamer_avatar(sprite_name)\n return get_item_sprite(sprite_type, sprite_name)\n except:\n pass\n return None\n\n\ndef get_streamer_avatar(streamer):\n check_output_folder(f\"sprites/avatars\")\n file_path = f\"sprites/avatars/{streamer}.png\"\n\n if os.path.isfile(file_path):\n return open(file_path, \"rb\")\n\n res = requests.get(f\"https://www.twitch.tv/{streamer}\")\n soup = BeautifulSoup(res.text, \"html.parser\")\n avatar = soup.find(\"meta\", {\"name\": \"twitter:image\"})\n\n res = requests.get(avatar[\"content\"])\n with open(file_path, \"wb\") as o:\n o.write(res.content)\n\n im = Image.open(file_path)\n im = im.resize((64, 64))\n im.save(file_path)\n\n return open(file_path, \"rb\")\n\n\ndef get_item_sprite(sprite_type, sprite_name):\n check_output_folder(f\"sprites/{sprite_type}\")\n\n file_path = f\"sprites/{sprite_type}/{sprite_name}\"\n\n if os.path.isfile(file_path + \".png\"):\n return open(file_path + \".png\", \"rb\")\n\n url = f\"https://poketwitch.bframework.de/static/twitchextension/items/{sprite_type}/{sprite_name}\"\n try:\n\n res = requests.get(url + \".svg\")\n\n soup = BeautifulSoup(res.text, \"html.parser\")\n svg = soup.find(\"image\")\n\n if svg is None:\n with open(file_path + \".svg\", \"wb\") as o:\n o.write(res.content)\n\n drawing = svg2rlg(file_path + \".svg\")\n renderPM.drawToFile(drawing, file_path + \".png\", fmt=\"PNG\")\n\n else:\n href = svg[\"href\"]\n\n s = href.split(\"base64,\")[1]\n\n img_data = s.encode()\n content = base64.b64decode(img_data)\n with open(file_path + \".png\", \"wb\") as o:\n o.write(content)\n except:\n url = url + \".png\"\n\n res = requests.get(url)\n content = res.content\n\n if res.status_code != 200:\n return None\n\n with open(file_path + \".png\", \"wb\") as o:\n o.write(content)\n\n file_path = file_path + \".png\"\n\n if sprite_type != \"pokemon\":\n im = Image.open(file_path)\n im = im.resize((64, 64))\n im.save(file_path)\n\n return open(file_path, \"rb\")\n","repo_name":"Dominic-Santos/twitch_bot","sub_path":"TwitchChannelPointsMinerLocal/classes/entities/Pokemon/Utils.py","file_name":"Utils.py","file_ext":"py","file_size_in_byte":3627,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"22089813319","text":"with open(\"input2.txt\") as f:\n reader = f.readlines()\n\n#movimiento = {\"forward\": 0,\"down\": 0, \"up\": 0}\n\n# for the first part\n#for linea in reader:\n# comando, n = linea.rstrip().split()\n# n = int(n)\n# movimiento[comando] += n\n#distancia = movimiento[\"forward\"]\n#profundidad = movimiento[\"down\"] - movimiento[\"up\"]\n#print(distancia*profundidad)\n\nhorizontal, profundidad, aim = 0, 0, 0\nfor linea in reader:\n comando, n = linea.rstrip().split()\n n = int(n)\n if comando == \"down\":\n aim += n\n elif comando == \"up\":\n aim -= n\n else:\n horizontal += n\n profundidad += aim*n\n\nprint(profundidad*horizontal)\n\n","repo_name":"EverVino/coding_challenge","sub_path":"dia2/dia2.py","file_name":"dia2.py","file_ext":"py","file_size_in_byte":652,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"35415847265","text":"from flask import Flask, render_template, flash, request, session, redirect, url_for\nfrom pony.orm import Database, Optional, Required, PrimaryKey, db_session, sql_debug, select\nimport datetime as dt\nfrom pathlib import Path\n#import statistics as st\nimport os\nimport pony\nimport pprint\nimport logging\nfrom logging.handlers import RotatingFileHandler\nfrom forms import DataEntryForm, SelectReadingForm, EditReadingForm\nfrom functions import decimalAverage\nfrom collections import namedtuple\n\n# App config.\n##DEBUG = True\napp = Flask(__name__)\napp.config.from_object(__name__)\napp.config['SECRET_KEY'] = os.urandom(512)\n\n@app.errorhandler(405)\ndef methodNotAllowed(e):\n f = request.full_path.split('?', 1)[0]\n return render_template('405.jinja2', **locals()), 405\n\ndbFile = \"/home/bill/glucose2/glucose.db\"\ndbPath = Path(dbFile)\ndb = Database()\n\nclass Readings(db.Entity):\n date = PrimaryKey(str) #(dt.datetime)\n average = Optional(float)\n comment = Optional(str)\n hold = Optional(float)\n\ndb.bind(provider='sqlite', filename=str(dbPath), create_db=False)\ndb.generate_mapping(create_tables=False)\n\n@app.route(\"/\", methods=['GET'])\ndef home():\n flash(dbPath)\n with db_session:\n numberOfHeldReadings = len(Readings.select(lambda c: c.hold is not None))\n if numberOfHeldReadings == 0:\n flash('There are no partial readings.')\n elif numberOfHeldReadings == 1:\n flash('There is one partial reading.')\n else:\n flash(f'There are {numberOfHeldReadings} partial readings.')\n return render_template('Home.jinja2', **locals())\n\n@app.route(\"/enter\", methods=['GET', 'POST'])\ndef enter():\n\n flash(dbPath)\n\n if request.method == 'GET':\n form = DataEntryForm(request.form)\n return render_template('EnterReading.jinja2', **locals())\n\n elif request.method == 'POST':\n form = DataEntryForm(request.form)\n reqdate = request.form['ddate']\n comment = request.form['annotation']\n morning = request.form['amreading']\n evening = request.form['pmreading']\n if evening == '':\n average = None\n hold = morning\n else:\n average = decimalAverage(morning, evening)\n hold = None\n try:\n with db_session:\n Readings(date = reqdate, average = average, comment = comment, hold = hold)\n except Exception as e:\n flash(f'ERROR (DataEnbry.py:64): {e}')\n return render_template('EnterReading.jinja2', **locals())\n\n else:\n return \"fall through\"\n\n@app.route(\"/select\", methods=['GET'])\ndef select():\n\n flash(dbPath)\n\n with db_session:\n heldReadings = Readings.select(lambda c: c.hold is not None).order_by(1)\n numberOfHeldReadings = len(heldReadings)\n heldReadingsList = list(heldReadings)\n if numberOfHeldReadings > 0:\n heldReadingDates = []\n index = 1\n for heldReading in heldReadingsList:\n heldReadingDates.append((f'D{index}', heldReading.date))\n index += 1\n form = SelectReadingForm()\n form.helddateslist.choices = heldReadingDates\n session['heldDates'] = heldReadingDates\n return render_template('SelectReading.jinja2', **locals()) # form=heldForm)\n else:\n return render_template('NoneHeld.jinja2', **locals())\n\n@app.route(\"/edit\", methods=['POST'])\ndef edit():\n\n flash(dbPath)\n\n form = SelectReadingForm(request.form)\n FormIndex = form.data['helddateslist']\n heldReadingDates = session['heldDates']\n # session.pop('heldDates')\n heldReadingDates = dict(heldReadingDates)\n WorkingDate = heldReadingDates[FormIndex]\n session['WorkingDate'] = WorkingDate\n with db_session:\n reading = Readings[WorkingDate]\n heldReading = namedtuple('heldReading', ['readingDate', 'amreading', 'annotation'])\n hr = heldReading(WorkingDate, reading.hold, reading.comment)\n form = EditReadingForm(obj=hr)\n return render_template('EditReading.jinja2', **locals())\n\n@app.route(\"/update\", methods=['POST'])\ndef update():\n WorkingDate = session['WorkingDate']\n session.pop('WorkingDate')\n form = EditReadingForm(request.form)\n evening = form.data['pmreading']\n if evening is None:\n return render_template('NoEvening.jinja2', **locals())\n with db_session:\n reading = Readings[WorkingDate]\n morning = reading.hold\n reading.hold = None\n reading.average = decimalAverage(morning, evening)\n reading.comment = form.data['annotation']\n return redirect(url_for('home'))\n\n\nif __name__ == \"__main__\":\n\n # handler = RotatingFileHandler('foo.log', maxBytes=10000, backupCount=1)\n # handler.setLevel(logging.DEBUG)\n # handler.setLevel(logging.INFO)\n # app.logger.addHandler(handler)\n # app.logger.setLevel(logging.DEBUG)\n # l = app.logger.info\n\n app.debug = True\n app.logger = True\n\n app.run(host='wtrenker.com', port=7000, debug = True, use_reloader=False)\n# use_reloader=False is the key to getting multi-threaded debug working in PyCharm\n","repo_name":"wtrenker/DataEntry","sub_path":"DataEntry.py","file_name":"DataEntry.py","file_ext":"py","file_size_in_byte":5135,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"11941637505","text":"#!/usr/bin/env python3\n# coding:utf-8\n\nimport tkinter as tk\n\nwindow = tk.Tk() # 窗口 object\nwindow.title(\"my window\") # 标题\nwindow.geometry(\"300x200\") # 窗口大小\n\nl = tk.Label(window, bg=\"yellow\", \n width=20, text=\"empty\") # width 是字符的宽度\nl.pack()\n\ndef print_selection_1():\n if (var1.get()==1) & (var2.get()==0):\n # & 按位与运算符\n # var1.get() == 1 就是 var1 获得的变量 onvalue=1，\n # var1.get() == 0 即是 var1 获得的变量 offvalu=0\n l.config(text=\"I love only Python\")\n elif (var1.get()==0) & (var2.get()==1):\n l.config(text=\"I love only C++\")\n elif (var1.get()==0) & (var2.get()==0):\n l.config(text=\"I do not love either\")\n else:\n l.config(text=\"I love both\")\n\ndef print_selection_2(): # 用 and 效果一样\n if var1.get()==1 and var2.get()==0:\n l.config(text=\"I love only Python\")\n elif var1.get()==0 and var2.get()==1:\n l.config(text=\"I love only C++\")\n elif var1.get()==0 and var2.get()==0:\n l.config(text=\"I do not love either\")\n else:\n l.config(text=\"I love both\")\n\nvar1 = tk.IntVar()\nvar2 = tk.IntVar()\nc1 = tk.Checkbutton(window, text=\"Python\", variable=var1, onvalue=1, offvalue=0, command=print_selection_2)\n # onvalue 和 Tkinter_05.py 的部件 radiobutton 中的 value 相似，\n # 当我们选中了这个 checkbutton，onvalue 的值 1 就会放入到 var1 中，然后 var1 将其赋值给参数 variable\n # offvalue 用法相似，\n # 但是 offvalue 是在没有选中这个 checkbutton 时，把值 0 放入 var1，然后赋值给参数 variable\nc2 = tk.Checkbutton(window, text=\"C++\", variable=var2, onvalue=1, offvalue=0, command=print_selection_2)\nc1.pack()\nc2.pack()\n\nwindow.mainloop() # 循环刷新\n\n","repo_name":"YorkFish/learning_notes","sub_path":"Python3/MorvanZhou/Tkinter/Tkinter_07.py","file_name":"Tkinter_07.py","file_ext":"py","file_size_in_byte":2019,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"11726437502","text":"from django.urls import path\nfrom api.views \\\nimport student, curriculum, \\\ncourse, university, section, \\\ngrade_stats, feedback, grade_stats, \\\ndepartment, staff_member, utils\n\n\nurlpatterns = [\n path('login', student.StudentLogin.as_view()),\n path('students', student.StudentList.as_view()),\n path('students/', student.StudentDetail.as_view()),\n path('students//sections/', section.EnrollStudent.as_view()),\n path('send-activation-email', student.send_activation_email),\n path('departments', department.DepartmentList.as_view()),\n path('instructors/', staff_member.StaffMemberDetail.as_view()),\n \n path('universities', university.UniversityList.as_view()),\n path('universities/', university.UniversityDetail.as_view()),\n path('universities//curriculums', curriculum.CurriculumList.as_view()),\n path('universities//courses', course.CourseList.as_view()),\n path('universities//sections-terms', section.get_sections_terms_by_university),\n path('universities//departments', department.get_departments_by_university),\n path('universities//instructors', staff_member.StaffMemberList.as_view()),\n\n path('departments//courses', course.get_courses_by_department_id),\n path('courses', course.CourseList.as_view()),\n path('courses/', course.CourseDetail.as_view()),\n\n path('sections', section.SectionList.as_view()),\n path('sections/', section.SectionDetail.as_view()),\n path('sections//feedbacks', feedback.FeedbackList.as_view()),\n path('sections//grade-stats', grade_stats.GradeStatsDetail.as_view()),\n\n path('loaderio-2cfac6fc22e5d24034475d0fcb53e6e1/', utils.get_loader_io),\n path('feedbacks/', feedback.FeedbackDetail.as_view()),\n path('students//feedbacks//like', feedback.FeedbackLike.as_view()),\n path('students//feedbacks//report', feedback.report_feedback),\n\n\n path('curriculums/', curriculum.CurriculumDetail.as_view()),\n path('curriculums', curriculum.CurriculumList.as_view()),\n path('activate-user//', student.activate_student, name='activate')\n]\n","repo_name":"felixdasta/MyUniPerformance","sub_path":"backend/api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2253,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"12307628225","text":"# Inicializa variables y lista con números\nfibo = [0]\ni = 0\nnum = 1\n\n#Genera ciclo\nwhile num < 1000:\n\tfibo.append(num)\n\ti += 1\n\tnum = fibo[i] + fibo[i-1]\n\n#Imprime resultado\nfor num in fibo:\n\tprint(num)","repo_name":"ldanielher/BEDU-Python","sub_path":"Retos/2SESION_Reto5.py","file_name":"2SESION_Reto5.py","file_ext":"py","file_size_in_byte":203,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"72844874404","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\ndef main():\n box_width = 30\n focal_length = 1000\n principal_point = np.array((300.0, 300))\n distance = 100\n basic_box = box_width/2 * np.array(((1,1,1.0), (1,1,-1), (1,-1,1), (1,-1,-1), (-1,1,1),(-1,1,-1),(-1,-1,1), (-1,-1,-1)))\n box1 = np.array(((0,0,distance))) + basic_box\n c30 = np.cos(30/180*np.pi)\n s30 = np.sin(30/180*np.pi)\n R = np.array(((c30, 0, s30), (0,1,0), (-s30, 0, c30)))\n # box2 = np.matmul(R,box1.transpose()).transpose()\n # bbx1 = draw_box(box1, focal_length, principal_point)\n # bbx2 = draw_box(box2, focal_length, principal_point, 'g')\n # plt.plot((principal_point[0]), (principal_point[1]), 'rx')\n # plt.gca().set_aspect('equal', adjustable='box')\n # plt.xlim(0, 1300)\n # plt.ylim(0, 600)\n # plt.show()\n # draw_birds_eye(box1, 'b')\n # draw_birds_eye(box2, 'g')\n # draw_birds_camera()\n # plt.gca().set_aspect('equal', adjustable='box')\n # plt.xlim(np.min(box1[:, 0])-5, np.max(box2[:, 0])+5)\n # plt.ylim(-5, np.max(box1[:, 2])+5)\n # plt.show()\n\n # # crop box1\n # draw_box(box1, focal_length, principal_point, draw_bbx=False)\n # plt.xlim(bbx1[0], bbx1[1])\n # plt.ylim(bbx1[2], bbx1[3])\n # plt.show()\n # plt.gca().set_aspect('equal', adjustable='box')\n # draw_birds_eye(box1, 'b')\n # draw_birds_camera()\n # plt.show()\n\n # draw_birds_eye(box1, 'g')\n # plt.gca().set_aspect('equal', adjustable='box')\n # draw_birds_camera()\n # plt.show()\n\n # # crop box 2\n # draw_box(box2, focal_length, principal_point, 'g', draw_bbx=False)\n # plt.xlim(bbx2[0], bbx2[1])\n # plt.ylim(bbx2[2], bbx2[3])\n # plt.show()\n # # second case\n # rot_box = np.matmul(np.linalg.inv(R),basic_box.transpose()).transpose()\n # box3 = np.array(((0,0,distance))) + rot_box\n # box4 = np.matmul(R,box3.transpose()).transpose()\n # bbx3 = draw_box(box3, focal_length, principal_point)\n # bbx4 = draw_box(box4, focal_length, principal_point, 'g')\n # plt.plot((principal_point[0]), (principal_point[1]), 'rx')\n # plt.gca().set_aspect('equal', adjustable='box')\n # plt.xlim(0, 1300)\n # plt.ylim(0, 600)\n # plt.show()\n # draw_birds_eye(box3, 'b')\n # draw_birds_eye(box4, 'g')\n # draw_birds_camera()\n # plt.gca().set_aspect('equal', adjustable='box')\n # plt.xlim(np.min(box3[:, 0])-5, np.max(box4[:, 0])+5)\n # plt.ylim(-5, np.max(box3[:, 2])+5)\n # plt.show()\n # #crop box 3\n # draw_box(box3, focal_length, principal_point, 'g', draw_bbx=False)\n # plt.xlim(bbx3[0], bbx3[1])\n # plt.ylim(bbx3[2], bbx3[3])\n # plt.show()\n # draw_birds_eye(box3, 'b')\n # draw_birds_camera()\n # plt.gca().set_aspect('equal', adjustable='box')\n # plt.show()\n # # crop box 4\n # draw_box(box4, focal_length, principal_point, 'g', draw_bbx=False)\n # plt.xlim(bbx4[0], bbx4[1])\n # plt.ylim(bbx4[2], bbx4[3])\n # plt.show()\n # draw_birds_eye(box3, 'g')\n # draw_birds_camera()\n # plt.gca().set_aspect('equal', adjustable='box')\n # plt.show()\n\n # case 3\n skew_box = np.array(((1,1,1.0), (1,1,-1), (1,-1,1), (1,-1,-1), (-1,1,1),(-1,1,-1),(-1,-1,1), (-1,-1,-1)))\n skew_box[:,2] -= 0.6+skew_box[:,0]*0.4\n skew_box *= box_width/2\n\n box5 = np.array(((0,0,distance))) + skew_box\n box6 = np.matmul(R,box5.transpose()).transpose()\n bbx5 = draw_box(box5, focal_length, principal_point)\n bbx6 = draw_box(box6, focal_length, principal_point, 'g')\n plt.plot((principal_point[0]), (principal_point[1]), 'rx')\n plt.gca().set_aspect('equal', adjustable='box')\n plt.xlim(0, 1300)\n plt.ylim(0, 600)\n plt.show()\n draw_birds_eye(box5, 'b')\n draw_birds_eye(box6, 'g')\n draw_birds_camera()\n plt.gca().set_aspect('equal', adjustable='box')\n plt.xlim(np.min(box5[:, 0])-5, np.max(box6[:, 0])+5)\n plt.ylim(-5, np.max(box5[:, 2])+5)\n plt.show()\n #crop box 5\n draw_box(box5, focal_length, principal_point, 'b', draw_bbx=False)\n plt.xlim(bbx5[0], bbx5[1])\n plt.ylim(bbx5[2], bbx5[3])\n plt.show()\n draw_birds_eye(box5, 'b')\n draw_birds_camera()\n plt.gca().set_aspect('equal', adjustable='box')\n plt.show()\n # crop box 6\n draw_box(box6, focal_length, principal_point, 'g', draw_bbx=False)\n plt.xlim(bbx6[0], bbx6[1])\n plt.ylim(bbx6[2], bbx6[3])\n plt.show()\n draw_birds_eye(box5, 'g')\n draw_birds_camera()\n plt.gca().set_aspect('equal', adjustable='box')\n plt.show()\n \n\ndef draw_box(box, f, pp,c='b', draw_bbx=True):\n edges = ((0,1), (0,2), (0,4), (1,3), (1, 5), (2, 3), (2,6), (3, 7), (4,5), (4,6), (5, 7), (6,7))\n box_hom = box[:, :2]/box[:,2].reshape(-1,1)*f+pp\n for i, j in edges:\n plt.plot((box_hom[i,0], box_hom[j,0]), (box_hom[i,1], box_hom[j,1]), c)\n xmin = np.min(box_hom[:,0])-5\n xmax = np.max(box_hom[:,0])+5\n ymin = np.min(box_hom[:,1])-5\n ymax = np.max(box_hom[:,1])+5\n plt.plot((xmin, xmax, xmax, xmin, xmin), (ymin, ymin, ymax, ymax, ymin),'r')\n return (xmin, xmax, ymin, ymax)\n\ndef draw_birds_eye(box, c='r'):\n edges = ((0,1), (0,2), (0,4), (1,3), (1, 5), (2, 3), (2,6), (3, 7), (4,5), (4,6), (5, 7), (6,7))\n for i, j in edges:\n plt.plot((box[i,0], box[j,0]), (box[i,2], box[j,2]), c)\n\ndef draw_birds_camera():\n length = 10\n angle = 45\n ca = np.cos(angle*np.pi/180)\n sa = np.sin(angle*np.pi/180)\n plt.plot((-sa*length, 0, sa*length), (ca*length, 0, ca*length),'r')\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"Davmo049/public_probabilistic_3d_regression","sub_path":"scripts/visualize_normalized_camera.py","file_name":"visualize_normalized_camera.py","file_ext":"py","file_size_in_byte":5495,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"38978588182","text":"import datetime\nfrom sqlalchemy import select, func, and_\nfrom database.database_support_core import AREA, GALAXY\nfrom sqlalchemy.engine import create_engine\nfrom config import BOINC_DB_LOGIN, PROCESSED, COMPUTING\nfrom database.boinc_database_support_core import RESULT\nfrom utils.logging_helper import config_logger\n\nfrom utils.shutdown_detection import shutdown\n\nLOG = config_logger(__name__)\n\n\nclass CacheGalaxy:\n def __init__(self, galaxy_name, galaxy_id, area_min, area_max, ignore):\n self.galaxy_name = galaxy_name\n self.galaxy_id = galaxy_id\n self.area_min = area_min\n self.area_max = area_max\n self.ignore = ignore\n\n def __str__(self):\n return '{0}, {1}, {2}, {3}, {4}'.format(self.galaxy_name, self.galaxy_id, self.area_min, self.area_max, self.ignore)\n\n\ndef build_key(galaxy_name, galaxy_id):\n return '{0}_{1}'.format(galaxy_name, galaxy_id)\n\n\ndef get_cached_galaxy(cache_data, galaxy_name, area_number):\n \"\"\"\n Get any cached data for this name\n :param cache_data:\n :param galaxy_name:\n :param area_number:\n :return:\n \"\"\"\n cache_galaxies = cache_data.get(galaxy_name)\n if cache_galaxies is not None:\n for cached_galaxy in cache_galaxies:\n if cached_galaxy.area_min <= area_number <= cached_galaxy.area_max:\n return cached_galaxy\n\n return None\n\n\ndef sort_data(connection, current_jobs, modulus, remainder):\n \"\"\"\n Sort the list of jobs\n\n :param current_jobs:\n :return:\n \"\"\"\n cache_data = {}\n return_data = {}\n for job_name in current_jobs:\n #LOG.info('Checking {0}'.format(job_name))\n index = job_name.index('_area')\n galaxy_name = job_name[0:index]\n\n index1 = job_name.index('_', index + 5)\n area_number = job_name[index + 5: index1]\n\n cached_galaxy = get_cached_galaxy(cache_data, galaxy_name, int(area_number))\n #LOG.info('Cache check = {0}'.format(cached_galaxy))\n\n if cached_galaxy is None:\n # Get the area\n LOG.info('Area Number = {0}'.format(area_number))\n area = connection.execute(select([AREA]).where(AREA.c.area_id == area_number)).first()\n if area is None:\n LOG.info('Area with id={0} does not exist (Job: {1})'.format(area_number, job_name))\n continue\n ignore = True\n galaxy_id = int(area[AREA.c.galaxy_id])\n if modulus is None or galaxy_id % modulus == remainder:\n ignore = False\n key = build_key(galaxy_name, galaxy_id)\n areas = return_data.get(key)\n if areas is None:\n areas = []\n return_data[key] = areas\n\n areas.append(area_number)\n\n # Add this galaxy to the cache\n min_max = connection.execute(select([func.min(AREA.c.area_id), func.max(AREA.c.area_id)]).where(AREA.c.galaxy_id == galaxy_id)).first()\n # LOG.info('Adding to cache = {0} {1} {2}'.format(galaxy_name, min_max, ignore))\n list_galaxies = cache_data.get(galaxy_name)\n if list_galaxies is None:\n list_galaxies = []\n cache_data[galaxy_name] = list_galaxies\n\n list_galaxies.append(CacheGalaxy(galaxy_name, galaxy_id, min_max[0], min_max[1], ignore))\n\n else:\n if not cached_galaxy.ignore:\n key = build_key(galaxy_name, cached_galaxy.galaxy_id)\n areas = return_data.get(key)\n areas.append(area_number)\n\n return return_data\n\n\ndef finish_processing(galaxy_name, galaxy_id, sorted_data):\n \"\"\"\n Have we finished processing yet\n :param galaxy_id:\n :param galaxy_name:\n :param sorted_data:\n :return:\n \"\"\"\n return sorted_data.get(build_key(galaxy_name, galaxy_id)) is None\n\n\ndef processed_data(connection, modulus, remainder):\n \"\"\"\n Work out which galaxies have been processed\n\n :param connection:\n :return:\n \"\"\"\n # Get the work units still being processed\n engine = create_engine(BOINC_DB_LOGIN)\n connection_boinc = engine.connect()\n current_jobs = []\n LOG.info('Getting results from BOINC')\n # The use of appid ensures MySQL uses an index otherwise it does a full table scan\n for result in connection_boinc.execute(select([RESULT]).where(and_(RESULT.c.server_state != 5, RESULT.c.appid == 1))):\n current_jobs.append(result[RESULT.c.name])\n\n connection_boinc.close()\n LOG.info('Got results')\n\n sorted_data = sort_data(connection, current_jobs, modulus, remainder)\n for key in sorted(sorted_data.iterkeys()):\n LOG.info('{0}: {1} results'.format(key, len(sorted_data[key])))\n\n # Get the galaxies we know are still processing\n processed = []\n for galaxy in connection.execute(select([GALAXY]).where(GALAXY.c.status_id == COMPUTING)):\n if modulus is None or int(galaxy[GALAXY.c.galaxy_id]) % modulus == remainder:\n if finish_processing(galaxy[GALAXY.c.name], galaxy[GALAXY.c.galaxy_id], sorted_data):\n processed.append(galaxy[GALAXY.c.galaxy_id])\n LOG.info('%d %s has completed', galaxy[GALAXY.c.galaxy_id], galaxy[GALAXY.c.name])\n\n for galaxy_id in processed:\n connection.execute(GALAXY.update().where(GALAXY.c.galaxy_id == galaxy_id).values(status_id=PROCESSED, status_time=datetime.datetime.now()))\n\n if shutdown() is True:\n raise SystemExit\n\n LOG.info('Marked %d galaxies ready for archiving', len(processed))\n LOG.info('%d galaxies are still being processed', len(sorted_data))\n","repo_name":"ICRAR/boinc-magphys","sub_path":"server/src/archive/processed_galaxy_mod.py","file_name":"processed_galaxy_mod.py","file_ext":"py","file_size_in_byte":5609,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"52"} +{"seq_id":"73993507686","text":"import os\nimport requests\nimport json\nimport ast\nfrom dotenv import load_dotenv\nfrom datetime import datetime\nfrom flask import Flask, redirect, render_template, request, session, url_for\nfrom flask_pymongo import PyMongo\nfrom bson.objectid import ObjectId\n#loading enviorement virables \nload_dotenv()\nauth_string= os.getenv(\"auth_string\")\nauth_data = os.getenv(\"auth_data\")\nauth_data= ast.literal_eval(auth_data)\nauth_string=ast.literal_eval(auth_string)\nDBNAME = os.getenv(\"DBNAME\")\nDATABASEURI = os.getenv(\"URI\")\n\n#calling api for spotify token\ntoken_response = requests.post('https://accounts.spotify.com/api/token',\n data = auth_data,\n headers = auth_string,) \ntoken_response_data = token_response.json()\n#assiging token to a virable\nthe_token = token_response_data['access_token']\n\napp = Flask(__name__)\n\n#Connecting to database\napp.config[\"MONGO_DBNANE\"] = DBNAME\napp.config[\"MONGO_URI\"] = DATABASEURI\n\nmongo = PyMongo(app)\n\n\n@app.route('/')\n@app.route('/home')\n#Display function is responsible for getting images from Spotify API and displaying records from the database to a flask template\ndef display():\n #getting images from spotify api\n def get_playlist_image(id):\n image_response = requests.get('https://api.spotify.com/v1/playlists/' + id + '/images',\n headers={'Authorization':'Bearer '+ the_token})\n #checking if connection is succesful\n if(image_response.status_code == 200):\n image_response_data = image_response.json()\n image_url = image_response_data[0]['url']\n return image_url\n else:\n #displaying a placeholder image if connection failed'''\n image_url='./static/img/missing.jpg'\n return image_url\n #database requests\n playlists=mongo.db.playlists.find() \n is_empty = playlists.count()\n all_categories = mongo.db.categories.find() \n #sending virables to be used in flask template file\n return render_template(\"home.html\", is_empty=is_empty, playlists=playlists, get_playlist_image=get_playlist_image, categories=all_categories)\n\n@app.route('/category/')\n#displaying all items from database under category table\ndef display_category(category_id):\n #finding category by id\n the_categories = mongo.db.categories.find_one({'_id': ObjectId(category_id)})\n # we want to display an error message if category is empty\n #for this reason we are counting records found by find()\n #then we are passing is_empty to flask which will display an error is value is =0\n filter_query = { \"category\": the_categories['category_name']}\n filtered_playlists = mongo.db.playlists.find(filter_query)\n is_empty = filtered_playlists.count()\n #as above getting the images from spotify API\n def get_playlist_image(id):\n image_response = requests.get('https://api.spotify.com/v1/playlists/' + id + '/images',\n headers={'Authorization':'Bearer '+ the_token})\n if(image_response.status_code == 200):\n image_response_data = image_response.json()\n image_url = image_response_data[0]['url']\n return image_url\n else:\n image_url='./static/img/missing.jpg'\n return image_url\n all_categories = mongo.db.categories.find()\n\n return render_template(\"category.html\", is_empty=is_empty, playlists=filtered_playlists, get_playlist_image=get_playlist_image, category=the_categories, categories=all_categories)\n\n@app.route('/add')\ndef add():\n return render_template('add.html',\n categories=mongo.db.categories.find())\n\n@app.route('/insert', methods=['POST','GET'])\n#inseting newly added record trough flask template to database\ndef insert_playlist():\n playlists=mongo.db.playlists\n playlists.insert_one(request.form.to_dict())\n return redirect(url_for('display'))\n\n\n@app.route('/edit/')\ndef edit_playlist(playlist_id):\n the_playlist = mongo.db.playlists.find_one({'_id': ObjectId(playlist_id)})\n all_categories = mongo.db.categories.find()\n return render_template('edit.html', playlist=the_playlist,\n categories=all_categories)\n\n@app.route('/update_playlist/',methods=['POST'])\n#updating newly edited record trough flask template to database\ndef update_playlist(playlist_id):\n playlist = mongo.db.playlists \n playlist.update(\n {'_id': ObjectId(playlist_id)},\n {\n 'name':request.form.get('name'),\n 'category':request.form.get('category_name'),\n 'description': request.form.get('description'),\n 'spotify_id':request.form.get('spotify_id'),\n 'spotify_link':request.form.get('spotify_link'),\n }\n )\n return redirect(url_for('display'))\n\n@app.route('/delete_playlist/')\n#deleting chosen record \ndef delete_playlist(playlist_id):\n mongo.db.playlists.remove({'_id': ObjectId(playlist_id)})\n return redirect(url_for('display'))\n\n@app.route('/playlist/')\ndef display_tracks(playlist_id):\n the_playlist = mongo.db.playlists.find_one({'_id': ObjectId(playlist_id)})\n def status_check(id):\n status_response = requests.get('https://api.spotify.com/v1/playlists/'+ id +'/', headers={'Authorization':'Bearer '+ the_token})\n if(status_response.status_code == 200):\n return True\n else:\n return False\n\n def get_playlist_tracklist(id):\n tracklist_response = requests.get('https://api.spotify.com/v1/playlists/'+ id +'/tracks', headers={'Authorization':'Bearer '+ the_token})\n track_list = []\n count = 0\n #checking if connection is established \n #if not flask will display an error message to user \n if(tracklist_response.status_code==200):\n tracklist_response_data = tracklist_response.json()\n maxium_of_values = len(tracklist_response_data['items'])\n #writing data to virable\n while count < maxium_of_values:\n track_list.append(tracklist_response_data['items'][count]['track'])\n count+=1\n else:\n return track_list\n else:\n track_list = False\n return render_template('playlist_details.html', playlist=the_playlist, get_playlist_tracklist=get_playlist_tracklist, status_check=status_check)\n\n\nif __name__ == '__main__':\n app.run(host=os.environ.get('IP'),\n port=(os.environ.get('PORT')),\n debug=True)","repo_name":"delegacz/md-milestone-project-3","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":6549,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"42091832688","text":"from pickle import load\nfrom numpy import array\nfrom numpy import argmax\nfrom numpy import asarray\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\nfrom keras.models import load_model\nfrom nltk.translate.bleu_score import corpus_bleu\n\n\n# load a clean dataset\ndef load_clean_sentences(filename):\n return load(open('pickle/' + filename, 'rb'))\n\n\n# fit a tokenizer\ndef create_tokenizer(lines):\n tokenizer = Tokenizer()\n tokenizer.fit_on_texts(lines)\n return tokenizer\n\n\n# max sentence length\ndef max_length(lines):\n return max(len(line.split()) for line in lines)\n\n\n# encode and pad sequences\ndef encode_sequences(tokenizer, length, lines):\n # integer encode sequences\n X = tokenizer.texts_to_sequences(lines)\n # pad sequences with 0 values\n X = pad_sequences(X, maxlen=length, padding='post')\n return X\n\n\n# map an integer to a word\ndef word_for_id(integer, tokenizer):\n for word, index in tokenizer.word_index.items():\n if index == integer:\n return word\n return None\n\n\n# generate target given source sequence\ndef predict_sequence(model, tokenizer, source):\n prediction = model.predict(source, verbose=0)[0]\n integers = [argmax(vector) for vector in prediction]\n target = list()\n for i in integers:\n word = word_for_id(i, tokenizer)\n if word is None:\n break\n target.append(word)\n return ' '.join(target)\n\n\n# evaluate the skill of the model\ndef evaluate_model(model, tokenizer, sources, raw_dataset):\n actual, predicted = list(), list()\n with open('fr_en_translations2.txt', 'w') as all_translations:\n for i, source in enumerate(sources):\n # translate encoded source text\n source = source.reshape((1, source.shape[0])) # convert vector to array for predict method\n translation = predict_sequence(model, tokenizer, source)\n raw_target, raw_src = raw_dataset[i]\n if i < 10:\n print('src=[%s], target=[%s], predicted=[%s]' %\n (raw_src, raw_target, translation))\n all_translations.write('src=[%s], target=[%s], predicted=[%s]\\n' %\n (raw_src, raw_target, translation))\n actual.append([raw_target.split()])\n predicted.append(translation.split())\n # calculate BLEU score\n print('BLEU-1: %f' % corpus_bleu(actual, predicted, weights=(1.0, 0, 0, 0)))\n print('BLEU-2: %f' % corpus_bleu(actual, predicted, weights=(0.5, 0.5, 0, 0)))\n print('BLEU-3: %f' % corpus_bleu(actual, predicted, weights=(0.3, 0.3, 0.3, 0)))\n print('BLEU-4: %f' % corpus_bleu(actual, predicted, weights=(0.25, 0.25, 0.25, 0.25)))\n\n\n# evaluate the skill of the model\ndef user_evaluate_model(model, tokenizer, source, raw_src):\n # translate encoded source text\n # source = source.reshape((1, source.shape[0])) # convert vector to array for predict method\n translation = predict_sequence(model, tokenizer, source)\n print('src=[%s], predicted=[%s]' % (raw_src, translation))\n\n\ntarget_language = 'french'\n# load datasets\ndataset = asarray(load_clean_sentences('english-%s-both.pkl' % target_language))\ntrain = asarray(load_clean_sentences('english-%s-train.pkl' % target_language))\ntest = asarray(load_clean_sentences('english-%s-test.pkl' % target_language))\n\n# prepare english tokenizer\n# eng_tokenizer = create_tokenizer(dataset[:, 0])\neng_tokenizer = load(open('pickle/english_tokenizer.pkl', 'rb'))\neng_vocab_size = len(eng_tokenizer.word_index) + 1\neng_length = max_length(dataset[:, 0])\n# prepare target tokenizer\n# ger_tokenizer = create_tokenizer(dataset[:, 1])\nger_tokenizer = load(open('pickle/%s_tokenizer.pkl' % target_language, 'rb'))\nger_vocab_size = len(ger_tokenizer.word_index) + 1\nger_length = max_length(dataset[:, 1])\n# prepare data, column 0 is english, 1 is german\ntrainX = encode_sequences(ger_tokenizer, ger_length, train[:, 1])\ntestX = encode_sequences(ger_tokenizer, ger_length, test[:, 1])\n\n# load model\nmodel = load_model('models/english_%s_model.h5' % target_language)\n# test on some training sequences\n# print('train')\n# evaluate_model(model, eng_tokenizer, trainX, train)\n# test on some test sequences\nprint('test')\nevaluate_model(model, eng_tokenizer, testX, test)\n# phrase = 'de rien'\n# user_evaluate_model(model, eng_tokenizer, encode_sequences(ger_tokenizer, ger_length, [phrase]), phrase)\n","repo_name":"Harshwin/Comparison_statistical_and_neural_network_for_machine_translation","sub_path":"tester.py","file_name":"tester.py","file_ext":"py","file_size_in_byte":4439,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"13115225412","text":"from sys import stdin\nfrom collections import deque\n\nn, k = map(int, stdin.readline().split())\nvirus = []\nd = [[] for _ in range(k + 1)]\ncnt = 0\n\nfor i in range(n): # 지도 입력 받음\n input = list(map(int, stdin.readline().split()))\n virus.append(input)\n for j in range(n):\n if input[j]:\n d[input[j]].append((i, j))\n\ns, x, y = map(int, stdin.readline().split())\n\ndx = [-1, 1, 0, 0]\ndy = [0, 0, -1, 1]\n\n\ndef bfs(k, tuple):\n queue = deque()\n while tuple:\n queue.append(tuple.pop())\n while queue:\n x, y = queue.popleft()\n for i in range(4):\n nx = x + dx[i]\n ny = y + dy[i]\n if nx < 0 or ny < 0 or nx >= n or ny >= n: # 범위 넘는 경우 패스\n continue\n if virus[nx][ny] == 0:\n virus[nx][ny] = k\n tuple.append((nx, ny))\n\n\nfor _ in range(s):\n for i in range(1, k + 1):\n bfs(i, d[i])\n\nprint(virus[x-1][y-1])\n","repo_name":"olive-su/1day_1Algorithm","sub_path":"22.02_PS/0220_경쟁적_전염.py","file_name":"0220_경쟁적_전염.py","file_ext":"py","file_size_in_byte":970,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"19032878395","text":"# -*- coding: utf-8 -*-\nfrom odoo import api, models, fields, _\nfrom odoo.exceptions import UserError\nimport logging\n_logger = logging.getLogger(__name__)\n\n\nclass Tracking(models.Model):\n _inherit = ['mail.thread', 'mail.activity.mixin']\n _name = 'lgps.tracking'\n\n name = fields.Char(\n required=True,\n string=_(\"Tracking Id\"),\n default=\"Autogenerated on Save\"\n )\n\n client_id = fields.Many2one(\n comodel_name=\"res.partner\",\n string=_(\"Client Account\"),\n domain=[\n ('customer', '=', True),\n ('active', '=', True),\n ('is_company', '=', True)\n ],\n required=True,\n )\n\n gpsdevice_id = fields.Many2one(\n comodel_name=\"lgps.gpsdevice\",\n string=_(\"Gps Device\"),\n index=True,\n required=True,\n )\n\n applicant = fields.Char(\n string=_(\"Applicant\"),\n )\n\n state = fields.Selection(\n [\n ('registered', _('Registrado')),\n ('active', _('Activo')),\n ('paused', _('Detenido')),\n ('finished', _('Finalizado')),\n ('billed', _('Facturado')),\n ('cancelled', _('Cancelled')),\n ],\n string=_(\"State\"),\n default=\"registered\",\n track_visibility='onchange',\n required=True,\n )\n\n category = fields.Selection(\n [\n ('event', _('Per Event')),\n ('permanent', _('Permanent')),\n ('uninterrupted ', _('Uninterrupted')),\n ],\n string=_(\"Category\"),\n default=\"event\",\n required=True,\n index=True,\n )\n\n driver = fields.Char(\n string=_(\"Driver\"),\n )\n\n phone = fields.Text(\n string=_(\"Phones\"),\n )\n\n notifications = fields.Char(\n string=_(\"Email Notifications\"),\n )\n\n notify = fields.Boolean(\n string=_(\"Notify Client\"),\n default=False\n )\n\n initial_date = fields.Datetime(\n default=fields.Datetime.now,\n string=_(\"Initial Date\"),\n )\n\n final_date = fields.Datetime(\n string=_(\"Final Date\"),\n )\n\n origin = fields.Text(\n string=_(\"Origin\"),\n required=True,\n )\n\n destination = fields.Text(\n string=_(\"Destination\"),\n required=True,\n )\n\n route = fields.Text(\n string=_(\"Route\"),\n )\n\n observations = fields.Text(\n string=_(\"Observations\"),\n )\n\n emails = fields.Text(\n string=_(\"Additional Emails\"),\n )\n\n start_date = fields.Datetime(\n string=_(\"Activity Started at\"),\n readonly=True\n )\n\n end_date = fields.Datetime(\n string=_(\"Activity finished at\"),\n readonly=True\n )\n\n tracking_log_ids = fields.One2many(\n comodel_name=\"lgps.tracking_logs\",\n inverse_name=\"tracking_id\",\n string=_(\"Logs\"),\n )\n\n freight_weight = fields.Float(\n digits=(10, 1),\n string=_(\"Load Weight\"),\n help=_(\"Freight weight in Kilos\")\n )\n\n freight_dimensions = fields.Float(\n digits=(10, 1),\n string=_(\"Freight dimensions\"),\n help=_(\"Freight dimensions in meters\")\n )\n\n freight_cost = fields.Float(\n digits=(10, 2),\n string=_(\"Freight cost\"),\n help=_(\"Freight costs\")\n )\n\n comment_period = fields.Integer(\n string=_('Comment Period'),\n default=0,\n )\n\n active = fields.Boolean(default=True)\n\n @api.model\n def create(self, vals):\n seq = self.env['ir.sequence'].next_by_code('lgps.tracking') or '/'\n vals['name'] = seq\n return super(Tracking, self).create(vals)\n\n def button_do_active(self):\n for tracking in self:\n # tracking.state = 'active'\n _logger.warning('self %', self)\n user = self.env['res.users'].search([('name', '=', self.env.user.name)])\n _logger.warning('user: %', user)\n # _logger.warning('user name: %', user.name)\n # _logger.warning('user id: %', user.id)\n # log_object = self.env['lgps.tracking_logs']\n # user.id\n # employee_id = self.env[''].search()\n raise UserError('Stop execution')\n\n # dictionary = {\n # 'name': 'Automatic Generated',\n # 'comment': 'Monitoreo Iniciado',\n # 'comment_date': fields.Datetime.now,\n # 'employee_id': device.client_id.id,\n # 'email_sent': device.id,\n # 'tracking_id': self.destination_gpsdevice_ids.id,\n # 'vehicle_location': self.operation_mode,\n # }\n # device_log = log_object.create(dictionary)\n\n return True\n\n def button_do_pause(self):\n for tracking in self:\n tracking.state = 'paused'\n return True\n\n def button_do_finish(self):\n for tracking in self:\n tracking.state = 'finished'\n return True\n\n def button_do_cancel(self):\n for tracking in self:\n tracking.state = 'cancelled'\n tracking.active = False\n return True\n","repo_name":"intralix/lgps","sub_path":"lgps/models/tracking.py","file_name":"tracking.py","file_ext":"py","file_size_in_byte":5066,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"38247010397","text":"from random import randint\nimport turtle\n\nwidth = 1000\nheight = 750\n\n# Create a canvas instance.\nmyturtle = turtle.Turtle()\n# Creating a screen instance to control turtle canvas size.\nscreen = turtle.Screen()\n\nscreen.setup(width, height)\n\n# Go to a certain coordinate\nmyturtle.goto(50, 75)\n\nmyturtle.forward(100)\nmyturtle.left(90)\nmyturtle.forward(200)\nmyturtle.left(90)\nmyturtle.forward(100)\nmyturtle.left(90)\nmyturtle.forward(200)\n\n\nclass Point:\n # What is self\n # Self in a class is a variable that holds the object instance.\n # Self here would be described as Point.\n # __init__ is a special method for classes.\n def __init__(self, x, y):\n # print(\"Hey I am __init__\") # Whenever we create an object instance __init__ will execute.\n self.x = x # Another way to say this is point.x = x.\n self.y = y # Or you could say point.y = y.\n\n def distance(self):\n return self.x + self.y\n\n # Ordinary methods will only be called if you call them explicitly.\n def falls_in_rectangle(self, numx, numy):\n print(\"I am ordinary\")\n if numx > self.x or numy > self.y:\n return True\n else:\n return False\n\n def game(self, rectangle):\n if (\n rectangle.lowleft.x < self.x < rectangle.upright.x\n and rectangle.lowleft.y < self.y < rectangle.upright.y\n ):\n return True\n else:\n return False\n\n\nclass Rectangle:\n def __init__(self, lowleft, upright):\n self.lowleft = lowleft\n self.upright = upright\n\n def point(self):\n print(self.lowleft.x, self.lowleft.y, self.upright.x, self.upright.y)\n print(self.lowleft.x + self.lowleft.y + self.upright.x, self.upright.y)\n\n\nclass GuiRectangle(Rectangle):\n def draw(self, canvas):\n\n canvas.forward(100)\n canvas.left(90)\n canvas.forward(200)\n canvas.left(90)\n canvas.forward(100)\n canvas.left(90)\n canvas.forward(200)\n\n\nguirectangle = GuiRectangle(\n Point(randint(0, 400), randint(0, 400)), Point(randint(10, 400), randint(10, 400))\n)\n\n\nguirectangle.draw(canvas=myturtle)\n\npoint1 = Point(10, 20)\npoint2 = Point(1, 2)\n# So when we create a Object Instance self is our Object.\nprint(point1.x, point1.y)\n\nprint(point1.falls_in_rectangle(9, 12))\n\nprint(point1.distance())\n\nrec = Rectangle(Point(5, 6), Point(8, 9))\nrec2 = Rectangle(Point(100, 100), Point(200, 200))\nrec.point()\nrec2.point()\n\nrectangle = Rectangle(\n Point(randint(0, 400), randint(0, 400)), Point(randint(10, 400), randint(10, 400))\n)\nprint(\n rectangle.lowleft.x, rectangle.lowleft.y, rectangle.upright.x, rectangle.upright.y\n)\n\n# user_point = Point(float(input(\"Guess X: \")), float(input(\"Guess Y: \")))\n\n# print(\"Your point was inside the rectangle: \", user_point.game(rectangle))\n","repo_name":"0xlvl3/oop","sub_path":"project1/geometry_game.py","file_name":"geometry_game.py","file_ext":"py","file_size_in_byte":2818,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"34078952330","text":"from mongoengine import connect\n\n__author__ = 'Enis Simsar'\n\nfrom time import sleep\nfrom decouple import config\n\nfrom listen_module.twitter_stream_thread import StreamCreator\nfrom models.Topic import Topic\nfrom models.Tweet import Tweet\nfrom bson.objectid import ObjectId\n\n\nclass TwitterListen:\n def __init__(self):\n self.topic_dic = {}\n self.thread = None\n\n def setup(self, topic_list):\n if len(topic_list) != 0:\n for topic in topic_list:\n if str(topic['id']) not in self.topic_dic:\n self.topic_dic[str(topic['id'])] = topic\n self.thread = StreamCreator(self.topic_dic)\n self.thread.start()\n\n def restart(self, topic_list):\n self.topic_dic = {}\n if self.thread is not None:\n self.kill()\n if len(topic_list) != 0:\n for topic in topic_list:\n if str(topic['id']) not in self.topic_dic:\n self.topic_dic[str(topic['id'])] = topic\n self.thread = StreamCreator(self.topic_dic)\n self.thread.start()\n\n def kill(self):\n self.thread.terminate()\n del self.thread\n self.thread = None\n\n\ndef get_last_sequence_id():\n last_tweet = Tweet.objects.order_by('-_id').first()\n return last_tweet.id if last_tweet is not None else ObjectId()\n\n\ndef main():\n connect(\n config('MONGODB_DB'),\n username=config('MONGODB_USER'),\n password=config('MONGODB_PASSWORD'),\n host=config('MONGODB_HOST'),\n port=config('MONGODB_PORT', cast=int),\n authentication_source='admin',\n connect=False\n )\n\n running_topic_list = [topic.to_dict() for topic in Topic.objects.filter(is_active=True)]\n twitter_module = TwitterListen()\n twitter_module.setup(running_topic_list)\n\n last_sequence_id = get_last_sequence_id()\n\n count = 0\n while True:\n print(\"Loop is continuing. count = {0}\".format(count))\n count += 1\n sleep(300)\n new_running_topic_list = [topic.to_dict() for topic in Topic.objects.filter(is_active=True)]\n\n current_keywords = [i['keywords'] for i in running_topic_list]\n current_languages = [i['languages'] for i in running_topic_list]\n\n new_keywords = [i['keywords'] for i in new_running_topic_list]\n new_languages = [i['languages'] for i in new_running_topic_list]\n\n if current_keywords != new_keywords or current_languages != new_languages:\n running_topic_list = new_running_topic_list\n print(\"Restarting Twitter Module!\")\n twitter_module.restart(new_running_topic_list)\n\n if count % 6 == 0:\n new_last_sequence_id = get_last_sequence_id()\n print(\"last_id = {0}, new_last_id = {1}\".format(last_sequence_id, new_last_sequence_id))\n if last_sequence_id == new_last_sequence_id:\n running_topic_list = new_running_topic_list\n print(\"Unexpectedly Stopped Module, Restarting...\")\n twitter_module.restart(new_running_topic_list)\n last_sequence_id = get_last_sequence_id()\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"openmaker-eu/watchtower-news","sub_path":"listen_module/listen_controller.py","file_name":"listen_controller.py","file_ext":"py","file_size_in_byte":3161,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"36942134523","text":"from typing import Dict, Union\nfrom pydantic import BaseModel, Extra\nimport xarray as xr\nfrom tsdat import DataReader\nimport os\nimport getpass\nfrom datetime import datetime\nimport pandas as pd\nimport numpy as np\nfrom mhkit.wave.resource import energy_flux, energy_period\nfrom mhkit.wave.performance import capture_length_matrix\n\n\nclass MbariWecDataReader(DataReader):\n \"\"\"---------------------------------------------------------------------------------\n Custom Data Reader for MBARI WEC 2021 dataset.\n\n Implementation based on https://github.com/SNL-WaterPower/fbWecCntrl/blob/master/mbari_wec/mbari_wec_2021_example.py\n\n ---------------------------------------------------------------------------------\"\"\"\n\n class Parameters(BaseModel, extra=Extra.forbid):\n \"\"\"\n density [km/m^3]: water density\n depth [m]: depth\n gravity [m/s^2]: earth's gravity acceleration\n \"\"\"\n\n density: float = 1025.0\n depth: float = 100.0\n gravity: float = 9.81\n\n parameters: Parameters = Parameters()\n \"\"\"Extra parameters that can be set via the retrieval configuration file. If you opt\n to not use any configuration parameters then please remove the code above.\"\"\"\n\n def read(self, input_key: str) -> Union[xr.Dataset, Dict[str, xr.Dataset]]:\n file_name = input_key\n rho = self.parameters.density\n h = self.parameters.depth\n g = self.parameters.gravity\n\n #%% Read bulk parameters\n date_parser = lambda epoch: pd.to_datetime(epoch, unit=\"s\")\n dat = pd.read_csv(\n file_name,\n index_col=3,\n usecols=np.insert(np.arange(13), -1, [364, 365, 366]),\n parse_dates=[\"Epoch Time\"],\n date_parser=date_parser,\n )\n b = dat.to_xarray()\n\n #%% Frequency array\n dat1 = pd.read_csv(\n file_name,\n index_col=[],\n usecols=np.arange(13, 13 + 38 + 1),\n )\n freq_array = dat1.iloc[0].to_xarray()\n freq_array.name = \"Frequency\"\n\n dat2 = pd.read_csv(\n file_name,\n index_col=[],\n usecols=np.arange(13 + 38 + 1, 13 + 2 * (38 + 1)),\n )\n df_array = (\n dat2.iloc[0]\n .to_xarray()\n .assign_coords(dict(index=freq_array.values))\n .rename(dict(index=\"Frequency\"))\n )\n df_array.name = \"df\"\n df_array.attrs[\"long_name\"] = \"Frequency spacing\"\n df_array.attrs[\"units\"] = \"Hz\"\n\n #%% a and b parameters\n names = [\"a1\", \"b1\", \"a2\", \"b2\"]\n tmp_list = []\n for idx, name in enumerate(names):\n dat_tmp = pd.read_csv(\n file_name,\n index_col=[0],\n usecols=np.insert(\n np.arange(13 + (2 + idx) * (38 + 1), 13 + (3 + idx) * (38 + 1)),\n 0,\n 3,\n ),\n date_parser=date_parser,\n )\n tmp_da = dat_tmp.to_xarray().to_array(dim=\"Frequency\", name=name)\n tmp_da = tmp_da.assign_coords({\"Frequency\": freq_array.values})\n tmp_list.append(tmp_da)\n\n ab_ds = xr.merge(tmp_list)\n\n #%% Spectral density, spreading, etc.\n dat_S = pd.read_csv(\n file_name,\n index_col=[0],\n usecols=np.insert(np.arange(13 + 6 * (38 + 1), 13 + 7 * (38 + 1)), 0, 3),\n date_parser=date_parser,\n )\n S = dat_S.to_xarray().to_array(dim=\"Frequency\", name=\"Variance density\")\n S = S.assign_coords({\"Frequency\": freq_array.values})\n\n dat_dir = pd.read_csv(\n file_name,\n index_col=[0],\n usecols=np.insert(np.arange(13 + 7 * (38 + 1), 13 + 8 * (38 + 1)), 0, 3),\n date_parser=date_parser,\n )\n Dir = dat_dir.to_xarray().to_array(dim=\"Frequency\", name=\"Direction\")\n Dir = Dir.assign_coords({\"Frequency\": freq_array.values})\n\n dat_spread = pd.read_csv(\n file_name,\n index_col=[0],\n usecols=np.insert(np.arange(13 + 8 * (38 + 1), 13 + 9 * (38 + 1)), 0, 3),\n date_parser=date_parser,\n )\n spread = dat_spread.to_xarray().to_array(\n dim=\"Frequency\", name=\"Directional spread\"\n )\n spread = spread.assign_coords({\"Frequency\": freq_array.values})\n\n #%% Combine, clean up\n\n da = xr.merge([b, ab_ds, S, Dir, spread, df_array])\n da[\"Battery Voltage (V)\"].attrs[\"units\"] = \"V\"\n da[\"Battery Voltage (V)\"].attrs[\"long_name\"] = \"Battery voltage\"\n\n da[\"Power (W)\"].attrs[\"units\"] = \"W\"\n da[\"Power (W)\"].attrs[\"long_name\"] = \"Battery power\"\n\n da[\"Humidity (%rel)\"].attrs[\"units\"] = \"1\"\n da[\"Humidity (%rel)\"].attrs[\"standard_name\"] = \"relative_humidity\"\n da[\"Humidity (%rel)\"].attrs[\"long_name\"] = \"Relative humidity\"\n\n da[\"Significant Wave Height (m)\"].attrs[\"units\"] = \"m\"\n da[\"Significant Wave Height (m)\"].attrs[\n \"standard_name\"\n ] = \"sea_surface_wave_significant_height\"\n da[\"Significant Wave Height (m)\"].attrs[\"long_name\"] = \"Significant wave height\"\n\n da[\"Direction\"].attrs[\"units\"] = \"degree\"\n da[\"Direction\"].attrs[\"long_name\"] = \"\"\n\n da[\"Peak Period (s)\"].attrs[\"units\"] = \"s\"\n da[\"Peak Period (s)\"].attrs[\n \"standard_name\"\n ] = \"sea_surface_wave_period_at_variance_spectral_density_maximum\"\n da[\"Peak Period (s)\"].attrs[\"long_name\"] = \"Peak period\"\n\n da[\"Mean Period (s)\"].attrs[\"units\"] = \"s\"\n da[\"Mean Period (s)\"].attrs[\n \"standard_name\"\n ] = \"sea_surface_wave_zero_upcrossing_period\"\n da[\"Mean Period (s)\"].attrs[\"long_name\"] = \"Mean period\"\n\n da[\"Peak Direction (deg)\"].attrs[\"units\"] = \"degree\"\n da[\"Peak Direction (deg)\"].attrs[\n \"standard_name\"\n ] = \"sea_surface_wave_from_direction_at_variance_spectral_density_maximum\"\n da[\"Peak Direction (deg)\"].attrs[\"long_name\"] = \"Peak direction\"\n\n da[\"Peak Directional Spread (deg)\"].attrs[\"units\"] = \"degree\"\n da[\"Peak Directional Spread (deg)\"].attrs[\n \"standard_name\"\n ] = \"sea_surface_wave_directional_spread_at_variance_spectral_density_maximum\"\n da[\"Peak Directional Spread (deg)\"].attrs[\n \"long_name\"\n ] = \"Peak directional spread\"\n\n da[\"Mean Direction (deg)\"].attrs[\"units\"] = \"degree\"\n da[\"Mean Direction (deg)\"].attrs[\n \"standard_name\"\n ] = \"sea_surface_wave_from_direction\"\n da[\"Mean Direction (deg)\"].attrs[\"long_name\"] = \"Mean direction\"\n\n da[\"Mean Directional Spread (deg)\"].attrs[\"units\"] = \"degree\"\n da[\"Mean Directional Spread (deg)\"].attrs[\n \"long_name\"\n ] = \"Mean directional spread\"\n\n da[\"Latitude (deg)\"].attrs[\"units\"] = \"degree_north\"\n da[\"Latitude (deg)\"].attrs[\"standard_name\"] = \"latitude\"\n da[\"Latitude (deg)\"].attrs[\"long_name\"] = \"Latitude\"\n\n da[\"Longitude (deg)\"].attrs[\"units\"] = \"degree_east\"\n da[\"Longitude (deg)\"].attrs[\"standard_name\"] = \"longitude\"\n da[\"Longitude (deg)\"].attrs[\"long_name\"] = \"Longitude\"\n\n da[\"Wind Speed (m/s)\"].attrs[\"units\"] = \"m/s\"\n da[\"Wind Speed (m/s)\"].attrs[\"standard_name\"] = \"wind_speed\"\n da[\"Wind Speed (m/s)\"].attrs[\"long_name\"] = \"Wind speed\"\n\n da[\"Wind Direction (deg)\"].attrs[\"units\"] = \"degree\"\n da[\"Wind Direction (deg)\"].attrs[\"standard_name\"] = \"wind_from_direction\"\n da[\"Wind Direction (deg)\"].attrs[\"long_name\"] = \"Wind direction\"\n\n da[\"Surface Temperature (°C)\"] = 274.15 * da[\"Surface Temperature (°C)\"]\n da[\"Surface Temperature (°C)\"].attrs[\"units\"] = \"K\"\n da[\"Surface Temperature (°C)\"].attrs[\n \"standard_name\"\n ] = \"sea_surface_temperature\"\n da[\"Surface Temperature (°C)\"].attrs[\"long_name\"] = \"Surface temperature\"\n\n da[\"Frequency\"].attrs[\"units\"] = \"Hz\"\n da[\"Frequency\"].attrs[\"standard_name\"] = \"wave_frequency\"\n da[\"Frequency\"].attrs[\"long_name\"] = \"Frequency\"\n\n da[\"Variance density\"].attrs[\"units\"] = \"m^2/Hz\"\n da[\"Variance density\"].attrs[\n \"standard_name\"\n ] = \"sea_surface_wave_variance_spectral_density\"\n da[\"Variance density\"].attrs[\"long_name\"] = \"Spectral density\"\n\n da[\"Directional spread\"].attrs[\"units\"] = \"degree\"\n da[\"Directional spread\"].attrs[\n \"standard_name\"\n ] = \"sea_surface_wave_directional_spread\"\n da[\"Directional spread\"].attrs[\"long_name\"] = \"Directional spreading\"\n\n da = da.rename(\n {\n \"Epoch Time\": \"time\",\n \"Frequency\": \"freq\",\n \"Battery Voltage (V)\": \"batter_voltage\",\n \"Variance density\": \"S\",\n \"Direction\": \"wave_dir\",\n \"Power (W)\": \"battery_power\",\n \"Humidity (%rel)\": \"humidity\",\n \"Significant Wave Height (m)\": \"Hm0\",\n \"Peak Period (s)\": \"Tp\",\n \"Mean Period (s)\": \"Tm\",\n \"Peak Direction (deg)\": \"peak_dir\",\n \"Peak Directional Spread (deg)\": \"peak_spread\",\n \"Mean Direction (deg)\": \"mean_dir\",\n \"Mean Directional Spread (deg)\": \"mean_spread\",\n \"Directional spread\": \"spread\",\n \"Latitude (deg)\": \"spot_lat\",\n \"Longitude (deg)\": \"spot_lon\",\n \"Wind Speed (m/s)\": \"wind_speed\",\n \"Wind Direction (deg)\": \"wind_dir\",\n \"Surface Temperature (°C)\": \"temperature\",\n }\n )\n\n J = xr.DataArray(\n np.array(\n [\n energy_flux(\n da.isel(time=idx)[\"S\"].to_pandas(), h=h, rho=rho\n ).values[0][0]\n for idx in range(len(da.time))\n ]\n ),\n dims=\"time\",\n name=\"J\",\n ).assign_coords(dict(time=da.time.values))\n J.attrs[\"units\"] = \"W/m\"\n J.attrs[\"long_name\"] = \"Energy flux\"\n\n Te = xr.DataArray(\n np.array(\n [\n energy_period(\n da.isel(time=idx)[\"S\"].to_pandas(),\n )\n for idx in range(len(da.time))\n ]\n ).squeeze(),\n dims=\"time\",\n name=\"Te\",\n ).assign_coords(dict(time=da.time.values))\n Te.attrs[\"units\"] = \"s\"\n Te.attrs[\"long_name\"] = \"Energy period\"\n\n da = xr.merge([da, J, Te])\n\n da.time.attrs[\"long_name\"] = \"Epoch time\"\n\n da.attrs[\n \"institution\"\n ] = \"Sandia National Laboratories and Monterey Bay Aquarium Research Institute\"\n da.attrs[\"Conventions\"] = \"CF-1.8\"\n da.attrs[\"title\"] = file_name\n da.attrs[\"source\"] = \"Sofar spotter buoy\"\n da.attrs[\"history\"] = \"generated {:} by {:}\".format(\n datetime.now().strftime(\"%Y-%m-%d @ %H:%M:%S\"), getpass.getuser()\n )\n da.attrs[\n \"references\"\n ] = \"https://content.sofarocean.com/hubfs/Technical_Reference_Manual.pdf\"\n da = da.sortby(\"time\")\n da = da.drop_isel(time=0) # first sample appears anomalous\n\n ds = da\n\n return ds\n","repo_name":"paulohy/ncei-global-marine-data-ingest","sub_path":"pipelines/mbari_wec/readers.py","file_name":"readers.py","file_ext":"py","file_size_in_byte":11375,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"37147572518","text":"\"\"\"\r\nThis is the main monitoring program\r\ncron: 1 1 1 1 1\r\nnew Env('tg监控程序')\r\n\"\"\"\r\n\r\nimport datetime\r\nimport re\r\nfrom telethon import events, TelegramClient\r\nimport asy\r\nfrom ql_invoke import Invoke\r\n\r\napi_id = asy.read_config(\"api_id\")\r\napi_hash = asy.read_config(\"api_hash\")\r\nclient = TelegramClient(\"session_name\", api_id, api_hash)\r\n\r\n\r\n# 获取消息\r\n@client.on(events.NewMessage(chats=asy.read_config(\"sur_channel_id\")))\r\nasync def get_the_message(event):\r\n val = event.message.message\r\n await judgment_variables(val)\r\n # 采用监听方式采集消息\r\n\r\n\r\nasync def judgment_variables(val):\r\n part_text = re.compile(r'export (.*?)=')\r\n value_list = part_text.findall(val)\r\n rule = re.compile(r'export (.*\")')\r\n variables_list = rule.findall(val)\r\n script_file = await condition(value_list, variables_list)\r\n if script_file:\r\n task_info = await invoke.get_task_info(script_file)\r\n if await invoke.task_status(task_info['id'], value_list, variables_list):\r\n await send_message(\"开始运行任务，没有任务结束提示，后期更新加上\")\r\n else:\r\n print(\"没有发现变量可调用的脚本，可能是脚本库没有标注出来，自行手动添加即可，功能待完善\")\r\n\r\n\r\nasync def send_message(message):\r\n await client.send_message(asy.read_config(\"log_channel_id\"), message)\r\n\r\n\r\nasync def condition(cod1, cod2):\r\n if cod1 and cod2 is not None:\r\n script_file = await asy.read_varname(cod1[0])\r\n if script_file:\r\n return script_file\r\n # if await invoke.save_config(variables=cod1, value=cod2):\r\n # await send_message(f\"已将 {cod2} 写入配置文件中\")\r\n # return script_file\r\n # else:\r\n # await send_message(f\"{cod2} 配置文件更新失败\")\r\n # return None\r\n else:\r\n await send_message(f\"未匹配到 {cod1} 可能是脚本库没有标注出来，自行手动添加即可\")\r\n return None\r\n\r\n\r\nif __name__ == '__main__':\r\n # 获取当前时间\r\n now = datetime.datetime.now()\r\n print(\"------开始运行！-------\")\r\n now_time = now.strftime(\"%Y-%m-%d %H:%M:%S\")\r\n print(f\"运行时间于：{now_time}\")\r\n invoke = Invoke()\r\n with client:\r\n client.loop.run_until_complete(send_message(f\"hello!您在{now_time}上线啦\"))\r\n client.run_until_disconnected()\r\n","repo_name":"wangquanfugui233/JDVariableMonitoring","sub_path":"surveillance/surveillance_tg.py","file_name":"surveillance_tg.py","file_ext":"py","file_size_in_byte":2439,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"39065184748","text":"import string\n\nclass Lexer:\n '''\n This module is designed to be capable of reading through and interpreting\n expressions of propositional logic. There are two core functionalities of\n this module:\n 1.) To determine whether a given input is a valid expression in the\n language of propositional logic,\n and,\n 2.) To perform the shunting-yard algorithm on such a valid expression in order\n to obtain the postfix form (e.g. `P -> Q` becomes `P Q ->`)\n Two functions comprise the API: lexify() and lexify_exp(), the first\n latter will take in a valid expression (e.g. `P -> Q`) and return the postfix;\n If a postfix expression is returned, then the program successfully passed the\n lexing stage and the expression considered can be considered acceptable.\n Calling lexify() will normalize any input to remove newlines and empty strings,\n and then pass each subsequent expression to lexify_exp()\n '''\n def __init__(self):\n self.next = None\n self.feed = ''\n self.open_paren = 0\n self.clos_paren = 0\n self.build = ''\n self.postfix = []\n self.op_stack = []\n self.oparen = '('\n self.binary_op = ['~', '^', 'v', '->', '<->']\n self.prec = ['(', '~', '^', 'v', '->', '<->']\n self.terms = list(string.ascii_uppercase)\n\n def read(self):\n ''' Grab the next character '''\n if not self.feed:\n return None\n # save the first element\n character = self.feed[0]\n # overwrite the feed with the tail\n self.feed = self.feed[1:]\n return character\n\n def scan(self):\n ''' Advance through the string '''\n self.next = self.read()\n if self.next is None:\n return\n while self.next.isspace():\n self.next = self.read()\n\n def get_precedence(self, operator):\n ''' return the operator's precedence '''\n return self.prec.index(operator)\n\n def lexify(self, args):\n ''' Pass each expression through the lexify_exp function '''\n # normalize input string by newlines\n args = args.split('\\n')\n # filter out any empty strings\n args = list(filter(bool, args))\n # append operators to the output\n output = []\n for arg in args:\n output += self.lexify_exp(arg)\n return output\n\n def lexify_exp(self, exp):\n ''' Try to read the input into postfix '''\n # set the feed to the expression\n self.feed = exp\n # get the first character\n self.scan()\n # try to match an expression\n self.exp()\n # reset the feed\n self.feed = ''\n # if number of parentheses don't match, raise an error\n if self.next == ')' and (self.open_paren - (self.clos_paren + 1)) != 0:\n raise Exception(\"Error: unaccompanied closing brace\")\n # return the final postfix representation\n return self.pop_stack()\n\n def exp(self):\n ''' Match an expression '''\n # match a unary expression\n if self.next == \"~\":\n self.unary()\n #match a binary expression\n else:\n self.binary()\n while self.next == '^':\n self.scan()\n self.binary()\n\n def unary(self):\n ''' Match unary function, negation (~) '''\n # append ~ to the stack and try to match its corresponding expression\n self.scan()\n self.op_stack.append('~')\n if self.next == '(':\n self.binary()\n else:\n self.exp() \n while self.next == 'v' or self.next == '^':\n self.process_op(self.next)\n self.scan()\n self.atomic()\n\n def binary(self):\n ''' Match binary functions: ^, v, ->, <-> '''\n # match the left argument\n self.atomic()\n self.build = ''\n # try to match biconditional head\n if self.next == '<':\n self.build += self.next\n self.scan()\n self.biconditional()\n # try to match conditional tail\n elif self.next == '-':\n self.build += self.next\n self.scan()\n self.conditional()\n # match the right argument\n while self.next == 'v' or self.next == '^':\n self.process_op(self.next)\n self.scan()\n self.atomic()\n\n def conditional(self):\n ''' Match a conditional expression '''\n if self.next == '>':\n # update operator to either -> or <->\n self.build += self.next\n # add operator to op_stack or postfix\n self.process_op(self.build)\n # scan the next character\n self.scan()\n # try to match an atomic statement\n self.atomic()\n # otherwise, operator is not in our language\n else:\n raise Exception(\"Error parsing conditional; invalid char: \" + str(self.next))\n\n def biconditional(self):\n ''' Match a biconditional expression '''\n if self.next == '-':\n # update the operator to either - or <-\n self.build += self.next\n # scan the next character\n self.scan()\n # try to match a conditional\n self.conditional()\n # otherwise, operator is not in out language\n else:\n raise Exception('Error parsing biconditional; invalid char: ' + str(self.next))\n\n def atomic(self):\n ''' Match atomic sentences '''\n # if the next character is a negation, try to match a negated expression\n if self.next == '~':\n #self.exp()\n self.unary()\n # if the next character is an uppercase term [A-Z], add to output\n # and scan for the next character\n elif self.next.isalpha() and self.next.isupper():\n self.postfix += self.next\n if self.op_stack:\n if self.op_stack[-1] == '~':\n # append the term's negation\n self.op_stack.pop(-1)\n self.postfix += '~'\n self.scan()\n # if the next character is an open parenthesis, adjust the\n # stack accordingly, increment counter, and scan for next character\n elif self.next == '(':\n self.o_paren()\n self.open_paren += 1\n self.scan()\n # try to match another expression\n self.exp()\n # if the next character is an closing parenthesis, pop operators\n # from the stack, increment counter, and scan for next character\n if self.next == ')':\n self.c_paren()\n self.clos_paren += 1\n self.scan()\n else:\n raise Exception('Error: unaccompanied opening brace')\n else:\n raise Exception('Error: not a valid expression: ' + str(self.next))\n\n def o_paren(self):\n ''' handle opening parentheses '''\n # if the stack is not empty, remove the top element\n if self.op_stack:\n prev_op = self.op_stack.pop()\n # if previous operator precedence is less, while less,\n # append previous operator to output\n if self.get_precedence(prev_op) < self.get_precedence(self.oparen):\n while self.get_precedence(prev_op) < self.get_precedence(self.oparen):\n self.postfix.append(prev_op)\n # if the stack is not empty, pop it\n if self.op_stack:\n prev_op = self.op_stack.pop()\n # otherwise, add the parenthesis to the stack\n else:\n self.op_stack.append(self.oparen)\n # otherwise, add both the previous operator and the parenthesis to the stack\n else:\n self.op_stack.append(prev_op)\n self.op_stack.append(self.oparen)\n # otherwise add paranthesis to the stack\n else:\n self.op_stack.append(self.oparen)\n\n def c_paren(self):\n ''' handle closing parentheses '''\n # if stack is not empty, remove the top operator\n if self.op_stack:\n prev_op = self.op_stack.pop()\n # while we have not found an opening paren, append operator to output\n while prev_op != '(':\n self.postfix.append(prev_op)\n # get the next stack operator\n if self.op_stack:\n prev_op = self.op_stack.pop()\n # if the stack is empty, raise an error\n else:\n raise Exception('No opening brace detected\\n')\n\n def process_op(self, operator):\n ''' process operators into the postfix expression '''\n # if the stack is not empty, remove the top element\n if self.op_stack:\n prev_op = self.op_stack.pop()\n # if stack operator has lower precedence, add both to the stack\n if self.get_precedence(prev_op) <= self.get_precedence(operator):\n self.op_stack.append(prev_op)\n self.op_stack.append(operator)\n # otherwise, append the stack operator to output and push op to stack\n else:\n self.postfix.append(prev_op)\n self.op_stack.append(operator)\n # otherwise, add operator to stack\n else:\n self.op_stack.append(operator)\n\n def pop_stack(self):\n ''' pop the remainder of the stack to the output queue '''\n output = []\n # while stack isn't empty, remove it and add to the output\n while self.op_stack:\n operator = self.op_stack.pop()\n self.postfix.append(operator)\n # append the operator lists\n output += self.postfix\n # erase postfix state\n self.postfix = []\n return output\n","repo_name":"gavinbarrett/SL_Engine","sub_path":"src/lexer.py","file_name":"lexer.py","file_ext":"py","file_size_in_byte":9869,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"44775981936","text":"import os\nimport cv2\nfrom tqdm import tqdm\nfrom pathlib import Path\nfrom IPython import display as ipythondisplay\nimport base64\nimport numpy as np\ndef evaluate_real(model, env, n_eval_episodes=3, episode_length=100, video_rate=None, video_prefix=\"\"):\n\n episodic_rewards = []\n for i in range(n_eval_episodes):\n print(f\"Start evaluation episode {i} of {n_eval_episodes}\")\n\n episodic_reward = 0\n obs = env.reset()\n for _step in tqdm(range(episode_length)):\n print(\"episode: \", i,)\n action, _ = model.predict(obs, deterministic=True)\n obs, reward, done, info = env.step(action)\n episodic_reward += reward\n\n episodic_rewards.append(episodic_reward)\n mean_episodic_reward = np.mean(episodic_rewards)\n print(f\"Finished evaluation with mean episodic reward: {mean_episodic_reward}\")\ndef evaluate(model, env, n_eval_episodes=3, episode_length=100, video_rate=None, video_prefix=\"\"):\n video_folder = \"videos/\"\n # Create output folder if needed\n os.makedirs(video_folder, exist_ok=True)\n\n episodic_rewards = []\n for i in range(n_eval_episodes):\n print(f\"Start evaluation episode {i} of {n_eval_episodes}\")\n img_array = []\n episodic_reward = 0\n obs = env.reset()\n for _step in tqdm(range(episode_length)):\n action, _ = model.predict(obs, deterministic=True)\n obs, reward, done, info = env.step(action)\n episodic_reward += reward\n if video_rate is not None:\n img = env.render(\"rgb_array\")\n if 0 not in img.shape:\n img_array.append(img)\n\n if video_rate is not None:\n video_file = f\"{video_prefix}_{i}\"\n path = Path(video_folder) / video_file\n print(\"Start video writer\")\n height, width, _ = img_array[-1].shape\n size = (width, height)\n fourcc = cv2.VideoWriter_fourcc(*\"mp4v\")\n out = cv2.VideoWriter(f\"{video_folder}/temp.mp4\", fourcc, video_rate, size)\n\n for img in img_array:\n img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n out.write(img)\n out.release()\n\n os.system(\n f\"ffmpeg -y -i {video_folder}/temp.mp4 -vcodec libx264 -f mp4 {path}.mp4 >> /tmp/ffmpeg_{video_prefix}_{i}.txt 2>&1\"\n )\n\n print(f\"Showing episode {i} with episodic reward: {episodic_reward}\")\n show_video(video_file=video_file, video_folder=video_folder)\n\n os.remove(f\"{video_folder}/temp.mp4\")\n\n episodic_rewards.append(episodic_reward)\n mean_episodic_reward = np.mean(episodic_rewards)\n print(f\"Finished evaluation with mean episodic reward: {mean_episodic_reward}\")\n\ndef show_video(video_file, video_folder=\"videos/\"):\n \"\"\"\n Adapted from https://colab.research.google.com/github/Stable-Baselines-Team/rl-colab-notebooks/blob/sb3/stable_baselines_getting_started.ipynb\n\n which was taken from https://github.com/eleurent/highway-env\n\n :param video_path: (str) Path to the folder containing videos\n :param prefix: (str) Filter the video, showing only the only starting with this prefix\n \"\"\"\n html = []\n mp4 = Path(video_folder) / f\"{video_file}.mp4\"\n video_b64 = base64.b64encode(mp4.read_bytes())\n html.append(\n \"\"\"\"\"\".format(\n mp4, video_b64.decode(\"ascii\")\n )\n )\n ipythondisplay.display(ipythondisplay.HTML(data=\"
\".join(html)))","repo_name":"runyuma/eagerx_sideproject","sub_path":"helper.py","file_name":"helper.py","file_ext":"py","file_size_in_byte":3675,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"14804295146","text":"#screenshot application by okay.anshu\nimport time \nimport pyautogui\nfrom PIL import Image\nimport tkinter as tk\n\n#creating a function for taking the screenshot\n\ndef screenshot():\n #picking a random name for the ss image\n \n name = int(round(time.time()*1000))\n \n #storing the image file into an address\n #user need to create a folder for storing the clicked img\n #then replace this file address to your file address.\n name = f\"D:/Coding files/pyhton/screenshot/screenshot_saved/{name}.png\"\n \n #calling the predefined screenshot method from pyautogui package \n \n img = pyautogui.screenshot(name)\n #using the Pillow module to show the image\n img.show()\n \n#working for the gui \n\n#creating a basic frame and adding two buttons one is for take ss one is for exit.\n\nroot = tk.Tk()\nframe = tk.Frame(root)\nframe.pack()\n#assigning the screenshot command to the take screenshot button\nbutton = tk.Button(\n frame,\n text=\"Take Screenshot\",\n command=screenshot\n)\n#assigning the exit command to the exit button\nbutton.pack(side=tk.LEFT)\nclose=tk.Button(\n frame,\n text= \"EXIT\",\n command=quit\n)\n\nclose.pack(side=tk.LEFT)\n\nroot.mainloop()\n\n#this code is contributed by Himanshu\n","repo_name":"nycanshu/take-ss","sub_path":"screenshot .py","file_name":"screenshot .py","file_ext":"py","file_size_in_byte":1222,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"13459467490","text":"#SIMULATION SCRIPT\n#Thsic script read the data from the file and send it by MQTT\n\nimport csv\nimport sys\nimport time\nimport paho.mqtt.client as paho\nbroker = \"127.0.0.1\" #\"172.25.102.21\" #host name\nport = 1883\n\ndef on_publish(client,userdata,result): #create function for callback\n print(\"data published \\n\")\n\nclient1 = paho.Client(\"user\") #create client object\nclient1.on_publish = on_publish #assign function to callback\nclient1.connect(broker,port) #establish connection\n\ncsvfile = open('profilo_carichi.csv','r', newline='')\nobj = csv.reader(csvfile)\nc = 0\nfor row in obj:\n print (row)\n if c != 0:\n mex = '{\"ts\":\"'+row[0]+'\",\"value\":\"'+row[1]+'\"}'\n ret= client1.publish(\"home_sim/load/active_power\",mex) #publish\n if c>5:\n sys.exit(0)\n c = c+1\n time.sleep(2)\n\n","repo_name":"tommasopessina/BlockchainSystem","sub_path":"Python script/publishData.py","file_name":"publishData.py","file_ext":"py","file_size_in_byte":890,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"21079172249","text":"\"\"\"\nto do: \n finish system tests for:\n request_current_sensor_present\n request_current_sensor_value\n request_current_sensor_nominal\n request_display_leds_present\n request_display_solenoids_present\n\ntopics subscribed:\n cmd_all_off\n cmd_play_score\n cmd_set_number\n cmd_set_phrase\n connected\n request_computer_details\n request_current_sensor_nominal\n request_current_sensor_present\n request_current_sensor_value\n request_display_leds_present\n request_display_solenoids_present\n request_system_tests\n\ntopics published:\n\n connected\n response_computer_details\n response_current_sensor_nominal*\n response_current_sensor_present*\n response_current_sensor_value*\n response_display_leds_present\n response_display_solenoids_present\n\n\"\"\"\nimport importlib\nimport os\nimport queue\nimport RPi.GPIO as GPIO\nimport sys\nimport threading\nimport time\nimport traceback\n\napp_path = os.path.dirname((os.path.dirname(os.path.dirname(os.path.realpath(__file__)))))\nsys.path.append(os.path.split(app_path)[0])\n\nimport settings\nimport common.deadman as deadman\nfrom thirtybirds3 import thirtybirds\nfrom thirtybirds3.adapters.gpio.hc595 import HC595_shift_reg as hc595\n\nfrom thirtybirds3.adapters.sensors import ina260_current_sensor as ina260\n# scores are located outside this script because they're voluminous\nimport roles.display.score_by_mode.system_test as system_test_scores\nimport roles.display.score_by_mode.singles as single_notes\n\nscores = {\n \"system_test_scores_descending_scale\":system_test_scores.descending_scale,\n \"c_piano\":single_notes.c_piano,\n \"c_mezzo\":single_notes.c_mezzo,\n \"c_forte\":single_notes.c_forte,\n \"asharp_piano\":single_notes.asharp_piano,\n \"asharp_mezzo\":single_notes.asharp_mezzo,\n \"asharp_forte\":single_notes.asharp_forte,\n \"gsharp_piano\":single_notes.gsharp_piano,\n \"gsharp_mezzo\":single_notes.gsharp_mezzo,\n \"gsharp_forte\":single_notes.gsharp_forte,\n \"g_piano\":single_notes.g_piano,\n \"g_mezzo\":single_notes.g_mezzo,\n \"g_forte\":single_notes.g_forte,\n \"f_piano\":single_notes.f_piano,\n \"f_mezzo\":single_notes.f_mezzo,\n \"f_forte\":single_notes.f_forte,\n}\n\n###########################\n# S Y S T E M T E S T S #\n###########################\n\n# machine measurements #\n# Check communication with TLC5947\n# Check communication with HC595\n# [unit 1 only] measure 24V current \n \n# tests for human observation #\n# [one station at a time] cycle through all digits and phrases\n# [one station at a time] cycle through all 5 pitches\n\n###################\n# ACRYLIC DISPLAY #\n###################\n\nclass Acrylic_Display():\n \"\"\" This class is the hardware init and control for the acrylic displays and \n their shift registers. It also acts as simple receiver of commands. \n All animations and sophisticated behaviors reside elsewhere and call this over thirtybirds\n \"\"\"\n def __init__(self):\n self.current_phrase = \"juega\"\n self.current_number = 0\n self.shift_register_states = [0x00,0x00,0x00,0x00,0x00]\n self.shift_register_chain = hc595.HC595(bus=0,deviceId=0)\n self.Display_LED_Mapping = {\n \"digit\": {\n \"a\": {\n 0: {\"bit\": 0, \"shift_register_index\": 0},\n 1: {\"bit\": 1, \"shift_register_index\": 0},\n 2: {\"bit\": 2, \"shift_register_index\": 0},\n 3: {\"bit\": 3, \"shift_register_index\": 0},\n 4: {\"bit\": 4, \"shift_register_index\": 0},\n 5: {\"bit\": 5, \"shift_register_index\": 0},\n 6: {\"bit\": 6, \"shift_register_index\": 0},\n 7: {\"bit\": 7, \"shift_register_index\": 0},\n 8: {\"bit\": 0, \"shift_register_index\": 1},\n 9: {\"bit\": 1, \"shift_register_index\": 1}\n },\n \"b\": {\n 0: {\"bit\": 2, \"shift_register_index\": 1},\n 1: {\"bit\": 3, \"shift_register_index\": 1},\n 2: {\"bit\": 4, \"shift_register_index\": 1},\n 3: {\"bit\": 5, \"shift_register_index\": 1},\n 4: {\"bit\": 6, \"shift_register_index\": 1},\n 5: {\"bit\": 7, \"shift_register_index\": 1},\n 6: {\"bit\": 0, \"shift_register_index\": 2},\n 7: {\"bit\": 1, \"shift_register_index\": 2},\n 8: {\"bit\": 2, \"shift_register_index\": 2},\n 9: {\"bit\": 3, \"shift_register_index\": 2}\n },\n \"c\": {\n 0: {\"bit\": 0, \"shift_register_index\": 3},\n 1: {\"bit\": 1, \"shift_register_index\": 3},\n 2: {\"bit\": 2, \"shift_register_index\": 3},\n 3: {\"bit\": 3, \"shift_register_index\": 3},\n 4: {\"bit\": 4, \"shift_register_index\": 3},\n 5: {\"bit\": 5, \"shift_register_index\": 3},\n 6: {\"bit\": 6, \"shift_register_index\": 3},\n 7: {\"bit\": 7, \"shift_register_index\": 3},\n 8: {\"bit\": 0, \"shift_register_index\": 4},\n 9: {\"bit\": 1, \"shift_register_index\": 4}\n }\n },\n \"display_phrase\": {\n \"como\": {\"bit\": 2, \"shift_register_index\": 4},\n \"fue\":{\"bit\": 3, \"shift_register_index\": 4},\n \"dinero\": {\"bit\": 4, \"shift_register_index\": 4},\n \"trueque\": {\"bit\": 5, \"shift_register_index\": 4},\n \"juega\": {\"bit\": 6, \"shift_register_index\": 4}\n }\n }\n\n def set_phrase(self, phrase): # [ \"\"| juega | trueque | dinero | como | que ]\n self.current_phrase = phrase\n self.update_display()\n \n def generate_phrase_bytes(self):\n if self.current_phrase != \"\":\n shift_register_index = self.Display_LED_Mapping[\"display_phrase\"][self.current_phrase][\"shift_register_index\"]\n bit = self.Display_LED_Mapping[\"display_phrase\"][self.current_phrase][\"bit\"]\n self.shift_register_states[shift_register_index] = self.shift_register_states[shift_register_index] + (1 << bit)\n\n def set_number(self, number):\n if number > 999:\n number = number % 1000\n self.current_number = number\n self.update_display()\n\n def generate_number_bytes(self):\n a,b,c = '{:>03d}'.format(self.current_number)\n\n shift_register_index = self.Display_LED_Mapping[\"digit\"][\"a\"][int(a)][\"shift_register_index\"]\n bit = self.Display_LED_Mapping[\"digit\"][\"a\"][int(a)][\"bit\"]\n self.shift_register_states[shift_register_index] += (1 << bit)\n\n shift_register_index = self.Display_LED_Mapping[\"digit\"][\"b\"][int(b)][\"shift_register_index\"]\n bit = self.Display_LED_Mapping[\"digit\"][\"b\"][int(b)][\"bit\"]\n self.shift_register_states[shift_register_index] += (1 << bit)\n\n shift_register_index = self.Display_LED_Mapping[\"digit\"][\"c\"][int(c)][\"shift_register_index\"]\n bit = self.Display_LED_Mapping[\"digit\"][\"c\"][int(c)][\"bit\"]\n self.shift_register_states[shift_register_index] += (1 << bit)\n\n def set_all_off(self):\n self.shift_register_states = [0x00] * len(self.shift_register_states)\n self.shift_register_chain.write(self.get_inverted_shift_register_states())\n #self.shift_register_chain.write(self.shift_register_states)\n\n def update_display(self):\n self.set_all_off()\n if self.current_number > -1:\n self.generate_number_bytes()\n if self.current_phrase != \"\":\n self.generate_phrase_bytes()\n self.shift_register_chain.write(self.get_inverted_shift_register_states())\n\n def get_inverted_shift_register_states(self):\n mask = 0b11111111\n inverted_shift_register_states = []\n for shift_register_state in self.shift_register_states:\n inverted_shift_register_states.append(shift_register_state ^ mask)\n return inverted_shift_register_states\n\n\n###############\n# C H I M E S #\n###############\n\nclass Chime(threading.Thread):\n def __init__(self, gpio_number):\n threading.Thread.__init__(self)\n self.queue = queue.Queue()\n self.gpio_number = gpio_number\n GPIO.setup(gpio_number, GPIO.OUT)\n self.stop_power()\n self.start()\n\n def stop_power(self):\n print(\"stop_power\", self.gpio_number)\n GPIO.output(self.gpio_number, GPIO.LOW)\n\n def start_power(self):\n print(\"start_power\", self.gpio_number)\n GPIO.output(self.gpio_number, GPIO.HIGH)\n\n def add_pulse_to_queue(self, pulse_duration):\n self.queue.put(pulse_duration)\n\n def run(self):\n while True:\n try:\n pulse_duration = self.queue.get(True)\n if pulse_duration < 0.1: # saftey check\n self.start_power()\n time.sleep(pulse_duration)\n self.stop_power()\n except Exception as e:\n print(e)\n self.stop_power()\n finally:\n self.stop_power()\n\nclass Chime_Player(threading.Thread):\n def __init__(self):\n threading.Thread.__init__(self)\n self.current_score = queue.Queue() # queue can act as interrupt or stop message\n GPIO.setmode(GPIO.BCM)\n self.gpios = [6,16,5,17,22]\n self.chimes = []\n for gpio in self.gpios: # how can this be a comprehension\n self.chimes.append(Chime(gpio))\n self.start()\n\n def stop_all_chime_power(self):\n self.play_score(\"blank\")\n for chime in self.chimes:\n chime.stop_power()\n\n def play_score(self,score_name):\n print(\"score_name=\",score_name)\n self.current_score.put(score_name)\n\n def run(self):\n while True:\n try:\n #score_name = self.current_score.get(True)\n #score = scores[score_name]\n score = self.current_score.get(True)\n score = scores[score]\n default_beat_period = score[\"default_beat_period\"]\n beats = score[\"beats\"]\n interrupt = False\n for beat in beats:\n print(\"beat=\", beat)\n if interrupt:\n break\n #print(\"beat\",beat)\n for notes in beat:\n #print(\"notes\",notes)\n if interrupt:\n break\n for note in notes:\n #print(\"note\",note) \n self.chimes[note[\"pitch\"]].add_pulse_to_queue(note[\"period\"])\n if not self.current_score.empty():\n interrupt = True\n break\n time.sleep(default_beat_period)\n except Exception as e:\n print(e)\n #self.stop_all_chime_power()\n\n###########\n# M A I N #\n###########\n\nclass Main(threading.Thread):\n def __init__(self):\n threading.Thread.__init__(self)\n self.tb = thirtybirds.Thirtybirds(\n settings, \n app_path,\n self.network_message_handler,\n self.network_status_change_handler,\n self.exception_handler\n )\n self.deadman = deadman.Deadman_Switch(self.tb)\n self.queue = queue.Queue()\n self.hostname = self.tb.get_hostname()\n\n self.tb.subscribe_to_topic(\"cmd_all_off\")\n self.tb.subscribe_to_topic(\"cmd_play_score\")\n self.tb.subscribe_to_topic(\"cmd_set_phrase\")\n self.tb.subscribe_to_topic(\"cmd_set_number\")\n self.tb.subscribe_to_topic(\"request_display_leds_present\")\n self.tb.subscribe_to_topic(\"request_display_solenoids_present\")\n self.tb.subscribe_to_topic(\"request_system_tests\")\n self.tb.subscribe_to_topic(\"request_computer_details\")\n self.tb.subscribe_to_topic(\"request_current_sensor_nominal\")\n self.tb.subscribe_to_topic(\"request_current_sensor_present\")\n self.tb.subscribe_to_topic(\"request_current_sensor_value\")\n\n self.tb.publish(\"connected\", True)\n self.chime_player = Chime_Player()\n self.acrylic_display = Acrylic_Display()\n if self.tb.get_hostname() == 'pinball1game':\n self.power_sensor = ina260.INA260()\n self.tb.subscribe_to_topic(\"get_current\")\n self.start()\n\n\n def request_system_tests(self):\n # computer details\n self.tb.publish(\n topic=\"response_current_sensor_nominal\",\n message=self.request_current_sensor_nominal()\n )\n self.tb.publish(\n topic=\"response_display_leds_present\", \n message=self.request_display_leds_present()\n )\n self.tb.publish(\n topic=\"response_display_solenoids_present\", \n message=self.response_display_solenoids_present()\n )\n\n def request_display_solenoids_present(self):\n # This needs to be finished after the current sensor works\n return [True,True,True,True,True]\n\n def request_display_leds_present(self):\n # This needs to be finished after the current sensor works\n return {\n \"phrases\":[True,True,True,True,True],\n \"numerals\":{\n \"a\":[True,True,True,True,True,True,True,True,True,True],\n \"b\":[True,True,True,True,True,True,True,True,True,True],\n \"c\":[True,True,True,True,True,True,True,True,True,True],\n }\n }\n\n def request_computer_details(self):\n return {\n \"df\":self.tb.get_system_disk(),\n \"cpu_temp\":self.tb.get_core_temp(),\n \"pinball_git_timestamp\":self.tb.app_get_git_timestamp(),\n \"tb_git_timestamp\":self.tb.tb_get_git_timestamp(),\n }\n\n def request_current_sensor_nominal(self):\n # TODO: Make the ACTUAL tests here.\n return True\n\n def request_current_sensor_present(self):\n # TODO: Make the ACTUAL tests here.\n return True\n\n def request_current_sensor_value(self):\n # TODO: Make the ACTUAL tests here.\n return 0.0\n\n def status_receiver(self, msg):\n print(\"status_receiver\", msg)\n def network_message_handler(self, topic, message, origin, destination):\n self.add_to_queue(topic, message, origin, destination)\n def exception_handler(self, exception):\n print(\"exception_handler\",exception)\n def network_status_change_handler(self, status, hostname):\n print(\"network_status_change_handler\", status, hostname)\n def add_to_queue(self, topic, message, origin, destination):\n self.queue.put((topic, message, origin, destination))\n\n def run(self):\n while True:\n try:\n topic, message, origin, destination = self.queue.get(True)\n print(topic, message)\n if topic == b'cmd_all_off':\n self.acrylic_display.set_all_off()\n self.chime_player.stop_all_chime_power()\n\n if topic == b'cmd_play_score':\n if destination == self.tb.get_hostname():\n self.chime_player.play_score(message)\n\n if topic == b'cmd_set_number':\n if destination == self.tb.get_hostname():\n self.acrylic_display.set_number(message)\n \n if topic == b'cmd_set_phrase':\n if destination == self.tb.get_hostname():\n self.acrylic_display.set_phrase(message)\n\n if topic == b'request_computer_details':\n self.tb.publish(\n topic=\"response_computer_details\", \n message=self.request_computer_details()\n )\n\n if topic == b'request_current_sensor_nominal':\n self.tb.publish(\n topic=\"response_current_sensor_nominal\",\n message=self.request_current_sensor_nominal()\n )\n\n if topic == b'request_current_sensor_present':\n self.tb.publish(\n topic=\"response_current_sensor_present\",\n message=self.request_current_sensor_present()\n )\n\n if topic == b'request_current_sensor_value':\n self.tb.publish(\n topic=\"response_current_sensor_value\",\n message=self.request_current_sensor_value()\n )\n\n if topic == b'request_display_leds_present':\n self.tb.publish(\n topic=\"response_display_leds_present\", \n message=self.request_display_leds_present()\n )\n \n if topic == b'request_display_solenoids_present':\n self.tb.publish(\n topic=\"response_display_solenoids_present\", \n message=self.request_display_solenoids_present()\n )\n\n if topic == b'request_system_tests':\n self.request_system_tests()\n\n except Exception as e:\n exc_type, exc_value, exc_traceback = sys.exc_info()\n print(e, repr(traceback.format_exception(exc_type, exc_value,exc_traceback)))\n\nmain = Main()\n","repo_name":"andycavatorta/pinball","sub_path":"roles/display/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":17516,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"6072657375","text":"from main import COLORS,WIDTH,HEIGHT,pygame\r\n\r\ndef handle_paddle_movement(keys, left_paddle, right_paddle):\r\n if keys[pygame.K_z] and left_paddle.y-left_paddle.V>=0 :\r\n left_paddle.move(True)\r\n elif keys[pygame.K_s] and left_paddle.y+left_paddle.V+left_paddle.height<=HEIGHT:\r\n left_paddle.move(False)\r\n elif keys[pygame.K_UP]and right_paddle.y-right_paddle.V>=0:\r\n right_paddle.move(True)\r\n elif keys[pygame.K_DOWN] and right_paddle.y+right_paddle.V+right_paddle.height<=HEIGHT:\r\n right_paddle.move(False)\r\n\r\ndef handle_collision(ball, left_paddle, right_paddle):\r\n if ball.y+ball.radius>=HEIGHT: ball.y_V*=-1\r\n elif ball.y-ball.radius<=0: ball.y_V *= -1\r\n\r\n if ball.x_V < 0:\r\n if left_paddle.y <= ball.y <= left_paddle.y + left_paddle.height:\r\n if ball.x - ball.radius <= left_paddle.x + left_paddle.width:\r\n ball.x_V *= -1\r\n\r\n difference_in_y = (left_paddle.y + left_paddle.height / 2) - ball.y\r\n red_factor = (left_paddle.height / 2) / ball.Max_V\r\n y_V = difference_in_y / red_factor\r\n ball.y_V = -1 * y_V\r\n\r\n else:\r\n if right_paddle.y <= ball.y <= right_paddle.y + right_paddle.height:\r\n if ball.x + ball.radius >= right_paddle.x:\r\n ball.x_V *= -1\r\n\r\n difference_in_y = (right_paddle.y + right_paddle.height / 2) - ball.y\r\n red_factor = (right_paddle.height / 2) / ball.Max_V\r\n y_V = difference_in_y / red_factor\r\n ball.y_V = -1 * y_V","repo_name":"karkouri-zakaria/Academic_projects","sub_path":"MyProjects/Pong/Movement_and_collisions.py","file_name":"Movement_and_collisions.py","file_ext":"py","file_size_in_byte":1577,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"70485631526","text":"import geojson\nfrom sqlalchemy import (\n create_engine,\n Column,\n Integer,\n String,\n Float,\n)\nfrom geoalchemy2 import Geometry\nfrom sqlalchemy.dialects.postgresql import JSONB\nfrom sqlalchemy.orm import sessionmaker, declarative_base\n\nconnection_url = 'postgresql://postgres_user:geodata1234@127.0.0.1:5444/postgres' # noqa\nengine = create_engine(\n connection_url,\n pool_size=10,\n max_overflow=2,\n pool_recycle=300,\n pool_pre_ping=True,\n pool_use_lifo=True\n # echo=True,\n)\nSession = sessionmaker(bind=engine)\nsession = Session()\nBase = declarative_base()\n\n\nclass GeoData(Base):\n __tablename__ = 'geotable'\n\n id = Column(Integer, primary_key=True)\n geometry = Column(Geometry(srid=4326))\n area_ha = Column(Float)\n crop = Column(String)\n history = Column(JSONB)\n productivity = Column(String)\n region = Column(String)\n score = Column(String)\n type = Column(String)\n\n\nGeoData.__table__.drop(bind=engine)\nGeoData.__table__.create(bind=engine)\n\n\ndef geometry_to_ewkt(geom_dict):\n geom_type = geom_dict['type'].upper()\n if geom_type == 'POINT':\n coords = geom_dict['coordinates']\n return f'SRID=4326;POINT({coords[0]} {coords[1]})'\n elif geom_type == 'MULTIPOLYGON':\n polys = ','.join([','.join(\n [f\"(({','.join([' '.join(map(str, point)) for point in points])}))\"\n for points in poly]) for poly in geom_dict['coordinates']])\n return f'SRID=4326;MULTIPOLYGON({polys})'\n\ncount = 0\nwith engine.connect() as connection:\n with open('tools/fr-subset.geojsons') as f:\n for line in f:\n data = geojson.loads(line)\n\n geometry = None\n if data.get('geometry') is not None:\n geometry = geometry_to_ewkt(data['geometry'])\n\n geo_data = GeoData(\n geometry=geometry,\n id=data['properties']['id'],\n crop=data['properties']['crop'],\n productivity=data['properties']['productivity'],\n area_ha=float(data['properties']['area_ha']) if data['properties']['area_ha'] else None, # noqa\n history=data['properties']['history'],\n region=data['properties']['region'],\n score=data['properties']['score'],\n type=data['geometry']['type'].upper(),\n )\n session.add(geo_data)\n count += 1\n\n session.commit()\n\nprint(f'total docs: {count}', 'Data imported successfully!')\n","repo_name":"Oless-N/search_geo_data","sub_path":"tools/import_geo_data.py","file_name":"import_geo_data.py","file_ext":"py","file_size_in_byte":2496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"20975176754","text":"######################### LEXER.py ###############################\n\n\n\n# Given a regex description of the language syntax and parsing priority\n# tokenize the input file and return a iterator for parsing\n\n\n\n#-----------------------------------------------------------------------------------------------------------#\n\n# Import some tools for lexical parsing escaped characters\nimport esc_tools\n\n\n# For each piece of programming syntax describe:\n# --> A regex expression\n# --> Key to pass on the parser (ID)\n# --> Any other additional info ( ie an action )\n\n#List of dictionaries\n\nlanguage = [{'regex':'int|void', 'key':'TYPE'},\n {'regex':'return', 'key':'RETURN'},\n {'regex':';', 'key':'END STATEMENT'},\n {'regex':'[\\\\(\\\\){}\\\\[\\\\]]', 'key':'BRACKETS'},\n {'regex':'[0-9]+', 'key':'INTEGER'},\n {'regex':'[0-9]+.[0-9]+', 'key':'FLOAT'},\n {'regex':'[a-zA-Z][a-zA-Z0-9]*', 'key':'IDENTIFIER'}\n]\n\n## 0-9: $ a-z:% A-Z: @\n\nlowercase_alphabet = list('abcdefghijklmnopqrstuvwxyz')\nuppercase_alphabet = list('ABCDEFGHIJKLMNOPQRSTUVWXYZ')\ndigits = list('0123456789')\n\n# Compute the character set\ncharacter_set = set()\nfor expression in language:\n # Expand the short hand notation\n new_chars = expression['regex'].replace('0-9',''.join(digits)\\\n ).replace('a-z',''.join(lowercase_alphabet)\\\n ).replace('A-Z',''.join(uppercase_alphabet))\n # get rid of symbols that are inherintly regex defined\n for regex_symbol in '[]()+|*':\n new_chars = esc_tools.replace_with_esc(new_chars, regex_symbol, '')\n\n # add the new characters\n for char in new_chars:\n character_set.add(char)\n\n\n\n#-----------------------------------------------------------------------------------------------------------#\n\n\n# How to create a NFA \n\n# Todo:\n# Create NFA data structure\n# Parse Regex Expression\n# Modify the NFA using the language definition regex\n\n\n#-----------------------------------------------------------------------------------------------------------#\n\n# Create NFA data structure\n\n# NFA is a directed graph with numbered vertices\n\n# For each regex operator define how it is turned into a NFA graph\n# define a function for each regex operator that takes parameters:\n# regex_op(NFA_graph, input_node, regex_parameters)\n# and modifies the NFA appropriately returning the new node index\n# Note: the function can recursively handle the regex nesting\n\n\n# The graph data structure as a dictionery indexed by tuples where\n# (s,d) -> referes to connection from s to d\n# The values stored will either be EPSILON or a character from the language\n# in a list naming all the connections\n\n# Example NFA\n\n# REGEX: (a|b)c\n# NFA:\n# e e a\n# ------> 1 -----> 2 ----> 3 -----|\n# | |\n# |-----> 4 ---> 5 ----> 6\n# e b c\n#\n# REPRESENTATION:\n# graph = {(1,2):[e],\n# (2,3):[e],\n# (2,4):[e],\n# (3,5):['a'],\n# (4,5):['b'],\n# (5,6):['c']\n# }\n\n\n# The graph data structure with be held within a Graph object which will\n# have methods for adding edges and finding neighbours\n\nclass Graph():\n\n def __init__(self, size=0):\n # Define the graph structure as above\n self.graph = {}\n\n # The node names should be numbered between 0 and the graph size\n # Defaults to 0\n self.size = size\n\n def add_node(self):\n # Returns a number to identify a new graph node\n\n new_node = self.size # The new number is the size of the graph\n # ie when the graph is empty the first node\n # created will be named 0 then 1,2,3...\n self.size = self.size + 1 # Increase the size by 1\n\n return new_node\n\n def add_edge(self, source, target, value):\n # The add edge method for our graph structure\n # (s,d) -> referes to connection from s to d\n\n #Check the source and target values are in range\n assert 0 <= source and source < self.size, 'Source value out of graph range'\n assert 0 <= target and target < self.size, 'Target value out of graph range'\n\n # Create the edge\n self.graph[(source, target)] = self.graph.get((source,target),[])+[value]\n\n def get_neighbours(self, node, value):\n # Return all the edges a node has with a given edge value\n neighbours = [] # Init an array to collect neighbours\n\n for i in range(self.size): # For ever other node identifier\n # Get the edge from the graph\n edge = self.graph.get((node, i), [])\n if value in edge: # If the edge exists and has the correct value\n neighbours.append(i) # Add the node id to array\n \n return neighbours\n\n\n\n\n# Create the placeholder for Epsilon values\nEPSILON = 'EPSILON'\n\n \n\n#-----------------------------------------------------------------------------------------------------------#\n\n\n# Parse Regex Expression\n\n# Regex can be parsed to create a NFA graph in four basic ways\n#\n# ab: Creates a chain link in the graph\n# a|b: Creates two paths which have each option\n# a*: Creates a loop back to initial node\n# a+: Creates a loop with extra link and a one way connection (so must run at least once)\n\n# Regex operators have a precedence\n# 0: Brackets\n# 1: *,+ Operator\n# 2: Concatenation \n# 3: | Operator\n\n# However our Regex expressions use more complex operators to simplify\n# what would be complicated regex expressions\n# More compilcated Regex:\n# Square Brackets [abcd] ::= (a|b|c|d)\n# Square Brackets with range [0-9] ::= (0|1|2|3|4|5|6|7|8|9) (Only comes as a-z, A-Z, 0-9)\n\n# Additionally if the regex operations can be escaped by the backslash to be used as normal strings\n\n# Define a function that will reduce the regex to the four basic operations\n\ndef reduce_regex(exp):\n # We only should find the most deeply nested square brackets first\n # Search for the first close bracket\n # We will recursively eliminate the most deeply nested brackets and from left to right\n\n # We need to be careful to avoid square brackets that have been escaped in the regex strings\n\n # Get index of the first closing bracket ']'\n end_brck_inx = esc_tools.find_with_esc(exp, ']')\n\n # First the base case where the regex has no brackets\n if end_brck_inx == -1:\n return exp\n\n # Divide the expression into 2 parts around the closing bracket\n tail_end = exp[(end_brck_inx+1):]\n front_end = exp[:end_brck_inx]\n \n # Now we should search the front end for the last opening bracket to get a pair\n strt_brck_inx = esc_tools.rfind_with_esc(front_end, '[')\n\n # Now we can get three pieces\n inside_exp = front_end[strt_brck_inx+1:]\n front_end = front_end[:strt_brck_inx]\n # and tail_end\n\n #print('a',front_end,'n', inside_exp, 'f',tail_end)\n\n # Now we should expand the notation in inside expression\n # We will create a list of all the possible options and then we will concatenate at the end\n options = []\n\n # First we should check for our range notation\n if 'a-z' in inside_exp:\n # get rid of our shorthand notation and add the lowercase alphabet to our list\n inside_exp = inside_exp.replace('a-z','')\n options.extend(lowercase_alphabet)\n\n if 'A-Z' in inside_exp:\n # get rid of shorthand and add uppercase alphabet\n inside_exp = inside_exp.replace('A-Z','')\n options.extend(uppercase_alphabet)\n\n if '0-9' in inside_exp:\n # get rid of shorthand and add digits\n inside_exp = inside_exp.replace('0-9','')\n options.extend(digits)\n\n # now we can add the other characters in our brackets and add them to options\n # because of escaped characters we dont know how wide each element is and\n # we will iterate with a while loop\n # Our implementation assumes no repeats but a repeat will not raise error\n i = 0\n while i DFA node\n # set_hash x where x is a set ---> int\n # Since sets are not hashable we define a function set_hash to turn them into unique hashable tuples\n\n set_hash = lambda x: tuple(sorted(list(x)))\n\n DFA_nodes = {}\n\n # Create a stack of NFA possiblilities to compute further from\n # Implement with a set of tuple(set())'s\n # For every element in here, elm, tuple(elm) will be in DFA_nodes\n compute_stack = set()\n\n\n # We start with the input node to the NFA and its epsilon-closure\n DFA_in = epsilon_closure(NFA, {NFA_input_node})\n # Add it to the computation stack\n compute_stack.add(set_hash(DFA_in))\n # create a DFA node for the current NFA possibility\n DFA_in_node = DFA.add_node()\n # and add this node to the DFA node dictionary\n DFA_nodes[set_hash(DFA_in)] = DFA_in_node\n\n\n while len(compute_stack) != 0: # While the computation stack is not empty\n\n # get a random possibility to compute its links\n current_DFA_pos = set(compute_stack.pop())\n\n # get the assigned node from the dict\n current_DFA_node = DFA_nodes[set_hash(current_DFA_pos)]\n\n #print('\\nWe calculating with DFA_node ',current_DFA_node)\n\n # For each of the characters in the character set calculate a possible NFA state\n for character in character_set:\n\n new_pos = epsilon_closure(NFA, move(current_DFA_pos, NFA, character))\n\n if not new_pos == set(): # If the set is non empty\n new_pos_tup = set_hash(new_pos)\n if DFA_nodes.get(new_pos_tup, None) is None:\n #print('_'+character+'_', end='')\n # This possibility has never been seen before\n # Create a new DFA node to refer to it\n new_node = DFA.add_node()\n # and add it to the dictionary\n DFA_nodes[new_pos_tup] = new_node\n\n print(new_node, end='')\n\n # Now connect it to the node it was connected to\n DFA.add_edge(current_DFA_node, new_node, character)\n\n # Now add it to the computation stack so that its connections can be computed\n compute_stack.add(new_pos_tup)\n else:\n #print(character, end='')\n # This node has been seen before so simply add a connection to it\n # Get the posibility that it points to by DFA_nodes\n points_to = DFA_nodes[new_pos_tup]\n DFA.add_edge(current_DFA_node, points_to, character)\n\n\n return DFA_nodes\n\n\n\n\n\n#-----------------------------------------------------------------------------------------------------------#\n \n\nif __name__=='__main__':\n # Create the NFA graph\n NFA = Graph()\n\n input_node = NFA.add_node()\n \n for expression in language:\n print('Computing the NFA for',expression['regex'])\n cur_output_node = NFA.add_node()\n expression['output_node'] = cur_output_node\n regex = expression['regex']\n regex_to_NFA(regex, NFA, input_node, cur_output_node)\n\n DFA = Graph()\n\n print('NFA to DFA...')\n print('Creating DFA nodes')\n w = NFA_to_DFA(DFA, NFA, input_node)\n print('')\n\n \n ","repo_name":"IainMcWhinnie/Compiler-Project","sub_path":"lexer.py","file_name":"lexer.py","file_ext":"py","file_size_in_byte":25552,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"28450848157","text":"# -*- coding:utf-8 -*-\r\n#@InnerAc\r\n#getNum.py\r\n\"\"\"\r\nBy this program, you can get the JSON file of nodes and edges.\r\n\"\"\"\r\nimport sqlite3\r\nimport math\r\nimport json\r\nimport re\r\n\r\nconn = None\r\n\r\ndef init(path):\r\n\tglobal conn\r\n\tconn = sqlite3.connect(path)\r\ndef getNodes():\r\n\tglobal conn\r\n\tcursor = conn.execute(\"SELECT url, name, size FROM node\")\r\n\tnodes = {}\r\n\ti = 0;\r\n\tfor row in cursor:\r\n\t\turl = row[0]\r\n\t\tname = row[1]\r\n\t\tsize = row[2]\r\n\t\tnodes[url] = {\"name\":name,\"size\":size}\r\n\t\ti += 1\r\n\t\tif(i == 50):\r\n\t\t\tbreak;\r\n\t\t# if i == 10:\r\n\t\t# \tbreak;\r\n\treturn nodes\r\ndef getLinks(nodes):\r\n\tglobal conn\r\n\tcursor = conn.execute(\"SELECT name_one,name_two,value FROM value\")\r\n\tedges = []\r\n\ti = 0;\r\n\tfor row in cursor:\r\n\t\tone = row[0]\r\n\t\ttwo = row[1]\r\n\t\tvalue = row[2]\r\n\t\tif one in nodes or two in nodes:\r\n\t\t\tedges.append({\"source\": one,\"target\": two,\"weight\": value})\r\n\t\t# i += 1\r\n\t\t# if i == 10:\r\n\t\t\t# break;\r\n\treturn edges\r\ndef close():\r\n\tglobal conn\r\n\tconn.close()\t\r\nif __name__ == '__main__':\r\n\tinit(\"person.db\")\r\n\tinfo = {}\r\n\tnodes = getNodes()\r\n\tedges = getLinks(nodes)\r\n\tinfo[\"nodes\"] = nodes\r\n\tinfo[\"edges\"] = edges\r\n\tf = open('info.json','w')\r\n\tf.write(json.dumps(info))\r\n\tf.close()\r\n\tclose()\r\n","repo_name":"InnerAc/githubRelational","sub_path":"getJson.py","file_name":"getJson.py","file_ext":"py","file_size_in_byte":1193,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"71806144484","text":"# View all recorded frames and classify them. This will create the dataset for the neural network\n# To classify a frame, user will input a key, corresponding to the finger that is currently being pressed\n# the key-finger relationship is the same as putting fingers on a keyboard in the standard position, \n# in the middle row. The only exception, for pratical reasons is the thumbs, which correspond to \n# 'c' or 'v' instead of spacebar, which is reserved for 'raised' status.\n# Additionally it's also possible to undo last classification by pressing 2, and change the classification\n# by pressing 'q'\n#\n# Results are stored in the file pushing.txt\n\nimport os\nimport cv2\n\nl_mig = []\nl_anu = []\nl_mid = []\nl_ind = []\nthumb = []\nr_mig = []\nr_anu = []\nr_mid = []\nr_ind = []\nraised = []\n\nnames = os.listdir('./ds_building/inputs/pictures')\nnames.sort()\n\ncv2.namedWindow('Finger Picker')\n\ni = 0\nmaxi = i + 1000\nwhile (i < maxi ):\n try:\n fname = names[names.index(str(i) + '.png')]\n img = cv2.imread('./ds_building/inputs/pictures/' + fname)\n except:\n i+=1\n continue\n if(img is None):\n continue\n\n cv2.imshow(str(i), cv2.resize(img,(1280,720)))\n \n k = cv2.waitKey(0)\n if (k == ord('q')):\n i = i - 2\n elif (k == ord('a')):\n l_mig.append(fname)\n elif (k == ord('s')):\n l_anu.append(fname)\n elif (k == ord('d')):\n l_mid.append(fname)\n elif (k == ord('f')):\n l_ind.append(fname)\n elif (k == ord('c') or k == ord('c')):\n thumb.append(fname)\n elif (k == ord('j')):\n r_ind.append(fname)\n elif (k == ord('k')):\n r_mid.append(fname)\n elif (k == ord('l')):\n r_anu.append(fname)\n elif (k == ord(';')):\n r_mig.append(fname)\n elif (k == ord('2')):\n os.remove('./ds_building/inputs/pictures/' + fname)\n os.remove('./ds_building/inputs/results/' + fname[:-4])\n \n i += 1\n\n cv2.destroyAllWindows()\n\n\nlists = [l_mig,l_anu,l_mid,l_ind,thumb,r_ind,r_mid,r_anu,r_mig, raised]\n\nwith open('./ds_building/pushing.txt','w') as f:\n for i in range(len(lists)):\n for img in lists[i]: \n f.write(img +' '+str(i) +'\\n') ","repo_name":"ianirolab/invisible_keyboard","sub_path":"ds_building/finger_picker.py","file_name":"finger_picker.py","file_ext":"py","file_size_in_byte":2182,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"19056792410","text":"from odoo import http\nfrom odoo.http import request\nfrom odoo import api, fields, models, _, tools\nfrom datetime import datetime, date\nfrom odoo.exceptions import UserError, ValidationError\nimport pytz\nfrom dateutil import tz\n\nclass HelpdeskBackend(http.Controller):\n\n\t@http.route('/hr/fetch_dashboard_data', type=\"json\", auth='user')\n\tdef fetch_dashboard_data(self):\n\t\t\n\t\tuser_id = request.env.user.id\n\t\tdashboard_data = {}\n\t\tnow = datetime.now()\n\t\temployee = request.env['hr.employee'].sudo()\n\t\tannouncement = request.env['hr.announcement'].sudo()\n\t\tattendance = request.env['hr.attendance'].sudo()\n\t\texpense = request.env['hr.expense'].sudo()\n\t\tleaves = request.env['hr.leave'].sudo()\n\t\tpayslip = request.env['hr.payslip'].sudo()\n\t\tcontract = request.env['hr.contract'].sudo()\n\t\tdata = {}\n\t\tbirthdate_data = {}\n\t\tfor datas in employee.search([]):\n\t\t\tif datas.birthday:\n\t\t\t\tif datas.birthday.day <= now.date().day and datas.birthday.month <= now.date().month:\n\t\t\t\t\tbirthdate_data[datas.id] = datas.name\n\t\tdashboard_data['birthday'] = birthdate_data\n\n\t\tannouncement_data = {}\n\t\tfor datas in announcement.search([]):\n\t\t\tannouncement_data[str(datas.announcement_date)] = datas.name\n\t\tdashboard_data['announcement'] = announcement_data\n\n\t\tleave_count = {}\n\t\tleave_count['pending'] = leaves.search_count([('employee_id.name','=', request.env.user.name),('state','=','confirm')])\n\t\tleave_count['approved'] = leaves.search_count([('employee_id.name','=', request.env.user.name),('state','=','validate')])\n\t\tleave_count['cancelled'] = leaves.search_count([('employee_id.name','=', request.env.user.name),('state','=','cancel')])\n\t\tleave_count['refused'] = leaves.search_count([('employee_id.name','=', request.env.user.name),('state','=','refuse')])\n\n\t\ttotal_leave = {}\n\n\t\tapproved_leave = 0.0\n\t\tallocated_leave = 0.0\n\t\tif request.env.user.has_group('base.user_admin'):\n\t\t\tleaves_id = request.env['hr.leave.allocation'].search([])\n\t\telse:\n\t\t\tleaves_id = request.env['hr.leave.allocation'].search([('employee_id.name','=', request.env.user.name)])\n\n\t\tfor leave in leaves_id:\n\t\t\tif (leave.state == 'validate'):\n\t\t\t\tif leave.type_request_unit != 'hour':\n\t\t\t\t\tapproved_leave += leave.number_of_days_display\n\t\t\tif leave.type_request_unit != 'hour':\n\t\t\t\t\tallocated_leave += leave.number_of_days_display\n\n\t\ttotal_leave['leave_approved'] = approved_leave\n\t\ttotal_leave['leave_allocated'] = allocated_leave\n\t\t\n\t\tpayslip_vals = {}\n\t\tfor pay in payslip.search([('employee_id.name','=', request.env.user.name)]):\n\t\t\tpay_slip = {}\n\t\t\tpay_slip['number'] = pay.number if pay.number else '' \n\t\t\tpay_slip['date_from'] = pay.date_from\n\t\t\tpay_slip['date_to'] = pay.date_to\n\t\t\tpay_slip['state'] = dict(pay._fields['state'].selection).get(pay.state)\n\t\t\tpayslip_vals[pay.id] = pay_slip\n\n\t\texpenses_vals = {}\n\t\tfor expenses in expense.search([('employee_id.name','=', request.env.user.name)]):\n\t\t\texpense_dict = {}\n\t\t\texpense_dict['date'] = expenses.date\n\t\t\texpense_dict['name'] = expenses.name\n\t\t\texpense_dict['total'] = expenses.total_amount\n\t\t\texpense_dict['state'] = dict(expenses._fields['state'].selection).get(expenses.state)\n\t\t\texpenses_vals[expenses.id] = expense_dict\n\n\t\tattendance_vals = {}\n\n\t\tfor attendances in attendance.search([('employee_id.name','=', request.env.user.name)]):\n\t\t\tattendance_dict = {}\n\n\t\t\tuser_tz = pytz.timezone(request.env.context.get('tz') or request.env.user.tz or 'UTC')\n\t\t\tcheck_in_time = pytz.UTC.localize(fields.Datetime.from_string(attendances.check_in))\n\t\t\tcheck_in = check_in_time.astimezone(user_tz)\n\n\t\t\tif attendances.check_out:\n\t\t\t\tcheck_out_time = pytz.UTC.localize(fields.Datetime.from_string(attendances.check_out))\n\t\t\t\tcheck_out = check_out_time.astimezone(user_tz)\n\n\t\t\tattendance_dict['date'] = attendances.check_in.date() if attendances.check_in else ''\n\t\t\tattendance_dict['check_in'] = check_in.time() if attendances.check_in else ''\n\t\t\tattendance_dict['check_out'] = check_out.time() if attendances.check_out else '' \n\t\t\tattendance_vals[attendances.id] = attendance_dict\n\n\t\tleaves_vals = {}\n\t\tfor leaves in leaves.search([('employee_name.name','=', request.env.user.name)]):\n\t\t\tleave_dict = {}\n\t\t\tleave_dict['request_out'] = leaves.request_date_from\n\t\t\tleave_dict['request_in'] = leaves.request_date_to\n\t\t\tleave_dict['leave_type'] = leaves.holiday_status_id.name\n\t\t\tleave_dict['state'] = dict(leaves._fields['state'].selection).get(leaves.state)\n\t\t\tleaves_vals[leaves.id] = leave_dict\n\t\tcount = {}\n\t\tcount['payslip'] = payslip.search_count([('employee_id.name','=', request.env.user.name)])\n\n\t\tcount['attendance'] = attendance.search_count([('employee_id.name','=', request.env.user.name)])\n\t\ttotal = 0.0\n\t\tfor data in expense.search([('employee_id.name','=', request.env.user.name)]): \n\t\t\ttotal += data.total_amount\n\n\t\tcount['contract'] = contract.search_count([('employee_id.name','=', request.env.user.name)])\n\t\tcount['expense'] = '{0:.2f}'.format(total)\n\t\tdashboard_data['user'] = request.env.user.name\n\t\tdashboard_data['leaves_total'] = total_leave\n\t\tdashboard_data['leaves_count'] = leave_count\n\t\tdashboard_data['count'] = count\n\t\tdashboard_data['payslip'] = payslip_vals\n\t\tdashboard_data['expense'] = expenses_vals\n\t\tdashboard_data['attendance'] = attendance_vals\n\t\tdashboard_data['leaves'] = leaves_vals\n\n\t\treturn dashboard_data\n\n\t@http.route('/hr/fetch_birthdate_data', type=\"json\", auth='user')\n\tdef fetch_birthday_data(self, data):\n\t\tdashboard_data = {}\n\t\t# now = datetime.now()\n\t\ttry:\n\t\t\tdatetime.strptime(data, '%d/%m/%Y')\n\t\texcept ValueError:\n\t\t\traise UserError(_(\"Incorrect data format\"))\n\t\tdate_time_obj = datetime.strptime(data, '%d/%m/%Y')\n\t\temployee = request.env['hr.employee']\n\t\tbirthdate_data = {}\n\t\tfor datas in employee.search([]):\t\n\t\t\tif datas.birthday:\n\t\t\t\tif datas.birthday.day == date_time_obj.date().day and datas.birthday.month == date_time_obj.date().month:\n\t\t\t\t\tbirthdate_data[datas.id] = datas.name\n\t\tdashboard_data['birthday'] = birthdate_data\n\n\t\treturn dashboard_data\n\t\t","repo_name":"prointec/demoprueba","sub_path":"hr_dashboard_app_ent/controllers/backend.py","file_name":"backend.py","file_ext":"py","file_size_in_byte":5935,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"11231641586","text":"from src.db_helper import session, User, MessageQueue, UserMessage\nimport src.logging_helper as logging\n\nfrom telethon.errors import FloodWaitError\nimport asyncio\nimport os\nimport configparser\nimport datetime\nfrom sqlalchemy.sql.expression import func\n\n\nconfig = configparser.ConfigParser(os.environ)\nconfig_path = os.path.dirname(__file__) + '/../config/' #we need this trick to get path to config folder\nconfig.read(config_path + 'settings.ini')\n\nlogger = logging.get_logger()\n\nasync def add_message_to_queue(message, is_test=False, session=None):\n new_message = MessageQueue(message=message, is_test=is_test)\n session.add(new_message)\n session.commit()\n\n if is_test == True: #if it's test message, send it only to admin\n users = session.query(User).filter(User.id == int(config['TELEGRAM']['ADMIN_ID'])).all()\n else:\n users = session.query(User).all()\n for user in users:\n user_message = UserMessage(user_id=user.id, message_queue_id=new_message.id, status='queued')\n session.add(user_message)\n session.commit()\n\n\nasync def process_message_queue(client, messages_to_send=10, delay_between_messages=10, session=None):\n unsent_user_messages = (\n session.query(UserMessage)\n .filter(UserMessage.status == 'queued')\n .join(MessageQueue)\n .order_by(func.random()) # Add this line to randomize the order of the rows\n .limit(messages_to_send)\n .all()\n )\n\n dialogs = await client.get_dialogs()\n\n for user_message in unsent_user_messages:\n message = user_message.message_queue\n user = session.query(User).filter(User.id == user_message.user_id).first()\n\n message_processed = False\n\n for dialog in dialogs:\n if dialog.id == user.id:\n try:\n await client.send_message(user.id, message.message, link_preview=False)\n user_message.sent_at = datetime.datetime.utcnow()\n user_message.status = 'sent'\n message_processed = True\n except Exception as e:\n if \"Too many requests (caused by SendMessageRequest)\" in str(e):\n if user_message.error_count < int(config['ANNOUNCE']['MAX_ERROR_COUNT']):\n user_message.error_count += 1\n session.commit()\n logger.warning(f\"Too many requests, stopping the script. Error details: {e}\")\n message_processed = True\n return\n else:\n logger.warning(f\"Too many requests more then MAX_ERROR_COUNT={int(config['ANNOUNCE']['MAX_ERROR_COUNT'])}. Error details: {e}\")\n user_message.status = 'error'\n message_processed = True\n return\n else:\n logger.error(f\"Error sending message to user {user.id}: {e}\")\n user_message.status = 'error'\n finally:\n break\n else:\n continue\n\n if message_processed == False: #if we haven't find user in the list of dialogs, try to send anyway\n try:\n await client.send_message(user.id, message.message, link_preview=False)\n user_message.sent_at = datetime.datetime.utcnow()\n user_message.status = 'sent'\n except Exception as e:\n if \"Too many requests (caused by SendMessageRequest)\" in str(e):\n if user_message.error_count < int(config['ANNOUNCE']['MAX_ERROR_COUNT']):\n user_message.error_count += 1\n session.commit()\n logger.warning(f\"Too many requests, stopping the script. Error details: {e}\")\n return\n else:\n logger.warning(f\"Too many requests more then MAX_ERROR_COUNT={int(config['ANNOUNCE']['MAX_ERROR_COUNT'])}. Error details: {e}\")\n user_message.status = 'error'\n return\n else:\n logger.error(f\"Error sending message to user {user.id}: {e}\")\n user_message.status = 'error'\n\n session.commit()\n\n # You can add a delay here if needed to avoid being flagged as spam\n await asyncio.sleep(delay_between_messages)","repo_name":"rvnikita/RvChat_bot","sub_path":"src/announce_helper.py","file_name":"announce_helper.py","file_ext":"py","file_size_in_byte":4490,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"52"} +{"seq_id":"5297633404","text":"\"\"\"Base module for the Python Durable functions.\n\nExposes the different API components intended for public consumption\n\"\"\"\nfrom .orchestrator import Orchestrator\nfrom .entity import Entity\nfrom .models.utils.entity_utils import EntityId\nfrom .models.DurableOrchestrationClient import DurableOrchestrationClient\nfrom .models.OrchestrationRuntimeStatus import OrchestrationRuntimeStatus\nfrom .models.DurableOrchestrationContext import DurableOrchestrationContext\nfrom .models.DurableEntityContext import DurableEntityContext\nfrom .models.RetryOptions import RetryOptions\nfrom .models.TokenSource import ManagedIdentityTokenSource\nimport json\nfrom pathlib import Path\nimport sys\nimport warnings\n\n\ndef validate_extension_bundles():\n \"\"\"Raise a warning if host.json contains bundle-range V1.\n\n Effects\n ------\n Warning: Warning prompting the user to update to bundles V2\n \"\"\"\n # No need to validate if we're running tests\n if \"pytest\" in sys.modules:\n return\n\n host_path = \"host.json\"\n bundles_key = \"extensionBundle\"\n version_key = \"version\"\n host_file = Path(host_path)\n\n if not host_file.exists():\n # If it doesn't exist, we ignore it\n return\n\n with open(host_path) as f:\n host_settings = json.loads(f.read())\n try:\n version_range = host_settings[bundles_key][version_key]\n except Exception:\n # If bundle info is not available, we ignore it.\n # For example: it's possible the user is using a manual extension install\n return\n # We do a best-effort attempt to detect bundles V1\n # This is the string hard-coded into the bundles V1 template in VSCode\n if version_range == \"[1.*, 2.0.0)\":\n message = \"Your application is currently configured to use Extension Bundles V1.\"\\\n \" Durable Functions for Python works best with Bundles V2,\"\\\n \" which provides additional features like Durable Entities, better performance,\"\\\n \" and is actively being developed.\"\\\n \" Please update to Bundles V2 in your `host.json`.\"\\\n \" You can set extensionBundles version to be: [2.*, 3.0.0)\"\n warnings.warn(message)\n\n\n# Validate that users are not in extension bundles V1\nvalidate_extension_bundles()\n\n__all__ = [\n 'Orchestrator',\n 'Entity',\n 'EntityId',\n 'DurableOrchestrationClient',\n 'DurableEntityContext',\n 'DurableOrchestrationContext',\n 'ManagedIdentityTokenSource',\n 'OrchestrationRuntimeStatus',\n 'RetryOptions'\n]\n\ntry:\n # disabling linter on this line because it fails to recognize the conditional export\n from .decorators.durable_app import (DFApp, Blueprint) # noqa\n __all__.append('DFApp')\n __all__.append('Blueprint')\nexcept ModuleNotFoundError:\n pass\n","repo_name":"Azure/azure-functions-durable-python","sub_path":"azure/durable_functions/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2829,"program_lang":"python","lang":"en","doc_type":"code","stars":113,"dataset":"github-code","pt":"52"} +{"seq_id":"27767095977","text":"import re\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport os\n\n__FILENAMES__ = {\n \"foreign\": \"-foreign-\",\n \"self\": \"-self-\",\n \"download\": \"-throughput-download-\",\n \"upload\": \"-throughput-upload-\",\n \"granular\": \"-throughput-granular-\",\n}\n\n\ndef seconds_since_start(dfs, start, column_name=\"SecondsSinceStart\"):\n \"\"\"\n Adds \"Seconds Since Start\" column to all DataFrames in List of DataFrames,\n based on \"CreationTime\" column within them and start time passed.\n\n :param dfs: List of DataFrames. Each DataFrame MUST contain DateTime column named \"CreationTime\"\n :param start: DateTime start time\n :param column_name: String of column name to add, default \"SecondsSinceStart\"\n :return: Inplace addition of column using passed column name\n \"\"\"\n for df in dfs:\n df[column_name] = (df[\"CreationTime\"]-start).apply(pd.Timedelta.total_seconds)\n\n\ndef find_earliest(dfs):\n \"\"\"\n Returns earliest DateTime in List of DataFrames based on \"CreationTime\" column within them.\n ASSUMES DATAFRAMES ARE SORTED\n\n :param dfs: List of DataFrames. Each DataFrame MUST contain DateTime column named \"CreationTime\" and MUST BE SORTED by it.\n :return: DateTime of earliest time within all dfs.\n \"\"\"\n earliest = dfs[0][\"CreationTime\"].iloc[0]\n for df in dfs:\n if df[\"CreationTime\"].iloc[0] < earliest:\n earliest = df[\"CreationTime\"].iloc[0]\n return earliest\n\n\ndef timeSinceStart(dfs, start):\n \"\"\"\n Adds \"TimeSinceStart\" column to all dataframes\n :param dfs:\n :param start:\n :return:\n \"\"\"\n for df in dfs:\n df[\"TimeSinceStart\"] = df[\"CreationTime\"]-start\n\ndef probeClean(df):\n # ConnRTT and ConnCongestionWindow refer to Underlying Connection\n df.columns = [\"CreationTime\", \"NumRTT\", \"Duration\", \"ConnRTT\", \"ConnCongestionWindow\", \"Type\", \"Empty\"]\n df = df.drop(columns=[\"Empty\"])\n df[\"CreationTime\"] = pd.to_datetime(df[\"CreationTime\"], format=\"%m-%d-%Y-%H-%M-%S.%f\")\n df[\"Type\"] = df[\"Type\"].apply(str.strip)\n df[\"ADJ_Duration\"] = df[\"Duration\"] / df[\"NumRTT\"]\n df = df.sort_values(by=[\"CreationTime\"])\n return df\n\n\ndef throughputClean(df):\n df.columns = [\"CreationTime\", \"Throughput\", \"NumberConnections\", \"Empty\"]\n df = df.drop(columns=[\"Empty\"])\n df[\"CreationTime\"] = pd.to_datetime(df[\"CreationTime\"], format=\"%m-%d-%Y-%H-%M-%S.%f\")\n df[\"ADJ_Throughput\"] = df[\"Throughput\"] / 1000000\n df = df.sort_values(by=[\"CreationTime\"])\n return df\n\n\ndef granularClean(df):\n df.columns = [\"CreationTime\", \"Throughput\", \"ID\", \"Type\", \"Empty\"]\n df = df.drop(columns=[\"Empty\"])\n df[\"CreationTime\"] = pd.to_datetime(df[\"CreationTime\"], format=\"%m-%d-%Y-%H-%M-%S.%f\")\n df[\"Type\"] = df[\"Type\"].apply(str.strip)\n df[\"ADJ_Throughput\"] = df[\"Throughput\"] / 1000000\n df = df.sort_values(by=[\"CreationTime\"])\n return df\n\n\ndef make90Percentile(df):\n df = df.sort_values(by=[\"ADJ_Duration\"])\n df = df.reset_index()\n df = df.iloc[:int(len(df)*.9)]\n df = df.sort_values(by=[\"CreationTime\"])\n return df\n\n\ndef main(title, paths):\n # Data Ingestion\n foreign = pd.read_csv(paths[\"foreign\"])\n self = pd.read_csv(paths[\"self\"])\n download = pd.read_csv(paths[\"download\"])\n upload = pd.read_csv(paths[\"upload\"])\n granular = pd.read_csv(paths[\"granular\"])\n\n # Data Cleaning\n foreign = probeClean(foreign)\n self = probeClean(self)\n download = throughputClean(download)\n upload = throughputClean(upload)\n granular = granularClean(granular)\n\n # Data Separation\n selfUp = self[self[\"Type\"] == \"SelfUp\"]\n selfUp = selfUp.reset_index()\n selfDown = self[self[\"Type\"] == \"SelfDown\"]\n selfDown = selfDown.reset_index()\n granularUp = granular[granular[\"Type\"] == \"Upload\"]\n granularUp = granularUp.reset_index()\n granularDown = granular[granular[\"Type\"] == \"Download\"]\n granularDown = granularDown.reset_index()\n\n\n\n # Moving Average\n foreign[\"DurationMA5\"] = foreign[\"ADJ_Duration\"].rolling(window=5).mean()\n selfUp[\"DurationMA5\"] = selfUp[\"ADJ_Duration\"].rolling(window=5).mean()\n selfDown[\"DurationMA5\"] = selfDown[\"ADJ_Duration\"].rolling(window=5).mean()\n\n # Normalize\n dfs = [foreign, selfUp, selfDown, download, upload, granularUp, granularDown]\n timeSinceStart(dfs, find_earliest(dfs))\n seconds_since_start(dfs, find_earliest(dfs))\n\n yCol = \"SecondsSinceStart\"\n\n def GraphNormal():\n ########## Graphing Complete\n fig, ax = plt.subplots()\n ax.set_title(title)\n ax.plot(foreign[yCol], foreign[\"ADJ_Duration\"], \"b.\", label=\"foreign\")\n ax.plot(selfUp[yCol], selfUp[\"ADJ_Duration\"], \"r.\", label=\"selfUP\")\n ax.plot(selfDown[yCol], selfDown[\"ADJ_Duration\"], \"c.\", label=\"selfDOWN\")\n ax.plot(foreign[yCol], foreign[\"DurationMA5\"], \"b--\", label=\"foreignMA\")\n ax.plot(selfUp[yCol], selfUp[\"DurationMA5\"], \"r--\", label=\"selfUPMA\")\n ax.plot(selfDown[yCol], selfDown[\"DurationMA5\"], \"c--\", label=\"selfDOWNMA\")\n ax.set_ylim([0, max(foreign[\"ADJ_Duration\"].max(), self[\"ADJ_Duration\"].max())])\n ax.legend(loc=\"upper left\")\n\n secax = ax.twinx()\n secax.plot(download[yCol], download[\"ADJ_Throughput\"], \"g-\", label=\"download (MB/s)\")\n secax.plot(granularDown[granularDown[\"ID\"] == 0][yCol], granularDown[granularDown[\"ID\"] == 0][\"ADJ_Throughput\"], \"g--\", label=\"Download Connection 0 (MB/S)\")\n secax.plot(upload[yCol], upload[\"ADJ_Throughput\"], \"y-\", label=\"upload (MB/s)\")\n secax.plot(granularUp[granularUp[\"ID\"] == 0][yCol], granularUp[granularUp[\"ID\"] == 0][\"ADJ_Throughput\"], \"y--\", label=\"Upload Connection 0 (MB/S)\")\n secax.legend(loc=\"upper right\")\n #GraphNormal()\n\n def StackedThroughput():\n ########## Graphing Stacked\n fig, ax = plt.subplots()\n ax.set_title(title + \" Granular Throughput\")\n # ax.plot(foreign[yCol], foreign[\"ADJ_Duration\"], \"b.\", label=\"foreign\")\n # ax.plot(selfUp[yCol], selfUp[\"ADJ_Duration\"], \"r.\", label=\"selfUP\")\n # ax.plot(selfDown[yCol], selfDown[\"ADJ_Duration\"], \"c.\", label=\"selfDOWN\")\n # ax.plot(foreign[yCol], foreign[\"DurationMA5\"], \"b--\", label=\"foreignMA\")\n # ax.plot(selfUp[yCol], selfUp[\"DurationMA5\"], \"r--\", label=\"selfUPMA\")\n # ax.plot(selfDown[yCol], selfDown[\"DurationMA5\"], \"c--\", label=\"selfDOWNMA\")\n # ax.set_ylim([0, max(foreign[\"ADJ_Duration\"].max(), self[\"ADJ_Duration\"].max())])\n # ax.legend(loc=\"upper left\")\n\n secax = ax.twinx()\n secax.plot(download[yCol], download[\"ADJ_Throughput\"], \"g-\", label=\"download (MB/s)\")\n secax.plot(upload[yCol], upload[\"ADJ_Throughput\"], \"y-\", label=\"upload (MB/s)\")\n\n granularDown[\"bucket\"] = granularDown[\"SecondsSinceStart\"].round(0)\n buckets = pd.DataFrame(granularDown[\"bucket\"].unique())\n buckets.columns = [\"bucket\"]\n buckets = buckets.set_index(\"bucket\")\n buckets[\"SecondsSinceStart\"] = granularDown.drop_duplicates(subset=[\"bucket\"]).reset_index()[\"SecondsSinceStart\"]\n buckets[\"bottom\"] = 0\n for id in sorted(granularDown[\"ID\"].unique()):\n secax.bar(granularDown[yCol][granularDown[\"ID\"] == id] + .05,\n granularDown[\"ADJ_Throughput\"][granularDown[\"ID\"] == id],\n width=.09, bottom=buckets.iloc[len(buckets) - len(granularDown[granularDown[\"ID\"] == id]):][\"bottom\"]\n )\n # ,label=f\"Download Connection {id}\")\n buckets[\"toadd_bottom\"] = (granularDown[granularDown[\"ID\"] == id]).set_index(\"bucket\")[\"ADJ_Throughput\"]\n buckets[\"toadd_bottom\"] = buckets[\"toadd_bottom\"].fillna(0)\n buckets[\"bottom\"] += buckets[\"toadd_bottom\"]\n\n\n granularUp[\"bucket\"] = granularUp[\"SecondsSinceStart\"].round(0)\n buckets = pd.DataFrame(granularUp[\"bucket\"].unique())\n buckets.columns = [\"bucket\"]\n buckets = buckets.set_index(\"bucket\")\n buckets[\"SecondsSinceStart\"] = granularUp.drop_duplicates(subset=[\"bucket\"]).reset_index()[\"SecondsSinceStart\"]\n buckets[\"bottom\"] = 0\n for id in sorted(granularUp[\"ID\"].unique()):\n secax.bar(granularUp[yCol][granularUp[\"ID\"] == id] - .05, granularUp[\"ADJ_Throughput\"][granularUp[\"ID\"] == id],\n width=.09, bottom=buckets.iloc[len(buckets) - len(granularUp[granularUp[\"ID\"] == id]):][\"bottom\"]\n )\n #,label=f\"Upload Connection {id}\")\n buckets[\"toadd_bottom\"] = (granularUp[granularUp[\"ID\"] == id]).set_index(\"bucket\")[\"ADJ_Throughput\"]\n buckets[\"toadd_bottom\"] = buckets[\"toadd_bottom\"].fillna(0)\n buckets[\"bottom\"] += buckets[\"toadd_bottom\"]\n secax.legend(loc=\"upper right\")\n\n\n secax.legend(loc=\"upper left\")\n\n #StackedThroughput()\n stacked_bar_throughput(upload, granularUp, \"SecondsSinceStart\", \"ADJ_Throughput\", title + \" Upload Stacked\",\n \"Upload Throughput MB/s\")\n stacked_bar_throughput(download, granularDown, \"SecondsSinceStart\", \"ADJ_Throughput\", title + \" Download Stacked\",\n \"Download Throughput MB/s\")\n\n def Percent90():\n ######### Graphing Removing 90th Percentile\n nonlocal selfUp\n nonlocal selfDown\n nonlocal foreign\n selfUp = make90Percentile(selfUp)\n selfDown = make90Percentile(selfDown)\n foreign = make90Percentile(foreign)\n\n # Recalculate MA\n foreign[\"DurationMA5\"] = foreign[\"ADJ_Duration\"].rolling(window=5).mean()\n selfUp[\"DurationMA5\"] = selfUp[\"ADJ_Duration\"].rolling(window=5).mean()\n selfDown[\"DurationMA5\"] = selfDown[\"ADJ_Duration\"].rolling(window=5).mean()\n\n # Graphing Complete\n fig, ax = plt.subplots()\n ax.set_title(title + \" 90th Percentile (ordered lowest to highest duration)\")\n # ax.plot(foreign[yCol], foreign[\"ADJ_Duration\"], \"b.\", label=\"foreign\")\n # ax.plot(selfUp[yCol], selfUp[\"ADJ_Duration\"], \"r.\", label=\"selfUP\")\n # ax.plot(selfDown[yCol], selfDown[\"ADJ_Duration\"], \"c.\", label=\"selfDOWN\")\n ax.plot(foreign[yCol], foreign[\"DurationMA5\"], \"b--\", label=\"foreignMA\")\n ax.plot(selfUp[yCol], selfUp[\"DurationMA5\"], \"r--\", label=\"selfUPMA\")\n ax.plot(selfDown[yCol], selfDown[\"DurationMA5\"], \"c--\", label=\"selfDOWNMA\")\n ax.set_ylim([0, max(foreign[\"ADJ_Duration\"].max(), selfUp[\"ADJ_Duration\"].max(), selfDown[\"ADJ_Duration\"].max())])\n ax.legend(loc=\"upper left\")\n\n secax = ax.twinx()\n secax.plot(download[yCol], download[\"ADJ_Throughput\"], \"g-\", label=\"download (MB/s)\")\n secax.plot(granularDown[granularDown[\"ID\"] == 0][yCol], granularDown[granularDown[\"ID\"] == 0][\"ADJ_Throughput\"],\n \"g--\", label=\"Download Connection 0 (MB/S)\")\n secax.plot(upload[yCol], upload[\"ADJ_Throughput\"], \"y-\", label=\"upload (MB/s)\")\n secax.plot(granularUp[granularUp[\"ID\"] == 0][yCol], granularUp[granularUp[\"ID\"] == 0][\"ADJ_Throughput\"], \"y--\",\n label=\"Upload Connection 0 (MB/S)\")\n secax.legend(loc=\"upper right\")\n\n Percent90()\n\ndef stacked_bar_throughput(df, granular, xcolumn, ycolumn, title, label):\n fig, ax = plt.subplots()\n ax.set_title(title)\n\n secax = ax.twinx()\n ax.get_yaxis().set_visible(False)\n ax.set_xlabel(\"Seconds Since Start (s)\")\n secax.set_ylabel(\"Throughput (MB/s)\")\n # secax.set_xticks(range(0, round(granular[xcolumn].max()) + 1)) # Ticks every 1 second\n\n # Plot Main Throughput\n secax.plot(df[xcolumn], df[ycolumn], \"k--\", label=label)\n\n df_gran = granular.copy()\n # df_gran[\"bucket\"] = df_gran[xcolumn].round(0) # With rounding\n df_gran[\"bucket\"] = df_gran[xcolumn] # Without rounding (csv creation time points need to be aligned)\n buckets = pd.DataFrame(df_gran[\"bucket\"].unique())\n buckets.columns = [\"bucket\"]\n buckets = buckets.set_index(\"bucket\")\n buckets[xcolumn] = df_gran.drop_duplicates(subset=[\"bucket\"]).reset_index()[xcolumn]\n buckets[\"bottom\"] = 0\n for id in sorted(df_gran[\"ID\"].unique()):\n secax.bar(df_gran[xcolumn][df_gran[\"ID\"] == id],\n df_gran[ycolumn][df_gran[\"ID\"] == id],\n width=.25, bottom=buckets.iloc[len(buckets) - len(df_gran[df_gran[\"ID\"] == id]):][\"bottom\"]\n )\n # ,label=f\"Download Connection {id}\")\n buckets[\"toadd_bottom\"] = (df_gran[df_gran[\"ID\"] == id]).set_index(\"bucket\")[ycolumn]\n buckets[\"toadd_bottom\"] = buckets[\"toadd_bottom\"].fillna(0)\n buckets[\"bottom\"] += buckets[\"toadd_bottom\"]\n\n secax.legend(loc=\"upper right\")\n\n\ndef findFiles(dir):\n matches = {}\n\n files = os.listdir(dir)\n for file in files:\n if os.path.isfile(dir+file):\n for name in __FILENAMES__:\n regex = \"(?P.*)(?P\" + __FILENAMES__[name] + \")(?P.*)\"\n match = re.match(regex, file)\n if match is not None:\n start = match.group(\"start\")\n end = match.group(\"end\")\n if start not in matches:\n matches[start] = {}\n if end not in matches[start]:\n matches[start][end] = {}\n if name in matches[start][end]:\n print(\"ERROR ALREADY FOUND A FILE THAT HAS THE SAME MATCHING\")\n matches[start][end][name] = dir+file\n return matches\n\ndef generatePaths():\n return {\n \"foreign\": \"\",\n \"self\": \"\",\n \"download\": \"\",\n \"upload\": \"\",\n \"granular\": \"\",\n }\n\ndef makeGraphs(files):\n for start in files:\n x = 0\n for end in files[start]:\n # Check if it contains all file fields\n containsALL = True\n for key in __FILENAMES__:\n if key not in files[start][end]:\n containsALL = False\n # If we don't have all files then loop to next one\n if not containsALL:\n continue\n\n main(start + \" - \" + str(x), files[start][end])\n x += 1\n\n# Press the green button in the gutter to run the script.\nif __name__ == '__main__':\n paths = generatePaths()\n\n files = findFiles(\"./Data/WillTest/\")\n print(files)\n makeGraphs(files)\n\n plt.show()","repo_name":"Schickendantzj/RPMGraph","sub_path":"main_with_granular.py","file_name":"main_with_granular.py","file_ext":"py","file_size_in_byte":14336,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"11607106412","text":"# -*- coding: utf-8 -*-\n# @Time : 2019/4/7 19:20\n# @Author : XiaTian\n# @File : 客户端.py\n\nimport socket\n\n# 1、创建套接字（买手机）\nphone = socket.socket(family=socket.AF_INET,type=socket.SOCK_STREAM)\n\n# 2、拨号\nphone.connect(('127.0.0.1',8080))\n\n# 3、发、收消息\na = {}\nphone.send(str(a).encode('utf-8')) # 网络通信传的的二进制，传消息时要先转换成二进制\ndata = phone.recv(1024)\nprint(data)\n\n# 4、关闭\nphone.close()\n\n\n'''\n 客户端只有一套套接字phone，用于发起建立链接请求，发收消息\n'''","repo_name":"summer5625/Mygit","sub_path":"第三模块_面向对象_网络编程基础/网络编程/day1/一次通信/客户端.py","file_name":"客户端.py","file_ext":"py","file_size_in_byte":568,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"29223214494","text":"from flask import Flask, request, send_from_directory, jsonify\nimport yaml\nfrom dotenv import load_dotenv\nfrom oauth import request_oauth_token\nfrom datetime import datetime\nimport logging\nfrom logging.handlers import RotatingFileHandler\n\napp = Flask(__name__, static_folder='public')\nload_dotenv()\n\n# Configure logging\nlogging.basicConfig(filename='app.log', level=logging.INFO, format='%(asctime)s [Process %(process)d] [%(levelname)s] - %(message)s')\n\nhandler = RotatingFileHandler('app.log', maxBytes=5*1024*1024, backupCount=5)\nlogging.getLogger().addHandler(handler)\n\nlogger = logging.getLogger(__name__)\n\nclass Person:\n def __init__(self, username, real_name, first_chatted, last_chatted, location='', chatter_type='unknown', interaction_count=0, aspirations='', journal=[], message_count=0):\n self.username = username\n self.realName = real_name\n self.location = location\n self.chatter_type = chatter_type\n self.interaction_count = interaction_count\n self.aspirations = aspirations\n self.message_count = message_count\n self.journal = journal\n self.first_chatted = first_chatted\n self.last_chatted = last_chatted\n\n\ndef load_people_data_from_yaml(yaml_str):\n data = yaml.load(yaml_str, Loader=yaml.FullLoader)\n\n people = []\n for person_data in data.get('data', []):\n person = Person(\n person_data.get('username'),\n person_data.get('realName'),\n person_data.get('first_chatted'),\n person_data.get('last_chatted'),\n person_data.get('location'),\n person_data.get('chatter_type'),\n person_data.get('interaction_count', 0),\n person_data.get('aspirations'),\n person_data.get('journal'),\n person_data.get('message_count', 0)\n )\n people.append(person)\n return people\n\ndef reset_messages_count():\n filename = 'people.yaml'\n try:\n with open(filename, 'r') as file:\n data = yaml.load(file, Loader=yaml.FullLoader)\n print('Resetting message counts ...')\n\n for person_data in data.get('data', []):\n # Reset the message_count to 0\n person_data['message_count'] = 0\n\n # Write the updated data back to the same file\n with open(filename, 'w') as file:\n yaml.dump(data, file, default_flow_style=False)\n\n print(f\"Message counts reset and saved to {filename}\")\n except Exception as e:\n print(f\"An error occurred: {str(e)}\")\n\n@app.route('/')\ndef index():\n return send_from_directory('public', 'index.html')\n\n@app.route('/')\ndef serve_static_file(filename):\n return send_from_directory(app.static_folder, filename)\n\n@app.route('/log', methods=['GET'])\ndef log():\n data = request.json\n print('Data logged from client:', data)\n\n yaml_str = yaml.dump(data)\n with open('update.yaml', 'w') as file:\n file.write(yaml_str)\n return '', 200\n\n@app.route('/message_count', methods=['POST'])\ndef message_count():\n data = request.json\n username = data.get('username')\n\n if not username:\n return jsonify({\"error\": \"Username not provided\"}), 400\n\n with open('people.yaml', 'r') as file:\n people_list = yaml.safe_load(file)['data']\n\n user_found = False\n for person in people_list:\n if person['username'].lower() == username.lower():\n user_found = True\n if person['chatter_type'] != 'bot':\n person['message_count'] += 1\n person['last_chatted'] = datetime.now().strftime(\"%Y-%m-%d\")\n action = \"incremented\"\n logger.info(f\"Message count incremented for user {username}, and last_chatted updated\")\n else:\n logger.info(f\"{person['username'].lower()} was a bot, no changes were made\")\n return jsonify({\"message\": f\"User {username} was a bot, no changes were made\"}), 200\n\n if not user_found and username.lower() != 'bot':\n current_date = datetime.now().strftime(\"%Y-%m-%d\")\n new_person = {\n 'username': username,\n 'chatter_type': 'known',\n 'message_count': 1,\n 'first_chatted': current_date,\n 'last_chatted': current_date,\n 'journal': []\n }\n people_list.append(new_person)\n action = \"added\"\n\n updated_yaml_str = yaml.dump({\"data\": people_list})\n with open('people.yaml', 'w') as file:\n file.write(updated_yaml_str)\n\n if action == \"incremented\":\n return jsonify({\"message\": f\"Message count incremented for user {username}, and last_chatted updated\"}), 200\n elif action == \"added\":\n return jsonify({\"message\": f\"User {username} added and message count incremented\"}), 200\n\n@app.route('/journal', methods=['GET', 'POST'])\ndef journal():\n\n if request.method == 'GET':\n return \"I am up\", 200\n\n data = request.json\n username = data.get('username')\n entry = data.get('entry')\n\n if not username or not entry:\n return jsonify({\"error\": \"Username or entry not provided\"}), 400\n\n with open('people.yaml', 'r') as file:\n people_list = yaml.safe_load(file)['data']\n\n user_found = False\n for person in people_list:\n if person['username'].lower() == username.lower():\n user_found = True\n journal_entry = {\n 'date': datetime.now().strftime(\"%Y-%m-%d\"),\n 'entry': entry\n }\n person['journal'].append(journal_entry)\n person['chatter_type'] = 'known'\n break\n\n if not user_found:\n return jsonify({\"error\": f\"No user found with username {username}\"}), 404\n\n updated_data = {\"data\": people_list}\n updated_yaml_str = yaml.dump(updated_data)\n\n with open('people.yaml', 'w') as file:\n file.write(updated_yaml_str)\n\n return jsonify({\"message\": f\"Journal updated for user {username}\"}), 200\n\n\n@app.route('/people', methods=['GET'])\ndef people():\n with open('people.yaml', 'r') as file:\n yaml_str = file.read()\n parsed_yaml = yaml.load(yaml_str, Loader=yaml.FullLoader)\n return jsonify(parsed_yaml.get('data', []))\n\nif __name__ == '__main__':\n reset_messages_count()\n # Start the Flask app in a separate thread\n from threading import Thread\n def start_app():\n app.run(port=3000)\n\n app_thread = Thread(target=start_app)\n app_thread.start()\n","repo_name":"justinhennessy/twitch-contacts","sub_path":"backend.py","file_name":"backend.py","file_ext":"py","file_size_in_byte":6465,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"38780289363","text":"from pathlib import Path\r\nimport comtypes.client\r\nfrom glob import glob\r\n\r\nwd_format_pdf = 17\r\n\r\ninput_files = glob(\".\\doc\\*.docx\")\r\n\r\nword = comtypes.client.CreateObject(\"Word.Application\")\r\n\r\nfor file in input_files:\r\n in_file = str(Path(file).absolute())\r\n out_file = str(Path(file).absolute()).replace(\"docx\",\"pdf\")\r\n doc = word.Documents.Open(in_file)\r\n doc.SaveAs(out_file, FileFormat=wd_format_pdf)\r\n doc.Close()\r\n # print(out_file)\r\n\r\nword.Quit()","repo_name":"stepheku/powerplan-rtf-gen","sub_path":"convert_docx_to_pdf.py","file_name":"convert_docx_to_pdf.py","file_ext":"py","file_size_in_byte":472,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"23668001260","text":"from functools import wraps\nfrom flask_jwt_extended import verify_jwt_in_request, get_jwt_identity\nfrom ..services import conta_service, operacao_service\nfrom flask import make_response, jsonify\n\ndef user_conta(view_function):\n @wraps(view_function)\n def decorator_funtion(*args, **kwargs):\n verify_jwt_in_request() #verificar se existe uma chave token\n usuario_logado = get_jwt_identity()\n conta = conta_service.listar_conta_id(kwargs['id'])\n if conta is None:\n return make_response(jsonify(\"Conta não existe\"), 404)\n elif conta.usuario_id == usuario_logado:\n return view_function(*args, **kwargs)\n else:\n return make_response(jsonify(\"Esta conta não pertence ao usuário logado\"), 403)\n return decorator_funtion\n\ndef user_operacao(view_function):\n @wraps(view_function)\n def decorator_funtion(*args, **kwargs):\n verify_jwt_in_request()\n usuario_logado = get_jwt_identity()\n operacao = operacao_service.listar_operacao_id(kwargs['id'])\n if operacao is None:\n return make_response(jsonify(\"Operação não encontrada\"), 404)\n else:\n conta = conta_service.listar_conta_id(operacao.conta_id)\n if conta.usuario_id == usuario_logado:\n return view_function(*args, **kwargs)\n else:\n return make_response ( jsonify ( \"Esta operação não pertence ao usuário logado\" ), 403 )\n return decorator_funtion","repo_name":"Marilainny/python-app-flutter","sub_path":"api/decorators/autorizacao.py","file_name":"autorizacao.py","file_ext":"py","file_size_in_byte":1499,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"36981368004","text":"\"\"\"\nFaça uma lista de comprar com listas\nO usuário deve ter a possibilidade de \ninserir, apagar e listar valores de sua lista\nNão permita que i programa quebre com\nerros de índices inexistente na lista\n\"\"\"\nimport os # limpar o terminal \n\nlista = [] # Criar uma lista vazia \n\nwhile True: # laço com as opções.\n print('Selecione uma opção')\n opcao = input('[i]nserir [a]pagar [l]istar: ')\n\n if opcao == 'i':\n os.system('cls')\n valor = input('Valor: ')\n lista.append(valor)\n elif opcao == 'a':\n indece_str = input(\n 'Escolha o índece para apagar: '\n )\n try: # Tratamento de erro \n indice = int(indece_str)\n del lista[indice]\n except ValueError: # passando o erro no except \n print('Por favor, digite um número int.')\n except IndexError:\n print('Índice não existe na lista')\n except Exception:\n print('Erro desconhecido') \n elif opcao == 'l':\n os.system('cls')\n \n if len(lista) == 0:\n print('Nada para listar')\n for i, valor in enumerate(lista):\n print(i, valor) \n else:\n print('Por favor, escolha i, a ou l.') \n","repo_name":"eleao83/Projeto.py","sub_path":"aula54.py","file_name":"aula54.py","file_ext":"py","file_size_in_byte":1245,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"24732431566","text":"class Node:\n def __init__(self, data) -> None:\n self.data = data\n self.next = None\n\n\na = Node('A')\nb = Node('B')\nc = Node('C')\nd = Node('D')\n\na.next = b\nb.next = c\nc.next = d\n\n\ndef reverse_linked_list(head, prev=None):\n if head == None: return prev\n next = head.next\n head.next = prev\n return reverse_linked_list(next, head)\n\n\n\nresult = reverse_linked_list(a)\n\ncurrent = result\nwhile current:\n print(current.data)\n current = current.next","repo_name":"kadimasum/BridgePy","sub_path":"linked_lists/reverse_list_recur.py","file_name":"reverse_list_recur.py","file_ext":"py","file_size_in_byte":472,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"46218596656","text":"class siteSelection:\n\n def __init__(self, driver) -> None:\n self.driver = driver\n\n def findCampground(self):\n self.driver.find_element_by_id('list-view-button').click()\n avali = self.driver.find_elements_by_css_selector('span mat-panel-description div')\n for ava in avali:\n if ava.text == 'Available':\n ava.click()\n break\n self.driver.find_element_by_css_selector(\"button[aria-label='Expand park alerts']\").click()\n\n def findSite(self):\n siteFound = False\n siteNum = 0\n while siteFound == False:\n avali = self.driver.find_elements_by_css_selector('mat-accordion mat-expansion-panel')\n for ava in avali:\n siteNum = (ava.text).split()\n siteNum = int(siteNum[1])\n if 'Available' in ava.text and not('Partially' in ava.text):\n ava.click()\n siteFound = True\n break\n if siteFound:\n break\n self.driver.find_element_by_id('loadMoreButton').click()\n \n reserveBtn = self.driver.find_element_by_id('reserveButton-' + str(siteNum-1))\n reserveBtn.click()","repo_name":"TheKinshu/ParkReservation","sub_path":"pageDetail/siteSelection.py","file_name":"siteSelection.py","file_ext":"py","file_size_in_byte":1229,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"9227115544","text":"\"\"\"\nThis script runs the signal_timestamps function, that gets the duration of each \nrecording + all the timestamps of the recoring, and saves the info in the \ndatabase.\n\"\"\"\n\nimport os\nimport sqlite3\n\nfrom birdsong.data_preparation.audio_conversion.signal_extraction import signal_timestamps\n\nif 'HOSTNAME' in os.environ:\n # script runs on server\n STORAGE_DIR = '/storage/step1_wav/'\n DATABASE_DIR = '/storage/db.sqlite'\nelse:\n # script runs locally\n STORAGE_DIR = 'storage/step1_wav/'\n DATABASE_DIR = 'storage/db.sqlite'\n\n# Get a list of files that are downloaded\ndownloaded_files = os.listdir(STORAGE_DIR)\nprint('list with downloaded files made')\nprint(len(downloaded_files))\n\n# Get the recording ID's from the filenames\ndownloaded_ids = [int(x[:-4]) for x in downloaded_files]\n\n# Get all the recordings that were already processed before\nconn = sqlite3.connect(DATABASE_DIR)\nprint('database loaded')\nc = conn.cursor()\nq = '''\nSELECT id FROM recordings\nWHERE step1 = 1 AND duration IS NOT NULL\n'''\nc.execute(q)\nprocessed_ids = [i[0] for i in c.fetchall()]\nprint('list of already processed recordings')\nprint(len(processed_ids))\n\n# Remove the already processed recordings from the ones we want to process\nto_process = [x for x in downloaded_ids if x not in processed_ids]\nprint('list of files to process')\nprint(len(to_process))\n\n# Processing\nq = '''\nUPDATE recordings\nSET duration = ?, sum_signal = ?, timestamps = ? \nWHERE id = ?\n'''\nbatch = []\nfor i, rec_id in enumerate(to_process):\n rec = str(rec_id) + '.wav'\n print(rec)\n try:\n duration, sum_signal, timestamps = signal_timestamps(\n STORAGE_DIR + rec)\n batch.append((duration, sum_signal, timestamps, rec_id))\n if len(batch) % 50 == 0:\n print(f\"batch {i} full\")\n c.executemany(q, batch)\n conn.commit()\n batch = []\n except:\n print(f'could not get info of recording {rec}')\n pass\nc.executemany(q, batch)\nconn.commit()\nconn.close()\n","repo_name":"multavici/DSR-Bird-Song","sub_path":"birdsong/data_preparation/audio_conversion/get_timestamps.py","file_name":"get_timestamps.py","file_ext":"py","file_size_in_byte":2006,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"11627617837","text":"import tensorflow as tf\n\nclass siamese:\n# create model\n\n def __init__(self, trigram_size):\n self.q1 = tf.placeholder(tf.float32, [None, trigram_size]) # question input\n self.q2 = tf.placeholder(tf.float32, [None, trigram_size]) # training : answer, testing : question\n with tf.variable_scope(\"siamese\") as scope:\n self.o1 = self.network(self.q1)\n scope.reuse_variables()\n self.o2 = self.network(self.q2)\n # with tf.variable_scope(\"siamese1\"):\n # self.o1 = self.network(self.q1)\n # with tf.variable_scope(\"siamese2\"):\n # self.o2 = self.network(self.q2)\n\n # Create loss\n self.label = tf.placeholder(tf.float32, [None, 1]) # change the input to\n self.bm_score = tf.placeholder(tf.float32, [None, 1])\n self.test_len = tf.placeholder(tf.int32)\n self.loss = self.similarity_loss()\n self.sim = self.cosine_sim()\n self.cs = self.combined_score()\n self.acc = self.accuracy()\n self.pred_label = self.get_predicted_label()\n self.map = self.mean_average_precision()\n self.mrr = self.mean_reciprocal_rank()\n self.patk = self.precision_at_1()\n self.cmap = self.comb_mean_average_precision()\n self.cmrr = self.comb_mean_reciprocal_rank()\n self.cpatk = self.comb_precision_at_1()\n\n def network(self, x):\n x = tf.expand_dims(x, 2) # adding image height\n x = tf.expand_dims(x, 3) # adding image channels\n with tf.name_scope(\"model\") as scope:\n with tf.variable_scope(\"conv_layer\") as scope:\n out = tf.contrib.layers.conv2d(inputs=x, num_outputs=10, kernel_size=[1, 10], activation_fn=tf.nn.relu, padding='SAME',\n weights_initializer=tf.contrib.layers.xavier_initializer_conv2d(),scope=scope)\n out = tf.contrib.layers.max_pool2d(out, [1, 100], padding='SAME')\n # Flatten the output\n with tf.variable_scope(\"flatten_layer\") as scope:\n out = tf.contrib.layers.flatten(inputs=out)\n # Final FC Layer\n with tf.variable_scope(\"fully_connected\") as scope:\n out = tf.contrib.layers.fully_connected(inputs=out, num_outputs= 128, activation_fn=tf.nn.relu,\n weights_initializer=tf.contrib.layers.xavier_initializer(), scope=scope)\n\n return out\n\n def similarity_loss(self):\n m = 0.5 # arbitrary\n cos_dist = tf.losses.cosine_distance(tf.nn.l2_normalize(self.o1, 1), tf.nn.l2_normalize(self.o2, 1), dim=1,\n reduction=\"none\")\n pos = tf.subtract(1.0,cos_dist)\n neg = tf.maximum(0.0, tf.subtract(cos_dist,m))\n L = self.label * pos + (tf.subtract(1.0,self.label)) * neg\n loss = tf.reduce_mean(L)\n return loss\n\n def cosine_sim(self):\n cos_dist = tf.losses.cosine_distance(tf.nn.l2_normalize(self.o1, 1), tf.nn.l2_normalize(self.o2, 1), dim=1,\n reduction=\"none\")\n return tf.subtract(1.0,cos_dist)\n #return cos_dist\n\n def combined_score(self):\n alpha = 0.2\n cos_dist = tf.losses.cosine_distance(tf.nn.l2_normalize(self.o1, 1), tf.nn.l2_normalize(self.o2, 1), dim=1,\n reduction=\"none\")\n cos_dist = tf.subtract(1.0,cos_dist)\n return tf.multiply(alpha, self.bm_score) + tf.multiply(1 - alpha, cos_dist)\n\n def accuracy(self):\n val = tf.cast(tf.less_equal(self.sim, 0.5), tf.int32) # self.sim is distance, not similarity\n val = tf.cast(val, tf.float32)\n return tf.reduce_sum(tf.cast(tf.equal(val, self.label), tf.int32))\n\n def get_predicted_label(self):\n val = tf.cast(tf.less_equal(self.sim, 0.5), tf.int32) # self.sim is distance, not similarity\n return tf.cast(val, tf.float32)\n\n # source : https://medium.com/@pds.bangalore/mean-average-precision-abd77d0b9a7e\n def mean_average_precision(self):\n sim_score = self.sim\n _, indices = tf.nn.top_k(tf.transpose(sim_score),k=self.test_len) # get the indices sorted by the similarity score\n indices = tf.transpose(indices)\n relevant_labels = tf.gather(self.label, indices=indices) # sort the labels by the sorted indices\n precision_at_k = tf.cumsum(relevant_labels)\n index_rel_labels = tf.expand_dims(tf.range(1, self.test_len+1),1) # get the index of the labels\n average_precision = tf.divide(tf.cast(precision_at_k, tf.float32), tf.cast(index_rel_labels, tf.float32)) # compute AP\n return tf.reduce_mean(average_precision)\n\n # source : https://machinelearning.wtf/terms/mean-reciprocal-rank-mrr/\n def mean_reciprocal_rank(self):\n sim_score = self.sim\n _, indices = tf.nn.top_k(tf.transpose(sim_score), k=self.test_len) # get the indices sorted by the similarity score\n indices = tf.transpose(indices)\n relevant_labels = tf.gather(self.label, indices=indices) # sort the labels by the sorted indices\n rank = tf.argmax(relevant_labels) # get the position of the highest predicted labels\n mrr = tf.divide(1,tf.add(rank,1))\n return mrr\n\n def precision_at_1(self):\n sim_score = self.sim\n _, indices = tf.nn.top_k(tf.transpose(sim_score), k=self.test_len) # get the indices sorted by the similarity score\n indices = tf.transpose(indices)\n relevant_labels = tf.gather(self.label, indices=indices) # sort the labels by the sorted indices\n precision1 = tf.gather(tf.squeeze(relevant_labels), indices=[0])\n return precision1\n\n # source : https://medium.com/@pds.bangalore/mean-average-precision-abd77d0b9a7e\n def comb_mean_average_precision(self):\n sim_score = self.cs\n _, indices = tf.nn.top_k(tf.transpose(sim_score),\n k=self.test_len) # get the indices sorted by the similarity score\n indices = tf.transpose(indices)\n relevant_labels = tf.gather(self.label, indices=indices) # sort the labels by the sorted indices\n precision_at_k = tf.cumsum(relevant_labels)\n index_rel_labels = tf.expand_dims(tf.range(1, self.test_len + 1), 1) # get the index of the labels\n average_precision = tf.divide(tf.cast(precision_at_k, tf.float32),\n tf.cast(index_rel_labels, tf.float32)) # compute AP\n return tf.reduce_mean(average_precision)\n\n # source : https://machinelearning.wtf/terms/mean-reciprocal-rank-mrr/\n def comb_mean_reciprocal_rank(self):\n sim_score = self.cs\n _, indices = tf.nn.top_k(tf.transpose(sim_score),\n k=self.test_len) # get the indices sorted by the similarity score\n indices = tf.transpose(indices)\n relevant_labels = tf.gather(self.label, indices=indices) # sort the labels by the sorted indices\n rank = tf.argmax(relevant_labels) # get the position of the highest predicted labels\n mrr = tf.divide(1, tf.add(rank, 1))\n return mrr\n\n def comb_precision_at_1(self):\n sim_score = self.cs\n _, indices = tf.nn.top_k(tf.transpose(sim_score),\n k=self.test_len) # get the indices sorted by the similarity score\n indices = tf.transpose(indices)\n relevant_labels = tf.gather(self.label, indices=indices) # sort the labels by the sorted indices\n precision1 = tf.gather(tf.squeeze(relevant_labels), indices=[0])\n return precision1\n","repo_name":"rezkaaufar/siamese-convnet","sub_path":"siamese.py","file_name":"siamese.py","file_ext":"py","file_size_in_byte":7101,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"18725607857","text":"import json\n\ndata = {\n 'no':1,\n 'acc':2\n}\n#json.dumps(): 对数据进行编码\n#json.loads(): 对数据进行解码\n\n\njson_str = json.dumps(data)\nprint(data)\nprint(json_str)\n\n# 如果你要处理的是文件而不是字符串，你可以使用 json.dump() 和 json.load() 来编码和解码JSON数据\n# 写入 JSON 数据\n# with open('data.json', 'w') as f:\n# # json.dump(data, f)\n# #\n# # # 读取数据\n# # with open('data.json', 'r') as f:\n# # data = json.load(f)","repo_name":"lizhilizhi/python-study","sub_path":"12_json.py","file_name":"12_json.py","file_ext":"py","file_size_in_byte":479,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"769838941","text":"#! /usr/bin/env python3\n\n'''\nhypersphere.py is a python script that can find the volume of an n-dimensional hypersphere using the Monte Carlo\nmean-value method to approximate the value of the integral (specifically found for 0-12 dimensions) and plots the\nvolume as a function of dimension. Also finds the error using different number of sample points for a 10-d\nhypersphere and plots this error as a function of number of sample points.\n\nJaniris Rodriguez\nPHZ 4151C\nFeb 21, 2021\n'''\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef withinSphere(arg, dim):\n '''\n evaluates whether given point is within a {dim}-dimenional sphere\n input:\n arguement (list of floats): represents point to evaluate\n dim (int): number of elements in arguements, represents number of dimensions for sphere\n returns:\n 1 if the point (arguement) is within the sphere, 0 if it isn't\n '''\n\n # value will store the result of the sum of the arguements squared to check if it would be inside the {dim}-dimensional sphere\n value = 0\n\n for i in range(dim):\n value += (arg[i]**2)\n\n # checks to see if this point is within the sphere, it is within the sphere if it less than or equal to 1\n # if it is, result of function is 1, if not, it is 0\n if value <= 1:\n result = 1\n else:\n result = 0\n\n return result\n\n\ndef montecarloIntegration(func, dim, lim, N):\n '''\n based on textbook function for the Monte Carlo mean-value integration method of any dimension\n input:\n func (function): user-defined function to be integrated over, takes in a list as an arguement\n dim (int): number of dimensions being integrated over\n lim (2-d list of floats): stores limits of integration for dim dimensions. first index represents the dimension,\n second index represents either the lower limit (element 0) or upper limit (element 1) of integration\n N (int): number of sample points\n returns:\n result of integral and a list of lists of random values used as arguements for the function\n '''\n I = 1.0 / N # stores value of integral, starts by storing 1/N, which helps set up finding average of function\n summation = 0 # stores value of summation for N random arguemnts sent to function to find average value of function\n argList = [] # stores lists of random values used as arguements for the function\n\n # multiplies inetgral value by the subtraction of lower limit of integral from upper limit of integral for the ith dimension\n for i in range(dim):\n I *= (lim[i][1] - lim[i][0])\n\n for i in range(N):\n # stores arguements to send to function for one iteration (ie sample point)\n arg = []\n\n # generating dim number of random arguements within the bounds of integration for the jth dimension\n # to send to function being integrated\n for j in range(dim):\n arg.append(np.random.uniform(lim[j][0], lim[j][1]))\n\n # adds the result of function with these randomly produced arguements\n summation += func(arg, dim)\n\n #stores this iteration's line up of random numbers to find error\n argList.append(arg)\n\n # multiplies result of sum into value of integral\n I *= summation\n\n return I, argList\n\ndef errorMonteCarlo(func, arg, dim, lim, N):\n '''\n find the error on the integral when using the Monte Carlo mean-value method\n input:\n func (function): user-defined function to be integrated over, takes in a list as an arguement\n arg (2-d list): stores arguements used to find value of integral using the montecarloIntegration function. first index\n represents which sample point out of N is being evaluated, the second index represents a specific\n coordinate value for that sample point\n dim (int): number of dimensions being integrated over\n lim (2-d list): stores limits of integration for dim dimensions. first index represents the dimension,\n second index represents either the lower limit (element 0) or upper limit (element 1) of integration\n N (int): number of sample points\n returns:\n error on the integral estimation\n '''\n\n var_term1 = 1. / N # will store the expectation value of func^2 to find variance of func\n var_term2 = 1. / (N**2) # will store the expectation value squared of func to find variance of func\n summation1 = 0 # will store sum of func values for given arg values to calculate 1st term of variance of func\n summation2 = 0. # will store sum of func values for given arg values to calculate 2nd term of variance of func\n sigma = 1. / np.sqrt(N) # will store error\n\n # multiplies error value by the subtraction of lower limit of integral from upper limit of integral for the ith dimension\n for j in range(dim):\n sigma *= (lim[j][1] - lim[j][0])\n\n # finds values of function for a given arguement to add to summations for the terms in thhe variance of func\n for i in range(int(N)):\n val = func(arg[j], dim)\n summation1 += val ** 2\n summation2 += val\n\n # multiply in the respective summations to find the two terms of the variance\n var_term1 *= summation1\n var_term2 *= summation2\n\n # combine the two variance terms and take square root to find the error on func\n sigma *= np.sqrt(var_term1 - var_term2)\n\n return sigma\n\n\n# main code block\nnumPoints = 1000000 # number of sampling points for integration\nnumDimension = 10 # number of dimensions hypersphere is\nlimit = [-1,1] # bounds of integration, -1 to 1 for all dimensions\n\nlimit_10d = [] # list containing limits of integration for 10 dimensions\narguements_10d = [] # list containing arguements used for the integration in dimensions\n\n# limits of integration for all 10 dimensions are -1 to 1\nfor i in range(numDimension):\n limit_10d.append(limit)\n\n# calculate volume of 10-d hypersphere\nvolume_10d, arguements_10d = montecarloIntegration(withinSphere, numDimension, limit_10d, numPoints)\n\nprint(f'The volume of a 10-d hypersphere is {volume_10d}\\n')\n\nlowerDim = 0\nupperDim = 12\ndimensionRange = [*range(lowerDim, upperDim)] # range of dimensions from 0 to 12\nvolumes = [] # store volumes of spheres for given dimensions in dimensionRange\narguments = [] # stores arguements used to calculate the volume of spheres for given dimensions\n\nfor i in dimensionRange:\n # creating a temporary list of limits for each iteration\n limit_list = []\n # appends appropriate number of limits for a given dimension to send into Monte Carlo function\n for j in range(len(dimensionRange)):\n limit_list.append(limit)\n # find volumes for each dimension in dimensionRange\n # saves these to temporary variables to then append to their respective lists\n tempVolume, tempArg = montecarloIntegration(withinSphere, dimensionRange[i], limit_list, numPoints)\n volumes.append(tempVolume)\n arguments.append(tempArg)\n\n print(f'The (hyper)volume of a {i}-d sphere is approximately {volumes[i]:.4f}')\n\n\n#graph of hypervolume v. dimension\nplt.plot(dimensionRange, volumes)\nplt.xlabel('Dimension')\nplt.ylabel('Hypervolume')\nplt.title('Hypervolume v. Number of Dimension')\nax = plt.gca() # gets the axes for this specific plot\nax.spines['top'].set_visible(False) # gets rid of top and right axes (purely for aesthetics)\nax.spines['right'].set_visible(False)\nplt.savefig('dimension_hypervolume_plot.png') # saves figure\nplt.show()\nplt.clf()\n\nlowerNumPoints = 20000 # bottom of range for number of sampling points\nstep = 20000 # step between different point values\nnumber = int((numPoints - lowerNumPoints) / step) # calculates how many different numbers of sample points there should be for given step value\n\n# generates sample point range from {lowerNumPoints} to {numPoints} in steps of {step} as a list\nnumPointsRange = np.linspace(lowerNumPoints, numPoints, number).tolist()\n\nerrors_10d = [] # stores the errors in the estimation of integral for 10-d hypersphere\n\nprint('\\nFor a 10-d hypersphere, the error on the volume is:')\n\ncounter = 0 # for the purpose of printing out errors\n\nfor i in numPointsRange:\n # find error in integral result for 10-d hypersphere for i sample points\n errors_10d.append(errorMonteCarlo(withinSphere, arguements_10d, numDimension, limit_10d, i))\n print(f'{errors_10d[int(counter)]} for {i} steps')\n counter += 1\n\n#graph of error v. sample points\nplt.plot(numPointsRange, errors_10d)\nplt.xlabel('Number of Sample Points')\nplt.ylabel('Error on Volume')\nplt.title('Error on Volume of 10-d Hypershpere v. N')\nax = plt.gca() # gets the axes for this specific plot\nax.spines['top'].set_visible(False) # gets rid of top and right axes (purely for aesthetics)\nax.spines['right'].set_visible(False)\nplt.savefig('error_N_plot.png') # saves figure\nplt.show()\nplt.clf()\n","repo_name":"janirisrodriguez/Compuational-Physics-HW","sub_path":"hypersphere.py","file_name":"hypersphere.py","file_ext":"py","file_size_in_byte":9400,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"6238186700","text":"import strawberry\nimport datetime\nimport typing\nimport uuid\n\nfrom typing import Annotated\n\nfrom utils.Dataloaders import getLoadersFromInfo, getUserFromInfo\nfrom .BaseGQLModel import BaseGQLModel\n\nfrom GraphTypeDefinitions._GraphResolvers import (\n resolve_id,\n resolve_name,\n resolve_name_en,\n resolve_changedby,\n resolve_created,\n resolve_lastchange,\n resolve_createdby,\n resolve_rbacobject,\n createRootResolver_by_id,\n createRootResolver_by_page\n)\n\nPartGQLModel = Annotated[\"PartGQLModel\", strawberry.lazy(\".PartGQLModel\")]\nItemTypeGQLModel = Annotated[\"ItemTypeGQLModel\", strawberry.lazy(\".ItemTypeGQLModel\")]\n\n@strawberry.input\nclass ItemUpdateGQLModel:\n lastchange: datetime.datetime\n name: typing.Optional[str] = None\n order: typing.Optional[int] = None\n value: typing.Optional[str] = None\n type_id: typing.Optional[uuid.UUID] = None\n\n@strawberry.federation.type(\n keys=[\"id\"], \n name=\"FormItemGQLModel\",\n description=\"\"\"Type representing an item in the form\"\"\"\n)\nclass ItemGQLModel(BaseGQLModel):\n @classmethod\n def getLoader(cls, info):\n return getLoadersFromInfo(info).items\n \n # @classmethod\n # async def resolve_reference(cls, info: strawberry.types.Info, id: uuid.UUID):\n # implementation is inherited\n\n id = resolve_id\n name = resolve_name\n changedby = resolve_changedby\n lastchange = resolve_lastchange\n created = resolve_created\n createdby = resolve_createdby\n name_en = resolve_name_en\n rbacobject = resolve_rbacobject\n\n @strawberry.field(description=\"\"\"Item's order\"\"\")\n def order(self) -> int:\n return self.order if self.order else 0\n\n @strawberry.field(description=\"\"\"Item's value \"\"\")\n def value(self) -> str:\n return self.value\n\n @strawberry.field(description=\"Retrieves the part owning the item\")\n async def part(self, info: strawberry.types.Info) -> typing.Optional[\"PartGQLModel\"]:\n from .PartGQLModel import PartGQLModel\n result = await PartGQLModel.resolve_reference(info, self.part_id)\n return result\n\n @strawberry.field(description=\"Retrieves the item type\")\n async def type(self, info: strawberry.types.Info) -> typing.Optional[\"ItemTypeGQLModel\"]:\n from .ItemTypeGQLModel import ItemTypeGQLModel\n result = None if self.type_id is None else await ItemTypeGQLModel.resolve_reference(info=info, id=self.type_id)\n return result\n\n#############################################################\n#\n# Queries\n#\n#############################################################\n\n@strawberry.field(description=\"Retrieves the item type\")\nasync def item_by_id(\n self, info: strawberry.types.Info, id: uuid.UUID\n) -> typing.Optional[ItemGQLModel]:\n result = await ItemGQLModel.resolve_reference(info=info, id=id)\n return result\n\nfrom dataclasses import dataclass\nfrom uoishelpers.resolvers import createInputs\n\n@createInputs\n@dataclass\nclass FormItemWhereFilter:\n name: str\n name_en: str\n type_id: uuid.UUID\n value: str\n\n@strawberry.field(description=\"Retrieves the item type\")\nasync def item_page(\n self, info: strawberry.types.Info, skip: int = 0, limit: int = 0,\n where: typing.Optional[FormItemWhereFilter] = None\n) -> typing.List[ItemGQLModel]:\n loader = getLoadersFromInfo(info).items\n wf = None if where is None else strawberry.asdict(where)\n result = await loader.page(skip, limit, where = wf)\n return result\n#############################################################\n#\n# Mutations\n#\n#############################################################\n\n@strawberry.input(description=\"Input structure - C operation\")\nclass FormItemInsertGQLModel:\n name: str = strawberry.field(description=\"Item name\")\n part_id: uuid.UUID = strawberry.field(description=\"id of parent entity\")\n\n id: typing.Optional[uuid.UUID] = strawberry.field(description=\"primary key (UUID), could be client generated\", default=None)\n value: typing.Optional[str] = None\n order: typing.Optional[int] = strawberry.field(description=\"Position in parent entity\", default=None)\n type_id: typing.Optional[uuid.UUID] = None\n createdby: strawberry.Private[uuid.UUID] = None \n rbacobject: strawberry.Private[uuid.UUID] = None \n \n\n@strawberry.input(description=\"Input structure - U operation\")\nclass FormItemUpdateGQLModel:\n lastchange: datetime.datetime = strawberry.field(description=\"timestamp of last change = TOKEN\")\n id: uuid.UUID = strawberry.field(description=\"primary key (UUID), identifies object of operation\")\n\n name: typing.Optional[str] = strawberry.field(description=\"Item name\", default=None)\n value: typing.Optional[str] = None\n order: typing.Optional[int] = strawberry.field(description=\"Position in parent entity\", default=None)\n type_id: typing.Optional[uuid.UUID] = None\n changedby: strawberry.Private[uuid.UUID] = None\n \n@strawberry.type(description=\"Result of CU operations\")\nclass FormItemResultGQLModel:\n id: uuid.UUID = strawberry.field(description=\"primary key of CU operation object\")\n msg: str = strawberry.field(description=\"\"\"Should be `ok` if descired state has been reached, otherwise `fail`.\nFor update operation fail should be also stated when bad lastchange has been entered.\"\"\")\n\n @strawberry.field(description=\"\"\"Result of item operation\"\"\")\n async def item(self, info: strawberry.types.Info) -> typing.Optional[ItemGQLModel]:\n result = await ItemGQLModel.resolve_reference(info, self.id)\n return result\n \n\n@strawberry.field(\n description=\"\"\"Updates a section.\"\"\"\n)\nasync def item_update(self, info: strawberry.types.Info, item: FormItemUpdateGQLModel) -> \"FormItemResultGQLModel\":\n user = getUserFromInfo(info)\n item.changedby = uuid.UUID(user[\"id\"])\n\n result = FormItemResultGQLModel(id=item.id, msg=\"ok\")\n loader = getLoadersFromInfo(info).items\n row = await loader.update(item)\n result.msg = \"fail\" if row is None else \"ok\"\n return result \n\n\n@strawberry.field(\n description=\"\"\"C operation\"\"\"\n)\nasync def item_insert(self, info: strawberry.types.Info, item: FormItemInsertGQLModel) -> \"FormItemResultGQLModel\":\n user = getUserFromInfo(info)\n item.createdby = uuid.UUID(user[\"id\"])\n\n # form as the parent of new section is checked\n # rbacobject is retrieved and assigned to section.rbacobject\n # rbacobject is shared among form, its sections and parts\n partloader = getLoadersFromInfo(info).parts\n part = await partloader.load(item.part_id)\n assert part is not None, f\"{item.part_id} is unknown part (of section) (during item insert)\"\n item.rbacobject = part.rbacobject\n\n loader = getLoadersFromInfo(info).items\n row = await loader.insert(item)\n result = FormItemResultGQLModel(id=item.id, msg=\"ok\")\n result.msg = \"fail\" if row is None else \"ok\"\n result.id = None if row is None else row.id\n return result ","repo_name":"hrbolek/gql_forms","sub_path":"GraphTypeDefinitions/ItemGQLModel.py","file_name":"ItemGQLModel.py","file_ext":"py","file_size_in_byte":6901,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"6583138844","text":"# 31. 下一个排列\n# 实现获取 下一个排列 的函数，算法需要将给定数字序列重新排列成字典序中下一个更大的排列（即，组合出下一个更大的整数）。\n#\n# 如果不存在下一个更大的排列，则将数字重新排列成最小的排列（即升序排列）。\n#\n# 必须 原地 修改，只允许使用额外常数空间。\n#\n#\n#\n# 示例 1：\n#\n# 输入：nums = [1,2,3]\n# 输出：[1,3,2]\n# 示例 2：\n#\n# 输入：nums = [3,2,1]\n# 输出：[1,2,3]\n# 示例 3：\n#\n# 输入：nums = [1,1,5]\n# 输出：[1,5,1]\n# 示例 4：\n#\n# 输入：nums = [1]\n# 输出：[1]\n#\n#\n# 提示：\n#\n# 1 <= nums.length <= 100\n# 0 <= nums[i] <= 100\n\nclass Solution(object):\n def __init__(self):\n pass\n\n def get_next(self, nums):\n if not nums or len(nums) == 1:\n return\n cnt = len(nums)\n # 找到 要换的那个值：从后往前第一个降序中小的那个值\n idx = -1\n for i in range(cnt - 1, 0, -1):\n if nums[i] > nums[i - 1]:\n idx = i - 1\n break\n print(idx)\n if idx == -1: # 完全逆序从大到小，直接返回\n nums.reverse()\n return nums\n\n # 交换 idx 与 后面第一个大于它的值交换\n for i in range(cnt - 1, idx, -1):\n if nums[i] > nums[idx]:\n nums[idx], nums[i] = nums[i], nums[idx]\n break\n # print(nums)\n\n # idx 后面的数据翻转\n nums[idx + 1:] = nums[cnt:idx:-1]\n return nums\n\n\nnums = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\nsolu = Solution()\nfor i in range(10):\n print(\"res:\", solu.get_next(nums))\n","repo_name":"StevenZhou5/CuteCodeV2","sub_path":"Algorithm/normal/31.py","file_name":"31.py","file_ext":"py","file_size_in_byte":1679,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"39404382983","text":"from simple_colors import *\nimport random\nfrom termcolor import colored\n\n\n#Allows player to actually setup the game themselves\ndef setup():\n global n\n global d\n global numb\n global life\n life = 10\n print(\"\\nWelcome to the \" +\n colored(\"Number Guessing game\", 'yellow', attrs=['bold']))\n print(\"First off, I want you to choose your range \")\n n = input(\"\\nType in the minimum number you want me to guess from\\n\")\n d = input(\"Now type in the maximum number you want me to guess from \\n\")\n numb = random.randint(int(n), int(d))\n\n\n#Game itself\ndef guessing():\n global life\n guess = input(\"\\nTry guess a number between \" + str(n) + \" and \" + str(d) +\n \"\\n\")\n\n while int(guess) != int(numb) and life > 1:\n if int(guess) < int(numb):\n print(\"\\nThat guess is too low\")\n life = life - 1\n print(\" You now have \" +\n colored(str(life), 'red', attrs=['bold']) + \" Lives\")\n guess = input(\"\\n Try again\\n\")\n elif int(guess) > int(numb):\n print(\"\\nThat guess is\", red(\"too high\", [\"bold\"]))\n life = life - 1\n print(\" You now have \" +\n colored(str(life), 'green', attrs=['bold']) + \" Lives\")\n guess = input(\"\\n Try again\\n\")\n\n\n#Response once player has either won or lost all lives\ndef checking():\n global again\n if int(life) > 1:\n print(\"\\nWELL DONE you finished with \" + str(life) + \" Lives left\", )\n print(\" The number was \" + str(numb))\n again = input(\"\\nWanna play again? y/n\\n\")\n else:\n print(\"\\nLMFAO YOU DIDN'T GET IT\")\n print(\" The number was \" + str(numb))\n again = input(\"\\nWanna play again? y/n\\n\")\n\n\n#Beginning of the actual Code\nsetup()\nguessing()\nchecking()\n\n#Restart Game or Close Program options here\nwhile again.lower() == \"y\":\n setup()\n guessing()\n checking()\n\nif again.lower() == \"n\":\n print(\"\\nThanks for Playing, Have a good rest of your day,\", cyan(\"Bye bye now \",['bold']))\n","repo_name":"ZaidMGames/Number-Guessing-Game","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2064,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"40003553406","text":"from django.shortcuts import render, redirect\nfrom django.shortcuts import render, redirect\nfrom django.contrib import messages\nfrom .models import User, Book, Author, Review\nimport bcrypt\n\n\n\n\ndef index(request):\n return render(request, 'login.html')\n\n\ndef new_user(request):\n errors = User.objects.new_validator(request.POST)\n if len(errors) > 0:\n for key, value in errors.items():\n messages.error(request, value)\n return redirect('/')\n else:\n name= request.POST['name']\n user_name= request.POST['user_name']\n email= request.POST['email']\n password= request.POST['password']\n pw_hash = bcrypt.hashpw(password.encode(), bcrypt.gensalt()).decode()\n new_user = User.objects.create(name=name, user_name=user_name, email=email, password=pw_hash)\n request.session['user_id'] = new_user.id\n\n return redirect(f'/all_books/{new_user.id}')\n\n\n\ndef login(request):\n \n errors = User.objects.return_validator(request.POST)\n if len(errors) > 0:\n for key, value in errors.items():\n messages.error(request, value)\n return redirect('/')\n login_user_list = User.objects.filter(email=request.POST['email']) \n logged_in_user = login_user_list[0]\n request.session['user_id'] = logged_in_user.id\n return redirect(f'/all_books/{logged_in_user.id}')\n\n\n\ndef all_books(request, user_id):\n if 'user_id' not in request.session:\n return redirect('/') \n \n last_three = Review.objects.all().order_by('-id')[:3]\n context = {\n 'user' : User.objects.get(id=request.session['user_id']),\n 'details' : Review.objects.all(),\n 'books' : Book.objects.all(),\n 'last_three' : last_three\n\n }\n return render(request, 'all_books.html', context)\n\n\n\ndef add_book(request):\n if 'user_id' not in request.session:\n return redirect('/')\n\n author = Author.objects.all()\n context={\n 'user' : User.objects.get(id=request.session['user_id']),\n 'author' : author\n }\n \n return render(request, 'add_book.html', context)\n\n\n\ndef process_new(request):\n\n errors = User.objects.book_validator(request.POST)\n if len(errors) > 0:\n for key, value in errors.items():\n messages.error(request, value)\n return redirect('/add_book')\n\n title = request.POST['title']\n review = request.POST['review']\n rating = request.POST['rating']\n user_id = request.session['user_id']\n \n if request.POST['new_auth'] == '':\n author = request.POST['author']\n author= Author.objects.get(id=author)\n this_book = Book.objects.create(title=title, author=author)\n else:\n added_auth = request.POST['new_auth']\n new_auth = Author.objects.create(name=added_auth)\n this_book = Book.objects.create(title=title, author=new_auth)\n\n this_book_id = this_book.id\n review= Review.objects.create(comment=review, rating=rating, user=User.objects.get(id=user_id), book=this_book)\n\n return redirect(f'/one_book/{this_book_id}')\n\n\n\ndef one_book(request, this_book_id):\n if 'user_id' not in request.session:\n return redirect('/') \n\n user = User.objects.get(id=request.session['user_id'])\n\n book = Book.objects.get(id=this_book_id)\n author = book.author.name\n review = book.books.all()\n\n context = {\n 'book' : book,\n 'author' : author,\n 'review' : review,\n 'user' : user,\n }\n return render(request, 'one_book.html', context)\n\n\n\ndef add_review(request):\n user = User.objects.get(id=request.session['user_id'])\n comment = request.POST['comment']\n rating = request.POST['rating']\n book_id = request.POST['book_id']\n\n book_updated = Book.objects.get(id=book_id)\n Review.objects.create(comment=comment, rating=rating, user=user, book=book_updated)\n\n return redirect(f'/one_book/{book_id}')\n\n\n\ndef user_page(request, user_id):\n if 'user_id' not in request.session:\n return redirect('/')\n\n user = User.objects.get(id=user_id)\n review_count = user.users.all()\n print(review_count)\n \n count = 0\n for x in review_count:\n if [x]:\n count = count + 1\n \n context={\n 'user' : user,\n 'books' :user.users.all(),\n 'count' : count\n }\n\n return render(request, 'user_page.html', context)\n\n\n\ndef logout(request):\n request.session.flush()\n return redirect('/')","repo_name":"R2DEV0/dojo_reads","sub_path":"main_app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4425,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"69901791204","text":"#!/usr/bin/env python\n\n# This file is part of the Printrun suite.\n#\n# Printrun 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# Printrun 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 Printrun. If not, see .\n\nimport sys\n\ntry:\n import wx\nexcept:\n print(\"wxPython is not installed. This program requires wxPython to run.\")\n if sys.version_info.major >= 3:\n print(\"\"\"\\\nAs you are currently running python3, this is most likely because wxPython is\nnot yet available for python3. You should try running with python2 instead.\"\"\")\n sys.exit(-1)\n else:\n raise\n\nfrom printrun.pronterface import PronterApp\n\nif __name__ == '__main__':\n app = PronterApp(False)\n try:\n app.MainLoop()\n except KeyboardInterrupt:\n pass\n del app\n","repo_name":"algspd/asimov","sub_path":"software/Printrun_resin/pronterface.py","file_name":"pronterface.py","file_ext":"py","file_size_in_byte":1255,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"52"} +{"seq_id":"39080786745","text":"import os\nimport pdfkit\nfrom PyPDF2 import PdfFileMerger\n\nclass PDFMerger:\n def __init__(self, src_dir, pathpdf):\n self.src_dir = src_dir\n self.pathpdf = pathpdf\n self.pname = []\n\n def get_name(self):\n string_parts = self.src_dir.split('_')\n self.pname = (string_parts[2], string_parts[1])\n\n def convert_html_to_pdf(self):\n enc_folder = os.listdir(self.src_dir)\n for folder in enc_folder: \n path = os.path.join(self.src_dir, folder)\n for html in os.listdir(path):\n if html.endswith(\".html\"):\n # Convert HTML file to PDF\n html_path = os.path.join(path, html)\n pdf_file = os.path.splitext(html)[0] + \".pdf\"\n pdf_path = os.path.join(self.pathpdf, pdf_file)\n try:\n pdfkit.from_file(html_path, pdf_path)\n except PermissionError:\n print(f\"Permission denied: {html_path}\")\n continue\n except Exception as e:\n print(f\"Error occurred when converting {html} to PDF: {e}\")\n continue\n\n def merge_pdfs(self):\n output_dir = 'D:\\\\MergedHTMLs' # replace with the path to your output folder\n output_filename = os.path.join(output_dir, self.pname[0] + \"_\" + self.pname[1] + \"_ENC.pdf\") # replace with your desired output filename and path\n\n pdf_merger = PdfFileMerger()\n\n # Merge all PDF files in the folder into one document\n for filename in os.listdir(self.pathpdf):\n if filename.endswith('.pdf'):\n filepath = os.path.join(self.pathpdf, filename)\n with open(filepath, 'rb') as f:\n pdf_merger.append(f)\n\n # Write the merged document to a new file in the output directory\n with open(output_filename, 'wb') as f:\n pdf_merger.write(f)\n\n def reset(self):\n for f in os.listdir(self.pathpdf):\n filepath = os.path.join(self.pathpdf, f)\n os.remove(filepath)\n\nsrc_dir = \"D:\\\\ProgressNotes\"\npathpdf = \"D:\\\\MergedPDFs\"\n\nmerger = PDFMerger(src_dir, pathpdf)\n\nfor folder in os.listdir(src_dir):\n merger.src_dir = os.path.join(src_dir, folder)\n merger.get_name()\n merger.convert_html_to_pdf()\n merger.merge_pdfs()\n merger.reset()\n","repo_name":"Anish-I/EHRFileMigration","sub_path":"Filemerger.py","file_name":"Filemerger.py","file_ext":"py","file_size_in_byte":2413,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"22009166944","text":"from dataclasses import asdict, dataclass\nfrom typing import Tuple\n\nfrom elasticsearch import Elasticsearch\nfrom state_controller import StateController\n\n\n@dataclass\nclass ElasticsearchLoader:\n \"\"\"Класс для загрузки данных в ElasticSearch\"\"\"\n\n es: Elasticsearch\n es_sc: StateController\n page_size: int = 500\n\n def load(self, index: str,\n data: Tuple):\n ready_data = []\n self.es_sc.get_state()\n\n for i in range(len(data)):\n ready_data += [{'index': {'_index': index,\n '_id': str(data[i].id)}},\n asdict(data[i])]\n\n ready_data = ready_data[self.es_sc.state * 2:]\n\n while len(ready_data) > 0:\n if self.page_size * 2 > len(ready_data):\n ready_data, list_to_load = [], ready_data\n else:\n ready_data = ready_data[self.page_size * 2:]\n list_to_load = (ready_data[:self.page_size * 2])\n\n self.es.bulk(body=list_to_load)\n\n self.es_sc.state += self.page_size\n self.es_sc.set_state()\n","repo_name":"Wiped-Out/yandex_16_team_work","sub_path":"docker_compose/etl/elasticsearch_loader.py","file_name":"elasticsearch_loader.py","file_ext":"py","file_size_in_byte":1131,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"52"} +{"seq_id":"35604862809","text":"import requests\r\nfrom time import sleep\r\n\r\nuuid = input('Enter your R6 Tab UUID:')\r\nstart = input('Type \"Start\" to begin sending:')\r\nsentBots = 0\r\nif start != 'Start':\r\n print('Invalid Line. Quitting.')\r\n sleep(2.0)\r\n exit()\r\nelse:\r\n while True:\r\n WebRequest = requests.get('https://r6tab.com/mainpage.php?page='+uuid)\r\n sentBots = sentBots + 1\r\n print(f'[{sentBots}]: Bot Sent Successfully')\r\n \r\n \r\n","repo_name":"Inq-B/R6S-Tab-Botter","sub_path":"Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":447,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"4713386923","text":"import numpy as np\nimport tensorflow as tf\nfrom baselines.common import tf_util as U\nfrom baselines.common.tests.test_with_mpi import with_mpi\nfrom baselines import logger\ntry:\n from mpi4py import MPI\nexcept ImportError:\n MPI = None\n\nclass MpiAdamOptimizer(tf.train.AdamOptimizer):\n \"\"\"Adam optimizer that averages gradients across mpi processes.\"\"\"\n def __init__(self, comm, grad_clip=None, mpi_rank_weight=1, **kwargs):\n self.comm = comm\n self.grad_clip = grad_clip\n self.mpi_rank_weight = mpi_rank_weight\n tf.train.AdamOptimizer.__init__(self, **kwargs)\n def compute_gradients(self, loss, var_list, **kwargs):\n grads_and_vars = tf.train.AdamOptimizer.compute_gradients(self, loss, var_list, **kwargs)\n grads_and_vars = [(g, v) for g, v in grads_and_vars if g is not None]\n flat_grad = tf.concat([tf.reshape(g, (-1,)) for g, v in grads_and_vars], axis=0) * self.mpi_rank_weight\n shapes = [v.shape.as_list() for g, v in grads_and_vars]\n sizes = [int(np.prod(s)) for s in shapes]\n\n total_weight = np.zeros(1, np.float32)\n self.comm.Allreduce(np.array([self.mpi_rank_weight], dtype=np.float32), total_weight, op=MPI.SUM)\n total_weight = total_weight[0]\n\n buf = np.zeros(sum(sizes), np.float32)\n countholder = [0] # Counts how many times _collect_grads has been called\n stat = tf.reduce_sum(grads_and_vars[0][1]) # sum of first variable\n def _collect_grads(flat_grad, np_stat):\n if self.grad_clip is not None:\n gradnorm = np.linalg.norm(flat_grad)\n if gradnorm > 1:\n flat_grad /= gradnorm\n logger.logkv_mean('gradnorm', gradnorm)\n logger.logkv_mean('gradclipfrac', float(gradnorm > 1))\n self.comm.Allreduce(flat_grad, buf, op=MPI.SUM)\n np.divide(buf, float(total_weight), out=buf)\n if countholder[0] % 100 == 0:\n check_synced(np_stat, self.comm)\n countholder[0] += 1\n return buf\n\n avg_flat_grad = tf.py_func(_collect_grads, [flat_grad, stat], tf.float32)\n avg_flat_grad.set_shape(flat_grad.shape)\n avg_grads = tf.split(avg_flat_grad, sizes, axis=0)\n avg_grads_and_vars = [(tf.reshape(g, v.shape), v)\n for g, (_, v) in zip(avg_grads, grads_and_vars)]\n return avg_grads_and_vars\n\ndef check_synced(localval, comm=None):\n \"\"\"\n It's common to forget to initialize your variables to the same values, or\n (less commonly) if you update them in some other way than adam, to get them out of sync.\n This function checks that variables on all MPI workers are the same, and raises\n an AssertionError otherwise\n\n Arguments:\n comm: MPI communicator\n localval: list of local variables (list of variables on current worker to be compared with the other workers)\n \"\"\"\n comm = comm or MPI.COMM_WORLD\n vals = comm.gather(localval)\n if comm.rank == 0:\n assert all(val==vals[0] for val in vals[1:]),\\\n 'MpiAdamOptimizer detected that different workers have different weights: {}'.format(vals)\n\n@with_mpi(timeout=5)\ndef test_nonfreeze():\n np.random.seed(0)\n tf.set_random_seed(0)\n\n a = tf.Variable(np.random.randn(3).astype('float32'))\n b = tf.Variable(np.random.randn(2,5).astype('float32'))\n loss = tf.reduce_sum(tf.square(a)) + tf.reduce_sum(tf.sin(b))\n\n stepsize = 1e-2\n # for some reason the session config with inter_op_parallelism_threads was causing\n # nested sess.run calls to freeze\n config = tf.ConfigProto(inter_op_parallelism_threads=1)\n sess = U.get_session(config=config)\n update_op = MpiAdamOptimizer(comm=MPI.COMM_WORLD, learning_rate=stepsize).minimize(loss)\n sess.run(tf.global_variables_initializer())\n losslist_ref = []\n for i in range(100):\n l,_ = sess.run([loss, update_op])\n print(i, l)\n losslist_ref.append(l)\n","repo_name":"openai/baselines","sub_path":"baselines/common/mpi_adam_optimizer.py","file_name":"mpi_adam_optimizer.py","file_ext":"py","file_size_in_byte":3976,"program_lang":"python","lang":"en","doc_type":"code","stars":14949,"dataset":"github-code","pt":"52"} +{"seq_id":"25987144499","text":"import re\nfrom SearchEngineBase import SearchEngineBase\nfrom SearchEngineBase import main\n\n\nclass BOWInvertedIndexEngine(SearchEngineBase):\n def __init__(self):\n super(BOWInvertedIndexEngine, self).__init__()\n self.inverted_index = {}\n\n def process_corpus(self, id, text):\n words = self.parse_text_to_words(text)\n for word in words:\n if word not in self.inverted_index:\n self.inverted_index[word] = []\n self.inverted_index[word].append(id)\n\n def search(self, query):\n query_words = list(self.parse_text_to_words(query))\n query_words_index = list()\n for query_word in query_words:\n query_words_index.append(0)\n\n for query_word in query_words:\n if query_word not in self.inverted_index:\n return []\n\n result = []\n while True:\n current_ids = []\n for idx, query_word in enumerate(query_words):\n current_index = query_words_index[idx]\n current_inverted_list = self.inverted_index[query_word]\n\n if current_index >= len(current_inverted_list):\n return result\n\n current_ids.append(current_inverted_list[current_index])\n\n if all(x == current_ids[0] for x in current_ids):\n result.append(current_ids[0])\n query_words_index = [x + 1 for x in query_words_index]\n continue\n\n min_val = min(current_ids)\n min_val_pos = current_ids.index(min_val)\n query_words_index[min_val_pos] += 1\n\n @staticmethod\n def parse_text_to_words(text):\n # Use regular expressions to remove punctuation and line breaks\n text = re.sub(r'[^\\w]', ' ', text)\n text = text.lower()\n # Generate a list of all words\n word_list = text.split(' ')\n # Removing blank words\n word_list = filter(None, word_list)\n # Return a set of words\n return set(word_list)\n\n\nsearch_engine = BOWInvertedIndexEngine()\nmain(search_engine)","repo_name":"chaihuoa/Little-Search-Engine","sub_path":"BOWInvertedIndexEngine.py","file_name":"BOWInvertedIndexEngine.py","file_ext":"py","file_size_in_byte":2081,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"70212545446","text":"import asyncio\nimport sqlite3\nimport schedule\nfrom config import DIRECTORY\n\ndirectory = r'{0}\\posts.db'.format(DIRECTORY)\nconnection = sqlite3.connect(directory)\ncursor = connection.cursor()\n\ncursor.execute('''CREATE TABLE IF NOT EXISTS posts (\n id INTEGER PRIMARY KEY AUTOINCREMENT,organization TEXT NOT NULL,date TEXT NOT NULL,\n description TEXT NOT NULL,address TEXT NOT NULL,lat FLOAT NOT NULL,lon FLOAT NOT NULL,media_id TEXT);''')\n\nconnection.commit()\n\ncursor.close()\nconnection.close()\n\n\ndef insert(organization, date, description, address, lat, lon):\n con = sqlite3.connect(directory)\n cur = con.cursor()\n\n cur.execute(\"INSERT INTO posts (organization,date,description,address,lat,lon) VALUES (?, ?, ?, ?, ?, ?)\",\n (organization, date, description, address, lat, lon))\n result = cur.lastrowid\n\n con.commit()\n\n cur.close()\n con.close()\n\n return result\n\n\ndef fetch(post_id):\n con = sqlite3.connect(directory)\n cur = con.cursor()\n\n data = cur.execute(\"SELECT * FROM posts WHERE id = ?\", (post_id,)).fetchone()\n\n cur.close()\n con.close()\n\n return data\n\n\ndef fetch_by_organization(org):\n con = sqlite3.connect(directory)\n cur = con.cursor()\n\n data = cur.execute(\"SELECT * FROM posts WHERE organization = ?\", (org,)).fetchone()\n\n cur.close()\n con.close()\n\n return data\n\n\ndef nearest(lon, lat):\n con = sqlite3.connect(directory)\n cur = con.cursor()\n \n data = cur.execute(\"SELECT id, date, lat, lon FROM posts ORDER BY date\").fetchall()\n\n cur.close()\n con.close()\n\n grouped_data_by_date = {}\n\n for row in data:\n distance = (((row[2]) - lat) ** 2 + ((row[3]) - lon) ** 2) ** 0.5\n if row[1] in grouped_data_by_date:\n grouped_data_by_date[row[1]].append([row[0], distance])\n else:\n grouped_data_by_date[row[1]] = [[row[0], distance]]\n\n sorted_data = {\n key: sorted(value, key=lambda x: x[1])\n for key, value in grouped_data_by_date.items()\n }\n print(sorted_data)\n result_string = ','.join(str(item[0]) for value in sorted_data.values() for item in value)\n\n return result_string\n\n\ndef size():\n con = sqlite3.connect(directory)\n cur = con.cursor()\n\n result = len(cur.execute(\"SELECT * FROM posts\").fetchall())\n \n cur.close()\n con.close()\n \n return result\n\n\ndef delete_posts():\n con = sqlite3.connect(directory)\n cur = con.cursor()\n\n cur.execute(\"DELETE FROM posts WHERE date < date('now')\")\n\n con.commit()\n \n cur.close()\n con.close()\n\n\n# Запускаем задачу удалени�� ежедневно в определенное время\nschedule.every().day.at(\"00:00\").do(delete_posts)\n\n\nasync def schedule():\n while True:\n schedule.run_pending()\n await asyncio.sleep(1)\n\n\nif __name__ == '__main__':\n schedule()\n","repo_name":"ha1mg/partyOnBot","sub_path":"db/posts.py","file_name":"posts.py","file_ext":"py","file_size_in_byte":2874,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"40016801663","text":"#!/usr/bin/python3\nimport string\nimport re\n\ndef chiffrement(alphabet,texte ,chiffrer, cle):\n \"\"\"\n Chiffre/Dechiffre un texte par la méthode de Vigenere\n\n Args:\n - alphabet (string ou liste de caractere): l'alphabet\n - texte (string) : le texte à chiffrer/dechiffrer\n - cle (string) : la clé utilisée pour chiffrer/dechiffrer le texte\n - chiffrer (booleen) : indique si l'on veut effectuer un chiffrement (True) ou un dechiffrement (False).\n Returns:\n - resultat (string) : le texte obtenu après le chiffrement/dechiffrement\n \"\"\"\n # on supprime toutes les ponctuations et les espaces\n texte = re.sub(r'[^\\w\\s]','',texte)\n texte = texte.replace(\" \",\"\")\n cle = re.sub(r'[^\\w\\s]','',cle)\n cle = cle.replace(\" \",\"\")\n\n taille_cle = len(cle)\n\n # le ième caractère du texte\n index_caractere_courant_texte = 0\n\n resultat = \"\"\n\n for caractere_texte in texte:\n if caractere_texte.lower() in alphabet:\n # on trouve le caractere de la clé à utiliser\n caractere_cle = cle[index_caractere_courant_texte%taille_cle].lower()\n\n # cas où le caractere de la clé à utiliser est dans l'alphabet\n if caractere_cle in alphabet:\n # on recupere l'index du caractere_cle et du caractere courant du texte dans l'alphabet\n index_caractere_cle = alphabet.index(caractere_cle)\n index_caractere = alphabet.index(caractere_texte.lower())\n\n # on calcule l'index du nouveau caractere obtenu apres chiffrement\n if chiffrer == True:\n index_nouveau_caractere = (index_caractere+index_caractere_cle)%len(alphabet)\n else:\n index_nouveau_caractere = (index_caractere-index_caractere_cle)%len(alphabet)\n\n # cas où le caractere du texte est une lettre majuscule\n if caractere_texte.isupper():\n resultat+=alphabet[index_nouveau_caractere].upper()\n else:\n resultat+=alphabet[index_nouveau_caractere]\n else:\n resultat += caractere_texte\n else:\n resultat += caractere_texte\n\n index_caractere_courant_texte += 1\n\n return resultat\n\ndef chiffrer_texte(chiffrer):\n \"\"\"\n Chiffre/Dechiffre un texte saisi par l'utilisateur par la méthode de Vigenere\n\n Args:\n - chiffrer (booleen) : indique si l'on veut effectuer un chiffrement (True) ou un dechiffrement (False).\n \"\"\"\n alphabet = string.ascii_lowercase\n\n if chiffrer:\n print(\"Chiffrer un texte\\n*********************\")\n texte_input = \"\\tSaisissez le texte à chiffrer\\n\\t>\"\n texte_resultat = \"\\tTexte chiffré\"\n else:\n print(\"Déchiffrer un texte\\n**********************\")\n texte_input = \"\\tSaisissez le texte à déchiffrer\\n\\t>\"\n texte_resultat = \"\\tTexte déchiffré\"\n\n\n cle=input(\"\\tQuelle clé(un caractere, un mot, une phrase, un texte,..) souhaitez-vous utiliser?\\n\\t>\")\n while not cle:\n print(\"\\tLa clé ne doit pas etre vide\")\n cle=input(\"\\tQuelle clé(un caractere, un mot, une phrase, un texte,..) souhaitez-vous utiliser?\\n\\t>\")\n\n print(\"\\tLe texte est formé à partir des lettres de l'alphabet et peut contenir des lettres majuscules.\")\n texte=input(texte_input)\n while not texte:\n print(\"\\tLe texte ne doit pas etre vide\")\n texte=input(texte_input)\n\n resultat=chiffrement(alphabet, texte, chiffrer, cle)\n print(texte_resultat,\"\\n\\t>\",resultat)\n\n# print(chiffrement.__doc__)\n# print(chiffrer_texte.__doc__)\n","repo_name":"christVic/Cryptologie-Vigenere","sub_path":"chiffrement.py","file_name":"chiffrement.py","file_ext":"py","file_size_in_byte":3693,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"41643213411","text":"\"\"\"This file contains utility classes for exploring complex instance graphs.\nThis is handy for deeply nested ``Stash`` instances.\n\n\"\"\"\n__author__ = 'Paul Landes'\n\nimport dataclasses\nfrom typing import Set, Type, Any\nimport logging\nimport sys\nimport collections\nfrom io import TextIOBase\nfrom zensols.config import ClassResolver, Writable\n\nlogger = logging.getLogger(__name__)\n\n\nclass ClassExplorer(Writable):\n \"\"\"A utility class that recursively reports class metadata in an object graph.\n\n \"\"\"\n ATTR_META_NAME = 'ATTR_EXP_META'\n \"\"\"The attribute name set on classes to find to report their fields. When the\n value of this is set as a class attribute, each of that object instances'\n members are pretty printed. The value is a tuple of string attribute\n names.\n\n \"\"\"\n\n def __init__(self, include_classes: Set[Type],\n exclude_classes: Set[Type] = None,\n indent: int = 4, attr_truncate_len: int = 80,\n include_dicts: bool = False,\n include_private: bool = False,\n dictify_dataclasses: bool = False):\n self.include_classes = include_classes\n if exclude_classes is None:\n self.exclude_classes = set()\n else:\n self.exclude_classes = exclude_classes\n self.indent = indent\n self.attr_truncate_len = attr_truncate_len\n self.include_dicts = include_dicts\n self.include_private = include_private\n self.dictify_dataclasses = dictify_dataclasses\n\n def get_metadata(self, inst: Any) -> dict:\n self.visited = set()\n try:\n include_classes = set(self.include_classes | set([inst.__class__]))\n meta = self._get_metadata(\n inst, tuple(include_classes), tuple(self.exclude_classes))\n finally:\n del self.visited\n return meta\n\n def _get_dict(self, inst: dict, include_classes: Set[Type],\n exclude_classes: Set[Type]) -> dict:\n oid = id(inst)\n if oid not in self.visited:\n children = []\n self.visited.add(oid)\n for k, v in inst.items():\n if logger.isEnabledFor(logging.DEBUG):\n logger.debug(f'desc {k} -> {type(v)}')\n v = self._get_metadata(v, include_classes, exclude_classes)\n if v is not None:\n children.append({'attr': k, 'child': v})\n return {'class_name': '', 'children': children}\n\n def _is_traversable(self, inst: Any, include_classes: Set[Type],\n exclude_classes: Set[Type]) -> bool:\n return isinstance(inst, include_classes) and \\\n not isinstance(inst, exclude_classes)\n\n def _get_metadata(self, inst: Any, include_classes: Set[Type],\n exclude_classes: Set[Type]) -> dict:\n oid = id(inst)\n if oid in self.visited:\n return None\n self.visited.add(oid)\n dat = None\n if self.include_dicts and isinstance(inst, dict):\n dat = self._get_dict(inst, include_classes, exclude_classes)\n elif self._is_traversable(inst, include_classes, exclude_classes):\n dat = collections.OrderedDict()\n cls = inst.__class__\n class_name = ClassResolver.full_classname(cls)\n children = []\n dat['class_name'] = class_name\n is_dataclass = self.dictify_dataclasses and \\\n dataclasses.is_dataclass(inst)\n has_attr_meta = hasattr(cls, self.ATTR_META_NAME)\n if hasattr(inst, 'name'):\n dat['name'] = getattr(inst, 'name')\n if has_attr_meta or is_dataclass:\n attrs = {}\n dat['attrs'] = attrs\n if not has_attr_meta and is_dataclass:\n try:\n attr_names = dataclasses.asdict(inst)\n except Exception as e:\n logger.info(\n f'can not get attr names for {type(inst)}: {e}')\n attr_names = ()\n elif has_attr_meta:\n attr_names = getattr(cls, self.ATTR_META_NAME)\n # TODO: skip attributes that will or have already been\n # traversed as a \"traversable\" object on a recursion\n for attr in attr_names:\n v = getattr(inst, attr)\n if isinstance(v, dict):\n v = self._get_dict(v, include_classes, exclude_classes)\n if v is not None:\n children.append({'attr': attr, 'child': v})\n else:\n attrs[attr] = v\n for attr in inst.__dir__():\n if logger.isEnabledFor(logging.DEBUG):\n logger.debug(f'desc meta: {type(inst)}.{attr}')\n if self.include_private or not attr.startswith('_'):\n try:\n child_inst = getattr(inst, attr)\n except Exception as e:\n msg = f'error: can not traverse attribute {attr}: {e}'\n logger.info(msg)\n child_inst = msg\n if isinstance(child_inst, dict):\n child = self._get_dict(\n child_inst, include_classes, exclude_classes)\n else:\n child = self._get_metadata(\n child_inst, include_classes, exclude_classes)\n if child is not None:\n children.append({'attr': attr, 'child': child})\n if len(children) > 0:\n dat['children'] = children\n return dat\n\n def write(self, inst: Any, depth: int = 0,\n writer: TextIOBase = sys.stdout):\n meta = self.get_metadata(inst)\n self._write(meta, depth, None, writer)\n\n def write_metadata(self, depth: int = 0,\n writer: TextIOBase = sys.stdout,\n metadata: dict = None):\n self._write(metadata, depth, None, writer)\n\n def _write(self, metadata: dict, depth: int, attr: str, writer):\n cn = f'{attr}: ' if attr is not None else ''\n name = f\" ({metadata['name']})\" if 'name' in metadata else ''\n sp = self._sp(depth)\n sp2 = self._sp(depth + 1)\n writer.write(f\"{sp}{cn}{metadata['class_name']}{name}\\n\")\n if 'attrs' in metadata:\n for k, v in metadata['attrs'].items():\n v = self._trunc(str(v), max_len=self.attr_truncate_len)\n writer.write(f'{sp2}{k}: {v}\\n')\n if 'children' in metadata:\n for c in metadata['children']:\n self._write(c['child'], depth + 1, c['attr'], writer)\n","repo_name":"plandes/util","sub_path":"src/python/zensols/config/meta.py","file_name":"meta.py","file_ext":"py","file_size_in_byte":6849,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"70570908965","text":"import sys\ninput = sys.stdin.readline\n\nn = list(map(int, input().strip()))\nn.sort(reverse=True)\n\nif n[-1] == 0:\n if sum(n) % 3 == 0:\n answer = map(str, n)\n print(\"\".join(answer))\n else:\n print(-1)\nelse:\n print(-1)\n","repo_name":"jeanP-tech/Algorithms","sub_path":"[10610] 30.py","file_name":"[10610] 30.py","file_ext":"py","file_size_in_byte":244,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"24262728067","text":"import pdb\nfrom collections import defaultdict\n\nFNAME = \"in14.txt\"\n\ndata = open(FNAME).read().split('\\n\\n')\npdb.set_trace()\nbase = data[0]\nproduct = {}\nfor line in data[1].splitlines():\n a, b = line.split(\" -> \")\n product[a] = b\n\n# iterate the comopund forward one generation\n# e.g. HH -> N means HH -> HNH\ndef step(compound):\n out = ''\n for i in range(len(compound) - 1):\n out += compound[i] + product[compound[i:i+2]]\n out += compound[-1]\n return out\n \n# the original way for Part 1 - obsoleced but maintained for reference\n#compound = base\n#for _ in range(10):\n# compound = step(compound)\n#\n#counts = [(compound.count(i), i) for i in compound]\n#counts.sort()\n#print(\"Part 1:\", counts[-1][0] - counts[0][0])\n\n# return the count profile of a compound x steps from now\n# will use recursion with memoization\nmemo = {}\ndef profile(compound, steps):\n \n if (compound, steps) in memo:\n return memo[(compound, steps)]\n \n # large compounds can just be broken into small compounds and recombined\n if len(compound) > 2:\n counts = defaultdict(int)\n for i in range(len(compound) - 1):\n subcounts = profile(compound[i:i+2], steps)\n for k, v in subcounts.items():\n counts[k] += v\n for c in compound[1:-1]: # correct for double counting the overlap\n counts[c] -= 1\n return counts \n \n # other than at the top level, we can just deal with compounds of size 2\n assert len(compound) == 2\n \n # base cases\n if steps == 1:\n countdict = defaultdict(int)\n for c in step(compound):\n countdict[c] += 1\n memo[(compound, steps)] = countdict\n return countdict\n \n # for a new compound, iterate it forward a generation,\n # then find the counts of its children and recombine\n counts = defaultdict(int)\n nextgen = step(compound)\n left = nextgen[:2]\n right = nextgen[-2:]\n countsleft = profile(left, steps - 1)\n countsright = profile(right, steps - 1)\n for k, v in countsleft.items():\n counts[k] += v\n for k, v in countsright.items():\n counts[k] += v\n counts[nextgen[1]] -= 1 # correct for double counting the middle character\n memo[(compound, steps)] = counts # save it for later!\n return counts\n \ncounts = profile(base, 10)\nprint(\"Part 1:\", max(counts.values()) - min(counts.values()))\ncounts = profile(base, 40)\nprint(\"Part 2:\", max(counts.values()) - min(counts.values()))\n\n#pdb.set_trace()\n","repo_name":"stoberc/aoc2021","sub_path":"p14.py","file_name":"p14.py","file_ext":"py","file_size_in_byte":2525,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"16411252732","text":"import torch\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\nclass SimpleFunction(torch.nn.Module):\n def __init__(self, x=0., y=0.):\n super().__init__()\n self.x = torch.nn.Parameter(torch.Tensor([x]))\n self.register_parameter(\"param_x\", self.x)\n self.y = torch.nn.Parameter(torch.Tensor([y]))\n self.register_parameter(\"param_y\", self.y)\n\n def forward(self):\n return (1.5-self.x+self.x*self.y)**2+(2.25-self.x+self.x*self.y*self.y)**2+(2.625-self.x+self.x*self.y*self.y*self.y)**2\n\n\ntotal_epochs = 100\npropagation_times = 5\nlr = 0.001\n\nif __name__ == \"__main__\":\n x, y = 2.45, 1.23\n model = SimpleFunction(x, y)\n\n epoch_idx = np.arange(1, total_epochs+1)\n losses = np.zeros(total_epochs, dtype=np.float32)\n opti = torch.optim.SGD(model.parameters(), lr=lr)\n\n model.train()\n for epoch in range(total_epochs):\n loss = model()\n # print(loss)\n losses[epoch] = loss\n opti.zero_grad()\n loss.backward()\n opti.step()\n print(f\"Epoch {epoch} :\")\n # print(f\"x = {x} y = {y}\")\n print(f\"f(x,y) = {loss[0]}\")\n\n model.eval()\n res = model()\n print(f\"Test: {res[0]}\")\n\n plt.title(\"Backward Propagation Loss\")\n plt.xlabel(\"Epoch\")\n plt.ylabel(\"Loss\")\n plt.plot(epoch_idx, losses)\n plt.show()\n","repo_name":"CaptainHarryChen/forward-propagation","sub_path":"BackwardPropagation/simple-function.py","file_name":"simple-function.py","file_ext":"py","file_size_in_byte":1335,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"36333301450","text":"from django.db import models\nfrom mptt.models import MPTTModel, TreeForeignKey\nfrom django.conf import settings\nfrom django.utils.translation import gettext_lazy as _\nfrom django.utils import timezone\n\n\nclass Category(MPTTModel):\n name = models.CharField(\n verbose_name=_(\"Category Name\"),\n help_text=_(\"Required and unique\"),\n max_length=255,\n unique=True,\n )\n slug = models.SlugField(\n verbose_name=_(\"Category safe URL\"),\n unique=True,\n max_length=255,\n )\n parent = TreeForeignKey(\n \"self\",\n on_delete=models.CASCADE,\n null=True,\n blank=True,\n related_name=\"children\",\n )\n is_active = models.BooleanField(default=False)\n\n class MPTTMeta:\n order_insertion_by = (\"name\",)\n\n class Meta:\n verbose_name = _(\"Category\")\n verbose_name_plural = _(\"Categories\")\n\n def __str__(self):\n return self.name\n\n\nclass Brand(models.Model):\n name = models.CharField(\n verbose_name=_(\"Brand Name\"),\n help_text=_(\"Required and unique\"),\n max_length=255,\n unique=True,\n )\n slug = models.SlugField(\n verbose_name=_(\"Brand safe URL\"),\n unique=True,\n max_length=255,\n )\n is_active = models.BooleanField(default=False)\n\n class Meta:\n verbose_name = _(\"Brand\")\n verbose_name_plural = _(\"Brands\")\n\n def __str__(self):\n return self.name\n\n\nclass ProductType(models.Model):\n name = models.CharField(\n max_length=255,\n verbose_name=_(\"Name\"),\n help_text=_(\"Required\"),\n )\n is_active = models.BooleanField(default=True)\n\n class Meta:\n verbose_name = _(\"Product Type\")\n verbose_name_plural = _(\"Product Types\")\n\n def __str__(self):\n return self.name\n\n\nclass ProductSpecification(models.Model):\n product_type = models.ForeignKey(\n ProductType,\n on_delete=models.RESTRICT,\n )\n name = models.CharField(\n verbose_name=_(\"Name\"),\n help_text=_(\"Required\"),\n unique=True,\n max_length=255,\n )\n\n class Meta:\n verbose_name = _(\"Product Specification\")\n verbose_name_plural = _(\"Product Specifications\")\n\n def __str__(self):\n return self.name\n\n\nclass Product(models.Model):\n brand = models.ForeignKey(\n Brand,\n on_delete=models.RESTRICT,\n null=True,\n blank=True,\n )\n product_type = models.ForeignKey(\n ProductType,\n on_delete=models.RESTRICT,\n )\n category = models.ForeignKey(\n Category,\n on_delete=models.RESTRICT,\n )\n title = models.CharField(\n max_length=255,\n verbose_name=_(\"Title\"),\n help_text=_(\"Required\"),\n )\n short_description = models.CharField(\n max_length=255,\n verbose_name=_(\"Short description\"),\n help_text=_(\"Required\"),\n )\n description = models.TextField(\n blank=True,\n verbose_name=_(\"Description\"),\n help_text=_(\"Not Required\"),\n )\n slug = models.SlugField(max_length=255)\n regular_price = models.DecimalField(\n verbose_name=_(\"Regular price\"),\n help_text=_(\"Maximum 4999.99$\"),\n error_messages={\n \"name\": {\n \"max_length\": _(\"The price must be between 0 and 4999.99$\"),\n },\n },\n max_digits=6,\n decimal_places=2,\n )\n discount_price = models.DecimalField(\n verbose_name=_(\"Discount price\"),\n help_text=_(\"Maximum 4999.99$\"),\n error_messages={\n \"name\": {\n \"max_length\": _(\"The price must be between 0 and 4999.99$\"),\n },\n },\n max_digits=6,\n decimal_places=2,\n )\n is_active = models.BooleanField(\n verbose_name=_(\"Product visibility\"),\n help_text=_(\"Change product visibility\"),\n default=True,\n )\n created_at = models.DateTimeField(\n _(\"Created at\"),\n auto_now_add=True,\n editable=False,\n )\n updated_at = models.DateTimeField(\n _(\"Updated at\"),\n auto_now=True,\n )\n\n class Meta:\n ordering = (\"created_at\",)\n verbose_name = _(\"Product\")\n verbose_name_plural = _(\"Products\")\n\n def __str__(self):\n return self.title\n\n\nclass ProductSpecificationValue(models.Model):\n product = models.ForeignKey(\n Product,\n on_delete=models.CASCADE,\n )\n specification = models.ForeignKey(\n ProductSpecification,\n on_delete=models.RESTRICT,\n )\n value = models.CharField(\n max_length=255,\n verbose_name=_(\"Value\"),\n help_text=_(\"Product specification value (maximum of 255\"),\n )\n\n class Meta:\n verbose_name = _(\"Product Specification Value\")\n verbose_name_plural = _(\"Product Specification Values\")\n\n def __str__(self):\n return self.value\n\n\nclass ProductImage(models.Model):\n product = models.ForeignKey(Product, on_delete=models.CASCADE)\n image = models.ImageField(\n verbose_name=_(\"Image\"),\n help_text=_(\"Upload a product image\"),\n upload_to=\"images/products/\",\n default=\"images/products/default.png\",\n )\n alt_text = models.CharField(\n max_length=255,\n null=True,\n blank=True,\n verbose_name=_(\"Alternative text\"),\n help_text=_(\"Please add alternative text\"),\n )\n if_feature = models.BooleanField(default=False)\n created_at = models.DateTimeField(\n _(\"Created at\"),\n auto_now_add=True,\n editable=False,\n )\n updated_at = models.DateTimeField(\n _(\"Updated at\"),\n auto_now=True,\n )\n\n class Meta:\n verbose_name = _(\"Product image\")\n verbose_name_plural = _(\"Product images\")\n\n\nclass ProductComment(models.Model):\n user = models.ForeignKey(\n settings.AUTH_USER_MODEL,\n on_delete=models.CASCADE,\n )\n product = models.ForeignKey(\n Product,\n on_delete=models.CASCADE,\n null=True,\n blank=True,\n )\n reply = models.ForeignKey(\n \"ProductComment\",\n on_delete=models.CASCADE,\n related_name=\"children\",\n null=True,\n blank=True,\n )\n text = models.TextField()\n created_at = models.DateTimeField(default=timezone.now, editable=False)\n\n class Meta:\n verbose_name = _(\"Comment\")\n verbose_name_plural = _(\"Comments\")\n\n def __str__(self):\n return \"{} - {}\".format(self.user.__str__(), self.product.title)\n","repo_name":"SorooshDaryabari/Ecommerce","sub_path":"products/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":6507,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"20398666715","text":"n, l = map(int, input().split())\nboard = []\nfor _ in range(n):\n tmp = list(map(int, input().split()))\n board.append(tmp)\n\n\ndef search(line):\n setting = []\n for i in range(0, n - 1):\n if line[i] - line[i + 1] == 0:\n continue\n elif line[i] - line[i + 1] == -1:\n if i - l + 1 < 0:\n return 0\n for index in range(i - l + 1, i+1):\n if line[index] == line[i]:\n if index in setting:\n return 0\n setting.append(index)\n else:\n return 0\n elif line[i] - line[i + 1] == 1:\n if i + l + 1 > n:\n return 0\n for index in range(i + 1, i + l + 1):\n if line[index] == line[i + 1]:\n if index in setting:\n return 0\n setting.append(index)\n else:\n return 0\n else:\n return 0\n return 1\n\n\ncount = 0\nfor check_line in board:\n count += search(check_line)\nfor check_line in zip(*board):\n count += search(check_line)\n\nprint(count)\n","repo_name":"KhelKim/CodingPractice","sub_path":"BackJoon/simulation/14890.py","file_name":"14890.py","file_ext":"py","file_size_in_byte":1163,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"41236598388","text":"import json\r\nimport logging\r\nimport os\r\nimport tempfile\r\nimport asyncio\r\nimport platform\r\nimport aiohttp\r\nfrom yarl import URL\r\n\r\nimport pytest\r\nfrom aiohttp import ClientSession\r\nfrom injector import (ClassAssistedBuilder, Injector, Module, inject, provider,\r\n singleton)\r\n\r\nfrom backup.config import Config, Setting\r\nfrom backup.model import Coordinator\r\nfrom dev.simulationserver import SimulationServer\r\nfrom backup.drive import DriveRequests, DriveSource, FolderFinder, AuthCodeQuery\r\nfrom backup.util import GlobalInfo, Estimator, Resolver, DataCache\r\nfrom backup.ha import HaRequests, HaSource, HaUpdater\r\nfrom backup.logger import reset\r\nfrom backup.model import DummyBackup, DestinationPrecache, Model\r\nfrom backup.time import Time\r\nfrom backup.module import BaseModule\r\nfrom backup.debugworker import DebugWorker\r\nfrom backup.creds import Creds\r\nfrom backup.server import ErrorStore\r\nfrom backup.ha import AddonStopper\r\nfrom backup.ui import UiServer\r\nfrom backup.watcher import Watcher\r\nfrom .faketime import FakeTime\r\nfrom .helpers import Uploader, createBackupTar\r\nfrom dev.ports import Ports\r\nfrom dev.simulated_google import SimulatedGoogle\r\nfrom dev.request_interceptor import RequestInterceptor\r\nfrom dev.simulated_supervisor import SimulatedSupervisor\r\n\r\n\r\n@singleton\r\nclass FsFaker():\r\n @inject\r\n def __init__(self):\r\n self.bytes_free = 1024 * 1024 * 1024\r\n self.bytes_total = 1024 * 1024 * 1024\r\n self.old_method = None\r\n\r\n def start(self):\r\n if platform.system() != \"Windows\":\r\n self.old_method = os.statvfs\r\n os.statvfs = self._hijack\r\n\r\n def stop(self):\r\n if platform.system() != \"Windows\":\r\n os.statvfs = self.old_method\r\n\r\n def _hijack(self, path):\r\n return os.statvfs_result((0, 1, int(self.bytes_total), int(self.bytes_free), int(self.bytes_free), 0, 0, 0, 0, 255))\r\n\r\n def setFreeBytes(self, bytes_free, bytes_total=1):\r\n self.bytes_free = bytes_free\r\n self.bytes_total = bytes_total\r\n if self.bytes_free > self.bytes_total:\r\n self.bytes_total = self.bytes_free\r\n\r\n\r\nclass ReaderHelper:\r\n def __init__(self, session, ui_port, ingress_port):\r\n self.session = session\r\n self.ui_port = ui_port\r\n self.ingress_port = ingress_port\r\n self.timeout = aiohttp.ClientTimeout(total=20)\r\n\r\n def getUrl(self, ingress=True, ssl=False):\r\n if ssl:\r\n protocol = \"https\"\r\n else:\r\n protocol = \"http\"\r\n if ingress:\r\n return protocol + \"://localhost:\" + str(self.ingress_port) + \"/\"\r\n else:\r\n return protocol + \"://localhost:\" + str(self.ui_port) + \"/\"\r\n\r\n async def getjson(self, path, status=200, json=None, auth=None, ingress=True, ssl=False, sslcontext=None):\r\n async with self.session.get(self.getUrl(ingress, ssl) + path, json=json, auth=auth, ssl=sslcontext, timeout=self.timeout) as resp:\r\n assert resp.status == status\r\n return await resp.json()\r\n\r\n async def get(self, path, status=200, json=None, auth=None, ingress=True, ssl=False):\r\n async with self.session.get(self.getUrl(ingress, ssl) + path, json=json, auth=auth, timeout=self.timeout) as resp:\r\n if resp.status != status:\r\n import logging\r\n logging.getLogger().error(resp.text())\r\n assert resp.status == status\r\n return await resp.text()\r\n\r\n async def postjson(self, path, status=200, json=None, ingress=True):\r\n async with self.session.post(self.getUrl(ingress) + path, json=json, timeout=self.timeout) as resp:\r\n assert resp.status == status\r\n return await resp.json()\r\n\r\n async def assertError(self, path, error_type=\"generic_error\", status=500, ingress=True, json=None):\r\n logging.getLogger().info(\"Requesting \" + path)\r\n data = await self.getjson(path, status=status, ingress=ingress, json=json)\r\n assert data['error_type'] == error_type\r\n\r\n\r\n# This module should onyl ever have bindings that can also be satisfied by MainModule\r\nclass TestModule(Module):\r\n def __init__(self, config: Config, ports: Ports):\r\n self.ports = ports\r\n self.config = config\r\n\r\n @provider\r\n @singleton\r\n def getDriveCreds(self, time: Time) -> Creds:\r\n return Creds(time, \"test_client_id\", time.now(), \"test_access_token\", \"test_refresh_token\", \"test_client_secret\")\r\n\r\n @provider\r\n @singleton\r\n def getTime(self) -> Time:\r\n return FakeTime()\r\n\r\n @provider\r\n @singleton\r\n def getPorts(self) -> Ports:\r\n return self.ports\r\n\r\n @provider\r\n @singleton\r\n def getConfig(self) -> Config:\r\n return self.config\r\n\r\n\r\n@pytest.fixture\r\ndef event_loop():\r\n if platform.system() == \"Windows\":\r\n asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())\r\n return asyncio.new_event_loop()\r\n\r\n\r\n@pytest.fixture\r\nasync def generate_config(server_url: URL, ports, cleandir):\r\n return Config.withOverrides({\r\n Setting.DRIVE_URL: str(server_url),\r\n Setting.SUPERVISOR_URL: str(server_url) + \"/\",\r\n Setting.AUTHORIZATION_HOST: str(server_url),\r\n Setting.TOKEN_SERVER_HOSTS: str(server_url),\r\n Setting.DRIVE_REFRESH_URL: str(server_url.with_path(\"/oauth2/v4/token\")),\r\n Setting.DRIVE_AUTHORIZE_URL: str(server_url.with_path(\"/o/oauth2/v2/auth\")),\r\n Setting.DRIVE_TOKEN_URL: str(server_url.with_path(\"/token\")),\r\n Setting.DRIVE_DEVICE_CODE_URL: str(server_url.with_path(\"/device/code\")),\r\n Setting.SUPERVISOR_TOKEN: \"test_header\",\r\n Setting.SECRETS_FILE_PATH: \"secrets.yaml\",\r\n Setting.CREDENTIALS_FILE_PATH: \"credentials.dat\",\r\n Setting.FOLDER_FILE_PATH: \"folder.dat\",\r\n Setting.RETAINED_FILE_PATH: \"retained.json\",\r\n Setting.ID_FILE_PATH: \"id.json\",\r\n Setting.DATA_CACHE_FILE_PATH: \"data_cache.json\",\r\n Setting.STOP_ADDON_STATE_PATH: \"stop_addon.json\",\r\n Setting.INGRESS_TOKEN_FILE_PATH: \"ingress.dat\",\r\n Setting.DEFAULT_DRIVE_CLIENT_ID: \"test_client_id\",\r\n Setting.DEFAULT_DRIVE_CLIENT_SECRET: \"test_client_secret\",\r\n Setting.BACKUP_DIRECTORY_PATH: os.path.join(cleandir, \"backups\"),\r\n Setting.PORT: ports.ui,\r\n Setting.INGRESS_PORT: ports.ingress,\r\n Setting.BACKUP_STARTUP_DELAY_MINUTES: 0,\r\n Setting.PING_TIMEOUT: 0.1,\r\n })\r\n\r\n\r\n@pytest.fixture\r\nasync def injector(cleandir, ports, generate_config):\r\n drive_creds = Creds(FakeTime(), \"test_client_id\", None, \"test_access_token\", \"test_refresh_token\")\r\n\r\n os.mkdir(os.path.join(cleandir, \"backups\"))\r\n with open(os.path.join(cleandir, \"secrets.yaml\"), \"w\") as f:\r\n f.write(\"for_unit_tests: \\\"password value\\\"\\n\")\r\n\r\n with open(os.path.join(cleandir, \"credentials.dat\"), \"w\") as f:\r\n f.write(json.dumps(drive_creds.serialize()))\r\n\r\n return Injector([BaseModule(), TestModule(generate_config, ports)])\r\n\r\n\r\n@pytest.fixture\r\nasync def ui_server(injector, server):\r\n os.mkdir(\"static\")\r\n server = injector.get(UiServer)\r\n await server.run()\r\n yield server\r\n await server.shutdown()\r\n\r\n\r\n@pytest.fixture\r\ndef reader(server, ui_server, session, ui_port, ingress_port):\r\n return ReaderHelper(session, ui_port, ingress_port)\r\n\r\n\r\n@pytest.fixture\r\nasync def uploader(injector: Injector, server_url):\r\n return injector.get(ClassAssistedBuilder[Uploader]).build(host=str(server_url))\r\n\r\n\r\n@pytest.fixture\r\nasync def google(injector: Injector):\r\n return injector.get(SimulatedGoogle)\r\n\r\n\r\n@pytest.fixture\r\nasync def interceptor(injector: Injector):\r\n return injector.get(RequestInterceptor)\r\n\r\n\r\n@pytest.fixture\r\nasync def supervisor(injector: Injector, server, session):\r\n return injector.get(SimulatedSupervisor)\r\n\r\n\r\n@pytest.fixture\r\nasync def addon_stopper(injector: Injector):\r\n return injector.get(AddonStopper)\r\n\r\n\r\n@pytest.fixture\r\nasync def server(injector, port, drive_creds: Creds, session):\r\n server = injector.get(SimulationServer)\r\n\r\n # start the server\r\n logging.getLogger().info(\"Starting SimulationServer on port \" + str(port))\r\n await server.start(port)\r\n yield server\r\n await server.stop()\r\n\r\n\r\n@pytest.fixture\r\nasync def data_cache(injector):\r\n return injector.get(DataCache)\r\n\r\n\r\n@pytest.fixture\r\nasync def session(injector):\r\n async with injector.get(ClientSession) as session:\r\n yield session\r\n\r\n\r\n@pytest.fixture\r\nasync def precache(injector):\r\n return injector.get(DestinationPrecache)\r\n\r\n\r\n@pytest.fixture\r\nasync def backup(coord, source, dest):\r\n await coord.sync()\r\n assert len(coord.backups()) == 1\r\n return coord.backups()[0]\r\n\r\n\r\n@pytest.fixture\r\nasync def fs(injector):\r\n faker = injector.get(FsFaker)\r\n faker.start()\r\n yield faker\r\n faker.stop()\r\n\r\n\r\n@pytest.fixture\r\nasync def estimator(injector, fs):\r\n return injector.get(Estimator)\r\n\r\n\r\n@pytest.fixture\r\nasync def device_code(injector):\r\n return injector.get(AuthCodeQuery)\r\n\r\n\r\n@pytest.fixture\r\nasync def error_store(injector):\r\n return injector.get(ErrorStore)\r\n\r\n\r\n@pytest.fixture\r\nasync def model(injector):\r\n return injector.get(Model)\r\n\r\n\r\n@pytest.fixture\r\nasync def global_info(injector):\r\n return injector.get(GlobalInfo)\r\n\r\n\r\n@pytest.fixture\r\nasync def server_url(port):\r\n return URL(\"http://localhost:\").with_port(port)\r\n\r\n\r\n@pytest.fixture\r\nasync def ports(unused_tcp_port_factory):\r\n return Ports(unused_tcp_port_factory(), unused_tcp_port_factory(), unused_tcp_port_factory())\r\n\r\n\r\n@pytest.fixture\r\nasync def port(ports: Ports):\r\n return ports.server\r\n\r\n\r\n@pytest.fixture\r\nasync def ui_url(ports: Ports):\r\n return URL(\"http://localhost\").with_port(ports.ingress)\r\n\r\n\r\n@pytest.fixture\r\nasync def ui_port(ports: Ports):\r\n return ports.ui\r\n\r\n\r\n@pytest.fixture\r\nasync def ingress_port(ports: Ports):\r\n return ports.ingress\r\n\r\n\r\n@pytest.fixture\r\nasync def coord(injector):\r\n return injector.get(Coordinator)\r\n\r\n\r\n@pytest.fixture()\r\nasync def updater(injector):\r\n return injector.get(HaUpdater)\r\n\r\n\r\n@pytest.fixture()\r\nasync def cleandir():\r\n newpath = tempfile.mkdtemp()\r\n os.chdir(newpath)\r\n return newpath\r\n\r\n\r\n@pytest.fixture\r\nasync def time(injector):\r\n reset()\r\n return injector.get(Time)\r\n\r\n\r\n@pytest.fixture\r\nasync def config(injector):\r\n return injector.get(Config)\r\n\r\n\r\n@pytest.fixture\r\nasync def drive_creds(injector):\r\n return injector.get(Creds)\r\n\r\n\r\n@pytest.fixture\r\nasync def drive(injector, server, session):\r\n return injector.get(DriveSource)\r\n\r\n\r\n@pytest.fixture\r\nasync def ha(injector, server, session):\r\n return injector.get(HaSource)\r\n\r\n\r\n@pytest.fixture\r\nasync def ha_requests(injector, server):\r\n return injector.get(HaRequests)\r\n\r\n\r\n@pytest.fixture\r\nasync def drive_requests(injector, server):\r\n return injector.get(DriveRequests)\r\n\r\n\r\n@pytest.fixture\r\nasync def resolver(injector):\r\n return injector.get(Resolver)\r\n\r\n\r\n@pytest.fixture\r\nasync def client_identifier(injector):\r\n return injector.get(Config).clientIdentifier()\r\n\r\n\r\n@pytest.fixture\r\nasync def debug_worker(injector):\r\n return injector.get(DebugWorker)\r\n\r\n\r\n@pytest.fixture()\r\nasync def folder_finder(injector):\r\n return injector.get(FolderFinder)\r\n\r\n\r\n@pytest.fixture()\r\nasync def watcher(injector):\r\n watcher = injector.get(Watcher)\r\n yield watcher\r\n await watcher.stop()\r\n\r\n\r\nclass BackupHelper():\r\n def __init__(self, uploader, time):\r\n self.time = time\r\n self.uploader = uploader\r\n\r\n async def createFile(self, size=1024 * 1024 * 2, slug=\"testslug\", name=\"Test Name\"):\r\n from_backup: DummyBackup = DummyBackup(\r\n name, self.time.toUtc(self.time.local(1985, 12, 6)), \"fake source\", slug)\r\n data = await self.uploader.upload(createBackupTar(slug, name, self.time.now(), size))\r\n return from_backup, data\r\n\r\n\r\n@pytest.fixture\r\ndef backup_helper(uploader, time):\r\n return BackupHelper(uploader, time)\r\n","repo_name":"sabeechen/hassio-google-drive-backup","sub_path":"hassio-google-drive-backup/tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":12022,"program_lang":"python","lang":"en","doc_type":"code","stars":2613,"dataset":"github-code","pt":"52"} +{"seq_id":"15260815229","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri May 28 10:46:49 2021\r\n\r\n@author: Selim Arslan\r\nRechteck-, Trapez- und Simpsonregel\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport sympy as sp\r\nimport matplotlib.pyplot as plt\r\n\r\n\"\"\"\r\nExakte Integration\r\n\r\nm = 10 kg\r\nv = 20 m /s\r\na = 5 m/s\r\n\r\nt = Integral 10 / (-v*(v**(1/2))) dv -> [20 / v**0.5] \r\na = 5 und b = 20 bestimtes Integrall berechnen von [20 / v**0.5] \r\n4.472135954999580\r\n\"\"\"\r\n#%% Summierte Rechtecksformel\r\ndef sum_Rechteck(f, a, b, n):\r\n h = (b-a) / n\r\n res = 0.\r\n for i in range(n):\r\n res = res + f(a + (i + 0.5) * h)\r\n return h * res\r\n \r\n \r\ndef f(v):\r\n return 10 / (-v*(v**(1/2)))\r\n\r\na = 20\r\nb = 5\r\nn = 5\r\nI = 0.544987\r\n\r\nrf = sum_Rechteck(f, a, b, n)\r\nabsoluterFehler = np.absolute(I - rf)\r\n\r\nprint('Rf = ', rf)\r\nprint('Absulter Fehler = ',absoluterFehler)\r\n\r\n#%% Summierte Trapezregel\r\n\r\n\r\ndef sum_Trapezregel(f, a, b, n):\r\n h = (b-a) / n\r\n res = 0.\r\n fTemp = (f(a) + f(b)) / 2\r\n for i in range(1,n):\r\n res = res + f(a + (i * h))\r\n return h * (fTemp + res)\r\n\r\ndef f(v):\r\n return 10 / (-v*(v**(1/2)))\r\n\r\na = 20\r\nb = 5\r\nn = 5\r\nI = 0.544987\r\n\r\ntf = sum_Rechteck(f, a, b, n)\r\nabsoluterFehler = np.absolute(I - tf)\r\n\r\nprint('Tf = ', tf)\r\nprint('Absulter Fehler = ',absoluterFehler)\r\n\r\n#%% Summierte Simpsonregel\r\n\r\ndef sum_simpson(f, a, b, n):\r\n h = (b - a)/n\r\n res = 0.\r\n for i in range(n):\r\n res = res + f(a + (i + 0.5) * h)\r\n res = 2 * res\r\n for i in range(1,n):\r\n res = res + f(a + i * h)\r\n res = res + 0.5 * (f(a) + f(b))\r\n return res *h/3.\r\n\r\ndef f(v):\r\n return 10 / (-v*(v**(1/2)))\r\n\r\na = 20\r\nb = 5\r\nn = 5\r\nI = 0.544987\r\n\r\nsf = sum_simpson(f, a, b, n)\r\nabsoluterFehler = np.absolute(I - sf)\r\n\r\nprint('Tf = ', sf)\r\nprint('Absulter Fehler = ',absoluterFehler)\r\n\r\n#%% Summierte Trapezregel mit Daten und nicht äquidistante Werte\r\nr = np.array([0, 800, 1200, 1400, 2000, 3000, 3400, 3600, 4000, 5000, 5500, 6370], dtype=\"float64\")* 1000\r\nroh = np.array([13000, 12900, 12700, 12000, 11650, 10600, 9900, 5500, 5300, 4750, 4500, 3300], dtype=\"float64\")\r\n\r\n\r\n#Summierte Trapezformel für nicht äquidistante Werte\r\ndef sum_Trapez(x, y):\r\n Tf_neq = 0\r\n if len(x) != len(y):\r\n raise AttributeError('Länge der Array stimmen nicht überrein')\r\n for i in range(len(x)-1):\r\n Tf_neq = Tf_neq + ((y[i] + y[i + 1]) / 2 * (x[i + 1] - x[i]))\r\n return Tf_neq\r\n\r\ndef f(r, roh):\r\n masse = roh * 4 * np.pi * r**2\r\n return masse\r\n\r\nTf_neq = sum_Trapez(r, f(r, roh))\r\n\r\n#Konstante für die Erdmasse\r\nErdmasse = 5.9723e24\r\n\r\n#Fehlerberechnung\r\nabsoluterFehler = np.absolute(Tf_neq - Erdmasse)\r\nrelativerFehler = np.absolute(absoluterFehler / Erdmasse * 100)\r\n\r\nprint('Erdmasse= ', Erdmasse)\r\nprint('Tf_neq= ', Tf_neq)\r\nprint('Absoluter Fehler= ', absoluterFehler)\r\nprint('Relativer Fehler= ', relativerFehler)\r\n#%% ","repo_name":"selArslan/PythonData","sub_path":"Rechteck_Trapez_SimpsonRegel.py","file_name":"Rechteck_Trapez_SimpsonRegel.py","file_ext":"py","file_size_in_byte":2876,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"73056735204","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport os\nimport re\nimport math\nimport time\nimport json\n\nfrom config import NOTLOCAL\n\nEPSILON = 0.0000001\nLOGDIR = os.environ['OPENSHIFT_LOG_DIR']\nMAX_CHAR = 4000\nCHAR_RATIO = 0.6643902034970293\n\nif not NOTLOCAL:\n LOGDIR = os.path.join(LOGDIR, '../server/logs')\n\njswriteto = lambda s: s[:-4] + '.js'\n_curpath = os.path.normpath(\n os.path.join(os.getcwd(), os.path.dirname(__file__)))\n\n\nclass SimpleLinearRegression:\n\n \"\"\" tool class as help for calculating a linear function \"\"\"\n\n def __init__(self, data):\n \"\"\" initializes members with defaults \"\"\"\n self.data = data # list of (x,y) pairs\n self.a = 0 # \"a\" of y = a + b*x\n self.b = 0 # \"b\" of y = a + b*x\n self.r = 0 # coefficient of correlation\n\n def run(self):\n \"\"\" calculates coefficient of correlation and\n the parameters for the linear function \"\"\"\n sumX, sumY, sumXY, sumXX, sumYY = 0, 0, 0, 0, 0\n n = float(len(self.data))\n\n for x, y in self.data:\n sumX += x\n sumY += y\n sumXY += x * y\n sumXX += x * x\n sumYY += y * y\n\n denominator = math.sqrt(\n (sumXX - 1 / n * sumX ** 2) * (sumYY - 1 / n * sumY ** 2))\n if denominator < EPSILON:\n return False\n\n # coefficient of correlation\n self.r = (sumXY - 1 / n * sumX * sumY)\n self.r /= denominator\n\n # is there no relationship between 'x' and 'y'?\n if abs(self.r) < EPSILON:\n return False\n\n # calculating 'a' and 'b' of y = a + b*x\n self.b = sumXY - sumX * sumY / n\n self.b /= (sumXX - sumX ** 2 / n)\n\n self.a = sumY - self.b * sumX\n self.a /= n\n return True\n\n def function(self, x):\n \"\"\" linear function (be aware of current\n coefficient of correlation \"\"\"\n return self.a + self.b * x\n\n def __repr__(self):\n \"\"\" current linear function for print \"\"\"\n return \"y = f(x) = %(a)f + %(b)f*x\" % self.__dict__\n\nre_log = re.compile(r'^(.+?),(.+?),(\\d+),(\\d+),(\\d+),(.+)$')\nre_js = re.compile(r'(var kc =) \\d+(, bc =) \\d+(, km =) \\d+(, bm =) \\d+')\n\nstriplines = lambda s: '\\n'.join(l.strip() for l in s.splitlines())\n\n\ndef parselog(fromtime):\n logfile = os.path.join(LOGDIR, 'mosesserver.log')\n fromtime = time.strptime(fromtime, '%Y-%m-%d %H:%M:%S')\n validlinec = []\n validlinem = []\n with open(logfile, 'r', encoding='utf-8', errors='ignore') as f:\n for ln in f:\n l = ln.strip()\n m = re_log.match(l)\n if not m:\n continue\n try:\n if time.strptime(m.group(1), '%Y-%m-%d %H:%M:%S') < fromtime:\n continue\n realchar = int(m.group(5)) * int(m.group(3)) / float(m.group(4))\n usedtime = float(m.group(6))\n except Exception:\n continue\n if m.group(2) == 'c2m':\n validlinec.append((realchar, usedtime))\n else:\n validlinem.append((realchar, usedtime))\n lrc = SimpleLinearRegression(validlinec)\n lrm = SimpleLinearRegression(validlinem)\n kc, bc, km, bm = 28, 8883, 28, 8883\n if lrc.run():\n kc, bc = int(lrc.b * 1000), int(lrc.a * 1000)\n if lrm.run():\n km, bm = int(lrm.b * 1000), int(lrm.a * 1000)\n return kc, bc, km, bm\n\n# min: -24.219574739049666\n\njoinlist = lambda l: ''.join(chr(min(32 + int(-n * 3.9), 126))\n for n in l).replace('\\\\', '\\\\\\\\').replace('\"', r'\\\"')\n\n\ndef writejs(value, jsfile):\n writeto = jswriteto(jsfile)\n js_template = ('var zhcmodel = \"%s\";\\nvar zhmmodel = \"%s\";\\n'\n 'var zhclen = %s, zhmlen = %s;\\n%s')\n zhmodel = json.load(open(os.path.join(_curpath, 'modelzh.json'), 'r'))\n f = striplines(js_template % (\n joinlist(zhmodel['zhc']), joinlist(zhmodel['zhm']),\n int(MAX_CHAR * CHAR_RATIO), MAX_CHAR,\n re_js.sub(r'\\1 %s\\2 %s\\3 %s\\4 %s' % value, open(jsfile, 'r').read())\n )) + '\\n'\n with open(writeto, 'w') as w:\n w.write(f)\n\nif __name__ == '__main__':\n REPODIR = os.environ['OPENSHIFT_REPO_DIR']\n writejs(parselog('2020-01-01 00:00:00'),\n os.path.join(REPODIR, 'diy/static/wenyan_.js'))\n","repo_name":"gumblex/pywebapps","sub_path":"diy/speedestimate.py","file_name":"speedestimate.py","file_ext":"py","file_size_in_byte":4346,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"52"} +{"seq_id":"4146388725","text":"import json\n\nfrom functions import dkufood\nfrom .loadresmsg import restList, keyboard\n\n\n# 요청한 식당의 식단 메뉴를 가져옵니다..\ndef getRestMenu(topic):\n \"\"\"\n 카카오톡에서 사용자의 요청을 받아 원하는 식당 정보를 받고,\n 메시지로 반환하는 함수\n\n :type topic: string\n :param topic: 메뉴 이름\n :return: 카카오톡에 일반 메시지로 반환\n \"\"\"\n\n with open('data/restMenu.json') as msg_File:\n data = json.load(msg_File)\n\n if topic == \"처음으로..\":\n data[topic]['keyboard'] = keyboard\n return data[topic]\n\n _request = {\n \"location\": {\n \"campus\": topic[1:3],\n \"restaurant\": topic[4:]\n }\n }\n\n result = dkufood.requestFoodMenu(_request)\n\n data[topic]['message']['text'] = topic + \"\\n\\n\" + result['message']\n data[topic]['keyboard'] = restList\n\n return data[topic]","repo_name":"NEONKID/DLUGBot","sub_path":"messages/foodmessage.py","file_name":"foodmessage.py","file_ext":"py","file_size_in_byte":922,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"26928093050","text":"a=[1,2,3,4,5,6,7,8,9,10]\nb=[a[2],a[4],a[6]]\n\nfor item,i in enumerate(b):\n# if item==2 or item==4 or item==6: --> alternate method\n print(item,i)\n\n\n\n\n\n\n\n\n\n","repo_name":"HarshalAtre/My-codes","sub_path":"python/ch12/12--ques 2.py","file_name":"12--ques 2.py","file_ext":"py","file_size_in_byte":161,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"27614718057","text":"import typing as t\n\nimport libtmux\nfrom libtmux._compat import LegacyVersion as Version\nfrom libtmux.common import get_version\n\nfrom .__about__ import __version__\nfrom .exc import TmuxpPluginException\n\n#: Minimum version of tmux required to run libtmux\nTMUX_MIN_VERSION = \"1.8\"\n\n#: Most recent version of tmux supported\nTMUX_MAX_VERSION = None\n\n#: Minimum version of libtmux required to run libtmux\nLIBTMUX_MIN_VERSION = \"0.8.3\"\n\n#: Most recent version of libtmux supported\nLIBTMUX_MAX_VERSION = None\n\n#: Minimum version of tmuxp required to use plugins\nTMUXP_MIN_VERSION = \"1.6.0\"\n\n#: Most recent version of tmuxp\nTMUXP_MAX_VERSION = None\n\n\nif t.TYPE_CHECKING:\n from libtmux.session import Session\n from libtmux.window import Window\n from typing_extensions import TypedDict, TypeGuard, Unpack\n\n from ._types import PluginConfigSchema\n\n class VersionConstraints(TypedDict):\n version: t.Union[Version, str]\n vmin: str\n vmax: t.Optional[str]\n incompatible: t.List[t.Union[t.Any, str]]\n\n class TmuxpPluginVersionConstraints(TypedDict):\n tmux: VersionConstraints\n tmuxp: VersionConstraints\n libtmux: VersionConstraints\n\n\nclass Config(t.TypedDict):\n plugin_name: str\n tmux_min_version: str\n tmux_max_version: t.Optional[str]\n tmux_version_incompatible: t.Optional[t.List[str]]\n libtmux_min_version: str\n libtmux_max_version: t.Optional[str]\n libtmux_version_incompatible: t.Optional[t.List[str]]\n tmuxp_min_version: str\n tmuxp_max_version: t.Optional[str]\n tmuxp_version_incompatible: t.Optional[t.List[str]]\n\n\nDEFAULT_CONFIG: \"Config\" = {\n \"plugin_name\": \"tmuxp-plugin\",\n \"tmux_min_version\": TMUX_MIN_VERSION,\n \"tmux_max_version\": TMUX_MAX_VERSION,\n \"tmux_version_incompatible\": None,\n \"libtmux_min_version\": LIBTMUX_MIN_VERSION,\n \"libtmux_max_version\": LIBTMUX_MAX_VERSION,\n \"libtmux_version_incompatible\": None,\n \"tmuxp_min_version\": TMUXP_MIN_VERSION,\n \"tmuxp_max_version\": TMUXP_MAX_VERSION,\n \"tmuxp_version_incompatible\": None,\n}\n\n\ndef validate_plugin_config(config: \"PluginConfigSchema\") -> \"TypeGuard[Config]\":\n return isinstance(config, dict)\n\n\ndef setup_plugin_config(\n config: \"PluginConfigSchema\", default_config: \"Config\" = DEFAULT_CONFIG\n) -> \"Config\":\n new_config = config.copy()\n for default_key, default_value in default_config.items():\n if default_key not in new_config:\n new_config[default_key] = default_value # type:ignore\n\n assert validate_plugin_config(new_config)\n\n return new_config\n\n\nclass TmuxpPlugin:\n def __init__(self, **kwargs: \"Unpack[PluginConfigSchema]\") -> None:\n \"\"\"\n Initialize plugin.\n\n The default version values are set to the versions that the plugin\n system requires.\n\n Parameters\n ----------\n plugin_name : str\n Name of the child plugin. Used in error message plugin fails to\n load\n\n tmux_min_version : str\n Min version of tmux that the plugin supports\n\n tmux_max_version : str\n Min version of tmux that the plugin supports\n\n tmux_version_incompatible : list\n Versions of tmux that are incompatible with the plugin\n\n libtmux_min_version : str\n Min version of libtmux that the plugin supports\n\n libtmux_max_version : str\n Max version of libtmux that the plugin supports\n\n libtmux_version_incompatible : list\n Versions of libtmux that are incompatible with the plugin\n\n tmuxp_min_version : str\n Min version of tmuxp that the plugin supports\n\n tmuxp_max_version : str\n Max version of tmuxp that the plugin supports\n\n tmuxp_version_incompatible : list\n Versions of tmuxp that are incompatible with the plugin\n\n \"\"\"\n config = setup_plugin_config(config=kwargs)\n self.plugin_name = config[\"plugin_name\"]\n\n # Dependency versions\n self.tmux_version = get_version()\n self.libtmux_version = libtmux.__about__.__version__\n self.tmuxp_version = Version(__version__)\n\n self.version_constraints: \"TmuxpPluginVersionConstraints\" = {\n \"tmux\": {\n \"version\": self.tmux_version,\n \"vmin\": config[\"tmux_min_version\"],\n \"vmax\": config[\"tmux_max_version\"],\n \"incompatible\": config[\"tmux_version_incompatible\"]\n if config[\"tmux_version_incompatible\"]\n else [],\n },\n \"libtmux\": {\n \"version\": self.libtmux_version,\n \"vmin\": config[\"libtmux_min_version\"],\n \"vmax\": config[\"libtmux_max_version\"],\n \"incompatible\": config[\"libtmux_version_incompatible\"]\n if config[\"libtmux_version_incompatible\"]\n else [],\n },\n \"tmuxp\": {\n \"version\": self.tmuxp_version,\n \"vmin\": config[\"tmuxp_min_version\"],\n \"vmax\": config[\"tmuxp_max_version\"],\n \"incompatible\": config[\"tmuxp_version_incompatible\"]\n if config[\"tmuxp_version_incompatible\"]\n else [],\n },\n }\n\n self._version_check()\n\n def _version_check(self) -> None:\n \"\"\"\n Check all dependency versions for compatibility.\n \"\"\"\n for dep, constraints in self.version_constraints.items():\n assert isinstance(constraints, dict)\n try:\n assert self._pass_version_check(**constraints)\n except AssertionError as e:\n raise TmuxpPluginException(\n \"Incompatible {dep} version: {version}\\n{plugin_name} \"\n \"requirements:\\nmin: {vmin} | max: {vmax} | \"\n \"incompatible: {incompatible}\\n\".format(\n dep=dep, plugin_name=self.plugin_name, **constraints\n )\n ) from e\n\n def _pass_version_check(\n self,\n version: t.Union[str, Version],\n vmin: str,\n vmax: t.Optional[str],\n incompatible: t.List[t.Union[t.Any, str]],\n ) -> bool:\n \"\"\"\n Provide affirmative if version compatibility is correct.\n \"\"\"\n if vmin and version < Version(vmin):\n return False\n if vmax and version > Version(vmax):\n return False\n if version in incompatible:\n return False\n\n return True\n\n def before_workspace_builder(self, session: \"Session\") -> None:\n \"\"\"\n Provide a session hook previous to creating the workspace.\n\n This runs after the session has been created but before any of\n the windows/panes/commands are entered.\n\n Parameters\n ----------\n session : :class:`libtmux.Session`\n session to hook into\n \"\"\"\n\n def on_window_create(self, window: \"Window\") -> None:\n \"\"\"\n Provide a window hook previous to doing anything with a window.\n\n This runs runs before anything is created in the windows, like panes.\n\n Parameters\n ----------\n window: :class:`libtmux.Window`\n window to hook into\n \"\"\"\n\n def after_window_finished(self, window: \"Window\") -> None:\n \"\"\"\n Provide a window hook after creating the window.\n\n This runs after everything has been created in the window, including\n the panes and all of the commands for the panes. It also runs after the\n ``options_after`` has been applied to the window.\n\n Parameters\n ----------\n window: :class:`libtmux.Window`\n window to hook into\n \"\"\"\n\n def before_script(self, session: \"Session\") -> None:\n \"\"\"\n Provide a session hook after the workspace has been built.\n\n This runs after the workspace has been loaded with ``tmuxp load``. It\n augments instead of replaces the ``before_script`` section of the\n workspace data.\n\n This hook provides access to the LibTmux.session object for any\n behavior that would be used in the ``before_script`` section of the\n workspace file that needs access directly to the session object.\n This runs after the workspace has been loaded with ``tmuxp load``.\n\n The hook augments, rather than replaces, the ``before_script`` section\n of the workspace. While it is possible to do all of the\n ``before_script`` workspace in this function, if a shell script\n is currently being used for the workspace, it would be cleaner to\n continue using the script in the ``before_section``.\n\n If changes to the session need to be made prior to\n anything being built, please use ``before_workspace_builder`` instead.\n\n Parameters\n ----------\n session : :class:`libtmux.Session`\n session to hook into\n \"\"\"\n\n def reattach(self, session: \"Session\") -> None:\n \"\"\"\n Provide a session hook before reattaching to the session.\n\n Parameters\n ----------\n session : :class:`libtmux.Session`\n session to hook into\n \"\"\"\n","repo_name":"tmux-python/tmuxp","sub_path":"src/tmuxp/plugin.py","file_name":"plugin.py","file_ext":"py","file_size_in_byte":9217,"program_lang":"python","lang":"en","doc_type":"code","stars":3824,"dataset":"github-code","pt":"52"} +{"seq_id":"34098126472","text":"# -*- coding: utf-8 *-*\n\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nfrom builtins import str, bytes, dict, int\n\nimport os\nimport sys\nsys.path.insert(0, os.path.join(os.path.dirname(__file__), \"..\", \"..\"))\n\nfrom pattern.web import DBPedia\n\ndbp = DBPedia()\n\n# DBPedia is a database of structured information mined from Wikipedia.\n# DBPedia data is stored as RDF triples: (subject, predicate, object),\n# e.g., X is-a Actor, Y is-a Country, Z has-birthplace Country, ...\n# If you know about pattern.graph (or graphs in general),\n# this triple format should look familiar.\n\n# DBPedia can be queried using SPARQL:\n# http://dbpedia.org/sparql\n# http://www.w3.org/TR/rdf-sparql-query/\n# A SPARQL query yields rows that match all triples in the WHERE clause.\n# A SPARQL query uses ?wildcards in triple subject/object to select fields.\n\n# 1) Search DBPedia for actors.\n\n# Variables are indicated with a \"?\" prefix.\n# Variables will be bound to the corresponding part of each matched triple.\n# The \"a\" is short for \"is of the class\".\n# The \"prefix\" statement creates a shorthand for a given namespace.\n# To see what semantic constraints are available in \"dbo\" (for example):\n# http://dbpedia.org/ontology/\nq = \"\"\"\nprefix dbo: \nselect ?actor where { \n ?actor a dbo:Actor.\n}\n\"\"\"\nfor result in dbp.search(q, start=1, count=10):\n print(result.actor)\nprint(\"\")\n\n# You may notice that each Result.actor is of the form:\n# \"http://dbpedia.org/resource/[NAME]\"\n# This kind of string is a subclass of unicode: DBPediaResource.\n# DBPediaResource has a DBPediaResource.name property (see below).\n\n# 2) Search DBPedia for actors and their place of birth.\n\nq = \"\"\"\nprefix dbo: \nselect ?actor ?place where { \n ?actor a dbo:Actor.\n ?actor dbo:birthPlace ?place.\n}\norder by ?actor\n\"\"\"\nfor r in dbp.search(q, start=1, count=10):\n print(\"%s (%s)\" % (r.actor.name, r.place.name))\nprint(\"\")\n\n# You will notice that the results now include duplicates,\n# the same actor with a city name, and with a country name.\n# We could refine ?place by including the following triple:\n# \"?place a dbo:Country.\"\n\n# 3) Search DBPedia for actors born in 1970.\n\n# Each result must match both triples, i.e.,\n# X is an actor + X is born on Y.\n# We don't want to filter by month and day (e.g., \"1970-12-31\"),\n# so we use a regular expression instead with filter():\nq = \"\"\"\nprefix dbo: \nselect ?actor ?date where { \n ?actor a dbo:Actor.\n ?actor dbo:birthDate ?date. \n filter(regex(str(?date), \"1970-..-..\"))\n}\norder by ?date\n\"\"\"\nfor r in dbp.search(q, start=1, count=10):\n print(\"%s (%s)\" % (r.actor.name, r.date))\nprint(\"\")\n\n# We could also make this query shorter,\n# by combining the two ?actor triples into one:\n# \"?actor a dbo:Actor; dbo:birthDate ?date.\"\n\n# 4) A more advanced example, in German:\n\nq = \"\"\"\nprefix dbo: \nprefix rdfs: \nselect ?actor ?place where { \n ?_actor a dbo:Actor.\n ?_actor dbo:birthPlace ?_place.\n ?_actor rdfs:label ?actor.\n ?_place rdfs:label ?place.\n filter(lang(?actor) = \"de\" && lang(?place) = \"de\")\n}\norder by ?actor\n\"\"\"\nfor r in dbp.search(q, start=1, count=10):\n print(\"%s (%s)\" % (r.actor, r.place))\nprint(\"\")\n\n# This extracts a German label for each matched DBPedia resource.\n# - X is an actor,\n# - X is born in Y,\n# - X has a German label A,\n# - Y has a German label B,\n# - Retrieve A and B.\n\n# For example, say one of the matched resources was:\n# \"\"\n# If you open this URL in a browser,\n# you will see all the available semantic properties and their values.\n# One of the properties is \"rdfs:label\": a human-readable & multilingual label.\n\n# 5) Find triples involving cats.\n\n# \n# means: \"is in the category of\".\nq = \"\"\"\nprefix dbo: \nprefix rdfs: \nselect ?cat ?relation ?concept where {\n ?cat .\n ?cat ?_relation ?_concept.\n ?_relation rdfs:label ?relation.\n ?_concept rdfs:label ?concept.\n filter(lang(?relation) = \"en\" && lang(?concept) = \"en\")\n} order by ?cat\n\"\"\"\nfor r in dbp.search(q, start=1, count=10):\n print(\"%s ---%s--> %s\" % (r.cat.name, r.relation.ljust(10, \"-\"), r.concept))\nprint(\"\")\n\n# 6) People whose first name includes \"Édouard\"\n\nq = \"\"\"\nprefix dbo: \nprefix foaf: \nselect ?person ?name where { \n ?person a dbo:Person.\n ?person foaf:givenName ?name.\n filter(regex(?name, \"Édouard\"))\n}\n\"\"\"\nfor result in dbp.search(q, start=1, count=10, cached=False):\n print(\"%s (%s)\" % (result.person.name, result.name))\nprint(\"\")\n","repo_name":"clips/pattern","sub_path":"examples/01-web/10-dbpedia.py","file_name":"10-dbpedia.py","file_ext":"py","file_size_in_byte":4869,"program_lang":"python","lang":"en","doc_type":"code","stars":8585,"dataset":"github-code","pt":"52"} +{"seq_id":"70051688165","text":"import random\nfrom django.conf import settings\nfrom django.db import models\nfrom django.db.models import Q\n\nUser = settings.AUTH_USER_MODEL\n\nclass TweetLike(models.Model):\n user = models.ForeignKey(User, on_delete=models.CASCADE)\n tweet = models.ForeignKey(\"Tweet\", on_delete=models.CASCADE)\n timestamp = models.DateTimeField(auto_now_add=True)\n\nclass TweetQuerySet(models.QuerySet):\n def by_username(self, username):\n return self.filter(user__username__iexact=username)\n\n def feed(self, user):\n profiles_exist = user.following.exists()\n followed_users_id = []\n if profiles_exist:\n followed_users_id = user.following.values_list(\"user__id\", flat=True) # [x.user.id for x in profiles]\n return self.filter(\n Q(user__id__in=followed_users_id) |\n Q(user=user)\n ).distinct().order_by(\"-timestamp\")\n\nclass TweetManager(models.Manager):\n def get_queryset(self, *args, **kwargs):\n return TweetQuerySet(self.model, using=self._db)\n\n def feed(self, user):\n return self.get_queryset().feed(user)\n\nclass Tweet(models.Model):\n # Maps to SQL data\n # id = models.AutoField(primary_key=True)\n parent = models.ForeignKey(\"self\", null=True, on_delete=models.SET_NULL)\n user = models.ForeignKey(User, on_delete=models.CASCADE, related_name=\"tweets\") # many users can many tweets\n likes = models.ManyToManyField(User, related_name='tweet_user', blank=True, through=TweetLike)\n content = models.TextField(blank=True, null=True)\n image = models.FileField(upload_to='images/', blank=True, null=True)\n timestamp = models.DateTimeField(auto_now_add=True)\n\n objects = TweetManager()\n # def __str__(self):\n # return self.content\n \n class Meta:\n ordering = ['-id']\n \n @property\n def is_retweet(self):\n return self.parent != None\n \n def serialize(self):\n '''\n Feel free to delete!\n '''\n return {\n \"id\": self.id,\n \"content\": self.content,\n \"likes\": random.randint(0, 200)\n }","repo_name":"codingforentrepreneurs/Tweetme-2","sub_path":"tweets/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2094,"program_lang":"python","lang":"en","doc_type":"code","stars":401,"dataset":"github-code","pt":"52"} +{"seq_id":"11286993894","text":"import json\nfrom pathlib import Path\nimport os\n\nimport cv2\n\n\ndef main():\n os.makedirs(out_dir, exist_ok=True)\n for i, sample in enumerate(Path(in_dir).glob('*.jpg')):\n sample = str(sample)\n json_path = f'{sample[:-3]}json'\n img = cv2.imread(sample)\n with open(json_path) as f:\n label = json.load(f)['shapes']\n for k, rec in enumerate(label):\n crop = img[round(rec['points'][0][1]):round(rec['points'][1][1]), round(rec['points'][0][0]):round(rec['points'][1][0])]\n cv2.imwrite(f'{out_dir}/{i}{k}.jpg', crop)\n\nif __name__ == '__main__':\n in_dir = 'data/test_data/test_images'\n out_dir = 'data/crop'\n main()","repo_name":"hoangphucITJP/eklipse","sub_path":"crop_avatar.py","file_name":"crop_avatar.py","file_ext":"py","file_size_in_byte":688,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"19098417475","text":"from decimal import Decimal\n\nfrom django.db import transaction\nfrom rest_framework import serializers\n\nfrom app.models import Transaction, Wallet\n\n\nclass WalletSerializer(serializers.ModelSerializer):\n class Meta:\n model = Wallet\n fields = ['id', 'label', 'balance']\n read_only_fields = ['balance']\n\n\nclass TransactionSerializer(serializers.ModelSerializer):\n class Meta:\n model = Transaction\n fields = ['id', 'wallet_id', 'txid', 'amount']\n\n def create(self, validated_data):\n with transaction.atomic():\n instance = super().create(validated_data)\n self._add_new_transaction_to_wallet(\n wallet_id=instance.wallet_id.id,\n amount=instance.amount\n )\n return instance\n\n def update(self, instance, validated_data):\n with transaction.atomic():\n old_amount = instance.amount\n instance = super().update(instance, validated_data)\n self._update_transaction_in_wallet(\n wallet_id=instance.wallet_id.id,\n old_amount=old_amount,\n new_amount=instance.amount,\n )\n\n return instance\n\n def _update_transaction_in_wallet(\n self, wallet_id: str, old_amount: Decimal, new_amount: Decimal\n ):\n wallet = Wallet.objects.select_for_update().get(pk=wallet_id)\n balance = wallet.balance or 0\n wallet.balance = balance - old_amount + new_amount\n wallet.save()\n\n def _add_new_transaction_to_wallet(self, wallet_id: str, amount: Decimal):\n wallet = Wallet.objects.select_for_update().get(pk=wallet_id)\n balance = wallet.balance or 0\n wallet.balance = balance + amount\n wallet.save()\n","repo_name":"Mexamos/b2broker-test-task","sub_path":"b2broker/app/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":1752,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"38486571039","text":"#! /usr/bin/pytdon\r\n# -*- coding:utf-8 -*- \r\n\r\n'''\r\ntcp服务提供者基类\r\n\r\n'''\r\n\r\nimport threading\r\nimport socketserver\r\nfrom . import tcpcli\r\nfrom .cloudapp import getCloudApp\r\nfrom .provider import ProviderBase\r\nfrom .svrstat import svrstat\r\nfrom .const import CMD_TCP_SVR_REQ, CMDID_MID\r\n\r\n\r\nclass TcpProviderBase(socketserver.BaseRequestHandler, ProviderBase):\r\n protocol = 1\r\n handleMap = None\r\n server = None\r\n thread = None\r\n async_response = False\r\n \r\n # 绑定消息处理函数\r\n @classmethod\r\n def BindCmdHandle(cls, cmdid, func):\r\n cls.handleMap[cmdid] = func\r\n\r\n \r\n # 服务提供者构造器(对外)\r\n # 调用前请确保已初始化CloudApp实例\r\n @classmethod\r\n def Start(cls):\r\n if not cls.Regist(False): return None\r\n\r\n cls.handleMap = {}\r\n cls.BindCmdHandle(CMD_TCP_SVR_REQ, cls.onRequest)\r\n\r\n socketserver.ThreadingTCPServer.daemon_threads = True\r\n cls.server = socketserver.ThreadingTCPServer((cls.host, cls.port), cls)\r\n\r\n cls.thread = threading.Thread(target=cls.server.serve_forever, \r\n name='PrvdServer:'+cls.regname)\r\n #cls.thread.setDaemon(True)\r\n cls.thread.start()\r\n cls.regProvider()\r\n return cls\r\n\r\n @classmethod\r\n def Shutdown(cls):\r\n print((\"Shutdown Provider \" + cls.url))\r\n tcpcli.TellExit()\r\n cls.server.shutdown()\r\n cls.server.server_close()\r\n cls.server = None\r\n cls.thread.join() # daemon 可不join\r\n print(\"Shutdown Provider join\" )\r\n cls.thread = None\r\n\r\n ## override // 默认的tcp微服务请求处理方法（即请求cmdid=CMD_TCP_SVR_REQ时的回调）\r\n def onRequest(self):\r\n print('no implete...\\n')\r\n\r\n def close(self):\r\n setattr(self, 'closeFlag', True)\r\n \r\n\r\n #def setError(self, err):\r\n # self.handleerr = err\r\n\r\n # 返回数据给客户调用者， 在事件处理函数中调用\r\n def response(self, rspmsg, err = 0):\r\n asyncmode = hasattr(self, 'async_response_mutex')\r\n asyncmode and self.async_response_mutex.acquire()\r\n ret = tcpcli.Send(self.request, \r\n self.reqcmdid|CMDID_MID, self.reqseqid, rspmsg)\r\n asyncmode and self.async_response_mutex.release()\r\n\r\n svrstat.addPrvdCount(self, 0 == err)\r\n if 0 == ret:\r\n self.close()\r\n return ret\r\n \r\n # 异步响应，返回的闭包可以在以某个时间某个线程中调用，给予响应\r\n def response_async(self):\r\n rspcmdid = self.reqcmdid|CMDID_MID\r\n rspseqid = self.reqseqid\r\n sock = self.request\r\n if not hasattr(self, 'async_response_mutex'):\r\n self.async_response_mutex = threading.Lock()\r\n self.async_response = True\r\n async_response_mutex = self.async_response_mutex\r\n\r\n def respsor(rspmsg, err = 0):\r\n try:\r\n async_response_mutex.acquire()\r\n tcpcli.Send(sock, rspcmdid, rspseqid, rspmsg)\r\n svrstat.addPrvdCount(self, 0 == err)\r\n finally:\r\n async_response_mutex.release()\r\n\r\n return respsor\r\n\r\n\r\n ## override\r\n def handle(self):\r\n print(\"begin handle\")\r\n threading.currentThread().setName('handle:' + str(self.request.getpeername()))\r\n while not getattr(self, 'closeFlag', False):\r\n result,recvBytes, cmdid,seqid,body = tcpcli.Recv(self.request, False)\r\n if 0 == result:\r\n self.recvBytes = recvBytes\r\n self.reqcmdid = cmdid\r\n self.reqseqid = seqid\r\n self.reqbody = body\r\n self.handleerr = 0\r\n func = self.handleMap.get(cmdid)\r\n if func:\r\n '''\r\n def respsor(msg, err = 0):\r\n rspid = cmdid|CMDID_MID\r\n rspseqid = seqid\r\n if 0 == tcpcli.Send(self.request, rspid, rspseqid, msg):\r\n self.close()\r\n svrstat.addPrvdCount(self, 0 == err)\r\n func(self, respsor)\r\n '''\r\n func(self)\r\n else:\r\n print((\"No CmdFunc cmd=0x%x(%d)\" % (cmdid, seqid)))\r\n self.handleerr = 1\r\n \r\n else:\r\n self.close()\r\n \r\n\r\n\r\n \r\n","repo_name":"kawloong/CppCloud","sub_path":"python_sdk/cppcloud/tcpprovider.py","file_name":"tcpprovider.py","file_ext":"py","file_size_in_byte":4456,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"52"} +{"seq_id":"32223823330","text":"import tkinter\nfrom tkinter import *\nfrom tkinter import ttk\nfrom tkinter import messagebox\nfrom PIL import ImageTk, Image\nimport pyexcel as pe\nimport python_project\nimport getdotaz_new\n\n\ndef kresgraf(xvalue, yvalue):\n def zaznamenej(nametext, xvalue, yvalue):\n try:\n sheet = pe.get_sheet(file_name=\"data.ods\")\n except:\n pe.save_as(array=[[\"Jméno\", \"OsaX\", \"OsaY\"]], dest_file_name=\"data.ods\")\n sheet = pe.get_sheet(file_name=\"data.ods\")\n\n data = [str(nametext), xvalue, yvalue]\n\n sheet.row += data\n sheet.save_as(\"data.ods\")\n\n messagebox.showinfo(message=\"Data byla uložena do souboru data.ods ve složce projektu\")\n root.destroy()\n\n ratio = 0.80 # Ratio mění velikost dle procent (hodnota * 100%)\n root = Tk()\n canvas = Canvas(root, width=round(930*ratio), height=round(807*ratio))\n canvas.pack()\n\n img = Image.open(\"polcompass.jpg\")\n img2 = ImageTk.PhotoImage(img.resize((round(930*ratio), round(807*ratio))))\n canvas.create_image(0, 0, anchor=NW, image=img2)\n\n xpos = (img2.width() / 2 - round(14*ratio))\n ypos = (img2.height() / 2 + round(1*ratio))\n\n # 36x36 velikost bloku kompasu\n # xhodnoty = 0\n # yhodnoty = 0\n\n dot = Image.open(\"dot.png\")\n dotres = ImageTk.PhotoImage(dot.resize((round(25*ratio), round(25*ratio)))) # 12 je ještě akceptovatelné v poměru velikost-přesnost\n canvas.create_image(xpos + (xvalue * round(36*ratio)), ypos - (yvalue * round(36*ratio)),\n image=dotres) # začátek střed, 1 v hodnotách je jeden blok\n\n strany = [\"KSČM\", \"SocDem\", \"Zelení\", \"Piráti\", \"KDU-ČSL\", \"ANO\", \"STAN\", \"TOP 09\", \"ODS\", \"SPD\", \"Trikolóra\", \"Svobodní\" ]\n souradnice = [[-7,6],[-5, -2],[-2,-5],[-1,-6],[1,5],[2,4],[2,-3],[5,-2],[6,3],[3,6],[6,5],[7,2]] \n \n min_rozdil = 100\n min_rozdil_strana = \"\"\n for i in range(len(strany)):\n aktualnix = souradnice[i][0]\n aktualniy = souradnice[i][1]\n \n #print(aktualnix)\n #print(aktualniy)\n \n rozdil_x = abs(xvalue - aktualnix)\n rozdil_y = abs(yvalue - aktualniy)\n rozdil_celkem = rozdil_x + rozdil_y\n \n if(rozdil_celkem < min_rozdil):\n min_rozdil = rozdil_celkem\n min_rozdil_strana = strany[i]\n \n #print(min_rozdil, min_rozdil_strana)\n\n nejblizsi_strana = tkinter.Label(root,font=(\"Arial\", 25), text=\"Vaše nejbližší strana: \" + min_rozdil_strana)\n nejblizsi_strana.pack()\n\n nametext = tkinter.Label(root, text=\"Zadejte jméno\")\n nametext.pack()\n\n nameentry = ttk.Entry(root)\n nameentry.pack()\n\n enter = tkinter.Button(root, text=\"Poslat\", font=(\"Arial\", 10), height=1, width=5, command=lambda: zaznamenej(nameentry.get(), xvalue, yvalue) )\n enter.pack()\n\n mainloop()\n","repo_name":"DavidPCZ/python_projekt","sub_path":"graf.py","file_name":"graf.py","file_ext":"py","file_size_in_byte":2862,"program_lang":"python","lang":"cs","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"4298255365","text":"import cv2\n\ncap = cv2.VideoCapture(\"C:\\\\vscode\\\\Teknofest\\\\klasor11\\\\car.mp4\")\n\n\ncircles = []\ndef mouse(event,x,y,lags,params): # event maosue haerketi x y oluşacak çemberin merkezi\n if event == cv2.EVENT_LBUTTONDOWN:\n circles.append((x,y))\n\ncv2.namedWindow(\"Frame\")\ncv2.setMouseCallback(\"Frame\",mouse) # frame üzerine yapılan işleri belirtir.\n\nwhile 1 :\n _,frame = cap.read()\n frame = cv2.resize(frame,(640,480))\n\n for center in circles:\n cv2.circle(frame,center,20,(255,0,0),-1)\n\n cv2.imshow(\"Frame\",frame)\n\n key = cv2.waitKey(1)\n\n if key == 27:\n break\n elif key == ord(\"h\"): # ekranı temizlememk için\n circles = []\n\n\ncap.release()\ncv2.destroyAllWindows()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"Yusufygc/GoruntuIsleme","sub_path":"klasor11/fare_kullanımı.py","file_name":"fare_kullanımı.py","file_ext":"py","file_size_in_byte":736,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"27300749332","text":"from time import time\nimport sklearn.ensemble as ske\nfrom clustering_cdhit import readJson\nimport numpy as np\nimport sys, os\nimport pickle\n \n\n\ndef train_model(numpy_matrix, region_type, samples_file, samples_sp2): \n \n samples_dic = readJson(samples_file)\n samplesnumber = len(samples_dic.keys())\n gene_matrix=np.load(numpy_matrix)\n\n Y = np.zeros((1,samplesnumber))\n Y[:,0:samples_sp2]=1\n print (Y)\n Y=Y.T\n print (Y.shape)\n\n t0 = time()\n model = ske.RandomForestRegressor(bootstrap=True, n_estimators=1000, n_jobs= 16, max_depth=None, min_samples_split=1.0, random_state=0)\n model.fit(gene_matrix, Y.ravel())\n print(\"analysis done in in %0.3fs.\" % (time() - t0)) \n\n try:\n os.stat(\"models\")\n except:\n os.mkdir(\"models\")\n\n # save the model to disk\n filename = 'models/finalized_model_%s.sav' %region_type\n pickle.dump(model, open(filename, 'wb'))\n\n\n\nif __name__ == \"__main__\":\n\n numpy_matrix = sys.argv[1]\n region_type = sys.argv[2]\n samples_file = sys.argv[3]\n samples_sp2 = sys.argv[4]\n\n train_model(numpy_matrix, region_type, samples_file, int(samples_sp2))\n #train_model('../matrix/0.7_PERC_gene.npy', 'gene', '../dics/samples.json', 2)","repo_name":"ivyduk/identiclust-train","sub_path":"scripts/train_model.py","file_name":"train_model.py","file_ext":"py","file_size_in_byte":1334,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"16250654530","text":"import os\n\nimport pytest\n\nimport audb\n\n\n@pytest.fixture(scope='package', autouse=True)\ndef cache(tmpdir_factory):\n r\"\"\"Provide a reusable cache for docstring tests.\n\n As we rely on emodb from the public repo,\n it makes sense to cache it\n across all docstring tests.\n\n \"\"\"\n cache = tmpdir_factory.mktemp('cache')\n # We use the environment variable here\n # to ensure audb.config.CACHE_ROOT\n # does still return the default config value\n # in the doctest\n env_cache = os.environ.get('AUDB_CACHE_ROOT', None)\n env_shared_cache = os.environ.get('AUDB_SHARED_CACHE_ROOT', None)\n os.environ['AUDB_CACHE_ROOT'] = str(cache)\n os.environ['AUDB_SHARED_CACHE_ROOT'] = str(cache)\n\n yield\n\n if env_cache is None:\n del os.environ['AUDB_CACHE_ROOT']\n else: # pragma: nocover\n os.environ['AUDB_CACHE_ROOT'] = env_cache\n\n if env_shared_cache is None:\n del os.environ['AUDB_SHARED_CACHE_ROOT']\n else: # pragma: nocover\n os.environ['AUDB_SHARED_CACHE_ROOT'] = env_shared_cache\n\n\n@pytest.fixture(autouse=True)\ndef public_repository(doctest_namespace):\n r\"\"\"Provide access to the public Artifactory repository.\n\n Some tests in the docstrings need access to the emodb database.\n As all the unit tests defined under ``tests/*``\n should not be able to see the public repository\n as the number of available databases would then not be deterministic.\n We provide this access here\n with the help of the ``doctest_namespace`` fixture.\n\n The ``conftest.py`` file has to be in the same folder\n as the code file where the docstring is defined.\n\n \"\"\"\n audb.config.REPOSITORIES = [\n audb.Repository(\n name='data-public',\n host='https://audeering.jfrog.io/artifactory',\n backend='artifactory',\n ),\n ]\n doctest_namespace['audb'] = audb\n\n yield\n\n # Remove public repo\n audb.config.REPOSITORIES.pop()\n","repo_name":"audeering/audb","sub_path":"audb/core/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":1945,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"52"} +{"seq_id":"71781671204","text":"import time\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\nimport os\nimport platform\nsystem = platform.platform()\ndriverpath = \"/home/user/Schreibtisch/ChromeDriver_91.0.4472.19/chromedriver\"\nif 'Linux' in system:\n tmp = \"/home/user\"\n slashes = '/'\nif 'Windows' in system:\n tmp = os.environ.get('TMP')\n slashes = '\\\\'\n\n# erstelle datei wenn nicht vorhanden\ndef in_datei_schreiben():\n now = time.strftime(\"%m-%d-%y_%H-%M-%S\")\n file = open(newpath + slashes + username_chat.text + '.txt', 'a+')\n file.write(username_chat.text + ' ' + 'zuletzt online:' + now + \"\\n\")\n file.close()\n\n# erstelle ordner zum speichern\nnewpath = tmp + slashes +'whatsapp_timestamps'\nif not os.path.exists(newpath):\n os.makedirs(newpath)\n\n# setze wendriver auf chrome debugger fenster\n\n#windows\ndef webdriver_win():\n chrome_options = Options()\n chrome_options.add_experimental_option(\"debuggerAddress\", \"127.0.0.1:9222\")\n chrome_driver = r\"C:\\Users\\danie\\AppData\\Local\\Programs\\Python\\chromedriver_win32_v90\\chromedriver.exe\"\n browser = webdriver.Chrome(chrome_driver, options=chrome_options)\n return browser\n#linux\ndef webdriver_linux():\n options = webdriver.ChromeOptions()\n options.add_experimental_option(\"debuggerAddress\", \"127.0.0.1:9222\")\n browser = webdriver.Chrome(options=options, executable_path=driverpath)\n return browser\nif 'Linux' in system:\n browser = webdriver_linux()\nif 'Windows' in system:\n browser = webdriver_win()\n# testet ob gelbe Warnung das handy nicht verbunden ist existiert und bleibt in der schleife bis connection wieder da ist\ndef test_connection():\n oflinetester = browser.find_element_by_xpath(\"html/body/div/div[1]/div[1]/div[3]/div\") # .find_elements_by_tag_name(\"span\")\n while \"Telefon nicht verbunden\" in oflinetester.text:\n print(\"Telefon nicht verbunden...warte\")\n time.sleep(3)\n#div wert falls wieder probleme auftreten\ndiv_wert = 3\n\n#xpath variablen\nleft_sidebar_all_xpath = \"html/body/div/div[1]/div[1]/div[3]/div\"\nchatlist_xpath = left_sidebar_all_xpath + \"/div[2]/div[\" + str(div_wert) + \"]/div/div\"\narchiviert_xpath = left_sidebar_all_xpath + \"/div[2]/div[1]\"\nchat_xpath = \"html/body/div/div/div/div[4]/div/header/div[2]\"\nchat_username_xpath = chat_xpath + \"/div\"\nonlinestatus_xpath = chat_xpath + \"/div[2]/span\"\n\n# wechselt zu aktuell offenen tab (sollte hierbei Whatsapp sein)\nbrowser.switch_to.window(browser.window_handles[0])\ntest_connection()\n\n# ermittelt nutzeranzahl\nchatlist = browser.find_element_by_xpath(chatlist_xpath)\nnutzeranzahl = int(chatlist.get_attribute(\"aria-rowcount\"))\ni = 1\n\n# voreinstellung der browser-fenster-höhen\n# da bei scrollbarem chatverlauf unerwünschte effekte auftreten (nicht alle chats werden mitgenommen)\n# browserseitiges Zoomen ist nicht zu empfehlen da dadurch .click() nicht jeden nutzer mitnimmt und das script mit den usern durcheinander kommt\nbrowser.set_window_size(0, 0) # verkleinerung auf 0 um wirkliche Windowgröße zu ermitteln mit nachfolgendem browser.get_window_size().get(\"height\")\nchrome_hoehe = browser.get_window_size().get(\"height\")\nheader_hoehe = browser.find_element_by_xpath(left_sidebar_all_xpath + \"/header\").rect.get(\"height\")\noffline_banner = 107 # bleibt erst mal hardgecocodet da eine parametrisierung zwar möglich aber wenig sinnvoll ist\nsucher_hoehe = browser.find_element_by_xpath(left_sidebar_all_xpath + \"/div[1]\").rect.get(\"height\")\narchive_hoehe = browser.find_element_by_xpath(archiviert_xpath).rect.get(\"height\")\ngesamt_vor_hoehe = chrome_hoehe + header_hoehe + offline_banner + sucher_hoehe + archive_hoehe\nchat_hoehe = browser.find_element_by_xpath(chatlist_xpath + \"/div[1]\").rect.get(\"height\")\nbrowser.set_window_size(600, gesamt_vor_hoehe + chat_hoehe * nutzeranzahl)\n\nprint(\"--------------------------------\")\n\nwhile i > 0:\n # prüfe alle nutzer absteigend durch (aufsteigend müsste auch gehen, wurde aber noch nicht getestet)\n for j in range(nutzeranzahl, 0, -1):\n test_connection()\n # ermittelt username aus chatverlauf\n username_chatverlauf = browser.find_element_by_xpath(chatlist_xpath + \"/div[\" + str(j) + \"]/div/div/div[2]/div[1]/div[1]/span/span\")\n print(str(j))\n print(\"listenname: \" + username_chatverlauf.text)\n username_chatverlauf.click()\n # ermittelt username aus angeklickten chat\n chat = browser.find_element_by_xpath(chat_xpath)\n username_chat = browser.find_element_by_xpath(chat_username_xpath)\n # zeigt chatheader noch einmal an um überprüfen zu können ob script sauber läuft\n print(\"chatkopf: \" + username_chat.text)\n # ermittelt onlinestatus\n print(\"Online-Status: \")\n time.sleep(0.1)\n anzahl = chat.find_elements_by_tag_name(\"div\")\n # wenn zweites div element vorhanden ist wird geprüft ob es das wort \"online\" ist oder etwas anderes, bei online logge aktuelle zeit\n if len(anzahl) == 3:\n now = time.strftime(\"%m-%d-%y_%H-%M-%S\")\n status = browser.find_element_by_xpath(onlinestatus_xpath).text\n if status == \"online\":\n print('zuletzt online:' + now)\n in_datei_schreiben()\n else:\n print(\"offline\")\n else:\n print(\"offline\")\n print(\"--------------------------------\")\n# time.sleep(1)\n","repo_name":"DanielMueller1309/Python","sub_path":"sonstiges/wa_online_anzeiger/onliner.py","file_name":"onliner.py","file_ext":"py","file_size_in_byte":5387,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"70132564646","text":"from athenataf.lib.functionality.test.ConfigurationTest import ConfigurationTest\n\nclass AccessRule(ConfigurationTest):\n\t'''\n\tTest class for Access Rule\n\t'''\n\t\n\tdef test_ath_10992_tcp_port_range_validation(self):\n\t\tself.NetworkPage.delete_network_if_present()\n\t\tbasic_info = self.NetworkPage.create_new_network()\n\t\tvirtual_lan = basic_info.employee_network_info()\n\t\tsecurity = virtual_lan.use_vlan_defaults()\n\t\taccess = security.configure_employee_security()\n\t\taccess.tcp_port_range_validation()\n\t\t\n\tdef test_ath_10993_mac_address_and_dhcp_options_attribute_to_be_disabled_in_wired_networks(self):\n\t\t'''\n\t\tbug\n\t\t'''\n\t\tself.NetworkPage.delete_wired_network_if_present()\n\t\tbasic_info = self.NetworkPage.create_new_network()\n\t\tvlan_obj = basic_info.wired_employee_network_info()\n\t\tsecurity = vlan_obj.wired_network_vlan_defaults()\n\t\taccess = security.wired_security_defaults()\n\t\taccess.click_role_access()\n\t\taccess.check_mac_address_and_dhcp_option_in_wired_network()\n\t\t\n\tdef test_ath_10986_role_with_mac_address_and_dhcp_options_matches_regular_expression(self):\n\t\tconf = self.config.config_vars\n\t\tself.NetworkPage.delete_network_if_present()\n\t\tself.NetworkPage.delete_custom_guest_network_if_present()\n\t\tself.take_s1_snapshot()\n\t\tbasic_info = self.NetworkPage.create_new_network()\n\t\tvlan_obj = basic_info.guest_network_info()\n\t\tsecurity = vlan_obj.use_vlan_defaults()\n\t\taccess = security.use_security_default()\n\t\taccess.click_role_access()\n\t\taccess.create_access_role_assignment_rule(str=conf.max_client_threshold, attribute=conf.dropdown_mac_address_and_dhcp_options)\n\t\taccess.finish_network_setup()\n\t\tbasic_info = self.NetworkPage.create_new_network()\n\t\tvlan_obj = basic_info.employee_network_info_with_specific_name(conf.guest_network_name, True)\n\t\tsecurity = vlan_obj.use_vlan_defaults()\n\t\tsecurity._set_phrase()\n\t\taccess = security.move_to_next_page()\n\t\taccess.click_role_access()\n\t\taccess.create_access_role_assignment_rule(str=conf.max_client_threshold, attribute=conf.dropdown_mac_address_and_dhcp_options)\n\t\taccess.finish_network_setup()\n\t\tself.take_s2_snapshot()\n\t\tself.NetworkPage.delete_network_if_present()\n\t\tself.NetworkPage.delete_custom_guest_network_if_present()\n\t\tself._delete_network_auth_server()\n\t\tself.take_s3_snapshot()\n\t\tself.assert_s1_s2_diff(0)\n\t\tself.assert_s1_s3_diff()\n\t\tself.clear()\n\t","repo_name":"cash2one/reautomation_handoff","sub_path":"athena_automation/athenataf/tests/configuration/access_rules/AccessRule.py","file_name":"AccessRule.py","file_ext":"py","file_size_in_byte":2312,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"28861414880","text":"# 0403:faf0\nimport sys\nimport time\nfrom thorpy.comm.discovery import discover_stages\nimport os\n# from thorpy.thorpy.comm.discovery import discover_stages\n# from thorpy.thorpy.message import *\n\n\ndef set_position(s, position):\n try:\n s.position = position\n except:\n pass\n\n\ndef get_position(s):\n\n return str(s.position) + '\\n'\n\n\ndef print_results(results):\n '''\n print to the results to stdout if needed\n and return the constructed string\n '''\n # if 'REQUEST_URI' in os.environ:\n # out_string = gen_web_results(results)\n # else:\n # out_string = gen_cons_results(results)\n # if not g_silent_mode:\n sys.stdout.write(str(results))\n return results\n\n\ndef run(verbose=False):\n # print(loc[0])\n\n stages = list(discover_stages())\n # print(stages)\n s = stages[0]\n\n s.home()\n # s.print_state()\n loc = sys.argv[1:]\n\n if loc[0].lower() == 'sp':\n pos = s.position\n position = float(loc[1])\n if verbose:\n print(position)\n set_position(s, position)\n\n if position < 0:\n while not (position * 0.95 > pos > position * 1.05):\n time.sleep(0.5)\n pos = s.position\n if verbose:\n print(pos)\n elif position > 0:\n while not (position * 0.95 < pos < position * 1.05):\n time.sleep(0.5)\n pos = s.position\n if verbose:\n print(pos)\n elif position == 0:\n while not -0.005 < pos < 0.005:\n time.sleep(0.5)\n pos = s.position\n if verbose:\n print(pos)\n return print_results(str(get_position(s)))\n\n elif loc[0].lower() == 'gp':\n return print_results(str(get_position(s)))\n # s._port.send_message(MGMSG_HW_STOP_UPDATEMSGS())\n\n\nif __name__ == \"__main__\":\n run()\n # print(\"Lior\")","repo_name":"lsasi/EyeC_System","sub_path":"Equipment/motor_control.py","file_name":"motor_control.py","file_ext":"py","file_size_in_byte":1934,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"13434894159","text":"from collections import OrderedDict\nfrom urllib.parse import urlparse\n\nimport binascii\n'''\ntitle : blockchain.py\ndescription : A blockchain implemenation\nauthor : Adil Moujahid\ndate_created : 20180212\ndate_modified : 20180309\nversion : 0.5\nusage : python blockchain.py\n python blockchain.py -p 5000\n python blockchain.py --port 5000\npython_version : 3.6.1\nComments : The blockchain implementation is mostly based on [1]. \n I made a few modifications to the original code in order to add RSA encryption to the transactions \n based on [2], changed the proof of work algorithm, and added some Flask routes to interact with the \n blockchain from the dashboards\nReferences : [1] https://github.com/dvf/blockchain/blob/master/blockchain.py\n [2] https://github.com/julienr/ipynb_playground/blob/master/bitcoin/dumbcoin/dumbcoin.ipynb\n'''\n\nimport Crypto\nimport Crypto.Random\nfrom Crypto.Hash import SHA\nfrom Crypto.PublicKey import RSA\nfrom Crypto.Signature import PKCS1_v1_5\n\nimport hashlib\nimport json\n\nfrom time import time\nfrom uuid import uuid4\n\nMINING_SENDER = \"THE BLOCKCHAIN\"\nMINING_REWARD = 100\nMINING_DIFFICULTY = 2\n\nclass Blockchain:\n\n def __init__(self):\n \n self.votes = []\n self.chain = []\n self.nodes = set()\n #Generate random number to be used as node_id\n self.node_id = str(uuid4()).replace('-', '')\n #Create genesis block\n self.create_block(0, '00')\n\n def register_node(self, node_url):\n \"\"\"\n Add a new node to the list of nodes\n \"\"\"\n #Checking node_url has valid format\n parsed_url = urlparse(node_url)\n if parsed_url.netloc:\n self.nodes.add(parsed_url.netloc)\n elif parsed_url.path:\n # Accepts an URL without scheme like '192.168.0.5:5000'.\n self.nodes.add(parsed_url.path)\n else:\n raise ValueError('Invalid URL')\n\n def verify_vote_signature(self, voter_id, signature, vote):\n \"\"\"\n Check that the provided signature corresponds to vote\n signed by the public key (voter_id)\n \"\"\"\n public_key = RSA.importKey(binascii.unhexlify(voter_id))\n verifier = PKCS1_v1_5.new(public_key)\n h = SHA.new(str(vote).encode('utf8'))\n return verifier.verify(h, binascii.unhexlify(signature))\n\n def submit_vote(self, voter_id, poll_id, value, signature):\n \"\"\"\n Add a transaction to transactions array if the signature verified\n \"\"\"\n vote = OrderedDict({'voter_id': voter_id, \n 'poll_id': poll_id,\n 'value': value})\n\n # Reward for mining a block\n if voter_id == MINING_SENDER:\n self.votes.append(vote)\n return len(self.chain) + 1\n # Manages votess from wallet to another wallet\n else:\n vote_verification = self.verify_vote_signature(voter_id, signature, vote)\n if vote_verification:\n self.votes.append(vote)\n return len(self.chain) + 1\n else:\n return False\n\n def create_block(self, nonce, previous_hash):\n \"\"\"\n Add a block of votes to the blockchain\n \"\"\"\n block = {'block_number': len(self.chain) + 1,\n 'timestamp': time(),\n 'votes': self.votes,\n 'nonce': nonce,\n 'previous_hash': previous_hash}\n\n # Reset the current list of votes\n self.votes = []\n\n self.chain.append(block)\n return block\n\n def hash(self, block):\n \"\"\"\n Create a SHA-256 hash of a block\n \"\"\"\n # We must make sure that the Dictionary is Ordered, or we'll have inconsistent hashes\n block_string = json.dumps(block, sort_keys=True).encode()\n \n return hashlib.sha256(block_string).hexdigest()\n\n def proof_of_work(self):\n \"\"\"\n Proof of work algorithm\n \"\"\"\n last_block = self.chain[-1]\n last_hash = self.hash(last_block)\n\n nonce = 0\n while self.valid_proof(self.votes, last_hash, nonce) is False:\n nonce += 1\n\n return nonce\n\n def valid_proof(self, votes, last_hash, nonce, difficulty=MINING_DIFFICULTY):\n \"\"\"\n Check if a hash value satisfies the mining conditions. This function is used within the proof_of_work function.\n \"\"\"\n guess = (str(votes)+str(last_hash)+str(nonce)).encode()\n guess_hash = hashlib.sha256(guess).hexdigest()\n return guess_hash[:difficulty] == '0'*difficulty\n\n def valid_chain(self, chain):\n \"\"\"\n Check if a bockchain is valid\n \"\"\"\n last_block = chain[0]\n current_index = 1\n\n while current_index < len(chain):\n block = chain[current_index]\n #print(last_block)\n #print(block)\n #print(\"\\n-----------\\n\")\n # Check that the hash of the block is correct\n if block['previous_hash'] != self.hash(last_block):\n return False\n\n # Check that the Proof of Work is correct\n #Delete the reward vote\n votes = block['votes'][:-1]\n # Need to make sure that the dictionary is ordered. Otherwise we'll get a different hash\n vote_elements = ['voter_id', 'poll_id', 'value']\n votes = [OrderedDict((k, vote[k]) for k in vote_elements) for vote in votes]\n\n if not self.valid_proof(votes, block['previous_hash'], block['nonce'], MINING_DIFFICULTY):\n return False\n\n last_block = block\n current_index += 1\n\n return True\n\n def resolve_conflicts(self):\n \"\"\"\n Resolve conflicts between blockchain's nodes\n by replacing our chain with the longest one in the network.\n \"\"\"\n neighbours = self.nodes\n new_chain = None\n\n # We're only looking for chains longer than ours\n max_length = len(self.chain)\n\n # Grab and verify the chains from all the nodes in our network\n for node in neighbours:\n print('http://' + node + '/chain')\n response = requests.get('http://' + node + '/chain')\n\n if response.status_code == 200:\n length = response.json()['length']\n chain = response.json()['chain']\n\n # Check if the length is longer and the chain is valid\n if length > max_length and self.valid_chain(chain):\n max_length = length\n new_chain = chain\n\n # Replace our chain if we discovered a new, valid chain longer than ours\n if new_chain:\n self.chain = new_chain\n return True\n\n return False","repo_name":"wpine215/p2poll","sub_path":"example/blockchain/blockchain.py","file_name":"blockchain.py","file_ext":"py","file_size_in_byte":6905,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"40923181108","text":"\nfrom train_inits import *\nfrom utils.dynamic_graph_transformer_utils import *\n\nimport math\n\ndef train_current_time_step(FLAGS, graphs, adjs, device, res_id=None, model_path=None):\n \"\"\"\n Setup\n \"\"\"\n\n # Set random seed\n random.seed(FLAGS.seed)\n np.random.seed(FLAGS.seed)\n torch.manual_seed(FLAGS.seed)\n torch.cuda.manual_seed(FLAGS.seed)\n print(FLAGS)\n\n # Recursively make directories if they do not exist\n if res_id is None:\n FLAGS.res_id = 'Final_%s_%s_seed_%d_time_%d'%(FLAGS.model_name, FLAGS.dataset, FLAGS.seed, FLAGS.time_step)\n else:\n FLAGS.res_id = 'Final_%s_%s_seed_%d_time_%d_resid_%s'%(FLAGS.model_name, FLAGS.dataset, FLAGS.seed, FLAGS.time_step, res_id)\n\n log_file, output_dir = create_logger(FLAGS)\n print('Savr dir:', output_dir)\n logging.basicConfig(filename=log_file, level=logging.INFO,\n format='%(asctime)s - %(levelname)s: %(message)s',\n datefmt='%m/%d/%Y %H:%M:%S')\n logging.info(vars(FLAGS))\n\n cur_window, train_start_time, eval_time = get_train_time_interval(FLAGS)\n print(cur_window, train_start_time, eval_time)\n\n FLAGS.cur_window = cur_window\n FLAGS.eval_time = eval_time\n FLAGS.train_start_time = train_start_time\n\n FLAGS.edge_encode_num = 2\n FLAGS.edge_dist_encode_num = FLAGS.max_dist + 2\n\n graphs_train = graphs[train_start_time : eval_time]\n adjs_train = adjs[train_start_time : eval_time]\n\n graphs_eval = graphs[eval_time]\n adjs_eval = adjs[eval_time]\n\n num_nodes_graph_eval = len(graphs_eval.nodes)\n feats = get_feats(adjs_train, num_nodes_graph_eval, 0, eval_time, FLAGS)\n FLAGS.num_features = feats[0].shape[1]\n\n print('Train graphs size')\n for graph in graphs_train:\n print(\"Node %d, edges %d\"%(len(graph.nodes), len(graph.edges)))\n\n print('Eval graphs size')\n print(\"Node %d, edges %d\"%(len(graphs_eval.nodes), len(graphs_eval.edges)))\n\n ###\n data_dict = compute_node_edge_encoding(graphs_train, FLAGS, force_regen=FLAGS.force_regen)\n print(data_dict.keys())\n\n train_edges, train_edges_false, val_edges, val_edges_false, test_edges, test_edges_false = \\\n get_evaluation_data(adjs_train[-1], adjs_eval, FLAGS.time_step, FLAGS.dataset, force_regen=FLAGS.force_regen)\n\n print(train_edges.shape, train_edges_false.shape, val_edges.shape, val_edges_false.shape, test_edges.shape, test_edges_false.shape)\n\n activate_eval_nodes = np.concatenate([train_edges, train_edges_false,\n val_edges, val_edges_false,\n test_edges, test_edges_false], axis=0)\n activate_eval_nodes = np.unique(activate_eval_nodes)\n num_activate_eval_nodes = len(activate_eval_nodes)\n activate_eval_nodes_splits = np.array_split(activate_eval_nodes, math.ceil(num_activate_eval_nodes/FLAGS.eval_batch_size))\n print('Eval split size', len(activate_eval_nodes_splits))\n ### get new indicies\n eval_all_nodes_to_new_index = dict(zip(activate_eval_nodes, np.arange(num_activate_eval_nodes)))\n train_edges_new = translate(train_edges, eval_all_nodes_to_new_index, shape='Nx2') # edges [Nx2] -> [2xN]\n train_edges_false_new = translate(train_edges_false, eval_all_nodes_to_new_index, shape='Nx2')\n val_edges_new = translate(val_edges, eval_all_nodes_to_new_index, shape='Nx2')\n val_edges_false_new = translate(val_edges_false, eval_all_nodes_to_new_index, shape='Nx2')\n test_edges_new = translate(test_edges, eval_all_nodes_to_new_index, shape='Nx2')\n test_edges_false_new = translate(test_edges_false, eval_all_nodes_to_new_index, shape='Nx2')\n\n\n print(\"# train: {}, # val: {}, # test: {}\\n\".format(len(train_edges), len(val_edges), len(test_edges)))\n logging.info(\"# train: {}, # val: {}, # test: {}\".format(len(train_edges), len(val_edges), len(test_edges)))\n\n eval_edge_encode, eval_PPR, eval_edge_dist_encode = data_dict['st_%d_et_%d'%(train_start_time, eval_time)]\n # print(eval_edge_encode.shape, eval_PPR.shape, eval_edge_dist_encode.shape)\n\n if FLAGS.supervised:\n from utils.minibatch_sup import NodeMinibatchIterator\n minibatchIterator = NodeMinibatchIterator(\n negative_mult_training=FLAGS.neg_sample_size, # negative sample size\n graphs=graphs_train, # graphs (total)\n adjs=adjs_train, # adjs (total)\n start_time=FLAGS.train_start_time\n )\n else: # from DySAT paper\n pass\n\n model = load_model(FLAGS, device)\n print(model)\n if model_path:\n print('Load parameters from ', model_path)\n model.load_state_dict(torch.load(model_path), strict=False)\n optimizer = optim.Adam(model.parameters(), lr=FLAGS.learning_rate, weight_decay=FLAGS.weight_decay)\n\n # Setup result accumulator variables.\n epochs_test_result = defaultdict(lambda: [])\n epochs_val_result = defaultdict(lambda: [])\n\n \"\"\"\n Training starts\n \"\"\"\n epoch_train_loss_all = []\n best_valid_result = 0\n best_valid_epoch = 0\n best_valid_model_path = os.path.join(output_dir, 'best_valid_model_{}.pt'.format(FLAGS.dataset))\n best_valid_epoch_predict_true = None\n\n total_epoch_time = 0.0\n for epoch in range(FLAGS.num_epoches):\n model.train()\n minibatchIterator.shuffle()\n epoch_train_loss = []\n epoch_time = 0.0\n it = 0\n\n ### Start forward\n t = time.time()\n\n iter_loss = 0\n while not minibatchIterator.end():\n if FLAGS.supervised:\n train_start, train_end, pos_edges, neg_edges = minibatchIterator.next_minibatch_feed_dict()\n edge_encode, PPR, edge_dist_encode = data_dict['st_%d_et_%d'%(train_start, train_end)]\n\n active_nodes = np.unique(np.concatenate([pos_edges, neg_edges], axis=1))\n node_feats_np, edge_encode_np, edge_dist_encode_np, target_node_size, context_nodes_size, all_nodes_to_new_index = get_common_neighbors(active_nodes,\n edge_encode, PPR, edge_dist_encode,\n feats[train_end-1-train_start], FLAGS)\n node_feats_th = torch.FloatTensor(node_feats_np).to(device)\n edge_encode_th = torch.LongTensor(edge_encode_np).to(device)\n edge_dist_encode_th = torch.LongTensor(edge_dist_encode_np).to(device)\n\n ###\n output, output_unsupervised = model(node_feats_th, edge_encode_th, edge_dist_encode_th, target_node_size, context_nodes_size, device)\n loss = torch.tensor(0.0, device=device)\n\n if FLAGS.supervised_loss:\n pos_edges_new = translate(pos_edges, all_nodes_to_new_index, shape='2xN')\n neg_edges_new = translate(neg_edges, all_nodes_to_new_index, shape='2xN')\n loss += link_forecast_loss(output, pos_edges_new, neg_edges_new, FLAGS.neg_weight, device)\n\n if FLAGS.unsupervised_loss:\n sampled_temporal_edges = generate_temporal_edges(edge_encode_np, target_node_size, FLAGS.neg_sample_size)\n # print(output_unsupervised.shape)\n for t, (pos_edges, neg_edges) in enumerate(sampled_temporal_edges):\n loss += link_forecast_loss(output_unsupervised[t, :, :], pos_edges, neg_edges, FLAGS.neg_weight, device)/output_unsupervised.size(0)\n\n else:\n pass\n\n iter_loss += loss.item()\n optimizer.zero_grad()\n loss.backward()\n nn.utils.clip_grad_norm_(model.parameters(), FLAGS.max_gradient_norm)\n optimizer.step()\n\n epoch_train_loss.append(iter_loss)\n # track current time\n epoch_time += time.time() - t\n # logging\n logging.info(\"Mini batch Iter: {} train_loss= {:.5f}\".format(it, loss.data.item()))\n logging.info(\"Time for Mini batch Iter {}: {}\".format(it, time.time() - t))\n it += 1\n\n epoch_train_loss_all.append(np.mean(epoch_train_loss))\n print(\"Train Loss at epoch {} : {}\".format(epoch, np.mean(epoch_train_loss)))\n print(\"Time for epoch \", epoch_time)\n total_epoch_time += epoch_time\n logging.info(\"Time for epoch : {}\".format(epoch_time))\n\n\n # Validation in training:\n if (epoch + 1) % FLAGS.test_freq == 0:\n model.eval() # disable dropout in model\n\n emb = []\n for cur_activate_eval_nodes in activate_eval_nodes_splits:\n\n node_feats_np, edge_encode_np, edge_dist_encode_np, target_node_size, context_nodes_size, eval_all_nodes_to_new_index = get_common_neighbors(cur_activate_eval_nodes,\n eval_edge_encode, eval_PPR, eval_edge_dist_encode,\n feats[-1], FLAGS, deterministic_neighbor_sampling=True)\n node_feats_th = torch.FloatTensor(node_feats_np).to(device)\n edge_encode_th = torch.LongTensor(edge_encode_np).to(device)\n edge_dist_encode_th = torch.LongTensor(edge_dist_encode_np).to(device)\n\n #########\n output, _ = model(node_feats_th, edge_encode_th, edge_dist_encode_th, target_node_size, context_nodes_size, device)\n emb.append(output[:target_node_size, :].detach().cpu().numpy())\n emb = np.concatenate(emb, axis=0)\n\n # Use external classifier to get validation and test results.\n\n val_results, test_results, val_pred_true, test_pred_true = evaluate_classifier(\n train_edges_new, train_edges_false_new,\n val_edges_new, val_edges_false_new,\n test_edges_new, test_edges_false_new,\n emb, emb)\n\n val_HAD = val_results[\"HAD\"][0]\n test_HAD = test_results[\"HAD\"][0]\n val_SIGMOID = val_results[\"SIGMOID\"][0]\n test_SIGMOID = test_results[\"SIGMOID\"][0]\n\n print(\"Epoch %d, Val AUC_HAD %.4f, Test AUC_HAD %.4f, Val AUC_SIGMOID %.4f, Test AUC_SIGMOID %.4f, \"%(epoch, val_HAD, test_HAD, val_SIGMOID, test_SIGMOID))\n logging.info(\"Epoch %d, Val AUC_HAD %.4f, Test AUC_HAD %.4f, Val AUC_SIGMOID %.4f, Test AUC_SIGMOID %.4f, \"%(epoch, val_HAD, test_HAD, val_SIGMOID, test_SIGMOID))\n\n epochs_test_result[\"HAD\"].append(test_HAD)\n epochs_val_result[\"HAD\"].append(val_HAD)\n epochs_test_result[\"SIGMOID\"].append(test_SIGMOID)\n epochs_val_result[\"SIGMOID\"].append(val_SIGMOID)\n\n if val_HAD > best_valid_result:\n best_valid_result = val_HAD\n best_valid_epoch = epoch\n best_valid_epoch_predict_true = val_pred_true[\"HAD\"],\n torch.save(model.state_dict(), best_valid_model_path)\n\n if epoch - best_valid_epoch > 100: # FLAGS.num_epoches/2:\n break\n\n \"\"\"\n Done training: choose best model by validation set performance.\n \"\"\"\n best_epoch = epochs_val_result[\"HAD\"].index(max(epochs_val_result[\"HAD\"]))\n logging.info(\"Total used time is: {}\\n\".format(total_epoch_time))\n print(\"Total used time is: {}\\n\".format(total_epoch_time))\n\n print(\"Best epoch \", best_epoch)\n logging.info(\"Best epoch {}\".format(best_epoch))\n\n val_results, test_results = epochs_val_result[\"HAD\"][best_epoch], epochs_test_result[\"HAD\"][best_epoch]\n\n print(\"Best epoch val results {}\".format(val_results))\n print(\"Best epoch test results {}\".format(test_results))\n\n logging.info(\"Best epoch val results {}\\n\".format(val_results))\n logging.info(\"Best epoch test results {}\\n\".format(test_results))\n\n \"\"\"\n Get final results\n \"\"\"\n result = {\n 'id': FLAGS.res_id,\n 'best_epoch': best_epoch,\n 'best_valid_epoch_result': val_results,\n 'best_test_epoch_result': test_results,\n 'valid_epoch_auc': epochs_val_result[\"HAD\"],\n 'test_epoch_auc': epochs_test_result[\"HAD\"],\n 'epoch_train_loss': epoch_train_loss_all\n }\n\n with open(os.path.join(output_dir, 'result_{}.json'.format(FLAGS.dataset)), 'w') as outfile:\n json.dump(result, outfile)\n np.save(os.path.join(output_dir, 'test_pred_true.npy'), np.array(best_valid_epoch_predict_true))\n\n\ndef get_common_neighbors(active_nodes, edge_encode_list, PPR, edge_dist_encode, feats, FLAGS, deterministic_neighbor_sampling=None):\n\n if deterministic_neighbor_sampling == None:\n deterministic_neighbor_sampling = FLAGS.deterministic_neighbor_sampling\n\n all_nodes, all_nodes_to_new_index, target_node_size_1, context_nodes_size_1 = sample_joint_neighbors(active_nodes, PPR, \n deterministic_neighbor_sampling, \n FLAGS.two_steam_model,\n FLAGS.max_neighbors)\n # print(target_node_size_1, context_nodes_size_1)\n all_node_size = target_node_size_1 + context_nodes_size_1\n\n ###\n time_size = len(edge_encode_list)\n\n edge_encode_np_1 = np.zeros((all_node_size, all_node_size, time_size))\n for t in range(time_size):\n edge_encode_np_1[:, :, t] = 2*t\n edge_encode_tmp = edge_encode_list[t]\n edge_encode_tmp = edge_encode_tmp[all_nodes, :][:, all_nodes].tocoo()\n edge_encode_np_1[edge_encode_tmp.row, edge_encode_tmp.col, t] = 2*t+1\n\n\n ###\n edge_dist_encode_np_1 = np.ones((all_node_size, all_node_size)) * (FLAGS.max_dist+1)\n edge_dist_encode_tmp = edge_dist_encode[all_nodes, :][:, all_nodes].tocoo()\n edge_dist_encode_np_1[edge_dist_encode_tmp.row, edge_dist_encode_tmp.col] = edge_dist_encode_tmp.data\n\n\n # print(FLAGS.max_dist+1, np.max(edge_dist_encode_tmp.data), np.max(edge_dist_encode_np_1))\n ###\n node_feats_np_1 = feats[all_nodes].toarray()\n # print('>>>',node_feats_th.size(), edge_encode_th.size(), edge_dist_encode_th.size(), target_node_size_1, context_nodes_size_1)\n return node_feats_np_1, edge_encode_np_1, edge_dist_encode_np_1, target_node_size_1, context_nodes_size_1, all_nodes_to_new_index\n\nif __name__ == '__main__':\n\n \"\"\"\n Run GraphTransformer (our proposal)\n \"\"\"\n\n model_name = 'DyGraphTransformer_two_stream'\n\n FLAGS = flags()\n FLAGS.supervised=True\n FLAGS.supervised_loss=True\n FLAGS.unsupervised_loss=False\n\n FLAGS.model_name = model_name\n\n FLAGS = update_args(FLAGS)\n FLAGS.max_neighbors = -1\n FLAGS.deterministic_neighbor_sampling = True\n\n if FLAGS.dataset in ['Enron_16', 'Enron_92']:\n\n FLAGS.two_steam_model = False\n else:\n FLAGS.two_steam_model = True\n\n\n for time_step in reversed(range(FLAGS.min_time+1, FLAGS.max_time)):\n FLAGS.time_step = time_step\n\n \"\"\"\n Setup layers and res_id\n \"\"\"\n # RES_ID = '%d_layer_model'%FLAGS.num_layers\n RES_ID = '%d_layer_model_window_%d'%(FLAGS.num_layers, FLAGS.window)\n\n device = get_device(FLAGS)\n # # load graphs\n graphs, adjs = load_graphs(FLAGS.dataset)\n\n for seed in [123, 321, 231]:\n FLAGS.seed = seed\n train_current_time_step(FLAGS, graphs, adjs, device, RES_ID)\n torch.cuda.empty_cache()\n","repo_name":"CongWeilin/DyFormer","sub_path":"link_pred_pytorch/train_models_graph_trans.py","file_name":"train_models_graph_trans.py","file_ext":"py","file_size_in_byte":16015,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"52"} +{"seq_id":"8362756674","text":"from rest_framework import serializers\nfrom .models import Book, Comment\n\n\n\nclass CommentSerializer(serializers.ModelSerializer):\n class Meta:\n model = Comment\n exclude = []\n\nclass BookSerializer(serializers.ModelSerializer):\n book_comments = CommentSerializer(many=True, read_only=True)\n status = serializers.CharField(required=False)\n checkOutBy = serializers.CharField(required=False)\n checkOutDate = serializers.DateTimeField(required=False, allow_null=True)\n addedDate = serializers.DateTimeField(required=False)\n class Meta:\n model = Book\n exclude = ['statusBeforeCheckout']\n","repo_name":"macofiloteo/library-drf","sub_path":"api/books/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":630,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"1387750679","text":"\n# !pip install tensorflow==2.4.1 tensorflow-gpu==2.4.1 opencv-python mediapipe sklearn matplotlib\n\nimport cv2\nimport numpy as np\nimport os\nfrom matplotlib import pyplot as plt\nimport time\nimport mediapipe as mp\nfrom keras.models import load_model\n\n\"\"\"# 2. Keypoints using MP Holistic\"\"\"\n\nmp_holistic = mp.solutions.holistic # Holistic model\nmp_drawing = mp.solutions.drawing_utils # Drawing utilities\n\ndef mediapipe_detection(image, model):\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # COLOR CONVERSION BGR 2 RGB\n image.flags.writeable = False # Image is no longer writeable\n results = model.process(image) # Make prediction\n image.flags.writeable = True # Image is now writeable \n image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) # COLOR COVERSION RGB 2 BGR\n return image, results\n\ndef draw_landmarks(image, results):\n mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACEMESH_TESSELATION) # Draw face connections\n mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS) # Draw pose connections\n mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS) # Draw left hand connections\n mp_drawing.draw_landmarks(image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS) # Draw right hand connections\n\ndef draw_styled_landmarks(image, results):\n # Draw face connections\n mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACEMESH_TESSELATION, \n mp_drawing.DrawingSpec(color=(80,110,10), thickness=1, circle_radius=1), \n mp_drawing.DrawingSpec(color=(80,256,121), thickness=1, circle_radius=1)\n ) \n # Draw pose connections\n mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS,\n mp_drawing.DrawingSpec(color=(80,22,10), thickness=2, circle_radius=4), \n mp_drawing.DrawingSpec(color=(80,44,121), thickness=2, circle_radius=2)\n ) \n # Draw left hand connections\n mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n mp_drawing.DrawingSpec(color=(121,22,76), thickness=2, circle_radius=4), \n mp_drawing.DrawingSpec(color=(121,44,250), thickness=2, circle_radius=2)\n ) \n # Draw right hand connections \n mp_drawing.draw_landmarks(image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=4), \n mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2)\n )\n\n# cap = cv2.VideoCapture(0)\ncap = cv2.VideoCapture(\"final.mp4\")\n# cap = cv2.VideoCapture(\"1 (2).mp4\")\n\n# Set mediapipe model \nwith mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n while cap.isOpened():\n\n # Read feed\n ret, frame = cap.read()\n\n # Make detections\n image, results = mediapipe_detection(frame, holistic)\n print(results)\n \n # Draw landmarks\n draw_styled_landmarks(image, results)\n\n # Show to screen\n cv2.imshow('OpenCV Feed', image)\n\n # Break gracefully\n if cv2.waitKey(10) & 0xFF == ord('q'):\n break\n cap.release()\n cv2.destroyAllWindows()\n\n# print(len(results.left_hand_landmarks.landmark))\n\n# print(results)\n# print(results.face_landmarks)\n\n# draw_landmarks(frame, results)\n\n# plt.imshow(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n\n\"\"\"# 3. Extract Keypoint Values\"\"\"\n\n# len(results.left_hand_landmarks.landmark)\n\n# pose = []\n# for res in results.pose_landmarks.landmark:\n# test = np.array([res.x, res.y, res.z, res.visibility])\n# pose.append(test)\n\n## In one Line below\n\n# pose = np.array([[res.x, res.y, res.z, res.visibility] for res in results.pose_landmarks.landmark]).flatten() if results.pose_landmarks else np.zeros(132)\n# face = np.array([[res.x, res.y, res.z] for res in results.face_landmarks.landmark]).flatten() if results.face_landmarks else np.zeros(1404)\n# lh = np.array([[res.x, res.y, res.z] for res in results.left_hand_landmarks.landmark]).flatten() if results.left_hand_landmarks else np.zeros(21*3)\n# rh = np.array([[res.x, res.y, res.z] for res in results.right_hand_landmarks.landmark]).flatten() if results.right_hand_landmarks else np.zeros(21*3)\n\n## In function \n\ndef extract_keypoints(results):\n pose = np.array([[res.x, res.y, res.z, res.visibility] for res in results.pose_landmarks.landmark]).flatten() if results.pose_landmarks else np.zeros(33*4)\n face = np.array([[res.x, res.y, res.z] for res in results.face_landmarks.landmark]).flatten() if results.face_landmarks else np.zeros(468*3)\n lh = np.array([[res.x, res.y, res.z] for res in results.left_hand_landmarks.landmark]).flatten() if results.left_hand_landmarks else np.zeros(21*3)\n rh = np.array([[res.x, res.y, res.z] for res in results.right_hand_landmarks.landmark]).flatten() if results.right_hand_landmarks else np.zeros(21*3)\n return np.concatenate([pose, face, lh, rh])\n\n# result_test = extract_keypoints(results)\n\n# print(result_test)\n## 468*3+33*4+21*3+21*3 = 1662 key point ##\n\n# np.save('0', result_test)\n\n# np.load('0.npy')\n\n\"\"\"# 4. Setup Folders for Collection\"\"\"\n\n# Path for exported data, numpy arrays\nDATA_PATH = os.path.join('Action') \n\n# Actions that we try to detect\nactions = np.array(['Fighting', 'Walking', 'Running'])\n\n# 60 videos worth of data\nno_sequences = 60\n\n# Videos are going to be 30 frames in length\nsequence_length = 30\n# hello\n## 0\n## 1\n## 2\n## ...\n## 29\n# thanks\n\nfor action in actions: \n for sequence in range(no_sequences):\n try: \n os.makedirs(os.path.join(DATA_PATH, action, str(sequence)))\n print('Done')\n except:\n print('Already here')\n pass\n\n\"\"\"# 5. Collect Keypoint Values for Training and Testing\"\"\"\n\ncap = cv2.VideoCapture('final.mp4')\n# Set mediapipe model \nwith mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n \n # NEW LOOP\n # Loop through actions\n for action in actions:\n # Loop through sequences aka videos\n for sequence in range(no_sequences):\n # Loop through video length aka sequence length\n for frame_num in range(sequence_length):\n\n # Read feed\n ret, frame = cap.read()\n\n # Make detections\n image, results = mediapipe_detection(frame, holistic)\n# print(results)\n\n # Draw landmarks\n draw_styled_landmarks(image, results)\n \n # NEW Apply wait logic\n if frame_num == 0: \n cv2.putText(image, 'STARTING COLLECTION', (120,200), \n cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255, 0), 4, cv2.LINE_AA)\n cv2.putText(image, 'Collecting frames for {} Video Number {}'.format(action, sequence), (15,12), \n cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1, cv2.LINE_AA)\n # Show to screen\n cv2.imshow('OpenCV Feed', image)\n # cv2.waitKey(1000)\n else: \n cv2.putText(image, 'Collecting frames for {} Video Number {}'.format(action, sequence), (15,12), \n cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1, cv2.LINE_AA)\n # Show to screen\n cv2.imshow('OpenCV Feed', image)\n \n # cv2.waitKey(1000)\n \n # NEW Export keypoints\n keypoints = extract_keypoints(results)\n npy_path = os.path.join(DATA_PATH, action, str(sequence), str(frame_num))\n np.save(npy_path, keypoints)\n\n # Break gracefully\n if cv2.waitKey(10) & 0xFF == ord('q'):\n break\n \n cap.release()\n cv2.destroyAllWindows()\n\ncap.release()\ncv2.destroyAllWindows()\n\n\"\"\"# 6. Preprocess Data and Create Labels and Features\"\"\"\n\nfrom sklearn.model_selection import train_test_split\nfrom tensorflow.keras.utils import to_categorical\n\nlabel_map = {label:num for num, label in enumerate(actions)}\n\nprint(label_map)\n# >>> { 'Fighting':0, 'Walking':1 'Running':2}\n\nsequences, labels = [], []\nfor action in actions:\n for sequence in range(no_sequences):\n window = []\n for frame_num in range(sequence_length):\n res = np.load(os.path.join(DATA_PATH,action,str(sequence),\"{}.npy\".format(frame_num)))\n window.append(res)\n sequences.append(window)\n labels.append(label_map[action])\n\n# np.array(sequences).shape\n\n# np.array(labels).shape\n\nX = np.array(sequences)\n\n# X.shape\n\ny = to_categorical(labels).astype(int)\n\n# y\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05)\n\n# y_test.shape\n\n\"\"\"# 7. Build and Train LSTM Neural Network\"\"\"\n\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import LSTM, Dense\nfrom tensorflow.keras.callbacks import TensorBoard\n\nlog_dir = os.path.join('Logs')\ntb_callback = TensorBoard(log_dir=log_dir)\n\nmodel = Sequential()\nmodel.add(LSTM(64, return_sequences=True, activation='relu', input_shape=(30,1662)))\nmodel.add(LSTM(128, return_sequences=True, activation='relu'))\nmodel.add(LSTM(64, return_sequences=False, activation='relu'))\nmodel.add(Dense(64, activation='relu'))\nmodel.add(Dense(32, activation='relu'))\nmodel.add(Dense(actions.shape[0], activation='softmax'))\n\n# res = [.7, 0.2, 0.1]\n\n# actions[np.argmax(res)]\n\nmodel.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['categorical_accuracy'])\n\nmodel.fit(X_train, y_train, epochs=2000, callbacks=[tb_callback]) ## fit for Tranning \n\nmodel.summary()\n\n\"\"\"# 8. Make Predictions\"\"\"\n\nres = model.predict(X_test)\n\nactions[np.argmax(res[4])]\n\nactions[np.argmax(y_test[4])]\n\n\"\"\"# 9. Save Weights\"\"\"\n\nmodel.save('action.h5')\n\ndel model\n\n\nmodel = load_model('action.h5')\n# model.load_weights('action.h5')\n\n\"\"\"# 10. Evaluation using Confusion Matrix and Accuracy\"\"\"\n\nfrom sklearn.metrics import multilabel_confusion_matrix, accuracy_score\nyhat = model.predict(X_train)\n# yhat = model.predict(X_test)\n\nytrue = np.argmax(y_train, axis=1).tolist()\n# ytrue = np.argmax(y_test, axis=1).tolist()\nyhat = np.argmax(yhat, axis=1).tolist()\n\nprint(multilabel_confusion_matrix(ytrue, yhat))\n\nprint(accuracy_score(ytrue, yhat))\n","repo_name":"yap-yeasin/Human_motion_analysis","sub_path":"action_train.py","file_name":"action_train.py","file_ext":"py","file_size_in_byte":10760,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"25393305392","text":"from datetime import datetime\n\nfrom celery import shared_task\nfrom celery.utils.log import get_task_logger\n\nfrom management.investiments.models import Gain, Investiment, InvestimentStatus\n\nlogger = get_task_logger(__name__)\n\n\n@shared_task\ndef generate_gains():\n # import ipdb; ipdb.set_trace()\n investiments = Investiment.objects.filter(status=InvestimentStatus.ACTIVE)\n\n for investiment in investiments:\n gain = investiment.gains.last()\n today = datetime.now()\n\n difference = 0\n if gain:\n difference = today.month - gain.created_at.month\n else:\n difference = today.month - investiment.created_at.month\n\n if difference:\n new_gain = Gain.objects.create(\n origin_investiment=investiment,\n amount=Gain.calculate_gain(investiment=investiment)\n )\n\n logger.info(f\"{investiment.id} generated new gain of {new_gain.amount}.\")\n","repo_name":"ftbevi/backend-test","sub_path":"management/investiments/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":954,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"52"} +{"seq_id":"9884718420","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Apr 13 18:24:13 2017\n\n@author: gualandi\n\"\"\"\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\ndef DrawMatrix(M, name):\n # Crea una figura, disegna l'immagine data dalla matrice, aggiungi la colorbar sulla destra\n plt.figure(figsize=(6,6))\n # Uso la colormap 'gray' per avere la schacchiera in bianco&nero\n plt.imshow(M, cmap='gray')\n plt.savefig(\"prova.pdf\", dpi=300)\n plt.show()\n\n#------------------------------------------\n# MAIN ENTRY POINT\n#------------------------------------------\nif __name__ == \"__main__\":\n filename1 = 'D:\\Ricerca\\DOTA\\data\\DOTmark_1.0\\Data\\ClassicImages\\data32_1001.csv'\n M1 = np.loadtxt(open(filename1, \"rb\"), delimiter=\",\")\n DrawMatrix(M1, )\n\n filename2 = 'D:\\Ricerca\\DOTA\\data\\DOTmark_1.0\\Data\\ClassicImages\\data64_1001.csv'\n M2 = np.loadtxt(open(filename2, \"rb\"), delimiter=\",\")\n DrawMatrix(M2)\n \n filename2 = 'D:\\Ricerca\\DOTA\\data\\DOTmark_1.0\\Data\\ClassicImages\\data128_1001.csv'\n M2 = np.loadtxt(open(filename2, \"rb\"), delimiter=\",\")\n DrawMatrix(M2)\n \n filename2 = 'D:\\Ricerca\\DOTA\\data\\DOTmark_1.0\\Data\\ClassicImages\\data256_1001.csv'\n M2 = np.loadtxt(open(filename2, \"rb\"), delimiter=\",\")\n DrawMatrix(M2)\n \n filename2 = 'D:\\Ricerca\\DOTA\\data\\DOTmark_1.0\\Data\\ClassicImages\\data512_1001.csv'\n M2 = np.loadtxt(open(filename2, \"rb\"), delimiter=\",\")\n DrawMatrix(M2)","repo_name":"stegua/dotlib","sub_path":"python/view_matrix.py","file_name":"view_matrix.py","file_ext":"py","file_size_in_byte":1428,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"52"} +{"seq_id":"41621957023","text":"import torch\r\nimport numpy as np\r\nfrom tqdm.auto import tqdm\r\n\r\ndevice = 'cuda' if torch. cuda.is_available() else 'cpu'\r\ntorch.manual_seed(777)\r\nif device == 'cuda':\r\n torch.cuda.manual_seed_all(777)\r\n\r\ndef train_one_epoch(model, train_loader, optimizer, criterion):\r\n model.train()\r\n running_loss = 0.0\r\n correct = 0\r\n total = 0\r\n\r\n for inputs, labels in tqdm(train_loader):\r\n inputs, labels = inputs.to(device), labels.to(device)\r\n\r\n optimizer.zero_grad()\r\n outputs = model(inputs)\r\n loss = criterion(outputs, labels)\r\n loss.backward()\r\n optimizer.step()\r\n running_loss += loss.item()\r\n\r\n _, predicted = torch.max(outputs.data, 1)\r\n total += labels.size(0)\r\n correct += (predicted == labels).sum().item()\r\n\r\n return 100 * correct / total, running_loss / len(train_loader)\r\n\r\ndef evaluate(model, data_loader, criterion):\r\n model.eval()\r\n val_loss = 0.0\r\n correct = 0\r\n total = 0\r\n \r\n with torch.no_grad():\r\n for inputs, labels in tqdm(data_loader):\r\n inputs, labels = inputs.to(device), labels.to(device)\r\n \r\n outputs = model(inputs)\r\n loss = criterion(outputs, labels)\r\n val_loss += loss.item()\r\n\r\n _, predicted = torch.max(outputs.data, 1)\r\n total += labels.size(0)\r\n correct += (predicted == labels).sum().item()\r\n\r\n return 100 * correct / total, val_loss / len(data_loader)\r\n\r\ndef SVM_data_setting(data_loader):\r\n input_list = []\r\n label_list = []\r\n \r\n for inputs, labels in tqdm(data_loader):\r\n inputs = np.array(inputs).flatten()\r\n input_list.append(inputs)\r\n label_list.append(labels)\r\n \r\n input_list = np.array(input_list).reshape(len(data_loader), -1)\r\n label_list = np.array(label_list)\r\n \r\n return input_list, label_list","repo_name":"sumin303/23_2_AI_Midterm","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":1890,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"74426860003","text":"import SIS\r\nimport os, requests, sqlite3, json, random\r\nfrom datetime import datetime\r\nfrom PyQt5.QtWidgets import QWidget, QPushButton, QHBoxLayout, QVBoxLayout, QLabel, QComboBox, QLineEdit, \\\r\n QMessageBox, QTextBrowser, QTabWidget, QTableWidget, QTableWidgetItem, QHeaderView, QListWidget, QListView, \\\r\n QListWidgetItem, QDialog, QSlider\r\nfrom PyQt5.QtCore import Qt, QSize, pyqtSignal, QUrl\r\nfrom PyQt5.Qt import QPixmap, QDesktopServices, QIcon, QScrollArea\r\n\r\n\r\n\r\nclass SISMainWindow(QWidget):\r\n def __init__(self, parent=None):\r\n super().__init__(parent)\r\n # self.check_databases()\r\n self.init_ui()\r\n\r\n def init_ui(self):\r\n self.setToolTip('SIS Torrents Downloader '\r\n '\\n第一会所论坛种子下载器')\r\n self.downloaderWidget = DownloaderWidget()\r\n self.browserWidget = BrowserWidget()\r\n\r\n # tab widget\r\n tabWidget = QTabWidget()\r\n tabWidget.addTab(self.downloaderWidget, 'Downloader 下载器')\r\n tabWidget.addTab(self.browserWidget, 'Browser 浏览器')\r\n tabWidget.tabBarClicked.connect(self.whenTabClicked)\r\n\r\n allLayout = QVBoxLayout()\r\n allLayout.addWidget(tabWidget)\r\n\r\n self.setLayout(allLayout)\r\n\r\n self.setWindowTitle('SIS Downloader by Fyang (肥羊)')\r\n self.setMinimumWidth(800)\r\n self.setMinimumHeight(600)\r\n\r\n def whenTabClicked(self, n_tab):\r\n if n_tab == 1 and 'Start' in self.downloaderWidget.start_btn.text():\r\n connect = sqlite3.connect(SIS.DBPATH + 'SISDB.sqlite')\r\n self.browserWidget.b_start_load.setEnabled(True)\r\n self.browserWidget.result_label.setText('')\r\n self.browserWidget.setTypeCombox(\r\n [x[0] for x in connect.cursor().execute('select type from SIStops group by type').fetchall()])\r\n self.browserWidget.b_count_label\\\r\n .setText(' {} in stock'.format(connect.cursor().execute('select count(tid) from SIStops').fetchone()[0]))\r\n connect.close()\r\n elif 'Download' in self.downloaderWidget.start_btn.text():\r\n self.browserWidget.b_start_load.setEnabled(False)\r\n self.browserWidget.result_label.setText('Disabled searching while downloading, 下载中搜索不可用.')\r\n\r\n\r\nclass DownloaderWidget(QWidget):\r\n stop_thread_signal = pyqtSignal(bool)\r\n\r\n def __init__(self, parent=None):\r\n super(DownloaderWidget, self).__init__(parent)\r\n # this list store all topics that download from topic_downloader thread.\r\n self.cookies = None\r\n self.initUI()\r\n # set proxies updater threads.\r\n for which in range(1, 3):\r\n proxiesoperator = SIS.ProxiesThread(which, self)\r\n proxiesoperator.start()\r\n # set sql operator\r\n self.sqloperator = SIS.SISSql(self)\r\n self.sqloperator.start()\r\n self.pages_generator = None\r\n self.startTimer(5000)\r\n\r\n def initUI(self):\r\n #################################################\r\n # downloader stuff below\r\n self.info_box = QHBoxLayout()\r\n self.info_label = QLabel()\r\n self.info_label.setText('This application is supposed to private use, DO NOT share to anyone!'\r\n '\\n考虑到法律问题，本软件仅作为个人学习研究使用，请不要向他人传播。\\n')\r\n self.info_box.addWidget(self.info_label)\r\n\r\n self.url_box = QHBoxLayout()\r\n self.url_label_title = QLabel('SiS Address (站点地址) ')\r\n self.url_line = QLineEdit()\r\n self.url_line.setMinimumWidth(260)\r\n self.url_line.setText(get_forum_address())\r\n self.url_label = QLabel('Unkown')\r\n self.url_label.setFixedWidth(50)\r\n # self.check_url()\r\n self.url_test_btn = QPushButton('Test(测试)')\r\n self.url_test_btn.clicked.connect(self.check_url)\r\n self.url_box.addWidget(self.url_label_title)\r\n self.url_box.addWidget(self.url_line)\r\n self.url_box.addWidget(self.url_label)\r\n self.url_box.addWidget(self.url_test_btn)\r\n self.url_box.addStretch(100)\r\n\r\n self.login_box = QHBoxLayout()\r\n login_id_label = QLabel('UserName')\r\n self.login_id_line = QLineEdit()\r\n login_pw_label = QLabel('Password')\r\n self.login_pw_line = QLineEdit()\r\n self.login_box.addWidget(login_id_label)\r\n self.login_box.addWidget(self.login_id_line)\r\n self.login_box.addWidget(login_pw_label)\r\n self.login_box.addWidget(self.login_pw_line)\r\n login_btn = QPushButton('Login')\r\n login_btn.clicked.connect(self.get_sis_cookies)\r\n self.login_box.addWidget(login_btn)\r\n self.login_status_label = QLabel('')\r\n self.login_status_label.setFixedWidth(100)\r\n self.login_box.addWidget(self.login_status_label)\r\n self.login_box.addStretch(1)\r\n\r\n self.forum_select_box = QHBoxLayout()\r\n self.forum_info = QLabel('Choose sub-forum (选择子版块)')\r\n self.forum_menu = QComboBox()\r\n self.forum_menu.addItem('Asia Uncensored Authorship Seed 亚洲无码原创区')\r\n self.forum_menu.addItem('Asia Censored Authorship Seed 亚洲有码原创区')\r\n self.forum_menu.addItem('Western Uncensored Authorship Seed 欧美无码原创区')\r\n self.forum_menu.addItem('Anime Authorship Seed 成人游戏动漫原创区')\r\n self.forum_menu.addItem('Asia Uncensored Section 亚洲无码转帖区 ')\r\n self.forum_menu.addItem('Asia Censored Section 亚洲有码转帖区')\r\n self.forum_menu.addItem('Western Uncensored 欧美无码转帖区')\r\n self.forum_menu.addItem('Anime Fans Castle 成人游戏动漫转帖区')\r\n self.forum_menu.setMaximumWidth(350)\r\n self.forum_select_box.addWidget(self.forum_info)\r\n self.forum_select_box.addWidget(self.forum_menu)\r\n self.forum_select_box.addStretch(1)\r\n\r\n self.pages_box = QHBoxLayout()\r\n self.pages_label = QLabel(\"How many pages to download (下载页数，每页有30-40个种子)\")\r\n self.pages_line_start = QLineEdit('1')\r\n self.pages_line_start.setFixedWidth(40)\r\n self.pages_line = QLineEdit()\r\n self.pages_line.setText('9999')\r\n self.pages_line.setFixedWidth(40)\r\n self.pages_box.addWidget(self.pages_label)\r\n self.pages_box.addWidget(self.pages_line_start)\r\n self.pages_box.addWidget(self.pages_line)\r\n self.pages_box.addStretch(1)\r\n\r\n # self.thread_box = QHBoxLayout()\r\n # self.thread_label = QLabel('How many downloading threads (开启线程数，线程太多容易崩溃)')\r\n # self.thread_menu = QComboBox()\r\n # for each in range(1, 11):\r\n # self.thread_menu.addItem(str(each))\r\n # self.thread_box.addWidget(self.thread_label)\r\n # self.thread_box.addWidget(self.thread_menu)\r\n # self.thread_box.addStretch(1)\r\n\r\n btnlayout = QHBoxLayout()\r\n self.start_btn = QPushButton('Start', self)\r\n self.start_btn.clicked.connect(self.start_btn_clicked)\r\n self.start_btn.setFixedWidth(180)\r\n self.stop_btn = QPushButton('Stop', self)\r\n self.stop_btn.setFixedWidth(180)\r\n self.stop_btn.clicked.connect(self.stop_btn_clicked)\r\n btnlayout.addWidget(self.start_btn)\r\n btnlayout.addWidget(self.stop_btn)\r\n btnlayout.addStretch(100)\r\n\r\n self.main_box = QVBoxLayout()\r\n self.main_box.addLayout(self.info_box)\r\n self.main_box.addLayout(self.url_box)\r\n self.main_box.addLayout(self.login_box)\r\n self.main_box.addLayout(self.forum_select_box)\r\n self.main_box.addLayout(self.pages_box)\r\n # self.main_box.addLayout(self.thread_box)\r\n self.main_box.addLayout(btnlayout)\r\n\r\n threads_control_layout = QVBoxLayout()\r\n\r\n pages_threads_layout = QHBoxLayout()\r\n pages_threads_layout.addWidget(QLabel('PgThreads'))\r\n self.pages_threads_slider = QSlider(Qt.Horizontal)\r\n self.pages_threads_slider.setRange(0, 30)\r\n self.pages_threads_slider.setValue(2)\r\n pages_threads_layout.addWidget(self.pages_threads_slider)\r\n page_t_label = QLabel(str(self.pages_threads_slider.value()))\r\n page_t_label.setFixedWidth(20)\r\n pages_threads_layout.addWidget(page_t_label)\r\n self.pages_threads_slider.valueChanged.connect(lambda x: page_t_label.setText(str(x)))\r\n threads_control_layout.addLayout(pages_threads_layout)\r\n\r\n topics_threads_layout = QHBoxLayout()\r\n topics_threads_layout.addWidget(QLabel('TpThreads'))\r\n self.tops_threads_silder = QSlider(Qt.Horizontal)\r\n self.tops_threads_silder.setRange(0, 50)\r\n self.tops_threads_silder.setValue(15)\r\n topics_threads_layout.addWidget(self.tops_threads_silder)\r\n topics_t_label = QLabel(str(self.tops_threads_silder.value()))\r\n topics_t_label.setFixedWidth(20)\r\n topics_threads_layout.addWidget(topics_t_label)\r\n self.tops_threads_silder.valueChanged.connect(lambda x: topics_t_label.setText(str(x)))\r\n threads_control_layout.addLayout(topics_threads_layout)\r\n\r\n tors_threads_layout = QHBoxLayout()\r\n tors_threads_layout.addWidget(QLabel('TrThreads'))\r\n self.tors_threads_silder = QSlider(Qt.Horizontal)\r\n self.tors_threads_silder.setRange(0, 100)\r\n self.tors_threads_silder.setValue(15)\r\n tors_threads_layout.addWidget(self.tors_threads_silder)\r\n tors_t_label = QLabel(str(self.tors_threads_silder.value()))\r\n tors_t_label.setFixedWidth(20)\r\n tors_threads_layout.addWidget(tors_t_label)\r\n self.tors_threads_silder.valueChanged.connect(lambda x: tors_t_label.setText(str(x)))\r\n threads_control_layout.addLayout(tors_threads_layout)\r\n\r\n pics_thread_layout = QHBoxLayout()\r\n pics_thread_layout.addWidget(QLabel('PcThreads'))\r\n self.pics_threads_silder = QSlider(Qt.Horizontal)\r\n self.pics_threads_silder.setRange(0, 500)\r\n self.pics_threads_silder.setValue(250)\r\n pics_thread_layout.addWidget(self.pics_threads_silder)\r\n pics_t_label = QLabel(str(self.pics_threads_silder.value()))\r\n pics_t_label.setFixedWidth(20)\r\n pics_thread_layout.addWidget(pics_t_label)\r\n self.pics_threads_silder.valueChanged.connect(lambda x: pics_t_label.setText(str(x)))\r\n threads_control_layout.addLayout(pics_thread_layout)\r\n\r\n self.output_box = QVBoxLayout()\r\n self.output_window = QTextBrowser()\r\n self.output_window.setMinimumWidth(640)\r\n self.output_window.document().setMaximumBlockCount(1000)\r\n self.progress_label = QLabel()\r\n self.progress_box = QHBoxLayout()\r\n self.progress_box.addWidget(self.progress_label)\r\n self.output_box.addWidget(self.output_window)\r\n self.output_box.addLayout(self.progress_box)\r\n\r\n upper_layout = QHBoxLayout()\r\n upper_layout.addLayout(self.main_box)\r\n upper_layout.addLayout(threads_control_layout)\r\n\r\n downloaderLayout = QVBoxLayout()\r\n downloaderLayout.addLayout(upper_layout)\r\n downloaderLayout.addLayout(self.output_box)\r\n\r\n self.setLayout(downloaderLayout)\r\n # downloader stuff above\r\n #################################################\r\n\r\n def check_url(self):\r\n if len(self.proxies_pool):\r\n proxy = random.choice(SIS.SIS_POOLS['proxies'])\r\n else:\r\n return\r\n try:\r\n req = requests.head(self.url_line.text(), proxies=proxy, timeout=2)\r\n except requests.exceptions.RequestException:\r\n self.url_label.setText('Failed')\r\n return\r\n if req.ok:\r\n self.url_label.setText('OK')\r\n with open('sis_addr.dat', 'w') as f:\r\n f.write(self.url_line.text())\r\n else:\r\n self.url_label.setText('Failed')\r\n failed_msg = 'The address is not available now, try to find other one.' \\\r\n '\\nAddress must be under the correct form: http://example/forum/' \\\r\n '\\n前论坛地址已经不可用了，请输入新的地址。\\n新地址输入，请务必保持正确的格式，包括符号！'\r\n QMessageBox().warning(self, 'Address Failed', failed_msg, QMessageBox.Ok)\r\n \r\n def get_sis_cookies(self):\r\n loginname = self.login_id_line.text()\r\n password = self.login_pw_line.text()\r\n if loginname is '' or password is '':\r\n return None\r\n login_data = {'action': 'login',\r\n 'loginsubmit': 'true',\r\n '62838ebfea47071969cead9d87a2f1f7': loginname,\r\n 'c95b1308bda0a3589f68f75d23b15938': password}\r\n\r\n cookies = requests.post(self.url_line.text() + 'logging.php', data=login_data).cookies\r\n if len(cookies.values()[0]) > 10:\r\n self.cookies = cookies\r\n self.login_status_label.setText('Success')\r\n else:\r\n self.login_status_label.setText('Failed')\r\n return\r\n\r\n def getUndownloadedPic(self):\r\n connect = sqlite3.connect(SIS.DBPATH + 'SISDB.sqlite')\r\n ret = connect.cursor().execute('SELECT * FROM SISpic WHERE picb IS NULL').fetchall()\r\n connect.close()\r\n return ret\r\n\r\n def start_btn_clicked(self):\r\n if 'Start' not in self.start_btn.text():\r\n self.check_url()\r\n if 'Start' in self.start_btn.text():\r\n # if 'OK' not in self.url_label.text():\r\n # QMessageBox().critical(self, 'URL error', 'In case the url is not correct or did not verify, try again.\\n'\r\n # '站点还未连通或者不可用，不能下载，请重试。')\r\n # return\r\n if self.pages_line.text().isdigit() is False:\r\n QMessageBox().critical(self, 'Pages error', 'How many pages you want to download?\\n请输入正确的下载页数')\r\n return\r\n self.start_btn.setText('Downloading...')\r\n self.pages_generator = (self.url_line.text() + self.sub_forum_addr() + '{}.html'.format(each)\r\n for each in range(int(self.pages_line_start.text()), int(self.pages_line.text()) + 1))\r\n\r\n def stop_btn_clicked(self):\r\n self.start_btn.setText('Start')\r\n self.stop_thread_signal.emit(False)\r\n\r\n def timerEvent(self, QTimerEvent):\r\n self.progress_label.setText('Tops: {}/{} Tors: {}/{} Pics: {}/{} Tp-SQL: {} Tr-SQL: {} Pic-SQL: {} \\n'\r\n 'Page-Th: {} Topic-Th: {} Tor-Th: {} Pic-Th: {} Proxies: {}'\r\n .format(SIS.Finished_jobs['top'], len(SIS.SIS_POOLS['tops queue']),\r\n SIS.Finished_jobs['tor'], len(SIS.SIS_POOLS['tors queue']),\r\n SIS.Finished_jobs['pic'], len(SIS.SIS_POOLS['pics queue']),\r\n len(SIS.SIS_Queries['top']), len(SIS.SIS_Queries['tor']), len(SIS.SIS_Queries['pic']),\r\n SIS.Working_threads['page'],\r\n SIS.Working_threads['top'],\r\n SIS.Working_threads['tor'],\r\n SIS.Working_threads['pic'],\r\n len(SIS.SIS_POOLS['proxies'])))\r\n with open('Jobs.json', 'w') as f:\r\n f.write(json.dumps(SIS.SIS_POOLS))\r\n \r\n if 'Download' not in self.start_btn.text():\r\n return\r\n if len(SIS.SIS_POOLS['pics queue']) > 0 and SIS.Working_threads['pic'] < self.pics_threads_silder.value():\r\n th = SIS.SISPicLoader(self)\r\n self.stop_thread_signal.connect(th.setRunning)\r\n th.start()\r\n\r\n if len(SIS.SIS_POOLS['tors queue']) > 0 and SIS.Working_threads['tor'] < self.tors_threads_silder.value():\r\n tth = SIS.SISTorLoader(self.cookies, self)\r\n tth.send_text.connect(self.infoRec)\r\n self.stop_thread_signal.connect(tth.setRunning)\r\n tth.start()\r\n\r\n if len(SIS.SIS_POOLS['tops queue']) > 0 and SIS.Working_threads['top'] < self.tops_threads_silder.value():\r\n th = SIS.SISTopicLoader(self.cookies, self)\r\n th.send_text.connect(self.infoRec)\r\n self.stop_thread_signal.connect(th.setRunning)\r\n th.start()\r\n\r\n if len(SIS.SIS_POOLS['tops queue']) == 0 and SIS.Working_threads['page'] < self.pages_threads_slider.value():\r\n td = SIS.SISPageLoader(self.pages_generator, self.cookies, self)\r\n td.send_text.connect(self.infoRec)\r\n self.stop_thread_signal.connect(td.setRunning)\r\n td.start()\r\n\r\n def infoRec(self, info):\r\n self.output_window.append(info)\r\n\r\n def sub_forum_addr(self):\r\n index = self.forum_menu.currentIndex()\r\n return {0: 'forum-143-', 1: 'forum-230-', 2: 'forum-229-', 3: 'forum-231-',\r\n 4: 'forum-25-', 5: 'forum-58-', 6: 'forum-77-', 7: 'forum-27-'}[index]\r\n\r\n\r\nclass BrowserWidget(QWidget):\r\n def __init__(self, parent=None):\r\n super(BrowserWidget, self).__init__(parent)\r\n self.initUI()\r\n self.currentIndex = 0\r\n\r\n def initUI(self):\r\n # browser stuff below\r\n b_menu_layout1 = QHBoxLayout()\r\n self.b_search_line = QLineEdit()\r\n self.b_search_line.setMinimumWidth(200)\r\n self.b_type_combox = QComboBox()\r\n self.b_category_combox = QComboBox()\r\n self.b_category_combox.addItems(('所有', '亚洲', '欧美', '动漫'))\r\n self.b_censor_combox = QComboBox()\r\n self.b_censor_combox.addItems(('所有', '无码', '有码', '未知'))\r\n self.b_count_label = QLabel()\r\n self.b_count_label.setFixedWidth(100)\r\n b_menu_layout1.addWidget(QLabel('关键字'))\r\n b_menu_layout1.addWidget(self.b_search_line)\r\n b_menu_layout1.addWidget(self.b_type_combox)\r\n b_menu_layout1.addWidget(self.b_category_combox)\r\n b_menu_layout1.addWidget(self.b_censor_combox)\r\n b_menu_layout1.addWidget(self.b_count_label)\r\n\r\n b_menu_layout2 = QHBoxLayout()\r\n self.prev_page_btn = QPushButton(' << ')\r\n self.prev_page_btn.setEnabled(False)\r\n self.prev_page_btn.clicked.connect(self.prevClicked)\r\n self.next_page_btn = QPushButton(' >> ')\r\n self.next_page_btn.setEnabled(False)\r\n self.next_page_btn.clicked.connect(self.nextClicked)\r\n self.result_label = QLabel()\r\n # self.result_label.setFixedWidth(200)\r\n self.b_topic_each_page_slider = QSlider(Qt.Horizontal)\r\n self.b_topic_each_page_slider.setRange(50, 5000)\r\n silderLabel = QLabel('50条/每页')\r\n silderLabel.setFixedWidth(100)\r\n silderLabel.setAlignment(Qt.AlignCenter)\r\n self.b_topic_each_page_slider.valueChanged.connect(lambda x: silderLabel.setText('{} 条/每页'.format(x)))\r\n self.b_start_load = QPushButton('Load 加载')\r\n self.b_start_load.clicked.connect(self.startQuery)\r\n b_menu_layout2.addWidget(self.prev_page_btn)\r\n b_menu_layout2.addWidget(self.next_page_btn)\r\n b_menu_layout2.addWidget(self.result_label)\r\n b_menu_layout2.addStretch(1)\r\n b_menu_layout2.addWidget(self.b_start_load)\r\n b_menu_layout2.addWidget(self.b_topic_each_page_slider)\r\n b_menu_layout2.addWidget(silderLabel)\r\n\r\n self.b_table_view = myTable()\r\n self.b_table_view.itemClicked.connect(self.whenItemClicked)\r\n self.b_table_view.setEditTriggers(QTableWidget.NoEditTriggers)\r\n self.b_table_view.setSortingEnabled(True)\r\n self.b_table_view.setSelectionBehavior(QTableWidget.SelectRows)\r\n self.b_table_view.setSelectionMode(QTableWidget.SingleSelection)\r\n self.b_table_view.horizontalHeader().setSectionResizeMode(QHeaderView.Stretch)\r\n\r\n b_layout = QVBoxLayout()\r\n b_layout.addLayout(b_menu_layout1)\r\n b_layout.addLayout(b_menu_layout2)\r\n b_layout.addWidget(self.b_table_view)\r\n\r\n self.magLine = QLineEdit('下载链接')\r\n b_layout.addWidget(self.magLine)\r\n\r\n self.setLayout(b_layout)\r\n # browser stuff above\r\n #################################################\r\n\r\n def setTypeCombox(self, typs):\r\n self.b_type_combox.clear()\r\n self.b_type_combox.addItem('所有')\r\n self.b_type_combox.addItems(typs)\r\n\r\n def startQuery(self):\r\n if self.b_topic_each_page_slider.value() > 1000:\r\n reply = QMessageBox().warning(self, '提示', '每页显示大于1000，可能会造成卡顿，要继续吗？',\r\n QMessageBox.Yes | QMessageBox.No)\r\n if reply == QMessageBox.No:\r\n return\r\n query = 'SELECT * FROM SIStops'\r\n if self.b_search_line.text() is not '':\r\n query += ' WHERE name GLOB \"*{}*\"'.format(self.b_search_line.text())\r\n if self.b_type_combox.currentIndex() != 0:\r\n if 'WHERE' in query:\r\n query += ' AND type=\"{}\"'.format(self.b_type_combox.currentText())\r\n else:\r\n query += ' WHERE type=\"{}\"'.format(self.b_type_combox.currentText())\r\n if self.b_category_combox.currentIndex() != 0:\r\n if 'WHERE' in query:\r\n query += ' AND category=\"{}\"'.format(self.b_category_combox.currentIndex())\r\n else:\r\n query += ' WHERE category=\"{}\"'.format(self.b_category_combox.currentIndex())\r\n if self.b_censor_combox.currentIndex() != 0:\r\n if 'WHERE' in query:\r\n query += ' AND censor={}'.format(self.b_type_combox.currentIndex() - 1)\r\n else:\r\n query += ' WHERE censor={}'.format(self.b_type_combox.currentIndex() - 1)\r\n connect = sqlite3.connect(SIS.DBPATH + 'SISDB.sqlite')\r\n result = connect.cursor().execute(query).fetchall()\r\n connect.close()\r\n self.searchResult(result)\r\n\r\n\r\n def searchResult(self, result):\r\n if len(result):\r\n spliter = self.b_topic_each_page_slider.value()\r\n self.result_container = [result[i:i+spliter] for i in range(0, len(result), spliter)]\r\n self.placeItem(self.result_container[0])\r\n else:\r\n return\r\n if len(self.result_container) > 1:\r\n self.next_page_btn.setEnabled(True)\r\n self.currentIndex = 0\r\n self.prev_page_btn.setEnabled(False)\r\n self.result_label.setText('{}/{}'.format(self.currentIndex+1, len(self.result_container)))\r\n\r\n def placeItem(self, items):\r\n self.b_table_view.clear()\r\n self.b_table_view.setRowCount(0)\r\n base_url = get_forum_address()\r\n self.b_table_view.setColumnCount(10)\r\n self.b_table_view.setHorizontalHeaderLabels(('类型', '片名', '有无码', '赞', '日期',\r\n '地区', '磁力链', '截图', '原址', ''))\r\n self.b_table_view.setRowCount(len(items))\r\n\r\n for row, each in enumerate(items):\r\n for _n, item in enumerate(each[1:]):\r\n tableitem = QTableWidgetItem()\r\n tableitem.setTextAlignment(Qt.AlignCenter)\r\n if _n == 1:\r\n tableitem.setData(Qt.DisplayRole, item)\r\n tableitem.setToolTip('{}'.format(item))\r\n elif _n == 2:\r\n if item == 0:\r\n tableitem.setData(Qt.DisplayRole, '无')\r\n elif item == 1:\r\n tableitem.setData(Qt.DisplayRole, '有')\r\n else:\r\n tableitem.setData(Qt.DisplayRole, '未知')\r\n elif _n == 4:\r\n try:\r\n tableitem.setData(Qt.DisplayRole, datetime.fromtimestamp(float(item)).strftime('%Y-%m-%d'))\r\n except ValueError:\r\n pass\r\n elif _n == 5:\r\n cate = {1:'亚洲', 2:'欧美', 3:'动漫', 0:'未知'}\r\n category = cate[item]\r\n tableitem.setText(category)\r\n else:\r\n tableitem.setData(Qt.DisplayRole, item)\r\n self.b_table_view.setItem(row, _n, tableitem)\r\n connect = sqlite3.connect(SIS.DBPATH + 'SISDB.sqlite')\r\n tors = [x[0] for x in connect.cursor().execute('SELECT magnet from SISmags WHERE tid=?', (each[0],)).fetchall()]\r\n pics = connect.cursor().execute('SELECT tid, path from PicPath WHERE tid=?', (each[0],)).fetchall()\r\n\r\n\r\n if tors:\r\n tableitem = QTableWidgetItem('打开')\r\n tableitem.setTextAlignment(Qt.AlignCenter)\r\n tableitem.setData(1000, tors)\r\n self.b_table_view.setItem(row, 6, tableitem)\r\n\r\n if len(pics):\r\n tableitem = QTableWidgetItem('显示')\r\n tableitem.setTextAlignment(Qt.AlignCenter)\r\n tableitem.setData(1000, pics)\r\n self.b_table_view.setItem(row, 7, tableitem)\r\n connect.close()\r\n\r\n tableitem = QTableWidgetItem('...')\r\n tableitem.setTextAlignment(Qt.AlignCenter)\r\n tableitem.setToolTip(base_url + 'thread-' + each[0] + '.html')\r\n self.b_table_view.setItem(row, 8, tableitem)\r\n\r\n tableitem = QTableWidgetItem('删')\r\n tableitem.setToolTip(each[0])\r\n # put id info in to item\r\n tableitem.setData(1000, each[0])\r\n # put pic path info in to item\r\n tableitem.setData(1001, pics)\r\n tableitem.setTextAlignment(Qt.AlignCenter)\r\n self.b_table_view.setItem(row, 9, tableitem)\r\n\r\n for _n in range(10):\r\n if _n == 1:\r\n continue\r\n self.b_table_view.horizontalHeader().setSectionResizeMode(_n, QHeaderView.ResizeToContents)\r\n\r\n def whenItemClicked(self, item):\r\n if item.column() == 6:\r\n magtext = ''\r\n for each in item.data(1000):\r\n magtext += each + '\\t'\r\n self.magLine.setText(magtext)\r\n elif item.column() == 7 and item.data(1000) is not None:\r\n pics = item.data(1000)\r\n sw = SisPicWin(pics, self)\r\n sw.show()\r\n elif item.column() == 8:\r\n QDesktopServices().openUrl(QUrl(item.toolTip()))\r\n elif item.column() == 9:\r\n reply = QMessageBox().question(self, '删除确认', '移除\\n{}'.format(item.toolTip()), QMessageBox.Ok | QMessageBox.Cancel)\r\n if reply == QMessageBox.Ok:\r\n tid = item.data(1000)\r\n picpath = item.data(1001)\r\n connect = sqlite3.connect(SIS.DBPATH + 'SISDB.sqlite')\r\n for each in ('SIStops', 'SISmags', 'PicPath'):\r\n connect.cursor().execute('DELETE FROM {} WHERE tid=\"{}\"'.format(each, tid))\r\n for each in picpath:\r\n connect.cursor().execute('DELETE FROM PicMD5 WHERE path=?', (each[1],))\r\n connect.cursor().execute('DELETE FROM PicPath WHERE path=?', (each[1],))\r\n del_localpic(each[1])\r\n connect.commit()\r\n self.b_table_view.removeRow(item.row())\r\n connect.close()\r\n\r\n def prevClicked(self):\r\n self.currentIndex -= 1\r\n self.placeItem(self.result_container[self.currentIndex])\r\n if self.currentIndex == 0:\r\n self.prev_page_btn.setEnabled(False)\r\n if self.next_page_btn.isEnabled() is False:\r\n self.next_page_btn.setEnabled(True)\r\n self.result_label.setText('{}/{}'.format(self.currentIndex + 1, len(self.result_container)))\r\n\r\n def nextClicked(self):\r\n self.currentIndex += 1\r\n self.placeItem(self.result_container[self.currentIndex])\r\n if len(self.result_container) == self.currentIndex + 1:\r\n self.next_page_btn.setEnabled(False)\r\n if self.prev_page_btn.isEnabled() is False:\r\n self.prev_page_btn.setEnabled(True)\r\n self.result_label.setText('{}/{}'.format(self.currentIndex + 1, len(self.result_container)))\r\n\r\n\r\nclass myTable(QTableWidget):\r\n def mousePressEvent(self, *args, **kwargs):\r\n if args[0].button() == Qt.RightButton:\r\n try:\r\n clickedItem = self.itemAt(args[0].x(), args[0].y())\r\n if clickedItem.column() == 7:\r\n clickedRow = clickedItem.row()\r\n replay = QMessageBox().question(self, '删除图片',\r\n '【{}】的图片将会被移除'.format(self.itemAt(clickedRow, 2).text()),\r\n QMessageBox.Ok|QMessageBox.Cancel)\r\n if replay == QMessageBox.Ok:\r\n self.clearPicFromTable(clickedItem)\r\n\r\n except AttributeError:\r\n super().mousePressEvent(*args, **kwargs)\r\n return\r\n super().mousePressEvent(*args, **kwargs)\r\n\r\n def clearPicFromTable(self, item):\r\n pics = item.data(1000)\r\n self.deletePicturesFromDB(pics)\r\n item.setData(1000, None)\r\n item.setText('')\r\n\r\n def deletePicturesFromDB(self, pics):\r\n connect = sqlite3.connect(SIS.DBPATH + 'SISDB.sqlite')\r\n try:\r\n for each in pics:\r\n connect.cursor().execute('DELETE FROM PicPath WHERE path=?', (each[1],))\r\n connect.cursor().execute('DELETE FROM PicMD5 WHERE path=?', (each[1],))\r\n del_localpic(each[1])\r\n connect.commit()\r\n except sqlite3.IntegrityError as err:\r\n print(err)\r\n finally:\r\n connect.close()\r\n\r\n\r\nclass SisPicWin(QDialog):\r\n def __init__(self, pics, parent=None):\r\n super().__init__(parent)\r\n layout = QVBoxLayout()\r\n self.pics = pics\r\n # self.selected = 0\r\n self.m_pListWidget = picList()\r\n self.m_pListWidget.setIconSize(QSize(100, 100))\r\n self.m_pListWidget.setResizeMode(QListView.Adjust)\r\n self.m_pListWidget.setViewMode(QListView.IconMode)\r\n self.m_pListWidget.setMovement(QListView.Static)\r\n self.m_pListWidget.setSpacing(5)\r\n self.m_pListWidget.itemClicked.connect(self.item_clicked)\r\n self.m_pListWidget.delete_signal.connect(self.deletePic)\r\n self.m_pListWidget.setFixedHeight(110)\r\n layout.addWidget(self.m_pListWidget)\r\n self.imageLabel = QLabel()\r\n scroll = QScrollArea()\r\n scroll.setWidget(self.imageLabel)\r\n layout.addWidget(scroll)\r\n self.placeItems()\r\n self.setLayout(layout)\r\n # self.setWindowFlags(Qt.WindowMinimizeButtonHint)\r\n self.setWindowFlags(Qt.Window)\r\n self.resize(800, 600)\r\n\r\n def placeItems(self):\r\n self.m_pListWidget.clear()\r\n for index, each in enumerate(self.pics):\r\n pix = QPixmap(SIS.DBPATH + each[1])\r\n icon = QIcon()\r\n icon.addPixmap(pix)\r\n listItem = QListWidgetItem()\r\n listItem.setIcon(icon)\r\n listItem.setSizeHint(QSize(90, 90))\r\n listItem.setData(1000, index)\r\n listItem.setData(1001, pix)\r\n listItem.setData(1002, each[1])\r\n self.m_pListWidget.insertItem(index, listItem)\r\n if index == 0:\r\n self.imageLabel.setPixmap(pix)\r\n self.imageLabel.resize(pix.size().width(), pix.size().height())\r\n\r\n def item_clicked(self, item):\r\n if item:\r\n pix = item.data(1001)\r\n self.imageLabel.setPixmap(pix)\r\n\r\n def deletePic(self, item):\r\n index = item.data(1000)\r\n reply = QMessageBox().question(self, '删除图片', '{} 号图片将会被移除'.format(index+1),\r\n QMessageBox.Ok | QMessageBox.Cancel)\r\n if reply == QMessageBox.Cancel:\r\n return\r\n connect = sqlite3.connect(SIS.DBPATH + 'SISDB.sqlite')\r\n try:\r\n self.pics.pop(item.data(1000))\r\n picpath = item.data(1002)\r\n connect.cursor().execute('DELETE FROM PicMD5 WHERE path=?', (picpath,))\r\n connect.cursor().execute('DELETE FROM PicPath WHERE path=?', (picpath,))\r\n del_localpic(picpath)\r\n connect.commit()\r\n except sqlite3.IntegrityError as err:\r\n print(err)\r\n finally:\r\n connect.close()\r\n self.m_pListWidget.clear()\r\n self.placeItems()\r\n\r\n\r\nclass picList(QListWidget):\r\n delete_signal = pyqtSignal(QListWidgetItem)\r\n\r\n def mousePressEvent(self, event):\r\n item = self.itemAt(event.x(), event.y())\r\n if event.button() == Qt.RightButton and item:\r\n self.delete_signal.emit(item)\r\n return\r\n super().mousePressEvent(event)\r\n\r\n\r\ndef get_forum_address():\r\n with open('sis_addr.dat', 'r') as f:\r\n return f.readline()\r\n\r\n\r\ndef del_localpic(picpath):\r\n try:\r\n re_path = SIS.DBPATH + picpath.replace('\\\\', os.sep).replace('/', os.sep).replace(':', os.sep)\r\n os.remove(re_path)\r\n dirpath = os.path.split(re_path)[0]\r\n if len(os.listdir(dirpath)) == 0:\r\n os.removedirs(dirpath)\r\n except FileNotFoundError:\r\n pass\r\n","repo_name":"FYoungLee/SiSTor","sub_path":"SISUI.py","file_name":"SISUI.py","file_ext":"py","file_size_in_byte":33677,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"43445701795","text":"import win32com.client\nimport os\nimport Cybos_function\nimport sqlite3\n\nos.chdir(\"C:\\\\Users\\\\S\\\\Desktop\\\\바탕화면(임시)\\\\ETF\\\\\")\nos.getcwd()\n\n# Cybos Plus연결 여부 체크\nCybos_function.Get_login_status()\n\n# ETF code, name 불러오기\nETF_code_list, ETF_name_list = Cybos_function.Get_ETF_code_name()\n\n# ETF 코드 및 종목명 txt파일로 저장\nwith open(\"ETF_code_list.txt\", \"w\") as f :\n for code in ETF_code_list :\n f.write(code + \"\\n\")\nwith open(\"ETF_name_list.txt\", \"w\") as f :\n for name in ETF_name_list :\n f.write(name + \"\\n\")\n\n# ETF 일일데이터 객체 생성\nobjETF = win32com.client.Dispatch(\"Dscbo1.CpSvr7246\")\n\n# 데이터 수신 및 DB에 저장\ncount = 1\ncon = sqlite3.connect(\"tmp/ETF.db\")\nfor code in ETF_code_list :\n try :\n print(count, \" / \" , len(ETF_code_list))\n count += 1\n ETF_df = Cybos_function.ETF_GetData(objETF, code)\n ETF_df.to_sql(code, con, index=False)\n\n except :\n print(\"오류 : \", code)\n continue\ncon.close()\n\n\n","repo_name":"harimha/PycharmProjects","sub_path":"python32bit_project/Using_Cybos_ETF.py","file_name":"Using_Cybos_ETF.py","file_ext":"py","file_size_in_byte":1029,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"74142350245","text":"#!/usr/bin/python3.8\r\n# coding=utf-8\r\nimport pandas as pd\r\nimport geopandas\r\nimport matplotlib.pyplot as plt\r\nimport contextily\r\nimport sklearn.cluster\r\nimport numpy as np\r\n# muzeze pridat vlastni knihovny\r\n\r\n\r\n\r\ndef make_geo(df: pd.DataFrame) -> geopandas.GeoDataFrame:\r\n \"\"\" Konvertovani dataframe do geopandas.GeoDataFrame se spravnym kodovani\"\"\"\r\n # drop NaNs in coordination columns\r\n df.dropna(subset= [\"d\"], inplace=True)\r\n df.dropna(subset= [\"e\"], inplace=True)\r\n # create GeoDataFrame\r\n gdf = geopandas.GeoDataFrame(df, geometry=geopandas.points_from_xy(df['d'], df['e']), crs=\"EPSG:5514\")\r\n return gdf\r\n\r\n\r\ndef plot_geo(gdf: geopandas.GeoDataFrame, constrained_layout=True, fig_location: str = None,\r\n show_figure: bool = False):\r\n \"\"\" Vykresleni grafu s dvemi podgrafy podle lokality nehody \"\"\"\r\n # do not show plot immediately\r\n plt.ioff()\r\n # plot two subplots side by side\r\n fig, ax = plt.subplots(1,2, constrained_layout=constrained_layout, figsize=(14,8))\r\n # do not plot axises\r\n ax[0].axis(\"off\")\r\n ax[1].axis(\"off\")\r\n # select only ZLK region\r\n gdf_zlk = gdf[gdf[\"region\"] == \"ZLK\"]\r\n # convert projection\r\n gdf_zlk = gdf_zlk.to_crs(epsg=3857)\r\n # plot accidents\r\n gdf_zlk[gdf_zlk[\"p5a\"] == 1].plot(ax=ax[0], markersize=6, color=\"tab:red\", label=\"Nehody v obci\")\r\n gdf_zlk[gdf_zlk[\"p5a\"] == 2].plot(ax=ax[1], markersize=6, color=\"tab:green\", label=\"Nehody mimo obec\")\r\n # set titles\r\n ax[0].set_title('Nehody ve Zlinskem kraji: v obci')\r\n ax[1].set_title('Nehody ve Zlinskem kraji: mimo obec')\r\n # set map\r\n contextily.add_basemap(ax[0], crs=gdf_zlk.crs.to_string(), source=contextily.providers.Stamen.Toner, alpha=0.9)\r\n contextily.add_basemap(ax[1], crs=gdf_zlk.crs.to_string(), source=contextily.providers.Stamen.Toner, alpha=0.9)\r\n # save figure to fig_location\r\n if fig_location is not None:\r\n plt.savefig(fig_location)\r\n # show plot\r\n if show_figure:\r\n plt.show()\r\n\r\n\r\ndef plot_cluster(gdf: geopandas.GeoDataFrame, fig_location: str = None,\r\n show_figure: bool = False):\r\n \"\"\" Vykresleni grafu s lokalitou vsech nehod v kraji shlukovanych do clusteru \"\"\"\r\n # do not show plot immediately\r\n plt.ioff()\r\n \r\n # select JHC region\r\n gdf_c = gdf[gdf[\"region\"] == \"JHC\"]\r\n # set geometry to centroid and convert projection\r\n gdf_c = gdf_c.set_geometry(gdf_c.centroid).to_crs(epsg=3857)\r\n # set coordinates\r\n coords = np.dstack([gdf_c.geometry.x, gdf_c.geometry.y]).reshape(-1, 2)\r\n # k-means to get clusters\r\n db = sklearn.cluster.MiniBatchKMeans(n_clusters=25).fit(coords)\r\n # create copy of gdc and insert column cluster\r\n gdf_jhc = gdf_c.copy()\r\n gdf_jhc[\"cluster\"] = db.labels_\r\n # group gdf_jhc by cluster where accicents are counted\r\n gdf_jhc = gdf_jhc.dissolve(by=\"cluster\", aggfunc={\"p1\": \"count\"}).rename(columns=dict(p1=\"cnt\"))\r\n # get coordinates and merge with gdf_jhc\r\n gdf_coords = geopandas.GeoDataFrame(geometry=geopandas.points_from_xy(db.cluster_centers_[:, 0], db.cluster_centers_[:, 1]))\r\n gdf_points = gdf_jhc.merge(gdf_coords, left_on=\"cluster\", right_index=True).set_geometry(\"geometry_y\")\r\n\r\n # setup plot graph\r\n plt.figure(figsize=(20,10))\r\n ax = plt.gca()\r\n plt.axis(\"off\")\r\n ax.set_title(\"Nehody ve zlinskem kraji\")\r\n # plot result\r\n gdf_c.plot(ax=ax, color=\"tab:red\", markersize=20, alpha=0.2)\r\n gdf_points.plot(ax=ax, markersize=gdf_points[\"cnt\"]*2, column=\"cnt\", legend=True, alpha=0.7)\r\n # add map as background\r\n contextily.add_basemap(ax, crs=\"epsg:3857\", source=contextily.providers.Stamen.Toner, alpha=0.6)\r\n\r\n # save figure to fig_location if location is given\r\n if fig_location is not None:\r\n plt.savefig(fig_location)\r\n # show plot if requested\r\n if show_figure:\r\n plt.show()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n # zde muzete delat libovolne modifikace\r\n gdf = make_geo(pd.read_pickle(\"accidents.pkl.gz\"))\r\n plot_geo(gdf, \"geo1.png\", True)\r\n plot_cluster(gdf, \"geo2.png\", True)\r\n\r\n","repo_name":"ttribula/izv-projects","sub_path":"final/geo.py","file_name":"geo.py","file_ext":"py","file_size_in_byte":4101,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"8832828889","text":"\"\"\"\nMessenger objects for communication intra, interprocess and intercomputer\n\"\"\"\n\nfrom typing import Dict, Callable, Optional\nfrom enum import Enum, auto\nfrom collections import deque\nimport threading\n\nimport zmq\nfrom zmq.eventloop.zmqstream import ZMQStream\nfrom tornado.ioloop import IOLoop\n\nfrom perceptivo.root import Perceptivo_Object\nfrom perceptivo.types.networking import Socket\nfrom perceptivo.networking.messages import Message\n\n\nclass Node(Perceptivo_Object):\n\n class Poll_Mode(Enum):\n IOLOOP = auto()\n DEQUE = auto()\n NONE = auto()\n\n def __init__(self,\n socket:Socket,\n poll_mode:Poll_Mode = Poll_Mode.IOLOOP,\n callback:Optional[Callable]=None,\n to:Optional[str]=None,\n deque_size: int = 256):\n \"\"\"\n Wrapper around zmq sockets to send and receive messages\n\n\n Args:\n socket (:class:`.types.Socket`): Socket descriptor (see :class:`~.types.Socket`)\n poll_mode (:class:`.Poll_Mode`): Strategy for polling messages.\n\n * ``IOLOOP`` - uses tornado's IOloop and ZMQStreams to poll for messages.\n Needs to be given ``callback`` as well, which will be called with the received message as the only argument\n * ``DEQUE`` - a thread is spawned to poll the socket and add any message to ``deque``\n * ``NONE`` - interact with the socket manually\n\n callback (typing.Callable): A callable object that will be called with a received\n message as its only argument if ``poll_mode == IOLOOP``\n \"\"\"\n self.id = socket.id\n self.socket_type = socket.socket_type\n self.protocol = socket.protocol\n self.mode = socket.mode\n self.port = socket.port\n self.ip = socket.ip\n self.poll_mode = poll_mode\n self.callback = callback\n self.deque = deque(maxlen=deque_size)\n if to is None:\n self.to = socket.to\n else:\n self.to = to\n\n super(Node, self).__init__()\n\n self._stopping = threading.Event()\n\n self.socket: zmq.Socket = self._init_socket()\n\n self.logger.info(f'Socket initialized - id: {self.id}')\n\n def _init_socket(self) -> zmq.Socket:\n ctx = zmq.Context.instance()\n socket = ctx.socket(getattr(zmq, self.socket_type))\n socket.setsockopt_string(zmq.IDENTITY, self.id)\n\n if self.mode == 'bind':\n if self.ip == '':\n self.ip = '*'\n socket.bind(self.address)\n elif self.mode == 'connect':\n socket.connect(self.address)\n else:\n raise ValueError(f'mode needs to be one of types.ZMQ_MODE, got {self.mode}')\n\n if self.poll_mode == self.Poll_Mode.IOLOOP:\n socket = ZMQStream(socket)\n if not callable(self.callback):\n raise ValueError(f'Must provide a callback for poll_mode == IOLoop, got {self.callback}')\n socket.on_recv(self.callback)\n loop = IOLoop.current()\n threading.Thread(target=self._start_ioloop, args=(loop,), daemon=True).start()\n\n elif self.poll_mode == self.Poll_Mode.DEQUE:\n threading.Thread(target=self._start_polling, daemon=True).start()\n\n return socket\n\n @property\n def address(self) -> str:\n \"\"\"\n The full address, including protocol, ip, port, or endpoint, depending on\n the protocol\n\n Returns:\n str\n \"\"\"\n if self.protocol == 'tcp':\n return f\"tcp://{self.ip}:{self.port}\"\n elif self.protocol == 'ipc':\n return f\"ipc:///tmp/{self.port}\"\n else:\n raise NotImplementedError('Only tcp and ipc modes are implemented!')\n\n def send(self, msg:Optional[Message] = None, to:Optional[str]=None, **kwargs):\n \"\"\"for now just wrapping the socket\"\"\"\n if msg is None:\n if len(kwargs) == 0:\n raise ValueError(f'Need a message or kwargs that can be used to create a message')\n msg = Message(**kwargs)\n\n if self.socket_type in ('ROUTER','DEALER'):\n if to is None and self.to is None:\n error_msg = 'With Router/Dealer sockets, need to explicitly pass \"to\" in send or init'\n self.logger.exception(error_msg)\n raise ValueError(error_msg)\n elif to is None:\n to = self.to\n\n multipart = [to.encode('utf-8'), msg.serialize()]\n self.socket.send_multipart(multipart)\n else:\n self.socket.send(msg.serialize())\n self.logger.debug(f'Sent message number {msg.message_number}')\n\n def _start_ioloop(self, loop:IOLoop):\n \"\"\"spawn a tornado ioloop\"\"\"\n try:\n loop.start()\n except RuntimeError:\n # loop already started\n pass\n\n def _start_polling(self):\n \"\"\"spawn a thread to poll the socket and add incoming messages to the queue\"\"\"\n while not self._stopping.is_set():\n msg = Message.from_serialized(self.socket.recv())\n self.deque.append(msg)\n\n def release(self):\n self._stopping.set()\n self.socket.close()\n","repo_name":"perceptivo/perceptivo","sub_path":"perceptivo/networking/node.py","file_name":"node.py","file_ext":"py","file_size_in_byte":5256,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"4813554629","text":"import random\n\nclass songTracking:\n nice = [1,2,3,4,5,14,21,30,32,33,34]\n cozy = [6,7,8,11,12,13,18,35,36,37,38,39,40]\n storm = [15,16,17,24,25,26,41,42]\n winter = [19,20,21,22,23,29,31,43,44]\n\n def track(self, currentVibe, mySpot):\n if(currentVibe in self.nice):\n fi = \"/home/pi/jackbot/nice.txt\"\n elif(currentVibe in self.cozy):\n fi = \"/home/pi/jackbot/cozy.txt\"\n elif(currentVibe in self.storm):\n fi = \"/home/pi/jackbot/storm.txt\"\n else:\n fi = \"/home/pi/jackbot/winter.txt\"\n\n found = False\n print(fi)\n f = open(fi)\n countent = f.readlines()\n for i in range(0,len(countent)):\n curr = countent[i].split(',')\n if(curr[0] == mySpot.id()):\n found = True\n countent[i] = curr[0] + ',' + str(int(curr[1])+1) + '\\n'\n break\n if (found == False):\n countent = countent+ [str(mySpot.id() + ',1\\n')]\n \n f.close()\n f = open(fi, \"w\")\n f.writelines(countent)\n f.close()\n \n def rec(self,currentVibe,mySpot):\n if(currentVibe in self.nice):\n fi = \"/home/pi/jackbot/nice.txt\"\n elif(currentVibe in self.cozy):\n fi = \"/home/pi/jackbot/cozy.txt\"\n elif(currentVibe in self.storm):\n fi = \"/home/pi/jackbot/storm.txt\"\n else:\n fi = \"/home/pi/jackbot/winter.txt\"\n f = open(fi)\n choices = []\n countent = f.readlines()\n for i in range(0,len(countent)):\n curr = countent[i].split(',')\n if(int(curr[1]) >= 5):\n for j in range(0,int(curr[1])):\n choices.append(curr[0])\n if(len(choices) > 0): \n choice = random.randint(0,(len(choices)-1)) \n print(choices[choice]) \n mySpot.play_track([choices[choice]])\n f.close()\n\n\n\n\n\n","repo_name":"abneyjw/jackbot","sub_path":"songtracking.py","file_name":"songtracking.py","file_ext":"py","file_size_in_byte":1985,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"72325841445","text":"'''Create a program that reads the name and two grades of several students and stores them all in a composite list. \nAt the end, show a bulletin containing the average of each one and allow the user to show the grades of each student\nindividually.'''\n# Creating a list to store the data\nstudents = []\n# Creating a while loop to repeat the data entry\nwhile True:\n # Requesting the name of the student\n name = input('Name: ')\n # Requesting the first grade of the student\n grade1 = float(input('Grade 1: '))\n # Requesting the second grade of the student\n grade2 = float(input('Grade 2: '))\n # Calculating the average between the two grades\n avg = (grade1 + grade2) / 2\n # Adding the name, grades and average to the list\n students.append([name, [grade1, grade2], avg])\n # Asking if the user wants to add another student\n cont = input('Do you want to continue? [Y/N] ')\n # If the user doesn't want to add another student, stop the while loop\n if cont in 'Nn':\n break\n# Printing a line to separate the data from the header\nprint('-=' * 30)\n# Printing the header\nprint(f'{\"No.\":<4}{\"NAME\":<10}{\"AVERAGE\":>8}')\n# Printing a line to separate the header from the data\nprint('-' * 26)\n# Iterating over the students list, printing the student number, name and average\nfor i, student in enumerate(students):\n print(f'{i:<4}{student[0]:<10}{student[2]:>8.1f}')\n# Creating another while loop to show the grades of a student\nwhile True:\n # Printing a line to separate the data from the header\n print('-' * 35)\n # Requesting the student number\n option = int(input('Show grades of which student? (999 to stop): '))\n # If the user types 999, stop the while loop\n if option == 999:\n break\n # If the student number is valid, print the name of the student and the grades\n if option <= len(students) - 1:\n print(f'Grades of {students[option][0]} are {students[option][1]}')\n# Printing a message to indicate the end of the program\nprint('FINISH')","repo_name":"arthur-americo/Python_Study","sub_path":"53 Bulletin with composite lists.py","file_name":"53 Bulletin with composite lists.py","file_ext":"py","file_size_in_byte":2012,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"35443731898","text":"import requests\n\nrequest_id = input(\"Введите искомый id: \")\n\nresponse = requests.get(\n \"http://127.0.0.1:5000/api/problems\", {\"id\": request_id}\n)\n\njson = response.json()\nfor i in json.keys():\n print(json[i])\n","repo_name":"Yabra/BeginnerCodeQuest","sub_path":"api_tests/show_problems.py","file_name":"show_problems.py","file_ext":"py","file_size_in_byte":228,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"32366611129","text":"import sys\nimport re\nimport random\n\n#an engine is expected to keep track of the following state information\nboardsize = 19\nkomi = 6.5\nprotocol_version = 2\nname = 'Go Away'\nversion = '1.0'\ncommands = ['protocol_version', #supported commands\n 'name',\n 'version',\n 'known_command',\n 'list_commands',\n 'quit',\n 'boardsize',\n 'clear_board',\n 'komi',\n 'play',\n 'genmove']\n\ndef quit(): #function for the engine to close the connection\n sys.stdout.write('engine closed\\n')\n\ndef clear_board(): #funtion for clearing the board\n sys.stdout.write('board has been cleared\\n')\n\ndef resign(): #function for resigning operation\n sys.stdout.write('one player resigns the game\\n')\n\ndef play(color, coord):\n #here should be code for adding the stone to board\n\n sys.stdout.write(color + ' play at ' + str(coord) + '\\n')\n\ndef genmove(color):\n num = random.randint(0, boardsize * boardsize + 1)\n #here should be code for determing where the next stone is to be put\n\n if num == 0:\n return 'resign'\n else:\n return color + ' ' + num_to_coor(num)\n\ndef num_to_coor(num):\n if num == boardsize * boardsize + 1:\n return 'pass'\n row = num // boardsize\n col = num % boardsize\n return chr(col + 96) + str(boardsize - row)\n\ndef color_parser(arguments):\n if len(arguments) == 1:\n color = arguments[0].lower()\n if color in ['w', 'white']:\n return 'w'\n elif color in ['b', 'black']:\n return 'b'\n\ndef move_parser(arguments):\n if len(arguments) == 2:\n color = arguments[0].lower()\n vertex = arguments[1].lower()\n\n if color in ['w', 'white']:\n color = 'w'\n elif color in ['b', 'black']:\n color = 'b'\n else:\n return None\n\n if vertex == 'pass':\n return [color, 362]\n\n col = ord(vertex[0]) - 96\n if vertex[1:].isdigit():\n row = int(vertex[1:])\n print(row, col, boardsize)\n if col >= 1 and col <= boardsize and row >= 1 and row <= boardsize:\n return [color, (boardsize - row) * boardsize + col]\n\ndef gtp_parser():\n global boardsize\n while True:\n id = ''\n raw_input = sys.stdin.readline().strip(' \\n')\n if '#' in raw_input:\n raw_input = raw_input[:raw_input.find('#')]\n cmd = raw_input.split(' ')\n if cmd[0].isdigit():\n id = cmd[0]\n command_name = cmd[1]\n arguments = cmd[2:]\n else:\n command_name = cmd[0]\n arguments = cmd[1:]\n # sys.stdout.write(id + command_name + str(arguments))\n if command_name == 'play':\n if arguments == ['resign']:\n resign()\n sys.stdout.write('=' + id + ' \\n\\n')\n else:\n move = move_parser(arguments)\n if move:\n play(move[0], move[1])\n sys.stdout.write('=' + id + ' \\n\\n')\n else:\n sys.stdout.write('?' + id + ' invalid command format\\n\\n')\n elif command_name == 'genmove':\n color = color_parser(arguments)\n if color:\n reply = genmove(color)\n sys.stdout.write('=' + id + ' ' + reply + '\\n\\n')\n if reply == 'resign':\n break\n else:\n sys.stdout.write('?' + id + ' invalid command format\\n\\n')\n elif command_name == 'protocol_version':\n if arguments == []:\n sys.stdout.write('=' + id + ' ' + str(protocol_version) + '\\n\\n')\n else:\n sys.stdout.write('?' + id + ' invalid command format\\n\\n')\n elif command_name == 'name':\n if arguments == []:\n sys.stdout.write('=' + id + ' ' + str(name) + '\\n\\n')\n else:\n sys.stdout.write('?' + id + ' invalid command format\\n\\n')\n elif command_name == 'version':\n if arguments == []:\n sys.stdout.write('=' + id + ' ' + str(version) + '\\n\\n')\n else:\n sys.stdout.write('?' + id + ' invalid command format\\n\\n')\n elif command_name == 'known_command':\n if len(arguments) == 1:\n if arguments[0] in commands:\n sys.stdout.write('=' + id + ' true\\n\\n')\n else:\n sys.stdout.write('=' + id + ' false\\n\\n')\n else:\n sys.stdout.write('?' + id + ' invalid command format\\n\\n')\n elif command_name == 'list_commands':\n if arguments == []:\n sys.stdout.write('=' + id + ' ')\n for command in commands:\n sys.stdout.write(command + '\\n')\n sys.stdout.write('\\n')\n else:\n sys.stdout.write('?' + id + ' invalid command format\\n\\n')\n elif command_name == 'quit':\n if arguments == []:\n quit()\n sys.stdout.write('=' + id + ' \\n\\n')\n break\n else:\n sys.stdout.write('?' + id + ' invalid command format\\n\\n')\n elif command_name == 'boardsize':\n if len(arguments) == 1 and arguments[0].isdigit():\n size = int(arguments[0])\n if size >= 2 and size <= 25:\n boardsize = size\n sys.stdout.write('=' + id + ' \\n\\n')\n else:\n sys.stdout.write('?' + id + ' unacceptable size\\n\\n')\n else:\n sys.stdout.write('?' + id + ' invalid command format\\n\\n')\n elif command_name == 'clear_board':\n if arguments == []:\n clear_board()\n sys.stdout.write('=' + id + ' \\n\\n')\n else:\n sys.stdout.write('?' + id + ' invalid command format\\n\\n')\n elif command_name == 'komi':\n value = re.compile(r'^[-+]?[0-9]+\\.[0-9]+$')\n if len(arguments) == 1 and value.match(arguments[0]):\n komi = float(arguments[0])\n sys.stdout.write('=' + id + ' \\n\\n')\n else:\n sys.stdout.write('?' + id + ' invalid command format\\n\\n')\n else:\n sys.stdout.write('?' + id + ' unknown command\\n\\n')\n\ngtp_parser()\n","repo_name":"gms2009/GTP-Go-Text-Protocol","sub_path":"gtp.py","file_name":"gtp.py","file_ext":"py","file_size_in_byte":6540,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"29003530265","text":"#!/usr/bin/env python3\n\n# Find errors in Postfix log SQLite DB\n\nimport re\nimport sys\nfrom collections import defaultdict\nfrom sqlite3 import connect\n\nclass PostfixSearch(object):\n\n \"\"\"\n \"\"\"\n\n def __init__(self):\n self._sql = None\n\n def load_db(self, dbpath):\n self._sql = connect(dbpath)\n\n def find_msg_by_subject(self, sender, subject):\n request = \"\"\"\n SELECT raddr.email, msg.qid, st.status\n FROM email_insertion msg\n INNER JOIN email_recipient rcv ON msg.qid = rcv.qid\n INNER JOIN email_sender snd ON msg.qid = snd.qid\n INNER JOIN email_address saddr ON snd.emailid = saddr.id\n INNER JOIN email_address raddr ON rcv.emailid = raddr.id\n INNER JOIN email_subject esub ON esub.qid = msg.qid\n INNER JOIN subject sub ON sub.id = esub.subjectid\n INNER JOIN email_status est ON est.qid = msg.qid\n INNER JOIN status st ON est.statusid = st.id\n WHERE saddr.email = :sender\n AND sub.text LIKE :subject\n ORDER BY raddr.email\n \"\"\"\n c = self._sql.cursor()\n c.execute(request, {'sender': sender, 'subject': subject})\n for row in c.fetchall():\n yield row\n\n def find_reason_by_qid(self, qid):\n request = \"\"\"\n SELECT est.msg, datetime(est.date, 'unixepoch')\n FROM email_status est\n WHERE est.qid=:qid\n \"\"\"\n c = self._sql.cursor()\n c.execute(request, {'qid': qid})\n return c.fetchone()\n\n\nHOSTERROR_CRE = re.compile(r'^host\\s([\\w\\.\\-]+)'\n r'\\[(\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3})\\]\\s'\n r'said:\\s(\\d{3})\\s(.*)$')\n\ndef main(dbpath, sender, subject):\n ps = PostfixSearch()\n ps.load_db(dbpath)\n addresses = defaultdict(list)\n for email, qid, status in ps.find_msg_by_subject(sender, subject):\n addresses[email].append((status, qid))\n invalid = 0\n issues = {}\n for addr in sorted(addresses, key=lambda a: a.lower()):\n statuses = addresses[addr]\n if 'sent' not in [s[0] for s in statuses]:\n qid = [s[1] for s in statuses if s[0] == 'bounced']\n if not qid:\n # likely to be invalid addresses\n # print(addr, statuses)\n invalid +=1 \n else:\n reason, dt = ps.find_reason_by_qid(qid[0])\n issues[addr] = (dt, reason)\n #print(addr)\n for addr in sorted(issues, key=lambda a: issues[a][0]):\n dt, reason = issues[addr]\n # print(\"%-32s %-20s %s\" % (addr, dt, reason))\n mo = HOSTERROR_CRE.match(reason)\n if mo:\n reason = mo.group(4)\n code = int(mo.group(3))\n else:\n reason = reason\n code = 500\n print(\"%s,%d,%s\" % (addr, code, reason))\n\n\nif __name__ == '__main__':\n if len(sys.argv) < 2:\n print(\"Missing DB\", file=sys.stderr)\n exit(1)\n #main(sys.argv[1], 'Lettre%%Décembre 2015')\n main(sys.argv[1], 'paca@ml.ffplum.info', 'Newsletter Radios 8.33')\n","repo_name":"ebouaziz/miscripts","sub_path":"Python/email/findfail.py","file_name":"findfail.py","file_ext":"py","file_size_in_byte":3111,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"5575798319","text":"import json\nimport requests\nfrom bs4 import BeautifulSoup\n\nclass Wrapper:\n \"\"\"\n Wrapper to return a ticker, based on ISIN input.\n Data from Morningstar.com\n \"\"\"\n\n # If True prints some basic feedback from the Wrapper\n debug = False\n\n def __init__(self, ISIN=''):\n self.ISIN = ISIN.upper()\n\n if self.ISIN:\n # Opens search URL and returns a Requests-object\n r = self.startConnection()\n\n # Creates soup and finds the correct result data based on a request-object\n self.makeSoup(r)\n\n def setISIN(self, ISIN):\n \"\"\" Sets the value of ISIN, it's required to set this, either in the __init__ or with this function \"\"\"\n self.ISIN = ISIN.upper()\n\n def startConnection(self):\n \"\"\" Stars the connection to the search URL and returns a request-object if succeeded \"\"\"\n PREFIX = 'http://www.morningstar.com/search.html?q='\n search_url = f'{PREFIX}+{self.ISIN}'\n if self.debug:\n print(search_url)\n r = requests.get(search_url)\n if r.status_code == 200:\n return r\n else:\n return None\n\n def makeSoup(self, request):\n \"\"\" Creates a BS object and filters the data we need \"\"\"\n soup = BeautifulSoup(request.text, 'html.parser')\n\n for div in soup.find_all('div'):\n\n # Limit our search to the search results\n if div.get('class') == ['search-list-content']:\n\n # Check if we found correct result, containing our ISIN\n if div.get('data-key').strip() == self.ISIN:\n\n self.data = div.get('data-initialdata')\n break\n\n def getTicker(self):\n \"\"\" Returns a ticker based on the filtered data, which is collected in makeSoup() \"\"\"\n if self.debug:\n print('DATA: ' + self.data)\n\n myJSON = json.loads(self.data)\n\n if self.debug:\n print('JSON: ' + str(myJSON))\n print('Result part: ' + str(myJSON['result']))\n\n new_dict = myJSON['m']\n\n if self.debug:\n print('NEW DICT: ', end='')\n print(list(new_dict))\n\n # The ticker is part of a large string, followed by the OS001-tag.\n pos_tag = str(new_dict).find('OS001')\n pos_comma = str(new_dict)[pos_tag:].find(',')\n\n ticker = str(new_dict)[pos_tag + 9:pos_tag + pos_comma - 1]\n return ticker","repo_name":"promwarm/ISIN_to_ticker","sub_path":"MorningStar_wrapper.py","file_name":"MorningStar_wrapper.py","file_ext":"py","file_size_in_byte":2436,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"52"} +{"seq_id":"40011795572","text":"from flask import Flask, render_template, request, redirect\nimport sqlite3\n\napp = Flask(__name__)\n\n# brings you to default home page\n@app.route('/', methods=['GET', 'POST'])\ndef index():\n return render_template(\"home.html\")\n\n\n# retrieves the url, stores it, gets the base 62 value\n@app.route('/urlenter', methods=['GET', 'POST'])\ndef home():\n global characters\n global Base\n global originalURL\n\n # the characters in base62\n characters = '0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'\n # variable holding length of 62\n Base = len(characters)\n\n # open,execute and close database connection\n conn = sqlite3.connect('urlHolder.db')\n c = conn.cursor()\n originalURL = request.form['longurl']\n # Insert the url into database\n c.execute(\"INSERT INTO urlInfo (longurl) values (?)\", (originalURL,))\n conn.commit()\n url = str(originalURL)\n # select the id where the url name matches\n c.execute(\"SELECT id FROM urlInfo WHERE longurl=?\", (url,))\n # retrieves that db row\n k = c.fetchone()\n\n for value in k:\n temp = value\n\n tempvalue = ''\n # retrive the base 62\n while temp != 0:\n tempvalue = (characters[temp % Base]) + tempvalue\n temp = int(temp / Base)\n # set the new link value\n newlink = tempvalue\n # hold to return the new link value\n newurl = 'http://localhost:5000/' + str(newlink)\n\n return render_template(\"short.html\", newUrl=newurl)\n\n\n# converts the base 62 back to get the original url link to redirect the user\n@app.route('/')\ndef convert(newlink):\n holder = 0\n default = 1\n\n for value in reversed(newlink):\n holder += default * characters.index(value)\n default *= Base\n assign = holder\n # open and execute database connection\n conn = sqlite3.connect('urlHolder.db')\n c = conn.cursor()\n c.execute(\"SELECT * FROM urlInfo WHERE id=?\", (assign,))\n\n # grab all and assign index 1, the original url to the redirect\n for data in c.fetchall():\n normalurl = data[1]\n return redirect(normalurl)\n\n\nif __name__ == \"__main__\":\n app.run(debug=True)\n","repo_name":"JHutnick/link-shortener","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2131,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"10553640717","text":"import cv2\nimport pafy\nimport requests\nfrom datetime import datetime\nfrom moviepy.editor import VideoFileClip\nfrom modules.storage.models import VideoModel\n\n\ndef pull_videos(url):\n \"\"\"\n Return a list of video urls from the server\n \"\"\"\n video = pafy.new(url)\n metadata_url = None\n for s in video.streams:\n if s.resolution == \"1280x720\":\n metadata_url = s.url\n\n r = requests.get(metadata_url)\n videos = r.text.split()\n videos = [v for v in videos if not v.startswith('#')]\n return videos\n\n\ndef download_video(url):\n \"\"\"Download video and return filename\"\"\"\n path = \"test/temp/downloaded.mpeg\"\n r = requests.get(url, stream=True)\n if r.status_code == 200:\n with open(path, 'wb') as f:\n for chunk in r:\n f.write(chunk)\n return path\n\n\n\ndef convert_video(filename):\n \"\"\"Convert the video and return the new filename\"\"\"\n output = filename.replace('mpeg','mp4')\n clip = VideoFileClip(filename)\n clip.write_videofile(output, audio=False, fps=clip.fps) # default codec: 'libx264', 24 fps\n\n return output\n\n\n\ndef downloader(camera,url):\n \"\"\"Continuously download videos from a source\"\"\"\n urls = pull_videos(url)\n for i,url in enumerate(urls):\n try:\n print(\"Downloading \",url)\n filename = download_video(url)\n filename = convert_video(filename)\n timestamp = datetime.now()\n model = VideoModel.create(camera,timestamp,filename)\n model.save()\n except Exception as e:\n print(e)\n\n\nif __name__ == \"__main__\":\n camera = \"alabama\"\n link = \"https://www.youtube.com/watch?v=cjuskMMYlLA\"\n downloader(camera,link)","repo_name":"maxkferg/smart-city-model","sub_path":"downloader.py","file_name":"downloader.py","file_ext":"py","file_size_in_byte":1709,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"23485089331","text":"#-*- coding: utf-8 -*-\nfrom __future__ import print_function\nimport os\nimport rsa\nimport json\nimport time\nimport binascii\nimport requests\n\n\nclass KsherPay(object):\n __nonce_str = (''.join(map(lambda x: (hex(x)[2:]), os.urandom(17))))[:32]\n __time_stamp = time.strftime('%Y%m%d%H%M%S%SS')\n __DOMAIN = 'https://api.mch.ksher.net/KsherPay'\n __GATEWAY_DOMAIN = 'https://gateway.ksher.com/api'\n\n def __init__(self, appid='', privatekey='', pubkey='', version='3.0.0'):\n self.appid = appid # ksher appid\n self.privatekey = privatekey # 私钥\n self.pubkey = pubkey # ksher公钥\n self.version = version # SDK版本\n\n def __new__(cls, *args, **kwargs):\n if not hasattr(KsherPay, \"_instance\"):\n KsherPay.__instance = object.__new__(cls)\n return KsherPay.__instance\n\n\n def __ksher_sign(self, kwargs):\n \"\"\"\n 签名\n :param kwargs:\n :return:\n \"\"\"\n alist = sorted([\"%s=%s\" % (key, str(value)) for key, value in kwargs.items()])\n predata = \"\".join(alist).encode('utf8')\n keydata = ''\n with open(self.privatekey) as f:\n keydata = f.read()\n privkeystr = rsa.PrivateKey.load_pkcs1(keydata, 'PEM')\n signature = rsa.sign(predata, privkeystr, 'MD5')\n signature = binascii.hexlify(signature)\n return signature\n\n def __verify_ksher_sign(self, signature, kwargs):\n \"\"\"\n 验签\n :param signature:\n :param kwargs:\n :return:\n \"\"\"\n predata = \"\".join(sorted([\"%s=%s\" % (key, kwargs[key]) for key in kwargs.keys()])).encode('utf-8')\n pubkey = ''\n\n try:\n with open(self.pubkey) as f:\n pubkey = rsa.PublicKey.load_pkcs1(f.read())\n ecodesign = binascii.unhexlify(signature)\n if rsa.verify(predata, ecodesign, pubkey):\n print('验签通过......')\n return True\n except Exception as e:\n # if e.message == 'Verification failed':\n print('验签失败......')\n return False\n\n def _request(self, url, data, m=\"\"):\n sign = self.__ksher_sign(data)\n data.update({'sign': sign.decode()})\n print('请求{}接口的请求数据:\\n {}'.format(url, json.dumps(data, sort_keys=True, indent=4)))\n if m == \"POST\":\n r = requests.post(url, data, timeout=60)\n else:\n r = requests.get(url, params=data, timeout=60)\n response = r.text\n if r.status_code == 200:\n response = r.json()\n if response.get('code') == 0:\n signature = response.get('sign', '')\n verified = self.__verify_ksher_sign(signature, response.get('data'))\n verified = True\n if not verified:\n response = {\n \"code\": 0,\n \"data\": {\n \"err_code\": \"VERIFY_KSHER_SIGN_FAIL\",\n \"err_msg\": \"verify signature failed\",\n \"result\": \"FAIL\"\n },\n \"msg\": \"ok\",\n \"sign\": \"\",\n \"status_code\": \"\",\n \"status_msg\": \"\",\n \"time_stamp\": self.__time_stamp,\n \"version\": self.version\n }\n return response\n print('请求{}接口的响应数据:\\n {}'.format(url, json.dumps(response, sort_keys=True, indent=4)))\n return response\n\n\n def quick_pay(self, **kwargs):\n \"\"\"\n B扫C支付\n :param kwargs:\n Mandatory params:\n mch_order_no\n total_fee\n fee_type\n auth_code\n channel\n Optional Params\n operator_id\n :return:\n \"\"\"\n kwargs.update({'appid': self.appid, 'nonce_str': self.__nonce_str, 'time_stamp': self.__time_stamp})\n response = self._request(url='{}/quick_pay'.format(self.__DOMAIN), data=kwargs, m=\"POST\")\n return response\n\n\n def jsapi_pay(self, **kwargs):\n \"\"\"\n C扫B支付\n :param kwargs:\n Mandatory params:\n mch_order_no\n total_fee\n fee_type\n channel\n Optional Params:\n redirect_url\n notify_url\n paypage_title\n operator_id\n :return:\n \"\"\"\n kwargs.update({'appid': self.appid, 'nonce_str': self.__nonce_str, 'time_stamp': self.__time_stamp})\n response = self._request(url='{}/jsapi_pay'.format(self.__DOMAIN), data=kwargs)\n return response\n\n def native_pay(self, **kwargs):\n \"\"\"\n 动态码支付\n :param kwargs:\n Mandatory params:\n mch_order_no\n total_fee\n fee_type\n channel\n Optional Params:\n redirect_url\n notify_url\n paypage_title\n product\n attach\n operator_id\n device_id\n img_type\n :return:\n \"\"\"\n kwargs.update({'appid': self.appid, 'nonce_str': self.__nonce_str, 'time_stamp': self.__time_stamp})\n response = self._request(url='{}/native_pay'.format(self.__DOMAIN), data=kwargs, m=\"POST\")\n return response\n\n def minipro_pay(self, **kwargs):\n \"\"\"\n 小程序支付\n :param kwargs:\n Mandatory params:\n mch_order_no\n total_fee\n fee_type\n channel\n sub_openid\n channel_sub_appid\n\n Optional Params:\n redirect_url\n notify_url\n paypage_title\n product\n operator_id\n :return:\n \"\"\"\n kwargs.update({'appid': self.appid, 'nonce_str': self.__nonce_str, 'time_stamp': self.__time_stamp})\n response = self._request(url='{}/mini_program_pay'.format(self.__DOMAIN), data=kwargs)\n return response\n\n def app_pay(self, **kwargs):\n \"\"\"\n app支付\n :param kwargs:\n Mandatory params:\n mch_order_no\n total_fee\n fee_type\n channel\n sub_openid\n channel_sub_appid\n\n Optional Params:\n redirect_url\n notify_url\n paypage_title\n product\n attach\n operator_id\n refer_url 仅当channel为alipay时需要\n :return:\n \"\"\"\n kwargs.update({'appid': self.appid, 'nonce_str': self.__nonce_str, 'time_stamp': self.__time_stamp})\n response = self._request(url='{}/app_pay'.format(self.__DOMAIN), data=kwargs)\n return response\n\n def wap_pay(self, **kwargs):\n \"\"\"\n H5支付，仅支持channel=alipay\n :param kwargs:\n Mandatory params:\n mch_order_no\n total_fee\n fee_type\n channel\n\n Optional Params:\n redirect_url\n notify_url\n paypage_title\n product\n attach\n operator_id\n device_id\n refer_url\n :return:\n \"\"\"\n kwargs.update({'appid': self.appid, 'nonce_str': self.__nonce_str, 'time_stamp': self.__time_stamp})\n response = self._request(url='{}/wap_pay'.format(self.__DOMAIN), data=kwargs)\n return response\n\n def web_pay(self, **kwargs):\n \"\"\"\n PC网站支付，仅支持channel=alipay\n :param kwargs:\n Mandatory params:\n mch_order_no\n total_fee\n fee_type\n channel\n\n Optional Params:\n redirect_url\n notify_url\n paypage_title\n product\n attach\n operator_id\n device_id\n refer_url\n :return:\n \"\"\"\n kwargs.update({'appid': self.appid, 'nonce_str': self.__nonce_str, 'time_stamp': self.__time_stamp})\n response = self._request(url='{}/web_pay'.format(self.__DOMAIN), data=kwargs)\n return response\n\n def order_query(self, **kwargs):\n \"\"\"\n 订单查询\n :param kwargs:\n Mandatory params:\n mch_order_no、ksher_order_no、channel_order_no三选一\n :return:\n \"\"\"\n kwargs.update({'appid': self.appid, 'nonce_str': self.__nonce_str, 'time_stamp': self.__time_stamp})\n response = self._request(url='{}/order_query'.format(self.__DOMAIN), data=kwargs, m=\"GET\")\n return response\n\n def order_close(self, **kwargs):\n \"\"\"\n 订单关闭\n :param kwargs:\n Mandatory params:\n mch_order_no、ksher_order_no、channel_order_no三选一\n Optional Params:\n operator_id\n :return:\n \"\"\"\n kwargs.update({'appid': self.appid, 'nonce_str': self.__nonce_str, 'time_stamp': self.__time_stamp})\n response = self._request(url='{}/order_close'.format(self.__DOMAIN), data=kwargs)\n return response\n\n def order_reverse(self, **kwargs):\n \"\"\"\n 订单撤销\n :param kwargs:\n Mandatory params:\n mch_order_no、ksher_order_no、channel_order_no三选一\n Optional Params:\n operator_id\n :return:\n \"\"\"\n kwargs.update({'appid': self.appid, 'nonce_str': self.__nonce_str, 'time_stamp': self.__time_stamp})\n response = self._request(url='{}/order_reverse'.format(self.__DOMAIN), data=kwargs)\n return response\n\n def order_refund(self, **kwargs):\n \"\"\"\n 订单退款\n :param kwargs:\n Mandatory params:\n total_fee\n fee_type\n refund_fee\n mch_refund_no\n mch_order_no、ksher_order_no、channel_order_no三选一\n Optional Params:\n operator_id\n :return:\n \"\"\"\n kwargs.update({'appid': self.appid, 'nonce_str': self.__nonce_str, 'time_stamp': self.__time_stamp})\n response = self._request(url='{}/order_refund'.format(self.__DOMAIN), data=kwargs)\n return response\n\n def refund_query(self, **kwargs):\n \"\"\"\n 退款查询\n :param kwargs:\n Mandatory params:\n mch_refund_no、ksher_refund_no、channel_refund_no三选一\n mch_order_no、ksher_order_no、channel_order_no三选一\n :return:\n \"\"\"\n kwargs.update({'appid': self.appid, 'nonce_str': self.__nonce_str, 'time_stamp': self.__time_stamp})\n response = self._request(url='{}/refund_query'.format(self.__DOMAIN), data=kwargs, m=\"GET\")\n return response\n\n def rate_query(self, **kwargs):\n \"\"\"\n 汇率查询\n :param kwargs:\n Mandatory params:\n channel\n fee_type\n date\n :return:\n \"\"\"\n kwargs.update({'appid': self.appid, 'nonce_str': self.__nonce_str, 'time_stamp': self.__time_stamp})\n response = self._request(url='{}/rate_query'.format(self.__DOMAIN), data=kwargs,m=\"GET\")\n return response\n\n def gateway_order_query(self, **kwargs):\n \"\"\"\n 聚合支付商户查询订单支付状态\n :param kwargs:\n Mandatory params:\n mch_order_no\n :return:\n \"\"\"\n kwargs.update({'appid': self.appid, 'nonce_str': self.__nonce_str, 'time_stamp': self.__time_stamp})\n response = self._request(url='{}/gateway_order_query'.format(self.__GATEWAY_DOMAIN), data=kwargs, m=\"GET\")\n return response\n\n def gateway_pay(self, **kwargs):\n \"\"\"\n Gateway Pay API params\n 聚合支付商户通过API提交数据\n :param kwargs:\n mandatory parmas:\n \n mch_order_no: 商户订单号 str\n total_fee: 金额(分) int\n fee_type: 货币种类 st\n channel_list: 支付通道 str\n mch_code: 商户订单code str\n mch_redirect_url: 商户通知url str\n mch_redirect_url_fail: 失败回调网址 str\n product_name: 商品描述 str\n refer_url: 商家refer str\n device: 设备名称(PC or H5) str\n \n Optional params:\n \n color: 横幅颜色 str\n background: 横幅背景图片 str\n payment_color: 支付按钮颜色 str\n ksher_explain: 最下方文案 str\n hide_explain: 是否显示最下方文案(1显示 0不显示) int\n expire_time: 订单过期时间(min) int\n hide_exp_time: 是否显示过期时间(1显示 0不显示) int\n logo: 横幅logo str\n lang: 语言(en,cn,th) str\n shop_name: logo旁文案 str\n attach: 商户附加信息 str\n :return:\n {'pay_content': 'https://gateway.ksher.com/mindex?order_uuid=订单uuid'}\n \"\"\"\n kwargs.update({'appid': self.appid, 'nonce_str': self.__nonce_str, 'time_stamp': self.__time_stamp})\n response = self._request(url='{}/gateway_pay'.format(self.__GATEWAY_DOMAIN), data=kwargs, m=\"POST\")\n return response\n\n def merchant_info(self):\n \"\"\"\n merchant_info\n :param kwargs:\n Mandatory params: -\n :return:\n {\n\t \"code\": 0,\n\t \"status_code\": \"\",\n\t \"status_msg\": \"\",\n\t \"sign\": \"6d85861b1c69373ab97f1d1a96a1a0495307061759085f13fdcf2d933fe9ed7651e2c33d9923ee252942b83831d521eef11369fe2ae455ef0e11acd40c0473c7\",\n\t \"version\": \"2.0.0\",\n\t \"msg\": \"ok\",\n\t \"time_stamp\": \"2020-09-11T11:07:19.969854+08:00\",\n\t \"data\": {\n\t \t\"nonce_str\": \"V8ULba969xAu07hSkmivC0B67HLXeNEB\",\n\t \t\"country_support_channel\": [\"airpay\", \"alipay\", \"promptpay\", \"card\", \"linepay\", \"truemoney\", \"wechat\"], // 国家支持的通道\n\t \t\"mch_appid\": \"mch28811\",\n\t \t\"business_mode\": \"offline\", // 线上online 线下 offline\n\t \t\"mch_support_channel\": [{\n\t \t\t\"day_sum_limit\": -1, // 当日限额 -1 表示不限额 其他表示限额金额\n\t \t\t\"pay_channel\": \"alipay\", //通道\n\t \t\t\"settlement_time_type\": \"T+1\", // 结算组\n\t \t\t\"single_order_limit\": -1 // 单笔限额 -1 表示不限额\n\t \t}, {\n\t \t\t\"day_sum_limit\": -1,\n\t \t\t\"pay_channel\": \"linepay\",\n\t \t\t\"settlement_time_type\": \"T+2\",\n\t \t\t\"single_order_limit\": -1\n\t \t}, {\n\t \t\t\"day_sum_limit\": -1,\n\t \t\t\"pay_channel\": \"airpay\",\n\t \t\t\"settlement_time_type\": \"T+2\",\n\t \t\t\"single_order_limit\": -1\n\t \t}, {\n\t \t\t\"day_sum_limit\": -1,\n\t \t\t\"pay_channel\": \"wechat\",\n\t \t\t\"settlement_time_type\": \"T+1\",\n\t \t\t\"single_order_limit\": -1\n\t \t}],\n\t \t\"mch_id\": \"28811\",\n\t \t\"allow_remote_pay\": \"Y\", // 是否支持远程收款 Y支持 N不支持\n\t \t\"allow_partial_refund\": \"N\", // 是否支持部分退款 Y支持 N不支持\n\t \t\"master_mch_id\": \"\",\n\t \t\"bank_info\": {\n\t \t\t\"swift_code\": \"BKKBTHBK\", // 开户行的全球银行编码\n\t \t\t\"account_name\": \"hello word\", // 银行户名\n\t \t\t\"bank_name\": \"004,KASIKORNBANK PUBLIC COMPANY LIMITED\", //开户行\n\t \t\t\"account_id\": \"86656664448\" // 银行账号\n\t \t},\n\t \t\"logo_url\": \"\", // 商户logo\n\t \t\"account_type\": \"Formal\", // Formal 正式商户 Test 测试商户\n\t \t\"country_info\": {\n\t \t\t\"country_name\": \"Thailand\", // 国家名称\n\t \t\t\"phone_code\": \"+66\" // 国家区号\n\t \t},\n\t \t\"mch_notify_url\": \"\", // 商户通知URL\n\t \t\"is_master_mch\": \"N\", // 是否是父商户 Y是 N不是\n\t \t\"scan_type\": \"2\",\n\t \t\"wht\": 0, // ith holding tax计算\n\t \t\"bd_info\": {\n\t \t\t\"mobile\": \"13031028803\",\n\t \t\t\"bd_name\": \"vicky\",\n\t \t\t\"country_id\": \"2\",\n\t \t\t\"bd_id\": \"213\",\n\t \t\t\"add_time\": \"\"\n\t \t},\n\t \t\"has_open_use_coupon\": \"N\", // 是否开通优惠券核销 Y开通 N不开通\n\t \t\"price_fee_type\": \"THB\", // 标价币种\n\t \t\"rigist_time\": \"2020-04-01 17:54:14\",\n\t \t\"settlement_fee_type\": \"THB\", //结算币种\n\t \t\"agency_info\": {\n\t \t\t\"mobile\": \"136101101101\",\n\t \t\t\"agency_name\": \"54321@ksher.net\",\n\t \t\t\"email\": \"vicky@ksher.net\",\n\t \t\t\"agency_id\": \"10119\",\n\t \t\t\"regist_time\": \"2019-10-11 10:39:11\"\n\t \t},\n\t \t\"mobile\": \"shhhh123456\", // 商户手机号\n\t \t\"time_zone\": \"+07:00\", // 时区\n\t \t\"mch_name\": {\n\t \t\t\"mch_brief_name\": \"uvuvuvt++1\", // 商户简称\n\t \t\t\"merchant_name\": \"vyvycyct++1\" // 商户名称\n\t \t},\n\t \t\"contact\": \"dhhhshyd\", // 商户联系人名称\n\t \t\"operator_list\": [{\n\t \t\t\"operator_id\": \"13994\",\n\t \t\t\"mch_id\": \"28811\",\n\t \t\t\"state\": \"1\",\n\t \t\t\"operator\": \"dhhhshyd\",\n\t \t\t\"rule_id\": \"0\", // 0 表示老板 1 表示普通收银员\n\t \t\t\"add_time\": \"2020-08-13 16:43:54\"\n\t \t}]\n\t }\n }\n \"\"\"\n kwargs={'appid': self.appid, 'nonce_str': self.__nonce_str, 'time_stamp': self.__time_stamp}\n response = self._request(url='{}/merchant_info'.format(self.__DOMAIN), data=kwargs)\n return response\n\n def settlement_info(self, **kwargs):\n \"\"\"\n settlement_info\n :param kwargs:\n Mandatory params:\n begin_date\n end_date\n Optional params: \n pay_channel\n :return:\n {\n \"code\": 0,\n \"data\": {\n \"begin_date\": \"2020-07-01\",\n \"end_date\": \"2020-09-10\",\n \"nonce_str\": \"1At1iDK27LQxVnIvSwzqjlzgEZCsXTsj\",\n \"settlement_data\": [\n {\n \"begin_date\": \"2020-08-18\",\n \"commission_fee\": 0,\n \"end_date\": \"2020-08-18\",\n \"pay_channel\": \"wechat\",\n \"settlement_amount\": 10780,\n \"settlement_date\": \"2020-08-19\",\n \"transfer_amount\": 0,\n \"transfer_date\": \"\",\n \"vat\": 0\n },\n {\n \"begin_date\": \"2020-08-19\",\n \"commission_fee\": 0,\n \"end_date\": \"2020-08-19\",\n \"pay_channel\": \"wechat\",\n \"settlement_amount\": 2940,\n \"settlement_date\": \"2020-08-20\",\n \"transfer_amount\": 0,\n \"transfer_date\": \"\",\n \"vat\": 0\n },\n {\n \"begin_date\": \"2020-08-20\",\n \"commission_fee\": -57,\n \"end_date\": \"2020-08-20\",\n \"pay_channel\": \"wechat\",\n \"settlement_amount\": -2939,\n \"settlement_date\": \"2020-08-21\",\n \"transfer_amount\": 0,\n \"transfer_date\": \"\",\n \"vat\": -4\n }\n ]\n },\n \"msg\": \"ok\",\n \"sign\": \"17b62a22612f5639cfb584c437d7f70d3f9da6e2a0bdcb12334ccda34eb3cde2f7d781b0352474ab689c2d9b16a7aa78bef64afbb85110fd47f468e2be091eab\",\n \"status_code\": \"\",\n \"status_msg\": \"\",\n \"time_stamp\": \"2020-09-16T11:08:31.484616+08:00\",\n \"version\": \"2.0.0\"\n }\n \"\"\"\n kwargs.update({'mch_appid': self.appid, 'nonce_str': self.__nonce_str, 'time_stamp': self.__time_stamp})\n response = self._request(url='{}/get_settlement_info'.format(self.__DOMAIN), data=kwargs, m=\"GET\")\n return response\n\n def get_payout_balance(self, **kwargs):\n \"\"\"\n get_payout_balance\n :param kwargs:\n Mandatory params: \n \"fee_type\": \"THB\"\n :return:\n {\n \"code\": 0,\n \"data\": {\n \"appid\": \"mch29989\",\n \"balance\": 15880,\n \"nonce_str\": \"7cfd8667342f855a22ebdeafef394cb1\",\n \"result\": \"SUCCESS\"\n },\n \"msg\": \"ok\",\n \"sign\": \"7472ba0f983329fb6f3aee10624f6a19ede9ed28ed20289d8b0ad49cd95d1777dbdfafba18220883a6de823cd759db491fd49993d208db6429e666c50e4be054\",\n \"status_code\": \"\",\n \"status_msg\": \"\",\n \"time_stamp\": \"2022-01-12T19:16:45.920757+08:00\",\n \"version\": \"3.0.0\"\n }\n \"\"\"\n kwargs.update({'appid': self.appid, 'nonce_str': self.__nonce_str, 'time_stamp': self.__time_stamp})\n response = self._request(url='{}/get_payout_balance'.format(self.__DOMAIN), data=kwargs, m=\"GET\")\n return response\n\n def payout(self, **kwargs):\n \"\"\"\n payout\n :param kwargs:\n Mandatory params: \n \"fee_type\": \"THB\"\n :return:\n {\n \"code\": 0,\n \"data\": {\n \"appid\": \"mch29989\",\n \"channel_order_no\": \"2022011216521223001652907\",\n \"ksher_order_no\": \"80020220112175206651966\",\n \"mch_order_no\": \"20210110121104\",\n \"mdr\": 260,\n \"nonce_str\": \"7cfd8667342f855a22ebdeafef394cb1\",\n \"receiver_name\": \"MISS TEST_P0170109 P0170109\",\n \"result\": \"SUCCESS\",\n \"total_fee\": 100,\n \"trade_type\": \"PAYOUT\"\n },\n \"msg\": \"ok\",\n \"sign\": \"24983cb648731139e166fca476015adc6ec5c2550ee5f873ddaafc90a68cc5d5628f6808f01939b00b615ac9b5121527e13abd003b0d8bfb6310517fb28bd4ef\",\n \"status_code\": \"\",\n \"status_msg\": \"\",\n \"time_stamp\": \"2022-01-12T17:52:13.001998+08:00\",\n \"version\": \"3.0.0\"\n }\n \"\"\"\n kwargs.update({'appid': self.appid, 'nonce_str': self.__nonce_str, 'time_stamp': self.__time_stamp})\n response = self._request(url='{}/payout'.format(self.__DOMAIN), data=kwargs, m=\"POST\")\n return response\n\n def order_query_payout(self, **kwargs):\n \"\"\"\n order_query_payout\n :param kwargs:\n Mandatory params: \n \"channel\"\n \"mch_order_no\"\n :return:\n {\n \"code\": 0,\n \"data\": {\n \"appid\": \"mch29989\",\n \"attach\": \"test BANK\",\n \"cash_fee\": 100,\n \"cash_fee_type\": \"THB\",\n \"channel\": \"payout\",\n \"channel_order_no\": \"2022011216521223001652907\",\n \"device_id\": \"\",\n \"fee_type\": \"THB\",\n \"ksher_order_no\": \"80020220112175206651966\",\n \"mch_order_no\": \"20210110121104\",\n \"mdr\": 260,\n \"nonce_str\": \"7cfd8667342f855a22ebdeafef394cb1\",\n \"openid\": \"\",\n \"operation\": \"ORDER-QUERY\",\n \"operator_id\": 15030,\n \"rate\": \"1.000000\",\n \"raw_total_fee\": 100,\n \"result\": \"SUCCESS\",\n \"time_end\": \"2022-01-12 16:52:06\",\n \"total_fee\": 100\n },\n \"msg\": \"ok\",\n \"sign\": \"6c9ce516a981275cb4f2934fafeeaf6e66ba678acc6df39328ee880035234cfa7b25a82c0c28e1b0ca016cbead243c1ca22d360d43687e3d0f5597b32f8a377f\",\n \"status_code\": \"\",\n \"status_msg\": \"\",\n \"time_stamp\": \"2022-01-12T18:44:18.769151+08:00\",\n \"version\": \"3.0.0\"\n }\n \"\"\"\n kwargs.update({'appid': self.appid, 'nonce_str': self.__nonce_str, 'time_stamp': self.__time_stamp})\n response = self._request(url='{}/order_query_payout'.format(self.__DOMAIN), data=kwargs, m=\"GET\")\n return response","repo_name":"ksher-api/ksher-sdk","sub_path":"python/ksher_pay_sdk.py","file_name":"ksher_pay_sdk.py","file_ext":"py","file_size_in_byte":24077,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"41393514066","text":"import wx\nfrom coinpy.tools.observer import Observable\nfrom coinpy_client.view.guithread import guithread\n\nclass ReceiveView(wx.Dialog, Observable):\n EVT_SET_LABEL= Observable.createevent()\n \n def __init__(self, parent, size=(300, 200)):\n wx.Dialog.__init__(self, parent, size=size, title=\"Receive\", style=wx.DEFAULT_DIALOG_STYLE|wx.RESIZE_BORDER)\n Observable.__init__(self)\n\n # Create Controls\n self.address_label = wx.StaticText(self, -1, \"Address:\")\n self.address_textctrl = wx.TextCtrl(self, -1, \"\", size=(250,-1), style=wx.TE_READONLY|wx.BORDER_SIMPLE)\n self.address_textctrl.SetBackgroundColour((196,196,196))\n self.label_label = wx.StaticText(self, -1, \"Label:\")\n self.label_textctrl = wx.TextCtrl(self, -1, \"\", size=(80,-1))\n\n # Setup Sizers\n sizer = wx.BoxSizer(wx.VERTICAL)\n formsizer = wx.FlexGridSizer(2, 2)\n formsizer.Add(self.address_label, 0, wx.LEFT|wx.ALL, 5)\n formsizer.Add(self.address_textctrl, 1, wx.LEFT|wx.ALL|wx.EXPAND, 5)\n formsizer.Add(self.label_label, 0, wx.LEFT|wx.ALL, 5)\n formsizer.Add(self.label_textctrl, 1, wx.LEFT|wx.ALL|wx.EXPAND, 5)\n formsizer.AddGrowableCol(1)\n \n sizer.Add(formsizer, 1, wx.EXPAND|wx.TOP|wx.BOTTOM, 20)\n btnsizer = wx.StdDialogButtonSizer()\n ok_button = wx.Button(self, wx.ID_OK)\n ok_button.SetDefault()\n btnsizer.AddButton(ok_button)\n\n cancel_button = wx.Button(self, wx.ID_CANCEL)\n btnsizer.AddButton(cancel_button)\n btnsizer.Realize()\n\n sizer.Add(btnsizer, 0, wx.ALIGN_RIGHT|wx.ALL, 5)\n self.SetSizer(sizer)\n \n # Bind Events\n ok_button.Bind(wx.EVT_BUTTON, self.on_ok)\n \n @guithread \n def open(self):\n self.Show(True)\n\n def on_ok(self, event):\n self.Show(False)\n self.fire(self.EVT_SET_LABEL)\n\n def on_cancel(self):\n self.Show(False)\n \n def set_receive_address(self, address):\n self.address_textctrl.SetValue(address)\n \n def get_receive_address(self):\n return self.address_textctrl.GetValue()\n \n def get_label(self):\n return self.label_textctrl.GetValue()\n \n \nif __name__ == '__main__':\n from coinpy.tools.reactor.reactor import Reactor\n app = wx.App(False)\n r = ReceiveView(None)\n r.set_receive_address(\"mhFwRrjRNt8hYeWtm9LwqCpCgXjF38RJqn\")\n r.open()\n app.MainLoop()\n\n","repo_name":"sirk390/coinpy","sub_path":"coinpy-client/src/coinpy_client/view/wallet/receive_view.py","file_name":"receive_view.py","file_ext":"py","file_size_in_byte":2463,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"52"} +{"seq_id":"13093379835","text":"from odoo import _, api, fields, models\nfrom odoo.exceptions import UserError, ValidationError\nimport logging\n\n_logger = logging.getLogger(__name__)\n\n\nclass ProductTempExt(models.Model):\n _inherit = 'product.template'\n\n is_sweep_product = fields.Boolean(string=\"Is a Sweep Product\")\n sweep_account_id = fields.Many2one('account.account')\n\n\nclass AccountInvoiceExt(models.Model):\n _inherit = 'account.invoice'\n\n def cron_sweep_entries(self):\n logging.info(\"Hamza Ilyas ----> cron_sweep_entries called\")\n invoices = self.env['account.invoice'].search([('type', '=', 'out_invoice'), ('state', 'in', ('paid', 'open'))])\n for invoice in invoices:\n logging.info(\"Hamza Ilyas ----> <<<<<<<<<>>>>>>>>>\")\n if invoice.move_id and invoice.invoice_line_ids:\n\n logging.info(\"Hamza Ilyas ----> <<<<<<<<<< Invoice is open or paid and have lines >>>>>>>>>>\")\n for invoice_line in invoice.invoice_line_ids:\n if not invoice_line.swept:\n logging.info(\"Hamza Ilyas ----> <<<<<<<<<< Invoice line is not swept >>>>>>>>>>\")\n expense = self.check_ili_in_expenses(invoice_line)\n if expense:\n self.swept_journal_entry(expense)\n invoice_line.swept = True\n continue\n\n receipt_line = self.check_ili_in_purchase_receipts(invoice_line)\n if receipt_line:\n self.swept_journal_entry(receipt_line)\n invoice_line.swept = True\n continue\n\n bill_line = self.check_ili_in_vendor_bills(invoice_line)\n if bill_line:\n self.swept_journal_entry(bill_line)\n invoice_line.swept = True\n continue\n\n def swept_journal_entry(self, record):\n logging.info(\"Hamza Ilyas ----> swept_journal_entry Called\")\n journal = self.env['sweep.journal'].search([])\n sweep_journal = False\n if journal:\n sweep_journal = journal.journal_id\n if record._name == 'hr.expense':\n logging.info(\"Hamza Ilyas ----> Expense Model\")\n self.create_swept_journal_entry(record.name, False, False, sweep_journal, record.total_amount, record.product_id,\n record.analytic_account_id)\n elif record._name == 'account.invoice.line':\n logging.info(\"Hamza Ilyas ----> Vendor Bill Line Model\")\n self.create_swept_journal_entry(record.invoice_id.number, record, False, sweep_journal, record.price_unit,\n record.product_id, record.account_analytic_id)\n elif record._name == 'account.voucher.line':\n logging.info(\"Hamza Ilyas ----> Purchase Receipt Line Model\")\n self.create_swept_journal_entry(record.voucher_id.number, False, record, sweep_journal, record.price_unit,\n record.product_id, record.account_analytic_id)\n\n def create_swept_journal_entry(self, je_ref, vendor_bill_line, voucher_line, je_journal, ji_amount, ji_product, analytic_account):\n logging.info(\"Hamza Ilyas ----> create_swept_journal_entry Called\")\n logging.info(\"Hamza Ilyas ----> <<<<<<<<>>>>>>>>\")\n logging.info(ji_amount)\n if ji_product.property_account_expense_id:\n # create move\n if vendor_bill_line:\n vendor_bill_line = vendor_bill_line.id\n if voucher_line:\n voucher_line = voucher_line.id\n move = self.env['account.move'].create({'ref': je_ref, 'journal_id': je_journal.id, 'state': 'draft',\n 'is_a_sweep_je': True, 'vendor_bill_line_id': vendor_bill_line,\n 'voucher_line_id': voucher_line})\n move.write({'line_ids': [\n (0, 0, {'account_id': ji_product.property_account_expense_id.id,\n 'name': ji_product.name,\n 'debit': ji_amount,\n 'credit': 0.0,\n 'move_id': move.id,\n 'analytic_account_id': analytic_account.id,\n 'swept': True,\n }),\n (0, 0, {'account_id': ji_product.sweep_account_id.id,\n 'name': ji_product.name,\n 'debit': 0.0,\n 'credit': ji_amount,\n 'move_id': move.id,\n 'analytic_account_id': analytic_account.id,\n 'swept': True,\n }),\n ], })\n if move:\n move.post()\n logging.info(\"Hamza Ilyas ----> Swept Journal Entry Created & posted Successfully\")\n\n def check_ili_in_expenses(self, invoice_line):\n logging.info(\"Hamza Ilyas ----> check_ili_in_expenses Called\")\n expenses = self.env['hr.expense'].search([('state', 'in', ('done', 'approve', 'post'))])\n for expense in expenses:\n if expense.product_id == invoice_line.product_id and \\\n expense.analytic_account_id == invoice_line.account_analytic_id and not expense.swept:\n expense.swept = True\n expense.invoice_line_id = invoice_line\n return expense\n else:\n continue\n\n def check_ili_in_purchase_receipts(self, invoice_line):\n logging.info(\"Hamza Ilyas ----> check_ili_in_purchase_receipts Called\")\n purchase_receipts = self.env['account.voucher'].search([('state', '=', 'posted')])\n for receipt in purchase_receipts:\n for receipt_line in receipt.line_ids:\n if receipt_line.product_id == invoice_line.product_id and \\\n receipt_line.account_analytic_id == invoice_line.account_analytic_id and not receipt_line.swept:\n receipt_line.swept = True\n receipt_line.invoice_line_id = invoice_line.id\n return receipt_line\n else:\n continue\n\n def check_ili_in_vendor_bills(self, invoice_line):\n logging.info(\"Hamza Ilyas ----> check_ili_in_vendor_bills Called\")\n vendor_bills = self.env['account.invoice'].search([('state', 'in', ('paid', 'open')), ('type', '=', 'in_invoice')])\n for bill in vendor_bills:\n for bill_line in bill.invoice_line_ids:\n if bill_line.product_id == invoice_line.product_id and \\\n bill_line.account_analytic_id == invoice_line.account_analytic_id and not bill_line.swept:\n bill_line.swept = True\n bill_line.invoice_line_id = invoice_line.id\n return bill_line\n else:\n continue\n\n\nclass AccountInvoiceLineExt(models.Model):\n _inherit = 'account.invoice.line'\n\n swept = fields.Boolean(\"Swept\")\n\n invoice_line_id = fields.Many2one(\"account.invoice.line\", \"Invoice Line\")\n\n line_type = fields.Selection([\n ('out_invoice', 'Customer Invoice'),\n ('in_invoice', 'Vendor Bill'),\n ('out_refund', 'Customer Refund'),\n ('in_refund', 'Vendor Refund')], invisible=True, related='invoice_id.type')\n\n @api.onchange('product_id')\n def onchange_product_id(self):\n logging.info(\"Hamza Ilyas ----> onchange_product_id Called on account invoice line\")\n if self.invoice_id.type == 'in_invoice':\n if self.product_id:\n if self.product_id.is_sweep_product:\n self.account_id = self.product_id.sweep_account_id\n\n\nclass HrExpenseExt(models.Model):\n _inherit = 'hr.expense'\n\n swept = fields.Boolean(string=\"Swept\", readonly=True)\n invoice_line_id = fields.Many2one(\"account.invoice.line\", \"Invoice Line\")\n\n @api.onchange('product_id')\n def onchange_product_id(self):\n logging.info(\"Hamza Ilyas ----> onchange_product_id Called on hr.expense\")\n if self.product_id:\n if self.product_id.is_sweep_product:\n self.account_id = self.product_id.sweep_account_id\n\n\nclass AccountVoucherExt(models.Model):\n _inherit = 'account.voucher.line'\n\n swept = fields.Boolean(string=\"Swept\", readonly=True)\n invoice_line_id = fields.Many2one(\"account.invoice.line\", \"Invoice Line\")\n\n\n # @api.model\n # def create(self, vals):\n # if 'product_id' in vals:\n # if vals['product_id']:\n # product = self.env['product.product'].search([('id', '=', vals['product_id'])])\n # if product:\n # if product.is_sweep_product and product.sweep_account_id:\n # vals['account_id'] = product.sweep_account_id.id\n #\n # rec = super(AccountVoucherExt, self).create(vals)\n # return rec\n\n #@api.multi\n # def write(self, vals):\n # return super(AccountVoucherExt, self).write(vals)\n\n @api.onchange('product_id')\n def onchange_product_id(self):\n logging.info(\"Hamza Ilyas ----> onchange_product_id Called on account voucher line\")\n if self.product_id:\n temp = self.product_id\n self.product_id = self.env['product.product'].search([])[1]\n self.product_id = temp\n if self.product_id.is_sweep_product:\n logging.info(\"Hamza Ilyas ----> is_sweep_product\")\n self.account_id = self.product_id.sweep_account_id.id\n self.write({\n 'account_id': self.product_id.sweep_account_id.id\n })\n self.account_id = self.product_id.sweep_account_id.id\n logging.info(\"Hamza Ilyas ----> account_id set as :\")\n logging.info(self.account_id.name)\n # self.check_account_id(self.product_id, self.account_id)\n\n def check_account_id(self, product_id, account_id):\n logging.info(\"Hamza Ilyas ----> check_account_id Called\")\n if product_id.is_sweep_product and product_id.sweep_account_id:\n logging.info(\"11\")\n if product_id.sweep_account_id.id != account_id.id:\n logging.info(\"22\")\n self.onchange_product_id()\n logging.info(\"Hamza Ilyas ----> finish check_account_id\")\n\n\nclass SweepJournal(models.Model):\n _name = 'sweep.journal'\n\n journal_id = fields.Many2one('account.journal', string='Journal')\n\n\nclass AccountMoveLineExt(models.Model):\n _inherit = 'account.move.line'\n\n swept = fields.Boolean(string=\"Swept\", readonly=True)\n\n\nclass AccountMoveExt(models.Model):\n _inherit = 'account.move'\n\n is_a_sweep_je = fields.Boolean()\n voucher_line_id = fields.Many2one('account.voucher.line', \"Voucher Line\")\n vendor_bill_line_id = fields.Many2one('account.invoice.line', \"Vendor Bill Line\")\n\n @api.multi\n def button_cancel(self):\n logging.info(\"Hamza Ilyas ----> button_cancel Called\")\n for move in self:\n if not move.journal_id.update_posted:\n raise UserError(\n _('You cannot modify a posted entry of this journal.\\nFirst you should set the journal to allow cancelling entries.'))\n else:\n if move.is_a_sweep_je:\n if move.ref:\n bill = self.env['account.invoice'].search([('number', '=', move.ref)])\n if bill:\n self.unswept_bill_lines(move)\n else:\n receipt = self.env['account.voucher'].search([('number', '=', move.ref)])\n if receipt:\n self.unswept_voucher_lines(move)\n else:\n expense = self.env['hr.expense'].search([('name', '=', move.ref)])\n if expense:\n self.unswept_expense(expense)\n # for line in move.line_ids:\n # line.swept = False\n\n if self.ids:\n self.check_access_rights('write')\n self.check_access_rule('write')\n self._check_lock_date()\n self._cr.execute('UPDATE account_move ' \\\n 'SET state=%s ' \\\n 'WHERE id IN %s', ('draft', tuple(self.ids),))\n self.invalidate_cache()\n self._check_lock_date()\n\n # on cancelling of sweep journal entry it will be removed\n if self.is_a_sweep_je:\n logging.info(\"Hamza Ilyas ----> unlinking swept journal entry\")\n self.unlink()\n return True\n\n def unswept_bill_lines(self, move):\n # for line in bill.invoice_line_ids:\n # for move_line in move.line_ids:\n # if move_line.product_id.name == line.name and move_line.analytical_account_id == line.account_analytic_id:\n line = move.vendor_bill_line_id\n line.swept = False\n line.invoice_line_id.swept = False\n line.invoice_id.swept = False\n\n def unswept_voucher_lines(self, move):\n # for line in recipt.line_ids:\n # for move_line in move.line_ids:\n # if move_line.name == line.name and move_line.analytical_account_id == line.account_analytic_id:\n line = move.voucher_line_id\n line.swept = False\n line.invoice_line_id.swept = False\n line.voucher_id.swept = False\n\n def unswept_expense(self, expense):\n expense.swept = False\n expense.invoice_line_id.swept = False\n","repo_name":"HamzaIlyas/sweep_v10","sub_path":"sweep/models/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":13920,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"1066424980","text":"import requests\nimport json\nfrom fastapi.responses import JSONResponse\nfrom fastapi import Depends\nfrom schemas import schemas\n# from fastapi.security import \n\n# oauth2_scheme=O\n\nauth_token='eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1c2VkSUQiOiJubkBleGFtcGxlLmNvbSIsImV4cGlyeSI6MTY2NTk2MDMwMi4yNjE5NjkzfQ.jyX5N9_vLrJEA4sXr5m-96nBOhWrcCE4Ut-Pw7MvqIw'\nhed = {'Content-type': 'application/json','Authorization': 'Bearer ' + auth_token}\n\ndef order_dispatched(url:str):\n data= schemas.Order()\n r = requests.post(url, json=json.loads(data.json()), headers=hed)\n return JSONResponse(content=None, status_code=200) if r.status_code==200 else None\n\n\ndef order_cancelled(url:str):\n data=schemas.Order_canceller()\n r = requests.post(url, json=json.loads(data.json()), headers=hed)\n return JSONResponse(content=None, status_code=200) if r.status_code==200 else None\n\nurl = \"https://webhook.site/88d825cd-ee99-4806-b67e-6a14a4021018\"\nprint(order_dispatched(url))\nprint(order_cancelled(url))\n# https://webhook.site/88d825cd-ee99-4806-b67e-6a14a4021018","repo_name":"NZere/webhook","sub_path":"webhook.py","file_name":"webhook.py","file_ext":"py","file_size_in_byte":1041,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"38776709061","text":"from collections import namedtuple\nimport inspect\nimport random\n\nimport libcst as cst\n\nimport pytest\n\nfrom ast_tools.common import exec_def_in_file\nfrom ast_tools.passes.ssa import ssa\nfrom ast_tools.passes import apply_passes, debug\nfrom ast_tools.stack import SymbolTable\n\n\n\nNTEST = 16\n\nbasic_template = '''\\\ndef basic(x):\n if x:\n {} 0\n else:\n {} 2\n {}\n'''\n\ntemplate_options = ['r =', 'return']\n\ndef _do_ssa(func, strict, **kwargs):\n for dec in (\n begin_rewrite(),\n debug(**kwargs),\n ssa(strict),\n debug(**kwargs),\n end_rewrite()):\n func = dec(func)\n return func\n\n\n@pytest.mark.parametrize('strict', [True, False])\n@pytest.mark.parametrize('a', template_options)\n@pytest.mark.parametrize('b', template_options)\ndef test_basic_if(strict, a, b):\n if a == b == 'return':\n final = ''\n else:\n final = 'return r'\n\n src = basic_template.format(a, b, final)\n tree = cst.parse_statement(src)\n env = SymbolTable({}, {})\n basic = exec_def_in_file(tree, env)\n ssa_basic = apply_passes([ssa(strict)])(basic)\n\n for x in (False, True):\n assert basic(x) == ssa_basic(x)\n\n\nnested_template = '''\\\ndef nested(x, y):\n if x:\n if y:\n {} 0\n else:\n {} 1\n else:\n if y:\n {} 2\n else:\n {} 3\n {}\n'''\n\n@pytest.mark.parametrize('strict', [True, False])\n@pytest.mark.parametrize('a', template_options)\n@pytest.mark.parametrize('b', template_options)\n@pytest.mark.parametrize('c', template_options)\n@pytest.mark.parametrize('d', template_options)\ndef test_nested(strict, a, b, c, d):\n if a == b == c == d == 'return':\n final = ''\n else:\n final = 'return r'\n\n src = nested_template.format(a, b, c, d, final)\n tree = cst.parse_statement(src)\n env = SymbolTable({}, {})\n nested = exec_def_in_file(tree, env)\n ssa_nested = apply_passes([ssa(strict)])(nested)\n\n for x in (False, True):\n for y in (False, True):\n assert nested(x, y) == ssa_nested(x, y)\n\nimbalanced_template = '''\\\ndef imbalanced(x, y):\n {} -1\n if x:\n {} -2\n if y:\n {} 0\n else:\n {} 1\n return r\n'''\n\ninit_template_options = ['r = ', '0']\n\n@pytest.mark.parametrize('strict', [True, False])\n@pytest.mark.parametrize('a', init_template_options)\n@pytest.mark.parametrize('b', init_template_options)\n@pytest.mark.parametrize('c', template_options)\n@pytest.mark.parametrize('d', template_options)\ndef test_imbalanced(strict, a, b, c, d):\n src = imbalanced_template.format(a, b, c, d)\n tree = cst.parse_statement(src)\n env = SymbolTable({}, {})\n imbalanced = exec_def_in_file(tree, env)\n can_name_error = False\n for x in (False, True):\n for y in (False, True):\n try:\n imbalanced(x, y)\n except NameError:\n can_name_error = True\n break\n\n if can_name_error and strict:\n with pytest.raises(SyntaxError):\n ssa_imbalanced = apply_passes([ssa(strict)])(imbalanced)\n else:\n ssa_imbalanced = apply_passes([ssa(strict)])(imbalanced)\n for x in (False, True):\n for y in (False, True):\n try:\n assert imbalanced(x, y) == ssa_imbalanced(x, y)\n except NameError:\n assert can_name_error\n\n\n\ndef test_reassign_arg():\n def bar(x):\n return x\n\n @apply_passes([ssa()], metadata_attr='metadata')\n def foo(a, b):\n if b:\n a = len(a)\n return a\n assert inspect.getsource(foo) == '''\\\ndef foo(a, b):\n _cond_0 = b\n a_0 = len(a)\n a_1 = a_0 if _cond_0 else a\n __0_return_0 = a_1\n return __0_return_0\n'''\n symbol_tables = foo.metadata['SYMBOL-TABLE']\n assert len(symbol_tables) == 1\n assert symbol_tables[0][0] == ssa\n symbol_table = symbol_tables[0][1]\n assert symbol_table == {\n 2: {\n 'a': 'a',\n 'b': 'b',\n },\n 3: {\n 'a': 'a',\n 'b': 'b',\n },\n 4: {\n 'a': 'a_0',\n 'b': 'b',\n },\n 5: {\n 'a': 'a_1',\n 'b': 'b',\n },\n }\n\n\ndef test_double_nested_function_call():\n def bar(x):\n return x\n\n def baz(x):\n return x + 1\n\n @apply_passes([ssa()], metadata_attr='metadata') # 1\n def foo(a, b, c): # 2\n if b: # 3\n a = bar(a) # 4\n else: # 5\n a = bar(a) # 6\n if c: # 7\n b = bar(b) # 8\n else: # 9\n b = bar(b) # 10\n return a, b # 11\n assert inspect.getsource(foo) == '''\\\ndef foo(a, b, c): # 2\n _cond_0 = b\n a_0 = bar(a) # 4\n a_1 = bar(a) # 6\n a_2 = a_0 if _cond_0 else a_1\n _cond_1 = c\n b_0 = bar(b) # 8\n b_1 = bar(b) # 10\n b_2 = b_0 if _cond_1 else b_1\n __0_return_0 = a_2, b_2 # 11\n return __0_return_0\n'''\n\n symbol_tables = foo.metadata['SYMBOL-TABLE']\n assert len(symbol_tables) == 1\n assert symbol_tables[0][0] == ssa\n symbol_table = symbol_tables[0][1]\n gold_table = {i: {\n 'a': 'a' if i < 4 else 'a_0' if i < 6 else 'a_1' if i < 7 else 'a_2',\n 'b': 'b' if i < 8 else 'b_0' if i < 10 else 'b_1' if i < 11 else 'b_2',\n 'c': 'c',\n } for i in range(2, 12)}\n assert symbol_table == gold_table\n\nclass Thing:\n def __init__(self, x=None):\n self.x = x\n\n def __eq__(self, other):\n if isinstance(other, type(self)):\n return self.x == other.x\n else:\n return self.x == other\n\n def __ne__(self, other):\n return not (self == other)\n\n def __repr__(self):\n return f'Thing({self.x})'\n\n@pytest.mark.parametrize('strict', [True, False])\ndef test_attrs_basic(strict):\n def f1(t, cond):\n old = t.x\n if cond:\n t.x = 1\n else:\n t.x = 0\n return old\n\n f2 = apply_passes([ssa(strict)])(f1)\n\n t1 = Thing()\n t2 = Thing()\n\n assert t1 == t2 == None\n f1(t1, True)\n assert t1 != t2\n f2(t2, True)\n assert t1 == t2 == 1\n f1(t1, False)\n assert t1 != t2\n f2(t2, False)\n assert t1 == t2 == 0\n\n\n@pytest.mark.parametrize('strict', [True, False])\ndef test_attrs_returns(strict):\n def f1(t, cond1, cond2):\n if cond1:\n t.x = 1\n if cond2:\n return 0\n else:\n t.x = 0\n if cond2:\n return 1\n return -1\n\n f2 = apply_passes([ssa(strict)])(f1)\n\n t1 = Thing()\n t2 = Thing()\n assert t1 == t2\n\n for _ in range(NTEST):\n c1 = random.randint(0, 1)\n c2 = random.randint(0, 1)\n o1 = f1(t1, c1, c2)\n o2 = f2(t2, c1, c2)\n assert o1 == o2\n assert t1 == t2\n\n\n@pytest.mark.parametrize('strict', [True, False])\ndef test_attrs_class(strict):\n class Counter1:\n def __init__(self, init, max):\n self.cnt = init\n self.max = max\n\n def __call__(self, en):\n if en and self.cnt < self.max - 1:\n self.cnt = self.cnt + 1\n elif en:\n self.cnt = 0\n\n class Counter2:\n __init__ = Counter1.__init__\n __call__ = apply_passes([ssa(strict)])(Counter1.__call__)\n\n c1 = Counter1(3, 5)\n c2 = Counter2(3, 5)\n\n assert c1.cnt == c2.cnt\n\n for _ in range(NTEST):\n e = random.randint(0, 1)\n o1 = c1(e)\n o2 = c2(e)\n assert o1 == o2\n assert c1.cnt == c2.cnt\n\n@pytest.mark.parametrize('strict', [True, False])\ndef test_attrs_class_methods(strict):\n class Counter1:\n def __init__(self, init, max):\n self.cnt = init\n self.max = max\n\n def __call__(self, en):\n if en and self.cnt < self.max - 1:\n self.cnt = self.cnt + self.get_step(self.cnt)\n elif en:\n self.cnt = 0\n\n def get_step(self, cnt):\n return (cnt % 2) + 1\n\n class Counter2:\n __init__ = Counter1.__init__\n __call__ = apply_passes([ssa(strict)])(Counter1.__call__)\n get_step = Counter1.get_step\n\n c1 = Counter1(3, 5)\n c2 = Counter2(3, 5)\n\n assert c1.cnt == c2.cnt\n\n for _ in range(NTEST):\n e = random.randint(0, 1)\n o1 = c1(e)\n o2 = c2(e)\n assert o1 == o2\n assert c1.cnt == c2.cnt\n\n\ndef test_nstrict():\n # This function would confuse strict ssa in so many ways\n def f1(cond):\n if cond:\n if cond:\n return 0\n elif not cond:\n z = 1\n\n if not cond:\n x = z\n return x\n\n f2 = apply_passes([ssa(False)])(f1)\n assert inspect.getsource(f2) == '''\\\ndef f1(cond):\n _cond_2 = cond\n _cond_0 = cond\n __0_return_0 = 0\n _cond_1 = not cond\n z_0 = 1\n _cond_3 = not cond\n x_0 = z_0\n __0_return_1 = x_0\n return __0_return_0 if _cond_2 and _cond_0 else __0_return_1\n'''\n for cond in [True, False]:\n assert f1(cond) == f2(cond)\n\n\ndef test_attr():\n bar = namedtuple('bar', ['x', 'y'])\n\n def f1(x, y):\n z = bar(1, 0)\n if x:\n a = z\n else:\n a = y\n a.x = 3\n return a\n\n f2 = apply_passes([ssa(False)])(f1)\n assert inspect.getsource(f2) == '''\\\ndef f1(x, y):\n _attr_a_x_0 = a.x\n z_0 = bar(1, 0)\n _cond_0 = x\n a_0 = z_0\n a_1 = y\n a_2 = a_0 if _cond_0 else a_1\n _attr_a_x_1 = 3\n __0_final_a_x_0 = _attr_a_x_1; __0_return_0 = a_2\n a_2.x = __0_final_a_x_0\n return __0_return_0\n'''\n\n\ndef test_call():\n def f1(x):\n x = 2\n return g(x=x)\n\n f2 = apply_passes([ssa(False)])(f1)\n assert inspect.getsource(f2) == '''\\\ndef f1(x):\n x_0 = 2\n __0_return_0 = g(x=x_0)\n return __0_return_0\n'''\n\n\ndef ident(x): return x\n\nsig_template = '''\\\ndef f(x{}, y{}) -> ({}, {}):\n return x, y\n'''\n\n\ntemplate_options = ['', 'int', 'ident(int)', 'ident(x=int)']\n\n@pytest.mark.parametrize('strict', [True, False])\n@pytest.mark.parametrize('x', template_options)\n@pytest.mark.parametrize('y', template_options)\ndef test_call_in_annotations(strict, x, y):\n r_x = x if x else 'int'\n r_y = y if y else 'int'\n x = f': {x}' if x else x\n y = f': {y}' if y else y\n src = sig_template.format(x, y, r_x, r_y)\n tree = cst.parse_statement(src)\n env = SymbolTable(locals(), globals())\n f1 = exec_def_in_file(tree, env)\n f2 = apply_passes([ssa(strict)])(f1)\n\n\n@pytest.mark.parametrize('strict', [True, False])\ndef test_issue_79(strict):\n class Wrapper:\n def __init__(self, val):\n self.val = val\n def apply(self, f):\n return f(self.val)\n\n def f1(x):\n return x.apply(lambda x: x+1)\n\n f2 = apply_passes([ssa(strict)])(f1)\n\n for _ in range(8):\n x = Wrapper(random.randint(0, 1<<10))\n assert f1(x) == f2(x)\n","repo_name":"leonardt/ast_tools","sub_path":"tests/test_ssa.py","file_name":"test_ssa.py","file_ext":"py","file_size_in_byte":10958,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"52"} +{"seq_id":"41881079438","text":"N=int(input())\nS=list(map(int, input().split()))\ncan_list = [False] * 1001\nfor a in range(1,500):\n for b in range(1,500):\n tmp = 4*a*b + 3*a + 3*b\n if tmp <= 1000:can_list[tmp] = True\nng = 0\nfor s in S:\n if not can_list[s]:ng+=1\nprint(ng)","repo_name":"tokumaru-y/competitive_program_python","sub_path":"atcoder/contested/227/b.py","file_name":"b.py","file_ext":"py","file_size_in_byte":258,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"30710117381","text":"n = int(input(\"ingrese numero para lista: \"))\nlista = []\n\nfor i in range (n):\n\n p = input(\"ingrese una palabra: \")\n lista.append(p)\n print (f\"{lista[i]} --- posicion {i}\") \n\nprint (f\"{lista}\") \n\ny = int(input(\"Que posicion desea cambiar? \"))\n\nz = input(\"Que palabra desea poner en su lugar? \")\n\nlista.pop(y)\nlista.insert(y, z)\n\nprint (f\"{lista}\") ","repo_name":"MarielKov/Mate3","sub_path":"Unsam.Clase.3/listaej3.py","file_name":"listaej3.py","file_ext":"py","file_size_in_byte":368,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"25939590499","text":"import pandas as pd\r\nimport numpy as np\r\n\r\ncount = 0\r\ndef userInfo():\r\n global count\r\n print('Choose your type of process & number of procese')\r\n type = ['prs','type','CPUs','RAM(GB)']\r\n print(type[0],type[1],type[2],type[3])\r\n p1 = ['t2nano',1,0.5]\r\n p2 = ['t2macro',1,1]\r\n p3 = ['t2small',1,2]\r\n p4 = ['t2medium',2,4]\r\n p5 = ['t2large',2,8]\r\n print('t1',p1[0],p1[1],p1[2])\r\n print('t2',p2[0],p2[1],p2[2])\r\n print('t3',p3[0],p3[1],p3[2])\r\n print('t4',p4[0],p4[1],p4[2])\r\n print('t5',p5[0],p5[1],p5[2])\r\n\r\n dta = pd.read_csv('data.csv',header=None)\r\n\r\n print('log in Here')\r\n\r\n users = dta.iloc[:,0].values\r\n\r\n\r\n name = input('Enter your name ')\r\n flag=0\r\n for i in users:\r\n if name==i:\r\n flag=1\r\n break\r\n if flag==1:\r\n instance = input(\"Enter type of instance \")\r\n num_instance = int(input(\"Enter number of instance \"))\r\n pid = count\r\n count+=1\r\n return name, pid, instance, num_instance\r\n else:\r\n print(\"Invalid User\")\r\n\r\n# count=0\r\n# userInfo()\r\n","repo_name":"Singham12345/Cloud-Project","sub_path":"c_project.py","file_name":"c_project.py","file_ext":"py","file_size_in_byte":1086,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"7196386214","text":"from flask import request, jsonify\nimport jwt\nimport datetime\nimport functools\nimport config\nimport logging\n\n\ndef get_token(user):\n data = {\n \"user_id\": user['user_id'],\n \"email\": user['email'],\n \"exp\": datetime.datetime.utcnow() + datetime.timedelta(hours=24)\n }\n return jwt.encode(data, config.JWT_SECRET, algorithm=\"HS256\")\n\n\ndef authenticated(func):\n @functools.wraps(func)\n def wrapper(*args, **kwargs):\n token = request.headers.get('Authorization')\n\n if not token or token == 'Bearer null':\n logging.error('Token no proporcionado')\n return jsonify({'message': 'Token no proporcionado'}), 401\n\n try:\n decoded = jwt.decode(\n token[7:], config.JWT_SECRET, algorithms=['HS256'])\n request.user = decoded\n return func(*args, **kwargs)\n except jwt.ExpiredSignatureError as e:\n logging.exception(e)\n return jsonify({'message': 'Token expirado'}), 401\n except jwt.InvalidTokenError as e:\n logging.exception(e)\n return jsonify({'message': 'Token inválido'}), 401\n\n return wrapper\n","repo_name":"alexiscomerci/tr_back","sub_path":"utils/auth_utils.py","file_name":"auth_utils.py","file_ext":"py","file_size_in_byte":1165,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"73122040486","text":"from __future__ import division, print_function\n\nimport datetime as dt\nimport numpy as np\nimport numbers\n\nfrom BDProjects import datetime_to_float, float_to_datetime\nfrom BDProjects.Entities import ParameterType, Parameter\n\nfrom .EntityManager import EntityManager\nfrom ._helpers import require_signed_in\n\ndefault_parameter_types = {'Generic': 'Unspecified parameter',\n # Single value parameters\n 'Numeric value': 'Single numeric value',\n 'String value': 'String value',\n 'Boolean value': 'Boolean value',\n 'DateTime value': 'Single DateTime value',\n # dictionary\n 'Dictionary': 'Dictionary',\n # ranges\n 'Numeric range': 'Numeric values range',\n 'DateTime range': 'DateTime values range',\n # grids\n 'Uniform numeric grid': 'Uniform numeric grid',\n 'NonUniform numeric grid': 'NonUniform numeric grid',\n }\n\n\nclass ParameterManager(EntityManager):\n\n def __init__(self, session_manager):\n super(ParameterManager, self).__init__(session_manager)\n\n @require_signed_in\n def create_parameter_type(self, parameter_type, description=None):\n parameter_type_object, parameter_type_exists = self._check_parameter_type_name(parameter_type, description)\n if isinstance(parameter_type_object, ParameterType):\n if not parameter_type_exists:\n parameter_type_object.user_id = self.session_manager.user.id\n self.session.add(parameter_type_object)\n self.session.commit()\n if parameter_type_object.name not in default_parameter_types:\n record = 'Parameter type \"%s\" successfully created' % parameter_type_object.name\n self.session_manager.log_manager.log_record(record=record, category='Information')\n return parameter_type_object\n else:\n self.session.rollback()\n if parameter_type_object.name not in default_parameter_types:\n record = 'Parameter type \"%s\" is already registered' % parameter_type_object.name\n self.session_manager.log_manager.log_record(record=record, category='Warning')\n return self.session.query(ParameterType).filter(\n ParameterType.name == parameter_type_object.name).one()\n else:\n record = 'Wrong Parameter Type argument'\n self.session_manager.log_manager.log_record(record=record, category='Warning')\n\n @require_signed_in\n def delete_parameter_type(self, parameter_type):\n if not isinstance(parameter_type, ParameterType):\n record = 'Wrong argument for Parameter Type delete operation'\n self.session_manager.log_manager.log_record(record=record, category='Warning')\n return False\n self.session.delete(parameter_type)\n self.session.commit()\n record = 'Parameter type \"%s\" deleted' % parameter_type.name\n self.session_manager.log_manager.log_record(record=record, category='Information')\n return True\n\n @require_signed_in\n def _create_parameter(self, name, parameter_type, float_value=None, string_value=None,\n index=0, unit_name=None, description=None, parent=None, commit=True,\n suppres_log_message=False):\n parameter_type_object, parameter_type_exists = self._check_parameter_type_name(parameter_type, description)\n if unit_name is None:\n unit_name = ''\n else:\n unit_name = str(unit_name)\n if not parameter_type_exists:\n record = 'Parameter type \"%s\" not exist' % parameter_type_object.name\n self.session_manager.log_manager.log_record(record=record, category='Warning')\n else:\n parameter_type_id = parameter_type_object.id\n parent_id = None\n if parent is not None:\n if isinstance(parent, Parameter):\n existing_parameter = self.session.query(Parameter).filter(Parameter.id == parent.id).all()\n if existing_parameter:\n parent_id = existing_parameter[0].id\n else:\n record = 'Parameter \"%s\", id=%d not exist' % (parent.name, parent.id)\n self.session_manager.log_manager.log_record(record=record, category='Warning')\n elif isinstance(parent, int):\n existing_parameter = self.session.query(Parameter).filter(Parameter.id == parent).all()\n if existing_parameter:\n parent_id = existing_parameter[0].id\n else:\n record = 'Parameter with id=%d not exist' % parent\n self.session_manager.log_manager.log_record(record=record, category='Warning')\n else:\n record = 'Parent must be valid Parameter or its ID'\n self.session_manager.log_manager.log_record(record=record, category='Warning')\n parameter = Parameter(name=name, type_id=parameter_type_id, index=index,\n session_id=self.session_manager.session_data.id)\n parameter.unit_name = unit_name\n if parent_id:\n parameter.parent_id = parent_id\n if string_value is not None:\n parameter.string_value = string_value\n if float_value is not None:\n parameter.float_value = float_value\n if commit:\n self.commit_parameter(parameter)\n if not suppres_log_message:\n if parent_id:\n record = 'Parameter \"%s\"->\"%s\" created' % (parameter.parent.name, parameter.name)\n else:\n record = 'Parameter \"%s\" created' % parameter.name\n self.session_manager.log_manager.log_record(record=record, category='Information')\n return parameter\n\n @require_signed_in\n def commit_parameter(self, parameter):\n if isinstance(parameter, Parameter):\n self.session.add(parameter)\n self.session.commit()\n\n @require_signed_in\n def delete_parameter(self, parameter):\n if not isinstance(parameter, Parameter):\n record = 'Wrong argument for Parameter delete operation'\n self.session_manager.log_manager.log_record(record=record, category='Warning')\n return False\n if parameter.parent:\n if 'range' in parameter.parent.type.name and (parameter.name == 'start' or parameter.name == 'stop'):\n record = 'Can not delete START or STOP from RANGE parameter. Delete RANGE parameter itself.'\n self.session_manager.log_manager.log_record(record=record, category='Warning')\n return False\n self.session.delete(parameter)\n self.session.commit()\n record = 'Parameter \"%s\" deleted' % parameter.name\n self.session_manager.log_manager.log_record(record=record, category='Information')\n return True\n\n @require_signed_in\n def copy_parameter(self, parameter, suppress_parent_warning=False, commit=True):\n if not isinstance(parameter, Parameter):\n record = 'Wrong argument for Parameter copy operation'\n self.session_manager.log_manager.log_record(record=record, category='Warning')\n return None\n if parameter.parent and not suppress_parent_warning:\n record = 'Parameter belongs to its PARENT. This information will be lost in parameter copy.'\n self.session_manager.log_manager.log_record(record=record, category='Warning')\n new = self._create_parameter(name=parameter.name,\n parameter_type=parameter.type,\n float_value=parameter.float_value,\n string_value=parameter.string_value,\n index=parameter.index,\n unit_name=parameter.unit_name,\n description=parameter.description,\n parent=None,\n commit=commit,\n suppres_log_message=True)\n for child in parameter.children:\n child_copy = self.copy_parameter(child, suppress_parent_warning=True, commit=commit)\n child_copy.parent_id = new.id\n if commit:\n self.session.commit()\n record = 'Parameter \"%s\" copied' % parameter.name\n self.session_manager.log_manager.log_record(record=record, category='Information')\n return new\n\n def _check_parameter_type_name(self, parameter_type, description=None):\n parameter_type_exists = False\n if isinstance(parameter_type, str):\n parameter_type_object = ParameterType(name=parameter_type, description=description)\n elif isinstance(parameter_type, ParameterType):\n parameter_type_object = parameter_type\n else:\n parameter_type_object = None\n return parameter_type_object, parameter_type_exists\n existing_parameter_type = \\\n self.session.query(ParameterType).filter(ParameterType.name == parameter_type_object.name).all()\n if existing_parameter_type:\n parameter_type_object = existing_parameter_type[0]\n parameter_type_exists = True\n return parameter_type_object, parameter_type_exists\n\n def get_parameter_types(self, name=None):\n q = self.session.query(ParameterType)\n if name is not None and len(str(name)) > 2:\n template = '%' + str(name) + '%'\n q = q.filter(ParameterType.name.ilike(template))\n return q.all()\n\n def create_generic_parameter(self, name, float_value=None, string_value=None,\n unit_name=None, description=None, parent=None, commit=True):\n if not isinstance(float_value, numbers.Number):\n raise ValueError('Expected numeric value for a float_value')\n return self._create_parameter(name, parameter_type='Generic',\n float_value=np.float64(float_value),\n string_value=string_value,\n unit_name=unit_name, description=description,\n parent=parent,\n commit=commit)\n\n def create_dict_parameter(self, name, description=None, parent=None, commit=True):\n return self._create_parameter(name, parameter_type='Dictionary',\n description=description,\n parent=parent,\n commit=commit)\n\n def create_numeric_parameter(self, name, value, unit_name=None, description=None, parent=None, commit=True):\n if not isinstance(value, numbers.Number):\n raise ValueError('Expected numeric value for a parameter')\n return self._create_parameter(name, parameter_type='Numeric value',\n float_value=np.float64(value), unit_name=unit_name, description=description,\n parent=parent, commit=commit)\n\n def create_string_parameter(self, name, value, unit_name=None, description=None, parent=None, commit=True):\n if not isinstance(value, str):\n raise ValueError('Expected string value for a parameter')\n return self._create_parameter(name, parameter_type='String value',\n string_value=value, unit_name=unit_name, description=description,\n parent=parent, commit=commit)\n\n def create_boolean_parameter(self, name, value, description=None, parent=None, commit=True):\n if not isinstance(value, numbers.Number):\n raise ValueError('Expected boolean or numeric value for a parameter')\n return self._create_parameter(name, parameter_type='Boolean value',\n float_value=np.float64(bool(value)), description=description,\n parent=parent, commit=commit)\n\n def create_datetime_parameter(self, name, value, description=None, parent=None, commit=True):\n if not isinstance(value, dt.datetime):\n raise ValueError('Expected datetime value for a parameter')\n td = datetime_to_float(value)\n return self._create_parameter(name, parameter_type='DateTime value',\n float_value=td, description=description, parent=parent, commit=commit)\n\n def create_numeric_range_parameter(self, name, start, stop, description=None, parent=None, commit=True):\n if not (isinstance(start, numbers.Number) and isinstance(stop, numbers.Number)):\n raise ValueError('Expected numeric value for start and stop')\n range_parameter = self._create_parameter(name, parameter_type='Numeric range',\n description=description, parent=parent, commit=commit)\n self.create_numeric_parameter(name='start', value=start, parent=range_parameter, commit=commit)\n self.create_numeric_parameter(name='stop', value=stop, parent=range_parameter, commit=commit)\n return range_parameter\n\n def create_datetime_range_parameter(self, name, start, stop, description=None, parent=None, commit=True):\n if not (isinstance(start, dt.datetime) and isinstance(stop, dt.datetime)):\n raise ValueError('Expected datetime value for start and stop')\n range_parameter = self._create_parameter(name, parameter_type='DateTime range',\n description=description, parent=parent, commit=commit)\n self.create_datetime_parameter(name='start', value=start, parent=range_parameter, commit=commit)\n self.create_datetime_parameter(name='stop', value=stop, parent=range_parameter, commit=commit)\n return range_parameter\n\n @require_signed_in\n def get_dangling_parameters(self, delete=False):\n q = self.session.query(Parameter).filter(Parameter.parent == None)\n q = q.filter(Parameter.measurements == None)\n q = q.filter(Parameter.samples == None)\n q = q.filter(Parameter.equipment == None)\n q = q.filter(Parameter.data_channels == None)\n if delete:\n result = q.delete(synchronize_session='fetch')\n self.session.commit()\n record = 'Deleted %i dangling Parameters' % result\n self.session_manager.log_manager.log_record(record=record, category='Information')\n else:\n result = q.all()\n self.session.commit()\n record = 'Found %i dangling Parameters' % len(result)\n self.session_manager.log_manager.log_record(record=record, category='Information')\n return result\n\n\ndef get_range_parameter_value(parameter):\n if not isinstance(parameter, Parameter):\n raise ValueError('Expected valid Parameter object')\n if parameter.type.name not in ['DateTime range', 'Numeric range']:\n raise ValueError('Expected valid Parameter object of range type')\n start = None\n stop = None\n for child in parameter.children:\n if child.name == 'start':\n start = child.float_value\n elif child.name == 'stop':\n stop = child.float_value\n if parameter.type.name == 'DateTime range':\n start = float_to_datetime(start)\n stop = float_to_datetime(stop)\n return start, stop\n","repo_name":"bond-anton/BDProjects","sub_path":"BDProjects/EntityManagers/ParameterManager.py","file_name":"ParameterManager.py","file_ext":"py","file_size_in_byte":15991,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"72631077924","text":"from collections import defaultdict\n\ndef longest_consecutive_sequence(nums):\n counts = defaultdict(lambda: 0)\n repeat_remover = set()\n max_counts = 0\n for n in nums:\n if n in repeat_remover:\n continue\n repeat_remover.add(n)\n worth = 1\n if n - 1 in counts:\n worth += counts[n - 1]\n if n + 1 in counts:\n worth += counts[n + 1]\n counts[n] = worth\n if n - 1 in counts:\n l_worth = counts[n - 1]\n counts[n - l_worth] = worth\n if n + 1 in counts:\n r_worth = counts[n + 1]\n counts[n + r_worth] = worth\n max_counts = max(max_counts, worth)\n return max_counts\n\n\nif __name__ == '__main__':\n arr = [100, 4, 200, 1, 3, 2, 0, 7, 90, 5, 34, 6]\n print(longest_consecutive_sequence(arr))","repo_name":"clarencenhuang/programming-challenges","sub_path":"leetcode/128_longest_consecutive_sequence.py","file_name":"128_longest_consecutive_sequence.py","file_ext":"py","file_size_in_byte":831,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"33997394040","text":"__author__ = [\"fkiraly\"]\n\n__all__ = [\n \"check_is_mtype\",\n \"check_raise\",\n \"mtype\",\n]\n\nfrom typing import List, Union\n\nimport numpy as np\n\nfrom sktime.datatypes._alignment import check_dict_Alignment\nfrom sktime.datatypes._common import _metadata_requested, _ret\nfrom sktime.datatypes._hierarchical import check_dict_Hierarchical\nfrom sktime.datatypes._panel import check_dict_Panel\nfrom sktime.datatypes._proba import check_dict_Proba\nfrom sktime.datatypes._registry import AMBIGUOUS_MTYPES, SCITYPE_LIST, mtype_to_scitype\nfrom sktime.datatypes._series import check_dict_Series\nfrom sktime.datatypes._table import check_dict_Table\n\n# pool convert_dict-s\ncheck_dict = dict()\ncheck_dict.update(check_dict_Series)\ncheck_dict.update(check_dict_Panel)\ncheck_dict.update(check_dict_Hierarchical)\ncheck_dict.update(check_dict_Alignment)\ncheck_dict.update(check_dict_Table)\ncheck_dict.update(check_dict_Proba)\n\n\ndef _check_scitype_valid(scitype: str = None):\n \"\"\"Check validity of scitype.\"\"\"\n valid_scitypes = list({x[1] for x in check_dict.keys()})\n\n if not isinstance(scitype, str):\n raise TypeError(f\"scitype should be a str but found {type(scitype)}\")\n\n if scitype is not None and scitype not in valid_scitypes:\n raise TypeError(scitype + \" is not a supported scitype\")\n\n\ndef _coerce_list_of_str(obj, var_name=\"obj\"):\n \"\"\"Check whether object is string or list of string.\n\n Parameters\n ----------\n obj - object to check\n var_name: str, optional, default=\"obj\" - name of input in error messages\n\n Returns\n -------\n list of str\n equal to obj if was a list; equal to [obj] if obj was a str\n note: if obj was a list, return is not a copy, but identical\n\n Raises\n ------\n TypeError if obj is not a str or list of str\n \"\"\"\n if isinstance(obj, str):\n obj = [obj]\n elif isinstance(obj, list):\n if not np.all([isinstance(x, str) for x in obj]):\n raise TypeError(f\"{var_name} must be a string or list of strings\")\n else:\n raise TypeError(f\"{var_name} must be a string or list of strings\")\n\n return obj\n\n\ndef check_is_mtype(\n obj,\n mtype: Union[str, List[str]],\n scitype: str = None,\n return_metadata=False,\n var_name=\"obj\",\n):\n \"\"\"Check object for compliance with mtype specification, return metadata.\n\n Parameters\n ----------\n obj - object to check\n mtype: str or list of str, mtype to check obj as\n valid mtype strings are in datatypes.MTYPE_REGISTER (1st column)\n scitype: str, optional, scitype to check obj as; default = inferred from mtype\n if inferred from mtype, list elements of mtype need not have same scitype\n valid mtype strings are in datatypes.SCITYPE_REGISTER (1st column)\n return_metadata - bool, str, or list of str, optional, default=False\n if False, returns only \"valid\" return\n if True, returns all three return objects\n if str, list of str, metadata return dict is subset to keys in return_metadata\n var_name: str, optional, default=\"obj\" - name of input in error messages\n\n Returns\n -------\n valid: bool - whether obj is a valid object of mtype/scitype\n msg: str or list of str - error messages if object is not valid, otherwise None\n str if mtype is str; list of len(mtype) with message per mtype if list\n returned only if return_metadata is True or str, list of str\n metadata: dict - metadata about obj if valid, otherwise None\n returned only if return_metadata is True or str, list of str\n Keys populated depend on (assumed, otherwise identified) scitype of obj.\n Always returned:\n \"mtype\": str, mtype of obj (assumed or inferred)\n \"scitype\": str, scitype of obj (assumed or inferred)\n For scitype \"Series\":\n \"is_univariate\": bool, True iff series has one variable\n \"is_equally_spaced\": bool, True iff series index is equally spaced\n \"is_empty\": bool, True iff series has no variables or no instances\n \"has_nans\": bool, True iff the series contains NaN values\n For scitype \"Panel\":\n \"is_univariate\": bool, True iff all series in panel have one variable\n \"is_equally_spaced\": bool, True iff all series indices are equally spaced\n \"is_equal_length\": bool, True iff all series in panel are of equal length\n \"is_empty\": bool, True iff one or more of the series in the panel are empty\n \"is_one_series\": bool, True iff there is only one series in the panel\n \"has_nans\": bool, True iff the panel contains NaN values\n \"n_instances\": int, number of instances in the panel\n For scitype \"Table\":\n \"is_univariate\": bool, True iff table has one variable\n \"is_empty\": bool, True iff table has no variables or no instances\n \"has_nans\": bool, True iff the panel contains NaN values\n \"n_instances\": int, number of instances/rows in the table\n For scitype \"Alignment\":\n currently none\n\n Raises\n ------\n TypeError if no checks defined for mtype/scitype combination\n TypeError if mtype input argument is not of expected type\n \"\"\"\n mtype = _coerce_list_of_str(mtype, var_name=\"mtype\")\n\n valid_keys = check_dict.keys()\n\n # we loop through individual mtypes in mtype and see whether they pass the check\n # for each check we remember whether it passed and what it returned\n msg = []\n found_mtype = []\n found_scitype = []\n\n for m in mtype:\n if scitype is None:\n scitype_of_m = mtype_to_scitype(m)\n else:\n _check_scitype_valid(scitype)\n scitype_of_m = scitype\n key = (m, scitype_of_m)\n if (m, scitype_of_m) not in valid_keys:\n raise TypeError(f\"no check defined for mtype {m}, scitype {scitype_of_m}\")\n\n res = check_dict[key](obj, return_metadata=return_metadata, var_name=var_name)\n\n if _metadata_requested(return_metadata):\n check_passed = res[0]\n else:\n check_passed = res\n\n if check_passed:\n found_mtype.append(m)\n found_scitype.append(scitype_of_m)\n final_result = res\n elif _metadata_requested(return_metadata):\n msg.append(res[1])\n\n # there are three options on the result of check_is_mtype:\n # a. two or more mtypes are found - this is unexpected and an error with checks\n if len(found_mtype) > 1:\n raise TypeError(\n f\"Error in check_is_mtype, more than one mtype identified: {found_mtype}\"\n )\n # b. one mtype is found - then return that mtype\n elif len(found_mtype) == 1:\n if _metadata_requested(return_metadata):\n # add the mtype return to the metadata\n final_result[2][\"mtype\"] = found_mtype[0]\n final_result[2][\"scitype\"] = found_scitype[0]\n # final_result already has right shape and dependency on return_metadata\n return final_result\n else:\n return True\n # c. no mtype is found - then return False and all error messages if requested\n else:\n if len(msg) == 1:\n msg = msg[0]\n\n return _ret(False, msg, None, return_metadata)\n\n\ndef check_raise(obj, mtype: str, scitype: str = None, var_name: str = \"input\"):\n \"\"\"Check object for compliance with mtype specification, raise errors.\n\n Parameters\n ----------\n obj - object to check\n mtype: str or list of str, mtype to check obj as\n valid mtype strings are in datatypes.MTYPE_REGISTER (1st column)\n scitype: str, optional, scitype to check obj as; default = inferred from mtype\n if inferred from mtype, list elements of mtype need not have same scitype\n valid mtype strings are in datatypes.SCITYPE_REGISTER (1st column)\n var_name: str, optional, default=\"input\" - name of input in error messages\n\n Returns\n -------\n valid: bool - True if obj complies with the specification\n same as when return argument of check_is_mtype is True\n otherwise raises an error\n\n Raises\n ------\n TypeError with informative message if obj does not comply\n TypeError if no checks defined for mtype/scitype combination\n ValueError if mtype input argument is not of expected type\n \"\"\"\n obj_long_name_for_avoiding_linter_clash = obj\n valid, msg, _ = check_is_mtype(\n obj=obj_long_name_for_avoiding_linter_clash,\n mtype=mtype,\n scitype=scitype,\n return_metadata=[],\n var_name=var_name,\n )\n\n if valid:\n return True\n else:\n raise TypeError(msg)\n\n\ndef mtype(\n obj,\n as_scitype: Union[str, List[str]] = None,\n exclude_mtypes=AMBIGUOUS_MTYPES,\n):\n \"\"\"Infer the mtype of an object considered as a specific scitype.\n\n Parameters\n ----------\n obj : object to infer type of - any type, should comply with some mtype spec\n if as_scitype is provided, this needs to be mtype belonging to scitype\n as_scitype : str, list of str, or None, optional, default=None\n name of scitype(s) the object \"obj\" is considered as, finds mtype for that\n if None (default), does not assume a specific as_scitype and tests all mtypes\n generally, as_scitype should be provided for maximum efficiency\n valid scitype type strings are in datatypes.SCITYPE_REGISTER (1st column)\n exclude_mtypes : list of str, default = AMBIGUOUS_MTYPES\n which mtypes to ignore in inferring mtype, default = ambiguous ones\n\n Returns\n -------\n str - the inferred mtype of \"obj\", a valid mtype string\n or None, if obj is None\n mtype strings with explanation are in datatypes.MTYPE_REGISTER\n\n Raises\n ------\n TypeError if no type can be identified, or more than one type is identified\n \"\"\"\n if obj is None:\n return None\n\n if as_scitype is not None:\n as_scitype = _coerce_list_of_str(as_scitype, var_name=\"as_scitype\")\n for scitype in as_scitype:\n _check_scitype_valid(scitype)\n\n m_plus_scitypes = [\n (x[0], x[1]) for x in check_dict.keys() if x[0] not in exclude_mtypes\n ]\n\n if as_scitype is not None:\n m_plus_scitypes = [(x[0], x[1]) for x in m_plus_scitypes if x[1] in as_scitype]\n\n # collects mtypes that are tested as valid for obj\n mtypes_positive = []\n\n # collects error messages from mtypes that are tested as invalid for obj\n mtypes_negative = dict()\n\n for m_plus_scitype in m_plus_scitypes:\n valid, msg, _ = check_is_mtype(\n obj,\n mtype=m_plus_scitype[0],\n scitype=m_plus_scitype[1],\n return_metadata=[],\n )\n if valid:\n mtypes_positive += [m_plus_scitype[0]]\n else:\n mtypes_negative[m_plus_scitype[0]] = msg\n\n if len(mtypes_positive) > 1:\n raise TypeError(\n f\"Error in check_is_mtype, more than one mtype identified:\"\n f\" {mtypes_positive}\"\n )\n\n if len(mtypes_positive) < 1:\n msg = \"\"\n for mtype, error in mtypes_negative.items():\n msg += f\"{mtype}: {error}\\r\\n\"\n msg = (\n f\"No valid mtype could be identified for object of type {type(obj)}. \"\n f\"Errors returned are as follows, in format [mtype]: [error message] \\r\\n\"\n ) + msg\n raise TypeError(msg)\n\n return mtypes_positive[0]\n\n\ndef check_is_scitype(\n obj,\n scitype: Union[str, List[str]],\n return_metadata=False,\n var_name=\"obj\",\n exclude_mtypes=AMBIGUOUS_MTYPES,\n):\n \"\"\"Check object for compliance with scitype specification, return metadata.\n\n Parameters\n ----------\n obj - object to check\n scitype: str or list of str, scitype to check obj as\n valid mtype strings are in datatypes.SCITYPE_REGISTER\n return_metadata - bool, optional, default=False\n if False, returns only \"valid\" return\n if True, returns all three return objects\n if str, list of str, metadata return dict is subset to keys in return_metadata\n var_name: str, optional, default=\"obj\" - name of input in error messages\n exclude_mtypes : list of str, default = AMBIGUOUS_MTYPES\n which mtypes to ignore in inferring mtype, default = ambiguous ones\n\n Returns\n -------\n valid: bool - whether obj is a valid object of mtype/scitype\n msg: dict[str, str] or None\n error messages if object is not valid, otherwise None\n keys are all mtypes tested, value for key is error message for that key\n metadata: dict - metadata about obj if valid, otherwise None\n returned only if return_metadata is True\n Fields depend on scitpe.\n Always returned:\n \"mtype\": str, mtype of obj (assumed or inferred)\n mtype strings with explanation are in datatypes.MTYPE_REGISTER\n \"scitype\": str, scitype of obj (assumed or inferred)\n scitype strings with explanation are in datatypes.SCITYPE_REGISTER\n For scitype \"Series\":\n \"is_univariate\": bool, True iff series has one variable\n \"is_equally_spaced\": bool, True iff series index is equally spaced\n \"is_empty\": bool, True iff series has no variables or no instances\n \"has_nans\": bool, True iff the series contains NaN values\n For scitype \"Panel\":\n \"is_univariate\": bool, True iff all series in panel have one variable\n \"is_equally_spaced\": bool, True iff all series indices are equally spaced\n \"is_equal_length\": bool, True iff all series in panel are of equal length\n \"is_empty\": bool, True iff one or more of the series in the panel are empty\n \"is_one_series\": bool, True iff there is only one series in the panel\n \"has_nans\": bool, True iff the panel contains NaN values\n \"n_instances\": int, number of instances in the panel\n For scitype \"Table\":\n \"is_univariate\": bool, True iff table has one variable\n \"is_empty\": bool, True iff table has no variables or no instances\n \"has_nans\": bool, True iff the panel contains NaN values\n For scitype \"Alignment\":\n currently none\n Raises\n ------\n TypeError if scitype input argument is not of expected type\n \"\"\"\n scitype = _coerce_list_of_str(scitype, var_name=\"scitype\")\n\n for x in scitype:\n _check_scitype_valid(x)\n\n valid_keys = check_dict.keys()\n\n # find all the mtype keys corresponding to the scitypes\n keys = [x for x in valid_keys if x[1] in scitype and x[0] not in exclude_mtypes]\n\n # storing the msg return\n msg = {}\n found_mtype = []\n found_scitype = []\n\n for key in keys:\n res = check_dict[key](obj, return_metadata=return_metadata, var_name=var_name)\n\n if _metadata_requested(return_metadata):\n check_passed = res[0]\n else:\n check_passed = res\n\n if check_passed:\n final_result = res\n found_mtype.append(key[0])\n found_scitype.append(key[1])\n elif _metadata_requested(return_metadata):\n msg[key[0]] = res[1]\n\n # there are three options on the result of check_is_mtype:\n # a. two or more mtypes are found - this is unexpected and an error with checks\n if len(found_mtype) > 1:\n raise TypeError(\n f\"Error in check_is_mtype, more than one mtype identified: {found_mtype}\"\n )\n # b. one mtype is found - then return that mtype\n elif len(found_mtype) == 1:\n if _metadata_requested(return_metadata):\n # add the mtype return to the metadata\n final_result[2][\"mtype\"] = found_mtype[0]\n # add the scitype return to the metadata\n final_result[2][\"scitype\"] = found_scitype[0]\n # final_result already has right shape and dependency on return_metadata\n return final_result\n else:\n return True\n # c. no mtype is found - then return False and all error messages if requested\n else:\n return _ret(False, msg, None, return_metadata)\n\n\ndef scitype(obj, candidate_scitypes=SCITYPE_LIST, exclude_mtypes=AMBIGUOUS_MTYPES):\n \"\"\"Infer the scitype of an object.\n\n Parameters\n ----------\n obj : object to infer type of - any type, should comply with some mtype spec\n if as_scitype is provided, this needs to be mtype belonging to scitype\n candidate_scitypes: str or list of str, scitypes to pick from\n valid scitype strings are in datatypes.SCITYPE_REGISTER\n exclude_mtypes : list of str, default = AMBIGUOUS_MTYPES\n which mtypes to ignore in inferring mtype, default = ambiguous ones\n valid mtype strings are in datatypes.MTYPE_REGISTER\n\n Returns\n -------\n str - the inferred sciype of \"obj\", a valid scitype string\n or None, if obj is None\n scitype strings with explanation are in datatypes.SCITYPE_REGISTER\n\n Raises\n ------\n TypeError if no type can be identified, or more than one type is identified\n \"\"\"\n candidate_scitypes = _coerce_list_of_str(\n candidate_scitypes, var_name=\"candidate_scitypes\"\n )\n\n valid_scitypes = []\n\n for scitype in candidate_scitypes:\n valid = check_is_scitype(\n obj,\n scitype=scitype,\n return_metadata=False,\n exclude_mtypes=exclude_mtypes,\n )\n if valid:\n valid_scitypes += [scitype]\n\n if len(valid_scitypes) > 1:\n raise TypeError(\n \"Error in function scitype, more than one valid scitype identified:\"\n f\"{ valid_scitypes}\"\n )\n if len(valid_scitypes) == 0:\n raise TypeError(\n \"Error in function scitype, no valid scitype could be identified.\"\n )\n\n return valid_scitypes[0]\n","repo_name":"sktime/sktime","sub_path":"sktime/datatypes/_check.py","file_name":"_check.py","file_ext":"py","file_size_in_byte":17871,"program_lang":"python","lang":"en","doc_type":"code","stars":7028,"dataset":"github-code","pt":"52"} +{"seq_id":"37021333331","text":"#Embedded file name: e:\\jenkins\\workspace\\client_SERENITY\\branches\\release\\SERENITY\\packages\\evegraphics\\ui\\controlPaths.py\r\nfrom carbon.common.script.util.mathUtil import RayToPlaneIntersection\r\nimport geo2\r\nimport lineController\r\n_PATH_STATE_PICK_XZ = 0\r\n_PATH_STATE_PICK_Y = 1\r\n_PATH_STATE_INACTIVE = 2\r\n_PATH_STATE_DONE = 3\r\n\r\nclass SinglePointPath:\r\n\r\n def __init__(self):\r\n self.pathColor = (1, 1, 1, 0.5)\r\n self.movementColor = (1, 1, 1, 1)\r\n self.linePath = None\r\n self.movementLine = None\r\n self.mouseDown = False\r\n self.state = _PATH_STATE_INACTIVE\r\n self.planarPosition = (0, 0, 0)\r\n self.finalPosition = (0, 0, 0)\r\n\r\n def Start(self):\r\n lc = lineController.LineController.GetGlobalInstance()\r\n self.linePath = lc.CreateLinePath(self.pathColor)\r\n self.linePath.BeginPath()\r\n self.state = _PATH_STATE_PICK_XZ\r\n self.planarPosition = (0, 0, 0)\r\n self.finalPosition = (0, 0, 0)\r\n self.movementLine = self.linePath.lineSet.AddLine((0, 0, 0), (0, 0, 0), self.movementColor)\r\n\r\n def Abort(self):\r\n self.linePath.EndPath()\r\n self.state = _PATH_STATE_INACTIVE\r\n self.movementLine = None\r\n\r\n def AddPoint(self):\r\n if self.state == _PATH_STATE_PICK_XZ:\r\n self.state = _PATH_STATE_PICK_Y\r\n self.linePath.ConfirmPoint()\r\n elif self.state == _PATH_STATE_PICK_Y:\r\n self.state = _PATH_STATE_DONE\r\n self.linePath.EndPath()\r\n\r\n def UpdatePosition(self, cameraController):\r\n if self.state == _PATH_STATE_PICK_XZ:\r\n ray_dir, ray_start = cameraController.GetPickVector()\r\n multiplier = ray_start[1] / -ray_dir[1]\r\n self.planarPosition = geo2.Vec3Add(ray_start, geo2.Vec3Scale(ray_dir, multiplier))\r\n self.linePath.SetPosition(self.planarPosition)\r\n self.movementLine.SetEndPosition(self.planarPosition)\r\n elif self.state == _PATH_STATE_PICK_Y:\r\n viewTransform = cameraController.GetCamera().viewMatrix.transform\r\n plane_dir = (viewTransform[0][2], 0, viewTransform[2][2])\r\n ray_dir, start = cameraController.GetPickVector()\r\n plane_position = RayToPlaneIntersection(start, ray_dir, self.planarPosition, plane_dir)\r\n self.finalPosition = (self.planarPosition[0], plane_position[1], self.planarPosition[2])\r\n self.linePath.SetPosition(self.finalPosition)\r\n self.movementLine.SetEndPosition(self.finalPosition)\r\n\r\n def IsComplete(self):\r\n return self.state == _PATH_STATE_DONE\r\n\r\n def GetEndPosition(self):\r\n if self.state == _PATH_STATE_DONE:\r\n return self.finalPosition\r\n return self.linePath.GetEndPosition()\r\n","repo_name":"connoryang/dec-eve-serenity","sub_path":"client/evegraphics/ui/controlPaths.py","file_name":"controlPaths.py","file_ext":"py","file_size_in_byte":2784,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"12607985650","text":"\"\"\"\nModule that contains an object for manipulating DNA strings\n\"\"\"\n\nimport logging\nfrom pathlib import Path\n\n# Use logging rather than print statements to track workflows and record exceptions.py\ncurrent_directory = str(Path(__file__).resolve().parent)\nlogging.basicConfig(level=logging.DEBUG, # DEBUG, INFO, WARN, ERROR\n format=\"%(asctime)s [%(levelname)s] %(message)s\",\n handlers=[logging.FileHandler(f\"{current_directory}/logs/transribe_n_translate.log\"),\n logging.StreamHandler()],)\n\n\n# Custom exceptions.py\nclass SequenceError(Exception):\n pass\n\n\n\"\"\"\n\n\"\"\"\n\n\nclass SequenceTools:\n def __init__(self):\n self.translation_codes = {}\n self.base_complement = {\"G\": \"C\", \"T\": \"A\", \"A\": \"T\", \"C\": \"G\"}\n self.dna_alphabet = \"GATC\"\n self.rna_alphabet = \"gauc\"\n\n def is_dna(self, dna_sequence):\n if not dna_sequence.isupper():\n raise SequenceError(\"DNA sequences should be Upper Case according to the IUPAC Alphabet\")\n position = 0\n for i in dna_sequence:\n position += 1\n if i not in self.dna_alphabet:\n raise SequenceError(\"Non DNA base {} at position {}\".format(i, str(position)))\n return True\n\n def is_rna(self, rna_sequence):\n if not rna_sequence.islower():\n raise SequenceError(\"RNA sequences should be Lower Case according to the IUPAC Alphabet\")\n position = 0\n for i in rna_sequence:\n position += 1\n if i not in self.rna_alphabet:\n raise SequenceError(\"Non RNA base {} at position {}\".format(i, str(position)))\n return True\n\n def reverse_complement(self, dna_string):\n\n # Check the string is DNA\n self.is_dna(dna_string)\n\n # Reverse the string using slice offset to -1 for the full string and list to convert into a list\n reverse_bases = list(dna_string[::-1])\n\n # This line of code joins together a for loop that returns a list that complements the bases while iterating\n complement = \"\".join([self.base_complement[base] for base in reverse_bases])\n\n return complement\n\n\nif __name__ == '__main__':\n \"\"\"\n This code allows me to directly call the script and use a test string e.g. the one below\n \"\"\"\n # st = SequenceTools()\n # dna = \"GATC\"\n # rna = \"aggagtaagcccttgcaactggaaatacacccattg\"\n # print(st.transcribe(dna))\n\n # Create object\n seq_tools = SequenceTools()\n\n dna_sequence = \"GCTGAGACTTCCTGGACGGGGGACAGGCTGTGGGGTTTCTCAGATAACTGGGCCCCTGCGCTCAGGAGGC\"\n reverse_complement = seq_tools.reverse_complement(dna_sequence)\n print(reverse_complement)\n","repo_name":"Peter-J-Freeman/stp_tutorials","sub_path":"STP_Tutorials/modules/seq_tools.py","file_name":"seq_tools.py","file_ext":"py","file_size_in_byte":2695,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"20530387945","text":"# 2140. Solving Questions With Brainpower\n \nclass Solution:\n def mostPoints(self, questions: List[List[int]]) -> int:\n N = len(questions)\n @cache\n def helper(idx):\n if idx >= N:\n return 0\n count = 0\n pts, bp = questions[idx]\n count = 0\n #print(idx, cd)\n count = max(pts + helper(idx+bp+1), helper(idx+1))\n return count\n \n return helper(0)\n \n def mostPoints(self, qs):\n N = len(qs)\n dp = [0] * N\n \n def helper(idx):\n if idx >= N:\n return 0\n if dp[idx]:\n return dp[idx]\n count = 0\n pts, bp = qs[idx]\n count = max(pts + helper(idx+bp+1), helper(idx+1))\n dp[idx] = count\n return dp[idx]\n \n return helper(0)","repo_name":"zerobcpp/Code-practices","sub_path":"2140.py","file_name":"2140.py","file_ext":"py","file_size_in_byte":887,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"71770451366","text":"# -*- coding: utf-8 -*-\nimport json\nfrom pathlib import Path\nfrom queue import Queue\n\nfrom pytest import Pytester\n\nfrom xvirt.events import EvtCollectionFinish\nfrom xvirt.remote import RemoteEndpoint\n\n_queue = Queue()\n\n\ndef remote_transport_send(payload: bytes) -> None:\n _queue.put(payload)\n\n\ndef test_module_full_name():\n \"\"\"just a reminder that __name__ has the full package path :)\"\"\"\n assert __name__ == 'tests.' + Path(__file__).stem\n assert remote_transport_send.__name__ == ''\n\n\ndef test_send_events(pytester: Pytester):\n # Controlled()\n target = EvtCollectionFinish(['ciao1', 'ciao2'])\n string = json.dumps((target, target))\n assert string == ''\n\n remote_transport = __name__ + '.' + remote_transport_send.__name__\n\n # pytester.runpytest_inprocess(f'--xvirt-remote-transport={remote_transport}')\n\n\ndef test_bar_fixture(testdir):\n \"\"\"Make sure that pytest accepts our fixture.\"\"\"\n\n # create a temporary pytest test module\n testdir.makepyfile(\"\"\"\n def test_sth(bar):\n assert bar == \"europython2015\"\n \"\"\")\n\n # run pytest with the following cmd args\n result = testdir.runpytest(\n '--foo=europython2015',\n '-v'\n )\n\n # fnmatch_lines does an assertion internally\n result.stdout.fnmatch_lines([\n '*::test_sth PASSED*',\n ])\n\n # make sure that that we get a '0' exit code for the testsuite\n assert result.ret == 0\n\n\ndef test_help_message(testdir):\n result = testdir.runpytest(\n '--help',\n )\n # fnmatch_lines does an assertion internally\n result.stdout.fnmatch_lines([\n 'xvirt:',\n '*--foo=DEST_FOO*Set the value for the fixture \"bar\".',\n ])\n\n\ndef test_hello_ini_setting(testdir):\n testdir.makeini(\"\"\"\n [pytest]\n HELLO = world\n \"\"\")\n\n testdir.makepyfile(\"\"\"\n import pytest\n\n @pytest.fixture\n def hello(request):\n return request.config.getini('HELLO')\n\n def test_hello_world(hello):\n assert hello == 'world'\n \"\"\")\n\n result = testdir.runpytest('-v')\n\n # fnmatch_lines does an assertion internally\n result.stdout.fnmatch_lines([\n '*::test_hello_world PASSED*',\n ])\n\n # make sure that that we get a '0' exit code for the testsuite\n assert result.ret == 0\n","repo_name":"simonegiacomelli/pytest-xvirt-old","sub_path":"tests/test_xvirt.py","file_name":"test_xvirt.py","file_ext":"py","file_size_in_byte":2290,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"27032317094","text":"import warnings\n\nimport matplotlib\n\nmatplotlib.use('Agg')\nmatplotlib.rcParams['font.size'] = 10\nmatplotlib.rcParams['font.family'] = 'serif'\n\nfrom utils.utils_plot import *\nfrom utils.utils_plot_synth import *\nimport yaml\n\ndefault_hparams = {\n 'logistic_k': 100,\n 'logistic_k_dafi': 10000,\n 'regularization_penalty': 0,\n 'negative_box_penalty': 0.0,\n 'negative_proportion_default': 0.0001,\n 'positive_box_penalty': 0.0,\n 'corner_penalty': .0,\n 'feature_diff_penalty': 0.,\n 'gate_size_penalty': .0,\n 'gate_size_default': (0.5, 0.5),\n 'load_from_pickle': True,\n 'dafi_init': False,\n 'optimizer': \"Adam\", # or Adam, SGD\n 'loss_type': 'logistic', # or MSE\n 'n_epoch_eval': 100,\n 'n_mini_batch_update_gates': 50,\n 'learning_rate_classifier': 0.05,\n 'learning_rate_gates': 0.05,\n 'batch_size': 10,\n 'n_epoch': 1000,\n 'seven_epochs_for_gate_motion_plot': [0, 50, 100, 200, 300, 400, 500],\n 'test_size': 0.20,\n 'experiment_name': 'default',\n 'random_state': 123,\n 'n_run': 2,\n 'init_type': 'random_corner',\n 'corner_init_deterministic_size': .75,\n 'train_alternate': True,\n 'run_logistic_to_convergence': False,\n 'output': {\n 'type': 'full'\n },\n 'annealing': {\n 'anneal_logistic_k': False,\n 'final_k': 1000,\n 'init_k': 1\n },\n 'two_phase_training': {\n 'turn_on': False,\n 'num_only_log_loss_epochs': 50\n },\n 'plot_params': {\n 'figsize': [10, 10],\n 'marker_size': .01,\n },\n 'use_out_of_sample_eval_data': False,\n}\n\n\ndef make_dev_data_plots():\n model_path = '../output/single_two_phase_gs=10/model.pkl'\n cell_sz = .1\n with open('../data/cll/x_dev_4d_1p.pkl', 'rb') as f:\n x_dev_list = pickle.load(f)\n\n with open('../data/cll/y_dev_4d_1p.pkl', 'rb') as f:\n labels = pickle.load(f)\n\n feature_names = ['CD5', 'CD19', 'CD10', 'CD79b']\n feature2id = dict((feature_names[i], i) for i in range(len(feature_names)))\n x_dev_list, offset, scale = dh.normalize_x_list(x_dev_list)\n\n # get_dafi_gates(offset, scale, feature2id)\n with open(model_path, 'rb') as f:\n model = pickle.load(f)\n\n DAFI_GATES = get_dafi_gates(offset, scale, feature2id)\n\n plot_samples_and_gates_cll_4d_dev(x_dev_list, labels, model, DAFI_GATES, cell_sz=cell_sz)\n\n\ndef make_accs_and_losses_final_model():\n savefolder = '../output/CV_neg=0.001_diff=0.001_FINAL_OOS_seed0/'\n tracker_train_path = savefolder + 'tracker_train_m.pkl'\n tracker_eval_path = savefolder + 'tracker_eval_m.pkl'\n\n plot_accs_and_losses(tracker_train_path, tracker_eval_path, savefolder=savefolder)\n\n\ndef make_synth_plot(hparams, model_paths, cells_to_plot=100000, device=1):\n with open(model_paths['init'], 'rb') as f:\n model_init = pickle.load(f).cuda(device)\n if hparams['dictionary_is_broken']:\n model_init.fix_children_dict_synth()\n\n with open(model_paths['final'], 'rb') as f:\n model_final = pickle.load(f).cuda(device)\n if hparams['dictionary_is_broken']:\n model_final.fix_children_dict_synth()\n\n models = {'init': model_init, 'final': model_final}\n\n synth_input = SynthInput(hparams, device=device)\n data = [x.cpu().detach().numpy() for x in synth_input.x_train]\n data_pos = [x for x, y in zip(data, synth_input.y_train) if y == 1.]\n data_neg = [x for x, y in zip(data, synth_input.y_train) if y == 0.]\n catted_data_pos = np.concatenate(data_pos)\n shuffled_idxs = np.random.permutation(len(catted_data_pos))\n print('max is', np.max(catted_data_pos[shuffled_idxs][0:cells_to_plot]))\n plot_synth_data_with_gates(models, catted_data_pos[shuffled_idxs][0:cells_to_plot], hparams,\n {'title': 'Class 1 Results'})\n savepath = '../output/%s/synth_gates_pos.png' % hparams['experiment_name']\n plt.tight_layout()\n plt.savefig(savepath, dpi=300, bbox_inches='tight')\n plt.clf()\n\n catted_data_neg = np.concatenate(data_neg)\n shuffled_idxs = np.random.permutation(len(catted_data_neg))\n plot_synth_data_with_gates(models, catted_data_neg[shuffled_idxs][0:cells_to_plot], hparams,\n {'title': 'Class 2 Results'})\n savepath = '../output/%s/synth_gates_neg.png' % hparams['experiment_name']\n plt.tight_layout()\n plt.savefig(savepath, dpi=300, bbox_inches='tight')\n plt.clf()\n\n\ndef make_model_plot(hparams, path_to_model, device):\n # make a run that takes in just a model rather than a model chekcpoint dict\n run_model_single_iter_pos_and_neg_gates(hparams, path_to_model, device_data=device)\n\n\ndef make_model_plots_both_panels(hparams, path_to_model_checkpoints, num_iters=120):\n with open(path_to_model_checkpoints, 'rb') as f:\n model_checkpoints = pickle.load(f)\n model_init = model_checkpoints[0]\n model_final = model_checkpoints[num_iters]\n run_both_panels_pos_and_neg_gates(model_init, hparams, savename='pos_and_neg_plots_both_init.png')\n run_both_panels_pos_and_neg_gates(model_final, hparams, savename='pos_and_neg_plots_both_final.png')\n\n\ndef make_dafi_plot(hparams):\n run_dafi_single_iter_pos_and_neg_gates(hparams, device_data=0)\n\n\ndef make_model_loss_plots(output_dir, figsize=(9, 3)):\n with open(os.path.join(output_dir, 'tracker_train_m.pkl'), 'rb') as f:\n tracker_train = pickle.load(f)\n with open(os.path.join(output_dir, 'tracker_eval_m.pkl'), 'rb') as f:\n tracker_eval = pickle.load(f)\n log_loss_train = tracker_train.log_loss\n log_loss_eval = tracker_eval.log_loss\n\n acc_train = tracker_train.acc\n acc_eval = tracker_eval.acc\n\n reg_train = [n + f for n, f in zip(tracker_train.neg_prop_loss, tracker_train.feature_diff_loss)]\n reg_eval = [n + f for n, f in zip(tracker_eval.neg_prop_loss, tracker_eval.feature_diff_loss)]\n\n fig, axes = plt.subplots(1, 3, figsize=figsize)\n x_ticks = np.arange(len(log_loss_train))\n axes[0].plot(x_ticks, acc_train, color='b', label='Train')\n axes[1].plot(x_ticks, log_loss_train, color='b', label='Train')\n axes[2].plot(x_ticks, reg_train, color='b', label='Train')\n\n axes[0].plot(x_ticks, acc_eval, color='tab:orange', label='Test')\n axes[1].plot(x_ticks, log_loss_eval, color='tab:orange', label='Test')\n axes[2].plot(x_ticks, reg_eval, color='tab:orange', label='Test')\n savepath = os.path.join(output_dir, 'diagnostics.png')\n plt.tight_layout()\n plt.savefig(savepath, dpi=300, bbox_inches='tight')\n\n\nif __name__ == '__main__':\n warnings.filterwarnings(\"ignore\")\n # experiment_yaml_file = '../configs/testing_corner_init.yaml'\n # experiment_yaml_file = '../configs/testing_overlaps.yaml'\n # experiment_yaml_file = '../configs/testing_my_heuristic_init.yaml'\n\n # for both panels plots\n # path_to_model_checkpoints = '../output/Both_Panels_CV_neg=0.001_diff=0.001_seed1/model_checkpoints.pkl'\n # yaml_filename = '../configs/both_panels.yaml'\n\n # for dafi/model plots\n # path_to_saved_model = '../output/FINAL_MODEL_neg=0.001_diff=0.001_seed0/init_model.pkl'\n # yaml_filename = '../configs/OOS_Final_Model.yaml'\n model_paths_synth = \\\n {\n 'init': '../output/Synth_same_reg_as_alg_seed0/model_init_seed0.pkl',\n 'final': '../output/Synth_same_reg_as_alg_seed0/model_final_seed0.pkl'\n }\n\n # make_accs_and_losses_final_model()\n\n # for synth plots\n yaml_filename = '../configs/synth_plot.yaml'\n\n # for dafi/model plots\n # path_to_saved_model = '../output/FINAL_MODEL_neg=0.001_diff=0.001_seed0/init_model.pkl'\n # path_to_saved_model = '../output/Middle_neg=0.001_diff=0.001_FINAL_OOS_seed0/model.pkl'\n # yaml_filename = '../configs/OOS_Final_Model.yaml'\n # yaml_filename = '../configs/FINAL_MODEL_middle_init.yaml'\n hparams = default_hparams\n with open(yaml_filename, \"r\") as f_in:\n yaml_params = yaml.safe_load(f_in)\n hparams.update(yaml_params)\n # make_dafi_plot(hparams)\n make_synth_plot(hparams, model_paths_synth)\n # make_model_plot(hparams, path_to_saved_model, 0)\n # make_model_loss_plots('../output/CV_neg=0.001_diff=0.001_FINAL_OOS_seed0')\n # make_model_plots_both_panels(hparams, path_to_model_checkpoints)\n\n # run_gate_motion_from_saved_results(experiment_yaml_file)\n# run_leaf_gate_plots(experiment_yaml_file)\n# run_single_iter_pos_and_neg_gates_plot(experiment_yaml_file)\n# make_dev_data_plots(hparams)\n","repo_name":"disiji/fc_differentiable","sub_path":"src/make_plots.py","file_name":"make_plots.py","file_ext":"py","file_size_in_byte":8405,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"10545874686","text":"from django.urls import path\nfrom . import views\n\napp_name ='core'\n\nurlpatterns = [\n path('', views.index, name='index'),\n path('perfil/', views.perfil, name='perfil'),\n path('perfil//', views.perfil, name='perfil'),\n path('reservar_hora/', views.reserva_de_hora, name='reserva_de_hora'),\n path('listar_reserva/', views.listar_reserva_hora, name='listar_reserva'),\n path('listar_reserva//', views.listar_reserva_hora, name='listar_reserva'),\n path('modificar_reserva//', views.modificar_reserva, name='modificar'),\n path('eliminar_reserva//', views.eliminar_reserva, name='eliminar'),\n path('nuestros_servicios/', views.nuestros_servicios, name='servicios'),\n path('afinacion/', views.afinacion, name='afinacion'),\n path('aire_acondicionado/', views.aire_acondicionado, name='aire_acondicionado'),\n path('alineacion/', views.alineacion, name='alineacion'),\n path('banda_distribucion/', views.banda_de_distribucion, name='banda_distribucion'),\n path('bateria/', views.bateria, name='bateria'),\n path('bomba_agua/', views.bomba_agua, name='bomba_agua'),\n path('caja_transferencia/', views.caja_transferencia, name='caja_transferencia'),\n path('cambio_aceite/', views.cambio_aceite, name='cambio_aceite'),\n path('cambio_llantas/', views.cambio_llantas, name='cambio_llantas'),\n path('frenos/', views.frenos, name='frenos'),\n path('direccion_hidraulica/', views.direccion_hidraulica, name='direccion_hidraulica'),\n path('luces/', views.luces, name='luces'),\n path('boleta//', views.boleta, name='boleta'),\n path('mensaje_reserva/', views.mensaje_reserva, name='mensaje_reserva'),\n path('mensaje_mod_reserva/', views.mensaje_modificar_reserva, name='mensaje_modificar_reserva'),\n]\n","repo_name":"niickoh/portafolio-duoc","sub_path":"appwebserviexpress-main/Portafolios/core/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1764,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"12332147579","text":"import os\nimport sys\nimport time\nimport json\nimport decimal\nimport difflib\nimport sqlite3\nimport datetime\nimport traceback\n\nfrom hashlib import md5\n\n# pylint: disable=wildcard-import\n# pylint: disable=unused-wildcard-import\nfrom idc import *\nfrom idaapi import *\nfrom idautils import *\n# pylint: enable=unused-wildcard-import\n# pylint: enable=wildcard-import\n\nimport idaapi\n\nidaapi.require(\"diaphora\")\n\ntry:\n import ida_hexrays as hr\n\n HAS_HEXRAYS = True\nexcept ImportError:\n HAS_HEXRAYS = False\n\nsys.path.append(os.path.join(os.path.dirname(__file__), \"codecut\"))\nfrom codecut import lfa\n\nfrom pygments import highlight\nfrom pygments.lexers import NasmLexer, CppLexer, DiffLexer\nfrom pygments.formatters import HtmlFormatter\n\nimport diaphora_config as config\n\nfrom others.tarjan_sort import strongly_connected_components, robust_topological_sort\n\nfrom jkutils.factor import primesbelow\nfrom jkutils.graph_hashes import CKoretKaramitasHash\n\ntry:\n from jkutils.IDAMagicStrings import get_source_strings\n\n HAS_GET_SOURCE_STRINGS = True\nexcept ImportError:\n print(f\"Error loading IDAMagicStrings.py: {str(sys.exc_info()[1])}\")\n HAS_GET_SOURCE_STRINGS = False\n\n# pylint: disable-next=wrong-import-order\nfrom PyQt5 import QtWidgets\n\n#-------------------------------------------------------------------------------\n# Chooser items indices. They do differ from the CChooser.item items that are\n# handled in diaphora.py.\nimport diaphora\n\nCHOOSER_ITEM_MAIN_EA = diaphora.ITEM_MAIN_EA + 1\nCHOOSER_ITEM_MAIN_NAME = diaphora.ITEM_MAIN_NAME + 1\nCHOOSER_ITEM_DIFF_EA = diaphora.ITEM_DIFF_EA + 1\nCHOOSER_ITEM_DIFF_NAME = diaphora.ITEM_DIFF_NAME + 1\nCHOOSER_ITEM_RATIO = diaphora.ITEM_RATIO\n\n# Constants unexported in IDA Python\nPRTYPE_SEMI = 0x0008\n\n# Messages\nMSG_RELAXED_RATIO_ENABLED = \"\"\"AUTOHIDE DATABASE\\n\nRelaxed ratio calculations can be enabled. It will ignore many small\nmodifications to functions and will match more functions with higher ratios.\nEnable this option if you're only interested in the new functionality. Disable\nit for patch diffing if you're interested in small modifications (like buffer\nsizes).\n\nYou can disable it by un-checking the 'Relaxed calculations of differences\nratios' option.\"\"\"\n\nMSG_FUNCTION_SUMMARIES_ONLY = \"\"\"AUTOHIDE DATABASE\\n\nDo not export basic blocks or instructions will be enabled. It will not export\nthe information relative to basic blocks or instructions and 'Diff assembly in a\ngraph' will not be available.\n\nThis is automatically done for exporting huge databases with more than 100,000\nfunctions. You can disable it by un-checking the 'Do not export basic blocks or\ninstructions' option.\"\"\"\n\nLITTLE_ORANGE = 0x026AFD\n\n\n#-------------------------------------------------------------------------------\n# Python linter specific things to disable (temporarily, I guess...)\n#\n# pylint: disable=missing-class-docstring\n# pylint: disable=missing-function-docstring\n# pylint: disable=protected-access\n\n#-------------------------------------------------------------------------------\ndef log(message):\n \"\"\"\n Print a message\n \"\"\"\n print(f\"[Diaphora: {time.asctime()}] {message}\")\n\n\n#-------------------------------------------------------------------------------\ndef log_refresh(message, show=False, do_log=True):\n \"\"\"\n Print a message and refresh the UI.\n \"\"\"\n if show:\n show_wait_box(message)\n else:\n replace_wait_box(message)\n\n if user_cancelled():\n raise Exception(\"Cancelled\")\n\n if do_log:\n log(message)\n\n\n#-------------------------------------------------------------------------------\ndef debug_refresh(message):\n \"\"\"\n Print a debugging message if debugging is enabled.\n \"\"\"\n if os.getenv(\"DIAPHORA_DEBUG\"):\n log(message)\n\n\n#-------------------------------------------------------------------------------\ndiaphora.log = log\ndiaphora.log_refresh = log_refresh\n\n#-------------------------------------------------------------------------------\n# pylint: disable=global-variable-not-assigned\ng_bindiff = None\n\n\ndef show_choosers():\n \"\"\"\n Show the non empty choosers.\n \"\"\"\n if g_bindiff is not None:\n g_bindiff.show_choosers(False)\n\n\n#-------------------------------------------------------------------------------\ndef save_results():\n \"\"\"\n Show the dialogue to save the diffing results.\n \"\"\"\n if g_bindiff is not None:\n filename = ask_file(1, \"*.diaphora\", \"Select the file to store diffing results\")\n if filename is not None:\n g_bindiff.save_results(filename)\n\n\n# pylint: enable=global-variable-not-assigned\n\n\n#-------------------------------------------------------------------------------\ndef load_and_import_all_results(filename, main_db, diff_db):\n \"\"\"\n Load the diffing results and import all matches.\n \"\"\"\n tmp_diff = CIDABinDiff(\":memory:\")\n\n if os.path.exists(filename) and os.path.exists(main_db) and os.path.exists(diff_db):\n tmp_diff.load_and_import_all_results(filename, main_db, diff_db)\n\n idaapi.qexit(0)\n\n\n#-------------------------------------------------------------------------------\ndef load_results():\n \"\"\"\n Load previously saved diffing results.\n \"\"\"\n tmp_diff = CIDABinDiff(\":memory:\")\n filename = ask_file(0, \"*.diaphora\", \"Select the file to load diffing results\")\n if filename is not None:\n tmp_diff.load_results(filename)\n\n\n#-------------------------------------------------------------------------------\ndef import_definitions():\n \"\"\"\n Import *only* the definitions (struct, enums and unions).\n \"\"\"\n tmp_diff = CIDABinDiff(\":memory:\")\n message = \"Select the file to import structures, unions and enumerations from\"\n filename = ask_file(0, \"*.sqlite\", message)\n if filename is not None:\n message = \"HIDECANCEL\\nDo you really want to import all structures, unions and enumerations?\"\n if ask_yn(1, message) == 1:\n tmp_diff.import_definitions_only(filename)\n\n\n#-------------------------------------------------------------------------------\ndef diaphora_decode(ea):\n \"\"\"\n Wrapper for IDA's decode_insn\n \"\"\"\n ins = idaapi.insn_t()\n decoded_size = idaapi.decode_insn(ins, ea)\n return decoded_size, ins\n\n\n#-------------------------------------------------------------------------------\n# pylint: disable=redefined-outer-name\n# pylint: disable=arguments-renamed\n# pylint: disable=attribute-defined-outside-init\n# pylint: disable=c-extension-no-member\nclass CHtmlViewer(PluginForm):\n \"\"\"\n Class used to graphically show the differences.\n \"\"\"\n\n def OnCreate(self, form):\n self.parent = self.FormToPyQtWidget(form)\n self.PopulateForm()\n\n self.browser = None\n self.layout = None\n return 1\n\n def PopulateForm(self):\n self.layout = QtWidgets.QVBoxLayout()\n self.browser = QtWidgets.QTextBrowser()\n self.browser.setLineWrapMode(QtWidgets.QTextEdit.FixedColumnWidth)\n self.browser.setLineWrapColumnOrWidth(150)\n self.browser.setHtml(self.text)\n self.browser.setReadOnly(True)\n self.layout.addWidget(self.browser)\n self.parent.setLayout(self.layout)\n\n def Show(self, text, title):\n self.text = text\n return PluginForm.Show(self, title)\n\n\n# pylint: enable=c-extension-no-member\n# pylint: enable=attribute-defined-outside-init\n# pylint: enable=arguments-renamed\n# pylint: enable=redefined-outer-name\n\n\n#-------------------------------------------------------------------------------\nclass CBasicChooser(Choose):\n def __init__(self, title):\n Choose.__init__(\n self,\n title,\n [[\"Id\", 10 | Choose.CHCOL_PLAIN], [\"Name\", 30 | Choose.CHCOL_PLAIN]],\n )\n self.items = []\n\n def OnGetSize(self):\n return len(self.items)\n\n def OnGetLine(self, n):\n return self.items[n]\n\n\n#-------------------------------------------------------------------------------\n# Hex-Rays finally removed AddCommand(). Now, instead of a 1 line call, we need\n# 2 classes...\nclass command_handler_t(ida_kernwin.action_handler_t):\n def __init__(self, obj, cmd_id, num_args=2):\n self.obj = obj\n self.cmd_id = cmd_id\n self.num_args = num_args\n ida_kernwin.action_handler_t.__init__(self)\n\n def activate(self, ctx):\n if self.num_args == 1:\n return self.obj.OnCommand(self.cmd_id)\n if len(self.obj.selected_items) == 0:\n sel = 0\n else:\n sel = self.obj.selected_items[0]\n return self.obj.OnCommand(sel, self.cmd_id)\n\n def update(self, ctx):\n return idaapi.AST_ENABLE_ALWAYS\n\n\n#-------------------------------------------------------------------------------\n# Support for the removed AddCommand() API\n\n\n# pylint: disable=super-init-not-called\n# pylint: disable=arguments-differ\nclass CDiaphoraChooser(diaphora.CChooser, Choose):\n def __init__(self, title, bindiff, show_commands=True):\n diaphora.CChooser.__init__(self, title, bindiff, show_commands)\n self.actions = []\n\n def AddCommand(self, menu_name, shortcut=None):\n if menu_name is not None:\n tmp = menu_name.replace(\" \", \"\")\n action_name = f\"Diaphora:{tmp}\"\n else:\n action_name = None\n self.actions.append([len(self.actions), action_name, menu_name, shortcut])\n return len(self.actions) - 1\n\n def OnPopup(self, widget, popup_handle):\n for num, action_name, menu_name, shortcut in self.actions:\n if menu_name is None:\n ida_kernwin.attach_action_to_popup(widget, popup_handle, None)\n else:\n handler = command_handler_t(self, num, 2)\n desc = ida_kernwin.action_desc_t(\n action_name, menu_name, handler, shortcut\n )\n ida_kernwin.attach_dynamic_action_to_popup(widget, popup_handle, desc)\n\n\n# pylint: enable=arguments-differ\n# pylint: enable=super-init-not-called\n\n\n#-------------------------------------------------------------------------------\nclass CIDAChooser(CDiaphoraChooser):\n \"\"\"\n Wrapper class for IDA choosers\n \"\"\"\n\n # pylint: disable=non-parent-init-called\n def __init__(self, title, bindiff, show_commands=True):\n CDiaphoraChooser.__init__(self, title, bindiff, show_commands)\n if title.startswith(\"Unmatched in\"):\n Choose.__init__(\n self,\n title,\n [[\"Line\", 8], [\"Address\", 10], [\"Name\", 20]],\n Choose.CH_MULTI,\n )\n else:\n columns = [\n [\"Line\", 8],\n [\"Address\", 10],\n [\"Name\", 20],\n [\"Address 2\", 10],\n [\"Name 2\", 20],\n [\"Ratio\", 8],\n [\"BBlocks 1\", 5],\n [\"BBlocks 2\", 5],\n [\"Description\", 30],\n ]\n Choose.__init__(self, title, columns, Choose.CH_MULTI)\n\n # pylint: enable=non-parent-init-called\n\n def OnSelectLine(self, sel):\n item = self.items[sel[0]]\n if self.primary:\n jump_ea = int(item[CHOOSER_ITEM_MAIN_EA], 16)\n # Only jump for valid addresses\n if is_mapped(jump_ea):\n jumpto(jump_ea)\n else:\n self.bindiff.show_asm(self.items[sel[0]], self.primary)\n\n def OnGetLine(self, n):\n return self.items[n]\n\n def OnGetSize(self):\n return len(self.items)\n\n def OnDeleteLine(self, sel):\n for n in sorted(sel, reverse=True):\n if n >= 0:\n\n def get_item(n, index):\n try:\n return self.items[n][index]\n except IndexError:\n return None\n\n name1 = get_item(n, CHOOSER_ITEM_MAIN_NAME)\n name2 = get_item(n, CHOOSER_ITEM_DIFF_NAME)\n\n del self.items[n]\n\n if name1 in self.bindiff.matched_primary:\n del self.bindiff.matched_primary[name1]\n if name2 in self.bindiff.matched_secondary:\n del self.bindiff.matched_secondary[name2]\n\n return [Choose.ALL_CHANGED] + sel\n\n def show(self, force=False):\n \"\"\"\n Sort items, add menu items and show the chooser.\n \"\"\"\n if self.show_commands:\n self.items = sorted(\n self.items,\n key=lambda x: decimal.Decimal(x[CHOOSER_ITEM_RATIO]),\n reverse=True,\n )\n\n t = self.Show()\n if t < 0:\n return False\n\n # pylint: disable=attribute-defined-outside-init\n\n if self.show_commands and (self.cmd_diff_asm is None or force):\n # create aditional actions handlers\n self.cmd_rediff = self.AddCommand(\"Diff again\")\n self.cmd_save_results = self.AddCommand(\"Save results\")\n self.cmd_add_manual_match = self.AddCommand(\"Add manual match\")\n self.AddCommand(None)\n self.cmd_diff_asm = self.AddCommand(\"Diff assembly\")\n self.cmd_diff_microcode = self.AddCommand(\"Diff microcode\")\n self.cmd_diff_c = self.AddCommand(\"Diff pseudo-code\")\n self.cmd_diff_graph = self.AddCommand(\"Diff assembly in a graph\")\n self.cmd_diff_graph_microcode = self.AddCommand(\"Diff microcode in a graph\")\n self.cmd_diff_external = self.AddCommand(\"Diff using an external tool\")\n self.cmd_diff_asm_patch = self.AddCommand(\"Show assembly patch\")\n self.cmd_diff_c_patch = self.AddCommand(\"Show pseudo-code patch\")\n self.cmd_view_callgraph_context = self.AddCommand(\n \"Show callers and callees graph\"\n )\n self.AddCommand(None)\n self.cmd_import_selected = self.AddCommand(\"Import selected\", \"Ctrl+Alt+i\")\n self.cmd_import_selected_auto = self.AddCommand(\"Import selected sub_*\")\n self.cmd_import_all = self.AddCommand(\"Import *all* functions\")\n self.cmd_import_all_funcs = self.AddCommand(\n \"Import *all* data for sub_* functions\"\n )\n self.AddCommand(None)\n self.cmd_highlight_functions = self.AddCommand(\"Highlight matches\")\n self.cmd_unhighlight_functions = self.AddCommand(\"Unhighlight matches\")\n elif not self.show_commands and (self.cmd_show_asm is None or force):\n self.cmd_show_asm = self.AddCommand(\"Show assembly\")\n self.cmd_show_pseudo = self.AddCommand(\"Show pseudo-code\")\n\n # pylint: enable=attribute-defined-outside-init\n\n return True\n\n def OnCommand(self, n, cmd_id):\n \"\"\"\n Aditional right-click-menu commands handles.\n \"\"\"\n if cmd_id == self.cmd_show_asm:\n self.bindiff.show_asm(self.items[n], self.primary)\n elif cmd_id == self.cmd_show_pseudo:\n self.bindiff.show_pseudo(self.items[n], self.primary)\n elif cmd_id == self.cmd_import_all:\n text = \"HIDECANCEL\\n\"\n text += \"Do you want to import all functions, comments, prototypes and definitions?\"\n if ask_yn(1, text) == 1:\n self.bindiff.import_all(self.items)\n elif cmd_id == self.cmd_import_all_funcs:\n if (\n ask_yn(\n 1,\n \"HIDECANCEL\\nDo you really want to import all IDA named matched functions, comments, prototypes and definitions?\",\n )\n == 1\n ):\n self.bindiff.import_all_auto(self.items)\n elif (\n cmd_id == self.cmd_import_selected\n or cmd_id == self.cmd_import_selected_auto\n ):\n if len(self.selected_items) <= 1:\n self.bindiff.import_one(self.items[n])\n else:\n if (\n ask_yn(\n 1,\n \"HIDECANCEL\\nDo you really want to import all selected IDA named matched functions, comments, prototypes and definitions?\",\n )\n == 1\n ):\n self.bindiff.import_selected(\n self.items,\n self.selected_items,\n cmd_id == self.cmd_import_selected_auto,\n )\n elif cmd_id == self.cmd_diff_c:\n self.bindiff.show_pseudo_diff(self.items[n])\n elif cmd_id == self.cmd_diff_c_patch:\n self.bindiff.show_pseudo_diff(self.items[n], html=False)\n elif cmd_id == self.cmd_diff_asm_patch:\n self.bindiff.show_asm_diff(self.items[n], html=False)\n elif cmd_id == self.cmd_diff_asm:\n self.bindiff.show_asm_diff(self.items[n])\n elif cmd_id == self.cmd_diff_microcode:\n self.bindiff.show_microcode_diff(self.items[n])\n elif cmd_id == self.cmd_highlight_functions:\n if (\n ask_yn(\n 1,\n \"HIDECANCEL\\nDo you want to change the background color of each matched function?\",\n )\n == 1\n ):\n color = self.get_color()\n for item in self.items:\n ea = int(item[CHOOSER_ITEM_MAIN_EA], 16)\n if not set_color(ea, CIC_FUNC, color):\n # pylint: disable-next=consider-using-f-string\n print(\"Error setting color for %x\" % ea)\n self.Refresh()\n elif cmd_id == self.cmd_unhighlight_functions:\n for item in self.items:\n ea = int(item[CHOOSER_ITEM_MAIN_EA], 16)\n if not set_color(ea, CIC_FUNC, 0xFFFFFF):\n # pylint: disable-next=consider-using-f-string\n print(\"Error setting color for %x\" % ea)\n self.Refresh()\n elif cmd_id == self.cmd_diff_graph:\n item = self.items[n]\n ea1 = int(item[CHOOSER_ITEM_MAIN_EA], 16)\n name1 = item[CHOOSER_ITEM_MAIN_NAME]\n ea2 = int(item[CHOOSER_ITEM_DIFF_EA], 16)\n name2 = item[CHOOSER_ITEM_DIFF_NAME]\n # pylint: disable-next=consider-using-f-string\n log(\"Diff graph for 0x%x - 0x%x\" % (ea1, ea2))\n self.bindiff.graph_diff(ea1, name1, ea2, name2)\n elif cmd_id == self.cmd_diff_graph_microcode:\n item = self.items[n]\n ea1 = int(item[CHOOSER_ITEM_MAIN_EA], 16)\n name1 = item[CHOOSER_ITEM_MAIN_NAME]\n ea2 = int(item[CHOOSER_ITEM_DIFF_EA], 16)\n name2 = item[CHOOSER_ITEM_DIFF_NAME]\n # pylint: disable-next=consider-using-f-string\n log(\"Diff microcode graph for 0x%x - 0x%x\" % (ea1, ea2))\n self.bindiff.graph_diff_microcode(ea1, name1, ea2, name2)\n elif cmd_id == self.cmd_view_callgraph_context:\n item = self.items[n]\n ea1 = int(item[CHOOSER_ITEM_MAIN_EA], 16)\n name1 = item[CHOOSER_ITEM_MAIN_NAME]\n ea2 = int(item[CHOOSER_ITEM_DIFF_EA], 16)\n name2 = item[CHOOSER_ITEM_DIFF_NAME]\n # pylint: disable-next=consider-using-f-string\n log(\"Showing call graph context for 0x%x - 0x%x\" % (ea1, ea2))\n self.bindiff.show_callgraph_context(name1, name2)\n elif cmd_id == self.cmd_save_results:\n filename = ask_file(\n 1, \"*.diaphora\", \"Select the file to store diffing results\"\n )\n if filename is not None:\n self.bindiff.save_results(filename)\n elif cmd_id == self.cmd_add_manual_match:\n self.add_manual_match()\n elif cmd_id == self.cmd_rediff:\n self.bindiff.db.execute(\"detach diff\")\n timeraction_t(self.bindiff.re_diff, None, 1000)\n elif cmd_id == self.cmd_diff_external:\n self.bindiff.diff_external(self.items[n])\n\n return True\n\n def get_diff_functions(self):\n \"\"\"\n Return the functions rows for the diff database\n \"\"\"\n cur = self.bindiff.db_cursor()\n try:\n cur.execute(\"select cast(id as text), name, address from diff.functions order by id\")\n rows = list(cur.fetchall())\n rows = list(map(list, rows))\n finally:\n cur.close()\n\n return rows\n\n def add_manual_match_internal(self, ea1, ea2):\n \"\"\"\n Internal function, add a manual match directly to the partial chooser.\n \"\"\"\n main_row = self.bindiff.get_function_row_by_ea(ea1)\n if main_row is None:\n warning(\"Can't find the local function!\")\n return\n\n diff_row = self.bindiff.get_function_row_by_ea(ea2, \"diff\")\n if diff_row is None:\n warning(\"Can't find the foreign function!\")\n return\n\n ratio = self.bindiff.compare_function_rows(main_row, diff_row)\n\n ea1 = main_row[\"address\"]\n name1 = main_row[\"name\"]\n ea2 = diff_row[\"address\"]\n name2 = diff_row[\"name\"]\n desc = \"Manual match\"\n bb1 = main_row[\"nodes\"]\n bb2 = diff_row[\"nodes\"]\n\n if name1 in self.bindiff.matched_primary or name2 in self.bindiff.matched_secondary:\n line = (\n \"Either the local function or the foreign function is already matched!\\n\"\n + \"Please remove the previously assigned match before adding a manual match.\"\n )\n warning(line)\n return\n\n self.bindiff.partial_chooser.add_item(\n diaphora.CChooser.Item(ea1, name1, ea2, name2, desc, ratio, bb1, bb2)\n )\n self.bindiff.matched_primary[name1] = {\"name\": name2, \"ratio\": ratio}\n self.bindiff.matched_secondary[name2] = {\"name\": name1, \"ratio\": ratio}\n self.bindiff.partial_chooser.Refresh()\n\n def add_manual_match(self):\n \"\"\"\n Menu item handler for adding a manual match.\n \"\"\"\n f = choose_func(\"Select a function from the current database...\", 0)\n if f is not None:\n diff_chooser = CBasicChooser(\n \"Select a function from the external database...\"\n )\n diff_funcs = self.get_diff_functions()\n diff_chooser.items = diff_funcs\n ret = diff_chooser.Show(modal=True)\n if ret > -1:\n ea1 = f.start_ea\n ea2 = int(diff_funcs[ret][2])\n # pylint: disable-next=consider-using-f-string\n log(\"Adding manual match between 0x%08x and 0x%08x\" % (ea1, ea2))\n self.add_manual_match_internal(ea1, ea2)\n\n def OnSelectionChange(self, sel):\n self.selected_items = sel\n\n def seems_false_positive(self, item):\n \"\"\"\n Check if it looks like a false positive because the names are different.\n \"\"\"\n name1 = item[CHOOSER_ITEM_MAIN_NAME]\n name2 = item[CHOOSER_ITEM_DIFF_NAME]\n\n name1 = name1.rstrip(\"_0\")\n name2 = name2.rstrip(\"_0\")\n\n if not name1.startswith(\"sub_\") and not name2.startswith(\"sub_\"):\n if name1 != name2:\n if name2.find(name1) == -1 and not name1.find(name2) == -1:\n return True\n\n return False\n\n def OnGetLineAttr(self, n):\n if not self.title.startswith(\"Unmatched\"):\n item = self.items[n]\n ratio = float(item[CHOOSER_ITEM_RATIO])\n if self.seems_false_positive(item):\n return [LITTLE_ORANGE, 0]\n else:\n red = int(164 * (1 - ratio))\n green = int(128 * ratio)\n blue = int(255 * (1 - ratio))\n # pylint: disable-next=consider-using-f-string\n color = int(\"0x%02x%02x%02x\" % (blue, green, red), 16)\n return [color, 0]\n return [0xFFFFFF, 0]\n\n\n#-------------------------------------------------------------------------------\n# pylint: disable=no-member\nclass CBinDiffExporterSetup(Form):\n \"\"\"\n IDA class to build the export dialogue.\n \"\"\"\n\n def __init__(self):\n s = r\"\"\"Diaphora\n Please select the path to the SQLite database to save the current IDA database and the path of the SQLite database to diff against.\n If no SQLite diff database is selected, it will just export the current IDA database to SQLite format. Leave the 2nd field empty if you are exporting the first database.\n\n SQLite databases: Export filter limits:\n <#Select a file to export the current IDA database to SQLite format#Export IDA database to SQLite :{iFileSave}> <#Minimum address to find functions to export#From address:{iMinEA}>\n <#Select the SQLite database to diff against #SQLite database to diff against:{iFileOpen}> <#Maximum address to find functions to export#To address :{iMaxEA}>\n\n Export options:\n \n <#Enable this option to disable exporting microcode#Export microcode instructions and basic blocks:{rExportMicrocode}>\n \n <#Enable if you want neither sub_* functions nor library functions to be exported#Export only non-IDA generated functions:{rNonIdaSubs}>\n <#Export only function summaries, not all instructions. Showing differences in a graph between functions will not be available.#Do not export instructions and basic blocks:{rFuncSummariesOnly}>\n <#Enable this option to ignore thunk functions, nullsubs, etc....#Ignore small functions:{rIgnoreSmallFunctions}>{cGroupExport}>|\n\n Diffing options:\n \n \n <#Enable this option if you aren't interested in small changes#Relaxed calculations of differences ratios:{rRelaxRatio}>\n \n <#Enable this option to ignore sub_* names for the 'Same name' heuristic.#Ignore automatically generated names:{rIgnoreSubNames}>\n <#Enable this option to ignore all function names for the 'Same name' heuristic.#Ignore all function names:{rIgnoreAllNames}>{cGroup1}>\n\n Project specific rules:\n <#Select the project specific Python script rules#Python script:{iProjectSpecificRules}>\n\n NOTE: Don't select IDA database files (.IDB, .I64) as only SQLite databases are considered.\n\"\"\"\n args = {\n \"iFileSave\": Form.FileInput(save=True, swidth=40, hlp=\"SQLite database (*.sqlite)\"),\n \"iFileOpen\": Form.FileInput(open=True, swidth=40, hlp=\"SQLite database (*.sqlite)\"),\n \"iMinEA\": Form.NumericInput(tp=Form.FT_HEX, swidth=22),\n \"iMaxEA\": Form.NumericInput(tp=Form.FT_HEX, swidth=22),\n \"cGroupExport\": Form.ChkGroupControl(\n (\n \"rUseDecompiler\",\n \"rExcludeLibraryThunk\",\n \"rIgnoreSmallFunctions\",\n \"rExportMicrocode\",\n \"rNonIdaSubs\",\n \"rFuncSummariesOnly\",\n )\n ),\n \"cGroup1\": Form.ChkGroupControl(\n (\n \"rUnreliable\",\n \"rSlowHeuristics\",\n \"rRelaxRatio\",\n \"rExperimental\",\n \"rIgnoreSubNames\",\n \"rIgnoreAllNames\",\n )\n ),\n \"iProjectSpecificRules\": Form.FileInput(\n open=True, hlp=\"Python scripts (*.py)\"\n ),\n }\n\n Form.__init__(self, s, args)\n\n def set_options(self, opts):\n \"\"\"\n Set the configuration options from opts.\n \"\"\"\n if opts.file_out is not None:\n self.iFileSave.value = opts.file_out\n if opts.file_in is not None:\n self.iFileOpen.value = opts.file_in\n if opts.project_script is not None:\n self.iProjectSpecificRules.value = opts.project_script\n\n self.rUseDecompiler.checked = opts.use_decompiler\n self.rExcludeLibraryThunk.checked = opts.exclude_library_thunk\n self.rUnreliable.checked = opts.unreliable\n self.rSlowHeuristics.checked = opts.slow\n self.rRelaxRatio.checked = opts.relax\n self.rExperimental.checked = opts.experimental\n self.iMinEA.value = opts.min_ea\n self.iMaxEA.value = opts.max_ea\n self.rNonIdaSubs.checked = not opts.ida_subs\n self.rIgnoreSubNames.checked = opts.ignore_sub_names\n self.rIgnoreAllNames.checked = opts.ignore_all_names\n self.rIgnoreSmallFunctions.checked = opts.ignore_small_functions\n self.rFuncSummariesOnly.checked = opts.func_summaries_only\n self.rExportMicrocode.checked = opts.export_microcode\n\n def get_options(self):\n \"\"\"\n Get a dictionary with the configuration options.\n \"\"\"\n opts = dict(\n file_out=self.iFileSave.value,\n file_in=self.iFileOpen.value,\n use_decompiler=self.rUseDecompiler.checked,\n exclude_library_thunk=self.rExcludeLibraryThunk.checked,\n unreliable=self.rUnreliable.checked,\n slow=self.rSlowHeuristics.checked,\n relax=self.rRelaxRatio.checked,\n experimental=self.rExperimental.checked,\n min_ea=self.iMinEA.value,\n max_ea=self.iMaxEA.value,\n ida_subs=self.rNonIdaSubs.checked is False,\n ignore_sub_names=self.rIgnoreSubNames.checked,\n ignore_all_names=self.rIgnoreAllNames.checked,\n ignore_small_functions=self.rIgnoreSmallFunctions.checked,\n func_summaries_only=self.rFuncSummariesOnly.checked,\n project_script=self.iProjectSpecificRules.value,\n export_microcode=self.rExportMicrocode.checked,\n )\n return BinDiffOptions(**opts)\n\n\n# pylint: enable=no-member\n\n\n#-------------------------------------------------------------------------------\nclass timeraction_t(object):\n def __init__(self, func, args, interval):\n self.func = func\n self.args = args\n self.interval = interval\n self.obj = idaapi.register_timer(self.interval, self)\n if self.obj is None:\n raise RuntimeError(\"Failed to register timer\")\n\n def __call__(self):\n if self.args is not None:\n self.func(self.args)\n else:\n self.func()\n return -1\n\n\n#-------------------------------------------------------------------------------\nclass uitimercallback_t(object):\n def __init__(self, g, interval):\n self.interval = interval\n self.obj = idaapi.register_timer(self.interval, self)\n if self.obj is None:\n raise RuntimeError(\"Failed to register timer\")\n self.g = g\n\n def __call__(self):\n f = find_widget(self.g._title)\n activate_widget(f, 1)\n process_ui_action(\"GraphZoomFit\", 0)\n return -1\n\n\n#-------------------------------------------------------------------------------\nclass CDiffGraphViewer(GraphViewer):\n \"\"\"\n Class used to show graphs.\n \"\"\"\n\n def __init__(self, title, g, colours):\n try:\n GraphViewer.__init__(self, title, False)\n self.graph = g[0]\n self.relations = g[1]\n self.nodes = {}\n self.colours = colours\n except:\n warning(\"CDiffGraphViewer: OnInit!!! \" + str(sys.exc_info()[1]))\n\n def OnRefresh(self):\n try:\n self.Clear()\n self.nodes = {}\n\n for key in self.graph:\n self.nodes[key] = self.AddNode([key, self.graph[key]])\n\n for key in self.relations:\n if key not in self.nodes:\n self.nodes[key] = self.AddNode([key, [[0, 0, \"\"]]])\n parent_node = self.nodes[key]\n for child in self.relations[key]:\n if child not in self.nodes:\n self.nodes[child] = self.AddNode([child, [[0, 0, \"\"]]])\n child_node = self.nodes[child]\n self.AddEdge(parent_node, child_node)\n\n return True\n except:\n print(\"GraphViewer Error:\", sys.exc_info()[1])\n return True\n\n def OnGetText(self, node_id):\n try:\n ea, rows = self[node_id]\n if ea in self.colours:\n colour = self.colours[ea]\n else:\n colour = 0xFFFFFF\n ret = []\n for row in rows:\n ret.append(row[2])\n label = \"\\n\".join(ret)\n return (label, colour)\n except:\n print(\"GraphViewer.OnGetText:\", sys.exc_info()[1])\n return (\"ERROR\", 0x000000)\n\n def Show(self):\n return GraphViewer.Show(self)\n\n\n#-------------------------------------------------------------------------------\nclass CCallGraphViewer(GraphViewer):\n def __init__(self, title, callers, callees, target):\n GraphViewer.__init__(self, title, False)\n self.target = target\n self.callers = callers\n self.callees = callees\n\n self.root = None\n self.nodes = {}\n self.node_types = {\n \"target\": config.CALLGRAPH_COLOR_TARGET,\n \"callee\": config.CALLGRAPH_COLOR_CALLEE,\n \"caller\": config.CALLGRAPH_COLOR_CALLER,\n }\n\n def OnRefresh(self):\n self.Clear()\n self.root = self.AddNode(self.target)\n self.nodes[self.root] = [self.target, \"target\"]\n\n for caller in self.callers:\n name = caller[\"name1\"]\n node = self.AddNode(name)\n self.AddEdge(node, self.root)\n self.nodes[node] = [name, \"caller\"]\n\n for callee in self.callees:\n name = callee[\"name1\"]\n node = self.AddNode(name)\n self.AddEdge(self.root, node)\n self.nodes[node] = [name, \"callee\"]\n\n return True\n\n def OnGetText(self, node_id):\n node = self.nodes[node_id]\n name, node_type = node\n colour = self.node_types[node_type]\n return name, colour\n\n def OnHint(self, node_id):\n node = self.nodes[node_id]\n _, node_type = node\n return node_type\n\n def Show(self):\n return GraphViewer.Show(self)\n\n\n#-------------------------------------------------------------------------------\nclass CIdaMenuHandlerShowChoosers(idaapi.action_handler_t):\n def __init__(self):\n idaapi.action_handler_t.__init__(self)\n\n def activate(self, ctx):\n show_choosers()\n return 1\n\n def update(self, ctx):\n return idaapi.AST_ENABLE_ALWAYS\n\n\n#-------------------------------------------------------------------------------\nclass CIdaMenuHandlerSaveResults(idaapi.action_handler_t):\n def __init__(self):\n idaapi.action_handler_t.__init__(self)\n\n def activate(self, ctx):\n save_results()\n return 1\n\n def update(self, ctx):\n return idaapi.AST_ENABLE_ALWAYS\n\n\n#-------------------------------------------------------------------------------\nclass CIdaMenuHandlerLoadResults(idaapi.action_handler_t):\n def __init__(self):\n idaapi.action_handler_t.__init__(self)\n\n def activate(self, ctx):\n load_results()\n return 1\n\n def update(self, ctx):\n return idaapi.AST_ENABLE_ALWAYS\n\n\n#-------------------------------------------------------------------------------\nclass CExternalDiffingDialog(Form):\n def __init__(self):\n self.iStrCommand = None\n Form.__init__(\n self,\n r\"\"\"STARTITEM 0\nBUTTON YES* Diff Pseudo-code\nBUTTON NO Diff Assembler\nExternal Diffing Tool\n<#Hint1#Enter command line:{iStrCommand}>\n\"\"\",\n {\n \"iStrCommand\": Form.StringInput(),\n },\n )\n\n\n#-------------------------------------------------------------------------------\nclass CPrinter_t(hr.vd_printer_t):\n \"\"\"Converts microcode output to an array of strings.\"\"\"\n\n def __init__(self, *args):\n hr.vd_printer_t.__init__(self)\n self.mc = []\n\n def get_mc(self):\n return self.mc\n\n # pylint: disable-next=arguments-differ\n # pylint: disable-next=unexpected-keyword-arg\n def _print(self, _, line):\n self.mc.append(line)\n return 1\n\n#-------------------------------------------------------------------------------\nclass CIDABinDiff(diaphora.CBinDiff):\n \"\"\"\n The main binary diffing class.\n \"\"\"\n\n def __init__(self, db_name):\n diaphora.CBinDiff.__init__(self, db_name, chooser=CIDAChooser)\n self.decompiler_available = config.EXPORTING_USE_DECOMPILER\n self.names = dict(Names())\n self.min_ea = get_inf_attr(INF_MIN_EA)\n self.max_ea = get_inf_attr(INF_MAX_EA)\n\n self.microcode_ins_list = self.get_microcode_instructions()\n\n self.project_script = None\n self.hooks = None\n\n def clear_pseudo_fields(self):\n self.pseudo = {}\n self.pseudo_hash = {}\n self.pseudo_comments = {}\n self.microcode = {}\n\n def refresh(self):\n idaapi.request_refresh(0xFFFFFFFF)\n\n def show_choosers(self, force=False):\n \"\"\"\n Show all non empty choosers.\n \"\"\"\n CHOOSERS = [\n self.best_chooser,\n self.partial_chooser,\n self.multimatch_chooser,\n self.unreliable_chooser,\n self.unmatched_primary,\n self.unmatched_second,\n self.interesting_matches,\n ]\n\n for chooser in CHOOSERS:\n if chooser is not None and len(chooser.items) > 0:\n chooser.show(force)\n\n def diff(self, db):\n if user_cancelled():\n return None\n\n res = diaphora.CBinDiff.diff(self, db)\n if res:\n # And, finally, show the list of best and partial matches and\n # register the hotkey for re-opening results\n self.show_choosers()\n self.register_menu()\n hide_wait_box()\n return res\n\n def get_last_crash_func(self):\n \"\"\"\n Get the last inserted row before IDA or Diaphora crashed.\n \"\"\"\n sql = \"select address from functions order by id desc limit 1\"\n cur = self.db_cursor()\n try:\n cur.execute(sql)\n\n row = cur.fetchone()\n if not row:\n return None\n\n address = int(row[0])\n finally:\n cur.close()\n\n return address\n\n def recalculate_primes(self):\n \"\"\"\n Recalculate the primes assigned to a function.\n \"\"\"\n sql = \"select primes_value from functions\"\n\n callgraph_primes = 1\n callgraph_all_primes = {}\n\n cur = self.db_cursor()\n try:\n cur.execute(sql)\n for row in cur.fetchall():\n ret = row[0]\n callgraph_primes *= decimal.Decimal(row[0])\n try:\n callgraph_all_primes[ret] += 1\n except KeyError:\n callgraph_all_primes[ret] = 1\n finally:\n cur.close()\n\n return callgraph_primes, callgraph_all_primes\n\n def commit_and_start_transaction(self):\n self.db.commit()\n self.db.execute(\"PRAGMA synchronous = OFF\")\n self.db.execute(\"PRAGMA journal_mode = MEMORY\")\n self.db.execute(\"BEGIN transaction\")\n\n def do_export(self, crashed_before=False):\n \"\"\"\n Internal use, export the database.\n \"\"\"\n callgraph_primes = 1\n callgraph_all_primes = {}\n # pylint: disable-next=consider-using-f-string\n log(\"Exporting range 0x%08x - 0x%08x\" % (self.min_ea, self.max_ea))\n func_list = list(Functions(self.min_ea, self.max_ea))\n total_funcs = len(func_list)\n t = time.monotonic()\n\n if crashed_before:\n start_func = self.get_last_crash_func()\n if start_func is None:\n warning(\n \"Diaphora cannot resume the previous crashed session, the export process will start from scratch.\"\n )\n crashed_before = False\n else:\n callgraph_primes, callgraph_all_primes = self.recalculate_primes()\n\n self.commit_and_start_transaction()\n\n i = 0\n self._funcs_cache = {}\n for func in func_list:\n if user_cancelled():\n raise Exception(\"Canceled.\")\n\n i += 1\n if (total_funcs >= 100) and i % (int(total_funcs / 100)) == 0 or i == 1:\n line = \"Exported %d function(s) out of %d total.\\nElapsed %d:%02d:%02d second(s), remaining time ~%d:%02d:%02d\"\n elapsed = time.monotonic() - t\n remaining = (elapsed / i) * (total_funcs - i)\n\n m, s = divmod(remaining, 60)\n h, m = divmod(m, 60)\n m_elapsed, s_elapsed = divmod(elapsed, 60)\n h_elapsed, m_elapsed = divmod(m_elapsed, 60)\n replace_wait_box(\n line % (i, total_funcs, h_elapsed, m_elapsed, s_elapsed, h, m, s)\n )\n\n if crashed_before:\n rva = func - self.get_base_address()\n if rva != start_func:\n continue\n\n # When we get to the last function that was previously exported, switch\n # off the 'crash' flag and continue with the next row.\n crashed_before = False\n continue\n\n props = self.read_function(func)\n self.clear_pseudo_fields()\n if props is False:\n continue\n\n ret = props[11]\n callgraph_primes *= decimal.Decimal(ret)\n try:\n callgraph_all_primes[ret] += 1\n except KeyError:\n callgraph_all_primes[ret] = 1\n \n self.save_function(props)\n\n # Try to fix bug #30 and, also, try to speed up operations as doing a\n # commit every 10 functions, as before, is overkill.\n if total_funcs > config.EXPORTING_FUNCTIONS_TO_COMMIT:\n if i % (total_funcs / 10) == 0:\n self.commit_and_start_transaction()\n\n md5sum = GetInputFileMD5()\n self.save_callgraph(\n str(callgraph_primes), json.dumps(callgraph_all_primes), md5sum\n )\n self.export_structures()\n try:\n self.export_til()\n except:\n log(f\"Error reading type libraries: {str(sys.exc_info()[1])}\")\n self.save_compilation_units()\n\n log_refresh(\"Creating indices...\")\n self.create_indices()\n\n def export(self):\n \"\"\"\n Export the current database. Call script hooks if there is any.\n \"\"\"\n if self.project_script is not None:\n if not self.load_hooks():\n return False\n\n crashed_before = False\n crash_file = f\"{self.db_name}-crash\"\n if os.path.exists(crash_file):\n log(\"Resuming a previously crashed session...\")\n crashed_before = True\n\n log(f\"Creating crash file {crash_file}...\")\n with open(crash_file, \"wb\") as f:\n f.close()\n\n try:\n show_wait_box(\"Exporting database\")\n try:\n self.do_export(crashed_before)\n except:\n log(f\"Error: {str(sys.exc_info()[1])}\")\n traceback.print_exc()\n if self.hooks is not None:\n if \"on_export_crash\" in dir(self.hooks):\n ret = self.hooks.on_export_crash()\n if not ret:\n raise\n finally:\n hide_wait_box()\n\n self.db.commit()\n log(f\"Removing crash file {self.db_name}-crash...\")\n os.remove(f\"{self.db_name}-crash\")\n\n cur = self.db_cursor()\n try:\n cur.execute(\"analyze\")\n finally:\n cur.close()\n\n self.db_close()\n\n def import_til(self):\n \"\"\"\n Import IDA's Type Libraries.\n \"\"\"\n log(\"Importing type libraries...\")\n cur = self.db_cursor()\n try:\n sql = \"select name from diff.program_data where type = 'til'\"\n cur.execute(sql)\n for row in cur.fetchall():\n til = row[\"name\"]\n if isinstance(til, bytes):\n til = til.decode(\"utf-8\")\n\n try:\n add_default_til(til)\n except:\n log(f'Error loading TIL {row[\"name\"]}: {str(sys.exc_info()[1])}')\n finally:\n cur.close()\n\n auto_wait()\n\n def import_definitions(self):\n \"\"\"\n Import structs, enums and unions\n \"\"\"\n cur = self.db_cursor()\n try:\n sql = \"select type, name, value from diff.program_data where type in ('structure', 'struct', 'enum', 'union')\"\n cur.execute(sql)\n rows = diaphora.result_iter(cur)\n\n new_rows = set()\n for row in rows:\n if row[\"name\"] is None:\n continue\n\n the_name = row[\"name\"].split(\" \")[0]\n if get_struc_id(the_name) == BADADDR:\n type_name = \"struct\"\n if row[\"type\"] == \"enum\":\n type_name = \"enum\"\n elif row[\"type\"] == \"union\":\n type_name = \"union\"\n\n new_rows.add(row)\n line = f\"{type_name} {row['name']};\"\n try:\n ret = idc.parse_decls(line)\n if ret != 0:\n pass\n except:\n log(f\"Error importing type: {str(sys.exc_info()[1])}\")\n\n for _ in range(10):\n for row in new_rows:\n if row[\"name\"] is None:\n continue\n\n the_name = row[\"name\"].split(\" \")[0]\n if (\n get_struc_id(the_name) == BADADDR\n and get_struc_id(row[\"name\"]) == BADADDR\n ):\n definition = self.get_valid_definition(row[\"value\"])\n ret = idc.parse_decls(\n definition\n ) # Remove the \"idc.\" to reproduce some strange behaviour\n if ret != 0:\n pass\n finally:\n cur.close()\n\n auto_wait()\n\n def reinit(self, main_db, diff_db, create_choosers=True):\n \"\"\"\n Reinitialize databases.\n \"\"\"\n log(f\"Main database '{main_db}'.\")\n log(f\"Diff database '{diff_db}'.\")\n\n self.__init__(main_db)\n self.attach_database(diff_db)\n self.last_diff_db = diff_db\n\n if create_choosers:\n self.create_choosers()\n\n def import_definitions_only(self, filename):\n \"\"\"\n Import only the definitions (TIL and structs/enums/unions).\n \"\"\"\n self.reinit(\":memory:\", filename)\n self.import_til()\n self.import_definitions()\n\n def generate_asm_diff_internal(self, ea1, ea2, field, title_fmt, html=True, error_func=log):\n \"\"\"\n Internal use, generate a HTML table with the assembly differences.\n \"\"\"\n cur = self.db_cursor()\n try:\n sql = f\"\"\"select *\n from (\n select prototype, {field}, name, 1\n from functions\n where address = ?\n and {field} is not null\n union select prototype, {field}, name, 2\n from diff.functions\n where address = ?\n and {field} is not null)\n order by 4 asc\"\"\"\n ea1 = str(int(ea1, 16))\n ea2 = str(int(ea2, 16))\n cur.execute(sql, (ea1, ea2))\n rows = cur.fetchall()\n res = None\n if len(rows) != 2:\n message = f\"Sorry, there is no {field} available for either the first or the second database.\"\n error_func(message)\n else:\n row1 = rows[0]\n row2 = rows[1]\n\n html_diff = CHtmlDiff()\n asm1 = self.prettify_asm(row1[field])\n asm2 = self.prettify_asm(row2[field])\n buf1 = f'{row1[\"name\"]} proc near\\n{asm1}\\n{row1[\"name\"]} endp'\n buf2 = f'{row2[\"name\"]} proc near\\n{asm2}\\n{row2[\"name\"]} endp'\n\n fmt = HtmlFormatter()\n fmt.noclasses = True\n fmt.linenos = False\n fmt.nobackground = True\n if not html:\n uni_diff = difflib.unified_diff(buf1.split(\"\\n\"), buf2.split(\"\\n\"))\n tmp = []\n for line in uni_diff:\n tmp.append(line.strip(\"\\n\"))\n tmp = tmp[2:]\n buf = \"\\n\".join(tmp)\n\n src = highlight(buf, DiffLexer(), fmt)\n else:\n src = html_diff.make_file(\n buf1.split(\"\\n\"), buf2.split(\"\\n\"), fmt, NasmLexer()\n )\n\n title = title_fmt % (row1[\"name\"], row2[\"name\"])\n res = (src, title)\n finally:\n cur.close()\n\n return res\n\n def generate_asm_diff(self, ea1, ea2, html=True, error_func=log):\n return self.generate_asm_diff_internal(\n ea1, ea2, \"assembly\", \"Diff assembly %s - %s\", html, error_func\n )\n\n def generate_microcode_diff(self, ea1, ea2, error_func=log):\n return self.generate_asm_diff_internal(\n ea1, ea2, \"microcode\", \"Diff microcode %s - %s\", error_func\n )\n\n def show_asm_diff(self, item, html=True):\n res = self.generate_asm_diff(item[1], item[3], html=html, error_func=warning)\n if res:\n (src, title) = res\n cdiffer = CHtmlViewer()\n cdiffer.Show(src, title)\n\n def show_microcode_diff(self, item):\n res = self.generate_microcode_diff(item[1], item[3], error_func=warning)\n if res:\n (src, title) = res\n cdiffer = CHtmlViewer()\n cdiffer.Show(src, title)\n\n def save_asm_diff(self, ea1, ea2, filename):\n res = self.generate_asm_diff(ea1, ea2)\n if res:\n (src, _) = res\n with open(filename, \"w\", encoding=\"utf8\") as f:\n f.write(src)\n\n def import_one(self, item):\n ret = ask_yn(\n 1,\n \"AUTOHIDE DATABASE\\nDo you want to import all the type libraries, structs and enumerations?\",\n )\n\n if ret == 1:\n # Import all the type libraries from the diff database\n self.import_til()\n # Import all the struct and enum definitions\n self.import_definitions()\n elif ret == -1:\n return\n\n # Import just the selected item\n ea1 = str(int(item[1], 16))\n ea2 = str(int(item[3], 16))\n self.do_import_one(ea1, ea2, True)\n\n new_func = self.read_function(str(ea1))\n self.delete_function(ea1)\n self.save_function(new_func)\n self.db.commit()\n\n self.update_choosers()\n\n def show_asm(self, item, primary):\n cur = self.db_cursor()\n try:\n if primary:\n db = \"main\"\n else:\n db = \"diff\"\n ea = str(int(item[1], 16))\n sql = \"select prototype, assembly, name from %s.functions where address = ?\"\n sql = sql % db\n cur.execute(sql, (ea,))\n row = cur.fetchone()\n if row is None:\n warning(\n \"Sorry, there is no assembly available for the selected function.\"\n )\n else:\n fmt = HtmlFormatter()\n fmt.noclasses = True\n fmt.linenos = True\n asm = self.prettify_asm(row[\"assembly\"])\n final_asm = f'; {row[\"prototype\"]}\\n{row[\"name\"]} proc near\\n{asm}\\n{row[\"name\"]} endp\\n'\n src = highlight(final_asm, NasmLexer(), fmt)\n title = f'Assembly for {row[\"name\"]}'\n cdiffer = CHtmlViewer()\n cdiffer.Show(src, title)\n finally:\n cur.close()\n\n def show_pseudo(self, item, primary):\n cur = self.db_cursor()\n try:\n if primary:\n db = \"main\"\n else:\n db = \"diff\"\n ea = str(int(item[1], 16))\n sql = (\n \"select prototype, pseudocode, name from %s.functions where address = ?\"\n )\n sql = sql % db\n cur.execute(sql, (str(ea),))\n row = cur.fetchone()\n if row is None or row[\"prototype\"] is None or row[\"pseudocode\"] is None:\n warning(\n \"Sorry, there is no pseudo-code available for the selected function.\"\n )\n else:\n fmt = HtmlFormatter()\n fmt.noclasses = True\n fmt.linenos = True\n func = f'{row[\"prototype\"]}\\n{row[\"pseudocode\"]}'\n src = highlight(func, CppLexer(), fmt)\n title = f'Pseudo-code for {row[\"name\"]}'\n cdiffer = CHtmlViewer()\n cdiffer.Show(src, title)\n finally:\n cur.close()\n\n def generate_pseudo_diff(self, ea1, ea2, html=True, error_func=log):\n cur = self.db_cursor()\n try:\n sql = \"\"\"select *\n from (\n select prototype, pseudocode, name, 1\n from functions\n where address = ?\n and pseudocode is not null\n union\n select prototype, pseudocode, name, 2\n from diff.functions\n where address = ?\n and pseudocode is not null)\n order by 4 asc\"\"\"\n ea1 = str(int(ea1, 16))\n ea2 = str(int(ea2, 16))\n cur.execute(sql, (ea1, ea2))\n rows = cur.fetchall()\n res = None\n if len(rows) != 2:\n error_func(\n \"Sorry, there is no pseudo-code available for either the first or the second database.\"\n )\n else:\n row1 = rows[0]\n row2 = rows[1]\n\n html_diff = CHtmlDiff()\n proto1 = self.decompile_and_get(int(ea1))\n if proto1:\n buf1 = proto1 + \"\\n\" + \"\\n\".join(self.pseudo[int(ea1)])\n else:\n log(\n \"warning: cannot retrieve the current pseudo-code for the function, using the previously saved one...\"\n )\n buf1 = row1[\"prototype\"] + \"\\n\" + row1[\"pseudocode\"]\n buf2 = row2[\"prototype\"] + \"\\n\" + row2[\"pseudocode\"]\n\n if buf1 == buf2:\n error_func(\"Both pseudo-codes are equal.\")\n\n fmt = HtmlFormatter()\n fmt.noclasses = True\n fmt.linenos = False\n fmt.nobackground = True\n if not html:\n uni_diff = difflib.unified_diff(buf1.split(\"\\n\"), buf2.split(\"\\n\"))\n tmp = []\n for line in uni_diff:\n tmp.append(line.strip(\"\\n\"))\n tmp = tmp[2:]\n buf = \"\\n\".join(tmp)\n\n src = highlight(buf, DiffLexer(), fmt)\n else:\n src = html_diff.make_file(\n buf1.split(\"\\n\"), buf2.split(\"\\n\"), fmt, CppLexer()\n )\n\n title = f'Diff pseudo-code {row1[\"name\"]} - {row2[\"name\"]}'\n res = (src, title)\n finally:\n cur.close()\n return res\n\n def show_pseudo_diff(self, item, html=True):\n res = self.generate_pseudo_diff(item[1], item[3], html=html, error_func=warning)\n if res:\n (src, title) = res\n cdiffer = CHtmlViewer()\n cdiffer.Show(src, title)\n\n def save_pseudo_diff(self, ea1, ea2, filename):\n res = self.generate_pseudo_diff(ea1, ea2, html=True)\n if res:\n (src, _) = res\n with open(filename, \"w\", encoding=\"utf8\") as f:\n f.write(src)\n\n def diff_external(self, item):\n cmd_line = None\n f = CExternalDiffingDialog()\n f.Compile()\n cmd = reg_read_string(\"diaphora_external_command\")\n if cmd == \"\" or cmd is None:\n cmd = \"your_command $1 $2\"\n f.iStrCommand.value = cmd\n ok = f.Execute()\n if ok == 0:\n cmd_line = f.iStrCommand.value\n diff_asm = True\n elif ok == 1:\n cmd_line = f.iStrCommand.value\n diff_asm = False\n\n f.Free()\n if cmd_line is None:\n return\n\n reg_write_string(\"diaphora_external_command\", cmd_line)\n if diff_asm:\n ret = self.diff_external_asm(item, cmd_line)\n else:\n ret = self.diff_external_pseudo(item, cmd_line)\n print(\"External command returned\", ret)\n\n def diff_external_asm(self, item, cmd_line):\n ret = None\n try:\n cur = self.db_cursor()\n sql = \"\"\"select *\n from (\n select prototype, assembly, name, 1\n from functions\n where address = ?\n and assembly is not null\n union\n select prototype, assembly, name, 2\n from diff.functions\n where address = ?\n and assembly is not null)\n order by 4 asc\"\"\"\n ea1 = str(int(item[1], 16))\n ea2 = str(int(item[3], 16))\n cur.execute(sql, (ea1, ea2))\n rows = cur.fetchall()\n if len(rows) != 2:\n warning(\n \"Sorry, there is no assembly available for either the first or the second database.\"\n )\n else:\n row1 = rows[0]\n row2 = rows[1]\n\n asm1 = self.prettify_asm(row1[\"assembly\"])\n asm2 = self.prettify_asm(row2[\"assembly\"])\n buf1 = f'{row1[\"name\"]} proc near\\n{asm1}\\n{row1[\"name\"]} endp'\n buf2 = f'{row2[\"name\"]} proc near\\n{asm2}\\n{row2[\"name\"]} endp'\n\n filename1 = f\"main_{item[1]}.asm\"\n filename2 = f\"diff_{item[3]}.asm\"\n\n with open(filename1, \"w\", encoding=\"utf8\") as f_source:\n with open(filename2, \"w\", encoding=\"utf8\") as f_dest:\n f_source.writelines(buf1)\n f_dest.writelines(buf2)\n\n line = cmd_line.replace(\"$1\", filename1)\n line = line.replace(\"$2\", filename2)\n ret = os.system(line)\n finally:\n cur.close()\n\n return ret\n\n def diff_external_pseudo(self, item, cmd_line):\n ret = None\n cur = self.db_cursor()\n try:\n sql = \"\"\"select *\n from (\n select prototype, pseudocode, address, 1\n from functions\n where address = ?\n and pseudocode is not null\n union\n select prototype, pseudocode, address, 2\n from diff.functions\n where address = ?\n and pseudocode is not null)\n order by 4 asc\"\"\"\n ea1 = str(int(item[1], 16))\n ea2 = str(int(item[3], 16))\n cur.execute(sql, (ea1, ea2))\n rows = cur.fetchall()\n if len(rows) != 2:\n warning(\n \"Sorry, there is no pseudo-code available for either the first or the second database.\"\n )\n else:\n row1 = rows[0]\n row2 = rows[1]\n\n filename1 = f\"main_{item[1]}.cpp\"\n filename2 = f\"diff_{item[3]}.cpp\"\n\n with open(filename1, \"w\", encoding=\"utf8\") as f_source:\n with open(filename2, \"w\", encoding=\"utf8\") as f_dest:\n f_source.writelines(f'{row1[\"prototype\"]}\\n{row1[\"pseudocode\"]}')\n f_dest.writelines(f'{row2[\"prototype\"]}\\n{row2[\"pseudocode\"]}')\n\n line = cmd_line.replace(\"$1\", filename1)\n line = line.replace(\"$2\", filename2)\n ret = os.system(line)\n finally:\n cur.close()\n return ret\n\n def graph_diff(self, ea1, name1, ea2, name2):\n g1 = self.get_graph(str(ea1), True)\n g2 = self.get_graph(str(ea2))\n\n if g1 == ({}, {}) or g2 == ({}, {}):\n warning(\n \"Sorry, graph information is not available for one of the databases.\"\n )\n return False\n\n colours = self.compare_graphs(g1, g2)\n\n title1 = f\"Graph for {name1} (primary)\"\n title2 = f\"Graph for {name2} (secondary)\"\n graph1 = CDiffGraphViewer(title1, g1, colours[0])\n graph2 = CDiffGraphViewer(title2, g2, colours[1])\n graph1.Show()\n graph2.Show()\n\n set_dock_pos(title2, title1, DP_RIGHT)\n uitimercallback_t(graph1, 100)\n uitimercallback_t(graph2, 100)\n\n def graph_diff_microcode(self, ea1, name1, ea2, name2):\n g1 = self.get_graph(str(ea1), True, \"microcode\")\n g2 = self.get_graph(str(ea2), False, \"microcode\")\n\n if g1 == ({}, {}) or g2 == ({}, {}):\n warning(\n \"Sorry, graph information is not available for one of the databases.\"\n )\n return False\n\n colours = self.compare_graphs(g1, g2)\n\n title1 = f\"Microcode graph for {name1} (primary)\"\n title2 = f\"Microcode graph for {name2} (secondary)\"\n graph1 = CDiffGraphViewer(title1, g1, colours[0])\n graph2 = CDiffGraphViewer(title2, g2, colours[1])\n graph1.Show()\n graph2.Show()\n\n set_dock_pos(title2, title1, DP_RIGHT)\n uitimercallback_t(graph1, 100)\n uitimercallback_t(graph2, 100)\n\n def get_calls_graph(self, name, mtype, db):\n \"\"\"\n Get the call graph for the given function.\n \"\"\"\n cur = self.db_cursor()\n rows = []\n try:\n sql = f\"\"\"select cg.type type, f2.address ea1, f2.name name1,\n f1.address ea2, f1.name name2\n from {db}.callgraph cg,\n {db}.functions f1,\n {db}.functions f2\n where f1.name = ?\n and cg.func_id = f1.id\n and cg.type = ?\n and f2.address = cg.address \"\"\"\n cur.execute(sql, (name, mtype))\n rows = cur.fetchall()\n finally:\n cur.close()\n\n return rows\n\n def build_calls_graph(self, title, callers, callees, name):\n \"\"\"\n Build the CCallGraphViewer objects.\n \"\"\"\n g = CCallGraphViewer(title, callers, callees, name)\n return g\n\n def show_callgraph_context(self, name1, name2):\n \"\"\"\n Show the callers and the callees for the given functions.\n \"\"\"\n main_callers = self.get_calls_graph(name1, \"caller\", \"main\")\n main_callees = self.get_calls_graph(name1, \"callee\", \"main\")\n diff_callers = self.get_calls_graph(name2, \"caller\", \"diff\")\n diff_callees = self.get_calls_graph(name2, \"callee\", \"diff\")\n\n base_title = \"Call graph context for {name}\"\n title1 = base_title.format(name=name1)\n title2 = base_title.format(name=name2)\n g1 = self.build_calls_graph(title1, main_callers, main_callees, name1)\n g2 = self.build_calls_graph(title2, diff_callers, diff_callees, name2)\n g1.Show()\n g2.Show()\n\n set_dock_pos(title2, title1, DP_RIGHT)\n uitimercallback_t(g1, 100)\n uitimercallback_t(g2, 100)\n\n def import_instruction(self, ins_data1, ins_data2):\n \"\"\"\n Try to import an instruction.\n \"\"\"\n ea1 = self.get_base_address() + int(ins_data1[0])\n _, cmt1, cmt2, operand_names, name, mtype, _, mcmt, mitp = ins_data2\n if operand_names is None:\n operand_names = []\n\n # Set instruction level comments\n if cmt1 is not None and get_cmt(ea1, 0) is None:\n set_cmt(ea1, cmt1, 0)\n\n if cmt2 is not None and get_cmt(ea1, 1) is None:\n set_cmt(ea1, cmt2, 1)\n\n for operand_name in operand_names:\n index, name = operand_name\n if name:\n ida_bytes.set_forced_operand(ea1, index, name)\n\n if mcmt is not None:\n cfunc = decompile(ea1)\n if cfunc is not None:\n tl = idaapi.treeloc_t()\n tl.ea = ea1\n #\n # We might have a pseudo-code comment without item_preciser_t information\n # (it's None). In such cases, simply don't assign anything to the member\n # treeloc_t.itp or it will trigger an error.\n #\n # Fix for issue #269.\n #\n if mitp is not None:\n tl.itp = mitp\n\n comment = mcmt\n cfunc.set_user_cmt(tl, comment)\n cfunc.save_user_cmts()\n\n tmp_ea = None\n the_type = False\n data_refs = list(DataRefsFrom(ea1))\n if len(data_refs) > 0:\n # Global variables\n tmp_ea = data_refs[0]\n if tmp_ea in self.names:\n curr_name = get_ea_name(tmp_ea)\n if curr_name != name and self.is_auto_generated(curr_name):\n set_name(tmp_ea, name, SN_CHECK)\n the_type = False\n else:\n # If it's an object, we don't want to rename the offset, we want to\n # rename the true global variable.\n if is_off(get_full_flags(tmp_ea), OPND_ALL):\n tmp_ea = next(DataRefsFrom(tmp_ea), tmp_ea)\n\n set_name(tmp_ea, name, SN_CHECK)\n the_type = True\n else:\n # Functions\n code_refs = list(CodeRefsFrom(ea1, 0))\n if len(code_refs) == 0:\n code_refs = list(CodeRefsFrom(ea1, 1))\n\n if len(code_refs) > 0:\n curr_name = get_ea_name(code_refs[0])\n if curr_name != name and self.is_auto_generated(curr_name):\n set_name(code_refs[0], name, SN_CHECK)\n tmp_ea = code_refs[0]\n the_type = True\n\n if tmp_ea is not None and the_type:\n if mtype is not None and idc.get_type(tmp_ea) != mtype:\n if isinstance(mtype, bytes):\n mtype = mtype.decode(\"utf-8\")\n SetType(tmp_ea, mtype)\n\n def row_is_importable(self, ea2, import_syms):\n \"\"\"\n Check if the given row is importable.\n \"\"\"\n ea = str(ea2)\n if ea not in import_syms:\n return False\n\n operand_names = import_syms[ea][3]\n\n # Has cmt1\n if import_syms[ea][1] is not None:\n return True\n\n # Has cmt2\n if import_syms[ea][2] is not None:\n return True\n\n # Has operand names\n operand_names = import_syms[ea][3]\n if operand_names is not None:\n for operand_name in operand_names:\n if operand_name[1] != \"\":\n return True\n\n # Has a name\n if import_syms[ea][4] is not None:\n return True\n\n # Has pseudocode comment\n if import_syms[ea][6] is not None:\n return True\n\n return False\n\n def do_import_instruction_level_item(self, diff_rows, import_syms, matched_syms):\n \"\"\"\n Internal use, import everything that can be imported at assembly instruction\n level.\n \"\"\"\n lines1 = diff_rows[0][\"assembly\"]\n lines2 = diff_rows[1][\"assembly\"]\n\n address1 = json.loads(diff_rows[0][\"assembly_addrs\"])\n address2 = json.loads(diff_rows[1][\"assembly_addrs\"])\n\n diff_list = difflib._mdiff(lines1.splitlines(1), lines2.splitlines(1))\n for x in diff_list:\n left, right, _ = x\n left_line = left[0]\n right_line = right[0]\n\n if right_line == \"\" or left_line == \"\":\n continue\n\n # At this point, we know which line number matches with\n # which another line number in both databases.\n ea1 = address1[int(left_line) - 1]\n ea2 = address2[int(right_line) - 1]\n changed = left[1].startswith(\"\\x00-\") and right[1].startswith(\"\\x00+\")\n is_importable = self.row_is_importable(ea2, import_syms)\n if changed or is_importable:\n ea1 = str(ea1)\n ea2 = str(ea2)\n if ea1 in matched_syms and ea2 in import_syms:\n self.import_instruction(matched_syms[ea1], import_syms[ea2])\n if ea2 in matched_syms and ea1 in import_syms:\n self.import_instruction(matched_syms[ea2], import_syms[ea1])\n\n def import_instruction_level(self, ea1, ea2, cur):\n cur = self.db_cursor()\n try:\n # Check first if we have any importable items\n sql = \"\"\" select distinct ins.address ea, ins.disasm dis, ins.comment1 cmt1, ins.comment2 cmt2, ins.operand_names operand_names, ins.name name, ins.type type, ins.pseudocomment cmt, ins.pseudoitp itp\n from diff.function_bblocks bb,\n diff.functions f,\n diff.bb_instructions bbi,\n diff.instructions ins\n where f.id = bb.function_id\n and bbi.basic_block_id = bb.basic_block_id\n and ins.id = bbi.instruction_id\n and f.address = ?\n and (ins.comment1 is not null\n or ins.comment2 is not null\n or ins.operand_names is not null\n or ins.name is not null\n or pseudocomment is not null) \"\"\"\n cur.execute(sql, (str(ea2),))\n import_rows = cur.fetchall()\n if len(import_rows) > 0:\n import_syms = {}\n for row in import_rows:\n operand_names = row[\"operand_names\"]\n if operand_names is not None:\n operand_names = json.loads(operand_names)\n import_syms[row[\"ea\"]] = [\n row[\"ea\"],\n row[\"cmt1\"],\n row[\"cmt2\"],\n operand_names,\n row[\"name\"],\n row[\"type\"],\n row[\"dis\"],\n row[\"cmt\"],\n row[\"itp\"],\n ]\n\n # Check in the current database\n sql = \"\"\" select distinct ins.address ea, ins.disasm dis, ins.comment1 cmt1, ins.comment2 cmt2, ins.operand_names operand_names, ins.name name, ins.type type, ins.pseudocomment cmt, ins.pseudoitp itp\n from function_bblocks bb,\n functions f,\n bb_instructions bbi,\n instructions ins\n where f.id = bb.function_id\n and bbi.basic_block_id = bb.basic_block_id\n and ins.id = bbi.instruction_id\n and f.address = ?\"\"\"\n cur.execute(sql, (str(ea1),))\n match_rows = cur.fetchall()\n if len(match_rows) > 0:\n matched_syms = {}\n for row in match_rows:\n operand_names = row[\"operand_names\"]\n if operand_names is not None:\n operand_names = json.loads(operand_names)\n matched_syms[row[\"ea\"]] = [\n row[\"ea\"],\n row[\"cmt1\"],\n row[\"cmt2\"],\n operand_names,\n row[\"name\"],\n row[\"type\"],\n row[\"dis\"],\n row[\"cmt\"],\n row[\"itp\"],\n ]\n\n # We have 'something' to import, let's diff the assembly...\n sql = \"\"\"select *\n from (\n select assembly, assembly_addrs, 1\n from functions\n where address = ?\n and assembly is not null\n union select assembly, assembly_addrs, 2\n from diff.functions\n where address = ?\n and assembly is not null)\n order by 2 asc\"\"\"\n cur.execute(sql, (str(ea1), str(ea2)))\n diff_rows = cur.fetchall()\n if len(diff_rows) > 0:\n try:\n self.do_import_instruction_level_item(\n diff_rows, import_syms, matched_syms\n )\n except:\n log(f\"Error importing item: {str(sys.exc_info()[1])}\")\n traceback.print_exc()\n finally:\n cur.close()\n\n def do_import_one(self, ea1, ea2, force=False):\n cur = self.db_cursor()\n try:\n sql = \"select prototype, comment, mangled_function, function_flags from diff.functions where address = ?\"\n cur.execute(sql, (str(ea2),))\n row = cur.fetchone()\n if row is not None:\n proto = row[\"prototype\"]\n comment = row[\"comment\"]\n name = row[\"mangled_function\"]\n flags = row[\"function_flags\"]\n\n ea1 = int(ea1)\n if not name.startswith(\"sub_\") or force:\n if not set_name(ea1, name, SN_NOWARN | SN_NOCHECK):\n for i in range(10):\n if set_name(ea1, f\"{name}_{i}\", SN_NOWARN | SN_NOCHECK):\n break\n\n if proto is not None and proto != \"int()\":\n SetType(ea1, proto)\n\n if comment is not None and comment != \"\":\n func = get_func(ea1)\n if func is not None:\n set_func_cmt(func, comment, 1)\n\n if flags is not None:\n set_func_attr(ea1, FUNCATTR_FLAGS, flags)\n\n self.import_instruction_level(ea1, ea2, cur)\n finally:\n cur.close()\n\n def import_selected(self, items, selected, only_auto):\n log_refresh(\"Importing selected row(s)...\")\n\n # Import all the type libraries from the diff database\n self.import_til()\n # Import all the struct and enum definitions\n self.import_definitions()\n\n new_items = []\n for index in selected:\n item = items[index]\n name1 = item[2]\n if not only_auto or name1.startswith(\"sub_\"):\n new_items.append(item)\n self.import_items(new_items)\n\n def import_items(self, items):\n to_import = set()\n # Import all the function names and comments\n for item in items:\n ea1 = str(int(item[1], 16))\n ea2 = str(int(item[3], 16))\n self.do_import_one(ea1, ea2)\n to_import.add(ea1)\n\n try:\n show_wait_box(\"Updating primary database...\")\n total = 0\n for ea in to_import:\n ea = str(ea)\n new_func = self.read_function(ea)\n self.delete_function(ea)\n self.save_function(new_func)\n total += 1\n\n self.db.execute(\"analyze\")\n self.db.commit()\n\n # Update the choosers after importing\n self.update_choosers()\n finally:\n hide_wait_box()\n\n def update_choosers(self):\n for chooser in [\n self.best_chooser,\n self.partial_chooser,\n self.unreliable_chooser,\n ]:\n for i, item in enumerate(chooser.items):\n ea = int(item[1], 16)\n name = item[2]\n func_name = get_func_name(ea)\n if func_name is not None and func_name != \"\" and func_name != name:\n chooser.items[i][2] = func_name\n chooser.Refresh()\n\n def do_import_all(self, items):\n # Import all the type libraries from the diff database\n self.import_til()\n # Import all the struct and enum definitions\n self.import_definitions()\n # Import all the items in the chooser\n self.import_items(items)\n\n def do_import_all_auto(self, items):\n # Import all the type libraries from the diff database\n self.import_til()\n # Import all the struct and enum definitions\n self.import_definitions()\n\n # Import all the items in the chooser for sub_* functions\n new_items = []\n for item in items:\n name1 = item[2]\n if name1.startswith(\"sub_\"):\n new_items.append(item)\n\n self.import_items(new_items)\n\n def import_all(self, items):\n try:\n self.do_import_all(items)\n except:\n log(f\"import_all(): {str(sys.exc_info()[1])}\")\n traceback.print_exc()\n\n def import_all_auto(self, items):\n try:\n self.do_import_all_auto(items)\n except:\n log(f\"import_all(): {str(sys.exc_info()[1])}\")\n traceback.print_exc()\n\n def do_decompile(self, f):\n # pylint: disable-next=unexpected-keyword-arg\n return decompile(f, flags=DECOMP_NO_WAIT)\n\n def get_plain_microcode_line(self, color_line):\n \"\"\"\n Remove colors, trailing spaces and the basic block numbers from a microcode\n line.\n \"\"\"\n plain_line = ida_lines.tag_remove(color_line)\n plain_line = plain_line.strip(\" \")\n\n mnem = None\n tokens = plain_line.split(\" \")\n for _, x in enumerate(tokens[1:]):\n if not x.isdigit():\n mnem = x\n pos = plain_line.find(x)\n plain_line = plain_line[pos:]\n break\n return plain_line, mnem\n\n def get_microcode_bblocks(self, mba):\n mba.build_graph()\n total = mba.qty\n bblocks = {}\n for i in range(total):\n if i == 0:\n continue\n\n block = mba.get_mblock(i)\n if block.type == hr.BLT_STOP:\n continue\n\n vp = hr.qstring_printer_t(None, True)\n block._print(vp)\n src = vp.s\n lines = src.splitlines()\n\n new_lines = []\n for line in lines:\n color_line = line.strip(\"\\n\").strip(\" \")\n pos = color_line.find(\";\")\n line_ea = None\n comments = None\n if pos > -1:\n comments = color_line[pos + 1:].strip(\" \")\n line_ea = int(comments.split(\" \")[0], 16)\n color_line = color_line[:pos]\n plain_line, mnem = self.get_plain_microcode_line(color_line)\n new_lines.append(\n {\n \"address\": line_ea,\n \"line\": plain_line,\n \"mnemonic\": mnem,\n \"color_line\": color_line,\n \"comments\": comments,\n }\n )\n\n bblocks[i] = {\"start\": block.start, \"end\": block.end, \"lines\": new_lines}\n\n bb_relations = {}\n for i in range(total):\n if i == 0 or i not in bblocks:\n continue\n\n block = mba.get_mblock(i)\n for succ in block.succset:\n try:\n bb_relations[i].append(succ)\n except KeyError:\n bb_relations[i] = [succ]\n\n return bblocks, bb_relations\n\n def get_microcode(self, f, ea):\n if not self.decompiler_available or not self.export_microcode:\n return [], []\n\n mbr = hr.mba_ranges_t(f)\n hf = hr.hexrays_failure_t()\n ml = hr.mlist_t()\n vp = CPrinter_t()\n mba = hr.gen_microcode(mbr, hf, ml, hr.DECOMP_WARNINGS, hr.MMAT_GENERATED)\n if mba is None:\n return [], []\n\n mba._print(vp)\n bblocks = []\n bb_relations = []\n bblocks, bb_relations = self.get_microcode_bblocks(mba)\n\n self.microcode[ea] = []\n for line in vp.mc:\n line = ida_lines.tag_remove(line).strip(\"\\n\")\n pos = line.find(\";\")\n if pos > -1:\n line = line[:pos]\n\n tokens = re.split(r\"\\W+\", line)\n tokens = list(filter(None, tokens))\n if len(tokens) > 2:\n self.microcode[ea].append(line.strip(\" \"))\n return bblocks, bb_relations\n\n def decompile_and_get(self, ea):\n if not self.decompiler_available or is_spec_ea(ea):\n return False\n\n # Workaround for a bug in IDA that might trigger the following error:\n #\n # max non-trivial tinfo_t count has been reached\n #\n if os.getenv(\"DIAPHORA_WORKAROUND_MAX_TINFO_T\") is not None:\n idaapi.clear_cached_cfuncs()\n\n decompiler_plugin = os.getenv(\"DIAPHORA_DECOMPILER_PLUGIN\")\n if decompiler_plugin is None:\n decompiler_plugin = \"hexrays\"\n if not init_hexrays_plugin() and not (\n load_plugin(decompiler_plugin) and init_hexrays_plugin()\n ):\n self.decompiler_available = False\n return False\n\n f = get_func(ea)\n if f is None:\n return False\n\n cfunc = self.do_decompile(f)\n if cfunc is None:\n # Failed to decompile\n return False\n\n visitor = CAstVisitor(cfunc)\n visitor.apply_to(cfunc.body, None)\n self.pseudo_hash[ea] = visitor.primes_hash\n\n cmts = idaapi.restore_user_cmts(cfunc.entry_ea)\n if cmts is not None:\n for tl, cmt in cmts.items():\n self.pseudo_comments[tl.ea - self.get_base_address()] = [\n str(cmt),\n tl.itp,\n ]\n\n sv = cfunc.get_pseudocode()\n self.pseudo[ea] = []\n first_line = None\n for sline in sv:\n line = tag_remove(sline.line)\n if line.startswith(\"//\"):\n continue\n\n if first_line is None:\n first_line = line\n else:\n self.pseudo[ea].append(line)\n\n self.microcode[ea] = []\n self.get_microcode(f, ea)\n return first_line\n\n def guess_type(self, ea):\n t = guess_type(ea)\n if not self.use_decompiler:\n return t\n else:\n try:\n ret = self.decompile_and_get(ea)\n if ret:\n t = ret\n except:\n # pylint: disable-next=consider-using-f-string\n log(\"Cannot decompile 0x%x: %s\" % (ea, str(sys.exc_info()[1])))\n return t\n\n def register_menu_action(self, action_name, action_desc, handler, hotkey=None):\n show_choosers_action = idaapi.action_desc_t(\n action_name, action_desc, handler, hotkey, None, -1\n )\n idaapi.register_action(show_choosers_action)\n idaapi.attach_action_to_menu(\n f\"Edit/Plugins/{action_desc}\", action_name, idaapi.SETMENU_APP\n )\n\n def register_menu(self):\n # pylint: disable-next=global-statement\n global g_bindiff\n g_bindiff = self\n\n menu_items = [\n [\n \"diaphora:show_results\",\n \"Diaphora - Show results\",\n CIdaMenuHandlerShowChoosers(),\n \"F3\",\n ],\n [\n \"diaphora:save_results\",\n \"Diaphora - Save results\",\n CIdaMenuHandlerSaveResults(),\n None,\n ],\n [\n \"diaphora:load_results\",\n \"Diaphora - Load results\",\n CIdaMenuHandlerLoadResults(),\n None,\n ],\n ]\n for item in menu_items:\n action_name, action_desc, action_handler, hotkey = item\n self.register_menu_action(action_name, action_desc, action_handler, hotkey)\n\n warning(\n \"\"\"AUTOHIDE REGISTRY\\nIf you close one tab you can always re-open it by pressing F3\nor selecting Edit -> Plugins -> Diaphora - Show results\"\"\"\n )\n\n # Ripped out from REgoogle (which is dead since long ago...)\n def constant_filter(self, value):\n \"\"\"Filter for certain constants/immediate values. Not all values should be\n taken into account for searching. Especially not very small values that may\n just contain the stack frame size.\n \"\"\"\n # no small values\n if value < 0x1000:\n return False\n\n if (\n value & 0xFFFFFF00 == 0xFFFFFF00\n or value & 0xFFFF00 == 0xFFFF00\n or value & 0xFFFFFFFFFFFFFF00 == 0xFFFFFFFFFFFFFF00\n or value & 0xFFFFFFFFFFFF00 == 0xFFFFFFFFFFFF00\n ):\n return False\n\n # no single bits sets - mostly defines / flags\n for i in range(64):\n if value == (1 << i):\n return False\n\n return True\n\n def is_constant(self, oper, ea):\n value = oper.value\n # make sure, its not a reference but really constant\n if value in DataRefsFrom(ea):\n return False\n\n return True\n\n def get_disasm(self, ea):\n mnem = print_insn_mnem(ea)\n op1 = print_operand(ea, 0)\n op2 = print_operand(ea, 1)\n line = f\"{mnem.ljust(8)} {op1}\"\n if op2 != \"\":\n line += f\", {op2}\"\n return line\n\n def get_function_names(self, f):\n name = get_func_name(int(f))\n true_name = name\n demangle_named_name = demangle_name(name, INF_SHORT_DN)\n if demangle_named_name == \"\":\n demangle_named_name = None\n\n if demangle_named_name is not None:\n name = demangle_named_name\n\n return name, true_name, demangle_named_name\n\n def extract_function_callers(self, f):\n # Calculate the callers *but* considering data references to functions from\n # functions as code references.\n callers = list()\n refs = list(CodeRefsTo(f, 0))\n refs.extend(DataRefsTo(f))\n for caller in refs:\n caller_func = get_func(caller)\n if caller_func and caller_func.start_ea not in callers:\n callers.append(caller_func.start_ea)\n return callers\n\n def extract_function_constants(self, ins, x, constants):\n for operand in ins.ops:\n if operand.type == o_imm:\n if self.is_constant(operand, x) and self.constant_filter(operand.value):\n constants.append(operand.value)\n elif operand.type == o_displ:\n if self.constant_filter(operand.addr):\n constants.append(operand.addr)\n\n drefs = DataRefsFrom(x)\n for dref in drefs:\n if get_func(dref) is None:\n str_constant = get_strlit_contents(dref, -1, -1)\n if str_constant is not None:\n str_constant = str_constant.decode(\"utf-8\", \"backslashreplace\")\n if str_constant not in constants:\n constants.append(str_constant)\n return constants\n\n def extract_function_switches(self, x, switches):\n switch = get_switch_info(x)\n if switch:\n switch_cases = switch.get_jtable_size()\n results = calc_switch_cases(x, switch)\n\n if results is not None:\n # It seems that IDAPython for idaq64 has some bug when reading\n # switch's cases. Do not attempt to read them if the 'cur_case'\n # returned object is not iterable.\n can_iter = False\n switch_cases_values = set()\n for cur_case in results.cases:\n if \"__iter__\" not in dir(cur_case):\n break\n\n can_iter |= True\n for case_id in cur_case:\n switch_cases_values.add(case_id)\n\n if can_iter:\n switches.append([switch_cases, list(switch_cases_values)])\n\n return switches\n\n def extract_function_mdindex(\n self, bb_topological, bb_topological_sorted, bb_edges, bb_topo_num, bb_degree\n ):\n md_index = 0\n if bb_topological:\n bb_topo_order = {}\n for i, scc in enumerate(bb_topological_sorted):\n for bb in scc:\n bb_topo_order[bb] = i\n tuples = []\n for src, dst in bb_edges:\n tuples.append(\n (\n bb_topo_order[bb_topo_num[src]],\n bb_degree[src][0],\n bb_degree[src][1],\n bb_degree[dst][0],\n bb_degree[dst][1],\n )\n )\n rt2, rt3, rt5, rt7 = (decimal.Decimal(p).sqrt() for p in (2, 3, 5, 7))\n emb_tuples = (\n sum((z0, z1 * rt2, z2 * rt3, z3 * rt5, z4 * rt7))\n for z0, z1, z2, z3, z4 in tuples\n )\n md_index = sum((1 / emb_t.sqrt() for emb_t in emb_tuples))\n md_index = str(md_index)\n return md_index\n\n def extract_function_pseudocode_features(self, f):\n pseudo = None\n pseudo_hash1 = None\n pseudo_hash2 = None\n pseudo_hash3 = None\n pseudo_lines = 0\n pseudocode_primes = None\n if f in self.pseudo:\n pseudo = \"\\n\".join(self.pseudo[f])\n pseudo_lines = len(self.pseudo[f])\n pseudo_hash1, pseudo_hash2, pseudo_hash3 = self.kfh.hash_bytes(\n pseudo\n ).split(\";\")\n if pseudo_hash1 == \"\":\n pseudo_hash1 = None\n if pseudo_hash2 == \"\":\n pseudo_hash2 = None\n if pseudo_hash3 == \"\":\n pseudo_hash3 = None\n pseudocode_primes = str(self.pseudo_hash[f])\n return (\n pseudo,\n pseudo_lines,\n pseudo_hash1,\n pseudocode_primes,\n pseudo_hash2,\n pseudo_hash3,\n )\n\n def extract_function_assembly_features(self, assembly, f, image_base):\n asm = []\n keys = list(assembly.keys())\n keys.sort()\n\n # Collect the ordered list of addresses, as shown in the assembly\n # viewer (when diffing). It will be extremely useful for importing\n # stuff later on.\n assembly_addrs = []\n\n # After sorting our the addresses of basic blocks, be sure that the\n # very first address is always the entry point, no matter at what\n # address it is.\n base = f - image_base\n keys.remove(base)\n keys.insert(0, base)\n for key in keys:\n for line in assembly[key]:\n assembly_addrs.append(line[0])\n asm.append(line[1])\n asm = \"\\n\".join(asm)\n return asm, assembly_addrs\n\n def get_decoded_instruction(self, x):\n decoded_size, ins = diaphora_decode(x)\n if ins.ops[0].type in [o_mem, o_imm, o_far, o_near, o_displ]:\n decoded_size -= ins.ops[0].offb\n if ins.ops[1].type in [o_mem, o_imm, o_far, o_near, o_displ]:\n decoded_size -= ins.ops[1].offb\n if decoded_size <= 0:\n decoded_size = 1\n\n return ins, decoded_size\n\n def extract_function_topological_information(self, bb_relations, bb_topological):\n loops = 0\n strongly_connected = None\n strongly_connected_spp = 0\n bb_topological_sorted = None\n try:\n strongly_connected = strongly_connected_components(bb_relations)\n bb_topological_sorted = robust_topological_sort(bb_topological)\n bb_topological = json.dumps(bb_topological_sorted)\n strongly_connected_spp = 1\n for item in strongly_connected:\n val = len(item)\n if val > 1:\n strongly_connected_spp *= self.primes[val]\n except RecursionError:\n # XXX: FIXME: The original implementation that we're using is recursive\n # and can fail. We really need to create our own non recursive version.\n strongly_connected = []\n bb_topological = None\n except:\n traceback.print_exc()\n raise\n\n loops = 0\n for sc in strongly_connected:\n if len(sc) > 1:\n loops += 1\n else:\n if sc[0] in bb_relations and sc[0] in bb_relations[sc[0]]:\n loops += 1\n\n return (\n bb_topological,\n bb_topological_sorted,\n strongly_connected,\n loops,\n strongly_connected_spp,\n )\n\n def extract_line_mnem_disasm(self, x):\n mnem = print_insn_mnem(x)\n try:\n disasm = GetDisasm(x)\n except UnicodeDecodeError:\n # This is a workaround for a rare error getting the disassembly for a\n # line with some UTF-8 characters that Python fails to handle properly\n disasm = self.get_disasm(x)\n\n return mnem, disasm\n\n def extract_microcode(self, f):\n micro = None\n micro_spp = 1\n clean_micro = None\n mnemonics = set()\n if self.export_microcode and f in self.microcode:\n micro = \"\\n\".join(self.microcode[f])\n ret = []\n for line in self.microcode[f]:\n tokens = line.split(\" \")\n for x in tokens[1:]:\n if not x.isdigit():\n mnem = x\n mnemonics.add(mnem)\n pos = mnem.find(\".\")\n if pos > -1:\n mnem = mnem[:pos]\n break\n\n line = line[line.find(mnem):]\n ret.append(line)\n if mnem in self.microcode_ins_list:\n micro_spp *= self.primes[self.microcode_ins_list.index(mnem)]\n else:\n log(\n f\"Warning: Mnemonic {repr(mnem)} not found in the list of microcode instructions!\"\n )\n clean_micro = self.get_cmp_asm_lines(\"\\n\".join(ret))\n return micro, clean_micro, micro_spp\n\n def get_microcode_instructions(self):\n if HAS_HEXRAYS:\n instructions = []\n for x in dir(hr):\n if x.startswith(\"m_\"):\n mnem = x[2:]\n if mnem not in instructions:\n instructions.append(x[2:])\n\n instructions.sort()\n return instructions\n return []\n\n def read_function(self, ea):\n \"\"\"\n Extract anything and everything we (might) need from a function.\n\n This is a horribly big (read: huge) function that, from time to time, I try\n to make a bit smaller. Feel free to do the same...\n \"\"\"\n name, true_name, demangle_named_name = self.get_function_names(ea)\n # Call hooks immediately after we have a proper function name\n if self.hooks is not None:\n if \"before_export_function\" in dir(self.hooks):\n ret = self.hooks.before_export_function(ea, name)\n if not ret:\n return False\n\n f = int(ea)\n func = get_func(f)\n if not func:\n # pylint: disable-next=consider-using-f-string\n log(\"Cannot get a function object for 0x%x\" % f)\n return False\n\n export_time = time.monotonic()\n flow = FlowChart(func)\n size = 0\n\n if not self.ida_subs:\n # Unnamed function, ignore it...\n if (\n name.startswith(\"sub_\")\n or name.startswith(\"j_\")\n or name.startswith(\"unknown\")\n or name.startswith(\"nullsub_\")\n ):\n debug_refresh(f\"Skipping function {repr(name)}\")\n return False\n\n # Already recognized runtime's function?\n flags = get_func_attr(f, FUNCATTR_FLAGS)\n if flags & FUNC_LIB or flags == -1:\n debug_refresh(f\"Skipping library function {repr(name)}\")\n return False\n\n if self.exclude_library_thunk:\n # Skip library and thunk functions\n flags = get_func_attr(f, FUNCATTR_FLAGS)\n if flags & FUNC_LIB or flags & FUNC_THUNK or flags == -1:\n debug_refresh(f\"Skipping thunk function {repr(name)}\")\n return False\n\n if name.startswith(\"nullsub_\"):\n debug_refresh(f\"Skipping nullsub function {repr(name)}\")\n return False\n\n image_base = self.get_base_address()\n nodes = 0\n edges = 0\n instructions = 0\n mnems = []\n dones = {}\n names = set()\n bytes_hash = []\n bytes_sum = 0\n function_hash = []\n outdegree = 0\n indegree = len(list(CodeRefsTo(f, 1)))\n assembly = {}\n basic_blocks_data = {}\n bb_relations = {}\n bb_topo_num = {}\n bb_topological = {}\n switches = []\n bb_degree = {}\n bb_edges = []\n constants = []\n\n # The callees will be calculated later\n callees = []\n callers = self.extract_function_callers(f)\n\n mnemonics_spp = 1\n cpu_ins_list = GetInstructionList()\n cpu_ins_list.sort()\n\n current_head = BADADDR\n for block in flow:\n if block.end_ea == 0 or block.end_ea == BADADDR:\n # pylint: disable-next=consider-using-f-string\n print(\"0x%08x: Skipping bad basic block\" % f)\n continue\n\n nodes += 1\n instructions_data = []\n\n block_ea = block.start_ea - image_base\n idx = len(bb_topological)\n bb_topological[idx] = []\n bb_topo_num[block_ea] = idx\n\n for current_head in list(Heads(block.start_ea, block.end_ea)):\n mnem, disasm = self.extract_line_mnem_disasm(current_head)\n size += get_item_size(current_head)\n instructions += 1\n\n if mnem in cpu_ins_list:\n mnemonics_spp *= self.primes[cpu_ins_list.index(mnem)]\n\n try:\n assembly[block_ea].append([current_head - image_base, disasm])\n except KeyError:\n if nodes == 1:\n assembly[block_ea] = [[current_head - image_base, disasm]]\n else:\n assembly[block_ea] = [\n # pylint: disable-next=consider-using-f-string\n [current_head - image_base, \"loc_%x:\" % current_head],\n [current_head - image_base, disasm],\n ]\n\n ins, decoded_size = self.get_decoded_instruction(current_head)\n constants = self.extract_function_constants(ins, current_head, constants)\n\n curr_bytes = get_bytes(current_head, decoded_size, False)\n if curr_bytes is None or len(curr_bytes) != decoded_size:\n # pylint: disable-next=consider-using-f-string\n log(\"Failed to read %d bytes at [%08x]\" % (decoded_size, current_head))\n continue\n\n bytes_hash.append(curr_bytes)\n bytes_sum += sum(curr_bytes)\n\n function_hash.append(get_bytes(current_head, get_item_size(current_head), False))\n outdegree += len(list(CodeRefsFrom(current_head, 0)))\n mnems.append(mnem)\n op_value = get_operand_value(current_head, 1)\n if op_value == -1:\n op_value = get_operand_value(current_head, 0)\n\n tmp_name = None\n if op_value != BADADDR and op_value in self.names:\n tmp_name = self.names[op_value]\n demangle_named_name = demangle_name(tmp_name, INF_SHORT_DN)\n if demangle_named_name is not None:\n tmp_name = demangle_named_name\n pos = tmp_name.find(\"(\")\n if pos > -1:\n tmp_name = tmp_name[:pos]\n\n if not tmp_name.startswith(\"sub_\") and not tmp_name.startswith(\"nullsub_\"):\n names.add(tmp_name)\n\n # Calculate the callees\n refs = list(CodeRefsFrom(current_head, 0))\n for callee in refs:\n callee_func = get_func(callee)\n if callee_func and callee_func.start_ea != func.start_ea:\n if callee_func.start_ea not in callees:\n callees.append(callee_func.start_ea)\n\n if len(refs) == 0:\n refs = DataRefsFrom(current_head)\n\n tmp_type = None\n for ref in refs:\n if ref in self.names:\n tmp_name = self.names[ref]\n tmp_type = idc.get_type(ref)\n\n ins_cmt1 = GetCommentEx(current_head, 0)\n ins_cmt2 = GetCommentEx(current_head, 1)\n\n operands_names = []\n # save operands_names\n for index, _ in enumerate(ins.ops):\n if ida_bytes.is_forced_operand(ins.ip, index):\n operand_name = (\n ida_bytes.get_forced_operand(ins.ip, index)\n if ida_bytes.is_forced_operand(ins.ip, index)\n else \"\"\n )\n operands_names.append([index, operand_name])\n\n instructions_data.append(\n [\n current_head - image_base,\n mnem,\n disasm,\n ins_cmt1,\n ins_cmt2,\n operands_names,\n tmp_name,\n tmp_type,\n ]\n )\n\n switches = self.extract_function_switches(current_head, switches)\n\n basic_blocks_data[block_ea] = instructions_data\n bb_relations[block_ea] = []\n if block_ea not in bb_degree:\n # bb in degree, out degree\n bb_degree[block_ea] = [0, 0]\n\n for succ_block in block.succs():\n if succ_block.end_ea == 0:\n continue\n\n succ_base = succ_block.start_ea - image_base\n bb_relations[block_ea].append(succ_base)\n bb_degree[block_ea][1] += 1\n bb_edges.append((block_ea, succ_base))\n if succ_base not in bb_degree:\n bb_degree[succ_base] = [0, 0]\n bb_degree[succ_base][0] += 1\n\n edges += 1\n indegree += 1\n if succ_block.id not in dones:\n dones[succ_block] = 1\n\n for pred_block in block.preds():\n if pred_block.end_ea == 0:\n continue\n\n try:\n bb_relations[pred_block.start_ea - image_base].append(\n block.start_ea - image_base\n )\n except KeyError:\n bb_relations[pred_block.start_ea - image_base] = [\n block.start_ea - image_base\n ]\n\n edges += 1\n outdegree += 1\n if pred_block.id not in dones:\n dones[pred_block] = 1\n\n for block in flow:\n if block.end_ea == 0:\n continue\n\n block_ea = block.start_ea - image_base\n for succ_block in block.succs():\n if succ_block.end_ea == 0:\n continue\n\n succ_base = succ_block.start_ea - image_base\n bb_topological[bb_topo_num[block_ea]].append(bb_topo_num[succ_base])\n\n topological_data = self.extract_function_topological_information(\n bb_relations, bb_topological\n )\n (\n bb_topological,\n bb_topological_sorted,\n strongly_connected,\n loops,\n strongly_connected_spp,\n ) = topological_data\n\n asm, assembly_addrs = self.extract_function_assembly_features(\n assembly, f, image_base\n )\n try:\n clean_assembly = self.get_cmp_asm_lines(asm)\n except:\n clean_assembly = \"\"\n # pylint: disable-next=consider-using-f-string\n print(\"Error getting assembly for 0x%x\" % f)\n\n cc = edges - nodes + 2\n proto = self.guess_type(f)\n proto2 = idc.get_type(f)\n try:\n prime = str(self.primes[cc])\n except:\n # pylint: disable-next=consider-using-f-string\n log(\"Cyclomatic complexity too big: 0x%x -> %d\" % (f, cc))\n prime = 0\n\n comment = idc.get_func_cmt(f, 1)\n bytes_hash = md5(b\"\".join(bytes_hash)).hexdigest()\n function_hash = md5(b\"\".join(function_hash)).hexdigest()\n\n function_flags = get_func_attr(f, FUNCATTR_FLAGS)\n (\n pseudo,\n pseudo_lines,\n pseudo_hash1,\n pseudocode_primes,\n pseudo_hash2,\n pseudo_hash3,\n ) = self.extract_function_pseudocode_features(f)\n microcode, clean_microcode, microcode_spp = self.extract_microcode(f)\n microcode_bblocks, microcode_bbrelations = self.get_microcode(func, ea)\n\n clean_pseudo = self.get_cmp_pseudo_lines(pseudo)\n\n md_index = self.extract_function_mdindex(\n bb_topological, bb_topological_sorted, bb_edges, bb_topo_num, bb_degree\n )\n seg_rva = current_head - get_segm_start(current_head)\n\n kgh = CKoretKaramitasHash()\n kgh_hash = kgh.calculate(f)\n\n rva = f - self.get_base_address()\n\n # It's better to have names sorted\n names = list(names)\n names.sort()\n\n export_time = time.monotonic() - export_time\n export_time = str(export_time)\n\n props_list = (\n name,\n nodes,\n edges,\n indegree,\n outdegree,\n size,\n instructions,\n mnems,\n names,\n proto,\n cc,\n prime,\n f,\n comment,\n true_name,\n bytes_hash,\n pseudo,\n pseudo_lines,\n pseudo_hash1,\n pseudocode_primes,\n function_flags,\n asm,\n proto2,\n pseudo_hash2,\n pseudo_hash3,\n len(strongly_connected),\n loops,\n rva,\n bb_topological,\n strongly_connected_spp,\n clean_assembly,\n clean_pseudo,\n mnemonics_spp,\n switches,\n function_hash,\n bytes_sum,\n md_index,\n constants,\n len(constants),\n seg_rva,\n assembly_addrs,\n kgh_hash,\n None,\n None,\n microcode,\n clean_microcode,\n microcode_spp,\n export_time,\n microcode_bblocks,\n microcode_bbrelations,\n callers,\n callees,\n basic_blocks_data,\n bb_relations,\n )\n\n if self.hooks is not None:\n if \"after_export_function\" in dir(self.hooks):\n d = self.create_function_dictionary(props_list)\n d = self.hooks.after_export_function(d)\n props_list = self.get_function_from_dictionary(d)\n\n return props_list\n\n def get_base_address(self):\n return idaapi.get_imagebase()\n\n def get_modules_using_lfa(self):\n # First, try to guess modules areas\n _, lfa_modules = lfa.analyze()\n\n # Next, using IDAMagicStrings, try to guess file names using some heuristics\n func_modules = {}\n if HAS_GET_SOURCE_STRINGS:\n d, _ = get_source_strings(4, [0, 1])\n\n # First, put names found with IDAMagicStrings to anonymous modules found\n # with LFA\n for source_file in d:\n for _, func_name, mod_name in d[source_file]:\n func_ea = idc.get_name_ea_simple(func_name)\n if func_ea not in func_modules:\n # print(\"0x%08x:%s -> %s\" % (func_ea, func_name, mod_name))\n func_modules[func_ea] = mod_name\n for module in lfa_modules:\n if func_ea >= module.start and func_ea <= module.end:\n if module.name == \"\":\n module.name = mod_name\n break\n\n #\n # Next sub-step: find the limits of modules with the same name that appear\n # multiple times and update the end address to the last found one.\n #\n new_modules = []\n named_modules = {}\n for module in lfa_modules:\n if module.name != \"\":\n if module.name not in named_modules:\n named_modules[module.name] = {\n \"start\": module.start,\n \"end\": module.end,\n }\n else:\n named_modules[module.name][\"end\"] = module.end\n\n areas = []\n for name, module in named_modules.items():\n new_modules.append(\n {\"name\": name, \"start\": module[\"start\"], \"end\": module[\"end\"]}\n )\n areas.append([module[\"start\"], module[\"end\"]])\n\n #\n # Next step: find all the modules, discard anonymous modules that fall in\n # the area (start and end addres) of named modules and return a list with\n # the proper modules, both anonymous and otherwise.\n #\n for module in lfa_modules:\n if module.name != \"\":\n continue\n\n found = False\n for start, end in areas:\n if module.start >= start and module.end <= end:\n found = True\n break\n\n if not found:\n d = {\"name\": module.name, \"start\": module.start, \"end\": module.end}\n new_modules.append(d)\n\n for module in new_modules:\n module[\"total\"] = 0\n module[\"primes\"] = 1\n module[\"pseudo_primes\"] = 1\n\n iterations = 1\n for func, item in self._funcs_cache.items():\n if iterations % 1000 == 0:\n log_refresh(\"Calculating modules weights...\")\n\n for module in new_modules:\n if module[\"start\"] <= func <= module[\"end\"]:\n _, primes_value, pseudocode_primes = item\n module[\"total\"] += 1\n if primes_value is not None:\n module[\"primes\"] *= int(primes_value)\n if pseudocode_primes is not None:\n module[\"pseudo_primes\"] *= int(pseudocode_primes)\n break\n\n for module in new_modules:\n module[\"total\"] = str(module[\"total\"])\n module[\"primes\"] = str(module[\"primes\"])\n module[\"pseudo_primes\"] = str(module[\"pseudo_primes\"])\n\n return new_modules\n\n def save_compilation_units(self):\n lfa_modules = self.get_modules_using_lfa()\n\n sql1 = \"\"\"insert into compilation_units (name, start_ea, end_ea)\n values (?, ?, ?)\"\"\"\n sql2 = \"\"\"insert or ignore into compilation_unit_functions(\n cu_id, func_id)\n values (?, ?)\"\"\"\n sql3 = \"\"\" update compilation_units\n set primes_value = ?,\n pseudocode_primes = ?,\n functions = ?\n where id = ? \"\"\"\n sql4 = \"\"\" update functions set source_file = ? where id = ? \"\"\"\n cur = self.db_cursor()\n try:\n dones = set()\n for module in lfa_modules:\n module_name = None\n if module[\"name\"] != \"\":\n module_name = module[\"name\"]\n\n vals = (module[\"name\"], str(module[\"start\"]), str(module[\"end\"]))\n cur.execute(sql1, vals)\n cu_id = cur.lastrowid\n\n for values in self._funcs_cache.values():\n func_id = values[0]\n if func_id not in dones:\n dones.add(func_id)\n cur.execute(sql2, (cu_id, func_id))\n cur.execute(sql4, (module_name, func_id))\n\n cur.execute(\n sql3,\n [module[\"primes\"], module[\"pseudo_primes\"], module[\"total\"], cu_id],\n )\n if cur.rowcount == 0:\n raise Exception(\n \"Unable to UPDATE the primes for a compilation unit!\"\n )\n except:\n print(f\"ERROR saving compilation unit: {str(sys.exc_info()[1])}\")\n raise\n finally:\n cur.close()\n\n def save_callgraph(self, primes, all_primes, md5sum):\n cur = self.db_cursor()\n try:\n sql = \"insert into main.program (callgraph_primes, callgraph_all_primes, processor, md5sum) values (?, ?, ?, ?)\"\n proc = idaapi.get_idp_name()\n if BADADDR == 0xFFFFFFFFFFFFFFFF:\n proc += \"64\"\n cur.execute(sql, (primes, all_primes, proc, md5sum))\n finally:\n cur.close()\n\n def GetLocalType(self, ordinal, flags):\n ret = get_local_tinfo(ordinal)\n if ret is not None:\n (stype, fields) = ret\n if stype:\n name = idc.get_numbered_type_name(ordinal)\n try:\n return idc_print_type(stype, fields, name, flags)\n except:\n log(f\"Error: {str(sys.exc_info()[1])}\")\n return \"\"\n return \"\"\n\n def export_structures(self):\n # It seems that get_ordinal_qty, sometimes, can return negative\n # numbers, according to one beta-tester. My guess is that it's a bug\n # in IDA. However, as we cannot reproduce, at least handle this\n # condition.\n local_types = idc.get_ordinal_qty()\n if (local_types & 0x80000000) != 0:\n # pylint: disable-next=consider-using-f-string\n message = \"warning: get_ordinal_qty returned a negative number (0x%x)!\" % local_types\n log(message)\n return\n\n for i in range(local_types):\n name = idc.get_numbered_type_name(i + 1)\n definition = self.GetLocalType(\n i + 1, PRTYPE_MULTI | PRTYPE_TYPE | PRTYPE_SEMI | PRTYPE_PRAGMA\n )\n if definition is None:\n continue\n\n type_name = \"struct\"\n if definition.startswith(\"enum\"):\n type_name = \"enum\"\n elif definition.startswith(\"union\"):\n type_name = \"union\"\n\n # For some reason, IDA my return types with the form \"__int128 unsigned\",\n # we want it the right way \"unsigned __int128\".\n if name and name.find(\" \") > -1:\n names = name.split(\" \")\n name = names[0]\n if names[1] == \"unsigned\":\n name = f\"unsigned {name}\"\n\n self.add_program_data(type_name, name, definition)\n\n def get_til_names(self):\n idb_path = get_idb_path()\n filename, _ = os.path.splitext(idb_path)\n til_path = f\"{filename}.til\"\n\n with open(til_path, \"rb\") as f:\n line = f.readline()\n pos = line.find(b\"Local type definitions\")\n if pos > -1:\n tmp = line[pos + len(b\"Local type definitions\") + 1:]\n pos = tmp.find(b\"\\x00\")\n if pos > -1:\n defs = tmp[:pos].split(b\",\")\n return defs\n return None\n\n def export_til(self):\n til_names = self.get_til_names()\n if til_names is not None:\n for til in til_names:\n self.add_program_data(\"til\", til, None)\n\n def load_and_import_all_results(self, filename, main_db, diff_db):\n results_db = diaphora.sqlite3_connect(filename)\n\n cur = results_db.cursor()\n try:\n sql = \"select main_db, diff_db, version from config\"\n cur.execute(sql)\n rows = cur.fetchall()\n if len(rows) != 1:\n Warning(\"Malformed results database!\")\n return False\n\n row = rows[0]\n version = row[\"version\"]\n if version != diaphora.VERSION_VALUE:\n message = f\"The version of the diff results is {version} and current version is {diaphora.VERSION_VALUE}, there can be some incompatibilities.\"\n Warning(message)\n\n self.reinit(main_db, diff_db)\n\n min_ratio = float(self.get_value_for(\"MINIMUM_IMPORT_RATIO\", 0.5))\n log(f\"Minimum import threshold {min_ratio}\")\n\n sql = \"select * from results\"\n cur.execute(sql)\n for row in diaphora.result_iter(cur):\n if row[\"type\"] == \"best\":\n choose = self.best_chooser\n elif row[\"type\"] == \"partial\":\n choose = self.partial_chooser\n else:\n choose = self.unreliable_chooser\n\n ea1 = int(row[\"address\"], 16)\n name1 = row[\"name\"]\n ea2 = int(row[\"address2\"], 16)\n name2 = row[\"name2\"]\n desc = row[\"description\"]\n ratio = float(row[\"ratio\"])\n\n if ratio < min_ratio:\n log(f\"Match {name1}-{name2} is excluded\")\n continue\n\n bb1 = int(row[\"bb1\"])\n bb2 = int(row[\"bb2\"])\n\n choose.add_item(\n diaphora.CChooser.Item(\n ea1, name1, ea2, name2, desc, ratio, bb1, bb2\n )\n )\n\n sql = \"select * from unmatched\"\n cur.execute(sql)\n for row in diaphora.result_iter(cur):\n if row[\"type\"] == \"primary\":\n choose = self.unmatched_primary\n else:\n choose = self.unmatched_second\n choose.add_item(\n diaphora.CChooser.Item(int(row[\"address\"], 16), row[\"name\"])\n )\n\n self.import_all_auto(self.best_chooser.items)\n self.import_all_auto(self.partial_chooser.items)\n\n return True\n finally:\n cur.close()\n results_db.close()\n\n return False\n\n def load_results(self, filename):\n results_db = diaphora.sqlite3_connect(filename)\n\n ret = False\n cur = results_db.cursor()\n try:\n sql = \"select main_db, diff_db, version from config\"\n cur.execute(sql)\n rows = cur.fetchall()\n if len(rows) != 1:\n warning(\"Malformed results database!\")\n msg(\"Malformed results database!\")\n return False\n\n row = rows[0]\n version = row[\"version\"]\n if version != diaphora.VERSION_VALUE:\n line = \"The version of the diff results is %s and current version is %s, there can be some incompatibilities.\"\n warning(line % (version, diaphora.VERSION_VALUE))\n msg(line)\n\n main_db = row[\"main_db\"]\n diff_db = row[\"diff_db\"]\n if not os.path.exists(main_db):\n log(f\"Primary database {main_db} not found.\")\n main_db = ask_file(0, main_db, \"Select the primary database path\")\n if main_db is None:\n return False\n\n if not os.path.exists(diff_db):\n diff_db = ask_file(0, main_db, \"Select the secondary database path\")\n if diff_db is None:\n return False\n\n self.reinit(main_db, diff_db)\n\n sql = \"select * from results\"\n cur.execute(sql)\n for row in diaphora.result_iter(cur):\n if row[\"type\"] == \"best\":\n choose = self.best_chooser\n elif row[\"type\"] == \"partial\":\n choose = self.partial_chooser\n elif row[\"type\"] == \"multimatch\":\n choose = self.multimatch_chooser\n else:\n choose = self.unreliable_chooser\n\n ea1 = int(row[\"address\"], 16)\n name1 = row[\"name\"]\n ea2 = int(row[\"address2\"], 16)\n name2 = row[\"name2\"]\n desc = row[\"description\"]\n ratio = float(row[\"ratio\"])\n bb1 = int(row[\"bb1\"])\n bb2 = int(row[\"bb2\"])\n\n choose.add_item(\n diaphora.CChooser.Item(\n ea1, name1, ea2, name2, desc, ratio, bb1, bb2\n )\n )\n\n sql = \"select * from unmatched\"\n cur.execute(sql)\n for row in diaphora.result_iter(cur):\n if row[\"type\"] == \"primary\":\n choose = self.unmatched_primary\n else:\n choose = self.unmatched_second\n choose.add_item(\n diaphora.CChooser.Item(int(row[\"address\"], 16), row[\"name\"])\n )\n\n log(\"Showing diff results.\")\n self.show_choosers()\n ret = True\n finally:\n cur.close()\n results_db.close()\n\n return ret\n\n def re_diff(self):\n self.best_chooser.Close()\n self.partial_chooser.Close()\n self.multimatch_chooser.Close()\n if self.unreliable_chooser is not None:\n self.unreliable_chooser.Close()\n if self.unmatched_primary is not None:\n self.unmatched_primary.Close()\n if self.unmatched_second is not None:\n self.unmatched_second.Close()\n\n _diff_or_export(\n use_ui=True, file_in=self.last_diff_db, project_script=self.project_script\n )\n\n def equal_db(self):\n are_equal = diaphora.CBinDiff.equal_db(self)\n if are_equal:\n if (\n ask_yn(\n 0,\n \"HIDECANCEL\\nThe databases seems to be 100% equal. Do you want to continue anyway?\",\n )\n != 1\n ):\n self.do_continue = False\n return are_equal\n\n\n#-------------------------------------------------------------------------------\ndef _diff_or_export(use_ui, **options):\n # pylint: disable-next=global-statement\n global g_bindiff\n total_functions = len(list(Functions()))\n if get_idb_path() == \"\" or total_functions == 0:\n warning(\n \"No IDA database opened or no function in the database.\\nPlease open an IDA database and create some functions before running this script.\"\n )\n return None\n\n opts = BinDiffOptions(**options)\n\n if use_ui:\n x = CBinDiffExporterSetup()\n x.Compile()\n x.set_options(opts)\n\n if not x.Execute():\n return None\n\n opts = x.get_options()\n\n if opts.file_out == opts.file_in:\n warning(\"Both databases are the same file!\")\n return None\n elif opts.file_out == \"\" or len(opts.file_out) < 5:\n warning(\n \"No output database selected or invalid filename. Please select a database file.\"\n )\n return None\n elif is_ida_file(opts.file_in) or is_ida_file(opts.file_out):\n warning(\n \"One of the selected databases is an IDA file. Please select only database files.\"\n )\n return None\n\n export = True\n if os.path.exists(opts.file_out):\n crash_file = f\"{opts.file_out}-crash\"\n resume_crashed = False\n crashed_before = False\n if os.path.exists(crash_file):\n crashed_before = True\n ret = ask_yn(\n 1,\n \"The previous export session crashed. Do you want to resume the previous crashed session?\",\n )\n if ret == -1:\n log(\"Cancelled\")\n return None\n elif ret == 1:\n resume_crashed = True\n\n if not resume_crashed and not crashed_before:\n ret = ask_yn(\n 0, \"Export database already exists. Do you want to overwrite it?\"\n )\n if ret == -1:\n log(\"Cancelled\")\n return None\n\n if ret == 0:\n export = False\n\n if export:\n if g_bindiff is not None:\n g_bindiff = None\n\n if not resume_crashed:\n remove_file(opts.file_out)\n log(f\"Database {repr(opts.file_out)} removed\")\n if os.path.exists(crash_file):\n os.remove(crash_file)\n\n t0 = time.monotonic()\n try:\n bd = CIDABinDiff(opts.file_out)\n bd.use_decompiler = opts.use_decompiler\n bd.exclude_library_thunk = opts.exclude_library_thunk\n bd.unreliable = opts.unreliable\n bd.slow_heuristics = opts.slow\n bd.relaxed_ratio = opts.relax\n bd.experimental = opts.experimental\n bd.min_ea = opts.min_ea\n bd.max_ea = opts.max_ea\n bd.ida_subs = opts.ida_subs\n bd.ignore_sub_names = opts.ignore_sub_names\n bd.ignore_all_names = opts.ignore_all_names\n bd.ignore_small_functions = opts.ignore_small_functions\n bd.function_summaries_only = opts.func_summaries_only\n bd.export_microcode = opts.export_microcode\n bd.sql_max_processed_rows = config.SQL_MAX_PROCESSED_ROWS\n bd.timeout = config.SQL_TIMEOUT_LIMIT * max(total_functions / 20000, 1)\n bd.project_script = opts.project_script\n\n if export:\n exported = False\n if os.getenv(\"DIAPHORA_PROFILE\") is not None:\n log(\"*** Profiling export ***\")\n # pylint: disable-next=import-outside-toplevel\n import cProfile\n\n profiler = cProfile.Profile()\n profiler.runcall(bd.export)\n exported = True\n profiler.print_stats(sort=\"time\")\n else:\n try:\n bd.export()\n exported = True\n except KeyboardInterrupt:\n log(f\"Aborted by user, removing crash file {opts.file_out}-crash...\")\n os.remove(f\"{opts.file_out}-crash\")\n\n if exported:\n final_t = time.monotonic() - t0\n # pylint: disable-next=consider-using-f-string\n log(f\"Database exported, time taken: {datetime.timedelta(seconds=final_t)}.\")\n hide_wait_box()\n\n if opts.file_in != \"\":\n if os.getenv(\"DIAPHORA_PROFILE\") is not None:\n log(\"*** Profiling diff ***\")\n # pylint: disable-next=import-outside-toplevel\n import cProfile\n\n profiler = cProfile.Profile()\n profiler.runcall(bd.diff, opts.file_in)\n profiler.print_stats(sort=\"time\")\n else:\n bd.diff(opts.file_in)\n except:\n print((f\"Error: {sys.exc_info()[1]}\"))\n traceback.print_exc()\n finally:\n hide_wait_box()\n\n return bd\n\n\n#-------------------------------------------------------------------------------\ndef _generate_html(db1, diff_db, ea1, ea2, html_asm, html_pseudo):\n bd = CIDABinDiff(db1)\n bd.db = diaphora.sqlite3_connect(db1)\n bd.load_results(diff_db)\n bd.save_pseudo_diff(ea1, ea2, html_pseudo)\n bd.save_asm_diff(ea1, ea2, html_asm)\n\n\n#-------------------------------------------------------------------------------\nclass BinDiffOptions:\n def __init__(self, **kwargs):\n total_functions = len(list(Functions()))\n sqlite_db = os.path.splitext(get_idb_path())[0] + \".sqlite\"\n self.file_out = kwargs.get(\"file_out\", sqlite_db)\n self.file_in = kwargs.get(\"file_in\", \"\")\n self.use_decompiler = kwargs.get(\n \"use_decompiler\", config.EXPORTING_USE_DECOMPILER\n )\n self.exclude_library_thunk = kwargs.get(\n \"exclude_library_thunk\", config.EXPORTING_EXCLUDE_LIBRARY_THUNK\n )\n\n self.relax = kwargs.get(\"relax\")\n if self.relax:\n warning(MSG_RELAXED_RATIO_ENABLED)\n\n self.unreliable = kwargs.get(\"unreliable\", config.DIFFING_ENABLE_UNRELIABLE)\n self.slow = kwargs.get(\n \"slow\", total_functions <= config.MIN_FUNCTIONS_TO_DISABLE_SLOW\n )\n self.experimental = kwargs.get(\n \"experimental\", config.DIFFING_ENABLE_EXPERIMENTAL\n )\n self.min_ea = kwargs.get(\"min_ea\", get_inf_attr(INF_MIN_EA))\n self.max_ea = kwargs.get(\"max_ea\", get_inf_attr(INF_MAX_EA))\n self.ida_subs = kwargs.get(\"ida_subs\", config.EXPORTING_ONLY_NON_IDA_SUBS)\n self.ignore_sub_names = kwargs.get(\n \"ignore_sub_names\", config.DIFFING_IGNORE_SUB_FUNCTION_NAMES\n )\n self.ignore_all_names = kwargs.get(\n \"ignore_all_names\", config.DIFFING_IGNORE_ALL_FUNCTION_NAMES\n )\n self.ignore_small_functions = kwargs.get(\n \"ignore_small_functions\", config.DIFFING_IGNORE_SMALL_FUNCTIONS\n )\n\n # Enable, by default, exporting only function summaries for huge dbs.\n too_big_db = total_functions > config.MIN_FUNCTIONS_TO_CONSIDER_HUGE\n self.func_summaries_only = kwargs.get(\"func_summaries_only\", too_big_db)\n if too_big_db:\n warning(MSG_FUNCTION_SUMMARIES_ONLY)\n\n # Python script to run for both the export and diffing process\n self.project_script = kwargs.get(\"project_script\")\n\n # Microcode slows down the export process and might cause false positives\n # with big to huge databases, disable it by default for 'big' databases\n medium_db = total_functions <= config.MIN_FUNCTIONS_TO_CONSIDER_MEDIUM\n self.export_microcode = kwargs.get(\"export_microcode\", medium_db)\n\n\n#-------------------------------------------------------------------------------\nclass CHtmlDiff:\n \"\"\"A replacement for difflib.HtmlDiff that tries to enforce a max width\n\n The main challenge is to do this given QTextBrowser's limitations. In\n particular, QTextBrowser only implements a minimum of CSS.\n \"\"\"\n\n _html_template = \"\"\"\n \n \n \n \n \n \n %(rows)s\n
\n \n \n \"\"\"\n\n _style = (\n \"\"\"\n table.diff_tab {\n font-family: Courier, monospace;\n table-layout: fixed;\n width: 100%;\n }\n\n .diff_add {\n background-color: \"\"\"\n + config.DIFF_COLOR_ADDED\n + \"\"\";\n }\n .diff_chg {\n background-color: \"\"\"\n + config.DIFF_COLOR_CHANGED\n + \"\"\";\n }\n .diff_sub {\n background-color: \"\"\"\n + config.DIFF_COLOR_SUBTRACTED\n + \"\"\";\n }\n .diff_lineno {\n text-align: right;\n background-color: \"\"\"\n + config.DIFF_COLOR_LINE_NO\n + \"\"\";\n }\n \"\"\"\n )\n\n _row_template = \"\"\"\n \n %s\n %s\n %s\n %s\n \n \"\"\"\n\n _rexp_too_much_space = re.compile(\"^\\t[.\\\\w]+ {8}\")\n\n def make_file(self, lhs, rhs, fmt, lex):\n rows = []\n for left, right, changed in difflib._mdiff(lhs, rhs):\n lno, ltxt = left\n rno, rtxt = right\n\n if not changed:\n ltxt = highlight(ltxt, lex, fmt)\n rtxt = highlight(rtxt, lex, fmt)\n else:\n ltxt = self._stop_wasting_space(ltxt)\n rtxt = self._stop_wasting_space(rtxt)\n\n ltxt = ltxt.replace(\" \", \" \")\n rtxt = rtxt.replace(\" \", \" \")\n ltxt = ltxt.replace(\"<\", \"<\")\n ltxt = ltxt.replace(\">\", \">\")\n rtxt = rtxt.replace(\"<\", \"<\")\n rtxt = rtxt.replace(\">\", \">\")\n\n row = self._row_template % (str(lno), ltxt, str(rno), rtxt)\n rows.append(row)\n\n all_the_rows = \"\\n\".join(rows)\n all_the_rows = (\n all_the_rows.replace(\"\\x00+\", '')\n .replace(\"\\x00-\", '')\n .replace(\"\\x00^\", '')\n .replace(\"\\x01\", \"\")\n .replace(\"\\t\", 4 * \" \")\n )\n\n res = self._html_template % {\"style\": self._style, \"rows\": all_the_rows}\n return res\n\n def _stop_wasting_space(self, s):\n \"\"\"I never understood why you'd want to have 13 spaces between instruction and args'\"\"\"\n m = self._rexp_too_much_space.search(s)\n if m:\n mlen = len(m.group(0))\n return s[: mlen - 4] + s[mlen:]\n else:\n return s\n\n\n#-------------------------------------------------------------------------------\ntry:\n\n class CAstVisitorInherits(ctree_visitor_t):\n pass\n\nexcept:\n\n class CAstVisitorInherits:\n pass\n\n\n#-------------------------------------------------------------------------------\n# pylint: disable=super-init-not-called\n# pylint: disable=non-parent-init-called\n# pylint: disable=arguments-differ\nclass CAstVisitor(CAstVisitorInherits):\n def __init__(self, cfunc):\n self.primes = primesbelow(4096)\n ctree_visitor_t.__init__(self, CV_FAST)\n self.cfunc = cfunc\n self.primes_hash = 1\n return\n\n def visit_expr(self, expr):\n try:\n self.primes_hash *= self.primes[expr.op]\n except:\n traceback.print_exc()\n return 0\n\n def visit_insn(self, ins):\n try:\n self.primes_hash *= self.primes[ins.op]\n except:\n traceback.print_exc()\n return 0\n\n# pylint: enable=arguments-differ\n# pylint: enable=non-parent-init-called\n# pylint: enable=super-init-not-called\n\n#-------------------------------------------------------------------------------\ndef is_ida_file(filename):\n filename = filename.lower()\n return (\n filename.endswith(\".idb\")\n or filename.endswith(\".i64\")\n or filename.endswith(\".til\")\n or filename.endswith(\".id0\")\n or filename.endswith(\".id1\")\n or filename.endswith(\".nam\")\n )\n\n\n#-------------------------------------------------------------------------------\ndef remove_file(filename):\n try:\n os.remove(filename)\n except:\n # Fix for Bug #5: https://github.com/joxeankoret/diaphora/issues/5\n #\n # For some reason, in Windows, the handle to the SQLite database is\n # not closed, and I really try to be sure that all the databases are\n # detached, no cursor is leaked, etc... So, in case we cannot remove\n # the database file because it's still being used by IDA in Windows\n # for some unknown reason, just drop the database's tables and after\n # that continue normally.\n with sqlite3.connect(filename, check_same_thread=False) as db:\n cur = db.cursor()\n try:\n funcs = [\n \"functions\",\n \"program\",\n \"program_data\",\n \"version\",\n \"instructions\",\n \"basic_blocks\",\n \"bb_relations\",\n \"bb_instructions\",\n \"function_bblocks\",\n \"compilation_units\",\n \"compilation_unit_functions\",\n ]\n for func in funcs:\n db.execute(f\"drop table if exists {func}\")\n finally:\n cur.close()\n\n\n#-------------------------------------------------------------------------------\ndef main():\n # pylint: disable-next=global-statement\n global g_bindiff\n\n # EXPORT\n if os.getenv(\"DIAPHORA_AUTO\") is not None:\n file_out = os.getenv(\"DIAPHORA_EXPORT_FILE\")\n if file_out is None:\n raise Exception(\"No export file specified!\")\n\n use_decompiler = os.getenv(\"DIAPHORA_USE_DECOMPILER\")\n if use_decompiler is None:\n use_decompiler = False\n\n auto_wait()\n\n if os.path.exists(file_out):\n if g_bindiff is not None:\n g_bindiff = None\n\n remove_file(file_out)\n log(f\"Database {repr(file_out)} removed\")\n\n bd = CIDABinDiff(file_out)\n project_script = os.getenv(\"DIAPHORA_PROJECT_SCRIPT\")\n if project_script is not None:\n bd.project_script = project_script\n bd.use_decompiler = use_decompiler\n\n bd.exclude_library_thunk = bd.get_value_for(\n \"exclude_library_thunk\", bd.exclude_library_thunk\n )\n bd.ida_subs = bd.get_value_for(\"ida_subs\", bd.ida_subs)\n bd.ignore_sub_names = bd.get_value_for(\"ignore_sub_names\", bd.ignore_sub_names)\n bd.function_summaries_only = bd.get_value_for(\n \"function_summaries_only\", bd.function_summaries_only\n )\n bd.min_ea = int(bd.get_value_for(\"from_address\", \"0\"), 16)\n bd.export_microcode = bd.get_value_for(\n \"self.export_microcode\", bd.export_microcode\n )\n\n _to_ea = bd.get_value_for(\"to_address\", None)\n if _to_ea is not None:\n bd.max_ea = int(_to_ea, 16)\n\n try:\n bd.export()\n except KeyboardInterrupt:\n log(f\"Aborted by user, removing crash file {file_out}-crash...\")\n os.remove(f\"{file_out}-crash\")\n\n idaapi.qexit(0)\n\n # DIFF-SHOW\n elif os.getenv(\"DIAPHORA_AUTO_HTML\") is not None:\n debug_refresh(\"Handling DIAPHORA_AUTO_HTML\")\n debug_refresh(f'DIAPHORA_AUTO_HTML={os.getenv(\"DIAPHORA_AUTO_HTML\")}')\n debug_refresh(f'DIAPHORA_DB1={os.getenv(\"DIAPHORA_DB1\")}')\n debug_refresh(f'DIAPHORA_DB2={os.getenv(\"DIAPHORA_DB2\")}')\n debug_refresh(f'DIAPHORA_DIFF={os.getenv(\"DIAPHORA_DIFF\")}')\n debug_refresh(f'DIAPHORA_EA1={os.getenv(\"DIAPHORA_EA1\")}')\n debug_refresh(f'DIAPHORA_EA2={os.getenv(\"DIAPHORA_EA2\")}')\n debug_refresh(f'DIAPHORA_HTML_ASM={os.getenv(\"DIAPHORA_HTML_ASM\")}')\n debug_refresh(f'DIAPHORA_HTML_PSEUDO={os.getenv(\"DIAPHORA_HTML_PSEUDO\")}')\n db1 = os.getenv(\"DIAPHORA_DB1\")\n if db1 is None:\n raise Exception(\"No database file specified!\")\n diff_db = os.getenv(\"DIAPHORA_DIFF\")\n if diff_db is None:\n raise Exception(\"No diff database file for diff specified!\")\n ea1 = os.getenv(\"DIAPHORA_EA1\")\n if ea1 is None:\n raise Exception(\"No address 1 specified!\")\n ea2 = os.getenv(\"DIAPHORA_EA2\")\n if ea2 is None:\n raise Exception(\"No address 2 specified!\")\n html_asm = os.getenv(\"DIAPHORA_HTML_ASM\")\n if html_asm is None:\n raise Exception(\"No html output file for asm specified!\")\n html_pseudo = os.getenv(\"DIAPHORA_HTML_PSEUDO\")\n if html_pseudo is None:\n raise Exception(\"No html output file for pseudo specified!\")\n _generate_html(db1, diff_db, ea1, ea2, html_asm, html_pseudo)\n idaapi.qexit(0)\n else:\n _diff_or_export(True)\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"joxeankoret/diaphora","sub_path":"diaphora_ida.py","file_name":"diaphora_ida.py","file_ext":"py","file_size_in_byte":122228,"program_lang":"python","lang":"en","doc_type":"code","stars":3283,"dataset":"github-code","pt":"52"} +{"seq_id":"17551613670","text":"import sys\r\nimport pygame as pg\r\nimport numpy as np\r\n\r\n\r\nclass Simulation:\r\n def __init__(self, flowers):\r\n pg.init()\r\n self.clock = pg.time.Clock()\r\n self.screen = pg.display.set_mode(screen_size)\r\n self.screen.fill(pg.Color('black'))\r\n self.flowers = flowers\r\n\r\n def update(self):\r\n running = True\r\n while running:\r\n for event in pg.event.get():\r\n if event.type == pg.QUIT:\r\n pg.quit()\r\n sys.exit()\r\n self.logic()\r\n self.screen.fill(pg.Color('green'))\r\n self.draw()\r\n pg.display.update()\r\n self.clock.tick(60)\r\n\r\n def draw(self):\r\n for flower in self.flowers:\r\n flower.draw(self.screen)\r\n\r\n def logic(self):\r\n for flower in self.flowers:\r\n flower.rotate()\r\n\r\n\r\nclass Flower:\r\n def __init__(self, pos, radius):\r\n self.pos = pos\r\n self.radius = radius\r\n self.color = np.random.randint(0, 255, 3)\r\n self.petal_color = np.random.randint(0, 255, 3)\r\n self.number_of_petals = np.random.randint(4, 7)\r\n self.angle = np.random.uniform(0, np.pi * 2)\r\n self.instant_radius = 0\r\n\r\n def draw(self, screen):\r\n # Current Radius\r\n self.instant_radius += 0.25\r\n if self.instant_radius >= self.radius:\r\n self.instant_radius = self.radius\r\n\r\n # Draw Petals\r\n angle_diff = 2 * np.pi / self.number_of_petals\r\n petal_dist = self.instant_radius * np.array([np.cos(self.angle), np.sin(self.angle)])\r\n for i in range(self.number_of_petals):\r\n petal_dist = np.matmul(petal_dist, self.rotation_matrix(angle_diff))\r\n petal_loc = petal_dist + self.pos\r\n pg.draw.circle(screen, self.petal_color, petal_loc, self.instant_radius)\r\n pg.draw.circle(screen, pg.Color('black'), petal_loc, self.instant_radius, 2)\r\n\r\n # Draw Center\r\n pg.draw.circle(screen, self.color, self.pos, self.instant_radius)\r\n pg.draw.circle(screen, pg.Color('black'), self.pos, self.instant_radius, 2)\r\n\r\n def rotate(self):\r\n self.angle += 0.0\r\n\r\n @staticmethod\r\n def rotation_matrix(angle):\r\n rot = np.array([[np.cos(angle), -np.sin(angle)],\r\n [np.sin(angle), np.cos(angle)]])\r\n return rot\r\n\r\n\r\nif __name__ == '__main__':\r\n screen_size = np.array([640, 640])\r\n spacing = 40\r\n position = np.array([20, 20])\r\n my_flowers = []\r\n x, y = 0, 0\r\n while x < 16:\r\n while y < 16:\r\n my_flowers.append(Flower(position + np.array([x * spacing, y * spacing]), 8))\r\n print(position + np.array([x * spacing, y * spacing]))\r\n y += 1\r\n x += 1\r\n y = 0\r\n simulation = Simulation(my_flowers)\r\n simulation.update()\r\n","repo_name":"HanTheDestroyer/Flower_and_Circle_Packing","sub_path":"Flower Field.py","file_name":"Flower Field.py","file_ext":"py","file_size_in_byte":2864,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"6708344897","text":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.15.2\n# kernelspec:\n# display_name: env\n# language: python\n# name: python3\n# ---\n\n# ## Importing Libraries\n\n# +\n## Importing Libraries\nimport os\nimport gc\nimport glob\nimport random\nimport numpy as np \nimport pandas as pd\nimport seaborn as sns\nfrom tqdm import tqdm\nfrom scipy import stats\nfrom pathlib import Path\nfrom itertools import groupby\nimport matplotlib.pyplot as plt\n\nimport joblib\nimport pickle\n\nfrom sklearn.model_selection import KFold\nfrom sklearn.preprocessing import OneHotEncoder, OrdinalEncoder\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.pipeline import make_pipeline\nimport warnings\nfrom sklearn.exceptions import ConvergenceWarning\n\nwarnings.filterwarnings(\"ignore\", category=FutureWarning)\nwarnings.filterwarnings(\"ignore\", category=ConvergenceWarning)\nfrom sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet, SGDRegressor\nfrom sklearn.linear_model import PassiveAggressiveRegressor, ARDRegression, RidgeCV\nfrom sklearn.linear_model import TheilSenRegressor, RANSACRegressor, HuberRegressor\nfrom sklearn.neural_network import MLPRegressor\nfrom sklearn.svm import SVR, LinearSVR\nfrom sklearn.neighbors import KNeighborsRegressor\nfrom sklearn.gaussian_process import GaussianProcessRegressor\nfrom sklearn.tree import DecisionTreeRegressor\nfrom sklearn.experimental import enable_hist_gradient_boosting\nfrom sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, ExtraTreesRegressor, HistGradientBoostingRegressor\nfrom sklearn.ensemble import BaggingRegressor, GradientBoostingRegressor, VotingRegressor, StackingRegressor\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.cross_decomposition import PLSRegression\n\nreg_dict = {\"LinearRegression\": LinearRegression(),\n \"Ridge\": Ridge(),\n \"Lasso\": Lasso(),\n \"ElasticNet\": ElasticNet(), \n \"Polynomial_deg2\": Pipeline([('poly', PolynomialFeatures(degree=2)),('linear', LinearRegression())]),\n \"Polynomial_deg3\": Pipeline([('poly', PolynomialFeatures(degree=3)),('linear', LinearRegression())]),\n \"Polynomial_deg4\": Pipeline([('poly', PolynomialFeatures(degree=4)),('linear', LinearRegression())]),\n \"Polynomial_deg5\": Pipeline([('poly', PolynomialFeatures(degree=5)),('linear', LinearRegression())]),\n \"KNeighborsRegressor\": KNeighborsRegressor(n_neighbors=3),\n \"DecisionTreeRegressor\": DecisionTreeRegressor(),\n \"RandomForestRegressor\": RandomForestRegressor(),\n \"SVR\": SVR(kernel='rbf', C=1e3, gamma=0.1, epsilon=0.1),\n \"GaussianProcessRegressor\": GaussianProcessRegressor(),\n \"SGDRegressor\": SGDRegressor(),\n \"MLPRegressor\": MLPRegressor(hidden_layer_sizes=(10,10), max_iter=100, early_stopping=True, n_iter_no_change=5),\n \"ExtraTreesRegressor\": ExtraTreesRegressor(n_estimators=100), \n \"PLSRegression\": PLSRegression(n_components=10),\n \"PassiveAggressiveRegressor\": PassiveAggressiveRegressor(max_iter=100, tol=1e-3),\n \"TheilSenRegressor\": TheilSenRegressor(random_state=0),\n \"RANSACRegressor\": RANSACRegressor(random_state=0),\n \"HistGradientBoostingRegressor\": HistGradientBoostingRegressor(),\n \"AdaBoostRegressor\": AdaBoostRegressor(random_state=0, n_estimators=100),\n \"BaggingRegressor\": BaggingRegressor(base_estimator=SVR(), n_estimators=10),\n \"GradientBoostingRegressor\": GradientBoostingRegressor(random_state=0),\n \"VotingRegressor\": VotingRegressor([('lr', LinearRegression()), ('rf', RandomForestRegressor(n_estimators=10))]),\n \"StackingRegressor\": StackingRegressor(estimators=[('lr', RidgeCV()), ('svr', LinearSVR())], final_estimator=RandomForestRegressor(n_estimators=10)),\n \"ARDRegression\": ARDRegression(),\n \"HuberRegressor\": HuberRegressor(),\n }\n\n# -\n\n# ## Read a data\n\nst = pd.read_csv('../archive/shopping_trends.csv')\nst.sample(5)\n\n# ## Data Preprocessing\n# Cheking data types, checking data loss, any nun? \n\nst.head()\n\nst.dtypes\n\n# +\ncolumns = ['Gender', 'Item Purchased', 'Category', 'Location', 'Size', 'Color', 'Season', 'Review Rating',\n 'Subscription Status', 'Shipping Type', 'Discount Applied', 'Promo Code Used', \n 'Payment Method', 'Frequency of Purchases']\n\ndf = st[columns]\ndf.head(-5)\n# -\n\ndf.dtypes\n\ndf.isnull().sum()\n#データ内に欠損値があるかどうかを確認\n\ntrain_x = df.copy()#コピーを作成する\ntrain_x = train_x.drop([], axis=1)# 指定した行を削除\ndisplay(train_x)#データを表示\n\ntrain_x.isnull().sum()\n\n# +\nfrom sklearn.preprocessing import LabelEncoder\nnon_numeric_columns = ['Gender', 'Item Purchased', 'Category', 'Location', 'Size', 'Color', 'Season',\n 'Subscription Status', 'Shipping Type', 'Discount Applied', 'Promo Code Used', \n 'Payment Method', 'Frequency of Purchases']\n\nfor col in non_numeric_columns:\n encoder = LabelEncoder()\n encoder.fit(df[col])\n df[col] = encoder.transform(df[col])\n train_x[col] = encoder.transform(train_x[col])\n\ndf\n# -\n\n# ## Feature Engineering\n\ndf\n\n\ndef calculate_mae_and_mrrmse(y_true, y_pred, scaler=None):\n if scaler:\n y_true = scaler.inverse_transform(y_true)\n y_pred = scaler.inverse_transform(y_pred)\n\n # Calculate Mean Rowwise Root Mean Squared Error (MRRMSE)\n rowwise_rmse = np.sqrt(np.mean(np.square(y_true - y_pred), axis=1))\n mrrmse_score = np.mean(rowwise_rmse)\n else:\n # Calculate Mean Rowwise Root Mean Squared Error (MRRMSE)\n rowwise_rmse = np.sqrt(np.mean(np.square(y_true - y_pred), axis=1))\n mrrmse_score = np.mean(rowwise_rmse)\n \n # Print the results\n print(f\"MRRMSE Score: {mrrmse_score}\")\n\n\n# +\nkf = KFold(n_splits=6, shuffle=True, random_state=None)\n\n# Create k-fold columns\nfor fold, (train_index, test_index) in enumerate(kf.split(df)):\n df.loc[test_index, 'kfold'] = fold\n \n \n # plt.figure(figsize=(10, 6))\n # for fold, (_, test_index) in enumerate(kf.split(df)):\n # plt.scatter(test_index, [fold] * len(test_index), label=f'Fold {fold}')\n\n # plt.title('KFold Cross-Validation')\n # plt.xlabel('Data Index')\n # plt.ylabel('Fold Number')\n # plt.legend()\n # plt.show()\n\ndf['kfold'] = df['kfold'].astype(int)\ncols = ['kfold'] + [col for col in df if col != 'kfold']\ndf = df[cols]\n\ndf\n# -\n\n# ## Training \n\n# +\ntest_data = df[df[\"kfold\"]==5].sample(frac=1, random_state=6174)\ntrain_data = df[df[\"kfold\"]!=5].sample(frac=1, random_state=6174)\ntrain_data\n\nplt.figure(figsize=(14, 8))\nfor i in range(5):\n plt.subplot(2, 3, i+1)\n sns.histplot(train_data[train_data[\"kfold\"]!=i].index, bins=30, kde=True, color='blue', label='Train')\n sns.histplot(train_data[train_data[\"kfold\"]==i].index, bins=30, kde=True, color='orange', label='Validation')\n plt.title(f'Training Data - Fold {i}')\n plt.xlabel('Data Index')\n plt.ylabel('Count')\n plt.legend()\n\nplt.tight_layout()\nplt.show()\n\n\n# +\ndef calculate_statistic(df, group_col, cols, stat_func, stat_name):\n stat_df = df.groupby(group_col)[cols].apply(stat_func)\n stat_df.columns = [f'{group_col}_{stat_name}_{col}' for col in cols]\n return stat_df.reset_index().astype({group_col: str})\n\nreview_rating_mean = calculate_statistic(df, 'Item Purchased', 'Review Rating', lambda x: x.mean(), 'mean')\nreview_rating_std = calculate_statistic(df, 'Item Purchased', 'Review Rating', lambda x: x.std(), 'std')\nreview_rating_min = calculate_statistic(df, 'Item Purchased', 'Review Rating', lambda x: x.min(), 'min')\nreview_rating_max = calculate_statistic(df, 'Item Purchased', 'Review Rating', lambda x: x.max(), 'max')\nreview_rating_median = calculate_statistic(df, 'Item Purchased', 'Review Rating', lambda x: x.median(), 'median')\nreview_rating_skew = calculate_statistic(df, 'Item Purchased', 'Review Rating', lambda x: x.skew(), 'skew')\nreview_rating_kurt = calculate_statistic(df, 'Item Purchased', 'Review Rating', lambda x: x.kurt(), 'kurt')\nreview_rating_ratio_mean_std = calculate_statistic(df, 'Item Purchased', 'Review Rating', lambda x: x.mean()/x.std(), 'ratio_mean_std')\n# -\n\nreview_rating_std\n\ndf\n\n# +\n# features = ['Item_Purchased', 'Review Rating']\n# df.reset_index(inplace=True)\n\nX = df.copy()\nX = pd.merge(X, review_rating_mean, on='Review Rating', how='left', suffixes=('_orig', '_mean'))\nX = pd.merge(X, review_rating_std, on='Review Rating', how='left', suffixes=('_orig', '_std'))\n# X = pd.merge(X, review_rating_min, on='Review Rating', how='left', suffixes=('_orig', '_min'))\n# X = pd.merge(X, review_rating_max, on='Review Rating', how='left', suffixes=('_orig', '_max'))\n# X = pd.merge(X, review_rating_median, on='Review Rating', how='left', suffixes=('_orig', '_median'))\n# X = pd.merge(X, review_rating_skew, on='Review Rating', how='left', suffixes=('_orig', '_skew'))\n\n# Repeat for 'Item Purchased'\n# X = pd.merge(X, review_rating_mean, on='Item Purchased', how='left', suffixes=('_item', '_mean'))\n# X = pd.merge(X, review_rating_std, on='Item Purchased', how='left', suffixes=('_item', '_std'))\n# X = pd.merge(X, review_rating_min, on='Item Purchased', how='left', suffixes=('_item', '_min'))\n# X = pd.merge(X, review_rating_max, on='Item Purchased', how='left', suffixes=('_item', '_max'))\n# X = pd.merge(X, review_rating_median, on='Item Purchased', how='left', suffixes=('_item', '_median'))\n# X = pd.merge(X, review_rating_skew, on='Item Purchased', how='left', suffixes=('_item', '_skew'))\n\n# X.drop(features, axis=1, inplace=True)\nX\n\n# +\nindex = de_train[de_train[\"cell_type\"] == \"NK cells\"].index\nindex = index.difference(all_de_train.index)\n\nX_test = X.loc[index]\ny_test = y.loc[index]\n\n# Drop cell_type = 'NK cells'\nX_train = X.drop(index)\ny_train = y.drop(index)\n\n# +\nn_components = 37\nalpha = 0.5\n\nmodels = []\nsvds = []\ny_preds = []\n\nkf = KFold(n_splits=5, shuffle=True, random_state=6174)\nfor fold, (train_index, val_index) in enumerate(kf.split(X_train, y_train)):\n X_k_train = X_train.iloc[train_index].sample(frac=1, random_state=6174)\n X_k_val = X_train.iloc[val_index].sample(frac=1, random_state=6174)\n\n y_k_train = y_train.iloc[train_index].sample(frac=1, random_state=6174)\n y_k_val = y_train.iloc[val_index].sample(frac=1, random_state=6174) \n\n # genes\n pred_df = y_test.copy()\n pred_df[genes] = 0\n\n # Model\n model = make_pipeline(\n ColumnTransformer([('ohe', OneHotEncoder(handle_unknown='ignore'), ['sm_name'])]),\n LinearRegression()\n )\n svd = TruncatedSVD(n_components=n_components, random_state=6174)\n z_tr = svd.fit_transform(y_k_train)\n model.fit(X_k_train, z_tr)\n models.append(model)\n svds.append(svd)\n\n y_pred = svd.inverse_transform(model.predict(X_test))\n y_preds.append(y_pred)\n pred_df[genes] = y_pred\n\n # Get validation score\n calculate_mae_and_mrrmse(y_test[genes].values, pred_df[genes].values)\n\ny_preds = np.array(y_preds)\nprint(\"\\nEnsemble\")\ncalculate_mae_and_mrrmse(y_test[genes].values, y_preds.mean(axis=0))\n","repo_name":"shoot649854/CSTD","sub_path":"src/nb-s-001.py","file_name":"nb-s-001.py","file_ext":"py","file_size_in_byte":11372,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"20553866361","text":"#\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.\nimport functools\n\nfrom oslo_config import cfg\nfrom oslo_log import log as logging\nimport oslo_messaging as messaging\nfrom sqlalchemy import exc as s_exc\n\nfrom warre import app\nfrom warre.extensions import db\nfrom warre import models\n\n\nCONF = cfg.CONF\nLOG = logging.getLogger(__name__)\n\n\ndef app_context(f):\n @functools.wraps(f)\n def decorated(self, *args, **kwargs):\n with self.app.app_context():\n return f(self, *args, **kwargs)\n return decorated\n\n\nclass NotificationEndpoints(object):\n\n def __init__(self):\n self.app = app.create_app(init_config=False)\n\n def sample(self, ctxt, publisher_id, event_type, payload, metadata):\n try:\n LOG.debug('Processing notification for payload %s', payload)\n traits = {d[0]: d[2] for d in payload[0]['traits']}\n event_type = payload[0].get('event_type')\n if event_type == 'lease.event.end_lease':\n self._lease_end(traits['lease_id'])\n elif event_type == 'lease.event.start_lease':\n self._lease_start(traits['lease_id'])\n else:\n LOG.debug(\"Received unhandled event %s\", event_type)\n except Exception:\n LOG.exception('Unable to handle notification: %s', payload)\n\n return messaging.NotificationResult.HANDLED\n\n def _lease_end(self, lease_id):\n self._update_lease(lease_id, models.Reservation.COMPLETE)\n\n def _lease_start(self, lease_id):\n self._update_lease(lease_id, models.Reservation.ACTIVE)\n\n @app_context\n def _update_lease(self, lease_id, status):\n try:\n reservation = db.session.query(models.Reservation) \\\n .filter_by(lease_id=lease_id).one()\n except s_exc.InvalidRequestError:\n LOG.error(\"No reservation with lease ID %s\", lease_id)\n else:\n reservation.status = status\n db.session.add(reservation)\n db.session.commit()\n LOG.info(\"Updated reservation %s to %s\", reservation.id, status)\n","repo_name":"NeCTAR-RC/warre","sub_path":"warre/notification/endpoints.py","file_name":"endpoints.py","file_ext":"py","file_size_in_byte":2613,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"23755423576","text":"class Solution:\n def addTwoNumbers(self, l1: ListNode, l2: ListNode) -> ListNode:\n\n # ==================================================\n # Linked List (Recursive) =\n # ==================================================\n # time : O(m+n)\n # space : O(abs(m-n))\n\n len1, len2 = self.length(l1), self.length(l2)\n if len1 > len2: l2 = self.fill(l2, len1 - len2)\n elif len2 > len1: l1 = self.fill(l1, len2 - len1)\n\n carry, ret = self.merge(l1, l2)\n if carry: ret = ListNode(1, ret)\n\n return ret\n\n # ==================================================\n # Reverse Linked List =\n # ==================================================\n # time : O(m+n)\n # space : O(1)\n\n l1, l2 = self.reverse(l1), self.reverse(l2)\n return self.reverse(self.add(l1, l2))\n\n # ==================================================\n # Linked List + Stack =\n # ==================================================\n # time : O(m+n)\n # space : O(m+n)\n\n return self.stack(l1, l2)\n\n def length(self, l: ListNode) -> int:\n length, tmp = 0, l\n while tmp:\n length += 1\n tmp = tmp.next\n return length\n\n def fill(self, l: ListNode, num: int) -> ListNode:\n ret = head = ListNode(-1)\n for i in range(num):\n head.next = ListNode(0)\n head = head.next\n head.next = l\n return ret.next\n\n def merge(self, l1: ListNode, l2: ListNode) -> ListNode:\n \"\"\"\n Recursive to merge two lists while summing nodes' values\n \"\"\"\n if not l1 and not l2: return (0, None)\n carry, next = self.merge(l1.next, l2.next)\n curSum = l1.val + l2.val + carry\n node = ListNode(curSum % 10, next)\n return (curSum // 10, node)\n\n def reverse(self, head: ListNode) -> ListNode:\n pre = None\n while head:\n tmp = head.next\n head.next = pre\n pre, head = head, tmp\n\n return pre\n\n def add(self, l1: ListNode, l2: ListNode) -> ListNode:\n ret = cur = ListNode(0)\n\n carry = 0\n while l1 or l2:\n if l1: n1, l1 = l1.val, l1.next\n else: n1 = 0\n if l2: n2, l2 = l2.val, l2.next\n else: n2 = 0\n\n total = n1 + n2 + carry\n carry = total // 10\n total %= 10\n\n cur.next = ListNode(total)\n cur = cur.next\n\n if carry: cur.next = ListNode(1)\n\n return ret.next\n\n def stack(self, l1: ListNode, l2: ListNode) -> ListNode:\n stack1, stack2 = [], []\n while l1:\n stack1.append(l1.val)\n l1 = l1.next\n while l2:\n stack2.append(l2.val)\n l2 = l2.next\n\n carry, ret = 0, None\n while stack1 or stack2 or carry:\n val1 = stack1.pop() if stack1 else 0\n val2 = stack2.pop() if stack2 else 0\n curSum = val1 + val2 + carry\n carry = curSum // 10\n curSum = curSum % 10\n\n ret = ListNode(curSum, ret)\n\n return ret\n","repo_name":"yylou/leetcode","sub_path":"0445-add-two-numbers-ii/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":3246,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"50"} +{"seq_id":"25312296044","text":"# Root Finding - Secant Method\r\n\r\n# FUNCTIONS\r\n\r\n\r\ndef f(x): # function calculation based on given x value\r\n result = 0 # initial sum = 0\r\n for i in range(degree, 0, -1):\r\n temp = x # exponent base\r\n for j in range(i, 1, -1): # power calculation\r\n temp *= x\r\n result += coeff[i]*temp\r\n return result + coeff[0] # add with constant\r\n\r\n\r\ndef secant(x1, x2, iterations): # secant iteration\r\n while(not(f(x1) < precision and f(x1) > -precision)):\r\n print(\"Iteration\", iterations, \"x1 =\", x1, \"f(x1) =\",\r\n f(x1), \":: x2 =\", x2, \"f(x2) =\", f(x2))\r\n m = (f(x2)-f(x1))/(x2-x1) # finding gradient\r\n x2 = x1 # new x2 is old x1\r\n x1 = -f(x1)/m + x1 # new x1\r\n iterations += 1 # counting iterations\r\n if(iterations > 900): # exception : iteration limit/no solution\r\n return(print(\"Iteration limit exceeded, bad guess or no real roots\"))\r\n return(secant(x1, x2, iterations)) # recursion\r\n print(\"Iteration\", iterations, \"x1 =\", x1, \"f(x1) =\", # solution print\r\n f(x1), \":: x2 =\", x2, \"f(x2) =\", f(x2))\r\n print(\"\\nOne of the root(s) is approximately\", x1)\r\n\r\n\r\n# BODY\r\n# declaring degree of polynomial\r\ndegree = int(input(\"Enter the degree of the polynomial : \"))\r\n\r\ncoeff = [0]*(degree+1) # preparing list by filling with 0\r\n\r\n# entering the coefficients of the polynomial\r\nprint(\"\\nEnter the coefficients for : \")\r\nfor i in range(degree, -1, -1):\r\n print(\"x^\"+str(i), \":\", end=\" \")\r\n coeff[i] = float(input())\r\n\r\n# displaying function f(x)\r\nprint(\"\\nFunction f(x)= \", end=\"\")\r\nfor i in range(degree, 0, -1):\r\n if(coeff[i] > 0):\r\n print(coeff[i], end=\"\")\r\n print(\"x^\"+str(i), end=\" + \")\r\nprint(coeff[0])\r\n\r\n# guessing x1\r\nh = 0.1 # step-difference\r\nprecision = 0.01 # set precision of the result\r\nprint(\"Pick 2 points =\")\r\nx1 = float(input(\"Pick x1 : \"))\r\nx2 = float(input(\"Pick x2 : \"))\r\n\r\n# exception : guessed right\r\nif(f(x1) < precision and f(x1) > -precision):\r\n print(\"Guessed right, one of the root(s) is approximately\", x1)\r\nelif(f(x2) < precision and f(x2) > -precision):\r\n print(\"Guessed right, one of the root(s) is approximately\", x2)\r\nelse:\r\n # initiallizing iterations\r\n iterations = 0\r\n secant(x1, x2, iterations)","repo_name":"Vidiskiu/root_finding","sub_path":"Root Finding Secant Method.py","file_name":"Root Finding Secant Method.py","file_ext":"py","file_size_in_byte":2301,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"6719457168","text":"import fileinput\nfrom typing import Generator, Iterable\n\n\ndef priority(item: str) -> int:\n order = ord(item)\n if order < ord(\"a\"):\n return order - ord(\"A\") + 27\n return order - ord(\"a\") + 1\n\n\ndef halves(line: str) -> tuple[str, str]:\n half = len(line) // 2\n return line[:half], line[half:]\n\n\ndef triplets(lines: list[str]) -> Generator[tuple[str, str, str], None, None]:\n for i in range(0, len(lines), 3):\n yield lines[i], lines[i + 1], lines[i + 2]\n\n\ndef itemize(group: Iterable[str]) -> tuple[str, ...]:\n return tuple(set(items) for items in group)\n\n\ndef common(*sacks: set[str]) -> str:\n sacks = iter(sacks)\n intersection = next(sacks)\n for sack in sacks:\n intersection &= sack\n return intersection.pop()\n\n\nif __name__ == \"__main__\":\n lines = [line.strip() for line in fileinput.input(\"-\")]\n compartment_items = map(itemize, (halves(line) for line in lines))\n group_items = map(itemize, (triplet for triplet in triplets(lines)))\n print(sum(priority(common(*items)) for items in compartment_items))\n print(sum(priority(common(*items)) for items in group_items))\n","repo_name":"technomunk/advent-of-code","sub_path":"2022/03_rucksack_reorganization.py","file_name":"03_rucksack_reorganization.py","file_ext":"py","file_size_in_byte":1133,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"28721688086","text":"from tkinter import *\r\n\r\ndef func_table():\r\n num = int(entry.get())\r\n strg = \" Table of \" + str(num) + \"\\n-----------------\\n\"\r\n for i in range(1, 11):\r\n strg =strg + \" \" + str(num) + \" x \" + str(i) + \" = \" + str(num * i) + \"\\n\"\r\n\r\n output_table.configure(text=strg, justify=LEFT, relief=RIDGE, bg=\"pink\", pady=0, padx=0)\r\n\r\nroot = Tk()\r\nroot.geometry(\"688x733\")\r\nroot.maxsize(788, 833)\r\nroot.title(\"Table Generator Pro - V1\")\r\nroot.wm_iconbitmap(\"table.ico\")\r\n\r\nf = Label(root, text=\"Table Generator Pro\", font=\"ubuntu 40 bold\", relief=SUNKEN, bg=\"dark blue\", fg=\"white\")\r\nf.pack(pady=40, fill=BOTH)\r\nentry = Entry(root, font=\"lucida 20 bold\", justify=CENTER)\r\nentry.pack()\r\nbutton = Button(root, text=\"Generate Table\", font=\"lucida 15 bold\", command=func_table, bg=\"indian red\", fg=\"white\")\r\nbutton.pack(pady=20)\r\noutput_table = Label(root, font=\"lucida 22 bold\")\r\noutput_table.pack()\r\nstatusvar = StringVar()\r\nstatusvar.set(\"Developed by- Kunwar Pratap\")\r\nsbar = Label(root, textvariable=statusvar, relief=SUNKEN, font=\"lucida 11 bold\", bg=\"sky blue\", padx=10)\r\nsbar.pack(side=BOTTOM, fill=X)\r\n\r\nroot.mainloop()\r\n","repo_name":"Kunwar-Pratap/Table-Generator","sub_path":"Table Generator.py","file_name":"Table Generator.py","file_ext":"py","file_size_in_byte":1136,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"15470826098","text":"import asyncio\nfrom website import Website\nfrom console_writer import ConsoleWriter\nimport sys\nimport signal\nimport functools\n\n\nclass App:\n def __init__(self):\n\n # Single instance of ConsoleWriter for application.\n # Only object writing to the console\n self.console_writer = ConsoleWriter()\n self.console_writer.greet()\n\n # User input\n self.websites_to_monitor = self.get_websites_to_monitor()\n\n async def attach_shutdown_signals(self):\n # Only functional in linux\n signals = [\"SIGTERM\", \"SIGINT\"]\n loop = asyncio.get_running_loop()\n\n for signame in signals:\n\n loop.add_signal_handler(\n getattr(signal, signame), functools.partial(self.shutdown, loop)\n )\n\n await asyncio.sleep(0)\n\n def shutdown(self, loop):\n \"\"\"\n Shutdown function. Called automatically on \n linux devices on console termination. API not implemented for Windows.\n\n PARAMETERS: loop, the current event loop.\n \"\"\"\n\n tasks = [t for t in asyncio.all_tasks() if t is not asyncio.current_task()]\n\n for task in tasks:\n task.cancel()\n\n asyncio.gather(*tasks)\n loop.stop()\n\n def start_app(self, schedules):\n\n # Reporting frequency and timeframe\n self.schedules = schedules\n\n # Start an event loop running the\n # highest level coroutine in the app\n try:\n asyncio.run(self.monitor_websites())\n except KeyboardInterrupt:\n # Only rough shutdown for Windows\n pass\n finally:\n self.console_writer.goodbye()\n\n def get_websites_to_monitor(self):\n \"\"\"\n Enables user to input website url and check_interval\n which are used to create Website instances. These are\n returned as the list variable, websites.\n\n PARAMETERS: None\n RETURNS: list\n \"\"\"\n websites = []\n\n while True:\n\n website_instance = self.create_website()\n if website_instance:\n websites.append(website_instance)\n self.console_writer.add_dashboard(website_instance.dashboard)\n print(f\"Your website has been added to the monitoring list\")\n\n if not self.user_wants_more(websites):\n break\n\n return websites\n\n def create_website(self):\n \"\"\"\n Creates an instance of the Website class based on\n user input.\n\n PARAMETERS: None\n RETURNS: Website instance or None\n depending on success of instantiation\n \"\"\"\n url = self.get_user_url()\n check_interval = self.get_user_check_interval()\n website = self.get_website(url, check_interval)\n\n if website:\n return website\n\n def user_wants_more(self, websites):\n \"\"\"\n Displays current number of items in the websites parameter\n and asks use for input.\n\n RETURNS: True is user inputs something equating to 'yes' else False\n \"\"\"\n\n print(f\"In total that makes {len(websites)}. Add another? [y/n]\")\n\n return input().lower() in [\"yes\", \"y\"]\n\n def get_website(self, url: str, check_interval: int):\n \"\"\"\n Instantiates Website instance. Safely returns \n instance or None depending on success.\n\n PARAMETERS: check_interval: Positive integer in seconds.\n Ping refresh freuency e.g. 30 would \n equate to check every 30 seconds\n url: String e.g. http://google.fr\n Instantiates Website instance.\n\n RETURNS: Website instance or None.\n \"\"\"\n try:\n\n website = Website(url=url, check_interval=check_interval)\n\n except Exception:\n print(\n \"I wasn't able to connect with that URL.\\n\"\n + \"Please revise it, including 'http://'\"\n + \" or 'https://' as appropriate).\"\n )\n return None\n\n return website\n\n def get_user_url(self):\n \"\"\"\n Returns user input after being asked for a url.\n Checking is done on Website isntance instatiation\n but user url is expected to be of the form\n http://google.fr\n\n RETURNS: String\n \"\"\"\n print(\"Please enter the a website you wish to monitor\")\n url = input().lower()\n return url\n\n def get_user_check_interval(self):\n \"\"\"\n Get check_interval from user input.\n\n PARAMETERS: None\n RETURNS: Int or None depending on input capture success\n \"\"\"\n check_interval = None\n print(\"How many seconds between consequetive checks?:\")\n while not check_interval:\n try:\n check_interval = int(input())\n except ValueError:\n print(\"That doesn't look like a number. Try again please.\")\n continue\n return check_interval\n\n async def monitor_websites(self):\n \"\"\"\n Collects all coroutines in to a \n list and creates tasks to run them\n in the existing event loop.\n \"\"\"\n\n # website.all_async_tasks kicks off all\n # async processes necessary to monitor\n # and report on that website instance\n coros = [\n asyncio.create_task(\n website.all_async_tasks(self.schedules, self.console_writer)\n )\n for website in self.websites_to_monitor\n ]\n\n print(f\"Beginning website monitoring...\")\n\n # Better shutdozn for linux users\n if \"linux\" in sys.platform:\n coros.append(self.attach_shutdown_signals())\n\n await asyncio.gather(*coros)\n\n\nif __name__ == \"__main__\":\n\n # Define reporting schedules (in seconds))\n schedule1 = {\"frequency\": 10, \"timeframe\": -600}\n schedule2 = {\"frequency\": 60, \"timeframe\": -60 * 60}\n schedules = [schedule1, schedule2]\n\n # Instantiate app\n app = App()\n app.start_app(schedules=schedules)\n\n","repo_name":"DanielJohnHarty/website_monitoring_app","sub_path":"website_monitoring_app.py","file_name":"website_monitoring_app.py","file_ext":"py","file_size_in_byte":6042,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"10691365912","text":"import argparse\nfrom sklearn.datasets import load_svmlight_file\n\ndefaultPath = \".\"\nCLI=argparse.ArgumentParser()\nCLI.add_argument(\n \"--data_path\",\n type=str,\n default=defaultPath\n)\n\nargs = CLI.parse_args()\n\nX,y = load_svmlight_file(args.data_path + \"/data/epsilon_normalized\")\n\n# Make the train-test split\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\n\n# Convert to dense\nimport numpy as np\nX_train = np.array(X_train.todense())\nX_test = np.array(X_test.todense())\n\n# Write to binary numpy files\nnp.save(args.data_path + \"/data/epsilon.X_train\", X_train)\nnp.save(args.data_path + \"/data/epsilon.X_test\", X_test)\nnp.save(args.data_path + \"/data/epsilon.y_train\", y_train)\nnp.save(args.data_path + \"/data/epsilon.y_test\", y_test)\n","repo_name":"choudary21/WML-CE-RAPIDS-PAI4SK","sub_path":"preprocess-epsilon.py","file_name":"preprocess-epsilon.py","file_ext":"py","file_size_in_byte":835,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"40153496800","text":"import FWCore.ParameterSet.Config as cms\n\nprocess = cms.Process( \"GctDigiToRaw\" )\n\nprocess.source = cms.Source( \"PoolSource\",\n fileNames = cms.untracked.vstring( \"file:gctDigis.root\" )\n)\n\nprocess.maxEvents = cms.untracked.PSet( input = cms.untracked.int32 ( 10 ) )\n\nprocess.load( \"EventFilter/GctRawToDigi/gctDigiToRaw_cfi\" )\nprocess.gctDigiToRaw.verbose = cms.untracked.bool ( False )\n\nprocess.dump = cms.OutputModule( \"DumpFEDRawDataProduct\",\n feds = cms.untracked.vint32( 745 ),\n dumpPayload = cms.untracked.bool( True )\n)\n\nprocess.p = cms.Path( process.gctDigiToRaw * process.dump )\n\nprocess.output = cms.OutputModule( \"PoolOutputModule\",\n fileName = cms.untracked.string( \"gctDigiToRaw.root\" )\n)\n\nprocess.out = cms.EndPath( process.output )\n","repo_name":"cms-sw/cmssw","sub_path":"EventFilter/GctRawToDigi/test/gctDigiToRaw_cfg.py","file_name":"gctDigiToRaw_cfg.py","file_ext":"py","file_size_in_byte":750,"program_lang":"python","lang":"en","doc_type":"code","stars":985,"dataset":"github-code","pt":"50"} +{"seq_id":"22429614033","text":"#models.py\n\nfrom datetime import datetime\nfrom django.core.urlresolvers import reverse\nfrom django.http import HttpResponseRedirect\nfrom django.db import models as m\n \nclass Post(m.Model): \n content = m.CharField(max_length=256) \n created_at = m.DateTimeField('Datetime created') \n \n \ndef post_upload(request):\n if request.method == 'GET':\n return render(request, 'post/upload.html', {})\n elif request.method == 'POST':\n post = m.Post.objects.create(content=request.POST['content'],\n created_at=datetime.utcnow())\n # No need to call post.save() at this point -- it's already saved.\n return HttpResponseRedirect(reverse('post_detail', kwargs={'post_id': post.id}))\n\nclass Document(m.Model):\n docfile = m.FileField(upload_to='documents/%Y/%m/%d')\n #created_at = m.DateTimeField('Datetime created') \n\ndef display(self):\n with open(self.text.path) as f:\n return f.read().replace('\\n', '
')\n\n\n","repo_name":"sahaana/nginx-django-uwsgi","sub_path":"box/mysite/mysite/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1405,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"31641731433","text":"'''\nAuthor: your name\nDate: 2021-09-08 14:43:39\nLastEditTime: 2021-09-10 09:18:44\nLastEditors: Please set LastEditors\nDescription: In User Settings Edit\nFilePath: \\prometheus\\attention\\pytorch\\senet.py\n'''\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\n\nclass SeNet(nn.Module):\n \"\"\"\n reference https://github.com/moskomule/senet.pytorch\n \"\"\"\n def __init__(self, channels, reduction=16):\n super(SeNet, self).__init__()\n assert (channels // reduction) > 0, \"channels // reduction should bigger than zero\"\n self.__avg_pool = nn.AdaptiveAvgPool2d(1)\n self.__fc = nn.Sequential(\n nn.Linear(channels, channels // reduction, bias=False),\n nn.ReLU(inplace=True),\n nn.Linear(channels // reduction, channels, bias=False),\n nn.Sigmoid()\n )\n def forward(self, input):\n n, c, _, _ = input.size()\n t = self.__avg_pool(input).view(n, c)\n t = self.__fc(t).view(n, c, 1, 1)\n return input * t.expand_as(input)\n\nif __name__ == '__main__':\n torch.manual_seed(seed = 2021)\n data_in = torch.randn(1, 3, 256, 256)\n se = SeNet(3, 1)\n data_out = se(data_in)\n print(data_in)\n print(data_out)\n \n \n \n \n \n \n \n","repo_name":"degawong/prometheus","sub_path":"attention/pytorch/senet.py","file_name":"senet.py","file_ext":"py","file_size_in_byte":1291,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"11713509341","text":"# CS4102 Spring 2022 - Unit B Programming\n#################################\n# Collaboration Policy: You are encouraged to collaborate with up to 3 other\n# students, but all work submitted must be your own independently written\n# solution. List the computing ids of all of your collaborators in the\n# comments at the top of each submitted file. Do not share written notes,\n# documents (including Google docs, Overleaf docs, discussion notes, PDFs), or\n# code. Do not seek published or online solutions, including pseudocode, for\n# this assignment. If you use any published or online resources (which may not\n# include solutions) when completing this assignment, be sure to cite them. Do\n# not submit a solution that you are unable to explain orally to a member of\n# the course staff. Any solutions that share similar text/code will be\n# considered in breach of this policy. Please refer to the syllabus for a\n# complete description of the collaboration policy.\n#################################\n# Your Computing ID: cms4ub\n# Collaborators:\n# Sources: Introduction to Algorithms, Cormen\n#################################\n\nimport numpy as np\n\nclass Supply:\n def __init__(self):\n return\n\n # This is the method that should set off the computation\n # of the supply chain problem. It takes as input a list containing lines of input\n # as strings. You should parse that input and then call a\n # subroutine that you write to compute the total edge-weight sum\n # and return that value from this method\n #\n # @return the total edge-weight sum of a tree that connects nodes as described\n # in the problem statement\n def compute(self, file_data):\n edgeWeightSum = 0\n\n line = file_data[0].split()\n nodesCount = int(line[0])\n linkCount = int(line[1])\n\n nodes = np.zeros(nodesCount)\n nodeNames = []\n nodeListByName = {}\n nodeListByIndex = {}\n links = np.zeros((linkCount,3))\n distCenter = -1\n for i in range(nodesCount):\n line = file_data[i + 1].split()\n nodes[i] = i\n nodeNames.append(line[0])\n type = 0\n if (line[1] == \"rail-hub\"):\n type = 1\n elif (line[1] == \"dist-center\"):\n type = 2\n distCenter = i\n elif (line[1] == \"store\"):\n type = 3\n nodeListByName[line[0]] = [i,type, distCenter]\n nodeListByIndex[i] = [i,type, distCenter]\n\n for i in range(linkCount):\n line = file_data[i + nodesCount + 1].split()\n node1 = nodeListByName[line[0]]\n node2 = nodeListByName[line[1]]\n\n validLink = False\n if (((node1[1] == 0 or node1[1] == 1) and (node2[1] == 1 or node2[1] == 2)) or ((node2[1] == 0 or node2[1] == 1) and (node1[1] == 1 or node1[1] == 2)) or (node1[1] == 2 and node2[1] == 3 and node2[2] == node1[0]) or (node1[1] == 3 and node2[1] == 2 and node1[2] == node2[0]) or (node1[1] == 3 and node2[1] == 3)):\n #if (((node1[1] == 0 or node1[1] == 1) and (node2[1] == 1 or node2[1] == 2)) or ((node2[1] == 0 or node2[1] == 1) and (node1[1] == 1 or node1[1] == 2)) or (node1[1] == 2 and node2[1] == 3) or (node1[1] == 3 and node2[1] == 2) or (node1[1] == 3 and node2[1] == 3)):\n validLink = True\n\n if validLink:\n links[i][0] = node1[0]\n links[i][1] = node2[0]\n links[i][2] = int(line[2])\n else:\n links[i][0] = -1\n links[i][1] = -1\n links[i][2] = -1\n\n\n links = np.delete(links,np.argwhere(links[:,0] == -1),axis=0)\n links = links[np.argsort(links, axis = 0)[:,2]]\n\n edgeWeightSum = self.kruskal(links, nodeListByIndex, nodes, nodesCount, linkCount)\n\n # your function to compute the result should be called here\n\n return edgeWeightSum\n\n def kruskal(self, links, nodeListByIndex, nodes, nodesCount, linkCount):\n disjointsets = nodes\n edgesAccepted = 0\n edgeWeightSum = 0\n print(nodeListByIndex)\n while (edgesAccepted < nodesCount - 1):\n print(edgesAccepted)\n print(links)\n print(nodes)\n #print(links[0][0])\n #print(nodeListByIndex)\n node1List = nodeListByIndex[links[0][0]]\n node1 = node1List[0]\n node2List = nodeListByIndex[links[0][1]]\n node2 = node2List[0]\n #if ((nodeListByIndex[node1][2] != -1 and nodeListByIndex[node2][1] != 3) or (nodeListByIndex[node2][2] != -1 and nodeListByIndex[node1][1] != 3)): #or (nodeListByIndex[node1][2] != -1 and nodeListByIndex[node2][2] != -1)):\n # links = np.delete(links, 0, axis = 0)\n #continue\n if (nodes[node1] == nodes[node2]):\n links = np.delete(links, 0, axis=0)\n continue\n #if (nodeListByIndex[node1][1] == 3):\n # nodeListByIndex[node1][1] = node2\n #if (nodeListByIndex[node2][1] == 3):\n #nodeListByIndex[node2][1] = node1\n #print(nodes)\n #print(node1,node2)\n nodes = self.union(node1,node2,nodes)\n #links = np.delete(links, 0, axis=0)\n #print(nodes)\n edgeWeightSum += links[0][2]\n edgesAccepted += 1\n\n return edgeWeightSum\n\n def union(self,node1Index,node2Index,nodes):\n #setIndices1 = np.argwhere(nodes == node1[0])\n setIndices2 = np.argwhere(nodes == nodes[node2Index])\n for i in setIndices2:\n nodes[i] = nodes[node1Index]\n return nodes\n\n\n","repo_name":"CooperSc/Algorithms","sub_path":"Supply.py","file_name":"Supply.py","file_ext":"py","file_size_in_byte":5673,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"9765572855","text":"values = []\n\nlists = [[] for _ in range(12)]\n\nwith open('input.txt') as f:\n for line in f:\n values.append(line)\n for index, val in enumerate(line.strip()):\n lists[index].append(val)\n\nmost_frequent = []\n\nfor l in lists:\n most_frequent.append(max(set(l), key = l.count))\n\ntemp = \"\"\ntemp2 = \"\"\n\ngamma_rate = temp.join(most_frequent)\nopposite = []\n\nfor i in range(len(gamma_rate)):\n val = '0' if gamma_rate[i] == '1' else '1'\n opposite.append(val)\n\nepsilon_rate = temp2.join(opposite)\n\nprint(int(gamma_rate, 2) * int(epsilon_rate, 2))\n\ngenerator_ratings = values\nscrubber_ratings = values\n\nfor i in range(12):\n zeros = sum([1 for rating in generator_ratings if rating[i] == '0'])\n ones = len(generator_ratings) - zeros\n if len(generator_ratings) == 1:\n break\n if ones >= zeros:\n generator_ratings = [rating for rating in generator_ratings if rating[i] == '1']\n else:\n generator_ratings = [rating for rating in generator_ratings if rating[i] == '0']\n\nfor i in range(12):\n zeros = sum([1 for rating in scrubber_ratings if rating[i] == '0'])\n ones = len(scrubber_ratings) - zeros\n if len(scrubber_ratings) == 1:\n break\n if ones < zeros:\n scrubber_ratings = [rating for rating in scrubber_ratings if rating[i] == '1']\n else:\n scrubber_ratings = [rating for rating in scrubber_ratings if rating[i] == '0']\n\ngenerator_rating = int(generator_ratings[0], 2)\nscrubber_rating = int(scrubber_ratings[0], 2)\n\nprint(generator_rating * scrubber_rating)","repo_name":"aleksihamalainen/advent-of-code-2021","sub_path":"day3/day3.py","file_name":"day3.py","file_ext":"py","file_size_in_byte":1543,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"30907006467","text":"import logging\nfrom typing import Dict, List\n\nfrom moodle_dl.config import ConfigHelper\nfrom moodle_dl.moodle.mods import MoodleMod\nfrom moodle_dl.moodle.moodle_constants import moodle_html_footer, moodle_html_header\nfrom moodle_dl.moodle.request_helper import RequestRejectedError\nfrom moodle_dl.types import Course, File\nfrom moodle_dl.utils import PathTools as PT\n\n\nclass QuizMod(MoodleMod):\n MOD_NAME = 'quiz'\n MOD_PLURAL_NAME = 'quizzes'\n MOD_MIN_VERSION = 2016052300 # 3.1\n\n @classmethod\n def download_condition(cls, config: ConfigHelper, file: File) -> bool:\n return config.get_download_quizzes() or (not (file.module_modname.endswith(cls.MOD_NAME) and file.deleted))\n\n async def real_fetch_mod_entries(self, courses: List[Course]) -> Dict[int, Dict[int, Dict]]:\n quizzes = (\n await self.client.async_post(\n 'mod_quiz_get_quizzes_by_courses', self.get_data_for_mod_entries_endpoint(courses)\n )\n ).get('quizzes', [])\n\n result = {}\n for quiz in quizzes:\n course_id = quiz.get('course', 0)\n\n quiz_files = quiz.get('introfiles', [])\n self.set_props_of_files(quiz_files, type='quiz_introfile')\n\n quiz_intro = quiz.get('intro', '')\n if quiz_intro != '':\n quiz_files.append(\n {\n 'filename': 'Quiz intro',\n 'filepath': '/',\n 'description': quiz_intro,\n 'type': 'description',\n }\n )\n\n self.add_module(\n result,\n course_id,\n quiz.get('coursemodule', 0),\n {\n 'id': quiz.get('id', 0),\n 'name': quiz.get('name', 'unnamed quiz'),\n 'files': quiz_files,\n },\n )\n\n await self.add_quizzes_files(result)\n return result\n\n async def add_quizzes_files(self, quizzes: Dict[int, Dict[int, Dict]]):\n \"\"\"\n Fetches for the quizzes list the quizzes files\n @param quizzes: Dictionary of all quizzes, indexed by courses, then module id\n \"\"\"\n if not self.config.get_download_quizzes():\n return\n\n if self.version < 2016052300: # 3.1\n return\n\n await self.run_async_load_function_on_mod_entries(quizzes, self.load_quiz_files)\n\n async def load_quiz_files(self, quiz: Dict):\n data = {'quizid': quiz.get('id', 0), 'userid': self.user_id, 'status': 'all'}\n attempts = (await self.client.async_post('mod_quiz_get_user_attempts', data)).get('attempts', [])\n quiz_name = quiz.get('name', '')\n for attempt in attempts:\n attempt['_quiz_name'] = quiz_name\n\n quiz['files'] += await self.run_async_collect_function_on_list(\n attempts,\n self.load_files_of_attempt,\n 'attempt',\n {'collect_id': 'id', 'collect_name': '_quiz_name'},\n )\n\n async def load_files_of_attempt(self, attempt: Dict) -> List[Dict]:\n result = []\n\n attempt_id = attempt.get('id', 0)\n attempt_state = attempt.get('state', 'unknown')\n quiz_name = attempt.get('_quiz_name', '')\n\n attempt_filename = PT.to_valid_name(\n quiz_name + ' (attempt ' + str(attempt_id) + ' ' + attempt_state + ')', is_file=False\n )\n\n data = {'attemptid': attempt_id}\n try:\n if attempt_state == 'finished':\n questions = (await self.client.async_post('mod_quiz_get_attempt_review', data)).get('questions', [])\n elif attempt_state == 'inprogress':\n questions = (await self.client.async_post('mod_quiz_get_attempt_summary', data)).get('questions', [])\n else:\n return result\n except RequestRejectedError:\n logging.debug(\"No access rights for quiz attempt %d\", attempt_id)\n return result\n\n # build quiz HTML\n quiz_html = moodle_html_header\n for question in questions:\n question_html = question.get('html', '').split('\n # \"\"\"\n\n event_data_time = f'''send reminder email'''\n event_data_zipcode = i[5]\n if not event_data_zipcode: # if zipcode in logistics form exists, use that, else use zipcode in account\n event_data_zipcode = i[4]\n event_data_location = zipcode_dict[event_data_zipcode]\n event_data += \"\" + \\\n i[-2]+\", \"+event_data_location+\", \" + \\\n event_data_time+\": \"+i[2]+\"
\"\n\n event_data += \"\"\n # print('event_data in try:', event_data)\n\n html += event_data\n # print('date_events_dict date entry to delete:',date_events_dict[curr_date])\n del date_events_dict[curr_date]\n # print('date_events_dict after deletion:')\n # print('done with 1 day')\n # html+=\"\"+str(curr_mo_date_events_dict[curr_date])+\"\"\n # curr_event_row.append(curr_mo_date_events_dict[curr_date])\n except Exception as e:\n # print(e)\n # print('except')\n html += \"\"\n # curr_event_row.append([])\n # print('curr_event_row',curr_event_row)\n # del date_events_dict[date]\n html += \"\"\n # print('done with 1 day')\n event_rows.append(curr_event_row)\n # print('event_rows',event_rows)\n # print(\"\\n\")\n\n if curr_mo_index == len(months)-1: # at last month, or months[11]='12'\n curr_mo_index = 0 # reset to months[0]\n curr_year = str(int(curr_year)+1)\n else:\n curr_mo_index += 1\n\n ######## end of new ########\n\n html += \"\"\n # print('done with calendar page html')\n # print()\n\n # if date is in current month, proceed to add date numbers and corresponding events on each date\n # if date is in next month, add month header, then sat-sun row, then add date numbers and corresponding events\n\n return html\n # return(render_template('calendar.html'))\n\n\n@app.route('/sendreminderemail', methods=[\"GET\", \"POST\"])\n# def makecalevents(ltype): # include this in return of oauth2callback\ndef sendreminderemail():\n\n if 'credentials' not in flask.session:\n return flask.redirect('authorize')\n\n # Load credentials from the session.\n credentials = google.oauth2.credentials.Credentials(\n **flask.session['credentials'])\n # print('credentials loaded from session')\n\n # def create_and_send_email():\n try:\n service = build('gmail', 'v1', credentials=credentials)\n # print('service built')\n\n # if window.confirm(\"Click OK to send email\"):\n message = EmailMessage()\n\n etype = request.args.get('etype')\n recipient = request.args.get('recipient')\n items = request.args.get('items')\n # remove ids from items\n pattern = r'\\([0-9]+\\)\\ '\n if len(re.findall(pattern, items)) > 1:\n plural = 1\n else:\n plural = 0\n items = re.sub(pattern, '', items)\n\n ymd = request.args.get('ymd')\n dt = datetime.datetime(\n int(ymd.split('-')[0]), int(ymd.split('-')[1]), int(ymd.split('-')[2]))\n dt_to_weekday = {0: 'Monday', 1: 'Tuesday', 2: 'Wednesday',\n 3: 'Thursday', 4: 'Friday', 5: 'Saturday', 6: 'Sunday'}\n weekday = dt_to_weekday[dt.weekday()]\n mdy = ymd.split('-')[1]+'/'+ymd.split('-')[2]+'/'+ymd.split('-')[0]\n first_name = request.args.get('first_name')\n\n if etype == \"dropoff\":\n body_html = f\"\"\"

Hi {first_name},

\n

Hope you're doing well! We're reaching out regarding the upcoming start of your rental of {items} on {weekday} {mdy}. Please use this link to schedule your drop-off. Let us know if you have any questions.

\n

All the best,

\n

Team Hubbub

\n \"\"\"\n body = MIMEText(body_html, 'html')\n message.set_content(body)\n # if one item, don't use plural\n if plural == 1:\n message['Subject'] = '[Hubbub] Scheduling drop-offs for your rentals'\n else:\n message['Subject'] = '[Hubbub] Scheduling drop-off for your rental'\n\n elif etype == \"pickup\":\n body_html = f\"\"\"

Hi {first_name},

\n

Hope you're doing well and enjoying your rental!

\n

We're reaching out regarding the upcoming end of your rental of {items} on {weekday} {mdy}. Please schedule your pick-up at this link, or use the link to set up a rental extension. Let us know if you have any questions.

\n

All the best,

\n

Team Hubbub

\n \"\"\"\n body = MIMEText(body_html, 'html')\n message.set_content(body)\n\n message['Subject'] = '[Hubbub] End of Rental Logistics'\n\n message['To'] = recipient\n message['From'] = 'hello@hubbub.shop'\n # message['Subject'] = 'Automated draft'\n\n # encoded message\n encoded_message = base64.urlsafe_b64encode(message.as_bytes()) \\\n .decode()\n\n create_message = {\n 'raw': encoded_message\n }\n # pylint: disable=E1101\n send_message = (service.users().messages().send\n (userId=\"me\", body=create_message).execute())\n print(F'Message Id: {send_message[\"id\"]}')\n except HttpError as error:\n print(F'An error occurred: {error}')\n send_message = None\n\n return \"email sent\"\n\n\nif __name__ == \"__main__\":\n app.run(host=\"127.0.0.1\", port=8080, debug=True, ssl_context='adhoc')\n","repo_name":"angihe93/auto-ops","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":49713,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"74332756965","text":"import nltk\nimport numpy as np\nimport random\nimport string\nimport re \nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.metrics.pairwise import cosine_similarity\n\nf = open('info.txt', 'r')\nraw = f.read()\n\nsentence_tokens = nltk.sent_tokenize(raw)\n\nlemmer = nltk.stem.WordNetLemmatizer()\n\n\ndef LemTokens(tokens):\n return [lemmer.lemmatize(token) for token in tokens]\n\nremove_punct_dict = dict((ord(punct), None) for punct in string.punctuation)\n\n\ndef LemNormalize(text):\n return LemTokens(nltk.word_tokenize(text.lower().translate(remove_punct_dict)))\n\nGREETING_INPUTS = (\"hello\", \"hi\", \"greetings\", \"sup\", \"hey\", \"what's up\", \"what's popping\")\nGREETING_RESPONSE = (\"hi\", \"how are you doing\", \"i'm sleeping\", \"thank you for waisting your time with me today\", \"how are the kids\")\n\ndef greeting(sentence):\n sentence = re.sub(r'[^\\w\\s]', '', sentence)\n for word in sentence.split():\n if word.lower() in GREETING_INPUTS:\n return random.choice(GREETING_RESPONSE)\n\ndef response(userInput):\n sentence_tokens.append(userInput)\n TfidVec = TfidfVectorizer(tokenizer=LemNormalize)\n tfidf = TfidVec.fit_transform(sentence_tokens)\n\n vals = cosine_similarity(tfidf[-1], tfidf)\n flat = vals.flatten()\n idx = flat.argsort()[-2]\n\n flat.sort()\n best_response = flat[-2]\n\n if best_response == 0:\n return \"I'm sorry, I don't know that yet :(\"\n else:\n return sentence_tokens[idx]\n\n\nprint(\"ROBOTBOT: My name is RobotBot. I will answer queries about airplanes. If you want to exit type bye\")\nname = input(\"What's your name?\")\nwhile True:\n userInput = input(name + \": \").lower()\n print(\"ROBOTBOT: \", end=\"\")\n if userInput != 'bye':\n if greeting(userInput) is not None:\n print(greeting(userInput))\n else:\n print(response(userInput))\n sentence_tokens.remove(userInput)\n else:\n print(\"Goodbye! Thank you for waisting a perfectly good minute of your life with us. We sincerely hope you did not enjoy it. Please do hesitate to come back.\")\n break \n\n\n\n\n\n\n \n\n","repo_name":"cedim04/Machine-Learning","sub_path":"naturalchat/naturalchat.py","file_name":"naturalchat.py","file_ext":"py","file_size_in_byte":2100,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"34611800886","text":"\"\"\"\nGiven two integers a and b, which can be positive or negative, find the sum of all the integers between and including them and return it. If the two numbers are equal return a or b.\n\nNote: a and b are not ordered!\n\nExamples\nget_sum(1, 0) == 1 // 1 + 0 = 1\nget_sum(1, 2) == 3 // 1 + 2 = 3\nget_sum(0, 1) == 1 // 0 + 1 = 1\nget_sum(1, 1) == 1 // 1 Since both are same\nget_sum(-1, 0) == -1 // -1 + 0 = -1\nget_sum(-1, 2) == 2 // -1 + 0 + 1 + 2 = 2\n\"\"\"\n\"\"\"\n-2 -1 0 1 2 3 4 \n\"\"\"\n\n\"\"\"\ndef get_sum(a,b):\n print([x for x in range(a, b+1)])\n\n\n\nget_sum(-2, 2)\n\"\"\"\n\n\ndef get_sum(a, b):\n if a <= b:\n c = a\n v = b\n else:\n v = a\n c = b\n\n z = 0\n if a != b:\n for i in range(c, v+1):\n z += i\n else:\n z = a\n\n return z\n\n\nprint(get_sum(0, -1))\n\n\"\"\"\ndef get_sum(a,b):\n return sum(xrange(min(a,b), max(a,b)+1))\n\"\"\"","repo_name":"Zagumionnov/codewars","sub_path":"2. Sum of Numbers.py","file_name":"2. Sum of Numbers.py","file_ext":"py","file_size_in_byte":879,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"9434030439","text":"import numpy as np\nimport random\nfrom os import listdir\nimport os\nimport json\nimport time\nimport copy\nfrom operator import itemgetter\n\nfrom visualising import plot_grids\nfrom gridRL import pad_grid, GridRL, flatten, build_flat_model\nfrom DSL_functions import get_func_list, get_num_list, evaluate, logical_and, tile, upsample, evaluate_flat, color_map, enclosed, rectangulate\n\nimport torch\n\n\ndef total_fitness(p, e):\n if len(p.shape) == 1:\n p = np.expand_dims(p, axis=0)\n shape = (min(p.shape[0], e.shape[0]), min(p.shape[1], e.shape[1]))\n incorrect = (p[0:shape[0], 0:shape[1]] != e[0:shape[0], 0:shape[1]]).sum()\n misshape = (abs(p.shape[0]-e.shape[0]) * e.shape[1]) + (abs(p.shape[1] - e.shape[1]) * e.shape[0])\n # print(p.shape, e.shape, shape, incorrect, misshape)\n return incorrect + misshape\n\n\ndef evaluate_fitness(program, task):\n \"\"\" Take a program and a task, and return its fitness score as a tuple. \"\"\"\n score = 0\n\n # For each sample\n for sample in task:\n i = np.array(sample['input'])\n if i.shape[0] == 0 or i.shape[1] == 0:\n return np.inf\n o = np.array(sample['output'])\n\n # For each fitness function\n images = evaluate_flat(program, i)\n score += total_fitness(images, o)\n\n return score\n\n\ndef solve(task, seconds, model, temp):\n start_time = time.time()\n best_score = np.inf\n best_program = None\n\n in_image = np.array(task['train'][0]['input'])\n in_image = pad_grid(in_image)\n out_image = np.array(task['train'][0]['output'])\n out_image = pad_grid(out_image)\n\n while time.time() - start_time < seconds:\n\n program = build_flat_model(model, in_image, out_image, temp=temp)\n while len(program) > 40:\n program = build_flat_model()\n\n score = evaluate_fitness(program, task['train'])\n\n if score < best_score:\n best_score = score\n best_program = program\n start_time = time.time()\n\n if best_score == 0:\n final_score = evaluate_fitness(best_program, task['test'])\n print(time.time() - start_time, best_program)\n\n if final_score == 0:\n return 10, final_score\n else:\n return 1, final_score\n\n if best_score < 5:\n print('got close, scored ', evaluate_fitness(best_program, task['test']))\n\n # input_im = np.array(task['train'][1]['input'])\n # output_im = np.array(task['train'][1]['output'])\n # plot_grids([input_im, evaluate_flat(best_program, input_im), output_im])\n\n final_score = evaluate_fitness(best_program, task['test'])\n print(best_program)\n return 0, final_score\n\n\nif __name__ == \"__main__\":\n\n # Setting paths for getting files.\n training_dir = \"./data/training\"\n training_files = sorted(listdir(training_dir))\n\n # Getting task ids for tasks already solved.\n if os.path.isfile('success.txt'):\n with open('success.txt', 'r+') as f:\n ids = f.read()\n success_ids = ids.splitlines()\n else:\n success_ids = []\n\n # Getting all tasks and compiling into a list.\n tasks = []\n for task_file in training_files:\n with open(\"/\".join([training_dir, task_file])) as f:\n task_id = task_file.split(\".\")[0]\n tasks.append((task_id, json.load(f)))\n\n # Setting arguments for solving the model.\n model_path = 'models/20200508.201046'\n wait_time = 5\n\n\n\n # Setting up model.\n func_list = get_func_list()\n num_list = get_num_list()\n model = GridRL(num_list, func_list)\n model.load_state_dict(torch.load(model_path))\n model.eval()\n\n # in_im = np.array(tasks[1][1]['train'][0]['input'])\n # x = evaluate_flat(train001, in_im)\n # plot_grids([in_im, x])\n\n # Attempting to solve each task in turn.\n\n temps = [1, 10, 100, 1000]\n\n for temp in temps:\n solves = 0\n scores = []\n for i, task in enumerate(tasks[:80]):\n complete, score = solve(task[1], wait_time, model, temp)\n solves += complete\n scores.append(score)\n\n # Notifying if a previously unsolved task is completed.\n if complete and task[0] not in success_ids:\n with open('success.txt', 'a+') as f:\n f.write(str(i) + ' ' + task[0] + '\\n')\n print('NEW SOLVE WOOOOOOOOOOO')\n print(i, solves, np.mean(np.sqrt(scores)))\n\n\n# train000 = [upsample, 3, 3, logical_and, tile, 3, 3]\n# train001 = [colormap, [[4], [], [[enclosed, [], [], []]]]]\n","repo_name":"HoagyC/ARC","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4541,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"43133005434","text":"## modification of rlym-tools/examples/sb3_multi_example.py based on youtube guide\nimport numpy as np\nimport os, glob\n\nfrom pyparsing import Combine\nfrom rlgym.envs import Match\nfrom rlgym.utils.action_parsers import DiscreteAction\nfrom stable_baselines3 import PPO\nfrom stable_baselines3.common.callbacks import CheckpointCallback\nfrom stable_baselines3.common.vec_env import VecMonitor, VecNormalize, VecCheckNan\nfrom stable_baselines3.ppo import MlpPolicy\n\nfrom rlgym.utils.obs_builders import AdvancedObs\nfrom rlgym.utils.state_setters import DefaultState\nfrom rlgym.utils.terminal_conditions.common_conditions import TimeoutCondition, GoalScoredCondition\nfrom rlgym_tools.sb3_utils import SB3MultipleInstanceEnv\nfrom rlgym.utils.reward_functions.common_rewards.misc_rewards import EventReward\nfrom rlgym.utils.reward_functions.common_rewards.player_ball_rewards import VelocityPlayerToBallReward\nfrom rlgym.utils.reward_functions.common_rewards.ball_goal_rewards import VelocityBallToGoalReward\nfrom rlgym.utils.reward_functions import CombinedReward\n\nimport torch.nn as nn\n\ndef find_last_ckpt(save_dir):\n fl = glob.glob(os.path.join(save_dir, '*'))\n if len(fl) == 0:\n print('No previously saveed model found.')\n return None\n ckpts = [int(str(fname).split(\"_\")[-2]) for fname in fl]\n ckptname = fl[np.argmax(ckpts)]\n return ckptname\n\nif __name__ == '__main__': # Required for multiprocessing\n frame_skip = 8 # Number of ticks to repeat an action\n half_life_seconds = 5 # Easier to conceptualize, after this many seconds the reward discount is 0.5\n\n fps = 120 / frame_skip\n gamma = np.exp(np.log(0.5) / (fps * half_life_seconds)) # Quick mafs\n\n target_steps = 100_000\n agents_per_match = 2 # for 1vs1\n num_instances = 4 # #RL instances to run at the same time\n steps = target_steps // (num_instances * agents_per_match)\n batch_size = steps\n wait_time= 1*60 # time to wait between each individual RL instance\n save_dir = \"./models\"\n model_name= \"rl_model\"\n ckpt_freq = 500_000\n print(f\"fps={fps}, gamma={gamma})\")\n\n reward_function = CombinedReward(\n reward_functions=(\n VelocityPlayerToBallReward(),\n VelocityBallToGoalReward(),\n EventReward(\n team_goal=100., \n concede=-100., #getting scored on\n shot=5., \n save=30., \n demo=10.0),\n ),\n reward_weights=(0.1, 1., 1.),\n )\n\n\n def get_match(): # Need to use a function so that each instance can call it and produce their own objects\n return Match(\n # team_size=3, # 3v3 to get as many agents going as possible, will make results more noisy\n team_size=1, #1vs1\n tick_skip=frame_skip,\n reward_function=reward_function,\n self_play=True,\n terminal_conditions=[TimeoutCondition(round(fps * 30)), GoalScoredCondition()], # Some basic terminals\n obs_builder=AdvancedObs(), # Not that advanced, good default\n state_setter=DefaultState(), # Resets to kickoff position\n action_parser=DiscreteAction() # Discrete > Continuous don't @ me\n )\n\n env = SB3MultipleInstanceEnv(get_match, num_instances, wait_time=wait_time)# Start num_instances instances, waiting 60 seconds between each\n env = VecCheckNan(env) # Optional\n env = VecMonitor(env) # Recommended, logs mean reward and ep_len to Tensorboard\n env = VecNormalize(env, norm_obs=False, gamma=gamma) # Highly recommended, normalizes rewards\n\n ckptname = find_last_ckpt(save_dir)\n if ckptname is not None:\n print('Ckpt %s is found. Continuing training' % ckptname)\n model = PPO.load(\n ckptname,\n env,\n device=\"auto\", # Need to set device again (if using a specific one)\n # force_reset=True # Make SB3 reset the env so it doesn't think we're continuing from last state\n )\n else: #start from scratch \n policy_kwargs = dict(\n activation_fn=nn.Tanh,\n net_arch=[512, 512, dict(\n pi=[256, 256, 256], # controls actions of the model\n vf=[256, 256, 256] # value function \n )],\n )\n\n model = PPO(\n MlpPolicy,\n env,\n n_epochs=1, # PPO calls for multiple epochs\n policy_kwargs=policy_kwargs, #\n learning_rate=5e-5, # Around this is fairly common for PPO\n ent_coef=0.01, # From PPO Atari\n vf_coef=1., # From PPO Atari\n gamma=gamma, # Gamma as calculated using half-life\n verbose=3, # Print out all the info as we're going\n batch_size=batch_size, # Batch size as high as possible within reason\n n_steps=steps, # Number of steps to perform before optimizing network\n tensorboard_log=\"logs\", # `tensorboard --logdir logs` in terminal to see graphs\n device=\"auto\" # Uses GPU if available\n )\n\n # Save model every so often\n # Divide by num_envs (number of agents) because callback only increments every time all agents have taken a step\n # This saves to specified folder with a specified name\n callback = CheckpointCallback(round(ckpt_freq / env.num_envs), save_path=save_dir, name_prefix=model_name)\n\n while True:\n model.learn(total_timesteps=25_000_000, callback=callback)\n model.save(save_dir+\"/exit_save.zip\")\n model.save(f\"mmr_models/{model.num_timesteps}\")\n ","repo_name":"firatozdemir/RL-RL","sub_path":"train_example.py","file_name":"train_example.py","file_ext":"py","file_size_in_byte":5719,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"37835225201","text":"import os\r\nfrom sklearn import cluster, feature_extraction, metrics\r\nimport matplotlib.pyplot as plt\r\n\r\n# %%\r\n#dir = \"C:\\\\Users\\\\usuario\\\\Desktop\\\\stories\"\r\ndir = \"C:\\\\Leo\\\\NLP\\\\skill-test-armorblx\\\\04_cluster_stories\\\\stories\"\r\nfor root, dirs, files in os.walk(dir, topdown=False):\r\n for name in files:\r\n print(os.path.join(root, name))\r\n\r\n# %%\r\ncorpus = []\r\nfor root, dirs, files in os.walk(dir):\r\n for name in files:\r\n with open(os.path.join(root, name), errors=\"ignore\") as f:\r\n #print(f.read())\r\n corpus.append(f.read())\r\n\r\n# %%\r\nprint(corpus[1])\r\n\r\n# %%\r\nVectorizer = feature_extraction.text.TfidfVectorizer(stop_words='english', min_df=2, max_features=1000)\r\nX = Vectorizer.fit_transform(corpus)\r\n\r\n# %%\r\navgs = []\r\ncentroids = []\r\nmax_k = 30\r\nfor k in range(2, max_k):\r\n Model = cluster.KMeans(n_clusters=k)\r\n Model.fit(X)\r\n avgs.append(metrics.silhouette_score(X, Model.labels_))\r\n centroids.append(Model.cluster_centers_)\r\n\r\n# %%\r\n#plt.plot(avgs)\r\nplt.scatter(range(2, max_k), avgs)\r\n\r\n# %%\r\ndeltas = []\r\nfor i in range(len(avgs)-1):\r\n delta = avgs[i+1] - avgs[i]\r\n deltas.append((delta, i + 1))\r\n\r\n# %%\r\nfrom heapq import nlargest\r\nnlargest(3, deltas, key=lambda x: x[0])\r\n\r\n\r\n# %%\r\n","repo_name":"pistolilla/jupyter","sub_path":"skill-test-armorblx/04_cluster_stories/cluster-kmeans.py","file_name":"cluster-kmeans.py","file_ext":"py","file_size_in_byte":1251,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"880276800","text":"from os.path import abspath, exists\nfrom os import getcwd, mkdir\nfrom utils.args import config_args\nfrom utils.Scanner import *\nfrom utils.Scraper import *\n\n# Main function\ndef run_spyder():\n args = config_args()\n printer = Printer(silent=args.silent)\n # Recursive Scraper Mode\n if args.recursive != None and args.file == None:\n # Url validation\n url_scan = checkHttpUrl(args.recursive)\n # Level validation\n lvl_scan = 5\n if args.level != None:\n if checkNumLevel(args.level):\n lvl_scan = int(args.level)\n else: \n printer.messageError(msg=' Invalid value of level, default value [5] charged!', time=2)\n # Path validation\n path_scan = getDefaultDataPath()\n if args.path != None:\n if exists(args.path):\n path_scan = args.path\n else:\n printer.messageError(msg='Invalid value of path, default path [../data] charged.')\n printer.messageWarning(msg=' Try with \\'/home/$(user)/path\\'.', time=2)\n checkDataPath(path_scan)\n # Stating scanner\n scan = Scanner(url=url_scan, depth=lvl_scan)\n scan.depthScan(args.silent)\n # Starting scraper\n scrap = Scraper(scan.parse_url.netloc, path_scan)\n scrap.scrapResources(scan.resources, silent=args.silent)\n\n # File Scraper Mode\n if args.recursive == None and args.file != None:\n path_scan = getDefaultDataPath()\n checkDataPath(path_scan)\n url_res = checkHttpUrl(args.file)\n # Starting scraper\n scrp = Scraper(url_res, path_scan)\n scrp.scrapResourceFromUrl(silent=args.silent)\n\n# Function that checks the syntax 'http' of a url [origin]\ndef checkHttpUrl(origin):\n parser = urlparse(origin)\n if parser.scheme == '':\n url = 'http://'+parser.path\n return url\n else:\n return origin\n\n# Function that checks flag level [-l]\ndef checkNumLevel(level):\n try:\n if int(level) > 0:\n return True\n return False\n except:\n return False\n\n# Function that checks if the path exists and if it does not exist it creates it\ndef checkDataPath(path):\n try:\n while exists(path) == False:\n mkdir(path)\n return True\n except:\n return False\n\n# Function that returns the default path\ndef getDefaultDataPath():\n current_path = abspath(getcwd())\n spl_current = current_path.replace('/',' ').split()\n default = ''\n for i in range(0,(len(spl_current)-1)):\n default = default + '/' +spl_current[i]\n return default + '/data'\n\n# Run main function\nif __name__=='__main__':\n run_spyder()\n","repo_name":"goldcod3/Arachnida","sub_path":"spider/spider.py","file_name":"spider.py","file_ext":"py","file_size_in_byte":2704,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"71209184166","text":"post = {}\nfrom flask import Flask,jsonify,request\n\napp = Flask(__name__)\n\n@app.route(\"/\")\ndef welcome():\n return \"Hello, World!\"\n\n@app.route(\"/create\",methods = [\"POST\"])\ndef create_post():\n try:\n data = request.get_json()\n post[data[\"name\"]] = data[\"caption\"]\n post[\"likes\"] = 0\n return jsonify({\"msg\":\"Post has been added successfully\"})\n except:\n return jsonify({\"msg\" : \"adding post has been failed\"})\n \n@app.route(\"/read\",methods = [\"GET\"])\ndef READ_post():\n try:\n return jsonify({\"msg\": post})\n except:\n return jsonify({\"msg\" : \"READING post has been failed\"})\n \n@app.route(\"/update\",methods = [\"PATCH\"])\ndef READ_post():\n try:\n data = request.get_json()\n name = data[\"name\"]\n caption = data[\"caption\"]\n for i in post:\n if name == i:\n post[name] = caption\n break\n return jsonify({\"msg\": \"post is updated successfully\"})\n except:\n return jsonify({\"msg\" : \"post is not updated\"})\n \n@app.route(\"/delete\",methods = [\"DELETE\"])\ndef READ_post():\n try:\n data = request.get_json()\n name = data[\"name\"]\n for i in post:\n if name == i:\n del post[name]\n break\n return jsonify({\"msg\": \"post is deleted successfully\"})\n except:\n return jsonify({\"msg\" : \"post is not deleted\"})\n\nif __name__ == \"__main__\":\n app.run()","repo_name":"KoushikNiyogi/GA201_mock1","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1454,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"24843566140","text":"# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, val=0, left=None, right=None):\n# self.val = val\n# self.left = left\n# self.right = right\nclass Solution:\n def verticalTraversal(self, root: Optional[TreeNode]) -> List[List[int]]:\n \n queue = deque()\n queue.append((root, 0, 0))\n hashMap = defaultdict(list)\n minimum = math.inf\n maximum = -math.inf\n \n while queue:\n \n node, vlevel, hlevel = queue.popleft()\n \n hashMap[vlevel].append((hlevel, node.val))\n minimum = min(minimum, vlevel)\n maximum = max(maximum, vlevel)\n \n if node.left:\n queue.append((node.left, vlevel-1, hlevel+1))\n \n if node.right:\n queue.append((node.right, vlevel+1, hlevel+1))\n \n result = []\n for i in range(minimum, maximum+1):\n temp = []\n for j in sorted(hashMap[i], key = lambda x:(x[0],x[1])):\n temp.append(j[1])\n result.append(temp)\n \n return result\n \n \n \n \n \n ","repo_name":"ParshvaTimbadia/LeetCode","sub_path":"987-vertical-order-traversal-of-a-binary-tree/987-vertical-order-traversal-of-a-binary-tree.py","file_name":"987-vertical-order-traversal-of-a-binary-tree.py","file_ext":"py","file_size_in_byte":1243,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"74508950563","text":"import sys\ndef input():\n return sys.stdin.readline().rstrip()\n#값에대한 idx를 찾는것이 아닌 값에대해 모든 arr를 돌면서 몫cnt를 m개와 비교를해서\n# cnt가 m보다 크면 숫자를 높여야하므로 left초기화 반대는 right초기화를 한다\n# cnt가 m보다 작은경우에는 해당사항이 아니므로 answer를 초기화하지 않는다.\nm, n = map(int, input().split())\narr = list(sorted(map(int, input().split())))\nleft, right = 0, arr[-1]\nanswer = 0\n\nwhile left <= right:\n mid = (left+right)//2\n cnt = 0\n if mid==0:\n print(0)\n exit(0)\n for i in range(n):\n cnt += (arr[i]//mid)\n\n if cnt >= m:\n left = mid + 1\n answer = mid\n else:\n right = mid -1\n\nprint(answer)\n","repo_name":"young0264/hellopycharm","sub_path":"백준/16401_과자나눠주기.py","file_name":"16401_과자나눠주기.py","file_ext":"py","file_size_in_byte":766,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"25064181107","text":"\n\nfrom datetime import date\nimport random\n\ndb = \"C:/Users/m_gray.DESKTOP-Q2TTLA7/Desktop/gratitude.txt\"\nday = \"\"\ntoday = date.today()\n\nrandom_entry = \"\"\nall_entries = []\n\nprint()\n# print entry from random date\ndef rand_selection():\n with open(db, 'r') as f:\n count = 0\n for entry in f.readlines():\n count += 1\n all_entries.append(entry)\n selection = random.randrange(count)\n all_entries_strip = [i.strip('\\n') for i in all_entries]\n return all_entries_strip[selection]\nprint(rand_selection())\nprint()\n\nstuff = input('what are you grateful for today? -> ')\ninfo = [str(today) + ': ' + stuff]\n\ndef append(text):\n with open(db, 'a') as f:\n text = print(info, file=f)\n\ndef read():\n with open(db, 'r') as f:\n count = 1\n for i in f.readlines():\n print(str(count) + ' ' + i)\n count += 1\n input('...')\n\ndef run():\n if stuff == 'READ':\n print()\n print('-------------------------------------------------------------------------------------------------------')\n print()\n print(read())\n else:\n append(info)\n\n# read()\nrun()\n# read()\n\n","repo_name":"mechanicalmatt/Gratitude","sub_path":"gratitude_main.py","file_name":"gratitude_main.py","file_ext":"py","file_size_in_byte":1170,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"7089584968","text":"# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, val=0, left=None, right=None):\n# self.val = val\n# self.left = left\n# self.right = right\nclass Solution:\n def deepestLeavesSum(self, root: Optional[TreeNode]) -> int:\n fringe = [(root, 0)] if root else []\n next_level = []\n \n latest_level = fringe\n \n while fringe:\n next_level = []\n \n for node, level in fringe:\n if node.left:\n next_level.append((node.left, level + 1))\n if node.right:\n next_level.append((node.right, level + 1))\n \n latest_level = fringe\n fringe = next_level\n\n return sum(node.val for node, level in latest_level)\n \n \n \n \n ","repo_name":"ffekirnew/a2sv-competitive-programming","sub_path":"1302-deepest-leaves-sum/1302-deepest-leaves-sum.py","file_name":"1302-deepest-leaves-sum.py","file_ext":"py","file_size_in_byte":891,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"31502642398","text":"\nimport numpy as np\nimport trajectory_planning_helpers as tph\n\n\ndef compute_stats(result_file):\n # Load the CSV with the optimized trajectory\n results = np.loadtxt(result_file, delimiter=\",\")\n\n # Compute distances between trajectory points\n distances = np.zeros((len(results), 1))\n distance_vectors = np.zeros((len(results), 2))\n distance_vectors[0:-2, :] = results[1:-1, :] - results[0:-2, :]\n distance_vectors[-2, :] = results[-2, :] - results[-1, :]\n distance_vectors[-1, :] = results[-1, :] - results[0, :]\n distances[:, 0] = np.linalg.norm(distance_vectors, axis=1)\n\n #print(distances)\n\n # Compute curvatures\n headings, curvatures = tph.calc_head_curv_num.calc_head_curv_num(\n path=results,\n el_lengths=distances,\n is_closed=True\n )\n total_curvature = np.sum(curvatures)\n\n # Import vehicle dynamics\n ggv, ax_max_machines = tph.import_veh_dyn_info.import_veh_dyn_info(\n ggv_import_path=\"eval/veh_dyn_info/ggv.csv\",\n ax_max_machines_import_path=\"eval/veh_dyn_info/ax_max_machines.csv\"\n )\n\n # Compute velocity and acceleration profile\n vel_profile = tph.calc_vel_profile.calc_vel_profile(\n ggv=ggv,\n ax_max_machines=ax_max_machines,\n v_max=70.0,\n kappa=curvatures,\n el_lengths=distances,\n closed=True,\n filt_window=None,\n dyn_model_exp=1.0,\n drag_coeff=0.75,\n m_veh=1200.0\n )\n\n vel_profile_loop = np.append(vel_profile, vel_profile[0])\n acc_profile = tph.calc_ax_profile.calc_ax_profile(\n vx_profile=vel_profile_loop,\n el_lengths=distances,\n eq_length_output=False\n )[0]\n\n # Calculate laptime\n laptime = tph.calc_t_profile.calc_t_profile(\n vx_profile=vel_profile,\n ax_profile=acc_profile,\n el_lengths=distances\n )[-1]\n\n return total_curvature, laptime, vel_profile, acc_profile\n\n\nif __name__ == \"__main__\":\n results_centerline = compute_stats(\"output/centerline.csv\")\n results_optimization_based = compute_stats(\"output/optimization_based.csv\")\n results_pi = compute_stats(\"output/pi_results.csv\")\n\n print(\"| Results | Lap Time |\")\n print(\"| ----------------------- | -------- |\")\n print(\"| Centerline | %.2fs |\" % results_centerline[1])\n print(\"| Optimization-based | %.2fs |\" % results_optimization_based[1])\n print(\"| Probabilistic Inference | %.2fs |\" % results_pi[1])\n \n np.savetxt(\"output/pi_results_vel_profile.csv\", results_pi[2], delimiter=\"\\n\")\n np.savetxt(\"output/pi_results_acc_profile.csv\", results_pi[3], delimiter=\"\\n\")\n\n","repo_name":"Ashn-1/pi-racing","sub_path":"src/eval/compute_stats.py","file_name":"compute_stats.py","file_ext":"py","file_size_in_byte":2639,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"30810115350","text":"import logging\nfrom functools import wraps\nfrom tkinter import Frame, Button, Label, colorchooser, messagebox\n\nfrom LabExT.Utils import get_visa_address\nfrom LabExT.View.Controls.InstrumentSelector import InstrumentRole, InstrumentSelector\n\ncolors = {\n 0: 'red',\n 1: 'purple',\n 2: 'blue',\n 3: 'green',\n 4: 'skyblue',\n 5: 'black',\n 6: 'orange',\n 7: 'yellow'\n}\n\n\ndef show_errors_as_popup(caught_err_classes=(Exception,)):\n \"\"\" Catches the error classes specified in the argument and shows a TKinter messagebox. \"\"\"\n\n def decorator(func):\n @wraps(func)\n def new_func(*args, **kwargs):\n try:\n retval = func(*args, **kwargs)\n return retval\n except caught_err_classes as E:\n # If an error which is a (sub-)class of any in caught_err_classes, we catch it and display it in an\n # message box and put an error message to the logger.\n try:\n # This line works if this decorator is used for CardFrame sub-classes.\n parent = args[0].controller.view.main_window\n except:\n # If this decorator is used outside, we cannot find the parent window reference for now.\n # Setting the parent to None displays the message box ontop the Tk root (= LabExT main window)\n parent = None\n logging.getLogger().debug('Decorator show_errors_as_popup is used outside CardFrame! '\n 'Displaying messagebox with Tk-root as parent.')\n logging.getLogger().error(str(E))\n messagebox.showerror('Error', str(E), parent=parent)\n\n return new_func\n\n return decorator\n\n\nclass CardFrame(Frame):\n \"\"\"\n Parent class for all cards. Contains functions that inherited card types must follow.\n \"\"\"\n\n INSTRUMENT_TYPE = 'FILL ME'\n PLOTTING_ENABLED = True\n\n default_parameters = {}\n\n def __init__(self, parent, controller, model, index):\n\n # refs to LiveViewer objects\n self.model = model\n self.controller = controller\n # my index\n self.index = index\n # root window where I will be placed\n self._root = parent\n # logger, is always handy\n self.logger = logging.getLogger()\n # plot color\n self.color = colors.get(index, 'gray')\n # keep my references which buttons to gray out when\n self.buttons_active_when_settings_enabled = []\n self.buttons_inactive_when_settings_enabled = []\n\n # setup my main frame, its 3 rows by 3 columns\n Frame.__init__(self, parent, relief=\"groove\", borderwidth=2)\n self.columnconfigure(0, weight=4)\n self.columnconfigure(1, minsize=100)\n self.columnconfigure(2, weight=4)\n self.columnconfigure(3, weight=4)\n\n #\n # defining common GUI elements + frame for GUI elements set in sub-class\n #\n\n # row 0: title\n self.label = Label(self, text=self.INSTRUMENT_TYPE)\n self.label.grid(row=0, column=0, padx=2, pady=2, sticky='NSW')\n\n # row 0: color selection button\n if self.PLOTTING_ENABLED:\n self.color_button = Button(self, text=\"\", command=self.choose_color, bg=self.color)\n self.color_button.grid(row=0, column=1, sticky='NESW')\n\n # row 0: remove card button\n self.remove_button = Button(self, text=\"X\", command=lambda: controller.remove_card(self.index))\n self.remove_button.grid(row=0, column=3, sticky='NE')\n\n # row 1: instrument selector\n self.available_instruments = dict()\n io_set = get_visa_address(self.INSTRUMENT_TYPE)\n self.available_instruments.update({self.INSTRUMENT_TYPE: InstrumentRole(self._root, io_set)})\n\n try:\n for role_name in self.model.old_instr[index]:\n if role_name in self.available_instruments:\n self.available_instruments[role_name].create_and_set(self.model.old_instr[index][role_name])\n except IndexError:\n pass\n\n self.instr_selec = InstrumentSelector(self)\n self.instr_selec.title = 'Instrument'\n self.instr_selec.instrument_source = self.available_instruments\n self.instr_selec.grid(row=1, column=0, columnspan=4, padx=2, pady=2, sticky='NESW')\n\n # row 2: user's content frame, filled by subclasses\n self.content_frame = Frame(self)\n self.content_frame.grid(row=2, column=0, columnspan=4, padx=2, pady=2, sticky='NESW')\n\n # define the card-related attributes\n self.instrument = None\n self.polling_function = None\n self.active_thread = None\n self.enabled = None\n self.paused = False\n self.thread_finished = True\n self.plot_data = None\n\n self.string_var = None\n self.initialized = False\n\n self.last_instrument_type = \"\"\n\n def load_instrument_instance(self):\n \"\"\"\n Use this function to get an instance from the instrument selector dropdown.\n \"\"\"\n selected_instruments = dict()\n for role_name, role_instrs in self.available_instruments.items():\n selected_instruments.update({role_name: role_instrs.choice})\n\n # do not let card load an instrument if any other card already has the same instrument active\n for i, (_, card) in enumerate(self.model.cards):\n if card is self:\n continue\n if card.INSTRUMENT_TYPE is not self.INSTRUMENT_TYPE:\n continue\n if not card.enabled:\n continue\n if card.instrument is None:\n continue\n self_desc = role_instrs.choice\n foreign_desc = card.instrument.instrument_config_descriptor\n if self_desc['visa'] == foreign_desc['visa'] \\\n and self_desc['class'] == foreign_desc['class'] \\\n and self_desc['channel'] == foreign_desc['channel']:\n raise RuntimeError('This instrument is already active in another card.')\n\n loaded_instr = self.controller.experiment_manager.instrument_api.create_instrument_obj(self.INSTRUMENT_TYPE,\n selected_instruments,\n {})\n\n self.last_instrument_type = loaded_instr.instrument_parameters['class']\n return loaded_instr\n\n def enable_settings_interaction(self):\n # call this function when you want to enable GUI elements for changing settings\n # e.g. on stopping live-polling or closing the instrument connection\n for b in self.buttons_active_when_settings_enabled:\n b[\"state\"] = \"active\"\n for b in self.buttons_inactive_when_settings_enabled:\n b[\"state\"] = \"disable\"\n self.instr_selec.enabled = True\n\n def disable_settings_interaction(self):\n # call this function when you want to disable GUI elements for changing settings\n # e.g. on starting live-polling or opening the instrument connection\n for b in self.buttons_active_when_settings_enabled:\n b[\"state\"] = \"disable\"\n for b in self.buttons_inactive_when_settings_enabled:\n b[\"state\"] = \"active\"\n self.instr_selec.enabled = False\n\n def choose_color(self):\n \"\"\"\n Displays the color selection tool.\n \"\"\"\n if not self.PLOTTING_ENABLED:\n raise NotImplementedError('Plotting is not implemented for this card. Cannot choose color.')\n # variable to store hexadecimal code of color\n color_code = colorchooser.askcolor(title=\"Choose color\")\n self.color = color_code[1]\n self.controller.show_main_window()\n self.color_button.configure(bg=color_code[1])\n self.controller.update_color(self.index, self.color)\n\n def stop_instr(self):\n raise NotImplementedError\n\n def tear_down(self):\n raise NotImplementedError\n","repo_name":"LabExT/LabExT","sub_path":"LabExT/View/LiveViewer/Cards/CardFrame.py","file_name":"CardFrame.py","file_ext":"py","file_size_in_byte":8181,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"52"} +{"seq_id":"73697873764","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport re\nimport sys\nimport tarfile\n\nfrom six.moves import urllib\nimport tensorflow as tf\nimport read_image\n\nFLAGS = tf.app.flags.FLAGS\n\n# Basic model parameters.\ntf.app.flags.DEFINE_integer('batch_size', 128,\n \"\"\"Number of images to process in a batch.\"\"\")\n#tf.app.flags.DEFINE_string('data_dir', '/tmp/cifar10_data',\n #\"\"\"Path to the CIFAR-10 data directory.\"\"\")\ntf.app.flags.DEFINE_boolean('use_fp16', False,\n \"\"\"Train the model using fp16.\"\"\")\n\n# Global constants describing the CIFAR-10 data set.\nIMAGE_SIZE = 128\nNUM_CLASSES = read_image.NUM_CLASS\nNUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 500\nNUM_EXAMPLES_PER_EPOCH_FOR_EVAL = 100\n\n\n# Constants describing the training process.\nMOVING_AVERAGE_DECAY = 0.9999 # The decay to use for the moving average.\nNUM_EPOCHS_PER_DECAY = 350.0 # Epochs after which learning rate decays.\nLEARNING_RATE_DECAY_FACTOR = 0.1 # Learning rate decay factor.\nINITIAL_LEARNING_RATE = 0.1 # Initial learning rate.\n\n# If a model is trained with multiple GPUs, prefix all Op names with tower_name\n# to differentiate the operations. Note that this prefix is removed from the\n# names of the summaries when visualizing a model.\nTOWER_NAME = 'tower'\n\nDATA_URL = 'http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz'\n\ndef _activation_summary(x):\n \"\"\"Helper to create summaries for activations.\n\n Creates a summary that provides a histogram of activations.\n Creates a summary that measures the sparsity of activations.\n\n Args:\n x: Tensor\n Returns:\n nothing\n \"\"\"\n # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training\n # session. This helps the clarity of presentation on tensorboard.\n tensor_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', x.op.name)\n tf.summary.histogram(tensor_name + '/activations', x)\n tf.summary.scalar(tensor_name + '/sparsity',\n tf.nn.zero_fraction(x))\n\n\ndef _variable_on_cpu(name, shape, initializer):\n \"\"\"Helper to create a Variable stored on CPU memory.\n\n Args:\n name: name of the variable\n shape: list of ints\n initializer: initializer for Variable\n\n Returns:\n Variable Tensor\n \"\"\"\n with tf.device('/cpu:0'):\n dtype = tf.float16 if FLAGS.use_fp16 else tf.float32\n var = tf.get_variable(name, shape, initializer=initializer, dtype=dtype)\n return var\n\n\ndef _variable_with_weight_decay(name, shape, stddev, wd):\n \"\"\"Helper to create an initialized Variable with weight decay.\n\n Note that the Variable is initialized with a truncated normal distribution.\n A weight decay is added only if one is specified.\n\n Args:\n name: name of the variable\n shape: list of ints\n stddev: standard deviation of a truncated Gaussian\n wd: add L2Loss weight decay multiplied by this float. If None, weight\n decay is not added for this Variable.\n\n Returns:\n Variable Tensor\n \"\"\"\n dtype = tf.float16 if FLAGS.use_fp16 else tf.float32\n var = _variable_on_cpu(\n name,\n shape,\n tf.truncated_normal_initializer(stddev=stddev, dtype=dtype))\n if wd is not None:\n weight_decay = tf.multiply(tf.nn.l2_loss(var), wd, name='weight_loss')\n tf.add_to_collection('losses', weight_decay)\n return var\n\ndef deconv1(output, kernel):\n trans = tf.nn.conv2d_transpose(output, kernel, output_shape=[FLAGS.batch_size, IMAGE_SIZE, IMAGE_SIZE, 3], strides=[1,1,1,1], padding='SAME')\n return trans\n\ndef unpool1(pool, kernel1):\n #let me use image resize to temporarily achieve this, since I can't find a tf method to do it\n unpooled = tf.image.resize_images(pool, [IMAGE_SIZE, IMAGE_SIZE])\n pool1_trans = deconv1(unpooled, kernel1)\n return pool1_trans\n\ndef devonv2(output, kernel2, kernel1):\n trans = tf.nn.conv2d_transpose(output, kernel2, output_shape=[FLAGS.batch_size, int(IMAGE_SIZE/2), int(IMAGE_SIZE/2), 64], strides=[1,1,1,1], padding='SAME')\n trans = unpool1(trans, kernel1)\n return trans\n\ndef unpool2(pool, kernel2, kernel1):\n #let me use image resize to temporarily achieve this, since I can't find a tf method to do it\n unpooled = tf.image.resize_images(pool, [int(IMAGE_SIZE/2), int(IMAGE_SIZE/2)])\n pool2_trans = deconv2(unpooled, kernel2, kernel1)\n return pool2_trans\n\ndef inference(images):\n \"\"\"Build the CIFAR-10 model.\n Args:\n images: Images returned from distorted_inputs() or inputs().\n Returns:\n Logits.\n \"\"\"\n # conv1 \n with tf.variable_scope('conv1') as scope:\n kernel1 = _variable_with_weight_decay('weights',\n shape=[5, 5, 3, 64],\n stddev=5e-2,\n wd=0.0)\n conv = tf.nn.conv2d(images, kernel1, [1, 1, 1, 1], padding='SAME')\n biases1 = _variable_on_cpu('biases', [64], tf.constant_initializer(0.0))\n pre_activation = tf.nn.bias_add(conv, biases1)\n conv1 = tf.nn.relu(pre_activation, name=scope.name)\n _activation_summary(conv1)\n \n with tf.variable_scope('conv1_visualization'):\n #16 activations\n # scale weights to [0 1], type is still float\n x_min = tf.reduce_min(kernel1)\n x_max = tf.reduce_max(kernel1)\n kernel_0_to_1 = (kernel1 - x_min) / (x_max - x_min)\n # to tf.image_summary format [batch_size, height, width, channels]\n kernel_transposed = tf.transpose (kernel_0_to_1, [3, 0, 1, 2])\n # this will display random 3 filters from the 64 in conv1\n tf.summary.image('conv1/filters', kernel_transposed, max_outputs=3)\n layer1_image1 = conv1[0:1, :, :, 0:16]\n layer1_image1 = tf.transpose(layer1_image1, perm=[3,1,2,0])\n tf.summary.image(\"filtered_images_layer1\", layer1_image1, max_outputs=16)\n \n #conv1 = tf.subtract(conv1, biases1)\n #conv1 = tf.nn.relu(conv1, name=scope.name)\n conv_trans = tf.nn.conv2d_transpose(conv1, kernel1, output_shape=[FLAGS.batch_size, IMAGE_SIZE, IMAGE_SIZE, 3], strides=[1,1,1,1], padding='SAME')\n tf.summary.image(\"reverse_conv1\", conv_trans)\n #find strongest activation here?\n #what is a strongest activation? contrast?\n #how? anyway let's play\n flattened_conv1 = tf.reshape(conv1, [FLAGS.batch_size, IMAGE_SIZE*IMAGE_SIZE, 64])#[1,1024,64]\n mean, variance = tf.nn.moments(flattened_conv1, 1)\n max_index = tf.argmax(variance, 1)#[1,64] in, out [1, 1]\n #remove all other filters and revert\n zeros = tf.zeros([FLAGS.batch_size, IMAGE_SIZE, IMAGE_SIZE, 64])\n size_temp = tf.convert_to_tensor([FLAGS.batch_size,IMAGE_SIZE,IMAGE_SIZE])\n max_index = tf.cast(max_index, tf.int32)\n lower_half_size = tf.concat([size_temp, max_index], 0)\n upper_half_size = tf.concat([size_temp, 64-max_index-1], 0)\n lower_half = tf.slice(zeros, [0,0,0,0], lower_half_size)\n max_index = tf.squeeze(max_index)# out int\n upper_half = tf.slice(zeros, [0,0,0,max_index+1], upper_half_size)\n max_slice = tf.slice(conv1, [0,0,0,max_index], [FLAGS.batch_size,IMAGE_SIZE,IMAGE_SIZE,1]) \n #can't use max_index in these places\n #lower_half = tf.concat([zeros for x in range(64-max_index-1)], axis=3)\n #upper_half = tf.concat([zeros for x in range(max_index)], axis=3)\n #lower_half = tf.slice(conv1, [0,0,0,0], [FLAGS.batch_size,IMAGE_SIZE,IMAGE_SIZE,max_index])\n #upper_half = tf.slice(conv1, [0,0,0,max_index+1], [FLAGS.batch_size,IMAGE_SIZE,IMAGE_SIZE,64-max_index-1])\n #lower_half = tf.zeros(shape=[FLAGS.batch_size,IMAGE_SIZE,IMAGE_SIZE, max_index], dtype=tf.float32)\n #upper_half = tf.zeros(shape=[FLAGS.batch_size,IMAGE_SIZE,IMAGE_SIZE, 64-max_index-1], dtype=tf.float32) \n max_filters = tf.concat([lower_half, max_slice, upper_half], axis=3)\n #if stack: output shape:[1,32,32,64,1]\n #max_filters = tf.concat([max_slice for x in range(64)], axis=3)\n #虽然说这样看起来更有道理，但是图片看起来实在没什么区别\n max_trans = tf.nn.conv2d_transpose(max_filters, kernel1, output_shape=[FLAGS.batch_size, IMAGE_SIZE, IMAGE_SIZE, 3], strides=[1,1,1,1], padding='SAME')\n tf.summary.image(\"reverse_max_conv1\", max_trans, max_outputs=16)\n #image input: [batch_size, height, width, channels]\n\n # pool1\n pool1, argmax= tf.nn.max_pool_with_argmax(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],\n padding='SAME', name='pool1')\n with tf.name_scope('pool1_visualization'):\n '''\n flattened_pool1 = tf.reshape(pool1, [FLAGS.batch_size, 16*16*64])\n flattened_argmax = tf.reshape(pool1, [FLAGS.batch_size, 16*16*64])\n raw = [[0 for x in range(FLAGS.batch_size)] for y in range(32*32*64)] \n for i in range(16*16*64):\n for j in range(FLAGS.batch_size):\n raw[j][flattened_argmax[j][i]] = flattened_pool1[j][i]\n unpooled = tf.reshape([FLAGS.batch_size, 32, 32, 64])\n #neurons = tf.split(argmax, 64, axis=3)\n #neurons = tf.squeeze(neurons)\n #tf.summary.image(\"argmax_pool1\", )\n '''\n #return tf.reshape(pool1, [FLAGS.batch_size, 16*16*64]), tf.reshape(pool1, [FLAGS.batch_size, 16*16*64])\n #let me use image resize to temporarily achieve this, since I can't find a tf method to do it\n unpooled = tf.image.resize_images(pool1, [IMAGE_SIZE, IMAGE_SIZE])\n pool1_trans = tf.nn.conv2d_transpose(unpooled, kernel1, output_shape=[FLAGS.batch_size, IMAGE_SIZE, IMAGE_SIZE, 3], strides=[1,1,1,1], padding='SAME')\n tf.summary.image(\"reverse_pool1\", pool1_trans, max_outputs=16)\n \n # norm1\n norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75,\n name='norm1')\n #???what is it? just ignore it and see what will happen\n\n # conv2\n with tf.variable_scope('conv2') as scope:\n kernel2 = _variable_with_weight_decay('weights',\n shape=[5, 5, 64, 64],\n stddev=5e-2,\n wd=0.0)\n conv = tf.nn.conv2d(norm1, kernel2, [1, 1, 1, 1], padding='SAME')\n biases2 = _variable_on_cpu('biases', [64], tf.constant_initializer(0.1))\n pre_activation = tf.nn.bias_add(conv, biases2)\n conv2 = tf.nn.relu(pre_activation, name=scope.name)\n _activation_summary(conv2)\n with tf.name_scope(\"conv2_visualization\"):\n #find strongest activation here?\n #what is a strongest activation? contrast?\n #how? anyway let's play\n #conv2 = tf.subtract(conv2, biases2)\n flattened_conv2 = tf.reshape(conv2, [FLAGS.batch_size, int(IMAGE_SIZE/2)**2, 64])#[1,1024,64]\n mean, variance = tf.nn.moments(flattened_conv2, 1)\n max_index = tf.argmax(variance, 1)#[1,64] in, int out\n #remove all other filters and revert\n max_index = tf.squeeze(max_index)\n max_slice = tf.slice(conv2, [0,0,0,max_index], [FLAGS.batch_size,int(IMAGE_SIZE/2),int(IMAGE_SIZE/2),1])\n #max_filters = tf.concat([max_slice for x in range(64)], axis=3) output shape:[1,32,32,64,1]\n max_filters = tf.concat([max_slice for x in range(64)], axis=3)\n max_trans = tf.nn.conv2d_transpose(max_filters, kernel2, \n output_shape=[FLAGS.batch_size, int(IMAGE_SIZE/2), int(IMAGE_SIZE/2), 64], strides=[1,1,1,1], padding='SAME')\n trans = unpool1(max_trans, kernel1)\n tf.summary.image(\"reverse_max_conv2\", trans, max_outputs=16)\n\n for y in range(16):\n max_index = y\n max_slice = tf.slice(conv2, [0,0,0,max_index], [FLAGS.batch_size,int(IMAGE_SIZE/2),int(IMAGE_SIZE/2),1])\n #max_filters = tf.concat([max_slice for x in range(64)], axis=3) output shape:[1,32,32,64,1]\n max_filters = tf.concat([max_slice for x in range(64)], axis=3)\n max_trans = tf.nn.conv2d_transpose(max_filters, kernel2, \n output_shape=[FLAGS.batch_size, int(IMAGE_SIZE/2), int(IMAGE_SIZE/2), 64], strides=[1,1,1,1], padding='SAME')\n trans = unpool1(max_trans, kernel1)\n tf.summary.image(\"filtered_images_layer2\", trans, max_outputs=16)\n #image input: [batch_size, height, width, channels]\n\n # norm2\n norm2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75,\n name='norm2')\n # pool2\n pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1],\n strides=[1, 2, 2, 1], padding='SAME', name='pool2')\n with tf.name_scope('pool2_visualization'):\n unpooled = tf.image.resize_images(pool2, [int(IMAGE_SIZE/2), int(IMAGE_SIZE/2)])\n pool2_trans = devonv2(unpooled, kernel2, kernel1)\n tf.summary.image(\"reverse_pool2\", pool2_trans, max_outputs=16)\n\n # local3\n with tf.variable_scope('local3') as scope:\n # Move everything into depth so we can perform a single matrix multiply.\n reshape = tf.reshape(pool2, [FLAGS.batch_size, -1])\n dim = reshape.get_shape()[1].value\n weights = _variable_with_weight_decay('weights', shape=[dim, 384],\n stddev=0.04, wd=0.004)\n biases = _variable_on_cpu('biases', [384], tf.constant_initializer(0.1))\n local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name)\n _activation_summary(local3)\n '''\n with tf.name_scope('local3_visualization'):\n delocal3 = tf.subtract(local3, biases)\n #weights are not invertable\n delocal3 = tf.matmul(delocal3, tf.matrix_inverse(weights))\n delocal3 = tf.reshape(delocal3, [FLAGS.batch_size, 8, 8, 64])\n tf.summary.image(\"reverse_local3\", unpool2(delocal3, kernel2, kernel1), max_outputs=16)\n '''\n # local4\n with tf.variable_scope('local4') as scope:\n weights = _variable_with_weight_decay('weights', shape=[384, 192],\n stddev=0.04, wd=0.004)\n biases = _variable_on_cpu('biases', [192], tf.constant_initializer(0.1))\n local4 = tf.nn.relu(tf.matmul(local3, weights) + biases, name=scope.name)\n _activation_summary(local4)\n\n # linear layer(WX + b),\n # We don't apply softmax here because\n # tf.nn.sparse_softmax_cross_entropy_with_logits accepts the unscaled logits\n # and performs the softmax internally for efficiency.\n with tf.variable_scope('softmax_linear') as scope:\n weights = _variable_with_weight_decay('weights', [192, NUM_CLASSES],\n stddev=1/192.0, wd=0.0)\n biases = _variable_on_cpu('biases', [NUM_CLASSES],\n tf.constant_initializer(0.0))\n softmax_linear = tf.add(tf.matmul(local4, weights), biases, name=scope.name)\n _activation_summary(softmax_linear)\n\n return softmax_linear\n\n\ndef loss(logits, labels):\n \"\"\"Add L2Loss to all the trainable variables.\n\n Add summary for \"Loss\" and \"Loss/avg\".\n Args:\n logits: Logits from inference().\n labels: Labels from distorted_inputs or inputs(). 1-D tensor\n of shape [batch_size]\n\n Returns:\n Loss tensor of type float.\n \"\"\"\n # Calculate the average cross entropy loss across the batch.\n labels = tf.cast(labels, tf.int64)\n cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(\n labels=labels, logits=logits, name='cross_entropy_per_example')\n cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')\n tf.add_to_collection('losses', cross_entropy_mean)\n\n # The total loss is defined as the cross entropy loss plus all of the weight\n # decay terms (L2 loss).\n return tf.add_n(tf.get_collection('losses'), name='total_loss')\n\n\ndef _add_loss_summaries(total_loss):\n \"\"\"Add summaries for losses in CIFAR-10 model.\n\n Generates moving average for all losses and associated summaries for\n visualizing the performance of the network.\n\n Args:\n total_loss: Total loss from loss().\n Returns:\n loss_averages_op: op for generating moving averages of losses.\n \"\"\"\n # Compute the moving average of all individual losses and the total loss.\n loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')\n losses = tf.get_collection('losses')\n loss_averages_op = loss_averages.apply(losses + [total_loss])\n\n # Attach a scalar summary to all individual losses and the total loss; do the\n # same for the averaged version of the losses.\n for l in losses + [total_loss]:\n # Name each loss as '(raw)' and name the moving average version of the loss\n # as the original loss name.\n tf.summary.scalar(l.op.name + ' (raw)', l)\n tf.summary.scalar(l.op.name, loss_averages.average(l))\n\n return loss_averages_op\n\ndef accuracy(logits, labels):\n #labels=tf.convert_\n #labels_tensor.cast(labels_tensor, tf.int64)\n correct_prediction = tf.equal(tf.argmax(logits, 1), labels)\n accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n #top_k_op = tf.nn.in_top_k(logits, labels, 1)\n return accuracy\n\ndef train(total_loss, global_step):\n \"\"\"Train CIFAR-10 model.\n\n Create an optimizer and apply to all trainable variables. Add moving\n average for all trainable variables.\n\n Args:\n total_loss: Total loss from loss().\n global_step: Integer Variable counting the number of training steps\n processed.\n Returns:\n train_op: op for training.\n \"\"\"\n # Variables that affect learning rate.\n num_batches_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN / FLAGS.batch_size\n decay_steps = int(num_batches_per_epoch * NUM_EPOCHS_PER_DECAY)\n\n # Decay the learning rate exponentially based on the number of steps.\n lr = tf.train.exponential_decay(INITIAL_LEARNING_RATE,\n global_step,\n decay_steps,\n LEARNING_RATE_DECAY_FACTOR,\n staircase=True)\n tf.summary.scalar('learning_rate', lr)\n\n # Generate moving averages of all losses and associated summaries.\n loss_averages_op = _add_loss_summaries(total_loss)\n\n # Compute gradients.\n with tf.control_dependencies([loss_averages_op]):\n opt = tf.train.GradientDescentOptimizer(lr)\n grads = opt.compute_gradients(total_loss)\n\n # Apply gradients.\n apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)\n\n # Add histograms for trainable variables.\n for var in tf.trainable_variables():\n tf.summary.histogram(var.op.name, var)\n\n # Add histograms for gradients.\n for grad, var in grads:\n if grad is not None:\n tf.summary.histogram(var.op.name + '/gradients', grad)\n\n # Track the moving averages of all trainable variables.\n variable_averages = tf.train.ExponentialMovingAverage(\n MOVING_AVERAGE_DECAY, global_step)\n variables_averages_op = variable_averages.apply(tf.trainable_variables())\n\n with tf.control_dependencies([apply_gradient_op, variables_averages_op]):\n train_op = tf.no_op(name='train')\n\n return train_op\n\n\ndef maybe_download_and_extract():\n \"\"\"Download and extract the tarball from Alex's website.\"\"\"\n dest_directory = FLAGS.data_dir\n if not os.path.exists(dest_directory):\n os.makedirs(dest_directory)\n filename = DATA_URL.split('/')[-1]\n filepath = os.path.join(dest_directory, filename)\n if not os.path.exists(filepath):\n def _progress(count, block_size, total_size):\n sys.stdout.write('\\r>> Downloading %s %.1f%%' % (filename,\n float(count * block_size) / float(total_size) * 100.0))\n sys.stdout.flush()\n filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress)\n print()\n statinfo = os.stat(filepath)\n print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')\n extracted_dir_path = os.path.join(dest_directory, 'cifar-10-batches-bin')\n if not os.path.exists(extracted_dir_path):\n tarfile.open(filepath, 'r:gz').extractall(dest_directory)\n\n","repo_name":"calthaz/PanguML","sub_path":"PythonWorksp/TensorFlow/visualization/general.py","file_name":"general.py","file_ext":"py","file_size_in_byte":19371,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"39260852661","text":"import numpy as np\nfrom sklearn.metrics import accuracy_score\nimport matplotlib.pyplot as plt\n\nSHOW_WRONG_DIGITS = True\nSHOW_GOOD_DIGITS = True\n\ndef sigmoid(x):\n return 1 / (1 + np.exp(-x))\n\n\ndef softmax(x):\n exp_x = np.exp(x)\n return exp_x / np.sum(exp_x, axis=1, keepdims=True)\n\n\nclass NeuralNetwork:\n def __init__(self, layer_sizes):\n self.layer_sizes = layer_sizes\n self.num_layers = len(layer_sizes)\n self.weights = []\n self.biases = []\n for i in range(1, self.num_layers):\n prev_size = layer_sizes[i-1]\n curr_size = layer_sizes[i]\n self.weights.append(np.random.randn(prev_size, curr_size))\n self.biases.append(np.zeros((1, curr_size)))\n\n def der_sigmoid(self, x):\n return sigmoid(x) * (1 - sigmoid(x))\n\n def forward(self, X):\n self.activations = [X]\n self.z_values = []\n\n for i in range(self.num_layers - 2):\n z = np.dot(self.activations[i], self.weights[i]) + self.biases[i]\n self.z_values.append(z)\n activation = sigmoid(z)\n self.activations.append(activation)\n\n z_out = np.dot(self.activations[-1], self.weights[-1]) + self.biases[-1]\n self.z_values.append(z_out)\n output_activation = softmax(z_out)\n self.activations.append(output_activation)\n\n return self.activations[-1]\n\n def backpropagation(self, X, y, learning_rate):\n m = X.shape[0]\n delta = (self.activations[-1] - y) / m\n\n d_weights = []\n d_biases = []\n\n d_weights.append(np.dot(self.activations[-2].T, delta))\n d_biases.append(np.sum(delta, axis=0, keepdims=True))\n\n for i in range(self.num_layers - 3, -1, -1):\n delta = np.dot(delta, self.weights[i + 1].T) * self.der_sigmoid(self.z_values[i])\n d_weights.insert(0, np.dot(self.activations[i].T, delta))\n d_biases.insert(0, np.sum(delta, axis=0))\n\n for i in range(self.num_layers - 1):\n self.weights[i] -= learning_rate * d_weights[i]\n self.biases[i] -= learning_rate * d_biases[i]\n\n\n def train(self, X, y, learning_rate, epochs, X_test, y_test):\n accuracy_scores = []\n error_rates = []\n display_images = False\n for epoch in range(epochs):\n y_pred = self.forward(X)\n self.backpropagation(X, y, learning_rate)\n if epoch == epochs - 1:\n display_images = True\n accuracy, error = self.performance_metrics(X_test, y_test, display_images)\n accuracy_scores.append(accuracy)\n error_rates.append(error)\n print(f\"Iteration {epoch}\")\n print(f\"Accuracy: {accuracy}\")\n print(f\"Error rate: {error}\")\n\n return accuracy_scores, error_rates\n\n\n def display_misclassified_images(self, X, predicted_labels, true_labels):\n misclassified_indices = np.where(predicted_labels != true_labels)[0]\n\n if misclassified_indices.size > 0:\n print(\"Błędnie sklasyfikowane cyfry:\")\n num_bad_images = min(6, misclassified_indices.size)\n rows = int(np.ceil(num_bad_images / 3))\n fig, axes = plt.subplots(rows, 3, figsize=(12, 8))\n\n for i in range(num_bad_images):\n index = misclassified_indices[i]\n predicted_label = predicted_labels[index]\n true_label = true_labels[index]\n misclassified_image = X[index].reshape(28, 28)\n\n row = i // 3\n col = i % 3\n axes[row, col].imshow(misclassified_image, cmap='gray')\n axes[row, col].set_title(f\"Przewidziana: {predicted_label}, Oczekiwana: {true_label}\")\n axes[row, col].axis('off')\n\n plt.tight_layout()\n plt.show()\n\n def display_classified_images(self, X, predicted_labels, true_labels):\n classified_indices = np.where(predicted_labels == true_labels)[0]\n if classified_indices.size > 0:\n print(\"Poprawnie sklasyfikowane cyfry:\")\n num_good_images = min(6, classified_indices.size)\n rows = int(np.ceil(num_good_images / 3))\n fig, axes = plt.subplots(rows, 3, figsize=(12, 8))\n\n for i in range(num_good_images):\n index = classified_indices[i]\n predicted_label = predicted_labels[index]\n true_label = true_labels[index]\n classified_image = X[index].reshape(28, 28)\n\n row = i // 3\n col = i % 3\n axes[row, col].imshow(classified_image, cmap='gray')\n axes[row, col].set_title(f\"Przewidziana: {predicted_label}, Oczekiwana: {true_label}\")\n axes[row, col].axis('off')\n\n plt.tight_layout()\n plt.show()\n\n def performance_metrics(self, X, y, display_images):\n predictions = self.forward(X)\n predicted_labels = np.argmax(predictions, axis=1)\n true_labels = np.argmax(y, axis=1)\n\n error_output = ((predicted_labels - true_labels) ** 2).mean()\n accuracy = accuracy_score(true_labels, predicted_labels)\n\n if display_images and SHOW_WRONG_DIGITS:\n self.display_misclassified_images(X, predicted_labels, true_labels)\n\n if display_images and SHOW_GOOD_DIGITS:\n self.display_classified_images(X, predicted_labels, true_labels)\n\n print(\"------------------------------\")\n return accuracy, error_output\n\n","repo_name":"jkwiatk1/WSI","sub_path":"Lab5/network.py","file_name":"network.py","file_ext":"py","file_size_in_byte":5525,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"36204348052","text":"from django.db import models\nfrom django.core.validators import MaxValueValidator, MinValueValidator\n#from section.models import Section\n#from account.models import Teacher\n# Create your models here.\nclass Subject(models.Model):\n Department_List = (('CSE', 'CSE'),('IT','IT'),)\n Subject_Code = models.CharField(max_length = 10,primary_key = True)\n Subject_Name = models.CharField(max_length = 50)\n Semester = models.IntegerField(default=1,validators=[MaxValueValidator(8), MinValueValidator(1)])\n Department = models.CharField(max_length=10, choices=Department_List)\n#class Time_Table(models.Model):\n# Day_List = (('MONDAY','MONDAY'),('TUESDAY','TUESDAY'),('WEDNESDAY','WEDNESDAY'),('THURSDAY','THURSDAY'),('FRIDAY','FRIDAY'),('SATURDAY','SATURDAY'))\n# Day = models.CharField(max_length=10, choices=Day_List)\n# Time = models.TimeField()\n# Section = models.ForeignKey(Section,on_delete = models.SET_NULL,null=True)\n# Teacher = models.ForeignKey(Teacher,on_delete = models.SET_NULL,null=True)\n# Subject = models.ForeignKey(Subject,on_delete = models.SET_NULL,null=True)\n","repo_name":"mynk26/Acute-Vision","sub_path":"Acute-Vision/time_table/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1101,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"73475942246","text":"import redis\nfrom redis.exceptions import ConnectionError, ResponseError, NoPermissionError\nfrom pylambdalib.element_objects import Val\nfrom pylambdalib.lambdaexceptions import EnviromentVariableNotFoundError, IncorrectRedisUserOrPassword, NoPermissionForRedisUser\n# from dotenv import load_dotenv\nimport os\n\ndef get_connection(host, port):\n try:\n db = redis.Redis(host=host, port=int(port), decode_responses=True,encoding=\"ISO-8859-1\")\n db.ping()\n return db\n except ConnectionError:\n return None\n\n# USERENV = \"REDIS-USER\"\n# PASSENV = \"REDIS-PASSWORD\"\n\n# prueba la conexion, si anda sin problemas la retorna\n# def get_connection(host, port, username = None, password = None):\n# if username is None or password is None:\n# load_dotenv()\n# username = os.getenv(USERENV)\n# password = os.getenv(PASSENV)\n# if username is None:\n# raise EnviromentVariableNotFoundError(USERENV)\n# if password is None:\n# raise EnviromentVariableNotFoundError(PASSENV)\n# auth_command = f\"AUTH {username} {password}\"\n# try:\n# db = redis.Redis(\n# host=host,\n# port=int(port),\n# decode_responses=True,\n# encoding=\"ISO-8859-1\"\n# )\n# db.execute_command(auth_command)\n# db.ping()\n# return db\n# except NoPermissionError:\n# raise NoPermissionForRedisUser(username)\n# except ConnectionError:\n# return None\n# except ResponseError:\n# raise IncorrectRedisUserOrPassword\n\ndef get_company_name(db, company_id):\n for s_val in db.smembers(\"0.2.\"+str(company_id) + \":val\"):\n val = Val(s_val)\n if val.var == \"@name\":\n return val.val\n return \"Unknown company\"\n\ndef get_area_name(db,area_id):\n for s_val in db.smembers(str(area_id)+\":val\"):\n val = Val(s_val)\n if val.is_up() and val.var == \"@oName\":\n return val.val\n return \"Unknown area\"\n\ndef check_quotes(s):\n s = s.replace('\"', '')\n s = s.replace(\"'\", \"\")\n s = s.replace(\"\\t\", \" \")\n s = s.replace(\"\\n\",\" \")\n s = s.replace(\"\\r\", \" \")\n s = s.replace(\":\", \" \")\n s = s.replace(\"|\", \" \")\n s = s.replace('Á','A')\n s = s.replace('á','a')\n s = s.replace('É', 'E')\n s = s.replace('é', 'e')\n s = s.replace('Í', 'I')\n s = s.replace('í', 'i')\n s = s.replace('Ó', 'O')\n s = s.replace('ó', 'o')\n s = s.replace('Ú', 'U')\n s = s.replace('ú', 'u')\n s = s.replace('Ñ', 'N')\n s = s.replace('ñ', 'n')\n return s\n\ndef get_foSide_key(s):\n return s[s.index('-') + 1:]\n\n# recibe un string del tipo clave:smth y retorna solamente la clave\ndef get_key_only(key):\n return key[:key.index(':')]","repo_name":"if777-1/pylambdalib","sub_path":"pylambdalib/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2729,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"26145440057","text":"\nimport nltk\nnltk.download('punkt')\n\n#retourne la distance du mot par rapport a l'aspect\ndef get_word_aspect_distance(aspect_start_position,aspect_end_position, word_position):\n if word_position < aspect_start_position:\n return - (aspect_start_position - word_position)\n elif word_position > aspect_end_position:\n return - (aspect_end_position - word_position)\n else:\n return 0\n\n#returne la liste des mots d'une phrase et la position de chaque mot\ndef get_word_with_indice(sentence):\n # tokens = ['je', 'mange', 'la', 'banane']\n tokens = nltk.word_tokenize(sentence)\n data = []\n i = 0\n for token in tokens:\n data.append({'word': token, 'position': i})\n i = i+1\n return data\n\n#deternime les coordonnee de l'aspet dans la phrase\ndef get_aspect_position(sentence, aspect):\n tokens = nltk.word_tokenize(sentence)\n aspect_token = nltk.word_tokenize(aspect)\n aspect_length = len(aspect_token)\n start_positio = 0\n end_positio = 0\n for i in range(len(tokens)):\n # print(tokens[i], aspect_token[0])\n if tokens[i] == aspect_token[0]:\n match = True\n for j in range(len(aspect_token)):\n # print(tokens[i+j], aspect_token[j])\n if tokens[i+j] != aspect_token[j]:\n match = False\n if match:\n start_positio = i\n end_positio = aspect_length+i-1\n return start_positio, end_positio\n\n\n\n# Sentence reprensentation contain sentence, aspect, polarity, start and end\ndef get_sentence_vector(sentence_representatio):\n result = []\n for sent_rep in sentence_representatio:\n sentence = sent_rep['sentence']\n aspect = sent_rep['aspect']\n data = get_word_with_indice(sentence)\n start, end = get_aspect_position(sentence, aspect)\n oneElmt = []\n for elm in data:\n oneElmt.append(get_word_aspect_distance(aspect_start_position=start, aspect_end_position=end,\n word_position=elm['position']))\n result.append(oneElmt)\n return result","repo_name":"germes96/SemEval_absa_parser","sub_path":"data_process.py","file_name":"data_process.py","file_ext":"py","file_size_in_byte":2116,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"43703094272","text":"import argparse\nimport email.policy\nimport os.path\nimport datetime\nimport sys\n\nfrom dateutil import parser\nfrom email.parser import BytesHeaderParser\nfrom email.errors import MissingHeaderBodySeparatorDefect\nimport glob\n\n\ndef arg_parser(arguments):\n argparser = argparse.ArgumentParser(arguments)\n argparser.add_argument(\"eml_folder\", help=\"Folder with eml files\")\n return argparser\n\n\ndef parse_str_to_datetime(data_str, ignoretz=False):\n return parser.parse(data_str, fuzzy=True, ignoretz=ignoretz).strftime(\"%d/%m/%Y %H:%M:%S %z\")\n\n\ndef eml_chg_date(folder):\n \"\"\"\n This script takes all *.eml files from eml_folder (input),\n fixes the dates to one standard format: %d/%m/%Y %H:%M:%S %z\n and places them in (input)/fixed folder.\n \"\"\"\n y = 0, 0\n new_policy = email.policy.compat32.clone(raise_on_defect=True)\n\n if not glob.glob(args.eml_folder + \"/*.eml\"):\n sys.exit(\"No .eml files here\")\n\n try:\n os.mkdir(folder + \"/fixed\")\n except FileExistsError:\n print(\"Folder \"\"fixed\"\" exists... continuing\")\n pass\n\n print(\"EML DATE FIXER STARTS\")\n\n for file in glob.glob(folder + \"/*.eml\"):\n with open(file, \"rb\") as fp:\n try:\n msg = BytesHeaderParser(policy=new_policy).parse(fp)\n except MissingHeaderBodySeparatorDefect:\n print(\"MissingHeaderBodySeparatorDefect : fixing : \" + fp.name)\n # return to the first byte of fp\n fp.seek(0)\n fp_corrected = fp.read().replace(b\"\\r\", b\" \")\n msg = BytesHeaderParser().parsebytes(fp_corrected)\n\n try:\n date_parsed = parse_str_to_datetime(msg.get(\"Date\"))\n\n except parser.ParserError:\n print(\"ParseError: Date: fixing : \" + fp.name)\n date_parsed = msg.get(\"Date\").split(\" \")[:-1]\n date_parsed = \" \".join(date_parsed)\n date_parsed = parse_str_to_datetime(date_parsed)\n\n except ValueError:\n date_parsed = parse_str_to_datetime(msg.get(\"Date\"), True)\n\n except Exception as error:\n with open(\"logs.txt\", 'a') as fl:\n fl.write(str(datetime.datetime.now()) + \" \" + fp.name + \": \" + type(error).__name__ + str(\n error.args) + \"\\n\")\n continue\n\n msg.replace_header(\"Date\", date_parsed)\n y += 1\n\n mail_fixed = os.path.join(folder + \"/fixed\", os.path.basename(file))\n with open(mail_fixed, 'wb') as fp2:\n fp2.write(msg.as_bytes())\n\n print(\"DATES FIXED: \", y)\n print(\"FILES in root folder: \", len(os.listdir(folder)) - 1) # minus fixed folder\n print(\"FILES in fixed folder: \", len(os.listdir(folder + \"/fixed\")))\n\n\nif __name__ == '__main__':\n\n print(eml_chg_date.__doc__)\n argparser = arg_parser(sys.argv)\n args = argparser.parse_args()\n\n eml_chg_date(args.eml_folder)\n","repo_name":"michuhu/eml-date-fix","sub_path":"eml-date-fix.py","file_name":"eml-date-fix.py","file_ext":"py","file_size_in_byte":2971,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"73688639205","text":"# This is a sample Python script.\n\n# Press Shift+F10 to execute it or replace it with your code.\n# Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings.\nfrom matplotlib import image as mpig\nimport scipy\nimport os\nimport numpy as np\nfrom sklearn.cluster import KMeans\nfrom sklearn.cluster import kmeans_plusplus\nfrom sklearn.cluster import AgglomerativeClustering\nfrom sklearn.cluster import DBSCAN\nfrom sklearn.mixture import GaussianMixture\nfrom sklearn.metrics import silhouette_score\nfrom sklearn.metrics import calinski_harabasz_score\n\n\nclass PictureUtils:\n def __init__(self, read, save, sub=0):\n \"\"\"\n :param read: read document path\n :param save: write document path\n :param sub: is subsampling\n \"\"\"\n self.read = read\n self.save = save\n self.img_shape = [0, 0, 0]\n self.picture = None\n self.sub = sub\n\n def read_picture(self):\n self.picture = mpig.imread(self.read)\n if self.sub == 0:\n self.img_shape = list(np.shape(self.picture))\n print(self.img_shape)\n else:\n self.picture = self.subsample()\n self.img_shape = list(np.shape(self.picture))\n print(self.img_shape)\n\n stand_picture = np.reshape(self.picture, [-1, self.img_shape[-1]])\n print(np.unique(stand_picture, axis=0),np.size(np.unique(stand_picture, axis=0)))\n return stand_picture\n\n def subsample(self):\n sub_picture = self.picture[::self.sub, ::self.sub]\n return sub_picture\n\n def compress_picture(self, cluster, cluster_label):\n print(cluster, cluster_label,np.size(np.unique(cluster, axis=0)))\n clip_cluster = list(np.clip(np.array(cluster), 0, 1))\n compressPicture = []\n for label in cluster_label:\n compressPicture.append(clip_cluster[label])\n compressPicture = np.resize(compressPicture, self.img_shape)\n # print(compressPicture, np.shape(compressPicture))\n mpig.imsave(self.save, arr=compressPicture)\n\n\nclass ModelUtils:\n def __init__(self, modelName, **params):\n params = params['params']\n self.modelName = modelName\n model_dict = {'k_means': KMeans,\n 'GM': GaussianMixture}\n if modelName == 'k_means':\n self.model = model_dict[modelName](init=params['init'], n_clusters=params['n_clusters'])\n elif modelName == 'GM':\n self.model = model_dict[modelName](n_components=params['n_clusters'],covariance_type=params['covariance_type'])\n self.paramsDict = params\n\n self.data = None\n self.clusterslabel = None\n\n def get_cluster_center(self):\n if self.modelName == 'k_means':\n return self.model.cluster_centers_\n elif self.modelName == 'GM':\n return self.model.means_\n\n def fit_predict(self, data):\n self.data = data\n self.clusterslabel = self.model.fit_predict(data)\n return self.get_cluster_center(), self.clusterslabel\n\n def evaluate_score(self):\n sc = silhouette_score(self.data, self.clusterslabel, sample_size=40)\n print(\"sc finished\")\n ch = calinski_harabasz_score(self.data, self.clusterslabel )\n print(\"ch finished\")\n return sc,ch\n\n\ndef compressFunc(read_path, save_path, **params):\n\n pictureUtils = PictureUtils(read_path, save_path, 0)\n image = pictureUtils.read_picture()\n params = params['params']\n print(params)\n modelUtils = ModelUtils(modelName=params['modelName'], params=params)\n cluster_centers, clusters_label = modelUtils.fit_predict(image)\n\n print(\"training finished\")\n sc, ch = modelUtils.evaluate_score()\n print(params['modelName'],\":score sc\", sc, 'ch', ch)\n pictureUtils.compress_picture(cluster_centers, clusters_label)\n\n\nif __name__ == '__main__':\n read = 'picture/tiger.png'\n save = 'GenerateDir/newtiger_gm32.png'\n param = {\n 'n_clusters': 32,\n 'covariance_type': 'spherical',\n 'modelName':'GM'\n }\n compressFunc(read_path=read, save_path=save, params=param)\n\n save = 'GenerateDir/newtiger_k32.png'\n param = {'init': 'k-means++',\n 'n_clusters': 32,\n 'modelName': 'k_means'\n }\n compressFunc(read_path=read, save_path=save, params = param)\n\n","repo_name":"FindTheTruth/Machine-learning","sub_path":"clusters algorithm/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4345,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"72361429926","text":"import numpy as np\nimport os\nimport shutil\n\nif os.path.isdir('val_imgs/'):\n shutil.rmtree('val_imgs/')\nos.mkdir('val_imgs/')\n\nimg_files = os.listdir('imgs/')\ntest_files = np.random.choice(img_files, size=int(0.1 * len(img_files)), replace=False)\n\nfor file in test_files:\n shutil.move('imgs/' + file, 'val_imgs/')","repo_name":"cpgaffney1/cs231n","sub_path":"split_val.py","file_name":"split_val.py","file_ext":"py","file_size_in_byte":318,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"44764283132","text":"import setuptools\n\nwith open(\"README.md\", \"r\") as fh:\n long_description = fh.read()\n\nsetuptools.setup(\n name=\"pyfizi\",\n version=\"0.7.1\",\n author=\"Nicholas Mancuso, Megan Roytman\",\n author_email=\"nicholas.mancuso@med.usc.edu\",\n description=\"Impute GWAS summary statistics using reference genotype data\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n url=\"https://github.com/bogdanlab/fizi\",\n packages=[\"pyfizi\"],\n python_requires='>=3',\n install_requires=[\n \"numpy>=1.14.5\",\n \"scipy>=1.2.0\",\n \"pandas>=1.2.0\",\n \"pandas-plink\",\n ],\n scripts=[\n \"bin/fizi\",\n ],\n classifiers=[\n \"Programming Language :: Python :: 3\",\n \"License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)\",\n \"Operating System :: OS Independent\",\n ],\n)\n","repo_name":"bogdanlab/fizi","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":885,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"52"} +{"seq_id":"11072923694","text":"from typing import Type\n\nimport requests\nfrom maubot import Plugin\nfrom maubot.handlers import command\nfrom mautrix.types import MessageEvent\n\nfrom help.config import Config\n\n\nclass HelpPlugin(Plugin):\n commands: object\n categories: object\n standard_category: str\n\n async def start(self) -> None:\n self.config.load_and_update()\n self.standard_category = self.config[\"standard_category\"]\n await self.fetch_help()\n\n async def fetch_help(self):\n self.log.info(\"Loading help data..\")\n commands = requests.request(\"GET\", self.config['command_index'], headers={}, data=\"\")\n self.commands = commands.json()\n self.log.info(f\"Command infos loaded. The command index contains {len(self.commands.keys())} commands.\")\n\n categories = requests.request(\"GET\", self.config['category_index'], headers={}, data=\"\")\n self.categories = categories.json()\n self.log.info(f\"Category infos loaded. The category index contains {len(self.categories.keys())} categories.\")\n\n @command.new(\"help\", aliases=[\"hilfe\"])\n @command.argument(\"name\", pass_raw=True, required=False)\n async def help_command(self, evt: MessageEvent, name: str):\n if name == \"\" or name == \" \":\n command_index: dict = {}\n used_commands: list = []\n for cat in sorted(self.categories):\n command_index[cat] = self.categories[cat]\n for cmd in self.categories[cat]:\n used_commands.append(cmd)\n if self.standard_category not in command_index:\n command_index[self.standard_category] = []\n for key in sorted(self.commands):\n if key not in used_commands:\n command_index[self.standard_category].append(key)\n msg: str = \"

Liste der Befehle:

\\n
    \\n\"\n\n for key in sorted(command_index):\n if key == self.standard_category or len(command_index[key]) == 0:\n continue\n msg = msg + f\"
  1. {key}\\n
      \"\n for cmd in sorted(command_index[key]):\n msg = msg + f\"
    • {cmd}
      • \\n\"\n msg = msg + \"
    2. \\n\"\n if len(command_index[self.standard_category]) != 0:\n msg = msg + f\"
  2. {self.standard_category}\\n
      \\n\"\n for cmd in sorted(command_index[self.standard_category]):\n msg = msg + f\"
    • {cmd}
      • \"\n msg = msg + \"
    3. \\n\"\n msg = msg + \"

Nähere Informationen über einen Befehl erhälst du mit !help \" \\\n \"<Befehl>.

\"\n await evt.respond(msg, markdown=False, allow_html=True)\n\n elif name == \"reload\":\n if evt.sender in self.config['devs']:\n await self.fetch_help()\n await evt.respond(\"Die Hilfenachrichten wurden neu geladen.\")\n else:\n await evt.respond(\"Nur Developer können die Hilfenachrichten neu laden, um Spam vorzubeugen.\")\n else:\n if self.commands[name] is None:\n await evt.respond(\n f\"Für den Befehl `{name}` gibt es keine Informationen. Entweder exsistiert dieser Befehl nicht, \"\n f\"oder die Developer haben vergessem, diesen zu registrieren.\")\n else:\n await evt.respond(f\"**Hilfe:** `!{name}`\\n\\n*{self.commands[name]}*\")\n\n @classmethod\n def get_config_class(cls) -> Type['BaseProxyConfig']:\n return Config\n","repo_name":"drdeee/maubot-help","sub_path":"help/helpplugin.py","file_name":"helpplugin.py","file_ext":"py","file_size_in_byte":3601,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"4997913684","text":"import requests\r\nfrom bs4 import BeautifulSoup\r\nfrom apscheduler.schedulers.asyncio import AsyncIOScheduler\r\nfrom dataclasses import dataclass\r\nimport asyncio\r\nimport nest_asyncio\r\n\r\n\r\n@dataclass\r\nclass CurrencyCourses:\r\n dollar_to_rub_course: float\r\n rub_to_dollar_course: float\r\n eur_to_rub_course: float\r\n rub_to_eur_course: float\r\n dollar_to_eur_course: float\r\n eur_to_dollar_course: float\r\n\r\n\r\nscheduler = AsyncIOScheduler()\r\n\r\ndollar_course = 0\r\neuro_course = 0\r\n\r\nurl = 'http://www.cbr.ru/scripts/XML_daily.asp'\r\n\r\ncurrency_courses_data: CurrencyCourses\r\n\r\nasync def get_exchange_rates(url):\r\n global dollar_course, euro_course, currency_courses_data\r\n\r\n response = requests.get(url)\r\n\r\n xml_page = BeautifulSoup(response.text, features='lxml-xml')\r\n\r\n all_valutes = xml_page.find_all('Valute')\r\n\r\n for valute in all_valutes:\r\n char_code = valute.find('CharCode').text\r\n\r\n if char_code == 'USD':\r\n dollar_course = float(valute.find('Value').text.replace(',', '.'))\r\n\r\n if char_code == 'EUR':\r\n euro_course = float(valute.find('Value').text.replace(',', '.'))\r\n\r\n dollar_to_rub_course = dollar_course - 1\r\n rub_to_dollar_course = round(1 / dollar_course - 0.0003, 4)\r\n\r\n eur_to_rub_course = euro_course - 1\r\n rub_to_eur_course = round(1 / euro_course - 0.0003, 4)\r\n\r\n dollar_to_eur_course = round(dollar_course / euro_course + 0.0003, 4)\r\n eur_to_dollar_course = round(euro_course / dollar_course - 0.0003, 4)\r\n\r\n currency_courses_data = CurrencyCourses(dollar_to_rub_course, rub_to_dollar_course, eur_to_rub_course, rub_to_eur_course, dollar_to_eur_course, eur_to_dollar_course)\r\n\r\n\r\nloop = asyncio.get_event_loop()\r\n\r\nnest_asyncio.apply()\r\nentering_status = loop.run_until_complete(get_exchange_rates(url))\r\n\r\nscheduler.add_job(get_exchange_rates, 'interval', hours=2, args=(url,))","repo_name":"NumizGame/TradeBot","sub_path":"exchange_rates.py","file_name":"exchange_rates.py","file_ext":"py","file_size_in_byte":1886,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"21931347930","text":"import os\n\npreFabFilePath = \"D:\\\\luaSample\\\\LuaProject\\\\UIClass\\\\Achievement\"\nuiFormFilePath = \"D:\\\\luaSample\\\\LuaProject\\\\UIClass\"\ncode_temp_path = \"D:\\\\luaSample\\\\LuaProject\\\\LuaTeamplate.txt\"\n\n\ndef get_folder_name(path):\n folder_name = os.path.basename(path)\n print(\"Folder name:\", folder_name)\n return folder_name\n\n\ndef replace_ClassName_PanelName(className, prefabName, lua_temp, des_file):\n for line in lua_temp:\n if \"ClassName\" in line:\n temp_line_replace = line.replace(\"ClassName\", className)\n if \"PanelName\" in temp_line_replace:\n des_file.write(temp_line_replace.replace(\n \"PanelName\", prefabName))\n else:\n des_file.write(temp_line_replace)\n\n elif \"PanelName\" in line:\n des_file.write(line.replace(\"PanelName\", prefabName))\n print(\"Replace {}\".format(line.replace(\"PanelName\", prefabName)))\n print(\"prefabName ={}\".format(prefabName))\n else:\n des_file.write(line)\n\n\ndef write_template_to_file(file_path):\n className = os.path.basename(file_path).split(\".lua\")[0]\n prefabName = className.replace(\"UIForm\", \"Panel\")\n with open(code_temp_path, 'r') as lua_temp:\n with open(file_path, 'w') as des_file:\n replace_ClassName_PanelName(\n className, prefabName, lua_temp, des_file)\n print(\"Template written successfully!\")\n\n\ndef create_prefab_folder(path):\n if not os.path.exists(path):\n os.makedirs(path)\n prefix_file = get_folder_name(path)\n file_name = path + \"\\\\{}Panel.prefab\".format(prefix_file)\n open(file_name, \"w\").close()\n print(\"Folder created successfully!\")\n else:\n print(\"Folder already exists!\")\n\n\ndef create_uiform_folder(path):\n if not os.path.exists(path):\n os.makedirs(path)\n prefix_file = get_folder_name(path)\n file_name = path + \"\\\\{}UIPanel.prefab\".format(prefix_file)\n open(file_name, \"w\").close()\n print(\"Folder created successfully!\")\n else:\n print(\"Folder already exists!\")\n\n\ndef check_file_exists(folder_path, file_name):\n file_path = os.path.join(folder_path, file_name)\n if not os.path.exists(file_path):\n open(file_path, \"w\").close()\n write_template_to_file(file_path)\n print(\"File created successfully!\")\n else:\n print(\"File already exists!\")\n\n\n# with open(code_temp_path, 'r', encoding='UTF-8') as lua_temp:\n# with open(\"D:\\\\luaSample\\\\LuaProject\\\\UIClass\\\\AchievementUIForm.lua\", 'w', encoding='UTF-8') as des_file:\n# replace_ClassName_PanelName(\n# \"AchievementUIForm\", \"AchievementPanel\", lua_temp, des_file)\n\nif __name__ == \"__main__\":\n create_prefab_folder(preFabFilePath)\n check_file_exists(uiFormFilePath, \"{}UIForm.lua\".format(\n get_folder_name(preFabFilePath)))\n","repo_name":"aWhalefall/LuaProject","sub_path":"CodeTemp.py","file_name":"CodeTemp.py","file_ext":"py","file_size_in_byte":2881,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"20719548840","text":"#!/usr/bin/env python\n\nfrom __future__ import print_function\n\nimport distutils.dir_util\nimport glob\nimport os.path\n\n\nDOTFILE_DIR = os.path.dirname(os.path.realpath(__file__))\nHOME_DIR = os.environ['HOME']\nUNIVERSAL_CTAGS_CONFIG_DIR = os.path.join(HOME_DIR, '.ctags.d')\nFISH_CONFIG_DIR = os.path.join(HOME_DIR, '.config/fish')\nFISH_FUNC_DIR = os.path.join(FISH_CONFIG_DIR, 'functions')\n\n\ndef yes_no_prompt(prompt):\n result = raw_input('%s [Y/N]\\n' % prompt)\n return result.lower() in ['y', 'yes']\n\n\ndef install_symlink(src, dest_dir):\n distutils.dir_util.mkpath(dest_dir)\n dest_path = os.path.join(dest_dir, os.path.basename(src))\n abs_src_path = os.path.join(DOTFILE_DIR, src)\n if os.path.lexists(dest_path):\n if os.readlink(dest_path) == abs_src_path:\n # Symlink already exists and points to target file.\n return\n if not yes_no_prompt('Replace existing file \\'%s\\'?' % dest_path):\n return\n os.unlink(dest_path)\n os.symlink(abs_src_path, dest_path)\n print('Created symlink %s -> %s' % (abs_src_path, dest_path))\n\n\ndef setup_fish():\n install_symlink('config.fish', FISH_CONFIG_DIR)\n install_symlink('fish_prompt.fish', FISH_FUNC_DIR)\n install_symlink('setup_gpg_agent.fish', FISH_FUNC_DIR)\n\n\ndef setup_ctags():\n install_symlink('config.ctags', UNIVERSAL_CTAGS_CONFIG_DIR)\n\n\ndef symlink_dotfiles():\n dotfiles = glob.glob('.*')\n for dotfile in dotfiles:\n install_symlink(dotfile, HOME_DIR)\n\n\ndef main():\n symlink_dotfiles()\n setup_ctags()\n setup_fish()\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"robertknight/mandrawer","sub_path":"dotfiles/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1602,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"52"} +{"seq_id":"35545427344","text":"import time\nimport coloredlogs\nimport logging\nimport sys\n\nimport schedule\nfrom . import config, lmtp, smtp\n\ncoloredlogs.install(stream=sys.stdout, level=logging.INFO, isatty=True)\nlogging.getLogger(\"mail.log\").setLevel(logging.WARNING)\nlogging.getLogger(\"asyncio\").setLevel(logging.WARNING)\n\n\nif __name__ == '__main__':\n conf = config.load()\n\n smtp_client = smtp.SMTPClient(conf)\n\n handler = lmtp.LMTPHandler(conf, smtp_client)\n controller = lmtp.LMTPController(handler, conf)\n\n @schedule.repeat(schedule.every(15).minutes)\n def update_lists():\n logging.getLogger(\"schedule\").info(\"Executing reload\")\n conf.get_provider().update_lists()\n conf.get_mta().reconfigure()\n\n controller.start()\n\n while True:\n schedule.run_pending()\n time.sleep(1)\n\n controller.stop()\n\n","repo_name":"TheStaticTurtle/porcumail","sub_path":"porcumail/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":824,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"34359390032","text":"\"\"\"\nTESTS is a dict with all of your tests.\nKeys for this will be the categories' names.\nEach test is a dict with\n \"input\" -- input data for a user function\n \"answer\" -- your right answer\n \"explanation\" -- not necessarily a key, it's used for an additional info in animation.\n\"\"\"\n\n\nTESTS = {\n \"Basics\": [\n {\n \"input\": ['a1', 1],\n \"answer\": ['b3', 'c2']\n },\n {\n \"input\": ['h8', 2],\n \"answer\": ['d6', 'd8', 'e5', 'e7', 'f4', 'f7', 'f8', 'g5', 'g6', 'h4', 'h6', 'h8']\n }\n ],\n \"Extra\": [\n {\n \"input\": ['e5', 1],\n \"answer\": ['c4', 'c6', 'd3', 'd7', 'f3', 'f7', 'g4', 'g6']\n }\n ]\n}\n","repo_name":"XanderVi/checkio-mission-chess-knight","sub_path":"verification/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":705,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"72248278245","text":"# ##### BEGIN GPL LICENSE BLOCK #####\n#\n# This program is free software; you can redistribute it and/or\n# modify it under the terms of the GNU General Public License\n# as published by the Free Software Foundation; either version 2\n# of the License, or (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program; if not, write to the Free Software Foundation,\n# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.\n#\n# ##### END GPL LICENSE BLOCK #####\n\nbl_info = {\n \"name\": \"Pipe Joints\",\n \"author\": \"Buerbaum Martin (Pontiac)\",\n \"version\": (0, 10, 7),\n \"blender\": (2, 5, 9),\n \"api\": 39685,\n \"location\": \"View3D > Add > Mesh > Pipe Joints\",\n \"description\": \"Add different types of pipe joints\",\n \"warning\": \"\",\n \"wiki_url\": \"http://wiki.blender.org/index.php/Extensions:2.5/Py/\"\\\n \"Scripts/Add_Mesh/Add_Pipe_Joints\",\n \"tracker_url\": \"https://projects.blender.org/tracker/index.php?\"\\\n \"func=detail&aid=21443\",\n \"category\": \"Add Mesh\"}\n\nimport bpy\nfrom math import *\nfrom bpy.props import *\n\n\n# Create a new mesh (object) from verts/edges/faces.\n# verts/edges/faces ... List of vertices/edges/faces for the\n# new mesh (as used in from_pydata).\n# name ... Name of the new mesh (& object).\ndef create_mesh_object(context, verts, edges, faces, name):\n # Create new mesh\n mesh = bpy.data.meshes.new(name)\n\n # Make a mesh from a list of verts/edges/faces.\n mesh.from_pydata(verts, edges, faces)\n\n # Update mesh geometry after adding stuff.\n mesh.update()\n\n from bpy_extras import object_utils\n return object_utils.object_data_add(context, mesh, operator=None)\n\n# A very simple \"bridge\" tool.\n# Connects two equally long vertex rows with faces.\n# Returns a list of the new faces (list of lists)\n#\n# vertIdx1 ... First vertex list (list of vertex indices).\n# vertIdx2 ... Second vertex list (list of vertex indices).\n# closed ... Creates a loop (first & last are closed).\n# flipped ... Invert the normal of the face(s).\n#\n# Note: You can set vertIdx1 to a single vertex index to create\n# a fan/star of faces.\n# Note: If both vertex idx list are the same length they have\n# to have at least 2 vertices.\ndef createFaces(vertIdx1, vertIdx2, closed=False, flipped=False):\n faces = []\n\n if not vertIdx1 or not vertIdx2:\n return None\n\n if len(vertIdx1) < 2 and len(vertIdx2) < 2:\n return None\n\n fan = False\n if (len(vertIdx1) != len(vertIdx2)):\n if (len(vertIdx1) == 1 and len(vertIdx2) > 1):\n fan = True\n else:\n return None\n\n total = len(vertIdx2)\n\n if closed:\n # Bridge the start with the end.\n if flipped:\n face = [\n vertIdx1[0],\n vertIdx2[0],\n vertIdx2[total - 1]]\n if not fan:\n face.append(vertIdx1[total - 1])\n faces.append(face)\n\n else:\n face = [vertIdx2[0], vertIdx1[0]]\n if not fan:\n face.append(vertIdx1[total - 1])\n face.append(vertIdx2[total - 1])\n faces.append(face)\n\n # Bridge the rest of the faces.\n for num in range(total - 1):\n if flipped:\n if fan:\n face = [vertIdx2[num], vertIdx1[0], vertIdx2[num + 1]]\n else:\n face = [vertIdx2[num], vertIdx1[num],\n vertIdx1[num + 1], vertIdx2[num + 1]]\n faces.append(face)\n else:\n if fan:\n face = [vertIdx1[0], vertIdx2[num], vertIdx2[num + 1]]\n else:\n face = [vertIdx1[num], vertIdx2[num],\n vertIdx2[num + 1], vertIdx1[num + 1]]\n faces.append(face)\n\n return faces\n\n\nclass AddElbowJoint(bpy.types.Operator):\n # Create the vertices and polygons for a simple elbow (bent pipe).\n '''Add an Elbow pipe mesh'''\n bl_idname = \"mesh.primitive_elbow_joint_add\"\n bl_label = \"Add Pipe Elbow\"\n bl_options = {'REGISTER', 'UNDO'}\n\n radius = FloatProperty(name=\"Radius\",\n description=\"The radius of the pipe\",\n default=1.0,\n min=0.01,\n max=100.0,\n unit=\"LENGTH\")\n div = IntProperty(name=\"Divisions\",\n description=\"Number of vertices (divisions)\",\n default=32, min=3, max=256)\n\n angle = FloatProperty(name=\"Angle\",\n description=\"The angle of the branching pipe (i.e. the 'arm' - \" \\\n \"Measured from the center line of the main pipe\",\n default=radians(45.0),\n min=radians(-179.9),\n max=radians(179.9),\n unit=\"ROTATION\")\n\n startLength = FloatProperty(name=\"Length Start\",\n description=\"Length of the beginning of the pipe\",\n default=3.0,\n min=0.01,\n max=100.0,\n unit=\"LENGTH\")\n endLength = FloatProperty(name=\"End Length\",\n description=\"Length of the end of the pipe\",\n default=3.0,\n min=0.01,\n max=100.0,\n unit=\"LENGTH\")\n\n def execute(self, context):\n\n radius = self.radius\n div = self.div\n\n angle = self.angle\n\n startLength = self.startLength\n endLength = self.endLength\n\n verts = []\n faces = []\n\n loop1 = [] # The starting circle\n loop2 = [] # The elbow circle\n loop3 = [] # The end circle\n\n # Create start circle\n for vertIdx in range(div):\n curVertAngle = vertIdx * (2.0 * pi / div)\n locX = sin(curVertAngle)\n locY = cos(curVertAngle)\n locZ = -startLength\n loop1.append(len(verts))\n verts.append([locX * radius, locY * radius, locZ])\n\n # Create deformed joint circle\n for vertIdx in range(div):\n curVertAngle = vertIdx * (2.0 * pi / div)\n locX = sin(curVertAngle)\n locY = cos(curVertAngle)\n locZ = locX * tan(angle / 2.0)\n loop2.append(len(verts))\n verts.append([locX * radius, locY * radius, locZ * radius])\n\n # Create end circle\n baseEndLocX = -endLength * sin(angle)\n baseEndLocZ = endLength * cos(angle)\n for vertIdx in range(div):\n curVertAngle = vertIdx * (2.0 * pi / div)\n # Create circle\n locX = sin(curVertAngle) * radius\n locY = cos(curVertAngle) * radius\n locZ = 0.0\n\n # Rotate circle\n locZ = locX * cos(pi / 2.0 - angle)\n locX = locX * sin(pi / 2.0 - angle)\n\n loop3.append(len(verts))\n # Translate and add circle vertices to the list.\n verts.append([baseEndLocX + locX, locY, baseEndLocZ + locZ])\n\n # Create faces\n faces.extend(createFaces(loop1, loop2, closed=True))\n faces.extend(createFaces(loop2, loop3, closed=True))\n\n base = create_mesh_object(context, verts, [], faces, \"Elbow Joint\")\n\n return {'FINISHED'}\n\n\nclass AddTeeJoint(bpy.types.Operator):\n # Create the vertices and polygons for a simple tee (T) joint.\n # The base arm of the T can be positioned in an angle if needed though.\n '''Add a Tee-Joint mesh'''\n bl_idname = \"mesh.primitive_tee_joint_add\"\n bl_label = \"Add Pipe Tee-Joint\"\n bl_options = {'REGISTER', 'UNDO'}\n\n radius = FloatProperty(name=\"Radius\",\n description=\"The radius of the pipe\",\n default=1.0,\n min=0.01,\n max=100.0,\n unit=\"LENGTH\")\n div = IntProperty(name=\"Divisions\",\n description=\"Number of vertices (divisions)\",\n default=32,\n min=4,\n max=256)\n\n angle = FloatProperty(name=\"Angle\",\n description=\"The angle of the branching pipe (i.e. the 'arm' - \" \\\n \"Measured from the center line of the main pipe\",\n default=radians(90.0),\n min=radians(0.1),\n max=radians(179.9),\n unit=\"ROTATION\")\n\n startLength = FloatProperty(name=\"Length Start\",\n description=\"Length of the beginning of the\" \\\n \" main pipe (the straight one)\",\n default=3.0,\n min=0.01,\n max=100.0,\n unit=\"LENGTH\")\n endLength = FloatProperty(name=\"End Length\",\n description=\"Length of the end of the\" \\\n \" main pipe (the straight one)\",\n default=3.0,\n min=0.01,\n max=100.0,\n unit=\"LENGTH\")\n branchLength = FloatProperty(name=\"Arm Length\",\n description=\"Length of the arm pipe (the bent one)\",\n default=3.0,\n min=0.01,\n max=100.0,\n unit=\"LENGTH\")\n\n def execute(self, context):\n\n radius = self.radius\n div = self.div\n\n angle = self.angle\n\n startLength = self.startLength\n endLength = self.endLength\n branchLength = self.branchLength\n\n if (div % 2):\n # Odd vertice number not supported (yet).\n return {'CANCELLED'}\n\n verts = []\n faces = []\n\n # List of vert indices of each cross section\n loopMainStart = [] # Vert indices for the\n # beginning of the main pipe.\n loopJoint1 = [] # Vert indices for joint that is used\n # to connect the joint & loopMainStart.\n loopJoint2 = [] # Vert indices for joint that is used\n # to connect the joint & loopArm.\n loopJoint3 = [] # Vert index for joint that is used\n # to connect the joint & loopMainEnd.\n loopArm = [] # Vert indices for the end of the arm.\n loopMainEnd = [] # Vert indices for the\n # end of the main pipe.\n\n # Create start circle (main pipe)\n for vertIdx in range(div):\n curVertAngle = vertIdx * (2.0 * pi / div)\n locX = sin(curVertAngle)\n locY = cos(curVertAngle)\n locZ = -startLength\n loopMainStart.append(len(verts))\n verts.append([locX * radius, locY * radius, locZ])\n\n # Create deformed joint circle\n vertTemp1 = None\n vertTemp2 = None\n for vertIdx in range(div):\n curVertAngle = vertIdx * (2.0 * pi / div)\n locX = sin(curVertAngle)\n locY = cos(curVertAngle)\n\n if vertIdx == 0:\n vertTemp1 = len(verts)\n if vertIdx == div / 2:\n # @todo: This will possibly break if we\n # ever support odd divisions.\n vertTemp2 = len(verts)\n\n loopJoint1.append(len(verts))\n if (vertIdx < div / 2):\n # Straight side of main pipe.\n locZ = 0\n loopJoint3.append(len(verts))\n else:\n # Branching side\n locZ = locX * tan(angle / 2.0)\n loopJoint2.append(len(verts))\n\n verts.append([locX * radius, locY * radius, locZ * radius])\n\n # Create 2. deformed joint (half-)circle\n loopTemp = []\n for vertIdx in range(div):\n if (vertIdx > div / 2):\n curVertAngle = vertIdx * (2.0 * pi / div)\n locX = sin(curVertAngle)\n locY = -cos(curVertAngle)\n locZ = -(radius * locX * tan((pi - angle) / 2.0))\n loopTemp.append(len(verts))\n verts.append([locX * radius, locY * radius, locZ])\n\n loopTemp2 = loopTemp[:]\n\n # Finalise 2. loop\n loopTemp.reverse()\n loopTemp.append(vertTemp1)\n loopJoint2.reverse()\n loopJoint2.extend(loopTemp)\n loopJoint2.reverse()\n\n # Finalise 3. loop\n loopTemp2.append(vertTemp2)\n loopTemp2.reverse()\n loopJoint3.extend(loopTemp2)\n\n # Create end circle (branching pipe)\n baseEndLocX = -branchLength * sin(angle)\n baseEndLocZ = branchLength * cos(angle)\n for vertIdx in range(div):\n curVertAngle = vertIdx * (2.0 * pi / div)\n # Create circle\n locX = sin(curVertAngle) * radius\n locY = cos(curVertAngle) * radius\n locZ = 0.0\n\n # Rotate circle\n locZ = locX * cos(pi / 2.0 - angle)\n locX = locX * sin(pi / 2.0 - angle)\n\n loopArm.append(len(verts))\n\n # Add translated circle.\n verts.append([baseEndLocX + locX, locY, baseEndLocZ + locZ])\n\n # Create end circle (main pipe)\n for vertIdx in range(div):\n curVertAngle = vertIdx * (2.0 * pi / div)\n locX = sin(curVertAngle)\n locY = cos(curVertAngle)\n locZ = endLength\n loopMainEnd.append(len(verts))\n verts.append([locX * radius, locY * radius, locZ])\n\n # Create faces\n faces.extend(createFaces(loopMainStart, loopJoint1, closed=True))\n faces.extend(createFaces(loopJoint2, loopArm, closed=True))\n faces.extend(createFaces(loopJoint3, loopMainEnd, closed=True))\n\n base = create_mesh_object(context, verts, [], faces, \"Tee Joint\")\n\n return {'FINISHED'}\n\n\nclass AddWyeJoint(bpy.types.Operator):\n '''Add a Wye-Joint mesh'''\n bl_idname = \"mesh.primitive_wye_joint_add\"\n bl_label = \"Add Pipe Wye-Joint\"\n bl_options = {'REGISTER', 'UNDO'}\n\n radius = FloatProperty(name=\"Radius\",\n description=\"The radius of the pipe\",\n default=1.0,\n min=0.01,\n max=100.0,\n unit=\"LENGTH\")\n div = IntProperty(name=\"Divisions\",\n description=\"Number of vertices (divisions)\",\n default=32,\n min=4,\n max=256)\n\n angle1 = FloatProperty(name=\"Angle 1\",\n description=\"The angle of the 1. branching pipe \" \\\n \"(measured from the center line of the main pipe)\",\n default=radians(45.0),\n min=radians(-179.9),\n max=radians(179.9),\n unit=\"ROTATION\")\n angle2 = FloatProperty(name=\"Angle 2\",\n description=\"The angle of the 2. branching pipe \" \\\n \"(measured from the center line of the main pipe) \",\n default=radians(45.0),\n min=radians(-179.9),\n max=radians(179.9),\n unit=\"ROTATION\")\n\n startLength = FloatProperty(name=\"Length Start\",\n description=\"Length of the beginning of the\" \\\n \" main pipe (the straight one)\",\n default=3.0,\n min=0.01,\n max=100.0,\n unit=\"LENGTH\")\n branch1Length = FloatProperty(name=\"Length Arm 1\",\n description=\"Length of the 1. arm\",\n default=3.0,\n min=0.01,\n max=100.0,\n unit=\"LENGTH\")\n branch2Length = FloatProperty(name=\"Length Arm 2\",\n description=\"Length of the 2. arm\",\n default=3.0,\n min=0.01,\n max=100.0,\n unit=\"LENGTH\")\n\n def execute(self, context):\n\n radius = self.radius\n div = self.div\n\n angle1 = self.angle1\n angle2 = self.angle2\n\n startLength = self.startLength\n branch1Length = self.branch1Length\n branch2Length = self.branch2Length\n\n if (div % 2):\n # Odd vertice number not supported (yet).\n return {'CANCELLED'}\n\n verts = []\n faces = []\n\n # List of vert indices of each cross section\n loopMainStart = [] # Vert indices for\n # the beginning of the main pipe.\n loopJoint1 = [] # Vert index for joint that is used\n # to connect the joint & loopMainStart.\n loopJoint2 = [] # Vert index for joint that\n # is used to connect the joint & loopArm1.\n loopJoint3 = [] # Vert index for joint that is\n # used to connect the joint & loopArm2.\n loopArm1 = [] # Vert idxs for end of the 1. arm.\n loopArm2 = [] # Vert idxs for end of the 2. arm.\n\n # Create start circle\n for vertIdx in range(div):\n curVertAngle = vertIdx * (2.0 * pi / div)\n locX = sin(curVertAngle)\n locY = cos(curVertAngle)\n locZ = -startLength\n loopMainStart.append(len(verts))\n verts.append([locX * radius, locY * radius, locZ])\n\n # Create deformed joint circle\n vertTemp1 = None\n vertTemp2 = None\n for vertIdx in range(div):\n curVertAngle = vertIdx * (2.0 * pi / div)\n locX = sin(curVertAngle)\n locY = cos(curVertAngle)\n\n if vertIdx == 0:\n vertTemp2 = len(verts)\n if vertIdx == div / 2:\n # @todo: This will possibly break if we\n # ever support odd divisions.\n vertTemp1 = len(verts)\n\n loopJoint1.append(len(verts))\n if (vertIdx > div / 2):\n locZ = locX * tan(angle1 / 2.0)\n loopJoint2.append(len(verts))\n else:\n locZ = locX * tan(-angle2 / 2.0)\n loopJoint3.append(len(verts))\n\n verts.append([locX * radius, locY * radius, locZ * radius])\n\n # Create 2. deformed joint (half-)circle\n loopTemp = []\n angleJoint = (angle2 - angle1) / 2.0\n for vertIdx in range(div):\n if (vertIdx > div / 2):\n curVertAngle = vertIdx * (2.0 * pi / div)\n\n locX = (-sin(curVertAngle) * sin(angleJoint)\n / sin(angle2 - angleJoint))\n locY = -cos(curVertAngle)\n locZ = (-(sin(curVertAngle) * cos(angleJoint)\n / sin(angle2 - angleJoint)))\n\n loopTemp.append(len(verts))\n verts.append([locX * radius, locY * radius, locZ * radius])\n\n loopTemp2 = loopTemp[:]\n\n # Finalise 2. loop\n loopTemp.append(vertTemp1)\n loopTemp.reverse()\n loopTemp.append(vertTemp2)\n loopJoint2.reverse()\n loopJoint2.extend(loopTemp)\n loopJoint2.reverse()\n\n # Finalise 3. loop\n loopTemp2.reverse()\n loopJoint3.extend(loopTemp2)\n\n # Create end circle (1. branching pipe)\n baseEndLocX = -branch1Length * sin(angle1)\n baseEndLocZ = branch1Length * cos(angle1)\n for vertIdx in range(div):\n curVertAngle = vertIdx * (2.0 * pi / div)\n # Create circle\n locX = sin(curVertAngle) * radius\n locY = cos(curVertAngle) * radius\n locZ = 0.0\n\n # Rotate circle\n locZ = locX * cos(pi / 2.0 - angle1)\n locX = locX * sin(pi / 2.0 - angle1)\n\n loopArm1.append(len(verts))\n # Add translated circle.\n verts.append([baseEndLocX + locX, locY, baseEndLocZ + locZ])\n\n # Create end circle (2. branching pipe)\n baseEndLocX = branch2Length * sin(angle2)\n baseEndLocZ = branch2Length * cos(angle2)\n for vertIdx in range(div):\n curVertAngle = vertIdx * (2.0 * pi / div)\n # Create circle\n locX = sin(curVertAngle) * radius\n locY = cos(curVertAngle) * radius\n locZ = 0.0\n\n # Rotate circle\n locZ = locX * cos(pi / 2.0 + angle2)\n locX = locX * sin(pi / 2.0 + angle2)\n\n loopArm2.append(len(verts))\n # Add translated circle\n verts.append([baseEndLocX + locX, locY, baseEndLocZ + locZ])\n\n # Create faces\n faces.extend(createFaces(loopMainStart, loopJoint1, closed=True))\n faces.extend(createFaces(loopJoint2, loopArm1, closed=True))\n faces.extend(createFaces(loopJoint3, loopArm2, closed=True))\n\n base = create_mesh_object(context, verts, [], faces, \"Wye Joint\")\n\n return {'FINISHED'}\n\n\nclass AddCrossJoint(bpy.types.Operator):\n '''Add a Cross-Joint mesh'''\n # Create the vertices and polygons for a coss (+ or X) pipe joint.\n bl_idname = \"mesh.primitive_cross_joint_add\"\n bl_label = \"Add Pipe Cross-Joint\"\n bl_options = {'REGISTER', 'UNDO'}\n\n radius = FloatProperty(name=\"Radius\",\n description=\"The radius of the pipe\",\n default=1.0,\n min=0.01,\n max=100.0,\n unit=\"LENGTH\")\n div = IntProperty(name=\"Divisions\",\n description=\"Number of vertices (divisions)\",\n default=32,\n min=4,\n max=256)\n\n angle1 = FloatProperty(name=\"Angle 1\",\n description=\"The angle of the 1. arm (from the main axis)\",\n default=radians(90.0),\n min=radians(-179.9),\n max=radians(179.9),\n unit=\"ROTATION\")\n angle2 = FloatProperty(name=\"Angle 2\",\n description=\"The angle of the 2. arm (from the main axis)\",\n default=radians(90.0),\n min=radians(-179.9),\n max=radians(179.9),\n unit=\"ROTATION\")\n angle3 = FloatProperty(name=\"Angle 3 (center)\",\n description=\"The angle of the center arm (from the main axis)\",\n default=radians(0.0),\n min=radians(-179.9),\n max=radians(179.9),\n unit=\"ROTATION\")\n\n startLength = FloatProperty(name=\"Length Start\",\n description=\"Length of the beginning of the \" \\\n \"main pipe (the straight one)\",\n default=3.0,\n min=0.01,\n max=100.0,\n unit=\"LENGTH\")\n branch1Length = FloatProperty(name=\"Length Arm 1\",\n description=\"Length of the 1. arm\",\n default=3.0,\n min=0.01,\n max=100.0,\n unit=\"LENGTH\")\n branch2Length = FloatProperty(name=\"Length Arm 2\",\n description=\"Length of the 2. arm\",\n default=3.0,\n min=0.01,\n max=100.0,\n unit=\"LENGTH\")\n branch3Length = FloatProperty(name=\"Length Arm 3 (center)\",\n description=\"Length of the center arm\",\n default=3.0,\n min=0.01,\n max=100.0,\n unit=\"LENGTH\")\n\n def execute(self, context):\n\n radius = self.radius\n div = self.div\n\n angle1 = self.angle1\n angle2 = self.angle2\n angle3 = self.angle3\n\n startLength = self.startLength\n branch1Length = self.branch1Length\n branch2Length = self.branch2Length\n branch3Length = self.branch3Length\n if (div % 2):\n # Odd vertice number not supported (yet).\n return {'CANCELLED'}\n\n verts = []\n faces = []\n\n # List of vert indices of each cross section\n loopMainStart = [] # Vert indices for the\n # beginning of the main pipe.\n loopJoint1 = [] # Vert index for joint that is used\n # to connect the joint & loopMainStart.\n loopJoint2 = [] # Vert index for joint that is used\n # to connect the joint & loopArm1.\n loopJoint3 = [] # Vert index for joint that is used\n # to connect the joint & loopArm2.\n loopJoint4 = [] # Vert index for joint that is used\n # to connect the joint & loopArm3.\n loopArm1 = [] # Vert idxs for the end of the 1. arm.\n loopArm2 = [] # Vert idxs for the end of the 2. arm.\n loopArm3 = [] # Vert idxs for the center arm end.\n\n # Create start circle\n for vertIdx in range(div):\n curVertAngle = vertIdx * (2.0 * pi / div)\n locX = sin(curVertAngle)\n locY = cos(curVertAngle)\n locZ = -startLength\n loopMainStart.append(len(verts))\n verts.append([locX * radius, locY * radius, locZ])\n\n # Create 1. deformed joint circle\n vertTemp1 = None\n vertTemp2 = None\n for vertIdx in range(div):\n curVertAngle = vertIdx * (2.0 * pi / div)\n locX = sin(curVertAngle)\n locY = cos(curVertAngle)\n\n if vertIdx == 0:\n vertTemp2 = len(verts)\n if vertIdx == div / 2:\n # @todo: This will possibly break if we\n # ever support odd divisions.\n vertTemp1 = len(verts)\n\n loopJoint1.append(len(verts))\n if (vertIdx > div / 2):\n locZ = locX * tan(angle1 / 2.0)\n loopJoint2.append(len(verts))\n else:\n locZ = locX * tan(-angle2 / 2.0)\n loopJoint3.append(len(verts))\n\n verts.append([locX * radius, locY * radius, locZ * radius])\n\n # loopTemp2 = loopJoint2[:] # UNUSED\n\n # Create 2. deformed joint circle\n loopTempA = []\n loopTempB = []\n angleJoint1 = (angle1 - angle3) / 2.0\n angleJoint2 = (angle2 + angle3) / 2.0\n for vertIdx in range(div):\n curVertAngle = vertIdx * (2.0 * pi / div)\n\n # Skip pole vertices\n # @todo: This will possibly break if\n # we ever support odd divisions.\n if not (vertIdx == 0) and not (vertIdx == div / 2):\n\n if (vertIdx > div / 2):\n angleJoint = angleJoint1\n angle = angle1\n Z = -1.0\n loopTempA.append(len(verts))\n\n else:\n angleJoint = angleJoint2\n angle = angle2\n Z = 1.0\n loopTempB.append(len(verts))\n\n locX = (sin(curVertAngle) * sin(angleJoint)\n / sin(angle - angleJoint))\n locY = -cos(curVertAngle)\n locZ = (Z * (sin(curVertAngle) * cos(angleJoint)\n / sin(angle - angleJoint)))\n\n verts.append([locX * radius, locY * radius, locZ * radius])\n\n loopTempA2 = loopTempA[:]\n loopTempB2 = loopTempB[:]\n loopTempB3 = loopTempB[:]\n\n # Finalise 2. loop\n loopTempA.append(vertTemp1)\n loopTempA.reverse()\n loopTempA.append(vertTemp2)\n loopJoint2.reverse()\n loopJoint2.extend(loopTempA)\n loopJoint2.reverse()\n\n # Finalise 3. loop\n loopJoint3.extend(loopTempB3)\n\n # Finalise 4. loop\n loopTempA2.append(vertTemp1)\n loopTempA2.reverse()\n loopTempB2.append(vertTemp2)\n loopJoint4.extend(reversed(loopTempB2))\n loopJoint4.extend(loopTempA2)\n\n # Create end circle (1. branching pipe)\n baseEndLocX = -branch1Length * sin(angle1)\n baseEndLocZ = branch1Length * cos(angle1)\n for vertIdx in range(div):\n curVertAngle = vertIdx * (2.0 * pi / div)\n # Create circle\n locX = sin(curVertAngle) * radius\n locY = cos(curVertAngle) * radius\n locZ = 0.0\n\n # Rotate circle\n locZ = locX * cos(pi / 2.0 - angle1)\n locX = locX * sin(pi / 2.0 - angle1)\n\n loopArm1.append(len(verts))\n # Add translated circle.\n verts.append([baseEndLocX + locX, locY, baseEndLocZ + locZ])\n\n # Create end circle (2. branching pipe)\n baseEndLocX = branch2Length * sin(angle2)\n baseEndLocZ = branch2Length * cos(angle2)\n for vertIdx in range(div):\n curVertAngle = vertIdx * (2.0 * pi / div)\n # Create circle\n locX = sin(curVertAngle) * radius\n locY = cos(curVertAngle) * radius\n locZ = 0.0\n\n # Rotate circle\n locZ = locX * cos(pi / 2.0 + angle2)\n locX = locX * sin(pi / 2.0 + angle2)\n\n loopArm2.append(len(verts))\n # Add translated circle\n verts.append([baseEndLocX + locX, locY, baseEndLocZ + locZ])\n\n # Create end circle (center pipe)\n baseEndLocX = branch3Length * sin(angle3)\n baseEndLocZ = branch3Length * cos(angle3)\n for vertIdx in range(div):\n curVertAngle = vertIdx * (2.0 * pi / div)\n # Create circle\n locX = sin(curVertAngle) * radius\n locY = cos(curVertAngle) * radius\n locZ = 0.0\n\n # Rotate circle\n locZ = locX * cos(pi / 2.0 + angle3)\n locX = locX * sin(pi / 2.0 + angle3)\n\n loopArm3.append(len(verts))\n # Add translated circle\n verts.append([baseEndLocX + locX, locY, baseEndLocZ + locZ])\n\n # Create faces\n faces.extend(createFaces(loopMainStart, loopJoint1, closed=True))\n faces.extend(createFaces(loopJoint2, loopArm1, closed=True))\n faces.extend(createFaces(loopJoint3, loopArm2, closed=True))\n faces.extend(createFaces(loopJoint4, loopArm3, closed=True))\n\n base = create_mesh_object(context, verts, [], faces, \"Cross Joint\")\n\n return {'FINISHED'}\n\n\nclass AddNJoint(bpy.types.Operator):\n '''Add a N-Joint mesh'''\n # Create the vertices and polygons for a regular n-joint.\n bl_idname = \"mesh.primitive_n_joint_add\"\n bl_label = \"Add Pipe N-Joint\"\n bl_options = {'REGISTER', 'UNDO'}\n\n radius = FloatProperty(name=\"Radius\",\n description=\"The radius of the pipe\",\n default=1.0,\n min=0.01,\n max=100.0,\n unit=\"LENGTH\")\n div = IntProperty(name=\"Divisions\",\n description=\"Number of vertices (divisions)\",\n default=32,\n min=4,\n max=256)\n number = IntProperty(name=\"Arms/Joints\",\n description=\"Number of joints/arms\",\n default=5,\n min=2,\n max=99999)\n length = FloatProperty(name=\"Length\",\n description=\"Length of each joint/arm\",\n default=3.0,\n min=0.01,\n max=100.0,\n unit=\"LENGTH\")\n\n def execute(self, context):\n radius = self.radius\n div = self.div\n number = self.number\n length = self.length\n\n if (div % 2):\n # Odd vertice number not supported (yet).\n return {'CANCELLED'}\n\n if (number < 2):\n return {'CANCELLED'}\n\n verts = []\n faces = []\n\n loopsEndCircles = []\n loopsJointsTemp = []\n loopsJoints = []\n\n vertTemp1 = None\n vertTemp2 = None\n\n angleDiv = (2.0 * pi / number)\n\n # Create vertices for the end circles.\n for num in range(number):\n circle = []\n # Create start circle\n angle = num * angleDiv\n\n baseEndLocX = length * sin(angle)\n baseEndLocZ = length * cos(angle)\n for vertIdx in range(div):\n curVertAngle = vertIdx * (2.0 * pi / div)\n # Create circle\n locX = sin(curVertAngle) * radius\n locY = cos(curVertAngle) * radius\n locZ = 0.0\n\n # Rotate circle\n locZ = locX * cos(pi / 2.0 + angle)\n locX = locX * sin(pi / 2.0 + angle)\n\n circle.append(len(verts))\n # Add translated circle\n verts.append([baseEndLocX + locX, locY, baseEndLocZ + locZ])\n\n loopsEndCircles.append(circle)\n\n # Create vertices for the joint circles.\n loopJoint = []\n for vertIdx in range(div):\n curVertAngle = vertIdx * (2.0 * pi / div)\n locX = sin(curVertAngle)\n locY = cos(curVertAngle)\n\n skipVert = False\n # Store pole vertices\n if vertIdx == 0:\n if (num == 0):\n vertTemp2 = len(verts)\n else:\n skipVert = True\n elif vertIdx == div / 2:\n # @todo: This will possibly break if we\n # ever support odd divisions.\n if (num == 0):\n vertTemp1 = len(verts)\n else:\n skipVert = True\n\n if not skipVert:\n if (vertIdx > div / 2):\n locZ = -locX * tan((pi - angleDiv) / 2.0)\n loopJoint.append(len(verts))\n\n # Rotate the vert\n cosAng = cos(-angle)\n sinAng = sin(-angle)\n LocXnew = locX * cosAng - locZ * sinAng\n LocZnew = locZ * cosAng + locX * sinAng\n locZ = LocZnew\n locX = LocXnew\n\n verts.append([\n locX * radius,\n locY * radius,\n locZ * radius])\n else:\n # These two vertices will only be\n # added the very first time.\n if vertIdx == 0 or vertIdx == div / 2:\n verts.append([locX * radius, locY * radius, locZ])\n\n loopsJointsTemp.append(loopJoint)\n\n # Create complete loops (loopsJoints) out of the\n # double number of half loops in loopsJointsTemp.\n for halfLoopIdx in range(len(loopsJointsTemp)):\n if (halfLoopIdx == len(loopsJointsTemp) - 1):\n idx1 = halfLoopIdx\n idx2 = 0\n else:\n idx1 = halfLoopIdx\n idx2 = halfLoopIdx + 1\n\n loopJoint = []\n loopJoint.append(vertTemp2)\n loopJoint.extend(reversed(loopsJointsTemp[idx2]))\n loopJoint.append(vertTemp1)\n loopJoint.extend(loopsJointsTemp[idx1])\n\n loopsJoints.append(loopJoint)\n\n # Create faces from the two\n # loop arrays (loopsJoints -> loopsEndCircles).\n for loopIdx in range(len(loopsEndCircles)):\n faces.extend(\n createFaces(loopsJoints[loopIdx],\n loopsEndCircles[loopIdx], closed=True))\n\n base = create_mesh_object(context, verts, [], faces, \"N Joint\")\n\n return {'FINISHED'}\n\n\nclass INFO_MT_mesh_pipe_joints_add(bpy.types.Menu):\n # Define the \"Pipe Joints\" menu\n bl_idname = \"INFO_MT_mesh_pipe_joints_add\"\n bl_label = \"Pipe Joints\"\n\n def draw(self, context):\n layout = self.layout\n layout.operator_context = 'INVOKE_REGION_WIN'\n layout.operator(\"mesh.primitive_elbow_joint_add\",\n text=\"Pipe Elbow\")\n layout.operator(\"mesh.primitive_tee_joint_add\",\n text=\"Pipe T-Joint\")\n layout.operator(\"mesh.primitive_wye_joint_add\",\n text=\"Pipe Y-Joint\")\n layout.operator(\"mesh.primitive_cross_joint_add\",\n text=\"Pipe Cross-Joint\")\n layout.operator(\"mesh.primitive_n_joint_add\",\n text=\"Pipe N-Joint\")\n\n################################\n\n\n# Define \"Pipe Joints\" menu\ndef menu_func(self, context):\n self.layout.menu(\"INFO_MT_mesh_pipe_joints_add\", icon=\"PLUGIN\")\n\n\ndef register():\n bpy.utils.register_module(__name__)\n\n # Add \"Pipe Joints\" menu to the \"Add Mesh\" menu\n bpy.types.INFO_MT_mesh_add.append(menu_func)\n\n\ndef unregister():\n bpy.utils.unregister_module(__name__)\n\n # Remove \"Pipe Joints\" menu from the \"Add Mesh\" menu.\n bpy.types.INFO_MT_mesh_add.remove(menu_func)\n\n\nif __name__ == \"__main__\":\n register()\n","repo_name":"damiles/blendocv","sub_path":"release/scripts/addons/add_mesh_pipe_joint.py","file_name":"add_mesh_pipe_joint.py","file_ext":"py","file_size_in_byte":35356,"program_lang":"python","lang":"en","doc_type":"code","stars":42,"dataset":"github-code","pt":"52"} +{"seq_id":"39489039974","text":"# 给定一个非负整数数组和一个整数m 将这个数组分成m个非空的连续子数组 设计一个算法让这m个数字各自和的最大值最小\n# 这个需要对思路进行一个转化 二分法分的不是数组 而是划分最大值的区间 和吃香蕉的问题有一点类似\n# 和最大值所在的空间就是[max(nums),sum(nums)] 如果取mid无法达成，那意味着结果值在右侧 否则在左侧\nfrom typing import List\nclass Solution:\n def splitArray(self,nums:List[int], m:int) -> int:\n def help(ans:int)->bool:\n cnt,cur=1,0\n for num in nums:\n if (cur + num) > ans:\n cur = num\n cnt += 1\n else:\n cur += num\n return cnt <= m\n l, h = max(nums),sum(nums)\n while l<=h:\n mid = l + (h-l)//2\n if l==h:\n return l\n # 如果中间值可以分割 意味着答案在左侧\n elif help(mid):\n h = mid\n else:\n l = mid+1\n return -1","repo_name":"JudgesL/python-learning","sub_path":"算法通关之路/第6章 二分法/分割数组的最大值.py","file_name":"分割数组的最大值.py","file_ext":"py","file_size_in_byte":1101,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"31161488529","text":"from odoo import api, models, fields, exceptions, tools, _\nimport datetime\n\n\nclass SupplierRating(models.Model):\n _name = 'supplier.rating'\n\n @api.depends('rating_lines','rating_lines.rating_id')\n def _compute_average_rating(self):\n for rec in self:\n if rec.rating_lines:\n len_rating = 0\n value_ratings = 0.0\n rec_id = 0\n for rating in rec.rating_lines:\n value_ratings += rating.value.value\n len_rating += 1\n rec_id = rating.rating_id.id\n rec.average_rating = value_ratings / len_rating\n average_rating_next = rec.average_rating / len_rating\n if rec_id:\n\n self._cr.execute('UPDATE supplier_rating SET average_rating_next=%s WHERE id = %s',\n (average_rating_next, rec_id,))\n\n else:\n rec.average_rating = 0\n rec.average_rating_next = 0\n\n @api.depends('rating_lines', 'rating_lines.value')\n def _compute_total_score(self):\n for rec in self:\n total = 0.0\n for rating in rec.rating_lines:\n total += rating.score\n rec.total_score = total\n\n name = fields.Char(string=\"Supplier Rating\", index=True, default='New', copy=False)\n date_rating = fields.Datetime('Rating Date', default=fields.Date.today)\n partner_id = fields.Many2one('res.partner', 'Vendor')\n # rating_lines = fields.One2many('ratings.lines', 'rating_id', 'Ratings', default=_get_rating_lines)\n rating_lines = fields.One2many('ratings.lines', 'rating_id', 'Ratings', copy=True)\n state = fields.Selection([\n ('draft', 'Draft'), ('validate', 'Validate')], default='draft')\n average_rating = fields.Float('Average Rating', compute='_compute_average_rating')\n average_rating_next = fields.Float('Average Rating')\n total_score = fields.Float('Total Score', compute='_compute_total_score')\n\n @api.onchange('partner_id')\n def onchange_partner(self):\n for rec in self:\n list_lines = []\n for line in rec.partner_id.ratings_weightage_ids:\n list_lines.append((0, 0, {'name': line.rating_id.name,\n 'description': line.rating_id.description,\n 'config_id': line.rating_id.id,\n 'compulsory': line.rating_id.compulsory,\n 'weightage': line.weightage,\n }))\n rec.rating_lines = list_lines\n\n\n @api.model\n def create(self, vals):\n if vals.get('name', _('New')) == _('New'):\n seq_supp_rate = self.env['ir.sequence'].next_by_code('supplier.rating') or _('New')\n vals['name'] = seq_supp_rate[:2] + str(datetime.datetime.now().year)[-2:] + seq_supp_rate[-3:]\n result = super(SupplierRating, self).create(vals)\n self._cr.commit()\n list_line = []\n for line in result.rating_lines:\n list_line.append(line.config_id.id)\n list_config = self.env['rating.configuration'].search([]).ids\n diff_list = list(set(list_line) - set(list_config))\n if not diff_list:\n diff_list = list(set(list_config) - set(list_line))\n if diff_list:\n rec_config = self.env['rating.configuration'].search([('id','in', diff_list),\n ('compulsory','=',True)])\n if rec_config:\n for rec in rec_config:\n result.write({'rating_lines': [(0,0,{'name': rec.name,\n 'description': rec.description,\n 'config_id': rec.id,\n 'compulsory': rec.compulsory})]})\n self._cr.commit()\n raise exceptions.Warning(_(rec.name + ' is Compulsory!'))\n return result\n\n @api.multi\n def validate_rating(self):\n for rec in self:\n for line in rec.rating_lines:\n if not line.value:\n raise exceptions.Warning(_('Please enter Ratings Value for %s')%line.name)\n else:\n rec.state = 'validate'\n\n\n\nclass RatingLines(models.Model):\n _name = 'ratings.lines'\n _rec_name = 'value'\n\n @api.onchange('value')\n def onchange_value(self):\n if self.value:\n self.value_value = self.value.value\n\n rating_id = fields.Many2one('supplier.rating', 'Rating')\n config_id = fields.Many2one('rating.configuration', 'Rating Name')\n name = fields.Char('Name')\n value = fields.Many2one('rating.value', 'Rating')\n value_value = fields.Float('Rating Value')\n rating_value = fields.Float('Rating Value', related='value.value')\n description = fields.Text('Description')\n compulsory = fields.Boolean('Compulsory')\n weightage = fields.Integer('Weightage')\n score = fields.Float('Score',compute='_get_score')\n score_one = fields.Float('Score One')\n\n @api.depends('value','config_id','score','score_one')\n def _get_score(self):\n for rec in self:\n if rec.config_id.id:\n self._cr.execute(\"\"\"\n select max(value) from rating_value where rating_config_id = %s\"\"\", (rec.config_id.id,))\n res = self._cr.fetchone()\n if res[0] and rec.value.value and rec.weightage:\n rec.score = (rec.value.value/res[0]) * rec.weightage\n\n if rec.id:\n self._cr.execute(\"\"\"update ratings_lines set score_one = %s where rating_id = %s and id = %s\"\"\",\n (rec.score,rec.rating_id.id, rec.id))\n\n @api.multi\n def unlink(self):\n if self.config_id.compulsory:\n raise exceptions.Warning(_(self.config_id.name + ' is Compulsory!'))\n return super(RatingLines, self).unlink()\n\n @api.multi\n def write(self, values):\n if values.get('value', False):\n values.update({'value_value':self.env['rating.value'].browse(values.get('value')).value})\n res = super(RatingLines, self).write(values)\n for rating_rec in self:\n rating = 0\n for rec in rating_rec.rating_id.rating_lines:\n if rec.value:\n rating += rec.value.value\n rating_rec.rating_id.average_rating = rating/len(rating_rec.rating_id.rating_lines)\n return res\n","repo_name":"Muhammad-SF/Test","sub_path":"core/supplier_rating/supplier_rating.py","file_name":"supplier_rating.py","file_ext":"py","file_size_in_byte":6607,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"20546492980","text":"import keyboard as kb\nimport pyautogui as auto\n\n# initial points for calculating other points based on resolution, these points are taken on a display of 1920x1080 resolution tested on all resolution below\n# it but not above 1920x1080 resolution\n\npoint0 = (271,995)\npoint1 = (368,995)\nskipcall = (1310,823)\ncall1 = (1046,823)\n\n# calculing the factors and new points based on that factor and resolution of current display\n\nfactorx0 = 1920/point0[0]\nfactory0 = 1080/point0[1]\nfactorx1 = 1920/point1[0]\nfactory1 = 1080/point1[1]\nfactorxskip = 1920/skipcall[0]\nfactoryskip = 1080/skipcall[1]\nfactorxcall = 1920/call1[0]\nfactorycall = 1080/call1[1]\n\nwidth, height = auto.size() # current resolution\n\nnewpoint0 = (int(width/factorx0), int(height/factory0))\nnewpoint1 = (int(width/factorx1), int(height/factory1))\nnewskip = (int(width/factorxskip), int(height/factoryskip))\nnewcall = (int(width/factorxcall), int(height/factorycall))\n\ntiledifference = newpoint1[0]-newpoint0[0]\ncalldifference = newskip[0]-newcall[0]\ntrigger = 0\n\nwhile True:\n if kb.is_pressed('1'):\n trigger=1\n auto.moveTo(newpoint0[0],newpoint0[1],0)\n# elif auto.position()[1] != newpoint0[1]:\n# trigger=0\n \n if trigger is 1:\n if kb.is_pressed('left'):\n if auto.position()[1] == newpoint0[1]:\n if auto.position()[0] == newpoint0[0]:\n auto.moveTo(newpoint0[0]+(13*tiledifference),newpoint0[1],0)\n else:\n auto.move(0-tiledifference,0)\n\n elif auto.position()[1] == newskip[1]:\n if auto.position()[0] == newskip[0]-calldifference:\n auto.moveTo(newskip[0],newskip[1],0)\n else:\n auto.move(0-calldifference,0)\n\n \n \n \n elif kb.is_pressed('right'):\n if auto.position()[1] == newpoint0[1]:\n if auto.position()[0] == newpoint0[0]+(13*tiledifference):\n auto.moveTo(newpoint0[0],newpoint0[1],0)\n else:\n auto.move(tiledifference,0)\n\n elif auto.position()[1] == newskip[1]:\n if auto.position()[0] == newskip[0]:\n auto.moveTo(newskip[0]-calldifference,newskip[1],0)\n else:\n auto.move(calldifference,0)\n \n elif kb.is_pressed('up'):\n if auto.position()[1] == newskip[1]:\n auto.moveTo(newpoint0[0],newpoint0[1],0)\n else:\n auto.moveTo(newskip[0],newskip[1],0)\n \n elif kb.is_pressed('down'):\n if auto.position()[1] == newpoint0[1]:\n auto.moveTo(newskip[0],newskip[1],0)\n else:\n auto.moveTo(newpoint0[0],newpoint0[1],0) \n \n \n \n elif kb.is_pressed('z'):\n auto.click()\n else:\n pass","repo_name":"neel-bp/Majsoul-gtaste-style-keys","sub_path":"g-taste.pyw","file_name":"g-taste.pyw","file_ext":"pyw","file_size_in_byte":2886,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"29885312175","text":"#!/usr/bin/python3\n\n#\n# Strobe through GPIO pins to find LEDs\n#\n\nimport RPi.GPIO as GPIO\nimport time,sys,argparse,signal\n\n# Get LEDs from the command line.\nparser = argparse.ArgumentParser()\nparser.add_argument('-l',action=\"append\", nargs=\"+\", type=int)\nargs = parser.parse_args()\nleds = args.l[0]\nprint(type(leds))\n\n# Signal handler for when user quits.\ndef signal_handler(signal, frame):\n\tsys.exit(0)\n\nGPIO.setmode(GPIO.BCM)\nfor led in leds:\n\tprint(led)\n\tGPIO.setup(led,GPIO.OUT)\n\nprint(\"^C to exit\")\n\nsignal.signal(signal.SIGINT, signal_handler)\n\nwhile True:\n\tfor led in leds:\n\t\tGPIO.output(led,GPIO.HIGH)\n\t\tprint(led,end='')\n\t\ttime.sleep(1)\n\t\tGPIO.output(led,GPIO.LOW)\n\t\tprint(' ',end=\"\\r\")\n\n","repo_name":"chrisgilldc/brickmaster","sub_path":"diag/led_test.py","file_name":"led_test.py","file_ext":"py","file_size_in_byte":712,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"32603810129","text":"#!/usr/bin/env python3\n\nimport sys\nimport re\nimport subprocess\n\nf = sys.stdin\n\n\nfor line in f:\n comm = re.findall(r'<(!?)(.*?)>',line)\n for cmds in comm:\n if cmds[0] == \"!\":\n \n temp = ''.join(cmds).strip()\n temp = \"<\"+temp\n temp = temp + \">\"\n # print(temp)\n # print(line)\n cont = (subprocess.run(cmds[1], shell = True, capture_output=True, text = True).stdout)\n # if re.search(temp, line):\n # print(\"got it\")\n # line = re.sub(temp,\\\n # cont, line)\n line = line.replace(temp,cont)\n\n else:\n temp = ''.join(cmds).strip()\n temp = \"<\"+temp\n temp = temp + \">\"\n # line = re.sub(temp,\\\n # subprocess.run(f\"cat {cmds[1]}\", shell = True, capture_output=True, text = True).stdout,\\\n # line)\n\n line = line.replace(temp, subprocess.run(f\"cat {cmds[1]}\", shell = True, capture_output=True, text = True).stdout)\n print(line, end= '')\n\n\n\n\n\n\n\n# for line in f:\n# cmds = re.findall(r'<(!?)(.*?)>',line)\n# if len(cmds) == 0:\n# #print(\"no neeed\")\n# print(line, end = '')\n# continue\n# if cmds[0][0] == '!':\n# res = subprocess.run(f\"{cmds[0][1]}\" ,\\\n# capture_output=True, text = True, shell = True).stdout\n# else:\n# #print(cmds[0][1])\n# res = subprocess.run(f\"cat {cmds[0][1]}\", capture_output=True, text = True, shell = True).stdout.strip()\n# temp = ''.join(cmds[0]).strip()\n# temp = \"<\"+temp\n# temp = temp + \">\"\n \n# output = re.sub(temp, res, line)\n# print(output, end= '')\n","repo_name":"Shaddock-L/comp9044","sub_path":"exam_sample/exam_practice/practice_q9.py","file_name":"practice_q9.py","file_ext":"py","file_size_in_byte":1698,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"14557807937","text":"from account import Account\nfrom login import Login\nfrom personal_cabinet import PersonalCabinet\n\nimport sqlite3\n\nconn = sqlite3.connect('card.s3db')\ncur = conn.cursor()\n\n#cur.execute(\"\"\"CREATE TABLE IF NOT EXISTS card\n#(\n# id INTEGER,\n# \"number\" TEXT,\n# pin TEXT,\n# balance INTEGER DEFAULT 0\n#);\"\"\")\n#conn.commit()\n\n\nclass StartMenu:\n def __init__(self):\n self.menu_options = {\n 1: 'Create an account',\n 2: 'Log into account',\n 0: 'Exit'\n }\n\n def print_menu(self):\n for key in self.menu_options.keys():\n print(f'{key}. {self.menu_options[key]}')\n\n @staticmethod\n def create_account():\n new_card = Account()\n\n # find last id in db\n cur.execute('select COALESCE(max(id), 0) + 1 from card')\n last_id = cur.fetchone()[0]\n\n # insert new account in db\n cur.execute(f\"\"\"\n INSERT INTO card\n (id, \"number\", pin, balance)\n VALUES({last_id}, {new_card.get_card_number()}, {new_card.get_pin()}, {new_card.get_balance()});\n \"\"\")\n conn.commit()\n print('Your card has been created')\n print('Your card number:')\n print(new_card.get_card_number())\n print('Your card PIN:')\n print(new_card.get_pin())\n\n @staticmethod\n def log_into_account():\n log = Login()\n checking = log.check_creds()\n if checking:\n print('You have successfully logged in!')\n personal_cabinet = PersonalCabinet(card_id=checking[0])\n while True:\n personal_cabinet.print_menu()\n option = int(input())\n if option == 1:\n personal_cabinet.get_balance()\n elif option == 2:\n personal_cabinet.add_income()\n elif option == 3:\n personal_cabinet.do_transfer()\n elif option == 4:\n personal_cabinet.close_account()\n elif option == 5:\n personal_cabinet.log_out()\n else:\n personal_cabinet.exit()\n else:\n print('Wrong card number or PIN!')\n\n @staticmethod\n def exit():\n print('Bye')\n exit()\n","repo_name":"ruffury/simple-banking-system","sub_path":"start_menu.py","file_name":"start_menu.py","file_ext":"py","file_size_in_byte":2285,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"34518839863","text":"class Sing:\n def __init__(self, text):\n self.text_list = text.splitlines()\n\n def __iter__(self):\n self.pointer = 0\n return self\n\n def __next__(self):\n if self.pointer >= len(self.text_list):\n raise StopIteration\n result = self.text_list[self.pointer]\n self.pointer += 1\n return result\n\n\nsong = '''So close, no matter how far\nCouldn't be much more from the heart\nForever trusting who we are\nAnd nothing else matters\nNever opened myself this way\nLife is ours, we live it our way\nAll these words, I don't just say\nAnd nothing else matters\nTrust I seek and I find in you\nEvery day for us something new\nOpen mind for a different view\nAnd nothing else matters\nNever cared for what they do\nNever cared for what they know\nBut I know\nSo close, no matter how far\nIt couldn't be much more from the heart\nForever trusting who we are\nAnd nothing else matters\n'''\n\ndef sing():\n text = Sing(song)\n for line in text:\n yield line\n\n\nfor i in sing():\n print(i)","repo_name":"Maciej-Glowacki/Python_exercises","sub_path":"exe_91_song_generator.py","file_name":"exe_91_song_generator.py","file_ext":"py","file_size_in_byte":1026,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"860717896","text":"from datetime import *\nfilenya = open('dataperpus.txt','w+')\nisian = []\nwhile True:\n kode = input('Masukkan Kode Member\t: ')\n name = input('Masukkan Nama Member\t: ')\n judul = input('Masukkan Judul Buku\t: ')\n option = input('Ulangi lagi (y/n)\t: ')\n gabung = kode + \"|\" + name + \"|\" + judul + \"|\" + str(datetime.date(datetime.now())) + \"|\" + str((datetime.date(datetime.now()+timedelta(days=7))))\n isian.append(gabung)\n if option in ('n','N'):\n for i in range(len(isian)):\n filenya.write(str(isian[i]) + '\\n')\n filenya.close()\n break","repo_name":"Dzazyy/python_project","sub_path":"Chapter 11/Python Project 2.py","file_name":"Python Project 2.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"19342844499","text":"import os\nfrom json import load\n\n__all__ = ['Planet']\n\nplanets_path = os.path.join(os.path.dirname(__file__), 'data', 'planets.json')\n\n\nclass Planet(object):\n \"\"\"\n Transiting planet parameters.\n\n This is meant to be a duck-type drop-in for the ``batman`` package's\n transiting exoplanet parameters ``TransitParams`` object.\n \"\"\"\n def __init__(self, per=None, t0=None, inc=None, rp=None, ecc=None, w=None,\n a=None, u=None, fp=None, t_secondary=None,\n limb_dark='quadratic'):\n self.per = per\n self.t0 = t0\n self.inc = inc\n self.rp = rp\n self.ecc = ecc\n self.w = w\n self.a = a\n self.u = u\n self.limb_dark = limb_dark\n self.fp = fp\n self.t_secondary = t_secondary\n\n @classmethod\n def from_name(cls, name):\n \"\"\"\n Initialize a Planet instance from the target name.\n\n There's a small (but growing?) database of planets pre-defined in the\n ``linea/data/planets.json`` file. If your favorite planet is missing,\n pull requests are welcome!\n\n Parameters\n ----------\n name : str (i.e.: \"55 Cnc e\" or \"WASP-189 b\")\n Name of the planet\n \"\"\"\n with open(planets_path, 'r') as f:\n planets = load(f)\n\n return cls(**planets[name])\n","repo_name":"bmorris3/linea","sub_path":"linea/planets.py","file_name":"planets.py","file_ext":"py","file_size_in_byte":1345,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"13024916725","text":"import pickle\n\nfrom board import Board\nfrom cmdline import CmdLine\nfrom command import Command, fetch_command\nfrom copy import deepcopy\nfrom curses_adapter import CursesAdapter, Color\nfrom hello import Hello\nfrom itertools import product\nfrom random import shuffle\nfrom ship import generate_ships\n\n\ndef _mod(a: int, b: int) -> int:\n return (a + b) % b\n\n\ndef _stack2num(stack: [int]) -> int:\n val = 0\n for digit in stack:\n val = val * 10 + digit\n return val\n\n\ndef _fill_rectangle(screen, y, x, h, w, *, sym=\" \"):\n for yy in range(y, y + h):\n screen.addstr(yy, x, sym * w)\n\n\nclass Game:\n def __init__(self, adapter: CursesAdapter, height: int, width: int):\n ships = generate_ships(height, width)\n self.userboard = Board(height, width, deepcopy(ships))\n self.compboard = Board(height, width, ships)\n self.adapter = adapter\n self.adapter.game_mode()\n self.screen = self.adapter.get_screen()\n self.h, self.w = self.screen.getmaxyx()\n self.w -= 1\n self.hello = Hello(height, width)\n self.cmdln = CmdLine()\n\n if not self._check_menu_required_size() or not self._check_game_required_size():\n self.adapter.quit()\n raise Exception(\"Screen too small, try to increase screen size\")\n\n def run(self):\n \"Запустить основное меню игры\"\n self.screen.addstr(0, 0, self.hello.tostr(self.h - 1, self.w))\n self.screen.addstr(self.h - 1, 0, self.cmdln.tostr_prompt(1, self.w))\n self.screen.keypad(True)\n run = False\n\n while True:\n if not run:\n key = self.screen.getkey()\n if run or key == \"\\n\":\n self.run_game()\n self.adapter.quit()\n break\n elif key == \":\":\n cmd = fetch_command(self.adapter, self.cmdln)\n if self._execute_command(cmd):\n break\n if cmd.command == Command.LOAD:\n run = True\n\n def run_game(self):\n \"Запустить саму игру\"\n curses = self.adapter.get_curses()\n digitstack = []\n crsy, crsx = 0, 0\n\n is_over = False\n while True:\n self.draw_screen(crsy, crsx)\n c = self.screen.getch()\n savestack = False\n if not is_over and c == curses.KEY_LEFT:\n crsx = _mod(crsx - 1, self.compboard.w)\n elif not is_over and c == curses.KEY_RIGHT:\n crsx = _mod(crsx + 1, self.compboard.w)\n elif not is_over and c == curses.KEY_UP:\n crsy = _mod(crsy - 1, self.compboard.h)\n elif not is_over and c == curses.KEY_DOWN:\n crsy = _mod(crsy + 1, self.compboard.h)\n elif not is_over and ord(\"0\") <= c <= ord(\"9\"):\n savestack = True\n d = c - ord(\"0\")\n digitstack.append(d)\n newy = _stack2num(digitstack) - 1\n if newy >= self.userboard.h:\n digitstack = [d]\n newy = d - 1\n if 0 <= newy < self.userboard.h:\n crsy = newy\n elif not is_over and ord(\"a\") <= c <= ord(\"z\") or ord(\"A\") <= c <= ord(\"Z\"):\n newx = c - (ord(\"a\") if ord(\"a\") <= c <= ord(\"z\") else ord(\"A\"))\n if 0 <= newx < self.compboard.w:\n crsx = newx\n elif (\n not is_over\n and (c == ord(\" \") or c == ord(\"\\n\"))\n and (crsy, crsx) not in self.compboard.shots\n ):\n is_over = self._turn(crsy, crsx)\n elif c == ord(\":\") and self._execute_command(\n fetch_command(self.adapter, self.cmdln)\n ):\n break\n\n if not savestack:\n digitstack.clear()\n\n def draw_screen(self, crsy, crsx):\n \"Нарисовать поля\"\n curses = self.adapter.get_curses()\n bh, bw = self.userboard.required_size()\n vspace = self.h - bh\n vspaces = [vspace - vspace // 2, vspace // 2]\n hspace = self.w - bw * 2\n hspaces = [hspace // 3] * 3\n if hspace % 3 > 0:\n hspaces[0] += 1\n if hspace % 3 > 1:\n hspaces[1] += 1\n\n self.userboard.draw(self.screen, vspaces[0], hspaces[0], False)\n\n _fill_rectangle(\n self.screen, vspaces[0], hspaces[0] + bw - 1, bh, hspaces[1] + 1\n )\n\n self.compboard.draw(self.screen, vspaces[0], sum(hspaces[:2]) + bw, True)\n\n crsy, crsx = self.userboard.board_to_screen(\n crsy, crsx, vspaces[0], sum(hspaces[:2]) + bw\n )\n\n self.screen.chgat(crsy, crsx, 1, curses.color_pair(Color.CURSOR))\n\n def _execute_command(self, cmd: Command) -> bool:\n \"Return True if need to quit program\"\n if cmd.command == Command.QUIT:\n self.adapter.quit()\n return True\n elif cmd.command == Command.LOAD:\n self._load_game(cmd.arg)\n elif cmd.command == Command.SAVE:\n self._save_game(cmd.arg)\n return False\n\n def _load_game(self, filename):\n try:\n (\n self.h,\n self.w,\n self.userboard,\n self.compboard,\n self.hello,\n self.cmdln,\n ) = pickle.load(open(filename, \"rb\"))\n self.screen.addstr(\n self.h - 1, 0, self.cmdln.answer(1, self.w, \"Game loaded\")\n )\n except Exception as e:\n self.screen.addstr(\n self.h - 1,\n 0,\n self.cmdln.answer(\n 1, self.w, \"Can't find file '%s' or invalid format\" % cmd.arg\n ),\n )\n\n def _save_game(self, filename):\n pickle.dump(\n (\n self.h,\n self.w,\n self.userboard,\n self.compboard,\n self.hello,\n self.cmdln,\n ),\n open(filename, \"bw\"),\n )\n self.screen.addstr(self.h - 1, 0, self.cmdln.answer(1, self.w, \"Game saved\"))\n\n def _turn(self, crsy, crsx) -> bool:\n \"Возвращает True, если игра окончена\"\n if self.compboard.shot(crsy, crsx):\n if self.compboard.is_over():\n self.screen.addstr(1, 0, \"You win!!!\".center(self.w, \" \"))\n return True\n else:\n while self._compshot():\n if self.userboard.is_over():\n self.screen.addstr(1, 0, \"You lose...r\".center(self.w, \" \"))\n return True\n return False\n\n def _compshot(self) -> bool:\n probs = list(product(range(self.userboard.h), range(self.userboard.w)))\n shuffle(probs)\n for (y, x) in probs:\n if (y, x) not in self.userboard.shots:\n return self.userboard.shot(y, x)\n raise Exception(\"Error: no space to shot\")\n\n def _check_menu_required_size(self) -> bool:\n hh, hw = self.hello.required_size()\n ch, cw = self.cmdln.required_size()\n return hh + ch <= self.h and max(hw, cw) <= self.w\n\n def _check_game_required_size(self) -> bool:\n bh, bw = self.userboard.required_size()\n ch, cw = self.cmdln.required_size()\n return bh + ch <= self.h and max(cw, bw * 2 + 3) <= self.w\n","repo_name":"nvxden/tinkoff2022","sub_path":"1/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":7481,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"16744234380","text":"from distutils.file_util import copy_file\nimport sys, os\nimport numpy as np\nimport scipy\nfrom scipy import interpolate\nimport matplotlib.pyplot as plt\nimport ast\nimport astropy.constants as const\nimport astropy.units as units\nfrom cosmosis.datablock import names, option_section\nfrom cosmosis.datablock.cosmosis_py import errors\n\nsys.path.insert(0, os.environ['COSMOSIS_SRC_DIR'] + '/gen_moments/cosmosis/src/')\nsys.path.insert(0, os.environ['COSMOSIS_SRC_DIR'] + '/gen_moments/data/')\nimport get_unique_comb_kp2_kp3 as get_unique_ind\nimport numpy as np\n# Finally we can now import camb\nimport camb\n\ncosmo_sec = names.cosmological_parameters\nIA_sec = names.intrinsic_alignment_parameters\nimport healpy as hp\nimport pickle as pk\n\nsys.path.insert(0, '/global/cfs/cdirs/lsst/www/shivamp/env/CS2_cori/lib/python3.9/site-packages')\nimport dill\n\n\ndef save_obj(name, obj):\n with open(name + '.pkl', 'wb') as f:\n pk.dump(obj, f, protocol=2)\n\n\ndef load_obj(name):\n with open(name + '.pkl', 'rb') as f:\n return pk.load(f)\n\n\ndef setup(options):\n\n measure_fname = options.get_string(\n option_section,\n \"measure_fname\",\n default=\n '/global/cfs/cdirs/des/shivamp/cosmosis2p0/cosmosis-standard-library/gen_moments/cosmosis/src/measurements_auto_smbin_gtsc.pk'\n )\n probe_comb = options.get_string(option_section, \"probe_comb\", default='kp2_kp3')\n dv_data_type = options.get_string(option_section, \"dv_data_type\", default='theory')\n\n df_measure = dill.load(open(measure_fname, 'rb'))\n\n if probe_comb == 'kp2':\n if dv_data_type == 'theory':\n dv_measure = df_measure['kp2_fid_normalised_pcaonly']\n if dv_data_type == 'sims_mean':\n dv_measure = df_measure['mean_kp2sims_pca']\n cov_mat = df_measure['cov_mat_kp2sims_pca']\n ind_sel = df_measure['ind_filter_kp2']\n norm_func_probe = df_measure['scaler_kp2']\n pca_func_probe = df_measure['pca_kp2']\n\n if probe_comb == 'kp3':\n if dv_data_type == 'theory':\n dv_measure = df_measure['kp3_fid_normalised_pcaonly']\n if dv_data_type == 'sims_mean':\n dv_measure = df_measure['mean_kp3sims_pca']\n cov_mat = df_measure['cov_mat_kp3sims_pca']\n ind_sel = df_measure['ind_filter_kp3']\n norm_func_probe = df_measure['scaler_kp3']\n pca_func_probe = df_measure['pca_kp3']\n\n if probe_comb == 'kp2_kp3':\n if dv_data_type == 'theory':\n dv_measure = df_measure['kp2_kp3_fid_normalised_pcaonly']\n if dv_data_type == 'sims_mean':\n dv_measure = df_measure['mean_kp2kp3sims_pca']\n cov_mat = df_measure['cov_mat_kp2kp3sims_pca']\n ind_sel = df_measure['ind_filter_kp2_kp3']\n norm_func_probe = df_measure['scaler_kp2_kp3']\n pca_func_probe = df_measure['pca_kp2_kp3']\n\n config_out = {}\n config_out['dv_measure'] = dv_measure\n config_out['cov_mat'] = cov_mat\n config_out['inv_cov_mat'] = np.linalg.inv(cov_mat)\n config_out['ind_sel'] = ind_sel\n config_out['norm_func_probe'] = norm_func_probe\n config_out['pca_func_probe'] = pca_func_probe\n config_out['probe_comb'] = probe_comb\n return config_out\n\n\ndef execute(block, config):\n\n if config['probe_comb'] == 'kp2':\n dv_theory_inp = block[('gen_moments_theory', 'kp2_all_array')][config['ind_sel']]\n\n if config['probe_comb'] == 'kp3':\n dv_theory_inp = block[('gen_moments_theory', 'kp3_all_array')][config['ind_sel']]\n\n if config['probe_comb'] == 'kp2_kp3':\n dv_theory_inp = block[('gen_moments_theory', 'kp2_kp3_all_array')][config['ind_sel']]\n\n dv_theory_scaled = config['norm_func_probe'].transform(np.array([dv_theory_inp]))\n dv_theory_pca = config['pca_func_probe'].transform(dv_theory_scaled)\n\n delta_dv = dv_theory_pca - config['dv_measure']\n chi2 = np.dot(np.dot(delta_dv, config['inv_cov_mat']), delta_dv.T)[0][0]\n like_val = -0.5 * chi2\n likes = names.likelihoods\n block[likes, 'GM_LIKE'] = like_val\n block['data_vector', 'GM_chi2'] = chi2\n return 0\n","repo_name":"des-science/gen_moments","sub_path":"cosmosis/src/get_like.py","file_name":"get_like.py","file_ext":"py","file_size_in_byte":4058,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"30402790169","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[19]:\n\n\n#Lab 10\nclass stack:\n def __init__(self,size):\n self.data = [0 for i in range(size)]\n self.top = 0\n self.size = size\n \n def isEmpty(self):\n return self.top == 0\n \n def push(self,val):\n if self.top != self.size:\n self.data[self.top] = val\n self.top += 1\n else:\n return \"stack overflow\"\n \n def pop(self):\n if not self.isEmpty():\n x = self.data[self.top-1]\n self.top -= 1\n return x\n else:\n return \"stack underflow\"\n \n def peek(self):\n if not self.isEmpty():\n return self.data[self.top-1]\n \n def count(self):\n return self.top\n \nclass Queue:\n def __init__(self,size):\n self.size = size\n self.data = [0 for i in range(size)]\n self.rare = 0\n self.front = 0\n self.count = 0\n \n def isEmpty(self):\n if self.count == 0:\n return True\n return False\n \n def enqueue(self,val):\n if self.count != self.size:\n self.data[self.rare] = val\n self.rare = (self.rare + 1) % self.size\n self.count += 1\n else:\n return \"Queue Overflow\"\n \n def dequeue(self):\n if self.count != 0:\n x = self.data[self.front]\n self.front = (self.front + 1) % self.size\n self.count -= 1\n return x\n else:\n return \"Queue Underflow\"\n \n def peek(self):\n if not self.isEmpty():\n return self.data[self.rare]\n return \"Queue is empty\"\n \n def Count(self):\n return self.count\n \nclass Graph:\n def __init__(self,v):\n self.v = v\n self.adjmat = [[0 for i in range(v)]for j in range(v)]\n \n def addEdge(self,source,dest):\n if source == dest:\n return \"source and destination can not be same\"\n else:\n self.adjmat[source][dest] = 1\n self.adjmat[dest][source] = 1\n \n def getNeighbours(self,vertex):\n temp = []\n for i in range(len(self.adjmat[vertex])):\n if self.adjmat[vertex][i] == 1:\n temp.append(i)\n return temp\n \n def printMat(self):\n for i in self.adjmat:\n for j in i:\n print(j,end=\" \")\n print()\n \n def BFS(self,source):\n visited_Q = []\n obj_Q = Queue(self.v)\n obj_Q.enqueue(source)\n visited_Q.append(source)\n while not obj_Q.isEmpty():\n x = obj_Q.dequeue()\n print(\"Visited :\",x+1)\n neg = self.getNeighbours(x)\n for i in neg:\n if i not in visited_Q:\n obj_Q.enqueue(i)\n visited_Q.append(i)\n \n def DFS(self,source):\n visited_stk = []\n obj_stk = stack(self.v)\n obj_stk.push(source)\n visited_stk.append(source)\n while not obj_stk.isEmpty():\n x = obj_stk.pop()\n print(\"Visited :\",x+1)\n neg = self.getNeighbours(x)\n for i in neg:\n if i not in visited_stk:\n obj_stk.push(i)\n visited_stk.append(i)\n \ng1 = Graph(10)\ng1.addEdge(0,1)\ng1.addEdge(0,3)\ng1.addEdge(3,2)\ng1.addEdge(1,2)\ng1.addEdge(2,9)\ng1.addEdge(2,8)\ng1.addEdge(1,7)\ng1.addEdge(1,6)\ng1.addEdge(1,4)\ng1.addEdge(4,5)\ng1.addEdge(4,6)\ng1.addEdge(7,6)\nprint(\"Adjacent Matrix: \\n\")\ng1.printMat()\nprint(\"\\nBFS: \\n\")\ng1.BFS(0)\nprint(\"\\nDFS: \\n\")\ng1.DFS(0)\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"farooq98/DFS-BFS","sub_path":"DFS & BFS/DFS and BFS.py","file_name":"DFS and BFS.py","file_ext":"py","file_size_in_byte":3676,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"75310422884","text":"import cv2\nimport numpy as np\n\n\"\"\"\nLEDのカラーと\n\nauthor Riku Yamamoto\n\"\"\"\nclass ColorLEDDetecter(object):\n\n \"\"\"\n inputは，実行するファイルだけでなく，\"rtsp://[ip_address]/[access_point].sdp\"\n などの指定でIPカメラにも対応可能\n \"\"\"\n def __init__(self, input=\"test.mov\"):\n self.cap = cv2.VideoCapture(input)\n self.mask = cv2.imread(\"mask.png\", cv2.IMREAD_GRAYSCALE)\n\n \"\"\"\n HSV領域をカラーによって指定する．opencvでは色相は360°ではなく180°で考える．\n \"\"\"\n def _get_range(self, color):\n if color == \"blue\":\n return self._hf(120)\n if color == \"green\":\n return self._hf(60)\n if color == \"red\":\n return self._hf(0)\n if color == \"skyblue\":\n return self._hf(90)\n if color == \"yellow\":\n return self._hf(30)\n if color == \"purple\":\n return self._hf(135)\n else:\n return (0, 0, 0), (0, 0, 0)\n\n \"\"\"\n HSVの値域，lowerとupperのフォーマットを設定する．\n \"\"\"\n def _hf(self, value, margin=10):\n return (value - margin, 100, 50), (value + margin, 255, 255)\n\n \"\"\"\n 輪郭検出．座標を抽出する．\n \"\"\"\n def contours(self, org_image, mask, color, options=\"time\"):\n # 輪郭の選択　もっとも外側のみ\n retr = cv2.RETR_EXTERNAL\n # 外郭点の保持の方法．近似\n approx = cv2.CHAIN_APPROX_SIMPLE\n _, contours, history = cv2.findContours(mask, cv2.RETR_EXTERNAL, approx)\n for i , cnt in enumerate(contours):\n # 次元削減\n cnt = np.squeeze(cnt, axis=1)\n # log\n print(\"[{}]{}: {}\".format(options, color, cnt[0]))\n # 描画関数．\n if org_image is not None:\n fontType = cv2.FONT_HERSHEY_SIMPLEX\n try:\n cv2.putText(org_image, \"[{}]{}\".format(i, color),(cnt[0][0], cnt[0][1]), fontType, 1, (0, 0, 255), 1)\n except:\n pass\n\n\n \"\"\"\n 画像の前処理 (ぼかしてノイズを減らす)\n \"\"\"\n def smooth(self, img, box=(11, 11)):\n # 平均フィルタ を使った平滑化\n # im_smooth = cv2.blur(img, box)\n\n # 中央値フィルタ 　を使った平滑化\n # im_smooth = cv2.medianBlur(img, box[0])\n\n # ガウスフィルタによる平滑化\n im_smooth = cv2.GaussianBlur(img, box, 0)\n return im_smooth\n\n \"\"\"\n video output\n \"\"\"\n def write_video(self, path=\"output.mp4\"):\n fourcc = cv2.VideoWriter_fourcc(*\"H264\")\n fps = self.cap.get(cv2.CAP_PROP_FPS)\n width = int(self.cap.get(3))\n height = int(self.cap.get(4))\n writer = cv2.VideoWriter(path, fourcc, fps, (width, height))\n\n while self.cap.isOpened():\n ret, frame = self.cap.read()\n if ret:\n frame = self.smooth(frame, box=(15, 15))\n # HSV空間に変換\n frame_hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n # 各色を検出する．\n mask = np.zeros(frame_hsv.shape[:2], dtype=np.uint8)\n # 各色に適応\n for color in list([\"green\", \"blue\", \"red\", \"skyblue\", \"yellow\", \"purple\"]):\n # color情報を取得し，画像の色調範囲を指定する．\n lower, upper = self._get_range(color)\n m = cv2.inRange(frame_hsv, lower, upper)\n # 必要ならマスク演算を行う (意図しない部分を消す )\n m = cv2.bitwise_and(m, m, mask=self.mask)\n\n # 領域抽出によって対象座標を見つける．\n self.contours(frame, m, color, options=str(self.cap.get(1)))\n # 各カラーのmaskを合算する．\n #mask = cv2.bitwise_or(mask, m)\n # 全てのmaskを適用\n #frame = cv2.bitwise_and(frame, frame, mask=mask)\n # 動画に出力する．\n writer.write(frame)\n else:\n break\n\n\n\nif __name__ == \"__main__\":\n detecter = ColorLEDDetecter()\n detecter.write_video()\n","repo_name":"ymmtr6/LEDDetect","sub_path":"led_detect.py","file_name":"led_detect.py","file_ext":"py","file_size_in_byte":4302,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"43606343215","text":"\"\"\"\nSample script to train the model on MNIST Dataset.\n\"\"\"\nfrom models import MLPMixer\nfrom models import MNISTData\nimport pytorch_lightning as pl\n\ndatamodule = MNISTData(data_dir='/mnt/Media/datasets', num_workers=4)\n\nmodel = MLPMixer(\n nb_channels=1,\n inp_size=28,\n patch_size=7,\n nb_classes=10,\n nb_layers=4,\n hidden_dim=768,\n token_mlp_dim=256,\n ch_mlp_dim=1024,\n lr=0.001)\n\n# print(model)\n\n# set gpus=0 for cpu training\ntrainer = pl.Trainer(gpus=1, max_epochs=2)\n\ntrainer.fit(model, datamodule)\n\ntrainer.test(model, datamodule=datamodule)\n","repo_name":"thisis-nkul/MLPMixer","sub_path":"mnist_train.py","file_name":"mnist_train.py","file_ext":"py","file_size_in_byte":571,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"30810128800","text":"from LabExT.Measurements.MeasAPI import MeasParamInt, MeasParamFloat\n\nfrom LabExT.ViewModel.Utilities.ObservableList import ObservableList\n\n\nclass LiveViewerModel:\n \"\"\"\n Model class for the live viewer. Contains all data needed for the operation of the liveviewer.\n \"\"\"\n def __init__(self, root):\n \"\"\"Constructor.\n\n Parameters\n ----------\n root : Tk\n Tkinter root window.\n \"\"\"\n\n # these are the general settings\n self.general_settings = {\n # number of points kept\n 'number of points kept': MeasParamInt(value=100),\n 'minimum y-axis span': MeasParamFloat(value=4.0),\n }\n\n # the plot collection, which is used for the plotting frame\n self.plot_collection = ObservableList()\n\n # the options when selecting a new card\n # this is dynamically filled in during start of the live viewer\n self.options = {}\n\n # the cards list\n self.cards = []\n # the old parameters, used when loading from an existing instance of the liveviewer\n self.old_params = []\n self.old_instr = []\n\n # the current live plot\n self.live_plot = None\n\n # the number of points kept\n self.plot_size = 100\n\n # the minimum y span\n self.min_y_span = 4.0\n","repo_name":"LabExT/LabExT","sub_path":"LabExT/View/LiveViewer/LiveViewerModel.py","file_name":"LiveViewerModel.py","file_ext":"py","file_size_in_byte":1333,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"52"} +{"seq_id":"75169108324","text":"import pydub\nimport numpy as np\n\n\ndef load_audio(file, sample_rate=16000, n_channels=1):\n \"\"\"Функция для загрузки аудио из файла\n\n Args:\n file (str): Путь до файла\n sample_rate (int, optional): Частота дискретизации. Defaults to 16000.\n n_channels (int, optional): Каналы. Defaults to 1.\n\n Returns:\n _type_: _description_\n \"\"\"\n audio = pydub.AudioSegment.from_file(file).set_sample_width(2)\n if sample_rate is not None:\n audio = audio.set_frame_rate(sample_rate)\n audio = audio.set_channels(n_channels)\n\n return audio\n\n # нормализация аудио, временно отключена\n # audio = np.array(audio.get_array_of_samples(), dtype=np.float32).reshape(\n # (-1, audio.channels)\n # ) / (1 << (8 * audio.sample_width - 1))\n\n # return np.squeeze(audio)\n\n\ndef load_binary_audio(file, sample_rate=16000, n_channels=1):\n \"\"\"Загрузка аудио с переводом в байтовую строку\n\n Args:\n file (str): Путь до файла\n sample_rate (int, optional): Частота дискретизации. Defaults to 16000.\n n_channels (int, optional): Каналы. Defaults to 1.\n\n Returns:\n _type_: _description_\n \"\"\"\n audio = load_audio(file, sample_rate, n_channels)\n return (\n np.array(audio.get_array_of_samples(), dtype=np.float32)\n .reshape((-1, audio.channels))\n .tobytes()\n )\n # return audio.tobytes()\n","repo_name":"pavviaz/remAInder","sub_path":"models/loading_audio.py","file_name":"loading_audio.py","file_ext":"py","file_size_in_byte":1564,"program_lang":"python","lang":"ru","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"40446015374","text":"\"\"\"\nStore a session's track history.\n\"\"\"\n\nimport time\n\ndef create_track_log(db, sessionID):\n \"\"\"\n Instantiate the Track History Collection.\n :param db: The database object.\n :param sessionID: Current user's session ID.\n :return: The Track History Collection object.\n \"\"\"\n\n collection_name = 'track_history_' + sessionID\n track_collection = db[collection_name]\n return track_collection\n\ndef update_track_log(db, sessionID, trackID):\n \"\"\"\n Add new track entries in the Track History Collection.\n :param db: The database object.\n :param sessionID: Current user's session ID.\n :param trackID: Current track's ID.\n :return: Nothing.\n \"\"\"\n\n track_collection = db['track_history_' + sessionID]\n\n try:\n track_collection.insert_one({\n \"timestamp\" : time.time(),\n \"trackID\" : trackID\n })\n except:\n print('Unable to update collection: track_collection!')\n\ndef delete_track_log(db, sessionID):\n \"\"\"\n Drop the Track History Collection.\n :param db: The database object.\n :param sessionID: Current user's session ID.\n :return: Nothing.\n \"\"\"\n\n collection_name = 'track_history_' + sessionID\n try:\n db[collection_name].drop()\n except:\n print('Could not drop collection: track_collection!')\n\n\ndef get_track_info(db, sessionID, trackID):\n \"\"\"\n Query the Collection for a given song.\n :param db: The database object.\n :param sessionID: Current user's session ID.\n :param trackID: The song's unique identifier.\n :return: Collection entry matching the query.\n \"\"\"\n\n track_collection = db['track_history_' + sessionID]\n\n try:\n query = track_collection.find_one({\n 'trackID': trackID\n })\n except:\n print('Error querying collection: track_collection!')\n\n return query\n\ndef get_track_log(db, sessionID, start = '', end = ''):\n \"\"\"\n Retrieve all Collection entries in a given time frame.\n :param db: The database object.\n :param sessionID: Current user's session ID.\n :param start: (Optional) The start of the time frame.\n :param end: (Optional) The end of the time frame.\n :return: A list of entries in a given time frame.\n \"\"\"\n\n track_collection = db['track_history_' + sessionID]\n\n result = []\n\n if start == '':\n start = 0.0\n if end == '':\n end = time.time()\n\n query = track_collection.find({\n \"$and\":[\n {\"timestamp\" : {\"$gt\": start}},\n {\"timestamp\" : {\"$lt\": end}}\n ]\n })\n\n for entry in query:\n result.append(entry)\n\n return result\n\n","repo_name":"edim98/Feedback-Music-Box-EMMA","sub_path":"app/track_history.py","file_name":"track_history.py","file_ext":"py","file_size_in_byte":2621,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"39390833366","text":"# # # Distribution Statement A. Approved for public release. Distribution unlimited.\n# # #\n# # # Author:\n# # # Naval Research Laboratory, Marine Meteorology Division\n# # #\n# # # This program is free software: you can redistribute it and/or modify it under\n# # # the terms of the NRLMMD License included with this program. This program is\n# # # distributed WITHOUT ANY WARRANTY; without even the implied warranty of\n# # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the included license\n# # # for more details. If you did not receive the license, for more information see:\n# # # https://github.com/U-S-NRL-Marine-Meteorology-Division/\n\n\"\"\"Matplotlib information for standard imagery with an existing system colormap.\"\"\"\nimport logging\n\nfrom matplotlib.colors import Normalize\nfrom matplotlib import cm\n\nfrom geoips.image_utils.colormap_utils import from_ascii\nfrom geoips.geoips_utils import find_ascii_palette\nfrom geoips.interfaces import colormappers\n\nLOG = logging.getLogger(__name__)\n\ninterface = \"colormappers\"\nfamily = \"matplotlib\"\nname = \"matplotlib_linear_norm\"\n\n\ndef call(\n data_range=None,\n cmap_name=\"Greys\",\n cmap_source=\"matplotlib\",\n cmap_path=None,\n create_colorbar=True,\n cbar_label=None,\n cbar_ticks=None,\n cbar_tick_labels=None,\n cbar_spacing=\"proportional\",\n cbar_full_width=False,\n colorbar_kwargs=None,\n set_ticks_kwargs=None,\n set_label_kwargs=None,\n):\n \"\"\"Set the matplotlib colors information for matplotlib linear norm cmaps.\n\n This information used in both colorbar and image production throughout\n GeoIPS image output specifications.\n\n Parameters\n ----------\n data_range : list, default=None\n * [min_val, max_val], matplotlib.colors.Normalize(vmin=min_val, vmax=max_val)\n * If data_range not specified, vmin and vmax both None.\n cmap_name : str, default=\"Greys\"\n * Specify the name of the resulting matplotlib colormap.\n * If no ascii_path specified, will use builtin matplotlib\n colormap of name cmap_name.\n ascii_path : str, default=None\n * Specify full path to ASCII palette to convert to matplotlib\n colormap.\n * If not specified, use internal matplotlib colormap \"cmap_name\".\n cbar_label : str, default=None\n * Positional parameter passed to cbar.set_label\n * If specified, use cbar_label string as colorbar label.\n create_colorbar : bool, default=True\n * Specify whether the image should contain a colorbar or not.\n cbar_ticks : list, default=None\n * Positional parameter passed to cbar.set_ticks\n * Specify explicit list of ticks to include for colorbar.\n * None indicates ticks at int(min) and int(max) values\n cbar_tick_labels : list, default=None\n * \"labels\" argument to pass to cbar.set_ticks.\n * can also specify directly within \"set_ticks_kwargs\"\n cbar_spacing : string, default=\"proportional\"\n * \"spacing\" argument to pass to fig.colorbar\n * can also specify directly within \"colorbar_kwargs\"\n cbar_full_width : bool, default=True\n * Extend the colorbar across the full width of the image.\n colorbar_kwargs : dict, default=None\n * keyword arguments to pass through directly to \"fig.colorbar\"\n set_ticks_kwargs : dict, default=None\n * keyword arguments to pass through directly to \"cbar.set_ticks\"\n set_label_kwargs : dict, default=None\n * keyword arguments to pass through directly to \"cbar.set_label\"\n\n Returns\n -------\n mpl_colors_info : dict\n * Specifies matplotlib Colors parameters for use in both plotting and\n colorbar generation\n\n See Also\n --------\n :ref:`api`\n See geoips.image_utils.mpl_utils.create_colorbar for field descriptions.\n \"\"\"\n min_val = None\n max_val = None\n if data_range is not None:\n min_val = data_range[0]\n max_val = data_range[1]\n\n if cmap_source == \"matplotlib\":\n try:\n mpl_cmap = cm.get_cmap(cmap_name)\n except ValueError:\n raise ValueError(f\"Colormap {cmap_name} not found in source {cmap_source}\")\n elif cmap_source == \"geoips\":\n cmap_plugin = colormappers.get_plugin(cmap_name)\n # Just get the cmap out of mpl_colors_info to use here.\n mpl_cmap = cmap_plugin()[\"cmap\"]\n elif cmap_source == \"ascii\":\n if cmap_path is not None:\n mpl_cmap = from_ascii(cmap_path, cmap_name=cmap_name)\n else:\n try:\n ascii_path = find_ascii_palette(cmap_name)\n mpl_cmap = from_ascii(ascii_path, cmap_name=cmap_name)\n except ValueError:\n raise ValueError(\n \"Colormap {cmap_name} not found in source {cmap_source}\"\n )\n else:\n raise ValueError(\n \"Uknown colormap source {cmap_source}, must be one of \"\n \"'matplotlib', 'geoips', or 'ascii'\"\n )\n\n LOG.info(\"Setting norm\")\n\n mpl_norm = Normalize(vmin=min_val, vmax=max_val)\n if cbar_ticks:\n mpl_ticks = cbar_ticks\n elif min_val is not None:\n mpl_ticks = [int(min_val), int(max_val)]\n else:\n mpl_ticks = None\n\n if cbar_tick_labels is None:\n mpl_tick_labels = mpl_ticks\n\n # Must be uniform or proportional, None not valid for Python 3\n mpl_boundaries = None\n\n mpl_colors_info = {\n \"cmap\": mpl_cmap,\n \"norm\": mpl_norm,\n \"boundaries\": mpl_boundaries,\n \"colorbar\": create_colorbar,\n \"cbar_ticks\": mpl_ticks,\n \"cbar_tick_labels\": mpl_tick_labels,\n \"cbar_label\": cbar_label,\n \"cbar_spacing\": cbar_spacing,\n \"cbar_full_width\": cbar_full_width,\n \"colorbar_kwargs\": colorbar_kwargs,\n \"set_ticks_kwargs\": set_ticks_kwargs,\n \"set_label_kwargs\": set_label_kwargs,\n }\n\n return mpl_colors_info\n","repo_name":"NRLMMD-GEOIPS/geoips","sub_path":"geoips/plugins/modules/colormappers/matplotlib_linear_norm.py","file_name":"matplotlib_linear_norm.py","file_ext":"py","file_size_in_byte":5880,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"52"} +{"seq_id":"27494601668","text":"from entsoe import EntsoePandasClient\nimport pandas as pd\n\n\nclient = EntsoePandasClient(api_key='794ce2cd-1b5a-42b8-9a50-f52076ca828d')\nstart = pd.Timestamp('20230614', tz='Europe/Warsaw')\nend = pd.Timestamp('20230616', tz='Europe/Warsaw')\ncountry_code = 'PT' # country code\ncountry_code_from = 'FR' # France\ncountry_code_to = 'PL_CZ' # Germany-Luxembourg\ntype_marketagreement_type = 'A01'\ncontract_marketagreement_type = \"A01\"\n\ndef get_data_fromENTSOE_green(country_code,timeframe): #as timeframe as tuple like ('20230614', '20230615')\n print(timeframe)\n start=pd.Timestamp(str(timeframe[0]), tz='Europe/Warsaw')\n end = pd.Timestamp(str(timeframe[1]), tz='Europe/Warsaw')\n ts = client.query_wind_and_solar_forecast(country_code, start=start,end=end)\n print(ts)\n return ts\n\ndef get_data_fromENTSOE_all(country_code,timeframe): #as timeframe as tuple like ('20230614', '20230615')\n print(timeframe)\n start=pd.Timestamp(str(timeframe[0]), tz='Europe/Warsaw')\n end = pd.Timestamp(str(timeframe[1]), tz='Europe/Warsaw')\n ts = client.query_generation(country_code, start=start,end=end)\n print(ts)\n return ts\n","repo_name":"jpiedrafita/hackaton_bsh_2023","sub_path":"datacharts/entso_data.py","file_name":"entso_data.py","file_ext":"py","file_size_in_byte":1143,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"71068688806","text":"import pytest\nimport requests\n\nfrom utilities.random_user import Random_user\nfrom utilities.ReadConfiguration import *\n\n@pytest.fixture(autouse=True)\ndef create_and_delete_user(request, random_data):\n global user\n user = None\n url = f\"{get_base_url()}/users\"\n headers = get_headers()\n user = {\"name\": f\"{random_data.first_name} {random_data.last_name}\", \"email\": random_data.email, \"gender\": random_data.gender, \"status\": random_data.status}\n response = requests.post(url=url, headers=headers, json=user)\n print(f\"Create user:\\n{response.json()}\")\n user = response.json()\n request.cls.user = user\n yield user\n delete = requests.delete(url=f\"{url}/{user['id']}\", headers=headers)\n print(f\"Delete code: \\n{delete.status_code}\")\n\n@pytest.fixture(autouse=True)\ndef random_data():\n return Random_user()","repo_name":"leraroy/pytest-api","sub_path":"tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":836,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"8343089998","text":"import tkinter as tk\nimport numpy as np\nfrom tkinter import filedialog as fd\nwin = tk.Tk()\n\nWIDTH = 500\nHEIGHT = 500\nvs = 10 #veľkosť bunky\nabvs = vs\ncells = np.zeros((WIDTH // vs, HEIGHT // vs), dtype = int) #dvojrozmerný zoznam\ncells_new = np.zeros((WIDTH // vs, HEIGHT // vs), dtype = int)\n\ndef create_stage():\n for x in range(WIDTH // vs):\n canvas.create_line(x * vs, 0, x * vs, HEIGHT)\n for y in range(HEIGHT // vs):\n canvas.create_line(0, y * vs, WIDTH, y * vs)\n\ndef get_neighbours(x, y):\n total = 0\n if x > 0:\n total += cells[x - 1, y]\n if x > 0 and y > 0:\n total += cells[x - 1, y - 1]\n if x < (WIDTH // abvs - 1) and y < (HEIGHT // abvs - 1):\n total += cells[x + 1, y + 1]\n if x > 0 and y < (HEIGHT // abvs - 1):\n total += cells[x - 1, y + 1]\n if x < (WIDTH // abvs - 1):\n total += cells[x + 1, y]\n if y > 0:\n total += cells[x, y - 1]\n if y < (HEIGHT // abvs - 1):\n total += cells[x, y + 1]\n if y > 0 and x < (WIDTH // abvs - 1):\n total += cells[x + 1, y - 1]\n return total\n\ndef recalculate():\n global cells, cells_new\n for y in range(HEIGHT // abvs):\n for x in range(WIDTH // abvs):\n temp = get_neighbours(x, y)\n if temp == 2 and cells[x, y] == 1:\n cells_new[x, y] = 1\n if temp == 3:\n cells_new[x, y] = 1\n if temp < 2 or temp > 3:\n cells_new[x, y] = 0\n cells = cells_new.copy()\n canvas.delete(\"all\")\n create_stage()\n redraw_cell()\n\ndef slider_changer(e):\n global vs\n canvas.delete(\"all\")\n vs = w.get()\n create_stage()\n redraw_cell()\n\ndef create_cell(e):\n global cells\n tx = e.x // vs\n ty = e.y // vs\n x = tx * vs\n y = ty * vs\n canvas.create_oval(x + vs//5, y + vs//5, x+vs - vs//5, y+vs - vs//5, fill = \"purple\")\n cells[tx, ty] = 1\n #print(get_neighbours(tx, ty))\n #print(cells)\n\ndef redraw_cell():\n for x in range(WIDTH // vs):\n for y in range(HEIGHT // vs):\n if cells[x, y] == 1:\n canvas.create_oval(x * vs + vs//5, y * vs + vs//5, x * vs + vs - vs//5, y * vs + vs - vs//5, fill = \"purple\")\n\ndef loop():\n if b_stop.config(\"text\")[4] == \"STOP\":\n recalculate()\n win.after(300, loop)\n\ndef start_the_loop():\n if b_stop.config(\"text\")[4] == \"START\":\n b_stop.config(text = \"STOP\")\n loop()\n else:\n b_stop.config(text = \"START\")\n\ndef openfile():\n global cells, cells_new\n file = fd.askopenfile()\n new_input = []\n counter = 0\n for line in file:\n line = line.split()\n for character in line:\n new_input.append(character)\n counter += 1\n\n if counter <= 2500:\n for i in range(counter):\n for j in range(len(new_input[i])):\n if new_input[i][j] == \"1\":\n cells_new[i, j] = 1\n else:\n cells_new[i, j] = 0\n cells = cells_new.copy()\n canvas.delete(\"all\")\n create_stage()\n redraw_cell()\n else:\n print(\"ERROR: File size must be less than 50x50\")\n\n\ncanvas = tk.Canvas(width = WIDTH, height = HEIGHT, bg = \"white\")\ncanvas.pack()\n\nw = tk.Scale(win, from_= 10, to = 50, orient = \"horizontal\", command = slider_changer, length = 500)\nw.pack()\n\nb_next_gen = tk.Button(win, text = \"NEXT GENERATION\", command = recalculate)\nb_next_gen.pack(side = tk.LEFT)\n\nb_file = tk.Button(win, text = \"OPEN MY GAME FILE\", command = openfile)\nb_file.pack(side = tk.RIGHT)\n\nb_stop = tk.Button(win, text = \"START\", command = start_the_loop)\nb_stop.pack(side = tk.BOTTOM)\n\ncreate_stage()\ncanvas.bind(\"\", create_cell)\n\nwin.mainloop()\n","repo_name":"notsoDminor/Game-of-Life","sub_path":"Kód.py","file_name":"Kód.py","file_ext":"py","file_size_in_byte":3729,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"13492683426","text":"def particle_placement(rij,numPart):\n\tLoc = []\n\tex = rij*math.sqrt(3)/2\n\tey = rij/2.\n\t'''Place first six particles around [0,0]'''\n\ttheta = 2*math.pi/6\n\tbeta = math.pi/6\n\tfor num in range(6):\n\t\tLoc.append([rij*math.cos(theta*num + beta),rij*math.sin(theta*num + beta)])\n\n\t'''Do the next few sets of four particles extending to the right'''\n\t'''Need to define a new center for each set of four'''\n\n\t\n","repo_name":"montoni/magnetic_plasmons","sub_path":"code_base/general.py","file_name":"general.py","file_ext":"py","file_size_in_byte":399,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"22037871926","text":"# %%\nimport numpy as np\nimport pandas as pd\nimport xarray as xr\nfrom . import aero\n\n\nclass Grid:\n def __init__(self, local_store: str = None) -> None:\n self.remote = None\n self.local = None\n\n self.local_store = local_store\n\n self.features = [\n \"u_component_of_wind\",\n \"v_component_of_wind\",\n \"temperature\",\n \"specific_humidity\",\n ]\n\n def set_local_path(self, local_store: str) -> None:\n self.local_store = local_store\n\n def set_remote(self) -> None:\n # remote google era5 zarr cloud storage\n self.remote = xr.open_zarr(\n \"gs://gcp-public-data-arco-era5/ar/full_37-1h-0p25deg-chunk-1.zarr-v3/\",\n chunks={\"time\": 48},\n consolidated=True,\n )\n\n def select_remote_hour(self, hour: np.datetime64) -> xr.Dataset:\n if self.remote is None:\n self.set_remote()\n\n features = [\n \"u_component_of_wind\",\n \"v_component_of_wind\",\n \"temperature\",\n \"specific_humidity\",\n ]\n\n selected = self.remote.sel(time=slice(hour, hour), level=slice(100, 700))[\n features\n ]\n\n return selected\n\n def sync_local(self, start: np.datetime64, stop: np.datetime64):\n # open local zarr storage, create if not exist\n try:\n self.local = xr.open_zarr(self.local_store, consolidated=True)\n except KeyError:\n print(f\"init local zarr from google arco era5, hour: {start.floor('1h')}\")\n selected = self.select_remote_hour(start.round(\"1h\").to_datetime64())\n selected.to_zarr(self.local_store, mode=\"w\", consolidated=True)\n self.local = xr.open_zarr(self.local_store, consolidated=True)\n\n # ensure the data is available locally\n for hour_dt in pd.date_range(start.floor(\"1h\"), stop.ceil(\"1h\"), freq=\"1h\"):\n hour = hour_dt.to_datetime64()\n if self.local.sel(time=self.local.time.isin(hour)).time.size > 0:\n continue\n\n print(f\"syncing zarr from google arco-era5, hour: {hour_dt}\")\n selected = self.select_remote_hour(hour)\n\n if selected.time.size == 0:\n RuntimeWarning(\n f\"data from {start} to {stop} is not available from google arco-era5.\"\n )\n else:\n selected.to_zarr(\n self.local_store, mode=\"a\", append_dim=\"time\", consolidated=True\n )\n\n # close to ensure the write is complete\n self.local.close()\n\n def interpolate(self, flight: pd.DataFrame) -> pd.DataFrame:\n times = pd.to_datetime(flight.timestamp)\n\n flight = flight.reset_index(drop=True).assign(\n longitude_360=lambda d: d.longitude % 360\n )\n\n start = times.min()\n stop = times.max()\n\n self.sync_local(start, stop)\n\n self.local = xr.open_zarr(self.local_store, consolidated=True)\n\n era5_cropped = self.local.sel(\n time=self.local.time.isin(\n pd.date_range(start.floor(\"1h\"), stop.ceil(\"1h\"), freq=\"1h\").to_numpy(\n dtype=\"datetime64\"\n )\n ),\n latitude=slice(flight.latitude.max() + 1, flight.latitude.min() - 1),\n longitude=slice(\n flight.longitude_360.min() - 1, flight.longitude_360.max() + 1\n ),\n )\n\n if era5_cropped.time.size == 0:\n RuntimeWarning(f\"data from {start} to {stop} is not available.\")\n return flight\n\n coords = {\n \"time\": ((\"points\",), flight.timestamp.to_numpy(dtype=\"datetime64[ns]\")),\n \"latitude\": ((\"points\",), flight.latitude.values),\n \"longitude\": ((\"points\",), flight.longitude_360.values),\n \"level\": (\n (\"points\",),\n aero.pressure(flight.altitude * aero.ft) / 100,\n ),\n }\n\n ds = xr.Dataset(coords=coords)\n\n new_params = era5_cropped.interp(\n ds.coords, method=\"linear\", assume_sorted=False\n ).to_dataframe()[self.features]\n\n flight_new = pd.concat([flight, new_params], axis=1).drop(\n columns=\"longitude_360\"\n )\n return flight_new\n","repo_name":"junzis/fastmeteo","sub_path":"fastmeteo/grid.py","file_name":"grid.py","file_ext":"py","file_size_in_byte":4300,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"52"} +{"seq_id":"28442716287","text":"import pickle\nimport os\nimport pandas as pd\nimport numpy as np\nfrom covid_extract.hosp_consts import states\nfrom eval_metrics import rmse, nrmse, mape, crps_samples, get_pr\nimport pudb\nfrom torch.utils.tensorboard import SummaryWriter\nfrom optparse import OptionParser\nfrom tqdm import tqdm\nimport pudb\nimport matplotlib.pyplot as plt\nimport sys\n\nparser = OptionParser()\nparser.add_option(\"-s\", \"--epiweek-start\", dest=\"epiweek_start\", default=\"202232\", type=\"string\")\nparser.add_option(\"-e\", \"--epiweek-end\", dest=\"epiweek_end\", default=\"202306\", type=\"string\")\nparser.add_option(\"-m\", \"--model_type\", dest=\"model_type\", default=\"normal\", type=\"string\") # normal, cnn, slidingwindow, preprocess, slidingwindow_cnn, rag, slidingwindow_rag\nparser.add_option(\"--size\", dest=\"window_size\", default=10, type=\"int\")\nparser.add_option(\"--stride\", dest=\"window_stride\", default=10, type=\"int\")\nparser.add_option(\"--seed\", dest=\"seed\", default=0, type=\"int\")\n# parser.add_option(\"-f\", \"--files\", dest=\"file_names\", default=\"sliding_model_\"+str(options.epiweek)+\"_True_0.001_500_4\", type=\"string\")\n# parser.add_option(\"-f\", \"--files\", dest=\"file_names\", default=\"sliding_model_\"+str(options.epiweek)+\"_True_0.001_500_4\", type=\"string\")\n# parser.add_option(\"-s\", \"--state\", dest=\"state\", default=\"AR\", type=\"string\")\n(options, args) = parser.parse_args()\nplot_count = 0\nstates = [\n \"DC\", \"MA\", \"FL\", \"GA\", \"IL\", \"NY\", \"NJ\", \"PA\", \"TX\", \"WA\", \"CA\", \"X\"\n]\nall_yps = []\nall_devs = []\nall_yts = []\n\nstatewise_yps = [[] for state in states]\nstatewise_devs = [[] for state in states]\nstatewise_yts = [[] for state in states]\n\nweekwise_data = [] # list of lists of lists with each element of weekwise_data being [ahead_wise_week_yps, aheadwise_week_devs, aheadwise_week_yts]\n\nif \"2023\" in options.epiweek_end:\n epiweeks = list(range(int(options.epiweek_start), 202252)) + list(range(202301, int(options.epiweek_end)))\nelse:\n epiweeks = list(range(int(options.epiweek_start), int(options.epiweek_end)))\n\nrmse_all, crps_all = [], []\nfor epiweek in tqdm(epiweeks):\n week_ahead = [1,2,3]\n plot_dict = {}\n heat_map_means = {}\n heat_map_stds = {}\n\n for state in states:\n plot_dict[state] = [None, None, None] # [pred_mean, pred_stddev, label]\n\n # yp_this_week, y_this_week = [], []\n # counter = -1\n for ah in week_ahead:\n if \"slidingwindow\" in options.model_type or \"preprocess\" in options.model_type:\n if \"autosize\" in options.model_type:\n save_model = f\"slidingwindow_disease_covid_epiweek_\"+str(epiweek)+\"_weekahead_\"+str(ah)+\"_autosize_\"+str(options.model_type[-1])\n else:\n save_model = f\"slidingwindow_disease_covid_epiweek_\"+str(epiweek)+\"_weekahead_\"+str(ah)+\"_wsize_\"+str(options.window_size)+\"_wstride_\"+str(options.window_stride)\n\n if \"preprocess\" in options.model_type:\n save_model = f\"slidingwindowpreprocessed_disease_covid_epiweek_\"+str(epiweek)+\"_weekahead_\"+str(ah)+\"_wsize_\"+str(options.window_size)+\"_wstride_\"+str(options.window_stride)\n \n if \"cnn\" in options.model_type:\n save_model = \"cnn_\" + save_model\n elif \"rag\" in options.model_type:\n save_model = \"rag_\" + save_model\n else:\n save_model = \"disease_covid_epiweek_\"+str(epiweek)+\"_weekahead_\"+str(ah)\n if \"cnn\" in options.model_type:\n save_model = \"cnn_\"+save_model\n elif \"rag\" in options.model_type:\n save_model = \"rag_\"+save_model\n else:\n save_model = \"normal_\"+save_model\n \n if options.seed != 0:\n save_model = save_model + \"_seed_\"+str(options.seed)\n\n disease_here = \"covid\"\n if \"cnn\" in options.model_type:\n disease_here = disease_here + \"_cnn\"\n if \"rag\" in options.model_type:\n disease_here = disease_here + \"_rag\"\n \n file_to_load = save_model + \"_predictions.pkl\"\n if \"slidingwindow\" in options.model_type or \"preprocess\" in options.model_type:\n directory = \"./\"+disease_here+\"_val_predictions_slidingwindow\"\n else:\n directory = \"./\"+disease_here+\"_val_predictions_normal\"\n with open(directory+\"/\"+file_to_load, \"rb\") as f:\n data_pickle = pickle.load(f)\n\n yp = data_pickle[list(data_pickle.keys())[-1]][\"pred\"]\n y = data_pickle[list(data_pickle.keys())[-1]][\"gt\"]\n\n # yp_this_week\n\n\n rmse_here = rmse(yp,y)\n crps_here = crps_samples(yp, y)\n\n rmse_all.append(rmse_here)\n crps_all.append(crps_here)\n\nrmse_avg = np.mean(rmse_all)\ncrps_avg = np.mean(crps_all)\n\nprint(\"RMSE avg: \"+str(rmse_avg))\nprint(\"CRPS avg: \"+str(crps_avg))\n\n","repo_name":"americast/FNPpp","sub_path":"Model_Training/CAMul-deploy-hosp/eval_covid_plots_val.py","file_name":"eval_covid_plots_val.py","file_ext":"py","file_size_in_byte":4755,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"32605948504","text":"from __future__ import (\n absolute_import,\n division,\n print_function,\n)\n\nimport base64\nimport io\nimport json\nimport os\n\ntry:\n # python 2\n from pipes import quote as shell_quote\nexcept ImportError:\n # python 3\n from shlex import quote as shell_quote\nimport re\nimport signal\nimport subprocess\nimport sys\ntry:\n # python2\n from urllib import urlencode as urllib_urlencode\nexcept ImportError:\n # python3\n from urllib.parse import urlencode as urllib_urlencode\n\nfrom pcs import settings\nfrom pcs.common import pcs_pycurl as pycurl\nfrom pcs.common.tools import (\n join_multilines,\n simple_cache,\n)\nfrom pcs.lib import reports\nfrom pcs.lib.errors import LibraryError, ReportItemSeverity\n\n\n\n_chkconfig = settings.chkconfig_binary\n_service = settings.service_binary\n_systemctl = settings.systemctl_binary\n\nclass ManageServiceError(Exception):\n #pylint: disable=super-init-not-called\n def __init__(self, service, message=None, instance=None):\n self.service = service\n self.message = message\n self.instance = instance\n\nclass DisableServiceError(ManageServiceError):\n pass\n\nclass EnableServiceError(ManageServiceError):\n pass\n\nclass StartServiceError(ManageServiceError):\n pass\n\nclass StopServiceError(ManageServiceError):\n pass\n\nclass KillServicesError(ManageServiceError):\n pass\n\n\ndef is_dir_nonempty(path):\n if not os.path.exists(path):\n return False\n if not os.path.isdir(path):\n return True\n return len(os.listdir(path)) > 0\n\n\ndef _get_service_name(service, instance=None):\n return \"{0}{1}.service\".format(\n service, \"\" if instance is None else \"@{0}\".format(instance)\n )\n\ndef ensure_is_systemd():\n \"\"\"\n Ensure if current system is systemd system. Raises Library error if not.\n \"\"\"\n if not is_systemctl():\n raise LibraryError(\n reports.unsupported_operation_on_non_systemd_systems()\n )\n\n\n\n@simple_cache\ndef is_systemctl():\n \"\"\"\n Check whenever is local system running on systemd.\n Returns True if current system is systemctl compatible, False otherwise.\n \"\"\"\n systemd_paths = [\n '/run/systemd/system',\n '/var/run/systemd/system',\n ]\n for path in systemd_paths:\n if os.path.isdir(path):\n return True\n return False\n\n\ndef disable_service(runner, service, instance=None):\n \"\"\"\n Disable specified service in local system.\n Raise DisableServiceError or LibraryError on failure.\n\n runner -- CommandRunner\n service -- name of service\n instance -- instance name, it ha no effect on not systemd systems.\n If None no instance name will be used.\n \"\"\"\n if not is_service_installed(runner, service, instance):\n return\n if is_systemctl():\n stdout, stderr, retval = runner.run([\n _systemctl, \"disable\", _get_service_name(service, instance)\n ])\n else:\n stdout, stderr, retval = runner.run([_chkconfig, service, \"off\"])\n if retval != 0:\n raise DisableServiceError(\n service,\n join_multilines([stderr, stdout]),\n instance\n )\n\n\ndef enable_service(runner, service, instance=None):\n \"\"\"\n Enable specified service in local system.\n Raise EnableServiceError or LibraryError on failure.\n\n runner -- CommandRunner\n service -- name of service\n instance -- instance name, it ha no effect on not systemd systems.\n If None no instance name will be used.\n \"\"\"\n if is_systemctl():\n stdout, stderr, retval = runner.run([\n _systemctl, \"enable\", _get_service_name(service, instance)\n ])\n else:\n stdout, stderr, retval = runner.run([_chkconfig, service, \"on\"])\n if retval != 0:\n raise EnableServiceError(\n service,\n join_multilines([stderr, stdout]),\n instance\n )\n\n\ndef start_service(runner, service, instance=None):\n \"\"\"\n Start specified service in local system\n CommandRunner runner\n string service service name\n string instance instance name, it ha no effect on not systemd systems.\n If None no instance name will be used.\n \"\"\"\n if is_systemctl():\n stdout, stderr, retval = runner.run([\n _systemctl, \"start\", _get_service_name(service, instance)\n ])\n else:\n stdout, stderr, retval = runner.run([_service, service, \"start\"])\n if retval != 0:\n raise StartServiceError(\n service,\n join_multilines([stderr, stdout]),\n instance\n )\n\n\ndef stop_service(runner, service, instance=None):\n \"\"\"\n Stop specified service in local system\n CommandRunner runner\n string service service name\n string instance instance name, it ha no effect on not systemd systems.\n If None no instance name will be used.\n \"\"\"\n if is_systemctl():\n stdout, stderr, retval = runner.run([\n _systemctl, \"stop\", _get_service_name(service, instance)\n ])\n else:\n stdout, stderr, retval = runner.run([_service, service, \"stop\"])\n if retval != 0:\n raise StopServiceError(\n service,\n join_multilines([stderr, stdout]),\n instance\n )\n\n\ndef kill_services(runner, services):\n \"\"\"\n Kill specified services in local system\n CommandRunner runner\n iterable services service names\n \"\"\"\n # make killall not report that a process is not running\n stdout, stderr, retval = runner.run(\n [\"killall\", \"--quiet\", \"--signal\", \"9\", \"--\"] + list(services)\n )\n # If a process isn't running, killall will still return 1 even with --quiet.\n # We don't consider that an error, so we check for output string as well.\n # If it's empty, no actuall error happened.\n if retval != 0:\n message = join_multilines([stderr, stdout])\n if message:\n raise KillServicesError(list(services), message)\n\n\ndef is_service_enabled(runner, service, instance=None):\n \"\"\"\n Check if specified service is enabled in local system.\n\n runner -- CommandRunner\n service -- name of service\n \"\"\"\n if is_systemctl():\n dummy_stdout, dummy_stderr, retval = runner.run(\n [_systemctl, \"is-enabled\", _get_service_name(service, instance)]\n )\n else:\n dummy_stdout, dummy_stderr, retval = runner.run([_chkconfig, service])\n\n return retval == 0\n\n\ndef is_service_running(runner, service, instance=None):\n \"\"\"\n Check if specified service is currently running on local system.\n\n runner -- CommandRunner\n service -- name of service\n \"\"\"\n if is_systemctl():\n dummy_stdout, dummy_stderr, retval = runner.run([\n _systemctl,\n \"is-active\",\n _get_service_name(service, instance)\n ])\n else:\n dummy_stdout, dummy_stderr, retval = runner.run(\n [_service, service, \"status\"]\n )\n\n return retval == 0\n\n\ndef is_service_installed(runner, service, instance=None):\n \"\"\"\n Check if specified service is installed on local system.\n\n runner -- CommandRunner\n service -- name of service\n instance -- systemd service instance\n \"\"\"\n if not is_systemctl():\n return service in get_non_systemd_services(runner)\n service_name = \"{0}{1}\".format(service, \"\" if instance is None else \"@\")\n return service_name in get_systemd_services(runner)\n\n\ndef get_non_systemd_services(runner):\n \"\"\"\n Returns list of all installed services on non systemd system.\n\n runner -- CommandRunner\n \"\"\"\n if is_systemctl():\n return []\n\n stdout, dummy_stderr, return_code = runner.run([_chkconfig])\n if return_code != 0:\n return []\n\n service_list = []\n for service in stdout.splitlines():\n service = service.split(\" \", 1)[0]\n if service:\n service_list.append(service)\n return service_list\n\n\ndef get_systemd_services(runner):\n \"\"\"\n Returns list of all systemd services installed on local system.\n\n runner -- CommandRunner\n \"\"\"\n if not is_systemctl():\n return []\n\n stdout, dummy_stderr, return_code = runner.run([\n _systemctl, \"list-unit-files\", \"--full\"\n ])\n if return_code != 0:\n return []\n\n service_list = []\n for service in stdout.splitlines():\n match = re.search(r'^([\\S]*)\\.service', service)\n if match:\n service_list.append(match.group(1))\n return service_list\n\n\ndef is_cman_cluster(runner):\n \"\"\"\n Detect if underlaying locally installed cluster is CMAN based\n \"\"\"\n # Checking corosync version works in most cases and supports non-rhel\n # distributions as well as running (manually compiled) corosync2 on rhel6.\n # - corosync2 does not support cman at all\n # - corosync1 runs with cman on rhel6\n # - corosync1 can be used without cman, but we don't support it anyways\n # - corosync2 is the default result if errors occur\n stdout, dummy_stderr, retval = runner.run([\n os.path.join(settings.corosync_binaries, \"corosync\"),\n \"-v\"\n ])\n if retval != 0:\n return False\n match = re.search(r\"version\\D+(\\d+)\", stdout)\n return match is not None and match.group(1) == \"1\"\n\n\ndef is_proxy_set(env_dict):\n \"\"\"\n Returns True whenever any of proxy environment variables (https_proxy,\n HTTPS_PROXY, all_proxy, ALL_PROXY) are set in env_dict. False otherwise.\n\n env_dict -- environment variables in dict\n \"\"\"\n proxy_list = [\"https_proxy\", \"all_proxy\"]\n for var in proxy_list + [v.upper() for v in proxy_list]:\n if env_dict.get(var, \"\") != \"\":\n return True\n return False\n\n\nclass CommandRunner(object):\n def __init__(self, logger, reporter, env_vars=None):\n self._logger = logger\n self._reporter = reporter\n # Reset environment variables by empty dict is desired here. We need\n # to get rid of defaults - we do not know the context and environment\n # where the library runs. We also get rid of PATH settings, so all\n # executables must be specified with full path unless the PATH variable\n # is set from outside.\n self._env_vars = env_vars if env_vars else dict()\n self._python2 = sys.version[0] == \"2\"\n\n @property\n def env_vars(self):\n return self._env_vars.copy()\n\n def run(\n self, args, stdin_string=None, env_extend=None, binary_output=False\n ):\n # Allow overriding default settings. If a piece of code really wants to\n # set own PATH or CIB_file, we must allow it. I.e. it wants to run\n # a pacemaker tool on a CIB in a file but cannot afford the risk of\n # changing the CIB in the file specified by the user.\n env_vars = self._env_vars.copy()\n env_vars.update(\n dict(env_extend) if env_extend else dict()\n )\n\n log_args = \" \".join([shell_quote(x) for x in args])\n self._logger.debug(\n \"Running: {args}\\nEnvironment:{env_vars}{stdin_string}\".format(\n args=log_args,\n stdin_string=(\"\" if not stdin_string else (\n \"\\n--Debug Input Start--\\n{0}\\n--Debug Input End--\"\n .format(stdin_string)\n )),\n env_vars=(\"\" if not env_vars else (\n \"\\n\" + \"\\n\".join([\n \" {0}={1}\".format(key, val)\n for key, val in sorted(env_vars.items())\n ])\n ))\n )\n )\n self._reporter.process(\n reports.run_external_process_started(\n log_args, stdin_string, env_vars\n )\n )\n\n try:\n process = subprocess.Popen(\n args,\n # Some commands react differently if they get anything via stdin\n stdin=(subprocess.PIPE if stdin_string is not None else None),\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n preexec_fn=(\n lambda: signal.signal(signal.SIGPIPE, signal.SIG_DFL)\n ),\n close_fds=True,\n shell=False,\n env=env_vars,\n # decodes newlines and in python3 also converts bytes to str\n universal_newlines=(not self._python2 and not binary_output)\n )\n out_std, out_err = process.communicate(stdin_string)\n retval = process.returncode\n except OSError as e:\n raise LibraryError(\n reports.run_external_process_error(log_args, e.strerror)\n )\n\n self._logger.debug(\n (\n \"Finished running: {args}\\nReturn value: {retval}\"\n + \"\\n--Debug Stdout Start--\\n{out_std}\\n--Debug Stdout End--\"\n + \"\\n--Debug Stderr Start--\\n{out_err}\\n--Debug Stderr End--\"\n ).format(\n args=log_args,\n retval=retval,\n out_std=out_std,\n out_err=out_err\n )\n )\n self._reporter.process(reports.run_external_process_finished(\n log_args, retval, out_std, out_err\n ))\n return out_std, out_err, retval\n\n\n# deprecated\nclass NodeCommunicationException(Exception):\n # pylint: disable=super-init-not-called\n def __init__(self, node, command, reason):\n self.node = node\n self.command = command\n self.reason = reason\n\n\n# deprecated\nclass NodeConnectionException(NodeCommunicationException):\n pass\n\n\n# deprecated\nclass NodeAuthenticationException(NodeCommunicationException):\n pass\n\n\n# deprecated\nclass NodePermissionDeniedException(NodeCommunicationException):\n pass\n\n# deprecated\nclass NodeCommandUnsuccessfulException(NodeCommunicationException):\n pass\n\n# deprecated\nclass NodeUnsupportedCommandException(NodeCommunicationException):\n pass\n\n\n# deprecated\nclass NodeConnectionTimedOutException(NodeCommunicationException):\n pass\n\n\n# deprecated\ndef node_communicator_exception_to_report_item(\n e, severity=ReportItemSeverity.ERROR, forceable=None\n):\n \"\"\"\n Transform NodeCommunicationException to ReportItem\n \"\"\"\n if isinstance(e, NodeCommandUnsuccessfulException):\n return reports.node_communication_command_unsuccessful(\n e.node,\n e.command,\n e.reason\n )\n exception_to_report = {\n NodeAuthenticationException:\n reports.node_communication_error_not_authorized,\n NodePermissionDeniedException:\n reports.node_communication_error_permission_denied,\n NodeUnsupportedCommandException:\n reports.node_communication_error_unsupported_command,\n NodeCommunicationException:\n reports.node_communication_error_other_error,\n NodeConnectionException:\n reports.node_communication_error_unable_to_connect,\n NodeConnectionTimedOutException:\n reports.node_communication_error_timed_out,\n }\n if e.__class__ in exception_to_report:\n return exception_to_report[e.__class__](\n e.node,\n e.command,\n e.reason,\n severity,\n forceable\n )\n raise e\n\n# deprecated, use pcs.common.node_communicator.Communicator\nclass NodeCommunicator(object):\n \"\"\"\n Sends requests to nodes\n \"\"\"\n\n @classmethod\n def format_data_dict(cls, data):\n \"\"\"\n Encode data for transport (only plain dict is supported)\n \"\"\"\n return urllib_urlencode(data)\n\n @classmethod\n def format_data_json(cls, data):\n \"\"\"\n Encode data for transport (more complex data than in format_data_dict)\n \"\"\"\n return json.dumps(data)\n\n def __init__(\n self, logger, reporter, auth_tokens, user=None, groups=None,\n request_timeout=None\n ):\n \"\"\"\n auth_tokens authorization tokens for nodes: {node: token}\n user username\n groups groups the user is member of\n request_timeout -- positive integer, time for one reqest in seconds\n \"\"\"\n self._logger = logger\n self._reporter = reporter\n self._auth_tokens = auth_tokens\n self._user = user\n self._groups = groups\n self._request_timeout = request_timeout\n\n @property\n def request_timeout(self):\n return (\n settings.default_request_timeout\n if self._request_timeout is None\n else self._request_timeout\n )\n\n def call_node(self, node_addr, request, data, request_timeout=None):\n \"\"\"\n Send a request to a node\n node_addr destination node, instance of NodeAddresses\n request command to be run on the node\n data command parameters, encoded by format_data_* method\n \"\"\"\n return self.call_host(node_addr.ring0, request, data, request_timeout)\n\n def call_host(self, host, request, data, request_timeout=None):\n \"\"\"\n Send a request to a host\n host host address\n request command to be run on the host\n data command parameters, encoded by format_data_* method\n request timeout float timeout for request, if not set object property\n will be used\n \"\"\"\n def __debug_callback(data_type, debug_data):\n prefixes = {\n pycurl.DEBUG_TEXT: b\"* \",\n pycurl.DEBUG_HEADER_IN: b\"< \",\n pycurl.DEBUG_HEADER_OUT: b\"> \",\n pycurl.DEBUG_DATA_IN: b\"<< \",\n pycurl.DEBUG_DATA_OUT: b\">> \",\n }\n if data_type in prefixes:\n debug_output.write(prefixes[data_type])\n debug_output.write(debug_data)\n if not debug_data.endswith(b\"\\n\"):\n debug_output.write(b\"\\n\")\n\n output = io.BytesIO()\n debug_output = io.BytesIO()\n cookies = self.__prepare_cookies(host)\n timeout = (\n request_timeout\n if request_timeout is not None\n else self.request_timeout\n )\n url = \"https://{host}:2224/{request}\".format(\n host=(\"[{0}]\".format(host) if \":\" in host else host),\n request=request\n )\n\n handler = pycurl.Curl()\n handler.setopt(pycurl.PROTOCOLS, pycurl.PROTO_HTTPS)\n handler.setopt(pycurl.TIMEOUT_MS, int(timeout * 1000))\n handler.setopt(pycurl.URL, url.encode(\"utf-8\"))\n handler.setopt(pycurl.WRITEFUNCTION, output.write)\n handler.setopt(pycurl.VERBOSE, 1)\n handler.setopt(pycurl.DEBUGFUNCTION, __debug_callback)\n handler.setopt(pycurl.SSL_VERIFYHOST, 0)\n handler.setopt(pycurl.SSL_VERIFYPEER, 0)\n handler.setopt(pycurl.NOSIGNAL, 1) # required for multi-threading\n if cookies:\n handler.setopt(pycurl.COOKIE, \";\".join(cookies).encode(\"utf-8\"))\n if data:\n handler.setopt(pycurl.COPYPOSTFIELDS, data.encode(\"utf-8\"))\n\n msg = \"Sending HTTP Request to: {url}\"\n if data:\n msg += \"\\n--Debug Input Start--\\n{data}\\n--Debug Input End--\"\n self._logger.debug(msg.format(url=url, data=data))\n self._reporter.process(\n reports.node_communication_started(url, data)\n )\n result_msg = (\n \"Finished calling: {url}\\nResponse Code: {code}\"\n + \"\\n--Debug Response Start--\\n{response}\\n--Debug Response End--\"\n )\n\n try:\n handler.perform()\n response_data = output.getvalue().decode(\"utf-8\")\n response_code = handler.getinfo(pycurl.RESPONSE_CODE)\n self._logger.debug(result_msg.format(\n url=url,\n code=response_code,\n response=response_data\n ))\n self._reporter.process(reports.node_communication_finished(\n url, response_code, response_data\n ))\n if response_code == 400:\n # old pcsd protocol: error messages are commonly passed in plain\n # text in response body with HTTP code 400\n # we need to be backward compatible with that\n raise NodeCommandUnsuccessfulException(\n host, request, response_data.rstrip()\n )\n elif response_code == 401:\n raise NodeAuthenticationException(\n host, request, \"HTTP error: {0}\".format(response_code)\n )\n elif response_code == 403:\n raise NodePermissionDeniedException(\n host, request, \"HTTP error: {0}\".format(response_code)\n )\n elif response_code == 404:\n raise NodeUnsupportedCommandException(\n host, request, \"HTTP error: {0}\".format(response_code)\n )\n elif response_code >= 400:\n raise NodeCommunicationException(\n host, request, \"HTTP error: {0}\".format(response_code)\n )\n return response_data\n except pycurl.error as e:\n # In pycurl versions lower then 7.19.3 it is not possible to set\n # NOPROXY option. Therefore for the proper support of proxy settings\n # we have to use environment variables.\n if is_proxy_set(os.environ):\n self._logger.warning(\"Proxy is set\")\n self._reporter.process(\n reports.node_communication_proxy_is_set()\n )\n errno, reason = e.args\n msg = \"Unable to connect to {node} ({reason})\"\n self._logger.debug(msg.format(node=host, reason=reason))\n self._reporter.process(\n reports.node_communication_not_connected(host, reason)\n )\n if errno == pycurl.E_OPERATION_TIMEDOUT:\n raise NodeConnectionTimedOutException(host, request, reason)\n else:\n raise NodeConnectionException(host, request, reason)\n finally:\n debug_data = debug_output.getvalue().decode(\"utf-8\", \"ignore\")\n self._logger.debug(\n (\n \"Communication debug info for calling: {url}\\n\"\n \"--Debug Communication Info Start--\\n\"\n \"{data}\\n\"\n \"--Debug Communication Info End--\"\n ).format(url=url, data=debug_data)\n )\n self._reporter.process(\n reports.node_communication_debug_info(url, debug_data)\n )\n\n def __prepare_cookies(self, host):\n # Let's be safe about characters in variables (they can come from env)\n # and do base64. We cannot do it for CIB_user however to be backward\n # compatible so we at least remove disallowed characters.\n cookies = []\n if host in self._auth_tokens:\n cookies.append(\"token={0}\".format(self._auth_tokens[host]))\n if self._user:\n cookies.append(\"CIB_user={0}\".format(\n re.sub(r\"[^!-~]\", \"\", self._user).replace(\";\", \"\")\n ))\n if self._groups:\n cookies.append(\"CIB_user_groups={0}\".format(\n # python3 requires the value to be bytes not str\n base64.b64encode(\n \" \".join(self._groups).encode(\"utf-8\")\n ).decode(\"utf-8\")\n ))\n return cookies\n","repo_name":"wangww631/pcs","sub_path":"pcs/lib/external.py","file_name":"external.py","file_ext":"py","file_size_in_byte":23397,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"52"} +{"seq_id":"19046546290","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nfrom __future__ import division\nimport itertools\nimport warnings\nwarnings.filterwarnings(\"ignore\")\nimport matplotlib.pyplot as plt\nimport matplotlib.dates as mdates\nfrom numpy import linspace, loadtxt, ones, convolve\nfrom sklearn.ensemble import IsolationForest\nimport numpy as np\nimport pandas as pd\nimport collections\nimport math\nfrom sklearn import metrics\nfrom random import randint\nfrom matplotlib import style\nimport seaborn as sns\n# style.use('fivethirtyeight')\nget_ipython().run_line_magic('matplotlib', 'inline')\n\npd.plotting.register_matplotlib_converters()\n\n\n# In[4]:\n\n\ndef make_log_data():\n '''This function reads in the names of the columns and the csv holding the anonymized curriculum data to make a dataframe.'''\n colnames = ['date', 'endpoint', 'user_id', 'cohort_id', 'source_ip']\n df = pd.read_csv(\"anonymized-curriculum-access.txt\", \n sep=\"\\s\", \n header=None, \n names = colnames, \n usecols=[0, 2, 3, 4, 5])\n return df\ndf= make_log_data()\ndf.head()\n\n\n# In[6]:\n\n\ndef prep_log_data(user, span, weight):\n ''' This function uses the dataframe created previously, allows the user to be specified, converts the date column to a date/time column, makes the index column the date,\n and returns a pd.series called pages which shows the total pages accessed by the user.''' \n df=make_log_data()\n df = df[df.user_id == user]\n df.date = pd.to_datetime(df.date)\n df = df.set_index(df.date)\n pages = df['endpoint'].resample('d').count()\n return df, pages\n\n\n# In[ ]:\n\n\ndef compute_bollinger(pages, span, weight, user):\n \n ''' This function calculates the lower, mid, and upper bands and the standard deviation. \n The function then concats the bands and the pages to the dataframe. Finally a new dataframe with\n the pages and bands is returned.\n '''\n \n midband = pages.ewm(span=span).mean()\n stdev = pages.ewm(span=span).std()\n ub = midband + stdev*weight\n lb = midband - stdev*weight\n bb = pd.concat([ub, lb], axis=1)\n bol_df = pd.concat([pages, midband, bb], axis=1)\n bol_df.columns = ['pages', 'midband', 'ub', 'lb']\n bol_df['pct_b'] = (bol_df['pages'] - bol_df['lb'])/(bol_df['ub'] - bol_df['lb'])\n bol_df['user_id'] = user\n \n return bol_df\n\n\n# In[ ]:\n\n\ndef plt_bands(b_df, user):\n \n ''' This functions will plot the upper, mid, and lower bands and original count of page accessess for every user.'''\n \n fig, ax = plt.subplots(figsize=(12,8))\n ax.plot(b_df.index, b_df.pages, label='Number of Pages, User: '+str(user))\n ax.plot(b_df.index, b_df.midband, label = 'Middle band')\n ax.plot(b_df.index, b_df.ub, label = 'Upper Band')\n ax.plot(b_df.index, b_df.lb, label = 'Lower Band')\n ax.set_ylabel('Number of Pages Accessed')\n \n return plt.show()\n \n\n","repo_name":"GabbyBarajasBroussard/anomaly-detection-exercises","sub_path":"TSA-exercises.py","file_name":"TSA-exercises.py","file_ext":"py","file_size_in_byte":2889,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"4024327369","text":"import matplotlib.pyplot as plt\nimport pandas as pd\nfrom sklearn import datasets, manifold\n\nfrom mds import mds_custom, mds_sklearn\n\n\ndef isomap(data, k):\n embedding = manifold.Isomap(n_components=2, n_neighbors=k)\n return embedding.fit_transform(data)\n\n\ndef tsne(data):\n embedding = manifold.TSNE(n_components=2)\n return embedding.fit_transform(data)\n\n\ndef lle(data):\n embedding = manifold.LocallyLinearEmbedding(n_components=2, n_neighbors=7)\n return embedding.fit_transform(data)\n\n\ndef plot_data(data, title, labels=None, colors=None):\n plt.title(title)\n plt.scatter(data[:, 0], data[:, 1], c=colors)\n if labels is not None:\n for i, txt in enumerate(labels):\n plt.text(data[i, 0], data[i, 1], txt, va=\"center\")\n\n\ndef make_experiment(data, name=None, labels=None, figsize=(20, 10), colors=None):\n plt.figure(figsize=figsize)\n plt.subplot(231)\n Y = mds_custom(data)\n plot_data(Y, 'MDS classic (pca)', labels, colors)\n plt.subplot(232)\n Y = mds_sklearn(data)\n plot_data(Y, 'MDS sklearn (smacof)', labels, colors)\n plt.subplot(233)\n Y = isomap(data, 3)\n plot_data(Y, 'Isomap (k=3)', labels, colors)\n plt.subplot(234)\n Y = isomap(data, 5)\n plot_data(Y, 'Isomap (k=5)', labels, colors)\n plt.subplot(235)\n Y = tsne(data)\n plot_data(Y, 'Tsne', labels, colors)\n plt.subplot(236)\n Y = lle(data)\n plt.suptitle(name)\n plot_data(Y, 'lle', labels, colors)\n plt.savefig(f'plots/{name}.jpg')\n\n\ndef main():\n # cars\n data = pd.read_csv('data/cars.csv', index_col=False, quotechar=\"'\", header=None)\n labels = data[0]\n data = data.values[:, 1:]\n make_experiment(data, 'Cars', labels)\n # swissroll\n data, t = datasets.make_swiss_roll()\n make_experiment(data, \"Swissroll\", colors=t)\n # pokemon\n data = pd.read_csv('data/pokemon.csv', delimiter=',', index_col=0)\n labels = data[data['Generation'] == 1]['Name']\n data = data.drop('Name', axis=1)\n strengths = pd.read_csv('data/strengths.csv', index_col=0)\n data['Type 2'] = data['Type 2'].fillna(data['Type 1']) # double pokemon type if only one is present\n data = data.join(strengths, on='Type 1', rsuffix=\"_t1\")\n data = data.join(strengths, on='Type 2', rsuffix=\"_t2\")\n data = data.drop(columns=['Type 1', 'Type 2'])\n\n data = data[data['Generation'] == 1]\n data['Legendary'] = data['Legendary'].apply(lambda x: 1 if x else 0)\n make_experiment(data, \"Pokemon\", labels, figsize=(40, 30))\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"mrugacz95/data_visualization","sub_path":"visualization.py","file_name":"visualization.py","file_ext":"py","file_size_in_byte":2526,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"21664545660","text":"#!/usr/bin/env python\n\"\"\"\nThis is a simple tic-tac-toe game implimented in python\n\n\"\"\"\nimport os\n#global variables\nmy_list = [' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ']\ngeneral_arr = [[0,1,2],[3,4,5],[6,7,8],[0,3,6],[0,4,8],[1,4,7],[2,5,8],[2,4,6]]\nplayer1_arr = []\nplayer2_arr = []\ntotal_arr = []\n#print game_board\ndef print_board(my_list):\n # For Windows\n if os.name == 'nt':\n _ = os.system('cls')\n # For macOS and Linux\n else:\n _ = os.system('clear')\n\n print(f'-----------------------------------------')\n print(f' {my_list[0]} | {my_list[1]} | {my_list[2]}')\n print(f' | | ')\n print(f'-----------------------------------------')\n print(f' | | ')\n print(f' {my_list[3]} | {my_list[4]} | {my_list[5]}')\n print(f' | | ')\n print(f'-----------------------------------------')\n print(f' | | ')\n print(f' {my_list[6]} | {my_list[7]} | {my_list[8]}')\n print(f'-----------------------------------------')\n#prompt player to select either 'X' or 'O'\ndef play(print_board,the_list):\n d = {'player1': '', 'player2': ''}\n while (d['player1']).upper() not in ['X', 'O']:\n d['player1'] = input(\"Please will you like to play as 'X' or 'O'? \")\n d['player1'] = (d['player1']).upper()\n if (d['player1']) == 'X':\n d['player2'] = 'O'\n else:\n d['player2'] = 'X'\n print(f'Player 1, You will be playing as {d[\"player1\"]} and Player 2 as {d[\"player2\"]}')\n start_game(print_board,d) \n#start game\ndef start_game(print_board,d):\n moves = 0\n player = ''\n answer = input(f'Shall we start the game?\\nPlease enter \"Y\" or \"N\" ')\n if answer.lower() == 'y':\n while moves < 9:\n if moves%2==0:\n player = d['player1']\n players_position(print_board,player,moves)\n moves += 1\n else:\n player = d['player2']\n players_position(print_board,player,moves)\n moves += 1\n else:\n print(\"Good-Bye :( \")\n exit()\n#prompt player to select position\ndef players_position(print_board,player,moves):\n position = ''\n while not position.isdigit() or int(position) not in range(1, 10) or int(position) - 1 in total_arr:\n position = input(\"Please enter your position from [1-9]: \")\n position = int(position) - 1\n total_arr.append(position)\n \n my_list[position] = player\n print_board(my_list)\n update_game(moves, position)\n#update game with players position\ndef update_game(moves,position):\n if moves >= 9:\n end_game()\n else:\n if moves%2==0:\n if len(player1_arr) < 3:\n player1_arr.append(position)\n elif len(player1_arr) == 3:\n player1_arr.pop(0)\n player1_arr.append(position)\n else:\n print(\"Error\")\n exit()\n else:\n if len(player2_arr) < 3:\n player2_arr.append(position)\n elif len(player2_arr) == 3:\n player2_arr.pop(0)\n player2_arr.append(position)\n else:\n print(\"Error\")\n exit()\n check_win(player1_arr,player2_arr)\n#check for winner\ndef check_win(player1_arr,player2_arr):\n if len(total_arr) >= 9:\n print(\"Draw Game!!!\")\n end_game()\n else:\n for items in general_arr:\n if set(items) == set(player1_arr):\n print(\"Player 1 Win!!!\")\n end_game()\n elif set(items) == set(player2_arr):\n print(\"Player 2 Win!!!\")\n end_game()\n else:\n pass\n#end and restart function\ndef end_game():\n global my_list, total_arr, player1_arr, player2_arr\n my_list = [' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ']\n total_arr, player1_arr, player2_arr = [], [], []\n replay = input(\"Will you like to play Again?\\nEnter 'Y' for Yes or 'N' for No \")\n if replay.lower() == 'y':\n play(print_board,my_list)\n else:\n print(\"Good-Bye :(\")\n exit()\n#start game\nplay(print_board,my_list)","repo_name":"akubosilver/Python_Stuffs","sub_path":"milestone1.py","file_name":"milestone1.py","file_ext":"py","file_size_in_byte":4315,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"31588926749","text":"from .compute_instance_group_rollout_policy import ComputeInstanceGroupRolloutPolicy\nfrom oci.util import formatted_flat_dict, NONE_SENTINEL, value_allowed_none_or_none_sentinel # noqa: F401\nfrom oci.decorators import init_model_state_from_kwargs\n\n\n@init_model_state_from_kwargs\nclass ComputeInstanceGroupLinearRolloutPolicyByCount(ComputeInstanceGroupRolloutPolicy):\n \"\"\"\n Specifies a linear rollout strategy for a compute instance group rolling deployment stage.\n \"\"\"\n\n def __init__(self, **kwargs):\n \"\"\"\n Initializes a new ComputeInstanceGroupLinearRolloutPolicyByCount object with values from keyword arguments. The default value of the :py:attr:`~oci.devops.models.ComputeInstanceGroupLinearRolloutPolicyByCount.policy_type` attribute\n of this class is ``COMPUTE_INSTANCE_GROUP_LINEAR_ROLLOUT_POLICY_BY_COUNT`` and it should not be changed.\n The following keyword arguments are supported (corresponding to the getters/setters of this class):\n\n :param policy_type:\n The value to assign to the policy_type property of this ComputeInstanceGroupLinearRolloutPolicyByCount.\n Allowed values for this property are: \"COMPUTE_INSTANCE_GROUP_LINEAR_ROLLOUT_POLICY_BY_COUNT\", \"COMPUTE_INSTANCE_GROUP_LINEAR_ROLLOUT_POLICY_BY_PERCENTAGE\"\n :type policy_type: str\n\n :param batch_delay_in_seconds:\n The value to assign to the batch_delay_in_seconds property of this ComputeInstanceGroupLinearRolloutPolicyByCount.\n :type batch_delay_in_seconds: int\n\n :param batch_count:\n The value to assign to the batch_count property of this ComputeInstanceGroupLinearRolloutPolicyByCount.\n :type batch_count: int\n\n \"\"\"\n self.swagger_types = {\n 'policy_type': 'str',\n 'batch_delay_in_seconds': 'int',\n 'batch_count': 'int'\n }\n\n self.attribute_map = {\n 'policy_type': 'policyType',\n 'batch_delay_in_seconds': 'batchDelayInSeconds',\n 'batch_count': 'batchCount'\n }\n\n self._policy_type = None\n self._batch_delay_in_seconds = None\n self._batch_count = None\n self._policy_type = 'COMPUTE_INSTANCE_GROUP_LINEAR_ROLLOUT_POLICY_BY_COUNT'\n\n @property\n def batch_count(self):\n \"\"\"\n **[Required]** Gets the batch_count of this ComputeInstanceGroupLinearRolloutPolicyByCount.\n The number that will be used to determine how many instances will be deployed concurrently.\n\n\n :return: The batch_count of this ComputeInstanceGroupLinearRolloutPolicyByCount.\n :rtype: int\n \"\"\"\n return self._batch_count\n\n @batch_count.setter\n def batch_count(self, batch_count):\n \"\"\"\n Sets the batch_count of this ComputeInstanceGroupLinearRolloutPolicyByCount.\n The number that will be used to determine how many instances will be deployed concurrently.\n\n\n :param batch_count: The batch_count of this ComputeInstanceGroupLinearRolloutPolicyByCount.\n :type: int\n \"\"\"\n self._batch_count = batch_count\n\n def __repr__(self):\n return formatted_flat_dict(self)\n\n def __eq__(self, other):\n if other is None:\n return False\n\n return self.__dict__ == other.__dict__\n\n def __ne__(self, other):\n return not self == other\n","repo_name":"oracle/oci-python-sdk","sub_path":"src/oci/devops/models/compute_instance_group_linear_rollout_policy_by_count.py","file_name":"compute_instance_group_linear_rollout_policy_by_count.py","file_ext":"py","file_size_in_byte":3365,"program_lang":"python","lang":"en","doc_type":"code","stars":345,"dataset":"github-code","pt":"52"} +{"seq_id":"8380088436","text":"# 4x4 matrix\ninput_data = [\n#job 0, 1, 2, 3 \n [9, 2, 7, 8], # Worker 0\n [6, 4, 3, 7], # Worker 1\n [5, 8, 1, 8], # Worker 2\n [7, 6, 9, 4] # Worker 3\n]\n\ndef find_least_cost(matrix):\n worker_legnth = len(matrix)\n least_cost = float('inf') # Initialize with positive infinity\n assigned_worker = -1\n assigned_job = -1\n\n for worker in range(worker_legnth):\n job = matrix[worker].index(min(matrix[worker])) # Find the index of the minimum value in the worker's row\n cost = matrix[worker][job]\n\n if cost < least_cost:\n least_cost = cost\n assigned_worker = worker\n assigned_job = job\n return least_cost, assigned_worker, assigned_job\n\ncost, worker, job = find_least_cost(input_data)\nprint(f\"Assigned Worker {worker} to job {job} with cost {cost} \")\n","repo_name":"mooorkey/ProjectI","sub_path":"BranchAndBound.py","file_name":"BranchAndBound.py","file_ext":"py","file_size_in_byte":826,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"24297321466","text":"import webbrowser, time, random\nfrom sad import depression\n\ndef create_magic():\n \n stack = depression()\n \n for x in range(len(stack)):\n stack.append(\"http://www.pickuplinegenerator.com/\")\n \n #print len(stack)\n return stack\n\ndef haha():\n \n stack = create_magic()\n \n for num in range(3):\n \n url = random.choice(stack)\n webbrowser.open(url)\n time.sleep(3)\n \nif __name__ == '__main__':\n haha()","repo_name":"crazcalm/HackFacebook","sub_path":"funny.py","file_name":"funny.py","file_ext":"py","file_size_in_byte":471,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"11711128710","text":"class Solution:\n def singleNumber(self, nums):\n s = reduce(xor, nums)\n nz = s & (s-1) ^ s\n num1 = reduce(xor, filter(lambda n: n & nz, nums))\n return(num1, s ^ num1)\n\n\n\n\n \n\n\n\n#Complexity: time complexity is O(n), where we do 2 passes over data. Additional space complexity is O(1), we use just several additional variables.\n","repo_name":"MohamedHamisa/Single-Number-III","sub_path":"code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":362,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"43368738032","text":"import os\nfrom aws_cdk import Stack, aws_lambda, aws_apigateway\nfrom constructs import Construct\n\n\nclass MacLookupCdkStack(Stack):\n def __init__(self, scope: Construct, construct_id: str, **kwargs) -> None:\n super().__init__(scope, construct_id, **kwargs)\n\n lookup_lmbda = aws_lambda.Function(\n self,\n \"MacLookupLambda\",\n runtime=aws_lambda.Runtime.PYTHON_3_8,\n code=aws_lambda.Code.from_asset(\"lambda\"),\n handler=\"lookup.handler\",\n )\n\n apigw = aws_apigateway.LambdaRestApi(self, \"Endpoint\", handler=lookup_lmbda)\n\n api = apigw.root.add_resource(\"api\")\n v1 = api.add_resource(\"v1\")\n\n items = v1.add_resource(\"macs\")\n items.add_method(\"GET\")\n\n item = items.add_resource(\"{mac}\")\n item.add_method(\"GET\")\n","repo_name":"xjulio/mac-lookup-cdk","sub_path":"mac_lookup_cdk/mac_lookup_cdk_stack.py","file_name":"mac_lookup_cdk_stack.py","file_ext":"py","file_size_in_byte":830,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"3492199640","text":"import bladeRF\nfrom bladeRF import _cffi\nfrom bladeRF._cffi import ffi, cdef, ptop\n\ncdef(\"\"\"\ntypedef enum\n{\n BLADERF_MODULE_RX, /**< Receive Module */\n BLADERF_MODULE_TX /**< Transmit Module */\n} bladerf_module;\n\ntypedef enum {\n BLADERF_SAMPLING_UNKNOWN, /**< Unable to determine connection type */\n BLADERF_SAMPLING_INTERNAL, /**< Sample from RX/TX connector */\n BLADERF_SAMPLING_EXTERNAL /**< Sample from J60 or J61 */\n} bladerf_sampling;\n\ntypedef enum {\n BLADERF_LNA_GAIN_UNKNOWN, /**< Invalid LNA gain */\n BLADERF_LNA_GAIN_BYPASS, /**< LNA bypassed - 0dB gain */\n BLADERF_LNA_GAIN_MID, /**< LNA Mid Gain (MAX-6dB) */\n BLADERF_LNA_GAIN_MAX /**< LNA Max Gain */\n} bladerf_lna_gain;\n\ntypedef enum {\n BLADERF_LPF_NORMAL, /**< LPF connected and enabled */\n BLADERF_LPF_BYPASSED, /**< LPF bypassed */\n BLADERF_LPF_DISABLED /**< LPF disabled */\n} bladerf_lpf_mode;\n\n\n/** Minimum RXVGA1 gain, in dB */\n#define BLADERF_RXVGA1_GAIN_MIN ...\n\n/** Maximum RXVGA1 gain, in dB */\n#define BLADERF_RXVGA1_GAIN_MAX ...\n\n/** Minimum RXVGA2 gain, in dB */\n#define BLADERF_RXVGA2_GAIN_MIN ...\n\n/** Maximum RXVGA2 gain, in dB */\n#define BLADERF_RXVGA2_GAIN_MAX ...\n\n/** Minimum TXVGA1 gain, in dB */\n#define BLADERF_TXVGA1_GAIN_MIN ...\n\n/** Maximum TXVGA1 gain, in dB */\n#define BLADERF_TXVGA1_GAIN_MAX ...\n\n/** Minimum TXVGA2 gain, in dB */\n#define BLADERF_TXVGA2_GAIN_MIN ...\n\n/** Maximum TXVGA2 gain, in dB */\n#define BLADERF_TXVGA2_GAIN_MAX ...\n\n/** Minimum sample rate, in Hz */\n#define BLADERF_SAMPLERATE_MIN ...\n\n/** Maximum recommended sample rate, in Hz */\n#define BLADERF_SAMPLERATE_REC_MAX ...\n\n/** Minimum bandwidth, in Hz */\n#define BLADERF_BANDWIDTH_MIN ...\n\n/** Maximum bandwidth, in Hz */\n#define BLADERF_BANDWIDTH_MAX ...\n\n/** Minimum tunable frequency (without an XB-200 attached), in Hz */\n#define BLADERF_FREQUENCY_MIN ...\n\n/** Maximum tunable frequency, in Hz */\n#define BLADERF_FREQUENCY_MAX ...\n\"\"\")\n\n@cdef('int bladerf_enable_module(struct bladerf *dev, '\n 'bladerf_module m, bool enable);')\ndef enable_module(dev, module, enable):\n err = _cffi.lib.bladerf_enable_module(dev, module, enable)\n bladeRF.errors.check_retcode(err)\n\n\n@cdef('int bladerf_set_sample_rate(struct bladerf *dev, '\n 'bladerf_module module, unsigned int rate, unsigned int *actual);')\ndef set_sample_rate(dev, module, rate):\n actual = ffi.new('unsigned int*')\n err = _cffi.lib.bladerf_set_sample_rate(dev, module, int(rate), actual)\n bladeRF.errors.check_retcode(err)\n return int(actual[0])\n\n\n@cdef('int bladerf_set_sampling(struct bladerf *dev, bladerf_sampling sampling);')\ndef set_sampling(dev, sampling):\n err = _cffi.lib.bladerf_set_sampling(dev, sampling)\n bladeRF.errors.check_retcode(err)\n\n\n@cdef('int bladerf_get_sampling(struct bladerf *dev, bladerf_sampling *sampling);')\ndef get_sampling(dev):\n sampling = ffi.new('bladerf_sampling*')\n err = _cffi.lib.bladerf_get_sampling(dev, sampling)\n bladeRF.errors.check_retcode(err)\n return int(sampling[0])\n\n\n@cdef('int bladerf_get_sample_rate(struct bladerf *dev, bladerf_module module, unsigned int *rate);')\ndef get_sample_rate(dev, module):\n rate = ffi.new('unsigned int *')\n err = _cffi.lib.bladerf_get_sample_rate(dev, module, rate)\n bladeRF.errors.check_retcode(err)\n return int(rate[0])\n\n\n@cdef('int bladerf_get_rational_sample_rate(struct bladerf *dev,'\n 'bladerf_module module, struct bladerf_rational_rate *rate);')\ndef get_rational_sample_rate(dev, module):\n pass\n\n\n@cdef('int bladerf_set_txvga2(struct bladerf *dev, int gain);')\ndef set_txvga2(dev, gain):\n err = _cffi.lib.bladerf_set_txvga2(dev, gain)\n bladeRF.errors.check_retcode(err)\n\n\n@cdef('int bladerf_get_txvga2(struct bladerf *dev, int *gain);')\ndef get_txvga2(dev):\n gain = ffi.new('int *')\n err = _cffi.lib.bladerf_get_txvga2(dev, gain)\n bladeRF.errors.check_retcode(err)\n return int(gain[0])\n\n\n@cdef('int bladerf_set_txvga1(struct bladerf *dev, int gain);')\ndef set_txvga1(dev, gain):\n err = _cffi.lib.bladerf_set_txvga1(dev, gain)\n bladeRF.errors.check_retcode(err)\n \n\n@cdef('int bladerf_get_txvga1(struct bladerf *dev, int *gain);')\ndef get_txvga1(dev):\n gain = ffi.new('int *')\n err = _cffi.lib.bladerf_get_txvga1(dev, gain)\n bladeRF.errors.check_retcode(err)\n return int(gain[0])\n\n\n@cdef('int bladerf_set_lna_gain(struct bladerf *dev, bladerf_lna_gain gain);')\ndef set_lna_gain(dev, gain):\n err = _cffi.lib.bladerf_set_lna_gain(dev, gain)\n bladeRF.errors.check_retcode(err)\n\n\n@cdef('int bladerf_get_lna_gain(struct bladerf *dev, bladerf_lna_gain *gain);')\ndef get_lna_gain(dev):\n gain = ffi.new('bladerf_lna_gain *')\n err = _cffi.lib.bladerf_get_lna_gain(dev, gain)\n bladeRF.errors.check_retcode(err)\n return int(gain[0])\n\n\n@cdef('int bladerf_set_rxvga1(struct bladerf *dev, int gain);')\ndef set_rxvga1(dev, gain):\n err = _cffi.lib.bladerf_set_rxvga1(dev, gain)\n bladeRF.errors.check_retcode(err)\n\n\n@cdef('int bladerf_get_rxvga1(struct bladerf *dev, int *gain);')\ndef get_rxvga1(dev):\n gain = ffi.new('int *')\n err = _cffi.lib.bladerf_get_rxvga1(dev, gain)\n bladeRF.errors.check_retcode(err)\n return int(gain[0])\n\n\n@cdef('int bladerf_set_rxvga2(struct bladerf *dev, int gain);')\ndef set_rxvga2(dev, gain):\n err = _cffi.lib.bladerf_set_rxvga2(dev, gain)\n bladeRF.errors.check_retcode(err)\n\n\n@cdef('int bladerf_get_rxvga2(struct bladerf *dev, int *gain);')\ndef get_rxvga2(dev):\n gain = ffi.new('int *')\n err = _cffi.lib.bladerf_get_rxvga2(dev, gain)\n bladeRF.errors.check_retcode(err)\n return int(gain[0])\n\n\n@cdef('int bladerf_set_bandwidth(struct bladerf *dev, bladerf_module module, '\n 'unsigned int bandwidth, unsigned int *actual);')\ndef set_bandwidth(dev, module, bandwidth):\n actual = ffi.new('unsigned int *')\n err = _cffi.lib.bladerf_set_bandwidth(dev, module, bandwidth, actual)\n bladeRF.errors.check_retcode(err)\n return int(actual[0])\n\n\n@cdef('int bladerf_get_bandwidth(struct bladerf *dev, bladerf_module module, '\n 'unsigned int *bandwidth);')\ndef get_bandwidth(dev, module):\n bandwidth = ffi.new('unsigned int *')\n err = _cffi.lib.bladerf_get_bandwidth(dev, module, bandwidth)\n bladeRF.errors.check_retcode(err)\n return int(bandwidth[0])\n\n\n@cdef('int bladerf_set_lpf_mode(struct bladerf *dev, bladerf_module module, '\n 'bladerf_lpf_mode mode);')\ndef set_lpf_mode(dev, module, mode):\n err = _cffi.lib.bladerf_set_lpf_mode(dev, module, mode)\n bladeRF.errors.check_retcode(err)\n\n\n@cdef('int bladerf_get_lpf_mode(struct bladerf *dev, bladerf_module module,'\n 'bladerf_lpf_mode *mode);')\ndef get_lpf_mode(dev, module):\n mode = ffi.new('bladerf_lpf_mode *')\n err = _cffi.lib.bladerf_get_lpf_mode(dev, module, mode)\n bladeRF.errors.check_retcode(err)\n return int(mode[0])\n\n\n@cdef('int bladerf_select_band(struct bladerf *dev, bladerf_module module,'\n 'unsigned int frequency);')\ndef select_band(dev, module, frequency):\n err = _cffi.lib.bladerf_select_band(dev, module, frequency)\n bladeRF.errors.check_retcode(err)\n\n\n@cdef('int bladerf_set_frequency(struct bladerf *dev, '\n 'bladerf_module module, unsigned int frequency);')\ndef set_frequency(dev, module, frequency):\n err = _cffi.lib.bladerf_set_frequency(dev, module, int(frequency))\n bladeRF.errors.check_retcode(err)\n\n\n@cdef('int bladerf_get_frequency(struct bladerf *dev,'\n 'bladerf_module module, unsigned int *frequency);')\ndef get_frequency(dev, module):\n frequency = ffi.new('unsigned int *')\n err = _cffi.lib.bladerf_get_frequency(dev, module, frequency)\n bladeRF.errors.check_retcode(err)\n return int(frequency[0])\n\n","repo_name":"michelp/pybladeRF","sub_path":"bladeRF/ctrl.py","file_name":"ctrl.py","file_ext":"py","file_size_in_byte":7724,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"52"} +{"seq_id":"73266924644","text":"import requests \n\nclass ZipCodes:\n def __init__(self):\n\n # Get zip code api key from file\n key_file = open(\"zip_code_api_key\",\"r\") \n self.key = key_file.read()\n\n def get_zip_codes_in_radius(self, zip_code, radius):\n if radius is None:\n radius = 0\n url = \"https://www.zipcodeapi.com/rest/\"+self.key+\"/radius.json/\"+str(zip_code)+\"/\"+str(radius)+\"/miles\"\n r = requests.get(url = url)\n\n return r.json()['zip_codes']","repo_name":"dylanhantula/core","sub_path":"back-end/zip_code_api.py","file_name":"zip_code_api.py","file_ext":"py","file_size_in_byte":478,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"32177471942","text":"import random\r\n\r\n\r\nmy_file = open(\"wordle.txt\", 'r')\r\nwordle = my_file.read()\r\nwordle_list = wordle.split(\",\")\r\nmy_file.close()\r\n\r\nanswer = str(random.choice(wordle_list))\r\nanswer_list=list(answer)\r\nc = []\r\nattempts = 0\r\nsmol = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z']\r\n\r\n\r\nwhile attempts < 7:\r\n attempt = str(input(f'Guess a 5 letter word! {6-attempts} guesses only :^) ' + '\\n'+'All letters start with caps! Not the bald Caps from LEC KEKW' + '\\n' + '\\n'))\r\n \r\n if len(attempt) != 5:\r\n print('This is not 5 letters you bonobo!')\r\n continue\r\n elif attempt[0] in smol:\r\n print ('Starts with caps sir... ')\r\n continue\r\n elif attempt not in wordle_list:\r\n print ('Not a valid word!')\r\n continue\r\n \r\n if attempt == answer:\r\n print(f'Congratulations! {attempt} is correct!')\r\n break\r\n else:\r\n attempts+=1\r\n print('You have '+ str(6-attempts) + ' attempt(s) ' + 'left. ')\r\n for word in range(len(answer)):\r\n if attempt[word] == answer[word]:\r\n print(f'{attempt[word]} is correct')\r\n if attempt[word] != answer[word]:\r\n c.append(attempt[word])\r\n c=list(set(c))\r\n \r\n for word in c: \r\n if word in answer_list:\r\n print(f'{word} is correct but in the wrong spot')\r\n \r\n if word not in answer_list:\r\n print(f'{word} is wrong')\r\n list.clear(c)\r\n \r\n \r\n if attempts > 5:\r\n print()\r\n print(f'You ran out of guesses. The correct word is {answer} ')\r\n break \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","repo_name":"yunhong97/Simple-Wordle-Python-Code","sub_path":"wordle.py","file_name":"wordle.py","file_ext":"py","file_size_in_byte":1922,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"4883798511","text":"import logging\nimport time\nfrom typing import Dict\nfrom multiprocessing import Queue\nimport threading\n\nimport memcache\n\nfrom utils import apps_group_to_dict\n\n\nclass Writer(threading.Thread):\n def __init__(\n self,\n queue: Queue,\n memc_client: memcache.Client,\n max_retry: int,\n time_retry: int,\n f_stats_error: Dict[str, int],\n f_stats_success: Dict[str, int],\n dry: bool\n ):\n super(Writer, self).__init__()\n self.queue = queue\n self.client = memc_client\n self.max_retry = max_retry\n self.time_retry = time_retry\n self.f_stats_error = f_stats_error\n self.f_stats_success = f_stats_success\n self.dry = dry\n\n def run(self):\n while True:\n if not self.queue.empty():\n apps_group = self.queue.get()\n self.queue.task_done()\n if isinstance(apps_group, str):\n if apps_group == 'quit':\n break\n\n out_dict = apps_group_to_dict(apps_group)\n\n failures = []\n for _ in range(self.max_retry):\n if self.dry:\n self.write_dry(out_dict)\n # вроде как try не нужен, поскольку клиент ловит ошибки\n failures = self.client.set_multi(out_dict)\n if len(failures) < len(out_dict):\n break\n time.sleep(self.time_retry)\n\n self.f_stats_error[apps_group.f_name] += len(failures)\n self.f_stats_success[apps_group.f_name] += len(out_dict) - len(failures)\n\n def write_dry(self, out_dict):\n for key, val in out_dict.items():\n logging.info(\"%s - %s -> %s\" % (self.client.servers[0].address, key, str(val).replace(\"\\n\", \" \")))\n","repo_name":"dmitriyStepanenko/otus_hw12","sub_path":"writer.py","file_name":"writer.py","file_ext":"py","file_size_in_byte":1923,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"192839234","text":"from dataclasses import dataclass\nfrom uuid import UUID\n\nfrom fastapi.testclient import TestClient\n\nfrom mealie.repos.repository_factory import AllRepositories\nfrom mealie.schema.recipe.recipe import Recipe\nfrom tests.utils import api_routes, random_string\nfrom tests.utils.fixture_schemas import TestUser\n\n\n@dataclass(slots=True)\nclass SimpleCase:\n value: str\n is_valid: bool\n\n\ndef test_validators_username(api_client: TestClient, unique_user: TestUser):\n users = [\n SimpleCase(value=unique_user.username, is_valid=False),\n SimpleCase(value=random_string(), is_valid=True),\n ]\n\n for user in users:\n response = api_client.get(api_routes.validators_user_name + \"?name=\" + user.value)\n assert response.status_code == 200\n response_data = response.json()\n assert response_data[\"valid\"] == user.is_valid\n\n\ndef test_validators_email(api_client: TestClient, unique_user: TestUser):\n emails = [\n SimpleCase(value=unique_user.email, is_valid=False),\n SimpleCase(value=f\"{random_string()}@example.com\", is_valid=True),\n ]\n\n for user in emails:\n response = api_client.get(api_routes.validators_user_email + \"?email=\" + user.value)\n assert response.status_code == 200\n response_data = response.json()\n assert response_data[\"valid\"] == user.is_valid\n\n\ndef test_validators_group_name(api_client: TestClient, unique_user: TestUser, database: AllRepositories):\n group = database.groups.get_one(unique_user.group_id)\n\n groups = [\n SimpleCase(value=group.name, is_valid=False),\n SimpleCase(value=random_string(), is_valid=True),\n ]\n\n for user in groups:\n response = api_client.get(api_routes.validators_group + \"?name=\" + user.value)\n assert response.status_code == 200\n response_data = response.json()\n assert response_data[\"valid\"] == user.is_valid\n\n\n@dataclass(slots=True)\nclass RecipeValidators:\n name: str\n group: UUID | str\n is_valid: bool\n\n\ndef test_validators_recipe(api_client: TestClient, random_recipe: Recipe):\n recipes = [\n RecipeValidators(name=random_recipe.name, group=random_recipe.group_id, is_valid=False),\n RecipeValidators(name=random_string(), group=random_recipe.group_id, is_valid=True),\n RecipeValidators(name=random_string(), group=random_recipe.group_id, is_valid=True),\n ]\n\n for recipe in recipes:\n response = api_client.get(api_routes.validators_recipe + f\"?group_id={recipe.group}&name={recipe.name}\")\n assert response.status_code == 200\n response_data = response.json()\n assert response_data[\"valid\"] == recipe.is_valid\n","repo_name":"mealie-recipes/mealie","sub_path":"tests/integration_tests/test_validators.py","file_name":"test_validators.py","file_ext":"py","file_size_in_byte":2663,"program_lang":"python","lang":"en","doc_type":"code","stars":3977,"dataset":"github-code","pt":"52"} +{"seq_id":"19361451140","text":"# encoding:utf-8\nimport sys\n\nsys.path.append(\"..\") # 将该目录加入到环境变量\nimport numpy as np\nfrom mf import MF\nfrom reader.trust import TrustGetter\n\n\nclass SocialMF(MF):\n \"\"\"\n docstring for SocialMF\n\n Jamali M, Ester M. A matrix factorization technique with trust propagation for recommendation in social networks[C]//Proceedings of the fourth ACM conference on Recommender systems. ACM, 2010: 135-142.\n \"\"\"\n\n def __init__(self):\n super(SocialMF, self).__init__()\n # self.config.lr=0.0001\n self.config.alpha = 1 # 0.8 rmse=0.87605\n self.tg = TrustGetter() # loading trust data\n self.init_model()\n\n def train_model(self):\n iteration = 0\n while iteration < self.config.maxIter:\n self.loss = 0\n for index, line in enumerate(self.rg.trainSet()):\n user, item, rating = line\n u = self.rg.user[user]\n i = self.rg.item[item]\n error = rating - self.predict(user, item)\n self.loss += error ** 2\n p, q = self.P[u], self.Q[i]\n\n total_weight = 0.0\n social_term = np.zeros(self.config.factor)\n followees = self.tg.get_followees(user) # 获得u所关注的用户列表\n for followee in followees:\n weight = followees[followee]\n if self.rg.containsUser(followee):\n uk = self.P[self.rg.user[followee]]\n social_term += weight * uk\n total_weight += weight\n\n if total_weight != 0:\n social_term = p - social_term / total_weight\n\n social_term_a = np.zeros(self.config.factor)\n total_count = 0\n followers = self.tg.get_followers(user)\n for follower in followers:\n if self.rg.containsUser(follower):\n total_count += 1\n uv = self.P[self.rg.user[follower]]\n social_term_m = np.zeros(self.config.factor)\n total_weight = 0.0\n followees = self.tg.get_followees(follower) # 获得u所关注的用户列表\n for followee in followees:\n weight = followees[followee]\n if self.rg.containsUser(followee):\n uw = self.P[self.rg.user[followee]]\n social_term_m += weight * uw\n total_weight += weight\n if total_weight != 0:\n social_term_a += uv - social_term_m / total_weight\n if total_count != 0:\n social_term_a /= total_count\n\n # update latent vectors\n self.P[u] += self.config.lr * (\n error * q - self.config.alpha * social_term + self.config.alpha * social_term_a - self.config.lambdaP * p) #\n self.Q[i] += self.config.lr * (error * p - self.config.lambdaQ * q)\n\n self.loss += self.config.alpha * social_term.dot(social_term).sum()\n\n self.loss += self.config.lambdaP * (self.P * self.P).sum() + self.config.lambdaQ * (self.Q * self.Q).sum()\n\n iteration += 1\n if self.isConverged(iteration):\n break\n\n\nif __name__ == '__main__':\n smf = SocialMF()\n smf.train_model()\n","repo_name":"ProQwest/Recommender-System","sub_path":"model/social_mf.py","file_name":"social_mf.py","file_ext":"py","file_size_in_byte":3504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"52"} +{"seq_id":"4633120190","text":"import unittest\n\nfrom env import veripb\nfrom veripb.parser import *\nfrom veripb.rules import *\nfrom veripb.rules_register import get_registered_rules\nfrom veripb.constraints import Term\nfrom veripb.constraints import CppIneqFactory as IneqFactory\n\n\nclass DummyContext:\n pass\n\ndef lit2str(lit):\n if lit < 0:\n return \"~x%i\"%(-lit)\n else:\n return \"x%i\"%(lit)\n\nclass TestParsing(unittest.TestCase):\n\n def checkParsing(self, terms, degree, eq = False):\n termString = \" \".join([\"%i %s\" % (coeff, lit2str(lit)) for coeff, lit in terms])\n op = \"=\" if eq else \">=\"\n string = \"%s %s %i ;\" % (termString, op, degree)\n terms = [Term(coeff, lit) for coeff, lit in terms]\n expect = []\n expect.append(self.ineqFactory.fromTerms(terms, degree))\n if eq:\n for term in terms:\n term.coefficient = -term.coefficient\n degree = -degree\n expect.append(self.ineqFactory.fromTerms(terms, degree))\n\n res = self.ineqFactory.parseString(string, allowMultiple = True)\n assert res == expect\n\n def setUp(self):\n self.ineqFactory = IneqFactory(False)\n self.context = DummyContext()\n self.context.ineqFactory = self.ineqFactory\n\n def test_header(self):\n parser = RuleParser(DummyContext())\n print(parser.parse(get_registered_rules(), [\"pseudo-Boolean proof version 1.0\"]))\n\n def test_OPB_line_1(self):\n res = self.ineqFactory.parseString(\"3 x1 >= 2 ;\", allowMultiple = True)\n assert res == [self.ineqFactory.fromTerms([Term(3,1)], 2)]\n\n def test_OPB_line_2(self):\n res = OPBParser(self.ineqFactory).parseOPB(\"+2 x1 -4 x2 = -42;\".split())\n assert res == [\n self.ineqFactory.fromTerms([Term(2,1), Term(-4,2)], -42),\n self.ineqFactory.fromTerms([Term(-2,1), Term(4,2)], 42)]\n\n def test_eq(self):\n res = OPBParser(self.ineqFactory).parseOPB(\"1 x2 -2 x1 = 2;\".split())\n\n if self.ineqFactory.freeNames:\n ineq1 = self.ineqFactory.fromTerms([Term(-2,2), Term(1,1)], 2)\n ineq2 = self.ineqFactory.fromTerms([Term(2,2), Term(-1,1)], -2)\n else:\n ineq1 = self.ineqFactory.fromTerms([Term(-2,1), Term(1,2)], 2)\n ineq2 = self.ineqFactory.fromTerms([Term(2,1), Term(-1,2)], -2)\n\n assert res == [ineq1, ineq2]\n\n def test_CNF_line(self):\n res = OPBParser(self.ineqFactory).parseCNF(\"-1 2 0\".split())\n assert res == [self.ineqFactory.fromTerms([Term(1,2), Term(1,-1)], 1)]\n\n def test_equals(self):\n rule = ConstraintEquals.parse(\"42 3 x1 >= 2;\", self.context)\n assert rule == ConstraintEquals(42, self.ineqFactory.fromTerms([Term(3,1)], 2))\n\n def test_implies(self):\n rule = ConstraintImplies.parse(\"42 3 x1 >= 2;\", self.context)\n assert rule == ConstraintImplies(42, self.ineqFactory.fromTerms([Term(3,1)], 2))\n\n def test_contradiction(self):\n rule = IsContradiction.parse(\"42 0\", self.context)\n assert rule == IsContradiction(42)\n\n def test_clause_1(self):\n self.checkParsing([(1, 1)], 1)\n self.checkParsing([(1, 1)], 1, eq = True)\n\n def test_clause_2(self):\n self.checkParsing([(1, 1), (1, 2)], 1)\n self.checkParsing([(1, 1), (1, 2)], 1, eq = True)\n\n def test_clause_3(self):\n self.checkParsing([(1, 1), (1, -2) ], 1)\n self.checkParsing([(1, 1), (1, -2) ], 1, eq = True)\n\n def test_clause_4(self):\n self.checkParsing([(1, 1), (-1, -2)], 0)\n self.checkParsing([(1, 1), (-1, -2)], 0, eq = True)\n\n def test_ineq_1(self):\n val = 214748364\n self.checkParsing([(val, 1)], val)\n self.checkParsing([(val, 1)], val, eq = True)\n\n def test_ineq_1(self):\n val = 214748364\n self.checkParsing([(-val, 1)], val)\n self.checkParsing([(-val, 1)], val, eq = True)\n\n def test_ineq_1(self):\n val = 214748364\n self.checkParsing([(-val, -1)], val)\n self.checkParsing([(-val, -1)], val, eq = True)\n\n def test_int32_max_almost(self):\n val = 2147483647 - 1\n self.checkParsing([(val, 1)], val)\n self.checkParsing([(val, 1)], val, eq = True)\n\n def test_int32_max(self):\n val = 2147483647\n self.checkParsing([(val, 1)], val)\n self.checkParsing([(val, 1)], val, eq = True)\n\n def test_int32_max_more(self):\n val = 2147483647 + 1\n self.checkParsing([(val, 1)], val)\n self.checkParsing([(val, 1)], val, eq = True)\n\n def test_small_large_coeff(self):\n val = 2147483647 + 1\n self.checkParsing([(1, 2), (val, 1)], val)\n self.checkParsing([(1, 2), (val, 1)], val, eq = True)\n\n def test_small_large_degree(self):\n val = 2147483647 + 1\n self.checkParsing([(1, 2), (1, 1)], val)\n self.checkParsing([(1, 2), (1, 1)], val, eq = True)\n\n def test_degree_overflow(self):\n val = 2147483647\n self.checkParsing([(-val//2, 1), (-val//2, 2), (-val//2, 3), (-val//2, 4)], 0)\n self.checkParsing([(-val//2, 1), (-val//2, 2), (-val//2, 3), (-val//2, 4)], 0, eq = True)\n\n def test_implication_1(self):\n string = \"1 x1 1 x2 1 x3 >= 2 <== x4 ;\"\n expect = self.ineqFactory.fromTerms([(1,1), (1,2), (1,3), (2,-4)], 2)\n result = self.ineqFactory.parseString(string, allowMultiple = True)\n assert result[0] == expect\n\n def test_implication_2(self):\n string = \"1 x1 1 x2 1 x3 >= 2 ==> x4 ;\"\n expect = self.ineqFactory.fromTerms([(1,-1), (1,-2), (1,-3), (2, 4)], 2)\n result = self.ineqFactory.parseString(string, allowMultiple = True)\n assert result[0] == expect\n\n def test_implication_3(self):\n string = \"-2 x1 1 x2 1 x3 >= 0 ==> x4 ;\"\n expect = self.ineqFactory.fromTerms([(2,1), (1,-2), (1,-3), (3,4)], 3)\n result = self.ineqFactory.parseString(string, allowMultiple = True)\n assert result[0] == expect\n\nclass TestWordParser(unittest.TestCase):\n def test_working_1(self):\n line = \"this is a test sentence\"\n with WordParser(line) as words:\n for word in words:\n pass\n\n def test_working_2(self):\n line = \"fancy 1 2 3 4\"\n with WordParser(line) as words:\n it = iter(words)\n assert(next(it) == \"fancy\")\n nums = list(map(int, it))\n assert(nums == [1,2,3,4])\n\n def test_exception_int(self):\n try:\n line = \" fancy 1 2 a 4\"\n with WordParser(line) as words:\n it = iter(words)\n assert(next(it) == \"fancy\")\n nums = list(map(int, it))\n except ParseError as e:\n assert(e.column == 14)\n else:\n assert(False)\n\n\n def test_exception_manual(self):\n try:\n line = \" this notis a test sentence\"\n with WordParser(line) as words:\n it = iter(words)\n assert(next(it) == \"this\")\n if next(it) != \"is\":\n raise ValueError(\"Expected is.\")\n except ParseError as e:\n assert(e.column == 9)\n else:\n assert(False)\n","repo_name":"StephanGocht/VeriPB","sub_path":"tests/test_parsing.py","file_name":"test_parsing.py","file_ext":"py","file_size_in_byte":7174,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"52"} +{"seq_id":"1381757365","text":"import sys, os\nsys.path.append(os.path.join(os.path.dirname(__file__), '..', 'common'))\nfrom common import open_file\n\ncan_drop_to = lambda x, y: grid[y][x] == '.'\n\ndef fill_lines(this_x, this_y, next_x, next_y):\n if this_x > next_x:\n for x in range(int(this_x), int(next_x)-1, -1): grid[this_y][x] = '#'\n elif this_x < next_x:\n for x in range(int(this_x), int(next_x)+1): grid[this_y][x] = '#'\n elif this_y > next_y:\n for y in range(int(this_y), int(next_y)-1, -1): grid[y][this_x] = '#'\n else:\n for y in range(int(this_y), int(next_y)+1): grid[y][this_x] = '#'\n\ndef print_grid():\n for y in range(200):\n for x in range(400, 720): print(grid[y][x], end='')\n print()\n\ndef determine_abyss_row():\n for y in range(len(grid)-1, 0, -1):\n if '#' in grid[y]: return y\n\ndef drop_sand():\n sand_location = (500, 0)\n while sand_location[1] < abyss_row:\n if can_drop_to(sand_location[0], sand_location[1]+1):\n sand_location = (sand_location[0], sand_location[1]+1)\n elif can_drop_to(sand_location[0]-1, sand_location[1]+1):\n sand_location = (sand_location[0]-1, sand_location[1]+1)\n elif can_drop_to(sand_location[0]+1, sand_location[1]+1):\n sand_location = (sand_location[0]+1, sand_location[1]+1)\n else:\n grid[sand_location[1]][sand_location[0]] = 'o'\n return True\n if sand_location[1] >= abyss_row:\n print(\"IN THE ABYSS\")\n return False\n return True\n\ndef flush_sand():\n for y in range(len(grid)):\n for x in range(len(grid[y])):\n if grid[y][x] == 'o': grid[y][x] = '.'\n\ngrid = [['.' for x in range(1000)] for y in range(1000)]\nfor line in open_file(\"input.txt\"):\n coords = line.strip().split(' -> ')\n for index, coord in enumerate(coords):\n if index < len(coords)-1:\n this_x, this_y = coord.split(',')\n next_x, next_y = coords[index+1].split(',')\n fill_lines(int(this_x), int(this_y), int(next_x), int(next_y))\n\nabyss_row = determine_abyss_row()\nsand_dropped = 0\nwhile True:\n if not drop_sand():\n print(f\"Part 1: Successfully dropped {sand_dropped} pieces of sand\") # 578\n break\n sand_dropped += 1\n\n# PART 2: Re-initialise grid (remove sand, and draw floor)\nflush_sand()\nabyss_row += 2\ngrid[abyss_row] = ['#' for x in range(1000)]\nsand_dropped = 0\nwhile grid[0][500] != \"o\":\n sand_dropped += 1\n drop_sand()\nprint(f\"Part 2: Successfully dropped {sand_dropped} pieces of sand\") # 24377","repo_name":"neilstudd/advent-of-code","sub_path":"2022/day14/day14.py","file_name":"day14.py","file_ext":"py","file_size_in_byte":2539,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"4374460454","text":"#!/usr/bin/env python\nimport rospy\nimport sys\nimport pickle\nimport socket\nimport numpy as np\nfrom visualization_msgs.msg import Marker\nfrom geometry_msgs.msg import PoseWithCovarianceStamped, Point, PointStamped\nimport tf\n\n\n# This node will periodically send its position to the CC.\n# It will also send a list of the messes that it has discovered to the CC.\nclass ClientNode():\n def __init__(self):\n rospy.init_node('location_node')\n rospy.Subscriber('/visualization_marker', Marker, self.marker_callback, queue_size=1)\n rospy.Subscriber('/amcl_pose', PoseWithCovarianceStamped, self.amcl_callback)\n\n host = \"134.126.125.125\" # ip of server\n port = 13000\n self.addr = (host, port)\n self.messes = []\n self.tf_listener = tf.TransformListener()\n self.position = None\n\n try:\n self.s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n except:\n rospy.loginfo(\"FAILED TO CREATE SOCKET\")\n sys.exit()\n\n self.ROBOT_ID = 0\n self.pos = [0,0]\n self.mess = [None,None]\n rate = rospy.Rate(5)\n\n rospy.loginfo(\"LOCATION_CLIENT SETUP COMPLETE\")\n self.old_marker = Point()\n self.old_marker.x = 100000\n self.old_marker.y = 100000\n while not rospy.is_shutdown():\n mess_x = self.mess[0]\n mess_y = self.mess[1]\n\n try:\n self.s.sendto(pickle.dumps([0,self.ROBOT_ID, self.pos[0], self.pos[1]]), self.addr)\n except Exception as e:\n s.close()\n rospy.loginfo(\"LOCATION_CLIENT ERROR. CLOSING SOCKET\")\n rospy.loginfo(e)\n sys.exit()\n\n rate.sleep()\n\n def is_new_mess(self, xone, yone, xtwo, ytwo):\n return np.sqrt((xone - xtwo)**2 + (yone - ytwo)**2) < .2\n\n # Callback for 'amcl_pose' topic\n def amcl_callback(self, amcl_msg):\n self.position = amcl_msg.pose.pose\n self.pos[0] = amcl_msg.pose.pose.position.x\n self.pos[1] = amcl_msg.pose.pose.position.y\n\n def marker_callback(self, marker):\n \"\"\" The marker callback to see the ar_markers\"\"\"\n marker_point = PointStamped()\n marker_point.header.frame_id = 'camera_rgb_optical_frame'\n marker_point.header.stamp = rospy.get_rostime()\n marker_point.point.x = marker.pose.position.x\n marker_point.point.y = marker.pose.position.y\n marker_point.point.z = marker.pose.position.z\n\n try: # transform marker position into the map frame\n marker_point.header.stamp = rospy.get_rostime()\n self.tf_listener.waitForTransform('camera_rgb_optical_frame',\n 'map',\n marker_point.header.stamp,\n rospy.Duration(1.0))\n # get the point transform\n marker_point = self.tf_listener.transformPoint('map', marker_point) \n except tf.Exception as e:\n print(e)\n print(\"ERROR in marker_callback\")\n\n new = True\n if len(self.messes) != 0:\n for mess in self.messes:\n if (self.close_enough(mess.x, mess.y, marker_point.point.x, marker_point.point.y, .15)):\n new = False\n else:\n if self.close_enough(self.position.position.x, self.position.position.y, marker_point.point.x, marker_point.point.y, 1):\n new = False\n rospy.loginfo(\"In second one\")\n self.messes.append(marker_point.point)\n self.s.sendto(pickle.dumps([1, self.ROBOT_ID, marker_point.point.x, marker_point.point.y]), self.addr)\n\n if self.close_enough(self.position.position.x, self.position.position.y, marker_point.point.x, marker_point.point.y, 1) and new:\n rospy.loginfo(\"In third one\")\n self.messes.append(marker_point.point)\n self.s.sendto(pickle.dumps([1, self.ROBOT_ID, marker_point.point.x, marker_point.point.y]), self.addr)\n\n \n \n\n def close_enough(self, x_one, y_one, x_two, y_two, threshold):\n \"\"\" Checks to see if its close enough to a goal\"\"\"\n return (np.sqrt((x_one - x_two)**2 + (y_one - y_two)**2) < threshold)\n\n \nif __name__ == \"__main__\":\n ClientNode()\n","repo_name":"JoeyNeidigh/robo_cleanup","sub_path":"scripts/location_client.py","file_name":"location_client.py","file_ext":"py","file_size_in_byte":4351,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"52"} +{"seq_id":"1844978227","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Dec 10 17:27:50 2018\r\n\r\n@author: User\r\n\"\"\"\r\n\r\nimport requests\r\nimport lxml.html as lh\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom bs4 import BeautifulSoup\r\n\r\nurl='http://s.cafef.vn/bao-cao-tai-chinh/ABC/BSheet/2018/0/0/1/0/bao-cao-tai-chinh-cong-ty-co-phan-truyen-thong-vmg.chn'\r\n\r\n#Create a handle, page, to handle the contents of the website\r\nimport time \r\nfrom selenium import webdriver\r\nfrom selenium.webdriver.common.keys import Keys\r\n\r\ndriver = webdriver.Chrome() \r\n\r\ndriver.get(url)\r\n\r\n# run\r\nstart_time = time.time()\r\ncks = ['hng', 'hsg']\r\nloadcafe(cks,driver)\r\nend_time = round((time.time() - start_time)/60,2)\r\nprint(\"TOTAL_TIME--- %s Minute ---\" %end_time,'all complete') \r\n\r\n\r\n\r\ndef loadcafe(cks,driver, outcd , outhd, outlctt):\r\n for ck in cks:\r\n ppTrucT = False\r\n # ghi ten ma chứng khoan\r\n ma_ck = driver.find_element_by_xpath(\"//input[@name='txtKeyword']\")\r\n ma_ck.clear()\r\n ma_ck.send_keys(ck)\r\n # tim ma chung khoan\r\n driver.find_element_by_xpath(\"//img[@id='btSearch']\").click()\r\n # chọn kỳ theo nam\r\n driver.find_element_by_xpath(\"//input[@id='rdo0']\").click()\r\n # truoc = driver.find_element_by_xpath(\"//img[@alt='Xem dữ liệu trước']\") \r\n # sau = driver.find_element_by_xpath(\"//img[@alt='Xem dữ liệu tiếp']\")\r\n # cac muc bao cao\r\n \r\n #----------KẾT QUẢ KINH DOANH\r\n driver.find_element_by_xpath(\"//a[@id='aNhom2']\").click()\r\n kqkd_0 = find_table(driver)\r\n \r\n #----------LƯU CHUYỂN TIỀN TỆ GIÁN TIẾP\r\n driver.find_element_by_xpath(\"//a[@id='aNhom3']\").click()\r\n lctt_gt_0 = find_table(driver)\r\n \r\n if np.isnan(lctt_gt_0[lctt_gt_0.columns[4]][1]):\r\n ppTrucT = True\r\n \r\n #----------LƯU CHUYỂN TIỀN TỆ TRỰC TIẾP\r\n if ppTrucT :\r\n driver.find_element_by_xpath(\"//a[@id='aNhom4']\").click()\r\n lctt_tt_0 = find_table(driver)\r\n \r\n #----------CÂN ĐỐI KẾ TOÁN\r\n driver.find_element_by_xpath(\"//a[@id='aNhom1']\").click() # chon cdkt truoc\r\n driver.find_element_by_link_text('Mở rộng').click()\r\n cdkt_0 = find_table(driver)\r\n #---------------------------------------------------------------\r\n lable_cdkt = cdkt_0.ix[:, 0:1] # nhan cho cac teu chi\r\n re_cdkt = cdkt_0.ix[:, 1:5] # lay noi dung cac nam\r\n \r\n lable_kqkd = kqkd_0.ix[:, 0:1] # nhan cho cac teu chi\r\n re_kqkd = kqkd_0.ix[:, 1:5] # lay noi dung cac nam\r\n \r\n lable_lctt_gt = lctt_gt_0.ix[:, 0:1] # nhan cho cac teu chi\r\n re_lctt_gt = lctt_gt_0.ix[:, 1:5] # lay noi dung cac nam\r\n if ppTrucT :\r\n lable_lctt_tt = lctt_tt_0.ix[:, 0:1] # nhan cho cac teu chi\r\n re_lctt_tt = lctt_tt_0.ix[:, 1:5] # lay noi dung cac nam\r\n \r\n \r\n conti = True\r\n count = 0\r\n while conti :\r\n for i in range(4):\r\n driver.find_element_by_xpath(\"//img[@alt='Xem dữ liệu trước']\").click()\r\n count +=4\r\n # cdkt\r\n driver.find_element_by_xpath(\"//a[@id='aNhom1']\").click()\r\n new_cdkt = find_table(driver)\r\n new_cdkt = new_cdkt.ix[:, 1:5]\r\n re_cdkt = pd.concat([ new_cdkt,re_cdkt], axis=1, sort=False)\r\n # kqkd\r\n driver.find_element_by_xpath(\"//a[@id='aNhom2']\").click()\r\n new_khkd = find_table(driver)\r\n new_khkd= new_khkd.ix[:, 1:5]\r\n re_kqkd = pd.concat([ new_cdkt,re_kqkd], axis=1, sort=False)\r\n # lctt_gt\r\n driver.find_element_by_xpath(\"//a[@id='aNhom3']\").click()\r\n new_lctt_gt = find_table(driver)\r\n new_lctt_gt= new_lctt_gt.ix[:, 1:5]\r\n re_lctt_gt = pd.concat([ new_cdkt,re_lctt_gt], axis=1, sort=False)\r\n \r\n # lctt_tt\r\n if ppTrucT :\r\n driver.find_element_by_xpath(\"//a[@id='aNhom4']\").click()\r\n new_lctt_1t = find_table(driver)\r\n new_lctt_tt= new_lctt_gt.ix[:, 1:5]\r\n re_lctt_tt = pd.concat([ new_cdkt,re_lctt_tt], axis=1, sort=False)\r\n \r\n if np.isnan(new_cdkt[new_cdkt.columns[1]][0]):\r\n conti = False\r\n \r\n re_cdkt = pd.concat([ lable_cdkt,re_cdkt], axis=1, sort=False) \r\n re_kqkd = pd.concat([ lable_kqkd,re_kqkd], axis=1, sort=False)\r\n re_lctt_gt = pd.concat([ lable_lctt_gt,re_lctt_gt], axis=1, sort=False)\r\n if ppTrucT :\r\n re_lctt_tt = pd.concat([ lable_lctt_tt,re_lctt_tt], axis=1, sort=False)\r\n \r\n \r\n re_cdkt.to_csv(ck +\"cdkt.csv\",index = False)\r\n re_kqkd.to_csv(ck +\"kqkd.csv\",index = False) \r\n re_lctt_gt.to_csv(ck +\"lctt_gt.csv\",index = False)\r\n if ppTrucT :\r\n re_lctt_tt.to_csv(ck +\"lctt_tt.csv\",index = False) \r\n for cl in range(count):\r\n driver.find_element_by_xpath(\"//img[@alt='Xem dữ liệu tiếp']\").click()\r\n \r\n#------------------------------------------------------------------------------------ \r\n\r\n\r\n \r\ndef find_table(driver): \r\n table = driver.find_element_by_id(\"tableContent\").get_attribute('outerHTML')\r\n col_year = [\"tiêu chí\"]\r\n years = driver.find_elements_by_class_name(\"h_t\")\r\n for y in years:\r\n col_year.append((y.text))\r\n df = pd.read_html(str(table))\r\n tb1 = df[0]\r\n tb1 = tb1.dropna(subset =[ 0])\r\n tb1= tb1.ix[:, 0:4]\r\n tb1.columns = col_year\r\n tb1 =tb1.reset_index(drop=True)\r\n return tb1\r\n\r\n","repo_name":"mis-drug-dealer/anythings_py","sub_path":"autoCafef.py","file_name":"autoCafef.py","file_ext":"py","file_size_in_byte":5682,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"19583959872","text":"#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# --------------------------------------------------\n# Description: Matrix Factorization With Tensorflow\n# --------------------------------------------------\n# Author: Roy Lei \n# Created Date : April 31st 2020\n# --------------------------------------------------\nimport requests\nimport pandas as pd\nCOURSERA_PATH = \"https://lua9b20g37-2.algolianet.com/1/indexes/*/queries?x-algolia-agent=Algolia%20for%20vanilla%20JavaScript%20(lite)%203.30.0%3Breact-instantsearch%205.2.3%3BJS%20Helper%202.26.1&x-algolia-application-id=LUA9B20G37&x-algolia-api-key=dcc55281ffd7ba6f24c3a9b18288499b\"\n\nclass Scraper(object):\n def __init__(self, path=\"\"):\n self.path = path\n self.responses = {}\n\n def _get_headers(self):\n return {\n \"Accept\": \"application/json\",\n \"Content-Type\": \"application/json\"\n }\n\n def _get_payloads(self):\n return {\n \"requests\": [\n {\n \"indexName\": \"DO_NOT_DELETE_PLACEHOLDER_INDEX_NAME\",\n \"params\": \"query=&page=0&highlightPreTag=%3Cais-highlight-0000000000%3E&highlightPostTag=%3C%2Fais-highlight-0000000000%3E&optionalFilters=query%3Aapi&facets=%5B%5D&tagFilters=\"\n },\n {\n \"indexName\": \"prod_all_products_term_optimization\",\n \"params\": \"query=&hitsPerPage=100&page=0&highlightPreTag=%3Cais-highlight-0000000000%3E&highlightPostTag=%3C%2Fais-highlight-0000000000%3E&optionalFilters=query%3Aapi&ruleContexts=%5B%22en%22%5D&facets=%5B%5D&tagFilters=\"\n },\n {\n \"indexName\": \"test_suggestions\",\n \"params\": \"query=&hitsPerPage=7&page=0&highlightPreTag=%3Cais-highlight-0000000000%3E&highlightPostTag=%3C%2Fais-highlight-0000000000%3E&optionalFilters=query%3Aapi&facets=%5B%5D&tagFilters=\"\n }\n ]\n }\n\n def _fliter(self, items):\n dataframe = self._create_pandas()\n\n for item in items:\n # print(item)\n row = dict()\n for column in self._get_columns():\n if column == \"description\":\n row[column] = item[\"_snippetResult\"][\"description\"][\"value\"]\n else:\n row[column] = item[column]\n \n dataframe = dataframe.append(row, ignore_index=True)\n return dataframe\n\n def _get_columns(self):\n return [\n \"name\",\n \"productDifficultyLevel\",\n \"enrollments\",\n \"language\",\n \"numProductRatings\",\n \"avgProductRating\",\n \"skills\",\n \"imageUrl\",\n \"description\"\n ]\n \n def _create_pandas(self):\n return pd.DataFrame(columns=self._get_columns())\n\n def call(self):\n self.responses = requests.post(\n self.path, json=self._get_payloads(), headers=self._get_headers())\n return self.responses.json()\n\n def result(self):\n return self._fliter(self.responses.json()[\"results\"][1][\"hits\"])\n\ndef main():\n scraper = Scraper(COURSERA_PATH)\n scraper.call()\n scraper.result().to_csv(\"./dataset/courses.csv\", encoding='utf-8', index=False)\n\nif __name__ == \"__main__\":\n main()","repo_name":"1997roylee/Final-Year-Project","sub_path":"src/algorithm/CourseraScraper.py","file_name":"CourseraScraper.py","file_ext":"py","file_size_in_byte":3316,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"18359318535","text":"from webbrowser import open as go\nfrom googletrans import Translator\n\ntrans = Translator()\n\nfrom flet import(\n Page, app,\n MainAxisAlignment, CrossAxisAlignment,\n Row, Column,\n AppBar, Dropdown, dropdown,\n Icon, icons,\n IconButton, FloatingActionButton, ButtonStyle,\n #PopupMenuButton, PopupMenuItem,\n Text, TextField, SnackBar,\n colors,\n NavigationRail, NavigationRailLabelType, NavigationRailDestination,\n VerticalDivider,\n AlertDialog, ElevatedButton, OutlinedButton, ProgressBar,\n Ref\n)\n\nclass Refrences:\n GET_WORD = Ref[TextField]()\n FROM_LANG = Ref[Dropdown]()\n TO_LANG = Ref[Dropdown]()\n\ndef main(page: Page):\n \n page.title = \"Dictionary Application\"\n page.vertical_alignment = \"center\"\n\n \n page.window_width = 800\n page.window_height = 500\n page.window_resizable = False\n\n def toggleTheme(e):\n e.control.selected = not e.control.selected\n e.control.update()\n \n \n def openMsgBarAfterDeny():\n page.snack_bar = SnackBar(\n Text(\"Backed To Application, Enjoy !\"),\n bgcolor = colors.GREEN_800,\n )\n page.snack_bar.open = True\n page.update()\n \n \n \n def getUserWord(e):\n user_word = Refrences.GET_WORD.current.value\n\n From_lang = Refrences.FROM_LANG.current.value\n\n To_lang = Refrences.TO_LANG.current.value\n \n Refrences.GET_WORD.current.value = \"\"\n Refrences.FROM_LANG.current.value = \"\"\n Refrences.TO_LANG.current.value = \"\"\n \n page.update()\n\n result = trans.translate(\n user_word,\n To_lang,\n From_lang\n )\n \n def close_dialog(e):\n trans_dialog.open = False\n page.update()\n \n \n trans_dialog = AlertDialog(\n modal = True,\n title = Text(\"Translation Is Done\"),\n content = Text(f\"{result.origin} ({result.src}) -> {result.text} ({result.dest})\"),\n \n actions = [\n OutlinedButton(\n \"Cool, Thanks\",\n on_click = close_dialog,\n icon = icons.CHECK,\n icon_color = colors.GREEN_600\n ),\n ],\n actions_alignment = MainAxisAlignment.END,\n )\n \n def trans_dialog_open():\n page.dialog = trans_dialog\n trans_dialog.open = True\n page.update()\n \n trans_dialog_open()\n\n \n \n def window_event(e):\n if e.data == \"close\":\n page.dialog = confirm_dialog\n confirm_dialog.open = True\n page.update()\n\n page.window_prevent_close = True\n page.on_window_event = window_event\n\n\n def confirmExit(e):\n page.window_destroy()\n\n\n def denyExit(e):\n confirm_dialog.open = False\n page.update()\n openMsgBarAfterDeny()\n\n\n confirm_dialog = AlertDialog(\n modal = True,\n title = Text(\"Confirm Your Action\"),\n content = Text(\"Do you Want To Exit Program?\"),\n \n actions = [\n ElevatedButton(\n \"Quit\", \n on_click = confirmExit,\n bgcolor = colors.RED_600,\n color = colors.WHITE\n ),\n OutlinedButton(\n \"No, Stay\", \n on_click = denyExit,\n icon = icons.CHECK,\n icon_color = colors.GREEN_600\n ),\n ],\n actions_alignment = MainAxisAlignment.END,\n )\n \n \n page.appbar = AppBar(\n leading = Icon(icons.BOOKMARK),\n leading_width = 40,\n \n title = Text(\"Dictionary Application\"),\n center_title = False,\n \n bgcolor = colors.SURFACE_VARIANT,\n \n actions = [\n IconButton(\n icon = icons.WB_SUNNY_OUTLINED,\n \n selected = False,\n selected_icon = icons.MODE_NIGHT,\n \n on_click = toggleTheme,\n tooltip = \"Change Theme\",\n \n style = ButtonStyle(\n color = {\n \"selected\" : colors.BLUE_400,\n \"\" : colors.YELLOW_900\n },\n ),\n ),\n \n IconButton(\n icon = icons.LINK_ROUNDED,\n icon_color = colors.CYAN_700,\n tooltip = \"Github\",\n on_click = lambda _: go(\"https://github.com/ardavan8102\")\n ),\n \n \n IconButton(\n icon = icons.SETTINGS,\n tooltip = \"Settings\",\n ),\n ],\n )\n \n searchField = Row(\n controls = [\n TextField(\n ref = Refrences.GET_WORD,\n label = \"Search A Word\",\n hint_text = \"Type Your Word ...\",\n ),\n \n Dropdown(\n ref = Refrences.FROM_LANG,\n width = 120,\n label = \"From\",\n options = [\n dropdown.Option(\"English\"),\n dropdown.Option(\"Persian\"),\n dropdown.Option(\"Spanish\"),\n ],\n ),\n \n Dropdown(\n ref = Refrences.TO_LANG,\n width = 120,\n label = \"To\",\n options = [\n dropdown.Option(\"English\"),\n dropdown.Option(\"Persian\"),\n dropdown.Option(\"Spanish\"),\n ],\n ),\n ],\n )\n \n rail = NavigationRail(\n selected_index = 0,\n label_type = NavigationRailLabelType.ALL,\n \n min_width = 100,\n min_extended_width = 400,\n \n leading = FloatingActionButton(\n icon = icons.SEARCH_OUTLINED, \n text = \"Translate\",\n on_click = getUserWord\n ),\n \n \n group_alignment = -0.9,\n \n destinations = [\n NavigationRailDestination(\n icon = icons.FAVORITE_BORDER, \n selected_icon = icons.FAVORITE, \n label = \"Likes\"\n ),\n \n NavigationRailDestination(\n icon_content = Icon(icons.BOOKMARK_BORDER),\n selected_icon_content = Icon(icons.BOOKMARK),\n label = \"Saved\",\n ),\n ]\n )\n\n page.add(\n \n \n Row(\n controls = [\n rail,\n VerticalDivider( width = 2 ),\n \n Row(\n controls = [\n \n searchField,\n \n ], \n alignment = \"spaceAround\",\n expand = True\n ),\n ],\n \n expand = True,\n \n )\n )\n\n\nif __name__ == \"__main__\":\n app(target = main)","repo_name":"ardavan8102/DictionaryApp","sub_path":"dic.py","file_name":"dic.py","file_ext":"py","file_size_in_byte":7126,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"52"} +{"seq_id":"9311527661","text":"import re\nimport os\nimport time\nimport poplib\nfrom django.core import mail\nfrom selenium.webdriver.common.keys import Keys\n\nfrom .base import FunctionalTest\n\nSUBJECT = 'Your login link for Superlists'\n\nclass LoginTest(FunctionalTest):\n\n def wait_for_mail(self, test_email, subject):\n\n if not self.staging_server:\n\n email = mail.outbox[0]\n self.assertIn(test_email, email.to)\n self.assertEqual(email.subject, subject)\n return email.body\n \n email_id = None\n start = time.time()\n inbox = poplib.POP3_SSL('pop.mail.yahoo.com')\n\n try:\n inbox.user(test_email)\n inbox.pass_(os.environ.get('YAHOO_PASSWORD'))\n\n while (time.time() - start) < 60:\n # get 10 newest messages\n count, _ = inbox.stat()\n for i in reversed(range(max(1, count - 10), count + 1)):\n print('getting message', i)\n _, lines, _ = inbox.retr(i)\n lines = [l.decode('utf-8') for l in lines]\n \n if f'Subject: {subject}' in lines:\n email_id = 1\n body = '\\n'.join(lines)\n return body\n time.sleep(5)\n \n finally:\n if email_id:\n inbox.dele(email_id)\n inbox.quit()\n\n def test_can_get_email_link_to_log_in(self):\n # Bob goes to the website and finds there's a new feature to log in.\n # He is prompted to enter the email and so does he do.\n if self.staging_server:\n test_email = 'testinggoatisthebest@yahoo.com'\n else:\n test_email = 'bob@example.com'\n self.browser.get(self.live_server_url)\n self.browser.find_element_by_name('email').send_keys(test_email)\n self.browser.find_element_by_name('email').send_keys(Keys.ENTER)\n\n # A message appears that a email has been sent to him with the log in\n # URL\n self.wait_for(lambda: self.assertIn(\n 'Check your email',\n self.browser.find_element_by_tag_name('body').text\n ))\n\n # She checks her mail and finds a message\n body = self.wait_for_mail(test_email, SUBJECT)\n\n # It has a a URL link in it\n self.assertIn('Use this link to log in', body)\n url_search = re.search(r'http://.+/.+$', body)\n\n if not url_search:\n self.fail(f'Could not find url in email body:\\n{body}')\n \n url = url_search.group(0)\n self.assertIn(self.live_server_url, url)\n\n # He clicks it\n self.browser.get(url)\n\n # He is logged in!\n self.wait_to_be_logged_in(test_email) \n\n # He now logs out of it\n self.browser.find_element_by_link_text('Log out').click()\n\n # He is logged out\n self.wait_to_be_logged_out(test_email)","repo_name":"adityaprakash-bobby/py-tdd","sub_path":"functional_tests/test_login.py","file_name":"test_login.py","file_ext":"py","file_size_in_byte":2927,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"42659124275","text":"#!/usr/bin/env python3\nimport os\nfrom Bio import SeqIO\n\nos.chdir('/Users/kika/ownCloud/blasto_comparative/proteins/')\ngenome = SeqIO.parse('Oeli_companion.CDS_cdseq_OK.fna', 'fasta')\n\nwith open('Oeli_companion.CDS_cdseq_OK_l30.fna', 'w') as out:\n\tfor seq in genome:\n\t\tif len(seq) >= 90:\n\t\t\tout.write('>{}\\n{}\\n'.format(seq.description, seq.seq))\n\t\telse:\n\t\t\tprint(seq.description)\n","repo_name":"kikinocka/ngs","sub_path":"py_scripts/len_filter.py","file_name":"len_filter.py","file_ext":"py","file_size_in_byte":380,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"30937344367","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# ## Sentiment Analsysis\n\n# In[1]:\n\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\n# #### Read Data set\n\n# In[2]:\n\n\ndf_review = pd.read_csv('sample30.csv')\nprint('Shape of dataset- ',df_review.shape)\ndf_review.head(5)\n\n\n# In[3]:\n\n\n# % of null values\nnull_values_per_column = (100*round((df_review.isnull().sum())/len(df_review),2)).sort_values(ascending=False)\nprint(\"% of null values per column:\")\nprint(null_values_per_column)\n\n\n# ### Here we see reviews_userProvince and reviews_userCity has ,more than 90% missing values and is not useful for our analysis, so removing these two columns\n\n# In[4]:\n\n\ndf_review = df_review.drop('reviews_userProvince',axis=1)\ndf_review = df_review.drop('reviews_userCity',axis=1)\n\n\n# In[5]:\n\n\ndf_review['reviews_doRecommend'].value_counts()\n\n\n# ### Here we also see. reviews_doRecommend has 9% of null valus, we shall fill this false\n\n# In[6]:\n\n\ndf_review.reviews_doRecommend = df_review['reviews_doRecommend'].fillna(False)\ndf_review.head()\n\n\n# ### As we are recommending products based on review sentiment, we dont require following columns, so we'll drop it\n# ##### 1. Id\n# ##### 2. brand\n# ##### 3. categories\n# ##### 4. manufacturer\n# ##### 5. reviews_date\n# ##### 6. reviews_title\n\n# In[7]:\n\n\ndf_review.drop(['id','brand','categories','manufacturer','reviews_date','reviews_title'],axis=1,inplace=True)\ndf_review.head()\n\n\n# ### We will also drop reviews_didPurchase as it has half of the values as null\n\n# In[8]:\n\n\ndf_review = df_review.drop('reviews_didPurchase',axis=1)\n\n\n# In[9]:\n\n\ndf_review.reviews_rating.value_counts()\n\n\n# #### Graph showing users rating on all products\n\n# In[10]:\n\n\n# Neighborhood also effects the price of the house and now we'll see the no. of sales in different neighboorhood\nplt.figure(figsize=(12,7))\nplt.hist(df_review.reviews_rating,bins=10)\nplt.xticks(rotation=90)\nplt.xlabel('ratings',fontsize=12)\nplt.ylabel('Count',fontsize=12)\nplt.show() \n\n\n# ### We have 20000+ 5 - ratings across the products\n\n# In[11]:\n\n\nReviews_text = df_review.reviews_text\nReviews_text\n\n\n# #### We'll process the text data\n\n# In[12]:\n\n\nimport nltk\nfrom nltk.corpus import stopwords\nfrom nltk.tokenize import word_tokenize\nnltk.download('punkt')\nnltk.download('stopwords')\nimport string\nimport re\nfrom nltk.stem import PorterStemmer\nfrom textblob import TextBlob\n\n\n# In[13]:\n\n\n# Removing punctations in reviews\ndf_review.reviews_text = df_review.reviews_text.apply(lambda x: x.translate(x.maketrans('', '', string.punctuation)))\n\n\n# In[14]:\n\n\n# Removing Special Characters in reviews\ndf_review.reviews_text = df_review.reviews_text.apply(lambda x: re.sub('[^a-zA-Z0-9 \\n\\.]', '', x))\n\n\n# In[15]:\n\n\n# Removing Emojis\nemoji_pattern = re.compile(\"[\"\n u\"\\U0001F600-\\U0001F64F\" # emoticons\n u\"\\U0001F300-\\U0001F5FF\" # symbols & pictographs\n u\"\\U0001F680-\\U0001F6FF\" # transport & map symbols\n u\"\\U0001F1E0-\\U0001F1FF\" # flags (iOS)\n \"]+\", flags=re.UNICODE)\ndf_review.reviews_text = df_review.reviews_text.apply(lambda x: emoji_pattern.sub(r'', x))\n\n\n# In[16]:\n\n\n# converting reviews to lower case\ndf_review.reviews_text = df_review.reviews_text.apply(lambda x: x.lower())\n\n\n# In[17]:\n\n\n# Removing stop words\ndf_review.reviews_text = df_review.reviews_text.apply(lambda x: \" \".join([word for word in nltk.tokenize.word_tokenize(x) if word not in stopwords.words('english')]))\n\n\n# In[18]:\n\n\n# Performing stemming on the review text\nps = PorterStemmer()\ndf_review.reviews_text = df_review.reviews_text.apply(lambda x: \" \".join([ps.stem(word) for word in nltk.tokenize.word_tokenize(x)]))\n\n\n# In[19]:\n\n\ndf_review.reviews_text\n\n\n# In[20]:\n\n\n# Performing spelling corrections\ndf_review.reviews_text = df_review.reviews_text.apply(lambda x: TextBlob(x).correct())\n\n\n# In[21]:\n\n\ndf_review.reviews_text = df_review.reviews_text.apply(lambda x: str(x))\n\n\n# In[22]:\n\n\ndf_review\n\n\n# In[23]:\n\n\n# Separating features and response variables\nX = df_review['reviews_text']\nY = df_review['user_sentiment']\n\n\n# In[24]:\n\n\nY = Y.apply(lambda x: 1 if x == 'Positive' else 0)\nY.value_counts()\n\n\n# In[25]:\n\n\n# Spliting dataset into train and test\nfrom sklearn.model_selection import train_test_split\nx_train,x_test,y_train,y_test = train_test_split(X,Y, test_size=0.30, random_state=44)\n\n\n# In[26]:\n\n\ny_train.value_counts()\nX\n\n\n# In[27]:\n\n\n# Building Model\ndef buildModel(Model, Xtrain, Xtest):\n # Instantiate the model\n model = Model\n \n # Fitting model to the Training set (all features)\n model.fit(Xtrain, y_train)\n \n global y_pred\n # Predicting the Test set results\n y_pred = model.predict(Xtest)\n return model\n\n\n# ## We have imbalanced data in the target variable, so we use class_weight as balanced in our models\n\n# #### In order to help our model focus more on meaningful words, we can use a TF-IDF score (Term Frequency, Inverse Document Frequency). TF-IDF weighs words by how rare they are in our dataset, discounting words that are too frequent and just add to the noise.\n\n# In[28]:\n\n\n# Create the word vector with TF-IDF Vectorizer\nfrom sklearn.feature_extraction.text import TfidfVectorizer\n\n\ntfidf_vect = TfidfVectorizer(ngram_range=(1, 1))\ntfidf_vect_train = tfidf_vect.fit_transform(x_train)\ntfidf_vect_train = tfidf_vect_train.toarray()\ntfidf_vect_test = tfidf_vect.transform(x_test)\ntfidf_vect_test = tfidf_vect_test.toarray()\n\n\n# In[29]:\n\n\nfrom xgboost import XGBClassifier\n\nXGBoost = buildModel(XGBClassifier(class_weight='balanced'), tfidf_vect_train, tfidf_vect_test)\n\n# Assign y_pred to a variable for further process\ny_pred_tfidf_xgb = y_pred\n\n\n# In[31]:\n\n\nfrom sklearn.metrics import classification_report\nfrom sklearn.metrics import confusion_matrix\n\n\nprint('Confusion matrix: \\n',confusion_matrix(y_test,y_pred_tfidf_xgb))\nprint(classification_report(y_test,y_pred))\n\n\n# ## Recommendation System\n\n# In[32]:\n\n\nratings = pd.read_csv('sample30.csv')\nprint('Shape of dataset- ',ratings.shape)\n\n\n# In[33]:\n\n\nratings.head()\n\n\n# In[34]:\n\n\n# Dividing the dataset into trian and test\nfrom sklearn.model_selection import train_test_split\ntrain, test = train_test_split(ratings, test_size=0.30, random_state=44)\n\n\n# In[35]:\n\n\nprint('train_shape - ',train.shape)\nprint('test_shape - ',test.shape)\n\n\n# In[37]:\n\n\nratings.reviews_username = ratings.reviews_username.astype(str)\n\n\n# In[38]:\n\n\nratings.reviews_username.size\n\n\n# In[39]:\n\n\n# Pivot the train ratings' dataset into matrix format in which columns are products and the rows are user IDs.\ndf_pivot = train.pivot_table(\n index='reviews_username',\n columns='name',\n values='reviews_rating'\n).fillna(0)\n\n\n# ### Creating dummy train & dummy test dataset\n# These dataset will be used for prediction \n# - Dummy train will be used later for prediction of the products which has not been rated by the user. To ignore the products rated by the user, we will mark it as 0 during prediction. The products not rated by user is marked as 1 for prediction in dummy train dataset. \n# \n# - Dummy test will be used for evaluation. To evaluate, we will only make prediction on the products rated by the user. So, this is marked as 1. This is just opposite of dummy_train.\n\n# In[40]:\n\n\n# Copy the train dataset into dummy_train\ndummy_train = train.copy()\ndummy_train.head()\n\n\n# In[41]:\n\n\n# The products not rated by user is marked as 1 for prediction. \ndummy_train['reviews_rating'] = dummy_train['reviews_rating'].apply(lambda x: 0 if x>=1 else 1)\n\n\n# In[42]:\n\n\n# Convert the dummy train dataset into matrix format.\ndummy_train = dummy_train.pivot_table(\n index='reviews_username',\n columns='name',\n values='reviews_rating'\n).fillna(1)\ndummy_train.head()\ndummy_train.shape\n\n\n# # User Similarity Matrix\n\n# ### Using Cosine Similarity\n\n# In[43]:\n\n\nfrom sklearn.metrics.pairwise import pairwise_distances\n\n# Creating the User Similarity Matrix using pairwise_distance function.\nuser_correlation = 1 - pairwise_distances(df_pivot, metric='cosine')\nuser_correlation[np.isnan(user_correlation)] = 0\nprint(user_correlation)\n\n\n# In[44]:\n\n\nuser_correlation.shape\n\n\n# ### Using adjusted cosine\n\n# #### Here, we are not removing the NaN values and calculating the mean only for the products rated by the user\n\n# In[45]:\n\n\n# Create a user-product matrix.\ndf_pivot = train.pivot_table(\n index='reviews_username',\n columns='name',\n values='reviews_rating'\n)\n\n\n# In[46]:\n\n\ndf_pivot.head()\n\n\n# ### Normalising the rating of the products for each user around 0 mean\n\n# In[47]:\n\n\nmean = np.nanmean(df_pivot, axis=1)\ndf_subtracted = (df_pivot.T-mean).T\n\n\n# In[48]:\n\n\ndf_subtracted.head()\n\n\n# ### Now finding cosine similarity\n\n# In[49]:\n\n\n# Creating the User Similarity Matrix using pairwise_distance function.\nuser_correlation = 1 - pairwise_distances(df_subtracted.fillna(0), metric='cosine')\nuser_correlation[np.isnan(user_correlation)] = 0\nprint(user_correlation)\n\n\n# In[50]:\n\n\nuser_correlation.shape\n\n\n# ## Prediction User - User\n\n# ##### Doing the prediction for the users which are positively related with other users, and not the users which are negatively related as we are interested in the users which are more similar to the current users. So, ignoring the correlation for values less than 0. \n\n# In[51]:\n\n\nuser_correlation[user_correlation<0]=0\nuser_correlation\n\n\n# ##### Rating predicted by the user (for products rated as well as not rated) is the weighted sum of correlation with the product rating (as present in the rating dataset). \n\n# In[52]:\n\n\nuser_predicted_ratings = np.dot(user_correlation, df_pivot.fillna(0))\nuser_predicted_ratings\n\n\n# In[53]:\n\n\nuser_predicted_ratings.shape\n\n\n# ##### Since we are interested only in the products not rated by the user, we will ignore the products rated by the user by making it zero. \n\n# In[54]:\n\n\nuser_final_rating = np.multiply(user_predicted_ratings,dummy_train)\nuser_final_rating.head()\n\n\n# #### Finding top20 recommendations for user\n\n# In[55]:\n\n\n# Take the user ID as input.\nuser_input = str(input(\"Enter your user name:- \"))\nprint(user_input)\n\n\n# In[57]:\n\n\n# d = user_final_rating.loc[user_input].sort_values(ascending=False)[0:10]\nd = user_final_rating.loc[user_input].sort_values(ascending=False)[0:20]\nd\n\n\n# In[88]:\n\n\nimport joblib\n\n# Saving the model as a pickle in a file\njoblib.dump(XGBoost,'XGBoost.pkl')\n\n\n# In[89]:\n\n\ndef get_top_20_recommended_products(username):\n d = user_final_rating.loc[username].sort_values(ascending=False)[0:20]\n return d\n\n\n# In[90]:\n\n\ndef get_reviews(product):\n return df_review[df_review['name']==product].reviews_text\n\n\n# In[91]:\n\n\ndict = {'product':[],\n 'Sentiment':[]\n }\ndef get_top_5_products_using_sentiment_analysis(recommendations):\n predictions = pd.DataFrame(dict)\n for product in recommendations.index:\n reviews = get_reviews(product)\n transformed = tfidf_vect.transform(reviews)\n # predicting sentiment of reviews\n pred = XGBoost.predict(transformed)\n # calculating the +ve reviews percentage\n sentiment = 100*np.sum(pred)/pred.size\n predictions.loc[len(predictions.index)] = [product, sentiment]\n # Returning the top 5 +ve reviewed products\n return predictions.sort_values(['Sentiment'],ascending=False).head(5)['product']\n\n\n# In[92]:\n\n\nprint('Top 20 Recommendations:-')\nrecommendations = get_top_20_recommended_products('joshua')\nrecommendations\n\n\n# In[93]:\n\n\nprint('Top 5 recommendations using sentiment analysis:- ')\nprint(get_top_5_products_using_sentiment_analysis(recommendations))\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"somaravt/heroku-deployment","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":11535,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"9664654900","text":"import typing\n\nimport discord\nfrom discord.ext import commands\n\nfrom jishaku.flags import Flags\nfrom jishaku.hljs import get_language, guess_file_traits\nfrom jishaku.shim.paginator_base import EmojiSettings\nfrom jishaku.types import ContextA\n\n# Version detection\nif discord.version_info >= (2, 0, 0):\n from jishaku.shim.paginator_200 import PaginatorEmbedInterface, PaginatorInterface\nelse:\n from jishaku.shim.paginator_170 import PaginatorEmbedInterface, PaginatorInterface\n\n__all__ = ('EmojiSettings', 'PaginatorInterface', 'PaginatorEmbedInterface',\n 'WrappedPaginator', 'FilePaginator', 'use_file_check')\n\n\nclass WrappedPaginator(commands.Paginator):\n \"\"\"\n A paginator that allows automatic wrapping of lines should they not fit.\n\n This is useful when paginating unpredictable output,\n as it allows for line splitting on big chunks of data.\n\n Delimiters are prioritized in the order of their tuple.\n\n Parameters\n -----------\n wrap_on: tuple\n A tuple of wrapping delimiters.\n include_wrapped: bool\n Whether to include the delimiter at the end of a wrapped line.\n force_wrap: bool\n If this is True, lines will be split at their maximum points should trimming not be possible\n with any provided delimiter.\n \"\"\"\n\n def __init__(\n self,\n *args: typing.Any,\n wrap_on: typing.Tuple[str, ...] = ('\\n', ' '),\n include_wrapped: bool = True,\n force_wrap: bool = False,\n **kwargs: typing.Any\n ):\n super().__init__(*args, **kwargs)\n self.wrap_on = wrap_on\n self.include_wrapped = include_wrapped\n self.force_wrap = force_wrap\n\n def add_line(self, line: str = '', *, empty: bool = False):\n true_max_size = self.max_size - self._prefix_len - self._suffix_len - 2 * self._linesep_len\n start = 0\n needle = 0\n last_delimiter = -1\n last_space = -1\n\n while needle < len(line):\n if needle - start >= true_max_size:\n if last_delimiter != -1:\n if self.include_wrapped and line[last_delimiter] != '\\n':\n super().add_line(line[start:last_delimiter + 1])\n needle = last_delimiter + 1\n start = last_delimiter + 1\n else:\n super().add_line(line[start:last_delimiter])\n needle = last_delimiter + 1\n start = last_delimiter + 1\n elif last_space != -1:\n super().add_line(line[start:last_space])\n needle = last_space + 1\n start = last_space\n else:\n super().add_line(line[start:needle])\n start = needle\n\n last_delimiter = -1\n last_space = -1\n\n if line[needle] in self.wrap_on:\n last_delimiter = needle\n elif line[needle] == ' ':\n last_space = needle\n\n needle += 1\n\n last_line = line[start:needle]\n if last_line:\n super().add_line(last_line)\n\n if empty:\n self._current_page.append('')\n self._count += self._linesep_len\n\n\nclass FilePaginator(commands.Paginator):\n \"\"\"\n A paginator of syntax-highlighted codeblocks, read from a file-like.\n\n Parameters\n -----------\n fp\n A file-like (implements ``fp.read``) to read the data for this paginator from.\n line_span: Optional[Tuple[int, int]]\n A linespan to read from the file. If None, reads the whole file.\n language_hints: Tuple[str, ...]\n A tuple of strings that may hint to the language of this file.\n This could include filenames, MIME types, or shebangs.\n A shebang present in the actual file will always be prioritized over this.\n \"\"\"\n\n def __init__(\n self,\n fp: typing.BinaryIO,\n line_span: typing.Optional[typing.Tuple[int, int]] = None,\n language_hints: typing.Tuple[str, ...] = (),\n **kwargs: typing.Any\n ):\n language = ''\n\n for hint in language_hints:\n language = get_language(hint)\n\n if language:\n break\n\n if not language:\n try:\n language = get_language(fp.name)\n except AttributeError:\n pass\n\n content, _, file_language = guess_file_traits(fp.read())\n\n language = file_language or language\n lines = content.split('\\n')\n\n super().__init__(prefix=f'```{language}', suffix='```', **kwargs)\n\n if line_span:\n if line_span[1] < line_span[0]:\n line_span = (line_span[1], line_span[0])\n\n if line_span[0] < 1 or line_span[1] > len(lines):\n raise ValueError(\"Linespan goes out of bounds.\")\n\n lines = lines[line_span[0] - 1:line_span[1]]\n\n for line in lines:\n self.add_line(line)\n\n\nclass WrappedFilePaginator(FilePaginator, WrappedPaginator):\n \"\"\"\n Combination of FilePaginator and WrappedPaginator.\n In other words, a FilePaginator that supports line wrapping.\n \"\"\"\n\n\ndef use_file_check(\n ctx: ContextA,\n size: int\n) -> bool:\n \"\"\"\n A check to determine if uploading a file and relying on Discord's file preview is acceptable over a PaginatorInterface.\n \"\"\"\n\n return all([\n size < 50_000, # Check the text is below the Discord cutoff point;\n not Flags.FORCE_PAGINATOR, # Check the user hasn't explicitly disabled this;\n (\n # Ensure the user isn't on mobile\n not ctx.author.is_on_mobile()\n if ctx.guild and ctx.bot.intents.presences and isinstance(ctx.author, discord.Member)\n else True\n )\n ])\n","repo_name":"hifthot/skidcity","sub_path":"blood/.local/lib/python3.10/site-packages/jishaku/paginators.py","file_name":"paginators.py","file_ext":"py","file_size_in_byte":5811,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"52"} +{"seq_id":"34113758151","text":"import math\r\nclass Solution:\r\n # use a stack to track the process. because input is guaranteed to be valid, \r\n # when there is an operator we just have to do operations on two numbers before it\r\n # after the operation, we push the result on to the stack for future record\r\n def evalRPN(self, tokens: List[str]) -> int:\r\n ops = set(['+', '-', '*', '/'])\r\n st = []\r\n for i in range(len(tokens)):\r\n if tokens[i] not in ops:\r\n st.append(tokens[i])\r\n else:\r\n y = int(st.pop())\r\n x = int(st.pop())\r\n st.append(str(calc(x, tokens[i], y)))\r\n return st[-1]\r\n \r\n \r\ndef calc(x, op, y):\r\n if op == '+':\r\n return x + y\r\n elif op == '-':\r\n return x - y\r\n elif op == '*':\r\n return x * y\r\n elif op == '/':\r\n tmp = x / y\r\n if tmp > 0:\r\n return math.floor(tmp)\r\n else:\r\n return math.ceil(tmp)","repo_name":"HaoboChen1887/leetcode","sub_path":"stack_queue/150_evaluate_reverse_polish_notation/150.py","file_name":"150.py","file_ext":"py","file_size_in_byte":1002,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"30401404734","text":"# -*- coding: utf-8 -*-\nimport logging\nimport json\nimport requests\n\nfrom ..filter import RsaRuleFilter, AnonymizationFilter\nfrom ..inspector import CognitiveInspector, TopologyInspector, SchemaInspector\n\nfrom chariot_base.utilities import Traceable\nfrom chariot_base.utilities.iotlwrap import IoTLWrapper\n\nfrom influxdb import InfluxDBClient\n\nfrom datetime import datetime, timedelta\n\nclass Engine(Traceable):\n def __init__(self, options={}):\n self.tracer = None\n self.southbound = None\n self.northbound = None\n self.schema = []\n self.db = self.init_db(**options['cognitive']['database'])\n\n self.iotl = None\n self.session = requests.Session()\n self.session.trust_env = False\n self.options = options\n self.last_sync_datetime = None\n\n self.inspectors = [\n CognitiveInspector(self),\n TopologyInspector(self),\n SchemaInspector(self)\n ]\n self.filters = [\n AnonymizationFilter(self),\n RsaRuleFilter(self)\n ]\n\n self.prepare_global_mute_options()\n\n def inject(self, southbound, northbound):\n self.southbound = southbound\n self.northbound = northbound\n\n def start(self):\n self.subscribe_to_southbound()\n self.subscribe_to_northbound()\n\n def inject_iotl(self, iotl):\n self.iotl = iotl\n\n def set_up_iotl_url(self, iotl_url):\n self.iotl_url = iotl_url\n\n def subscribe_to_southbound(self):\n self.southbound.subscribe_to_topics()\n\n def subscribe_to_northbound(self):\n pass\n\n def apply(self, message, child_span):\n span = self.start_span('apply', child_span)\n self.sync_iotl(span)\n self.filter(message, span)\n self.inspect(message, span)\n self.close_span(span)\n return 0\n\n def inspect(self, message, child_span):\n for _inspector in self.inspectors:\n if self.is_not_muted(_inspector.human_name):\n if self.is_not_muted_for_sensor(_inspector.human_name, message):\n span = self.start_span(f'filter_{_inspector.human_name}', child_span)\n _inspector.check(message, span)\n self.close_span(span)\n else:\n logging.debug(f'Rule {_inspector.human_name} is muted for sensor \"{message.sensor_id}\"')\n else:\n logging.debug(f'Rule {_inspector.human_name} is muted')\n\n def filter(self, message, child_span):\n messages = [message]\n for _filter in self.filters:\n if self.is_not_muted(_filter.human_name):\n if self.is_not_muted_for_sensor(_filter.human_name, message):\n span = self.start_span(f'filter_{_filter.human_name}', child_span)\n messages = _filter.do(messages, span)\n self.close_span(span)\n else:\n logging.debug(f'Rule {_filter.human_name} is muted for sensor \"{message.sensor_id}\"')\n else:\n logging.debug(f'Rule {_filter.human_name} is muted')\n\n for filtered_message in messages:\n self.publish(filtered_message, span)\n\n def publish(self, message, span):\n m = self.inject_to_message(span, message.dict())\n self.southbound.publish('northbound', json.dumps(m))\n logging.debug(f'Publish message from \"{message.sensor_id}\" to \"{message.destination}\"')\n\n def raise_alert(self, alert, span):\n m = json.dumps(self.inject_to_message(span, alert.dict()))\n logging.debug(m)\n self.northbound.publish('alerts', m)\n\n def get_acl(self, span, message):\n return self.iotl.acl(message.sensor_id)\n\n def get_params(self, span, destination):\n return self.iotl.params(destination)\n\n def is_sensitive(self, span, message):\n return self.iotl.isSensitive(message.sensor_id)\n\n def is_match(self, span, schema, message):\n return self.iotl.is_match(schema, message.value)\n\n def execute(self, payload):\n span = self.start_span(f'execute_command')\n msg = payload.decode('utf-8')\n command = json.loads(msg)\n \n name = command['name']\n logging.debug(f'executing command {name}')\n if name == 'refresh_iotl':\n self.execute_sync_iotl(span)\n else:\n logging.debug('Unkwown command');\n self.close_span(span)\n \n def execute_sync_iotl(self, span):\n if self.iotl_url is None:\n return\n\n logging.debug('Sync topology')\n url = self.iotl_url\n headers = self.inject_to_request_header(span, url)\n self.set_tag(span, 'url', url)\n result = self.session.get(url, headers=headers)\n current_iotl = result.json()\n self.iotl.load(current_iotl['code'])\n self.schema = self.iotl.schema(True)\n\n self.last_sync_datetime = datetime.now()\n\n def sync_iotl(self, span):\n if self.iotl_url is None:\n return\n\n if self.should_sync_iotl():\n self.execute_sync_iotl(span)\n\n def should_sync_iotl(self):\n if self.last_sync_datetime is None:\n return True\n return datetime.now() - self.last_sync_datetime >= timedelta(seconds=self.options.get('iotl_sync_delay', 60))\n\n def is_not_muted(self, id):\n return not self.muted_options[id]\n\n def is_not_muted_for_sensor(self, id, message):\n muted = self.iotl.params(message.sensor_id).get('mute', {})\n return muted.get(id, 0) == 0\n\n def prepare_global_mute_options(self):\n self.muted_options = self.options.get('mute', {})\n for _inspector in self.inspectors:\n if _inspector.human_name not in self.muted_options:\n self.muted_options[_inspector.human_name] = False\n for _filters in self.filters:\n if _filters.human_name not in self.muted_options:\n self.muted_options[_filters.human_name] = False\n\n def init_db(self, host, port, username, password, database, path, duration='4w'):\n logging.debug(f'{host}/{path}:{port} <{username}> ({database})')\n db = InfluxDBClient(host=host, port=port, username=username, password=password, database=database, path=path)\n db.create_database(database)\n db.create_retention_policy('awesome_policy', duration, 3, default=True)\n return db\n\n def save_instance(self, span, message):\n is_sensitive = self.is_sensitive(span, message)\n sensor_id = message.sensor_id\n timestamp = message.timestamp\n values = json.loads(message.value)\n\n points = []\n\n for k in values.keys():\n v = values[k]\n points.append({\n 'measurement': 'instances',\n 'tags': {\n 'sensor_id': sensor_id,\n 'value_name': k,\n },\n 'time': timestamp,\n 'fields': {\n 'sensor_id': sensor_id,\n 'value_name': k,\n 'value': str(v),\n 'is_sensitive': is_sensitive\n }\n })\n\n return self.db.write_points(points, protocol='json', retention_policy='awesome_policy')","repo_name":"charIoT-h2020/chariot-privacy-engine","sub_path":"chariot_privacy_engine/engine/engine.py","file_name":"engine.py","file_ext":"py","file_size_in_byte":7250,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"3002356018","text":"import uuid\nfrom http import HTTPStatus\nfrom typing import List, Optional\n\nfrom api.v1.films import FilmList\nfrom fastapi import APIRouter, Depends, HTTPException, Query\nfrom pydantic import BaseModel\nfrom services.film import FilmService, get_film_service\nfrom services.persons import PersonService, get_person_service\n\n# Объект router, в котором регистрируем обработчики\nrouter = APIRouter()\n\n\n# Модель ответа API\nclass Person(BaseModel):\n uuid: uuid.UUID\n full_name: str\n role: Optional[List[str]]\n film_ids: Optional[List]\n\n\n@router.get(\n \"/search\",\n response_model=List[Person],\n summary=\"Поиск персон\",\n description=\"Полнотекстовый поиск по персонам\",\n response_description=\"Полное имя, роль и id фильмов\",\n)\nasync def person_search(\n query: str = \"\",\n number: int = Query(default=1, alias=\"page[number]\"),\n size: int = Query(default=20, alias=\"page[size]\"),\n person_service: PersonService = Depends(get_person_service),\n) -> List[Person]:\n persons = await person_service.search(query, number, size)\n\n if not persons:\n # Если фильм не найден, отдаём 404 статус\n raise HTTPException(status_code=HTTPStatus.NOT_FOUND, detail=\"person not found\")\n\n return [\n Person(\n uuid=uuid.UUID(person.uuid),\n full_name=person.full_name,\n role=person.role,\n film_ids=person.film_ids,\n )\n for person in persons\n ]\n\n\n@router.get(\n \"/{person_id}/film\",\n response_model=List[FilmList],\n summary=\"Выдача фильмов персоны по ID\",\n description=\"Выдача фильмов персоны по ID\",\n response_description=\"Название и рейтинг фильмов\",\n)\nasync def person_films(\n person_id: str,\n person_service: PersonService = Depends(get_person_service),\n film_service: FilmService = Depends(get_film_service),\n) -> List[FilmList]:\n person = await person_service.get_by_id(person_id)\n if not person:\n raise HTTPException(status_code=HTTPStatus.NOT_FOUND, detail=\"person not found\")\n films = await film_service.get_by_ids(person.film_ids)\n if not films:\n raise HTTPException(status_code=HTTPStatus.NOT_FOUND, detail=\"films not found\")\n return [\n FilmList(\n uuid=uuid.UUID(film.uuid),\n title=film.title,\n imdb_rating=film.imdb_rating,\n )\n for film in films\n ]\n\n\n@router.get(\n \"/{person_id}\",\n response_model=Person,\n summary=\"Выдача персоны по ID\",\n description=\"Выдача персоны по ID\",\n response_description=\"Полное имя, роль и ID фильмов\",\n)\nasync def person_details(\n person_id: str,\n person_service: PersonService = Depends(get_person_service),\n) -> Person:\n person = await person_service.get_by_id(person_id)\n if not person:\n raise HTTPException(status_code=HTTPStatus.NOT_FOUND, detail=\"person not found\")\n return Person(\n uuid=uuid.UUID(person.uuid),\n full_name=person.full_name,\n role=person.role,\n film_ids=person.film_ids,\n )\n","repo_name":"LiubovAnt/backend_example","sub_path":"movie_api/app/api/v1/persons.py","file_name":"persons.py","file_ext":"py","file_size_in_byte":3258,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"12345804618","text":"from models.fridge import Fridge\n\n\nclass MedicalFridge(Fridge):\n \"\"\"\n Class that describe MedicalFridge\n \"\"\"\n def __init__(self, brand, model, capacity, is_defrosting, energy_efficiency_class, set_price,\n type_of_doors, type_of_fridge, number_of_doors):\n \"\"\"\n\n :param brand: The brand of the fridge.\n :param model: The model of the fridge.\n :param capacity: The capacity of the fridge.\n :param is_defrosting: Defrost state of the fridge.\n :param energy_efficiency_class: Energy efficiency of fridge.\n :param type_of_doors: Which type of doors has fridge.\n :param type_of_fridge: Type of fridge.\n :param number_of_doors: How many doors has fridge.\n \"\"\"\n self.type_of_doors = type_of_doors\n self.type_of_fridge = type_of_fridge\n self.number_of_doors = number_of_doors\n super().__init__(brand, model, capacity, is_defrosting, energy_efficiency_class, set_price)\n\n def get_max_usable_capacity(self):\n \"\"\"\n Overriding abstract method\n :return: Returns a list of the maximum useful volume of products that can be contained in the fridges.\n \"\"\"\n return self.capacity * self.number_of_doors\n\n def __str__(self):\n \"\"\"\n The method returns a string view of object\n return: String view of object\n \"\"\"\n return f'brand: {self.brand}, model: {self.model}, capacity: {self.capacity}, is_defrosting:' \\\n f' {self.is_defrosting},' f' energy_efficiency_class: {self.energy_efficiency_class}' \\\n f' type_of_doors: {self.type_of_doors}, ' f' type_of_fridge: {self.type_of_fridge}' \\\n f' number_of_doors: {self.number_of_doors}'\n","repo_name":"yarynakos/python-iot","sub_path":"models/medical_fridge.py","file_name":"medical_fridge.py","file_ext":"py","file_size_in_byte":1754,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"9636794392","text":"import secrets\r\nimport random\r\nimport time\r\nimport numpy as np\r\n\r\nmutationRate = 0.4\r\nbinList = []\r\nt1 = time.time()\r\nfor i in range (0,100):\r\n threshold = secrets.SystemRandom().uniform(0.000000000,1.0000000)\r\n if threshold >= 0.5:\r\n if threshold <= mutationRate:\r\n binList.append(-1)\r\n binList.append(1)\r\n elif threshold < 0.5:\r\n if threshold <= mutationRate:\r\n binList.append(-1)\r\n binList.append(0)\r\nt2 = time.time()\r\ntotalTime = t2 - t1\r\nprint(binList)\r\nprint(\"The time for that was: \", totalTime, \"s\")\r\n\r\n","repo_name":"MichaelNorberto/NTRUPythonImplementation","sub_path":"Random Polynomial Generation.py","file_name":"Random Polynomial Generation.py","file_ext":"py","file_size_in_byte":570,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"8789178260","text":"input_data = input()\nrow = int(input_data[1])\ncolumn = int(ord(input_data[0])) - int(ord('a')) + 1\n# column = ord(input_data[0]) - ord('a') + 1\n\nsteps = [(-2, -1), (-1, -2), (1, -2), (2, -1), (2, 1), (1, 2), (-1, 2), (-2, 1)]\n\nresult = 0\nfor step in steps:\n next_row = row + step[0]\n next_column = column + step[1]\n # 하나라도 False면 X\n if 1 <= next_row <= 8 and 1 <= next_column <= 8:\n result += 1\n\nprint(result)\n","repo_name":"kkmjkim/python-for-coding-test","sub_path":"ch04/4-3.py","file_name":"4-3.py","file_ext":"py","file_size_in_byte":439,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"20287139230","text":"\nimport os\nfrom flask import Flask, render_template, jsonify, request, send_file, send_from_directory, Response, abort\nfrom werkzeug.utils import secure_filename\nfrom flask import Flask\nfrom flask_restful import Api\nimport base64\nfrom edge_detection import edge_detection\nimport cv2\n\napp = Flask(__name__)\n\nINPUT_DIR_NAME = 'input/'\nOUTPUT_DIR_NAME = 'output/'\n\n@app.route('/')\ndef hello_world():\n return render_template('index.html')\n\n# static 파일 전달에 필요한 라우팅 \n@app.route('/static/')\ndef getResource(filename):\n if os.path.isfile('static/{}'.format(filename)):\n return send_file('static/{}'.format(filename))\n else:\n return abort(404, description=\"Resource not found\")\n\n\n\n\n#이미지 업로드에 사용되는\n@app.route('/uploadImage', methods = ['POST'])\ndef uploadImage():\n\n #파일 저장 이후,\n f = request.files['file']\n f.save(INPUT_DIR_NAME + f.filename)\n\n\n processed_filename = \"edge_{}.jpg\".format(f.filename)\n #엣지 디텍션 한다.\n print(\"이미지 읽는중..\")\n color_img = cv2.imread(INPUT_DIR_NAME + f.filename, cv2.IMREAD_COLOR) # 이미지 읽기\n print(\"엣지 디텍션중..\")\n edge = edge_detection(color_img) # 엣지 검출\n print(\"엣지 디텍션된 이미지 쓰는중중..\")\n cv2.imwrite(OUTPUT_DIR_NAME + processed_filename, edge) # 검출된 엣지 이미지 저장\n\n\n return send_file(OUTPUT_DIR_NAME + processed_filename, as_attachment=True)\n\n\nif __name__ == '__main__':\n app.run(host='0.0.0.0',port=20000, debug=True)\n","repo_name":"nacl1119/SportRecogProject","sub_path":"edge_detection/start_edge_extractor_svr.py","file_name":"start_edge_extractor_svr.py","file_ext":"py","file_size_in_byte":1561,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"43098594959","text":"import disnake\r\nimport config\r\nfrom disnake.ext import commands\r\nfrom db import DataBase\r\n\r\nclass Data(commands.Cog):\r\n def __init__(self , bot):\r\n self.bot = bot\r\n self.DataBase = DataBase('db.db')\r\n\r\n @commands.slash_command(description=\"Показывает ваш профиль\")\r\n async def profile(self , ctx):\r\n if self.DataBase.check_user(ctx.author.id) == True:\r\n username , balance , level , bank , work = self.DataBase.data(ctx.author.id)\r\n embed = disnake.Embed(\r\n color=disnake.Color.green(),\r\n title=f\"__Информация о пользователе {username}__\",\r\n description=f\"\"\"\r\n Имя: {username}\r\n Работа: {work}\r\n Банк: {bank}🍬\r\n Ваш счет: {balance}🍬\r\n Уровень: {level}\r\n\r\n Присоединился: {ctx.author.joined_at}\r\n \"\"\"\r\n )\r\n\r\n await ctx.send(embed=embed)\r\n\r\n else:\r\n self.DataBase.create(ctx.author.id , ctx.author.name)\r\n await ctx.send(f\"Вы были успешно зарегистрированы, теперь можете отправить команды повторно\")\r\n\r\n\r\ndef setup(bot):\r\n bot.add_cog(Data(bot))","repo_name":"Bogdan11212/chillbot","sub_path":"cogs/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":1281,"program_lang":"python","lang":"ru","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"71981462244","text":"import pandas as pd\r\n\r\ndef sentiments():\r\n data=pd.read_excel('E:\\\\python\\\\project\\\\user_reviews.xlsx')\r\n sentiment=data[\"Sentiment\"].tolist()\r\n Apps=data[\"App\"].tolist()\r\n apps=set()\r\n strr1=[]\r\n strr2=[]\r\n c=1\r\n strr2.append(\"Apps with same number of positive & negative sentiments are\")\r\n print(\"Apps with same number of positive & negative sentiments are\")\r\n for app in Apps:\r\n apps.add(app)\r\n track1={}\r\n track2={}\r\n for i in apps:\r\n start_index= Apps.index(i)\r\n length=Apps.count(i)\r\n track1.update({i:sentiment[start_index:start_index+length].count('Positive')})\r\n track2.update({i:sentiment[start_index:start_index+length].count('Negative')})\r\n if track1[i]==track2[i]:\r\n print(i)\r\n if c<=10:\r\n if track1[i]==track2[i]:\r\n strr2.append(i)\r\n c+=1\r\n #print(i)\r\n max1=max(track1,key=track1.get)\r\n max2=max(track2,key=track2.get)\r\n strr1.append(\"Most Positive sentiment is {} and corresponding App is {}\".format(track1[max1],max1))\r\n strr1.append(\"Most Negative sentiment is {} and corresponding App is {}\".format(track2[max2],max2))\r\n strr2.append(\"Etc. \")\r\n return strr1,strr2\r\n#sentiments()\r\n","repo_name":"ManasiHodavdekar/Google-Play-Store-Case-Study","sub_path":"prob12.py","file_name":"prob12.py","file_ext":"py","file_size_in_byte":1275,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"30527385499","text":"# https://leetcode.com/problems/add-two-numbers/description/\n\n\n# Definition for singly-linked list.\nclass ListNode:\n def __init__(self, x):\n self.val = x\n self.next = None\n\n\nclass Solution:\n def addTwoNumbers(self, l1, l2):\n \"\"\"\n :type l1: ListNode\n :type l2: ListNode\n :rtype: ListNode\n \"\"\"\n dummy = ListNode(0)\n p, q = l1, l2\n curr = dummy\n carry = 0\n while p is not None or q is not None:\n x = p.val if p is not None else 0\n y = q.val if q is not None else 0\n sum = x + y + carry\n carry = sum // 10\n curr.next = ListNode(sum % 10)\n curr = curr.next\n if p is not None:\n p = p.next\n if q is not None:\n q = q.next\n\n if carry == 1:\n curr.next = ListNode(carry)\n\n return dummy.next\n\n\nl1 = ListNode(2)\nl1.next = ListNode(4)\nl1.next.next = ListNode(3)\nl2 = ListNode(5)\nl2.next = ListNode(6)\nl2.next.next = ListNode(4)\ns = Solution()\nresult = s.addTwoNumbers(l1, l2)\nresult_list = []\ncurr = result\nwhile curr is not None:\n result_list.append(curr.val)\n curr = curr.next\nprint(result_list)\n","repo_name":"mookrs/leetcode","sub_path":"0002_add_two_numbers.py","file_name":"0002_add_two_numbers.py","file_ext":"py","file_size_in_byte":1223,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"14002538139","text":"# 5. ** Даны два файла, в каждом из которых находится запись многочленов.\n# Задача - сформировать файл, содержащий сумму многочленов.\n# in\n# \"poly.txt\"\n# \"poly_2.txt\"\n# out in the file\n# 3*x^9 + 3*x^8 - 2*x^6 + 1*x^5 - 3*x^4 - 3*x^2 + 3 + 2*x^2 + 2*x^1 + 2 = 0\n# 4*x^5 + 1*x^4 - 3*x^3 - 3 + 3*x^3 - 4*x^2 - 2*x^1 - 4 = 0\n# 4*x^2 - 4 + 3*x^6 - 4*x^5 - 4*x^4 - 4*x^3 + 3*x^2 - 2*x^1 - 0 = 0\n\n# in\n# \"poly.txt\"\n# \"poly_2.txt\"\n# out\n# The contents of the files do not match!\n\nfrom random import choice\n\n\ndef sum_polynomials(file_1, file_2):\n with open(file_1) as f_1, open(file_2) as f_2:\n a, b = f_1.readlines(), f_2.readlines()\n if len(a) == len(b):\n with open('sum_file.txt', 'a') as sum:\n for i, line in enumerate(a):\n sum.write(line[:-5] + ' + ' + b[i])\n else:\n print(\"The contents of the files do not match!\")\n\n\nsum_polynomials('poly.txt', 'poly_2.txt')\n","repo_name":"OlegKhudoerko/HW_Sem_4","sub_path":"HW_Sem_4/task_4_5.py","file_name":"task_4_5.py","file_ext":"py","file_size_in_byte":1054,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"14687632630","text":"import repackage\n\nrepackage.up()\n\nimport os\n\nimport json\n\nimport random\n\nimport datetime\nimport time\n\nimport discord\nfrom discord.ext import commands\n\nimport genanki\n\nimport tempfile\n\nfrom libs.GoogleDriveManager import GoogleDriveManager\nfrom libs.tools import replace_latex_delimiters, remove_text\n\nwith open(\"config.json\", \"r\") as f:\n config = json.load(f)\n\nintents = discord.Intents.default()\nintents.message_content = True\n\nbot = commands.Bot(command_prefix=config[\"bot_prefix\"], intents=intents)\n\ngoogle_drive_manager = GoogleDriveManager(\n credentials_file_path=config[\"credentials_file_path\"],\n data_folder_path=config[\"data_folder_path\"],\n)\n\n\n@bot.event\nasync def on_ready() -> None:\n print(\"------\")\n print(\"Discord Bot: Logged in as\")\n print(bot.user.name)\n print(bot.user.id)\n print(\"------\")\n\n\n@bot.command()\nasync def ping(ctx) -> None:\n await ctx.send(\"pong\")\n\n\n@bot.command()\nasync def update(ctx) -> None:\n google_drive_manager.get_files(folder_id=config[\"folder_id\"])\n\n embed = discord.Embed(\n title=\"Database updated\",\n color=discord.Color.purple(),\n )\n\n embed.set_footer(text=\"Made with 💜 by bonsainoodle\")\n\n await ctx.send(embed=embed)\n\n\n@bot.command()\nasync def rev(ctx, num_cards: int, *args: str) -> None:\n with open(f\"{config['data_folder_path']}/data.json\", \"r\", encoding=\"utf-8\") as f:\n data = json.load(f)\n\n random.shuffle(data)\n\n selected_cards = []\n tags = []\n backlinks = []\n include_tags = False\n include_backlinks = False\n\n for arg in args:\n arg = arg.replace(\"=\", \" \")\n arg = arg.lower()\n\n if arg.isdigit():\n num_cards = int(arg)\n elif arg.startswith(\"#\"):\n tags.append(arg[1:])\n elif arg.startswith(\"[[\"):\n backlinks.append(arg[2:-2])\n elif arg == \"include_tags\":\n include_tags = True\n elif arg == \"include_backlinks\":\n include_backlinks = True\n\n for card in data:\n if len(args) == 0:\n selected_cards.append(card)\n\n if (not tags or any(tag in card[\"tags\"] for tag in tags)) and (\n not backlinks\n or any(backlink in card[\"backlinks\"] for backlink in backlinks)\n ):\n if (include_tags and any(tag in card[\"tags\"] for tag in tags)) or (\n include_backlinks\n and any(backlink in card[\"backlinks\"] for backlink in backlinks)\n ):\n selected_cards.append(card)\n\n if len(selected_cards) >= num_cards:\n break\n\n if len(selected_cards) == 0:\n embed = discord.Embed(\n title=\"No card found\",\n description=\"Try to change your parameters\",\n color=discord.Color.purple(),\n )\n\n embed.set_footer(text=\"Made with 💜 by bonsainoodle\")\n\n await ctx.send(embed=embed)\n\n return\n\n now = datetime.datetime.now()\n file_name = f\"{now.strftime('%Y-%m-%d-%H-%M-%S')}.apkg\"\n\n anki_model = genanki.Model(\n random.randrange(1 << 30, 1 << 31),\n file_name,\n fields=[\n {\"name\": \"ContentBefore\"},\n {\"name\": \"ContentAfter\"},\n ],\n templates=[\n {\n \"name\": \"Card 1\",\n \"qfmt\": \"{{ContentBefore}}\",\n \"afmt\": '{{FrontSide}}
{{ContentAfter}}',\n },\n ],\n )\n\n anki_deck = genanki.Deck(random.randrange(1 << 30, 1 << 31), file_name)\n\n for card in selected_cards:\n my_note = genanki.Note(\n model=anki_model,\n fields=[\n remove_text(replace_latex_delimiters(card[\"content_before\"])),\n remove_text(replace_latex_delimiters(card[\"content_after\"])),\n ],\n tags=card[\"tags\"],\n )\n\n anki_deck.add_note(my_note)\n\n anki_package = genanki.Package(anki_deck)\n\n temp_dir = tempfile.mkdtemp(prefix=\"anki_export_\")\n\n output_file_path = os.path.join(temp_dir, file_name)\n\n anki_package.write_to_file(output_file_path)\n\n with open(output_file_path, \"rb\") as apkg_file:\n apkg_content = apkg_file.read()\n\n os.remove(output_file_path)\n\n os.rmdir(temp_dir)\n\n google_drive_manager.upload_file(\n parent_folder_id=config[\"rev_folder_id\"],\n file_name=file_name,\n content=apkg_content,\n )\n\n time.sleep(1)\n\n file_link = google_drive_manager.get_file_link(\n parent_folder_id=config[\"rev_folder_id\"], file_name=file_name\n )\n\n embed = discord.Embed(\n title=f\"Created a new file with {len(selected_cards)} cards\",\n description=f\"[{file_name}]({file_link})\",\n color=discord.Color.purple(),\n )\n\n embed.set_footer(text=\"Made with 💜 by bonsainoodle\")\n\n await ctx.send(embed=embed)\n\n\nbot.run(config[\"bot_token\"])\n","repo_name":"bonsainoodle/obsidianToAnki","sub_path":"bot/bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":4833,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"52"} +{"seq_id":"10751371856","text":"\"\"\"\n# Definition for a Node.\nclass Node:\n def __init__(self, val=None, children=None):\n self.val = val\n self.children = children\n\"\"\"\n\nclass Solution:\n def preorder(self, root: 'Node') -> List[int]:\n \n ans = []\n def getChildVal(root):\n if root:\n ans.append(root.val)\n \n for child in root.children:\n getChildVal(child)\n \n return\n \n getChildVal(root)\n return ans\n ","repo_name":"vaibhav0077/leetcode","sub_path":"0589-n-ary-tree-preorder-traversal/0589-n-ary-tree-preorder-traversal.py","file_name":"0589-n-ary-tree-preorder-traversal.py","file_ext":"py","file_size_in_byte":523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"23161092188","text":"class State():\n\n def __init__(self):\n self.area = input(\"Enter Area of State \")\n self.name = input(\"Enter State name \")\n self.capital = input(\"Enter State Capital \")\n\nclass Country():\n\n def __init__(self):\n self.area = input(\"Enter Area \")\n self.name = input(\"Enter Country name \")\n self.capital = input(\"Enter Capital name \")\n self.number_of_states = input(\"Enter number of states \")\n\n self.state_objects = []\n for idx in range(0, int(self.number_of_states)):\n print(\"Data being entered for state number\" + str(idx))\n state = State()\n self.state_objects.append(state)\n\nperson_object = Country()\nprint(\"\\n\" + person_object.name + \"\\n\" + str(person_object.area) + \"\\n\" + person_object.capital + \"\\n\")\n\n# state_object = State()\n# print(str(state_object.area) + \"\\n\" + state_object.name + \"\\n\" + state_object.capital)\n","repo_name":"manishkumar0282/python_tutorial_practice","sub_path":"firstSample/Lecture5.py","file_name":"Lecture5.py","file_ext":"py","file_size_in_byte":917,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"17272316348","text":"# boot.py -- run on boot-up\nimport config\nconfig = config.config()\n\nfrom machine import WDT, deepsleep, pin_sleep_wakeup\nwdt = WDT(timeout=config.WATCHDOG_TIMEOUT_MS)\nfrom utime import ticks_ms, ticks_diff, sleep_ms\nstart_ticks = ticks_ms\nfrom utility import log, releasePinHold, setPinHold, changePin, setPin\nfrom utility import blinkLED\nfrom pycom import heartbeat\nheartbeat(False)\n\n# variables\nled_time = 250\nbutton_push_period = 0\nforced_reset = False\nblinkLED(\"blue\",led_time,1)\nfrom os import dupterm\nfrom machine import UART, Pin\nfrom sys import print_exception\n\nprimaryUART = UART(0, 115200)\ndupterm(primaryUART)\n\nlog(\"Boot: Starting Bee-IoT-Scale-App-v1.0 (2022-03-14)...\")\n\n# import libaries\nfrom machine import reset_cause, wake_reason\nfrom utility import setToNVRAM, getFromNVRAM, getBootCountFromNVRAM, setBootCountToNVRAM\nimport machine\n\ntry:\n bootCount = getBootCountFromNVRAM() + 1\n reset_cause = reset_cause()\n wake_reason = wake_reason()\n\n log(\"Main: Reset cause: %s\" % (reset_cause))\n log(\"Main: Wake up reason: %s %s\" % (wake_reason[0],wake_reason[1]))\n\n if(reset_cause == machine.PWRON_RESET):\n log(\"Main: Forced system reset..(PWRON_RESET).\")\n forced_reset = True\n bootCount = 1\n elif(reset_cause == machine.HARD_RESET):\n log(\"Main: Forced system reset (HARD_RESET)....\")\n forced_reset = True\n bootCount = 1\n elif(reset_cause == machine.WDT_RESET):\n log(\"Main: Forced system reset (WDT_RESET)...\")\n forced_reset = True\n elif(reset_cause == machine.DEEPSLEEP_RESET):\n log(\"Main: Deepsleep reset, this is expected behaviour\")\n forced_reset = False\n elif(reset_cause == machine.SOFT_RESET):\n log(\"Main: SOFT Reset...\")\n elif(reset_cause == machine.BROWN_OUT_RESET):\n log(\"Main: Brown out reset...\")\n forced_reset = True\n\n if wake_reason[0] == machine.PWRON_WAKE:\n log(\"Main: Woke up by reset button\")\n elif wake_reason[0] == machine.PIN_WAKE:\n log(\"Main: Woke up by external pin (external interrupt)\")\n read_sensors = True\n elif wake_reason[0] == machine.RTC_WAKE:\n log(\"Main: Woke up by RTC (timer ran out)\")\n elif wake_reason[0] == machine.ULP_WAKE:\n log(\"Main: Woke up by ULP (capacitive touch)\")\nexcept Exception as e:\n print_exception(e)\n\nlog(\"Main: bootCount: %d\" % (bootCount))\nsetBootCountToNVRAM(bootCount)\nsleep_time = config.MEASUREMENT_INTERVAL\n","repo_name":"Lapland-UAS-Tequ/tequ-bee-nest-scale","sub_path":"boot.py","file_name":"boot.py","file_ext":"py","file_size_in_byte":2450,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"69883619685","text":"import potential_portfolios\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\nclass PortfolioGraph:\n def __init__(self):\n self.client = potential_portfolios.client\n\n\ndata = pd.read_csv(\"Visualization.csv\", index_col=False)\ndata[\"period\"] = data[\"YEAR\"].astype(str) + data[\"QUARTER\"]\nuser_portfolio = [\"BF\", \"AF\", \"BE\", \"AI\", \"M\"]\npotential_portfolio = [\"BF\", \"AF\", \"BJ\", \"AL\", \"AY\"]\n\n\n# Original Portfolio\nop_data = data.TICKER.isin(user_portfolio)\nop = data[op_data]\n# Points by portfolio\nop_agg1 = op.groupby('period').agg({'MONEY': 'mean'}).reset_index()\nop_agg2 = op.groupby('period').agg({'CARBON_FOOTPRINT': 'mean'}).reset_index()\n\n# Potential Portfolio\npp_data = data.TICKER.isin(potential_portfolio)\npp = data[pp_data]\npp_agg1 = pp.groupby('period').agg({'MONEY': 'mean'}).reset_index()\npp_agg2 = pp.groupby('period').agg({'CARBON_FOOTPRINT': 'mean'}).reset_index()\n\n# create figure and axis objects with subplots\nfig, ax = plt.subplots()\nax2 = ax.twinx()\n\n# Original Plot\nplt.plot(op_agg2.period, op_agg2.CARBON_FOOTPRINT, marker=\"o\", label=\"Current Carbon Footprint\")\nplt.plot(op_agg1.period, op_agg1.MONEY, marker=\"o\", label=\"Current Returns\")\n\n# Potential Portfolio Plot\nplt.plot(pp_agg2.period, pp_agg2.CARBON_FOOTPRINT, linestyle='dashed', marker=\"o\", label=\"Potential Carbon Footprint\")\nplt.plot(pp_agg1.period, pp_agg1.MONEY, linestyle='dashed', marker=\"o\", label=\"Potential Returns\")\n\n# Labelling\nax.set_xlabel(\"Time Periods\", fontsize=14)\nax.set_ylabel(\"Carbon Footprint(tCO\\u2082e/bnUSD)\", color=\"red\", fontsize=14)\nax2.set_ylabel(\"Returns\", color=\"blue\", fontsize=14)\n\nax.legend()\nplt.legend()\nplt.show()\n","repo_name":"bpk1bpk1/JUMP-2022","sub_path":"visualization.py","file_name":"visualization.py","file_ext":"py","file_size_in_byte":1638,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"17964881150","text":"\"\"\"Music Generation Functionality\"\"\"\n# pylint: disable=W0718\nfrom typing import Literal, Tuple, Callable, Optional\nimport gc\nimport warnings\nimport numpy as np\nfrom tqdm.auto import tqdm\nfrom audiocraft.models import MusicGen\nimport torch\nwarnings.filterwarnings('ignore')\n\nDEFAULT_MODEL = 'medium'\nDEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'\nMODELS = ['small','medium','large','melody']\n\nclass MusicGenerator:\n \"\"\"Class for Music Generation\"\"\"\n def __init__(\n self,\n pretrained:Literal['small','medium','large','melody'] = DEFAULT_MODEL,\n device:str = DEVICE\n ) -> None:\n self.pretrained = pretrained\n self.device = device\n self.model = None\n self.loaded = False\n self.load_error = None\n self.pbar = None\n\n def load_model(\n self,\n pretrained:Literal['small','medium','large','melody'] = None,\n device:str = None\n ) -> bool:\n \"\"\"Load a pre-trained model for music generation.\n\n Args:\n pretrained (Literal['small','medium','large','melody'], optional): The size of\n the pre-trained model to load. Defaults to None.\n device (str, optional): The device to use for model inference. Defaults to None.\n\n Returns:\n bool: True if the model is successfully loaded, False otherwise.\n \"\"\"\n if self.loaded is True:\n self.delete_model()\n\n if pretrained is not None:\n self.pretrained = pretrained\n if device is not None:\n self.device = device\n try:\n pt_fullname = f\"facebook/musicgen-{self.pretrained}\"\n self.model = MusicGen.get_pretrained(pt_fullname,\\\n device=self.device)\n self.loaded = True\n except Exception as err:\n self.load_error = err\n self.loaded = False\n\n return self.loaded\n\n def delete_model(\n self\n ) -> None:\n \"\"\"Deletes the model and frees up memory.\n\n This function deletes the model object, performs garbage collection, empties the\n CUDA cache, and resets the device, model, loaded flag, load error, and progress\n bar attributes.\n\n Returns:\n None\n \"\"\"\n del self.model\n gc.collect()\n torch.cuda.empty_cache()\n self.device = None\n self.model = None\n self.loaded = False\n self.load_error = None\n self.pbar = None\n\n def reset_progress_bar(\n self\n ) -> None:\n \"\"\"Resets the progress bar.\n\n This function resets the progress bar by setting the total value to 1 and updating the\n description to 'Generating'.\n\n Returns:\n None\n \"\"\"\n self.pbar = tqdm(total=1, dynamic_ncols=True,\\\n desc='Generating')\n\n def set_progress_bar_cb(\n self,\n callback:Callable = None\n ) -> None:\n \"\"\"Sets the progress bar callback function.\n\n Args:\n callback (Callable, optional): The callback function to be set. Defaults to None.\n\n Returns:\n None\n\n Example:\n set_progress_bar_cb(callback=my_callback_function)\n \"\"\"\n if callback is None:\n self.reset_progress_bar()\n callback = self.cb_progress_bar\n self.model.set_custom_progress_callback(callback)\n\n def cb_progress_bar(\n self,\n p:int,\n t:int\n ) -> None:\n \"\"\"Updates the progress bar with the given progress and total values.\n\n Args:\n p (int): The current progress value.\n t (int): The total value.\n\n Raises:\n ValueError: If the progress bar is not initialized.\n\n Returns:\n None\n \"\"\"\n if self.pbar is None:\n raise ValueError(\"Progress Bar must be initialized first\")\n if self.pbar.total != t:\n self.pbar.total = t\n self.pbar.reset(t)\n self.pbar.update(p)\n if self.pbar.total == p:\n self.pbar.close()\n else:\n self.pbar.refresh()\n\n def set_model_params(\n self,\n use_sample:bool=True,\n top_k:int = 250,\n top_p:float = 0.0,\n temp:float = 1,\n duration:int = 8,\n cfg_coef:int = 3\n ) -> None:\n \"\"\"Sets the parameters for generating music using the model.\n\n Args:\n use_sample (bool): Use sampling if True, else do argmax decoding. Defaults to True.\n top_k (int): Top-k is a setting in text and music generation models that limits the\n number of choices (tokens) the model considers at each step. A smaller\n top-k leads to more predictable outputs, while a larger top-k allows\n for more variety. Defaults to 250.\n top_p (float): Top-p, also called nucleus sampling, is a method in text generation\n where the model chooses from a set of tokens based on their combined\n likelihood, ensuring a balance between diversity and coherence in\n the output. Unlike top-k, which picks a fixed number of tokens,\n top-p's selection varies based on their probabilities. Defaults to\n 0.0, when set to 0 top_k is used.\n temp (float): Softmax temperature parameter. In music generation, the temperature\n setting controls how predictable or varied the music is. A higher\n temperature results in more random and diverse music, while a lower\n temperature creates more consistent and less varied music. Defaults\n to 1.0.\n duration (int): Duration of the generated waveform. Defaults to 30.0.\n cfg_coef (int): Coefficient used for classifier free guidance. This technique in\n music generation uses an additional classifier network to guide\n the music creation process towards specific characteristics or\n styles. It provides more detailed control, allowing users to\n influence the style and features of the generated music. Defaults\n to 3.0.\n\n Raises:\n ValueError: If the model is not loaded.\n\n Returns:\n None\n \"\"\"\n if self.model is None:\n raise ValueError(\"Model must be loaded first\")\n self.model.set_generation_params(use_sample, top_k, top_p, temp,\\\n duration, cfg_coef)\n\n def text_cond_generate(\n self,\n prompt:str\n ) -> Tuple[int, np.ndarray]:\n \"\"\"Text conditional music generation using the loaded model.\n\n Args:\n prompt (str): The prompt text for generating the waverform.\n\n Returns:\n Tuple[int, np.ndarray]: A tuple containing the sample rate and the generated\n waveform as a numpy array.\n\n Raises:\n ValueError: If the model is not loaded.\n \"\"\"\n if self.model is None:\n raise ValueError(\"Model must be loaded first\")\n\n # Generate music based on prompt\n wav = self.model.generate([prompt], progress=True)\n wav = wav.cpu().flatten().numpy()\n\n return self.model.sample_rate, wav\n\n @staticmethod\n def prepare_input_audio(\n input_audio:Tuple[int, np.ndarray] = None\n ) -> Tuple[int, np.ndarray]:\n \"\"\"Prepares the input audio for further processing.\n\n Args:\n input_audio (Tuple[int, np.ndarray], optional): A tuple containing the sample\n rate and waveform of the input\n audio. Defaults to None.\n\n Returns:\n Tuple[int, np.ndarray]: A tuple containing the sample rate and processed\n waveform.\n\n Note:\n - If `input_audio` is not provided, the function returns a tuple with sample\n rate 0 and waveform None.\n - If `input_audio` is provided, the function converts the waveform to a torch\n tensor and performs additional processing if necessary.\n \"\"\"\n if input_audio is not None:\n sample_rate, wav = input_audio\n wav = torch.tensor(wav)\n if wav.dtype == torch.int16:\n wav = wav.float() / 32767.0\n if wav.dim() == 2 and wav.shape[1] == 2:\n wav = wav.mean(dim=1)\n else:\n sample_rate, wav = 0, None\n\n return sample_rate, wav\n\n def melody_cond_generate(\n self,\n input_audio:Tuple[int, np.ndarray],\n prompt:Optional[str] = ''\n ) -> Tuple[int, np.ndarray]:\n \"\"\"Melody conditional music generation on an input audio with the loaded model.\n\n Args:\n input_audio (Tuple[int, np.ndarray]): The input audio as a tuple of sample\n rate and waveform.\n prompt (Optional[str]): The prompt for generating the waveform. Defaults to\n an empty string.\n\n Returns:\n Tuple[int, np.ndarray]: A tuple containing the sample rate and the generated\n melody waveform.\n\n Raises:\n ValueError: If the model is not loaded.\n\n Note:\n - If no prompt is provided, it will be set to None.\n - The input audio will be prepared using the MusicGenerator class.\n - The generated melody will be conditioned on the input audio using the model's\n generate_with_chroma method.\n - The generated waveform will be converted to a numpy array.\n \"\"\"\n if self.model is None:\n raise ValueError(\"Model must be loaded first\")\n\n if not prompt:\n prompt = None\n sample_rate, wav = MusicGenerator.prepare_input_audio(input_audio)\n wav = wav[None].expand(1, -1, -1)\n # Generate music based on melody of existing clip (`wav`) and prompt\n wav = self.model.generate_with_chroma([prompt], wav,\\\n sample_rate, progress=True)\n wav = wav.cpu().flatten().numpy()\n\n return self.model.sample_rate, wav\n\n def cont_generate(\n self,\n input_audio:Tuple[int, np.ndarray],\n prompt:Optional[str] = ''\n ) -> Tuple[int, np.ndarray]:\n \"\"\"Generates a music continuation to an existing audio clip based on a prompt.\n\n Args:\n input_audio (Tuple[int, np.ndarray]): The input audio clip as a tuple of\n sample rate and waveform.\n prompt (Optional[str], optional): The prompt for generating the music\n continuation. Defaults to ''.\n\n Returns:\n Tuple[int, np.ndarray]: The sample rate and waveform of the generated\n music continuation.\n\n Raises:\n ValueError: If the model is not loaded.\n\n Note:\n - If no prompt is provided, the prompt will be set to None.\n - The input audio clip is prepared using the `prepare_input_audio` method of the\n `MusicGenerator` class.\n - The generated music continuation is obtained by calling the `generate_continuation`\n method of the model.\n - The generated waveform is converted to a numpy array.\n \"\"\"\n\n if self.model is None:\n raise ValueError(\"Model must be loaded first\")\n\n if not prompt:\n prompt = None\n sample_rate, wav = MusicGenerator.prepare_input_audio(input_audio)\n wav = wav[None].expand(1, -1, -1)\n # Generate music continuation to existing clip (`wav`) based on prompt\n wav = self.model.generate_continuation(wav, sample_rate,\\\n [prompt], progress=True)\n wav = wav.cpu().flatten().numpy()\n\n return self.model.sample_rate, wav\n\n def uncond_generate(\n self\n ) -> Tuple[int, np.ndarray]:\n \"\"\"Generates music unconditionally.\n\n This function generates music unconditionally using a pre-loaded model. It returns the\n sample rate and the generated waveform.\n\n Returns:\n Tuple[int, np.ndarray]: A tuple containing the sample rate (int) and the generated\n waveform (np.ndarray).\n\n Raises:\n ValueError: If the model is not loaded.\n \"\"\"\n\n if self.model is None:\n raise ValueError(\"Model must be loaded first\")\n\n # Generate music unconditionally (based on nothing)\n wav = self.model.generate_unconditional(1, progress=True)\n wav = wav.cpu().flatten().numpy()\n\n return self.model.sample_rate, wav\n\n def generate(\n self,\n prompt:Optional[str] = '',\n input_audio:Optional[Tuple[int, np.ndarray]] = None,\n use_sample:Optional[bool] = True,\n top_k:Optional[int] = 250,\n top_p:Optional[float] = 0.0,\n temp:Optional[float] = 1,\n duration:Optional[int] = 8,\n cfg_coef:Optional[int] = 3,\n progress:Optional[Callable] = None\n ) -> Tuple[int, np.ndarray]:\n \"\"\"Generate audio using the model.\n\n Args:\n prompt (Optional[str]): The prompt text to generate audio from. Default is an empty\n string.\n input_audio (Optional[Tuple[int, np.ndarray]]): The input audio to condition the\n generation on. Default is None.\n use_sample (Optional[bool]): Whether to use sampling during generation. Default is\n True.\n top_k (Optional[int]): The number of top tokens to consider during sampling. Default\n is 250.\n top_p (Optional[float]): The cumulative probability threshold for top-k sampling.\n Default is 0.0.\n temp (Optional[float]): The temperature value for sampling. Default is 1.\n duration (Optional[int]): The duration of the generated audio in seconds. Default\n is 8.\n cfg_coef (Optional[int]): The coefficient for controlling the model configuration.\n Default is 3.\n progress (Optional[Callable]): A callback function to track the progress of\n generation. Default is None.\n\n Returns:\n Tuple[int, np.ndarray]: A tuple containing the sample rate and the generated\n audio waveform.\n \"\"\"\n self.set_model_params(use_sample, top_k, top_p, temp, duration, cfg_coef)\n\n if progress is not None:\n def progress_callback(p, t):\n progress((p, t), desc='Generating')\n else:\n progress_callback = None\n\n self.set_progress_bar_cb(progress_callback)\n\n input_audio_not_none = input_audio is not None\n\n if input_audio_not_none and (self.pretrained == 'melody'):\n sample_rate, wav = self.melody_cond_generate(input_audio, prompt)\n elif input_audio_not_none:\n self.set_model_params(use_sample, top_k, top_p, temp, duration, cfg_coef)\n sample_rate, wav = self.cont_generate(input_audio, prompt)\n elif not prompt:\n sample_rate, wav = self.uncond_generate()\n else:\n sample_rate, wav = self.text_cond_generate(prompt)\n\n return sample_rate, wav\n","repo_name":"smasis001/Music-GenAI-App","sub_path":"music_generation/music_gen.py","file_name":"music_gen.py","file_ext":"py","file_size_in_byte":16237,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"14500021264","text":"from tkinter import Tk, filedialog, END\nfrom p_view import PdfView\nfrom p_model import Pdf\nfrom PyPDF2 import PdfFileReader, PdfFileMerger\nfrom contextlib import ExitStack\nimport re\n\nclass PdfController():\n #Initializes the GUI\n def __init__(self):\n self.root = Tk()\n self.view = PdfView(self.root)\n self.pdfs = []\n self.view.add_btn['command'] = self.add_file\n self.view.delete_btn['command'] = self.delete\n self.view.path_list_view.bind(\"<>\", self.get_info)\n self.view.merge_btn['command'] = self.merge\n self.view.up_btn['command'] = self.moveup\n self.view.down_btn['command'] = self.movedown\n \n # Starts the Gui for the entire program\n def run(self):\n self.root.title(\"PMerge - PDF file Merger\")\n self.root.geometry('700x700')\n self.root.mainloop()\n\n #opens the Filename open dialog box and sets the import_view to the selected file\n #TODO: Don't forget to change the initialdir to C:/ for production\n def add_file(self):\n file_count = 1\n filepath = filedialog.askopenfilenames(title=\"Select PDF\",\n initialdir=\"C:\\\\\",\n filetypes=[('pdf files', '.pdf')])\n for file in filepath:\n pdf_obj = Pdf()\n with open(file, 'rb') as file_binary:\n pdf = PdfFileReader(file_binary)\n pdf_obj.info = pdf.getDocumentInfo()\n pdf_obj.page_num = pdf.getNumPages()\n pdf_obj.filepath = file\n self.view.path_list_view.insert(END, file)\n self.pdfs.append(pdf_obj)\n file_binary.close()\n\n #build the filetree of files to be manipulated\n #TODO: Add Filetree version to do manipulations like split and merge page by page\n def build_filetree(self, filename='', num_of_pages=1):\n file_count = len(self.view.filetree.get_children())\n folder = self.view.filetree.insert(\"\", file_count,\n \"\", text=filename)\n \n\n\n #deletes the selected files in path_list_view\n #TODO Delete multiple files at once\n def delete(self):\n files = self.view.path_list_view.curselection()\n for file in files:\n filepath = self.view.path_list_view.get(file)\n pdf = self.pdfs[file] # using the indices to grab the filepath from the listbox\n self.pdfs.remove(pdf) # removing the filepath from the pdfs list\n self.view.path_list_view.delete(file) # delete filepath from listbox\n self.clear_feedback()\n\n # Merge Files\n #TODO\n def merge(self):\n with ExitStack() as stack:\n new_filepath = filedialog.asksaveasfilename(title=\"Select PDF\",\n initialdir=\"C:\\\\\",\n filetypes=[('pdf files', '.pdf'),('all files', '*.*')])\n if not re.search(\".pdf$\",new_filepath):\n new_filepath = new_filepath + \".pdf\"\n \n pdfMerger = PdfFileMerger()\n files = [stack.enter_context(open(pdf.filepath, 'rb')) for pdf in self.pdfs]\n #open a read binary of all the filepaths currently loaded into PMerger and merge them into the pdfMerger object\n for pdf in files:\n pdfMerger.append(pdf)\n \n #open the new filepath to as a write binary file and write all of the files that were previously loaded into pdfMerger\n with open(new_filepath, 'wb') as final_filepath:\n pdfMerger.write(final_filepath)\n self.clear_feedback()\n self.append_feedback(\"Files have been merged\")\n \n #adds text to the feedback view\n def append_feedback(self, text):\n output_text = self.view.feedback_text.get() + text\n self.view.feedback_text.set(output_text)\n\n # Clears the Feedback view of all text\n def clear_feedback(self):\n self.view.feedback_text.set(\"\")\n\n # Displays PDF obj Items in the Object list in the feedback view\n def get_info(self, event):\n for index in self.view.path_list_view.curselection():\n pdf = self.pdfs[index]\n self.clear_feedback()\n self.append_feedback(pdf.filepath)\n self.append_feedback(\"\\nNumber of Pages: {}\".format(pdf.page_num))\n self.append_feedback(\"\\nTitle: {}\".format(pdf.info.title))\n self.append_feedback(\"\\nAuthor: {}\".format(pdf.info.author))\n self.append_feedback(\"\\nCreator: {}\".format(pdf.info.creator))\n self.append_feedback(\"\\nProducer: {}\".format(pdf.info.producer))\n self.append_feedback(\"\\nSubject: {}\".format(pdf.info.subject))\n\n #move file one up the list on the file order\n def moveup(self):\n if self.view.path_list_view.curselection():\n file_index = self.view.path_list_view.curselection()[0]\n else:\n self.clear_feedback()\n self.append_feedback(\"\\nNo file has been selected\")\n return\n if file_index == 0:\n self.clear_feedback()\n self.append_feedback(\"\\nFile already at the top of the list\")\n else:\n self.pdfs.insert(file_index-1, self.pdfs.pop(file_index))\n file = self.view.path_list_view.get(file_index)\n self.view.path_list_view.delete(file_index)\n self.view.path_list_view.insert(file_index-1, file)\n self.view.path_list_view.select_set(file_index-1)\n\n #move file one down the list on the file order\n def movedown(self):\n if self.view.path_list_view.curselection():\n file_index = self.view.path_list_view.curselection()[0]\n list_len = self.view.path_list_view.size()\n else:\n self.clear_feedback()\n self.append_feedback(\"\\nNo file has been selected\")\n return\n if file_index == list_len - 1:\n self.clear_feedback()\n self.append_feedback(\"\\nFile already at the bottom of the merge file stack\")\n else:\n self.pdfs.insert(file_index+1, self.pdfs.pop(file_index))\n file = self.view.path_list_view.get(file_index)\n self.view.path_list_view.delete(file_index)\n self.view.path_list_view.insert(file_index+1, file)\n self.view.path_list_view.select_set(file_index+1)\n \n\nif __name__ == \"__main__\":\n print(\"Please run the p_merge.py file as the main program\")","repo_name":"cortezcodes/p_merge","sub_path":"p_controller.py","file_name":"p_controller.py","file_ext":"py","file_size_in_byte":6532,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"39764536153","text":"import torch.nn as nn\nimport torch.nn.functional as F\n\nclass CNN(nn.Module):\n def __init__(self):\n super(CNN, self).__init__()\n \n self.conv1 = nn.Conv2d(3, 10, 5)\n self.conv2 = nn.Conv2d(10, 20, 5)\n self.conv3 = nn.Conv2d(20, 30, 5)\n \n def forward(self, i):\n x = i.view(-1, i.shape[2], i.shape[3], i.shape[4])\n x = F.relu(self.conv1(x))\n x = F.relu(self.conv2(x))\n x = F.relu(self.conv3(x))\n x = nn.AvgPool2d(4)(x)\n x = x.view(i.shape[0], i.shape[1], -1)\n return x\n \nclass CNNLSTM(nn.Module):\n def __init__(self, seq_length, num_classes):\n super(CNNLSTM, self).__init__()\n self.net_cnn = CNN()\n self.lstm = nn.LSTM(750, 100)\n self.fc = nn.Linear(100*seq_length, num_classes)\n \n def forward(self, x):\n x = self.net_cnn(x)\n x, _ = self.lstm(x)\n x = x.view(x.shape[0], -1)\n x = self.fc(x)\n return x ","repo_name":"medengessia/KTH-Action-Recognition","sub_path":"models/cnn_lstm.py","file_name":"cnn_lstm.py","file_ext":"py","file_size_in_byte":972,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"70362227044","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n path('', views.index),\n path('frame', views.frame),\n path('sign_in', views.sign_in),\n path('sign_up', views.sign_up),\n path('sign_out', views.sign_out),\n]\n","repo_name":"eldarian-1/remote_cam","sub_path":"server/main/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":237,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"39468418349","text":"from fastapi.testclient import TestClient\nimport sys\nfrom pathlib import Path\n\nsys.path.append(str(Path(__file__).resolve().parents[1]))\nfrom main import app\n\nclient = TestClient(app)\n\ndef test_read_root():\n response = client.get(\"/\")\n assert response.status_code == 200\n assert response.json() == {\"Hello\": \"World\"}\n\ndef test_read_main():\n\tresponse = client.get(\"/hello\")\n\tassert response.status_code == 200\n\tassert response.json() == {\"pease\": \"hello\"}","repo_name":"HermitBroccoli/Applicant365_server","sub_path":"test/test_one.py","file_name":"test_one.py","file_ext":"py","file_size_in_byte":463,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"11908211563","text":"from typing import List\nfrom collections import deque\n\ndef get_neighbors(node: List[int], image: List[List[int]]):\n neighbors = []\n delta_col = [-1, 0, 1, 0]\n delta_row = [0, -1, 0, 1]\n for i in range(4):\n new_row = node[0] + delta_row[i] \n new_col = node[1] + delta_col[i]\n if new_row < 0 or new_row >= len(image): continue\n if new_col < 0 or new_col >= len(image[0]): continue\n neighbors.append([new_row, new_col])\n return neighbors\n\ndef flood_fill(r: int, c: int, replacement: int, image: List[List[int]]) -> List[List[int]]:\n queue = deque([[r, c]])\n original = image[r][c]\n image[r][c] = replacement\n while len(queue) > 0:\n node = queue.popleft()\n for neighbor in get_neighbors(node, image):\n if image[neighbor[0]][neighbor[1]] == original:\n queue.append(neighbor)\n image[neighbor[0]][neighbor[1]] = replacement\n return image\n\nif __name__ == '__main__':\n r = int(input())\n c = int(input())\n replacement = int(input())\n image = [[int(x) for x in input().split()] for _ in range(int(input()))]\n res = flood_fill(r, c, replacement, image)\n for row in res:\n print(' '.join(map(str, row)))\n\n","repo_name":"yashanand1910/solutions","sub_path":"algomonster/graph/flood_fill.py","file_name":"flood_fill.py","file_ext":"py","file_size_in_byte":1236,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"53222852","text":"from __future__ import print_function\nfrom keras.preprocessing import sequence\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Embedding\nfrom keras.layers import LSTM\nfrom keras.layers.wrappers import TimeDistributed\nfrom keras.models import load_model\nimport pandas as pa\nimport numpy as np\nfrom numpy import argmax\n\nword_max_features = 1116\nmaxlen = 29 # cut texts after this number of words (among top max_features most common words)\nbatch_size = 20\ntrain_data_df= pa.read_csv('sequenceData/trainSentenceSequence.txt',header=None)\ntrain_label_df=pa.read_csv('sequenceData/trainLabelSequence.txt',header=None)\ntest_data_df= pa.read_csv('sequenceData/testSentenceSequence.txt',header=None)\ntest_label_df=pa.read_csv('sequenceData/testLabelSequence.txt',header=None)\n\nx_train = np.array(train_data_df.iloc[:,0:29])\ny_train = np.array(train_label_df.iloc[:,0:8])\nx_test = np.array(test_data_df.iloc[:,0:29])\ny_test = np.array(test_label_df.iloc[:,0:8])\n\"\"\"\ntrain_test_df= pa.read_csv('test_sequence.csv',header=None)\nlabel_test_df=pa.read_csv('test_label.csv',header=None)\nprint('Loading data...')\n\n\n\n\nprint('Pad sequences (samples x time)')\nx_train = sequence.pad_sequences(x_train, maxlen=maxlen)\nx_test = sequence.pad_sequences(x_test, maxlen=8)\n\"\"\"\nprint('x_train shape:', x_train.shape)\n\nprint('y_train shape:', y_train.shape)\nprint('x_test shape:', x_test.shape)\n\nprint('y_test shape:', y_test.shape)\nprint('Build model...')\nmodel = Sequential()\nmodel.add(Embedding(word_max_features, 128))\nmodel.add(LSTM(256,return_sequences=True, dropout=0.2, recurrent_dropout=0.2))\nmodel.add(Dense(32, activation='sigmoid'))\nmodel.add(LSTM(256, dropout=0.2, recurrent_dropout=0.2))\nmodel.add(Dense(8, activation='softmax'))\n\n# try using different optimizers and different optimizer configs\n\nmodel.compile(loss='binary_crossentropy',\n optimizer='adam',\n metrics=['accuracy'])\n\nprint('Train...')\n\n#model.fit(x_train, y_train,batch_size=batch_size,validation_split=0.1,epochs=40)\n\n\n#model.save('model/test.mod')\n\n#model=load_model('model/test.mod')\n\nscore, acc = model.evaluate(x_test, y_test)\nprint('Test score:', score)\nprint('Test accuracy:', acc)\n\nfor i,data in enumerate(x_test):\n\tres = model.predict(data)\n\tprint (argmax(res))\n\t#print(res.shape)","repo_name":"wmmks/SemanticParser","sub_path":"intent/IntentClassification.py","file_name":"IntentClassification.py","file_ext":"py","file_size_in_byte":2288,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"27917410400","text":"from datetime import datetime\n\nimport app.utils.date_formater\nfrom app.calculator.day_work import DayWork\n\n\ndef test_day_work_create_class(work_day1_fixture):\n expected_day = 'MO'\n expected_start_hour = datetime.strptime(\"10:00\", '%H:%M')\n expected_finish_hour = datetime.strptime(\"12:00\", '%H:%M')\n assert work_day1_fixture.day == expected_day\n assert work_day1_fixture.start_time == expected_start_hour\n assert work_day1_fixture.finish_time == expected_finish_hour\n\n\ndef test_get_day(work_day1_fixture: DayWork) -> None:\n expected_day = 'MO'\n day = work_day1_fixture.get_day(\"MO10:00-12:00\")\n assert day == expected_day\n\n\ndef test_get_start_hour(mocker: any, work_day1_fixture: DayWork) -> None:\n expected_start_hour = datetime.strptime(\"10:00\", '%H:%M')\n mocker.patch(\"app.utils.date_formater.to_date\", return_value=datetime.strptime(\"10:00\", '%H:%M'))\n start_hour = work_day1_fixture.get_start_hour(\"MO10:00-12:00\")\n assert start_hour == expected_start_hour\n\n\ndef test_finish_hour(mocker: any, work_day1_fixture: DayWork) -> None:\n expected_finish_hour = datetime.strptime(\"12:00\", '%H:%M')\n mocker.patch(\"app.utils.date_formater.to_date\", return_value=datetime.strptime(\"12:00\", '%H:%M'))\n finish_hour = work_day1_fixture.get_finish_hour(\"MO12:00-12:00\")\n assert finish_hour == expected_finish_hour\n","repo_name":"diegocanepa/acme-exercise","sub_path":"tests/app/calculator/test_day_work.py","file_name":"test_day_work.py","file_ext":"py","file_size_in_byte":1355,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"3229429678","text":"from features.locators import google_loc\n\nlocator = {\n **google_loc\n}\n\n\ndef get_loc(loc_name):\n select = locator.get(loc_name)\n if not select:\n raise Warning(f'+++ Поле {loc_name} не найдено +++')\n return select\n","repo_name":"SergeyMihalich/beHaveTest","sub_path":"features/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":244,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"5700859197","text":"from pyspark.sql import SparkSession\nimport yaml\nimport os.path\nimport src.utils.aws_utils as ut\n\nif __name__ == '__main__':\n\n os.environ[\"PYSPARK_SUBMIT_ARGS\"] = (\n '--jars \"https://s3.amazonaws.com/redshift-downloads/drivers/jdbc/1.2.36.1060/RedshiftJDBC42-no-awssdk-1.2.36.1060.jar\"\\\n --packages \"org.apache.spark:spark-avro_2.11:2.4.2,io.github.spark-redshift-community:spark-redshift_2.11:4.0.1,org.apache.hadoop:hadoop-aws:2.7.4\" pyspark-shell'\n )\n\n # Create the SparkSession\n sparkSession = SparkSession \\\n .builder \\\n .appName(\"Read from enterprise applications\") \\\n .master('local[*]') \\\n .getOrCreate()\n\n current_dir = os.path.abspath(os.path.dirname(__file__))\n appConfigFilePath = os.path.abspath(current_dir + \"/../../../../\"+\"application.yml\")\n\n with open(appConfigFilePath) as conf:\n doc = yaml.load(conf,Loader=yaml.FullLoader)\n\n # Setup spark to use s3\n hadoop_conf = sparkSession.sparkContext._jsc.hadoopConfiguration()\n hadoop_conf.set(\"fs.s3.impl\", \"org.apache.hadoop.fs.s3a.S3AFileSystem\")\n hadoop_conf.set(\"fs.s3a.access.key\", doc[\"s3_conf\"][\"access_key\"])\n hadoop_conf.set(\"fs.s3a.secret.key\", doc[\"s3_conf\"][\"secret_access_key\"])\n hadoop_conf.set(\"fs.s3a.endpoint\", \"s3-eu-west-1.amazonaws.com\")\n\n print(\"\\nCreating Dataframe from txn_fact dataset,\")\n txnDf = sparkSession.read\\\n .option(\"header\",\"true\")\\\n .option(\"delimiter\", \"|\")\\\n .csv(\"s3a://\"+doc[\"s3_conf\"][\"s3_bucket\"]+\"/txn_fct.csv\")\n\n txnDf.show(5,False)\n\n print(\"Writing txn_fact dataframe to AWS Redshift Table >>>>>>>\")\n\n jdbcUrl = ut.getRedshiftJdbcUrl(doc)\n print(jdbcUrl)\n\n txnDf.write\\\n .format(\"io.github.spark_redshift_community.spark.redshift\") \\\n .option(\"url\", jdbcUrl) \\\n .option(\"tempdir\", \"s3a://\"+doc[\"s3_conf\"][\"s3_bucket\"]+\"/temp\") \\\n .option(\"forward_spark_s3_credentials\", \"true\") \\\n .option(\"dbtable\", \"PUBLIC.TXN_FCT\") \\\n .mode(\"overwrite\")\\\n .save()\n\n print(\"Completed <<<<<<<<<\")\n\n","repo_name":"roshith-mambatta/dataframe-pyexamples","sub_path":"src/dataframe/from/other/systems/Write2Redshift.py","file_name":"Write2Redshift.py","file_ext":"py","file_size_in_byte":2076,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"19024356390","text":"import cv2\nimport numpy as np\nimport math\nimport random\n\n\nclass ValueNoise:\n def __init__(self, height, width, lattice, seed):\n self.height = height\n self.width = width\n self.lattice = lattice\n random.seed(seed)\n ilen = int(self.height / self.lattice) + 1\n jlen = int(self.width / self.lattice) + 1\n self.suppleMat = [[0 for i in range(ilen)] for i in range(jlen)]\n for i in range(ilen):\n for j in range(jlen):\n self.suppleMat[i][j] = (int)(255 * random.random())\n\n def fade_old(self, t):\n return t * t * (3 - 2 * t)\n\n def fade(self, t):\n return t * t * t * (t * (t * 6 - 15) + 10)\n\n def value_noise(self, smooth=0):\n dst = np.zeros((self.height, self.width), np.uint8)\n for i in range(0, self.height):\n for j in range(0, self.width):\n x = i / self.lattice\n y = j / self.lattice\n\n sx = math.floor(x)\n sy = math.floor(y)\n fx = x - sx\n fy = y - sy\n if smooth == 0:\n cbufx = [math.floor((1.0 - fx) * 2048)]\n cbufx.append(2048 - cbufx[0])\n cbufy = [math.floor((1.0 - fy) * 2048)]\n cbufy.append(2048 - cbufy[0])\n elif smooth == 1:\n cbufx = [math.floor(self.fade_old(1.0 - fx) * 2048)]\n cbufx.append(2048 - cbufx[0])\n cbufy = [math.floor(self.fade_old(1.0 - fy) * 2048)]\n cbufy.append(2048 - cbufy[0])\n else:\n cbufx = [math.floor(self.fade(1.0 - fx) * 2048)]\n cbufx.append(2048 - cbufx[0])\n cbufy = [math.floor(self.fade(1.0 - fy) * 2048)]\n cbufy.append(2048 - cbufy[0])\n dst[i, j] = (self.suppleMat[sx][sy] * cbufx[0] * cbufy[0] +\n self.suppleMat[sx + 1][sy] * cbufx[1] * cbufy[0] +\n self.suppleMat[sx][sy + 1] * cbufx[0] * cbufy[1] +\n self.suppleMat[sx + 1][sy + 1] * cbufx[1] * cbufy[1]) >> 22\n return dst\n\n def show_pic(self):\n value_noise_no_fade = self.value_noise()\n value_noise_old_fade = self.value_noise(1)\n value_noise_new_fade = self.value_noise(2)\n cv2.imshow(\"value_noise_no_fade\", value_noise_no_fade)\n cv2.imshow(\"value_noise_old_fade\", value_noise_old_fade)\n cv2.imshow(\"value_noise_new_fade\", value_noise_new_fade)\n\n\nclass PerlinNoise:\n def __init__(self, height, width, lattice):\n self.height = height\n self.width = width\n self.lattice = lattice\n self.p = []\n self.premutation = []\n '''\n self.premutation = [151, 160, 137, 91, 90, 15,\n 131, 13, 201, 95, 96, 53, 194, 233, 7, 225, 140, 36, 103, 30, 69, 142, 8, 99, 37, 240, 21,\n 10,\n 23,\n 190, 6, 148, 247, 120, 234, 75, 0, 26, 197, 62, 94, 252, 219, 203, 117, 35, 11, 32, 57, 177,\n 33,\n 88, 237, 149, 56, 87, 174, 20, 125, 136, 171, 168, 68, 175, 74, 165, 71, 134, 139, 48, 27,\n 166,\n 77, 146, 158, 231, 83, 111, 229, 122, 60, 211, 133, 230, 220, 105, 92, 41, 55, 46, 245, 40,\n 244,\n 102, 143, 54, 65, 25, 63, 161, 1, 216, 80, 73, 209, 76, 132, 187, 208, 89, 18, 169, 200,\n 196,\n 135, 130, 116, 188, 159, 86, 164, 100, 109, 198, 173, 186, 3, 64, 52, 217, 226, 250, 124,\n 123,\n 5, 202, 38, 147, 118, 126, 255, 82, 85, 212, 207, 206, 59, 227, 47, 16, 58, 17, 182, 189,\n 28, 42,\n 223, 183, 170, 213, 119, 248, 152, 2, 44, 154, 163, 70, 221, 153, 101, 155, 167, 43, 172, 9,\n 129, 22, 39, 253, 19, 98, 108, 110, 79, 113, 224, 232, 178, 185, 112, 104, 218, 246, 97,\n 228,\n 251, 34, 242, 193, 238, 210, 144, 12, 191, 179, 162, 241, 81, 51, 145, 235, 249, 14, 239,\n 107,\n 49, 192, 214, 31, 181, 199, 106, 157, 184, 84, 204, 176, 115, 121, 50, 45, 127, 4, 150, 254,\n 138, 236, 205, 93, 222, 114, 67, 29, 24, 72, 243, 141, 128, 195, 78, 66, 215, 61, 156, 180]\n '''\n for i in range(0, 256):\n self.premutation.append(i)\n random.seed(0)\n random.shuffle(self.premutation)\n for i in range(0, 512):\n self.p.append(self.premutation[i % 256])\n\n def fade_old(self, t):\n return t * t * (3 - 2 * t)\n\n def fade(self, t):\n return t * t * t * (t * (t * 6 - 15) + 10)\n\n def lerp(self, t, a, b):\n return a + t * (b - a)\n\n def grad(self, has, x, y):\n h = has & 3\n if h == 0:\n return x\n elif h == 1:\n return -x\n elif h == 2:\n return y\n elif h == 3:\n return -y\n else:\n return 0\n\n def grad_old(self, has, x, y):\n h = has & 7\n if h == 0:\n return x\n elif h == 1:\n return -x\n elif h == 2:\n return y\n elif h == 3:\n return -y\n elif h == 4:\n return x + y\n elif h == 5:\n return x - y\n elif h == 6:\n return -x + y\n elif h == 7:\n return -x - y\n return 0\n\n def perlin_noise(self, x, y, smooth, dir):\n X = math.floor(x) & 255\n Y = math.floor(y) & 255\n x -= math.floor(x)\n y -= math.floor(y)\n if smooth == 0:\n u = self.fade(x)\n v = self.fade(y)\n else:\n u = self.fade_old(x)\n v = self.fade_old(y)\n\n AA = self.p[self.p[X] + Y]\n AB = self.p[self.p[X] + Y + 1]\n BA = self.p[self.p[X + 1] + Y]\n BB = self.p[self.p[X + 1] + Y + 1]\n if dir == 0:\n x1 = self.lerp(u, self.grad(AA, x, y), self.grad(BA, x - 1, y))\n x2 = self.lerp(u, self.grad(AB, x, y - 1), self.grad(BB, x - 1, y - 1))\n else:\n x1 = self.lerp(u, self.grad_old(AA, x, y), self.grad_old(BA, x - 1, y))\n x2 = self.lerp(u, self.grad_old(AB, x, y - 1), self.grad_old(BB, x - 1, y - 1))\n y = self.lerp(v, x1, x2)\n return (y + 1) / 2\n\n def octave_perlin_noise(self, x, y, octaves, persistence,smooth,dir):\n total = 0.0\n frequency = 4.0\n amplitude = 128.0\n maxValue = 0.0\n for i in range(0, octaves):\n total += self.perlin_noise(x * frequency, y * frequency,smooth,dir) * amplitude\n maxValue += amplitude\n amplitude *= persistence\n frequency *= 2\n return total / maxValue\n\n def perlin_noise_main(self, smooth=0, dir=0):\n dst = np.zeros((self.height, self.width), np.uint8)\n for i in range(0, self.height):\n for j in range(0, self.width):\n x = i / lattice\n y = j / lattice\n dst[i, j] = 255 * self.octave_perlin_noise(x, y, 6,0.5,smooth, dir)\n return dst\n\n def show_pic(self):\n # perlin_noise_old_fade = self.perlin_noise_main(1)\n # perlin_noise_old_dir = self.perlin_noise_main(0, 1)\n # perlin_noise_old = self.perlin_noise_main(1, 1)\n perlin_noise_new = self.perlin_noise_main()\n # cv2.imshow(\"perlin_noise_old_fade\", perlin_noise_old_fade)\n # cv2.imshow(\"perlin_noise_old_dir\", perlin_noise_old_dir)\n # cv2.imshow(\"perlin_noise_old\", perlin_noise_old)\n cv2.imshow(\"perlin_noise_new\", perlin_noise_new)\n\n\n# All_Used\ndstWidth = 600\ndstHeight = 600\nlattice = 300\nvn = ValueNoise(dstHeight, dstWidth, lattice, 0)\n# vn.show_pic()\n\npn = PerlinNoise(dstHeight, dstWidth, lattice)\npn.show_pic()\ncv2.waitKey(0)\n# for lattice in range(40,100):\n# pn = PerlinNoise(256,256,lattice)\n# pn.show_pic()\n# print(\"lattice:\",lattice)\n# cv2.waitKey(600)\n","repo_name":"Alethu/Easy2DPerlinNoisePicGeneration","sub_path":"PerlinNoise.py","file_name":"PerlinNoise.py","file_ext":"py","file_size_in_byte":8279,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"6604833604","text":"import cv2\r\nimport numpy as np\r\n\r\n# 0 means black\r\n# 1 means white\r\n\r\nimg_black = np.zeros((640,480)) # this is a grayscale img since it has only 2 dimensions\r\nprint(img_black.shape)\r\n\r\nimg_black = np.zeros((640,480,3),np.uint8) # this is a not a grayscale img since it has only 3 dimensions\r\n# here 640 is the height and 480 is the width\r\nprint(img_black.shape)\r\n# cv2.imshow('Black img',img_black)\r\n\r\nimg = np.zeros((640,480,3),np.uint8)\r\nimg[:] = 255,0,0 # : means that we want the entire img to be of that color\r\n# we can also color a particular part of the img by specifying the height and width range\r\ncv2.imshow('image',img)\r\n\r\ncv2.line(img_black,(0,0),(300,300),(0,255,0),3)\r\n# this means that a green line of thickness 3 is to be drawn from (0,0) to (300,300)\r\n\r\ncv2.line(img,(0,0),(img.shape[1],img.shape[0]),(0,255,0),3)\r\n# here since we are passing coordinates we have to follow the format (width,height)\r\n\r\ncv2.rectangle(img,(0,0),(300,400),(0,0,255),2)\r\n\r\nimg_filled = np.zeros((640,480,3),np.uint8)\r\ncv2.rectangle(img_filled,(0,0),(300,400),(0,0,255),cv2.FILLED)\r\ncv2.imshow('Filled image',img_filled)\r\n\r\ncv2.circle(img,(400,50),30,(255,0,255),5)\r\n\r\ncv2.putText(img,'OpenCV shapes & text',(50,100),cv2.FONT_HERSHEY_COMPLEX,1,(0,200,0),2)\r\n\r\ncv2.imshow('Black img',img_black)\r\ncv2.imshow('image',img)\r\ncv2.waitKey(0)\r\n","repo_name":"arshde3p/OpenCV","sub_path":"OpenCV/6_shapes_&_text.py","file_name":"6_shapes_&_text.py","file_ext":"py","file_size_in_byte":1332,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"4124919830","text":"import matplotlib.pyplot as plt\r\nimport pyautogui as py\r\nimport csv\r\nimport time\r\n\r\ncount=10\r\n\r\nmouse_co=[['X','Y','TIME']]\r\n\r\nx=[]\r\ny=[]\r\nztime=[]\r\nc=0\r\nfor i in range(10000):\r\n\ta=time.time()\r\n\ttime.sleep(0.001)\r\n\ttemp=[]\r\n\tmouse=py.position()\r\n\tb=time.time()\r\n\tc+=b-a\r\n\tztime.append(c)\r\n\ttemp.append(mouse.x)\r\n\tx.append(mouse.x)\r\n\ttemp.append(mouse.y)\r\n\ty.append(mouse.y)\r\n\ttemp.append(c)\r\n\tprint(f\"{i} : {temp}\")\r\n\tmouse_co.append(temp)\r\n\r\nprint(\"Out of the loop...\")\r\n\r\nfilename = f\"Mouse XY Data {count}.csv\"\r\n\t\r\nwith open(filename, 'w') as csvfile:\r\n\tcsvwriter = csv.writer(csvfile)\r\n\tcsvwriter.writerows(mouse_co)\r\n\r\nprint(\"Done Writing...\")\r\nax = plt.figure().add_subplot(projection='3d')\r\n\r\nax.plot(x, y, ztime)\r\nax.set_xlabel('X')\r\nax.set_ylabel('Y')\r\nax.set_zlabel('Time')\r\n# ax.savefig(f'Mouse XY Graph {count}.png')\r\nplt.show()\r\n","repo_name":"joabeliot/JERRY","sub_path":"Module/Mouse Movements/MouseMovementDataCollector.py","file_name":"MouseMovementDataCollector.py","file_ext":"py","file_size_in_byte":842,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"19359694485","text":"# -*- coding: utf-8 -*-\nimport logging\nfrom datetime import datetime\n\nfrom scrapy.linkextractors import LinkExtractor\nfrom scrapy.spiders import Rule\n\nfrom news_all.tools.others import to_list\n\nfrom news_all.spider_models import NewsRCSpider, NewsSpider, NewsCrawlSpider\n\n\n\n\nclass ZhwSpider(NewsRCSpider):\n \"\"\"中华网\"\"\"\n name = 'zhw'\n mystart_urls = {\n 'http://military.china.com/news/index.html': 18962, # 中华网-军事-中国军情\n 'http://military.china.com/jswh/': 18965, # 中华网-军事-军事文化动态-左侧列表采集\n 'http://military.china.com/important/': 18961, # 中华网-军事-军事要闻\n # 'http://military.china.com/news/index.html': 18963, # 中华网-军事-国际军情 #重复\n 'http://ent.china.com/tv/index.html': 18959, # 中华网-娱乐-影视\n 'https://ent.china.com/star/news/': 18957, # 中华网-娱乐-明星\n 'http://ent.china.com/movie/index.html': 18958, # 中华网-娱乐-电影\n 'http://money.china.com/': 1200899, # 中华网-投资-需要确定采集区域\n 'http://culture.china.com/expo/': 18992, # 中华网-文化-博览-左侧列表抓取\n 'http://culture.china.com/art/': 18991, # 中华网-文化-艺文-左侧列表采集\n 'http://auto.china.com/zhuanzai/newcar/': 1200734, # 中华网-新车\n 'http://auto.china.com/15yuanchuang/': 18966, # 中华网-汽车-原创-左侧列表采集\n # 'http://auto.china.com/zhuanzai/newcar/': 18970, # 中华网-汽车-新车 重复\n 'http://auto.china.com/zhuanzai/hangye/': 1200735, # 中华网-汽车资讯-原创行业新车导购四块\n 'http://economy.china.com/industrial/': 18974, # 中华网-经济-产经商贸\n 'http://economy.china.com/domestic/': 18972, # 中华网-经济-国内宏观\n 'http://economy.china.com/consume/': 18975, # 中华网-经济-时尚消费-左侧列表采集\n 'http://economy.china.com/global/': 18973, # 中华网-经济-海外经济-抓取不到\n # 'http://military.china.com/news/index.html': 1200319, # 中华网军事-中国军情 重复\n 'http://military.china.com/photo/1': 1200292, # 中华网军事-军事图片-左侧列表\n # 'http://military.china.com/jswh/': 1200293, # 中华网军事-自媒体-全部抓取 #重复\n\n }\n rules = (\n # https://military.china.com/news/568/20190609/36357095.html\n #https://military.china.com/jswh/figure/11163298/20190608/36354564.html\n #https://military.china.com/important/11132797/20190612/36381445.html\n #https://ent.china.com/movie/tv/11015529/20190612/36382362.html\n #https://ent.china.com/star/news/11052670/20190612/36383637.html\n #https://ent.china.com/movie/news/205/20190612/36382020.html\n #https://money.china.com/toutiao/2019/0612/13329.html\n #https://culture.china.com/expo/11171063/20190611/36373370.html\n #https://culture.china.com/art/11159887/20190612/36381396.html\n #https://auto.china.com/new/11294.html\n #https://economy.china.com/industrial/11173306/20190612/36386194.html\n #https://economy.china.com/domestic/11173294/20190612/36385763.html\n #https://economy.china.com/consume/11173302/20190611/36371915.html\n #https://economy.china.com/global/11173292/20190611/36374785.html\n #https://military.china.com/jctp/tuku/11172988/20190531/36308184.html --图集 未解\n\n\n\n\n # todo\n Rule(LinkExtractor(allow=(r'china.com.*?/\\d+.html'),\n ), callback='parse_item',\n follow=False),\n )\n\n # todo\n #https://military.china.com/jswh/figure/11163298/20190608/36354564.html\n def parse_item(self, response):\n xp = response.xpath\n try:\n title = xp(\"//h1[@class='article-main-title']/text()\").extract_first()\n # source = xp(\"//div[@class='time-source']\")[0]\n content_div = xp(\"//div[@id='chan_newsDetail']\")[0]\n\n pubtime = xp(\"//div[@class='time-source']/span[@class='time']\").re(r'\\d{2,4}-\\d{1,2}-\\d{1,2}')[0]\n print(\"=========\"+pubtime)\n # pubtime = xp(\"//div[@class='Remark']/span/text()\").extract_first().split('|')[0]\n # \n # return\n \n \n origin_name = xp('//span[@class=\"source\"]/a/text()').extract_first('')\n except:\n\n return self.parse_item_2(response)\n\n # 过滤视频\n # if self.video_filter(content_div) or self.page_turn_filter(content_div):\n # return\n\n content, media, _, _ = self.content_clean(content_div)\n\n return self.produce_item(\n response=response,\n title=title,\n # self.get_page_title(response).split('_')[0]\n pubtime=pubtime,\n origin_name=origin_name,\n \n content=content,\n media=media\n )\n\n #https://ent.china.com/movie/tv/11015529/20190612/36382362.html\n def parse_item_2(self, response):\n \n xp = response.xpath\n try:\n title = xp(\"//h1[@id='chan_newsTitle']/text()\").extract_first()\n # source = xp(\"//div[@class='chan_newsInfo_source']\")[0]\n content_div = xp(\"//div[@id='chan_newsDetail']\")[0]\n\n pubtime = xp(\"//div[@class='chan_newsInfo_source']/span[@class='time']\").re(r'\\d{2,4}-\\d{1,2}-\\d{1,2}')[0]\n print(\"==========\"+pubtime)\n # pubtime = xp(\"//div[@class='Remark']/span/text()\").extract_first().split('|')[0]\n # \n # return\n \n \n origin_name = xp('//span[@class=\"source\"]/a/text()').extract_first('')\n except:\n\n return self.parse_item_3(response)\n\n # 过滤视频\n # if self.video_filter(content_div) or self.page_turn_filter(content_div):\n # return\n\n content, media, _, _ = self.content_clean(content_div)\n\n return self.produce_item(\n response=response,\n title=title,\n # self.get_page_title(response).split('_')[0]\n pubtime=pubtime,\n origin_name=origin_name,\n \n content=content,\n media=media\n )\n\n #https://money.china.com/toutiao/2019/0612/13329.html\n def parse_item_3(self, response):\n \n xp = response.xpath\n try:\n title = xp(\"//h1[@id='chan_newsTitle']/text()\").extract_first()\n # source = xp(\"//div[@id='chan_newsInfo']\")[0]\n content_div = xp(\"//div[@id='chan_newsDetail']\")[0]\n\n pubtime = xp(\"//div[@id='chan_newsInfo']\").re(r'\\d{2,4}-\\d{1,2}-\\d{1,2}')[0]\n # pubtime = xp(\"//div[@class='Remark']/span/text()\").extract_first().split('|')[0]\n # \n # return\n \n \n origin_name = xp('//div[@id=\"chan_newsInfo\"]/a/text()').extract_first('')\n except:\n\n return self.parse_item_4(response)\n\n # 过滤视频\n # if self.video_filter(content_div) or self.page_turn_filter(content_div):\n # return\n\n content, media, _, _ = self.content_clean(content_div)\n\n return self.produce_item(\n response=response,\n title=title,\n # self.get_page_title(response).split('_')[0]\n pubtime=pubtime,\n origin_name=origin_name,\n \n content=content,\n media=media\n )\n\n #https://auto.china.com/new/11294.html\n def parse_item_4(self, response):\n \n xp = response.xpath\n try:\n title = xp(\"//h1[@id='arti-title']/text()\").extract_first()\n # source = xp(\"//div[@id='chan_newsInfo']\")[0]\n content_div = xp(\"//div[@id='js-arti-detail']\")[0]\n\n pubtime = xp(\"//div[@class='arti-info']/span[@class='time']/text()\").extract_first()\n # pubtime = xp(\"//div[@class='Remark']/span/text()\").extract_first().split('|')[0]\n # \n # return\n \n \n origin_name = xp('//span[@class=\"source\"]/text()').extract_first('')\n except:\n return self.produce_debugitem(response, \"xpath error\")\n # return self.parse_item_4(response)\n\n # 过滤视频\n # if self.video_filter(content_div) or self.page_turn_filter(content_div):\n # return\n\n content, media, _, _ = self.content_clean(content_div)\n\n return self.produce_item(\n response=response,\n title=title,\n # self.get_page_title(response).split('_')[0]\n pubtime=pubtime,\n origin_name=origin_name,\n \n content=content,\n media=media\n )\n\n","repo_name":"Pintrue/news_all","sub_path":"news_all/spiders_old/zhw_all.py","file_name":"zhw_all.py","file_ext":"py","file_size_in_byte":8785,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"10395304540","text":"\"\"\"\n Frames -> means creating 3D border with different types of borders\n make Frames, add Frames in a Window\n\"\"\"\n\nfrom tkinter import *\n\nwindow = Tk()\nwindow.title(\"Frames\")\n\n# creating widgets of frame\n# relief means type of border u want for ur frame -> raised,flat,sunken,groove\nfirst_frame = Frame(borderwidth=4, bg=\"red\", relief=\"raised\")\nsecond_frame = Frame(borderwidth=4, bg=\"green\", relief=\"flat\")\n\n# lbl1 is packed inside first_name\nlbl1 = Label(master=first_frame, text=\"This is First Frame\")\nlbl1.pack()\n\n# lbl2 is packed inside second_name\nlbl2 = Label(master=second_frame, text=\"This is Second Frame\")\nlbl2.pack()\n\n# now packing the frames in our window\n# side -> on which side ur frame should be packed/named.\nfirst_frame.pack(side=LEFT)\nsecond_frame.pack(side=RIGHT)\n\nwindow.mainloop()","repo_name":"dikshagautam74/DG2021PY","sub_path":"Session25.7.py","file_name":"Session25.7.py","file_ext":"py","file_size_in_byte":803,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"70469388005","text":"\nimport numpy as np\nimport tensorflow as tf\nfrom keras import backend as K\n\ndef getTargetedActivation(model,layerDict,layerName,inputTensor,Image):\n layerOutput = layerDict[layerName].output\n iterate = K.function([inputTensor], [layerOutput])\n return iterate([Image])\n\nfrom keras.layers import Input, Lambda\n\n \ndef getIterateFunctionStyle(model,layerDict, layerName, inputTensor,Image,a,b,c,styleIndex):\n \n layerOutput = layerDict[layerName].output[0,:,:,:]\n arrayTarget = np.squeeze(getTargetedActivation(model,layerDict,layerName,inputTensor,Image)[0])\n print(\"max target\",np.max(abs(arrayTarget)))\n layerTarget = tf.convert_to_tensor(arrayTarget)\n print(layerOutput)\n print(layerTarget)\n print(layerOutput.shape)\n print(layerTarget.shape)\n #subtract_layer = Lambda(lambda inputs: inputs[0] - inputs[1],output_shape=lambda shapes: shapes[0])\n #subtract = K.function([inputTensor],[subtract_layer([inputTensor,layerTarget])])\n #difference = subtract[layerOutput]\n difference=tf.math.subtract(layerOutput,layerTarget)\n #difference=K.bias_add(layerOutput[:,:,:,:],-1*getTargetedActivation(model,layerDict,layerName,inputTensor,Image))\n loss_content=K.sum(K.square(difference))/(128*259*64)\n \n loss_style= - tf.math.log(K.mean(model.output[:,styleIndex]))\n \n Regularization_term = K.mean(K.square(inputTensor))\n \n loss=a*loss_content+b*loss_style+c*Regularization_term\n #loss=loss_content\n # compute the gradient of the input picture wrt this loss\n grads = K.gradients(loss, inputTensor)[0]\n\n\n \n iterate = K.function([inputTensor], [loss, grads,loss_content,loss_style,Regularization_term])\n \n return iterate\n \ndef getIterateFunction(model,layerDict, layerName, inputTensor,Image):\n \n layerOutput = layerDict[layerName].output[0,:,:,:]\n arrayTarget = np.squeeze(getTargetedActivation(model,layerDict,layerName,inputTensor,Image)[0])\n layerTarget = tf.convert_to_tensor(arrayTarget)\n\n difference=tf.math.subtract(layerOutput,layerTarget)\n\n loss=K.sum(K.square(difference))/(128*259*64)\n\n grads = K.gradients(loss, inputTensor)[0]\n\n iterate = K.function([inputTensor], [loss, grads])\n \n return iterate\n\n\n##first attempt : kind of Adagrad\ndef gradientDescent(iterate, inputImgData, step):\n k=5\n lossValue=2000\n for i in range(1,300):\n if(lossValue/512>=3):\n lossValue, gradsValue = iterate([inputImgData])\n gradsValue /= (K.sqrt(K.max(K.square(gradsValue))) + 1e-10)/20\n print(lossValue,np.max(abs(gradsValue))*step/(1+k*i))\n inputImgData+=gradsValue*step/(1+k*i)*(-1)\n return lossValue\n\ndef SGD_Nesterov(iterate,img,lr0,momentum,early_stopping,factor=0.5,epochs=20,niter=200):\n #initialization\n lossValue=early_stopping+1\n v=np.zeros(img.shape)\n lr=lr0\n #update of img\n for i in range(1,niter):\n if(lossValue>early_stopping):\n lossValue, gradsValue = iterate([img])\n #step decay for learning rate\n lr=lr*(factor**(i%epochs==0))\n #Nesterov momentum + SGD\n v_prec=v.copy()\n v=momentum*v-lr*gradsValue\n img+= (-1*momentum)*v_prec + (1+momentum)*v\n print(i,lossValue,np.max(abs(v)),np.max(abs(gradsValue)))\n \n return(lossValue)\n \ndef Adam(iterate,img,lr0,early_stopping,factor,epochs,niter,beta1=0.9,beta2=0.999,eps=1e-8):\n #initialization\n lossValue=early_stopping+1\n v=np.zeros(img.shape)\n m=np.zeros(img.shape)\n lr=lr0\n\n #update of img\n for i in range(1,niter):\n if(lossValue>early_stopping):\n liste=iterate([img]) \n longueur=len(liste)\n if(longueur==5):\n lossValue, gradsValue,loss_content,loss_style,reg = liste\n elif(longueur==2):\n lossValue,gradsValue=liste\n #step decay for learning rate\n lr=lr*(factor**(i%epochs==0))\n #ADAM\n m=beta1*m + (1-beta1)*gradsValue\n mi=m/(1-beta1**i)\n v=beta2*v + (1-beta2)*(gradsValue**2)\n vi=v/(1-beta2**i)\n img+= (-1*lr)*mi / (np.sqrt(vi)+eps)\n if(i%1==0):\n print(i,lossValue,np.max(abs((-1*lr)*mi / (np.sqrt(vi)+eps))))\n if(longueur==5):\n print(loss_content,loss_style,reg)\n \n return(lossValue)","repo_name":"clementpiat/Audio-style-transfer","sub_path":"optimization_content_extraction.py","file_name":"optimization_content_extraction.py","file_ext":"py","file_size_in_byte":4404,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"29889148122","text":"\nif __name__=='__main__':\n n = int(raw_input())\n orders = []\n for i in range(1, n+1):\n t, d = map(int, raw_input().split(' '))\n orders.append([ t+d, i ])\n orders.sort()\n orders = map(lambda order:order[1], orders)\n print(' '.join(map(str, orders)))\n","repo_name":"melug/Algorithms","sub_path":"jim_and_burgers.py","file_name":"jim_and_burgers.py","file_ext":"py","file_size_in_byte":281,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"19691740015","text":"###############################################\r\n# Mise en place d'un client simple\r\n# simulation d'une connexion client/serveur\r\n#\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\" version basique \"\"\"\"\"\"\"\"\"\"\"#\r\n\r\nimport socket, sys, select, threading, getopt\r\n\r\n# la fonction lit un message d'une socket \"socket\" et le renvoi décodé.\r\ndef read_message_from_socket(socket):\r\n BUFF_SIZE = 4096 # 4 KiB\r\n data = b''\r\n while True:\r\n part = socket.recv(BUFF_SIZE)\r\n data += part\r\n if len(part) < BUFF_SIZE:\r\n # either 0 or end of data\r\n break\r\n return data.decode()\r\n\r\ndef read_and_send(sock, clientname):\r\n while True: \r\n # On lit une ligne sur l'input, bloquant.\r\n msgClient=input(\"\")\r\n \r\n # On recupere les messages et on les affiche.\r\n # Idéalement devrait etre dans un autre thread que l'input et le send plus bas. \r\n read_and_write(sock)\r\n\r\n # On envoie le message au serveur.\r\n msgClient=msgClient.encode()\r\n sock.send(clientname.encode() + b\" :\" + msgClient)\r\n\r\n # Si on a ecris FIN sur l'input, on sort.\r\n if msgClient.upper()==b\"FIN\":\r\n break\r\n\r\ndef read_and_write(sock):\r\n # On ecoute sur la socket serveur pendant 50ms max.\r\n try:\r\n sockets_to_read, writable, errors = select.select([sock],[], [sock], 0.05)\r\n except select.error:\r\n pass\r\n else: \r\n for client_error in errors:\r\n client_error.close()\r\n print(\"Client déconnecté du à une error\")\r\n sys.exit()\r\n # On parcourt la liste des clients à lire\r\n for socket in sockets_to_read:\r\n try:\r\n message = read_message_from_socket(socket)\r\n except ConnectionError:\r\n socket.close()\r\n print(\"Client déconnecté\")\r\n sys.exit()\r\n else: \r\n print(\"recieved msg :\", message)\r\n\r\ndef main(clientname):\r\n # création d'un socket pour la connexion avec le serveur en local\r\n sock=socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\n\r\n try:\r\n # connexion au serveur, bloc surveillé, et gestion de l'exception\r\n sock.connect(('127.0.0.1',12807))\r\n\r\n except socket.error:\r\n print(\"la connexion a échoué.......\")\r\n sys.exit()\r\n\r\n print(\">>> Connexion établie avec le serveur...\")\r\n read_and_send(sock, clientname)\r\n sock.close()\r\n\r\nif len(sys.argv) >= 2:\r\n main(sys.argv[1])\r\nelse:\r\n print('Client.py [NameOfClient]')","repo_name":"Syugeek/TP-Python","sub_path":"Client.py","file_name":"Client.py","file_ext":"py","file_size_in_byte":2535,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"30814131431","text":"# -*- encoding: utf-8 -*-\nfrom openerp import models, fields, api\nclass Invoiceaddress(models.Model):\n _inherit = 'account.invoice'\n\n @api.one\n def _compute_check(self):\n if self.state == 'open' and self.type == 'out_invoice':\n if self.amount_total == self.importe_abonado:\n query = \"\"\" UPDATE account_invoice SET state='paid' WHERE id = %s\"\"\"\n self._cr.execute(query, (self.id,))\n self.check_status = True\n\n @api.one\n def _compute_get_nota(self):\n if self.type == 'out_invoice':\n inv_obj_refund = self.env[\"account.invoice\"].search([('type', '=', 'out_refund'), ('origin', '=', self.number)])\n if inv_obj_refund:\n for refund in inv_obj_refund:\n if refund.state == 'open' or refund.state == 'paid':\n self.total_ncredito += refund.amount_total\n\n optional_address = fields.Text(\"Dirección de Factura\", help=\"Si existe direeción de facturación, esta sera la dirección que se mostrara en la factura\")\n total_ncredito = fields.Float(\"Total de Nota de Credito\", domain=[('type','=','out_invoice')], compute='_compute_get_nota')\n check_status = fields.Boolean(\"Factura Pagada\", compute='_compute_check')\n nota_debito = fields.Boolean(\"Nota de Debito\")\n tipo_nota_credito = fields.Many2one(\"tipo.nota.debito.factura\", \"Tipo nota crédito\")\n","repo_name":"hondurasopen/delivery_cost","sub_path":"models/invoice_address.py","file_name":"invoice_address.py","file_ext":"py","file_size_in_byte":1416,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"37353750257","text":"# -*- coding: utf-8 -*-\n##############################################################################\n#\n# Account Bank Match\n# © 2016 1200 Web Development \n# © 2016 Therp BV \n#\n# This program is free software: you can redistribute it and/or modify\n# it under the terms of the GNU Affero General Public License as\n# published by the Free Software Foundation, either version 3 of the\n# License, or (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU Affero General Public License for more details.\n#\n# You should have received a copy of the GNU Affero General Public License\n# along with this program. If not, see .\n#\n##############################################################################\n\nfrom openerp import api, models\n\n\nclass AccountBankStatementImport(models.TransientModel):\n _inherit = 'account.bank.statement.import'\n\n @api.model\n def _complete_statement(self, stmt_vals, journal_id, account_number):\n \"\"\"Override to set statement name from journal sequence.\n\n Also delete spurious info from ref in statement line, and complete\n statement line with partner_name and possibly so_ref\n (sales order reference).\n\n Also empty name field (that is, just use '/'), so this field will\n be available to fill in with invoice reference (this is copied more or\n less from existing code).\n \"\"\"\n\n def empty_name(transaction):\n \"\"\"Empty name, moving possible info to ref field.\"\"\"\n # Make absolutely sure transaction['ref'] contains a string:\n if 'ref' not in transaction or not transaction['ref']:\n transaction['ref'] = ''\n if (transaction['name'] and transaction['name'] != '/' and\n transaction['name'] != transaction['ref']):\n ref_trans = (transaction['ref'] and ' ' or '') + \\\n transaction['name']\n if transaction['ref'] not in ref_trans or not transaction['ref'] and ref_trans:\n transaction['ref'] += ref_trans\n transaction['name'] = '/'\n\n stmt_vals = super(AccountBankStatementImport, self)._complete_statement(stmt_vals, journal_id, account_number)\n # set a custom statement name:\n if journal_id:\n sequence = self.env['account.journal'].browse(journal_id).sequence_id\n stmt_vals['name'] = sequence._next()\n # Update transactions:\n for transaction in stmt_vals['transactions']:\n if transaction.get('name') in ['NONREF', 'NOTPROVIDED']:\n transaction['name'] = ''\n if transaction.get('ref') in ['NONREF', 'NOTPROVIDED']:\n transaction['ref'] = ''\n transaction['name'] = '\\n'.join(\n line\n for line in (transaction.get('name') or '').split('\\n')\n if line not in [\n 'Referenz NOTPROVIDED',\n 'Verwendungszweck',\n ])\n empty_name(transaction)\n # Fill partner_name and counterparty_name:\n if transaction.get('partner_name'):\n transaction['counterparty_name'] = transaction['partner_name']\n return stmt_vals\n","repo_name":"shouyejing/1200wd_addons","sub_path":"account_bank_match/models/account_bank_statement_import.py","file_name":"account_bank_statement_import.py","file_ext":"py","file_size_in_byte":3504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"52"} +{"seq_id":"72178440164","text":"from advanced_bot import AdvancedBot, Colors\nfrom pid import PID\nfrom ev3dev2.motor import OUTPUT_B, OUTPUT_C, OUTPUT_A\nfrom ev3dev2.sensor import INPUT_1, INPUT_2, INPUT_4\n\n\nif __name__ == \"__main__\":\n regulator = PID(kp=1.2, ki=0, kd=0.2, integral_reset_count=5)\n bot = AdvancedBot(OUTPUT_C, OUTPUT_B, OUTPUT_A, INPUT_2, INPUT_1, INPUT_4, regulator, False, False)\n colors = [Colors.BLUE, Colors.GREEN]\n\n try:\n bot.run_loader_job(colors)\n except KeyboardInterrupt:\n bot.turn_off_all_motors()\n except Exception as e:\n print(e)\n\n bot.turn_off_all_motors()\n print(\"Stopped\")\n","repo_name":"VisteK528/WR","sub_path":"SemiAdvancedBot/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":618,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"4815313890","text":"from difflib import get_close_matches\nimport json\ndata = json.load(open(\"data.json\"))\n\ndef search(word):\n if word in data:\n \n result= data[word]\n if len(result)>0:\n if len(result)==1:\n return result\n else:\n for i in range(0,len(result)-1):\n print (result[i], \"/n\")\n return (result[i+1])\n\n \n elif len(get_close_matches(word,data.keys())) > 0:\n print (\"Did you mean: \" , get_close_matches(word,data.keys())[0])\n decision = input(\"Enter Y if yes, N if no: \")\n if decision == \"Y\":\n result = data[get_close_matches(word,data.keys())[0]]\n if len(result)>0:\n if len(result)==1:\n return result\n else:\n for i in range(0,len(result)-1):\n print (result[i], \"/n\")\n return (result[i+1])\n elif decision == \"N\":\n return (\"Sorry, I'm unable to find the word that you were searchning for\")\n else: \n return (\"Sorry, I'm unable to find the word that you were searchning for\")\n\nword=input(\"Enter the word to search: \")\nword = str.lower(word)\nprint(search(word))","repo_name":"vigneshethiraj/Dictionary","sub_path":"code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":1247,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"32899260900","text":"#!/user/bin/env python\n# coding:utf-8\nfrom decimal import Decimal\nimport pdfplumber\nfrom pdfplumber import utils\nimport re\n\n\ndef get_header(pages):\n header = []\n page1_header_words = pages[0].extract_words()[0:3]\n page2_header_words = pages[1].extract_words()[0:3]\n page3_header_words = pages[2].extract_words()[0:3]\n for i, word in enumerate(page1_header_words):\n if word.get(\"text\") == page2_header_words[i].get(\"text\") \\\n and word.get(\"text\") == page3_header_words[i].get(\"text\"):\n header.append(word.get(\"text\"))\n\n page1_header_words = pages[-1].extract_words()[0:3]\n page2_header_words = pages[-2].extract_words()[0:3]\n page3_header_words = pages[-3].extract_words()[0:3]\n for i, word in enumerate(page1_header_words):\n if word.get(\"text\") == page2_header_words[i].get(\"text\") \\\n and word.get(\"text\") == page3_header_words[i].get(\"text\"):\n header.append(word.get(\"text\"))\n return header\n\n\ndef filter_header(word, header, page_width):\n my_top = word.get(\"top\")\n # my_x1 = word.get(\"x1\") or str(word.get(\"text\")).isdigit() and (my_x1/page_width - Decimal('0.5')) < 0.1\n is_filter = (word.get(\"text\") in header and my_top < 80)\n return is_filter\n\n\nTITLE_STYLE = [\n u\"^(第[\\d一二三四五六七八九十]+章、?).+\", # 0 例：第一章 XXX；第一章、XXX； 第一章.XXX；第1章 XXX；第1章、XXX； 第1章.XXX\n u\"^(第[\\d一二三四五六七八九十]+节、?).+\", # 1 例：第一节 XXX；第一节、XXX； 第一节.XXX；第1节 XXX；第1节、XXX； 第1节.XXX\n u\"^(第[\\d一二三四五六七八九十]+项、?(?!标准)).+\", # 2 例：第一项 XXX；第一项、XXX； 第一项.XXX；第1项 XXX；第1项、XXX； 第1项.XXX\n u\"^(附注\\s*[\\d一二三四五六七八九十]+．?).+\", # 3 例：附注一；附注二\n u\"^([一二三四五六七八九十]+[、\\.]).+\", # 4 例：一、XXX\n u\"^([\\(（][一二三四五六七八九十]+[\\)）](?!、)).+\", # 5 例：(一) XXX；（一） XXX\n u\"^(注释\\s*[\\d一二三四五六七八九十]+[．\\.、]?).+\", # 6 例：注释1；注释2\n u\"^([\\(（][一二三四五六七八九十]+[\\)）]、).+\", # 7 例：(一) 、XXX；（一）、XXX\n u\"^(\\d{1,2}、).+\", # 8 例：1、XXX 与7例的位置进行了调换 公司：永力达\n u\"^(\\d{1,2}[\\.．](?!\\d{1,3}\\D+|\\d{1,2}(?=20[1-5]\\d))).+\", # 9 例：1.XXX\n u\"^([1-4]?\\d(?![kK][wW]|号|名|世纪|%|年以[内上]|年[至到]|年|）|\\)|\\.|．)(?=\\D{2,})).+\", # 10 例：1XXX\n u\"^(\\d{1,2}[\\.．]\\d{1,2}(?:[\\.．](?!\\d{1,3}\\D)|(?=\\D{2,})|(?=20[1-5]\\d))).+\",\n # 11 例：1.1 XXX；1.1.XXX （注意：1.2017年11月27日，海口市制药厂有限公司被海南省科技厅）\n u\"^(\\d{1,2}[\\.．]\\d{1,2}[\\.．]\\d{1,2}(?:[\\.．](?!\\d{1,3}\\D)|(?=\\D{2,}))).+\", # 12 例：1.1.1 XXX；1.1.1.XXX\n u\"^(\\d{1,2}[\\.．]\\d{1,2}[\\.．]\\d{1,2}[\\.．]\\d{1,2}(?:[\\.．](?!\\d{1,3}\\D)|(?=\\D{2,}))).+\",\n # 13 例：1.1.1.1 XXX；1.1.1.1.XXX\n u\"^([\\(（]\\d{1,2}[\\)）](?!、|=)).+\", # 14 例：(1) XXX；（1） XXX\n u\"^([\\(（]\\d{1,2}[\\)）]、).+\", # 15 例：(1)、XXX；（1）、XXX\n u\"^((? len(cur_cols):\n for i, l_col in enumerate(last_cols):\n for col in cur_cols:\n if col.get(\"x0\") == l_col.get(\"x0\") and i > 0:\n last_txt_cols[i] = last_txt_cols[i] + \"\\n\" + col.get(\"text\")\n break\n last_cols[i] = cur_cols\n\n\nif __name__ == \"__main__\":\n path = u'test.pdf'\n line_list = list()\n paragraph_list = list()\n table_list = list()\n header = []\n col_line_list = []\n row_line_list = []\n cols = []\n rows = []\n with pdfplumber.open(path) as pdf:\n print(pdf.pages[36].extract_tables()[0])\n # table = pdf.pages[3].extract_tables()[0]\n header = get_header(pdf.pages)\n first_page = pdf.pages[36]\n first_page.extract_text() # 将页面的所有字符对象整理到一个字符串中。添加x1一个字符的字符与下一个字符的字符之间的差x0大于的空格x_tolerance。添加换行符doctop，其中一个字符的字符与下一个字符的字符之间的差doctop大于y_tolerance\n # rects = first_page.within_bbox()\n page_width = first_page.width\n print(\"width:\", first_page.width)\n print(\"height:\", first_page.height)\n im = first_page.to_image(resolution=300)\n # dict1 = {'x0': int('10'), 'x1': int('550'), 'top': int('54'), 'bottom': int('57')}\n # dict1 = {'x0': Decimal('10'), 'x1': Decimal('550.983'), 'top': Decimal('53.850'), 'bottom': Decimal('56.850')}\n # (x0, top, x1, bottom)\n # bbox1 = (Decimal('10'), Decimal('53.850'), Decimal('550.983'), Decimal('56.850'))\n bbox1 = (Decimal('50.66'), Decimal('249.819'), Decimal('245.420'), Decimal('255.379'))\n result = first_page.within_bbox(bbox1) # 裁剪坐标内的文本\n print(result)\n # print(first_page.extract_words()[0])\n im.draw_rect(bbox1, stroke_width=1)\n im.save(\"D:\\\\a.png\", format=\"PNG\")\n # exit()\n # print(first_page.lines)\n # print(first_page.objects.get(\"line\", ''))\n line_dict, table_pos = {}, []\n table_lines = {}\n table_line_in = False\n # align_list, align = [], {}\n words = first_page.extract_words()\n l_x0, l_x1, l_top, bottom, line_width, line, paragraph = 0, 0, 0, 0, 0, \"\", \"\"\n for index, word in enumerate(list(words)):\n print(word)\n word_text = word.get(\"text\")\n if filter_header(word, header, page_width): # 页码和表头退出\n continue\n # l_top: 上一个word的top值\n # 当前的wod的top值和上一个word的top差值小于2，说明就在同一行\n if (word.get(\"top\") - l_top) < 2 or l_x0 == 0: # 拼接同一行的数据\n # x0：word的最小left值，\n # l_x1：上一个word的最大left值，即word.left + word.width\n # word.get(\"x0\") 当前word的left值\n #\n is_need_blank = bool(re.search(\"^[A-Za-z]+$\", word_text) or (word.get(\"x0\") - l_x1) / 2 > 7 and l_x1 > 0)\n line = line + (\" \" if is_need_blank else \"\") + word_text\n line_width = line_width + (\n word.get(\"x1\") - word.get(\"x0\")) # (word.get(\"x1\") - word.get(\"x0\")) 计算当前word的width\n if l_x0 == 0:\n line_dict.update({\"x0\": word.get(\"x0\")})\n else:\n if len(line) > 0:\n line_dict.update({\"x1\": l_x1, \"line\": line, \"line_width\": line_width})\n line_list.append(line_dict)\n line_dict = {}\n line_dict.update({\"x0\": word.get(\"x0\")})\n line = word_text\n line_width = (word.get(\"x1\") - word.get(\"x0\"))\n l_x0 = word.get(\"x0\")\n l_x1 = word.get(\"x1\")\n l_top = word.get(\"top\")\n bottom = word.get(\"bottom\")\n\n pre_word = words[index - 1]\n pre_x0 = pre_word.get(\"x0\")\n pre_x1 = pre_word.get(\"x1\")\n pre_top = pre_word.get(\"top\")\n if (word.get(\"top\") - pre_top) < 2 and not re.search(title_pattern, line) and not table_line_in: # 拼接同一行的数据\n if word.get(\"x0\") - pre_x1 > 15: # 表格行的处理，word之间间隔大于15就当表格处理\n table_line_in = True\n table_lines.update({word.get(\"top\"): [words[index - 1], word]}) # 当前值和左边值\n elif table_line_in and word.get(\"top\") - pre_top <= 20:\n col_list = table_lines.get(word.get(\"top\")) # 同一行的数据\n if col_list:\n col_index = len(col_list) + 1\n pre_key = list(table_lines.keys())[-2]\n pre_col_list = table_lines.get(pre_key)\n if tmp_word.get(\"x0\") > word.get(\"x1\"): # tmp_word 距离最近的数据\n pre_col_list.insert(len(col_list), {})\n elif tmp_word.get(\"x1\") < word.get(\"x0\"):\n pre_col_list.insert(len(col_list) + 1, {}) # col_list：和当前word同一水平的全部数据\n col_list.append(word)\n table_lines.update({word.get(\"top\"): col_list})\n else:\n pre_key = list(table_lines.keys())[-1]\n pre_col_list = table_lines.get(pre_key)\n tmp_word = pre_col_list[0] # 倒着读的，所以要取最左边的数据\n # if relation([tmp_word.get(\"x0\"), tmp_word.get(\"x1\")], [word.get(\"x0\"), word.get(\"x1\")]) == 1:\n if tmp_word.get(\"x0\") > word.get(\"x1\"):\n pre_col_list.insert(0, {})\n elif tmp_word.get(\"x1\") < word.get(\"x0\"):\n pre_col_list.insert(1, {})\n table_lines.update({word.get(\"top\"): [word]})\n\n\n elif word.get(\"top\") - pre_top > 20:\n table_line_in = False\n\n # col_line_list.append(words[index - 1])\n # cols.append(words[index - 1].get(\"text\"))\n # next_word = words[index + 1]\n # if next_word.get(\"top\") - l_top > 2: # 最后一个了\n # col_line_list.append(word)\n # cols.append(word.get(\"text\"))\n # row_line_list.append(col_line_list)\n # rows.append(cols)\n # col_line_list, cols = [], []\n # if len(row_line_list) > 1: reset_cols(row_line_list, rows)\n\n print(\"\\n\")\n\n print(\"****************************************\")\n print(rows)\n for i, line in enumerate(line_list):\n print(str(i) + \"------->>>>\", line)\n\n paragraph = \"\"\n for i, line_dict in enumerate(line_list):\n line = line_dict.get(\"line\")\n line_width = line_dict.get(\"line_width\")\n x0 = line_dict.get(\"x0\")\n\n if line_width / page_width < 0.7 and i == 0:\n paragraph = line\n paragraph_list.append(paragraph)\n paragraph = \"\"\n else:\n pre_dict = line_list[i - 1]\n pre_x0 = pre_dict.get(\"x0\")\n pre_line = pre_dict.get(\"line\")\n pre_line_width = pre_dict.get(\"line_width\")\n find_list = re.search(title_pattern, line)\n if line_width / page_width < 0.7 and pre_line_width / page_width < 0.7 \\\n or \".....\" in line and str(line[-1]).isdigit(): # 上下超短行、目录行\n if bool(paragraph):\n paragraph_list.append(paragraph)\n paragraph = \"\"\n continue\n # paragraph = line\n if i == len(line_list) - 1 or not bool(paragraph): # 最后一个多输出一个\n paragraph_list.append(line)\n elif bool(find_list) and bool(abs(x0 - pre_x0) > 18):\n if bool(paragraph):\n paragraph_list.append(paragraph)\n if line_width / page_width < 0.7:\n paragraph_list.append(line)\n paragraph = \"\"\n else:\n paragraph = line\n elif line_width / page_width > 0.7 and bool(abs(x0 - pre_x0) > 18) and re.search(\"[。.；]$\", pre_line):\n if bool(paragraph):\n paragraph_list.append(paragraph)\n paragraph = line\n elif line_width / page_width < 0.7 and re.search(\"[。.；]$\", line): # 结尾行\n paragraph = paragraph + line\n paragraph_list.append(paragraph)\n paragraph = \"\"\n else:\n paragraph = paragraph + line\n\n # paragraph = paragraph + line\n\n print(\"-------------------------------------\")\n for i, p in enumerate(paragraph_list):\n print(str(i) + \"------->>>>\", p)\n","repo_name":"zengzih/analysisPdf","sub_path":"pdf_extractor.py","file_name":"pdf_extractor.py","file_ext":"py","file_size_in_byte":14128,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"31574693959","text":"import sys\n\n\nif sys.version_info < (3, 0):\n PY2 = True\n PY3 = False\n string_types = (str, unicode)\n text_type = unicode\n iteritems = dict.iteritems\nelse:\n PY2 = False\n PY3 = True\n string_types = (bytes, str)\n text_type = str\n iteritems = dict.items\n","repo_name":"oracle/oci-python-sdk","sub_path":"src/oci/_vendor/chardet/compat.py","file_name":"compat.py","file_ext":"py","file_size_in_byte":279,"program_lang":"python","lang":"en","doc_type":"code","stars":345,"dataset":"github-code","pt":"52"} +{"seq_id":"19422780364","text":"# 1. librerias\n\n\n# 2. constantes\n\n# 3. Funciones y/o Clases\n\ndef ingreso_entero(msg :str = 'Ingrese un número entero: '):\n try:\n numero = int(input(msg))\n return numero\n except:\n print('Error! Vuelva a intentar')\n return ingreso_entero(msg)\n\n# 4. mi Programa\n\nn = ingreso_entero()\n\n# calculo del factorial\nfactorial = 1\nfor i in range(2,n+1):\n factorial = i * factorial\n\nprint(f'el factorial de {n}! es {factorial}')\n\n\n\n\n\n\n\n\n\n","repo_name":"Daniexel/PythonFundamentos","sub_path":"Modulo3/scripts/manejo_errores/manejo_errores2.py","file_name":"manejo_errores2.py","file_ext":"py","file_size_in_byte":464,"program_lang":"python","lang":"pt","doc_type":"code","dataset":"github-code","pt":"52"} +{"seq_id":"38461239303","text":"#!/usr/bin/env python3\n\n\"\"\"\nanother version: bruhat/morse.py\n\"\"\"\n\n\nfrom collections import namedtuple\nfrom functools import reduce\nfrom operator import mul\nfrom random import shuffle, seed\n\nimport numpy\nfrom numpy import concatenate as cat\n\nfrom qupy.util import mulclose_fast\nfrom qupy.argv import argv\nfrom qupy.smap import SMap\nfrom qupy.ldpc.solve import zeros2, shortstr, parse, pseudo_inverse, int_scalar, dot2\nfrom qupy.ldpc.css import CSSCode\n#from qupy.ldpc.decoder import StarDynamicDistance\n\nfrom qupy.argv import argv\nfrom qupy.smap import SMap\n\ntry:\n from pyx import canvas, path, deco, trafo, style, text, color, deformer, unit\n from pyx.color import rgb\n \n black = rgb(0., 0., 0.)\n blue = rgb(0.1, 0.1, 0.9)\n red = lred = rgb(1., 0., 0.)\n green = rgb(0.0, 0.6, 0.0)\n white = rgb(1., 1., 1.)\n #shade = rgb(0.75, 0.55, 0)\n grey = rgb(0.75, 0.75, 0.75)\n yellow = rgb(1., 1., 0.)\n \n st_dashed = [style.linestyle.dashed]\n st_dotted = [style.linestyle.dotted]\n st_round = [style.linecap.round]\n \n st_thick = [style.linewidth.thick]\n st_Thick = [style.linewidth.Thick]\n st_THick = [style.linewidth.THick]\n st_THICK = [style.linewidth.THICK]\n #print dir(style.linewidth)\n\n import boxs\n\nexcept ImportError:\n print(\"warning: pyx not found\")\n\n\n\nclass Matrix(object):\n \"\"\" rows and cols : list of hashable items \"\"\"\n\n def __init__(self, rows, cols, elements={}):\n rows = list(rows)\n cols = list(cols)\n self.rows = rows\n self.cols = cols\n self.set_rows = set(rows)\n self.set_cols = set(cols)\n #self.row_lookup = dict((row, idx) for (idx, row) in rows)\n #self.col_lookup = dict((col, idx) for (idx, col) in cols)\n self.elements = dict(elements)\n\n def copy(self):\n return Matrix(self.rows, self.cols, self.elements)\n\n def __len__(self):\n return len(self.rows)\n\n def __str__(self):\n return \"Matrix(%d, %d)\"%(len(self.rows), len(self.cols))\n\n def __eq__(self, other):\n return self.elements == other.elements\n\n def __ne__(self, other):\n return self.elements != other.elements\n\n @classmethod\n def identity(cls, items):\n elements = dict(((i, i), 1) for i in items)\n A = cls(items, items, elements)\n return A\n\n @classmethod\n def inclusion(cls, rows, cols):\n A = cls(rows, cols)\n for i in cols:\n A[i, i] = 1\n return A\n\n @classmethod\n def retraction(cls, rows, cols):\n A = cls(rows, cols)\n for i in rows:\n A[i, i] = 1\n return A\n\n def __setitem__(self, key, value):\n row, col = key\n #idx = self.row_lookup[row]\n #jdx = self.col_lookup[col]\n assert row in self.set_rows\n assert col in self.set_cols\n elements = self.elements\n if value != 0:\n elements[row, col] = value\n elif (row, col) in elements:\n del elements[row, col]\n\n def __getitem__(self, item):\n row, col = item\n \n if isinstance(row, slice) and isinstance(col, slice):\n assert row == slice(None)\n assert col == slice(None)\n value = self\n\n elif isinstance(col, slice):\n assert col == slice(None)\n value = []\n assert row in self.set_rows\n for (r,c) in self.elements.keys():\n if r == row:\n value.append(c)\n\n elif isinstance(row, slice):\n assert row == slice(None)\n value = []\n assert col in self.set_cols\n for (r,c) in self.elements.keys():\n if c == col:\n value.append(r)\n\n else:\n #idx = self.row_lookup[row]\n #jdx = self.col_lookup[col]\n assert row in self.set_rows\n assert col in self.set_cols\n value = self.elements.get((row, col), 0)\n return value\n\n def __add__(self, other):\n assert self.set_cols == other.set_rows\n A = self.copy()\n # could optimize, but do we care...\n for i,j in other.elements.keys():\n A[i, j] += other[i, j]\n return A\n\n def __mul__(self, other):\n assert self.set_cols == other.set_rows\n A = Matrix(self.rows, other.cols)\n # could optimize, but do we care...\n #for i in self.rows:\n # for j in self.cols:\n for (i, j) in self.elements.keys():\n for k in other.cols:\n A[i, k] = A[i, k] + self[i, j] * other[j, k]\n return A\n\n def sum(self):\n return sum(self.elements.values())\n\n def todense(self, rows=None, cols=None):\n if rows is None:\n rows = self.rows\n if cols is None:\n cols = self.cols\n assert set(rows) == self.set_rows, \"%s\\n%s\"%(set(rows), self.set_rows)\n assert set(cols) == self.set_cols, \"%s\\n%s\"%(set(cols), self.set_cols)\n row_lookup = dict((row, idx) for (idx, row) in enumerate(rows))\n col_lookup = dict((col, idx) for (idx, col) in enumerate(cols))\n A = numpy.zeros((len(rows), len(cols)), dtype=int_scalar)\n elements = self.elements\n for (row, col) in elements.keys():\n value = elements[row, col]\n A[row_lookup[row], col_lookup[col]] = value\n return A\n\n\nclass Cell(object):\n\n def __init__(self, grade, bdy=[], key=None):\n self.grade = grade\n self.bdy = list(bdy)\n self.key = key\n\n def __repr__(self):\n return \"Cell(%d, %s, %s)\"%(self.grade, self.bdy, self.key)\n\n def __str__(self):\n return \"Cell(%d, %s)\"%(self.grade, self.key)\n\n def check(self):\n grade = self.grade\n for cell in self.bdy:\n assert cell.grade == grade-1\n\n def append(self, cell):\n assert isinstance(cell, Cell)\n assert cell.grade == self.grade-1\n self.bdy.append(cell)\n\n def __len__(self):\n return len(self.bdy)\n\n def __getitem__(self, idx):\n return self.bdy[idx]\n\n def __lt__(self, other):\n return self.key < other.key\n\n def __le__(self, other):\n return self.key <= other.key\n\n #def get_dual(self):\n\n\n\nclass Complex(object):\n\n def __init__(self):\n self.lookup = {0:{}, 1:{}, 2:{}}\n\n def set(self, key, cell):\n assert isinstance(cell, Cell)\n lookup = self.lookup\n cells = lookup.setdefault(cell.grade, {})\n assert cells.get(key) is None\n cells[key] = cell\n assert cell.key is None\n cell.key = key\n\n def get_cells(self, grade):\n cells = list(self.lookup[grade].values())\n cells.sort()\n return cells\n\n# def __XX__get_code(self):\n# verts = self.lookup[0]\n# edges = self.lookup[1]\n# faces = self.lookup[2]\n# \n# n = len(edges)\n# mx = len(verts)\n# mz = len(faces)\n# Hx = zeros2(mx, n)\n# Hz = zeros2(mz, n)\n#\n# cols = list(edges.keys())\n# cols.sort()\n# rows = list(verts.keys())\n# rows.sort()\n#\n# for j, col in enumerate(cols):\n# edge = edges[col]\n# for i, row in enumerate(rows): # ugh\n# vert = verts[row]\n# if vert in edge.bdy:\n# Hx[i, j] = 1\n# \n# rows = list(faces.keys())\n# rows.sort()\n#\n# for j, col in enumerate(cols):\n# edge = edges[col]\n# for i, row in enumerate(rows): # ugh\n# face = faces[row]\n# if edge in face.bdy:\n# Hz[i, j] = 1\n#\n# #print()\n# #print(shortstr(Hx))\n# #print()\n# #print(shortstr(Hz))\n#\n# code = CSSCode(Hx=Hx, Hz=Hz)\n# return code\n \n def get_bdymap(self, grade):\n # grade -> grade-1\n cols = self.get_cells(grade)\n rows = self.get_cells(grade-1)\n A = Matrix(rows, cols)\n for col in cols:\n assert col.grade == grade\n for row in col:\n assert row.grade == grade-1\n A[row, col] = (A[row, col] + 1)%2\n return A\n\n def get_parity_checks(self, grade=1):\n verts = self.get_cells(grade-1)\n edges = self.get_cells(grade)\n faces = self.get_cells(grade+1)\n \n Hzt = self.get_bdymap(grade+1)\n Hzt = Hzt.todense(edges, faces)\n Hz = Hzt.transpose()\n\n Hx = self.get_bdymap(grade)\n Hx = Hx.todense(verts, edges)\n return Hz, Hx\n\n def get_code(self, grade=1):\n Hz, Hx = self.get_parity_checks(grade)\n code = CSSCode(Hx=Hx, Hz=Hz)\n return code\n \n def build_surface(self, top_left, bot_right, \n open_top=False, open_bot=False, open_left=False, open_right=False):\n # open boundary is \"rough\". default to smooth\n\n i0, j0 = top_left\n i1, j1 = bot_right\n\n # build verts\n for i in range(i0, i1):\n for j in range(j0, j1):\n if i==i0 and open_top:\n continue\n if i==i1-1 and open_bot:\n continue\n if j==j0 and open_left:\n continue\n if j==j1-1 and open_right:\n continue\n cell = Cell(0)\n key = (i, j)\n self.set(key, cell)\n\n verts = self.lookup[0]\n\n # build edges\n for i in range(i0, i1):\n for j in range(j0, j1):\n ks = \"hv\" # horizontal, _vertical edge\n if i==i1-1 and j==j1-1:\n continue\n elif i==i1-1:\n ks = \"h\"\n elif j==j1-1:\n ks = \"v\"\n for k in ks:\n bdy = []\n vert = verts.get((i, j))\n if vert is not None:\n bdy.append(vert)\n if k == \"h\":\n vert = verts.get((i, j+1))\n else:\n vert = verts.get((i+1, j))\n if vert is not None:\n bdy.append(vert)\n if len(bdy)==0:\n continue\n cell = Cell(1, bdy)\n key = (i, j, k)\n self.set(key, cell)\n\n edges = self.lookup[1]\n\n # build faces\n for i in range(i0, i1-1):\n for j in range(j0, j1-1):\n top = edges.get((i, j, \"h\"))\n left = edges.get((i, j, \"v\"))\n bot = edges.get((i+1, j, \"h\"))\n right = edges.get((i, j+1, \"v\"))\n bdy = [top, left, bot, right]\n bdy = [cell for cell in bdy if cell]\n if len(bdy)==0:\n continue\n cell = Cell(2, bdy)\n self.set((i, j), cell)\n\n def build_torus(self, rows, cols):\n\n # build verts\n for i in range(rows):\n for j in range(cols):\n cell = Cell(0)\n key = (i, j)\n self.set(key, cell)\n\n verts = self.lookup[0]\n\n # build edges\n for i in range(rows):\n for j in range(cols):\n ks = \"hv\" # horizontal, _vertical edge\n for k in ks:\n bdy = []\n vert = verts.get((i, j))\n assert vert is not None\n bdy.append(vert)\n if k == \"h\":\n vert = verts.get((i, (j+1)%cols))\n else:\n vert = verts.get(((i+1)%rows, j))\n assert vert is not None\n bdy.append(vert)\n assert len(bdy)==2\n cell = Cell(1, bdy)\n key = (i, j, k)\n self.set(key, cell)\n\n edges = self.lookup[1]\n\n # build faces\n for i in range(rows):\n for j in range(cols):\n top = edges.get((i, j, \"h\"))\n left = edges.get((i, j, \"v\"))\n bot = edges.get(((i+1)%rows, j, \"h\"))\n right = edges.get((i, (j+1)%cols, \"v\"))\n bdy = [top, left, bot, right]\n bdy = [cell for cell in bdy if cell]\n assert len(bdy)==4\n cell = Cell(2, bdy)\n self.set((i, j), cell)\n\n def get_coord(self, i, j, k=None):\n mul = 2\n if k is None:\n row = mul*i\n col = mul*j\n else:\n assert k in \"hv\"\n k = \"hv\".index(k)\n row = mul*i + k\n col = mul*j + (1-k)\n return row, col\n\n def get_smap(self):\n get_coord = self.get_coord\n smap = SMap()\n\n verts = self.lookup[0]\n edges = self.lookup[1]\n faces = self.lookup[2]\n for key in verts.keys():\n i, j = key\n row, col = get_coord(i, j)\n smap[row, col] = \".\"\n\n for key in edges.keys():\n i, j, k = key\n row, col = get_coord(i, j, k)\n c = {\"h\":\"-\", \"v\":\"|\"}[k]\n smap[row, col] = c\n\n for key in faces.keys():\n i, j = key\n row, col = get_coord(i, j)\n smap[row+1, col+1] = \"o\"\n return smap\n\n def __str__(self):\n smap = self.get_smap()\n return str(smap)\n\n\nclass Flow(object):\n def __init__(self, cx):\n self.cx = cx\n self.pairs = {}\n\n def add(self, src, tgt):\n assert isinstance(src, Cell)\n assert isinstance(tgt, Cell)\n assert src.grade == tgt.grade-1\n assert src in tgt\n pairs = self.pairs.setdefault(src.grade, [])\n pairs.append((src, tgt))\n\n def remove(self, src, tgt):\n self.pairs[src.grade].remove((src, tgt))\n\n def get_pairs(self):\n pairs = []\n cx = self.cx\n edges = cx.get_cells(1)\n faces = cx.get_cells(2)\n for edge in edges:\n for vert in edge:\n pairs.append((vert, edge))\n for face in faces:\n for edge in face:\n pairs.append((edge, face))\n return pairs\n\n def get_critical(self, grade):\n cx = self.cx\n remove = set()\n for _grade, pairs in self.pairs.items():\n for src, tgt in pairs:\n remove.add(src)\n remove.add(tgt)\n critical = []\n cells = cx.get_cells(grade)\n for cell in cells:\n if cell not in remove:\n critical.append(cell)\n return critical\n\n def get_flowmap(self, grade):\n # grade --> grade+1\n cx = self.cx\n edges = cx.get_cells(grade)\n faces = cx.get_cells(grade+1)\n A = Matrix(faces, edges)\n pairs = self.pairs.setdefault(grade, [])\n for src, tgt in pairs:\n assert src.grade == grade\n A[tgt, src] += 1\n return A\n\n# def get_adj(self, grade):\n# \"return flow (adjacency) matrix for this grade\"\n# cells = self.cx.get_cells(grade)\n# lookup = dict((cell, idx) for (idx, cell) in enumerate(cells))\n# N = len(cells)\n# A = numpy.zeros((N, N), dtype=int_scalar)\n# pairs = self.pairs[grade-1]\n# flow = dict((src, tgt) for src, tgt in pairs)\n# for i, cell in enumerate(cells):\n# for src in cell.bdy:\n# tgt = flow.get(src)\n# if tgt != None:\n# j = lookup[tgt]\n# if i!=j:\n# A[j, i] = 1\n# return A, lookup\n\n def get_adj(self, grade):\n \"return flow (adjacency) matrix for this grade\"\n cx = self.cx\n A = cx.get_bdymap(grade) # grade --> grade-1\n B = self.get_flowmap(grade-1) # grade-1 --> grade\n C = B*A # grade --> grade\n for cell in cx.get_cells(grade):\n C[cell, cell] = 0\n return C\n\n def accept(self, src, tgt):\n assert isinstance(src, Cell)\n assert isinstance(tgt, Cell)\n assert src.grade == tgt.grade-1\n pairs = self.pairs.setdefault(src.grade, [])\n for pair in pairs:\n if pair[0] == src or pair[1] == tgt:\n return False\n pairs = self.pairs.setdefault(src.grade-1, [])\n for pair in pairs:\n if pair[1] == src:\n return False\n pairs = self.pairs.setdefault(src.grade+1, [])\n for pair in pairs:\n if pair[0] == tgt:\n return False\n\n cx = self.cx\n self.add(src, tgt)\n for grade in (1, 2):\n A = self.get_adj(grade)\n #print(shortstr(A))\n #N = len(A)\n B = A.copy()\n cells = cx.get_cells(grade)\n while B.sum():\n #B = numpy.dot(A, B)\n B = A*B\n #for j in range(N):\n for j in cells:\n if B[j, j]:\n self.remove(src, tgt)\n return False\n self.remove(src, tgt)\n return True\n\n def __str__(self):\n items = ['%s->%s' % (src.key, tgt.key) for (src, tgt) in self.pairs[0]]\n items += ['%s->%s' % (src.key, tgt.key) for (src, tgt) in self.pairs[1]]\n s = \", \".join(items)\n return \"Flow(%s)\"%(s,)\n\n def build(self):\n pairs = self.get_pairs()\n shuffle(pairs)\n \n idx = 0\n while idx < len(pairs):\n src, tgt = pairs[idx]\n if self.accept(src, tgt):\n self.add(src, tgt)\n idx += 1\n\n def morse_homology(self, grade=1):\n cx = self.cx\n cells = {}\n crit = {}\n for grade in [2, 1, 0]:\n cells[grade] = cx.get_cells(grade)\n crit[grade] = self.get_critical(grade)\n\n chain = []\n for grade in [2, 1]:\n bdy = Matrix(crit[grade-1], crit[grade])\n A = self.get_adj(grade) # grade --> grade-1 --> grade\n B = cx.get_bdymap(grade) # grade --> grade-1\n C = Matrix.identity(cells[grade])\n while C.sum():\n #print()\n #print(shortstr(C.todense()))\n D = B*C # grade --> grade-1\n for a in crit[grade-1]:\n for b in crit[grade]:\n if D[a, b]:\n bdy[a, b] += D[a, b]\n\n C = A*C\n\n #print(bdy.todense())\n chain.append(bdy)\n return chain\n\n def render(self, cvs=None, name=None, size=1.0, double=False):\n if cvs is None:\n cvs = canvas.canvas()\n\n dx, dy = size, -size\n r = 0.05 * size\n mul = 1.5 # critical radius\n\n cx = self.cx\n critical = self.get_critical(0)\n cells = cx.get_cells(0)\n for cell in cells:\n i, j = cell.key\n x, y = dx*j, dy*i\n cell.pos = (x, y)\n if cell in critical:\n cvs.stroke(path.circle(x, y, mul*r), [blue]+st_thick)\n else:\n cvs.fill(path.circle(x, y, r))\n\n critical = self.get_critical(1)\n cells = cx.get_cells(1)\n for cell in cells:\n i, j, k = cell.key\n assert k in \"hv\"\n x0, y0 = dx*j, dy*i\n if k==\"h\":\n x1, y1 = dx*(j+1), dy*i\n else:\n x1, y1 = dx*j, dy*(i+1)\n\n x = 0.5*(x0+x1)\n y = 0.5*(y0+y1)\n if cell in critical:\n cvs.stroke(path.circle(x, y, mul*r), [red]+st_thick)\n else:\n cvs.fill(path.circle(x, y, 1*r), [red]+st_thick)\n cvs.stroke(path.line(x0, y0, x1, y1))\n cell.pos = (x0, y0, x1, y1, x, y)\n\n critical = self.get_critical(2)\n cells = cx.get_cells(2)\n for cell in cells:\n i, j = cell.key\n x, y = dx*(j+0.5), dy*(i+0.5)\n cell.pos = (x, y)\n if cell in critical:\n cvs.stroke(path.circle(x, y, mul*r), [green]+st_thick)\n\n for src, tgt in self.pairs[0]:\n i0, j0 = src.key # vert\n i1, j1, k = tgt.key # edge\n if k==\"h\":\n assert i0 == i1 # same row\n if j0==j1:\n pass\n elif j0-1==j1:\n pass\n elif j0i0:\n i0 = i1+1\n\n x0, y0 = dx*j0, dy*i0\n if k==\"h\":\n x1, y1 = dx*(j1+0.5), dy*i1\n else:\n x1, y1 = dx*j1, dy*(i1+0.5)\n if double:\n dx1, dy1 = x1-x0, y1-y0\n x1 += dx1\n y1 += dy1\n cvs.stroke(path.line(x0, y0, x1, y1), [deco.earrow(), green]+st_THick)\n\n for src, tgt in self.pairs[1]:\n i0, j0, k = src.key # edge\n i1, j1 = tgt.key # face\n\n if k==\"h\":\n assert j0==j1 # same col\n if i0==i1 or i0-1==i1:\n pass\n elif i0 < i1:\n #i0 = i1+1\n i1 = i0-1\n if k==\"v\":\n assert i0==i1 # same row\n if j0==j1 or j0-1==j1:\n pass\n elif j0 < j1:\n #j0 = j1+1\n j1 = j0-1\n\n x0, y0 = dx*j0, dy*i0\n if k==\"h\":\n x1, y1 = dx*(j0+1), dy*i0\n else:\n x1, y1 = dx*j0, dy*(i0+1)\n x0 = 0.5*(x0+x1)\n y0 = 0.5*(y0+y1)\n x1, y1 = dx*(j1+0.5), dy*(i1+0.5)\n\n if double:\n dx1, dy1 = x1-x0, y1-y0\n x0 -= dx1\n y0 -= dy1\n cvs.stroke(path.line(x0, y0, x1, y1), [deco.earrow(), blue]+st_THick)\n\n if name:\n cvs.writePDFfile(name)\n cvs.writeSVGfile(name)\n print(\"saved\", name)\n\n return cvs\n\n\n\ndef render_flow():\n m, n = argv.get(\"m\", 3), argv.get(\"n\", 3) \n nrows, ncols = argv.get(\"nrows\", 1), argv.get(\"ncols\", 1) \n double = argv.double\n name = argv.get(\"name\", \"output\")\n\n cx = Complex()\n\n if argv.torus:\n cx.build_torus(m, n)\n signature = (1, 2, 1)\n\n elif argv.disc:\n cx.build_surface((0, 0), (m+1, n+1))\n signature = (1, 0, 0)\n\n else:\n \n cx.build_surface((0, 0), (m+1, n+1), open_top=True, open_bot=True)\n signature = (0, 1, 0)\n\n if argv.showcode:\n code = cx.get_code()\n print(code)\n return\n\n rows = []\n row = []\n while len(rows) < nrows:\n flow = Flow(cx)\n flow.build()\n \n dims = [len(flow.get_critical(grade)) for grade in [0, 1, 2]]\n print(\"dims:\", dims)\n if signature is not None and tuple(dims) != signature:\n continue\n\n #flow.render(name=\"output\")\n row.append(flow.render(size=1.3, double=double))\n if len(row)==ncols:\n rows.append(row)\n row = []\n print(len(row), len(rows))\n\n PAD = 2.0\n rows = [boxs.HBox([cvs for cvs in row], pad=PAD) for row in rows]\n box = boxs.VBox(rows, pad=PAD)\n box.save(name)\n\n print(\"OK\")\n\n\ndef test_flow():\n\n seed(3)\n\n m, n = argv.get(\"m\", 3), argv.get(\"n\", 3) \n cx = Complex()\n\n if argv.disc:\n cx.build_surface((0, 0), (m, n))\n k = 0\n elif argv.surface:\n cx.build_surface((0, 0), (m, n), open_top=True, open_bot=True)\n k = 0\n elif argv.torus:\n cx.build_torus(m, n)\n k = 2\n else:\n cx.build_torus(m, n)\n k = 2\n \n #print(cx)\n\n for trial in range(10):\n\n flow = Flow(cx)\n flow.build()\n\n if argv.render:\n flow.render(name=\"output\")\n break\n\n chain = flow.morse_homology(1)\n A, B = chain\n BA = B*A\n for key,v in BA.elements.items():\n assert v%2 == 0\n #print(BA.todense())\n\n Hzt = A.todense() % 2\n Hz = Hzt.transpose()\n Hx = B.todense() % 2\n\n assert dot2(Hx, Hzt).sum() == 0\n code = CSSCode(Hx=Hx, Hz=Hz)\n #print(\"k =\", code.k)\n assert code.k == k, (code.k, k)\n\n\n\n\ndef test_surface():\n\n rows, cols = 4, 3\n\n cx = Complex()\n cx.build_surface((0, 0), (rows, cols))\n edges = cx.lookup[1].values()\n for cell in edges:\n assert len(cell)==2, cell\n code = cx.get_code()\n assert code.n == 17\n assert code.k == 0\n assert code.mx == 11\n assert code.mz == 6\n\n cx = Complex()\n cx.build_surface((0, 0), (rows, cols), open_top=True, open_bot=True)\n code = cx.get_code()\n assert code.n == 13\n assert code.k == 1\n assert code.mx == 6\n assert code.mz == 6\n\n cx = Complex()\n cx.build_surface((0, 0), (rows, cols), open_left=True, open_right=True)\n code = cx.get_code()\n assert code.n == 11\n assert code.k == 1\n assert code.mx == 4\n assert code.mz == 6\n\n cx = Complex()\n cx.build_surface((0, 0), (rows, cols), \n open_top=True, open_bot=True, open_left=True, open_right=True)\n code = cx.get_code()\n assert code.n == 7\n assert code.k == 0\n assert code.mx == 2\n assert code.mz == 5\n\n cx = Complex()\n cx.build_torus(rows, cols)\n #print(cx)\n code = cx.get_code()\n #print(code)\n\n\n\nif __name__ == \"__main__\":\n\n name = argv.next()\n\n if argv.profile:\n import cProfile as profile\n profile.run(\"%s()\"%name)\n\n elif name:\n\n fn = eval(name)\n fn()\n\n else:\n\n test_surface()\n test_flow()\n\n","repo_name":"punkdit/qupy","sub_path":"qupy/ldpc/cell.py","file_name":"cell.py","file_ext":"py","file_size_in_byte":25293,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"71230414886","text":"import setuptools\n\nwith open(\"README.md\", \"r\") as fh:\n long_description = fh.read()\n\nsetuptools.setup(\n name=\"k8s-healthcheck\",\n version=\"0.0.1\",\n author=\"Chris McCoy\",\n author_email=\"chris@chr.is\",\n description=\"A quick and dirty health checker\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n url=\"https://github.com/chris-mccoy/k8s-healthcheck\",\n packages=setuptools.find_packages(),\n classifiers=[\n \"Programming Language :: Python :: 3\",\n \"License :: OSI Approved :: MIT License\",\n \"Operating System :: OS Independent\",\n ],\n python_requires='>=3.6',\n)\n\n","repo_name":"chris-mccoy/k8s-healthcheck","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":655,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"31612822105","text":"from cs50 import get_int\n\n\ndef main():\n\n cardNumber = get_int(\"Type credit card number:\")\n\n while cardNumber < 0: # prompt an non-negative number input for user\n\n cardNumber = get_int(\"Type credit card number:\")\n\n cardLenght = countCardLenght(cardNumber)\n\n if checkCardLenght(cardLenght) == True:\n\n if checkSum(cardNumber, cardLenght) == True:\n\n checkAndprintlag(cardNumber, cardLenght)\n\n else:\n\n print(\"INVALID\\n\")\n\n else:\n\n print(\"INVALID\\n\")\n\n\ndef countCardLenght(cN): # counts card number's lenght\n\n for i in range(13, 17, 1):\n\n powerX = (10 ** i)\n rest = cN / powerX\n\n if int(rest) == 0:\n\n return i\n\n return 0\n\n\ndef checkCardLenght(cL): # checks if card number's lenght iguals 13, 15 ou 16\n\n if cL == 13 or cL == 15 or cL == 16:\n\n return True\n\n else:\n\n return False\n\n\ndef checkSum(cN, cL): # calculates and verify checkSum Luhn's Algorithm\n\n firstSum = 0 # first Sum for checkSum Luhn's Algorithm\n secondSum = 0 # second Sum for checkSum Luhn's Algorithm\n toggle = False # boolean to alternate between sums.\n\n for i in range(cL):\n\n digit = cN % 10\n cN = cN / 10\n\n if toggle == True: # calculate first sum\n\n digit = int(digit) * 2 # each single digit from card Number\n\n if digit >= 10:\n\n secondDigit = int(digit) # second digit if first digit > 9 after times 2\n secondDigit = int(secondDigit) / 10\n digit = int(digit) % 10\n digit += int(secondDigit)\n\n firstSum += int(digit)\n toggle = False\n\n else: # calculate second sum\n\n secondSum += int(digit)\n toggle = True\n \n secondSum += int(firstSum) # final sum for second sum\n\n if secondSum % 10 == 0:\n\n return True\n\n else:\n\n return False\n\n\ndef checkAndprintlag(cN, cL): # checks first digits and prints flag for AMEX, MASTERCARD and VISA\n\n checkAndprintlagLenght13(cN, cL)\n checkAndprintlagLenght15(cN, cL)\n checkAndprintlagLenght16(cN, cL)\n\n\ndef checkAndprintlagLenght13(cN, cL): # checks first digits and prints flag for leght 13 card's size\n\n if cL == 13:\n\n fD = cN / (10 ** 12)\n if int(fD) == 4:\n\n print(\"VISA\\n\")\n\n else:\n\n print(\"INVALID\\n\")\n\n\ndef checkAndprintlagLenght15(cN, cL): # checks first digits and prints flag for leght 15 card's size\n\n if cL == 15:\n\n fD = cN / (10 ** 13) # first digits\n\n if int(fD) == 34 or int(fD) == 37:\n\n print(\"AMEX\\n\")\n\n else:\n\n print(\"INVALID\\n\")\n\n\ndef checkAndprintlagLenght16(cN, cL): # checks first digits and prints flag for leght 16 card's size\n\n if cL == 16:\n\n fD = cN / (10 ** 14) # first digits\n\n if int(fD) == 51 or int(fD) == 52 or int(fD) == 53 or int(fD) == 54 or int(fD) == 55:\n\n print(\"MASTERCARD\\n\")\n\n else:\n\n fD = cN / (10 ** 15)\n if int(fD) == 4:\n\n print(\"VISA\\n\")\n\n else:\n\n print(\"INVALID5\\n\")\n\n\nif __name__ == \"__main__\":\n main()","repo_name":"LBcheche/CS50-2022","sub_path":"Week6/sentimental-credit/credit.py","file_name":"credit.py","file_ext":"py","file_size_in_byte":3182,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"36154462176","text":"#!/usr/bin/python3\n\"\"\"This module defines a base class for all models in our hbnb clone\"\"\"\nimport uuid\nfrom datetime import datetime\n\n\nclass BaseModel:\n \"\"\"A base class for all hbnb models\"\"\"\n def __init__(self, *args, **kwargs):\n \"\"\"Instatntiates a new model\"\"\"\n from models import storage\n self.id = str(uuid.uuid4())\n self.created_at = self.updated_at = datetime.now()\n if len(kwargs) != 0:\n self._assign_att(kwargs)\n storage.new(self)\n else:\n storage.new(self)\n def _assign_att(self, att):\n \"\"\"\n Assigns in attrubutes for kwargs\n \"\"\"\n str_format = \"%Y-%m-%dT%H:%M:%S.%f\"\n for key, value in att.items():\n if key == \"created_at\" or key == \"updated_at\":\n t = self.__dict__[key]\n t = datetime.strptime(value, str_format)\n else:\n t = value\n\n def __str__(self):\n \"\"\"Returns a string representation of the instance\"\"\"\n cls = (str(type(self)).split('.')[-1]).split('\\'')[0]\n return '[{}] ({}) {}'.format(cls, self.id, self.__dict__)\n\n def save(self):\n \"\"\"Updates updated_at with current time when instance is changed\"\"\"\n from models import storage\n self.updated_at = datetime.now()\n storage.save()\n \n def delete(self):\n \"\"\"\n Delete instance from storage by calling its delete method\n \"\"\"\n models.storage.delete(self)\n\n def to_dict(self):\n \"\"\"Convert instance into dict format\"\"\"\n dictionary = {}\n dictionary.update(self.__dict__)\n dictionary.update({\"__class__\": (str(type(self)).split(\".\")[-1]).split(\"'\")[0]})\n dictionary['created_at'] = self.created_at.isoformat()\n dictionary['updated_at'] = self.updated_at.isoformat()\n return dictionary\n","repo_name":"Tassuo7/AirBnB_clone_v2","sub_path":"models/base_model.py","file_name":"base_model.py","file_ext":"py","file_size_in_byte":1866,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"52"} +{"seq_id":"13451695989","text":"import gzip\nimport json\nimport os\nimport pickle\nimport shutil\nfrom collections import defaultdict, Counter\nfrom pprint import pprint\nimport math\nimport numpy as np\nimport scipy\nfrom scipy import stats\nimport argparse\nfrom pathlib import Path\n\nfrom matplotlib import cm\nfrom transforms3d.axangles import axangle2mat\nfrom util.transforms import hmg, dot\nimport torch\nimport cv2\nfrom PIL import Image\nfrom tqdm import tqdm\nfrom dataset.preprocessing.sens_reader.SensorData import SensorData\nfrom util.distinct_colors import DistinctColors\nfrom util.metrics import ConfusionMatrix\nfrom util.misc import visualize_mask, create_box, get_boundary_mask\nfrom util.panoptic_quality import panoptic_quality, panoptic_quality_match, _panoptic_quality_compute\n\n\ndef get_keyframe_indices(filenames, window_size):\n \"\"\"\n select non-blurry images within a moving window\n \"\"\"\n scores = []\n for filename in tqdm(filenames, 'processing keyframes'):\n img = cv2.imread(str(filename))\n blur_score = compute_blur_score_opencv(img)\n scores.append(blur_score)\n\n keyframes = [i + np.argmin(scores[i:i + window_size]) for i in range(0, len(scores), window_size)]\n return keyframes, scores\n\n\ndef compute_blur_score_opencv(image):\n \"\"\"\n Estimate the amount of blur an image has with the variance of the Laplacian.\n Normalize by pixel number to offset the effect of image size on pixel gradients & variance\n https://github.com/deepfakes/faceswap/blob/ac40b0f52f5a745aa058f92339302065177dd28b/tools/sort/sort.py#L626\n \"\"\"\n if image.ndim == 3:\n image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n blur_map = cv2.Laplacian(image, cv2.CV_32F)\n score = np.var(blur_map) / np.sqrt(image.shape[0] * image.shape[1])\n return 1.0 - score\n\n\ndef subsample_scannet(src_folder, rate):\n \"\"\"\n sample every nth frame from scannet\n \"\"\"\n all_frames = sorted(list(x.stem for x in (src_folder / 'pose').iterdir()), key=lambda y: int(y) if y.isnumeric() else y)\n total_sampled = int(len(all_frames) * rate)\n sampled_frames = [all_frames[i * (len(all_frames) // total_sampled)] for i in range(total_sampled)]\n unsampled_frames = [x for x in all_frames if x not in sampled_frames]\n for frame in sampled_frames:\n if 'inf' in Path(src_folder / \"pose\" / f\"{frame}.txt\").read_text():\n unsampled_frames.append(frame)\n folders = [\"color\", \"depth\", \"instance\", \"pose\", \"semantics\"]\n exts = ['jpg', 'png', 'png', 'txt', 'png']\n for folder, ext in tqdm(zip(folders, exts), desc='sampling'):\n assert (src_folder / folder).exists(), src_folder\n for frame in unsampled_frames:\n if (src_folder / folder / f'{frame}.{ext}').exists():\n os.remove(str(src_folder / folder / f'{frame}.{ext}'))\n else:\n print(str(src_folder / folder / f'{frame}.{ext}'), \"already exists!\")\n\n\ndef subsample_scannet_blur_window(src_folder, min_frames):\n \"\"\"\n sample non blurry frames from scannet\n \"\"\"\n all_frames = sorted(list(x.stem for x in (src_folder / 'pose').iterdir()), key=lambda y: int(y) if y.isnumeric() else y)\n all_frame_paths = sorted(list(x for x in (src_folder / 'color').iterdir()), key=lambda y: int(y.stem) if y.stem.isnumeric() else y.stem)\n if len(all_frame_paths) <= min_frames:\n sampled_frames = all_frames\n else:\n window_size = max(2, int(math.ceil(len(all_frames) / min_frames)))\n frame_indices, _ = get_keyframe_indices(all_frame_paths, window_size)\n print(\"Using a window size of\", window_size, \"got\", len(frame_indices), \"frames\")\n sampled_frames = [all_frames[i] for i in frame_indices]\n unsampled_frames = [x for x in all_frames if x not in sampled_frames]\n for frame in sampled_frames:\n if 'inf' in Path(src_folder / \"pose\" / f\"{frame}.txt\").read_text():\n unsampled_frames.append(frame)\n folders = [\"color\", \"depth\", \"instance\", \"pose\", \"semantics\"]\n exts = ['jpg', 'png', 'png', 'txt', 'png']\n for folder, ext in tqdm(zip(folders, exts), desc='sampling'):\n assert (src_folder / folder).exists(), src_folder\n for frame in unsampled_frames:\n if (src_folder / folder / f'{frame}.{ext}').exists():\n os.remove(str(src_folder / folder / f'{frame}.{ext}'))\n else:\n print(str(src_folder / folder / f'{frame}.{ext}'), \"already exists!\")\n\n\n# manual fix for objects labeled incorrectly / ambigiously in scannet scenes\nscene_specific_fixes_objectid = {\n \"scene0050_02\": {\n 24: 37,\n 26: 37,\n 12: 6,\n 1: 6,\n 16: 9\n },\n \"scene0144_01\": {\n 4: 3,\n 13: 3,\n 5: 3\n },\n \"scene0221_01\": {\n 15: 7,\n 36: 15,\n 37: 1,\n 38: 1\n },\n \"scene0300_01\": {\n 13: 25,\n 14: 25,\n 20: 37\n },\n \"scene0389_00\": {\n 19: 37,\n 20: 3,\n 21: 3,\n 28: 37\n },\n \"scene0423_02\": {\n 6: 7\n },\n \"scene0616_00\": {\n 21: 1,\n 22: 1,\n 24: 1,\n 25: 1,\n 30: 1,\n 31: 1,\n },\n \"scene0645_02\": {\n 5: 7,\n 6: 3,\n 25: 5,\n 41: 5,\n 27: 37,\n 32: 37,\n 34: 37,\n 60: 0,\n 61: 37\n },\n \"scene0693_00\": {\n 1: 1,\n 4: 1,\n 6: 3,\n 11: 8,\n 20: 40,\n }\n}\nmmdet_export_fixes = {\n \"office_020737\": {\n \"rotation\": [math.radians(0.997707), -0.017, -0.065, -0.001],\n \"translation\": [0, 0, 1],\n \"scale\": 0.33\n },\n \"office_0213meeting\": {\n \"rotation\": None,\n \"translation\": [0, 0, 0.75],\n \"scale\": 0.33\n },\n \"koenig_0200\": {\n \"rotation\": [math.radians(0.9999), -0.034897, -0.013082, -0.000457],\n \"translation\": [0, 0, 1.1],\n \"scale\": 0.25\n }\n}\n\n\ndef extract_scan(path_sens_root, path_dest):\n sd = SensorData(str(path_sens_root / f'{path_sens_root.stem}.sens'))\n sd.export_depth_images(str(path_dest / 'depth'))\n sd.export_color_images(str(path_dest / 'color'))\n sd.export_poses(str(path_dest / 'pose'))\n sd.export_intrinsics(str(path_dest / 'intrinsic'))\n\n\ndef extract_labels(path_sens_root, path_dest):\n os.system(f'unzip {str(path_sens_root / f\"{path_sens_root.stem}_2d-label-filt.zip\")} -d {str(path_dest)}')\n os.system(f'unzip {str(path_sens_root / f\"{path_sens_root.stem}_2d-instance-filt.zip\")} -d {str(path_dest)}')\n if (path_dest / \"instance\").exists():\n shutil.rmtree(str(path_dest / \"instance\"))\n if (path_dest / \"semantics\").exists():\n shutil.rmtree(str(path_dest / \"semantics\"))\n os.rename(str(path_dest / \"instance-filt\"), str(path_dest / \"instance\"))\n os.rename(str(path_dest / \"label-filt\"), str(path_dest / \"semantics\"))\n\n\ndef visualize_mask_folder(path_to_folder, offset=0):\n (path_to_folder.parent / f\"visualized_{path_to_folder.stem}\").mkdir(exist_ok=True)\n for f in tqdm(list(path_to_folder.iterdir()), desc='visualizing masks'):\n visualize_mask(np.array(Image.open(f)) + offset, path_to_folder.parent / f\"visualized_{path_to_folder.stem}\" / f.name)\n\n\ndef visualize_confidence_notta(path_to_confidence_file):\n data = torch.load(gzip.open(path_to_confidence_file), map_location='cpu')\n semantics = np.array(Image.open(path_to_confidence_file.parents[1] / \"m2f_notta_semantics\" / f\"{path_to_confidence_file.stem}.png\"))\n probability, confidence, confidence_notta = data['probabilities'], data['confidences'], data['confidences_notta']\n confidence_notta[semantics == 0] = 0\n Image.fromarray((cm.get_cmap('gray')(confidence_notta) * 255).astype(np.uint8)).save(f\"{path_to_confidence_file.stem}.png\")\n\n\ndef visualize_confidence(path_to_confidence_file):\n data = torch.load(gzip.open(path_to_confidence_file), map_location='cpu')\n semantics = np.array(Image.open(path_to_confidence_file.parents[1] / \"m2f_semantics\" / f\"{path_to_confidence_file.stem}.png\"))\n probability, confidence, confidence_notta = data['probabilities'], data['confidences'], data['confidences_notta']\n confidence[semantics == 0] = 0\n Image.fromarray((cm.get_cmap('gray')(confidence) * 255).astype(np.uint8)).save(f\"{path_to_confidence_file.stem}_tta.png\")\n\n\ndef visualize_labels(src_folder):\n visualize_mask_folder(src_folder / \"instance\")\n visualize_mask_folder(src_folder / \"semantics\")\n\n\ndef get_scannet_to_nyu_map():\n scannetid_to_nyuid = {int(x.split('\\t')[0]): x.split('\\t')[4] for x in Path(\"resources/scannet-labels.combined.tsv\").read_text().splitlines()[1:]}\n scannetid_to_nyuid[0] = 0\n scannetid_to_nyuid_arr = np.ones(1280, dtype=np.int32) * 40\n\n for scid, nyuid in scannetid_to_nyuid.items():\n if nyuid == '':\n nyuid = 40\n else:\n nyuid = int(nyuid)\n scannetid_to_nyuid_arr[scid] = nyuid\n return scannetid_to_nyuid_arr\n\n\ndef scannet_to_nyu(semantics):\n scannetid_to_nyuid_arr = get_scannet_to_nyu_map()\n nyu_semantics = semantics.reshape(-1)\n nyu_semantics = scannetid_to_nyuid_arr[nyu_semantics.tolist()]\n return nyu_semantics.reshape(semantics.shape)\n\n\ndef get_reduce_and_fold_map():\n all_classes = []\n for cllist in [x.strip().split(',') for x in Path(\"resources/scannet_to_reduced_scannet.csv\").read_text().strip().splitlines()]:\n all_classes.append(cllist[0])\n reduce_map = np.zeros(41).astype(np.int)\n for idx, cllist in enumerate([x.strip().split(',') for x in Path(\"resources/scannet_to_reduced_scannet.csv\").read_text().strip().splitlines()]):\n if cllist[1] != '':\n reduce_map[idx + 1] = all_classes.index(cllist[1]) + 1\n else:\n reduce_map[idx + 1] = idx + 1\n fold_map = np.zeros(41).astype(np.int)\n for idx, cllist in enumerate([x.strip().split(',') for x in Path(\"resources/scannet_reduced_to_coco.csv\").read_text().strip().splitlines()]):\n fold_map[all_classes.index(cllist[0]) + 1] = idx + 1\n return reduce_map, fold_map\n\n\ndef fold_scannet_classes(src_folder):\n reduce_map, fold_map = get_reduce_and_fold_map()\n output_folder = src_folder / \"rs_semantics\"\n output_folder.mkdir(exist_ok=True)\n for f in tqdm((src_folder / \"semantics\").iterdir(), desc='folding semantics'):\n arr = scannet_to_nyu(np.array(Image.open(f)))\n ins_arr = np.array(Image.open(src_folder / \"instance\" / f.name))\n if src_folder.stem in scene_specific_fixes_objectid:\n for ob_id in scene_specific_fixes_objectid[src_folder.stem]:\n arr[ins_arr == ob_id] = scene_specific_fixes_objectid[src_folder.stem][ob_id]\n shape = arr.shape\n Image.fromarray(fold_map[reduce_map[arr.flatten()]].reshape(shape).astype(np.int8)).save(output_folder / f.name)\n\n\ndef get_thing_semantics(sc_classes='reduced'):\n thing_semantics = [False]\n for cllist in [x.strip().split(',') for x in Path(f\"resources/scannet_{sc_classes}_things.csv\").read_text().strip().splitlines()]:\n thing_semantics.append(bool(int(cllist[1])))\n return thing_semantics\n\n\ndef get_classnames(sc_classes='reduced'):\n classnames = [\"void\"]\n for cllist in [x.strip().split(',') for x in Path(f\"resources/scannet_{sc_classes}_things.csv\").read_text().strip().splitlines()]:\n classnames.append(cllist[0])\n return classnames\n\n\ndef renumber_instances(src_folder, prefix='rs'):\n all_frame_names = sorted([x.stem for x in (src_folder / f\"color\").iterdir() if x.name.endswith('.jpg')], key=lambda y: int(y))\n thing_semantics = get_thing_semantics()\n print('len thing_semantics', len(thing_semantics))\n semantics, instances = [], []\n for frame_name in tqdm(all_frame_names, desc='read labels'):\n semantics.append(torch.from_numpy(np.array(Image.open(src_folder / f\"{prefix}_semantics\" / f\"{frame_name}.png\"))))\n instances.append(torch.from_numpy(np.array(Image.open(src_folder / f\"instance\" / f\"{frame_name}.png\"))))\n semantics = torch.stack(semantics, 0)\n instances = torch.stack(instances, 0)\n\n instance_semantics_counts = defaultdict(Counter)\n unique_instances = torch.unique(instances)\n for instance in unique_instances:\n usem, uctr = torch.unique(semantics[instances == instance], return_counts=True)\n for usem_idx in range(usem.shape[0]):\n instance_semantics_counts[instance.item()][usem[usem_idx].item()] += uctr[usem_idx].item()\n instance_to_semantic = {}\n for instance in instance_semantics_counts:\n instance_to_semantic[instance] = instance_semantics_counts[instance].most_common(1)[0][0]\n\n instance_to_remapped_instance = {}\n remapped_instance_to_instance = {0: 0}\n new_instance_id = 1\n for instance in sorted(instance_to_semantic.keys()):\n if thing_semantics[instance_to_semantic[instance]]:\n instance_to_remapped_instance[instance] = new_instance_id\n remapped_instance_to_instance[new_instance_id] = instance\n new_instance_id += 1\n else:\n instance_to_remapped_instance[instance] = 0\n\n pprint(instance_to_remapped_instance)\n\n remapped_instances = torch.zeros_like(instances)\n for uinst in unique_instances:\n remapped_instances[instances == uinst.item()] = instance_to_remapped_instance[uinst.item()]\n\n if (src_folder / 'segmentation_data.pkl').exists():\n export_dict = pickle.load(open(src_folder / 'segmentation_data.pkl', 'rb'))\n else:\n export_dict = {}\n export_dict['num_semantic_classes'] = len(thing_semantics)\n export_dict['fg_classes'] = [i for i, is_thing in enumerate(thing_semantics) if is_thing]\n export_dict['bg_classes'] = [i for i, is_thing in enumerate(thing_semantics) if not is_thing]\n instance_to_semantic[0] = 0\n remapped_instance_to_semantic = {k: instance_to_semantic[remapped_instance_to_instance[k]] for k in range(new_instance_id)}\n export_dict[f'{prefix}_instance_to_semantic'] = remapped_instance_to_semantic\n\n Path(src_folder / f\"{prefix}_instance\").mkdir(exist_ok=True)\n\n # save instances\n for iidx in range(remapped_instances.shape[0]):\n Image.fromarray(remapped_instances[iidx].numpy()).save(src_folder / f\"{prefix}_instance\" / f\"{all_frame_names[iidx]}.png\")\n # save bboxes\n pickle.dump(export_dict, open(src_folder / 'segmentation_data.pkl', 'wb'))\n\n\ndef create_inconsistent_instance_map_dataset(src_folder, prefix='rs'):\n all_frame_names = sorted([x.stem for x in (src_folder / \"color\").iterdir() if x.name.endswith('.jpg')], key=lambda y: int(y))\n sample_indices = list(range(len(all_frame_names)))\n export_dict = pickle.load(open(src_folder / 'segmentation_data.pkl', 'rb'))\n semantics, instances = [], []\n for frame_name in tqdm(all_frame_names, desc='read labels'):\n semantics.append(torch.from_numpy(np.array(Image.open(src_folder / f\"{prefix}_semantics\" / f\"{frame_name}.png\"))))\n instances.append(torch.from_numpy(np.array(Image.open(src_folder / f\"{prefix}_instance\" / f\"{frame_name}.png\"))))\n semantics = torch.stack(semantics, 0)\n instances = torch.stack(instances, 0)\n\n instance_to_semantics = export_dict[f'{prefix}_instance_to_semantic']\n fg_classes = export_dict['fg_classes']\n\n print(instance_to_semantics)\n remapped_instances_inc = instances.clone().long()\n remapped_instances_sem = instances.clone()\n remapped_instance_to_semantics_inc = {}\n new_instance_ctr = 1\n\n for sidx in tqdm(sorted(list(set(instance_to_semantics.values())))):\n for iidx in range(instances.shape[0]):\n for inst_id in [x for x in sorted(torch.unique(instances[iidx]).tolist()) if x != 0]:\n if instance_to_semantics[inst_id] == sidx:\n remapped_instances_inc[iidx][instances[iidx] == inst_id] = new_instance_ctr\n remapped_instance_to_semantics_inc[new_instance_ctr] = sidx\n new_instance_ctr += 1\n\n print(remapped_instances_inc.max())\n\n for i in range(len(fg_classes)):\n remapped_instances_sem[semantics == fg_classes[i]] = i + 1\n\n Path(src_folder / f\"{prefix}_instance_inc\").mkdir(exist_ok=True)\n Path(src_folder / f\"{prefix}_instance_sem\").mkdir(exist_ok=True)\n\n for iidx in tqdm(range(remapped_instances_inc.shape[0])):\n sample_index = sample_indices[iidx]\n Image.fromarray(remapped_instances_inc[iidx].numpy().astype(np.uint16)).save(src_folder / f\"{prefix}_instance_inc\" / f\"{all_frame_names[sample_index]}.png\")\n Image.fromarray(remapped_instances_sem[iidx].numpy()).save(src_folder / f\"{prefix}_instance_sem\" / f\"{all_frame_names[sample_index]}.png\")\n\n export_dict[f'{prefix}_instance_to_semantic_inc'] = remapped_instance_to_semantics_inc\n pickle.dump(export_dict, open(src_folder / 'segmentation_data.pkl', 'wb'))\n\n\ndef convert_from_mask_to_semantics_and_instances(original_mask, segments, coco_to_scannet, is_thing, instance_ctr, instance_to_semantic):\n id_to_class = torch.zeros(1024).int()\n instance_mask = torch.zeros_like(original_mask)\n invalid_mask = original_mask == 0\n for s in segments:\n if s['category_name'] in coco_to_scannet:\n id_to_class[s['id']] = coco_to_scannet[s['category_name']]\n if is_thing[coco_to_scannet[s['category_name']]]:\n instance_mask[original_mask == s['id']] = instance_ctr\n instance_to_semantic[instance_ctr] = coco_to_scannet[s['category_name']]\n instance_ctr += 1\n return id_to_class[original_mask.flatten().numpy().tolist()].reshape(original_mask.shape), instance_mask, invalid_mask, instance_ctr, instance_to_semantic\n\n\ndef convert_from_mask_to_semantics_and_instances_no_remap(original_mask, segments, _coco_to_scannet, is_thing, instance_ctr, instance_to_semantic):\n id_to_class = torch.zeros(1024).int()\n instance_mask = torch.zeros_like(original_mask)\n invalid_mask = original_mask == 0\n for s in segments:\n id_to_class[s['id']] = s['category_id']\n if is_thing[s['category_id']]:\n instance_mask[original_mask == s['id']] = instance_ctr\n instance_to_semantic[instance_ctr] = s['category_id']\n instance_ctr += 1\n return id_to_class[original_mask.flatten().numpy().tolist()].reshape(original_mask.shape), instance_mask, invalid_mask, instance_ctr, instance_to_semantic\n\n\ndef map_panoptic_coco(src_folder, sc_classes='reduced', undistort=False):\n coco_to_scannet = {}\n thing_semantics = get_thing_semantics(sc_classes)\n for cidx, cllist in enumerate([x.strip().split(',') for x in Path(f\"resources/scannet_{sc_classes}_to_coco.csv\").read_text().strip().splitlines()]):\n for c in cllist[1:]:\n coco_to_scannet[c.split('/')[1]] = cidx + 1\n instance_ctr = 1\n instance_to_semantic = {}\n instance_ctr_notta = 1\n segment_ctr = 1\n instance_to_semantic_notta = {}\n (src_folder / \"m2f_instance\").mkdir(exist_ok=True)\n (src_folder / \"m2f_semantics\").mkdir(exist_ok=True)\n (src_folder / \"m2f_notta_instance\").mkdir(exist_ok=True)\n (src_folder / \"m2f_notta_semantics\").mkdir(exist_ok=True)\n (src_folder / \"m2f_feats\").mkdir(exist_ok=True)\n (src_folder / \"m2f_probabilities\").mkdir(exist_ok=True)\n (src_folder / \"m2f_invalid\").mkdir(exist_ok=True)\n (src_folder / \"m2f_segments\").mkdir(exist_ok=True)\n\n if undistort:\n transforms = json.loads((src_folder / \"transforms.json\").read_text())\n if \"camera_model\" in transforms and transforms[\"camera_model\"] == \"OPENCV_FISHEYE\":\n h, w, cx, cy, k1, k2, k3, k4 = int(transforms[\"h\"]), int(transforms[\"w\"]), transforms[\"cx\"], transforms[\"cy\"], transforms[\"k1\"], transforms[\"k2\"], transforms[\"k3\"], transforms[\"k4\"]\n distortion_params = np.array([k1, k2, k3, k4])\n mtx = np.array([[transforms[\"fl_x\"], 0, cx], [0, transforms[\"fl_y\"], cy], [0, 0, 1]])\n newcameramtx = cv2.fisheye.estimateNewCameraMatrixForUndistortRectify(mtx, distortion_params, [w, h], np.eye(3), balance=1)\n mapx, mapy = cv2.fisheye.initUndistortRectifyMap(mtx, distortion_params, np.eye(3), newcameramtx, [w, h], cv2.CV_16SC2)\n mask = np.ones([h, w]).astype(np.uint8) * 255\n distorted_mask = 255 - ((cv2.remap(mask, mapx, mapy, interpolation=cv2.INTER_CUBIC) > 0) * 255).astype(np.uint8)\n roi = [0, 0, w - 1, h - 1]\n else:\n undistort = False\n\n for idx, fpath in enumerate(tqdm(sorted(list((src_folder / \"color\").iterdir()), key=lambda x: x.stem), desc='map labels')):\n data = torch.load(gzip.open(src_folder / \"panoptic\" / f'{fpath.stem}.ptz'), map_location='cpu')\n probability, confidence, confidence_notta = data['probabilities'], data['confidences'], data['confidences_notta']\n\n if undistort:\n probability_numpy = probability.cpu().numpy()\n probability_undistorted = np.zeros_like(probability_numpy)\n for cidx in range(probability_numpy.shape[-1]):\n probability_undistorted[:, :, cidx] = cv2.remap(probability_numpy[:, :, cidx], mapx, mapy, cv2.INTER_CUBIC)\n confidence_undistorted = cv2.remap(confidence.cpu().numpy(), mapx, mapy, cv2.INTER_CUBIC)\n confidence_notta_undistorted = cv2.remap(confidence_notta.cpu().numpy(), mapx, mapy, cv2.INTER_CUBIC)\n probability, confidence, confidence_notta = torch.clip(torch.from_numpy(probability_undistorted), 0, 1), torch.clip(torch.from_numpy(confidence_undistorted), 0, 1), torch.clip(torch.from_numpy(confidence_notta_undistorted), 0, 1)\n\n semantic, instance, invalid_mask, instance_ctr, instance_to_semantic = convert_from_mask_to_semantics_and_instances_no_remap(data['mask'], data['segments'], coco_to_scannet, thing_semantics, instance_ctr, instance_to_semantic)\n semantic_notta, instance_notta, _, instance_ctr_notta, instance_to_semantic_notta = convert_from_mask_to_semantics_and_instances_no_remap(data['mask_notta'], data['segments_notta'], coco_to_scannet, thing_semantics,\n instance_ctr_notta, instance_to_semantic_notta)\n segment_mask = torch.zeros_like(data['mask'])\n for s in data['segments']:\n segment_mask[data['mask'] == s['id']] = segment_ctr\n segment_ctr += 1\n Image.fromarray(segment_mask.numpy().astype(np.uint16)).save(src_folder / \"m2f_segments\" / f\"{fpath.stem}.png\")\n Image.fromarray(semantic.numpy().astype(np.uint16)).save(src_folder / \"m2f_semantics\" / f\"{fpath.stem}.png\")\n Image.fromarray(instance.numpy()).save(src_folder / \"m2f_instance\" / f\"{fpath.stem}.png\")\n Image.fromarray(semantic_notta.numpy().astype(np.uint16)).save(src_folder / \"m2f_notta_semantics\" / f\"{fpath.stem}.png\")\n Image.fromarray(instance_notta.numpy()).save(src_folder / \"m2f_notta_instance\" / f\"{fpath.stem}.png\")\n Image.fromarray(invalid_mask.numpy().astype(np.uint8) * 255).save(src_folder / \"m2f_invalid\" / f\"{fpath.stem}.png\")\n # interpolated_p = torch.nn.functional.interpolate(torch.cat([probability.permute((2, 0, 1)), confidence.unsqueeze(0), confidence_notta.unsqueeze(0)], 0).unsqueeze(0), size=(256, 256), mode='bilinear', align_corners=False).squeeze(0)\n # probability, confidence, confidence_notta = interpolated_p[:-2, :, :].permute((1, 2, 0)).cpu(), interpolated_p[-2, :, :].cpu(), interpolated_p[-1, :, :].cpu()\n np.savez_compressed(src_folder / \"m2f_probabilities\" / f\"{fpath.stem}.npz\", probability=probability.float().numpy(), confidence=confidence.float().numpy(), confidence_notta=confidence_notta.float().numpy())\n \n if undistort:\n to_undistort = [\n src_folder / \"m2f_segments\" / f\"{fpath.stem}.png\",\n src_folder / \"m2f_semantics\" / f\"{fpath.stem}.png\",\n src_folder / \"m2f_instance\" / f\"{fpath.stem}.png\",\n src_folder / \"m2f_notta_semantics\" / f\"{fpath.stem}.png\",\n src_folder / \"m2f_notta_instance\" / f\"{fpath.stem}.png\",\n src_folder / \"m2f_invalid\" / f\"{fpath.stem}.png\"\n ]\n for img_p in to_undistort:\n img = np.array(Image.open(img_p))\n img = cv2.remap(img, mapx, mapy, cv2.INTER_NEAREST)\n img[distorted_mask > 0] = 0\n img = img[roi[1]: roi[3] + 1, roi[0]: roi[2] + 1]\n Image.fromarray(img).save(img_p)\n\n # feats = data['feats']\n # np.savez_compressed(src_folder / \"m2f_feats\" / f\"{fpath.stem}.npz\", feats=feats.float().numpy())\n\n export_dict = pickle.load(open(src_folder / 'segmentation_data.pkl', 'rb'))\n export_dict[f'm2f_instance_to_semantic'] = instance_to_semantic\n export_dict[f'm2f_notta_instance_to_semantic'] = instance_to_semantic\n pprint(instance_to_semantic)\n pickle.dump(export_dict, open(src_folder / 'segmentation_data.pkl', 'wb'))\n\n\ndef map_gt_bboxes(path_sens_root, src_folder):\n reduce_map, fold_map = get_reduce_and_fold_map()\n thing_semantics = get_thing_semantics()\n distinct_colors = DistinctColors()\n bboxes = {}\n valid_boxid = 0\n (src_folder / \"visualized_gtboxes\").mkdir(exist_ok=True)\n pkl_segmentation_data = pickle.load(open(src_folder / f'segmentation_data.pkl', 'rb'))\n bbox_annot = np.load(path_sens_root / f\"{path_sens_root.stem}_bbox.npy\")\n for bbox_idx in range(bbox_annot.shape[0]):\n position = bbox_annot[bbox_idx][0:3]\n orientation = np.eye(3)\n extent = bbox_annot[bbox_idx][3:6]\n instance_id = int(bbox_annot[bbox_idx][7]) + 1\n if src_folder.stem in scene_specific_fixes_objectid:\n if instance_id in scene_specific_fixes_objectid[src_folder.stem]:\n bbox_annot[bbox_idx][6] = scene_specific_fixes_objectid[src_folder.stem][instance_id]\n label = fold_map[reduce_map[int(bbox_annot[bbox_idx][6])]]\n if thing_semantics[label]:\n bboxes[valid_boxid] = {\n 'position': position,\n 'orientation': orientation,\n 'extent': extent,\n 'class': label\n }\n create_box(position, extent, orientation, distinct_colors.get_color_fast_numpy(label)).export(src_folder / \"visualized_gtboxes\" / f\"{label}_{valid_boxid}.obj\")\n valid_boxid += 1\n pkl_segmentation_data['gt_bboxes'] = bboxes\n pickle.dump(pkl_segmentation_data, open(src_folder / f'segmentation_data.pkl', 'wb'))\n\n\ndef map_imvoxnet_boxes(path_bboxes, src_folder, class_set=\"reduced\"):\n mmdet_to_scannet_reduced = {}\n for cidx, cllist in enumerate([x.strip().split(',') for x in Path(f\"resources/scannet_mmdet_to_scannet_{class_set}.csv\").read_text().strip().splitlines()]):\n mmdet_to_scannet_reduced[cllist[0]] = cllist[1]\n classes = [\"\"]\n for idx, cllist in enumerate([x.strip().split(',') for x in Path(f\"resources/scannet_{class_set}_to_coco.csv\").read_text().strip().splitlines()]):\n classes.append(cllist[0])\n thing_semantics = get_thing_semantics()\n distinct_colors = DistinctColors()\n bboxes = {}\n valid_boxid = 0\n (src_folder / \"visualized_mmdetboxes\").mkdir(exist_ok=True)\n pkl_segmentation_data = pickle.load(open(src_folder / f'segmentation_data.pkl', 'rb'))\n bbox_annot = json.loads(Path(path_bboxes).read_text())\n for bbox in bbox_annot:\n corners = np.array(bbox['corners'])\n if src_folder.stem in mmdet_export_fixes:\n rotation_fix = np.eye(4)\n axangle = mmdet_export_fixes[src_folder.stem][\"rotation\"]\n if axangle is not None:\n rotation_fix[:3, :3] = axangle2mat(axangle[1:4], axangle[0])\n translation_fix = hmg(np.eye(3))\n translation_fix[:3, 3] = np.array(mmdet_export_fixes[src_folder.stem][\"translation\"])\n scale_fix = hmg(np.eye(3) * mmdet_export_fixes[src_folder.stem][\"scale\"])\n corners = dot(np.linalg.inv(translation_fix @ scale_fix @ rotation_fix), corners)\n cmin = np.min(corners, axis=0)\n cmax = np.max(corners, axis=0)\n position = (cmax + cmin) / 2\n orientation = np.eye(3)\n label = classes.index(mmdet_to_scannet_reduced[bbox['label']].lower())\n extent = cmax - cmin\n if thing_semantics[label]:\n bboxes[valid_boxid] = {\n 'position': position,\n 'orientation': orientation,\n 'extent': extent,\n 'class': label\n }\n create_box(position, extent, orientation, distinct_colors.get_color_fast_numpy(label)).export(src_folder / \"visualized_mmdetboxes\" / f\"{label}_{valid_boxid}.obj\")\n valid_boxid += 1\n pkl_segmentation_data['mmdet_bboxes'] = bboxes\n pickle.dump(pkl_segmentation_data, open(src_folder / f'segmentation_data.pkl', 'wb'))\n\n\ndef read_and_resize_labels(path, size):\n image = Image.open(path)\n return np.array(image.resize(size, Image.NEAREST))\n\n\ndef calculate_iou_folders_image_wise(path_pred, path_target, image_size, pred_offset=0):\n num_semantic_classes = 1 + len(Path(\"resources/scannet_reduced_to_coco.csv\").read_text().strip().splitlines())\n iou_avg = 0\n val_set = json.loads(Path(path_target.parent / \"splits.json\").read_text())['test']\n val_paths = [y for y in sorted(list(path_pred.iterdir()), key=lambda x: int(x.stem)) if y.stem in val_set]\n faulty_gt_classes = [0]\n for p in tqdm(val_paths):\n img_pred = read_and_resize_labels(p, image_size) + pred_offset\n img_target = read_and_resize_labels(path_target / p.name, image_size)\n valid_mask = ~np.isin(img_target, faulty_gt_classes)\n train_cm = ConfusionMatrix(num_classes=num_semantic_classes, ignore_class=[])\n iou = train_cm.add_batch(img_pred[valid_mask], img_target[valid_mask], return_miou=True)\n iou_avg += iou\n iou_avg /= len(val_paths)\n return iou_avg\n\n\ndef calculate_iou_folders(path_pred, path_target, image_size, pred_offset=0):\n num_semantic_classes = 1 + len(Path(\"resources/scannet_reduced_to_coco.csv\").read_text().strip().splitlines())\n val_set = json.loads(Path(path_target.parent / \"splits.json\").read_text())['test']\n val_paths = [y for y in sorted(list(path_pred.iterdir()), key=lambda x: int(x.stem)) if y.stem in val_set]\n faulty_gt_classes = [0]\n train_cm = ConfusionMatrix(num_classes=num_semantic_classes, ignore_class=[])\n for p in tqdm(val_paths):\n img_pred = read_and_resize_labels(p, image_size) + pred_offset\n img_target = read_and_resize_labels(path_target / p.name, image_size)\n valid_mask = ~np.isin(img_target, faulty_gt_classes)\n train_cm.add_batch(img_pred[valid_mask], img_target[valid_mask], return_miou=False)\n return train_cm.get_miou()\n\n\ndef calculate_panoptic_quality_folders_image_wise(path_pred_sem, path_pred_inst, path_target_sem, path_target_inst, image_size):\n is_thing = get_thing_semantics()\n val_set = json.loads(Path(path_target_sem.parent / \"splits.json\").read_text())['test']\n faulty_gt = [0]\n things = set([i for i in range(len(is_thing)) if is_thing[i]])\n stuff = set([i for i in range(len(is_thing)) if not is_thing[i]])\n pq_avg, sq_avg, rq_avg = 0, 0, 0\n val_paths = [y for y in sorted(list(path_pred_sem.iterdir()), key=lambda x: int(x.stem)) if y.stem in val_set]\n for p in tqdm(val_paths):\n img_target_sem = read_and_resize_labels((path_target_sem / p.name), image_size)\n valid_mask = ~np.isin(img_target_sem, faulty_gt)\n img_pred_sem = torch.from_numpy(read_and_resize_labels(p, image_size)[valid_mask]).unsqueeze(-1)\n img_target_sem = torch.from_numpy(img_target_sem[valid_mask]).unsqueeze(-1)\n img_pred_inst = torch.from_numpy(read_and_resize_labels((path_pred_inst / p.name), image_size)[valid_mask]).unsqueeze(-1)\n img_target_inst = torch.from_numpy(read_and_resize_labels((path_target_inst / p.name), image_size)[valid_mask]).unsqueeze(-1)\n pred = torch.cat([img_pred_sem, img_pred_inst], dim=1).cuda()\n target = torch.cat([img_target_sem, img_target_inst], dim=1).cuda()\n pq, sq, rq = panoptic_quality(pred, target, things, stuff, allow_unknown_preds_category=True)\n pq_avg += pq.item()\n sq_avg += sq.item()\n rq_avg += rq.item()\n pq_avg /= len(val_paths)\n sq_avg /= len(val_paths)\n rq_avg /= len(val_paths)\n return pq_avg, sq_avg, rq_avg\n\n\ndef calculate_panoptic_quality_folders(path_pred_sem, path_pred_inst, path_target_sem, path_target_inst, image_size):\n is_thing = get_thing_semantics()\n val_set = json.loads(Path(path_target_sem.parent / \"splits.json\").read_text())['test']\n faulty_gt = [0]\n things = set([i for i in range(len(is_thing)) if is_thing[i]])\n stuff = set([i for i in range(len(is_thing)) if not is_thing[i]])\n val_paths = [y for y in sorted(list(path_pred_sem.iterdir()), key=lambda x: int(x.stem)) if y.stem in val_set]\n pred, target = [], []\n for p in tqdm(val_paths):\n img_target_sem = read_and_resize_labels((path_target_sem / p.name), image_size)\n valid_mask = ~np.isin(img_target_sem, faulty_gt)\n img_pred_sem = torch.from_numpy(read_and_resize_labels(p, image_size)[valid_mask]).unsqueeze(-1)\n img_target_sem = torch.from_numpy(img_target_sem[valid_mask]).unsqueeze(-1)\n img_pred_inst = torch.from_numpy(read_and_resize_labels((path_pred_inst / p.name), image_size)[valid_mask]).unsqueeze(-1)\n img_target_inst = torch.from_numpy(read_and_resize_labels((path_target_inst / p.name), image_size)[valid_mask]).unsqueeze(-1)\n pred_ = torch.cat([img_pred_sem, img_pred_inst], dim=1).reshape(-1, 2)\n target_ = torch.cat([img_target_sem, img_target_inst], dim=1).reshape(-1, 2)\n pred.append(pred_)\n target.append(target_)\n pq, sq, rq = panoptic_quality(torch.cat(pred, dim=0).cuda(), torch.cat(target, dim=0).cuda(), things, stuff, allow_unknown_preds_category=True)\n return pq.item(), sq.item(), rq.item()\n\n\ndef calculate_panoptic_quality_per_frame_folders(path_pred_sem, path_pred_inst, path_target_sem, path_target_inst, image_size):\n is_thing = get_thing_semantics()\n val_set = json.loads(Path(path_target_sem.parent / \"splits.json\").read_text())['test']\n faulty_gt = [0]\n things = set([i for i in range(len(is_thing)) if is_thing[i]])\n stuff = set([i for i in range(len(is_thing)) if not is_thing[i]])\n val_paths = [y for y in sorted(list(path_pred_sem.iterdir()), key=lambda x: int(x.stem)) if y.stem in val_set]\n things_, stuff_, iou_sum_, true_positives_, false_positives_, false_negatives_ = set(), set(), [], [], [], []\n for p in tqdm(val_paths):\n img_target_sem = read_and_resize_labels((path_target_sem / p.name), image_size)\n valid_mask = ~np.isin(img_target_sem, faulty_gt)\n img_pred_sem = torch.from_numpy(read_and_resize_labels(p, image_size)[valid_mask]).unsqueeze(-1)\n img_target_sem = torch.from_numpy(img_target_sem[valid_mask]).unsqueeze(-1)\n img_pred_inst = torch.from_numpy(read_and_resize_labels((path_pred_inst / p.name), image_size)[valid_mask]).unsqueeze(-1)\n img_target_inst = torch.from_numpy(read_and_resize_labels((path_target_inst / p.name), image_size)[valid_mask]).unsqueeze(-1)\n pred_ = torch.cat([img_pred_sem, img_pred_inst], dim=1).reshape(-1, 2)\n target_ = torch.cat([img_target_sem, img_target_inst], dim=1).reshape(-1, 2)\n _things, _stuff, _iou_sum, _true_positives, _false_positives, _false_negatives = panoptic_quality_match(pred_, target_, things, stuff, True)\n things_.union(_things)\n stuff_.union(_stuff)\n iou_sum_.append(_iou_sum)\n true_positives_.append(_true_positives)\n false_positives_.append(_false_positives)\n false_negatives_.append(_false_negatives)\n results = _panoptic_quality_compute(things_, stuff_, torch.cat(iou_sum_, 0), torch.cat(true_positives_, 0), torch.cat(false_positives_, 0), torch.cat(false_negatives_, 0))\n return results[\"all\"][\"pq\"].item(), results[\"all\"][\"sq\"].item(), results[\"all\"][\"rq\"].item()\n\n\ndef create_validation_set(src_folder, fraction):\n all_frames = [x.stem for x in sorted(list((src_folder / \"color\").iterdir()), key=lambda x: int(x.stem))]\n selected_val = [all_frames[i] for i in range(0, len(all_frames), int(1 / fraction))]\n selected_train = [x for x in all_frames if x not in selected_val]\n print(len(selected_train), len(selected_val))\n Path(src_folder / \"splits.json\").write_text(json.dumps({\n 'train': selected_train,\n 'test': selected_val\n }))\n\n\ndef create_mask2former_split_data(src_folder):\n all_frame_names = sorted([x.stem for x in (src_folder / f\"color\").iterdir() if x.name.endswith('.jpg')], key=lambda y: int(y) if y.isnumeric() else y)\n thing_semantics = get_thing_semantics()\n print('len thing_semantics', len(thing_semantics))\n semantics = []\n for frame_name in tqdm(all_frame_names, desc='read labels'):\n semantics.append(torch.from_numpy(np.array(Image.open(src_folder / f\"m2f_semantics\" / f\"{frame_name}.png\"))))\n\n semantics = torch.stack(semantics, 0)\n export_dict = pickle.load(open(src_folder / 'segmentation_data.pkl', 'rb'))\n instance_to_semantics = export_dict[f'm2f_instance_to_semantic']\n fg_classes = export_dict['fg_classes']\n\n print(instance_to_semantics)\n remapped_instances_sem = torch.zeros_like(semantics)\n\n sem_instance_to_semantics = {0: 0}\n for i in range(len(fg_classes)):\n remapped_instances_sem[semantics == fg_classes[i]] = i + 1\n sem_instance_to_semantics[i + 1] = fg_classes[i]\n\n Path(src_folder / f\"m2f_instance_sem\").mkdir(exist_ok=True)\n\n for iidx in tqdm(range(semantics.shape[0])):\n Image.fromarray(remapped_instances_sem[iidx].numpy()).save(src_folder / f\"m2f_instance_sem\" / f\"{all_frame_names[iidx]}.png\")\n\n export_dict['m2f_sem_instance_to_semantics'] = sem_instance_to_semantics\n pickle.dump(export_dict, open(src_folder / f'segmentation_data.pkl', 'wb'))\n\n\ndef create_m2f_used_instances(src_folder):\n export_dict = pickle.load(open(src_folder / 'segmentation_data.pkl', 'rb'))\n instance_to_semantics = export_dict[f'm2f_sem_instance_to_semantics']\n print(instance_to_semantics)\n all_frame_names = sorted([x.stem for x in (src_folder / f\"color\").iterdir() if x.name.endswith('.jpg')], key=lambda y: int(y) if y.isnumeric() else y)\n frame_counts = {k: 0 for k in instance_to_semantics.keys()}\n dims = Image.open(src_folder / f\"m2f_instance_sem\" / f\"{all_frame_names[0]}.png\").size\n for frame_name in tqdm(all_frame_names, desc='read labels'):\n uinsts, ucounts = torch.from_numpy(np.array(Image.open(src_folder / f\"m2f_instance_sem\" / f\"{frame_name}.png\"))).unique(return_counts=True)\n for iidx in range(len(uinsts)):\n percinst = ucounts[iidx] / (dims[0] * dims[1])\n if percinst > 0.005:\n frame_counts[uinsts[iidx].item()] += 1\n is_valid_instance = {}\n for k in frame_counts:\n is_valid_instance[k] = True if frame_counts[k] > len(all_frame_names) * 0.01 else False\n print(is_valid_instance)\n export_dict['m2f_sem_valid_instance'] = is_valid_instance\n pickle.dump(export_dict, open(src_folder / f'segmentation_data.pkl', 'wb'))\n\n\ndef create_instances_for_dmnerf(src_folder, correspondences, class_set='reduced'):\n suffix_o = \"_no_correspondences\" if not correspondences else \"\"\n suffix_i = \"_correspondences\" if correspondences else \"\"\n color_folder = src_folder / \"color\"\n semantics_folder = src_folder / \"m2f_notta_semantics\"\n instance_folder = src_folder / f\"m2f_notta_instance{suffix_i}\"\n output_folder = src_folder / f\"m2f_notta_dmnerf{suffix_o}\"\n if output_folder.exists():\n shutil.rmtree(output_folder)\n output_folder.mkdir(exist_ok=True)\n is_thing_class = get_thing_semantics(sc_classes=class_set)\n stuff_classes = [i for i in range(len(is_thing_class)) if not is_thing_class[i]]\n instance_to_semantics = {}\n for f in tqdm(list(color_folder.iterdir()), desc='creating new mask'):\n semantics = np.array(Image.open(semantics_folder / f\"{f.stem}.png\"))\n instance = np.array(Image.open(instance_folder / f\"{f.stem}.png\"))\n classes = np.unique(semantics)\n new_instance = np.zeros_like(instance)\n for c in classes:\n if c in stuff_classes:\n assigned_index = stuff_classes.index(c)\n new_instance[semantics == c] = assigned_index\n instance_to_semantics[assigned_index] = int(c)\n else:\n uniques = np.unique(instance[semantics == c])\n for u in uniques:\n if u != 0:\n assigned_index = len(stuff_classes) + u\n new_instance[instance == u] = assigned_index\n instance_to_semantics[assigned_index] = int(c)\n Image.fromarray(new_instance).save(output_folder / f\"{f.stem}.png\")\n pickle.dump(instance_to_semantics, open(src_folder / f\"dmnerf_i2s{suffix_o}.pkl\", \"wb\"))\n\n\ndef from_ours_to_replica_traj_w_c(src_folder):\n poses = sorted(list((src_folder / \"pose\").iterdir()), key=lambda x: int(x.stem) if x.stem.isnumeric() else x.stem)\n traj_w_c_string = \"\"\n for pose_file in poses:\n RT = np.array([[float(y.strip()) for y in x.strip().split()] for x in Path(pose_file).read_text().splitlines() if x != ''])\n traj_w_c_string += f\"\"\"{RT[0, 0]} {RT[0, 1]} {RT[0, 2]} {RT[0, 3]} {RT[1, 0]} {RT[1, 1]} {RT[1, 2]} {RT[1, 3]} {RT[2, 0]} {RT[2, 1]} {RT[2, 2]} {RT[2, 3]} 0.00 0.00 0.00 1.00\\n\"\"\"\n (src_folder / \"traj_w_c.txt\").write_text(traj_w_c_string)\n\n\ndef from_trajectory_to_replica_traj_blend(src_folder):\n traj_w_c_string = \"\"\n with open(src_folder / \"trajectories\" / f\"trajectory_blender.pkl\", \"rb\") as fptr:\n trajectories = pickle.load(fptr)\n for i in range(len(trajectories)):\n RT = trajectories[i]\n traj_w_c_string += f\"\"\"{RT[0, 0]} {RT[0, 1]} {RT[0, 2]} {RT[0, 3]} {RT[1, 0]} {RT[1, 1]} {RT[1, 2]} {RT[1, 3]} {RT[2, 0]} {RT[2, 1]} {RT[2, 2]} {RT[2, 3]} 0.00 0.00 0.00 1.00\\n\"\"\"\n (src_folder / \"traj_blender.txt\").write_text(traj_w_c_string)\n\n\ndef debug_dump_instances_for_scene(path):\n instance = np.array(Image.open(path))\n u, c = np.unique(instance, return_counts=True)\n for uin in u:\n visualize_mask((instance == uin).astype(int), f\"inst_{uin}.png\")\n\n\ndef export_all_for_semantic_nerf(src_folder):\n base_dir = src_folder.parent\n all_scenes = [x for x in base_dir.iterdir() if x.name.startswith(\"scene\")]\n for scene in tqdm(all_scenes):\n out_dir = base_dir / \"raw\" / \"from_semantic_nerf\" / scene.name / \"Sequence_1\"\n if out_dir.exists():\n shutil.rmtree(out_dir)\n out_dir.mkdir(parents=True)\n # copy color -> rgb\n # remake splits\n splits = json.loads((base_dir / scene.name / \"splits.json\").read_text())\n for split in [\"train\", \"val\"]:\n splits[split] = [f\"{int(x):04d}\" for x in splits[split]]\n Path(out_dir / \"splits.json\").write_text(json.dumps(splits))\n # copy intrinsics\n shutil.copyfile(base_dir / scene.name / \"intrinsic\" / \"intrinsic_color.txt\", out_dir / \"intrinsic_color.txt\")\n # make trajectory and copy\n from_ours_to_replica_traj_w_c(base_dir / scene.name)\n shutil.copyfile(base_dir / scene.name / \"traj_w_c.txt\", out_dir / \"traj_w_c.txt\")\n (out_dir / \"rgb\").mkdir()\n for f in (base_dir / scene.name / \"color\").iterdir():\n shutil.copyfile(f, out_dir / \"rgb\" / f\"{int(f.stem):04d}.jpg\")\n # copy depth -> depth\n if not (out_dir / \"depth\").exists():\n shutil.copytree(base_dir / scene.name / \"depth\", out_dir / \"depth\")\n\n\ndef export_all_for_dmnerf(src_folder):\n base_dir = src_folder.parent\n all_scenes = [x for x in base_dir.iterdir() if x.name.startswith(\"scene\")]\n for scene in tqdm(all_scenes):\n dm_nerf_path = Path(\"/cluster/gimli/ysiddiqui/dm-nerf-data/scannet\") / scene.name\n out_dir = dm_nerf_path\n if not out_dir.exists():\n shutil.copytree(base_dir / \"raw\" / \"from_semantic_nerf\" / scene.name / \"Sequence_1\", out_dir)\n create_instances_for_dmnerf(base_dir / scene.name, correspondences=False)\n suffix = \"_no_correspondences\"\n output_folder = dm_nerf_path / f\"semantic_instance_m2f{suffix}\"\n output_folder.mkdir(exist_ok=True)\n input_folder = base_dir / scene.name / f\"m2f_notta_dmnerf{suffix}\"\n input_names = sorted(list(input_folder.iterdir()), key=lambda x: int(x.stem))\n output_names = [f\"semantic_instance_{int(x.stem)}\" for x in input_names]\n for idx in range(len(input_names)):\n shutil.copyfile(input_names[idx], output_folder / f\"{output_names[idx]}.png\")\n\n\ndef render_mesh(src_sens_path):\n import trimesh\n import pyrender\n\n def create_groups():\n seg_file = src_sens_path / f\"{sens_root.stem}_vh_clean.segs.json\"\n seg_indices = np.array(json.loads(Path(seg_file).read_text())[\"segIndices\"])\n face_seg_ids = np.concatenate([seg_indices[scannet_mesh.faces[:, 0:1]], seg_indices[scannet_mesh.faces[:, 1:2]], seg_indices[scannet_mesh.faces[:, 2:3]]], axis=-1)\n face_seg_ids = stats.mode(face_seg_ids, axis=1).mode[:, 0]\n vertex_reseg = np.zeros_like(seg_indices)\n vertex_reseg[scannet_mesh.faces[:, 0]] = face_seg_ids\n vertex_reseg[scannet_mesh.faces[:, 1]] = face_seg_ids\n vertex_reseg[scannet_mesh.faces[:, 2]] = face_seg_ids\n # colors = (distinct_colors.get_color_fast_numpy(vertex_reseg) * 255).astype(np.uint8)\n # trimesh.Trimesh(vertices=scannet_mesh.vertices, faces=scannet_mesh.faces, vertex_colors=colors).export(\"test_seg.obj\")\n colors = np.concatenate([x[:, np.newaxis] for x in [(vertex_reseg // 256 ** 2) % 256, (vertex_reseg // 256) % 256, vertex_reseg % 256]], axis=-1)\n return trimesh.Trimesh(vertices=scannet_mesh.vertices, faces=scannet_mesh.faces, vertex_colors=colors)\n\n flip_mat = np.array([\n [1, 0, 0, 0],\n [0, -1, 0, 0],\n [0, 0, -1, 0],\n [0, 0, 0, 1]\n ])\n src_folder = Path(\"data/scannet/\", sens_root.stem)\n with open(src_folder / \"trajectories\" / f\"trajectory_blender.pkl\", \"rb\") as fptr:\n trajectories = pickle.load(fptr)\n scannet_mesh = trimesh.load(src_sens_path / \"scene0050_02_vh_clean.ply\", process=False)\n mesh = pyrender.Mesh.from_trimesh(create_groups())\n scene = pyrender.Scene()\n scene.add(mesh)\n for i, pose in enumerate(tqdm(trajectories)):\n camera = pyrender.PerspectiveCamera(yfov=np.pi / 3.0, aspectRatio=640 / 480)\n camera_pose = pose @ flip_mat\n camera_node = scene.add(camera, pose=camera_pose)\n r = pyrender.OffscreenRenderer(640, 480)\n color, depth = r.render(scene, pyrender.constants.RenderFlags.FLAT)\n Image.fromarray(color).save(f\"runs/scannet_render/segments/{i:04d}.png\")\n scene.remove_node(camera_node)\n\n\ndef map_rendered_mesh(src_sens_path):\n import inflect\n inflect_engine = inflect.engine()\n root = Path(\"runs/scannet_render\")\n out_sem = root / \"semantics\"\n out_ins = root / \"instance\"\n seg_file = src_sens_path / f\"{sens_root.stem}.aggregation.json\"\n seg_groups = json.loads(Path(seg_file).read_text())[\"segGroups\"]\n segment_to_label = np.zeros(2 ** 24).astype(np.int32)\n segment_to_id = np.zeros(2 ** 24).astype(np.int32)\n distinct_colors = DistinctColors()\n scannetlabel_to_nyuid = {x.split('\\t')[1]: x.split('\\t')[4] for x in Path(\"resources/scannet-labels.combined.tsv\").read_text().splitlines()[1:]}\n scannetlabel_to_nyuid['object'] = 40\n for group in seg_groups:\n label = group['label']\n if inflect_engine.singular_noun(group['label']):\n label = inflect_engine.singular_noun(group['label'])\n segment_to_label[group[\"segments\"]] = scannetlabel_to_nyuid[label]\n segment_to_id[group[\"segments\"]] = group['id'] + 1\n bg_classes = [i for i, x in enumerate(get_thing_semantics(\"reduced\")) if not x]\n for item in tqdm(sorted(list((root / \"segments\").iterdir()), key=lambda x: int(x.stem))):\n segment = np.array(Image.open(item))\n segment = segment[:, :, 0] * 256 ** 2 + segment[:, :, 1] * 256 + segment[:, :, 2]\n ids = segment_to_id[segment]\n segment = segment_to_label[segment]\n ids[segment == 0] = -1\n segment[segment == 0] = -1\n segment[segment == (256 * 256 * 255 + 256 * 255 + 255)] = 0\n\n for i in range(1):\n arr_t, arr_r, arr_b, arr_l = segment[1:, :], segment[:, 1:], segment[:-1, :], segment[:, :-1]\n arr_t_1, arr_r_1, arr_b_1, arr_l_1 = segment[2:, :], segment[:, 2:], segment[:-2, :], segment[:, :-2]\n\n arr_t = np.concatenate([arr_t, segment[-1, :][np.newaxis, :]], axis=0)\n arr_r = np.concatenate([arr_r, segment[:, -1][:, np.newaxis]], axis=1)\n arr_b = np.concatenate([segment[0, :][np.newaxis, :], arr_b], axis=0)\n arr_l = np.concatenate([segment[:, 0][:, np.newaxis], arr_l], axis=1)\n\n arr_t_1 = np.concatenate([arr_t_1, segment[-2, :][np.newaxis, :], segment[-1, :][np.newaxis, :]], axis=0)\n arr_r_1 = np.concatenate([arr_r_1, segment[:, -2][:, np.newaxis], segment[:, -1][:, np.newaxis]], axis=1)\n arr_b_1 = np.concatenate([segment[0, :][np.newaxis, :], segment[1, :][np.newaxis, :], arr_b_1], axis=0)\n arr_l_1 = np.concatenate([segment[:, 0][:, np.newaxis], segment[:, 1][:, np.newaxis], arr_l_1], axis=1)\n\n segment[np.logical_and(segment == -1, arr_t != -1)] = arr_t[np.logical_and(segment == -1, arr_t != -1)]\n segment[np.logical_and(segment == -1, arr_r != -1)] = arr_r[np.logical_and(segment == -1, arr_r != -1)]\n segment[np.logical_and(segment == -1, arr_b != -1)] = arr_b[np.logical_and(segment == -1, arr_b != -1)]\n segment[np.logical_and(segment == -1, arr_l != -1)] = arr_l[np.logical_and(segment == -1, arr_l != -1)]\n\n segment[np.logical_and(segment == -1, arr_t_1 != -1)] = arr_t_1[np.logical_and(segment == -1, arr_t_1 != -1)]\n segment[np.logical_and(segment == -1, arr_r_1 != -1)] = arr_r_1[np.logical_and(segment == -1, arr_r_1 != -1)]\n segment[np.logical_and(segment == -1, arr_b_1 != -1)] = arr_b_1[np.logical_and(segment == -1, arr_b_1 != -1)]\n segment[np.logical_and(segment == -1, arr_l_1 != -1)] = arr_l_1[np.logical_and(segment == -1, arr_l_1 != -1)]\n\n arr_t, arr_r, arr_b, arr_l = ids[1:, :], ids[:, 1:], ids[:-1, :], ids[:, :-1]\n arr_t_1, arr_r_1, arr_b_1, arr_l_1 = ids[2:, :], ids[:, 2:], ids[:-2, :], ids[:, :-2]\n\n arr_t = np.concatenate([arr_t, ids[-1, :][np.newaxis, :]], axis=0)\n arr_r = np.concatenate([arr_r, ids[:, -1][:, np.newaxis]], axis=1)\n arr_b = np.concatenate([ids[0, :][np.newaxis, :], arr_b], axis=0)\n arr_l = np.concatenate([ids[:, 0][:, np.newaxis], arr_l], axis=1)\n\n arr_t_1 = np.concatenate([arr_t_1, ids[-2, :][np.newaxis, :], ids[-1, :][np.newaxis, :]], axis=0)\n arr_r_1 = np.concatenate([arr_r_1, ids[:, -2][:, np.newaxis], ids[:, -1][:, np.newaxis]], axis=1)\n arr_b_1 = np.concatenate([ids[0, :][np.newaxis, :], ids[1, :][np.newaxis, :], arr_b_1], axis=0)\n arr_l_1 = np.concatenate([ids[:, 0][:, np.newaxis], segment[:, 1][:, np.newaxis], arr_l_1], axis=1)\n\n ids[np.logical_and(ids == -1, arr_t != -1)] = arr_t[np.logical_and(ids == -1, arr_t != -1)]\n ids[np.logical_and(ids == -1, arr_r != -1)] = arr_r[np.logical_and(ids == -1, arr_r != -1)]\n ids[np.logical_and(ids == -1, arr_b != -1)] = arr_b[np.logical_and(ids == -1, arr_b != -1)]\n ids[np.logical_and(ids == -1, arr_l != -1)] = arr_l[np.logical_and(ids == -1, arr_l != -1)]\n\n ids[np.logical_and(ids == -1, arr_t_1 != -1)] = arr_t_1[np.logical_and(ids == -1, arr_t_1 != -1)]\n ids[np.logical_and(ids == -1, arr_r_1 != -1)] = arr_r_1[np.logical_and(ids == -1, arr_r_1 != -1)]\n ids[np.logical_and(ids == -1, arr_b_1 != -1)] = arr_b_1[np.logical_and(ids == -1, arr_b_1 != -1)]\n ids[np.logical_and(ids == -1, arr_l_1 != -1)] = arr_l_1[np.logical_and(ids == -1, arr_l_1 != -1)]\n\n segment[segment == -1] = 0\n ids[ids == -1] = 0\n\n if src_sens_path.stem in scene_specific_fixes_objectid:\n for ob_id in scene_specific_fixes_objectid[src_sens_path.stem]:\n segment[ids == ob_id] = scene_specific_fixes_objectid[src_sens_path.stem][ob_id]\n\n reduce_map, fold_map = get_reduce_and_fold_map()\n segment = fold_map[reduce_map[segment.flatten()]].reshape(segment.shape).astype(np.int8)\n semantic_bg = np.isin(segment, bg_classes)\n ids[semantic_bg] = 0\n\n alpha = 0.75\n\n ids = cv2.medianBlur(ids.astype(np.uint8), 5)\n segment = cv2.medianBlur(segment.astype(np.uint8), 5)\n\n boundaries_semantics = get_boundary_mask(segment)\n boundaries_instance = get_boundary_mask(ids)\n\n segment = (distinct_colors.get_color_fast_numpy(segment.reshape(-1)).reshape(list(segment.shape) + [3]) * 255).astype(np.uint8)\n ids = (distinct_colors.get_color_fast_numpy(ids.reshape(-1), override_color_0=True).reshape(list(ids.shape) + [3]) * 255).astype(np.uint8)\n color_image = np.array(Image.open(root / \"rgb\" / f\"{item.stem}.jpg\"))\n\n segment = segment * alpha + color_image * (1 - alpha)\n ids = ids * alpha + color_image * (1 - alpha)\n segment[boundaries_semantics > 0, :] = 0\n ids[boundaries_instance > 0, :] = 0\n\n Image.fromarray(ids.astype(np.uint8)).save(out_ins / item.name)\n Image.fromarray(segment.astype(np.uint8)).save(out_sem / item.name)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description='scannet preprocessing')\n parser.add_argument('--sens_root', required=False, default='/cluster/gimli/ysiddiqui/scannet_val_scans/scene0050_02', help='sens file path')\n parser.add_argument('--n', required=False, type=int, default=1, help='num proc')\n parser.add_argument('--p', required=False, type=int, default=0, help='current proc')\n args = parser.parse_args()\n\n sens_root = Path(args.sens_root)\n dest = Path(\"data/scannet/\", sens_root.stem)\n dest.mkdir(exist_ok=True)\n print('#' * 80)\n print(f'extracting sens from {str(sens_root)} to {str(dest)}...')\n extract_scan(sens_root, dest)\n extract_labels(sens_root, dest)\n print('#' * 80)\n print('subsampling...')\n subsample_scannet_blur_window(dest, min_frames=900)\n visualize_labels(dest)\n print('#' * 80)\n print('mapping labels...')\n fold_scannet_classes(dest)\n visualize_mask_folder(dest / \"rs_semantics\")\n print('#' * 80)\n print('renumbering instances...')\n renumber_instances(dest)\n visualize_mask_folder(dest / \"rs_instance\")\n print('#' * 80)\n print('please run the following command for mask2former to generate machine generated panoptic segmentation')\n print(\n f'python demo.py --config-file ../configs/coco/panoptic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_100ep.yaml --input {str(dest.absolute())}/color --output {str(dest.absolute())}/panoptic --predictions {str(dest.absolute())}/panoptic --opts MODEL.WEIGHTS ../checkpoints/model_final_f07440.pkl')\n print('#' * 80)\n print('mapping coco labels...')\n map_panoptic_coco(dest)\n visualize_mask_folder(dest / \"m2f_semantics\")\n visualize_mask_folder(dest / \"m2f_instance\")\n visualize_mask_folder(dest / \"m2f_segments\")\n print('creating validation set')\n print('#' * 80)\n print('creating validation set')\n create_validation_set(dest, 0.20)\n print('#' * 80)\n","repo_name":"nihalsid/panoptic-lifting","sub_path":"dataset/preprocessing/preprocess_scannet.py","file_name":"preprocess_scannet.py","file_ext":"py","file_size_in_byte":55899,"program_lang":"python","lang":"en","doc_type":"code","stars":266,"dataset":"github-code","pt":"52"} +{"seq_id":"70763610725","text":"import pandas as pd\n\ndf = pd.read_csv('rank_signals8.csv')\nend_df = pd.DataFrame()\n\nuniq_permno = df.permno.unique()\n\nfor i in range(2001,2021):\n temp_df = df[df['year'] == i]\n df2 = pd.DataFrame()\n \n #go through each permno\n for j in uniq_permno:\n try:\n temp_df1 = temp_df[temp_df['permno'] == j]\n df2 = temp_df1.iloc[0]\n ret = 1\n\n #get the cumulative return over the year\n for k in range(0, len(temp_df1)):\n ret *= (1+temp_df['RET'].iloc[k])\n\n ret -= 1\n df2['RET'] = ret\n \n end_df = end_df.append(df2)\n except:\n pass\n\nprint(end_df)\n\n#Output to a seperate file\n# end_df.to_csv('1_year_data.csv')","repo_name":"charles001203/big_data_in_finance","sub_path":"get_one_year_data.py","file_name":"get_one_year_data.py","file_ext":"py","file_size_in_byte":746,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"23142590457","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n\nimport re\nfrom lxml.html import fromstring\nfrom lxml.etree import tostring\nfrom lxml.cssselect import CSSSelector\nfrom tag_exceptions import DoesNotHaveTagException, FoundManyTagsException\n\nclass HtmlSpec(object):\n html = None\n tree = None\n node = None\n\n def __init__(self, html, node=None):\n self.html = html\n self.tree = fromstring(str(html))\n self.node = node\n \n if(self.node == None):\n self.node = self.tree\n\n def __find__(self, tag_name):\n return self.tree.cssselect(tag_name)\n\n def has(self, tag_name, count=1):\n result = self.__find__(tag_name)\n\n if len(result) == 0:\n raise DoesNotHaveTagException('Html does not have tag %s' % tag_name)\n elif len(result) != count:\n raise FoundManyTagsException('Expected %d founded %d' % (count, len(result)))\n\n if len(result) == 1:\n self.node = result[0]\n return HtmlSpec(tostring(self.node), node = self.node)\n else:\n html_specs = []\n for r in result:\n html_specs.append(HtmlSpec(tostring(r), node=r))\n return html_specs\n \n def with_tag(self, tag_name):\n self.__find__(tag_name)\n\n if self.node != None:\n return HtmlSpec(self.html)\n else:\n raise DoesNotHaveTagException('Html does not have tag %s' % tag_name)\n","repo_name":"rafaelmws/html_spec","sub_path":"html_spec/html_spec.py","file_name":"html_spec.py","file_ext":"py","file_size_in_byte":1447,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"5293071335","text":"#Note - reuses some code from chapter 3\nfrom pyspark.sql import SparkSession\nfrom pyspark import SparkContext\nfrom pyspark.sql.types import StringType\nfrom pyspark.sql import functions as f\n\n\n# Create spark context\nsc = SparkContext(\"local\", \"Ch6BasicExampleApp\")\n# Get spark session\nspark = SparkSession.builder.getOrCreate()\n\n# Get the data and place it in a spark dataframe\ndata = spark.read.format(\"csv\").option(\"sep\", \";\").option(\"inferSchema\", \"true\").option(\"header\", \"true\").load(\n \"../chapter1/stream-classifier/data/bank/bank.csv\")\n\n# map target to numerical category\n#data = data.withColumn('label', f.when((f.col(\"y\") == \"yes\"), 1).otherwise(0))\n\ndata.printSchema()\n\ndata.show()\n\n# UDF\nimport datetime\ndef month_as_int(month):\n month_number = datetime.datetime.strptime(month, \"%b\").month\n return month_number\n\nspark.udf.register(\"monthAsInt\", month_as_int, StringType())\n\n\n# Apply in spark sql\ndata.createOrReplaceTempView('bank_data_view')\n\nspark.sql('''\nselect *, monthAsInt(month) as month_as_int from bank_data_view\n''').show()\n\n\n# Apply on dataframe\nfrom pyspark.sql.functions import udf\nmonth_as_int_udf = udf(month_as_int, StringType())\n\ndf = spark.table(\"bank_data_view\")\ndf.withColumn('month_as_int', month_as_int_udf(\"month\")).show()\n\n# Create with decorator syntax\n@udf(\"string\")\ndef month_as_int_udf(month):\n month_number = datetime.datetime.strptime(month, \"%b\").month\n return month_number\n\ndf.withColumn('month_as_int', month_as_int_udf(\"month\")).show()\n\n\nfrom pyspark.sql.functions import pandas_udf, PandasUDFType\n\n# @pandas_udf('string')\n# def month_as_int(month_series):\n# return datetime.datetime.strptime()","repo_name":"PacktPublishing/Machine-Learning-Engineering-with-Python","sub_path":"Chapter06/spark_example_udfs.py","file_name":"spark_example_udfs.py","file_ext":"py","file_size_in_byte":1658,"program_lang":"python","lang":"en","doc_type":"code","stars":143,"dataset":"github-code","pt":"52"} +{"seq_id":"72316570725","text":"import logging\nimport sys\n\nLOG_FILE = 'log.txt'\nLOG_FORMAT = '[%(asctime)s] %(levelname)-8s %(message)s'\n\nLOGGER = None\n\n\ndef load_logger():\n logger = logging.getLogger()\n logger.setLevel(logging.INFO)\n\n console_handler = logging.StreamHandler(sys.stdout)\n console_handler.setLevel(logging.INFO)\n console_handler.setFormatter(logging.Formatter(LOG_FORMAT))\n\n logger.addHandler(console_handler)\n return logger\n\n\ndef get_logger():\n global LOGGER\n if not LOGGER:\n LOGGER = load_logger()\n return LOGGER\n","repo_name":"k-joel/yangbot","sub_path":"logger.py","file_name":"logger.py","file_ext":"py","file_size_in_byte":536,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"74900913444","text":"import os\n\nfrom selenium import webdriver\n\nfrom core.singlenton.app_path import AppPath\nfrom core.singlenton.logger import Logger\n\n\nclass WebDriver:\n class __WebDriver:\n def __init__(self):\n self.options = webdriver.ChromeOptions()\n self.options.add_argument(\"--start-maximized\")\n # self.options.add_argument(\"--headless\")\n # self.options.headless = True\n self.driver_path = os.path.abspath(AppPath.path + '//driver//chromedriver.exe')\n self.driver = webdriver.Chrome(executable_path=self.driver_path, chrome_options=self.options)\n self.driver.get('https://www.kickstarter.com/')\n\n def close_webdriver(self):\n print('close chrome')\n self.driver.quit()\n\n driver = None\n\n def __new__(cls):\n if not WebDriver.driver:\n try:\n WebDriver.driver = WebDriver.__WebDriver().driver\n except Exception as e:\n Logger().error('ERROR Starting webdriver')\n Logger().error(str(e))\n\n return WebDriver.driver\n","repo_name":"jdieguezbean/kickstarterScraping","sub_path":"core/singlenton/webdriver.py","file_name":"webdriver.py","file_ext":"py","file_size_in_byte":1080,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"18740631415","text":"import pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom matplotlib import rcParams\n\ndf = pd.read_csv(\"ShivamStudySchedule.csv\")\n#print(df)\nqsn = df[\"NoOfQuestions\"]\n#print(qsn)\ntopic = df[\"MathsTopic\"]\n#print(topic)\ndate = df[\"Date\"]\n#print(date)\nsns.set_theme(style=\"whitegrid\")\nrcParams[\"figure.figsize\"]= 10,10\nsns.barplot(x='Date', y='NoOfQuestions', hue='MathsTopic', data=df)\n\nx_range = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]\nplt.yticks(x_range)\n\nplt.xlabel(\"Dates\", fontsize = 16)\nplt.ylabel(\"Number of questions\", fontsize = 16)\n\nplt.show()\n\n\n","repo_name":"Pragati8626/shivanstudy","sub_path":"ShivamStudySchedule_barChart.py","file_name":"ShivamStudySchedule_barChart.py","file_ext":"py","file_size_in_byte":573,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"3429207912","text":"# -*- coding: utf-8 -*-\n\"\"\"\nSpyder Editor\n\nThis is a temporary script file.\n\"\"\"\nx= int (input())\ni=0\nl=[]\nwhile i< (x):\n y=int (input())\n l.insert(i,y)\n i=i+1\n \nind=int(input())\nl.sort(reverse=True)\nprint (l[x-ind]) \n \n ","repo_name":"NourAdel/Concepts_Of_Promgramming_Languages","sub_path":"ass1/problem1.py","file_name":"problem1.py","file_ext":"py","file_size_in_byte":234,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"8498017953","text":"#!/usr/bin/env python3\n# -*- coding: UTF-8 -*-\n# 搬运色块_1\n\nimport cv2\nimport numpy as np\nimport time\nimport threading\nimport signal\n# import LeArm\n# import kinematics as kin\n# import RPi.GPIO as GPIO\ndef test(aaa):\n print(\"hello world\"+aaa)\n time.sleep(1000)\n print('aaaa')\ndebug = True\n\nstream = \"http://127.0.0.1:8080/?action=stream?dummy=param.mjpg\"\ncap = cv2.VideoCapture(stream)\n\norgFrame = None\nRunning = False\n\n# 校准按键\nGPIO.setmode(GPIO.BCM)\nGPIO.setwarnings(False)\nkey = 22\nGPIO.setup(key, GPIO.IN, GPIO.PUD_UP)\n# 校准标志\ncorrection_flag = False\n\n# # 要识别的颜色字典\n# color_dist = {'red': {'Lower': np.array([0, 60, 60]), 'Upper': np.array([6, 255, 255])},\n# 'blue': {'Lower': np.array([100, 80, 46]), 'Upper': np.array([124, 255, 255])},\n# 'green': {'Lower': np.array([35, 43, 35]), 'Upper': np.array([90, 255, 255])},\n# }\n# 色块颜色，位置列表\nposition_color_list = []\n# 识别到色块标志\ncv_blocks_ok = False\n# 搬运步骤\nstep = 0\nnum_random = None\n# 识别次数\ncv_count = 0\n# 用于判读色块是否稳定\nlast_blocks = []\nlast_x = 0\nstable = False\n# 存储色块， 用于判读色块 Y轴的远近， 机械臂先取近的\nstorage_blocks = []\n\n# 暂停信号的回调\ndef cv_stop(signum, frame):\n global Running\n\n print(\"Stop \")\n if Running is True:\n Running = False\n cv2.destroyAllWindows()\n\n# 继续信号的回调\ndef cv_continue(signum, frame):\n global stream\n global Running\n global cap\n\n if Running is False:\n cap = cv2.VideoCapture(stream)\n Running = True\n\n# 注册信号回调\nsignal.signal(signal.SIGTSTP, cv_stop)\nsignal.signal(signal.SIGCONT, cv_continue)\n\n# 数值映射\n# 将一个数从一个范围映射到另一个范围\ndef leMap(x, in_min, in_max, out_min, out_max):\n return (x - in_min) * (out_max - out_min) / (in_max - in_min) + out_min\n\ndef move_blocks(position_color_list):\n global cv_blocks_ok,step\n #\n while True:\n while cv_blocks_ok is True:\n if len(position_color_list) == 1: #\n # 数据处理\n print (position_color_list, 'pos')\n if step == 0:\n x_pix_cm = position_color_list['x_pic']#position_color_list[0][1]\n y_pix_cm = position_color_list['y_pic']#position_color_list[0][2]\n angle = position_color_list['angle']#position_color_list[0][3]\n # 数据映射\n n_x = int(leMap(x_pix_cm, 0.0, 320.0, -1250.0, 1250.0)) * 1.0\n n_y = int(leMap(240 - y_pix_cm, 0.0, 240.0, 1250, 3250.0)) * 1.0\n # 需要根据实际情况调整，偏差主要来自舵机的虚位\n if n_x < -100:\n n_x -= 120 # 偏差\n LeArm.setServo(1, 700, 500)\n time.sleep(0.5)\n step = 1\n elif step == 1:\n # 机械臂下去\n if kin.ki_move(n_x, n_y, 200.0, 1500):\n step = 2\n else:\n step = 6\n elif step == 2:\n # 根据方块的角度转动爪子\n if angle <= -45:\n angle = -(90 + angle)\n n_angle = leMap(angle, 0.0, -45.0, 1500.0, 1750.0)\n if n_x > 0:\n LeArm.setServo(2, 3000 - n_angle, 500)\n else:\n LeArm.setServo(2, n_angle, 500)\n time.sleep(0.5)\n step = 3\n elif step == 3:\n # 抓取\n print ('3 ok')\n LeArm.setServo(1, 1200, 500)\n time.sleep(0.5)\n step = 4\n elif step == 4: # 将方块提起\n print ('4 ok')\n kin.ki_move(n_x, n_y, 700.0, 1000)\n step = 5\n elif step == 5: # 抓取成功，放置方块\n print ('5 ok')\n if position_color_list['field'] == 'red':\n LeArm.runActionGroup('red', 1)\n elif position_color_list['field']== 'blue':\n LeArm.runActionGroup('blue', 1)\n elif position_color_list['field']== 'green':\n LeArm.runActionGroup('green', 1)\n step = 6\n elif step == 6: # 复位机械臂\n print ('6 ok')\n LeArm.runActionGroup('rest', 1)\n #threadLock.acquire()\n position_color_list = []\n cv_blocks_ok = False\n #threadLock.release()\n step = 0\n else:\n time.sleep(0.01)\n\n\n# 启动动作在运行线程\n# th1 = threading.Thread(target=move_blocks)\n# th1.setDaemon(True)\n# th1.start()\n#\n# # 线程锁\n# threadLock = threading.Lock()\n\n# 镜头畸变系数\nlens_mtx = np.array([\n [993.17745922, 0., 347.76412756],\n [0., 992.6210587, 198.08924031],\n [0., 0., 1.],\n ])\nlens_dist = np.array([[-2.22696961e-01, 3.34897836e-01, 1.43573965e-03, -5.99140365e-03, -2.03168813e+00]])\n\n\n# 镜头畸变调整\ndef lens_distortion_adjustment(image):\n global lens_mtx, lens_dist\n h, w = image.shape[:2]\n newcameramtx, roi = cv2.getOptimalNewCameraMatrix(lens_mtx, lens_dist, (w, h), 0, (w, h)) # 自由比例参数\n dst = cv2.undistort(image, lens_mtx, lens_dist, None, newcameramtx)\n return dst\n\n\n# 机械臂位置校准\ndef Arm_Pos_Corr():\n LeArm.setServo(1, 1200, 500)\n time.sleep(0.5)\n kin.ki_move(0, 2250, 200.0, 1500)\n\nif debug:\n Running = True\nelse:\n Running = False\n\n# 运行程序前按下KEY2,进入校准机械臂位置， 校准完成后，再按下KEY退出\nrun_corr_one = 0\n\n# 初始化机械臂位置\nLeArm.runActionGroup('rest', 1)\n# while True:\n # if GPIO.input(key) == 0:\n # time.sleep(0.1)\n # if GPIO.input(key) == 0:\n # correction_flag = not correction_flag\n # if correction_flag is False:\n # LeArm.runActionGroup('rest', 1)\n # if correction_flag is False:\n # run_corr_one = 0\n # if Running:\n # if cap.isOpened():\n # ret, orgFrame = cap.read()\n # if ret:\n # t1 = cv2.getTickCount()\n # try:\n # orgFrame = cv2.resize(orgFrame, (320,240), interpolation = cv2.INTER_CUBIC) #将图片缩放到 320*240\n # except Exception as e:\n # print(e)\n # continue\n # if orgFrame is not None :\n # orgFrame = lens_distortion_adjustment(orgFrame)\n # img_h, img_w = orgFrame.shape[:2]\n# # #\n#\n#\n\n\n\n # # 获取图像中心点坐标x, y\n # img_center_x = img_w / 2\n # img_center_y = img_h / 2\n # if cv_blocks_ok is False:\n # # 高斯模糊\n # gs_frame = cv2.GaussianBlur(orgFrame, (5, 5), 0)\n # # 转换颜色空间\n # hsv = cv2.cvtColor(gs_frame, cv2.COLOR_BGR2HSV)\n # for i in color_dist:\n # # 查找字典颜色\n # mask = cv2.inRange(hsv, color_dist[i]['Lower'], color_dist[i]['Upper'])\n # # 腐蚀\n # mask = cv2.erode(mask, None, iterations=2)\n # # 膨胀\n # kernel = np.ones((5, 5), np.uint8)\n # mask = cv2.dilate(mask, kernel, iterations=2)\n # # 查找轮廓\n # # cv2.imshow('mask', mask)\n # cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]\n # if len(cnts) > 0:\n # # 找出最大的区域\n # c = max(cnts, key=cv2.contourArea)\n # # 返回的值 中心坐标（x, y）,（w，h）,角度\n # rect = cv2.minAreaRect(c)\n # # 获取最小外接矩形的4个顶点\n # box = cv2.boxPoints(rect)\n # # 数据类型转换\n # # 绘制轮廓\n # cv2.drawContours(orgFrame, [np.int0(box)], -1, (0, 255, 255), 2)\n # # 找色块中心点\n # c_x, c_y = rect[0]\n # h, w = rect[1]\n # c_angle = rect[2]\n # if h * w >= 1350: # 色块面积限制\n # # 绘制中心点\n # cv2.circle(orgFrame, (int(c_x), int(c_y)), 3, (216, 0, 255), -1)\n # # print 'nnn', int(c_x), int(c_y)\n # # print c_angle\n # # 存储用于判读X是否稳定的列表\n # last_blocks.append([int(c_x), i])\n # if stable:\n # # 存储 稳定后的数据\n # storage_blocks.append((int(c_y), int(c_x), i, int(c_angle)))\n # '''\n # 检测色块位置步骤：\n # 1、 判读色块 X 轴数据是否稳定\n # 2、 稳定后，存储数据\n # 3、 判读稳定后的数据， 找出Y轴色块 距离机械臂最近的\n # '''\n # stable = False\n # if len(last_blocks) > 0:\n # if -10 <= int(last_blocks[len(last_blocks) - 1][0] - last_x) <= 10: # 只判读最后一个方块是否稳定\n # print (cv_count)\n # cv_count += 1\n # else:\n # cv_count = 0\n # last_x = int(last_blocks[len(last_blocks) - 1][0])\n # last_blocks = []\n # if cv_count >= 5:\n # cv_count = 0\n # stable = True # 数据稳定后，开始取数据\n # # 稳定后的数据发送给搬运进程\n # if len(storage_blocks) > 0:\n # max_y = storage_blocks.index(max(storage_blocks))\n # # 存储稳定后的数据， 颜色， X, Y, 色块角度\n # position_color_list.append((storage_blocks[max_y][2], storage_blocks[max_y][1],\n # storage_blocks[max_y][0], storage_blocks[max_y][3]))\n # storage_blocks = []\n # cv_blocks_ok = True # 开始搬运\n # # 画图像中心点\n # cv2.line(orgFrame, (int(img_w / 2) - 20, int(img_h / 2)), (int(img_w / 2) + 20, int(img_h / 2)), (0, 0, 255), 1)\n # cv2.line(orgFrame, (int(img_w / 2), int(img_h / 2) - 20), (int(img_w / 2), int(img_h / 2) + 20), (0, 0, 255), 1)\n # t2 = cv2.getTickCount()\n # time_r = (t2 - t1) / cv2.getTickFrequency()\n # fps = 1.0/time_r\n # if debug:\n # cv2.putText(orgFrame, \"fps:\" + str(int(fps)),\n # (10, orgFrame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.65, (0, 0, 255), 2)#(0, 0, 255)BGR\n # cv2.imshow(\"orgFrame\", orgFrame)\n # cv2.waitKey(1)\n # else:\n # time.sleep(0.01)\n # else:\n # if correction_flag and run_corr_one == 0:\n # run_corr_one += 1\n # Arm_Pos_Corr()\n # else:\n # time.sleep(0.01)\n #\n #\n","repo_name":"zsyu9779/machine","sub_path":"Handling_color_blocks.py","file_name":"Handling_color_blocks.py","file_ext":"py","file_size_in_byte":12524,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"45560907796","text":"import math\n\nfrom ....utilities.byte_io_mdl import ByteIO\n\n\nclass Quat:\n @staticmethod\n def read(reader: ByteIO):\n raise NotImplementedError('Override me')\n\n\nclass Quat64(Quat):\n @staticmethod\n def read(reader: ByteIO):\n b0 = reader.read_uint32()\n b1 = reader.read_uint32()\n xs = b0 & 0x1FFFFF\n ys = (((b1 & 0x03FF) << 11) | (b0 >> 21)) >> 0\n zs = (b1 >> 10) & 0x1FFFFF\n wn = -1 if (b1 & 0x80000000) else 1\n\n x = (xs - 0x100000) / 0x100000\n y = (ys - 0x100000) / 0x100000\n z = (zs - 0x100000) / 0x100000\n w = wn * math.sqrt(1.0 - x * x - y * y - z * z)\n return x, y, z, w\n\n\nclass Quat48(Quat):\n @staticmethod\n def read(reader: ByteIO):\n x = (reader.read_uint16() - 32768) / 32768\n y = (reader.read_uint16() - 32768) / 32768\n raw_z = reader.read_uint16()\n z = ((raw_z & 0x7FFF) - 16384) / 16384\n w_neg = (raw_z >> 15) & 0x1\n w = math.sqrt(1 - x * x - y * y - z * z)\n if w_neg:\n w *= -1\n return x, y, z, w\n","repo_name":"stepa2/SourceIO","sub_path":"source1/mdl/structs/compressed_vectors.py","file_name":"compressed_vectors.py","file_ext":"py","file_size_in_byte":1073,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"52"} +{"seq_id":"20872698426","text":"# coding:utf-8\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nfrom sklearn.datasets import fetch_mldata\r\nfrom chainer import cuda, Variable, FunctionSet,\\\r\n optimizers,Chain,serializers\r\nimport chainer.functions as F\r\nimport sys\r\n\r\nimport CNNcifar10Train\r\n\r\n\r\nif __name__==\"__main__\":\r\n\r\n unitSet = [512] #中間層ユニット数\r\n\r\n N = 1000 #テストするサンプル数\r\n N_train = 500 #訓練データの数\r\n N_test = N - N_train #テストデータの数\r\n\r\n tate = 4\r\n yoko = 4\r\n n_config = tate*yoko\r\n\r\n category= [\"Airplane\",\"Automobile\",\r\n \"Bird\",\"Cat\",\"Deer\",\"Dog\",\r\n \"Frog\",\"Horse\",\"Ship\",\"Truck\"]\r\n\r\n KeepTimeList = [\"2016-10-03-21-54\",\r\n \"2016-10-04-00-32\",\r\n \"2016-10-04-03-21\",\r\n \"2016-10-04-06-34\",\r\n \"2016-10-04-09-50\",\r\n \"2016-10-04-13-12\",\r\n \"2016-10-05-13-07\"]\r\n\r\n for val in range(6,7):\r\n if val == 2:\r\n NetName = \"conv2\"\r\n Title=NetName\r\n KeepTime = \"\"\r\n elif val == 7:\r\n NetName = \"Cifar_Liner\"\r\n KeepTime = KeepTimeList[val-1]\r\n else:\r\n NetName = \"Cifar_conv%d\"%(val)\r\n KeepTime = KeepTimeList[val-1]\r\n\r\n Cifar10 = CNNcifar10Train.Cifar10CNN(N,N_train,N_test,unitSet,NetName)\r\n serializers.load_npz\\\r\n ('./modelkeep/'+NetName+'_Model_'+KeepTime,Cifar10.model)\r\n #Cifar10.draw_1image(0)\r\n Cifar10.draw_answerChack(category,tate,yoko)\r\n #Cifar10.predict(n_config)\r\n plt.show()\r\n","repo_name":"ysys1005/Gon","sub_path":"CNNcifar10Test.py","file_name":"CNNcifar10Test.py","file_ext":"py","file_size_in_byte":1656,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"47949676308","text":"# to open/load image\r\nfrom PIL import Image,ImageFilter\r\n# to render image\r\nimport matplotlib.pyplot as plt\r\n# to make kernels and noise matrix\r\nimport numpy as np\r\n\r\n# A helper function to show images side by\r\n\r\ndef show_side_by_side(im1,title1,im2,title2,gray=False):\r\n CMAP = 'viridis' if not gray else 'gray'\r\n plt.subplot(1,2,1)\r\n plt.title(title1)\r\n plt.imshow(im1,cmap=CMAP)\r\n\r\n plt.subplot(1,2,2)\r\n plt.title(title2)\r\n plt.imshow(im2,cmap=CMAP)\r\n \r\n plt.show()\r\n\r\nim = Image.open('lenna.png')\r\nrows,cols = im.size\r\n# Okay now lets create some noise\r\nnoise = np.random.normal(0,15,size=(rows,cols,3)).astype(np.uint8)\r\n\r\nnoised_im_arr = im + noise\r\n\r\nplt.figure(figsize=(7,7))\r\n\r\nshow_side_by_side(im,'Original',noised_im_arr,'Noised Image')\r\n\r\n# Now lets filter the noise\r\n# mean filtering\r\nnoised_im = Image.fromarray(noised_im_arr)\r\n\r\nkernel = np.ones((5,5))/36\r\nkernel_filter = ImageFilter.Kernel((5,5),kernel.flatten())\r\n\r\nfiltered_img = noised_im.filter(kernel_filter)\r\n\r\nshow_side_by_side(noised_im,'Noised Image',filtered_img,'Filtered Image')\r\n\r\n# Smaller kernel can make a sharp image but trade off the noise reduction capabilities\r\nsmall_kernel = np.ones((3,3))/9\r\nsmall_kernel_filte = ImageFilter.Kernel((3,3),small_kernel.flatten())\r\n\r\nfiltered_img_small_kernel = noised_im.filter(small_kernel_filte)\r\n\r\nshow_side_by_side(filtered_img,'5 x 5 kernel',filtered_img_small_kernel,'3 x 3 kernel')\r\n# It is clearly visible that her shoulder is now sharper but the green spots are brighter too\r\n\r\n# Noise reduction can be achieved with Gaussian Blur too\r\n# ImageFilter.GaussianBlur\r\n# Default Radius is 2\r\n\r\ngaussian_img = noised_im.filter(ImageFilter.GaussianBlur)\r\nshow_side_by_side(noised_im,'Noised Image',gaussian_img,'Gaussian Blurred Img')\r\n\r\n# Sharpening Images\r\n# For image sharpening we will use commonly used kernel\r\n# -1 -1 -1\r\n# -1 9 -1\r\n# -1 -1 -1\r\n\r\nsharpen_kernel = np.array([[-1,-1,-1],\r\n [-1, 9,-1],\r\n [-1,-1,-1]])\r\n\r\nsharpen_filter = ImageFilter.Kernel((3,3),sharpen_kernel.flatten())\r\n\r\nsharped_gaussian = gaussian_img.filter(sharpen_filter)\r\nsharpen_img = im.filter(sharpen_filter)\r\nshow_side_by_side(gaussian_img,'Gaussian blurred Img',sharped_gaussian,'Sharpened Gaussian Img')\r\nshow_side_by_side(im,'orginal image',sharpen_img,'Sharpened Image')\r\n\r\n# There's a SHARPEN filter provided by PIL by default\r\nPIL_sharpen_img = im.filter(ImageFilter.SHARPEN)\r\nshow_side_by_side(sharpen_img,'Sharpen by manual',PIL_sharpen_img,'Sharpen by PIL')\r\n\r\n# Edge Detection\r\n# Before Finding Edge Use ImageFilter.ENHANCE_EDGE filter to enhance the edges of an images so that you can find more and more edges\r\nimg = Image.open('barbara.png')\r\nenhanced_egde = img.filter(ImageFilter.EDGE_ENHANCE)\r\n\r\nshow_side_by_side(img,'Original',enhanced_egde,'Enhanced Edges',gray=True)\r\n\r\n# Now we have enhanced edges we can start finding the edges in the image\r\n\r\nfind_edge = img.filter(ImageFilter.FIND_EDGES)\r\nshow_side_by_side(img,'Original Image',find_edge,'Edges in the Image',gray=True)\r\n\r\n# Median Filter\r\n# Median Filter takes the median of the neighbouring pixel and replace the middle one with median\r\n\r\nim2 = Image.open('cameraman.jpeg')\r\n\r\nmedian_filtered = im2.filter(ImageFilter.MedianFilter)\r\n# median filters are good for increasing the segmentation within the images\r\nshow_side_by_side(im2,'Original',median_filtered,'Median Filtered')\r\n","repo_name":"zee-1/ComputerVision","sub_path":"linearFilteringPIL.py","file_name":"linearFilteringPIL.py","file_ext":"py","file_size_in_byte":3479,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"15159800175","text":"import pyupbit\nimport pandas as pd\n\n\naccess = \"AkLyRC85lejerj1JTAv011KXt3m1w2XJOHsqi74F\"\nsecret = \"xuQZJHTY8j75kxagCNSYjxOiUt6i95IicCsFV8Ux\"\nupbit = pyupbit.Upbit(access, secret)\n\ndef GetRSI(ohlcv, period, st):\n '''\n RSI지표 수치를 구해준다. 첫번째: 분봉/일봉 정보, 두번째: 기간, 세번째: 기준 날짜\n ohlcv : Open, High, Low, Close, Volume의 약자\n '''\n ohlcv[\"close\"] = ohlcv[\"close\"]\n delta = ohlcv[\"close\"].diff()\n up, down = delta.copy(), delta.copy()\n up[up < 0] = 0\n down[down > 0] = 0\n _gain = up.ewm(com=(period - 1), min_periods=period).mean()\n _loss = down.abs().ewm(com=(period - 1), min_periods=period).mean()\n RS = _gain / _loss\n return float(pd.Series(100 - (100 / (1 + RS)), name=\"RSI\").iloc[st])\n\n#이동평균선 수치를 구해준다 첫번째: 분봉/일봉 정보, 두번째: 기간, 세번째: 기준 날짜\n\n\ndef GetMA(ohlcv, period, st):\n close = ohlcv[\"close\"]\n ma = close.rolling(period).mean()\n return float(ma[st])\n\n\n#비트코인의 일봉(캔들) 정보를 가져온다.\ndf = pyupbit.get_ohlcv(\"KRW-BTC\", interval=\"minute240\")\n","repo_name":"parade621/monarchBot","sub_path":"upbit_auto_btc.py","file_name":"upbit_auto_btc.py","file_ext":"py","file_size_in_byte":1135,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"32709088658","text":"from api_models.metadata_detection_point import MetadataDetectionPointCreate\nfrom db import crud\n\n\ndef test_create(db):\n obj = crud.metadata_detection_point.create_or_read(\n model=MetadataDetectionPointCreate(description=\"test description\", value=\"test value\"), db=db\n )\n\n assert obj.description == \"test description\"\n assert obj.value == \"test value\"\n\n\ndef test_create_duplicate_value(db):\n obj1 = crud.metadata_detection_point.create_or_read(model=MetadataDetectionPointCreate(value=\"test\"), db=db)\n assert obj1\n\n # Ensure that you get the same object back if you try to create a duplicate value\n obj2 = crud.metadata_detection_point.create_or_read(model=MetadataDetectionPointCreate(value=obj1.value), db=db)\n assert obj2.description == obj1.description\n assert obj2.uuid == obj1.uuid\n assert obj2.value == obj1.value\n","repo_name":"seanmcfeely/ace2-ams","sub_path":"db/app/db/tests/test_crud/metadata_detection_point/test_create.py","file_name":"test_create.py","file_ext":"py","file_size_in_byte":861,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"13097366415","text":"from sys import stdin\nfrom itertools import product\n\n\ndef solution(N, board):\n\n def domain_length(domain):\n return domain[1] - domain[0]\n\n def color_check(row_domain, col_domain):\n ref_color = board[row_domain[0]][col_domain[0]]\n for x, y in product(range(*row_domain), range(*col_domain)):\n if board[x][y] != ref_color:\n return False\n return ref_color\n\n def compress(row_domain, col_domain):\n is_mono = color_check(row_domain, col_domain)\n if is_mono:\n return is_mono\n\n string = '('\n half_row_len, half_col_len = domain_length(row_domain)//2, domain_length(col_domain)//2\n for i in range(4):\n # check how output should be ordered and decide placing 'i//2', 'i%2'\n row_part = row_domain[0] + half_row_len*(i//2), row_domain[0] + half_row_len*(i//2+1)\n col_part = col_domain[0] + half_col_len*(i%2), col_domain[0] + half_col_len*(i%2+1)\n string += compress(row_part, col_part)\n return string + ')'\n\n return compress((0, N), (0, N))\n\n\nN = int(stdin.readline())\nboard = [list(stdin.readline().strip()) for _ in range(N)]\n\nprint(solution(N, board))\n","repo_name":"grasshopperTrainer/coding_practice","sub_path":"baekjoon/accepted/1992 쿼드트리.py","file_name":"1992 쿼드트리.py","file_ext":"py","file_size_in_byte":1208,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"74416490085","text":"\"\"\"\nStreamlit app to display a Sunburst chart using the custom component.\n\n* Run it as: `streamlit run app_sunburst.py`\n* Before first run, compile the frontend component by navigating to the `frontend` folder, \nand running: `pnpm run build`. \n\"\"\"\n\nimport os\nimport pyarrow as pa\nimport pyarrow.parquet\nimport awkward as ak\nimport streamlit as st\n\nfrom st_sunburst_component.utils import hierarchy as h\nfrom st_sunburst_component import sunburst_chart\n\n\nst.set_page_config(layout=\"wide\")\n\n\n@st.experimental_memo\ndef read_hierarchy_table():\n \"\"\"This routine takes a couple seconds to run. When running multiple times, store\n the hierarchy table one time, and load it there on.\n \"\"\"\n dataset = pa.parquet.read_table(\"../datasets/tennis_shot_by_shot.parquet\")\n evt_hierarchy, evt_names = h.convert_to_hierarchy(dataset)\n return evt_hierarchy, evt_names\n\n\nnum_levels_to_plot = 10\nevt_hierarchy, event_names = read_hierarchy_table()\n\nif \"anchor_index\" not in st.session_state:\n st.session_state[\"anchor_index\"] = 0\n\nst.title(\"Sunburst Chart\")\n# print(\"RENDERING anchor index: \", st.session_state[\"anchor_index\"])\n\nancestors, subtree = h.get_ancestors_n_subtree(\n evt_hierarchy, st.session_state.anchor_index, num_levels_to_plot - 1\n)\n\nnew_anchor_index = sunburst_chart(\n event_names, st.session_state.anchor_index, ancestors, subtree\n)\n# print(\"DESIRED anchor index: \", new_anchor_index)\n\nif new_anchor_index is not None and new_anchor_index != st.session_state.anchor_index:\n st.session_state[\"anchor_index\"] = new_anchor_index\n st.experimental_rerun()\n","repo_name":"ananis25/seqmatcher","sub_path":"experiments/app_sunburst.py","file_name":"app_sunburst.py","file_ext":"py","file_size_in_byte":1582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"24474501005","text":"from rest_framework import serializers\n\nfrom openapi.models import GraphQuery\nfrom openapi.models import PredictionGraph\nfrom openapi.models import PredictionGraphPoint\n\nfrom openapi.models import HeatMapLocalQuery\nfrom openapi.models import HeatMapLocal\nfrom openapi.models import HeatMapLocalPoint\n\nfrom openapi.models import HeatMapPostcodeQuery\nfrom openapi.models import HeatMapPostcode\nfrom openapi.models import HeatMapPostcodePoint\n\nfrom openapi.models import GridSquareData\n\nfrom openapi.models import PROPERTY_CHOICES, ESTATE_CHOICES\n\n# Serialzers for the Models, using DRF Serializers\n\n#-------------------------------------------------------------------------------\nclass HeatMapPostcodePointSerializer(serializers.ModelSerializer):\n class Meta:\n model = HeatMapPostcodePoint\n fields = ('postcode', 'value') \n\nclass HeatMapPostcodeSerializer(serializers.ModelSerializer):\n points = HeatMapPostcodePointSerializer(many=True, read_only=True)\n class Meta:\n model = HeatMapPostcode\n fields = ('id','points')\n\nclass HeatMapPostcodeQuerySerializer(serializers.ModelSerializer): \n heat_map = HeatMapPostcodeSerializer(read_only=True) \n class Meta:\n model = HeatMapPostcodeQuery\n fields = ('loaded', 'estate_type','property_type', 'date','heat_map')\n\nclass HeatMapLocalPointSerializer(serializers.ModelSerializer):\n class Meta:\n model = HeatMapLocalPoint\n fields = ('longitude','latitude', 'value') \n\nclass HeatMapLocalPointPlusSerializer(serializers.ModelSerializer):\n class Meta:\n model = HeatMapLocalPoint\n fields = ('longitude','latitude', 'date','estate_type','property_type', 'value') \n\nclass HeatMapLocalSerializer(serializers.ModelSerializer):\n points = HeatMapLocalPointSerializer(many=True, read_only=True)\n class Meta:\n model = HeatMapLocal\n fields = ('points',)\n\nclass HeatMapLocalQuerySerializer(serializers.ModelSerializer): \n heat_map = HeatMapLocalSerializer(read_only=True) \n class Meta:\n model = HeatMapLocalQuery\n fields = ('loaded','longitude','latitude','estate_type','property_type', 'date', 'radius','heat_map')\n\n\n\n#-------------------------------------------------------------------------------\nclass PredictionGraphPointSerializer(serializers.ModelSerializer):\n class Meta:\n model = PredictionGraphPoint\n fields = ('point_type', 'price', 'sigma', 'time') \n\nclass PredictionGraphSerializer(serializers.ModelSerializer):\n points = PredictionGraphPointSerializer(many=True, read_only=True)\n class Meta:\n model = PredictionGraph\n fields = ('id','points')\n\nclass GraphQuerySerializer(serializers.ModelSerializer): \n graph = PredictionGraphSerializer(read_only=True) \n class Meta:\n model = GraphQuery\n fields = ('id','loaded','longitude','latitude','estate_type','property_type','graph')\n \n \n#-------------------------------------------------------------------------------\n\nclass GridSquareDataSerializer(serializers.ModelSerializer):\n class Meta:\n model = GridSquareData\n fields = ('id', 'longitude', 'latitude')\n","repo_name":"suryarastogi/ImperialProjects","sub_path":"Group Project/ghostapi/openapi/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":3173,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"35203754303","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Jul 24 00:08:47 2016\r\n\r\n@author: aditya\r\n\"\"\"\r\n\r\nimport numpy as np\r\nfrom dateutil.parser import parse\r\nimport dateutil.parser\r\nimport matplotlib.pyplot as plt\r\nfrom datetime import datetime\r\nimport pandas as pd\r\n\r\ntest = pd.read_csv('D:\\Projects\\The S R\\Test.csv')\r\n\r\ntest['Application_Receipt_Date'] = test['Application_Receipt_Date'].apply(parse)\r\ntest['App_Receipt_Month'] = test['Application_Receipt_Date'].dt.month\r\ntest.groupby('App_Receipt_Month').ID.nunique().plot()\r\nQ_Res = []\r\nfor i in test['App_Receipt_Month']:\r\n if i <= 3:\r\n Q_Res.append('Q4')\r\n elif i <=6:\r\n Q_Res.append('Q1')\r\n elif i <=9:\r\n Q_Res.append('Q2')\r\n elif i <=12:\r\n Q_Res.append('Q3')\r\ntest['Applied_Quarter'] = Q_Res\r\ndel test['Application_Receipt_Date']\r\ndel test['App_Receipt_Month']\r\n\r\n\r\ntest.groupby('Office_PIN').ID.nunique().plot()\r\noffice = pd.DataFrame(test.groupby('Office_PIN').ID.nunique())\r\noffice['Office_PIN'] = office.index\r\n\r\noffice.columns = ('No_Offices','Office')\r\nadi = pd.DataFrame(office.loc[test['Office_PIN'],'No_Offices'])\r\nadi = adi.reset_index()\r\ntest['Tot_Applications'] = adi['No_Offices']\r\nOff_Cat = []\r\nfor i in test['Tot_Applications']:\r\n if i <= 93:\r\n Off_Cat.append('D')\r\n elif i <=123:\r\n Off_Cat.append('C')\r\n elif i <= 172:\r\n Off_Cat.append('B')\r\n elif i <=397:\r\n Off_Cat.append('A')\r\ntest['Company_Level'] = Off_Cat \r\ndel test['Tot_Applications']\r\n\r\ntest.groupby('Applicant_Gender').ID.nunique()\r\ntest['Applicant_Gender'] = test['Applicant_Gender'].fillna('unknown')\r\n\r\n\r\ntest['Applicant_BirthDate'] = test['Applicant_BirthDate'].fillna('7/24/2016')\r\ntest['Applicant_BirthDate'] = test['Applicant_BirthDate'].apply(parse)\r\nage_applicant = (datetime.today().date()-test.Applicant_BirthDate)\r\nage_ap = np.ravel(age_applicant)\r\nage_ap = age_ap.astype('timedelta64[D]')\r\nage_int_years = np.int64(age_ap/365)\r\ntest['Age_Applicant'] = age_int_years\r\nmedian = test['Age_Applicant'].median()\r\n\r\nAge_app = []\r\nfor i in test['Age_Applicant']:\r\n if i == 0:\r\n Age_app.append(38)\r\n else:\r\n Age_app.append(i)\r\ntest['Age_Applicant'] = Age_app\r\nage = []\r\ntest['Age_Applicant'].describe()\r\ntest['Age_Applicant'].mean()\r\nfor i in test['Age_Applicant']:\r\n if i <=33 :\r\n age.append('L33')\r\n elif i <= 38 :\r\n age.append('L38')\r\n elif i < 65 :\r\n age.append('L46')\r\n elif i >=65 :\r\n age.append('L86')\r\npd.DataFrame.to_csv(test,'test.csv')\r\nage = pd.DataFrame(age)\r\n\r\ntest['Applicant_Age_Cat'] = age\r\n\r\ntest['Applicant_City_PIN'] = test['Applicant_City_PIN'].fillna('Missing')\r\n\r\nlocal = []\r\nfor i in test.index:\r\n if test.Applicant_City_PIN[i] == test.Office_PIN[i]:\r\n local.append('Local')\r\n elif test.Applicant_City_PIN[i] == 'Missing':\r\n local.append('Missing')\r\n else:\r\n local.append('Non_local')\r\n \r\ntest['local'] = local\r\n\r\ntest['Manager_Grade'] = test['Manager_Grade'].fillna('Direct')\r\ntest['Manager_Current_Designation'] = test['Manager_Current_Designation'].fillna('Direct')\r\ntest['Manager_Status'] = test['Manager_Status'].fillna('Direct')\r\ntest['Manager_Gender'] = test['Manager_Gender'].fillna('Direct')\r\n\r\ntest['Manager_Joining_Designation'] = test['Manager_Joining_Designation'].fillna('Direct')\r\ntest.groupby('Manager_Joining_Designation').ID.nunique()\r\n\r\ncurrent_d = []\r\nfor i in test.index:\r\n if test.Manager_Joining_Designation[i] == 'Level 6':\r\n current_d.append('Level 5')\r\n elif test.Manager_Joining_Designation[i] == 'Level 7':\r\n current_d.append('Level 5')\r\n elif test.Manager_Joining_Designation[i] == 'Level 5':\r\n current_d.append('Level 5')\r\n elif test.Manager_Joining_Designation[i] == 'Level 4':\r\n current_d.append('Level 4')\r\n elif test.Manager_Joining_Designation[i] == 'Level 3':\r\n current_d.append('Level 3')\r\n elif test.Manager_Joining_Designation[i] == 'Level 2':\r\n current_d.append('Level 2')\r\n elif test.Manager_Joining_Designation[i] == 'Level 1':\r\n current_d.append('Level 1')\r\n else: \r\n current_d.append('Direct')\r\n \r\ntest['Manager_Joining_Designation'] = current_d\r\n\r\n#Manager_Grade Feature\r\n\r\ntest['Manager_Grade'] = test['Manager_Grade'].fillna('Direct')\r\ngrade = []\r\nfor i in test.index:\r\n if test.Manager_Grade[i] <= 2:\r\n grade.append('L2')\r\n elif test.Manager_Grade[i] == 3:\r\n grade.append('L3')\r\n elif test.Manager_Grade[i] <= 5:\r\n grade.append('L5')\r\n elif test.Manager_Grade[i] <= 10:\r\n grade.append('L10') \r\n else: \r\n grade.append('Direct')\r\ntest['Manager_Grade'] = grade\r\n\r\n#Applicant_Occupation feature\r\n\r\ntest['Applicant_Occupation'] = test['Applicant_Occupation'].fillna('Others')\r\ntest.groupby('Applicant_Occupation').ID.nunique()\r\n\r\n\r\n#Managers Age\r\n\r\ntest['Manager_DoB'] = test['Manager_DoB'].fillna('7/24/2016')\r\ntest['Manager_DoB'] = test['Manager_DoB'].apply(parse)\r\nage_manager = (datetime.today().date()-test.Manager_DoB)\r\nage_mgr = np.ravel(age_manager)\r\nage_mgr = age_mgr.astype('timedelta64[D]')\r\nage_int_years = np.int64(age_mgr/365)\r\ntest['Age_Manager'] = age_int_years\r\ntest['Age_Manager'].median()\r\nAge_man = []\r\nfor i in test['Age_Manager']:\r\n if i == 0:\r\n Age_man.append(42)\r\n else:\r\n Age_man.append(i)\r\ntest['Age_Manager'] = Age_man\r\ntest.groupby('Age_Manager').ID.nunique()\r\ntest['Age_Manager'].describe\r\nage = []\r\nfor i in test['Age_Manager']:\r\n if i <=35 :\r\n age.append('L35')\r\n elif i <= 40 :\r\n age.append('L40')\r\n elif i <= 45 :\r\n age.append('L45')\r\n elif i <=50 :\r\n age.append('L50')\r\n elif i <=55 :\r\n age.append('L55')\r\n elif i <=60 :\r\n age.append('L60')\r\n else:\r\n age.append('G60')\r\n\r\ntest['Manager_Age_Cat'] = age\r\n\r\n\r\n#Managers Experience in years\r\n\r\ntest['Manager_DOJ'] = test['Manager_DOJ'].fillna('7/24/2016')\r\ntest['Manager_DOJ'] = test['Manager_DOJ'].apply(parse)\r\nexp_manager = (datetime.today().date()-test.Manager_DOJ)\r\nexp_mgr = np.ravel(exp_manager)\r\nexp_mgr = exp_mgr.astype('timedelta64[D]')\r\nexp_int_years = np.int64(exp_mgr/365)\r\ntest['Exp_Manager'] = exp_int_years\r\ntest['Exp_Manager'].median()\r\nExp_man = []\r\nfor i in test['Exp_Manager']:\r\n if i == 0:\r\n Exp_man.append(9)\r\n else:\r\n Exp_man.append(i)\r\ntest['Exp_Manager'] = Exp_man\r\ntest.groupby('Exp_Manager').ID.nunique()\r\n\r\nexp = []\r\n\r\nfor i in test['Exp_Manager']:\r\n if i <=10 :\r\n exp.append('L10')\r\n elif i <=12 :\r\n exp.append('L12')\r\n elif i <=14 :\r\n exp.append('L14')\r\n else:\r\n exp.append('G14')\r\n \r\ntest['Exp_Mgr'] = exp\r\n\r\n#Manager No_of Producsts feature\r\ntest['Manager_Num_Products'].median()\r\ntest['Manager_Num_Products'] = test['Manager_Num_Products'].fillna(2)\r\ntest.Manager_Num_Products.describe()\r\ntest.groupby('Manager_Num_Products').ID.nunique()\r\n\r\npro = []\r\n\r\nfor i in test['Manager_Num_Products']:\r\n if i == 0 :\r\n pro.append('NoSale')\r\n elif i <=4 :\r\n pro.append('L4')\r\n elif i == 5 :\r\n pro.append('L5')\r\n elif i <=15 :\r\n pro.append('L15')\r\n elif i <=25 :\r\n pro.append('L25')\r\n elif i <=40 :\r\n pro.append('L40')\r\n else:\r\n pro.append('G40')\r\n\r\ntest['product1'] = pro\r\n\r\n#Manager No_of Producsts feature in last 3 months\r\n\r\ntest.Manager_Num_Products2.describe()\r\ntest.groupby('Manager_Num_Products2').ID.nunique()\r\ntest['Manager_Num_Products2'] = test['Manager_Num_Products2'].fillna(5)\r\n\r\npro = []\r\nfor i in test['Manager_Num_Products2']:\r\n if i == 0 :\r\n pro.append('NoSale')\r\n elif i <=4 :\r\n pro.append('L4')\r\n elif i == 5 :\r\n pro.append('L5')\r\n elif i <=10 :\r\n pro.append('L10')\r\n elif i <=15 :\r\n pro.append('L15')\r\n else:\r\n pro.append('G15')\r\n\r\ntest['product2'] = pro\r\n\r\n\r\n## Number of agents sourced by Manager\r\n\r\ntest.Manager_Num_Application.describe()\r\ntest.groupby('Manager_Num_Application').ID.nunique()\r\ntest['Manager_Num_Application'] = test['Manager_Num_Application'].fillna(2)\r\n\r\npro = []\r\n\r\nfor i in test['Manager_Num_Application']:\r\n if i == 0 :\r\n pro.append('None')\r\n elif i == 1 :\r\n pro.append('One')\r\n elif i == 2 :\r\n pro.append('Two')\r\n elif i == 3 :\r\n pro.append('Three')\r\n elif i <= 5 :\r\n pro.append('L5')\r\n elif i <= 10 :\r\n pro.append('L10')\r\n else:\r\n pro.append('G10')\r\n\r\ntest['ManagerTotalAgent'] = pro\r\n\r\ntest.groupby('ManagerTotalAgent').ID.nunique()\r\n\r\n\r\n## Number of agents sourced by Manager in last 3 months\r\n\r\ntest.Manager_Num_Coded.describe()\r\ntest.groupby('Manager_Num_Coded').ID.nunique()\r\ntest['Manager_Num_Coded'] = test['Manager_Num_Coded'].fillna(1)\r\n\r\npro = []\r\n\r\nfor i in test['Manager_Num_Coded']:\r\n if i == 0 :\r\n pro.append('None')\r\n elif i == 1 :\r\n pro.append('One')\r\n elif i == 2 :\r\n pro.append('Two')\r\n elif i <= 4 :\r\n pro.append('L4')\r\n else:\r\n pro.append('G4')\r\n\r\ntest['ManagerTotalAgentL3'] = pro\r\n\r\ntest.groupby('ManagerTotalAgentL3').ID.nunique()\r\n\r\n## Manager business last 3 months\r\n\r\ntest.Manager_Business.mean()\r\ntest.Manager_Business.describe()\r\ntest.groupby('Manager_Business').ID.nunique()\r\ntest['Manager_Business'] = test['Manager_Business'].fillna(111575)\r\ntest['Manager_Business'] = abs(test['Manager_Business'])\r\n\r\npro = []\r\n\r\nfor i in test['Manager_Business']:\r\n if i == 0 :\r\n pro.append('No_business')\r\n elif i <= 100000 :\r\n pro.append('L1')\r\n elif i <= 500000 :\r\n pro.append('L5')\r\n elif i <= 1000000 :\r\n pro.append('L10')\r\n elif i <= 1500000 :\r\n pro.append('L15') \r\n else:\r\n pro.append('G15')\r\n\r\ntest['Manager_Business'] = pro\r\n\r\ntest.groupby('Manager_Business').ID.nunique()\r\n\r\n#Applicant Qualification feature\r\n\r\ntest.groupby('Applicant_Qualification').ID.nunique()\r\ntest['Applicant_Qualification'] = test['Applicant_Qualification'].fillna('unknown')\r\nqual = pd.DataFrame(test.groupby('Applicant_Qualification').ID.nunique())\r\n\r\nqual = qual.reset_index()\r\n\r\nqual0 = qual.Applicant_Qualification[0]\r\nqual1 = qual.Applicant_Qualification[1]\r\nqual2 = qual.Applicant_Qualification[2]\r\nqual3 = qual.Applicant_Qualification[3]\r\nqual4 = qual.Applicant_Qualification[4]\r\nqual5 = qual.Applicant_Qualification[5]\r\nqual6 = qual.Applicant_Qualification[6]\r\nqual7 = qual.Applicant_Qualification[7]\r\nqual8 = qual.Applicant_Qualification[8]\r\n\r\ntest['Applicant_Qualification1'] = test['Applicant_Qualification'] \r\n\r\n\r\nqualific = []\r\n\r\nfor i in test['Applicant_Qualification']:\r\n if i == qual0 :\r\n qualific.append('Prof_Q') \r\n elif i == qual1 :\r\n qualific.append('Prof_Q')\r\n elif i == qual7 :\r\n qualific.append('Prof_Q')\r\n elif i == qual2 :\r\n qualific.append(qual2)\r\n elif i == qual3 :\r\n qualific.append(qual3)\r\n elif i == qual4 :\r\n qualific.append(qual4)\r\n elif i == qual5 :\r\n qualific.append(qual5)\r\n elif i == qual6 :\r\n qualific.append(qual6)\r\n elif i == qual8 :\r\n qualific.append(qual8)\r\n\r\ntest['Applicant_Qualification'] = qualific\r\n\r\n","repo_name":"aditya436/The_SR_AV","sub_path":"Test_Clean.py","file_name":"Test_Clean.py","file_ext":"py","file_size_in_byte":11163,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"37385906343","text":"from sys import stdin\ninput = stdin.readline\n\ndef bellman_from_src(src, edges, n):\n # return True if graph contains negative cycle\n dist = [float('inf')] * (n+1)\n dist[src] = 0\n\n for _ in range(n-1):\n for s, e, d in edges:\n dist[e] = min(dist[e], dist[s] + d)\n\n # run one more time and check updated\n for s, e, d in edges:\n if dist[e] > dist[s] + d:\n return True\n return False\n\ndef solve():\n n, m, w = map(int, input().split())\n edges = []\n\n for _ in range(m):\n s, e, d = map(int, input().split())\n edges.append((s,e,d))\n edges.append((e,s,d))\n\n for _ in range(w):\n s, e, d = map(int, input().split())\n edges.append((s,e,-d))\n\n return bellman_from_src(1, edges, n)\n\nTC = int(input())\nfor _ in range(TC):\n print('YES' if solve() else 'NO')\n","repo_name":"storykim/problem-solving","sub_path":"boj/1865.py","file_name":"1865.py","file_ext":"py","file_size_in_byte":849,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"72446906085","text":"import sys \nimport subprocess\nimport os\nimport configparser\nimport re\nimport glob\nfrom PyQt5.QtWidgets import QApplication, QGraphicsOpacityEffect, QDialog, QLabel, QLineEdit\nfrom PyQt5 import QtCore\nfrom PyQt5.Qt import Qt\n\no = \"\"\ncmd = \"\"\n\nclass Example(QDialog):\n\t\n\tdef __init__(self):\n\t\tsuper().__init__()\n\t\tself.setWindowOpacity(0)\n\t\tself.rpath = None\n\t\tself.path = None\n\t\tself.appname = None\n\t\tself.lineEntry = QLineEdit(self)\n\t\tself.lineEntry.returnPressed.connect(self.run)\n\n\t\tself.lineEntry.move(16,16)\n\t\tself.lineEntry.resize(200,40)\n\n\t\tself.qlabel = QLabel(self)\n\t\tself.qlabel.move(16,64)\n\n\t\tself.lineEntry.textChanged.connect(self.onChanged)\n\t\t\n\t\tself.setGeometry(50,50,320,200)\n\t\tself.setWindowTitle(\"QLineEdit Example\")\n\t\tself.show()\n\t\tself.paths()\n\t\t\n\t\n\tdef paths(self):\n\t\tself.qlabel.adjustSize()\t\t\n\t\tconfig = configparser.RawConfigParser()\n\t\tprint(config.sections())\n\t\t#saves all .desktop files to an array\n\t\tpath = glob.glob(f'/usr/share/applications/*.desktop')\n\t\tnames = []\n\t\tfor e in path:\n\t\t\tconfig.read(e)\n\t\t\tname = (config['Desktop Entry']['Name'])\n\t\t\ta = f'{name }={e}'\n\t\t\tnames.append(a)\n\t\tsnames = '\\n'.join(names)\n\t\tprint(snames)\n\t\tself.paths = snames\n\t\t\n\n\tdef onChanged(self, text):\n\t\trpath=\"\"\n\t\tappname = \"\"\n\t\tprint(self.paths)\n\t\tsnames = self.paths\n\t\t\n\t\tcinput = text.capitalize()\n\t\tfor line in snames.split('\\n'):\n\t\t\tif cinput in line:\n\t\t\t\tt = re.sub(r'^.*?=', '=', line)\n\t\t\t\trpath = t.replace(\"=\", \"\") \n\t\t\t\tprint(rpath)\n\t\t\t\tbreak\n\t\tfor line in snames.split('\\n'):\n\t\t\tif cinput in line:\n\t\t\t\tsline = ''.join(line)\n\t\t\t\tformat = sline.split(\"=\", 1)\n\t\t\t\tappname = format[0] \n\t\t\t\tprint(appname)\n\t\t\t\tbreak\n\t\tself.rpath = rpath\n\t\tself.appname = appname\n\t\t\n\t\tpath = os.path.join('/tmp', 'ls.txt')\n\t\tfile = open(path, \"w\")\n\t\tfile.write(self.appname)\n\t\tos.system('sh ~/code/appLauncher/barupdate.sh ')\n\n\tdef bar(self):\n\t\tpath = os.path.join('/tmp', 'ls.txt')\n\t\tfile = open(path, \"w\")\n\t\tfile.write(self.appname)\n\t\tos.system('echo hook:module/demo2 >>/tmp/polybar_mqueue.*')\n\n\tdef run(self):\n\t\tconfig = configparser.RawConfigParser()\n\t\tprint(self.rpath)\n\t\tconfig.read(self.rpath)\n\t\texec = (config['Desktop Entry']['Exec'])\n\t\tprint(exec)\n\t\tsubprocess.run(f'{exec} & disown', shell=True)\n\t\texit()\n\t\t\n\nif __name__ == '__main__':\n\tapp = QApplication(sys.argv)\n\tex = Example()\n\tsys.exit(app.exec_())","repo_name":"becker63/PolyLauncher","sub_path":"applauncher.py","file_name":"applauncher.py","file_ext":"py","file_size_in_byte":2314,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"33643551442","text":"# -*- coding: utf-8 -*-\nimport os\nimport sys\nimport time\nimport urllib\nfrom urllib.request import urlretrieve\n\nimport arxiv\nimport click\nfrom dateutil.parser import parse\nfrom logzero import logger\nfrom pybtex import database\nfrom lxml import etree\n\n\ndef reporthook(count, block_size, total_size):\n global start_time\n if count == 0:\n start_time = time.time()\n return\n duration = time.time() - start_time\n progress_size = int(count * block_size)\n speed = int(progress_size / (1024 * duration))\n percent = int(count * block_size * 100 / total_size)\n sys.stdout.write(\n \"\\r{}%, {} KB, {} KB/s, {:.1f} seconds passed\".format(min(percent, 100), progress_size / 1024, speed, duration))\n sys.stdout.flush()\n\n\ndef download_from_arxiv(url, dirpath='.'):\n if url.endswith('.pdf'):\n paper_id = os.path.splitext(os.path.basename(url))[0]\n else:\n paper_id = os.path.basename(url)\n paper = arxiv.query(id_list=[paper_id])[0]\n\n def custom_slugify(obj):\n author_last_name = obj['authors'][0].strip().split(' ')[-1]\n year = parse(obj['published']).year\n title = obj['title'].strip().replace('\\n', '')\n logger.info('Download \"{}\" from \"{}\"'.format(title, obj['pdf_url']))\n return '[{}+{}] {}'.format(author_last_name, year, title)\n\n if not paper.get('pdf_url', ''):\n print(\"Object has no PDF URL.\")\n return\n\n path = os.path.join(dirpath, custom_slugify(paper) + '.pdf')\n urlretrieve(paper['pdf_url'], path, reporthook=reporthook)\n return path\n\n\ndef download_from_acl(url, dirpath='.'):\n if url.endswith('.pdf'):\n url = url[:-4] # strip '.pdf'\n\n # get filename\n bib_url = url.strip('\\n').rstrip('/') + '.bib'\n bib = urllib.request.urlopen(bib_url).read().decode('utf-8')\n bib_database = database.parse_string(bib, bib_format='bibtex')\n author_lastname = bib_database.entries.values()[0].persons['author'][0].last()[0]\n year = bib_database.entries.values()[0].fields['year'].strip()\n title = bib_database.entries.values()[0].fields['title'].strip()\n out_name = '[{}+{}] {}.pdf'.format(author_lastname, year, title).replace('{', '').replace('}', '')\n\n # get authorname\n path = os.path.join(dirpath, out_name)\n pdf_url = url.strip('\\n').rstrip('/') + '.pdf'\n logger.info('Download \"{}\" from \"{}\"'.format(title, pdf_url))\n urlretrieve(pdf_url, path, reporthook=reporthook)\n return path\n\n\ndef download_from_openreview(url, dirpath='.'):\n url = url.rstrip('\\n')\n if '/pdf?' in url:\n url = url.replace('/pdf?', '/forum?')\n page_source = urllib.request.urlopen(url).read().decode('utf-8')\n xml = etree.fromstring(page_source, parser=etree.HTMLParser())\n bib = xml.xpath('//a[@class=\"action-bibtex-modal\"]/@data-bibtex')[0]\n bib_database = database.parse_string(bib, bib_format='bibtex')\n author_lastname = bib_database.entries.values()[0].persons['author'][0].last()[0]\n year = bib_database.entries.values()[0].fields['year'].strip()\n title = bib_database.entries.values()[0].fields['title'].strip()\n out_name = '[{}+{}] {}.pdf'.format(author_lastname, year, title).replace('{', '').replace('}', '')\n\n path = os.path.join(dirpath, out_name)\n pdf_url = url.replace('/forum?', '/pdf?')\n logger.info('Download \"{}\" from \"{}\"'.format(title, pdf_url))\n urlretrieve(pdf_url, path, reporthook=reporthook)\n return path\n\n\n@click.command()\n@click.argument('urls', type=str, nargs=-1)\n@click.option(\n '-o',\n '--out',\n default=None,\n type=click.Path(),\n help='path to save pdf'\n)\ndef main(urls, out):\n if out is None:\n if 'ARXIV_OUT' in os.environ:\n out = os.environ['ARXIV_OUT']\n else:\n out = '.'\n logger.info('Save PDF(s) to {}'.format(out))\n\n for url in urls:\n if 'arxiv' in url:\n download_from_arxiv(url, dirpath=out)\n elif 'aclweb' in url:\n download_from_acl(url, dirpath=out)\n elif 'openreview' in url:\n download_from_openreview(url, dirpath=out)\n else:\n raise NotImplementedError\n","repo_name":"butsugiri/download_arxiv","sub_path":"download_arxiv.py","file_name":"download_arxiv.py","file_ext":"py","file_size_in_byte":4117,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"10445010448","text":"from rest_framework import serializers\n\nfrom apps.visums.models import LinkedCategory\nfrom apps.visums.models.enums import CheckState\nfrom apps.visums.serializers import CategorySerializer, LinkedSubCategorySerializer\n\n\n# LOGGING\nimport logging\nfrom scouts_auth.inuits.logging import InuitsLogger\n\nlogger: InuitsLogger = logging.getLogger(__name__)\n\n\nclass LinkedCategorySerializer(serializers.ModelSerializer):\n\n parent = CategorySerializer()\n sub_categories = LinkedSubCategorySerializer(many=True)\n\n class Meta:\n model = LinkedCategory\n exclude = [\"category_set\"]\n\n def to_representation(self, obj: LinkedCategory) -> dict:\n if obj.is_archived:\n return None\n\n # logger.debug(\"LINKED CATEGORY TO_REPRESENTATION: %s\", obj)\n\n data = super().to_representation(obj)\n\n # logger.debug(\"LINKED CATEGORY TO_REPRESENTATION: %s\", obj)\n\n # data[\"state\"] = CheckState.CHECKED\n # for sub_category in data.get(\"sub_categories\", []):\n # if CheckState.is_unchecked(sub_category.get(\"state\", CheckState.UNCHECKED)):\n # data[\"state\"] = CheckState.UNCHECKED\n # break\n data[\"state\"] = obj.check_state\n data[\"readable_name\"] = obj.readable_name\n\n data[\"camp\"] = {}\n data[\"visum\"] = {}\n\n visum = obj.category_set.visum\n\n data[\"camp\"][\"name\"] = visum.name\n data[\"visum\"][\"id\"] = visum.id\n\n return data\n","repo_name":"ScoutsGidsenVL/kampvisum-backend","sub_path":"scouts_kampvisum_api/apps/visums/serializers/linked_category_serializer.py","file_name":"linked_category_serializer.py","file_ext":"py","file_size_in_byte":1457,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"33262346247","text":"\"\"\"empty message\n\nRevision ID: 7086759e272f\nRevises: e017ed70b3f8\nCreate Date: 2016-10-15 09:31:31.262126\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = '7086759e272f'\ndown_revision = 'e017ed70b3f8'\n\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import postgresql\n\ndef upgrade():\n ### commands auto generated by Alembic - please adjust! ###\n op.add_column('users', sa.Column('login_type', sa.String(length=12), nullable=True))\n op.drop_column('users', 'login_method')\n ### end Alembic commands ###\n\n\ndef downgrade():\n ### commands auto generated by Alembic - please adjust! ###\n op.add_column('users', sa.Column('login_method', postgresql.ENUM('local', 'oauth', name='login_method'), autoincrement=False, nullable=True))\n op.drop_column('users', 'login_type')\n ### end Alembic commands ###\n","repo_name":"mosegontar/chessquick","sub_path":"migrations/versions/7086759e272f_.py","file_name":"7086759e272f_.py","file_ext":"py","file_size_in_byte":851,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"21482725961","text":"import os\nimport pathlib\nimport tempfile\n\nfrom c3s_eqc_automatic_quality_control import dashboard\n\n\ndef test_get_logger() -> None:\n logger_name = \"test-logger\"\n logger = dashboard.get_logger(logger_name)\n assert logger.name == logger_name\n\n\ndef test_set_log_file() -> None:\n logger_name = \"test-logger\"\n logger_text = \"TEST EQC\"\n logger = dashboard.get_logger(logger_name)\n with tempfile.NamedTemporaryFile(delete=False) as t:\n name = t.name\n logger = dashboard.set_logfile(logger, pathlib.Path(name))\n logger.info(logger_text)\n with open(name) as f:\n assert logger_text in f.readline()\n os.remove(name)\n assert not os.path.exists(name)\n\n\ndef test_ensure_log_dir() -> None:\n with tempfile.TemporaryDirectory() as tempdir:\n original_eqc_etc = os.environ.get(dashboard.EQC_AQC_ENV_VARNAME)\n os.environ[dashboard.EQC_AQC_ENV_VARNAME] = tempdir\n log_dir = dashboard.ensure_log_dir()\n assert log_dir.is_dir()\n if original_eqc_etc is None:\n os.environ.pop(dashboard.EQC_AQC_ENV_VARNAME)\n else:\n os.environ[dashboard.EQC_AQC_ENV_VARNAME] = original_eqc_etc\n\n\ndef test_get_eqc_run_logger() -> None:\n logger_name = \"logger-name\"\n logger = dashboard.get_eqc_run_logger(logger_name)\n assert f\"_{logger_name}.log\" in getattr(logger.handlers[0], \"baseFilename\")\n","repo_name":"bopen/c3s-eqc-automatic-quality-control","sub_path":"tests/test_50_dashboard.py","file_name":"test_50_dashboard.py","file_ext":"py","file_size_in_byte":1383,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"52"} +{"seq_id":"38428362038","text":"from keras.applications.resnet50 import ResNet50\nfrom keras.preprocessing import image\nfrom keras.applications.resnet50 import preprocess_input, decode_predictions\nimport tensorflow\nimport numpy as np\nfrom matplotlib import pyplot\nimport pandas as pd\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom sklearn.datasets import make_regression\nfrom sklearn.preprocessing import MinMaxScaler\n\nfrom keras.applications.vgg19 import VGG19\n# from keras.applications.densenet.DenseNet169 import DenseNet169\nfrom keras_applications import densenet\n\nfrom sklearn.decomposition import PCA\n\nfrom matplotlib import pyplot\nfrom mpl_toolkits.mplot3d import Axes3D\nimport matplotlib.pyplot as plt\n\n\n\n\nfrom keras.applications.vgg16 import VGG16\nfrom keras.preprocessing import image\nfrom keras.applications.vgg16 import preprocess_input\nimport numpy as np\n\n\n# def CALC_SHOW_PCA_3D(Data, STRING_MODEL):\n# pca = PCA(n_components=3)\n# DataPCA = pca.fit(Data).transform(Data)\n# target_names = ['MRI', 'CT']\n# group1 = np.full((len(ImagesOfMri)), 0)#Mri Group=0\n# group2 = np.full((len(ImagesOfCt)), 1)#ct Group=0\n# Group = np.concatenate((group1, group2), axis=0, out=None)#all Groups\n# x = [x for (x ,y , z) in DataPCA]\n# y = [y for (x ,y , z) in DataPCA]\n# z=[z for (x ,y , z) in DataPCA]\n#\n# fig = pyplot.figure()\n# ax = Axes3D(fig)\n# colors = ['red', 'blue']\n# lw = 2\n#\n# for color, i, target_name in zip(colors, [0, 1], target_names):#Draw a 3D graph by the colored groups\n# ax.scatter(DataPCA[Group == i, 0], DataPCA[Group == i, 1], DataPCA[Group == i, 2], color=color, alpha=.8, lw=lw, label=target_name)\n# plt.legend(loc='best', shadow=False, scatterpoints=1)\n# plt.title('PCA of mri/ct '+STRING_MODEL)\n#\n# pyplot.show()\n\n# model = VGG16(weights='imagenet', include_top=False)\n# model = ResNet50(weights='imagenet')\n# model = VGG19(weights='imagenet')\nmodel = densenet.DenseNet169(weights='imagenet')\n\n\ndef get_features_images(images):\n features=[]\n for img in images:\n x = image.img_to_array(img)\n x = np.expand_dims(x, axis=0)\n x = preprocess_input(x)\n features.append(model.predict(x)[0])\n return features\n\n\n\n\ndf = pd.read_csv('InputFiles/dataset.csv', names=['Images', 'Questions', 'Answers']) # open csv file and rename columns\n# predictions = pd.read_csv('InputFiles/VQAM.csv', names=['Images', 'Questions', 'Answers']) # open csv file and rename columns\n\n\n# # dictionary of replaceable words\nreplace_dict = {\"magnetic resonance imaging\": \"mri\",\n \"mri scan\": 'mri',\n \"MRI\": \"mri\",\n \"shows\": \"show\",\n \"reveal\": \"show\",\n \"demonstrate\": \"show\",\n \"CT\": \"ct\",\n \"ct scan\": \"ct\",\n \"does\": \"\", \"do \": \"\", \"the\": \"\",\n # \" a \":' ',' is ':' ',\n }\ndf.replace(to_replace=replace_dict, inplace=True, regex=True) # replace word\n\n\n\nImagesOfMri = df[(~df['Questions'].str.contains('mri|ct') & df['Questions'].str.contains('what') & df[\n 'Answers'].str.contains('mri')) == True]['Images']\nImagesOfCt = df[(~df['Questions'].str.contains('mri|ct') & df['Questions'].str.contains('what') & df[\n 'Answers'].str.contains('ct')) == True]['Images']\n\nImagesMri = [image.load_img(\"images\\Train-images\\\\\" + img + \".jpg\", target_size=(224, 224)) for img in ImagesOfMri]\nImagesCt = [image.load_img(\"images\\Train-images\\\\\" + img + \".jpg\", target_size=(224, 224)) for img in ImagesOfCt]\n\n\n\n\nfeaturesMri=get_features_images(ImagesMri)\nfeaturesCt=get_features_images(ImagesCt)\n\nsizeMri=len(featuresMri)\nsizeCt=len(featuresCt)\n\n\nTrainingData = np.concatenate((featuresMri[:int(sizeMri*0.8)], featuresCt[:int(sizeCt*0.8)]), axis=0, out=None)\ny = np.full((len(featuresMri[:int(sizeMri*0.8)])), 0)\ny1 = np.full((len(featuresCt[:int(sizeCt*0.8)])), 1)\nY = np.concatenate((y, y1), axis=0, out=None)\n# print(len(featuresCt))\n\n\n\n\n\n\nsizefeatur=len(featuresCt[0])\n\n# create model\nmodel = Sequential()\nmodel.add(Dense(12, input_dim=sizefeatur, init='uniform', activation='relu'))\nmodel.add(Dense(sizefeatur, init='uniform', activation='relu'))\nmodel.add(Dense(1, init='uniform', activation='sigmoid'))\n# Compile model\nmodel.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n# Fit the model-The training proces\nmodel.fit(TrainingData, Y, epochs=150, batch_size=10, verbose=2)\n# calculate predictions\npredictions = model.predict(TrainingData)\n# round predictions\nrounded = [round(x[0]) for x in predictions]\nprint(rounded)\n\n\ny = np.full((len(featuresMri[int(sizeMri*0.8):])), 0)\ny1 = np.full((len(featuresCt[int(sizeCt*0.8):])), 1)\nYreal= np.concatenate((y, y1), axis=0, out=None)\n\n# new instances where we do not know the answer\nnewData= np.concatenate((featuresMri[int(sizeMri*0.8):], featuresCt[int(sizeCt*0.8):]), axis=0, out=None)\n\n# make a prediction-predict the class\nynew = model.predict_classes(newData)\n# show the inputs and predicted outputs\n#\nfor i in range(len(newData)):\n\t# print(\"X=%s, Predicted=%s\" % (newData[i], ynew[i]))\n\tprint(\"Real=%s,Predicted=%s\" % (Yreal[i], ynew[i]))\n # if Yreal[i]==ynew[i]:\n # n+=1\nn=[1 for i in range(len(newData)) if Yreal[i]==ynew[i]]\ncorrect=sum(n)\nprint(\"%s /%s Are correct \"%(correct,len(newData)))\n\n\n\n\n\n\n\n\n# CALC_SHOW_PCA_3D(dataFeatures,\"DL-VGG16\")\n\n\n","repo_name":"shoamco/VQA-of-CT-and-MRI-in-deep-learning","sub_path":"Research Process/DeepLearning.py","file_name":"DeepLearning.py","file_ext":"py","file_size_in_byte":5377,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"23211664193","text":"\"\"\"\nCP1404 - Prac07\nconsole program to test programming language class\n\"\"\"\n\nfrom prac07.programminglanguage import ProgrammingLanguage\n\n\ndef main():\n ruby = ProgrammingLanguage(\"Ruby\", \"Dynamic\", True, 1995)\n python = ProgrammingLanguage(\"Python\", \"Dynamic\", True, 1991)\n vb = ProgrammingLanguage(\"Visual Basic\", \"Static\", False, 1991)\n print(python)\n language_list = [ruby, python, vb]\n print(\"The dynamically typed languages are:\")\n for language in language_list:\n if language.typing == \"Dynamic\":\n print(language.name)\n\nmain()\n","repo_name":"KeeganBentley/CP1404-Pracs","sub_path":"prac07/languageprogram.py","file_name":"languageprogram.py","file_ext":"py","file_size_in_byte":569,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"6204096150","text":"from torch import nn\nfrom torch.nn import init\nimport torch.nn.functional as F\n\n\n# Convolution Auto Encoder\nclass LeNetAE28(nn.Module):\n def __init__(self):\n super(LeNetAE28, self).__init__()\n\n # Feature Extractor\n self.feature_extractor = nn.Sequential(\n nn.Conv2d(1, 20, 5, stride=1), # (b, 20, 24, 24)\n nn.ReLU(),\n nn.MaxPool2d(2, stride=2), # (b, 20, 12, 12)\n nn.Conv2d(20, 50, 5, stride=1), # (b, 50, 8, 8)\n nn.ReLU(),\n nn.MaxPool2d(2, stride=2) # (b, 50, 4, 4)\n )\n\n self.fc = nn.Sequential(\n nn.Linear(800, 500),\n nn.ReLU(),\n nn.Linear(500, 500),\n nn.ReLU(),\n nn.Linear(500, 10)\n )\n\n for module in self.modules():\n if isinstance(module, nn.Conv2d):\n init.xavier_uniform_(module.weight)\n\n def forward(self, x):\n feature = self.feature_extractor(x)\n feature = feature.flatten(start_dim=1)\n res = self.fc(feature)\n return res\n\nclass CUDNN(nn.Module):\n def __init__(self):\n super(CUDNN, self).__init__()\n\n # Feature Extractor\n self.feature_extractor = nn.Sequential(\n nn.Conv2d(3, 64, 5),\n nn.ReLU(),\n nn.MaxPool2d(2),\n # (b, 64, 14, 14)\n nn.Dropout2d(.25),\n nn.Conv2d(64, 128, 3),\n nn.ReLU(),\n nn.MaxPool2d(2),\n # (b, 128, 6, 6)\n nn.Dropout2d(.25),\n nn.Conv2d(128, 256, 3),\n nn.ReLU(),\n nn.MaxPool2d(2),\n # (b, 256, 2, 2)\n nn.Dropout2d(.25),\n nn.Conv2d(256, 128, 2),\n nn.ReLU()\n # (b, 128, 1, 1)\n )\n\n self.fc = nn.Sequential(\n nn.Linear(128, 256),\n nn.ReLU(),\n nn.Linear(256, 256),\n nn.ReLU(),\n nn.Linear(256, 10)\n )\n\n for module in self.modules():\n if isinstance(module, nn.Conv2d):\n init.xavier_uniform_(module.weight)\n\n def forward(self, x):\n feature = self.feature_extractor(x)\n feature = feature.flatten(start_dim=1)\n res = self.fc(feature)\n return res\n\n\nclass IRNet(nn.Module):\n def __init__(self, fe=None, num_mac=4, inter_dim=50):\n super(IRNet, self).__init__()\n\n # Feature Extractor\n self.feature_extractor = fe\n self.mac_layer = nn.MaxPool2d(num_mac, stride=1)\n self.dim = inter_dim\n\n for module in self.modules():\n if isinstance(module, nn.Conv2d):\n init.xavier_uniform_(module.weight)\n\n def forward(self, x):\n if isinstance(x, tuple):\n x1, x2 = x\n feature_x = self.feature_extractor(x1)\n feature_y = self.feature_extractor(x2)\n mvx = self.mac_layer(feature_x).view(-1, self.dim)\n mvy = self.mac_layer(feature_y).view(-1, self.dim)\n mvx = F.normalize(mvx, p=2, dim=1)\n mvy = F.normalize(mvy, p=2, dim=1)\n\n return mvx, mvy\n else:\n feature = self.feature_extractor(x)\n mac_vector = self.mac_layer(feature)\n mac_vector = mac_vector.view(-1, self.dim)\n mac_vector = F.normalize(mac_vector, p=2, dim=1)\n\n return mac_vector\n\n\n","repo_name":"voidstrike/AdversarialAttack","sub_path":"src/Model.py","file_name":"Model.py","file_ext":"py","file_size_in_byte":3361,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"22417013976","text":"# HELP THE BOOK SELLER - 6 kyu\n# https://www.codewars.com/kata/54dc6f5a224c26032800005c\n\n#, \"(A : 200) - (B : 1140)\"\n\nb = [\"ABAR 200\", \"CDXE 500\", \"BKWR 250\", \"BTSQ 890\", \"DRTY 600\"]\nc = [\"A\", \"B\"]\n\ndic = {key:0 for key in c}\n\nfor i in b:\n if i[0] in dic:\n dic[i[0]] += int(i.split()[1])\n \nout = \"\"\nfor x in dic:\n if len(out) != 0:\n out += \" - \"\n out += f\"({x} : {dic[x]})\"\n\n","repo_name":"j0rdiC/codewars","sub_path":"f.help-book-seller.py","file_name":"f.help-book-seller.py","file_ext":"py","file_size_in_byte":409,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"40174712815","text":"#\n# @lc app=leetcode id=11 lang=python3\n#\n# [11] Container With Most Water\n#\n\n# @lc code=start\nclass Solution:\n def maxArea(self, height: List[int]) -> int:\n start = 0\n end = len(height) - 1\n maxWater = 0\n while start < end:\n water = (end-start) * min(height[start], height[end])\n maxWater = max(water, maxWater)\n if height[start] < height[end]: start += 1\n else: end -= 1\n \n return maxWater\n# @lc code=end\n\n","repo_name":"kazuma-shinomiya/leetcode","sub_path":"11.container-with-most-water.py","file_name":"11.container-with-most-water.py","file_ext":"py","file_size_in_byte":497,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"1703820477","text":"from gi.repository import Gtk\n# from .spec_filechoosers import ExposureLoader, FileEntryWithButton\n\nclass PleaseWaitDialog(Gtk.Dialog):\n def __init__(self, title='Data reduction running, please wait...', parent=None, flags=Gtk.DialogFlags.DESTROY_WITH_PARENT | Gtk.DialogFlags.MODAL, buttons=(Gtk.STOCK_CANCEL, Gtk.ResponseType.CANCEL)):\n Gtk.Dialog.__init__(self, title, parent, flags, buttons)\n vb = self.get_content_area()\n self.pbar = Gtk.ProgressBar()\n self.pbar.set_text('Working...')\n self.label = Gtk.Label()\n self.label.set_line_wrap(True)\n vb.pack_start(self.pbar, False, True, 0)\n vb.pack_start(self.label, True, True, 0)\n vb.show_all()\n def set_label_text(self, msg):\n self.label.set_text(msg)\n self.pbar.pulse()\n \nclass PleaseWaitInfoBar(Gtk.InfoBar):\n def __init__(self, buttons=(Gtk.STOCK_CANCEL, Gtk.ResponseType.CANCEL)):\n Gtk.InfoBar.__init__(self)\n for i in range(len(buttons) / 2):\n self.add_button(buttons[2 * i], buttons[2 * i + 1])\n self.set_message_type(Gtk.MessageType.INFO)\n vb = self.get_content_area()\n self.label = Gtk.Label('Data reduction running...')\n self.pbar = Gtk.ProgressBar()\n self.pbar.set_text('Working...')\n vb.pack_start(self.label, False, True, 0)\n vb.pack_start(self.pbar, False, True, 0)\n self.show_all()\n def set_label_text(self, msg):\n self.pbar.set_text(msg)\n self.pbar.pulse()\n def set_n_jobs(self, n):\n self.label.set_text('%d data reduction job(s) running...' % n)\n\n","repo_name":"awacha/SAXSCtrl","sub_path":"retired_code/data_reduction_setup.py","file_name":"data_reduction_setup.py","file_ext":"py","file_size_in_byte":1618,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"32378334235","text":"__author__ = \"Trần Văn Hạnh\"\nfrom math import * \n\"\"\"\nLập phương trình thực hiện công việc sau:\n 1. Nhập ba số a, b, c bất kỳ từ bàn phím\n 2. Giải và biện luận nghiệm của phương trình bậc 2: ax^2 + bx + c =0 ( kể cả trường hợp nghiệm phức)\n 3. Đưa kết quả ra màn hình\n\"\"\"\ndef solveFirstEqua():\n\ttry: \n\t\ta = float(input('Vui lòng nhập hệ số a: ')) \n\t\tb = float(input('Vui lòng nhập hệ số b: ')) \n\n\t\tif a != 0 and b != 0:\n\t\t\tx = round(-b/a,2)\n\t\t\tprint(\"Nghiệm của phương trình là: x = %s\" %x)\n\t\telif a != 0 and b == 0:\n\t\t\tx = 0\n\t\t\tprint(\"Nghiệm của phương trình là: x = %s\" %x)\n\t\telse:\n\t\t\tprint(\"Không phải là phương trình\")\n\texcept ValueError:\n\t\tprint(\"Vui lòng nhập lại giá trị số tự nhiên: \")\n\ndef solveSecondEqua():\n\ttry: \n\t\ta = float(input('Vui lòng nhập hệ số a: ')) \n\t\tb = float(input('Vui lòng nhập hệ số b: ')) \n\t\tc = float(input('Vui lòng nhập hệ số c: ')) \n\n\t\tif(a != 0 and b != 0 and c != 0):\n\t\t\t# calculate the discriminant \n\t\t\td = (b**2) - (4*a*c) \n\t\t\t \n\t\t\t# find two solutions \n\t\t\tif d < 0:\n\t\t\t print(\"Phương trình này vô nghiệm\")\n\t\t\telif d == 0:\n\t\t\t x = round((-b + sqrt(b**2-4*a*c))/2*a, 2)\n\t\t\t print(\"Phương trình này có một nghiệp duy nhất: \", x)\n\t\t\telse:\n\t\t\t x1 = round((-b + sqrt(b**2-4*a*c))/2*a, 2)\n\t\t\t x2 = round((-b - sqrt(b**2-4*a*c))/2*a, 2)\n\t\t\t sp = (\" \")*10\n\t\t\t print(\"Phương trình này có 2 nghiệm: \\n{sp} x1 = {x1}\\n{sp} x2 = {x2} \".format(x1 = x1, x2 = x2, sp = sp))\n\t\telif a != 0 and b != 0 and c == 0:\n\t\t\tx1 = 0\n\t\t\tx2 = round(-b/a, 2)\n\t\t\tsp = (\" \")*10\n\t\t\tprint(\"Phương trình này có 2 nghiệm: \\n{sp} x1 = {x1}\\n{sp} x2 = {x2} \".format(x1 = x1, x2 = x2, sp = sp))\n\t\telse:\n\t\t\tprint(\"Không phải là phương trình mà bạn muốn giải!\")\n\texcept ValueError:\n\t\tprint(\"Vui lòng nhập lại giá trị số tự nhiên: \")\n# ---------------------------------------------------------------\nprint(\"-------- Giải phương trình bậc nhất và bậc hai ----------\")\nprint(\"Bạn muốn giải phương trình nào ?\\n- Nhập 1 cho bậc phương trình bậc 1 \\n- Nhập 2 cho phương trình bậc 2\")\ntry:\n\tx = int(input())\n\tif x == 1:\n\t solveFirstEqua()\n\telse:\n\t solveSecondEqua()\nexcept:\n\tprint(\"Vui lòng nhập lại sự lựa chọn:\")\n","repo_name":"tranhanhIT/PythonPlusPlus","sub_path":"exEquation.py","file_name":"exEquation.py","file_ext":"py","file_size_in_byte":2394,"program_lang":"python","lang":"vi","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"38806963483","text":"\"\"\"\nFaça um programa que pergunte a hora ao usuário e, baseando-se no horário descrito, exiba a saudação apropriada. Ex. \nBom dia 0-11, Boa tarde 12-17 e Boa noite 18-23.\n\"\"\"\n\n#------------------MINHA SOLUÇÃO-------------------\n\nhora = input('Que horas são? ')\n\nif hora.isdigit() == True:\n hora_float = float(hora)\n if hora_float <= 12 and hora_float > 4:\n print('Bom dia!')\n elif hora_float <= 18:\n print('Boa tarde!')\n elif hora_float >= 19:\n print('Boa noite!')\n elif hora_float <= 4 and hora_float > 0:\n print('Está de madrugada')\nelse:\n print('Não é um número')\n\n#---------------CORREÇÃO-------------","repo_name":"euToni/UdEmyPY","sub_path":"aulas 31 ex/ex02.py","file_name":"ex02.py","file_ext":"py","file_size_in_byte":665,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"40835718539","text":"import numpy as np\n\ndef sigmoid_(x):\n\tif type(x) == list:\n\t\tfor i in range(len(x)):\n\t\t\tx[i] = 1 / (1 + np.exp(-x[i]))\n\t\treturn x\n\telse:\n\t\treturn 1 / (1 + np.exp(-x))\n\tlog_loss.py","repo_name":"flavienfr/bootcamp_ml","sub_path":"day02/ex00/sigmoid.py","file_name":"sigmoid.py","file_ext":"py","file_size_in_byte":178,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"36679555557","text":"import csv\n\nList=['Name', 'Email','Mobile', 'University', 'Major']\n\ndef mainFunc():\n\n var=True\n d=dict()\n x=[]\n print(\"Please enter your info\")\n while 1:\n name=input(\"Name: \")\n if (name==\"stop\"):\n break\n x.append(name)\n email = input(\"Email: \")\n if (email == \"stop\"):\n break\n x.append(email)\n mobile = input(\"Mobile: \")\n if (mobile == \"stop\"):\n break\n x.append(mobile)\n uni = input(\"University: \")\n if (uni == \"stop\"):\n break\n x.append(uni)\n major = input(\"major: \")\n if (major == \"stop\"):\n break\n x.append(major)\n j=0\n csvInit()\n for i in range(len(x)):\n if x[i]==\"stop\":\n break\n\n d[List[j]] = x[i]\n j=j+1\n if j==5:\n j=0\n csvWrite(d)\n\n print(d)\n\n\ndef csvInit():\n f = open('names.csv', 'w')\n\n with f:\n headers = ['Name', 'Email', 'Mobile', 'University', 'Major']\n writer = csv.DictWriter(f, fieldnames=headers)\n\n writer.writeheader()\n\ndef csvWrite(d):\n f = open('names.csv', 'a')\n\n with f:\n headers = ['Name', 'Email', 'Mobile', 'University', 'Major']\n writer = csv.DictWriter(f, fieldnames=headers)\n\n\n writer.writerow(d)\n\nmainFunc()\n","repo_name":"habibaashraf/RT---Task-7","sub_path":"Task7.py","file_name":"Task7.py","file_ext":"py","file_size_in_byte":1336,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"7896504269","text":"# 격자판의 각 점은 두 정수의 쌍 (x,y)로 나타낸다\n# 제일 처음 수빈이의 위치는 (xs, yx), 집이 위치한 (xe, ye)로 이동하려 한다\n# 두 가지 방법으로 이동 가능\n # 1. 점프 - 상하좌우 점프 (1초 걸림)\n # 2. 텔레포트 - 정해진 좌표로 이동 (10초 걸림)\n# 집에 갈 수 있는 가장 빠른 시간은?\n\nimport sys\ninput = sys.stdin.readline\n\ndef calc_time(lst):\n time = 0\n x, y = xs, ys\n for (x1, y1, x2, y2) in lst:\n time += abs(x1-x) + abs(y1-y) + 10\n x, y = x2, y2\n time += abs(xe-x) + abs(ye-y)\n return time\n\ndef dfs(n, lst):\n global ans\n\n if n == 3: # 마지막 텔포 사용 시점(종료 시점)\n t = calc_time(lst)\n # 최소값 갱신\n if t < ans:\n ans = t\n return\n\n for k in range(3):\n if not used[k]:\n used[k] = True # 사용 표시\n x1, y1, x2, y2 = telpo_lst[k] # 사용할 텔포 좌표(출발, 도착)\n dfs(n+1, lst+[(x1, y1, x2, y2)]) # (x1,y1) => (x2,y2)로 이동\n dfs(n+1, lst+[(x2, y2, x1, y1)]) # (x2,y2) => (x1,y1)로 이동\n dfs(n+1, lst) # 텔레포트 사용 X\n used[k] = False # 사용 표시 제거\n\nxs, ys = map(int, input().split()) # 출발 좌표\nxe, ye = map(int, input().split()) # 도착 좌표\ntelpo_lst = []\nfor _ in range(3):\n x1, y1, x2, y2 = map(int, input().split())\n telpo_lst.append([x1, y1, x2, y2])\nused = [False] * 3\nans = abs(xs-xe) + abs(ys-ye) # 최소 시간(기본값: 출발지점~도착지점)\n\ndfs(0, [])\n\nprint(ans)","repo_name":"Going777/Algorithm","sub_path":"BOJ/Gold/12908_텔레포트 3.py","file_name":"12908_텔레포트 3.py","file_ext":"py","file_size_in_byte":1660,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"1626100910","text":"import os\n\nfrom sqlalchemy.exc import OperationalError, ProgrammingError\nfrom tqdm import tqdm\n\n\nclass ETLProcess(object):\n def __init__(self, read_db, write_db, write_table_name):\n self.read_db = read_db\n self.write_db = write_db\n self.write_table_name = write_table_name\n self._reset()\n\n def __unicode__(self):\n return \"ETLProcess: (table: {})\".format(self.write_table_name)\n\n def extract(self, sql, write_pk_field=None, types=None):\n self.types = types\n if sql.endswith('.sql'):\n with open(sql, 'r') as f:\n sql = f.read()\n if write_pk_field:\n self.extract_method = (self._format_sql, (sql, write_pk_field))\n else:\n self.extract_method = (self.read_db.query, (sql,))\n\n def transform(self, *fields):\n self.transform_pipeline.fields = fields\n return self.transform_pipeline\n\n def load(self, upsert_fields=None, ensure=None, safe=False):\n method, args = self.extract_method\n if os.getenv(\"VERBOSE\"):\n print(\"Querying data...\")\n results = method(*args)\n results = self._apply_middleware(results)\n if results:\n if os.getenv(\"VERBOSE\"):\n print(\"Loading {}\".format(self.write_table_name))\n results = tqdm(results)\n\n table = self.write_db[self.write_table_name]\n self._write_rows(table, results, upsert_fields, ensure, safe)\n self._reset()\n\n def extract_override(self, f):\n self.extract_method = (f, tuple())\n\n def link(self, field, target, table_name, child_field, name=None):\n self.links.append(\n (field, target, table_name, child_field, {\"name\": name}))\n\n def link_closest(self, field, target, table_name, child_field, name=None,\n method=\">=\"):\n self.links.append((field, target, table_name, child_field,\n {'closest_method': method, 'name': name}))\n\n def middleware(self, f):\n self._middleware.append(f)\n\n def ignore(self, *args):\n self._ignored += args\n\n def _reset(self):\n self.transform_pipeline = TransformPipeline()\n self.transform_pipeline._reset()\n self.links = []\n self._middleware = []\n self._ignored = []\n self.types = None\n\n def _apply_middleware(self, results):\n for middleware in self._middleware:\n results = middleware(results)\n return results\n\n def _format_sql(self, sql, write_pk_field):\n last_pk = 0\n try:\n rows = self.write_db.query(\"SELECT MAX({}) max FROM {};\".format(\n write_pk_field, self.write_table_name\n ))\n except (OperationalError, ProgrammingError):\n pass\n else:\n last_pk = next(rows)['max'] or last_pk\n return self.read_db.query(sql.format(last_pk))\n\n def _write_rows(self, table, rows, upsert_fields, ensure=None, safe=False):\n dropped = False\n for row in rows:\n row_data = self._update_row(row)\n if upsert_fields:\n table.upsert(row_data, upsert_fields, ensure=None)\n else:\n table.insert(row_data, ensure=ensure)\n if not dropped and not safe:\n self._drop_old_columns(table, row_data.keys())\n dropped = True\n\n def _update_row(self, row):\n row_data = self._type_format(row)\n row_data = self.transform_pipeline.transform(row)\n row_data = self._make_links(row_data)\n row_data = self._remove_ignored(row_data)\n return row_data\n\n def _remove_ignored(self, row):\n for field in self._ignored:\n row.pop(field)\n return row\n\n def _drop_old_columns(self, table, current_columns):\n current_columns += ['id']\n for column in table.columns:\n if column not in current_columns:\n table.drop_column(column)\n\n def _make_links(self, row_data):\n for field, target, table_name, child_field, options in self.links:\n closest_method = options.get('closest_method')\n if closest_method:\n query = \"SELECT id FROM {0} WHERE {1} \" + closest_method + \" \\\n {2} ORDER BY {1};\"\n if closest_method.startswith(\"<\"):\n query = query[:-1] + \" DESC;\"\n else:\n query = \"SELECT id FROM {0} WHERE {1} = {2};\"\n query = query.format(table_name, child_field, row_data[target])\n res = self.write_db.query(query)\n try:\n id = next(res)['id']\n except StopIteration:\n id = None\n row_data[options.get(\"name\", field)] = id\n return row_data\n\n def _type_format(self, row):\n if self.types:\n for k, type_ in self.types.items():\n row[k] = type_(row[k])\n return row\n\n\ndef default(data):\n def inner(default_value):\n return data or default_value\n return inner\n\n\ndef func(data):\n def inner(f):\n return f(data)\n return inner\n\n\nclass TransformPipeline(object):\n builtin_methods = {\n 'default': default,\n 'func': func,\n }\n\n def __init__(self):\n self.fields = []\n self.pipeline = {}\n\n def __unicode__(self):\n return \"TransformPipeline (fields: {})\".format(self.fields)\n\n def __getattr__(self, method, *args, **kwargs):\n def inner(*args, **kwargs):\n for field in self.fields:\n if field not in self.pipeline:\n self.pipeline[field] = []\n self.pipeline[field].append((method, args, kwargs))\n return self\n return inner\n\n def transform(self, row):\n for field, methods in self.pipeline.items():\n row[field] = self._update(row[field], methods)\n return row\n\n def _update(self, data, methods):\n for method, args, kwargs in methods:\n try:\n f = getattr(data, method)\n except AttributeError:\n f = self.builtin_methods[method](data)\n data = f(*args, **kwargs)\n return data\n\n def _reset(self):\n self.fields = []\n self.pipeline = {}\n","repo_name":"exit99/easy-etl","sub_path":"easy_etl/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":6285,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"52"} +{"seq_id":"5572648419","text":"print(\"Program started\")\n\n# First, we import the initialized sat helper class.\nfrom src.sathelper import SatHelper\nprint(\"SatHelper imported\")\n\n#variables\nsh = SatHelper()\n\n#initialize variables\nwidth = 2\nheight = 5\n\nnumberOfIs = 2\npieces = []\nlistOfIs = []\nnumberOfPixelsForOneI = 4\nfor indexI in range(numberOfIs):\n for indexPixelI in range(1, numberOfPixelsForOneI + 1):\n listOfIs.append(\"I\" + str(indexI+1) + \"P\" + str(indexPixelI))\n\npieces += listOfIs\n\nprint(\"list of Is \" , listOfIs)\n#pieces = [\"I\", \"IB1\", \"IB2\", \"IB3\", \"IB4\", \"IB5\" \n# \"T\", \"TB\",\n# \"L\", \"LB\",\n# \"S\", \"SB\",\n# \"O\", \"OB\",\n# \"NaN\"] # begin I, begin T, etc. \n# begin O2, Begin O2, Begin O3, Begin I1.\n#add variables for field\nfor x in range(width):\n for y in range(height):\n for piece in pieces:\n print(\"p\"+str(x)+str(y)+str(piece))\n sh.declareVariable(name=\"p\"+str(x)+str(y)+str(piece))\nprint(\"field variables added\")\n\nfor x in range(width):\n for y in range(height):\n piecesArray = [f\"p\"+str(x)+str(y)+str(i) for i in pieces]\n i = 0\n for piece in piecesArray:\n for letter in pieces:\n piece += letter\n\n print(piecesArray)\n sh.addAtMostOne(piecesArray) # maximum eins\n sh.addClause(piecesArray) # clausel minimum eins stelle sicher das genau 1 da ist\nprint(\"field atMostOne & addClause clausel added\")\n\n#I = 1 x 4 \n# 0\n# 0 X\n# 1 X\n# 2 X\n# 3 X\n# needed variables = 4\n# I = [0,0], [0,1], [0,2], [0,3] // [X,Y] = [right, bottom]\n# you don't need to add variables for the I \n# you just need to add implications\ni_height = 4\ni_width = 1\n\n# add Klausel for I\nfor x in range(width):\n for y in range(height):\n for beginIndex in range(1,numberOfIs+1):\n if x + i_width <= width:\n if y + i_height <= height:\n #sh.addImplies(\"p\"+str(x)+str(y)+\"IB\"+str(beginIndex),\n # \"p\"+str(x)+str(y)+\"IB\")\n sh.addEquivalent(\"p\"+str(x)+str(y)+\"I\"+str(beginIndex)+\"P1\",\n \"p\"+str(x)+str(y+1)+\"I\"+str(beginIndex)+\"P2\")\n sh.addEquivalent(\"p\"+str(x)+str(y)+\"I\"+str(beginIndex)+\"P1\",\n \"p\"+str(x)+str(y+2)+\"I\"+str(beginIndex)+\"P3\")\n sh.addEquivalent(\"p\"+str(x)+str(y)+\"I\"+str(beginIndex)+\"P1\",\n \"p\"+str(x)+str(y+3)+\"I\"+str(beginIndex)+\"P4\")\n print(\"p\"+str(x)+str(y)+\"I\"+str(beginIndex)+\"P1\", \" <=> \",\n \"p\"+str(x)+str(y+1)+\"I\"+str(beginIndex)+\"P2\")\n print(\"p\"+str(x)+str(y)+\"I\"+str(beginIndex)+\"P1\", \" <=> \",\n \"p\"+str(x)+str(y+2)+\"I\"+str(beginIndex)+\"P3\")\n print(\"p\"+str(x)+str(y)+\"I\"+str(beginIndex)+\"P1\", \" <=> \",\n \"p\"+str(x)+str(y+3)+\"I\"+str(beginIndex)+\"P4\")\n \nfor indexI in range(1, numberOfIs+1):\n listForI = []\n for x in range(width): \n for y in range(height):\n if x + i_width <= width:\n if y + i_height <= height:\n listForI.append(\"p\"+str(x)+str(y)+\"I\"+str(indexI)+\"P1\")\n print(\"listForI \" , listForI)\n sh.addClause(listForI)\n\nprint(\"I clausel added\")\n\n\n\n\n#O = 2 x 2\n# 01\n# 0 XX\n# 1 XX\n# needed variables = 4 // [X,Y] = [right, bottom]\n# O = [0,0], [1,0], [0,1], [1,1]\n# o_height = 2\n# o_width = 2\n# for x in range(o_width):\n# for y in range(o_height):\n# print(\"O\"+str(x)+str(y))\n# sh.declareVariable(\"O\"+str(x)+str(y))\n# print(\"O variable added\")\n\n# # add Klausel for O\n# for x in range(width):\n# for y in range(height):\n# if x + o_width <= width: \n# if y + o_height <= height:\n# sh.addClause([\"p\"+str(x)+str(y)+\"O\", \n# \"p\"+str(x+1)+str(y)+\"O\", \n# \"p\"+str(x)+str(y+1)+\"O\", \n# \"p\"+str(x+1)+str(y+1)+\"O\"])\n# print([\"p\"+str(x)+str(y)+\"O\", \n# \"p\"+str(x+1)+str(y)+\"O\", \n# \"p\"+str(x)+str(y+1)+\"O\", \n# \"p\"+str(x+1)+str(y+1)+\"O\"])\n# print(\"O clausel added\")\n\n\n# #T = 1x3 verknüpfung mit 1x1 an position [1,1]\n# # 012\n# # 0 XXX\n# # 1 X\n# # needed variables = 4 // [X,Y] = [right, bottom]\n# # T = [0,0], [1,0], [2,0], [1,1]\n# t_height = 2\n# t_width = 3\n# for x in range(t_width):\n# print(\"T\"+str(x)+\"0\")\n# sh.declareVariable(\"T\"+str(x)+\"0\")\n# print(\"T11\")\n# sh.declareVariable(\"T11\")\n\n# print(\"T variable added\")\n\n# # add Klausel for T\n# for x in range(width):\n# for y in range(height):\n# if x + t_width <= width: \n# if y + t_height <= height:\n# sh.addClause([\"p\"+str(x)+str(y)+\"T\", \n# \"p\"+str(x+1)+str(y)+\"T\", \n# \"p\"+str(x+2)+str(y)+\"T\", \n# \"p\"+str(x+1)+str(y+1)+\"T\"])\n# print([\"p\"+str(x)+str(y)+\"T\", \n# \"p\"+str(x+1)+str(y)+\"T\", \n# \"p\"+str(x+2)+str(y)+\"T\", \n# \"p\"+str(x+1)+str(y+1)+\"T\"])\n# print(\"T clausel added\")\n\n\n# #S = 2 x 1 verknüpfungmit 2x1 (um 1 nach rechts & 1 nach oben verschoben)\n# # 012\n# # 0 XX\n# # 1 XX\n# # needed variables = 4 // [X,Y] = [right, bottom]\n# # S = [0,1], [1,1], [1,0], [2,0]\n# s_height = 2\n# s_width = 3\n# for x in range(2):\n# print(\"S\"+str(x)+\"1\")\n# sh.declareVariable(\"S\"+str(x)+\"1\")\n\n# for x in range(1,3):\n# print(\"S\"+str(x)+\"0\")\n# sh.declareVariable(\"S\"+str(x)+\"0\")\n# print(\"S variable added\")\n\n# # add Klausel for S\n# for x in range(width):\n# for y in range(height):\n# if x + s_width <= width: \n# if y + s_height <= height:\n# sh.addClause([\"p\"+str(x)+str(y+1)+\"S\", \n# \"p\"+str(x+1)+str(y+1)+\"S\", \n# \"p\"+str(x+1)+str(y)+\"S\", \n# \"p\"+str(x+2)+str(y)+\"S\"])\n# print([\"p\"+str(x)+str(y+1)+\"S\", \n# \"p\"+str(x+1)+str(y+1)+\"S\", \n# \"p\"+str(x+1)+str(y)+\"S\", \n# \"p\"+str(x+2)+str(y)+\"S\"])\n# print(\"S clausel added\")\n\n# #L = 3 x 2\n# # 012\n# # 0 X\n# # 1 XXX\n# # needed variables = 4\n# # L = [0,0], [0,1], [1,1], [2,1]\n# l_height = 2\n# l_width = 3\n# for x in range(l_width):\n# print(\"L\"+str(x)+\"1\")\n# sh.declareVariable(\"L\"+str(x)+\"1\")\n\n# print(\"L00\")\n# sh.declareVariable(\"L00\")\n# print(\"L variable added\")\n\n\n# # add Klausel for L\n# for x in range(width):\n# for y in range(height):\n# if x + l_width <= width: \n# if y + l_height <= height:\n# sh.addClause([\"p\"+str(x)+str(y)+\"L\", \n# \"p\"+str(x)+str(y+1)+\"L\", \n# \"p\"+str(x+1)+str(y+1)+\"L\", \n# \"p\"+str(x+2)+str(y+1)+\"L\"])\n# print([\"p\"+str(x)+str(y)+\"L\", \n# \"p\"+str(x)+str(y+1)+\"L\", \n# \"p\"+str(x+1)+str(y+1)+\"L\", \n# \"p\"+str(x+2)+str(y+1)+\"L\"])\n# print(\"L clausel added\")\n\n#sh.printFormulaFile()\nsh.printFormula()\n#print(\"printed formula\")\n\nsh.solveSat()\nprint(\"SAT solved\")\n\nprint(\"Program finished\")","repo_name":"bester2112/SAT-Solving-basiertes-Object-Packing","sub_path":"Hauptprogramm/code/tests-scripts/06_3rd_try.py","file_name":"06_3rd_try.py","file_ext":"py","file_size_in_byte":7363,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"24331088946","text":"import time\nimport asyncio\n\nfrom bentoml.marshal.marshal import ParadeDispatcher\n\n\nasync def test_parade_dispatcher():\n MAX_LATENCY = 0.1\n\n def _test_func(i):\n return i * 2\n\n @ParadeDispatcher(MAX_LATENCY, max_size=1000)\n async def _do_sth_slow(input_list):\n await asyncio.sleep(0.1)\n return [_test_func(i) for i in input_list]\n\n inputs = [i for i in range(100)]\n time_st = time.time()\n outputs = await asyncio.gather(*(_do_sth_slow(i) for i in inputs))\n assert time.time() - time_st < 1\n assert [o == _test_func(i) for i, o in zip(inputs, outputs)]\n\n\nif __name__ == \"__main__\":\n asyncio.get_event_loop().run_until_complete(test_parade_dispatcher())\n","repo_name":"chengs369/BentoML","sub_path":"tests/marshal/test_marshal.py","file_name":"test_marshal.py","file_ext":"py","file_size_in_byte":701,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"52"} +{"seq_id":"22680521003","text":"from qdrant_client import QdrantClient\nfrom qdrant_client.http.models import SearchRequest\nfrom sentence_transformers import SentenceTransformer\nimport streamlit as st\nimport os\nimport logging\nfrom itertools import chain\n\n\nlogging.basicConfig(level=logging.WARNING)\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.WARNING)\n\nCOLLECTION_NAME = \"restaurant_review_answers\"\n\n\n@st.cache_resource\ndef get_qdrant_connection():\n host = os.getenv(\"QDRANT_HOST\")\n port = os.getenv(\"QDRANT_PORT\")\n return QdrantClient(host, port=port)\n\n\n@st.cache_resource\ndef get_embedding_model():\n return SentenceTransformer(\"all-MiniLM-L6-v2\")\n\n\ndef search(selected_list, df):\n if not selected_list:\n return\n\n qdrant = get_qdrant_connection()\n model = get_embedding_model()\n\n # Take text\n selected_rows = df.iloc[selected_list, :]\n selected_rows_list = selected_rows[\"body\"].to_list()\n\n # Transform text\n embeddings = model.encode(selected_rows_list)\n\n logger.debug(f\"embeddings length: {len(embeddings)}\")\n\n # Prepare and send requests\n requests = [\n SearchRequest(vector=vector.tolist(), limit=1, with_payload=True)\n for vector in embeddings\n ]\n\n hits_not_flattened = qdrant.search_batch(\n collection_name=COLLECTION_NAME,\n requests=requests,\n )\n\n # Hits flatten\n hits = list(chain.from_iterable(hits_not_flattened))\n\n if logger.level == logging.DEBUG:\n logger.debug(\"PRINTING HITS\")\n for i, hit in enumerate(hits, 1):\n logger.debug(f\"hit {i}: {hit}\")\n\n result = [\n {\"Message\": text, \"Score\": hit.score, \"Answer\": hit.payload[\"answer\"]}\n for text, hit in zip(selected_rows_list, hits)\n ]\n\n if logger.level == logging.DEBUG:\n logger.debug(\"PRINTING Result\")\n for i, item in enumerate(result, 1):\n logger.debug(f\"result {i}: {item}\")\n\n return result\n","repo_name":"lince098/MBD_NLP_Challenge_Restaurant_Reviews","sub_path":"challenge_functions/answer_reviews.py","file_name":"answer_reviews.py","file_ext":"py","file_size_in_byte":1913,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"20884322098","text":"import typing as tp\n\n\ndef filter_list_by_list(a: tp.Sequence[int], b: tp.Sequence[int]) \\\n -> tp.List[int]:\n \"\"\"\n Filter first sorted lists by other sorted list\n :param a: first sorted list\n :param b: second sorted list\n :return: filtered sorted list\n \"\"\"\n arr = []\n p1 = 0\n p2 = 0\n while p1 < len(a) and p2 < len(b):\n if a[p1] < b[p2]:\n arr.append(a[p1])\n p1 += 1\n elif a[p1] == b[p2]:\n p1 += 1\n else:\n p2 += 1\n else:\n while p1 < len(a):\n arr.append(a[p1])\n p1 += 1\n return arr\n","repo_name":"PredelinaAsya/Python_course_hw","sub_path":"filter_list_by_list/filter_list_by_list.py","file_name":"filter_list_by_list.py","file_ext":"py","file_size_in_byte":616,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"33861387642","text":"\"\"\"\n1510. Stone Game IV\nhttps://leetcode.com/problems/stone-game-iv/\n\nAlice and Bob take turns playing a game, with Alice starting first.\n\nInitially, there are n stones in a pile. On each player's turn, that player makes a move consisting of removing any non-zero square number of stones in the pile.\n\nAlso, if a player cannot make a move, he/she loses the game.\n\nGiven a positive integer n. Return True if and only if Alice wins the game otherwise return False, assuming both players play optimally.\n\n \n\nExample 1:\n\nInput: n = 1\nOutput: true\nExplanation: Alice can remove 1 stone winning the game because Bob doesn't have any moves.\n\n\"\"\"\n\n\nclass Solution:\n def winnerSquareGame(self, n: int) -> bool:\n \n if int(math.sqrt(n)) ** 2 == n:\n return True\n \n dp = [None] * (n+1)\n dp[0] = False\n steps = sorted([ele ** 2 for ele in range(1, int(math.sqrt(n)) + 1)], reverse=True)\n \n for i in range(n):\n for step in steps:\n if i + step <= n:\n if dp[i + step]:\n continue\n else:\n dp[i + step] = (False if dp[i] else True)\n \n if dp[-1]:\n return True\n return False\n \n \n\n","repo_name":"EvanTian233/Leetcode-solutions","sub_path":"Python_Solutions/Algorithms/4_DynamicProgramming/WinnerSquareGame.py","file_name":"WinnerSquareGame.py","file_ext":"py","file_size_in_byte":1313,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"44820377788","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\n视频信息爬取脚本\r\n参数为某视频的BV号\r\n因标签无法与视频信息同时获得，故需另外爬取。\r\n且MySQL数据库无法存储列表，故将标签列表放置与video_tag表中。\r\nvideo_info表与video_tag通过BV_bid进行连接\r\nget_tag_video_info为通过api获得视频标签\r\nget_tag_video_info_1_list、get_tag_video_info_1_data为通过网页原码获得视频标签\r\n\"\"\"\r\n__author__ = 'OtakuNio'\r\n\r\nimport time\r\nimport pandas as pd\r\nfrom lxml import etree\r\nfrom sqlalchemy import create_engine\r\nfrom get_response_with_proxy_ip import get_response\r\n\r\n\r\ndef video_info_main(bid):\r\n print(\"video_info\")\r\n my_url1 = 'https://api.bilibili.com/x/web-interface/view?bvid={}'.format(bid)\r\n # my_url2 = 'https://api.bilibili.com/x/tag/archive/tags?bvid={}'.format(bid)\r\n my_url3 = 'https://www.bilibili.com/video/{}'.format(bid)\r\n save_to_database_video_info(pd.DataFrame(data=get_data_video_info(get_response(my_url1).json()['data']), index=[1]),\r\n 'video_info')\r\n save_to_database_video_info(\r\n pd.DataFrame(data=get_tag_data_video_info(get_tag_list_video_info(get_response(my_url3)), bid), index=[1]),\r\n 'video_tag')\r\n print(\"video_info-finish\")\r\n\r\n\r\ndef get_data_video_info(response_json):\r\n video_info_data = {\r\n 'BV_bid': response_json['bvid'],\r\n 'aid': response_json['aid'],\r\n 'title': response_json['title'],\r\n 'uploader': response_json['owner']['name'],\r\n 'UP_mid': response_json['owner']['mid'],\r\n 'description': response_json['desc'],\r\n 'pubdate': time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(response_json['pubdate'])),\r\n 'duration_time': response_json['duration'],\r\n 'view': response_json['stat']['view'],\r\n 'danmu': response_json['stat']['danmaku'],\r\n 'reply': response_json['stat']['reply'],\r\n 'favorite': response_json['stat']['favorite'],\r\n 'coin': response_json['stat']['coin'],\r\n 'share': response_json['stat']['share'],\r\n 'like': response_json['stat']['like'],\r\n 'now_rank': response_json['stat']['now_rank'],\r\n 'history_rank': response_json['stat']['his_rank']\r\n }\r\n return video_info_data\r\n\r\n\r\ndef get_tag_video_info(response_json, BV_bid):\r\n tag_list = []\r\n for tag in response_json:\r\n tag_list.append(tag['tag_name'])\r\n while len(tag_list) < 10:\r\n tag_list.append('')\r\n tag_data = {\r\n 'BV_bid': BV_bid,\r\n 'tag0': tag_list[0],\r\n 'tag1': tag_list[1],\r\n 'tag2': tag_list[2],\r\n 'tag3': tag_list[3],\r\n 'tag4': tag_list[4],\r\n 'tag5': tag_list[5],\r\n 'tag6': tag_list[6],\r\n 'tag7': tag_list[7],\r\n 'tag8': tag_list[8],\r\n 'tag9': tag_list[9],\r\n }\r\n return tag_data\r\n\r\n\r\ndef get_tag_list_video_info(response):\r\n tag_list = []\r\n tree = etree.HTML(response.text)\r\n raw_tag_list = tree.xpath(\"//ul[@class='tag-area clearfix']/li\")\r\n count = 0\r\n for li in raw_tag_list:\r\n if len(li.xpath(\"./div/a/span/text()\")) != 0:\r\n tag_list.append((li.xpath(\"./div/a/span/text()\")[0].strip().replace(' ', '')))\r\n elif len(li.xpath(\"./a/span/text()\")) != 0:\r\n tag_list.append((li.xpath(\"./a/span/text()\")[0].strip().replace(' ', '')))\r\n elif len(li.xpath(\"./div/a/text()\")) != 0:\r\n tag_list.append((li.xpath(\"./div/a/text()\")[0].strip().replace(' ', '')))\r\n count = count + 1\r\n if count == 10:\r\n break\r\n while count < 10:\r\n tag_list.append('')\r\n count += 1\r\n return tag_list\r\n\r\n\r\ndef get_tag_data_video_info(tag_list, BV_bid):\r\n tag_data = {\r\n 'BV_bid': BV_bid,\r\n 'tag0': tag_list[0],\r\n 'tag1': tag_list[1],\r\n 'tag2': tag_list[2],\r\n 'tag3': tag_list[3],\r\n 'tag4': tag_list[4],\r\n 'tag5': tag_list[5],\r\n 'tag6': tag_list[6],\r\n 'tag7': tag_list[7],\r\n 'tag8': tag_list[8],\r\n 'tag9': tag_list[9],\r\n }\r\n return tag_data\r\n\r\n\r\ndef save_to_database_video_info(df, table_name):\r\n # conn = create_engine(\"mysql+pymysql://{}:{}@{}:{}/{}?charset={}\".format('用户名', '密码', '数据库IP地址', '端口号', '数据库名',\r\n # '字符编码'))\r\n is_table_exist = True\r\n is_data_exist = True\r\n conn = create_engine(\r\n \"mysql+pymysql://{}:{}@{}:{}/{}?charset={}\".format('root', '123456', 'localhost', '3306', 'test', 'utf8mb4'))\r\n try:\r\n sql = \"select * from {} where Bv_bid='{}';\".format(table_name, df.loc[1, 'BV_bid'])\r\n if pd.read_sql_query(sql, conn).empty:\r\n is_data_exist = False\r\n except Exception:\r\n is_table_exist = False\r\n if (is_table_exist == False) or (is_data_exist == False):\r\n df.to_sql(table_name, conn, if_exists='append', index=False)\r\n else:\r\n try:\r\n sql = \"delete from {} where Bv_bid='{}'\".format(table_name, df.loc[1, 'BV_bid'])\r\n pd.read_sql_query(sql, conn)\r\n except Exception:\r\n df.to_sql(table_name, conn, if_exists='append', index=False)\r\n","repo_name":"OtakuNio/bilibili-crawler","sub_path":"video_part/video_info.py","file_name":"video_info.py","file_ext":"py","file_size_in_byte":5140,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"74421338405","text":"# boilerplate code\nimport numpy as np\nimport random\nimport cv2\nimport tensorflow as tf\nimport DisplayImages\n\nmodel_fn = 'inception5h/tensorflow_inception_graph.pb'\n\n# creating TensorFlow session and loading the model\ngraph = tf.Graph()\nsess = tf.InteractiveSession(graph=graph)\nwith tf.gfile.FastGFile(model_fn, 'rb') as f:\n graph_def = tf.GraphDef()\n graph_def.ParseFromString(f.read())\nt_input = tf.placeholder(np.float32, name='input') # define the input tensor\nimagenet_mean = 117.0\nt_preprocessed = tf.expand_dims(t_input - imagenet_mean, 0)\ntf.import_graph_def(graph_def, {'input': t_preprocessed})\n\nlayers = [op.name for op in graph.get_operations() if op.type == 'Conv2D' and 'import/' in op.name]\nfeature_nums = [int(graph.get_tensor_by_name(name + ':0').get_shape()[-1]) for name in layers]\n\nprint('Number of layers', len(layers))\nprint('Total number of feature channels:', sum(feature_nums))\n\n\n# Picking some internal layer. Note that we use outputs before applying the ReLU nonlinearity\n# to have non-zero gradients for features with negative initial activations.\nlayer = 'mixed4d_3x3_bottleneck_pre_relu'\nchannel = 139 # picking some feature channel to visualize\n\n# start with a gray image with a little noise\nimg_noise = np.zeros(shape=(224, 224, 3)) + 100.0 # .random.uniform(size=(224, 224, 3)) + 100.0\n\ndisplay = DisplayImages.DisplayImages(full_screen=True)\n\ndef showarray(a):\n a = np.uint8(np.clip(a, 0, 1) * 255)\n image = display.pad_image_to_screen_aspect(a)\n display.show_image(image)\n\n\ndef T(layer):\n # Helper for getting layer output tensor'''\n return graph.get_tensor_by_name(\"import/%s:0\" % layer)\n\n\ndef tffunc(*argtypes):\n # Helper that transforms TF-graph generating function into a regular one.\n # See \"resize\" function below.\n\n placeholders = list(map(tf.placeholder, argtypes))\n\n def wrap(f):\n out = f(*placeholders)\n\n def wrapper(*args, **kw):\n return out.eval(dict(zip(placeholders, args)), session=kw.get('session'))\n\n return wrapper\n\n return wrap\n\n\n# Helper function that uses TF to resize an image\ndef resize(img, size):\n img = tf.expand_dims(img, 0)\n return tf.image.resize_bilinear(img, size)[0, :, :, :]\n\n\nresize = tffunc(np.float32, np.int32)(resize)\n\n\ndef calc_grad_tiled(img, t_grad, tile_size=200):\n # '''Compute the value of tensor t_grad over the image in a tiled way.\n # Random shifts are applied to the image to blur tile boundaries over\n # multiple iterations.'''\n sz = tile_size\n h, w = img.shape[:2]\n sx, sy = np.random.randint(sz, size=2)\n img_shift = np.roll(np.roll(img, sx, 1), sy, 0)\n grad = np.zeros_like(img)\n for y in range(0, max(h - sz // 2, sz), sz):\n for x in range(0, max(w - sz // 2, sz), sz):\n sub = img_shift[y:y + sz, x:x + sz]\n g = sess.run(t_grad, {t_input: sub})\n grad[y:y + sz, x:x + sz] = g\n return np.roll(np.roll(grad, -sx, 1), -sy, 0)\n\n\ndef render_deepdream(t_obj, img0=img_noise,\n iter_n=100, step=1.5, octave_n=4, octave_scale=1.4):\n t_score = tf.reduce_mean(t_obj) # defining the optimization objective\n t_grad = tf.gradients(t_score, t_input)[0] # behold the power of automatic differentiation!\n\n # split the image into a number of octaves\n img = img0\n octaves = []\n for i in range(octave_n - 1):\n hw = img.shape[:2]\n lo = resize(img, np.int32(np.float32(hw) / octave_scale))\n hi = img - resize(lo, hw)\n img = lo\n octaves.append(hi)\n\n # generate details octave by octave\n for octave in range(octave_n):\n if octave > 0:\n hi = octaves[-octave]\n img = resize(img, hi.shape[:2]) + hi\n for i in range(iter_n):\n g = calc_grad_tiled(img, t_grad)\n img += g * (step / (np.abs(g).mean() + 1e-7))\n print('.', end=' ')\n showarray(img / 255.0)\n\n return img\n\n\ndef get_image(path):\n img0 = cv2.imread(path)\n img0 = np.float32(img0)\n return img0\n\n\n# img0 = get_image('ImagesIn/profile800.jpg')\n# render_deepdream(T(layer)[:,:,:,139], img0)\n# render_deepdream(tf.square(T('mixed4c')), img0)\n\nimg_profile = get_image('ImagesIn/profile740.jpg')\nimg_parasol = get_image('ImagesIn/parasolSmall.jpg')\nimg_eye = get_image('ImagesIn/eye740.jpg')\nimg_leaves = get_image('ImagesIn/leaves.jpg')\nimg_roses = get_image('ImagesIn/roses.jpg')\n\nchannels = [1, 16, 18, 7, 24, 4, 11, 19, 23, 111, 30, 36, 31, 42, 41, 44, 123, 108, 109, 47, 45, 51, 52, 127, 128, 134,\n 57, 58, 53, 60, 139, 140, 112,\n 61, 75, 101, 100, 98, 90, 136, 114, 122, 115, 82, 70, 138, 97, 116, 117, 87, 141, 86, 83, 143]\n\nwhile True:\n for channel in channels:\n which_image = random.randint(1, 5)\n\n if which_image == 1:\n render_deepdream(T(layer)[:, :, :, channel], img_eye)\n elif which_image == 2:\n render_deepdream(T(layer)[:, :, :, channel], img_parasol)\n elif which_image == 3:\n render_deepdream(T(layer)[:, :, :, channel], img_leaves)\n elif which_image == 4:\n render_deepdream(T(layer)[:, :, :, channel], img_roses)\n else:\n render_deepdream(T(layer)[:, :, :, channel], img_profile)\n\n # path = 'ImagesOut/mixed' + str(i) + '.jpg'\n # print(path)\n # cv2.imwrite(path, img)\n","repo_name":"chipgarner/DreamsTensorflow","sub_path":"MyDream.py","file_name":"MyDream.py","file_ext":"py","file_size_in_byte":5356,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"36358775658","text":"import logging\n\nfrom fastapi import FastAPI, Depends, HTTPException\nfrom sqlalchemy.orm import Session\nfrom starlette import status\nfrom starlette.status import HTTP_200_OK, HTTP_204_NO_CONTENT\n\nfrom config import WebConfig\nfrom database import get_db\nfrom errors import NotFoundException, WrongDifficultyException\nfrom services.category_service import CategoryService\nfrom services.question_service.question_service import QuestionService\nfrom web.schemas import CategoryListResponse, CategoryResponse, QuestionResponse, AnswerRequest, TermResponse\n\napp = FastAPI()\n\nlogging.getLogger().setLevel(logging.INFO)\n\n\n@app.get(f\"{WebConfig.ROUTE_PREFIX}/categories\", status_code=HTTP_200_OK, response_model=CategoryListResponse)\nasync def get_categories(db: Session = Depends(get_db)):\n service = CategoryService.build(db)\n\n categories = service.get_all()\n res = [CategoryResponse(id=category.id, name=category.name) for category in categories]\n\n return CategoryListResponse(categories=res)\n\n\n@app.get(WebConfig.ROUTE_PREFIX + \"/questions/{category_id}\", status_code=HTTP_200_OK, response_model=QuestionResponse)\nasync def get_question(category_id: int, db: Session = Depends(get_db)):\n service = QuestionService.build(db)\n try:\n question = service.create_question(category_id)\n except NotFoundException:\n raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=\"wrong category id\")\n\n return QuestionResponse(\n question=question.question,\n correct=TermResponse(id=question.correct.id, name=question.correct.name),\n answers=question.answers\n )\n\n\n@app.post(f\"{WebConfig.ROUTE_PREFIX}/answers\", status_code=HTTP_204_NO_CONTENT)\nasync def get_question(data: AnswerRequest, db: Session = Depends(get_db)):\n service = QuestionService.build(db)\n try:\n service.answer_question(data)\n db.commit()\n except NotFoundException:\n raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=\"wrong term id\")\n finally:\n db.rollback()\n\n\n@app.get(WebConfig.ROUTE_PREFIX + \"/questions/\", status_code=HTTP_200_OK, response_model=QuestionResponse)\nasync def get_question_with_difficulty(category_id: int, difficulty: float, db: Session = Depends(get_db)):\n service = QuestionService.build(db)\n try:\n question = service.create_question_with_given_difficulty(category_id, difficulty)\n except NotFoundException:\n raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=\"wrong category id\")\n except WrongDifficultyException:\n raise HTTPException(status_code=status.HTTP_422_UNPROCESSABLE_ENTITY,\n detail=\"difficulty should be between 0 and 1\")\n return QuestionResponse(\n question=question.question,\n correct=TermResponse(id=question.correct.id, name=question.correct.name),\n answers=question.answers\n )\n","repo_name":"elacymerys/thesis","sub_path":"thesis-api/main/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":2888,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"72594329445","text":"# Написать два алгоритма нахождения i-го по счёту простого числа.\n# Без использования Решета Эратосфена;\n# Использовать алгоритм решето Эратосфена\nimport cProfile\n\n\n# решение без решета эратосфена\n\ndef prime_number_search(prime_number_pos):\n dividers = [2]\n prime_numbers = [2]\n candidate = prime_numbers[0]\n k = 1\n while k < prime_number_pos:\n candidate += 1\n dividers += [candidate]\n # проверяем делимость кандидата\n for divider in dividers[:-1]:\n if candidate % divider == 0:\n break\n else:\n prime_numbers += [candidate]\n k += 1\n return prime_numbers[prime_number_pos - 1]\n\n\n# решение с решетом эратосфена\n\n\ndef prime_number_search_eratosthenes(prime_number_pos):\n prime_numbers_count = 1\n numbers = [0, 0, 2]\n while prime_numbers_count < prime_number_pos+1:\n bottom = numbers[-1]+1\n top = bottom+prime_number_pos\n numbers += list(range(bottom, top))\n m = 2\n while m < top:\n if numbers[m] != 0:\n j = m * 2\n while j < top:\n numbers[j] = 0\n j += m\n m += 1\n while numbers[-1] == 0:\n del numbers[-1]\n prime_numbers_count = len(set(numbers))\n numbers = [number for number in numbers if number != 0]\n return numbers[prime_number_pos-1]\n\n\ncProfile.run('print(prime_number_search_eratosthenes(1000))')\ncProfile.run('print(prime_number_search(1000))')\n# 500ое простое число равно 3571 согласно википедии\n\n# на моей машине prime_number_search_eratosthenes(1000) занимает 0.011\n# на моей машине prime_number_search(1000) занимает 0.242\n# Вывод: алгоритм эратосфена эффективней\n\n\n","repo_name":"VFarsiyants/training_repo","sub_path":"AlgorithmsAndDataStructursOnPython/Lesson4_PracticalTasks/task2.py","file_name":"task2.py","file_ext":"py","file_size_in_byte":2044,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"12307711615","text":"class Punto:\n def __init__(self,x = 0,y = 0):\n self.x = x\n self.y = y\n def __add__(self,other):\n x = self.x + other.x\n y = self.y + other.y\n return Punto(x, y)\n\npunto1 = Punto(4,6)\npunto2 = Punto(1,-2)\nsuma = (punto1 + punto2)\nprint(f\"{suma.x} - {suma.y}\")","repo_name":"ldanielher/BEDU-Python","sub_path":"SESION5/poli.py","file_name":"poli.py","file_ext":"py","file_size_in_byte":297,"program_lang":"python","lang":"it","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"1730010226","text":"\"\"\"\nThis script is helpful if you have a lot of bookmark folders in chrome and often have a tough time finding a URL in all the folders.\nThis search is case insensitive.\nTo run this script, please do 'pip install chrome-bookmarks' (without the quotes) on the terminal in PyCharm.\nSearch for a bookmark name, even if you know only a part of the name and it will give you all corresponding URLs associated with the bookmark name.\n\"\"\"\nimport chrome_bookmarks\n\ndef chrome_bookmark_finder(keyword):\n bookmarks_dict = {}\n\n # Getting all the bookmark names and URLs and storing them in a dictionary\n for url in chrome_bookmarks.urls:\n bookmarks_dict.update({url.name:url.url})\n\n print(\"The list of bookmarks are: \" + str(bookmarks_dict))\n\n # Searching (case insensitive) for the keyword (bookmark name) in the dictionary (bookmarks)\n res = [val for key, val in bookmarks_dict.items() if keyword.lower() in key.lower()]\n\n # Printing URLs that match with the search keyword\n print(f'URLs that match with {keyword.upper()} are : {str(res)}')\n\ndef main():\n keyword = input(\"Search for a bookmarked URL with a keyword: \")\n chrome_bookmark_finder(keyword)\n\nif __name__ == '__main__':\n main()","repo_name":"tejak18d/pythonprojects","sub_path":"ChromeBookmarkFinderFinal.py","file_name":"ChromeBookmarkFinderFinal.py","file_ext":"py","file_size_in_byte":1218,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"21970794180","text":"def crealista():\n respuestas=[]\n for x in range(5):\n palabra= input(\"\")\n respuestas.append(palabra)\n return respuestas\n\ndef createxto():\n file = open(\"Data.txt\",\"r\")\n numeros = []\n for x in range(5):\n numeros.append(file.readline())\n file.close()\n return numeros\n \ndef prom(numeros):\n acum = 0\n for x in range(5):\n acum = acum + int(numeros[x])\n result= acum/5\n resulta=str(result)\n return resulta\n\ndef main():\n result=prom(createxto())\n resultad=open(\"Resultados.txt\",\"w+\")\n resultad.write(result)\n resultad.close()\n print(result)\nmain()","repo_name":"Abrahamcepedao/ITC","sub_path":"Semestre1/Pensamiento computacional/archivos py/proyecto/calcula_promedio.py","file_name":"calcula_promedio.py","file_ext":"py","file_size_in_byte":631,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"73304890405","text":"from django.conf import settings\nfrom django.core.mail import EmailMessage\n\n\n\n# EMAIL OTP TEMPLATE - FINFLO TESTING ENV\n\n\ndef email_to(to_email,key):\n \n subject = \"OTP for Finflo Login \"\n\n message = \"\"\"\n Dear {0},\n your otp to login in finflo is {1} .\n if any support needed contact support@finflo.com\n \"\"\".format(str(to_email),str(key))\n\n email_from = settings.EMAIL_HOST_USER\n recipient_list = [to_email]\n cc_list = [\"anand98.ar@gmail.com\"]\n\n email = EmailMessage(\n subject,\n message,\n email_from,\n recipient_list,\n cc_list,\n )\n email.send(fail_silently=False)","repo_name":"anandrajB/procure_to_pay","sub_path":"accounts/email.py","file_name":"email.py","file_ext":"py","file_size_in_byte":650,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"72088012964","text":"def number(digital):\n largest = digital[0]\n\n for num in digital:\n if num % 2 != 0:\n if num > largest:\n largest = num\n\n return largest\n\n\nodd = [709, 703, 705, 707, 600, 7, 105]\ncall = number(odd)\nprint(f\"Largest: {call}\")\n","repo_name":"KevinEjiofor/python_practice","sub_path":"project/HigestOdd.py","file_name":"HigestOdd.py","file_ext":"py","file_size_in_byte":263,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"40830949074","text":"# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, val=0, left=None, right=None):\n# self.val = val\n# self.left = left\n# self.right = right\nclass Solution:\n def preorderTraversal(self, root: TreeNode) -> List[int]:\n return self.preOrderTraversal(root, [])\n \n def preOrderTraversal(self, root: TreeNode, result):\n if root:\n result.append(root.val)\n self.preOrderTraversal(root.left, result)\n self.preOrderTraversal(root.right, result)\n if result:\n return result\n \n \n\n# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, val=0, left=None, right=None):\n# self.val = val\n# self.left = left\n# self.right = right\nclass Solution:\n def inorderTraversal(self, root: TreeNode) -> List[int]:\n return self.inOrderTraversal(root, [])\n \n def inOrderTraversal(self, root: TreeNode, result):\n if root:\n self.inOrderTraversal(root.left, result)\n result.append(root.val)\n self.inOrderTraversal(root.right, result)\n if result:\n return result\n \n \n# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, val=0, left=None, right=None):\n# self.val = val\n# self.left = left\n# self.right = right\nclass Solution:\n def postorderTraversal(self, root: TreeNode) -> List[int]:\n return self.postOrderTraversal(root, [])\n \n def postOrderTraversal(self, root: TreeNode, result):\n if root:\n self.postOrderTraversal(root.left, result)\n self.postOrderTraversal(root.right, result)\n result.append(root.val) \n if result:\n return result\n","repo_name":"rasedul506/code-practice","sub_path":"BinarySearchTree/TreeTraversal.py","file_name":"TreeTraversal.py","file_ext":"py","file_size_in_byte":1828,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"29125465189","text":"import uvicorn\nfrom fastapi import FastAPI\nfrom fastapi_sqlalchemy import DBSessionMiddleware, db\n\nfrom schemas.schema import Book as SchemaBook\nfrom schemas.schema import Author as SchemaAuthor\n\nfrom schemas.schema import Book\nfrom schemas.schema import Author\n\nfrom models.models import Book as ModelBook\nfrom models.models import Author as ModelAuthor\n\nimport os\nfrom dotenv import load_dotenv\n\nload_dotenv('.env')\n\n\napp = FastAPI()\n\n# to avoid csrftokenError\napp.add_middleware(DBSessionMiddleware, db_url='postgresql://postgres:postgres@localhost/FastAPIDB')\n\n@app.get(\"/\")\nasync def root():\n return {\"message\": \"CICD Implementation\"}\n\n\n@app.post('/book/', response_model=SchemaBook)\nasync def book(book: SchemaBook):\n db_book = ModelBook(title=book.title, rating=book.rating, author_id = book.author_id)\n db.session.add(db_book)\n db.session.commit()\n return db_book\n\n@app.get('/book/')\nasync def book():\n book = db.session.query(ModelBook).all()\n return book\n\n\n \n@app.post('/author/', response_model=SchemaAuthor)\nasync def author(author:SchemaAuthor):\n db_author = ModelAuthor(name=author.name, age=author.age)\n db.session.add(db_author)\n db.session.commit()\n return db_author\n\n@app.get('/author/')\nasync def author():\n author = db.session.query(ModelAuthor).all()\n return author\n\n\n# To run locally\nif __name__ == '__main__':\n uvicorn.run(app, host='0.0.0.0', port=8000)\n","repo_name":"jitendra-meena/FastAPI","sub_path":"app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1421,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"52"} +{"seq_id":"16761609264","text":"#codechef - lost guy radhu\nt=int(input(\"enter the input please\"))\nstr1,str2=map(int,input(\"enter the name imout\").split(\" \"))\nlist2=[]\nlist3=[]\nlist4=[]\nprint(list1)\nprint(list1[0])\ni=0\nfor z in range(t):\n for i in range(list1[0]):\n A=int(input(\"scores in games played \"))\n list2.append(A)\n print(list2)\n for k in range(list1[1]):\n B=int(input(\"quries please enter\"))\n list3.append(B)\n\n print(list3)\n for l in list3:\n\n for k in range(l):\n list4.append(list2[k])\n print(max(list4))\n\n while 1:\n if i in list4:\n list4.pop()\n i+=1\n break","repo_name":"jagdishwar/CP","sub_path":"codechef1.py","file_name":"codechef1.py","file_ext":"py","file_size_in_byte":848,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"32813837260","text":"from numpy import delete\n\ndef fieldsOF():\n \"\"\"\n List of OpenFOAM fields\n\n :return: vector_fields\n :return: scalar_fields\n :return: tensor_fields\n \"\"\"\n scalar_fields = ['p', 'pMean', 'nuSgs', 'k', 'kMean', 'ppMean']\n vector_fields = ['U', 'UMean', 'omega', 'upMean']\n tensor_fields = ['RMean', 'uuMean', 'uuRTotal', 'UPrime2Mean']\n\n return scalar_fields, vector_fields, tensor_fields\n\n\ndef fieldsComponentsOF():\n \"\"\"\n List of OpenFOAM fields components\n\n :return: vector_components\n :return: tensor_components\n \"\"\"\n scalar_fields, vector_fields, tensor_fields = fieldsOF()\n\n vector_components = []\n for vector_field in vector_fields:\n x_components = vector_field + 'x'\n y_components = vector_field + 'y'\n z_components = vector_field + 'z'\n vector_components.append(x_components)\n vector_components.append(y_components)\n vector_components.append(z_components)\n\n tensor_components = []\n for tensor_field in tensor_fields:\n xx_components = tensor_field + 'xx'\n yy_components = tensor_field + 'yy'\n zz_components = tensor_field + 'zz'\n xy_components = tensor_field + 'xy'\n xz_components = tensor_field + 'xz'\n yz_components = tensor_field + 'yz'\n tensor_components.append(xx_components)\n tensor_components.append(yy_components)\n tensor_components.append(zz_components)\n tensor_components.append(xy_components)\n tensor_components.append(xz_components)\n tensor_components.append(yz_components)\n\n return vector_components, tensor_components\n\ndef cleanDoubleData(dataArray, coordinateArray1, coordinateArray2, coordinateArray3):\n \"\"\" Remove duplicate data and retain their indexes\"\"\"\n index = []\n for pos in range(1, len(dataArray)):\n if dataArray[pos] == dataArray[pos-1]:\n index.append(pos)\n\n newData = delete(dataArray, index)\n newCoordinate1 = delete(coordinateArray1, index)\n newCoordinate2 = delete(coordinateArray2, index)\n newCoordinate3 = delete(coordinateArray3, index)\n\n return newData, newCoordinate1, newCoordinate2, newCoordinate3\n","repo_name":"GiordiR/pySOWFA","sub_path":"pySOWFA/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2167,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"77182858","text":"from PIL import Image\nimport sys, os\nfrom struct import pack as pk\n\nSPLASH_SCREEN_WIDTH = 1280\nSPLASH_SCREEN_HEIGHT = 720\n\nSPLASH_SCREEN_STRIDE = 768\n\ndef convert_image(image_fn):\n splash = Image.open(image_fn, 'r')\n w, h = splash.size\n if w == 1280 and h == 720:\n splash = splash.transpose(Image.ROTATE_90)\n w, h = splash.size\n assert w == 720\n assert h == 1280\n splash = splash.convert('RGBA')\n splash_bin = bytearray()\n for row in range(SPLASH_SCREEN_WIDTH):\n for col in range(SPLASH_SCREEN_HEIGHT):\n r, g, b, a = splash.getpixel((col, row))\n splash_bin += pk('3:\n self.huMoments[0]=4\n if self.huMoments[1]<7:\n self.huMoments[1]=6\n if self.huMoments[1]>8:\n self.huMoments[1]=9\n if self.huMoments[2]<10:\n self.huMoments[2]=9\n if self.huMoments[2]>19:\n self.huMoments[2]=20\n if self.huMoments[3]<10:\n self.huMoments[3]=9\n if self.huMoments[3]>15:\n self.huMoments[3]=16\n if self.huMoments[4]<21:\n self.huMoments[4]=20\n if self.huMoments[4]>31:\n self.huMoments[4]=32\n if self.huMoments[5]<14:\n self.huMoments[5]=13\n if self.huMoments[5]>19:\n self.huMoments[5]=20\n if self.huMoments[6]<21:\n self.huMoments[6]=20\n if self.huMoments[6]>31:\n self.huMoments[6]=32\n","repo_name":"Koanne/SZI","sub_path":"ImageExample.py","file_name":"ImageExample.py","file_ext":"py","file_size_in_byte":5807,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"26789020045","text":"import os\nfrom importlib import import_module\nfrom inspect import getmembers, isclass\nfrom yaml import safe_load\nimport warnings\nfrom collections import OrderedDict\nfrom six import string_types\n\n# FIXME: Bad practice\n# Logger settings should not be defined in a module, but once by the\n# application developer. Thus outside of basil. Otherwise multiple calls to\n# the basic config are possible. This is left here at the moment for backward\n# compatibility and since our logging format is the same everywhere (?).\nimport logging\nlogging.basicConfig(level=logging.INFO, format=\"%(asctime)s - %(name)s - [%(levelname)-8s] (%(threadName)-10s) %(message)s\")\n\n\nclass Base(object):\n def __init__(self, conf):\n self.name = None\n self.version = None\n self.conf_path = None\n self.parent = None\n self._init = {}\n self._conf = self._open_conf(conf)\n if 'name' in self._conf:\n self.name = self._conf['name']\n if 'version' in self._conf:\n self.version = self._conf['version']\n if 'conf_path' in self._conf:\n self.conf_path = self._conf['conf_path']\n if 'parent' in self._conf:\n self.parent = self._conf['parent']\n if 'init' in self._conf:\n self._update_init(self._conf['init'])\n\n def _open_conf(self, conf):\n def isFile(f):\n return hasattr(f, 'read')\n\n conf_dict = {}\n if not conf:\n pass\n elif isinstance(conf, string_types): # parse the first YAML document in a stream\n if os.path.isfile(conf):\n with open(conf, 'r') as f:\n conf_dict.update(safe_load(f))\n conf_dict.update(conf_path=f.name)\n else: # YAML string\n try:\n conf_dict.update(safe_load(conf))\n except ValueError: # invalid path/filename\n raise IOError(\"File not found: %s\" % conf)\n elif isFile(conf): # parse the first YAML document in a stream\n conf_dict.update(safe_load(conf))\n conf_dict.update(conf_path=conf.name)\n else: # conf is already a dict\n conf_dict.update(conf)\n return conf_dict\n\n def _update_init(self, init_conf=None, **kwargs):\n init_conf = self._open_conf(init_conf)\n if init_conf:\n self._init.update(init_conf)\n self._init.update(kwargs)\n\n def init(self):\n self._initialized = True\n\n @property\n def is_initialized(self):\n if \"_initialized\" in self.__dict__ and self._initialized:\n return True\n else:\n return False\n\n def close(self):\n self._initialized = False\n\n def set_configuration(self, conf):\n raise NotImplementedError(\"set_configuration() not implemented\")\n\n def get_configuration(self):\n raise NotImplementedError(\"get_configuration() not implemented\")\n\n\nclass Dut(Base):\n '''Device\n '''\n def __init__(self, conf):\n super(Dut, self).__init__(conf)\n self._transfer_layer = None\n self._hardware_layer = None\n self._registers = None\n self.load_hw_configuration(self._conf)\n\n def init(self, init_conf=None, **kwargs):\n super(Dut, self).init()\n init_conf = self._open_conf(init_conf)\n\n def update_init(mod):\n if init_conf:\n if mod.name in init_conf:\n mod._update_init(init_conf[mod.name])\n if mod.name in kwargs:\n mod._update_init(kwargs[mod.name])\n\n def catch_exception_on_init(mod):\n try:\n mod.init()\n except NotImplementedError:\n pass\n\n for item in self._transfer_layer.values():\n update_init(item)\n catch_exception_on_init(item)\n for item in self._hardware_layer.values():\n update_init(item)\n catch_exception_on_init(item)\n for item in self._registers.values():\n update_init(item)\n catch_exception_on_init(item)\n\n def close(self):\n def catch_exception_on_close(mod):\n if mod.is_initialized:\n try:\n mod.close()\n except Exception: # if close() failed\n # restore status after close() failed\n mod._is_initialized = True\n\n for item in self._registers.values():\n catch_exception_on_close(item)\n for item in self._hardware_layer.values():\n catch_exception_on_close(item)\n for item in self._transfer_layer.values():\n catch_exception_on_close(item)\n\n def set_configuration(self, conf):\n conf = self._open_conf(conf)\n if conf:\n for item, item_conf in conf.items():\n if item != 'conf_path':\n try:\n self[item].set_configuration(item_conf)\n except NotImplementedError:\n pass\n\n def get_configuration(self):\n conf = {}\n for key, value in self._registers.items():\n try:\n conf[key] = value.get_configuration()\n except NotImplementedError:\n conf[key] = {}\n for key, value in self._hardware_layer.items():\n try:\n conf[key] = value.get_configuration()\n except NotImplementedError:\n conf[key] = {}\n for key, value in self._transfer_layer.items():\n try:\n conf[key] = value.get_configuration()\n except NotImplementedError:\n conf[key] = {}\n return conf\n\n def load_hw_configuration(self, conf, extend_config=False):\n conf = self._open_conf(conf)\n if extend_config:\n self._conf.update(conf)\n else:\n self._conf = conf\n\n if not extend_config:\n if 'name' in self._conf:\n self.name = self._conf['name']\n else:\n self.name = None\n if 'version' in self._conf:\n self.version = self._conf['version']\n else:\n self.version = None\n self._transfer_layer = OrderedDict()\n self._hardware_layer = OrderedDict()\n self._registers = OrderedDict()\n\n if 'transfer_layer' in conf:\n for intf in conf['transfer_layer']:\n intf['parent'] = self\n kargs = {}\n kargs['conf'] = intf\n self._transfer_layer[intf['name']] = self._factory('basil.TL.' + intf['type'], *(), **kargs)\n\n if 'hw_drivers' in conf:\n if conf['hw_drivers']:\n for hwdrv in conf['hw_drivers']:\n hwdrv['parent'] = self\n kargs = {}\n if 'interface' in hwdrv:\n if hwdrv['interface'].lower() == 'none':\n kargs['intf'] = None\n else:\n kargs['intf'] = self._transfer_layer[hwdrv['interface']]\n elif 'hw_driver' in hwdrv:\n kargs['intf'] = self._hardware_layer[hwdrv['hw_driver']]\n else:\n kargs['intf'] = None\n kargs['conf'] = hwdrv\n self._hardware_layer[hwdrv['name']] = self._factory('basil.HL.' + hwdrv['type'], *(), **kargs)\n\n if 'user_drivers' in conf:\n warnings.warn(\"Deprecated: user_drivers move modules to hw_drivers\", DeprecationWarning)\n if conf['user_drivers']:\n for userdrv in conf['user_drivers']:\n userdrv['parent'] = self\n kargs = {}\n kargs['intf'] = self._hardware_layer[userdrv['hw_driver']]\n kargs['conf'] = userdrv\n self._hardware_layer[userdrv['name']] = self._factory('basil.HL.' + userdrv['type'], *(), **kargs)\n\n if 'registers' in conf:\n if conf['registers']:\n for reg in conf['registers']:\n reg['parent'] = self\n kargs = {}\n if 'driver' in reg:\n if not reg['driver'] or reg['driver'].lower() == 'none':\n kargs['driver'] = None\n else:\n kargs['driver'] = self._hardware_layer[reg['driver']]\n kargs['conf'] = reg\n self._registers[reg['name']] = self._factory('basil.RL.' + reg['type'], *(), **kargs)\n elif 'hw_driver' in reg:\n kargs['driver'] = self._hardware_layer[reg['hw_driver']]\n kargs['conf'] = reg\n self._registers[reg['name']] = self._factory('basil.RL.' + reg['type'], *(), **kargs)\n else:\n raise ValueError('No driver specified for register: %s' % (reg['name'],))\n\n def _factory(self, importname, *args, **kargs):\n splitted_import_name = importname.split('.')\n\n def is_basil_base_class(item):\n return isclass(item) and issubclass(item, Base) and item.__module__ == importname\n\n try:\n mod = import_module(importname)\n except ImportError: # give it another try\n if len(splitted_import_name) > 2 and splitted_import_name[0] == 'basil':\n importname = '.'.join(splitted_import_name[2:]) # remove \"basil.RL.\" etc.\n mod = import_module(importname)\n else: # raise initial exception\n raise\n basil_base_classes = getmembers(mod, is_basil_base_class)\n cls = None\n if not basil_base_classes: # found no base class\n raise ValueError('Found no matching class in %s.' % importname)\n elif len(basil_base_classes) > 1: # found more than 1 base class\n mod_name = splitted_import_name[-1]\n for basil_base_class in basil_base_classes:\n if mod_name == basil_base_class[0]: # check for base class name\n cls = basil_base_class[1]\n break\n if cls is None:\n raise ValueError('Found more than one matching class in %s.' % importname)\n else: # found single class\n cls = basil_base_classes[0][1]\n return cls(*args, **kargs)\n\n def __getitem__(self, item):\n if item in self._registers:\n return self._registers[item]\n elif item in self._hardware_layer:\n return self._hardware_layer[item]\n elif item in self._transfer_layer:\n return self._transfer_layer[item]\n raise KeyError('Item not existing: %s' % (item,))\n\n def get_modules(self, type_name):\n '''Getting modules by type name.\n\n Parameters\n ----------\n type_name : string\n Type name of the modules to be returned.\n\n Returns\n -------\n List of modules of given type name else empty list.\n '''\n modules = []\n for module in self:\n if module.__class__.__name__ == type_name:\n modules.append(module)\n return modules\n\n def __iter__(self):\n for item in self._registers.values():\n yield item\n for item in self._hardware_layer.values():\n yield item\n for item in self._transfer_layer.values():\n yield item\n\n # TODO:\n def __setitem__(self, key, value):\n self._registers[key].set(value)\n\n def __repr__(self):\n return str(self.get_configuration())\n","repo_name":"SiLab-Bonn/basil","sub_path":"basil/dut.py","file_name":"dut.py","file_ext":"py","file_size_in_byte":11675,"program_lang":"python","lang":"en","doc_type":"code","stars":38,"dataset":"github-code","pt":"52"} +{"seq_id":"3015093172","text":"asking = True\nwhile asking:\n\tchar_code = int(input(\"Enter ASCII code: \"))\n\tif char_code <= 31:\n\t\tprint(\"Unprintable! Used to control peripherals such as printers: \" + chr(char_code))\n\telif char_code == 127:\n\t\tprint(\"Probably unprintable as this code represents delete: \" + chr(char_code))\n\telif 127 < char_code < 160:\n\t\tprint(\"Non-standard displayable characters, may not be displayable: \" + chr(char_code))\n\telif char_code > 255:\n\t\tprint(\"Not ASCII code! (probably)\")\n\telse:\n\t\tprint(\"Character: \" + chr(char_code))\n\t\tasking = False","repo_name":"sp0002/cp2019","sub_path":"p01/q6_find_ascii_char.py","file_name":"q6_find_ascii_char.py","file_ext":"py","file_size_in_byte":532,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"73339825125","text":"\"\"\"Get most recent team data for NYUrban.\"\"\"\nfrom datetime import datetime, timezone\nfrom urllib.parse import parse_qs\nimport re\n\nfrom bs4 import BeautifulSoup\nfrom selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.common.exceptions import SessionNotCreatedException, WebDriverException\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as e_c\nfrom selenium.webdriver.chrome.options import Options\n\nimport os\n\nfrom config import Config\n\nDRIVER_URL = \"https://sites.google.com/chromium.org/driver/\"\n\n\nclass VbScrape:\n def __init__(self, cnf: Config):\n self.cnf = cnf\n self.url = self.cnf.cnf_value(\"scrape.url\")\n self.log_scrape = self.cnf.cnf_value(\"flag.log_scrape\")\n self.scrape_headless = self.cnf.cnf_value(\"flag.scrape_headless\")\n self.driver = None\n self.html_doc = None\n self.team_name = None\n self.games = []\n self.gym = {}\n\n def get_schedule(self) -> list:\n \"\"\"Get schedule for the current NY Urban season.\"\"\"\n try:\n options = Options()\n if self.scrape_headless:\n options.headless = True\n if self.log_scrape:\n print(\n f\"...setting up {'headless ' if self.scrape_headless else ''}driver\"\n )\n self.driver = webdriver.Chrome(\n executable_path=self.get_chromedriver_path(), options=options\n )\n try:\n self.login()\n self.get_latest_team_page()\n self.parse_team_data()\n self.parse_gym_info()\n finally:\n self.driver.close()\n self.driver = None\n except SessionNotCreatedException as e:\n print(\n f\">>>ERROR: {e.msg}Get the version matching your Chrome browser and place it in '/vendor' from:\\n{DRIVER_URL}\"\n )\n except WebDriverException as e:\n print(\n f\">>>ERROR: Get the latest 'chromedriver' binary from the link below and place it in '/vendor':\\n{DRIVER_URL}\"\n )\n except Exception as e:\n print(f\">>> Some other exception with selenium: {e}\")\n return [self.team_name, self.games, self.gym]\n\n def get_chromedriver_path(self) -> str:\n return os.path.join(\n os.path.dirname(os.path.abspath(__file__)),\n \"vendor\",\n self.cnf.cnf_value(\"scrape.chromedriver\"),\n )\n\n def login(self) -> None:\n \"\"\"Authenticate into NY Urban.\"\"\"\n if not self.driver:\n return None\n if self.log_scrape:\n print(f\"...logging in: {self.url}\")\n self.driver.get(self.url)\n assert \"Volleyball League\" in self.driver.title\n user = self.driver.find_element_by_id(\"username\")\n user.clear()\n user.send_keys(self.cnf.cnf_value(\"scrape.user\"))\n pwd = self.driver.find_element_by_id(\"password\")\n pwd.clear()\n pwd.send_keys(self.cnf.cnf_value(\"scrape.pwd\"))\n pwd.send_keys(Keys.RETURN)\n if self.driver.title == \"Login Problems\":\n raise ValueError(\"invalid credentials\")\n WebDriverWait(self.driver, 10).until(e_c.title_is(\"Team Listing\"))\n\n def get_latest_team_page(self) -> None:\n \"\"\"Get the page source for the most recent team.\"\"\"\n if not self.driver:\n return\n if self.log_scrape:\n print(\"...navigating to latest team details\")\n assert \"Team Listing\" in self.driver.title\n team_link = self.driver.find_element_by_xpath(\"//tbody/tr[2]/td/a\")\n team_link.click()\n WebDriverWait(self.driver, 10).until(e_c.title_is(\"Team Detail\"))\n source = self.driver.page_source\n assert \"Division:\" in source\n self.html_doc = source\n\n def parse_team_data(self) -> None:\n \"\"\"\n Parse team data into a list.\n\n Returns a list of games. Each game is a list with the following items:\n [datetime, location, opponent]\n \"\"\"\n if self.log_scrape:\n print(\"...retrieving schedule\")\n soup = BeautifulSoup(self.html_doc, \"html.parser\")\n self.team_name: str = soup.find(\"div\", class_=\"team\").h1.span.text.strip()\n team_table = soup.find(\"div\", class_=\"team_div\").div.table.tbody\n rows = team_table.findChildren(\"tr\", recursive=False)\n for row in rows[1:]:\n cols = row.findChildren(\"td\", recursive=False)\n if \"No Game This Week\" not in cols[3].text:\n self.games.append(\n [\n # TODO check for next-year rollover\n datetime.strptime(\n f\"{cols[0].text.split()[1].strip()} {datetime.now().year} {cols[2].text.strip()}PM -0500\",\n \"%m/%d %Y %I:%M%p %z\",\n ),\n cols[1].div.a.text.strip(),\n next(sub for sub in re.split(\"\\n|\\t\", cols[3].text) if sub),\n ]\n )\n\n def parse_gym_info(self) -> None:\n \"\"\"Get a dict mapping of gym code to name and address.\"\"\"\n if self.log_scrape:\n print(\"...retrieving gyms\")\n soup = BeautifulSoup(self.html_doc, \"html.parser\")\n gym_table = soup.find(\"div\", class_=\"locationcontent\").div.table.tbody\n rows = gym_table.findChildren(\"tr\", recursive=False)\n for row in rows[1:]:\n cols = row.findChildren(\"td\", recursive=False)\n gym_code = cols[0].string\n if gym_code in self.gym:\n continue\n gym_name = cols[1].contents[0].text.strip()\n try:\n address = parse_qs(cols[2].a.attrs.get(\"href\")).get(\"address\")[0]\n except Exception:\n address = None\n self.gym[gym_code] = [gym_name, address]\n\n @staticmethod\n def parse_schedule(schedule: list, upcoming_only=False) -> list:\n \"\"\"Get the schedule with gym names and locations.\"\"\"\n from_ts = datetime.now(timezone.utc) if upcoming_only else datetime(2000, 1, 1)\n games = []\n for one_game in schedule[1]:\n if one_game[0] < from_ts:\n continue\n one_game.extend(schedule[2][one_game[1]])\n games.append(one_game)\n return [schedule[0], games]\n","repo_name":"ericgarig/vb_gcal","sub_path":"vb_scrape.py","file_name":"vb_scrape.py","file_ext":"py","file_size_in_byte":6456,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"94492834","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport os\nimport re\nimport sys\nimport logging\nimport argparse\nimport matplotlib\nmatplotlib.use('Agg')\n\nimport numpy as np\nfrom collections import OrderedDict\nfrom matplotlib import pyplot as plt\n\nLOG = logging.getLogger(__name__)\n\n__version__ = \"1.0.0\"\n__author__ = (\"Xingguo Zhang\",)\n__email__ = \"invicoun@foxmail.com\"\n__all__ = []\n\n\ndef read_tsv(file):\n\n for line in open(file):\n line = line.strip()\n\n if not line or line.startswith(\"#\"):\n continue\n line = line.replace(\",\", \"\")\n\n yield line.split(\"\\t\")\n\n\ndef deal_with(files, types=\"all\", model=\"length\"):\n\n r = {}\n if types==\"all\":\n types = [\"MITE\", \"LTR\", \"SINE\", \"TER\", \"HELITRON\"]\n\n if model== \"length\":\n model = 4\n elif model== \"number\":\n model = 2\n else:\n model = -1\n\n for file in files:\n for line in read_tsv(file):\n if line[0] not in r:\n r[line[0]] = [float(line[5]), []]\n if line[1] not in types:\n continue\n r[line[0]][1].append((line[1], float(line[model])))\n\n pr = OrderedDict()\n sample = []\n for name, values in sorted(r.items(), key=lambda d:d[1][0], reverse=True):\n sample.append(name.replace(\"_\", \" \").capitalize())\n for types, dv in values[1]:\n if types not in pr:\n pr[types] = []\n pr[types].append(dv)\n return sample, pr\n\n\ndef plot_transposon(files, types=\"all\", model=\"length\"):\n\n sample, pr = deal_with(files, types, model)\n if model == \"length\":\n yl = \"Transposon length(pb)\"\n elif model == \"number\":\n yl = \"Transposon number\"\n else:\n yl = \"% transposon\"\n #PCOLOR = [\"#E6FA3C\", \"#E6AD67\", \"#FA2883\", \"#524CE6\", \"#6AFFE0\"]\n #PCOLOR = [\"#8975FA\", \"#3BA5F0\", \"#91FAD7\", \"#83E673\", \"#FFFA4D\"]\n #PCOLOR = [\"#FA2843\", \"#62F0A3\", \"#FA7D5A\", \"#5AEAE3\", \"#FF8619\"]\n #PCOLOR = [\"#0780cf\", \"#765005\", \"#fa6d1d\", \"#0e2c82\", \"#b6b51f\"]\n PCOLOR = [\"#015699\", \"#FAC00F\", \"#F3764A\", \"#5FC6C9\", \"#4F596D\"]\n\n fig = plt.figure(figsize=[10,8.5])\n ax = fig.add_subplot(1,1,1,) \n \n ax.spines['top'].set_visible(False)#去掉上边边框\n ax.spines['bottom'].set_visible(False)#去掉下方边边框\n ax.spines['right'].set_visible(False)#去掉右边边框\n ax.spines['left'].set_visible(False)#去掉左边边框\n ax.grid(True, 'major', 'y', ls='--', lw=.5, c='black', alpha=.3)\n ax.xaxis.set_major_formatter(plt.FuncFormatter(''.format)) #X轴不显示刻度\n ax.xaxis.set_minor_formatter(plt.FuncFormatter(''.format))\n\n plt.tick_params(bottom=False, top=False, left=False, right=False, labelsize=12)\n plt.subplots_adjust(left=0.15, right=0.95, top=0.95, bottom=0.25)\n n = 0\n x = np.array(sample)\n\n for i in pr:\n y = np.array(pr[i])\n if n == 0:\n ax.bar(x, y, label=i, color=PCOLOR[n])\n h = y\n else:\n ax.bar(x, y, bottom=h, label=i, color=PCOLOR[n])\n h = h+y\n n += 1\n\n for a,b in zip(x, sample):\n ax.text(a, -2, b, ha='right', va='top', fontsize=12, rotation=45)\n\n plt.legend(loc=\"upper right\", frameon=False)\n plt.ylabel(yl, fontsize=14)\n plt.savefig(\"%s_transposon.png\" % types, dpi=500)\n plt.savefig(\"%s_transposon.pdf\" % types)\n\n return 0\n\n\ndef add_args(parser):\n\n parser.add_argument(\"input\", nargs='+', metavar='FILE', type=str,\n help='Input the statistical results of each sample transposon, stat_transposon.tsv.')\n parser.add_argument('-t', '--types', choices=[\"MITE\", \"LTR\", \"SINE\", \"TER\", \"HELITRON\", \"all\"], default=\"all\",\n help='Set the type of transposon displayed, default=all.')\n parser.add_argument('-m', '--model', choices=[\"length\", \"number\", \"percentage\"], default=\"length\",\n help='Set the type of data displayed, default=length.')\n\n return parser\n\n\ndef main():\n\n logging.basicConfig(\n stream=sys.stderr,\n level=logging.INFO,\n format=\"[%(levelname)s] %(message)s\"\n )\n\n parser = argparse.ArgumentParser(\n formatter_class=argparse.RawDescriptionHelpFormatter,\n description=\"\"\"\nname:\n plot_transposon.py: Draw a picture of the transposon\nattention:\n plot_transposon.py *.stat_transposon.tsv\nversion: %s\ncontact: %s <%s>\\\n \"\"\" % (__version__, \" \".join(__author__), __email__))\n\n parser = add_args(parser)\n args = parser.parse_args()\n\n plot_transposon(args.input, args.types, args.model)\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"zxgsy520/grandTE","sub_path":"scripts/plot_transposon.py","file_name":"plot_transposon.py","file_ext":"py","file_size_in_byte":4547,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"38168317141","text":"rooms = int(input())\nfree_chairs = 0\nflag = True\n\nfor i in range(rooms):\n lst = input().split()\n if len(lst[0]) < int(lst[1]):\n print(f'{int(lst[1]) - len(lst[0])} more chairs needed in room {i + 1}')\n flag = False\n else:\n free_chairs += len(lst[0]) - int(lst[1])\n\nif flag:\n print(f'Game On, {free_chairs} free chairs left')\n","repo_name":"lubodonchev/SoftUni_Coursework","sub_path":"First_Project/SoftUni Python Fundamentals May 2023/Lists Advanced Exercise/office_chairs.py","file_name":"office_chairs.py","file_ext":"py","file_size_in_byte":358,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"35099113308","text":"from enum import Enum\n\n\nclass Headers(Enum):\n FileUploadHeaders = {\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) '\n 'Chrome/95.0.4638.69 Safari/537.36',\n }\n\n CommonHeaders = {\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) '\n 'Chrome/95.0.4638.69 Safari/537.36',\n 'Content-Type': 'application/json;charset=UTF-8',\n }\n\n\nclass AnswerPathAndUploadUrl(Enum):\n items = {\n 'url': {\n 'loginUrl': 'https://yk.myunedu.com/yunkai/sys/identification/login',\n 'courseListSearchUrl': 'https://yk.myunedu.com/yunkai/web/study/userPracticeScheme/overview',\n 'homeworkListSearchUrl': 'https://yk.myunedu.com/yunkai/web/student/task/list',\n 'uploadUrl': 'https://yk.myunedu.com/yunkai/file/upload',\n 'answerUrl': 'https://yk.myunedu.com/yunkai/web/student/task/submit/answer',\n 'infoUrl': 'https://yk.myunedu.com/yunkai/web/student/task/info',\n },\n \"课程实践\": {\n 'commonFilename': \"小组讨论.pdf\",\n 'imgFilename': '小组讨论.png',\n },\n \"小组学习\": {\n 'commonFilename': \"小组讨论.pdf\",\n 'imgFilename': '小组讨论.png',\n },\n \"线下作业1\": {\n 'commonFilename': \"线下作业1.docx\",\n 'imgFilename': '小组讨论.png',\n },\n \"线下作业2\": {\n 'commonFilename': \"线下作业2.docx\",\n 'imgFilename': '小组讨论.png',\n },\n \"线下作业3\": {\n 'commonFilename': \"线下作业3.docx\",\n 'imgFilename': '小组讨论.png',\n },\n \"线下作业4\": {\n 'commonFilename': \"线下作业4.docx\",\n 'imgFilename': '小组讨论.png',\n },\n '离线作业1': {\n 'commonFilename': \"离线作业1.docx\",\n 'imgFilename': '小组讨论.png',\n },\n '离线作业2': {\n 'commonFilename': \"离线作业2.docx\",\n 'imgFilename': '小组讨论.png',\n },\n '离线作业3': {\n 'commonFilename': \"离线作业3.docx\",\n 'imgFilename': '小组讨论.png',\n },\n '离线作业4': {\n 'commonFilename': \"离线作业4.docx\",\n 'imgFilename': '小组讨论.png',\n },\n \"线上作业\": {\n 'startUrl': 'https://yk.myunedu.com/yunkai/web/examPaper/start',\n 'updateUrl': 'https://yk.myunedu.com/yunkai/web/examPaper/update',\n 'submitUrl': 'https://yk.myunedu.com/yunkai/web/examPaper/submit',\n },\n '在线作业': {\n 'startUrl': 'https://yk.myunedu.com/yunkai/web/examPaper/start',\n 'updateUrl': 'https://yk.myunedu.com/yunkai/web/examPaper/update',\n 'submitUrl': 'https://yk.myunedu.com/yunkai/web/examPaper/submit',\n }\n }\n\n info_find_items = {\n 'answerSuccessUrl': 'https://yk.myunedu.com/yunkai/web/student/task/info'\n }\n\n\nclass BrushClassUrl(Enum):\n items = {\n 'add_progress': 'https://yk.myunedu.com/yunkai/admin/userstudyrecord/addVideoProgress',\n 'add_time': 'https://yk.myunedu.com/yunkai/admin/userstudyrecord/addVideoTime',\n 'course_list': 'https://yk.myunedu.com/yunkai/web/study/userPracticeScheme/overview',\n 'course_video_info': 'https://yk.myunedu.com/yunkai/web/charterSection/charterSectionList',\n 'live_video_info': 'https://yk.myunedu.com/yunkai/web/study/liveLessons'\n }\n\n\nclass TaskReturnMessage(Enum):\n ImgTextFindSuccess = {'imgStatus': None, 'textStatus': None, 'taskStatus': True, 'msg': '图片、文字填充-查询成功'}\n ImgTextFindFailing = {'taskStatus': False, 'msg': '图片、文字填充查询-连续请求不成功'}\n ImgTextFindFailingDetail = {'taskStatus': False, 'msg': '图片、文字填充查询-数据缺失'}\n\n FileNotFound = {'taskStatus': False, 'msg': f\"文件不存在：\"}\n FileExists = {'taskStatus': True, 'msg': f\"文件存在\"}\n\n FileUploadSuccess = {'taskStatus': True, 'msg': '文件上传成功'}\n FileUploadFailing = {'taskStatus': False, 'msg': '文件上传失败 连续请求不成功'}\n\n LastFileUploadSuccess = {'taskStatus': True, 'msg': '总提交成功-pdf、word、img答案提交阶段'}\n LastFileUploadFailing = {'taskStatus': False, 'msg': '总提交失败-pdf、word、img答案提交阶段 连续请求不成功'}\n\n LastTextUploadSuccess = {'taskStatus': True, 'msg': f'总提交成功-文本填充阶段'}\n LastTextUploadFailing = {'taskStatus': False, 'msg': f'总提交失败-文本填充阶段 连续请求不成功'}\n\n LastOnlineHomeWorkSuccess = {'taskStatus': True, 'msg': '总提交成功-线上作业'}\n LastOnlineHomeWorkFailing = {'taskStatus': False, 'msg': '总提交失败-线上作业 连续请求不成功'}\n LastOnlineHomeWorkFailingDetail = {'taskStatus': False, 'msg': '总提交失败-线上作业'}\n\n ResponseDataLost = {'taskStatus': False, 'msg': '题目答案搜索-缺失答案数据'}\n\n ChildHomeWorkFailing = {'taskStatus': False, 'msg': '子问题 连续请求不成功'}\n ChildHomeWorkSuccess = {'taskStatus': True, 'msg': '子问题 提交成功'}\n\n OnlineHomeWorkFinished = {'taskStatus': True, 'msg': '选择题已完成'}\n\n QuestionAnswerSearchDataLost = {'taskStatus': False, 'msg': '线上作业 答案搜索-缺失答案数据'}\n QuestionAnswerSearchRequestFailing = {'taskStatus': False, 'msg': '线上作业 答案搜索-请求不成功'}\n\n\nclass LogMessage(Enum):\n CourseListSearchLogItem = {\n 'content': \"{} courseListSearch 连续请求不成功!\\n\",\n 'loggerItem': {\n 'one': None, 'two': 'null', 'three': 'null', 'four': '连续请求不成功', 'five': '课程列表搜索阶段'\n }\n }\n\n HomeworkListSearchLogItem = { # 日志对象\n 'content': \"{} homeworkListSearch 连续请求不成功!\\n\",\n 'loggerItem': {\n 'one': None, 'two': None, 'three': 'null', 'four': '连续请求不成功', 'five': '子作业列表搜索阶段'\n }\n }\n\n ChildHomeWorkFinished = {\n 'content': \"已经完成：{} {} {}\\n\",\n 'loggerItem': {\n 'one': None, 'two': None, 'three': None, 'four': '已经完成', 'five': '科目子作业存储阶段'\n }\n }\n\n ChildHomeWorkSaved = {\n 'content': \"已经完成：{} {} {}\\n\",\n 'loggerItem': {\n 'one': None, 'two': None, 'three': None, 'four': '存储成功', 'five': '科目子作业存储阶段'\n }\n }\n\n\n# print(Headers.FileUploadHeaders.value)\n# # AnswerPathAndUploadUrl.items.value['url']['uploadUrl'] += '13rqwrqr'\n# # print(AnswerPathAndUploadUrl.items.value['url']['uploadUrl'])\n# # print(TaskReturnMessage.LastOnlineHomeWorkFailingDetail.value['msg'].format(\"xiaoz\"))\n# items = TaskReturnMessage.LastOnlineHomeWorkFailingDetail.value\n# items['msg'] = items['msg'].format('xiaozhou')\n# print(items)\n\n","repo_name":"Exixiaozhou/YkStudyAnswer","sub_path":"MyLib/constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":7326,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"12500694994","text":"#!/usr/bin/python3\n## author: jinchoiseoul@gmail.com\n## brief: 1st interview quiz from serverless.kakaogroup.com\n## Try not to use list-like collections\n## In other words, use space of O(1) complexity\n\n\ndef fibonacci_iter1(n):\n ''' naive implementation '''\n if n<=0:\n return\n assert n>=1\n if n==1:\n yield 1\n return\n if n==2:\n yield 1\n yield 1\n return\n \n yield 1\n yield 1\n n -= 2\n p2, p1, cur = 1, 1, 2\n while n > 0:\n yield cur\n p2, p1 = p1, cur\n cur = p2 + p1\n n -= 1\n \n\ndef fibonacci_iter2(n):\n ''' More compressive way to implement\n (1, 0,) 1, 1, 2, 3, 5. 8, ,,, '''\n if n<=0:\n return\n assert n>=1\n p2, p1 = 1, 0\n while n > 0:\n cur = p2 + p1\n yield cur\n p2, p1 = p1, cur\n n -= 1\n\n\ndef fibonacci_iter3(n):\n def fib():\n a, b = 1, 1\n while True:\n yield a\n a, b = b, a+b\n\n it = fib()\n for _ in range(n):\n yield next(it)\n\n\nimport unittest\n\n\nclass SolutionTest(unittest.TestCase):\n\n def test_basics(self):\n self.assertFib(0, [])\n self.assertFib(1, [1])\n self.assertFib(2, [1,1])\n self.assertFib(3, [1,1,2])\n self.assertFib(4, [1,1,2,3])\n self.assertFib(5, [1,1,2,3,5])\n \n def assertFib(self, n, expected):\n fibs = [fibonacci_iter1, fibonacci_iter2, fibonacci_iter3]\n for fib in fibs:\n actual = list(fib(n))\n self.assertEqual(actual, expected)\n\n\nif __name__ == \"__main__\":\n\n unittest.main()\n","repo_name":"JiniousChoi/encyclopedia-in-code","sub_path":"quizzes/etc/arrays/fibonacci_iter.py","file_name":"fibonacci_iter.py","file_ext":"py","file_size_in_byte":1591,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"22491602297","text":"# Definition for singly-linked list.\n# class ListNode(object):\n# def __init__(self, val=0, next=None):\n# self.val = val\n# self.next = next\n\nclass Solution(object):\n def addTwoNumbers(self, l1, l2):\n \"\"\"\n :type l1: ListNode\n :type l2: ListNode\n :rtype: ListNode\n \"\"\"\n #Create 2 strings that we are going to convert into integers and do the sum. \n current_node = l1\n num1 = \"\"\n num2 = \"\"\n \n #we loop until the node before the last node\n while current_node.next != None:\n num1 += str(current_node.val) \n #convert the value in the node into a string.\n current_node = current_node.next \n #we set the current_node to be the next node.\n num1 += str(current_node.val) \n #for the last node, we add it separetely outside of the for loop\n \n #we repeat the same operation for the second number.\n \n current_node = l2\n while current_node.next != None:\n num2 += str(current_node.val)\n current_node = current_node.next\n num2 += str(current_node.val)\n \n res = str(int(num1[::-1]) + int(num2[::-1]))[::-1]\n # After we sum, we will convert the integers into a string because it's easier to manipulate for linkedlists.\n \n #We create the head of the linkedlist, called linkedlist.\n linkedlist = ListNode(val=res[0])\n current_node = linkedlist\n \n idx = 1 #initalize the index before the while loop\n while idx < len(res):\n current_node.next = ListNode(val = res[idx])\n current_node = current_node.next\n idx += 1\n #Finally we return the head of the list. As we return the head, the whole list will be returned. \n \n return linkedlist # as we return the head of the linkedlist, the whoe list will be changed\n ","repo_name":"ronaldngounou/LeetCode-Practice","sub_path":"0002-add-two-numbers/0002-add-two-numbers.py","file_name":"0002-add-two-numbers.py","file_ext":"py","file_size_in_byte":1944,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"18001677751","text":"#!/usr/bin/python3\n\"\"\"Emulate dpkg --info package.deb control-file\"\"\"\n\nfrom __future__ import print_function\n\nimport sys\n\nfrom apt_inst import DebFile\n\n\ndef main():\n \"\"\"Main function.\"\"\"\n if len(sys.argv) < 3:\n print('Usage: tool file.deb control-file\\n', file=sys.stderr)\n sys.exit(0)\n fobj = open(sys.argv[1])\n try:\n print(DebFile(fobj).control.extractdata(sys.argv[2]))\n finally:\n fobj.close()\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"PikaOS-Linux/pkgs-baseos","sub_path":"python-apt/python-apt/doc/source/examples/dpkg-info.py","file_name":"dpkg-info.py","file_ext":"py","file_size_in_byte":480,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"52"} +{"seq_id":"640506339","text":"#!/usr/bin/env python3.7\n__author__ = \"Emiliano Sauvisky\"\n__version__ = \"1.0\"\n__license__ = \"MIT\"\n\nimport argparse\nimport asyncio\nimport random\nimport re\nimport scipy.stats\nimport time\n\n# import numpy as np\n# import matplotlib.pyplot as plt\n\n\nfrom aiorun import run\nfrom logzero import logger\nfrom pyautogui import hotkey, press, typewrite\nfrom num2words import num2words\n\nRE_INTEGER_RANGE = r'[0-9]+\\.\\.\\.[0-9]+'\n\n\ndef trunc_gauss_int(bottom, top):\n '''\n Generates a random integer following a personal gaussian distribution,\n using the mean of both values as mu, and 0.7 as the standard deviation\n whilst truncating values outside the range.\n '''\n lower = -1\n upper = 1\n mu = 0 # 422516lu\n sigma = 0.7\n\n total_possibilities = abs(top - bottom) / 2\n median_value = (bottom + top) / 2\n\n return int(median_value + (total_possibilities * scipy.stats.truncnorm.rvs((lower-mu)/sigma,(upper-mu)/sigma,loc=mu,scale=sigma,size=1)))\n\nclass AnswerWasAlreadyUsed(Exception):\n pass\n\nclass Main(object):\n def __init__(self, args):\n self.items = args.items\n self.ignore_spaces = args.ignore_spaces\n self.time_interval = float(args.time)\n self.write_for_extense = args.write_for_extense\n self.leading_zeroes = args.leading_zeroes\n self.diminishing_time = args.diminishing_time\n self.previous_checks = []\n self.start_time = time.time() - self.time_interval\n\n self.words = []\n with open(args.file, 'r') as file:\n for line in file:\n if not line.startswith('#') or not line.strip():\n self.words.append(line.strip('\\n '))\n\n def generate_random_word(self):\n return random.choice(self.words).casefold()\n\n def generate_random_number(self, full_range):\n try:\n first_number, last_number = str(full_range).split('...', 1)\n except:\n logger.error('I cannot understand what you mean with: ' + str(full_range))\n quit()\n else:\n first_number = min(int(first_number), int(last_number))\n last_number = max(int(first_number), int(last_number))\n\n if self.write_for_extense:\n return str(num2words(trunc_gauss_int(first_number, last_number))).replace('-', ' ').replace(',', '')\n if self.leading_zeroes:\n integer = trunc_gauss_int(first_number, last_number)\n ret_len = max(len(str(first_number)), len(str(last_number)))\n return str(\"{:0{precision}d}\".format(integer, precision=ret_len))\n else:\n return str(trunc_gauss_int(first_number, last_number))\n\n async def compile_new_answer(self):\n while True:\n answer_items = []\n for item in self.items:\n if re.match(RE_INTEGER_RANGE, item):\n answer_items.append(self.generate_random_number(item))\n elif item == '...':\n answer_items.append(self.generate_random_word())\n else:\n answer_items.append(item)\n\n if self.ignore_spaces:\n answer = ''.join(answer_items)\n else:\n answer = ' '.join(answer_items)\n\n # Bails out if answer was already used and it's on memory (so we don't have to load the file again)\n if answer in self.previous_checks:\n logger.error('Answer {} was already used before (memory)'.format(answer))\n continue\n\n # Otherwise load messages.log and check if anyone has sent an identical message before.\n # If so, bail out.\n with open('messages.log', 'r') as file:\n try:\n for line in file:\n if answer.casefold() == line.casefold().strip():\n self.previous_checks.append(answer)\n logger.error('Answer {} was already used before (file)'.format(answer))\n raise Exception\n # elif answer.casefold() in line.casefold():\n # self.previous_checks.append(answer)\n # logger.error('Answer {} was already used before, but partially (file)'.format(answer))\n # # print(answer, line)\n # # print(answer.casefold(), line.casefold())\n # raise Exception\n except:\n continue\n\n logger.warning('Generated answer: ' + answer)\n self.previous_checks.append(answer)\n return answer\n\n async def send_answer(self, answer):\n hotkey('ctrl', 'a')\n typewrite(answer)\n hotkey('Enter')\n\n async def start(self):\n # while True:\n while True:\n # print(time.time() - self.start_time)\n if time.time() - self.start_time >= self.time_interval:\n # Get a brand new answer that wasn't used before\n self.start_time = time.time()\n answer = await self.compile_new_answer()\n await self.send_answer(answer)\n if self.diminishing_time and self.time_interval > 1.5:\n self.time_interval = self.time_interval * 0.95\n\n\nif __name__ == '__main__':\n def cmdline_args():\n p = argparse.ArgumentParser()\n\n p.add_argument('-f', '--file', help='File from where to pick answers.', default='answers.txt')\n p.add_argument('-x', '--write-for-extense', help='Write numbers for extense.', action='store_true')\n p.add_argument('-z', '--leading-zeroes', help='Keep leading zeroes', action='store_true')\n p.add_argument('-n', '--ignore-spaces', help='Ignore spaces between items (i.e.: \"100...300 5\" would output numbers from 1000 to 3000 ending in 5.', action='store_true')\n p.add_argument('-d', '--diminishing-time', help='Diminish [default = until 1.5s]', action='store_true')\n p.add_argument('-t', '--time', help='Minimum time to wait between each message (in seconds). Accepts floats. [default = 2]', default=2)\n p.add_argument('items', nargs='+', help='List of items to input. Use three dots (...) as a placeholder for a randomly chosen item.'\n + ' If one item is of the format of int...int (e.g.: 1000...3000), then the script will fetch a'\n + ' random integer between the two adjacent numbers.')\n\n res = p.parse_args()\n return res\n\n args = cmdline_args()\n asyncio.run(Main(args).start())\n","repo_name":"esauvisky/GiveawayPwner","sub_path":"giveaway.py","file_name":"giveaway.py","file_ext":"py","file_size_in_byte":6580,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"22030813547","text":"from typing import Tuple, Iterator\nfrom contextlib import contextmanager\nfrom pprint import pprint\nimport os\nimport signal\nimport subprocess\nimport time\n\nimport boggart\nimport bugzoo\nimport rooibos\n\n\n@contextmanager\ndef test_environment(port_bugzoo: int = 6060,\n port_rooibos: int = 8888,\n port_boggart: int = 8000,\n verbose: bool = True\n ) -> Iterator[Tuple[boggart.Client, bugzoo.Client]]:\n url_bugzoo = \"http://127.0.0.1:{}\".format(port_bugzoo)\n with boggart.server.ephemeral_stack(port_boggart=port_boggart,\n port_rooibos=port_rooibos,\n port_bugzoo=port_bugzoo,\n verbose=verbose) as client_boggart:\n client_bugzoo = bugzoo.Client(url_bugzoo)\n print(client_boggart)\n print(client_bugzoo)\n yield client_boggart, client_bugzoo\n\n\ndef generate_mutant():\n with test_environment() as (client_boggart, client_bugzoo):\n snapshot = client_bugzoo.bugs[\"tse2012:gcd\"]\n location = boggart.FileLocationRange.from_string(\"gcd.c@10:3::10:15\")\n mutation = boggart.Mutation(\"NEGATE_IF_CONDITION_CSTYLE\", 0, location, {})\n mutations = [mutation]\n mutant = client_boggart.mutate(snapshot, mutations)\n print(mutant)\n print(mutant.uuid)\n print(mutant.to_dict())\n\n\ndef generate_mutations():\n with test_environment() as (client_boggart, client_bugzoo):\n snapshot = client_bugzoo.bugs[\"tse2012:gcd\"]\n filepath = \"gcd.c\"\n for mutation in client_boggart.mutations(snapshot, filepath):\n pprint(mutation.to_dict())\n\n\nif __name__ == '__main__':\n generate_mutations()\n","repo_name":"squaresLab/boggart","sub_path":"examples/mutant.py","file_name":"mutant.py","file_ext":"py","file_size_in_byte":1769,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"12611762316","text":"from BSTNode import BSTNode\n\nclass BSTMap:\n def __init__(self):\n \"\"\"Creates a new BSTMap\"\"\"\n self._root = None\n\n def put(self, key, value):\n \"\"\"adds k:v pair to BSTMap\"\"\"\n if self._root is None:\n self._root = BSTNode(key, value)\n return\n else:\n self._root = self._root.put(key, value)\n\n def get(self, key):\n \"\"\"Returns value associated w/ key\"\"\"\n if self._root is None: raise KeyError\n\n return self._root.get(key)\n\n def __contains__(self, key):\n \"\"\"Returns true if key is in BSTMap, false otherwise\"\"\"\n if self._root is None: return False\n\n return key in self._root\n\n def __repr__(self):\n \"\"\"Returns string representation of BSTMap\"\"\"\n return repr(self._root)\n\nif __name__ == '__main__':\n \n bst1 = BSTMap()\n for i in range(8):\n assert not i in bst1 # test contains (false)\n bst1.put(i, str(i))\n assert bst1.get(i) == str(i) # tests put/get \n assert i in bst1 # tests contains (true)\n\n print(bst1)\n\n# 0 <--bst1._root\n# \\\n# 1\n# \\\n# 2 \n# /\n# 1.5","repo_name":"Nicholas-Couillard/cse2050","sub_path":"Lecture/Archive/BSTMap.py","file_name":"BSTMap.py","file_ext":"py","file_size_in_byte":1157,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"9019487810","text":"import requests\nimport time\n\n#criar decorator calclar tempo\ndef calcular_tempo(funcao):\n def wrapper():\n tempo_inicial = time.time()\n print(\"vou pegar a cotacao\")\n funcao()\n print(\"peguei a cotacao\")\n tempo_final = time.time()\n print(f\"tempo de operacao foi:{tempo_final - tempo_inicial} segundos\")\n return wrapper\n\n\n@calcular_tempo\ndef pegar_cotacao_dolar():\n link = f'https://economia.awesomeapi.com.br/last/USD-BRL'\n requisicao = requests.get(link)\n requisicao = requisicao.json()\n print(requisicao['USDBRL']['bid'])\n\npegar_cotacao_dolar()\n","repo_name":"RicardoSilvaSoares/python","sub_path":"src/Flask/Decorator_aprendendo.py","file_name":"Decorator_aprendendo.py","file_ext":"py","file_size_in_byte":603,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"16186909422","text":"# -*- coding: utf-8 -*-\n__author__ = 'dizcza'\n\n# THIS IS A FILE TO TUN THE PROJECT JourneyClassifier\n# For more info go to https://github.com/dizcza/JourneyClassifier\nimport time\nimport csv\nfrom datetime import datetime\n\n# run a timer and import data from journeyhandler.py\ntime_start = time.time()\nimport journeyhandler\nfrom journeyhandler import COUNTRY_BOUNDARIES as BOUNDARIES\n\n\ndef classify(years, journey_zones):\n \"\"\"\n\t Classify data into group of journeys with the same year and the same zone (country).\n\t All countries borders must be stored in 'borders'.\n\t\"\"\"\n try:\n data_length = journeyhandler.get_data_length([years, journey_zones])\n if data_length == 0:\n raise Exception\n except:\n print(\"\\n\\t(!) %s: Invalid data input.\\n\" % journeyhandler.get_func_name())\n return\n\n group_list = [0 for dummy_checkin in range(data_length)]\n\n # Set first unique IDs for each set of journeys, taken during one year.\n ids = 1\n first_year = min(years)\n last_year = max(years)\n for year in range(last_year, first_year - 1, -1):\n for country_name in BOUNDARIES.keys():\n # If data sample with current zone in current year is found, checker 'isfound' becomes True.\n isfound = False\n for checkin in range(data_length - 1, -1, -1):\n # Loop from the end to the beginning of data samples for each year and possible localization zone.\n if journey_zones[checkin] != 'usa' and journey_zones[checkin] == country_name and years[\n checkin] == year:\n group_list[checkin] = ids\n isfound = True\n if isfound:\n # If matching is found on the last steps, look for another zone\n # in the current year. Thus, we must change the ID to the new\n # travelling group and reset found matches.\n ids += 1\n\n print(\"OUTPUT: Have found %d groups of travellings.\" % max(group_list))\n return group_list\n\n\nprint(\"\\n\" + \"*\" * 20 + \" Begin to operate data. \" + \"*\" * 20)\n\ncsvfile = open('checkins.csv', 'r')\ndata = csv.reader(csvfile, delimiter=',')\n\nID = []\ntimeunix = []\nyears = []\nlatitude = []\nlongitude = []\n\nfor checkin, row in enumerate(data):\n ID.append(str(row[0]))\n timeunix.append(int(row[1]))\n year = int(datetime.fromtimestamp(timeunix[-1]).strftime('%Y'))\n years.append(year)\n latitude.append(float(row[2]))\n longitude.append(float(row[3]))\ncsvfile.close()\n\n# Make data immutable.\nlatitude = tuple(latitude)\nlongitude = tuple(longitude)\nNUM_OF_CHECKINS = len(ID)\n\n# Finding home location.\nhomeloc, native_country_name = journeyhandler.figure_out_home([latitude, longitude])\nprint(\"Home location: %s \\t\" % native_country_name, homeloc)\n\n# Visualize the data, if you want.\njourneyhandler.visualize_data(latitude, longitude, home=homeloc)\n\n# Journey zones classifier.\njourney_zones = journeyhandler.setzones([latitude, longitude])\n\n# Classify data into set of journeys with unique IDs\ngroup_list = classify(years, journey_zones)\n\n# Wrap data into 'checkins_upd.csv'\ntry:\n csvfile = open('checkins_upd.csv', 'wb')\n outputfile = csv.writer(csvfile, delimiter=',')\n for checkin in range(NUM_OF_CHECKINS):\n outputfile.writerow(\n [ID[checkin], timeunix[checkin], latitude[checkin], longitude[checkin], group_list[checkin]])\nexcept:\n print(\"\\nOUTPUT ERROR: Cannot write the results into the output file.\\n\")\nfinally:\n csvfile.close()\n\ntime_end = time.time()\nprint(\"*\" * 25 + \" THE END. \" + \"*\" * 25 + \"\\n\")\nprint(\"Execution time: %g sec.\" % (time_end - time_start))\n","repo_name":"dizcza/JourneyClassifier","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3646,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"5018737066","text":"#lista = [\"b\" , \"c\" , \"d\" , \"e\"]\n#numeros = list(range(0,11))\nlista = []\nnumeros = []\n\n#lista.append(\"f\")\n#lista.insert(0,\"a\")\n#del lista[0]\n\n#if \"a\" in lista:\n# lista.remove(\"a\")\n\nfor cont in range (0,5):\n lista.append(input(\"Digite uma letra: \"))\n numeros.append(input(\"Digite uns números: \"))\n\nfor c, v in enumerate(lista):\n print(\"{} - {}\".format(c,v))\nprint(\"------------------------------------------\")\nfor c, v in enumerate(numeros):\n print(\"{} - {}\".format(c,v))\n\n \n\n\n\n#print(lista)\n#print(numeros)\n#print(len(lista))\n#print(len(numeros))\n\n\n","repo_name":"Lucasvirtual/Python-modulo-3","sub_path":"aula 17.py","file_name":"aula 17.py","file_ext":"py","file_size_in_byte":568,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"5880658710","text":"from flask import render_template, request, redirect, url_for, flash, send_file, safe_join\nfrom app import app, Users, db, login_manager, create_project, allowed_file, classification, Projects, csv_writer\nfrom flask_login import LoginManager, UserMixin, login_required, login_user, current_user, logout_user\nimport os\nfrom werkzeug.utils import secure_filename\n\nlist_project = []\n\n@app.route('/', methods=['POST', 'GET'])\n@login_required\ndef index():\n\tlist_project = os.listdir('project/'+current_user.user_name)\n\tif request.method == \"POST\":\n\t\tquery = Projects.query.filter_by(title_project=request.form['title_card'], project_username=current_user.user_name).all()\n\t\treturn redirect(f\"/create_project/{current_user.user_name}/{query[0].title_project}/upload_files/add_classes/data_classification/{query[0].file_name}\")\n\n\treturn render_template(\"base.html\", list=list_project)\n\n@app.route('/login/register', methods=[\"POST\", \"GET\"])\ndef register():\n\tif request.method == \"POST\":\n\t\ttry:\n\t\t\tsearch_username = Users.query.filter_by(user_name = request.form['user_name']).all()\n\t\t\tsearch_email = Users.query.filter_by(email = request.form['email']).all()\n\t\t\tif request.form['user_name'] != \"\" and request.form['email'] != \"\" and request.form['psw'] != \"\":\n\t\t\t\tif search_username == []:\n\t\t\t\t\tif search_email == []:\n\t\t\t\t\t\tu = Users(user_name=request.form['user_name'],email=request.form['email'])\n\t\t\t\t\t\tu.set_password(request.form['psw'])\n\t\t\t\t\t\tdb.session.add(u)\n\t\t\t\t\t\tdb.session.flush()\n\t\t\t\t\t\tdb.session.commit()\n\t\t\t\t\t\tos.mkdir('project/'+str(request.form['user_name']))\n\t\t\t\t\t\treturn redirect(url_for('login'))\n\t\t\t\t\telse:\n\t\t\t\t\t\tflash(\"Email is already in use\")\n\t\t\t\telse:\n\t\t\t\t\tflash(\"Username is already in use\")\n\t\t\telse:\n\t\t\t\tflash(\"Fill in all the fields\")\n\n\t\texcept:\n\t\t\tdb.session.rollback()\n\t\t\tprint(\"Error\")\n\n\treturn render_template(\"register.html\")\n\n@app.route('/login', methods=['POST', \"GET\"])\ndef login():\n\tif request.method == \"POST\":\n\t\tuser = db.session.query(Users).filter(Users.user_name == request.form['user_name']).first()\n\t\tif user and user.check_password(request.form['psw']):\n\t\t\trm = True if request.form.get('cb_remember') else False\n\t\t\tlogin_user(user, remember=rm)\n\t\t\treturn redirect(\"/\")\n\t\telse:\n\t\t\tflash(\"Invalid username/password\", 'error')\n\t\t\treturn redirect(url_for('login'))\n\treturn render_template(\"login.html\")\n\n@app.route('/logout/')\n@login_required\ndef logout():\n logout_user()\n flash(\"You have been logged out.\")\n return redirect(url_for('login'))\n\n@app.route('/create_project/', methods=['POST', \"GET\"])\n@login_required\ndef project():\n\tif request.method == \"POST\":\n\t\ttry:\n\t\t\tcreate_project(current_user.user_name, request.form['title_project'])\n\t\t\treturn redirect(f\"/create_project/{current_user.user_name}/{request.form['title_project']}/upload_files/\")\n\t\texcept FileExistsError:\n\t\t\tflash(\"Проект с таким именем уже существует\", 'error')\n\n\treturn render_template('create_project.html', name=current_user.user_name)\n\n@app.route('/create_project///upload_files/', methods=['POST', \"GET\"])\n@login_required\ndef upload_files(name_user, name_project):\n\tif request.method == \"POST\":\n\t\tif \"file\" not in request.files:\n\t\t\tflash('No file part')\n\t\t\treturn redirect(request.url)\n\t\tfile = request.files[\"file\"]\n\t\tif file.filename == '':\n\t\t\tflash('Not selected file')\n\t\t\treturn redirect(request.url)\n\t\tif file and allowed_file(file.filename):\n\t\t\tfilename = secure_filename(file.filename)\n\t\t\tfile.save(os.path.join('project/'+current_user.user_name+'/'+name_project, filename))\n\t\t\treturn redirect(f'add_classes/{filename}')\n\t\telse:\n\t\t\tflash('Invalid file extension')\n\n\treturn render_template('add_files.html')\n\n@app.route('/create_project///upload_files/add_classes/', methods=['POST', \"GET\"])\n@login_required\ndef add_classes(name_user, name_project, filename):\n\tu = Users.query.filter_by(id = 1).all()\n\tp = Projects(title_project=name_project, file_name=filename, project_username=current_user.user_name, u_id=u[0].id, class_list=\"\")\n\tsearch_project = Projects.query.filter_by(title_project=name_project, project_username=current_user.user_name).all()\n\tif search_project != []:\n\t\tif request.method == \"POST\":\n\t\t\tif '+' in request.form:\n\t\t\t\tif request.form['title_class'] == \"\":\n\t\t\t\t\tflash(\"Название класса не может быть пустым\")\n\t\t\t\telse:\n\t\t\t\t\tquery = Projects.query.filter_by(title_project=name_project, project_username=current_user.user_name).all()\n\t\t\t\t\tlist_=query[0].class_list.split()\n\t\t\t\t\tlist_.append(request.form['title_class'])\n\t\t\t\t\tl = \" \".join(list_)\n\t\t\t\t\tProjects.query.filter_by(title_project=name_project, project_username=current_user.user_name).update({\"class_list\": l})\n\t\t\t\t\tdb.session.commit()\n\t\t\tif 'continue' in request.form:\n\t\t\t\treturn redirect(f\"data_classification/{filename}\")\n\telse:\n\t\tdb.session.add(p)\n\t\tdb.session.commit()\n\n\tquery = Projects.query.filter_by(title_project=name_project, project_username=current_user.user_name).all()\n\tlocal_list = query[0].class_list.split()\n\treturn render_template('add_classes.html', classes=local_list)\n\n@app.route('/create_project///upload_files/add_classes/data_classification/', methods=['POST', \"GET\"])\n@login_required\ndef data_classification(name_user, name_project, filename):\n\tpath = f\"project/{current_user.user_name}/{name_project}/{filename}\"\n\tsorted_path=\"sorted.txt\"\n\tdata_csv, column_names = classification(path)\n\tdata_csv_changed = []\n\tfor item in data_csv:\n\t\tr = \" \".join(item)\n\t\tdata_csv_changed.append(r)\n\tquery = Projects.query.filter_by(title_project=name_project, project_username=current_user.user_name).all()\n\tlocal_list = query[0].class_list.split()\n\n\tif request.method == \"POST\":\n\t\tdict = {}\n\t\tk = []\n\t\tsorted_list = []\n\t\tfor item in data_csv_changed:\n\t\t\td1 = {str(item): str(request.form.get(item))}\n\t\t\tdict.update(d1)\n\t\tfor i in sorted(dict.items(), key=lambda para: para[1]):\n\t\t\tk.append(data_csv_changed.index(i[0]))\n\t\tfor i in k:\n\t\t\tsorted_list.append(data_csv[i])\n\t\tcsv_writer(sorted_list, current_user.user_name, name_project, column_names)\n\t\tdownload_path=f\"project/{name_user}/{name_project}/sorted.csv\"\n\t\ttry:\n\t\t\treturn send_file(download_path, as_attachment=True)\n\t\texcept Exception as e:\n\t\t\treturn str(e)\n\n\treturn render_template(\"data_classification.html\", data = data_csv_changed, classes=local_list, paht=sorted_path)","repo_name":"mDmitriy2001/project_practice","sub_path":"view.py","file_name":"view.py","file_ext":"py","file_size_in_byte":6401,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"10352891305","text":"\"\"\"\nNama : Yuda Hadi Prasetyo\nNIM : 065002100004\nJurusan Sistem Informasi\nAlgoritma dan pemrograman \nTugas Mandiri 2\n8 oktober 2021\n\"\"\"\n#Mengolah Rata-Rata Nilai\nx,jumlah,i = 0,0,0\njawab = \"ya\"\n\nwhile jawab==\"ya\":\n x = int(input('Masukkan Nilai Mahasiswa: '))\n while (x!=-1) and not (x>100):\n i+=1\n jumlah+=x\n x=int(input('Masukkan Nilai Mahasiswa: '))\n\n if (i!=0):\n ratarata = jumlah/i\n print('Total nilai mahasiswa: ',jumlah)\n print('Jumlah mahasiswa: ',i)\n print('Nilai rata-rata mahasiswa tersebut: ',ratarata)\n\n else:\n print('tidak ada nilai rata-rata') \n t = input(\"Apakah anda ingin melanjutkan program ini?(ya/tidak): \")\n if t==\"tidak\" and t!=\"ya\":\n print('program terhenti')\n break;\nprint ('Terimakasih telah menggunakan program ini')\n","repo_name":"yudahadi79/Algoritma-Pemrograman","sub_path":"Tugas Pak Anung/Tugas Mandiri 2.py","file_name":"Tugas Mandiri 2.py","file_ext":"py","file_size_in_byte":818,"program_lang":"python","lang":"id","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"28170603871","text":"from dungeon_level.dungeon_tiles import Tiles\nfrom scipy.ndimage.measurements import label as label_connected_components\nimport numpy as np\n\nclass LevelTweaker:\n @staticmethod\n def tweak_level(level, tweaker_aesthetic):\n if tweaker_aesthetic.should_fill_unused_space:\n LevelTweaker.fill_unused_space(level)\n\n @staticmethod\n def fill_unused_space(level):\n start_position = np.argwhere(level.upper_layer == Tiles.player)[0]\n non_wall_mask = (level.upper_layer != Tiles.wall).astype(int)\n connected_components, component_count = label_connected_components(non_wall_mask)\n used_space_component = connected_components[tuple(start_position)]\n level.upper_layer[connected_components != used_space_component] = Tiles.wall","repo_name":"bjatkin/dungeon-design","sub_path":"generation/level_tweaker.py","file_name":"level_tweaker.py","file_ext":"py","file_size_in_byte":778,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"33033330500","text":"from flask import Flask\nfrom flask_cors import CORS\nfrom flask_restful import reqparse, abort, Api, Resource\nimport datetime as dt\n\nfrom pymongo import MongoClient\nimport pymongo\nimport pandas as pd\nimport unicodedata\n\nMongoIPClient = '34.238.43.143'\nPort = 27025\n\napp = Flask(__name__)\nCORS(app)\napi = Api(app)\n\n# des listes permettant de tester la coherences des donnees en entree\nCountry_list = ['AF', 'AL', 'DZ', 'AS', 'AD', 'AO', 'AI', 'AQ', 'AG', 'AR', 'AM', 'AW', 'AU', 'AT', 'AZ', 'BS', 'BH', 'BD', 'BB', 'BY',\n 'BE', 'BZ', 'BJ', 'BM', 'BT', 'BO', 'BQ', 'BA', 'BW', 'BV', 'BR', 'IO', 'BN', 'BG', 'BF', 'BI', 'KH', 'CM', 'CA', 'CV',\n 'KY', 'CF', 'TD', 'CL', 'CN', 'CX', 'CC', 'CO', 'KM', 'CG', 'CD', 'CK', 'CR', 'HR', 'CU', 'CW', 'CY', 'CZ', 'CI', 'DK',\n 'DJ', 'DM', 'DO', 'EC', 'EG', 'SV', 'GQ', 'ER', 'EE', 'ET', 'FK', 'FO', 'FJ', 'FI', 'FR', 'GF', 'PF', 'TF', 'GA', 'GM',\n 'GE', 'DE', 'GH', 'GI', 'GR', 'GL', 'GD', 'GP', 'GU', 'GT', 'GG', 'GN', 'GW', 'GY', 'HT', 'HM', 'VA', 'HN', 'HK', 'HU', \n 'IS', 'IN', 'ID', 'IR', 'IQ', 'IE', 'IM', 'IL', 'IT', 'JM', 'JP', 'JE', 'JO', 'KZ', 'KE', 'KI', 'KP', 'KR', 'KW', 'KG', \n 'LA', 'LV', 'LB', 'LS', 'LR', 'LY', 'LI', 'LT', 'LU', 'MO', 'MK', 'MG', 'MW', 'MY', 'MV', 'ML', 'MT', 'MH', 'MQ', 'MR', \n 'MU', 'YT', 'MX', 'FM', 'MD', 'MC', 'MN', 'ME', 'MS', 'MA', 'MZ', 'MM', 'NR', 'NP', 'NL', 'NC', 'NZ', 'NI', 'NE', 'NG', \n 'NU', 'NF', 'MP', 'NO', 'OM', 'PK', 'PW', 'PS', 'PA', 'PG', 'PY', 'PE', 'PH', 'PN', 'PL', 'PT', 'PR', 'QA', 'RO', 'RU', \n 'RW', 'RE', 'BL', 'SH', 'KN', 'LC', 'MF', 'PM', 'VC', 'WS', 'SM', 'ST', 'SA', 'SN', 'RS', 'SC', 'SL', 'SG', 'SX', 'SK', \n 'SI', 'SB', 'SO', 'ZA', 'GS', 'SS', 'ES', 'LK', 'SD', 'SR', 'SJ', 'SZ', 'SE', 'CH', 'SY', 'TW', 'TJ', 'TZ', 'TH', 'TL', \n 'TG', 'TK', 'TO', 'TT', 'TN', 'TR', 'TM', 'TC', 'TV', 'UG', 'UA', 'AE', 'GB', 'US', 'UM', 'UY', 'UZ', 'VU', 'VE', 'VN', \n 'VG', 'VI', 'WF','EH','YE','ZM','ZW']\n\n\nHorizon_considered = ['201701', '201712']\n\nHorizon_considered = [dt.datetime.strptime(date, '%Y%m') for date in Horizon_considered]\n\n## foncitons permettant de retourner des messages d'erreurs dans un dictionaire sans faire crasher le front\ndef abort_if_country_doesnt_exist(country):\n if country not in Country_list:\n abort(404, message=\"Country {} doesn't exist\".format(country))\n\ndef abort_if_date_format_is_invalid(month):\n try :\n month = dt.datetime.strptime(month, '%Y%m')\n except ValueError:\n abort(404, message=\"Date_format {} or {} doesn't exist\".format(month))\n if month < Horizon_considered[0]:\n abort(404, message=\"Date {} too young\".format(month))\n if month > Horizon_considered[1]:\n abort(404, message=\"Date {} too old\".format(month))\n\n# Requête\nclass api_beahaviour(Resource):\n def get(self, country1, country2, month):\n\n ### On test les variables, init_date, end_date\n abort_if_country_doesnt_exist(country1)\n abort_if_country_doesnt_exist(country2)\n abort_if_date_format_is_invalid(month)\n\n ### Create the request with pymongo (output : les données agregées)\n def Impact(country1, country2, month):\n [imp_pos, imp_neg, mention] = [0, 0, 0]\n \n client = MongoClient(MongoIPClient, Port)\n\n # On recupere le dataset\n db = client.gdelt\n\n # On recupere les donnees de country1 sur country2 au mois month sur la collection 'events'\n table = db.events.find({'Actor1Geo_CountryCode': country1, \n 'Actor2Geo_CountryCode': country2, \n 'MonthYear': month})\n \n # On aggrège nos données\n\n for el in table:\n Gold = el['GoldsteinScale']\n NumMen = el['NumMentions']\n\n if Gold > 0:\n imp_pos = imp_pos + (Gold * NumMen)\n else:\n imp_neg = imp_neg + (abs(Gold) * NumMen)\n mention = mention + NumMen\n\n #table = pd.DataFrame(list(table))\n #imp_pos = (table['GoldsteinScale'][table['GoldsteinScale'] > 0].astype(int) * table['NumMentions'][table['GoldsteinScale'] > 0]).sum()\n #imp_neg = (table['GoldsteinScale'][table['GoldsteinScale'] < 0].astype(int) * table['NumMentions'][table['GoldsteinScale'] < 0]).sum()\n\n #mention = table['NumMentions'].sum()\n\n if mention != 0:\n imp_pos = round(imp_pos / mention, 2)\n imp_neg = round(imp_neg / mention, 2)\n\n return imp_pos, imp_neg , mention\n \n ### Add value to the request\n imp1_C1_C2_pos, imp1_C1_C2_neg, mention_C1_C2= Impact(country1, country2, int(month))\n imp1_C2_C1_pos, imp1_C2_C1_neg, mention_C2_C1 = Impact(country2, country1, int(month))\n\n ### Create the answer\n awnser = {'pays1' : country1,\n 'pays2' : country2,\n 'imp1_C1_C2_pos': imp1_C1_C2_pos,\n 'imp1_C1_C2_neg' : imp1_C1_C2_neg,\n 'mention_C1_C2' : mention_C1_C2,\n 'imp1_C2_C1_pos': imp1_C2_C1_pos,\n 'imp1_C2_C1_neg' : imp1_C2_C1_neg,\n 'mention_C2_C1' : mention_C2_C1\n }\n\n return awnser, 201\n \n## Actually setup the Api resource routing here\n\napi.add_resource(api_beahaviour, '///')\n\nif __name__ == '__main__':\n app.run(debug = True)\n","repo_name":"qdonnars/No_SQL_GDELT_Project","sub_path":"api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":5710,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"35929865920","text":"\"\"\"\nThis module scrapes the distrowatch.com website for the top linux distros.\nORIGINAL REPO: https://github.com/dani0105/free-datasets\nDATE: 2022-10-04\nREQUIREMENTS: requests, bs4, pandas, re\n\"\"\"\n\n\n# IMPORTS\n\n# Imports or providing the absolute date\nimport datetime\nfrom dateutil.relativedelta import relativedelta\n\n# Imports or handling the data\nimport pandas as pd\nimport requests\nimport re\nfrom bs4 import BeautifulSoup\n\n\n# Define rs and bs4 objects\nreq = requests.get(\"https://distrowatch.com/dwres.php?resource=popularity\", timeout=5)\nsoup = BeautifulSoup(req.content, \"html.parser\")\n\n# Prevents repeating this twice\nphr3 = soup.find_all(\"td\", class_=\"phr3\")\n\n# Actual data\ndistro_names = [i.text for i in soup.find_all(\"td\", class_=\"phr2\")]\nscores = [i.text for i in phr3]\nchange = [re.search(r\"/a(.*?).png\", i.findNext(\"img\")[\"src\"]).group(1) for i in phr3]\n\n# Get absolute date for each set\ntwelve = (datetime.datetime.now() - relativedelta(months=12)).strftime(\"%Y-%m-%d\")\nsix = (datetime.datetime.now() - relativedelta(months=6)).strftime(\"%Y-%m-%d\")\nthree = (datetime.datetime.now() - relativedelta(months=3)).strftime(\"%Y-%m-%d\")\none = (datetime.datetime.now() - relativedelta(months=1)).strftime(\"%Y-%m-%d\")\n\n# Create groups based on timeframe\n\ntwelve_mons = list(\n zip(\n distro_names[0:266],\n [twelve] * 266,\n [datetime.datetime.now().strftime(\"%Y-%m-%d\")] * 266,\n scores[0:266],\n change[0:266],\n )\n)\nsix_mons = list(\n zip(\n distro_names[266:532],\n [six] * 266,\n [datetime.datetime.now().strftime(\"%Y-%m-%d\")] * 266,\n scores[266:532],\n change[266:532],\n )\n)\nthree_mons = list(\n zip(\n distro_names[532:798],\n [three] * 266,\n [datetime.datetime.now().strftime(\"%Y-%m-%d\")] * 266,\n scores[532:798],\n change[532:798],\n )\n)\none_mon = list(\n zip(\n distro_names[798:1064],\n [one] * 266,\n [datetime.datetime.now().strftime(\"%Y-%m-%d\")] * 266,\n scores[798:1064],\n change[798:1064],\n )\n)\n\n# Create dataframe from groups\ndf = pd.DataFrame(\n twelve_mons + six_mons + three_mons + one_mon,\n columns=[\"distro\", \"start_date\", \"end_date\", \"score\", \"change\"],\n)\n\n# Save to csv (this will save in the same directory as where the script is run)\n\ndf.to_csv(\"LINUX_DISTROS.csv\", encoding=\"utf-8\", index=False)\n","repo_name":"dani0105/free-datasets","sub_path":"datasets/linux-distros/distro_scraper.py","file_name":"distro_scraper.py","file_ext":"py","file_size_in_byte":2374,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"42097653849","text":"from interaction.protocols.baseprotocol import BaseProtocol\nfrom datetime import datetime\nimport struct\n\n\nclass Modbus(BaseProtocol):\n\n def form_request(self):\n addr = self.dev.addr\n self.mess += addr.to_bytes(1, 'big')\n func = self.qry.func\n self.mess += bytes.fromhex(func)\n req = self.qry.req\n self.mess += bytes.fromhex(req)\n self.mess += self.calc_crc(self.mess)\n\n def response_processing(self):\n if self.check_response():\n func = getattr(self, 'resp_' + self.qry.handler, None)\n if func is not None:\n func()\n else:\n self.db_logger.warning(f'Method resp_{self.qry.handler} for class {self.__class__.__name__} not found')\n else:\n self.db_logger.error(f'Device {self.dev.id} request {self.qry.id}. Response error')\n\n def calc_crc(self, data: bytes) -> bytes:\n crc = 0xFFFF\n poly = 0xA001\n for byte in data:\n crc ^= byte\n for _ in range(8):\n temp = crc & 0x0001\n crc >>= 1\n if temp:\n crc ^= poly\n\n crc16modbus = crc.to_bytes(2, 'little')\n return crc16modbus\n\n def check_response(self) -> bool:\n check = False\n lenresp = len(self.resp)\n if lenresp > 2:\n check = self.calc_crc(self.resp[:lenresp-2]) == self.resp[-2:]\n return check\n\n def rounding(self, factor):\n return len(str(factor).split('.')[1]) if (factor - int(factor)) != 0 else 0\n\n def resp_uint4(self):\n res = self.resp[3:3 + self.resp[2]]\n value = struct.unpack('>I', res[2:] + res[:2])[0]\n if self.qry.factor is not None and self.qry.factor != 1.0:\n value *= self.qry.factor\n value = round(value, self.rounding(self.qry.factor))\n self.tosave = str(value)\n\n def resp_sint2(self):\n res = self.resp[3:3 + self.resp[2]]\n value = struct.unpack('>h', res)[0]\n if self.qry.factor is not None and self.qry.factor != 1.0:\n value *= self.qry.factor\n value = round(value, self.rounding(self.qry.factor))\n self.tosave = str(value)\n\n def resp_datetime4(self):\n res = self.resp[3:3 + self.resp[2]]\n value = struct.unpack('>I', res[2:] + res[:2])[0]\n self.tosave = datetime.utcfromtimestamp(value).strftime('%d.%m.%Y %H:%M:%S')\n","repo_name":"KonstantinQQ/metersmonitor","sub_path":"interaction/protocols/modbus.py","file_name":"modbus.py","file_ext":"py","file_size_in_byte":2424,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"75336566563","text":"import logging\nimport logging.handlers\n\nfrom flask import Flask, render_template\n\nfrom . import web\nfrom . import apiv1\n\nfrom .manager import Manager\n\n\nclass Server():\n\n '''\n This is the database server Class. \n It initializes flask app, database backends, logging system and so on.\n '''\n \n def __init__(self, addr='127.0.0.1:5000', backend='sqlite:///test.db',\n enable_retry=False, debug=False, logfile='', email_config=None, slack_config =None):\n pos = addr.rfind(':')\n self.host, self.port = addr[:pos], addr[pos+1:]\n self.port = int(self.port)\n self.addr = addr if addr.startswith('http') else 'http://' + addr\n self.debug = debug\n self.backend = backend\n self.logfile = logfile\n self.email_config = email_config\n self.slack_config = slack_config\n self.enable_retry = enable_retry\n \n self._init_manager()\n self._init_app()\n\n \n def _init_manager(self):\n Manager.set(backend=self.backend, enable_retry=self.enable_retry, logfile=self.logfile, email=self.email_config, slack=self.slack_config)\n Manager.get().logger.info('using {} as backend'.format(self.backend))\n\n def _init_app(self):\n self.app = Flask(__name__)\n self.app.config['addr'] = self.addr\n @self.app.route('/')\n def index():\n \t return render_template('index.html')\n self.app.register_blueprint(web.bp, url_prefix='/web')\n self.app.register_blueprint(apiv1.bp, url_prefix='/api/v1')\n Manager.get().logger.info('start database service at {}'.format(self.addr))\n \n def run(self):\n self.app.run(host=self.host, port=self.port, debug=True)\n\n","repo_name":"janelia-flyem/neutuse","sub_path":"neutuse/database/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1708,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"52"} +{"seq_id":"31912904971","text":"from numpy import ones_like\nfrom numpy.random import seed, normal\nfrom scipy.stats import linregress\nfrom sklearn.metrics import r2_score\nfrom matplotlib import pyplot as plt\n\n\nSTRIDE = 4\n\n\ndef data():\n # creates the data set\n seed(0x2AFFFFFF)\n x = normal(3, 1, 256)\n y = normal(150, 40, 256) / x\n X = 1/x\n return (X, y)\n\n\ndef test():\n # creates the testing set\n X, y = data()\n test_X = X[::STRIDE]\n test_y = y[::STRIDE]\n return (test_X, test_y)\n\n\ndef train():\n # creates the training set\n X, y = data()\n\n mask = ones_like(X)\n mask = (mask == 1)\n mask[::STRIDE] = False\n\n train_X = X[mask]\n train_y = y[mask]\n return (train_X, train_y)\n\n\ndef linearFit(X, y):\n slope, intercept, r, p, stderr = linregress(X, y)\n return (slope, intercept, r, p, stderr)\n\n\ndef linearModel(X, y):\n slope, intercept, r, p, stderr = linearFit(X, y)\n return (lambda x: slope * x + intercept)\n\n\n\nX, y = data()\ntrain_X, train_y = train()\ntest_X, test_y = test()\n\n\nplt.close('all')\nplt.ion()\nfig, ax = plt.subplots()\nax.scatter(X, y)\nax.set_title('original set')\n\n\nfig, ax = plt.subplots()\nax.scatter(test_X, test_y)\nax.set_title('testing set')\n\n\nfig, ax = plt.subplots()\nax.scatter(train_X, train_y)\nax.set_title('training set')\n\n\n# determines the usefulness of the linear model for predicting future values\nslope, intercept, r, p, stderr = linearFit(train_X, train_y)\n\nmodel = linearModel(train_X, train_y)\nr2 = r2_score(train_y, model(train_X))\n\n# expected output R**2 ~ 0.8 (good enough)\nout = (\n f'Training Set:\\n'\n f'R**2: {r**2} from the linear regression\\n'\n f'R**2: {r**2} by the r2-score of sklean\\n'\n)\n\nprint(out)\n\n\n# determines the usefulness of the linear model for predicting future values\nslope, intercept, r, p, stderr = linearFit(test_X, test_y)\n\nmodel = linearModel(test_X, test_y)\nr2 = r2_score(test_y, model(test_X))\n\n# expected output R**2 ~ 0.8 (good enough)\nout = (\n f'Testing Set:\\n'\n f'R**2: {r**2} from the linear regression\\n'\n f'R**2: {r**2} by the r2-score of sklean\\n'\n)\n\nprint(out)\n","repo_name":"misael-diaz/machine-learning","sub_path":"training-and-testing/train-test.py","file_name":"train-test.py","file_ext":"py","file_size_in_byte":2073,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"17801555022","text":"#\n# classify iris_data from 2 different features with different Algorithms.\n# Logistic Regression\n# Random Forest\n# SVM.SVC\n# EnsembleVoteClassifier\n#\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\nimport itertools\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC\nfrom sklearn.ensemble import RandomForestClassifier\nfrom mlxtend.classifier import EnsembleVoteClassifier\nfrom mlxtend.data import iris_data\nfrom mlxtend.plotting import plot_decision_regions\n\n# Initializing Classifiers\nclf1 = LogisticRegression(random_state=0)\nclf2 = RandomForestClassifier(random_state=0)\nclf3 = SVC(random_state=0, probability=True)\neclf = EnsembleVoteClassifier(clfs=[clf1, clf2, clf3],\n weights=[2, 1, 1], voting='soft')\n\n# Loading some example data\nX, y = iris_data()\n#X = X[:,[0, 2]] # Select 2 features, since visulization is 2D which just require 2 axes\nXX = [X[:,[i,j]] for i in range(4) for j in range(i+1,4)] # combinations of X[:0:4]\n\n# Plotting Decision Regions\n\ngs = gridspec.GridSpec(2, 2)\nfig = plt.figure(figsize=(10, 8))\n\nclfs = [clf1, clf2, clf3, eclf]\nlabels = ['Logistic Regression',\n 'Random Forest',\n 'RBF kernel SVM',\n 'Ensemble']\n\nfor row,X in enumerate(XX):\n for clf, lab, grd in zip(clfs,\n labels,\n itertools.product([0, 1],\n repeat=2)):\n clf.fit(X, y)\n ax = plt.subplot(gs[grd[0], grd[1]])\n fig = plot_decision_regions(X=X, y=y,\n clf=clf, legend=2)\n plt.title(lab) \n plt.show()\n","repo_name":"Paul0M/Machine-Learning","sub_path":"Classifier/Diff_Algorithm.py","file_name":"Diff_Algorithm.py","file_ext":"py","file_size_in_byte":1676,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"13218306209","text":"from django.shortcuts import render, HttpResponse, redirect\nfrom .models import Article, Person\nfrom . import forms\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.forms import User\nfrom django.core.paginator import Paginator\n\n\ndef articles_list(request):\n articles = Article.nodes.order_by('-date').all()\n article_list = []\n for article in articles:\n author = article.author.single()\n article_data = {\n 'title': article.title,\n 'body': article.body,\n 'slug': article.slug,\n 'date': article.date,\n 'author': author.name,\n 'snippet': article.snippet()\n }\n article_list.append(article_data)\n args = {'articles': article_list}\n return render(request, 'articles/articleslist.html', args)\n\n\ndef article_detail(request, slug):\n # return HttpResponse(slug)\n article = Article.nodes.get(slug=slug)\n author = article.author.single()\n return render(request, 'articles/article_detail.html', {'article': article, 'author': author})\n\n\n@login_required(login_url='/accounts/login')\ndef create_article(request):\n if request.method == 'POST':\n form = forms.CreateArticle(request.POST, request.FILES)\n if form.is_valid:\n instance = form.save()\n account = Person.nodes.get(name=request.user.username)\n instance.author.connect(account)\n instance.save()\n return redirect('articles:list')\n else:\n form = forms.CreateArticle()\n return render(request, 'articles/create_article.html', {'form': form})\n","repo_name":"mamtech-A/djangoBlogneo","sub_path":"articles/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1617,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"24786266446","text":"from __future__ import annotations\n\nfrom collections import OrderedDict\n\nimport pandas as pd\nimport pytest\n\nimport ibis\nimport ibis.expr.datatypes as dt\n\ndd = pytest.importorskip(\"dask.dataframe\")\n\nfrom dask.dataframe.utils import tm # noqa: E402\n\nfrom ibis.backends.dask.execution import execute # noqa: E402\n\n\n@pytest.fixture(scope=\"module\")\ndef value():\n return OrderedDict([(\"fruit\", \"pear\"), (\"weight\", 0)])\n\n\n@pytest.fixture(scope=\"module\")\ndef struct_client(value, npartitions):\n df = dd.from_pandas(\n pd.DataFrame(\n {\n \"s\": [\n OrderedDict([(\"fruit\", \"apple\"), (\"weight\", None)]),\n value,\n OrderedDict([(\"fruit\", \"pear\"), (\"weight\", 1)]),\n ],\n \"key\": list(\"aab\"),\n \"value\": [1, 2, 3],\n }\n ),\n npartitions=npartitions,\n )\n return ibis.dask.connect({\"t\": df})\n\n\n@pytest.fixture\ndef struct_table(struct_client):\n return struct_client.table(\n \"t\",\n schema={\n \"s\": dt.Struct.from_tuples([(\"fruit\", dt.string), (\"weight\", dt.int8)])\n },\n )\n\n\ndef test_struct_field_literal(value):\n struct = ibis.literal(value)\n assert struct.type() == dt.Struct.from_tuples(\n [(\"fruit\", dt.string), (\"weight\", dt.int8)]\n )\n\n expr = struct[\"fruit\"]\n result = execute(expr.op())\n assert result == \"pear\"\n\n expr = struct[\"weight\"]\n result = execute(expr.op())\n assert result == 0\n\n\ndef test_struct_field_series(struct_table):\n t = struct_table\n expr = t.s[\"fruit\"]\n result = expr.compile()\n expected = dd.from_pandas(\n pd.Series([\"apple\", \"pear\", \"pear\"], name=\"fruit\"),\n npartitions=1,\n )\n tm.assert_series_equal(result.compute(), expected.compute(), check_index=False)\n\n\ndef test_struct_field_series_group_by_key(struct_table):\n t = struct_table\n expr = t.group_by(t.s[\"fruit\"]).aggregate(total=t.value.sum())\n result = expr.compile()\n expected = dd.from_pandas(\n pd.DataFrame([(\"apple\", 1), (\"pear\", 5)], columns=[\"fruit\", \"total\"]),\n npartitions=1,\n )\n tm.assert_frame_equal(\n result.compute().reset_index(drop=True),\n expected.compute().reset_index(drop=True),\n )\n\n\ndef test_struct_field_series_group_by_value(struct_table):\n t = struct_table\n expr = t.group_by(t.key).aggregate(total=t.s[\"weight\"].sum())\n result = expr.compile()\n # these are floats because we have a NULL value in the input data\n expected = dd.from_pandas(\n pd.DataFrame([(\"a\", 0.0), (\"b\", 1.0)], columns=[\"key\", \"total\"]),\n npartitions=1,\n )\n tm.assert_frame_equal(\n result.compute().reset_index(drop=True),\n expected.compute().reset_index(drop=True),\n )\n","repo_name":"ibis-project/ibis","sub_path":"ibis/backends/dask/tests/execution/test_structs.py","file_name":"test_structs.py","file_ext":"py","file_size_in_byte":2790,"program_lang":"python","lang":"en","doc_type":"code","stars":3246,"dataset":"github-code","pt":"52"} +{"seq_id":"566308160","text":"# coding: utf-8\n\n__author__ = \"Korantin\"\n\n\nimport ply.lex as lex\n\ntokens = (\n 'UNSPEC',\n 'AFFIX',\n 'CLITIC',\n 'REDUPL',\n 'GINFIX',\n 'DINFIX',\n 'CIRCONF1',\n 'ABLAUT',\n 'POLYSE',\n 'GMORPH0',\n 'DMORPH0',\n 'PORTMA',\n 'GINHER',\n 'DINHER',\n 'PHRASE',\n 'TRAIT',\n 'LEMME',\n\t'CIRCONF2'\n)\n\n\nt_UNSPEC = r'\\:'\nt_AFFIX = r'\\-'\nt_CLITIC = r'\\='\nt_REDUPL = r'\\~'\nt_GINFIX = r'\\<'\nt_DINFIX = r'\\>'\nt_CIRCONF1 = r'[0-9]'\nt_ABLAUT = r'\\\\'\nt_POLYSE = r'\\/'\nt_GMORPH0 = r'\\['\nt_DMORPH0 = r'\\]'\nt_PORTMA = r'\\.'\nt_GINHER = r'\\('\nt_DINHER = r'\\)'\nt_PHRASE = r'\\_'\nt_CIRCONF2 = r'\\+'\n\ndef t_LEMME(t):\n r'[a-zéàè][a-zéàè]*'\n return t\n\ndef t_TRAIT(t):\n r'[A-Z1-9][A-Z]*'\n return t\n\ndef t_newline(t):\n r'\\n+'\n t.lexer.lineno += len(t.value)\n\nt_ignore = ' \\t'\n\ndef t_error(t):\n print(\"Illegal character '%s'\" % t.value[0])\n t.lexer.skip(1)\n\nlex.lex(optimize=1,lextab=\"gloseur\")\n\n# data = 'pomme(M)-P'\n#\n# lexer.input(data)\n#\n#\n# while True:\n# tok = lexer.token()\n# if not tok:\n# break # No more input\n# print(tok)","repo_name":"Krolov18/Languages","sub_path":"Gloses/MoodleProject/Gloseur/GloseurLexer.py","file_name":"GloseurLexer.py","file_ext":"py","file_size_in_byte":1136,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"42699221417","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom rom.models import CharacterBase, CharacterJob\nfrom rom.form_character import CharacterForm, CharacterWGForm\nfrom django.db.models import Q\nfrom guild.models import WAR_TYPE\n\n\nclass CharacterManagement(object):\n def __init__(self, user, base_id=None):\n self.user = user\n if base_id is not None:\n try:\n self.base = self.user.bases.get(pk=base_id)\n except CharacterBase.DoesNotExist:\n raise Exception(\"%s DoesNotExist\" % base_id)\n else:\n self.base = None\n\n def push_base(self, form_json, hash_form):\n ign = form_json['ign']\n base_level = form_json['base_level']\n contribution = form_json['contribution']\n gold_medal = form_json['gold_medal']\n job_ids = form_json['jobs']\n\n self.base = CharacterBase(\n member=self.user,\n ign=ign,\n base_level=base_level,\n contribution=contribution,\n gold_medal=gold_medal,\n )\n\n data = dict()\n data['hash_form'] = hash_form\n self.base.update_data(data_dict=data)\n self.base.save()\n\n self.__push_jobs(job_ids=job_ids)\n return self.base\n\n def update_base(self, form_json, hash_form):\n if self.base is not None:\n ign = form_json['ign']\n base_level = form_json['base_level']\n contribution = form_json['contribution']\n gold_medal = form_json['gold_medal']\n job_ids = form_json['jobs']\n\n self.base.member = self.user\n self.base.ign = ign\n self.base.base_level = base_level\n self.base.contribution = contribution\n self.base.gold_medal = gold_medal\n\n data = dict()\n data['hash_form'] = hash_form\n self.base.update_data(data_dict=data)\n self.base.save()\n\n self.__push_jobs(job_ids=job_ids)\n if self.__guild_exists():\n woe = form_json['woe_job']\n woc = form_json['woc_job']\n zone = form_json['zone_job']\n self.__push_war_job(woe=woe, woc=woc, zone=zone)\n\n return self.base\n else:\n raise Exception(\"Please init CharacterManagement with base_id\")\n\n def delete_base(self):\n if self.base is not None:\n self.base.delete()\n return 0\n else:\n raise Exception(\"Please init CharacterManagement with base_id\")\n\n def __push_jobs(self, job_ids):\n job_ids = [int(job_id) for job_id in job_ids]\n if self.base is not None:\n job_ids_old = [[job_ch.job.pk, job_ch] for job_ch in self.base.jobs.all()]\n\n for job_id_old, job_ch in job_ids_old:\n if job_id_old not in job_ids:\n job_ch.delete()\n\n for job_id in job_ids:\n self.__push_job(\n job_id=job_id,\n )\n else:\n raise Exception(\"Please init CharacterManagement with base_id\")\n\n def __push_job(self, job_id):\n if not self.base.check_job(job_id=job_id):\n job = CharacterJob(\n base=self.base,\n job_id=job_id,\n )\n job.save()\n return job\n\n def __push_war_job(self, woe, woc, zone):\n war_woe = self.base.guild_war_jobs.filter(war=0).first()\n war_woe.job = self.base.jobs.get(job_id=woe).job\n war_woe.save()\n war_woc = self.base.guild_war_jobs.filter(war=1).first()\n war_woc.job = self.base.jobs.get(job_id=woc).job\n war_woc.save()\n war_zone = self.base.guild_war_jobs.filter(war=2).first()\n war_zone.job = self.base.jobs.get(job_id=zone).job\n war_zone.save()\n\n def get_base(self):\n if self.base is not None:\n return self.__base_dto(self.base)\n else:\n raise Exception(\"Please init CharacterManagement with base_id\")\n\n def get_bases(self, filter_list=[]):\n bases = list()\n bases_obj = self.user.bases.all()\n\n for f, v in filter_list:\n if f == 'join_guild':\n bases_obj = bases_obj.filter(Q(waiting__guild=v) | Q(waiting__isnull=True), guild__isnull=True)\n\n for b in bases_obj:\n bases.append(self.__base_dto(b))\n return bases\n\n @staticmethod\n def __base_dto(base):\n base_dto = dict()\n base_dto['id'] = base.pk\n base_dto['ign'] = base.ign\n base_dto['base_level'] = base.base_level\n base_dto['contribution'] = base.contribution\n base_dto['gold_medal'] = base.gold_medal\n\n jobs_ch = base.jobs.all()\n jobs = []\n for j in jobs_ch:\n job = dict()\n job['id'] = j.pk\n job['job_id'] = j.job.pk\n job['job_name'] = j.job.name\n job['job_image'] = j.job.image\n jobs.append(job)\n\n base_dto['jobs'] = jobs\n\n guild_m = base.guild.first()\n guild_obj = None\n if guild_m:\n guild_obj = guild_m.guild\n if guild_obj is not None:\n guild = dict()\n guild['guild_id'] = guild_obj.pk\n guild['guild_name'] = guild_obj.name\n guild['guild_image'] = guild_obj.image\n guild['invite_code'] = guild_obj.invite_code\n guild['guild_data'] = guild_obj.get_data_json()\n for war_id, war in WAR_TYPE:\n if base.guild_war_jobs.filter(war=war_id).first().job:\n guild[war] = base.guild_war_jobs.filter(war=war_id).first().job.pk\n else:\n guild[war] = None\n else:\n guild = None\n base_dto['guild'] = guild\n\n waiting_approve = base.waiting.first()\n w_guild_obj = None\n if waiting_approve:\n w_guild_obj = waiting_approve.guild\n if w_guild_obj is not None:\n waiting = dict()\n waiting['guild_id'] = w_guild_obj.pk\n waiting['guild_name'] = w_guild_obj.name\n waiting['guild_image'] = w_guild_obj.image\n waiting['invite_code'] = w_guild_obj.invite_code\n waiting['guild_data'] = w_guild_obj.get_data_json()\n else:\n waiting = None\n base_dto['waiting'] = waiting\n\n return base_dto\n\n def __guild_exists(self):\n return self.base.guild.all().exists()\n\n def get_request_form(self, request):\n if self.base is not None:\n if self.__guild_exists():\n return CharacterWGForm(request.POST)\n return CharacterForm(request.POST)\n else:\n raise Exception(\"Please init CharacterManagement with base_id\")\n\n def get_form_base(self):\n if self.base is not None:\n if self.__guild_exists():\n return self.__generate_form_wg()\n return self.__generate_form()\n else:\n raise Exception(\"Please init CharacterManagement with base_id\")\n\n def __generate_form(self):\n base_dto = self.get_base()\n\n initial = dict()\n initial['ign'] = base_dto['ign']\n initial['base_level'] = base_dto['base_level']\n initial['contribution'] = base_dto['contribution']\n initial['gold_medal'] = base_dto['gold_medal']\n job_ids = list()\n for job in base_dto['jobs']:\n job_ids.append(job['job_id'])\n initial['jobs'] = job_ids\n\n form = CharacterForm(initial=initial)\n return form\n\n def __generate_form_wg(self):\n base_dto = self.get_base()\n\n initial = dict()\n initial['ign'] = base_dto['ign']\n initial['base_level'] = base_dto['base_level']\n initial['contribution'] = base_dto['contribution']\n initial['gold_medal'] = base_dto['gold_medal']\n job_ids = list()\n for job in base_dto['jobs']:\n job_ids.append(job['job_id'])\n initial['jobs'] = job_ids\n initial['woe_job'] = base_dto['guild']['woe']\n initial['woc_job'] = base_dto['guild']['woc']\n initial['zone_job'] = base_dto['guild']['zone']\n\n form = CharacterWGForm(initial=initial)\n return form\n\n def get_hash_form(self):\n return self.base.get_data_json('hash_form')\n","repo_name":"iArraylist/jgm","sub_path":"rom/services/character_management.py","file_name":"character_management.py","file_ext":"py","file_size_in_byte":8325,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"21437229902","text":"import glob\nimport pandas as pd\nimport numpy as np\nimport statsmodels.api as sm\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom matplotlib.lines import Line2D\nfrom tqdm import tqdm\nimport torch\nimport argparse\nimport torch.nn.functional as F\nfrom torch.distributions import Bernoulli\n\nconditions = [\"equal\", \"unequal\"]\nagents = [\"human\", \"raw\", \"fitted\"]\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--agent\", type=str, required=True, choices=agents)\nparser.add_argument(\"--condition\", type=str, required=True, choices=conditions)\nargs = parser.parse_args()\n\nif args.agent == 'raw':\n probs = torch.stack([torch.load('data/loo_llama_fixed_model=65B_fold=' + str(i) + '.pth', map_location='cpu')[1] for i in range(100)], dim=-1)\n splits = torch.load('data/splits.pth')\n action_mat = Bernoulli(probs=probs).sample()\n actions = torch.zeros(probs.numel())\n counter = 0\n for i in range(probs.shape[0]):\n for j in range(probs.shape[1]):\n actions[splits[i, j]] = action_mat[i, j]\n\n\nif args.agent == 'fitted':\n logits = torch.stack([torch.load('data/loo_centaur_fixed_model=65B_fold=' + str(i) + '.pth', map_location='cpu')[2] for i in range(100)], dim=-1)\n splits = torch.load('data/splits.pth')\n action_mat = Bernoulli(logits=logits).sample()\n actions = torch.zeros(logits.numel())\n counter = 0\n for i in range(logits.shape[0]):\n for j in range(logits.shape[1]):\n actions[splits[i, j]] = action_mat[i, j]\n\n# preprocess data\ndf = pd.read_csv('data/exp1.csv')\nforced_choices_1 = np.zeros(len(df))\nchoices_raw = np.zeros(len(df))\nchoices_fitted = np.zeros(len(df))\nnum_participants = df.participant.max() + 1\nnum_tasks = df.task.max() + 1\nrow_counter1 = 0\nrow_counter2 = 0\nfor participant in tqdm(range(num_participants)):\n df_participant = df[df['participant'] == participant]\n for task in range(num_tasks):\n df_task = df_participant[df_participant['task'] == task]\n choices_1 = df_task[df_task.trial < 4].choice.sum()\n num_trials = df_task.trial.max() + 1\n row_counter1 = row_counter1 + 4\n for trial in range(4, num_trials):\n forced_choices_1[row_counter1] = choices_1\n if args.agent == 'raw':\n choices_raw[row_counter1] = actions[row_counter2].item()\n if args.agent == 'fitted':\n choices_fitted[row_counter1] = actions[row_counter2].item()\n row_counter1 = row_counter1 + 1\n row_counter2 = row_counter2 + 1\n\ndf['forced_choices_1'] = forced_choices_1.astype('int')\nif args.agent == 'raw':\n df['choice'] = choices_raw\nif args.agent == 'fitted':\n df['choice'] = choices_fitted\nif args.condition == 'equal':\n # extract data\n df = df[(df.trial == 4) & (df.forced_choices_1 == 2)]\n reward_differences = (df.expected_reward0 - df.expected_reward1).to_numpy().astype(float)\n choices = 1 - df.choice.to_numpy()\n horizon = (df[(df.trial == 4) & (df.forced_choices_1 == 2)].horizon == 10).to_numpy().astype(float)\n interaction = horizon * reward_differences\n\n log_reg = sm.Logit(choices, np.stack((reward_differences, horizon, interaction, np.ones(reward_differences.shape)), axis=-1)).fit()\n\nelif args.condition == 'unequal':\n # case: x3 1\n df_31 = df[(df.trial == 4) & (df.forced_choices_1 == 3)]\n reward_differences_31 = (df_31.expected_reward0 - df_31.expected_reward1).to_numpy().astype(float)\n choices_31 = 1 - df_31.choice.to_numpy()\n horizons_31 = (df[(df.trial == 4) & (df.forced_choices_1 == 3)].horizon == 10).to_numpy().astype(float)\n\n # case: x3 0\n df_13 = df[(df.trial == 4) & (df.forced_choices_1 == 1)]\n reward_differences_13 = (df_13.expected_reward1 - df_13.expected_reward0).to_numpy().astype(float)\n choices_13 = df_13.choice.to_numpy()\n horizons_13 = (df[(df.trial == 4) & (df.forced_choices_1 == 1)].horizon == 10).to_numpy().astype(float)\n\n choices = np.concatenate((choices_31, choices_13), axis=0)\n reward_differences = np.concatenate((reward_differences_31, reward_differences_13), axis=0)\n horizon = np.concatenate((horizons_31, horizons_13), axis=0)\n interaction = horizon * reward_differences\n\n log_reg = sm.Logit(choices, np.stack((reward_differences, horizon, interaction, np.ones(reward_differences.shape)), axis=-1)).fit()\n\nx_reward_differences = np.linspace(-30, 30, 1000)\nx_horizon6 = np.ones(1000)\nx_6 = np.stack((x_reward_differences, x_horizon6, x_horizon6 * x_reward_differences, np.ones(1000)), axis=-1)\ny_6 = log_reg.predict(x_6)\n\nx_reward_differences = np.linspace(-30, 30, 1000)\nx_horizon1 = np.zeros(1000)\nx_1 = np.stack((x_reward_differences, x_horizon1, x_horizon1 * x_reward_differences, np.ones(1000)), axis=-1)\ny_1 = log_reg.predict(x_1)\n\n# make plot\nplt.rcParams[\"figure.figsize\"] = (1.8,2.0)\nplt.plot(x_1[:, 0], y_1, color='C0' if args.condition == 'equal' else 'C1')\nplt.plot(x_6[:, 0], y_6, color='C0' if args.condition == 'equal' else 'C1', ls='--')\nsns.despine()\nplt.xlabel('Reward difference', size=9)\nif args.condition == 'equal':\n plt.ylabel('p(first option)', size=9)\nelse:\n plt.ylabel('p(more informative)', size=9)\n\nplt.legend(['Horizon 1', 'Horizon 6',], frameon=False, bbox_to_anchor=(0.0, 1.02, 1, 0.2), loc=\"lower left\", borderaxespad=0, handletextpad=0.5, columnspacing=0.6, ncol=1, prop={'size': 9})\nplt.ylim(0, 1)\nplt.xlim(-30, 30)\nplt.yticks(size=9)\nplt.xticks(size=9)\nplt.tight_layout()\nplt.savefig('figures/probs_agent=' + args.agent + '_condition=' + args.condition + '.pdf', bbox_inches='tight')\nplt.show()\n","repo_name":"marcelbinz/CENTaUR","sub_path":"HorizonTask/plot_choice_probabilities.py","file_name":"plot_choice_probabilities.py","file_ext":"py","file_size_in_byte":5560,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"52"} +{"seq_id":"9680100789","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\nfrom bs4 import BeautifulSoup\nfrom django.shortcuts import render\nimport re\nfrom xml.dom.minidom import parse, parseString\nfrom .models import Bsi, Bsm, Bsds, Bshf, Cci, Ccm, Ccds, Cchf, Chi, Chm, Chds, Chhf, Mi, Mm, Mds, Mhf\n#remember to import models\n\n\ndef index(request):\n \"\"\"\n View function for home page of site.\n \"\"\"\n # Render the HTML template index.html with the data in the context variable\n htmlDoc = open(\"infopage/templates/index.html\")\n soup = BeautifulSoup(htmlDoc, 'lxml')\n findBsi = soup.find(id='bsi')\n findBsm = soup.find(id='bsm')\n findBsds = soup.find(id='bsds')\n findBshf = soup.find(id='bshf')\n findCci = soup.find(id='cci')\n findCcm = soup.find(id='ccm')\n findCcds = soup.find(id='ccds')\n findCchf = soup.find(id='cchf')\n findChi = soup.find(id='chi')\n findChm = soup.find(id='chm')\n findChds = soup.find(id='chds')\n numBsi = Bsi.objects.count()\n numBsm = Bsm.objects.count()\n numBsds = Bsds.objects.count()\n numBshf = Bshf.objects.count()\n numCci = Cci.objects.count()\n numCcm = Ccm.objects.count()\n numCcds = Ccds.objects.count()\n numCchf = Cchf.objects.count()\n numChi = Chi.objects.count()\n numChm = Chm.objects.count()\n numChds = Chds.objects.count()\n print(numCci)\n\n\n if numBsi > 0 and numBsi <= 1:\n addAttBsi = findBsi['style'] = \"top: 4px; left: 50px; width: 15px; height: 15px;\"\n elif numBsi >= 2 and numBsi <=3:\n addAttBsi = findBsi['style'] = \"top: 1px; left: 46px; width: 25px; height: 25px;\"\n elif numBsi > 3:\n addAttBsi = findBsi['style'] = \"top: -3px; left: 40px; width: 35px; height: 35px;\"\n else:\n addAttBsi = findBsi['style'] = \"width: 0px; height: 0px;\"\n\n if numBsm > 0 and numBsm <= 1:\n addAttBsm = findBsm['style'] = \"top: 4px; left: 95px; width: 15px; height: 15px;\"\n elif numBsm >= 2 and numBsm <= 3:\n addAttBsm = findBsm['style'] = \"top: 1px; left: 90px; width: 25px; height: 25px;\"\n elif numBsm > 3:\n addAttBsm = findBsm['style'] = \"top: -3px; left: 85px; width: 35px; height: 35px;\"\n else:\n addAttBsm = findBsm['style'] = \"width: 0px; height: 0px;\"\n\n if numBsds > 0 and numBsds <= 1:\n addAttBsds = findBsds['style'] = \"top: 4px; left: 140px; width: 15px; height: 15px;\"\n elif numBsds >= 2 and numBsds <= 3:\n addAttBsds = findBsds['style'] = \"top: 1px; left: 135px; width: 25px; height: 25px;\"\n elif numBsds > 3:\n addAttBsds = findBsds['style'] = \"top: -3px; left: 130px; width: 35px; height: 35px;\"\n else:\n addAttBsds = findBsds['style'] = \"width: 0px; height: 0px;\"\n\n if numBshf > 0 and numBshf <= 1:\n addAttBshf = findBshf['style'] = \"top: 4px; left: 185px; width: 15px; height: 15px;\"\n elif numBshf >= 2 and numBshf <= 3:\n addAttBshf = findBshf['style'] = \"top: 1px; left: 180px; width: 25px; height: 25px;\"\n elif numBshf > 3:\n addAttBshf = findBshf['style'] = \"top: -3px; left: 175px; width: 35px; height: 35px;\"\n else:\n addAttBshf = findBshf['style'] = \"width: 0px; height: 0px;\"\n\n\n if numCci > 0 and numCci <= 1:\n addAttCci = findCci['style'] = \"top: 49px; left: 50px; width: 15px; height: 15px;\"\n elif numCci >= 2 and numCci <= 3:\n addAttCci = findCci['style'] = \"top: 44px; left: 46px; width: 25px; height: 25px;\"\n elif numCci > 3:\n addAttCci = findCci['style'] = \"top: 39px; left: 40px; width: 35px; height: 35px;\"\n else:\n addAttCci = findCci['style'] = \"width: 0px; height: 0px;\"\n\n if numCcm > 0 and numCcm <= 1:\n addAttCcm = findCcm['style'] = \"top: 49px; left: 95px; width: 15px; height: 15px;\"\n elif numCci >= 2 and numCci <= 3:\n addAttCcm = findCcm['style'] = \"top: 44px; left: 140px; width: 25px; height: 25px;\"\n elif numCcm > 3:\n addAttCcm = findCcm['style'] = \"top: 39px; left: 185px; width: 35px; height: 35px;\"\n else:\n addAttCcm = findCcm['style'] = \"width: 0px; height: 0px;\"\n\n if numCcds > 0 and numCcds <= 1:\n addAttCcds = findCcds['style'] = \"top: 49px; left: 140px; width: 15px; height: 15px;\"\n elif numCcds >= 2 and numCcds <= 3:\n addAttCcds = findCcds['style'] = \"top: 44px; left: 135px; width: 25px; height: 25px;\"\n elif numCcds > 3:\n addAttCcds = findCcds['style'] = \"top: 39px; left: 130px; width: 35px; height: 35px;\"\n else:\n addAttCcds = findCcds['style'] = \"width: 0px; height: 0px;\"\n\n if numCchf > 0 and numCchf <= 1:\n addAttCchf = findCchf['style'] = \"top: 49px; left: 185px; width: 15px; height: 15px;\"\n elif numCchf >= 2 and numCchf <= 3:\n addAttCchf = findCchf['style'] = \"top: 44px; left: 180px; width: 25px; height: 25px;\"\n elif numCchf > 3:\n addAttCchf = findCchf['style'] = \"top: 39px; left: 175px; width: 35px; height: 35px;\"\n else:\n addAttCchf = findCchf['style'] = \"width: 0px; height: 0px;\"\n\n if numChi > 0 and numChi <= 1:\n addAttChi = findChi['style'] = \"top: 94px; left: 50px; width: 15px; height: 15px;\"\n elif numChi >= 2 and numChi <= 3:\n addAttChi = findChi['style'] = \"top: 139px; left: 46px; width: 25px; height: 25px;\"\n elif numChi > 3:\n addAttChi = findChi['style'] = \"top: 184px; left: 40px; width: 35px; height: 35px;\"\n else:\n addAttChi = findChi['style'] = \"width: 0px; height: 0px;\"\n\n if numChm > 0 and numChm <= 1:\n addAttChm = findChm['style'] = \"top: 94px; left: 95px; width: 15px; height: 15px;\"\n elif numChm >= 2 and numChm <= 3:\n addAttChm = findChm['style'] = \"top: 139px; left: 135px; width: 25px; height: 25px;\"\n elif numChm > 3:\n addAttChm = findChm['style'] = \"top: 184px; left: 180px; width: 35px; height: 35px;\"\n else:\n addAttChm = findChm['style'] = \"width: 0px; height: 0px;\"\n\n if numChds > 0 and numChds <= 1:\n addAttChds = findChds['style'] = \"top: 94px; left: 95px; width: 15px; height: 15px;\"\n elif numChds >= 2 and numChds <= 3:\n addAttChds = findChds['style'] = \"top: 139px; left: 135px; width: 25px; height: 25px;\"\n elif numChds > 3:\n addAttChds = findChds['style'] = \"top: 184px; left: 180px; width: 35px; height: 35px;\"\n else:\n addAttChds = findChds['style'] = \"width: 0px; height: 0px;\"\n\n\n\n htmlDoc.close()\n html = soup.prettify(\"utf-8\")\n\n with open(\"infopage/templates/index.html\", \"wb\") as output:\n output.write(html)\n\n\n return render(\n request,\n 'index.html',\n context={},\n )\n","repo_name":"KrystalPhuar/digitalhealth","sub_path":"Desktop/dhWithYing/digitalhealth/infopage/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6546,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"24947176960","text":"\"\"\"\nDetails the various flask endpoints for processing and retrieving\ncommand details as well as a swagger spec endpoint\n\"\"\"\n\nfrom multiprocessing import Process, Queue\nimport sys\nfrom flask import Flask, request, jsonify\nfrom flask_swagger import swagger\nimport json\nfrom db import session, engine\nfrom base import Base, Command\nfrom command_parser import get_valid_commands, process_command_output\nimport os\napp = Flask(__name__)\n\n\n@app.route('/commands', methods=['GET'])\ndef get_command_output():\n \"\"\"\n Returns as json the command details that have been processed\n ---\n tags: [commands]\n responses:\n 200:\n description: Commands returned OK\n 400:\n description: Commands not found\n \"\"\"\n commands = session.query(Command)\n result=session.execute(commands)\n json_data=[]\n for r in result:\n json_data.append({\n 'id' : r[0],\n 'command_string' : r[1],\n 'length' : r[2],\n 'duration' : r[3],\n 'output' : r[4].decode()\n })\n if not json_data:\n return \"Commands not found\"\n json_data = json.dumps(json_data)\n return jsonify(json.loads(json_data))\n\n\n@app.route('/commands/', methods=['GET'])\ndef get_command_id(command_id):\n \"\"\"\n Returns as json the command details that have been processed\n ---\n tags: [commands]\n responses:\n 200:\n description: Commands returned OK\n 400:\n description: Commands not found\n \"\"\"\n commands = session.query(Command)\n commands=(str(commands))\n commands+=\" where commands.id={0}\".format(command_id)\n result = session.execute(commands)\n json_data = []\n for r in result:\n json_data.append({\n 'id': r[0],\n 'command_string': r[1],\n 'length': r[2],\n 'duration': r[3],\n 'output': r[4].decode()\n })\n if not json_data:\n return \"Commands not found\"\n json_data = json.dumps(json_data)\n return jsonify(json.loads(json_data))\n # json_data=json.dumps([dict(r) for r in result])\n\n\n@app.route('/commands', methods=['POST'])\ndef process_commands():\n \"\"\"\n Processes commmands from a command list\n ---\n tags: [commands]\n parameters:\n - name: filename\n in: formData\n description: filename of the commands text file to parse\n required: true\n type: string\n responses:\n 200:\n description: Processing OK\n \"\"\"\n fi = request.args.get('filename')\n print(fi)\n file_data = request.args.get('file_data')\n print(file_data)\n if file_data is not None:\n fi=\"commands_data.txt\"\n with open(fi,'w') as f:\n file_data=file_data.split(\"\\\\n\")\n print(file_data)\n for data in file_data:\n f.write(data+\"\\n\")\n if fi is None:\n return \"Processing Error\"\n queue = Queue()\n get_valid_commands(queue, fi)\n processes = [Process(target=process_command_output, args=(queue,session,))\n for num in range(3)]\n for process in processes:\n process.start()\n #for process in processes:\n # process.join()\n if file_data is not None:\n os.remove(\"commands_data.txt\")\n pass\n return 'Successfully processed commands.'\n\n\n@app.route('/database', methods=['POST'])\ndef make_db():\n \"\"\"\n Creates database schema\n ---\n tags: [db]\n responses:\n 200:\n description: DB Creation OK\n \"\"\"\n Base.metadata.create_all(engine)\n return 'Database creation successful.'\n\n\n@app.route('/database', methods=['DELETE'])\ndef drop_db():\n \"\"\"\n Drops all db tables\n ---\n tags: [db]\n responses:\n 200:\n description: DB table drop OK\n \"\"\"\n Base.metadata.drop_all(engine)\n return 'Database deletion successful.'\n\n\nif __name__ == '__main__':\n \"\"\"\n Starts up the flask server\n \"\"\"\n port = 8080\n use_reloader = True\n\n # provides some configurable options\n for arg in sys.argv[1:]:\n if '--port' in arg:\n port = int(arg.split('=')[1])\n elif '--use_reloader' in arg:\n use_reloader = arg.split('=')[1] == 'true'\n\n app.run(port=port, debug=True, use_reloader=use_reloader)\n\n\n@app.route('/spec')\ndef swagger_spec():\n \"\"\"\n Display the swagger formatted JSON API specification.\n ---\n tags: [docs]\n responses:\n 200:\n description: OK status\n \"\"\"\n spec = swagger(app)\n spec['info']['title'] = \"Nervana cloud challenge API\"\n spec['info']['description'] = (\"Nervana's cloud challenge \" +\n \"for interns and full-time hires\")\n spec['info']['license'] = {\n \"name\": \"Nervana Proprietary License\",\n \"url\": \"http://www.nervanasys.com\",\n }\n spec['info']['contact'] = {\n \"name\": \"Nervana Systems\",\n \"url\": \"http://www.nervanasys.com\",\n \"email\": \"info@nervanasys.com\",\n }\n spec['schemes'] = ['http']\n spec['tags'] = [\n {\"name\": \"db\", \"description\": \"database actions (create, delete)\"},\n {\"name\": \"commands\", \"description\": \"process and retrieve commands\"}\n ]\n return jsonify(spec)\n","repo_name":"kasirajanss93/intel-cloud-challenge","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5176,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"32265486136","text":"import numpy as np\r\nimport sklearn.mixture as skm\r\nimport cv2\r\n\r\ndef update_data_loss(data_loss, pixels, pcoors, gmms):\r\n for i in range(2):\r\n _ds = -gmms[i].score_samples(pixels)\r\n for j, coors in enumerate(pcoors):\r\n data_loss[coors[0], coors[1], i] = _ds[j]\r\n\r\ndef update_ks(ks, im, gmms, alphas):\r\n for i in range(2):\r\n _coors = np.where(alphas == i)\r\n _pxs = im[_coors]\r\n _ks = gmms[i].predict(_pxs)\r\n for j, _k in enumerate(_ks):\r\n ks[_coors[0][j], _coors[1][j]] = _k\r\n\r\ndef update_rows(alphas, data_loss, ws_hor, ws_ver, flag, s, h, w):\r\n for i in range(h):\r\n if flag[i]:\r\n ds_temp = np.zeros((w, 2))\r\n #es_temp = np.zeros((w, 2))\r\n pre = np.zeros((w, 2))\r\n ds_temp[:, 0] += data_loss[i, :, 0]\r\n ds_temp[:, 1] += data_loss[i, :, 1]\r\n if i > 0:\r\n ds_temp[:, 0] += ws_ver[i - 1, :] * s[(alphas[i - 1, :], np.zeros(w).astype(np.int))]\r\n ds_temp[:, 1] += ws_ver[i - 1, :] * s[(alphas[i - 1, :], np.ones(w).astype(np.int))]\r\n if i < h - 1:\r\n ds_temp[:, 0] += ws_ver[i, :] * s[(alphas[i + 1, :], np.zeros(w).astype(np.int))]\r\n ds_temp[:, 1] += ws_ver[i, :] * s[(alphas[i + 1, :], np.ones(w).astype(np.int))]\r\n #es_temp = ds_temp[:]\r\n for j in range(1, w):\r\n if ds_temp[j - 1, 0] < ds_temp[j - 1, 1]:\r\n ds_temp[j, 0] += ds_temp[j - 1, 0]\r\n pre[j, 0] = 0\r\n smth = ds_temp[j - 1, 0] + ws_hor[i, j - 1]\r\n if ds_temp[j - 1, 1] < smth:\r\n ds_temp[j, 1] += ds_temp[j - 1, 1]\r\n pre[j, 1] = 1\r\n else:\r\n ds_temp[j, 1] += smth\r\n pre[j, 1] = 0\r\n else:\r\n ds_temp[j, 1] += ds_temp[j - 1, 1]\r\n pre[j, 1] = 1\r\n smth = ds_temp[j - 1, 1] + ws_hor[i, j - 1]\r\n if ds_temp[j - 1, 0] < smth:\r\n ds_temp[j, 0] += ds_temp[j - 1, 0]\r\n pre[j, 0] = 0\r\n else:\r\n ds_temp[j, 0] += smth\r\n pre[j, 0] = 1\r\n new_alphas = np.array([-1]*w)\r\n if ds_temp[-1, 0] < ds_temp[-1, 1]:\r\n new_alphas[-1] = 0\r\n else:\r\n new_alphas[-1] = 1\r\n for col in range(2, w + 1):\r\n new_alphas[-col] = pre[-col + 1, new_alphas[-col + 1]]\r\n alphas[i] = new_alphas[:]\r\n\r\ndef update_cols(alphas, data_loss, ws_hor, ws_ver, flag, s, h, w):\r\n for i in range(w):\r\n if flag[i]:\r\n ds_temp = np.zeros((h, 2))\r\n #es_temp = np.zeros((w, 2))\r\n pre = np.zeros((h, 2))\r\n ds_temp[:, 0] += data_loss[:, i, 0]\r\n ds_temp[:, 1] += data_loss[:, i, 1]\r\n if i > 0:\r\n ds_temp[:, 0] += ws_hor[:, i - 1] * s[(alphas[:, i - 1], np.zeros(h).astype(np.int))]\r\n ds_temp[:, 1] += ws_hor[:, i - 1] * s[(alphas[:, i - 1], np.ones(h).astype(np.int))]\r\n if i < w - 1:\r\n ds_temp[:, 0] += ws_hor[:, i] * s[(alphas[:, i + 1], np.zeros(h).astype(np.int))]\r\n ds_temp[:, 1] += ws_hor[:, i] * s[(alphas[:, i + 1], np.ones(h).astype(np.int))]\r\n #es_temp = ds_temp[:]\r\n for j in range(1, h):\r\n if ds_temp[j - 1, 0] < ds_temp[j - 1, 1]:\r\n ds_temp[j, 0] += ds_temp[j - 1, 0]\r\n pre[j, 0] = 0\r\n smth = ds_temp[j - 1, 0] + ws_ver[j - 1, i]\r\n if ds_temp[j - 1, 1] < smth:\r\n ds_temp[j, 1] += ds_temp[j - 1, 1]\r\n pre[j, 1] = 1\r\n else:\r\n ds_temp[j, 1] += smth\r\n pre[j, 1] = 0\r\n else:\r\n ds_temp[j, 1] += ds_temp[j - 1, 1]\r\n pre[j, 1] = 1\r\n smth = ds_temp[j - 1, 1] + ws_ver[j - 1, i]\r\n if ds_temp[j - 1, 0] < smth:\r\n ds_temp[j, 0] += ds_temp[j - 1, 0]\r\n pre[j, 0] = 0\r\n else:\r\n ds_temp[j, 0] += smth\r\n pre[j, 0] = 1\r\n new_alphas = np.array([-1]*h)\r\n if ds_temp[-1, 0] < ds_temp[-1, 1]:\r\n new_alphas[-1] = 0\r\n else:\r\n new_alphas[-1] = 1\r\n for row in range(2, h + 1):\r\n new_alphas[-row] = pre[-row + 1, new_alphas[-row + 1]]\r\n alphas[:, i] = new_alphas[:]\r\n\r\ndef energy(alphas, data_loss, ws_hor, ws_ver, s, h, w):\r\n de = 0.\r\n se = 0.\r\n for i in range(h):\r\n for j in range(w):\r\n de += data_loss[i, j, alphas[i, j]]\r\n if i < h - 1:\r\n se += s[alphas[i, j], alphas[i + 1, j]] * ws_ver[i, j]\r\n if j < w - 1:\r\n se += s[alphas[i, j], alphas[i, j + 1]] * ws_hor[i, j]\r\n return de, se\r\n\r\ndef update_gmms(gmms, im, ks, alphas, n_compo):\r\n pis = np.zeros((2, n_compo))\r\n for a in range(2):\r\n for k in range(n_compo):\r\n k_pixels = im[np.logical_and(ks == k, alphas == a)]\r\n pis[a, k] = k_pixels.shape[0]\r\n gmms[a].means_[k] = np.mean(k_pixels, axis=0)\r\n gmms[a].covariances_[k] = np.cov(k_pixels.T)\r\n pis /= np.tile(np.sum(pis, axis=-1, keepdims=True), (1, n_compo))\r\n gmms[0].weights_ = pis[0]\r\n gmms[1].weights_ = pis[1]\r\n\r\nn_compo = 5\r\nmax_iter = 100\r\nlmbd = 10\r\nlmbd2 = 10\r\nsamples = ['flower', 'sponge', 'person']\r\n\r\nfor sample in samples:\r\n print(sample)\r\n im = cv2.imread('{}.png'.format(sample)).astype(np.float)\r\n height, width = im.shape[0], im.shape[1]\r\n fg_mask = cv2.imread('{}_fg.png'.format(sample))\r\n bg_mask = cv2.imread('{}_bg.png'.format(sample))\r\n fg_coors = (np.where(fg_mask > 0)[0][::3], np.where(fg_mask > 0)[1][::3])\r\n fg_coors_zipped = list(zip(fg_coors[0], fg_coors[1]))\r\n bg_coors = (np.where(bg_mask > 0)[0][::3], np.where(bg_mask > 0)[1][::3])\r\n bg_coors_zipped = list(zip(bg_coors[0], bg_coors[1]))\r\n fg_pixels = im[fg_coors] #np.reshape(im[fg_coors], (-1, 3))\r\n bg_pixels = im[bg_coors] #np.reshape(im[bg_coors], (-1, 3))\r\n fg_gmm = skm.GaussianMixture(n_components=n_compo, max_iter=200)\r\n bg_gmm = skm.GaussianMixture(n_components=n_compo, max_iter=200)\r\n fg_gmm.fit(fg_pixels)\r\n bg_gmm.fit(bg_pixels)\r\n ks = np.empty((height, width))\r\n ks_fg_prior = fg_gmm.predict(fg_pixels)\r\n ks_bg_prior = bg_gmm.predict(bg_pixels)\r\n for i, k in enumerate(ks_fg_prior):\r\n ks[fg_coors_zipped[i][0], fg_coors_zipped[i][1]] = k\r\n for i, k in enumerate(ks_bg_prior):\r\n ks[bg_coors_zipped[i][0], bg_coors_zipped[i][1]] = k\r\n alphas = np.array([[-1] * width] * height)\r\n alphas[fg_coors] = 0\r\n alphas[bg_coors] = 1\r\n\r\n uninit_coors = [(i, j) for i in range(height) for j in range(width) if alphas[i, j] == -1]\r\n uninit = np.array([im[coors[0], coors[1], :] for coors in uninit_coors])\r\n\r\n ks_fg = fg_gmm.predict(uninit)\r\n ds_fg = -fg_gmm.score_samples(uninit)\r\n ks_bg = bg_gmm.predict(uninit)\r\n ds_bg = -bg_gmm.score_samples(uninit)\r\n ks_fg_bg = np.array([ks_fg, ks_bg])\r\n fg_or_bg = [0 if ds_fg[i] < ds_bg[i] else 1 for i in range(len(uninit))]\r\n\r\n for i, fob in enumerate(fg_or_bg):\r\n alphas[uninit_coors[i][0], uninit_coors[i][1]] = fob\r\n ks[uninit_coors[i][0], uninit_coors[i][1]] = ks_fg_bg[fob, i]\r\n #dataLoss[uninit_coors[i][0], uninit_coors[i][1], 0] = ds_fg[i]\r\n #dataLoss[uninit_coors[i][0], uninit_coors[i][1], 1] = ds_bg[i]\r\n bayes_seg = (np.ones_like(alphas) - alphas) * 255\r\n cv2.imwrite('{}_bayes.png'.format(sample), bayes_seg.astype(np.uint8))\r\n\r\n ws_hor = np.zeros((height, width))\r\n ws_ver = np.zeros((height, width))\r\n temp = 0.\r\n count = 0\r\n for i in range(height):\r\n for j in range(width):\r\n if i + 1 < height:\r\n temp += np.sum((im[i, j] - im[i + 1, j]) ** 2)\r\n count += 1\r\n if j + 1 < width:\r\n temp += np.sum((im[i, j] - im[i, j + 1]) ** 2)\r\n count += 1\r\n beta = (2 * temp / count) ** -1\r\n ws_hor[:, :-1] = (np.exp(-beta * np.sum((im[:, 1:, :] - im[:, :-1, :]) ** 2, axis=-1)) + lmbd2) * lmbd\r\n ws_ver[:-1, :] = (np.exp(-beta * np.sum((im[1:, ...] - im[:-1, ...]) ** 2, axis=-1)) + lmbd2) * lmbd\r\n\r\n sij = np.ones((2,2)) - np.eye(2)\r\n\r\n update_gmms([fg_gmm, bg_gmm], im, ks, alphas, n_compo)\r\n\r\n data_loss = np.empty((height, width, 2))\r\n data_loss[fg_coors + (np.zeros(len(fg_coors_zipped)).astype(np.int),)] = 0.\r\n data_loss[fg_coors + (np.ones(len(fg_coors_zipped)).astype(np.int),)] = np.inf\r\n data_loss[bg_coors + (np.zeros(len(bg_coors_zipped)).astype(np.int),)] = np.inf\r\n data_loss[bg_coors + (np.ones(len(bg_coors_zipped)).astype(np.int),)] = 0.\r\n\r\n update_data_loss(data_loss, uninit, uninit_coors, [fg_gmm, bg_gmm])\r\n\r\n flag_rows = np.array([True] * height)\r\n flag_cols = np.array([True] * width)\r\n\r\n old_alphas = alphas[:]\r\n\r\n update_rows(alphas, data_loss, ws_hor, ws_ver, flag_rows, sij, height, width)\r\n update_cols(alphas, data_loss, ws_hor, ws_ver, flag_cols, sij, height, width)\r\n\r\n new_de, new_se = energy(alphas, data_loss, ws_hor, ws_ver, sij, height, width)\r\n new_energy = new_de + new_se\r\n print('Iteration 0 --- Energy: {} (DE={}, SE={})'.format(new_energy, new_de, new_se))\r\n\r\n #tolerance = 0\r\n\r\n for i in range(1, max_iter):\r\n old_energy = new_energy\r\n\r\n #'''\r\n old_flag_rows = flag_rows[:]\r\n old_flag_cols = flag_cols[:]\r\n flag_rows[:] = False\r\n flag_cols[:] = False\r\n for j in range(height):\r\n if old_flag_rows[j]:\r\n if np.any(old_alphas[j] != alphas[j]):\r\n if j > 0:\r\n flag_rows[j - 1] = True\r\n if j < height - 1:\r\n flag_rows[j + 1] = True\r\n for j in range(width):\r\n if old_flag_cols[j]:\r\n if np.any(old_alphas[:, j] != alphas[:, j]):\r\n if j > 0:\r\n flag_cols[j - 1] = True\r\n if j < width - 1:\r\n flag_cols[j + 1] = True\r\n #'''\r\n\r\n update_ks(ks, im, [fg_gmm, bg_gmm], alphas)\r\n update_gmms([fg_gmm, bg_gmm], im, ks, alphas, n_compo)\r\n update_data_loss(data_loss, uninit, uninit_coors, [fg_gmm, bg_gmm])\r\n\r\n old_alphas = alphas[:]\r\n\r\n update_rows(alphas, data_loss, ws_hor, ws_ver, flag_rows, sij, height, width)\r\n update_cols(alphas, data_loss, ws_hor, ws_ver, flag_cols, sij, height, width)\r\n\r\n new_de, new_se = energy(alphas, data_loss, ws_hor, ws_ver, sij, height, width)\r\n new_energy = new_de + new_se\r\n print('Iteration {} --- Energy: {} (DE={}, SE={})'.format(i, new_energy, new_de, new_se))\r\n\r\n if new_energy >= old_energy:\r\n #tolerance += 1\r\n #if tolerance > 3:\r\n break\r\n\r\n final_segs = (np.ones_like(alphas) - alphas) * 255\r\n cv2.imwrite('{}_seg.png'.format(sample), final_segs.astype(np.uint8))\r\n\r\n gt = cv2.imread('{}_gt.png'.format(sample)).astype(np.float)\r\n acc = float(np.sum(gt[..., 0]==final_segs)) / height / width\r\n print('acc: {}'.format(acc))\r\n print('\\n')","repo_name":"zehzhang/MRF_BCD","sub_path":"mrfBCD.py","file_name":"mrfBCD.py","file_ext":"py","file_size_in_byte":11595,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"52"} +{"seq_id":"72438329444","text":"import requests\n\nheaders = {\n 'Content-Type': 'application/json',\n 'Authorization': 'Token 96f4a5ea160a345defdfff0320144e7c469ce70f'\n}\n\n\ndef get_meta_data(ticker):\n url = \"https://api.tiingo.com/tiingo/daily/{}\".format(ticker)\n response = requests.get(url, headers=headers)\n return response.json()\n\n\ndef get_price_data(ticker):\n url = \"https://api.tiingo.com/tiingo/daily/{}/prices\".format(ticker)\n response = requests.get(url, headers=headers)\n return response.json()[0]\n\n\ndef get_histo_price_data(ticker):\n url = \"https://api.tiingo.com/tiingo/daily/{}/prices?startDate=2012-1-1&endDate=2022-3-3\".format(ticker)\n response = requests.get(url, headers=headers)\n return response.json()","repo_name":"CHAMS1110/stockapp","sub_path":"apps/home/tiingo.py","file_name":"tiingo.py","file_ext":"py","file_size_in_byte":717,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"6732880384","text":"import os\nfrom pathlib import Path\nfrom shutil import copy, copytree\nimport plistlib\nimport requests\nfrom urllib.parse import urlparse\nimport zipfile\nimport sys\nimport subprocess\nimport tempfile\nimport platform\nimport argparse\nfrom glob import glob\n\nfrom utils.copy import Copy\nfrom utils.downloader import DpkgDeb, Ldid\nfrom utils.hash import LdidHash\n\n\"\"\" Functions \"\"\"\ndef cmd_in_path(args, cmd):\n if args.debug:\n print(f\"[DEBUG] Checking if command {cmd} is in PATH...\")\n \n if cmd == \"ldid\":\n if is_ios():\n if args.debug:\n print(f\"[DEBUG] Checking for ldid on iOS\")\n \n if is_dpkg_installed(\"ldid\"):\n if args.debug:\n print(f\"[DEBUG] ldid is installed via dpkg\")\n \n return True\n else:\n print(\"[-] ldid is required on iOS, but it is not installed. Please install it from Procursus.\")\n exit(1)\n \n try:\n if args.debug:\n print(f\"[DEBUG] Checking ldid output...\")\n \n ldid_out = subprocess.check_output([\"ldid\"], stderr=subprocess.STDOUT)\n if \"procursus\" not in ldid_out.decode(\"utf-8\"):\n if args.debug:\n print(f\"[DEBUG] ldid installed is not from Procursus... skipping.\")\n \n return False\n else:\n if args.debug:\n print(f\"[DEBUG] ldid installed is from Procursus!\")\n \n return True\n except(Exception,):\n return False\n \n if args.debug:\n print(f\"[DEBUG] ldid is not in PATH... skipping.\")\n \n try:\n which_cmd = subprocess.check_output([\"which\", f\"{cmd}\"], stderr=subprocess.STDOUT)\n except:\n return False\n \n return True\n \n # if not f\"{cmd} not found\" in subprocess.run(f\"which {cmd}\".split(), capture_output=True, text=True).stdout:\n # return True\n \n #return which(cmd) is not None\n \ndef is_macos():\n if platform.machine().startswith(\"i\"):\n return False\n \n return sys.platform == \"darwin\"\n\ndef is_linux():\n return sys.platform == \"linux\"\n\ndef is_ios():\n if not sys.platform == \"darwin\":\n return False\n \n return platform.machine().startswith(\"i\")\n\ndef is_dpkg_installed(pkg):\n return (os.system(\"dpkg -s \" + pkg + \"> /dev/null 2>&1\")) == 0\n \n\n\"\"\" Main Function \"\"\"\ndef main(args):\n print(f\"IPA Permasigner | Version {subprocess.check_output(['git', 'rev-parse', '--abbrev-ref', 'HEAD']).decode('ascii').strip()}-{subprocess.check_output(['git', 'rev-parse', '--short', 'HEAD']).decode('ascii').strip()}\")\n print(\"Program created by Nebula | Original scripts created by zhuowei | CoreTrust bypass by Linus Henze\")\n print()\n \n # Check if script is running on Windows, if so, fail\n if sys.platform == \"windows\":\n print(\"[-] Script must be ran on macOS or Linux.\")\n exit(1)\n \n # Check if codesign is added on Linux\n if args.codesign:\n if is_linux():\n print(\"[-] You cannot use codesign on Linux, remove the argument and it'll use ldid instead.\")\n exit(1)\n \n # Auto download ldid\n if not cmd_in_path(args, \"ldid\"):\n if Path(f\"{os.getcwd()}/ldid\").exists():\n if is_linux() and platform.machine() == \"x86_64\":\n if args.debug:\n print(f\"[DEBUG] On Linux x86_64, ldid not found...\")\n \n if not LdidHash.check_linux_64(args):\n print(\"[*] ldid is outdated or malformed, downloading latest version...\")\n os.remove(f\"{os.getcwd()}/ldid\")\n Ldid.download_linux_64()\n elif is_linux() and platform.machine() == \"aarch64\":\n if args.debug:\n print(f\"[DEBUG] On Linux aarch64, ldid not found...\")\n \n if not LdidHash.check_linux_arm64(args):\n print(\"[*] ldid is outdated or malformed, downloading latest version...\")\n os.remove(f\"{os.getcwd()}/ldid\")\n Ldid.download_linux_arm64()\n elif is_macos() and platform.machine() == \"x86_64\":\n if args.debug:\n print(f\"[DEBUG] On macOS x86_64, ldid not found...\")\n \n if not LdidHash.check_macos_64(args):\n print(\"[*] ldid is outdated or malformed, downloading latest version...\")\n os.remove(f\"{os.getcwd()}/ldid\")\n Ldid.download_macos_64()\n elif is_macos() and platform.machine() == \"arm64\":\n if args.debug:\n print(f\"[DEBUG] On macOS arm64, ldid not found...\")\n \n if not LdidHash.check_macos_arm64(args):\n print(\"[*] ldid is outdated or malformed, downloading latest version...\")\n os.remove(f\"{os.getcwd()}/ldid\")\n Ldid.download_macos_arm64()\n else:\n print(\"[*] ldid not found, downloading.\")\n if is_linux() and platform.machine() == \"x86_64\":\n Ldid.download_linux_64(args)\n elif is_linux() and platform.machine() == \"aarch64\":\n Ldid.download_linux_arm64(args)\n elif is_macos() and platform.machine() == \"x86_64\":\n Ldid.download_macos_64(args)\n elif is_macos() and platform.machine() == \"arm64\":\n Ldid.download_macos_arm64(args)\n \n # Auto download dpkg-deb on Linux\n if not cmd_in_path(args, \"dpkg-deb\") and is_linux():\n if not Path(f\"{os.getcwd()}/dpkg-deb\").exists():\n if platform.machine() == \"x86_64\":\n if args.debug:\n print(f\"[DEBUG] On Linux x86_64, dpkg-deb not found...\")\n \n print(\"[*] dpkg-deb not found, downloading.\")\n DpkgDeb.download_linux_64(args)\n print()\n elif platform.machine() == \"aarch64\":\n if args.debug:\n print(f\"[DEBUG] On Linux aarch64, dpkg-deb not found...\")\n \n print(\"[*] dpkg-deb not found, downloading.\")\n DpkgDeb.download_linux_arm64(args)\n print()\n \n if is_macos():\n try:\n which_dpkg = subprocess.check_output([\"which\", \"dpkg\"], stderr=subprocess.STDOUT)\n except:\n if args.debug:\n print(f\"[DEBUG] On macOS x86_64, dpkg-deb not found...\")\n print(\"[-] dpkg is not installed and is required on macOS. Install it though brew or Procursus to continue.\")\n exit(1)\n \n # Prompt the user if they'd like to use an external IPA or a local IPA\n if not (args.url or args.path):\n option = input(\"[?] Would you like to use an IPA stored on the web, or on your system? [external, local] \")\n option = option.lower()\n\n global out_deb_name\n \n with tempfile.TemporaryDirectory() as tmpfolder:\n print(\"[*] Created temporary directory.\")\n print()\n \n # If the user's choice is external, download an IPA\n # Otherwise, copy the IPA to the temporary directory\n if args.url:\n url = args.url\n \n if not os.path.splitext(urlparse(url).path)[1] == \".ipa\":\n print(\"[-] URL provided is not an IPA, make sure to provide a direct link.\")\n exit(1)\n \n res = requests.get(url, stream=True)\n \n try:\n if res.status_code == 200:\n print(f\"[*] Downloading file...\")\n \n with open(f\"{tmpfolder}/app.ipa\", \"wb\") as f:\n f.write(res.content)\n else:\n print(f\"[-] URL provided is not reachable. Status code: {res.status_code}\")\n exit(1)\n except requests.exceptions.RequestException as err:\n print(f\"[-] URL provided is not reachable. Error: {err}\")\n exit(1)\n elif args.path:\n path = args.path\n \n if path.strip()[-1] == \" \":\n path = path.strip()[:-1]\n \n if Path(path).exists():\n copy(path, f\"{tmpfolder}/app.ipa\")\n else:\n print(\"[-] That file does not exist! Make sure you're using a direct path to the IPA file.\")\n exit(1)\n elif option == \"external\":\n url = input(\"[?] Paste in the *direct* path to an IPA online: \")\n \n if not os.path.splitext(urlparse(url).path)[1] == \".ipa\":\n print(\"[-] URL provided is not an IPA, make sure to provide a direct link.\")\n exit(1)\n \n res = requests.get(url, stream=True)\n \n try:\n if res.status_code == 200:\n print(f\"[*] Downloading file...\")\n \n with open(f\"{tmpfolder}/app.ipa\", \"wb\") as f:\n f.write(res.content)\n else:\n print(f\"[-] URL provided is not reachable. Status code: {res.status_code}\")\n exit(1)\n except requests.exceptions.RequestException as err:\n print(f\"[-] URL provided is not reachable. Error: {err}\")\n exit(1) \n elif option == \"local\":\n path = input(\"[?] Paste in the path to an IPA in your file system: \")\n \n if path.strip()[-1] == \" \":\n path = path.strip()[:-1]\n \n if Path(path).exists():\n copy(path, f\"{tmpfolder}/app.ipa\")\n else:\n print(\"[-] That file does not exist! Make sure you're using a direct path to the IPA file.\")\n exit(1)\n else:\n print(\"[-] That is not a valid option!\")\n exit(1)\n print()\n \n # Unzip the IPA file\n print(\"[*] Unzipping IPA...\")\n with zipfile.ZipFile(f\"{tmpfolder}/app.ipa\", 'r') as f:\n os.makedirs(f\"{tmpfolder}/app\", exist_ok=False)\n f.extractall(f\"{tmpfolder}/app\")\n print()\n \n # Read data from the plist\n print(\"[*] Reading plist...\")\n global folder, app_name, app_bundle, app_version, app_min_ios, app_author, app_executable\n \n if Path(f\"{tmpfolder}/app/Payload\").exists():\n for fname in os.listdir(path=f\"{tmpfolder}/app/Payload\"):\n if fname.endswith(\".app\"):\n folder = fname\n print(\"Found plist!\")\n else:\n print(\"[-] IPA is not valid!\")\n exit(1)\n \n if Path(f\"{tmpfolder}/app/Payload/{folder}/Info.plist\").exists():\n with open(f\"{tmpfolder}/app/Payload/{folder}/Info.plist\", 'rb') as f:\n info = plistlib.load(f)\n app_name = info[\"CFBundleName\"]\n app_bundle = info[\"CFBundleIdentifier\"]\n app_version = info[\"CFBundleShortVersionString\"]\n app_min_ios = info[\"MinimumOSVersion\"]\n app_author = app_bundle.split(\".\")[1]\n if info[\"CFBundleExecutable\"]:\n app_executable = info[\"CFBundleExecutable\"]\n print(\"Executable found.\")\n else:\n app_executable = None\n print(\"No executable found.\")\n print(\"Found information about the app!\")\n print()\n \n # Get the deb file ready\n print(\"[*] Preparing deb file...\")\n print(\"Making directories...\")\n os.makedirs(f\"{tmpfolder}/deb/Applications\", exist_ok=False)\n os.makedirs(f\"{tmpfolder}/deb/DEBIAN\", exist_ok=False)\n print(\"Copying deb file scripts and control...\")\n Copy.copy_postrm(f\"{tmpfolder}/deb/DEBIAN/postrm\", app_name)\n Copy.copy_postinst(f\"{tmpfolder}/deb/DEBIAN/postinst\", app_name)\n Copy.copy_control(f\"{tmpfolder}/deb/DEBIAN/control\", app_name, app_bundle, app_version, app_min_ios, app_author)\n print(\"Copying app files...\")\n copytree(f\"{tmpfolder}/app/Payload/{folder}\", f\"{tmpfolder}/deb/Applications/{folder}\")\n print(\"Changing deb file scripts permissions...\")\n subprocess.run(f\"chmod 0755 {tmpfolder}/deb/DEBIAN/postrm\".split(), stdout=subprocess.DEVNULL)\n subprocess.run(f\"chmod 0755 {tmpfolder}/deb/DEBIAN/postinst\".split(), stdout=subprocess.DEVNULL)\n if app_executable is not None:\n print(\"Changing app executable permissions...\")\n full_path = f\"'{tmpfolder}/deb/Applications/{folder}/{app_executable}'\"\n os.system(\"chmod 0755 \" + full_path)\n print()\n \n # Sign the app\n print(\"[*] Signing app...\")\n Copy.copy_entitlements(f\"{tmpfolder}/entitlements.plist\", app_bundle)\n if args.codesign:\n print(\"Signing with codesign as it was specified...\")\n subprocess.run(f\"security import ./dev_certificate.p12 -P password -A\".split(), stdout=subprocess.DEVNULL)\n full_path = f\"'{tmpfolder}/deb/Applications/{folder}'\"\n frameworks_path = f\"{tmpfolder}/deb/Applications/{folder}/Frameworks\"\n \n os.system(f\"codesign -s 'We Do A Little Trolling iPhone OS Application Signing' --force --deep --preserve-metadata=entitlements '{full_path}'\")\n \n if Path(frameworks_path).exists():\n if args.debug:\n print(\"[DEBUG] Frameworks path exists\")\n \n for file in os.listdir(frameworks_path):\n if file.endswith(\".dylib\"):\n print(f\"Signing dylib {file}...\")\n os.system(f\"codesign -s 'We Do A Little Trolling iPhone OS Application Signing' --force --deep '{frameworks_path}/{file}'\")\n \n for fpath in glob(frameworks_path + '/*.framework'):\n fname = os.path.basename(fpath)\n if Path(f\"{fpath}/Info.plist\").exists():\n with open(f\"{fpath}/Info.plist\", 'rb') as f:\n info = plistlib.load(f)\n if info[\"CFBundleExecutable\"]:\n f_executable = info[\"CFBundleExecutable\"]\n if args.debug:\n print(f\"[DEBUG] Executable found in the {fname}\")\n else:\n f_executable = None\n if args.debug:\n print(f\"[DEBUG] No executable found in the {fname}\")\n if f_executable is not None:\n print(f\"Signing executable in {fname}\")\n exec_path = os.path.join(fpath, f_executable)\n if args.debug:\n print(f\"[DEBUG] Running command: codesign -s 'We Do A Little Trolling iPhone OS Application Signing' --force --deep {exec_path}\")\n subprocess.run(f\"codesign -s 'We Do A Little Trolling iPhone OS Application Signing' --force --deep '{exec_path}'\", shell=True)\n else:\n print(\"Signing with ldid...\")\n full_path = f\"'{tmpfolder}/deb/Applications/{folder}'\"\n frameworks_path = f\"{tmpfolder}/deb/Applications/{folder}/Frameworks\"\n if cmd_in_path(args, \"ldid\"):\n if args.debug:\n print(f\"[DEBUG] Running command: ldid -S{tmpfolder}/entitlements.plist -M -Kdev_certificate.p12 '{full_path}'\")\n \n os.system(f\"ldid -S{tmpfolder}/entitlements.plist -M -Kdev_certificate.p12 '{full_path}'\")\n else:\n subprocess.run(\"chmod +x ldid\".split(), stdout=subprocess.DEVNULL)\n if args.debug:\n print(f\"[DEBUG] Running command: ./ldid -S{tmpfolder}/entitlements.plist -M -Kdev_certificate.p12 '{full_path}'\")\n \n os.system(f\"./ldid -S{tmpfolder}/entitlements.plist -M -Kdev_certificate.p12 '{full_path}'\")\n \n if Path(frameworks_path).exists():\n if args.debug:\n print(\"[DEBUG] Frameworks path exists\")\n \n for file in os.listdir(frameworks_path):\n if file.endswith(\".dylib\"):\n print(f\"Signing dylib {file}...\")\n if cmd_in_path(args, \"ldid\"):\n if args.debug:\n print(f\"[DEBUG] Running command: ldid -Kdev_certificate.p12 '{frameworks_path}/{file}'\")\n \n os.system(f\"ldid -Kdev_certificate.p12 '{frameworks_path}/{file}'\")\n else:\n if args.debug:\n print(f\"[DEBUG] Running command: ./ldid -Kdev_certificate.p12 '{frameworks_path}/{file}'\")\n \n os.system(f\"./ldid -Kdev_certificate.p12 '{frameworks_path}/{file}'\")\n \n for fpath in glob(frameworks_path + '/*.framework'):\n fname = os.path.basename(fpath)\n if Path(f\"{fpath}/Info.plist\").exists():\n with open(f\"{fpath}/Info.plist\", 'rb') as f:\n info = plistlib.load(f)\n if info[\"CFBundleExecutable\"]:\n f_executable = info[\"CFBundleExecutable\"]\n if args.debug:\n print(f\"[DEBUG] Executable found in the {fname}\")\n else:\n f_executable = None\n if args.debug:\n print(f\"[DEBUG] No executable found in the {fname}\")\n if f_executable is not None:\n print(f\"Signing executable in {fname}\")\n exec_path = os.path.join(fpath, f_executable)\n if cmd_in_path(args, \"ldid\"):\n if args.debug:\n print(f\"[DEBUG] Running command: ldid -Kdev_certificate.p12 {exec_path}\")\n subprocess.run(f\"ldid -Kdev_certificate.p12 '{exec_path}'\", shell=True)\n else:\n if args.debug:\n print(f\"[DEBUG] Running command: ./ldid -Kdev_certificate.p12 {exec_path}\")\n subprocess.run(f\"./ldid -Kdev_certificate.p12 '{exec_path}'\", shell=True)\n print()\n\n # Package the deb file\n print(\"[*] Packaging the deb file...\")\n os.makedirs(\"output\", exist_ok=True)\n if Path(f\"output/{app_name}.deb\").exists():\n os.remove(f\"output/{app_name}.deb\")\n \n out_deb_name = app_name.replace(' ', '')\n\n if cmd_in_path(args, \"dpkg-deb\"):\n if args.debug:\n print(f\"[DEBUG] Running command: dpkg-deb -Zxz --root-owner-group -b {tmpfolder}/deb output/{app_name.replace(' ', '')}.deb\")\n \n subprocess.run(f\"dpkg-deb -Zxz --root-owner-group -b {tmpfolder}/deb output/{app_name.replace(' ', '')}.deb\".split(), stdout=subprocess.DEVNULL)\n else:\n if args.debug:\n print(f\"[DEBUG] Running command: ./dpkg-deb -Zxz --root-owner-group -b {tmpfolder}/deb output/{app_name.replace(' ', '')}.deb\")\n \n subprocess.run(f\"./dpkg-deb -Zxz --root-owner-group -b {tmpfolder}/deb output/{app_name.replace(' ', '')}.deb\".split(), stdout=subprocess.DEVNULL)\n\n # Done!!!\n print()\n print(\"[*] We are finished!\")\n print(\"[*] Copy the newly created deb from the output folder to your jailbroken iDevice and install it!\")\n print(\"[*] The app will continue to work when rebooted to stock.\")\n print(f\"[*] Output file: output/{out_deb_name}.deb\")\n \nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('-c', '--codesign', action='store_true', help=\"uses codesign instead of ldid.\")\n parser.add_argument('-d', '--debug', action='store_true', help=\"shows some debug info, only useful for testing.\")\n parser.add_argument('-u', '--url', type=str, help=\"the direct URL of the IPA to be signed.\")\n parser.add_argument('-p', '--path', type=str, help=\"the direct local path of the IPA to be signed.\")\n args = parser.parse_args()\n \n main(args)\n\n","repo_name":"CrackerCat/permasigner","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":21058,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"52"} +{"seq_id":"29892295417","text":"d = {\"a\": 1, \"b\": [1, 2]}\n\nd[\"a\"]\nd.get(\"a\")\n\nd[\"c\"]\nd.get(\"c\", \"return if cant find key\")\n\nd[\"a\"] = 12\nd[\"a\"]\n\nd.update({\"a\": 13})\nd[\"a\"]\n\nd.__setitem__(\"c\", 3)\n\nd[\"d\"] = 4\n\nd\n\nd[\"a\"]\n\n\"a\" in d\nd.keys()\nd.values()\n\nd.get(\"b\")\nd.__getitem__(\"b\")\n\nlist(d.keys())\nlist(d.keys())[2]\n\nlist(d.values())\n\nfor i in enumerate(d):\n print(i)\n","repo_name":"stenhaug/learnex","sub_path":"Py/dicts.py","file_name":"dicts.py","file_ext":"py","file_size_in_byte":335,"program_lang":"python","lang":"vi","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"20530414845","text":"class SmallestInfiniteSet1:\n\n def __init__(self):\n self.heap = [i for i in range(1, 1001)]\n self.map = {i : 1 for i in self.heap}\n \n\n def popSmallest(self) -> int:\n if self.heap:\n idx = heapq.heappop(self.heap)\n del self.map[idx]\n return idx\n return -1\n \n\n def addBack(self, num: int) -> None:\n if num not in self.map:\n self.map[num] = 1\n heapq.heappush(self.heap, num)\n \n \nclass SmallestInfiniteSet:\n\n def __init__(self):\n self.n = 1\n self.seen = set()\n self.heap = []\n \n\n def popSmallest(self) -> int:\n if self.heap:\n res = heappop(self.heap)\n self.seen.remove(res)\n else:\n res = self.n\n self.n += 1\n return res\n \n\n def addBack(self, num: int) -> None:\n if num not in self.seen and num < self.n:\n self.seen.add(num)\n heappush(self.heap, num)\n \n \n \n \n \n\n\n# Your SmallestInfiniteSet object will be instantiated and called as such:\n# obj = SmallestInfiniteSet()\n# param_1 = obj.popSmallest()\n# obj.addBack(num)","repo_name":"zerobcpp/Code-practices","sub_path":"2336.py","file_name":"2336.py","file_ext":"py","file_size_in_byte":1209,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"31576792159","text":"from oci.util import formatted_flat_dict, NONE_SENTINEL, value_allowed_none_or_none_sentinel # noqa: F401\nfrom oci.decorators import init_model_state_from_kwargs\n\n\n@init_model_state_from_kwargs\nclass StaticPublicKey(object):\n \"\"\"\n A static public key which is used to verify the JWT signature.\n \"\"\"\n\n #: A constant which can be used with the format property of a StaticPublicKey.\n #: This constant has a value of \"JSON_WEB_KEY\"\n FORMAT_JSON_WEB_KEY = \"JSON_WEB_KEY\"\n\n #: A constant which can be used with the format property of a StaticPublicKey.\n #: This constant has a value of \"PEM\"\n FORMAT_PEM = \"PEM\"\n\n def __init__(self, **kwargs):\n \"\"\"\n Initializes a new StaticPublicKey object with values from keyword arguments. This class has the following subclasses and if you are using this class as input\n to a service operations then you should favor using a subclass over the base class:\n\n * :class:`~oci.apigateway.models.JsonWebKey`\n * :class:`~oci.apigateway.models.PemEncodedPublicKey`\n\n The following keyword arguments are supported (corresponding to the getters/setters of this class):\n\n :param kid:\n The value to assign to the kid property of this StaticPublicKey.\n :type kid: str\n\n :param format:\n The value to assign to the format property of this StaticPublicKey.\n Allowed values for this property are: \"JSON_WEB_KEY\", \"PEM\", 'UNKNOWN_ENUM_VALUE'.\n Any unrecognized values returned by a service will be mapped to 'UNKNOWN_ENUM_VALUE'.\n :type format: str\n\n \"\"\"\n self.swagger_types = {\n 'kid': 'str',\n 'format': 'str'\n }\n\n self.attribute_map = {\n 'kid': 'kid',\n 'format': 'format'\n }\n\n self._kid = None\n self._format = None\n\n @staticmethod\n def get_subtype(object_dictionary):\n \"\"\"\n Given the hash representation of a subtype of this class,\n use the info in the hash to return the class of the subtype.\n \"\"\"\n type = object_dictionary['format']\n\n if type == 'JSON_WEB_KEY':\n return 'JsonWebKey'\n\n if type == 'PEM':\n return 'PemEncodedPublicKey'\n else:\n return 'StaticPublicKey'\n\n @property\n def kid(self):\n \"\"\"\n **[Required]** Gets the kid of this StaticPublicKey.\n A unique key ID. This key will be used to verify the signature of a\n JWT with matching \\\"kid\\\".\n\n\n :return: The kid of this StaticPublicKey.\n :rtype: str\n \"\"\"\n return self._kid\n\n @kid.setter\n def kid(self, kid):\n \"\"\"\n Sets the kid of this StaticPublicKey.\n A unique key ID. This key will be used to verify the signature of a\n JWT with matching \\\"kid\\\".\n\n\n :param kid: The kid of this StaticPublicKey.\n :type: str\n \"\"\"\n self._kid = kid\n\n @property\n def format(self):\n \"\"\"\n **[Required]** Gets the format of this StaticPublicKey.\n The format of the public key.\n\n Allowed values for this property are: \"JSON_WEB_KEY\", \"PEM\", 'UNKNOWN_ENUM_VALUE'.\n Any unrecognized values returned by a service will be mapped to 'UNKNOWN_ENUM_VALUE'.\n\n\n :return: The format of this StaticPublicKey.\n :rtype: str\n \"\"\"\n return self._format\n\n @format.setter\n def format(self, format):\n \"\"\"\n Sets the format of this StaticPublicKey.\n The format of the public key.\n\n\n :param format: The format of this StaticPublicKey.\n :type: str\n \"\"\"\n allowed_values = [\"JSON_WEB_KEY\", \"PEM\"]\n if not value_allowed_none_or_none_sentinel(format, allowed_values):\n format = 'UNKNOWN_ENUM_VALUE'\n self._format = format\n\n def __repr__(self):\n return formatted_flat_dict(self)\n\n def __eq__(self, other):\n if other is None:\n return False\n\n return self.__dict__ == other.__dict__\n\n def __ne__(self, other):\n return not self == other\n","repo_name":"oracle/oci-python-sdk","sub_path":"src/oci/apigateway/models/static_public_key.py","file_name":"static_public_key.py","file_ext":"py","file_size_in_byte":4112,"program_lang":"python","lang":"en","doc_type":"code","stars":345,"dataset":"github-code","pt":"52"} +{"seq_id":"19057957355","text":"import dataclasses\nimport datetime\nimport typing\n\nimport discord\n\nfrom database.models.model import Model\n\n\n@dataclasses.dataclass()\nclass ReactionRole(Model):\n message_id: int = None\n emoji: str = None\n guild: int = None\n created: datetime.datetime = dataclasses.field(default_factory=datetime.datetime.utcnow)\n author: int = None\n role: int = None\n\n def get_guild(self) -> typing.Optional[discord.Guild]:\n return self.bot.get_guild(self.guild)\n\n def get_role(self) -> typing.Optional[discord.Role]:\n guild = self.get_guild()\n if guild:\n return guild.get_role(self.role)\n\n @classmethod\n def setup_table(cls) -> str:\n return \"\"\"\n CREATE TABLE IF NOT EXISTS reaction_role(\n id bigint primary key ,\n message_id bigint,\n emoji VARCHAR(256),\n guild bigint references guilds(id) on DELETE CASCADE,\n created timestamp,\n author bigint,\n role bigint,\n unique (message_id,emoji)\n )\n \"\"\"\n","repo_name":"LucasCoderT/iceteabot","sub_path":"database/models/reaction_role.py","file_name":"reaction_role.py","file_ext":"py","file_size_in_byte":1024,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"31174357580","text":"from django.contrib import admin\nfrom django.urls import path\nfrom . import views\n\nurlpatterns = [\n path('', views.index, name=''),\n path('login', views.ulogin, name='login'),\n path('register', views.uregister, name='register'),\n path('dashboard', views.udashbaord, name='dashboard'),\n path('add-consignment', views.addconsign, name='add-consignment'),\n path('logout', views.ulogout, name='logout'),\n\n path('payment-success//', views.paystatus, name=\"payment-success\"),\n\n\n\n # Url for Vendor Operations and view\n\n path('vendor-login', views.vendorlogin, name='vendor-login'),\n path('vendor-register', views.vendorregister, name='vendor-register'),\n path('vendor', views.vendor, name='vendor'),\n path('add-vehicle', views.addvehicle, name='add-vehicle'),\n path('add-driver', views.add_driver, name='add-driver'),\n path('update-status//', views.update_status, name='update-status'),\n path('add-tvride', views.add_tvride, name='add-tvride'),\n\n\n\n]\n","repo_name":"Creative-Me-Sanket/SmartTransportManagementSystem","sub_path":"userapp/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":996,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"29262916116","text":"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import, division, print_function\n\nimport json\nimport logging\nimport random\n\nfrom django.conf import settings\n\nfrom libs.utils import get_hmac\nfrom projects.paths import get_project_repos_path\nfrom spawners.base import get_pod_volumes\nfrom spawners.project_spawner import ProjectSpawner\nfrom spawners.templates import constants\nfrom spawners.templates import deployments\nfrom spawners.templates import pods\nfrom spawners.templates import services\nfrom spawners.templates import ingresses\n\nlogger = logging.getLogger('polyaxon.spawners.notebook')\n\n\nclass NotebookSpawner(ProjectSpawner):\n NOTEBOOK_JOB_NAME = 'notebook'\n PORT = 8888\n\n def get_notebook_url(self):\n return self._get_service_url(self.NOTEBOOK_JOB_NAME)\n\n def get_notebook_token(self):\n return get_hmac(settings.APP_LABELS_NOTEBOOK, self.project_uuid)\n\n @staticmethod\n def get_notebook_code_volume():\n volume = pods.get_volume(volume=constants.REPOS_VOLUME,\n claim_name=settings.REPOS_CLAIM_NAME,\n volume_mount=settings.REPOS_ROOT)\n\n volume_mount = pods.get_volume_mount(volume=constants.REPOS_VOLUME,\n volume_mount=settings.REPOS_ROOT)\n return volume, volume_mount\n\n def request_notebook_port(self):\n if not self._use_ingress():\n return self.PORT\n\n labels = 'app={},role={}'.format(settings.APP_LABELS_NOTEBOOK,\n settings.ROLE_LABELS_DASHBOARD)\n ports = [service.spec.ports[0].port for service in self.list_services(labels)]\n port = random.randint(*settings.NOTEBOOK_PORT_RANGE)\n while port in ports:\n port = random.randint(*settings.NOTEBOOK_PORT_RANGE)\n return port\n\n def start_notebook(self, image, resources=None):\n ports = [self.request_notebook_port()]\n target_ports = [self.PORT]\n volumes, volume_mounts = get_pod_volumes()\n code_volume, code_volume_mount = self.get_notebook_code_volume()\n volumes.append(code_volume)\n volume_mounts.append(code_volume_mount)\n deployment_name = constants.DEPLOYMENT_NAME.format(\n project_uuid=self.project_uuid, name=self.NOTEBOOK_JOB_NAME)\n notebook_token = self.get_notebook_token()\n notebook_url = self._get_proxy_url(\n namespace=self.namespace,\n job_name=self.NOTEBOOK_JOB_NAME,\n deployment_name=deployment_name,\n port=ports[0])\n notebook_dir = get_project_repos_path(self.project_name)\n notebook_dir = '{}/{}'.format(notebook_dir, notebook_dir.split('/')[-1])\n deployment = deployments.get_deployment(\n namespace=self.namespace,\n app=settings.APP_LABELS_NOTEBOOK,\n name=self.NOTEBOOK_JOB_NAME,\n project_name=self.project_name,\n project_uuid=self.project_uuid,\n volume_mounts=volume_mounts,\n volumes=volumes,\n image=image,\n command=[\"/bin/sh\", \"-c\"],\n args=[\n \"jupyter notebook \"\n \"--no-browser \"\n \"--port={port} \"\n \"--ip=0.0.0.0 \"\n \"--allow-root \"\n \"--NotebookApp.token={token} \"\n \"--NotebookApp.trust_xheaders=True \"\n \"--NotebookApp.base_url={base_url} \"\n \"--NotebookApp.notebook_dir={notebook_dir} \".format(\n port=self.PORT,\n token=notebook_token,\n base_url=notebook_url,\n notebook_dir=notebook_dir)],\n ports=target_ports,\n container_name=settings.CONTAINER_NAME_PLUGIN_JOB,\n resources=resources,\n role=settings.ROLE_LABELS_DASHBOARD,\n type=settings.TYPE_LABELS_EXPERIMENT)\n deployment_labels = deployments.get_labels(app=settings.APP_LABELS_NOTEBOOK,\n project_name=self.project_name,\n project_uuid=self.project_uuid,\n role=settings.ROLE_LABELS_DASHBOARD,\n type=settings.TYPE_LABELS_EXPERIMENT)\n\n self.create_or_update_deployment(name=deployment_name, data=deployment)\n service = services.get_service(\n namespace=self.namespace,\n name=deployment_name,\n labels=deployment_labels,\n ports=ports,\n target_ports=target_ports,\n service_type=self._get_service_type())\n\n self.create_or_update_service(name=deployment_name, data=service)\n\n if self._use_ingress():\n annotations = json.loads(settings.K8S_INGRESS_ANNOTATIONS)\n paths = [{\n 'path': '/notebook/{}'.format(self.project_name.replace('.', '/')),\n 'backend': {\n 'serviceName': deployment_name,\n 'servicePort': ports[0]\n }\n }]\n ingress = ingresses.get_ingress(namespace=self.namespace,\n name=deployment_name,\n labels=deployment_labels,\n annotations=annotations,\n paths=paths)\n self.create_or_update_ingress(name=deployment_name, data=ingress)\n\n def stop_notebook(self):\n deployment_name = constants.DEPLOYMENT_NAME.format(project_uuid=self.project_uuid,\n name=self.NOTEBOOK_JOB_NAME)\n self.delete_deployment(name=deployment_name)\n self.delete_service(name=deployment_name)\n if self._use_ingress():\n self.delete_ingress(name=deployment_name)\n","repo_name":"MiguelPeralvo/polyaxon","sub_path":"polyaxon/spawners/notebook_spawner.py","file_name":"notebook_spawner.py","file_ext":"py","file_size_in_byte":5952,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"52"} +{"seq_id":"44012901654","text":"# -*- coding:utf-8 -*-\n\nimport pika\nimport time\n\nusername = \"faith\"\npwd = \"qq2921481\"\nuser_pwd = pika.PlainCredentials(username, pwd)\n\nconnection = pika.BlockingConnection(pika.ConnectionParameters(host='172.16.54.130', credentials=user_pwd))\nchannel = connection.channel()\n\n# 声明 queue，因为不确定生产者先执行还是消费者先执行\nchannel.queue_declare(queue='hello', durable=True)\n\n\ndef callback(ch, method, properties, body):\n \"\"\" 回调函数，有点像事件驱动模型 \"\"\"\n print(ch, method, properties) # ch -》 channel\n time.sleep(10)\n print(\"收到消息\", body)\n ch.basic_ack(delivery_tag=method.delivery_tag)\n\n\n#\nchannel.basic_qos(prefetch_count=1) # 只要有一个队列在等待就不给你发了\n\nchannel.basic_consume(callback,\n queue='hello') # 不确认no_ack=True，默认是确认的no_ack=False，默认客户端不确认，就不删队列，保证消息被完整消费,全部挂了重启还会重新消费未消费完毕的队列\n\nchannel.start_consuming()\n\nprint(\"生产者等待消息中，阻塞\") # 默认是轮询机制，开启多个server 轮询接受\n\n\"\"\" \n rabbitmqctl stop \n rabbitmq-server start & \n rabbitmqctl list_queues\n rabbitmq-plugins enable rabbitmq_management # 开启web管理界面 \n Exchange 定义的时候是有类型的，以决定到底是哪些Queue符合条件，可以接受信息\n fanout 所有bind到此exchange的queue都可以接收消息\n direct 通过routingkey 和 exchange 决定的哪个唯一的queue可以接受消息\n topic：所有符合routingkey（此时可以是一个表达式）的routeKey 所bind得queue可以接受到消息\n headers： 通过headers决定发给谁\n\n\"\"\"\n","repo_name":"qq453388937/calc_mmd","sub_path":"rbmq/02_rb_server_consumer.py","file_name":"02_rb_server_consumer.py","file_ext":"py","file_size_in_byte":1750,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"70360301286","text":"import time\nimport math\nimport random\nfrom . import synthizer\nsynthizer.initialize()\n#Sound buffer class, for handling synthizer sound buffers.\nclass sound_buffer(object):\n\tdef __init__(self,filename,buffer):\n\t\tself.filename=filename\n\t\tself.buffer=buffer\n\n\tdef destroy(self):\n\t\tself.buffer.destroy()\n\n#Sound buffer manager class, for passing already loaded sound buffers if they exist, else creating new ones.\nclass sound_buffer_manager(object):\n\tdef __init__(self):\n\t\tself.buffers=[]\n\n\tdef buffer(self,filename):\n\t\tfor i in self.buffers:\n\t\t\tif i.filename==filename:\n#Our sound is already loaded into a buffer, so return it.\n\t\t\t\treturn i.buffer\n#Our sound is not loaded, so load it, add the buffer to the buffers list and return it.\n\t\ttmp=synthizer.Buffer.from_file(filename)\n\t\tself.buffers.append(sound_buffer(filename,tmp))\n\t\treturn tmp\n\n\tdef destroy(self,buffer):\n#Not used anywhere yet, todo.\n\t\tbuffer.destroy()\n\t\tself.buffers.remove(self)\n\ngsbm=sound_buffer_manager()\n#The actual sound3D class.\nclass sound3d(object):\n\tdef __init__(self, type,context):\n\t\tself.context=context\n\t\tself.type=type\n\t\tself.vol=0\n\t\tself.handle=0\n\t\tself.paused=False\n\t\tself.filename=\"\"\n\t\tself.buffer=None\n\t\tself.source=None\n\t\tself.generator=None\n\t\tself.length=None\n\n\tdef load(self, filename):\n\t\tif self.handle!=None: self.close()\n\t\tif isinstance(filename, str): # Asume path on disk.\n\t\t\tself.generator=synthizer.BufferGenerator(self.context)\n\t\t\tself.buffer=gsbm.buffer(filename)\n\t\t\tself.length=self.buffer.get_length_in_seconds()\n\t\t\tself.generator.buffer.value=self.buffer\n\t\t\tif self.type==\"3d\":\n\t\t\t\tself.source = synthizer.Source3D(self.context)\n\t\t\telif self.type==\"direct\":\n\t\t\t\tself.source = synthizer.DirectSource(self.context)\n\t\t\telif self.type==\"panned\":\n\t\t\t\tself.source = synthizer.PannedSource(self.context)\n\t\tif self.is_active:\n\t\t\tself.filename=filename\n\t\t\treturn True\n\t\treturn False\n\n\tdef close(self):\n\t\tif not self.is_active():\n\t\t\treturn False\n\t\tself.source.remove_generator(self.generator)\n\t\tself.source.destroy()\n\t\tself.generator.destroy()\n\t\tself.source=None\n\t\tself.buffer=None\n\t\tself.generator=None\n\t\tself.filename=\"\"\n\n\tdef play(self):\n\t\tif not self.is_active():\n\t\t\treturn False\n\t\tself.generator.looping.value=False\n\t\tself.source.add_generator(self.generator)\n\t\tself.paused=False\n\t\tself.looping=False\n\t\treturn True\n\n\tdef play_looped(self):\n\t\tif not self.is_active():\n\t\t\treturn False\n\t\tself.generator.looping.value=True\n\t\tself.source.add_generator(self.generator)\n\t\tself.paused=False\n\t\tself.looping=True\n\t\treturn True\n\n\tdef play_wait(self):\n\t\tif not self.is_active():\n\t\t\treturn False\n\t\tself.generator.looping=False\n\t\tself.play()\n\t\twhile self.is_playing():\n\t\t\ttime.sleep(0.005)\n\t\treturn True\n\n\tdef is_playing(self):\n\t\treturn self.position<=self.length-0.005\n\n\tdef pause(self):\n\t\tif not self.is_active():\n\t\t\treturn False\n\t\tself.source.remove_generator(self.generator)\n\t\tself.paused=True\n\n\tdef stop(self):\n\t\tif not self.is_active():\n\t\t\treturn False\n\t\tself.source.remove_generator(self.generator)\n\t\tself.generator.playback_position.value=0\n\t\tself.paused=False\n\n\tdef get_position(self):\n\t\tif not self.is_active():\n\t\t\treturn -1\n\t\treturn self.generator.playback_position\n\n\tdef set_position(self, position):\n\t\tif not self.is_active():\n\t\t\treturn False\n\t\tself.generator.playback_position=position\n\t\treturn True\n\n\tdef get_volume(self):\n\t\tif not self.is_active():\n\t\t\treturn 0\n\t\treturn self.vol\n\n\tdef set_volume(self, volume):\n\t\tif not self.is_active():\n\t\t\treturn False\n\t\tif volume>0: volume=0\n\t\tself.vol=volume\n#using formula from the example code to convert to DB\n\t\tself.source.gain=10**(volume/20)\n\n\tdef get_pitch(self):\n\t\tif not self.is_active():\n\t\t\treturn 100\n\t\treturn self.generator.pitch_bend*100\n\n\tdef set_pitch(self, pitch):\n\t\tif not self.is_active():\n\t\t\treturn False\n\t\tfreq=(float(pitch)/100)\n\t\tif freq>10: freq=10\n\t\tif freq<0.1: freq=0.1\n\t\tself.generator.pitch_bend=freq\n\n\tdef get_pan(self):\n\t\tif not self.is_active():\n\t\t\treturn 0\n\t\tif self.type==\"panned\":\n\t\t\treturn int(self.source.panning_scaler*100)\n\t\telse:\n\t\t\treturn 0\n\n\tdef set_pan(self, pan):\n\t\tif not self.is_active():\n\t\t\treturn False\n\t\tif self.type!=\"panned\":\n\t\t\treturn False\n\t\tself.source.panning_scalar=pan/100\n\n\tdef is_active(self):\n\t\tif self.source!=None:\n\t\t\ttry:\n\t\t\t\tpb=self.generator.playback_position.value\n\t\t\texcept: return False\n\t\t\treturn True\n\t\treturn False\n\n\tpan=property(get_pan, set_pan)\n\tpitch=property(get_pitch, set_pitch)\n\tvolume=property(get_volume, set_volume)\n\tposition=property(get_position, set_position)\n\tactive=property(is_active)\n\n#Fade a sound.\n\tdef fade(self,dest_volume, time_per_fade):\n\t\twhile self.volume!=dest_volume:\n\t\t\tif self.volumedest_volume: self.volume=self.volume-1\n\t\t\ttime.sleep(time_per_fade/1000)\n\t\tself.stop()\n\t\treturn True\n\ncontext = synthizer.Context()\nreverb = synthizer.GlobalFdnReverb(context)","repo_name":"Brynify/Unspoken","sub_path":"addon/globalPlugins/Unspoken/sound.py","file_name":"sound.py","file_ext":"py","file_size_in_byte":4847,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"52"} +{"seq_id":"26531503570","text":"# Run in terminal after setting up virtual envr - \n#python3 -m pip install schedule\n#python3 -m pip install requests\n\nimport schedule # pip3 install schedule\nimport requests # pip3 install request\nimport time\nimport json\n\n#having issues importing schedule and requests\n # Fixed, only had to run 'pip3 install request' in terminal\n\ndef pull_currency_data(site_url, apikey, file_name):\n \"\"\"\n :param site_url: URL to which requests get should hit\n :param apikey: Key from your account\n :param file_name: location and file name where output should be saved/appended\n \"\"\"\n global data\n data = requests.get(url=site_url+apikey)\n file = open(file_name, \"a\")\n currency = json.loads(data.content)\n output = data.headers['Date'] + '|' + '1' + '|' + str(currency['USD']) + '|' + str(currency['JPY']) + '|' + \\\n str(currency['EUR']) + '|' + str(currency['NGN']) + '\\n'\n file.write(output)\n file.close()\n\nkey = '2447285030a725a86d4ef48ea1336edb9c67189caa0750fb8ab4697799861dc8'\nurl = 'https://min-api.cryptocompare.com/data/price?fsym=BTC&tsyms=USD,JPY,EUR,NGN&api_key='\nfile_name = './currency_extract.csv'\n\n# Refresh rate is every 60seconds\nschedule.every(60).seconds.do(pull_currency_data, url, key, file_name)\n\nwhile True:\n schedule.run_pending()\n time.sleep(1)","repo_name":"KushimoKola/Python_Ingestion","sub_path":"exchange_data.py","file_name":"exchange_data.py","file_ext":"py","file_size_in_byte":1307,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"36993524112","text":"\n# Dnešní projekty jsou součástí projektu 1D Piškvorky. Dělej je jeden po druhém.\n# 0. Rozděl 1D Piškvorky na čtyři moduly:\n# • ai.py, kde bude funkce tah_pocitace,\n# • piskvorky.py, kde budou ostatní funkce,\n# • hra.py, kde bude import a volání hlavní funkce z piskvorky.py (a nic jiného),\n# • test_piskvorky.py, kde budou testy.\n# Připomínám: Až to bude fungovat, dej to do Gitu! A kdybys něco nedopatřením rozbila, git diff HEAD ukáže změny od poslední\n# revize.\n# 1. Napiš aspoň dva testy na každou funkci z Piškvorek, ke které testy napsat umíš.\n# Testy pravděpodobně neumíš napsat na funkci input a všechny funkce, které ji (byť nepřímo) volají. A taky asi neumíš otestovat\n# účinek funkce print.\n# 2. Zkus program přepsat t\n# from random import randrange\n\n\n#pocitac má hraje s o, hráč s x\nfrom ai import tah_pocitace\n \ndef tah_hrace(pole, cislo_policka):\n \"Vrátí herní pole se symbolem x umístěným na danou pozici podle zadání hráče\"\n return pole[:(cislo_policka - 1)] + 'x' + pole[(cislo_policka):]\n\ndef zadej_pole(pole):\n \"Hráč zadá políčko, na kterém chce hrát\"\n while True:\n vstup_uzivatele = input(\"Zadej číslo pole v rozmezí od 1 do 20: \")\n try:\n cislo_policka = int(vstup_uzivatele)\n except ValueError:\n print(\"Nejedná se o číslo ve správném formátu\")\n continue\n \n if cislo_policka not in range(1, 21, 1):\n print('Nejedná se o číslo v rozmezí 1 - 20')\n continue\n elif pole[cislo_policka - 1] == 'o' or pole[cislo_policka - 1] == 'x':\n print('Na tomto políčku se nemůže hrát.')\n continue\n else:\n break\n \n return cislo_policka\n \ndef stav_hry(pole, hrac):\n \"Vrátí True, pokud jsou tři stejné znaky za sebou a False, pokud ne\"\n # stačí mi, když kontroluji do třetího znaku u konce\n for i in range(len(pole) - 2):\n if pole[i] == hrac and pole[i + 1] == hrac and pole[i + 2] == hrac:\n return True\n return False\n\ndef vyhodnot(pole):\n \"Vrátí, v jakém stavu je pole, jestli někdo vyhrál, zda hra skončila nebo stále pokračuje\"\n if stav_hry(pole, 'x'): # vyhrál hráč\n return 'x'\n elif stav_hry(pole, 'o'): #vyhrál počítač\n return 'o'\n elif '-' in pole: #hra pokračuje\n return '-'\n else:\n return '!' #došlo k remíze\n\ndef piskvorky1d():\n \"Vytvoří řetězec s herním polem a střídavě volá funkce tah_hrace a tah_pocitace, dokud někdo nevyhraje nebo nedojde k remíze\"\n pole = '--------------------'\n\n while True:\n\n pole = tah_hrace(pole, zadej_pole(pole))\n print(pole) \n if not vyhodnot(pole) == '-':\n break\n\n pole = tah_pocitace(pole)\n print(pole)\n if not vyhodnot(pole) == '-':\n break\n \n if vyhodnot(pole) == 'x':\n print(\"Vyhrál jsi!\")\n elif vyhodnot(pole) == 'o':\n print(\"Počítač vyhrál!\")\n else:\n print(\"Došlo k remíze!\")\n \n return None\n\n","repo_name":"EvaSedlarova/piskvorky","sub_path":"hra.py","file_name":"hra.py","file_ext":"py","file_size_in_byte":3146,"program_lang":"python","lang":"cs","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"14475991412","text":"import os\r\nimport sqlite3 as sl\r\nimport sys\r\nimport time\r\nfrom datetime import date\r\nfrom flask import Flask, flash, request, redirect, url_for, session, render_template\r\nfrom werkzeug.utils import secure_filename\r\nfrom modules.dummies import generate_dummypage\r\nfrom modules.database import *\r\nfrom modules.dummies import *\r\nfrom modules.constants import *\r\nfrom modules.likes import *\r\nimport bcrypt\r\n\r\napp = Flask(__name__, template_folder='templates')\r\nCURRENT_ADDRESS = \"http://127.0.0.1:5000/\"\r\n\r\n\r\napp.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER\r\napp.config['UPLOAD_AVATAR_FOLDER'] = UPLOAD_AVATAR_FOLDER\r\napp.config['SECRET_KEY'] = 'a03cb5d6aa4399201f230dedcbbb3ed8bec0018d19db9521415b547a'\r\n\r\n\r\ndef allowed_file(filename):\r\n return '.' in filename and \\\r\n filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS\r\n\r\n\r\n# The page for meme uploading\r\n@app.route(\"/uploads\", methods=['GET', 'POST'])\r\n@app.route(\"/upload\", methods=['GET', 'POST'])\r\n@app.route(\"/post\", methods=['GET', 'POST'])\r\n@app.route(\"/new\", methods=['GET', 'POST'])\r\ndef upload_meme():\r\n if 'login' not in session:\r\n return redirect(url_for('login_user'))\r\n if not is_user_active():\r\n log_out()\r\n return redirect(url_for('login_user'))\r\n if request.method == 'POST':\r\n # check if the post request has the file part\r\n if 'file' in request.files:\r\n username = session['login']\r\n file = request.files['file']\r\n if file.filename != '' and allowed_file(file.filename):\r\n filename = str(date.today()) + \"_time_\" + str(time.time()) + '.' + file.filename.rsplit('.', 1)[-1]\r\n file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\r\n connection = create_connection(DATABASE_PATH)\r\n add_data(connection=connection, tablename='post',\r\n dataclass_element=Post(id=0, author_id=get_userid_byname(username, DATABASE_PATH),\r\n text=request.form.get('comment'), image=filename,\r\n date=str(date.today()), like=0, dislike=0))\r\n return redirect(url_for('show_feed'))\r\n avatar = 'images/avatars/' + get_user_avatar_bId(session['id'], DATABASE_PATH)\r\n return render_template('upload.html', avatar=avatar)\r\n\r\n\r\n# The feed page\r\n@app.route('/')\r\n@app.route(\"/feed\", methods=['GET', 'POST'])\r\n@app.route(\"/index\", methods=['GET', 'POST'])\r\n@app.route(\"/main\", methods=['GET', 'POST'])\r\ndef show_feed(page=1):\r\n if 'login' not in session:\r\n return redirect(url_for('login_user'))\r\n if not is_user_active():\r\n log_out()\r\n return redirect(url_for('login_user'))\r\n if request.method == \"POST\":\r\n if 'delete' in request.form.keys():\r\n id = request.form['id']\r\n author_id = list(get_authorid_by_post(create_connection(DATABASE_PATH), id))[0][0]\r\n if session['admin'] >= 1 or session['id'] == author_id:\r\n delete_post_bID(id, create_connection(DATABASE_PATH), UPLOAD_FOLDER)\r\n return redirect(url_for('show_feed'))\r\n else:\r\n return render_template(\"403.html\")\r\n if 'dislike' in request.form.keys():\r\n id = request.form['id']\r\n import_history(id, session['id'], 2)\r\n if 'like' in request.form.keys():\r\n id = request.form['id']\r\n import_history(id, session['id'], 1)\r\n if request.method == \"GET\" and request.args.get('page'):\r\n page = int(request.args.get('page'))\r\n dataposts = list(get_all_tabledata(create_connection(DATABASE_PATH), 'Post'))\r\n pages, limit = calc_pages_and_limit(dataposts, page)\r\n posts = generate_posts(dataposts[::-1], page, limit)\r\n avatar = 'images/avatars/' + get_user_avatar_bId(session['id'], DATABASE_PATH)\r\n return render_template(\"index.html\", posts=posts, pages=pages, avatar=avatar)\r\n\r\n\r\ndef process_useraction(action, nickname):\r\n if action == 'Delete':\r\n delete_user(create_connection(DATABASE_PATH), nickname)\r\n elif action == 'Make admin':\r\n make_admin(create_connection(DATABASE_PATH), nickname)\r\n elif action == 'Make moderator':\r\n make_moderator(create_connection(DATABASE_PATH), nickname)\r\n elif action == 'Make user':\r\n make_user(create_connection(DATABASE_PATH), nickname)\r\n elif action == 'Ban':\r\n ban_user()\r\n else:\r\n return render_template('500.html')\r\n\r\n\r\n@app.route('/admin', methods=['GET', 'POST'])\r\n@app.route('/adminpannel', methods=['GET', 'POST'])\r\n@app.route('/adminpanel', methods=['GET', 'POST'])\r\n@app.route('/moderator', methods=['GET', 'POST'])\r\n@app.route('/moder', methods=['GET', 'POST'])\r\ndef admin_page():\r\n if 'login' not in session:\r\n return redirect(url_for('login_user'))\r\n if not is_user_active():\r\n log_out()\r\n return redirect(url_for('login_user'))\r\n if int(list(get_admin_status_bId(session['id'], DATABASE_PATH))[0][0]) == 0:\r\n randomizer = random.randint(1,3)\r\n if randomizer == 1:\r\n return render_template(\"notadmin_page.html\")\r\n return render_template(\"403.html\")\r\n return redirect(url_for('user_page'))\r\n \r\n\r\n@app.route('/me', methods=['GET', 'POST'])\r\n@app.route('/userpage', methods=['GET', 'POST'])\r\n@app.route('/user', methods=['GET', 'POST'])\r\ndef user_page():\r\n if 'login' not in session:\r\n return redirect(url_for('login_user'))\r\n if not is_user_active():\r\n log_out()\r\n return redirect(url_for('login_user'))\r\n userid = session['id']\r\n page = 1\r\n if request.method == \"GET\":\r\n if 'id' in request.args.keys():\r\n userid = int(request.args['id'])\r\n elif request.args.get('page'):\r\n page = int(request.args.get('page'))\r\n if request.method == 'POST':\r\n if 'exit' in request.form.keys():\r\n log_out()\r\n return redirect(url_for('login_user'))\r\n else:\r\n action = request.form['action']\r\n nickname = request.form['nickname']\r\n process_useraction(action, nickname)\r\n dataposts = list(get_author_posts(create_connection(DATABASE_PATH), userid))\r\n pages, limit = calc_pages_and_limit(dataposts, page)\r\n posts = generate_posts(dataposts[::-1], page, limit)\r\n avatar = 'images/avatars/' + get_user_avatar_bId(userid, DATABASE_PATH)\r\n userdata = {'id': userid, 'login': get_username_bId(userid, DATABASE_PATH),\r\n 'admin': int(list(get_admin_status_bId(userid, DATABASE_PATH))[0][0])}\r\n return render_template('userpage.html', posts=posts, pages=pages, avatar=avatar, userdata=userdata)\r\n\r\ndef is_user_active():\r\n \"\"\"Check if user exists in database\"\"\"\r\n con = create_connection(DATABASE_PATH)\r\n res = con.execute(f\"SELECT id,login FROM Users WHERE id={session['id']} and login='{session['login']}'\")\r\n return len(list(res)) == 1\r\n\r\ndef log_out():\r\n \"\"\"Clear a user session\"\"\"\r\n keys = list(session.keys())[:]\r\n for key in keys:\r\n session.pop(key)\r\n\r\ndef generate_posts(dataposts, page, limit):\r\n \"\"\"Returns array of dictionaries for posts\"\"\"\r\n posts = []\r\n # Generate posts\r\n for i in range((page - 1) * PAGES_POSTS, limit):\r\n author_name = get_username_bId(dataposts[i][1], DATABASE_PATH)\r\n avatar = get_user_avatar_bId(dataposts[i][1], DATABASE_PATH)\r\n reactions = list(get_reaction_bId(dataposts[i][0], DATABASE_PATH))\r\n post = {'id': dataposts[i][0], 'avatar': AVATAR_FOLDER + avatar, 'author_id': dataposts[i][1],\r\n 'author_name': author_name, 'date': dataposts[i][4], 'comment': dataposts[i][2],\r\n 'image': MEMES_FOLDER + dataposts[i][3], 'likes': reactions[0][0], 'dislikes': reactions[0][1]\r\n }\r\n posts.append(post)\r\n return posts\r\n\r\ndef calc_pages_and_limit(dataposts, page):\r\n \"\"\"Returns max pages and limit for current page\"\"\"\r\n pages = len(dataposts) // PAGES_POSTS\r\n if len(dataposts) % PAGES_POSTS != 0:\r\n pages += 1\r\n limit = page * PAGES_POSTS\r\n if limit > len(dataposts):\r\n limit = len(dataposts)\r\n return pages, limit\r\n\r\n\r\n@app.errorhandler(404)\r\ndef page_notexist(e):\r\n return render_template('404.html')\r\n\r\n@app.errorhandler(403)\r\ndef page_access_denied(e):\r\n return render_template('403.html')\r\n\r\n@app.errorhandler(500)\r\ndef internal_server_error(e):\r\n return render_template('500.html')\r\n\r\n@app.errorhandler(400)\r\ndef bad_request(e):\r\n return render_template('400.html')\r\n\r\n\r\n\r\n# Register page\r\n@app.route(\"/register\", methods=['POST', 'GET'])\r\n@app.route(\"/signup\", methods=['POST', 'GET'])\r\ndef register_user():\r\n if 'login' in session:\r\n return render_template(\"welcome_page.html\")\r\n if request.method == 'POST':\r\n login = request.form['login']\r\n password = bcrypt.hashpw(request.form['password'].encode(), bcrypt.gensalt())\r\n # email = request.form['email']\r\n file = ''\r\n if 'avatar' in request.files:\r\n file = request.files['avatar']\r\n filename = 'ava.jpg'\r\n path = ''\r\n if file == '':\r\n path = DEFAULT_AVATAR\r\n elif allowed_file(file.filename):\r\n filename = secure_filename(login + '_' + file.filename)\r\n path = os.path.join(app.config['UPLOAD_AVATAR_FOLDER'], filename)\r\n file.save(path)\r\n con = sl.connect(DATABASE_PATH)\r\n email = f\"user_email_{login}{path[len(path) - 4:]}@mail.ru\"\r\n print(filename)\r\n val = add_data(connection=con, tablename='users', dataclass_element=User(0, 0, login, password, filename, email),\r\n individual_fields=USERS_INDIVIDUAL_FIELDS)\r\n if val == 100:\r\n return render_template('cant_register.html')\r\n res = list(con.execute(f\"SELECT id,admin FROM Users WHERE login='{login}'\"))[0]\r\n session['id'] = res[0]\r\n session['login'] = login\r\n session['admin'] = res[1]\r\n return redirect(url_for('show_feed'))\r\n return render_template('register.html')\r\n\r\n\r\n# Login page\r\n@app.route(\"/login\", methods=['POST', 'GET'])\r\n@app.route(\"/auth\", methods=['POST', 'GET'])\r\n@app.route(\"/authorize\", methods=['POST', 'GET'])\r\ndef login_user():\r\n if 'login' in session:\r\n return render_template('welcome_page.html')\r\n if request.method == 'POST':\r\n login = request.form.get('login')\r\n password = request.form.get('password').encode()\r\n con = sl.connect(DATABASE_PATH)\r\n sql = f\"SELECT password,id, admin FROM users WHERE `login`='{login}'\"\r\n result = list(con.execute(sql))\r\n if len(result) == 0 or password != result[0][0]:\r\n return render_template(\"cant_login.html\")\r\n print(result[0][0])\r\n if bcrypt.checkpw(password, result[0][0]):\r\n session['login'] = login\r\n session['id'] = result[0][1]\r\n session['admin'] = result[0][2]\r\n return render_template(\"welcome_page.html\")\r\n return render_template('auth.html')\r\n\r\n\r\n# Programm run\r\n#if __name__ == '__main__':\r\n# app.run(host=\"0.0.0.0\", debug=True)\r\n","repo_name":"mrglaster/flask-retromemes-app","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":11180,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"52"} +{"seq_id":"72947300644","text":"'''\nCreated on Apr 10, 2014\n\n@author: Harvey\n'''\nimport pulp\nimport openpyxl\n\ndef optionmargin(exchange, fwd, strikes, prem, otype):\n if otype == 'c':\n vv = [max(k-fwd,0) for k in strikes];\n else:\n vv = [max(fwd-k,0) for k in strikes];\n \n if exchange == 'CFFE':\n marginRatio = 0.15;\n minRatio = 0.667; \n margin=[p+max(marginRatio*fwd-v, minRatio*marginRatio*fwd) for (p, v) in zip(prem, vv)];\n \n return margin; \n\ndef strat2pos(init_strat): \n uc = init_strat['callsprd'];\n up = init_strat['putsprd'];\n vc = init_strat['callfly'];\n vp = init_strat['putfly'];\n wf = init_strat['callput'];\n nSize = len(wf);\n call = [0.0]*nSize;\n put = [0.0]*nSize;\n call[0] = uc[0] + vc[1] + wf[0];\n call[nSize-1] =-uc[nSize-2] + vc[nSize-2] + wf[nSize-1];\n put[0] =-up[0] + vp[1] - wf[0];\n put[nSize-1] = up[nSize-2] + vp[nSize-2] - wf[nSize-1];\n \n for i in range(1, nSize-1):\n call[i] = uc[i]-uc[i-1] + wf[i] + vc[i-1]-2*vc[i]+vc[i+1];\n put[i] =-up[i]+up[i-1] - wf[i] + vp[i-1]-2*vp[i]+vp[i+1]; \n return {'call':call, 'put':put}\n\ndef arboptimizer( mktdata, exchange, init_strat, capital_limit, fwdmargin ):\n fwdBid = mktdata['fwdbid'];\n fwdAsk = mktdata['fwdask'];\n fwdMid = (fwdBid + fwdAsk)/2.0;\n strikes = mktdata['strike'];\n callBids = mktdata['callbid'];\n callAsks = mktdata['callask'];\n callMids = [ (a + b)/2 for (a,b) in zip(callBids, callAsks)];\n putBids = mktdata['putbid'];\n putAsks = mktdata['putask'];\n putMids = [ (a + b)/2 for (a,b) in zip(putBids, putAsks)];\n nSize = len(strikes);\n \n uc0 = init_strat['callsprd'];\n up0 = init_strat['putsprd'];\n vc0 = init_strat['callfly'];\n vp0 = init_strat['putfly'];\n wf0 = init_strat['callput']; \n\n margin_c = optionmargin(exchange, fwdMid, strikes, callMids, 'c');\n margin_p = optionmargin(exchange, fwdMid, strikes, putMids, 'p');\n prob = pulp.LpProblem(\"ArbAllocator\",pulp.LpMaximize);\n\n uc = pulp.LpVariable.dict('uc_%s', range(nSize-1));\n up = pulp.LpVariable.dict('up_%s', range(nSize-1));\n vc = pulp.LpVariable.dict('vc_%s', range(nSize));\n vp = pulp.LpVariable.dict('vp_%s', range(nSize));\n wf = pulp.LpVariable.dict('wf_%s', range(nSize)); \n xuc = pulp.LpVariable.dict('xuc_%s', range(nSize-1), lowBound=0.0);\n xup = pulp.LpVariable.dict('xup_%s', range(nSize-1), lowBound=0.0); \n yc = pulp.LpVariable.dict('yc_%s', range(nSize), lowBound=0.0);\n yp = pulp.LpVariable.dict('yp_%s', range(nSize), lowBound=0.0);\n yf = pulp.LpVariable('yf', lowBound = 0.0, cat='Continuous'); \n zc = pulp.LpVariable.dict('zc_%s', range(nSize), lowBound=0.0);\n zp = pulp.LpVariable.dict('zp_%s', range(nSize), lowBound=0.0);\n zf = pulp.LpVariable('zf', lowBound = 0.0, cat='Continuous');\n \n for i in vc.viewkeys():\n vc[i].lowBound = -vc0[i];\n \n for i in vp.viewkeys():\n vp[i].lowBound = -vp0[i];\n\n prob += sum([(uc0[i]+uc[i])*(callMids[i+1]-callMids[i]+strikes[i+1]-strikes[i]) \\\n -(strikes[i+1]-strikes[i])*xuc[i] for i in range(nSize-1)]) \\\n + sum([(up0[i]+up[i])*(putMids[i]-putMids[i+1]+strikes[i+1]-strikes[i]) \\\n -(strikes[i+1]-strikes[i])*xup[i] for i in range(nSize-1)]) \\\n + sum([(vc0[i]+vc[i])*(2*callMids[i]-callMids[i-1]-callMids[i+1]) for i in range(1, nSize-1)]) \\\n + sum([(vp0[i]+vp[i])*(2*putMids[i] - putMids[i-1] - putMids[i+1]) for i in range(1, nSize-1)]) \\\n + sum([(wf0[i]+wf[i])*(putMids[i]-callMids[i]+fwdMid-strikes[i]) for i in range(nSize)]) \\\n - sum([yc[i]*(callAsks[i]-callMids[i]) for i in range(nSize)]) \\\n - sum([yp[i]*(putAsks[i]-putMids[i]) for i in range(nSize)]) \\\n - yf*(fwdAsk-fwdMid); \n \n prob += sum([zc[i]*margin_c[i]+zp[i]*margin_p[i] for i in range(nSize)])+ \\\n sum([(uc0[i]+uc[i])*(callMids[i]-callMids[i+1]) for i in range(nSize-1)]) + \\\n sum([(up0[i]+up[i])*(putMids[i+1]-putMids[i]) for i in range(nSize-1)]) + \\\n sum([(vc0[i]+vc[i])*(callMids[i-1]+callMids[i+1]-2*callMids[i]) for i in range(1, nSize-1)]) + \\\n sum([(vp0[i]+vp[i])*(putMids[i-1] +putMids[i+1] -2*putMids[i] ) for i in range(1, nSize-1)]) + \\\n sum([(wf0[i]+wf[i])*(callMids[i] - putMids[i]) for i in range(nSize)]) + \\\n + zf*fwdmargin*fwdMid <=capital_limit;\n \n prob += zf - sum([wf[i]+wf0[i] for i in range(nSize)]) >= 0;\n prob += zf + sum([wf[i]+wf0[i] for i in range(nSize)]) >= 0;\n prob += yf - sum([wf[i] for i in range(nSize)]) >= 0;\n prob += yf + sum([wf[i] for i in range(nSize)]) >= 0;\n \n prob += (uc0[0]+uc[0])+ (vc0[1]+vc[1]) + (wf0[0]+wf[0]) + zc[0] >= 0;\n prob += -(up0[0]+up[0])+ (vp0[1]+vp[1]) - (wf0[0]+wf[0]) + zp[0] >= 0;\n prob += -uc0[nSize-2]+vc0[nSize-2]+wf0[nSize-1] - uc[nSize-2]+vc[nSize-2]+wf[nSize-1] + zc[nSize-1] >= 0;\n prob += up0[nSize-2]+vp0[nSize-2]-wf0[nSize-1] + up[nSize-2]+vp[nSize-2]-wf[nSize-1] + zp[nSize-1] >= 0;\n \n prob += yc[0]+uc[0]+vc[1]+wf[0] >= 0;\n prob += yc[0]-uc[0]-vc[1]-wf[0] >= 0;\n prob += yp[0]-up[0]+vp[1]-wf[0] >= 0;\n prob += yp[0]+up[0]-vp[1]+wf[0] >= 0;\n prob += yc[nSize-1]-uc[nSize-2]+vc[nSize-2]+wf[nSize-1] >= 0;\n prob += yc[nSize-1]+uc[nSize-2]-vc[nSize-2]-wf[nSize-1] >= 0;\n prob += yp[nSize-1]+up[nSize-2]+vp[nSize-2]-wf[nSize-1] >= 0;\n prob += yp[nSize-1]-up[nSize-2]-vp[nSize-2]+wf[nSize-1] >= 0;\n prob += vc[0] == 0.0;\n prob += vc[nSize-1] == 0.0;\n prob += vp[0] == 0.0;\n prob += vp[nSize-1] == 0.0;\n \n for i in range(nSize-1):\n prob += xuc[i] - uc0[i] - uc[i] >= 0\n prob += xup[i] - up0[i] - up[i] >= 0\n \n for i in range(1, nSize-1): \n prob += uc0[i]-uc0[i-1]+vc0[i-1]+vc0[i+1]-2*vc0[i]+wf0[i]+uc[i]-uc[i-1]+vc[i-1]+vc[i+1]-2*vc[i]+wf[i]+zc[i] >= 0; \n prob += up0[i-1]-up0[i]+vp0[i-1]+vp0[i+1]-2*vp0[i]-wf0[i]+up[i-1]-up[i]+vp[i-1]+vp[i+1]-2*vp[i]-wf[i]+zp[i] >= 0;\n prob += yc[i]+uc[i]-uc[i-1]+vc[i-1]+vc[i+1]-2*vc[i]+wf[i] >= 0;\n prob += yc[i]-uc[i]+uc[i-1]-vc[i-1]-vc[i+1]+2*vc[i]-wf[i] >= 0;\n prob += yp[i]+up[i-1]-up[i]+vp[i-1]+vp[i+1]-2*vp[i]-wf[i] >= 0;\n prob += yp[i]-up[i-1]+up[i]-vp[i-1]-vp[i+1]+2*vp[i]+wf[i] >= 0;\n\n prob.solve(); \n solutions = {};\n for v in prob.variables():\n solutions[v.name] = v.varValue\n \n callsprd=[ solutions['uc_'+str(i)] for i in range(nSize-1)] + [0];\n putsprd =[ solutions['up_'+str(i)] for i in range(nSize-1)] + [0];\n callput= [ solutions['wf_'+str(i)] for i in range(nSize)]; \n callfly =[ solutions['vc_'+str(i)] for i in range(nSize) ];\n putfly = [ solutions['vp_'+str(i)] for i in range(nSize) ]; \n res = {'callput': callput, \n 'callsprd':callsprd, \n 'putsprd': putsprd, \n 'callfly': callfly, \n 'putfly' : putfly, \n 'value' : prob.objective.value(), \n 'status': pulp.LpStatus[prob.status]};\n return res;\n \ndef arboptimizer2( mktdata, exchange, capital_limit, fwdmargin ):\n fwdBid = mktdata['fwdbid'];\n fwdAsk = mktdata['fwdask'];\n fwdMid = (fwdBid + fwdAsk)/2.0;\n strikes = mktdata['strike'];\n callBids = mktdata['callbid'];\n callAsks = mktdata['callask'];\n callMids = [ (a + b)/2 for (a,b) in zip(callBids, callAsks)];\n putBids = mktdata['putbid'];\n putAsks = mktdata['putask'];\n putMids = [ (a + b)/2 for (a,b) in zip(putBids, putAsks)];\n nSize = len(strikes);\n\n margin_c = optionmargin(exchange, fwdMid, strikes, callMids, 'c');\n margin_p = optionmargin(exchange, fwdMid, strikes, putMids, 'p');\n prob = pulp.LpProblem(\"ArbAllocator\",pulp.LpMaximize);\n\n wc = pulp.LpVariable.dict('wc_%s', range(nSize-1), lowBound=0.0);\n wp = pulp.LpVariable.dict('wp_%s', range(nSize-1), lowBound=0.0);\n uc = pulp.LpVariable.dict('uc_%s', range(nSize-1), lowBound=0.0);\n up = pulp.LpVariable.dict('up_%s', range(nSize-1), lowBound=0.0);\n vc = pulp.LpVariable.dict('vc_%s', range(nSize), lowBound=0.0);\n vp = pulp.LpVariable.dict('vp_%s', range(nSize), lowBound=0.0);\n rc = pulp.LpVariable.dict('rc_%s', range(nSize), lowBound=0.0);\n rp = pulp.LpVariable.dict('rp_%s', range(nSize), lowBound=0.0);\n zc = pulp.LpVariable.dict('zc_%s', range(nSize), lowBound=0.0);\n zp = pulp.LpVariable.dict('zp_%s', range(nSize), lowBound=0.0);\n zf = pulp.LpVariable('zf', lowBound = 0.0, cat='Continuous');\n\n prob += sum([wc[i]*(callBids[i+1]-callAsks[i]) for i in range(nSize-1)]) + \\\n sum([wp[i]*(putBids[i]-putAsks[i+1]) for i in range(nSize-1)]) + \\\n sum([uc[i]*(callBids[i]-callAsks[i+1]-strikes[i+1]+strikes[i]) for i in range(nSize-1)]) + \\\n sum([up[i]*(putBids[i+1]-putAsks[i]-strikes[i+1]+strikes[i]) for i in range(nSize-1)]) + \\\n sum([vc[i]*(2*callBids[i] - callAsks[i-1] - callAsks[i+1]) for i in range(1, nSize-1)]) + \\\n sum([vp[i]*(2*putBids[i] - putAsks[i-1] - putAsks[i+1]) for i in range(1, nSize-1)]) + \\\n sum([rc[i]*(putBids[i] - callAsks[i] + fwdBid - strikes[i]) for i in range(nSize)]) + \\\n sum([rp[i]*(callBids[i] - putAsks[i] - fwdAsk + strikes[i]) for i in range(nSize)])\n \n prob += sum([zc[i]*margin_c[i]+zp[i]*margin_p[i] for i in range(nSize)])+ \\\n sum([(wc[i]-uc[i])*(callMids[i]-callMids[i+1]) for i in range(nSize-1)]) + \\\n sum([(wp[i]-up[i])*(putMids[i+1]-putMids[i]) for i in range(nSize-1)]) + \\\n sum([vc[i]*(callMids[i-1]+callMids[i+1]-2*callMids[i]) for i in range(1, nSize-1)]) + \\\n sum([vp[i]*(putMids[i-1] +putMids[i+1] -2*putMids[i] ) for i in range(1, nSize-1)]) + \\\n sum([(rc[i]-rp[i])*(callMids[i] - putMids[i]) for i in range(nSize)]) + \\\n + zf*fwdmargin*fwdMid <=capital_limit;\n prob += sum([rp[i]-rc[i] for i in range(nSize)]) - zf <= 0;\n prob += sum([rc[i]-rp[i] for i in range(nSize)]) - zf <= 0;\n prob += wc[0]-uc[0]+vc[1]+rc[0]-rp[0] + zc[0] >= 0;\n prob += -wp[0]+up[0]+vp[1]+rp[0]-rc[0]+ zp[0] >= 0;\n prob += -wc[nSize-2]+uc[nSize-2]+vc[nSize-2]+rc[nSize-1]-rp[nSize-1] + zc[nSize-1] >= 0;\n prob += wp[nSize-2]-up[nSize-2]+vp[nSize-2]+rp[nSize-1]-rc[nSize-1]+ zp[nSize-1] >= 0;\n prob += vc[0] == 0.0;\n prob += vc[nSize-1] == 0.0;\n prob += vp[0] == 0.0;\n prob += vp[nSize-1] == 0.0;\n for i in range(1, nSize-1):\n prob += wc[i]-uc[i]-(wc[i-1]-uc[i-1])+vc[i-1]+vc[i+1]-2*vc[i]+rc[i]-rp[i]+zc[i] >= 0; \n prob += wp[i-1]-up[i-1]-(wp[i]-up[i])+vp[i-1]+vp[i+1]-2*vp[i]+rp[i]-rc[i]+zp[i] >= 0;\n\n prob.solve(); \n solutions = {};\n for v in prob.variables():\n solutions[v.name] = v.varValue\n \n callsprd=[ solutions['wc_'+str(i)]-solutions['uc_'+str(i)] for i in range(nSize-1)];\n putsprd =[ solutions['wp_'+str(i)]-solutions['up_'+str(i)] for i in range(nSize-1)];\n callput=[ solutions['rc_'+str(i)]-solutions['rp_'+str(i)] for i in range(nSize)]; \n callfly = [ solutions['vc_'+str(i)] for i in range(1,nSize-1) ];\n putfly = [ solutions['vp_'+str(i)] for i in range(1, nSize-1) ]; \n res = {'callput': callput, \n 'callsprd':callsprd, \n 'putsprd': putsprd, \n 'callfly': callfly, \n 'putfly' : putfly, \n 'value' : prob.objective.value(), \n 'status': pulp.LpStatus[prob.status]};\n return res;\n\nif __name__ == \"__main__\":\n dataFile = 'test.xlsx';\n sheetName = 'mktData';\n wb = openpyxl.load_workbook('test.xlsx', data_only=True);\n ws = wb[sheetName];\n \n mktRange = 'A6:E18';\n wr = ws.range(mktRange);\n mktdata = {};\n mktdata['fwdbid'] = float(ws.cell(row=2,column=0).value);\n mktdata['fwdask'] = float(ws.cell(row=2,column=1).value); \n for i in range(len(wr)):\n row = wr[i];\n for j in range(len(row)):\n colname = str(wr[0][j].value).lower();\n if i == 0:\n mktdata[colname] = [];\n else:\n mktdata[colname].append(float(wr[i][j].value)); \n\n# init_strat = {'callsprd':[0]*8,\n# 'putsprd':[0]*8,\n# 'callfly':[0]*8,\n# 'putfly':[0]*8,\n# 'callput':[0,0,0,0,0,-1500,0,0],\n# }\n init_strat = {};\n posRange = 'G6:K18';\n wr = ws.range(posRange); \n for i in range(len(wr)):\n row = wr[i];\n for j in range(len(row)):\n colname = str(wr[0][j].value).lower();\n if i == 0:\n init_strat[colname] = [];\n else:\n init_strat[colname].append(float(wr[i][j].value));\n \n exchange = 'CFFE';\n fwdmargin = 0.12;\n capital_limit = 1000000;\n\n res1 = arboptimizer( mktdata, exchange, init_strat, capital_limit, fwdmargin );\n #res2 = arboptimizer2( mktdata, exchange, capital_limit, fwdmargin );\n pos = strat2pos(res1);\n res1['call'] = pos['call'];\n res1['put'] = pos['put'];\n outfile = \"test_output.xlsx\";\n outRange = 'N6:T18';\n wr = ws.range(outRange); \n for i, col in enumerate(['callsprd','putsprd','callfly','putfly','callput','call','put']):\n wr[0][i].value = col;\n for j, pos in enumerate(res1[col]):\n wr[j+1][i].value = pos; \n \n wb.save(outfile); \n pass\n ","repo_name":"harvey1673/pyktrader","sub_path":"tools/arboptimizer.py","file_name":"arboptimizer.py","file_ext":"py","file_size_in_byte":13349,"program_lang":"python","lang":"en","doc_type":"code","stars":160,"dataset":"github-code","pt":"52"} +{"seq_id":"37743609267","text":"# 12. 1列目をcol1.txtに，2列目をcol2.txtに保存\n# 各行の1列目だけを抜き出したものをcol1.txtに，2列目だけを抜き出したものをcol2.txtとしてファイルに保存せよ．確認にはcutコマンドを用いよ．\n\npath_input = \"./popular-names.txt\"\n\ncol1 = \"./col1.txt\"\ncol2 = \"./col2.txt\"\n\nlist1 = []\nlist2 = []\n\nwith open(path_input) as f:\n list_input = f.readlines()\n\n# 1列目\n\nfor line in list_input:\n list_split1 = line.split(\"\\t\")\n list1.append(list_split1[0] + \"\\n\")\n\nwith open(col1, mode=\"w\") as f1:\n f1.writelines(list1)\n\n# 2列目\n\nfor line in list_input:\n list_split2 = line.split(\"\\t\")\n list2.append(list_split2[1] + \"\\n\")\n\nwith open(col2, mode=\"w\") as f2:\n f2.writelines(list2)\n\n# cut -f 1 popular-names.txt > col1_cut.txt\n# cut -f 2 popular-names.txt > col2_cut.txt\n","repo_name":"sky-y/nlp100","sub_path":"chap2/12.py","file_name":"12.py","file_ext":"py","file_size_in_byte":842,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"5041397542","text":"import data_handlers\nimport cnn_model\nimport torch\nimport torch.nn as nn\nfrom matplotlib import pyplot as plt\nimport numpy as np\nimport time\nfrom scipy.special import softmax\nimport os\nslide = 120\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\nmodel = torch.load(\"model.pt\").to(device)\nmodel.eval()\n\ndef find_bobber(img, slide = 120):\n result = np.zeros((((img.shape[0] - 150) // slide + 1), ((img.shape[1] - 150) // slide + 1)))\n for i in range(0, img.shape[0] - 150, slide):\n batch = np.zeros((((img.shape[1] - 150) // slide + 1), 3, 150, 150))\n for j in range(0, img.shape[1] - 150, slide):\n window = img[i : 150 + i, j : 150 + j]\n batch[j // slide, 0] = window[:,:,0]\n batch[j // slide, 1] = window[:,:,1]\n batch[j // slide, 2] = window[:,:,2]\n out = model(torch.Tensor(batch).float().to(device))\n temp = out.cpu().detach().numpy()\n result[i // slide] = softmax(temp, axis = 1)[:, 0]\n return result\n\ndef find_clicking_point(img, base_size = 25, starti = None, startj = None, endi = None, endj = None):\n if starti == None:\n starti, startj = 0,0\n endi, endj = img.shape[:2]\n if endi - starti < base_size and endj - startj < base_size:\n return starti, endi, startj, endj\n img2 = np.zeros(img.shape)\n nninput = np.empty((1, 3, 150, 150))\n while base_size < endi - starti or base_size < endj - startj:\n if endj - startj == endi - starti:\n midi = (endi + starti) // 2\n midj = (endj + startj) // 2\n tests = [(starti, midi, startj, endj), (midi, endi, startj, endj), (starti, endi, startj, midj), (starti, endi, midj, endj), ((starti + endi) // 4, (starti + endi) // 4 + (endi - starti) // 2, startj, endj)]\n elif endj - startj > endi - starti:\n width = endi - starti\n tests = [(starti, endi, startj, startj + width), (starti, endi, endj - width, endj), (starti, endi, (startj + endj) // 4, (startj + endj) // 4 + width)]\n else:\n width = endj - startj\n tests = [(starti, starti + width, startj, endj), (endi - width, endi, startj, endj), ((starti + endi) // 4, (starti + endi) // 4 + width, startj, endj)]\n grades = []\n for i1, i2, j1, j2 in tests:\n img2[i1:i2, j1:j2] = img[i1:i2, j1:j2]\n nninput[0, 0] = img2[:,:,0]\n nninput[0, 1] = img2[:,:,1]\n nninput[0, 2] = img2[:,:,2]\n out = model(torch.Tensor(nninput).float().to(device))\n temp = out.cpu().detach().numpy()[0]\n grades.append(softmax(temp)[0])\n img2[i1:i2, j1:j2] = 0\n starti, endi, startj, endj = tests[np.argmax(grades)]\n img2[starti:endi,startj:endj] = img[starti:endi,startj:endj]\n return (starti + endi) // 2, (startj + endj) // 2\n \n \n\ndef find_bobber_pos(img):\n result = find_bobber(img, 120)\n f = np.argmax(result)\n columns = ((img.shape[1] - 150) // slide + 1)\n i,j = f // columns, f % columns\n i1, j1 = find_clicking_point(img[i * 120 : i * 120 + 150, j * 120 : j * 120 + 150])\n return i * 120 + i1, j * 120 + j1\n\n \nif __name__ == \"__main__\":\n count = 0\n for i in os.listdir(\"images\"):\n count += 1\n img = plt.imread(\"images/{}\".format(i))[:,:,:3]\n plt.imshow(img)\n plt.show()\n result = find_bobber(img, 120)\n plt.imshow(result, cmap='gray')\n plt.show()\n f = np.argmax(result)\n columns = ((img.shape[1] - 150) // slide + 1)\n i,j = f // columns, f % columns\n find_clicking_point(img[i * 120 : i * 120 + 150, j * 120 : j * 120 + 150])\n #plt.imshow(img[i * 120 : i * 120 + 150, j * 120 : j * 120 + 150])\n #plt.show()\n\n \n","repo_name":"Dspil/fishing_bot","sub_path":"find_bobber.py","file_name":"find_bobber.py","file_ext":"py","file_size_in_byte":3769,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"38091732836","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.nn import init\n\nEPSILON = 1e-12\n\nclass BasicConv2d(nn.Module):\n\n def __init__(self, in_channels, out_channels, **kwargs):\n super(BasicConv2d, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)\n self.bn = nn.BatchNorm2d(out_channels, eps=0.001)\n\n def forward(self, x):\n x = self.conv(x)\n x = self.bn(x)\n return F.relu(x, inplace=True)\n\nclass Fusion(nn.Module):\n def __init__(self, feature_dim):\n super(Fusion, self).__init__()\n\n self.linear = nn.Linear(8*2048, feature_dim, bias=False)\n self.bn = nn.BatchNorm1d(feature_dim)\n self.pool = nn.AdaptiveMaxPool2d(1)\n self.M = 8\n self.attentions = BasicConv2d(2048, self.M, kernel_size=1)\n self.linear.weight.data.normal_(0, 0.001)\n init.normal_(self.bn.weight.data, 1.0, 0.02)\n init.constant_(self.bn.bias.data, 0.0)\n\n def forward(self, feat, feat2):\n feat2_att = self.attentions(feat2)\n\n B, C, H, W = feat.size()\n _, M, AH, AW = feat2_att.size()\n\n x = (torch.einsum('imjk,injk->imn', (feat2_att, feat)) / float(H * W)).view(B, -1)\n x = torch.sign(x) * torch.sqrt(torch.abs(x) + EPSILON)\n x = F.normalize(x, dim=-1)\n x = self.linear(x)\n x = self.bn(x)\n\n return x","repo_name":"bar371/ReFace","sub_path":"ReIDModules/AIM_CCReID/models/Fusion.py","file_name":"Fusion.py","file_ext":"py","file_size_in_byte":1403,"program_lang":"python","lang":"en","doc_type":"code","stars":44,"dataset":"github-code","pt":"52"} +{"seq_id":"38461408773","text":"#!/usr/bin/env python3\n\nimport numpy\n\nfrom qupy.ldpc.solve import parse, zeros2, span, dot2, row_reduce, array2\nfrom qupy.ldpc.css import CSSCode\n\n\ndef z_weld(acode, bcode, pairs):\n\n for c in [acode, bcode]:\n print(\"Lx:\")\n print(c.Lx)\n print(\"Lz:\")\n print(c.Lz)\n print(\"Hx:\")\n print(c.Hx)\n print(\"Hz:\")\n print(c.Hz)\n print(\"-------------------\")\n\n mx = acode.mx + bcode.mx\n #for (i, j) in pairs:\n assert len(set(pairs)) == len(pairs) # uniq\n\n n = acode.n + bcode.n - len(pairs)\n\n# Hx = zeros2(mx, n)\n# Hx[:acode.mx, :acode.n] = acode.Hx\n# Hx[acode.mx:, acode.n-len(pairs):] = bcode.Hx\n# \n# az = acode.mz + bcode.mz + acode.k + bcode.k\n# Az = zeros2(az, n)\n# r0, r1 = 0, acode.mz\n# Az[r0:r1, :acode.n] = acode.Hx; r0, r1 = r1, r1+len(acode.Hx)\n# Az[r0:r1, acode.n-len(pairs):] = bcode.Hx; r0, r1 = r1, r1+len(bcode.Hx)\n## Az[r0:r1, :acode.n] = acode.Lz; r0, r1 = r1, r1+len(acode.Lz)\n## #assert r1 == len(Az), (r1, len(Az))\n## Az[r0:r1, acode.n-len(pairs):] = bcode.Lz; r0, r1 = r1, r1+len(bcode.Lz)\n#\n# print(\"Az:\")\n# print(Az)\n\n #print(Az)\n Hz = []\n for z in span(Az):\n #print(z)\n #print( dot2(Hx, z.transpose()))\n if dot2(Hx, z.transpose()).sum() ==0:\n Hz.append(z)\n Hz = array2(Hz)\n Hz = row_reduce(Hz)\n \n print(\"Hx:\")\n print(Hx)\n print(\"Hz:\")\n print(Hz)\n\n\ndef test():\n\n Hx = parse(\"1.11. .11.1\")\n Hz = parse(\"111.. ..111\")\n\n code = CSSCode(Hx=Hx, Hz=Hz)\n\n print(code)\n\n z_weld(code, code, [(3, 0), (4, 1)])\n\n\n\nif __name__ == \"__main__\":\n test()\n\n\n","repo_name":"punkdit/qupy","sub_path":"qupy/ldpc/weld.py","file_name":"weld.py","file_ext":"py","file_size_in_byte":1655,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"42297270676","text":"import torch\nimport torch.nn as nn\n\n\nclass Discriminator(nn.Module):\n def __init__(self, in_channels=1, img_size=32):\n super(Discriminator, self).__init__()\n \n self.model = nn.Sequential(\n *self._block(in_channels, 16, normalize=False),\n *self._block(16, 32),\n *self._block(32, 64),\n *self._block(64, 128)\n )\n \n self.fc = nn.Linear(128*(img_size//2**4)**2, 1)\n \n self.sigmoid = nn.Sigmoid()\n \n def _block(self, in_channels, out_channels, normalize=True):\n layers = [\n nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=2, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Dropout2d(0.2)\n ]\n \n if normalize:\n layers.append(nn.BatchNorm2d(out_channels))\n \n return layers\n \n def forward(self, img):\n x = self.model(img)\n x = x.view(x.shape[0], -1)\n x = self.fc(x)\n x = self.sigmoid(x)\n return x\n \n \nclass Generator(nn.Module):\n def __init__(self, in_features=100, img_channels=1, img_size=32):\n super(Generator, self).__init__()\n self.img_size = img_size\n \n self.fc = nn.Linear(in_features, 128*((img_size//4)**2))\n \n self.model = nn.Sequential(\n nn.BatchNorm2d(128),\n *self._block(128, 128),\n *self._block(128, 64),\n nn.Conv2d(64, img_channels, kernel_size=3, padding=1),\n nn.Tanh()\n )\n \n def _block(self, in_channels, out_channels):\n layers = [\n nn.Upsample(scale_factor=2),\n nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),\n nn.BatchNorm2d(out_channels),\n nn.LeakyReLU(0.2, inplace=True)\n ]\n \n return layers\n \n def forward(self, z):\n z = self.fc(z)\n z = z.view(z.shape[0], 128, self.img_size//4, self.img_size//4)\n z = self.model(z)\n return z\n \n \nif __name__ == '__main__':\n img = torch.randn(1, 1, 32, 32)\n z = torch.randn(1, 100)\n \n generator = Generator()\n discriminator = Discriminator()\n \n assert generator(z).shape == img.shape\n assert discriminator(img).shape == (1, 1)","repo_name":"onebottlekick/bhban_ai_pytorch","sub_path":"CNN/gan/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2310,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"22081581832","text":"import sys\nimport re\nimport abc\nimport six\nfrom decimal import Decimal\nfrom datetime import datetime\n\nfrom bs4 import BeautifulSoup\n\nif sys.version_info[0] == 3:\n from urllib.request import urlopen\nelif sys.version_info[0] == 2:\n from urllib import urlopen\nelse:\n raise Exception('Python version 2 or 3 required')\n\n\ndef make_soup(url, parser=\"html.parser\"):\n response = urlopen(url)\n soup = BeautifulSoup(response, parser)\n return soup\n\n\n@six.add_metaclass(abc.ABCMeta)\nclass SecurityPage(object):\n\n @classmethod\n def from_url(cls, url):\n if '/uk/funds/snapshot/snapshot' in url:\n return FundsPage(url)\n elif '/uk/stockreport/' in url:\n return StockPage(url)\n elif '/uk/etf/' in url:\n return ETFPage(url)\n\n def __init__(self, url):\n self.url = url\n cls_name = self.__class__.__name__\n security_type = cls_name[:cls_name.find(\"Page\")]\n self.data_ = {\"type\": security_type, \"url\": self.url}\n\n def get_data(self):\n soup = make_soup(self.url)\n self._update_data(soup)\n return self.data_\n\n @abc.abstractmethod\n def _update_data(self, soup):\n \"\"\"\"\"\"\n\n\nclass FundsPage(SecurityPage):\n \"\"\"\n http://www.morningstar.co.uk/uk/funds/snapshot/snapshot.aspx?id=F00000NGEH\n \"\"\"\n def _update_data(self, soup):\n text = soup.find_all('div', class_='snapshotTitleBox')[0].h1.text\n self.data_[\"name\"] = str(text)\n table = soup.find_all('table', class_='overviewKeyStatsTable')[0]\n for tr in table.find_all('tr'):\n tds = tr.find_all('td')\n if len(tds) != 3:\n continue\n if tds[0].text.startswith('NAV'):\n date = tds[0].span.text\n (currency, value) = tds[2].text.split()\n if tds[0].text.startswith('Day Change'):\n change = tds[2].text.strip()\n if tds[0].text.startswith('ISIN'):\n isin = tds[2].text.strip()\n result = {\n 'value': Decimal(value),\n 'currency': currency,\n 'change': change,\n 'date': datetime.strptime(date, '%d/%m/%Y').date(),\n 'ISIN': isin\n }\n self.data_.update(result)\n\n\nclass StockPage(SecurityPage):\n def _update_data(self, soup):\n title = soup.find_all('span', class_='securityName')[0].text\n value = soup.find_all('span', id='Col0Price')[0].text\n change = soup.find_all('span', id='Col0PriceDetail')[0].text\n change = change.split('|')[1].strip()\n date = soup.find_all('p', id='Col0PriceTime')[0].text[6:16]\n currency = soup.find_all('p', id='Col0PriceTime')[0].text\n currency = re.search(r'\\|\\s([A-Z]{3,4})\\b', currency).group(1)\n isin = soup.find_all('td', id='Col0Isin')[0].text\n return {\n 'name': title,\n 'value': Decimal(value),\n 'currency': currency,\n 'change': change,\n 'date': datetime.strptime(date, '%d/%m/%Y').date(),\n 'ISIN': isin\n }\n\n\nclass ETFPage(SecurityPage):\n def _update_data(self, soup):\n text = soup.find_all('div', class_='snapshotTitleBox')[0].h1.text\n self.data_[\"name\"] = text.split('|')[0].strip()\n self.data_[\"ticker\"] = text.split('|')[1].strip()\n for keyword in [\"Exchange\", \"ISIN\"]:\n line = soup.find(text=keyword)\n if line is None:\n continue\n text = line.parent.nextSibling.nextSibling.text\n self.data_[keyword] = str(text)\n line = soup.find(text=\"Closing Price\")\n if line is not None:\n self.data_[\"currency\"] = \\\n line.parent.nextSibling.nextSibling.text[:3]\n","repo_name":"chrpinedo/MorningScraper","sub_path":"morningscraper/security.py","file_name":"security.py","file_ext":"py","file_size_in_byte":3748,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"52"} +{"seq_id":"12521203376","text":"import RPi.GPIO as GPIO\nimport os,time,threading,sys,signal\nfrom Biblio.pn532 import *\nfrom Biblio.lcd import Lcd\nfrom Biblio.Button import Button\nfrom datetime import datetime\n\n#Thread qui va récuperer la valeur de la carte\nclass CardThread(threading.Thread):\n\n\tdef __init__(self,C1,C2):\n\t\tthreading.Thread.__init__(self)\n\t\tself.valCard = '' #La valeur de la carte sera mise ici\n\t\tself.C1 = C1 #code de la carte 1 \n\t\tself.C2 = C2 #code de la carte 2\n\n\tdef run(self):\n\t\tcmd = \"sudo python3 DetectCard.py \"+self.C1+\" \"+self.C2 # prépare la commande pour récupèrer la valeur de la carte\n\t\tprint(cmd) \n\t\tres = os.popen(cmd) #lance la commande dans un autre processus\n\t\tself.valCard = res.read().split(\"\\n\") #Attends de recevoir la réponse\n\n#Classe utilisant le composant lecteur NFC\nclass NFC:\n\tdef __init__(self,hexaCarte1,hexaCarte2):\n\t\tself.C1 = hexaCarte1 #code de la carte 1\n\t\tself.C2 = hexaCarte2 #code de la carte 2\n\n\t#Fonction permettant de lancer le thread et de modifier l'affichage du LCD en fonction du temps passé à attendre\n\t#Elle renvoie la valeur de la carte mise sur le lecteur, ou -2 si le temps est dépassé \n\tdef ProgDetectCard(self):\n\t\tself.th = CardThread(str(self.C1),str(self.C2)) #Initialise le Thread\n\t\tself.th.start() #Le lance (sa fonction run())\n\t\tlcd = Lcd(1)\n\t\tbutCache = Button(5)\n\t\testAppuie = False\n\t\tafficheTemps = False\n\t\ttempsAttente = datetime.now()\n\t\tlcd.setText(\"En attente de carte ...\")\n\t\tlcd.setColor(\"bleu\")\n\t\twhile self.th.valCard == \"\": #Attends qu'il récupère sa valeur \n\t\t\tif butCache.estAppuie() and not estAppuie:\n\t\t\t\testAppuie = True\n\t\t\telif not butCache.estAppuie() and estAppuie:\n\t\t\t\treturn -2\n\t\t\t#Si ça fait déjà 4 secondes que le joueur n'a pas mit la carte on annonce que c'est bientôt fini\n\t\t\telif not afficheTemps and 4 == int((datetime.now() - tempsAttente).total_seconds()):\n\t\t\t\tlcd.setText(\"Bientot fini...\")\n\t\t\t\tlcd.setColor(\"rouge\")\n\t\t\t\tafficheTemps = True\n\t\tprint(\"test : \",self.th.valCard[2])\n\t\tif len(self.th.valCard[2]) > 2 : # Pas de carte au bout de 10 seconde\n\t\t\treturn -2\n\t\telse :\n\t\t\treturn self.th.valCard[2] #La retourne\n\n\t#Fonction qui attend que le joueur place une carte\n\t#Si au bout de 7 secondes, la fonction n'a toujours rien reçu, on arrête la fonction grâce au signal alarm.\n\t#Sinon affiche le numéro de carte (1 ou 2) placé par le joueur. \n\tdef DetectCard(self):\n\t\ttry:\n\t\t\tpn532 = PN532_I2C(debug=False, reset=20, req=16)\n\t\t\tic, ver, rev, support = pn532.get_firmware_version()\n\t\t\tprint('Found PN532 with firmware version: {0}.{1}'.format(ver, rev))\n\n\t\t\t# Configure PN532 to communicate with MiFare cards\n\t\t\tpn532.SAM_configuration()\n\t\t\t#Fonction du signal\n\t\t\tdef fin_signal_ALRM(sig,ignore):\n\t\t\t\tprint(\"ça fait 7 secondes, on kill\")\n\t\t\t\tsys.exit()\n\n\t\t\tsignal.signal(signal.SIGALRM,fin_signal_ALRM)\n\t\t\tsignal.alarm(7) #Lance le signal alarm sur 7 secondes\n\n\t\t\tprint('Waiting for RFID/NFC card...')\n\t\t\t# Check if a card is available to read\n\t\t\twhile True :\n\t\t\t\tuid = pn532.read_passive_target(timeout=0.5) #Attend que le joueur place une carte\n\t\t\t\n\t\t\t\tif uid is None:\n \t\t\t\t\tcontinue\n\t\t\t\t\n\t\t\t\ttabHexa = [hex(i) for i in uid]\n\t\t\n\t\t\t\tcarte1 = self.C1[1:-1].split(\",\") # Transforme en tableau\t\t\t\t\n\t\t\t\tcarte2 = self.C2[1:-1].split(\",\") # Transforme en tableau\n\t\t\t\testC1 = True\t\n\t\t\t\testC2 = True\n\t\n\t\t\t\tfor i in range(len(tabHexa)):\n\t\t\t\t\tif tabHexa[i] != carte1[i]:\t\n\t\t\t\t\t\testC1 = False\n\t\t\t\t\tif tabHexa[i] != carte2[i]:\n\t\t\t\t\t\testC2 = False\n\t\t\t\t\n\t\t\t\tif(estC1):\n\t\t\t\t\tprint(\"1\")\n\t\t\t\t\treturn 1\n\t\t\t\telif(estC2):\n\t\t\t\t\tprint(\"2\")\n\t\t\t\t\treturn 2\n\t\t\t\telse:\n\t\t\t\t\tprint(\"-1\")\n\t\t\t\t\treturn -1\t\t\n\t\texcept KeyboardInterrupt: #Ctrl C\n\t\t\tprint(\"stop\")\n\t\texcept Exception as e:\n\t\t\tprint(e)\n\t\tfinally:\n\t\t\tGPIO.cleanup()\n","repo_name":"Sebastien-Gineste/Raspberry","sub_path":"Biblio/NFC.py","file_name":"NFC.py","file_ext":"py","file_size_in_byte":3696,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"74663655843","text":"# print a string using plus signs\n\nanimal = 'chicken'\n\nprint('the ' + animal + \" crossed the road\")\n\n# print using f string - i could understand its a little bit more scalable\nprint(f'the {animal} crossed the road')\n\n#-------------------------------------------------------------------------------------------------------------\n\n# performance of a for loop vs. a while loop\n\nanimals = ['chicken', 'cow', 'pig', 'horse', 'sheep']\n\n# find a Horse\n\n\n#-------------------------------------------------------------------------------------------------------------\n\n# multiple assignments?\nanimals = ['chicken', 'cow', 'pig', 'horse', 'sheep']\n\nanimal_1 = animals[0]\nanimal_2 = animals[1]\n\nanimals_1,animals_2 = animals[0],animals[1]\n\n#-------------------------------------------------------------------------------------------------------------\n\n# this seems to be different pattern - start executing here vs. start from the top\ndef myfunction():\n print('this executes a function instead of starting at main')\n\n\nif __name__ == '__main__':\n print('hello world')\n\n myfunction()\n\n","repo_name":"itglueguy/100daysofcode","sub_path":"noob_practices/noob_practices.py","file_name":"noob_practices.py","file_ext":"py","file_size_in_byte":1080,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"4953846105","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 ]\n\n operations = [\n migrations.CreateModel(\n name='reading',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('Bookname', models.CharField(max_length=60, verbose_name='\\u4e66\\u540d')),\n ('Author', models.CharField(max_length=60, verbose_name='\\u4f5c\\u8005')),\n ('ISBN', models.CharField(max_length=60, verbose_name='ISBN')),\n ('Category', models.CharField(max_length=60, null=True, verbose_name='\\u7c7b\\u522b', blank=True)),\n ('Picture', models.ImageField(null=True, upload_to=b'pictures', blank=True)),\n ('Grade', models.IntegerField(default=0, verbose_name='\\u8bc4\\u5206')),\n ('Ranking', models.DateTimeField(auto_now_add=True, verbose_name='\\u4e0a\\u699c\\u65f6\\u95f4', null=True)),\n ('Price', models.CharField(default=0, max_length=60, verbose_name='\\u4ef7\\u683c')),\n ('Postage', models.CharField(default=0, max_length=60, verbose_name='\\u90ae\\u8d39')),\n ('Abstract', models.CharField(max_length=130, verbose_name='\\u7b80\\u4ecb')),\n ('Comment', models.CharField(max_length=130, null=True, verbose_name='\\u4e66\\u8bc4', blank=True)),\n ('Quantity', models.IntegerField(default=0, null=True, verbose_name='\\u6570\\u91cf')),\n ('createTime', models.DateTimeField(auto_now_add=True, verbose_name='\\u521b\\u5efa\\u65f6\\u95f4', null=True)),\n ('website', models.URLField()),\n ],\n ),\n ]\n","repo_name":"RomanceSky/DoubanBak","sub_path":"Read/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":1789,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"23859706800","text":"#check if string is digits\n\n#if digits: convert to int and check if greater than 99\n\n#if greater than 99 print a message about a big number\n\n#if not greater than 99 print message about small number\n\n# check if string isalpha then \n\n#if isalpha print message about being all alpha\n\n#if not is alpha print a message about being neither all alpha nor all digit\n\n#call the function with a string from user input\n\n\n#Defining the function that takes one arguement\ndef str_analysis(user_input):\n #checks if the user input is empty\n while user_input != \"\":\n #checks if user input contains is all digits, if true continue \n if user_input.isdigit() == True:\n user_num = int(user_input)\n if user_num > 99:\n print(\"The number entered:\",user_num,\"is a big number\")\n elif user_num < 99:\n print(\"The number entered:\",user_num,\"is a small number\")\n #checks if user input is all alphabetic\n elif user_input.isalpha() == True:\n print(\"The input is all alphabetic\")\n #If the user input is neither all alphabetic, not empty, or all digits, then return it has multiple character types.\n elif user_input.isalpha() == False:\n print(\"The input is neither all alpha nor all digits, it has multiple character types\")\n\nuser_input = input(\"Please enter a string for analysis: \")\n\nstr_analysis(user_input)\n","repo_name":"ZHANGW31/Learning-Python","sub_path":"Mod4.py","file_name":"Mod4.py","file_ext":"py","file_size_in_byte":1410,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"13658462749","text":"'''\nAuthor: Cao Shixin\nDate: 2023-04-06 09:34:01\nLastEditors: Cao Shixin\nLastEditTime: 2023-04-14 16:11:09\nDescription: \n'''\nfrom django.urls import path, re_path\nfrom app2 import views\n\nurlpatterns = [\n path('app2/index/',view=views.index),\n # 动态路由配置（路由参数）\n path('app2/show//',view= views.show),\n # 参数数据类型，int（匹配0和正整数）、str（任意非空字符串，不包含”/“，默认类型）、slug（匹配任何ASCII字符串、连接线和下划线）、uuid（uuid字符串，必须包含“-”，所有字母必须小写）\n path('app2/article//',view=views.show_uuid,name='show_uuid'),\n path('app2/article//',view=views.show_slug,name='show_slug'),\n \n # 灵活性更高的re_path()\n re_path('app2/list/(?P\\d{4})/',view= views.article_list),\n re_path('app2/list/(?P\\d+)&key=(?P\\w+)',view=views.article_page,name='article_page'),\n \n # 反向解析路由，即使用name字段，（项目中最好后面也是name不变，只调整path的route即可）\n # name规则：应用名+配置项名称\n path('app2/url_reverse/',view=views.url_reverse,name='app2_url_reverse'),\n \n path('app2/test_get/',view=views.test_get,name='test_get'),\n path('app2/hello_world/',view=views.hello_world,name='app2_hello_world'),\n \n path('app2/test_post/',view=views.test_post,name='app2_test_post'),\n path('app2/test_response/',view=views.test_response,name='app2_test_response'),\n \n path('app2/test_render/',view=views.test_render,name='app2_test_render'),\n path('app2/test_redirect_model//',view=views.test_redirect_model,name='app2_test_redirect_model'),\n path('app2/userinfo/',view=views.userinfo,name='app2_userinfo'),\n]\n","repo_name":"KirstenDunst/CSXPythonFile","sub_path":"Django/myshop-test/app2/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1788,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"21830007651","text":"from hashlib import sha1\nimport hmac\nimport os\nfrom time import time\n\nfrom libcloud.utils.py3 import httplib\nfrom libcloud.utils.py3 import urlencode\n\ntry:\n import simplejson as json\nexcept ImportError:\n import json\n\nfrom libcloud.utils.py3 import PY3\nfrom libcloud.utils.py3 import b\nfrom libcloud.utils.py3 import urlquote\n\nif PY3:\n from io import FileIO as file\n\nfrom libcloud.utils.files import read_in_chunks\nfrom libcloud.common.types import MalformedResponseError, LibcloudError\nfrom libcloud.common.base import Response, RawResponse\n\nfrom libcloud.storage.providers import Provider\nfrom libcloud.storage.base import Object, Container, StorageDriver\nfrom libcloud.storage.types import ContainerAlreadyExistsError\nfrom libcloud.storage.types import ContainerDoesNotExistError\nfrom libcloud.storage.types import ContainerIsNotEmptyError\nfrom libcloud.storage.types import ObjectDoesNotExistError\nfrom libcloud.storage.types import ObjectHashMismatchError\nfrom libcloud.storage.types import InvalidContainerNameError\nfrom libcloud.common.types import LazyList\nfrom libcloud.common.openstack import OpenStackBaseConnection\nfrom libcloud.common.openstack import OpenStackDriverMixin\n\nfrom libcloud.common.rackspace import (\n AUTH_URL_US, AUTH_URL_UK)\n\nCDN_HOST = 'cdn.clouddrive.com'\nAPI_VERSION = 'v1.0'\n\n\nclass CloudFilesResponse(Response):\n valid_response_codes = [httplib.NOT_FOUND, httplib.CONFLICT]\n\n def success(self):\n i = int(self.status)\n return i >= 200 and i <= 299 or i in self.valid_response_codes\n\n def parse_body(self):\n if not self.body:\n return None\n\n if 'content-type' in self.headers:\n key = 'content-type'\n elif 'Content-Type' in self.headers:\n key = 'Content-Type'\n else:\n raise LibcloudError('Missing content-type header')\n\n content_type = self.headers[key]\n if content_type.find(';') != -1:\n content_type = content_type.split(';')[0]\n\n if content_type == 'application/json':\n try:\n data = json.loads(self.body)\n except:\n raise MalformedResponseError('Failed to parse JSON',\n body=self.body,\n driver=CloudFilesStorageDriver)\n elif content_type == 'text/plain':\n data = self.body\n else:\n data = self.body\n\n return data\n\n\nclass CloudFilesRawResponse(CloudFilesResponse, RawResponse):\n pass\n\n\nclass CloudFilesConnection(OpenStackBaseConnection):\n \"\"\"\n Base connection class for the Cloudfiles driver.\n \"\"\"\n\n auth_url = AUTH_URL_US\n responseCls = CloudFilesResponse\n rawResponseCls = CloudFilesRawResponse\n\n def __init__(self, user_id, key, secure=True, **kwargs):\n super(CloudFilesConnection, self).__init__(user_id, key, secure=secure,\n **kwargs)\n self.api_version = API_VERSION\n self.accept_format = 'application/json'\n self.cdn_request = False\n\n if self._ex_force_service_region:\n self.service_region = self._ex_force_service_region\n\n def get_endpoint(self):\n # First, we parse out both files and cdn endpoints\n # for each auth version\n if '2.0' in self._auth_version:\n eps = self.service_catalog.get_endpoints(\n service_type='object-store',\n name='cloudFiles')\n cdn_eps = self.service_catalog.get_endpoints(\n service_type='object-store',\n name='cloudFilesCDN')\n elif ('1.1' in self._auth_version) or ('1.0' in self._auth_version):\n eps = self.service_catalog.get_endpoints(name='cloudFiles')\n cdn_eps = self.service_catalog.get_endpoints(name='cloudFilesCDN')\n\n # if this is a CDN request, return the cdn url instead\n if self.cdn_request:\n eps = cdn_eps\n\n if self.service_region:\n _eps = []\n for ep in eps:\n if ep['region'].lower() == self.service_region.lower():\n _eps.append(ep)\n eps = _eps\n\n if len(eps) == 0:\n raise LibcloudError('Could not find specified endpoint')\n\n ep = eps[0]\n if 'publicURL' in ep:\n return ep['publicURL']\n else:\n raise LibcloudError('Could not find specified endpoint')\n\n def request(self, action, params=None, data='', headers=None, method='GET',\n raw=False, cdn_request=False):\n if not headers:\n headers = {}\n if not params:\n params = {}\n\n self.cdn_request = cdn_request\n params['format'] = 'json'\n\n if method in ['POST', 'PUT'] and 'Content-Type' not in headers:\n headers.update({'Content-Type': 'application/json; charset=UTF-8'})\n\n return super(CloudFilesConnection, self).request(\n action=action,\n params=params, data=data,\n method=method, headers=headers,\n raw=raw)\n\n\nclass CloudFilesUSConnection(CloudFilesConnection):\n \"\"\"\n Connection class for the Cloudfiles US endpoint.\n \"\"\"\n\n auth_url = AUTH_URL_US\n\n\nclass CloudFilesUKConnection(CloudFilesConnection):\n \"\"\"\n Connection class for the Cloudfiles UK endpoint.\n \"\"\"\n\n auth_url = AUTH_URL_UK\n\n\nclass CloudFilesSwiftConnection(CloudFilesConnection):\n \"\"\"\n Connection class for the Cloudfiles Swift endpoint.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n self.region_name = kwargs.pop('ex_region_name', None)\n super(CloudFilesSwiftConnection, self).__init__(*args, **kwargs)\n\n def get_endpoint(self, *args, **kwargs):\n if '2.0' in self._auth_version:\n endpoint = self.service_catalog.get_endpoint(\n service_type='object-store',\n name='swift',\n region=self.region_name)\n elif ('1.1' in self._auth_version) or ('1.0' in self._auth_version):\n endpoint = self.service_catalog.get_endpoint(\n name='swift', region=self.region_name)\n\n if 'publicURL' in endpoint:\n return endpoint['publicURL']\n else:\n raise LibcloudError('Could not find specified endpoint')\n\n\nclass CloudFilesStorageDriver(StorageDriver, OpenStackDriverMixin):\n \"\"\"\n Base CloudFiles driver.\n\n You should never create an instance of this class directly but use US/US\n class.\n \"\"\"\n name = 'CloudFiles'\n website = 'http://www.rackspace.com/'\n\n connectionCls = CloudFilesConnection\n hash_type = 'md5'\n supports_chunked_encoding = True\n\n def __init__(self, *args, **kwargs):\n OpenStackDriverMixin.__init__(self, *args, **kwargs)\n super(CloudFilesStorageDriver, self).__init__(*args, **kwargs)\n\n def list_containers(self):\n response = self.connection.request('')\n\n if response.status == httplib.NO_CONTENT:\n return []\n elif response.status == httplib.OK:\n return self._to_container_list(json.loads(response.body))\n\n raise LibcloudError('Unexpected status code: %s' % (response.status))\n\n def list_container_objects(self, container):\n value_dict = {'container': container}\n return LazyList(get_more=self._get_more, value_dict=value_dict)\n\n def get_container(self, container_name):\n response = self.connection.request('/%s' % (container_name),\n method='HEAD')\n\n if response.status == httplib.NO_CONTENT:\n container = self._headers_to_container(\n container_name, response.headers)\n return container\n elif response.status == httplib.NOT_FOUND:\n raise ContainerDoesNotExistError(None, self, container_name)\n\n raise LibcloudError('Unexpected status code: %s' % (response.status))\n\n def get_object(self, container_name, object_name):\n container = self.get_container(container_name)\n response = self.connection.request('/%s/%s' % (container_name,\n object_name),\n method='HEAD')\n if response.status in [httplib.OK, httplib.NO_CONTENT]:\n obj = self._headers_to_object(\n object_name, container, response.headers)\n return obj\n elif response.status == httplib.NOT_FOUND:\n raise ObjectDoesNotExistError(None, self, object_name)\n\n raise LibcloudError('Unexpected status code: %s' % (response.status))\n\n def get_container_cdn_url(self, container):\n container_name = container.name\n response = self.connection.request('/%s' % (container_name),\n method='HEAD',\n cdn_request=True)\n\n if response.status == httplib.NO_CONTENT:\n cdn_url = response.headers['x-cdn-uri']\n return cdn_url\n elif response.status == httplib.NOT_FOUND:\n raise ContainerDoesNotExistError(value='',\n container_name=container_name,\n driver=self)\n\n raise LibcloudError('Unexpected status code: %s' % (response.status))\n\n def get_object_cdn_url(self, obj):\n container_cdn_url = self.get_container_cdn_url(container=obj.container)\n return '%s/%s' % (container_cdn_url, obj.name)\n\n def enable_container_cdn(self, container, ex_ttl=None):\n \"\"\"\n @inherits: L{StorageDriver.enable_container_cdn}\n\n @param ex_ttl: cache time to live\n @type ex_ttl: C{int}\n \"\"\"\n container_name = container.name\n headers = {'X-CDN-Enabled': 'True'}\n\n if ex_ttl:\n headers['X-TTL'] = ex_ttl\n\n response = self.connection.request('/%s' % (container_name),\n method='PUT',\n headers=headers,\n cdn_request=True)\n\n return response.status in [httplib.CREATED, httplib.ACCEPTED]\n\n def create_container(self, container_name):\n container_name = self._clean_container_name(container_name)\n response = self.connection.request(\n '/%s' % (container_name), method='PUT')\n\n if response.status == httplib.CREATED:\n # Accepted mean that container is not yet created but it will be\n # eventually\n extra = {'object_count': 0}\n container = Container(name=container_name,\n extra=extra, driver=self)\n\n return container\n elif response.status == httplib.ACCEPTED:\n error = ContainerAlreadyExistsError(None, self, container_name)\n raise error\n\n raise LibcloudError('Unexpected status code: %s' % (response.status))\n\n def delete_container(self, container):\n name = self._clean_container_name(container.name)\n\n # Only empty container can be deleted\n response = self.connection.request('/%s' % (name), method='DELETE')\n\n if response.status == httplib.NO_CONTENT:\n return True\n elif response.status == httplib.NOT_FOUND:\n raise ContainerDoesNotExistError(value='',\n container_name=name, driver=self)\n elif response.status == httplib.CONFLICT:\n # @TODO: Add \"delete_all_objects\" parameter?\n raise ContainerIsNotEmptyError(value='',\n container_name=name, driver=self)\n\n def download_object(self, obj, destination_path, overwrite_existing=False,\n delete_on_failure=True):\n container_name = obj.container.name\n object_name = obj.name\n response = self.connection.request('/%s/%s' % (container_name,\n object_name),\n method='GET', raw=True)\n\n return self._get_object(\n obj=obj, callback=self._save_object, response=response,\n callback_kwargs={'obj': obj,\n 'response': response.response,\n 'destination_path': destination_path,\n 'overwrite_existing': overwrite_existing,\n 'delete_on_failure': delete_on_failure},\n success_status_code=httplib.OK)\n\n def download_object_as_stream(self, obj, chunk_size=None):\n container_name = obj.container.name\n object_name = obj.name\n response = self.connection.request('/%s/%s' % (container_name,\n object_name),\n method='GET', raw=True)\n\n return self._get_object(obj=obj, callback=read_in_chunks,\n response=response,\n callback_kwargs={'iterator': response.response,\n 'chunk_size': chunk_size},\n success_status_code=httplib.OK)\n\n def upload_object(self, file_path, container, object_name, extra=None,\n verify_hash=True):\n \"\"\"\n Upload an object.\n\n Note: This will override file with a same name if it already exists.\n \"\"\"\n upload_func = self._upload_file\n upload_func_kwargs = {'file_path': file_path}\n\n return self._put_object(container=container, object_name=object_name,\n upload_func=upload_func,\n upload_func_kwargs=upload_func_kwargs,\n extra=extra, file_path=file_path,\n verify_hash=verify_hash)\n\n def upload_object_via_stream(self, iterator,\n container, object_name, extra=None):\n if isinstance(iterator, file):\n iterator = iter(iterator)\n\n upload_func = self._stream_data\n upload_func_kwargs = {'iterator': iterator}\n\n return self._put_object(container=container, object_name=object_name,\n upload_func=upload_func,\n upload_func_kwargs=upload_func_kwargs,\n extra=extra, iterator=iterator)\n\n def delete_object(self, obj):\n container_name = self._clean_container_name(obj.container.name)\n object_name = self._clean_object_name(obj.name)\n\n response = self.connection.request(\n '/%s/%s' % (container_name, object_name), method='DELETE')\n\n if response.status == httplib.NO_CONTENT:\n return True\n elif response.status == httplib.NOT_FOUND:\n raise ObjectDoesNotExistError(value='', object_name=object_name,\n driver=self)\n\n raise LibcloudError('Unexpected status code: %s' % (response.status))\n\n def ex_get_meta_data(self):\n \"\"\"\n Get meta data\n\n @rtype: C{dict}\n \"\"\"\n response = self.connection.request('', method='HEAD')\n\n if response.status == httplib.NO_CONTENT:\n container_count = response.headers.get(\n 'x-account-container-count', 'unknown')\n object_count = response.headers.get(\n 'x-account-object-count', 'unknown')\n bytes_used = response.headers.get(\n 'x-account-bytes-used', 'unknown')\n temp_url_key = response.headers.get(\n 'x-account-meta-temp-url-key', None)\n\n return {'container_count': int(container_count),\n 'object_count': int(object_count),\n 'bytes_used': int(bytes_used),\n 'temp_url_key': temp_url_key}\n\n raise LibcloudError('Unexpected status code: %s' % (response.status))\n\n def ex_multipart_upload_object(self, file_path, container, object_name,\n chunk_size=33554432, extra=None,\n verify_hash=True):\n object_size = os.path.getsize(file_path)\n if object_size < chunk_size:\n return self.upload_object(file_path, container, object_name,\n extra=extra, verify_hash=verify_hash)\n\n iter_chunk_reader = FileChunkReader(file_path, chunk_size)\n\n for index, iterator in enumerate(iter_chunk_reader):\n self._upload_object_part(container=container,\n object_name=object_name,\n part_number=index,\n iterator=iterator,\n verify_hash=verify_hash)\n\n return self._upload_object_manifest(container=container,\n object_name=object_name,\n extra=extra,\n verify_hash=verify_hash)\n\n def ex_enable_static_website(self, container, index_file='index.html'):\n \"\"\"\n Enable serving a static website.\n\n @param container: Container instance\n @type container: L{Container}\n\n @param index_file: Name of the object which becomes an index page for\n every sub-directory in this container.\n @type index_file: C{str}\n\n @rtype: C{bool}\n \"\"\"\n container_name = container.name\n headers = {'X-Container-Meta-Web-Index': index_file}\n\n response = self.connection.request('/%s' % (container_name),\n method='POST',\n headers=headers,\n cdn_request=False)\n\n return response.status in [httplib.CREATED, httplib.ACCEPTED]\n\n def ex_set_error_page(self, container, file_name='error.html'):\n \"\"\"\n Set a custom error page which is displayed if file is not found and\n serving of a static website is enabled.\n\n @param container: Container instance\n @type container: L{Container}\n\n @param file_name: Name of the object which becomes the error page.\n @type file_name: C{str}\n\n @rtype: C{bool}\n \"\"\"\n container_name = container.name\n headers = {'X-Container-Meta-Web-Error': file_name}\n\n response = self.connection.request('/%s' % (container_name),\n method='POST',\n headers=headers,\n cdn_request=False)\n\n return response.status in [httplib.CREATED, httplib.ACCEPTED]\n\n def ex_set_account_metadata_temp_url_key(self, key):\n \"\"\"\n Set the metadata header X-Account-Meta-Temp-URL-Key on your Cloud\n Files account.\n\n @param key: X-Account-Meta-Temp-URL-Key\n @type key: C{str}\n\n @rtype: C{bool}\n \"\"\"\n headers = {'X-Account-Meta-Temp-URL-Key': key}\n\n response = self.connection.request('',\n method='POST',\n headers=headers,\n cdn_request=False)\n\n return response.status in [httplib.OK, httplib.NO_CONTENT,\n httplib.CREATED, httplib.ACCEPTED]\n\n def ex_get_object_temp_url(self, obj, method='GET', timeout=60):\n \"\"\"\n Create a temporary URL to allow others to retrieve or put objects\n in your Cloud Files account for as long or as short a time as you\n wish. This method is specifically for allowing users to retrieve\n or update an object.\n\n @param obj: The object that you wish to make temporarily public\n @type obj: L{Object}\n\n @param method: Which method you would like to allow, 'PUT' or 'GET'\n @type method: C{str}\n\n @param timeout: Time (in seconds) after which you want the TempURL\n to expire.\n @type timeout: C{int}\n\n @rtype: C{bool}\n \"\"\"\n self.connection._populate_hosts_and_request_paths()\n expires = int(time() + timeout)\n path = '%s/%s/%s' % (self.connection.request_path,\n obj.container.name, obj.name)\n try:\n key = self.ex_get_meta_data()['temp_url_key']\n assert key is not None\n except Exception:\n raise KeyError('You must first set the ' +\n 'X-Account-Meta-Temp-URL-Key header on your ' +\n 'Cloud Files account using ' +\n 'ex_set_account_metadata_temp_url_key before ' +\n 'you can use this method.')\n hmac_body = '%s\\n%s\\n%s' % (method, expires, path)\n sig = hmac.new(b(key), b(hmac_body), sha1).hexdigest()\n params = urlencode({'temp_url_sig': sig,\n 'temp_url_expires': expires})\n\n temp_url = 'https://%s/%s/%s?%s' %\\\n (self.connection.host + self.connection.request_path,\n obj.container.name, obj.name, params)\n\n return temp_url\n\n def _upload_object_part(self, container, object_name, part_number,\n iterator, verify_hash=True):\n upload_func = self._stream_data\n upload_func_kwargs = {'iterator': iterator}\n part_name = object_name + '/%08d' % part_number\n extra = {'content_type': 'application/octet-stream'}\n\n self._put_object(container=container,\n object_name=part_name,\n upload_func=upload_func,\n upload_func_kwargs=upload_func_kwargs,\n extra=extra, iterator=iterator,\n verify_hash=verify_hash)\n\n def _upload_object_manifest(self, container, object_name, extra=None,\n verify_hash=True):\n extra = extra or {}\n meta_data = extra.get('meta_data')\n\n container_name_cleaned = self._clean_container_name(container.name)\n object_name_cleaned = self._clean_object_name(object_name)\n request_path = '/%s/%s' % (container_name_cleaned, object_name_cleaned)\n\n headers = {'X-Auth-Token': self.connection.auth_token,\n 'X-Object-Manifest': '%s/%s/' %\n (container_name_cleaned,\n object_name_cleaned)}\n\n data = ''\n response = self.connection.request(request_path,\n method='PUT', data=data,\n headers=headers, raw=True)\n\n object_hash = None\n\n if verify_hash:\n hash_function = self._get_hash_function()\n hash_function.update(b(data))\n data_hash = hash_function.hexdigest()\n object_hash = response.headers.get('etag')\n\n if object_hash != data_hash:\n raise ObjectHashMismatchError(\n value=('MD5 hash checksum does not match (expected=%s, ' +\n 'actual=%s)') %\n (data_hash, object_hash),\n object_name=object_name, driver=self)\n\n obj = Object(name=object_name, size=0, hash=object_hash, extra=None,\n meta_data=meta_data, container=container, driver=self)\n\n return obj\n\n def _get_more(self, last_key, value_dict):\n container = value_dict['container']\n params = {}\n\n if last_key:\n params['marker'] = last_key\n\n response = self.connection.request('/%s' % (container.name),\n params=params)\n\n if response.status == httplib.NO_CONTENT:\n # Empty or inexistent container\n return [], None, True\n elif response.status == httplib.OK:\n objects = self._to_object_list(json.loads(response.body),\n container)\n\n # TODO: Is this really needed?\n if len(objects) == 0:\n return [], None, True\n\n return objects, objects[-1].name, False\n\n raise LibcloudError('Unexpected status code: %s' % (response.status))\n\n def _put_object(self, container, object_name, upload_func,\n upload_func_kwargs, extra=None, file_path=None,\n iterator=None, verify_hash=True):\n extra = extra or {}\n container_name_cleaned = self._clean_container_name(container.name)\n object_name_cleaned = self._clean_object_name(object_name)\n content_type = extra.get('content_type', None)\n meta_data = extra.get('meta_data', None)\n\n headers = {}\n if meta_data:\n for key, value in list(meta_data.items()):\n key = 'X-Object-Meta-%s' % (key)\n headers[key] = value\n\n request_path = '/%s/%s' % (container_name_cleaned, object_name_cleaned)\n result_dict = self._upload_object(\n object_name=object_name, content_type=content_type,\n upload_func=upload_func, upload_func_kwargs=upload_func_kwargs,\n request_path=request_path, request_method='PUT',\n headers=headers, file_path=file_path, iterator=iterator)\n\n response = result_dict['response'].response\n bytes_transferred = result_dict['bytes_transferred']\n server_hash = result_dict['response'].headers.get('etag', None)\n\n if response.status == httplib.EXPECTATION_FAILED:\n raise LibcloudError(value='Missing content-type header',\n driver=self)\n elif verify_hash and not server_hash:\n raise LibcloudError(value='Server didn\\'t return etag',\n driver=self)\n elif (verify_hash and result_dict['data_hash'] != server_hash):\n raise ObjectHashMismatchError(\n value=('MD5 hash checksum does not match (expected=%s, ' +\n 'actual=%s)') % (result_dict['data_hash'], server_hash),\n object_name=object_name, driver=self)\n elif response.status == httplib.CREATED:\n obj = Object(\n name=object_name, size=bytes_transferred, hash=server_hash,\n extra=None, meta_data=meta_data, container=container,\n driver=self)\n\n return obj\n else:\n # @TODO: Add test case for this condition (probably 411)\n raise LibcloudError('status_code=%s' % (response.status),\n driver=self)\n\n def _clean_container_name(self, name):\n \"\"\"\n Clean container name.\n \"\"\"\n if name.startswith('/'):\n name = name[1:]\n name = urlquote(name)\n\n if name.find('/') != -1:\n raise InvalidContainerNameError(value='Container name cannot'\n ' contain slashes',\n container_name=name, driver=self)\n\n if len(name) > 256:\n raise InvalidContainerNameError(\n value='Container name cannot be longer than 256 bytes',\n container_name=name, driver=self)\n\n return name\n\n def _clean_object_name(self, name):\n name = urlquote(name)\n return name\n\n def _to_container_list(self, response):\n # @TODO: Handle more then 10k containers - use \"lazy list\"?\n containers = []\n\n for container in response:\n extra = {'object_count': int(container['count']),\n 'size': int(container['bytes'])}\n containers.append(Container(name=container['name'], extra=extra,\n driver=self))\n\n return containers\n\n def _to_object_list(self, response, container):\n objects = []\n\n for obj in response:\n name = obj['name']\n size = int(obj['bytes'])\n hash = obj['hash']\n extra = {'content_type': obj['content_type'],\n 'last_modified': obj['last_modified']}\n objects.append(Object(\n name=name, size=size, hash=hash, extra=extra,\n meta_data=None, container=container, driver=self))\n\n return objects\n\n def _headers_to_container(self, name, headers):\n size = int(headers.get('x-container-bytes-used', 0))\n object_count = int(headers.get('x-container-object-count', 0))\n\n extra = {'object_count': object_count,\n 'size': size}\n container = Container(name=name, extra=extra, driver=self)\n return container\n\n def _headers_to_object(self, name, container, headers):\n size = int(headers.pop('content-length', 0))\n last_modified = headers.pop('last-modified', None)\n etag = headers.pop('etag', None)\n content_type = headers.pop('content-type', None)\n\n meta_data = {}\n for key, value in list(headers.items()):\n if key.find('x-object-meta-') != -1:\n key = key.replace('x-object-meta-', '')\n meta_data[key] = value\n\n extra = {'content_type': content_type, 'last_modified': last_modified}\n\n obj = Object(name=name, size=size, hash=etag, extra=extra,\n meta_data=meta_data, container=container, driver=self)\n return obj\n\n def _ex_connection_class_kwargs(self):\n return self.openstack_connection_kwargs()\n\n\nclass CloudFilesUSStorageDriver(CloudFilesStorageDriver):\n \"\"\"\n Cloudfiles storage driver for the US endpoint.\n \"\"\"\n\n type = Provider.CLOUDFILES_US\n name = 'CloudFiles (US)'\n connectionCls = CloudFilesUSConnection\n\n\nclass CloudFilesSwiftStorageDriver(CloudFilesStorageDriver):\n \"\"\"\n Cloudfiles storage driver for the OpenStack Swift.\n \"\"\"\n type = Provider.CLOUDFILES_SWIFT\n name = 'CloudFiles (SWIFT)'\n connectionCls = CloudFilesSwiftConnection\n\n def __init__(self, *args, **kwargs):\n self._ex_region_name = kwargs.get('ex_region_name', 'RegionOne')\n super(CloudFilesSwiftStorageDriver, self).__init__(*args, **kwargs)\n\n def openstack_connection_kwargs(self):\n rv = super(CloudFilesSwiftStorageDriver,\n self).openstack_connection_kwargs()\n rv['ex_region_name'] = self._ex_region_name\n return rv\n\n\nclass CloudFilesUKStorageDriver(CloudFilesStorageDriver):\n \"\"\"\n Cloudfiles storage driver for the UK endpoint.\n \"\"\"\n\n type = Provider.CLOUDFILES_UK\n name = 'CloudFiles (UK)'\n connectionCls = CloudFilesUKConnection\n\n\nclass FileChunkReader(object):\n def __init__(self, file_path, chunk_size):\n self.file_path = file_path\n self.total = os.path.getsize(file_path)\n self.chunk_size = chunk_size\n self.bytes_read = 0\n self.stop_iteration = False\n\n def __iter__(self):\n return self\n\n def next(self):\n if self.stop_iteration:\n raise StopIteration\n\n start_block = self.bytes_read\n end_block = start_block + self.chunk_size\n if end_block >= self.total:\n end_block = self.total\n self.stop_iteration = True\n self.bytes_read += end_block - start_block\n return ChunkStreamReader(file_path=self.file_path,\n start_block=start_block,\n end_block=end_block,\n chunk_size=8192)\n\n def __next__(self):\n return self.next()\n\n\nclass ChunkStreamReader(object):\n def __init__(self, file_path, start_block, end_block, chunk_size):\n self.fd = open(file_path, 'rb')\n self.fd.seek(start_block)\n self.start_block = start_block\n self.end_block = end_block\n self.chunk_size = chunk_size\n self.bytes_read = 0\n self.stop_iteration = False\n\n def __iter__(self):\n return self\n\n def next(self):\n if self.stop_iteration:\n self.fd.close()\n raise StopIteration\n\n block_size = self.chunk_size\n if self.bytes_read + block_size >\\\n self.end_block - self.start_block:\n block_size = self.end_block - self.start_block - self.bytes_read\n self.stop_iteration = True\n\n block = self.fd.read(block_size)\n self.bytes_read += block_size\n return block\n\n def __next__(self):\n return self.next()\n","repo_name":"ConPaaS-team/conpaas","sub_path":"conpaas-director/cpsdirector/iaas/libcloud/storage/drivers/cloudfiles.py","file_name":"cloudfiles.py","file_ext":"py","file_size_in_byte":32660,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"52"} +{"seq_id":"23220796917","text":"# Exercise 18: Volume of a cylinder function\r\nimport math\r\ndef volumeOfCyl(radius,height):\r\n area = math.pi*radius**2\r\n volume = area*height\r\n print(\"The volume of a cylinder with the given parameters is:\",volume)\r\n return None\r\n\r\nradiusInput = float(input(\"Please enter a radius to compute with: \"))\r\nheightInput = float(input(\"Please enter a height to compute with: \"))\r\n\r\nvolumeOfCyl(radiusInput,heightInput)\r\n","repo_name":"harrisonpearson18/Python-Projects","sub_path":"2021 Intro to Prog Files from Home/Homework-4 Harrison Pearson/Exercise 18 Function - Volume of a Cylinder.py","file_name":"Exercise 18 Function - Volume of a Cylinder.py","file_ext":"py","file_size_in_byte":425,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"29442737154","text":"import torch\nimport torchvision\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torchvision import models\nfrom util import *\n\n\"\"\"\nOur modified resnet18 model without transfer learning\n\"\"\"\nclass ResNet18(nn.Module):\n def __init__(self):\n super(ResNet18, self).__init__()\n self.features = models.resnet18()\n self.features.layer3 = nn.Sequential()\n self.features.layer4 = nn.Sequential()\n self.features.fc = nn.Linear(128, 10)\n self.features.relu = nn.LeakyReLU(inplace=True)\n self.features = self.features\n def forward(self, x):\n x = self.features(x)\n return x\n \n \n\"\"\"\nOur modified resnet18 model with transfer learning.\n\"\"\"\nclass ResNet18_tl(nn.Module):\n def __init__(self):\n super(ResNet18_tl, self).__init__()\n model_ft = models.resnet18(pretrained=True)\n set_parameter_requires_grad(model_ft, True)\n self.features = nn.Sequential(\n *list(model_ft.children())[:6]\n )\n self.conv1 = nn.Conv2d(in_channels = 128, out_channels = 256, kernel_size = 2, padding = 1)\n self.conv2 = nn.Conv2d(in_channels = 256, out_channels = 256, kernel_size = 3, padding = 1)\n self.bh1 = nn.BatchNorm2d(num_features = 256)\n self.pool3 = nn.AvgPool2d(kernel_size = (2,2))\n \n self.lin1 = nn.Linear(in_features = 1024, out_features =500)\n self.lin2 = nn.Linear(in_features = 500, out_features = 10)\n\n def forward(self, x):\n x = self.features(x)\n x = self.conv1(x)\n x = self.conv2(x)\n x = F.relu(self.bh1(x))\n x = self.pool3(x)\n x = torch.flatten(x, start_dim = 1)\n x = self.lin1(x)\n x = self.lin2(x) \n return x\n","repo_name":"michael4706/resnet18_model","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":1762,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"880290250","text":"import requests\nfrom bs4 import BeautifulSoup\nfrom urllib.parse import urlparse\nfrom urllib3.exceptions import InsecureRequestWarning\n\n# Disable insecure request warnings\nrequests.packages.urllib3.disable_warnings(InsecureRequestWarning)\n\nclass Request:\n\n # Constructor of Request\n def __init__(self, url, head):\n self.url = url\n self.header = head\n self.content = None\n self.status_code = None\n self.reason = ''\n\n # Function that obtains html code from url - Request\n def getContent(self, printer):\n try:\n target = checkUrl(self.url)\n req = requests.get(target, headers=self.header, timeout=2, verify=False,)\n self.status_code = req.status_code\n self.reason = req.reason\n if req.status_code == 200:\n html = req.content\n soup = BeautifulSoup(html,'lxml')\n self.content = soup\n except Exception:\n printer.messageError('[ERROR] Domain not found --> {}'.format(target))\n\n # Function that obtains the content of image to download\n def getImage(self, printer):\n try:\n target = checkUrl(self.url)\n req = requests.get(target, headers=self.header, verify=False)\n self.status_code = req.status_code\n self.reason = req.reason\n if req.status_code == 200:\n self.content = req.content\n except Exception:\n printer.check_status_code(target, req.status_code, req.reason)\n\n # Function that obtains the content of file to download\n def getFile(self, printer):\n try:\n target = checkUrl(self.url)\n req = requests.get(target, headers=self.header, verify=False, stream=True)\n self.status_code = req.status_code\n self.reason = req.reason\n if req.status_code == 200:\n self.content = req\n except Exception:\n printer.check_status_code(target, req.status_code, req.reason)\n\n # Function that obtains links to pages to be scanned\n def getLinksHrefs(self):\n links = list()\n if self.content != None:\n elements = self.content.findAll('a')\n for a in elements:\n href = a.get('href')\n links.append(href)\n return links\n\n # Function that obtains links to img to be downloaded\n def getLinksImg(self):\n links = list()\n if self.content != None:\n elements = self.content.findAll('img')\n for img in elements:\n src = img.get('src')\n links.append(src)\n return links\n\n# Function that checks the syntax 'http' of a url [origin]\ndef checkUrl(origin):\n parser = urlparse(origin)\n if parser.scheme == '':\n url = 'http://'+parser.path\n return url\n else:\n return origin\n","repo_name":"goldcod3/Arachnida","sub_path":"spider/utils/Request.py","file_name":"Request.py","file_ext":"py","file_size_in_byte":2881,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"7315827594","text":"import requests\nfrom ratelimit import limits, sleep_and_retry\nfrom bs4 import BeautifulSoup\nfrom retry import retry\nimport urllib.parse\n \nREQUEST_WAIT_SECONDS = 10\n\n@retry(requests.exceptions.RequestException, tries=3) # robustness\ndef get_page(link):\n \"\"\"Get HTML page at given link.\n Raises PageDoesNotExistError if it doesn't exist.\n Raises generic Exception if unexpected HTTP codes are encountered.\"\"\"\n response = requests.get(link)\n if response.status_code == 200:\n return response\n if response.status_code == 404:\n raise PageDoesNotExistError(\"{} does not exist\".format(link))\n raise Exception(f\"Failed to get page {link}, got HTTP status code {response.status_code}.\")\n\nclass GoogleLyricsScraper:\n @sleep_and_retry\n @limits(calls=1, period=REQUEST_WAIT_SECONDS)\n def scrape_lyrics(self, artist, name):\n pg = get_page(\"https://www.google.com/search?q=\"\n + urllib.parse.quote(\" \".join((artist, name, \"lyrics\"))))\n soup = BeautifulSoup(pg.content, 'html.parser')\n lyric_blocks = []\n is_first = True\n for e in soup.select(\"div.BNeawe.tAd8D.AP7Wnd\"):\n # Lyrics appear to be nested in 2 layers of divs with\n # these class tags. And they're repeated, for some reason.\n for lyric_e in e.select(\"div.BNeawe.tAd8D.AP7Wnd\"):\n if is_first:\n is_first = False\n else:\n lyric_blocks.append(lyric_e.get_text())\n if not lyric_blocks:\n raise LyricsNotFoundError(\"Couldn't find lyrics for song {}, by {}\".format(name, artist))\n return \"\\n\".join(lyric_blocks)\n\nclass GeniusScraper:\n def scrape(self, link):\n pg = get_page(link)\n soup = BeautifulSoup(pg.content, 'html.parser')\n divs = soup.select(\"div.lyrics\")\n if not divs:\n raise LyricsNotFoundError(\"Couldn't find lyrics div on genius.com page.\")\n return divs[0].get_text()\n\nclass Top40dbScraper:\n def scrape(self, link):\n pg = get_page(link)\n soup = BeautifulSoup(pg.content, 'html.parser')\n for e in soup.find_all([\"small\", \"script\"]):\n e.decompose() # remove the log-in message and ad shit\n divs = soup.select(\"div#divTOP40DB_LYRICS\")\n if not divs:\n raise LyricsNotFoundError(\"Couldn't find lyrics div on top40db.net page.\")\n return divs[0].get_text()\n\nclass LyricsFreakScraper:\n def scrape(self, link):\n pg = get_page(link)\n soup = BeautifulSoup(pg.content, 'html.parser')\n divs = soup.select(\"div#content\")\n if not divs:\n raise LyricsNotFoundError(\"Couldn't find lyrics div on lyricsfreak.com page.\")\n return divs[0].get_text()\n\nclass AzLyricsScraper:\n @sleep_and_retry\n @limits(calls=1, period=REQUEST_WAIT_SECONDS)\n def scrape_lyrics(self, song_link):\n \"\"\"Get lyrics from azlyrics.com at the given link.\n\n Raises PageDoesNotExist exception if the page doesn't exist.\n \"\"\"\n lyrics_page = get_page(song_link)\n soup = BeautifulSoup(lyrics_page.content, 'html.parser')\n lyrics_div = soup.find(\"div\", class_=\"ringtone\").find_next_sibling(\"div\")\n return lyrics_div.text.strip()\n\nclass GenericLyricsScraper:\n def __init__(self):\n \"\"\"Unlike the Google scraper, doesn't scrape directly from\n the lyrics displayed by Google. Looks through search results\n and matches them with the appropriate scraper. Eg if there's\n a link for genius.com, passes that to the genius.com scraper.\"\"\"\n self._scrapers = [\n (\"genius.com\", GeniusScraper()),\n (\"top40db.net\", Top40dbScraper()),\n (\"lyricsfreak.com\", LyricsFreakScraper()),\n (\"azlyrics.com\", AzLyricsScraper())\n ]\n\n @sleep_and_retry\n @limits(calls=1, period=REQUEST_WAIT_SECONDS)\n def scrape_lyrics(self, artist, name):\n pg = get_page(\"https://www.google.com/search?q=\"\n + urllib.parse.quote(\" \".join((artist, name, \"lyrics\"))))\n soup = BeautifulSoup(pg.content, 'html.parser')\n for element in soup.select(\"a\"):\n print(\"========TRYING LINK=========\")\n link = element.get(\"href\")\n try:\n print(\"Processing link:\", link)\n # The parent div should contain the title of the page -- which, for\n # all of the sites we're using, should in turn contain the name of\n # the song.\n if not fuzzy_matches(name, element.parent.get_text()):\n # skip this link to ensure that we're not just getting the lyrics of\n # a random song by the same artist. Don't bother to check that the\n # artist is the same; in all likelihood, if the artist is\n # different, it's a different version of the same song.\n print(\"song name does not match:\", element.parent.get_text())\n continue\n for site_name, scraper in self._scrapers:\n if site_name in link:\n print(\"Matches with scraper for\", site_name)\n if link.startswith(\"/\"):\n link = \"https://www.google.com\" + link\n print(\"Trying to scrape from link\", link)\n return scraper.scrape(link)\n except Exception as e:\n continue # try the next link\n raise LyricsNotFoundError(\"Could not scrape lyrics from any website in first page of results.\")\n\ndef fuzzy_matches(important_s, s):\n important_tokens = set([token.lower() for token in important_s.strip().split()])\n tokens = set([token.lower() for token in s.strip().split()])\n # threshold chosen arbitrarily, pretty much\n return len(important_tokens.intersection(tokens)) / len(important_tokens) >= 0.49\n\nclass LyricsNotFoundError(Exception):\n def __init__(self, msg):\n super().__init__(msg)\n\nclass PageDoesNotExistError(Exception):\n def __init__(self, msg):\n super().__init__(msg)\n","repo_name":"Kevinpgalligan/BeatlesAndYou","sub_path":"scraping.py","file_name":"scraping.py","file_ext":"py","file_size_in_byte":6133,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"28756787330","text":"import openai\r\nimport config\r\nimport typer\r\nfrom rich import print\r\nfrom rich.table import Table\r\n\r\n\r\n\r\ndef main():\r\n openai.api_key = config.api_key\r\n\r\n #la parte que se mostrara primero del programa\r\n print(\"[bold green]ChatGPT con python[/bold green]\")\r\n\r\n #tabla de opciones \r\n table = Table(\"Comando\", \"Descripcion\")\r\n table.add_row(\"break\", \"salir del chat\")\r\n print(table)\r\n \r\n while True:\r\n text = input(\"Introduce algo: \")\r\n \r\n if text == \"break\":\r\n break\r\n \r\n # crear la solicitud de completado utilizando el estado actual del modelo\r\n completion = openai.Completion.create(\r\n engine=\"text-davinci-003\",\r\n prompt=text,\r\n max_tokens=2048\r\n )\r\n \r\n # imprimir la respuesta del modelo de IA\r\n message = completion.choices[0].text\r\n print(message)\r\n\r\n\r\n \r\n \r\nif __name__ == \"__main__\":\r\n typer.run(main)","repo_name":"eduardoLOEZ/chat-gpt","sub_path":"gpt.py","file_name":"gpt.py","file_ext":"py","file_size_in_byte":965,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"32688636501","text":"from bs4 import BeautifulSoup\nimport requests\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# Requests the raw HTML from the site\nwebpage = requests.get('https://content.codecademy.com/courses/beautifulsoup/cacao/index.html')\n\n\n# Creates BeautifulSoup object that will traverse the HTML\nsoup = BeautifulSoup(webpage.content, \"html.parser\")\n\n# -- START Making a historgram of the ratings data--\n# Pull all ratings into a list\nratings = soup.find_all(attrs = {\n \"class\" : \"Rating\"\n})\n\n# Pull all ratings into a list\nratings_list = []\n\n#append to ratings_list each rating from ratings and convert the values to float and start at element 1 of the list since it has the word \"Rating\"\nfor rating in ratings[1:]:\n ratings_list.append(float(rating.get_text()))\n\n# print(ratings_list)\n# print(ratings)\n\n#use Matplotlib to create a histogram of ratings_list\nplt.hist(ratings_list)\nplt.show()\n\n# END -- Making a historgram of the ratings data--\n\n#Find top 10 highest rated cholatier companies\n# alt method:\n# company_tags = soup.find_all(attrs = {\n# \"class\": \"Company\"\n# })\n\ncompany_tags = soup.select(\".Company\")\ncompany_names = []\nfor company in company_tags[1:]:\n company_names.append(company.get_text())\n\n# print(company_tags)\n# print(company_names)\n\n# #creates dictionary \n# company_rating = {\"Company\": company_names, \"Rating\": ratings_list}\n# #creates dataframe \n# company_rating_df = pd.DataFrame.from_dict(company_rating)\n# # print(company_rating_df)\n\n# #creates dataframe of top 10\n# mean_ratings = company_rating_df.groupby(\"Company\").Rating.mean()\n# top_ten = mean_ratings.nlargest(10)\n# # print(top_ten)\n\n#scrape cocoa perecent ratings\ncocoa_percent_tags = soup.select(\".CocoaPercent\")\ncocoa_percents = []\n\nfor cocoa_percentage in cocoa_percent_tags[1:]:\n cocoa_percents.append(float(cocoa_percentage.get_text().strip(\"%\")))\n# print(cocoa_percents)\n\n#create add columns to dataframe\ncompany_rating = {\"Company\": company_names, \"Rating\": ratings_list, \"CocoaPercentage\": cocoa_percents}\ncompany_rating_df = pd.DataFrame.from_dict(company_rating)\n# print(company_rating_df)\n\n#make scatterplot of ratings vs cocoa percentage\nplt.scatter(company_rating_df.CocoaPercentage, company_rating_df.Rating)\n\nz = np.polyfit(company_rating_df.CocoaPercentage, company_rating_df.Rating, 1)\nline_function = np.poly1d(z)\nplt.plot(company_rating_df.CocoaPercentage, line_function(company_rating_df.CocoaPercentage), \"r--\")\n\nplt.show()\nplt.clf()\n","repo_name":"ughdeeb/beautifulsoup-codecademy-project","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2465,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"33539351683","text":"def merge(left: list, right: list):\n result = []\n while left and right:\n if left[0] <= right[0]:\n result.append(left[0])\n left.pop(0)\n else:\n result.append(right[0])\n right.pop(0)\n\n result += left\n result += right\n return result\n\n\ndef merge_sort(a):\n len_ = len(a)\n if len_ < 2:\n return a\n right = a[len_ // 2:]\n left = a[:len_ // 2]\n\n right = merge_sort(right)\n left = merge_sort(left)\n\n return merge(left, right)\n","repo_name":"NLincoln/sorting-project","sub_path":"sorting/MergeSort.py","file_name":"MergeSort.py","file_ext":"py","file_size_in_byte":516,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"72479410724","text":"# Databricks notebook source\r\n# MAGIC %run ../custom_reader_notebook/delta/delta_add_date\r\n\r\n# COMMAND ----------\r\n\r\nfrom runtime.nutterfixture import NutterFixture, tag\r\nclass DateTestFixture(NutterFixture):\r\n def assertion_date(self):\r\n date = generate_date() # from notebook\r\n date_str = date.strftime(\"%B\")\r\n assert date_str==\"May\"\r\n\r\nresult = DateTestFixture().execute_tests()\r\nprint(result.to_string())\r\nis_job = dbutils.notebook.entry_point.getDbutils().notebook().getContext().currentRunId().isDefined()\r\nprint(is_job)\r\nresult.exit(dbutils)\r\nif is_job:\r\n print(\"ici\")\r\n result.exit(dbutils)\r\n\r\n\r\n# COMMAND ----------\r\n\r\n\r\n","repo_name":"GreGGus/custom_reader_notebook","sub_path":"tests/test_nutter_date.py","file_name":"test_nutter_date.py","file_ext":"py","file_size_in_byte":653,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"23704765400","text":"\"\"\"\n=============================================================\n~/tfat/tests/api/test_encounter_filters.py\nCreated: Jul-05-2021 15:32\nDESCRIPTION:\n\n The tests in this file ensure that the filters available for\n tag Encounter endpoint function as expected.\n\n Filters:\n\n + year\n + year__gte\n + year__lte\n + year__gt\n + year__lt\n + lake\n + lake__not\n + spc\n + spc__not\n + tagid\n + tagid__like\n + tagdoc\n + tagdoc__not\n + tagdoc__like\n + tag_origin\n + tag_origin__not\n + tag_position\n + tag_position__not\n + tag_type\n + tag_type__not\n + tag_colour\n + tag_colour__not\n + tagstat\n + tagstat__not\n + tlen\n + tlen__gte\n + tlen__lte\n + tlen__gt\n + tlen__lt\n + flen\n + flen__gte\n + flen__lte\n + flen__gt\n + flen__lt\n + rwt\n + rwt__null\n + rwt__gte\n + rwt__lte\n + rwt__gt\n + rwt__lt\n + sex\n + sex__not\n + sex__null\n + clipc\n + clipc__not\n + clipc__null\n + clipc__like\n + clipc__not_like\n + fate\n + fate__not\n\n\n\nA. Cottrill\n=============================================================\n\"\"\"\n\n\nfrom datetime import datetime\n\nimport pytest\nimport pytz\nfrom django.urls import reverse\nfrom rest_framework import status\n\nfrom ..factories import EncounterFactory, LakeFactory, ProjectFactory, SpeciesFactory\n\n\n@pytest.fixture\ndef encounters():\n\n superior = LakeFactory(abbrev=\"SU\", lake_name=\"Lake Superior\")\n huron = LakeFactory(abbrev=\"HU\", lake_name=\"Lake Huron\")\n ontario = LakeFactory(abbrev=\"ON\", lake_name=\"Lake Ontario\")\n\n walleye = SpeciesFactory(spc=\"334\", spc_nmco=\"walleye\")\n lake_trout = SpeciesFactory(spc=\"081\", spc_nmco=\"lake trout\")\n perch = SpeciesFactory(spc=\"331\", spc_nmco=\"yellow perch\")\n\n project0 = ProjectFactory(\n year=\"2012\", prj_cd=\"LSA_IS12_123\", prj_nm=\"Superior Index\", lake=superior\n )\n\n project1 = ProjectFactory(\n year=\"2015\",\n prj_cd=\"LHA_IA15_ABC\",\n prj_nm=\"Huron Community Index Netting\",\n lake=huron,\n )\n\n project2 = ProjectFactory(\n year=\"2017\",\n prj_cd=\"LOA_SC17_XYZ\",\n prj_nm=\"Lake Ontario Sport Creel\",\n lake=ontario,\n )\n\n fish0 = EncounterFactory(\n species=walleye,\n project=project0,\n observation_date=datetime(2013, 11, 11).replace(tzinfo=pytz.UTC),\n flen=225,\n tlen=250,\n rwt=None,\n clipc=\"0\",\n sex=9,\n fate=\"K\",\n tagstat=\"C\",\n tagid=\"1111\",\n tagdoc=\"25012\",\n # _tag_type=\"2\",\n # _tag_position=\"5\",\n # _tag_origin=\"01\",\n # _tag_colour=\"2\",\n )\n fish0.save()\n\n fish1 = EncounterFactory(\n species=perch,\n project=project0,\n observation_date=datetime(2013, 11, 11).replace(tzinfo=pytz.UTC),\n flen=275,\n tlen=300,\n rwt=500,\n clipc=\"2\",\n sex=1,\n fate=\"K\",\n tagstat=\"C\",\n tagid=\"2222\",\n tagdoc=\"45042\",\n # _tag_type=\"4\",\n # _tag_position=\"5\",\n # _tag_origin=\"04\",\n # _tag_colour=\"2\",\n )\n fish1.save()\n\n fish2 = EncounterFactory(\n species=walleye,\n project=project2,\n observation_date=datetime(2017, 11, 11).replace(tzinfo=pytz.UTC),\n flen=375,\n tlen=400,\n rwt=1000,\n clipc=\"5\",\n sex=1,\n fate=\"K\",\n tagstat=\"C\",\n tagid=\"3333\",\n tagdoc=\"34013\",\n # _tag_type=\"3\",\n # _tag_position=\"4\",\n # _tag_origin=\"01\",\n # _tag_colour=\"3\",\n )\n fish2.save()\n\n fish3 = EncounterFactory(\n species=lake_trout,\n project=project1,\n observation_date=datetime(2015, 11, 11).replace(tzinfo=pytz.UTC),\n flen=375,\n tlen=400,\n rwt=1100,\n clipc=\"0\",\n sex=2,\n fate=\"R\",\n tagstat=\"C\",\n tagid=\"4444\",\n tagdoc=\"43043\",\n # _tag_type=\"4\",\n # _tag_position=\"3\",\n # _tag_origin=\"04\",\n # _tag_colour=\"3\",\n )\n fish3.save()\n\n fish4 = EncounterFactory(\n species=lake_trout,\n project=project2,\n observation_date=datetime(2017, 11, 11).replace(tzinfo=pytz.UTC),\n flen=325,\n tlen=350,\n rwt=800,\n clipc=\"23\",\n sex=None,\n fate=\"R\",\n tagstat=\"A\",\n tagid=\"4444\",\n tagdoc=\"43994\",\n # _tag_type=\"4\",\n # _tag_position=\"3\",\n # _tag_origin=\"99\",\n # _tag_colour=\"4\",\n )\n fish4.save()\n\n fish5 = EncounterFactory(\n species=perch,\n project=project1,\n observation_date=datetime(2015, 11, 11).replace(tzinfo=pytz.UTC),\n flen=225,\n tlen=250,\n rwt=500,\n clipc=None,\n sex=None,\n fate=\"R\",\n tagstat=\"A\",\n tagid=\"5511\",\n tagdoc=\"25044\",\n # _tag_type=\"2\",\n # _tag_position=\"5\",\n # _tag_origin=\"04\",\n # _tag_colour=\"4\",\n )\n fish5.save()\n\n return [fish0, fish1, fish2, fish3, fish4, fish5]\n\n\nfilter_args = [\n # where, when, what and how:\n ({\"lake\": \"ON\"}, [2, 4]),\n ({\"lake__not\": \"ON\"}, [0, 1, 3, 5]),\n ({\"lake\": \"ON,HU\"}, [2, 3, 4, 5]),\n ({\"lake__not\": \"ON,HU\"}, [0, 1]),\n ({\"year\": \"2015\"}, [3, 5]),\n ({\"year__gte\": \"2015\"}, [2, 3, 4, 5]),\n ({\"year__lte\": \"2015\"}, [0, 1, 3, 5]),\n ({\"year__gt\": \"2015\"}, [2, 4]),\n ({\"year__lt\": \"2015\"}, [0, 1]),\n ({\"spc\": \"081\"}, [3, 4]),\n ({\"spc_not\": \"081\"}, [0, 1, 2, 5]),\n ({\"spc\": \"081,334\"}, [0, 2, 3, 4]),\n ({\"spc_not\": \"081,334\"}, [1, 5]),\n # fish attributes\n ({\"tlen\": 350}, [4]),\n ({\"tlen__gte\": 350}, [2, 3, 4]),\n ({\"tlen__lte\": 350}, [0, 1, 4, 5]),\n ({\"tlen__gt\": 350}, [2, 3]),\n ({\"tlen__lt\": 350}, [0, 1, 5]),\n ({\"flen\": 325}, [4]),\n ({\"flen__gte\": 325}, [2, 3, 4]),\n ({\"flen__lte\": 325}, [0, 1, 4, 5]),\n ({\"flen__gt\": 325}, [2, 3]),\n ({\"flen__lt\": 325}, [0, 1, 5]),\n ({\"rwt\": 800}, [4]),\n ({\"rwt__null\": True}, [0]),\n ({\"rwt__null\": False}, [1, 2, 3, 4, 5]),\n ({\"rwt__gte\": 800}, [2, 3, 4]),\n ({\"rwt__lte\": 800}, [1, 4, 5]),\n ({\"rwt__gt\": 800}, [2, 3]),\n ({\"rwt__lt\": 800}, [1, 5]),\n ({\"sex\": 1}, [1, 2]),\n ({\"sex\": \"1,9\"}, [0, 1, 2]),\n ({\"sex__not\": 1}, [0, 3, 4, 5]),\n ({\"sex__not\": \"1,9\"}, [3, 4, 5]),\n ({\"sex__null\": True}, [4, 5]),\n ({\"sex__null\": False}, [0, 1, 2, 3]),\n ({\"clipc\": 0}, [0, 3]),\n ({\"clipc\": \"2,5\"}, [1, 2]),\n ({\"clipc__not\": 0}, [1, 2, 4, 5]),\n ({\"clipc__not\": \"2,5\"}, [0, 3, 4, 5]),\n ({\"clipc__null\": True}, [5]),\n ({\"clipc__null\": False}, [0, 1, 2, 3, 4]),\n ({\"clipc__like\": 2}, [1, 4]),\n ({\"clipc__not_like\": 2}, [0, 2, 3, 5]),\n ({\"fate\": \"K\"}, [0, 1, 2]),\n ({\"fate__not\": \"K\"}, [3, 4, 5]),\n # tag attributes:\n ({\"tagid\": \"1111\"}, [0]),\n ({\"tagid__like\": \"11\"}, [0, 5]),\n ({\"tagdoc\": \"25012\"}, [0]),\n ({\"tagdoc__not\": \"25012\"}, [1, 2, 3, 4, 5]),\n ({\"tagdoc__like\": \"250\"}, [0, 5]),\n # TODO: refactor encounter model to include separate fields for tag attributes:\n # ({\"tag_origin\": \"01\"}, [0, 2]),\n # ({\"tag_origin__not\": \"01\"}, [1, 3, 4, 5]),\n # ({\"tag_origin\": \"01,99\"}, [0, 2, 4]),\n # ({\"tag_origin__not\": \"01,99\"}, [1, 3, 5]),\n # ({\"tag_position\": \"3\"}, [3, 4]),\n # ({\"tag_position\": \"3,4\"}, [2, 3, 4]),\n # ({\"tag_position__not\": \"3\"}, [0, 1, 2, 5]),\n # ({\"tag_position__not\": \"3,4\"}, [0, 1, 5]),\n # ({\"tag_type\": \"4\"}, [1, 3, 4]),\n # ({\"tag_type__not\": \"4\"}, [0, 2, 5]),\n # ({\"tag_type\": \"3,4\"}, [1, 2, 3, 4]),\n # ({\"tag_type__not\": \"3,4\"}, [0, 5]),\n # ({\"tag_colour\": \"2\"}, [0, 1]),\n # ({\"tag_colour__not\": \"2\"}, [2, 3, 4, 5]),\n # ({\"tag_colour\": \"2,3\"}, [0, 1, 2, 3]),\n # ({\"tag_colour__not\": \"2,3\"}, [4, 5]),\n ({\"tagstat\": \"A\"}, [4, 5]),\n ({\"tagstat__not\": \"A\"}, [0, 1, 2, 3]),\n ({\"tagstat\": \"C\"}, [0, 1, 2, 3]),\n ({\"tagstat\": \"A,C\"}, [0, 1, 2, 3, 4, 5]),\n]\n\n\n@pytest.mark.django_db\n@pytest.mark.parametrize(\"filter,expected\", filter_args)\ndef test_Encounter_filters(client, encounters, filter, expected):\n \"\"\"The readonly api endpoint for tag encounters accepts filters that are\n attributes of the project (lake, project code, proejct name, year) the\n encountered fish (species, size, sex, ect) as well as the tag attributes.\n\n This test is parameterized to accept a list of two element tuples, the first\n element is the filter to apply, the second is the list of indices that\n correspond to the angler records that should be returned in the response.\n The indices are used to extract the id from the fixture and compare those to\n the angler id's returned by the response.\n\n \"\"\"\n\n ids = []\n for i, x in enumerate(encounters):\n if i in expected:\n ids.append(x.id)\n\n url = reverse(\"tfat_api:encounter-list\")\n response = client.get(url, filter)\n assert response.status_code == status.HTTP_200_OK\n\n # pull out the ids from the response:\n payload = response.data[\"results\"]\n observed_ids = {x[\"id\"] for x in payload}\n\n assert len(payload) == len(expected)\n assert set(ids) == observed_ids\n","repo_name":"AdamCottrill/TFAT","sub_path":"tfat/tests/api/test_encounter_filters.py","file_name":"test_encounter_filters.py","file_ext":"py","file_size_in_byte":9213,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"21044756387","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Nov 23 13:51:41 2021\r\n\r\n@author: Adi Kurhade\r\n\"\"\"\r\n\r\nimport streamlit as st\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\n\r\ndef shorten_categories(categories, cutoff):\r\n categorical_map = {}\r\n for i in range(len(categories)):\r\n if categories.values[i] >= cutoff:\r\n categorical_map[categories.index[i]] = categories.index[i]\r\n else:\r\n categorical_map[categories.index[i]] = \"others\"\r\n return categorical_map\r\n\r\n\r\ndef clean_experience(years):\r\n if years == \"Less than 1 year\":\r\n return 0.5\r\n if years == \"More than 50 years\":\r\n return 50\r\n return float(years)\r\n\r\n\r\ndef clean_education(x):\r\n if 'Bachelor’s degree' in x:\r\n return 'Bachelor’s degree'\r\n if 'Master’s degree' in x:\r\n return 'Master’s degree'\r\n if 'Professional degree' in x or 'Other doctoral' in x:\r\n return 'Post grad'\r\n return 'Less than a Bachelors'\r\n\r\n@st.cache\r\ndef load_data():\r\n df = pd.read_csv(\"survey_results_public.csv\")\r\n df = df[[\"Country\",\r\n \"EdLevel\",\r\n \"YearsCodePro\",\r\n \"Employment\",\r\n \"ConvertedCompYearly\"\r\n ]]\r\n df = df.rename({\"ConvertedCompYearly\":\"Salary\"}, axis=1)\r\n df = df[df.Salary.notnull()]\r\n df = df.dropna()\r\n df1 = df[df[\"Employment\"]== \"Employed full-time\"]\r\n df1.drop(['Employment'],axis=1, inplace=True)\r\n \r\n country_map = shorten_categories(df1.Country.value_counts(), 400)\r\n df1['Country'] = df1['Country'].map(country_map)\r\n df1 = df1[df1[\"Salary\"] <= 250000]\r\n df1 = df1[df1[\"Salary\"] >= 10000]\r\n df1 = df1[df1['Country'] != 'Other']\r\n \r\n df1['YearsCodePro'] = df1['YearsCodePro'].apply(clean_experience)\r\n df1['Education'] = df1['EdLevel'].apply(clean_education)\r\n df1.drop(\"EdLevel\",axis =1, inplace=True)\r\n return df1\r\n\r\ndf = load_data()\r\n\r\ndef show_explore_page():\r\n st.title(\"Explore Software Engineer Salary\")\r\n st.write(\"\"\"\r\n ### Stack Overflow Developer Survey 2021\r\n \"\"\"\r\n )\r\n data = df['Country'].value_counts()\r\n\r\n fig1, ax1 = plt.subplots()\r\n ax1.pie(data, labels = data.index, autopct=\"%1.1f%%\",shadow = True,\r\n startangle =90)\r\n ax1.axis(\"equal\")\r\n\r\n st.write(\"\"\"### Number of Data from different Countries\"\"\")\r\n st.pyplot(fig1)\r\n\r\n st.write(\r\n \"\"\"\r\n ### Mean Salary based on country\r\n \"\"\"\r\n )\r\n\r\n data = df.groupby([\"Country\"])[\"Salary\"].mean().sort_values(ascending=True)\r\n st.bar_chart(data)\r\n \r\n st.write(\"\"\"\r\n ### Mean salary based on Experience\r\n \"\"\")\r\n data = df.groupby([\"YearsCodePro\"])[\"Salary\"].mean().sort_values(ascending=True)\r\n st.line_chart(data)\r\n \r\n \r\n \r\n\r\n","repo_name":"Adityakurhade/stackoverflow_data_salary_prediction","sub_path":"explore_page.py","file_name":"explore_page.py","file_ext":"py","file_size_in_byte":2777,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"72501995044","text":"def makeArrayConsecutive2(statues):\n\n s = sorted(statues[:])\n f = 1\n m = 0\n\n while f < len(s):\n if s[f] - s[f - 1] > 1:\n m += s[f] - s[f - 1] - 1\n\n f += 1\n\n return m\n","repo_name":"kanglicheng/codefights","sub_path":"ArcadeUniverse/Intro/EdgeOfTheOcean/MakeArrayConsecutive2.py","file_name":"MakeArrayConsecutive2.py","file_ext":"py","file_size_in_byte":206,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"11719435611","text":"import numpy as np\n\nfrom problems.toy_biobjective import circle_points, concave_fun_eval, create_pf\nfrom solvers import epo_search, pareto_mtl_search, linscalar, moo_mtl_search\n\nimport matplotlib.pyplot as plt\nfrom latex_utils import latexify\n\nif __name__ == '__main__':\n K = 4 # Number of trajectories\n n = 20 # dim of solution space\n m = 2 # dim of objective space\n rs = circle_points(K) # preference\n\n pmtl_K = 5\n pmtl_refs = circle_points(pmtl_K, 0, np.pi / 2)\n methods = ['EPO', 'PMTL', 'MOOMTL', 'LinScalar']\n latexify(fig_width=2., fig_height=1.55)\n ss, mi = 0.1, 100\n pf = create_pf()\n for method in methods:\n fig, ax = plt.subplots()\n fig.subplots_adjust(left=.12, bottom=.12, right=.97, top=.97)\n ax.plot(pf[:, 0], pf[:, 1], lw=2, c='k', label='Pareto Front')\n last_ls = []\n for k, r in enumerate(rs):\n r_inv = 1. / r\n ep_ray = 1.1 * r_inv / np.linalg.norm(r_inv)\n ep_ray_line = np.stack([np.zeros(m), ep_ray])\n label = r'$r^{-1}$ ray' if k == 0 else ''\n ax.plot(ep_ray_line[:, 0], ep_ray_line[:, 1], color='k',\n lw=1, ls='--', dashes=(15, 5), label=label)\n ax.arrow(.95 * ep_ray[0], .95 * ep_ray[1],\n .05 * ep_ray[0], .05 * ep_ray[1],\n color='k', lw=1, head_width=.02)\n # x0 = np.random.randn(n) * 0.4\n x0 = np.zeros(n)\n x0[range(0, n, 2)] = 0.3\n x0[range(1, n, 2)] = -.3\n x0 += 0.1 * np.random.randn(n)\n x0 = np.random.uniform(-0.6, 0.6, n) if method in [\"MOOMTL\", \"LinScalar\"] else x0\n if method == 'EPO':\n _, res = epo_search(concave_fun_eval, r=r, x=x0,\n step_size=ss, max_iters=100)\n if method == 'PMTL':\n _, res = pareto_mtl_search(concave_fun_eval,\n ref_vecs=pmtl_refs, r=r_inv, x=x0,\n step_size=0.2, max_iters=150)\n if method == 'LinScalar':\n _, res = linscalar(concave_fun_eval, r=r, x=x0,\n step_size=ss, max_iters=mi)\n if method == 'MOOMTL':\n _, res = moo_mtl_search(concave_fun_eval, x=x0,\n step_size=0.2, max_iters=150)\n last_ls.append(res['ls'][-1])\n last_ls = np.stack(last_ls)\n ax.scatter(last_ls[:, 0], last_ls[:, 1], s=40, c='b', alpha=1)\n ax.set_xlabel(r'$l_1$')\n ax.set_ylabel(r'$l_2$')\n ax.xaxis.set_label_coords(1.015, -0.03)\n ax.yaxis.set_label_coords(-0.01, 1.01)\n ax.spines['right'].set_color('none')\n ax.spines['top'].set_color('none')\n fig.savefig('figures/' + method + '.pdf')\n\n plt.show()\n","repo_name":"dbmptr/EPOSearch","sub_path":"toy_experiments/compare_solvers.py","file_name":"compare_solvers.py","file_ext":"py","file_size_in_byte":2878,"program_lang":"python","lang":"en","doc_type":"code","stars":46,"dataset":"github-code","pt":"52"} +{"seq_id":"27325634607","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Apr 29 18:33:29 2021\n\n@author: cijzendoornvan\n\"\"\"\n\n######################\n# PACKAGES\n######################\nimport yaml\nimport os\nimport pickle\nimport numpy as np\nimport matplotlib.pyplot as plt \nimport matplotlib.gridspec as gridspec\nfrom JAT.Jarkus_Analysis_Toolbox import Transects, Extraction\n\n######################\n# LOAD SETTINGS\n######################\nconfig = yaml.safe_load(open(r\"C:\\Users\\cijzendoornvan\\OneDrive - Delft University of Technology\\Documents\\DuneForce\\JARKUS\\JAT\\Examples\\03_extract_all\\jarkus_03.yml\"))\nlocation_filter = yaml.safe_load(open(config['inputdir'] + config['data locations']['LocFilter']))\nplot_titles = yaml.safe_load(open(config['inputdir'] + config['data locations']['Titles'])) \n \nDirFigures = config['outputdir'] + config['save locations']['DirFig']\nif os.path.isdir(DirFigures) == False:\n os.mkdir(DirFigures)\n\n# Load jarkus dataset\ndata = Transects(config)\nconversion_alongshore2ids, conversion_ids2alongshore = data.get_conversion_dicts()\n\ndef get_distribution_plot(variable, dimension, figure_title, colorbar_label, start_yr, end_yr, Dir):\n \n # Create an array with locations and an array with labels of the ticks\n ticks_x = [350, 1100, 1700]\n labels_x = ['Wadden Coast', 'Holland Coast', 'Delta Coast']\n \n years_requested = list(range(start_yr, end_yr+1))\n ticks_y = range(0, len(years_requested))[0::5]\n labels_y = [str(yr) for yr in years_requested][0::5]\n \n dimension.rename(columns = conversion_ids2alongshore, inplace = True)\n dimension = dimension.sort_index(axis=1) # Flip transects based on alongshore order instead of ids order.\n # dimension.rename(columns = conversion_alongshore2ids, inplace = True)\n \n # Calculate overall average and stddev, used for range of colorbar\n average = np.nanmean(dimension.values)\n stddev = np.nanstd(dimension.values, ddof=1)\n range_value = 2*stddev\n vmin = average - range_value\n vmax = average + range_value\n \n average_through_space = dimension.mean(axis=0)\n average_through_time = dimension.mean(axis=1)\n vmin_avg = average_through_time.min()\n vmax_avg = average_through_time.max()\n \n # Set-up of figure\n fig = plt.figure(figsize=(20,10)) \n fig.suptitle(figure_title, fontsize=24)\n gs = gridspec.GridSpec(2, 2, width_ratios=[3, 1], height_ratios=[3,2]) \n \n # PLOT TEMPORAL AND SPATIAL DISTRIBUTION OF VARIABLE\n ax1 = fig.add_subplot(gs[0])\n cmap = plt.cm.get_cmap('Greens') # Define color use for colorbar\n colorplot = plt.pcolor(dimension.loc[start_yr:end_yr], vmin=vmin, vmax=vmax, cmap=cmap)\n # Set labels and ticks of x and y axis\n plt.yticks(ticks_y, labels_y)\n plt.tick_params(axis='y', which='both',length=5, labelsize = 16)\n plt.xticks(ticks_x, labels_x) #rotation='vertical')\n plt.tick_params(axis='x', which='both',length=0, labelsize = 20)\n # plot boundaries between coastal regions\n plt.axvline(x=686, color='r') # Boundary kustvak 6 en 7, Den Helder, trsct 7000000, decameter 23417\n plt.axvline(x=1507, color='r')# Boundary kustvak 9 en 10, Hoek van Holland, trsct 10000000, decameter 35908\n \n # PLOT YEARLY AVERAGE OF VARIABLE\n ax2 = fig.add_subplot(gs[1])\n plt.plot(average_through_time, average_through_time.index, color='green')\n #plt.scatter(average_through_time, average_through_time.index, c=average_through_time, cmap=cmap, vmin=vmin, vmax=vmax)\n # Set labels and ticks of x and y axis\n ticks_y = average_through_time.index[0::5]\n plt.xlabel(colorbar_label)\n plt.yticks(ticks_y, labels_y)\n plt.tick_params(axis='y', which='both',length=5, labelsize = 16)\n plt.xlim([vmin_avg-0.75*stddev, vmax_avg+0.75*stddev])\n plt.tick_params(axis='x', which='both',length=5, labelsize = 16)\n \n # PLOT SPATIAL AVERAGES OF VARIABLE\n ax3 = fig.add_subplot(gs[2])\n plt.scatter(range(0, len(average_through_space)), average_through_space, c=average_through_space, cmap=cmap, vmin=vmin, vmax=vmax)\n # Set labels and ticks of x and y axis\n plt.xlim([0, len(average_through_space)])\n plt.xticks(ticks_x, labels_x) \n plt.ylabel(colorbar_label)\n plt.tick_params(axis='x', which='both',length=0, labelsize = 20)\n plt.ylim([vmin, vmax])\n plt.tick_params(axis='y', which='both',length=5, labelsize = 16)\n plt.axvline(x=686, color='r') # Boundary kustvak 6 en 7, Den Helder, trsct 7000000, decameter 23417\n plt.axvline(x=1507, color='r')# Boundary kustvak 9 en 10, Hoek van Holland, trsct 10000000, decameter 35908\n\n # Add colorbar\n fig.subplots_adjust(right=0.88)\n cbar_ax = fig.add_axes([0.90, 0.10, 0.02, 0.78])\n cbar = fig.colorbar(colorplot, cax=cbar_ax)\n cbar.set_label(colorbar_label,size=18, labelpad = 20)\n cbar.ax.tick_params(labelsize=16) \n \n plt.tight_layout\n \n manager = plt.get_current_fig_manager()\n manager.window.showMaximized()\n plt.show()\n\n plt.savefig(Dir + variable + '_distribution' + '_plot.png')\n\n plt.close()\n\n#%%##############################\n#### EXECUTE ####\n#################################\nstart_yr = 1965 \nend_yr = 2021\n\nextract = Extraction(data, config) # initalize the extra class \nvariables = extract.get_requested_variables() # get all variables that were requested (based on jarkus.yml file)\n\nfor variable in variables:\n print(variable)\n # Get title and label from yml file\n figure_title = plot_titles[variable][0]\n colorbar_label = plot_titles[variable][1]\n \n # Create distribution plots for unfiltered dataframes\n dimension_unfilt = pickle.load(open(config['outputdir'] + config['save locations']['DirD'] + variable + '_dataframe.pickle','rb')) \n DirFig_unfilt = config['outputdir'] + config['save locations']['DirFig'] + config['save locations']['DirD']\n if os.path.isdir(DirFig_unfilt) == False:\n os.mkdir(DirFig_unfilt)\n get_distribution_plot(variable, dimension_unfilt, figure_title, colorbar_label, start_yr, end_yr, DirFig_unfilt)\n\n # Create distribution plots for filtered dataframes\n dimension_filt = pickle.load(open(config['outputdir'] + config['save locations']['DirE'] + variable + '_filtered_dataframe.pickle','rb')) \n DirFig_filt = config['outputdir'] + config['save locations']['DirFig'] + config['save locations']['DirE']\n if os.path.isdir(DirFig_filt) == False:\n os.mkdir(DirFig_filt)\n get_distribution_plot(variable, dimension_filt, figure_title, colorbar_label, start_yr, end_yr, DirFig_filt)\n","repo_name":"christavanijzendoorn/JAT","sub_path":"Examples/03_extract_all/Distribution_plots.py","file_name":"Distribution_plots.py","file_ext":"py","file_size_in_byte":6560,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"52"} +{"seq_id":"17936337647","text":"# -*- coding: utf-8 -*-\nimport logging\nimport simplejson\n\nfrom django.shortcuts import render\nfrom django.http import HttpResponse\n\nfrom management.models import WorkRecord, Service\nfrom services.utils import get_resident, new_year_day\nfrom ehr.forms import BodyExamForm\nfrom ehr.models import BodyExam\n\nfrom .forms import AftercareForm\nfrom .models import Aftercare\n\ndebug = logging.getLogger('debug')\n\n\ndef aftercare_page(request):\n return render(request, 'diabetes/aftercare_page.html')\n\n\ndef aftercare_review(request):\n resident = get_resident(request)\n\n context = {'aftercare_1': None, 'aftercare_2': None,\n 'aftercare_3': None, 'aftercare_4': None}\n\n for aftercare in context:\n service_item = Service.items.get(alias=aftercare, service_type__alias='diabetes')\n try:\n record = WorkRecord.objects.get(resident=resident, service_item=service_item)\n except WorkRecord.DoesNotExist:\n pass\n else:\n context[aftercare] = Aftercare.objects.get(id=record.item_id)\n context['resident'] = resident\n return render(request, 'diabetes/aftercare_review.html', context)\n\n\ndef aftercare_form(request):\n aftercare = request.POST.get('aftercare')\n form = AftercareForm()\n resident = get_resident(request)\n template = 'diabetes/aftercare_%s_form_content.html' % aftercare\n\n #因为要算体质指数，所以要把一般体格检查里的身高取得\n record = WorkRecord.objects.filter(resident=resident, model_name='BodyExam',\n submit_time__gte=new_year_day()).first()\n if record:\n body_exam = BodyExam.objects.get(id=record.item_id)\n sign_height = body_exam.height\n else:\n sign_height = 0.0\n return render(request, template, {'form': form, 'resident': resident, 'sign_height': sign_height})\n\n\ndef aftercare_submit(request):\n success = False\n resident = get_resident(request)\n item_alias = request.POST.get('aftercare')\n service_item = Service.items.get(alias=item_alias, service_type__alias='diabetes')\n form = AftercareForm(request.POST)\n if form.is_valid():\n result = form.save()\n record = WorkRecord(provider=request.user, resident=resident, service_item=service_item,\n app_label='diabetes', model_name='Aftercare',\n item_id=result.id, service_item_alias=service_item.alias)\n record.save()\n success = True\n else:\n debug.info(form.errors.as_data())\n\n return HttpResponse(simplejson.dumps({'success': success}),\n content_type='text/html; charset=UTF-8')\n\n\ndef aftercare_supplement_page(request):\n return render(request, 'diabetes/aftercare_supplement_page.html')\n\n\ndef aftercare_supplement_review(request):\n resident = get_resident(request)\n context = {'aftercare_5': None, 'aftercare_6': None}\n for aftercare in context:\n service_item = Service.items.get(alias=aftercare, service_type__alias='diabetes')\n try:\n record = WorkRecord.objects.get(resident=resident, service_item=service_item)\n except WorkRecord.DoesNotExist:\n pass\n else:\n context[aftercare] = Aftercare.objects.get(id=record.item_id)\n context['resident'] = resident\n\n return render(request, 'diabetes/aftercare_supplement_review.html', context)\n\n\ndef body_exam_page(request):\n return render(request, 'diabetes/body_exam_page.html')\n\n\ndef body_exam_form(request):\n resident = get_resident(request)\n records = WorkRecord.objects.filter(resident=resident,\n model_name='BodyExam',\n submit_time__gte=new_year_day())\n if records.count():\n result = BodyExam.objects.get(id=records[0].item_id)\n form = BodyExamForm(instance=result)\n else:\n form = BodyExamForm()\n\n return render(request, 'ehr/body_exam_form.html', {'form': form, 'resident': resident,\n 'type_alias': 'diabetes'})\n\n\ndef body_exam_submit(request):\n submit_data = request.POST.copy()\n if 'csrfmiddlewaretoken' in submit_data:\n submit_data.pop('csrfmiddlewaretoken')\n\n if submit_data:\n resident = get_resident(request)\n record = WorkRecord.objects.filter(resident=resident, model_name='BodyExam',\n submit_time__gte=new_year_day()).first()\n if record:\n result, created = BodyExam.objects.update_or_create(id=record.item_id, defaults=submit_data)\n success = True\n else:\n form = BodyExamForm(submit_data)\n if form.is_valid():\n result = form.save(commit=False)\n result.resident = resident\n result.save()\n success = True\n else:\n success, message = False, u'数据保存到数据库时失败'\n if success:\n service_item = Service.items.get(alias='physical_examination',\n service_type__alias='diabetes')\n WorkRecord.objects.create(provider=request.user, resident=resident, service_item=service_item,\n app_label='diabetes', model_name='BodyExam', item_id=result.id,\n service_item_alias=service_item.alias)\n message = u'记录保存成功'\n else:\n success, message = False, u'没有提交任何数据结果'\n\n return HttpResponse(simplejson.dumps({'success': success, 'message': message}),\n content_type='text/html; charset=UTF-8')\n","repo_name":"1330018801/public_health","sub_path":"diabetes/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5706,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"1483964003","text":"from flask_restful import Resource\nfrom flask_security import current_user\nfrom ..utils.response_format import build_data_response\n\nclass UserGetView(Resource):\n def get(self):\n if current_user.is_anonymous:\n response = build_data_response({\"user_id\": None, \"username\": None, \"email\": None}, 200)\n else:\n response = build_data_response({\n \"user_id\": current_user.id,\n \"username\": current_user.username,\n \"email\": current_user.email,\n },\n 200,)\n return response\n\n","repo_name":"xemedo/flask-angular-security","sub_path":"app/views/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":575,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"9622585813","text":"\"\"\"Search related views.\"\"\"\nfrom django import http\nfrom django import views\n\nfrom core.search import search_api\nfrom core import data_api\n\n\nclass SearchView(views.View):\n \"\"\"Searches for works similar to a given set.\"\"\"\n\n MAX_QUERY_WORKS = 200\n\n def get(self, request):\n work_info_dicts = []\n work_ids = self._extract_work_ids(request)\n if work_ids:\n similar_work_ids, trope_id_to_weight = (\n search_api.get_similar_books(\n work_ids))\n if similar_work_ids:\n work_info_dicts = data_api.get_work_info_dicts_by_id(\n similar_work_ids,\n allowed_trope_id_to_weight=trope_id_to_weight)\n return http.JsonResponse(\n {'results': work_info_dicts})\n\n def _extract_work_ids(self, request):\n \"\"\"Turns query parameter into a set of work ids.\"\"\"\n work_ids = set([])\n if request.GET.getlist('works'):\n work_ids = work_ids.union(set(\n data_api.get_work_ids_by_name(\n request.GET.getlist('works'))))\n query = request.GET.get('query')\n if query:\n work_id = search_api.get_work_id_for_search_query(query)\n if work_id is not None:\n work_ids.add(work_id)\n return set(list(work_ids)[:SearchView.MAX_QUERY_WORKS])\n\n\nclass AutocompleteView(views.View):\n \"\"\"Autocompleter for work names.\"\"\"\n\n def get(self, request):\n query = request.GET.get('query')\n if query:\n suggestions = search_api.get_autocomplete_suggestions(query)\n else:\n suggestions = []\n return http.JsonResponse(\n {'suggestions': suggestions})\n","repo_name":"romack77/bookslikethis","sub_path":"bookslikethis/core/views/search_views.py","file_name":"search_views.py","file_ext":"py","file_size_in_byte":1732,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"13995086662","text":"import json\nfrom path import Path as path\nimport pandas as pd\nfrom collections import defaultdict, Counter\n\ndef convert_fname(fname):\n output = fname\n output = output.replace(\":\",\"\")\n output = output.replace(\"_\",\"__\")\n output = output.replace(\"/\",\"_\")\n output = output.replace(\"\\\\\",\"_\")\n output = str(path(output).stem) + \".jpg\"\n return output\n\ndef txt_to_list(fname):\n with open(fname,'r', encoding='utf-8') as f:\n return f.read().split(\"\\n\")\n \ndef list_to_txt(mylist, fname):\n with open(fname,'w', encoding='utf-8') as f:\n f.write(\"\\n\".join(mylist))\n \ndef read_json(jsonfile):\n with open(jsonfile, 'r', encoding='utf-8') as f:\n return json.loads(f.read())\n \ndef write_json(h, jsonfile):\n with open(jsonfile,'w', encoding='utf-8') as f:\n f.write(json.dumps(h))\n \ndef folder_report(folder, prefix, n):\n counts = defaultdict(int)\n files = [f for f in path(folder).files() if f.name.startswith(prefix)]\n print(len(files))\n for f in files:\n key = f.split(\"_\")[n]\n counts[key] += 1\n keys = list(counts)\n values = [counts[k] for k in keys]\n df = pd.DataFrame()\n df['keys'] = keys\n df['values'] = values\n df.sort_values('values', axis=0, ascending= False, inplace = True)\n return df\n\ndef find_and_add_substrings(s, t):\n result = []\n start = 0\n while True:\n index = s.find(t, start)\n if index == -1:\n break\n result.append(s[:index + len(t)])\n start = index + 1\n return result\n\ndef get_most_common_folders(file_paths, N):\n folder_counts = Counter()\n \n # Count occurrences of each folder\n for file_path in file_paths:\n for folder in find_and_add_substrings(file_path, \"/\"):\n folder_counts[folder] += 1\n \n # Sort folders by counts in descending order\n sorted_folders = sorted(folder_counts.items(), key=lambda x: x[1], reverse=True)\n \n # Select top N folders\n most_common_folders = sorted_folders[:N]\n \n return most_common_folders","repo_name":"xjdeng/sd_tools","sub_path":"filetools.py","file_name":"filetools.py","file_ext":"py","file_size_in_byte":2049,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"74101819365","text":"# coding: utf-8\n\n# In[1]:\n\n\n# ----------------------------------拟合圆，得到圆心和半径------------------------------------------#\nfrom numpy import * # 导入numpy的库函数\n\n\n\ndef circleLeastFit(points):\n center_x = 0.0\n center_y = 0.0\n radius = 0.0\n\n sum_x = sum_y = 0.0\n sum_x2 = sum_y2 = 0.0\n sum_x3 = sum_y3 = 0.0\n sum_xy = sum_x1y2 = sum_x2y1 = 0.0\n N = len(points)\n for i in range(1, N):\n x = points[i][0]\n y = points[i][1]\n x2 = x * x\n y2 = y * y\n sum_x += x\n sum_y += y\n sum_x2 += x2\n sum_y2 += y2\n sum_x3 += x2 * x\n sum_y3 += y2 * y\n sum_xy += x * y\n sum_x1y2 += x * y2\n sum_x2y1 += x2 * y\n\n C = D = E = G = H = 0.0\n a = b = c = 0.0\n C = N * sum_x2 - sum_x * sum_x\n D = N * sum_xy - sum_x * sum_y\n E = N * sum_x3 + N * sum_x1y2 - (sum_x2 + sum_y2) * sum_x\n G = N * sum_y2 - sum_y * sum_y\n H = N * sum_x2y1 + N * sum_y3 - (sum_x2 + sum_y2) * sum_y\n a = (H * D - E * G) / (C * G - D * D)\n b = (H * C - E * D) / (D * D - G * C)\n c = -(a * sum_x + b * sum_y + sum_x2 + sum_y2) / N\n\n center_x = a / (-2)\n center_y = b / (-2)\n radius = sqrt(a * a + b * b - 4 * c) / 2\n\n return center_x, center_y, round(radius, 2)\n\n\n# In[2]:\n\n\n# ----------------------------------分析图片，得到响应json--------------------------------------#\n# -*- coding: utf-8 -*-\nimport urllib.request\nimport urllib.error\nimport time\nimport json\n\n\ndef faceIdentify(fr):\n http_url = 'https://api-cn.faceplusplus.com/facepp/v3/detect'\n key = \"enFC8NNZcf19CNRZGhGMvGKPHtMgVkfB\"\n secret = \"kIV15LIXTSX0gKflGEYHXekj8-mgEuHE\"\n # filepath = r\"gg.jpg\"\n\n boundary = '----------%s' % hex(int(time.time() * 1000))\n data = []\n data.append('--%s' % boundary)\n data.append('Content-Disposition: form-data; name=\"%s\"\\r\\n' % 'api_key')\n data.append(key)\n\n data.append('--%s' % boundary)\n data.append('Content-Disposition: form-data; name=\"%s\"\\r\\n' % 'api_secret')\n data.append(secret)\n\n data.append('--%s' % boundary)\n data.append('Content-Disposition: form-data; name=\"%s\"; filename=\" \"' % 'image_file')\n data.append('Content-Type: %s\\r\\n' % 'application/octet-stream')\n data.append(fr.read())\n fr.close()\n data.append('--%s' % boundary)\n data.append('Content-Disposition: form-data; name=\"%s\"\\r\\n' % 'return_landmark')\n data.append('2')\n data.append('--%s' % boundary)\n data.append('Content-Disposition: form-data; name=\"%s\"\\r\\n' % 'return_attributes')\n data.append(\n \"gender,age,smiling,headpose,facequality,blur,eyestatus,emotion,ethnicity,beauty,mouthstatus,eyegaze,skinstatus\")\n data.append('--%s--\\r\\n' % boundary)\n\n for i, d in enumerate(data):\n if isinstance(d, str):\n data[i] = d.encode('utf-8')\n\n http_body = b'\\r\\n'.join(data)\n\n # build http request\n req = urllib.request.Request(url=http_url, data=http_body)\n\n # header\n req.add_header('Content-Type', 'multipart/form-data; boundary=%s' % boundary)\n\n try:\n # post data to server\n resp = urllib.request.urlopen(req, timeout=5)\n # get response\n qrcont = resp.read()\n # if you want to load as json, you should decode first,\n # for example: json.loads(qrcont.decode('utf-8'))\n text = json.loads(qrcont.decode('utf-8'))\n tt = text.get('faces')[0].get('landmark')\n return tt\n except urllib.error.HTTPError as e:\n print(e.read().decode('utf-8'))\n return 'wrong'\n\n\n# In[3]:\n\n\n# ---------------------------------------特征点分析----------------------------------------------#\nimport math\n\n\ndef qradium(a, b, t):\n list1 = []\n for i in range(a, b):\n tt = t.get('contour_left' + str(i))\n list1.append([tt.get('x'), tt.get('y')])\n x, y, r = circleLeastFit(list1)\n return r\n\n\ndef chinhu(t):\n list2 = []\n for i in range(14, 17):\n text2 = t.get('contour_left' + str(i))\n list2.append([text2.get('x'), text2.get('y')])\n text3 = t.get('contour_right' + str(i))\n list2.append([text3.get('x'), text3.get('y')])\n text4 = t.get('contour_chin')\n list2.append([text4.get('x'), text4.get('y')])\n x, y, r = circleLeastFit(list2);\n return r\n\n\n# -----------------------------调用这个方法得到七个特征---------------------------------#\ndef handle_features(text1):\n w1 = round(math.sqrt((text1.get('contour_right1').get('x') - text1.get('contour_left1').get('x')) ** 2 + (\n text1.get('contour_right1').get('y') - text1.get('contour_left1').get('y')) ** 2), 2)\n w2 = round(math.sqrt((text1.get('contour_right3').get('x') - text1.get('contour_left3').get('x')) ** 2 + (\n text1.get('contour_right3').get('y') - text1.get('contour_left3').get('y')) ** 2), 2)\n w3 = round(math.sqrt((text1.get('contour_right9').get('x') - text1.get('contour_left9').get('x')) ** 2 + (\n text1.get('contour_right9').get('y') - text1.get('contour_left9').get('y')) ** 2), 2)\n h = round(math.sqrt((text1.get('contour_chin').get('x') - text1.get('nose_bridge1').get('x')) ** 2 + (\n text1.get('contour_chin').get('y') - text1.get('nose_bridge1').get('y')) ** 2), 2)\n r1 = qradium(1, 7, text1)\n r2 = qradium(7, 14, text1)\n r3 = chinhu(text1)\n\n return w1 / h, w2 / h, w3 / h, h / h, r1 / h, r2 / h, r3 / h\n\n\nfrom sklearn.externals import joblib\nclf = joblib.load('./FHModel/faceShape/clf.pkl')\n\n\ndef get_face(img):\n x = []\n text = faceIdentify(img)\n w1, w2, w3, h, r1, r2, r3 = handle_features(text)\n x = [[w1 / h, w2 / h, w3 / h, h / h, r1 / h, r2 / h, r3 / h]]\n y = clf.predict(x)\n # print(y)\n return y\n","repo_name":"e-yi/hairBook","sub_path":"FHModel/faceShape/get_features.py","file_name":"get_features.py","file_ext":"py","file_size_in_byte":5723,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"74187643354","text":"import csv\nimport schedule\nimport time\n\n# def record(name, age):\n# with open('catalog.csv', 'a+') as f:\n# writer = csv.writer(f, delimiter='/')\n# writer.writerow((name, age))\n# con = input('con? ')\n# while con == 'yes':\n# record(input(), int(input()))\n\n\n\ndef write_csv():\n name = input('Enter name: ')\n age = input('Enter age: ')\n with open('users.csv', 'a') as f:\n writer = csv.writer(f, delimiter='/')\n writer.writerow(\n (name, age)\n )\n answ = input('Continue? y or n : ')\n if answ == 'y':\n write_csv()\n else:\n print('stop!')\n\nwrite_csv()\n\ndef mailing():\n with open('users.csv', 'r') as f:\n data = f.readlines()\n names = [i.replace('\\n','') for i in data]\n for i in names:\n name = i.split('/')\n if int(name[-1]) >= 18:\n print(f'Скидки сегодня! {name[0]}')\n\nschedule.every(3).second.do(mailing)\n\nwhile True:\n schedule.run_pending()\n time.sleep(1)\n\n","repo_name":"dasmatoxx/schedule_profect","sub_path":"git.py","file_name":"git.py","file_ext":"py","file_size_in_byte":1014,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"27214941116","text":"import pymysql\nimport threading\nimport configparser\nimport sys\nimport os\nimport os.path\n\ndef runOnWorker(worker, keyPath, command):\n print(\"########\"+worker['ipAddress']+\"########\")\n os.system(\"ssh -oStrictHostKeyChecking=no -i \\\"\"+keyPath+\"\\\" ubuntu@\"+worker['ipAddress']+\" '\"+command+\"'\")\n print(\"########\"+worker['ipAddress']+\"########\")\n\nparser = configparser.ConfigParser()\nparser.read(\"../halite.ini\")\nDB_CONFIG = parser[\"database\"]\n\nkeyPath = os.path.join(\"../\", parser[\"aws\"][\"keyfilepath\"])\n\ndb = pymysql.connect(host=DB_CONFIG[\"hostname\"], user=DB_CONFIG['username'], passwd=DB_CONFIG['password'], db=DB_CONFIG['name'], cursorclass=pymysql.cursors.DictCursor)\ncursor = db.cursor()\n\ncursor.execute(\"select * from Worker\")\nworkers = cursor.fetchall()\n\ncommand = sys.argv[1]\n\nisAsync = False if len(sys.argv) < 3 else int(sys.argv[2]) == 1\n\nif isAsync:\n threads = []\n for worker in workers:\n t = threading.Thread(target=runOnWorker, args = (worker, keyPath, command))\n t.daemon = True\n t.start()\n threads.append(t)\n\n for t in threads:\n t.join()\nelse:\n for worker in workers:\n runOnWorker(worker, keyPath, command)\n","repo_name":"HaliteChallenge/Halite","sub_path":"admin/commandRunner.py","file_name":"commandRunner.py","file_ext":"py","file_size_in_byte":1184,"program_lang":"python","lang":"en","doc_type":"code","stars":188,"dataset":"github-code","pt":"50"} +{"seq_id":"14070815598","text":"from logging import raiseExceptions\nimport numpy as np\n\nimport random\nimport pickle\n\nfrom collections import defaultdict, Counter\nfrom itertools import product\nimport gzip\n\nfrom google.cloud import storage\n\n\nclass MarkovGenerator:\n def __init__(self, n):\n self.n = n\n\n self.ngram = defaultdict(list)\n self.frequency_table = None\n self.probability_table = None\n self.vocab = set([\"$\", \"#\"])\n self.v2i = dict()\n self.g2i = dict()\n self.lite = False\n\n def add_gram(self, gram, next_state):\n if gram not in self.g2i:\n self.g2i[gram] = len(self.g2i)\n self.ngram[self.g2i[gram]].append(next_state)\n\n def add_the_rest_gram(self):\n non_terminal_vocab = self.vocab - set([\"$\", \"#\"])\n for i in range(1, self.n+1):\n all_gram = product(list(non_terminal_vocab), repeat=i)\n for gram in all_gram:\n gr = \"$\"*(self.n-i) + \"\".join(gram)\n if gr not in self.g2i:\n self.g2i[gr] = len(self.g2i)\n\n def fit(self, corpus, smooth=True):\n if self.lite:\n raise ValueError(\"Lite model cannot be updated\")\n\n for doc in corpus:\n self.vocab |= set(doc)\n for idx in range(self.n):\n start = \"$\"*(self.n-idx) + doc[:idx+1]\n self.add_gram(start[:self.n], start[-1])\n for idx in range(len(doc)-self.n):\n self.add_gram(doc[idx:idx+self.n],doc[idx+self.n])\n self.add_gram(doc[-(self.n):], \"#\")\n\n if self.v2i:\n for v in sorted(list(self.vocab)):\n if v not in self.v2i:\n self.v2i[v] = len(self.v2i)\n else:\n self.v2i = {v: i for i, v in enumerate(sorted(list(self.vocab)))}\n self.i2v = {i: v for i, v in enumerate(sorted(list(self.v2i)))}\n valid_gram = set()\n\n if smooth:\n # Insert Other valid ngram to g2i\n self.add_the_rest_gram()\n self.probability_table = np.ones(\n (len(self.g2i), len(self.vocab)), dtype=np.int32)\n self.probability_table[:, self.v2i[\"$\"]] = 0\n self.probability_table = np.cumsum(\n self.probability_table / len(self.vocab)-1, 1\n )\n else:\n self.probability_table = np.zeros(\n (len(self.g2i), len(self.vocab)), dtype=np.float32)\n \n for gram_idx, next_list in self.ngram.items():\n valid_gram.add(gram_idx)\n counter = Counter(next_list)\n frequency_table = np.full(\n (len(self.vocab),), int(smooth), dtype=np.int32)\n frequency_table[self.v2i[\"$\"]] = 0\n\n for ch, freq in counter.items():\n frequency_table[self.v2i[ch]] += freq\n self.probability_table[gram_idx] = np.cumsum(\n frequency_table /\n (len(next_list)+int(smooth)*(len(self.vocab)-1))\n )\n\n def generate(self, seed=\"\", max_length=100, max_words=99999):\n current_state = \"$\"*(self.n-len(seed)) + seed\n generated_string = seed\n white_space_counter = 0\n while (\n (current_state[-1] != \"#\") and\n (len(generated_string) <= max_length)\n ):\n rng = random.random()\n char_idx = np.argmax(self.probability_table[self.g2i[current_state]] > rng)\n char = self.i2v[char_idx]\n if char == \" \":\n white_space_counter += 1\n if white_space_counter >= max_words:\n char = \"#\"\n if char != \"#\":\n generated_string += char\n current_state = current_state[1:] + char\n return generated_string\n \n def save(self, fname, lite=False):\n param_to_save = {\n \"probability_table\": self.probability_table,\n \"n\": self.n,\n \"g2i\": self.g2i,\n \"i2v\": self.i2v,\n \"vocab\": set(),\n \"model\": defaultdict(list),\n \"v2i\": dict(),\n \"lite_mode\": lite\n }\n if lite:\n with gzip.open(fname, \"wb+\") as f:\n pickle.dump(param_to_save, f)\n return\n\n param_to_save[\"model\"] = self.ngram\n param_to_save[\"v2i\"] = self.v2i\n param_to_save[\"vocab\"] = self.vocab\n with open(fname, \"wb\") as f:\n pickle.dump(param_to_save, f)\n\n @staticmethod\n def load(fname, bucket_name=\"\"):\n if bucket_name:\n data = _open_gcs(fname, bucket_name)\n else:\n data = _open_file(fname)\n markov = MarkovGenerator(data[\"n\"])\n markov.ngram = data[\"model\"]\n markov.vocab = data[\"vocab\"]\n markov.probability_table = data[\"probability_table\"]\n markov.v2i = data[\"v2i\"]\n markov.i2v = data[\"i2v\"]\n markov.g2i = data[\"g2i\"]\n return markov\n\n\ndef _open_file(fname):\n try:\n with open(fname, \"rb\") as f:\n return pickle.load(f)\n except pickle.UnpicklingError:\n with gzip.open(fname, \"rb\") as f:\n return pickle.load(f)\n \n\ndef _open_gcs(fname, bucket_name):\n client = storage.Client()\n bucket = client.get_bucket(bucket_name)\n fblob = bucket.get_blob(fname)\n with fblob.open(\"rb\") as f:\n content = gzip.GzipFile(fileobj=f).read()\n return pickle.loads(content)\n","repo_name":"ajmalkurnia/name-generator","sub_path":"model/markov.py","file_name":"markov.py","file_ext":"py","file_size_in_byte":4740,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"35873857396","text":"import requests as re\r\nfrom bs4 import BeautifulSoup as bs\r\nfrom fake_useragent import UserAgent\r\nimport os\r\nfrom retrying import retry\r\nimport math\r\n\r\ntables_url = 'https://www.fsc.gov.tw/ch/home.jsp?id=3&parentpath=0&contentid=128&mcustomize=lawnew_list.jsp&pagesize=100&page='\r\ncontent_path = 'content/'\r\n\r\n\r\ndef request_data_num():\r\n # 取得共有幾筆資料\r\n soup = request_fun(tables_url + str(1))\r\n return int(soup.select('.red')[0].text)\r\n\r\n\r\ndef request_all_table():\r\n request_data_num()\r\n # 建立資料夾\r\n if not os.path.exists(content_path):\r\n os.mkdir(content_path)\r\n\r\n total_data = '
    '\r\n\r\n # 資料總數除以一頁幾筆資料，獲得共有幾頁\r\n page_num = math.ceil(request_data_num() / 100)\r\n\r\n for i in range(1, page_num + 1):\r\n print('start page', i)\r\n table_soup = request_fun(tables_url + str(i))\r\n\r\n # 取得列表中的每一條項目\r\n table = table_soup.select('.newslist')[0].select('li')\r\n for j in range(0, len(table)):\r\n # 每次的第一項為欄位名稱，ver1只會取一次\r\n if i != 1 and j == 0:\r\n continue\r\n\r\n # 將該項目加入自行生成的列表中\r\n total_data += str(table[j])\r\n\r\n if table[j].select('a'):\r\n # id為該連結內容的dataserno\r\n content_url_tmp = table[j].select('a')[0]['href']\r\n content_url = 'https://www.fsc.gov.tw/ch/' + content_url_tmp\r\n item_id = content_url_tmp[content_url_tmp.find('dataserno=')\r\n + len(str('dataserno=')):content_url_tmp.find('&dtable=')]\r\n print('start content', j)\r\n request_each_content(content_url, item_id)\r\n print('done content', j)\r\n\r\n print('done page', i)\r\n\r\n total_data += '
'\r\n\r\n # 所有資料存到該html裡，只會有一張列表\r\n file = open('total_table.html', 'w', encoding='utf-8')\r\n file.write(str(total_data))\r\n file.close()\r\n\r\n\r\ndef request_each_content(content_url, item_id):\r\n # 取得每條項目中連結的內容\r\n content_soup = request_fun(content_url)\r\n try:\r\n content = content_soup.select('.maincontent')[0]\r\n except:\r\n print('not web page or no content')\r\n return\r\n file = open(content_path + '/' + str(item_id) + '.html', 'w', encoding='utf-8')\r\n file.write(str(content))\r\n file.close()\r\n\r\n\r\n@retry(stop_max_attempt_number=3)\r\ndef request_fun(url):\r\n # 發出request都由這邊發出\r\n fake_header_can = UserAgent().random\r\n fake_header = {'user-agent': fake_header_can}\r\n request = re.get(url, headers=fake_header)\r\n return bs(request.text, 'html.parser')\r\n\r\n\r\nif __name__ == '__main__':\r\n request_all_table()\r\n","repo_name":"kimpao207/request_for_law","sub_path":"financial_supervisory_commission_ver1.py","file_name":"financial_supervisory_commission_ver1.py","file_ext":"py","file_size_in_byte":2881,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"22930435431","text":"#!/usr/bin/env python\n\nimport numpy as np\nimport h5py\nimport matplotlib.pyplot as plt\n\n# Static network weights of Braitenberg vehicle controller\n# Fig. 5.5\n\nweights_l = np.array(\t[[250., 250., 250., 500., 0., 0., 0., 0.],\n\t\t\t\t\t\t[250., 250., 500., 1000., 0., 0., 0., 0.],\n\t\t\t\t\t\t[250., 500., 1000., 1500., 0., 0., 0., 0.],\n\t\t\t\t\t\t[500., 1000., 1500., 2000., 0., 0., 0., 0.]])\n\nweights_r = np.array(\t[[0., 0., 0., 0., 500., 250., 250., 250.],\n\t\t\t\t\t\t[0., 0., 0., 0., 1000., 500., 250., 250.],\n\t\t\t\t\t\t[0., 0., 0., 0., 1500., 1000., 500., 250.],\n\t\t\t\t\t\t[0., 0., 0., 0., 2000., 1500., 1000., 500.]])\n\nfig = plt.figure(figsize=(6,6))\n\nax1 = plt.subplot(211)\nplt.title('Left Weights')\nplt.imshow(weights_l, alpha=0.5)\nplt.axis('off')\nfor (j,i),label in np.ndenumerate(weights_l):\n\tax1.text(i,j,int(label),ha='center',va='center')\n\nax2 = plt.subplot(212)\nplt.title('Right Weights')\nplt.imshow(weights_r, alpha=0.5)\nplt.axis('off')\nfor (j,i),label in np.ndenumerate(weights_r):\n\tax2.text(i,j,int(label),ha='center',va='center')\n\nfig.tight_layout()\nplt.show()","repo_name":"clamesc/Training-Neural-Networks-for-Event-Based-End-to-End-Robot-Control","sub_path":"Controller/plot_braitenberg_weights.py","file_name":"plot_braitenberg_weights.py","file_ext":"py","file_size_in_byte":1045,"program_lang":"python","lang":"en","doc_type":"code","stars":49,"dataset":"github-code","pt":"50"} +{"seq_id":"34186597939","text":"#!/usr/bin/env python\n\"\"\"\nA one-off script to migrate documents from the ES5 metasearch index to ES6.\n\nThis is necessary because trying to republish all best bets from search-admin frequently fails due to transient network issues.\n\nRequires two Elasticsearch clients: client5 and client6. These use different versions of the python client.\n\nclient5 fetches a page of docs from the old index, and client6 POSTs them to the new index. Simple!\n\"\"\"\nfrom elasticsearch5 import Elasticsearch as Elasticsearch5, TransportError as TransportError5\nfrom elasticsearch6 import Elasticsearch as Elasticsearch6, TransportError as TransportError6\nfrom elasticsearch6.helpers import bulk\nfrom datetime import datetime\nimport os\n\n# Using the example indices and doc types from GOV.UK's search API\n# https://github.com/alphagov/rummager/tree/master/config/schema/indexes\n\nINDEX = 'metasearch'\nGENERIC_DOC_TYPE = 'generic-document'\n\nES5_HOST_PORT = os.getenv('ES5_ORIGIN_HOST', 'http://elasticsearch5:80')\nES6_TARGET_PORT = os.getenv('ES6_TARGET_HOST', 'http://elasticsearch6:80')\n\nes_client5 = Elasticsearch5([ES5_HOST_PORT])\nes_client6 = Elasticsearch6([ES6_TARGET_PORT])\n\n\ndef _prepare_docs_for_bulk_insert(docs):\n for doc in docs:\n yield {\n \"_id\": doc['_id'],\n \"_source\": doc['_source'],\n }\n\ndef bulk_index_documents_to_es6(documents):\n try:\n bulk(\n es_client6,\n _prepare_docs_for_bulk_insert(documents),\n index=INDEX,\n doc_type=GENERIC_DOC_TYPE,\n chunk_size=100\n )\n except TransportError6 as e:\n print(\"Failed to index documents: %s\", str(e))\n\n\ndef fetch_documents(from_=0, page_size=100, scroll_id=None):\n try:\n if scroll_id is None:\n results = es_client5.search(INDEX, GENERIC_DOC_TYPE, from_=from_, size=page_size, scroll='2m')\n scroll_id = results['_scroll_id']\n else:\n results = es_client5.scroll(scroll_id=scroll_id, scroll='2m')\n docs = results['hits']['hits']\n return (scroll_id, docs)\n except TransportError5 as e:\n print(\"Failed to fetch documents: %s\", str(e))\n return str(e), e.status_code\n\n\nif __name__ == '__main__':\n start = datetime.now()\n\n dcount = es_client5.count(index=INDEX, doc_type=GENERIC_DOC_TYPE)['count']\n\n print('Preparing to index {} document(s) from ES5'.format(dcount))\n\n offset = 0\n page_size = 250\n scroll_id = None\n while offset <= dcount:\n scroll_id, docs = fetch_documents(from_=offset, page_size=page_size, scroll_id=scroll_id)\n\n print('Indexing documents {} to {} into ES6'.format(offset, offset+page_size))\n bulk_index_documents_to_es6(docs)\n\n offset += page_size\n\n print('Finished in {} seconds'.format(datetime.now() - start))\n","repo_name":"alphagov-mirror/elasticsearch-migration-helpers","sub_path":"scripts/copy_es5_metaseach_to_es6.py","file_name":"copy_es5_metaseach_to_es6.py","file_ext":"py","file_size_in_byte":2809,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"50"} +{"seq_id":"31394735712","text":"def saddle_points(matrix):\n points = set()\n if matrix:\n first_column_length = len(matrix[0])\n for x_index, column in enumerate(matrix):\n if len(column) != first_column_length:\n raise ValueError(\"Matrix isn't square or rectangular.\")\n for y_index, element in enumerate(column):\n if element == max(column) and element == min([z[y_index] for z in matrix]):\n points.add((x_index+1, y_index+1))\n return points\n","repo_name":"coleberhorst/python-exercism","sub_path":"saddle-points/saddle_points.py","file_name":"saddle_points.py","file_ext":"py","file_size_in_byte":475,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"27964049508","text":"from flask import Flask, request\nfrom flask import render_template\nfrom morse import MorseCodeTranslator\n\napp = Flask(__name__)\n\n@app.route(\"/\")\ndef home():\n return render_template(\"index.html\")\n\n@app.route(\"/morse_decoding\", methods=[\"POST\", \"GET\"])\ndef morse_decoding():\n if request.method == \"POST\":\n inputMorse = request.form.get(\"inputMorse\")\n # 코드를 넣어주세요.\n morse_translator = MorseCodeTranslator() \n \n inputMorset_M = set(inputMorse) # 부호 확인용\n if inputMorset_M.issubset(['-','.',' ']): \n english_text = morse_translator.morse_to_english(inputMorse)\n print(\"Morse -> English\",english_text)\n program_result = english_text\n elif inputMorse.isalpha():\n morse_text = morse_translator.english_to_morse(inputMorse)\n print(\"Englist -> Morse\",morse_text)\n program_result = morse_text\n else:\n program_result='ERROR'\n #english_text = morse_translator.morse_to_english(inputMorse)\n #english_text = inputMorse\n\n return render_template(\"index.html\", result=program_result)\n\n\nif __name__ == \"__main__\":\n app.run(host=\"0.0.0.0\", port=5100)\n","repo_name":"tlagmlwns/Finish_etc","sub_path":"web_morse/morse/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":1219,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"3097595364","text":"\nfrom 数据库.UserinfoMysql import Userinfo\nfrom 数据库.Homemysql import Homeinfo\nfrom AI import ServerAI\n\n#这个类用来处理用户输入数据的\nclass Recvinfo:\n def __init__(self):\n #初始化用户在线字典用昵称(具有唯一性)：addr\n self.userdict = {}\n #初始化生成userinfo数据库查询类的对象\n self.usermysql = Userinfo()\n self.homemysql = Homeinfo()\n self.serverAI = ServerAI()\n self.dict_home_user ={}\n\n #接口获取函数\n def put(self,data,addr):\n #用户发送数据data\n self.data = data\n #用户的addr\n self.addr = addr\n \n #这是个获取在线用户信息的接口\n def get_userdict(self):\n self.userdict = self.homemysql.allonlineuser()\n return self.userdict\n\n\n #如果是登信息的话\n def userenter(self,addr):\n data = self.data.split(':')\n #用户帐号\n userID= data[1]\n #用户密码\n userpassword= data[2]\n #去数据库对比返回知为(密码,昵称)\n mysqldata = self.usermysql.aster_password(userID)\n if mysqldata[0] == userpassword:\n print('c错误的地方',mysqldata)\n #调用在线用户数据数据返回所有在线用户\n self.userdict=self.homemysql.allonlineuser()\n if self.userdict:\n #如果不再用户列表里就把它加入到列比里\n if mysqldata[1] in self.userdict:\n return '该用户已存在'+'#'+addr[0]+'#'+str(addr[1])\n #如果没有问题就返回Y加用户名\n return 'Y '+mysqldata[1]+'#'+addr[0]+'#'+str(addr[1])\n elif mysqldata == '没有这个用户':\n return mysqldata+'#'+addr[0]+'#'+str(addr[1])\n else:\n return '密码错误'+'#'+addr[0]+'#'+str(addr[1])\n\n #找回密码\n def getbackzhanghaomima(self,data,addr):\n s = self.usermysql.getbackzhanghaomima(data)\n info = s + '#' + addr[0] + '#' + str(addr[1])\n return info\n\n #注册用户\n def userenroll(self,addr):\n data = self.data.split(':')\n del data[0]\n IDnumber=data[3]\n #具体信息已逗号组合成字符串\n s = ''\n for x in data:\n s += \"'\"+str(x)+\"'\" + ','\n userinfo = s[:-1]\n # print(userinfo)\n #返回注册函数返回的结果加密码\n jiegu = str(self.usermysql.add_user_info(userinfo,IDnumber))+':'+data[0]\n jieguo = jiegu.split(':')\n print('&&&&&&&&&&',jieguo)\n # 如果输入有误\n if jieguo[0] == '昵称或者身份证重复了重复了!':\n s = jieguo[0] + '#' + addr[0] + '#' + str(addr[1])\n # sockfd.sendto(jieguo[0].encode(),addr)\n else:\n sss = 'Y 你的账号请牢记\\n账号:' + jieguo[0] + '\\n密码:' + jieguo[1]\n s = sss + '#' + addr[0] + '#' + str(addr[1])\n return s\n #如果是匿名聊天模式怎吧用户加入到匿名房间里\n def nimingmoshijinru(self,data,addr):\n self.homemysql.newuseradd(data,addr)\n\n # 获取房间的用户的详细信息用于客户端双击房间列表内的用户了\n def get_home_user_info(self,username,addr):\n # print('username', username)\n #返回值生成包���了age：sex:likes:ctiy:phonename:meiting\n t = self.usermysql.get_homeuser_info(username)\n s = '*获取用户信息:'+t+'#'+addr[0]+'#'+str(addr[1])\n print(s)\n return s\n #用于私聊模式获取用户信息\n def siliao_home_user_info(self,username, addr):\n t = self.usermysql.get_homeuser_info(username)\n s = '*私聊获取用户信息:' + t + '#' + addr[0] + '#' + str(addr[1])\n return s\n\n\n #获取房间列表\n def get_home_list(self,addr):\n data = self.homemysql.gain_home_list()\n return '*获取房间列表:'+data+'#'+addr[0]+'#'+str(addr[1])\n\n #退出房间通知\n def user_qiut_home(self,username,addr):\n addrname = '<用户 ' + username + ' 退出了 ' + self.dict_home_user[username] + ' 房间>\\n'\n #退出时返回用户以前的房间内内的所有成员信息（name,addr)\n allusername = self.homemysql.home_user_name_and_addr_from(self.dict_home_user[username])\n #只用来发送通知\n alluserlistmsg = []\n for uaddr in allusername:\n t = uaddr[1].split('&')\n useraddr = (t[0], int(t[1]))\n # 给每个用户发送房间列表\n # 给其他用户发送欢迎退出信息\n if useraddr != addr:\n msg = addrname + '#' + useraddr[0] + '#' + str(useraddr[1])\n alluserlistmsg.append(msg)\n\n return alluserlistmsg\n\n #欢迎房间信息\n def weclomehome(self,username,homename,addr):\n # 过来的用户的昵称\n addrname = '<欢迎用户 ' + username + ' 来到 ' + homename + ' 房间>\\n'\n # 返回该房间的所有用户名字和房间内所有的用户addr\n allusername = self.homemysql.gethomeusernickname(homename,username, addr)\n # s是所有用户名字用':'组合\n s = '*获取房间用户信息:'\n for x in allusername:\n s += str(x[0]) + ':'\n alluserlistmsg = []\n\n for uaddr in allusername:\n t = uaddr[1].split('&')\n useraddr = (t[0], int(t[1]))\n # 给每个用户发送房间列表\n alluserlistmsg.append(s + '#' + useraddr[0] + '#' + str(useraddr[1]))\n # 给其他用户发送欢迎退出信息\n if useraddr != addr:\n msg = addrname + '#' + useraddr[0] + '#' + str(useraddr[1])\n alluserlistmsg.append(msg)\n return alluserlistmsg\n\n\n #获取房间用户信息\n def get_home_user(self,addr,homeandusername):\n listquitfanhui=qiutfanhui = []\n homename = homeandusername.split(\":\")[0]\n username = homeandusername.split(\":\")[1]\n if username not in self.dict_home_user:\n self.dict_home_user[username] = homename\n msg = self.weclomehome(username, homename, addr)\n return msg\n else:\n #先更准备好欢迎语言并更新新用户数据库内的用户房间名字\n msg=self.weclomehome(username, homename, addr)\n\n # 发送退出房间通知听通知后更新新的字典\n m = self.user_qiut_home(username, addr)\n print('到这里了，这是发送户退出的通知msg=',msg,'**m=',m)\n self.dict_home_user[username] = homename\n return msg+m\n\n\n #机器人消息\n def robot_ai(self,data,addr):\n s = self.serverAI.robot_ai(data)\n #根据返回数据生成\n return_s = '*机器人消息:'+s+'#'+addr[0]+'#'+str(addr[1])\n return return_s\n #私聊机器人消息\n def si_robot_ai(self,data,addr):\n s = self.serverAI.robot_ai(data)\n return_s = '*私聊机器人消息:'+s+'#'+addr[0]+'#'+str(addr[1])\n return return_s\n\n #获取房间内用户信息发消息用的\n def infoyonggethomeuser(self,username,addr):\n data = self.homemysql.gethomeuser(username,addr)\n return data\n\n #用户退出消息\n def userquit(self,data):\n self.homemysql.userquit(data)\n # self.get_userdict()\n print('这是用户退出后的用户列表',self.userdict)\n\n #如果是建立私聊模式的消息addr是发送者的addr\n def createpribymoshi(self,data,addr):\n try:\n t = data.split(':')\n # print('t',t)\n #要建立连接的对象的名字\n othername = t[0]\n #发起者的名字\n myname = t[1]\n #获取ohernamde的addr还有\n otheradd = self.homemysql.siliaomishi_get_othername_addr(othername)\n otheraddr = otheradd[0].split('&')\n print('otheraddr',otheraddr)\n msg = '*与:'+myname+':建立私聊'+'#'+otheraddr[0]+'#'+str(otheraddr[1])\n print('建立私聊的用户的返回消息:',msg)\n return msg\n except:\n pass\n\n #私聊消息的haul\n def siliaomsg(self,data):\n #data=测试3:测试1:FASFASD\n s = data.split(':')\n\n otheradd = self.homemysql.siliaomishi_get_othername_addr(s[0])\n otheraddr = otheradd[0].split('&')\n # print('otheraddr', otheraddr)\n msg = '*私聊消息来自:' +s[1]+ ':'+s[2] + '#' + otheraddr[0] + '#' + str(otheraddr[1])\n return msg\n\n #根据其他用户名字获取addr并返回要给他发送文件\n def sendtofileother(self,data,addr):\n print('data',data)\n othername=data.split(':')[1]\n otheradd=self.homemysql.siliaomishi_get_othername_addr(othername)\n otheraddr=otheradd[0].split('&')\n print('获取到用户是：',otheraddr)\n #返回给目标用户数据准备接受文件+发送者addr+&+端口\n msg ='*准备接受文件:'+addr[0]+'&'+data.split(\":\")[0]+'#'+otheraddr[0]+'#'+str(otheraddr[1])\n return msg\n\n #用户创建房间的话\n def usercreatehome(self,data,addr):\n # 使用管理员广播功能给所有用户发送创建成功的消息，失败则返回创建用户\n t = data.split(':')\n username=t[0]\n homename=t[1]\n if len(t) == 3:\n homepwd=t[2]\n print('userinfoorder创建放假pwd',homepwd)\n createreturn = self.homemysql.createhome(homename,username,homepwd)\n # 用于创建房间的功能,房间名字，创建者，密码可以为空默认值\n else:\n createreturn = self.homemysql.createhome(homename,username)\n if createreturn == '创建失败':\n return ['*创建房间:创建失败'+'#'+addr[0]+'#'+str(addr[1])]\n else:\n #创建成功 则用管理员名义给所有用户发送管理员信息，并给用户发送成功数据\n return [homename,username,'*创建房间:创建成功'+'#'+addr[0]+'#'+str(addr[1])]","repo_name":"mlf-github/liantinshixin","sub_path":"聊天室服务器/userinfoorder.py","file_name":"userinfoorder.py","file_ext":"py","file_size_in_byte":10074,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"4005938424","text":"\ndef add_time(start_time, duration, weekday =\"none\"):\n\n # first step is to split the strings and save the items to a list to be able to iterate them\n time_list = start_time.split(' ')\n s_time = time_list[0].split(':')\n meridian = time_list[1]\n duration_list = duration.split(':')\n\n ## print('start time is {}, duration is {} and meridian {}'.format(s_time, duration_list, meridian))\n\n # second step is to sum the total time as integers\n total_time = [int(x)+int(y) for x, y in zip(s_time, duration_list)]\n ## print('total time is {}'.format(total_time))\n\n # third step is to format the minutes to transfrom into hours after crossing 60\n\n if total_time[1] > 60:\n add_hours = (total_time[1] // 60)\n total_time[1] = total_time[1] - ((total_time[1]//60) * 60)\n ## print('hours to add {}'.format(add_hours))\n total_time[0] = total_time[0] + add_hours # adding hours to total time\n\n # fourth step is calculate the hours\n total_hours = total_time[0]\n if total_time[0] > 12:\n total_time[0] = total_time[0] - ((total_time[0]//12) * 12)\n\n if total_time[0] == 0:\n total_time[0] = 12\n\n # fifth step is to format all list items to a string to add a 0 in front of ints from 1 to 9\n\n if total_time[1] < 10:\n total_time[1] = str('0{}'.format(total_time[1]))\n else:\n total_time[1] = str(total_time[1])\n\n total_time[0] = str(total_time[0])\n\n ##print('meridian time is {}'.format(total_time))\n\n\n\n # defining the right meridian\n\n half_days = total_hours // 12\n half_day_modulus = half_days % 2\n\n\n if half_days == 0:\n total_time.append(meridian)\n elif half_days > 0 and half_day_modulus == 1 and meridian == \"AM\":\n final_meridian = \"PM\"\n total_time.append(final_meridian)\n elif half_days > 0 and half_day_modulus == 0 and meridian == \"AM\":\n final_meridian = \"AM\"\n total_time.append(final_meridian)\n elif half_days > 0 and half_day_modulus == 1 and meridian == \"PM\":\n final_meridian = \"AM\"\n total_time.append(final_meridian)\n elif half_days > 0 and half_day_modulus == 0 and meridian == \"PM\":\n final_meridian = \"PM\"\n total_time.append(final_meridian)\n\n # calculating days later\n\n total_days = total_hours // 24 # calculating total days to check modulus\n ## print('total hours {} and total days {}'.format(total_hours, total_days))\n\n if meridian == 'PM' and 12 < total_hours < 25:\n total_time.append('next day')\n elif meridian == 'PM' and total_hours > 24:\n total_time.append('({} days later)'.format(total_days + 1))\n elif meridian == 'AM' and 24 < total_hours < 48:\n total_time.append('next day')\n elif meridian == 'AM' and total_hours > 48:\n total_time.append('({} days later)'.format(total_days))\n\n # dealing with weekday\n\n if meridian == \"PM\" and half_days > 0:\n total_days = total_days + 1\n\n day_definition = 0\n\n if weekday == 'Monday':\n day_definition = (total_days + 1) % 8\n elif weekday == 'Tuesday':\n day_definition = (total_days + 2) % 8\n elif weekday == 'Wednesday':\n day_definition = (total_days + 3) % 8\n elif weekday == 'Thursday':\n day_definition = (total_days + 4) % 8\n elif weekday == 'Friday':\n day_definition = (total_days + 5) % 8\n elif weekday == 'Saturday':\n day_definition = (total_days + 6) % 8\n elif weekday == 'Sunday':\n day_definition = (total_days + 7) % 8\n\n if day_definition == 1:\n total_time.append('Monday')\n elif day_definition == 2:\n total_time.append('Tuesday')\n elif day_definition == 3:\n total_time.append('Wednesday')\n elif day_definition == 4:\n total_time.append('Thursday')\n elif day_definition == 5:\n total_time.append('Friday')\n elif day_definition == 6:\n total_time.append('Saturday')\n elif day_definition == 7:\n total_time.append('Sunday')\n\n\n ## print('{} \\n'.format(total_time))\n\n new_time = ''\n if len(total_time) == 3:\n new_time = \"{}:{} {}\".format(total_time[0], total_time[1], total_time[2])\n elif len(total_time) == 4:\n new_time = \"{}:{} {} {}\".format(total_time[0], total_time[1], total_time[2], total_time[3])\n\n return new_time\n\n\nadd_time(\"3:00 PM\", \"3:10\")\n# Returns: 6:10 PM\n\nadd_time(\"11:30 AM\", \"2:32\", \"Monday\")\n# Returns: 2:02 PM, Monday\n\nadd_time(\"11:43 AM\", \"00:20\")\n# Returns: 12:03 PM\n\nadd_time(\"10:10 PM\", \"3:30\")\n# Returns: 1:40 AM (next day)\n\nadd_time(\"11:43 PM\", \"24:20\", \"Tuesday\")\n# Returns: 12:03 AM, Thursday (2 days later)\n\nadd_time(\"6:30 PM\", \"205:12\")\n# Returns: 7:42 AM (9 days later)\n","repo_name":"llasso05/free_code_camp","sub_path":"time_calculator.py","file_name":"time_calculator.py","file_ext":"py","file_size_in_byte":4675,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"16611227428","text":"import pprint\n\ncode = \"\"\"\n\nnook x 1\nspot\n + x 1\n hopsame outspot x 10\nback\nspot outspot\n\n\"\"\"\n\ndef valuify(value):\n if value.isnumeric():\n return int(value)\n else:\n return value\n\n# NOOK determines memory location\n# SPOT determines loop back point\n# MEMORY determines where in memory a thing is\n# HOPSAME if the same, go to location\n# HOPMORE if more, go to location\n# HOPLESS if less, go to location\n# SETSAME if the same, set context\n# SETMORE if more, set context\n# SETLESS if less, set context\n\ndataset = []\ncommand = [\n '+',\n '-',\n '=',\n '*',\n '%',\n '/',\n 'nook',\n 'spot',\n 'back',\n 'pocket',\n 'hop',\n 'hopsame']\n\nnooks = []\nnook_count = 0\n\ndef split_complex(base_cmd):\n global nook_count\n\n if base_cmd[0] not in command:\n base_cmd = [\"=\"] + base_cmd\n \n if base_cmd[0] in command:\n if len(base_cmd) > 1:\n if base_cmd[0] == \"nook\":\n nooks.append(base_cmd[1])\n\n if base_cmd[1] not in nooks and base_cmd[1].isnumeric():\n dataset.append([\"nook\", \"t\" + str(nook_count)])\n base_cmd.insert(1,\"t\"+str(nook_count))\n nooks.append(\"t\"+str(nook_count))\n nook_count += 1\n\n return base_cmd\n\ndef layer(depth,layers=0):\n global dataset\n\n spa = ''.join(['\\t' for t in range(layers)])\n\n paren_layer = 0\n internal_layers = layers\n\n d = 0\n length = len(depth)\n base_cmd = []\n while d < length:\n char = depth[d]\n #print(spa + char + '\\t\\t' + str(paren_layer))\n \n if char == \" \" and paren_layer == 0:\n base_cmd.append(depth[:d])\n depth = depth[d+1:]\n #print(spa + \"Depth: $\" + depth + \"$\")\n d = 0\n\n if char == \"(\":\n\n paren_layer += 1\n\n elif char == \")\":\n paren_layer -= 1\n if paren_layer == 0:\n internal_layers += 1\n #print(spa + \"Running: $\" + str(depth[2:d]) + \"$\")\n layer(depth=depth[2:d],layers=internal_layers)\n depth = dataset[-1][1] + depth[d+1:]\n d = 0\n\n d += 1\n length = len(depth)\n base_cmd.append(depth)\n\n base_cmd = split_complex(base_cmd)\n #print(spa + str(base_cmd))\n dataset.append(base_cmd)\n\nfor c in code.split('\\n'):\n c = c.strip()\n if c != \"\":\n layer(depth=c)\n\n#print('\\n\\n')\npprint.pprint(dataset)","repo_name":"EHowardHill/hanabi","sub_path":"新しいフォルダー/img/firstbit.py","file_name":"firstbit.py","file_ext":"py","file_size_in_byte":2468,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"27075655493","text":"import time\nimport json\nimport os\n\nTIME_INTERVAL = int(os.environ['TIME_INTERVAL'])\nREQUEST_COUNT = int(os.environ['REQUEST_COUNT'])\n\n\nclass RedisMiddleWare:\n\n redis_reference = None\n\n @staticmethod\n def set_redis(redis_ref):\n RedisMiddleWare.redis_reference = redis_ref\n\n @staticmethod\n def get_redis():\n return RedisMiddleWare.redis_reference\n\n @staticmethod\n def check_limit(request_body):\n client_id = request_body.get('clientId')\n url = request_body.get('url')\n redis_con = RedisMiddleWare.get_redis()\n max_time = int(time.time())\n min_time = int(max_time-TIME_INTERVAL)\n existing_count = redis_con.zcount(client_id, min_time, max_time)\n if existing_count < REQUEST_COUNT:\n pickled_data = json.dumps({'url': url, 'time': max_time})\n redis_con.zadd(client_id, {pickled_data: max_time})\n redis_con.execute_command('EXPIRE ' + client_id + ' 60')\n return False\n return True\n\n\n","repo_name":"arpitx165/proxy-service","sub_path":"middleware.py","file_name":"middleware.py","file_ext":"py","file_size_in_byte":1012,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"4129234649","text":"from app.auth import crud\nfrom app.auth.schemas import UserCreate, UserUpdate\n\nfrom app.tests.factories import UserFactory\n\n\ndef test_create_and_get_users(db):\n user_data = UserFactory.random_user_data()\n\n # test create user\n user_created = crud.create_user(db, UserCreate(**user_data))\n\n assert user_created.full_name == user_data.get(\"full_name\")\n assert user_created.email == user_data.get(\"email\")\n\n # test get user\n user_in_db = crud.get_user_by_email(db, user_data.get(\"email\"))\n\n assert user_in_db.full_name == user_data.get(\"full_name\")\n\n user_in_db = crud.get_user_by_id(db, user_created.id)\n\n assert user_in_db.full_name == user_created.full_name\n assert user_in_db.email == user_created.email\n\n\ndef test_list_users(db):\n UserFactory.clear_users(db)\n\n users_in_db = crud.list_users(db)\n\n assert len(users_in_db) == 0\n\n quantity_user = 12\n users_data = UserFactory.random_user_data(quantity_user)\n\n for user_data in users_data:\n crud.create_user(db, UserCreate(**user_data))\n\n users_in_db = crud.list_users(db)\n\n assert len(users_in_db) == len(users_data)\n\n\ndef test_update_user(db):\n user, _ = UserFactory.sample_user(db)\n\n name_changed = user.full_name + \"__updated\"\n crud.update_user(\n db, user.id, UserUpdate(full_name=name_changed)\n )\n\n user_updated = crud.get_user_by_id(db, user.id)\n\n assert user_updated.id == user.id\n assert user_updated.full_name == name_changed\n","repo_name":"CrissAlvarezH/api-template","sub_path":"app/tests/integration/auth/test_crud.py","file_name":"test_crud.py","file_ext":"py","file_size_in_byte":1474,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"20641718989","text":"from hanoi.tower import Tower\nfrom hanoi.state import State\nfrom hanoi.hanoi import HanoiGame\nfrom hanoi.hanoi_exception import HanoiException\n\n\ndef negative_disc_input():\n\n # TEST NUMBER 1 -- OK\n try:\n hanoiGame = HanoiGame(-1)\n assert (False)\n\n except HanoiException:\n pass\n\n\ndef check_wrong_finished_game():\n # TEST NUMBER 2 -- OK\n\n hanoi_game = HanoiGame(3)\n hanoi_game.move(0, 2)\n hanoi_game.move(0, 1)\n assert (hanoi_game.is_finished() is False)\n\n\ndef check_wrong_movement():\n # TEST NUMBER 3 -- OK\n\n hanoi_game = HanoiGame(3)\n try:\n hanoi_game.move(2, 1)\n assert (False)\n\n except HanoiException:\n pass\n\n\ndef check_wrong_tower_init():\n\n # TEST NUMBER 4 -- OK\n\n # Something is really wrong if this test is not working\n tower = Tower()\n assert (tower.is_empty() == True)\n\n\ndef check_push_pop_tower():\n # TEST NUMBER 5 -- OK\n\n tower = Tower()\n tower.push_disc(2)\n assert (tower.pop_disc() == 2)\n assert (tower.is_empty() == True)\n\n\ndef check_total_states(discs=10):\n # TEST NUMBER 6 -- OK\n\n # Initial state plus 2^n - 1 states with moves\n hanoi_game = HanoiGame(discs)\n assert (hanoi_game.get_n_states() == 1024)\n\n\ndef retrieve_tower_from_state():\n # TEST NUMBER 7 -- OK\n\n # You need to be able to retrieve the state of a tower in a state, to see the number of elements that are there\n hanoi_game = HanoiGame(3)\n state = hanoi_game.get_state(5)\n\n assert (sum(1 for element in state.get_tower(1) if element != 0) == 1)\n assert (sum(1 for element in state.get_tower(0) if element != 0) == 1)\n assert (sum(1 for element in state.get_tower(2) if element != 0) == 1)\n\n\ndef check_pop_from_empty_tower():\n # TEST NUMBER 8 -- OK\n\n tower = Tower()\n try:\n tower.pop_disc()\n assert (False)\n\n except HanoiException:\n pass\n\n\ndef check_pushed_discs():\n # TEST NUMBER 9 -- OK\n\n tower = Tower()\n tower.push_disc(3)\n assert (tower.pop_disc() == 3)\n\n\ndef check_output():\n # TEST NUMBER 10 -- OK\n\n # Note that we are accessing the first state. No moves have been done yet, So no header or last move line needs to be present\n expected = \"\"\"\n....#|#.... .....|..... .....|..... \n...##|##... .....|..... .....|..... \n..###|###.. .....|..... .....|..... \n.####|####. .....|..... .....|..... \n#####|##### .....|..... .....|..... \n Tower 1 Tower 2 Tower 3 \n\"\"\"\n\n hanoi_game = HanoiGame(5)\n state = hanoi_game.get_state(0)\n assert (expected == str(state))\n\n\ndef show_standard_optimal_state():\n # TEST NUMBER 11\n\n # Show a standard state from the optimal solution\n expected = \"\"\"\nMove id 7 Rec Depth 3\nLast move: 1 Disk, from 1 to 3\n...|... ...|... ..#|#.. \n...|... ...|... .##|##. \n...|... ...|... ###|### \nTower 1 Tower 2 Tower 3 \n\"\"\"\n hanoi_game = HanoiGame(3)\n state = hanoi_game.get_state(7)\n assert (expected == str(state))\n\n\ndef show_current_playing():\n # TEST NUMBER 12\n\n # Show the current state, when playing manually\n hanoi_game = HanoiGame(3)\n hanoi_game.move(0, 2)\n hanoi_game.move(0, 1)\n\n state = hanoi_game.get_current_state()\n\n expected = \"\"\"\n...|... ...|... ...|... \n...|... ...|... ...|... \n###|### .##|##. ..#|#.. \nTower 1 Tower 2 Tower 3 \n\"\"\"\n assert (expected == str(state))\n\n\nif __name__ == '__main__':\n\n negative_disc_input()\n check_wrong_finished_game()\n check_wrong_movement()\n check_wrong_tower_init()\n check_push_pop_tower()\n check_total_states(10)\n retrieve_tower_from_state()\n check_pop_from_empty_tower()\n check_pushed_discs()\n check_output()\n show_standard_optimal_state()\n show_current_playing()\n\n\n","repo_name":"AdrianMedico/HanoiTowers","sub_path":"test suite/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":3768,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"9269980596","text":"i = 0\n# create an empty list\nnumbers = []\n# create a variable for while loop, using input\nn = int(input('>>> '))\n\n\n# convert while loop to a function\ndef sth(i, numbers):\n while i < n:\n print(f\"At the top i is {i}\")\n # adding to the empty list a new number (i)\n numbers.append(i)\n\n i += 2\n print(\"Number now: \", numbers)\n print(f\"At the bottom i is {i}\")\n\n\nsth(i, numbers)\n\nprint(\"The numbers: \")\n\nfor num in numbers:\n print(num)\n","repo_name":"melfaberlioz/Python_Learning","sub_path":"ex33_while.py","file_name":"ex33_while.py","file_ext":"py","file_size_in_byte":479,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"21276128547","text":"from django.shortcuts import render\nfrom django.views.generic import ListView,DetailView\nfrom .models import Course,Topic,CourseCategory\n# Create your views here.\n\n\nclass CourseListView(ListView):\n model = Course\n context_object_name = \"course\"\n template_name = \"learn.html\"\n\n def get_context_data(self,**kwargs):\n context = super(CourseListView,self).get_context_data(**kwargs)\n context['category'] = CourseCategory.objects.all()\n return context\n\nclass CourseDetailView(DetailView):\n model = Course\n context_object_name = \"course\"\n template_name = \"learn-detail.html\"\n\nclass TopicDetailView(DetailView):\n model = Topic\n context_object_name = \"topic\"\n template_name = \"topic-detail.html\"","repo_name":"sammagafu/sammagafu","sub_path":"learn/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":738,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"26286581768","text":"import openai\nimport os\nimport requests\nimport tempfile\nimport asyncio\n\nfrom core.cartridges import addCartridge, update_cartridge_field\nfrom file_handling.s3 import write_file, read_file\nfrom tools.debug import eZprint\n\nopenai.api_key = os.getenv('OPENAI_API_KEY', default=None)\n\nasync def generate_temp_image(prompt):\n DEBUG_KEYS = ['FILE_HANDLING', 'IMAGE_GENERATION']\n eZprint(f'Generating image with prompt: {prompt}', DEBUG_KEYS)\n try:\n loop = asyncio.get_event_loop()\n response = await loop.run_in_executor(None, lambda: openai.Image.create(\n prompt=prompt,\n n=1,\n size='1024x1024'\n ))\n except Exception as e:\n eZprint(f'Error generating image: {e}', DEBUG_KEYS)\n return None\n \n image_url = response['data'][0]['url']\n response = requests.get(image_url)\n eZprint(f'Image URL: {image_url}', DEBUG_KEYS)\n processed_media = tempfile.NamedTemporaryFile(suffix=\".png\", delete=False)\n processed_media.write(response.content)\n processed_media.close()\n return processed_media\n \n\nasync def generate_images(prompts, sessionID, convoID, loadout):\n\n tasks = [generate_image(prompt, sessionID, convoID, loadout) for prompt in prompts]\n images = await asyncio.gather(*tasks)\n images_str = ', '.join(images)\n return images_str\n\n\nasync def generate_image(prompt, sessionID, convoID, loadout):\n\n loop = asyncio.get_event_loop()\n response = await loop.run_in_executor(None, lambda: openai.Image.create(\n prompt=prompt,\n n=1,\n size='1024x1024'\n ))\n \n image_url = response['data'][0]['url']\n print(f'Image URL: {image_url}')\n response = requests.get(image_url)\n \n name = prompt + '.png'\n cartVal = {\n 'label' : name,\n # 'text' : str(transcriptions),\n 'description' : 'Image generated by openAI with prompt: ' + prompt,\n 'file' : name,\n 'extension' : 'image/png',\n # 'media_url' : url,\n 'type' : 'media',\n 'enabled' : True,\n }\n\n cartKey = await addCartridge(cartVal, sessionID, loadout, convoID )\n url = await write_file(response.content, cartKey) \n print(url)\n await update_cartridge_field({'sessionID': sessionID, 'cartKey' : cartKey, 'fields': {'media_url': url}}, convoID, loadout, True)\n\n return name\n","repo_name":"kpister/prompt-linter","sub_path":"data/scraping/repos/samueltates~nova/file_handling~image_handling.py","file_name":"file_handling~image_handling.py","file_ext":"py","file_size_in_byte":2344,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"11778222674","text":"\nimport math\nfrom Ch04DecisionTree import Dataset\n\n\nclass TreeNode:\n \"\"\"\n 决策树结点类\n \"\"\"\n current_index = 0\n\n def __init__(self, parent=None, attr_name=None, children=None, judge=None, split=None, data_index=None,\n attr_value=None, rest_attribute=None):\n \"\"\"\n 决策树结点类初始化方法\n :param parent: 父节点\n \"\"\"\n self.parent = parent # 父节点，根节点的父节点为 None\n self.attribute_name = attr_name # 本节点上进行划分的属性名\n self.attribute_value = attr_value # 本节点上划分属性的值，是与父节点的划分属性名相对应的\n self.children = children # 孩子结点列表\n self.judge = judge # 如果是叶子结点，需要给出判断\n self.split = split # 如果是使用连续属性进行划分，需要给出分割点\n self.data_index = data_index # 对应训练数据集的训练索引号\n self.index = TreeNode.current_index # 当前结点的索引号，方便输出时查看\n self.rest_attribute = rest_attribute # 尚未使用的属性列表\n TreeNode.current_index += 1\n\n def to_string(self):\n \"\"\"用一个字符串来描述当前结点信息\"\"\"\n this_string = 'current index : ' + str(self.index) + \";\\n\"\n if not (self.parent is None):\n parent_node = self.parent\n this_string = this_string + 'parent index : ' + str(parent_node.index) + \";\\n\"\n this_string = this_string + str(parent_node.attribute_name) + \" : \" + str(self.attribute_value) + \";\\n\"\n this_string = this_string + \"data : \" + str(self.data_index) + \";\\n\"\n if not(self.children is None):\n this_string = this_string + 'select attribute is : ' + str(self.attribute_name) + \";\\n\"\n child_list = []\n for child in self.children:\n child_list.append(child.index)\n this_string = this_string + 'children : ' + str(child_list)\n if not (self.judge is None):\n this_string = this_string + 'label : ' + self.judge\n return this_string\n\n\ndef is_number(s):\n \"\"\"判断一个字符串是否为数字\"\"\"\n try:\n float(s)\n return True\n except ValueError:\n pass\n return False\n\n\ndef ent(labels):\n \"\"\"\n 样本集合的信息熵\n :param labels: 样本集合中数据的类别标签\n :return:\n \"\"\"\n label_name = []\n label_count = []\n\n for item in labels:\n if not(item in label_name):\n label_name.append(item)\n label_count.append(1)\n else:\n index = label_name.index(item)\n label_count[index] = label_count[index] + 1\n\n n = sum(label_count)\n entropy = 0.0\n for item in label_count:\n p = item / n\n entropy = entropy - p*math.log(p, 2)\n\n return entropy\n\n\ndef gain(attribute, labels, is_value=False):\n \"\"\"\n 计算信息增益\n :param attribute: 集合中样本该属性的值列表\n :param labels: 集合中样本的数据标签\n :return:\n \"\"\"\n # is_value = False # 当前属性是离散的形容词还是连续的数值\n info_gain = ent(labels)\n n = len(labels)\n split_value = None # 如果是连续值的话，也需要返回分隔界限的值\n\n if is_value:\n # print('attribute', attribute)\n # 属性值是连续的数值，首先应该使用二分法寻找最佳分割点\n sorted_attribute = attribute.copy()\n sorted_attribute.sort()\n split = [] # 候选的分隔点\n for i in range(0, n-1):\n temp = (sorted_attribute[i] + sorted_attribute[i+1]) / 2\n split.append(temp)\n info_gain_list = []\n # print('split', split)\n for temp_split in split:\n low_labels = []\n high_labels = []\n for i in range(0, n):\n if attribute[i] <= temp_split:\n low_labels.append(labels[i])\n else:\n high_labels.append(labels[i])\n temp_gain = info_gain - len(low_labels)/n*ent(low_labels) - len(high_labels)/n*ent(high_labels)\n info_gain_list.append(temp_gain)\n\n # print('info_gain_list', info_gain_list)\n info_gain = max(info_gain_list)\n max_index = info_gain_list.index(info_gain)\n split_value = split[max_index]\n else:\n # 属性值是离散的值\n attribute_dict = {}\n label_dict = {}\n index = 0\n for item in attribute:\n if attribute_dict.__contains__(item):\n attribute_dict[item] = attribute_dict[item] + 1\n label_dict[item].append(labels[index])\n else:\n attribute_dict[item] = 1\n label_dict[item] = [labels[index]]\n index += 1\n\n for key, value in attribute_dict.items():\n info_gain = info_gain - value/n * ent(label_dict[key])\n\n return info_gain, split_value\n\n\ndef finish_node(current_node, data, label):\n \"\"\"\n 完成当前结点的后续计算，包括选择属性，划分子节点等\n :param current_node: 当前的结点\n :param data: 数据集\n :param label: 数据集的 label\n :return:\n \"\"\"\n n = len(label)\n\n # 判断当前结点的数据是否属于同一类，如果是，直接标记为叶子结点并返回\n one_class = True\n\n this_data_index = current_node.data_index\n for i in this_data_index:\n for j in this_data_index:\n if label[i] != label[j]:\n one_class = False\n break\n if not one_class:\n break\n if one_class:\n current_node.judge = label[this_data_index[0]]\n return\n\n rest_title = current_node.rest_attribute # 候选属性\n if len(rest_title) == 0: # 如果候选属性为空，则是个叶子结点。需要选择最多的那个类作为该结点的类\n label_count = {}\n temp_data = current_node.data_index\n for index in temp_data:\n if label_count.__contains__(label[index]):\n label_count[label[index]] += 1\n else:\n label_count[label[index]] = 1\n final_label = max(label_count)\n current_node.judge = final_label\n return\n\n title_gain = {} # 记录每个属性的信息增益\n title_split_value = {} # 记录每个属性的分隔值，如果是连续属性则为分隔值，如果是离散属性则为None\n for title in rest_title:\n attr_values = []\n current_label = []\n for index in current_node.data_index:\n this_data = data[index]\n attr_values.append(this_data[title])\n current_label.append(label[index])\n temp_data = data[0]\n this_gain, this_split_value = gain(attr_values, current_label, is_number(temp_data[title])) # 如果属性值为数字，则认为是连续的\n title_gain[title] = this_gain\n title_split_value[title] = this_split_value\n\n best_attr = max(title_gain, key=title_gain.get) # 信息增益最大的属性名\n current_node.attribute_name = best_attr\n current_node.split = title_split_value[best_attr]\n rest_title.remove(best_attr)\n\n a_data = data[0]\n if is_number(a_data[best_attr]): # 如果是该属性的值为连续数值\n split_value = title_split_value[best_attr]\n small_data = []\n large_data = []\n for index in current_node.data_index:\n this_data = data[index]\n if this_data[best_attr] <= split_value:\n small_data.append(index)\n else:\n large_data.append(index)\n small_str = '<=' + str(split_value)\n large_str = '>' + str(split_value)\n small_child = TreeNode(parent=current_node, data_index=small_data, attr_value=small_str,\n rest_attribute=rest_title.copy())\n large_child = TreeNode(parent=current_node, data_index=large_data, attr_value=large_str,\n rest_attribute=rest_title.copy())\n current_node.children = [small_child, large_child]\n\n else: # 如果该属性的值是离散值\n best_titlevalue_dict = {} # key是属性值的取值，value是个list记录所包含的样本序号\n for index in current_node.data_index:\n this_data = data[index]\n if best_titlevalue_dict.__contains__(this_data[best_attr]):\n temp_list = best_titlevalue_dict[this_data[best_attr]]\n temp_list.append(index)\n else:\n temp_list = [index]\n best_titlevalue_dict[this_data[best_attr]] = temp_list\n\n children_list = []\n for key, index_list in best_titlevalue_dict.items():\n a_child = TreeNode(parent=current_node, data_index=index_list, attr_value=key,\n rest_attribute=rest_title.copy())\n children_list.append(a_child)\n current_node.children = children_list\n\n # print(current_node.to_string())\n for child in current_node.children: # 递归\n finish_node(child, data, label)\n\n\ndef id3_tree(Data, title, label):\n \"\"\"\n id3方法构造决策树，使用的标准是信息增益（信息熵）\n :param Data: 数据集，每个样本是一个 dict(属性名：属性值)，整个 Data 是个大的 list\n :param title: 每个属性的名字，如 色泽、含糖率等\n :param label: 存储的是每个样本的类别\n :return:\n \"\"\"\n n = len(Data)\n rest_title = title.copy()\n root_data = []\n for i in range(0, n):\n root_data.append(i)\n\n root_node = TreeNode(data_index=root_data, rest_attribute=title.copy())\n finish_node(root_node, Data, label)\n\n return root_node\n\n\ndef print_tree(root=TreeNode()):\n \"\"\"\n 打印输出一颗树\n :param root: 根节点\n :return:\n \"\"\"\n node_list = [root]\n while(len(node_list)>0):\n current_node = node_list[0]\n print('--------------------------------------------')\n print(current_node.to_string())\n print('--------------------------------------------')\n children_list = current_node.children\n if not (children_list is None):\n for child in children_list:\n node_list.append(child)\n node_list.remove(current_node)\n\n\ndef evaluation(root=TreeNode(), true_labels=[]):\n \"\"\"\n 计算决策树在训练数据集上的分类正确率\n :param root: 决策树的根节点\n :param true_labels: 训练数据集的\n :return:\n \"\"\"\n node_list = [root]\n n = len(true_labels)\n count = 0\n\n while(len(node_list)>0):\n current_node = node_list[0]\n children_list = current_node.children\n\n if not (children_list is None):\n for child in children_list:\n node_list.append(child)\n\n if not (current_node.judge is None):\n for index in current_node.data_index:\n if current_node.judge == true_labels[index]:\n count += 1\n\n node_list.remove(current_node)\n\n return count / n\n\n\ndef run_test():\n watermelon, title, title_full = Dataset.watermelon3()\n\n # 先处理数据结构\n data = [] # 存放数据\n label = [] # 存放标签\n for melon in watermelon:\n a_dict = {}\n dim = len(melon) - 1\n for i in range(0, dim):\n a_dict[title[i]] = melon[i]\n data.append(a_dict)\n label.append(melon[dim])\n\n decision_tree = id3_tree(data, title, label)\n print_tree(decision_tree)\n accuracy = evaluation(root=decision_tree, true_labels=label)\n print('正确率是：'+str(accuracy*100)+\"%\")\n\n\nif __name__ == '__main__':\n run_test()\n\n","repo_name":"sdubrz/MachineLearningStudy","sub_path":"Ch04DecisionTree/id3.py","file_name":"id3.py","file_ext":"py","file_size_in_byte":11746,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"50"} +{"seq_id":"74817538396","text":"from autobahn.wamp.types import PublishOptions\n\nfrom crossbar._util import dump_json\nfrom crossbar.bridge.rest.common import _CommonResource\n\n__all__ = ('PublisherResource', )\n\n\nclass PublisherResource(_CommonResource):\n \"\"\"\n A HTTP/POST to WAMP-Publisher bridge.\n \"\"\"\n def _process(self, request, event):\n\n if 'topic' not in event:\n return self._deny_request(request, 400, key=\"topic\", log_category=\"AR455\")\n\n topic = event.pop('topic')\n\n args = event['args'] if 'args' in event and event['args'] else []\n kwargs = event['kwargs'] if 'kwargs' in event and event['kwargs'] else {}\n options = event['options'] if 'options' in event and event['options'] else {}\n\n publish_options = PublishOptions(acknowledge=True,\n forward_for=options.get('forward_for', None),\n retain=options.get('retain', None),\n exclude_me=options.get('exclude_me', None),\n exclude_authid=options.get('exclude_authid', None),\n exclude_authrole=options.get('exclude_authrole', None),\n exclude=options.get('exclude', None),\n eligible_authid=options.get('eligible_authid', None),\n eligible_authrole=options.get('eligible_authrole', None),\n eligible=options.get('eligible', None))\n\n kwargs['options'] = publish_options\n\n # http://twistedmatrix.com/documents/current/web/howto/web-in-60/asynchronous-deferred.html\n\n d = self._session.publish(topic, *args, **kwargs)\n\n def on_publish_ok(pub):\n res = {'id': pub.id}\n body = dump_json(res, True).encode('utf8')\n self._complete_request(request, 200, body, log_category=\"AR200\", reason=\"OK\")\n\n def on_publish_error(err):\n self._fail_request(request, failure=err, log_category=\"AR456\")\n\n return d.addCallbacks(on_publish_ok, on_publish_error)\n","repo_name":"crossbario/crossbar","sub_path":"crossbar/bridge/rest/publisher.py","file_name":"publisher.py","file_ext":"py","file_size_in_byte":2162,"program_lang":"python","lang":"en","doc_type":"code","stars":2045,"dataset":"github-code","pt":"50"} +{"seq_id":"14232481944","text":"from tkinter import *\nimport time\n\n\ntry:\n strt=int(input(\"Please write secret code to launch our downloder program : \"))\n print(\"welcome to your yt lite downloder..\")\n user=input(\"Enter your good name : \")\n print(\"welcome to digital next genration mini py YT 2.01 videos downloder<>, \",user)\nexcept ValueError:\n time.sleep(3)\n print(\"may be some mistake in your input , Please recheck Cearfully,.. secret code, hint:= {sum of first 2 digts of latitude & longitude of in which city your are in.. }\")\n userdata=input(\"enter again > \")\n print(\"congrats!\")\n\ndef ytube():\n from pytube import YouTube\n link=(str(hd.get()))\n yt=YouTube(link)\n stm=yt.streams.first()\n stm.download()\n print(\"downloder done vro\")\n print(\"enjoy your video!\")\n time.sleep(3)\n print(\"If any thing you want to say about this program or py so, Pease write here , and we definatily work on this and make more effective for you : \")\n feed=input(\"Write here your : \")\n print(\"please wait we are uploading your risponse.. ... ....\")\n time.sleep(3)\n print(\"we are work on your valuable feed thankyou for using..\",user)\n\nasr=Tk()\nasr.title(\"Youtube DOWNLODER 2.0\")\nasr.geometry(\"1300x700+00+00\")\nfilebg=PhotoImage(file='C:\\\\Users\\\\neha\\\\Links\\\\tubedownloder\\\\pikrepo.png')\nl_fr_bg=Label(asr,image=filebg)\nl_fr_bg.place(x=0,y=0)\nlabel=Label(text=\"Hey Buddy, Welcome,, Insert your link below\",fg=\"purple\",font=\"200x300\",bg='steel blue').pack()\nlbel2=Label(text=\"hey user there is some changes in your program you can'nt paste youtube link by mouse or touch, but you can paste link by 'CLTRL + V ' keys\",fg=\"yellow\",bg='purple' ).pack()\nbutton1=Button(text=\"download\",font=\"150x160\",bg=\"green\",fg=\"yellow\",command=ytube).pack()\nhd=StringVar()\nvalid=Entry(textvariable=hd,bd=4,bg='powder blue').pack()\n\nasr.mainloop()\n\n","repo_name":"Anuragsingh2003/Ytube_v_downloder_v3.1_py","sub_path":"youtune downdloderrr.py","file_name":"youtune downdloderrr.py","file_ext":"py","file_size_in_byte":1842,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"36762342879","text":"import json\nimport logging\n\ntry:\n\tfrom urllib.request import Request, urlopen # Python 3\nexcept ImportError:\n\tfrom urllib2 import Request, urlopen # Python 2\n\nfrom django.conf import settings\n\ndef infoip(func):\n\tdef wrap(request, *args, **kwargs):\n\t\tinfo = None\n\t\ttry:\n\t\t\tinfo = __get_geoip_info(request)\n\t\texcept Exception as e:\n\t\t\tlogging.error(e)\n\n\t\trequest.infoip = info\n\n\t\treturn func(request)\n\treturn wrap\n\ndef __get_client_ip(request):\n\tx_forwarded_for = request.META.get('HTTP_X_FORWARDED_FOR')\n\tif x_forwarded_for:\n\t\tip = x_forwarded_for.split(',')[0]\n\telse:\n\t\tip = request.META.get('REMOTE_ADDR')\n\treturn ip\n\ndef __get_geoip_info(request):\n\tprotocol = \"http\"\n\tif hasattr(settings, 'INFOIP_USE_HTTPS') and settings.INFOIP_USE_HTTPS:\n\t\tprotocol = \"https\"\n\n\turl = \"{0}://api.infoip.io/{1}\".format(protocol, __get_client_ip(request))\n\t\n\tq = Request(url)\n\n\tif hasattr(settings, 'INFOIP_API_KEY') and settings.INFOIP_API_KEY:\n\t\tq.add_header('x-infoip-token', settings.INFOIP_API_KEY)\n\telse:\n\t\tlogging.debug(\"No infoip API key found. Rate limiting may occur!\")\n\n\tdata = urlopen(q).read().decode(\"utf-8\")\n\treturn json.loads(data)","repo_name":"ciokan/infoip-geoip-python-integrations","sub_path":"infoip/django.py","file_name":"django.py","file_ext":"py","file_size_in_byte":1133,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"19869290834","text":"# -*- coding: utf-8 -*-\n# -*- python -*-\n# Author: Luo Jinghua\nimport os\n\nenv = Environment(tools = ['default', 'convlib'],\n ENV = os.environ)\nbareenv = env.Clone()\nsrcs = ['codecs_cn.c', 'codecs_hk.c', 'codecs_iso2022.c',\n 'codecs_jp.c', 'codecs_kr.c', 'codecs_tw.c',\n 'multibytecodec.c']\nsrcs += Glob('*-table.c')\nsrcs += ['singlebytetables.c', 'singlebytecodec.c', 'uniconv.c',\n 'charsetalias.c', 'unicode.c', 'converter.c',\n 'tableconverter.c', 'utfconverter.c']\nenv.AppendUnique(CCFLAGS = ['-Wall', '-g'],\n LINKFLAGS = ['-g'],\n\t\t CPPPATH = ['.'])\n\nuniconv = env.ConvenienceLibrary ('uniconv', srcs)\n\niconv_env = env.Clone()\niconv_src = ['iconv.c']\nif iconv_env['PLATFORM'] == 'win32':\n iconv_def = os.path.join('win32', 'iconv.def')\n if 'gcc' in iconv_env['CC']:\n iconv_src.append(iconv_def)\n else:\n iconv_env.AppendUnique(LINKFLAGS = ['/DEF:' + iconv_def])\n iconv_env.AppendUnique(CPPDEFINES = ['UNICONV_REPLACE_ICONV'])\n\niconv = iconv_env.SharedLibrary('iconv', iconv_src,\n LIBS = ['uniconv'],\n LIBPATH = ['.'],\n CPPPATH = ['.'])\n\ntest = env.Program ('test', 'main.c',\n LIBS = ['uniconv'],\n LIBPATH = ['.'],\n CPPPATH = ['.'])\nconv = env.Program ('uconv', 'uconv.c',\n LIBS = ['uniconv'],\n LIBPATH = ['.'],\n CPPPATH = ['.'])\n\nconv2_env = bareenv.Clone()\nif env['PLATFORM'] == 'win32':\n conv2_env.AppendUnique(CPPPATH = ['win32'],\n LIBS = ['iconv'],\n LIBPATH = ['.'])\nconv2 = conv2_env.Program ('uconv2', 'uconv2.c')\n","repo_name":"LolHacksRule/uniconv","sub_path":"SConstruct","file_name":"SConstruct","file_ext":"","file_size_in_byte":1774,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"1686846456","text":"continous_features = range(1, 14)\ncategorial_features = range(14, 40)\ncontinous_clip = [20, 600, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50]\n\n\nclass Dataset:\n def __init__(self):\n pass\n\n\nclass CriteoDataset(Dataset):\n def __init__(self, sparse_feature_dim):\n self.cont_min_ = [0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n self.cont_max_ = [\n 20, 600, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50\n ]\n self.cont_diff_ = [\n 20, 603, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50\n ]\n self.hash_dim_ = sparse_feature_dim\n # here, training data are lines with line_index < train_idx_\n self.train_idx_ = 41256555\n self.continuous_range_ = range(1, 14)\n self.categorical_range_ = range(14, 40)\n\n def _reader_creator(self, file_list, is_train, trainer_num, trainer_id):\n def reader():\n print(file_list)\n for file in file_list:\n with open(file, 'r') as f:\n print(\"open file success\")\n line_idx = 0\n for line in f:\n line_idx += 1\n features = line.rstrip('\\n').split('\\t')\n dense_feature = []\n sparse_feature = []\n for idx in self.continuous_range_:\n if features[idx] == '':\n dense_feature.append(0.0)\n else:\n dense_feature.append((float(features[idx]) -\n self.cont_min_[idx - 1]) /\n self.cont_diff_[idx - 1])\n for idx in self.categorical_range_:\n sparse_feature.append([\n hash(str(idx) + features[idx]) % self.hash_dim_\n ])\n\n label = [int(features[0])]\n yield [dense_feature] + sparse_feature + [label]\n\n return reader\n\n def train(self, file_list, trainer_num, trainer_id):\n return self._reader_creator(file_list, True, trainer_num, trainer_id)\n\n def test(self, file_list):\n return self._reader_creator(file_list, False, 1, 0)\n\n def infer(self, file_list):\n return self._reader_creator(file_list, False, 1, 0)\n","repo_name":"PaddlePaddle/continuous_integration","sub_path":"distributed/dist_fleet/thirdparty/ctr/py_reader_generator.py","file_name":"py_reader_generator.py","file_ext":"py","file_size_in_byte":2447,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"50"} +{"seq_id":"37737599729","text":"#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n# author: Olivier Noguès\r\n\r\n\r\nimport os\r\nimport time\r\nimport inspect\r\nimport json\r\nimport importlib\r\n\r\nimport ares.Lib.js.fncs\r\nimport ares.Lib.js.objects\r\nimport ares.utils.AresSiphash\r\n\r\n\r\n# Factory wii all the javascript information\r\nfactory = None\r\n\r\n\r\nDSC = {\r\n 'eng': '''\r\n:dsc:\r\nThe Ares Js object is the object in charge of defining the functions to apply to the different recordSets once they are \r\ndefined on the Javascript side. This object will not be in charge of encoding the data (this is done as part of the \r\npandas DataFrame in the module **AresFilePandas.py**), it will be on charge of ensuring that all your containers using the same \r\ndata will rely on the same javascript variable. The only differences will be the javascript transformation to end up \r\nto an object definition which will fit the containers.\r\n__\r\nEach chart and table containers will have specific requirements and this specific transformation is done in objects/ conversion folder. \r\nThose conversion are defined based on the output function in the Js Object and each chart family will use it to type the data.\r\n__\r\nFor example for the **ChartJs object** you get the below data conversion\r\n\r\n```python\r\ndef plotChartJs(self, chartType, data=None, width=100, widthUnit=\"%\", height=300, heightUnit='px', title='', chartOptions=None, toolsbar=None, htmlCode=None, globalFilter=None):\r\n \r\nparams = (list(data._schema['values']), list(data._schema['keys']))\r\nreturn self.add(graph.AresHtmlGraphChartJs.Chart(self, chartType, data.output('ChartJs', chartType, params), width, widthUnit, height, heightUnit, title, chartOptions, toolsbar, htmlCode, globalFilter), sys._getframe().f_code.co_name)\r\n```\r\n\r\nThe data.output() function in this case will type the object to be a ChartJs one. The definition of the conversion from a recordSet to a ChartJs object is done in the module **jsCharts.py** specific to the ChartJs framework.\r\nThe Javascript part used to convert the data to this container is then defined in a static and generic manner in the value string variable.\r\n\r\n```javascript\r\nvar temp = {}; var labels = []; var uniqLabels = {};\r\nseriesNames.forEach(function(series){temp[series] = {}}) ;\r\ndata.forEach(function(rec) { \r\n seriesNames.forEach(function(name){\r\n if(rec[name] !== undefined) {\r\n if (!(rec[xAxis] in uniqLabels)){labels.push(rec[xAxis]); uniqLabels[rec[xAxis]] = true};\r\n temp[name][rec[xAxis]] = rec[name]}})\r\n});\r\nresult = {datasets: [], labels: labels};\r\nseriesNames.forEach(function(series){\r\n dataSet = {label: series, data: []};\r\n labels.forEach(function(x){\r\n if (temp[series][x] == undefined) {dataSet.data.push(null)} else {dataSet.data.push(temp[series][x])}\r\n }); result.datasets.push(dataSet)})\r\n```\r\n\r\n## How to add Javascript transformations\r\n\r\n\r\n## How to use the debug flag\r\n\r\n\r\n## Use and Create a regression test \r\n\r\nThis part is still in progress (do not hesitate to help), but the idea is quite simple to add various tests to your javascript functions.\r\nIndeed any changes in the Javascript is quite painful and difficult to test / debug, the idea with this is to store all the relevant input and output in a text file automatically leveraging on the AReS architecture to make this easier.\r\n\r\nA possible extension to this model could be to create specificity per type of browser in order to have a font end code which can adapt according to the browser.\r\n\r\n'''}\r\n\r\n\r\ndef load(forceReload=False):\r\n \"\"\"\r\n :category: Javascript\r\n :rubric: JS\r\n :type: Factory\r\n :dsc:\r\n Load the factory with the all the different Javascript functions defined in the framework.\r\n It will store all the different Javascript function used to transform the recordSet or the ones in charge\r\n of changing the recordSet to fit the different containers in the AReS components.\r\n :return: The factory data\r\n \"\"\"\r\n global factory\r\n\r\n if factory is None or forceReload:\r\n tempFactory, aliasChecks = {}, {}\r\n jsStructures = {'fncs': ares.Lib.js.fncs, 'objs': ares.Lib.js.objects}\r\n for jsType, jsMod in jsStructures.items():\r\n for mod in os.listdir(os.path.dirname(jsMod.__file__)):\r\n if not mod.endswith('.py') or mod == '__init__.py':\r\n continue\r\n\r\n pyMod = importlib.import_module('%s.%s' % (jsMod.__name__, mod.replace(\".py\", \"\")))\r\n for name, obj in inspect.getmembers(pyMod):\r\n if inspect.isclass(obj) and obj.alias is not None:\r\n if hasattr(obj, 'alias') and hasattr(obj, 'value'):\r\n inst = obj()\r\n alias = [inst.alias] if not hasattr(inst, 'chartTypes') else [\"%s_%s\" % (inst.alias, cType.replace(\"-\", \"\")) for cType in inst.chartTypes]\r\n params = \"data\" if getattr(inst, 'params', None) is None else \"data, %s\" % \", \".join(inst.params)\r\n for a in alias:\r\n if jsType == 'fncs':\r\n a = \"ares_%s\" % a.replace(\"-\", \"\")\r\n if a in aliasChecks:\r\n raise Exception(\"Alias %s found two time in the framework %s and %s\" % (a, obj.__name__, aliasChecks[a]))\r\n\r\n aliasChecks[a] = obj.__name__\r\n text = \"\".join([jsLine.strip() for jsLine in inst.value.strip().split(\"\\n\")])\r\n tempFactory.setdefault(jsType, {})[a] = {\"text\": text, 'params': params, 'module': pyMod.__name__, 'class': obj}\r\n factory = tempFactory\r\n return factory\r\n\r\n\r\nclass Js(object):\r\n \"\"\"\r\n :category:\r\n :rubric:\r\n :type:\r\n :dsc:\r\n\r\n \"\"\"\r\n\r\n def __init__(self, aresObj, pyDf, keys=None, values=None, debug=False):\r\n load()\r\n self._schema = {'fncs': [], 'out': None, 'post': [], 'keys': set() if keys is None else set(keys),\r\n 'values': set() if values is None else set(values), 'debug': getattr(aresObj, 'DEBUG', debug)}\r\n self._dataId = id(pyDf) # Store the memory ID of the original object (the one known by all the components\r\n if not hasattr(pyDf, 'htmlCode'):\r\n dataCode = None\r\n # For input data not defined as dataframe\r\n for key, dataSrc in aresObj.jsSources.items():\r\n if dataSrc.get('dataId') == self._dataId:\r\n dataCode = key\r\n break\r\n\r\n pyDf = aresObj.df(pyDf, htmlCode=\"ares_id_%s\" % len(aresObj.jsSources) if dataCode is None else dataCode)\r\n self._jqId = pyDf.htmlCode # Original recordSet, this will never change\r\n self.aresObj, self.jqId, self._data = aresObj, pyDf.htmlCode, pyDf\r\n\r\n def setId(self, jqId):\r\n \"\"\"\r\n :category: Javascript Object\r\n :rubric: JS\r\n :type: System\r\n :dsc:\r\n Change the Id variable name for the javascript data source.\r\n :return: The Python object\r\n \"\"\"\r\n self.jqId = jqId if jqId is not None else self._jqId\r\n return self\r\n\r\n def attach(self, htmlObj):\r\n \"\"\"\r\n :category: Javascript Object\r\n :rubric: JS\r\n :type: Front End\r\n :dsc:\r\n Attach the Dataframe to a HTML Object. This function is normally used in the different components in order\r\n to guarantee the link of the data. This will also ensure that the same data set will be store only once in the page\r\n \"\"\"\r\n if not self._jqId in self.aresObj.jsSources:\r\n self.aresObj.jsSources[self._jqId] = {'dataId': self._dataId, 'containers': [], 'data': self._data}\r\n self.aresObj.jsSources[self._jqId]['containers'].append(htmlObj)\r\n self.aresObj.jsSources[self._jqId]['data'] = self._data # In case of replacements\r\n\r\n def output(self, outFamily, outType, args):\r\n \"\"\"\r\n :category: Formatting\r\n :rubric: JS\r\n :type: Front End\r\n :dsc:\r\n Format the recordSet to a defined container.\r\n \"\"\"\r\n self._schema['out'] = {\"family\": outFamily, 'type': outType, 'params': args}\r\n self._schema['out']['name'] = \"%s_%s\" % (outFamily, outType.replace(\"-\", \"\")) if \"%s_%s\" % (outFamily, outType.replace(\"-\", \"\")) in factory['objs'] else outFamily\r\n return self\r\n\r\n def fncs(self, fncNames, systemInfo=None):\r\n \"\"\"\r\n :category: Formatting\r\n :rubric: JS\r\n :type: Front End\r\n :dsc:\r\n Post process functions on the recordSet. It will potentially enhance the columns to be processed if the systemInfo parameters are defined.\r\n This will allow to pass extra parameters to the different functions in the framework according to the system.\r\n Information can be computed in the bespoke reports or coming directly from the source system.\r\n In order to use this the category should be defined in the Javascript function defined in the module jsFncsRecords.\r\n :return: The Python Js object\r\n \"\"\"\r\n if not fncNames:\r\n return self\r\n\r\n if not isinstance(fncNames, list):\r\n fncNames = [fncNames]\r\n for fncName in fncNames:\r\n args = None\r\n if isinstance(fncName, tuple):\r\n # This mean that some parameters are expected in the configuration\r\n if fncName[0] == 'order':\r\n countPerAxis, orderSeries = {}, []\r\n self._data['order'] = 0\r\n if fncName[1] is not None:\r\n for rec in self._data[fncName[1]]:\r\n countPerAxis[rec] = countPerAxis.get(rec, -1) + 1\r\n orderSeries.append(countPerAxis[rec])\r\n self._data['order'] = orderSeries\r\n continue\r\n\r\n args = list(fncName)[1:]\r\n fncName = \"ares_%s\" % fncName[0].replace(\"-\", \"\")\r\n factory['fncs'][fncName]['class'].extendColumns(self._schema, args)\r\n if systemInfo is not None:\r\n for category, sysCols in systemInfo.items():\r\n args = factory['fncs'][fncName]['class'].extendArgs(category, args, sysCols)\r\n self._schema['fncs'].append({'name': fncName, 'args': args})\r\n return self\r\n\r\n def post(self, fncNames):\r\n \"\"\"\r\n :category: Formatting\r\n :rubric: JS\r\n :type: Front End\r\n :dsc:\r\n\r\n :return:\r\n \"\"\"\r\n if not isinstance(fncNames, list):\r\n fncNames = [fncNames]\r\n for fncName in fncNames:\r\n args = None\r\n if isinstance(fncName, tuple):\r\n # This mean that some parameters are expected in the configuration\r\n args = list(fncName)[1:]\r\n fncName = fncName[0].replace(\"-\", \"\")\r\n self._schema['post'].append({'name': \"ares_%s\" % fncName, 'args': args})\r\n return self\r\n\r\n def getJs(self, fncs=None):\r\n \"\"\"\r\n :category: Formatting\r\n :rubric: JS\r\n :type: Front End\r\n :dsc:\r\n\r\n :return:\r\n \"\"\"\r\n val = self.jqId\r\n if fncs is not None:\r\n self.post(fncs)\r\n fncContentTemplate = [\"var result = []\", \"if (data !== null){%s} else {data = []}\", \"return result\"]\r\n if self._schema['debug']:\r\n fncContentTemplate = [\"var t0 = performance.now()\", \"var result = []\", \"if (data !== null){%s} else {data = []}\",\r\n \"console.log('Function: '+ arguments.callee.name +', count records: '+ data.length +', time: '+ (performance.now()-t0) +' ms.')\",\r\n \"console.log('Arguments -')\",\r\n \"for(var i = 1; i < arguments.length; i++){console.log(' ' + i +': '+ JSON.stringify(arguments[i]))}\",\r\n \"console.log()\",\r\n \"console.log('Input Data -')\", \"console.log(arguments[0])\", \"console.log()\",\r\n \"console.log('Output Data -')\", \"console.log(result)\", \"console.log()\", \"return result\"]\r\n fncContentTemplate = \"; \".join(fncContentTemplate)\r\n # Add the different filtering rules\r\n filters, jsFncs = [], self._schema['fncs']\r\n for k, v in self.aresObj.jsSources.get(self.jqId, {}).get('filters', {}).items():\r\n if k == 'allIfEmpty':\r\n v = list(v)\r\n filters.append(\"'%s': %s\" % (k, v))\r\n if len(filters) > 0:\r\n jsFncs = [{'args_js': ['{%s}' % \", \".join(filters)], 'name': 'ares_filter'}] + jsFncs\r\n # Set all the Javascript functions\r\n for fnc in jsFncs:\r\n if fnc['name'] in factory['fncs']:\r\n if hasattr(self.aresObj, \"jsGlobal\"):\r\n self.aresObj.jsGlobal.fnc(\"%s(%s)\" % (fnc['name'], factory['fncs'][fnc['name']]['params']), fncContentTemplate % factory['fncs'][fnc['name']]['text'])\r\n else:\r\n print(\"function %s(%s) {var result = []; %s;return result; };\" % (fnc['name'], factory['fncs'][fnc['name']]['params'], factory['fncs'][fnc['name']]['text']))\r\n if fnc.get('args') is not None:\r\n val = \"%s(%s, %s)\" % (fnc['name'], val, \", \".join([json.dumps(a) for a in fnc['args']]))\r\n elif fnc.get('args_js') is not None:\r\n # TODO: Remove this hack done for the filter function\r\n val = \"%s(%s, %s)\" % (fnc['name'], val, \", \".join([a for a in fnc['args_js']]))\r\n else:\r\n val = \"%s(%s)\" % (fnc['name'], val)\r\n # Container formatting\r\n if self._schema['out'] is not None:\r\n if self._schema['out']['name'] in factory['objs']:\r\n if hasattr(self.aresObj, \"jsGlobal\"):\r\n self.aresObj.jsGlobal.fnc(\"Ares%s(%s)\" % (self._schema['out']['name'], factory['objs'][self._schema['out']['name']]['params']), fncContentTemplate % factory['objs'][self._schema['out']['name']]['text'])\r\n else:\r\n print(\"function Ares%s(%s) {%s};\" % (self._schema['out']['name'], fncContentTemplate % factory['objs'][self._schema['out']]['params'], factory['objs'][self._schema['out']['name']]['text']))\r\n params = [json.dumps(a) for a in self._schema['out']['params']]\r\n val = \"Ares%s(%s, %s)\" % (self._schema['out']['name'], val, \", \".join(params))\r\n # Post process function\r\n for fnc in self._schema['post']:\r\n fncName = fnc['name']\r\n if fncName in factory['fncs']:\r\n if hasattr(self.aresObj, \"jsGlobal\"):\r\n self.aresObj.jsGlobal.fnc(\"%s(%s)\" % (fncName, factory['fncs'][fncName]['params']), fncContentTemplate % factory['fncs'][fncName]['text'])\r\n else:\r\n print(\"function %s(%s) {%s\" % (fncName, factory['fncs'][fncName]['params'], fncContentTemplate % factory['fncs'][fncName]['text']))\r\n if fnc['args']:\r\n val = \"%s(%s, %s)\" % (fncName, val, \", \".join([json.dumps(a) for a in fnc['args']]))\r\n else:\r\n val = \"%s(%s)\" % (fncName, val)\r\n return val\r\n\r\n def toTsv(self, process='input'):\r\n \"\"\"\r\n :category:\r\n :rubric:\r\n :type:\r\n :dsc:\r\n\r\n :return: A String with the Javascript function to be used\r\n \"\"\"\r\n tsv = ares.Lib.js.objects.jsText.JsTextTsv()\r\n self.aresObj.jsGlobal.fnc(\"ToTsv(data, colNames)\", \"%s; return result\" % tsv.value)\r\n return \"ToTsv(%s, %s)\" % (self.jqId, json.dumps(list(self._schema['keys'] | self._schema['values'])))\r\n\r\n # --------------------------------------------------------------------------------------------------------------\r\n #\r\n # Testing part\r\n # --------------------------------------------------------------------------------------------------------------\r\n def addTest(self, fncName, data, outpath=None):\r\n \"\"\"\r\n :category:\r\n :type:\r\n :rubric:\r\n :dsc:\r\n\r\n \"\"\"\r\n self.fncs(fncName)\r\n if outpath is None:\r\n outpath = os.path.join(os.path.dirname(__file__), \"tests\")\r\n fncName, module = self._fncs[0]['name'], factory['fncs'][self._fncs[0]['name']]['module'].split(\".\")[-1]\r\n testFilePath = os.path.join(outpath, \"%s_%s.json\" % (module, fncName))\r\n if os.path.exists(testFilePath):\r\n inFile = open(testFilePath)\r\n testObj = json.loads(inFile.read())\r\n inFile.close()\r\n else:\r\n testObj = {\"fnc\": fncName, \"tests\": {}, 'author': ''}\r\n for alias in self._fncs:\r\n testObj[\"def\"] = \"function %s(%s) {var result = []; %s ;return result; };\" % (\r\n fncName, factory['fncs'][fncName]['params'], factory['fncs'][fncName]['text'])\r\n testObj['module'] = factory['fncs'][fncName]['module']\r\n #\r\n hashObj = ares.utils.AresSiphash.SipHash()\r\n testObj['tests'][hashObj.hashId(json.dumps(data))] = {'data': data, \"time\": time.time()}\r\n\r\n with open(r\"%s\\%s_%s.json\" % (outpath, module, fncName), \"w\") as f:\r\n json.dump(testObj, f, indent=2)\r\n\r\n def doReg(self, moduleName, fncName, outpath=None):\r\n \"\"\"\r\n :category:\r\n :type:\r\n :rubric:\r\n :dsc:\r\n\r\n \"\"\"\r\n if outpath is None:\r\n outpath = os.path.join(os.path.dirname(__file__), \"tests\")\r\n testFilePath = os.path.join(outpath, \"%s_%s.json\" % (moduleName, fncName))\r\n inFile = open(testFilePath)\r\n testObj = json.loads(inFile.read())\r\n inFile.close()\r\n for testId, testDef in testObj['tests'].items():\r\n print(\"var data_%s = %s\" % (testId, testDef['data']))\r\n print(\"%s(data_%s)\" % (testObj['fnc'], testId))\r\n\r\n\r\ndef docEnum(aresObj, outStream, lang='eng'):\r\n \"\"\"\r\n :category: Javascript\r\n :rubric: PY\r\n :type: Documentation\r\n \"\"\"\r\n outStream.link(\"Breadcrumb definition\", \"\")\r\n outStream.link(\"Markdown definition\", \"\")\r\n outStream.link(\"RecordSets Functions\", \"\")\r\n outStream.link(\"Date Functions\", \"\")\r\n outStream.link(\"Charts Functions\", \"\")\r\n outStream.link(\"Polyfill Functions\", \"\")\r\n outStream.link(\"Regression Test\", \"\")\r\n\r\n\r\nif __name__ == \"__main__\":\r\n data = [\r\n {\"name\": \"Olivier\", \"job\": \"BNP\", \"value\": 11, \"value2\": 2},\r\n {\"name\": \"Olivier\", \"job\": \"BNP\", \"value\": 4, \"value2\": 3},\r\n {\"name\": \"Aurelie\", \"job\": \"BNP\", \"value\": 4, \"value2\": None},\r\n ]\r\n jsObj = Js(None, 'jsYoupi')\r\n jsObj.doReg('jsRecords', 'sum')\r\n #jsObj.addTest(('sum', ['name'], ['value']), data)\r\n #print( jsObj.fncs([('sum', ['name'], ['value']), ('rename', {'count': 'youpi'})]).output(('C3', [''], [''])).getJs() )\r\n\r\n# data = [\r\n# {\"name\": \"A\", \"job\": \"BNP\", \"value\": 3, \"value2\": 2},\r\n# {\"name\": \"A\", \"job\": \"BNP\", \"value\": 4, \"value2\": 3},\r\n# {\"name\": \"B\", \"job\": \"BNP\", \"value\": 4, \"value2\": None},\r\n# ]\r\n#\r\n# jsObj = Js(None)\r\n#\r\n# # [(\"sum\", ), \"rename\"])\r\n# #jsObj.fncs( [ (\"sum\", ['name'], ['value']), ('rename', {\"value\": \"label\"} )] )\r\n# #jsObj.fncs( [(\"count(distinct)\", ['name', 'value2'] ), ('rename', {'count': 'youpi'}), ('C3', [''], ['']) ] )\r\n# #jsObj.fncs( [(\"sum\", ['name'], ['value', 'value2'] ) ] )\r\n#\r\n# print(os.path.dirname(__file__))\r\n# outpath = r'C:\\Users\\olivier\\Documents\\youpi\\ares\\Lib\\js\\tests'\r\n# #jsObj.fncs([(\"stats(Column)\", 'value2')])\r\n# jsObj.addTest(\"toMarkUp\", data=\"test data **youpi**\")\r\n#\r\n# #jsObj.output(('D3_bubble', ['value2'], 'name'))\r\n# #jsObj.fncs( [(\"sum\", ['name'], ['value', 'value2'] )] )\r\n#\r\n# # jsObj.test(outpath)\r\n#\r\n","repo_name":"jeamick/ares-visual","sub_path":"Lib/js/AresJs.py","file_name":"AresJs.py","file_ext":"py","file_size_in_byte":18274,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"34232362834","text":"#! /usr/bin/env python\n#Ryan A. Melnyk\n#schmelnyk@gmail.com\n\nimport os, argparse, subprocess, errno, sys, string\nimport multiprocessing as mp\nimport pyparanoid.pyparanoid as pp\nfrom Bio import SeqIO\n\ndef parse_args():\n\tparser = argparse.ArgumentParser(description='''\nTakes a complete PyParanoid directory (base and propagated) and generate list of orthologs. Using the 'threshold'\nargument relaxes the cutoff and includes homologs that occur exactly once in some fraction of all strains (e.g. 90%).\n\t''')\n\tparser.add_argument('outdir', type=str,help='path to PyParanoid folder')\n\tparser.add_argument('prefix',type=str,help='output folder for data')\n\tparser.add_argument('--threshold',type=float,help='proportion of strains to be considered an ortholog')\n\tparser.add_argument('--cpus',type=int,help='number of CPUs to use for tasks. Defaults to # of cores available.')\n\tparser.add_argument('--clean',action=\"store_true\",help=\"clean up intermediate files\")\n\tparser.add_argument('--strains',type=str,help='specify if a subset of strains are to be identified')\n\tparser.add_argument('--orthos',type=str,help=\"specify to use previously calculated groups\")\n\tparser.add_argument('--use_MP',action=\"store_true\",help=\"use the python multiprocessing module for dramatic speed up\")\n\treturn parser.parse_args()\n\n\ndef parse_matrix(strains):\n\torthos = []\n\tprint(\"Parsing matrix to identify orthologs...\")\n\theader = open(os.path.join(outdir,\"homolog_matrix.txt\")).readline().rstrip().split(\"\\t\")\n\ttry:\n\t\tindices = [header.index(s) for s in strains]\n\texcept:\n\t\tprint(s,\"not found in matrix. Check strainlist.\")\n\tfor line in open(os.path.join(outdir,\"homolog_matrix.txt\")):\n\t\tvals = line.rstrip().split(\"\\t\")\n\t\tif vals[0] == \"\":\n\t\t\tcontinue\n\t\telse:\n\t\t\tstrain_vals = [vals[i] for i in indices]\n\t\t\tif set(strain_vals[1:]) == set([\"1\"]):\n\t\t\t\torthos.append(vals[0])\n\tprint(len(orthos), \"orthologs found.\")\n\treturn orthos\n\ndef parse_threshold_matrix(t,strains):\n\torthos = []\n\tprint(\"Parsing matrix to identify orthologs...\")\n\theader = open(os.path.join(outdir,\"homolog_matrix.txt\")).readline().rstrip().split(\"\\t\")\n\ttry:\n\t\tindices = [header.index(s) for s in strains]\n\texcept:\n\t\tprint(s,\"not found in matrix. Check strainlist.\")\n\tfor line in open(os.path.join(outdir,\"homolog_matrix.txt\")):\n\t\tvals = line.rstrip().split(\"\\t\")\n\t\tif vals[0] == \"\":\n\t\t\tcontinue\n\t\telse:\n\t\t\tstrain_vals = [vals[i] for i in indices]\n\t\t\tif float(strain_vals.count(\"1\"))/float(len(strain_vals)) > t:\n\t\t\t\torthos.append(vals[0])\n\tprint(len(orthos), \"orthologs found.\")\n\treturn orthos\n\ndef get_orthos(orthos,strains):\n\tseqdata = {}\n\tprint(\"Parsing homolog.faa...\")\n\tfor seq in SeqIO.parse(open(os.path.join(outdir,\"homolog.faa\"),'r'),'fasta'):\n\t\tvals = seq.id.split(\"|\")\n\t\tif vals[0] in strains:\n\t\t\tif vals[2] in orthos:\n\t\t\t\tif vals[2] in seqdata:\n\t\t\t\t\tpass\n\t\t\t\telse:\n\t\t\t\t\tseqdata[vals[2]] = {}\n\t\t\t\tif vals[0] not in seqdata[vals[2]]:\n\t\t\t\t\tseq.id = vals[0]\n\t\t\t\t\tseqdata[vals[2]][vals[0]] = seq\n\tprint(\"Parsing prop_homolog.faa...\")\n\tfor seq in SeqIO.parse(open(os.path.join(outdir,\"prop_homolog.faa\"),'r'),'fasta'):\n\t\tvals = seq.id.split(\"|\")\n\t\tif vals[0] in strains:\n\t\t\tif vals[2] in orthos:\n\t\t\t\tif vals[2] in seqdata:\n\t\t\t\t\tpass\n\t\t\t\telse:\n\t\t\t\t\tseqdata[vals[2]] = {}\n\t\t\t\tif vals[0] not in seqdata[vals[2]]:\n\t\t\t\t\tseq.id = vals[0]\n\t\t\t\t\tseqdata[vals[2]][vals[0]] = seq\n\tfor group in seqdata:\n\t\to = open(os.path.join(prefix,\"orthos\",group+\".faa\"),'w')\n\t\tfor s in seqdata[group]:\n\t\t\tSeqIO.write(seqdata[group][s],o,'fasta')\n\t\to.close()\n\treturn\n\ndef align_orthos(orthos,cpus):\n\tcount = len(orthos)\n\tprint(\"Aligning {} ortholog files...\".format(str(count)))\n\tif use_MP:\n\t\tpool = mp.Pool(processes=cpus)\n\t\t[pool.apply_async(hmmalign, args=(o,)) for o in orthos]\n\t\tpool.close()\n\t\tpool.join()\n\telse:\n\t\tfor o in orthos:\n\t\t\thmmalign(o)\n\t\t\tcount -= 1\n\treturn\n\ndef hmmalign(o):\n\tcmds = \"hmmalign -o {} {} {}\".format(os.path.join(prefix,\"ortho_align\",o+\".sto\"),os.path.join(prefix,\"hmms\",o+\".hmm\"),os.path.join(prefix,\"orthos\",o+\".faa\"))\n\tproc = subprocess.Popen(cmds.split())\n\tproc.wait()\n\treturn\n\ndef extract_hmms(orthos):\n\tcount = len(orthos)\n\tpresent = [f.split(\".\")[0] for f in os.listdir(os.path.join(prefix,\"hmms\"))]\n\tprint(\"Extracting {} HMM files...{} already found.\".format(str(count),str(len(present))))\n\tFNULL = open(os.devnull, 'w')\n\tfor o in orthos:\n\t\tcount -= 1\n\t\tif o in present:\n\t\t\tpass\n\t\telse:\n\t\t\tcmds = \"hmmfetch -o {} {} {}\".format(os.path.join(prefix,\"hmms\",o+\".hmm\"),os.path.join(outdir,\"all_groups.hmm\"),o)\n\t\t\tproc = subprocess.Popen(cmds.split(),stdout=FNULL,stderr=FNULL)\n\t\t\tproc.wait()\n\t\tif count % 100 == 0:\n\t\t\tprint(\"\\t\"+str(count), \"remaining...\")\n\t\telse:\n\t\t\tpass\n\tif count == 0:\n\t\tprint(\"\\tDone!\")\n\tFNULL.close()\n\treturn\n\ndef create_master_alignment(orthos,strains):\n\n\talign_data = {k : [] for k in strains}\n\tcount = len(orthos)\n\ttotal_leng = 0 ###DEBUG\n\tprint(\"Creating master alignment...Parsing {} homologs...\".format(str(count)))\n\ttranslation = str.maketrans('','',string.ascii_lowercase+\".\")\n\tfor o in orthos:\n\t\tcount -= 1\n\t\tpresent = []\n\t\tfor line in open(os.path.join(prefix,\"hmms\",o+\".hmm\")):\n\t\t\tif line.startswith(\"LENG\"):\n\t\t\t\tlength = int(line.rstrip().split()[1])\n\t\t\t\ttotal_leng += length ###DEBUG\n\t\t\t\tbreak\n\t\tfor line in open(os.path.join(prefix,\"ortho_align\",o+\".sto\")):\n\t\t\tif line.startswith(\"#\") or line.startswith(\"//\"):\n\t\t\t\tcontinue\n\t\t\telse:\n\t\t\t\tvals = line.rstrip().split()\n\t\t\t\tif len(vals) < 1:\n\t\t\t\t\tcontinue\n\t\t\t\telif vals[0] in align_data:\n\t\t\t\t\talign_data[vals[0]].append(vals[1].translate(translation))\n\t\t\t\t\tif vals[0] not in present:\n\t\t\t\t\t\tpresent.append(vals[0])\n\t\t\t\telse:\n\t\t\t\t\talign_data[vals[0]] = [vals[1].translate(translation)]\n\t\t\t\t\tif vals[0] not in present:\n\t\t\t\t\t\tpresent.append(vals[0])\n\t\tfor s in strains:\n\t\t\tif s not in present:\n\t\t\t\talign_data[s].append(\"-\"*length)\n\t\t\tif len(\"\".join(align_data[s])) != total_leng:\n\t\t\t\tprint(s, \"is short!\")\n\t\t\t\tprint(total_leng, len(\"\".join(align_data[s])))\n\t\t\t\tprint(align_data[s])\n\t\t\t\tprint(o)\n\t\t\t\tsys.exit()\n\t\tif count % 100 == 0:\n\t\t\tprint(\"\\t\"+str(count), \"remaining...\")\n\t\telse:\n\t\t\tpass\n\tprint(\"Done!\")\n\tprint(\"Writing alignment...\")\n\tout = open(os.path.join(prefix,\"master_alignment.faa\"),'w')\n\tfor a in align_data:\n\t\tout.write(\">{}\\n{}\\n\".format(a,\"\".join(align_data[a]).upper().replace(\".\",\"-\")))\n\tout.close()\n\tout = open(os.path.join(prefix,\"orthos.txt\"),'w')\n\t[out.write(\"{}\\n\".format(orth)) for orth in orthos]\n\tout.close()\n\treturn\n\ndef get_strains():\n\tstrains = [line.rstrip() for line in open(os.path.join(outdir,\"strainlist.txt\"))]\n\t[strains.append(s) for s in [line.rstrip() for line in open(os.path.join(outdir,\"prop_strainlist.txt\"))]]\n\treturn strains\n\ndef index_hmms():\n\tprint(\"Indexing all_groups.hmm...\")\n\tcmds = \"hmmfetch --index {}\".format(os.path.join(outdir,\"all_groups.hmm\"))\n\tproc = subprocess.Popen(cmds.split())\n\tproc.wait()\n\treturn\n\ndef main():\n\targs = parse_args()\n\tglobal prefix\n\tprefix = os.path.abspath(args.prefix)\n\tglobal outdir\n\toutdir = os.path.abspath(args.outdir)\n\n\tpp.createdirs(prefix,[\"orthos\",\"ortho_align\",\"hmms\"])\n\tif args.strains:\n\t\tstrains = [line.rstrip() for line in open(os.path.abspath(args.strains),'r')]\n\telse:\n\t\tstrains = get_strains()\n\n\tif args.orthos:\n\t\torthos = [line.rstrip() for line in open(os.path.abspath(args.orthos),'r')]\n\telse:\n\t\tif args.threshold:\n\t\t\torthos = parse_threshold_matrix(args.threshold,strains)\n\t\telse:\n\t\t\torthos = parse_matrix(strains)\n\n\tif args.cpus:\n\t\tcpus = args.cpus\n\telse:\n\t\tcpus = mp.cpu_count()\n\n\tglobal use_MP\n\tif args.use_MP:\n\t\tuse_MP = True\n\telse:\n\t\tuse_MP = False\n\n\tindex_hmms()\n\textract_hmms(orthos)\n\n\tget_orthos(orthos,strains)\n\talign_orthos(orthos,cpus)\n\tcreate_master_alignment(orthos,strains)\n\tif args.clean:\n\t\tpp.cleanup(os.path.join(prefix,\"ortho_align\"))\n\t\tpp.cleanup(os.path.join(prefix,\"orthos\"))\n\t\tpp.cleanup(os.path.join(prefix,\"hmms\"))\n\n\nif __name__ == '__main__':\n\tmain()\n","repo_name":"ryanmelnyk/PyParanoid","sub_path":"IdentifyOrthologs.py","file_name":"IdentifyOrthologs.py","file_ext":"py","file_size_in_byte":7808,"program_lang":"python","lang":"en","doc_type":"code","stars":32,"dataset":"github-code","pt":"50"} +{"seq_id":"18513197193","text":"from __future__ import print_function\nimport tensorflow as tf\nimport numpy as np\nimport os\nimport csv\n\ndef write_result(name, predictions):\n \"\"\"\n \"\"\"\n if predictions is None:\n raise Exception('need predictions')\n\n predictions = predictions.flatten()\n\n if not os.path.exists('./results/'):\n os.makedirs('./results/')\n\n path = os.path.join('./results/', name)\n\n with open(path, 'wt', encoding='utf-8', newline='') as csv_target_file:\n target_writer = csv.writer(csv_target_file, lineterminator='\\n')\n\n header = [\n 'user_id',\n 'time_slot_0', 'time_slot_1', 'time_slot_2', 'time_slot_3',\n 'time_slot_4', 'time_slot_5', 'time_slot_6', 'time_slot_7',\n 'time_slot_8', 'time_slot_9', 'time_slot_10', 'time_slot_11',\n 'time_slot_12', 'time_slot_13', 'time_slot_14', 'time_slot_15',\n 'time_slot_16', 'time_slot_17', 'time_slot_18', 'time_slot_19',\n 'time_slot_20', 'time_slot_21', 'time_slot_22', 'time_slot_23',\n 'time_slot_24', 'time_slot_25', 'time_slot_26', 'time_slot_27',\n ]\n\n target_writer.writerow(header)\n\n for i in range(0, len(predictions), 28):\n # NOTE: 57159 is the offset of user ids\n userid = [57159 + i // 28]\n labels = predictions[i:i+28].tolist()\n\n target_writer.writerow(userid + labels)\n\ntf.reset_default_graph()\nwith tf.device('/CPU:0'):\n sess = tf.Session()\n\n # 載入模型\n saver = tf.train.import_meta_graph('./model_2/cnn_0.8575981259346008_0.2087835669517517.ckpt-8401.meta')\n saver.restore(sess, tf.train.latest_checkpoint('./model_2/'))\n\n # 載入graph\n y = tf.get_collection('outputs')[0]\n graph = tf.get_default_graph()\n\n xs = graph.get_tensor_by_name(\"inputs/xs:0\")\n keep_prob = graph.get_tensor_by_name(\"inputs/keep_prob:0\")\n\n dataset = np.load('./datasets/v0_eigens.npz')\n\n # NOTE: read features of test set\n test_eigens = dataset['issue_eigens'][:, :-28].reshape(-1, 896)\n # test_eigens = test_eigens[:, -28:]\n\n result = sess.run(y, feed_dict = {xs: test_eigens, keep_prob: 1})\n # result3 = sess.run(tf.where(tf.less(result, 0.03), tf.zeros_like(result), tf.ones_like(result)))\n # result2 = sess.run(tf.where(tf.less(result, 0.02), tf.zeros_like(result), tf.ones_like(result)))\n \n write_result('result_cnn_3.csv', result)\n # write_result('result_7_002.csv', result2)","repo_name":"shao-chi/KKStream-Deep-Learning-Workshop","sub_path":"cnn_predict.py","file_name":"cnn_predict.py","file_ext":"py","file_size_in_byte":2450,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"26860049297","text":"import re\nimport threading\nimport dataclasses\nfrom queue import Queue\nfrom typing import Optional\n\nimport pandas as pd\n\nfrom utils import Logger\n\nCOLUMNS = [\"Title\", \"Url\", \"Image\", \"Price\"]\n\nNON_NULL_COLUMNS = [\"Title\", \"Image\", \"Price\"]\n\n@dataclasses.dataclass\nclass Columns:\n \"\"\"Stores columns information\"\"\"\n title: str\n url: str\n image: str\n price: str\n\n@dataclasses.dataclass\nclass FileStats:\n \"\"\"Store file descriptions i.e. file name, products before and products after\"\"\"\n website: str\n category: str\n output_path: str\n products_count_before: Optional[int] = None\n products_count_after: Optional[int] = None\n\nclass DataHandler:\n \"\"\"Removes any rows with blank values for title, price or image\"\"\"\n def __init__(self, stats: list[FileStats]) -> None:\n self.logger = Logger(__class__.__name__)\n\n self.queue = Queue()\n\n self.stats = stats\n\n [threading.Thread(target=self.__work, daemon=True).start() for _ in range(3)]\n\n @staticmethod\n def __get_columns(columns: list[str]) -> Optional[Columns]: \n for column in columns:\n if re.search(r\"title\", column, re.I):\n title = column\n elif re.search(r\"url\", column, re.I):\n url = column\n elif re.search(r\"image\", column, re.I):\n image = column\n elif re.search(r\"price\", column, re.I):\n price = column\n \n try:\n return Columns(title=title, url=url, image=image, price=price)\n except: pass\n\n @staticmethod\n def __rename_columns(df: pd.DataFrame, columns: Columns) -> pd.DataFrame:\n return df.rename(columns={columns.title: COLUMNS[0],\n columns.url: COLUMNS[1],\n columns.image: COLUMNS[2],\n columns.price: COLUMNS[-1]})\n \n def __read_file(self, file_path: str) -> Optional[pd.DataFrame]:\n if re.search(r\".xlsx$\", file_path, re.I):\n return pd.read_excel(file_path)\n elif re.search(r\".csv$\", file_path, re.I):\n return pd.read_csv(file_path)\n elif re.search(r\".json$\", file_path, re.I):\n return pd.read_json(file_path)\n elif re.search(r\".xml$\", file_path, re.I):\n return pd.read_xml(file_path)\n elif re.search(r\".tsv$\", file_path, re.I):\n return pd.read_csv(file_path, sep=\"\\t\")\n \n extension = re.search(r\"\\.[a-zA-Z]{1,6}$\", file_path)\n\n self.logger.warn(\"Skipping removal of blanks as the script is not \"\n \"configured to handle {} file type\".format(extension))\n \n def __save(self, df: pd.DataFrame, file_path: str, name: str) -> None:\n if re.search(r\".xlsx$\", file_path, re.I):\n df.to_excel(file_path, index=False)\n elif re.search(r\".csv$\", file_path, re.I):\n df.to_csv(file_path, index=False)\n elif re.search(r\".json$\", file_path, re.I):\n df.to_json(file_path, index=False)\n elif re.search(r\".xml$\", file_path, re.I):\n df.to_xml(file_path, index=False)\n elif re.search(r\".tsv$\", file_path, re.I):\n df.to_csv(file_path, sep=\"\\t\")\n \n filename = file_path\n\n if re.search(r\"\\/\", file_path):\n filename = file_path.split(\"/\")[-1]\n elif re.search(r\"\\\\\", file_path):\n filename = file_path.split(\"\\\\\")[-1]\n\n stats = [dataclasses.asdict(record) for record in self.stats]\n\n [record.pop(\"output_path\") for record in stats]\n\n pd.DataFrame(stats).to_csv(\"./stats/stats.csv\", index=False)\n\n self.logger.info(\"{}: Non-null records saved to {}\".format(name, filename))\n \n def __work(self) -> None:\n while True:\n name, output_path = self.queue.get()\n\n stats = self.__get_stats(output_path)\n\n df = self.__read_file(output_path)\n\n stats.products_count_before = len(df)\n\n if df is None:\n self.queue.task_done()\n\n continue\n\n columns = self.__get_columns(list(df.columns.values))\n\n df = self.__rename_columns(df, columns)\n\n [df.dropna(subset=column, inplace=True) for column in NON_NULL_COLUMNS]\n\n stats.products_count_after = len(df)\n\n self.__save(df, output_path, name)\n\n self.queue.task_done()\n \n def __get_stats(self, output_path: str) -> FileStats:\n for stats in self.stats:\n if stats.output_path == output_path: return stats","repo_name":"Manue-Towett/webharvy","sub_path":"apps/data_handler.py","file_name":"data_handler.py","file_ext":"py","file_size_in_byte":4581,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"30868119829","text":"# крч эта прога выдаёт график зависимости среднего значения очков игрального кубика от кол-ва бросков\r\nfrom random import randint\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\n\r\nclass Die:\r\n def __init__(self, count):\r\n self.count = count\r\n\r\n def random_score(self):\r\n return randint(1, 6)\r\n\r\n def randoming(self):\r\n z = 0\r\n summa = 0\r\n while z <= self.count:\r\n summa += randint(1, 6)\r\n z += 1\r\n return summa / self.count\r\n\r\n\r\nx = np.linspace(1, 5001, 5000)\r\ni = 0\r\ny = []\r\nfor i in range(1, 5001):\r\n ThisClass = Die(i)\r\n y.append(ThisClass.randoming())\r\nplt.plot(x, y)\r\nplt.show()\r\nJustDie = Die(1000)\r\nprint(JustDie.random_score())\r\n","repo_name":"GlavnyNegodyai/test-ish-_repository","sub_path":"code/rn.py","file_name":"rn.py","file_ext":"py","file_size_in_byte":809,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"9975427825","text":"from pydantic import BaseModel\nfrom enum import Enum\n\n\nclass DogType(str, Enum):\n terrier = \"terrier\"\n bulldog = \"bulldog\"\n dalmatian = \"dalmatian\"\n\n\nclass Dog(BaseModel):\n name: str\n pk: int\n kind: DogType\n\n\nclass Timestamp(BaseModel):\n id: int\n timestamp: int","repo_name":"Floly/devtools_hw2","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":285,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"8709266345","text":"\n# inmporting nessesry libreries\nfrom collections import OrderedDict\nfrom ast import Try\nfrom operator import concat\nimport numpy as np\nimport pandas as pd\nfrom openpyxl.styles.borders import Border, Side\nfrom openpyxl import load_workbook\n\n#load excel file\ntry:\n book = load_workbook(filename=\"octant_input.xlsx\")\nexcept:\n print(\"unable to find the input file\")\n exit()\n \n#open workbook\nsheet = book.active\n \n#definging the property of border\nthin_border = Border(left=Side(style='thin'), \n right=Side(style='thin'), \n top=Side(style='thin'), \n bottom=Side(style='thin'))\n\n# ------------------------------------------------taking input file---------------------------------------------------------------\n# appling try and except for checking the file \ntry:\n #reading the input file and storing its value in the df \n df = pd.read_excel('octant_input.xlsx')\nexcept:\n print(\"unable to find the input file\")\n exit()\n\nprint(\"Column headings:\")\nprint(df)\n# ----------------------------------------------calculating the value of mean of column U,V and W-----------------------------------\ntry:\n meanU=df['U'].mean()\n meanV=df['V'].mean()\n meanW=df['W'].mean()\nexcept:\n print(\"unable to find the mean\")\n\nsheet = book.active\n\n\nmeanU=df['U'].mean()\nmeanV=df['V'].mean()\nmeanW=df['W'].mean()\n# adding the mean value to the new column\nsheet.cell(row=1, column=5).value =\"Uavg\"\nsheet.cell(row=2, column=5).value =meanU\n\nsheet.cell(row=1, column=6).value =\"Vavg\"\nsheet.cell(row=2, column=6).value =meanV\n\nsheet.cell(row=1, column=7).value =\"Wavg\"\nsheet.cell(row=2, column=7).value =meanW \n\n\n# saving the output to the output file\nbook.save(\"octant_output_ranking_excel.xlsx\")\n# reading the same saved output value to new again and store it into df\ndf = pd.read_excel('octant_output_ranking_excel.xlsx')\n# ---------------------------------------------- calculating error in U,V and W----------------------------------\ndf['U-Uavg']=df['U']-meanU\ndf['V-Vavg']=df['V']-meanV\ndf['W-Wavg']=df['W']-meanW\n# ----------------------------------------------below code for desiding the octant----------------------------------------------------------\n # creating a list for octant values\noctant=[] \n # calculating the Octant value .\ntry:\n for (l, m, q) in zip(df['U-Uavg'],df['V-Vavg'],df['W-Wavg']):\n # appanding the octannt values to the list\n try:\n if l>0 and m>0 and q<0:\n octant.append(-1)\n elif l>0 and m>0 and q>0:\n octant.append(1)\n elif l<0 and m>0 and q<0:\n octant.append(-2)\n elif l<0 and m>0 and q>0:\n octant.append(2)\n elif l<0 and m<0 and q<0:\n octant.append(-3)\n elif l<0 and m<0 and q>0:\n octant.append(3)\n elif l>=0 and m<=0 and q<0:\n octant.append(-4)\n elif l>0 and m<0 and q>0:\n octant.append(4) \n except:\n print(\"error in appending the value\")\n df['octant'] = octant\nexcept:\n print(\"somting wrong with the columns U-Uavg,V-Vavg,W-Wavg \")\n\n# saving the file in out put file\nwith pd.ExcelWriter('octant_output_ranking_excel.xlsx') as writer:\n\n\n df.to_excel(writer, sheet_name='Sheet_name_1',index=False)\nprint(\"sucssesfully done!\")\n# reading the same file again in workbook\ntry:\n book = load_workbook(filename=\"octant_output_ranking_excel.xlsx\")\nexcept:\n print(\"error in readin the book\")\n exit()\n#open workbook\nsheet = book.active\n\n# making the desire heading\nsheet.cell(row=4, column=12).value =\"User input\"\nsheet.cell(row=4, column=12).border = thin_border\n\n\n# mod value\nmod=5000\nsheet = book.active\nsheet.cell(row=4, column=13).value =\"mod \"+str(mod)\nsheet.cell(row=4, column=13).border = thin_border\n\n# making our desire desired heading\n\n\n\nsheet.cell(row=2, column=13).value =\"Octant ID\"\nsheet.cell(row=2, column=13).border = thin_border\n\nsheet.cell(row=2, column=14).value =1\nsheet.cell(row=2, column=14).border = thin_border\n\nsheet.cell(row=2, column=15).value =-1\nsheet.cell(row=2, column=15).border = thin_border\n\nsheet.cell(row=2, column=16).value =2\nsheet.cell(row=2, column=16).border = thin_border\n\nsheet.cell(row=2, column=17).value =-2\nsheet.cell(row=2, column=17).border = thin_border\n\nsheet.cell(row=2, column=18).value =3\nsheet.cell(row=2, column=18).border = thin_border\n\nsheet.cell(row=2, column=19).value =-3\nsheet.cell(row=2, column=19).border = thin_border\n\nsheet.cell(row=2, column=20).value =4\nsheet.cell(row=2, column=20).border = thin_border\n\nsheet.cell(row=2, column=21).value =-4\nsheet.cell(row=2, column=21).border = thin_border\n\nsheet.cell(row=1, column=22).value =1\nsheet.cell(row=1, column=22).border = thin_border\nsheet.cell(row=2, column=22).value =\"Rank 1\"\nsheet.cell(row=2, column=22).border = thin_border\n\nsheet.cell(row=1, column=23).value =-1\nsheet.cell(row=1, column=23).border = thin_border\nsheet.cell(row=2, column=23).value =\"Rank 2\"\nsheet.cell(row=2, column=23).border = thin_border\n\nsheet.cell(row=1, column=24).value =2\nsheet.cell(row=1, column=24).border = thin_border\nsheet.cell(row=2, column=24).value =\"Rank 3\"\nsheet.cell(row=2, column=24).border = thin_border\n\nsheet.cell(row=1, column=25).value =-2\nsheet.cell(row=1, column=25).border = thin_border\nsheet.cell(row=2, column=25).value =\"Rank 4\"\nsheet.cell(row=2, column=25).border = thin_border\n\nsheet.cell(row=1, column=26).value =3\nsheet.cell(row=1, column=26).border = thin_border\nsheet.cell(row=2, column=26).value =\"Rank 5\"\nsheet.cell(row=2, column=26).border = thin_border\n\nsheet.cell(row=1, column=27).value =-3\nsheet.cell(row=1, column=27).border = thin_border\nsheet.cell(row=2, column=27).value =\"Rank 6\"\nsheet.cell(row=2, column=27).border = thin_border\n\nsheet.cell(row=1, column=28).value =4\nsheet.cell(row=1, column=28).border = thin_border\nsheet.cell(row=2, column=28).value =\"Rank 7\"\nsheet.cell(row=2, column=28).border = thin_border\n\nsheet.cell(row=1, column=29).value =-4\nsheet.cell(row=1, column=29).border = thin_border\nsheet.cell(row=2, column=29).value =\"Rank 8\"\nsheet.cell(row=2, column=29).border = thin_border\n\nsheet.cell(row=2, column=30).value =\"Rank1 Octant ID\"\nsheet.cell(row=2, column=30).border = thin_border\n\nsheet.cell(row=2, column=31).value =\"Rank1 Octant Name\"\nsheet.cell(row=2, column=31).border = thin_border\n\nsheet.cell(row=3, column=13).value =\"Overall Count\"\nsheet.cell(row=3, column=13).border = thin_border\n\n# below code is for calculating the longest subsequence ans its count\ndict = {}\nfor k in range(1,5):\n maxo=0\n maxi=0\n for i in range(14,22,2):\n \n if(sheet.cell(row=2, column=i).value==k):\n sheet.cell(row=3, column=i).value =df['octant'].value_counts()[k]\n sheet.cell(row=3, column=i).border = thin_border\n dict[df['octant'].value_counts()[k]]=k\n for i in range(15,22,2):\n \n if(sheet.cell(row=2, column=i).value==-1*k):\n sheet.cell(row=3, column=i).value =df['octant'].value_counts()[-1*k]\n sheet.cell(row=3, column=i).border = thin_border\n dict[df['octant'].value_counts()[-1*k]]=-1*k\n\n# Creates a sorted dictionary (sorted by key)\n\n# sorting the disctionery\ndict1 = OrderedDict(sorted(dict.items()))\nk=8\nq=0\ndict2 ={}\nfor key in dict1:\n\n dict2[dict1[key]]=k\n k=k-1\nfor k in range(1,5):\n for i in range(23,30,2):\n if(sheet.cell(row=1, column=i).value==-1*k):\n sheet.cell(row=3, column=i).value =dict2[-1*k]\n sheet.cell(row=3, column=i).border = thin_border\n\n for i in range(22,30,2):\n if(sheet.cell(row=1, column=i).value==k):\n sheet.cell(row=3, column=i).value =dict2[k]\n sheet.cell(row=3, column=i).border = thin_border\n if(dict2[k]==1):\n q=k \n if(dict2[-1*k]==1):\n q=k \n\ndict0={1:\"Internal outward interaction\",-1:\"External outward interaction\",2:\"External Ejection\",-2:\"Internal Ejection\",3:\"External inward interaction\",-3:\"Internal inward interaction\",4:\"Internal sweep\",-4:\"External sweep\"}\nsheet.cell(row=3, column=30).value =q\nsheet.cell(row=3, column=30).border = thin_border\nsheet.cell(row=3, column=31).value =dict0[q]\nsheet.cell(row=3, column=31).border = thin_border\n \ntry:\n leng=df['octant'].count()\nexcept:\n print(\"errpr in leng\")\n\n\nprint(leng) \n\n\n \n# putting the value of mod in k\nk=mod\n\n\nz=int(leng/k)+1\n\n# no rows in output\nR = int(z)\n\n# column where my loop ends\nC = int(22)\n \n\n# creating the list for octant value\nnumb=[1,-1,2,-2,3,-3,4,-4]\n \n\n\n\n# making a desire dectionary calulation of the output below box\ndict5={\"Internal outward interaction\":0,\"External outward interaction\":0,\"External Ejection\":0,\"Internal Ejection\":0,\"External inward interaction\":0,\"Internal inward interaction\":0,\"Internal sweep\":0,\"External sweep\":0}\n\n\nfor i in range(R):\n \n \n dict = {}\n for j in range(13,C): \n if j==13:\n # condition for last row of output to cheak the length of input so it stop when input length reached\n if i==R-1:\n u=str(i*k)+\"-\"+str(leng) \n sheet.cell(row=i+5, column=j).value =u\n sheet.cell(row=i+5, column=j).border = thin_border\n else: \n u=str(i*k)+\"-\"+str(k*(i+1)-1) \n sheet.cell(row=i+5, column=j).value =u\n sheet.cell(row=i+5, column=j).border = thin_border\n else: \n # for counting the values of diffrent octant i am making a new variable count\n count=0\n for xx in range(i*k,k*(i+1)):\n if xx==leng:\n break\n \n if df['octant'][xx]==numb[j-14]:\n count=count+1\n # below code for storing the value of noumber of count of certain octant in a dectionary count:octant manner\n dict[count]=numb[j-14]\n sheet.cell(row=i+5, column=j).value =count\n sheet.cell(row=i+5, column=j).border = thin_border\n\n \n \n# sorting the dectionary by its value order\n dict1 = OrderedDict(sorted(dict.items()))\n # using this vable to find the rank in a order\n kk=8\n# below variable is for Octant name \n q=0\n# making new dectionary for storing the rank in desired order\n dict2 ={}\n dict3={1:\"Internal outward interaction\",-1:\"External outward interaction\",2:\"External Ejection\",-2:\"Internal Ejection\",3:\"External inward interaction\",-3:\"Internal inward interaction\",4:\"Internal sweep\",-4:\"External sweep\"}\n for key in dict1:\n # storing the octant value with value equal its rank decreasing order\n dict2[dict1[key]]=kk\n kk=kk-1\n\n # below code for the matrix adter input value \n for qq in range(1,5):\n # positive 1,2,3,4\n for j in range(23,30,2):\n if(sheet.cell(row=1, column=j).value==-1*qq):\n sheet.cell(row=i+5, column=j).value =dict2[-1*qq]\n sheet.cell(row=i+5, column=j).border = thin_border\n# for negative -1,-2,-3,-4\n for j in range(22,30,2):\n if(sheet.cell(row=1, column=j).value==qq):\n sheet.cell(row=i+5, column=j).value =dict2[qq]\n sheet.cell(row=i+5, column=j).border = thin_border\n # below for rank 1 octant value\n if(dict2[qq]==1):\n q=qq\n if(dict2[-1*qq]==1):\n q=-1*qq \n # wite in a cell\n sheet.cell(row=i+5, column=30).value =q\n sheet.cell(row=i+5, column=30).border = thin_border\n \n sheet.cell(row=i+5, column=31).value =dict3[q]\n dict5[dict3[q]]+=1\n sheet.cell(row=i+5, column=31).border = thin_border\n\n\n\n# below variable treck record of no colmns for helping the postion for below matrix\nRR=R+8\n# below matrix where its colmns starts\ncolnew=14\n\n# heading of below matrix\nsheet.cell(row=RR, column=14).value =\"Octant ID\"\nsheet.cell(row=RR, column=14).border = thin_border\n\nsheet.cell(row=RR, column=15).value =\"Octant Name \"\nsheet.cell(row=RR, column=15).border = thin_border\n\nsheet.cell(row=RR, column=16).value =\"Count of Rank 1 Mod Values\"\nsheet.cell(row=RR, column=16).border = thin_border\n# keep tracing no rows\nRR+=1\n\n# below matrix vlue calculating\nfor i in range(1,5):\n# for positve value i\n sheet.cell(row=RR, column=14).value =i\n sheet.cell(row=RR, column=14).border = thin_border\n\n sheet.cell(row=RR, column=15).value =dict3[i]\n sheet.cell(row=RR, column=15).border = thin_border\n\n sheet.cell(row=RR, column=16).value =dict5[dict3[i]]\n sheet.cell(row=RR, column=16).border = thin_border\n # keep tracing no rows\n RR+=1\n# for negative vlue of i\n sheet.cell(row=RR, column=14).value =-1*i\n sheet.cell(row=RR, column=14).border = thin_border\n\n sheet.cell(row=RR, column=15).value =dict3[-1*i]\n sheet.cell(row=RR, column=15).border = thin_border\n\n sheet.cell(row=RR, column=16).value =dict5[dict3[-1*i]]\n sheet.cell(row=RR, column=16).border = thin_border\n# keep tracing no rows\n RR+=1\n\nbook.save(\"octant_output_ranking_excel.xlsx\")","repo_name":"Rohit312003/2001EE56_2022","sub_path":"tut05/tut05.py","file_name":"tut05.py","file_ext":"py","file_size_in_byte":13337,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"24622332451","text":"#-*- coding: utf-8 -*-\nfrom __future__ import print_function\n\nimport unittest\n\nfrom easydatalab.common.configuration import AppConfiguration\nfrom easydatalab.common.exceptions import ExecutionError\n\ndef customize_fun(configuration):\n configuration.add_parameter('a1', configuration.get_parameter('A:a1'))\n\nclass TestAppConfiguration(unittest.TestCase):\n\n def test_constructor(self):\n cfgFile = 'easydatalab/tests/resources/config/config_for_unittests.cfg'\n appConfiguration = AppConfiguration(cfgFile)\n self.assertEqual( repr(appConfiguration), 'AppConfiguration: from=easydatalab/tests/resources/config/config_for_unittests.cfg' )\n\n def test_get_parameter_loaded(self):\n cfgFile = 'easydatalab/tests/resources/config/config_for_unittests.cfg'\n appConfiguration = AppConfiguration(cfgFile)\n appConfiguration.__enter__()\n value = appConfiguration.get_parameter('A:a1')\n self.assertEqual( value, 'val_a1' )\n\n\n def test_get_parameter_added(self):\n cfgFile = 'easydatalab/tests/resources/config/config_for_unittests.cfg'\n appConfiguration = AppConfiguration(cfgFile)\n appConfiguration.__enter__()\n appConfiguration.add_parameter('z1', 'val_z1')\n value = appConfiguration.get_parameter('z1')\n self.assertEqual( value, 'val_z1' )\n\n def test_get_parameter_not_found(self):\n cfgFile = 'easydatalab/tests/resources/config/config_for_unittests.cfg'\n appConfiguration = AppConfiguration(cfgFile)\n appConfiguration.__enter__()\n with self.assertRaises(ExecutionError) as ctx:\n value = appConfiguration.get_parameter('x1')\n raise ExecutionError('AppConfiguration', 'key x1 was not found')\n\n def test_get_parameter_loaded(self):\n cfgFile = 'easydatalab/tests/resources/config/config_for_unittests.cfg'\n appConfiguration = AppConfiguration(cfgFile)\n appConfiguration.__enter__()\n appConfiguration.customize(customize_fun)\n value = appConfiguration.get_parameter('a1')\n self.assertEqual( value, 'val_a1' )\n","repo_name":"cfalguiere/easydatalab","sub_path":"easydatalab/tests/common/test_configuration.py","file_name":"test_configuration.py","file_ext":"py","file_size_in_byte":2097,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"10702564379","text":"\"\"\"\nTests for the tedana stats module\n\"\"\"\nimport random\n\nimport numpy as np\nimport pytest\n\nfrom tedana.stats import computefeats2, get_coeffs, getfbounds\n\n\ndef test_break_computefeats2():\n \"\"\"\n Ensure that computefeats2 fails when input data do not have the right\n shapes.\n \"\"\"\n n_samples, n_vols, n_comps = 10000, 100, 50\n data = np.empty((n_samples, n_vols))\n mmix = np.empty((n_vols, n_comps))\n mask = np.empty((n_samples))\n\n data = np.empty((n_samples))\n with pytest.raises(ValueError):\n computefeats2(data, mmix, mask, normalize=True)\n\n data = np.empty((n_samples, n_vols))\n mmix = np.empty((n_vols))\n with pytest.raises(ValueError):\n computefeats2(data, mmix, mask, normalize=True)\n\n mmix = np.empty((n_vols, n_comps))\n mask = np.empty((n_samples, n_vols))\n with pytest.raises(ValueError):\n computefeats2(data, mmix, mask, normalize=True)\n\n mask = np.empty((n_samples + 1))\n with pytest.raises(ValueError):\n computefeats2(data, mmix, mask, normalize=True)\n data.shape[1] != mmix.shape[0]\n mask = np.empty((n_samples))\n mmix = np.empty((n_vols + 1, n_comps))\n with pytest.raises(ValueError):\n computefeats2(data, mmix, mask, normalize=True)\n\n\ndef test_smoke_computefeats2():\n \"\"\"\n Ensures that computefeats2 works with random inputs and different optional parameters\n \"\"\"\n n_samples, n_times, n_components = 100, 20, 6\n data = np.random.random((n_samples, n_times))\n mmix = np.random.random((n_times, n_components))\n mask = np.random.randint(2, size=n_samples)\n\n assert computefeats2(data, mmix) is not None\n assert computefeats2(data, mmix, mask=mask) is not None\n assert computefeats2(data, mmix, normalize=False) is not None\n\n\ndef test_get_coeffs():\n \"\"\"\n Check least squares coefficients.\n \"\"\"\n # Simulate one voxel with 40 TRs\n data = np.empty((2, 40))\n data[0, :] = np.arange(0, 200, 5)\n data[1, :] = np.arange(0, 200, 5)\n X = np.arange(0, 40)[:, np.newaxis]\n mask = np.array([True, False])\n\n betas = get_coeffs(data, X, mask=None, add_const=False)\n betas = np.squeeze(betas)\n assert np.allclose(betas, np.array([5.0, 5.0]))\n\n betas = get_coeffs(data, X, mask=None, add_const=True)\n betas = np.squeeze(betas)\n assert np.allclose(betas, np.array([5.0, 5.0]))\n\n betas = get_coeffs(data, X, mask=mask, add_const=False)\n betas = np.squeeze(betas)\n assert np.allclose(betas, np.array([5, 0]))\n\n betas = get_coeffs(data, X, mask=mask, add_const=True)\n betas = np.squeeze(betas)\n assert np.allclose(betas, np.array([5, 0]))\n\n\ndef test_break_get_coeffs():\n \"\"\"\n Ensure that get_coeffs fails when input data do not have the right\n shapes.\n \"\"\"\n n_samples, n_echos, n_vols, n_comps = 10000, 5, 100, 50\n data = np.empty((n_samples, n_vols))\n X = np.empty((n_vols, n_comps))\n mask = np.empty((n_samples))\n\n data = np.empty((n_samples))\n with pytest.raises(ValueError):\n get_coeffs(data, X, mask, add_const=False)\n\n data = np.empty((n_samples, n_vols))\n X = np.empty((n_vols))\n with pytest.raises(ValueError):\n get_coeffs(data, X, mask, add_const=False)\n\n data = np.empty((n_samples, n_echos, n_vols + 1))\n X = np.empty((n_vols, n_comps))\n with pytest.raises(ValueError):\n get_coeffs(data, X, mask, add_const=False)\n\n data = np.empty((n_samples, n_echos, n_vols))\n mask = np.empty((n_samples, n_echos, n_vols))\n with pytest.raises(ValueError):\n get_coeffs(data, X, mask, add_const=False)\n\n mask = np.empty((n_samples + 1, n_echos))\n with pytest.raises(ValueError):\n get_coeffs(data, X, mask, add_const=False)\n\n\ndef test_smoke_get_coeffs():\n \"\"\"\n Ensure that get_coeffs returns outputs with different inputs and optional paramters\n \"\"\"\n n_samples, _, n_times, n_components = 100, 5, 20, 6\n data_2d = np.random.random((n_samples, n_times))\n x = np.random.random((n_times, n_components))\n mask = np.random.randint(2, size=n_samples)\n\n assert get_coeffs(data_2d, x) is not None\n # assert get_coeffs(data_3d, x) is not None TODO: submit an issue for the bug\n assert get_coeffs(data_2d, x, mask=mask) is not None\n assert get_coeffs(data_2d, x, add_const=True) is not None\n\n\ndef test_getfbounds():\n good_inputs = range(1, 12)\n\n for n_echos in good_inputs:\n getfbounds(n_echos)\n\n\ndef test_smoke_getfbounds():\n \"\"\"\n Ensures that getfbounds returns outputs when fed in a random number of echo\n \"\"\"\n n_echos = random.randint(3, 10) # At least two echos!\n f05, f025, f01 = getfbounds(n_echos)\n\n assert f05 is not None\n assert f025 is not None\n assert f01 is not None\n","repo_name":"ME-ICA/tedana","sub_path":"tedana/tests/test_stats.py","file_name":"test_stats.py","file_ext":"py","file_size_in_byte":4716,"program_lang":"python","lang":"en","doc_type":"code","stars":149,"dataset":"github-code","pt":"50"} +{"seq_id":"22497253973","text":"from datetime import date\nimport pandas as pd\nimport yfinance as yf\n\nclass YahooFiManager:\n def get_stock_df(self, tickers:str, start: date, end: date) -> pd.DataFrame:\n '''takes in all caps string of stock tickers separated by spaces, retur'''\n return yf.download(tickers=tickers, \n period='5d',\n interval='1m',\n start=start, \n end=end)","repo_name":"bbeckenb/de_tools_playground","sub_path":"dags/common/classes/YahooFiManager.py","file_name":"YahooFiManager.py","file_ext":"py","file_size_in_byte":458,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"41093156840","text":"from fastapi import APIRouter, Depends, HTTPException\nfrom sqlalchemy import select, update\n\nfrom bad_responses import ErrorOrSucess\nfrom config import STATIC_ACCESS_KEY\nfrom database import get_async_session\nfrom auth.models import user\nfrom auth.base_config import fastapi_users\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\nfrom profile.schemas import GetUserProfile, UpdateUserProfileInfo\n\ncurrent_user = fastapi_users.current_user()\n# создаём новый роутер\nprofile_router = APIRouter(\n prefix='/profile',\n tags=['Profile']\n)\n\n\n@profile_router.get('/get_profile/{need_user_id}',\n responses={401: {'model': ErrorOrSucess, 'description': 'Unauthorized'},\n 400: {'model': ErrorOrSucess, 'description': \"User doesn't exist\"}},\n response_model=GetUserProfile)\nasync def get_user_profile(need_user_id: int, person: user = Depends(current_user),\n session: AsyncSession = Depends(get_async_session)) -> dict:\n \"\"\"Получение информации из профиля пользователя по id\"\"\"\n\n # проверка на то что id пользователя неотрицательный\n if need_user_id < 0:\n raise HTTPException(status_code=400, detail=\"User doesn't exist\")\n # делаем запрос к бд\n query = select(user).where(user.id == need_user_id)\n sqlalchemy_engine = await session.execute(query)\n data = sqlalchemy_engine.fetchall()\n # смотрим что пользователь есть в бд\n if len(data) > 0:\n # забираем все интересующие нас данные\n user_obj = data[0][0]\n ans = dict()\n ans['last_name'] = user_obj.last_name\n ans['first_name'] = user_obj.first_name\n ans['patronymic'] = user_obj.patronymic\n ans['username'] = user_obj.username\n ans['email'] = user_obj.email\n ans['desc'] = user_obj.description\n ans['birthday'] = user_obj.birthday\n ans['position'] = user_obj.position\n ans['experience_in_company'] = user_obj.experience_in_company\n ans['phone_number'] = user_obj.phone_number\n ans['about_me'] = user_obj.about_me\n ans['work_place'] = user_obj.work_place\n return ans\n raise HTTPException(status_code=400, detail=\"User doesn't exist\")\n\n\n@profile_router.post('/change_profile_info',\n responses={200: {'model': ErrorOrSucess, 'description': 'OK'},\n 401: {'model': ErrorOrSucess, 'description': \"User doesn't exist\"}})\nasync def post_user_profile(user_data: UpdateUserProfileInfo,\n person: user = Depends(current_user), session: AsyncSession = Depends(get_async_session)):\n \"\"\"Изменение информации в профиле пользователя\"\"\"\n data_in_dict_format = dict(user_data)\n stmt = update(user).where(user.id == person.id).values(**data_in_dict_format)\n await session.execute(stmt)\n await session.commit()\n raise HTTPException(status_code=200, detail='OK')\n\n\n@profile_router.put('/become_manager', responses={200: {'model': ErrorOrSucess, 'description': 'OK'},\n 401: {'model': ErrorOrSucess, 'description': \"User doesn't exist\"},\n 400: {'model': ErrorOrSucess, 'description': \"Already admin\"}})\nasync def expand_rights(access_key: str, person: user = Depends(current_user),\n session: AsyncSession = Depends(get_async_session)):\n \"\"\"Расширить права став администратором (менеджером)\"\"\"\n query = select(user).where(user.id == person.id)\n stmt = update(user).where(user.id == person.id).values(is_superuser=True)\n # выдача прав на суперпользователя только в том случае если пользователь ввёл правильный ключ\n if access_key == STATIC_ACCESS_KEY:\n data_sup_user = await session.execute(query)\n # проверяем что пользователь ещё не менеджер\n if not data_sup_user.fetchall()[0][0].is_superuser:\n await session.execute(stmt)\n await session.commit()\n return {'detail': 'you get more rights access'}\n else:\n raise HTTPException(status_code=400, detail=\"you are already an admin, re-granting rights is not possible\")\n else:\n raise HTTPException(status_code=400, detail=\"don't correct access key\")\n","repo_name":"Raisin228/smart_office_hackathon_mobile_app","sub_path":"backend/src/profile/router.py","file_name":"router.py","file_ext":"py","file_size_in_byte":4644,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"17992566597","text":"#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\n# code is far away from bugs with the god animal protect\n# I love animals. They taste delicious.\n# ┌─┐ ┌─┐\n# ┌──┘ ┴───────┘ ┴──┐\n# │ │\n# │ ─── │\n# │ ─┬┘ └┬─ │\n# │ │\n# │ ─┴─ │\n# │ │\n# └───┐ ┌───┘\n# │ │\n# │ │\n# │ │\n# │ └──────────────┐\n# │ │\n# │ Gods Bless ├─┐\n# │ Never Bugs ┌─┘\n# │ │\n# └─┐ ┐ ┌───────┬──┐ ┌──┘\n# │ ─┤ ─┤ │ ─┤ ─┤\n# └──┴──┘ └──┴──┘\n\nfrom __future__ import absolute_import, annotations, print_function\n\nfrom src.constants import KEY_MMAP_ADDR, KEY_BOARD_INT_NUM, SCREEN_MMAP_ADDR\nfrom src.keyboards import KeyBoard\nfrom src.memory import Memory\nfrom src.opcode import OPCode\nfrom src.screen import Screen\nfrom src.constants import SCREEN_WIDTH, SCREEN_HEIGHT\nfrom src.sign_extend import SignExtend\n\n\nclass CPU(object):\n\n def __init__(self):\n self.PC = 0x7C00\n self.BP = 0x0\n self.SP = 0x0\n self.PSW = 0b10 # 默认允许中断\n self.IDTR = 0x0\n self.CR3 = 0x0\n self.CR2 = 0x0\n self.CR1 = 0x0\n self.CR0 = 0x0\n self.regs = [0] * 32\n self.f_regs = [0.0] * 4\n self.memory = None\n self.keyboard = None\n self.screen = None\n\n def attach_memory(self, memory: Memory):\n self.memory = memory\n self.SP = self.memory.size - 4\n self.BP = self.SP\n\n def set_keyboard_memory(self, key_code: int):\n \"\"\"\n 暂时不设置缓冲区深度了。\n :param key_code: key的ascii码\n :return:\n \"\"\"\n self.memory.write8(KEY_MMAP_ADDR, key_code)\n\n def read_keyboard(self):\n return self.memory.read8(KEY_MMAP_ADDR)\n\n def get_screen_memory(self):\n result = []\n for y in range(0, SCREEN_HEIGHT):\n for x in range(0, SCREEN_WIDTH):\n result.append(self.memory.read8(SCREEN_MMAP_ADDR + (y * SCREEN_WIDTH) + x))\n return result\n\n def set_keyboard_interrupt_flag(self):\n self.PSW |= 0b100 # 设置中断标志\n low_16_bit = self.PSW & 0xffff # 设置中断号\n high_8_bit = self.PSW & 0xff000000\n self.PSW = high_8_bit << 24 | KEY_BOARD_INT_NUM << 16 | low_16_bit\n\n def attach_keyboard(self, keyboard: KeyBoard) -> KeyBoard:\n self.keyboard = keyboard\n self.keyboard.init_set_memory_handler(self.set_keyboard_memory)\n self.keyboard.init_set_cpu_interrupt_flag(self.set_keyboard_interrupt_flag)\n return self.keyboard\n\n def attach_screen(self, screen: Screen):\n self.screen = screen\n self.screen.init_get_memory_handler(self.get_screen_memory)\n return self.screen\n\n def cli(self):\n low_2_bit = self.PSW & 0b11\n self.PSW = ((self.PSW >> 3) << 3) | low_2_bit\n\n @property\n def is_enable_protection(self):\n return self.CR0 & 0b1\n\n def fetch(self):\n if self.is_enable_protection:\n # 保护模式待实现\n ...\n else:\n instruction = self.memory.read32(self.PC)\n self.PC += 4\n return instruction\n\n def enter_interrupt(self, int_num: int):\n # push PWS\n self.SP -= 4\n self.memory.write32(self.SP, self.PSW)\n # push pc\n self.SP -= 4\n self.memory.write32(self.SP, self.PC)\n self.PC = self.IDTR + int_num * 4\n\n def run(self):\n while True:\n instruction = self.fetch()\n op_code = instruction >> 26\n RS_INDEX = (instruction & 0b000000_11111_11111_11111_1111_1111_111) >> 21\n RT_INDEX = (instruction & 0b000000_00000_11111_11111_1111_1111_111) >> 16\n RD_INDEX = (instruction & 0b000000_00000_00000_11111_1111_1111_111) >> 11\n SHORT_IMMEDIATE_NUM = instruction & 0b000000_00000_00000_11111_1111_1111_111\n LONG_IMMEDIATE_NUM = instruction & 0b000000_11111_11111_11111_1111_1111_111\n\n if op_code in (OPCode.ADD, ):\n self.regs[RD_INDEX] = self.regs[RS_INDEX] + self.regs[RT_INDEX]\n elif op_code in (OPCode.ADDI, ):\n self.regs[RS_INDEX] = self.regs[RS_INDEX] + SHORT_IMMEDIATE_NUM\n elif op_code in (OPCode.SUB, ):\n self.regs[RD_INDEX] = self.regs[RS_INDEX] - self.regs[RT_INDEX]\n elif op_code in (OPCode.MUL, ):\n self.regs[RD_INDEX] = self.regs[RS_INDEX] * self.regs[RT_INDEX]\n elif op_code in (OPCode.DIV, ):\n self.regs[RD_INDEX] = int(self.regs[RS_INDEX] / self.regs[RT_INDEX])\n elif op_code == OPCode.MOD:\n _, self.regs[RD_INDEX] = divmod(self.regs[RS_INDEX], self.regs[RT_INDEX])\n elif op_code in (OPCode.ADD, OPCode.FADD):\n self.f_regs[RD_INDEX] = self.f_regs[RS_INDEX] + self.f_regs[RT_INDEX]\n elif op_code in (OPCode.SUB, OPCode.FSUB):\n self.f_regs[RD_INDEX] = self.f_regs[RS_INDEX] - self.f_regs[RT_INDEX]\n elif op_code in (OPCode.MUL, OPCode.FMUL):\n self.f_regs[RD_INDEX] = self.f_regs[RS_INDEX] * self.f_regs[RT_INDEX]\n elif op_code in (OPCode.DIV, OPCode.FDIV):\n self.f_regs[RD_INDEX] = int(self.f_regs[RS_INDEX] / self.f_regs[RT_INDEX])\n\n elif op_code == OPCode.AND:\n self.regs[RD_INDEX] = self.regs[RS_INDEX] & self.regs[RT_INDEX]\n elif op_code == OPCode.OR:\n self.regs[RD_INDEX] = self.regs[RS_INDEX] | self.regs[RT_INDEX]\n elif op_code == OPCode.XOR:\n self.regs[RD_INDEX] = self.regs[RS_INDEX] ^ self.regs[RT_INDEX]\n elif op_code == OPCode.NOT:\n self.regs[RT_INDEX] = ~self.regs[RS_INDEX]\n elif op_code == OPCode.SLL:\n self.regs[RD_INDEX] = self.regs[RS_INDEX] << self.regs[RT_INDEX]\n elif op_code == OPCode.SLR:\n self.regs[RD_INDEX] = self.regs[RS_INDEX] >> self.regs[RT_INDEX]\n elif op_code == OPCode.LT:\n if self.regs[RS_INDEX] < self.regs[RT_INDEX]:\n self.PC += SignExtend.extend_16bit(SHORT_IMMEDIATE_NUM) * 4\n elif op_code == OPCode.GT:\n if self.regs[RS_INDEX] > self.regs[RT_INDEX]:\n self.PC += SignExtend.extend_16bit(SHORT_IMMEDIATE_NUM) * 4\n elif op_code == OPCode.LTE:\n if self.regs[RS_INDEX] <= self.regs[RT_INDEX]:\n self.PC += SignExtend.extend_16bit(SHORT_IMMEDIATE_NUM) * 4\n elif op_code == OPCode.GTE:\n if self.regs[RS_INDEX] >= self.regs[RT_INDEX]:\n self.PC += SignExtend.extend_16bit(SHORT_IMMEDIATE_NUM) * 4\n elif op_code == OPCode.EQ:\n if self.regs[RS_INDEX] == self.regs[RT_INDEX]:\n self.PC += SignExtend.extend_16bit(SHORT_IMMEDIATE_NUM) * 4\n elif op_code == OPCode.NEQ:\n if self.regs[RS_INDEX] != self.regs[RT_INDEX]:\n self.PC += SignExtend.extend_16bit(SHORT_IMMEDIATE_NUM) * 4\n elif op_code == OPCode.JMP:\n self.PC = self.regs[RS_INDEX]\n elif op_code == OPCode.JMPI:\n self.PC += SignExtend.extend_26bit(LONG_IMMEDIATE_NUM) * 4\n elif op_code == OPCode.MOV:\n self.regs[RT_INDEX] = self.regs[RS_INDEX]\n elif op_code == OPCode.LOAD:\n self.regs[RT_INDEX] = self.memory.read8(self.regs[RS_INDEX])\n elif op_code == OPCode.STORE:\n self.memory.write8(self.regs[RT_INDEX], self.regs[RS_INDEX])\n elif op_code == OPCode.LUI:\n imm_num = instruction & 0xffff\n self.regs[RS_INDEX] = imm_num << 16\n elif op_code == OPCode.CALL:\n self.SP -= 4\n self.memory.write32(self.SP, self.PC)\n self.PC = self.regs[RS_INDEX]\n elif op_code == OPCode.CALLI:\n self.SP -= 4\n self.memory.write32(self.SP, self.PC)\n self.PC += SignExtend.extend_26bit(LONG_IMMEDIATE_NUM) * 4\n elif op_code == OPCode.RET:\n self.PC = self.memory.read32(self.SP)\n self.SP += 4\n elif op_code == OPCode.PUSH:\n self.SP -= 4\n self.memory.write32(self.SP, self.regs[RS_INDEX])\n elif op_code == OPCode.PUSH:\n self.regs[RS_INDEX] = self.memory.read32(self.SP)\n self.SP += 4\n elif op_code == OPCode.INT:\n int_num = instruction & 0xff\n self.enter_interrupt(int_num=int_num)\n self.PSW &= ~0b00 # 不再允许中断和单步调试\n elif op_code == OPCode.HALT:\n self.PC = self.PC - 4\n elif op_code == OPCode.IRET:\n self.PC = self.memory.read32(self.SP)\n self.SP += 4\n self.PSW = self.memory.read32(self.SP)\n self.SP += 4\n elif op_code == OPCode.LIDT:\n self.IDTR = instruction & 0x00ff_ffff\n elif op_code == OPCode.LCR:\n cr_num = RT_INDEX\n if cr_num == 0:\n self.CR0 = self.regs[RS_INDEX]\n elif cr_num == 1:\n self.CR1 = self.regs[RS_INDEX]\n elif cr_num == 2:\n self.CR2 = self.regs[RS_INDEX]\n elif cr_num == 3:\n self.CR3 = self.regs[RS_INDEX]\n else:\n # 触发中断\n pass\n\n elif op_code == OPCode.STI:\n self.PSW |= 0b10\n elif op_code == OPCode.CLI:\n self.PSW &= 0xffff_fffd\n elif op_code == OPCode.NOP:\n pass # do nothing\n\n enable_interrupt = (self.PSW & 0b10) >> 1\n if enable_interrupt:\n interrupt_flag = (self.PSW & 0b100) >> 2\n if interrupt_flag:\n self.cli()\n int_num = (self.PSW >> 16) & 0xff\n self.enter_interrupt(int_num=int_num)","repo_name":"likeyiyy/rsic_emulator","sub_path":"src/cpu.py","file_name":"cpu.py","file_ext":"py","file_size_in_byte":10672,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"1541303935","text":"import numpy\n\na, b, c = tuple(numpy.array(list(map(\n float, input('z y x\\n').strip().rstrip().split(' ')))) / 180 * numpy.pi)\n\nRz = numpy.matrix([[numpy.cos(a), -numpy.sin(a), 0],\n [numpy.sin(a), numpy.cos(a), 0],\n [0, 0, 1]])\nRy = numpy.matrix([[numpy.cos(b), 0, numpy.sin(b)],\n [0, 1, 0],\n [-numpy.sin(b), 0, numpy.cos(b)]])\nRx = numpy.matrix([[1, 0, 0],\n [0, numpy.cos(c), -numpy.sin(c)],\n [0, numpy.sin(c), numpy.cos(c)]])\n\nxz = numpy.matrix([[1, 0, 0],\n [0, 0, 0],\n [0, 0, 1]])\n\nA = numpy.matrix(numpy.identity(3) * 0.5) * 2\n\nA2 = Rz * Ry * Rx * A\nprint(A2)\nprint(numpy.linalg.det(A2))\n\nA3 = xz * A2\nprint(A3)\n\nA4 = numpy.matrix([[A3[0, 0], A3[0, 2]],\n [A3[2, 0], A3[2, 2]]])\nprint(A4)\nprint(numpy.linalg.det(A4))\n","repo_name":"JoeyTeng/OI","sub_path":"Google Code Jam/2018/Qualification Round/p4.tesing.py","file_name":"p4.tesing.py","file_ext":"py","file_size_in_byte":884,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"32029347095","text":"from datetime import timedelta\n\n\nclass BusinessPeriod(object):\n\n def __init__(self, period='', years=0, quarters=0, months=0, weeks=0, days=0, businessdays=0):\n \"\"\" class to store and calculate date periods as combinations of days, weeks, years etc.\n\n :param str period: encoding a business period.\n Such is given by a sequence of digits as :class:`int` followed by a :class:`char` -\n indicating the number of\n years **Y**,\n quarters **Q** (which is equivalent to 3 month),\n month **M**,\n weeks **W** (which is equivalent to 7 days),\n days **D**,\n business days **B**.\n E.g. **1Y2W3D** what gives a period of 1 year plus 2 weeks and 3 days\n (see :doc:`tutorial ` for details).\n\n :param int years: number of years in the period (equivalent to 12 months)\n :param int quarters: number of quarters in the period (equivalent to 3 months)\n :param int months: number of month in the period\n :param int weeks: number of weeks in the period (equivalent to 7 days)\n :param int days: number of days in the period\n :param int businessdays: number of business days,\n i.e. days which are neither weekend nor :class:`holidays `, in the period.\n Only either `businessdays` or the others can be given.\n Both at the same time is not allowed.\n\n \"\"\"\n if period and any((years, months, days, businessdays)):\n raise ValueError(\n \"Either string or argument input only for %s\" % self.__class__.__name__)\n\n super(BusinessPeriod, self).__init__()\n if isinstance(period, BusinessPeriod):\n years = period.years\n months = period.months\n days = period.days\n businessdays = period.businessdays\n elif isinstance(period, timedelta):\n days = period.days\n elif period is None:\n pass\n elif isinstance(period, str):\n if period.upper() == '':\n pass\n elif period.upper() == '0D':\n pass\n elif period.upper() == 'ON':\n businessdays = 1\n elif period.upper() == 'TN':\n businessdays = 2\n elif period.upper() == 'DD':\n businessdays = 3\n else:\n s, y, q, m, w, d, f = BusinessPeriod._parse_ymd(period)\n # no final businesdays allowed\n if f:\n raise ValueError(\"Unable to parse %s as %s\" % (period, self.__class__.__name__))\n # except the first non vanishing of y,q,m,w,d must have positive sign\n sgn = [int(x / abs(x)) for x in (y, q, m, w, d) if x]\n if [x for x in sgn[1:] if x < 0]:\n raise ValueError(\n \"Except at the beginning no signs allowed in %s as %s\" % (str(period), self.__class__.__name__))\n y, q, m, w, d = (abs(x) for x in (y, q, m, w, d))\n # use sign of first non vanishing of y,q,m,w,d\n sgn = sgn[0] if sgn else 1\n businessdays, years, quarters, months, weeks, days = s, sgn * y, sgn * q, sgn * m, sgn * w, sgn * d\n else:\n raise TypeError(\n \"%s of Type %s not valid to create BusinessPeriod.\" %(str(period), period.__class__.__name__))\n\n self._months = 12 * years + 3 * quarters + months\n self._days = 7 * weeks + days\n self._businessdays = businessdays\n\n if businessdays and (self._months or self._days):\n raise ValueError(\n \"Either (years,months,days) or businessdays must be zero for %s\" % self.__class__.__name__)\n if self._months and not self._days / self._months >= 0:\n ymd = self.years, self.months, self.days\n raise ValueError(\n \"(years, months, days)=%s must have equal sign for %s\" % (str(ymd), self.__class__.__name__))\n\n @property\n def years(self):\n return int(-1 * (-1 * self._months // 12) if self._months < 0 else self._months // 12)\n\n @property\n def months(self):\n return int(-1 * (-1 * self._months % 12) if self._months < 0 else self._months % 12)\n\n @property\n def days(self):\n return int(self._days)\n\n @property\n def businessdays(self):\n return int(self._businessdays)\n\n # --- validation and information methods ---------------------------------\n\n @classmethod\n def _parse_ymd(cls, period):\n # can even parse strings like '-1B-2Y-4Q+5M' but also '0B', '-1Y2M3D' as well.\n period = period.upper().replace(' ', '')\n period = period.replace('BUSINESSDAYS', 'B')\n period = period.replace('YEARS', 'Y')\n period = period.replace('QUARTERS', 'Q')\n period = period.replace('MONTHS', 'M')\n period = period.replace('WEEKS', 'W')\n period = period.replace('DAYS', 'D')\n\n def _parse(p, letter):\n if p.find(letter) >= 0:\n s, p = p.split(letter, 1)\n s = s[1:] if s.startswith('+') else s\n sgn, s = (-1, s[1:]) if s.startswith('-') else (1, s)\n if not s.isdigit():\n raise ValueError(\"Unable to parse %s in %s as %s\" % (s, p, cls.__name__))\n return sgn * int(s), p\n return 0, p\n\n p = period.upper()\n\n # p[-1] is not 'B', p.strip('0123456789+-B')==''\n s, p = _parse(p, 'B') if not p[-1]=='B' else (0, p)\n s, p = _parse(p, 'B') if not p.strip('0123456789+-B') else (s, p)\n s, p = _parse(p, 'B') if p.count('B') > 1 else (s, p)\n y, p = _parse(p, 'Y')\n q, p = _parse(p, 'Q')\n m, p = _parse(p, 'M')\n w, p = _parse(p, 'W')\n d, p = _parse(p, 'D')\n f, p = _parse(p, 'B')\n if not p == '':\n raise ValueError(\"Unable to parse %s as %s\" % (p, cls.__name__))\n return s, y, q, m, w, d, f\n\n @classmethod\n def is_businessperiod(cls, period):\n \"\"\" returns true if the argument can be understood as :class:`BusinessPeriod` \"\"\"\n if period is None:\n return False\n if isinstance(period, (int, float, list, set, dict, tuple)):\n return False\n if isinstance(period, (timedelta, BusinessPeriod)):\n return True\n if period in ('', '0D', 'ON', 'TN', 'DD'):\n return True\n if isinstance(period, str):\n if period.isdigit():\n return False\n #if period.upper().strip('+-0123456789BYQMWD'):\n # return False\n try: # to be removed\n BusinessPeriod._parse_ymd(period)\n except ValueError:\n return False\n return True\n return False\n\n # --- operator methods ---------------------------------------------------\n\n def __repr__(self):\n return self.__class__.__name__ + \"('%s')\" % str(self)\n\n def __str__(self):\n\n if self.businessdays:\n period_str = str(self.businessdays) + 'B'\n else:\n period_str = '-' if self.years < 0 or self.months < 0 or self.days < 0 else ''\n if self.years:\n period_str += str(abs(self.years)) + 'Y'\n if self.months:\n period_str += str(abs(self.months)) + 'M'\n if self.days:\n period_str += str(abs(self.days)) + 'D'\n\n if not period_str:\n period_str = '0D'\n return period_str\n\n def __abs__(self):\n ymdb = self.years, self.months, self.days, self.businessdays\n y,m,d,b = tuple(map(abs, ymdb))\n return self.__class__(years=y, months=m, days=d, businessdays=b)\n\n def __cmp__(self, other):\n other = self.__class__() if other == 0 else other\n if not isinstance(other, BusinessPeriod):\n other = BusinessPeriod(other)\n if self.businessdays:\n if other and not other.businessdays:\n # log warning on non compatible pair\n return None\n return self.businessdays - other.businessdays\n m = 12 * (self.years - other.years) + self.months - other.months\n d = self.days - other.days\n if m * 28 < -d < m * 31:\n p = self.__class__(months=m)\n if p.min_days() <= -d <= p.max_days():\n # log warning on non orderable pair\n return None\n return m * 30.5 + d\n\n def __eq__(self, other):\n if isinstance(other, type(self)):\n attr = 'years', 'months', 'days', 'businessdays'\n return all(getattr(self, a) == getattr(other, a) for a in attr)\n return False\n\n def __ne__(self, other):\n return not self.__eq__(other)\n\n def __le__(self, other):\n cmp = self.__cmp__(other)\n cmp = self.__cmp__(other + '1D') if cmp is None else cmp\n return cmp if cmp is None else cmp <= 0\n\n def __lt__(self, other):\n cmp = self.__cmp__(other)\n return cmp if cmp is None else cmp < 0\n\n def __ge__(self, other):\n lt = self.__lt__(other)\n return None if lt is None else not lt\n\n def __gt__(self, other):\n le = self.__le__(other)\n return None if le is None else not le\n\n def __hash__(self):\n return hash(repr(self))\n\n def __nonzero__(self):\n # return any((self.years, self.months, self.days, self.businessdays))\n return self.__bool__()\n\n def __bool__(self):\n # return self.__nonzero__()\n return any((self._months, self._days, self._businessdays))\n\n def __add__(self, other):\n if isinstance(other, (list, tuple)):\n return [self + o for o in other]\n if BusinessPeriod.is_businessperiod(other):\n p = BusinessPeriod(other)\n y = self.years + p.years\n m = self.months + p.months\n d = self.days + p.days\n b = self.businessdays + p.businessdays\n return self.__class__(years=y, months=m, days=d, businessdays=b)\n raise TypeError('addition of BusinessPeriod cannot handle objects of type %s.' % other.__class__.__name__)\n\n def __sub__(self, other):\n if isinstance(other, (list, tuple)):\n return [self - o for o in other]\n if BusinessPeriod.is_businessperiod(other):\n return self + (-1 * BusinessPeriod(other))\n raise TypeError('subtraction of BusinessPeriod cannot handle objects of type %s.' % other.__class__.__name__)\n\n def __mul__(self, other):\n if isinstance(other, (list, tuple)):\n return [self * o for o in other]\n if isinstance(other, int):\n m = other * self._months\n d = other * self._days\n b = other * self._businessdays\n return BusinessPeriod(months=m, days=d, businessdays=b)\n raise TypeError(\"expected int type but got %s\" % other.__class__.__name__)\n\n def __rmul__(self, other):\n return self.__mul__(other)\n\n def max_days(self):\n if self._months < 0:\n sgn = -1\n days_in_month = 31, 30, 31, 30, 31, 31, 30, 31, 30, 31, 31, 28 # days from mar to feb forwards\n else:\n sgn = 1\n days_in_month = 31, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31, 28\n m = sgn * self._months\n # days from jan to feb backwards\n days = 0\n for i in range(m):\n days += days_in_month[int(i % 12)]\n days += 1 if int(i % 48) == 11 else 0\n return sgn * days + self._days\n\n def min_days(self):\n if self._months < 0 :\n sgn = -1\n days_in_month = 28, 31, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31 # days from feb to jan backwards\n else:\n sgn = 1\n days_in_month = 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31, 31 # days from feb to jan forwards\n m = sgn * self._months\n days = 0\n for i in range(m):\n days += days_in_month[int(i % 12)]\n days += 1 if int(i % 48) == 36 else 0\n return sgn * days + self._days\n","repo_name":"sonntagsgesicht/businessdate","sub_path":"businessdate/businessperiod.py","file_name":"businessperiod.py","file_ext":"py","file_size_in_byte":12099,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"50"} +{"seq_id":"31618877189","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\n\n##########################################################\n## global paraemters for the model\n##########################################################\n\nnt = 30 # num of temp points\nN = 50 # size of lattice\nequil_steps = 1000 # num of equil steps\nmc_steps = 1000 # num of mc steps\n\ntemp = 1.0\n\n\n##########################################################\n## the model code\n##########################################################\n\ndef initial_state(N):\n ''' gen random spin config for lattice size N x N with values -1 or 1 '''\n state = 2*np.random.randint(2, size=(N, N))-1\n return state\n\ndef mc_moves(config, N, temp):\n ''' execute mc moves using metropolis alg '''\n for i in range(N):\n for j in range(N):\n ## random site i in spot [a, b] that we assign to spin s\n a = np.random.randint(0, N)\n b = np.random.randint(0, N)\n s = config[a, b]\n ## calculating delta E = 2 * s * (top, bot, left, right, H=0)\n ## including mod N if a or b are at the boundaries\n ## they lap over to the other side\n neighbors = config[(a+1)%N,b] + config[a,(b+1)%N] + config[(a-1)%N,b] + config[a,(b-1)%N]\n energy_change = 2*s*neighbors\n ## if energy change is <= 0, accept\n if energy_change <= 0:\n s *= -1\n ## elif accept with prob A = e^(-energy_change/T)\n elif np.random.rand() < np.exp(-energy_change/temp):\n s *= -1\n ## apply change to the spot\n config[a, b] = s\n return config\n\n##########################################################\n## calculating quantitites\n##########################################################\n\n## quantities can all be calculated with energy and mag\n\ndef calc_energy(config):\n energy = 0\n for i in range(len(config)):\n for j in range(len(config)):\n S = config[i,j]\n nb = config[(i+1)%N, j] + config[i,(j+1)%N] + config[(i-1)%N, j] + config[i,(j-1)%N]\n energy += -nb*S\n return energy/4.\n\n\ndef calc_mag(config):\n ## simply sum all values\n return np.sum(config)\n\n\nTemp = np.linspace(1, 4, nt)\nEnergy = np.zeros(nt)\nMagnetization = np.zeros(nt)\nSpecificHeat = np.zeros(nt)\nSusceptibility = np.zeros(nt)\n\n\n##########################################################\n## running the code / plotting\n##########################################################\n\n## gen model\nmodel2 = initial_state(N)\nprint(model2)\n\ncount = 0\nfor i in range(equil_steps):\n mc_moves(model2, N, temp)\n print(count)\n count += 1\n\n## plotting stuff\ncmap = mpl.colors.ListedColormap(['yellow', 'blue'])\nbounds = [-N, -N, N, N]\nnorm = mpl.colors.BoundaryNorm(bounds, cmap.N)\n\nimg = plt.imshow(model2, interpolation='none', cmap = cmap)\n\nplt.show()\n\n## iterate monte carlo moves\n# for m in range(len(Temp)):\n# e0 = 0\n# e1 = 0\n# m0 = 0\n# m1 = 0\n# model = initial_state(N)\n# E = calc_energy(model)\n# M = calc_mag(model)\n#\n# for i in range(equil_steps):\n# mc_moves(model, N, Temp[m])\n#\n# for i in range(mc_steps):\n# mc_moves(model, N, Temp[m])\n# E = calc_energy(model)\n# M = calc_mag(model)\n#\n#\n# print(E,M,m,i)\n#\n# # iterate\n# e0 = e0 + E\n# m0 = m0 + M\n# m1 = m1 + (M*M)\n# e1 = e1 + (E*E)\n#\n# # change values in the arrays to the values\n# Energy[m] = e0/(mc_steps*N*N)\n# Magnetization[m] = m0/(mc_steps*N*N)\n# SpecificHeat[m] = ( e1/mc_steps - e0*e0/(mc_steps*mc_steps) )/(N*Temp[m]*Temp[m])\n#\n# Susceptibility[m] = ( m1/mc_steps - m0*m0/(mc_steps*mc_steps))\n\n\n\n\n\n#######\n\n# f = plt.figure(figsize=(18, 10), dpi=80, facecolor='w', edgecolor='k');\n#\n# sp = f.add_subplot(2, 2, 1 );\n# plt.plot(Temp, Energy, 'o', color=\"red\");\n# plt.xlabel(\"Temperature (T)\");\n# plt.ylabel(\"Energy \", fontsize=20);\n#\n# sp = f.add_subplot(2, 2, 2 );\n# plt.plot(Temp, abs(Magnetization), 'o', color=\"blue\");\n# plt.xlabel(\"Temperature (T)\");\n# plt.ylabel(\"Magnetization \");\n#\n#\n# sp = f.add_subplot(2, 2, 3 );\n# plt.plot(Temp, SpecificHeat, 'o', color=\"black\");\n# plt.xlabel(\"Temperature (T)\");\n# plt.ylabel(\"Specific Heat \");\n#\n#\n# sp = f.add_subplot(2, 2, 4 );\n# plt.plot(Temp, Susceptibility, 'o', color=\"green\");\n# plt.xlabel(\"Temperature (T)\");\n# plt.ylabel(\"Susceptibility\");\n# plt.show()\n# plt.legend(loc='best', fontsize=15);\n","repo_name":"kevinbelleville/Computational-Physics","sub_path":"project_3/kevin.py","file_name":"kevin.py","file_ext":"py","file_size_in_byte":4593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"9349029011","text":"from chrome import chrome_purchase as cPurchase\nfrom flask import Flask, Response, request\n\napp = Flask(__name__)\n\n@app.route('/purchase', methods=['POST'])\ndef purchase_event():\n\tprint(request.form)\n\turl = request.form.get('url')\n\tif url is not None:\n\t\tsPurchase = cPurchase.ChromePurchase()\n\t\tmsg = sPurchase.addToShoppingCard(url)\n\t\treturn Response(msg)\n\telse:\n\t\treturn \"Not purchase URL\", 400\t\n\n\t\n\nif __name__ == \"__main__\":\n\tapp.run(host='0.0.0.0', threaded=False, port = 5001)\n","repo_name":"toponsky/h_project","sub_path":"purchase/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":483,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"27220339832","text":"from discord.ext.commands import Bot as BotBase\nfrom discord.ext.commands import Context\nfrom discord.ext.commands import CommandNotFound, BadArgument\nfrom discord.ext.commands import MissingRequiredArgument, MissingRole, MissingPermissions\nfrom discord.errors import Forbidden, HTTPException\nfrom discord import Embed, File\nfrom glob import glob\nfrom apscheduler.schedulers.asyncio import AsyncIOScheduler\nfrom apscheduler.triggers.cron import CronTrigger\nfrom datetime import datetime \nfrom asyncio import sleep\nfrom ..db import db\n\nPREFIX = \"!\"\nOWNER_IDS = [572353145963806721]\nCOGS = [path.split(\"\\\\\")[-1][:-3] for path in glob(\"./lib/cogs/*.py\")]\nIGNORE_EXCEPTIONS = [CommandNotFound, BadArgument]\n\nclass Ready(object):\n def __init__(self):\n for cog in COGS:\n setattr(self, cog, False)\n def ready_up(self, cog):\n setattr(self, cog, True)\n print(f\"{cog} cog is ready\")\n\n def all_ready(self):\n return all([getattr(self, cog) for cog in COGS])\n\nclass Bot(BotBase):\n def __init__(self):\n self.PREFIX = PREFIX\n self.ready = False\n self.cogs_ready = Ready()\n self.guild = None\n self.scheduler = AsyncIOScheduler()\n db.autosave(self.scheduler)\n\n super().__init__(command_prefix=PREFIX, OWNER_ID=OWNER_IDS)\n\n def setup(self):\n for cog in COGS:\n self.load_extension(f\"lib.cogs.{cog}\")\n print(f\"{cog} cog Loaded\")\n\n def run(self, version):\n self.VERSION = version\n print(\"running setup\")\n self.setup()\n\n with open(\"./lib/bot/token\", \"r\", encoding=\"utf-8\") as tf:\n self.TOKEN = tf.read()\n\n print(\"Running Shinobu...\")\n super().run(self.TOKEN, reconnect=True)\n\n async def process_commands(self,message):\n ctx = await self.get_context(message, cls=Context)\n if ctx.command is not None and ctx.guild is not None:\n if self.ready:\n await self.invoke(ctx)\n else:\n await self.send(\"Wait for Oneechan to be ready!! \")\n \n async def on_connect(self):\n print(\"Ara Ara!\")\n\n async def on_disconnect(self):\n print(\"Ara Ara Sionara!\")\n\n async def on_error(self, err, *args, **kwargs):\n if err == 'on_command_error':\n await args[0].send(\"Somthing went wrong.\")\n channel = self.get_channel(710051662563115052)\n await channel.send(\"An error Occurrred\")\n raise \n\n async def on_command_error(self, ctx, exc):\n if any([isinstance(exc, error) for error in IGNORE_EXCEPTIONS]):\n pass\n\n elif isinstance(exc, MissingRequiredArgument):\n await ctx.send(\"One or More Argument missing in the command\")\n # elif hasattr(exc, \"original\"):\n # raise exc.original\n elif isinstance(exc, MissingPermissions):\n await ctx.send(\"You are not allowed to Use This command\")\n\n elif isinstance(exc, MissingRole):\n await ctx.send(\"You do not have the necessary role to use this command\")\n \n elif hasattr(exc, \"original\"):\n if isinstance(exc.original, Forbidden):\n await ctx.send(\"Shinobu doesn't have permission to do that!!\")\n elif isinstance(exc.original, HTTPException):\n await ctx.send(\"Unable To send Messages\")\n else:\n raise exc.original\n else:\n raise exc\n\n async def rules_reminder(self):\n self.stdout.send(\"Remember to follow the rules\")\n\n async def on_ready(self):\n if not self.ready:\n self.guild = self.get_guild(710051662563115049)\n self.stdout = self.get_channel(710051662563115052)\n self.scheduler.start()\n self.scheduler.add_job(self.rules_reminder, CronTrigger(day_of_week=0, hour=12))\n\n # embed = Embed(title=\"Ara Ara!!\", description=\"Shinobu is now here\", colour=0xFF0000, timestamp=datetime.utcnow())\n # embed.add_field(name=\"Name\", value=\"Value\",inline=False)\n # embed.set_author(name=\"LunaticSatoshi\", icon_url=self.guild.icon_url)\n # embed.set_thumbnail(url=self.guild.icon_url)\n # embed.set_image(url=self.guild.icon_url)\n # embed.set_footer(text=\"This is a footer\")\n # await channel.send(embed=embed)\n while not self.cogs_ready.all_ready():\n await sleep(0.5)\n self.ready = True\n print(\"Shinobu ready\")\n await self.stdout.send(\"Now Online\")\n else:\n print(\"Shinobu reconnected\")\n\n async def on_message(self, message):\n if not message.author.bot:\n await self.process_commands(message)\n\nbot = Bot()","repo_name":"Lunaticsatoshi/Shinobu","sub_path":"lib/bot/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4729,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"17660044938","text":"cokgenTip = int(input(\"\"\"Çokgenin kaç kenarlı olduğunu belirtiniz\r\nÜçgen ise 3\r\nDörtgen ise 4 giriniz\\n\\n\"\"\"))\r\n\r\nif cokgenTip == 3:\r\n taban = int(input(\"Taban kenar uzunluğunu giriniz\\n\"))\r\n yukseklik = int(input(\"Yüksekliği giriniz\\n\"))\r\n alan = (taban*yukseklik) / 2\r\n print (f\"Seçilen çokgen bir üçgendir. Alanı {alan} br karedir\")\r\n\r\nelif cokgenTip==4:\r\n dortgentip = int(input(\"\"\"Dörtgenin kare veya dikdörtgen olduğunu belirtiniz\r\n Kare ise 1\r\n Dikdörtgen ise 2 giriniz\\n\\n\"\"\"))\r\n if dortgentip == 1:\r\n kenar = int(input(\"Kenar uzunluğunu giriniz\\n\"))\r\n alan = kenar**2\r\n print(f\"Seçilen çokgen bir karedir. Alanı {alan} br karedir\")\r\n elif dortgentip == 2:\r\n taban = int(input(\"Taban kenar uzunluğunu giriniz\\n\"))\r\n yukseklik = int(input(\"Yüksekliği giriniz\\n\"))\r\n alan = taban * yukseklik\r\n print(f\"Seçilen çokgen bir dikdörtgendir. Alanı {alan} br karedir\")\r\n else:\r\n print(\"Girilen değerler hatalıdır\")\r\n\r\nelse:\r\n print(\"Girilen çokgen tipi hatalıdır\")","repo_name":"tolgaeken/beltek-python","sub_path":"belteklab/bltk313.py","file_name":"bltk313.py","file_ext":"py","file_size_in_byte":1088,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"9304635865","text":"from __future__ import absolute_import\n\nfrom contextlib import contextmanager\nimport copy\n\nimport dbt.compat\nimport dbt.deprecations\nimport dbt.exceptions\nimport dbt.schema\nimport dbt.flags as flags\nimport dbt.clients.gcloud\nimport dbt.clients.agate_helper\n\nfrom dbt.adapters.postgres import PostgresAdapter\nfrom dbt.adapters.bigquery.relation import BigQueryRelation\nfrom dbt.contracts.connection import Connection\nfrom dbt.logger import GLOBAL_LOGGER as logger\n\nimport google.auth\nimport google.api_core\nimport google.oauth2\nimport google.cloud.exceptions\nimport google.cloud.bigquery\n\nimport time\nimport agate\n\n\nclass BigQueryAdapter(PostgresAdapter):\n\n config_functions = [\n # deprecated -- use versions that take relations instead\n \"query_for_existing\",\n \"execute_model\",\n \"create_temporary_table\",\n \"drop\",\n \"execute\",\n \"quote_schema_and_table\",\n \"make_date_partitioned_table\",\n \"already_exists\",\n \"expand_target_column_types\",\n \"load_dataframe\",\n \"get_missing_columns\",\n \"cache_new_relation\",\n\n \"create_schema\",\n \"alter_table_add_columns\",\n\n # versions of adapter functions that take / return Relations\n \"get_relation\",\n \"drop_relation\",\n \"rename_relation\",\n\n \"get_columns_in_table\",\n\n # formerly profile functions\n \"add_query\",\n ]\n\n SCOPE = ('https://www.googleapis.com/auth/bigquery',\n 'https://www.googleapis.com/auth/cloud-platform',\n 'https://www.googleapis.com/auth/drive')\n\n RELATION_TYPES = {\n 'TABLE': BigQueryRelation.Table,\n 'VIEW': BigQueryRelation.View,\n 'EXTERNAL': BigQueryRelation.External\n }\n\n QUERY_TIMEOUT = 300\n Relation = BigQueryRelation\n Column = dbt.schema.BigQueryColumn\n\n @classmethod\n def handle_error(cls, error, message, sql):\n logger.debug(message.format(sql=sql))\n logger.debug(error)\n error_msg = \"\\n\".join(\n [item['message'] for item in error.errors])\n\n raise dbt.exceptions.DatabaseException(error_msg)\n\n @contextmanager\n def exception_handler(self, sql, model_name=None,\n connection_name='master'):\n try:\n yield\n\n except google.cloud.exceptions.BadRequest as e:\n message = \"Bad request while running:\\n{sql}\"\n self.handle_error(e, message, sql)\n\n except google.cloud.exceptions.Forbidden as e:\n message = \"Access denied while running:\\n{sql}\"\n self.handle_error(e, message, sql)\n\n except Exception as e:\n logger.debug(\"Unhandled error while running:\\n{}\".format(sql))\n logger.debug(e)\n raise dbt.exceptions.RuntimeException(dbt.compat.to_string(e))\n\n @classmethod\n def type(cls):\n return 'bigquery'\n\n @classmethod\n def date_function(cls):\n return 'CURRENT_TIMESTAMP()'\n\n def begin(self, name):\n pass\n\n def commit(self, connection):\n pass\n\n @classmethod\n def get_status(cls, cursor):\n raise dbt.exceptions.NotImplementedException(\n '`get_status` is not implemented for this adapter!')\n\n @classmethod\n def get_bigquery_credentials(cls, profile_credentials):\n method = profile_credentials.method\n creds = google.oauth2.service_account.Credentials\n\n if method == 'oauth':\n credentials, project_id = google.auth.default(scopes=cls.SCOPE)\n return credentials\n\n elif method == 'service-account':\n keyfile = profile_credentials.keyfile\n return creds.from_service_account_file(keyfile, scopes=cls.SCOPE)\n\n elif method == 'service-account-json':\n details = profile_credentials.keyfile_json\n return creds.from_service_account_info(details, scopes=cls.SCOPE)\n\n error = ('Invalid `method` in profile: \"{}\"'.format(method))\n raise dbt.exceptions.FailedToConnectException(error)\n\n @classmethod\n def get_bigquery_client(cls, profile_credentials):\n project_name = profile_credentials.project\n creds = cls.get_bigquery_credentials(profile_credentials)\n\n return google.cloud.bigquery.Client(project_name, creds)\n\n @classmethod\n def open_connection(cls, connection):\n if connection.state == 'open':\n logger.debug('Connection is already open, skipping open.')\n return connection\n\n try:\n handle = cls.get_bigquery_client(connection.credentials)\n\n except google.auth.exceptions.DefaultCredentialsError as e:\n logger.info(\"Please log into GCP to continue\")\n dbt.clients.gcloud.setup_default_credentials()\n\n handle = cls.get_bigquery_client(connection.credentials)\n\n except Exception as e:\n raise\n logger.debug(\"Got an error when attempting to create a bigquery \"\n \"client: '{}'\".format(e))\n\n connection.handle = None\n connection.state = 'fail'\n\n raise dbt.exceptions.FailedToConnectException(str(e))\n\n connection.handle = handle\n connection.state = 'open'\n return connection\n\n @classmethod\n def close(cls, connection):\n connection.state = 'closed'\n\n return connection\n\n def _link_cached_relations(self, manifest, schemas):\n pass\n\n def _list_relations(self, schema, model_name=None):\n connection = self.get_connection(model_name)\n client = connection.handle\n\n bigquery_dataset = self.get_dataset(schema, model_name)\n\n all_tables = client.list_tables(\n bigquery_dataset,\n # BigQuery paginates tables by alphabetizing them, and using\n # the name of the last table on a page as the key for the\n # next page. If that key table gets dropped before we run\n # list_relations, then this will 404. So, we avoid this\n # situation by making the page size sufficiently large.\n # see: https://github.com/fishtown-analytics/dbt/issues/726\n # TODO: cache the list of relations up front, and then we\n # won't need to do this\n max_results=100000)\n\n # This will 404 if the dataset does not exist. This behavior mirrors\n # the implementation of list_relations for other adapters\n try:\n return [self._bq_table_to_relation(table) for table in all_tables]\n except google.api_core.exceptions.NotFound as e:\n return []\n\n def get_relation(self, schema, identifier, model_name=None):\n if self._schema_is_cached(schema, model_name):\n # if it's in the cache, use the parent's model of going through\n # the relations cache and picking out the relation\n return super(BigQueryAdapter, self).get_relation(\n schema=schema,\n identifier=identifier,\n model_name=model_name\n )\n\n table = self._get_bq_table(schema, identifier)\n return self._bq_table_to_relation(table)\n\n def drop_relation(self, relation, model_name=None):\n if self._schema_is_cached(relation.schema, model_name):\n self.cache.drop(relation)\n\n conn = self.get_connection(model_name)\n client = conn.handle\n\n dataset = self.get_dataset(relation.schema, model_name)\n relation_object = dataset.table(relation.identifier)\n client.delete_table(relation_object)\n\n def rename(self, schema, from_name, to_name, model_name=None):\n raise dbt.exceptions.NotImplementedException(\n '`rename` is not implemented for this adapter!')\n\n def rename_relation(self, from_relation, to_relation, model_name=None):\n raise dbt.exceptions.NotImplementedException(\n '`rename_relation` is not implemented for this adapter!')\n\n @classmethod\n def get_timeout(cls, conn):\n credentials = conn['credentials']\n return credentials.get('timeout_seconds', cls.QUERY_TIMEOUT)\n\n def materialize_as_view(self, dataset, model):\n model_name = model.get('name')\n model_alias = model.get('alias')\n model_sql = model.get('injected_sql')\n\n conn = self.get_connection(model_name)\n client = conn.handle\n\n view_ref = dataset.table(model_alias)\n view = google.cloud.bigquery.Table(view_ref)\n view.view_query = model_sql\n view.view_use_legacy_sql = False\n\n logger.debug(\"Model SQL ({}):\\n{}\".format(model_name, model_sql))\n\n with self.exception_handler(model_sql, model_name, model_name):\n client.create_table(view)\n\n return \"CREATE VIEW\"\n\n @classmethod\n def poll_until_job_completes(cls, job, timeout):\n retry_count = timeout\n\n while retry_count > 0 and job.state != 'DONE':\n retry_count -= 1\n time.sleep(1)\n job.reload()\n\n if job.state != 'DONE':\n raise dbt.exceptions.RuntimeException(\"BigQuery Timeout Exceeded\")\n\n elif job.error_result:\n raise job.exception()\n\n def make_date_partitioned_table(self, dataset_name, identifier,\n model_name=None):\n conn = self.get_connection(model_name)\n client = conn.handle\n\n dataset = self.get_dataset(dataset_name, identifier)\n table_ref = dataset.table(identifier)\n table = google.cloud.bigquery.Table(table_ref)\n table.partitioning_type = 'DAY'\n\n return client.create_table(table)\n\n def materialize_as_table(self, dataset, model, model_sql,\n decorator=None):\n model_name = model.get('name')\n model_alias = model.get('alias')\n\n conn = self.get_connection(model_name)\n client = conn.handle\n\n if decorator is None:\n table_name = model_alias\n else:\n table_name = \"{}${}\".format(model_alias, decorator)\n\n table_ref = dataset.table(table_name)\n job_config = google.cloud.bigquery.QueryJobConfig()\n job_config.destination = table_ref\n job_config.write_disposition = 'WRITE_TRUNCATE'\n\n logger.debug(\"Model SQL ({}):\\n{}\".format(table_name, model_sql))\n query_job = client.query(model_sql, job_config=job_config)\n\n # this waits for the job to complete\n with self.exception_handler(model_sql, model_alias,\n model_name):\n query_job.result(timeout=self.get_timeout(conn))\n\n return \"CREATE TABLE\"\n\n def execute_model(self, model,\n materialization, sql_override=None,\n decorator=None, model_name=None):\n\n if sql_override is None:\n sql_override = model.get('injected_sql')\n\n if flags.STRICT_MODE:\n connection = self.get_connection(model.get('name'))\n Connection(**connection)\n\n model_name = model.get('name')\n model_schema = model.get('schema')\n\n dataset = self.get_dataset(model_schema, model_name)\n\n if materialization == 'view':\n res = self.materialize_as_view(dataset, model)\n elif materialization == 'table':\n res = self.materialize_as_table(\n dataset, model,\n sql_override, decorator)\n else:\n msg = \"Invalid relation type: '{}'\".format(materialization)\n raise dbt.exceptions.RuntimeException(msg, model)\n\n return res\n\n def raw_execute(self, sql, model_name=None, fetch=False, **kwargs):\n conn = self.get_connection(model_name)\n client = conn.handle\n\n logger.debug('On %s: %s', model_name, sql)\n\n job_config = google.cloud.bigquery.QueryJobConfig()\n job_config.use_legacy_sql = False\n query_job = client.query(sql, job_config)\n\n # this blocks until the query has completed\n with self.exception_handler(sql, model_name):\n iterator = query_job.result()\n\n return query_job, iterator\n\n def create_temporary_table(self, sql, model_name=None, **kwargs):\n\n # BQ queries always return a temp table with their results\n query_job, _ = self.raw_execute(sql, model_name)\n bq_table = query_job.destination\n\n return self.Relation.create(\n project=bq_table.project,\n schema=bq_table.dataset_id,\n identifier=bq_table.table_id,\n quote_policy={\n 'schema': True,\n 'identifier': True\n },\n type=BigQueryRelation.Table)\n\n def alter_table_add_columns(self, relation, columns, model_name=None):\n\n logger.debug('Adding columns ({}) to table {}\".'.format(\n columns, relation))\n\n conn = self.get_connection(model_name)\n client = conn.handle\n\n dataset = self.get_dataset(relation.schema, model_name)\n\n table_ref = dataset.table(relation.name)\n table = client.get_table(table_ref)\n\n new_columns = [col.to_bq_schema_object() for col in columns]\n new_schema = table.schema + new_columns\n\n new_table = google.cloud.bigquery.Table(table_ref, schema=new_schema)\n client.update_table(new_table, ['schema'])\n\n def execute(self, sql, model_name=None, fetch=None, **kwargs):\n _, iterator = self.raw_execute(sql, model_name, fetch, **kwargs)\n\n if fetch:\n res = self.get_table_from_response(iterator)\n else:\n res = dbt.clients.agate_helper.empty_table()\n\n # If we get here, the query succeeded\n status = 'OK'\n return status, res\n\n def execute_and_fetch(self, sql, model_name, auto_begin=None):\n status, table = self.execute(sql, model_name, fetch=True)\n return status, table\n\n @classmethod\n def get_table_from_response(cls, resp):\n column_names = [field.name for field in resp.schema]\n rows = [dict(row.items()) for row in resp]\n return dbt.clients.agate_helper.table_from_data(rows, column_names)\n\n # BigQuery doesn't support BEGIN/COMMIT, so stub these out.\n\n def add_begin_query(self, name):\n pass\n\n def add_commit_query(self, name):\n pass\n\n def create_schema(self, schema, model_name=None):\n logger.debug('Creating schema \"%s\".', schema)\n\n conn = self.get_connection(model_name)\n client = conn.handle\n\n dataset = self.get_dataset(schema, model_name)\n\n # Emulate 'create schema if not exists ...'\n try:\n client.get_dataset(dataset)\n except google.api_core.exceptions.NotFound:\n with self.exception_handler('create dataset', model_name):\n client.create_dataset(dataset)\n\n def drop_tables_in_schema(self, dataset):\n conn = self.get_connection()\n client = conn.handle\n\n for table in client.list_tables(dataset):\n client.delete_table(table.reference)\n\n def drop_schema(self, schema, model_name=None):\n logger.debug('Dropping schema \"%s\".', schema)\n\n if not self.check_schema_exists(schema, model_name):\n return\n\n conn = self.get_connection(model_name)\n client = conn.handle\n\n dataset = self.get_dataset(schema, model_name)\n with self.exception_handler('drop dataset', model_name):\n self.drop_tables_in_schema(dataset)\n client.delete_dataset(dataset)\n\n def get_existing_schemas(self, model_name=None):\n conn = self.get_connection(model_name)\n client = conn.handle\n\n with self.exception_handler('list dataset', model_name):\n all_datasets = client.list_datasets(include_all=True)\n return [ds.dataset_id for ds in all_datasets]\n\n def get_columns_in_table(self, schema_name, table_name,\n database=None, model_name=None):\n\n # BigQuery does not have databases -- the database parameter is here\n # for consistency with the base implementation\n\n conn = self.get_connection(model_name)\n client = conn.handle\n\n try:\n dataset_ref = client.dataset(schema_name)\n table_ref = dataset_ref.table(table_name)\n table = client.get_table(table_ref)\n return self.get_dbt_columns_from_bq_table(table)\n\n except (ValueError, google.cloud.exceptions.NotFound) as e:\n logger.debug(\"get_columns_in_table error: {}\".format(e))\n return []\n\n def get_dbt_columns_from_bq_table(self, table):\n \"Translates BQ SchemaField dicts into dbt BigQueryColumn objects\"\n\n columns = []\n for col in table.schema:\n # BigQuery returns type labels that are not valid type specifiers\n dtype = self.Column.translate_type(col.field_type)\n column = self.Column(\n col.name, dtype, col.fields, col.mode)\n columns.append(column)\n\n return columns\n\n def check_schema_exists(self, schema, model_name=None):\n conn = self.get_connection(model_name)\n client = conn.handle\n\n with self.exception_handler('get dataset', model_name):\n all_datasets = client.list_datasets(include_all=True)\n return any([ds.dataset_id == schema for ds in all_datasets])\n\n def get_dataset(self, dataset_name, model_name=None):\n conn = self.get_connection(model_name)\n dataset_ref = conn.handle.dataset(dataset_name)\n return google.cloud.bigquery.Dataset(dataset_ref)\n\n def _bq_table_to_relation(self, bq_table):\n if bq_table is None:\n return None\n\n return self.Relation.create(\n project=bq_table.project,\n schema=bq_table.dataset_id,\n identifier=bq_table.table_id,\n quote_policy={\n 'schema': True,\n 'identifier': True\n },\n type=self.RELATION_TYPES.get(bq_table.table_type))\n\n def _get_bq_table(self, dataset_name, identifier, model_name=None):\n conn = self.get_connection(model_name)\n\n dataset = self.get_dataset(dataset_name, model_name)\n\n table_ref = dataset.table(identifier)\n\n try:\n return conn.handle.get_table(table_ref)\n except google.cloud.exceptions.NotFound:\n return None\n\n @classmethod\n def warning_on_hooks(hook_type):\n msg = \"{} is not supported in bigquery and will be ignored\"\n dbt.ui.printer.print_timestamped_line(msg.format(hook_type),\n dbt.ui.printer.COLOR_FG_YELLOW)\n\n def add_query(self, sql, model_name=None, auto_begin=True,\n bindings=None, abridge_sql_log=False):\n if model_name in ['on-run-start', 'on-run-end']:\n self.warning_on_hooks(model_name)\n else:\n raise dbt.exceptions.NotImplementedException(\n '`add_query` is not implemented for this adapter!')\n\n @classmethod\n def is_cancelable(cls):\n return False\n\n @classmethod\n def quote(cls, identifier):\n return '`{}`'.format(identifier)\n\n def quote_schema_and_table(self, schema, table, model_name=None):\n return self.render_relation(self.quote(schema), self.quote(table))\n\n def render_relation(cls, schema, table):\n connection = self.get_connection()\n project = connection.credentials.project\n return '{}.{}.{}'.format(self.quote(project), schema, table)\n\n @classmethod\n def convert_text_type(cls, agate_table, col_idx):\n return \"string\"\n\n @classmethod\n def convert_number_type(cls, agate_table, col_idx):\n decimals = agate_table.aggregate(agate.MaxPrecision(col_idx))\n return \"float64\" if decimals else \"int64\"\n\n @classmethod\n def convert_boolean_type(cls, agate_table, col_idx):\n return \"bool\"\n\n @classmethod\n def convert_datetime_type(cls, agate_table, col_idx):\n return \"datetime\"\n\n @classmethod\n def _agate_to_schema(cls, agate_table, column_override):\n bq_schema = []\n for idx, col_name in enumerate(agate_table.column_names):\n inferred_type = cls.convert_agate_type(agate_table, idx)\n type_ = column_override.get(col_name, inferred_type)\n bq_schema.append(\n google.cloud.bigquery.SchemaField(col_name, type_))\n return bq_schema\n\n def load_dataframe(self, schema, table_name, agate_table,\n column_override, model_name=None):\n bq_schema = self._agate_to_schema(agate_table, column_override)\n dataset = self.get_dataset(schema, None)\n table = dataset.table(table_name)\n conn = self.get_connection(None)\n client = conn.handle\n\n load_config = google.cloud.bigquery.LoadJobConfig()\n load_config.skip_leading_rows = 1\n load_config.schema = bq_schema\n\n with open(agate_table.original_abspath, \"rb\") as f:\n job = client.load_table_from_file(f, table, rewind=True,\n job_config=load_config)\n\n with self.exception_handler(\"LOAD TABLE\"):\n self.poll_until_job_completes(job, self.get_timeout(conn))\n\n def expand_target_column_types(self, temp_table,\n to_schema, to_table, model_name=None):\n # This is a no-op on BigQuery\n pass\n\n def _flat_columns_in_table(self, table):\n \"\"\"An iterator over the flattened columns for a given schema and table.\n Resolves child columns as having the name \"parent.child\".\n \"\"\"\n for col in self.get_dbt_columns_from_bq_table(table):\n flattened = col.flatten()\n for subcol in flattened:\n yield subcol\n\n @classmethod\n def _get_stats_column_names(cls):\n \"\"\"Construct a tuple of the column names for stats. Each stat has 4\n columns of data.\n \"\"\"\n columns = []\n stats = ('num_bytes', 'num_rows', 'location', 'partitioning_type',\n 'clustering_fields')\n stat_components = ('label', 'value', 'description', 'include')\n for stat_id in stats:\n for stat_component in stat_components:\n columns.append('stats:{}:{}'.format(stat_id, stat_component))\n return tuple(columns)\n\n @classmethod\n def _get_stats_columns(cls, table, relation_type):\n \"\"\"Given a table, return an iterator of key/value pairs for stats\n column names/values.\n \"\"\"\n column_names = cls._get_stats_column_names()\n\n # agate does not handle the array of column names gracefully\n clustering_value = None\n if table.clustering_fields is not None:\n clustering_value = ','.join(table.clustering_fields)\n # cast num_bytes/num_rows to str before they get to agate, or else\n # agate will incorrectly decide they are booleans.\n column_values = (\n 'Number of bytes',\n str(table.num_bytes),\n 'The number of bytes this table consumes',\n relation_type == 'table',\n\n 'Number of rows',\n str(table.num_rows),\n 'The number of rows in this table',\n relation_type == 'table',\n\n 'Location',\n table.location,\n 'The geographic location of this table',\n True,\n\n 'Partitioning Type',\n table.partitioning_type,\n 'The partitioning type used for this table',\n relation_type == 'table',\n\n 'Clustering Fields',\n clustering_value,\n 'The clustering fields for this table',\n relation_type == 'table',\n )\n return zip(column_names, column_values)\n\n def get_catalog(self, manifest):\n connection = self.get_connection('catalog')\n client = connection.handle\n\n schemas = {\n node.to_dict()['schema']\n for node in manifest.nodes.values()\n }\n\n column_names = (\n 'table_schema',\n 'table_name',\n 'table_type',\n 'table_comment',\n # does not exist in bigquery, but included for consistency\n 'table_owner',\n 'column_name',\n 'column_index',\n 'column_type',\n 'column_comment',\n )\n all_names = column_names + self._get_stats_column_names()\n columns = []\n\n for schema_name in schemas:\n relations = self.list_relations(schema_name)\n for relation in relations:\n\n # This relation contains a subset of the info we care about.\n # Fetch the full table object here\n dataset_ref = client.dataset(relation.schema)\n table_ref = dataset_ref.table(relation.identifier)\n table = client.get_table(table_ref)\n\n flattened = self._flat_columns_in_table(table)\n relation_stats = dict(self._get_stats_columns(table,\n relation.type))\n\n for index, column in enumerate(flattened, start=1):\n column_data = (\n relation.schema,\n relation.name,\n relation.type,\n None,\n None,\n column.name,\n index,\n column.data_type,\n None,\n )\n column_dict = dict(zip(column_names, column_data))\n column_dict.update(copy.deepcopy(relation_stats))\n\n columns.append(column_dict)\n\n return dbt.clients.agate_helper.table_from_data(columns, all_names)\n","repo_name":"alexpatton/dbt","sub_path":"dbt/adapters/bigquery/impl.py","file_name":"impl.py","file_ext":"py","file_size_in_byte":25792,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"50"} +{"seq_id":"7670907406","text":"import os\nimport argparse\nimport json\nimport numpy as np\nimport cv2\nfrom thermal_faces.convolution import detect_heads\nfrom thermal_faces.seq_reader import extract_metadata, convert_to_temperature, seq_frames\nfrom thermal_faces.utils import save_image, JsonNumpyEncoder\nfrom tqdm import tqdm\n\n# Find heads in the average temperature over the entire video. This should\n# find the most stable head locations, and these people are most likely\n# watching the performance.\n\ndef main():\n parser = argparse.ArgumentParser(description=\"Calculate the average temperature in a SEQ-video file and find faces in the average temperature. These are recorded in a json file for later use.\")\n parser.add_argument(\"-f\", \"--filename\", required=True, help=\"The name of the SEQ video file.\")\n parser.add_argument(\"-o\", \"--outfile\", default=\"head_locations.json\", help=\"The output json file name.\")\n parser.add_argument(\"-i\", \"--imagefile\", default=\"mean.png\", help=\"The output image file name.\")\n parser.add_argument(\"-p\", \"--npy\", default=\"mean.npy\", help=\"The name of the pickled numpy file containing the mean temperature.\")\n parser.add_argument(\"--firstframe\", default=0, help=\"First frame to include.\")\n parser.add_argument(\"--lastframe\", default=None, help=\"Last frame to include.\")\n parser.add_argument(\"--minwidth\", default=30, help=\"Minimum head width.\")\n parser.add_argument(\"--maxwidth\", default=50, help=\"Maximum head width.\")\n parser.add_argument(\"--threshhold\", default=2, help=\"Threshhold for detecting a head. Lower threshold means more heads and more false positives.\")\n parser.add_argument(\"--keep\", default=[], nargs='*', help=\"A list of subject ids to keep. By default keep all.\")\n args = parser.parse_args()\n\n keep = [int(k) for k in args.keep]\n first_frame = int(args.firstframe)\n if args.lastframe is None:\n last_frame = float(\"inf\")\n else:\n last_frame = int(args.lastframe)\n\n # Video file data\n metadata = extract_metadata(args.filename)\n width = int(metadata[\"Raw Thermal Image Width\"])\n height = int(metadata[\"Raw Thermal Image Height\"])\n bitdepth = 16\n frame_size = width * height * (bitdepth // 8)\n\n # If not already done, find the mean temperature.\n if not os.path.isfile(args.npy):\n frame_index = 0\n temperature_sum = None\n for frame in tqdm(seq_frames(args.filename)):\n # read temperature data\n raw_data = np.frombuffer(frame[len(frame)-frame_size:], dtype=np.uint16).reshape(height, width)\n\n if frame_index > first_frame and frame_index < last_frame:\n temperature = convert_to_temperature(raw_data, metadata)\n if temperature_sum is None:\n temperature_sum = temperature\n else:\n temperature_sum += temperature\n\n if frame_index % 1000 == 0:\n temperature = convert_to_temperature(raw_data, metadata)\n if not os.path.exists('temperature_frames'):\n os.makedirs('temperature_frames')\n save_image(temperature, f\"temperature_frames/{frame_index}.png\", scaled=True)\n\n frame_index += 1\n\n if args.lastframe is None:\n last_frame = frame_index\n \n frames = last_frame - first_frame\n\n # Get the mean\n temperature_mean = temperature_sum/frames\n np.save(args.npy, temperature_mean)\n\n else:\n temperature_mean = np.load(args.npy)\n\n\n heads = detect_heads(\n temperature_mean,\n min_width=int(args.minwidth),\n max_width=int(args.maxwidth),\n threshold=float(args.threshhold)\n )\n\n if keep:\n heads = [h for i, h in enumerate(heads) if i in keep]\n\n for i, head in enumerate(heads):\n y, x = head[\"y\"], head[\"x\"]\n h, w = head[\"height\"]//2, head[\"width\"]//2\n y_max, x_max = head[\"max temp y\"], head[\"max temp x\"]\n cv2.ellipse(temperature_mean, (x,y), (w, h), 0, 0, 360, thickness=2, color=255)\n cv2.circle(temperature_mean, (x_max, y_max), 0, thickness=5, color=255)\n cv2.putText(temperature_mean, str(i), (x+w,y+h), cv2.FONT_HERSHEY_PLAIN, 3, 255, 2, cv2.LINE_AA)\n\n head[\"subject_id\"] = i\n\n save_image(temperature_mean, args.imagefile, scaled=True)\n\n with open(args.outfile, \"w\") as file:\n json.dump(heads, file, indent=4, cls=JsonNumpyEncoder)\n\n\n\nif __name__ == \"__main__\":\n main()\n\n","repo_name":"rantahar/seq_file","sub_path":"mean_temp_image.py","file_name":"mean_temp_image.py","file_ext":"py","file_size_in_byte":4450,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"39917231290","text":"# Calculadora fracciones | Jesús Domínguez\n\n# Clase creadora de fracciones\nclass Fraccionarios():\n \n # Constructor\n def __init__(self):\n # Atributos por defecto\n self.numerador = 0\n self.denominador = 0\n\n # Set()\n def setNumerador(self, x:int):\n self.numerador = x\n\n def setDenominador(self, x:int):\n self.denominador = x\n\n # Get()\n def getNumerador(self):\n return self.numerador\n\n def getDenominador(self):\n return self.denominador\n\n# Clase operadora de fracciones\nclass CalculadoraFraccionarios():\n \n # Constructor\n def __init__(self):\n # f1 y f2 son atributos\n # del objeto cálculo\n self.f1 = Fraccionarios()\n self.f2 = Fraccionarios()\n\n # Set()\n def setF1(self, x:Fraccionarios()):\n self.f1 = x\n\n def setF2(self, x:Fraccionarios()):\n self.f2 = x\n\n # Get()\n def getF1(self):\n return self.f1\n\n def getF2(self):\n return self.f2\n \n # Suma HETEROGÉNEA\n def sumaFraccionesHeterogeneas(self):\n numerador = (self.f1.getNumerador() * self.f2.getDenominador()) + (self.f1.getDenominador() * self.f2.getNumerador())\n denominador = self.f1.getDenominador() * self.f2.getDenominador()\n print(f\"{numerador} / {denominador} = {round(numerador / denominador, 2)}\")\n\n # Suma HOMOGÉNEAS\n def sumaFraccionesHomogeneas(self):\n numerador = self.f1.getNumerador() + self.f2.getNumerador()\n denominador = self.f1.getDenominador()\n print(f\"{numerador} / {denominador} = {round(numerador / denominador, 2)}\")\n\n # Resta HETEROGÉNEA\n def restaFraccionesHeterogeneas(self):\n numerador = (self.f1.getNumerador() * self.f2.getDenominador()) - (self.f1.getDenominador() * self.f2.getNumerador())\n denominador = self.f1.getDenominador() * self.f2.getDenominador()\n print(f\"{numerador} / {denominador} = {round(numerador / denominador, 2)}\")\n\n # Resta HOMOGÉNEAS\n def restaFraccionesHomogeneas(self):\n numerador = self.f1.getNumerador() - self.f2.getNumerador()\n denominador = self.f1.getDenominador()\n print(f\"{numerador} / {denominador} = {round(numerador / denominador, 2)}\")\n\n # Multiplicación\n def multiplicacionFracciones(self):\n numerador = self.f1.getNumerador() * self.f2.getNumerador()\n denominador = self.f1.getDenominador() * self.f2.getDenominador()\n print(f\"{numerador} / {denominador} = {round(numerador / denominador, 2)}\")\n\n # División\n def divisionFracciones(self):\n numerador = self.f1.getNumerador() * self.f2.getDenominador()\n denominador = self.f1.getDenominador() * self.f2.getNumerador()\n print(f\"{numerador} / {denominador} = {round(numerador / denominador, 2)}\")\n\n\n# Método principal para pruebas\ndef main():\n\n print(\"- - SUMA HETEROGÉNEA - -\")\n # Fracción 1\n f1 = Fraccionarios() # Instancia de clase\n f1.setNumerador(10) # Asignar atributo\n f1.setDenominador(20) # Asignar atributo\n\n # Fracción 2\n f2 = Fraccionarios() # Instancia de clase\n f2.setNumerador(30) # Asignar atributo\n f2.setDenominador(40) # Asignar atributo\n\n # Imprimir\n print(f\"Fracción 1: {f1.getNumerador()} / {f1.getDenominador()}\")\n print(f\"Fracción 2: {f2.getNumerador()} / {f2.getDenominador()}\")\n\n # Suma heterogénea\n calculo1 = CalculadoraFraccionarios()\n calculo1.setF1(f1)\n calculo1.setF2(f2)\n calculo1.sumaFraccionesHeterogeneas()\n\n print(\"- - SUMA HOMOGÉNEA - -\")\n # Fracción 3\n f3 = Fraccionarios() # Instancia de clase\n f3.setNumerador(3) # Asignar atributo\n f3.setDenominador(10) # Asignar atributo\n\n # Fracción 4\n f4 = Fraccionarios() # Instancia de clase\n f4.setNumerador(5) # Asignar atributo\n f4.setDenominador(10) # Asignar atributo\n\n # Imprimir\n print(f\"Fracción 3: {f3.getNumerador()} / {f3.getDenominador()}\")\n print(f\"Fracción 4: {f4.getNumerador()} / {f4.getDenominador()}\")\n\n # Suma homogénea\n calculo2 = CalculadoraFraccionarios()\n calculo2.setF1(f3)\n calculo2.setF2(f4)\n calculo2.sumaFraccionesHomogeneas()\n\n print(\"- - RESTA HETEROGÉNEA - -\")\n # Fracción 5\n f5 = Fraccionarios() # Instancia de clase\n f5.setNumerador(4) # Asignar atributo\n f5.setDenominador(7) # Asignar atributo\n\n # Fracción 6\n f6 = Fraccionarios() # Instancia de clase\n f6.setNumerador(2) # Asignar atributo\n f6.setDenominador(5) # Asignar atributo\n\n # Imprimir\n print(f\"Fracción 5: {f5.getNumerador()} / {f5.getDenominador()}\")\n print(f\"Fracción 6: {f6.getNumerador()} / {f6.getDenominador()}\")\n\n # Resta heterogénea\n calculo3 = CalculadoraFraccionarios()\n calculo3.setF1(f5)\n calculo3.setF2(f6)\n calculo3.restaFraccionesHeterogeneas()\n\n print(\"- - RESTA HOMOGÉNEA - -\")\n # Fracción 7\n f7 = Fraccionarios() # Instancia de clase\n f7.setNumerador(10) # Asignar atributo\n f7.setDenominador(45) # Asignar atributo\n\n # Fracción 8\n f8 = Fraccionarios() # Instancia de clase\n f8.setNumerador(20) # Asignar atributo\n f8.setDenominador(45) # Asignar atributo\n\n # Imprimir\n print(f\"Fracción 7: {f7.getNumerador()} / {f7.getDenominador()}\")\n print(f\"Fracción 8: {f8.getNumerador()} / {f8.getDenominador()}\")\n\n # Resta homogénea\n calculo4 = CalculadoraFraccionarios()\n calculo4.setF1(f7)\n calculo4.setF2(f8)\n calculo4.restaFraccionesHomogeneas()\n\n print(\"- - MULTIPLICACIÓN - -\")\n # Fracción 9\n f9 = Fraccionarios() # Instancia de clase\n f9.setNumerador(9) # Asignar atributo\n f9.setDenominador(12) # Asignar atributo\n\n # Fracción 10\n f10 = Fraccionarios() # Instancia de clase\n f10.setNumerador(2) # Asignar atributo\n f10.setDenominador(2) # Asignar atributo\n\n # Imprimir\n print(f\"Fracción 9: {f9.getNumerador()} / {f9.getDenominador()}\")\n print(f\"Fracción 10: {f10.getNumerador()} / {f10.getDenominador()}\")\n\n # Multiplicación\n calculo5 = CalculadoraFraccionarios()\n calculo5.setF1(f9)\n calculo5.setF2(f10)\n calculo5.multiplicacionFracciones()\n\n print(\"- - DIVISIÓN - -\")\n # Fracción 11\n f11 = Fraccionarios() # Instancia de clase\n f11.setNumerador(12) # Asignar atributo\n f11.setDenominador(9) # Asignar atributo\n\n # Fracción 12\n f12 = Fraccionarios() # Instancia de clase\n f12.setNumerador(2) # Asignar atributo\n f12.setDenominador(5) # Asignar atributo\n\n # Imprimir\n print(f\"Fracción 11: {f11.getNumerador()} / {f11.getDenominador()}\")\n print(f\"Fracción 12: {f12.getNumerador()} / {f12.getDenominador()}\")\n\n # División\n calculo6 = CalculadoraFraccionarios()\n calculo6.setF1(f11)\n calculo6.setF2(f12)\n calculo6.divisionFracciones()\n\n# Llamar método principal de pruebas\nmain()","repo_name":"jesusdominguez2004/pruebas","sub_path":"algoritmos-1/Semana 7/Jesús Domínguez.py","file_name":"Jesús Domínguez.py","file_ext":"py","file_size_in_byte":6885,"program_lang":"python","lang":"es","doc_type":"code","stars":4,"dataset":"github-code","pt":"50"} +{"seq_id":"7692174538","text":"#!/usr/bin/python3\n\n\nclass Person:\n def __init__(self, name, surname):\n self.name = name\n self.surname = surname\n self.calcTable = {'A':1,'Á':1, 'J':1, 'S':1,'Š':1, 'B':2, 'K':2, 'T':2, 'Ť':2,'C':3, 'Č':3, 'L':3, 'U':3, 'Ú':3, 'Ů':3, 'D':4, 'Ď':4, 'M':4, 'V':4,'E':5, 'É':5, 'Ě':5,\n 'N':5,'Ň':5, 'W':5,'Ö':5, 'F':6, 'O':6,'Ó':6, 'X':6,'G':7, 'P':7, 'Y':7, 'Ý':7,'H':8, 'Q':8, 'Z':8,'Ž':8,'I':9,'Í':9, 'Ü':9, 'R':9, 'Ř':9}\n self.risks = [7, 16, 25, 34, 52, 61, 70, 79, 88, 92]\n self.highrisks = [29, 40, 43]\n self.problematic = 0\n \n def gcalc(self, word): \n \"\"\"Calculate the gross number from the word.\"\"\"\n word = list(word)\n total = 0\n for letter in word:\n value = self.calcTable[letter]\n total = total + value\n return(total)\n \n def reduce(self, number): \n \"\"\"Reduce gross numbers to netto numbers.\"\"\"\n if number == 11 or number == 22: # Numbers 11 or 22 should not be reduced.\n total = number\n else:\n total = number\n size = len(str(total)) \n if size > 1:\n while size > 1: # Repeats until the number is not fully reduced to one digit only.\n if total == 11 or total == 22: #Numbers 11 or 22 should not be reduced.\n break\n word = str(total)\n word = list(word)\n total = 0 \n for letter in word:\n total = total +int(letter)\n size = len(str(total))\n \n else:\n total = number\n return(total)\n \n def ncalc(self, word): \n \"\"\"Calculate the netto numbers.\"\"\"\n word = list(word)\n total = self.gcalc(word)\n total = self.reduce(total)\n return(total)\n \n def getInNum(self): \n \"\"\"Return the numeric value of the first letter in a name.\"\"\"\n nletter = list(self.name)\n nletter = nletter[0]\n total = self.calcTable[nletter]\n return(total)\n \n def getIsNum(self): \n \"\"\"Return the numeric values of the first letter in a surname.\"\"\"\n sletter = list(self.surname)\n sletter = sletter[0]\n total = self.calcTable[sletter]\n return(total)\n \n \n def getWest(self):\n \"\"\"Return the number for the western part of cross.\"\"\"\n west = self.ncalc(self.name)\n west = self.reduce(west)\n return(west)\n \n def getEast(self):\n \"\"\"Return the number for the eastern part of cross.\"\"\"\n east = self.ncalc(self.surname)\n east = self.reduce(east)\n return(east)\n \n def getSubNorth(self):\n subnorth = self.getInNum()+self.getIsNum()\n return(subnorth)\n \n def getNorth(self):\n \"\"\"Return the personal number.\"\"\"\n north = self.getSubNorth()\n north = self.reduce(north)\n return(north)\n \n def getFirst(self):\n \"\"\"Return the number of the first life trimester.\"\"\"\n first = self.gcalc(self.name) + self.gcalc(self.surname)\n return(first)\n \n def getSecond(self):\n \"\"\"Return the number of the second life trimester.\"\"\"\n second = self.getNorth() + self.getFirst()\n return(second)\n \n def getThird(self):\n \"\"\"Return the number of the third life trimester.\"\"\"\n third = (self.gcalc(self.name)+self.getSubNorth()) + (self.gcalc(self.surname)+self.getSubNorth())\n return(third)\n \n def getSouth(self):\n \"\"\"Return the number in the southern part of cross.\"\"\"\n south = self.reduce(self.getThird())\n return(south)\n \n def calcCross(self):\n \"\"\"Return the values of the horizontal and vertical additions on the cross.\"\"\"\n horizontal = self.getWest()+self.getEast()\n vertical = self.getNorth()+self.getSouth()\n middle = horizontal + vertical\n return(horizontal,vertical,middle)\n \n def getLowest(self):\n number = self.getThird()\n size = len(str(number))\n if size > 1:\n number = list(str(number))\n total = 0\n for num in number:\n total = total + int(num)\n else:\n total = number\n return(total)\n \n def getRisk(self,number):\n \"\"\"Find out whether a number is a risk number.\"\"\"\n if number in self.risks:\n risk = '!'\n elif number in self.highrisks:\n risk = 'X'\n else:\n risk = ''\n return(risk)\n \n def getFlow(self, number):\n \"\"\"Find out whether there is an energy leak.\"\"\"\n if number == 8:\n flow = 'EF'\n elif number%8 == 0:\n flow = 'PEF'\n else:\n flow = ''\n return(flow)\n\nclass PrintCross:\n \"\"\"Print out all values in the cross (no background though).\"\"\"\n def __init__(self, person):\n self.person = person\n \n def printCross(self):\n print(' {} '.format(self.person.getNorth()))\n print(' ')\n print(' {} {} '.format(self.person.getInNum(), self.person.getIsNum()))\n print(' ') \n print('{} {}'.format(self.person.getWest(), self.person.getEast()))\n print(' {} '.format(self.person.getFirst()))\n print(' ')\n print(' {} {} {} '.format(self.person.gcalc(name), self.person.getSecond(), self.person.gcalc(surname)))\n print(' ')\n print(' {} '.format(self.person.getThird()))\n print(' ')\n print(' {} {} '.format((self.person.gcalc(name)+self.person.getNorth()), (self.person.gcalc(surname)+self.person.getNorth())))\n print(' {} '.format(self.person.getLowest()))\n print(' ')\n print(' {} '.format(self.person.getSouth()))\n\nclass PrintNums:\n def __init__(self, person):\n self.person = person\n \n def printNums(self):\n print(\"Výpočet pro jméno:\",self.person.name, self.person.surname)\n print(\"=============================================================\")\n print(\"Hrubé číslo jména:\", self.person.gcalc(name), self.person.getRisk(self.person.gcalc(name)))\n print(\"Hrubé číslo příjmení:\", self.person.gcalc(surname), self.person.getRisk(self.person.gcalc(surname)))\n print(\"-------------------------------------------------------------\")\n print(\"Západ:\", self.person.getWest(), self.person.getRisk(self.person.getWest()))\n print(\"Východ:\", self.person.getEast(), self.person.getRisk(self.person.getEast()))\n print(\"Sever:\", self.person.getNorth(), self.person.getRisk(self.person.getNorth()))\n print(\"Jih:\", self.person.getSouth(), self.person.getRisk(self.person.getSouth()))\n print(\"-------------------------------------------------------------\")\n print(\"Číslo iniciály jména: \", self.person.getInNum(), self.person.getRisk(self.person.getInNum()))\n print(\"Číslo iniciály příjmení: \", self.person.getIsNum(), self.person.getRisk(self.person.getIsNum()))\n print(\"Číslo osobnosti: \",self.person.getSubNorth(),self.person.getRisk(self.person.getSubNorth()))\n print(\"-------------------------------------------------------------\")\n print(\"Číslo první fáze života: \", self.person.getFirst(), self.person.getRisk(self.person.getFirst()))\n print(\"Číslo druhé fáze života: \", self.person.getSecond(), self.person.getRisk(self.person.getSecond()))\n print(\"Číslo třetí fáze života: \", self.person.getThird(), self.person.getRisk(self.person.getThird()))\n print(\"Pomocné číslo je: \", self.person.getLowest(), self.person.getRisk(self.person.getLowest()))\n print(\"-------------------------------------------------------------\")\n kname = self.person.gcalc(name)+self.person.getSubNorth()\n ksurname = (self.person.gcalc(surname)+self.person.getSubNorth())\n print(\"Číslo kombinace jména a osobnosti (vlevo dole):\", kname, self.person.getRisk(kname))\n print(\"Číslo kombinace příjmení a osobnosti (vpravo dole):\", ksurname, self.person.getRisk(ksurname))\n print(\"-------------------------------------------------------------\")\n hor, ver, sum = self.person.calcCross()\n print(\"Horizontální výpočet kříže: \", hor, self.person.getRisk(hor), self.person.getFlow(hor))\n print(\"Vertikální výpočet kříže: \", ver, self.person.getRisk(ver), self.person.getFlow(ver))\n print(\"Součet kříže: \", sum, self.person.getRisk(sum), self.person.getFlow(sum))\n print(\"=============================================================\")\n\n\nincoming = input(\"Zadejte vaše jméno a příjmení (Jan Novák): \")\nincoming = incoming.upper()\nincoming = incoming.split(\" \")\n\nname = incoming[0]\nsurname = incoming[1]\n\nperson = Person(name, surname)\ncalculation = PrintNums(person)\ncalculation.printNums()\n\nchoice = input(\"Chcete zobrazit kříž pro toto jméno? (a/n): \")\nchoice = choice.upper()\nif choice == 'A':\n cross = PrintCross(person)\n cross.printCross()\nelse:\n pass\n","repo_name":"lruzicka/numerology","sub_path":"numerology.py","file_name":"numerology.py","file_ext":"py","file_size_in_byte":10116,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"50"} +{"seq_id":"25953113191","text":"from loanOBJS import loanOBJ\nfrom loans import mycursor,db\nfrom loanFunc import *\nfrom switch import Switch\nloan=[]\ndef row_exists(row):\n global mycursor\n query=(\"SELECT EXISTS(SELECT * from loan WHERE name= %s)\")\n mycursor.execute(query,(row, ))\n return mycursor.fetchone()[0]\ndef create():\n loan.append(input(\"Give me a name:\").strip())\n name=loan[len(loan)-1]\n globals()[name]=loanOBJ()\n globals()[name].name=name\n if row_exists(globals()[name].name):\n globals()[name].name=name_check()\n if globals()[name].name:\n pass\n else:\n exec(\"continue\")\n globals()[name].rate=rate_check(input(\"what is the interest rate?:\"))\n while True:\n globals()[name].type=input(\"'monthly' or 'annual' rates?:\").strip()\n if globals()[name].type=='monthly' or globals()[name].type=='annual':\n break\n print(\"INPUT ERROR!!\")\n\n globals()[name].deadline=date_check(input(\"input the deadline(formatted as m-d-y or 02-21-2004):\").strip())\n globals()[name].amount=amount_check(input(\"How hard did you fuck yourself over?(amount):\"))\n print(globals()[name].name,\" \",globals()[name].type,\" \",globals()[name].rate,\" \",globals()[name].deadline,\" \",globals()[name].amount)\n print(row_exists(globals()[name].name))\n mycursor.execute('INSERT INTO loan(name,amount,deadline,type,rate) VALUES (%s,%s,%s,%s,%s)', (globals()[name].name,globals()[name].amount,globals()[name].deadline,globals()[name].type,globals()[name].rate))\n print(\"data committed\")\n db.commit()\ndef promiscuo(loanList,loanNames):\n count=0\n print(\"choose one:\")\n for x in loanList:\n print(count,\"-\",x.name,\" \",end='')\n count+=1\n loanNames.append(x.name)\n print()\n while True:\n name=int(input(\"\"))\n if name not in range(len(loanNames)):\n print(\"shitty choice, try again\")\n pass\n else:\n break\n print(\"day\")\n day=input()\n\n rateCalc=diff_dates(date(int(loanList[name].deadline.split('-')[2]),int(loanList[name].deadline.split('-')[0]),int(loanList[name].deadline.split('-')[1])),date(int(day.split('-')[2]),int(day.split('-')[0]),int(day.split('-')[1])))\n #i have zero clue how loans work so im pretty certain this thing doesnt work, its not a bug its just me genuinely having 0 clue what im even supposed to do\n if loanList[name].type == 'monthly':\n rateCalc=round(rateCalc/ 30.437)\n print(rateCalc)\n new_loan=loanList[name].amount\n decRate=loanList[name].rate\n print(\"huh\",loanList[name].rate)\n while decRate>1:\n print(\"what\",decRate)\n decRate/=10\n print(\"who\",decRate)\n print(\"uhuh \",rateCalc)\n for x in range(rateCalc):\n print(new_loan*decRate,\" \",decRate)\n new_loan+=new_loan*decRate\n print(new_loan)\n new_loan/=2\n else:\n rateCalc=round(rateCalc/365)\n #nah\ndef query(type,query):\n with Switch(type) as case:\n if case('name'):\n mycursor.execute('select * from loan where name = %s',(query, ))\n return mycursor.fetchall()\n","repo_name":"Gohostieee/loanSQL","sub_path":"loanCommands.py","file_name":"loanCommands.py","file_ext":"py","file_size_in_byte":3164,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"23420599162","text":"import loader\nfrom functools import reduce\n\ndef checkSlope(_data, _right, _down):\n WIDTH=len(_data[0].strip())\n\n count=0\n pos=0\n for i in range(0, len(_data), _down):\n if _data[i][pos]=='#':\n count+=1\n pos+=_right\n pos%=WIDTH\n \n return count\n\nif __name__ == '__main__':\n data=loader.loadInLines('day3.in')\n\n # PART 1\n print(checkSlope(data,3,1))\n\n # PART 2\n tocheck=[\n # Right 1, down 1.\n (1,1),\n # Right 3, down 1. (This is the slope you already checked.)\n (3,1),\n # Right 5, down 1.\n (5,1),\n # Right 7, down 1.\n (7,1),\n # Right 1, down 2.\n (1,2)\n ]\n\n answers=[checkSlope(data,i[0],i[1]) for i in tocheck]\n out=reduce(lambda a, b: a*b, answers)\n print(out)\n ","repo_name":"natfaulk/Advent_of_code_2020","sub_path":"src/day3.py","file_name":"day3.py","file_ext":"py","file_size_in_byte":719,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"43204050905","text":"\"\"\"\nStep 1 \n\nStep by step codes of the ideal rankine cycle simulator to demonstrate: \n\n Data Structures+ Algorithms = Programs\n\n list, dict,function\n\nThe ideal rankine cycle as \n \n ┌─── Node 0 ── Turbine ── Node 1 ──┐\n │ │\n Boiler Condenser\n │ │\n └─── Node 3 ── Pump ── Node 2 ───┘ \n\n Michael J . Mora. \n Fundamentals of Engineering Thermodynamics(7th Edition). John Wiley & Sons, Inc. 2011\n Chapter 8 : Vapour Power Systems Example 8.1:Analyzing an Ideal Rankine Cycle Page 438\n\nLicense: this code is in the public domain\n\nAuthor:Cheng Maohua\nEmail: cmh@seu.edu.cn\n\"\"\"\nfrom seuif97 import *\n\ndef CalCycle(h,Wcycledot):\n \n results={}\n \n # Part(a)\n # Mass and energy rate balances for control volumes \n # around the turbine and pump give, respectively\n\n # turbine\n results['wtdot'] = h[0] - h[1]\n # pump\n results['wpdot'] = h[3]-h[2]\n\n # The rate of heat transfer to the working fluid as it passes \n # through the boiler is determined using mass and energy rate balances as\n results['qindot'] = h[0]-h[3]\n\n # thermal efficiency\n results['eta'] = ( results['wtdot'] -results['wpdot'])/ results['qindot'] \n\n # Part(b)\n # back work ratio：bwr, defined as the ratio of the pump work input to the work \n # developed by the turbine.\n results['bwr'] =results['wpdot']/results['wtdot'] # \n\n # Part(c)\n results['mdot'] = (Wcycledot*10**3*3600)/((h[0]-h[1])-(h[3]-h[2])) # mass flow rate in kg/h\n \n # Part(d)\n results['Qindot'] = results['mdot']*results['qindot']/(3600*10**3) # in MW\n\n # Part(e)\n results['Qoutdot'] = results['mdot']*(h[1]-h[2])/(3600*10**3) # in MW\n return results\n\ndef CalCondenser(Qoutdot):\n # Part(f)\n # Given: \n tcwin=15\n tcwout=35\n\n hcwout=tx2h(tcwout,0) # From table A-2,hcwout= 146.68 kj/kg \n hcwin= tx2h(tcwin,0) # hcwin 62.99 \n mcwdot= (Qoutdot*10**3*3600)/(hcwout-hcwin) \n return mcwdot \n","repo_name":"862337210/PyRankine","sub_path":"step1/calcycle.py","file_name":"calcycle.py","file_ext":"py","file_size_in_byte":2207,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"40163124310","text":"import FWCore.ParameterSet.Config as cms\n\n# With an ideal geometry (for the reconstruction)\nMagneticFieldMapESProducer = cms.ESProducer(\"MagneticFieldMapESProducer\")\n\n# The same as above but with a misaligned tracker geometry (for the simulation)\nmisalignedMagneticFieldMap = cms.ESProducer(\"MagneticFieldMapESProducer\",\n trackerGeometryLabel = cms.untracked.string('MisAligned'),\n appendToDataLabel = cms.string('MisAligned')\n)\n\n\n","repo_name":"cms-sw/cmssw","sub_path":"FastSimulation/ParticlePropagator/python/MagneticFieldMapESProducer_cfi.py","file_name":"MagneticFieldMapESProducer_cfi.py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"en","doc_type":"code","stars":985,"dataset":"github-code","pt":"50"} +{"seq_id":"15950108784","text":"import matplotlib.pyplot as plt\nimport pandas as pd\n\ndataset = 'CIFAR100'\nbatch_size = 256\ntrials = 10\nEPOCHS = 200\nfinish_parallel_by=50\n\ndef plot(epoch_range, train, test, heading, extra = 1):\n epochs = range(1,epoch_range+1)\n plt.plot(epochs, train, 'g', label='Training ' + heading)\n plt.plot(epochs, test, 'b', label='Test ' + heading)\n plt.title('Training and Test ' + heading)\n plt.xlabel('Epochs')\n plt.ylabel(heading)\n plt.legend()\n plt.savefig(heading + str(extra) + '.png')\n plt.close()\n\ndef write_list_to_csv(dictionary, heading):\n dataframe = pd.DataFrame(dictionary)\n dataframe.to_csv(heading + '.csv')\n","repo_name":"myisu-rese-arch/pytorch-cifar","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":650,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"50"} +{"seq_id":"39602703042","text":"from flask import Flask, request, jsonify\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_marshmallow import Marshmallow\nfrom sqlalchemy.orm import relationship\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy import Table, Column, Integer, ForeignKey\nfrom sqlalchemy import text\nimport json\nimport os\n\nBase = declarative_base()\n\n#init app\napp = Flask(__name__)\n\nbasedir = os.path.abspath(os.path.dirname(__file__))\n\n#Database\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///' + os.path.join(basedir, 'test3000.sqlite')\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\n\n#Init db\ndb = SQLAlchemy(app)\n\n#Init ma\nma = Marshmallow(app)\n\n\n\n#entitati\n\nclass Pacient(db.Model):\n __tablename__ = 'pacienti'\n id_pacient = db.Column(db.Integer, primary_key = True)\n nume_pacient = db.Column(db.String(100))\n copil = relationship(\"Disease\")\n\n def __init__(self, id_pacient, nume_pacient):\n self.id_pacient = id_pacient\n self.nume_pacient = nume_pacient\n\n\n\nclass Disease(db.Model):\n __tablename__ = 'boli'\n id_boala = db.Column(db.Integer, primary_key = True)\n id_pacient = Column(db.Integer, ForeignKey('pacienti.id_pacient'))\n nume = db.Column(db.String(50))\n descriere = db.Column(db.String(100))\n\n def __init__(self, id_boala, id_pacient, nume, descriere):\n self.id_boala = id_boala\n self.id_pacient = id_pacient\n self.nume = nume\n self.descriere = descriere\n\n\nclass PacientDoctorTable(db.Model):\n __tablename__ = \"asocieri\"\n id = db.Column(db.Integer, primary_key = True)\n id_doctor = db.Column(db.Integer, ForeignKey('doctor.doctor_id'))\n id_pacient = db.Column(db.Integer, ForeignKey('pacienti.id_pacient'))\n\n def __init__(self,id_doctor, id_pacient):\n self.id_doctor = id_doctor\n self.id_pacient = id_pacient\n\n# class PacientDiseaseTable:\n# __tablename__ = 'asociere_boala_pacient'\n# id_boala= db.Column(db.Integer, ForeignKey('boli.id_boala'))\n# id_pacient = db.Column(db.Integer, ForeignKey('pacient.id_pacient')) \n\n# def __init__(self, id_boala, id_pacient):\n# self.id_boala = id_boala\n# self.id_pacient = id_pacient\n\n\nclass Doctor(db.Model):\n\n doctor_id = db.Column(db.Integer, primary_key = True)\n nume = db.Column(db.String(100))\n specializare = db.Column(db.String(50))\n email = db.Column(db.String(50))\n parola = db.Column(db.String(50))\n \n def __init__(self,doctor_id, nume,specializare,email, parola):\n self.doctor_id = doctor_id\n self.nume = nume\n self.specializare = specializare\n self.email = email\n self.parola = parola\n\n #sfarsit entitati \n\n\n\nclass DoctorSchema(ma.Schema):\n class Meta:\n fields = ('doctor_id', 'nume','specializare','email','parola')\n\nclass DiseaseSchema(ma.Schema):\n class Meta:\n fields = ('id_boala','id_pacient','nume','descriere')\n\nclass PacientSchema(ma.Schema):\n class Meta:\n fields = ('id_pacient','nume_pacient') \n\nclass DoctorPatientSchema(ma.Schema):\n class Meta:\n fields = ('id_doctor', 'id_pacient')\n\n\ndoctor_schema = DoctorSchema()\ndoctors_schema = DoctorSchema(many=True)\n\ndisease_schema = DiseaseSchema()\ndiseases_schema = DiseaseSchema(many = True)\n\n\npatient_schema = PacientSchema()\npatients_schema = PacientSchema(many=True)\n\npatient_doctor_single = DoctorPatientSchema()\npatient_doctor_schema = DoctorPatientSchema(many = True)\n\n\n#ruta verificare doctor pacient\n@app.route('/relatii', methods = ['GET'])\ndef getRelatie():\n relatii = PacientDoctorTable.query.all()\n\n result = patient_doctor_schema.dump(relatii)\n\n return jsonify(result)\n\n#ruta de logare\n@app.route('/doctor//

', methods=['GET'])\ndef get_doctor(e, p):\n query = \"select * from doctor WHERE email = :em LIMIT 1\"\n \n doctor = db.session.query(Doctor).filter_by(email = e).filter(parola = p).first()\n db.session.commit()\n dt = doctor_schema.dump(doctor)\n print(dt)\n fl = open('cookie.json','w')\n json.dump(dt,fl)\n return jsonify(dt)\n\n@app.route('/isLoggedIn',methods=['GET'])\ndef isLoggedIn():\n fl = open('cookie.json','r')\n txt_Data=fl.read()\n if txt_Data != '':\n txt_Data = json.loads(txt_Data)\n else:\n txt_Data = json.loads('{}')\n return jsonify(txt_Data)\n\n#ruta de selectare a pacientilor in functie de doctorul care este logat\n@app.route('/myPacients/')\ndef get_my_doctors(doctor_id):\n \n query_asociere = db.session.query(PacientDoctorTable).filter_by(id_doctor = doctor_id)\n\n #array \n result = patient_doctor_schema.dump(query_asociere)\n\n pacient_arr = []\n pacienti = []\n for i in result:\n query_pt = db.session.query(Pacient).filter_by(id_pacient = i['id_pacient'])\n # db.session.commit()\n res = patients_schema.dump(query_pt)\n # print(res)\n if len(res) > 0:\n pacienti.append(res[0])\n\n return jsonify(pacienti)\n\n#ruta pentru register\n@app.route('/addDoctor', methods=['POST'])\ndef add_doctor():\n doctor_id = request.json['doctor_id']\n nume = request.json['nume']\n specializare = request.json['specializare']\n email = request.json['email']\n password = request.json['parola']\n \n new_doctor = Doctor(doctor_id, nume, specializare,email, password)\n\n db.session.add(new_doctor)\n db.session.commit()\n\n return doctor_schema.jsonify(new_doctor)\n\n\n\n#ruta de popularea a tabelei de asociere intre doctor si pacient\n@app.route('/insertRelationship', methods = ['POST'])\ndef insert_relationship():\n id_doctor = request.json['id_doctor']\n id_patient = request.json['id_pacient']\n\n rel = PacientDoctorTable(id_doctor,id_patient)\n\n db.session.add(rel)\n db.session.commit()\n\n\n return patient_doctor_single.jsonify(rel)\n\n\n#ruta de creare a pacientului\n@app.route('/addPacient', methods=['POST'])\ndef addPacient():\n pacient_id = request.json['id_pacient']\n nume = request.json['nume_pacient']\n\n pacient = Pacient(pacient_id, nume)\n db.session.add(pacient)\n db.session.commit()\n\n return patient_schema.jsonify(pacient)\n\n\n#ruta de creeare a unei boli\n@app.route('/addDisease', methods=['POST'])\ndef addDisease():\n id_boala = request.json['id_boala']\n id_pacient = request.json['id_pacient']\n nume = request.json['nume']\n descriere = request.json['descriere']\n\n disease = Disease(id_boala, id_pacient, nume, descriere)\n\n db.session.add(disease)\n db.session.commit()\n\n return disease_schema.jsonify(disease)\n\n\n#route for test\n@app.route('/', methods=['GET'])\ndef hello():\n return \"Hello World\"\n\n\n#ruta de selectare a tuturor doctorilor\n@app.route('/doctors', methods=['GET'])\ndef get_doctors():\n all_doctors = Doctor.query.all()\n \n result = doctors_schema.dump(all_doctors)\n\n return jsonify(result)\n\n\n#ruta de selectare a pecientilor\n@app.route('/pacients', methods=['GET'])\ndef get_patients():\n all_patients = Pacient.query.all()\n\n result = patients_schema.dump(all_patients)\n\n return jsonify(result)\n\n\n#ruta de selectare a bolilor\n@app.route('/diseases', methods=['GET'])\ndef get_diseases():\n all_diseases = Disease.query.all()\n\n result = diseases_schema.dump(all_diseases)\n\n return jsonify(result)\n\n\n#run server\n\n\nif __name__ == '__main__':\n app.run(host = '0.0.0.0',debug=True)\n","repo_name":"BaditaGeorge/Licenta","sub_path":"REST API Licenta/.venv/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":7326,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"43321918678","text":"import time\n\nimport pytest\nfrom selenium import webdriver\n\nfrom pageObjects.Frames import Frame\nfrom pageObjects.Homepage import HomePage\nfrom Utilities.BaseClass import BaseClass\nfrom Utilities.Screenshot import Screenshot\n\n\ndef pytest_addoption(parser):\n parser.addoption(\n \"--browser_name\", action=\"store\", default=\"chrome\"\n )\n\n\n\n@pytest.fixture(scope=\"class\")\ndef setup(request):\n browser_name = request.config.getoption(\"browser_name\") # to get the browser name during runtime\n ss_path = \"/conftest/\"\n\n if browser_name == \"chrome\":\n options = webdriver.ChromeOptions()\n options.add_argument(\"--disable-popup-blocking\")\n driver = webdriver.Chrome(executable_path=\"C:\\\\Users\\\\indrasen\\\\Documents\\\\chromedriver_win32\\\\chromedriver.exe\",chrome_options=options)\n\n elif browser_name == \"firefox\": # firefox gecko driver\n # driver = webdriver.Firefox(executable_path=\"C:\\\\Users\\\\indrasen\\\\Documents\\\\geckodriver-v0.26.0-win64\\\\geckodriver.exe\")\n driver = webdriver.Firefox(executable_path=\"C:\\\\Users\\\\indrasen\\\\Downloads\\\\geckodriver-v0.26.0-win32\\\\geckodriver.exe\")\n\n driver.get(\"https://c6.avaamo.com/web_channels/444588bc-92fe-477f-87c1-88a92946346a/demo.html?theme=avm-messenger&banner=true&demo=true&banner_text=%20&banner_title=This%20is%20how%20the%20chat%20agent%20shows%20up\")\n\n baseclass = BaseClass()\n logs = baseclass.getLogger()\n ss = Screenshot(driver)\n\n expected_title = \"Test agent - IRA\"\n\n try:\n assert driver.title == expected_title\n logs.info(\"Webpage loaded successfully\")\n\n except Exception as e:\n logs.info(\"Webpage failed to load\",e)\n ss.screenShot(ss_path+\"webpage_load_fail.png\")\n\n driver.maximize_window()\n driver.implicitly_wait(4)\n\n home_page = HomePage(driver)\n\n try:\n if home_page.IraIcon().is_displayed():\n home_page.notificationButton().click()\n\n except Exception as e:\n logs.critical(\"IraIcon is not displayed in the webpage\",e)\n ss.screenShot(ss_path+\"IraIcon_Unavailable.png\")\n\n request.cls.driver = driver\n yield\n driver.close()","repo_name":"indrakumar0812/Avaamo","sub_path":"testcases/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":2127,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"18023886271","text":"# -*- coding:utf-8 -*-\n\"\"\"\n@author: guoxiaorui\n@file: 2_add_spaces\n@time: 2021/12/19 11:00 上午\n@desc: \n\"\"\"\nfrom typing import List\n\n\nclass Solution:\n def addSpaces(self, s: str, spaces: List[int]) -> str:\n s_array = []\n point = 0\n for i in range(len(s)):\n if point < len(spaces) and i == spaces[point]:\n s_array.append(\" \")\n point += 1\n s_array.append(s[i])\n return \"\".join(s_array)\n\n\nif __name__ == '__main__':\n s = \"LeetcodeHelpsMeLearn\"\n spaces = [8, 13, 15]\n so = Solution()\n print(so.addSpaces(s, spaces))\n","repo_name":"sun10081/leetcode_practice_xiaorui","sub_path":"questions/week/2021/2021_12_19/2_add_spaces.py","file_name":"2_add_spaces.py","file_ext":"py","file_size_in_byte":609,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"1798866127","text":"# search tables\n# pre-calculated data\n\nfact = [0] * 100\n\ndef factorial(n):\n if n <= 1:\n return 1\n f = fact[n - 1] * n\n return f\n\nfor i in range(100):\n fact[i] = factorial(i)\n\nprint(fact[5])\n","repo_name":"ash/python-tut","sub_path":"60.py","file_name":"60.py","file_ext":"py","file_size_in_byte":209,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"34197035144","text":"__author__ = 'wenzou'\n\nUNLIMITED_POSITIVE = 10000000000000000000000\n\nSYST_CONSTRAINTS = (0,1000)\nDIAST_CONSTRAINTS = (0,1000)\n\n\nTEMPERATURE_UNIT_CELSIUS_SLUG='celsius'\nTEMPERATURE_UNIT_FAHRENHEIT_SLUG='fahrenheit'\n\nWEIGHT_UNIT_POUNDS_SLUG = 'pounds'\nWEIGHT_UNIT_KILOGRAMS_SLUG = 'kg'\nWEIGHT_UNIT_STONES_SLUG = 'stone'\n\nSTEPS_UNIT_SLUG = 'steps'\nBOOLEAN_UNIT_SLUG = 'boolean'\nPPM_UNIT_SLUG = 'ppm'\nBPM_UNIT_SLUG = 'beats_per_minute'\nML_CUBED_PER_SECOND_UNIT_SLUG = 'ml3s'\nPERCENTAGE_UNIT_SLUG = 'percentage'\nMG_PER_DL_UNIT_SLUG = 'mgdl'\nMMHG_UNIT_SLUG = 'mmHg'\nTIMES_UNIT_SLUG = 'times'\n\nBODY_WEIGHT_SLUG = \"body_weight\"\nGLUCOSE_SLUG = \"glucose\"\nDIASTOLIC_SLUG = \"diastolic\"\nSYSTOLIC_SLUG = \"systolic\"\nOXYGEN_SLUG = \"oxygen\"\nAIRFLOW_SLUG = \"airflow\"\nSTEPS_SLUG = \"steps\"\nBODY_TEMP_SLUG = \"body_temp\"\nHEART_RATE_SLUG = \"heartrate\" # deprecated\nPULSE_SLUG = \"pulse\"\nROOM_TEMP_SLUG = \"room_temp\"\n### 5/6/13 - these slugs are rarely referenced so far (if it should be renamed, then rename them now) ###\nCARBON_MONOXIDE_SLUG = \"carbon_monoxide\"\nSCALE_SLUG = \"scale\"\nPANIC_SLUG = \"panic\"\nPRESSURE_SLUG = \"pressure_pad\"\nTOILET_SLUG = \"toilet\"\nDOOR_SENSOR_SLUG = \"door\"\nMOTION_SENSOR_SLUG = \"motion\"\nBED_SLUG = \"bed\"\nSLEEP_SLUG = \"sleep\"\nFIRE_SLUG = \"fire\"\n###\n\nDEFAULT_RANGES = 'DEFAULT_RANGES'\nRANGE_SPEC_BY_UNIT = {\n DEFAULT_RANGES: {\n BOOLEAN_UNIT_SLUG: (0, 1, 'True', 'False', '1', '0'),\n WEIGHT_UNIT_KILOGRAMS_SLUG: (27, 180, 'kilograms'),\n WEIGHT_UNIT_POUNDS_SLUG: (60, 399, 'pounds'),\n WEIGHT_UNIT_STONES_SLUG: (4, 30, 'stones'),\n TEMPERATURE_UNIT_CELSIUS_SLUG: (95, 105, 'farenheit'),\n TEMPERATURE_UNIT_FAHRENHEIT_SLUG: (35, 40, 'celsius'),\n PERCENTAGE_UNIT_SLUG: (70, 100),\n MMHG_UNIT_SLUG: (40, 250),\n MG_PER_DL_UNIT_SLUG: (40, 999),\n BPM_UNIT_SLUG: (30, 150),\n PPM_UNIT_SLUG: (0, 100),\n STEPS_UNIT_SLUG: (1, UNLIMITED_POSITIVE),\n ML_CUBED_PER_SECOND_UNIT_SLUG: (0, UNLIMITED_POSITIVE)\n },\n SCALE_SLUG : {\n WEIGHT_UNIT_KILOGRAMS_SLUG: (27, 180, 'kilograms'),\n WEIGHT_UNIT_POUNDS_SLUG: (60, 399, 'pounds'),\n WEIGHT_UNIT_STONES_SLUG: (4, 30, 'stones')\n },\n BODY_TEMP_SLUG: {\n TEMPERATURE_UNIT_CELSIUS_SLUG: (35, 40, 'celsius'),\n TEMPERATURE_UNIT_FAHRENHEIT_SLUG: (95, 105, 'farenheit')\n },\n ROOM_TEMP_SLUG: {\n TEMPERATURE_UNIT_CELSIUS_SLUG: (-60, 80, 'celsius'),\n TEMPERATURE_UNIT_FAHRENHEIT_SLUG: (-76, 176, 'farenheit')\n },\n GLUCOSE_SLUG: {\n MG_PER_DL_UNIT_SLUG: (40, 999)\n },\n DIASTOLIC_SLUG: {\n MMHG_UNIT_SLUG: (40, 140)\n },\n SYSTOLIC_SLUG: {\n MMHG_UNIT_SLUG: (60, 250)\n },\n OXYGEN_SLUG: {\n PERCENTAGE_UNIT_SLUG: (70, 100)\n },\n AIRFLOW_SLUG: {\n ML_CUBED_PER_SECOND_UNIT_SLUG: (0, UNLIMITED_POSITIVE)\n },\n STEPS_SLUG: {\n STEPS_UNIT_SLUG: (1, UNLIMITED_POSITIVE)\n },\n HEART_RATE_SLUG: {\n BPM_UNIT_SLUG: (30, 150)\n },\n PULSE_SLUG: {\n BPM_UNIT_SLUG: (30, 150)\n },\n CARBON_MONOXIDE_SLUG: {\n PPM_UNIT_SLUG: (0, 100)\n },\n PANIC_SLUG: {\n \n },\n PRESSURE_SLUG: {\n \n },\n TOILET_SLUG: {\n \n },\n DOOR_SENSOR_SLUG: {\n \n },\n MOTION_SENSOR_SLUG: {\n \n },\n BED_SLUG: {\n \n },\n SLEEP_SLUG: {\n \n },\n FIRE_SLUG: {\n \n },\n BODY_WEIGHT_SLUG : {\n WEIGHT_UNIT_KILOGRAMS_SLUG: (27, 180, 'kilograms'),\n WEIGHT_UNIT_POUNDS_SLUG: (60, 399, 'pounds'),\n WEIGHT_UNIT_STONES_SLUG: (4, 30, 'stones')\n }\n}\n\n#body wei\nWEIGHT_SPEC = {WEIGHT_UNIT_KILOGRAMS_SLUG: RANGE_SPEC_BY_UNIT[SCALE_SLUG][WEIGHT_UNIT_KILOGRAMS_SLUG],\n WEIGHT_UNIT_POUNDS_SLUG: RANGE_SPEC_BY_UNIT[SCALE_SLUG][WEIGHT_UNIT_POUNDS_SLUG],\n WEIGHT_UNIT_STONES_SLUG: RANGE_SPEC_BY_UNIT[SCALE_SLUG][WEIGHT_UNIT_STONES_SLUG]}\n\nTEMP_SPEC = {TEMPERATURE_UNIT_CELSIUS_SLUG: RANGE_SPEC_BY_UNIT[BODY_TEMP_SLUG][TEMPERATURE_UNIT_CELSIUS_SLUG],\n TEMPERATURE_UNIT_FAHRENHEIT_SLUG: RANGE_SPEC_BY_UNIT[BODY_TEMP_SLUG][TEMPERATURE_UNIT_FAHRENHEIT_SLUG]}\n\n\nACCEPTED_UNITS_BY_READING_TYPE = {\n SCALE_SLUG: {WEIGHT_UNIT_POUNDS_SLUG, WEIGHT_UNIT_KILOGRAMS_SLUG, WEIGHT_UNIT_STONES_SLUG},\n BODY_TEMP_SLUG: {TEMPERATURE_UNIT_CELSIUS_SLUG, TEMPERATURE_UNIT_FAHRENHEIT_SLUG},\n ROOM_TEMP_SLUG: {TEMPERATURE_UNIT_CELSIUS_SLUG, TEMPERATURE_UNIT_FAHRENHEIT_SLUG},\n GLUCOSE_SLUG: {MG_PER_DL_UNIT_SLUG},\n DIASTOLIC_SLUG: {MMHG_UNIT_SLUG},\n SYSTOLIC_SLUG: {MMHG_UNIT_SLUG},\n OXYGEN_SLUG: {PERCENTAGE_UNIT_SLUG},\n AIRFLOW_SLUG: {ML_CUBED_PER_SECOND_UNIT_SLUG},\n STEPS_SLUG: {STEPS_UNIT_SLUG},\n HEART_RATE_SLUG: {BPM_UNIT_SLUG},\n PULSE_SLUG: {BPM_UNIT_SLUG},\n CARBON_MONOXIDE_SLUG: {PPM_UNIT_SLUG},\n PANIC_SLUG: {BOOLEAN_UNIT_SLUG},\n PRESSURE_SLUG: {BOOLEAN_UNIT_SLUG},\n TOILET_SLUG: {BOOLEAN_UNIT_SLUG},\n DOOR_SENSOR_SLUG: {BOOLEAN_UNIT_SLUG},\n MOTION_SENSOR_SLUG: {BOOLEAN_UNIT_SLUG},\n BED_SLUG: {BOOLEAN_UNIT_SLUG},\n SLEEP_SLUG: {BOOLEAN_UNIT_SLUG},\n FIRE_SLUG: {BOOLEAN_UNIT_SLUG},\n BODY_WEIGHT_SLUG: {WEIGHT_UNIT_POUNDS_SLUG, WEIGHT_UNIT_KILOGRAMS_SLUG, WEIGHT_UNIT_STONES_SLUG}\n}\n\nDEFAULT_UNITS_BY_READING_TYPE = {\n SCALE_SLUG: WEIGHT_UNIT_POUNDS_SLUG,\n BODY_TEMP_SLUG: TEMPERATURE_UNIT_FAHRENHEIT_SLUG,\n ROOM_TEMP_SLUG: TEMPERATURE_UNIT_FAHRENHEIT_SLUG,\n GLUCOSE_SLUG: MG_PER_DL_UNIT_SLUG,\n DIASTOLIC_SLUG: MMHG_UNIT_SLUG,\n SYSTOLIC_SLUG: MMHG_UNIT_SLUG,\n OXYGEN_SLUG: PERCENTAGE_UNIT_SLUG,\n AIRFLOW_SLUG: ML_CUBED_PER_SECOND_UNIT_SLUG,\n STEPS_SLUG: STEPS_UNIT_SLUG,\n HEART_RATE_SLUG: BPM_UNIT_SLUG,\n PULSE_SLUG: BPM_UNIT_SLUG,\n CARBON_MONOXIDE_SLUG: PPM_UNIT_SLUG,\n PANIC_SLUG: BOOLEAN_UNIT_SLUG,\n PRESSURE_SLUG: BOOLEAN_UNIT_SLUG,\n TOILET_SLUG: BOOLEAN_UNIT_SLUG,\n DOOR_SENSOR_SLUG: BOOLEAN_UNIT_SLUG,\n MOTION_SENSOR_SLUG: BOOLEAN_UNIT_SLUG,\n BED_SLUG: BOOLEAN_UNIT_SLUG,\n SLEEP_SLUG: BOOLEAN_UNIT_SLUG,\n FIRE_SLUG: BOOLEAN_UNIT_SLUG,\n BODY_WEIGHT_SLUG: WEIGHT_UNIT_POUNDS_SLUG\n}\n\nREADING_TYPE_SLUG_LIST = [BODY_WEIGHT_SLUG, GLUCOSE_SLUG, DIASTOLIC_SLUG,\n SYSTOLIC_SLUG, OXYGEN_SLUG, AIRFLOW_SLUG, STEPS_SLUG, BODY_TEMP_SLUG,\n PULSE_SLUG, ROOM_TEMP_SLUG, CARBON_MONOXIDE_SLUG,\n SCALE_SLUG, PANIC_SLUG, PRESSURE_SLUG,\n TOILET_SLUG, DOOR_SENSOR_SLUG, MOTION_SENSOR_SLUG,\n BED_SLUG, SLEEP_SLUG, FIRE_SLUG]\n\nACKNOWLEDGE_KEY = 'acknowledgement'\nSOURCE_TYPE_KEY = 'source_type'\nDEVICE_ID_KEY = 'device_id'\nAPI_KEY_KEY = \"api_key\"\nORIGINAL_VALUE_KEY = 'value'\nREADING_TYPE_ID_KEY = 'reading_type_id'\nORIGINAL_UNIT_KEY = 'unit'\nPREFERRED_VALUE_KEY = 'pref_value'\nPREFERRED_UNIT_KEY = 'pref_unit'\nUSER_EVENT_KEY = 'user_event'\nTAG_KEY = 'tag'\nLATITUDE_KEY = 'latitude'\nLONGITUDE_KEY = 'longitude'\nDURATION_KEY = 'duration'\nCREATED_READING_KEY = 'created_reading'\nCREATED_TRANSMISSION_KEY = 'created_transmission'\nDATA_KEY = 'data'\nREADINGS_JSON_KEY = 'readings'\nEXTRA_FIELDS_KEY = 'extra_fields'\nSOURCE_DEVELOPER_KEY = 'source_developer'\nCARE_RECEIVER_KEY = 'care_receiver'\nSOURCE_MANUFACTURER_KEY = 'source_manufacturer'\nASSOCIATED_READINGS_KEY = 'associated_readings'\nPHONE_SOURCE_KEY = 'phone'\nDEVICE_SOURCE_KEY = 'device'\nCONFIRM_KEY = 'confirm'\nPENDING_KEY = 'pending'\nREMOVE_KEY = 'remove'\n\n\n#name of the task\nAPI_PROCESS_TASK = 'independa.api.reading.process'","repo_name":"Independa/uds_module","sub_path":"validate/constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":7466,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"3884253696","text":"from PyQt5.QtCore import *\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtGui import *\n\nfrom components.translator import tr\n\n\nclass Ui_settingsDialog(object):\n def setupUi(self, settingsDialog):\n settingsDialog.setObjectName(\"settingsDialog\")\n settingsDialog.resize(351, 251)\n settingsDialog.setModal(True)\n self.verticalLayout_2 = QVBoxLayout(settingsDialog)\n self.verticalLayout_2.setObjectName(\"verticalLayout_2\")\n self.frame = QFrame(settingsDialog)\n self.frame.setFrameShape(QFrame.Box)\n self.frame.setFrameShadow(QFrame.Sunken)\n self.frame.setObjectName(\"frame\")\n self.verticalLayout = QVBoxLayout(self.frame)\n self.verticalLayout.setObjectName(\"verticalLayout\")\n self.fileBrowserLbl = QLabel(self.frame)\n font = QFont()\n font.setBold(True)\n font.setWeight(75)\n self.fileBrowserLbl.setFont(font)\n self.fileBrowserLbl.setObjectName(\"fileBrowserLbl\")\n self.verticalLayout.addWidget(self.fileBrowserLbl)\n self.followSymChBox = QCheckBox(self.frame)\n self.followSymChBox.setChecked(True)\n self.followSymChBox.setObjectName(\"followSymChBox\")\n self.verticalLayout.addWidget(self.followSymChBox)\n self.sessionLbl = QLabel(self.frame)\n font = QFont()\n font.setBold(True)\n font.setWeight(75)\n self.sessionLbl.setFont(font)\n self.sessionLbl.setObjectName(\"sessionLbl\")\n self.verticalLayout.addWidget(self.sessionLbl)\n self.sessionLayout = QHBoxLayout()\n self.sessionLayout.setObjectName(\"sessionLayout\")\n self.saveRestoreSessionChBox = QCheckBox(self.frame)\n sizePolicy = QSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)\n sizePolicy.setHorizontalStretch(0)\n sizePolicy.setVerticalStretch(0)\n sizePolicy.setHeightForWidth(self.saveRestoreSessionChBox.sizePolicy().hasHeightForWidth())\n self.saveRestoreSessionChBox.setSizePolicy(sizePolicy)\n self.saveRestoreSessionChBox.setChecked(True)\n self.saveRestoreSessionChBox.setObjectName(\"saveRestoreSessionChBox\")\n self.sessionLayout.addWidget(self.saveRestoreSessionChBox)\n # self.clearSessionBtn = QPushButton(self.frame)\n # self.clearSessionBtn.setObjectName(\"clearSessionBtn\")\n # self.clearSessionBtn.setObjectName(\"clearSessionBtn\")\n # self.sessionLayout.addWidget(self.clearSessionBtn)\n self.verticalLayout.addLayout(self.sessionLayout)\n self.updaterLbl = QLabel(self.frame)\n font = QFont()\n font.setBold(True)\n font.setWeight(75)\n self.updaterLbl.setFont(font)\n self.updaterLbl.setObjectName(\"updaterLbl\")\n self.verticalLayout.addWidget(self.updaterLbl)\n self.horizontalLayout = QHBoxLayout()\n self.horizontalLayout.setSpacing(6)\n self.horizontalLayout.setObjectName(\"horizontalLayout\")\n self.checkUpdatesChBox = QCheckBox(self.frame)\n self.checkUpdatesChBox.setChecked(True)\n self.checkUpdatesChBox.setObjectName(\"checkUpdatesChBox\")\n self.horizontalLayout.addWidget(self.checkUpdatesChBox)\n spacerItem = QSpacerItem(40, 20, QSizePolicy.Expanding, QSizePolicy.Minimum)\n self.horizontalLayout.addItem(spacerItem)\n self.channelLbl = QLabel(self.frame)\n self.channelLbl.setObjectName(\"channelLbl\")\n self.horizontalLayout.addWidget(self.channelLbl)\n self.channelCombo = QComboBox(self.frame)\n self.channelCombo.setObjectName(\"channelCombo\")\n self.channelCombo.addItem(\"\")\n self.channelCombo.addItem(\"\")\n self.horizontalLayout.addWidget(self.channelCombo)\n self.verticalLayout.addLayout(self.horizontalLayout)\n self.downUpdatesChBox = QCheckBox(self.frame)\n self.downUpdatesChBox.setChecked(True)\n self.downUpdatesChBox.setObjectName(\"downUpdatesChBox\")\n self.verticalLayout.addWidget(self.downUpdatesChBox)\n self.horizontalLayout_2 = QHBoxLayout()\n self.horizontalLayout_2.setObjectName(\"horizontalLayout_2\")\n self.languageLbl = QLabel(self.frame)\n font = QFont()\n font.setBold(True)\n font.setWeight(75)\n self.languageLbl.setFont(font)\n self.languageLbl.setObjectName(\"languageLbl\")\n self.horizontalLayout_2.addWidget(self.languageLbl)\n self.languageCombo = QComboBox(self.frame)\n self.languageCombo.setObjectName(\"languageCombo\")\n self.horizontalLayout_2.addWidget(self.languageCombo)\n self.verticalLayout.addLayout(self.horizontalLayout_2)\n self.verticalLayout_2.addWidget(self.frame)\n self.buttonBox = QDialogButtonBox(settingsDialog)\n self.buttonBox.setOrientation(Qt.Horizontal)\n self.buttonBox.setStandardButtons(QDialogButtonBox.Cancel|QDialogButtonBox.RestoreDefaults|QDialogButtonBox.Save)\n self.buttonBox.setObjectName(\"buttonBox\")\n self.buttonBox.button(QDialogButtonBox.Cancel).setText(tr['BUTTON_CANCEL'])\n self.buttonBox.button(QDialogButtonBox.Save).setText(tr['BUTTON_SAVE'])\n self.buttonBox.button(QDialogButtonBox.RestoreDefaults).setText(tr['BUTTON_RESTORE_DEFAULTS'])\n self.verticalLayout_2.addWidget(self.buttonBox)\n\n settingsDialog.setWindowTitle(tr['SETTINGS_TITLE'])\n self.fileBrowserLbl.setText(tr['SETTINGS_FILE_BROWSER'])\n self.followSymChBox.setText(tr['SETTINGS_FOLLOW_SYMLINKS'])\n self.sessionLbl.setText(tr['SETTINGS_SESSION'])\n self.saveRestoreSessionChBox.setText(tr['SETTINGS_SAVE_RESTORE_SESSION'])\n # self.clearSessionBtn.setText(tr['SETTINGS_CLEAR_SESSION'])\n self.updaterLbl.setText(tr['SETTINGS_UPDATER'])\n self.checkUpdatesChBox.setText(tr['SETTINGS_CHECK_UPDATES'])\n self.channelLbl.setText(tr['SETTINGS_UPDATER_CHANNEL'])\n self.channelCombo.setItemText(0, tr['SETTINGS_STABLE'])\n self.channelCombo.setItemText(1, tr['SETTINGS_PRERELEASE'])\n self.downUpdatesChBox.setText(tr['SETTINGS_AUTO_UPDATES'])\n self.languageLbl.setText(tr['SETTINGS_LANGUAGE'])\n\n self.buttonBox.accepted.connect(settingsDialog.accept)\n self.buttonBox.rejected.connect(settingsDialog.reject)\n QMetaObject.connectSlotsByName(settingsDialog)\n","repo_name":"m1lhaus/woofer","sub_path":"forms/setting_form.py","file_name":"setting_form.py","file_ext":"py","file_size_in_byte":6287,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"50"} +{"seq_id":"30121077240","text":"from pyspark.sql import SparkSession\nfrom pyspark.sql.types import StructField, StructType, IntegerType, StringType, DoubleType\nimport time\n\n#Q3: What was the best in term of revenue Animation movie of 1995?\n\nstart = time.perf_counter()\n\nsc = SparkSession \\\n .builder \\\n .appName(\"SQL Spark CSV query 3 execution\") \\\n .getOrCreate() \n\nmovie_schema = StructType([\n StructField(\"movie_id\", IntegerType(), True),\n StructField(\"name\", StringType(), True),\n StructField(\"description\", StringType(), True),\n StructField(\"year\", StringType(), True),\n StructField(\"duration\", IntegerType(), True),\n StructField(\"cost\", IntegerType(), True),\n StructField(\"revenue\", IntegerType(), True),\n StructField(\"rating\", DoubleType(), True)])\n\nmovies_df = sc.read.format('csv').options(header='false').schema(movie_schema).load(\"hdfs://master:9000/home/user/project/csv/movies.csv\") \nmovies_df.registerTempTable(\"movies\")\n\n\nmovie_genre_schema = StructType([\n StructField(\"movie_id\", IntegerType(), True),\n \t StructField(\"genre\", StringType(), True)])\n\nmovie_genres_df = sc.read.format('csv').options(header='false').schema(movie_genre_schema).load(\"hdfs://master:9000/home/user/project/csv/movie_genres.csv\") \nmovie_genres_df.registerTempTable(\"movie_genres\")\n\n#find best in term of revenue Animation movie of 1995\nquery = \"SELECT movies.name FROM movies INNER JOIN movie_genres ON movies.movie_id = movie_genres.movie_id WHERE movie_genres.genre = 'Animation' and movies.year = 1995 and movies.revenue > 0 ORDER BY movies.revenue DESC\"\nmovie = sc.sql(query)\n\nprint(\"The best in term of revenue Animation movie of 1995 was\",movie.collect()[0][0])\n\n# Time\ntime_avg = time.perf_counter() - start\nwith open('time.txt', 'a') as fd:\n fd.write('Q3: ')\n fd.write(str(time_avg))\n fd.write('\\n')","repo_name":"geo-chalk/Msc-Data-Science-AUEB","sub_path":"Large Scale Data Management/Project/Part1/Part1_Task4/4_2csv/Code/4_2csv_Q3.py","file_name":"4_2csv_Q3.py","file_ext":"py","file_size_in_byte":1860,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"17096234390","text":"from datetime import *\n\n# The Spark Challenge Fall 2021\n# Student Check-In Validation and Tracking\n# Date Modified : 12/06/2021\n# Import ECE Student PUID from verification file\n# Import Matching Data From Records\n# Track the number of visits\n# Logs the transcript of visits\n\n# Time Check\n# now = datetime.now()\n# current_time = now.strftime(TIME_FORMAT)\n\n# Visit Limit\nCOUNT_LIMIT = 1\n\n# Import verification\nSTUDENT_ID_FILE = 'verification.txt'\n\n# Visit Logs\nVISITATION_LOG_FILE = 'checkout_logs.txt'\n\n# datetime standard format\nTIME_FORMAT = '%H:%M:%S'\n\n\n# Data Structures\nclass student:\n def __init__(self, puid):\n self.puid = puid\n self.count = 1\n\n#Import Student ID's from verification file\n\ndef initialize():\n print('Reading the Database...')\n\n # Load ECE student IDs from file\n with open(STUDENT_ID_FILE, 'r') as myFile:\n for line in myFile.readlines():\n ECEStudents.append(line.strip())\n\n print('Validating visit history...')\n # Load visit transcript file - populate student dictionary\n with open(VISITATION_LOG_FILE, 'r') as myFile:\n for line in myFile.readlines():\n line = line.split('-')\n\n # Input verification check\n if(len(line) != 3):\n print('ERROR READING IN STUDENT HISTORY DATABASE')\n return\n\n #Extract information from line\n [PUID, count, time] = line\n\n if PUID in studentDB.keys():\n # Student previously added - UPDATE COUNT\n if int(count) > studentDB[PUID].count:\n studentDB[PUID].count = int(count)\n else:\n # New Student, add to dictionary\n newstudent = student(PUID.strip())\n studentDB[PUID.strip()] = newstudent\n studentDB[PUID].count = int(count)\n\n #populates the student list with checkin_puid.txt\n with open(\"checkin_logs.txt\", 'r') as myFile:\n for line in myFile.readlines():\n line = line.split('-')\n\n # Input verification check\n if(len(line) != 3):\n print('ERROR READING IN STUDENT HISTORY DATABASE')\n return\n #Extract information from line\n [PUID, count, time] = line\n\n if PUID in studentDB.keys():\n # Student previously added - UPDATE COUNT\n if int(count) > studentDB[PUID].count:\n studentDB[PUID].count = int(count)\n else:\n # New Student, add to dictionary\n newstudent = student(PUID.strip())\n studentDB[PUID.strip()] = newstudent\n studentDB[PUID].count = int(count)\n\n print(studentDB.keys);\n\ndef verfifyStudent():\n PUID = ''\n count = 0\n\n # Get PUID Number from scanner\n while PUID == '':\n PUID = input('Swipe PUID...')\n PUID = PUID.strip()\n PUID = PUID.split('=')\n\n try:\n PUID = PUID[2]\n except IndexError:\n print('ERROR READING CARD')\n PUID = '' # Your while loop is running when PUID is empty --> Need to reset to prevent continuing to next part even though the swipe failed because it still produces something\n \n PUID = PUID[1:] #commented out so I don't have to go brain damage mode to input my stuff and test\n #uncomment the following ^ line based on verification file: PUID = PUID[1:] if verification file is in the format:\n # 12312312 as opposed to 0012312312\n\n if PUID == 'exit':\n print('Exit Success')\n exit(0)\n\n print(studentDB.keys())\n # Check if student has visited before\n if PUID in studentDB.keys():\n # Verify visit is valid\n if studentDB[PUID].count >= 2:\n print('\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n!!!Already Checked-Out!!!\\n')\n print(f'Swipe Count More Than {studentDB[PUID].count}\\n')\n else:\n #Add Vivek's code here after checking with altay\n #if time <30 mins, send a print statement that asks if they are sure they want to check out\n #If response == no|NO|No|nO or whatever, break, do not execute the rest of the loop\n #Else respnse == YESSIR IDGAF ABOUT MY GRADE, excecute the below statement\n #checkout() should be its own helper function rather than a subfunction of checkin.py\n #Is this the checkout?\n\n ##Gets the amount of time the student has been there\n with open('checkin_logs.txt', 'r') as logs:\n for line in logs.readlines():\n # Finding check in time based on PUID\n if (line.split('-')[0] == PUID):\n checkinTime = line.split('-')[2][:-1]\n checkoutTime = datetime.now().strftime(TIME_FORMAT)\n checkoutTime = datetime.strptime(checkoutTime, TIME_FORMAT)\n checkinTime = datetime.strptime(checkinTime, TIME_FORMAT)\n diff = checkoutTime - checkinTime\n\n if diff.total_seconds() > 1800: #If the student has been there for more than 30(1800s) minutes, they can checkout\n studentDB[PUID].count += 1\n count = studentDB[PUID].count\n print('\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nCheck-Out Successful!')\n print(f'Swipe Count: {studentDB[PUID].count}')\n\n checkoutTime = datetime.now().strftime(TIME_FORMAT)\n print(f'\\nCheckout Time: {checkoutTime}')\n \n # Converts to datetime object\n checkoutTime = datetime.strptime(checkoutTime, TIME_FORMAT)\n \n # Add visit information to transcript log\n with open('checkout_logs.txt', 'a') as myFile:\n myFile.writelines(f'{PUID}-{count}-{datetime.now().strftime(TIME_FORMAT)}\\n')\n\n with open('checkout_puid.txt', 'a') as myFile:\n myFile.writelines(f'{PUID}\\n')\n\n with open('checkout_time.txt', 'a') as myFile:\n myFile.writelines(f'{checkoutTime}\\n')\n\n # Opening previous logs with PUID, num checkin, and time checkin\n with open('checkin_logs.txt', 'r') as logs:\n for line in logs.readlines():\n # Finding check in time based on PUID\n if (line.split('-')[0] == PUID):\n checkinTime = line.split('-')[2][:-1] # The [:-2] ignores the last two characters \"\\n\"\n print(f'Checkin Time: {checkinTime}')\n\n # Converts to datetime object\n checkinTime = datetime.strptime(checkinTime, TIME_FORMAT)\n\n # Calculates difference between checkout time and check in time\n diff = checkoutTime - checkinTime\n print(f'Time Stayed: {diff}')\n\n # Write the time difference to file\n with open('checkout_difference.txt', 'a') as myFile:\n myFile.writelines(f'{PUID}-{str(diff)}\\n')\n else: #Print saying what conditions are not met\n print(\"\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n30 minutes have not elapsed yet.\\nTime left till 30 minutes have elapsed:\", (int)(30 - diff.total_seconds()/60))\n \n # New Student visitor, verify ECE student and add to transcript\n else:\n if PUID in ECEStudents:\n checkinTime = datetime.now().strftime(TIME_FORMAT)\n studentDB[PUID] = student(PUID)\n\n print('\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nCheck-In Successful!')\n print(f'Check-In Time : {checkinTime}')\n print(f'Swipe Count : {studentDB[PUID].count}\\n')\n\n with open('checkin_logs.txt', 'a') as myFile:\n myFile.writelines(f'{PUID}-1-{checkinTime}\\n')\n\n with open('checkin_puid.txt', 'a') as myFile:\n myFile.writelines(f'{PUID}\\n')\n\n with open('checkin_time.txt', 'a') as myFile:\n myFile.writelines(f'{checkinTime}\\n')\n else:\n print('\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n!!!NOT AN ECE STUDENT!!!')\n\nif __name__ == \"__main__\":\n # Establish Data Structures\n studentDB = {}\n ECEStudents = []\n\n # Initialize\n print('Spark Check-In Script Initalizing')\n initialize()\n\n print('System initialized successfully. Type exit to halt program safely.\\n\\n')\n # Begin program\n while(True):\n response = verfifyStudent()\n \n","repo_name":"altayatik/sparkchallenge","sub_path":"checkin.py","file_name":"checkin.py","file_ext":"py","file_size_in_byte":8852,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"11485225179","text":"#用Adaboost和决策树的模型比较\n#在sklearn中的Adaboost默认采用的是决策树的模型，我们可以随机生成一些数据，然后对比下Adaboost中的弱分类器，决策树分类器和Adaboost模型在分类准确率上的表现\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import zero_one_loss\nfrom sklearn import datasets\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import AdaBoostClassifier\n\n#设置Adaboost迭代次数\nn_estimators = 200\n#使用\nx,y = datasets.make_hastie_10_2(n_samples=12000,random_state=1)\n#从12000个数据中取前2000进行测试集，其余作为训练集\ntrain_x,train_y = x[2000:],y[2000:]\ntest_x,test_y = x[:2000],y[:2000]\n#弱分类器\ndt_stump = DecisionTreeClassifier(max_depth=1,min_samples_leaf=1)\ndt_stump.fit(train_x,train_y)\ndt_stump_err = 1.0-dt_stump.score(test_x,test_y)\n#决策树分类器\ndt = DecisionTreeClassifier()\ndt.fit(train_x,train_y)\ndt_err = 1.0-dt.score(test_x,test_y)\n#AdaBoost分类器\nada = AdaBoostClassifier(base_estimator=dt_stump,n_estimators=n_estimators)\nada.fit(train_x,train_y)\n#三个分类器的错误率可视化\nfig = plt.figure()\n#设置plt正确显示中文\nplt.rcParams['font.sans-serif'] = ['SimHei']\nax = fig.add_subplot(111)\nax.plot([1,n_estimators],[dt_stump_err]*2,'k-',label = '决策树弱分类器 错误率')\nax.plot([1,n_estimators],[dt_err]*2,'k--',label = '决策树模型 错误率')\nada_err = np.zeros((n_estimators,))\n#遍历每次迭代的结果i为迭代次数，pred_y为预测结果\nfor i,pred_y in enumerate(ada.staged_predict(test_x)):\n #统计错误率\n ada_err[i] = zero_one_loss(pred_y,test_y)\n#绘制每次迭代的Adaboost错误率\nax.plot(np.arange(n_estimators)+1,ada_err,label = 'Adaboost Test 错误率',color='R')\nax.set_xlabel('迭代次数')\nax.set_ylabel('错误率')\nleg = ax.legend(loc='upper right',fancybox=True)\nplt.show()","repo_name":"13060923171/Data_analysis-Algorithmic_combat","sub_path":"实战-AdaBoost算法/main2.py","file_name":"main2.py","file_ext":"py","file_size_in_byte":1919,"program_lang":"python","lang":"zh","doc_type":"code","stars":2,"dataset":"github-code","pt":"50"} +{"seq_id":"40133276070","text":"import FWCore.ParameterSet.Config as cms\n\nfrom DQMServices.Core.DQMEDAnalyzer import DQMEDAnalyzer\nl1TdeCSCTF = DQMEDAnalyzer('L1TdeCSCTF',\n\toutFile=cms.untracked.string(\"\"),\n\tDQMStore=cms.untracked.bool(True),\n\tDQMFolder=cms.untracked.string(\"L1TEMU/CSCTFexpert\"),\n\tdisableROOToutput=cms.untracked.bool(True),\n\tdataTrackProducer=cms.InputTag(\"csctfDigis\"),\n\temulTrackProducer=cms.InputTag(\"valCsctfTrackDigis\"),\n\tlctProducer=cms.InputTag(\"csctfDigis\"),\n\tdataStubProducer=cms.InputTag(\"csctfDigis\",\"DT\"),\n\temulStubProducer=cms.InputTag(\"dttfDigis\"),\n\tPTLUT=cms.PSet(\n\t\tLowQualityFlag=cms.untracked.uint32(4),\n\t\tReadPtLUT=cms.bool(False),\n\t\tPtMethod=cms.untracked.uint32(1),\n\t)\n)\n\n","repo_name":"cms-sw/cmssw","sub_path":"DQM/L1TMonitor/python/L1TdeCSCTF_cfi.py","file_name":"L1TdeCSCTF_cfi.py","file_ext":"py","file_size_in_byte":680,"program_lang":"python","lang":"en","doc_type":"code","stars":985,"dataset":"github-code","pt":"50"} +{"seq_id":"71786685274","text":"# 카카오 블라인드\ndef solution(numbers):\n answer = []\n for n in numbers:\n if n == 1:\n answer.append(1)\n continue\n b = format(n, 'b')\n flag = isPerfect(b)\n l = len(b)\n if not flag[0]:\n b = \"0\" * (2 ** flag[1] - l - 1) + b\n \n if b[len(b)//2] == \"0\":\n if \"1\" in b:\n answer.append(0)\n else:\n answer.append(1)\n continue\n if checkbinary(b, 0, len(b)//2-1, flag[1], 2) and checkbinary(b, len(b)//2+1, len(b)-1, flag[1], 2):\n answer.append(1)\n else:\n answer.append(0)\n \n return answer\n\ndef isPerfect(b):\n i = 1\n while 2**i-1 < len(b):\n i += 1\n return [2**i-1 == len(b), i]\n\ndef checkbinary(bi, start, end, end_level, level):\n if end_level == level:\n return True\n if bi[(end+start) // 2] == \"0\":\n if (\"1\" in bi[start:(end+start)//2]) or (\"1\" in bi[(end+start)//2+1:end+1]):\n return False\n return checkbinary(bi, start, (end+start)//2-1, end_level, level+1) and checkbinary(bi, (end+start)//2+1, end, end_level, level+1)\n\n# 시험 당시에는 쉽게 풀었는데...왜 다시푸니까 더 오래걸렸지?\n# 풀이 방식\n# 주어진 10진수를 2진수로 변경한다.\n# 완전 이진트리가 되도록 0을 2진수 앞에 붙여준다.(노드의 개수가 2**n-1 에 맞도록)\n# 노드들을 탐색한다.\n# 만약 해당노드가 리프노드가 아니고 더미노드이면 그 노드기준 왼쪽과 오른쪽 서브트리에 1이 있는지 확인하고 있으면 이진트리가 아니므로 False를 리턴한다.\n# 리프노드면 0이든 1이든 상관없기 때문에 True를 리턴한다.\n# 그렇게 모든 노드를 탐색하고 False를 리턴하지 않았으면 answer에 1을 넣는다.","repo_name":"JH-TT/Coding_Practice","sub_path":"Programmers/Graph_P/150367.py","file_name":"150367.py","file_ext":"py","file_size_in_byte":1846,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"19879074359","text":"import re\n\nstrings_id = 0\nstrings = {}\ndef get_string_id(str):\n\tif not strings.has_key(str):\n\t\tglobal strings_id\n\t\tstrings_id += 1\n\t\tstrings[str] = strings_id\n\treturn strings[str]\n\nmatcher = {}\n\ndef add_matcher(ending, type):\n\tending = list(ending)\n\tending.reverse()\n\tending.append('')\n\tm = matcher\n\tfor c in ending:\n\t\tif len(c) == 0:\n\t\t\tc = '.'\n\t\t\tassert not m.has_key(c)\n\t\t\tm[c] = get_string_id(type)\n\t\telse:\n\t\t\tif not m.has_key(c):\n\t\t\t\tm[c] = {}\n\t\t\telse:\n\t\t\t\tassert isinstance(m[c], dict)\n\t\t\tm = m[c]\n\ndef print_matcher(m, move = False):\n\tif isinstance(m, int):\n\t\treturn \"return str_%d;\" % m\n\telse:\n\t\tc = m.keys()\n\t\tc.sort()\n\t\tdef case_matcher(ch, m):\n\t\t\treturn \" case '%s': %s\" % (ch, print_matcher(m, True))\n\t\tchm = ''\n\t\tif move:\n\t\t\tchm = '++ch; '\n\t\treturn \"\\n%sswitch(*ch) {%s default: return 0; }\" % (chm, ''.join([case_matcher(ch, m[ch]) for ch in c]))\n\nfor line in open(\"mime.types\"):\n\tif None != re.search(r'^\\s*#', line):\n\t\tcontinue\n\n\ttoks = re.split(r'\\s+', line)[:-1]\n\tif len(toks) >= 2:\n\t\tfor ending in toks[1:]:\n\t\t\tadd_matcher(ending, toks[0])\n\n# http://support.microsoft.com/kb/288102\nfor t in [\n\t('asf', 'video/x-ms-asf'),\n\t('asx', 'video/x-ms-asf'),\n\t('wma', 'audio/x-ms-wma'),\n\t('wax', 'audio/x-ms-wax'),\n\t('wmv', 'audio/x-ms-wmv'),\n\t('wvx', 'video/x-ms-wvx'),\n\t('wm', 'video/x-ms-wm'),\n\t('wmx', 'video/x-ms-wmx'),\n\t('wmz', 'application/x-ms-wmz'),\n\t('wmd', 'application/x-ms-wmd')\n\t]:\n\t\tadd_matcher(t[0], t[1])\n\ncpp = open(\"netek_mimetype.cpp\", \"wb\")\ncpp.write('#include \"netek_mimetype.h\"\\n\\n');\n\nstrs = strings.keys()\nstrs.sort()\nfor s in strs:\n\tcpp.write('static const char str_%d[] = \"%s\";\\n' % (strings[s], s))\n\ncpp.write('''\nconst char *neteK::mimeTypeSearch(const char *ch)\n{%s}\n''' % print_matcher(matcher));\n","repo_name":"BackupTheBerlios/netek-svn","sub_path":"trunk/mime_types_generate.py","file_name":"mime_types_generate.py","file_ext":"py","file_size_in_byte":1737,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"31032634812","text":"import argparse\nimport datetime as dt\nimport os\nimport warnings\nfrom pathlib import Path\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport soundfile as sf\nimport torch\n\nfrom matcha.hifigan.config import v1\nfrom matcha.hifigan.denoiser import Denoiser\nfrom matcha.hifigan.env import AttrDict\nfrom matcha.hifigan.models import Generator as HiFiGAN\nfrom matcha.models.matcha_tts import MatchaTTS\nfrom matcha.text import sequence_to_text, text_to_sequence\nfrom matcha.utils.utils import assert_model_downloaded, get_user_data_dir, intersperse\n\nMATCHA_URLS = {\n \"matcha_ljspeech\": \"https://drive.google.com/file/d/1BBzmMU7k3a_WetDfaFblMoN18GqQeHCg/view?usp=drive_link\",\n \"matcha_vctk\": \"https://drive.google.com/file/d/1enuxmfslZciWGAl63WGh2ekVo00FYuQ9/view?usp=drive_link\",\n}\n\nVOCODER_URLS = {\n \"hifigan_T2_v1\": \"https://drive.google.com/file/d/14NENd4equCBLyyCSke114Mv6YR_j_uFs/view?usp=drive_link\",\n \"hifigan_univ_v1\": \"https://drive.google.com/file/d/1qpgI41wNXFcH-iKq1Y42JlBC9j0je8PW/view?usp=drive_link\",\n}\n\nMULTISPEAKER_MODEL = {\n \"matcha_vctk\": {\"vocoder\": \"hifigan_univ_v1\", \"speaking_rate\": 0.85, \"spk\": 0, \"spk_range\": (0, 107)}\n}\n\nSINGLESPEAKER_MODEL = {\"matcha_ljspeech\": {\"vocoder\": \"hifigan_T2_v1\", \"speaking_rate\": 0.95, \"spk\": None}}\n\n\ndef plot_spectrogram_to_numpy(spectrogram, filename):\n fig, ax = plt.subplots(figsize=(12, 3))\n im = ax.imshow(spectrogram, aspect=\"auto\", origin=\"lower\", interpolation=\"none\")\n plt.colorbar(im, ax=ax)\n plt.xlabel(\"Frames\")\n plt.ylabel(\"Channels\")\n plt.title(\"Synthesised Mel-Spectrogram\")\n fig.canvas.draw()\n plt.savefig(filename)\n\n\ndef process_text(i: int, text: str, device: torch.device):\n print(f\"[{i}] - Input text: {text}\")\n x = torch.tensor(\n intersperse(text_to_sequence(text, [\"english_cleaners2\"]), 0),\n dtype=torch.long,\n device=device,\n )[None]\n x_lengths = torch.tensor([x.shape[-1]], dtype=torch.long, device=device)\n x_phones = sequence_to_text(x.squeeze(0).tolist())\n print(f\"[{i}] - Phonetised text: {x_phones[1::2]}\")\n\n return {\"x_orig\": text, \"x\": x, \"x_lengths\": x_lengths, \"x_phones\": x_phones}\n\n\ndef get_texts(args):\n if args.text:\n texts = [args.text]\n else:\n with open(args.file) as f:\n texts = f.readlines()\n return texts\n\n\ndef assert_required_models_available(args):\n save_dir = get_user_data_dir()\n if not hasattr(args, \"checkpoint_path\") and args.checkpoint_path is None:\n model_path = args.checkpoint_path\n else:\n model_path = save_dir / f\"{args.model}.ckpt\"\n assert_model_downloaded(model_path, MATCHA_URLS[args.model])\n\n vocoder_path = save_dir / f\"{args.vocoder}\"\n assert_model_downloaded(vocoder_path, VOCODER_URLS[args.vocoder])\n return {\"matcha\": model_path, \"vocoder\": vocoder_path}\n\n\ndef load_hifigan(checkpoint_path, device):\n h = AttrDict(v1)\n hifigan = HiFiGAN(h).to(device)\n hifigan.load_state_dict(torch.load(checkpoint_path, map_location=device)[\"generator\"])\n _ = hifigan.eval()\n hifigan.remove_weight_norm()\n return hifigan\n\n\ndef load_vocoder(vocoder_name, checkpoint_path, device):\n print(f\"[!] Loading {vocoder_name}!\")\n vocoder = None\n if vocoder_name in (\"hifigan_T2_v1\", \"hifigan_univ_v1\"):\n vocoder = load_hifigan(checkpoint_path, device)\n else:\n raise NotImplementedError(\n f\"Vocoder {vocoder_name} not implemented! define a load_<> method for it\"\n )\n\n denoiser = Denoiser(vocoder, mode=\"zeros\")\n print(f\"[+] {vocoder_name} loaded!\")\n return vocoder, denoiser\n\n\ndef load_matcha(model_name, checkpoint_path, device):\n print(f\"[!] Loading {model_name}!\")\n model = MatchaTTS.load_from_checkpoint(checkpoint_path, map_location=device)\n _ = model.eval()\n\n print(f\"[+] {model_name} loaded!\")\n return model\n\n\ndef to_waveform(mel, vocoder, denoiser=None):\n audio = vocoder(mel).clamp(-1, 1)\n if denoiser is not None:\n audio = denoiser(audio.squeeze(), strength=0.00025).cpu().squeeze()\n\n return audio.cpu().squeeze()\n\n\ndef save_to_folder(filename: str, output: dict, folder: str):\n folder = Path(folder)\n folder.mkdir(exist_ok=True, parents=True)\n plot_spectrogram_to_numpy(np.array(output[\"mel\"].squeeze().float().cpu()), f\"{filename}.png\")\n np.save(folder / f\"{filename}\", output[\"mel\"].cpu().numpy())\n sf.write(folder / f\"{filename}.wav\", output[\"waveform\"], 22050, \"PCM_24\")\n return folder.resolve() / f\"{filename}.wav\"\n\n\ndef validate_args(args):\n assert (\n args.text or args.file\n ), \"Either text or file must be provided Matcha-T(ea)TTS need sometext to whisk the waveforms.\"\n assert args.temperature >= 0, \"Sampling temperature cannot be negative\"\n assert args.steps > 0, \"Number of ODE steps must be greater than 0\"\n\n if args.checkpoint_path is None:\n # When using pretrained models\n if args.model in SINGLESPEAKER_MODEL.keys():\n args = validate_args_for_single_speaker_model(args)\n\n if args.model in MULTISPEAKER_MODEL:\n args = validate_args_for_multispeaker_model(args)\n else:\n # When using a custom model\n if args.vocoder != \"hifigan_univ_v1\":\n warn_ = \"[-] Using custom model checkpoint! I would suggest passing --vocoder hifigan_univ_v1, unless the custom model is trained on LJ Speech.\"\n warnings.warn(warn_, UserWarning)\n if args.speaking_rate is None:\n args.speaking_rate = 1.0\n\n if args.batched:\n assert args.batch_size > 0, \"Batch size must be greater than 0\"\n assert args.speaking_rate > 0, \"Speaking rate must be greater than 0\"\n\n return args\n\n\ndef validate_args_for_multispeaker_model(args):\n if args.vocoder is not None:\n if args.vocoder != MULTISPEAKER_MODEL[args.model][\"vocoder\"]:\n warn_ = f\"[-] Using {args.model} model! I would suggest passing --vocoder {MULTISPEAKER_MODEL[args.model]['vocoder']}\"\n warnings.warn(warn_, UserWarning)\n else:\n args.vocoder = MULTISPEAKER_MODEL[args.model][\"vocoder\"]\n\n if args.speaking_rate is None:\n args.speaking_rate = MULTISPEAKER_MODEL[args.model][\"speaking_rate\"]\n\n spk_range = MULTISPEAKER_MODEL[args.model][\"spk_range\"]\n if args.spk is not None:\n assert (\n args.spk >= spk_range[0] and args.spk <= spk_range[-1]\n ), f\"Speaker ID must be between {spk_range} for this model.\"\n else:\n available_spk_id = MULTISPEAKER_MODEL[args.model][\"spk\"]\n warn_ = f\"[!] Speaker ID not provided! Using speaker ID {available_spk_id}\"\n warnings.warn(warn_, UserWarning)\n args.spk = available_spk_id\n\n return args\n\n\ndef validate_args_for_single_speaker_model(args):\n if args.vocoder is not None:\n if args.vocoder != SINGLESPEAKER_MODEL[args.model][\"vocoder\"]:\n warn_ = f\"[-] Using {args.model} model! I would suggest passing --vocoder {SINGLESPEAKER_MODEL[args.model]['vocoder']}\"\n warnings.warn(warn_, UserWarning)\n else:\n args.vocoder = SINGLESPEAKER_MODEL[args.model][\"vocoder\"]\n\n if args.speaking_rate is None:\n args.speaking_rate = SINGLESPEAKER_MODEL[args.model][\"speaking_rate\"]\n\n if args.spk != SINGLESPEAKER_MODEL[args.model][\"spk\"]:\n warn_ = f\"[-] Ignoring speaker id {args.spk} for {args.model}\"\n warnings.warn(warn_, UserWarning)\n args.spk = SINGLESPEAKER_MODEL[args.model][\"spk\"]\n\n return args\n\n\n@torch.inference_mode()\ndef cli():\n parser = argparse.ArgumentParser(\n description=\" ðŸ�µ Matcha-TTS: A fast TTS architecture with conditional flow matching\"\n )\n parser.add_argument(\n \"--model\",\n type=str,\n default=\"matcha_ljspeech\",\n help=\"Model to use\",\n choices=MATCHA_URLS.keys(),\n )\n\n parser.add_argument(\n \"--checkpoint_path\",\n type=str,\n default=None,\n help=\"Path to the custom model checkpoint\",\n )\n\n parser.add_argument(\n \"--vocoder\",\n type=str,\n default=None,\n help=\"Vocoder to use (default: will use the one suggested with the pretrained model))\",\n choices=VOCODER_URLS.keys(),\n )\n parser.add_argument(\"--text\", type=str, default=None, help=\"Text to synthesize\")\n parser.add_argument(\"--file\", type=str, default=None, help=\"Text file to synthesize\")\n parser.add_argument(\"--spk\", type=int, default=None, help=\"Speaker ID\")\n parser.add_argument(\n \"--temperature\",\n type=float,\n default=0.667,\n help=\"Variance of the x0 noise (default: 0.667)\",\n )\n parser.add_argument(\n \"--speaking_rate\",\n type=float,\n default=None,\n help=\"change the speaking rate, a higher value means slower speaking rate (default: 1.0)\",\n )\n parser.add_argument(\"--steps\", type=int, default=10, help=\"Number of ODE steps (default: 10)\")\n parser.add_argument(\"--cpu\", action=\"store_true\", help=\"Use CPU for inference (default: use GPU if available)\")\n parser.add_argument(\n \"--denoiser_strength\",\n type=float,\n default=0.00025,\n help=\"Strength of the vocoder bias denoiser (default: 0.00025)\",\n )\n parser.add_argument(\n \"--output_folder\",\n type=str,\n default=os.getcwd(),\n help=\"Output folder to save results (default: current dir)\",\n )\n parser.add_argument(\"--batched\", action=\"store_true\", help=\"Batched inference (default: False)\")\n parser.add_argument(\n \"--batch_size\", type=int, default=32, help=\"Batch size only useful when --batched (default: 32)\"\n )\n\n args = parser.parse_args()\n\n args = validate_args(args)\n device = get_device(args)\n print_config(args)\n paths = assert_required_models_available(args)\n\n if args.checkpoint_path is not None:\n print(f\"[ðŸ�µ] Loading custom model from {args.checkpoint_path}\")\n paths[\"matcha\"] = args.checkpoint_path\n args.model = \"custom_model\"\n\n model = load_matcha(args.model, paths[\"matcha\"], device)\n vocoder, denoiser = load_vocoder(args.vocoder, paths[\"vocoder\"], device)\n\n texts = get_texts(args)\n\n spk = torch.tensor([args.spk], device=device, dtype=torch.long) if args.spk is not None else None\n if len(texts) == 1 or not args.batched:\n unbatched_synthesis(args, device, model, vocoder, denoiser, texts, spk)\n else:\n batched_synthesis(args, device, model, vocoder, denoiser, texts, spk)\n\n\nclass BatchedSynthesisDataset(torch.utils.data.Dataset):\n def __init__(self, processed_texts):\n self.processed_texts = processed_texts\n\n def __len__(self):\n return len(self.processed_texts)\n\n def __getitem__(self, idx):\n return self.processed_texts[idx]\n\n\ndef batched_collate_fn(batch):\n x = []\n x_lengths = []\n\n for b in batch:\n x.append(b[\"x\"].squeeze(0))\n x_lengths.append(b[\"x_lengths\"])\n\n x = torch.nn.utils.rnn.pad_sequence(x, batch_first=True)\n x_lengths = torch.concat(x_lengths, dim=0)\n return {\"x\": x, \"x_lengths\": x_lengths}\n\n\ndef batched_synthesis(args, device, model, vocoder, denoiser, texts, spk):\n total_rtf = []\n total_rtf_w = []\n processed_text = [process_text(i, text, \"cpu\") for i, text in enumerate(texts)]\n dataloader = torch.utils.data.DataLoader(\n BatchedSynthesisDataset(processed_text),\n batch_size=args.batch_size,\n collate_fn=batched_collate_fn,\n num_workers=8,\n )\n for i, batch in enumerate(dataloader):\n i = i + 1\n start_t = dt.datetime.now()\n output = model.synthesise(\n batch[\"x\"].to(device),\n batch[\"x_lengths\"].to(device),\n n_timesteps=args.steps,\n temperature=args.temperature,\n spks=spk,\n length_scale=args.speaking_rate,\n )\n\n output[\"waveform\"] = to_waveform(output[\"mel\"], vocoder, denoiser)\n t = (dt.datetime.now() - start_t).total_seconds()\n rtf_w = t * 22050 / (output[\"waveform\"].shape[-1])\n print(f\"[ðŸ�µ-Batch: {i}] Matcha-TTS RTF: {output['rtf']:.4f}\")\n print(f\"[ðŸ�µ-Batch: {i}] Matcha-TTS + VOCODER RTF: {rtf_w:.4f}\")\n total_rtf.append(output[\"rtf\"])\n total_rtf_w.append(rtf_w)\n for j in range(output[\"mel\"].shape[0]):\n base_name = f\"utterance_{j:03d}_speaker_{args.spk:03d}\" if args.spk is not None else f\"utterance_{j:03d}\"\n length = output[\"mel_lengths\"][j]\n new_dict = {\"mel\": output[\"mel\"][j][:, :length], \"waveform\": output[\"waveform\"][j][: length * 256]}\n location = save_to_folder(base_name, new_dict, args.output_folder)\n print(f\"[ðŸ�µ-{j}] Waveform saved: {location}\")\n\n print(\"\".join([\"=\"] * 100))\n print(f\"[ðŸ�µ] Average Matcha-TTS RTF: {np.mean(total_rtf):.4f} ± {np.std(total_rtf)}\")\n print(f\"[ðŸ�µ] Average Matcha-TTS + VOCODER RTF: {np.mean(total_rtf_w):.4f} ± {np.std(total_rtf_w)}\")\n print(\"[ðŸ�µ] Enjoy the freshly whisked ðŸ�µ Matcha-TTS!\")\n\n\ndef unbatched_synthesis(args, device, model, vocoder, denoiser, texts, spk):\n total_rtf = []\n total_rtf_w = []\n for i, text in enumerate(texts):\n i = i + 1\n base_name = f\"utterance_{i:03d}_speaker_{args.spk:03d}\" if args.spk is not None else f\"utterance_{i:03d}\"\n\n print(\"\".join([\"=\"] * 100))\n text = text.strip()\n text_processed = process_text(i, text, device)\n\n print(f\"[ðŸ�µ] Whisking Matcha-T(ea)TS for: {i}\")\n start_t = dt.datetime.now()\n output = model.synthesise(\n text_processed[\"x\"],\n text_processed[\"x_lengths\"],\n n_timesteps=args.steps,\n temperature=args.temperature,\n spks=spk,\n length_scale=args.speaking_rate,\n )\n output[\"waveform\"] = to_waveform(output[\"mel\"], vocoder, denoiser)\n # RTF with HiFiGAN\n t = (dt.datetime.now() - start_t).total_seconds()\n rtf_w = t * 22050 / (output[\"waveform\"].shape[-1])\n print(f\"[ðŸ�µ-{i}] Matcha-TTS RTF: {output['rtf']:.4f}\")\n print(f\"[ðŸ�µ-{i}] Matcha-TTS + VOCODER RTF: {rtf_w:.4f}\")\n total_rtf.append(output[\"rtf\"])\n total_rtf_w.append(rtf_w)\n\n location = save_to_folder(base_name, output, args.output_folder)\n print(f\"[+] Waveform saved: {location}\")\n\n print(\"\".join([\"=\"] * 100))\n print(f\"[ðŸ�µ] Average Matcha-TTS RTF: {np.mean(total_rtf):.4f} ± {np.std(total_rtf)}\")\n print(f\"[ðŸ�µ] Average Matcha-TTS + VOCODER RTF: {np.mean(total_rtf_w):.4f} ± {np.std(total_rtf_w)}\")\n print(\"[ðŸ�µ] Enjoy the freshly whisked ðŸ�µ Matcha-TTS!\")\n\n\ndef print_config(args):\n print(\"[!] Configurations: \")\n print(f\"\\t- Model: {args.model}\")\n print(f\"\\t- Vocoder: {args.vocoder}\")\n print(f\"\\t- Temperature: {args.temperature}\")\n print(f\"\\t- Speaking rate: {args.speaking_rate}\")\n print(f\"\\t- Number of ODE steps: {args.steps}\")\n print(f\"\\t- Speaker: {args.spk}\")\n\n\ndef get_device(args):\n if torch.cuda.is_available() and not args.cpu:\n print(\"[+] GPU Available! Using GPU\")\n device = torch.device(\"cuda\")\n else:\n print(\"[-] GPU not available or forced CPU run! Using CPU\")\n device = torch.device(\"cpu\")\n return device\n\n\nif __name__ == \"__main__\":\n cli()\n","repo_name":"shivammehta25/Matcha-TTS","sub_path":"matcha/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":15278,"program_lang":"python","lang":"en","doc_type":"code","stars":212,"dataset":"github-code","pt":"50"} +{"seq_id":"22867255519","text":"from math import gcd\nfrom functools import reduce\nfrom warnings import warn\nimport argparse\nfrom recip import RecipParser\n\ndef lcm(a, b):\n return (a*b) // gcd(a, b)\n\ndef run():\n parser = argparse.ArgumentParser()\n parser.add_argument('mode',\n choices=['violations', 'meter', 'timebase'])\n parser.add_argument('recip_file')\n parser.add_argument('--timebase', type=int, default=1,\n help='represent durations on timebase, '\n 'incompatible with timebase mode')\n args = parser.parse_args()\n # Read the data\n with open(args.recip_file) as f:\n recip_lines = [l.strip() for l in f]\n\n parser = RecipParser(timebase=args.timebase)\n\n tolerance = 10e-9 # floating-point error tolerance\n report = {}\n note_denominators = {1} # set of note denominators\n if args.mode == 'meter':\n print('name,meter_segment,start_time,numerator,denominator')\n for i, line in enumerate(recip_lines):\n name, *tokens = line.split(' ')\n violations = []\n period, last_barline, last_meter_time, last_meter = None, None, None, None\n meter_segment, bar_position, absolute_time = -1, 0, 0\n #if name != 'NLB122808_01': continue\n for parsed in parser.parse(tokens):\n #print(parsed)\n token_type = parsed['type']\n if token_type == METER:\n if args.mode == 'meter':\n # Deal with a bunch of stupid edge case\n meter = (parsed['numerator'], parsed['denominator'])\n if last_meter_time != absolute_time and meter != last_meter:\n meter_segment += 1\n # Only update when the meter has changed\n if meter != last_meter:\n print('{},{},{},{},{}'.format(name, meter_segment, absolute_time, meter[0], meter[1]))\n last_meter_time = absolute_time\n last_meter = meter\n period = parser.meter_period(parsed)\n if period is not None:\n if token_type in DURATION_TYPES:\n dt = parser.duration(parsed)\n bar_position += dt\n absolute_time += dt\n #print(absolute_time)\n denom = parsed['value']\n if bar_position > period + tolerance:\n violation = 'Duration of bar {} exceeded: {}'.format(('initial' if last_barline is None else last_barline['number']), bar_position)\n violations.append(violation)\n if denom % 1 != 0:\n warn('Non-integer denominator encountered')\n elif denom != 0:\n note_denominators.add(int(denom))\n #print(bar_position)\n if token_type in BARLINE_TYPES:\n barnum = parsed['number']\n if parsed['final']:\n barnum = 'final'\n if last_barline is None: \n if absolute_time > 0:\n absolute_time = period # we're at the end of a pickup \n barnum = 'initial'\n if barnum not in ['final', 'initial'] and bar_position + tolerance < period:\n violation = ('Bar {} is incomplete: ' .format(barnum) +\n 'bar position ({}) not equal to period ({})'.format(bar_position, period))\n violations.append(violation)\n bar_position = 0\n last_barline = parsed\n #print(absolute_time)\n if len(violations) > 0:\n report[name, i+1] = violations\n\n if args.mode == 'timebase':\n print(reduce(lcm, note_denominators))\n if args.mode == 'violations':\n print('name')\n for key in report.keys():\n print(key[0])\n # print('Number of violations: {}'.format(len(report)))\n # print('Files with violations: {}'.format(', '.join(map(lambda x: x[0], report.keys()))))\n # print('Violations per file:\\n{}'.format(\n # '\\n'.join('{}:\\n{}'.format(key, '\\n'.join('\\t{}'.format(v) for v in report[key])) for key in report.keys())))\n\nif __name__ == '__main__':\n report = run()\n","repo_name":"bjvanderweij/rhythm","sub_path":"rhythm/reciptools.py","file_name":"reciptools.py","file_ext":"py","file_size_in_byte":4321,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"34470995537","text":"# -*- coding: utf-8 -*-\r\n# 六面体数据\r\n# ------------------------------------------------------\r\n# v4----- v5\r\n# /| /|\r\n# v0------v1|\r\n# | | | |\r\n# | v7----|-v6\r\n# |/ |/\r\n# v3------v2\r\n\r\n# 顶点集\r\n\r\n\r\nfrom OpenGL.GL import *\r\nfrom OpenGL.GLU import *\r\nfrom OpenGL.GLUT import *\r\nfrom OpenGL.arrays import vbo\r\nimport numpy as np\r\n\r\nglutInit()\r\nglutCreateWindow(b'Example 3')\r\n\r\nvertices = np.array([\r\n -0.5, 0.5, 0.5,\r\n 0.5, 0.5, 0.5,\r\n 0.5, -0.5, 0.5,\r\n -0.5, -0.5, 0.5,\r\n -0.5, 0.5, -0.5,\r\n 0.5, 0.5, -0.5,\r\n 0.5, -0.5, -0.5,\r\n -0.5, -0.5, -0.5\r\n], dtype=np.float32)\r\n\r\n# 索引集\r\n\r\nindices = np.array([\r\n 0, 1, 2, 3, # v0-v1-v2-v3 (front)\r\n 4, 5, 1, 0, # v4-v5-v1-v0 (top)\r\n 3, 2, 6, 7, # v3-v2-v6-v7 (bottom)\r\n 5, 4, 7, 6, # v5-v4-v7-v6 (back)\r\n 1, 5, 6, 2, # v1-v5-v6-v2 (right)\r\n 4, 0, 3, 7 # v4-v0-v3-v7 (left)\r\n], dtype=np.int)\r\n\r\nvbo_vertices = vbo.VBO(vertices)\r\nvbo_vertices.bind()\r\nglInterleavedArrays(GL_V3F, 0, None)\r\n\r\nvbo_indices = vbo.VBO(indices, target=GL_ELEMENT_ARRAY_BUFFER)\r\nvbo_indices.bind()\r\n\r\n\r\ndef draw():\r\n glDrawElements(GL_QUADS, int(vbo_indices .size/4), GL_UNSIGNED_INT, None)\r\n glutSwapBuffers() # 切换缓冲区，以显示绘制内容\r\n\r\n\r\nif __name__ == '__main__':\r\n glutDisplayFunc(draw)\r\n glutMainLoop()\r\n","repo_name":"listenzcc/CT-Display","sub_path":"opengl-examples/example3.py","file_name":"example3.py","file_ext":"py","file_size_in_byte":1359,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"39690873246","text":"from discord.ext import commands\nimport discord\nimport time\nimport requests\nimport aiohttp\nimport sys\nimport os\nfrom textwrap import dedent\nfrom utils import presence, settings\nimport psutil\nimport logging\nimport traceback\n\n\nclass General(commands.Cog):\n def __init__(self, bot):\n self.bot = bot\n\n def get_bot_uptime(self, start_time):\n t = time.gmtime(time.time() - start_time)\n return f\"{t.tm_mday - 1} days, {t.tm_hour} hours, and {t.tm_min} minutes\"\n\n @commands.cooldown(rate=1, per=3)\n @commands.command()\n async def info(self, ctx):\n async with ctx.channel.typing():\n e = discord.Embed(\n color=discord.Color(0x6441A4),\n title=\"Luna Bot | Discord Bot For Gaming Community\"\n )\n uptime = self.get_bot_uptime(self.bot.uptime)\n mem = psutil.virtual_memory()\n e.add_field(\n name=\"Uptime\",\n value=uptime,\n inline=False\n )\n e.add_field(\n name=\"Version\",\n value=dedent(f\"\"\"\\\n **·** Python {sys.version.split(' ')[0]}\n **·** discord.py {discord.__version__}\n **·** Luna Bot {settings.Version}\n \"\"\")\n )\n if ctx.guild is None:\n e.add_field(\n name=\"Shard Info\",\n value=dedent(f\"\"\"\\\n **·** Shard latency: {round(self.bot.latency*1000)}ms\n **·** Total shards: {self.bot.shard_count}\n \"\"\")\n )\n else:\n e.add_field(\n name=\"Shard Info\",\n value=dedent(f\"\"\"\\\n **·** Current shard: {ctx.guild.shard_id}\n **·** Shard latency: {round(self.bot.latency*1000)}ms\n **·** Total shards: {self.bot.shard_count}\n \"\"\")\n )\n e.add_field(\n name=\"System\",\n value=dedent(f\"\"\"\\\n **·** {psutil.cpu_percent(interval=1)}% CPU\n **·** {round(mem.used/1000000)}/{round(mem.total/1000000)}MB RAM used\n \"\"\")\n )\n e.add_field(\n name=\"Developer\",\n value=\"MetaLuna#1999\",\n inline=False\n )\n await ctx.send(embed=e)\n\n @commands.cooldown(rate=1, per=3)\n @commands.command(pass_context=True)\n async def ping(self, ctx):\n t = time.time()\n await ctx.trigger_typing()\n t2 = round((time.time() - t) * 1000)\n await ctx.send(\"Pong! {}ms\".format(t2))\n\n @commands.command(pass_context=True)\n async def invite(self, ctx):\n await ctx.send(\"https://discordapp.com/api/oauth2/authorize?scope=bot&permissions=8&client_id=272451861636841482\")\n\n\ndef setup(bot):\n bot.add_cog(General(bot))\n","repo_name":"metalun4/LunaBot-v0.0.3","sub_path":"cogs/general.py","file_name":"general.py","file_ext":"py","file_size_in_byte":3006,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"71384830235","text":"''' Script to dump contents from Ntuples into CSV format '''\n\n# -- Import libraries -- #\nimport ROOT as r\nimport os, re, time, sys, pickle\nfrom optparse import OptionParser\nfrom root_numpy import tree2array\nimport numpy as np\nimport pandas as pd\nimport multiprocessing as mp\nimport utils.baseDumper as bd\nfrom concentrator import combine_dataframes\nimport time\n\ndef addbaseDumperOptions(pr):\n pr.add_option(\"-v\",\"--verbose\" , dest=\"verbose\" , action=\"store_true\", default=False, help=\"If activated, print verbose output\")\n pr.add_option('--outpath', '-o', type=\"string\", dest = \"outpath\", default = \"./results/\")\n pr.add_option('--inputFolder', '-i', type=\"string\", dest = \"inputFolder\", default = \".\")\n pr.add_option('--nevents', '-n', type=\"int\", metavar=\"nevents\", dest=\"nevents\", default = 100)\n pr.add_option(\"--njobs\", dest=\"njobs\", type=\"int\", help=\"Number of cores to use for multiprocess mode.\", default = 1)\n return \n\ndef submit(args):\n ''' Function to submit jobs '''\n DTdumper = bd.baseDumper(args[0], args[1])\n DTdumper.run()\n return\n\ndef main_run(opts, classtype): \n # Function for job submission \n\n files_ = [f for f in os.listdir(options.inputFolder) if \".root\" in f] \n t0 = time.time()\n with mp.Pool(processes = opts.njobs) as pool:\n pool.map( submit, ((opts, f) for f in files_))\n tf = time.time()\n print(\"I has taken %3.2f minutes (%3.2f seconds) to write these files.\"%(abs(t0-tf)/60, abs(t0-tf)))\n return\n\nif __name__ == \"__main__\":\n # parser inputs\n pr = OptionParser(usage=\"%prog [options]\")\n\n addbaseDumperOptions(pr)\n (options,args) = pr.parse_args()\n\n main_run(options, bd.baseDumper)\n","repo_name":"Cvico/DTPatternRecognition","sub_path":"dumpDTDigisFromNtuples.py","file_name":"dumpDTDigisFromNtuples.py","file_ext":"py","file_size_in_byte":1645,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"22370248390","text":"\"\"\"SustainAnalytics website Scrape\n\nThis script allows the user to scrape the companies' ESG ratings from the\nSustainAnalytics website\nWebsite link: \"https://www.sustainalytics.com/esg-ratings\"\n\nThis tool accepts Company's names list in comma separated value\nfile (.csv) format as input.\n\nThis script requires that `pandas` be installed within the Python\nenvironment you are running this script in.\n\nThe output is a .csv file with Company name and its corresponding ESG ratings\n\"\"\"\n\nimport pandas as pd\nfrom selenium.common.exceptions import NoSuchElementException\nfrom time import sleep\nfrom tqdm import tqdm\nfrom .scraper import WebScraper\n\n\ndef append_dict(temp: str) -> str:\n ''' Append the SustainAnalytics dictionary with Company name, Industry\\\n Name, and its ESG Risk rating\n\n Parameters\n ----------\n temp : str\n The previous company name appended to the dictionary\n\n Returns\n -------\n str\n The latest company name appended to the dictionary\n '''\n if temp == company:\n bot.append_empty_values(san)\n\n else:\n san['SA_Company'].append(company.text)\n san['SA_ESG_Risk'].append(esg_score.text)\n san['SA_Industry'].append(industry.text)\n temp = company\n return temp\n\n\n# Read input companies dataset\ncompanies_filename = WebScraper._get_filename()\nheader_name = WebScraper._get_headername()\nexport_path = WebScraper._get_exportpath()\ndf = pd.read_csv(companies_filename)\ndata_length = len(df)\n\n# Set up the webdriver\nURL = \"https://www.sustainalytics.com/esg-ratings\"\nbot = WebScraper(URL)\n\n# Scrape the website. Extract company names and their respective ESG score\n# and store it in the dictionary\ntemp = 0\nfor i in tqdm(range(data_length)):\n san = {'SA_Company': [], 'SA_ESG_Risk': [], 'SA_Industry': []}\n # Starting the search by finding the search bar and searching for the\n # company\n search_bar = bot.send_request_to_search_bar(\n header_name, df, i, xpath='//*[@id=\"searchInput\"]')\n\n try:\n key = bot.find_element('.//div[@class=\"list-group-item\"]')\n key.click()\n sleep(3)\n xpath = '/html/body/section[2]/section[1]/div/div[1]/div[1]/div[3]/ \\\n div[1]/div[1]/div[1]/span'\n esg_score = bot.find_element(xpath)\n company = bot.find_element(\n '/html/body/section[2]/section[1]/div/div[1]/div[1]/div[1]/div/h2')\n industry = bot.find_element(\n '/html/body/section[2]/section[1]/div/div[1]/div[1]/div[2]/ \\\n div[1]/p/strong')\n temp = append_dict(temp)\n\n except NoSuchElementException:\n bot.append_empty_values(san)\n\n # Save the data into a csv file\n df1 = bot.convert_dict_to_csv(san, export_path)\n","repo_name":"shweta-29/Companies_ESG_Scraper","sub_path":"esgmetrics/esgscraper/sustainanalytics.py","file_name":"sustainanalytics.py","file_ext":"py","file_size_in_byte":2726,"program_lang":"python","lang":"en","doc_type":"code","stars":28,"dataset":"github-code","pt":"50"} +{"seq_id":"20058510734","text":"\nimport numpy as np\nimport argparse\nimport cv2\nimport os\n\n# Bağımsız komut satırı argümanları belirlenir\nap = argparse.ArgumentParser()\nap.add_argument(\"-i\", \"--image\", required=True, help=\"programa girilen resim dizinine giden yol\")\nap.add_argument(\"-f\", \"--face\", required=True, help=\"yüz dedektörü model dizinine giden yol\")\nap.add_argument(\"-a\", \"--age\", required=True, help=\"yaş dedektörü model dizinine giden yol\")\nap.add_argument(\"-g\", \"--gender\", required=True, help=\"cinsiyet dedektörü model dizinine giden yol\")\nap.add_argument(\"-c\", \"--confidence\", type=float, default=0.5, help=\"zayıf tespitleri filtreleme için minimum olasılık\")\nargs = vars(ap.parse_args())\n\n# Dedektörün tahmin edeceği yaş sınıfları tanımlanır\nAGE_BUCKETS = [\"(0-2)\", \"(4-6)\", \"(8-12)\", \"(15-20)\", \"(25-32)\", \"(38-43)\", \"(48-53)\", \"(60-100)\"]\nGENDER_BUCKETS = [\"Kadin\", \"Erkek\"]\n\n# Modeller yüklenir\nprint(\"[BILGI] Yüz dedektör modeli yükleniyor...\")\nprototxtPath = os.path.sep.join([args[\"face\"], \"deploy.prototxt\"])\nweightsPath = os.path.sep.join([args[\"face\"], \"res10_300x300_ssd_iter_140000.caffemodel\"])\nfaceNet = cv2.dnn.readNet(prototxtPath, weightsPath)\nprint(\"[BILGI] Yaş dedektör modeli yükleniyor...\")\nprototxtPath = os.path.sep.join([args[\"age\"], \"age_deploy.prototxt\"])\nweightsPath = os.path.sep.join([args[\"age\"], \"age_net.caffemodel\"])\nageNet = cv2.dnn.readNet(prototxtPath, weightsPath)\nprint(\"[BILGI] Cinsiyet dedektör modeli yükleniyor...\")\nprototxtPath = os.path.sep.join([args[\"gender\"], \"gender.prototxt\"])\nweightsPath = os.path.sep.join([args[\"gender\"], \"gender_net.caffemodel\"])\ngenderNet = cv2.dnn.readNet(prototxtPath, weightsPath)\n\n# Resim yüklenir ve resim için bir input blobu oluşturulur\nimage = cv2.imread(args[\"image\"])\n(h, w) = image.shape[:2]\nblob = cv2.dnn.blobFromImage(image, 1.0, (300, 300), (104.0, 177.0, 123.0))\n\n# Blob ağa verilir ve yüz tespit edilir\nprint(\"[BILGI] Yüz algılama...\")\nfaceNet.setInput(blob)\ndetections = faceNet.forward()\n\n# Bulununan yüz döngüye verilir\nfor i in range(0, detections.shape[2]):\n\t# tahminle ilişkili güven (yani olasılık) çıkarılır\n\tconfidence = detections[0, 0, i, 2]\n\n\t# Güvenin(confidence) minimum güvenden daha yüksek olması sağlanarak zayıf algılamalar filtrelenir\n\tif confidence > args[\"confidence\"]:\n\t\t# Nesne için sınırla kutunun (x, y) koordinatlarını hesaplayın\n\t\tbox = detections[0, 0, i, 3:7] * np.array([w, h, w, h])\n\t\t(startX, startY, endX, endY) = box.astype(\"int\")\n\n\t\t# Yüzün ROI'sini çıkarılır ve yüz ROI'sinden bir blob oluşturulur\n\t\tface = image[startY:endY, startX:endX]\n\t\tfaceBlob = cv2.dnn.blobFromImage(face, 1.0, (227, 227), (78.4263377603, 87.7689143744, 114.895847746), swapRB=False)\n\t\tglobal j\n\t\tj = i\n\t\t# Sınıflara ait tahminler bulunur ve en büyük olasılığa sahip yaş ve cinsiyet sınıfları belirlenir\n\t\tageNet.setInput(faceBlob)\n\t\tpredsA = ageNet.forward()\n\t\ti = predsA[0].argmax()\n\t\tage = AGE_BUCKETS[i]\n\t\tageConfidence = predsA[0][i]\n\t\tgenderNet.setInput(faceBlob)\n\t\tpredsG = genderNet.forward()\n\t\tj = predsG[0].argmax()\n\t\tgender = GENDER_BUCKETS[j]\n\t\tgenderConfidence = predsG[0][j]\n\n\t\t# Tahminler terminale yazdırılır\n\t\ttext = \"{}: {:.2f}%\".format(age, ageConfidence * 100) + \" \" + \"{}: {:.2f}%\".format(gender, genderConfidence * 100)\n\t\tprint(\"[BILGI] {}\".format(text))\n\n\t\t# İlgili sınıflar yazdırılır ve bulunan yüzün kutu sınırları çizilir\n\t\ty = startY - 20 if startY - 20 > 20 else startY + 20\n\t\tcv2.rectangle(image, (startX, startY), (endX, endY), (255, 255, 0), 3)\n\t\tcv2.putText(image, text, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.85, (255, 255, 0), 2)\n\n# Çıktı(output) resmi ekranda gösterilir\ncv2.imshow(\"Image\", image)\ncv2.waitKey(0)","repo_name":"fadimanakilci/Age-And-Gender-Prediction","sub_path":"detect_age.py","file_name":"detect_age.py","file_ext":"py","file_size_in_byte":3739,"program_lang":"python","lang":"tr","doc_type":"code","stars":2,"dataset":"github-code","pt":"50"} +{"seq_id":"26269984468","text":"import math\nimport time\nimport random\nimport openai\nimport json\nimport os\nfrom dotenv import load_dotenv\nfrom pythonosc import udp_client\nfrom pythonosc import osc_server\nfrom pythonosc.dispatcher import Dispatcher\nfrom threading import Thread\nimport tkinter as tk\nimport subprocess\nimport webbrowser\nfrom playsound import playsound\n\n# UPD Client for world map visualisation\nclient = udp_client.SimpleUDPClient(\"127.0.0.1\", 12000)\n\n# Imports Catastrophe class\nfrom catastrophe import Catastrophe\n\n# Gets headline constructors\nfrom construct_headline import construct_start_headline, construct_end_headline, get_source\n\n# Loads in .env file which needs to be located in the same folder as this file\nload_dotenv()\n# Fetches api key from .env file (can be generated at https://platform.openai.com/account/api-keys)\nopenai.api_key = os.getenv(\"OPENAI_API_KEY\")\n\n# Fetches IP address from .env file\nip_address = os.getenv(\"IP_ADDRESS\")\n\nkey_mapping = [\"a\", \"s\", \"d\", \"f\", \"g\", \"h\", \"j\", \"k\", \"l\", \"ö\", \"ä\", \"#\"]\n\nprint(f\"Momentane IP Adresse: {ip_address}\")\n\n\nclass Symptoms:\n def __init__(self):\n # Start values\n self.prompt = \"Generiere 25 kurze, fiktive & sarkastische Schlagzeilen über den Klimawandel. Die Schlagzeilen sollen keine Jahreszahlen oder den Begriff Klimawandel beinhalten. Geb die Schlagzeilen als Liste mit dem key 'headlines' in einer JSON zurück\"\n self.is_game_running = False\n self.are_headlines_loaded = True\n self.start_year = 2025\n self.year = self.start_year\n self.count = 0\n self.death_count = 0\n self.temperature = 1\n self.free_regions = [\"na1\", \"na2\", \"eu1\", \"sa1\", \"sa2\", \"af1\", \"af2\", \"af3\", \"as1\", \"as2\", \"as3\", \"oc1\"]\n self.occupied_regions = set()\n self.region_data = {\n \"na1\": {\n \"is_active\": False,\n \"type\": \"\",\n \"resolution_percentage\": 0,\n },\n \"na2\": {\n \"is_active\": False,\n \"type\": \"\",\n \"resolution_percentage\": 0,\n },\n \"eu1\": {\n \"is_active\": False,\n \"type\": \"\",\n \"resolution_percentage\": 0,\n },\n \"sa1\": {\n \"is_active\": False,\n \"type\": \"\",\n \"resolution_percentage\": 0,\n },\n \"sa2\": {\n \"is_active\": False,\n \"type\": \"\",\n \"resolution_percentage\": 0,\n },\n \"af1\": {\n \"is_active\": False,\n \"type\": \"\",\n \"resolution_percentage\": 0,\n },\n \"af2\": {\n \"is_active\": False,\n \"type\": \"\",\n \"resolution_percentage\": 0,\n },\n \"af3\": {\n \"is_active\": False,\n \"type\": \"\",\n \"resolution_percentage\": 0,\n },\n \"as1\": {\n \"is_active\": False,\n \"type\": \"\",\n \"resolution_percentage\": 0,\n },\n \"as2\": {\n \"is_active\": False,\n \"type\": \"\",\n \"resolution_percentage\": 0,\n },\n \"as3\": {\n \"is_active\": False,\n \"type\": \"\",\n \"resolution_percentage\": 0,\n },\n \"oc1\": {\n \"is_active\": False,\n \"type\": \"\",\n \"resolution_percentage\": 0,\n },\n }\n self.headline_reserve = []\n self.used_headlines = []\n self.sensor_values: list[int] = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n self.has_first_catastrophe_happened = False\n self.annihilation_triggered = False\n\n def reset_attributes(self):\n # Preserves headlines before resetting all attributes\n generated_headlines = self.headline_reserve\n self.__init__()\n self.headline_reserve = generated_headlines\n\n def get_inputs(self):\n # Writes sensor input from Pi Cap into variable\n def get_diff_values(unused_addr, *args):\n self.sensor_values = args\n\n # Maps dispatcher to path of diff values\n dispatcher = Dispatcher()\n dispatcher.map(\"/diff*\", get_diff_values)\n\n # Initiates OSC server\n server = osc_server.BlockingOSCUDPServer((ip_address, 3000), dispatcher)\n server.serve_forever()\n\n def send_data(self):\n tick_count = 0\n while True:\n if tick_count > 5:\n # Sends data to p5project\n client.send_message('/death_count', str(int(self.death_count)))\n client.send_message(\"/are_headlines_loaded\", self.are_headlines_loaded)\n tick_count = 0\n tick_count += 1\n\n region_json = json.dumps(self.region_data, indent=4)\n client.send_message(\"/region_data\", region_json)\n\n client.send_message(\"/is_game_running\", self.is_game_running)\n time.sleep(0.03)\n\n def generate_headlines(self, verbose):\n while True:\n if len(self.headline_reserve) < 100:\n if verbose:\n print(\"Filling up headlines... (currently \" + str(len(self.headline_reserve)) + \"/100)\")\n try:\n # Calls GPT API and requests headlines\n gpt_response = openai.ChatCompletion.create(\n model=\"gpt-3.5-turbo\",\n messages=[\n {\"role\": \"user\",\n \"content\": self.prompt}\n ]\n )\n # Converts response into JSON\n headlines_json = json.loads(gpt_response.choices[0].message.content)\n # Adds new headlines to headlines array\n for headline in headlines_json['headlines']:\n self.headline_reserve.append({\"headline\": headline, \"source\": get_source()})\n # Rests for 5 seconds\n time.sleep(5)\n except Exception as e:\n # Catches bad response from GPT API\n print(\"Error while generating headlines:\", e)\n\n def set_temperature(self):\n # Temperature graph\n self.temperature = 1.5 * math.cos(0.04 * (self.year - self.start_year) + math.pi) + 2.5\n\n def trigger_headline(self):\n if len(self.headline_reserve) > 0:\n # Randomly picks headline from array\n index = random.randrange(0, len(self.headline_reserve))\n headline = self.headline_reserve[index]\n print(headline[\"headline\"] + \" - \" + headline[\"source\"])\n\n self.used_headlines.insert(0, headline)\n # Removes chosen headline from array\n del self.headline_reserve[index]\n else:\n print(\"--- Blank (headline) ---\")\n\n def trigger_catastrophe(self):\n if len(self.free_regions) != 0:\n # Moves region from free to occupied\n selected_region = random.choice(self.free_regions)\n self.occupied_regions.add(selected_region)\n self.free_regions.remove(selected_region)\n\n # Initialises the catastrophe based on selected region and current temperature\n catastrophe = Catastrophe(selected_region, self.temperature)\n\n # Constructs starting headline based on type and region\n start_headline = {\n \"headline\": construct_start_headline(selected_region, catastrophe.type),\n \"source\": get_source()\n }\n self.used_headlines.insert(0, start_headline)\n # playsound(\"audio/alert.wav\", block=False)\n print(\n \"════════════════════════════════════════════════════════════════════════════════════════════════════════════\")\n print(\n f\"!!! CATASTROPHE - {selected_region} - {catastrophe.type} - {float(catastrophe.wind_up):.3}s wind_up - {float(catastrophe.duration):.3}s dur - {float(catastrophe.resolution_time):.3}s res_time - {int(catastrophe.deaths_per_second):,} d_p_s !!!\")\n print(start_headline[\"headline\"] + \" - \" + start_headline[\"source\"])\n print(\"!!! On electrode \" + str(catastrophe.electrode_index) + \" - \" + key_mapping[\n catastrophe.electrode_index] + \" !!!\")\n print(\n \"════════════════════════════════════════════════════════════════════════════════════════════════════════════\")\n\n # Changes region data\n self.region_data[selected_region][\"is_active\"] = True\n self.region_data[selected_region][\"type\"] = catastrophe.type\n self.region_data[selected_region][\"resolution_percentage\"] = 1\n\n # Sets starting parameters for catastrophe\n current_windup = 0\n current_duration = 0\n current_resolution_time = 0\n current_death_count = 0\n resolved_by_player = False\n\n # Wind up period of catastrophe\n while current_windup < catastrophe.wind_up and self.is_game_running is True:\n if self.sensor_values[catastrophe.electrode_index] > 15:\n current_resolution_time += 0.01\n self.region_data[selected_region][\n \"resolution_percentage\"] = 1 - current_resolution_time / catastrophe.resolution_time\n if current_resolution_time >= catastrophe.resolution_time:\n resolved_by_player = True\n playsound(\"audio/resolved.wav\", block=False)\n break\n current_windup += 0.01\n time.sleep(0.01)\n\n # Main duration of catastrophe if it hasn't been resolved yet\n if catastrophe.resolution_time >= current_resolution_time:\n while current_duration < catastrophe.duration and self.is_game_running is True:\n if self.sensor_values[catastrophe.electrode_index] > 15:\n current_resolution_time += 0.01\n self.region_data[selected_region][\n \"resolution_percentage\"] = 1 - current_resolution_time / catastrophe.resolution_time\n if current_resolution_time >= catastrophe.resolution_time:\n resolved_by_player = True\n playsound(\"audio/resolved.wav\", block=False)\n break\n self.death_count += catastrophe.deaths_per_second * 0.01\n current_death_count += catastrophe.deaths_per_second * 0.01\n current_duration += 0.01\n time.sleep(0.01)\n\n # Changes region data\n self.region_data[selected_region][\"is_active\"] = False\n\n # Constructs ending headline\n end_headline = {\n \"headline\": construct_end_headline(selected_region, catastrophe.type, current_death_count),\n \"source\": get_source()\n }\n self.used_headlines.insert(0, end_headline)\n print(\n \"════════════════════════════════════════════════════════════════════════════════════════════════════════════\")\n print(\n f\">>> RESOLVED - {selected_region} - {catastrophe.type} - resolved by player? {resolved_by_player} <<<\")\n print(end_headline[\"headline\"] + \" - \" + end_headline[\"source\"])\n print(\n \"════════════════════════════════════════════════════════════════════════════════════════════════════════════\")\n\n if self.is_game_running is True:\n # Puts region on 2 second cooldown\n time.sleep(2)\n\n # Moves region back from occupied to free\n self.free_regions.append(selected_region)\n self.occupied_regions.remove(selected_region)\n else:\n print(\"--- Blank (catastrophe) ---\")\n\n def trigger_annihilation(self):\n print(\"☁☢☁ Started annihilation event ☁☢☁\")\n # Occupies regions until it reaches four occupied\n war_regions = []\n while len(war_regions) < 4:\n if len(self.free_regions) > 0:\n selected_region = self.free_regions[0]\n self.free_regions.remove(selected_region)\n self.occupied_regions.add(selected_region)\n print(f\"☁☢☁ Added {selected_region} to annihilation event ☁☢☁\")\n war_regions.append(selected_region)\n\n # Gets sensor value indexes for all four occupied regions\n region_indexes = []\n for region in war_regions:\n catastrophe = Catastrophe(region, self.temperature)\n region_indexes.append(catastrophe.electrode_index)\n self.region_data[region][\"is_active\"] = True\n self.region_data[region][\"type\"] = \"annihilation\"\n self.region_data[region][\"resolution_percentage\"] = 1\n\n # Constructs starting headline for the nuclear war\n start_headline = {\n \"headline\": \"Nach Monaten der Anspannung - DEFCON 1 erreicht: Das Zeitalter der Atomkriege beginnt\",\n \"source\": \"Tiffany\"\n }\n self.used_headlines.insert(0, start_headline)\n playsound(\"audio/annihilation.wav\", block=False)\n print(\n \"☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢\")\n print(f\"☢☢☢ NUCLEAR WAR - {str(war_regions)} ☢☢☢\")\n print(start_headline[\"headline\"] + \" - \" + start_headline[\"source\"])\n print(\"☢☢☢ On electrodes \" + str(region_indexes) + \" ☢☢☢\")\n print(\n \"☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢\")\n\n # Sets starting parameters for annihilation\n resolution_time = 4\n deaths_per_second = 500_000_000\n current_death_count = 0\n current_resolution_time = 0\n\n # Annihilation loop which runs until death_count reaches 10 billion, the game ends or the player resolves the event\n while self.death_count < 10_000_000_000 and self.is_game_running is True:\n if self.sensor_values[region_indexes[0]] > 15 and self.sensor_values[region_indexes[1]] > 15 and \\\n self.sensor_values[region_indexes[2]] > 15 and self.sensor_values[region_indexes[3]] > 15:\n current_resolution_time += 0.01\n for region in war_regions:\n self.region_data[region][\n \"resolution_percentage\"] = 1 - current_resolution_time / resolution_time\n if current_resolution_time >= resolution_time:\n playsound(\"audio/resolved.wav\", block=False)\n break\n self.death_count += deaths_per_second * 0.01\n current_death_count += deaths_per_second * 0.01\n time.sleep(0.01)\n\n # Changes region data\n for region in war_regions:\n self.region_data[region][\"is_active\"] = False\n\n # Sends ending headline\n end_headline = {\n \"headline\": f\"Ein Wunder: Der Atomkrieg ist vorbei! Für den Frieden mussten nur {int(current_death_count):,} Personen sterben\",\n \"source\": \"Tiffany\"\n }\n self.used_headlines.insert(0, end_headline)\n print(\n \"☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢\")\n print(end_headline[\"headline\"] + \" - \" + end_headline[\"source\"])\n print(\n \"☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢☁☢\")\n\n if self.is_game_running is True:\n # Puts regions on 2 second cooldown\n time.sleep(2)\n\n # Moves regions back from occupied to free\n for region in war_regions:\n self.free_regions.append(region)\n self.occupied_regions.remove(region)\n\n def trigger_event(self):\n # Chance of nuclear war\n chance_annihilation = 0.001 * (self.death_count / 10_000_000)\n # Limits chance of nuclear war to 4 %\n if chance_annihilation > 0.04:\n chance_annihilation = 0.04\n # Prevents further nuclear wars if one has already been triggered\n if self.annihilation_triggered is True:\n chance_annihilation = 0\n # Chance of headline occurring\n chance_headline = 0.15\n # Base chance of catastrophe occurring\n base_chance_catastrophe = 0.10\n # Temperature increase since game start\n temperature_delta = self.temperature - 1\n # Chance of nothing happening\n chance_remaining = 1 - chance_headline - base_chance_catastrophe - chance_annihilation\n # Chance of catastrophe occurring depending on temperature\n catastrophe_function = 0.7 * (math.cos(math.pi + (temperature_delta / 3) * math.pi) + 1)\n if catastrophe_function > 1:\n catastrophe_function = 1\n chance_catastrophe = base_chance_catastrophe + catastrophe_function * chance_remaining\n # Increase chance of first catastrophe\n if self.has_first_catastrophe_happened is False:\n chance_catastrophe = 0.5\n\n # Picks random number\n random_number = random.randrange(0, 1000000) / 1000000\n\n # Triggers headline\n if random_number < chance_headline:\n self.trigger_headline()\n\n # Triggers catastrophe\n elif random_number < (chance_headline + chance_catastrophe):\n self.has_first_catastrophe_happened = True\n Thread(target=self.trigger_catastrophe, daemon=True).start()\n\n elif random_number < (chance_headline + chance_catastrophe + chance_annihilation):\n if self.annihilation_triggered is False:\n self.annihilation_triggered = True\n Thread(target=self.trigger_annihilation, daemon=True).start()\n\n # Triggers nothing\n else:\n print(\"--- Blank ---\")\n\n def run(self, skip_headlines):\n while True:\n # Game starts when any of the sensors are touched by the player\n print(\"\\nTouch any electrode to start game.\\n\")\n while self.is_game_running is False:\n if any(sensor > 15 for sensor in self.sensor_values):\n # Clears all attributes except headlines\n self.reset_attributes()\n self.is_game_running = True\n break\n\n # Waits for headline generation until at least 20 are available\n if len(self.headline_reserve) < 20 and not skip_headlines:\n self.are_headlines_loaded = False\n print(\"Waiting for GPT to return headlines...\\n\")\n while len(self.headline_reserve) < 20 and not skip_headlines:\n pass\n\n self.are_headlines_loaded = True\n\n print(\"/// SYMPTOMS startet ///\")\n print(\"\\n\")\n time.sleep(1)\n\n # Main game loop\n while self.year < 2100 and self.death_count < 10_000_000_000:\n self.trigger_event()\n self.count += 1\n if self.count == 10:\n self.year += 1\n self.count = 0\n self.set_temperature()\n print(\n \"┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄\")\n print(\n f\"JAHR {self.year} - {float(self.temperature):.2}°C - {int(self.death_count):,} TOTE - {len(self.occupied_regions)} AKTIVE REGION(EN) - ATOMKRIEG WAHRSCHEINLICHKEIT {(0.001 * (self.death_count / 10_000_000)):.2%}\")\n print(\n \"┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄\")\n time.sleep(0.3)\n self.is_game_running = False\n time.sleep(1)\n if self.death_count >= 10_000_000_000:\n print(f\"\\n// MENSCHHEIT AUSGESTORBEN, SPIEL ZU ENDE: {int(self.death_count):,} TOTE //\")\n else:\n print(f\"\\n// SPIEL ZU ENDE: {int(self.death_count):,} TOTE //\")\n if self.annihilation_triggered:\n print(\"★ DU HAST DIE ZERSTÖRUNG DER MENSCHHEIT DURCH DEN ATOMKRIEG VERHINDERT. GUT GEMACHT! ★\")\n time.sleep(4)\n\n def gui(self):\n def update_labels():\n \n year_label.config(text=str(self.year))\n temperature_label.config(text=f\"Aktuelle Erderwärmung: {self.temperature:.2f}°C\")\n\n headline_strings = self.used_headlines[:25]\n headlines_text = \"\"\n for headline in headline_strings:\n headlines_text += headline[\"headline\"] + \"\\n\" + \" - \" + headline[\"source\"] + \"\\n\\n\\n\"\n \n # if headline_strings is empty, UI shows starting condition\n if not headline_strings:\n placeholder_text = \"Kannst du die Welt retten? Versuch es sofort und berühre einen der leuchtenden Punkte\"\n headline_list_text.delete(1.0, tk.END)\n headline_list_text.insert(tk.END, placeholder_text)\n elif self.year == 2100 or self.death_count >= 10_000_000_000:\n end_game_text = f\"{int(self.death_count):,} Menschen sind durch die Folgen des Klimawandels umgekommen\"\n headline_list_text.delete(1.0, tk.END)\n headline_list_text.insert(tk.END, end_game_text)\n\n # closes Window after delay of 20 seconds\n # window.after(20000, window.destroy)\n else:\n current_text = headline_list_text.get(1.0, tk.END).strip()\n if current_text == \"Kannst du die Welt retten? Versuch es sofort und berühre einen der leuchtenden Punkte\":\n headline_list_text.delete(1.0, tk.END)\n # Insert the headlines text into the headline_list_text\n headline_list_text.delete(1.0, tk.END)\n headline_list_text.insert(tk.END, headlines_text)\n\n window.after(100, update_labels)\n\n window = tk.Tk()\n window.configure(bg='#DFE9F6')\n\n # Newsfeed according to screensize\n screen_width = window.winfo_screenwidth()\n screen_height = window.winfo_screenheight()\n\n window_width = screen_width \n news_wrap = screen_width // 1.5\n titel_size = screen_width // 40\n text_size = screen_width // 80\n news_size = screen_width // 120\n\n window.geometry(f\"{window_width}x{screen_height}\")\n\n # MainFrame\n frame = tk.Frame(window, bg='#DFE9F6', pady=24, padx=32)\n frame.grid(sticky='nsew') # Using grid instead of pack\n\n window.grid_rowconfigure(0, weight=1) # Make the frame expandable\n window.grid_columnconfigure(0, weight=1)\n\n # Header\n header = tk.Frame(frame, bg=window.cget(\"bg\"))\n header.grid(sticky='ew') # Header occupies the entire width\n\n # Logo\n logo_image = tk.PhotoImage(file=\"./assets/SYMPTOMS.png\")\n logo_label = tk.Label(header, image=logo_image, bg=window.cget(\"bg\"), pady=12)\n logo_label.pack()\n\n # Label for year variable\n year_label = tk.Label(header, text=str(self.year), font=(\"Inter\", titel_size), fg=\"#262626\",\n bg=window.cget(\"bg\"))\n year_label.pack(side=tk.LEFT)\n\n # Image for header\n image = tk.PhotoImage(file=\"./assets/Sorting.png\")\n resized_image = image.subsample(2, 2)\n image_label = tk.Label(header, image=resized_image, bg=window.cget(\"bg\"))\n image_label.pack(side=tk.RIGHT)\n\n # Frame for temperature_label\n temperature_frame = tk.Frame(frame, bg=window.cget(\"bg\"))\n temperature_frame.grid(sticky='ew') # temperature_frame occupies the entire width\n\n # Label for temperature variable\n temperature_label = tk.Label(temperature_frame, text=f\"Aktuelle Erderwärmung: {self.temperature:.2f}°C\",\n font=(\"Inter\", text_size), fg=\"#262626\", bg=window.cget(\"bg\"))\n temperature_label.pack(side=tk.TOP, anchor='w')\n\n # Newsframe\n newsframe = tk.Frame(frame, bg=window.cget(\"bg\"), pady=12, padx=24)\n newsframe.grid(sticky='nsew') # Newsframe fills the remaining space\n\n frame.grid_rowconfigure(2, weight=1) # Make the newsframe expandable\n frame.grid_columnconfigure(0, weight=1)\n\n # Postframe\n postframe = tk.Frame(newsframe, bg='#FFFFFF', padx=24)\n postframe.pack(side=tk.LEFT, anchor='n', expand=True)\n\n # TODO: Zum Schluss Windows_width auf 100% stellen\n # Label for the list of headlines\n headline_list_label = tk.Label(newsframe, text=\"Dein Newsfeed\", width=screen_width, font=(\"Inter\", news_size), fg=\"#262626\",\n wraplength=news_wrap, bg=window.cget(\"bg\"))\n headline_list_label.pack(fill=tk.Y, anchor='w')\n\n headline_list_text = tk.Text(newsframe, width=screen_width, pady=24, padx=24, font=(\"Inter\", news_size), fg=\"#262626\", wrap=tk.WORD)\n headline_list_text.pack(fill=tk.BOTH, expand=True) # Changed to fill both sides and expand\n\n update_labels()\n window.title(\"Symptoms\")\n window.mainloop()\n\n def main(self, skip_headlines=False, test_auto_start=False, start_p5=True, verbose=False):\n if start_p5 is True:\n # Starts osc bridge and p5 sketch\n subprocess.Popen(\"bridge.bat\")\n subprocess.Popen(\"serve.bat\")\n\n # Opens sketch in Browser window\n webbrowser.open_new(\"http://127.0.0.1:5000\")\n\n # Starts GUI with headlines, year & temperature\n Thread(target=self.gui, daemon=True).start()\n\n # Headline generation thread\n if not skip_headlines:\n Thread(target=self.generate_headlines, args=(verbose,), daemon=True).start()\n\n # Input fetching thread\n Thread(target=self.get_inputs, daemon=True).start()\n\n # Sends data to p5\n Thread(target=self.send_data, daemon=True).start()\n\n # Automatically starts game if enabled (does not reset game data after game ends!)\n if test_auto_start is True:\n self.is_game_running = True\n\n # Runtime\n self.run(skip_headlines)\n\n\nsymptoms = Symptoms()\n\n# Props:\n# skip_headlines: Whether headline generation is skipped (defaults to False)\n# test_auto_start: Immediately starts game (defaults to False)\n# start_p5: Starts p5 sketch & bridge and opens browser window (defaults to True)\n# verbose: Prints progress of headline generation (defaults to True)\nsymptoms.main(skip_headlines=False, test_auto_start=False, start_p5=True, verbose=False)\n","repo_name":"kpister/prompt-linter","sub_path":"data/scraping/repos/m-m-mic~symptoms/installation~runtime~game.py","file_name":"installation~runtime~game.py","file_ext":"py","file_size_in_byte":28829,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"9469632474","text":"from docplex.mp.model import Model\nfrom collections import namedtuple\nfrom sys import stdout\n\n# Initializing data\n# Cloud (c0) and cloudlets\n# units: storage(MB), cpu(MIPS), RAM(MB)\nCLOUDLETS = [\n (\"C0\", 500000, 3000000, 120000),\n (\"C1\", 510, 3100, 420),\n (\"C2\", 520, 3100, 420),\n (\"C3\", 530, 5300, 680)\n]\n\n# units: storage(MB), cpu(MIPS), RAM(MB), delayThreshold(ms)\nVMs = [\n (\"V1\", 'U1', 150, 2100, 120, 5000),\n (\"V2\", 'U1', 150, 2200, 220, 5000),\n (\"V3\", 'U1', 170, 2300, 320, 5000),\n (\"V4\", 'U1', 180, 2400, 420, 5000),\n (\"V5\", 'U1', 190, 2500, 520, 5000)\n]\n\n# Data tuple\nCloudlet = namedtuple(\"Cloudlet\", [\"c_name\", \"c_storage\", \"c_CPU\", \"c_RAM\"])\nVM = namedtuple(\"VM\", [\"v_name\", \"v_user\",\"v_storage\", \"v_CPU\", \"v_RAM\", \"v_delayTreshold\"])\n\n# COnnecting the data with the tuple\ncloudlets = [Cloudlet(*c) for c in CLOUDLETS]\nvms = [VM(*v) for v in VMs]\nidx = [(n.c_name,v.v_name) for n in cloudlets for v in vms]\n\nmodl = Model(\"Cloudlet-VM Allocation\")\n\n# creating variables\nx = modl.binary_var_dict(idx, name=\"allocate\")\n\n# storage contraint\nfor n in range(0, len(cloudlets)):\n modl.add_constraint(\n modl.sum(\n vms[v].v_storage*x[cloudlets[n].c_name, vms[v].v_name] for v in range(0, len(vms))\n ) <= cloudlets[n].c_storage\n , ctname=\"storageConstr\")\n\n# cpu contraint\nfor n in range(0, len(cloudlets)):\n modl.add_constraint(\n modl.sum(\n vms[v].v_CPU*x[cloudlets[n].c_name, vms[v].v_name] for v in range(0, len(vms))\n ) <= cloudlets[n].c_CPU\n , ctname=\"cpuConstr\")\n\n# ram contraint\nfor n in range(0, len(cloudlets)):\n modl.add_constraint(\n modl.sum(\n vms[v].v_RAM*x[cloudlets[n].c_name, vms[v].v_name] for v in range(0, len(vms))\n ) <= cloudlets[n].c_RAM\n , ctname=\"ramConstr\")\n\n# allocation constraint: a VM must be allocated (even in cloud), \n# but only in one place (i.e., a VM must not be allocated in two places)\nfor v in range(0, len(vms)):\n modl.add_constraint(\n modl.sum(\n x[cloudlets[n].c_name, vms[v].v_name] for n in range(0, len(cloudlets))\n ) == 1\n , ctname=\"AllocationConst\")\n\n# objective function\nmodl.minimize(modl.sum(x[cloudlets[0].c_name, vms[v].v_name] for v in range(0, len(vms))))\n\n# export the Linear Program model and solve\nmodl.export_as_lp(\"/home/jps/pli/cplex\")\nsolution = modl.solve()\n\n# print solution\nmodl.print_information()\nmodl.print_solution()","repo_name":"jpfsilvaa/AllocationILP","sub_path":"cplex/p1_cplex.py","file_name":"p1_cplex.py","file_ext":"py","file_size_in_byte":2492,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"38325039237","text":"# -*- coding: utf-8 -*-\n#\n# This file is part of the OpenHandWrite project software.\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 3 of the License, or\n# (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program. If not, see .\n#\nfrom __future__ import print_function\n\nfrom collections import OrderedDict\n\nfrom psychopy import gui\nfrom psychopy.iohub.client import launchHubServer\nfrom constants import *\n\n#\n### Misc. utility functions used by the python experiment template.\n#\n\ndef getImageFilePath(file_name):\n '''\n Returns the full, absolute, path to the image named file_name. file_name\n must be an image file located in the resources\\image folder of the project.\n If the file can not be found, None is returned.\n\n :param file_name: image file name\n :return: full path to image file in project, or None if file not found.\n '''\n pth = os.path.join(IMAGE_FOLDER,file_name)\n if os.path.exists(pth):\n return pth\n return None\n\ndef getAudioFilePath(file_name):\n '''\n Returns the full, absolute, path to the audio file named file_name.\n file_name must be an audio file located in the resources\\audio\n folder of the project. If the file can not be found, None is returned.\n\n :param file_name: audio file name\n :return: full path to audio file in project, or None if file not found.\n '''\n pth = os.path.join(AUDIO_FOLDER,file_name)\n if os.path.exists(pth):\n return pth\n return None\n\ndef getAvailableConditionsFileNames():\n '''\n Return a list of all .xlsx experiment condition file names that are in the\n projects conditions subfolder.\n :return: list of condition file name str\n '''\n if os.path.exists(CONDITIONS_FOLDER):\n import glob\n cvfile_paths = glob.glob(CONDITIONS_FOLDER+os.path.sep+'*.xlsx')\n return [ os.path.split(fpath)[1] for fpath in glob.glob(CONDITIONS_FOLDER+os.path.sep+'*.xlsx')]\n return []\n\ndef isImageFileCandidate(file_name):\n '''\n Returns True if the file_name str should be considered an image file name\n for use by an image stim graphic in the experiment. Otherwise returns False.\n :param file_name: candidate image name string\n :return: boolean\n '''\n try:\n fname, fext = file_name.rsplit('.')\n if fext in ACCEPTED_IMAGE_FORMATS:\n return True\n return False\n except:\n return False\n\ndef showSessionInfoDialog():\n '''\n Display a dialog to collect session or participant level information\n at the start of an experiment.\n\n If the dialog OK button is pressed, a dictionary with the values entered\n for each dialog input is returned. If thew dialogs Cancel button is pressed,\n None is returned.\n :return: dict of session info, or None if dialog was cancelled\n '''\n info = OrderedDict()\n info['Session Code'] = DEFAULT_SESSION_CODE\n info['Conditions File'] = getAvailableConditionsFileNames()\n# info['ExpName'] =EXP_NAME\n# info['ExpVersion'] = EXP_VERSION\n infoDlg = gui.DlgFromDict(dictionary=info,\n title='{} (v{})'.format(EXP_NAME, EXP_VERSION),\n order = info.keys(),\n )\n # fixed=['ExpName','ExpVersion'])\n if infoDlg.OK:\n return info\n return None\n\ndef start_iohub(sess_info):\n '''\n Starts the iohub server process, using data from the dict returned by\n showSessionInfoDialog() to create the hdf5 file name. If the file\n already exists, the existing file is renamed so that it is not\n overwritten by the current sessions data.\n\n iohub device configuration information is read from an\n 'iohub_config.yaml' file which must be in the same folder as this file.\n\n The created ioHubConnection object is returned after the iohub\n server has started and is ready for interaction with the experiment\n runtime.\n\n :param sess_info: dict returned from showSessionInfoDialog()\n :return: ioHubConnection object\n '''\n import os, shutil\n\n save_to = os.path.join(os.path.dirname(__file__),u'results',\n sess_info['Session Code'])\n save_to = os.path.normpath(save_to)\n if not save_to.endswith('.hdf5'):\n save_to = save_to+u'.hdf5'\n\n fdir, sess_code = os.path.split(save_to)\n if not os.path.exists(fdir):\n os.mkdir(fdir)\n\n #TODO: Ask if file should be overwritten, or new session code entered.\n si = 1\n save_dest = save_to\n while os.path.exists(save_dest):\n sname, sext = sess_code.rsplit(u'.',1)\n save_dest = os.path.join(fdir, u\"{}_{}.{}\".format(sname,si,sext))\n si+=1\n\n if save_dest is not save_to:\n shutil.move(save_to,save_dest)\n\n sess_code=sess_code[0:min(len(sess_code),24)]\n if sess_code.endswith(u'.hdf5'):\n sess_code = sess_code[:-5]\n if save_to.endswith(u'.hdf5'):\n save_to = save_to[:-5]\n\n kwargs={'experiment_code':EXP_NAME,\n 'session_code':sess_code,\n 'datastore_name':save_to,\n 'iohub_config_name': 'iohub_config.yaml'\n }\n return launchHubServer(**kwargs)\n\ndef saveWintabDeviceHardwareInfo(io):\n '''\n Save all available wintab device hardware information to the sessions .hdf5\n file as a series of experiment message events. This function is called at\n the start of the experiment, after the start_iohub() function has returned\n the created iohub connection object.\n\n The following areas of information are saved:\n\n * wintab device hardware model information\n * the availability, data range, etc, for each axis of the wintab device\n * wintab context values read from the C CONTEXT struct at device init\n\n :param io: ioHubConnection instance\n :return: None\n '''\n wtdev = io.devices.tablet\n\n io.sendMessageEvent(text=\"START WINTAB HW MODEL INFO\")\n for k, v in wtdev.model.items():\n io.sendMessageEvent(text=\"{}: {}\".format(k,v))\n io.sendMessageEvent(text=\"STOP WINTAB HW MODEL INFO\")\n\n io.sendMessageEvent(text=\"START WINTAB AXIS INFO\")\n for axname, axinfo in wtdev.axis.items():\n io.sendMessageEvent(text=\"{} Axis:\".format(axname))\n for k, v in axinfo.items():\n io.sendMessageEvent(text=\"{}: {}\".format(k,v))\n io.sendMessageEvent(text=\"END WINTAB AXIS INFO\")\n\n io.sendMessageEvent(text=\"START WINTAB CONTEXT INFO\")\n for k, v in wtdev.context.items():\n io.sendMessageEvent(text=\"{}: {}\".format(k,v))\n io.sendMessageEvent(text=\"END WINTAB CONTEXT INFO\")\n\n","repo_name":"isolver/OpenHandWrite","sub_path":"distribution/getwrite/experiments/ExperimentTemplate/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":7017,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"50"} +{"seq_id":"26641901066","text":"from appium import webdriver\nfrom appium.webdriver.common.touch_action import TouchAction\n\ndesired_caps = {}\ndesired_caps['platformName'] = 'Android'\ndesired_caps['platdormVersion'] = '6.0'\ndesired_caps['deviceName'] = '192.168.208.101:5555'\ndesired_caps['unicodeKeyboard'] = True\ndesired_caps['resetKeyboard'] = True\ndriver = webdriver.Remote('http://localhost:4723/wd/hub',desired_caps)\ndriver.implicitly_wait(10)\n# print(driver.current_package)\n# print(driver.current_activity)\ndriver.start_activity('com.android.settings','.ChooseLockPattern')\n# TouchAction(driver).press(x=210,y=258).move_to(x=90,y=0).move_to(x=90,y=0).move_to(x=0,y=90).move_to(x=0,y=90).move_to(x=-90,y=0).move_to(x=0,y=-90).perform()\n# TouchAction(driver)\\\n# .press(x=210,y=258)\\\n# .move_to(x=300,y=258)\\\n# .move_to(x=390,y=258)\\\n# .move_to(x=390,y=348)\\\n# .move_to(x=390,y=438)\\\n# .move_to(x=300,y=438)\\\n# .move_to(x=300,y=348)\\\n# .move_to(x=210,y=438)\\\n# .release().perform()\n\n(TouchAction(driver)\n .press(x=210,y=258)\n .move_to(x=300,y=258)\n .move_to(x=390,y=258)\n .move_to(x=390,y=348)\n .move_to(x=390,y=438)\n .move_to(x=300,y=438)\n .move_to(x=300,y=348)\n .move_to(x=210,y=438)\n .release().perform())\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"sunny970/phone_automation","sub_path":"移动自动化/appium/test06_fingeraction.py","file_name":"test06_fingeraction.py","file_ext":"py","file_size_in_byte":1286,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"16033635972","text":"from movements import possible_movements, get_random_movement\nfrom validations import valid_move\n\nbord = [\n ['A1', 'B1', 'C1', 'D1', 'E1', 'F1', 'G1', 'H1'],\n ['A2', 'B2', 'C2', 'D2', 'E2', 'F2', 'G2', 'H2'],\n ['A3', 'B3', 'C3', 'D3', 'E3', 'F3', 'G3', 'H3'],\n ['A4', 'B4', 'C4', 'D4', 'E4', 'F4', 'G4', 'H4'],\n ['A5', 'B5', 'C5', 'D5', 'E5', 'F5', 'G5', 'H5'],\n ['A6', 'B6', 'C6', 'D6', 'E6', 'F6', 'G6', 'H6'],\n ['A7', 'B7', 'C7', 'D7', 'E7', 'F7', 'G7', 'H7'],\n ['A8', 'B8', 'C8', 'D8', 'E8', 'F8', 'G8', 'H8']\n]\n\n\ndef horse_walk():\n current_position = [0, 0]\n visited_squares = [[0, 0]]\n flag = True\n while flag:\n movements = possible_movements(current_position)\n movement = get_random_movement(movements)\n\n if not valid_move(movement, visited_squares):\n\n while not valid_move(movement, visited_squares):\n movement = get_random_movement(movements)\n valid_move(movement, visited_squares)\n\n if len(movements) == 0:\n flag = False\n break\n\n if valid_move(movement, visited_squares):\n visited_squares.append(movement)\n current_position = movement\n\n print(visited_squares)\n for position in visited_squares:\n print(f'{position} = {bord[position[0]][position[1]]}')\n\n print(len(visited_squares))\n\n\nhorse_walk()\n","repo_name":"ArturStefan/horse-problem","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1402,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"26799599736","text":"#!/usr/bin/python3\n\ndef first():\n var1=input(\"Enter the first number: \")\n var2=input(\"Enter the second number: \")\n\n print(var1/var2)\n\ndef second():\n var1=int(input(\"Enter the first number: \"))\n var2=int(input(\"Enter the second number: \"))\n\n print(var1/var2)\n\ndef third():\n retvalue1=\"\"\n while(not retvalue1.isnumeric()):\n retvalue1=input(\"Type in first a number: \") \n retvalue2=\"\"\n while(not retvalue2.isnumeric()):\n retvalue2=input(\"Type in a second number: \") \n retvalue1=int(retvalue1)\n retvalue2=int(retvalue2)\n print(retvalue1/retvalue2)\n\ndef fourth():\n while True:\n try:\n val1 = int(input(\"Enter the first number: \"))\n val2 = int(input(\"Enter the second number: \"))\n break\n except:\n print(\"You did not enter a number\")\n continue\n print(val1/val2)\n\ndef fith():\n mylist = [\"obj1\",\"obj2\",\"obj3\"]\n myvar1 = 32\n myvar2 = \"Something else\"\n print('{} {}'.format(myvar1,myvar2))\n print('{1} {0}'.format(myvar1,myvar2))\n print('{} {}'.format(mylist[1],mylist[2]))\n\ndef sixth():\n for i in range(1,13):\n print(\"{:3d} {:4d} {:5d}\".format(i, i**2, i**3))\n\nif __name__==\"__main__\":\n# first()\n# second()\n# third()\n# fourth()\n# fith()\n sixth()\n","repo_name":"apariyar/Den-Core-2019","sub_path":"Systems/secureprog/input_validation.py","file_name":"input_validation.py","file_ext":"py","file_size_in_byte":1308,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"30409593071","text":"import numpy as np\nfrom sigmoid import * \n\ndef costFunction(theta, X, y, lam):\n X = np.c_[np.ones(X.shape[0]), X]\n m = y.shape[0]\n J = 0\n\n HX = sigmoid(X.dot(theta.T))\n \n firstPartOfCost = -( (y.T).dot(np.log(HX)))\n secondPartOfCost = ((1.0 - y).T).dot(np.log(1.0-HX))\n regularizationTerm = (lam/(2.0 * m)) * np.sum( np.power(theta[1:],2)) \n \n J = ((1.0/m) * (firstPartOfCost - secondPartOfCost)) + regularizationTerm\n return J[0]\n\ndef gradients(theta, X, y, lam):\n X = np.c_[np.ones(X.shape[0]), X]\n m = y.shape[0]\n J = 0\n grads = np.zeros(theta.shape[0])\n\n HX = sigmoid(X.dot(theta.T))\n\n grads[0] = (1.0/m) * ((HX - y.T).dot(X[:,0]))\n grads[1:] = ((1.0/m) * ((HX - y.T).dot(X[:,1:]))) + ((lam/m) * theta[1:])\n return grads\n","repo_name":"vimalvnair/purple-horse","sub_path":"stop-signal-identification/cost_function.py","file_name":"cost_function.py","file_ext":"py","file_size_in_byte":782,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"26941666670","text":"import numpy as np\nimport pylab as pl\nimport scipy.signal as signal\nimport fir1\n\nfs = 1000\n\nNTAPS = 100\nLEARNING_RATE = 0.0005\n\necg = np.loadtxt(\"ecg50hz.dat\")\npl.figure(1)\npl.plot(ecg)\n\nf = fir1.Fir1(NTAPS)\nf.setLearningRate(LEARNING_RATE);\n\ny= np.empty(len(ecg))\nfor i in range(len(ecg)):\n ref_noise = np.sin(2.0 * np.pi / 20.0 * i);\n canceller = f.filter(ref_noise)\n output_signal = ecg[i] - canceller\n f.lms_update(output_signal)\n y[i] = output_signal\n\npl.figure(2)\npl.plot(y)\npl.show()\n","repo_name":"jys168723/fir1","sub_path":"demo/lms_50Hz_ecg_filter.py","file_name":"lms_50Hz_ecg_filter.py","file_ext":"py","file_size_in_byte":506,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"50"} +{"seq_id":"6312371323","text":"import demistomock as demisto # noqa: F401\nfrom CommonServerPython import * # noqa: F401\nfrom typing import Tuple\n\nBELOW_THRESHOLD_ITEMS_CONTEXT_PATH = 'LowerSimilarityIncidents'\nABOVE_THE_THRESHOLD_ITEMS_CONTEXT_PATH = 'incidents'\n\n\ndef save_to_context(items: list, context_path: str, delete_existing: bool = False, is_sub_playbook: str = 'auto',\n table_header='Incidents Result'):\n if delete_existing:\n res = demisto.executeCommand('DeleteContext', {\"key\": context_path, \"subplaybook\": is_sub_playbook})\n if is_error(res):\n return_error('Failed to delete current context. Error details:\\n{}'.format(get_error(res)))\n\n return CommandResults(\n outputs_prefix=context_path,\n outputs=items,\n readable_output=tableToMarkdown(table_header, items))\n\n\ndef _get_incident_campaign(_id: int):\n res = demisto.executeCommand('getIncidents', {'id': _id})\n\n if is_error(res):\n return\n\n res_custom_fields = res[0]['Contents']['data'][0]['CustomFields']\n return res_custom_fields['partofcampaign'] if 'partofcampaign' in res_custom_fields else None\n\n\ndef filter_by_threshold(context: list, threshold: float) -> Tuple[list, list]:\n low = []\n high = []\n for item in context:\n if item.get('similarity') >= threshold:\n high.append(item)\n else:\n campaign = _get_incident_campaign(item['id'])\n if campaign:\n high.append(item)\n else:\n low.append(item)\n return low, high\n\n\ndef main():\n input_args = demisto.args()\n # If user did not provide a lower threshold then split is not needed.\n threshold = input_args.get('similarity_threshold')\n\n try:\n threshold = float(threshold)\n except ValueError as e:\n raise DemistoException(f'Could not use threshold: {threshold}. Error: {e}')\n\n root_context_path = 'EmailCampaign'\n above_threshold_context_path = f'{root_context_path}.{ABOVE_THE_THRESHOLD_ITEMS_CONTEXT_PATH}'\n below_threshold_context_path = f'{root_context_path}.{BELOW_THRESHOLD_ITEMS_CONTEXT_PATH}'\n context = demisto.get(demisto.context(), f'{above_threshold_context_path}')\n\n # If there are no incident to split\n if not context:\n return\n only_lower_values, only_higher_values = filter_by_threshold(context, threshold)\n result = []\n result.append(save_to_context(only_lower_values, below_threshold_context_path,\n table_header='Low Similarity Incidents Result'))\n result.append(save_to_context(only_higher_values, above_threshold_context_path, True,\n table_header='High Similarity Incidents Result'))\n return_results(result)\n\n\nif __name__ in ['__main__', '__builtin__', 'builtins']:\n main()\n","repo_name":"demisto/content","sub_path":"Packs/Campaign/Scripts/SplitCampaignContext/SplitCampaignContext.py","file_name":"SplitCampaignContext.py","file_ext":"py","file_size_in_byte":2801,"program_lang":"python","lang":"en","doc_type":"code","stars":1023,"dataset":"github-code","pt":"50"} +{"seq_id":"7507196809","text":"#!/usr/bin/env python\n\n\"\"\"The setup script.\"\"\"\n\nfrom setuptools import setup, find_packages\n\nwith open('README.rst') as readme_file:\n readme = readme_file.read()\n\nwith open('HISTORY.rst') as history_file:\n history = history_file.read()\n\nrequirements = ['Click>=7.0', ]\n\ntest_requirements = ['pytest>=3', ]\n\nsetup(\n author=\"Robert G Hennessy\",\n author_email='robertghennessy@gmail.com',\n python_requires='>=3.6',\n classifiers=[\n 'Development Status :: 2 - Pre-Alpha',\n 'Intended Audience :: Developers',\n 'License :: OSI Approved :: GNU General Public License v3 (GPLv3)',\n 'Natural Language :: English',\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3.6',\n 'Programming Language :: Python :: 3.7',\n 'Programming Language :: Python :: 3.8',\n ],\n description=\"Send out notifications for delays and next bbus or train arrival for transit operators using SIRI API (ex. Caltrain and SFMTA)\",\n entry_points={\n 'console_scripts': [\n 'transit_notification=transit_notification.cli:main',\n ],\n },\n install_requires=requirements,\n license=\"GNU General Public License v3\",\n long_description=readme + '\\n\\n' + history,\n include_package_data=True,\n keywords='transit_notification',\n name='transit_notification',\n packages=find_packages(include=['transit_notification', 'transit_notification.*']),\n test_suite='tests',\n tests_require=test_requirements,\n url='https://github.com/robertghennessy/transit_notification',\n version='0.1.0',\n zip_safe=False,\n)\n","repo_name":"robertghennessy/transit_notification","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1617,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"30978170420","text":"import math\nimport random\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\ndef cut_batch(tensor, batch_size, model):\n '''\n tensor - len x long x *\n cut long into batch_sizes and feed in model\n '''\n lon = tensor.shape[1]\n num = int(math.ceil(float(lon) / batch_size))\n outs = []\n for j in range(num):\n start = j*batch_size\n end = min((j+1)*batch_size, lon)\n ten = tensor[:,start:end] # len x batch x *\n out = model(ten) # len x batch x *\n outs.append(out)\n outs = torch.cat(outs, dim=1) # len x long x *\n return outs \n\ndef run_pretrain_transformer(conj, deps, step, neg_step, pt, percent, device, loss_fn, return_outputs=False):\n '''\n pretrain using random mask char\n conj - batch x clen\n deps - [num_dep x dlen], len=batch\n step - [num_step x slen], len=batch\n pt - pretrain_transformer\n percent - masking percent\n '''\n # turn into tensors\n conj = torch.LongTensor(conj).to(device).transpose(0,1) # clen x batch\n b=conj.shape[1]\n deps = torch.cat([torch.LongTensor(d[random.randint(0, d.shape[0]-1)]).unsqueeze(1) for d in deps], dim=1).to(device) # 256 x b, randomly choose 1 for each sample\n step = torch.cat([torch.LongTensor(s[random.randint(0, s.shape[0]-1)]).unsqueeze(1) for s in step], dim=1).to(device) # 256 x b, randomly choose 1 for each sample\n neg_step = torch.cat([torch.LongTensor(s[random.randint(0, s.shape[0]-1)]).unsqueeze(1) for s in neg_step], dim=1).to(device) # 256 x b, randomly choose 1 for each sample\n\n sample = torch.cat([conj,deps,step,neg_step],dim=1) # 256 x 4b\n\n encoder = nn.DataParallel(pt['encoder'],[0,1,3],dim=1)\n decoder = nn.DataParallel(pt['decoder'],[0,1,3],dim=1)\n\n while 1:\n mask = torch.ones(sample.shape[0]*sample.shape[1]).long().to(device)\n mask[:int(math.ceil(len(mask)*percent))]=0\n mask = mask[torch.randperm(len(mask))]\n mask = mask.reshape(sample.shape)\n masked_sample = sample*mask\n if torch.sum(masked_sample != sample)>0 and masked_sample.sum(0).min() > 0:\n break\n hidden = encoder(masked_sample) # len x batch x channel\n output = decoder(hidden) # len x batch x vocab_size\n if return_outputs:\n return sample, mask, output\n res = loss_fn(sample, output, mask)\n if res == 0.0:\n return None\n loss, corrects, total = res\n pt['encoder'] = encoder.module\n pt['decoder'] = decoder.module\n return loss, corrects, total\n\ndef run_step_cls_transformer(conj, deps, step, labels, sct, use_deps, device, loss_fn):\n '''\n int-encoded inputs, 0 padded, numpy\n conj - batch x clen\n deps - [num_dep x dlen], len=batch\n step - batch x slen\n sct - step_cls_transformer\n use_deps - bool, used deps or not\n '''\n # turn into tensors\n conj = torch.LongTensor(conj).to(device).transpose(0,1) # clen x batch\n step = torch.LongTensor(step).to(device).transpose(0,1) # (1+slen) x batch\n b=conj.shape[1]\n if use_deps:\n deps = torch.cat([torch.LongTensor(d[random.randint(0, d.shape[0]-1)]).unsqueeze(1) for d in deps], dim=1).to(device) # 256 x b, randomly choose 1 for each sample\n\n conj_encoder = nn.DataParallel(sct['conj_encoder'],[0,1,3],dim=1)\n deps_encoder = nn.DataParallel(sct['deps_encoder'],[0,1,3],dim=1)\n step_decoder = nn.DataParallel(sct['step_decoder'],[0,1,3],dim=1)\n\n encoded_conj = conj_encoder(conj) # clen x batch x channel\n\n if use_deps:\n encoded_deps = deps_encoder(deps) # dlen x batch x channel\n memory = torch.cat([encoded_conj,encoded_deps], dim=0) # (256*2) x batch x channel\n else:\n memory = encoded_conj\n\n outputs = step_decoder(step ,memory, mask_tgt=True)[0] # batch x cls_num\n labels = torch.LongTensor(labels).to(device)\n loss, corrects, total = loss_fn(outputs, labels)\n return loss, corrects, total\n\ndef run_step_gen_transformer(conj, deps, step, sgt, d_model, device, loss_fn, return_outputs=False):\n '''\n conj - batch x clen\n deps - [num_dep x dlen], len=batch\n sgt - step_gen_transformer\n '''\n # turn into tensors\n conj = torch.LongTensor(conj).to(device).transpose(0,1) # clen x batch\n deps = torch.cat([torch.LongTensor(d[random.randint(0, d.shape[0]-1)]).unsqueeze(1) for d in deps], dim=1).to(device) # 256 x b, randomly choose 1 for each sample\n\n conj_encoder = nn.DataParallel(sgt['conj_encoder'],[0,1,3],dim=1)\n deps_encoder = nn.DataParallel(sgt['deps_encoder'],[0,1,3],dim=1)\n step_decoder = nn.DataParallel(sgt['step_decoder'],[0,1,3],dim=1)\n\n b=conj.shape[1]\n\n encoded_conj = conj_encoder(conj) # clen x batch x channel\n encoded_deps = deps_encoder(deps) # dlen x batch x channel\n\n memory = torch.cat([encoded_conj,encoded_deps],dim=0) # (256*2) x batch x channel\n\n tgt = torch.randn(conj.shape[0], b, d_model).to(device)\n outputs = step_decoder(tgt ,memory, mask_tgt=False) # tlen x batch x cls_num\n if return_outputs:\n return conj, deps, outputs\n else:\n step = [torch.LongTensor(s).to(device).transpose(0,1) for s in step] # [tlen' x num_dep]\n loss, corrects, total = loss_fn(outputs, step)\n return loss, corrects, total \n\nif __name__ == '__main__':\n from data_utils import DataParser\n from model import build_pretrain_transformer, build_step_cls_transformer, build_step_gen_transformer\n from loss import *\n\n dataparser = DataParser('../holstep', max_len=256, use_tokens=False, verbose=True, saved_vocab='vocab.pkl', saved_train_conj='train_conj.pkl', saved_val_conj='val_conj.pkl', saved_test_conj='test_conj.pkl', saved_max_len=57846)\n\n pre_train_gen = dataparser.conj_generator(split='train', batch_size=1, shuffle=True, load_neg_steps=True)\n pre_val_gen = dataparser.conj_generator(split='val', batch_size=1, shuffle=False, load_neg_steps=True)\n\n cls_train_gen= dataparser.steps_generator(split='train', batch_size=1, shuffle=True)\n cls_val_gen= dataparser.steps_generator(split='val', batch_size=1, shuffle=False)\n\n gen_train_gen= dataparser.conj_generator(split='train', batch_size=1, shuffle=True, load_neg_steps=False)\n gen_val_gen= dataparser.conj_generator(split='val', batch_size=1, shuffle=False, load_neg_steps=False)\n\n d_model = 8\n n_head=8\n n_hid=16\n n_layers=6\n pt = build_pretrain_transformer(dataparser.vocab_size+3, dataparser.max_len,d_model, n_head, n_hid, n_layers) # ,,\n sct = build_step_cls_transformer(dataparser.vocab_size+3, dataparser.max_len,d_model, n_head, n_hid, n_layers)\n sgt = build_step_gen_transformer(dataparser.vocab_size+3, dataparser.max_len, d_model, n_head, n_hid, n_layers)\n\n device = 'cuda:0' if torch.cuda.is_available() else 'cpu'\n '''\n for i, data in enumerate(cls_train_gen):\n print(i)\n loss = run_step_cls_transformer(*data, sct, device, cls_loss)\n break\n for i, data in enumerate(gen_train_gen):\n print(i)\n loss = run_step_gen_transformer(*data, sgt, d_model, device, gen_loss)\n break\n '''\n for i, data in enumerate(pre_train_gen):\n print(i)\n loss = run_pretrain_transformer(*data, pt, 0.2, device, pretrain_loss)\n break\n","repo_name":"arthurhero/HOL-transformer","sub_path":"run_models.py","file_name":"run_models.py","file_ext":"py","file_size_in_byte":7217,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"18270685733","text":"import numpy as np\nimport laspy as las\n\n# Determine if a point is inside a given polygon or not\n# Polygon is a list of (x,y) pairs. This function\n# returns True or False. The algorithm is called\n# the \"Ray Casting Method\".\n# the point_in_poly algorithm was found here:\n# http://geospatialpython.com/2011/01/point-in-polygon.html\ndef point_in_poly(x,y,poly):\n\n n = len(poly)\n inside = False\n\n p1x,p1y = poly[0]\n for i in range(n+1):\n p2x,p2y = poly[i % n]\n if y > min(p1y,p2y):\n if y <= max(p1y,p2y):\n if x <= max(p1x,p2x):\n if p1y != p2y:\n xints = (y-p1y)*(p2x-p1x)/(p2y-p1y)+p1x\n if p1x == p2x or x <= xints:\n inside = not inside\n p1x,p1y = p2x,p2y\n\n return inside\n\n# This one is my own version of the ray-trace algorithm which utilises the numpy arrays so that a list of x and y coordinates can be processed in one call and only points inside polygon are returned alongside the indices in case required for future referencing. This saves a fair bit of looping.\ndef points_in_poly(x,y,poly):\n n = len(poly)\n inside=np.zeros(x.size,dtype=bool)\n xints=np.zeros(x.size)\n\n p1x,p1y = poly[0]\n for i in range(n+1):\n p2x,p2y=poly[i % n]\n if p1y!=p2y:\n xints[np.all([y>min(p1y,p2y), y<=max(p1y,p2y), x<=max(p1x,p2x)],axis=0)] = (y[np.all([y>min(p1y,p2y), y<=max(p1y,p2y), x<=max(p1x,p2x)],axis=0)]-p1y)*(p2x-p1x)/(p2y-p1y)+p1x\n if p1x==p2x:\n inside[np.all([y>min(p1y,p2y), y<=max(p1y,p2y), x<=max(p1x,p2x)],axis=0)] = np.invert(inside[np.all([y>min(p1y,p2y), y<=max(p1y,p2y), x<=max(p1x,p2x)],axis=0)])\n else:\n inside[np.all([y>min(p1y,p2y), y<=max(p1y,p2y), x<=max(p1x,p2x),x<=xints],axis=0)] = np.invert(inside[np.all([y>min(p1y,p2y), y<=max(p1y,p2y), x<=max(p1x,p2x),x<=xints],axis=0)])\n p1x,p1y = p2x,p2y\n\n return x[inside],y[inside], inside\n\n# This retrieves all points within circular neighbourhood, Terget point is the location around which the neighbourhood search is conducted, for a specified search radius. x and y are vectors with the x and y coordinates of the test points\ndef points_in_radius(x,y,target_x, target_y,radius):\n inside=np.zeros(x.size,dtype=bool)\n d2=(x-target_x)**2+(y-target_y)**2\n inside = d2<=radius**2\n return x[inside],y[inside], inside\n\n# filter lidar wth polygon\n# This function has been updated to include an option to filter by first return location.\n# The reason for this is so full collections of returns associated with each LiDAR pulse\n# can be retrieved, which can be an issue at edges in multi-return analyses\ndef filter_lidar_data_by_polygon(in_pts,polygon,filter_by_first_return_location = False):\n pts = np.zeros((0,in_pts.shape[1]))\n if in_pts.shape[0]>0:\n if filter_by_first_return_location:\n # find first returns\n mask = in_pts[:,3]==1\n x_temp, y_temp, inside_temp = points_in_poly(in_pts[mask,0],in_pts[mask,1],polygon)\n shots = np.unique(in_pts[mask,6][inside_temp]) # index 6 refers to GPS time\n inside = np.in1d(in_pts[:,6],shots) # this function retrieves all points corresponding to this GPS time\n x = in_pts[inside,0]\n y = in_pts[inside,1]\n x_temp=None\n y_temp=None\n inside_temp=None\n else:\n x,y,inside = points_in_poly(in_pts[:,0],in_pts[:,1],polygon)\n pts = in_pts[inside,:]\n else:\n print(\"\\t\\t\\t no points in polygon\")\n return pts\n\n# filter lidar by circular neighbourhood\ndef filter_lidar_data_by_neighbourhood(in_pts,target_xy,radius):\n pts = np.zeros((0,in_pts.shape[1]))\n if in_pts.shape[0]>0:\n x,y,inside = points_in_radius(in_pts[:,0],in_pts[:,1],target_xy[0],target_xy[1],radius)\n pts = in_pts[inside,:]\n else:\n print( \"\\t\\t\\t no points in neighbourhood\")\n return pts\n","repo_name":"DTMilodowski/LiDAR_canopy","sub_path":"src/LiDAR_tools.py","file_name":"LiDAR_tools.py","file_ext":"py","file_size_in_byte":3971,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"50"} +{"seq_id":"24277283464","text":"import asyncio,aiohttp,ssl,random,datetime,time,re,json,collections,functools\r\nfrom faker import Faker\r\nimport urllib.parse as parse\r\n\r\nfaker = Faker('zh-cn')\r\n\r\nssl._create_default_https_context = ssl._create_unverified_context\r\nssl_context = ssl.create_default_context()\r\nssl_context.check_hostname = False\r\nssl_context.verify_mode = ssl.CERT_NONE\r\n\r\ndef get_ts():\r\n return datetime.datetime.now().isoformat(' ')\r\n\r\ndef get_ua():\r\n return faker.user_agent()\r\n\r\ndef get_id():\r\n return str(random.randint(1,8))\r\n\r\ndef get_phone():\r\n return str(faker.phone_number())\r\n\r\n@functools.lru_cache()\r\ndef load_citys(fn='citys.txt'):\r\n with open(fn,'r',encoding='utf-8') as f:\r\n return f.read().split()\r\n\r\ndef get_area():\r\n return random.choice(load_citys())\r\n\r\ndef get_qq():\r\n if random.random() < 0.8:\r\n a = random.randrange(10000,1000000000)\r\n else:\r\n a = random.randrange(1000000000,5000000000)\r\n return str(a)\r\n\r\ndef get_mobile_code():\r\n return ''.join(map(str,(random.randrange(10) for _ in range(6))))\r\n\r\ndef rand_bool():\r\n return random.random() < 0.5\r\n\r\ndef get_password():\r\n while True:\r\n k = dict(\r\n length = random.randint(8,16),\r\n special_chars = rand_bool(),\r\n digits = rand_bool(),\r\n upper_case = rand_bool(),\r\n lower_case = rand_bool(),\r\n )\r\n try:\r\n return faker.password(**k)\r\n except Exception:\r\n pass\r\n\r\ndef get_headers():\r\n return {\r\n 'User-Agent': faker.user_agent(),\r\n }\r\n\r\ndef auto_json(res):\r\n try:\r\n return json.loads(res)\r\n except Exception:\r\n return res\r\n\r\ndef detect_csrf(html):\r\n pt = r'\\'\r\n p = re.search(pt,html)\r\n if p:\r\n return p.group(1)\r\n\r\nclass worker:\r\n time_limit = random.uniform(5,6) * 3600\r\n def __init__(self,rand_mode=True):\r\n self.logd = {}\r\n self.tasks = []\r\n self.rand_mode = rand_mode\r\n \r\n def set_alive(self):\r\n self.alive = time.time()\r\n \r\n def is_alive(self):\r\n return time.time() - self.alive < 60\r\n \r\n def add_task(self,co):\r\n self.tasks.append(asyncio.Task(co))\r\n \r\n async def task_exit(self):\r\n await asyncio.sleep(self.time_limit)\r\n print('time limit! exiting...')\r\n exit()\r\n\r\n async def post(self,*a,csrf=None,headers=None,**k):\r\n session = self.session\r\n if csrf:\r\n headers = headers or {}\r\n headers = headers.copy()\r\n headers['X-CSRF-Token'] = csrf\r\n async with session.post(*a,headers=headers,**k,ssl=ssl_context) as response:\r\n res = await response.text()\r\n return auto_json(res)\r\n\r\n async def get(self,*a,**k):\r\n session = self.session\r\n async with session.get(*a,**k,ssl=ssl_context) as response:\r\n res = await response.text()\r\n return auto_json(res)\r\n \r\n async def query_qq_api(self,t):\r\n entry = 'https://apis.map.qq.com/'\r\n key = 'PTMBZ-GCQLW-SC2RG-R2FNI-HWPNQ-4PBQM'\r\n city = parse.quote(faker.city())\r\n kw = parse.quote(faker.word())\r\n x = random.uniform(0,90)\r\n y = random.uniform(0,180)\r\n x2 = random.uniform(0,90)\r\n y2 = random.uniform(0,180)\r\n if t == 0: # https://apis.map.qq.com/ws/location/v1/ip?key=PTMBZ-GCQLW-SC2RG-R2FNI-HWPNQ-4PBQM\r\n url = f'{entry}ws/location/v1/ip?key={key}'\r\n elif t == 1:\r\n url = f'{entry}ws/geocoder/v1/?address={city}&key={key}'\r\n elif t == 2:\r\n url = f'{entry}ws/place/v1/search?boundary=nearby({x},{y},1000)&keyword={kw}&page_size=10&page_index=1&key={key}'\r\n elif t == 3:\r\n url = f'{entry}ws/place/v1/suggestion/?region={city}&keyword={kw}&key={key}'\r\n elif t == 4:\r\n url = f'{entry}ws/geocoder/v1/?location={x},{y}&key={key}&get_poi=1'\r\n elif t == 5:\r\n url = f'{entry}ws/direction/v1/bicycling/?from={x},{y}&to={x2},{y2}&key={key}'\r\n elif t == 6:\r\n url = f'{entry}ws/direction/v1/ebicycling/?from={x},{y}&to={x2},{y2}&key={key}'\r\n elif t == 7:\r\n url = f'{entry}ws/direction/v1/transit/?from={x},{y}&to={x2},{y2}&key={key}'\r\n elif t == 8:\r\n url = f'{entry}ws/direction/v1/driving/?from={x},{y}&to={x2},{y2}&key={key}'\r\n elif t == 9:\r\n url = f'{entry}ws/direction/v1/walking/?from={x},{y}&to={x2},{y2}&key={key}'\r\n headers = get_headers()\r\n r = await self.get(url,headers=headers)\r\n try:\r\n m = r['message']\r\n except Exception:\r\n m = str(r)\r\n if '每日调用量已达到上限' in m:\r\n return 'limit_day'\r\n if '已达到上限' in m:\r\n return 'limit'\r\n return 'ok'\r\n \r\n async def task_qq_api(self,t):\r\n sd = self.qq_states[t]\r\n while True:\r\n await asyncio.sleep(0)\r\n try:\r\n m = await self.query_qq_api(t)\r\n except Exception as e:\r\n print('qq_api',t,repr(e))\r\n m = 'err'\r\n sd[m] += 1\r\n if sd['limit_day'] > 10:return\r\n if m == 'ok':\r\n self.set_alive()\r\n \r\n async def start_qq_api(self,tn=10,n_con=5):\r\n self.qq_states = [collections.defaultdict(int) for _ in range(tn)]\r\n for _ in range(n_con):\r\n for t in range(tn):\r\n self.add_task(self.task_qq_api(t))\r\n await asyncio.sleep(0.1)\r\n \r\n async def query_toupiao(self,host):\r\n headers = get_headers()\r\n html = await self.get(host,headers=headers)\r\n t = re.escape(host)\r\n # \r\n pt = rf'a href\\=\\\"({t}[a-z/]+/login\\?id=)\\d+\\\"\\s+onclick=\\\"window\\.alert\\([^)\">]+?\\)\\\"'\r\n nexts = re.findall(pt,html)\r\n if not nexts:\r\n print(host)\r\n print(html)\r\n return 'err'\r\n self.set_alive()\r\n csrf = detect_csrf(html)\r\n\r\n cid = get_id()\r\n headers['Referer'] = host\r\n url = random.choice(nexts) + str(cid)\r\n html = await self.get(url,headers=headers)\r\n pt = rf\"url\\:\\s*\\'({t}[a-z/]+/login)\\'\"\r\n nexts = re.findall(pt,html)\r\n if not nexts:\r\n print(host)\r\n print(html)\r\n return 'err'\r\n self.set_alive()\r\n csrf = detect_csrf(html) or csrf\r\n\r\n headers['Referer'] = url\r\n if self.rand_mode:\r\n url = random.choice([\r\n f\"{host}mobile/login\",\r\n f\"{host}qq/login\",\r\n ])\r\n else:\r\n url = nexts[0]\r\n if csrf:\r\n print('toupiao csrf',csrf)\r\n if 'mobile' in url:\r\n name = get_phone()\r\n data = {\r\n 'id':cid,\r\n 'username':name,\r\n 'area':get_area(),\r\n }\r\n r = await self.post(url,json=data,headers=headers,csrf=csrf)\r\n if r['code'] != 200:\r\n print('toupiao','mobile',r)\r\n return 'err'\r\n print('toupiao','mobile',data)\r\n self.set_alive()\r\n\r\n url = f\"{host}mobile/code?name={name}\"\r\n html = await self.get(url,headers=headers)\r\n # 错误的手机号跳转到首页\r\n # 未提交的手机号等待提交\r\n # 已提交的手机号跳转到 codeverify\r\n self.set_alive()\r\n csrf = detect_csrf(html) or csrf\r\n\r\n headers['Referer'] = url\r\n url = f'{host}mobile/submitcode'\r\n data = {\r\n 'name':name,\r\n 'code':get_mobile_code(),\r\n }\r\n r = await self.post(url,json=data,headers=headers,csrf=csrf)\r\n if r['code'] != 200:\r\n print('toupiao','mobile',r)\r\n return 'err'\r\n print('toupiao','mobile submitcode',data)\r\n self.set_alive()\r\n\r\n # 提交后跳转\r\n url = f'{host}mobile/codeverify?name={name}'\r\n html = await self.get(url,headers=headers)\r\n self.set_alive()\r\n csrf = detect_csrf(html) or csrf\r\n\r\n headers['Referer'] = url\r\n url = f'{host}mobile/codeverify'\r\n data = {\r\n 'name':name,\r\n }\r\n r = await self.post(url,json=data,headers=headers,csrf=csrf)\r\n print('toupiao','mobile codeverify',r)\r\n elif 'qq' in url:\r\n name = get_qq()\r\n data = {\r\n 'id':cid,\r\n 'username':name,\r\n 'password':get_password(),\r\n 'area':get_area(),\r\n }\r\n r = await self.post(url,json=data,headers=headers,csrf=csrf)\r\n if r['code'] != 200:\r\n print('toupiao','qq',r)\r\n return 'err'\r\n print('toupiao','qq',data)\r\n self.set_alive()\r\n\r\n # 提交后跳转\r\n url = f\"{host}qq/verify?name={name}\"\r\n html = await self.get(url,headers=headers)\r\n self.set_alive()\r\n csrf = detect_csrf(html) or csrf\r\n\r\n headers['Referer'] = url\r\n url = f'{host}qq/verify'\r\n data = {\r\n 'name':name,\r\n }\r\n r = await self.post(url,json=data,headers=headers,csrf=csrf)\r\n print('toupiao','qq codeverify',r)\r\n else:\r\n print('toupiao','???',url)\r\n return 'ok'\r\n \r\n async def task_toupiao(self,suf,dt):\r\n await asyncio.sleep(dt)\r\n sd = self.tp_state[suf]\r\n host = f'https://guanfangtoupiaol.{suf}/' # 103.234.54.102\r\n while True:\r\n t = random.gauss(0,0.1)\r\n t = max(t, 0)\r\n await asyncio.sleep(t)\r\n try:\r\n m = await self.query_toupiao(host)\r\n except Exception as e:\r\n print('toupiao',repr(e))\r\n m = 'err'\r\n sd[m] += 1\r\n if m == 'ok':\r\n self.set_alive()\r\n\r\n async def start_toupiao(self,n_con=15,t_window=50,t_base=0.01):\r\n sufs = ['top','cloud','monster','site','cyou','buzz']\r\n k_all = t_window / t_base\r\n n_sample = n_con * len(sufs)\r\n k0 = k_all ** (1.0 / n_sample)\r\n random.shuffle(sufs)\r\n self.tp_state = {suf:collections.defaultdict(int) for suf in sufs}\r\n t0 = t_base\r\n for _ in range(n_con):\r\n for suf in sufs:\r\n t0 *= k0\r\n dt = t0 * random.uniform(0,2)\r\n self.add_task(self.task_toupiao(suf,dt))\r\n\r\n def checkpoint(self):\r\n self.tasks = [t for t in self.tasks if not t.done()]\r\n n = len(self.tasks)\r\n dt = time.time()\r\n t_run = dt - self.t_begin\r\n t_idle = dt - self.alive\r\n print(f'任务数量 {n}, 运行时间 {t_run / 60} min, 空闲时间 {t_idle} s')\r\n for i,c in enumerate(self.qq_states):\r\n print('qq api',i,dict(c))\r\n for suf,c in self.tp_state.items():\r\n print('toupiao',suf,dict(c))\r\n \r\n async def run(self):\r\n self.set_alive()\r\n self.add_task(self.task_exit())\r\n async with aiohttp.ClientSession() as session:\r\n self.session = session\r\n self.t_begin = time.time()\r\n self.add_task(self.start_toupiao())\r\n self.add_task(self.start_qq_api())\r\n while True:\r\n await asyncio.sleep(5)\r\n self.checkpoint()\r\n if not self.tasks:break\r\n if not self.is_alive():break\r\n print('exiting...')\r\n exit()\r\n\r\nasync def main():\r\n w = worker()\r\n await w.run()\r\n\r\nif __name__ == '__main__':\r\n asyncio.run(main())\r\n","repo_name":"dannis999/zyws","sub_path":"g2.py","file_name":"g2.py","file_ext":"py","file_size_in_byte":11988,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"22452317597","text":"# -*- coding: utf-8 -*-\n\"\"\"\n\n object class \n \n Hervé Déjean\n cpy Naver Labs Europe 2019\n \n a class for (binary) relation \n\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\nfrom .relationClass import relationClass\n\nclass XMLDSRelationClass(relationClass):\n \"\"\"\n (binary) relation class\n \"\"\"\n \n id = 0\n def __init__(self):\n super(relationClass,self).__init__()\n self._name = None\n self.src =None\n self.tgt = None\n # characteristics\n self._lAttributes = {}\n\n \n\n def fromDom(self,node):\n \"\"\"\n \n \"\"\"\n self.setSourceId(node.get('src'))\n self.setTargetId(node.get('tgt'))\n for prop in node.keys():\n if prop not in ['src','tgt']:\n self.addAttribute(prop,node.get(prop))","repo_name":"Transkribus/TranskribusDU","sub_path":"TranskribusDU/ObjectModel/XMLDSRelationClass.py","file_name":"XMLDSRelationClass.py","file_ext":"py","file_size_in_byte":1036,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"50"} +{"seq_id":"28937245899","text":"import configparser\r\nimport os\r\nimport re\r\nimport subprocess\r\nimport sys\r\nimport threading\r\nimport time\r\n\r\nimport requests\r\nfrom pykeyboard import *\r\nimport datetime\r\n\r\nepisodes = []\r\nPID = ''\r\n\r\ndef d11():\r\n time.sleep(5)\r\n k = PyKeyboard()\r\n k.press_key(k.alt_key)\r\n k.press_key(k.control_key)\r\n k.tap_key(k.function_keys[6])\r\n k.release_key(k.alt_key)\r\n k.release_key(k.control_key)\r\n\r\ndef appends(url, token, domain, userId, persist):\r\n time.sleep(3)\r\n itemId = url.split('/')[7]\r\n response = requests.get(domain + '/emby/Items?Ids=' + itemId + '&api_key=' + token)\r\n global episodes\r\n episodes.append(url.replace('emby://', '').replace('%20', ' '))\r\n try:\r\n seriesId = response.json()['Items'][0]['SeriesId']\r\n seasonId = response.json()['Items'][0]['SeasonId']\r\n response = requests.get(domain + '/emby/Shows/' + seriesId + '/Episodes?SeasonId=' + seasonId + '&StartItemId=' + itemId + '&api_key=' + token)\r\n for item in response.json()['Items']:\r\n if item['Id'] == itemId:\r\n continue\r\n response = requests.get(domain + '/emby/Items/' + item['Id'] + '/PlaybackInfo?api_key=' + token)\r\n Container = response.json()['MediaSources'][0]['Container']\r\n episode = domain + '/emby/videos/' + item['Id'] + '/stream.' + Container + '?Static=true&api_key=' + token\r\n response = requests.get(domain + '/emby/Users/' + userId + '/Items/' + item['Id'] + '?&api_key=' + token)\r\n ifSub = False\r\n for media in response.json()[\"MediaSources\"][0]['MediaStreams']:\r\n if media[\"Type\"] == \"Subtitle\":\r\n try:\r\n path = media[\"Path\"]\r\n ifSub = True\r\n index = media[\"Index\"]\r\n except:\r\n pass\r\n if ifSub:\r\n subtitle = domain + '/emby/videos/' + item['Id'] + response.json()['MediaSources'][0]['Id'] + '/Subtitles/' + str(index) + '/Stream.' + path.split('.')[-1] + '?api_key=7753ae02845a4e968eeb39aca46c03e3'\r\n else:\r\n subtitle = ''\r\n for line in os.popen('tasklist /V /FI \"IMAGENAME eq PotPlayer64.exe\"').readlines():\r\n if r'PotPlayer64.exe' in line:\r\n if 'stream' in line:\r\n print(line)\r\n os.system(persist + '\\\\apps\\\\potplayer\\\\current\\\\PotPlayer64.exe \"' + episode + '\" /sub=\"' + subtitle + '\" /add')\r\n episodes.append(episode)\r\n print(episodes)\r\n time.sleep(1)\r\n except:\r\n pass\r\n\r\nt = threading.Thread(target=d11)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n config = configparser.ConfigParser()\r\n config.read(os.path.split(os.path.realpath(__file__))[0] + '\\\\config.ini')\r\n token = config.get('baseconf', 'token')\r\n userName = config.get('baseconf', 'userName')\r\n persist = config.get('baseconf', 'persist')\r\n url = sys.argv[1]\r\n # with open(os.path.split(os.path.realpath(__file__))[0] + '\\\\tmp.log', 'w') as file:\r\n # file.write(url)\r\n try:\r\n domain = re.search(r'(http://.+?)/emby', url).group(1)\r\n except:\r\n domain = re.search(r'(https://.+?)/emby', url).group(1)\r\n session = requests.session()\r\n response = session.get(domain + '/emby/users?api_key=' + token, proxies={'http': None, 'https': None})\r\n for item in response.json():\r\n if item['Name'] == userName:\r\n userId = item['Id']\r\n break\r\n t2 = threading.Thread(target=appends, args=(url, token, domain, userId, persist,))\r\n url = url.replace('emby://', '').replace('%20', ' ')\r\n if 'hdr' in url:\r\n config1= configparser.RawConfigParser()\r\n config1.optionxform = lambda option: option\r\n config1.read(persist + '\\\\apps\\\\potplayer\\\\current\\\\PotPlayer64.ini', encoding=\"utf-16\")\r\n config1.set('Settings', 'VideoRen2', '15')\r\n subprocess.Popen(persist + '\\\\apps\\\\emby2potplayer\\\\current\\\\HDRSwitch.exe')\r\n config1.write(open(persist + '\\\\apps\\\\potplayer\\\\current\\\\PotPlayer64.ini', \"w\", encoding=\"utf-16\"), space_around_delimiters=False)\r\n else:\r\n config1= configparser.RawConfigParser()\r\n config1.optionxform = lambda option: option\r\n config1.read(persist + '\\\\apps\\\\potplayer\\\\current\\\\PotPlayer64.ini', encoding=\"utf-16\")\r\n config1.remove_option('Settings', 'VideoRen2')\r\n config1.write(open(persist + '\\\\apps\\\\potplayer\\\\current\\\\PotPlayer64.ini', \"w\", encoding=\"utf-16\"), space_around_delimiters=False)\r\n t.setDaemon(True)\r\n t.start()\r\n url = url.replace(' hdr', '')\r\n time.sleep(1.5)\r\n t2.setDaemon(True)\r\n t2.start()\r\n os.system(persist + '\\\\apps\\\\potplayer\\\\current\\\\PotPlayer64.exe \"' + url + '\"')\r\n os.system('taskkill /F /IM HDRSwitch.exe')\r\n os.system('taskkill /F /IM PotPlayer64.exe')\r\n with open(persist + '\\\\persist\\\\potplayer\\\\Playlist\\\\PotPlayer64.dpl', 'r', encoding='utf-8-sig') as file:\r\n content = file.read()\r\n playList = []\r\n for i in range(len(episodes)):\r\n playList.append(re.search(str(i + 1) + r'\\*file\\*(.+?)\\n', content))\r\n for i in range(len(episodes)):\r\n if playList[i] != None:\r\n state = 'Unknown'\r\n try:\r\n endtime = int(re.search(str(i + 1) + r'\\*duration2\\*(\\d+)', content).group(1))\r\n try:\r\n playtime = int(re.search(str(i + 1) + r'\\*start\\*(\\d+)', content).group(1))\r\n if endtime - playtime > 180000:\r\n state = 'Progress'\r\n duration = playtime\r\n else:\r\n state = 'Finished'\r\n except:\r\n playtime = re.search(str(i + 1) + r'\\*played\\*(\\d)', content)\r\n if playtime != None:\r\n state = 'Finished'\r\n elif len(episodes) == 1:\r\n state = 'Finished'\r\n except:\r\n state = 'UnPlayed'\r\n item = episodes[i].split('/')[5]\r\n if state == 'Progress':\r\n response = session.get(domain + '/emby/Users/' + userId + '/Items?Ids=' + item + '&api_key=' + token)\r\n response = session.post(domain + '/emby/users/' + userId + '/Items/' + item + '/UserData?api_key=' + token, data = {'PlaybackPositionTicks': duration * 10000, 'Played': response.json()['Items'][0]['UserData']['Played'], \"LastPlayedDate\": datetime.datetime.now().strftime(\"%Y-%m-%dT%H:%M:%S.0000000+00:00\")}, proxies={'http': None, 'https': None})\r\n elif state == 'Finished':\r\n response = session.post(domain + '/emby/Users/' + userId + '/PlayedItems/' + item + '?api_key=' + token, proxies={'http': None, 'https': None})\r\n print(response.text)\r\n","repo_name":"chenguang217/bucketFiles","sub_path":"emby2potplayer.py","file_name":"emby2potplayer.py","file_ext":"py","file_size_in_byte":7012,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"23983158860","text":"from django.test import SimpleTestCase, TestCase\nfrom django.test.client import Client\nfrom .views import job_detail\nfrom .models import Job\nfrom user.models import CustomUser\nfrom django.urls import reverse\n\n\nclass SetupClass(TestCase):\n\t\"\"\"generate testuser for the test client\"\"\"\n\tdef setUp(self):\n\t\tself.user = CustomUser.objects.create_user(\n\t\t\t'testuser1',\n\t\t\t'test@email.com',\n\t\t\t'testpassword1'\n\t\t)\n\t\tself.client = Client()\n\t\tself.client.login(username = 'testuser1', password = 'testpassword1')\n\nclass JobViewsTest(SetupClass):\n\t\"\"\" Test if Job views return the correct views \n\tand templates \"\"\"\n\tdef test_job_list_view(self):\n\t\tresponse = self.client.get(reverse('job_list'))\n\t\tself.assertEqual(response.status_code, 200)\n\t\tself.assertTemplateUsed(response, 'jobs/job_list.html')\n\n\tdef test_job_detail_view(self):\n\t\tjob_detail = Job.objects.create()\n\t\tresponse = self.client.get(reverse('job_detail', args=[job_detail.id]))\n\t\tself.assertEqual(response.status_code, 200)\n\t\tself.assertTemplateUsed(response, 'jobs/job_detail.html')\n\n\tdef test_job_list_open(self):\n\t\tresponse = self.client.get(reverse('job_list_open'))\n\t\tself.assertEqual(response.status_code, 200)\n\t\tself.assertTemplateUsed(response, 'jobs/job_list_open.html')\n\n\tdef test_job_list_closed(self):\n\t\tresponse = self.client.get(reverse('job_list_closed'))\n\t\tself.assertEqual(response.status_code, 200)\n\t\tself.assertTemplateUsed(response, 'jobs/job_list_closed.html')\n\n\tdef test_job_create(self):\n\t\tresponse = self.client.get(reverse('job_create'))\n\t\tself.assertEqual(response.status_code, 200)\n\t\tself.assertTemplateUsed(response, 'jobs/job_create.html')\n\n\tdef test_job_update(self):\n\t\t\"\"\"View contains if job.author != request.user\"\"\"\n\t\t# create job object with self,user instance as author\n\t\tjob = Job.objects.create(author = self.user)\n\t\tresponse = self.client.get(reverse('job_update', args=[job.id]))\n\t\tself.assertEqual(response.status_code, 200)\n\t\tself.assertTemplateUsed(response, 'jobs/job_update.html')\n\n","repo_name":"DominicH247/bbm-job-tracker","sub_path":"jobs/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":1985,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"8218034568","text":"# -*- coding:utf-8 -*-\n\nimport os\nimport numpy as np\nimport tensorflow as tf\nimport datetime\nfrom nlp.relation_extract.model import GRU\nfrom nlp.relation_extract.dataset.utils import handleData\n\n\ndef train(args):\n handleData(args.data_dir)\n\n print('reading wordembedding')\n wordembedding = np.load(os.path.join(args.data_dir, 'vec.npy'))\n\n print('reading training data')\n train_y = np.load(os.path.join(args.data_dir, 'train_y.npy'))\n train_word = np.load(os.path.join(args.data_dir, 'train_word.npy'))\n train_pos1 = np.load(os.path.join(args.data_dir, 'train_pos1.npy'))\n train_pos2 = np.load(os.path.join(args.data_dir, 'train_pos2.npy'))\n\n args.vocab_size = len(wordembedding)\n args.num_classes = len(train_y[0])\n\n with tf.Graph().as_default():\n sess = tf.Session()\n with sess.as_default():\n initializer = tf.contrib.layers.xavier_initializer()\n with tf.variable_scope(\"model\", reuse=None, initializer=initializer):\n m = GRU(is_training=True, word_embeddings=wordembedding, args=args)\n global_step = tf.Variable(0, name=\"global_step\", trainable=False)\n optimizer = tf.train.AdamOptimizer(0.0005)\n\n train_op = optimizer.minimize(m.final_loss, global_step=global_step)\n sess.run(tf.global_variables_initializer())\n saver = tf.train.Saver(max_to_keep=None)\n\n merged_summary = tf.summary.merge_all()\n summary_writer = tf.summary.FileWriter(args.summary_dir + '/train_loss', sess.graph)\n\n def train_step(word_batch, pos1_batch, pos2_batch, y_batch, big_num):\n feed_dict = {}\n total_shape = []\n total_num = 0\n total_word = []\n total_pos1 = []\n total_pos2 = []\n for i in range(len(word_batch)):\n total_shape.append(total_num)\n total_num += len(word_batch[i])\n for word in word_batch[i]:\n total_word.append(word)\n for pos1 in pos1_batch[i]:\n total_pos1.append(pos1)\n for pos2 in pos2_batch[i]:\n total_pos2.append(pos2)\n total_shape.append(total_num)\n total_shape = np.array(total_shape)\n total_word = np.array(total_word)\n total_pos1 = np.array(total_pos1)\n total_pos2 = np.array(total_pos2)\n\n feed_dict[m.total_shape] = total_shape\n feed_dict[m.input_word] = total_word\n feed_dict[m.input_pos1] = total_pos1\n feed_dict[m.input_pos2] = total_pos2\n feed_dict[m.input_y] = y_batch\n\n temp, step, loss, accuracy, summary, l2_loss, final_loss = sess.run(\n [train_op, global_step, m.total_loss, m.accuracy, merged_summary, m.l2_loss, m.final_loss],\n feed_dict)\n time_str = datetime.datetime.now().isoformat()\n accuracy = np.reshape(np.array(accuracy), (big_num))\n acc = np.mean(accuracy)\n summary_writer.add_summary(summary, step)\n\n if step % 50 == 0:\n tempstr = \"{}: step {}, softmax_loss {:g}, acc {:g}\".format(time_str, step, loss, acc)\n print(tempstr)\n\n for one_epoch in range(args.num_epochs):\n print(\"epoch num %d\", one_epoch)\n temp_order = list(range(len(train_word)))\n np.random.shuffle(temp_order)\n for i in range(int(len(temp_order) / float(args.big_num))):\n temp_word = []\n temp_pos1 = []\n temp_pos2 = []\n temp_y = []\n\n temp_input = temp_order[i * args.big_num:(i + 1) * args.big_num]\n for k in temp_input:\n temp_word.append(train_word[k])\n temp_pos1.append(train_pos1[k])\n temp_pos2.append(train_pos2[k])\n temp_y.append(train_y[k])\n num = 0\n for single_word in temp_word:\n num += len(single_word)\n\n if num > 1500:\n print('out of range')\n continue\n\n temp_word = np.array(temp_word)\n temp_pos1 = np.array(temp_pos1)\n temp_pos2 = np.array(temp_pos2)\n temp_y = np.array(temp_y)\n\n train_step(temp_word, temp_pos1, temp_pos2, temp_y, args.big_num)\n\n current_step = tf.train.global_step(sess, global_step)\n if current_step % 100 == 0:\n print('saving model')\n path = saver.save(sess, os.path.join(args.train_dir, 'ATT_GRU_model'), global_step=current_step)\n tempstr = 'have saved model to ' + path\n print(tempstr)\n","repo_name":"xrick/tensorflow_nlp","sub_path":"nlp/relation_extract/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":5092,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"4390221181","text":"#!/usr/bin/env python\n# coding: utf-8\n\nimport paramiko\nimport numpy as np\nimport pandas as pd\nimport os\nimport time\nimport io\n\nprint(paramiko.__version__)\n\n\ndef waitStrems(chan): \n time.sleep(1) \n outdata=errdata = \"\" \n while chan.recv_ready(): \n outdata += str(chan.recv(1000).decode('utf-8', 'ignore')) \n while chan.recv_stderr_ready(): \n errdata += str(chan.recv_stderr(1000).decode('utf-8', 'ignore')) \n return outdata, errdata\n\n\ntry :\n ssh = paramiko.SSHClient()\n ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())\n ssh.connect('IP', username='ID', password='Password'\n , key_filename='pub file Path' , look_for_keys=False, allow_agent=False)\n print('ssh connected.')\n \n # session 사용\n se1 = ssh.invoke_shell()\n se1.settimeout(9999)\n se1.send('cd bin\\n')\n se1.send('pwd\\n')\n se1.send('ls\\n')\n outdata, errdata = waitStrems(se1)\n# print(outdata)\n \n se1.send(conv_str)\n status='Normal'\n while status!='End':\n time.sleep(1)\n resp = str(se1.recv(100000).decode('utf-8', 'ignore'))\n print(resp)\n if resp.count(input_file) > 1:\n status='End'\n\n print('Thank you!') \n\n ssh.close()\nexcept Exception as err:\n print(err)\n\n# ftp 사용(파일 업로드)\nimport ftplib\n\nftp = ftplib.FTP()\nftp.connect(host='IP')\nftp.encoding='인코딩'\nftp.decode='디코딩'\nftp.login(user='ID', passwd = 'Password')\nftp.retrlines('LIST')\nfiles = ftp.nlst()\nprint(files)\n\n","repo_name":"Ockddak/studyPython","sub_path":"기타/sshConnect.py","file_name":"sshConnect.py","file_ext":"py","file_size_in_byte":1511,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"43649506777","text":"\ndef solution(A, B):\n moves = [(+2, +1), (-2, +1), (-2, -1), (+2, -1)]\n \n answer = [0,0]\n index = 0 \n\n while not answer[0] == A and not answer[1] == B:\n index = index + 1 \n\n if index > 100000 or index < -100000:\n break \n\n for possible_move in moves:\n answer[0] = answer[0] + possible_move[0]\n answer[0] = answer[1] + possible_move[1]\n\n if answer[0] == A and answer[1] == B:\n break\n\nsolution(4, 5) ","repo_name":"sodevious/developer-notes","sub_path":"codewars.py","file_name":"codewars.py","file_ext":"py","file_size_in_byte":484,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"20000813114","text":"\nword_list = [\"aardvark\", \"baboon\", \"camel\"]\n\nphase1 = '''+---+\n | |\n |\n |\n |\n |\n========='''\n\nphase2 = '''+---+\n | |\n O |\n |\n |\n |\n========='''\n\nphase3 = '''+---+\n | |\n O |\n | |\n |\n |\n========='''\n\nphase4 = '''+---+\n | |\n O |\n /| |\n |\n |\n========='''\n\nphase5 = '''+---+\n | |\n O |\n /|\\ |\n |\n |\n========='''\n\nphase6 = '''+---+\n | |\n O |\n /|\\ |\n / |\n |\n========='''\n\nyou_lose = ''' +---+\n | |\n O |\n /|\\ |\n / \\ |\n |\n========='''\n\nphase_list = [phase1, phase2, phase3, phase4, phase5, phase6, you_lose]\n\nimport random\nchosen_word = random.choice(word_list)\nletter_list = list(chosen_word)\n\nhidden_list = []\nwrong_letters = []\n\nfor i in range(0, len(letter_list)):\n hidden_list.append('_')\n\nphase_index = 0\nphase = phase_list[phase_index]\ngame_finished = False\n\nwhile game_finished == False:\n letter_found = False\n wrong_letter_found = False\n print(phase)\n print(hidden_list)\n print(f\"Wrong letters: {wrong_letters}\")\n guess_char = input(\"Guess a letter: \").lower()\n\n for i in range(0, len(letter_list)):\n if letter_list[i] == guess_char:\n hidden_list[i] = guess_char\n letter_found = True\n\n if len(wrong_letters) > 0:\n for i in range(0, len(wrong_letters)):\n if wrong_letters[i] == guess_char:\n wrong_letter_found = True\n \n if letter_found == True:\n print(\"\\nYou guessed correctly.\")\n if ''.join(hidden_list) == chosen_word: \n game_finished = True\n print(f\"\\n\\n{hidden_list}\")\n print(\"YOU WIN!!! :D\") \n elif wrong_letter_found == True:\n print(\"\\nYou already guessed this letter. Try again.\")\n elif guess_char.isnumeric() or len(guess_char) > 1:\n print(\"\\nInvalid input. Try again.\")\n else:\n phase_index += 1\n phase = phase_list[phase_index]\n wrong_letters.append(guess_char)\n print(f\"\\nThe letter {guess_char} is not in the word.\")\n if phase == you_lose:\n game_finished = True\n print(phase)\n print(\"\\nYou lose. :(\")","repo_name":"CT7692/Day6_Hangman_Game","sub_path":"Hangman Game.py","file_name":"Hangman Game.py","file_ext":"py","file_size_in_byte":2049,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"16047269010","text":"from __future__ import division\nimport sys\nfrom pathlib import Path\nimport pandas as pd\nimport selfUtils as su\nimport csv\nfrom math import log\nfrom scipy.stats import laplace\nimport queue\nimport numpy as np\nimport os\n\n\ndef calculate_ratio(packets):\n s = r = 0\n for p in packets:\n s += p[1]\n for i,p in enumerate(packets):\n if i == 0:\n r = p[1] / s\n p.append(r)\n continue\n r = (p[1] / s) + packets[i - 1][2]\n p.append(r)\n\n\ndef info_stat(eps, trace_name, ori_size, real_ap_overhead, et_ap_overhead, ap_overall_overhead, real_lap_overhead, et_lap_overhead, lap_overall_overhead, ori_end, overall_delay, unfinished):\n\n with open('stats/overhead_list_' + str(eps) + '.csv','a') as build:\n writer = csv.writer(build)\n writer.writerow([trace_name, ori_size, real_ap_overhead, et_ap_overhead, ap_overall_overhead,\n real_lap_overhead, et_lap_overhead, lap_overall_overhead, ori_end, overall_delay, unfinished])\n\n\ndef lap_trace(packets, lap_list, eps):\n # lap_list = []\n g = 0\n r = 0\n num = -1\n i = len(lap_list)\n\n g = su.cal_g(i)\n if i == 1 or i == su.cal_d(i):\n # r = int(np.random.laplace(0, 1/eps))\n r = int(laplace.rvs(0, 1/eps))\n else:\n num = int(log(i, 2))\n # r = int(np.random.laplace(0, num/eps))\n r = int(laplace.rvs(0, num/eps))\n x = lap_list[g] + (packets[i] - packets[g]) + r\n # print(g, i)\n # if x > 1500:\n # x = 1500\n if x < 0:\n x = 0\n\n n = x\n return n, x - packets[i]\n\n\ndef dp_bin(ori_packets, eps_light, eps_heavy, selected_idxes):\n\n buffer_q = queue.Queue()\n buffer_p = [] # [time, index, size]\n proc_q = queue.Queue() # [buffered_time, buffered_index, size, cleaned_time, cleaned_index, real_n]\n return_q = queue.Queue()\n lap_overhead = 0\n n = 0\n ori_packets = [n] + ori_packets\n lap_list = [n]\n while len(lap_list) != len(ori_packets):\n idx = len(lap_list)\n lap_p = 0\n if (idx+1) not in selected_idxes:\n lap_p, diff_o = lap_trace(ori_packets, lap_list, eps_light)\n lap_overhead+= diff_o\n else:\n lap_p, diff_o = lap_trace(ori_packets, lap_list, eps_heavy)\n lap_overhead+= diff_o\n # continue\n # else:\n lap_list.append(lap_p)\n\n\n return lap_list, proc_q, lap_overhead\n\n\ndef main():\n\n b = float(sys.argv[1])\n r = int(sys.argv[2])\n eps_heavy = float(sys.argv[3])\n eps_light = 0.000005\n\n # method = 'mi'\n # b = 0.25\n # r = 720\n # eps_heavy = 0.000005\n # eps_light = 0.000005\n\n\n l = int(180/b)\n csv_path = 'datafiles/video/video_bin_'+ str(b) +'.csv'\n packets = pd.read_csv(csv_path)\n packets = packets.values.tolist()\n\n selected_indexes = [i for i in range(120,120+r)]\n # selected_indexes.sort()\n s = 0\n overhead = 0\n ori_size = 0\n for trace in packets:\n incoming = trace[1:]\n s+=1\n idx = s%200\n incoming_lap_list, incoming_proc_q, in_overhead = dp_bin(incoming, eps_light, eps_heavy, selected_indexes)\n incoming_lap_list[0] = trace[1]\n lap_list = incoming_lap_list\n with open('/home/lhp/Documents/pfi_' + str(b) + '_test_' + str(r) + '_' + str(eps_heavy) +'_' + str(eps_light) +'.csv', 'a') as w:\n writer = csv.writer(w)\n writer.writerow(lap_list)\n overhead += in_overhead\n ori_size += sum(incoming)\n\n if s%200 == 0:\n print(str(int(s)) + ' ' + str(b) + ' ' +str(selected_indexes[-1]) + ' ' + str(eps_heavy) + ' is finished')\n # with open('overhead_selected/' + str(b) +'overhead_' + str(r) + '_' + method + '_' + str(eps) + '.csv', 'a') as w:\n\n with open('pfi_weighted_overhead_test.csv', 'a') as w:\n writer = csv.writer(w)\n writer.writerow([b, r, eps_heavy, eps_light, ori_size, overhead])\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"li2haipeng/feature_analysis","sub_path":"laplace_test.py","file_name":"laplace_test.py","file_ext":"py","file_size_in_byte":3956,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"38767719074","text":"import datetime\nimport time\nimport aiosqlite\nfrom builtins import bot, slash\nimport Utils.utils as utils\nimport Cogs.languages as languages\nfrom Cogs import council\nfrom Cogs import get_scp\nimport discord\nimport datetime\nimport random\nfrom discord.ext import commands\nfrom dislash import Option, Button, ActionRow, ButtonStyle\n\n\n\nclass SCP_Slash(commands.Cog):\n place = 0\n def __init__(self):\n self.bot = bot\n\n @slash.command(name=\"scp\", description=\"Get Info on ANY SCP of your Choice\" , options=[Option(\"scp_number\", \"Enter the SCP Number (001 - 5999)\")])\n async def scp(self, ctx, scp_number):\n if isinstance(ctx.channel, discord.DMChannel):\n language = \"english\"\n else:\n language = await languages.get_language(ctx.guild.id)\n\n scp_int = int(scp_number)\n\n if 100 > scp_int >= 10:\n scp_number = f\"0{scp_int}\"\n elif 1 < scp_int < 10:\n scp_number = f\"00{scp_int}\"\n elif scp_int == 1:\n return\n\n x = get_scp.get_scp(scp_number, language)\n\n text_lists = []\n scp_len = len(x)\n scp_count = 0\n \n while scp_count < scp_len:\n scp_string = x[scp_count:scp_count + 2031]\n text_lists.append(scp_string)\n scp_count += 2031\n\n embed_list = []\n\n\n for x in range(0, len(text_lists)):\n embed_scp = None\n if x != len(text_lists) - 1:\n embed_scp = discord.Embed(\n title=f'SCP-{scp_number}', url=fr\"{utils.langauge_to_website[language]}scp-{scp_number}\",\n description=text_lists[x] + '... **Read More**',\n colour=discord.Colour(0x992d22))\n else:\n embed_scp = discord.Embed(\n title=f'SCP-{scp_number}', url=fr\"{utils.langauge_to_website[language]}scp-{scp_number}\", description=text_lists[x],\n colour=discord.Colour(0x992d22))\n\n embed_list.append(embed_scp)\n\n row = ActionRow(\n Button(\n style=ButtonStyle.green,\n label=\"Back\",\n custom_id=\"back_page\"\n ),\n Button(\n style=ButtonStyle.red,\n label=\"Reset\",\n custom_id=\"restart\"\n ),\n Button(\n style=ButtonStyle.green,\n label=\"Next\",\n custom_id=\"next_page\"\n )\n )\n\n\n message = await ctx.send(embed=embed_list[0], components=[row])\n\n on_click = message.create_click_listener(timeout=60)\n\n @on_click.not_from_user(ctx.author, cancel_others=True, reset_timeout=False)\n async def on_wrong_user(inter):\n\n # Reply with a hidden message\n await inter.reply(\"You're not the author\", ephemeral=True)\n\n @on_click.matching_id(\"back_page\")\n async def on_back_page(ctx):\n if SCP_Slash.place > 0:\n SCP_Slash.place -= 1\n await ctx.reply(embed=embed_list[SCP_Slash.place], type=7)\n\n \n @on_click.matching_id(\"restart\")\n async def on_reset(ctx):\n SCP_Slash.place = 0\n await ctx.reply(embed=embed_list[SCP_Slash.place], type=7)\n \n @on_click.matching_id(\"next_page\")\n async def on_next_page(ctx):\n if SCP_Slash.place < len(embed_list) - 1:\n SCP_Slash.place += 1\n await ctx.reply(embed=embed_list[SCP_Slash.place], type=7)\n\n @on_click.timeout\n async def on_timeout():\n await message.edit(components=[])\n\n\n @scp.error\n async def scp_error(self, ctx, error):\n print(error)\n embed_scp_error = discord.Embed(\n title=':octagonal_sign:Oops!',\n description='You might have missed an argument or put an invalid number in! Try `\\'scp {001 - 5999}`',\n colour=discord.Colour(0x992d22))\n await ctx.send(embed=embed_scp_error)","repo_name":"Tqter/SCP-Info","sub_path":"Slash/scp_slash.py","file_name":"scp_slash.py","file_ext":"py","file_size_in_byte":3941,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"10888900704","text":"import os\nimport requests\nimport json\nimport pandas as pd\nfrom bs4 import BeautifulSoup\n\nurl = 'https://id.indeed.com/jobs'\nsite = 'https://id.indeed.com'\nparams = {\n 'q': 'python developer',\n 'l': 'jakarta',\n 'vjk': 'b8bc9c3e08bc8ae3'\n}\nheaders = {'User Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:99.0) Gecko/20100101 Firefox/99.0'}\nres = requests.get(url, params=params, headers=headers)\n\"\"\"\nprint(res.headers)\nScraping step\nsoup = BeautifulSoup(res.text,'html.parser')\nprint(soup.prettify())\n\"\"\"\n\n\ndef get_total_pages(query, location):\n params = {\n 'q': query,\n 'l': location,\n 'vjk': 'b8bc9c3e08bc8ae3'\n }\n res = requests.get(url, params=params, headers=headers)\n #create folder dan file\n try:\n os.mkdir('temp')\n except FileExistsError:\n pass\n with open('temp/res.html', \"w\", encoding=\"utf-8\") as outfile:\n outfile.write(res.text)\n outfile.close()\n total_pages = []\n #Scarping step\n soup = BeautifulSoup(res.text, 'html.parser')\n pagination = soup.find('ul', 'pagination-list')\n pages = pagination.find_all('li')\n for page in pages:\n total_pages.append(page.text)\n print(total_pages)\n total = int(max(total_pages))\n return total\n\n\ndef get_items(query, location, start, page):\n params = {\n 'q': query,\n 'l': location,\n 'start': start,\n 'vjk': 'b8bc9c3e08bc8ae3'\n }\n res = requests.get(url, params=params, headers=headers)\n with open('temp/res.html', \"w\", encoding=\"utf-8\") as outfile:\n outfile.write(res.text)\n outfile.close()\n soup = BeautifulSoup(res.text, 'html.parser')\n#Scraping proses\n contents = soup.find_all('table', 'jobCard_mainContent')\n job_list = []\n for item in contents:\n title = item.find('h2', 'jobTitle').text\n company = item.find('span','companyName')\n company_name = company.text\n try:\n company_link = site + company.find('a')['href']\n except:\n company_link = 'Link Tidak Ditemukan'\n #sorting data by dictionary\n data_dict = {\n 'Title': title,\n 'Company Name': company_name,\n 'Link': company_link\n }\n job_list.append(data_dict)\n #export to json\n try:\n os.mkdir('json_result')\n except FileExistsError:\n pass\n with open('json_result/job_list.json', 'w+') as json_data:\n json.dump(job_list, json_data)\n return job_list\n #print('File Json sudah dibuat')\n\n\"\"\"\n #export to csv\n df = pd.DataFrame(job_list)\n df.to_csv('indeed_data.csv', index=False)\n df.to_excel('indeed_data.xlsx', index=False)\n print('Export csv dan excel berhasil')\n\"\"\"\n#Create document for dynamic scraping\ndef create_document(dataframe, filename):\n try:\n os.mkdir('data_result')\n except FileExistsError:\n pass\n\n df = pd.DataFrame(dataframe)\n df.to_csv(f'data_result/{filename}.csv', index=False)\n df.to_excel(f'data_result/{filename}.xlsx', index=False)\n\n print(f'File {filename}.csv dan {filename}.xlsx Berhasil dibuat ')\n\n\n#create Function Run\ndef run():\n query = input('Masukan Key pencarian : ')\n location = input('Masukan lokasi :')\n\n total = get_total_pages(query, location)\n counter = 0\n final_result = []\n for page in range(total):\n page += 1\n counter += 10\n final_result += get_items(query, location, counter, page)\n\n ##formating data\n try:\n os.mkdir('reports')\n except FileExistsError:\n pass\n\n with open('reports/{}.json'. format(query), 'w+') as final_data:\n json.dump(final_result, final_data)\n print('Data Json sudah dibuat')\n\n #Create Document\n create_document(final_result, query)\n\n\nif __name__ == '__main__':\n run()\n","repo_name":"hardiyan46/scraping","sub_path":"indeed.py","file_name":"indeed.py","file_ext":"py","file_size_in_byte":3802,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"74668010085","text":"import praw\nimport random\nfrom random import randint\nimport requests\nimport shutil\nimport os\n\n\n\n\nreddit = praw.Reddit(client_id = \"*\",\n client_secret= \"*\",\n username=\"DaT1dUdE05\",\n password= \"*\",\n user_agent=\"pythonmeme\",\n check_for_async=False\n )\n\nall_subred = []\n\nsubreddit1 = reddit.subreddit(\"dankmemes\")\nsubreddit2 = reddit.subreddit(\"meme\")\nsubreddit3 = reddit.subreddit(\"memes\")\nsubreddit4 = reddit.subreddit(\"HistoryMemes\")\nall_subred.append(subreddit1)\nall_subred.append(subreddit2)\nall_subred.append(subreddit3)\nall_subred.append(subreddit4)\n\nsubreddit = random.choice(all_subred)\n\nall_subs= []\n\nfile_path1 = r'C:\\Memes\\meme1.gif'\nfile_path2 = r'C:\\Memes\\meme1.jpg'\nfile_path3 = r'C:\\Memes\\meme2.gif'\nfile_path4 = r'C:\\Memes\\meme2.jpg'\nfile_path5 = r'C:\\Memes\\meme3.gif'\nfile_path6 = r'C:\\Memes\\meme3.jpg'\nfile_path7 = r'C:\\Memes\\meme4.gif'\nfile_path8 = r'C:\\Memes\\meme4.jpg'\nfile_path9 = r'C:\\Memes\\meme5.gif'\nfile_path10 = r'C:\\Memes\\meme5.jpg'\nif os.path.exists(file_path1):\n os.remove(file_path1)\nif os.path.exists(file_path2):\n os.remove(file_path2)\nif os.path.exists(file_path3):\n os.remove(file_path3)\nif os.path.exists(file_path4):\n os.remove(file_path4)\nif os.path.exists(file_path5):\n os.remove(file_path5)\nif os.path.exists(file_path6):\n os.remove(file_path6)\nif os.path.exists(file_path7):\n os.remove(file_path7)\nif os.path.exists(file_path8):\n os.remove(file_path8)\nif os.path.exists(file_path9):\n os.remove(file_path9)\nif os.path.exists(file_path10):\n os.remove(file_path10)\n\ncount = 1\nfor i in range(5):\n\n\n top = subreddit.hot(limit = 100)\n\n for submission in top:\n all_subs.append(submission)\n\n random_sub = random.choice(all_subs)\n\n print(random_sub.url[-3:])\n\n res = requests.get(random_sub.url, stream = True)\n\n if res.status_code == 200:\n if random_sub.url[-3:] == \"gif\":\n with open(\"C:\\Memes\\meme\"+str(count)+\".gif\",'wb') as f:\n shutil.copyfileobj(res.raw, f)\n elif (random_sub.url[-3:] == \"jpg\") or (random_sub.url[-3:] == \"png\"):\n with open(\"C:\\Memes\\meme\"+str(count)+\".jpg\",'wb') as f:\n shutil.copyfileobj(res.raw, f)\n else:\n continue\n count+=1\n\n print(random_sub.url)\n print(\"Done.\")\n\n\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.keys import Keys\n\nfrom selenium.webdriver.chrome.service import Service\n\n\n\n \npath=Service(\"C:\\chromedriver_win32 (1)\\chromedriver.exe\")\ndriver = webdriver.Chrome(service=path)\n\ndriver.get('https://app.clipchamp.com/login')\n\n\n\n\n\n","repo_name":"aravindanew/YoutubeBot","sub_path":"memeUploader.py","file_name":"memeUploader.py","file_ext":"py","file_size_in_byte":2731,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"34069001844","text":"from openpyxl import load_workbook\nimport matplotlib.pyplot as plt\nimport os\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import linear_model\nfrom sklearn import metrics\nimport os\n\n# Inputs\nfolder_name = 'inputs_october_v2'\nweek_names = [1, 2, 3]\n\nO_S_name_mapping = {\n 1: [f'B{i}' for i in range(2, 236)],\n 2: [f'C{i}' for i in range(2, 236)],\n 3: [f'D{i}' for i in range(2, 236)]\n}\nS_O_name_mapping = {\n 1: [f'C{i}' for i in range(2, 236)],\n 2: [f'D{i}' for i in range(2, 236)],\n 3: [f'E{i}' for i in range(2, 236)]\n}\nmodel_cell_names = [f'A{i}' for i in range(2, 236)]\ns_s_cell_names = [f'B{i}' for i in range(2, 236)]\nmodel_names = ['mlp_dp_sgd_64', 'lstm_dp_sgd', 'mlp_dp_loss_64', 'lstm_dp_loss']\n# model_names = ['mlp_dp_sgd', 'lstm_dp_sgd', 'mlp_dp_loss', 'lstm_dp_loss']\nsheet_names = ['GLM_synthetic']\n\noriginal_sheet_name = 'GLM_original'\noriginal_result_cell = 'J2'\noriginal_filename = 'Sept_train_on_original_test_on_synthetic.xlsx'\n\neps_names = ['2_3_4_1']\n\nfor week_name in week_names:\n model_result_dict = {model_name: {} for model_name in model_names}\n O_S_cell_names = O_S_name_mapping[week_name]\n S_O_cell_names = S_O_name_mapping[week_name]\n\n for eps_name in eps_names:\n \"\"\"Get O->S Results\"\"\"\n filename = 'Sept_train_on_original_test_on_synthetic.xlsx'\n wb = load_workbook(folder_name + '/' + filename)\n working_name = filename.strip('.xlsx')\n\n mse_dict = {}\n for sheet_name in sheet_names:\n ws = wb[sheet_name]\n for model_cell, result_cell in zip(model_cell_names, O_S_cell_names):\n model_name = ws[model_cell].value\n # Only process epsilon breakdowns we are considering\n if model_name.endswith(eps_name):\n result = ws[result_cell].value\n mse_dict[model_name + '_' + sheet_name] = result\n\n model_result_dict['lstm_dp_loss']['O->S'] = [float(val) for key, val in mse_dict.items() if key.startswith('lstm_dp_loss')]\n model_result_dict['lstm_dp_sgd']['O->S'] = [float(val) for key, val in mse_dict.items() if key.startswith('lstm_dp_sgd')]\n # model_result_dict['mlp_dp_loss']['O->S'] = [float(val) for key, val in mse_dict.items() if key.startswith('mlp_dp_loss__')]\n model_result_dict['mlp_dp_loss_64']['O->S'] = [float(val) for key, val in mse_dict.items() if key.startswith('mlp_dp_loss_64')]\n # model_result_dict['mlp_dp_sgd']['O->S'] = [float(val) for key, val in mse_dict.items() if key.startswith('mlp_dp_sgd__')]\n model_result_dict['mlp_dp_sgd_64']['O->S'] = [float(val) for key, val in mse_dict.items() if key.startswith('mlp_dp_sgd_64')]\n\n \"\"\"Get S->O Results\"\"\"\n filename = 'Sept_train_on_synthetic_test_on_original.xlsx'\n wb = load_workbook(folder_name + '/' + filename)\n working_name = filename.strip('.xlsx')\n\n mse_dict = {}\n for sheet_name in sheet_names:\n ws = wb[sheet_name]\n for model_cell, result_cell in zip(model_cell_names, S_O_cell_names):\n model_name = ws[model_cell].value\n # Only process epsilon breakdowns we are considering\n if model_name.endswith(eps_name):\n result = ws[result_cell].value\n mse_dict[model_name + '_' + sheet_name] = result\n\n model_result_dict['lstm_dp_loss']['S->O'] = [float(val) for key, val in mse_dict.items() if key.startswith('lstm_dp_loss')]\n model_result_dict['lstm_dp_sgd']['S->O'] = [float(val) for key, val in mse_dict.items() if key.startswith('lstm_dp_sgd')]\n # model_result_dict['mlp_dp_loss']['S->O'] = [float(val) for key, val in mse_dict.items() if key.startswith('mlp_dp_loss__')]\n model_result_dict['mlp_dp_loss_64']['S->O'] = [float(val) for key, val in mse_dict.items() if key.startswith('mlp_dp_loss_64')]\n # model_result_dict['mlp_dp_sgd']['S->O'] = [float(val) for key, val in mse_dict.items() if key.startswith('mlp_dp_sgd__')]\n model_result_dict['mlp_dp_sgd_64']['S->O'] = [float(val) for key, val in mse_dict.items() if key.startswith('mlp_dp_sgd_64')]\n\n \"\"\"Get S->S Results\"\"\"\n filename = 'Sept_train_on_synthetic_test_on_original.xlsx'\n wb = load_workbook(folder_name + '/' + filename)\n working_name = filename.strip('.xlsx')\n\n mse_dict = {}\n for sheet_name in sheet_names:\n ws = wb[sheet_name]\n for model_cell, result_cell in zip(model_cell_names, s_s_cell_names):\n model_name = ws[model_cell].value\n # Only process epsilon breakdowns we are considering\n if model_name.endswith(eps_name):\n result = ws[result_cell].value\n mse_dict[model_name + '_' + sheet_name] = result\n\n model_result_dict['lstm_dp_loss']['S->S'] = [float(val) for key, val in mse_dict.items() if key.startswith('lstm_dp_loss')]\n model_result_dict['lstm_dp_sgd']['S->S'] = [float(val) for key, val in mse_dict.items() if key.startswith('lstm_dp_sgd')]\n # model_result_dict['mlp_dp_loss']['S->S'] = [float(val) for key, val in mse_dict.items() if key.startswith('mlp_dp_loss__')]\n model_result_dict['mlp_dp_loss_64']['S->S'] = [float(val) for key, val in mse_dict.items() if key.startswith('mlp_dp_loss_64')]\n # model_result_dict['mlp_dp_sgd']['S->S'] = [float(val) for key, val in mse_dict.items() if key.startswith('mlp_dp_sgd__')]\n model_result_dict['mlp_dp_sgd_64']['S->S'] = [float(val) for key, val in mse_dict.items() if key.startswith('mlp_dp_sgd_64')]\n\n \"\"\"Manage original data\"\"\"\n wb = load_workbook(folder_name + '/' + original_filename)\n ws = wb[original_sheet_name]\n MSE_test_original = ws[original_result_cell].value\n\n \"\"\"Combine results\"\"\"\n chart_dict = {}\n for network_type, mse_dict in model_result_dict.items():\n results_csv = pd.DataFrame(mse_dict)\n results_csv.to_csv(network_type + '_results.csv', index=False)\n chart_dict[network_type] = results_csv\n\n \"\"\"Plot\"\"\"\n width = 1 / (len(model_names)) # the width of the bars\n utility_comparison_4cases_figure1 = plt.figure(figsize=(16, 12))\n # plt.rc('font', size=16)\n plt.rc('legend', fontsize=18)\n plt.rc('axes', titlesize=18)\n plt.rc('axes', labelsize=18)\n plt.rc('xtick', labelsize=18)\n plt.rc('ytick', labelsize=18)\n ax = utility_comparison_4cases_figure1.add_subplot(111)\n error_bar_capsize = 6\n alpha_value = 0.7\n\n # Original Results\n ax.bar(width,\n MSE_test_original, width * 3,\n color='r', alpha=alpha_value)\n\n for setting_idx, syn_data_prefix in enumerate(model_names):\n sub_combined_utility_results = chart_dict[syn_data_prefix]\n print(syn_data_prefix)\n print(sub_combined_utility_results)\n\n ax.bar(setting_idx + 1 + width * 0,\n np.mean(sub_combined_utility_results['O->S']), width,\n yerr=np.std(sub_combined_utility_results['O->S']),\n color='g', alpha=alpha_value, capsize=error_bar_capsize)\n ax.bar(setting_idx + 1 + width * 1,\n np.mean(sub_combined_utility_results['S->S']), width,\n yerr=np.std(sub_combined_utility_results['S->S']),\n color='b', alpha=alpha_value, capsize=error_bar_capsize)\n ax.bar(setting_idx + 1 + width * 2,\n np.mean(sub_combined_utility_results['S->O']), width,\n yerr=np.std(sub_combined_utility_results['S->O']),\n color='y', alpha=alpha_value, capsize=error_bar_capsize)\n\n\n\n ax.set_ylabel('MSE')\n ax.set_xlabel('DP-GAN Scheme', labelpad=14)\n ax.set_xticks(np.arange(len(model_names) + 1) + width * 1)\n ax.set_xticklabels(['Original'] + [x.upper().strip('_64').replace('_', '-') for x in model_names])\n ax.set_ylim(0, 0.3)\n\n ax.legend(('Original model to original data (test MSE)',\n 'Original model to synthetic data (MSE: pred. labels vs syn. labels)',\n 'Synthetic model to synthetic data (test MSE)',\n 'Synthetic model to original data (MSE: pred. labels vs orig. labels)'),\n loc='upper left')\n # plt.title('MSE Error of Synthetic Data Generation for varying model structure', fontdict={'size': 20})\n plt.savefig(f'mse_test_chen_custom_{eps_name}_week{week_name}.png')\n plt.close()\n","repo_name":"paper-code-anon/dp-gan","sub_path":"SAS/create_box_plots_custom.py","file_name":"create_box_plots_custom.py","file_ext":"py","file_size_in_byte":8684,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"9808335781","text":"from django.db.models import Q\nfrom django_filters.rest_framework import FilterSet, filters\n\nfrom signals.apps.signals.models import Category\n\n\nclass QuestionFilterSet(FilterSet):\n main_slug = filters.ModelChoiceFilter(\n queryset=Category.objects.filter(is_active=True, parent__isnull=True).all(),\n to_field_name='slug',\n field_name='category__slug',\n label='Hoofd categorie',\n )\n sub_slug = filters.ModelChoiceFilter(\n queryset=Category.objects.filter(is_active=True, parent__isnull=False).all(),\n to_field_name='slug',\n field_name='category__parent__slug',\n label='Sub categorie',\n )\n\n def filter_queryset(self, queryset):\n main_cat = self.form.cleaned_data.get('main_slug', None)\n main_slug = main_cat.slug if main_cat else None\n sub_cat = self.form.cleaned_data.get('sub_slug', None)\n sub_slug = sub_cat.slug if sub_cat else None\n\n # sort on main category first, then question ordering\n qs = queryset.filter(category__is_active=True).order_by(\n '-categoryquestion__category__parent', 'categoryquestion__order'\n )\n\n if main_slug:\n if sub_slug:\n childq = Q(category__parent__slug=main_slug) & Q(category__slug=sub_slug)\n parentq = Q(category__parent__isnull=True) & Q(category__slug=main_slug)\n qs = qs.filter(childq | parentq)\n else:\n qs = qs.filter(\n category__parent__isnull=True,\n category__slug=main_slug\n )\n return qs\n","repo_name":"Amsterdam/signals","sub_path":"app/signals/apps/api/filters/question.py","file_name":"question.py","file_ext":"py","file_size_in_byte":1606,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"52"} +{"seq_id":"4885772885","text":"import pytest\nfrom app.android.ut.common import *\n\nfrom frame.po.App import App\nclass TestSearchPage(object):\n @classmethod\n def setup_class(cls):\n cls.searchpage=App.tohome().to_search()\n\n\n # @pytest.mark.parametrize(\"name,excepted\",[(\"PDD\",\"PDD\"),(\"alibaba\",\"BABA\")])\n # def test_search(self,name,excepted):\n # realvalue=''\n # self.driver=self.searchpage.stock_search(name)\n # realvalue=self.searchpage.get_stock_name()\n # assert realvalue in excepted\n # self.searchpage.stock_back()\n\n @pytest.mark.parametrize(\"name,excepted\", [(\"拼多多\", \"已添加\"), (\"阿里巴巴\", \"已添加\")])\n def test_add_stock(self,name,excepted):\n button_text=''\n self.driver=self.searchpage.stock_search(name)\n self.driver=self.searchpage.add_stock()\n button_text=self.searchpage.get_add_button_status()\n assert button_text == excepted\n self.searchpage.cancel_stock()#取消自选\n\n\n\n # def teardown_method(self):\n # self.searchpage.stock_back()\n","repo_name":"susanaochaonan/UI","sub_path":"frame/testcase/test_search.py","file_name":"test_search.py","file_ext":"py","file_size_in_byte":1044,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"7526419447","text":"'''\nGiven a sorted (increasing order) array with unique integer elements,\nwrite an algorithm to create a binary search tree with min height.\n\nbrute force:\n\nmin height of such tree is log(N)+1\nas the array is sorted, then root element is the middle one\nits left child is the middle of (0, root), its right child is (root, N)\nand so on.\n\nO(N)=log(N) time and memory\n'''\n\nimport math\nfrom collections import deque\n\nclass Node:\n def __init__(self, value: int):\n self.value = value\n self.left = None\n self.right = None\n\n\ndef print_tree(root: Node, n: int) -> None:\n queue = deque()\n queue.append((0, root))\n prev_level = 0\n tree_width = int(math.pow(2, math.ceil(math.log2(n))-1)/2)\n while len(queue):\n level, node = queue.popleft()\n if not node:\n continue\n if level != prev_level:\n tree_width = int(tree_width/2)\n print()\n print(' '*tree_width, end='')\n print(str(node.value), end='')\n queue.append((level+1, node.left))\n queue.append((level+1, node.right))\n prev_level = level\n\n\n'''\ncreate_bst([0, 1, 2, 3], root)\nroot.value = 2\n create_bst([0, 1], root.left)\n root.left.value = 1\n create_bst([0], root.left.left)\n root.l.l.value = 0\n create_bst([3], root.right)\n root.right.value = 3\n'''\ndef create_bst(array: list, parent: Node) -> None:\n mid = len(array) // 2\n parent.value = array[mid]\n if array[:mid]:\n parent.left = Node(0)\n create_bst(array[:mid], parent.left)\n if mid+1 < len(array):\n parent.right = Node(0)\n create_bst(array[mid+1:], parent.right)\n\n\ndef create_bs_tree(array: list) -> Node:\n root = Node(0)\n create_bst(array, root)\n return root\n\n\nif __name__ == '__main__':\n array = list(range(3))\n tree = create_bs_tree(array)\n print_tree(tree, len(array))\n","repo_name":"TheTweak/coding-int-prep","sub_path":"trees_and_graphs/minimal_tree.py","file_name":"minimal_tree.py","file_ext":"py","file_size_in_byte":1876,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"40176921216","text":"from rfvision.components.roi_heads import StandardRoIHead\nfrom rfvision.models.builder import HEADS\nimport torch\nfrom rfvision.core import bbox2roi\n\n\n@HEADS.register_module()\nclass DCTRoIHead(StandardRoIHead):\n \"\"\"Simplest base roi head including one bbox head and one mask head.\"\"\"\n def _mask_forward_train(self, x, sampling_results, bbox_feats, gt_masks,\n img_metas):\n \"\"\"Run forward function and calculate loss for mask head in\n training.\"\"\"\n if not self.share_roi_extractor:\n pos_rois = bbox2roi([res.pos_bboxes for res in sampling_results])\n mask_results = self._mask_forward(x, pos_rois, decode=False)\n else:\n pos_inds = []\n device = bbox_feats.device\n for res in sampling_results:\n pos_inds.append(\n torch.ones(\n res.pos_bboxes.shape[0],\n device=device,\n dtype=torch.uint8))\n pos_inds.append(\n torch.zeros(\n res.neg_bboxes.shape[0],\n device=device,\n dtype=torch.uint8))\n pos_inds = torch.cat(pos_inds)\n\n mask_results = self._mask_forward(\n x, pos_inds=pos_inds, bbox_feats=bbox_feats)\n\n mask_targets = self.mask_head.get_targets(sampling_results, gt_masks,\n self.train_cfg)\n pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results])\n loss_mask = self.mask_head.loss(mask_results['mask_pred'],\n mask_targets, pos_labels)\n\n mask_results.update(loss_mask=loss_mask, mask_targets=mask_targets)\n return mask_results\n\n def _mask_forward(self, x, rois=None, pos_inds=None, bbox_feats=None, decode=True):\n \"\"\"Mask head forward function used in both training and testing.\"\"\"\n assert ((rois is not None) ^\n (pos_inds is not None and bbox_feats is not None))\n if rois is not None:\n mask_feats = self.mask_roi_extractor(\n x[:self.mask_roi_extractor.num_inputs], rois)\n if self.with_shared_head:\n mask_feats = self.shared_head(mask_feats)\n else:\n assert bbox_feats is not None\n mask_feats = bbox_feats[pos_inds]\n\n mask_pred = self.mask_head(mask_feats, decode=decode)\n mask_results = dict(mask_pred=mask_pred, mask_feats=mask_feats)\n return mask_results","repo_name":"mvig-robotflow/rfvision","sub_path":"rfvision/components/roi_heads/dct_roi_head.py","file_name":"dct_roi_head.py","file_ext":"py","file_size_in_byte":2584,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"52"} +{"seq_id":"70231138405","text":"import requests\nfrom bs4 import BeautifulSoup\nimport datetime\nimport sqlite3\n\n\n# 기사 데이터 추출\ndef crawling(link):\n link_req = requests.get(link).text\n soup = BeautifulSoup(link_req, \"html.parser\")\n div_title = soup.find(\"div\", class_= \"article_info\").h3\n div_contents = soup.find(\"div\", class_= \"articleCont\")\n span = soup.find(\"span\", class_= \"article_date\")\n\n id = link.split(\"article_id=\")[1].split(\"&\")[0]\n title = div_title.get_text().strip()\n contents = div_contents.get_text().replace(\"\\t\",\"\").replace(\"\\n\",\"\").strip()\n date = span.get_text()\n print(id, date, title, contents)\n return (id, date, title, contents)\n\n# 현재 페이지의 모든 기사 링크를 리스트로 추출\ndef get_link(url):\n req = requests.get(url).text\n soup = BeautifulSoup(req, 'html.parser')\n links = []\n for tag in soup.select('.articleSubject'):\n links.append(\"https://finance.naver.com/\" + tag.select_one('a')[\"href\"].split('§')[0])\n return links\n\n\n# 실시간 속보\ndef breaking_news():\n print(\"- 실시간 속보 -\")\n\n base_url = \"https://finance.naver.com/news/news_list.naver?mode=LSS2D§ion_id=101§ion_id2=258\"\n\n links = []\n result = []\n page = int(input(\"몇번째 페이지까지 크롤링하시겠습니까? \"))\n for current_page in range(1, page + 1):\n url = base_url + \"&page=\" + str(current_page)\n links += get_link(url)\n\n for link in links:\n result.append(crawling(link))\n return result\n\n# 주요뉴스\ndef main_news():\n print(\"- 주요뉴스 -\")\n\n base_url = \"https://finance.naver.com/news/mainnews.naver\"\n links = []\n result = []\n\n day = int(input((\"며칠 전 뉴스까지 크롤링 하시겠습니까? (0입력 시 오늘의 주요뉴스만): \")))\n today = datetime.datetime.now()\n\n for d in range(day+1):\n target_day = (today - datetime.timedelta(days=d)).strftime('%Y-%m-%d')\n url = base_url + \"?date=\" + str(target_day)\n links += get_link(url)\n\n for link in links:\n result.append(crawling(link))\n return result\n\n# 많이 본 뉴스\ndef most_viewed_news():\n print(\"- 많이 본 뉴스 -\")\n base_url = \"https://finance.naver.com/news/news_list.naver?mode=RANK\"\n links = []\n result = []\n\n day = int(input((\"며칠 전 뉴스까지 크롤링 하시겠습니까? (0입력 시 오늘의 많이 본 뉴스만): \")))\n today = datetime.datetime.now()\n\n for d in range(day+1):\n for page in range(1,5): # 4페이지까지\n target_day = (today - datetime.timedelta(days=d)).strftime('%Y%m%d')\n url = base_url + \"&date=\" + str(target_day) + \"&page=\" +str(page)\n req = requests.get(url).text\n soup = BeautifulSoup(req, 'html.parser')\n for tag in soup.select('.simpleNewsList'):\n for li in tag.select('li'):\n links.append(\"https://finance.naver.com/\" + li.select_one('a')[\"href\"].split('§')[0])\n\n for link in links:\n result.append(crawling(link))\n return result\n\n# 데이터 베이스 테이블 생성\ndef creatDB():\n con = sqlite3.connect(\"./news_data.db\")\n cur = con.cursor()\n sql = \"CREATE TABLE newsTable (id INTEGER PRIMARY KEY, date text, title text, contents text)\"\n try:\n cur.execute(sql)\n print(\"The table has been created successfully!\")\n except:\n print(\"table newsTable already exists!\")\n finally:\n con.close()\n\ndef main():\n data = []\n # creatDB()\n try:\n con = sqlite3.connect(\"./news_data.db\")\n cursor = con.cursor()\n print(\"Successfully connected to the database!\")\n except:\n print(\"Connection failed!\")\n\n while True:\n print(\"---------------------------------------\")\n category = int(input(\"1. 실시간 속보\\n2. 주요뉴스\\n3. 많이 본 뉴스\\n4. 종료\\n원하는 카테고리 번호를 입력하세요: \"))\n print(\"---------------------------------------\")\n\n if category==1:\n data += breaking_news()\n elif category == 2:\n data += main_news()\n elif category == 3:\n data += most_viewed_news()\n elif category == 4:\n break\n\n try:\n INSERT_SQL = \"INSERT INTO newsTable(id, date, title, contents) VALUES (?, ?, ?, ?);\"\n cursor.executemany(INSERT_SQL, data)\n print(\"The Data has been inserted Successfully!\")\n except Exception as e:\n print(e)\n\n con.commit()\n con.close()\n\n\nif __name__ == \"__main__\":\n main()","repo_name":"handevmin/moonybot","sub_path":"crawler/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4546,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"8438084657","text":"from mimetypes import init\nfrom re import T\nimport rclpy\nfrom rclpy.node import Node\nimport tf_transformations\nfrom cv_bridge import CvBridge, CvBridgeError\n\nfrom geometry_msgs.msg import Twist, Pose\nfrom sensor_msgs.msg import Imu\nfrom nav_msgs.msg import Odometry\nfrom sensor_msgs.msg import Range, Image\n\nfrom math import sqrt, sin, cos, atan2, pi\nfrom enum import Enum\nfrom rclpy.task import Future\nimport sys, os, cv2\n\n\nclass ThymioState(Enum):\n INIT = 0\n FOLLOWING_LINE = 1\n\nHOMEPATH = os.path.expanduser(\"~\")\nDATASET_PATH = HOMEPATH+'/dataset'\n\nclass ControllerNode(Node):\n def __init__(self):\n super().__init__('main_node')\n \n self.vel_publisher = self.create_publisher(Twist, 'cmd_vel', 10)\n self.odom_subscriber = self.create_subscription(Odometry, 'odom', self.odom_callback, 10)\n self.camera = self.create_subscription(Image, 'camera', self.img_callback, 10)\n\n self.current_state = ThymioState.INIT\n\n self.gl_sens = None\n self.gr_sens = None\n\n self.ground_l = self.create_subscription(Range, 'ground/left', self.ground_l_cb, 10)\n self.ground_r = self.create_subscription(Range, 'ground/right', self.ground_r_cb, 10)\n\n \n def start(self):\n # Create and immediately start a timer that will regularly publish commands\n self.timer = self.create_timer(1/60, self.update_callback)\n self.done_future = Future()\n \n return self.done_future\n \n def stop(self):\n # Set all velocities to zero\n cmd_vel = Twist()\n self.vel_publisher.publish(cmd_vel)\n \n def odom_callback(self, msg):\n self.odom_pose = msg.pose.pose\n self.odom_velocity = msg.twist.twist\n \n pose2d = self.pose3d_to_2d(self.odom_pose)\n\n def img_callback(self, msg):\n pass\n\n\n def ground_l_cb(self, msg):\n self.gl_sens = msg.range\n \n def ground_r_cb(self, msg):\n self.gr_sens = msg.range\n\n def pose3d_to_2d(self, pose3):\n quaternion = (\n pose3.orientation.x,\n pose3.orientation.y,\n pose3.orientation.z,\n pose3.orientation.w\n )\n\n roll, pitch, yaw = tf_transformations.euler_from_quaternion(quaternion)\n \n pose2 = (\n pose3.position.x, # x position\n pose3.position.y, # y position\n yaw # theta orientation\n )\n \n return pose2\n\n def init_state(self):\n cmd_vel = Twist()\n cmd_vel.linear.x = 2.0\n cmd_vel.angular.z = 0.0\n return cmd_vel\n\n\n def update_init_state(self):\n if self.current_state == ThymioState.INIT:\n if self.gr_sens == 1.0 or self.gr_sens == 1.0:\n self.get_logger().info(f\"Line detected!\")\n self.current_state = ThymioState.FOLLOWING_LINE\n self.get_logger().info(f\"Entered state {self.current_state}\")\n\n\n def follow_line(self):\n cmd_vel = Twist()\n if not self.gl_sens:\n cmd_vel.linear.x = 0.0\n cmd_vel.angular.z = -1.5\n elif not self.gr_sens:\n cmd_vel.linear.x = 0.0\n cmd_vel.angular.z = 1.5\n else:\n cmd_vel.linear.x = 2.0\n cmd_vel.angular.z = 0.0\n\n return cmd_vel\n\n\n def update_callback(self):\n if self.current_state == ThymioState.INIT:\n self.update_init_state()\n cmd_vel = self.init_state()\n\n if self.current_state == ThymioState.FOLLOWING_LINE:\n cmd_vel = self.follow_line()\n\n \n #self.get_logger().info(f\"Left: {self.gl_sens}, Right: {self.gr_sens}\")\n\n # Publish the command\n self.vel_publisher.publish(cmd_vel)\n\n \n\n\ndef main():\n # Initialize the ROS client library\n rclpy.init(args=sys.argv)\n \n # Create an instance of your node class\n node = ControllerNode()\n done = node.start()\n\n rclpy.spin_until_future_complete(node, done)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"marcolatella/Mighty-Thymio-Simulation","sub_path":"project_ml/project_ml/main_node.py","file_name":"main_node.py","file_ext":"py","file_size_in_byte":4022,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"25694044790","text":"# -*- coding: utf-8 -*-\n\nimport time\n\nfrom django.core import mail\nfrom django.core.urlresolvers import reverse\n\nfrom auf.django.loginguard.models import LoginEvent\nfrom auf.django.loginguard import conf\n\nfrom .common import CommonTest\n\n\nclass SettingTest(CommonTest):\n\n def _try_patched_login_ko(self, url_name):\n data = {'username': self.username,\n 'password': 'xxx', }\n url = reverse(url_name)\n return self.client.post(url, data)\n\n def test_flag_policy(self):\n \"\"\"\n no timecheck if flag is off, no log\n \"\"\"\n self.assertEqual(conf.LOGIN_GUARD_RETRY_POLICY_ON, False)\n response = self._try_patched_login_ko('retry_policy_off_login')\n self.assertEqual(response.status_code, 200)\n events = LoginEvent.objects.all()\n self.assertEqual(len(events), 0)\n response = self._try_patched_login_ko('retry_policy_off_login')\n self.assertEqual(response.status_code, 200)\n events = LoginEvent.objects.all()\n self.assertEqual(len(events), 0)\n\n def test_alert(self):\n \"\"\"\n no timecheck if flag is off, no log\n \"\"\"\n self.assertEqual(conf.LOGIN_GUARD_FREQUENCY_ALERT_ON, False)\n self._try_patched_login_ko('alert_off_login')\n time.sleep(3)\n self._try_patched_login_ko('alert_off_login')\n mails = [m.body for m in mail.outbox]\n self.assertEqual(len(mails), 0)\n","repo_name":"olarcheveque/auf.django.loginguard","sub_path":"auf/django/loginguard/tests/configuration.py","file_name":"configuration.py","file_ext":"py","file_size_in_byte":1433,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"72094733286","text":"\"\"\"def recherche_sequentielle_liste_triee(liste, elem):\n if (elem > liste[len(liste) - 1]):\n return (False);\n else :\n i = 0;\n while (liste[i] < elem):\n i += 1;\n if (liste [i] == elem):\n return (True);\n else :\n return (False);\n1: \n2: meilleur cas calcules de comlexcite = min(cout(d) * d e Dn) = 1\n3: pire cas calcules de comlexcite = max(cout(d) * d, d e Dn) = n * 3\n4: complexciter moyenne = E cout(d) * p(d)\n\"\"\"\ndef myster(liste, elem):\n for i in range(len(liste)):\n if (liste[i] == elem):\n resultat[0] = resultat[0] + 1;\n resultat[2] = i;\n if (resultat[1] == -1):\n resultat[1] = i;\n return (resultat);\n\n\"\"\"\n\"\"\"","repo_name":"babdenour/Python","sub_path":"FacPython/TD7/TP7.py","file_name":"TP7.py","file_ext":"py","file_size_in_byte":745,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"18495330317","text":"trials=int(input(\"Enter number of trials: \"))\nfor trial in range(trials):\n randc=(list(map(int,(input(f\"Enter row then space and column for trial {trial}: \").split(' ')))))\n rows,cols=randc[0],randc[1]\n table=[[0 for _ in range(cols)] for _ in range(rows)]\n for i in range(rows):\n input_row=list(input(f\"Enter the elements of row {i}: \"))\n for j in range(cols):\n table[i][j]=input_row[j]\n row_hashes={} \n col_hashes={} \n for k in range(rows):\n for m in range(cols):\n if table[k][m] == \"#\":\n if k not in row_hashes:\n row_hashes[k]=[]\n row_hashes[k].append(m)\n \n for l in range(cols):\n for n in range(rows):\n if table[n][l] == \"#\":\n if l not in col_hashes:\n col_hashes[l]=[]\n col_hashes[l].append(n)\n # print(row_hashes)\n # print(col_hashes)\n count=1\n num_counts=[] \n for num in row_hashes:\n for cons in row_hashes[num]:\n # print(f\"The list is {len(row_hashes[num])} items long\")\n # print(f\"The current value index is {cons}\")\n # print(f\"the list is {row_hashes[num]}\")\n # print(f\"the last value is {row_hashes[num][-1]}\")\n\n# check if cons is not greaterthan or equal to\n if cons < row_hashes[num][-1]:\n # print(f\"incrementin by one is {cons+1}\")\n indexof=row_hashes[num].index(cons)\n # print(f\"The index of is {indexof}\")\n # print(f\"The value to be compared with is {row_hashes[num][indexof+1]}\")\n if (cons+1) == (row_hashes[num][indexof+1]):\n count+=1 \n elif count > 1:\n num_counts.append(count)\n count=1\n\n # print(count) \n if count > 1:\n num_counts.append(count)\n else:\n num_counts.append(1) \n count=1 \n # print(num_counts)\n\n counter=1\n # print(\"........................................................................................\")\n for num in col_hashes:\n for cons in col_hashes[num]:\n # print(f\"The list is {len(col_hashes[num])} items long\")\n # print(f\"The current value index is {cons}\")\n # print(f\"the list is {col_hashes[num]}\")\n # print(f\"the last value is {col_hashes[num][-1]}\")\n# check if cons is not greaterthan or equal to\n if cons < (col_hashes[num][-1]):\n # print(f\"incrementin by one is {cons+1}\")\n indexof=col_hashes[num].index(cons)\n # print(f\"The value to be compared with is {col_hashes[num][indexof+1]}\")\n if (cons+1) == (col_hashes[num][indexof+1]):\n counter+=1\n elif counter > 1:\n num_counts.append(counter)\n counter=1 \n\n # print(counter) \n if counter > 1:\n num_counts.append(counter)\n else:\n num_counts.append(1) \n counter=1 \n # print(num_counts)\n print(max(num_counts))","repo_name":"SsuunaFran/hackerearth","sub_path":"maximum_borders.py","file_name":"maximum_borders.py","file_ext":"py","file_size_in_byte":3192,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"6286832477","text":"from skimage import io\nfrom AgentModel import DQNAgent, DroneEnv\nimport numpy as np\n\n\nif __name__ == '__main__':\n save_path = './img_save/'\n npy_path = './npy_save/'\n agent = DQNAgent(test_mode=True)\n # DroneEnv.get_dist()\n # need an api for distance info\n # api:\n # return: float\n env = DroneEnv()\n trial_len = 100\n current_state = env.reset()\n distance_list = []\n for step in range(trial_len):\n action = agent.act(current_state)\n io.imsave(save_path + '{}act{}.jpg'.format(step, action), current_state)\n distance_list.append(env.get_dist())\n new_state, reward, done = env.step(action)\n current_state = new_state\n if done:\n print('Finished in {} steps.'.format(step))\n break\n distance_list = np.array(distance_list)\n np.save(npy_path + '.npy', distance_list)\n","repo_name":"n0lean/AirSim_DroneLanding","sub_path":"ModelTest.py","file_name":"ModelTest.py","file_ext":"py","file_size_in_byte":868,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"20000869427","text":"import dps_client\nfrom datetime import datetime\n\nURL = 'http://bergerab.com/dps/db/api/v1/'\nclient = dps_client.connect(URL, 'Adam Test')\n\nbatch = client.make_batch(datetime.now())\nbatch.add('Signal A', 1.2)\nbatch.add('Signal B', 1.3)\n\nprint(client.send())\n","repo_name":"bergerab/dps","sub_path":"dps_client/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":257,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"5818352931","text":"from urllib.parse import urlparse\n\nimport re\nimport os\nimport wget\nimport gzip\nimport shutil\nimport zipfile\n\nimport subprocess as sp\n\n\nMETHODS = [\n 'Naive',\n 'Burkhardt',\n 'Cohen',\n 'Sandia',\n 'Sandia2',\n 'SandiaDot',\n 'SandiaDot2',\n]\n\nGRAPHS = {\n 'loc-brightkite_edges.txt': 'http://snap.stanford.edu/data/loc-brightkite_edges.txt.gz',\n 'amazon0302.txt': 'https://snap.stanford.edu/data/amazon0302.txt.gz',\n 'roadNet-PA.txt': 'https://snap.stanford.edu/data/roadNet-PA.txt.gz',\n 'amazon0505.txt': 'https://snap.stanford.edu/data/amazon0505.txt.gz',\n 'soc-Epinions1.txt': 'https://snap.stanford.edu/data/soc-Epinions1.txt.gz',\n 'email-EuAll.txt': 'https://snap.stanford.edu/data/email-EuAll.txt.gz',\n 'loc-gowalla_edges.txt': 'https://snap.stanford.edu/data/loc-gowalla_edges.txt.gz',\n 'soc-Slashdot0902.txt': 'https://snap.stanford.edu/data/soc-Slashdot0902.txt.gz',\n 'soc-Slashdot0811.txt': 'https://snap.stanford.edu/data/soc-Slashdot0811.txt.gz',\n}\n\nFULLGRAPH_POWS = [\n i\n for p in range(0, 4)\n for i in range(10 ** p, 10 ** (p + 1), 10 ** p)\n]\n\n\ndef download_graph(url):\n archive_path = './input/' + os.path.split(urlparse(url).path)[1]\n file_path = os.path.splitext(archive_path)[0]\n\n if os.path.exists(file_path) is False:\n wget.download(url, './input')\n\n with gzip.open(archive_path, 'rb') as f_in:\n with open(file_path, 'wb') as f_out:\n shutil.copyfileobj(f_in, f_out)\n\n content = None\n with open(file_path, 'r') as f_in:\n content = f_in.readlines()\n\n with open(file_path, 'w') as f_out:\n for line in content:\n if not line.startswith('#'):\n f_out.write(str.replace(line, '\t', ' '))\n\n os.remove(archive_path)\n\n\ndef create_fullgraph(n):\n file_path = f'./input/FullGraph/fullgraph_{n}.txt'\n if os.path.exists(file_path) is False:\n with open(file_path, 'w') as f_out:\n for i in range(0, n):\n for j in range(i + 1, n):\n f_out.write(f'{i} {j}\\n')\n\n\ndef init():\n sp.run(f'make', shell=True)\n for url in list(GRAPHS.values()):\n download_graph(url)\n for n in FULLGRAPH_POWS:\n create_fullgraph(n)\n\n\ndef get_time(file_path):\n result = {}\n content = None\n with open(file_path, 'r') as f_in:\n content = f_in.readlines()\n for method in METHODS:\n for line in content:\n if re.fullmatch(f'({method} used time \\(in seconds\\): )(.*)(\\n)', line) is not None:\n result[method] = re.sub(\n f'({method} used time \\(in seconds\\): )(.*)(\\n)', '\\g<2>', line)\n print(result)\n return result\n\n\ndef test_graph(file_path):\n results_path = './results/' + os.path.split(file_path)[1]\n\n res = sp.run(f'./main {file_path} > {results_path}', shell=True)\n\n print(res)\n\n return results_path\n\n\ndef test_all_fullgraphs(n=FULLGRAPH_POWS[-1]):\n with open('./fullgraph_results.md', 'w') as f_out:\n head = '| N |'\n grid = '|:-:|'\n for method in METHODS:\n head += f' {method} time (s) | '\n grid += f':-:|'\n f_out.write(f'{head}\\n')\n f_out.write(f'{grid}\\n')\n\n for n in list(filter(lambda x: x <= n, FULLGRAPH_POWS)):\n time = get_time(test_graph(f'./input/FullGraph/fullgraph_{n}.txt'))\n res = f'| {n} |'\n for method in METHODS:\n res += f' {time.get(method)} |'\n f_out.write(f'{res}\\n')\n\n\ndef test_all_stanford_graphs(n=-1):\n with open('./stanford_graph_results.md', 'w') as f_out:\n head = '| Name |'\n grid = '|:----:|'\n for method in METHODS:\n head += f' {method} time (s) | '\n grid += f':-:|'\n f_out.write(f'{head}\\n')\n f_out.write(f'{grid}\\n')\n\n for g in list(GRAPHS.keys()):\n print(g, n)\n if n == 0:\n break\n n -= 1\n time = get_time(test_graph(f'./input/{g}'))\n print(time)\n res = f'| {g} |'\n for method in METHODS:\n res += f' {time.get(method)} |'\n f_out.write(f'{res}\\n')\n\n\nif __name__ == '__main__':\n init()\n test_all_fullgraphs(1000)\n test_all_stanford_graphs()\n","repo_name":"Pogozhelskaya/Triangle_counting","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":4310,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"71853669926","text":"from scipy.special import binom\r\n\r\nfrom dataclasses import dataclass\r\nfrom functools import total_ordering\r\nfrom random import randint\r\n\r\n_pid = 0\r\n\r\nclass GameError(Exception):\r\n pass\r\n\r\n\r\n@total_ordering\r\n@dataclass\r\nclass Bet:\r\n amount: int\r\n face: int\r\n\r\n # A bet is less than another bet by checking the amount first, then the face value.\r\n def __lt__(self, other: \"Bet\"):\r\n if other is None:\r\n return other\r\n return (self.amount, self.face) < (other.amount, other.face)\r\n\r\n def __str__(self) -> str:\r\n return f\"{self.amount}x [{self.face}]\"\r\n\r\n\r\nclass Player:\r\n \"\"\"Represents one individual player.\"\"\"\r\n def __init__(self, name: str, dice_count = 5, dice_size = 6):\r\n global _pid\r\n self.id = _pid\r\n _pid += 1\r\n self.name = name\r\n self.dice_size = dice_size\r\n\r\n # Dice don't need to be special, a list of numbers is probably fine\r\n self.dice: list[int] = []\r\n for i in range(dice_count):\r\n self.dice.append(randint(1, self.dice_size))\r\n\r\n def roll(self):\r\n count = len(self.dice)\r\n self.dice.clear()\r\n for i in range(count):\r\n self.dice.append(randint(1, self.dice_size))\r\n\r\n def remove_die(self):\r\n self.dice.pop()\r\n\r\n # A Player loses when they run out of dice.\r\n @property\r\n def lost(self) -> bool:\r\n return len(self.dice) == 0\r\n\r\n @property\r\n def dice_string(self) -> str:\r\n s = \"\"\r\n for i in self.dice:\r\n s += f\" [{i}]\"\r\n return s.strip()\r\n\r\n def __str__(self) -> str:\r\n s = f\"{self.name:>10}: \"\r\n for i in self.dice:\r\n s += f\" [{i}]\"\r\n return s\r\n\r\n\r\nclass Game:\r\n \"\"\"Represents an entire game's backend logic and state-keeping.\"\"\"\r\n def __init__(self, player_names: list[str] = [], dice_count = 5, dice_size = 6):\r\n self.dice_count = dice_count\r\n self.dice_size = dice_size\r\n\r\n # Create players from names\r\n self.players: list[Player] = []\r\n for name in player_names:\r\n self.players.append(\r\n Player(name, self.dice_count, self.dice_size)\r\n )\r\n\r\n # Game state\r\n self.current_bet: Bet = None\r\n self.previous_player: Player = None\r\n self.current_player: Player = None\r\n\r\n @property\r\n def dice_in_play(self) -> int:\r\n \"\"\"Amount of dice in play.\"\"\"\r\n return sum((len(p.dice) for p in self.players))\r\n\r\n @property\r\n def all_dice(self) -> list[int]:\r\n \"\"\"Every individual die in play.\"\"\"\r\n d = []\r\n for p in self.players:\r\n d += p.dice\r\n return d\r\n\r\n @property\r\n def game_over(self) -> bool:\r\n \"\"\"Return True if only one player is left.\"\"\"\r\n return len(self.players) == 1\r\n\r\n def check_bet(self, bet: Bet) -> bool:\r\n \"\"\"Return True if a bet is valid against the current dice in play.\"\"\"\r\n return self.all_dice.count(bet.face) >= bet.amount\r\n\r\n def check_spot_on(self, bet: Bet) -> bool:\r\n \"\"\"Return True if a bet is spot-on against the current dice in play.\"\"\"\r\n return self.all_dice.count(bet.face) == bet.amount\r\n\r\n def spot_on_equation(self, die_face: int, dice_amount: int, dice_in_play: int, known_dice: list[int] = []) -> float:\r\n \"\"\"What are the odds of a spot-on bet being true?\"\"\"\r\n dice_amount -= known_dice.count(die_face)\r\n dice_in_play -= len(known_dice)\r\n q = dice_amount\r\n n = dice_in_play\r\n c = binom(n, q)\r\n return c * ((1/self.dice_size)**q) * (((self.dice_size - 1)/self.dice_size)**(n - q))\r\n\r\n def bet_equation(self, die_face: int, dice_amount: int, dice_in_play: int, known_dice: list[int] = []) -> float:\r\n \"\"\"What are the odds of a bet being true?\"\"\"\r\n dice_amount -= known_dice.count(die_face)\r\n dice_in_play -= len(known_dice)\r\n return sum([self.spot_on_equation(die_face, i, dice_in_play) for i in range(dice_amount, dice_in_play + 1)])\r\n\r\n def next_player(self) -> Player:\r\n \"\"\"Take a player off the beginning of the list,\r\n and return it after putting it back on the end.\"\"\"\r\n self.previous_player = self.current_player\r\n p = self.players.pop(0)\r\n self.players.append(p)\r\n self.current_player = p\r\n return p\r\n\r\n def remove_lost_players(self) -> list[Player]:\r\n \"\"\"Remove any players with zero dice from the game.\"\"\"\r\n lost_players = [p for p in self.players if p.lost]\r\n for lp in lost_players:\r\n self.players.remove(lp)\r\n return lost_players\r\n\r\n def setup(self):\r\n \"\"\"Called at the beginning of a new game. Get everything prepared.\"\"\"\r\n for player in self.players:\r\n player.roll()\r\n self.current_player = None\r\n self.previous_player = None\r\n self.current_bet = None\r\n self.next_player()\r\n\r\n def reset(self):\r\n \"\"\"Called at the beginning of a new round. Get everything prepared.\"\"\"\r\n for player in self.players:\r\n player.roll()\r\n self.current_bet = None\r\n\r\n # First bet works slightly differently to other bets so it's its own thing.\r\n def first_bet(self, bet: Bet):\r\n if bet.amount > self.dice_in_play:\r\n raise GameError(f\"Bet is for more dice than are on the table. ({bet.amount}).\")\r\n if bet.face > self.dice_size or 1 > bet.face:\r\n raise GameError(f\"Bet is for an invalid die face ({bet.face}).\")\r\n self.current_bet = bet\r\n\r\n def place_bet(self, bet: Bet):\r\n if bet.amount > self.dice_in_play:\r\n raise GameError(f\"Bet is for more dice than are on the table. ({bet.amount}).\")\r\n if bet.face > self.dice_size or 1 > bet.face:\r\n raise GameError(f\"Bet is for an invalid die face ({bet.face}).\")\r\n if bet.amount > self.current_bet.amount or (bet.amount == self.current_bet.amount\r\n and bet.face > self.current_bet.face):\r\n self.current_bet = bet\r\n else:\r\n raise GameError(\"Bet ({bet}) isn't better than the current bet ({self.current_bet})\")\r\n\r\n def call_bluff(self) -> bool:\r\n \"\"\"Call the previous player's bluff. Returns True if the current player was right.\"\"\"\r\n if self.check_bet(self.current_bet):\r\n self.current_player.remove_die()\r\n self.reset()\r\n return False\r\n else:\r\n self.previous_player.remove_die()\r\n self.reset()\r\n return True\r\n\r\n def call_spot_on(self) -> bool:\r\n \"\"\"Call the previous player's bet spot-on. Returns True if the current player was right.\"\"\"\r\n if self.check_spot_on(self.current_bet):\r\n for p in self.players:\r\n if p != self.current_player:\r\n p.remove_die()\r\n self.reset()\r\n return True\r\n else:\r\n self.current_player.remove_die()\r\n self.reset()\r\n return False\r\n\r\n def __str__(self) -> str:\r\n players = \"\\n\".join([str(p) for p in self.players])\r\n s = f\"Current Player: {self.current_player.name}\\nCurrent Bet: {self.current_bet}\\n\"\r\n s += players\r\n return s\r\n","repo_name":"DigiDuncan/LiarsDice","sub_path":"dice/lib/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":7293,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"24373526302","text":"#!/usr/bin/env pipenv run python\n\"\"\"Solutions to day 2 of Advent of Code\"\"\"\n\nfrom collections import defaultdict, Counter\nfrom get_input import get_input\n\ndef part1(lines):\n \"\"\"Solution to part 1\"\"\"\n checksum = defaultdict(int)\n for line in lines:\n digits = Counter(list(line))\n for i in range(2, 5):\n if i in digits.values():\n checksum[i] += 1\n result = 1\n for num in checksum.values():\n result *= num\n return result\n\n\ndef part2(lines):\n \"\"\"Solution to part 2\"\"\"\n for h_index, head in enumerate(lines):\n for tail in lines[:h_index]:\n common_letters = ''.join(\n h for h, t in zip(list(head), list(tail)) if h == t)\n if len(common_letters) + 1 == len(head):\n return common_letters\n raise Exception(\"Could not find soltution\")\n\nif __name__ == '__main__':\n LINES = get_input(day=2, year=2018).splitlines()\n print(\"Part 1: {}\".format(part1(LINES)))\n print(\"Part 2: {}\".format(part2(LINES)))\n","repo_name":"ThomasZumsteg/adventofcode2018","sub_path":"day02.py","file_name":"day02.py","file_ext":"py","file_size_in_byte":1029,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"72526115686","text":"import itertools as it\n\n\ndef LookSay():\n s = '1'\n while True:\n for item in s:\n yield int(item)\n s = \"\".join([str(len(list(l))) + str(k) for k, l in it.groupby(s)])\n # print(s)\n\n\n# for i, l in enumerate(LookSay()):\n# print(f\"{i}: {l}\")\n# if i > 10:\n# break\n","repo_name":"llilyshkall/MSU","sub_path":"python/day07/day07_task03.py","file_name":"day07_task03.py","file_ext":"py","file_size_in_byte":313,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"70781296166","text":"# Parsing each word\ndef TabularParser(word, TRIE, LeftToRight) :\n wordSize = len(word)\n result = []\n\n # create a empty matrix called result\n for i in range(wordSize+1) :\n result.append([])\n for j in range(wordSize+1) :\n result[i].append([])\n\n # Tabular Algorithm iteration\n for e in range(1,wordSize+1) : # end point\n for s in range(e-1, -1, -1) : # starting point\n # s ~ e = s~e + s~k + k~e\n # When the starting point and the end point are given, parse those interval.\n # result will change inside the ParseInside function.\n ParseInside(word, result, e, s, wordSize, TRIE, LeftToRight)\n\n return result\n\n\n\ndef ParseInside(word, result, e, s, wordSize, TRIE, LeftToRight) :\n # k between 's to e'\n for k in range(s+1, e+1) :\n # characters from s ~ e\n if ( k == e ) :\n stoeMorp = word[s:k]\n POS = TRIE.searchMorp(stoeMorp)\n if ( POS == False ) :\n continue\n else :\n for pos in POS :\n result[s][e].append([(stoeMorp, pos)])\n\n else :\n # Matrix entry of [s][k], [k][e]\n # leftTabular is an already-made morps cases that will be attached at the left side of the result.\n # RightTabular is an already-made morps cases taht will be attached at the right side of the result.\n leftTabular = result[s][k]\n rightTabular = result[k][e]\n if ( rightTabular == None or leftTabular == None ) :\n continue\n\n for lcandidate in leftTabular :\n for rcandidate in rightTabular :\n # GrammarCheck\n if GrammarCheck(lcandidate, rcandidate, LeftToRight) :\n new = lcandidate + rcandidate\n result[s][e].append(new)\n\n\n\ndef GrammarCheck(left, right, LeftToRight) :\n # if attaching characters are exactly the same (= same character, same POS which means that it came from itself)\n if (left[-1] == right[0] ) :\n return True\n\n if (left[-1][1] in LeftToRight) :\n if (right[0][1] in LeftToRight[left[-1][1]]) :\n return True\n\n return False\n\n\n\n\n","repo_name":"Linohong/Tabular-Parser","sub_path":"TabularParser.py","file_name":"TabularParser.py","file_ext":"py","file_size_in_byte":2249,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"4309717454","text":"import pandas as pd\r\nfrom bokeh.charts import Bar, output_file, show\r\nfrom bokeh.models import FuncTickFormatter\r\n\r\nskills_list = ['cheese making', 'squanching', 'leaving harsh criticisms']\r\npct_counts = [25, 40, 1]\r\ndf = pd.DataFrame({'skill':skills_list, 'pct jobs with skill':pct_counts})\r\np = Bar(df, 'index', values='pct jobs with skill', title=\"Top skills for ___ jobs\", legend=False)\r\nlabel_dict = {}\r\nfor i, s in enumerate(skills_list):\r\n label_dict[i] = s\r\n\r\nprint(label_dict)\r\n\r\np.xaxis.formatter = FuncTickFormatter(code=\"\"\"\r\n var labels = %s;\r\n return labels[tick];\r\n\"\"\" % label_dict)\r\n\r\noutput_file(\"bar.html\")\r\nshow(p)","repo_name":"DanielLongo/eegML","sub_path":"eegvis/experiments/fixed_ticks.py","file_name":"fixed_ticks.py","file_ext":"py","file_size_in_byte":641,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"52"} +{"seq_id":"40177349016","text":"\nfrom sys import argv\nfrom os import makedirs, path\nfrom fontTools.ttLib import TTFont\nfrom PIL import Image, ImageDraw, ImageFont\n\nif len(argv) < 4:\n print('''\n Usage:\\n\n py font_maker.py \n py font_maker.py all\n ''')\n exit()\n\nfont = ImageFont.truetype(argv[1], 68)\n\n\nif not path.exists(argv[2]):\n makedirs(argv[2])\n\nhstr = []\nif argv[3] == \"all\":\n fobj = TTFont(argv[1])\n dict = fobj.getBestCmap()\n for key, _ in dict.items():\n hstr.append(chr(key))\nelse:\n hstr = argv[3]\n\n\nfor c in hstr:\n try:\n size = font.getbbox(c)\n img = Image.new(\"RGBA\", (size[2], size[3]), (0, 0, 0, 0))\n draw = ImageDraw.Draw(img)\n draw.text((0, 0), c, font=font, fill='white')\n\n width, height = img.size\n minx = 99999\n miny = 99999\n maxx = -10\n maxy = -10\n pix = img.load()\n for x in range(0, width):\n for y in range(0, height):\n if pix[x, y][3] != 0:\n if minx > x:\n minx = x\n if miny > y:\n miny = y\n if maxx < x:\n maxx = x\n if maxy < y:\n maxy = y\n\n img.save(\n argv[2] + \"\\\\\" + str(ord(c)) + \".png\"\n )\n except Exception as r:\n print(r)\n\n","repo_name":"HKLab/HK.TMProGlyphSupplementation","sub_path":"font-maker.py","file_name":"font-maker.py","file_ext":"py","file_size_in_byte":1421,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"432515714","text":"import numpy as np\nimport re\n\nclass BoWInitializer:\n \n def __init__(self, features, keywords_f, kw_feats_f, kw_feats_bias_f, pca_mat_f, pca_mean_f):\n with open(keywords_f, 'r') as f:\n self._keywords = f.readlines()\n self._keywords = [x.strip() for x in self._keywords]\n self._keywords = [(x.split(\":\")[0], int(x.split(\":\")[1].strip())) for x in self._keywords]\n self._keywords.sort(key = lambda x: x[1], reverse=False)\n self._keywords = list(map(lambda x: x[0] ,self._keywords))\n \n self._kw_feats = np.fromfile(kw_feats_f, dtype=np.float32)\n self._kw_feats = np.reshape(self._kw_feats, [len(self._keywords), -1])\n self._kw_bias = np.fromfile(kw_feats_bias_f, dtype=np.float32)\n self._pca_mat = np.fromfile(pca_mat_f, dtype=np.float32)\n self._pca_mean = np.fromfile(pca_mean_f, dtype=np.float32)\n self._pca_mat = np.reshape(self._pca_mat, [-1,self._pca_mean.shape[0]])\n self._features = features\n \n \n def score(self, query):\n # Tokenize\n query = re.sub(\"[\\\\/?!,.'\\\"]\", \" \", query)\n query = query.split(\" \")\n # compute embeddings\n query_feats = np.copy(self._kw_bias)\n should_rescore = False\n for word in query:\n if word != \"\":\n exact = None\n exact_kw = None\n possible = None\n possible_kw = None\n for kw_i, kw in enumerate(self._keywords):\n if word in kw:\n index = kw.index(word)\n if index == 0 and (exact == None or kw < exact_kw ):\n exact = kw_i\n exact_kw = kw\n elif possible == None:\n possible = kw_i\n possible_kw = kw\n if exact != None:\n #print(exact, exact_kw)\n query_feats += self._kw_feats[exact]\n should_rescore = True\n elif possible != None:\n #print(possible, possible_kw) \n query_feats += self._kw_feats[possible]\n should_rescore = True\n \n if should_rescore:\n query_feats = np.tanh(query_feats)\n query_feats /= np.linalg.norm(query_feats)\n\n # Apply PCA\n query_feats -= self._pca_mean\n query_feats = self._pca_mat @ query_feats\n query_feats /= np.linalg.norm(query_feats)\n\n # Compute scores\n scores = (1 - np.dot(self._features, query_feats)) / 2\n scores = np.exp(scores * (-50))\n return scores\n else:\n return np.ones(self._features.shape[0], dtype=np.float32)\n \n \n","repo_name":"siret-junior/somhunter-simulator","sub_path":"initializers/bow_initializer.py","file_name":"bow_initializer.py","file_ext":"py","file_size_in_byte":2836,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"4384534597","text":"import tornado.web\nimport tornado.ioloop\nimport tornado.httpserver\n\n\nclass IndexHandler(tornado.web.RequestHandler):\n # 处理get请求的方法\n def get(self):\n # 给浏览器响应信息\n self.write(\"hello tornado web three!!!\")\n\n\nif __name__ == \"__main__\":\n app = tornado.web.Application(\n [(r\"/\", IndexHandler)]\n )\n # 实例化一个http服务对象\n httpServer = tornado.httpserver.HTTPServer(app)\n # 服务器绑定相应的端口\n httpServer.bind(8000)\n # 开启 多进程\n httpServer.start(5)\n # 值可以不写或None或负数，这些都认为开启服务器对于的cpu核心数\n # 虽然提供了多进程的方式，但是有问题，不建议上面的方式启动多进程\n # 使用手动启动多个进程，并可以绑定不能的端口\n # 有问题：\n # （1）每个子进程都会从父进程中复制中一份IOloop的实例，如果在创建子进程前修改IOLoop, 会影响子进程。\n # （2）所有的进程都是由一个命令启动的，无法做到在不停止服务的情况下修改代码。\n # （3）所有进程共享一个端口，想要分别监控��困难。\n tornado.ioloop.IOLoop.current().start()\n\n","repo_name":"Thousandhack/tornado_stu","sub_path":"test/server_03.py","file_name":"server_03.py","file_ext":"py","file_size_in_byte":1221,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"21892458687","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Aug 24 16:02:55 2022\r\n\r\n\r\n\"\"\"\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport re\r\nimport random\r\n#%%\r\n'''Potential Cell for getting data from the web'''\r\n# Can only call this cell with internet, so I sectioned this off from the rest of the data\r\nurl = 'https://raw.githubusercontent.com/PokeAPI/pokeapi/master/data/v2/csv/type_efficacy.csv'\r\ntypechart = pd.read_csv(url)\r\nurl1 = 'https://raw.githubusercontent.com/PokeAPI/pokeapi/master/data/v2/csv/items.csv'\r\nitems = pd.read_csv(url1)\r\nurl2 = 'https://raw.githubusercontent.com/PokeAPI/pokeapi/master/data/v2/csv/natures.csv'\r\nnatures = pd.read_csv(url2)\r\nurl3 = 'https://raw.githubusercontent.com/PokeAPI/pokeapi/master/data/v2/csv/abilities.csv'\r\nabilities = pd.read_csv(url3)\r\nurl4 = 'https://raw.githubusercontent.com/PokeAPI/pokeapi/master/data/v2/csv/pokemon_abilities.csv'\r\npkabilities = pd.read_csv(url4)\r\n#%%\r\n# Add nature into the stat calculator and add it as a input for the statistics method of pkstorage\r\nstats = pd.read_csv(\"C:\\\\Users\\\\cacru\\\\OneDrive\\\\Documents\\\\pokemon_stats.csv\")\r\nspecies = pd.read_csv(\"C:\\\\Users\\\\cacru\\\\OneDrive\\\\Documents\\\\pokemon_species.csv\")\r\ntypes = pd.read_csv(\"C:\\\\Users\\\\cacru\\\\OneDrive\\\\Documents\\\\types.csv\")\r\nptypes = pd.read_csv(\"C:\\\\Users\\\\cacru\\\\OneDrive\\\\Documents\\\\pokemon_types.csv\")\r\nmovesdf = pd.read_csv(\"C:\\\\Users\\\\cacru\\\\OneDrive\\\\Documents\\\\Data Analysis Practice\\\\moves.csv\")\r\nmovesdf.drop(columns='contest_type_id')\r\nmovesdf.drop(columns='contest_effect_id')\r\nmovesdf.drop(columns='super_contest_effect_id')\r\nstatsdf = pd.DataFrame(stats)\r\nspeciesdf = pd.DataFrame(species)\r\ntypesdf = pd.DataFrame(types)\r\n#%%\r\n# This cell, I want to combine species, ptypes, and stats all into one data frame.\r\n# First, lets work with adding the stats to the species df.\r\n# The complication with this is the way the stats df is organized vertically. \r\nf = statsdf.query('pokemon_id == 1')\r\nprint(f)\r\n\r\nptypesdf = pd.DataFrame(ptypes)\r\n\r\n#%%\r\n\r\n\r\n#%%\r\ndef bst(x):\r\n y = statsdf[statsdf['pokemon_id']==x]\r\n lst = list(y['base_stat'])\r\n bstsum = 0\r\n for i in lst:\r\n bstsum = bstsum + i\r\n print('BST =', bstsum)\r\ndef types(x):\r\n y = ptypesdf[ptypesdf['pokemon_id']==x]\r\n t = y['type_id'].values.tolist()\r\n if int(len(t)) == 1:\r\n k = typesdf[typesdf['id']==t[0]]\r\n s = k['identifier'].values.tolist()\r\n print('Type:', s)\r\n else:\r\n k = typesdf[typesdf['id']==t[0]]\r\n s = k['identifier'].values.tolist()\r\n print('Type 1:', s)\r\n k1 = typesdf[typesdf['id']==t[1]]\r\n s1 = k1['identifier'].values.tolist()\r\n print('Type 2:', s1)\r\ndef pokemoninfo(x):\r\n y = speciesdf[speciesdf['id']==x]\r\n print('Name:', y['identifier'].values.tolist())\r\n print('Generation:', str(y['generation_id'].values.tolist()))\r\n if int(y['gender_rate']) < 0:\r\n print('No Gender')\r\n else:\r\n print('Percentage Male:', round((int(y['gender_rate'])/8)*100,3))\r\n print('Capture Rate:', y['capture_rate'].values.tolist())\r\n bst(x) \r\ndef pokemoninfobyname(x):\r\n y = speciesdf[speciesdf['identifier']==x]\r\n print('Pokedex No.:', y['id'].values.tolist())\r\n x = int(y['id'])\r\n types(x)\r\n print('Generation:', str(y['generation_id'].values.tolist()))\r\n if int(y['gender_rate']) < 0:\r\n print('No Gender')\r\n else:\r\n print('Percentage Male:', round((int(y['gender_rate'])/8)*100,3))\r\n print('Capture Rate:', y['capture_rate'].values.tolist())\r\n bst(x)\r\ndef statfinder(x):\r\n y = speciesdf[speciesdf['identifier']==x]\r\n z = int(y['id'])\r\n #Level 50 min stats\r\n df1 = statsdf[statsdf['pokemon_id']==z]\r\n return df1\r\ndef fiftymin(x):\r\n df1 = statfinder(x)\r\n ### This section locates all the stat values from the csv files\r\n hp = df1['base_stat'][df1['stat_id']==1].values.tolist()\r\n atk = df1['base_stat'][df1['stat_id']==2].values.tolist()\r\n defc = df1['base_stat'][df1['stat_id']==3].values.tolist()\r\n spatk = df1['base_stat'][df1['stat_id']==4].values.tolist()\r\n spdef = df1['base_stat'][df1['stat_id']==5].values.tolist()\r\n spd = df1['base_stat'][df1['stat_id']==6].values.tolist()\r\n nm = (2*hp[0]+0+(int(0/4)))*50\r\n hp1 = int(nm/100 + 50 + 10)\r\n ### This is solving for all of the numerators in the stat calculation equations\r\n nm1 = (2*atk[0]+0+(int(0/4)))*50\r\n nm2 = (2*defc[0]+0+(int(0/4)))*50\r\n nm3 = (2*spatk[0]+0+(int(0/4)))*50\r\n nm4 = (2*spdef[0]+0+(int(0/4)))*50\r\n nm5 = (2*spd[0]+0+(int(0/4)))*50\r\n ### This solves the final values\r\n atk1 = int((nm1/100+5)*.9)\r\n def1 = int((nm2/100+5)*.9)\r\n spatk1 = int((nm3/100+5)*.9)\r\n spdef1 = int((nm4/100+5)*.9)\r\n spd1 = int((nm5/100+5)*.9)\r\n ### This prints out the final stats\r\n print('HP:',hp1)\r\n print('ATK:', atk1)\r\n print('DEF:', def1)\r\n print('SpATK:', spatk1)\r\n print('SpDEF:', spdef1)\r\n print('SPD:', spd1)\r\ndef fiftymax(x):\r\n\r\n df1 = statfinder(x)\r\n iv = 31\r\n ev = 252\r\n ### This section locates all the stat values from the csv files\r\n hp = df1['base_stat'][df1['stat_id']==1].values.tolist()\r\n atk = df1['base_stat'][df1['stat_id']==2].values.tolist()\r\n defc = df1['base_stat'][df1['stat_id']==3].values.tolist()\r\n spatk = df1['base_stat'][df1['stat_id']==4].values.tolist()\r\n spdef = df1['base_stat'][df1['stat_id']==5].values.tolist()\r\n spd = df1['base_stat'][df1['stat_id']==6].values.tolist()\r\n nm = (2*hp[0]+iv+(int(ev/4)))*50\r\n hp1 = int(nm/100 + 50 + 10)\r\n ### This is solving for all of the numerators in the stat calculation equations\r\n nm1 = (2*atk[0]+iv+(int(ev/4)))*50\r\n nm2 = (2*defc[0]+iv+(int(ev/4)))*50\r\n nm3 = (2*spatk[0]+iv+(int(ev/4)))*50\r\n nm4 = (2*spdef[0]+iv+(int(ev/4)))*50\r\n nm5 = (2*spd[0]+iv+(int(ev/4)))*50\r\n ### This solves the final values\r\n atk1 = int((nm1/100+5)*1.1)\r\n def1 = int((nm2/100+5)*1.1)\r\n spatk1 = int((nm3/100+5)*1.1)\r\n spdef1 = int((nm4/100+5)*1.1)\r\n spd1 = int((nm5/100+5)*1.1)\r\n ### This prints out the final stats\r\n print('HP:',hp1)\r\n print('ATK:', atk1)\r\n print('DEF:', def1)\r\n print('SpATK:', spatk1)\r\n print('SpDEF:', spdef1)\r\n print('SPD:', spd1)\r\ndef stats(x):\r\n df1 = statfinder(x)\r\n ### This section locates all the stat values from the csv files\r\n hp = df1['base_stat'][df1['stat_id']==1].values.tolist()\r\n atk = df1['base_stat'][df1['stat_id']==2].values.tolist()\r\n defc = df1['base_stat'][df1['stat_id']==3].values.tolist()\r\n spatk = df1['base_stat'][df1['stat_id']==4].values.tolist()\r\n spdef = df1['base_stat'][df1['stat_id']==5].values.tolist()\r\n spd = df1['base_stat'][df1['stat_id']==6].values.tolist()\r\n statdict = {'HP': hp, 'ATK': atk, 'DEF': defc, 'SpATK': spatk, 'SpDEF': spdef, 'SPD': spd}\r\n return statdict\r\ndef statgraph(x):\r\n# Need to input two word moves with a \"-\"\r\n name = x.title()\r\n df1 = statfinder(x)\r\n ### This section locates all the stat values from the csv files\r\n hp = df1['base_stat'][df1['stat_id']==1].values.tolist()\r\n atk = df1['base_stat'][df1['stat_id']==2].values.tolist()\r\n defc = df1['base_stat'][df1['stat_id']==3].values.tolist()\r\n spatk = df1['base_stat'][df1['stat_id']==4].values.tolist()\r\n spdef = df1['base_stat'][df1['stat_id']==5].values.tolist()\r\n spd = df1['base_stat'][df1['stat_id']==6].values.tolist()\r\n statdict = {'HP': hp, 'ATK': atk, 'DEF': defc, 'SpATK': spatk, 'SpDEF': spdef, 'SPD': spd}\r\n x = statdict\r\n labels = list(x.keys())\r\n values = list(x.values())\r\n values1 = []\r\n for i in values:\r\n it = i[0]\r\n values1.append(it)\r\n x = {labels[i]: values1[i] for i in range(len(labels))}\r\n bar = plt.bar(range(len(x)), values1, align='center')\r\n bar[0].set_color('r')\r\n bar[1].set_color('y')\r\n bar[3].set_color('purple')\r\n bar[4].set_color('g')\r\n bar[5].set_color('c')\r\n plt.xticks(range(len(x)), labels)\r\n plt.ylim(0,275)\r\n plt.xlabel('Base Stats')\r\n plt.ylabel('Value')\r\n plt.title(name)\r\n plt.show()\r\ndef moveinfo(x):\r\n y = movesdf[movesdf['identifier'] == x]\r\n power = y['power'].values.tolist()\r\n mtype = y['type_id'].values.tolist()\r\n types = typesdf[typesdf['id'] == mtype[0]]\r\n t = types['identifier'].values.tolist()\r\n sp = y['damage_class_id'].values.tolist()\r\n dmg = {1: 'Status', 2: 'Physical', 3: 'Special'}\r\n dtype = dmg[sp[0]]\r\n print(f'Name: {x}\\nType: {t[0]}\\nPower: {power[0]}\\nDamage Type: {dtype}')\r\ndef stat(x, level):\r\n df1 = statfinder(x)\r\n ### This section locates all the stat values from the csv files\r\n hp = df1['base_stat'][df1['stat_id']==1].values.tolist()\r\n atk = df1['base_stat'][df1['stat_id']==2].values.tolist()\r\n defc = df1['base_stat'][df1['stat_id']==3].values.tolist()\r\n spatk = df1['base_stat'][df1['stat_id']==4].values.tolist()\r\n spdef = df1['base_stat'][df1['stat_id']==5].values.tolist()\r\n spd = df1['base_stat'][df1['stat_id']==6].values.tolist()\r\n nm = (2*hp[0]+0+(int(0/4)))*level\r\n hp1 = int(nm/100 + level + 10)\r\n ### This is solving for all of the numerators in the stat calculation equations\r\n nm1 = (2*atk[0]+0+(int(0/4)))*level\r\n nm2 = (2*defc[0]+0+(int(0/4)))*level\r\n nm3 = (2*spatk[0]+0+(int(0/4)))*level\r\n nm4 = (2*spdef[0]+0+(int(0/4)))*level\r\n nm5 = (2*spd[0]+0+(int(0/4)))*level\r\n ### This solves the final values\r\n atk1 = int((nm1/100+5)*.9)\r\n def1 = int((nm2/100+5)*.9)\r\n spatk1 = int((nm3/100+5)*.9)\r\n spdef1 = int((nm4/100+5)*.9)\r\n spd1 = int((nm5/100+5)*.9)\r\n sts = [hp1, atk1, def1, spatk1, spdef1, spd1]\r\n return sts\r\ndef varstatsHP(species, ev, level, iv=16):\r\n df1 = statfinder(species)\r\n ### This section locates all the stat values from the csv files\r\n hp = df1['base_stat'][df1['stat_id']==1].values.tolist()\r\n nm = (2*hp[0]+iv+(int(ev/4)))*level\r\n hp1 = int(nm/100 + level + 10)\r\n return hp1\r\ndef varstats(species, ev, level, stat, iv=16):\r\n df1 = statfinder(species)\r\n ### This section locates all the stat values from the csv files\r\n spd = df1['base_stat'][df1['stat_id']==stat].values.tolist()\r\n nm5 = (2*spd[0]+iv+(int(ev/4)))*level\r\n ### This solves the final values\r\n spd1 = int((nm5/100+5))\r\n return spd1\r\ndef MoveCalc(move):\r\n blk = \"\"\r\n move = move.lower()\r\n if isinstance(move, (str)) == True:\r\n for i in range(len(move)):\r\n if bool(re.search(r\"\\s\", move)) == True:\r\n if move[i] == ' ':\r\n blk = blk + '-'\r\n else:\r\n blk = blk + move[i] \r\n else:\r\n pass\r\n else:\r\n raise TypeError('The input must be a string')\r\n y = movesdf[movesdf['identifier'] == blk]\r\n power = y['power'].values.tolist()\r\n mtype = y['type_id'].values.tolist()\r\n types = typesdf[typesdf['id'] == mtype[0]]\r\n t = types['identifier'].values.tolist()\r\n sp = y['damage_class_id'].values.tolist()\r\n dmg = {1: 'Status', 2: 'Physical', 3: 'Special'}\r\n dtype = dmg[sp[0]]\r\n movedct = {'Name': blk, 'Power': power, 'Type': t, 'Damage Type': dtype}\r\n return movedct\r\ndef DMGCalc(stata, statb, pwr, lvl):\r\n stata = int(stata)\r\n statb = int(statb)\r\n pwr = int(pwr)\r\n lvl = int(lvl)\r\n num1 = (((2*lvl)/5)+2)\r\n num1a = ((stata/statb))\r\n num2 = (num1*pwr*num1a)\r\n rand = random.uniform(0.85, 1)\r\n dmg = int(((num2/50)+2)*rand)\r\n return dmg\r\n#%%\r\n\r\nclass pkstorage():\r\n def __init__(self, name, move, level=50):\r\n self.name = name\r\n self.level = level\r\n # Creating a storing a moveset for the pokemon\r\n if isinstance(move, (list)) == True:\r\n if len(move) < 5:\r\n self.move = move\r\n else:\r\n raise ValueError('The total moves has to be 4 or less')\r\n else:\r\n raise TypeError('move must be a list')\r\n def moveset(self):\r\n # This section is going to store the moveset information for the pokemon\r\n move = self.move\r\n for i in move:\r\n i = i.lower()\r\n blk = \"\"\r\n # Checking if there is a space in the string since the function cannot use spaces\r\n # https://www.geeksforgeeks.org/python-check-for-spaces-in-string/\r\n # imported re at the top. /s looks for the spaces\r\n if bool(re.search(r\"\\s\", i)) == True:\r\n for item in range(len(i)):\r\n if i[item] == ' ':\r\n blk = blk + '-'\r\n else:\r\n blk = blk + i[item]\r\n print('-----------------------------')\r\n moveinfo(blk)\r\n else:\r\n print('-----------------------------')\r\n moveinfo(i)\r\n # This gives me the stats of the pokemon at its level with 0 IV's and EV's\r\n def minstats(self):\r\n # The three lines below reference the information stored in the class object\r\n name = self.name\r\n name1 = name.lower()\r\n level = self.level\r\n # Calls the stat functioned defined in the functions portion.\r\n sts = stat(name1, level)\r\n print('---------')\r\n print(f'Level: {level}\\n---------\\nHP: {sts[0]}\\nATK: {sts[1]}\\nDEF: {sts[2]}\\nSpATK: {sts[3]}\\nSpDEF: {sts[4]}\\nSPD: {sts[5]}')\r\n # This gives the pokemon information by calling the function defined in the functions section.\r\n def info(self):\r\n name = self.name\r\n name1 = name.lower()\r\n self.info = pokemoninfobyname(name1)\r\n def statistics(self,hp=0,atk=0,deff=0,spatk=0,spdef=0,spd=0):\r\n ev = [hp, atk, deff, spatk, spdef, spd]\r\n name = self.name\r\n name = name.lower()\r\n lvl = self.level\r\n for i in ev:\r\n if isinstance(i,(int)) == False:\r\n raise TypeError('Inputs for the EVs have to be an integer')\r\n elif i > 252:\r\n raise ValueError('Individual EVs cannot be higher than 252')\r\n elif i < 0:\r\n raise ValueError('Individual EVs cannot be negative')\r\n else:\r\n pass\r\n if sum(ev) > 510:\r\n raise ValueError('Total amount of EVs cannot be higher than 510')\r\n else:\r\n pass\r\n #HP \r\n hp1 = varstatsHP(species=name, level=lvl, ev=ev[0])\r\n print('----------------\\n',name.title(),'\\nLevel:',lvl, '\\n----------------')\r\n print(f'HP: {hp1} / EV: {ev[0]}\\n----------------')\r\n labels = ['ATK', 'DEF', 'SpATK', 'SpDEF', 'SPD']\r\n ev1 = ev[1:]\r\n pos = 0\r\n statlist = [hp1]\r\n for i in ev1:\r\n pos1 = pos + 2\r\n st = varstats(species=name, level=lvl, ev=i, stat=pos1)\r\n print(f'{labels[pos]}: {st} / EV: {i}')\r\n pos = pos + 1\r\n statlist.append(st)\r\n return statlist\r\n def sgraph(self):\r\n name = self.name\r\n name = name.lower()\r\n statgraph(name)\r\n def DamageCalc(self, pkatk, pkdef, move):\r\n attack_move = MoveCalc(move)\r\n level = self.level\r\n if attack_move['Damage Type'] == 'Physical':\r\n attacking_stat = pkatk[1]\r\n defending_stat = pkdef[2]\r\n elif attack_move['Damage Type'] == 'Special':\r\n attacking_stat = pkatk[3]\r\n defending_stat = pkdef[4]\r\n else:\r\n print('That is a status move')\r\n exit()\r\n dmg = attack_move['Power'][0]\r\n atktype = attack_move['Type']\r\n damage = DMGCalc(attacking_stat, defending_stat, dmg, level)\r\n print(damage)\r\n#%%\r\nmoveset1 = ['Earthquake', 'Scale Shot', 'Swords Dance', 'Outrage']\r\nmoveset2 = ['Behemoth Blade', 'Play Rough', 'Wild Charge', 'Swords Dance']\r\nslot1 = pkstorage(name='Garchomp', move=moveset1, level=100) \r\nslot2 = pkstorage(name='Zacian', move=moveset2, level=100)\r\n#%%\r\n'''TESTING CELL'''\r\nstat1 = slot2.statistics(hp=4,atk=252,spd=252)\r\nstat2 = slot1.statistics(atk=252,hp=252,deff=4)\r\nslot2.DamageCalc(pkatk=stat1, pkdef=stat2, move='Behemoth Bash')\r\n#%%\r\nstatsdf.info()\r\nspecies.info()\r\n\r\n#%%\r\ndef gen_analysis(x):\r\n print('Generation', x, 'Analysis')\r\n y = speciesdf.groupby('generation_id')['generation_id'].count()\r\n print('Number of Pokemon:', y[x])\r\n \r\ngen_analysis(7)\r\n","repo_name":"tljorda2/practice","sub_path":"Pokemon_Practice.py","file_name":"Pokemon_Practice.py","file_ext":"py","file_size_in_byte":16427,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"9809468271","text":"from django.test import override_settings\nfrom django.urls import include, path\nfrom django.utils import timezone\nfrom faker import Faker\nfrom freezegun import freeze_time\nfrom rest_framework.status import HTTP_200_OK, HTTP_401_UNAUTHORIZED\nfrom rest_framework.test import APITestCase\n\nfrom signals.apps.api.views import NamespaceView\nfrom signals.apps.my_signals.models import Token\nfrom signals.apps.signals.factories import SignalFactory\n\nurlpatterns = [\n path('v1/relations/', NamespaceView.as_view(), name='signal-namespace'),\n path('', include('signals.apps.my_signals.urls')),\n]\n\n\nclass NameSpace:\n urlpatterns: list\n\n\ntest_urlconf = NameSpace()\ntest_urlconf.urlpatterns = urlpatterns\n\n\nfake = Faker()\n\n\n@override_settings(ROOT_URLCONF=test_urlconf)\nclass TestMySignalsLoggedInReporterEndpoint(APITestCase):\n endpoint = '/my/signals/me'\n\n def test_me_endpoint(self):\n for _ in range(5):\n email = fake.free_email()\n SignalFactory.create(reporter__email=email)\n\n token = Token.objects.create(reporter_email=email)\n request_headers = {'HTTP_AUTHORIZATION': f'Token {token.key}'}\n\n response = self.client.get(self.endpoint, **request_headers)\n\n self.assertEqual(response.status_code, HTTP_200_OK)\n self.assertEqual(response.json()['email'], email)\n\n def test_me_endpoint_expired_token(self):\n email = fake.free_email()\n\n now = timezone.now()\n with freeze_time(now - timezone.timedelta(days=7)):\n token = Token.objects.create(reporter_email=email)\n\n request_headers = {'HTTP_AUTHORIZATION': f'Token {token.key}'}\n\n response = self.client.get(self.endpoint, **request_headers)\n self.assertEqual(response.status_code, HTTP_401_UNAUTHORIZED)\n self.assertEqual(response.json()['detail'], 'Invalid token.')\n","repo_name":"Amsterdam/signals","sub_path":"app/signals/apps/my_signals/tests/rest_framework/views/test_my_signals_logged_in_reporter.py","file_name":"test_my_signals_logged_in_reporter.py","file_ext":"py","file_size_in_byte":1866,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"52"} +{"seq_id":"72631106084","text":"\ndef merge2(a, m, b):\n assert isinstance(a, list)\n i, j = 0, 0\n while i < len(a) or j < len(b):\n if i < len(a) and (j > len(b) - 1 or a[i] < b[j]):\n i += 1\n else:\n a.insert(i, b[j])\n i += 1\n j += 1\n return a\n\n\nif __name__ == '__main__':\n print(merge2([1, 2, 3], [4, 5]))","repo_name":"clarencenhuang/programming-challenges","sub_path":"lintcode/merge_2_sorted_arrays.py","file_name":"merge_2_sorted_arrays.py","file_ext":"py","file_size_in_byte":343,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"17896103142","text":"import math\nprint(\"Hello, world! This is Python 3!\")\ndef leap_year(year):\n leap = False\n if (int(year) % 4 == 0 and int(year) % 100 != 0) or int(year) % 400 == 0:\n leap = True\n return leap\n\n\ndef rotate(s, n):\n Rfirst = s[0: len(s) - n]\n Rsecond = s[len(s) - n:]\n return Rsecond + Rfirst\n\ndef float_check(x):\n\n return (x.count('.') == 1 or x.count('.') < 1) and x.replace('.', '').isdigit()\n\ndef digit_count(n):\n odd_count = 0\n even_count = 0\n zero_count = 0\n z = str(math.floor(n))\n for i in z:\n if int(i) == 0:\n zero_count = zero_count + 1\n elif int(i) % 2 == 0:\n even_count = even_count + 1\n else:\n odd_count = odd_count + 1\n return even_count, odd_count, zero_count\n","repo_name":"ShahViraj11/CSE-231-Projects","sub_path":"Labs/Lab 04/Lab 04.py","file_name":"Lab 04.py","file_ext":"py","file_size_in_byte":772,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"13884447141","text":"import os\nfrom collections import Counter, defaultdict\nimport csv\nimport pandas as pd\nimport numpy as np\nfrom sklearn.metrics import precision_recall_fscore_support\n\n\ndef get_categories(prop, model_name):\n analysis_type = 'tfidf-raw-10000/each_target_vs_corpus_per_category'\n path_dir = f'../results/{model_name}/{analysis_type}'\n path_dir = f'{path_dir}/{prop}'\n categories = set()\n for d in os.listdir(path_dir):\n categories.add(d)\n return categories\n\ndef get_context_cnts(prop, cat, label, model_name):\n \n analysis_type = 'tfidf-raw-10000/each_target_vs_corpus_per_category'\n path_dir = f'../results/{model_name}/{analysis_type}'\n path_dir = f'{path_dir}/{prop}'\n path_label = f'{path_dir}/{cat}/{label}'\n \n context_cnt = Counter()\n for f in os.listdir(path_label):\n full_path = f'{path_label}/{f}'\n if full_path.endswith('.csv'):\n with open(full_path) as infile:\n data = list(csv.DictReader(infile))\n for d in data:\n context = d['']\n diff = float(d['diff'])\n if diff > 0:\n context_cnt[context] += 1\n return context_cnt\n \ndef get_n_concepts_total(prop, cat, model_name):\n \n analysis_type = 'tfidf-raw-10000/each_target_vs_corpus_per_category'\n path_dir = f'../results/{model_name}/{analysis_type}'\n path_dir = f'{path_dir}/{prop}'\n label = 'pos'\n path_pos = f'{path_dir}/{cat}/{label}'\n label = 'neg'\n path_neg = f'{path_dir}/{cat}/{label}'\n \n files_pos = [f for f in os.listdir(path_pos) if f.endswith('.csv')]\n files_neg = [f for f in os.listdir(path_neg) if f.endswith('.csv')]\n \n return len(files_pos), len(files_neg)\n\ndef get_f1_distinctiveness(n_pos, n_neg, total_pos, total_neg):\n \n \n total_instances = total_pos + total_neg\n labels = []\n [labels.append('pos') for i in range(total_pos)]\n [labels.append('neg') for i in range(total_neg)]\n pred_labels_pos = []\n for i in range(total_pos):\n if i < n_pos:\n pred_labels_pos.append('pos')\n else:\n pred_labels_pos.append('neg')\n# print(n_pos, total_pos)\n# print(pred_labels_pos.count('pos'), pred_labels_pos.count('neg'))\n \n pred_labels_neg = []\n for i in range(total_neg):\n if i < n_neg:\n pred_labels_neg.append('pos')\n else:\n pred_labels_neg.append('neg')\n# print(n_neg, total_neg)\n# print(pred_labels_neg.count('pos'), pred_labels_neg.count('neg'))\n \n predictions = pred_labels_pos + pred_labels_neg\n \n \n #print(len(labels), len(predictions))\n #print(pos_predictions, neg_predictions)\n \n p, r, f1, supp = precision_recall_fscore_support(labels, predictions, average = 'weighted', \n zero_division=0)\n #average='weighted'\n \n return p, r, f1\n\n\n \ndef aggregate_contexts(prop, cutoff, model_name):\n aggregation_name = 'aggregated-tfidf-raw-10000-categories'\n path_dir_agg = f'../analysis/{model_name}/{aggregation_name}/{prop}'\n os.makedirs(path_dir_agg, exist_ok = True)\n \n context_cnts_all = Counter()\n context_cat_dict = defaultdict(set)\n\n cats = get_categories(prop, model_name)\n\n for cat in cats:\n context_cnts_pos = get_context_cnts(prop, cat, 'pos', model_name)\n context_cnts_neg = get_context_cnts(prop, cat, 'neg', model_name)\n total_pos, total_neg = get_n_concepts_total(prop, cat, model_name)\n \n context_f1_dict = Counter()\n context_score_dict = defaultdict(dict)\n \n # get distinctiveness\n for c, cnt_pos in context_cnts_pos.most_common():\n cnt_neg = context_cnts_neg[c]\n p, r, f1 = get_f1_distinctiveness(cnt_pos, cnt_neg, total_pos, total_neg)\n context_f1_dict[c] = f1\n context_score_dict[c] = {'p': p,'r':r, 'f1': f1}\n \n table = []\n for c, f1 in context_f1_dict.most_common():\n scores = context_score_dict[c]\n d = dict()\n d['context'] = c\n d.update(scores)\n d['n_pos'] = context_cnts_pos[c]\n d['total_pos'] = total_pos\n d['n_neg'] = context_cnts_neg[c]\n d['total_neg'] = total_neg\n table.append(d)\n \n # collect and write to file\n f = f'{path_dir_agg}/{cat}.csv'\n \n header = table[0].keys()\n with open(f, 'w') as outfile:\n writer = csv.DictWriter(outfile, fieldnames = header)\n writer.writeheader()\n for d in table:\n writer.writerow(d)\n \n \ndef prepare_annotation(prop, model_name, cutoff=3, cutoff_concepts = 5):\n \n annotation_name = f'annotation-tfidf-top_{cutoff}_{cutoff_concepts}-raw-10000-categories'\n path_dir_annotation = f'../analysis/{model_name}/{annotation_name}/{prop}'\n os.makedirs(path_dir_annotation, exist_ok = True)\n f_annotation = f'../analysis/{model_name}/{annotation_name}/{prop}/annotation-updated.csv'\n \n # paths aggregated files:\n aggregation_name = 'aggregated-tfidf-raw-10000-categories'\n path_dir_agg = f'../analysis/{model_name}/{aggregation_name}/{prop}'\n\n \n # get categories\n cats = get_categories(prop, model_name)\n \n # collect all contexts and categories \n context_cats_dict = defaultdict(set)\n \n # load top per category\n for cat in cats:\n path = f'{path_dir_agg}/{cat}.csv'\n with open(path) as infile:\n data = list(csv.DictReader(infile))\n # sort by f1\n f1_dict = defaultdict(list)\n for d in data:\n f1 = d['f1']\n f1_dict[f1].append(d)\n scores = sorted(list(f1_dict.keys()), reverse=True)\n top_scores = scores[:cutoff]\n top_context_dicts = []\n for ts in top_scores:\n dicts = f1_dict[ts]\n for d in dicts:\n n_pos = int(d['n_pos'])\n if n_pos > cutoff_concepts:\n top_context_dicts.append(d)\n \n contexts = [d['context'] for d in top_context_dicts]\n # record categories\n for c in contexts:\n context_cats_dict[c].add(cat)\n \n with open(f_annotation, 'w') as outfile:\n outfile.write('context,evidence_type,categories\\n')\n for c, cats in context_cats_dict.items():\n outfile.write(f'{c}, ,{\" \".join(cats)}\\n')\n\n\n\n\n","repo_name":"PiaSommerauer/CorpusDiagnostics","sub_path":"scripts/prepare_annotations.py","file_name":"prepare_annotations.py","file_ext":"py","file_size_in_byte":6468,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"40714574383","text":"#%%\nfrom faust_macrogen_graph import parserutils, analyzeutils, graphutils, eades_fas\nfrom pathlib import Path\nimport pandas as pd\nfrom collections import Counter, OrderedDict\nimport networkx as nx\n\n\n \ndef gen_feature_dict(paramlist, special_researchers, tempsyn=False):\n \"\"\"Computes a dictionary with graph and source features as key-value-pairs.\n \n Args:\n paramlist (list): List with the parameters 'approach', 'skipignore' and 'MFAS approach'.\n special_resarchers (dict): Dictionary with sources (string) as keys and their publication year (int) as values.\n tempsyn (bool): If True, the tempsyn-relation-elements will added to the graph.\n Returns:\n Dictionary with graph and source features as key-value-pairs.\n \"\"\"\n \n #####\n # preparation & creation of graph\n #####\n \n #{fas, edges, nodes, cycles, df}\n feature_dict = {} \n fas_algorithm = paramlist[2]\n\n G = graphutils.gen_faustgraph(paramlist, special_researchers, tempsyn)\n G_fas = eades_fas.eades_FAS(G, fas_algorithm)\n \n #####\n # adding graph features to the feature_dict\n #####\n feature_dict[\"fas\"] = len(G_fas)\n feature_dict[\"edges\"] = len(G.edges())\n feature_dict[\"nodes\"] = len(G.nodes())\n feature_dict[\"nodeslist\"] = list(G.nodes())\n feature_dict[\"cycles\"] = len(list(nx.simple_cycles(G)))\n \n #####\n # analysis\n #####\n year_scores = analyzeutils.get_source_year(G, special_researchers)\n year_df = pd.DataFrame(year_scores.items(), columns=[\"source\", \"year\"])\n year_df.set_index(\"source\", inplace=True)\n \n #df with research count scores\n research_scores = analyzeutils.get_research_score(G)\n sorted_research_scores = {k: research_scores[k] \n for k in sorted(research_scores, key=research_scores.get, reverse=True)}\n research_df = pd.DataFrame(sorted_research_scores.items(), columns=[\"source\", \"year_frequency\"])\n research_df.set_index(\"source\", inplace=True)\n \n #df with normed research count scores\n norm_research_scores = analyzeutils.get_norm_research_score(G, special_researchers, 1770, 2017)\n sorted_norm_research_scores = {k: norm_research_scores[k]\n for k in sorted(norm_research_scores, key=norm_research_scores.get, reverse=True)}\n \n norm_research_df = pd.DataFrame(sorted_norm_research_scores.items(), columns=[\"source\", \"norm_year_frequency\"])\n norm_research_df.set_index(\"source\", inplace=True)\n \n #combinig the three dfs\n source_df = research_df.join(norm_research_df)\n \n #adding df with publication year of the source to the source_df\n year_scores = analyzeutils.get_source_year(G, special_researchers)\n year_df = pd.DataFrame(year_scores.items(), columns=[\"source\", \"pub_year\"])\n year_df.set_index(\"source\", inplace=True)\n \n source_df = source_df.join(year_df)\n \n fas_source_counter = Counter()\n for edge in G_fas:\n if G.has_edge(edge[0], edge[1]):\n edge_data = G.get_edge_data(edge[0], edge[1])\n key = edge_data[\"source\"]\n if fas_source_counter[key]:\n fas_source_counter[key] += 1\n else:\n fas_source_counter[key] = 1\n \n \n fasfrequency_df = pd.DataFrame.from_dict(OrderedDict(fas_source_counter.most_common()), \n orient=\"index\").reset_index()\n fasfrequency_df = fasfrequency_df.rename(columns={\"index\":\"source\", 0:\"fas_frequency\"})\n fasfrequency_df.set_index(\"source\", inplace=True)\n \n df = source_df.join(fasfrequency_df)\n df = df.dropna()\n \n percent_fas = (df[\"fas_frequency\"] / df[\"year_frequency\"]) * 100\n norm_percent_fas = (df[\"fas_frequency\"] / df[\"norm_year_frequency\"]) * 100\n percentfas_df = pd.concat([percent_fas, norm_percent_fas], axis=1, sort=True)\n percentfas_df = percentfas_df.rename(columns={0:\"percent_fas\", 1:\"norm_percent_fas\"})\n percentfas_df.sort_values(by=\"percent_fas\", ascending=False)\n df = df.join(percentfas_df, on=\"source\")\n \n \n feature_dict[\"source_df\"] = source_df\n feature_dict[\"fasfrequency_df\"] = fasfrequency_df\n feature_dict[\"percentfas_df\"] = percentfas_df\n \n \n return feature_dict\n\ndef compare_approaches(approaches, special_researchers, temppre=False):\n \"\"\"Computes a DataFrame where the number of nodes, edges, cycles and feedback edges of each approach from the\n approaches list will be listed.\n Args:\n approaches (list): List with the approaches names as strings.\n special_resarchers (dict): Dictionary with sources (string) as keys and their publication year (int) as values. \n temppre (bool): If True, the graph for the approaches will be computed by the combination of \n the temppre- and the dates-graph, if False, only the dates-graph.\n Return:\n DataFrame with the approaches as index and the features \"n nodes\", \"n edges\", \"n cycles\" and \"n feedback edges\" as columns.\n \"\"\"\n\n approaches_graphs = {}\n approaches_fas = {}\n \n filespath = Path('resources')\n date_items = parserutils.xmlparser(filespath, True, skipignore=False)\n \n for approach in approaches:\n \n if temppre:\n temppre_items = parserutils.xmlparser(filespath)\n temppreG = nx.DiGraph()\n for t in temppre_items:\n graphutils.add_egdes_from_node_list(temppreG, t)\n \n datesG = graphutils.graph_from_dates(date_items, approach, special_researchers)\n G = nx.compose(temppreG, datesG)\n \n else:\n G = graphutils.graph_from_dates(date_items, approach, special_researchers)\n \n approaches_graphs[approach] = G\n G_fas = eades_fas.eades_FAS(G, True)\n aG = G.copy()\n aG.remove_edges_from(G_fas)\n \n approaches_fas[approach] = G_fas\n \n graphs_approaches = {}\n columns = [\"n nodes\", \"n edges\", \"n cycles\", \"n feedback edges\"]\n \n for k, v in approaches_graphs.items():\n graphs_approaches[k] = [len(v.nodes()), len(v.edges()), len(list(nx.simple_cycles(v))), len(approaches_fas[k])]\n \n approach_df = pd.DataFrame(graphs_approaches)\n approach_df = approach_df.T\n approach_df.columns = columns\n \n return approach_df\n","repo_name":"realjanpaulus/faust_macrogen_graph","sub_path":"src/faust_macrogen_graph/comparisonutils.py","file_name":"comparisonutils.py","file_ext":"py","file_size_in_byte":6366,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"14873546602","text":"#%%\ndef add_category_lists_sv20classifer(int_dict,prediction,Detached_list,Resonant_list,Scattering_list,Classical_list):\n# Takes in a dictionary of class names.\n# Takes in a prediction (probabilities that an object belongs to each class).\n# Takes in lists of probabilities assigned to membership of each class, for each prior object.\n# Sorts out the probabilities and assigns them to the correct list,\n# ie, Classical probability to the Classical list, Resonant probability to the Resonant list, etc.\n if int_dict[0] == 'Detached':\n Detached_list.append(prediction[0][0])\n elif int_dict[0] == 'Resonant':\n Resonant_list.append(prediction[0][0])\n elif int_dict[0] == 'Scattering':\n Scattering_list.append(prediction[0][0])\n elif int_dict[0] == 'Classical':\n Classical_list.append(prediction[0][0])\n else:\n raise Exception('Unknown format in int_dict.',int_dict)\n if int_dict[1] == 'Detached':\n Detached_list.append(prediction[0][1])\n elif int_dict[1] == 'Resonant':\n Resonant_list.append(prediction[0][1])\n elif int_dict[1] == 'Scattering':\n Scattering_list.append(prediction[0][1])\n elif int_dict[1] == 'Classical':\n Classical_list.append(prediction[0][1])\n else:\n raise Exception('Unknown format in int_dict.',int_dict)\n if int_dict[2] == 'Detached':\n Detached_list.append(prediction[0][2])\n elif int_dict[2] == 'Resonant':\n Resonant_list.append(prediction[0][2])\n elif int_dict[2] == 'Scattering':\n Scattering_list.append(prediction[0][2])\n elif int_dict[2] == 'Classical':\n Classical_list.append(prediction[0][2])\n else:\n raise Exception('Unknown format in int_dict.',int_dict)\n if int_dict[3] == 'Detached':\n Detached_list.append(prediction[0][3])\n elif int_dict[3] == 'Resonant':\n Resonant_list.append(prediction[0][3])\n elif int_dict[3] == 'Scattering':\n Scattering_list.append(prediction[0][3])\n elif int_dict[3] == 'Classical':\n Classical_list.append(prediction[0][3])\n else:\n raise Exception('Unknown format in int_dict.',int_dict)\n return Detached_list,Resonant_list,Scattering_list,Classical_list\n#%%\ndef datestr(datevec):\n # datevec should be [yyyy,mm,dd,hh,mm,ss]\n datestr = ''\n for i in range(len(datevec)-1):\n thisstr = str(datevec[i])\n if len(thisstr) == 1:\n thisstr = '0' + thisstr\n if i == 0:\n datestr = datestr + thisstr\n else:\n datestr = datestr + '_' + thisstr\n thisstr = str(datevec[-1])\n if datevec[-1] < 10:\n thisstr = '0' + thisstr\n datestr = datestr + '_' + thisstr\n return datestr\n#%%\ndef get_GMdict():\n GM_sun = 1\n GM_mercury = 1/6023600\n GM_venus = 1/408523.71\n GM_earthmoon = 1/328900.56\n GM_mars = 1/3098708\n GM_jupiter = 1/1047.3486\n GM_saturn = 1/3497.898\n GM_uranus = 1/22902.98\n GM_neptune = 1/19412.24\n GMdict = {'sun':GM_sun,'mercury':GM_mercury,'venus':GM_venus,\\\n 'earth':GM_earthmoon,'mars':GM_mars,'jupiter':GM_jupiter,\\\n 'saturn':GM_saturn,'uranus':GM_uranus,'neptune':GM_neptune}\n return GMdict\n#%%\ndef get_sv20classifier():\n# Machine learning classifier for Kuiper belt objects. Code is unchanged from\n# code distributed with 'Machine learning classification of Kuiper belt populations'.\n# Smullen/Volk 2020 (sv20), MNRAS 497:2, September 2020, pg 1391-1403,\n# https://doi.org/10.1093/mnras/staa1935\n# Code is found at https://github.com/rsmullen/KBO_Classifier\n import pandas as pd\n from sklearn.model_selection import train_test_split\n from sklearn.ensemble import GradientBoostingClassifier\n training_file = '00_KBO_features.csv'\n all_KBOs = pd.read_csv(training_file,skipinitialspace=True)\n secure_KBOs = all_KBOs[all_KBOs['Securely Classified']==True]\n all_types = list(set(secure_KBOs['Class']))\n types_dict = { all_types[i] : i for i in range( len(all_types) ) }\n int_dict = { i : all_types[i] for i in range( len(all_types) ) }\n classes = secure_KBOs['Class'].map(types_dict)\n features_train, features_test, classes_train, classes_test = train_test_split(secure_KBOs, classes, test_size=0.3, random_state=30)\n features_train.drop(['MPC ID', 'Securely Classified', 'Class'], axis=1, inplace=True)\n features_train = features_train.to_numpy()\n features_test.drop(['MPC ID', 'Securely Classified', 'Class'], axis=1, inplace=True)\n features_test = features_test.to_numpy()\n classifier = GradientBoostingClassifier( learning_rate=0.1, loss='deviance', max_depth=3, max_features='log2', n_estimators=130, random_state=30 )\n classifier.fit(features_train, classes_train)\n return classifier, int_dict, types_dict\n#%%\ndef parsedata_sv20classifier(data,classifier,int_dict):\n# This function computes the necessary features to classify a KBO.\n# Data MUST be a 101 row x 6 column array.\n# Columns are t, a, e, i, Omega, omega.\n# Rows are different time outputs: MUST be 1000yr outputs, ie [0, 1E3, 2E3....99E3,100E3].\n# Returns features for classification.\n# This code is unchanged from sv20, ie\n# Smullen, Rachel A., and Kathryn Volk.\n# 'Machine learning classification of Kuiper belt populations.'\n# Monthly Notices of the Royal Astronomical Society 497.2 (2020): 1391-1403.\n# The classification simulation and data parsing is copied from sv20 code with\n# minimal changes to not have to query Horizons through Rebound when setting up a sim,\n# because Rebound's Horizons query is very slow.\n import numpy as np\n # Take stats of simulations.\n initials = data[0,1:] # a, e, i, Omega, omega\n finals = data[-1,1:]\n mins = np.amin(data[:,1:],axis = 0)\n maxes = np.amax(data[:,1:],axis = 0)\n dels = maxes-mins\n means = np.mean(data[:,1:],axis = 0)\n stdev = np.std(data[:,1:],axis = 0)\n # Take time derivatives.\n diffs = data[1:,:]-data[:-1,:]\n dxdt = diffs[:,1:]/diffs[:,0, np.newaxis] # Add on new axis to time to give same dimensionality as the numerator.\n mindxdt = np.amin(dxdt,axis = 0)\n meandxdt = np.mean(dxdt,axis = 0)\n maxdxdt = np.amax(dxdt,axis = 0)\n deldxdt = maxdxdt-mindxdt\n # Rearrange data into the order we want.\n arrs = [initials,finals,mins,means,maxes,stdev,dels,mindxdt,meandxdt,maxdxdt,deldxdt]\n inds = [0,1,2,3,4] # a, e, i, Omega, omega\n features = []\n ## Features contains all x values, then all y, etc: xi, xf, xmin, xmean, xmax, xsigma, Deltax, xdotmin, xdotmean, xdotmax.\n for i in inds:\n for a in arrs:\n features += [a[i]]\n features_out = np.array(features).reshape(1,-1) # Make sure features is a 2d array.\n prediction = classifier.predict_proba(features_out) # Predict the probabilities of class membership for object.\n if np.max(prediction) == prediction[0][0]:\n category = int_dict[0]\n elif np.max(prediction) == prediction[0][1]:\n category = int_dict[1]\n elif np.max(prediction) == prediction[0][2]:\n category = int_dict[2]\n elif np.max(prediction) == prediction[0][3]:\n category = int_dict[3]\n return category,prediction\n#%%\n# This file classifies all the clones of a single Kuiper belt object.\nTHIS_INSTANCE = 1\nimport time\nimport numpy as np\nimport pandas as pd\nimport rebound\nt0 = time.time()\n# Prime the machine learning classifier.\nclassifier, int_dict, types_dict = get_sv20classifier()\n# Get list of objects to classify.\ndate = '20221012'\nyear = 2022\nmonth = 1\nday = 1\nhour = 0\nminute = 0\nsecond = 0\ndatestr = datestr([year,month,day,hour,minute,second])\nelements_file = 'horizons_barycentric_nom_noclones_' + date + '_' + datestr + '.csv'\ndf = pd.read_csv(elements_file)\ndes_list = df['packed_designation'].tolist()\ndes = des_list[THIS_INSTANCE-1]\n# Read heliocentric orbital elements of clones.\nclones_input_file = 'clones_' + des + '.csv'\ndf = pd.read_csv(clones_input_file)\nNclones = df.shape[0]\nePh = df['ePh'].tolist() # eccentricity, Plutino, heliocentric\nqPh_au = df['qPh_au'].tolist() # perihelion distance, Plutino, heliocentric, au\ntpPh_jd = df['tpPh_jd'].tolist() # time of perihelion passage, Plutino, heliocentric, Julian date TDB\nWPh_deg = df['WPh_deg'].tolist() # longitude of ascending node, Plutino, heliocentric, degrees\nwPh_deg = df['wPh_deg'].tolist() # argument of perihelion, Plutino, heliocentric, degrees\niPh_deg = df['iPh_deg'].tolist() # inclination, Plutino, heliocentric, degrees\n# Read heliocentric orbital elements of planets.\nplanets_file = 'planets_for_' + des + '.csv'\ndf = pd.read_csv(planets_file)\nepochP = df['epochP_jd'][0] # epoch of orbital elements, Julian date\neJh = df['eJh'][0] # Jupiter\nqJh_au = df['qJh_au'][0]\ntpJh_jd = df['tpJh_jd'][0]\nWJh_deg = df['WJh_deg'][0]\nwJh_deg = df['wJh_deg'][0]\niJh_deg = df['iJh_deg'][0]\neSh = df['eSh'][0] # Saturn\nqSh_au = df['qSh_au'][0]\ntpSh_jd = df['tpSh_jd'][0]\nWSh_deg = df['WSh_deg'][0]\nwSh_deg = df['wSh_deg'][0]\niSh_deg = df['iSh_deg'][0]\neUh = df['eUh'][0] # Uranus\nqUh_au = df['qUh_au'][0]\ntpUh_jd = df['tpUh_jd'][0]\nWUh_deg = df['WUh_deg'][0]\nwUh_deg = df['wUh_deg'][0]\niUh_deg = df['iUh_deg'][0]\neNh = df['eNh'][0] # Neptune\nqNh_au = df['qNh_au'][0]\ntpNh_jd = df['tpNh_jd'][0]\nWNh_deg = df['WNh_deg'][0]\nwNh_deg = df['wNh_deg'][0]\niNh_deg = df['iNh_deg'][0]\n# Get masses of outer planets.\nGMdict = get_GMdict()\n# Create lists to store probabilities of class membership for each clone.\nclone_class_list = []\nclassical_list = []\nresonant_list = []\nscattering_list = []\ndetached_list = []\n# Run a Rebound simulation for each clone and classify it.\nfor iclone in range(Nclones):\n sim = rebound.Simulation()\n sim.add(m = 1,hash = '0')\n sim.integrator = 'ias15'\n epochobj = epochP\n # Build simulation, outer planets first.\n eobj = eJh # Jupiter\n qobj = qJh_au\n tpobj = tpJh_jd\n Wobj = np.radians(np.mod(WJh_deg,360))\n wobj = np.radians(np.mod(wJh_deg,360))\n iobj = np.radians(iJh_deg)\n aobj = qobj/(1-eobj)\n dt = epochobj - tpobj # time since pericenter passage in days\n dt = dt * (2*np.pi/365.25) # convert days to yr/(2pi)\n n = np.sqrt(1/aobj**3) # radians / (yr/2pi)\n Mobj = np.mod(n*dt,2*np.pi) # radians\n sim.add(primary=sim.particles[0],m=GMdict['jupiter'],hash='jupiter',\\\n a=aobj,e=eobj,inc=iobj,omega=wobj,Omega=Wobj,M=Mobj)\n eobj = eSh # Saturn\n qobj = qSh_au\n tpobj = tpSh_jd\n Wobj = np.radians(np.mod(WSh_deg,360))\n wobj = np.radians(np.mod(wSh_deg,360))\n iobj = np.radians(iSh_deg)\n aobj = qobj/(1-eobj)\n dt = epochobj - tpobj # time since pericenter passage in days\n dt = dt * (2*np.pi/365.25) # convert days to yr/(2pi)\n n = np.sqrt(1/aobj**3) # radians / (yr/2pi)\n Mobj = np.mod(n*dt,2*np.pi) # radians\n sim.add(primary=sim.particles[0],m=GMdict['saturn'],hash='saturn',\\\n a=aobj,e=eobj,inc=iobj,omega=wobj,Omega=Wobj,M=Mobj)\n eobj = eUh # Uranus\n qobj = qUh_au\n tpobj = tpUh_jd\n Wobj = np.radians(np.mod(WUh_deg,360))\n wobj = np.radians(np.mod(wUh_deg,360))\n iobj = np.radians(iUh_deg)\n aobj = qobj/(1-eobj)\n dt = epochobj - tpobj # time since pericenter passage in days\n dt = dt * (2*np.pi/365.25) # convert days to yr/(2pi)\n n = np.sqrt(1/aobj**3) # radians / (yr/2pi)\n Mobj = np.mod(n*dt,2*np.pi) # radians\n sim.add(primary=sim.particles[0],m=GMdict['uranus'],hash='uranus',\\\n a=aobj,e=eobj,inc=iobj,omega=wobj,Omega=Wobj,M=Mobj)\n eobj = eNh # Neptune\n qobj = qNh_au\n tpobj = tpNh_jd\n Wobj = np.radians(np.mod(WNh_deg,360))\n wobj = np.radians(np.mod(wNh_deg,360))\n iobj = np.radians(iNh_deg)\n aobj = qobj/(1-eobj)\n dt = epochobj - tpobj # time since pericenter passage in days\n dt = dt * (2*np.pi/365.25) # convert days to yr/(2pi)\n n = np.sqrt(1/aobj**3) # radians / (yr/2pi)\n Mobj = np.mod(n*dt,2*np.pi) # radians\n sim.add(primary=sim.particles[0],m=GMdict['neptune'],hash='neptune',\\\n a=aobj,e=eobj,inc=iobj,omega=wobj,Omega=Wobj,M=Mobj)\n # Add the Plutino clone.\n eobj = ePh[iclone]\n qobj = qPh_au[iclone]\n tpobj = tpPh_jd[iclone]\n Wobj = np.radians(np.mod(WPh_deg[iclone],360))\n wobj = np.radians(np.mod(wPh_deg[iclone],360))\n iobj = np.radians(iPh_deg[iclone])\n aobj = qobj/(1-eobj)\n dt = epochobj - tpobj # time since pericenter passage in days\n dt = dt * (2*np.pi/365.25) # convert days to yr/(2pi)\n n = np.sqrt(1/aobj**3) # radians / (yr/2pi)\n Mobj = np.mod(n*dt,2*np.pi) # radians\n sim.add(primary=sim.particles[0],m=0,hash='plutino',\\\n a=aobj,e=eobj,inc=iobj,omega=wobj,Omega=Wobj,M=Mobj)\n # Prepare to integrate simulation.\n sim.N_active = 5\n sim.move_to_com()\n time_outs = np.linspace(0,100E3,101)*2*np.pi # 100 kyr\n data = []\n for i,t in enumerate(time_outs):\n if t>0:\n sim.move_to_com()\n # Integrate to next output.\n sim.integrate(t, exact_finish_time=True)\n orbits = sim.calculate_orbits(primary=sim.calculate_com())\n o = orbits[-1] # take KBO\n # Save t, a, e, i, Omega, omega. Time in data needs to be in years, so divide by 2pi.\n step = np.array([t/2/np.pi, o.a, o.e, np.degrees(o.inc), np.degrees(o.Omega)%360, np.degrees(o.omega)%360])\n # Add step to data.\n if len(data)==0: data = step\n else: data = np.vstack((data,step))\n # Release memory so we don't accidentally keep integrating the same sim with a different clone.\n sim = None\n category,prediction = parsedata_sv20classifier(data,classifier,int_dict)\n clone_class_list.append(category)\n detached_list,resonant_list,scattering_list,classical_list = \\\n add_category_lists_sv20classifer(int_dict,prediction,detached_list,\\\n resonant_list,scattering_list,classical_list)\n# Make new, separate file of clone class lists.\ndf = pd.DataFrame()\ndf['category'] = clone_class_list\ndf['classical_probability'] = classical_list\ndf['resonant_probability'] = resonant_list\ndf['scattering_probability'] = scattering_list\ndf['detached_probability'] = detached_list\nclones_output_file = 'class_lists_' + des + '.csv'\ndf.to_csv(clones_output_file,index=False)\nt1 = time.time()\nelapsed_time_hours = (t1-t0)/3600\nprint('took',elapsed_time_hours,'hrs',THIS_INSTANCE,des)\n","repo_name":"iwygh/mmk23a","sub_path":"03a_classify_clones_template.py","file_name":"03a_classify_clones_template.py","file_ext":"py","file_size_in_byte":14275,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"3959285771","text":"# 주차 요금 계산\n# 2022-01-30\n\nfrom math import ceil\n\ndef solution(fees, records):\n answer = []\n rec_dict = {}\n for record in records:\n tmp = record.split()\n car_no = tmp[1]\n if car_no in rec_dict.keys():\n rec_dict[car_no].append(tmp[0])\n else:\n rec_dict[car_no] = [tmp[0]]\n rec_dict = sorted(rec_dict.items(), key=lambda x: x[0])\n\n for rec in rec_dict:\n tmp = 0\n if len(rec[1]) % 2:\n rec[1].append('23:59')\n while rec[1]:\n h, m = rec[1].pop(0).split(':')\n nh, nm = rec[1].pop(0).split(':')\n park_min = (int(nh) - int(h)) * 60 + (int(nm) - int(m))\n tmp += park_min\n answer.append(tmp)\n\n for i in range(0, len(answer)):\n if answer[i] <= fees[0]:\n answer[i] = fees[1]\n else:\n tmp = answer[i] - fees[0]\n answer[i] = fees[1] + fees[3] * ceil(tmp / fees[2])\n\n return answer\n\n\nprint(solution([180, 5000, 10, 600], [\"05:34 5961 IN\", \"06:00 0000 IN\", \"06:34 0000 OUT\", \"07:59 5961 OUT\", \"07:59 0148 IN\", \"18:59 0000 IN\", \"19:09 0148 OUT\", \"22:59 5961 IN\", \"23:00 5961 OUT\"]))","repo_name":"hybae430/Programmers","sub_path":"92341.py","file_name":"92341.py","file_ext":"py","file_size_in_byte":1174,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"34518761853","text":"class Cat:\n species = 'mammal'\n def __init__(self, name, age):\n self.name = name\n self.age = age\n\nfluffy = Cat(\"Fluffy\", 3)\nkuku = Cat(\"Kuku\", 5)\ncraken = Cat(\"Craken\", 1)\n\ndef get_oldest_cat(*args):\n return max(args)\n\nprint(f\"The oldest cat is {get_oldest_cat(fluffy.age, kuku.age, craken.age)} years old.\")","repo_name":"Maciej-Glowacki/Python_exercises","sub_path":"exe_65_class.py","file_name":"exe_65_class.py","file_ext":"py","file_size_in_byte":331,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"72075280805","text":"#Librerias a utilizar:\r\nimport os\r\nimport numpy as np\r\nimport cv2\r\nimport argparse\r\nfrom matplotlib import pyplot as plt\r\n\r\n\r\ndef image_stats(image):\r\n '''Esta función de el vector de características de la imagen,\r\n es decir vuelve la media y la desviación estandar de cada canal'''\r\n \r\n (l, a, b) = cv2.split(image)\r\n (l_mean, l_std) = (l.mean(), l.std())\r\n (a_mean, a_std) = (a.mean(), a.std())\r\n (b_mean, b_std) = (b.mean(), b.std())\r\n\r\n \r\n return (l_mean, l_std, a_mean, a_std, b_mean, b_std)\r\n\r\n\r\ndef extraigo_features_dataset():\r\n grupos=[]\r\n num_clusters=14\r\n for i in range(num_clusters):\r\n path='./styleLib/'+ str(i)\r\n dirs=os.listdir(path)\r\n g=[]\r\n for img in dirs:\r\n pic=cv2.imread(os.path.join(path,img))\r\n\r\n #Paso todas las imágenes a LAB\r\n pic=cv2.cvtColor(pic, cv2.COLOR_BGR2LAB)\r\n g.append(pic)\r\n grupos.append(g)\r\n\r\n #Extraigo el vector de características promedio en cada grupo:\r\n features=[]\r\n for k in range(num_clusters): \r\n features_group=[]\r\n for img in grupos[k]:\r\n f=image_stats(img) #(l_mean, l_std, a_mean, a_std, b_mean, b_std)\r\n features_group.append(f)\r\n features_group=np.array(features_group)\r\n\r\n L_m = np.mean( features_group[:,0] )\r\n L_std= np.mean( features_group[:,1] )\r\n a_m = np.mean( features_group[:,2] )\r\n a_std= np.mean( features_group[:,3] )\r\n b_m = np.mean( features_group[:,4] )\r\n b_std= np.mean( features_group[:,5] )\r\n\r\n features.append([L_m ,L_std,a_m,a_std,b_m,b_std])\r\n return features,grupos\r\n\r\ndef color_transfer(stat_source, img):\r\n \r\n '''Realiza la transferencia de color, \r\n stat_source son las vector de característica medio\r\n img: la imagen que queremos varia el color\r\n \r\n '''\r\n \r\n# img = cv2.cvtColor(img, cv2.COLOR_BGR2LAB).astype(\"float32\")\r\n\r\n # Describo características deseadas y las que tiene la imagen\r\n (l_mean_src, l_std_src, a_mean_src, a_std_src, b_mean_src, b_std_src) = stat_source\r\n (l_mean_dest, l_std_dest, a_mean_dest, a_std_dest, b_mean_dest, b_std_dest) = image_stats(img)\r\n\r\n # Resto el valor medio\r\n (l, a, b) = cv2.split(img)\r\n l -= l_mean_dest\r\n a -= a_mean_dest\r\n b -= b_mean_dest\r\n\r\n # Escalado\r\n l = (l_std_dest / l_std_src) * l\r\n a = (a_std_dest / a_std_src) * a\r\n b = (b_std_dest / b_std_src) * b\r\n\r\n # Agrego valor medio de source\r\n l += l_mean_src\r\n a += a_mean_src\r\n b += b_mean_src\r\n\r\n # Mantengo numeros entre 0 y 255\r\n l = np.clip(l, 0, 255)\r\n a = np.clip(a, 0, 255)\r\n b = np.clip(b, 0, 255)\r\n\r\n # Combino canales\r\n transfer = cv2.merge([l, a, b])\r\n #transfer = cv2.cvtColor(transfer.astype(\"uint8\"), cv2.COLOR_LAB2BGR)\r\n\r\n #Imagen modificada en color\r\n return transfer.astype(\"uint8\")\r\n\r\n\r\ndef show_image(title, image, width = 500):\r\n \r\n r = width / float(image.shape[1])\r\n dim = (width, int(image.shape[0] * r))\r\n image = cv2.cvtColor(image, cv2.COLOR_LAB2RGB)\r\n\r\n resized = cv2.resize(image, dim, interpolation = cv2.INTER_AREA)\r\n # Show the resized image\r\n plt.title(title)\r\n plt.imshow(resized)\r\n plt.axis('off')\r\n plt.show()\r\n \r\n \r\ndef Color_adjustment(img):\r\n #extraigo features del dataset:\r\n img = cv2.cvtColor(img, cv2.COLOR_BGR2LAB).astype(\"float32\")\r\n \r\n features_dataset, dataset= extraigo_features_dataset() #todo en espacio color LAB\r\n\r\n features_img=image_stats(img) #features de la imagen\r\n\r\n #me fijo la feature mas cercana:\r\n features_dataset=np.array(features_dataset)\r\n\r\n features_img=np.array(features_img)\r\n dist= np.sqrt(np.sum( (features_dataset- features_img )**2, axis=1))\r\n ind=np.argmin(dist)\r\n transferred = color_transfer(features_dataset[ind] , img) \r\n return transferred\r\n \r\n \r\n \r\n \r\n \r\n","repo_name":"tania-19/Watercolorization","sub_path":"funciones/colorAdj.py","file_name":"colorAdj.py","file_ext":"py","file_size_in_byte":3964,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"20360094897","text":"def check_palindrome(ss):\n middle = len(ss) // 2\n \n first_part = ss[0:middle]\n if len(ss) % 2 != 0:\n middle = middle + 1\n second_part = ss[middle:][::-1] # ss[-1:middle-1:-1]\n \n if first_part == second_part:\n return True\n else:\n return False\n \ndef check_palindrome2(ss):\n if ss == ss[::-1]:\n return True\n else:\n return False\n \n \nif __name__ == \"__main__\":\n print(check_palindrome('suggus'))\n print(check_palindrome('sugus'))\n print()\n print(check_palindrome2('suggus'))\n print(check_palindrome2('sugus'))","repo_name":"vladhondru25/Algorithms-DataStructures","sub_path":"Strings/palindrome.py","file_name":"palindrome.py","file_ext":"py","file_size_in_byte":594,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"11660067307","text":"from db import Db\nfrom sender import Sender\nfrom datetime import datetime\nfrom time import time, sleep\nfrom config import * \nimport json, re\nfrom requests_oauthlib import OAuth2Session\nfrom threading import Thread, active_count\nfrom rq import Queue\nfrom worker import conn\n\nclass User():\n \"\"\"\n Data type for users of this system.\n\n Public methods:\n * create\n * get_by_email\n * email\n * token\n * name\n * created_at\n * filters_made\n * make_filter\n * json\n * delete_filter\n * user_info\n \"\"\"\n\n def __init__(self, data=None):\n if data == None:\n self._email = None\n self._token = None\n self._filters_made = None\n self._name = None\n self._created_at = None\n else:\n data = json.loads(data)\n self._email = data['email']\n self._token = data['token']\n self._filters_made = data['filters_made']\n self._name = data['name']\n self._created_at = datetime.fromtimestamp(data['created_at'])\n\n def __repr__(self):\n return f\"{self.email()} - {self.name()}\"\n\n def create(self, email, name=None, token=None):\n created_at = datetime.now()\n sql = \"INSERT INTO participant (email, name, token, created_at) VALUES (%s, %s, %s, %s);\"\n data = [email, name, token, created_at]\n db = Db()\n try:\n db.query(sql, data)\n self._email = email\n self._name = name\n self._created_at = created_at\n self._token = token\n return True\n except Exception as e:\n print(e)\n return False\n\n\n def email(self):\n return self._email\n\n def token(self):\n return self._token\n\n def name(self):\n return self._name\n\n def created_at(self):\n return self._created_at\n\n def filters_made(self):\n return self._filters_made\n\n def get_by_email(self, email):\n db = Db()\n sql = \"SELECT * FROM participant WHERE email = %s;\"\n data = [email]\n participant = db.query(sql, data)\n if participant is None:\n return None\n self._email = participant['email']\n if participant['token'] is not None:\n self._token = json.loads(participant['token'])\n else:\n self._token = None\n self._filters_made = participant['filters_made']\n self._name = participant['name']\n self._created_at = participant['created_at']\n return self\n\n def json(self):\n _dict = {'email': self.email(), 'name': self.name(), 'token': self.token(), 'filters_made': self.filters_made(), 'created_at': self.created_at().timestamp()}\n return json.dumps(_dict)\n\n def make_filter(self, domain, wait_time=1):\n if self._email is None:\n raise Exception('No user specified: use .get_by_email() or .create() first')\n if self._token is None:\n raise Exception(\"User's Oauth2 token is None\")\n\n google = OAuth2Session(client_id, token=self.token())\n if self.token()['expires_at'] < time()+10:\n google = self.refresh_token()\n if google == 'refresh_error':\n return 'refresh_error'\n headers = {\"Content-Type\": \"application/json\"}\n params = {\n \"criteria\": {\n \"from\": domain\n },\n \"action\": {\n \"removeLabelIds\": [\"SPAM\"],\n \"addLabelIds\": [\"CATEGORY_PERSONAL\"]\n }\n }\n #print(domain)\n r = google.post(\"https://www.googleapis.com/gmail/v1/users/me/settings/filters\", data=json.dumps(params), headers=headers)\n\n if r.status_code == 200:\n filter_id = r.json()['id']\n #print(filter_id)\n db = Db()\n sql = \"INSERT INTO filter (filter_id, sender, participant, filter_made, created_at) VALUES (%s, %s, %s, %s, %s);\"\n data = [filter_id, domain, self.email(), True, datetime.now()]\n db.query(sql, data, True)\n return True\n elif r.status_code == 429:\n if wait_time <= 8:\n sleep(wait_time)\n return self.make_filter(wait_time*2)\n else:\n print(r.status_code, r.text)\n return False\n else:\n if wait_time <= 1:\n wait_time = 2\n return self.make_filter(domain, wait_time)\n else:\n sleep(1)\n print(r.text, r.status_code)\n return False\n\n \n def make_filters(self):\n db = Db()\n sql = \"SELECT * FROM sender;\"\n result = db.query(sql)\n for i, row in enumerate(result):\n self.make_filter(row['domain'])\n self._filters_made = True\n return self.set_filters_made(True)\n\n def set_filters_made(self, state):\n db = Db()\n sql = \"UPDATE participant SET filters_made = %s WHERE email = %s\"\n data = [state, self.email()]\n db.query(sql, data, True)\n return True\n\n def refresh_token(self):\n google = OAuth2Session(client_id, token=self.token())\n extra = {\n 'client_id': client_id,\n 'client_secret': client_secret,\n }\n if 'refresh_token' in self.token().keys():\n try:\n self.set_token(google.refresh_token(refresh_url, **extra))\n print('token updated!')\n return google\n except Exception as e:\n print('Error: ', e)\n return 'refresh_error'\n else:\n return 'refresh_error'\n\n def user_info(self, wait_time=1):\n google = OAuth2Session(client_id, token=self.token())\n if self.token()['expires_at'] < time() + 10:\n google = self.refresh_token()\n if google == 'refresh_error':\n return 'refresh_error'\n r = google.get('https://www.googleapis.com/oauth2/v1/userinfo')\n if r.status_code == 200:\n data = r.json()\n if self._name != data['name']:\n db = Db()\n sql = 'UPDATE participant SET name = %s WHERE email = %s;'\n params = [data['name'], self._email]\n db.query(sql, params)\n self._name = data['name']\n if self._email != data['email']:\n db = Db()\n sql = 'UPDATE participant SET name = %s WHERE email = %s;'\n params = [data['email'], self._email]\n db.query(sql, params)\n self._email = data['email']\n return r.json()\n elif r.status_code == 429:\n if wait_time <= 8:\n sleep(wait_time)\n return self.user_info(wait_time*2)\n else:\n print(r.status_code, r.text)\n return False\n else:\n print(r.status_code, r.text)\n return False\n\n def set_token(self, token):\n if self._email is None:\n raise Exception('Anonymous user, set email first')\n db = Db()\n sql = 'UPDATE participant set token = %s where email = %s;'\n data = [json.dumps(token), self.email()]\n db.query(sql, data)\n self._token = token\n return self\n\n def _get_filter(self, filter_id, wait_time=1):\n google = OAuth2Session(client_id, token=self.token())\n if self.token()['expires_at'] < time()+10:\n google = self.refresh_token()\n if google == 'refresh_error':\n return 'refresh_error'\n url = \"https://www.googleapis.com/gmail/v1/users/me/settings/filters/{}\".format(filter_id)\n r = google.get(url)\n if str(r.status_code)[0] == '2':\n return True\n elif r.status_code == 429:\n if wait_time <= 8:\n sleep(wait_time)\n return self._get_filter(wait_time*2)\n else:\n print(r.status_code, r.text)\n return False\n else:\n print('Filter not found in user account')\n self._reset_filter()\n print(r.status_code, r.text)\n return False\n\n def list_filters(self):\n db = Db()\n sql = \"select * from filter where participant = %s;\"\n data = [self.email()]\n result = db.query(sql, data)\n tmp_list = []\n for row in result:\n tmp_row = {}\n for k, v in row.items():\n tmp_row[k] = v\n tmp_list.append(tmp_row)\n return tmp_list\n\n\n def delete_filter(self, filter_id, wait_time=1):\n google = OAuth2Session(client_id, token=self.token())\n if self.token()['expires_at'] < time()+10:\n google = self.refresh_token()\n if google == 'refresh_error':\n return 'refresh_error'\n url = \"https://www.googleapis.com/gmail/v1/users/me/settings/filters/{}\".format(filter_id)\n r = google.delete(url)\n if str(r.status_code)[0] == '2':\n db = Db()\n sql = 'DELETE FROM filter WHERE filter_id = %s;'\n data = [filter_id]\n db.query(sql, data, True)\n return True\n elif r.status_code == 429:\n if wait_time <= 8:\n sleep(wait_time)\n return self.delete_filter(filter_id, wait_time*2)\n else:\n print(r.status_code, r.text)\n return False\n else:\n if wait_time <= 1:\n sleep(1)\n wait_time = 2\n return self.delete_filter(filter_id, wait_time)\n else:\n print(r.status_code, r.text)\n return False\n\n def delete_filters(self):\n #if self.filters_made != True:\n # raise Exception(\"Filters have not been made yet\")\n filters = self.list_filters()\n for f in filters:\n self.delete_filter(f['filter_id'])\n self._filters_made = False\n return self.set_filters_made(False)\n \n\nif __name__=='__main__':\n pass\n u = User()\n\n # get by email\n #u.get_by_email('graydenshand@gmail.com')\n #print(u.token())\n\n # create\n #u.create('graydenshand+test@gmail.com', 'Grayden Shand')\n #print(u)\n\n # to json --> from json\n #u.get_by_email('graydenshand@gmail.com')\n #string = u.json()\n #p = User(string)\n #print(p)\n\n # user_info \n #u.get_by_email('graydenshand@gmail.com')\n #print(u.user_info())\n\n # set_token\n #u.get_by_email('graydenshand@gmail.com')\n #u.set_token(token)\n #u.get_by_email('graydenshand@gmail.com')\n #print(u.token())\n\n # make_filter\n #u.get_by_email('graydenshand@gmail.com')\n #u.make_filter()\n #print(u.json())\n\n\n u.get_by_email('graydenshand@gmail.com')\n q = Queue(connection=conn)\n #print(u.json())\n #print(u.list_filters())\n from queue_functions import *\n #q.enqueue(make_filters, u.json())\n q.enqueue(delete_filters, u.json())\n\n # delete all filters\n \"\"\"\n db = Db()\n sql = \"select email from participant;\"\n result = db.query(sql)\n for i, row in enumerate(result):\n u = User().get_by_email(row['email'])\n print(u.json())\n u.delete_filter()\n \"\"\"\n\n\n\n\n\n\n\n\n\n\n","repo_name":"graydenshand/google_promotion_filter","sub_path":"user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":11332,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"52"} +{"seq_id":"11443473885","text":"# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, val=0, next=None):\n# self.val = val\n# self.next = next\nclass Solution:\n def addTwoNumbers(self, l1: Optional[ListNode], l2: Optional[ListNode]) -> Optional[ListNode]:\n num1 = str(l1.val)\n num2 = str(l2.val)\n list1 = l1\n list2 = l2\n while list1.next != None:\n num1 = str(list1.next.val) + num1\n list1 = list1.next\n while list2.next != None:\n num2 = str(list2.next.val) + num2\n list2 = list2.next \n \n result = str(int(num1) + int(num2))\n \n if len(result) == 1:\n return ListNode(int(result))\n elif len(result) == 2:\n return ListNode(int(result[-1]), ListNode(int(result[-2])))\n else:\n total = ListNode(int(result[-1]), ListNode(int(result[-2])))\n index = -3\n temp = total\n while index + len(result) != -1:\n if temp.next != None:\n temp = temp.next\n else:\n temp.next = ListNode(int(result[index]))\n index -= 1\n return total\n \n ","repo_name":"kwilliam777/CodingTestStudy","sub_path":"weekly assingment/week1/k_2.py","file_name":"k_2.py","file_ext":"py","file_size_in_byte":1234,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"16858753151","text":"\"\"\"\n Implementation of the Directional Gaussian Smoothing algorithm\n introduced in https://arxiv.org/abs/2002.03001\n The algorithm is implemented as a scipy.optimize method\n\n This particular algorithm will be modified to change sigma\n\"\"\"\n\nimport numpy as np\nfrom scipy.optimize import OptimizeResult\nfrom scipy.optimize._numdiff import approx_derivative\nfrom collections import deque\n\n\ndef ADGS(fun, x0, args=(), learning_rate=.01, sigma=10., gamma=.995, quad_points=7,\n maxiter=1000, xtol=1e-6, ftol=1e-4, gtol=1e-4, callback=None, **options):\n \"\"\"\n Minimize a scalar function using the ADGS optimizer.\n It is DGS but with exponential decay on sigma\n \"\"\"\n # initialize DGS variables\n dim = len(x0)\n xk = x0.copy()\n fk = fun(xk)\n t = 0\n ##fun_vals = deque([0]*10, maxlen=10)\n\n # establish search directions and quadrature points\n basis = np.eye(dim)\n gh_roots, gh_weights = np.polynomial.hermite.hermgauss(quad_points)\n\n def step(x):\n '''Perform a step of DGS optimizer'''\n nonlocal t\n t += 1\n # estimate smoothed directional derivative along each basis direction\n df_sigma_basis = np.zeros(dim)\n for d in range(dim):\n # estimate directional derivative via Gauss--Hermite quadrature\n f_d = lambda t: fun(x + t*basis[d])\n f_d_vals = np.array([f_d(sigma * p) for p in gh_roots])\n df_sigma_basis[d] = np.sum(gh_weights * gh_roots * f_d_vals)\\\n / (sigma * np.sqrt(np.pi)/2)\n # assemble smoothed gradient and update minimizer\n grad_sigma = np.matmul(basis, df_sigma_basis)\n x -= learning_rate * grad_sigma\n return x, grad_sigma\n\n # iteratively optimize target function\n success = False\n for _ in range(maxiter):\n x = xk.copy()\n fval = fk\n xk, gfk = step(x.copy())\n fk = fun(xk)\n if callback is not None:\n callback(xk)\n\n # check if sigma should be reduced\n sigma *= gamma\n ##fun_vals.append(fk)\n ##if all(abs(fk - fun_val) < 1e-3 for fun_val in fun_vals):\n ##print(f'iteration {t}, sigma is reduced: {sigma:.4f} --> {sigma/2:.4f}')\n ##print(fk, fun_vals, '\\n')\n ##sigma = max(sigma/2, 1e-6)\n ##fun_vals = deque([0]*10, maxlen=10)\n\n # check termination conditions\n if np.linalg.norm(gfk, np.inf) < gtol:\n msg = 'Optimization terminated succesfully.'\n success = True\n break\n if np.linalg.norm(x - xk, np.inf) < xtol:\n msg = 'Optimization terminated due to x-tolerance.'\n break\n if np.abs((fval - fk) / (fval + 1e-8)) < ftol:\n msg = 'Optimization terminated due to f-tolerance.'\n break\n if t >= maxiter:\n msg = 'The maximum number of iterations is reached.'\n break\n\n return OptimizeResult(x=xk, fun=fk, jac=gfk, nit=t, nfev=quad_points*xk.size*t,\n success=success, msg=msg)\n\n\nif __name__ == '__main__':\n\n fun = lambda x: np.sum(x**2)\n x0 = np.random.randn(100)\n vals = [fun(x0)]\n res = ADGS(fun, x0, callback=lambda x: vals.append(fun(x)))\n print(res)\n","repo_name":"sukiboo/smoothing_based_optimization","sub_path":"extra_scripts/adgs.py","file_name":"adgs.py","file_ext":"py","file_size_in_byte":3253,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"74416533605","text":"#!/usr/bin/python\n### before spark-submit: export PYTHONIOENCODING=utf8\n\nfrom pyspark import SparkContext, SparkConf\nfrom pyspark.sql import SparkSession\nfrom pyspark.sql.types import *\nfrom pyspark.sql.functions import *\nfrom pyspark.sql.functions import max as max_\ndef quiet_logs(sc):\n logger = sc._jvm.org.apache.log4j\n logger.LogManager.getLogger(\"org\"). setLevel(logger.Level.ERROR)\n logger.LogManager.getLogger(\"akka\").setLevel(logger.Level.ERROR)\n\nconf = SparkConf().setAppName(\"uni\").setMaster(\"spark://spark-master:7077\")\nsc = SparkContext(conf=conf)\nspark = SparkSession(sc)\n\nquiet_logs(spark)\n\nfrom pyspark.sql.types import *\n\n# //////////// READING FROM AUSTRALIA UV INDEX CSV FILE //////////////\nschemaString = \"timestamp Lat Lon UV_Index\"\nfields = [StructField(field_name, StringType(), True) for field_name in schemaString.split()]\nschema = StructType(fields)\ndf = spark.read.csv(\"hdfs://namenode:9000/datasets/australia_uv_index.csv\", header=True, mode=\"DROPMALFORMED\", schema=schema)\n\n# //////////// READING FROM CANCER INCIDENCE AND MORTALITY CSV FILE //////////////\nschemaStringMelanoma = \"Data_type Cancer_group Year Sex Territory Count Age_standardised_rate ICD10_codes\"\nfieldsMelanoma = [StructField(field_name, StringType(), True) for field_name in schemaStringMelanoma.split()]\nschemaMelanoma = StructType(fieldsMelanoma)\ndfMelanoma = spark.read.csv(\"hdfs://namenode:9000/datasets/cancer_incidence_and_mortality_by_state_and_territory.csv\", header=True, mode=\"DROPMALFORMED\", schema=schemaMelanoma)\n\n# extracting year, month, day from timestamp\ndf = df.withColumn(\"Year\", year(col(\"timestamp\")))\\\n .withColumn(\"Month\", month(col(\"timestamp\")))\\\n .withColumn(\"Day\", dayofyear(col(\"timestamp\")))\n\ndf = df.filter(col(\"Year\").isNotNull())\ndf = df.withColumn(\"UV_Index\", col(\"UV_Index\").cast(FloatType()))\n\n# adding territory column depending of Lat and Lon\ndf = df.withColumn(\"Territory\", expr(\"case when Lat = -34.04 and Lon = 151.1 then 'New South Wales' \" +\n \"when Lat = -34.92 and Lon = 138.62 then 'South Australia' \" +\n \"when Lat = -37.73 and Lon = 145.1 then 'Victoria' \" +\n \"when Lat = -27.45 and Lon = 153.03 then 'Queensland' \" +\n \"when Lat = -31.92 and Lon = 115.96 then 'Western Australia' \" +\n \"when Lat = -42.99 and Lon = 147.29 then 'Tasmania' \" +\n \"when Lat = -35.31 and Lon = 149.2 then 'Australian Capital Territory' \" +\n \"else 'Northern Territory' end\"))\n\ndf.show(truncate=False)\n# filtering second csv\ndfMelanoma = dfMelanoma.withColumn(\"Year\", col(\"Year\").cast(IntegerType()))\ndfMelanoma = dfMelanoma.filter(dfMelanoma[\"Cancer_group\"] == \"Melanoma of the skin\")\\\n .filter((dfMelanoma[\"Year\"]>2013) & (dfMelanoma[\"Year\"]<2016))\\\n .filter(dfMelanoma[\"Sex\"] == \"Persons\")\\\n .select(\"Territory\", \"Year\", \"Count\", \"Data_type\")\ndfMelanoma = dfMelanoma.withColumn(\"Count\", col(\"Count\").cast(FloatType()))\n\ndfMelanomaIncidence = dfMelanoma.filter(dfMelanoma[\"Data_type\"] == \"Incidence\").select(\"Territory\", \"Year\", \"Count\", \"Data_type\").orderBy('Count', ascending=False) \ndfMelanomaMortality = dfMelanoma.filter(dfMelanoma[\"Data_type\"] == \"Mortality\").select(\"Territory\", \"Year\", \"Count\", \"Data_type\").orderBy('Count', ascending=False) \n\ndfMelanomaIncidence.show(truncate=False)\ndfMelanomaMortality.show(truncate=False)\n\n#max and avg UV Index for each year in whole Australia\nprint(\"---------------------------------------------\")\nprint(\"Maximum and average UV index in Australia\")\ndfMaxAvgAustralia = df.groupBy(\"Year\")\\\n .agg(\n max(col(\"UV_Index\")).alias(\"max_UV_Index\"),\n avg(col(\"UV_Index\")).alias(\"avg_UV_Index\"),\n )\\\n .orderBy('Year', ascending=True)\ndfMaxAvgAustralia.show(truncate=False)\n\nprint(\"---------------------------------------------\")\nprint(\"Maximum UV Index for every territory in each year\")\ndfMaxTerritoryYear = df.groupBy(\"Territory\").pivot(\"Year\").max(\"UV_Index\")\ndfMaxTerritoryYear.show(truncate=False)\n#dfMaxTerritoryYear.repartition(1).write.csv(\"hdfs://namenode:9000/results/MaxTerritoryYear.csv\", sep='|')\n\nprint(\"---------------------------------------------\")\nprint(\"Average UV Index for every territory in each year\")\ndfAvgTerritoryYear = df.groupBy(\"Territory\").pivot(\"Year\").avg(\"UV_Index\")\ndfAvgTerritoryYear.show(truncate=False)\n#dfAvgTerritoryYear.repartition(1).write.csv(\"hdfs://namenode:9000/results/AvgTerritoryYear.csv\", sep='|')\n\nprint(\"---------------------------------------------\")\nprint(\"Maximum UV Index for every year and month\")\ndfMaxYearMonth = df.groupBy(\"Year\").pivot(\"Month\").max(\"UV_Index\").orderBy('Year', ascending=True)\ndfMaxYearMonth.show(truncate=False)\n#dfMaxYearMonth.repartition(1).write.csv(\"hdfs://namenode:9000/results/MaxYearMonth.csv\", sep='|')\n\n# max UV Index for every year and month\nprint(\"---------------------------------------------\")\nprint(\"Month with maximum UV Index in every year\")\ndfYearMax = df.groupBy(\"Year\").max(\"UV_Index\")\ndfMonthMax = dfYearMax.join(df, (dfYearMax[\"Year\"] == df[\"Year\"]) & \\\n (dfYearMax[\"max(UV_Index)\"] == df[\"UV_Index\"]) , \"inner\")\\\n .select(df[\"Year\"], \"Month\", \"max(UV_Index)\")\\\n .orderBy('Year', ascending=True)\ndfMonthMax.show(truncate=False)\n#dfMonthMax.repartition(1).write.csv(\"hdfs://namenode:9000/results/MonthMax.csv\", sep='|')\n\n# group by territory and year\ndf = df.groupBy(\"Territory\", \"Year\").max(\"UV_Index\")\n\n# join with dataset about risk and mortality\ndfJoinIncidence = df.join(dfMelanomaIncidence, (df[\"Territory\"] == dfMelanomaIncidence[\"Territory\"]) & \\\n (df[\"Year\"] == dfMelanomaIncidence[\"Year\"]) , \"inner\")\\\n .select(df[\"Territory\"], df[\"Year\"], \"max(UV_Index)\", \"Count\", \"Data_type\")\\\n .orderBy('Count', ascending=False)\ndfJoinMortality = df.join(dfMelanomaMortality, (df[\"Territory\"] == dfMelanomaMortality[\"Territory\"]) & \\\n (df[\"Year\"] == dfMelanomaMortality[\"Year\"]) , \"inner\")\\\n .select(df[\"Territory\"], df[\"Year\"], \"max(UV_Index)\", \"Count\", \"Data_type\")\\\n .orderBy('Count', ascending=False)\n#dfJoinIncidence.repartition(1).write.csv(\"hdfs://namenode:9000/results/JoinIncidence.csv\", sep='|')\n#dfJoinMortality.repartition(1).write.csv(\"hdfs://namenode:9000/results/JoinMortality.csv\", sep='|')\nprint(\"---------------------------------------------\")\nprint(\"Territory by year, avg UV index and number of incidence\")\ndfJoinIncidence.show(truncate=False)\n\nprint(\"---------------------------------------------\")\nprint(\"Territory by year, avg UV index and number of mortality\")\ndfJoinMortality.show(truncate=False)\n\nprint(\"---------------------------------------------\")\nprint(\"The five cities with the highest number of deaths caused by melanoma of skin\")\ndfJoinMortalityYear = dfJoinMortality.filter(df[\"Year\"] == \"2014\").orderBy('Count', ascending=False)\ndfJoinMortalityYear.show(n=5,truncate=False)","repo_name":"AnaNikolasevic/BigDataProject","sub_path":"batch/batch_uv_index.py","file_name":"batch_uv_index.py","file_ext":"py","file_size_in_byte":7455,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"29523466828","text":"import random\nimport back.tools\nfrom _md5 import md5\nimport config.settings\n\n\ncon = back.tools.con\n\n# {\n# payment_id: int\n# operation_id: int\n# price: int\n# chat_id: int\n# time: int\n# hash: varchar\n# success: bool\n# }\n\n\ndef generate_link(chat_id, price):\n print('generate_link')\n\n while True:\n payment_id = random.randint(10000, 99999)\n if back.tools.get('payments', id_=payment_id) is None:\n break\n\n hash_ = generate_hash(price, payment_id)\n\n with back.tools.DBConnection(config.settings.database_url) as con:\n curs = con.cursor()\n curs.execute(f'''INSERT INTO payments (id, price, chat_id, hash) VALUES ({payment_id}, {price}, {chat_id}, '{hash_}')''')\n curs.execute(f'UPDATE users SET payment_id = array_append(payment_id, {payment_id}) where id = {chat_id}')\n\n print('generate_link', 'm.chat.id', chat_id, 'payment_id', payment_id)\n\n link = f'{config.settings.payment_link}m={config.settings.shop_id}&oa={price}&o={payment_id}' \\\n f'&s={hash_}&us_chat_id={chat_id}&lang=ru&i=&em='\n return link\n\n\ndef generate_hash(price, payment_id):\n # ID Вашего магазина:Сумма платежа:Секретное слово:Номер заказа\n hash_ = md5(f\"{config.settings.shop_id}:{price}:{config.settings.secret}:{payment_id}\".encode('utf-8')).hexdigest()\n return hash_\n","repo_name":"artemmarkaryan/betmafia","sub_path":"back/payment.py","file_name":"payment.py","file_ext":"py","file_size_in_byte":1399,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"18220643659","text":"from PIL import Image\nimport pytesseract\nimport re\n\nimport requests\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\n\n# 이미지 로드\nimg = Image.open('image.jpg')\n# OCR 적용\ntext = pytesseract.image_to_string(img, lang='eng')\nprint(text)\n# 숫자와 문자가 섞인 간단한 주소를 찾는 정규표현식\nregex = r'\\d+ [\\w\\s]+'\naddresses = re.findall(regex, text)\nprint(addresses)\n\n\n\nfor address in addresses:\n # Google Maps Geocoding API로 주소를 위도, 경도로 변환\n geocoding_url = f\"https://maps.googleapis.com/maps/api/geocode/json?address={address}&key=YOUR_GEOCODING_API_KEY\"\n geocoding_response = requests.get(geocoding_url)\n geocode = geocoding_response.json()\n\n lat = geocode['results'][0]['geometry']['location']['lat']\n lng = geocode['results'][0]['geometry']['location']['lng']\n\n # Google Street View API를 통해 로드뷰 이미지를 가져옴\n street_view_url = f\"https://maps.googleapis.com/maps/api/streetview?size=600x300&location={lat},{lng}&fov=80&heading=70&pitch=0&key=YOUR_STREET_VIEW_API_KEY\"\n image_response = requests.get(street_view_url)\n\n # 로드뷰 이미지를 파일로 저장\n with open(f'roadview_{address}.jpg', 'wb') as f:\n f.write(image_response.content)\n\n # 이미지를 화면에 출력\n img = mpimg.imread(f'roadview_{address}.jpg')\n imgplot = plt.imshow(img)\n plt.show()","repo_name":"jongminKims/meta_EightProject","sub_path":"nic/MACRO/googleMacro.py","file_name":"googleMacro.py","file_ext":"py","file_size_in_byte":1402,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"44187025362","text":"\"\"\"dsrg_site URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/3.1/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.conf.urls.i18n import i18n_patterns\nfrom django.urls import include, path\nfrom django.contrib import admin\nfrom django.conf import settings\nfrom django.conf.urls.static import static\n#Index\nfrom website.views import index\n#News\nfrom website.views import news_list, news_list_filter_group, news_details\n#Profiles\nfrom website.views import profiles_list, profiles_details\n#Project\nfrom website.views import project_details, project_list, project_list_filter_group\n#Research\nfrom website.views import research_list, research_details, research_list_filter_group\n#Publication\nfrom website.views import publication_list\n#Resource\nfrom website.views import resource_list\n\nurlpatterns = [\n path('ckeditor/', include('ckeditor_uploader.urls')),\n]+static(settings.STATIC_URL,document_root=settings.STATICFILES_DIRS) + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n\nnews_patterns = ([\n path('', news_list, name='news'),\n path('', news_list_filter_group, name='news_group'),\n path('detail/',news_details, name='detail'),\n], 'news')\n\nprofiles_patterns = ([\n path('', profiles_list, name='profiles'),\n path('',profiles_details, name='detail'),\n], 'profiles')\n\nproject_patterns = ([\n path('', project_list, name='project'),\n path('', project_list_filter_group, name='project_group'),\n path('detail/',project_details, name='detail'),\n], 'project')\n\n\nresearch_patterns = ([\n path('', research_list, name='research'),\n path('', research_list_filter_group, name='research_group'),\n path('detail/',research_details, name='detail'),\n], 'research')\n\npublication_patterns = ([\n path('', publication_list, name='publication'),\n], 'publication')\n\nresource_patterns = ([\n path('', resource_list, name='resource'),\n], 'resource')\n\nurlpatterns += i18n_patterns(\n path('admin/', admin.site.urls),\n path('',view=index, name='home'),\n path('news/', include(news_patterns, namespace='news')),\n path('people/', include(profiles_patterns, namespace='profiles')),\n path('project/', include(project_patterns, namespace='project')),\n path('research/', include(research_patterns, namespace='research')),\n path('publication/', include(publication_patterns, namespace='publication')),\n path('resource/', include(resource_patterns, namespace='resource')),\n)","repo_name":"KadirKrykl/Ceng4001InternDjango","sub_path":"dsrg_site/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":3013,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"23585474661","text":"from django.test import TestCase\n\nfrom ..forms import StockForm\n\n\nclass StockFormTests(TestCase):\n def test_create_stock_form_required_fields(self):\n form = StockForm()\n self.assertTrue(form.fields[\"name\"].required)\n self.assertTrue(form.fields[\"price\"].required)\n\n def test_create_stock_form_field_error_messages(self):\n name_error_messages = {\n \"required\": \"Required.\",\n \"unique\": \"This name has already been registered.\",\n \"max_length\": \"Please use less than 100 characters.\",\n }\n price_error_messages = {\n \"required\": \"Required.\",\n \"invalid\": \"Please use digits.\",\n \"max_value\": \"Please enter a number between 0 and 999999999999.\",\n \"min_value\": \"Please enter a number between 0 and 999999999999.\",\n }\n form = StockForm()\n self.assertEqual(\n form.fields[\"name\"].error_messages, name_error_messages\n )\n self.assertEqual(\n form.fields[\"price\"].error_messages, price_error_messages\n )\n\n def test_create_stock_form_when_valid(self):\n form = StockForm(\n {\n \"name\": \"lemon\",\n \"price\": 200,\n }\n )\n\n self.assertTrue(form.is_bound)\n self.assertTrue(form.is_valid())\n self.assertEqual(form.errors, {})\n self.assertEqual(form.errors.as_text(), \"\")\n\n self.assertEqual(form.cleaned_data[\"name\"], \"lemon\")\n self.assertEqual(form.cleaned_data[\"price\"], \"200\")\n\n # boundデータを確認\n form_output = []\n\n for boundfield in form:\n form_output.append([boundfield.label, boundfield.data])\n\n expected_output = [\n [\"Name\", \"lemon\"],\n [\"Price\", 200],\n ]\n\n self.assertEqual(form_output, expected_output)\n\n def test_create_stock_form_when_empty(self):\n form = StockForm()\n self.assertFalse(form.is_bound)\n self.assertFalse(form.is_valid())\n with self.assertRaises(AttributeError):\n form.cleaned_data\n\n def test_create_stock_form_when_partially_empty(self):\n form = StockForm({\"name\": \"orange\"})\n self.assertEqual(form.errors[\"price\"], [\"Required.\"])\n self.assertFalse(form.is_valid())\n","repo_name":"4ka0/simple_inventory","sub_path":"stock/tests/test_stock_form.py","file_name":"test_stock_form.py","file_ext":"py","file_size_in_byte":2310,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"9220441750","text":"import copy\nimport logging\nfrom collections import OrderedDict\nfrom typing import List, Tuple, Any\n\nimport faiss\nimport more_itertools\nimport numpy as np\nimport torch\n\n\nlogger = logging.getLogger(__name__)\n\nclass OrderedDictCache:\n ''' Simple ordered dict.\n '''\n\n def __init__(self, capacity: int, buffer=10, lru: bool = True, callback=None):\n self.cache = OrderedDict()\n self.capacity = capacity\n self.buffer = buffer\n self.lru = lru\n self.callback = callback\n\n def get(self, key: Any) -> Any:\n if key not in self.cache:\n return None\n else:\n if self.lru:\n self.cache.move_to_end(key)\n return self.cache[key]\n\n def put(self, key: Any, value: Any) -> None:\n self.cache[key] = value\n\n if self.lru:\n self.cache.move_to_end(key)\n\n self._cleanup()\n\n def remove(self, key: Any) -> None:\n item = self.cache.pop(key)\n\n if self.callback is not None:\n self.callback([item])\n\n def _cleanup(self):\n\n if len(self.cache) >= (self.capacity + self.buffer):\n removed_list = []\n\n while len(self.cache) > self.capacity:\n removed = self.cache.popitem(last=False)\n removed_list.append(removed)\n\n if self.callback is not None:\n self.callback(removed_list)\n\n def __len__(self):\n return len(self.cache)\n\n def __iter__(self):\n for item in self.cache.items():\n yield item\n\nimport os\ndef parse_bool(b):\n return b == \"True\" or b == \"TRUE\" or b == \"true\" or b == \"1\"\n\nclass MemoryIndex:\n def __init__(self, embedding_dim=768, capacity: int = 9900, buffer=100, lru: bool = True, **kwargs):\n\n self.embedding_dim = embedding_dim\n self.id = 0\n\n ''' This is a hack so that memory has different id range from the knowledgebase.\n '''\n self.id_offset = int(1e9 + 1)\n\n self.init_index()\n\n def remove_from_cache(docs: List[Tuple]):\n doc_ids = [d[0] for d in docs]\n doc_ids_arr = np.asarray(doc_ids)\n self.remove_ids(doc_ids_arr)\n\n self.cache = OrderedDictCache(capacity=capacity, buffer=buffer, lru=lru, callback=remove_from_cache)\n\n self._random_retrieval = parse_bool(os.getenv(\"RANDOM_RETRIEVAL\", default=\"False\"))\n\n def init_index(self):\n \"\"\" Initialise the Faiss index.\n \"\"\"\n index = faiss.IndexFlatIP(self.embedding_dim)\n self.index = faiss.IndexIDMap2(index)\n\n def get_doc_dicts(self, doc_ids: np.ndarray) -> List[dict]:\n \"\"\" Get dictionary information from the requested docs.\n\n Args:\n doc_ids (ndarray):\n \"\"\"\n doc_ids = doc_ids.flatten()\n docs = []\n for id in doc_ids:\n\n id = int(id) - self.id_offset\n\n doc_dict = copy.deepcopy(self.cache.get(int(id)))\n if doc_dict is None:\n doc_dict = {\"id\": f\"{id}\", \"text\": \"\", \"title\": \"\",\n \"embeddings\": np.zeros(self.embedding_dim, dtype=np.float32)}\n else:\n try:\n doc_dict['id'] = f\"{int(doc_dict['id']) + self.id_offset}\"\n except TypeError:\n pass\n except ValueError:\n pass\n\n\n docs.append(doc_dict)\n\n logging.debug(f\"Doc Dicts: {doc_dict['id']}, {doc_dict['title']}, {doc_dict['text']}\")\n return docs\n\n def get_doc_dict(self, doc_id: int) -> List[dict]:\n \"\"\" Get dictionary information from the requested docs.\n\n Args:\n doc_ids (int):\n \"\"\"\n\n doc_id = int(doc_id) - self.id_offset\n\n doc_dict = copy.deepcopy(self.cache.get(int(doc_id)))\n if doc_dict is None:\n doc_dict = {\"id\": f\"{doc_id}\", \"text\": \" \", \"title\": \" \",\n \"embeddings\": np.zeros(self.embedding_dim, dtype=np.float32)}\n else:\n try:\n doc_dict['id'] = f\"{int(doc_dict['id']) + self.id_offset}\"\n except TypeError:\n pass\n except ValueError:\n pass\n\n logging.debug(f\"Doc Dicts: {doc_dict['id']}, {doc_dict['title']}, {doc_dict['text']}\")\n\n return doc_dict\n\n def get_top_docs(self, question_hidden_states: np.ndarray, n_docs: int = 5) -> Tuple[np.ndarray, np.ndarray]:\n \"\"\"\n Args:\n question_hidden_states (ndarray): Question states to match against the Faiss index.\n n_docs (int): Number of docs to retrieve.\n\n Returns:\n :obj:`np.ndarray` of shape :obj:`(batch_size, n_docs)`: A tensor of indices of retrieved documents.\n :obj:`np.ndarray` of shape :obj:`(batch_size, vector_size)`: A tensor of vector representations of retrieved documents.\n \"\"\"\n assert len(question_hidden_states.shape) == 2\n\n if self._random_retrieval:\n question_hidden_states= np.random.randn(*question_hidden_states.shape)\n\n distances, indices, = self.index.search(np.float32(question_hidden_states), n_docs)\n\n embeddings_list = []\n for ind in indices:\n nearest = []\n for nearest_ind in ind:\n item = self.cache.get(int(nearest_ind))\n\n if item is not None:\n nearest.append(item['embeddings'])\n else:\n nearest.append(np.zeros(self.embedding_dim, dtype=np.float32))\n\n embeddings_list.append(nearest)\n\n indices_array = np.asarray(indices)\n embeddings_array = np.asarray(embeddings_list)\n\n indices_array += self.id_offset\n\n logging.debug(f\"Top Docs: {indices}, {distances}\")\n return indices_array, embeddings_array, distances\n\n def add(self, context_dicts: List[dict], context_hidden_states: np.ndarray):\n \"\"\" Add vectors and dicts to the index.\n Args:\n context_dicts (List[dict]): A list of dictionaries with the representations. Must contain id, title and text fields.\n context_hidden_states (ndarray): The ndarray is batch size * dim.\n \"\"\"\n\n assert len(context_dicts) > 0\n assert len(context_hidden_states.shape) == 2\n\n context_hidden_states = np.float32(context_hidden_states)\n\n id_list = []\n for item, vec in zip(context_dicts, context_hidden_states):\n item['embeddings'] = vec.tolist()\n self.cache.put(self.id, item)\n id_list.append(self.id)\n self.id += 1\n\n ids = np.asarray(id_list)\n\n logger.debug(f\"Add ids to Faiss: {ids}\")\n self.index.add_with_ids(context_hidden_states, ids)\n\n ids += self.id_offset\n\n return ids\n\n def remove_ids(self, doc_ids: np.ndarray) -> List[dict]:\n \"\"\" Remove from the dictionary and the Faiss index.\n Args:\n doc_ids (ndarray): Ids to remove.\n \"\"\"\n logger.debug(f\"Remove from Faiss: {doc_ids}\")\n self.index.remove_ids(doc_ids)\n\n def clear_memory(self):\n \"\"\" Clear the Faiss index.\n \"\"\"\n for chunk in more_itertools.chunked(self.cache.cache.items(),100):\n ids = [c[0] for c in chunk]\n doc_ids = np.asarray(ids)\n print(f\"Clear doc_ids: {doc_ids}\")\n self.index.remove_ids(doc_ids)\n \n\n def __len__(self):\n return len(self.cache)\n\n def __iter__(self):\n for item in self.cache:\n yield item\n","repo_name":"dwlmt/story-fragments","sub_path":"story_fragments/modules/memory_cache_index.py","file_name":"memory_cache_index.py","file_ext":"py","file_size_in_byte":7523,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"40414472604","text":"import sys\nfrom pathlib import Path\nfrom typing import BinaryIO, Union\n\n\ndef is_silk_data(raw: bytes) -> bool:\n if len(raw) > 10:\n offset = 0\n if raw[0] == 2:\n offset = 1\n if raw[offset:10] == b\"#!SILK_V3\":\n return True\n return False\n\n\ndef get_file(file: Union[str, BinaryIO]) -> BinaryIO:\n if isinstance(file, str):\n path = Path(file)\n if not path.is_file():\n raise FileNotFoundError(file)\n return open(file, \"rb\")\n elif isinstance(file, BinaryIO):\n return file\n else:\n raise TypeError(file)\n\n\ndef force_quit():\n import os\n import multiprocessing\n os.kill(multiprocessing.current_process().pid, 15) # sigterm\n\n\ndef _play_sound(source: Union[str, bytes]):\n import winsound\n from threading import Thread\n\n t = Thread(\n target=winsound.PlaySound,\n name=\"PlayerThread\",\n args=(source, winsound.SND_FILENAME if isinstance(source, str) else winsound.SND_MEMORY),\n )\n t.start()\n try:\n while True:\n t.join(0.5)\n except KeyboardInterrupt:\n print(\"Interrupt received\")\n force_quit()\n\n\ndef play_audio(source: Union[str, bytes]):\n if sys.platform != \"win32\":\n raise RuntimeError(\"PlaySound only support windows\")\n\n _play_sound(source)\n","repo_name":"DCZYewen/Python-Silk-Module","sub_path":"src/pysilk/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1325,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"52"} +{"seq_id":"16169752684","text":"import matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\nx = np.linspace(0, 5, 10)\r\nfig, ax = plt.subplots()\r\n\r\nax.plot(x, x+1, color='red', alpha=0.5) #alpha 0.5 to półprzezroczystość\r\nax.plot(x, x+2, color='#1155dd')\r\nax.plot(x, x+3, color='#15cc55')\r\nax.legend(loc=2) #upper left corner\r\nax.set_xlabel(r'$\\alpha$')\r\nax.set_ylabel(r'$y$')\r\nax.set_title('jakis tytul tako')\r\n\r\nplt.show()","repo_name":"hura56/python","sub_path":"Wykres_kolorowyOOOO.py","file_name":"Wykres_kolorowyOOOO.py","file_ext":"py","file_size_in_byte":390,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"29608893250","text":"from email.mime import image\nfrom PIL import Image\n\ndef Images_Pdf(filename, output):\n images = []\n\n for file in filename:\n #pegando a imagem\n im = Image.open(file)\n #convertendo a imagem\n im = im.convert('RGB')\n images.append(im)\n\n images[0].save(output, save_all= True, append_images=images[1:])\n\nImages_Pdf([\"hotd.jpg\", \"dragon.jpg\"], \"output.pdf\")\n\n\n","repo_name":"juliagmf/ImgPdf-python","sub_path":"image_pdf.py","file_name":"image_pdf.py","file_ext":"py","file_size_in_byte":410,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"15487471940","text":"from django.shortcuts import render\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.template import loader\nfrom .models import Order\n# Create your views here.\ndef index(request):\n print('index() 실행')\n # return render(request,'board/index.html')\n # 반환되는 queryset에 대해서 order_by함수 이용하면 특정 필드 기준으로 정렬\n # order_by에 들어가는 필드 앞에 -를 붙이면 내림차순(desc) 아니면 오름차순\n\n Order_list = Order.objects.all().order_by('-id')\n\n context ={\n 'Order_list':Order_list\n }\n return render(request,'order/index.html', context)\n\ndef home(request):\n # 목록으로\n return HttpResponseRedirect(\"/order/\")\n\ndef order(request):\n if request.method =='GET': #요청방식이 get 방식이면 화면 표시\n return render(request,'order/add_order.html')\n else:\n order_text = request.POST['product_name']\n price = request.POST['price']\n\n # price = request.POST['price']\n address = request.POST['address']\n print(\"등록 완료\",order_text,price,address)\n Order.objects.create(\n order_text = order_text,\n price = price, #세션에 있는 값 저장\n address = address\n )\n return HttpResponseRedirect('/order/')\n\ndef list_order(request):\n # result = None # 필터링 된 리스트\n # context = {}\n\n # # print(request.GET)\n # #검색 조건과 검색 키워드가 있어야 필터링 실행\n # if 'searchType' in request.GET and 'searchWord' in request.GET:\n # search_type = request.GET['searchType'] # get안의 문자열은\n # search_word = request.GET['searchWord'] # html의 name속성과 일치해야함\n # print(\"searchType :{}, search_word : {}\".format(search_type,search_word))\n # order = Order.objects.all().order_by('id')\n context = {\n \"order_list\" : order\n }\n return render(request,'order/list_order.html',context)\n\n# def search_order(request):\n# search_name = request.POST['product_name']\n# check = request.POST['check']\n\n# if check == 'order_text':\n# order_list=Order.objects.filter(order_text__contains = search_name)\n# else:\n# order_list=Order.objects.filter(address__contains = search_name)\n# context={\n# 'order_list':order_list\n# }\n# return render(request,'order/list_order.html',context)\n\ndef search_order(request):\n input_name = request.POST['product_name']\n check = request.POST['option']\n\n oList = []\n if check ==\"order\":\n oList = Order.objects.filter(order_text__contains = input_name)\n elif check ==\"front_add\":\n oList = Order.objects.filter(address__startswith = input_name)\n elif check ==\"address\":\n oList = Order.objects.filter(address__contains = input_name)\n elif check ==\"price\":\n oList = Order.objects.filter(price__contains = input_name)\n\n return render(request,\"order/list_order.html\",{'order_list' : oList})\n\ndef read(request,id):\n print(\"read실행\")\n order = Order.objects.get(id = id)\n context ={\n 'order':order,\n 'oList' : order.order_text.split(\",\")\n }\n return render(request,'order/read.html', context)\n\ndef delete(request,id):\n Order.objects.get(id=id).delete()\n return HttpResponseRedirect('../../list_order')\n\ndef update(request,id):\n order = Order.objects.get(id = id)\n if request.method == \"GET\":\n #id로 찾은 친구 정보를 템플릿에 표시하기 위해서\n print(\"method>>\",request.method)\n context = {'order' : order}\n return render(request,'../update_order.html', context)\n else:\n # id로 찾은 객체에 대해서 폼의 값으로 원래 객체의 값 덮어쓰기\n print(\"method>>\",request.method)\n order.order_text = request.POST['order_text']\n order.price = request.POST['price']\n order.address = request.POST['address']\n\n order.save()\n #수정 후에 해당 글로 다시 이동\n redirect_url = '/order/' +str(id) +'/'\n\n return HttpResponseRedirect(redirect_url)","repo_name":"qhtneo/wecan","sub_path":"projects/myorder/order/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4130,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"19688576656","text":"from maliang import Maliang\n\napp = Maliang(buffer_proxy=True)\n\n\ndef on_draw():\n app.line(app.mouse_x, app.mouse_y, app.pmouse_x, app.pmouse_y)\n\napp.regist_event(\"on_draw\", on_draw)\n\napp.run()","repo_name":"MLGB-bot/maliang","sub_path":"examples/mouse/pmouse_xy.py","file_name":"pmouse_xy.py","file_ext":"py","file_size_in_byte":194,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"13242801259","text":"from __future__ import with_statement\n\nimport copy\nimport datetime\nimport getpass\nimport logging\nimport os\nimport py_compile\nimport shutil\nfrom StringIO import StringIO\nimport tempfile\nfrom testify import TestCase, assert_equal, assert_gt, assert_in, assert_not_in, assert_raises, setup, teardown, assert_not_equal\n\nfrom mrjob.conf import dump_mrjob_conf\nimport mrjob.emr\nfrom mrjob.emr import EMRJobRunner, describe_all_job_flows, parse_s3_uri\nfrom mrjob.parse import JOB_NAME_RE\nfrom mrjob.util import tar_and_gzip\nfrom tests.mockboto import MockS3Connection, MockEmrConnection, MockEmrObject, MockKey, add_mock_s3_data, DEFAULT_MAX_DAYS_AGO, DEFAULT_MAX_JOB_FLOWS_RETURNED, to_iso8601\nfrom tests.mr_two_step_job import MRTwoStepJob\nfrom tests.quiet import logger_disabled, no_handlers_for_logger\n\ntry:\n import boto\n from mrjob import botoemr\nexcept ImportError:\n boto = None\n botoemr = None\n\n\nclass MockEMRAndS3TestCase(TestCase):\n\n @setup\n def make_mrjob_conf(self):\n _, self.mrjob_conf_path = tempfile.mkstemp(prefix='mrjob.conf.')\n dump_mrjob_conf({'runners': {'emr': {\n 'check_emr_status_every': 0.01,\n 's3_scratch_uri': 's3://walrus/tmp',\n 's3_sync_wait_time': 0.01,\n }}}, open(self.mrjob_conf_path, 'w'))\n\n @setup\n def rm_mrjob_conf(self):\n os.unlink(self.mrjob_conf_path)\n\n @setup\n def sandbox_boto(self):\n self.mock_s3_fs = {}\n self.mock_emr_job_flows = {}\n self.mock_emr_failures = {}\n self.mock_emr_output = {}\n\n def mock_boto_connect_s3(*args, **kwargs):\n kwargs['mock_s3_fs'] = self.mock_s3_fs\n return MockS3Connection(*args, **kwargs)\n\n def mock_botoemr_EmrConnection(*args, **kwargs):\n kwargs['mock_s3_fs'] = self.mock_s3_fs\n kwargs['mock_emr_job_flows'] = self.mock_emr_job_flows\n kwargs['mock_emr_failures'] = self.mock_emr_failures\n kwargs['mock_emr_output'] = self.mock_emr_output\n return MockEmrConnection(*args, **kwargs)\n\n self._real_boto_connect_s3 = boto.connect_s3\n boto.connect_s3 = mock_boto_connect_s3\n\n self._real_botoemr_EmrConnection = botoemr.EmrConnection\n botoemr.EmrConnection = mock_botoemr_EmrConnection\n\n @teardown\n def unsandbox_boto(self):\n boto.connect_s3 = self._real_boto_connect_s3\n botoemr.EmrConnection = self._real_botoemr_EmrConnection\n\n def add_mock_s3_data(self, data):\n \"\"\"Update self.mock_s3_fs with a map from bucket name\n to key name to data.\"\"\"\n add_mock_s3_data(self.mock_s3_fs, data)\n\n\nclass EMRJobRunnerEndToEndTestCase(MockEMRAndS3TestCase):\n\n @setup\n def make_tmp_dir_and_mrjob_conf(self):\n self.tmp_dir = tempfile.mkdtemp()\n self.mrjob_conf_path = os.path.join(self.tmp_dir, 'mrjob.conf')\n dump_mrjob_conf({'runners': {'emr': {\n 'check_emr_status_every': 0.01,\n 's3_sync_wait_time': 0.01,\n 'aws_availability_zone': 'PUPPYLAND',\n 'additional_emr_info': {'key': 'value'},\n }}}, open(self.mrjob_conf_path, 'w'))\n\n @teardown\n def rm_tmp_dir(self):\n shutil.rmtree(self.tmp_dir)\n\n def test_end_to_end(self):\n # read from STDIN, a local file, and a remote file\n stdin = StringIO('foo\\nbar\\n')\n\n local_input_path = os.path.join(self.tmp_dir, 'input')\n with open(local_input_path, 'w') as local_input_file:\n local_input_file.write('bar\\nqux\\n')\n\n remote_input_path = 's3://walrus/data/foo'\n self.add_mock_s3_data({'walrus': {'data/foo': 'foo\\n'}})\n\n # setup fake output\n self.mock_emr_output = {('j-MOCKJOBFLOW0', 1): [\n '1\\t\"qux\"\\n2\\t\"bar\"\\n', '2\\t\"foo\"\\n5\\tnull\\n']}\n\n mr_job = MRTwoStepJob(['-r', 'emr', '-v',\n '-c', self.mrjob_conf_path,\n '-', local_input_path, remote_input_path,\n '--hadoop-input-format', 'FooFormat',\n '--hadoop-output-format', 'BarFormat'])\n mr_job.sandbox(stdin=stdin)\n\n local_tmp_dir = None\n results = []\n\n mock_s3_fs_snapshot = copy.deepcopy(self.mock_s3_fs)\n\n with mr_job.make_runner() as runner:\n assert isinstance(runner, EMRJobRunner)\n\n # make sure that initializing the runner doesn't affect S3\n # (Issue #50)\n assert_equal(mock_s3_fs_snapshot, self.mock_s3_fs)\n\n # make sure AdditionalInfo was JSON-ified from the config file.\n # checked now because you can't actually read it from the job flow\n # on real EMR.\n assert_equal(runner._opts['additional_emr_info'],\n '{\"key\": \"value\"}')\n\n runner.run()\n\n for line in runner.stream_output():\n key, value = mr_job.parse_output_line(line)\n results.append((key, value))\n\n local_tmp_dir = runner._get_local_tmp_dir()\n # make sure cleanup hasn't happened yet\n assert os.path.exists(local_tmp_dir)\n assert any(runner.ls(runner.get_output_dir()))\n\n emr_conn = runner.make_emr_conn()\n job_flow = emr_conn.describe_jobflow(runner.get_emr_job_flow_id())\n assert_equal(job_flow.state, 'COMPLETED')\n name_match = JOB_NAME_RE.match(job_flow.name)\n assert_equal(name_match.group(1), 'mr_two_step_job')\n assert_equal(name_match.group(2), getpass.getuser())\n\n # make sure our input and output formats are attached to\n # the correct steps\n assert_in('-inputformat', job_flow.steps[0].args())\n assert_not_in('-outputformat', job_flow.steps[0].args())\n assert_not_in('-inputformat', job_flow.steps[1].args())\n assert_in('-outputformat', job_flow.steps[1].args())\n\n # make sure mrjob.tar.gz is created and uploaded as\n # a bootstrap file\n assert runner._mrjob_tar_gz_path\n mrjob_tar_gz_file_dicts = [\n file_dict for file_dict in runner._files\n if file_dict['path'] == runner._mrjob_tar_gz_path]\n\n assert_equal(len(mrjob_tar_gz_file_dicts), 1)\n\n mrjob_tar_gz_file_dict = mrjob_tar_gz_file_dicts[0]\n assert mrjob_tar_gz_file_dict['name']\n assert_equal(mrjob_tar_gz_file_dict.get('bootstrap'), 'file')\n\n # shouldn't be in PYTHONPATH (we dump it directly in site-packages)\n pythonpath = runner._get_cmdenv().get('PYTHONPATH') or ''\n assert_not_in(mrjob_tar_gz_file_dict['name'],\n pythonpath.split(':'))\n\n assert_equal(sorted(results),\n [(1, 'qux'), (2, 'bar'), (2, 'foo'), (5, None)])\n\n # make sure cleanup happens\n assert not os.path.exists(local_tmp_dir)\n assert not any(runner.ls(runner.get_output_dir()))\n\n # job should get terminated\n emr_conn = runner.make_emr_conn()\n job_flow_id = runner.get_emr_job_flow_id()\n for i in range(10):\n emr_conn.simulate_progress(job_flow_id)\n\n job_flow = emr_conn.describe_jobflow(job_flow_id)\n assert_equal(job_flow.state, 'TERMINATED')\n\n def test_failed_job(self):\n mr_job = MRTwoStepJob(['-r', 'emr', '-v',\n '-c', self.mrjob_conf_path])\n mr_job.sandbox()\n\n self.add_mock_s3_data({'walrus': {}})\n self.mock_emr_failures = {('j-MOCKJOBFLOW0', 0): None}\n\n with mr_job.make_runner() as runner:\n assert isinstance(runner, EMRJobRunner)\n\n with logger_disabled('mrjob.emr'):\n assert_raises(Exception, runner.run)\n\n emr_conn = botoemr.EmrConnection()\n job_flow_id = runner.get_emr_job_flow_id()\n for i in range(10):\n emr_conn.simulate_progress(job_flow_id)\n\n job_flow = emr_conn.describe_jobflow(job_flow_id)\n assert_equal(job_flow.state, 'FAILED')\n\n # job should get terminated on cleanup\n emr_conn = runner.make_emr_conn()\n job_flow_id = runner.get_emr_job_flow_id()\n for i in range(10):\n emr_conn.simulate_progress(job_flow_id)\n\n job_flow = emr_conn.describe_jobflow(job_flow_id)\n assert_equal(job_flow.state, 'TERMINATED')\n\n def test_pick_scratch_uri(self):\n self.add_mock_s3_data({'mrjob-walrus': {}, 'zebra': {}})\n runner = EMRJobRunner(conf_path=False)\n\n assert_equal(runner._opts['s3_scratch_uri'],\n 's3://mrjob-walrus/tmp/')\n\n def test_create_scratch_uri(self):\n # \"walrus\" bucket will be ignored; it doesn't start with \"mrjob-\"\n self.add_mock_s3_data({'walrus': {}, 'zebra': {}})\n\n runner = EMRJobRunner(conf_path=False, s3_sync_wait_time=0.01)\n\n # bucket name should be mrjob- plus 16 random hex digits\n s3_scratch_uri = runner._opts['s3_scratch_uri']\n assert_equal(s3_scratch_uri[:11], 's3://mrjob-')\n assert_equal(s3_scratch_uri[27:], '/tmp/')\n\n # bucket shouldn't actually exist yet\n scratch_bucket, _ = parse_s3_uri(s3_scratch_uri)\n assert_not_in(scratch_bucket, self.mock_s3_fs.keys())\n\n # need to do something to ensure that the bucket actually gets\n # created. let's launch a (mock) job flow\n jfid = runner.make_persistent_job_flow()\n assert_in(scratch_bucket, self.mock_s3_fs.keys())\n runner.make_emr_conn().terminate_jobflow(jfid)\n\n # once our scratch bucket is created, we should re-use it\n runner2 = EMRJobRunner(conf_path=False)\n assert_equal(runner2._opts['s3_scratch_uri'], s3_scratch_uri)\n s3_scratch_uri = runner._opts['s3_scratch_uri']\n\n def test_bootstrap_files_only_get_uploaded_once(self):\n # just a regression test for Issue #8\n\n # use self.mrjob_conf_path because it's easier than making a new file\n bootstrap_file = self.mrjob_conf_path\n\n runner = EMRJobRunner(conf_path=False,\n bootstrap_files=[bootstrap_file])\n\n matching_file_dicts = [fd for fd in runner._files\n if fd['path'] == bootstrap_file]\n assert_equal(len(matching_file_dicts), 1)\n\n def test_attach_to_existing_job_flow(self):\n emr_conn = EMRJobRunner(conf_path=False).make_emr_conn()\n # set log_uri to None, so that when we describe the job flow, it\n # won't have the loguri attribute, to test Issue #112\n emr_job_flow_id = emr_conn.run_jobflow(\n name='Development Job Flow', log_uri=None)\n\n stdin = StringIO('foo\\nbar\\n')\n self.mock_emr_output = {(emr_job_flow_id, 1): [\n '1\\t\"bar\"\\n1\\t\"foo\"\\n2\\tnull\\n']}\n\n mr_job = MRTwoStepJob(['-r', 'emr', '-v',\n '-c', self.mrjob_conf_path,\n '--emr-job-flow-id', emr_job_flow_id])\n mr_job.sandbox(stdin=stdin)\n\n results = []\n with mr_job.make_runner() as runner:\n runner.run()\n\n # Issue 182: don't create the bootstrap script when\n # attaching to another job flow\n assert_equal(runner._master_bootstrap_script, None)\n\n for line in runner.stream_output():\n key, value = mr_job.parse_output_line(line)\n results.append((key, value))\n\n assert_equal(sorted(results),\n [(1, 'bar'), (1, 'foo'), (2, None)])\n\n def test_default_hadoop_version(self):\n stdin = StringIO('foo\\nbar\\n')\n mr_job = MRTwoStepJob(['-r', 'emr', '-v',\n '-c', self.mrjob_conf_path])\n mr_job.sandbox(stdin=stdin)\n\n with mr_job.make_runner() as runner:\n runner.run()\n\n emr_conn = runner.make_emr_conn()\n job_flow = emr_conn.describe_jobflow(runner.get_emr_job_flow_id())\n\n assert_equal(job_flow.hadoopversion, '0.18')\n\n def test_set_hadoop_version(self):\n stdin = StringIO('foo\\nbar\\n')\n mr_job = MRTwoStepJob(['-r', 'emr', '-v',\n '-c', self.mrjob_conf_path,\n '--hadoop-version', '0.20'])\n mr_job.sandbox(stdin=stdin)\n\n with mr_job.make_runner() as runner:\n runner.run()\n\n emr_conn = runner.make_emr_conn()\n job_flow = emr_conn.describe_jobflow(runner.get_emr_job_flow_id())\n\n assert_equal(job_flow.hadoopversion, '0.20')\n\n def test_availability_zone_config(self):\n # Confirm that the mrjob.conf option 'aws_availability_zone' was\n # propagated through to the job flow\n mr_job = MRTwoStepJob(['-r', 'emr', '-v',\n '-c', self.mrjob_conf_path])\n mr_job.sandbox()\n\n with mr_job.make_runner() as runner:\n runner.run()\n\n emr_conn = runner.make_emr_conn()\n job_flow_id = runner.get_emr_job_flow_id()\n\n job_flow = emr_conn.describe_jobflow(job_flow_id)\n assert_equal(job_flow.availabilityzone, 'PUPPYLAND')\n\n def test_debugging_works(self):\n mr_job = MRTwoStepJob(['-r', 'emr', '-v',\n '-c', self.mrjob_conf_path,\n '--enable-emr-debugging'])\n mr_job.sandbox()\n\n with mr_job.make_runner() as runner:\n runner.run()\n flow = runner.make_emr_conn().describe_jobflow(runner._emr_job_flow_id)\n assert_equal(flow.steps[0].name, 'Setup Hadoop Debugging')\n\n\nclass BucketRegionTestCase(MockEMRAndS3TestCase):\n\n @setup\n def make_dummy_data(self):\n self.add_mock_s3_data({'mrjob-1': {}})\n s3c = boto.connect_s3()\n self.bucket1 = s3c.get_bucket('mrjob-1')\n self.bucket1_uri = 's3://mrjob-1/tmp/'\n\n def test_region_nobucket_nolocation(self):\n # aws_region specified, no bucket specified, default bucket has no location\n j = EMRJobRunner(aws_region='PUPPYLAND',\n s3_endpoint='PUPPYLAND',\n conf_path=False)\n assert_not_equal(j._opts['s3_scratch_uri'], self.bucket1_uri)\n\n def test_region_nobucket_nomatchexists(self):\n # aws_region specified, no bucket specified, no buckets have matching region\n self.bucket1.set_location('PUPPYLAND')\n j = EMRJobRunner(aws_region='KITTYLAND',\n s3_endpoint='KITTYLAND',\n conf_path=False)\n assert_not_equal(j._opts['s3_scratch_uri'], self.bucket1_uri)\n\n def test_noregion_nobucket_nolocation(self):\n # aws_region not specified, no bucket specified, default bucket has no location\n j = EMRJobRunner(conf_path=False)\n assert_equal(j._opts['s3_scratch_uri'], self.bucket1_uri)\n\n def test_noregion_bucket_nolocation(self):\n # aws_region not specified, bucket specified without location\n j = EMRJobRunner(conf_path=False,\n s3_scratch_uri=self.bucket1_uri)\n assert_equal(j._opts['s3_scratch_uri'], self.bucket1_uri)\n\n def test_noregion_bucket_location(self):\n # aws_region not specified, bucket specified with location\n self.bucket1.set_location('PUPPYLAND')\n j = EMRJobRunner(conf_path=False)\n assert_equal(j._aws_region, 'PUPPYLAND')\n\n\nclass ExtraBucketRegionTestCase(MockEMRAndS3TestCase):\n\n @setup\n def make_dummy_data(self):\n self.add_mock_s3_data({'mrjob-1': {}})\n s3c = boto.connect_s3()\n self.bucket1 = s3c.get_bucket('mrjob-1')\n self.bucket1_uri = 's3://mrjob-1/tmp/'\n\n self.add_mock_s3_data({'mrjob-2': {}})\n self.bucket2 = s3c.get_bucket('mrjob-2')\n self.bucket2.set_location('KITTYLAND')\n self.bucket2_uri = 's3://mrjob-2/tmp/'\n\n def test_region_nobucket_matchexists(self):\n # aws_region specified, no bucket specified, bucket exists with matching region\n j = EMRJobRunner(aws_region='KITTYLAND',\n s3_endpoint='KITTYLAND',\n conf_path=False)\n assert_equal(j._opts['s3_scratch_uri'], self.bucket2_uri)\n\n def test_region_bucket_match(self):\n # aws_region specified, bucket specified with matching location\n j = EMRJobRunner(aws_region='PUPPYLAND',\n s3_endpoint='PUPPYLAND',\n s3_scratch_uri=self.bucket1_uri,\n conf_path=False)\n assert_equal(j._opts['s3_scratch_uri'], self.bucket1_uri)\n\n def test_region_bucket_doesnotmatch(self):\n # aws_region specified, bucket specified with incorrect location\n with no_handlers_for_logger():\n stderr = StringIO()\n log = logging.getLogger('mrjob.emr')\n log.addHandler(logging.StreamHandler(stderr))\n log.setLevel(logging.WARNING)\n\n j = EMRJobRunner(aws_region='PUPPYLAND',\n s3_endpoint='PUPPYLAND',\n s3_scratch_uri=self.bucket2_uri,\n conf_path=False)\n\n assert_in('does not match bucket region', stderr.getvalue())\n\n\nclass DescribeAllJobFlowsTestCase(MockEMRAndS3TestCase):\n\n def test_can_get_all_job_flows(self):\n now = datetime.datetime.utcnow()\n\n NUM_JOB_FLOWS = 2222\n assert_gt(NUM_JOB_FLOWS, DEFAULT_MAX_JOB_FLOWS_RETURNED)\n\n for i in range(NUM_JOB_FLOWS):\n jfid = 'j-%04d' % i\n self.mock_emr_job_flows[jfid] = MockEmrObject(\n creationdatetime=to_iso8601(now - datetime.timedelta(minutes=i)),\n jobflowid=jfid)\n\n emr_conn = EMRJobRunner(conf_path=False).make_emr_conn()\n\n # ordinary describe_jobflows() hits the limit on number of job flows\n some_jfs = emr_conn.describe_jobflows()\n assert_equal(len(some_jfs), DEFAULT_MAX_JOB_FLOWS_RETURNED)\n\n all_jfs = describe_all_job_flows(emr_conn)\n assert_equal(len(all_jfs), NUM_JOB_FLOWS)\n assert_equal(sorted(jf.jobflowid for jf in all_jfs),\n [('j-%04d' % i) for i in range(NUM_JOB_FLOWS)])\n\n\n### tests for error parsing ###\n\nBUCKET = 'walrus'\nBUCKET_URI = 's3://' + BUCKET + '/'\n\nLOG_DIR = 'j-JOBFLOWID/'\n\nGARBAGE = \\\n\"\"\"GarbageGarbageGarbage\n\"\"\"\n\nTRACEBACK_START = 'Traceback (most recent call last):\\n'\n\nPY_EXCEPTION = \\\n\"\"\" File \"\", line 1, in \nTypeError: 'int' object is not iterable\n\"\"\"\n\nCHILD_ERR_LINE = '2010-07-27 18:25:48,397 WARN org.apache.hadoop.mapred.TaskTracker (main): Error running child\\n'\n\nJAVA_STACK_TRACE = \"\"\"java.lang.OutOfMemoryError: Java heap space\n at org.apache.hadoop.mapred.IFile$Reader.readNextBlock(IFile.java:270)\n at org.apache.hadoop.mapred.IFile$Reader.next(IFile.java:332)\n\"\"\"\n\nHADOOP_ERR_LINE_PREFIX = '2010-07-27 19:53:35,451 ERROR org.apache.hadoop.streaming.StreamJob (main): '\n\nUSEFUL_HADOOP_ERROR = 'Error launching job , Output path already exists : Output directory s3://yourbucket/logs/2010/07/23/ already exists and is not empty'\n\nBORING_HADOOP_ERROR = 'Job not Successful!'\nTASK_ATTEMPTS_DIR = LOG_DIR + 'task-attempts/'\n\nATTEMPT_0_DIR = TASK_ATTEMPTS_DIR + 'attempt_201007271720_0001_m_000126_0/'\nATTEMPT_1_DIR = TASK_ATTEMPTS_DIR + 'attempt_201007271720_0001_m_000126_0/'\n\ndef make_input_uri_line(input_uri):\n return \"2010-07-27 17:55:29,400 INFO org.apache.hadoop.fs.s3native.NativeS3FileSystem (main): Opening '%s' for reading\\n\" % input_uri\n\n\nclass FindProbableCauseOfFailureTestCase(MockEMRAndS3TestCase):\n # We're mostly concerned here that the right log files are read in the\n # right order. parsing of the logs is handled by tests.parse_test\n\n @setup\n def make_runner(self):\n self.add_mock_s3_data({'walrus': {}})\n self.runner = EMRJobRunner(s3_sync_wait_time=0,\n s3_scratch_uri='s3://walrus/tmp',\n conf_path=False)\n self.runner._s3_job_log_uri = BUCKET_URI + LOG_DIR\n\n @teardown\n def cleanup_runner(self):\n self.runner.cleanup()\n\n def test_empty(self):\n self.add_mock_s3_data({'walrus': {}})\n assert_equal(self.runner._find_probable_cause_of_failure([1]), None)\n\n def test_python_exception(self):\n self.add_mock_s3_data({'walrus': {\n ATTEMPT_0_DIR + 'stderr':\n GARBAGE + TRACEBACK_START + PY_EXCEPTION + GARBAGE,\n ATTEMPT_0_DIR + 'syslog':\n make_input_uri_line(BUCKET_URI + 'input.gz'),\n }})\n assert_equal(self.runner._find_probable_cause_of_failure([1]),\n {'lines': list(StringIO(PY_EXCEPTION)),\n 's3_log_file_uri':\n BUCKET_URI + ATTEMPT_0_DIR + 'stderr',\n 'input_uri': BUCKET_URI + 'input.gz'})\n\n def test_python_exception_without_input_uri(self):\n self.add_mock_s3_data({'walrus': {\n ATTEMPT_0_DIR + 'stderr': (\n GARBAGE + TRACEBACK_START + PY_EXCEPTION + GARBAGE),\n }})\n assert_equal(self.runner._find_probable_cause_of_failure([1]),\n {'lines': list(StringIO(PY_EXCEPTION)),\n 's3_log_file_uri':\n BUCKET_URI + ATTEMPT_0_DIR + 'stderr',\n 'input_uri': None})\n\n def test_java_exception(self):\n self.add_mock_s3_data({'walrus': {\n ATTEMPT_0_DIR + 'stderr': GARBAGE + GARBAGE,\n ATTEMPT_0_DIR + 'syslog':\n make_input_uri_line(BUCKET_URI + 'input.gz') +\n GARBAGE +\n CHILD_ERR_LINE +\n JAVA_STACK_TRACE +\n GARBAGE,\n }})\n assert_equal(self.runner._find_probable_cause_of_failure([1]),\n {'lines': list(StringIO(JAVA_STACK_TRACE)),\n 's3_log_file_uri':\n BUCKET_URI + ATTEMPT_0_DIR + 'syslog',\n 'input_uri': BUCKET_URI + 'input.gz'})\n\n def test_java_exception_without_input_uri(self):\n self.add_mock_s3_data({'walrus': {\n ATTEMPT_0_DIR + 'syslog':\n CHILD_ERR_LINE +\n JAVA_STACK_TRACE +\n GARBAGE,\n }})\n assert_equal(self.runner._find_probable_cause_of_failure([1]),\n {'lines': list(StringIO(JAVA_STACK_TRACE)),\n 's3_log_file_uri':\n BUCKET_URI + ATTEMPT_0_DIR + 'syslog',\n 'input_uri': None})\n\n def test_hadoop_streaming_error(self):\n # we should look only at step 2 since the errors in the other\n # steps are boring\n #\n # we include input.gz just to test that we DON'T check for it\n self.add_mock_s3_data({'walrus': {\n LOG_DIR + 'steps/1/syslog':\n GARBAGE +\n HADOOP_ERR_LINE_PREFIX + BORING_HADOOP_ERROR + '\\n',\n LOG_DIR + 'steps/2/syslog':\n GARBAGE +\n make_input_uri_line(BUCKET_URI + 'input.gz') +\n HADOOP_ERR_LINE_PREFIX + USEFUL_HADOOP_ERROR + '\\n',\n LOG_DIR + 'steps/3/syslog':\n HADOOP_ERR_LINE_PREFIX + BORING_HADOOP_ERROR + '\\n',\n }})\n\n assert_equal(self.runner._find_probable_cause_of_failure([1, 2, 3]),\n {'lines': [USEFUL_HADOOP_ERROR + '\\n'],\n 's3_log_file_uri':\n BUCKET_URI + LOG_DIR + 'steps/2/syslog',\n 'input_uri': None})\n\n def test_later_task_attempt_steps_win(self):\n # should look at later steps first\n self.add_mock_s3_data({'walrus': {\n TASK_ATTEMPTS_DIR + 'attempt_201007271720_0001_r_000126_3/stderr':\n TRACEBACK_START + PY_EXCEPTION,\n TASK_ATTEMPTS_DIR + 'attempt_201007271720_0002_m_000004_0/syslog':\n CHILD_ERR_LINE + JAVA_STACK_TRACE,\n }})\n failure = self.runner._find_probable_cause_of_failure([1, 2])\n assert_equal(failure['s3_log_file_uri'],\n BUCKET_URI + TASK_ATTEMPTS_DIR +\n 'attempt_201007271720_0002_m_000004_0/syslog')\n\n def test_later_step_logs_win(self):\n self.add_mock_s3_data({'walrus': {\n LOG_DIR + 'steps/1/syslog':\n HADOOP_ERR_LINE_PREFIX + USEFUL_HADOOP_ERROR + '\\n',\n LOG_DIR + 'steps/2/syslog':\n HADOOP_ERR_LINE_PREFIX + USEFUL_HADOOP_ERROR + '\\n',\n }})\n failure = self.runner._find_probable_cause_of_failure([1, 2])\n assert_equal(failure['s3_log_file_uri'],\n BUCKET_URI + LOG_DIR + 'steps/2/syslog')\n\n def test_reducer_beats_mapper(self):\n # should look at reducers over mappers\n self.add_mock_s3_data({'walrus': {\n TASK_ATTEMPTS_DIR + 'attempt_201007271720_0001_m_000126_3/stderr':\n TRACEBACK_START + PY_EXCEPTION,\n TASK_ATTEMPTS_DIR + 'attempt_201007271720_0001_r_000126_3/syslog':\n CHILD_ERR_LINE + JAVA_STACK_TRACE,\n }})\n failure = self.runner._find_probable_cause_of_failure([1])\n assert_equal(failure['s3_log_file_uri'],\n BUCKET_URI + TASK_ATTEMPTS_DIR +\n 'attempt_201007271720_0001_r_000126_3/syslog')\n\n def test_more_attempts_win(self):\n # look at fourth attempt before looking at first attempt\n self.add_mock_s3_data({'walrus': {\n TASK_ATTEMPTS_DIR + 'attempt_201007271720_0001_m_000126_0/stderr':\n TRACEBACK_START + PY_EXCEPTION,\n TASK_ATTEMPTS_DIR + 'attempt_201007271720_0001_m_000004_3/syslog':\n CHILD_ERR_LINE + JAVA_STACK_TRACE,\n }})\n failure = self.runner._find_probable_cause_of_failure([1])\n assert_equal(failure['s3_log_file_uri'],\n BUCKET_URI + TASK_ATTEMPTS_DIR +\n 'attempt_201007271720_0001_m_000004_3/syslog')\n\n def test_py_exception_beats_java_stack_trace(self):\n self.add_mock_s3_data({'walrus': {\n ATTEMPT_0_DIR + 'stderr': TRACEBACK_START + PY_EXCEPTION,\n ATTEMPT_0_DIR + 'syslog': CHILD_ERR_LINE + JAVA_STACK_TRACE,\n }})\n failure = self.runner._find_probable_cause_of_failure([1])\n assert_equal(failure['s3_log_file_uri'],\n BUCKET_URI + ATTEMPT_0_DIR + 'stderr')\n\n def test_exception_beats_hadoop_error(self):\n self.add_mock_s3_data({'walrus': {\n TASK_ATTEMPTS_DIR + 'attempt_201007271720_0002_m_000126_0/stderr':\n TRACEBACK_START + PY_EXCEPTION,\n LOG_DIR + 'steps/1/syslog':\n HADOOP_ERR_LINE_PREFIX + USEFUL_HADOOP_ERROR + '\\n',\n }})\n failure = self.runner._find_probable_cause_of_failure([1, 2])\n assert_equal(failure['s3_log_file_uri'],\n BUCKET_URI + TASK_ATTEMPTS_DIR +\n 'attempt_201007271720_0002_m_000126_0/stderr')\n\n def test_step_filtering(self):\n # same as previous test, but step 2 is filtered out\n self.add_mock_s3_data({'walrus': {\n TASK_ATTEMPTS_DIR + 'attempt_201007271720_0002_m_000126_0/stderr':\n TRACEBACK_START + PY_EXCEPTION,\n LOG_DIR + 'steps/1/syslog':\n HADOOP_ERR_LINE_PREFIX + USEFUL_HADOOP_ERROR + '\\n',\n }})\n failure = self.runner._find_probable_cause_of_failure([1])\n assert_equal(failure['s3_log_file_uri'],\n BUCKET_URI + LOG_DIR + 'steps/1/syslog')\n\n def test_ignore_errors_from_steps_that_later_succeeded(self):\n # This tests the fix for Issue #31\n self.add_mock_s3_data({'walrus': {\n ATTEMPT_0_DIR + 'stderr':\n GARBAGE + TRACEBACK_START + PY_EXCEPTION + GARBAGE,\n ATTEMPT_0_DIR + 'syslog':\n make_input_uri_line(BUCKET_URI + 'input.gz'),\n ATTEMPT_1_DIR + 'stderr': '',\n ATTEMPT_1_DIR + 'syslog':\n make_input_uri_line(BUCKET_URI + 'input.gz'),\n }})\n assert_equal(self.runner._find_probable_cause_of_failure([1]), None)\n\n\nclass TestEMRandS3Endpoints(MockEMRAndS3TestCase):\n\n def test_no_region(self):\n runner = EMRJobRunner(conf_path=False)\n assert_equal(runner.make_emr_conn().endpoint,\n 'elasticmapreduce.amazonaws.com')\n assert_equal(runner.make_s3_conn().endpoint,\n 's3.amazonaws.com')\n assert_equal(runner._aws_region, '')\n\n def test_none_region(self):\n # blank region should be treated the same as no region\n runner = EMRJobRunner(conf_path=False, aws_region=None)\n assert_equal(runner.make_emr_conn().endpoint,\n 'elasticmapreduce.amazonaws.com')\n assert_equal(runner.make_s3_conn().endpoint,\n 's3.amazonaws.com')\n assert_equal(runner._aws_region, '')\n\n def test_blank_region(self):\n # blank region should be treated the same as no region\n runner = EMRJobRunner(conf_path=False, aws_region='')\n assert_equal(runner.make_emr_conn().endpoint,\n 'elasticmapreduce.amazonaws.com')\n assert_equal(runner.make_s3_conn().endpoint,\n 's3.amazonaws.com')\n assert_equal(runner._aws_region, '')\n\n def test_eu(self):\n runner = EMRJobRunner(conf_path=False, aws_region='EU')\n assert_equal(runner.make_emr_conn().endpoint,\n 'eu-west-1.elasticmapreduce.amazonaws.com')\n assert_equal(runner.make_s3_conn().endpoint,\n 's3-eu-west-1.amazonaws.com')\n\n def test_us_east_1(self):\n runner = EMRJobRunner(conf_path=False, aws_region='us-east-1')\n assert_equal(runner.make_emr_conn().endpoint,\n 'us-east-1.elasticmapreduce.amazonaws.com')\n assert_equal(runner.make_s3_conn().endpoint,\n 's3.amazonaws.com')\n\n def test_us_west_1(self):\n runner = EMRJobRunner(conf_path=False, aws_region='us-west-1')\n assert_equal(runner.make_emr_conn().endpoint,\n 'us-west-1.elasticmapreduce.amazonaws.com')\n assert_equal(runner.make_s3_conn().endpoint,\n 's3-us-west-1.amazonaws.com')\n\n def test_ap_southeast_1(self):\n runner = EMRJobRunner(conf_path=False, aws_region='ap-southeast-1')\n assert_equal(runner.make_s3_conn().endpoint,\n 's3-ap-southeast-1.amazonaws.com')\n assert_raises(Exception, runner.make_emr_conn)\n\n def test_bad_region(self):\n # should fail in the constructor because the constructor connects to S3\n assert_raises(Exception, EMRJobRunner,\n conf_path=False, aws_region='the-moooooooon-1')\n\n def test_case_sensitive(self):\n assert_raises(Exception, EMRJobRunner,\n conf_path=False, aws_region='eu')\n assert_raises(Exception, EMRJobRunner,\n conf_path=False, aws_region='US-WEST-1')\n\n def test_explicit_endpoints(self):\n runner = EMRJobRunner(conf_path=False, aws_region='EU',\n s3_endpoint='s3-proxy', emr_endpoint='emr-proxy')\n assert_equal(runner.make_emr_conn().endpoint, 'emr-proxy')\n assert_equal(runner.make_s3_conn().endpoint, 's3-proxy')\n\n\nclass TestLs(MockEMRAndS3TestCase):\n\n def test_s3_ls(self):\n self.add_mock_s3_data({'walrus': {'one': '', 'two': '', 'three': ''}})\n\n runner = EMRJobRunner(s3_scratch_uri='s3://walrus/tmp',\n conf_path=False)\n\n assert_equal(set(runner._s3_ls('s3://walrus/')),\n set(['s3://walrus/one',\n 's3://walrus/two',\n 's3://walrus/three',]))\n\n assert_equal(set(runner._s3_ls('s3://walrus/t')),\n set(['s3://walrus/two',\n 's3://walrus/three',]))\n\n assert_equal(set(runner._s3_ls('s3://walrus/t/')),\n set([]))\n\n # if we ask for a nonexistent bucket, we should get some sort\n # of exception (in practice, buckets with random names will\n # probably be owned by other people, and we'll get some sort\n # of permissions error)\n assert_raises(Exception, set, runner._s3_ls('s3://lolcat/'))\n\nclass TestNoBoto(TestCase):\n\n @setup\n def blank_out_boto(self):\n self._real_boto = mrjob.emr.boto\n mrjob.emr.boto = None\n self._real_botoemr = mrjob.emr.botoemr\n mrjob.emr.botoemr = None\n\n @teardown\n def restore_boto(self):\n mrjob.emr.boto = self._real_boto\n mrjob.emr.botoemr = self._real_botoemr\n\n def test_init(self):\n # merely creating an EMRJobRunner should raise an exception\n # because it'll need to connect to S3 to set s3_scratch_uri\n assert_raises(ImportError, EMRJobRunner, conf_path=False)\n\n def test_init_with_s3_scratch_uri(self):\n # this also raises an exception because we have to check\n # the bucket location\n assert_raises(ImportError, EMRJobRunner,\n conf_path=False, s3_scratch_uri='s3://foo/tmp')\n\n\nclass TestBootstrapScripts(MockEMRAndS3TestCase):\n\n @setup\n def make_tmp_dir(self):\n self.tmp_dir = tempfile.mkdtemp()\n\n @teardown\n def rm_tmp_dir(self):\n shutil.rmtree(self.tmp_dir)\n\n def test_master_bootstrap_script_is_valid_python(self):\n # create a fake src tarball\n with open(os.path.join(self.tmp_dir, 'foo.py'), 'w'): pass\n yelpy_tar_gz_path = os.path.join(self.tmp_dir, 'yelpy.tar.gz')\n tar_and_gzip(self.tmp_dir, yelpy_tar_gz_path, prefix='yelpy')\n\n # do everything\n runner = EMRJobRunner(conf_path=False,\n bootstrap_cmds=['echo \"Hi!\"', 'true', 'ls'],\n bootstrap_files=['/tmp/quz'],\n bootstrap_mrjob=True,\n bootstrap_python_packages=[yelpy_tar_gz_path],\n bootstrap_scripts=['speedups.sh', '/tmp/s.sh'])\n script_path = os.path.join(self.tmp_dir, 'b.py')\n runner._create_master_bootstrap_script(dest=script_path)\n\n assert os.path.exists(script_path)\n py_compile.compile(script_path)\n\n def test_no_bootstrap_script_if_not_needed(self):\n script_path = os.path.join(self.tmp_dir, 'b.py')\n\n runner = EMRJobRunner(conf_path=False,\n bootstrap_mrjob=False)\n runner._create_master_bootstrap_script(dest=script_path)\n assert not os.path.exists(script_path)\n\n # bootstrap actions don't figure into the master bootstrap script\n runner = EMRJobRunner(conf_path=False,\n bootstrap_mrjob=False,\n bootstrap_actions=['foo', 'bar baz'])\n runner._create_master_bootstrap_script(dest=script_path)\n assert not os.path.exists(script_path)\n\n def test_bootstrap_actions_get_added(self):\n bootstrap_actions = [\n 's3://elasticmapreduce/bootstrap-actions/configure-hadoop -m,mapred.tasktracker.map.tasks.maximum=1',\n 's3://foo/bar#xyzzy', # use alternate name for script\n ]\n\n runner = EMRJobRunner(conf_path=False,\n bootstrap_actions=bootstrap_actions,\n s3_sync_wait_time=0.01)\n\n job_flow_id = runner.make_persistent_job_flow()\n\n emr_conn = runner.make_emr_conn()\n job_flow = emr_conn.describe_jobflow(job_flow_id)\n actions = job_flow.bootstrapactions\n\n assert_equal(len(actions), 3)\n\n assert_equal(\n actions[0].path,\n 's3://elasticmapreduce/bootstrap-actions/configure-hadoop')\n assert_equal(\n actions[0].args,\n ['-m,mapred.tasktracker.map.tasks.maximum=1'])\n assert_equal(actions[0].name, 'configure-hadoop')\n\n assert_equal(actions[1].path, 's3://foo/bar')\n assert_equal(actions[1].args, [])\n assert_equal(actions[1].name, 'xyzzy')\n\n # check for master bootstrap script\n assert actions[2].path.startswith('s3://mrjob-')\n assert actions[2].path.endswith('b.py')\n assert_equal(actions[2].args, [])\n assert_equal(actions[2].name, 'master')\n\n # make sure master bootstrap script is on S3\n assert runner.path_exists(actions[2].path)\n\n def test_local_bootstrap_action(self):\n # make sure that local bootstrap action scripts get uploaded to S3\n action_path = os.path.join(self.tmp_dir, 'apt-install.sh')\n with open(action_path, 'w') as f:\n f.write('for $pkg in $@; do sudo apt-get install $pkg; done\\n')\n\n bootstrap_actions = [\n action_path + ' python-scipy mysql-server']\n\n runner = EMRJobRunner(conf_path=False,\n bootstrap_actions=bootstrap_actions,\n s3_sync_wait_time=0.01)\n\n job_flow_id = runner.make_persistent_job_flow()\n\n emr_conn = runner.make_emr_conn()\n job_flow = emr_conn.describe_jobflow(job_flow_id)\n actions = job_flow.bootstrapactions\n\n assert_equal(len(actions), 2)\n\n assert actions[0].path.startswith('s3://mrjob-')\n assert actions[0].path.endswith('/apt-install.sh')\n assert_equal(actions[0].name, 'apt-install.sh')\n assert_equal(actions[0].args, ['python-scipy', 'mysql-server'])\n\n # check for master boostrap script\n assert actions[1].path.startswith('s3://mrjob-')\n assert actions[1].path.endswith('b.py')\n assert_equal(actions[1].args, [])\n assert_equal(actions[1].name, 'master')\n\n # make sure master bootstrap script is on S3\n assert runner.path_exists(actions[1].path)\n","repo_name":"Jyrsa/mrjob","sub_path":"tests/emr_test.py","file_name":"emr_test.py","file_ext":"py","file_size_in_byte":37958,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"52"} +{"seq_id":"16727965345","text":"import nltk\nimport multiprocessing as mp\nimport pandas as pd\n\nfrom nltk.sentiment.vader import SentimentIntensityAnalyzer\n\nclass SentimentAnalyzer:\n def __init__(self):\n nltk.download('vader_lexicon')\n self.sid = SentimentIntensityAnalyzer()\n\n def analysedf(self,df):\n print(\"Sentiment analyzing df's text column.\")\n dfsent=df\n print(\"len(dfsent)\",len(dfsent))\n\n dfsent['sent_pos']=dfsent['text'].map(lambda x: self.sid.polarity_scores(x)['pos'])\n dfsent['sent_neg']=dfsent['text'].map(lambda x: self.sid.polarity_scores(x)['neg'])\n dfsent['sent_neu']=dfsent['text'].map(lambda x: self.sid.polarity_scores(x)['neu'])\n dfsent['sent_compound']=dfsent['text'].map(lambda x: self.sid.polarity_scores(x)['compound'])\n\n print(\"analysedf finished\")\n return dfsent\n\n def analyse_worker(self,args):\n print(\"Sentiment analyzing df's text column.\")\n df,newcolname,feature=args\n dfsent=df\n print(\"len(dfsent)\",len(dfsent))\n\n dfsent[newcolname]=dfsent['text'].map(lambda x: self.sid.polarity_scores(x)[feature])\n return dfsent\n\n\n def paralellanalyse(self,df):\n print(__name__)\n print(\"analyzing coin\")\n data=([df,'sent_pos','pos'],[df,'sent_neg','neg'],[df,'sent_neu','neu'],[df,'sent_compound','compound'])\n if __name__ == 'twitter.sentiment':\n with mp.Pool() as pool:\n dfsent = pool.map(self.analyse_worker, data)\n\n i=0\n for d in data:\n #print(dfsent[i][d[1]])\n df[d[1]]=dfsent[i][d[1]]\n i+=1\n return df\n\n def merge_tweets_with_retweets(self,coin):\n coin_retweet_sent=coin.retweets.merge(coin.tweets,how='left',left_on='orig_tweet_id',right_on='id')\n #print((coin_retweet_sent[coin_retweet_sent[\"sent_neg\"]-0.2>coin_retweet_sent['sent_pos']]['text']))\n setattr( coin, 'retweets', coin_retweet_sent)\n\n\n def sent_mul_retweet_followers(self,coin):\n coin_retweet_sent=coin.retweets\n f = lambda x, y : x*y\n coin_retweet_sent['posmulrfollower'] = coin_retweet_sent[['sent_pos','retweeter_followers']].apply(lambda x: f(*x), axis=1)\n coin_retweet_sent['negmulrfollower'] = coin_retweet_sent[['sent_neg','retweeter_followers']].apply(lambda x: f(*x), axis=1)\n coin_retweet_sent['neumulrfollower'] = coin_retweet_sent[['sent_neu','retweeter_followers']].apply(lambda x: f(*x), axis=1)\n coin_retweet_sent['compmulrfollower'] = coin_retweet_sent[['sent_compound','retweeter_followers']].apply(lambda x: f(*x), axis=1)\n\n def sent_mul_tweet_followers(self,coin):\n f = lambda x, y : x*y\n coin_tweet=coin.tweets\n coin_tweet['follower_count']=coin_tweet['follower_count'].astype(float)\n #print(coin_tweet.head())\n print(len(coin_tweet))\n if(len(coin_tweet) >0):\n coin_tweet['posmulfollower'] = coin_tweet[['sent_pos','follower_count']].apply(lambda x: f(*x), axis=1)\n coin_tweet['negmulfollower'] = coin_tweet[['sent_neg','follower_count']].apply(lambda x: f(*x), axis=1)\n coin_tweet['neumulfollower'] = coin_tweet[['sent_neu','follower_count']].apply(lambda x: f(*x), axis=1)\n coin_tweet['compmulfollower'] = coin_tweet[['sent_compound','follower_count']].apply(lambda x: f(*x), axis=1)\n else:\n coin_tweet['posmulfollower']=0\n coin_tweet['negmulfollower']=0\n coin_tweet['neumulfollower']=0\n coin_tweet['compmulfollower']=0\n\n\n\n def group_retweet_by_hour(self,coin):\n coin_retweet_sent=coin.retweets\n coin_retweet_sent['datetime']=pd.to_datetime(coin_retweet_sent['retweet_created_at'],format='%Y-%m-%d %X')\n times = pd.DatetimeIndex(coin_retweet_sent.datetime)\n grt=coin_retweet_sent.groupby([times.year, times.month, times.day,times.hour]).retweeter_followers.sum()\n grtdf=pd.DataFrame(grt)\n grtdf['max_datetime']=coin_retweet_sent.groupby([times.year, times.month, times.day,times.hour]).datetime.max()\n grtdf['retweet_count']=coin_retweet_sent.groupby([times.year, times.month, times.day,times.hour]).retweeter_followers.count()\n if(len(coin_retweet_sent) >0):\n grtdf['sum_posmulrfollower']=coin_retweet_sent.groupby([times.year, times.month, times.day,times.hour]).posmulrfollower.sum()\n grtdf['sum_negmulrfollower']=coin_retweet_sent.groupby([times.year, times.month, times.day,times.hour]).negmulrfollower.sum()\n grtdf['sum_neumulrfollower']=coin_retweet_sent.groupby([times.year, times.month, times.day,times.hour]).neumulrfollower.sum()\n grtdf['sum_compmulrfollower']=coin_retweet_sent.groupby([times.year, times.month, times.day,times.hour]).compmulrfollower.sum()\n else:\n grtdf['sum_posmulrfollower']=0\n grtdf['sum_negmulrfollower']=0\n grtdf['sum_neumulrfollower']=0\n grtdf['sum_compmulrfollower']=0\n setattr( coin, 'grtdf', grtdf)\n\n def group_tweet_by_hour(self,coin):\n coin_tweet=coin.tweets\n times = pd.DatetimeIndex(coin_tweet.tstamp)\n gt=coin_tweet.groupby([times.year, times.month, times.day,times.hour]).follower_count.sum()\n gtdf=pd.DataFrame(gt)\n gtdf['max_datetime']=coin_tweet.groupby([times.year, times.month, times.day,times.hour]).tstamp.max()\n gtdf['tweet_count']=coin_tweet.groupby([times.year, times.month, times.day,times.hour]).follower_count.count()\n if(len(coin_tweet) >0):\n gtdf['sum_posmulfollower']=coin_tweet.groupby([times.year, times.month, times.day,times.hour]).posmulfollower.sum()\n gtdf['sum_negmulfollower']=coin_tweet.groupby([times.year, times.month, times.day,times.hour]).negmulfollower.sum()\n gtdf['sum_neumulfollower']=coin_tweet.groupby([times.year, times.month, times.day,times.hour]).neumulfollower.sum()\n gtdf['sum_compmulfollower']=coin_tweet.groupby([times.year, times.month, times.day,times.hour]).compmulfollower.sum()\n else:\n gtdf['sum_posmulfollower']=0\n gtdf['sum_negmulfollower']=0\n gtdf['sum_neumulfollower']=0\n gtdf['sum_compmulfollower']=0\n setattr( coin, 'gtdf', gtdf)\n","repo_name":"mike576/crypto_predictions","sub_path":"twitter/sentiment.py","file_name":"sentiment.py","file_ext":"py","file_size_in_byte":6269,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"16797243522","text":"#!/usr/bin/env python\n\n##########################################################################################\n# Import libraries\n##########################################################################################\nimport time\nimport pigpio\n\n\n##########################################################################################\n# Perform initializations\n##########################################################################################\ni2c_bus_ID = 1\ni2c_address = 0x71\nenabled = True\ndelay = 0.2\n\n# instantiate the local RPi's GPIO\npi = pigpio.pi()\n# humidity and temperature sensor - I2C interface detailed on pg. 18 of si7021 datasheet\nseg7 = pi.i2c_open(i2c_bus_ID, i2c_address)\n\n\n##########################################################################################\n# Main logic\n##########################################################################################\n\n# Set cursor position to leftmost digit (position 0)\npi.i2c_write_device(seg7, [0x79]) # cursor control byte\npi.i2c_write_device(seg7, [0x00]) # data byte specifying pos. 0\n\nwhile True:\n # write \"helo\" if display enabled\n if (enabled):\n try:\n pi.i2c_write_device(seg7, ['H'])\n pi.i2c_write_device(seg7, ['E'])\n pi.i2c_write_device(seg7, ['L'])\n pi.i2c_write_device(seg7, ['O'])\n print(\"Hello\")\n except pigpio.error as e:\n print(\"ERROR: failed to write to seg7 over I2C\")\n\n # else clear the display\n else:\n try:\n pi.i2c_write_device(seg7, [0x76])\n print(\"Goodbye\")\n except pigpio.error as e:\n print(\"ERROR: failed to write to seg7 over I2C\")\n\n # toggle enabled\n enabled = not enabled\n\n # wait until next iteration\n time.sleep(delay)\n\n# house keeping\npi.i2c_close(seg7)\npi.stop()\n","repo_name":"mmwebster/rpi-remote-monitoring-and-control","sub_path":"code/test-scripts/seg7-test.py","file_name":"seg7-test.py","file_ext":"py","file_size_in_byte":1834,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"16512859415","text":"import asyncio\nimport json\nimport os\nimport signal\n\nimport pytest\nfrom mock import patch\n\nfrom i3pyblocks import blocks, core, types\nfrom i3pyblocks.blocks import basic\n\nDEFAULT_STATE = dict(\n separator=None,\n urgent=None,\n align=None,\n markup=None,\n)\n\n\n@pytest.mark.asyncio\nasync def test_runner(capsys, mock_stdin):\n class ValidPollingBlock(blocks.PollingBlock):\n def __init__(self, block_name, sleep=0.1):\n self.count = 0\n super().__init__(\n block_name=block_name,\n sleep=sleep,\n default_state=DEFAULT_STATE,\n )\n\n async def run(self):\n self.count += 1\n self.update(str(self.count))\n\n runner = core.Runner()\n\n instance_1 = ValidPollingBlock(block_name=\"instance_1\")\n instance_2 = basic.TextBlock(\"Hello!\", block_name=\"instance_2\")\n instance_3 = basic.TextBlock(\"Another hello!\", block_name=\"instance_3\")\n\n await runner.register_block(instance_1)\n await runner.register_block(instance_2)\n await runner.register_block(instance_3)\n\n await runner.start(timeout=0.5)\n\n captured = capsys.readouterr()\n\n output_lines = captured.out.split(\"\\n\")\n header = json.loads(output_lines[0])\n\n assert header == {\"version\": 1, \"click_events\": True}\n\n results = json.loads(\"\\n\".join(output_lines[1:]))\n\n for i, result in enumerate(results[:5], start=1):\n assert result == [\n {\n \"name\": \"instance_1\",\n \"instance\": str(instance_1.id),\n \"full_text\": str(i),\n },\n {\n \"name\": \"instance_2\",\n \"instance\": str(instance_2.id),\n \"full_text\": \"Hello!\",\n },\n {\n \"name\": \"instance_3\",\n \"instance\": str(instance_3.id),\n \"full_text\": \"Another hello!\",\n },\n ]\n\n assert results[5] is None\n\n\n@pytest.mark.asyncio\nasync def test_runner_with_fault_block(capsys, mock_stdin):\n class FaultPollingBlock(blocks.PollingBlock):\n def __init__(self, sleep=0.1):\n self.count = 0\n super().__init__(sleep=sleep, default_state=DEFAULT_STATE)\n\n async def run(self):\n self.count += 1\n if self.count > 4:\n raise Exception(\"Boom!\")\n self.update(str(self.count))\n\n runner = core.Runner()\n instance = FaultPollingBlock()\n await runner.register_block(instance)\n\n await runner.start(timeout=0.5)\n\n captured = capsys.readouterr()\n\n output_lines = captured.out.split(\"\\n\")\n\n results = json.loads(\"\\n\".join(output_lines[1:]))\n\n for i, result in enumerate(results[:4], start=1):\n assert result == [\n {\n \"name\": \"FaultPollingBlock\",\n \"instance\": str(instance.id),\n \"full_text\": str(i),\n },\n ]\n\n assert results[4] == [\n {\n \"name\": \"FaultPollingBlock\",\n \"instance\": str(instance.id),\n \"full_text\": \"Exception in FaultPollingBlock: Boom!\",\n \"urgent\": True,\n }\n ]\n\n\n@pytest.mark.asyncio\nasync def test_runner_with_signal_handler(capsys, mock_stdin):\n async def send_signal():\n await asyncio.sleep(0.1)\n os.kill(os.getpid(), signal.SIGUSR1)\n\n async def send_another_signal():\n await asyncio.sleep(0.2)\n os.kill(os.getpid(), signal.SIGUSR2)\n\n class ValidPollingBlockWithSignalHandler(blocks.PollingBlock):\n def __init__(self, sleep=0.1):\n self.count = 0\n super().__init__(sleep=sleep, default_state=DEFAULT_STATE)\n\n async def run(self):\n pass\n\n async def signal_handler(self, sig):\n if sig == signal.SIGUSR1:\n self.update(\"received_signal\")\n elif sig == signal.SIGUSR2:\n self.update(\"received_another_signal\")\n else:\n raise Exception(\"This shouldn't happen\")\n\n runner = core.Runner()\n instance = ValidPollingBlockWithSignalHandler()\n await runner.register_block(instance, signals=[signal.SIGUSR1, signal.SIGUSR2])\n\n runner.register_task(send_signal())\n runner.register_task(send_another_signal())\n\n await runner.start(timeout=0.5)\n\n captured = capsys.readouterr()\n\n output_lines = captured.out.split(\"\\n\")\n\n results = json.loads(\"\\n\".join(output_lines[1:]))\n\n assert results[0] == [\n {\n \"name\": \"ValidPollingBlockWithSignalHandler\",\n \"instance\": str(instance.id),\n \"full_text\": \"received_signal\",\n }\n ]\n\n assert results[1] == [\n {\n \"name\": \"ValidPollingBlockWithSignalHandler\",\n \"instance\": str(instance.id),\n \"full_text\": \"received_another_signal\",\n }\n ]\n\n\n@pytest.mark.asyncio\nasync def test_runner_with_signal_handler_exception(capsys, mock_stdin):\n async def send_signal():\n await asyncio.sleep(0.1)\n os.kill(os.getpid(), signal.SIGUSR1)\n\n class InvalidPollingBlockWithSignalHandler(blocks.PollingBlock):\n def __init__(self, sleep=0.1):\n self.count = 0\n super().__init__(sleep=sleep, default_state=DEFAULT_STATE)\n\n async def run(self):\n pass\n\n async def signal_handler(self, sig):\n raise Exception(\"Boom!\")\n\n runner = core.Runner()\n instance = InvalidPollingBlockWithSignalHandler()\n await runner.register_block(instance, signals=[signal.SIGUSR1])\n\n runner.register_task(send_signal())\n\n await runner.start(timeout=0.5)\n\n result = instance.result()\n\n assert (\n result[\"full_text\"]\n == \"Exception in InvalidPollingBlockWithSignalHandler signal handler: Boom!\"\n )\n\n assert result[\"urgent\"] is True\n\n\n@pytest.mark.asyncio\nasync def test_runner_with_click_event():\n class ValidPollingBlockWithClickHandler(blocks.PollingBlock):\n def __init__(self, sleep=0.1):\n super().__init__(sleep=sleep, default_state=DEFAULT_STATE)\n\n async def run(self):\n pass\n\n async def click_handler(\n self, x, y, button, relative_x, relative_y, width, height, modifiers\n ):\n self.update(\n f\"{x}-{y}-{button}-{relative_x}-{relative_y}-{width}-{height}-{modifiers}\"\n )\n\n runner = core.Runner()\n instance = ValidPollingBlockWithClickHandler()\n await runner.register_block(instance)\n\n click_event = json.dumps(\n {\n \"name\": \"ValidPollingBlockWithClickHandler\",\n \"instance\": str(instance.id),\n \"button\": types.MouseButton.LEFT_BUTTON,\n \"modifiers\": [types.KeyModifier.ALT, types.KeyModifier.SUPER],\n \"x\": 123,\n \"y\": 456,\n \"relative_x\": 12,\n \"relative_y\": 34,\n \"width\": 20,\n \"height\": 40,\n \"extra\": \"should be ignored\",\n }\n ).encode()\n\n await runner.click_event(click_event)\n result = instance.result()\n\n assert result == {\n \"name\": \"ValidPollingBlockWithClickHandler\",\n \"instance\": str(instance.id),\n \"full_text\": \"123-456-1-12-34-20-40-['Mod1', 'Mod4']\",\n }\n\n\n@pytest.mark.asyncio\nasync def test_runner_with_click_event_exception():\n class InvalidPollingBlockWithClickHandler(blocks.PollingBlock):\n def __init__(self, sleep=0.1):\n super().__init__(sleep=sleep, default_state=DEFAULT_STATE)\n\n async def run(self):\n pass\n\n async def click_handler(\n self, x, y, button, relative_x, relative_y, width, height, modifiers\n ):\n raise Exception(\"Boom!\")\n\n runner = core.Runner()\n instance = InvalidPollingBlockWithClickHandler()\n await runner.register_block(instance)\n\n click_event = json.dumps(\n {\"name\": \"InvalidPollingBlockWithClickHandler\", \"instance\": str(instance.id)}\n ).encode()\n\n await runner.click_event(click_event)\n\n result = instance.result()\n\n assert (\n result[\"full_text\"]\n == \"Exception in InvalidPollingBlockWithClickHandler click handler: Boom!\"\n )\n\n assert result[\"urgent\"] is True\n\n\n@pytest.mark.asyncio\nasync def test_runner_with_click_events(capsys):\n class ValidPollingBlockWithClickHandler(blocks.PollingBlock):\n def __init__(self, sleep=0.1):\n super().__init__(sleep=sleep, default_state=DEFAULT_STATE)\n\n async def run(self):\n pass\n\n async def click_handler(\n self, x, y, button, relative_x, relative_y, width, height, modifiers\n ):\n self.update(\n f\"{x}-{y}-{button}-{relative_x}-{relative_y}-{width}-{height}-{modifiers}\"\n )\n\n runner = core.Runner()\n instance = ValidPollingBlockWithClickHandler()\n await runner.register_block(instance)\n\n click_event = json.dumps(\n {\n \"name\": \"ValidPollingBlockWithClickHandler\",\n \"instance\": str(instance.id),\n \"button\": 1,\n \"modifiers\": [\"Mod1\"],\n \"x\": 123,\n \"y\": 456,\n \"relative_x\": 12,\n \"relative_y\": 34,\n \"width\": 20,\n \"height\": 40,\n \"extra\": \"should be ignored\",\n }\n ).encode()\n\n mock_input = [b\"[\\n\", click_event, b\",\"]\n\n with patch(\"i3pyblocks._internal.misc.get_aio_reader\") as get_aio_reader_mock:\n reader_mock = get_aio_reader_mock.return_value\n reader_mock.readline.return_value = mock_input[0]\n reader_mock.readuntil.side_effect = mock_input[1:]\n\n await runner.start(timeout=0.5)\n\n captured = capsys.readouterr()\n\n output_lines = captured.out.split(\"\\n\")\n\n results = json.loads(\"\\n\".join(output_lines[1:]))\n\n assert results[0] == [\n {\n \"name\": \"ValidPollingBlockWithClickHandler\",\n \"instance\": str(instance.id),\n \"full_text\": \"123-456-1-12-34-20-40-['Mod1']\",\n }\n ]\n","repo_name":"thiagokokada/i3pyblocks","sub_path":"tests/test_core.py","file_name":"test_core.py","file_ext":"py","file_size_in_byte":9964,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"52"} +{"seq_id":"14742917598","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time : 2018/8/2 17:49\n# @Author : zwh\n# @Site : \n# @File : baiduhandler.py\n# @Software: PyCharm\n\nimport pyaudio\nimport wave\nimport requests\nimport json\n\nclass useBaiduHandler:\n\n def __init__(self):\n # get the token\n self.token = self.gettoken()\n\n def gettoken(self):\n try:\n apiKey = \"BElGG5nsGL8oevAa3gMzMk4Y\"\n secretKey = \"uVla1FdpQ2HgmojeY9e6pobrS3lRGaeY\"\n\n auth_url = \"https://openapi.baidu.com/oauth/2.0/token?grant_type=client_credentials&client_id=\" + apiKey + \"&client_secret=\" + secretKey\n response = requests.get(url=auth_url)\n jsondata = response.text\n return json.loads(jsondata)['access_token']\n except Exception as e:\n raise Exception(\"Cannot get the token, the reason is {}\".format(e))\n\n def parse(self, wavefile='16k.wav'):\n try:\n fp = wave.open(wavefile, 'rb')\n # 已经录好音的音频片段内容\n nframes = fp.getnframes()\n filelength = nframes * 2\n audiodata = fp.readframes(nframes)\n\n # 百度语音接口的产品ID\n cuid = '7519663'\n server_url = 'http://vop.baidu.com/server_api' + '?cuid={}&token={}'.format(cuid, self.token)\n headers = {\n 'Content-Type': 'audio/pcm; rete=8000',\n 'Content-Length': '{}'.format(filelength),\n }\n\n response = requests.post(url=server_url, headers=headers, data=audiodata)\n print(response.text)\n data = json.loads(response.text)\n if data['err_msg'] == 'success.':\n return data['result']\n else:\n return '你说的啥啊，听不清听不清！'\n except Exception as e:\n raise Exception(\"Parsing wave file failed. The reason is {}\".format(e))\n","repo_name":"zhangwuhui212/MachineLearning","sub_path":"YuyinCenter/handler/baiduhandler.py","file_name":"baiduhandler.py","file_ext":"py","file_size_in_byte":1921,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"10501070048","text":"#Atomic operations are very important when you have shared data\n\nfrom threading import Thread\nimport time\nimport random\nimport queue\n\ncounter = 0\njob_queue = queue.Queue() #things to print out\ncounter_queue = queue.Queue() #amount by which to increment cuonter\n\n\ndef increment_manager():\n global counter\n while True:\n increment = counter_queue.get() #get() has the locking mechanism.\n old_counter = counter\n counter = old_counter + increment\n job_queue.put((f'Counter value is {counter}','----'))\n counter_queue.task_done() #this unlocks the queue\n\nThread(target=increment_manager, daemon=True).start()\n\ndef printer_manager():\n while True:\n for line in job_queue.get():\n print(line)\n job_queue.task_done()\n\nThread(target=printer_manager, daemon=True).start()\n\ndef increment_counter():\n counter_queue.put(1)\n\nworker_threads = [Thread(target=increment_counter) for thread in range(10)]\n\nfor thread in worker_threads:\n thread.start()\n\nfor thread in worker_threads:\n thread.join()\n\ncounter_queue.join()\njob_queue.join()","repo_name":"mayank4519/python_projects","sub_path":"concurrency/non_locked_threads.py","file_name":"non_locked_threads.py","file_ext":"py","file_size_in_byte":1093,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"42778022104","text":"ALPHABET_ENG:str = \"abcdefghijklmnopqrstuvwxyz\"\nALPHABET_RU:str = \"абвгдеёжзийклмнопрстуфхцчшщъыьэюя\" \n\n\ndef language(text:str) -> str:\n if text[0].lower() in ALPHABET_ENG:\n return ALPHABET_ENG\n else:\n return ALPHABET_RU \n\n\ndef register(literal:str, alphabet:str):\n if literal.islower():\n return alphabet\n elif literal.isupper():\n return alphabet.upper()\n else:\n return ''\n\n\ndef ceaser_cipher(text:str, literal_key:str):\n result = []\n dictionary:str = \"\"\n alphabet:str = language(text)\n key:int = 0\n\n if literal_key.isdigit():\n key = int(literal_key)\n else:\n key = alphabet.index(literal_key.lower()) + 1\n\n for i in range(len(text)):\n \n dictionary = register(text[i], alphabet)\n\n if dictionary == \"\":\n result.append(text[i])\n\n if text[i] in dictionary:\n for j in range(len(dictionary)):\n \n if 0 <= j + key < len(dictionary) and text[i] == dictionary[j]:\n result.append(dictionary[j + key])\n elif j + key >= len(dictionary) and text[i] == dictionary[j]:\n residue = len(dictionary) - j\n result.append(dictionary[key - residue])\n\n return ''.join(result)\n\n\ndef ceaser_decrypt(text:str):\n dictionary:str = \"\"\n alphabet:str = language(text)\n \n result:str = \"\"\n for i in range(len(alphabet)):\n string:str = \"\"\n for j in range(len(text)):\n\n dictionary = register(text[j], alphabet)\n\n if dictionary == \"\":\n string += text[j]\n\n index = dictionary.index(text[j])\n string += dictionary[index - i]\n result += string + '\\n'\n\n return result","repo_name":"Bunnyhug01/KMZI","sub_path":"Lab_1-2/components/ceaser_cipher.py","file_name":"ceaser_cipher.py","file_ext":"py","file_size_in_byte":1791,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"27199532874","text":"import cv2 as cv\nimport os.path\nimport numpy as np\nfrom matplotlib import pyplot as plt\nimport random\n\nimg = cv.imread(\"../Qwirkle/qwirkle.jpg\")\n# print(img.shape)\n\nw, h, d = img.shape\n\ndw = int(w/6)\ndh = int(h/6)\n\nprint(\"dw=\", dw, \"dh=\", dh)\n\nfig, axes = plt.subplots(6, 6)\nplt.xticks([])\nplt.yticks([])\n\nfor c in range(6):\n x = c*dw\n for r in range(6):\n y = r*dh\n # print(\"x=\", x, \"y=\", y)\n t = img[x:x+dw,y:y+dh,:]\n # t = cv.resize(t, (50, 50), interpolation=cv.INTER_AREA)\n axes[c, r].imshow(t)\n\nplt.show()\n","repo_name":"anumby-source/RobotServiceJeunesse2023","sub_path":"training/cut_tuiles.py","file_name":"cut_tuiles.py","file_ext":"py","file_size_in_byte":552,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"22085495868","text":"# yazi=\"benim adım sümeyye bazna. Ankara'da yaşıyorum.\".split()\r\n\r\n# print(yazi[0][2]) \r\n\r\nsayilar =[1,2,3,4,5,6]\r\nsonuc= sayilar\r\nsonuc=sayilar [2]\r\n\r\n\r\nisimler = [\"mert\", \"ceyda\", \"kerem\", \"ahmet\"]\r\nsonuc = isimler[0]\r\n\r\nprint(type(sonuc))\r\n\r\nlisteMert = [\"mert\", 18]\r\nlisteCeyda=[\"ceyda\",20]\r\n\r\nsonuc2 = listeMert[1]\r\nprint(sonuc2)\r\n\r\nogrenciler=[[\"mert\",18],[\"ceyda,20\"]]\r\nprint(ogrenciler[0])\r\n\r\n\r\nogrenciler2=[listeCeyda,listeMert]\r\nprint(ogrenciler2)\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"sumeyyebazna/Python_VSCode","sub_path":"lists.py","file_name":"lists.py","file_ext":"py","file_size_in_byte":473,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"40213077593","text":"# Amanda Chen\n# SoftDev1 pd1\n# K25 -- Getting More REST\n# 2019-11-13\n\nfrom flask import Flask, render_template, request, redirect, url_for\nimport json\nfrom urllib.request import urlopen\napp = Flask(__name__)\n\n\n@app.route(\"/\")\ndef root():\n return render_template('root.html')\n\n#Dark Sky API\n#API_KEY: 1975880ad720859dec79321ed72598c5\n#https://api.darksky.net/forecast/[key]/[latitude],[longitude]\n@app.route(\"/weather\")\ndef weather():\n print(app)\n u = urlopen(\"https://api.darksky.net/forecast/1975880ad720859dec79321ed72598c5/40.713051,-74.007233\")\n response = u.read();\n info = json.loads(response);\n #print(info['daily']['data'][0])\n return render_template('weather.html',\n lat = info['latitude'],\n long = info['longitude'],\n timezone = info['timezone'],\n sum = info['daily']['summary'],\n high = info['daily']['data'][0]['temperatureHigh'],\n low = info['daily']['data'][0]['temperatureLow'])\n\n#Rest Countries API\n#No API Key\n#https://restcountries.eu/rest/v2/\n@app.route(\"/country\")\ndef country():\n print(app)\n u = urlopen(\"https://restcountries.eu/rest/v2/name/France\")\n response = u.read();\n info = json.loads(response);\n #print(info['daily']['data'][0])\n return render_template('country.html',\n name = info[0]['name'],\n pop = info[0]['population'],\n cap = info[0]['capital'],\n cur = info[0]['currencies'][0]['name'],\n reg = info[0]['subregion'],\n area = info[0]['area'])\n\n#Unsplash\n#API_KEY: ae7faf91895099838aac4db456686b8c4dfc427579a7f9c88eaed9e6d3f04c59\n#https://api.unsplash.com/photos/random?client_id=ae7faf91895099838aac4db456686b8c4dfc427579a7f9c88eaed9e6d3f04c59\n@app.route(\"/photo\")\ndef photo():\n print(app)\n u = urlopen(\"https://api.unsplash.com/photos/random?client_id=ae7faf91895099838aac4db456686b8c4dfc427579a7f9c88eaed9e6d3f04c59\")\n response = u.read();\n info = json.loads(response);\n #print(info['daily']['data'][0])\n return render_template('photo.html',\n photo = info['urls']['small'],\n date = info['created_at'][:10],\n descrip = info['alt_description'].title(),\n name = info['user']['name'])\n\n\nif __name__ == \"__main__\":\n app.debug = True\n app.run()\n","repo_name":"achen27/SoftDev_Work","sub_path":"fall/25_restrio/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2569,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"40621446500","text":"\n# this program will open a .csv file, parse the attributes, and output an eagle script \n# that can be run in a schematic to update the attributes with the BOM data\n\nimport os, time, sys, re\nimport csv\n\ndefaultAttrs = [\"Part\", \"Value\", \"Device\", \"Package\", \"Description\"]\noutputAttrs = [\"MPN, MF, VOLTAGE, PACKAGE\"]\n\nclass schematicItem:\n def __init__(self, refdes, attrDict):\n self.refdes = refdes\n self.attrDict = attrDict\n\n def output_scr(self):\n # This function will output a string that can be written as a line for an Eagle script\n outstr = ''\n for key in self.attrDict:\n if key not in defaultAttrs: # don't output the default attributes, or attributes with empty strings as values\n if self.attrDict[key]:\n outstr += (\"ATTR \" + self.refdes + \" \" + str(key) + \" \\'\" + self.attrDict[key] + \"\\';\\n\")\n return outstr\n\n def output_scr_mpn(self):\n # This function will output a string that can be written as a line for an Eagle script\n outstr = ''\n for key in self.attrDict:\n if key =='MPN' and self.attrDict[key]:\n outstr += (\"ATTR \" + self.refdes + \" \" + str(key) + \" \\'\" + self.attrDict[key] + \"\\';\\n\")\n return outstr\n\ncurrentpath = os.path.dirname(os.path.abspath(__file__))\n\n# get file name from args, or try to find a bom in the current directory\nfile_path = \"\"\nif len(sys.argv) > 1:\n file_path = sys.argv[1]\nelse:\n for fname in os.listdir(currentpath):\n if fname.endswith(\".csv\") and \"bom\" in fname.lower():\n file_path = fname;\n\nprint(file_path)\n\noutputpath = currentpath + r'\\eaglebomattr.scr'\noutput = open(outputpath, 'w')\nheaders = []\ndatalist = []\ncounter = 0\nitemlist = [] # list of schematic Items\n\ntry: \n firstline = True\n with open(file_path) as csvfile:\n for row in csv.reader(csvfile):\n if len(row) < 5:\n continue # skip line if less than 5 columns\n if firstline:\n headers = row\n firstline = False\n continue\n datadic = {}\n refdes = row[0]\n \n for i in range(1, len(row) - 1):\n datadic[headers[i]] = row[i]\n \n schitm = schematicItem(refdes, datadic)\n itemlist.append(schitm)\n\n print(\"Finished reading file\")\n for item in itemlist:\n data = item.output_scr_mpn()\n output.write(data)\n\n output.write(\"GROUP ALL;\\nCHANGE DISPLAY OFF (>0 0);\")\n\nexcept:\n print(\"Unable to open file\")\n\noutput.close()\n","repo_name":"kbdavid15/eagle_lbr","sub_path":"python/eaglebom_update_attr/eaglebom_update_attr/eaglebom_update_attr.py","file_name":"eaglebom_update_attr.py","file_ext":"py","file_size_in_byte":2592,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"43932002597","text":"import os\nimport struct\n\nclass block_data(object):\n def __init__(self, block_name, export_directory, recording_columns):\n self.recording_columns = recording_columns\n self.block_name = block_name\n self.column_index = 0\n if recording_columns:\n self.column_file = open(os.path.join(export_directory, block_name + '.txt'), 'w')\n\nclass binaryHelper(object):\n \"\"\"Help with getting binary data from files\"\"\"\n\n def __init__(self, export_directory, file_content, file_index):\n self.export_dir = export_directory\n self.block_types = set()\n self.active_blocks = list()\n self.current_block = None\n self.file_content = file_content\n self.file_index = file_index\n self.file_length = len(file_content)\n\n def at_eof(self):\n return self.file_index >= self.file_length\n\n def start_block(self, block_type):\n if block_type in self.block_types:\n recording_columns = False\n else:\n recording_columns = True\n self.block_types.add(block_type)\n self.active_blocks.append(self.current_block)\n self.current_block = block_data(block_type, self.export_dir, recording_columns)\n\n def end_block(self):\n if self.current_block.recording_columns:\n self.current_block.column_file.close()\n self.current_block = self.active_blocks.pop()\n\n def record_column(self, column_label, data_type):\n if self.current_block.recording_columns:\n first_index = int(self.current_block.column_index / 26) - 1\n second_index = int(self.current_block.column_index % 26)\n column_id = chr(second_index + ord('A'))\n if first_index >= 0:\n column_id = chr(first_index + ord('A')) + column_id\n print('{0} {3} - {1} ({2})'.format(self.current_block.column_index, column_label, data_type, column_id), file=self.current_block.column_file)\n self.current_block.column_index += 1\n\n def read_block_header(self):\n block_header = struct.unpack('<4s', self.file_content[self.file_index:self.file_index+4])[0].decode('ASCII')\n self.file_index += 4\n return block_header\n\n def unwind_block_header(self):\n self.file_index -= 4\n\n def read_int16(self, column_label, csv_file):\n self.record_column(column_label, 'int16')\n value_read = struct.unpack('> :\", end=\" \")\n for i in train_Q:\n if TEXT.vocab.itos[i] == \"\":\n break\n print(TEXT.vocab.itos[i], end=\" \")\n\n print(\"\\n<> :\", end=\" \")\n for jj, jx in enumerate(dec_output[real_value_index]):\n if LABEL.vocab.itos[jx] == \"\":\n break\n print(LABEL.vocab.itos[jx], end=\" \")\n\n print(\"\\n<> :\", end=\" \")\n for jj, ix in enumerate(ix):\n if jj == args.max_len:\n break\n if LABEL.vocab.itos[ix] == '':\n break\n print(LABEL.vocab.itos[ix], end=\" \")\n print(\"\\n\")\n","repo_name":"BM-K/Styling-Chatbot-with-Transformer","sub_path":"Chatbot/metric.py","file_name":"metric.py","file_ext":"py","file_size_in_byte":1178,"program_lang":"python","lang":"en","doc_type":"code","stars":33,"dataset":"github-code","pt":"52"} +{"seq_id":"31512009069","text":"'''\n>>사막횡단 개요\n 차를 구매한 원택님. 사막횡단을 시작하다.\n\n 기름을 넣고 출발했는데, 사막에서 차 멈춤\n ->기름통 최대용량 3L\n 사막 여기저기에 주유소가 있음 그리고 마주칠때마다 넣기로함.\n\n 주유소는 요금체계가 단순\n -기름 꽉채우는것만 가능(물론 금액도 고정)\n (단, 주유소 마다 금액은 다를 수 있음)\n\n -> 주유소에 소비하는 돈 최대한 아끼는 경로를 찾는 프로그램.\n'''\nif __name__==\"__main__\":\n ci=int(input())\n cnt_oilzone = int(input())\n cnt_road=int(input())\n for x in range(cnt_oilzone):\n print(cnt_road)\n\n'''\n>>입력\n입력은 여러 개의 테스트 케이스로 구성되어있다. \n첫 줄에는 테스트 케이스의 개수 tc가 주어진다. \n다음 줄부터 각각의 테스트 케이스가 ��도의 줄에 주어진다.\n\n테스트 케이스의 첫 줄에는 사막에 있는 \n주유소의 개수 N (2 ≤ N ≤ 100)이 주어진다. \n두 번째 줄에는 각 주유소에서 \n기름을 넣는데 드는 비용 ci (0 ≤ ci ≤ 100)가 주어진다. \n세 번째 줄에는 서로 다른 주유소를 잇는 도로의 개수 M이 주어진다. \n그 다음부터 한 줄에 하나씩 길로 이어진 두 주유소의 번호가 주어진다. \n두 주유소 사이에는 최대 한 개의 도로만 있다.\n\n참고로 원택님이 현재 있는 주유소의 번호는 1번이고, \nN번 주유소는 사막의 끝이다.\n\n>>출력\n각각의 테스트 케이스에 대해 최소로 필요한 비용을 출력한다. \n만약 사막을 횡단하는 것이 불가능할 경우 -1을 출력한다.\n'''","repo_name":"namkiseung/python_BasicProject","sub_path":"question_python(Unresolved)/backjune_quest/st_codetest/test_14.py","file_name":"test_14.py","file_ext":"py","file_size_in_byte":1683,"program_lang":"python","lang":"ko","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"10487797590","text":"#!/usr/bin/env python\n##############################################\n# Author: Julian Alexander Murillo\n##############################################\n# Lexinerus (GitHub)\n##############################################\n# Application: Nopynews\n# Notify Python News\n# This application show feeds in notifications\n# from your favorite news sources\n##############################################\n# Date: 22-Oct-2011\n##############################################\nimport feedparser\nimport os\nimport sys\nimport settings\nimport config\nimport time\nimport urllib\nfrom subprocess import Popen, PIPE\n\nclass Nopynews:\n\t__source = settings.FEED_SOURCE\n\t\n\t# List of all tweets with specified word on\n\t# settings file.\n\tdef news_list(self):\n\t\tlst_news = []\n\t\t\n\t\tfor news in self.__source:\n\t\t\tfeed = feedparser.parse(news)\n\t\t\tlst_news += feed.entries\n\t\t\n\t\treturn lst_news\n\n\t# Show all news on notifications.\n\tdef show_news(self, news_list):\n\t\tfor entry in news_list:\n\t\t\ttitle = entry.title\n\t\t\tdescription = entry.description\n\t\t\tdescription = description[:config.DESCRIPTION_SIZE]\n\t\t\tdescription += config.SUSPENTION_POINTS\n\t\t\tself.show_news_notification(title, description)\n\t\t\ttime.sleep(settings.SECONDS_BETWEEN_NEWS)\n\n\t# Show news into a notification\n\tdef show_news_notification(self, title, description):\t\n\t\targs = [title, description]\n\t\tcommand = ExternalApp()\n\t\tcommand.executeCommand(config.NOTIFY_APP, args)\n\nclass ExternalApp:\n\tdef executeCommand(self, command, args):\n\t\treturn Popen([command] + args, stdin=PIPE, stdout=PIPE)\n\nif __name__ == '__main__':\n\tnopynews = Nopynews()\n\tlst_news = nopynews.news_list()\n\tnopynews.show_news(lst_news)\n\n","repo_name":"64lines/Nopynews","sub_path":"Nopynews.py","file_name":"Nopynews.py","file_ext":"py","file_size_in_byte":1634,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"9639352754","text":"from pymongo import MongoClient\n\nclass Client(MongoClient):\n\n \"\"\" Extending functionality of the MongoClient to support ORM models. \"\"\"\n\n def find_one(self, model, *args, db=None):\n\n \"\"\" Retrieve the first record in the database that matches the provided args \n \n Arguments:\n model - The Type of the model being requested.\n *args - Arguments to be sent to MongoCollection.find_one()\n db - Override the model's definition about what database to search\n\n Returns:\n If successful, returns an instance of type model populated with the document record.\n else, None\n \"\"\"\n\n ## Retrieve the database name\n db = db if db else model.__db_name__\n\n ## Retrieve the collection name\n collection_name = model.__collection_name__\n\n ## Retrieve the db.collection from the mongo client\n collection = self[db][collection_name]\n\n ## Find the document in the given mongo collection\n record = collection.find_one(*args)\n\n ## On failure, return None\n if not record: return None\n\n ## Create a instance of type model\n ret = model(db=db, **record)\n\n ## Set the connection details in the new instance.\n ret.__set_collection__(self)\n\n return ret\n\n def find(self, model, *args, db=None):\n\n \"\"\" Retrive a list of records from the database that match the provided args.\n\n Arguments:\n model - The type of the model being requested\n *args - Arguments to be sent to MongoCollection.find()\n db - Override the model's defined db location\n\n Returns:\n If successfull, returns a list of instance models populated with the document records.\n else, an empty list.\n \"\"\"\n\n ret = []\n\n ## Retrieve the database name\n db = db if db else model.__db_name__\n\n ## Retrieve the collection name\n collection_name = model.__collection_name__\n\n ## Retrieve the db.collection from the mongo client\n collection = self[db][collection_name]\n\n ## Find the document in the given mongo collection\n records = collection.find(*args)\n\n ## Loop through each result, create an instance and add to return\n for record in records:\n\n ## Create a instance of type model\n instance = model(db=db, **record)\n\n ## Set the connection details in the new instance.\n instance.__set_collection__(self)\n\n ## Add to the return list\n ret.append(instance)\n\n return ret\n\n def add(self, model):\n\n \"\"\" Associate the given model to the mongo database.\n\n This function does not save the data, it only associates the model.\n To save call model.save()\n \"\"\"\n\n ## Set the connection details in the new instance.\n model.__set_collection__(self)\n\n","repo_name":"en0/smorm","sub_path":"client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":2939,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"11716454910","text":"import collections\n\ndef roadsAndLibraries(n, c_lib, c_road, cities):\n if c_lib <= c_road:\n return c_lib * n\n adj = collections.defaultdict(list)\n for start, des in cities:\n adj[start].append(des)\n adj[des].append(start)\n components = 0\n def dfs(city, visited):\n if city in visited:\n return \n visited.add(city)\n for nei in adj.get(city,[]):\n dfs(nei, visited) \n visited = set() \n for i in range(1,n+1):\n if i not in visited:\n components += 1\n dfs(i, visited)\n res = ((n-components) * c_road) + (c_lib * components) \n return res \n","repo_name":"MohamedHamisa/Roads-And-Liabraries","sub_path":"code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":648,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"4689989479","text":"\"\"\"E_Voting URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/2.1/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 . import views\nfrom django.conf.urls import include\nfrom django.conf import settings\nfrom django.conf.urls.static import static\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('',views.HomePage.as_view(),name='home'),\n path('ajax/login/', views.LoginViewAjax.as_view(\n template_name='partial_login_ajax.html'),\n name='login_ajax'),\n path('ajax/logout/', views.logout_view_ajax, name='logout_ajax'),\n path('registration/',include('registration.urls',namespace='registration')),\n path('election/',include('election.urls',namespace='election')),\n path('reporting/',include('reporting.urls',namespace='reporting')),\n path('accounts/login/', views.LoginView.as_view(),\n name='login'),\n path('test/',\n views.TestPage.as_view(),\n name='test'\n ),\n]\n\nif settings.DEBUG:\n urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n","repo_name":"bryan1188/DNHS-EVS","sub_path":"E_Voting/E_Voting/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1615,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"19503042069","text":"import output\n#7-4\ningredient = input(\"Please input the ingredient that you want(input the 'quit' to quit). \")\nwhile ingredient != \"quit\":\n print(\"We will add \"+ingredient+\" to your pizza.\")\n ingredient = input(\"Please input the ingredient that you want(input the 'quit' to quit). \")\n#7-5\nage = int(input(\"How old are you? \"))\nwhile age > 0:\n if age < 3:\n cost = 0\n elif age>=3 and age<12:\n cost = 10\n else :\n cost = 15\n print(\"You need pay \"+str(cost)+\" dollars\")\n age = int(input(\"How old are you? \"))\n#7-6\n\"\"\"Try other ways to break the circulate.\"\"\"\n#7-7\nsomeone = \"You\"\nwhile someone == \"You\":\n output.right()\n\n\n\n\n\n\n\n\n\n","repo_name":"2648226350/Python_learn","sub_path":"pych-seven/7_4to7_7.py","file_name":"7_4to7_7.py","file_ext":"py","file_size_in_byte":669,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"9170127807","text":"#!/usr/bin/python\nusage =\"\"\"\n\nMakes a BED file of the cut sites of a specified restriction enzyme.\n\nExample usage:\n \n # Get BED file of DpnI sites in dm3.fa\n python restriction-finder.py --fasta dm3.fa --enzyme DpnI --bed DpnI-sites.bed\n\n # can pipe to BedTools to get, e.g, sites in genes::\n python restriction-finder.py --fasta myfasta.fa --enzyme DpnI | intersectBed -a stdin -b genes.bed > DpnI-in-genes.bed\n\n\nCreated 13 Aug 2010 by Ryan Dale\"\"\"\ntry:\n from Bio import SeqIO\n from Bio import Restriction\nexcept ImportError:\n sys.stderr.write(\"\\nPlease install BioPython to use this script \\n\")\nimport optparse\nimport sys\nimport os\n\nop = optparse.OptionParser(usage=usage)\nop.add_option('--fasta', help='Required FASTA file containing sequences to search')\nop.add_option('--enzyme', help='Required enzyme name, case sensitive (e.g., DpnI or EcoRI)')\nop.add_option('--bed',help='BED file to create. If not specified, output will print to stdout.')\noptions,args = op.parse_args()\n\n# Input error checking...\ndef err(s):\n op.print_help()\n sys.stderr.write('\\n***ERROR: %s***\\n\\n'%s)\n sys.exit(1)\n# Hack to import just the enzyme you want from the Restriction module\nif options.enzyme is None:\n err('Please specify an enzyme with --enzyme')\nif options.fasta is None:\n err('Please specify a FASTA file with --fasta')\ntry:\n exec('from Bio.Restriction import %s as restr' % options.enzyme)\nexcept ImportError:\n err('No restriction enzyme \"%s\" found!' % options.enzyme)\n\nif not os.path.exists(options.fasta):\n err('FASTA file %s not found'%options.fasta)\n\nif options.bed is None:\n fout = sys.stdout\nelse:\n fout = open(options.bed,'w')\n\n\n# Let BioPython do the work...\nparser = SeqIO.parse(options.fasta,'fasta')\nfor chrom in parser:\n sys.stderr.write(chrom.name+'\\n')\n hits = restr.search(chrom.seq)\n for hit in hits:\n values = [chrom.name,\n str(hit),\n str(hit+1)]\n fout.write('\\t'.join(values)+'\\n')\n fout.flush()\nif options.bed is not None:\n fout.close()\n","repo_name":"daler/rdbio-scripts","sub_path":"sequenceFiles/restriction-finder.py","file_name":"restriction-finder.py","file_ext":"py","file_size_in_byte":2108,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"52"} +{"seq_id":"72644367525","text":"#https://github.com/Asabeneh/30-Days-Of-Python/blob/master/07_Day_Sets/07_sets.md\n\n# sets\nit_companies = {'Facebook', 'Google', 'Microsoft', 'Apple', 'IBM', 'Oracle', 'Amazon'}\nA = {19, 22, 24, 20, 25, 26}\nB = {19, 22, 20, 25, 26, 24, 28, 27}\nage = [22, 19, 24, 25, 26, 24, 25, 24]\n\n\n\"\"\"\nExercises: Level 1\n\n Find the length of the set it_companies\n Add 'Twitter' to it_companies\n Insert multiple IT companies at once to the set it_companies\n Remove one of the companies from the set it_companies\n What is the difference between remove and discard\n\"\"\"\nlength = len(it_companies);\nprint(length);\n\nit_companies.add('Twitter');\nprint(it_companies);\n\nit_companies.update(['Threads', 'X', 'Uber', 'Brother']);\nprint(it_companies);\n\nit_companies.remove('Brother');\nprint(it_companies);\n\n#\"discard()\" will not give you an error if the item does not exist, but remove() will.\n\n\n\n\"\"\"\nExercises: Level 2\n\n Join A and B\n Find A intersection B\n Is A subset of B\n Are A and B disjoint sets\n Join A with B and B with A\n What is the symmetric difference between A and B\n Delete the sets completely\n\"\"\"\n\n\"\"\"\nExercises: Level 3\n\n Convert the ages to a set and compare the length of the list and the set, which one is bigger?\n Explain the difference between the following data types: string, list, tuple and set\n I am a teacher and I love to inspire and teach people. How many unique words have been used in the sentence? Use the split methods and set to get the unique words.\n\n\n\n\"\"\"\n\n\n\n\n\n\n","repo_name":"Brandon-E-Ramirez/30DaysOfPython","sub_path":"7 - Sets/7.py","file_name":"7.py","file_ext":"py","file_size_in_byte":1517,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"16303873395","text":"# --------------------------------------------------------------------------------------------\r\n# Copyright (c) Microsoft Corporation. All rights reserved.\r\n# Licensed under the MIT License. See License.txt in the project root for license information.\r\n# --------------------------------------------------------------------------------------------\r\n\r\nfrom __future__ import print_function\r\nimport re\r\nimport time\r\n\r\ntry:\r\n from urllib.parse import quote, urlparse\r\nexcept ImportError:\r\n from urllib import quote #pylint: disable=no-name-in-module\r\n from urlparse import urlparse #pylint: disable=import-error\r\nfrom vsts_info_provider import VstsInfoProvider\r\nfrom continuous_delivery import ContinuousDelivery\r\nfrom continuous_delivery.models import (AuthorizationInfo, AuthorizationInfoParameters, BuildConfiguration,\r\n CiArtifact, CiConfiguration, ProvisioningConfiguration,\r\n ProvisioningConfigurationSource, ProvisioningConfigurationTarget,\r\n SlotSwapConfiguration, SourceRepository)\r\nfrom vsts_accounts import Account\r\nfrom vsts_accounts.models import (AccountCreateInfoInternal)\r\n\r\n# Use this class to setup or remove continuous delivery mechanisms for Azure web sites using VSTS build and release\r\nclass ContinuousDeliveryManager(object):\r\n def __init__(self, progress_callback):\r\n \"\"\"\r\n Use this class to setup or remove continuous delivery mechanisms for Azure web sites using VSTS build and release\r\n :param progress_callback: method of the form func(count, total, message)\r\n \"\"\"\r\n self._update_progress = progress_callback or self._skip_update_progress\r\n self._azure_info = _AzureInfo()\r\n self._repo_info = _RepositoryInfo()\r\n\r\n def get_vsts_app_id(self):\r\n \"\"\"\r\n Use this method to get the 'resource' value for creating an Azure token to be used by VSTS\r\n :return: App id for VSTS\r\n \"\"\"\r\n return '499b84ac-1321-427f-aa17-267ca6975798'\r\n\r\n def set_azure_web_info(self, resource_group_name, website_name, credentials,\r\n subscription_id, subscription_name, tenant_id, webapp_location):\r\n \"\"\"\r\n Call this method before attempting to setup continuous delivery to setup the azure settings\r\n :param resource_group_name:\r\n :param website_name:\r\n :param credentials:\r\n :param subscription_id:\r\n :param subscription_name:\r\n :param tenant_id:\r\n :param webapp_location:\r\n :return:\r\n \"\"\"\r\n self._azure_info.resource_group_name = resource_group_name\r\n self._azure_info.website_name = website_name\r\n self._azure_info.credentials = credentials\r\n self._azure_info.subscription_id = subscription_id\r\n self._azure_info.subscription_name = subscription_name\r\n self._azure_info.tenant_id = tenant_id\r\n self._azure_info.webapp_location = webapp_location\r\n\r\n def set_repository_info(self, repo_url, branch, git_token):\r\n \"\"\"\r\n Call this method before attempting to setup continuous delivery to setup the source control settings\r\n :param repo_url:\r\n :param branch:\r\n :param git_token:\r\n :return:\r\n \"\"\"\r\n self._repo_info.url = repo_url\r\n self._repo_info.branch = branch\r\n self._repo_info.git_token = git_token\r\n\r\n def remove_continuous_delivery(self):\r\n \"\"\"\r\n To be Implemented\r\n :return:\r\n \"\"\"\r\n # TODO: this would be called by appservice web source-control delete\r\n return\r\n\r\n def setup_continuous_delivery(self, azure_deployment_slot, app_type, vsts_account_name, create_account,\r\n vsts_app_auth_token):\r\n \"\"\"\r\n Use this method to setup Continuous Delivery of an Azure web site from a source control repository.\r\n :param azure_deployment_slot: the slot to use for deployment\r\n :param app_type: the type of app that will be deployed. i.e. AspNetWap, AspNetCore, etc.\r\n :param vsts_account_name:\r\n :param create_account:\r\n :param vsts_app_auth_token:\r\n :return: a message indicating final status and instructions for the user\r\n \"\"\"\r\n\r\n branch = self._repo_info.branch or 'refs/heads/master'\r\n\r\n # Verify inputs before we start generating tokens\r\n source_repository, account_name, team_project_name = self._get_source_repository(self._repo_info.url,\r\n self._repo_info.git_token, branch, self._azure_info.credentials)\r\n self._verify_vsts_parameters(vsts_account_name, source_repository)\r\n vsts_account_name = vsts_account_name or account_name\r\n cd_project_name = team_project_name or self._azure_info.website_name\r\n account_url = 'https://{}.visualstudio.com'.format(quote(vsts_account_name))\r\n portalext_account_url = 'https://{}.portalext.visualstudio.com'.format(quote(vsts_account_name))\r\n\r\n account_created = False\r\n accountClient = Account('3.2-preview', None, self._azure_info.credentials)\r\n if create_account:\r\n # Try to create the account (already existing accounts are fine too)\r\n self._update_progress(0, 100, 'Creating or getting Team Services account information')\r\n properties = {}\r\n #TODO right now it is hard to match a random Azure region to a VSTS region\r\n #properties['Microsoft.VisualStudio.Services.Account.TfsAccountRegion'] = self._azure_info.webapp_location\r\n properties['Microsoft.VisualStudio.Services.Account.SignupEntryPoint'] = 'AzureCli'\r\n account_creation_parameters = AccountCreateInfoInternal(\r\n vsts_account_name, None, vsts_account_name, None, properties)\r\n creation_results = accountClient.create_account(account_creation_parameters, True)\r\n account_created = not creation_results.account_id == None\r\n if account_created:\r\n self._update_progress(5, 100, 'Team Services account created')\r\n else:\r\n # Verify that the account exists\r\n if not accountClient.account_exists(vsts_account_name):\r\n raise RuntimeError(\r\n \"'The Team Services url '{}' does not exist. Check the spelling and try again.\".format(account_url))\r\n\r\n # Create ContinuousDelivery client\r\n cd = ContinuousDelivery('3.2-preview.1', portalext_account_url, self._azure_info.credentials)\r\n\r\n # Construct the config body of the continuous delivery call\r\n build_configuration = self._get_build_configuration(app_type, None)\r\n source = ProvisioningConfigurationSource('codeRepository', source_repository, build_configuration)\r\n auth_info = AuthorizationInfo('Headers', AuthorizationInfoParameters('Bearer ' + vsts_app_auth_token))\r\n slot_name = azure_deployment_slot or 'staging'\r\n slot_swap = None # TODO SlotSwapConfiguration(slot_name)\r\n target = ProvisioningConfigurationTarget('azure', 'windowsAppService', 'production', 'Production',\r\n self._azure_info.subscription_id,\r\n self._azure_info.subscription_name, self._azure_info.tenant_id,\r\n self._azure_info.website_name, self._azure_info.resource_group_name,\r\n self._azure_info.webapp_location, auth_info, slot_swap)\r\n ci_config = CiConfiguration(CiArtifact(name=cd_project_name))\r\n config = ProvisioningConfiguration(None, source, [target], ci_config)\r\n\r\n # Configure the continuous deliver using VSTS as a backend\r\n response = cd.provisioning_configuration(config)\r\n if response.ci_configuration.result.status == 'queued':\r\n final_status = self._wait_for_cd_completion(cd, response)\r\n return self._get_summary(final_status, account_url, vsts_account_name, account_created, self._azure_info.subscription_id,\r\n self._azure_info.resource_group_name, self._azure_info.website_name)\r\n else:\r\n raise RuntimeError('Unknown status returned from provisioning_configuration: ' + response.ci_configuration.result.status)\r\n\r\n def _verify_vsts_parameters(self, cd_account, source_repository):\r\n # if provider is vsts and repo is not vsts then we need the account name\r\n if source_repository.type in ['Github', 'ExternalGit'] and not cd_account:\r\n raise RuntimeError('You must provide a value for cd-account since your repo-url is not a Team Services repository.')\r\n\r\n def _get_build_configuration(self, app_type, working_directory):\r\n build_configuration = None\r\n if app_type == 'AspNetWap':\r\n build_configuration = BuildConfiguration(app_type, working_directory)\r\n elif app_type == 'AspNetCore':\r\n build_configuration = BuildConfiguration(app_type, working_directory)\r\n elif app_type == 'NodeJSWithGulp':\r\n build_configuration = BuildConfiguration('NodeJS', working_directory, 'Gulp')\r\n elif app_type == 'NodeJSWithGrunt':\r\n build_configuration = BuildConfiguration('NodeJS', working_directory, 'Grunt')\r\n else:\r\n raise RuntimeError(\"The app_type '{}' was not understood. Accepted values: AspNetWap, AspNetCore, NodeJSWithGulp, NodeJSWithGrunt.\")\r\n return build_configuration\r\n\r\n def _get_source_repository(self, uri, token, branch, cred):\r\n # Determine the type of repository (TfsGit, github, tfvc, externalGit)\r\n # Find the identifier and set the properties; default to externalGit\r\n type = 'ExternalGit'\r\n identifier = uri\r\n account_name = None\r\n team_project_name = None\r\n auth_info = None\r\n match = re.match(r'[htps]+\\:\\/\\/(.+)\\.visualstudio\\.com.*\\/_git\\/(.+)', uri, re.IGNORECASE)\r\n if match:\r\n type = 'TfsGit'\r\n account_name = match.group(1)\r\n # we have to get the repo id as the identifier\r\n info = self._get_vsts_info(uri, cred)\r\n identifier = info.repository_info.id\r\n team_project_name = info.repository_info.project_info.name\r\n else:\r\n match = re.match(r'[htps]+\\:\\/\\/github\\.com\\/(.+)', uri, re.IGNORECASE)\r\n if match:\r\n type = 'Github'\r\n identifier = match.group(1)\r\n auth_info = AuthorizationInfo('PersonalAccessToken', AuthorizationInfoParameters(None, token))\r\n else:\r\n match = re.match(r'[htps]+\\:\\/\\/(.+)\\.visualstudio\\.com\\/(.+)', uri, re.IGNORECASE)\r\n if match:\r\n type = 'TFVC'\r\n identifier = match.group(2)\r\n account_name = match.group(1)\r\n sourceRepository = SourceRepository(type, identifier, branch, auth_info)\r\n return sourceRepository, account_name, team_project_name\r\n\r\n def _get_vsts_info(self, vsts_repo_url, cred):\r\n vsts_info_client = VstsInfoProvider('3.2-preview', vsts_repo_url, cred)\r\n return vsts_info_client.get_vsts_info()\r\n\r\n def _wait_for_cd_completion(self, cd, response):\r\n # Wait for the configuration to finish and report on the status\r\n step = 5\r\n max = 100\r\n self._update_progress(step, max, 'Setting up Team Services continuous deployment')\r\n config = cd.get_provisioning_configuration(response.id)\r\n while config.ci_configuration.result.status == 'queued' or config.ci_configuration.result.status == 'inProgress':\r\n step += 5 if step + 5 < max else 0\r\n self._update_progress(step, max, 'Setting up Team Services continuous deployment (' + config.ci_configuration.result.status + ')')\r\n time.sleep(2)\r\n config = cd.get_provisioning_configuration(response.id)\r\n if config.ci_configuration.result.status == 'failed':\r\n self._update_progress(max, max, 'Setting up Team Services continuous deployment (FAILED)')\r\n raise RuntimeError(config.ci_configuration.result.status_message)\r\n self._update_progress(max, max, 'Setting up Team Services continuous deployment (SUCCEEDED)')\r\n return config\r\n\r\n def _get_summary(self, provisioning_configuration, account_url, account_name, account_created, subscription_id, resource_group_name, website_name):\r\n summary = '\\n'\r\n if not provisioning_configuration: return None\r\n\r\n # Add the vsts account info\r\n if not account_created:\r\n summary += \"The Team Services account '{}' was updated to handle the continuous delivery.\\n\".format(account_url)\r\n else:\r\n summary += \"The Team Services account '{}' was created to handle the continuous delivery.\\n\".format(account_url)\r\n\r\n # Add the subscription info\r\n website_url = 'https://portal.azure.com/#resource/subscriptions/{}/resourceGroups/{}/providers/Microsoft.Web/sites/{}/vstscd'.format(\r\n quote(subscription_id), quote(resource_group_name), quote(website_name))\r\n summary += 'You can check on the status of the Azure web site deployment here:\\n'\r\n summary += website_url + '\\n'\r\n\r\n # setup the build url and release url\r\n build_url = ''\r\n release_url = ''\r\n if provisioning_configuration.ci_configuration and provisioning_configuration.ci_configuration.project:\r\n project_id = provisioning_configuration.ci_configuration.project.id\r\n if provisioning_configuration.ci_configuration.build_definition:\r\n build_url = '{}/{}/_build?_a=simple-process&definitionId={}'.format(\r\n account_url, quote(project_id), quote(provisioning_configuration.ci_configuration.build_definition.id))\r\n if provisioning_configuration.ci_configuration.release_definition:\r\n release_url = '{}/{}/_apps/hub/ms.vss-releaseManagement-web.hub-explorer?definitionId={}&_a=releases'.format(\r\n account_url, quote(project_id), quote(provisioning_configuration.ci_configuration.release_definition.id))\r\n\r\n return ContinuousDeliveryResult(account_created, account_url, resource_group_name,\r\n subscription_id, website_name, website_url, summary,\r\n build_url, release_url, provisioning_configuration)\r\n\r\n def _skip_update_progress(self, count, total, message):\r\n return\r\n\r\n\r\nclass _AzureInfo(object):\r\n def __init__(self):\r\n self.resource_group_name = None\r\n self.website_name = None\r\n self.credentials = None\r\n self.subscription_id = None\r\n self.subscription_name = None\r\n self.tenant_id = None\r\n self.webapp_location = None\r\n\r\n\r\nclass _RepositoryInfo(object):\r\n def __init__(self):\r\n self.url = None\r\n self.branch = None\r\n self.git_token = None\r\n\r\nclass ContinuousDeliveryResult(object):\r\n def __init__(self, account_created, account_url, resource_group, subscription_id, website_name, cd_url, message, build_url, release_url, final_status):\r\n self.vsts_account_created = account_created\r\n self.vsts_account_url = account_url\r\n self.vsts_build_def_url = build_url\r\n self.vsts_release_def_url = release_url\r\n self.azure_resource_group = resource_group\r\n self.azure_subscription_id = subscription_id\r\n self.azure_website_name = website_name\r\n self.azure_continuous_delivery_url = cd_url\r\n self.status = 'SUCCESS'\r\n self.status_message = message\r\n self.status_details = final_status\r\n","repo_name":"EnjoyLifeFund/macHighSierra-cellars","sub_path":"azure-cli/2.0.18/libexec/lib/python3.6/site-packages/vsts_cd_manager/continuous_delivery_manager.py","file_name":"continuous_delivery_manager.py","file_ext":"py","file_size_in_byte":15827,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"52"} +{"seq_id":"12332117989","text":"##############################################################################################\r\n# Copyright 2018 The Johns Hopkins University Applied Physics Laboratory LLC\r\n# All rights reserved.\r\n# Permission is hereby granted, free of charge, to any person obtaining a copy of this \r\n# software and associated documentation files (the \"Software\"), to deal in the Software \r\n# without restriction, including without limitation the rights to use, copy, modify, \r\n# merge, publish, distribute, sublicense, and/or sell copies of the Software, and to \r\n# permit persons to whom the Software is furnished to do so.\r\n#\r\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, \r\n# INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR \r\n# PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE \r\n# LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, \r\n# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE \r\n# OR OTHER DEALINGS IN THE SOFTWARE.\r\n#\r\n# HAVE A NICE DAY.\r\n\r\n################################################################################\r\n### Object File Boundary Detection in IDA Pro with Local Function Affinity ###\r\n################################################################################\r\n\r\n# LFA Metric\r\n# Local Function Affinity (LFA) is a measurement of the direction a function\r\n# is being \"pulled\" by the functions it calls and the functions that call it.\r\n# By looking at an average of the log of the distance between these functions\r\n# we get a measurement of whether the function is related to functions in the\r\n# positive or negative direction.\r\n\r\n# Edge Detection\r\n# In a standard C/C++ development environment, the project is divided into\r\n# multiple source files, which are compiled to object files, then linked into\r\n# the final binary in order. If external references are eliminated (LFA does\r\n# this imperfectly by just eliminating calls whose distance is above a chosen\r\n# threshold) we would expect to see LFA starting positive, switching to\r\n# negative over the course of a source file, then switching back to positive\r\n# at the beginning of the next file. So object file boundaries \r\n\r\n# What is code anyway?\r\n# Don't get too hung up on \"object file boundaries\" - for LFA (or any other\r\n# attempt to solve the problem) to be perfect, the design and implementation\r\n# of the code would have to be perfect. What LFA is really finding is clusters\r\n# of functionality, that should be more or less related to object files\r\n# but it will often break up large object files into multiple clusters or\r\n# detect 2 or 3 related object files as one file.\r\n\r\nIDA_VERSION = 7\r\nimport basicutils_7x as basicutils\r\n\r\n#External dependencies\r\nimport math\r\n\r\n#CodeCut dependencies\r\nimport cc_base\r\nimport module\r\n\r\n#Threshold above which a function call is considered \"external\"\r\n#For published research - 0x1000 = 4K\r\nMAX_CALL = 0x1000\r\n\r\n#This is a list of the LFA scores for all functions\r\ng_function_list = []\r\n\r\n#This is a list of modules a.k.a. object files after the edge_detect()\r\n#function is executed \r\ng_module_list = []\r\n\r\n\r\n#func_callers_weight(f):\r\n#Return the LFA score for functions that this functions calls (i.e. the \"calls from\" score)\r\n#If there are no references, return 0\r\ndef func_callers_weight(f):\r\n\tfc = 0\r\n\tfs = 0\r\n\tfor xref in basicutils.FuncXrefsFrom(f):\r\n\t\tdist = abs(xref - f)\r\n\t\t#print \"%08x: %08x %d \" % (f, xref, dist),\r\n\t\tif dist > MAX_CALL:\r\n\t\t\tcontinue\r\n\t\tif (dist != 0):\r\n\t\t\tlogdist = math.log(dist)\r\n\t\telse: #recursive function call\r\n\t\t\tlogdist = 0\r\n\t\tif (xref - f < 0):\r\n\t\t\to = -logdist\r\n\t\telse:\r\n\t\t\to = logdist\r\n\t\t\t#print \" %f \" % o,\r\n\t\tfs += o\r\n\t\tfc += 1\r\n\r\n\tif fc == 0:\r\n\t\tscore = 0\r\n\telse:\t\t\r\n\t\tscore = fs / fc\r\n\treturn score\r\n\r\n#func_callee_weight(f):\r\n#Return the LFA score for calls where this function is the \"callee\" (i.e. the \"calls to\" score)\r\n#If there are no references, return 0\r\ndef func_callee_weight(f):\r\n\tfc = 0\r\n\tfs = 0\r\n\ta = 0\r\n\tfor xref in basicutils.CodeRefsTo(f):\r\n\t\r\n\t\tdist = abs(xref - f)\r\n\t\t#print \"%08x: %08x %d \" % (f, xref, dist),\r\n\t\tif dist > MAX_CALL:\r\n\t\t\tcontinue\r\n\t\tif (dist != 0):\r\n\t\t\tlogdist = math.log(dist)\r\n\t\telse: #recursive function call\r\n\t\t\tlogdist = 0\r\n\t\tif (xref - f < 0):\r\n\t\t\to = -logdist\r\n\t\telse:\r\n\t\t\to = logdist\r\n\t\t\t#print \" %f \" % o,\r\n\t\tfs += o\r\n\t\tfc += 1\r\n\r\n\t\t\r\n\tif fc == 0:\r\n\t\tscore = 0\r\n\telse:\t\t\r\n\t\tscore = fs / fc\r\n\treturn score\r\n\t\r\n#func_call_weight(start,end):\r\n#Iterate over each function in the range and calculated the LFA scores\r\n# If both scores are 0, skip the function altogether, exclude it from the list\r\n# If one score is 0, interpolate that score from the previous score\t\r\ndef func_call_weight(f_start, f_end):\r\n\tglobal g_function_list\r\n\t\r\n\tc = 1\r\n\tf = f_start\r\n\tfe = f_end\r\n\t\r\n\tif f==0:\r\n\t\tf = basicutils.NextFunction(0)\r\n\t\tf_end = basicutils.BADADDR\r\n\t\r\n\tprevscore = 0\r\n\tprevscore_1 = 0\r\n\tprevscore_2 = 0\r\n\tz1 = 0\r\n\tz2 = 0\r\n\t\r\n\t#for each function in range\r\n\twhile (f < fe):\r\n\t\t\r\n\t\t#get both LFA scores for the function\r\n\t\tscore_1 = func_callers_weight(f)\r\n\t\tscore_2 = func_callee_weight(f)\r\n\r\n\t\t#if both scores are 0 (i.e. no references for the function or all refs are above the threshold)\r\n\t\t#then skip the function altogether\r\n\t\tif (score_1 == 0) and (score_2 == 0):\r\n\t\t\t#print(\"Skipping 0x%08x\\n\" % f)\r\n\t\t\tprevscore_1 = 0\r\n\t\t\tprevscore_2 = 0\r\n\t\t\tz1 = 1\r\n\t\t\tz2 = 1\r\n\t\t\tfinf = module.func_info(f,0,0)\r\n\t\t\tfinf.lfa_skip=1\r\n\t\t\tg_function_list.append(finf)\r\n\t\t\tf = basicutils.NextFunction(f)\r\n\t\t\tcontinue\r\n\t\t\r\n\t\t#if 1st or 2nd score is zero, interpolate using previous score and an assumed negative linear slope\r\n\t\t#otherwise use the score\r\n\t\tif (score_1 == 0):\r\n\t\t\tscore_1 = prevscore_1 - z1 * .4\r\n\t\t\tz1 += 1\r\n\t\telse:\r\n\t\t\tprevscore_1 = score_1\r\n\t\t\tz1 = 1\r\n\t\tif (score_2 == 0):\r\n\t\t\tscore_2 = prevscore_2 - z2 * .4\r\n\t\t\tz2 += 1\r\n\t\telse:\r\n\t\t\tprevscore_2 = score_2\r\n\t\t\tz2 = 1\r\n\t\t\r\n\t\ttotal_score = score_1 + score_2\r\n\t\t\r\n\t\t#Output scores in log window\r\n\t\t#print(\"0x%08x, %d , %f, %f, %f\" % (f, c,score_1, score_2, total_score))\r\n\t\t\r\n\t\t#Add scores to the global function score list\r\n\t\tfinf = module.func_info(f,score_1,score_2)\r\n\t\tfinf.lfa_skip=0\r\n\t\tg_function_list.append(finf)\r\n\t\t\r\n\t\tline = \"0x%08x, %d , %f, %f, %f\\n\" % (f,c,score_1, score_2, total_score)\r\n\t\tf=basicutils.NextFunction(f)\r\n\t\tc+=1\r\n\t\t\r\n#get_last _three and get_lfa_start:\r\n#Previously LFA would just skip functions if they had no caller or callee score\r\n#it would effectively drop them. This meant that when doing edge detection we\r\n#knew every function in the function list had a score. Now we're putting all\r\n#functions in the function list, and we have a \"skip\" field if LFA should skip it\r\n#for scoring purposes. So these functions help parse that skip field, since for\r\n#edge detection we look at the previous three scores.\r\ndef get_last_three(index):\r\n\tc=0\r\n\ti = index-1\r\n\tp=[]\r\n\twhile ((c<3) and (i>0)):\r\n\t\t#print \"get_last_3: %d,%d\" % (c,i)\r\n\t\tif (g_function_list[i].lfa_skip == 0):\r\n\t\t\tp.append(g_function_list[i])\r\n\t\t\tc+=1\r\n\t\ti-=1\r\n\tif (c==3):\r\n\t\treturn p[0],p[1],p[2]\r\n\telse:\r\n\t\tprint(\"Error: could not find 3 scored entries before index: %d (%d,%d)\" % (index, i, c))\r\n\t\treturn 0,0,0\r\n\r\ndef get_lfa_start():\r\n\tc=0;\r\n\ti=0;\r\n\twhile (c < 4):\r\n\t\t#print \"get_lfa_start: %d,%d\" % (c,i)\r\n\t\tif (g_function_list[i].lfa_skip==0):\r\n\t\t\tc+=1\r\n\t\ti+=1\r\n\treturn i\r\n\t\t\r\n#edge_detect():\r\n# Determine boundaries between object files\r\n# Edge condition is a delta of at least 2 where the current score is positive \r\n# and 2 of the last 3 scores were negative (negative trend) \t\t\r\ndef edge_detect():\r\n\tglobal g_function_list\r\n\tglobal g_module_list\r\n\t\r\n\t#For published research\r\n\tEDGE_THRESHOLD = 2\r\n\t\r\n\tc=get_lfa_start()\r\n\t#do edge detection\r\n\twhile (c 0) and ((s - p_1) > EDGE_THRESHOLD)):\r\n\t\t\t\t#if 2 of last 3 were negative\r\n\t\t\t\tm = sorted([p_1,p_2,p_3])\r\n\t\t\t\tif (m[1] < 0):\r\n\t\t\t\t\tg_function_list[c].edge[0]=1\r\n\t\tc+=1\r\n\t#assign modules based on where the edges are\r\n\tc=0\r\n\tmod_start = g_function_list[0].loc\r\n\twhile(c start:\n unit_amount = (stop1 - start).seconds / 3600\n vals = {'time_stop': round(stop, 14),\n 'unit_amount': unit_amount}\n line.write(vals)\n","repo_name":"avanzosc/hr-addons","sub_path":"hr_timesheet_usability/models/project_task.py","file_name":"project_task.py","file_ext":"py","file_size_in_byte":3074,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"39458067313","text":"import tkinter\nfrom tkinter import *\nimport random\n\nLIVE=0\nDEAD=1\nKEEP=2\n\nCOLOR = ['black', 'white']\n\nBASE_X=BASE_Y=4\n\nclass Area:\n _x=0\n _y=0\n _map_=[[]] \n\n def __init__(self, X, Y):\n self._map_ = [ [DEAD for i in range(X)] for i in range(Y)]\n self._x = X\n self._y = Y\n \n def get_stat(self, _x, _y):\n return self._map_[_x][_y]\n \n def set_stat(self, _x, _y, val):\n self._map_[_x][_y] = val\n \n def set_stats(self, pos_list, val):\n for pos in pos_list:\n self._map_[pos[0]][pos[1]] = val\n\n def init_map(self, init_pos):\n for pos in init_pos:\n self._map_[pos[0]][pos[1]] = LIVE\n\n def init_map_random(self):\n for i in range(1, self._x - 1, 1):\n for j in range(1, self._y - 1, 1):\n self._map_[i][j] = random.randint(LIVE, DEAD)\n\n def show(self):\n i=0\n j=0\n for i in range(X):\n for j in range(Y):\n print( \"%d\"%(self.get_stat(i,j)), end='') \n print(\"\\n\")\n \n def live_check(self, _x, _y):\n cnt = 0\n #do not check current centre point itself\n for i in range(- 1, 2, 1):\n for j in range(-1, 2, 1):\n if not (i == 0 and j == 0):\n if self._map_[i + _x][j + _y] == LIVE:\n cnt += 1\n \n if cnt < 2 and self._map_[_x][_y] == LIVE:\n return DEAD\n elif (cnt == 2 or cnt == 3) and self._map_[_x][_y] == LIVE:\n return KEEP\n elif cnt > 3 and self._map_[_x][_y] == LIVE:\n return DEAD\n elif cnt == 3 and self._map_[_x][_y] == DEAD:\n return LIVE\n else:\n return KEEP\n\ndef init_draw(tk, canvas, label, X, Y):\n print(\"Draw %d:%d map\"%(X, Y))\n label.config(text=str(0))\n\n for i in range(0, X, 1):\n for j in range(0, Y, 1):\n x_lu = i * 5 + BASE_X\n y_lu = j * 5 + BASE_Y\n canvas.create_rectangle(x_lu, y_lu, x_lu + 5, y_lu + 5, fill=COLOR[DEAD])\n\n tk.update() \n\ndef scan_and_draw(tk, canvas, label, _map, step):\n for i in range(_map._x - 1):\n for j in range(_map._y - 1):\n x_lu = i*5 + BASE_X\n y_lu = j*5 + BASE_Y\n fill_color = COLOR[DEAD]\n if _map.get_stat(i, j) == LIVE:\n fill_color = COLOR[LIVE]\n canvas.create_rectangle(x_lu, y_lu, x_lu+5, y_lu+5, fill=fill_color)\n\ndef do_life_switch(tk, canvas, _map, pos_list, life_stat):\n for pos in pos_list:\n fill_color = COLOR[LIVE]\n x_lu = pos[0] * 5 + BASE_X\n y_lu = pos[1] * 5 + BASE_Y \n if(life_stat == DEAD):\n fill_color = COLOR[DEAD]\n canvas.create_rectangle(x_lu, y_lu, x_lu+5, y_lu+5, fill = fill_color)\n\ndef go_life_game(tk, canvas, label, _map, _step):\n die_pos = []\n born_pos = []\n\n for i in range(1, _map._x - 1, 1):\n for j in range(1, _map._y - 1, 1):\n ret = _map.live_check(i, j)\n if ret == DEAD:\n die_pos.append([i, j])\n elif ret == LIVE:\n born_pos.append([i, j])\n else:\n pass\n \n #update map data\n _map.set_stats(die_pos, DEAD)\n _map.set_stats(born_pos, LIVE)\n\n #update map canvas\n do_life_switch(tk, canvas, _map, die_pos, DEAD)\n do_life_switch(tk, canvas, _map, born_pos, LIVE)\n\n label.config(text = str(_step))\n\n tk.update()\n\ndef do_end_draw(tk):\n tk.mainloop()\n \n\n \n\n","repo_name":"CaptainXK/life_game","sub_path":"draw_map.py","file_name":"draw_map.py","file_ext":"py","file_size_in_byte":3520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"32205770826","text":"# -*- coding : utf-8 -*-\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import average_precision_score\nfrom sklearn.metrics import precision_recall_curve\nfrom sklearn.feature_extraction import DictVectorizer\nimport matplotlib.pyplot as plt\nfrom chap_08 import ch08_78\n\"79. 適合率-再現率グラフの描画\"\n\n\ndef main():\n X_text,y = ch08_78.make_x_y_text()\n vec = DictVectorizer(sparse=False)\n X_array = vec.fit_transform(X_text)\n X_train, X_test, y_train, y_test = train_test_split(X_array, y, test_size=0.2)\n lr = LogisticRegression()\n lr.fit(X_train, y_train)\n y_score = lr.decision_function(X_test)\n average_precision = average_precision_score(y_test, y_score)\n precision, recall, _ = precision_recall_curve(y_test, y_score)\n\n # グラフ設定\n plt.step(recall, precision, color='b', alpha=0.2, where='post')\n plt.fill_between(recall, precision, step='post', alpha=0.2, color='b')\n\n plt.xlabel('Recall')\n plt.ylabel('Precision')\n plt.ylim([0.0, 1.05])\n plt.xlim([0.0, 1.0])\n plt.title('2-class Precision-Recall curve: AUC={0:0.2f}'.format(average_precision))\n plt.show()\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"shihono/100-knock","sub_path":"chap_08/ch08_79.py","file_name":"ch08_79.py","file_ext":"py","file_size_in_byte":1254,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"32162835735","text":"#! -*- coding: UTF-8 -*-\n\nimport logging\n\nfrom sqlalchemy.orm.exc import NoResultFound\n\nfrom skee_t.db import DbEngine\nfrom skee_t.db.models import WxAccessToken\nfrom skee_t.services import BaseService\n\n__author__ = 'rensikun'\n\n\nLOG = logging.getLogger(__name__)\n\n\nclass wxAccessTokenService(BaseService):\n \"\"\"\n\n \"\"\"\n\n def __init__(self):\n pass\n\n def add(self, uuid, access_token, expires_in):\n \"\"\"\n 创建用户方法\n :param dict_args:Map类型的参数，封装了由前端传来的用户信息\n :return:\n \"\"\"\n wxAccessToken = WxAccessToken(\n uuid=uuid,\n access_token=access_token,\n expires_in=expires_in\n )\n\n session = None\n rst_code = 0\n rst_desc = 'success'\n try:\n session = DbEngine.get_session_simple()\n session.add(wxAccessToken)\n session.commit()\n except Exception as e:\n LOG.exception(\"Create user information error.\")\n rst_code = '999999'\n rst_desc = e.message\n if session is not None:\n session.rollback()\n finally:\n session.close()\n return {'rst_code':rst_code, 'rst_desc':rst_desc}\n\n def query(self, state):\n \"\"\"\n 创建用户方法\n :param dict_args:Map类型的参数，封装了由前端传来的用户信息\n :return:\n \"\"\"\n session = None\n rst_code = 0\n rst_desc = 'success'\n\n try:\n session = DbEngine.get_session_simple()\n return session.query(WxAccessToken) \\\n .filter(WxAccessToken.state == state)\\\n .order_by(WxAccessToken.entry_time.desc()).first()\n except NoResultFound as e:\n LOG.exception(\"access token error.\")\n return None\n except (TypeError, Exception) as e:\n LOG.exception(\"List SkiResort information error.\")\n # 数据库异常\n rst_code = '999999'\n rst_desc = e.message\n finally:\n session.close()\n return {'rst_code': rst_code, 'rst_desc': rst_desc}\n\n def update(self, uuid, state):\n \"\"\"\n 创建用户方法\n :param dict_args:Map类型的参数，封装了由前端传来的用户信息\n :return:\n \"\"\"\n session = None\n rst_code = 0\n rst_desc = 'success'\n\n try:\n session = DbEngine.get_session_simple()\n session.query(WxAccessToken)\\\n .filter(WxAccessToken.uuid == uuid)\\\n .update({WxAccessToken.state:state}\n ,synchronize_session=False\n )\n session.commit()\n except NoResultFound as e:\n LOG.exception(\"get_user_auth_info error.\")\n return None\n except (TypeError, Exception) as e:\n LOG.exception(\"List SkiResort information error.\")\n # 数据库异常\n rst_code = '999999'\n rst_desc = e.message\n finally:\n session.close()\n return {'rst_code': rst_code, 'rst_desc': rst_desc}","repo_name":"skee-t/backend","sub_path":"skee_t/wx/services/accessToken.py","file_name":"accessToken.py","file_ext":"py","file_size_in_byte":3178,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"27546273876","text":"from typing import Optional\n\nimport tensorlayerx as tlx\n\nfrom gammagl.layers.conv import MessagePassing\nfrom gammagl.utils.get_laplacian import get_laplacian\nfrom gammagl.utils.loop import add_self_loops, remove_self_loops\n\n\nclass ChebConv(MessagePassing):\n r\"\"\"The chebyshev spectral graph convolutional operator from the\n `\"Convolutional Neural Networks on Graphs with Fast Localized Spectral\n Filtering\" `_ paper\n\n .. math::\n \\mathbf{X}^{\\prime} = \\sum_{k=1}^{K} \\mathbf{Z}^{(k)} \\cdot\n \\mathbf{\\Theta}^{(k)}\n\n where :math:`\\mathbf{Z}^{(k)}` is computed recursively by\n\n .. math::\n \\mathbf{Z}^{(1)} &= \\mathbf{X}\n\n \\mathbf{Z}^{(2)} &= \\mathbf{\\hat{L}} \\cdot \\mathbf{X}\n\n \\mathbf{Z}^{(k)} &= 2 \\cdot \\mathbf{\\hat{L}} \\cdot\n \\mathbf{Z}^{(k-1)} - \\mathbf{Z}^{(k-2)}\n\n and :math:`\\mathbf{\\hat{L}}` denotes the scaled and normalized Laplacian\n :math:`\\frac{2\\mathbf{L}}{\\lambda_{\\max}} - \\mathbf{I}`.\n\n Args:\n in_channels (int): Size of each input sample\n out_channels (int): Size of each output sample.\n K (int): Chebyshev filter size :math:`K`.\n normalization (str, optional): The normalization scheme for the graph\n Laplacian (default: :obj:`\"sym\"`):\n\n 1. :obj:`None`: No normalization\n :math:`\\mathbf{L} = \\mathbf{D} - \\mathbf{A}`\n\n 2. :obj:`\"sym\"`: Symmetric normalization\n :math:`\\mathbf{L} = \\mathbf{I} - \\mathbf{D}^{-1/2} \\mathbf{A} \\mathbf{D}^{-1/2}`\n\n 3. :obj:`\"rw\"`: Random-walk normalization\n :math:`\\mathbf{L} = \\mathbf{I} - \\mathbf{D}^{-1} \\mathbf{A}`\n\n You need to pass :obj:`lambda_max` to the :meth:`forward` method of\n this operator in case the normalization is non-symmetric.\n **kwargs (optional): Additional arguments of\n :class:`gammagl.layers.conv.MessagePassing`.\n\n Shapes:\n - **input:**\n node features :math:`(|\\mathcal{V}|, F_{in})`,\n edge indices :math:`(2, |\\mathcal{E}|)`,\n edge weights :math:`(|\\mathcal{E}|)` *(optional)*,\n maximum :obj:`lambda` value :math:`(|\\mathcal{G}|)` *(optional)*\n - **output:** node features :math:`(|\\mathcal{V}|, F_{out})`\n\n \"\"\"\n\n def __init__(self, in_channels: int, out_channels: int, K: int, normalization: Optional = 'sym', **kwargs):\n kwargs.setdefault('aggr', 'add')\n super(ChebConv, self).__init__()\n\n assert K > 0\n assert normalization in [None, 'sym', 'rw'], 'Invalid normalization'\n self.in_channels = in_channels\n self.out_channels = out_channels\n self.normalization = normalization\n self.lins = tlx.nn.ModuleList([\n tlx.layers.Linear(in_features=in_channels, out_features=out_channels) for _ in\n range(K)\n ])\n\n def __normal__(self, edge_index, num_nodes: Optional[int],\n edge_weight, normalization: Optional[str],\n lambda_max, batch=None):\n edge_index, edge_weight = remove_self_loops(edge_index,\n edge_weight)\n edge_index, edge_weight = get_laplacian(edge_index=tlx.convert_to_tensor(edge_index), num_nodes=num_nodes,\n edge_weight=tlx.convert_to_tensor(edge_weight),\n normalization=normalization)\n if batch is not None and lambda_max.numel() > 1:\n lambda_max = lambda_max[batch[edge_index[0]]]\n edge_weight = (2.0 * edge_weight) / lambda_max\n edge_index, edge_weight = add_self_loops(edge_index=tlx.convert_to_tensor(edge_index),\n edge_attr=edge_weight,\n fill_value=-1,\n num_nodes=num_nodes)\n assert edge_weight is not None\n return edge_index, edge_weight\n\n def forward(self, x, edge_index, num_nodes, edge_weight: Optional = None, lambda_max: Optional = None,\n batch: Optional = None):\n if self.normalization != 'sym' and lambda_max is None:\n raise ValueError('You need to pass `lambda_max` to `forward() in`'\n 'case the normalization is non-symmetric.')\n if lambda_max is None:\n lambda_max = tlx.convert_to_tensor(2.0)\n else:\n lambda_max = tlx.convert_to_tensor(lambda_max)\n assert lambda_max is not None\n edge_index, normal = self.__normal__(edge_index, num_nodes,\n edge_weight, self.normalization,\n lambda_max, batch=batch)\n Tx_0 = x\n Tx_1 = x\n out = self.lins[0](Tx_0)\n\n if len(self.lins) > 1:\n Tx_1 = self.propagate(x=x, edge_index=edge_index, edge_weight=normal)\n out = out + self.lins[1](Tx_1)\n\n for lin in self.lins[2:]:\n Tx_2 = self.propagate(x=Tx_1, edge_index=edge_index, edge_weight=normal)\n Tx_2 = 2 * Tx_2 - Tx_0\n out = out + lin.forward(Tx_2)\n Tx_0, Tx_1 = Tx_1, Tx_2\n\n return out\n","repo_name":"BUPT-GAMMA/GammaGL","sub_path":"gammagl/layers/conv/cheb_conv.py","file_name":"cheb_conv.py","file_ext":"py","file_size_in_byte":5367,"program_lang":"python","lang":"en","doc_type":"code","stars":157,"dataset":"github-code","pt":"52"} +{"seq_id":"71332733926","text":"import glob\nimport os\nimport shlex\nimport subprocess\nfrom subprocess import PIPE\n\nfrom receiver.config import dcmtk_config\n\nTAGS_TO_DELETE = [\n \"(0010,0010)\", # Name\n \"(0010,1001)\", # Other Patient Names\n \"(0010,0020)\", # ID\n \"(0010,0030)\", # Birthdate\n \"(0010,1010)\", # Age\n \"(0010,1020)\", # Size\n \"(0010,1030)\", # Weight\n \"(0010,1040)\", # Patient Address\n \"(0008,0080)\", # Institution Name\n \"(0008,0081)\", # Institution Address\n \"(0008,0090)\", # Referring Physician\n \"(0008,0094)\", # Referring Physician's Telephone numbers\n \"(0008,1070)\", # Operator Name\n \"(0010,1000)\", # Other Patient IDs\n \"(0008,0092)\", # Referring Physician's Address\n \"(0038,0010)\", # Admission ID\n]\n\nDELETE_TAGS_CMD = \"\".join([f' -ea \"{tag}\"' for tag in TAGS_TO_DELETE])\n\n\ndef run(cmd):\n completed = subprocess.run(cmd, stderr=subprocess.STDOUT, shell=False)\n return completed.returncode, completed.stdout\n\n\ndef create_dicom_anonymize_cmds(config, image_folder):\n dcmtk = dcmtk_config(config)\n dcmodify_cmd = dcmtk.dcmtk_bin + \"/dcmodify -q -ie -gin -nb \"\n files = glob.glob(os.path.join(image_folder, \"*\"))\n file_cmds = [f\"{dcmodify_cmd} {DELETE_TAGS_CMD} {filename}\" for filename in files]\n return file_cmds\n\n\ndef run_many(config, image_folder):\n cmds = create_dicom_anonymize_cmds(config, image_folder)\n for cmd in cmds:\n # We are not checking for errors because dcmodify complains if some\n # tags are not present\n subprocess.run(shlex.split(cmd), stderr=subprocess.STDOUT, shell=False)\n","repo_name":"pacs-ris-crawler/pacs-ris-crawler","sub_path":"receiver/receiver/executor.py","file_name":"executor.py","file_ext":"py","file_size_in_byte":1578,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"52"} +{"seq_id":"43355048617","text":"import sys\nimport json\nimport datetime\nfrom os import system\n\nfrom _utility.Logger import Logger\nfrom _utility.get_package_dir import get_output_dir\nfrom covid_crawlers._base_classes.OverseasDataSources import OverseasDataSources\nfrom covid_crawlers.oceania.au_data.StateDataSources import StateDataSources\nfrom covid_crawlers.oceania.au_data.InfrequentStateDataJobs import InfrequentStateDataJobs\n\nfrom covid_db.RevisionIDs import RevisionIDs\nfrom covid_db.DerivedData import DerivedData\nfrom covid_db.DataPointsDB import DataPointsDB\nfrom covid_db.delete_old_dbs import delete_old_dbs\nfrom covid_db.SQLiteDataRevisions import SQLiteDataRevisions\nfrom covid_db.output_compressor.output_revision_datapoints_to_zip import output_revision_datapoints_to_zip\n\nfrom data_export.push_to_github import push_to_github\nfrom data_export.output_geojson import output_geojson\nfrom data_export.output_source_info import output_source_info\nfrom data_export.output_csv_data import output_csv_data\n\n\n# Output stdout/stderr to log files\nstdout_logger = sys.stdout = Logger(sys.stdout, ext='stdout')\nstderr_logger = sys.stderr = Logger(sys.stderr, ext='stderr')\n\nOUTPUT_DIR = get_output_dir() / 'output'\nTIME_FORMAT = datetime.datetime.now().strftime('%Y_%m_%d')\nLATEST_REVISION_ID = RevisionIDs.get_latest_revision_id(TIME_FORMAT)\nRUN_INFREQUENT_JOBS = '--run-infrequent-jobs' in [i.strip() for i in sys.argv]\nSOURCE_INFO = []\n\n\ndef run_infrequent_jobs():\n \"\"\"\n Run infrequent tasks which require more resources\n Comment out any of these if they break!\n \"\"\"\n isdj = InfrequentStateDataJobs()\n isdj.update_wa_regions()\n isdj.update_vic_tableau()\n isdj.update_sa_regions()\n isdj.update_act_powerbi()\n return isdj.get_status()\n\n\ndef _rem_dupes(datapoints):\n \"\"\"\n Remove dupes!\n \"\"\"\n add_me = set()\n\n for datapoint in datapoints:\n if datapoint in add_me:\n continue\n add_me.add(datapoint)\n\n return list(add_me)\n\n\ndef output_overseas_data(dpdb: DataPointsDB):\n \"\"\"\n Output from overseas data\n \"\"\"\n ods = OverseasDataSources()\n\n for source_id, source_url, source_desc, datapoints in ods.iter_data_sources():\n SOURCE_INFO.append([source_id, source_url, source_desc])\n dpdb.extend(source_id, _rem_dupes(datapoints), is_derived=False)\n\n return ods.get_status_dict()\n\n\ndef output_state_data(dpdb: DataPointsDB):\n \"\"\"\n Output from state data\n \"\"\"\n sds = StateDataSources()\n\n for source_id, source_url, source_desc, datapoints in sds.iter_data_sources():\n SOURCE_INFO.append([source_id, source_url, source_desc])\n dpdb.extend(source_id, _rem_dupes(datapoints), is_derived=False)\n\n return sds.get_status_dict()\n\n\ndef run_crawlers(status: dict, dpdb: DataPointsDB):\n if RUN_INFREQUENT_JOBS:\n # Run infrequent jobs that need Selenium or other\n # high-processing tasks only a few times a day tops\n status.update(run_infrequent_jobs())\n\n # Output both state and overseas data from crawlers\n if True:\n print(\"Outputting state data...\")\n status.update(output_state_data(dpdb))\n print(\"State data done. Outputting overseas data...\")\n status.update(output_overseas_data(dpdb))\n print(\"Overseas data done. Migrating sources with errors...\")\n else:\n # WARNING!!!! THIS CODE HAS BUGS+DOESN'T OUTPUT THE CASES!!!! ===============================================\n print(\"Outputting state and overseas data...\")\n _output_state_data = lambda: status.update(output_state_data(dpdb))\n _output_overseas_data = lambda: status.update(output_overseas_data(dpdb))\n t1 = threading.Thread(target=_output_state_data, args=())\n t2 = threading.Thread(target=_output_overseas_data, args=())\n\n t1.start()\n t2.start()\n t1.join()\n t2.join()\n print(\"State and overseas data done. Migrating sources with errors...\")\n\n\ndef copy_failed_from_previous_revision(status: dict, dpdb: DataPointsDB):\n \"\"\"\n If any of them failed, copy them across from the previous revision.\n Note the previous revision might have failed too, but should have\n copied the values from the previous revision before that, etc\n (assuming the crawler worked in the past)\n \"\"\"\n migrate_source_ids = []\n for status_key, status_dict in status.items():\n if status_dict['status'] == 'ERROR':\n print(\"ERROR OCCURRED, reverting to previous source ID data:\", status_key)\n migrate_source_ids.append(status_key)\n\n revisions = SQLiteDataRevisions()\n rev_date, rev_subid, dt = revisions.get_revisions()[0]\n prev_revision_path = revisions.get_revision_path(rev_date, rev_subid)\n dpdb.migrate_source_ids(prev_revision_path, migrate_source_ids)\n\n\ndef main():\n status = {}\n\n # Open the new output SQLite database\n sqlite_path = RevisionIDs.get_path_from_id(\n TIME_FORMAT, LATEST_REVISION_ID, 'sqlite'\n )\n dpdb = DataPointsDB(sqlite_path)\n run_crawlers(status, dpdb)\n dpdb.create_indexes()\n copy_failed_from_previous_revision(status, dpdb)\n\n # Derive \"new cases\" from \"total cases\" when\n # they aren't explicitly specified, etc\n DerivedData(dpdb).add_derived()\n\n # Commit and close the DB\n print(\"Derived data outputted OK: committing and closing\")\n dpdb.commit()\n dpdb.close()\n\n # Output basic status info to a .json info\n # This also signifies to the web\n # interface that the import went OK\n print(\"Writing status JSON file\")\n status_json_path = RevisionIDs.get_path_from_id(TIME_FORMAT, LATEST_REVISION_ID, 'json')\n with open(status_json_path, 'w', encoding='utf-8') as f:\n f.write(json.dumps({'status': status}, indent=4))\n\n # Output datapoints to zip\n print(\"Outputting datapoints to zip...\")\n with open(get_output_dir() / 'output' / f'{TIME_FORMAT}-{LATEST_REVISION_ID}.zip', 'wb') as f:\n output_revision_datapoints_to_zip(f, TIME_FORMAT, LATEST_REVISION_ID)\n\n # Upload them to remote AWS instance\n print(\"Uploading zip file to remote server...\")\n system('/usr/bin/env bash /home/david/upload_to_remote.sh %s' % f'{TIME_FORMAT}-{LATEST_REVISION_ID}')\n\n # Clean up old DBs to save on space\n print(\"Deleting older DBs to save space..\")\n delete_old_dbs()\n\n # Update the csv output\n print(\"Outputting CSV files:\")\n output_csv_data(TIME_FORMAT, LATEST_REVISION_ID)\n print('CSV write done')\n\n # Output information about the sources to a markdown table/csv file\n print(\"Outputting source info...\")\n output_source_info(SOURCE_INFO)\n\n # Output GeoJSON\n print(\"Outputting geojson...\")\n output_geojson()\n\n # Commit to GitHub\n print(\"Pushing to GitHub...\")\n push_to_github()\n print(\"Push to GitHub done!\")\n\n print(\"[end of script]\")\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"mcyph/world_subnational_covid_crawler","sub_path":"output_data.py","file_name":"output_data.py","file_ext":"py","file_size_in_byte":6858,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"7380293358","text":"import torch\nfrom torch import nn\nimport torch.nn.functional as F\n\nfrom typing import List\n\n\n\nclass FeatureMatchingLoss(nn.Module):\n def __init__(self, loss_type = 'l1', ):\n super(FeatureMatchingLoss, self).__init__()\n self.loss_type = loss_type\n\n def forward(self, \n real_features: List[List[List[torch.Tensor]]], \n fake_features: List[List[List[torch.Tensor]]]\n ) -> torch.Tensor:\n \"\"\"\n features: a list of features of different inputs (the third layer corresponds to\n features of a separate input to each of these discriminators)\n \"\"\"\n loss = 0\n\n for real_feats_net, fake_feats_net in zip(real_features, fake_features):\n # *_feats_net corresponds to outputs of a separate discriminator\n loss_net = 0\n\n for real_feats_layer, fake_feats_layer in zip(real_feats_net, fake_feats_net):\n assert len(real_feats_layer) == 1 or len(real_feats_layer) == len(fake_feats_layer), 'Wrong number of real inputs'\n if len(real_feats_layer) == 1:\n real_feats_layer = [real_feats_layer[0]] * len(fake_feats_layer)\n\n for real_feats_layer_i, fake_feats_layer_i in zip(real_feats_layer, fake_feats_layer):\n if self.loss_type == 'l1':\n loss_net += F.l1_loss(fake_feats_layer_i, real_feats_layer_i)\n elif self.loss_type == 'l2':\n loss_net += F.mse_loss(fake_feats_layer_i, real_feats_layer_i)\n\n loss_net /= len(fake_feats_layer) # normalize by the number of inputs\n loss_net /= len(fake_feats_net) # normalize by the number of layers\n loss += loss_net\n\n loss /= len(real_features) # normalize by the number of networks\n\n return loss","repo_name":"SamsungLabs/rome","sub_path":"src/losses/feature_matching.py","file_name":"feature_matching.py","file_ext":"py","file_size_in_byte":1848,"program_lang":"python","lang":"en","doc_type":"code","stars":386,"dataset":"github-code","pt":"52"} +{"seq_id":"27546082886","text":"import tensorlayerx as tlx\nimport numpy as np\nfrom itertools import chain\nfrom scipy.sparse.csgraph import shortest_path\nfrom gammagl.utils import k_hop_subgraph, to_scipy_sparse_matrix\nfrom gammagl.data import Graph, InMemoryDataset\nfrom gammagl.transforms import RandomLinkSplit\n\nclass SEALDataset(InMemoryDataset):\n def __init__(self, dataset, env, num_hops, neg_sampling_ratio=1.0, split='train'):\n self.data = dataset[0]\n self.num_hops = num_hops\n self.neg_sampling_ratio = neg_sampling_ratio\n self.env = env\n super().__init__(dataset.root)\n index = ['train', 'val', 'test'].index(split)\n self.data, self.slices = tlx.files.load_npy_to_any(name=self.processed_paths[index])\n\n @property\n def processed_file_names(self):\n return [f'{self.env}_SEAL_train_data.npy', f'{self.env}_SEAL_val_data.npy', f'{self.env}_SEAL_test_data.npy']\n\n def process(self):\n transform = RandomLinkSplit(num_val=0.05, num_test=0.1, neg_sampling_ratio=self.neg_sampling_ratio,\n is_undirected=True, split_labels=True)\n train_data, val_data, test_data = transform(self.data)\n\n self._max_z = 0\n\n # Collect a list of subgraphs for training, validation and testing:\n train_pos_data_list = self.extract_enclosing_subgraphs(\n train_data.edge_index, train_data.pos_edge_label_index, 1.)\n train_neg_data_list = self.extract_enclosing_subgraphs(\n train_data.edge_index, train_data.neg_edge_label_index, 0.)\n\n val_pos_data_list = self.extract_enclosing_subgraphs(\n val_data.edge_index, val_data.pos_edge_label_index, 1.)\n val_neg_data_list = self.extract_enclosing_subgraphs(\n val_data.edge_index, val_data.neg_edge_label_index, 0.)\n\n test_pos_data_list = self.extract_enclosing_subgraphs(\n test_data.edge_index, test_data.pos_edge_label_index, 1.)\n test_neg_data_list = self.extract_enclosing_subgraphs(\n test_data.edge_index, test_data.neg_edge_label_index, 0.)\n\n # Convert node labeling to one-hot features.\n for data in chain(train_pos_data_list, train_neg_data_list,\n val_pos_data_list, val_neg_data_list,\n test_pos_data_list, test_neg_data_list):\n data.x = tlx.reshape(\n tlx.scatter_update(\n tlx.zeros((data.z.shape[0] * (self._max_z + 1),), dtype=tlx.float32),\n data.z + tlx.arange(0, data.z.shape[0], dtype=data.z.dtype) * (self._max_z + 1),\n tlx.ones((data.z.shape[0],), dtype=tlx.float32)\n ),\n (data.z.shape[0], self._max_z + 1)\n )\n\n tlx.files.save_any_to_npy(self.collate(train_pos_data_list + train_neg_data_list),\n self.processed_paths[0])\n tlx.files.save_any_to_npy(self.collate(val_pos_data_list + val_neg_data_list),\n self.processed_paths[1])\n tlx.files.save_any_to_npy(self.collate(test_pos_data_list + test_neg_data_list),\n self.processed_paths[2])\n\n def extract_enclosing_subgraphs(self, edge_index, edge_label_index, y):\n data_list = []\n for src, dst in tlx.convert_to_numpy(edge_label_index).T.tolist():\n sub_nodes, sub_edge_index, mapping, _ = k_hop_subgraph(\n [src, dst], self.num_hops, edge_index, relabel_nodes=True)\n src, dst = mapping.tolist()\n\n # Remove target link from the subgraph.\n mask1 = (sub_edge_index[0] != src) | (sub_edge_index[1] != dst)\n mask2 = (sub_edge_index[0] != dst) | (sub_edge_index[1] != src)\n sub_edge_index = tlx.mask_select(sub_edge_index, mask1 & mask2, axis=1)\n\n # Calculate node labeling.\n z = self.drnl_node_labeling(sub_edge_index, src, dst, num_nodes=sub_nodes.shape[0])\n\n data = Graph(x=tlx.gather(self.data.x, sub_nodes), z=z, edge_index=sub_edge_index, y=y)\n data_list.append(data)\n\n return data_list\n\n def drnl_node_labeling(self, edge_index, src, dst, num_nodes=None):\n # Double-radius node labeling (DRNL).\n src, dst = (dst, src) if src > dst else (src, dst)\n adj = to_scipy_sparse_matrix(edge_index, num_nodes=num_nodes).tocsr()\n\n idx = list(range(src)) + list(range(src + 1, adj.shape[0]))\n adj_wo_src = adj[idx, :][:, idx]\n\n idx = list(range(dst)) + list(range(dst + 1, adj.shape[0]))\n adj_wo_dst = adj[idx, :][:, idx]\n\n dist2src = shortest_path(adj_wo_dst, directed=False, unweighted=True,\n indices=src)\n dist2src = np.insert(dist2src, dst, 0, axis=0)\n\n dist2dst = shortest_path(adj_wo_src, directed=False, unweighted=True,\n indices=dst - 1)\n dist2dst = np.insert(dist2dst, src, 0, axis=0)\n\n dist = dist2src + dist2dst\n dist_over_2, dist_mod_2 = dist // 2, dist % 2\n\n z = 1 + np.minimum(dist2src, dist2dst)\n z += dist_over_2 * (dist_over_2 + dist_mod_2 - 1)\n z[src] = 1.\n z[dst] = 1.\n z = np.nan_to_num(z, nan=0.)\n z = tlx.convert_to_tensor(z, dtype=tlx.int64)\n\n self._max_z = max(tlx.reduce_max(z), self._max_z)\n\n return z","repo_name":"BUPT-GAMMA/GammaGL","sub_path":"examples/seal/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":5326,"program_lang":"python","lang":"en","doc_type":"code","stars":157,"dataset":"github-code","pt":"52"} +{"seq_id":"32758011691","text":"#!/usr/bin/env python\nimport socket, _thread, sys, json, os\nfrom game import Game\n\nserver = os.getenv('SERVER')\nport = int(os.getenv('PORT'))\n\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\ntry:\n s.bind((server, port))\nexcept socket.error as e:\n str(e)\n\ns.settimeout(1.0)\ns.listen(2)\nprint('Server has started, waiting for a connection...')\n\ngames : dict[int, Game] = {}\nid_count = 0\n\ndef threaded_client(conn: socket.socket):\n req = json.loads(conn.recv(4096).decode())\n if req['connection'] == 'create':\n # generate gameid and create game\n side = 1\n game_id = -1\n for i in range(100):\n if i not in games:\n game_id = i\n break\n if game_id == -1:\n conn.send(str.encode(json.dumps({\n 'status': 'server_is_busy'\n })))\n return\n games[game_id] = Game(game_id)\n else:\n game_id = req['game_id']\n if game_id not in games:\n conn.send(str.encode(json.dumps({\n 'status': 'game_does_not_exist'\n })))\n return\n side = -1\n\n conn.send(str.encode(json.dumps({\n 'status': 'success',\n 'side': side,\n 'game_id': game_id,\n })))\n \n while True:\n try:\n req = json.loads(conn.recv(4096).decode())\n if game_id in games:\n game = games[game_id]\n \n game.connected[side] = True\n \n if not all([connected for connected in game.connected.values()]):\n res = {\n 'status': 'wait'\n }\n elif 'method' in req:\n res = game.request(req)\n else:\n res = {\n 'status': 'success'\n }\n\n res = json.dumps(res)\n conn.sendall(str.encode(res))\n else:\n break\n except Exception as e: \n print('Exception in threaded_client()')\n print(e)\n break\n\n print('lost connection')\n try:\n del games[game_id]\n print(f'closing game {game_id}')\n except:\n pass\n conn.close()\n\nif __name__ == '__main__':\n while True:\n conn = None\n try:\n conn, addr = s.accept()\n print(f'Connected to {addr}')\n\n _thread.start_new_thread(threaded_client, (conn,))\n except socket.timeout:\n pass\n except KeyboardInterrupt:\n if conn is not None:\n conn.close()\n break","repo_name":"HuMangoPP/WizardsChess","sub_path":"src/server/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":2643,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"31901749523","text":"# Este programa muestra el uso de la función input para leer datos del teclado\nnombre = input('¿Cómo te llamas?')\nprint(\"El usuario dice que se llama \",nombre, \" tú ¿le crees?\")\nnumero = input(\"¿Cuantos años tienes?: \")\n# input devuelve lo tecleado como una cadena\n# Si queremos interpretarlo como número, debemos convertirlo. La función int\n# intenta convertir a entero el argumento que se le da\nen4años = int(numero) + 4\n# print() espera una cadena. El operador + concatena cadenas\n# str convierte su argumento a cadena\nprint(\"Al terminar tu carrera vas a tener \", en4años, \" años\")","repo_name":"youkaimx/programmingintro","sub_path":"source/entrada_salida01.py","file_name":"entrada_salida01.py","file_ext":"py","file_size_in_byte":596,"program_lang":"python","lang":"es","doc_type":"code","stars":2,"dataset":"github-code","pt":"50"} +{"seq_id":"42132817507","text":"from typing import Awaitable, Iterable\nimport uuid\nimport time\nimport six\nimport base64\nimport hashlib\nimport hmac\nimport aiohttp\nimport xml.etree.ElementTree as ET\nfrom itertools import islice\n\n# Weekday and month names for HTTP date/time formatting; always English!\n_weekdayname = [\"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\", \"Sun\"]\n_monthname = [\n None, # Dummy so we can use 1-based month numbers\n \"Jan\",\n \"Feb\",\n \"Mar\",\n \"Apr\",\n \"May\",\n \"Jun\",\n \"Jul\",\n \"Aug\",\n \"Sep\",\n \"Oct\",\n \"Nov\",\n \"Dec\",\n]\n\na = \"GET\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nx-ms-client-request-id:39d4de1c-1958-11ed-85ae-7a8abb192e12\\nx-ms-date:Thu, 11 Aug 2022 09:30:23 GMT\\nx-ms-version:2021-06-08\\n/test2azure/adfsource/03120ebd-54b8-4f10-bdb4-40a840b7529b\\ncomp:list\\nrestype:directory\"\n\n_default_headers = {\n \"x-ms-version\": \"2021-06-08\",\n \"Accept\": \"application/json\",\n \"User-Agent\": \"azsdk-python-storage-file-share/12.9.0 Python/3.9.13 (macOS-12.4-arm64-arm-64bit)\",\n}\n\n_params = {\"restype\": \"directory\", \"comp\": \"list\"}\n\n\ndef format_date_time(timestamp: float) -> str:\n year, month, day, hh, mm, ss, wd, y, z = time.gmtime(timestamp)\n return \"%s, %02d %3s %4d %02d:%02d:%02d GMT\" % (\n _weekdayname[wd],\n day,\n _monthname[month],\n year,\n hh,\n mm,\n ss,\n )\n\n\ndef encode_base64(data: bytes) -> str:\n if isinstance(data, six.text_type):\n data = data.encode(\"utf-8\")\n encoded = base64.b64encode(data)\n return encoded.decode(\"utf-8\")\n\n\ndef sign_string(key: str, string_to_sign: str) -> str:\n key = base64.b64decode(key.encode(\"utf-8\"))\n string_to_sign = string_to_sign.encode(\"utf-8\")\n signed_hmac_sha256 = hmac.HMAC(key, string_to_sign, hashlib.sha256)\n digest = signed_hmac_sha256.digest()\n encoded_digest = encode_base64(digest)\n return encoded_digest\n\n\ndef gen_auth_header(access_key: str, account_name: str, string_to_sign: str) -> str:\n signature = sign_string(access_key, string_to_sign)\n auth_string = \"SharedKey \" + account_name + \":\" + signature\n return auth_string\n\n\ndef gen_headers(access_key: str, account_name: str, uri: str, params: dict) -> dict:\n request_id = str(uuid.uuid1())\n current_time = format_date_time(time.time())\n\n param_string = \"\"\n\n # append params by alphabet sorted\n for param, value in sorted(params.items()):\n param_string += f\"\\n{param}:{value}\"\n\n string_to_sign = f\"GET\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nx-ms-client-request-id:{request_id}\\nx-ms-date:{current_time}\\nx-ms-version:2021-06-08\\n{uri}{param_string}\"\n auth_string = gen_auth_header(access_key, account_name, string_to_sign)\n\n return {\n \"x-ms-client-request-id\": request_id,\n \"x-ms-date\": current_time,\n \"Authorization\": auth_string,\n }\n\n\ndef parse_response(res: str) -> dict:\n root = ET.fromstring(res)\n\n return {\n \"dirs\": root.findall(\"./Entries/Directory/Name\"),\n \"files\": root.findall(\"./Entries/File/Name\"),\n \"marker\": root[-1].text,\n }\n\n\ndef chunk(it: Iterable, size: int):\n it = iter(it)\n return iter(lambda: tuple(islice(it, size)), ())\n\n\nasync def fetch_list(\n session: aiohttp.ClientSession, params: dict, fileshare_info: dict, directory: str\n) -> Awaitable:\n \"\"\"return the sets of specific directory and it's sub-directory\"\"\"\n file_sets = set()\n\n # root directory and sub-level directory has different conversion of name\n if directory:\n endpoint = f\"/{fileshare_info['account_name']}/{fileshare_info['fileshare_name']}{directory}\"\n uri = f\"/{fileshare_info['fileshare_name']}{directory}\"\n else:\n endpoint = (\n f\"/{fileshare_info['account_name']}/{fileshare_info['fileshare_name']}\"\n )\n uri = f\"/{fileshare_info['fileshare_name']}\"\n\n # generate http headers\n headers = gen_headers(\n fileshare_info[\"access_key\"], fileshare_info[\"account_name\"], endpoint, params\n )\n\n async with session.get(\n uri,\n params=params,\n headers={**headers, **_default_headers},\n ) as resp:\n content = await resp.text()\n data = parse_response(content)\n\n # for root level directory, we want fetch a list of directory for futher process\n if not directory:\n for d in data[\"dirs\"]:\n file_sets.add(f\"/{d.text}\")\n\n for f in data[\"files\"]:\n file_sets.add(f.text)\n\n # for sub level directory, traverse the directory to get a list of fqdn file path\n else:\n for f in data[\"files\"]:\n file_sets.add(f\"{directory}/{f.text}\")\n\n for d in data[\"dirs\"]:\n file_sets |= await fetch_list(\n session,\n params,\n fileshare_info,\n directory + f\"/{d.text}\",\n )\n\n # azure storage rest-api only return max 5000 results, so we loop over all slice by marker flag\n if data[\"marker\"]:\n slice_sets = await fetch_list(\n session, {**params, \"marker\": data[\"marker\"]}, fileshare_info, directory\n )\n file_sets |= slice_sets\n\n return file_sets\n","repo_name":"benchilian-tw/fileshareDiff","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":5186,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"13003644105","text":"# Input the length of array\nn = int(input())\n\n# Input the array as list\na = [int(x) for x in input().split()]\n\n# List to maintain output\nv = []\n\n# As we can only divide by 3, so the number will be arranged in order of highest power of 3 to lowest power of 3. For\n# the same power of 3 the condition shift to 2, so the value only can be multiplied so order will be lowest to highest.\nfor i in a:\n val = i\n count = 0\n\n # Each element of v will contain first a power of 3 in it and its value multiplied by -1.\n while val % 3 == 0:\n count += 1\n val //= 3\n v.append([count, -1 * i])\n\n# When sorting lowest power of 3 will be in order lowest to highest and value for elements with same power in order \n# most negative to least negative. \n\nv.sort()\nv.reverse()\nfor i in v:\n # Multiply by -1 to get the original number.\n print(-1 * i[1], end=\" \")","repo_name":"TLE-MEC/Hack-CP-DSA","sub_path":"Codeforces/Divide by three, multiply by two/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":873,"program_lang":"python","lang":"en","doc_type":"code","stars":180,"dataset":"github-code","pt":"50"} +{"seq_id":"12297494888","text":"import json\nimport sys\n\nfrom pyxact import serialize_json\nfrom pyxact import loggingdb\nimport example_schema, utils\n\nrecord_json = json.dumps(example_schema.test_transaction1.transaction,\n cls=serialize_json.PyxactEncoder,\n indent=4)\nrecordlist_json = json.dumps(example_schema.test_transaction1.journal_list,\n cls=serialize_json.PyxactEncoder,\n indent=4)\ntransaction_json = json.dumps(example_schema.test_transaction1,\n cls=serialize_json.PyxactEncoder,\n indent=4)\n\n# PyxactEncoder is a customised JSON encoder class that knows how to serialise SQLRecord,\n# SQLRecordList and SQLTransaction types into JSON.\n\ncustom_decoder = serialize_json.PyxactDecoder()\ncustom_decoder.register_sqlschema(example_schema.accounting)\ncustom_decoder.register_sqlrecordlist(example_schema.JournalList)\ncustom_decoder.register_sqltransaction(example_schema.AccountingTransaction)\n\n# PyxactDecoder can turn the output of PyxactEncoder back into Python objects, provided the relevant\n# classes have been registered with it.\n\nif __name__ == '__main__':\n\n conn = utils.process_command_line('Demonstrate usage of pyxact JSON serialisation')\n\n cursor = conn.cursor()\n example_schema.create_example_schema(cursor)\n example_schema.populate_example_schema(cursor)\n","repo_name":"jhumphry/pyxact","sub_path":"doc/examples/example_json.py","file_name":"example_json.py","file_ext":"py","file_size_in_byte":1411,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"17836258692","text":"def computepay(hrs, rate):\n if hrs > 40:\n othr = int(hrs) - 40\n reghours = hrs - othr\n pay = float(reghours) * f_rate\n otrate = float(rate) * 1.5\n otpay = float(othr) * float(otrate)\n return pay + otpay\n\nhrs = input(\"Enter Hours:\")\nrate = input(\"Enter Rate:\")\nf_hrs = float(hrs)\nf_rate = float(rate)\np = computepay(f_hrs, f_rate)\nprint(\"Pay\", p)","repo_name":"WitherspoonD/PythonForEveryone","sub_path":"PFE_ex_04_06.py","file_name":"PFE_ex_04_06.py","file_ext":"py","file_size_in_byte":390,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"31997701819","text":"#here we loop over all single mutations of the Wuhan variant and use the pre-trained GeoPPI to get the predicted change in binding energy\nimport pandas as pd\nimport os\nimport subprocess\nimport time\nfrom tqdm import tqdm\nimport csv \n\ndef get_muts(variant):\n \"\"\"This function returns the binding affiity of a given variant given its name\"\"\"\n variant_scores_path = \"../SARS-CoV-2-RBD_DMS_Omicron/results/final_variant_scores/final_variant_scores.csv\"\n\n variants_names = {\n \"Wuhan-Hu-1_v2\": \"Wuhan-Hu-1_v2\",\n \"N501Y\": \"Alpha\", \n \"Beta\": \"Beta\",\n \"Delta\": \"Delta\",\n \"E484K\": \"Eta\",\n \"Omicron_BA1\":\"Omicron_BA1\",\n \"Omicron_BA2\":\"Omicron_BA2\"\n }\n # Expression and Binding scores per variant background -- for the heatmap plots\n scores_df = (\n pd.read_csv(variant_scores_path)\n .rename(columns = {\"position\":\"site\",\n \"delta_expr\":\"RBD expression\", # rename for the selection menus \n \"delta_bind\":\"ACE2 binding\" # rename for the selection menus \n })\n .replace({\"target\":variants_names})\n )\n # Expression and Binding scores per variant background -- for the heatmap plots\n \n muts = scores_df.loc[scores_df[\"target\"] == variant][\"mutation\"]\n epression = scores_df.loc[scores_df[\"target\"] == variant][\"RBD expression\"]\n\n return muts, epression\n\n\ndef foldX_one_var(variant,start):\n\n muts, epression = get_muts(variant) \n\n row = [\"mut_name\", str(5), str(5)]\n with open('./stability_predictions_' + variant + '.csv', 'a') as f:\n writer = csv.writer(f)\n writer.writerow(row)\n\n for mut_name , value in tqdm(zip(muts,epression)):\n # some of these are not single sutants?... looks like tis a mutant of a mutant..\n # mutationinfo = mut_name[:1] + \"E\" + mut_name[1:] # add and E into the mutation name for GeoPPI to be compatable.\n try:\n # edited_mut_name = mut_name[1:-1] + '-' +mut_name[-1] #just for stipped part of the spike protein as the naming system is different. \n # print(edited_mut_name)\n # ignore nanss and loop over both with zip.. \n # pdbfile = \"spike_\" + edited_mut_name + \".pdb\"\n # pdb_dir = '/mnt/ncshare/ozkilim/charge_pca_deepmut/stripped_RBD_classical'\n \n pdb_dir = '../Wuhan_RBDs/structures/' + variant #must be callable for all files... this is the sticking point.\n pdbfile = start + mut_name +'.pdb' #uses start path infor as its different currently for each path.\n # save to some log file and then re-read it and delte it?\n comm = './foldx --command=Stability --pdb={} --pdb-dir={} --output-dir=./stability_output'.format(pdbfile,pdb_dir)\n os.system(comm) \n output_file = \"./stability_output/\" + start + mut_name + \"_0_ST.fxout\"\n file = pd.read_csv(output_file,header=None,sep=\"\\t\") #upack saved reasults from the API and repack for our own data anlysis.\n output = file.values.tolist()[0][1] \n\n row = [mut_name, str(value), str(output)]\n with open('./stability_predictions_' + variant + '.csv', 'a') as f:\n writer = csv.writer(f)\n writer.writerow(row)\n \n except:\n pass \n \n# automate all reasults for all varients for figure production. \ndef main():\n '''Repete FoldX predictionsof stability for each varient.'''\n varient_names = [\"Wuhan-Hu-1_v2\",\"Alpha\",\"Beta\",\"Delta\",\"Eta\",'Omicron_BA1',\"Omicron_BA2\"]\n start_path = [\"RBD_331_531_\",\"rot-Alpha_RBD_331_531_\",\"rot-Beta_RBD_331_531_\",\"rot-Delta_RBD_331_531_\",\"Eta_RBD_331_531_\",\"Omicron_RBD_331_531_\",\"rot-OmicronBA2_RBD_331_531_\"] \n for variant,start in zip(varient_names,start_path):\n foldX_one_var(variant,start) \n\nif __name__ == \"__main__\":\n main()\n \n","repo_name":"csabaiBio/RBD-AlphaFold2-structures-and-phenotypic-information","sub_path":"foldX_stability/foldx_expression_prediction.py","file_name":"foldx_expression_prediction.py","file_ext":"py","file_size_in_byte":3883,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"1128566587","text":"##a=int(input(\"enter the number\"))\n##b=0\n##for i in str(a):\n## c=a%10\n## b=(b*10)+c\n## a=a//10\n##print(b) \n##\n##for a in range(6):\n## for b in range(0,6):\n## print((a+b)%2, end=\" \")\n## print()\n##\n## \ncount=1\nfor i in range(1,6):\n for b in range(0,6):\n if(count==1):\n print(count,end=\"\")\n count-=1\n else:\n print(count,end=\"\")\n count+=1\n print() \n","repo_name":"aslam5176/volly.day","sub_path":"reverse for.py","file_name":"reverse for.py","file_ext":"py","file_size_in_byte":452,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"11627160341","text":"from brt.jit.codegen.storage import KernelStorager\nimport json\nfrom typing import Dict, List\nfrom collections import OrderedDict\n\nkernel_storager = KernelStorager()\ncursor = kernel_storager.cursor\n\nSELECT_ALL_CMD = r\"\"\"\nSELECT Key, Identifier, OpType \nFROM KernelCache\"\"\"\nSELECT_KEY_CMD = r\"\"\"\nSELECT Identifier\nFROM KernelCache\nWHERE Key = ?\"\"\"\nUPDATE_CMD = r\"\"\"\nUPDATE KernelCache\nSET Identifier = ?\nWHERE Key = ?\n\"\"\"\nqrs = cursor.execute(SELECT_ALL_CMD).fetchall()\nfor key, id, optype in qrs:\n id: Dict = json.loads(id)\n\n # Delete parameters.padding_mode\n popret = id['parameters'].pop('padding_mode', None)\n if popret is not None:\n print(id['parameters'])\n new_id = json.dumps(id)\n cursor.execute(UPDATE_CMD, (new_id, key))\n cursor.connection.commit()\n \n # Update output size of ConvTranspose2d\n # {\n # \"output_infos\": {\n # \"output_0\": [34, 3, 32, 32]\n # },\n # ...\n # }\n # if optype == 'ConvTranspose2dBias':\n # outinfo: List = id['output_infos']['output_0']\n # if outinfo is None:\n # id['output_infos']['output_0'] = [id['input_infos']['input_0'][0], 3, 128, 128]\n # else:\n # outinfo[:2].extend([128, 128])\n # id['output_infos']['output_0'] = outinfo\n # print(f\"{id['output_infos']['output_0']}\")\n\n \n # Sort id\n id['input_infos'] = OrderedDict(sorted(id['input_infos'].items()))\n id['output_infos'] = OrderedDict(sorted(id['output_infos'].items()))\n id['parameters'] = OrderedDict(sorted(id['parameters'].items()))\n id = OrderedDict(sorted(id.items()))\n\n\n id = json.dumps(id)\n # print(id)\n cursor.execute(UPDATE_CMD, (id, key))\n cursor.connection.commit()\n \n\n","repo_name":"Raphael-Hao/brainstorm","sub_path":"scripts/kernel_db_update.py","file_name":"kernel_db_update.py","file_ext":"py","file_size_in_byte":1757,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"50"} +{"seq_id":"23009994873","text":"__author__ = 'anastasiiakorosteleva'\n\next = input()\nindecies = set(text)\nvalues = (text.count(letter) for letter in indecies)\nanalize = dict(zip(indecies, values))\nb = analize.keys()\nb = list(b)\nb.sort()\nfor key in b:\n print(key +' '+ str(analize[key]))","repo_name":"AnastasiiaKorosteleva/Python","sub_path":"homeworks/Fall'15/homework1/task4.py","file_name":"task4.py","file_ext":"py","file_size_in_byte":256,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"71786712154","text":"def solution(book_time):\n answer = 0\n n = 24 * 60 + 10\n dp = [0] * n\n \n for t in book_time:\n start, end = t\n s_h, s_m = map(int, start.split(\":\"))\n e_h, e_m = map(int, end.split(\":\"))\n dp[s_h*60 + s_m] += 1\n dp[e_h*60 + e_m + 10] -= 1\n for i in range(n-1):\n dp[i+1] += dp[i]\n \n return max(dp)\n\n# start부분은 +1, end부분은 -1로 두어서 나중에 한번에 누적합을 구한다.\n# 그 중에 최댓값을 리턴","repo_name":"JH-TT/Coding_Practice","sub_path":"Programmers/Prefix_Sum_P/155651.py","file_name":"155651.py","file_ext":"py","file_size_in_byte":486,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"70720840475","text":"# https://www.delftstack.com/it/howto/python/python-get-hostname/\n\nimport socket\nimport uuid\nimport requests\n\n\n# Restituisce il nome host del sistema sotto il quale Python è attualmente in esecuzione\nhostIP = socket.gethostbyname(socket.gethostname())\nprint(\"Nome host sistema:\", hostIP)\n\n\n# Restituisce l'indirizzo IP privato\nst = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\ntry:\n st.connect(('255.255.255.0', 1)) #10.255.255.255\n IP = st.getsockname()[0]\nexcept Exception:\n IP = '127.0.0.1'\nfinally:\n st.close()\nprint(\"Indirizzo IP privato:\", IP)\n\n\n# Restituisce l'indirizzo IP pubblico\npublicIp = requests.get('https://checkip.amazonaws.com').text.strip()\nprint(\"Indirizzo IP pubblico:\", publicIp)\n\n\n# Restituisce l'indirizzo MAC del dispositivo\nmacAddress = hex(uuid.getnode())\nprint(\"Indirizzo MAC:\", macAddress)","repo_name":"IGOR10S/Codes","sub_path":"Python/IP/IP_Address.py","file_name":"IP_Address.py","file_ext":"py","file_size_in_byte":835,"program_lang":"python","lang":"it","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"31150424533","text":"from django.shortcuts import render\nfrom rest_framework.response import Response\nfrom IGL_account.models import User\nfrom .serializer import IconSerializer, IconTeamSerializer, IconTeamMemberSerializer\nfrom rest_framework import generics\nfrom .models import Icon, IconTeam, IconTeamMember\n\n\n# Create your views here.\n\nclass IconAPI(generics.GenericAPIView):\n serializer_class = IconSerializer\n\n def post(self, request):\n try:\n serializer = IconSerializer(data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response({\"data\": serializer.data})\n except:\n return Response({\"message\": \"Unable to create icon\"})\n\n def get(self, request, pk=None):\n user_id = pk\n try:\n if user_id is not None:\n user = Icon.objects.get(id=user_id)\n serializer = IconSerializer(user)\n return Response({\"data\": serializer.data})\n user = Icon.objects.all()\n serializer = IconSerializer(user, many=True)\n return Response({\"data\": serializer.data})\n except:\n return Response({\"message\": \"Unable to get the Icon details\"})\n\n\nclass IconTeamAPI(generics.GenericAPIView):\n serializer_class = IconTeamSerializer\n\n def post(self, request):\n try:\n serializer = IconTeamSerializer(data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response({\"data\": serializer.data})\n except:\n return Response({\"message\": \"Unable to create the Icon team details\"})\n\n def get(self, request, pk=None):\n try:\n user = IconTeam.objects.all()\n serializer = IconTeamSerializer(user, many=True)\n return Response({\"data\": serializer.data})\n except:\n return Response({\"message\": \"Unable to find Icon team details\"})\n\n\nclass IconTeamMemberAPI(generics.GenericAPIView):\n serializer_class = IconTeamMemberSerializer\n\n def post(self, request):\n try:\n serializer = IconTeamMemberSerializer(data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response({\"data\": serializer.data})\n except:\n return Response({\"message\": \"Unable to update the Team member registration\"})\n\n def get(self, request, pk=None):\n user_id = pk\n try:\n if user_id is not None:\n user = IconTeamMember.objects.get(id=user_id)\n serializer = IconTeamMemberSerializer(user)\n return Response({\"data\": serializer.data})\n user = IconTeamMember.objects.all()\n serializer = IconTeamMemberSerializer(user, many=True)\n return Response({\"data\": serializer.data})\n except:\n return Response({\"message\": \"Unable to find team member details\"})\n","repo_name":"pythondjangoproj/test-proj","sub_path":"iconApp-main/icon/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2950,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"35509400063","text":"import json\nimport requests\nimport pprint\n\nif __name__=='__main__':\n url='http://ws.audioscrobbler.com/2.0/?method=geo.getTopArtists&api_key=4beab33cc6d65b05800d51f5e83bde1b&country=Spain&format=json'\n data = requests.get(url).text\n data = json.loads(data)\n data = data['topartists']\n data = data['artist']\n topArtist = data[0]\n artist=topArtist['name']\n print(artist)\n #for x,y in topArtist.iteritems():\n # print(\"{0},{1}\".format(x,y))","repo_name":"kariato/DataSciIntro","sub_path":"lesson2-2.py","file_name":"lesson2-2.py","file_ext":"py","file_size_in_byte":469,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"5168260600","text":"# 进程是资源单位\n# 每个进程至少有一个线程\nfrom threading import Thread\n\ndef func(name):\n for i in range(1000):\n print(name,i)\n# differentiate between when the script is being run as the \n# main program and when it is being imported as a module.\nif __name__=='__main__':\n# 给线程安排任务\n# 给传参必须是元组\n t1=Thread(target=func,args=('asd',))\n t1.start()\n t2=Thread(target=func,args=('fgh',))\n# 让进程可以运行了,什么时候执行由cpu决定\n t2.start()\n # for i in range(1000):\n # print('main',i) \n # second method\nclass Mythread(Thread):\n def run(self):\n for i in range(1000):\n print('zixiancheng',i)\nif __name__=='__main__':\n# 给线程安排任务\n t=Mythread()\n# 注意run就是单线程了，一定是start\n t.start()\n for i in range(1000):\n print('main',i) \n\n","repo_name":"tenacioustommy/Crawler","sub_path":"multithread/multithread.py","file_name":"multithread.py","file_ext":"py","file_size_in_byte":893,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"26087349568","text":"import math\nimport torch\nimport sys\nsys.path.append(\"..\")\nimport d2lzh_pytorch as d2l\n\nfeatures, labels = d2l.get_data_ch7()\n\n# 从零开始实现\ndef init_adagrad_states():\n s_w = torch.zeros((features.shape[1], 1), dtype=torch.float32)\n s_b = torch.zeros(1, dtype=torch.float32)\n return (s_w, s_b)\n\ndef adagrad(params, states, hyperparams):\n eps = 1e-6\n for p, s in zip(params, states):\n s.data += (p.grad.data**2)\n p.data -= hyperparams['lr'] * p.grad.data / torch.sqrt(s + eps)\n\nd2l.train_ch7(adagrad, init_adagrad_states(), {'lr': 0.1}, features, labels)\n\n# PyTorch实现\nd2l.train_pytorch_ch7(torch.optim.Adagrad, {'lr': 0.1}, features, labels)\n","repo_name":"HK404/DeepLearning","sub_path":"05_Optimizer/25_Adagrad.py","file_name":"25_Adagrad.py","file_ext":"py","file_size_in_byte":680,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"16484568743","text":"import traceback\nimport numpy as np\nimport scipy\nimport xarray as xr\nimport argparse\nimport pandas as pd\nfrom pathlib import Path\nimport tool_fig_config\n\nfrom multiprocessing import Pool\nimport multiprocessing\nimport os.path\nimport os\n\nfrom WRFDiag import wrf_load_helper\nwrf_load_helper.engine = \"netcdf4\"\n\n\nimport MITgcmDiff.loadFunctions as lf\nimport MITgcmDiff.mixed_layer_tools as mlt\nimport MITgcmDiff.calBudget as cb\nimport MITgcmDiag.data_loading_helper as dlh\n\nM_H2O = 0.018\nM_DRY = 0.02896\n\nc_p = 1004\ng0 = 9.8\nrho_water = 1e3\nL_vap = 2.25e6\n\ndef intVert(q, p_w):\n dm = ( p_w[:-1, : , :] - p_w[1:, :, : ]) / g0\n\n print(q.shape)\n print(p_w.shape)\n\n return np.sum(c_p * dm * q, axis=0)\n\n\n\ndef saturation_vapor_pressure(T):\n T_degC = T - 273.15\n return 6.112 * np.exp(17.67 * T_degC / (T_degC + 243.5)) * 100.0\n\ndef getMixingRatio(T, p, RH):\n p_wv = saturation_vapor_pressure(T) * RH\n return p_wv / (p - p_wv) * M_H2O / M_DRY\n\nvec_getMixingRatio = np.vectorize(getMixingRatio)\n\ndef produceDiagQuantities(ds):\n \n\n MU = ds.MU + ds.MUB\n \n H_DIAB_TTL = - (MU * ds.H_DIABATIC * ds.DNW / g0 * c_p).sum(dim=\"bottom_top\").rename(\"H_DIAB_TTL\")\n \n PREC_ACC_DT = ds.attrs['PREC_ACC_DT'] * 60\n print(\"PREC_ACC_DT = \", PREC_ACC_DT)\n\n H_DIAB_RAIN = ( (ds.PREC_ACC_C + ds.PREC_ACC_NC) / 1e3 / PREC_ACC_DT * rho_water * L_vap ).rename(\"H_DIAB_RAIN\")\n\n SHFLX = ds.HFX.rename(\"SHFLX\")\n LHFLX = ds.LH.rename(\"LHFLX\")\n SWFLX = (ds.SWUPB - ds.SWDNB).rename(\"SWFLX\")\n LWFLX = (ds.LWUPB - ds.LWDNB).rename(\"LWFLX\")\n \n TTL_HFLX = SHFLX + LHFLX + SWFLX + LWFLX\n TTL_HFLX = TTL_HFLX.rename(\"TTL_HFLX\")\n \n SLHFLX = (ds.HFX + ds.LH).rename(\"SLHFLX\")\n\n AOTDIFF = ds.TSK - ds.T2\n AOTDIFF = AOTDIFF.rename(\"AOTDIFF\")\n\n U10 = ds.U10\n V10 = ds.V10\n\n WIND10 = (U10**2 + V10**2)**0.5\n WIND10 = WIND10.rename(\"WIND10\")\n\n\n SST = ds.TSK.to_numpy()\n PSFC = ds.PSFC.to_numpy()\n Q2 = ds.Q2.to_numpy()\n qv_sat_tmp = vec_getMixingRatio(SST, PSFC, 1.0)\n \n AOQVDIFF = ds.PSFC.copy()\n AOQVDIFF[:, :] = qv_sat_tmp - Q2\n AOQVDIFF = AOQVDIFF.rename(\"AOQVDIFF\")\n\n new_ds = xr.merge([\n H_DIAB_TTL, H_DIAB_RAIN, SLHFLX, SHFLX, LHFLX, SWFLX, LWFLX, TTL_HFLX, WIND10, AOTDIFF, AOQVDIFF,\n ])\n\n\n return new_ds\n\n\n\n\n\ndef produceDiagQuantities_ocn(data):\n\n Usfc = data[\"UVEL\"][0:5, :, :].mean(axis=0)\n Vsfc = data[\"VVEL\"][0:5, :, :].mean(axis=0)\n SSC = np.sqrt(Usfc**2 + Vsfc**2)\n\n data[\"SSC\"] = SSC\n data[\"Usfc\"] = Usfc\n data[\"Vsfc\"] = Vsfc\n\n return data\n\n\n\n\n\nparser = argparse.ArgumentParser(\n prog = 'plot_skill',\n description = 'Plot prediction skill of GFS on AR.',\n)\n\nparser.add_argument('--date-rng', type=str, nargs=2, help='Date range.', required=True)\nparser.add_argument('--skip-hrs', type=int, help='The skip in hours to do the next diag.', required=True)\nparser.add_argument('--avg-hrs', type=int, help='The length of time to do the average in hours.', default=np.nan)\n#parser.add_argument('--data-freq-hrs', type=int, help='The data frequency in hours.', required=True)\nparser.add_argument('--sim-names', type=str, nargs='*', help='Simulation names', default=[])\n\nparser.add_argument('--mitgcm-beg-date', type=str, help='The datetime of iteration zero in mitgcm.', required=True)\nparser.add_argument('--mitgcm-deltaT', type=float, help='The timestep (sec) of mitgcm (deltaT).', required=True)\nparser.add_argument('--mitgcm-dumpfreq', type=float, help='The timestep (sec) of mitgcm dump frequency.', required=True)\nparser.add_argument('--mitgcm-grid-dir', type=str, help='Grid directory of MITgcm.', default=\"\")\nparser.add_argument('--pressure-factor', type=float, help='Pressure factor', default=1.0)\n\n\nparser.add_argument('--input-dirs', type=str, nargs='+', help='Input dirs.', required=True)\nparser.add_argument('--output-dir', type=str, help='Output dir', default=\"output_figure\")\nparser.add_argument('--nproc', type=int, help='Number of processors.', default=1)\nparser.add_argument('--lat-rng', type=float, nargs=2, help='Latitudes in degree', default=[20, 52])\nparser.add_argument('--lon-rng', type=float, nargs=2, help='Longitudes in degree', default=[360-180, 360-144])\nparser.add_argument('--deg-lat-per-inch', type=float, help='Degree latitude per plot-inch.', default=10.0)\nparser.add_argument('--deg-lon-per-inch', type=float, help='Degree longitude per plot-inch', default=10.0)\nparser.add_argument('--pvalue-threshold', type=float, help='P value threshold.', default=0.10)\nparser.add_argument('--varnames', type=str, nargs='+', help='Plotted variable names', default=['TTL_HFLX',])\nparser.add_argument('--overwrite', action=\"store_true\")\nparser.add_argument('--no-display', action=\"store_true\")\nparser.add_argument('--is-ensemble', action=\"store_true\")\nparser.add_argument('--ensemble-members', type=int, help=\"Ensemble members. Assume equal sampling members.\", default=-1)\n\nargs = parser.parse_args()\nprint(args)\n\nif args.is_ensemble and args.ensemble_members == -1:\n raise Exception(\"The option `--is-ensemble` is set but `--ensemble-members` is not given.\")\n\nif np.isnan(args.avg_hrs):\n print(\"--avg-hrs is not set. Set it to --skip-hrs = %d\" % (args.skip_hrs,))\n args.avg_hrs = args.skip_hrs\n\nskip_hrs = pd.Timedelta(hours=args.skip_hrs)\navg_hrs = pd.Timedelta(hours=args.avg_hrs)\ndts = pd.date_range(args.date_rng[0], args.date_rng[1], freq=skip_hrs, inclusive=\"left\")\n\nargs.lon = np.array(args.lon_rng) % 360.0\n\nlat_n, lat_s = np.amax(args.lat_rng), np.amin(args.lat_rng)\nlon_w, lon_e = np.amin(args.lon_rng), np.amax(args.lon_rng)\n\n\nif len(args.sim_names) == 0:\n args.sim_names = args.input_dirs\nelif len(args.sim_names) != len(args.input_dirs):\n raise Exception(\"--sim-names is provided but the number of input does not match the --input-dirs\")\n\n# Plotting setup\nheat_flux_setting = dict( \n ctl = dict(\n contourf_cmap = \"bwr\",\n contourf_levs = np.linspace(-400, 400, 41),\n contourf_ticks = np.linspace(-400, 400, 9),\n ),\n\n diff = dict(\n contourf_cmap = \"bwr\",\n contourf_levs = np.linspace(-20, 20, 21),\n contourf_ticks = np.linspace(-20, 20, 9),\n )\n)\n\nlevs_ps = np.arange(980, 1040, 4)\nlevs_ps_diff = np.concatenate(\n (\n np.arange(-20, 0, 1),\n np.arange(1, 21, 1),\n )\n) * args.pressure_factor\n\n\nplot_infos = dict(\n\n atm = {\n\n \"PSFC\" : dict(\n factor = 100,\n label = \"$P_\\\\mathrm{sfc}$\",\n unit = \"$\\\\mathrm{hPa}$\",\n ctl = dict(\n contourf_cmap = \"bone_r\",\n contourf_levs = np.linspace(980, 1040, 16),\n contourf_ticks = np.linspace(980, 1040, 16),\n ),\n\n diff = dict(\n contourf_cmap = \"bwr\",\n contourf_levs = np.linspace(-4, 4, 21),\n contourf_ticks = np.linspace(-4, 4, 11),\n )\n ),\n\n\n \"WIND10\" : dict(\n factor = 1,\n label = \"$\\\\left|\\\\vec{U}_\\\\mathrm{10m}\\\\right|$\",\n unit = \"$\\\\mathrm{m}/\\\\mathrm{s}$\",\n ctl = dict(\n contourf_cmap = \"hot_r\",\n contourf_levs = np.linspace(0, 20, 21),\n contourf_ticks = np.linspace(0, 20, 11),\n ),\n diff = dict(\n contourf_cmap = \"bwr\",\n contourf_levs = np.linspace(-5, 5, 21),\n contourf_ticks = np.linspace(-5, 5, 11),\n )\n ),\n\n\n \"T2\" : dict(\n factor = 1,\n label = \"$T_{\\\\mathrm{2m}}$\",\n unit = \"$\\\\mathrm{K}$\",\n ctl = dict(\n contourf_cmap = \"Spectral_r\",\n# contourf_levs = np.arange(285.15, 293.15, 0.1),\n# contourf_ticks = np.arange(285.15, 293.15, 0.5),\n\n contourf_levs = np.arange(273.15, 300, .5),\n contourf_ticks = np.arange(273.15, 300, 2),\n ),\n diff = dict(\n contourf_cmap = \"bwr\",\n contourf_levs = np.linspace(-2, 2, 21),\n contourf_ticks = np.linspace(-2, 2, 11),\n )\n ),\n\n \"SST\" : dict(\n factor = 1,\n label = \"$\\\\mathrm{SST}$\",\n unit = \"$\\\\mathrm{K}$\",\n ctl = dict(\n contourf_cmap = \"Spectral_r\",\n contourf_levs = np.arange(273.15, 300, .5),\n contourf_ticks = np.arange(273.15, 300, 2),\n# contourf_levs = np.arange(285.15, 293.15, 0.1),\n# contourf_ticks = np.arange(285.15, 293.15, 0.5),\n ),\n diff = dict(\n contourf_cmap = \"bwr\",\n contourf_levs = np.linspace(-1, 1, 21),\n contourf_ticks = np.linspace(-1, 1, 11),\n )\n ),\n\n \"QFX\" : dict(\n factor = 1e-4,\n label = \"QFX\",\n unit = \"$\\\\mathrm{kg}/\\\\mathrm{s}/\\\\mathrm{m}^2$\",\n ctl = dict(\n contourf_cmap = \"bwr\",\n contourf_levs = np.linspace(-1, 1, 21),\n contourf_ticks = np.linspace(-1, 1, 11),\n ),\n diff = dict(\n contourf_cmap = \"bwr\",\n contourf_levs = np.linspace(-1, 1, 21),\n contourf_ticks = np.linspace(-1, 1, 11),\n )\n ),\n\n\n \"AOTDIFF\" : dict(\n factor = 1,\n label = \"$\\\\mathrm{SST} - T_{\\\\mathrm{2m}}$\",\n unit = \"$\\\\mathrm{K}$\",\n ctl = dict(\n contourf_cmap = \"bwr\",\n contourf_levs = np.linspace(-5, 5, 21),\n contourf_ticks = np.linspace(-5, 5, 11),\n ),\n diff = dict(\n contourf_cmap = \"bwr\",\n contourf_levs = np.linspace(-2, 2, 21),\n contourf_ticks = np.linspace(-2, 2, 11),\n )\n ),\n\n \"H_DIAB_RAIN\" : dict(\n factor = 1000,\n label = \"H_DIAB_RAIN\",\n unit = \"$\\\\mathrm{kW}/\\\\mathrm{m}^2$\",\n ctl = dict(\n contourf_cmap = \"bwr\",\n contourf_levs = np.linspace(-1, 1, 21) * 5,\n contourf_ticks = np.linspace(-1, 1, 11) * 5,\n ),\n diff = dict(\n contourf_cmap = \"bwr\",\n contourf_levs = np.linspace(-1, 1, 21) * 1,\n contourf_ticks = np.linspace(-1, 1, 11) * 1,\n )\n ),\n\n\n \"H_DIAB_TTL\" : dict(\n factor = 1,\n label = \"H_DIAB_TTL\",\n unit = \"$\\\\mathrm{W}/\\\\mathrm{m}^2$\",\n ctl = dict(\n contourf_cmap = \"bwr\",\n contourf_levs = np.linspace(-1, 1, 21) * 1000,\n contourf_ticks = np.linspace(-1, 1, 11) * 1000,\n ),\n diff = dict(\n contourf_cmap = \"bwr\",\n contourf_levs = np.linspace(-1, 1, 21) * 1000,\n contourf_ticks = np.linspace(-1, 1, 11) * 1000,\n )\n ),\n\n\n \"TTL_HFLX\" : dict(\n factor = 1,\n label = \"HFLX\",\n unit = \"$\\\\mathrm{W}/\\\\mathrm{m}^2$\",\n **heat_flux_setting,\n ),\n\n \"SLHFLX\" : dict(\n factor = 1,\n label = \"SHFLX + LHFLX\",\n unit = \"$\\\\mathrm{W}/\\\\mathrm{m}^2$\",\n **heat_flux_setting,\n ),\n\n \"SHFLX\" : dict(\n factor = 1,\n label = \"SHFLX\",\n unit = \"$\\\\mathrm{W}/\\\\mathrm{m}^2$\",\n **heat_flux_setting,\n ),\n\n \"LHFLX\" : dict(\n factor = 1,\n label = \"LHFLX\",\n unit = \"$\\\\mathrm{W}/\\\\mathrm{m}^2$\",\n **heat_flux_setting,\n ),\n\n \"SWFLX\" : dict(\n factor = 1,\n label = \"SWFLX\",\n unit = \"$\\\\mathrm{W}/\\\\mathrm{m}^2$\",\n **heat_flux_setting,\n ),\n\n \"LWFLX\" : dict(\n factor = 1,\n label = \"LWFLX\",\n unit = \"$\\\\mathrm{W}/\\\\mathrm{m}^2$\",\n **heat_flux_setting,\n ),\n\n },\n\n ocn = {\n \"SSC\" : dict(\n factor = 1,\n label = \"$\\\\left|\\\\vec{U}_\\\\mathrm{sfc}\\\\right|$\",\n unit = \"$\\\\mathrm{m}/\\\\mathrm{s}$\",\n ctl = dict(\n contourf_cmap = \"YlOrRd\",\n contourf_levs = np.linspace(0, 1, 21) * 0.5,\n contourf_ticks = np.linspace(0, 1, 11) * 0.5,\n ),\n diff = dict(\n contourf_cmap = \"bwr\",\n contourf_levs = np.linspace(-.5, .5, 21),\n contourf_ticks = np.linspace(-.5, .5, 11),\n )\n ),\n }\n\n)\n\nprint(\"Parse variables...\")\n\nplot_variables = []\nfor i, full_varname in enumerate(args.varnames):\n\n \n category, varname = full_varname.split(\".\")\n \n if not ( (category in plot_infos) and (varname in plot_infos[category]) ):\n \n raise Exception(\"Error: Varname '%s' has no corresponding plot info.\" % (varname,))\n\n plot_variables.append((category, varname))\n\n\n\nprint(\"==================================\")\nprint(\"Date range: \", dts[0], \" to \", dts[-1])\nprint(\"Skip : \", skip_hrs)\nprint(\"Avg : \", avg_hrs)\nprint(\"Latitude box: %.2f %.2f\" % (lat_s, lat_n))\nprint(\"Longitude box: %.2f %.2f\" % (lon_w, lon_e))\n\nfor i, (category, varname) in enumerate(plot_variables):\n print(\"The %d-th plotted variable: %s - %s\" % (i, category, varname,))\n\nfor i, input_dir in enumerate(args.input_dirs):\n print(\"The %d-th input folder: %s\" % (i, input_dir,))\n\n\nprint(\"==================================\")\n\n\n\nncol = len(args.input_dirs)\nnrow = len(args.varnames)\n\nprint(\"Load matplotlib...\")\n\nimport matplotlib as mpl\nif args.no_display is False:\n print(\"Load TkAgg\")\n mpl.use('TkAgg')\nelse:\n print(\"Load Agg\")\n mpl.use('Agg')\n mpl.rc('font', size=15)\n \n \nmpl.use('Agg')\n# This program plots the AR event\nimport matplotlib.pyplot as plt\nfrom matplotlib import cm\nfrom matplotlib.patches import Rectangle\nimport matplotlib.transforms as transforms\nfrom matplotlib.dates import DateFormatter\nimport matplotlib.ticker as mticker\nimport cartopy.crs as ccrs\nfrom cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n\nprint(\"done\")\n\nprint(\"Create dir: %s\" % (args.output_dir,))\nPath(args.output_dir).mkdir(parents=True, exist_ok=True)\n\n\n\ndef workWrap(*args):\n\n try:\n result = plot(*args)\n except:\n traceback.print_exc()\n \n result = \"ERROR\"\n result\n\n\ndef plot(beg_dt, end_dt, output_filename):\n \n beg_dtstr = beg_dt.strftime(\"%Y-%m-%d_%H\")\n end_dtstr = end_dt.strftime(\"%Y-%m-%d_%H\")\n \n print(\"Doing date range: [%s, %s]\" % (beg_dtstr, end_dtstr))\n\n data = []\n\n ref_time = None\n\n coords = dict(\n atm = dict(\n lat = None,\n lon = None,\n ),\n\n ocn = dict(\n lat = None,\n lon = None,\n )\n )\n for i, input_dir in enumerate(args.input_dirs):\n\n _data = dict(atm=None, ocn=None)\n\n\n print(\"Load the %d-th folder: %s\" % (i, input_dir,))\n\n _ds = wrf_load_helper.loadWRFDataFromDir(input_dir, prefix=\"wrfout_d01_\", time_rng=[beg_dt, end_dt], extend_time=pd.Timedelta(days=1))\n\n if i == 0:\n ref_time = _ds.time.to_numpy()\n coords['atm']['lon'] = _ds.coords[\"XLONG\"].isel(time=0)\n coords['atm']['lat'] = _ds.coords[\"XLAT\"].isel(time=0)\n\n print(\"Loaded time: \")\n print(ref_time)\n\n\n if i > 0:\n if any(ref_time != _ds.time.to_numpy()):\n raise Exception(\"Time is not consistent between %s and %s\" % (args.input_dirs[0], input_dir,))\n\n\n _ds = _ds.mean(dim=\"time\", keep_attrs=True)\n\n if not args.is_ensemble:\n _data['atm'] = xr.merge([_ds, produceDiagQuantities(_ds)])\n else:\n _data['atm'] = _ds\n \n _ds_std = None\n if args.is_ensemble:\n try:\n _ds_std = wrf_load_helper.loadWRFDataFromDir(input_dir, prefix=\"std_wrfout_d01_\", time_rng=[beg_dt, end_dt], extend_time=pd.Timedelta(days=0))\n\n except Exception as e:\n print(e)\n traceback.print_exc()\n print(\"Error happens when trying to get standard deviation file. Ignore this.\")\n\n _ds_std = None\n\n if _ds_std is not None:\n _ds_std = _ds_std.mean(dim=\"time\", keep_attrs=True)\n _data['atm_std'] = _ds_std\n\n print(\"Load ocean data\")\n\n try:\n if args.mitgcm_grid_dir != \"\":\n mitgcm_grid_dir = args.mitgcm_grid_dir\n else:\n mitgcm_grid_dir = input_dir\n \n msm = dlh.MITgcmSimMetadata(args.mitgcm_beg_date, args.mitgcm_deltaT, args.mitgcm_dumpfreq, input_dir, mitgcm_grid_dir)\n coo, crop_kwargs = lf.loadCoordinateFromFolderAndWithRange(msm.grid_dir, nlev=None, lat_rng=args.lat_rng, lon_rng=args.lon_rng)\n\n coords['ocn']['lat'] = coo.grid[\"YC\"][:, 0]\n coords['ocn']['lon'] = coo.grid[\"XC\"][0, :]\n\n #ocn_z_T = coo.grid[\"RC\"].flatten()\n #ocn_z_W = coo.grid[\"RF\"].flatten()\n #ocn_mask = coo.grid[\"maskInC\"]\n\n # Load average data\n datasets = [\"diag_state\"]\n data_ave = dlh.loadAveragedDataByDateRange(beg_dt, end_dt, msm, **crop_kwargs, datasets=datasets, inclusive=\"right\") # inclusive is right because output at time=t is the average from \"before\" to t\n _data['ocn'] = produceDiagQuantities_ocn(data_ave)\n\n \n # Load standard deviation\n if args.is_ensemble:\n new_datasets = []\n for j in range(len(datasets)): \n new_datasets.append(\"%s_%s\" % (\"std\", datasets[j],))\n\n data_std = dlh.loadAveragedDataByDateRange(beg_dt, end_dt, msm, **crop_kwargs, datasets=new_datasets, inclusive=\"right\") # inclusive is right because output at time=t is the average from \"before\" to t\n _data['ocn_std'] = data_std\n\n except Exception as e:\n traceback.print_exc()\n print(\"Loading ocean data error. Still keep going...\")\n\n _data['ocn'] = None\n\n data.append(_data)\n #data_ave = dlh.loadAveragedDataByDateRange(dt, dt + avg_hrs, msm, **crop_kwargs, datasets=[\"diag_Tbdgt\", \"diag_2D\", \"diag_state\",], inclusive=\"right\") # inclusive is right because output at time=t is the average from \"before\" to t\n print(\"Data loading complete.\")\n \n cent_lon = 180.0\n\n plot_lon_l = lon_w\n plot_lon_r = lon_e\n plot_lat_b = lat_s\n plot_lat_t = lat_n\n\n proj = ccrs.PlateCarree(central_longitude=cent_lon)\n proj_norm = ccrs.PlateCarree()\n\n \n thumbnail_height = (lat_n - lat_s) / args.deg_lat_per_inch\n thumbnail_width = (lon_e - lon_w) / args.deg_lon_per_inch\n\n figsize, gridspec_kw = tool_fig_config.calFigParams(\n w = thumbnail_width,\n h = thumbnail_height,\n wspace = 2.5,\n hspace = 0.5,\n w_left = 1.0,\n w_right = 1.5,\n h_bottom = 1.0,\n h_top = 1.0,\n ncol = ncol,\n nrow = nrow,\n )\n\n fig, ax = plt.subplots(\n nrow, ncol,\n figsize=figsize,\n subplot_kw=dict(projection=proj, aspect=\"auto\"),\n gridspec_kw=gridspec_kw,\n constrained_layout=False,\n squeeze=False,\n )\n \n \n fig.suptitle(\"%s ~ %s\" % ( beg_dtstr, end_dtstr, ))\n \n \n print(\"Plot control simulation: \", args.sim_names[0])\n ref_ax = ax[:, 0]\n ref_data = data[0]\n\n\n ps_ref = ref_data[\"atm\"][\"PSFC\"]\n for j, _ax in enumerate(ref_ax): \n \n category, varname = plot_variables[j]\n plot_info = plot_infos[category][varname]\n\n if ref_data[category] is None:\n print(\"Blank data encountered. Skip this one.\")\n fig.delaxes(_ax)\n continue\n\n\n var_ref = ref_data[category][varname] / plot_info['factor']\n\n mappable = _ax.contourf(\n coords[category]['lon'],\n coords[category]['lat'],\n var_ref,\n levels=plot_info['ctl']['contourf_levs'],\n transform=proj_norm,\n extend=\"both\",\n cmap=plot_info['ctl']['contourf_cmap'],\n )\n\n cax = tool_fig_config.addAxesNextToAxes(fig, _ax, \"right\", thickness=0.03, spacing=0.05)\n cb = plt.colorbar(mappable, cax=cax, ticks=plot_info['ctl']['contourf_ticks'], orientation=\"vertical\", pad=0.0)\n cb.ax.set_ylabel(\" %s [ %s ]\" % (plot_info[\"label\"], plot_info[\"unit\"]))\n\n\n # Extra plot for current\n if category == \"ocn\" and varname == \"SSC\":\n\n print(\"Plot STREAMPLOT for SSC\")\n \n _ax.streamplot(\n coords['ocn']['lon'],\n coords['ocn']['lat'],\n ref_data['ocn']['Usfc'],\n ref_data['ocn']['Vsfc'],\n color='dodgerblue',\n linewidth=1,\n transform=proj_norm,\n )\n\n\n # Extra plot for current\n if category == \"atm\" and varname == \"WIND10\":\n\n print(\"Plot STREAMPLOT for SSC\")\n \n _ax.streamplot(\n coords['atm']['lon'],\n coords['atm']['lat'],\n ref_data['atm']['U10'],\n ref_data['atm']['V10'],\n color='dodgerblue',\n linewidth=1,\n transform=proj_norm,\n )\n\n\n\n\n cs = _ax.contour(\n coords['atm']['lon'],\n coords['atm']['lat'],\n ps_ref / 1e2,\n levels=levs_ps,\n transform=proj_norm,\n colors=\"black\",\n linewidths = 1.0,\n )\n\n #plt.clabel(cs, fmt= ( \"%d\" if np.all(levs_ps % 1 != 0) else \"%.1f\" ))\n\n\n ref_ax[0].set_title(args.sim_names[0])\n\n\n for i in range(1, len(args.input_dirs)):\n\n print(\"Plot diff simulation: \", args.sim_names[i])\n \n case_ax = ax[:, i]\n _data = data[i]\n \n\n \n ps_diff = _data[\"atm\"][\"PSFC\"] - ps_ref \n for j, _ax in enumerate(case_ax): \n \n category, varname = plot_variables[j]\n plot_info = plot_infos[category][varname]\n\n if _data[category] is None:\n print(\"Blank data encountered. Skip this one.\")\n fig.delaxes(_ax)\n continue\n\n var_mean1 = ref_data[category][varname]\n var_mean2 = _data[category][varname]\n\n var_diff = ( var_mean2 - var_mean1 ) / plot_info['factor']\n mappable = _ax.contourf(\n coords[category]['lon'],\n coords[category]['lat'],\n var_diff,\n levels=plot_info['diff']['contourf_levs'],\n transform=proj_norm,\n extend=\"both\",\n cmap=plot_info['diff']['contourf_cmap'],\n )\n #cax = tool_fig_config.addAxesNextToAxes(fig, _ax, \"right\", thickness=0.03, spacing=0.05)\n #cb = plt.colorbar(mappable, cax=cax, ticks=plot_info['diff']['contourf_ticks'], orientation=\"vertical\", pad=0.0)\n #cb.ax.set_ylabel(\" %s [ %s ]\" % (plot_info[\"label\"], plot_info[\"unit\"]))\n\n cs = _ax.contour(\n coords['atm']['lon'],\n coords['atm']['lat'],\n ps_diff / 1e2, \n levels=levs_ps_diff, \n transform=proj_norm, \n extend=\"both\", \n colors=\"black\",\n linewidths = 1.0,\n )\n \n #plt.clabel(cs, fmt= ( \"%d\" if np.all(levs_ps % 1 != 0) else \"%.1f\" ))\n \n if args.is_ensemble:\n stdcat = \"%s_std\" % category\n if stdcat in ref_data and varname in ref_data[stdcat]:\n\n print(\"Plotting significancy ... of variable %s \" % (varname,))\n var_std1 = ref_data[stdcat][varname]\n var_std2 = _data[stdcat][varname]\n\n # Doing T-test\n _tscore, _pvalues = scipy.stats.ttest_ind_from_stats(\n var_mean1, var_std1, args.ensemble_members,\n var_mean2, var_std2, args.ensemble_members,\n equal_var=True,\n alternative='two-sided',\n )\n\n cs = _ax.contourf(\n coords[category]['lon'],\n coords[category]['lat'],\n _pvalues,\n cmap=None,\n colors='none',\n levels=[-1, args.pvalue_threshold],\n hatches=[\"..\"],\n transform=proj_norm, \n )\n\n # Remove the contour lines for hatches\n for _, collection in enumerate(cs.collections):\n collection.set_edgecolor(\"black\")\n collection.set_linewidth(0.) \n \n\n \n \n case_ax[0].set_title(args.sim_names[i])\n\n\n\n \"\"\"\n _ax.quiver(coords[\"lon\"], coords[\"lat\"], _data.u10.to_numpy(), _data.v10.to_numpy(), scale=200, transform=proj_norm)\n\n cs = _ax.contourf(coords[\"lon\"], coords[\"lat\"], _data['map'], colors='none', levels=[0, 0.5, np.inf], hatches=[None, \".\"], transform=proj_norm)\n\n # Remove the contour lines for hatches \n for _, collection in enumerate(cs.collections):\n collection.set_edgecolor(\"red\")\n \"\"\"\n \n \n\n for _ax in ax.flatten():\n\n if _ax is None:\n\n # This axis has been deleted\n continue\n\n _ax.set_global()\n #__ax.gridlines()\n _ax.coastlines()#color='gray')\n _ax.set_extent([plot_lon_l, plot_lon_r, plot_lat_b, plot_lat_t], crs=proj_norm)\n\n gl = _ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,\n linewidth=1, color='gray', alpha=0.5, linestyle='--')\n\n gl.xlabels_top = False\n gl.ylabels_right = False\n\n #gl.xlocator = mticker.FixedLocator(np.arange(-180, 181, 30))\n #gl.xlocator = mticker.FixedLocator([120, 150, 180, -150, -120])#np.arange(-180, 181, 30))\n #gl.ylocator = mticker.FixedLocator([10, 20, 30, 40, 50])\n \n gl.xformatter = LONGITUDE_FORMATTER\n gl.yformatter = LATITUDE_FORMATTER\n gl.xlabel_style = {'size': 10, 'color': 'black'}\n gl.ylabel_style = {'size': 10, 'color': 'black'}\n\n\n print(\"Output file: \", output_filename)\n fig.savefig(output_filename, dpi=200)\n\n if args.no_display is False:\n plt.show()\n \n plt.close(fig)\n\n return \"DONE\" \n\nfailed_dates = []\nwith Pool(processes=args.nproc) as pool:\n\n input_args = []\n for i, beg_dt in enumerate(dts):\n \n end_dt = beg_dt + avg_hrs\n beg_dtstr = beg_dt.strftime(\"%Y-%m-%d_%H\")\n end_dtstr = end_dt.strftime(\"%Y-%m-%d_%H\")\n\n output_filename = \"%s/wrf_comparison_avg-%d_%s.png\" % (args.output_dir, args.avg_hrs, beg_dtstr)\n\n if args.overwrite is False and os.path.exists(output_filename):\n print(\"[%s] File %s already exists. Do not do this job.\" % (beg_dtstr, output_filename))\n\n else:\n input_args.append((beg_dt, end_dt, output_filename))\n\n \n result = pool.starmap(workWrap, input_args)\n\n","repo_name":"meteorologytoday/MITgcm-diagnostics","sub_path":"useful_code/plot_code/plot_atm_ocn.py","file_name":"plot_atm_ocn.py","file_ext":"py","file_size_in_byte":27491,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"12907734957","text":"from playhouse.postgres_ext import *\n\nimport jesse.helpers as jh\nimport jesse.services.logger as logger\nimport jesse.services.selectors as selectors\nfrom jesse.config import config\nfrom jesse.services.notifier import notify\nfrom jesse.enums import order_statuses, order_submitted_via\nfrom jesse.services.db import database\n\n\nif database.is_closed():\n database.open_connection()\n\n\nclass Order(Model):\n # id generated by Jesse for database usage\n id = UUIDField(primary_key=True)\n trade_id = UUIDField(index=True, null=True)\n session_id = UUIDField(index=True)\n\n # id generated by market, used in live-trade mode\n exchange_id = CharField(null=True)\n # some exchanges might require even further info\n vars = JSONField(default={})\n symbol = CharField()\n exchange = CharField()\n side = CharField()\n type = CharField()\n reduce_only = BooleanField()\n qty = FloatField()\n filled_qty = FloatField(default=0)\n price = FloatField(null=True)\n status = CharField(default=order_statuses.ACTIVE)\n created_at = BigIntegerField()\n executed_at = BigIntegerField(null=True)\n canceled_at = BigIntegerField(null=True)\n\n # needed in Jesse, but no need to store in database(?)\n submitted_via = None\n\n class Meta:\n from jesse.services.db import database\n\n database = database.db\n indexes = ((('trade_id', 'exchange', 'symbol', 'status', 'created_at'), False),)\n\n def __init__(self, attributes: dict = None, should_silent=False, **kwargs) -> None:\n Model.__init__(self, attributes=attributes, **kwargs)\n\n if attributes is None:\n attributes = {}\n\n for a, value in attributes.items():\n setattr(self, a, value)\n\n if self.created_at is None:\n self.created_at = jh.now_to_timestamp()\n\n # if jh.is_live():\n # from jesse.store import store\n # self.session_id = store.app.session_id\n # self.save(force_insert=True)\n\n if not should_silent:\n if jh.is_live():\n self.notify_submission()\n\n if jh.is_debuggable('order_submission') and (self.is_active or self.is_queued):\n txt = f'{\"QUEUED\" if self.is_queued else \"SUBMITTED\"} order: {self.symbol}, {self.type}, {self.side}, {self.qty}'\n if self.price:\n txt += f', ${self.price}'\n logger.info(txt)\n\n # handle exchange balance for ordered asset\n e = selectors.get_exchange(self.exchange)\n e.on_order_submission(self)\n\n def notify_submission(self) -> None:\n if config['env']['notifications']['events']['submitted_orders'] and (self.is_active or self.is_queued):\n txt = f'{\"QUEUED\" if self.is_queued else \"SUBMITTED\"} order: {self.symbol}, {self.type}, {self.side}, {self.qty}'\n if self.price:\n txt += f', ${self.price}'\n notify(txt)\n\n @property\n def is_canceled(self) -> bool:\n return self.status == order_statuses.CANCELED\n\n @property\n def is_active(self) -> bool:\n return self.status == order_statuses.ACTIVE\n\n @property\n def is_cancellable(self):\n \"\"\"\n orders that are either active or partially filled\n \"\"\"\n return self.is_active or self.is_partially_filled or self.is_queued\n\n @property\n def is_queued(self) -> bool:\n \"\"\"\n Used in live mode only: it means the strategy has considered the order as submitted,\n but the exchange does not accept it because of the distance between the current\n price and price of the order. Hence it's been queued for later submission.\n\n :return: bool\n \"\"\"\n return self.status == order_statuses.QUEUED\n\n @property\n def is_new(self) -> bool:\n return self.is_active\n\n @property\n def is_executed(self) -> bool:\n return self.status == order_statuses.EXECUTED\n\n @property\n def is_filled(self) -> bool:\n return self.is_executed\n\n @property\n def is_partially_filled(self) -> bool:\n return self.status == order_statuses.PARTIALLY_FILLED\n\n @property\n def is_stop_loss(self):\n return self.submitted_via == order_submitted_via.STOP_LOSS\n\n @property\n def is_take_profit(self):\n return self.submitted_via == order_submitted_via.TAKE_PROFIT\n\n @property\n def to_dict(self):\n return {\n 'id': self.id,\n 'session_id': self.session_id,\n 'exchange_id': self.exchange_id,\n 'symbol': self.symbol,\n 'side': self.side,\n 'type': self.type,\n 'qty': self.qty,\n 'filled_qty': self.filled_qty,\n 'price': self.price,\n 'status': self.status,\n 'created_at': self.created_at,\n 'canceled_at': self.canceled_at,\n 'executed_at': self.executed_at,\n }\n\n @property\n def position(self):\n return selectors.get_position(self.exchange, self.symbol)\n\n @property\n def value(self) -> float:\n return abs(self.qty) * self.price\n\n @property\n def remaining_qty(self) -> float:\n return jh.prepare_qty(abs(self.qty) - abs(self.filled_qty), self.side)\n\n def queue(self):\n self.status = order_statuses.QUEUED\n self.canceled_at = None\n if jh.is_debuggable('order_submission'):\n txt = f'QUEUED order: {self.symbol}, {self.type}, {self.side}, {self.qty}'\n if self.price:\n txt += f', ${round(self.price, 2)}'\n logger.info(txt)\n self.notify_submission()\n\n def resubmit(self):\n # don't allow resubmission if the order is already active or cancelled\n if not self.is_queued:\n raise NotSupportedError(f'Cannot resubmit an order that is not queued. Current status: {self.status}')\n\n # regenerate the order id to avoid errors on the exchange's side\n self.id = jh.generate_unique_id()\n self.status = order_statuses.ACTIVE\n self.canceled_at = None\n if jh.is_debuggable('order_submission'):\n txt = f'SUBMITTED order: {self.symbol}, {self.type}, {self.side}, {self.qty}'\n if self.price:\n txt += f', ${self.price}'\n logger.info(txt)\n self.notify_submission()\n\n def cancel(self, silent=False, source='') -> None:\n if self.is_canceled or self.is_executed:\n return\n\n # to fix when the cancelled stream's lag causes cancellation of queued orders\n if source == 'stream' and self.is_queued:\n return\n\n self.canceled_at = jh.now_to_timestamp()\n self.status = order_statuses.CANCELED\n\n # if jh.is_live():\n # self.save()\n\n if not silent:\n txt = f'CANCELED order: {self.symbol}, {self.type}, {self.side}, {self.qty}'\n if self.price:\n txt += f', ${round(self.price, 2)}'\n if jh.is_debuggable('order_cancellation'):\n logger.info(txt)\n if jh.is_live():\n if config['env']['notifications']['events']['cancelled_orders']:\n notify(txt)\n\n # handle exchange balance\n e = selectors.get_exchange(self.exchange)\n e.on_order_cancellation(self)\n\n def execute(self, silent=False) -> None:\n if self.is_canceled or self.is_executed:\n return\n\n self.executed_at = jh.now_to_timestamp()\n self.status = order_statuses.EXECUTED\n\n # if jh.is_live():\n # self.save()\n\n if not silent:\n txt = f'EXECUTED order: {self.symbol}, {self.type}, {self.side}, {self.qty}'\n if self.price:\n txt += f', ${round(self.price, 2)}'\n # log\n if jh.is_debuggable('order_execution'):\n logger.info(txt)\n # notify\n if jh.is_live():\n if config['env']['notifications']['events']['executed_orders']:\n notify(txt)\n\n # log the order of the trade for metrics\n from jesse.store import store\n store.completed_trades.add_executed_order(self)\n\n # handle exchange balance for ordered asset\n e = selectors.get_exchange(self.exchange)\n e.on_order_execution(self)\n\n p = selectors.get_position(self.exchange, self.symbol)\n if p:\n p._on_executed_order(self)\n\n def execute_partially(self, silent=False) -> None:\n self.executed_at = jh.now_to_timestamp()\n self.status = order_statuses.PARTIALLY_FILLED\n\n # if jh.is_live():\n # self.save()\n\n if not silent:\n txt = f\"PARTIALLY FILLED: {self.symbol}, {self.type}, {self.side}, filled qty: {self.filled_qty}, remaining qty: {self.remaining_qty}, price: {self.price}\"\n # log\n if jh.is_debuggable('order_execution'):\n logger.info(txt)\n # notify\n if jh.is_live():\n if config['env']['notifications']['events']['executed_orders']:\n notify(txt)\n\n # log the order of the trade for metrics\n from jesse.store import store\n store.completed_trades.add_executed_order(self)\n\n p = selectors.get_position(self.exchange, self.symbol)\n\n if p:\n p._on_executed_order(self)\n\n\n# if database is open, create the table\nif database.is_open():\n Order.create_table()\n","repo_name":"jesse-ai/jesse","sub_path":"jesse/models/Order.py","file_name":"Order.py","file_ext":"py","file_size_in_byte":9432,"program_lang":"python","lang":"en","doc_type":"code","stars":4933,"dataset":"github-code","pt":"50"} +{"seq_id":"12000416747","text":"import sys\ninput = sys.stdin.readline\nn,k = map(int, input().split())\nS = list(map(int, input().split()))\n\nleft = right = 0\n'''\n 짝수 연속\n 둘다 하나씩 움직이면서 계산\n 두 사이 안의 홀수가 k개라면 다음으로 right만 이동\n 넘어가면 left가 홀수가 나올떄까지 이동\n'''\ncnt = length = 0\n\nwhile right < n:\n if cnt > k: # 홀수의 개수가 k개보다 커진다면\n # 그럼 left에서 계속해서 증가한다 \n if S[left] % 2 == 1:# 홀수이면\n cnt -= 1\n left += 1\n continue\n else: # cnt가 k랑 같거나 작다면\n if S[right] % 2 == 1:\n cnt += 1\n right += 1\n length = max(length, right-left-cnt)\nprint(length)\n\n\n","repo_name":"Naver-KaKao-LINE-Coupang-BaeMin/Codingtest","sub_path":"namhyo01/bj/가장 긴 짝수 연속한 부분 수열(large)/22862.py","file_name":"22862.py","file_ext":"py","file_size_in_byte":746,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"39099775342","text":"import plotly.express as px\nimport dash\nimport dash_core_components as dcc\nimport dash_html_components as html\nfrom dash.dependencies import Input, Output,State\nimport plotly.graph_objects as go , plotly.io as pio# or plotly.express as px\nimport pandas as pd\nimport numpy as np\nfrom mpl_toolkits.mplot3d import axes3d\n\npio.templates.default = 'plotly_white' \n#samples = pd.read_pickle(\"c:\\\\users\\\\liors\\\\source\\\\repos\\\\ctrm\\\\ctrm\\\\sample.pkl\")\nsamples = pd.read_pickle(\"c:\\\\users\\\\liors\\\\source\\\\repos\\\\ctrm\\\\ctrm\\\\grail.pkl\")\nRGlocations = pd.read_csv(\"C:\\\\Users\\\\liors\\\\source\\\\repos\\ctrm\\\\for_lior\\\\red_grail_locations.csv\").sort_values([\"X\"],ignore_index=True)\nRGlocations =RGlocations.loc[RGlocations.name.str.contains(\"GA\")].reset_index()\nRGlocations[\"Xfix\"] = RGlocations.X-RGlocations.X.min()\nRGlocations[\"Yfix\"] = RGlocations.Y-RGlocations.Y.min()\nRGlocations[\"Zfix\"] = RGlocations.alt-RGlocations.alt.min()\n\n\ngrouped = samples.groupby([\"IPindex\",\"x\" ,\"y\",\"radius\"]).agg({\"semb\": ['mean','min','max','median', 'std' ]}).droplevel(axis=1, level=0).reset_index()\n\n#data = [go.Scatter3d(x = grouped.x,y=grouped.y,z=grouped.radius, marker=dict(size=12, color=grouped.semb, colorscale='Reds', opacity=0.5))]\napp = dash.Dash(__name__, external_stylesheets=[\"https://codepen.io/chriddyp/pen/bWLwgP.css\"])\n\n\nlayout = go.Layout(\n #scene=go.layout.Scene(\n # xaxis=go.layout.scene.XAxis(\n # showspikes=True,\n # spikecolor='#1fe5bd',\n # spikethickness=10,\n # ),\n # yaxis=go.layout.scene.YAxis(\n # showspikes=False,\n # spikecolor='#1fe5bd',\n # spikethickness=6,range=[0,200],\n # ),\n # zaxis=go.layout.scene.ZAxis(\n # showspikes=False,\n # spikecolor='black',\n # spikethickness=10,\n # ),\n #),\n height = 700,width = 700\n)\n#grouped.loc[(grouped.x==96 )&( grouped.y==140),'semb'] = 1\n#fig = go.Figure(data =data, layout=layout)\n#fig = px.scatter_3d(grouped, x = 'x', y='y',z='radius', color = 'semb',color_continuous_scale = 'Reds', opacity = 0.3)\n#fig2d = px.scatter()\n#fig.update_scenes(zaxis_autorange=\"reversed\",yaxis_autorange=\"reversed\",xaxis_autorange=\"reversed\")\ncolorsc = 0;\ntheme = {\n 'dark': True,\n 'detail': '#007439',\n 'primary': '#00EA64',\n 'secondary': '#6E6E6E',\n}\n\napp = dash.Dash()\napp.layout = html.Div([\n html.Div([\n html.Div(dcc.Graph(id = \"2dplot\"),style={'display': 'inline-block'}),\n html.Div(dcc.Graph(id = \"3dplot\",figure = px.scatter_3d()),style={'display': 'inline-block'}), \n html.Div(dcc.Slider(id = \"bar\", value = 0,max = samples.semb.max(), step = samples.semb.max()/10,min = 0,updatemode='drag',vertical=True),style={'display': 'inline-block'}),\n html.Div(dcc.Dropdown(id = \"aggregation-dropdown\",options=[{'label': 'mean', 'value': 'mean'}, {'label': 'maximum', 'value': 'max'},{'label': 'std', 'value': 'std'},{'label': 'median', 'value': 'median'}], value='mean', clearable=False),style={'display': 'inline-block', 'width': \"5%\"}),\n html.Button('Submit', id='submit-val', n_clicks=0)\n ]),\n \n dcc.Input(id=\"avrWindow\", type = \"number\", placeholder=\"window size\",min = 1,max = 10,value = 1),\n \n dcc.Graph(id = \"ImagePointView\"),\n dcc.Store(id = \"radius\")])\n \n #html.Div([dcc.Slider(id='radius',min=1,max=35,value=1,marks={str(rad): str(rad) for rad in range(1,35)},step=None,vertical=True)]\n # ,style={'display': 'inline-block'})],style={'display': 'block','height':'100%'})\n\n@app.callback(\n Output(\"ImagePointView\", \"figure\"),\n Input('3dplot', 'clickData'),\n Input('2dplot', 'clickData'))\ndef updateIP(selectedPoint,selectedPoint2):\n callbackVal = dash.callback_context.triggered[0]\n print(callbackVal)\n x=y=radius =radius= 0;\n if callbackVal['value'] is not None: \n x,y = callbackVal['value']['points'][0]['x'],callbackVal['value']['points'][0]['y']\n if ((callbackVal[\"prop_id\"]== '3dplot.clickData') ):\n radius = callbackVal['value']['points'][0]['customdata'][0]\n else:\n radius = callbackVal['value']['points'][0]['customdata']\n\n print(x,y,radius)\n target = samples[(samples.x == x)&(samples.y == y)&(samples.radius==radius)]\n title = \"semblance for x: %d, y: %d, depth: %d\" %(x,y,radius)\n fig = px.line(target,'t', 'semb',title = title)\n return(fig)\n\n@app.callback(\n Output('2dplot', 'figure'),\n Output('radius', 'data'),\n Input('3dplot', 'clickData'),\n Input('3dplot',\"figure\"),\n State(\"aggregation-dropdown\",\"value\")) \ndef update_figure(selected_rad,dfig,aggregation):\n colorsc = dfig['layout']['coloraxis']['colorscale']\n radius = 0;\n if selected_rad is not None: \n radius = selected_rad['points'][0]['z']\n filtered = grouped.loc[grouped.radius==radius];\n fig2d = go.Figure(data=go.Heatmap(\n z=filtered[aggregation],\n x=filtered[\"x\"],\n y=filtered[\"y\"],\n zmin = min(grouped[aggregation]),\n zmax = max(grouped[aggregation]),\n colorscale=colorsc,zsmooth = 'best', customdata = np.ones(filtered.size)*radius))\n fig2d.add_trace(go.Scatter(x=RGlocations[\"Xfix\"], y=RGlocations[\"Yfix\"],mode = 'markers', marker = dict(color = \"Blue\")))\n #fig2d = px.imshow(filtered.pivot_table(index = 'y', columns = 'x',values = aggregation),color_continuous_scale =colorsc,origin = \"lower\",aspect = \"auto\", labels = {'customdata':np.ones(filtered.size)*radius})\n fig2d.update_layout(title = (\"radius: %d\" %radius), xaxis = {\"title\":\"x\"},yaxis = {\"title\":\"y\"})\n title=\"Plot Title\"\n #fig.add_mesh3d(x=[0,0,int(max(grouped.x)),int(max(grouped.x))],y=[0,int(max(grouped.y)),int(max(grouped.y)),0],z=[selected_rad,selected_rad,selected_rad,selected_rad])\n return(fig2d,{'radius':radius})\n@app.callback(\n Output(\"3dplot\", \"figure\"),\n Output(\"bar\", \"max\"),\n Output(\"bar\", \"step\"),\n Input(\"submit-val\",\"n_clicks\"),\n Input(\"bar\",\"value\"),\n State(\"aggregation-dropdown\",\"value\"),\n State(\"avrWindow\",\"value\"))\ndef update_3dplot(clicks,bar, selectedAgg,window): \n\n title = \"Results are aggregated with: %s\\n semblance > %s \" %(selectedAgg, bar)\n\n fig = px.scatter_3d(grouped, x = 'x', y='y',z='radius', color = selectedAgg,color_continuous_scale = 'Reds', opacity = 0.3, title = title ,template=\"plotly_white\",size = (grouped[selectedAgg].ravel()>bar)*1, hover_data = {'customdata':grouped.radius})\n #fig = go.Figure(go.Scatter3d(x = grouped.x, y=grouped.y,z=grouped.radius ,marker=dict(size = (grouped[selectedAgg].ravel()>bar)*5,color = grouped[selectedAgg], colorscale= \"Reds\"),mode = \"markers\"))\n fig.update_scenes(zaxis_autorange=\"reversed\",yaxis_autorange=\"reversed\",xaxis_autorange=\"reversed\")\n fig.update_traces(marker = dict(line=dict(width=0)))\n return (fig, grouped[selectedAgg].max(),grouped[selectedAgg].max()/10 )\n\n\napp.run_server(debug=True, use_reloader=True) # Turn off reloader if inside Jupyter\nfig = px.scatter_3d(grouped, x = 'x', y='y',z='radius', color = selectedAgg,color_continuous_scale = 'Reds', opacity = 0.3, title = title,template=\"plotly_white\",size = (grouped[selectedAgg].ravel()>0.5)*1)\nfig.update_scenes(zaxis_autorange=\"reversed\",yaxis_autorange=\"reversed\",xaxis_autorange=\"reversed\")\nfig.update_xaxis(range = [0,2])\nfig.update_layout(xaxis=dict(rangeslider=dict(visible=True)))\nfig.show()","repo_name":"liorstov/ctrmPublic","sub_path":"pythonProj/3dfile.py","file_name":"3dfile.py","file_ext":"py","file_size_in_byte":7384,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"31827979038","text":"from pathlib import Path\nfrom typing import Dict, Type\n\nfrom SourceIO2.shared.content_manager.providers.base_provider import IProvider\nfrom SourceIO2.shared.content_manager.detectors.s2_detector import Source2Detector\nfrom SourceIO2.shared.content_manager.providers.fs_provider import FileSystemProvider\nfrom SourceIO2.utils.path_utils import backwalk_resolve\n\n\nclass TestDataDetector(Source2Detector):\n @classmethod\n def register_common(cls, root_path: Path, content_providers: Dict[str, IProvider]):\n pass\n\n @classmethod\n def detect(cls: Type['TestDataDetector'], path: Path):\n root = None\n test_marker = backwalk_resolve(path, Path('TEST.TEST'))\n if test_marker is not None:\n root = test_marker.parent\n if root is None:\n return Path(), {}\n content_providers = {}\n gameinfo = backwalk_resolve(path, Path('gameinfo.gi'))\n for path_type, path in cls.scan_gameinfo(gameinfo, root):\n if path_type in ('game', 'mod', 'write'):\n if path.stem in content_providers:\n continue\n content_providers[path.stem] = FileSystemProvider(path)\n elif path_type == 'addonroot':\n for addon in path.iterdir():\n if addon.stem.startswith('.') or f'addon_{addon.stem}' in content_providers:\n continue\n content_providers[f'addon_{addon.stem}'] = FileSystemProvider(addon)\n\n return root, content_providers\n","repo_name":"REDxEYE/SourceIO2","sub_path":"shared/content_manager/detectors/test_data.py","file_name":"test_data.py","file_ext":"py","file_size_in_byte":1524,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"50"} +{"seq_id":"5783073589","text":"yy = {'Y1': 1984,\n 'Y2': 1988,\n 'Y3': 1992,\n 'Y4': 1996,\n 'Y5': 2000,\n 'Y6': 2004,\n 'Y7': 2008,\n 'Y8': 2012,\n 'Y9': 2016,\n 'Y10': 2020}\n\nss = {'S1': 'ALABAMA',\n 'S2': 'ALASKA',\n 'S3': 'ARIZONA',\n 'S4': 'ARKANSAS',\n 'S5': 'CALIFORNIA',\n 'S6': 'COLORADO',\n 'S7': 'CONNECTICUT',\n 'S8': 'DELAWARE',\n 'S9': 'FLORIDA',\n 'S10': 'GEORGIA',\n 'S11': 'HAWAII',\n 'S12': 'IDAHO',\n 'S13': 'ILLINOIS',\n 'S14': 'INDIANA',\n 'S15': 'IOWA',\n 'S16': 'KANSAS',\n 'S17': 'KENTUCKY',\n 'S18': 'LOUISIANA',\n 'S19': 'MAINE',\n 'S20': 'MARYLAND',\n 'S21': 'MASSACHUSETTS',\n 'S22': 'MICHIGAN',\n 'S23': 'MINNESOTA',\n 'S24': 'MISSISSIPPI',\n 'S25': 'MISSOURI',\n 'S26': 'MONTANA',\n 'S27': 'NEBRASKA',\n 'S28': 'NEVADA',\n 'S29': 'NEW HAMPSHIRE',\n 'S30': 'NEW JERSEY',\n 'S31': 'NEW MEXICO',\n 'S32': 'NEW YORK',\n 'S33': 'NORTH CAROLIN',\n 'S34': 'NORTH DAKOTA',\n 'S35': 'OHIO',\n 'S36': 'OKLAHOMA',\n 'S37': 'OREGON',\n 'S38': 'PENNSYLVANIA',\n 'S39': 'RHODE ISLAND',\n 'S40': 'SOUTH CAROLIN',\n 'S41': 'SOUTH DAKOTA',\n 'S42': 'TENNESSEE',\n 'S43': 'TEXAS',\n 'S44': 'UTAH',\n 'S45': 'VERMONT',\n 'S46': 'VIRGINIA',\n 'S47': 'WASHINGTON',\n 'S48': 'WEST VIRGINIA',\n 'S49': 'WISCONSIN',\n 'S50': 'WYOMING'}\n\n\nmdim = min(13, max([len(i) for i in ss.values()]))\nss = {k.rjust(mdim):v.rjust(mdim) for k, v in ss.items()}\nyy = {k.rjust(mdim):str(v).rjust(mdim) for k, v in yy.items()}\n\nf = open('sub/sub.txt', 'rt').read()\nfor k, v in {**yy, **ss}.items():\n f = f.replace(k, v)\ng = open('sub/subbed.txt', 'wt')\ng.write(f)\ng.close()\n\n","repo_name":"ranieri-unimi/us-vote-bastianin-2022","sub_path":"sub/sub.py","file_name":"sub.py","file_ext":"py","file_size_in_byte":1478,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"18131475507","text":"class Solution:\n\n # def maxProduct(self, nums: List[int]) -> int:\n # if not nums:\n # return 0\n\n # result = nums[0]\n\n # for i in range(len(nums)):\n # current = 1\n # for j in range(len(nums)):\n # if (i == j):\n # current = nums[i]\n # else:\n # current *= nums[j]\n\n # result = max(result, current)\n\n # return result\n\n # def maxProduct(self, nums: List[int]) -> int:\n\n # if not nums:\n # return 0\n\n # # dynamic programming\n # dp_max = [0] * len(nums)\n # dp_min = [0] * len(nums)\n\n # dp_max[0] = nums[0]\n # dp_min[0] = nums[0]\n\n # for i in range(1, len(nums)):\n # if nums[i] > 0:\n # dp_max[i] = max(dp_max[i - 1] * nums[i], nums[i])\n # dp_min[i] = min(dp_min[i - 1] * nums[i], nums[i])\n # else:\n # dp_max[i] = max(dp_max[i - 1] * nums[i], nums[i])\n # dp_min[i] = min(dp_min[i - 1] * nums[i], nums[i])\n\n # return max(dp_max)\n\n def maxProduct(self, nums: List[int]) -> int:\n if not nums:\n return 0\n\n result = nums[0]\n cur_max = nums[0]\n cur_min = nums[0]\n\n for num in nums[1:]:\n temp = cur_max\n\n # cur_min * num = negative * negative = positive\n cur_max = max(cur_max * num, cur_min * num, num)\n\n # cur_max(temp) * num = positive * negative = negative\n cur_min = min(temp * num, cur_min * num, num)\n\n result = max(result, cur_max)\n\n return result\n","repo_name":"snackd/hacker_rank","sub_path":"python/leetcode/Array/Maximum Product Subarray.py","file_name":"Maximum Product Subarray.py","file_ext":"py","file_size_in_byte":1658,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"9189323711","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[73]:\n\n\n# Importer la base de données \n\nimport pandas as pd\ndf = pd.read_csv(\"/Users/fadilamoussaoui/Desktop/Session H22/PSY4016 - Programmation/Projet/Base_de_données_travail.csv\", sep = \";\")\n\ndf\n\n\n# In[74]:\n\n\n# Nom des colonnes de la base de données \n\ndf.columns\n\n\n# In[75]:\n\n\n# Enelever les colonnes inutiles pour le projet \n\ndf_projet = df.drop(columns =['Age','Salary','Study_Level','Mood_Disorder','BMI','Sport','Music',\n 'Extraversion','Conscientiousness','Instability.Neuroticism','Openness_to_Experience.Intellect',\n 'Honesty.Humility','Detachment','Psychoticism','Negative_Affect','Antagonism'])\n\ndf_projet\n\n\n# In[76]:\n\n\n# Nom des colonnes de la base de données utiles au projet\n\ndf_projet.columns\n\n\n# In[77]:\n\n\n# Lire les données de la colonne \"Sex\"\n\nfor value in df_projet[\"Sex\"]:\n print(value)\n\n\n# In[78]:\n\n\n# Lire les données de la colonne \"Meditation\"\n\nfor value in df_projet[\"Meditation\"]:\n print(value)\n\n\n# In[79]:\n\n\n# Lire les données de la colonne \"Empathy.Agreeableness\"\n# !!! Il y a une donnée manquante \n\nfor value in df_projet[\"Empathy.Agreeableness\"]:\n print(value)\n \n\n\n# In[80]:\n\n\n# Lire les données de la colonne \"Disinhibition\"\n\nfor value in df_projet[\"Disinhibition\"]:\n print(value)\n \n\n\n# In[87]:\n\n\n# Coder la variable Méditation ( \"No\" = 0; \"Yes\" = 1)\nfor value in df_projet['Meditation'] : \n if value == \"No\":\n df_projet.replace(value, int(str(0)), inplace = True)\n \n if value == \"Yes\":\n df_projet.replace(value, int(str(1)), inplace = True)\n \n# Lire les données de la colonne \"Meditation\"\n\nfor value in df_projet[\"Meditation\"]:\n print(value)\n\n\n# In[90]:\n\n\n# Type de variables \n\nprint(type(\"Sex\"))\nprint(type(\"Meditation\"))\nprint(type(\"Empathy.Agreeableness\"))\nprint(type(\"Disinhibition\"))\n\n\n# In[ ]:\n\n\n# Transformer de chaîne à float\n\nfor col in df.columns:\n if Meditation in col:\n df_projet[Meditation] = df_projet[Meditation].astype(float)\n if Empathy.Agreeableness in col:\n df_projet[Empathy.Agreeableness] = df_projet[Empathy.Agreeableness].astype(float)\n if Disinhibition in col:\n df_projet[Disinhibition] = df_projet[Disinhibition].astype(float)\n\n\n# In[46]:\n\n\n# Gérer les données manquantes pour la colonne \"Empathy.Agreeableness\"\n\nimport sklearn \nimport seaborn\nfrom sklearn import impute\nimport numpy as np\n\nimp = sklearn.impute.SimpleImputer(missing_values = np.nan, strategy = \"mean\")\n\n\n# In[ ]:\n\n\n# Modification des valeurs manquantes pour la moyenne des scores\nimport numpy as np\nimport sklearn \nfrom sklearn import impute\nimp = sklearn.impute.SimpleImputer(missing_values = np.nan, strategy = \"mean\")\n\nfor col in [Empathy.Agreeableness]:\n \n for index in range(len(df)):\n \n for i in [df_projet[col][index]]: \n cell = i \n imp.fit(df[[col]])\n df[col] = imp.transform(df[[col]])\n\n\n# In[101]:\n\n\n# Créer les scores Z\n\ndef create_scoreZ():\n\n import numpy as np\n import sklearn \n from sklearn import preprocessing\n import pandas as pd\n scaler = sklearn.preprocessing.StandardScaler()\n\n\n\n columns = df_projet.columns.values.tolist()\n\n for col in df_projet.columns : \n \n for i in df[col]: \n x_value = df_projet[col].to_numpy() \n x_value = x_value[:, np.newaxis] \n scaled_value = scaler.fit(x_value) \n scaled_value = scaler.transform(x_value)\n \n df['Z_'+col] = scaled_value\n\n\n# In[102]:\n\n\n# Test T pour hypothèse 1\n \nimport scipy\nfrom scipy import stats\n \navec_meditation = df_projet[df_projet['Meditation'] == 'Yes'][\"Empathy.Agreeableness\"]\nsans_meditation = df_projet[df_projet['Meditation'] == 'No'][\"Empathy.Agreeableness\"]\n\nstats.ttest_ind(avec_meditation, sans_meditation)\n\nres = stats.ttest_ind(avec_meditation, sans_meditation)\nprint(res.statistic, res.pvalue)\n\n\n# In[ ]:\n\n\n# Graphique pour hypothèse 1\n\nimport seaborn as sns\nwith sns.axes_style(style='ticks'):\n g = sns.catplot(data=df_projet, x = 'Meditation', y = 'Empathy.Agreeableness', kind='box')\n g.set_axis_labels('Meditation', 'Empathy.Agreeableness')\n \n\n\n# In[44]:\n\n\n# Test T pour hypothèse 2\n\navec_meditation = df_projet[df_projet['Meditation'] == 'Yes'][\"Disinhibition\"]\nsans_meditation = df_projet[df_projet['Meditation'] == 'No'][\"Disinhibition\"]\n\nstats.ttest_ind(avec_meditation, sans_meditation)\n\nres = stats.ttest_ind(avec_meditation, sans_meditation)\nprint(res.statistic, res.pvalue)\n\n\n# In[ ]:\n\n\n# Graphique pour hypothèse 2 \n\nwith sns.axes_style(style='ticks'):\n sns.catplot(data=df_projet, x = 'Meditation', y = 'Disinhibition', kind='box')\n set_axis_labels('Meditation', 'Disinhibition') \n\n\n# In[104]:\n\n\n# Préalables AA supervisé\n\nimport pandas as pd\nimport numpy as np\nimport scipy\nfrom scipy import stats\nget_ipython().run_line_magic('matplotlib', 'inline')\nimport matplotlib\nimport matplotlib.pyplot as plt\nplt.rcParams[\"figure.figsize\"] = (8,5) \nimport seaborn as sns;\nimport sklearn\nfrom sklearn.datasets import fetch_lfw_people, make_blobs, make_circles, load_digits\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.model_selection import GridSearchCV, train_test_split\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\nfrom sklearn.svm import SVC \nfrom sklearn.decomposition import PCA\nfrom sklearn.neighbors import KNeighborsClassifier as KNN\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import BaggingClassifier, RandomForestClassifier, RandomForestRegressor\nfrom sklearn.metrics import classification_report, confusion_matrix, accuracy_score\nfrom IPython.display import Image\nfrom mpl_toolkits import mplot3d\nfrom ipywidgets import interact, fixed\nfrom math import sqrt\n\n\n# In[ ]:\n\n\n# AA supervisé code \n\nclass KNN_GRAPHIQUE:\n\n\tdef AA_supervise():\n\n\t\tX_data = pd.read_excel(\"/Users/fadilamoussaoui/Desktop/Session H22/PSY4016 - Programmation/Projet/Base_de_données_travail.csv\")\n\t\tX = X_data['Sex', 'Meditation', 'Empathy.Agreeableness', 'Disinhibition'].values\n\n\t\tMeditation = X_data.Meditation.to_list()\n\t\tle = LabelEncoder()\n\t\tlabel=le.fit_transform(Meditation)\n\t\ty = label\n\n\t\tknn = KNN(n_neighbors=3)\n\t\ty_pred = cross_validation_predict(knn, X, y, cv = 5)\n\n\t\tprint(sqrt(mean_squared_error(y,y_pred)))\n\t\tprint(r2_score(y,y_pred))\n\n\t\terror = []\n\t\tfor k in range(1,100):\n\t\t knn = KNN(n_neighbors=k)\n\t\t y_pred = cross_validation_predict(knn, X, y)\n\t\t error.append(mean_squared_error(y,y_pred))\n \n\n\n# In[ ]:\n\n\n# AA supervisé graphique\n\nax = plt.axes()\nax.plot(range(1,100),error, color=\"red\", linestyle=\"-\", marker=\"o\",\n markerfacecolor=\"blue\", markersize=10)\nax.set_title(\"KNN non standardisé\")\nax.set_xlabel(\"K\")\nax.set_ylabel(\"Erreur\")\nplt.show()\n\n\n# In[ ]:\n\n\n# Préalables AA non-supervisé\nimport sklearn\nimport warnings\nwarnings.filterwarnings(\"ignore\")\nwarnings.simplefilter(\"ignore\")\nprint('sklearn version:', sklearn.__version__)\nget_ipython().run_line_magic('matplotlib', 'inline')\nimport matplotlib.pyplot as plt\nimport seaborn as sns; sns.set()\nfrom sklearn.cluster import KMeans\nfrom sklearn.datasets import make_blobs\nfrom sklearn.decomposition import PCA\n\n\n# In[ ]:\n\n\n# AA non-supervisé code \n\ndef AA_non_supervise():\n\n X_data = pd.read_excel(\"/Users/fadilamoussaoui/Desktop/Session H22/PSY4016 - Programmation/Projet/Base_de_données_travail.csv\")\n X = X_data['Sex', 'Meditation', 'Empathy.Agreeableness', 'Disinhibition'].values\n\n x = X_data.loc[:, caractéristiques].values\n y = X_data.loc[:,[\"Meditation\"]].values\n\n x = StandardScaler().fit_transform(x)\n\n\n pca = PCA(n_components=2)\n principalComponents = pca.fit_transform(x)\n Df1 = pd.DataFrame(data = principalComponents\n , columns = ['principal component 1', 'principal component 2'])\n Df2 = pd.concat([Df1, X_data[['Meditation']]], axis = 1)\n\n\n# In[ ]:\n\n\n# AA non-supervisé graphique \n\nfig = plt.figure(figsize = (8,8))\nax = fig.add_subplot(1,1,1) \nax.set_xlabel('Principal Component 1', fontsize = 15)\nax.set_ylabel('Principal Component 2', fontsize = 15)\nax.set_title('2 component PCA', fontsize = 20)\n\ntargets = ['No', 'Yes']\ncolors = ['r', 'g']\nfor target, color in zip(targets,colors):\n indicesToKeep = finalDf['Meditation'] == target\n ax.scatter(Df2.loc[indicesToKeep, 'principal component 1']\n , Df2.loc[indicesToKeep, 'principal component 2']\n , c = color\n , s = 50)\n \nprint(pca.explained_variance_ratio_)\n\n\n# In[ ]:\n\n\n# Base de données sqlite avec une pipeline\n\nimport sqlite\nsqlite.try_sqlite()\n\n","repo_name":"fadilamoussaoui/psy4016","sub_path":"Projet_Final_Fadila_Moussaoui.py","file_name":"Projet_Final_Fadila_Moussaoui.py","file_ext":"py","file_size_in_byte":8785,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"37507564377","text":"# Project libraries\nfrom . import BaseResource\n\n\nclass SSLCert(BaseResource):\n \"\"\"SSL Certificate.\"\"\"\n\n _strs = [\"cert_domains\", \"issuer\", \"id\", \"subject\"]\n _dates = [\"expires_at\", \"starts_at\"]\n _pks = [\"id\"]\n _bools = [\"ca_signed?\", \"self_signed?\"]\n order_by = \"created_at\"\n\n def __init__(self):\n self.app = None\n super(SSLCert, self).__init__()\n\n def __repr__(self):\n return \"\".format(self.id)\n","repo_name":"martyzz1/heroku3.py","sub_path":"heroku3/models/ssl_cert.py","file_name":"ssl_cert.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"en","doc_type":"code","stars":118,"dataset":"github-code","pt":"50"} +{"seq_id":"17523890592","text":"# -*- coding: utf-8 -*-\n\n# from django.test import TestCase\n\n# Create your tests here.\nimport pytest\nfrom games.models import *\nfrom players.models import *\n\n\npytestmark = pytest.mark.django_db\n\n\nclass TestMatchModel():\n\n def test_tie(self):\n red, blue = self.build_players()\n response = self.factory(\n [red, blue],\n [['1', '1'], ['1', '1'], ['1', '1']]\n )\n assert response is None\n\n def test_red_win(self):\n red, blue = self.build_players()\n response = self.factory(\n [red, blue],\n [['1', '3'], ['1', '2'], ['1', '3']]\n )\n assert response == red\n\n def test_blue_win(self):\n red, blue = self.build_players()\n response = self.factory(\n [red, blue],\n [['1', '1'], ['1', '1'], ['1', '2']]\n )\n assert response == blue\n\n def test_not_finished_match(self):\n red, blue = self.build_players()\n response = self.factory(\n [red, blue],\n [['1', '3'], ['1', '2']],\n round_number=2\n )\n assert response is None\n\n def build_players(self):\n red = Player.objects.create(name='red_sox')\n blue = Player.objects.create(name='blue_sox')\n return red, blue\n\n def factory(self, players, sets, round_number=None):\n match = Match.objects.create(\n red_player=players[0],\n blue_player=players[1]\n )\n rounds = round_number if round_number else match.rounds_per_match\n for i in range(rounds):\n match_round = MatchRound.objects.create(\n match=match\n )\n red_event = MatchEvent.objects.create(\n player=match.red_player,\n choice=sets[i][0],\n match_round=match_round\n )\n blue_event = MatchEvent.objects.create(\n player=match.blue_player,\n choice=sets[i][1],\n match_round=match_round\n )\n return match.winner()\n","repo_name":"ppsirg/jajanken","sub_path":"jajanken/games/tests/test_models.py","file_name":"test_models.py","file_ext":"py","file_size_in_byte":2067,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"70942432795","text":"\"\"\"\nModule to make the image stacking / production occur.\n\"\"\"\n\n\nimport os\nfrom glob import glob\n\nimport astropy.io.fits as pyfits\nimport numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\nfrom astropy.convolution import Gaussian2DKernel, interpolate_replace_nans\n\nfrom . import plotting as pl\nfrom . import registration as reg\nfrom . import utils as u\nfrom . import contrast as contrast\n\n\nclass FlatOpeningError(ValueError):\n pass\n\n\ndef open_flats(flatfile):\n \"\"\"\n Opens flats files. Essentially a wrapper around pyfits.getdata that\n also includes a descriptive exception if the file doesn't exist.\n\n Inputs:\n :flatfile: (str) path to dark to be opened.\n\n Outputs:\n :dark: (array) data from darks FITS file.\n \"\"\"\n if flatfile[-4:] != \"fits\":\n raise FlatOpeningError(\n \"\"\"Currently, SImMER only supports flats in FITS files.\"\"\"\n )\n if not os.path.exists(flatfile):\n raise FlatOpeningError(\n \"\"\"The requested flat file can't be found. Please check that you have a flat\n file corresponding to every filter used in your observations.\"\"\"\n )\n else:\n flat = pyfits.getdata(flatfile, 0)\n return flat\n\n\ndef image_driver(raw_dir, reddir, config, inst, sep_skies=False, plotting_yml=None, verbose=False):\n \"\"\"Do flat division, sky subtraction, and initial alignment via coords in header.\n Returns Python list of each registration method used per star.\n\n Inputs:\n :raw_dir: (string) directory for the raw data\n :reddir: (string) directory for the reduced data\n :config: (pandas DataFrame) dataframe corresponding to config sheet for data.\n :inst: (Instrument object) instrument for which data is being reduced.\n :plotting_yml: (string) path to the plotting configuration file.\n\n \"\"\"\n # Save these images to the appropriate folder.\n\n if plotting_yml:\n pl.initialize_plotting(plotting_yml)\n\n if inst.take_skies:\n skies = config[config.Comments == \"sky\"]\n else:\n skies = config[\n (config.Object != \"flat\")\n & (config.Object != \"dark\")\n & (config.Object != \"setup\")\n ]\n stars = skies.Object.unique()\n sdirs = glob(reddir + \"*/\")\n\n #Make sure list of stars doesn't include sky frames taken by nodding\n if sep_skies == True:\n keep = np.zeros(len(stars)) + 1\n for kk in np.arange(len(keep)):\n if(\"sky\" in stars[kk]):\n keep[kk] = 0\n wstar = np.where(keep == 1)\n stars = stars[wstar]\n\n else:\n stars = stars\n\n methods = []\n\n for star in tqdm(\n np.unique(stars), desc=\"Running image driver\", position=0, leave=True\n ):\n s_dir = reddir + star + \"/\"\n if (\n s_dir not in sdirs\n ): # make sure there's a subdirectory for each star\n os.mkdir(s_dir)\n\n filts = skies[\n skies.Object == star\n ].Filter.values # array of filters as strings\n for n, filter_name in enumerate(filts):\n obj = config[config.Object == star]\n imlist = eval(\n obj[obj.Comments != \"sky\"].Filenums.values[n]\n ) # pylint: disable=eval-used # liter_eval issues\n # cast obj_methods as list so that elementwise comparison isn't performed\n obj_methods = config[config.Object == star].Method.values\n\n # use pd.isnull because it can check against strings\n if np.all(pd.isnull(obj_methods)):\n methods.append(\"quick_look\")\n else:\n obj_method = obj_methods[~pd.isnull(obj_methods)][0].lower()\n if \"saturated\" and \"separated\" in obj_method:\n methods.append(\"saturated separated\")\n elif \"saturated\" in obj_method and \"separated\" not in obj_method:\n methods.append(\"saturated\")\n elif \"saturated\" not in obj_method and \"separated\" in obj_method:\n methods.append(\"separated\")\n create_imstack(\n raw_dir, reddir, s_dir, imlist, inst, filter_name=filter_name\n )\n return methods\n\n\ndef create_imstack(\n raw_dir, reddir, s_dir, imlist, inst, plotting_yml=None, filter_name=None\n):\n \"\"\"Create the stack of images by performing flat division, sky subtraction.\n\n Inputs:\n :raw_dir: (string) path to directory containing raw data\n :reddir: (string) path to directory containing reduced data\n :s_dir: (string) path to directory corresponding to a specific star.\n :imlist: (list) list of strings of paths pointing to image files.\n :inst: (Instrument object) instrument for which data is being reduced.\n :plot: (bool) determines whether or not intermediate plots should be produced.\n :filter_name: (string) name of the filter used for the images in question.\n\n Outputs:\n :im_array: (3d array) array of 2d images.\n :shifts_all: recording of all the x-y shifts made\n \"\"\"\n if plotting_yml:\n pl.initialize_plotting(plotting_yml)\n\n nims = len(imlist)\n imfiles = u.make_filelist(raw_dir, imlist, inst)\n\n #Keep track of original filenames so that we can annotate the shift1_cube\n #image arrays and easily decide which images to exclude\n original_fnames=imfiles.copy()\n for jj in np.arange(len(imfiles)):\n original_fnames[jj] = os.path.basename(imfiles[jj]).split('.')[0]\n\n im_array = u.read_imcube(imfiles)\n\n im_array = inst.adjust_array(im_array, nims)\n\n head = inst.head(imfiles[0])\n filt = inst.filt(nims, head, filter_name)\n\n # if necessary, make directory for filter. Also grab correct flat file\n fdirs = glob(s_dir + \"*/\")\n\n sf_dir = s_dir + filt + \"/\"\n if sf_dir not in fdirs: # make a directory for each filt\n os.mkdir(sf_dir)\n\n flatfile = reddir + f\"flat_{filt}.fits\"\n if (\n inst.name == \"PHARO\" and filt == \"Br-gamma\"\n ): # not sure whether this is generalizable\n flatfile = reddir + \"flat_K_short.fits\"\n\n #For ShARCS, use Ks flat instead of BrG-2.16 if necessary\n if (inst.name == \"ShARCS\" and filt == \"BrG-2.16\"):\n if os.path.exists(flatfile) == False:\n flatfile = reddir + 'flat_Ks.fits'\n\n #For ShARCS, use J flat instead of J+Ch4-1.2 if necessary\n if (inst.name == \"ShARCS\" and filt == \"J+Ch4-1.2\"):\n if os.path.exists(flatfile) == False:\n flatfile = reddir + 'flat_J.fits'\n\n flat = open_flats(flatfile)\n\n skyfile = sf_dir + \"sky.fits\"\n sky = pyfits.getdata(skyfile, 0)\n sky[np.isnan(sky)] = 0.0 # set nans from flat=0 pixels to 0 in sky\n\n shifts_all = []\n for i in range(nims):\n # flat division and sky subtraction\n current_im = im_array[i, :, :]\n flat[flat == 0] = np.nan\n current_im = (\n current_im / flat\n ) - sky # where flat = 0, this will be nan\n current_head = pyfits.getheader(imfiles[i])\n\n # bad pixel correction\n current_im = inst.bad_pix(current_im)\n\n # now deal with headers and shifts\n shifted_im, shifts = reg.shift_bruteforce(\n current_im\n ) # put it at the center\n\n shifts_all.append(shifts)\n\n im_array[i, :, :] = shifted_im\n hdu = pyfits.PrimaryHDU(shifted_im, header=current_head)\n hdu.writeto(\n sf_dir + \"sh{:02d}.fits\".format(i),\n overwrite=True,\n output_verify=\"ignore\",\n )\n\n pl.plot_array(\n \"intermediate\", im_array, -10.0, 10000.0, sf_dir, \"shift1_cube.png\",snames=original_fnames\n )\n\n # write shifts to file\n textfile = open(sf_dir + \"shifts.txt\", \"w\")\n textfile.write(\"im, d_row, d_col\\n\")\n for i, shift in enumerate(shifts_all):\n textfile.write(\"{},{},{}\\n\".format(i, *shift))\n textfile.close()\n return im_array, shifts_all\n\n\ndef create_im(s_dir, ssize1, plotting_yml=None, fdirs=None, method=\"quick_look\", verbose=False):\n \"\"\"Take the shifted, cut down images from before, then perform registration\n and combine. Tests should happen before this, as this is a per-star basis.\n\n Inputs:\n :s_dir: (str) directory for the raw data\n :ssize1: (int) initial pixel search size of box.\n :plotting_yml: (str) path to the plotting configuration file.\n :fdirs: (list of str) file directories.\n :method: (str) image registration method.\n \"\"\"\n if plotting_yml:\n pl.initialize_plotting(plotting_yml)\n\n if not fdirs:\n fdirs = glob(s_dir + \"*/\")\n\n for sf_dir in fdirs: # each filter for each star\n #Only register star images, not sky images\n dirparts = sf_dir.split('/')\n if 'sky' in dirparts[len(dirparts)-3]:\n if verbose == True:\n print('this is a sky directory: ', sf_dir)\n continue\n\n if verbose == True:\n print('working on sf_dir ', sf_dir)\n\n files = glob(\n sf_dir + f\"sh*.fits\"\n ) # might need to change to file_prefix\n nims = len(files)\n\n frames = u.read_imcube(files)\n frames = frames.astype(float)\n\n arrsize1 = ssize1 * 2 + 1\n rots = np.zeros((nims, arrsize1, arrsize1))\n newshifts1 = []\n\n # if we're doing PSF-fitting, we do it across all the images at once\n if method == 'psf':\n frames = reg.register_psf_fit(frames)\n\n for i in range(nims): # each image\n image = frames[i, :, :]\n\n #Interpolate over NaNs so that scipy can shift images\n #without producing arrays that are completely NaN\n #Following this tutorial: https://docs.astropy.org/en/stable/convolution/index.html\n\n # Generate Gaussian kernel with x_stddev=1 (and y_stddev=1)\n # It is a 9x9 array\n kernel = Gaussian2DKernel(x_stddev=1)\n\n # Replace NaNs with interpolated values\n image = interpolate_replace_nans(image, kernel)\n\n if method == \"saturated\":\n image_centered, rot, newshifts1 = reg.register_saturated(\n image, ssize1, newshifts1\n )\n rots[i, :, :] = rot\n elif method == \"quick_look\":\n image[image < 0.0] = 0.0\n image_centered = reg.register_bruteforce(image)\n if len(image_centered) == 0:\n print(\"Resorting to saturated mode.\")\n image_centered, rot, newshifts1 = reg.register_saturated(\n image, ssize1, newshifts1\n )\n rots[i, :, :] = rot\n elif method == \"saturated separated\":\n rough_center = reg.find_wide_binary(image)\n image_centered, rot, newshifts1 = reg.register_saturated(\n image, ssize1, newshifts1, rough_center=rough_center\n )\n rots[i, :, :] = rot\n elif method == \"separated\":\n rough_center = reg.find_wide_binary(image)\n image_centered = reg.register_bruteforce(\n image, rough_center=rough_center\n )\n frames[i, :, :] = image_centered # newimage\n\n final_im = np.nanmedian(frames, axis=0)\n #Trim down to smaller final size\n final_im = final_im[100:700,100:700] #extract central 600x600 pixel region\n\n #Trim down to smaller final size\n cutsize = 600 #desired axis length of final cutout image\n astart = int(round((final_im.shape[0]-cutsize)/2.))\n bstart = int(round((final_im.shape[1]-cutsize)/2.))\n aend = astart+cutsize\n bend = bstart+cutsize\n if np.logical_or(aend > final_im.shape[0],bend > final_im.shape[1]):\n print('ERROR: Requested cutout is too large. Using full image instead.')\n print('Current image dimensions: ', final_im.shape)\n print('Desired cuts: ', astart, aend, bstart, bend)\n else:\n final_im = final_im[astart:astart+cutsize,bstart:bstart+cutsize] #extract central cutsize x cutsize pixel region from larger image\n\n head = pyfits.getheader(files[0])\n hdu = pyfits.PrimaryHDU(final_im, header=head)\n hdu.writeto(\n sf_dir + \"final_im.fits\", overwrite=True, output_verify=\"ignore\"\n )\n\n textfile1 = open(sf_dir + \"shifts2.txt\", \"w\")\n textfile1.write(\"im, d_row, d_col\\n\")\n for i, item in enumerate(newshifts1):\n textfile1.write(\"{},{},{}\\n\".format(i, *item))\n textfile1.close()\n pl.plot_array(\n \"rots\",\n rots,\n 0.0,\n 1.0,\n sf_dir,\n \"rots.png\",\n extent=[-ssize1, ssize1, -ssize1, ssize1],\n )\n\n final_vmin, final_vmax = np.percentile(final_im, [1,99])\n pl.plot_array(\n \"final_im\", final_im, final_vmin, final_vmax, sf_dir, \"final_image.png\"\n )\n frames_vmin, frames_vmax = np.percentile(frames, [1,99])\n pl.plot_array(\n \"intermediate\", frames, frames_vmin, frames_vmax, sf_dir, \"centers.png\"\n )\n","repo_name":"arjunsavel/SImMER","sub_path":"src/simmer/image.py","file_name":"image.py","file_ext":"py","file_size_in_byte":13162,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"50"} +{"seq_id":"30827662107","text":"from logger import Logger\n\n\nclass Server(Logger):\n IDLE = 'IDLE'\n BUSY = 'BUSY'\n\n def __init__(self, state=IDLE):\n Logger.__init__(self)\n self.state = state\n self.jobStartTime = 0.0\n\n def __repr__(self):\n return 'Server: %s' % self.state\n\n def setState(self, state, currentTime):\n if state == Server.BUSY:\n if self.state == Server.BUSY:\n self.departure(1, currentTime, self.jobStartTime)\n self.arrival(1 if self.state == Server.BUSY else 0, currentTime)\n self.jobStartTime = currentTime\n elif state == Server.IDLE:\n self.departure(1 if self.state == Server.BUSY else 0, currentTime, self.jobStartTime)\n self.state = state\n","repo_name":"dj-shin/queueing-system","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":745,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"5833564771","text":"#coding=utf-8\n\"\"\"\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n#\n# fileName : Merge.py\n# comment : Merge the Original Picture and Ouput Picture\n# version :\n# author : ArtyZe\n# date : \n#\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\"\"\"\nimport cv2\n\ndef Merge(img,img1):\n height,width, c = img1.shape\n for c in range(c):\n for i in range(0, height):\n for j in range(0, width):\n if(img[i,j] >90):\n #here 90 is Threshold for heatmap\n #print im1[i,j]\n img1[i,j,1] = 100+img1[i,j,1]\n\t\t\t\n cv2.imwrite(\"final.png\",img1)\n return img1\n \nim = cv2.imread(\"pred.png\",cv2.IMREAD_GRAYSCALE)\nim1 = cv2.imread(\"orig.png\")\n\n\nMerge(im, im1)","repo_name":"my-hello-world/yolo_segmentation","sub_path":"Merge.py","file_name":"Merge.py","file_ext":"py","file_size_in_byte":761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"50"} +{"seq_id":"37956119185","text":"NEGATIVE_TRIGGER_LABEL = 'O'\nNEGATIVE_ARGUMENT_LABEL = 'no_arg'\nNEGATIVE_ENTITY_LABEL = 'O'\nSD4M_RELATION_TYPES = ['Accident', 'CanceledRoute', 'CanceledStop', 'Delay',\n 'Obstruction', 'RailReplacementService', 'TrafficJam',\n NEGATIVE_TRIGGER_LABEL]\n# Removed SDW relations without a trigger type:\n# [\"OrganizationLeadership\", \"Acquisition\"]\nSDW_RELATION_TYPES = [\"Disaster\", \"Insolvency\", \"Layoffs\", \"Merger\", \"SpinOff\", \"Strike\",\n \"CompanyProvidesProduct\", \"CompanyUsesProduct\", \"CompanyTurnover\",\n \"CompanyRelationship\", \"CompanyFacility\", \"CompanyIndustry\",\n \"CompanyHeadquarters\", \"CompanyWebsite\", \"CompanyWikipediaSite\",\n \"CompanyNumEmployees\", \"CompanyCustomer\", \"CompanyProject\",\n \"CompanyFoundation\", \"CompanyTermination\", \"CompanyFinancialEvent\",\n NEGATIVE_TRIGGER_LABEL]\nROLE_LABELS = ['location', 'delay', 'direction',\n 'start_loc', 'end_loc',\n 'start_date', 'end_date', 'cause',\n 'jam_length', 'route', NEGATIVE_ARGUMENT_LABEL]\nSDW_ROLE_LABELS = ['num_striking', 'company', 'damage_costs', 'parent',\n 'child', 'old', 'new', 'location', 'date', 'union',\n 'num_laid_off', 'victims', 'striker', 'buyer', 'acquired',\n 'seller', 'price', 'organization', 'person', 'position',\n 'from', 'to', NEGATIVE_ARGUMENT_LABEL]\nfrom eventx.dataset_readers import *\nfrom eventx.models import *\nfrom eventx.predictors import *\nfrom eventx.util import *\n","repo_name":"DFKI-NLP/eventx","sub_path":"eventx/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1632,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"35879826514","text":"class Grafo:\n def __init__(self, vertices, direccionado=True):\n self.n= vertices\n self.nodositerar = range(self.n)\n self.dir = direccionado\n self.listaadya = {nodo: set() for nodo in self.nodositerar}\n\n\n\n def adicionar(self, u,v, peso=1):\n self.listaadya[u].add((v,peso))\n\n if not self.dir:\n self.listaadya[v].add((u, peso))\n\n def mostrar(self):\n for clave in self.listaadya.keys():#keys() es algo attribuido ya para diccionarios\n print(\"nodo\", clave, \": \", self.listaadya[clave])\n\ng=Grafo(5)\ng.adicionar(0,0,25)\ng.adicionar(0,2,10)\ng.adicionar(0,3,100)\ng.adicionar(2,4,26)\ng.mostrar()","repo_name":"NeoEzzio/algoritmos","sub_path":"Gráfos/gráfo2.py","file_name":"gráfo2.py","file_ext":"py","file_size_in_byte":665,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"11550339437","text":"import math\nfrom copy import deepcopy\nimport os\nimport shutil\nfrom typing import Callable, Tuple, Dict, Union, Any\nimport gc\nimport pathlib\nimport hashlib\nimport importlib.util\nimport sys\nimport numbers\n\nimport torch\nfrom torch.nn.parallel import DistributedDataParallel\nfrom torch import distributed as dist\nfrom torch.cuda.amp.grad_scaler import GradScaler\nfrom torch.cuda.amp.autocast_mode import autocast\nfrom torchdata.dataloader2 import DataLoader2, MultiProcessingReadingService\nfrom vlutils.base import Registry\nfrom vlutils.base.freqHook import ChainHook\nfrom vlutils.saver import Saver\nfrom vlutils.logger import trackingFunctionCalls\nfrom vlutils.base import Restorable\nfrom vlutils.runtime import relativePath\nfrom vlutils.config import summary\n\nimport modfire.utils.registry\nfrom modfire.utils.registry import OptimRegistry, SchdrRegistry, CriterionRegistry, ModelRegistry, DatasetRegistry, DataPipeRegistry\nfrom modfire import Consts\nfrom modfire.config import Config\nfrom modfire.train.hooks import getAllHooks\nfrom modfire.validate import Validator, metrics\nfrom modfire.utils import totalParameters, StrPath, getRichProgress, SafeTerminate\nfrom modfire.dataset import QuerySplit, Database, TrainSplit\n\nfrom .hooks import EpochFrequencyHook, checkHook, splitHooks\nfrom .utils import EMATracker, PrettyStep, getSaver, setWeightDecay\n\n\nclass TrainerBuilder(modfire.utils.registry.Registry[Callable[..., \"PalTrainer\"]]):\n pass\n\n\ndef _p(attr):\n def _decorate(self):\n msg = \"[%s] \" + attr\n return msg, self.PrettyStep\n return property(_decorate)\n\nclass LoggingTemplate:\n def __init__(self):\n self._step = 0\n self._prettyStep = PrettyStep()\n\n @property\n def PrettyStep(self):\n return self._prettyStep(self._step)\n\n\n ########### Templates ###########\n _LOG_InitSummary = _p(\"Here is the whole config during this run: \\r\\n%s\")\n _LOG_Bye = _p(\"Bye.\")\n _LOG_FindCkpt = _p(\"Found ckpt to resume at %s\")\n _LOG_ResumeAtEpoch = _p(\"Resume training at %d epochs.\")\n _LOG_StartTraining = _p(\"Start training.\")\n _LOG_RevealTensorboard = _p(\"See you at `%s`\")\n _LOG_EarlyStop = _p(\"Early stopped at epoch %4d.\")\n _LOG_SummaryInfoNomAP = _p(\"Total epoches: %d, total steps: %s, best mAP: N/A.\")\n _LOG_SummaryInfo = _p(\"Total epoches: %d, total steps: %s, best mAP: %.2f%%.\")\n _LOG_SummarySaveCkpt = _p(\"Model saved to %s`.\")\n\n\n _DEBUG_InitRank = _p(\"<%s> is located at rank `%d`\")\n _DEBUG_Inited = _p(\"<%s> created.\")\n _DEBUG_RestoreStateStart = _p(\"Restored state dict from `%s`\")\n _DEBUG_RestoreStateFinish = _p(\"Restore network parameters finished.\")\n _DEBUG_OptimizerReset = _p(\"Optimizer reset.\")\n _DEBUG_LRSchdrReset = _p(\"LR scheduler reset.\")\n _DEBUG_RegistrySummary = _p(\"Summary of %s: \\r\\n%s\")\n _DEBUG_CreateDatasetStart = _p(\"Create `config.Train.TrainSet` (\\\"%s\\\") with training pipeline: `%s`.\")\n _DEBUG_CreateQuerysetStart = _p(\"Create `config.Train.QuerySet` (\\\"%s\\\") with evaluation pipeline: `%s`.\")\n _DEBUG_CreateDatabaseStart = _p(\"Create `config.Train.Database` (\\\"%s\\\") with evaluation pipeline: `%s`.\")\n _DEBUG_CreateDatasetFinish = _p(\"Training dataset \\r\\n\\t%s \\r\\nmounted.\")\n _DEBUG_CreateDatasetMainFinish = _p(\"Train set \\r\\n\\t%s, \\r\\nquery set \\r\\n\\t%s and \\r\\ndatabase \\r\\n\\t%s \\r\\nmounted.\")\n _DEBUG_CreateModelStart = _p(\"Creating model...\")\n _DEBUG_CreateModelFinish = _p(\"Model created. #Params: %s.\")\n _DEBUG_CreateCriterionStart = _p(\"Creating criterion...\")\n _DEBUG_CreateCriterionFinish = _p(\"criterion created. #Params: %s.\")\n _DEBUG_CreateOptimizerStart = _p(\"Creating optimizer...\")\n _DEBUG_CreateOptimizerFinish = _p(\"Optimizer created.\")\n _DEBUG_CreateSchdrStart = _p(\"Creating LR scheduler...\")\n _DEBUG_CreateSchdrFinish = _p(\"LR scheduler created.\")\n _DEBUG_CallBeforRunStart = _p(\"Call `_beforeRun()`.\")\n _DEBUG_CallBeforRunFinish = _p(\"End call `_beforeRun()`.\")\n _DEBUG_CallAfterRunStart = _p(\"Call `_afterRun()`.\")\n _DEBUG_CallAfterRunFinish = _p(\"End call `_afterRun()`.\")\n _DEBUG_CallEpochBeginStart = _p(\"Epoch %4d started.\")\n _DEBUG_CallEpochBeginFinish = _p(\"End call `_epochStart()`.\")\n _DEBUG_CallEpochEndStart = _p(\"Epoch %4d finished.\")\n _DEBUG_CallEpochEndFinish = _p(\"End call `_epochFinish()`.\")\n _DEBUG_TrainLoopFinish = _p(\"Training loop finished.\")\n _DEBUG_ShowSchdrUpdate = _p(\"Lr is set to %.2e.\")\n _DEBUG_ShowTemperatureUpdate = _p(\"Temperature is set to %.2e.\")\n _DEBUG_Validation = _p(\"Start validation at epoch %4d.\")\n _DEBUG_FindOneEarlyStop = _p(\"Performance not improved for %d / %d epochs.\")\n _DEBUG_EndValidation = _p(\"End validation at epoch %4d.\")\n\n _CRITICAL_ProcessInterrupt = _p(\"Main process was interrupted, try to save necessary info.\")\n _CRITICAL_TimeoutInfo = _p(\"This post-process will be killed after %d secs if stuck.\")\n _CRITICAL_ShutdownMessage = _p(\"Find the last checkpoint at `%s`\")\n\n\nclass PalTrainer(Restorable, LoggingTemplate):\n def __init__(self, config: Config, loggingLevel: int):\n Restorable.__init__(self)\n LoggingTemplate.__init__(self)\n\n self._epoch = 0\n\n self.rank = dist.get_rank()\n self.worldSize = dist.get_world_size()\n torch.cuda.set_device(self.rank)\n self.config = config\n self.saver = getSaver(self.config.Train.SaveDir, saveName=\"saved.ckpt\", config=config.serialize(), loggerName=\"root\", reserve=False, loggingLevel=loggingLevel, disable=self.rank != 0)\n\n self.saver.info(*self._LOG_InitSummary, summary(config.serialize()))\n\n self.saver.debug(*self._DEBUG_InitRank, self.__class__.__name__, self.rank)\n\n # # Used for self.PrettyStep\n # self.lastFormatted = -1\n self._preRegistration()\n\n self._model = self._createModel()\n self._criterion = self._createCriterion()\n self._optimizer, self.optimFn = self._createOptimizer(self._model, self._criterion)\n self._scheduler, self.schdrFn = self._createScheduler(self._optimizer)\n\n self.earlyStopFlag = torch.tensor([False]).to(self.rank)\n\n self.saver.debug(*self._DEBUG_Inited, self.__class__.__name__)\n\n\n def save(self, path = None):\n self.saver.save(path, trainer=self, config=self.config.serialize())\n\n def done(self):\n self.saver.debug(summary(self.config.serialize()))\n self.saver.info(*self._LOG_Bye)\n\n def resume(self, path):\n self.saver.info(*self._LOG_FindCkpt, path)\n self.restoreStates(path)\n self.saver.info(*self._LOG_ResumeAtEpoch, self._epoch)\n\n def restoreStates(self, path: StrPath):\n self.saver.debug(*self._DEBUG_RestoreStateStart, path)\n self.saver.load(path, \"cpu\", logger=self.saver, trainer=self)\n self.saver.debug(*self._DEBUG_RestoreStateFinish)\n self.resetOptimizer()\n self.resetScheduler(*self._scheduler.last_epoch)\n\n def resetOptimizer(self):\n del self._optimizer\n self._optimizer = self.optimFn(self._model.parameters(), **self.config.Train.Optim.Params)\n\n for group in self._optimizer.param_groups:\n group.setdefault('initial_lr', group['lr'])\n\n self.saver.debug(self._DEBUG_OptimizerReset)\n\n def resetScheduler(self, lastEpoch=-1):\n del self._scheduler\n self._scheduler = self.schdrFn(self._optimizer, last_epoch=lastEpoch, **self.config.Train.Schdr.Params)\n self.saver.debug(self._DEBUG_LRSchdrReset)\n\n def train(self):\n beforeRunHook, afterRunHook, stepStartHook, stepFinishHook, epochStartHook, epochFinishHook = self._createHooks(self.config, self.saver, self._model, self._criterion)\n\n scaler = GradScaler()\n\n datasets = self._createDatasets()\n\n # The DistributedReadingService is too slow since it use only one worker per node.\n # NOTE: cancel the comment once if the above issue is fixed.\n # with DataLoader(datasets[\"trainSet\"].DataPipe, reading_service=DistributedReadingService()) as trainLoader:\n with DataLoader2(datasets[\"trainSet\"].DataPipe, reading_service=MultiProcessingReadingService(num_workers=min(int(math.sqrt(datasets[\"trainSet\"].BatchSize)), 16), pin_memory=True, persistent_workers=True)) as trainLoader:\n\n self._beforeRun(beforeRunHook, **datasets)\n\n batchesOneEpoch = math.ceil(len(datasets[\"trainSet\"]) / (datasets[\"trainSet\"].BatchSize * self.worldSize))\n totalBatches = batchesOneEpoch * self.config.Train.Epoch\n\n self._model.train()\n\n # A forever dataLoader\n for targets, images in trainLoader:\n if self._step % batchesOneEpoch == 0:\n self._epochStart(epochStartHook, **datasets)\n\n with autocast():\n\n # Main loop\n # Any dict used as args for model, criterion\n otherArgs = self._stepStart(stepStartHook, inputs=(targets, images))\n\n # A dict as keyword arguments for criterion\n outputs = self._model(images.to(self.rank, non_blocking=True, memory_format=torch.channels_last), **otherArgs)\n\n # loss: A scalar, stats: A dict as keyword arguments for logging\n loss, stats = self._criterion(**outputs, y=targets.to(self.rank, non_blocking=True), **otherArgs)\n\n scaler.scale(loss).backward()\n scaler.step(self._optimizer)\n scaler.update()\n self._optimizer.zero_grad()\n\n\n self._stepFinish(stepFinishHook, loss=loss, stats=stats, outputs=outputs, **otherArgs)\n if self._step % batchesOneEpoch == 0:\n try:\n self._epochFinish(epochFinishHook, **datasets)\n except StopIteration:\n break\n if self._step > totalBatches:\n break\n\n self._afterRun(afterRunHook, **datasets)\n\n\n def _preRegistration(self):\n otherPythonFiles = self.config.Train.ExternalLib\n for pyFile in otherPythonFiles:\n filePath = pathlib.Path(pyFile).absolute()\n # md5 of abs file path as module name\n moduleName = hashlib.md5(str(filePath).encode()).hexdigest()\n spec = importlib.util.spec_from_file_location(moduleName, pyFile)\n if spec is None:\n continue\n module = importlib.util.module_from_spec(spec)\n sys.modules[moduleName] = module\n spec.loader.exec_module(module)\n\n for reg in modfire.utils.registry.__all__:\n registry = getattr(modfire.utils.registry, reg)\n if issubclass(registry, Registry):\n self.saver.debug(*self._DEBUG_RegistrySummary, registry, registry.summary())\n\n @staticmethod\n def _createHooks(config: Config, saver: Saver, model, criterion):\n allHooks = getAllHooks(config.Train.Hooks)\n\n modelHooks = splitHooks(*[m.module if isinstance(m, DistributedDataParallel) else m for m in [model, criterion]])\n\n # allHooks = dict()\n for key in modelHooks.keys():\n allHooks[str(key)] = ChainHook(allHooks[str(key)], modelHooks[key])\n\n\n beforeRunHook, afterRunHook, stepStartHook, stepFinishHook, epochStartHook, epochFinishHook = allHooks[\"beforeRunHook\"], allHooks[\"afterRunHook\"], allHooks[\"stepStartHook\"], allHooks[\"stepFinishHook\"], allHooks[\"epochStartHook\"], allHooks[\"epochFinishHook\"]\n\n\n beforeRunHook = checkHook(beforeRunHook, \"BeforeRunHook\", saver)\n afterRunHook = checkHook(afterRunHook, \"AfterRunHook\", saver)\n stepStartHook = checkHook(stepStartHook, \"StepStartHook\", saver)\n stepFinishHook = checkHook(stepFinishHook, \"StepFinishHook\", saver)\n epochStartHook = checkHook(epochStartHook, \"EpochStartHook\", saver)\n epochFinishHook = checkHook(epochFinishHook, \"EpochFinishHook\", saver)\n return beforeRunHook, afterRunHook, stepStartHook, stepFinishHook, epochStartHook, epochFinishHook\n\n def _createDatasets(self) -> Dict[str, Union[TrainSplit, QuerySplit, Database]]:\n self.saver.debug(*self._DEBUG_CreateDatasetStart, self.config.Train.TrainSet.Key, self.config.Train.TrainSet.Pipeline.Key or \"default\")\n try:\n trainPipeline = trackingFunctionCalls(DataPipeRegistry.get(self.config.Train.TrainSet.Pipeline.Key), self.saver)(**self.config.Train.TrainSet.Pipeline.Params)\n except KeyError:\n trainPipeline = None\n trainSet = trackingFunctionCalls(DatasetRegistry.get(self.config.Train.TrainSet.Key), self.saver)(**self.config.Train.TrainSet.Params, pipeline=trainPipeline)\n self.saver.debug(*self._DEBUG_CreateDatasetFinish, trainSet)\n return {\n \"trainSet\": trainSet.Split\n }\n\n def _createModel(self) -> DistributedDataParallel:\n self.saver.debug(*self._DEBUG_CreateModelStart)\n modelFn = trackingFunctionCalls(ModelRegistry.get(self.config.Model.Key), self.saver)\n\n model = modelFn(**self.config.Model.Params)\n\n # EMA model for evaluation\n # deepcopy can't handle faiss objects. reject.\n # adjust = worldSize * config.Train.TrainSet.Params[\"batchSize\"] * config.Train.ModelEMASteps / config.Train.Epoch\n # alpha = 1.0 - config.Train.ModelEMADecay\n # alpha = min(1.0, alpha * adjust)\n # modelEMA = ExponentialMovingAverage(model, device=rank, decay=1.0 - alpha)\n # EMA model for evaluation\n\n model = DistributedDataParallel(model.to(memory_format=torch.channels_last).to(self.rank), device_ids=[self.rank], output_device=self.rank, find_unused_parameters=False)\n model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)\n self.saver.debug(*self._DEBUG_CreateModelFinish, totalParameters(model))\n return model\n\n def _createCriterion(self) -> DistributedDataParallel:\n self.saver.debug(*self._DEBUG_CreateCriterionStart)\n criterionFn = trackingFunctionCalls(CriterionRegistry.get(self.config.Train.Criterion.Key), self.saver)\n criterion = criterionFn(**self.config.Train.Criterion.Params).to(self.rank)\n if any((p.requires_grad for p in criterion.parameters())):\n criterion = DistributedDataParallel(criterion, device_ids=[self.rank], output_device=self.rank, find_unused_parameters=False)\n criterion = torch.nn.SyncBatchNorm.convert_sync_batchnorm(criterion)\n self.saver.debug(*self._DEBUG_CreateCriterionFinish, totalParameters(criterion))\n return criterion\n\n def _createOptimizer(self, model: DistributedDataParallel, criterion: DistributedDataParallel) -> Tuple[torch.optim.Optimizer, Callable[..., torch.optim.Optimizer]]:\n self.saver.debug(*self._DEBUG_CreateOptimizerStart)\n if \"lr\" in self.config.Train.Optim.Params and \"batchSize\" in self.config.Train.TrainSet.Params:\n batchSize = self.config.Train.TrainSet.Params[\"batchSize\"] * self.worldSize\n exponent = math.log2(batchSize)\n scale = 3 - exponent / 2\n optimCfg = deepcopy(self.config.Train.Optim)\n optimCfg.Params[\"lr\"] /= (2 ** scale)\n else:\n optimCfg = self.config.Train.Optim\n optimFn = trackingFunctionCalls(OptimRegistry.get(optimCfg.Key), self.saver)\n\n # remove weight_decay in any norm layers\n paramGroup = setWeightDecay(model, optimCfg.Params[\"weight_decay\"], 0.0) +\\\n setWeightDecay(criterion, optimCfg.Params[\"weight_decay\"], 0.0)\n\n optimizer = optimFn(paramGroup, **optimCfg.Params)\n self.saver.debug(*self._DEBUG_CreateOptimizerFinish)\n return optimizer, optimFn\n\n def _createScheduler(self, optimizer: torch.optim.Optimizer) -> Tuple[torch.optim.lr_scheduler._LRScheduler, Callable[..., torch.optim.lr_scheduler._LRScheduler]]:\n self.saver.debug(*self._DEBUG_CreateSchdrStart)\n schdrFn = trackingFunctionCalls(SchdrRegistry.get(self.config.Train.Schdr.Key), self.saver)\n scheduler = schdrFn(optimizer, **self.config.Train.Schdr.Params)\n self.saver.debug(*self._DEBUG_CreateSchdrFinish)\n return scheduler, schdrFn\n\n def _beforeRun(self, hook, *args, **kwArgs):\n self.saver.debug(*self._DEBUG_CallBeforRunStart)\n self.saver.info(*self._LOG_StartTraining)\n\n hook(self._step, self._epoch, self, *args, logger=self.saver, **kwArgs)\n\n self.saver.info(*self._LOG_RevealTensorboard, self.saver.TensorboardURL)\n self.saver.debug(*self._DEBUG_CallBeforRunFinish)\n\n def _afterRun(self, hook, *args, **kwArgs):\n self.saver.debug(*self._DEBUG_CallAfterRunStart)\n self.saver.debug(*self._DEBUG_TrainLoopFinish)\n hook(self._step, self._epoch, self, *args, logger=self.saver, **kwArgs)\n self.saver.debug(*self._DEBUG_CallAfterRunFinish)\n\n def _stepStart(self, hook, *args, **kwArgs) -> Dict[str, Any]:\n return hook(self._step, self._epoch, self, *args, logger=self.saver, **kwArgs) or dict()\n\n def _stepFinish(self, hook, *args, loss, **kwArgs):\n self._step += 1\n\n # Update AveragedModel\n # if self._step % self.config.Train.ModelEMASteps == 0:\n # self._modelEMA.update_parameters(self._model)\n # if \"warmupEpochs\" in self.config.Train.Schdr.Params and self._epoch < self.config.Train.Schdr.Params[\"warmupEpochs\"]:\n # # Reset ema buffer to keep copying weights during warmup period\n # self._modelEMA.n_averaged.fill_(0)\n\n hook(self._step, self._epoch, self, *args, logger=self.saver, loss=loss, **kwArgs)\n\n def _epochStart(self, hook, *args, **kwArgs):\n self.saver.debug(*self._DEBUG_CallEpochBeginStart, self._epoch + 1)\n\n gc.collect()\n gc.collect()\n hook(self._step, self._epoch, self, *args, logger=self.saver, **kwArgs)\n\n self.saver.debug(*self._DEBUG_CallEpochBeginFinish)\n\n def _epochFinish(self, hook, *args, **kwArgs):\n self._epoch += 1\n\n self.saver.debug(*self._DEBUG_CallEpochEndStart, self._epoch)\n\n\n dist.broadcast(self.earlyStopFlag, 0)\n if self.earlyStopFlag:\n self.saver.info(*self._LOG_EarlyStop, self._epoch)\n raise StopIteration\n\n self._scheduler.step()\n self._model.module.step()\n self.saver.debug(*self._DEBUG_ShowSchdrUpdate, self._scheduler.get_last_lr()[0])\n self.saver.debug(*self._DEBUG_ShowTemperatureUpdate, self._model.module.Temperature)\n\n hook(self._step, self._epoch, self, *args, logger=self.saver, **kwArgs)\n self.saver.debug(*self._DEBUG_CallEpochEndFinish)\n\n\n\nclass MainTrainer(PalTrainer, SafeTerminate):\n def __init__(self, config: Config, loggingLevel: int):\n PalTrainer.__init__(self, config, loggingLevel)\n SafeTerminate.__init__(self, self.saver)\n # Running depedencies\n self.progress = getRichProgress().__enter__()\n self.epochBar = self.progress.add_task(\"[----/----]\", start=False, progress=\"\", suffix=Consts.CDot * 10)\n self.trainingBar = self.progress.add_task(\"\", start=False, progress=\"[----/----]\", suffix=Consts.CDot * 10)\n\n self.validator = Validator(self.config.Train.NumReturns)\n\n self.diffTracker = EMATracker((), 0.99).cuda()\n\n # Logging and saving\n self.bestmAP = -1\n self.earlyStopCount = 0\n\n def onTerminate(self, signum, frame):\n self.saver.critical(*self._CRITICAL_ProcessInterrupt)\n self.saver.critical(*self._CRITICAL_TimeoutInfo, Consts.TimeOut)\n self.progress.__exit__(None, None, None)\n self.save(os.path.join(self.saver.SaveDir, \"last.ckpt\"))\n self.saver.critical(*self._CRITICAL_ShutdownMessage, relativePath(os.path.join(self.saver.SaveDir, \"last.ckpt\")))\n self.summary()\n\n def summary(self):\n if self.bestmAP < 0:\n self.saver.info(*self._LOG_SummaryInfoNomAP, self._epoch, self._step)\n else:\n self.saver.info(*self._LOG_SummaryInfo, self._epoch, self._step, self.bestmAP * 100)\n self.saver.info(*self._LOG_SummarySaveCkpt, relativePath(os.path.join(self.saver.SaveDir, \"[ONE_OF_A].ckpt\")))\n\n def _beforeRun(self, hook, *args, **kwArgs):\n self.progress.start_task(self.trainingBar)\n self.progress.start_task(self.epochBar)\n super()._beforeRun(hook, *args, **kwArgs)\n\n def _afterRun(self, hook, *args, **kwArgs):\n super()._afterRun(hook, *args, **kwArgs)\n self.progress.__exit__(None, None, None)\n self.summary()\n\n def _stepFinish(self, hook, *args, loss, stats, **kwArgs):\n super()._stepFinish(hook, *args, loss=loss, **kwArgs)\n\n moment = self.diffTracker(loss)\n\n task = self.progress.get_task(self.trainingBar)\n self.progress.update(self.trainingBar, advance=1, progress=f\"[{task.completed + 1:4d}/{task.total:4d}]\", suffix=f\"L = [b green]{moment:2.2f}[/]\")\n self.progress.update(self.epochBar, advance=1)\n\n if self._step % 10 != 0:\n return\n\n for key, value in stats.items():\n if isinstance(value, numbers.Number):\n self.saver.add_scalar(f\"Stat/{key}\", value, global_step=self._step)\n else:\n if value.numel() == 1:\n self.saver.add_scalar(f\"Stat/{key}\", value, global_step=self._step)\n elif len(value.shape) == 4:\n self.saver.add_images(f\"Stat/{key}\", value, global_step=self._step)\n elif len(value.shape) == 3:\n self.saver.add_image(f\"Stat/{key}\", value, global_step=self._step)\n else:\n self.saver.add_histogram(f\"Stat/{key}\", value, global_step=self._step)\n self.saver.add_scalar(\"Stat/Loss\", loss, global_step=self._step)\n self.saver.add_scalar(\"Stat/Lr\", self._scheduler.get_last_lr()[0], global_step=self._step)\n\n def _epochStart(self, hook, *args, trainSet: TrainSplit, **kwArgs):\n totalBatches = math.ceil(len(trainSet) / (trainSet.BatchSize * self.worldSize))\n self.progress.update(self.trainingBar, total=totalBatches)\n self.progress.update(self.epochBar, total=self.config.Train.Epoch * totalBatches, completed=self._step, description=f\"[{self._epoch + 1:4d}/{self.config.Train.Epoch:4d}]\")\n\n self.progress.reset(self.trainingBar)\n super()._epochStart(hook, *args, trainSet=trainSet, **kwArgs)\n\n def _createHooks(self, config: Config, saver: Saver, model, criterion):\n beforeRunHook, afterRunHook, stepStartHook, stepFinishHook, epochStartHook, epochFinishHook = super()._createHooks(config, saver, model, criterion)\n\n saver.debug(\"Add additional hooks in `MainTrainer`.\")\n\n epochFinishHook = checkHook(ChainHook(\n EpochFrequencyHook(\n (1, self.log), logger=saver\n ),\n EpochFrequencyHook(\n (config.Train.ValFreq, self.validate), logger=saver\n ), epochFinishHook), \"EpochFinishHook\", saver)\n\n beforeRunHook = checkHook(ChainHook(\n self.validate\n , beforeRunHook), \"BeforeRunHook\", saver)\n\n return beforeRunHook, afterRunHook, stepStartHook, stepFinishHook, epochStartHook, epochFinishHook\n\n def _createDatasets(self) -> Dict[str, Union[TrainSplit, QuerySplit, Database]]:\n self.saver.debug(*self._DEBUG_CreateDatasetStart, self.config.Train.TrainSet.Key, self.config.Train.TrainSet.Pipeline.Key or \"default\")\n try:\n trainPipeline = trackingFunctionCalls(DataPipeRegistry.get(self.config.Train.TrainSet.Pipeline.Key), self.saver)(**self.config.Train.TrainSet.Pipeline.Params)\n except KeyError:\n trainPipeline = None\n trainSet = trackingFunctionCalls(DatasetRegistry.get(self.config.Train.TrainSet.Key), self.saver)(**self.config.Train.TrainSet.Params, pipeline=trainPipeline)\n\n\n self.saver.debug(*self._DEBUG_CreateQuerysetStart, self.config.Train.QuerySet.Key, self.config.Train.QuerySet.Pipeline.Key or \"default\")\n try:\n queryPipeline = trackingFunctionCalls(DataPipeRegistry.get(self.config.Train.QuerySet.Pipeline.Key), self.saver)(**self.config.Train.QuerySet.Pipeline.Params)\n except KeyError:\n queryPipeline = None\n querySet = trackingFunctionCalls(DatasetRegistry.get(self.config.Train.QuerySet.Key), self.saver)(**self.config.Train.QuerySet.Params, pipeline=queryPipeline)\n\n\n self.saver.debug(*self._DEBUG_CreateDatabaseStart, self.config.Train.Database.Key, self.config.Train.Database.Key or \"default\")\n try:\n databasePipeline = trackingFunctionCalls(DataPipeRegistry.get(self.config.Train.Database.Pipeline.Key), self.saver)(**self.config.Train.Database.Pipeline.Params)\n except KeyError:\n databasePipeline = None\n database = trackingFunctionCalls(DatasetRegistry.get(self.config.Train.Database.Key), self.saver)(**self.config.Train.Database.Params, pipeline=databasePipeline)\n\n\n\n self.saver.debug(*self._DEBUG_CreateDatasetMainFinish, trainSet, database, querySet)\n return {\n \"trainSet\": trainSet.Split,\n \"database\": database.Split,\n \"querySet\": querySet.Split\n }\n\n def log(self, *_, **__):\n self.saver.add_scalar(\"Stat/Epoch\", self._epoch, self._step)\n # self.saver.add_images(\"Train/Raw\", tensorToImage(images), global_step=self._step)\n\n def validate(self, *_, database: Database, querySet: QuerySplit, **__):\n torch.cuda.empty_cache()\n\n self.saver.debug(*self._DEBUG_Validation, self._epoch)\n\n results, summary = self.validator.validate(self._model.module, database, querySet, self.progress)\n\n for metricModule in metrics.__all__:\n if metricModule != \"Visualization\":\n # [mAP, Precision, Recall]\n self.saver.add_scalar(f\"Eval/{metricModule}@{self.validator.numReturns}\", results[metricModule], global_step=self._step)\n # self.saver.add_images(f\"Eval/Visualization\", results[\"Visualization\"], global_step=self._step)\n\n self.save()\n\n self.saver.info(\"%s\", summary)\n\n mAP = results[\"mAP\"]\n if mAP > self.bestmAP:\n self.bestmAP = mAP\n self.progress.update(self.epochBar, suffix=f\"H = [b red]{self.bestmAP * 100:2.2f}[/]%\")\n shutil.copy2(self.saver.SavePath, os.path.join(self.saver.SaveDir, \"best.ckpt\"))\n self.earlyStopCount = 0\n else:\n self.earlyStopCount += 1\n self.saver.debug(*self._DEBUG_FindOneEarlyStop, self.earlyStopCount, self.config.Train.EarlyStop)\n if self.earlyStopCount >= self.config.Train.EarlyStop:\n self.earlyStop()\n\n self.saver.debug(*self._DEBUG_EndValidation, self._epoch)\n self._model.train()\n\n def earlyStop(self):\n self.earlyStopFlag.data.copy_(torch.tensor([True]))\n\n\n@TrainerBuilder.register(\"BaseTrainer\")\ndef getTrainer(rank: int, config: Config, loggingLevel: int):\n if rank == 0:\n return MainTrainer(config, loggingLevel)\n return PalTrainer(config, loggingLevel)\n","repo_name":"VL-Group/modfire","sub_path":"modfire/train/trainer.py","file_name":"trainer.py","file_ext":"py","file_size_in_byte":27429,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"50"} +{"seq_id":"12668647641","text":"import boto3,json,tabulate,pprint,itertools,os,random,string,pwd,grp\nfrom shutil import copyfile\n\nInstances=[]\nTemplateFile=\"./templates/node_exporter_dashboard.json\"\nGrafanaDashPath=\"/var/lib/grafana/dashboards\"\nGrafanaUID=pwd.getpwnam(\"grafana\").pw_uid\nGrafanaGID=grp.getgrnam(\"grafana\").gr_gid\nMYDashPath=GrafanaDashPath+\"/mydashboards\"\n\ndef createDirNPermissions(dirin):\n if not os.path.exists(dirin):\n os.makedirs(dirin)\n os.chown(dirin,GrafanaUID,GrafanaGID)\n\ndef randomString(stringLength=10):\n letters = string.ascii_lowercase\n return ''.join(random.choice(letters) for i in range(stringLength))\n\ndef replaceInFile(fin,key,val):\n with open(fin, 'r') as f:\n newlines = []\n for line in f.readlines():\n newlines.append(line.replace(key,val))\n with open(fin, 'w') as f:\n for line in newlines:\n f.write(line)\n\ncreateDirNPermissions(GrafanaDashPath)\ncreateDirNPermissions(MYDashPath)\n\nec2 = boto3.client('ec2')\nfilters = [\n {\n 'Name': 'tag:MACHINETAGKEY',\n 'Values': ['MACHINESTAGVAL']\n }\n]\nlist_instances=json.dumps(ec2.describe_instances(Filters=filters), indent=4, sort_keys=True, default=str)\ndata = json.loads(list_instances)\nidict={}\nilist=[]\n\nfor reservations in data['Reservations']:\n for instance in reservations['Instances']:\n for tag in instance['Tags']:\n if tag['Key'] == 'Name':\n iName = tag['Value']\n iName = iName.replace(\" \",\"\")\n iPrivateDNS = instance['PrivateDnsName']\n dashUid = randomString(10)\n path=MYDashPath+\"/\"+iName+\"-dashboards.json\"\n copyfile(TemplateFile,path)\n os.chown(path,GrafanaUID,GrafanaGID)\n Replacements={'MACHINENAME': iName, 'MACHINEIPADDR': iPrivateDNS, 'DASHBOARDUID': dashUid}\n for k,v in Replacements.items():\n replaceInFile(path,k,v)\n","repo_name":"wcampos/grafana_dynamic_node_exporter_dashboards","sub_path":"generate_dasboards.py","file_name":"generate_dasboards.py","file_ext":"py","file_size_in_byte":1936,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"10219521114","text":"with open(\"in.txt\", \"r\") as input_file:\n data = input_file.readlines()\n\noutput_data = []\nfor line in data:\n split_str = line.split()\n output_dict = {\n \"code\": split_str[0],\n \"lat\": split_str[1],\n \"long\": split_str[2],\n \"name\": \" \".join(split_str[3:])\n }\n output_data.append(output_dict)\n\nwith open(\"out.txt\", \"w\") as output_file:\n for dictionary in output_data:\n output_file.write(str(dictionary) + \",\\n\")\n","repo_name":"joshuan98/hawkhack","sub_path":"formatLatLong.py","file_name":"formatLatLong.py","file_ext":"py","file_size_in_byte":459,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"73892931354","text":"import cvlib as cv\nfrom cvlib.object_detection import draw_bbox\nimport cv2\nimport base64\nimport requests\nimport json\nimport time\nimport threading\n\ndef readData():\n url = 'https://firestore.googleapis.com/v1/projects/parkingfrontend/databases/(default)/documents/images/lotOne'\n \n #sending post request and saving response as response object \n\n while True:\n resp = requests.get(url=url)\n data = resp.json()\n imgStr = data[\"fields\"][\"imageData\"][\"stringValue\"]\n\n with open(\"imageToSave.png\", \"wb\") as fh:\n fh.write(base64.b64decode(imgStr))\n fh.close()\n time.sleep(3)\n\n\nthread1 = threading.Thread(target = readData, args = ())\nthread1.start()\n\n\n\ndef numberPost(n):\n url = \"http://hughboy.com:9999/number\"\n #url = \"https://parkingfrontend.firebaseio.com/carsNumber/data.json\"\n headers = {'Content-Type': 'application/json'}\n data = {'number':n}\t\n r = requests.post(url=url, headers=headers, data=json.dumps(data))\n\ndef numberImage(n):\n url = \"http://hughboy.com:9999/img.png\" \n #url = \"https://parkingfrontend.firebaseio.com/imageResult/data.json\"\n headers = {'Content-Type': 'application/json'}\n data = {'image':n}\t\n r = requests.post(url=url, headers=headers, data=json.dumps(data))\n\n\nwhile True:\n #read input image\n\n image = cv2.imread(\"imageToSave.png\")\n #image = cv2.imread(\"randomGooglePic.jpg\")\n\n #apply object detection\n bbox, label, conf = cv.detect_common_objects(image)\n\n\n\n #draw bounding box over detected objects\n out = draw_bbox(image, bbox, label, conf)\n cv2.imwrite('resultImage.png',image)\n with open(\"resultImage.png\", \"rb\") as image:\n bkl = base64.b64encode(image.read())\n bkl = str(bkl, 'utf-8')\n numberImage(bkl)\n image.close()\n \n # display output\n # press any key to close window \n #cv2.imshow(\"object_detection\", out)\n #cv2.waitKey()\n\n #save output to disk if needed\n\n \n countNumber = label.count(\"car\")+label.count(\"motorcycle\")+label.count(\"bus\")+label.count(\"truck\")\n numberPost(countNumber)\n print(label)\n print(\"Master Sam, I have counter that there is\",str(countNumber),\" cars!\")\n time.sleep(1)\n","repo_name":"cachecake404/Parkinator","sub_path":"ServerFiles/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2231,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"24695530037","text":"import argparse\nfrom tkinter import SEL_FIRST\n\n\nclass ReturnType:\n def __init__(self, args):\n self.rotated = args.rotation\n self.draw = args.draw\n self.instances = [\n args.instance] if args.instance is not None else range(1, 41)\n self.encodings = [args.encoding] if args.encoding is not None else [2]\n if args.all:\n self.encodings = range(1, 5)\n\n\ndef parsArguments() -> ReturnType:\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"-i\", \"--instance\", help=\"Instance on which to run the model, default is all\", type=int)\n parser.add_argument(\n \"-r\", \"--rotation\", help=\"Flag to decide whether it is possible use rotated circuits, default is not rotated\", action='store_true')\n parser.add_argument(\n \"-d\", \"--draw\", help=\"Flag to decide whether to draw a the solution, default is false\", action='store_true')\n parser.add_argument(\n \"-e\", \"--encoding\", help=\"The encoding of constraints (1-pairwise, 2-sequential, 3-bitwise, 4-heule)\", type=int)\n parser.add_argument(\n \"-all\", \"--all\", help=\"Flag to decide whether to try all encodings\", action='store_true')\n args = parser.parse_args()\n returnType = ReturnType(args)\n return returnType\n","repo_name":"MihailStamenov98/Combinatorial-Decision-Making-and-Optimization","sub_path":"SAT/src/argument_parser.py","file_name":"argument_parser.py","file_ext":"py","file_size_in_byte":1266,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"3374600806","text":"\"\"\"\nmath_competition URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/2.2/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\"\"\"\n\nfrom django.urls import path\nfrom . import views\n\nurlpatterns = [\n path('', views.index, name='index'),\n path('register/', views.register, name='register'),\n path('logout/', views.log_out, name='logout'),\n path('login/', views.log_in, name='login'),\n path('competition//', views.comp_detail, name='comp_detail'),\n path('problem//', views.prob_detail, name='prob_detail'),\n path('competition/register//', views.register_comp, name='register_comp'),\n path('competition//standings/', views.standings, name='standings'),\n path('competition/new/', views.new_comp, name='new_comp'),\n path('competition//new/', views.new_prob, name='new_prob'),\n]","repo_name":"xiaoyu2006/math-competition","sub_path":"competition/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1376,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"7313067464","text":"import argparse\nimport ast\nimport json\nimport re\nfrom pathlib import Path\n\nimport pandas as pd\nimport requests\nfrom bs4 import BeautifulSoup\nfrom tqdm import tqdm\n\nparties = [\"PS\", \"PSD\", \"BE\", \"CDS-PP\", \"PEV\", \"PCP\"]\nall_legs = [\"VII\", \"VIII\", \"IX\", \"X\", \"XI\", \"XII\", \"XIII\", \"XIV\", \"XV\"]\n\n\ndef roman_to_int(roman_str):\n roman_dict = {\"I\": 1, \"V\": 5, \"X\": 10, \"L\": 50, \"C\": 100, \"D\": 500, \"M\": 1000}\n int_val = 0\n for i in range(len(roman_str)):\n if i > 0 and roman_dict[roman_str[i]] > roman_dict[roman_str[i - 1]]:\n int_val += roman_dict[roman_str[i]] - 2 * roman_dict[roman_str[i - 1]]\n else:\n int_val += roman_dict[roman_str[i]]\n return int_val\n\n\ndef scrape_first_page(publicacao):\n pub_url_id = f\"01/{roman_to_int(publicacao['pubLeg']):02}/{int(publicacao['pubSL']):02}/{int(publicacao['pubNr']):03}/{publicacao['pubdt']}\"\n url = f\"https://debates.parlamento.pt/catalogo/r3/dar/{pub_url_id}?org=PLC\"\n print(url)\n\n page = requests.get(url)\n soup = BeautifulSoup(page.content, \"html.parser\")\n\n pagination = soup.find(\"ul\", {\"class\": \"pagination\"})\n children = pagination.findChildren(\"li\", recursive=False)\n first_page = int(children[0].text)\n\n return first_page\n\n\nclass ARValuesMissingException(Exception):\n pass\n\n\ndef process_vote_detail(detail):\n votes = {\"in_favour\": [], \"against\": [], \"abstention\": []}\n detail = detail.split(\"
\")\n if len(detail) == 1: # all in favour\n in_favour = re.findall(\"\\(.*?)\\\", detail[0])\n votes[\"in_favour\"] = list(map(str.strip, in_favour))\n if len(detail) == 2:\n in_favour = re.findall(\"\\(.*?)\\\", detail[0])\n votes[\"in_favour\"] = list(map(str.strip, in_favour))\n other = re.findall(\"\\(.*?)\\\", detail[1])\n if \"Contra:\" in detail[1]:\n votes[\"against\"] = list(map(str.strip, other))\n elif \"Abstenção:\" in detail[1]:\n votes[\"abstention\"] = list(map(str.strip, other))\n if len(detail) == 3:\n in_favour, against, abstention = detail\n in_favour = re.findall(\"\\(.*?)\\\", in_favour)\n against = re.findall(\"\\(.*?)\\\", against)\n abstention = re.findall(\"\\(.*?)\\\", abstention)\n votes[\"in_favour\"] = list(map(str.strip, in_favour))\n votes[\"against\"] = list(map(str.strip, against))\n votes[\"abstention\"] = list(map(str.strip, abstention))\n\n def clean_noisy_party_labels(votes_dict, key):\n parties_regex = rf\"^\\d+-({'|'.join(parties)})$\"\n votes_dict[key] = [i for i in votes[key] if not re.search(parties_regex, i)]\n\n clean_noisy_party_labels(votes, \"in_favour\")\n clean_noisy_party_labels(votes, \"against\")\n clean_noisy_party_labels(votes, \"abstention\")\n\n assert all(\n key in votes for key in [\"in_favour\", \"against\", \"abstention\"]\n ), f\"{votes}---{detail}\"\n\n return votes\n\n\ndef process_voting(votacao):\n row = []\n\n votacao = votacao[0][\"pt_gov_ar_objectos_VotacaoOut\"]\n\n if not all(key in votacao for key in [\"resultado\", \"detalhe\"]):\n raise ARValuesMissingException(\"Some vote required attributes are missing.\")\n\n vot_resultado = votacao[\"resultado\"]\n row.append(vot_resultado)\n vot_dict = process_vote_detail(votacao[\"detalhe\"])\n row.append(vot_dict[\"in_favour\"])\n row.append(vot_dict[\"against\"])\n row.append(vot_dict[\"abstention\"])\n\n return row\n\n\ndef add_ini_attributes(iniciativa):\n row = []\n ini_num = iniciativa.get(\"iniNr\")\n row.append(ini_num)\n ini_leg = iniciativa.get(\"iniLeg\")\n row.append(ini_leg)\n ini_tipo = iniciativa.get(\"iniDescTipo\")\n row.append(ini_tipo)\n ini_titulo = iniciativa.get(\"iniTitulo\")\n row.append(ini_titulo)\n ini_sessao = iniciativa.get(\"iniSel\")\n row.append(ini_sessao)\n dataInicioLeg = iniciativa.get(\"dataInicioleg\")\n row.append(dataInicioLeg)\n dataFimLeg = iniciativa.get(\"dataFimleg\")\n row.append(dataFimLeg)\n\n return row\n\n\ndef process_ini_authors(iniciativa):\n row = []\n\n if \"iniAutorDeputados\" in iniciativa:\n autores_deputados = iniciativa[\"iniAutorDeputados\"][\n \"pt_gov_ar_objectos_iniciativas_AutoresDeputadosOut\"\n ]\n if isinstance(autores_deputados, list):\n autores_deputados = [\n f\"{dep['idCadastro']}-{dep['nome']}-{dep['GP']}\"\n for dep in autores_deputados\n ]\n else:\n autores_deputados = f\"{autores_deputados['idCadastro']}-{autores_deputados['nome']}-{autores_deputados['GP']}\"\n else:\n autores_deputados = iniciativa[\"iniAutorOutros\"][\"nome\"]\n\n row.append(autores_deputados)\n\n return row\n\n\ndef process_publication(publicacao):\n row = []\n\n if not all(\n key in publicacao\n for key in [\"pubNr\", \"pubSL\", \"pubdt\", \"pubLeg\", \"pubTipo\", \"pag\"]\n ):\n raise ARValuesMissingException(\n \"Some publication required attributes are missing\"\n )\n\n pub_num = int(publicacao[\"pubNr\"])\n row.append(pub_num)\n pub_sessao = int(publicacao[\"pubSL\"])\n row.append(pub_sessao)\n row.append(publicacao[\"pubdt\"])\n pub_legislatura = publicacao[\"pubLeg\"]\n pub_serie = publicacao[\"pubTipo\"].split()[1]\n pages = publicacao[\"pag\"][\"string\"]\n if isinstance(pages, str):\n row.append([pages])\n else:\n row.append(pages)\n row.append(f\"dar_serie_{pub_serie}_{pub_legislatura}_{pub_sessao}_{pub_num:03}.pdf\")\n if pub_legislatura in [\"VII\", \"VIII\", \"IX\"]:\n row.append(scrape_first_page(publicacao))\n else:\n row.append(1)\n\n return row\n\n\ndef process_speaker(deputado):\n row = []\n\n row.append(deputado[\"idCadastro\"])\n row.append(deputado[\"nome\"])\n row.append(deputado[\"GP\"])\n\n return row\n\n\ndef literal_return(val):\n try:\n return ast.literal_eval(val)\n except (ValueError, SyntaxError) as e:\n return val\n\n\ndef create_vote_from_vote_lists(df):\n # explode all three vote columns\n df[\"vot_in_favour\"] = df[\"vot_in_favour\"].apply(literal_return)\n df[\"vot_against\"] = df[\"vot_against\"].apply(literal_return)\n df[\"vot_abstention\"] = df[\"vot_abstention\"].apply(literal_return)\n exp_df = (\n df.explode(\"vot_in_favour\").explode(\"vot_against\").explode(\"vot_abstention\")\n )\n # compare labels with votes to find matches\n exp_df = exp_df.eq(exp_df.pop(\"dep_parl_group\"), axis=0)\n # remove all False rows and get the matches in each row\n exp_df = exp_df[exp_df.any(1)].idxmax(1)\n # remove duplicated indices\n df[\"vote\"] = exp_df[~exp_df.index.duplicated()]\n\n\ndef get_value_from_key(key, dictionary):\n for k, v in (\n dictionary.items()\n if isinstance(dictionary, dict)\n else enumerate(dictionary)\n if isinstance(dictionary, list)\n else []\n ):\n if k == key:\n yield v\n elif isinstance(v, (dict, list)):\n for result in get_value_from_key(key, v):\n yield result\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Init metadata dataset\")\n parser.add_argument(\n \"--legs\",\n type=str,\n nargs=\"+\",\n default=all_legs,\n choices=all_legs,\n help=\"legislatures\",\n )\n parser.add_argument(\n \"--metadata_dir\",\n type=Path,\n default=\"data/initiatives\",\n help=\"file directory with initiatives metadata\",\n )\n parser.add_argument(\n \"--output_path\",\n type=str,\n default=\"data/initial_corpus_meta.csv\",\n help=\"output file path\",\n )\n args = parser.parse_args()\n\n return args\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n\n print(f\"Selected legs: {args.legs}\")\n\n rows = []\n ini_count = 0\n for leg_num in args.legs:\n print(f\"Processing Legislatura{leg_num}\")\n with open(args.metadata_dir / f\"Iniciativas{leg_num}.json\") as f:\n json_data = json.load(f)\n\n iniciativas = json_data[\n \"ArrayOfPt_gov_ar_objectos_iniciativas_DetalhePesquisaIniciativasOut\"\n ][\"pt_gov_ar_objectos_iniciativas_DetalhePesquisaIniciativasOut\"]\n\n for i, iniciativa in tqdm(\n enumerate(iniciativas),\n desc=\"Iterating over initiatives\",\n total=len(iniciativas),\n ):\n try:\n # skip joint initiatives\n conjuntas = get_value_from_key(\"iniciativasConjuntas\", iniciativa)\n if list(conjuntas):\n raise ARValuesMissingException(\"Joint initiatives.\")\n\n discursos = list(\n get_value_from_key(\n \"pt_gov_ar_objectos_peticoes_OradoresOut\", iniciativa\n )\n )\n if not discursos:\n raise ARValuesMissingException(\"No speeches.\")\n\n eventos = iniciativa[\"iniEventos\"][\n \"pt_gov_ar_objectos_iniciativas_EventosOut\"\n ]\n\n oradores, votacao = None, None\n # print(f\"{len(eventos)} events\")\n for evento in eventos:\n if evento[\"fase\"] == \"Discussão generalidade\":\n oradores = list(get_value_from_key(\"oradores\", evento))\n\n if evento[\"fase\"] == \"Votação na generalidade\":\n votacao = list(get_value_from_key(\"votacao\", evento))\n\n if not oradores or not votacao:\n print(\"Missing speaker or vote information.\")\n continue\n\n speakers = oradores[0][\"pt_gov_ar_objectos_peticoes_OradoresOut\"]\n if isinstance(speakers, dict):\n speakers = [speakers]\n\n # print(f\"{len(speakers)} speakers\")\n ini_count += 1\n\n for orador in speakers:\n row = []\n publicacao = list(get_value_from_key(\"publicacao\", orador))\n publicacao = publicacao[0][\"pt_gov_ar_objectos_PublicacoesOut\"]\n\n row.extend(add_ini_attributes(iniciativa))\n row.extend(process_ini_authors(iniciativa))\n row.extend(process_voting(votacao))\n row.extend(process_publication(publicacao))\n\n deputado = list(get_value_from_key(\"deputados\", orador))\n if not deputado:\n continue\n deputado = deputado[0]\n row.extend(process_speaker(deputado))\n\n # print(\"Adding row...\")\n rows.append(row)\n\n # print(f\"Initiative #{i + 1} done\")\n\n except ARValuesMissingException as e:\n print(f\"Skipping initiative #{i + 1}. {e}\")\n\n print(f\"Total initiatives processed: {ini_count}\")\n\n df = pd.DataFrame(\n rows,\n columns=[\n \"ini_num\",\n \"ini_leg\",\n \"ini_type\",\n \"ini_title\",\n \"ini_session\",\n \"leg_begin_date\",\n \"leg_end_date\",\n \"authors\",\n \"vot_results\",\n \"vot_in_favour\",\n \"vot_against\",\n \"vot_abstention\",\n \"pub_num\",\n \"pub_session\",\n \"pub_date\",\n \"pages\",\n \"pdf_file_path\",\n \"doc_first_page\",\n \"dep_id\",\n \"dep_name\",\n \"dep_parl_group\",\n ],\n )\n\n # process votes\n create_vote_from_vote_lists(df)\n\n df.to_csv(args.output_path, index=False)\n","repo_name":"afonso-sousa/pt_parliamentary_minutes","sub_path":"init_corpus_meta.py","file_name":"init_corpus_meta.py","file_ext":"py","file_size_in_byte":11545,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"17291383048","text":"\nimport select\nimport sys\nimport os\nimport threading\nfrom importlib import util\nfrom apphead import AppHeader\n\nclass MyServer:\n \"\"\" Class to encapsulate the server functions \"\"\"\n def __init__(self, hostname=''):\n self.hostname = hostname\n\n self.entradas = [sys.stdin]\n self.connections = {}\n self.connections_lock = threading.Lock()\n\n self.applications = {}\n\n self.quit_flag = False\n\n def __enter__(self):\n \"\"\" When the server is initialized, the internal sockets must be configured \"\"\"\n for entry in os.scandir():\n if entry.is_file():\n if entry.name != \"apphead.py\" and entry.name.startswith(\"app\"):\n port_num = entry.name.split(\".\")[0][4:]\n self.set_application(port_num, entry.name, entry.path)\n print(f\"Server started, {len(self.applications)} applications are available.\")\n return self\n\n def __exit__(self, exc_type, exc_value, traceback):\n \"\"\" When the server is finishing, the internal sockets must be closed \"\"\"\n for app in self.applications.values():\n app.close_connection()\n if app.is_running():\n app.stop(5)\n\n def start(self, app_name=\"app\", app_path=\"\"):\n \"\"\" method to start the server \"\"\"\n self.update(False)\n for port, app in self.applications.items():\n if app.is_executable():\n self._run_app(port, app);\n while not self.quit_flag:\n reciever_list, _, _ = select.select(self.entradas, [], [])\n for reciever in reciever_list:\n if reciever == sys.stdin:\n self._answer_commands()\n else:\n self._attend_client(reciever)\n\n def set_application(self, port_num, app_name, app_path):\n \"\"\" sets the application on a specified port \"\"\"\n if isinstance(port_num, str):\n port_num = int(port_num) # posso fazer isso ?\n self.applications[port_num] = AppHeader(port_num, app_name, app_path)\n self.applications[port_num].configure_socket(self.hostname)\n self.entradas.append(self.applications[port_num].app_socket)\n\n def update(self, verbose=True):\n \"\"\"\n in case the app() method has been redefined, the server can update itself to run the new app\n without having to close the socket that it currently reserves\n \"\"\"\n for app in self.applications.values():\n app.setup(MyServer.load_module(app.name, app.path, piece=\"App\"))\n if verbose:\n print(\"The server has been successfully updated.\")\n\n def _attend_client(self, socket):\n cliente = threading.Thread(target=self._method_caller, args=socket.accept())\n cliente.setDaemon(True)\n cliente.start()\n\n def _run_app(self, portnum, apphead):\n apphead.thread_obj = threading.Thread(target=apphead.obj.main)\n apphead.start()\n print(f\"Application on port {portnum} is now running.\")\n\n def _answer_commands(self):\n cmd = input()\n if cmd == \"exit\":\n if self.connections:\n print(\"Failed to exit the server: there are open connections\")\n else: \n self.quit_flag = True\n elif cmd == \"forceexit\":\n for connection in self.connections.copy():\n connection.close()\n self._unregister_connection(connection)\n self.quit_flag = True\n elif cmd == \"hist\":\n print(str(self.connections.values()))\n elif cmd == \"update\":\n self.update()\n elif cmd == \"ports\":\n print(list(self.applications.keys()))\n elif cmd.startswith(\"run\"):\n pcmd = cmd.split(\" \")\n if self.applications[int(pcmd[1])].is_running():\n print(f\"Requested application is already running.\")\n else:\n self._run_app(pcmd[1], self.applications[int(pcmd[1])])\n elif cmd.startswith(\"stop\"):\n pcmd = cmd.split(\" \")\n if self.applications[int(pcmd[1])].is_running():\n self.applications[int(pcmd[1])].stop()\n print(f\"Application on port {pcmd[1]} was stopped.\")\n else:\n print(f\"Requested application is not running.\")\n elif cmd == \"apps\":\n i = 0\n for app in self.applications.values():\n if app.is_running():\n i += 1\n print(f\"There are currently {i} apps running on this server.\")\n\n def _register_connection(self, conn, addr):\n self.connections_lock.acquire()\n self.connections[conn] = addr\n self.connections_lock.release()\n print(\"Accepted connection from: \", addr)\n\n def _unregister_connection(self, conn):\n self.connections_lock.acquire()\n del self.connections[conn]\n self.connections_lock.release()\n\n def _recvall(self, sock, length):\n buff = bytearray(length)\n pos = 0\n while pos < length:\n read = sock.recv_into(memoryview(buff)[pos:])\n if read == 0:\n raise EOFError\n pos += read\n return buff\n\n def _recv_ott(self, sock):\n size = int.from_bytes(self._recvall(sock, 2), \"big\")\n #size = ord(self._recvall(sock, 1))\n return self._recvall(sock, size)\n\n def _method_caller(self, conn, addr):\n self._register_connection(conn, addr)\n sock_id = conn.getsockname()[1]\n while True:\n data = bytearray()\n try:\n #data = conn.recv(self.applications[sock_id].obj.read_buffer())\n data = self._recv_ott(conn)\n except EOFError:\n print(str(addr) + \" session has ended.\")\n self._unregister_connection(conn)\n conn.close()\n sys.exit()\n response_data = self.applications[sock_id].obj.process(data.decode(\"utf-8\"), addr)\n if isinstance(response_data, str):\n response_data = response_data.encode('utf-8')\n conn.sendall(response_data)\n\n @staticmethod\n def load_module(module_name, module_path, piece=None):\n \"\"\" Method used for dinamic module importing \"\"\"\n # print(f\"Loading module {module_name}\")\n spec = util.spec_from_file_location(module_name, module_path)\n module = util.module_from_spec(spec)\n sys.modules[module_name] = module\n spec.loader.exec_module(module)\n\n if piece:\n return getattr(module, piece)\n","repo_name":"Delpreti/lightchat","sub_path":"server/myserver.py","file_name":"myserver.py","file_ext":"py","file_size_in_byte":6592,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"3632819395","text":"import argparse\nimport hashlib\nimport os\nimport sys\n\nimport prestodb\n\n\n# perf script -s perf-script.py -F+srcline --full-source-path --strip-cxx-templates\n\nBATCH_SIZE = 50\n\n\ndef get_random_table_name():\n return \"T\" + hashlib.sha1(os.urandom(512)).hexdigest()\n\n\nclass CLI:\n def __init__(self, args):\n self.args = args\n\n if self.args.table is None:\n self.args.table = get_random_table_name()\n\n def trace_begin(self):\n connection = prestodb.dbapi.connect(\n host=self.args.host,\n port=self.args.port,\n user=\"perf\",\n catalog=\"memory\",\n schema=\"default\",\n )\n\n self.cursor = connection.cursor()\n self.cursor.execute(\n f\"\"\"\n CREATE TABLE IF NOT EXISTS {self.args.table} (\n event VARCHAR,\n timestamp BIGINT, -- TODO: Figure out how to get real time timestamps.\n comm VARCHAR,\n stack ARRAY(VARCHAR),\n srclines ARRAY(VARCHAR)\n )\n \"\"\"\n )\n if self.cursor.fetchone()[0]:\n print(f\"New table created: {self.args.table}\")\n else:\n print(f\"Using existing table: {self.args.table}\")\n\n self.count = 0\n self.batch = []\n\n def process_event(self, event):\n assert \"callchain\" in event\n\n stack = []\n srclines = []\n for frame in reversed(event[\"callchain\"]):\n stack.append(frame.get(\"sym\", {}).get(\"name\", \"[unknown]\"))\n srcline = \"[unknown]\"\n if \"sym_srcline\" in frame:\n # There is a colon in the path, but realpath will end up ignoring it.\n srcline = os.path.realpath(frame[\"sym_srcline\"])\n srclines.append(srcline)\n\n self.batch.append(\n f\"\"\"(\n '{event[\"ev_name\"]}',\n {event.get(\"sample\", {}).get(\"time\", 0)},\n '{event[\"comm\"]}',\n ARRAY{stack},\n ARRAY{srclines}\n )\n \"\"\"\n )\n\n self.count += 1\n\n if self.count % BATCH_SIZE == 0:\n self.__batch()\n print(f\"Processed {self.count} rows\")\n\n def trace_end(self):\n if len(self.batch) > 0:\n self.__batch()\n self.cursor.close()\n print(f\"Inserted {self.count} rows into {self.args.table}\")\n\n def __batch(self):\n self.cursor.execute(\n f\"INSERT INTO {self.args.table} VALUES {','.join(self.batch)}\"\n )\n count = self.cursor.fetchone()[0]\n assert count == len(self.batch)\n self.batch.clear()\n\n\nif __name__ == \"__main__\":\n if \"PERF_EXEC_PATH\" not in os.environ:\n print(\"This must be run with perf script, e.g. perf script -s perf-script.py\")\n sys.exit(1)\n\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--host\", metavar=\"HOST\", type=str, default=\"localhost\")\n parser.add_argument(\"--port\", metavar=\"PORT\", type=int, default=8080)\n parser.add_argument(\n \"-t\",\n \"--table\",\n type=str,\n default=None,\n help=\"Insert entries into a specific table\",\n )\n\n cli = CLI(parser.parse_args())\n\n trace_begin = cli.trace_begin\n process_event = cli.process_event\n trace_end = cli.trace_end\n","repo_name":"nicovank/microperf","sub_path":"perf-script.py","file_name":"perf-script.py","file_ext":"py","file_size_in_byte":3310,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"29272129260","text":"import os\nimport logging\nfrom http.server import HTTPServer\nfrom http.server import BaseHTTPRequestHandler\n\n\nlogging.basicConfig(format='%(asctime)s - %(message)s', level=logging.INFO)\n\nCRLF = '\\r\\n'\n\n\nhome_page_content = b\"\"\nwith open(os.path.join(\"templates\", \"index.html\"), 'rb') as f:\n home_page_content = f.read()\n\ndef map_path_to_file(path):\n return path[1:].replace('/', '\\\\')\n\n\nclass ContentLengthFrontEndServer(BaseHTTPRequestHandler):\n def _set_headers(self):\n self.send_response(200)\n self.end_headers()\n\n def do_GET(self):\n self._set_headers()\n file = map_path_to_file(self.path)\n if os.path.exists(file):\n with open(file, 'rb') as f:\n self.wfile.write(f.read())\n else:\n self.wfile.write(home_page_content)\n\n def do_POST(self):\n self._set_headers()\n\n request_body = \"\"\n chunk_size = self.read_chunk_size()\n while chunk_size != 0:\n request_body += self.read_chunk(chunk_size)\n chunk_size = self.read_chunk_size()\n terminator = self.rfile.read(len(CRLF)).decode()\n request_body += terminator\n\n self.wfile.write(b\"It works!\")\n \n def read_chunk_size(self):\n line = self.rfile.readline().strip()\n return int(line, 16)\n \n def read_chunk(self, chunk_size):\n chunk = self.rfile.read(chunk_size).decode()\n chunk += self.rfile.read(len(CRLF)).decode()\n return chunk\n\n\ndef run(address):\n logging.info(\"server started on {}\".format(':'.join(map(lambda x: str(x), address))))\n httpd = HTTPServer(address, ContentLengthFrontEndServer)\n httpd.serve_forever()\n\nif __name__ == \"__main__\":\n from sys import argv\n\n if len(argv) == 2:\n run(('', int(argv[1])))\n\n elif len(argv) == 3:\n run((argv[1], int(argv[2])))\n \n else:\n run(('', 8081))\n","repo_name":"blabla1337/skf-labs","sub_path":"python/HTTP-desync-CLTE-backend-server/http-desync-clte-backend-server.py","file_name":"http-desync-clte-backend-server.py","file_ext":"py","file_size_in_byte":1894,"program_lang":"python","lang":"en","doc_type":"code","stars":409,"dataset":"github-code","pt":"50"} +{"seq_id":"69997854236","text":"#!/usr/bin/env python3\n\n\"\"\"\nExecutable. \nPlot the residuals from an OpenFOAM run. \nFirst run the following OpenFOAM utilities in the OpenFOAM case directory:\n >> foamLog \nWhere is the file where the log (screen output) was saved\n(redirected, e.g. log.simpleFoam in \"simpleFoam >> log.simpleFoam\")\n\"\"\"\n\nimport os\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\n# dir = input('logs directory: ')\ndir = 'logs'\n\nfiles = [\n 'Ux_0',\n 'Uy_0',\n 'Uz_0',\n 'p_0',\n 'k_0',\n 'epsilon_0',\n 'omega_0',\n 'Uax_0',\n 'Uay_0',\n 'Uaz_0',\n 'pa_0'\n ]\n\nplt.figure()\nfor file in files:\n try:\n data = np.loadtxt(os.path.join(dir, file))\n plt.semilogy(data[:, 0], data[:, 1], label=file, alpha=0.5)\n except:\n pass\nplt.legend()\n\nplt.savefig('residuals.pdf')\nplt.savefig('residuals.png')\nplt.show()","repo_name":"xiaoh/DAFI","sub_path":"ensemble-learning/code/utilities/plot_residuals.py","file_name":"plot_residuals.py","file_ext":"py","file_size_in_byte":860,"program_lang":"python","lang":"en","doc_type":"code","stars":38,"dataset":"github-code","pt":"50"} +{"seq_id":"798202195","text":"#!/usr/bin/python3\ndef weight_average(my_list=[]):\n if not isinstance(my_list, list) or len(my_list) == 0:\n return (0)\n\n average = 0\n size = 0\n for tuppl in my_list:\n average += (tuppl[0] * tuppl[1])\n size += tuppl[1]\n return (average / size)\n","repo_name":"itsoluwatobby/alx-higher_level_programming","sub_path":"0x04-python-more_data_structures/100-weight_average.py","file_name":"100-weight_average.py","file_ext":"py","file_size_in_byte":279,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"6630633407","text":"import pygame\n\nclass Wavepoint():\n\n\n def __init__(self, color, field, index):\n self.color = color\n self.window = field\n self.center = [0, 0]\n self.index = index\n\n\n def setPoint(self):\n end_loop = False\n coordinate = [0, 0]\n\n #draw circles that follow the mouse\n while not end_loop:\n for event in pygame.event.get():\n if event.type == pygame.QUIT: sys.exit()\n\n\n coordinate = pygame.mouse.get_pos()\n pygame.draw.circle(self.window.return_screen(), self.color, coordinate, 5)\n self.window.background_refresh()\n\n if pygame.key.get_pressed()[13]:\n end_loop = True\n\n\n #update the class variables\n self.center = coordinate\n self.window.clear()\n\n\n\n def draw_point(self):\n pygame.draw.circle(self.window.return_screen(), self.color, self.center, 5)\n self.window.background_refresh()\n\n\n def return_position(self):\n return self.center\n\n\n","repo_name":"Vergil-SLI/2022-pathing-simulation","sub_path":"wavepoint.py","file_name":"wavepoint.py","file_ext":"py","file_size_in_byte":1021,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"27132683488","text":"import argparse\nimport os\n\nimport gym\nimport numpy as np\nfrom tqdm import tqdm\nimport tensorflow as tf\n\nfrom network_models.policy_net import Policy_net\n\n\ndef argparser():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--modeldir\", help=\"\", default=\"trained_models\")\n parser.add_argument(\"--alg\", help=\"\", default=\"gail\")\n parser.add_argument(\"--model\", help=\"\", default=\"\")\n parser.add_argument(\"--logdir\", help=\"\", default=\"log/test\")\n parser.add_argument(\"--iteration\", help=\"\", default=int(1e3))\n parser.add_argument(\"--stochastic\", help=\"\", action=\"store_false\")\n parser.add_argument(\"--gpu_num\", help=\"specify GPU number\", default=\"0\", type=str)\n return parser.parse_args()\n\n\ndef main(args):\n # prepare log dir\n if not os.path.exists(args.logdir):\n os.makedirs(args.logdir)\n # gym環境作成\n env = gym.make(\"CartPole-v0\")\n # policy net\n Policy = Policy_net(\"policy\", env)\n # tensorflow saver\n saver = tf.train.Saver()\n\n # session config\n config = tf.ConfigProto(\n gpu_options=tf.GPUOptions(visible_device_list=args.gpu_num, allow_growth=True)\n )\n # start session\n with tf.Session(config=config) as sess:\n # summary writer\n writer = tf.summary.FileWriter(args.logdir, sess.graph)\n # Sessionの初期化\n sess.run(tf.global_variables_initializer())\n # 学習済みモデルの読み込み\n if args.model == \"\":\n saver.restore(sess, args.modeldir + \"/\" + args.alg + \"/\" + \"model.ckpt\")\n else:\n # モデル番号の選択\n saver.restore(\n sess, args.modeldir + \"/\" + args.alg + \"/\" + \"model.ckpt-\" + args.model\n )\n # 状態の初期化\n obs = env.reset()\n success_num = 0\n\n # episode loop\n for iteration in tqdm(range(args.iteration)):\n rewards = []\n run_policy_steps = 0\n # run episode\n while True:\n run_policy_steps += 1\n # prepare to feed placeholder Policy.obs\n # ネットワーク入力用にobsを変換\n obs = np.stack([obs]).astype(dtype=np.float32)\n # 行動と価値を推定\n act, _ = Policy.act(obs=obs, stochastic=args.stochastic)\n\n # 要素数が1の配列をスカラーに変換\n act = np.asscalar(act)\n\n # policy netで推定した行動で状態の更新\n next_obs, reward, done, info = env.step(act)\n\n # episodeの各変数を追加\n rewards.append(reward)\n\n # episode終了判定\n # episodeが終了していたら次のepisodeを開始\n if done:\n obs = env.reset()\n reward = -1\n break\n else:\n obs = next_obs\n\n # summary追加\n writer.add_summary(\n tf.Summary(\n value=[\n tf.Summary.Value(\n tag=\"episode_length\", simple_value=run_policy_steps\n )\n ]\n ),\n iteration,\n )\n writer.add_summary(\n tf.Summary(\n value=[\n tf.Summary.Value(\n tag=\"episode_reward\", simple_value=sum(rewards)\n )\n ]\n ),\n iteration,\n )\n\n # episode成功判定\n if sum(rewards) >= 195:\n success_num += 1\n # 連続で100回成功していればepisode loopを終了\n if success_num >= 100:\n print(\"Iteration: \", iteration)\n print(\"Clear!!\")\n break\n else:\n success_num = 0\n\n writer.close()\n\n\nif __name__ == \"__main__\":\n args = argparser()\n main(args)\n","repo_name":"ykamikawa/tensorflow-gail","sub_path":"test_policy.py","file_name":"test_policy.py","file_ext":"py","file_size_in_byte":4064,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"38167666161","text":"budget = float(input())\nstatisti_count = int(input())\nprice_cloth = float(input())\ndecor = 0.1 * budget\n\nif statisti_count > 150:\n price_cloth *= 0.9\n\ntotal_cost = statisti_count * price_cloth + decor\ndifference = abs(budget - total_cost)\n\nif total_cost > budget:\n print('Not enough money!')\n print(f'Wingard needs {difference:.2f} leva more.')\nelse:\n print('Action!')\n print(f'Wingard starts filming with {difference:.2f} leva left.')\n","repo_name":"lubodonchev/SoftUni_Coursework","sub_path":"First_Project/SoftUni Basics January 2023/Conditional_Statements_Exercises/godzilla.py","file_name":"godzilla.py","file_ext":"py","file_size_in_byte":451,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"15834121302","text":"import socket\nn = 0\nwhile n != 3:\n\n\tprint(\"\\n Menu: \\n 1. DNS 2. Reverse DNS 3. Exit \\n\")\n\tn = int(input(\"\\n ENter your choice\"))\n\t\n\tif n== 1:\n\t\tprint('Welcome to DNS to IP Adress')\n\t\tURL = input('Enter URL: ')\n\t\taddr1 = socket.gethostbyname(URL)\n\t\tprint(addr1)\n\telif n == 2:\n\t\tprint('Welcome IP address to DNS')\n\t\tIP = input('Enter IP adress: ')\n\t\taddr6 = socket.gethostbyaddr(IP)\n\t\tprint(addr6)\n","repo_name":"CadmusFlux/Algorithm-Data_Structures","sub_path":"Others/DNS_lookup.py","file_name":"DNS_lookup.py","file_ext":"py","file_size_in_byte":397,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"21228795248","text":"import numpy as np\r\nimport pandas as pd\r\n\r\ntime = 200\r\nd_mu = 100\r\nd_std = 25\r\nd = np.maximum(np.random.normal(d_mu, d_std, time).round(0).astype(int),0)\r\n\r\nfrom scipy.stats import norm\r\nL, R, alpha = 4, 1, 0.95\r\nz = norm.ppf(alpha)\r\nx_std = np.sqrt(L+R)*d_std\r\n\r\nSs = np.round(x_std*z).astype(int)\r\nCs = 1/2 * d_mu * R\r\nIs = d_mu * L\r\nS = Ss + 2*Cs + Is\r\n\r\nhand = np.zeros(time, dtype=int)\r\ntransit = np.zeros((time,L+1), dtype=int)\r\n\r\nstockout_period = np.full(time, False, dtype=bool)\r\nstockout_cycle = []\r\n\r\nhand[0] = S-d[0]\r\ntransit[1,-1]= d[0]\r\n\r\nfor t in range(1,time):\r\n if transit[t-1,0]>0:\r\n stockout_cycle.append(stockout_period[t-1])\r\n hand[t] = hand[t-1]- d[t] + transit[t-1,0]\r\n stockout_period[t] = hand[t] < 0\r\n transit[t,:-1] = transit[t-1,1:]\r\n if 0==t%R:\r\n net = hand[t] + transit[t].sum()\r\n transit[t,L] = S-net\r\n\r\n\r\ndf = pd.DataFrame(data= {'Demand':d, 'On−hand':hand, 'In−transit':list(transit)})\r\ndf = df.iloc[R+L:,:] #Remove initialization periods\r\nprint(df)\r\ndf['On−hand'].plot(title='Inventory Policy (%d,%d)' %(R,S), ylim=(0,S), legend=True)\r\n\r\nprint('Alpha:',alpha*100)\r\nSL_alpha = 1-sum(stockout_cycle)/len(stockout_cycle)\r\nprint('Cycle Service Level:', round(SL_alpha*100,1))\r\nSL_period = 1-sum(stockout_period)/time\r\nprint('Period Service Level:', round(SL_period*100,1))","repo_name":"Yunbo-max/Mphil-in-ISMM-from-University-of-Cambirge","sub_path":"Project2_Factory_optimization/inventory optimisation/safety stock.py","file_name":"safety stock.py","file_ext":"py","file_size_in_byte":1324,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"7464815936","text":"import torch\nfrom torch.nn.utils.rnn import pad_sequence\nfrom os.path import join\nfrom torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss\nfrom transformers import AutoModelWithLMHead, AutoTokenizer, top_k_top_p_filtering\n\nimport re\nimport datetime\n\nfrom transformers import AutoTokenizer\n\nfrom models import get_embedding_layer, create_model, _create_model\nfrom prompt_encoder import PromptEncoder\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nSMALL_CONST = 1e-10\nBIG_CONST = -1e15\n\nclass Distill_Tuning(torch.nn.Module):\n\n def __init__(self, args, template, label_token = None):\n super().__init__()\n self.args = args\n\n # load tokenizer\n self.tokenizer = AutoTokenizer.from_pretrained(self.args.model_name_or_path)\n self.tokenizer.pad_token = self.tokenizer.eos_token\n \n # model setting\n self.model = create_model(self.args)\n # self.model.resize_token_embeddings(len(self.tokenizer))\n self.model = self.model.to(self.args.device)\n for param in self.model.parameters():\n param.requires_grad = self.args.use_lm_finetune\n \n # get model's embeddings\n self.embeddings = self.model.get_input_embeddings()\n \n\n # label information\n self.label_token = label_token\n self.label_token_ids ={}\n \n for k, v in self.label_token.items():\n print(k,v,self.tokenizer.encode(v))\n \n self.label_token_ids[k] = self.tokenizer.encode(v)\n\n self.template = template\n # load prompt encoder\n self.hidden_size = self.embeddings.embedding_dim\n \n self.pseudo_token_id = self.tokenizer.convert_tokens_to_ids(self.args.pseudo_token)\n\n self.spell_length = sum(self.template)\n self.prompt_encoder = PromptEncoder(self.template, self.hidden_size, self.tokenizer, args)\n self.prompt_encoder = self.prompt_encoder.to(self.args.device)\n \n # self.fc_loss = CrossEntropyLoss(ignore_index = self.tokenizer.eos_token_id)\n \n ### load discriminator\n if self.args.disc_embedding_checkpoint != None:\n \n self.disc_model = _create_model(self.args.disc_embedding_checkpoint[:-5]).to(self.args.device)\n self.spell_length_disc = sum(self.args.template_disc)\n self.disc_embedding = self.disc_model.get_input_embeddings()\n self.prompt_encoder_disc = PromptEncoder(self.args.template_disc, self.disc_embedding.embedding_dim, self.tokenizer, args)\n self.prompt_encoder_disc = self.prompt_encoder_disc.to(self.args.device)\n self.prompt_encoder_disc.load_state_dict(self.load_prompt(self.args.disc_embedding_checkpoint))\n else :\n self.disc_model = self.model\n self.prompt_encoder_disc = self.prompt_encoder\n \n \n def load_prompt(self, embedding_checkpoint):\n checkpoint = torch.load(embedding_checkpoint)\n prompt_embedding = checkpoint['embedding']\n return prompt_embedding\n \n def generate_soft_tokens(self, generated_tokens, past_key_values= None, position = None):\n \n if past_key_values!= None:\n last_embeds =self.embeddings(generated_tokens[:, -1]).unsqueeze(1)#get its embeddings\n with torch.no_grad():\n outputs = self.model(inputs_embeds=last_embeds,\n past_key_values = past_key_values,\n position_ids = position[:, past_key_values[0][0].shape[-2]],\n return_dict=True)\n \n else:\n attention_mask = (generated_tokens!=self.tokenizer.eos_token_id).type(torch.uint8)\n position_ids = attention_mask.long().cumsum(-1)- 1\n position_ids.masked_fill_(attention_mask == 0, 0)\n last_embeds =self.embeddings(generated_tokens) #get its embeddings\n with torch.no_grad():\n outputs = self.model(inputs_embeds=last_embeds,\n past_key_values = past_key_values,\n attention_mask = attention_mask,\n position_ids = position_ids,\n return_dict=True)\n \n next_token_logits = outputs.logits[:, -1, :]\n \n next_token_logits = self.top_k_top_p_filtering(next_token_logits.squeeze(1), top_k=self.args.ranking_scope, top_p=self.args.top_p, filter_value=BIG_CONST)\n \n return next_token_logits, outputs.past_key_values\n \n \n def top_k_top_p_filtering(self,\n logits,\n top_k = 0,\n top_p = 1.0,\n filter_value = -1e15 ,\n min_tokens_to_keep = 1,\n ):\n \"\"\"Filter a distribution of logits using top-k and/or nucleus (top-p) filtering\n Args:\n logits: logits distribution shape (batch size, vocabulary size)\n if top_k > 0: keep only top k tokens with highest probability (top-k filtering).\n if top_p < 1.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).\n Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)\n Make sure we keep at least min_tokens_to_keep per batch example in the output\n From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317\n \"\"\"\n if top_k > 0:\n top_k = min(max(top_k, min_tokens_to_keep), logits.size(-1)) # Safety check\n # Remove all tokens with a probability less than the last token of the top-k\n indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]\n logits[indices_to_remove] = filter_value\n\n if top_p < 1.0:\n sorted_logits, sorted_indices = torch.sort(logits, descending=True)\n cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)\n\n # Remove tokens with cumulative probability above the threshold (token with 0 are kept)\n sorted_indices_to_remove = cumulative_probs > top_p\n if min_tokens_to_keep > 1:\n # Keep at least min_tokens_to_keep (set to min_tokens_to_keep-1 because we add the first one below)\n sorted_indices_to_remove[..., :min_tokens_to_keep] = 0\n # Shift the indices to the right to keep also the first token above the threshold\n sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()\n sorted_indices_to_remove[..., 0] = 0\n\n # scatter sorted tensors to original indexing\n indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)\n logits[indices_to_remove] = filter_value\n return logits\n \n \n\n \n def _predict_scores(self, x_hs, att_mask):\n bz = len(x_hs)\n # construct query ids\n prompt_tokens = [self.pseudo_token_id]\n \n queries = self.get_query(x_hs, prompt_tokens)\n # construct label ids\n attention_mask = torch.cat([att_mask, torch.ones([att_mask.shape[0], self.spell_length_disc]).long().to(self.args.device)], dim=1)\n # get embedded input\n inputs_embeds = self.embed_input(queries)\n \n position_ids = attention_mask.long().cumsum(-1)- 1\n position_ids.masked_fill_(attention_mask == 0, 0)\n \n with torch.no_grad():\n output = self.disc_model(inputs_embeds = inputs_embeds,\n attention_mask = attention_mask,\n position_ids = position_ids,\n labels=None)\n\n logits = output.logits[:,-1,:].squeeze(1)\n \n binary_prob = torch.softmax(logits[:,[11274,14774]], dim=-1)\n \n if self.args.corpus_type == \"negative\":\n return binary_prob[:,1]\n else:\n return binary_prob[:,0] \n \n def get_query(self, x_h, prompt_tokens, x_t = None):\n \n prompt_tensor = torch.tensor(prompt_tokens* (self.spell_length_disc)).to(self.args.device)\n prompt_tensor = prompt_tensor.expand(x_h.shape[0],-1)\n if x_t != None:\n x_t = x_t.unsqueeze(1)\n return torch.cat([x_h, prompt_tensor, x_t], dim =1)\n else:\n return torch.cat([x_h, prompt_tensor], dim =1)\n \n\n def embed_input(self, queries):\n bz = queries.shape[0]\n queries_for_embedding = queries.clone()\n raw_embeds = self.disc_embedding(queries_for_embedding)\n \n replace_embeds = self.prompt_encoder_disc()\n \n replace_embeds = replace_embeds.unsqueeze(0).expand(bz,-1, -1)\n \n raw_embeds[:,-self.prompt_encoder_disc.spell_length:,: ] = replace_embeds\n \n return raw_embeds\n \n \n def get_query_head(self, x_h, prompt_tokens, x_t = None):\n \n prompt_tensor_head = torch.tensor(prompt_tokens* (self.spell_length)).to(self.args.device)\n \n trans_inputs = []\n \n index_musk = (x_h == self.tokenizer.pad_token_id).type(torch.uint8) # only calculte the token which is not eos\n \n valid_number_length = torch.sum(index_musk, 1)\n \n for index, seq in zip(valid_number_length, x_h):\n if index == x_h.shape[1]:\n trans_inputs.append(torch.cat([prompt_tensor_head,seq]))\n else:\n trans_inputs.append(torch.cat([seq[:index], prompt_tensor_head, seq[index:]]))\n \n res = torch.stack(trans_inputs, dim=0)\n if x_t != None:\n # x_t = x_t.unsqueeze(1)\n return torch.cat([res, x_t], dim =1)\n else:\n return res \n \n \n def embed_input_head(self, queries):\n bz = queries.shape[0]\n queries_for_embedding = queries.clone()\n \n queries_for_embedding[(queries == self.pseudo_token_id)] = self.tokenizer.unk_token_id\n raw_embeds = self.embeddings(queries_for_embedding)\n\n blocked_indices = (queries == self.pseudo_token_id).type(torch.uint8).nonzero().reshape((bz, self.spell_length, 2))[:, :, 1] # bz\n \n replace_embeds = self.prompt_encoder()\n for bidx in range(bz):\n for i in range(self.prompt_encoder.spell_length):\n raw_embeds[bidx, blocked_indices[bidx, i], :] = replace_embeds[i, :]\n return raw_embeds\n \n \n def generate(self, prompts_ids, max_length, desired_att = None, beta = 0.5):\n \"\"\"\n generation forward based on given prompt tokens, \n Args:\n prompt_ids: the prompt tokens\n max_length: the max len of the generation\n Returns:\n generated_texts:[generated tokens]\n \"\"\"\n cur_len = prompts_ids.shape[1]\n logits = []\n output_ids = prompts_ids\n return_dict = {}\n eos_flag = torch.ones([prompts_ids.shape[0]]).type(torch.uint8).to(self.args.device)\n \n \n prompt_tokens = [self.pseudo_token_id]\n queries = self.get_query_head(prompts_ids, prompt_tokens)\n inputs_embeds = self.embed_input_head(queries)\n\n attention_mask = torch.cat([prompts_ids!= self.tokenizer.pad_token_id , torch.ones([prompts_ids.shape[0], self.prompt_encoder.spell_length + max_length-prompts_ids.shape[1]]).long().to(self.args.device)], dim=1)\n\n position_ids = attention_mask.long().cumsum(-1)- 1\n position_ids.masked_fill_(attention_mask == 0, 0)\n\n # start = datetime.datetime.now()\n # test generation time\n\n while cur_len <= max_length:\n outputs = self.model(inputs_embeds=inputs_embeds,\n attention_mask = attention_mask[:,:inputs_embeds.shape[1]],\n position_ids = position_ids[:,:inputs_embeds.shape[1]],\n return_dict=True)\n \n next_token_logits = outputs.logits[:, -1, :]\n next_token_logits_ = self.top_k_top_p_filtering(next_token_logits, top_k=self.args.ranking_scope, top_p=1.0, filter_value=BIG_CONST)\n \n next_token_logits_prob = torch.softmax(next_token_logits_, dim=1)\n next_tokens = torch.multinomial(next_token_logits_prob, num_samples=1).squeeze(1)\n \n eos_flag = eos_flag.mul((next_tokens != self.tokenizer.eos_token_id).type(torch.uint8))# if flag = 0, it means the generation is over \n next_tokens = next_tokens.mul(eos_flag)\n next_tokens[next_tokens == 0] = self.tokenizer.eos_token_id \n output_ids = torch.cat([output_ids, next_tokens.unsqueeze(1)], dim=1)\n inputs_embeds = torch.cat([inputs_embeds, self.embeddings(next_tokens).unsqueeze(1)], dim=1)\n\n print(\"cur_len is:\",cur_len)\n cur_len = cur_len + 1\n \n# end = datetime.datetime.now()\n# print(\"runing time is:\",end-start)\n \n return_dict = {\"generated_tokens\":output_ids}\n return return_dict\n\n \n def feedback_from_discriminator(self, input_ids, logits_seq, desired_att):\n \n top_logits, top_indices = torch.topk(logits_seq, self.args.ranking_scope) # batch x topk\n \n candidates = []\n for logit_id, ids in zip(top_indices, input_ids):\n data = ids.expand(self.args.ranking_scope, -1)\n new_input_candidates = torch.cat([data, logit_id.unsqueeze(1)], dim=1) # batch x topk x seq+1\n candidates.append(new_input_candidates)\n \n \n candidates = torch.cat(candidates, dim=0)\n \n if candidates.shape[1]<30:\n pad_tensor =torch.empty(candidates.shape[0],30 - candidates.shape[1]).long().fill_(self.tokenizer.eos_token_id).to(self.args.device)\n candidates = torch.cat([pad_tensor,candidates], dim=1)\n \n pred_scores = []\n for new_input_candidates in torch.split(candidates, 120, dim=0):\n musk = (new_input_candidates != self.tokenizer.eos_token_id).type(torch.uint8)\n pred_score = self._predict_scores(new_input_candidates, musk)\n pred_scores.append(pred_score)\n\n pred_scores = torch.cat(pred_scores, dim=0)\n pred_scores = pred_scores.reshape(input_ids.shape[0], -1)\n\n res_logits = logits_seq.clone().detach()\n res_logits.scatter_(-1, top_indices, pred_scores)\n return res_logits\n \n \n \n def get_ranked_logtis(self, inputs, logits, desired_att):\n \n return_logits = []\n for i in range(inputs.shape[1]):\n tmp = self.feedback_from_discriminator(inputs[:, :i+1], logits[:,i,:], desired_att)\n return_logits.append(tmp)\n \n return torch.stack(return_logits, dim=1).detach().clone()\n \n \n \n def KL_loss(self, input_x, input_y, attention_mask):\n \"\"\"\n compute the KL loss\n \"\"\"\n m = torch.flatten(attention_mask)\n indices = torch.nonzero(m).squeeze(-1)\n \n x = input_x.reshape(-1,input_x.shape[-1])\n x = torch.index_select(x, 0, indices)\n \n y = input_y.reshape(-1,input_y.shape[-1])\n y = torch.index_select(y, 0, indices)\n \n y_ = torch.softmax(y/self.args.temperature, dim = -1)\n loss_ = -(y_ * (x+1e-20).log()).sum() / x.size(0)\n \n _y = torch.softmax(((1-y).mul(y>0.0))/self.args.temperature, dim = -1)\n _loss = -(_y * (1-x+1e-20).log()).sum() / x.size(0)\n\n return loss_ + _loss\n\n\n \n def contrast_crossEntry_loss(self, logits_prob, x_hs, sentence_labels = None):\n\n shift_prob = logits_prob[..., :-1, :].contiguous()\n shift_labels = x_hs[..., 1:].contiguous()\n\n if sentence_labels != None:\n index_negative = (sentence_labels==14774)\n index = torch.nonzero(index_negative).squeeze(1)\n shift_prob[index] = 1-shift_prob[index]\n\n loss = F.nll_loss((shift_prob.view(-1, shift_prob.size(-1))+1e-20).log(), shift_labels.view(-1), ignore_index=-100)\n \n return loss\n \n \n def get_candidate_logits(self, x_hs, att_mask):\n \n position_ids = att_mask.long().cumsum(-1)- 1\n position_ids.masked_fill_(att_mask == 0, 0)\n \n with torch.no_grad():\n output = self.model(input_ids= x_hs,\n attention_mask=att_mask,\n position_ids=position_ids,\n labels= None)\n return output.logits.detach().clone()\n \n \n def forward(self, x_hs, x_ts, att_mask):\n # construct query ids\n prompt_tokens = [self.pseudo_token_id]\n queries = self.get_query_head(x_hs, prompt_tokens)\n # construct label ids\n attention_mask = torch.cat([att_mask, torch.ones([att_mask.shape[0], self.prompt_encoder.spell_length]).long().to(self.args.device)], dim=1)\n \n position_ids = attention_mask.long().cumsum(-1)- 1\n position_ids.masked_fill_(attention_mask == 0, 0)\n \n labels = torch.clone(queries)\n \n labels.masked_fill_(attention_mask==0, -100)\n labels.masked_fill_(queries == self.pseudo_token_id, -100)\n \n # get embedded input\n inputs_embeds = self.embed_input_head(queries)\n\n output = self.model(inputs_embeds=inputs_embeds,\n attention_mask=attention_mask,\n position_ids=position_ids,\n labels= None)\n \n output_logits = output.logits\n # ce_loss = self.contrast_crossEntry_loss(torch.softmax(output_logits, dim = -1), labels, sentence_labels = x_ts)\n \n _queries = queries.view(queries.size(0)*queries.size(1))\n _output_logits = output_logits.view(output_logits.size(0)*output_logits.size(1),-1)\n disc_logits = _output_logits.index_select(0, torch.nonzero(_queries != self.pseudo_token_id).squeeze(1)).view(output_logits.shape[0],-1, output_logits.shape[2])\n \n logits_candidate = self.get_candidate_logits(x_hs,att_mask)\n logits_candidate = self.top_k_top_p_filtering(logits_candidate.view(logits_candidate.shape[0]*logits_candidate.shape[1], -1), top_k= self.args.ranking_scope , top_p=self.args.top_p, filter_value=BIG_CONST).view(x_hs.shape[0],x_hs.shape[1], -1)\n\n reank_output = self.get_ranked_logtis(x_hs, logits_candidate.detach().clone(), desired_att=None)\n \n reank_output = (logits_candidate>BIG_CONST+10).mul(reank_output)\n\n kl_loss = self.KL_loss(torch.softmax(disc_logits, dim=-1), reank_output, att_mask)\n \n loss = kl_loss\n\n return loss\n \n \n\n \n","repo_name":"littlehacker26/Discriminator-Cooperative-Unlikelihood-Prompt-Tuning","sub_path":"distill_tuning.py","file_name":"distill_tuning.py","file_ext":"py","file_size_in_byte":19111,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"52"} +{"seq_id":"42479607125","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom utils.PropertiesReader import PropertiesReader\n\n\nclass Environnement:\n\n def __init__(self, w, h, SMA):\n self.w = w\n self.h = h\n self.grille2d = [[None for x in range(self.w)] for y in range(self.h)]\n self.SMA = SMA\n self.is_torrique = PropertiesReader.prop.toric()\n\n def set_agent(self, agent):\n self.grille2d[agent.y][agent.x] = agent\n\n def delete_agent(self, agent):\n if agent.previous_y or agent.previous_x:\n self.grille2d[agent.previous_y][agent.previous_x] = None\n\n def get_grille(self):\n return self.grille2d\n\n def is_their_a_collision(self, x, y):\n \"\"\"\n Methode pour voir si il y a une quelquonque collision\n :param agent:\n :param x: prochaine position x de l'agent\n :param y: prochaine position y de l'agent\n :return: un agent percute, soit un mur traverse, soit rien\n \"\"\"\n\n if y < 0 or x < 0 or x > self.w-1 or y > self.h-1:\n # si il y a collision avec un mur\n return self.wall_collision_direction(x, y, self.w-1, self.h-1)\n\n potential_agent = self.grille2d[y][x]\n if potential_agent:\n # si il y a collision avec un autre agent, on renvois cet agent\n return potential_agent\n\n @staticmethod\n def wall_collision_direction(x, y, max_x, max_y):\n \"\"\"\n\n :param x: prochaine position x de l'agent\n :param y: prochaine position y de l'agent\n :param max_x: borne max de x\n :param max_y: borne max de y\n :return: un tuple de boolean pour dire quels axes on inverse (x, y, ou les deux)\n \"\"\"\n\n inverse_x_dir = False\n inverse_y_dir = False\n if y < 0 or y > max_y:\n # murhautbas\n inverse_y_dir = True\n if x < 0 or x > max_x:\n # murscote\n inverse_x_dir = True\n\n return (inverse_x_dir, inverse_y_dir)\n","repo_name":"FlorianDoublet/SMA-discret","sub_path":"model/core/Environnement.py","file_name":"Environnement.py","file_ext":"py","file_size_in_byte":1975,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"41595323335","text":"# coding:utf-8\nimport time\nimport urllib\nimport urllib2\nimport json\nimport requests\nimport StringIO\nimport gzip\nfrom poster.encode import multipart_encode\nfrom poster.streaminghttp import register_openers\nfrom protos import log_request_pb2\n\n# register_openers()\n# datagen, headers = multipart_encode()\n# print datagen, headers\n\nurl = \"http://192.168.9.41:8080/m/v1/log/\"\nlr = log_request_pb2.LogRequest()\n# data = {\"sysVersion\": \"6.0\", \"apkVersion\": \"1.2.3\",\n# \"channelID\": \"843984932\", \"versionCode\": \"1\", \"data\": \"item0\"}\ndata = \"item2\"\narg = {\n \"application_id\": \"com.apkpure.aegon\",\n \"client_version\": int(883),\n \"device_brand\": \"google\",\n \"device_model\": \"Nexus 5\",\n \"flavor\": \"aa\",\n \"sdk_version\": int(23),\n # \"supported_abis[]\": [\"armeabi-v7a\", \"armeabi\"]\n # \"supported_abis\": [\"armeabi-v7a\", \"armeabi\", \"22\", \"33\"]\n}\n\n\ndef dataGzip(data):\n s = StringIO.StringIO()\n g = gzip.GzipFile(fileobj=s, mode='w')\n g.write(data)\n g.close()\n gzipped_body = s.getvalue()\n # print('len:',s.len)\n request_body = gzipped_body\n return request_body\n\n\ndef init():\n global lr\n for i in range(0, 20):\n l = log_request_pb2.Log()\n l.type = str(i)\n # l.logData = json.dumps(data)\n l.logData = str(data)\n # l.delayTimeMs = int(time.time())\n l.delayTimeMs = int(10)\n # l.delayTimeMs = 128\n # print(l.delayTimeMs)\n lr.logs._values.append(l)\n\n\ndef test_zip(lr):\n if not isinstance(lr, log_request_pb2.LogRequest):\n return\n s = lr.SerializeToString()\n # arg[\"argument\"] = s\n global url\n url = url + \"?\" + urllib.urlencode(arg)\n print(\"url:\", url)\n req = urllib2.Request(url)\n req.add_header('Accept-encoding', 'gzip')\n # 发送压缩后数据\n print(\"slenunzip:\", len(s))\n s = dataGzip(s)\n print(\"s:\", s)\n print(\"slenzip:\", len(s))\n resp = requests.post(url, s, headers=req.headers)\n print(resp.content)\n print(resp)\n\n\ndef test_zip_arg(lr):\n if not isinstance(lr, log_request_pb2.LogRequest):\n return\n s = lr.SerializeToString()\n global url\n url = url + \"?\" + urllib.urlencode(arg)\n url = \"http://localhost:8080/m/v1/log/?\" \\\n \"application_id=com.apkpure.aegon&device_model=Nexus%205&\" \\\n \"supported_abis=%27armeabi-v7a%27&supported_abis=%20%27armeabi%27&supported_abis=%20%2722%27&\" \\\n \"sdk_version=23&device_brand=google&client_version=111&flavor=aa\"\n req = urllib2.Request(url)\n print(\"url:\", url)\n req.add_header('Accept-encoding', 'gzip')\n s = dataGzip(s)\n resp = requests.post(url, s, headers=req.headers)\n print(resp.content)\n print(resp)\n\n\ndef test_zip_arg_json(lr):\n if not isinstance(lr, log_request_pb2.LogRequest):\n return\n # delay time\n # for l in lr.logs._values:\n #\n s = lr.SerializeToString()\n global url\n # url = \"{}?arg={}\".format(url,json.dumps(arg))\n req = urllib2.Request(url)\n req.add_header('Accept-encoding', 'gzip')\n # 发送压缩后数据\n s = dataGzip(s)\n resp = requests.post(url, s, headers=req.headers)\n print(resp.content)\n print(resp)\n pass\n\n\ndef main():\n global lr\n init()\n # test_zip(lr)\n test_zip_arg(lr)\n # test_zip_arg_son(lr)\n\nif __name__ == '__main__':\n main()\n","repo_name":"tphyhFighting/docker","sub_path":"protobuf/python/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":3301,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"6983085356","text":"from collections import deque\ndef bfs(i, j):\n q = deque()\n q.append([i, j])\n ally = []\n ally.append([i, j])\n while q:\n r, c = q.popleft()\n for i in range(4):\n nx = r + dx[i]\n ny = c + dy[i]\n if 0 <= nx < N and 0 <= ny < N and visit[nx][ny] == 0:\n gap = abs(graph[nx][ny] - graph[r][c])\n if L <= gap <= R:\n visit[nx][ny] = 1\n q.append([nx, ny])\n ally.append([nx, ny])\n return ally\ndx = [1, -1, 0, 0]\ndy = [0, 0, -1, 1]\nN, L, R = map(int, input().split())\ngraph = [list(map(int, input().split())) for _ in range(N)]\n\ncnt = 0\nwhile True:\n visit = [[0] * N for i in range(N)]\n isFinished = True\n for i in range(N):\n for j in range(N):\n if visit[i][j] == 0: #visited를 bfs 외부에서 마킹하여 여러곳에서의 인구이동을 병렬 처리 (서로 영향 있는 부분만 bfs 내부에서 마킹됨)\n visit[i][j] = 1\n ally = bfs(i, j) #동맹국 리스트\n if len(ally) > 1:\n isFinished = False\n total_num = 0\n for r, c in ally: #인구 이동 처리\n total_num += graph[r][c]\n num = int(total_num / len(ally))\n for r, c in ally:\n graph[r][c] = num\n if isFinished:\n break\n cnt += 1\nprint(cnt)","repo_name":"jeno8522/Coding-Test-Study","sub_path":"2022/9월/4주차/백준/BFS/16234_인구이동.py","file_name":"16234_인구이동.py","file_ext":"py","file_size_in_byte":1482,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"18393083330","text":"import base64\nimport copy\nimport json\nimport isodate\nfrom datetime import datetime\nfrom pyld import jsonld\nimport pytz\n\nfrom cryptography.hazmat.primitives import serialization\nfrom cryptography.hazmat.backends import default_backend\nfrom cryptography.hazmat.primitives import hashes\nfrom cryptography.hazmat.primitives.asymmetric import padding\nfrom cryptography.exceptions import InvalidSignature\n\nSECURITY_CONTEXT_URL = 'https://w3id.org/security/v1'\nSECURITY_CONTEXT = {\n \"@context\": {\n \"id\": \"@id\",\n \"type\": \"@type\",\n\n \"dc\": \"http://purl.org/dc/terms/\",\n \"sec\": \"https://w3id.org/security#\",\n \"xsd\": \"http://www.w3.org/2001/XMLSchema#\",\n\n \"EcdsaKoblitzSignature2016\": \"sec:EcdsaKoblitzSignature2016\",\n \"EncryptedMessage\": \"sec:EncryptedMessage\",\n \"GraphSignature2012\": \"sec:GraphSignature2012\",\n \"LinkedDataSignature2015\": \"sec:LinkedDataSignature2015\",\n \"LinkedDataSignature2016\": \"sec:LinkedDataSignature2016\",\n \"CryptographicKey\": \"sec:Key\",\n\n \"authenticationTag\": \"sec:authenticationTag\",\n \"canonicalizationAlgorithm\": \"sec:canonicalizationAlgorithm\",\n \"cipherAlgorithm\": \"sec:cipherAlgorithm\",\n \"cipherData\": \"sec:cipherData\",\n \"cipherKey\": \"sec:cipherKey\",\n \"created\": {\"@id\": \"dc:created\", \"@type\": \"xsd:dateTime\"},\n \"creator\": {\"@id\": \"dc:creator\", \"@type\": \"@id\"},\n \"digestAlgorithm\": \"sec:digestAlgorithm\",\n \"digestValue\": \"sec:digestValue\",\n \"domain\": \"sec:domain\",\n \"encryptionKey\": \"sec:encryptionKey\",\n \"expiration\": {\"@id\": \"sec:expiration\", \"@type\": \"xsd:dateTime\"},\n \"expires\": {\"@id\": \"sec:expiration\", \"@type\": \"xsd:dateTime\"},\n \"initializationVector\": \"sec:initializationVector\",\n \"iterationCount\": \"sec:iterationCount\",\n \"nonce\": \"sec:nonce\",\n \"normalizationAlgorithm\": \"sec:normalizationAlgorithm\",\n \"owner\": {\"@id\": \"sec:owner\", \"@type\": \"@id\"},\n \"password\": \"sec:password\",\n \"privateKey\": {\"@id\": \"sec:privateKey\", \"@type\": \"@id\"},\n \"privateKeyPem\": \"sec:privateKeyPem\",\n \"publicKey\": {\"@id\": \"sec:publicKey\", \"@type\": \"@id\"},\n \"publicKeyPem\": \"sec:publicKeyPem\",\n \"publicKeyService\": {\"@id\": \"sec:publicKeyService\", \"@type\": \"@id\"},\n \"revoked\": {\"@id\": \"sec:revoked\", \"@type\": \"xsd:dateTime\"},\n \"salt\": \"sec:salt\",\n \"signature\": \"sec:signature\",\n \"signatureAlgorithm\": \"sec:signingAlgorithm\",\n \"signatureValue\": \"sec:signatureValue\"}}\n\nIDENTITY_CONTEXT_URL = 'https://w3id.org/identity/v1'\nIDENTITY_CONTEXT = {\n \"@context\": {\n \"id\": \"@id\",\n \"type\": \"@type\",\n\n \"cred\": \"https://w3id.org/credentials#\",\n \"dc\": \"http://purl.org/dc/terms/\",\n \"identity\": \"https://w3id.org/identity#\",\n \"perm\": \"https://w3id.org/permissions#\",\n \"ps\": \"https://w3id.org/payswarm#\",\n \"rdf\": \"http://www.w3.org/1999/02/22-rdf-syntax-ns#\",\n \"rdfs\": \"http://www.w3.org/2000/01/rdf-schema#\",\n \"sec\": \"https://w3id.org/security#\",\n \"schema\": \"http://schema.org/\",\n \"xsd\": \"http://www.w3.org/2001/XMLSchema#\",\n\n \"Group\": \"https://www.w3.org/ns/activitystreams#Group\",\n\n \"claim\": {\"@id\": \"cred:claim\", \"@type\": \"@id\"},\n \"credential\": {\"@id\": \"cred:credential\", \"@type\": \"@id\"},\n \"issued\": {\"@id\": \"cred:issued\", \"@type\": \"xsd:dateTime\"},\n \"issuer\": {\"@id\": \"cred:issuer\", \"@type\": \"@id\"},\n \"recipient\": {\"@id\": \"cred:recipient\", \"@type\": \"@id\"},\n \"Credential\": \"cred:Credential\",\n \"CryptographicKeyCredential\": \"cred:CryptographicKeyCredential\",\n\n \"about\": {\"@id\": \"schema:about\", \"@type\": \"@id\"},\n \"address\": {\"@id\": \"schema:address\", \"@type\": \"@id\"},\n \"addressCountry\": \"schema:addressCountry\",\n \"addressLocality\": \"schema:addressLocality\",\n \"addressRegion\": \"schema:addressRegion\",\n \"comment\": \"rdfs:comment\",\n \"created\": {\"@id\": \"dc:created\", \"@type\": \"xsd:dateTime\"},\n \"creator\": {\"@id\": \"dc:creator\", \"@type\": \"@id\"},\n \"description\": \"schema:description\",\n \"email\": \"schema:email\",\n \"familyName\": \"schema:familyName\",\n \"givenName\": \"schema:givenName\",\n \"image\": {\"@id\": \"schema:image\", \"@type\": \"@id\"},\n \"label\": \"rdfs:label\",\n \"name\": \"schema:name\",\n \"postalCode\": \"schema:postalCode\",\n \"streetAddress\": \"schema:streetAddress\",\n \"title\": \"dc:title\",\n \"url\": {\"@id\": \"schema:url\", \"@type\": \"@id\"},\n \"Person\": \"schema:Person\",\n \"PostalAddress\": \"schema:PostalAddress\",\n \"Organization\": \"schema:Organization\",\n\n \"identityService\": {\"@id\": \"identity:identityService\", \"@type\": \"@id\"},\n \"idp\": {\"@id\": \"identity:idp\", \"@type\": \"@id\"},\n \"Identity\": \"identity:Identity\",\n\n \"paymentProcessor\": \"ps:processor\",\n \"preferences\": {\"@id\": \"ps:preferences\", \"@type\": \"@vocab\"},\n\n \"cipherAlgorithm\": \"sec:cipherAlgorithm\",\n \"cipherData\": \"sec:cipherData\",\n \"cipherKey\": \"sec:cipherKey\",\n \"digestAlgorithm\": \"sec:digestAlgorithm\",\n \"digestValue\": \"sec:digestValue\",\n \"domain\": \"sec:domain\",\n \"expires\": {\"@id\": \"sec:expiration\", \"@type\": \"xsd:dateTime\"},\n \"initializationVector\": \"sec:initializationVector\",\n \"member\": {\"@id\": \"schema:member\", \"@type\": \"@id\"},\n \"memberOf\": {\"@id\": \"schema:memberOf\", \"@type\": \"@id\"},\n \"nonce\": \"sec:nonce\",\n \"normalizationAlgorithm\": \"sec:normalizationAlgorithm\",\n \"owner\": {\"@id\": \"sec:owner\", \"@type\": \"@id\"},\n \"password\": \"sec:password\",\n \"privateKey\": {\"@id\": \"sec:privateKey\", \"@type\": \"@id\"},\n \"privateKeyPem\": \"sec:privateKeyPem\",\n \"publicKey\": {\"@id\": \"sec:publicKey\", \"@type\": \"@id\"},\n \"publicKeyPem\": \"sec:publicKeyPem\",\n \"publicKeyService\": {\"@id\": \"sec:publicKeyService\", \"@type\": \"@id\"},\n \"revoked\": {\"@id\": \"sec:revoked\", \"@type\": \"xsd:dateTime\"},\n \"signature\": \"sec:signature\",\n \"signatureAlgorithm\": \"sec:signatureAlgorithm\",\n \"signatureValue\": \"sec:signatureValue\",\n \"CryptographicKey\": \"sec:Key\",\n \"EncryptedMessage\": \"sec:EncryptedMessage\",\n \"GraphSignature2012\": \"sec:GraphSignature2012\",\n \"LinkedDataSignature2015\": \"sec:LinkedDataSignature2015\",\n\n \"accessControl\": {\"@id\": \"perm:accessControl\", \"@type\": \"@id\"},\n \"writePermission\": {\"@id\": \"perm:writePermission\", \"@type\": \"@id\"}\n }\n}\n \n\n_get_values = jsonld.JsonLdProcessor.get_values\ndef _get_value(obj, key):\n try:\n return _get_values(obj, key)[0]\n # A bit more accurate since we're trying to pull a value out of a specific\n # key, and nothing exists for this one\n except IndexError:\n raise KeyError(key)\n_has_value = jsonld.JsonLdProcessor.has_value\n\n\ndef _make_simple_loader(url_map, load_unknown_urls=True,\n cache_externally_loaded=True):\n def _make_context(url, doc):\n return {\n \"contextUrl\": None,\n \"documentUrl\": url,\n \"document\": doc}\n\n # Wrap in the structure that's expected to come back from the\n # documentLoader\n _pre_url_map = {}\n _pre_url_map.update(url_map)\n _url_map = {\n url: _make_context(url, doc)\n for url, doc in _pre_url_map.items()}\n\n def loader(url):\n if url in _url_map:\n return _url_map[url]\n elif load_unknown_urls:\n doc = jsonld.load_document(url)\n # @@: Is this optimization safe in all cases?\n if isinstance(doc[\"document\"], str):\n doc[\"document\"] = json.loads(doc[\"document\"])\n _url_map[url] = doc\n return doc\n else:\n raise jsonld.JsonLdError(\n \"url not found and loader set to not load unknown URLs.\",\n {'url': url})\n\n return loader\n\n_security_context_loader = _make_simple_loader(\n {SECURITY_CONTEXT_URL: SECURITY_CONTEXT,\n IDENTITY_CONTEXT_URL: IDENTITY_CONTEXT})\n\n# @@: Shouldn't this be a mapping from these names to their actual\n# functionality? Seems kludgy to have all these if-elif-else things\n# as interspersed through the document...\n# Okay, answer is yes\n\n# TODO: Make these JsonLdErrors\n# class LdsError(jsonld.JsonLdError): pass\n# class LdsTypeError(LdsError, TypeError): pass\nclass LdsError(Exception): pass\nclass LdsTypeError(LdsError, TypeError): pass\n\ndef is_valid_uri(obj):\n \"\"\"\n Check to see if OBJ is a valid URI\n\n (or at least do the best check we can: that it's a string, and that\n it contains the ':' character.)\n \"\"\"\n return isinstance(obj, str) and \":\" in obj\n\ndef sign(document, options):\n \"\"\"\n Signs a JSON-LD document using a digital signature.\n\n - input: the JSON-LD document to be signed.\n - options: options to use:\n [privateKeyPem] A PEM-encoded private key.\n [creator] the URL to the paired public key.\n [date] an optional date to override the signature date with.\n If provided, must have an \"aware\" timezone\n (.tzinfo not None)\n [domain] an optional domain to include in the signature.\n [nonce] an optional nonce to include in the signature.\n [algorithm] the algorithm to use, eg: 'GraphSignature2012',\n 'LinkedDataSignature2015' (default: 'GraphSignature2012').\n \"\"\"\n options = copy.deepcopy(options)\n\n # TODO: The spec says privateKey, but in jsonld-signatures.js there are\n # these two separate fields...\n options[\"date\"] = options.get(\"date\") or datetime.now(pytz.utc)\n options.setdefault(\"algorithm\", \"GraphSignature2012\")\n\n if not options[\"algorithm\"] in SUITES:\n raise LdsError(\n (\"[jsig.sign] Unsupported algorithm '%s'; options.algorithm must \"\n \"be one of: %s\") % (options[\"algorithm\"], SUITES.keys()))\n\n suite = SUITES[options[\"algorithm\"]]\n options = suite.signature_munge_verify_options(options)\n\n # @@: Do we need this in the sign thing?\n sig_options = {\n \"date\": options[\"date\"]\n }\n if \"nonce\" in options:\n sig_options[\"nonce\"] = options[\"nonce\"]\n if \"domain\" in options:\n sig_options[\"domain\"] = options[\"domain\"]\n formatted = suite.format_for_signature(document, sig_options, options)\n sig_val = suite.sign_formatted(formatted, options)\n\n signature = {\n \"@context\": SECURITY_CONTEXT_URL,\n \"type\": options[\"algorithm\"],\n \"creator\": options[\"creator\"],\n \"created\": options[\"date\"],\n \"signatureValue\": sig_val}\n if \"domain\" in options:\n signature[\"domain\"] = options[\"domain\"]\n if \"nonce\" in options:\n signature[\"nonce\"] = options[\"nonce\"]\n ctx = _get_values(document, \"@context\")\n compacted = jsonld.compact(\n {\"https://w3id.org/security#signature\": signature},\n ctx, options={\n \"documentLoader\": _security_context_loader})\n\n del compacted[\"@context\"]\n \n output = copy.deepcopy(document)\n # @@: Wow, this seems like a terribly kludgy way to get that key,\n # but that's what's done in jsonld-signatures.js. I mean,\n # I guess it should work. I guess this is to avoid that the name may\n # be either expanded or compacted at this point\n signature_key = list(compacted.keys())[0]\n # TODO: support multiple signatures.\n # Same warning as in jsonld-signatures.js! ;P\n # We could put this in the suite option?\n output[signature_key] = compacted[signature_key]\n return output\n\n\ndef _basic_rsa_signature(formatted, options):\n private_key = serialization.load_pem_private_key(\n options[\"privateKeyPem\"],\n password=None,\n backend=default_backend())\n signed = private_key.sign(\n formatted,\n # I'm guessing this is the right padding function...?\n padding.PSS(\n mgf=padding.MGF1(hashes.SHA256()),\n salt_length=padding.PSS.MAX_LENGTH),\n hashes.SHA256())\n return base64.b64encode(signed).decode(\"utf-8\")\n\n\ndef _getDataToHash_2012_2015(input, sig_options, options):\n # TODO: These are two separate algorithms, so we should separate them\n to_hash = \"\"\n if options[\"algorithm\"] == \"GraphSignature2012\":\n if \"nonce\" in sig_options:\n to_hash += sig_options[\"nonce\"]\n to_hash += sig_options[\"date\"]\n to_hash += input\n if \"domain\" in sig_options:\n to_hash += \"@\" + sig_options[\"domain\"]\n else:\n headers = {\n \"http://purl.org/dc/elements/1.1/created\": sig_options.get(\"date\"),\n \"https://w3id.org/security#domain\": sig_options.get(\"domain\"),\n \"https://w3id.org/security#nonce\": sig_options.get(\"nonce\")};\n # add headers in lexicographical order\n for key in sorted(headers.keys()):\n value = headers[key]\n if value is not None:\n to_hash += \"%s: %s\\n\" % (key, value)\n to_hash += input\n return to_hash.encode(\"utf-8\")\n\n\ndef _w3c_date(dt):\n # We may need to convert it to UTC\n if dt.tzinfo is not pytz.utc:\n dt = dt.astimezone(pytz.utc)\n\n return isodate.datetime_isoformat(dt)\n\n\f\n# Verification\n\ndef verify(signed_document, options):\n \"\"\"\n Signs a JSON-LD document using a digital signature.\n\n Args:\n - input: the JSON-LD document to be verified.\n - options:\n\n # TODO: Not all these are implemented yet, and some may be algorithm\n # specific\n Options:\n - publicKey(signature, options): A procedure which, if present, is called\n to retrieve the public key. Must do all validation that ownership\n correcly aligns.\n - checkNonce(nonce, options)] a procedure to check if the nonce (null\n if none) used in the signature is valid.\n - checkDomain(domain, options): a procedure to check if the domain used\n (null if none) is valid.\n - checkKey(key, options): a procedure to check if the key used to sign the\n message is trusted.\n - checkKeyOwner(owner, key, options): a procedure to check if the key's\n owner is trusted.\n - checkTimestamp: check signature timestamp (default: false).\n - maxTimestampDelta: signature must be created within a window of\n this many seconds (default: 15 minutes).\n - documentLoader(url): the document loader.\n - id the ID (full URL) of the node to check the signature of, if\n the input contains multiple signed nodes.\n \"\"\"\n options = copy.copy(options)\n loader = options.get(\"documentLoader\", _security_context_loader)\n options.setdefault(\"algorithm\", \"GraphSignature2012\")\n\n # Here's a TODO copy-pasta'ed from jsonld-signatures.js:\n # TODO: frame before getting signature, not just compact? considerations:\n # should the assumption be that the signature is on the top-level object\n # and thus framing is unnecessary?\n compacted = jsonld.compact(\n signed_document, SECURITY_CONTEXT_URL, options={\n \"documentLoader\": loader})\n\n try:\n signature = _get_values(compacted, \"signature\")[0]\n except IndexError:\n raise LdsError('[jsigs.verify] No signature found.')\n\n try:\n suite_name = _get_values(signature, \"type\")[0]\n except IndexError:\n suite_name = \"\"\n\n if not suite_name in SUITES:\n raise LdsError(\n (\"[jsigs.verify] Unsupported signature algorithm \\\"%s\\\"; \"\n \"supported algorithms are: %s\") % (suite_name,\n SUITES.keys()))\n suite = SUITES[suite_name]\n\n # TODO: Should we be framing here? According to my talks with Dave Longley\n # we probably should, though I don't know how well pyld supports framing\n # and I need to wrap my head around it better\n # @@: So here we have to extract the signature\n\n # @@: 3 before 1 and 2? Well we need it in 1 and 2 :P\n # SPEC (3): Remove any signature nodes from the default graph in\n # document and save it as signature.\n # @@: This isn't recursive, should it be? Also it just handles\n # one value for now.\n # SPEC (2): Let document be a copy of signed document. \n document = copy.deepcopy(compacted)\n signature = document.pop(\"signature\")\n\n # SPEC (1): Get the public key by dereferencing its URL identifier\n # in the signature node of the default graph of signed document.\n # @@: Rest of SPEC(1) in _get_public_key\n get_public_key = options.get(\"publicKey\", _get_public_key)\n public_key = get_public_key(signature, options)\n\n # SPEC (5): Create a value tbv that represents the data to be\n # verified, and set it to the result of running the Create Verify\n # Hash Algorithm, passing the information in signature.\n # TODO: This doesn't look like the same verification step\n # being done in the signature step as ported from jsonld-signatures.js\n # It looks like what step we do here should be farmed out depending\n # on the signature suite used.\n # @@: Maybe sig_options should be munged by the suite?\n sig_options = {}\n if \"publicKeyPem\" in public_key:\n sig_options[\"publicKeyPem\"] = _get_value(public_key, \"publicKeyPem\")\n if \"publicKeyWif\" in public_key:\n sig_options[\"publicKeyWif\"] = _get_value(public_key, \"publicKeyWif\")\n if \"nonce\" in signature:\n sig_options[\"nonce\"] = _get_value(signature, \"nonce\")\n if \"domain\" in signature:\n sig_options[\"domain\"] = _get_value(signature, \"domain\")\n # @@: Why isn't this also \"created\"?\n sig_options[\"date\"] = _get_value(signature, \"created\")\n\n tbv = suite.format_for_signature(document, sig_options, options)\n\n # SPEC (6): Pass the signatureValue, tbv, and the public key to\n # the signature algorithm (e.g. JSON Web Signature using\n # RSASSA-PKCS1-v1_5 algorithm). Return the resulting boolean\n # value.\n return suite.verify_formatted(signature, tbv, public_key, options)\n\n\ndef _get_public_key(signature, options):\n def _id_of(obj):\n if isinstance(obj, str):\n return obj\n return obj.get(\"@id\") or obj.get(\"id\")\n\n creator_id = _id_of(_get_value(signature, \"creator\"))\n if not creator_id:\n raise LdsError(\n '[jsigs.verify] creator not found on signature.')\n\n creator = _get_security_compacted_jsonld(creator_id, options)\n if not \"publicKey\" in creator:\n raise LdsError(\n '[jsigs.verify] publicKey not found on creator object')\n\n # @@: What if it's a fragment identifier on an embedded object?\n public_key_id = _get_value(creator, \"publicKey\")\n public_key = _get_security_compacted_jsonld(\n public_key_id, options)\n\n owners = _get_values(public_key, \"owner\")\n\n # SPEC (1): Confirm that the linked data document that describes\n # the public key specifies its owner and that its owner's URL\n # identifier can be dereferenced to reveal a bi-directional link\n # back to the key.\n if not creator_id in owners:\n raise LdsError(\n '[jsigs.verify] The public key is not owned by its declared owner.')\n\n # SPEC (1): Ensure that the key's owner is a trusted entity before\n # proceeding to the next step.\n check_key_owner = options.get(\"checkKeyOwner\")\n if check_key_owner and not check_key_owner(signature, public_key, options):\n raise LdsError(\n '[jsigs.verify] The owner of the public key is not trusted.')\n\n return public_key\n\n\ndef _security_compact(document, options):\n loader = options.get(\"documentLoader\", _security_context_loader)\n return jsonld.compact(document, SECURITY_CONTEXT_URL,\n options={\"documentLoader\": loader})\n\ndef _get_jsonld(id, options):\n if isinstance(id, dict):\n id = id.get(\"id\") or id.get(\"@id\")\n if not id:\n raise ValueError(\"Tried to fetch object with no id: %s\" % id)\n loader = options.get(\"documentLoader\", _security_context_loader)\n return loader(id)[\"document\"]\n\ndef _get_security_compacted_jsonld(id, options):\n return _security_compact(_get_jsonld(id, options), options)\n\n\n# TODO: Are we actually passing in multiple aglgorithms for message\n# canonicalization *and* message digest?\ndef create_verify_hash(document, suite, options,\n options_to_canonicalize):\n \"\"\"\n \n \"\"\"\n normalized_input = suite.normalize_jsonld(document, options)\n\n # SPEC (1): Let options be a copy of input options.\n options_to_canonicalize = copy.deepcopy(options_to_canonicalize)\n\n # SPEC (2): If type, id, or signatureValue exists in options,\n # remove the entry.\n # @@: Well since we're specifically passing these in to this procedure\n # I guess we don't need to do that...\n\n # SPEC (3): If created does not exist in options, add an entry\n # with a value that is an ISO8601 combined date and time string\n # containing the current date and time accurate to at least one\n # second, in Universal Time Code format. For example:\n # 2017-11-13T20:21:34Z.\n if not \"created\" in options_to_canonicalize:\n options_to_canonicalize[\"created\"] = _w3c_date(datetime.now(pytz.utc))\n\n # SPEC (4): Generate output by: \n # SPEC (4.1): Creating a canonicalized options document by\n # canonicalizing options according to the canonicalization\n # algorithm (e.g. the GCA2015 [RDF-DATASET-NORMALIZATION]\n # algorithm). \n # Well, we need to add the context first:\n options_to_canonicalize[\"@context\"] = SECURITY_CONTEXT_URL\n canonical_options = suite.normalize_jsonld(\n options_to_canonicalize, options)\n\n # SPEC (4.2): Hash canonicalized options document using the\n # message digest algorithm (e.g. SHA-256) and set output to the\n # result.\n output = suite.message_digest(canonical_options, options)\n\n # SPEC (4.3): Hash canonicalized document using the message digest\n # algorithm (e.g. SHA-256) and append it to output.\n output += suite.message_digest(normalized_input, options)\n\n # SPEC (5): Hash output using the message digest algorithm\n # (e.g. SHA-256) and replace it with the result.\n output = suite.message_digest(output, options)\n\n # SPEC (6): Return output. \n return output\n\ndef _rsa_verify_sig(sig_value, formatted, public_key_jsonld):\n \"\"\"\n - sig_value: data to be verified\n - public_key: creator of this document's public_key \n - tbv: to be verified\n \"\"\"\n # TODO: Support other formats than just PEM\n public_key = serialization.load_pem_public_key(\n _get_value(public_key_jsonld, \"publicKeyPem\").encode(\"utf-8\"),\n backend=default_backend())\n\n try:\n public_key.verify(\n base64.b64decode(sig_value.encode(\"utf-8\")), formatted,\n padding.PSS(\n mgf=padding.MGF1(hashes.SHA256()),\n salt_length=padding.PSS.MAX_LENGTH),\n hashes.SHA256())\n return True\n except InvalidSignature:\n return False\n\n\f\n# In the future, we'll be doing a lot more work based on what suite is\n# selected.\n\ndef signature_common_munge_verify(options):\n if not is_valid_uri(options[\"creator\"]):\n raise LdsTypeError(\n \"[jsig.sign] options.creator must be a URL string.\")\n\n if \"domain\" in options and not is_valid_uri(options[\"domain\"]):\n raise LdsTypeError(\n \"[jsig.sign] options.domain must be a string.\")\n\n if \"nonce\" in options and not is_valid_uri(options[\"nonce\"]):\n raise LdsTypeError(\n \"[jsig.sign] options.nonce must be a string.\")\n\n if not isinstance(options[\"date\"], str):\n options[\"date\"] = _w3c_date(options[\"date\"])\n\n return options\n\nclass SignatureSuite():\n name = None\n\n @classmethod\n def signature_munge_verify_options(cls, options):\n options = signature_common_munge_verify(options)\n return options\n\n @classmethod\n def normalize_jsonld(cls, document, options):\n raise NotImplementedError()\n\n @classmethod\n def format_for_signature(cls, document, sig_options, options):\n raise NotImplementedError()\n\n @classmethod\n def sign_formatted(cls, formatted, options):\n raise NotImplementedError()\n\n @classmethod\n def verify_formatted(cls, formatted, options):\n raise NotImplementedError()\n\n\ndef _format_gs_2012_ld_2015(suite, document, sig_options, options):\n normalized = suite.normalize_jsonld(document, options)\n\n if len(normalized) == 0:\n raise LdsError(\n ('[jsig.sign] '\n 'The data to sign is empty. This error may be because a '\n '\"@context\" was not supplied in the input thereby causing '\n 'any terms or prefixes to be undefined. '\n 'Input: %s') % (json.dumps(document)))\n return _getDataToHash_2012_2015(normalized, sig_options, options)\n\n\nclass GraphSignature2012(SignatureSuite):\n name = \"GraphSignature2012\"\n\n @classmethod\n def format_for_signature(cls, document, sig_options, options):\n return _format_gs_2012_ld_2015(cls, document, sig_options, options)\n\n @classmethod\n def normalize_jsonld(self, document, options):\n return jsonld.normalize(\n document,\n {\"algorithm\": \"URGNA2012\",\n \"format\": \"application/nquads\",\n \"documentLoader\": options.get(\"documentLoader\",\n _security_context_loader)})\n\n @classmethod\n def sign_formatted(cls, formatted, options):\n return _basic_rsa_signature(formatted, options)\n\n @classmethod\n def verify_formatted(cls, signature, formatted, public_key_jsonld, options):\n return _rsa_verify_sig(\n _get_value(signature, \"signatureValue\"),\n formatted, public_key_jsonld)\n\n\nclass LinkedDataSignature2015(SignatureSuite):\n name = \"LinkedDataSignature2015\"\n\n @classmethod\n def normalize_jsonld(cls, document, options):\n return jsonld.normalize(\n document, {\"algorithm\": \"URDNA2015\",\n \"format\": \"application/nquads\"})\n\n @classmethod\n def format_for_signature(cls, document, sig_options, options):\n return _format_gs_2012_ld_2015(cls, document, sig_options, options)\n\n @classmethod\n def sign_formatted(cls, formatted, options):\n return _basic_rsa_signature(formatted, options)\n\n\nclass EcdsaKoblitzSignature2016(SignatureSuite):\n name = \"EcdsaKoblitzSignature2016\"\n\n @classmethod\n def signature_munge_verify_options(cls, options):\n options = signature_common_munge_verify(options)\n\n if not isinstance(options.get(\"privateKeyWif\", str)):\n raise LdsTypeError(\n \"[jsig.sign] options.privateKeyWif must be a base 58 \"\n \"formatted string.\")\n elif not isinstance(options.get(\"privateKeyPem\"), str):\n raise LdsTypeError(\n \"[jsig.sign] options.privateKeyPem must be a PEM \"\n \"formatted string.\")\n\n return options\n\nclass LinkedDataSignature2016(SignatureSuite):\n name = \"LinkedDataSignature2016\"\n \n\n\n\nSUITES = {\n s.name: s\n for s in [GraphSignature2012,\n LinkedDataSignature2015,\n # EcdsaKoblitzSignature2016,\n ]}\n\n","repo_name":"Spec-Ops/pyld-signatures","sub_path":"pyld_sig/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":27253,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"52"} +{"seq_id":"21904644384","text":"import os\n\nimport pytest\n\nfrom spsdk.crypto.keys import EccCurve, PrivateKeyEcc, PrivateKeyRsa\nfrom spsdk.crypto.signature_provider import PlainFileSP\nfrom spsdk.crypto.symmetric import Counter\nfrom spsdk.crypto.utils import get_matching_key_id\nfrom spsdk.exceptions import SPSDKError, SPSDKValueError\n\n\ndef test_counter():\n \"\"\"Test of Counter.\"\"\"\n # simple counter with nonce only\n cntr = Counter(bytes([0] * 16))\n assert cntr.value == bytes([0] * 16)\n\n # counter with nonce and counter encoded as little endian\n cntr = Counter(bytes([0] * 16), ctr_value=0x01234567, ctr_byteorder_encoding=\"little\")\n assert cntr.value == bytes([0] * 12 + [0x67, 0x45, 0x23, 0x01])\n\n # counter with nonce and counter encoded as little endian\n cntr = Counter(bytes([0] * 16), ctr_value=0x01234567)\n assert cntr.value == bytes([0] * 12 + [0x67, 0x45, 0x23, 0x01])\n\n # counter with nonce and counter encoded as big endian\n cntr = Counter(bytes([0] * 16), ctr_value=1, ctr_byteorder_encoding=\"big\")\n assert cntr.value == bytes([0] * 15 + [1])\n\n # increment\n cntr.increment()\n assert cntr.value == bytes([0] * 15 + [2])\n cntr.increment(2)\n assert cntr.value == bytes([0] * 15 + [4])\n cntr.increment(256)\n assert cntr.value == bytes([0] * 14 + [1, 4])\n\n\ndef test_counter_invalid():\n with pytest.raises(SPSDKError, match=\"Wrong byte order\"):\n Counter(nonce=bytes(16), ctr_byteorder_encoding=\"BIG\")\n\n\n@pytest.mark.parametrize(\"length\", [(2048), (3072), (4096)])\ndef test_matching_keys_rsa(tmpdir, length):\n signature_providers = []\n pub_keys = []\n for i in range(4):\n prv_key = PrivateKeyRsa.generate_key(key_size=length)\n prv_key.save(os.path.join(tmpdir, f\"key{i}.pem\"))\n signature_providers.append(PlainFileSP(os.path.join(tmpdir, f\"key{i}.pem\")))\n pub_keys.append(prv_key.get_public_key())\n\n for i in range(4):\n assert i == get_matching_key_id(\n public_keys=pub_keys, signature_provider=signature_providers[i]\n )\n\n\n@pytest.mark.parametrize(\"curve\", [(curve_name) for curve_name in EccCurve])\ndef test_matching_keys_ecc(tmpdir, curve):\n signature_providers = []\n pub_keys = []\n for i in range(4):\n prv_key = PrivateKeyEcc.generate_key(curve_name=curve)\n prv_key.save(os.path.join(tmpdir, f\"key{i}.pem\"))\n signature_providers.append(PlainFileSP(os.path.join(tmpdir, f\"key{i}.pem\")))\n pub_keys.append(prv_key.get_public_key())\n\n for i in range(4):\n assert i == get_matching_key_id(\n public_keys=pub_keys, signature_provider=signature_providers[i]\n )\n\n\ndef test_matching_keys_unmatch(tmpdir):\n signature_providers = []\n pub_keys = []\n for i in range(4):\n prv_key = PrivateKeyRsa.generate_key()\n prv_key.save(os.path.join(tmpdir, f\"key{i}.pem\"))\n signature_providers.append(PlainFileSP(os.path.join(tmpdir, f\"key{i}.pem\")))\n pub_keys.append(prv_key.get_public_key())\n\n prv_key = PrivateKeyRsa.generate_key()\n prv_key.save(os.path.join(tmpdir, \"diff_key.pem\"))\n with pytest.raises(SPSDKValueError):\n get_matching_key_id(\n public_keys=pub_keys,\n signature_provider=PlainFileSP(os.path.join(tmpdir, \"diff_key.pem\")),\n )\n","repo_name":"nxp-mcuxpresso/spsdk","sub_path":"tests/utils/crypto/test_common.py","file_name":"test_common.py","file_ext":"py","file_size_in_byte":3274,"program_lang":"python","lang":"en","doc_type":"code","stars":37,"dataset":"github-code","pt":"52"} +{"seq_id":"39969923117","text":"import humanize\n\nfrom datetime import datetime\nfrom pytz import timezone\n\n_intervals = (\n ('w', 604800),\n ('d', 86400),\n ('h', 3600),\n ('m', 60),\n ('s', 1),\n)\n\n_barColors = {\n 'Error': \"ff0000ff\",\n 'Deleted': \"ff0000ff\",\n 'Uploading': \"007bffff\",\n 'PausedUP': \"00ff00ff\",\n 'QueuedUP': \"007bffff\",\n 'StalledUP': \"b0b0b0ff\",\n 'CheckingUP': \"007bffff\",\n 'ForcedUP': \"ff0000ff\",\n 'Allocating': \"b0b0b0ff\",\n 'Downloading': \"00ff00ff\",\n 'MetaDL': \"007bffff\",\n 'PausedDL': \"b0b0b0ff\",\n 'QueuedDL': \"b0b0b0ff\",\n 'StalledDL': \"b0b0b0ff\",\n 'CheckingDL': \"ff0000ff\",\n 'ForcedDL': \"ff0000ff\",\n 'CheckingResumeData': \"b0b0b0ff\",\n 'Moving': \"ff0000ff\",\n 'Unknown': \"ff0000ff\"\n}\n\n_show_seeders = [\n \"Allocating\",\n \"Downloading\",\n \"MetaDL\",\n \"PausedDL\",\n \"QueuedDL\",\n \"StalledDL\",\n \"CheckingDL\",\n \"ForcedDL\",\n \"CheckingResumeData\",\n \"Unknown\"\n]\n\n\ndef _display_time(seconds, granularity=2):\n result = []\n\n for name, count in _intervals:\n value = seconds // count\n if value:\n seconds -= value * count\n if value == 1:\n name = name.rstrip('s')\n result.append(\"{}{}\".format(value, name))\n return ' '.join(result[:granularity])\n\n\ndef torrent_format(tr_dict):\n rm_values = {}\n for i in range(4):\n rm_values[f'TorrentName{i}'] = {'Text': tr_dict[i]['name']}\n rm_values[f'TorrentName{i}']['ToolTipText'] = tr_dict[i]['name']\n rm_values[f'TorrentStatus{i}'] = {'Text': tr_dict[i]['state'][0].upper() + tr_dict[i]['state'][1:]}\n rm_values[f'TorrentDSpeed{i}'] = {'Text': \"Down speed: \" + humanize.naturalsize(tr_dict[i]['dlspeed']) + \"/s\"}\n if rm_values[f'TorrentStatus{i}']['Text'] in _show_seeders:\n rm_values[f'TorrentSeeds{i}'] = {'Text': f\"Seeds: {tr_dict[i]['num_complete']}({tr_dict[i]['num_seeds']})\"}\n else:\n rm_values[f'TorrentSeeds{i}'] = {'Text': \\\n f\"Leechs: {tr_dict[i]['num_incomplete']}({tr_dict[i]['num_leechs']})\"}\n rm_values[f'TorrentETA{i}'] = {'Text': \"ETA: \" + _display_time(tr_dict[i]['eta'])}\n rm_values[f'TorrentPercentage{i}'] = {'Text': f\"{tr_dict[i]['progress'] * 100:.1f}%\"}\n rm_values[f'TorrentProgress{i}'] = {'Text': \\\n humanize.naturalsize(tr_dict[i]['downloaded']) + \"/\" +\\\n humanize.naturalsize(tr_dict[i]['downloaded'] + tr_dict[i]['amount_left'])}\n rm_values[f'TorrentProgressBar{i}'] = {'BarColor': _barColors[rm_values[f'TorrentStatus{i}']['Text']]}\n rm_values[f'TorrentUSpeed{i}'] = {'Text': \"Up speed: \" + humanize.naturalsize(tr_dict[i]['upspeed']) + \"/s\"}\n rm_values[f'TorrentAddedOn{i}'] = {'Text': humanize.naturaltime(\n datetime.fromtimestamp(tr_dict[i]['added_on'], tz=timezone(\"US/Eastern\")).replace(tzinfo=None)\n )}\n rm_values[f'TorrentRatio{i}'] = {'Text': f\"Ratio: {tr_dict[i]['ratio']:.2f}\"}\n return rm_values\n","repo_name":"LOAFCLAN/QBT_rainmeter_skin","sub_path":"@Resources/Scripts/torrent_formatter.py","file_name":"torrent_formatter.py","file_ext":"py","file_size_in_byte":2989,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"43191499485","text":"#from preprocessing_phase import preprocess\r\nimport heapq\r\nimport nltk\r\nnltk.download('stopwords')\r\nimport random\r\n\r\nfrom textblob import TextBlob\r\nfrom textblob.translate import NotTranslated\r\n#Data Augmentation Technique\r\n\r\nstop_words = nltk.corpus.stopwords.words('greek')\r\nwith open('stopwords.txt') as f:\r\n stop_words = f.read().splitlines()\r\n\r\n\r\nlexicon = [\"άντρας\",\"γυναίκα\",\"παιδί\"]\r\nemb = {'άντρας': [0.5 , 0.3, 0.2], 'γυναίκα': [0.3 , 0.5 , 0.2], 'παιδί': [0.1 , 0.3 , 0.6]}\r\n\r\ntext = ['αλλά','άντρας']\r\n\r\ndef replaceBySynonym(text):\r\n newText = []\r\n for word in text:\r\n if(word in stop_words):\r\n newText.append(word)\r\n else:\r\n synonym_index = [emb[word].index(x) for x in sorted(emb[word], reverse=True)[:2]][1]\r\n synonym = lexicon[synonym_index]\r\n newText.append(synonym)\r\n\r\n return newText\r\n\r\n\r\n\r\ndef replaceByShuffling(text):\r\n newText = text.copy()\r\n random.shuffle(newText)\r\n return newText\r\n\r\ndef replaceByTranslation(text):\r\n newText = []\r\n sr = random.SystemRandom()\r\n for word in text:\r\n wordBlob = TextBlob(word)\r\n try:\r\n wordBlob = wordBlob.translate(to=\"en\") ## Converting to random langauge for meaningful variation\r\n wordBlob = wordBlob.translate(to=\"el\")\r\n except NotTranslated:\r\n pass\r\n\r\n newText.append(str(wordBlob).lower())\r\n\r\n return newText\r\n\r\nprint(text)\r\nprint(\"converts to: \")\r\nprint(replaceBySynonym(text))\r\nprint(replaceByShuffling(text))\r\nprint(replaceByTranslation(text))","repo_name":"vlavrent/NLP-Book-Classifier","sub_path":"balancing_phase/balancing.py","file_name":"balancing.py","file_ext":"py","file_size_in_byte":1609,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"38599416537","text":"\"\"\"\nThis is a daemon, that uploads CV to S3\n\"\"\"\n\nimport os\nimport sys\nfrom datetime import datetime\nfrom django.core.wsgi import get_wsgi_application\n\n# Environment can use the models as if inside the Django app\nsys.path.insert(0, '/'.join(os.getcwd().split('/')[:-1]))\nos.environ.setdefault('DJANGO_SETTINGS_MODULE', 'testing_webpage.settings')\napplication = get_wsgi_application()\n\nimport boto3\nimport time\nimport urllib.parse\nfrom botocore.exceptions import EndpointConnectionError\nfrom beta_invite.models import User\nfrom decouple import config\nfrom django.db.models import Q\nfrom queue import Queue\nfrom subscribe import helper as h\n\n\ndef cool_print(s):\n print(str(datetime.today()) + ': ' + s)\n\n\n# the actual upload\ndef upload_resource_to_s3(user):\n\n bucket = config('aws_s3_bucket')\n s3_key = user.curriculum_url\n\n session = boto3.session.Session(region_name='us-east-2',\n aws_access_key_id=config('aws_access_key'),\n aws_secret_access_key=config('aws_secret_access_key'))\n\n s3client = session.client('s3', config=boto3.session.Config(signature_version='s3v4'))\n\n try:\n s3client.upload_file(get_local_path(user), bucket, s3_key)\n s3_url = get_s3_path(bucket, s3_key)\n\n cool_print(\"Uploaded: {} to: {}\".format(get_local_path(user), s3_url))\n\n return s3_url\n except FileNotFoundError:\n cool_print('FileNotFoundError: {}'.format(get_local_path(user)))\n cool_print('daemon will continue...')\n return '#'\n except EndpointConnectionError:\n cool_print('EndpointConnectionError with: {}'.format(get_local_path(user)))\n cool_print('daemon will continue...')\n return '#'\n except UnicodeEncodeError:\n cool_print('UnicodeEncodeError with CV of user_id: {}'.format(user.id))\n cool_print('daemon will continue...')\n return '#'\n\n\ndef add_new_users(queue, created_since):\n \"\"\"\n Users with\n 1. missing a s3 url\n 2. having a local resource\n 3. text analysis already done\n :return:\n \"\"\"\n # TODO fix cv updates\n users = User.objects.filter(~Q(curriculum_url='#') &\n Q(curriculum_s3_url='#')).all()\n cool_print('total new users, to add on S3: {}'.format(len(users)))\n [queue.put(u) for u in users]\n\n created_since = created_since if len(users) == 0 else max({u.created_at for u in users})\n\n return created_since\n\n\ndef get_s3_path(bucket, s3_key):\n return urllib.parse.urljoin(config('s3_base_url'), bucket + '/' + s3_key)\n\n\ndef get_local_path(user):\n return os.path.join('../media', user.curriculum_url)\n\n\n# each worker does this job\ndef upload_users(users_queue, wait_time_workers, debug):\n \"\"\"\n Uploads a User to s3, then waits some time, and repeats...\n :return:\n \"\"\"\n user = users_queue.get()\n if debug:\n user.curriculum_s3_url = 'LE FINI'\n else:\n user.curriculum_s3_url = upload_resource_to_s3(user)\n user.save()\n time.sleep(wait_time_workers)\n\n\ndef upload_all():\n\n wait_time_workers = 10 # seconds\n wait_time_db = 60 # 10 minutes\n debug = False\n users_queue = Queue()\n\n created_since = datetime(day=9, month=4, year=1948)\n\n while True:\n created_since = add_new_users(users_queue, created_since)\n while not users_queue.qsize() == 0:\n upload_users(users_queue, wait_time_workers, debug)\n\n time.sleep(wait_time_db)\n\n\ndef run():\n with open('s3_uploader.log', 'a') as f:\n sys.stdout = h.Unbuffered(f)\n upload_all()\n\n\nif __name__ == '__main__':\n run()\n","repo_name":"palaciossruben/acerto","sub_path":"testing_webpage/tasks/s3_uploader.py","file_name":"s3_uploader.py","file_ext":"py","file_size_in_byte":3618,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"26435290252","text":"from Crawler import *\nfrom tooling import *\n#import requests\n#from bs4 import BeautifulSoup\n#import datetime\nimport pandas\nimport time\n#import re\n\n#import os\n\n'''\nplayer_list = [\"沙奎尔-奥尼尔\",\"阿伦-艾弗森\",\"科比-布莱恩特\"]\nlink_list = find_link(2000,\"湖人\",\"76人\")\nget_csv(link_list,player_list,\"test.csv\")\n\n#os.environ['HTTP_PROXY'] = 'http://127.0.0.1:1080'\n#os.environ['HTTPS_PROXY'] = 'http://127.0.0.1:1080'\n'''\n\ninput_list = [{\"year\": 2000 ,\"team\" : [\"湖人\",\"76人\"],\"player\" : [\"沙奎尔-奥尼尔\",\"阿伦-艾弗森\",\"科比-布莱恩特\"]}\n ,{\"year\": 2001 ,\"team\" : [\"湖人\",\"篮网\"],\"player\" : [\"沙奎尔-奥尼尔\",\"杰森-基德\",\"科比-布莱恩特\"]},\n {\"year\": 1999 ,\"team\" : [\"湖人\",\"步行者\"],\"player\" : [\"沙奎尔-奥尼尔\",\"雷吉-米勒\",\"科比-布莱恩特\"]}]\n\ntotal_data = []\nstart_time = time.time()\nfor list in input_list:\n print(\"start\")\n link_list = find_link(list[\"year\"],list[\"team\"][0],list[\"team\"][1])\n print(link_list)\n sum_data = get_csv_data(link_list,list[\"player\"])\n print(sum_data)\n total_data.extend(sum_data)\n\nprint(\"got total data\")\n\nget_csv(total_data,\"1total.csv\")\nend_time = time.time()\nprint(\"total consume time is \", end_time-start_time)","repo_name":"xiaomuding/project","sub_path":"NBA/NBA.py","file_name":"NBA.py","file_ext":"py","file_size_in_byte":1260,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"37689359949","text":"\"\"\"kanban 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.conf import settings\nfrom django.conf.urls.static import static\nfrom django.contrib import admin\nfrom django.urls import path, include\nfrom django.conf.urls import url\nfrom rest_framework import routers\n\nfrom apps.profile.views import IconViewSet, UserViewSet\nfrom apps.main.views import AppView\nfrom apps.kanban.views import (\n BoardViewSet,\n TagViewSet,\n LaneViewSet,\n CardViewSet,\n CommentViewSet,\n SortLaneAPIView,\n SortCardAPIView,\n)\n\nprofile_router = routers.DefaultRouter()\nprofile_router.register(r\"icons\", IconViewSet)\nprofile_router.register(r\"users\", UserViewSet)\n\nkanban_router = routers.DefaultRouter()\nkanban_router.register(r\"boards\", BoardViewSet)\nkanban_router.register(r\"tags\", TagViewSet)\nkanban_router.register(r\"lanes\", LaneViewSet)\nkanban_router.register(r\"cards\", CardViewSet)\nkanban_router.register(r\"comments\", CommentViewSet)\n\nurlpatterns = [\n # Auth urls\n path(\"api/auth/\", include(\"dj_rest_auth.urls\")),\n path(\"api/auth/register/\", include(\"dj_rest_auth.registration.urls\")),\n # Account urls\n path(\"api/profile/\", include(profile_router.urls)),\n # Kanban urls\n path(\"api/kanban/\", include(kanban_router.urls)),\n path(\"api/kanban/sort/lane/\", SortLaneAPIView.as_view(), name=\"sort-lane\"),\n path(\"api/kanban/sort/card/\", SortCardAPIView.as_view(), name=\"sort-card\"),\n # Secret backdoor url\n path(\"arcane/\", admin.site.urls),\n # Load app\n url(r\"^\", AppView.as_view(), name=\"app\"),\n] + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n","repo_name":"sajalshres/kanban-on-django-marionette","sub_path":"backend/config/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2174,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"22789686759","text":"#%%\nimport h5py\nimport numpy as np\n\ndef load_wavelets_and_outputs(path_in):\n '''loads wavelets and fourier freqs from input path and stores them as numpy arrays'''\n hdf5_file = h5py.File(path_in, mode='r')\n # print(hdf5_file['/inputs/'].keys())\n wavelets = np.array(hdf5_file['inputs/wavelets'])\n freq_decoder= np.array(hdf5_file['inputs/fourier_frequencies'])\n print('wavelets dimensionality:' + str(wavelets.shape))\n return(wavelets,freq_decoder)\n\n\n\npath_in='processed_R2478.h5'\nwavelets,freq_decoder=load_wavelets(path_in)\n\n\n\n\n\n\n\n# %%\n","repo_name":"tbhowe/deepinsight_transformer","sub_path":"h5_test.py","file_name":"h5_test.py","file_ext":"py","file_size_in_byte":559,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"69899231525","text":"import pytest\n\nfrom .utils import _expected_path, SAMPLES, _serialize, _process\n\nPAIRS = [(s, _expected_path(s)) for s in SAMPLES]\n\n\n@pytest.mark.parametrize(\"sample, expected\", PAIRS)\ndef test_corpus(sample, expected):\n processed = _process(sample)\n exported = _serialize(processed)\n\n assert expected.read_bytes() == exported\n","repo_name":"jupyter/papyri","sub_path":"papyri/tests/test_corpus.py","file_name":"test_corpus.py","file_ext":"py","file_size_in_byte":336,"program_lang":"python","lang":"en","doc_type":"code","stars":74,"dataset":"github-code","pt":"52"} +{"seq_id":"7164146483","text":"import win32com.client as wincl\nimport os, os.path\nimport pandas as pd\nfrom IPython.display import display\nfrom unittest import loader\nfrom jinja2 import FileSystemLoader, Environment\nfrom destinatarios import destinatarios\n\n\ndef lerExcel(tabela):\n dados = pd.read_excel(tabela)\n #display(dados)\n cont = 0\n dados_template = []\n for i, col in dados.iterrows():\n\n texto_var = f\"{col['VAR']:.2%}\"\n\n if col['MESA'] == 'SFF' and col['ALERTA'] > 0 and col['ALERTA'] < 3:\n dados_template.append([col['MESA'], col['ALERTA'], col['FUNDO'], texto_var])\n cont = cont + 1\n if cont > 1:\n print(\"Cheguei aqui\")\n template = carregarTemplate(dados_template)\n enviarEmail(destinatarios['SFF'], template)\n\ndef rodarMacro():\n\n excel_macro = wincl.DispatchEx(\"Excel.application\")\n excel_path = os.path.expanduser(\"C:\\Git-Projects\\\\automates\\\\teste envio de email\\\\rodarMacro.xlsm\")\n workbook = excel_macro.Workbooks.Open(Filename = excel_path) # ReadOnly =1\n excel_macro.Application.Run(\"rodarMacro.xlsm!Módulo1.rodarMacro\")\n #Save the results in case you have generated data\n workbook.Save()\n excel_macro.Application.Quit()\n del excel_macro\n\n\ndef carregarTemplate(dados_template):\n loader = FileSystemLoader('templates')\n env = Environment(loader=loader)\n template = env.get_template('index2.html')\n\n file = open('output/index.html', 'w')\n\n render = template.render(dados = dados_template)\n file.write(render)\n file.close()\n return render\n\ndef enviarEmail(destinatario, template):\n\n outlook = wincl.Dispatch('outlook.application')\n\n # criando e-mail a partir da ingregracao com outlook\n email = outlook.CreateItem(0)\n email.Display()\n # configurando informacoes do email\n # Para quem vai o e-mail // Assunto da mensagem\n\n email.To = destinatario + \"; jrds.contato@hotmail.com; jrds.contato@gmail.com\"\n email.Cc = \"jhonatan.loko25@gmail.com\"\n email.Subject = 'e-mail automático de teste do python'\n\n # Criando e adicionando anexo\n # anexo = \"C:\\Git-Projects\\\\automates\\\\teste envio de email\\email.xlsx\"\n # email.Attachments.Add(anexo)\n\n email.HTMLBody = template\n\n #email.Send()\n\nlerExcel(\"rodarMacro.xlsm\")","repo_name":"jrsantos1/estudo-email-marketing-python","sub_path":"rodarMacroComWin32.py","file_name":"rodarMacroComWin32.py","file_ext":"py","file_size_in_byte":2254,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"17606680841","text":"import os\nfrom datetime import datetime\nfrom pathlib import Path\n\nfrom data_generators import DataGen\nfrom utils import train, test\nfrom tensorflow.keras import Sequential, layers, losses, optimizers, callbacks\nfrom metrics import accuracy, specificity, sensitivity, f1_score\n\nDATA_DIR = os.getenv('DATA_DIR')\nRESULTS_BASE_DIR = Path(os.getenv('RESULTS_BASE_DIR'))\n\nMODEL_NAME = 'LeNet-5'\nSHAPE = int(os.getenv('SHAPE', 128))\nEPOCHS = int(os.getenv('EPOCHS', 30))\nBATCH_SIZE = int(os.getenv('BATCH_SIZE', 32))\nVALIDATION_SPLIT = float(os.getenv('VALIDATION_SPLIT', '0.2'))\n\nif __name__ == \"__main__\":\n cn_log_dir = RESULTS_BASE_DIR/MODEL_NAME / \\\n f\"{datetime.now().strftime('%Y%m%d_%H%M%S')}\"\n\n lenet_model = Sequential(name=MODEL_NAME)\n lenet_model.add(layers.Conv2D(filters=64, kernel_size=(9, 9), strides=2, activation='relu', input_shape=(SHAPE,SHAPE,1)))\n lenet_model.add(layers.AveragePooling2D())\n lenet_model.add(layers.Conv2D(filters=24, kernel_size=(3, 3), activation='relu'))\n lenet_model.add(layers.AveragePooling2D())\n lenet_model.add(layers.Flatten())\n lenet_model.add(layers.Dense(units=16, activation='relu'))\n lenet_model.add(layers.Dense(units=16, activation='relu'))\n lenet_model.add(layers.Dense(units=4, activation = 'softmax'))\n lenet_model.compile(loss=losses.squared_hinge, optimizer=optimizers.Adam(lr=0.001),\n metrics=[accuracy, specificity, sensitivity, f1_score])\n lenet_model.summary()\n\n data_gen = DataGen(\n batch_size=BATCH_SIZE,\n data_dir=DATA_DIR,\n target_size=(SHAPE, SHAPE),\n validation_split=VALIDATION_SPLIT\n )\n\n print(\"Training model\")\n\n cn_callbacks = [\n callbacks.CSVLogger(f\"{cn_log_dir}/log.csv\"),\n ]\n\n train(\n model=lenet_model,\n data_gen=data_gen,\n save_dir=cn_log_dir,\n epochs=EPOCHS,\n callbacks=cn_callbacks\n )\n","repo_name":"mrhhodd/capsnet-keras","sub_path":"run_cnn.py","file_name":"run_cnn.py","file_ext":"py","file_size_in_byte":1915,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"72249051685","text":"#!/usr/bin/python\nImport ('env')\n\nsources = 'DummyPhysicsEnvironment.cpp'\n\nincs = '. ../common'\n\ndefs = []\n\nif env['WITH_BF_CXX_GUARDEDALLOC']:\n defs.append('WITH_CXX_GUARDEDALLOC')\n incs += ' #intern/guardedalloc'\n\nenv.BlenderLib ( 'ge_phys_dummy', Split(sources), Split(incs), defs, libtype=['core','player'], priority=[350,60] )\n","repo_name":"damiles/blendocv","sub_path":"source/gameengine/Physics/Dummy/SConscript","file_name":"SConscript","file_ext":"","file_size_in_byte":338,"program_lang":"python","lang":"en","doc_type":"code","stars":42,"dataset":"github-code","pt":"52"} +{"seq_id":"19198722274","text":"from models import Validator\nfrom flask import Flask, request, jsonify, send_file\nfrom flask_cors import CORS\n\nimport pandas as pd\nimport io\n\napp = Flask(__name__)\nCORS(app, resources={r\"/upload\": {\"origins\": \"https://jetcalcship.web.app\"}})\n\n@app.route('/upload', methods=['POST'])\ndef upload_file():\n try:\n # Check if the POST request has the file part\n if 'file' not in request.files:\n return jsonify({'error': 'No file part'}), 400\n\n file = request.files['file']\n\n # Check if the file has a valid name and extension\n if file.filename == '':\n return jsonify({'error': 'No selected file'}), 400\n\n # Ensure the file is an Excel file\n if file and file.filename.endswith('.xlsx'):\n # Read the Excel file into a DataFrame\n excel_data = pd.read_excel(file)\n\n # Validator to check the excel data \n validator = Validator(dataframe=excel_data)\n validator.style_dataframe()\n\n # Check data\n excel_data = validator.get_dataframe()\n\n # Save the updated DataFrame to a new Excel file\n output = io.BytesIO()\n writer = pd.ExcelWriter(output, engine='openpyxl')\n\n excel_data.to_excel(writer, index=False)\n writer.close()\n output.seek(0)\n\n # Return the updated Excel file to the user\n return send_file(\n output,\n as_attachment=True,\n download_name=\"planilha_atualizada.xlsx\"\n )\n\n else:\n return jsonify({'error': 'Invalid file format, must be .xlsx'}), 400\n\n except Exception as e:\n return jsonify({'error': str(e)}), 500\n\nif __name__ == '__main__':\n app.run(debug=True)\n","repo_name":"zoddinGC/cepValidator","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1781,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"11380966178","text":"'''\nYou are asked to calculate factorials of some small positive integers.\n\nInput\nAn integer t, 1<=t<=100, denoting the number of testcases, followed by t lines, \neach containing a single integer n, 1<=n<=100.\n\nOutput\nFor each integer n given at input, display a line with the value of n!\n'''\n\nt=int(input())\na=0\ntotal=0\nfor x in range(t):\n fac=int(input())\n total=fac\n for y in range(1,fac):\n total*=fac-y\n print(total)\n ","repo_name":"RafaelFN1230/CodeChef","sub_path":"FCTRL2 - Small Factorials.py","file_name":"FCTRL2 - Small Factorials.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"26142607487","text":"import os\nimport random\nimport torch.utils.data as data\nfrom PIL import Image\nfrom utils import regular_exp\n\n\ndef pil_loader(path: str) -> Image.Image:\n with open(path, \"rb\") as f:\n img = Image.open(f)\n return img.convert(\"RGB\")\n\nclass MICLeDataset(data.Dataset):\n\n def __init__(self,\n img_dir,\n transform=None,\n augmentation = None):\n \"\"\"\n :param img_dir: Path to the folder with all subdirs, each subdir contains images of one subject.\n |--- img_dir\n | |--- subject 1\n | | |--- 1_1.jpg\n | | |--- 1_2.jpg\n | | |--- 1_3.jpg\n | |--- subject 2\n | | |--- 2_1.jpg\n | | |--- 2_2.jpg\n | | |--- 2_3.jpg\n | |--- subject 3 \n | | |--- 3_1.jpg\n | | |--- 3_2.jpg\n | | |--- 3_3.jpg\n :param transform: transform to be applied on the image\n :param augmentation: augmentation to be applied on the image\n \"\"\"\n\n super().__init__()\n\n self.img_dir = img_dir\n self.folder_list = os.listdir(img_dir)\n self.transform = transform\n\n self.augmentation = augmentation\n\n def __len__(self):\n return len(self.folder_list)\n\n def __getitem__(self, idx):\n folder_name = self.folder_list[idx]\n folder_path = os.path.join(self.img_dir, folder_name)\n fname_list = os.listdir(folder_path)\n if len(fname_list) >=2:\n two_random_imgs = random.sample(fname_list, 2)\n img_path1 = os.path.join(folder_path, two_random_imgs[0])\n img_path2 = os.path.join(folder_path, two_random_imgs[1])\n image1 = pil_loader(img_path1)\n image2 = pil_loader(img_path2)\n\n if self.transform:\n image1 = self.transform(image1)\n image2 = self.transform(image2)\n return image1, image2, two_random_imgs[0] + \"&\" + two_random_imgs[1]\n\n elif len(fname_list) == 1:\n img_path = os.path.join(folder_path, fname_list[0])\n image = pil_loader(img_path)\n\n if self.augmentation:\n imageA = self.augmentation(image)\n imageB = self.augmentation(image)\n else:\n imageA = None\n imageB = None\n\n return imageA, imageB, fname_list[0]","repo_name":"rjrobben/MICLe_pytorch","sub_path":"dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":2407,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"52"} +{"seq_id":"7123301435","text":"import json\nfrom fastapi import APIRouter, Depends, Response, HTTPException\nfrom starlette.status import HTTP_500_INTERNAL_SERVER_ERROR, \\\n HTTP_401_UNAUTHORIZED, HTTP_201_CREATED, HTTP_200_OK\nfrom app.models.index import Person, Admin, Name\nfrom fastapi_cache.backends.redis import RedisCacheBackend\nfrom app.helper import redis_cache\nfrom fastapi_jwt_auth import AuthJWT\nfrom app.utils import Constants\n\n\nrouter = APIRouter()\n\n\n@router.post(\n \"/person\",\n name=\"persons:create-user\",\n response_model_exclude_unset=True\n)\nasync def person(person: Person, response: Response, cache: RedisCacheBackend = Depends(redis_cache),\n Authorize: AuthJWT = Depends()) -> Response:\n \"\"\"\n\n :param cache:\n :param response:\n :param person:\n :type Authorize: object\n \"\"\"\n Authorize.jwt_required()\n try:\n await cache.set(\"{0}_{1}\".format(person.first_name.lower(),\n person.last_name.lower()), json.dumps(person.__dict__))\n return Response(status_code=HTTP_201_CREATED, content=json.dumps(person.__dict__))\n except Exception as e:\n print(e)\n return Response(status_code=HTTP_500_INTERNAL_SERVER_ERROR, content=\"Person creation failed\")\n\n\n@router.delete(\n \"/person\",\n name=\"persons:delete-user\",\n response_model_exclude_unset=True\n)\nasync def person(user: Name, response: Response, cache: RedisCacheBackend = Depends(redis_cache),\n Authorize: AuthJWT = Depends()) -> Response:\n \"\"\"\n\n :param cache:\n :param response:\n :param user:\n :type Authorize: object\n \"\"\"\n Authorize.jwt_required()\n try:\n await cache.delete(\"{0}_{1}\".format(user.first_name.lower(),\n user.last_name.lower()))\n response.status_code = HTTP_200_OK\n return response\n except Exception as e:\n return Response(status_code=HTTP_500_INTERNAL_SERVER_ERROR, content='0')\n\n\n@router.get(\n \"/person\",\n name=\"persons:fetch-user\",\n response_model_exclude_unset=True\n)\nasync def person(first_name: str, last_name: str, response: Response, cache: RedisCacheBackend = Depends(redis_cache),\n Authorize: AuthJWT = Depends()) -> Response:\n \"\"\"\n\n :param last_name:\n :param first_name:\n :param user:\n :param cache:\n :param response:\n :type Authorize: object\n \"\"\"\n Authorize.jwt_required()\n try:\n _persons = await cache.get(\"{0}_{1}\".format(first_name.lower(),\n last_name.lower()))\n response.status_code = HTTP_200_OK\n return Response(content=_persons)\n except Exception as e:\n return Response(status_code=HTTP_500_INTERNAL_SERVER_ERROR, content='0')\n\n\n@router.put(\n \"/person\",\n name=\"persons:update-user\",\n response_model_exclude_unset=True\n)\nasync def person(person: Person, response: Response, cache: RedisCacheBackend = Depends(redis_cache),\n Authorize: AuthJWT = Depends()) -> Person:\n \"\"\"\n\n :param cache:\n :param response:\n :param person:\n :type Authorize: object\n \"\"\"\n Authorize.jwt_required()\n try:\n await cache.set(\"{0}_{1}\".format(person.first_name.lower(),\n person.last_name.lower()), json.dumps(person.__dict__))\n response.status_code = HTTP_200_OK\n return response\n except Exception as e:\n return Response(status_code=HTTP_500_INTERNAL_SERVER_ERROR, content='0')\n\n\n@router.post('/login')\ndef login(admin: Admin, Authorize: AuthJWT = Depends()):\n \"\"\"\n\n :param Authorize:\n :type admin: object\n \"\"\"\n if admin.username != Constants.username.value or admin.password != Constants.password.value:\n raise HTTPException(status_code=HTTP_401_UNAUTHORIZED, detail=\"Bad username or password\")\n\n # subject identifier for who this token is for example id or username from database\n access_token = Authorize.create_access_token(subject=admin.username)\n return Response(content=json.dumps({\"access_token\": access_token}), status_code=HTTP_200_OK)\n","repo_name":"nithishmohan/EMBL_task","sub_path":"app/api/routes/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":4105,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"15061198306","text":"import torch\nimport datetime\nimport time\nimport sys\nimport logging\n#import argparse\nimport torch.nn as nn\nfrom torch.autograd import Variable\nfrom torch.utils.data import DataLoader\nfrom torchvision import datasets\nfrom torchvision.utils import save_image, make_grid\nfrom torch.utils.tensorboard import SummaryWriter\nfrom torch.cuda.amp import autocast\n\nfrom tqdm import tqdm\nimport numpy as np\nimport pandas as pd\nfrom sklearn import manifold\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport PIL\nfrom PIL import Image\nimport torchvision.transforms as transforms\nfrom argparse import ArgumentParser, Namespace\n\n#from models_resnet_pl import *\nfrom models_pl import *\nfrom datasets_resnet import *\n\nimport torch.distributed as dist\n#from parallel import DataParallelModel as DPM \n#from parallel import DataParallelCriterion as DPC\nfrom apex import *\nimport pytorch_ssim\nimport options\nimport save_im as sv\nimport post_process as pp\nimport pandas as pd\nimport csv\nimport cv2 as cv\n\nimport pytorch_lightning as pl\nfrom pytorch_lightning.core import LightningModule\nfrom pytorch_lightning.trainer import Trainer\nfrom pytorch_lightning.callbacks import Callback\nfrom pytorch_lightning.callbacks import ModelCheckpoint\nfrom collections import OrderedDict\nfrom pytorch_lightning.utilities import _OMEGACONF_AVAILABLE, rank_zero_only, rank_zero_warn\nimport os\n#os.environ[\"NCCL_DEBUG\"] = \"INFO\"\n\ncuda = True if torch.cuda.is_available() else False\n#device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n#print(\"GPU status: %d\"%torch.cuda.device_count())\nTensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor\nFloatTensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor\nLongTensor = torch.cuda.LongTensor if cuda else torch.LongTensor\n\n#dataroot = \"../data/ICT_10_26_mix_dataset\"\n\nclass UnNormalize(object):\n def __init__(self, mean, std):\n self.mean = mean\n self.std = std\n\n def __call__(self, tensor):\n \"\"\"\n Args:\n tensor (Tensor): Tensor image of size (C, H, W) to be normalized.\n Returns:\n Tensor: Normalized image.\n \"\"\"\n for t, m, s in zip(tensor, self.mean, self.std):\n t.mul_(s).add_(m)\n # The normalize code -> t.sub_(m).div_(s)\n return tensor\n\ndef clean_dir(folder):\n for filename in os.listdir(folder):\n file_path = os.path.join(folder, filename)\n os.remove(file_path)\n\nclass WaeGAN(LightningModule):\n\n def __init__(self, args):\n super().__init__()\n self.save_hyperparameters()\n self.latent_dim = args.n_z\n self.lr = args.lr\n self.n_critic = args.n_critic\n self.args = args\n self.smth = 0.45#args.smth\n #self.automatic_optimization=False\n #self.b1 = b1\n #self.b2 = b2\n self.batch_size = args.batch_size\n self.one = torch.tensor(1,dtype=torch.float)#.to(self.device)\n # if args.precision==16:\n # self.one = self.one.half()\n # else:\n # pass\n # self.mone = -1*self.one\n \n \n self.df_csv = f\"./csv/{args.date}_{args.dataset}.csv\"\n self.tmp_csv = f\"./tmp/{args.date}_{args.dataset}_result.csv\"\n self.tmp_pred = f\"./tmp/{args.date}_{args.dataset}_predict.csv\"\n # networks\n self.generator_unet = ResNetUNet(args)#.to(self.device)\n self.discriminator_unet = MultiDiscriminator(args)#.to(self.device)\n self.mse_loss = nn.MSELoss()#.to(self.device)\n self.adv_loss = torch.nn.BCEWithLogitsLoss()#.to(self.device)\n self.aux_loss = LabelSmoothingCrossEntropy(0.1)#LabelSmoothing(self.smth)#torch.nn.CrossEntropyLoss(label_smoothing=self.smth)# \n self.criterion = pytorch_ssim.SSIM()#.to(self.device)\n\n self.generator_unet.apply(weights_init_normal)\n self.discriminator_unet.apply(weights_init_normal)\n\n self.k_enc = args.gp_lambda*args.k_wass\n self.no_sample = 0\n self.sum_test = 0\n self.json_dir = f\"./tmp/{args.date}_{args.dataset}_json\"\n self.jpg_dir = f\"./tmp/{args.date}_{args.dataset}_jpg\"\n self.png_dir = f\"./tmp/{args.date}_{args.dataset}_png\"\n self.org_dir = f\"./tmp/{args.date}_{args.dataset}_org\"\n self.gt_dir = f\"./tmp/{args.date}_{args.dataset}_gt\"\n self.pred_dir = f\"./tmp/{args.date}_{args.dataset}_pred\"\n os.makedirs(self.json_dir, exist_ok=True)\n os.makedirs(self.jpg_dir, exist_ok=True)\n os.makedirs(self.png_dir, exist_ok=True)\n os.makedirs(self.org_dir, exist_ok=True)\n os.makedirs(self.gt_dir, exist_ok=True)\n os.makedirs(self.pred_dir, exist_ok=True)\n #clean_dir(self.json_dir)\n #clean_dir(self.jpg_dir)\n self.result = []\n self.inv_transform = transforms.Compose(\n [\n UnNormalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),\n transforms.ToPILImage(),\n ]\n )\n\n def forward(self, z):\n return self.generator_unet(z)\n\n def compute_gradient_penalty(self, real_samples, fake_samples):\n \"\"\"Calculates the gradient penalty loss for WGAN GP\"\"\"\n # Random weight term for interpolation between real and fake samples\n alpha = torch.Tensor(np.random.random((real_samples.size(0), 1, 1, 1))).to(self.device)\n # Get random interpolation between real and fake samples\n interpolates = (alpha * real_samples + ((1 - alpha) * fake_samples)).requires_grad_(True)\n interpolates = interpolates.to(self.device)\n d_interpolates = self.discriminator_unet.compute_out(interpolates)\n fake = torch.Tensor(real_samples.shape[0], 1).fill_(1.0).to(self.device)\n # Get gradient w.r.t. interpolates\n gradients = torch.autograd.grad(\n outputs=d_interpolates,\n inputs=interpolates,\n grad_outputs=fake,\n create_graph=True,\n retain_graph=True,\n only_inputs=True,\n )[0]\n gradients = gradients.view(gradients.size(0), -1).to(self.device)\n gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean()\n return gradient_penalty\n\n def training_step(self, batch, batch_idx, optimizer_idx):\n \n lambda_gp = self.args.gp_lambda\n real_A = Variable(batch[\"A\"],requires_grad=True)#.to(self.device)\n real_B = Variable(batch[\"B\"],requires_grad=True)#.to(self.device)\n aug_A = Variable(batch[\"aug_A\"],requires_grad=True)#.to(self.device)\n labels = Variable(LongTensor(batch[\"label\"]), requires_grad=False)\n \n gen_labels = Variable(LongTensor(np.random.randint(0, self.args.n_classes,real_A.shape[0])),requires_grad=False)\n\n\n if self.args.noise_in:\n noisy = sv.gaussian(real_A,mean=0,stddev=self.args.sigma)#.to(self.device)\n else:\n noisy = aug_A\n \n if optimizer_idx == 0:\n frozen_params(self.discriminator_unet)\n free_params(self.generator_unet)\n\n generated, encoded_, e1_, _ = self.generator_unet(real_A)\n _, z_, z1_, _ = self(noisy)\n \n self.target, _ = torch.mode(torch.argmax(e1_, dim=1))\n match = (torch.argmax(e1_, dim=1) == self.target).type(Tensor)\n label_loss = self.aux_loss(z1_,e1_)\n self.log(\"matching\",match.mean().item())\n self.log(\"label loss\",label_loss)\n m_loss = self.mse_loss(real_B, generated)\n if self.args.descending:\n s_r = (1-self.current_epoch/self.args.train_max)*self.args.style_ratio\n else:\n s_r = (self.current_epoch/self.args.train_max)*self.args.style_ratio\n\n style_loss = (s_r)*(1 - self.criterion(real_B, generated))\\\n + (1-s_r)* m_loss\n \n \n \n enc_loss =(self.mse_loss(encoded_ , z_)) \n self.log(\"enc loss\",enc_loss) \n enc_loss = self.args.k_wass*enc_loss \n\n h_loss = self.args.k_wass*self.discriminator_unet(generated)\n wass_loss = -torch.mean(h_loss)\n g_loss = (style_loss + wass_loss + enc_loss) \n \n self.log(\"style loss\",style_loss)\n self.log(\"mse loss\",m_loss)\n self.log(\"g_loss\",g_loss, sync_dist=True)\n self.log(\"wass loss\",wass_loss)\n g_loss += self.args.k_wass*label_loss\n g_loss = g_loss.float()\n tqdm_dict = {'g_loss': g_loss}\n output = OrderedDict({\n 'loss': g_loss,\n 'progress_bar': tqdm_dict,\n 'log': tqdm_dict\n })\n return output\n\n elif optimizer_idx == 1:\n free_params(self.discriminator_unet)\n free_params(self.generator_unet)\n valid = Variable(Tensor(real_A.shape[0], 1).fill_(1.0), requires_grad=False)\n fake = Variable(Tensor(real_A.shape[0], 1).fill_(0.0), requires_grad=False)\n\n noisy = sv.gaussian(real_A,mean=0,stddev=self.args.sigma)\n generated, encoded_, e1_, e2_ = self(real_A.detach())\n _, z_, z1_, z2_ = self.generator_unet(noisy)\n\n real_aux, real_adv = e1_, e2_\n labels_onehot= torch.nn.functional.one_hot( labels, num_classes=self.args.n_classes)\n real_loss = self.adv_loss(real_adv,valid) + self.aux_loss(real_aux, labels_onehot)\n \n fake_aux, fake_adv = z1_, z2_\n gen_labels_onehot= torch.nn.functional.one_hot(gen_labels, num_classes=self.args.n_classes)\n fake_loss = self.adv_loss(fake_adv,fake) + self.aux_loss(fake_aux, gen_labels_onehot)\n \n \n enc_loss =(self.mse_loss(encoded_ , z_))\n self.log(\"enc loss\",enc_loss) # just monitor\n gen_loss = self.args.k_wass*(real_loss + fake_loss)/4.0\n self.log(\"genenc loss\",gen_loss) \n f_loss = self.args.k_wass*self.discriminator_unet(real_B)\n h_loss = self.args.k_wass*self.discriminator_unet(generated)\n d_loss = (torch.mean(f_loss) - torch.mean(h_loss))#wasserstein loss\n \n if self.args.clip_weight:\n d_loss -= gen_loss#enc_loss# if self.args.gram else 0\n for p in self.discriminator_unet.parameters():\n p.data.clamp_(-self.args.clip_value, self.args.clip_value)\n else:\n gradient_penalty = self.compute_gradient_penalty(real_B.data, generated.data)\n d_loss -= gen_loss#enc_loss# if self.args.gram else 0\n d_loss -= self.args.gp_lambda* self.args.k_wass* gradient_penalty\n \n \n d_loss = -d_loss.float()\n self.log(\"discriminator loss\",d_loss, sync_dist=True)\n \n tqdm_dict = {'d_loss': d_loss}\n \n output = OrderedDict({\n 'loss': d_loss,\n 'progress_bar': tqdm_dict,\n 'log': tqdm_dict\n })\n return output\n\n # def backward(self, loss, optimizer, optimizer_idx):\n # # do a custom way of backward\n # with autocast():\n # one = torch.tensor(1, dtype=torch.float) \n # if optimizer_idx == 0:\n # with autocast():\n # loss.backward(one,retain_graph=True)#.to(self.device)\n # else:\n # with autocast():\n # loss.backward(-1*one,retain_graph=True)#.to(self.device)\n\n\n def configure_optimizers(self):\n \n lr = self.lr\n #b1 = self.b1\n #b2 = self.b2\n opt_g = torch.optim.Adam(self.generator_unet.parameters(), lr=lr)#, betas=(b1, b2))\n opt_d = torch.optim.Adam(self.discriminator_unet.parameters(), lr=lr)#, betas=(b1, b2))\n \n return (\n {'optimizer': opt_g, 'frequency': 1},\n {'optimizer': opt_d, 'frequency': self.n_critic},\n )\n\n def train_dataloader(self):\n input_shape = (self.args.n_channel, self.args.img_height, self.args.img_width)\n dataset = ImageDataset(\"%s/%s\" % (self.args.dataroot,self.args.dataset) , input_shape, mode='train')\n return DataLoader(dataset, batch_size=self.args.batch_size, num_workers=24, pin_memory=True)\n\n def test_dataloader(self):\n input_shape = (self.args.n_channel, self.args.img_height, self.args.img_width)\n mode = self.args.val_target\n dataset = ImageDataset(\"%s/%s\" % (self.args.dataroot,self.args.dataset), input_shape, mode=mode )\n return DataLoader(dataset, batch_size= self.args.test_batch_size, shuffle=False, num_workers=24)\n\n def predict_dataloader(self):\n input_shape = (self.args.n_channel, self.args.img_height, self.args.img_width)\n #dataset = UserDataset(\"../data/%s\" % self.args.dataset, input_shape, mode=\"test\")\n dataset = UserDataset(\"../data/temporary\", input_shape, mode=\"test\")\n #dataset = UserDataset(\"../data/SNUH_test0\", input_shape, mode=\"test\")\n return DataLoader(dataset, batch_size=self.args.test_batch_size, num_workers=16)\n \n def on_epoch_end(self):\n pass\n \n def on_fit_start(self) -> None:\n pl.seed_everything(42)\n return super().on_fit_start()\n\n def test_step(self, batch, batch_idx):\n for img_A, img_B, aug_A, pathA, pathB in zip(batch[\"A\"], batch[\"B\"], batch[\"aug_A\"], batch[\"pathA\"], batch[\"pathB\"]):\n self.no_sample += 1\n real_A = Variable(img_A).type(Tensor)#.cuda()\n real_A = real_A.unsqueeze(0)\n real_B = Variable(img_B).type(Tensor)#.cuda()\n real_B = real_B.unsqueeze(0)\n aug_A = Variable(aug_A).type(Tensor)#.cuda()\n aug_A = aug_A.unsqueeze(0)\n fake_B, e, e1, e2 = self(real_A)\n _, z, z1, z2 = self(aug_A)\n nz_f = self.mse_loss(e, z) #+ self.mse_loss(e1, z1) + self.mse_loss(e2, z2)\n nz_f = nz_f.item()\n val = e2.squeeze().item()\n test_loss = self.mse_loss(fake_B, real_B)\n values, indexes = torch.topk(e1, k=3, dim=-1)\n indexes = indexes.data.view(-1)\n mean_v = values.mean()\n values = values - mean_v\n values = values.data.view(-1)\n \n real_A = real_A.data[0]\n fake_B = fake_B.data[0]\n real_B = real_B.data[0] #torch.cat([x for x in real_B.data.cpu()], -1)\n \n img_org = self.inv_transform(real_A.detach().clone())\n img_org = np.asarray(img_org, dtype='uint8')\n \n img_seg = self.inv_transform(fake_B.detach().clone())\n img_seg = np.asarray(img_seg, dtype='uint8')\n \n img_gt = self.inv_transform(real_B.detach().clone())#image_B\n img_gt = np.asarray(img_gt, dtype='uint8')\n \n target, t_area, area_p, _, _ =pp.critic_segmentation(img_seg)\n tp = target if area_p > 0.05 else 0\n #area_pred = t_area*area_p\n \n #tp = self.args.n_class\n iou, iou_bb, dice, unc, area_int, area_pred, area_gt, cnts = pp.critic_segmentation_by_class(tp, img_seg, img_gt, self.args)\n pngpath = os.path.relpath(self.org_dir+f\"/{batch_idx}_{self.no_sample}.png\")\n pp.save_pic(img_org,pngpath,0)\n pngpath = os.path.relpath(self.gt_dir+f\"/{batch_idx}_{self.no_sample}.png\")\n pp.save_pic(img_gt,pngpath,0)\n pngpath = os.path.relpath(self.pred_dir+f\"/{batch_idx}_{self.no_sample}.png\")\n pp.save_pic(img_seg,pngpath,0) \n self.result.append([nz_f,tp, indexes[0].item(), indexes[1].item(), indexes[2].item(),values[0].item(), test_loss.item(), val, unc, area_int, area_pred, area_gt, iou_bb, dice, iou, area_p,pathA, pathB])\n self.sum_test += test_loss.item()\n \n #del real_A, real_B, fake_B, pathA, pathB, test_loss, z, aug_A, cnts, nz_f, e, e1, e2, z1, z2\n #torch.cuda.empty_cache()\n self.log(\"test loss\",self.sum_test/self.no_sample, sync_dist=True)\n \n\n tqdm_dict = {'test_loss': test_loss}\n \n output = OrderedDict({\n 'loss': test_loss,\n 'progress_bar': tqdm_dict,\n 'log': tqdm_dict\n })\n return output\n \n def predict_step(self, batch, batch_idx):\n for img_A, aug_A, pathA in zip(batch[\"A\"], batch[\"aug_A\"], batch[\"pathA\"]):\n self.no_sample += 1\n real_A = Variable(img_A).type(Tensor)#.cuda()\n real_A = real_A.unsqueeze(0)\n aug_A = Variable(aug_A).type(Tensor)#.cuda()\n aug_A = aug_A.unsqueeze(0)\n fake_B, e, e1, e2 = self(real_A)\n _, z, z1, z2 = self(aug_A)\n nz_f = self.mse_loss(e, z) #+ self.mse_loss(e1, z1) + self.mse_loss(e2, z2)\n nz_f = nz_f.item()\n \n fake_B = fake_B.data[0] \n real_A = real_A.data[0] \n img_seg = self.inv_transform(fake_B.detach().clone())\n img_seg = np.asarray(img_seg, dtype='uint8')\n img_org = self.inv_transform(real_A.detach().clone())\n img_org = np.asarray(img_org, dtype='uint8') \n #img_cat = cv.vconcat([img_seg, img_gt])\n \n #tp = self.args.n_class\n #tp = int(torch.argmax(e1.squeeze()).item())\n val = e2.squeeze().item()\n values, indexes = torch.topk(e1, k=3, dim=-1)\n indexes = indexes.data.view(-1)\n mean_v = values.mean()\n values = values - mean_v\n values = values.data.view(-1)\n target, area, area_p, cnts, clist =pp.critic_segmentation(img_seg) \n base = os.path.splitext(pathA)[0]\n _, basename = os.path.split(base)\n jsonpath = os.path.relpath(self.json_dir+\"/\"+basename+\".json\")\n picpath = os.path.relpath(self.jpg_dir+\"/\"+basename+\".jpg\")\n pngpath = os.path.relpath(self.png_dir+\"/\"+basename+\".png\")\n #pp.save_pic(img_seg,picpath,0)\n pp.save_pic(img_org+img_seg,picpath,0)\n if cnts is not None: #len(cnts) > 0:\n M = cv.moments(cnts[0])\n M0 = M['m00']\n cX = int(M['m10'] / M0) if M0 > 0 else 0\n cY = int(M['m01'] / M0) if M0 > 0 else 0\n polygon = pp.save_contour(cnts,area_p,target,jsonpath,self.args)\n img_cv = pp.draw_pic(target,polygon,self.args)\n #img_cat = cv.vconcat([img_cat, img_cv])\n pp.save_pic(img_cv,pngpath,0)\n else:\n cX = cY = 0\n self.result.append([target, indexes[0].item(), indexes[1].item(), indexes[2].item(),values[0].item(),val, nz_f, clist, area, area_p, cX, cY, pathA])\n\n tqdm_dict = {'predict_loss': nz_f}\n \n output = OrderedDict({\n 'loss': nz_f,\n 'progress_bar': tqdm_dict,\n 'log': tqdm_dict\n })\n return output\n\n @rank_zero_only\n def on_predict_end(self):\n #dist.all_gather(self.result, Tensor)\n #output = [None for _ in self.result]\n \n self.result.sort(reverse=False, key=lambda list: list[5])\n sys.stdout.write(\"Sorted!!\\n\")\n logging.info(\"Sorted!!\\n\")\n\n with open(self.tmp_pred,\"w\",newline=\"\") as f:\n writer = csv.writer(f)\n writer.writerows(self.result)\n\n @rank_zero_only\n def on_test_end(self):\n mean_test = self.sum_test / self.no_sample\n str = \"mean mse: {}\\n\".format(mean_test)\n sys.stdout.write(str)\n logging.info(str)\n\n self.result.sort(reverse=True, key=lambda list: list[0])\n sys.stdout.write(\"Sorted!!\\n\")\n logging.info(\"Sorted!!\\n\")\n\n with open(self.tmp_csv,\"w\",newline=\"\") as f:\n writer = csv.writer(f)\n writer.writerows(self.result)\n\n world = args.img_width * args.img_height\n #[nz_f,tp, indexes[0].item(), indexes[1].item(), indexes[2].item(),values[0].item(), test_loss.item(), pathA, pathB, unc, area_int, area_pred, area_gt, iou_bb, dice, iou]\n df = pd.read_csv(self.tmp_csv, names=['nz_f', 'class', 'pclass', '2nd','3rd', 'diff','loss','validity','uncertainty',\\\n 'intersection','pred','gt','iou bb','f1','iou','area_p','pathA','pathB'], header=None) \n\n df['intersection'].astype(float)\n df['pred'].astype(float)\n df['gt'].astype(float)\n \n df['FP'] = df['pred'].sub(df['intersection']) #Predict an event when there was no event.\n df['FN'] = df['gt'].sub(df['intersection']) #Predict no event when in fact there was an event.\n df['TP'] = df['intersection']\n df['TN'] = df['intersection'].add(world - df['pred'].add(df['gt']))\n\n df['FP'].astype(float)\n df['FN'].astype(float)\n df['TP'].astype(float)\n df['TN'].astype(float)\n\n # Sensitivity, hit rate, recall, or true positive rate\n df['TPR'] = df['TP'].div(df['TP'].add(df['FN']))\n # Specificity or true negative rate\n df['TNR'] = df['TN'].div(df['TN'].add(df['FP']))\n # Precision or positive predictive value\n df['PPV'] = df['TP'].div(df['TP'].add(df['FP']))\n # Negative predictive value\n df['NPV'] = df['TN'].div(df['TN'].add(df['FN']))\n # Fall out or false positive rate\n df['FPR'] = df['FP'].div(df['FP'].add(df['TN']))\n # False negative rate\n df['FNR'] = df['FN'].div(df['TP'].add(df['FN']))\n # False discovery rate\n df['FDR'] = df['FP'].div(df['TP'].add(df['FP']))\n # Overall accuracy for each class\n df['ACC'] = (df['TP'].add(df['TN'])).div((df['TP'].add(df['FP'].add(df['FN'].add(df['TN'])))))\n # Approx AUC\n df['mAUC'] = 0.5*(df['TPR'].add(df['FPR']))\n str = \"Count={}, F1={},TP={},TN={},FP={},FN={},\\nACC={},IoU={},bbIoU={},mAUC={}, nz_f={} {}, validity={} {}, area_p={}\\n\".format(len(df), df['f1'].mean(), df['TP'].mean(),\\\n df['TN'].mean(),df['FP'].mean(),df['FN'].mean(),df['ACC'].mean(),df['iou'].mean(),df['iou bb'].mean(),df['mAUC'].mean(),df['nz_f'].quantile(0.5),df['nz_f'].std(),df['validity'].mean(),df['validity'].std(),df['area_p'].mean())\n sys.stdout.write(str)\n logging.info(str)\n \n top5 = df['pclass'].value_counts().head()\n str = f\"1st top5: index counts\\n{top5}\\n\"\n sys.stdout.write(str)\n logging.info(str)\n top5 = df['2nd'].value_counts().head()\n str = f\"2nd top5: index counts\\n{top5}\\n\"\n sys.stdout.write(str)\n logging.info(str)\n top5 = df['3rd'].value_counts().head()\n str = f\"3rd top5: index counts\\n{top5}\\n\"\n sys.stdout.write(str)\n logging.info(str)\n\n df.to_csv(self.df_csv,index=False)\n\nclass SaveImage(Callback):\n def __init__(self,args) -> None:\n self.args = args\n input_shape = (self.args.n_channel, self.args.img_height, self.args.img_width)\n self.dataset = ImageDataset(\"%s/%s\" % (self.args.dataroot,self.args.dataset), input_shape, mode='test')\n self.test_loader = DataLoader(self.dataset, batch_size= self.args.test_batch_size, shuffle=True, num_workers=16)\n \n #@rank_zero_only\n def on_train_epoch_end(self, trainer: Trainer, pl_module: LightningModule) -> None:\n batches_done = pl_module.current_epoch\n pl_module.generator_unet.eval()\n val_loss, validity = sv.sample_images(batches_done, self.test_loader, self.args, pl_module.generator_unet, pl_module.mse_loss, Tensor)\n self.log(\"validation loss\",val_loss)\n self.log(\"validity\",validity)\n pl_module.generator_unet.train()\n\n\ndef main(args: Namespace) -> None:\n input_shape = (args.n_channel, args.img_height, args.img_width)\n if args.precision == 16:\n Tensor = torch.cuda.HalfTensor if cuda else torch.FloatTensor\n o_level = 'O0'\n else:\n Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor\n o_level = 'O1'\n amp_back = 'apex'\n # ------------------------\n # 1 INIT LIGHTNING MODEL\n # ------------------------\n model = WaeGAN(args)\n if amp_back == 'apex':\n model = amp.initialize(model.cuda(), opt_level=o_level,loss_scale=1.0)\n amp.state_dict()\n dataset = args.dataset\n date = args.date\n save_path = \"./save/{dataset}_{date}\".format(dataset=dataset,date=date)\n checkpoint_callback = ModelCheckpoint(monitor=\"mse loss\", dirpath=save_path,\n filename=\"waegan-{epoch:02d}\",\n save_top_k=3,\n mode=\"min\",\n save_last=True)\n saveim_callback = SaveImage(args)\n precision = args.precision\n accel = \"ddp\" if args.DDP else None\n callbacks = [checkpoint_callback,saveim_callback]\n logging.basicConfig(filename=\"./%s.log\" % args.date ,format='%(asctime)s %(levelname)-8s %(message)s',level=logging.INFO, datefmt='%Y-%m-%d %H:%M:%S')\n logging.info(args)\n logging.getLogger('PIL').setLevel(logging.WARNING)\n\n if args.epoch !=0:\n # Load pretrained models\n start_epoch = args.epoch - 1\n if args.last:\n ckpt = ModelCheckpoint(dirpath=save_path,filename=\"last\")\n else:\n ckpt = ModelCheckpoint(dirpath=save_path,filename=\"waegan-{epoch:02d}\")\n \n #ckpt = ModelCheckpoint(dirpath=save_path,filename=\"waegan-{epoch:02d}\")\n base = os.path.basename(ckpt.format_checkpoint_name(dict(epoch=start_epoch)))\n ckpt_path = os.path.join(save_path,base)\n trainer = Trainer(gpus=args.gpu,accelerator=accel,callbacks=callbacks,\\\n precision=precision, amp_level= o_level, amp_backend=amp_back,\\\n log_every_n_steps=10, auto_select_gpus=True, max_epochs= args.train_max,\\\n auto_scale_batch_size=\"binsearch\", accumulate_grad_batches=1,\n sync_batchnorm=True)#gradient_clip_val=args.gp_lambda,\n \n if args.train:\n #trainer.tune(model)\n if args.epoch !=0:\n model = model.load_from_checkpoint(ckpt_path)\n model.train()\n trainer.fit(model, ckpt_path=ckpt_path)\n else:\n trainer.fit(model)\n else:\n \n if args.val_target=='train':\n model = model.load_from_checkpoint(ckpt_path)\n model.eval()\n input_shape = (args.n_channel, args.img_height, args.img_width)\n dataset = ImageDataset(\"%s/%s\" % (args.dataroot,args.dataset) , input_shape, mode='train')\n train_dataloader = DataLoader(dataset, batch_size=args.batch_size, num_workers=24)\n trainer.test(model,dataloaders=train_dataloader)\n \n \n elif args.val_target=='test':\n\n model = model.load_from_checkpoint(ckpt_path)\n model.eval()\n \n trainer.test(model)\n \n elif args.val_target=='user':\n\n model = model.load_from_checkpoint(ckpt_path)\n model.eval()\n \n trainer.predict(model)\n \n else:\n print(\"Nothing to be done !\")\n\nif __name__ == '__main__':\n \n args = options.Options()\n args = options.Options.parse(args)\n sv.init_imdirs(args)\n main(args)\n\n\n\n\n","repo_name":"choib/waegan_pl","sub_path":"waegan_pl/waegan_pl.py","file_name":"waegan_pl.py","file_ext":"py","file_size_in_byte":27223,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"8565082251","text":"import pygame\n\nclass Player(pygame.sprite.Sprite):\n def __init__(self, x, y):\n super().__init__()\n self.image = pygame.image.load('red_ball.png').convert_alpha()\n self.rect = self.image.get_rect()\n self.rect.x = x\n self.rect.y = y\n\n def move_left(self, pixels):\n self.rect.x -= pixels\n if self.rect.x < 0:\n self.rect.x = 0\n\n def move_right(self, pixels):\n self.rect.x += pixels\n if self.rect.x > 640 - self.rect.width:\n self.rect.x = 640 - self.rect.width\n\n def jump(self):\n self.rect.y += 2\n platform_hit_list = pygame.sprite.spritecollide(self, self.level.platform_list, False)\n self.rect.y -= 2\n\n if len(platform_hit_list) > 0 or self.rect.bottom >= self.level.height:\n self.change_y = -10\n\n def update(self):\n self.change_y += self.level.gravity\n\n self.rect.y += self.change_y\n\n platform_hit_list = pygame.sprite.spritecollide(self, self.level.platform_list, False)\n for platform in platform_hit_list:\n if self.change_y > 0:\n self.rect.bottom = platform.rect.top\n elif self.change_y < 0:\n self.rect.top = platform.rect.bottom\n\n self.change_y = 0\n","repo_name":"daniil-novel/python-proj-3sem","sub_path":"All_practice/game/player.py","file_name":"player.py","file_ext":"py","file_size_in_byte":1280,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"39332659342","text":"run_my_solution = False\n\nimport os\nimport copy\nimport signal\nimport os\nimport numpy as np\n\nif run_my_solution:\n from A1mysolution import *\n print('##############################################')\n print(\"RUNNING INSTRUCTOR's SOLUTION!!!!!!!!!!!!\")\n print('##############################################')\n\nelse:\n \n print('\\n======================= Code Execution =======================\\n')\n\n assignmentNumber = '1'\n\n import subprocess, glob, pathlib\n nb_name = '*A{}*.ipynb'\n # nb_name = '*.ipynb'\n filename = next(glob.iglob(nb_name.format(assignmentNumber)), None)\n print('Extracting python code from notebook named \\'{}\\' and storing in notebookcode.py'.format(filename))\n if not filename:\n raise Exception('Please rename your notebook file to -A{}.ipynb'.format(assignmentNumber))\n with open('notebookcode.py', 'w') as outputFile:\n subprocess.call(['jupyter', 'nbconvert', '--to', 'script',\n nb_name.format(assignmentNumber), '--stdout'], stdout=outputFile)\n # from https://stackoverflow.com/questions/30133278/import-only-functions-from-a-python-file\n import sys\n import ast\n import types\n with open('notebookcode.py') as fp:\n tree = ast.parse(fp.read(), 'eval')\n print('Removing all statements that are not function or class defs or import statements.')\n for node in tree.body[:]:\n if (not isinstance(node, ast.FunctionDef) and\n not isinstance(node, ast.Import)): # and\n # not isinstance(node, ast.ClassDef) and\n # not isinstance(node, ast.ImportFrom)):\n tree.body.remove(node)\n # Now write remaining code to py file and import it\n module = types.ModuleType('notebookcodeStripped')\n code = compile(tree, 'notebookcodeStripped.py', 'exec')\n sys.modules['notebookcodeStripped'] = module\n exec(code, module.__dict__)\n # import notebookcodeStripped as useThisCode\n from notebookcodeStripped import *\n\n\n \nexec_grade = 0\n\nfor func in ['train', 'use', 'rmse']:\n if func not in dir() or not callable(globals()[func]):\n print('CRITICAL ERROR: Function named \\'{}\\' is not defined'.format(func))\n print(' Check the spelling and capitalization of the function name.')\n\n \nprint('''\\nTesting\n X = np.array([1, 2, 3, 4, 5, 8, 9, 11]).reshape((-1, 1))\n T = (X - 5) * 0.05 + 0.002 * (X - 8)**2\n model = train(X, T, 0.001, 1000, True)\n''')\n\ntry:\n pts = 20\n X = np.array([1, 2, 3, 4, 5, 8, 9, 11]).reshape((-1, 1))\n T = (X - 5) * 0.05 + 0.002 * (X - 8)**2\n model = train(X, T, 0.001, 1000, True)\n correct_w = [1.86e-5, 9.92e-1]\n if np.allclose(model['w'].reshape(-1), correct_w, 1e-2):\n exec_grade += pts\n print(f'\\n--- {pts}/{pts} points. Returned correct values.')\n else:\n print(f'\\n--- 0/{pts} points. Returned incorrect values. w should be')\n print(correct_w)\n print(f' Your values are')\n print(model['w'])\nexcept Exception as ex:\n print(f'\\n--- 0/{pts} points. train raised the exception\\n')\n print(ex)\n\n\ntry:\n pts = 10\n correct_xms = [5.375, 3.352]\n if np.allclose(np.hstack((model['Xmeans'], model['Xstds'])), correct_xms, 1e-2):\n exec_grade += pts\n print(f'\\n--- {pts}/{pts} points. Xmeans and Xstds are correct values.')\n else:\n print(f'\\n--- 0/{pts} points. Returned incorrect values. Xmeans, Xstds should be')\n print(correct_xms)\n print(f' Your values are')\n print(model['Xmeans'], model['Xstds'])\n\nexcept Exception as ex:\n print(f'\\n--- 0/{pts} points. model key indexing raised the exception\\n')\n print(ex)\n\n\ntry:\n pts = 10\n correct_Tms = [0.055, 0.1414]\n if np.allclose(np.hstack((model['Tmeans'], model['Tstds'])), correct_Tms, 1e-2):\n exec_grade += pts\n print(f'\\n--- {pts}/{pts} points. Tmeans and Tstds are correct values.')\n else:\n print(f'\\n--- 0/{pts} points. Returned incorrect values. Tmeans, Tstds should be')\n print(correct_Tms)\n print(f' Your values are')\n print(model['Tmeans'], model['Tstds'])\n\nexcept Exception as ex:\n print(f'\\n--- 0/{pts} points. model key indexing raised the exception\\n')\n print(ex)\n\n\n######################################################################\n\nprint('''\\nTesting\n Y = use(X, model)\n''')\n\ntry:\n pts = 10\n Y = use(X, model)\n correct_Y = np.array([[-0.12808275],\n [-0.08623466],\n [-0.04438657],\n [-0.00253849],\n [ 0.0393096 ],\n [ 0.16485386],\n [ 0.20670194],\n [ 0.29039811]])\n\n if np.allclose(Y, correct_Y, 1e-2):\n exec_grade += pts\n print(f'\\n--- {pts}/{pts} points. Returned correct values.')\n else:\n print(f'\\n--- 0/{pts} points. Returned incorrect values. Y should be')\n print(correct_Y)\n print(f' Your values are')\n print(Y)\n\nexcept Exception as ex:\n print(f'\\n--- 0/{pts} points. use raised the exception\\n')\n print(ex)\n\n######################################################################\n\nprint('''\\nTesting\n err = rmse(Y, T)\n''')\n\ntry:\n pts = 10\n err = rmse(Y, T)\n correct_err = 0.0176\n if np.allclose(err, correct_err, 1e-2):\n exec_grade += pts\n print(f'\\n--- {pts}/{pts} points. Returned correct values.')\n else:\n print(f'\\n--- 0/{pts} points. Returned incorrect values. err should be')\n print(correct_err)\n print(f' Your values are')\n print(err)\n\nexcept Exception as ex:\n print(f'\\n--- 0/{pts} points. rmse raised the exception\\n')\n print(ex)\n\n\n\n\nname = os.getcwd().split('/')[-1]\n\nprint()\nprint('='*70)\nprint('{} Execution Grade is {} / 60'.format(name, exec_grade))\nprint('='*70)\n\nprint('''\\n __ / 5 Reading in weather.data correctly.''')\n\nprint('''\\n __ / 5 Count missing values, to show there are some.''')\n\nprint('''\\n __ / 5 Remove samples with missing values. Count to show there are none.''')\n\nprint('''\\n __ / 5 Construct X and T matrices as specified.''')\n\nprint('''\\n __ / 15 Use your train function on X and T. Show results as RMSE values and as plots\n for several different values of learning_rate and n_epochs. Type your \n observations of RMSE values and of plots with at least five sentences.''')\n\nprint('''\\n __ / 5 Print the weight values with corresponding variable names. Discuss\n which input variablesa re most signficant in predicting tave values.''')\n\nprint()\nprint('='*70)\nprint('{} FINAL GRADE is _ / 100'.format(name))\nprint('='*70)\n\nprint('''\nExtra Credit:\nPredict the change in tave from one day to the next, instead of the actual tave.\nShow and discuss RMSE and plotting results for several values of learning_rate\nand n_epochs.''')\n\nprint('\\n{} EXTRA CREDIT is 0 / 1'.format(name))\n","repo_name":"tmoleary21/cs445","sub_path":"hw1/A1grader.py","file_name":"A1grader.py","file_ext":"py","file_size_in_byte":7034,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"40839417200","text":"#!/usr/bin/env python\nfrom operator import itemgetter\nfrom pathlib import Path\nfrom typing import Tuple, List\n\nfrom isotyper.statistics._settings import *\nfrom isotyper.utilities import (\n write_out,\n create_file,\n Tree,\n READ_NUMBER_DIVISION,\n)\n\n\ndef renyi_entropy(\n cpoints: List,\n cvdf: List,\n vpoints: List,\n vvdf: List,\n totalv: int,\n totalreads: int,\n) -> Tuple[float, float]:\n \"\"\"Renyi entropy\n\n Parameters\n ----------\n cpoints : List\n cluster sizes\n cvdf : List\n cluster size distribution\n vpoints : List\n vertex sizes\n vvdf : List\n vertex size distribution\n totalv : int\n total vertices\n totalreads : int\n total read counts\n\n Returns\n -------\n Tuple[float, float]\n vertex and cluster Renyi entropies.\n \"\"\"\n vrenyi = 0\n crenyi = 0\n tv = totalreads * totalreads * 1.0 # KT: what is this * 1.0 for?\n tc = totalv * totalv * 1.0\n for i in range(0, len(vpoints)):\n vrenyi += vvdf[i] * (vpoints[i] * vpoints[i] / tv)\n for i in range(0, len(cpoints)):\n crenyi += cvdf[i] * (cpoints[i] * cpoints[i] / tc)\n return (vrenyi, crenyi)\n\n\ndef vdf(n: List) -> Tuple[List, List]:\n \"\"\"distribution function.\n\n Parameters\n ----------\n n : List\n list of numbers\n\n Returns\n -------\n Tuple[List, List]\n size vector and cumulative distribution function\n \"\"\"\n points = sorted(list(set(n)))\n vdf = []\n for i in range(0, len(points)):\n vdf.append(n.count(points[i]))\n return (points, vdf)\n\n\ndef gini_index(\n cpoints: List, cvdf: List, vpoints: List, vvdf: List\n) -> Tuple[float, float]:\n \"\"\"Gini index caculator for vertex and cluster size.\n\n Parameters\n ----------\n cpoints : List\n cluster sizes\n cvdf : List\n cluster size distribution\n vpoints : List\n vertex sizes\n vvdf : List\n vertex size distribution\n\n Returns\n -------\n Tuple[float, float]\n vertex and cluster gini indices.\n \"\"\"\n (vgini) = get_gini(n=vpoints, v=vvdf)\n (cgini) = get_gini(n=cpoints, v=cvdf)\n return (vgini, cgini)\n\n\ndef get_gini(n: List, v: List) -> float:\n \"\"\"get gini.\n\n Parameters\n ----------\n n : List\n list of numbers 1\n v : List\n size distribution\n\n Returns\n -------\n float\n gini index value\n \"\"\"\n values = []\n for i in range(0, len(n)):\n for j in range(0, v[i]):\n values.append(n[i])\n n = len(values)\n assert n > 0, \"Empty list of values\"\n # Sort smallest to largest\n sortedValues = sorted(values)\n cumm = [0]\n for i in range(n):\n cumm.append(sum(sortedValues[0 : (i + 1)]))\n LorenzPoints = [[], []]\n # Some of all y values\n sumYs = 0\n # Robin Hood index max(x_i, y_i)\n robinHoodIdx = -1\n for i in range(1, n + 2):\n x = 100.0 * (i - 1) / n\n y = 100.0 * (cumm[i - 1] / float(cumm[n]))\n LorenzPoints[0].append(x)\n LorenzPoints[1].append(y)\n sumYs += y\n maxX_Y = x - y\n if maxX_Y > robinHoodIdx:\n robinHoodIdx = maxX_Y\n # Gini index\n giniIdx = 100 + (100 - 2 * sumYs) / n\n return giniIdx / 100\n\n\ndef get_network_statistics_per_chain(\n cluster_file: Path,\n sample_id: str,\n per_chain_repertoire_statistics_file: Path,\n):\n \"\"\"Summary\n\n Parameters\n ----------\n cluster_file : Path\n path to clustered file.\n sample_id : str\n name of sample.\n per_chain_repertoire_statistics_file : Path\n path to output statistics file.\n \"\"\"\n create_file(per_chain_repertoire_statistics_file)\n fh = open(cluster_file, \"r\")\n cluster = Tree()\n index, sizesv, c_sizes = 0, [], {}\n total_v, total_reads = [], []\n sizesv, c_sizes = {}, {}\n chains_short = []\n t1 = 0\n n = 0\n for l in fh:\n if l[0] != \"#\":\n l = l.strip().split()\n seq_id = l[2]\n chains, freq, id_short = (\n seq_id.split(\"|\")[1].split(\"_\"),\n list(\n map(\n int,\n seq_id.split(READ_NUMBER_DIVISION)[1]\n .split(\"|\")[0]\n .split(\"_\"),\n )\n ),\n seq_id.split(READ_NUMBER_DIVISION)[0],\n )\n t1 = t1 + sum(freq)\n n = n + 1\n if len(chains_short) == 0:\n for i in range(0, len(chains)):\n c = chains[i].split(\"*\")[0]\n if c not in chains_short:\n chains_short.append(c)\n non_zero = [i for i in range(len(freq)) if freq[i] != 0]\n if len(total_v) == 0:\n total_v, total_reads = [0] * len(chains_short), [0] * len(\n chains_short\n )\n for c in chains_short:\n sizesv[c], c_sizes[c] = [], []\n for i in non_zero:\n c = chains[i].split(\"*\")[0]\n cluster[c][l[1]][freq[i]][id_short].value = 1\n index = chains_short.index(c)\n total_v[index] = total_v[index] + 1\n sizesv[c] = sizesv[c] + [freq[i]]\n total_reads[index] = total_reads[index] + freq[i]\n fh.close()\n print(total_reads, t1, n)\n if t1 != sum(total_reads):\n print(\"ERROR IN COUNTING!!\")\n out = (\n \"#Id\\tIsotype\\tN reads\\tN vertices\\tVertex Gini Index\\t\"\n + \"Cluster Gini Index\\tLargest Cluster (%)\\t2nd Largest Cluster (%)\\n\"\n )\n for c1 in chains_short:\n cluster_sizes_sub = []\n for clus in cluster[c1]:\n f = 0\n for f1 in cluster[c1][clus]:\n f = f + (f1 * len(cluster[c1][clus][f1]))\n cluster_sizes_sub = cluster_sizes_sub + [f]\n if len(cluster_sizes_sub) > 0:\n (vpoints, vvdf) = vdf(n=sizesv[c1])\n (cpoints, cvdf) = vdf(n=cluster_sizes_sub)\n vgini, cgini = gini_index(\n cpoints=cpoints,\n cvdf=cvdf,\n vpoints=vpoints,\n vvdf=vvdf,\n )\n max_pop, max_1_pop = cpoints[len(cpoints) - 1] * 100.0 / sum(\n sizesv[c1]\n ), cpoints[len(cpoints) - 2] * 100.0 / sum(sizesv[c1])\n out = (\n out\n + str(sample_id)\n + \"\\t\"\n + c1\n + \"\\t\"\n + str(sum(sizesv[c1]))\n + \"\\t\"\n + str(len(sizesv[c1]))\n + \"\\t\"\n + str(vgini)\n + \"\\t\"\n + str(cgini)\n + \"\\t\"\n + str(max_pop)\n + \"\\t\"\n + str(max_1_pop)\n + \"\\n\"\n )\n write_out(out, per_chain_repertoire_statistics_file)\n\n\ndef get_cluster_vertex_distributions(cluster_file: Path) -> Tuple:\n \"\"\"Read clustered file and obtain stats\n\n Parameters\n ----------\n cluster_file : Path\n path to cluster file.\n\n Returns\n -------\n Tuple\n general statistics about cluster vertices,\n \"\"\"\n fh = open(cluster_file, \"r\")\n cluster = Tree()\n (\n index,\n totalc,\n totalv,\n totalreads,\n sizesv,\n c_sizes,\n vertices_in_max_cluster,\n ) = (0, 0, 0, 0, [], {}, 0)\n for l in fh:\n index = index + 1\n if index > 1:\n l = l.strip()\n l = l.split()\n cluster[l[1]][l[2]].value = 1\n size = int(l[3])\n sizesv.append(size)\n totalv = totalv + 1\n totalreads = totalreads + size\n if int(l[1]) == 1:\n vertices_in_max_cluster = vertices_in_max_cluster + 1\n if l[1] in c_sizes:\n c_sizes[l[1]] = c_sizes[l[1]] + size\n else:\n c_sizes[l[1]] = size\n fh.close()\n sizes = []\n totalc = len(cluster)\n for c in cluster:\n sizes.append(len(cluster[c]))\n (cpoints, cvdf) = vdf(sizes)\n (vpoints, vvdf) = vdf(sizesv)\n return (\n cpoints,\n cvdf,\n vpoints,\n vvdf,\n totalc,\n totalv,\n totalreads,\n c_sizes,\n vertices_in_max_cluster,\n )\n\n\ndef proportional_measures(\n c_sizes: List, totalreads: int\n) -> Tuple[float, float]:\n \"\"\"Summary\n\n Parameters\n ----------\n c_sizes : List\n lsit of sizes\n totalreads : int\n total read count\n\n Returns\n -------\n Tuple[float, float]\n proportions\n \"\"\"\n sizes = []\n for c in c_sizes:\n sizes.append((c, c_sizes[c]))\n s = sorted(sizes, key=itemgetter(1), reverse=True)\n (max_pop, max_1_pop) = (\n s[0][1] * 100.0 / totalreads,\n s[1][1] * 100.0 / totalreads,\n )\n return (max_pop, max_1_pop)\n\n\ndef print_distributions(points: List, cdf: List, file_out: Path):\n \"\"\"Print distributions.\n\n Parameters\n ----------\n points : List\n list of sizes\n cdf : List\n cumulative distribution\n file_out : Path\n path to output\n \"\"\"\n create_file(file_out)\n out = \"#size\\tfrequency of size\\n\"\n for i in range(0, len(points)):\n out = out + str(points[i]) + \"\\t\" + str(cdf[i]) + \"\\n\"\n write_out(out, file_out)\n\n\ndef get_network_statistics(\n cluster_file: Path,\n sample_id: str,\n network_statistics: Path,\n species: str,\n cluster_size_distribution: Path,\n vertex_size_distribution: Path,\n):\n \"\"\"Get network statistics\n\n Parameters\n ----------\n cluster_file : Path\n path to clustered file.\n sample_id : str\n name of sample.\n network_statistics : Path\n path to output statistics file.\n species : str\n organism type.\n cluster_size_distribution : Path\n path to output statistics file (cluster size distribution).\n vertex_size_distribution : Path\n path to output statistics file (vertex size distribution).\n \"\"\"\n create_file(network_statistics)\n (\n cpoints,\n cvdf,\n vpoints,\n vvdf,\n totalc,\n totalv,\n totalreads,\n c_sizes,\n vertices_in_max_cluster,\n ) = get_cluster_vertex_distributions(cluster_file=cluster_file)\n print_distributions(\n points=vpoints, cdf=vvdf, file_out=vertex_size_distribution\n )\n print_distributions(\n points=cpoints, cdf=cvdf, file_out=cluster_size_distribution\n )\n vrenyi, crenyi = renyi_entropy(\n cpoints=cpoints,\n cvdf=cvdf,\n vpoints=vpoints,\n vvdf=vvdf,\n totalv=totalv,\n totalreads=totalreads,\n )\n vgini, cgini = gini_index(\n cpoints=cpoints,\n cvdf=cvdf,\n vpoints=vpoints,\n vvdf=vvdf,\n )\n max_pop, max_1_pop = proportional_measures(\n c_sizes=c_sizes, totalreads=totalreads\n )\n out = (\n \"#Id\\tAnalysis\\tN reads\\tN vertices\\tVertex Gini Index\\tCluster Gini Index\\tLargest Cluster (%)\\t\"\n + \"2nd Largest Cluster (%)\\t% Vertices in largest cluster\\tVertex Renyi\\tCluster Renyi\\tGene\\tSpecies\\n\"\n )\n out = (\n out\n + str(sample_id)\n + \"\\tOVERALL\\t\"\n + str(totalreads)\n + \"\\t\"\n + str(totalv)\n + \"\\t\"\n + str(vgini)\n + \"\\t\"\n + str(cgini)\n + \"\\t\"\n + str(max_pop)\n + \"\\t\"\n + str(max_1_pop)\n + \"\\t\"\n + str(vertices_in_max_cluster * 100.0 / totalv)\n + \"\\t\"\n + str(vrenyi)\n + \"\\t\"\n + str(crenyi)\n + \"\\t\"\n + \"IGH\" # always IGH in Clatworthy lab\n + \"\\t\"\n + species\n + \"\\n\"\n )\n write_out(out, network_statistics)\n\n\ndef main():\n \"\"\"main function for step 4.\"\"\"\n OUT_STAT.mkdir(exist_ok=True, parents=True)\n get_network_statistics(\n cluster_file=CLUST_ID_FILE,\n sample_id=SAMPLE_ID,\n network_statistics=NETSTATS,\n species=ORG,\n cluster_size_distribution=CLUSTER_SIZE_DIST,\n vertex_size_distribution=VERTEX_SIZE_DIST,\n )\n get_network_statistics_per_chain(\n cluster_file=CLUST_ID_FILE,\n sample_id=SAMPLE_ID,\n per_chain_repertoire_statistics_file=NETSTATS_PER_CHAIN,\n )\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"clatworthylab/bulkBCRseq","sub_path":"isotyper/statistics/statistics.py","file_name":"statistics.py","file_ext":"py","file_size_in_byte":12311,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"8499056293","text":"#! python3\n# 删除CSV文件的第一行内容\n\nimport csv, os\n\nos.makedirs('headerRemoved', exist_ok=True)\nfilePath = 'Automate_the_Boring_Stuff_onlinematerials/automate_online-materials/removeCsvHeader/'\n\n# 遍历文件夹中的csv文件，不是csv文件则跳过\n# 先读文件，跳过第一行，将剩余内容写入一个新文件(reader和writer 对象)\n\nfor csvFileName in os.listdir(filePath):\n if not csvFileName.endswith('.csv'):\n continue\n print('Removing Header From ' + csvFileName + '...')\n\n # 读CSV文件，\n csvRows = []\n csvFileObj = open(filePath + csvFileName)\n readerObj = csv.reader(csvFileObj)\n for row in readerObj:\n if readerObj.line_num == 1:\n continue\n csvRows.append(row)\n csvFileObj.close()\n\n csvFileObj = open(os.path.join('headerRemoved', csvFileName), 'w', newline='')\n csvWriter = csv.writer(csvFileObj)\n for row in csvRows:\n csvWriter.writerow(row)\n csvFileObj.close()\n","repo_name":"zsyzzx/python_code","sub_path":"removeCsvHeader.py","file_name":"removeCsvHeader.py","file_ext":"py","file_size_in_byte":981,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"35109311011","text":"import tripplanner\nimport unittest\nimport json\nfrom pymongo import MongoClient\nimport base64\n\n\ndef auth_header(username, password):\n credentials = '{0}:{1}'.format(username, password).encode('utf-8')\n encode_login = base64.b64encode(credentials).decode()\n return dict(Authorization=\"Basic \" + encode_login)\n\n\nclass FlaskrTestCase(unittest.TestCase):\n\n def setUp(self):\n self.app = tripplanner.app.test_client()\n # Run app in testing mode to retrieve exceptions and stack traces\n tripplanner.app.config['TESTING'] = True\n\n # Inject test database into application\n mongo = MongoClient('localhost', 27017)\n db = mongo.test_database\n tripplanner.app.db = db\n\n # Drop collection (significantly faster than dropping entire db)\n db.drop_collection('users')\n db.drop_collection('trips')\n\n def test_userdb(self):\n response = self.app.post('/users/',\n data=json.dumps(dict(username='admin',\n password='secret')),\n content_type='application/json')\n responseJSON = json.loads(response.data.decode())\n self.assertEqual(response.status_code, 200)\n print(responseJSON)\n\n trip_data = dict(trip='africa', waypoints=['egypt', 'ethiopia', 'south africa'])\n response = self.app.post('/trips/',\n data=json.dumps(trip_data),\n content_type='application/json',\n headers=auth_header('admin', 'secret'))\n responseJSON = json.loads(response.data.decode())\n postedObjectID = responseJSON['_id']\n self.assertEqual(response.status_code, 200)\n print(responseJSON)\n\n response = self.app.get('users/'+postedObjectID,\n headers=auth_header('admin', 'secret'))\n responseJSON = json.loads(response.data.decode())\n self.assertEqual(response.status_code, 200)\n print(responseJSON)\n\n\n def test_tripdb_auth(self):\n # create new user\n response = self.app.post('/users/',\n data=json.dumps(dict(username='admin',\n password='secret')),\n content_type='application/json')\n responseJSON = json.loads(response.data.decode())\n self.assertEqual(response.status_code, 200)\n\n # post new trip with auth\n trip_data = dict(trip='europe', waypoints=['london', 'paris', 'milan'])\n response = self.app.post('/trips/',\n data=json.dumps(trip_data),\n content_type='application/json',\n headers=auth_header('admin', 'secret'))\n responseJSON = json.loads(response.data.decode())\n postedObjectID = responseJSON['_id']\n self.assertEqual(response.status_code, 200)\n print(responseJSON)\n\n # update existing trip with auth\n up_data = dict(trip='europe', waypoints=['brussels', 'paris', 'amsterdam'])\n response = self.app.put('trips/'+postedObjectID,\n data=json.dumps(up_data),\n content_type='application/json',\n headers=auth_header('admin', 'secret'))\n responseJSON = json.loads(response.data.decode())\n self.assertEqual(response.status_code, 200)\n print(responseJSON)\n\n # get existing trip with auth\n response = self.app.get('trips/'+postedObjectID,\n headers=auth_header('admin', 'secret'))\n responseJSON = json.loads(response.data.decode())\n self.assertEqual(response.status_code, 200)\n print(responseJSON)\n\n # get non existing trip\n response = self.app.get('trips/57389d84496254540367aa0d',\n headers=auth_header('admin', 'secret'))\n responseJSON = json.loads(response.data.decode())\n self.assertEqual(response.status_code, 404)\n print(responseJSON)\n\n # delete trip with auth\n response = self.app.delete('trips/'+postedObjectID,\n headers=auth_header('admin', 'secret'))\n self.assertEqual(response.status_code, 200)\n print(responseJSON)\n\n # delete non existing trip\n response = self.app.delete('trips/68389d84496254540367aa0d',\n headers=auth_header('admin', 'secret'))\n self.assertEqual(response.status_code, 200)\n print(responseJSON)\n\n # get deleted trip\n response = self.app.get('trips/'+postedObjectID,\n headers=auth_header('admin', 'secret'))\n self.assertEqual(response.status_code, 404)\n print(responseJSON)\n\n\nif __name__ == '__main__':\n unittest.main()\n unittest.main()\n","repo_name":"MakeSchool-17/trip-planner-flask-backend-kvncaldwll","sub_path":"tripuserauthtest.py","file_name":"tripuserauthtest.py","file_ext":"py","file_size_in_byte":4929,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"24318483522","text":"horizontal=0\ndepth=0\n\ninput = open('input.txt','r')\n\nline = input.readline()\n\nwhile line:\n edit = (line.strip(\"\\n\")).split(\" \")\n command = edit[0]\n amount = int(edit[1])\n\n if command == \"forward\":\n horizontal+=amount\n elif command == \"up\":\n depth-=amount\n elif command == \"down\":\n depth+=amount\n line=input.readline()\ninput.close()\n\nprint(horizontal,depth)\nprint(depth*horizontal)","repo_name":"TheusSales/AdventofCode2021","sub_path":"challenge02pt1.py","file_name":"challenge02pt1.py","file_ext":"py","file_size_in_byte":422,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"11793684984","text":"import requests\nfrom threading import Timer\nimport logging\nimport time\nimport json\nfrom six.moves import input\nfrom glob import glob\nimport livejson\nfrom moviepy.editor import ImageSequenceClip\nimport os\n\n\"\"\"\ncams.json Docs\n\n\nEach entry consists of a URL, Detetion Type , and finally a FPS (For end of day storage video)\n\nValid entries for type are as follows.\n\n\nmax-age which uses the max-age argument of the Cache-Control tag in the header to wait.\netag which uses the ETag argument in the header to decide if it is needed to download.\nmax-age-etag uses both ETag and max-age methods. (For those stuborn URLS)\n\nExample\n\n{'cam1':{'url':'http://example.com','type':'max-age','fps':10}}\n\n\"\"\"\n\nLogLevel = 20\n\n\nclass MaxAgeDownload(object):\n def __init__(self, url,name):\n logging.getLogger(name).setLevel(LogLevel)\n self._timer = None\n self.url = url\n self.is_running = False\n self.name = name\n self.maxage = 1\n self._timer = Timer(self.maxage, self._run)\n self._timer.setName(self.name)\n\n def _run(self):\n self.is_running = False\n rep = requests.get(self.url,stream=True)\n logging.getLogger(self.name).info(\"Downloading new Image.\")\n self.maxage = int(rep.headers[\"Cache-Control\"].split(\"max-age=\")[1].split(\",\")[0]) #Get the max-age using some hacky split strings\n self.start()\n img = open(self.name+\"/\"+str(int(time.time()))+\".jpg\",\"wb+\")\n img.write(rep.raw.read())\n img.close()\n\n def start(self):\n if not self.is_running:\n self._timer = Timer(self.maxage, self._run)\n self._timer.setName(self.name)\n self._timer.start()\n self.is_running = True\n \n def stop(self):\n self._timer.cancel()\n self.is_running = False\n \nclass ETagDownload(object):\n def __init__(self, url,name):\n logging.getLogger(name).setLevel(LogLevel)\n self._timer = None\n self.url = url\n self.is_running = False\n self.name = name\n self.ETag = \"\"\n self._timer = Timer(1, self._run)\n self._timer.setName(self.name)\n\n def _run(self):\n self.is_running = False\n rep = requests.get(self.url,stream=True,headers={\"If-None-Match\":self.ETag})\n \n if rep.status_code == 200:\n logging.getLogger(self.name).info(\"Downloading new Image.\")\n self.ETag = rep.headers[\"ETag\"]\n img = open(self.name+\"/\"+str(int(time.time()))+\".jpg\",\"wb+\")\n img.write(rep.raw.read())\n img.close()\n self.start()\n\n def start(self):\n if not self.is_running:\n self.is_running = True\n self._timer = Timer(60, self._run)\n self._timer.setName(self.name)\n self._timer.start()\n \n \n def stop(self):\n self._timer.cancel()\n self.is_running = False \n\nclass MaxAgeETagDownload(object):\n def __init__(self, url,name):\n logging.getLogger(name).setLevel(LogLevel)\n self._timer = None\n self.url = url\n self.is_running = False\n self.name = name\n self.etag = \"\"\n self.maxage = 1\n self._timer = Timer(self.maxage, self._run)\n self._timer.setName(self.name)\n\n def _run(self):\n self.is_running = False\n rep = requests.get(self.url,stream=True)\n logging.getLogger(self.name).info(\"Downloading new Image.\")\n self.maxage = int(rep.headers[\"Cache-Control\"].split(\"max-age=\")[1].split(\",\")[0]) #Get the max-age using some hacky split strings\n self.start()\n if not self.etag == rep.headers[\"ETag\"]:\n img = open(self.name+\"/\"+str(int(time.time()))+\".jpg\",\"wb+\")\n img.write(rep.raw.read())\n img.close()\n self.etag = rep.headers[\"ETag\"]\n\n def start(self):\n if not self.is_running:\n self._timer = Timer(self.maxage, self._run)\n self._timer.setName(self.name)\n self._timer.start()\n self.is_running = True\n \n def stop(self):\n self._timer.cancel()\n self.is_running = False\n \n\n \ndef main():\n logging.basicConfig(format = '%(asctime)s [%(name)s] [%(levelname)s] %(message)s')\n log = logging.getLogger(\"TimeLapse\")\n log.setLevel(LogLevel)\n log.info(\"TimeLapse Starting!\")\n \n #Read in the Cameras!\n camfile = open(\"cams.json\")\n cameras = json.load(camfile)\n camfile.close()\n \n for x in cameras.keys():\n try:\n os.mkdir(x)\n os.mkdir(x+\"-mp4\")\n except Exception as e:\n log.error(\"Could not create folder \"+x+\"!\")\n log.error(\"Exception: \"+str(e))\n log.info(\"Added Camera \"+x)\n if cameras[x][\"type\"] == \"etag\":\n cameras[x][\"instance\"] = ETagDownload(cameras[x][\"url\"],x)\n if cameras[x][\"type\"] == \"max-age\":\n cameras[x][\"instance\"] = MaxAgeDownload(cameras[x][\"url\"],x) \n if cameras[x][\"type\"] == \"max-age-etag\":\n cameras[x][\"instance\"] = MaxAgeETagDownload(cameras[x][\"url\"],x)\n cameras[x][\"instance\"].start()\n \n while True:\n command = input(\">\")\n if command == \"list-cams\":\n for x in cameras.keys():\n log.info(x +\": Running? \"+str(cameras[x][\"instance\"].is_running))\n elif command.startswith(\"stopcam\"):\n cam = command.split(\" \")[1]\n if not cam in cameras.keys():\n log.error(\"Invalid Camera name\")\n else:\n log.info(\"Camera \"+cam+\" stopped!\")\n cameras[cam][\"instance\"].stop()\n \n elif command.startswith(\"startcam\"):\n cam = command.split(\" \")[1]\n if not cam in cameras.keys():\n log.error(\"Invalid Camera name\")\n else:\n log.info(\"Camera \"+cam+\" started!\")\n cameras[cam][\"instance\"].start()\n elif command.startswith(\"save\"):\n for x in cameras.keys():\n cameras[x][\"instance\"].stop()\n log.info(\"All Camers Stopped!\") \n for x in cameras.keys():\n log.info(\"Saving \"+x)\n data = livejson.File(x+\"-mp4/cat.json\")\n t = str(int(time.time()))\n q = ImageSequenceClip(x,fps=cameras[x][\"fps\"])\n q.write_videofile(x+\"-mp4/\"+t+\".mp4\")\n data[t]=glob(x+\"/*.jpg\")\n for y in glob(x+\"/*.jpg\"):\n os.remove(y)\n log.info(\"All Cameras started!\")\n for x in cameras.keys():\n cameras[x][\"instance\"].start()\n elif command.startswith(\"stopcams\"):\n for x in cameras.keys():\n cameras[x][\"instance\"].stop()\n log.info(\"All Camers Stopped!\") \n elif command.startswith(\"startcams\"):\n for x in cameras.keys():\n cameras[x][\"instance\"].start()\n log.info(\"All Camers Stopped!\") \n elif command == \"stop\":\n log.info(\"Stopping all cameras and halting!\")\n for x in cameras.keys():\n cameras[x][\"instance\"].stop()\n break\n elif command == \"help\": \n log.info(\"help: Shows this.\")\n log.info(\"list-cams: Lists the current cameras.\")\n log.info(\"stopcam: Stops a cameras recording.\")\n log.info(\"startcam: Starts a camera recording.\")\n log.info(\"stop: Stops this program.\")\n \n else:\n log.error(\"Command not found! \" +command)\n \n \nif __name__ == \"__main__\":\n main()","repo_name":"wgaylord/TimeLapseChicago","sub_path":"timelapse.py","file_name":"timelapse.py","file_ext":"py","file_size_in_byte":7691,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"2437103627","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Jun 4 16:42:12 2021\r\n\r\n@author: Zsh\r\n\"\"\"\r\n\r\nimport librosa\r\nimport glob\r\nimport os\r\nimport pandas as pd\r\nimport random\r\nimport librosa.display\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nfrom sklearn.preprocessing import LabelEncoder\r\nfrom torch.utils.data import Dataset, DataLoader, TensorDataset\r\nimport torch\r\nimport torch.nn.functional as F\r\nimport torch.optim as optim\r\nimport pickle\r\nimport se_resnet\r\n\r\n# -------------------Data Preprocessing-------------------\r\n\r\ntrain=pd.read_csv(\"dataset\\\\train_list1.csv\",sep=\",\")\r\ntest=pd.read_csv(\"dataset\\\\test_list.csv\",sep=\",\")\r\n\r\n# Randomly plot an audio\r\n# i=random.choice(train.index)\r\n# audio_name=train.filename[i]\r\n# path=str(audio_name)\r\n# print(\"Label: \",train.label[i])\r\n# x, sr=librosa.load(str(audio_name))\r\n# plt.figure(figsize=(12,4))\r\n# librosa.display.waveplot(x,sr=16000)\r\n\r\n# -------------------Feature Extraction-------------------\r\n# def parser(row):\r\n# file_name=row.filename\r\n# try:\r\n# x, sr=librosa.load(file_name)\r\n# mfccs=np.mean(librosa.feature.mfcc(y=x, sr=sr, n_mfcc=90),axis=1)\r\n# stfts = np.abs(librosa.stft(x,n_fft=112))\r\n# # chromas = np.mean(librosa.feature.chroma_stft(S=stfts, sr=sr).T)\r\n# # mels = np.mean(librosa.feature.melspectrogram(x, sr=sr),axis=1)\r\n# # contrasts = np.mean(librosa.feature.spectral_contrast(S=stfts, sr=sr).T)\r\n# print(\"processing\")\r\n \r\n# except Exception as e:\r\n# print(\"error\")\r\n# return None,None\r\n# feature_mfcc=mfccs\r\n# feature_stft=np.mean(stfts,axis=1)\r\n# # feature_mel=mels\r\n# feature_both=np.concatenate((feature_mfcc,feature_stft),axis=0)\r\n# # feature_chroma=chromas\r\n# # feature_contrast=np.mean(contrasts)\r\n \r\n# label=row.label\r\n# return [feature_both,label]\r\n\r\n# temp_train=train.apply(parser,axis=1,result_type=\"expand\")\r\n# temp_test=test.apply(parser,axis=1,result_type=\"expand\")\r\n\r\n# Store temp data in plk file\r\n# with open(\"data_train_BOTH.plk\",'wb') as f:\r\n# pickle.dump(temp_train,f)\r\n# with open(\"data_test_BOTH.plk\",'wb') as f:\r\n# pickle.dump(temp_test,f)\r\n\r\n# # Save processed data to csv file\r\n# with open(\"data_train_THREE.plk\",'rb') as f:\r\n# temp_train=pickle.load(f)\r\n# temp_train.to_csv('test_THREE.csv', sep=',',header=False,index=False)\r\n# with open(\"data_test_BOTH.plk\",'rb') as f:\r\n# temp_test=pickle.load(f)\r\n# temp_test.to_csv('test_BOTH.csv', sep=',',header=False,index=False)\r\n\r\nclass Dataset(Dataset):\r\n def __init__(self,xy,use_gpu):\r\n xy=xy.values\r\n self.x=xy[:,[0]]\r\n self.y=xy[:,[1]]\r\n list_x=[]\r\n for row in self.x:\r\n list_x.append(row.tolist()[0])\r\n self.x=torch.tensor(list_x)\r\n \r\n list_y=[]\r\n for row in self.y:\r\n list_y.append(row.tolist()[0])\r\n self.y=torch.tensor(list_y)\r\n \r\n self.len=xy.shape[0]\r\n if use_gpu:\r\n self.x=self.x.to(\"cuda\")\r\n self.y=self.y.to(\"cuda\")\r\n \r\n def __getitem__(self,index):\r\n return self.x[index], self.y[index]\r\n\r\n def __len__(self):\r\n return self.len\r\n\r\nwith open(\"data_train_SC.plk\",'rb') as f:\r\n temp=pickle.load(f)\r\n# print(len(temp[0][0]))\r\ntrain_dataset=Dataset(temp,torch.cuda.is_available())\r\n\r\nwith open(\"data_test_SC.plk\",'rb') as f2:\r\n temp2=pickle.load(f2)\r\ntest_dataset=Dataset(temp2,torch.cuda.is_available())\r\n\r\ntrain_loader=DataLoader(dataset=train_dataset,batch_size=64,shuffle=True,num_workers=2)\r\ntest_loader=DataLoader(dataset=test_dataset,batch_size=64,shuffle=False,num_workers=2)\r\n\r\n# Channel Encoder (Classifier)\r\n# class Model(torch.nn.Module):\r\n# def __init__(self):\r\n# super(Model,self).__init__()\r\n# self.fc1=torch.nn.Linear(384,320)\r\n# self.fc2=torch.nn.Linear(320,256)\r\n# self.fc3=torch.nn.Linear(256,192)\r\n# self.fc4=torch.nn.Linear(192,128)\r\n# self.fc5=torch.nn.Linear(128,96)\r\n# self.fc6=torch.nn.Linear(96,80)\r\n# self.fc7=torch.nn.Linear(80,64)\r\n# self.fc8=torch.nn.Linear(64,50)\r\n \r\n# def forward(self,x):\r\n# x-x.view(-1,384)\r\n# x=F.relu(self.fc1(x))\r\n# # x=self.dp(x)\r\n# x=F.relu(self.fc2(x))\r\n# x=F.relu(self.fc3(x))\r\n# x=F.relu(self.fc4(x))\r\n# x=F.relu(self.fc5(x))\r\n# x=F.relu(self.fc6(x))\r\n# x=F.relu(self.fc7(x))\r\n# return self.fc8(x)\r\n\r\nmodel = getattr(se_resnet,\"se_resnet_50\")(num_classes = 50)\r\ncriterion=torch.nn.CrossEntropyLoss()\r\noptimizer=torch.optim.Adam(model.parameters())\r\n\r\ndef test():\r\n correct=0\r\n total=0\r\n\r\n with torch.no_grad():\r\n for data in test_loader:\r\n x,y_test=data\r\n x=x.view(-1,3,7,7)\r\n y_test_pred=model(x)\r\n _,predicted=torch.max(y_test_pred.data,dim=1)\r\n \r\n y_test_list=y_test.data.numpy()\r\n predict_list=predicted.data.numpy()\r\n \r\n total+=len(predict_list)\r\n for i in range(len(predict_list)):\r\n if y_test_list[i]==predict_list[i]:\r\n correct+=1\r\n acc=1.0*correct/total\r\n print('Accuracy: %.5f' % acc)\r\n return acc\r\n\r\nloss_value=[] \r\nacc_value=[]\r\nif __name__=='__main__':\r\n if torch.cuda.is_available():\r\n model=model.cuda()\r\n criterion=criterion.cuda()\r\n for epoch in range(100):\r\n batch_loss=0\r\n for batch_idx,data in enumerate(train_loader,0):\r\n inputs,labels=data\r\n inputs=inputs.view(-1,3,7,7)\r\n optimizer.zero_grad()\r\n y_pred=model(inputs)\r\n loss=criterion(y_pred,labels)\r\n \r\n batch_loss+=loss.item()\r\n \r\n loss.backward()\r\n optimizer.step()\r\n loss_value.append(batch_loss/64.0)\r\n print(\"epoch {}, Loss: {}\".format(epoch+1,batch_loss))\r\n acc=test()\r\n acc_value.append(acc)\r\n \r\n plt.plot(range(len(loss_value)),loss_value)\r\n plt.show()\r\n plt.plot(range(len(acc_value)),acc_value)\r\n plt.show()\r\n","repo_name":"hbcfzy/FRSC","sub_path":"FRSC-part1/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":6160,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"52"} +{"seq_id":"70000356004","text":"#!/usr/bin/python\n\n\nimport wx \n# always add this line, or your code won't compile - the wx module contains the GUI code you'll use\n\n#The start of our wxPython GUI tutorial\"\"\"\n\napp = wx.App(redirect=False) # create a wx application\n\nwindow = wx.Frame(None, title = 'Sample GUI App') # create a window\nbtn = wx.Button(window) \n# create a button 'widget' on the window - note how the button receives the window. This creates a tree-like structure where the window is the parent node and anything else like buttons are child nodes.\n\nwindow.Show() # make the frame (and hence button) visible\napp.MainLoop() # keep things going\n","repo_name":"daviddoria/Examples","sub_path":"Python/GUI/Test.py","file_name":"Test.py","file_ext":"py","file_size_in_byte":622,"program_lang":"python","lang":"en","doc_type":"code","stars":224,"dataset":"github-code","pt":"52"} +{"seq_id":"35944781969","text":"from time import sleep\n\n\ndef maior(*num):\n print('-='*20)\n print('Analisando os valores passados...')\n ma = cont = 0\n for c in num:\n print(c, end=' ')\n sleep(.5)\n if cont == 0:\n ma = c\n cont += 1\n elif c > ma:\n ma = c\n print(f'\\nForam informados {len(num)} valores ao todo.')\n print(f'O maior valor inserido foi o {ma}')\n\n\nmaior(2, 9, 4, 5, 7, 1)\nmaior(-11, -2, -5, -10)\nmaior(4, 7, 0)\nmaior(6)\nmaior()\n","repo_name":"Ryctorius/Curso-de-Python","sub_path":"PycharmProjects/CursoExercicios/ex099.py","file_name":"ex099.py","file_ext":"py","file_size_in_byte":482,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"43296032708","text":"\"\"\"\nThis is scheduler for Learning.\n\"\"\"\n\nimport datetime\nimport itertools\nimport time\n\nDAY_OF_WEEKS = [\"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\", \"Sun\"]\n\n\ndef events_generator(schedule: str, start_at: datetime):\n if not start_at:\n start_at = datetime.datetime.now()\n parts = schedule.split(',')\n schedule_elements = []\n for element in parts:\n element = element.strip()\n parts = element.split(' ')\n if len(parts) > 2:\n raise ValueError(\"Wrong schedule template\")\n\n day_of_week = parts[0]\n if day_of_week.startswith('+'):\n dow_index = None\n day_incrementor = int(day_of_week[1:])\n else:\n try:\n dow_index = DAY_OF_WEEKS.index(day_of_week)\n except ValueError:\n raise ValueError(f\"Unexpected day of week '{day_of_week}'. Possible values are: {DAY_OF_WEEKS}\")\n day_incrementor = None\n\n if len(parts) == 2:\n parsed = time.strptime(parts[1], \"%H:%M\")\n time_part = {\n 'hour': parsed.tm_hour,\n 'minute': parsed.tm_min,\n 'second': 0,\n 'microsecond': 0,\n }\n else:\n time_part = {\n 'hour': start_at.hour,\n 'minute': start_at.minute,\n 'second': start_at.second,\n 'microsecond': start_at.microsecond,\n }\n\n schedule_elements.append((dow_index, time_part, day_incrementor))\n\n current_day = start_at\n for schedule_element in itertools.cycle(schedule_elements):\n day_of_week, time_part, day_incrementor = schedule_element\n if day_incrementor is not None:\n current_day += datetime.timedelta(days=day_incrementor)\n current_day = current_day.replace(**time_part)\n yield current_day\n else:\n while day_of_week != current_day.weekday():\n current_day += datetime.timedelta(days=1)\n current_day = current_day.replace(**time_part)\n yield current_day\n current_day += datetime.timedelta(days=1)\n","repo_name":"bestchanges/studyworthy","sub_path":"djangoapps/lms/schedule.py","file_name":"schedule.py","file_ext":"py","file_size_in_byte":2135,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"37709292758","text":"\n'''\ncollections.OrderedDict\nAn OrderedDict is a dictionary that remembers the order of the keys that were inserted first. If a new entry overwrites an existing entry, the original insertion position is left unchanged.\n\nExample\n\nCode\n\n>>> from collections import OrderedDict\n>>> \n>>> ordinary_dictionary = {}\n>>> ordinary_dictionary['a'] = 1\n>>> ordinary_dictionary['b'] = 2\n>>> ordinary_dictionary['c'] = 3\n>>> ordinary_dictionary['d'] = 4\n>>> ordinary_dictionary['e'] = 5\n>>> \n>>> print ordinary_dictionary\n{'a': 1, 'c': 3, 'b': 2, 'e': 5, 'd': 4}\n>>> \n>>> ordered_dictionary = OrderedDict()\n>>> ordered_dictionary['a'] = 1\n>>> ordered_dictionary['b'] = 2\n>>> ordered_dictionary['c'] = 3\n>>> ordered_dictionary['d'] = 4\n>>> ordered_dictionary['e'] = 5\n>>> \n>>> print ordered_dictionary\nOrderedDict([('a', 1), ('b', 2), ('c', 3), ('d', 4), ('e', 5)])\n'''\n# Enter your code here. Read input from STDIN. Print output to STDOUT\nfrom collections import OrderedDict\nsomedict=OrderedDict()\nn=int(input())\nfor i in range(n):\n data=input()\n if data in somedict.keys():\n somedict[data]+=1\n else:\n somedict[data]=1\nprint('distinct words ',len(somedict.keys()))\nprint('count of words ',*(somedict.values()))\n\n'''\nSample Input\n\n4\nbcdef\nabcdefg\nbcde\nbcdef\nSample Output\n\n3\n2 1 1\n'''\n\n","repo_name":"skriLLeX123/Python","sub_path":"Python Collections/Python Collections Ordered Dict.py","file_name":"Python Collections Ordered Dict.py","file_ext":"py","file_size_in_byte":1295,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"73946633445","text":"from flask import Flask, render_template, jsonify, request, redirect\nimport pandas as pd\nimport pymongo\nimport re\n\napp = Flask(__name__)\n\n\n@app.route(\"/\")\ndef index():\n # \"\"\"Return the homepage.\"\"\"\n \n return render_template(\"index.html\")\n\n\n# Add any other routes here\n@app.route(\"/budget\")\ndef budgetpage():\n # \"\"\"Return the homepage.\"\"\"\n return render_template(\"budget.html\")\n \n@app.route(\"/price/\")\ndef winebudget(selected_budget):\n #create mongodb connection\n conn = 'mongodb://test:password1@ds123444.mlab.com:23444/heroku_3t530jfl'\n client = pymongo.MongoClient(conn)\n\n #extract to DB\n db = client.heroku_3t530jfl\n wine_collections = db.wine_db.find({}, {'_id': 0})\n df = pd.DataFrame(list(wine_collections))\n\n #sorted by review\n sorted_wine = df.sort_values(by=\"review\", ascending=False)\n sorted_wine.reset_index(inplace=True, drop=True)\n sorted_wine.head()\n\n #take only top 100 wines\n filtered_data = sorted_wine.loc[sorted_wine[\"price\"]<=int(selected_budget)]\n top_100 = filtered_data[:100]\n\n\n #return new data for graphing\n wine_name = list(top_100[\"brand_name\"])\n grape_type = list(top_100[\"grape_type\"])\n price = list(top_100[\"price\"])\n review = list(top_100[\"review\"])\n\n data = []\n i=0\n\n while i < len(wine_name):\n wine_info = {\n \"wine_name\": wine_name[i],\n \"grape_type\": grape_type[i],\n \"price\": int(price[i]),\n \"review\": int(review[i])\n }\n data.append(wine_info)\n i +=1\n return jsonify(data)\n\n@app.route(\"/taste\")\ndef tastepage():\n # \"\"\"Return the homepage.\"\"\"\n return render_template(\"taste.html\")\n\n@app.route(\"/alcohol/\")\ndef drinkbudget(selected_percent):\n #create mongodb connection\n conn = 'mongodb://test:password1@ds123444.mlab.com:23444/heroku_3t530jfl'\n client = pymongo.MongoClient(conn)\n\n #extract to DB\n db = client.heroku_3t530jfl\n wine_collections = db.wine_db.find()\n df = pd.DataFrame(list(wine_collections))\n\n sorted_wine = df.sort_values(by=\"review\", ascending=False)\n sorted_wine.reset_index(inplace=True, drop=True)\n\n filtered_data = sorted_wine.loc[sorted_wine[\"lower_alcohol\"]<=int(selected_percent)]\n top_10 = filtered_data[:10]\n\n wine_name = list(top_10[\"brand_name\"])\n grape_type = list(top_10[\"grape_type\"])\n alcohol = list(top_10[\"lower_alcohol\"])\n\n data = []\n i=0\n\n while i < len(wine_name):\n wine_info = {\n \"wine_name\": wine_name[i],\n \"grape_type\": grape_type[i],\n \"alcohol\": alcohol[i]\n }\n data.append(wine_info)\n i +=1\n return jsonify(data)\n\n\n@app.route(\"/food\")\ndef foodpage():\n conn = 'mongodb://test:password1@ds123444.mlab.com:23444/heroku_3t530jfl'\n client = pymongo.MongoClient(conn)\n\n db = client.heroku_3t530jfl\n food_items = db.wine_db.find({}, {'_id': False, 'food_pairing': True})\n food_list = pd.DataFrame(list(food_items))\n\n\n # \"\"\"Return the homepage.\"\"\"\n return render_template(\"food.html\", food_list=food_list['food_pairing'].unique())\n\n@app.route(\"/yummy/\")\ndef foodchart(selected_food):\n\n conn = 'mongodb://test:password1@ds123444.mlab.com:23444/heroku_3t530jfl'\n client = pymongo.MongoClient(conn)\n\n db = client.heroku_3t530jfl\n food_choice = db.wine_db.find({'food_pairing': re.compile('^' + re.escape(selected_food) + '$', re.IGNORECASE)}, {'_id': False})\n food_list = list(food_choice)\n\n df = pd.DataFrame(food_list)\n\n grape_type=df.groupby(\"grape_type\").nunique()\n new_df=pd.DataFrame(grape_type)\n \n completed_df = new_df[[\"brand_name\"]]\n new_df=completed_df.reset_index()\n\n grape_names = list(new_df[\"grape_type\"])\n wine_count = list(new_df[\"brand_name\"])\n\n data = []\n i=0\n while i < len(grape_names):\n dict = {\n \"grape_type\":grape_names[i],\n \"wine_count\":wine_count[i]\n }\n data.append(dict)\n i+=1\n\n \n return jsonify(data)\n\n \n\n@app.route(\"/about\")\ndef aboutpage():\n # \"\"\"Return the homepage.\"\"\"\n return render_template(\"about.html\")\n \n\nif __name__ == \"__main__\":\n app.run()\n\n\n","repo_name":"Donthave1/DrinkDrankDrunk","sub_path":"Archive/backup.py","file_name":"backup.py","file_ext":"py","file_size_in_byte":4285,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"12505962015","text":"import logging\nimport json\n\nfrom google.appengine.api import users\nfrom google.appengine.ext import ndb\n\nfrom dashboard.common import request_handler\nfrom dashboard.common import utils\nfrom dashboard.common import xsrf\nfrom dashboard.models import table_config\n\n\nclass CreateHealthReportHandler(request_handler.RequestHandler):\n\n def get(self):\n \"\"\"Renders the UI with the form fields.\"\"\"\n self.RenderStaticHtml('create_health_report.html')\n\n def post(self):\n \"\"\"POSTS the data to the datastore.\"\"\"\n\n user = users.get_current_user()\n if not user:\n self.response.out.write(json.dumps({'error': 'User not logged in.'}))\n return\n if not utils.IsInternalUser():\n self.response.out.write(json.dumps(\n {'error':\n 'Unauthorized access, please use chromium account to login.'}))\n return\n\n get_token = self.request.get('getToken')\n get_table_config_list = self.request.get('getTableConfigList')\n get_table_config_details = self.request.get('getTableConfigDetails')\n if get_token == 'true':\n values = {}\n self.GetDynamicVariables(values)\n self.response.out.write(json.dumps({\n 'xsrf_token': values['xsrf_token'],\n }))\n elif get_table_config_list:\n self._GetTableConfigList()\n elif get_table_config_details:\n self._GetTableConfigDetails(get_table_config_details)\n else:\n self._CreateTableConfig()\n\n def _GetTableConfigList(self):\n query = table_config.TableConfig.query()\n table_config_list = query.fetch(keys_only=True)\n return_list = []\n for config in table_config_list:\n return_list.append(config.id())\n self.response.out.write(json.dumps({\n 'table_config_list': return_list,\n }))\n\n def _GetTableConfigDetails(self, config_name):\n config_entity = ndb.Key('TableConfig', config_name).get()\n if config_entity:\n master_bot_list = []\n for bot in config_entity.bots:\n master_bot_list.append(bot.parent().string_id() + '/' + bot.string_id())\n self.response.out.write(json.dumps({\n 'table_name': config_name,\n 'table_bots': master_bot_list,\n 'table_tests': config_entity.tests,\n 'table_layout': config_entity.table_layout\n }))\n else:\n self.response.out.write(json.dumps({\n 'error': 'Invalid config name.'\n }))\n\n def _CreateTableConfig(self):\n \"\"\"Creates a table config. Writes a valid name or an error message.\"\"\"\n self._ValidateToken()\n name = self.request.get('tableName')\n master_bot = self.request.get('tableBots').splitlines()\n tests = self.request.get('tableTests').splitlines()\n table_layout = self.request.get('tableLayout')\n override = int(self.request.get('override'))\n user = users.get_current_user()\n if not name or not master_bot or not tests or not table_layout or not user:\n self.response.out.write(json.dumps({\n 'error': 'Please fill out the form entirely.'\n }))\n return\n\n try:\n created_table = table_config.CreateTableConfig(\n name=name, bots=master_bot, tests=tests, layout=table_layout,\n username=user.email(), override=override)\n except table_config.BadRequestError as error:\n self.response.out.write(json.dumps({\n 'error': error.message,\n }))\n logging.error(error.message)\n return\n\n\n if created_table:\n self.response.out.write(json.dumps({\n 'name': name,\n }))\n else:\n self.response.out.write(json.dumps({\n 'error': 'Could not create table.',\n }))\n logging.error('Could not create table.')\n\n def _ValidateToken(self):\n user = users.get_current_user()\n token = str(self.request.get('xsrf_token'))\n if not user or not xsrf._ValidateToken(token, user):\n self.abort(403)\n","repo_name":"kiwibrowser/src","sub_path":"third_party/catapult/dashboard/dashboard/create_health_report.py","file_name":"create_health_report.py","file_ext":"py","file_size_in_byte":3797,"program_lang":"python","lang":"en","doc_type":"code","stars":2475,"dataset":"github-code","pt":"52"} +{"seq_id":"25851350514","text":"import socket\n\ndef main():\n \"\"\"\n Main function to create and bind a UDP socket,\n receive messages, and send a response back to the client.\n \"\"\"\n # Create a UDP socket\n sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n\n # Bind the socket to a specific IP address and port\n server_address = ('localhost', 12345)\n sock.bind(server_address)\n\n while True:\n # Wait for a message to be received\n print('Waiting for a message...')\n data, address = sock.recvfrom(4096)\n print(f'Received message: {data.decode()}')\n\n # Send a response back to the client\n response = 'Message received'\n sock.sendto(response.encode(), address)\n\nif __name__ == '__main__':\n main()\n","repo_name":"pcrumbly/UDP-example-pcrumbly","sub_path":"UDPServer.py","file_name":"UDPServer.py","file_ext":"py","file_size_in_byte":741,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"26341592255","text":"import time\n\nimport pytest\n\nfrom ingenialink.ethernet.network import EthernetNetwork\n\n\nMONITORING_CH_DATA_SIZE = 4\nMONITORING_NUM_SAMPLES = 100\nDISTURBANCE_CH_DATA_SIZE = 4\n\n\n@pytest.mark.no_connection\ndef test_connect_to_virtual(virtual_drive, read_config):\n server = virtual_drive\n time.sleep(1)\n net = EthernetNetwork()\n protocol_contents = read_config[\"ethernet\"]\n servo = net.connect_to_slave(server.ip, protocol_contents[\"dictionary\"], server.port)\n servo.write(\"CL_AUX_FBK_SENSOR\", 4)\n servo.write(\"DIST_CFG_REG0_MAP\", 4, 0)\n\n\n@pytest.mark.parametrize(\n \"reg, value, subnode\", [(\"CL_AUX_FBK_SENSOR\", 4, 1), (\"DIST_CFG_REG0_MAP\", 4, 0)]\n)\n@pytest.mark.no_connection\ndef test_virtual_drive_write_read(virtual_drive, read_config, reg, value, subnode):\n server = virtual_drive\n\n virtual_net = EthernetNetwork()\n protocol_contents = read_config[\"ethernet\"]\n virtual_servo = virtual_net.connect_to_slave(\n server.ip, protocol_contents[\"dictionary\"], server.port\n )\n\n virtual_servo.write(reg, value, subnode)\n response = virtual_servo.read(reg, subnode)\n assert response == value\n\n\n@pytest.mark.ethernet\n@pytest.mark.parametrize(\n \"reg, value, subnode\", [(\"CL_AUX_FBK_SENSOR\", 4, 1), (\"DIST_CFG_REG0_MAP\", 4, 0)]\n)\ndef test_virtual_drive_write_read_compare_responses(\n connect_to_slave, virtual_drive, read_config, reg, value, subnode\n):\n servo, net = connect_to_slave\n server = virtual_drive\n\n virtual_net = EthernetNetwork()\n protocol_contents = read_config[\"ethernet\"]\n virtual_servo = virtual_net.connect_to_slave(\n server.ip, protocol_contents[\"dictionary\"], server.port\n )\n\n virtual_response = virtual_servo.write(reg, value, subnode)\n response = servo.write(reg, value, subnode)\n assert response == virtual_response\n\n response = servo.read(reg, subnode)\n virtual_response = virtual_servo.read(reg, subnode)\n assert response == virtual_response\n\n new_value = virtual_response + 1\n virtual_servo.write(reg, new_value, subnode)\n saved_value = virtual_servo.read(reg, subnode)\n assert saved_value == new_value\n\n\n@pytest.mark.no_connection\n@pytest.mark.parametrize(\"divisor\", [1, 2])\ndef test_virtual_monitoring(virtual_drive, read_config, divisor):\n server = virtual_drive\n\n net = EthernetNetwork()\n protocol_contents = read_config[\"ethernet\"]\n servo = net.connect_to_slave(server.ip, protocol_contents[\"dictionary\"], server.port)\n\n servo.monitoring_disable()\n registers_key = [\"CL_POS_SET_POINT_VALUE\", \"CL_VOL_Q_SET_POINT\"]\n subnode = 1\n for idx, key in enumerate(registers_key):\n reg = servo._get_reg(key, subnode=1)\n address = reg.address\n servo.monitoring_set_mapped_register(\n idx, address, subnode, reg.dtype.value, MONITORING_CH_DATA_SIZE\n )\n\n servo.write(\"MON_DIST_FREQ_DIV\", divisor, subnode=0)\n servo.write(\"MON_CFG_SOC_TYPE\", 1, subnode=0)\n servo.write(\"MON_CFG_WINDOW_SAMP\", MONITORING_NUM_SAMPLES, subnode=0)\n\n servo.monitoring_enable()\n servo.write(\"MON_CMD_FORCE_TRIGGER\", 1, subnode=0)\n time.sleep(0.1)\n servo.monitoring_disable()\n\n servo.monitoring_read_data()\n\n for idx, key in enumerate(registers_key):\n reg = servo._get_reg(key, subnode=1)\n address = reg.address\n subnode = reg.subnode\n data = servo.monitoring_channel_data(idx)\n expected_data = [\n subnode + address + i for i in range(0, MONITORING_NUM_SAMPLES * divisor, divisor)\n ]\n assert data == expected_data\n\n\n@pytest.mark.no_connection\ndef test_virtual_disturbance(virtual_drive, read_config):\n server = virtual_drive\n\n net = EthernetNetwork()\n protocol_contents = read_config[\"ethernet\"]\n servo = net.connect_to_slave(server.ip, protocol_contents[\"dictionary\"], server.port)\n servo.disturbance_disable()\n servo.disturbance_remove_all_mapped_registers()\n\n registers_key = [\"CL_POS_SET_POINT_VALUE\", \"CL_VOL_Q_SET_POINT\"]\n subnode = 1\n dtypes = []\n data_arr = []\n for idx, key in enumerate(registers_key):\n reg = servo._get_reg(key, subnode=1)\n address = reg.address\n servo.disturbance_set_mapped_register(idx, address, subnode, reg.dtype.value, 4)\n dtypes.append(reg.dtype)\n data_arr.append([0, -1, 2, 3])\n\n channels = list(range(len(registers_key)))\n servo.disturbance_write_data(channels, dtypes, data_arr)\n servo.disturbance_enable()\n\n for channel in range(len(registers_key)):\n assert server._VirtualDrive__disturbance.channels[channel][\"data\"] == data_arr[channel]\n","repo_name":"ingeniamc/ingenialink-python","sub_path":"tests/test_virtual_drive.py","file_name":"test_virtual_drive.py","file_ext":"py","file_size_in_byte":4605,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"52"} +{"seq_id":"3023249248","text":"# bin/bash python\n\nimport tensorflow as tf\nfrom tensorflow.examples.tutorials.mnist import input_data\nmnist = input_data.read_data_sets('MNIST_data', one_hot=True)\n\n# config\nbatch_size = 100\nlearning_rate = .5\ntraining_epochs = 10\nlogs_path = \"./log/\"\n\nwith tf.name_scope('input'):\n # batch can be any size, but will have 784 pixels (flattened image)\n x = tf.placeholder(tf.float32, shape=[None, 784])\n # target/output: 10 classes\n y_ = tf.placeholder(tf.float32, shape=[None, 10])\n\nwith tf.name_scope('weights'):\n # model parameters change, so we want tf.Variables\n W = tf.Variable(tf.zeros([784, 10]))\n b = tf.Variable(tf.zeros([10]))\n\nwith tf.name_scope('softmax'):\n # prediction\n y = tf.nn.softmax(tf.matmul(x, W) + b)\n\nwith tf.name_scope('cross-entropy'):\n cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))\n\nwith tf.name_scope('train'):\n train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)\n\n\nwith tf.name_scope('Accuracy'):\n correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_,1))\n accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n\n# track cost and accuracy\ntf.summary.scalar(\"cost\", cross_entropy)\ntf.summary.scalar(\"accuracy\", accuracy)\n\n# merge summaries for convenience\nsummary_op = tf.summary.merge_all(key=tf.GraphKeys.SUMMARIES)\n\n# start session to initialize tensorflow variables\nwith tf.Session() as sesh:\n sesh.run(tf.initialize_all_variables())\n # writer = tf.summary.FileWriter(\"./\", sesh.graph)\n writer = tf.summary.FileWriter(logs_path , sesh.graph)\n\n for epoch in range(training_epochs):\n batch_count = int(mnist.train.num_examples/batch_size)\n for i in range(batch_count):\n batch_x, batch_y = mnist.train.next_batch(batch_size)\n _, summary = sesh.run([train_op, summary_op], feed_dict={ x: batch_x, y_: batch_y })\n writer.add_summary(summary, epoch * batch_count + i)\n if epoch % 5 ==0:\n print(\"Epoch: \", epoch)\n \n print(\"Accuracy: \", accuracy.eval(feed_dict={ x: mnist.test.images, y_: mnist.test.labels }))\n print(\"done\")\n","repo_name":"max-hoffman/tf_practice","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2089,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"18075351374","text":"import pygame\nfrom datetime import date, datetime\n\n########## LOCAL MODULES ##########\nfrom files.vars import Scene\nimport files.bucle as b\nimport files.import_imp\nfrom files.fonts import *\nfrom files.UI.Text import Text\nimport files.calendar.calendar_engine as c\n\ndef update_current_date():\n\tglobal cur_year, cur_month, cur_day\n\tcurrent_date = date.today()\n\n\t# Keep updating current dates\n\tcur_year = int(current_date.strftime(\"%Y\"))\n\tcur_month = int(current_date.strftime(\"%m\"))\n\tcur_day = int(current_date.strftime(\"%d\"))\n\nupdate_current_date()\n\n# Month/Year where the user is\ncalendar_year = cur_year\ncalendar_month = cur_month\n\ncalendar_1 = c.Calendar(year=cur_year, month=cur_month, day=cur_day)\n\ndef Draw(events):\n\tglobal calendar_year, calendar_month, calendar_day\n\t\n\tupdate_current_date()\n\n\t# Update the month/year where the user is\n\tcalendar_1.set_UI_date(\"Month\", value=calendar_month, init=True)\n\tcalendar_1.set_UI_date(\"Year\", value=calendar_year, init=True)\n\t\n\t# Update the current date\n\tcalendar_1.set_real_date(\"Month\", value=cur_month)\n\tcalendar_1.set_real_date(\"Year\", value=cur_year)\n\tcalendar_1.set_real_date(\"Day\", value=cur_day)\n\n\tcalendar_1.update(events)\n\n\t# Draw time\n\ttime = datetime.now()\n\ttime_string = Text(0, 640, time.strftime(\"%H:%M:%S\"), Arial_40, (255,255,255), lock=\"x\")\n\ttime_string.draw()\n\n\n\tfor event in events:\n\n\t\tif event.type == KEYDOWN:\n\t\t\tif event.key == K_RIGHT:\n\t\t\t\tif calendar_month == 12:\n\t\t\t\t\tcalendar_year += 1\n\t\t\t\t\tcalendar_month = 1\n\n\t\t\t\telse:\n\t\t\t\t\tcalendar_month += 1\t\t\t\t\n\t\t\t\t\t\n\t\t\telif event.key == K_LEFT:\n\t\t\t\tif calendar_month == 1:\n\t\t\t\t\tcalendar_year -= 1\n\t\t\t\t\tcalendar_month = 12\n\n\t\t\t\telse:\n\t\t\t\t\tcalendar_month -= 1\n\n\t\t\t","repo_name":"EDUATO/calendar","sub_path":"files/draw.py","file_name":"draw.py","file_ext":"py","file_size_in_byte":1676,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"44327790367","text":"s = input().split('-')\r\nans = []\r\nfor i in s:\r\n cnt = 0\r\n n = i.split('+')\r\n for j in n:\r\n cnt += int(j)\r\n ans.append(cnt)\r\na = ans[0]\r\nfor i in range(1, len(ans)):\r\n a -= ans[i]\r\nprint(a)\r\n","repo_name":"ppparkta/BOJ","sub_path":"백준/Silver/1541. 잃어버린 괄호/잃어버린 괄호.py","file_name":"잃어버린 괄호.py","file_ext":"py","file_size_in_byte":212,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"2031908929","text":"import requests\nimport json\nfrom utils import *\nimport random\n\ndef refreshAuth():\n authurl = 'https://api.gfycat.com/v1/oauth/token'\n print(\"Refreshing OAUTH data.....\")\n with open('gifyCatData.txt') as json_file: \n data = json.load(json_file)\n \n payload = data['data'][0]\n auth = requests.post(authurl, data=str(payload))\n print(\"Status: \", auth.status_code)\n r = json.loads((auth.content).decode())\n accessToken = r['access_token']\n\n return accessToken\n\ndef makeSearchReq(searchWord):\n url = 'https://api.gfycat.com/v1/gfycats/search?search_text=' + searchWord\n data = open_file('currentGifKeys.txt')\n accessToken = data[0]\n\n headers = {\n \"Authorization\" : accessToken\n }\n\n searchReq = requests.get(url, headers=headers)\n\n while searchReq.status_code != 200:\n accessToken = refreshAuth()\n headers = {\n \"Authorization\" : accessToken\n }\n \n searchReq = requests.get(url, headers=headers)\n\n print(\"search successful...\")\n saveCurrentKey(accessToken)\n\n searchData = json.loads(searchReq.content.decode('utf-8'))\n \n return searchData\n\n\ndef saveCurrentKey(accessTok):\n temp = open('currentGifKeys.txt', 'w')\n temp.write(accessTok)\n\n\ndef getGifLink(searchResults):\n gifURLList = []\n try:\n for item in searchResults['gfycats']:\n for container in item:\n if container == 'url':\n gifURLList.append(item[container])\n\n except: \n return None\n\n if len(gifURLList) == 0:\n return None\n\n randNum = random.randint(0, len(gifURLList)-1)\n\n url = gifURLList[randNum]\n\n return url\n\n\ndef mainRun(searchWord):\n data = makeSearchReq(searchWord)\n url = getGifLink(data)\n\n return url","repo_name":"tehzwen/discordBot","sub_path":"gifs.py","file_name":"gifs.py","file_ext":"py","file_size_in_byte":1785,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"36395195655","text":"\n# Python program to \n# demonstrate merging \n# of two files \nimport sys\nx=sys.argv[1]\n \ndata = data2 = \"\" \n \n# Reading data from file1 \nwith open('final_enc','rb') as fp: \n data = fp.read() \n \n\nprint(\"len(data): \",len(data))\nprint(\"data: \",data)\nprint(\"type(data): \",type(data))\n\nd1=\"\"\nd2=\"\"\n\nprint(\"str(x): \",str(x))\n\nif(str(x)=='2048'):\n\td1=data[0:256]\n\td2=data[256:]\nelif(str(x)=='1024'):\n\tprint(\"z: \",z)\n\td1=z[0:128]\n\td2=z[128:]\t\n \nprint(\"len(d1): \",len(d1))\nprint(\"len(d2): \",len(d2))\n\nwith open ('rsa_encrypted_hash_RECV', 'wb') as fp: \n fp.write(d1)\n\nwith open ('plaintext_RECV', 'wb') as fp: \n fp.write(d2) \n","repo_name":"JustKshitijD/Encryption-and-Authentication-using-OpenSSL","sub_path":"AUIN/break_final_enc_for_decryption.py","file_name":"break_final_enc_for_decryption.py","file_ext":"py","file_size_in_byte":631,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"40189142063","text":"import bs4\nimport requests\nimport datetime\n\n# data = requests.get('http://kurstenge.kz/')\n#\n# # print(data.text)\n#\n# dom = bs4.BeautifulSoup(data.text)\n# print(dom.select('td')[0].getText)\n\n\nlistPeremena = [ '8:00-8:50','9:05-9:55','10:10-11:00','11:15-12:05','12:20-13:10','13:25-14:15','14:30-15:20','15:35-16:25','17:45-18:35']\n\n\n\n\n#\n# hourNew = 8\n#\n# minNew = 50\n#\n# hour = 8\n#\n# minute = 0\n#\n# time = int(time) + minute\n# if time > 60:\n# timeNew = int(time) - 60\n# hour = hour + 1\n# print('До конца осталось')\n# print(timeNew)\n#\n# print(str(hour) + ':' + str(timeNew))\n# minute = minute + time\n# minute = minNew - minute\n# print( 'До звонка осталось ' + str(minute) + ' минут')\n\nlists = [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50]\n\n# # nowHour = datetime.datetime.now()\n# # nowHour = nowHour.hour\n# nowHour = input('hourss')\n# Hours = int(nowHour) - 8\n# minute = lists[Hours]\n#\n# print(minute)\n#\n# now = datetime.datetime.now()\n#\n# myTime = now.minute\n#\n# newTime = myTime - 50 + int(minute)\n#\n# print('до звонка ' + str(newTime) + ' мин')\n\n\n\nlistZvonok = ['8:00','9:05' ,'10:10' ,'11:15' ,'12:20' ,'13:25' ,'14:30', '15:35', '16:40', '17:45', '18:50' ]\n\nlistMonth = ['января','февраля','марта','апреля','мая','июня','июля','августа','сентября','октября','ноября','декабря']\nnow = datetime.datetime.now()\nday = now.day\nmonth = now.month\nmonth = listMonth[month - 1]\nhour = now.hour\nminute = now.minute\nyear = now.year\nretrn = str(day) + ' ' + month.strip()+ ' ' + str(year) + ' года'+ ' ' + str(hour) + ':' + str(minute)\nprint(retrn)\n\n\n","repo_name":"KUUBIK/Telegram","sub_path":"PyTime.py","file_name":"PyTime.py","file_ext":"py","file_size_in_byte":1681,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"39617102626","text":"from typing import TYPE_CHECKING\n\nimport aio_pika\n\nif TYPE_CHECKING:\n from app.web.app import Application\n\n\nclass LoggerPublisher:\n def __init__(self, app: \"Application\"):\n self.app = app\n\n async def info(self, message: str):\n conn = await aio_pika.connect_robust(self.app.config.rabbit.url)\n async with conn:\n channel = await conn.channel()\n logs_exchange = await channel.declare_exchange(\"logs\")\n await logs_exchange.publish(\n message=aio_pika.Message(f'{message}'.encode()),\n routing_key=\"info\"\n )\n self.app.logger.info(\"Logger publisher send message\")","repo_name":"Pavel418890/aiohttp_tgbot","sub_path":"app/store/rabbitmq/publisher/logger.py","file_name":"logger.py","file_ext":"py","file_size_in_byte":670,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"29169839340","text":"# -*- tab-width: 4; indent-tabs-mode: nil; py-indent-offset: 4 -*-\n#\n# This file is part of the LibreOffice project.\n#\n# This Source Code Form is subject to the terms of the Mozilla Public\n# License, v. 2.0. If a copy of the MPL was not distributed with this\n# file, You can obtain one at http://mozilla.org/MPL/2.0/.\n#\n\nfrom uitest.framework import UITestCase\nfrom libreoffice.uno.propertyvalue import mkPropertyValues\nfrom uitest.uihelper.common import get_url_for_data_file\n\n#Bug 116474 - Undo/redo: The redo of adding caption to an image isn't working: no image\n\nclass tdf116474(UITestCase):\n\n def test_tdf116474_insert_caption_undo(self):\n with self.ui_test.create_doc_in_start_center(\"writer\") as document:\n text = document.getText()\n cursor = text.createTextCursor()\n textGraphic = document.createInstance('com.sun.star.text.TextGraphicObject')\n provider = self.xContext.ServiceManager.createInstance('com.sun.star.graphic.GraphicProvider')\n graphic = provider.queryGraphic( mkPropertyValues({\"URL\": get_url_for_data_file(\"LibreOffice.jpg\")}))\n textGraphic.Graphic = graphic\n text.insertTextContent(cursor, textGraphic, False)\n #select image\n document.getCurrentController().select(document.getDrawPage()[0])\n\n with self.ui_test.execute_dialog_through_command(\".uno:InsertCaptionDialog\") as xDialogCaption:\n\n xCapt = xDialogCaption.getChild(\"caption_edit\")\n xCapt.executeAction(\"TYPE\", mkPropertyValues({\"TEXT\":\"Caption\"}))\n\n\n self.assertEqual(document.TextFrames[0].Text.String, \"Figure 1: Caption\")\n self.assertEqual(document.GraphicObjects.getCount(), 1) #nr. of images\n #Undo, redo\n self.xUITest.executeCommand(\".uno:Undo\")\n self.xUITest.executeCommand(\".uno:Redo\")\n #Verify\n self.assertEqual(document.TextFrames[0].Text.String, \"Figure 1: Caption\")\n self.assertEqual(document.GraphicObjects.getCount(), 1) #nr. of images\n\n# vim: set shiftwidth=4 softtabstop=4 expandtab:\n","repo_name":"LibreOffice/core","sub_path":"sw/qa/uitest/writer_tests2/tdf116474.py","file_name":"tdf116474.py","file_ext":"py","file_size_in_byte":2122,"program_lang":"python","lang":"en","doc_type":"code","stars":2194,"dataset":"github-code","pt":"52"} +{"seq_id":"26477915986","text":"# read file\nwith open(\"/Users/thanhminn/data for data analyst/data_project/project thi học sinh giỏi cấp tphcm 2023/Main Data/final_data.csv\") as file:\n data = file.read().split(\"\\n\")\n\n#set data for code\nheader = data[0]\nstudents = data[1:]\ntotal_student = len(students)\n\n# split header\nheader = header.split(\",\")\narr = header[4:]\n\n# turn each student into a list\nfor i in range(len(students)):\n\tstudents[i] = students[i].split(\",\")\n\n# number of students who took 0,1,2,3,...12 \nnum_of_subject_taken = [0]*12\n\nfor student in students:\n\tcount = 0\n\tfor i in range(12):\n\t\tif student[i+4] != \"-1\":\n\t\t\tcount += 1\n\tnum_of_subject_taken[count] += 1\n \n#Drawing bar chart plot\nimport matplotlib.pyplot as plt\nimport numpy \n\n#create y axis label using arr list\narr = [\"0 Subject\",\"1 Subject\",\"2 Subject\",\"3 Subject\",\"4 Subject\",\"5 Subject\",\"6 Subject\",\"7 Subject\",\"8 Subject\",\"9 Subject\",\"10 Subject\",\"11 Subject\"]\nfig, ax = plt.subplots()\n\n# list form y label\ny_pos = numpy.arange(len(arr))\n\n# plot the bar chart using 2 list\nplt.bar(y_pos, num_of_subject_taken,color=(0.1, 0.1, 0.1, 0.1), edgecolor='black')\n\n# change horizontal category name\nplt.xticks(y_pos, arr,)\n\n# label and title\nplt.ylabel('Numbers of Student',fontdict={'fontname':'Comic Sans MS',})\nplt.title('Student Take 1,2,..... Subject ',fontdict={'fontname':'Comic Sans MS',})\n\n# draw number of student \nrects = ax.patches\nfor rect, label in zip(rects, num_of_subject_taken):\n height = rect.get_height()\n ax.text(rect.get_x() + rect.get_width() / 2, height + 0.75, label, ha='center', va='bottom')\n \n# show the plot\nplt.show()\n","repo_name":"minnnhoclamdataaa/Data_Project","sub_path":"student take 0,1,2,3,...,12 subject.py","file_name":"student take 0,1,2,3,...,12 subject.py","file_ext":"py","file_size_in_byte":1606,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"5596000128","text":"from rqalpha_plus.apis import *\n\n__config__ = {\n \"base\": {\n \"accounts\": {\n \"stock\": 1000000,\n },\n \"start_date\": \"20190601\",\n \"end_date\": \"20190610\"\n },\n \"mod\": {\n \"rqfactor\": {\n \"enabled\": True\n }\n }\n}\n\n\ndef GRAHAM_SIGNAL():\n Graham_number = (22.5 * Factor('earnings_per_share') * Factor('book_value_per_share')) ** (0.5)\n # print(\"Graham_number:\",Graham_number)\n\n return Graham_number\n\n\ndef init(context):\n context.s1 = '600000.XSHG'\n # 在初始化阶段注册指标函数\n reg_indicator('graham', GRAHAM_SIGNAL, '1d', win_size=10)\n\n\ndef handle_bar(context, bar_dict):\n # 获取指标结果\n gra = get_indicator(context.s1, 'graham')\n print(\"gra:\", gra)\n\n # 设置入场条件\n if gra > 0:\n order_percent(context.s1, 0.1)\n","repo_name":"Code-Xiang/Tensorflow_karas","sub_path":"RiceQuant/examples/rqfactor_test.py","file_name":"rqfactor_test.py","file_ext":"py","file_size_in_byte":840,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"31799026602","text":"def sum(num1, num2):\n num1 = num1[::-1]\n num2 = num2[::-1]\n res = []\n sobra = '0'\n for count in range(0, 4):\n if num1[count] == '1' and num2[count] == '1' and sobra == '0':\n res.append('0')\n sobra = '1'\n elif num1[count] == '1' and num2[count] == '0' and sobra == '0':\n res.append('1')\n sobra = '0'\n elif num1[count] == '0' and num2[count] == '1' and sobra == '0':\n res.append('1')\n sobra = '0'\n elif num1[count] == '1' and num2[count] == '1' and sobra == '1':\n res.append('1')\n sobra = '1'\n elif num1[count] == '1' and num2[count] == '0' and sobra == '1':\n res.append('0')\n sobra = '1'\n elif num1[count] == '0' and num2[count] == '1' and sobra == '1':\n res.append('0')\n sobra = '1'\n elif num1[count] == '0' and num2[count] == '0' and sobra == '0':\n res.append('0')\n sobra = '0'\n elif num1[count] == '0' and num2[count] == '0' and sobra == '1':\n res.append('1')\n sobra = '0'\n res = ''.join(res)\n res = res[::-1]\n return res\n\ndef complemento_2(num):\n res = []\n numero1 = '0001'\n for count in range(0, len(num)):\n if num[count] == '1':\n res.append('0') \n elif num[count] == '0':\n res.append('1')\n res = ''.join(res)\n res = sum(res, numero1)\n num = res\n return num\n\ndef sub(num1, num2):\n num2 = complemento_2(num2)\n res = sum(num1, num2)\n return res\n\n\n","repo_name":"filipeoioi/simulador_mips_python","sub_path":"checkpoin_01/sum_sub.py","file_name":"sum_sub.py","file_ext":"py","file_size_in_byte":1571,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"29931956694","text":"import xml.etree.ElementTree as ET\nimport json\nimport string\nfrom pinecone_db import PineconeDB\n\n\n#tree = ET.parse(\"fact_checks_20190605.txt\")\n#root = tree.getroot()\n\npcdb = PineconeDB()\nwith open(\"../fact_checks_20190605.txt\") as f:\n lines = f.readlines()\n\n num_errors = 0\n num_success = 0\n for line in lines:\n try:\n xml_root = ET.fromstring(line)\n\n json_data = json.loads(xml_root.text)\n\n # print(json_data[\"url\"])\n # print(json_data[\"claimReviewed\"])\n claim = json_data[\"claimReviewed\"].translate(str.maketrans('', '', string.punctuation))\n # print(claim)\n\n pcdb.add_strings([claim], json_data[\"url\"])\n\n num_success += 1\n except Exception as e:\n print(e)\n num_errors += 1\n\n\nprint(\"{} entries successfully uploaded and {} errors\".format(num_success, num_errors))\n\n","repo_name":"dtkettler/factchecker","sub_path":"crawlers/upload_fact_data.py","file_name":"upload_fact_data.py","file_ext":"py","file_size_in_byte":902,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"37530598841","text":"import json\nimport time\nimport os\nimport random\nimport re\n\nimport requests\nimport textwrap\nimport asyncio\nimport nest_asyncio\nimport httpx\nfrom PIL import Image, ImageDraw, ImageFont\nfrom miku.utils import FreqLimiter\nimport nonebot\nfrom miku.modules.sekaiutils import check_local_card_asset\nfrom miku.modules.sekaiutils import get_card_thb\nfrom miku.modules.sekaiutils import headers_sekaiviewer\nfrom miku.modules.sekaiutils import audio_update_assets\nfrom miku.modules.sekaiutils import audio_update_aliases\nfrom miku.modules.sekaiutils import audio_update_list\n\nheaders_pjsekai = {\n 'DNT': '1',\n 'Referer': 'https://pjsek.ai/',\n 'sec-ch-ua': '\" Not;A Brand\";v=\"99\", \"Google Chrome\";v=\"91\", \"Chromium\";v=\"91\"',\n 'sec-ch-ua-mobile': '?0',\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'\n}\n\n@nonebot.scheduler.scheduled_job(\n 'cron',\n # year=None,\n # month=None,\n # day=None,\n # week=None,\n # day_of_week=None,\n hour='14',\n minute='5',\n # second=None,\n # start_date=None,\n # end_date=None,\n # timezone=None,\n)\nasync def card_resources_scheduler():\n bot = nonebot.get_bot()\n try:\n time.sleep(random.randint(1, 20))\n url = 'https://sekai-world.github.io/sekai-master-db-diff/cards.json'\n raw_data = requests.get(url, headers=headers_sekaiviewer)\n data = json.loads(raw_data.content)\n cards_list_dir = os.path.join(os.path.dirname(__file__), '../metas/cards_list.json')\n with open(cards_list_dir, 'w') as f:\n json.dump(data, f, indent=2, ensure_ascii=False)\n await bot.send_group_msg(group_id=773737472, message=f\"sekai中已有{len(list(data))}张卡牌。\\n你今天想要谁的陪伴呢？\")\n time.sleep(100 + random.randint(1, 80))\n for item in data:\n asset_name = item['assetbundleName']\n if item['cardRarityType'] in ('rarity_1', 'rarity_2', 'rarity_birthday'):\n after_should_exist = 0\n asset_exist_normal = check_local_thb_asset(asset_name, 'normal')\n else:\n after_should_exist = 1\n asset_exist_normal = check_local_thb_asset(asset_name, 'normal')\n asset_exist_after_training = check_local_thb_asset(asset_name, 'after_training')\n if not asset_exist_normal:\n get_card_thb(asset_name, 'normal')\n if after_should_exist and not asset_exist_after_training:\n get_card_thb(asset_name, 'after_training')\n await bot.send_group_msg(group_id=773737472, message=\"所有卡牌头图更新完成。\")\n except Exception as identifier:\n print(identifier)\n\n@nonebot.scheduler.scheduled_job(\n 'cron',\n # year=None,\n # month=None,\n # day=None,\n # week=None,\n day_of_week='0,2,4,6',\n hour='18',\n minute='5',\n # second=None,\n # start_date=None,\n # end_date=None,\n # timezone=None,\n)\nasync def song_resources_scheduler():\n bot = nonebot.get_bot()\n try:\n time.sleep(random.randint(1, 20))\n song_list, asset_list = audio_update_list()\n get_song_info = (f\"sekai中现在有{len(set(song_list))}首歌曲，\\n\"\n f\"有{len(asset_list)}段不一样的歌声。\\n\"\n f\"你今天想要听到谁的思念呢？\")\n await bot.send_group_msg(group_id=773737472, message=get_song_info)\n time.sleep(100 + random.randint(1, 80))\n audio_update_assets(asset_list)\n await bot.send_group_msg(group_id=773737472, message='歌曲文件更新完成')\n time.sleep(100 + random.randint(1, 80))\n index_list = audio_update_aliases()\n get_titles_info = (f\"sekai中现在有{len(index_list)}首歌曲。\\n\"\n f\"添加歌曲别名请艾特我发送：\\n\"\n f\"歌曲标题也叫歌曲别名\")\n await bot.send_group_msg(group_id=773737472, message=get_titles_info)\n except Exception as identifier:\n print(identifier)\n","repo_name":"Phynon/mikubot","sub_path":"miku/modules/sekaischeduler/resource_scheduler.py","file_name":"resource_scheduler.py","file_ext":"py","file_size_in_byte":4088,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"22717796332","text":"\"\"\"\nadventofcode 2020, day 23\n\"\"\"\n\nclass Node:\n \"\"\"\n linked list node\n \"\"\"\n def __init__(self, value):\n self.next = None\n self.value = value\n\n def __repr__(self):\n return str(self.value)\n\ndef build_list(numbers):\n \"\"\"\n seed the linked list\n \"\"\"\n nodes = {}\n prev = None\n for num in numbers:\n cur = Node(num)\n if prev:\n prev.next = cur\n prev = cur\n nodes[num] = cur\n\n # make it circular\n head = nodes[numbers[0]]\n prev.next = head\n return head, nodes\n\ndef play_rounds(head, nodes, rounds):\n \"\"\"\n play the crabs cups game for {rounds} rounds\n \"\"\"\n max_value = len(nodes)\n cur = head\n for _ in range(rounds):\n val = cur.value\n\n # get the three next nodes\n pick_1 = cur.next\n pick_2 = pick_1.next\n pick_3 = pick_2.next\n # separate nodes from list\n cur.next = pick_3.next\n\n # figure out the destination (if destination becomes 0, use max_value)\n destination = val - 1 or max_value\n while destination in (pick_1.value, pick_2.value, pick_3.value):\n destination = destination - 1 or max_value\n\n # relink the list and fit in the picked nodes\n dest_node = nodes[destination]\n pick_3.next = dest_node.next\n dest_node.next = pick_1\n cur = cur.next\n\n # return the node for cup with label 1\n return nodes[1]\n\ndef part_one(numbers):\n \"\"\"\n play the game\n \"\"\"\n list_head, list_nodes = build_list(numbers)\n node_one = play_rounds(list_head, list_nodes, 100)\n\n cur = node_one.next\n answer = \"\"\n while cur.value != 1:\n answer += str(cur.value)\n cur = cur.next\n\n return answer\n\ndef part_two(numbers):\n \"\"\"\n play the real game\n \"\"\"\n list_head, list_nodes = build_list(numbers)\n node_one = play_rounds(list_head, list_nodes, 10000000)\n return node_one.next.value * node_one.next.next.value\n\nif __name__ == '__main__':\n cups_part_one = [int(n) for n in \"219748365\"]\n print(\"part1:\", part_one(cups_part_one))\n cups_part_two = cups_part_one + list(range(max(cups_part_one) + 1, 1000001))\n print(\"part2:\", part_two(cups_part_two))\n","repo_name":"emilwillems/adventofcode_2020","sub_path":"day23/day23.py","file_name":"day23.py","file_ext":"py","file_size_in_byte":2221,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"17366493431","text":"import requests\nimport time\nimport os\n\ndef fotoCek():\n os.system(\"fswebcam --save a.jpg\")\n files = {'file': open('a.jpg', 'rb')}\n z = requests.post(\"https://455.eu/\", files=files)\n print (z.content)\n\nwhile True:\n sT = open(\"x.txt\", \"r\")\n gf = sT.read()\n sT.close()\n\n lastFoto = requests.get(\"http://455.eu/static/t.txt\").content\n\n if gf in str(lastFoto):\n print (\"same\")\n else:\n print (lastFoto)\n saveTime = open(\"x.txt\", 'w')\n saveTime.write(lastFoto)\n saveTime.close()\n fotoCek()\n","repo_name":"niyoseris/flaskcam","sub_path":"client-side.py","file_name":"client-side.py","file_ext":"py","file_size_in_byte":554,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"8841641152","text":"import json\nimport requests\nimport time\nimport eel\neel.init(\"static\")\n\n@eel.expose\ndef send_sms(number,message):\n num=number.split(\",\")\n url=\"http://api.sparrowsms.com/v2/sms/?\"\n for element in num:\n params={\n 'token':'c6IdMv1UB0miVmGMOPgU',\n 'from':'Demo',\n 'to':element,\n 'text':message,\n }\n r=requests.get(url,params=params)\n status_code = r.status_code\n response = r.text\n response_json = r.json()\n print(f\"{status_code}, {response}, {response_json}\")\n eel.sends(str(response_json))\n eel.back()\neel.start(\"index.html\",size=(580,350))\n","repo_name":"Gonelastvirus/Bulk_sms_sender","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":634,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"22456497977","text":"from random import randint\nimport sys\n\n# This is a change.\n\n# Dice rolling function.\ndef diceRolls():\n rolls = []\n for roll in range(numberOfDice):\n rolls.append(randint(1, sizeOfDice))\n return rolls\n\n\n# Ask user for input.\nwhile True: # Main program loop\n try:\n diceStr = input(\n 'Enter something like \"3d6\" or \"1d10+2\".\\n>'\n ) # Prompt for input as string.\n if diceStr.upper() == \"QUIT\":\n sys.exit()\n\n # TODO: Add a help dialogue.\n\n # Clean up input.\n diceStr = diceStr.lower().replace(\" \", \"\")\n # Check for Advantage (roll twice and take the higher value).\n advIndex = diceStr.find(\"adv\")\n diceStr = diceStr.replace(\"adv\", \"\")\n # Check for Disadvantage (roll twice and take the lower value).\n disIndex = diceStr.find(\"dis\")\n diceStr = diceStr.replace(\"dis\", \"\")\n\n # Allow rolling multiple different dice at once. Ex: 1d4, 1d6\n diceStr = diceStr.split(\",\")\n\n for roll in diceStr:\n\n # Find the index of \"d\" in the input.\n dIndex = roll.find(\"d\") # The index of the \"d\" character.\n if dIndex == -1:\n raise Exception('Missing the \"d\" character.')\n\n # Find the number of dice.\n numberOfDice = int(roll[:dIndex])\n\n # Find the index of the modifier if one exists. Find the modifier.\n if \"+\" in roll: # There is a '+' modifier.\n modSignIndex = roll.find(\"+\")\n modSign = \"+\"\n diceMod = int(roll[modSignIndex + 1 :])\n elif \"-\" in roll: # There is a '-' modifier.\n modSignIndex = roll.find(\"-\")\n modSign = \"-\"\n diceMod = -int(roll[modSignIndex + 1 :])\n else: # There is no modifier.\n modSignIndex = -1\n modSign = \"+\"\n diceMod = 0\n\n # Find the size of the dice.\n if modSignIndex == -1: # There is no modifier to the roll.\n sizeOfDice = int(roll[dIndex + 1 :])\n else:\n sizeOfDice = int(roll[dIndex + 1 : modSignIndex])\n\n # Display the total.\n if advIndex != -1 or disIndex != -1:\n roll1 = diceRolls()\n roll2 = diceRolls()\n\n if advIndex != -1: # Roll with Advantage.\n rolls = max(roll1, roll2)\n print(f\"Total: {sum(rolls) + diceMod}\")\n advRolls = \"\\u0336\" + \"\\u0336\".join(str(min(roll1, roll2)))\n print(f\"{rolls}, {advRolls}\")\n\n elif disIndex != -1: # Roll with Disadvantage.\n rolls = min(roll1, roll2)\n print(f\"Total: {sum(rolls) + diceMod}\")\n disRolls = \"\\u0336\" + \"\\u0336\".join(str(max(roll1, roll2)))\n print(f\"{rolls}, {disRolls}\")\n\n else: # Roll normally.\n rolls = diceRolls()\n print(f\"Total: {sum(rolls) + diceMod}\")\n print(f\"Rolls: {rolls}\")\n\n if diceMod != 0:\n print(f\"Modifier: {modSign} {abs(diceMod)}\")\n print(\"\\n\")\n\n # Verbose testing.\n # print(f'modSignIndex Value: {modSignIndex}')\n # print(f'modSign Value: {modSign}')\n # print(f'diceMod Value: {diceMod}')\n\n except Exception as exc:\n # Catch any exceptions and display to user.\n print('Invalid input. Enter something like \"3d6\" or \"1d10+2\".')\n print(\"Input was invalid because: \" + str(exc))\n continue # Go back to the dice string prompt.\n","repo_name":"CourtSC/Dice-Roller","sub_path":"diceRoller.py","file_name":"diceRoller.py","file_ext":"py","file_size_in_byte":3625,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"24212153378","text":"from linked_list import Node\n\n\nclass Queue:\n def __init__(self):\n self.first = None\n self.last = None\n self._size = 0\n\n def push(self, elem):\n node = Node(elem)\n if self.last is None:\n self.last = node\n else:\n self.last.next = node\n self.last = node\n\n if self.first is None:\n self.first = node\n self._size += 1\n\n def pop(self):\n if self._size > 0:\n elem = self.first.data\n self.first = self.first.next\n self._size -= 1\n return elem\n raise IndexError(\"The queue is empty\")\n\n def peek(self):\n if self._size > 0:\n return self.first.data\n raise IndexError(\"The queue is empty\")\n\n def __len__(self):\n return self._size\n\n def __repr__(self):\n if self._size > 0:\n representation = \"\"\n ptr = self.first\n while (ptr):\n representation += f\"{ptr.data} \"\n ptr = ptr.next\n return representation\n raise IndexError(\"Empty queue\")\n\n\na = Queue()\na.push(5)\na.push(1)\na.push(7)\nprint(a)\nprint(a.pop())\nprint(a)\nprint(a.peek())\n","repo_name":"Alan-Gomes1/Algoritmos-e-Estrutura-de-Dados","sub_path":"Estrutura de Dados/queue.py","file_name":"queue.py","file_ext":"py","file_size_in_byte":1204,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"21420309889","text":"from src.enums.ConstraintChecker import ConstraintChecker\nfrom src.enums import TraceState\nfrom src.constants.constants import CONSTRAINT_CHECKER_FUNCTIONS\nfrom src.models.Prefix import *\nfrom src.machine_learning.encoding import *\nfrom src.machine_learning.apriori import generate_frequent_events_and_pairs\n#from src.machine_learning.decision_tree import generate_decision_tree, generate_paths, generate_boost_decision_tree\nfrom src.enums import PrefixType\nfrom src.machine_learning import fitnessEditDistance\nfrom sklearn.model_selection import train_test_split\nimport itertools\nfrom src.enums import TraceLabel\nimport pandas as pd\nimport numpy as np\nfrom src.machine_learning.encoding.Encoding_setting import trace_attributes, resource_attributes\nimport settings\nimport math\nimport re\n\ntrace_attributes_for_numb=trace_attributes\nresource_attributes_for_numb=resource_attributes\n# Definizione della funzione `gain` per il calcolo del guadagno\ndef gain(c, nc, pc, pnc):\n prob_pos_comp = (pc + settings.smooth_factor) / (c + settings.smooth_factor * settings.num_classes)\n prob_pos_non_comp = (pnc + settings.smooth_factor) / (nc + settings.smooth_factor * settings.num_classes)\n _gain = prob_pos_comp / prob_pos_non_comp\n return _gain\n\n# Definizione della funzione `matthews_corrcoef` per il calcolo del coefficiente di correlazione di Matthews\ndef matthews_corrcoef(tp, fp, fn, tn):\n num = tp * tn - fp * fn\n denom = math.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))\n if denom == 0:\n return 0\n else:\n return num / denom\n\n# Definizione della funzione `calcScore` per il calcolo dello score\ndef calcScore(path, pos_paths_total_samples_, weights):\n purity = 1 - path.impurity\n pos_probability = path.num_samples['positive'] / pos_paths_total_samples_\n w = np.array([0.8, 0.1, 0.1])\n w = np.array([0.0, 0, 0])\n\n if path.num_samples['node_samples'] > 2:\n w = weights\n\n return np.mean(w * np.array([path.fitness, purity, pos_probability]))\n\n# Definizione della funzione `calcPathFitnessOnPrefixGOOD` per il calcolo della fitness del percorso su un prefisso\ndef calcPathFitnessOnPrefixGOOD(prefix, path, rules, fitness_type):\n path_weights = []\n path_activated_rules = np.zeros(len(path.rules))\n fitness = None\n for rule_idx, rule in enumerate(path.rules):\n template, rule_state, _ = rule\n template_name, template_params = parse_method(template)\n\n result = None\n if template_name in [ConstraintChecker.EXISTENCE.value, ConstraintChecker.ABSENCE.value, ConstraintChecker.INIT.value, ConstraintChecker.EXACTLY.value]:\n result = CONSTRAINT_CHECKER_FUNCTIONS[template_name](prefix, True, template_params[0], rules)\n else:\n result = CONSTRAINT_CHECKER_FUNCTIONS[template_name](prefix, True, template_params[0], template_params[1], rules)\n\n if rule_state == result.state:\n path_activated_rules[rule_idx] = 1\n path_weights.append(1 / (rule_idx + 1))\n\n if fitness_type == 'mean':\n fitness = np.mean(path_activated_rules)\n elif fitness_type == 'wmean':\n fitness = np.sum(path_weights * path_activated_rules) / np.sum(path_weights)\n\n return fitness\n\n# Definizione della funzione `extract_numbers_from_string` per l'estrazione dei numeri da una stringa\ndef extract_numbers_from_string(input_string, log, trace_attributes_for_numb, resource_attributes_for_numb):\n # Filtra trace_attributes per il log specifico, escludendo gli attributi non desiderati\n for log, attributes_list in trace_attributes_for_numb.items():\n trace_attributes_for_log = [\n attribute for attribute in trace_attributes_for_numb.get(log, [])\n if attribute not in settings.excluded_attributes\n ]\n\n # Filtra resource_attributes per il log specifico, escludendo gli attributi non desiderati\n resource_attributes_for_log = [\n attribute for attribute in resource_attributes_for_numb.get(log, [])\n if attribute not in settings.excluded_attributes\n ]\n\n # Controlla il pattern \"prefix\"\n prefix_pattern = r\"prefix_(\\d+)_(\\d+)\"\n matches = re.findall(prefix_pattern, input_string)\n if matches:\n numbers = [(int(match[0]), int(match[1])) for match in matches]\n return numbers\n\n # Controlla i pattern in trace_attributes_for_log\n for attribute in trace_attributes_for_log:\n trace_pattern = rf\"{attribute}_(\\d+)\"\n matches = re.findall(trace_pattern, input_string)\n if matches:\n numbers = [(int(match[0]), int(match[1])) for match in matches]\n return numbers\n\n # Controlla i pattern in resource_attributes_for_log\n for attribute in resource_attributes_for_log:\n resource_pattern = rf\"{attribute}_(\\d+)_(\\d+)\"\n matches = re.findall(resource_pattern, input_string)\n if matches:\n numbers = [(int(match[0]), int(match[1])) for match in matches]\n return numbers\n # Nessuna corrispondenza trovata\n return None\n\n\n# Definizione della funzione `calcPathFitnessOnPrefix` per il calcolo della fitness del percorso su un prefisso\ndef calcPathFitnessOnPrefix(prefix, path, dt_input_trainval,log):\n\n prefixes = []\n for trace in prefix:\n prefixes.append(trace['concept:name'])\n\n num_prefixes = len(prefixes)\n\n prefixes = dt_input_trainval.encode(prefixes)\n\n hyp = []\n for column in prefixes.columns:\n hyp.extend(prefixes[column].values)\n hyp = np.array(hyp)\n hyp = hyp.tolist()\n\n rec = np.zeros(len(hyp), dtype=int)\n ref = rec.tolist()\n\n for rule in path.rules:\n feature, state, parent = rule\n\n numbers = extract_numbers_from_string(feature, log, trace_attributes_for_numb,resource_attributes_for_numb)\n for n1, n2 in numbers:\n num1 = n1\n num2 = n2\n\n if num1 >= num_prefixes:\n continue\n\n if state == TraceState.VIOLATED:\n if isinstance(ref[num1 - 1], list):\n ref[num1 - 1].append(-num2)\n else:\n ref[num1 - 1] = [-num2]\n else:\n ref[num1 - 1] = int(num2)\n\n return fitnessEditDistance.edit(ref, hyp)\n\n# Definizione della funzione `generate_prefixes` per la generazione dei prefissi\ndef generate_prefixes(log, prefixing):\n def only(n):\n prefixes = {n: []}\n for index, trace in enumerate(log):\n if len(trace) >= n:\n events = []\n for event in trace:\n events.append(event)\n if len(events) == n:\n prefix_model = Prefix(trace.attributes[\"concept:name\"], index, events.copy())\n prefixes[n].append(prefix_model)\n break\n\n return prefixes\n\n def up_to(n):\n prefixes = {\"UPTO\": []}\n for index, trace in enumerate(log):\n events = []\n for event in trace:\n events.append(event)\n prefix_model = Prefix(trace.attributes[\"concept:name\"], index, events.copy())\n prefixes[\"UPTO\"].append(prefix_model)\n if len(events) == n:\n break\n return prefixes\n\n n = prefixing[\"length\"]\n if prefixing[\"type\"] == PrefixType.ONLY:\n return only(n)\n else:\n return up_to(n)\n\n# Definizione della funzione `parse_method` per il parsing di un metodo\ndef parse_method(method):\n method_name = method.split(\"[\")[0]\n rest = method.split(\"[\")[1][:-1]\n if \",\" in rest:\n method_params = rest.split(\",\")\n else:\n method_params = [rest]\n return method_name, method_params\n\n# Definizione della funzione `generate_prefix_path` per la generazione del percorso del prefisso\ndef generate_prefix_path(prefix):\n current_prefix = \"\"\n for event in prefix:\n current_prefix += event[\"concept:name\"] + \", \"\n current_prefix = current_prefix[:-2]\n return current_prefix\n\n# Definizione della funzione `generate_rules` per la generazione delle regole\ndef generate_rules(rules):\n if rules.strip() == \"\":\n rules = \"True\"\n return rules\n if \"is\" in rules:\n rules = rules.replace(\"is\", \"==\")\n words = rules.split()\n for index, word in enumerate(words):\n if \"A.\" in word:\n words[index] = \"A[\\\"\" + word[2:] + \"\\\"]\"\n if not words[index + 2].isdigit():\n words[index + 2] = \"\\\"\" + words[index + 2] + \"\\\"\"\n elif \"T.\" in word:\n words[index] = \"T[\\\"\" + word[2:] + \"\\\"]\"\n if not words[index + 2].isdigit():\n words[index + 2] = \"\\\"\" + words[index + 2] + \"\\\"\"\n elif word == \"same\":\n words[index] = \"A[\\\"\" + words[index + 1] + \\\n \"\\\"] == T[\\\"\" + words[index + 1] + \"\\\"]\"\n words[index + 1] = \"\"\n words = list(filter(lambda word: word != \"\", words))\n rules = \" \".join(words)\n return rules\n","repo_name":"GoldenRose01/Thesis","sub_path":"src/machine_learning/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":8935,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"23871303300","text":"# Databricks notebook source\n# MAGIC %md \n# MAGIC # Importing datasets from databricks fileStore \n\n# COMMAND ----------\n\nimport datetime\nimport pandas as pd\nfrom math import *\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# COMMAND ----------\n\n# Air_compressor data\n\nfile_location = \"/FileStore/tables/Twinsburg_Main_Air_Compressor.csv\"\nfile_type = \"csv\"\n\n# CSV options\ninfer_schema = \"true\"\nfirst_row_is_header = \"true\"\ndelimiter = \",\"\n\n# The applied options are for CSV files. For other file types, these will be ignored.\ndf1 = spark.read.format(file_type) \\\n .option(\"inferSchema\", infer_schema) \\\n .option(\"header\", first_row_is_header) \\\n .option(\"sep\", delimiter) \\\n .load(file_location)\n\ncompressor_data = df1.toPandas()\n\n#dropping irrelevant data \ncompressor_data = compressor_data.drop(columns = ['Compressor Total Run Hours (Hours)','Compressed Air Desiccant Dryer Dew Point (F)','Outside_Humidity (% RH)','OutSide_Temp (F)', 'Compressed Air Flow near distribution header (SCFM)','Compressed Air Totalizer near distribution header (m3n)','_c15','_c16','_c17','SampleNumber'])\n\ncompressor_data.rename(columns ={'Time Stamp':'Time_Stamp'},inplace = True)\n\n# COMMAND ----------\n\n# Production data \nfile_location = \"/FileStore/tables/Barcode_Reflow_Oven_Scan_Times_July_2020_1.csv\"\nfile_type = \"csv\"\n\n# CSV options\ninfer_schema = \"true\"\nfirst_row_is_header = \"true\"\ndelimiter = \",\"\n\n# The applied options are for CSV files. For other file types, these will be ignored.\ndf2 = spark.read.format(file_type) \\\n .option(\"inferSchema\", infer_schema) \\\n .option(\"header\", first_row_is_header) \\\n .option(\"sep\", delimiter) \\\n .load(file_location)\nproduction_data = df2.toPandas()\n#dropping irrelevant data \nproduction_data = production_data.drop(columns = ['COMPLETE REASON','CONST TIME','PLANT','AREA','OPERATION','Unique Index'])\nproduction_data.rename(columns ={'PRIOR COMPLETION TIME':'Time_Stamp'},inplace = True)\n\n# COMMAND ----------\n\ndisplay(df2)\n\n# COMMAND ----------\n\n# Energy usage cost\nfile_location = \"/FileStore/tables/julyUsage.csv\"\nfile_type = \"csv\"\n\n# CSV options\ninfer_schema = \"true\"\nfirst_row_is_header = \"true\"\ndelimiter = \",\"\n\ndf3 = spark.read.format(file_type) \\\n .option(\"inferSchema\", infer_schema) \\\n .option(\"header\", first_row_is_header) \\\n .option(\"sep\", delimiter) \\\n .load(file_location)\nUsage_cost = df3.toPandas()\n\n# COMMAND ----------\n\n# Oven trend_data\nfile_location = \"/FileStore/tables/July2020_Oven_TrendData-2.csv\"\nfile_type = \"csv\"\n\n# CSV options\ninfer_schema = \"true\"\nfirst_row_is_header = \"true\"\ndelimiter = \",\"\n\ndf4 = spark.read.format(file_type) \\\n .option(\"inferSchema\", infer_schema) \\\n .option(\"header\", first_row_is_header) \\\n .option(\"sep\", delimiter) \\\n .load(file_location)\nOven_trend = df4.toPandas()\n#Cleaning and reorganizing data\nOven_trend = Oven_trend.iloc[4:]\nOven_trend.columns =['Time_Stamp',\t'Avg_Current_SM1','Avg_Voltage_SM1','Avg_Current_SM4','Avg_Voltage_SM4','Avg_Current_SM7','Avg_Voltage_SM7']\n\n# COMMAND ----------\n\n# meter data\nfile_location = \"/FileStore/tables/Meter_Jan1_to_July_28_2020_interval_data.csv\"\nfile_type = \"csv\"\n\n# CSV options\ninfer_schema = \"true\"\nfirst_row_is_header = \"true\"\ndelimiter = \",\"\n\n# The applied options are for CSV files. For other file types, these will be ignored.\ndf5 = spark.read.format(file_type) \\\n .option(\"inferSchema\", infer_schema) \\\n .option(\"header\", first_row_is_header) \\\n .option(\"sep\", delimiter) \\\n .load(file_location)\nmeter_data = df5.toPandas()\nmeter_data ['Time_Stamp'] = meter_data['Date']+' '+meter_data['Time']\n#dropping irrelevant data \nmeter_data = meter_data.drop(columns = ['CAP KVAR','CAP KVA','CAP PF','MV90 ID','Rate','Premise','Name','Address','Date','Time','Peak'])\n\n# COMMAND ----------\n\n# MAGIC %md\n# MAGIC # five dataset\n# MAGIC ####compressor_data - 1 min time interval - Energy Demand by compressor\n# MAGIC ####meter_data - 30 min interval - Facility energy usage\n# MAGIC ####Oven_trend - 15 min interval - Energy Demand by three ovens\n# MAGIC ####Usage_cost* - monthly cost of energy\n# MAGIC ####production_data - Time it takes to produce\n\n# COMMAND ----------\n\nUsage_cost.head()\n##Extract monthly rate from \n\n# COMMAND ----------\n\ncompressor_data.head()\n\n# COMMAND ----------\n\nmeter_data.head(10)\ndisplay(meter_data)\n\n# COMMAND ----------\n\nproduction_data.head()\n\n# COMMAND ----------\n\nOven_trend.head()\n\n# COMMAND ----------\n\n# MAGIC %md\n# MAGIC ## Calculating Energy (KWH)\n\n# COMMAND ----------\n\n#Changing datatypes \n\nOven_trend['Time_Stamp']=pd.to_datetime(Oven_trend['Time_Stamp'])\nOven_trend['Avg_Current_SM1'] = Oven_trend['Avg_Current_SM1'].astype(float)\nOven_trend['Avg_Current_SM4'] = Oven_trend['Avg_Current_SM4'].astype(float)\nOven_trend['Avg_Current_SM7'] = Oven_trend['Avg_Current_SM7'].astype(float)\nOven_trend['Avg_Voltage_SM1'] = Oven_trend['Avg_Voltage_SM1'].astype(float)\nOven_trend['Avg_Voltage_SM4'] = Oven_trend['Avg_Voltage_SM4'].astype(float)\nOven_trend['Avg_Voltage_SM7'] = Oven_trend['Avg_Voltage_SM7'].astype(float)\n\n## Energy consumed by each oven \nOven_trend['KWH_SM1'] = (Oven_trend['Avg_Current_SM1']*Oven_trend['Avg_Voltage_SM1'])/250\nOven_trend['KWH_SM4'] = (Oven_trend['Avg_Current_SM4']*Oven_trend['Avg_Voltage_SM4'])/250\nOven_trend['KWH_SM7'] = (Oven_trend['Avg_Current_SM7']*Oven_trend['Avg_Voltage_SM7'])/250\n\n\n# COMMAND ----------\n\n\n\ncompressor_data['Time_Stamp'] = pd.to_datetime(compressor_data['Time_Stamp'])\ncompressor_data['Compressor Running %'] = compressor_data['Compressor Running %'].astype(float)\ncompressor_data['Compressor Motor Current (Amps)'] = compressor_data['Compressor Motor Current (Amps)'].astype(float)\ncompressor_data['Compressed Motor Speed (RPM)'] = compressor_data['Compressed Motor Speed (RPM)'].astype(float)\ncompressor_data['Compressor Motor Voltage (Volts)'] = compressor_data['Compressor Motor Voltage (Volts)'].astype(float)\ncompressor_data['Compressor Discharge Pressure (PSI)'] = compressor_data['Compressor Discharge Pressure (PSI)'].astype(float)\ncompressor_data['Compressed Air Pressure near distribution header (PSI)'] = compressor_data['Compressed Air Pressure near distribution header (PSI)'].astype(float)\ncompressor_data['Compressed Air Stream Temperature near distribution header (F)'] = compressor_data['Compressed Air Stream Temperature near distribution header (F)'].astype(float)\n\n##Energy consumption by compressor\n\ncompressor_data['compressor_KWH'] = (compressor_data['Compressor Motor Current (Amps)']*compressor_data['Compressor Motor Voltage (Volts)'])/60000\n\n# COMMAND ----------\n\n# MAGIC %md\n# MAGIC #Data aggregation\n\n# COMMAND ----------\n\nproduction_data['Time_Stamp'] = pd.to_datetime(production_data['Time_Stamp'])\nproduction_data_ts = production_data.set_index('Time_Stamp')\ndf_production = production_data_ts.resample('30Min').mean()\ndf_production\n\n# COMMAND ----------\n\n#Resampling to 30 min\n\n\ncompressor_data_ts = compressor_data.set_index('Time_Stamp')\ndf_compressor = compressor_data_ts.resample('30Min').mean()\n\nOven_trend_ts = Oven_trend.set_index('Time_Stamp')\ndf_Oventren = Oven_trend_ts.resample('30Min').mean()\n\ndf_meter = meter_data.set_index('Time_Stamp')\n\n# COMMAND ----------\n\nmain_df = df_compressor.join(df_Oventren, how='outer').join(df_meter, how='outer').join(df_production, how='outer')\nmain_df1 = Main_df.reset_index()\nmain_df1.head(20)\n\n# COMMAND ----------\n\nmain_df1.to_csv('main.csv', index = False)\n\n# COMMAND ----------\n\n# MAGIC %md \n# MAGIC #Subsetting \n\n# COMMAND ----------\n\n#DataFrame for July\n\n","repo_name":"rnunoo/3E","sub_path":"notebooks/Users/rnunoo@rockwellautomation.com/Sustainability/sustain_EDA.py","file_name":"sustain_EDA.py","file_ext":"py","file_size_in_byte":7503,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"1688110705","text":"import sys\n\n\nif __name__ == '__main__':\n in_n, in_s = map(int, sys.stdin.readline().split())\n seq_n = list(map(int, sys.stdin.readline().split()))\n\n sum_n = [0 for _ in range(in_n + 1)]\n for i in range(1, in_n + 1):\n sum_n[i] = sum_n[i - 1] + seq_n[i - 1]\n\n start_n = 0\n end_n = 1\n min_len = int(1e6) + 1\n\n while start_n != in_n:\n if sum_n[end_n] - sum_n[start_n] >= in_s:\n min_len = min(min_len, end_n - start_n)\n start_n += 1\n else:\n if end_n < in_n:\n end_n += 1\n else:\n start_n += 1\n\n if min_len != int(1e6) + 1:\n print(min_len)\n else:\n print(0)\n","repo_name":"junho-devv/algorithm-study","sub_path":"BACKJOONㅣ백준/단계별로 풀어보기/26_투 포인터 단계/1806ㅣ부분합.py","file_name":"1806ㅣ부분합.py","file_ext":"py","file_size_in_byte":688,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"31264617840","text":"import pychrono as chrono\nimport pychrono.fea as fea\nimport pychrono.irrlicht as chronoirr\nimport pychrono.pardisomkl as mkl\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nchrono.SetChronoDataPath('./chrono_data/')\n\n# Unit conversion\nM_TO_L = 1e0\nKG_TO_W = 1e0\nS_TO_T = 1e0\nPI = np.pi\n\n# Constants\nbeam_thickness = 0.003*M_TO_L\nbeam_length = 0.50*M_TO_L\nbeam_height = 0.04*M_TO_L\n\ng = -9.81*M_TO_L/S_TO_T**2 # m/s^2\n\nrho = 1220*KG_TO_W/(M_TO_L**3) # kg/m^3\nE = 26e7*KG_TO_W/M_TO_L/S_TO_T**2 # kg*m/s^2/m^2\nnu = 0.38\nmb = 0.01\n\nfreq = 1.0\namplitude = PI/2/S_TO_T\n\nstep = 1e-3*S_TO_T\ntfinal = 5*S_TO_T\n\n# Helper functions\ndef cast_node(nb):\n feaNB = fea.CastToChNodeFEAbase(nb)\n nodeFead = fea.CastToChNodeFEAxyzD(feaNB)\n return nodeFead\n\n# Chrono Simulation\nmysystem = chrono.ChSystemSMC()\nmysystem.Set_G_acc(chrono.ChVectorD(0,0,g))\n\nground = chrono.ChBodyEasyBox(0.1, 0.1, 0.1, 0, False)\nground.SetPos(chrono.ChVectorD(0,0,0))\nground.SetBodyFixed(True)\nmysystem.Add(ground)\n\nslider = chrono.ChBodyEasyBox(0.01, 0.01, beam_height, 0, True)\nslider.SetPos(chrono.ChVectorD(-beam_length/2,0,0))\n# mslider.SetBodyFixed(True)\nmysystem.Add(slider)\n\nclass MotorSpeed(chrono.ChFunction) :\n def __init__(self):\n super().__init__()\n\n def Get_y(self, x) :\n period = 1/freq\n t = np.mod(x, period)\n\n if t < period/4 or t > period*3/4:\n return amplitude\n else:\n return -amplitude\n\nmotor = chrono.ChLinkMotorRotationSpeed()\nmotor.Initialize(\n slider,\n ground,\n chrono.ChFrameD(chrono.ChVectorD(-beam_length/2,0,0),chrono.QUNIT)\n)\nmotor_speed = chrono.ChFunction_Sine(0,freq,amplitude)\n# motor_speed = MotorSpeed()\nmotor.SetMotorFunction(motor_speed)\nmysystem.Add(motor)\n\nbody = fea.ChMesh()\n\nnum_div_x = 25\nnum_div_z = 2\nnum_node_x = num_div_x+1\nnum_node_z = num_div_z+1\n\nnum_elements = num_div_x*num_div_z\nnum_nodes = num_node_x*num_node_z\n\ndx = beam_length/num_div_x\ndy = beam_thickness\ndz = beam_height/num_div_z # rad\n\n# Nodes\nfor k in range(num_node_z):\n for i in range(num_node_x):\n # Position of node\n x = i*dx-beam_length/2\n y = 0\n z = k*dz\n\n dirX = 0\n dirY = -1\n dirZ = 0\n\n # If nodes added to element in CCW then -y\n node = fea.ChNodeFEAxyzD(\n chrono.ChVectorD(x,y,z),\n chrono.ChVectorD(dirX,dirY,dirZ),\n )\n node.SetMass(0)\n\n if i <= 1:\n joint = fea.ChLinkPointFrameGeneric(True,True,True)\n joint.Initialize(node,slider)\n mysystem.Add(joint)\n\n body.AddNode(node)\n\n\n# Elements\nmat = fea.ChMaterialShellANCF(rho, E, nu)\nfor k in range(num_div_z):\n for i in range(num_div_x):\n nodeA = i+k*num_node_x\n nodeB = i+k*num_node_x+1\n nodeC = i+(k+1)*num_node_x+1\n nodeD = i+(k+1)*num_node_x\n\n element = fea.ChElementShellANCF()\n element.SetNodes(\n cast_node(body.GetNode(nodeA)),\n cast_node(body.GetNode(nodeB)),\n cast_node(body.GetNode(nodeC)),\n cast_node(body.GetNode(nodeD))\n )\n element.SetDimensions(dx, dz)\n element.AddLayer(dy, 0*chrono.CH_C_DEG_TO_RAD, mat)\n\n element.SetAlphaDamp(mb)\n element.SetGravityOn(False)\n\n body.AddElement(element)\n\nmysystem.Add(body)\n\n# Visuals\nvbody = fea.ChVisualizationFEAmesh(body)\nvbody.SetFEMdataType(fea.ChVisualizationFEAmesh.E_PLOT_SURFACE)\nvbody.SetWireframe(True)\nbody.AddAsset(vbody)\n\n# Solver and stepper\nmkl_solver = mkl.ChSolverPardisoMKL()\nmysystem.SetSolver(mkl_solver)\n\nhht_stepper = chrono.ChTimestepperHHT(mysystem)\nhht_stepper.SetStepControl(False)\nmysystem.SetTimestepper(hht_stepper)\n\napplication = chronoirr.ChIrrApp(mysystem, \"Curve beam\", chronoirr.dimension2du(1024, 768), chronoirr.VerticalDir_Z)\napplication.AddTypicalSky()\napplication.AddTypicalLights()\napplication.AddTypicalCamera(chronoirr.vector3df(0, 0.5, 0.5),chronoirr.vector3df(0, 0, 0))\napplication.AssetBindAll()\napplication.AssetUpdateAll()\n# application.SetShowInfos(True)\n# application.SetVideoframeSaveInterval(int(1/step/25)) # N frame per unit time\n# application.SetVideoframeSave(True)\n\napplication.SetTimestep(step)\n\nt = []\nf = []\nwhile application.GetDevice().run():\n print('time: {:.4f}'.format(mysystem.GetChTime()))\n t.append(mysystem.GetChTime())\n motor_react_force = motor.Get_react_force()\n f.append([motor_react_force.x,motor_react_force.y,motor_react_force.z])\n\n application.BeginScene()\n application.DrawAll()\n\n # Draw axis for scale and orientation\n chronoirr.drawSegment(application.GetVideoDriver(),chrono.ChVectorD(0,0,0),chrono.ChVectorD(1,0,0),chronoirr.SColor(1,255,0,0))\n chronoirr.drawSegment(application.GetVideoDriver(),chrono.ChVectorD(0,0,0),chrono.ChVectorD(0,1,0),chronoirr.SColor(1,0,255,0))\n chronoirr.drawSegment(application.GetVideoDriver(),chrono.ChVectorD(0,0,0),chrono.ChVectorD(0,0,1),chronoirr.SColor(1,0,0,255))\n\n application.DoStep()\n application.EndScene()\n\n if mysystem.GetChTime() > tfinal: # in system seconds\n application.GetDevice().closeDevice()\n\nt = np.array(t)\nf = np.array(f)\n\nplt.figure()\nplt.plot(t,f[:,0])\n","repo_name":"idealabasu/code_pychrono_simulation","sub_path":"beam_ANCF.py","file_name":"beam_ANCF.py","file_ext":"py","file_size_in_byte":5170,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"13677933070","text":"import pandas as pd \nimport numpy as np\nimport matplotlib.pyplot as plt\n\nyears = []\n\n# read CSVs\nmovies = pd.read_csv('datas/movies.csv')\nratings = pd.read_csv('datas/ratings.csv')\n\n# merge on movieId column\ndata = pd.merge(left=movies, right=ratings, on='movieId')\nfor title in data['title']:\n year_subset = title[-5:-1]\n try: years.append(int(year_subset))\n except: years.append(9999)\n \ndata['moviePubYear'] = years\nprint(len(data[data['moviePubYear'] == 9999]))\n\ngenre_df = pd.DataFrame(data['genres'].str.split('|').tolist(), index=data['movieId']).stack()\ngenre_df = genre_df.reset_index([0, 'movieId'])\ngenre_df.columns = ['movieId', 'Genre']\ntab = data\n\nuseri,frequsers=np.unique(tab.userId,return_counts=True)#useri les id des users, frequsers les freq de chaque user\nitemi,freqitems=np.unique(tab.movieId,return_counts=True)#itemi les id des item, freqitem les freq de chaque item\nn_users=len(useri)\nn_items=len(itemi)\nprint(\"le nombre des utilisateurs est :\"+ str(n_users) + \" Et le nombre des items est: \"+ str(n_items))\n\n\"\"\"\nUn des problèmes que j'ai rencontré était le fait que les ids des users et des items n'était pas ordonnée.\nC'est à dire on peut trouver l'utilisateur 1,2,3,5 et 8 sans trouver les utilisateurs 4 ,6 et 7. \nCeci a posé un problème dans la création de la matrice user-item parce que on risque d'avoir plusieurs lignes et colonnes vides.\nPour ça, j'ai crée un tableau indice_user et un tableau indice_item qui contiennent les anciens id et les nouvelles id \npar expl (1,2,5,6)=>(1,2,3,4) puis j'ai ajouté deux colonnes sur le tableau principale qui contient ces nouveaux IDs.\n(ps) ce traitement est très couteux!\n\"\"\"\n\nindice_user = pd.DataFrame()\nindice_user[\"indice\"]=range(1,len(useri)+1)\nindice_user[\"useri\"]=useri\n\nindice_item = pd.DataFrame()\nindice_item[\"indice\"]=range(1,len(itemi)+1)\nindice_item[\"itemi\"]=itemi\n\n#create user_ID_new and Item_ID_new\nx=[]\ny=[]\nfor i in range(0,len(tab)):\n x.append((indice_user.indice[indice_user.useri==tab.userId[i]].axes[0]+1)[0])\n y.append((indice_item.indice[indice_item.itemi==tab.movieId[i]].axes[0]+1)[0])\n\ntab[\"User_ID_new\"]=x\ntab[\"Item_ID_new\"]=y\n\n#create a train_data and test_data\nfrom sklearn.model_selection import train_test_split\ntrain_data, test_data = train_test_split(tab[[\"User_ID_new\",\"Item_ID_new\",\"rating\"]], test_size=0.25,random_state=123)\n\n\n\"\"\"\nDans cette partie du projet, j'applique le deuxième sous-type du fitrage collaboratif : \"Model-based\". \nIl consiste à appliquer la matrice de factorisation (MF) : c'est une méthode d'apprentissage non supervisé de décomposition\net de réduction de dimensionnalité pour les variables cachées. \n\nLe but de la matrice de factorisation est d'apprendre les préférences cachées des utilisateurs et les attributs cachés des items\ndepuis les ratings connus dans notre jeu de données, pour enfin prédire les ratings inconnus en multipliant les matrices de varibales \ncachées des utilisateurs et des items. \n\nPour le projet, j'ai utilisé l'algorithme:\n- ALS : Alternating Least Squares\n\n\"\"\"\n\n\"\"\" \nPour le faire, j'ai commencé par créer les matrice user-item train et test. Ce sont les deux matrices qui vont croisé les notes de utilsiateurs et des items.\nPuis, j'ai créé une fonction pour faire les prédictions\n\n\"\"\"\ntrain_data_matrix = np.zeros((n_users, n_items))#matrice nulle de longuer tous les users et tous les items\nfor line in train_data.itertuples():#parcourire la ligne col par col\n train_data_matrix[line[1]-1, line[2]-1] = line[3] \n\ntest_data_matrix = np.zeros((n_users, n_items))\nfor line in test_data.itertuples():\n test_data_matrix[line[1]-1, line[2]-1] = line[3]\n\n# La fonction prediction permet de prédire les ratings inconnus en multipliant les matrices P et la transposée de Q\ndef prediction(P,Q):\n return np.dot(P.T,Q)\n\n\"\"\" Il existe plusieurs métriques d'évaluation, mais la plus populaire des métriques utilisée pour évaluer l'exactitude des ratings prédits\nest l'erreur quadratique moyenne (RMSE) que j'ai utilisé dans le projet :\nRMSE = RacineCarrée{(1/N) * sum (r_i -estimé{r_i})^2}\n\"\"\"\n\ndef rmse(I,R,Q,P):\n return np.sqrt(np.sum((I * (R - prediction(P,Q)))**2)/len(R[R > 0])) \n\n\n# Script for training model with Alternating Least Squares algorithm\ntrain_errors = []\ntest_errors = []\n\n# Index matrix for training data\nI = train_data_matrix.copy()\nI[I > 0] = 1\nI[I == 0] = 0\n\n# Index matrix for test data\nI2 = test_data_matrix.copy()\nI2[I2 > 0] = 1\nI2[I2 == 0] = 0\n\nlmbda = 0.1 \nk = 20 \nn_epochs = 2 # number of epochs\nm, n = train_data_matrix.shape # Number of users and items\nP = 3 * np.random.rand(k,m) # Latent user feature matrix\nQ = 3 * np.random.rand(k,n) # Latent item feature matrix\nQ[0,:] = train_data_matrix[train_data_matrix != 0].mean(axis=0) # Avg. rating for each movie\nE = np.eye(k) # (k x k)-dimensional idendity matrix\n\n# Repeat until convergence\nfor epoch in range(n_epochs):\n # Fix Q and estimate P\n for i, Ii in enumerate(I):\n nui = np.count_nonzero(Ii) # Number of items user i has rated\n if (nui == 0): nui = 1 # Be aware of zero counts!\n \n # Least squares solution\n Ai = np.dot(Q, np.dot(np.diag(Ii), Q.T)) + lmbda * nui * E\n Vi = np.dot(Q, np.dot(np.diag(Ii), train_data_matrix[i].T))\n P[:,i] = np.linalg.solve(Ai,Vi)\n \n # Fix P and estimate Q\n for j, Ij in enumerate(I.T):\n nmj = np.count_nonzero(Ij) # Number of users that rated item j\n if (nmj == 0): nmj = 1 # Be aware of zero counts!\n \n # Least squares solution\n Aj = np.dot(P, np.dot(np.diag(Ij), P.T)) + lmbda * nmj * E\n Vj = np.dot(P, np.dot(np.diag(Ij), train_data_matrix[:,j]))\n Q[:,j] = np.linalg.solve(Aj,Vj)\n \n train_rmse = rmse(I,train_data_matrix,Q,P)\n test_rmse = rmse(I2,test_data_matrix,Q,P)\n train_errors.append(train_rmse)\n test_errors.append(test_rmse)\n \n print(\"[Epoch %d/%d] train error: %f, test error: %f\"\n % (epoch+1, n_epochs, train_rmse, test_rmse))\n\nprint(\"Algorithm converged\")\n\n\n\n# Maitenant, après avoir obtenus toutes les valeurs de l'erreur à chaque étape,on peut tracer la courbe d'apprentissage.\n# ==> On Vérifie la performance en traçant les erreurs du train et du test\nplt.plot(range(n_epochs), train_errors, marker='o', label='Training Data')\nplt.plot(range(n_epochs), test_errors, marker='v', label='Test Data')\nplt.title('Courbe d apprentissage SGD')\nplt.xlabel('Nombre d etapes')\nplt.ylabel('RMSE')\nplt.legend()\nplt.grid()\nplt.show()","repo_name":"AimeKoffi/Systeme-de-recommandation-CF-Model-Based","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6540,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"8366396139","text":"import logging\nfrom pathlib import PosixPath\nfrom typing import List, Optional, Dict\n\nfrom dockerup.github import GithubApp, GitHubApi\nfrom dockerup.models import File, Image, Update\nfrom dockerup.settings import Settings\n\nlogger: logging.Logger = logging.getLogger(__name__)\n\n\nclass Updater:\n def __init__(self,\n path: PosixPath,\n settings: Settings,\n github_app: Optional[GithubApp]) -> None:\n self._path = path\n self._settings = settings\n self._github_app = github_app\n self._files: List[File] = []\n self._latest_tags: Dict[str, str] = {}\n\n def _get_latest_tag(self, image: str) -> str:\n repo = self._settings.supported_images[image]\n if repo not in self._latest_tags:\n latest_tag = GitHubApi(repo, 'main', self._github_app).get_latest_release()\n assert latest_tag is not None\n self._latest_tags[repo] = latest_tag\n return self._latest_tags[repo]\n\n def resolve_files(self) -> List[File]:\n # Search for files to update\n for file_path in self._settings.files:\n file = self._path / file_path\n if not file.is_file():\n logger.info(f'Skipping: {file}')\n continue\n\n logger.info(f'Discovered: {file}')\n with file.open('r') as fh:\n contents = fh.read()\n\n self._files.append(File(\n PosixPath(file_path),\n [\n Image(\n line.split(' ')[1].split(':')[0],\n line.split(' ')[1].split(':')[1] if ':' in line.split(' ')[1] else None,\n )\n for line in contents.splitlines()\n if line.startswith('FROM ')\n ],\n contents,\n []))\n return self._files\n\n def update_files(self) -> List[File]:\n _files: List[File] = []\n for file in self._files:\n updates = []\n for image in file.images:\n if image.image not in self._settings.supported_images:\n logger.info(f'Skipping: {image}')\n continue\n latest_tag = self._get_latest_tag(image.image)\n if image.tag != latest_tag:\n logger.info(f'Updating {image.image} from {image.tag} to {latest_tag}')\n updates.append(Update(image.image, image.tag, latest_tag))\n\n updates = list(set(updates))\n contents = file.contents\n for update in updates:\n contents = contents.replace(\n (\n f'FROM {update.image}:{update.previous_tag}'\n if update.previous_tag else\n f'FROM {update.image}'\n ),\n f'FROM {update.image}:{update.new_tag}'\n )\n _files.append(File(file.file_path, file.images, contents, updates))\n return _files\n","repo_name":"InfraBits/dockerup","sub_path":"dockerup/updater.py","file_name":"updater.py","file_ext":"py","file_size_in_byte":3044,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"14179962351","text":"import os\n\ndataParseDir = os.getcwd()\n# \"..\\\\..\\..\\\\Fall Internship\\\\Old Data\\\\Data - pre dt units adjustment\"\nos.chdir(\"..\\\\TBISimulator\\\\Input_Files\\\\Inputs_For_All_Data\\\\LRR13\")\n\ndirs = [dir for dir in os.listdir() if dir.split(\"_\")[1] == \"14Day\"]\n\nfor dir in dirs:\n sourceFile = dir #+ \"\\\\Run_1\\\\parameters20.txt\"\n fileIn = open(sourceFile, 'r')\n lines = fileIn.readlines()\n fileIn.close()\n\n #lines[14:16] = [\"SproutingRandNumMultiplier = 150\\n\", \"MinDistanceBetweenSprouts = 3.0\\n\"]\n lines[0] = \"LengthRadiusRatio = 20\\n\"\n #lines[9] = \"SimulationTime = 14.0\\n\"\n paramSpecs = dir.split('_')\n # newFileName = \"Input_14Day\"\n # for spec in paramSpecs[2:4]:\n # newFileName+= '_' + spec\n # newFileName+='_3MD_150R'\n # for spec in paramSpecs[6:]:\n # newFileName+= '_' + spec\n\n # newFileName+=\".txt\"\n #fileOut = open(dataParseDir + \"\\\\..\\\\TBISimulator\\\\Input_Files\\\\Inputs_For_All_Data\\\\\" + newFileName, 'w')\n fileOut = open(\"..\\\\..\\\\Inputs_For_All_Data\\\\\" + sourceFile, 'w')\n fileOut.writelines(lines)\n fileOut.close()\n\n","repo_name":"ajbaird/TBISimulator","sub_path":"CopyAndRenameInputs.py","file_name":"CopyAndRenameInputs.py","file_ext":"py","file_size_in_byte":1083,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"4373828203","text":"\n\nfrom os import *\nfrom sys import *\nfrom collections import *\nfrom math import *\n\n## Read input as specified in the question.\n## Print output as specified in the question.\n\ndef count_zeros(n):\n if n == 0:\n return 0\n # print(n)\n assum = count_zeros(n//10)\n # print(assum)\n if n%10==0:\n return 1+assum\n else:\n return assum\n\n\n\nn = int(input())\nif n ==0:\n print(1)\nelse:\n # print(n)\n print(count_zeros(n))","repo_name":"MrCoolest/coding-problems","sub_path":"Count Zeros.py","file_name":"Count Zeros.py","file_ext":"py","file_size_in_byte":450,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"25076369598","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# Created by Liuxiaozhe on 2020/9/25\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\nimport torch\nfrom torchvision import transforms\nimport numpy as np\nimport cv2\nfrom pfld import PFLDInference, MTCNN\nfrom utils import getDistance, glotPosecube\n\n\nclass FaceAttribute:\n def __init__(self):\n #人脸检测\n self.mtcnn = MTCNN()\n #人脸关键点定位模型\n self.pfldmodel = PFLDInference().cuda()\n self.pfldmodel.load_state_dict(torch.load(\"weights/keypoints.pth\"))\n self.pfldmodel = self.pfldmodel.cuda()\n self.pfldmodel.eval()\n self.transform = transforms.Compose([transforms.ToTensor()])\n\n def main(self,img):\n transform = transforms.Compose([transforms.ToTensor()])\n with torch.no_grad():\n height, width = img.shape[:2]\n img_det = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n det = self.mtcnn.detect_face(img_det)\n # '''\n # [{'box': (160, 48, 322, 270), 'cls': array([0.9666081], dtype=float32),\n # 'pts': {'leye': (214, 136), 'reye': (276, 121), 'nose': (257, 159), 'lmouse': (238, 208),\n # 'rmouse': (288, 197)}}]\n # '''\n for i in range(len(det)): #单张图片人脸数量\n box = det[i]['box'] #人脸框tuple\n #cls = result[i]['cls'] #置信度ndarry\n pts = det[i]['pts'] #五官坐标dict\n x1, y1, x2, y2 = box #左上右下\n dis = y2 - y1\n #cv2.rectangle(img, (x1, y1), (x2, y2), (255, 255, 25)) #天蓝色人脸框\n w = x2 - x1 + 1\n h = y2 - y1 + 1\n size_w = int(max([w, h])*0.9)\n size_h = int(max([w, h]) * 0.9)\n cx = x1 + w//2\n cy = y1 + h//2\n x1 = cx - size_w//2\n x2 = x1 + size_w\n y1 = cy - int(size_h * 0.4)\n y2 = y1 + size_h\n left = 0\n top = 0\n bottom = 0\n right = 0\n if x1 < 0:\n left = -x1\n if y1 < 0:\n top = -y1\n if x2 >= width:\n right = x2 - width\n if y2 >= height:\n bottom = y2 - height\n\n x1 = max(0, x1)\n y1 = max(0, y1)\n x2 = min(width, x2)\n y2 = min(height, y2)\n cropped = img[y1:y2, x1:x2] #裁剪出的人脸\n # print(cropped.shape)\n # np_img = img[int(y1/1):y2+int(y1/1),int(x1/1):x2+int(x1/1)]\n # cv2.imshow(str(numa),np_img)\n cropped = cv2.copyMakeBorder(cropped, top, bottom, left, right, cv2.BORDER_CONSTANT, 0)\n input = cv2.resize(cropped, (112, 112))\n input = cv2.cvtColor(input, cv2.COLOR_BGR2RGB)\n input = transform(input).unsqueeze(0).cuda()\n pose, landmarks = self.pfldmodel(input)\n #poses = pose.cpu().detach().numpy()[0] * 180 / np.pi\n # 长度3 pitch是围绕X轴旋转,也叫做俯仰角。 yaw是围绕Y轴旋转,也叫偏航角。 roll是围绕Z轴旋转,也叫翻滚角\n pre_landmark = landmarks[0]\n pre_landmark = pre_landmark.cpu().detach().numpy().reshape(-1, 2) * [size_w, size_h] # 长度98\n # cv2.rectangle(img,(x1, y1), (x2, y2),(255,0,0)) #蓝色正方形\n fatigue = []\n for num, (x, y) in enumerate(pre_landmark.astype(np.int32)):\n #cv2.circle(img, (x1 - left + x, y1 - bottom + y), 1, (255, 255, 0), 1)\n #if 59 < num < 76 or num in [96,97]: #眼眶坐标\n #眼镜轮廓坐标\n # 62 70\n # 60 64 68 72\n # 66 74\n\n if num in [60, 62, 64, 66, 68, 70, 72, 74]:\n cv2.circle(img, (x1 - left + x, y1 - bottom + y), 1, (255, 255, 0), 1)\n fatigue.append((x, y))\n # print(fatigue)\n rightrow = getDistance(fatigue[0], fatigue[2])\n rightcol = getDistance(fatigue[1],fatigue[3])\n leftrow = getDistance(fatigue[4],fatigue[6])\n leftcol = getDistance(fatigue[5],fatigue[7])\n numerator = rightcol+leftcol\n denominator = rightrow+leftrow\n distance = numerator/denominator\n print('dis:'+str(distance))\n text ,color= 'eyes closed!' if distance < 0.17 else 'eyes opened',\\\n (0, 0, 255) if distance < 0.17 else (0, 255, 0)\n\n\n\n img = cv2.putText(img, text, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1.2, color, 2)\n # another way 计算\n # eye = rightcol + leftcol\n # if eye / dis < 0.03:\n # print('dis:' + str(distance))\n # print('eyes closed!')\n # else:\n # print('ok')\n\n # plotPosecube(img, poses[0], poses[1], poses[2], tdx=pts['nose'][0], tdy=pts['nose'][1],\n # size=(x2 - x1) // 2)\n cv2.imshow('example', img)\n cv2.waitKey(0)\n\n\n\n\n\n\nif __name__ == \"__main__\":\n import glob\n F = FaceAttribute()\n f = glob.glob('D:\\points/face\\FaceAttributeClassiry-master/test/*')\n for i in f:\n img = cv2.imread(i)\n F.main(img)\n\n\n\n\n","repo_name":"lxzatwowone1/Driver-Fatigue-Monitoring","sub_path":"eye_close_detection.py","file_name":"eye_close_detection.py","file_ext":"py","file_size_in_byte":5679,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"50"} +{"seq_id":"13175215069","text":"# Import relevant Python modules\nimport operator\nimport math\nimport random\nimport numpy as np\nfrom matplotlib import pyplot\n\n# Import DEAP modules\nfrom deap import algorithms\nfrom deap import base\nfrom deap import creator\nfrom deap import tools\nfrom deap import gp\n\n# ====================================================================\n# PARAMETERS - change the parameters in this section\n# Population size: 500 vs 2000\n# Tournament Size: 2 vs 5\n# repeat at least 10 times per setting\n\nno_generations = 75 # number of generations\nno_population = 1000 # population size\nno_tournaments = 5 # tournament size\n\n# other parameters that you can change and explore\np_xo = 0.7 # XO rate\np_m = 0.3 # Mutation rate\nUseSqError = True # use Least Squares approach \n\n# ====================================================================\n# Define your Problems/Target Functions and create sample data\n# Assume you make measurments at specific test points:\n\n# test_points = np.linspace(-math.pi,math.pi, 65).tolist()\ntest_points = [2, 3, 4, 5, 6, 7]\n## Defining the functions. ##\n#measurement = lambda x: x**6-2*x**4-13*x**2 # Function 1\n#measurement = lambda x: math.sin(math.pi/4 + 3*x) # Function 2\n\n# ====================================================================\n\ntarget = [1606, 2398, 3399, 4277, 4956, 5752]\nmeasurement = lambda idx, x: target[idx] - x\n#target = np.empty(len(test_points))\n#for i in range(len(test_points)): target[i] = measurement(test_points[i])\n\nfig, ax = pyplot.subplots(figsize=(15,4))\nax.scatter(test_points, target)\nax.set_xlabel('Test points')\nax.set_ylabel('Measurements')\nax.set_title('Data set')\npyplot.show()\n\n# Define new functions\ndef protectedDiv(left, right):\n return left / right if right else 1 \n \n# create Primitive set & classes \nif \"pset\" not in globals():\n pset = gp.PrimitiveSet(\"MAIN\", 1)\n pset.addPrimitive(operator.add, 2)\n pset.addPrimitive(operator.sub, 2)\n pset.addPrimitive(operator.mul, 2)\n pset.addPrimitive(protectedDiv, 2)\n pset.addPrimitive(operator.neg, 1)\n pset.addPrimitive(math.cos, 1)\n pset.addPrimitive(math.sin, 1)\n pset.addTerminal(1)\n pset.addTerminal(-1) \n pset.addEphemeralConstant(\"rand101\", lambda: random.randint(-1,1))\n pset.renameArguments(ARG0='x')\n\n creator.create(\"FitnessMin\", base.Fitness, weights=(-1.0,))\n creator.create(\"Individual\", gp.PrimitiveTree, fitness=creator.FitnessMin)\n \ntoolbox = base.Toolbox()\ntoolbox.register(\"expr\", gp.genHalfAndHalf, pset=pset, min_=1, max_=2)\ntoolbox.register(\"individual\", tools.initIterate, creator.Individual, toolbox.expr)\ntoolbox.register(\"population\", tools.initRepeat, list, toolbox.individual)\ntoolbox.register(\"compile\", gp.compile, pset=pset)\n\n\ndef evalSymbReg(individual):\n # Transform the tree expression in a callable function\n func = toolbox.compile(expr=individual)\n\n if UseSqError:\n # squared error\n error = (abs(func(x) - measurement(idx, x))**2 for idx, x in enumerate(test_points)) \n else:\n # Absolute distance between target curve and solution\n error = (abs(func(x) - measurement(idx, x)) for idx, x in enumerate(test_points)) \n\n return math.fsum(error)/len(test_points),\n\n\ntoolbox.register(\"evaluate\", evalSymbReg)\ntoolbox.register(\"select\", tools.selTournament, tournsize=no_tournaments)\ntoolbox.register(\"mate\", gp.cxOnePoint)\ntoolbox.register(\"expr_mut\", gp.genFull, min_=0, max_=2)\ntoolbox.register(\"mutate\", gp.mutUniform, expr=toolbox.expr_mut, pset=pset)\n\ntoolbox.decorate(\"mate\", gp.staticLimit(key=operator.attrgetter(\"height\"), max_value=64))\ntoolbox.decorate(\"mutate\", gp.staticLimit(key=operator.attrgetter(\"height\"), max_value=64))\n\nrandom.seed()\n\npop = toolbox.population(n=no_population)\nhof = tools.HallOfFame(1)\n\nstats_fit = tools.Statistics(lambda ind: ind.fitness.values)\nstats_size = tools.Statistics(len)\nmstats = tools.MultiStatistics(fitness=stats_fit, size=stats_size)\nmstats.register(\"mdn\", np.median)\nmstats.register(\"avg\", np.mean)\nmstats.register(\"std\", np.std)\nmstats.register(\"min\", np.min)\nmstats.register(\"max\", np.max)\n\npop, log = algorithms.eaSimple(pop, toolbox, p_xo, p_m, no_generations, stats=mstats, halloffame=hof, verbose=True)\n\n# Plot Fitness and Size\nx = np.arange(0, no_generations+1)\ns = log.chapters['size'].select(\"mdn\")\nf = log.chapters['fitness'].select(\"mdn\")\n\nfig, ax = pyplot.subplots()\nax.plot(x, f/max(f), 'k--', label='Fitness')\nax.plot(x, s/max(s), 'k:', label='Size')\nax.set_xlabel('Generations')\nax.set_ylabel('Normalised Fitness/Size')\nax.set_title('Median')\nlegend = ax.legend(shadow=True, fontsize='x-large')\nprint('Fitnes: [' + str(min(f))+', '+str(max(f))+']')\nprint('Size: [' + str(min(s))+', '+str(max(s))+']')\nprint('Evaluations: ' +str(sum(log.select(\"nevals\"))))\n\npyplot.show()\n\n# Best individual \nprint(hof[0])\n\n# Plot comparison Tagret vs. evolved solution\n\nx = test_points + [8]\nf = toolbox.compile(expr=hof[0])\n\ny = np.empty(len(x))\nfor i in range(len(x)): y[i] = f(x[i])\n\nprint(x)\nprint(y)\n\ntarget2 = target + [0]\n\nfig, ax = pyplot.subplots()\nax.plot(x, y, 'r-', label='Best Solution')\nax.plot(x, target2, 'k-', label='Target func')\n#legend = ax.legend(loc='upper center', shadow=True, fontsize='x-large')\nlegend = ax.legend(shadow=True, fontsize='x-large')\n\npyplot.show()\n\nprint(\"8 Users\")\nprint(f(8))\nprint(\"200 Users\")\nprint(f(200))\n","repo_name":"EB1811/randomPythonScripts","sub_path":"GeneticRegression.py","file_name":"GeneticRegression.py","file_ext":"py","file_size_in_byte":5350,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"38953880538","text":"# --- VTK-PYTHON SCRIPT FOR READING VTP, INTEGRATING VARIABLES OF INTEREST USING TRAPEZOIDAL RULE\n# --- CALCULATES PRESSURE AT OUTLETS OF THE MODEL\n# --- NOTE: TO BE RUN FROM DIRECTORY CONTAINING all_results.vtp FILE AND mesh-complete/ DIRECTORY\n# --- NOTE: THIS SCRIPT ASSUMES A TRIANGULAR SURFACE MESH\n# --- BASED ON A SCRIPT BY JUSTIN TRAN\n# --- UPDATED 8/4/17 BY CASEY FLEETER\n\n\n#---------------------------------------------------------------------#\n# RUN THIS SCRIPT FROM THE SAME DIRECTORY AS all_results.vtp #\n# SCRIPT GENERATES all_results_pressures.dat #\n#---------------------------------------------------------------------#\n\nimport sys\nimport os\nimport vtk\nimport numpy as np\n\n#------------------------------------------------------------#\n# CHANGE THE START AND END TIMES AND TIME STEP INCREMENT #\n#------------------------------------------------------------#\n# SPECIFIY TIME POINTS TO DETERMINE PRESSURE, ALONG WITH INCREMENT IN RESTART FILES\nSTART_TIME = 160\nEND_TIME = 240\nINCREMENT = 10\n\n\n#---------------------------------------------------------------------#\n# CHANGE THE NAMES OF THE CAP FILES FROM THE MESH-COMPLETE FOLDER #\n#---------------------------------------------------------------------#\n# NAMES OF CAP GEOMETRY FILES OF MODEL, WITHOUT CAP_ PREFIX\ninput_filenames = ['celiac_trunk', 'celiac_branch', 'superior_mesentaric', \\\n 'renal_right', 'renal_left', 'right_internal_iliac', \\\n 'left_internal_iliac', 'right_iliac', 'aorta_2']\n\n# PATH OF OUTPUT FILE FOR RESULTS\noutput_filename = 'all_results_pressures.dat'\noutput_collection = []\n\nif __name__ == \"__main__\":\n\n for file in input_filenames:\n\n # Load in the mesh file for the outlet you want pressures at. This can be found in the mesh-surfaces folder\n command_string = 'cd mesh-complete/mesh-surfaces'\n print(command_string)\n os.chdir('mesh-complete/mesh-surfaces')\n\n # Read geometry (mesh) information from this cap\n outlet_reader = vtk.vtkXMLPolyDataReader() # Create vtk instance\n outlet_reader.SetFileName('cap_' + file + '.vtp') # Open file\n outlet_reader.Update()\n outlet_model = vtk.vtkPolyData()\n outlet_model = outlet_reader.GetOutput() # Read file into new variable for manipulation\n numPts = outlet_model.GetNumberOfPoints() # Determine number of points in the mesh at this outlet\n outlet_IDs = outlet_model.GetPointData().GetArray(\"GlobalNodeID\") # Extract node IDs to match with full model solution\n \n\n\n # Load in the pressure information from the vtp file\n command_string = 'cd ../..'\n print(command_string)\n os.chdir('../..')\n\n # First, read in the .vtp file containing your quantities of interest\n all_results_reader = vtk.vtkXMLPolyDataReader() # Create vtk instance\n all_results_reader.SetFileName('all_results.vtp') # Open file\n all_results_reader.Update()\n all_results_model = vtk.vtkPolyData() \n all_results_model = all_results_reader.GetOutput() # Read file into new variable for manipulation\n all_results_numPts = all_results_model.GetNumberOfPoints() # Determine number of points in the mesh of the entire model\n all_results_IDs = all_results_model.GetPointData().GetArray('GlobalNodeID') # Extract node IDs of full model solution\n \n\n\n # Find the nodes on all_results that correspond to the outlet of interest\n outlet_nodes = []\n for i_node in xrange(0, numPts):\n this_ID = outlet_IDs.GetTuple1(i_node)\n \n # iterate through all nodes in model\n for i_full in xrange(0, all_results_numPts):\n full_ID = all_results_IDs.GetTuple1(i_full)\n\n if(full_ID == this_ID):\n outlet_nodes.append(i_full)\n break\n \n # Just to make sure we found all the outlet nodes in all_results\n assert(len(outlet_nodes) == numPts)\n \n # Create a Python list to hold the pressure arrays from the all_results.vtp\n pressure_vectors = []\n # keep track of how many timesteps in solution\n timestep_count = 0\n for i_array in xrange(START_TIME, END_TIME+INCREMENT, INCREMENT):\n pressure_vectors.append(vtk.vtkDoubleArray())\n\n if i_array < 10:\n pressure_vectors[timestep_count] = all_results_model.GetPointData().GetArray('pressure_' + '0000' + str(i_array))\n elif i_array < 100:\n pressure_vectors[timestep_count] = all_results_model.GetPointData().GetArray('pressure_' + '000' + str(i_array))\n elif i_array < 1000:\n pressure_vectors[timestep_count] = all_results_model.GetPointData().GetArray('pressure_' + '00' + str(i_array))\n elif i_array < 10000:\n pressure_vectors[timestep_count] = all_results_model.GetPointData().GetArray('pressure_' + '0' + str(i_array))\n else:\n pressure_vectors[timestep_count] = all_results_model.GetPointData().GetArray('pressure_' + str(i_array))\n \n timestep_count = timestep_count + 1\n \n \n \n # Integrate pressures over the surface of the outlet face to get the pressure on this face at each time\n temp_press = np.zeros(timestep_count)\n\n for i_time in xrange(0, timestep_count):\n \n # Compute the integral using trapezoidal rule\n total_area = 0.0\n # store pressure information for the entire outlet face at this time step\n curr_press = 0.0\n\n # iterate over all mesh cells on outlet face\n for i_cell in xrange(0, outlet_model.GetNumberOfCells()):\n \n # extract information about cell vertices\n temp_cell = outlet_model.GetCell(i_cell)\n pts_cell = temp_cell.GetPointIds()\n cell_pts = temp_cell.GetPoints()\n p0 = cell_pts.GetPoint(0)\n p1 = cell_pts.GetPoint(1)\n p2 = cell_pts.GetPoint(2)\n \n # compute area of mesh cell (triangular mesh assumed) \n local_area = temp_cell.TriangleArea(p0, p1, p2)\n total_area = total_area + local_area\n \n local_temp_press = 0.0\n # add contributions from each vertex of cell\n for ipt in xrange(0, pts_cell.GetNumberOfIds()):\n \n iid = pts_cell.GetId(ipt) # get node number of this point\n temp_press_vec = float(pressure_vectors[i_time].GetTuple(iid)[0]) # get pressure at this point\n # add pressure contribution of this point to the total cell pressure\n local_temp_press = local_temp_press + temp_press_vec\n \n # Complete the trapezoidal rule integration for this cell by multiplying the sum of\n # the pressures by the local area and dividing by the number of vertices\n # Add the contribution of this cell to the curr_press for the entire outlet face\n curr_press = curr_press + local_temp_press*local_area/3.0\n \n # save pressure information at the outlet face (with pressure normalized by the total area \n # of the outlet face) for the current timestep \n temp_press[i_time] = curr_press/ total_area\n\n # save pressure information for all timesteps for the current outlet face\n output_collection.append(temp_press)\n\n\n\n # Now that we have looped over all our .vtp files of interest and integrated\n # the variables, it is time to save them to the output file.\n outfile = open(output_filename, 'w')\n \n # First print a header that tells what each integrated quantity of interest is\n out_string = 'Time Step '\n for iq in xrange(0, len(input_filenames)):\n out_string = out_string + input_filenames[iq] + ' '\n out_string = out_string + '\\n'\n outfile.write(out_string)\n\n # Now print the data for each quantity of interest at each time step\n for i_time in xrange(0, timestep_count):\n\n # Print time step\n out_string = str(i_time)\n \n # Print each quantity of interest at that timestep\n for i_file in xrange(0,len(input_filenames)): \n out_string = out_string + ' ' + str(output_collection[i_file][i_time])\n \n out_string = out_string + '\\n'\n outfile.write(out_string)\n\n outfile.close()\n ","repo_name":"melodydong/Post_Solve_Scripts","sub_path":"Postsolve_Pressures.py","file_name":"Postsolve_Pressures.py","file_ext":"py","file_size_in_byte":8035,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"40418865300","text":"import os\nos.environ.setdefault('DJANGO_SETTINGS_MODULE', 'first_project.settings')\nimport django\ndjango.setup()\nimport random\nfrom first_app.models import Webpage,Topic,AccessRecord\nfrom faker import Faker\n\nfakegen = Faker()\n\nsample_topics = ['topic_1','topic_2','topic_3','topic_4','topic_5']\n\ndef add_topic():\n t = Topic.objects.get_or_create(topic_name = random.choice(sample_topics))[0]\n\n t.save()\n return t\n\ndef populate(N=5):\n for entry in range(N):\n print('populating ',entry)\n fake_url = fakegen.url()\n fake_names = fakegen.company()\n fake_date = fakegen.date()\n wbpg = Webpage.objects.get_or_create(topic = add_topic(), name = fake_names, url = fake_url)[0]\n access_record = AccessRecord.objects.get_or_create(name = wbpg, date = fake_date)[0]\n\nif __name__ == \"__main__\":\n print(\"populating...\")\n populate(30)\n print('populated')","repo_name":"zhangboz/Django-learning-notes","sub_path":"Django Level 2/first_project/populate_first_app.py","file_name":"populate_first_app.py","file_ext":"py","file_size_in_byte":902,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"74612214876","text":"import pygame\nfrom pygame.sprite import Sprite\n\n\nclass Ship(Sprite):\n \"\"\"ship controls\"\"\"\n\n def __init__(self, ai_game) -> None:\n super().__init__()\n self.screen = ai_game.screen\n self.settings = ai_game.settings\n self.screen_rect = ai_game.screen.get_rect()\n\n # load image of ship\n self.image = pygame.image.load('imgs/player_ship.png')\n self.rect = self.image.get_rect()\n\n # appearance of a new spaceship\n self.rect.midbottom = self.screen_rect.midbottom\n\n # saving float part of moving\n self.x = float(self.rect.x)\n\n # move right flag\n self.moving_right = False\n\n # move left flag\n self.moving_left = False\n\n def update(self) -> None:\n \"\"\"\n renew the ship's position in case of flag\n :return: None\n \"\"\"\n if self.moving_right and self.rect.right < self.screen_rect.right:\n self.x += self.settings.ship_speed\n if self.moving_left and self.rect.left > 0:\n self.x -= self.settings.ship_speed\n\n # Update rect x coordinate\n self.rect.x = self.x\n\n def blitme(self) -> None:\n \"\"\"paint new spaceship\"\"\"\n self.screen.blit(self.image, self.rect)\n\n def center_ship(self) -> None:\n \"\"\"\n place a new ship in the MIDBOTTOM\n :return: None\n \"\"\"\n self.rect.midbottom = self.screen_rect.midbottom\n self.x = float(self.rect.x)\n","repo_name":"LikeKugi/Alien","sub_path":"ship.py","file_name":"ship.py","file_ext":"py","file_size_in_byte":1469,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"26208222068","text":"\"\"\"\nAbstract classes and wrappers for LLMs, chatbots, and prompts.\n\"\"\"\n\n# TODO Make all prompts to text completion be strings, so people can fill prompts in. Or support both if need be...\n# TODO Add tests, etc... Make sure everything still works.\n\nimport requests\nimport json\nimport re\n\n# Imports for external APIs\nimport openai\nimport cohere\n\n# Hugging Face and PyTorch imports\nfrom transformers import pipeline\nimport torch\n\ndef _clean_messages_to_prompt(messages):\n \"\"\"\n Converts an array of messages in the form {\"role\": , \"content\":} into a String.\n\n This is influened by the OpenAI chat completion API.\n \"\"\"\n out_text = \"\\n\".join([f\"{str(m['role'])}: {str(m['content'])}\" for m in messages])\n return out_text\n\ndef _get_stop_sequences_from_messages(messages):\n \"\"\"\n Generetes a list of strings of stop sequences from an array of messages in the form {\"role\": , \"content\":}.\n \"\"\"\n roles = set()\n for m in messages:\n roles.add(m[\"role\"])\n stop_sequences = [f\"\\n{r}:\" for r in roles]\n return stop_sequences\n\nclass LanguageModelWrapper():\n \"\"\"\n Abstract wrapper for large language models.\n \"\"\"\n\n def __init__(self):\n pass\n\n def __repr__(self):\n pass\n\n def complete_chat(self, messages):\n \"\"\"\n Takes an array of messages in the form {\"role\": , \"content\":} and generate a response.\n\n This is influened by the OpenAI chat completion API.\n \"\"\"\n pass\n\n def text_completion(self, prompt, stop_sequences=[]):\n \"\"\"\n Standardizes text completion for large language models.\n \"\"\"\n pass\n\nclass Prompt():\n \"\"\"\n Prompts are used to generate text completions. Prompts can be simple Strings. They can also include variables surrounded by curly braces. For example:\n Hello {name}!\n In this case, 'name' can be filled using the fill_prompts() function. This makes it easier to loop through prompts that follow a specific pattern or structure.\n \"\"\"\n\n def __init__(self, prompt):\n self.prompt = prompt\n\n def __repr__(self):\n return self.prompt\n\n def get_prompt(self):\n \"\"\"\n Return the raw prompt command (i.e., does not fill in variables.)\n \"\"\"\n return self.prompt\n \n def fill_prompts(self, **kwargs):\n \"\"\"\n Return a prompt with variables filled in.\n \"\"\"\n pattern = r'\\{\\s*[a-zA-Z0-9_]+\\s*\\}'\n matches = re.findall(pattern, self.prompt)\n new_prompt = self.prompt\n for m in matches:\n keyword = m.replace(\"{\", \"\").replace(\"}\", \"\").strip()\n if keyword in kwargs:\n new_prompt = new_prompt.replace(m, kwargs[keyword])\n return new_prompt\n\nclass BloomWrapper():\n \"\"\"\n Wrapper for Hugging Face's BLOOM model. Requires access to Hugging Face's inference API.\n \"\"\"\n def __init__(self, apikey):\n self.apikey = apikey\n\n def __repr__(self):\n return f\"BloomWrapper()\"\n\n def complete_chat(self, messages, append_role=None):\n \"\"\"\n Mimicks a chat scenario with BLOOM, via a list of {\"role\": , \"content\":} objects.\n \"\"\"\n\n prompt_preamble = \"You are a friendly chat assistant. You are speaking to the 'user' below and will respond at the end, where it says 'assistant'.\\n\"\n prompt_text = prompt_preamble + _clean_messages_to_prompt(messages)\n if append_role is not None and len(append_role) > 0:\n prompt_text += f\"\\n{append_role}:\"\n\n API_URL = \"https://api-inference.huggingface.co/models/bigscience/bloom\"\n headers = {\"Authorization\": f\"Bearer {self.apikey}\"}\n\n response = requests.post(API_URL, headers=headers, json={\"inputs\": prompt_text}).json()\n\n all_text = response[0]['generated_text']\n new_text = all_text[len(prompt_text):]\n\n # We only return the first line of text.\n newline_location = new_text.find(\"\\n\") \n if newline_location > 0:\n new_text = new_text[:newline_location]\n\n return new_text\n\n def text_completion(self, prompt):\n \"\"\"\n Completes text via BLOOM (Hugging Face).\n \"\"\"\n API_URL = \"https://api-inference.huggingface.co/models/bigscience/bloom\"\n headers = {\"Authorization\": f\"Bearer {self.apikey}\"}\n\n response = requests.post(API_URL, headers=headers, json={\"inputs\": prompt}).json()\n all_text = response[0]['generated_text']\n new_text = all_text[len(prompt):]\n return new_text\n\nclass OpenAIGPTWrapper():\n \"\"\"\n Wrapper for the OpenAI API. Supports all major text and chat completion models by OpenAI.\n \"\"\"\n\n def __init__(self, apikey, model=\"gpt-3.5-turbo\"):\n openai.api_key = apikey\n self.model = model\n\n def __repr__(self):\n return f\"OpenAIGPTWrapper(model={self.model})\"\n \n def complete_chat(self, messages, append_role=None):\n \"\"\"\n Completes chat with OpenAI. If using GPT 3.5 or 4, will simply send the list of {\"role\": , \"content\":} objects to the API.\n\n If using an older model, it will structure the messages list into a prompt first.\n \"\"\"\n\n if self.model.find('gpt-4') >= 0 or self.model.find('gpt-3.5') >= 0:\n\n response = openai.ChatCompletion.create(\n model=self.model,\n messages=messages\n )\n top_response_content = response['choices'][0]['message']['content']\n return top_response_content\n\n else:\n\n prompt_text = _clean_messages_to_prompt(messages)\n if append_role is not None and len(append_role) > 0:\n prompt_text += f\"\\n{append_role}: \"\n prompt_text = prompt_text.strip()\n\n response = openai.Completion.create(\n model=self.model,\n prompt=prompt_text,\n stop=_get_stop_sequences_from_messages(messages)\n )\n\n top_response_content = response['choices'][0]['text']\n return top_response_content\n \n # Note that this currently will error out with GPT 3.5 or above as they are chat models.\n # TODO Add error catching.\n def text_completion(self, prompt, stop_sequences=[]):\n \"\"\"\n Completes text via OpenAI. Note that this doesn't support GPT 3.5 or later.\n \"\"\"\n\n if len(stop_sequences) == 0:\n response = openai.Completion.create(\n model=self.model,\n prompt=prompt\n )\n else:\n response = openai.Completion.create(\n model=self.model,\n prompt=prompt,\n stop = stop_sequences\n )\n top_response_content = response['choices'][0]['text']\n return top_response_content\n\nclass ClaudeWrapper():\n \"\"\"\n Wrapper for Anthropic's Claude large language model.\n\n We've opted to call Anthropic's API directly rather than using their Python offering.\n \"\"\"\n\n def __init__(self, apikey, model=\"claude-v1\"):\n self.apikey = apikey\n self.model = model\n\n def __repr__(self):\n return f\"ClaudeWrapper(model={self.model})\"\n \n def complete_chat(self, messages, append_role=None):\n \"\"\"\n Completes chat with Claude. Since Claude doesn't support a chat interface via API, we mimick the chat via the a prompt.\n \"\"\"\n\n r_headers = {\"X-API-Key\":self.apikey, \"Accept\":\"application/json\"}\n\n prompt_text = _clean_messages_to_prompt(messages)\n if append_role is not None and len(append_role) > 0:\n prompt_text += f\"\\n{append_role}: \"\n\n r_data = {\"prompt\": prompt_text,\n \"model\": self.model,\n \"max_tokens_to_sample\": 500,\n \"stop_sequences\": _get_stop_sequences_from_messages(messages)\n }\n\n resp = requests.post(\"https://api.anthropic.com/v1/complete\", headers=r_headers, json=r_data)\n completion = json.loads(resp.text)[\"completion\"].strip()\n\n return completion\n \n def text_completion(self, prompt, stop_sequences=[]):\n \"\"\"\n Completes text based on provided prompt.\n \"\"\"\n\n r_headers = {\"X-API-Key\":self.apikey, \"Accept\":\"application/json\"}\n r_data = {\"prompt\": prompt,\n \"model\": self.model,\n \"max_tokens_to_sample\": 500,\n \"stop_sequences\": stop_sequences\n }\n\n resp = requests.post(\"https://api.anthropic.com/v1/complete\", headers=r_headers, json=r_data)\n completion = json.loads(resp.text)[\"completion\"].strip()\n return completion\n\n# TODO Might want to add stop sequences (new lines, roles) to make this better.\nclass GPT2Wrapper(LanguageModelWrapper):\n \"\"\"\n Wrapper for GPT-2 implementation (via Hugging Face). \n \"\"\"\n\n def __init__(self):\n self.model_name = \"GPT-2\"\n\n def __repr__(self):\n return f\"GPT2Wrapper()\"\n \n def complete_chat(self, messages, append_role=None, max_length=300):\n \"\"\"\n Mimicks a chat scenario via a list of {\"role\": , \"content\":} objects. \n \"\"\"\n\n prompt_preamble = \"You are a friendly chat assistant. You are speaking to the 'user' below and will respond at the end, where it says 'assistant'.\\n\"\n prompt_text = prompt_preamble + _clean_messages_to_prompt(messages)\n if append_role is not None and len(append_role) > 0:\n prompt_text += f\"\\n{append_role}:\"\n\n generator = pipeline('text-generation', model='gpt2')\n resps = generator(prompt_text, max_length=max_length, num_return_sequences=1)\n resp = resps[0]['generated_text']\n resp = resp[len(prompt_text):] # Strip out the original text.\n return resp\n\n def text_completion(self, prompt, max_length=200):\n \"\"\"\n Completes text via GPT-2.\n \"\"\"\n generator = pipeline('text-generation', model='gpt2')\n resps = generator(prompt, max_length=max_length, num_return_sequences=1)\n resp = resps[0]['generated_text']\n resp = resp[len(prompt):] # Strip out the original text.\n return resp\n\nclass DollyWrapper():\n \"\"\"\n Implementation of Dolly 2.0 (via Hugging Face).\n \"\"\"\n\n def __init__(self):\n self.model_name = 'dolly-v2-12b'\n self.generate_text = pipeline(model=\"databricks/dolly-v2-12b\", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map=\"auto\")\n\n def __repr__(self):\n return f\"DollyWrapper(model={self.model})\"\n\n def complete_chat(self, messages, append_role=None):\n \"\"\"\n Mimicks a chat scenario via a list of {\"role\": , \"content\":} objects. \n \"\"\"\n\n prompt_preamble = \"You are a friendly chat assistant. You are speaking to the 'user' below and will respond at the end, where it says 'assistant'.\\n\"\n\n prompt_text = prompt_preamble + _clean_messages_to_prompt(messages)\n if append_role is not None and len(append_role) > 0:\n prompt_text += f\"\\n{append_role}:\"\n\n resp = self.generate_text(prompt_text)\n\n return resp\n\n def text_completion(self, prompt):\n \"\"\"\n Complates\n \"\"\"\n resp = self.generate_text(prompt)\n return resp\n\nclass CohereWrapper():\n \"\"\"\n Wrapper for Cohere's API. Defaults to their 'xlarge' model.\n \"\"\"\n\n def __init__(self, apikey, model=\"xlarge\"):\n self.apikey = apikey\n self.model = model\n\n def __repr__(self):\n return f\"CohereWrapper(model={self.model})\"\n\n def complete_chat(self, messages, append_role=None):\n \"\"\"\n Mimicks a chat scenario via a list of {\"role\": , \"content\":} objects. \n \"\"\"\n\n prompt_text = _clean_messages_to_prompt(messages)\n if append_role is not None and len(append_role) > 0:\n prompt_text += f\"\\n{append_role}:\"\n\n co = cohere.Client(self.apikey)\n response = co.generate(\n prompt=prompt_text,\n max_tokens=300, \n stop_sequences=_get_stop_sequences_from_messages(messages)\n )\n\n resp = response.generations[0].text\n\n for s in _get_stop_sequences_from_messages(messages):\n resp = resp.replace(s, \"\").strip()\n\n return resp\n\n def text_completion(self, prompt, stop_sequences=[]):\n \"\"\"\n Completes text.\n \"\"\"\n co = cohere.Client(self.apikey)\n response = co.generate(\n prompt=prompt,\n max_tokens=300, \n stop_sequences=stop_sequences\n )\n resp = response.generations[0].text\n return resp\n\nclass ChatBot():\n \"\"\"\n Allows you to have a chat conversation with an LLM wrapper.\n\n In short, it manages the list of {\"role\": , \"content\":} objects for you, so you don't have to figure this out. It also interacts directly with the model.\n \"\"\"\n\n def __init__(self, llm, initial_system_prompt=\"You are a friendly chatbot assistant.\"):\n \"\"\"\n Initializes a ChatBot. Provide an initial_system_prompt value to request the type of chatbot you will be dealing with.\n \n Warning: not all LLMs are trained to use instructions provided in a system prompt.\n \"\"\"\n self.llm = llm \n self.messages = []\n self._append_message('system', initial_system_prompt)\n\n def _append_message(self, role, message):\n \"\"\"\n Saves a message to the chatbot's message queue.\n \"\"\"\n self.messages.append({\"role\":role, \"content\":message})\n\n def chat(self, message):\n \"\"\"\n Chats with the chatbot.\n \"\"\"\n self._append_message('user', message)\n response = self.llm.complete_chat(self.messages, \"assistant\")\n self._append_message('assistant', response)\n return response\n \n","repo_name":"kpister/prompt-linter","sub_path":"data/scraping/repos/DSamuelHodge~phasellm/phasellm~llms~llms.py","file_name":"phasellm~llms~llms.py","file_ext":"py","file_size_in_byte":13829,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"74224940315","text":"#!/usr/bin/python3\n\n\"\"\"\nUploads files/firmware to Jolt over YMODEM\n\nIf the file has name \"jolt_os.bin\" or \"jolt_os.bin.gz\", it will start an OS update.\n\nIf the file ends in \".patch\" it will be applied as a patch (triggering an OS update).\n\"\"\"\n\nimport argparse\nimport os, sys\nimport serial\nimport time\nimport logging\nfrom xmodem import YMODEM\n\n# Configure logging\nlogging.basicConfig(stream=sys.stdout, level=logging.DEBUG)\nlog = logging.getLogger('elf2jelf')\n\ndef parse_args():\n this_path = os.path.abspath(__file__)\n default_elf_fn = os.path.join(this_path, '..', 'build', 'jolt_os.bin')\n\n parser = argparse.ArgumentParser()\n parser.add_argument('input', type=str,\n help='File to send over.')\n parser.add_argument('--baudrate', type=int, default=230400,\n help=\"Baudrate\")\n parser.add_argument('--port', type=str, default='/dev/ttyUSB0',\n help=\"Serial Port\")\n parser.add_argument('--verbose', '-v', type=str, default='INFO',\n help='''\n Valid options:\n SILENT\n INFO\n DEBUG\n ''')\n parser.add_argument('--monitor', '-m', action='store_true',\n help=\"Monitor esp32 after transfer.\")\n args = parser.parse_args()\n dargs = vars(args)\n\n global log\n logging_level = args.verbose.upper()\n if logging_level == 'INFO':\n log.setLevel(logging.INFO)\n elif logging_level == 'DEBUG':\n log.setLevel(logging.DEBUG)\n else:\n raise(\"Invalid Logging Verbosity\")\n\n return (args, dargs)\n\ndef consume(ser):\n ser.read(size=100000, )\n\ndef main(args):\n args, dargs = parse_args()\n\n # need to clear DTR, then clear RTS to properly reset device\n ser = serial.Serial(dsrdtr=True)\n ser.baudrate = args.baudrate\n ser.port = args.port\n ser.dtr = 1\n ser.rts = 1\n ser.timeout = 0.5\n\n ser.open()\n\n ser.dtr = 0\n ser.rts = 0\n\n log.info(\"Waiting for device to boot\")\n time.sleep(5) # Wait for device to boot\n\n def getc(size, timeout=5):\n return ser.read(size=size)\n\n def putc(data, timeout=5):\n return ser.write(data)\n\n ymodem = YMODEM(getc, putc)\n\n firmware_names = ['jolt_os.bin', 'jolt_os.bin.gz', 'JoltOS.bin', 'JoltOS.bin.gz']\n\n basename = os.path.basename(args.input)\n log.info(\"Initiating Upload\")\n cmd = []\n if basename in firmware_names:\n cmd.append(\"upload_firmware\")\n elif basename.endswith(\".patch\"):\n cmd.append(\"upload_firmware\")\n cmd.append(\"--patch\")\n else:\n cmd.append(\"upload\")\n cmd.append(basename)\n\n cmd = ' '.join(cmd) + '\\n'\n log.debug('Sending \"%s\" command' % cmd.strip())\n ser.write(cmd.encode('ascii'));\n consume(ser)\n\n log.info(\"Sending File\")\n ymodem.send([args.input,], retry=60)\n\n if args.monitor:\n while(True):\n try:\n n = ser.inWaiting()\n if n:\n line = ser.read(n)\n print(line.strip().decode(\"utf-8\", errors='replace') )\n except IOError:\n raise IOError()\n except:\n pass\n\nif __name__ == '__main__':\n main(sys.argv)\n\n","repo_name":"joltwallet/jolt_wallet","sub_path":"pyutils/usb_upload.py","file_name":"usb_upload.py","file_ext":"py","file_size_in_byte":3169,"program_lang":"python","lang":"en","doc_type":"code","stars":53,"dataset":"github-code","pt":"50"} +{"seq_id":"32485584431","text":"from __future__ import division\r\n\r\nimport math\r\nimport numpy as np\r\nimport colorsys\r\n\r\ndef K_to_RGB(colour_temperature):\r\n if colour_temperature < 1000:\r\n colour_temperature = 1000\r\n elif colour_temperature > 40000:\r\n colour_temperature = 40000\r\n\r\n tmp_internal = colour_temperature / 100.0\r\n\r\n # red\r\n if tmp_internal <= 66:\r\n red = 255\r\n else:\r\n tmp_red = 329.698727446 * math.pow(tmp_internal - 60, -0.1332047592)\r\n if tmp_red < 0:\r\n red = 0\r\n elif tmp_red > 255:\r\n red = 255\r\n else:\r\n red = tmp_red\r\n\r\n # green\r\n if tmp_internal <=66:\r\n tmp_green = 99.4708025861 * math.log(tmp_internal) - 161.1195681661\r\n if tmp_green < 0:\r\n green = 0\r\n elif tmp_green > 255:\r\n green = 255\r\n else:\r\n green = tmp_green\r\n else:\r\n tmp_green = 288.1221695283 * math.pow(tmp_internal - 60, -0.0755148492)\r\n if tmp_green < 0:\r\n green = 0\r\n elif tmp_green > 255:\r\n green = 255\r\n else:\r\n green = tmp_green\r\n\r\n # blue\r\n if tmp_internal >=66:\r\n blue = 255\r\n elif tmp_internal <= 19:\r\n blue = 0\r\n else:\r\n tmp_blue = 138.5177312231 * math.log(tmp_internal - 10) - 305.0447927307\r\n if tmp_blue < 0:\r\n blue = 0\r\n elif tmp_blue > 255:\r\n blue = 255\r\n else:\r\n blue = tmp_blue\r\n\r\n return [red, green, blue]\r\n\r\n\r\ndef hex_to_RGB(hex):\r\n ''' \"FFFFFF\" -> [255,255,255] '''\r\n # Pass 16 to the integer function for change of base\r\n return [int(hex[i:i+2], 16) for i in range(0,5,2)]\r\n\r\n\r\ndef RGB_to_hex(RGB):\r\n ''' [255,255,255] -> \"FFFFFF\" '''\r\n # Components need to be integers for hex to make sense\r\n RGB = [int(x) for x in RGB]\r\n return \"\".join([\"0{0:x}\".format(v) if v < 16 else\r\n \"{0:x}\".format(v) for v in RGB])\r\n\r\ndef RGB_to_HSV(RGB):\r\n ''' [255,255,255] -> \"[0, 0, 1]\" '''\r\n RGB = [int(x)/255.0 for x in RGB]\r\n return colorsys.rgb_to_hsv(*RGB)\r\n\r\ndef HSV_to_RGB(HSV):\r\n RGB = colorsys.hsv_to_rgb(*HSV)\r\n return [x*255 for x in RGB]\r\n\r\ndef phase(px, phase):\r\n return np.moveaxis(px, phase, 0)\r\n\r\ndef linear_gradient(pixels, start_hex, n, finish_hex=\"FFFFFF\", list_offset=0):\r\n ''' returns a gradient list of (n) colors between\r\n two hex colors. start_hex and finish_hex\r\n should be the full six-digit color string,\r\n inlcuding the number sign (\"FFFFFF\") '''\r\n # Starting and ending colors in RGB form\r\n RGB_list = np.empty_like(pixels)\r\n s = hex_to_RGB(start_hex)\r\n f = hex_to_RGB(finish_hex)\r\n # Initilize a list of the output colors with the starting color\r\n\r\n # Calcuate a color at each evenly spaced value of t from 1 to n\r\n for t in range(1, n):\r\n # Interpolate RGB vector for color at the current value of t\r\n RGB_list[t-1+list_offset, 0] = int(s[0] + (float(t)/(n-1))*(f[0]-s[0]))\r\n RGB_list[t-1+list_offset, 1] = int(s[2] + (float(t)/(n-1))*(f[2]-s[2]))\r\n RGB_list[t-1+list_offset, 2] = int(s[1] + (float(t)/(n-1))*(f[1]-s[1]))\r\n\r\n return RGB_list\r\n\r\ndef multi_gradient(px, colors, n):\r\n s = int(float(n)/len(colors))\r\n for x in range(0, len(colors)-2):\r\n px = linear_gradient(px, colors[x], colors[x+1], s, (s*x)-5)\r\n\r\ndef triplecolor(px, colors, n):\r\n colorone = hex_to_RGB(colors[0])\r\n colorone[1], colorone[2] = colorone[2], colorone[1]\r\n\r\n colortwo = hex_to_RGB(colors[1])\r\n colortwo[1], colortwo[2] = colortwo[2], colortwo[1]\r\n\r\n colorthree = hex_to_RGB(colors[2])\r\n colorthree[1], colorthree[2] = colorthree[2], colorthree[1]\r\n\r\n for i in range(0, n):\r\n if i % 3 == 0:\r\n px[i] = colorone\r\n elif i % 3 == 1:\r\n px[i] = colortwo\r\n elif i % 3 == 2:\r\n px[i] = colorthree\r\n return px\r\n\r\n\r\ndef rainbow(px, phase, n, saturation=1):\r\n\r\n for i in range(0, n):\r\n hue = i*(1.0/n)\r\n #Apply phase to hue\r\n hue += phase/360\r\n if hue > 1:\r\n hue -= 1\r\n\r\n px[i] = HSV_to_RGB([hue, saturation, 1])\r\n\r\n return px\r\n\r\n\r\ndef solid_fill(px, hex, n):\r\n rgb = hex_to_RGB(hex)\r\n\r\n px[0] = [rgb[0]]*n\r\n px[1] = [rgb[2]]*n\r\n px[2] = [rgb[1]]*n\r\n\r\n return px\r\n","repo_name":"shebpamm/pixel-server","sub_path":"colortools.py","file_name":"colortools.py","file_ext":"py","file_size_in_byte":4269,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"28046750987","text":"from enum import unique\r\nfrom turtle import update\r\nfrom LoginPage import *\r\nfrom PySide2.QtCore import *\r\nfrom PySide2.QtGui import *\r\nfrom PySide2.QtWidgets import *\r\nfrom PySide2.QtCore import QTimer\r\nfrom PySide2.QtWidgets import QLCDNumber\r\nimport pandas as pd\r\nimport json\r\n\r\n\r\nclass Ui_widget_game(object):\r\n def setupUi(self, widget_game):\r\n if not widget_game.objectName():\r\n widget_game.setObjectName(u\"widget_game\")\r\n widget_game.resize(483, 550)\r\n self.frame_game = QFrame(widget_game)\r\n self.frame_game.setObjectName(u\"frame_game\")\r\n self.frame_game.setGeometry(QRect(10, 10, 461, 531))\r\n self.frame_game.setStyleSheet(u\"background-color: rgb(35, 35, 35);\\n\"\r\n\"font: 75 10pt \\\"Yu Gothic UI\\\";\\n\"\r\n\"border-radius:20px;\")\r\n self.frame_game.setFrameShape(QFrame.StyledPanel)\r\n self.frame_game.setFrameShadow(QFrame.Raised)\r\n self.label_card = QLabel(self.frame_game)\r\n self.label_card.setObjectName(u\"label_card\")\r\n\r\n self.label_card.setGeometry(QRect(50, 120, 361, 251))\r\n self.label_card.setStyleSheet(u\"color:black;\\n\"\r\n\"background-color: rgb(83, 83, 83);\\n\"\r\n\"font: 75 20pt \\\"Yu Gothic UI\\\";\\n\"\r\n\"\\n\"\r\n\"\")\r\n self.label_language = QLabel(self.frame_game)\r\n self.label_language.setObjectName(u\"label_language\")\r\n self.label_language.setGeometry(QRect(190, 70, 61, 41))\r\n self.label_language.setStyleSheet(u\"color:white;\\n\"\r\n\"font: 75 11pt \\\"Yu Gothic UI\\\";\\n\"\r\n\"\\n\"\r\n\"\")\r\n self.button_ok = QPushButton(self.frame_game)\r\n self.button_ok.setObjectName(u\"button_ok\")\r\n self.button_ok.setGeometry(QRect(60, 390, 161, 81))\r\n self.button_ok.setStyleSheet(u\"background-color: white;\\n\"\r\n\"color: rgb(0, 0, 0);\\n\"\r\n\"font: 75 12pt \\\"Yu Gothic UI\\\";\\n\"\r\n\"border:none;\\n\"\r\n\"border-radius:20px;\\n\"\r\n\"\")\r\n self.button_cancel = QPushButton(self.frame_game)\r\n self.button_cancel.setObjectName(u\"button_cancel\")\r\n self.button_cancel.setGeometry(QRect(240, 390, 161, 81))\r\n self.button_cancel.setStyleSheet(u\"background-color:white;\\n\"\r\n\"font: 75 12pt \\\"Yu Gothic UI\\\";\\n\"\r\n\"color: rgb(0, 0, 0);\\n\"\r\n\"border:none;\\n\"\r\n\"border-radius:20px;\\n\"\r\n\"\")\r\n self.label_time = QLabel(self.frame_game)\r\n self.label_time.setObjectName(u\"label_language\")\r\n self.label_time.setGeometry(QRect(367, 20, 71, 22))\r\n self.label_time.setStyleSheet(u\"color:white;\\n\"\r\n\"font: 75 11pt \\\"Yu Gothic UI\\\";\\n\"\r\n\"\\n\"\r\n\"\")\r\n self.button_back = QPushButton(self.frame_game)\r\n self.button_back.setObjectName(u\"button_back\")\r\n self.button_back.setGeometry(QRect(50, 20, 111, 41))\r\n self.button_back.setStyleSheet(u\"QPushButton{background-color: rgb(58, 58, 58);\\n\"\r\n\"color:white;\\n\"\r\n\"border:none;\\n\"\r\n\"border-radius:20px;}\\n\"\r\n\"QPushButton::hover{\\n\"\r\n\"color:red;}\")\r\n self.label_check = QLabel(self.frame_game)\r\n self.label_check.setObjectName(u\"label_check\")\r\n self.label_check.setGeometry(QRect(350, 130,60, 21))\r\n self.label_check.setStyleSheet(u\"background-color: rgb(83, 83, 83);\\n\"\r\n\"font: 75 12pt \\\"Yu Gothic UI\\\";\\n\"\r\n\"border:none;\\n\"\r\n\"border-radius:20px;\")\r\n\r\n self.label_word = QLabel(self.frame_game)\r\n self.label_word.setObjectName(u\"label_word\")\r\n self.label_word.setGeometry(QRect(190, 230, 70, 30))\r\n self.label_word.setStyleSheet(u\"background-color: rgb(83, 83, 83);\\n\"\r\n\"font: 75 12pt \\\"Yu Gothic UI\\\";\\n\"\r\n\"border:none;\\n\"\r\n\"border-radius:20px;\")\r\n\r\n self.retranslateUi(widget_game)\r\n\r\n QMetaObject.connectSlotsByName(widget_game)\r\n # setupUi\r\n\r\n def retranslateUi(self, widget_game):\r\n widget_game.setWindowTitle(QCoreApplication.translate(\"widget_game\", u\"FLASHCARDS FOR DUTCH LEARNING\", None))\r\n self.label_card.setText(\"\")\r\n self.label_language.setText(QCoreApplication.translate(\"widget_game\", u\"DUTCH\", None))\r\n self.button_ok.setText(QCoreApplication.translate(\"widget_game\", u\"OK\", None))\r\n self.button_cancel.setText(QCoreApplication.translate(\"widget_game\", u\"CANCEL\", None))\r\n #self.timeEdit.setDisplayFormat(QCoreApplication.translate(\"widget_game\", u\"mm:ss\", None))\r\n self.label_time.setText(QCoreApplication.translate(\"widget_game\", u\"TIME\", None))\r\n self.button_back.setText(QCoreApplication.translate(\"widget_game\", u\"BACK\", None))\r\n#if QT_CONFIG(whatsthis)\r\n self.label_check.setWhatsThis(QCoreApplication.translate(\"widget_game\", u\"


\", None))\r\n#endif // QT_CONFIG(whatsthis)\r\n self.label_check.setText(QCoreApplication.translate(\"widget_game\", u\"0/20\", None))\r\n self.label_word.setText(QCoreApplication.translate(\"widget_game\", u\"Vocabulary\", None))\r\n # retranslateUi \r\n self.label_word.setText(\"START!\")\r\n \r\n \r\nclass Get_GamePage(QWidget, Ui_widget_game):\r\n def __init__(self):\r\n super(Get_GamePage,self).__init__()\r\n self.setupUi(self)\r\n #buttons functions\r\n self.button_ok.clicked.connect(self.my_word)\r\n self.button_cancel.clicked.connect(self.my_canceled_word)\r\n #dataframe of the cards\r\n self.df=pd.read_json(\"C:/Users/conko/OneDrive/Desktop/FlashCards/FlashCards_Team1/translatedCards.json\")\r\n\r\n #get the current username\r\n my_login_object=Login_Page()\r\n self.username=my_login_object.username\r\n\r\n #get the current level of player\r\n url=\"C:/Users/conko/OneDrive/Desktop/FlashCards/FlashCards_Team1/Loginsystem.json\"\r\n with open(url, 'r+') as f:\r\n data = json.load(f)\r\n res=list(filter(lambda x: x[\"username\"] == self.username, data))\r\n self.df_level=res[0]['level']\r\n \r\n #create vocabulary lists\r\n self.woorden_lijst=self.df[self.df['Unnamed: 0']==f'Level{self.df_level}']['Nederlands']\r\n self.vocab_list=self.df[self.df['Unnamed: 0']==f'Level{self.df_level}']['English']\r\n print(self.woorden_lijst )\r\n\r\n #it adjust the counter according to length of the list \r\n c=self.df[self.df['Unnamed: 0']==f'Level{self.df_level}']['English'].index \r\n self.counter=c[0]\r\n\r\n #unknown vocaabulary\r\n self.unknown_words=[]\r\n\r\n #white button sign\r\n self.flag=True\r\n\r\n #other functions \r\n def update_background(self):\r\n #it makes white buttons colorful\r\n if self.flag:\r\n\r\n self.button_ok.setStyleSheet(\"background-color: white;\")\r\n self.button_cancel.setStyleSheet(\"background-color: white;\")\r\n else: \r\n self.button_ok.setStyleSheet(\"background-color: green ;\")\r\n self.button_cancel.setStyleSheet(\"background-color: red ;\")\r\n \r\n self.flag = not self.flag \r\n\r\n \r\n def configureButton(self, button, begin, duration):\r\n\r\n end = begin.addSecs(duration)#duration: configuration total duration\r\n \r\n now = QTime.currentTime()\r\n button.setEnabled(begin <= now <= end)\r\n\r\n #This part works after 3 secs are finished\r\n if now < begin: \r\n self.update_background()\r\n\r\n #Set enable button_ok and button_cancel\r\n QTimer.singleShot(\r\n now.msecsTo(begin), lambda: button.setEnabled(True))\r\n\r\n #activate Engels \r\n my_word=self.df[self.df['Unnamed: 0']==f'Level{self.df_level}']['English'][self.counter] \r\n QTimer.singleShot(\r\n now.msecsTo(begin),lambda: self.label_word.setText(my_word))\r\n\r\n #change Language Title to English\r\n change_title=\"ENGLISH\"\r\n QTimer.singleShot(\r\n now.msecsTo(begin),lambda: self.label_language.setText(change_title)) \r\n\r\n #This part works when 3 sec begins again / deactivate butons \r\n if now < end:\r\n self.update_background()\r\n QTimer.singleShot(\r\n now.msecsTo(end), lambda: button.setEnabled(False))\r\n \r\n\r\n def activateButton(self):\r\n #set 3 seconds to activate button_ok \r\n begin = QTime.currentTime().addSecs(1)#delay the begin time 3 sec\r\n self.configureButton(self.button_ok, begin, 120)#every activating has 120 sec total duration to keep in activate\r\n #set 3 seconds to activate button_cancel\r\n begin = QTime.currentTime().addSecs(1)\r\n self.configureButton(self.button_cancel, begin, 120)\r\n #change language title to Dutch\r\n change_title=\"DUTCH\"\r\n self.label_language.setText(change_title)\r\n print(\"activitate button calisti\")\r\n \r\n def flashcard(self): \r\n\r\n try: \r\n #activate the buttons with timer\r\n self.activateButton()\r\n \r\n #test \r\n print(self.woorden_lijst[self.counter]) \r\n \r\n #run the game/print word on the screen\r\n my_word=self.woorden_lijst[self.counter]\r\n self.label_word.setText(my_word)\r\n\r\n #check label update etme\r\n check_lbl=f\"{self.counter}\"+f\"/{self.woorden_lijst.index[19]}\"\r\n self.label_check.setText(check_lbl)\r\n\r\n #update counter\r\n self.counter +=1\r\n\r\n except KeyError:\r\n #Later we can add a back button\r\n print(\"Key Error Olustu\")\r\n self.label_word.setText(\"KLAAR\")\r\n print(self.unknown_words)\r\n #self.update_level()\r\n\r\n #create a new list \r\n def my_canceled_word(self):\r\n print(\"CANCEL CLICKED\")\r\n self.flashcard() \r\n self.unknown_words.append(self.df[self.df['Unnamed: 0']==f'Level{self.df_level}']['Nederlands'][self.counter-2])\r\n \r\n #update level in the json file\r\n def update_level(self):\r\n #set the current level to the json file\r\n url=\"C:/Users/conko/OneDrive/Desktop/FlashCards/FlashCards_Team1/Loginsystem.json\"\r\n with open(url, 'r+') as f:\r\n data = json.load(f)\r\n data[0]['level'] = self.df_level+1 \r\n f.seek(0) \r\n json.dump(data, f )\r\n f.truncate() \r\n\r\n #game works with this method\r\n def my_word(self):\r\n #call the flashcard function\r\n self.flashcard() \r\n\r\n #update list(unknown vocabulary list)\r\n def last_list(self):\r\n pass\r\n #update counter \r\n def last_counter(self):\r\n pass \r\n\r\n\r\ndef main():\r\n import sys\r\n app = QApplication(sys.argv)\r\n get_GamePage= Get_GamePage()\r\n get_GamePage.show()\r\n sys.exit(app.exec_())\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n \r\n ","repo_name":"emretmz/FlashCards","sub_path":"FlashCards_Team1/src/GamePage.py","file_name":"GamePage.py","file_ext":"py","file_size_in_byte":11922,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"38763990629","text":"class Solution:\n def threeSumClosest(self, nums: List[int], target: int) -> int:\n # find nums[i] + nums[j] + nums[k] ~= target\n # -> minimize abs(nums[i] + nums[j] + nums[k] - target)\n \n min_dist = float('inf')\n nums.sort()\n for i in range(len(nums) - 2):\n left, right = i + 1, len(nums) - 1\n while left < right:\n cur_sum = nums[i] + nums[left] + nums[right]\n cur_dist = abs(cur_sum - target)\n if cur_dist == 0:\n return target\n if cur_dist < min_dist:\n min_dist = cur_dist\n min_sum = cur_sum\n if cur_sum <= target:\n left += 1\n while left < right and nums[left] == nums[left - 1]:\n left += 1\n else:\n right -= 1\n while left < right and nums[right] == nums[right + 1]:\n right -= 1\n return min_sum","repo_name":"coldmanck/leetcode-python","sub_path":"0016_3Sum_Closest.py","file_name":"0016_3Sum_Closest.py","file_ext":"py","file_size_in_byte":1036,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"50"} +{"seq_id":"10159014913","text":"# สร้าง Exception ด้วย Raise\n\nwhile True:\n try:\n name=str(input(\"Ples input your name :\"))\n if name == \"kk\":\n raise Exception(\"มีผู้ใช้แล้ว\")\n \n x=int(input(\"Ples input number :\"))\n y=int(input(\"Ples input number :\"))\n if x == 0 and y == 0:\n break\n if x<0 or y<0 :\n raise Exception(\"ป้อนค่าติดลบไม่ได้\")\n result=x/y\n print(result)\n except Exception as a:\n print(a)","repo_name":"PunyawS/learn_Python","sub_path":"Python for Beginner Phase3/80.py","file_name":"80.py","file_ext":"py","file_size_in_byte":547,"program_lang":"python","lang":"th","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"30242928739","text":"import json\nimport requests\ndef post():\n dict_data={\"ename\":\"loo\",\"email\":\"p@mail.com\",\"eaddress\":\"ppm\",\"ephone_number\":123456,\n \"esalary\":50}\n dump_data=json.dumps(dict_data)\n response=requests.post(\"http://127.0.0.1:8000/register/singleurl/\",data=dump_data)\n print(response.status_code)\n print(response.json())\ndef getWith_id_or_none(id=None):\n data={}\n if id is not None:\n data={\"id\":id}\n response=requests.get(\"http://127.0.0.1:8000/register/singleurl/\",data=json.dumps(data))\n print(response.json())\n print(response.status_code)\ndef put(id):\n data={\"id\":id,\"ename\":\"ravi\",\"email\":\"h@hg.com\",\"eaddress\":\"tyj\",\"ephone_number\":123456}\n jason_dump=json.dumps(data)\n response=requests.put(\"http://127.0.0.1:8000/register/singleurl/\",data=jason_dump)\n print(response.json())\n print(response.status_code) \ndef delete(id):\n data={\"id\":id}\n response=requests.delete(\"http://127.0.0.1:8000/register/singleurl/\",data=json.dumps(data))\n print(response.json())\n print(response.status_code)\ndelete(1)","repo_name":"sunilpedada/djangoAPI-normal-","sub_path":"testpro/toTESTcrud.py","file_name":"toTESTcrud.py","file_ext":"py","file_size_in_byte":1058,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"26301982018","text":"#!/usr/bin/env python # pylint: disable=missing-module-docstring\n# -*- coding: utf-8 -*-\n# ***************************************************************************80*************************************120\n#\n# python ./adversarial_supervision\\\n# /scripts\\\n# /1_attack\\\n# /adversarial_attack.py # pylint: disable=invalid-name\n#\n# *********************************************************************************************************************\n\n# standard import\nimport uuid\nimport os\nfrom os.path import join, exists\nfrom datetime import datetime\nfrom multiprocessing import Pool\n\n# 3rd party imports\nimport click\nimport pandas\nimport openai\nimport requests\nimport numpy\n\nclass UnrecognisedEnvironmentException(Exception):\n \"\"\"This happens when entered environmenis neither dev nor prod\"\"\"\n\nclass UnscuccessfulAPIConnectionException(Exception):\n \"\"\"This happens when an API connection goes unsuccessful\"\"\"\n\nclass EmptyResponseException(Exception):\n \"\"\"This happens when a response generated is empty\"\"\"\n\nclass ServerOverloadException(Exception):\n \"\"\"This happens when Openai server is overloaded\"\"\"\n\nclass ResponseGeneratorMismatchException(Exception):\n \"\"\"This happens when response generator is neither openai nor charlie\"\"\"\n\nclass UnscuccessfulAPICallException(Exception):\n \"\"\"This happens when an API call goes unsuccessful\"\"\"\n\n@click.command()\n@click.option('-a', '--openai_api_key', type=str, required=True, help='OpenAI API key')\n@click.option('-f', '--file_path', type=str,\n default='./adversarial_supervision/dataset/transfer_expriment_behaviors.csv',\n help='Input CSV file path')\n@click.option('-b', '--adversarial_suffix_file_path', type=str,\n default='./adversarial_supervision/dataset/adv_suffix.txt',\n help=\"File path of adversarial Suffix\")\n@click.option('-d', '--dan_attack_file_path', type=str,\n default=\"./adversarial_supervision/dataset/jailbreak_dan_prefix.txt\",\n help=\"File path of DAN attack\")\n@click.option('-r', '--reply_folder_path', type=str, required=True,\n help='folder where all the replies are stored')\n@click.option('-u','--username' ,type=str,default=\"\",help='username of HTTP endpoint in Node-RED')\n@click.option('-p','--password' ,type=str,default=\"\",help='password of HTTP endpoint in Node-RED')\n@click.option('-s','--sample',type=int,default=4,help='n text to sample from dataset')\n@click.option('-n', '--num_cores', type=int, default=2, help='Number of cores for parallelisation')\n@click.option('-v', '--use_adv_suffix', is_flag=True, default=False, help='Flag for adversarial suffix usage')\n@click.option('-k', '--use_dan_attack_prefix', is_flag=True, default=False, help='Flag for DAN attack usage as prefix')\n@click.option('-e','--env' ,type=click.Choice(['dev', 'prod']),default='dev',help='Dev or prod to update')\n@click.option('-g','--get_response_from' ,\n type=click.Choice(['openai', 'charlie']),\n default='openai',\n help='Get response from OpenAI or Charlie')\n@click.option('-m', '--model', type=str, required=True, help='model name - gpt-3.5-turbo-0301 or gpt-3.5-turbo-0613')\ndef main(openai_api_key: str, file_path: str, adversarial_suffix_file_path: str,\n dan_attack_file_path: str, reply_folder_path: str, sample: int, num_cores: int,\n use_adv_suffix: bool, use_dan_attack_prefix: bool, get_response_from: str, env: str,\n username: str, password: str, model: str) -> None:\n '''Main Function'''\n\n process(openai_api_key,\n file_path,\n adversarial_suffix_file_path,\n dan_attack_file_path,\n reply_folder_path,\n sample,\n num_cores,\n use_adv_suffix,\n use_dan_attack_prefix,\n get_response_from,\n env,\n username,\n password,\n model)\n\n\ndef process(openai_api_key: str,\n file_path: str,\n adversarial_suffix_file_path: str,\n dan_attack_file_path: str,\n reply_folder_path: str,\n sample: int,\n num_cores,\n use_adv_suffix: bool,\n use_dan_attack_prefix: bool,\n get_response_from: str,\n env: str,\n username: str,\n password: str,\n model: str) -> None:\n '''calls_openai for the attack'''\n\n openai.api_key = openai_api_key\n\n # read dataset\n df = pandas.read_csv(file_path,encoding=\"utf8\",header=None)\n df.rename(columns={0:\"prompt_text\"},inplace=True)\n\n # set the GUID\n # GUIDs are read from a text file if it was already present otherwise it creates them and\n # writes it into a text file. This is because it helps in comparing the results of utterances\n # in multiple runs\n guid_filepath = file_path.replace(\".csv\",\"_guid.txt\")\n\n if exists(guid_filepath):\n with open(guid_filepath,mode=\"r\",encoding=\"utf8\") as f:\n guid_list = f.read()\n guid_list = guid_list.strip().split(\"\\n\")\n if df.shape[0] != len(guid_list):\n for _ in range(abs(df.shape[0] - len(guid_list))):\n guid_list.append(f\"{uuid.uuid4()}\")\n df[\"id\"] = guid_list\n else:\n df[\"id\"] = df.apply(set_guid,axis=1)\n\n guid_list = df[\"id\"].unique().tolist()\n with open(guid_filepath, mode=\"w\", encoding=\"utf8\") as f:\n f.write(\"\\n\".join(guid_list))\n\n # strip if text has any whitespaces prefixed or suffixed\n df[\"prompt_text\"] = df[\"prompt_text\"].apply(lambda x: x.strip())\n\n # reply path\n df[\"reply_path\"] = df[\"id\"].apply(lambda x: join(reply_folder_path,f\"{x}.txt\"))\n\n df[\"final_prompt\"] = df['prompt_text']\n\n if use_dan_attack_prefix:\n # read adversarial suffix\n with open(dan_attack_file_path, mode=\"r\", encoding=\"utf8\") as f:\n dan_attack = f.read()\n\n df[\"dan_attack_prefix\"] = dan_attack\n df[\"final_prompt\"] = df[\"dan_attack_prefix\"] + df[\"final_prompt\"]\n\n if use_adv_suffix:\n # read adversarial suffix\n with open(adversarial_suffix_file_path, mode=\"r\", encoding=\"utf8\") as f:\n adv_suffix = f.read()\n\n df[\"adv_suffix\"] = adv_suffix\n df[\"final_prompt\"] = df[\"final_prompt\"] + df[\"adv_suffix\"]\n\n\n # get all the ids and corresponding text which are used to generate response\n completed_text_ids, completed_texts = get_completed_text_ids(reply_folder_path)\n uncompleted_text_ids = list(set(guid_list) - set(completed_text_ids))\n\n df.set_index(\"id\",inplace=True, drop=True)\n\n uncompleted_df = df.loc[uncompleted_text_ids]\n uncompleted_df[\"response\"] = \"\"\n uncompleted_df[\"completed\"] = False\n\n completed_df = df.loc[completed_text_ids]\n completed_df[\"response\"] = completed_texts\n completed_df[\"completed\"] = True\n\n df = pandas.concat([completed_df,uncompleted_df])\n\n # set where to get the response from\n df[\"get_response_from\"] = get_response_from\n\n # set the model name\n df[\"model\"] = model\n\n if get_response_from == \"charlie\":\n\n # set username and password\n df[\"username\"] = username\n df[\"password\"] = password\n\n # set the url depending on which system is being used - dev | prod\n if env == 'dev':\n url = \"https://elb.devvending.com/api/predict\"\n elif env == 'prod':\n url = \"https://elb.cwrtvending.com/api/predict\"\n else:\n raise UnrecognisedEnvironmentException('Unrecognised environment')\n\n # set the api\n df[\"url\"] = url\n\n\n print(df[[\"final_prompt\",\"completed\"]])\n\n # sample n number of rows from dataset\n df = df if sample >= df.shape[0] else df.sample(sample)\n\n print(df[\"prompt_text\"])\n\n # parallelization\n pool = Pool(num_cores)\n dfs = numpy.array_split(df, num_cores)\n pool_results = pool.map(parallelise_calls, dfs)\n pool.close()\n pool.join()\n df = pandas.concat(pool_results)\n\n if get_response_from == \"charlie\":\n df.drop(columns=[\"username\",\"password\"],inplace=True)\n\n print(df[[\"response\",\"get_response_from\",\"completed\"]])\n\n\n # write the final result with the timestamp\n df.to_csv(join(reply_folder_path,\n f\"final_result_{datetime.now().isoformat()}.csv\"),\n sep=\",\",\n index=True,\n encoding=\"utf8\")\n print(f\"Results are stored in {reply_folder_path}\")\n\n\ndef set_guid(_: pandas.Series) -> str:\n \"\"\"Sets the GUID for a text\"\"\"\n\n return str(uuid.uuid4())\n\ndef get_completed_text_ids(output_file_path: str) -> tuple:\n '''Find ids that have already been created'''\n\n file_names = os.listdir(output_file_path)\n completed_texts = []\n completed_ids = []\n for file_name in file_names:\n if file_name.endswith(\".txt\"):\n with open(join(output_file_path,file_name), mode=\"r\", encoding=\"utf8\") as f:\n text = f.read().strip()\n if text != \"\":\n completed_ids.append(file_name[0:-4])\n completed_texts.append(text)\n return completed_ids, completed_texts\n\n\ndef parallelise_calls(df: pandas.DataFrame) -> pandas.DataFrame:\n '''Parallelise dataframe processing'''\n\n return df.apply(send_text, axis=1)\n\n\ndef send_text(row: pandas.Series) -> pandas.Series:\n \"\"\"Send text\"\"\"\n\n if row[\"get_response_from\"] == \"openai\":\n return send_text_to_openai(row)\n elif row[\"get_response_from\"] == \"charlie\":\n return send_text_to_charlie(row)\n else:\n raise ResponseGeneratorMismatchException(\n f\"The provided response generator - {row['get_response_from']} is neither openai nor charlie\")\n\n\ndef send_text_to_openai(row: pandas.Series) -> pandas.Series:\n '''Send text to OpenAI'''\n\n if not row[\"completed\"]:\n\n try:\n response = call_openai(row[\"final_prompt\"], row[\"model\"])\n if not response.choices[0].message.content:\n raise ServerOverloadException(\"Unsuccessful API Call - may be dude to server overload\")\n\n text = response.choices[0].message.content\n\n text = text.strip()\n if text == \"\":\n raise EmptyResponseException(f\"Empty response generated for the text - {row.text}\")\n\n # Writing to text file\n with open(row[\"reply_path\"],mode=\"w\",encoding=\"utf-8\") as f:\n f.write(text)\n row[\"response\"] = text\n row[\"completed\"] = True\n\n except (Exception, ServerOverloadException, EmptyResponseException) as e: # pylint: disable=broad-exception-caught\n print(f\"Rerunning {row.name} due to {e}\")\n row = send_text_to_openai(row) # rerun the text\n\n return row\n\ndef call_openai(text: str, model: str) -> str:\n '''Calling OpenAI'''\n\n response = openai.ChatCompletion.create(\n model=model,\n messages=[\n {\"role\": \"user\", \"content\": text}\n ],\n temperature=0.0,\n max_tokens=500,\n top_p=1, # default value\n frequency_penalty=0.0, # default value\n presence_penalty=0.0 # default value\n )\n\n return response\n\ndef send_text_to_charlie(row: pandas.Series) -> pandas.Series:\n \"\"\"Send text to Charlie\"\"\"\n\n if not row[\"completed\"]:\n data = {\n \"id\": row.name,\n \"text\": row[\"final_prompt\"]\n }\n\n response = requests.post(url=row.url, # pylint: disable=missing-timeout\n auth=(row.username,row.password),\n json=data)\n\n try:\n if response.status_code != 200:\n raise UnscuccessfulAPICallException(\n f\"Status Code :{response.status_code} \\n\\nResponse:\\n\\n{response.json()}\")\n\n text = response.text.strip()\n if text == \"\":\n raise EmptyResponseException(f\"Empty response generated for the text - {row.text}\")\n\n # Writing to text file\n with open(row[\"reply_path\"],mode=\"w\",encoding=\"utf-8\") as f:\n f.write(text)\n row[\"response\"] = text\n row[\"completed\"] = True\n\n except (Exception, UnscuccessfulAPICallException, EmptyResponseException) as e: # pylint: disable=broad-exception-caught\n print(f\"Rerunning {row.name} due to {e}\")\n row = send_text_to_charlie(row) # rerun the text\n\n return row\n\nif __name__==\"__main__\":\n main() # pylint: disable=no-value-for-parameter\n","repo_name":"kpister/prompt-linter","sub_path":"data/scraping/repos/zia-ai~academy/adversarial_supervision~scripts~1_attack~adversarial_attack.py","file_name":"adversarial_supervision~scripts~1_attack~adversarial_attack.py","file_ext":"py","file_size_in_byte":12590,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"8511548619","text":"import psycopg2\nfrom flask import Flask, render_template, request\n\napp = Flask(__name__)\n\ndef get_db_connection() -> psycopg2.extensions.connection:\n \"\"\"establishing connection with database\"\"\"\n conn = psycopg2.connect(\n database=\"test_database\",\n user=\"test_user\",\n password=\"test@123\",\n host=\"127.0.0.1\",\n port=\"5432\",\n )\n return conn\n\n\nSQL1 =\"\"\"\nPREPARE SQL (text) AS\nSELECT * FROM netbsd.feedbacks f WHERE f.confirmation_no=$1;\nEXECUTE SQL(%s);\n\"\"\"\n\n\nSQL2 = \"\"\"\nPREPARE SQL (text, bool, text, bool, text, bool, text) AS\nINSERT INTO netbsd.feedbacks VALUES($1, $2, $3, $4, $5, $6, $7);\nEXECUTE SQL(%s, %s, %s, %s, %s, %s, %s);\n\"\"\"\n\n\n@app.route('/')\ndef index() -> str:\n return render_template('index.html')\n\n\n@app.route('/validate', methods=['POST'])\ndef validate() -> str:\n fid=request.form['feed']\n conn = get_db_connection()\n cur = conn.cursor()\n cur.execute(SQL1,fid) ### type error\n identifier = cur.fetchall()\n cur.close()\n conn.close()\n if not identifier:\n return render_template('valid.html',fid=fid)\n return render_template('invalid.html',identifier=identifier)\n\n\n@app.route('/store/', methods=['POST'])\ndef store(fid: str) -> str:\n answer1=request.form[\"answer1\"]\n name=request.form[\"name\"]\n answer2=request.form[\"answer2\"]\n email=request.form[\"email\"]\n answer3=request.form[\"answer3\"]\n notification_email=request.form[\"email\"]\n conn = get_db_connection()\n cur = conn.cursor()\n cur.execute(SQL2,\n (\n fid,\n answer1,\n name,\n answer2,\n email,\n answer3,\n notification_email\n )\n )\n cur.close()\n conn.commit()\n conn.close()\n return render_template('thank_you.html')\n","repo_name":"VivekKSah/NetBSD_project_2022","sub_path":"feedback_site/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1816,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"12281139688","text":"class GazetteerBTreeIndex:\n '''This class defines basic btree indexes on a table'''\n\n def __init__(self, name, schema, table_name, columns,\n unique=False, where=None, fillfactor=100):\n\n self.name = name\n self.schema = schema\n self.table_name = table_name\n self.full_table_name = schema + '.' + table_name\n if isinstance(columns, str):\n self.columns = (columns, )\n else:\n self.columns = columns\n self.unique = unique\n self.where = where\n self.fillfactor = fillfactor\n\n def generate_sql(self, drop_existing=False):\n '''Return the text of a SQL statement that will create the index. If\n specified, drop the existing index first.'''\n\n result = ''\n\n if drop_existing:\n result += self.generate_drop_sql()\n\n unique_text = 'UNIQUE' if self.unique else ''\n\n result += 'CREATE {0} INDEX IF NOT EXISTS {1} ON {2}.{3} '\\\n 'USING btree\\n ('.format(unique_text,\n self.name,\n self.schema,\n self.table_name)\n\n for c in self.columns[:-1]:\n result += c + ',\\n '\n result += self.columns[-1] + '\\n )\\n'\n\n result += 'WITH (fillfactor = {})'.format(self.fillfactor)\n\n if self.where is not None:\n result += '\\n' + self.where\n\n result += ';\\n\\n'\n\n return result\n\n def generate_drop_sql(self):\n '''Return the text of a SQL statement that will drop the index.'''\n\n return 'DROP INDEX IF EXISTS {0}.{1} CASCADE;\\n\\n' \\\n .format(self.schema, self.name)\n\n\nclass GazetteerForeignKey:\n '''This class defines foreign keys on a table'''\n\n def __init__(self, name, schema, table_name, columns,\n foreign_schema, foreign_table_name, foreign_columns):\n\n self.name = name\n\n self.schema = schema\n self.table_name = table_name\n self.full_table_name = schema + '.' + table_name\n if isinstance(columns, str):\n self.columns = (columns, )\n else:\n self.columns = columns\n\n self.foreign_schema = foreign_schema\n self.foreign_table_name = foreign_table_name\n if isinstance(foreign_columns, str):\n self.foreign_columns = (foreign_columns, )\n else:\n self.foreign_columns = foreign_columns\n\n def generate_sql(self, drop_existing=False):\n '''Return the text of a SQL statement that will create the index. If\n specified, drop the existing index first.'''\n\n result = ''\n\n if drop_existing:\n result += self.generate_drop_sql()\n\n result += 'ALTER TABLE {0} ADD CONSTRAINT {1} FOREIGN KEY ('\\\n .format(self.full_table_name, self.name)\n\n for c in self.columns[:-1]:\n result += c + ',\\n '\n result += self.columns[-1] + ')\\n'\n\n result += 'REFERENCES {0}.{1} ('\\\n .format(self.foreign_schema, self.foreign_table_name)\n\n for c in self.foreign_columns[:-1]:\n result += c + ',\\n '\n result += self.foreign_columns[-1] + ');\\n'\n\n return result\n\n def generate_drop_sql(self):\n '''Return the text of a SQL statement that will drop the index.'''\n\n return 'ALTER TABLE {0} DROP CONSTRAINT IF EXISTS {1} CASCADE;\\n\\n' \\\n .format(self.full_table_name, self.name)\n","repo_name":"jhumphry/gazetteer_etl","sub_path":"gazetteer/indexes.py","file_name":"indexes.py","file_ext":"py","file_size_in_byte":3531,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"71734570714","text":"import json, webbrowser\nfrom tile.tile import Tile, RotateableTile\nfrom utils import utils\nimport math, pygame, os\n\nwith open(\"mod.json\") as file:\n data = json.load(file)\n namespace = data[\"namespace\"]\n _mod = data[\"mod\"]\n\nclass Mover(RotateableTile):\n mod = _mod\n id = namespace + \":mover\"\n texture_name = \"tiles/mover.png\"\n tags = RotateableTile.tags + [\"movable\"]\n def tick(self, world):\n super().tick(world)\n self.t_x = self.x\n self.t_y = self.y\n\n cm, sm = Mover.can_move(self.x, self.y, self.r, world)\n if not cm:\n return\n \n for o in sm:\n o.update_queue[\"x\"], o.update_queue[\"y\"] = utils.move(o.x, o.y, self.r)\n self.update_queue[\"x\"], self.update_queue[\"y\"] = utils.move(self.x, self.y, self.r)\n\n def can_move(_x, _y, _r, world):\n x, y = utils.move(_x, _y, _r)\n tx, ty = utils.move(_x, _y, _r)\n sm = []\n while world.exist(tx, ty):\n obj = world.get(tx, ty) \n if isinstance(obj, AxisMovableBlock):\n if obj.r == 0 and (_r == 0 or _r == 180):\n pass\n elif obj.r == 180 and (_r == 90 or _r == 270):\n pass\n else:\n return False, []\n if \"movable\" in obj.tags:\n obj2 = world.get(*utils.move(obj.x, obj.y, _r))\n if (isinstance(obj2, Mover)):\n if obj2.r == _r:\n c, m = Mover.can_move(obj2.x, obj2.y, obj2.r, world)\n if not c:\n return False, []\n sm += m\n return True, sm\n if obj2 == None:\n sm.append(obj)\n return True, sm\n elif \"movable\" in obj2.tags:\n sm.append(obj)\n else:\n return False, []\n else:\n return False, []\n tx, ty = utils.move(tx, ty, _r)\n return True, sm\n\nclass RickRoller(RotateableTile):\n mod = _mod\n id = namespace + \":rickroller\"\n texture_name = \"tiles/rickroller.png\"\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self._die = False\n def tick(self, world):\n if self._die:\n self.kill()\n super().tick(world)\n return\n self.t_x = self.x\n self.t_y = self.y\n self.update_queue[\"x\"], self.update_queue[\"y\"] = utils.move(self.x, self.y, self.r)\n super().tick(world)\n def onOverlay(self, tile):\n if tile == self:\n return\n webbrowser.open(\"https://www.youtube.com/watch?v=dQw4w9WgXcQ\")\n self._die = True\n\nclass SolidBlock(Tile):\n mod = _mod\n id = namespace + \":solidblock\"\n texture_name = \"tiles/solidblock.png\"\n\nclass MoveableBlock(Tile):\n mod = _mod\n id = namespace + \":moveableblock\"\n texture_name = \"tiles/moveableblock.png\"\n tags = [\"movable\", \"solid\"]\n\nclass LevelFinish(Tile):\n mod = _mod\n id = namespace + \":levelfinish\"\n texture_name = \"tiles/levelfinish.png\"\n def tick(self, world):\n super().tick(world)\n self.world = world\n def onHit(self, tile, side):\n try:\n self.world.objects.clear()\n except:\n pass\n\nclass Rotater(Tile):\n tags = RotateableTile.tags\n\n mod = _mod\n id = namespace + \":rotater\"\n texture_name = \"tiles/rotater.png\"\n\n textures = [pygame.surface.Surface((32, 32)) for _ in range(0, 2)]\n def __init__(self, x: int, y: int, r: float):\n super().__init__(x, y)\n self.r = r\n texture = pygame.image.load(os.path.join(\"mods\", self.__class__.mod, self.__class__.texture_name))\n self.textures[0] = texture.copy()\n self.textures[1] = pygame.transform.flip(texture, True, False)\n\n def onHit(self, tile, side):\n if side % 2:\n return\n if not \"rotatable\" in tile.tags:\n return\n if tile.id == self.id:\n return\n if self.r // 180:\n tile.update_queue[\"r\"] = (tile.r + 90) % 360\n else:\n tile.update_queue[\"r\"] = (tile.r - 90) % 360\n\n def get_texture(self):\n return self.textures[self.r // 180 > 0]\n def empty_instance(cl):\n return cl(0, 0, 0)\n\nclass Cloner(RotateableTile):\n mod = _mod\n id = namespace + \":cloner\"\n texture_name = \"tiles/cloner.png\"\n\n tags = RotateableTile.tags + [\"movable\"]\n\n def tick(self, world):\n super().tick(world)\n bx, by = utils.move(self.x, self.y, (self.r - 180) % 360)\n tx, ty = utils.move(self.x, self.y, self.r)\n if world.exist(bx, by) and not world.exist(tx, ty):\n obj = world.get(bx, by)\n obj2 = obj.copy()\n obj2.x, obj2.y = tx, ty\n obj2.should_tick = False\n world.objects.append(obj2)\n\nclass AxisMovableBlock(Tile):\n mod = _mod\n id = namespace + \":axismovableblock\"\n texture_name = \"tiles/axismovableblock.png\"\n\n tags = RotateableTile.tags + [\"movable\"]\n\n def __init__(self, x, y, r):\n super().__init__(x, y)\n self.r = r\n if self.r == 270:\n self.r = 180\n if self.r == 90:\n self.r = 0\n self.textures = [None, None]\n texture = pygame.image.load(os.path.join(\"mods\", self.__class__.mod, self.__class__.texture_name))\n self.textures[0] = texture.copy()\n self.textures[1] = pygame.transform.rotate(texture, 90)\n def get_texture(self):\n return self.textures[self.r // 180 > 0]\n def empty_instance(cl):\n return cl(0, 0, 0)\n\ndef get_tiles():\n return [Mover, RickRoller, SolidBlock, MoveableBlock, LevelFinish, Rotater, Cloner, AxisMovableBlock]\n","repo_name":"jakiki6/justastupidgame","sub_path":"mods/main/tiles.py","file_name":"tiles.py","file_ext":"py","file_size_in_byte":5811,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"50"} +{"seq_id":"25244473041","text":"# Manuely Meireles Lemos\n\ndef main():\n\n\t# Declaração de variáveis\n\ty = float()\n\tx = float()\n\tcont = int()\n\n\t# Entrada \n\ty = float(input())\n\tx = y/2\n\tcont = 0\n\n\t# Processamento\n\tif y > 0:\n\n\t\tprint(f\"Y={y:.5f}\")\n\t\tfor _ in range(1,20):\n\t\t\tx = ((x**2)+y)/(2*x)\n\t\t\tcont += 1\n\t\t\tprint(f\"{cont}ª APROXIMAÇÃO = {x:.5f}\") # Saída\n\telse:\n\t\tprint(\"Não existe raiz quadrada REAL\") # Saída\n\nif __name__ == \"__main__\":\n\tmain()","repo_name":"manaLow/exercicios-farrer","sub_path":"farrer54.py","file_name":"farrer54.py","file_ext":"py","file_size_in_byte":423,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"23135946169","text":"import requests\nfrom lxml import etree\nfrom hashlib import md5\n\nurl = 'http://www.biquge.info/32_32050/'\nheaders = {\n 'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:6.0) Gecko/20100101 Firefox/6.0'}\n\nhtml = requests.get(url=url, headers=headers).content.decode('utf-8', 'ignore')\n\np = etree.HTML(html)\nr_list = p.xpath('//div[@id=\"list\"]/dl//dd[1]/a/@href')\nprint(r_list)\nfor href in r_list:\n two_url = 'http://www.biquge.info/32_32050/' + href\n print(two_url)\n two_html=requests.get(url=two_url, headers=headers).content.decode('utf-8', 'ignore')\n new_p = etree.HTML(two_html)\n story_info = new_p.xpath('//div[@id=\"content\"]/text()')\n for item in story_info:\n print(item)\n","repo_name":"sjk052026/test2020","sub_path":"spider/day05/XiaoshuoSpider.py","file_name":"XiaoshuoSpider.py","file_ext":"py","file_size_in_byte":708,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"5375266859","text":"import os, re, json, sys\n\nfile_pattern = re.compile(r'^sw\\_([0-9]+?)\\_([0-9]+?)\\.utt\\.txt$')\nline_pattern = re.compile(r'^(.*?)\\t(.*?)\\t(.*?)$')\n\ntmp_da = {'A': None, 'B': None}\n\ndef preprocess(dir_path, filename):\n with open(os.path.join(dir_path, filename), 'r') as f, \\\n open(os.path.join('./data/corpus/', filename[:-7] + 'jsonlines'), 'w') as out_f:\n data = f.read().split('\\n')\n prev_caller = None\n das = []\n sentences = []\n\n for line in data:\n m = line_pattern.search(line)\n if not m is None:\n current_caller = m.group(1)\n if m.group(2) == '+':\n da = tmp_da[current_caller]\n else:\n da = m.group(2)\n tmp_da[current_caller] = da\n assert da is not None, filename\n if current_caller == prev_caller:\n das.append(da)\n sentences.append(m.group(3))\n else:\n if len(das) > 0 and len(sentences) > 0:\n out_f.write(json.dumps({'caller': prev_caller,\n 'DA': das,\n 'sentence': sentences}))\n out_f.write('\\n')\n das = [da]\n sentences = [m.group(3)]\n prev_caller = current_caller\n\ndef FileIter():\n for i in range(14):\n dir_path = os.path.join('./data/swda', 'sw{:02}utt'.format(i))\n print('preprocessing in {}'.format(dir_path))\n files = [f for f in os.listdir(dir_path) if file_pattern.match(f)]\n for i, filename in enumerate(files, 1):\n preprocess(dir_path, filename)\n print('\\rFinish preprocess {}/{} files'.format(i, len(files)), end='')\n print()\n\ndef test1file():\n preprocess('./data/swda/sw00utt', 'sw_0002_4330.utt.txt')\n\nif __name__ == '__main__':\n FileIter()","repo_name":"ananyaganesh/ftmp","sub_path":"preprocess.py","file_name":"preprocess.py","file_ext":"py","file_size_in_byte":1990,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"23375592890","text":"from Project.Classes.MotorChain import MotorChain\nfrom Project.Domain import MaterialsDomain\nimport math\nfrom scipy import optimize\n\nACCEPTABLE_ERROR = 0.0001\n\nclass ThickVessel (MotorChain):\n def calculateCircumferentialStress (self):\n p = self.workPressure\n ri = self.internalRadius\n re = self.internalRadius + self.thickness\n self.circumferentialStress = p * ((ri ** 2) / (re ** 2 - ri ** 2 )) \\\n * (1 + (re ** 2)/(ri ** 2))\n\n def calculateHeatCircumferentialStress (self):\n E = self.material['elasticityModule']\n a = self.material['thermalExpansioCoeficient']\n v = self.material['poissonRatio']\n ri = self.internalRadius\n re = ri + self.thickness\n dT = self.additionalHeatStress.temperatureVariation\n self.additionalHeatStress.circumferentialStress = (E*a*dT)/(2*(1-v)) \\\n * ((2*(re/ri) ** 2)/(-1 + (re/ri) ** 2) - (1/math.log(re/ri)))\n\n def calculateMaxRadialStress (self):\n p = self.workPressure\n ri = self.internalRadius\n re = self.internalRadius + self.thickness\n self.radialStress = p * ((ri ** 2) / (re ** 2 - ri ** 2 )) \\\n * (1 - (re ** 2)/(ri ** 2))\n\n def calculateHeatMaxRadialStress (self):\n self.additionalHeatStress.radialStress = 0\n\n\n def calculateLongitudinalStress (self):\n p = self.workPressure\n ri = self.internalRadius\n re = self.internalRadius + self.thickness\n self.longitudinalStress = (p * ri ** 2)/((re ** 2)- (ri ** 2))\n\n def calculateHeatLongitudinalStress (self):\n E = self.material['elasticityModule']\n a = self.material['thermalExpansioCoeficient']\n v = self.material['poissonRatio']\n ri = self.internalRadius\n re = ri + self.thickness\n dT = self.additionalHeatStress.temperatureVariation\n self.additionalHeatStress.longitudinalStress = (E*a*dT)/(2*(1-v)) \\\n * ((2*(re/ri) ** 2)/(-1 + (re/ri) ** 2) - (1/math.log(re/ri)))\n\n def calculateThickness (self):\n radiusRatio = optimize.newton(self.calculateVonMissesByRadiusRatio,1.1)\n self.thickness = (radiusRatio * self.internalRadius) - self.internalRadius\n\n def calculateThicknessWithHeatAddition (self):\n radiusRatio = optimize.newton(self.calculateVonMissesByRadiusRatioWithHeatAddition, 1.1)\n self.thickness = (radiusRatio * self.internalRadius) - self.internalRadius\n\n def calculatePrincipalStresses (self):\n self.calculateCircumferentialStress()\n self.calculateLongitudinalStress()\n self.calculateMaxRadialStress()\n self.calculateNozzleReinforcementThickness()\n if self.hasAditionalHeatStress:\n self.calculateAdditionHeatStress()\n self.calculateVonMisesStress()\n \n def calculateVonMissesByRadiusRatio(self, x):\n p = self.workPressure \n sigma_e = self.material['yeldStrength']/1.5\n return ((math.sqrt(3) * p * (x**2))/((x**2) -1)) - sigma_e\n\n def calculateVonMissesByRadiusRatioWithHeatAddition(self, x):\n p = self.workPressure \n sigma_e = self.material['yeldStrength']/1.5\n S = self.getThermicalS()\n f1 = ((x**4)*(3*p**2 + 6*p*S + 4*S**2))/((x**2 -1)**2)\n f2 = (S*(x**2)*(4*S + 3*p))/((x**2 -1)*math.log(x))\n f3 = (S**2)/(math.log(x)**2)\n return math.sqrt(f1 - f2 + f3) - sigma_e\n\n\n @classmethod\n def motorChainSMCalculation(cls, motorChain):\n newMotorChain = cls(motorChain)\n material = material = \\\n MaterialsDomain.Materials.getMaterialById(newMotorChain.materialId)\n newMotorChain.material = material\n newMotorChain.calculatePrincipalStresses()\n newMotorChain.calculateSM()\n return newMotorChain\n \n\n @classmethod\n def motorChainThicknessCalculation(cls, motorChain):\n newMotorChain = cls(motorChain)\n material = material = \\\n MaterialsDomain.Materials.getMaterialById(newMotorChain.materialId)\n newMotorChain.material = material\n if newMotorChain.hasAditionalHeatStress:\n newMotorChain.calculateThicknessWithHeatAddition()\n else:\n newMotorChain.calculateThickness()\n return newMotorChain","repo_name":"samuelrcm2/POCmf","sub_path":"POCmf/Project/Classes/ThickVessel.py","file_name":"ThickVessel.py","file_ext":"py","file_size_in_byte":4241,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"14838097785","text":"fruit = input()\r\nset_size = input()\r\nordered_set = int(input())\r\n\r\nprice = 0\r\nif fruit == \"Watermelon\":\r\n if set_size == \"small\":\r\n price = 56 * 2\r\n elif set_size == \"big\":\r\n price = 28.70 * 5\r\nelif fruit == \"Mango\":\r\n if set_size == \"small\":\r\n price = 36.66 * 2\r\n elif set_size == \"big\":\r\n price = 19.60 * 5\r\nelif fruit == \"Pineapple\":\r\n if set_size == \"small\":\r\n price = 42.10 * 2\r\n elif set_size == \"big\":\r\n price = 24.80 * 5\r\nelif fruit == \"Raspberry\":\r\n if set_size == \"small\":\r\n price = 20 * 2\r\n elif set_size == \"big\":\r\n price = 15.20 * 5\r\ntotal_price = ordered_set * price\r\n\r\nif 400 <= total_price <= 1000:\r\n total_price *= 0.85\r\nif total_price > 1000:\r\n total_price *= 0.50\r\n\r\nprint(f\"{total_price:.2f} lv.\")\r\n\r\n","repo_name":"Pavlina-G/Softuni-Programming-Basics","sub_path":"07. PB Exams/2020/03._energy_booster.py","file_name":"03._energy_booster.py","file_ext":"py","file_size_in_byte":807,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"13013521864","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Oct 27 23:11:05 2017\r\n\r\n@author: ATruong1\r\n\"\"\"\r\n#%%\r\n# -*- coding: utf-8 -*-\r\nimport pandas as pd\r\nfrom sklearn import preprocessing\r\nfrom sklearn.preprocessing import Imputer\r\nfrom pandas import get_dummies\r\nfrom sklearn.cluster import KMeans\r\nfrom sklearn import metrics\r\nfrom sklearn import decomposition\r\nfrom scipy.spatial.distance import pdist, squareform\r\nimport matplotlib.pyplot as plt\r\n#%%\r\ndef cleanAndSelect(data, var_selected):\r\n #selectionne les variables listées dans car_selected ['var1', 'var2', 'var3'], \r\n \r\n data = data[['movie_title','title_year']+var_selected] \r\n \r\n data = data.dropna()\r\n data = data.drop_duplicates(['movie_title','title_year'])\r\n data.index.name = 'film_id'\r\n data = data.reset_index()\r\n \r\n \r\n \r\n return data, data[var_selected].copy()\r\n#%%\r\ndef feat_to_drop(data, threshold):\r\n # renvoie une liste des variables à garder après condition de fréquence avec 'threshold'\r\n filtered = data.sum(axis = 0) < threshold \r\n return filtered.index[filtered].tolist()\r\ndef addColumnForEachWord(data,variable, threshold):\r\n #va créer une colonne pour chaque élément presént la colonne 'variable' (pour les listes séparées par '|')\r\n def getWords(data):\r\n # obtiends une liste de tous les genres présents dans la colonne 'Genres' (unique)\r\n serie = data[variable].str.split('|').dropna()\r\n serie_ = serie.agg(['sum'])\r\n return list(set(serie_.values[0]))\r\n def add_column_eachWord(data, words):\r\n # rajoute une colonne pour chaque genre de film\r\n for word in words:\r\n data[word] = 0\r\n return data\r\n def add_column_wordsSplit(data):\r\n word_split = data[data[variable].isnull()==False][variable].apply(split_)\r\n data['words_split'] = word_split\r\n return data\r\n def fill_column_eachWord(row):\r\n #remplis les colonnes avec des 1 et des 0 si le genre correspond au film\r\n if pd.isnull(row[variable]) == False:\r\n words = row.words_split\r\n for word in words:\r\n row[word] = 1\r\n return row\r\n def split(string , separator):\r\n # parse un string avec des séparateurs\r\n return string.split(separator)\r\n def split_(string):\r\n return split(string,'|')\r\n data_ = add_column_eachWord(data,getWords(data))\r\n data_ = add_column_wordsSplit(data)\r\n data_ = data_.apply(fill_column_eachWord, axis = 1)\r\n data_ = data_.drop(['words_split',variable], axis=1)\r\n data_ = data_.drop(feat_to_drop(data_[getWords(data)], threshold), axis = 1)\r\n return data_\r\ndef addColumnForEachContent(data,variable, threshold):\r\n # renvoie une colonne pour chque élément dans la colonne 'variable'\r\n data_sup = get_dummies(data[variable])\r\n data_ = pd.concat([data, data_sup], axis=1)\r\n data_ = data_.drop(variable, axis=1)\r\n data_ = data_.drop(feat_to_drop(data_sup, threshold), axis=1)\r\n return data_\r\ndef plotSilhouette(data, start = 200, end = 1000, step = 100):\r\n #plot silhouettes pour nb de centroids allant de 'start' à 'end'\r\n range_ = range(start, end, step)\r\n res =[]\r\n for k in range_:\r\n kmeans = KMeans(n_clusters=k).fit(data)\r\n res.append(metrics.silhouette_score(data_,kmeans.labels_))\r\n plt.plot(range_,res,marker='o')\r\n plt.xlabel('Score de Silhouette')\r\n plt.savefig('scoreSilhouette_{}_{}.png'.format(start,end))\r\n plt.show()\r\n return\r\ndef distance_matrix(data):\r\n #Calcule une matrice de distance euclidienne entre toutes les lignes de 'data'\r\n dist_ = pdist(data, 'euclidean')\r\n dist_ = pd.DataFrame(squareform(dist_))\r\n return dist_\r\ndef getRecommendation(index_bis , info, distanceMatrix):\r\n # renvoie les 5 éléments les plus proches de 'index_bis' dans 'distance_matrix'\r\n nsmallest_list = distanceMatrix.nsmallest(6, index_bis).index.values.tolist()\r\n del nsmallest_list[0] \r\n res = info.iloc[nsmallest_list]\r\n return res\r\ndef recommend(data, info, film_id, d_matrix):\r\n #Renvoie les films recommandés par la fonction 'getRecommend'\r\n index = info.index[info['film_id'] == film_id].tolist()\r\n if len(index) == 0:\r\n return None, None\r\n else:\r\n index_ = index[0]\r\n return info.iloc[[index_]] , getRecommendation(index_,info,d_matrix)\r\ndef print_( string ):\r\n # Format de print\r\n separator = \"---------------------------\"\r\n print(separator + \" \" + string + \" \" + separator)\r\n return\r\n#%% Chargement\r\npd.set_option('display.width', 1000)\r\ndata = pd.read_csv('movie_metadata.csv', sep=\",\")\r\n#%% Clean et Observation\r\ninfo, data_ = cleanAndSelect(data, ['num_voted_users','actor_1_name','actor_2_name','actor_3_name','imdb_score','genres','duration','gross','director_name','budget'])\r\ndf1 = info[['movie_title','film_id','genres','director_name','title_year']].head(10)\r\ndf2 = info[['movie_title','film_id','genres','director_name','title_year']].sample(10)\r\nprint(df1.append(df2))\r\n\r\n\r\n#%% Ajout des genres\r\nthreshold = 0\r\ndata_ = addColumnForEachWord(data_,'genres', threshold)\r\n#%% Ajout des mots clés\r\n#data_ = addColumnForEachWord(data_,'plot_keywords', threshold)\r\n#%% Ajout des réalisateurs\r\ndata_ = addColumnForEachContent(data_,'director_name', threshold)\r\n#%% Ajout des Acteurs (présence de l'acteur dans le film 1 ou 0, peu importe Acteur1, Acteur 2, Acteur3)\r\ndata_['actors']= data_['actor_1_name']+'|'+data_['actor_2_name']+'|'+data_['actor_3_name']\r\n\r\ndata_ = addColumnForEachWord(data_,'actors', threshold)\r\ndata_ = data_.drop(['actor_1_name','actor_3_name','actor_2_name'], axis=1) \r\n#%% nouveau score qui sublime les haut score avec beaucoup de votes et pénalise les score faibles avec beaucoup de vote\r\nscore_ = data['imdb_score'].divide(10)\r\nnum_voter_ = (data['num_voted_users']-data['num_voted_users'].mean()).divide(data['num_voted_users'].max())\r\ndata_['new_score'] = score_.multiply(num_voter_)\r\ndata_ = data_.drop(['num_voted_users','imdb_score'], axis = 1)\r\n#%% succès commmercial ou pas\r\ndata_['profitability'] = data_['gross'].divide(data_['budget'])\r\ndata_=data_.drop(data_['gross'])\r\n\r\n\r\n#%% rescaling des données et calcul dela matrice de distance\r\ndata_scaled = preprocessing.scale(data_)\r\n\r\ndmatrix = distance_matrix(data_scaled)\r\n\r\n#%% Recommandation avec la matrice de distance\r\n'''id du film'''\r\n# film_id = 9 Harry Potter\r\n# film_id = 3 The Dark Knight Rises\r\n# film_id = 2607 The King's Speech\r\n# 283 Gladiator\r\nfilm_id = 2607\r\n\r\nmovie, recommendations = recommend(data_, info, film_id, dmatrix)\r\nif movie is None or recommendations is None:\r\n print('Sorry, we are not able to recommend you a movie based on the selected movie')\r\nelse:\r\n selected_columns_display = ['movie_title', 'genres','director_name','title_year']\r\n print_(\"Selected Movie:\")\r\n print(movie[selected_columns_display].to_string(index=False,header=False))\r\n print_(\"Recommendations:\")\r\n print(recommendations[selected_columns_display].to_string(index=False,header=False))\r\n#%%\r\n#%%\r\nlis=[]\r\nfor k in range(2,1004,250):\r\n pca = decomposition.PCA(n_components = k)\r\n pca.fit(data_scaled)\r\n lis.append(pca.explained_variance_ratio_.sum())\r\nplt.plot(range(2,1004,250),lis, marker='o')\r\nplt.xlabel('Ratio de var expliquée vs nombre de dimensions')\r\n\r\n\r\n#%%\r\npca = decomposition.PCA(n_components = 1500) #70% expliqué\r\npca.fit(data_scaled)\r\ndata_trans = pca.transform(data_scaled)\r\npca.explained_variance_ratio_.sum()\r\n\r\n#%%\r\nplotSilhouette(data_trans, 2, 1003, 200)\r\n","repo_name":"eric-snapcar/projet4","sub_path":"Main_woNaN_v0.py","file_name":"Main_woNaN_v0.py","file_ext":"py","file_size_in_byte":7645,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"36901816613","text":"import argparse\nimport logging\nimport os\nimport sys\nimport textwrap\n\nfrom . import eval, install\n\n\ndef main(args=None):\n \"\"\"The main routine.\"\"\"\n\n # Add the current working directory to sys.path, similar to python's\n # unittesting frameworks.\n sys.path.insert(0, os.getcwd())\n\n if args is None:\n args = sys.argv[1:]\n\n parent_parser = argparse.ArgumentParser(add_help=False)\n parent_parser.add_argument(\n \"--verbose\",\n \"-v\",\n default=False,\n action=\"store_true\",\n help=\"Include additional details, including full stack traces on errors.\",\n )\n\n parser = argparse.ArgumentParser(\n description=textwrap.dedent(\n \"\"\"braintrust is a cli tool to work with Braintrust.\n To see help for a specific subcommand, run `braintrust --help`,\n e.g. `braintrust eval --help`\"\"\"\n )\n )\n subparsers = parser.add_subparsers(help=\"sub-command help\", dest=\"subcommand\", required=True)\n\n for module in [eval, install]:\n module.build_parser(subparsers, parent_parser)\n\n args = parser.parse_args(args=args)\n level = logging.DEBUG if args.verbose else logging.INFO\n logging.basicConfig(format=\"%(asctime)s %(levelname)s [%(name)s]: %(message)s\", level=level)\n\n return args.func(args)\n\n\nif __name__ == \"__main__\":\n sys.exit(main())\n","repo_name":"braintrustdata/braintrust-sdk","sub_path":"py/src/braintrust/cli/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":1347,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"50"} +{"seq_id":"35818526715","text":"#!/usr/bin/python3\n#use ransac on green and red using findMm() no left red 4/24/2023\n# find black\nimport numpy as np\nfrom sklearn import linear_model, datasets\nimport cv2\nimport rospy\nfrom std_msgs.msg import String\nfrom sensor_msgs.msg import CompressedImage\nimport time\nfrom cv_bridge import CvBridge, CvBridgeError\n\ncam_vs_file = \"FILE\" #CAM if camera, FILE if file\nXc = -1\nyc = -1\nnumberOfClicks = 0\n\n\ndef get_mouse_cb(event, x, y, flags, param):\n global xc, yc, numberOfClicks\n if event == cv2.EVENT_LBUTTONDOWN:\n xc = x\n yc = y\n numberOfClicks += 1\n\nclass TakePhoto:\n def __init__(self):\n self.image_received = False\n # img_topic = \"/raspicam_node/image/compressed\"\n img_topic = \"/camera/image/compressed\"\n self.image_sub = rospy.Subscriber(\n img_topic, CompressedImage, self.callback, queue_size=10)\n\n\n def callback(self, data):\n self.image_received = True\n np_arr = np.frombuffer(data.data, np.uint8)\n self.img = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)\n\n# cv2.imshow(\"Orignal\", self.img)\n# self.img = cv2.cvtColor(self.img, cv2.COLOR_BGR2HSV)\n\n def get_img(self):\n if self.image_received == False:\n print(\"None\")\n return\n return self.img\n\n def save_img(self, img_title):\n if self.image_received == False:\n print(\"None\")\n return\n cv2.imwrite(img_title, self.img)\n\n def disp_img(self, img_title):\n if self.image_received == False:\n print(\"None\")\n return\n cv2.imshow(img_title, self.img)\n\ndef jabs(a,b):\n if(a > b):\n ab = a-b\n else:\n ab = b-a\n return ab\n\ndef findMm(im, xgb,ygb, xgt, ygt,prt=0):\n H, S, V = im[ygb,xgb]\n if prt == 1:\n print(\"Initial HSV infindMm \",H,S,V)\n Hl = H\n Hh = H\n Sl = S\n Sh = S\n yp = ygb-1 #next line up\n xp = xgb\n yf = ygt-1\n while yp > yf:\n for xtst in range(xp-5,xp+5): #find closest Hue to test value H\n hq,sq,V = im[yp,xtst] #q question\n if(xtst == xp-5):\n he = jabs(hq,H) #e evaluate\n hxpt = xtst\n se = jabs(sq, S)\n if prt == 1:\n print(\"xtst,yp, hq,sq \", xtst,yp, hq,sq)\n else:\n if jabs(hq, H) < he:\n he = jabs(hq, H) \n hxpt = xtst\n if jabs(sq, S) < se:\n se = jabs(sq, S)\n xp = hxpt\n if Hh < hq:\n Hh = hq\n if Hl > hq: \n Hl = hq\n if Sh < sq:\n Sh = sq\n if Sl > sq:\n Sl = sq \n if prt == 1:\n print(\"hxpt, Hl,Hh,Sl,Sh \",hxpt, Hl,Hh, Sl, Sh)\n yp -= 1\n if prt == 1:\n print(\"inTest yp,xp \", yp,xp)\n print()\n return Hl, Hh, Sl, Sh\n\n\nrospy.init_node(\"Color_Lane_Following\")\ncamera = TakePhoto()\ntime.sleep(1)\nrate = rospy.Rate(10)\nif(cam_vs_file == \"CAM\"): #camera if CAM, or FILE from file\n img = camera.get_img()\n imgsz = img.shape\n print(\"img size \", imgsz)\n\t\nelse:\n # read image\n #img = cv2.imread(\"/home/parallels/Documents/color/Lab13/smallRoadFar.jpg\")\n #img=cv2.imread('/home/parallels/Documents/color/Lab13/smallRoadClose.jpg')\n #img = cv2.imread('/home/parallels/Documents/color/Lab13/black30.jpg')\n #img = cv2.imread('/home/parallels/Documents/color/Lab13/black20_5.jpg')\n img = cv2.imread('/home/parallels/Documents/color/Lab13/black15.jpg')\n #img = cv2.imread('/home/parallels/Documents/color/Lab13/black12.jpg')\n #img = cv2.imread('/home/parallels/Documents/color/Lab13/black8.jpg')\n\n\n imgsz = img.shape\n print(\"img size \", imgsz)\n\nimgHSV = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n# show image\ncv2.namedWindow(\"image\")\n\n# define the events for the\n# mouse_click.\n\n# array to store picture values\nrows, cols = (10, 3)\nhsvValues = np.zeros((rows, cols), dtype = int)\npixelXYCords = np.zeros((rows, 2), dtype = int)\nprint(\"************** Click Information ************\")\nold_num_clk = numberOfClicks\ncv2.setMouseCallback('image', get_mouse_cb)\nwhile numberOfClicks < 9:\n cv2.imshow(\"image\", img)\n key = cv2.waitKey(1) & 0xFF\n if key == ord(\"c\"):\n break\n if(old_num_clk < numberOfClicks):\n print(\"numberOfClicks \", numberOfClicks)\n r, g, b = img[yc, xc]\n h, s, v = imgHSV[yc,xc]\n hsvValues[numberOfClicks][0] = h\n hsvValues[numberOfClicks][1] = s\n hsvValues[numberOfClicks][2] = v\n pixelXYCords[numberOfClicks][0] = xc\n pixelXYCords[numberOfClicks][1] = yc\n print(\"Coordinates \", pixelXYCords[numberOfClicks]) #x,y\n a = (b, g, r)\n colr = np.uint8([[a]])\n hsvColr = cv2.cvtColor(colr, cv2.COLOR_BGR2HSV)\n print(\" RGB, HSV \", r, g, b, \" \",h,s,v)\n old_num_clk += 1\n print()\nprint(\"DONE Clicking Image\")\nprint(\"*********** Clicking Results ***************\")\nprint(\"pixelXYCords \", pixelXYCords)\nprint(\"hsvValues \", hsvValues)\nprint(\"********************************************\")\nstart = time.process_time()\n#Set Color Limits\n#scan green line from lower click to upper click\nxgl = pixelXYCords[1][0]\nygl = pixelXYCords[1][1]\nxgu = pixelXYCords[2][0]\nygu = pixelXYCords[2][1]\ngH = hsvValues[1][0]\ngS = hsvValues[1][1]\ngHl,gHh,gSl,gSh = findMm(imgHSV, xgl,ygl, xgu, ygu) #find Green M&m\nprint(\"findMm green\", gHl, gHh, gSl, gSh)\ngreen_hsv_lower = (75, 125, 0) #was 39,142\ngreen_hsv_upper = (81, 176, 255) #was 42,174\ngreen_hsv_lower = (gHl, gSl,0) #using routine\ngreen_hsv_upper = (gHh, gSh,255) #using routine\ngreen_hsv_lower = np.array(green_hsv_lower)\ngreen_hsv_upper = np.array(green_hsv_upper)\n\nxrl = pixelXYCords[3][0]\nyrl = pixelXYCords[3][1]\nxru = pixelXYCords[4][0]\nyru = pixelXYCords[4][1]\n\nrHl,rHh, rSl,rSh = findMm(imgHSV, xrl,yrl, xru, yru) #find Red M&m\nprint(\"findMm red \", rHl,rHh, rSl,rSh)\nred_hsv_lower = ( 172, 125, 0)\nred_hsv_upper = (176, 142, 255)\nred_hsv_lower = (rHl, rSl, 0)\nred_hsv_upper = (rHh, rSh, 255)\nred_hsv_lower = np.array(red_hsv_lower)\nred_hsv_upper = np.array(red_hsv_upper)\nblk_hsv_lower = (35, 28, 40)\nblk_hsv_upper = (90, 47,90)\n\nwht_hsv_lower = (50,3,0)\nwht_hsv_upper = (94, 46, 255)\nlower_color = np.array(green_hsv_lower)\nupper_color = np.array(green_hsv_upper)\n\n # Create a mask of all pixels that fall within the color range\nprint(\"************ Color Limits ****************\")\nprint(\"green_hsv_lower \", green_hsv_lower)\nprint(\"green_hsv_upper \", green_hsv_upper)\nprint()\nprint(\"red_hsv_lower \", red_hsv_lower)\nprint(\"red_hsv_upper \", red_hsv_upper)\nprint()\nprint(\"white_hsv_lower \", wht_hsv_lower)\nprint(\"white_hsv_upper \", wht_hsv_upper)\nprint()\nprint(\"black_hsv_lower \", blk_hsv_lower)\nprint(\"black_hsv_upper \", blk_hsv_upper)\n\nimgHSV[0:290,:] = 0\n#mask = cv2.inRange(imgHSV, lower_color, upper_color)\nmaskg = cv2.inRange(imgHSV, green_hsv_lower, green_hsv_upper)\nmaskr = cv2.inRange(imgHSV, red_hsv_lower, red_hsv_upper)\nmaskw = cv2.inRange(imgHSV, wht_hsv_lower, wht_hsv_upper)\nmaskbk= cv2.inRange(imgHSV, blk_hsv_lower, blk_hsv_upper)\n#width, height = mask.size\ncv2.imshow(\"HSV image \",imgHSV)\ncv2.imshow(\"green_mask\", maskg)\ncv2.imshow(\"red_mask\", maskr)\ncv2.imshow(\"White_mask\", maskw)\ncv2.imshow(\"Black_mask\", maskbk)\nprint()\n\n# get green straight line x = m*y + b x = [y 1] (m b)'\nprint(\"*********** Green ********************************\")\nV = cv2.findNonZero(maskg)\n(x,y) = (V[:,0,0],V[:,0,1])\n#Insert Ransac code\nX = x.reshape(-1,1)\nY = y.reshape(-1,1)\n# Fit line using all data\nlr = linear_model.LinearRegression()\nlr.fit(Y, X)\nprint(\"linear fit \", lr.coef_, lr.intercept_)\n\n# Robustly fit linear model with RANSAC algorithm\n#ransac = linear_model.RANSACRegressor(residual_threshold = 15.)\nransac = linear_model.RANSACRegressor()\nransac.fit(Y,X)\nmg = ransac.estimator_.coef_\ncg = ransac.estimator_.intercept_\nprint(\"ransac fit \", mg, cg)\ninlier_mask = ransac.inlier_mask_\niRmax = np.where(inlier_mask > 0)[0]\niRmin = np.where(inlier_mask == 0)[0] #Outliers\n#print(f\"number of non-zero: {np.count_nonzero(inlier_mask)}\" )\nprint(\"**** size inlier_mask ***\", len(iRmax), inlier_mask.shape)\nprint(\"**** size outlier_mask ***\", len(iRmin))\n\n#A = np.vstack([y, np.ones(len(y))]).T\n#x1,y1 = np.array(pixelXYCords[1], dtype=np.float32)\n#x2,y2 = np.array(pixelXYCords[2], dtype=np.float32)\n#print(\"int pixel XY Coor 1 \", pixelXYCords[1])\n#print(\"int pixel XY Coor 2 \", pixelXYCords[2])\n#mg = (x2-x1)/(y2-y1)\n#cg = x2 - mg*y2\n#print(\" mg, cg \" , mg,cg)\n #m, c = np.linalg.lstsq(A,x, rcond=None)[0]\n #print(\"m c \", m,c)\n#R = np.abs(x - A.dot([mg, cg]))\n\nxs = x[iRmax]\nys = y[iRmax]\nAs = np.vstack([ys, np.ones(len(ys))]).T\nmsg,csg = np.linalg.lstsq(As, xs, rcond=None)[0]\nprint(\"Green ms cs \",msg,csg)\nmaskgs = np.zeros_like(maskg)\nmaskgs[ys,xs] = 250\nxg_robot = msg*imgsz[0] + csg\nprint(\"Green at robot \",xg_robot)\ncv2.imshow(\"green mask sel \", maskgs)\n\nprint(\"*********** Red Right ************\")\nV = cv2.findNonZero(maskr)\n(x,y) = (V[:,0,0],V[:,0,1])\n\n#Insert Ransac code\nX = x.reshape(-1,1)\nY = y.reshape(-1,1)\n# Fit line using all data\nlr = linear_model.LinearRegression()\nlr.fit(Y, X)\nprint(\"linear fit \", lr.coef_, lr.intercept_)\n\n# Robustly fit linear model with RANSAC algorithm\n#ransac = linear_model.RANSACRegressor(residual_threshold = 15.)\nransac = linear_model.RANSACRegressor()\nransac.fit(Y,X)\nmr1 = ransac.estimator_.coef_\ncr1 = ransac.estimator_.intercept_\nprint(\"ransac fit \", mr1, cr1)\ninlier_mask = ransac.inlier_mask_\niRmax = np.where(inlier_mask > 0)[0]\niRmin = np.where(inlier_mask == 0)[0] #Outliers\n#print(f\"number of non-zero: {np.count_nonzero(inlier_mask)}\" )\nprint(\"**** size inlier_mask ***\", len(iRmax), inlier_mask.shape)\nprint(\"**** size outlier_mask ***\", len(iRmin))\n\n\nxs = x[iRmax]\nys = y[iRmax]\nAs = np.vstack([ys, np.ones(len(ys))]).T\nmsr1,csr1 = np.linalg.lstsq(As, xs, rcond=None)[0]\nprint(\"Red 1 ms cs \",msr1,csr1)\nmaskr1s = np.zeros_like(maskr)\nmaskr1s[ys,xs] = 250\nxr1_robot = msr1*imgsz[0] + csr1\nprint(\"Right red at robot \",xr1_robot)\ncv2.imshow(\"red Right mask sel \", maskr1s)\n\n#print(\"*********** Red Left ************\")\n#x = x[iRmin]\n#y = y[iRmin]\n#X = x.reshape(-1,1)\n#Y = y.reshape(-1,1)\n# Fit line using all data\n#lr = linear_model.LinearRegression()\n#lr.fit(Y, X)\n#print(\"linear fit \", lr.coef_, lr.intercept_)\n\n# Robustly fit linear model with RANSAC algorithm\n#ransac = linear_model.RANSACRegressor(residual_threshold = 15.)\n#ransac = linear_model.RANSACRegressor()\n#ransac.fit(Y,X)\n#mr2 = ransac.estimator_.coef_\n#cr2 = ransac.estimator_.intercept_\n#print(\"ransac fit \", mr2, cr2)\n#inlier_mask = ransac.inlier_mask_\n#iRmax = np.where(inlier_mask > 0)[0]\n#iRmin = np.where(inlier_mask == 0)[0] #Outliers\n##print(f\"number of non-zero: {np.count_nonzero(inlier_mask)}\" )\n#print(\"**** size inlier_mask ***\", len(iRmax), inlier_mask.shape)\n#print(\"**** size outlier_mask ***\", len(iRmin))\n\n#print(\"size iRmax \", len(iRmax), len(x))\n#xs = x[iRmax]\n#ys = y[iRmax]\n\n#As = np.vstack([ys, np.ones(len(ys))]).T\n#msr2,csr2 = np.linalg.lstsq(As, xs, rcond=None)[0]\n#print(\"Red 2 ms cs \",msr2,csr2)\n#maskr2s = np.zeros_like(maskr)\n#maskr2s[ys,xs] = 250\n#xr2_robot = msr2*imgsz[0] + csr2\n#print(\"Left red at robot \",xr2_robot)\n#cv2.imshow(\"red Left mask sel \", maskr2s)\n\n\n\n#\n## x = m*y + c\n## yo = (c2-c1)/(m1-m2)\n## xo = m1*yo + c1\n#\nyo = (csr1 - csg)/(msg - msr1)\nxo = msg*yo + csg\n#y1 = (csr2 - csg)/(msg - msr2)\n#x1 = msg*y1 + csg\n#y12= (csr2 - csr1)/(msr1 - msr2)\n#x12= msr2*y12 + csr2\nprint()\nprint(\"************* Vanishing Points **************\")\nprint(\"Right red and green vanishing point xo,yo \",xo, yo)\n#print(\"Left red and green vanishing point x1,y1 \", x1,y1)\n#print(\"Right and Left reds vanishing point x12,y12 \", x12,y12)\nprint()\nprint(\"Processing time \", time.process_time() - start)\nixo = int(xo)\niyo = int(yo)\nimage2 = cv2.circle(img, (ixo,iyo), radius=3, color=(0, 0, 255), thickness=-1)\ncv2.imshow(\"Drive To \", image2)\n\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n\n","repo_name":"leonardjd/lab13src","sub_path":"findLane.py","file_name":"findLane.py","file_ext":"py","file_size_in_byte":11954,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"42786474139","text":"from django.shortcuts import render\nfrom .parsing import record_db\nimport sqlite3\nfrom datetime import datetime\n\n\n#@cache_page(100)\ndef currencies(request):\n #record_db()\n content = get_currencies()\n last = len(content) - 26\n return render(request, 'base.html', {'content': content[last:]})\n\n\ndef get_currencies():\n date_now = str(datetime.now())[:10]\n conn = sqlite3.connect('db.sqlite3')\n cursor = conn.cursor()\n cursor.execute(f'SELECT country, unit, course, date FROM courses WHERE date LIKE \"{date_now}%\"')\n content = cursor.fetchall()\n conn.close()\n return content\n","repo_name":"Andrei-Red/parsing-currencies","sub_path":"parsing_currencies/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":604,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"21161201027","text":"import sys\nimport numpy as np\nimport matplotlib as mpl\nmpl.rcParams[\"svg.fonttype\"] = \"none\"\nmpl.rcParams[\"font.size\"] = 18\nmpl.rcParams[\"axes.unicode_minus\"]=False\nfrom matplotlib import pyplot as plt\nimport h5py as h5\nfrom scipy import integrate\n\nclass Spectrum:\n def __init__( self ):\n self.ref = []\n self.trans = []\n self.reflected = []\n self.boxScattered = []\n self.boxReference = []\n self.sourceFluxReference = []\n self.transFluxReference = []\n self.asymmetry = []\n self.Sr = []\n self.theta = []\n self.crossSectionInPx = -1.0\n self.freqmin = 0.0\n self.dfreq = 0.0\n self.Nfreq = 0.0\n self.uid = 0\n self.voxelSize = -1.0 # In nano meters\n\n def freqmax( self ):\n return self.freqmin + self.dfreq*self.Nfreq\n\n def plot( self, color=\"black\", lw=1 ):\n fig = plt.figure()\n ax = fig.add_subplot(1,1,1)\n f = np.linspace( self.freqmin, self.freqmax(), len( self.ref ) )\n wavelength = self.voxelSize/f\n ax.plot( wavelength, self.trans/self.ref, color=color, lw=lw )\n ax.set_xlabel(\"Wavelength (nm)\")\n ax.set_ylabel(\"Transmittivity (\\$I_e/I_0\\$)\")\n ax.spines[\"right\"].set_visible(False)\n ax.spines[\"top\"].set_visible(False)\n ax.yaxis.set_ticks_position(\"left\")\n ax.xaxis.set_ticks_position(\"bottom\")\n return fig, ax\n\n def asymmetryFactor( self, color=\"black\", lw=1, ax=None, label=\"\" ):\n if ( len(self.asymmetry) == 0 ):\n raise (Exception(\"Assymmetry not given!\"))\n f = f = np.linspace( self.freqmin, self.freqmax(), len(self.asymmetry) )\n wavelength = self.voxelSize/f\n if ( ax is None ):\n fig = plt.figure()\n ax = fig.add_subplot(1,1,1)\n ax.plot(wavelength, self.asymmetry, color=color, lw=lw, label=label)\n ax.set_xlabel(\"Wavelength (nm)\")\n ax.set_ylabel(\"Asymmetry factor, \\$g\\$\")\n return fig, ax\n\n def plotDiff( self ):\n fig = plt.figure()\n ax = fig.add_subplot(1,1,1)\n f = np.linspace( self.freqmin, self.freqmax(), len( self.ref) )\n T = self.trans/self.ref\n R = np.abs( self.reflected/self.ref )\n ax.plot( self.voxelSize/f, R+T, color=\"black\" )\n ax.set_xlabel( \"Wavelength (nm)\" )\n ax.set_ylabel(\"\\$R+T\\$\")\n return fig, ax\n\n def getScatteringCrossSecion( self ):\n intensityRatio = np.abs( (self.boxScattered-self.boxFluxRef)/self.sourceFluxReference )\n area = self.crossSectionInPx*self.voxelSize*self.voxelSize/1E6\n return intensityRatio*area\n\n def scatteringCrossSection( self, color=\"black\", lw=1, asymmetry=False, label=\"\" ):\n fig = plt.figure()\n ax = fig.add_subplot(1,1,1)\n if ( len(self.sourceFluxReference) == 0 ):\n print (\"No source flux given!\")\n return fig, ax\n if ( len(self.boxScattered) == 0 ):\n print (\"No scattered box flux given!\")\n if ( self.crossSectionInPx < 0.0 ):\n print (\"No cross section in pixels given!\")\n\n f = np.linspace( self.freqmin, self.freqmax(), len( self.ref ) )\n #intensityRatio = np.abs( (self.boxScattered-self.boxFluxRef)/self.sourceFluxReference )\n #area = self.crossSectionInPx*self.voxelSize*self.voxelSize/1E6\n\n wavelength = self.voxelSize/f\n ax.plot( wavelength, self.getScatteringCrossSecion(), color=color, lw=lw)\n ax.set_ylabel(\"Scattering cross section, \\$\\sigma_s\\$ (\\$\\SI{}{\\micro\\meter\\squared}\\$)\")\n ax.set_xlabel(\"Wavelength (nm)\")\n ax.spines[\"right\"].set_visible(False)\n ax.spines[\"top\"].set_visible(False)\n ax.xaxis.set_ticks_position(\"bottom\")\n ax.yaxis.set_ticks_position(\"left\")\n\n if ( asymmetry ):\n ax2 = ax.twinx()\n self.asymmetryFactor( color=\"#377eb8\", ax=ax2, label=\"g\" )\n ax2.plot([],[],color=color, label=label)\n ax2.legend(loc=\"lower right\", frameon=False, labelspacing=0.02)\n return fig, ax\n\n def getDimlessFreq( self, wavelenthInnm ):\n dimlessWave = wavelenthInnm/self.voxelSize\n return 1.0/dimlessWave\n\n def reflection( self, color=\"black\", lw=1 ):\n if ( len(self.ref) == 0 ):\n raise( Exception(\"Reflection not initialized!\") )\n fig = plt.figure()\n ax = fig.add_subplot(1,1,1)\n f = np.linspace( self.freqmin, self.freqmax(), len( self.ref ) )\n wavelength = self.voxelSize/f\n backScattered = np.abs( self.reflected/self.ref )\n ax.plot( wavelength, backScattered, color=color, lw=lw )\n ax.set_xlabel(\"Wavelength (nm)\")\n ax.set_ylabel(\"Reflectivity (\\$I_r/I_0\\$)\")\n return fig, ax\n\n def SrDistribution( self ):\n if ( len(self.Sr.shape) != 2 ):\n raise (Exception(\"Sr not given!\"))\n elif ( len(self.theta) == 0 ):\n raise (Exception(\"Theta values not given!\"))\n\n # Was a bug in the storing of theta\n if ( len(self.theta) != self.Sr.shape[0] ):\n self.theta = self.theta[:self.Sr.shape[0]]\n f = np.linspace( self.freqmin, self.freqmax(), len( self.ref ) )\n wavelength = self.voxelSize/f\n colors = [\"#1b9e77\", \"#d95f02\", \"#7570b3\", \"#e7298a\", \"#66a61e\"]\n N = 3\n fig = plt.figure()\n ax = fig.add_subplot(1,1,1, projection=\"polar\")\n #ax = fig.add_subplot(1,1,1)\n counter = 0\n for i in range(0, self.Sr.shape[1], int(self.Sr.shape[1]/N) ):\n ax.plot(self.theta, np.log(self.Sr[:,i]/np.min(self.Sr[:,i])), color=colors[counter%len(colors)], label=\"%d nm\"%(wavelength[i]))\n ax.plot(2.0*np.pi-self.theta, np.log(self.Sr[:,i]/np.min(self.Sr[:,i])), color=colors[counter%len(colors)] )\n counter += 1\n ax.legend(frameon=False, labelspacing=0.05)\n return fig, ax\n\ndef initSpectrum( fname ):\n specPlot = Spectrum()\n with h5.File( fname, 'r' ) as hf:\n specPlot.ref = np.array( hf.get(\"spectrumReference\"))\n specPlot.trans = np.array( hf.get(\"spectrumTransmitted\"))\n freqs = np.array( hf.get(\"spectrumFreqs\") )\n specPlot.freqmin = freqs[0]\n specPlot.dfreq = freqs[1]\n specPlot.Nfreq = freqs[2]\n if ( \"vxSizeNM\" in hf.keys() ):\n specPlot.voxelSize = np.array( hf.get(\"vxSizeNM\") )[0]\n\n if ( \"boxFluxScat\" in hf.keys() ):\n specPlot.boxScattered = np.array( hf.get(\"boxFluxScat\") )\n\n if ( \"reflPlaneRef\" in hf.keys() ):\n specPlot.sourceFluxReference = np.array( hf.get(\"reflPlaneRef\") )\n\n if ( \"geometrySourceVolume\" in hf.keys() ):\n val = np.array( hf.get(\"geometrySourceVolume\") )\n dx = val[3]-val[0]\n dy = val[4]-val[1]\n dz = val[5]-val[2]\n crossSections = np.array( [dx*dy,dx*dz,dy*dz] )\n specPlot.crossSectionInPx = np.max( crossSections )\n\n if ( \"reflPlaneScat\" in hf.keys() ):\n specPlot.reflected = np.array( hf.get(\"reflPlaneScat\") )\n\n if ( \"boxFluxRef\" in hf.keys() ):\n specPlot.boxFluxRef = np.array( hf.get(\"boxFluxRef\") )\n\n if ( \"Asymmetry\" in hf.keys() ):\n specPlot.asymmetry = np.array( hf.get(\"Asymmetry\") )\n\n if ( \"Sr\" in hf.keys() ):\n specPlot.Sr = np.array( hf.get(\"Sr\") )\n\n if ( \"SrTheta\" in hf.keys() ):\n specPlot.theta = np.array( hf.get(\"SrTheta\") )\n return specPlot\n\ndef main( argv ):\n if ( len(argv) != 1 ):\n print (\"Usage: python plotSpectrum.py \")\n return\n fname = argv[0]\n specPlot = initSpectrum( fname )\n\n # Tweak\n specPlot.voxelSize = 21.6\n fig, ax = specPlot.plot()\n fig2, ax2 = specPlot.plotDiff()\n fig3, ax3 = specPlot.scatteringCrossSection()\n try:\n fig4, ax4 = specPlot.reflection()\n except Exception as exc:\n print (str(exc))\n\n try:\n fig5, ax5 = specPlot.scatteringCrossSection( color=\"#e41a1c\", label=\"\\$\\sigma_s\\$\", asymmetry=True )\n except Exception as exc:\n print (str(exc))\n plt.show()\n\nif __name__ == \"__main__\":\n main( sys.argv[1:] )\n","repo_name":"davidkleiven/OptiX","sub_path":"Coccolith/Scripts/plotSpectrum.py","file_name":"plotSpectrum.py","file_ext":"py","file_size_in_byte":8168,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"13991963631","text":"from django.conf import settings\n\nfrom .compat import get_model\n\ntry:\n from django.contrib.auth import get_user_model\n UserModel = get_user_model\nexcept ImportError:\n UserModel = lambda: get_model('auth', 'User')\n\n\ndef UserModelString():\n try:\n return settings.AUTH_USER_MODEL\n except AttributeError:\n return 'auth.User'\n\n\ndef UsernameField():\n return getattr(UserModel(), 'USERNAME_FIELD', 'username')\n","repo_name":"ejakait/bitkoin","sub_path":"bitkoin/django-registration-master/registration/users.py","file_name":"users.py","file_ext":"py","file_size_in_byte":435,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"14568659281","text":"# import libraries\nimport pandas as pd\nimport numpy as np\nfrom sqlalchemy import create_engine\nimport re\nimport joblib\nimport string \nimport sys \n\nimport nltk\nnltk.download(['punkt', 'wordnet','stopwords'])\n\nfrom nltk.corpus import stopwords\nfrom nltk.tokenize import word_tokenize\nfrom nltk.stem import WordNetLemmatizer\nfrom nltk.stem.porter import PorterStemmer\n\nfrom sklearn.pipeline import Pipeline, FeatureUnion\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\nfrom sklearn.model_selection import train_test_split, GridSearchCV\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.multioutput import MultiOutputClassifier\nfrom sklearn.base import BaseEstimator, TransformerMixin\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, make_scorer\nfrom sklearn.metrics import classification_report\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.naive_bayes import BernoulliNB, MultinomialNB\nfrom scipy.sparse import csr_matrix\nfrom sklearn import svm\n\nfrom class_def import Lemmer\n\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import learning_curve\nfrom sklearn.model_selection import ShuffleSplit\nfrom sklearn.model_selection import validation_curve\n\n# Loading data lite from file path\ndef load_data(database_filepath, noDisaster):\n \"\"\"\n\tLoad database and get dataset\n\tArgs: \n\t\tdatabase_filepath (str): file path of sqlite database\n\tReturn:\n\t\tX (pandas dataframe): Features\n\t\ty (pandas dataframe): Targets/ Labels\n categories (list): List of categorical columns\n :param databse_filepath:\n \"\"\"\n engine = create_engine('sqlite:///../'+database_filepath)\n df = pd.read_sql_table('DS_messages', engine)\n engine.dispose()\n \n if noDisaster: df = df.drop('related',axis=1)\n \n X = df['message']\n y = df[df.columns[4:]]\n categories = y.columns.tolist()\n\n return X, y, categories\n\n# Loading original data from file path\ndef load_OrigData(noDisaster=True):\n \"\"\"\n\tLoad database and get dataset\n\tArgs: \n\t\tdatabase_filepath (str): file path of sqlite database\n\tReturn:\n\t\tX_train (pandas dataframe): train Features\n X_test (pandas dataframe): test Features\n\t\ty_train (pandas dataframe): Targets/ Labels for training\n y_test (pandas dataframe): Targets/ Labels for testing\n categories (list): List of categorical columns\n :param databse_filepath:\n \"\"\"\n path = '../../data/'\n filenames = ['disaster_response_messages_training.csv',\n 'disaster_response_messages_validation.csv',\n 'disaster_response_messages_test.csv',]\n for filename in filenames:\n df = pd.read_csv(path+filename, encoding = 'latin-1')\n df.drop(['id', 'split', 'original', 'genre', 'PII', 'offer', 'child_alone'], \n axis = 1, inplace = True)\n if noDisaster: df = df.drop('related', axis=1)\n if filename == 'disaster_response_messages_training.csv':\n X_train = df['message']\n y_train = df[df.columns[1:]]\n categories = y_train.columns.tolist()\n elif filename == 'disaster_response_messages_validation.csv':\n X_val = df['message']\n y_val = df[df.columns[1:]]\n elif filename == 'disaster_response_messages_test.csv':\n X_test = df['message']\n y_test = df[df.columns[1:]]\n\n return X_train, y_train, X_val, y_val, X_test, y_test, categories \n\n# Tokenizing\n# def tokenize(text):\n# # normalize text and remove punctuation\n# text = re.sub(r\"[^a-zA-Z0-9]\", \" \", text.lower())\n \n# # tokenize text\n# tokens = word_tokenize(text)\n# stop_words = stopwords.words(\"english\")\n# words = [w for w in tokens if w not in stop_words]\n \n# # Reduce words to their stems\n# stemmer = PorterStemmer()\n# stemmed = [stemmer.stem(w) for w in words]\n \n# # Reduce words to their root form\n# lemmatizer = WordNetLemmatizer()\n# lemmed = [lemmatizer.lemmatize(w) for w in stemmed]\n \n# return lemmed\n \n# Building model\ndef build_model():\n \"\"\"Returns the GridSearchCV model\n Args:\n None\n Returns:\n cv: Grid search model object\n \"\"\"\n #clf = svm.SVC() #MultinomialNB() #BernoulliNB()\n\n # The pipeline has tfidf, dimensionality reduction, and classifier\n pipeline = Pipeline([\n ('vect', CountVectorizer(tokenizer=Lemmer.tokenize)),\n ('tfidf', TfidfTransformer()),\n ('clf', MultiOutputClassifier(svm.SVC(gamma=10))) #MultinomialNB() # #BernoulliNB()\n ])\n\n # Parameters for GridSearchCV\n param_grid = {\n 'vect__max_df': (0.5, 0.75, 1.0),\n 'vect__ngram_range': ((1, 1), (1,2)),\n 'vect__max_features': (None, 5000,10000),\n 'tfidf__use_idf': (True, False)\n }\n\n cv = GridSearchCV(pipeline, param_grid)\n\n return cv\n\ndef evaluate_model(model, X_test, y_test, categories):\n \"\"\"Prints multi-output classification results\n Args:\n model (pandas dataframe): the scikit-learn fitted model\n X_text (pandas dataframe): The X test set\n y_test (pandas dataframe): the y test classifications\n category_names (list): the category names\n Returns:\n None\n \"\"\"\n\n # Generate predictions\n y_pred = model.predict(X_test)\n\n # Print out the full classification report\n print(classification_report(y_test, y_pred, target_names=categories))\n\n# Save model \ndef save_model(model, model_filepath):\n \"\"\"\n Dumps the model to given path \n Args: \n model: the fitted model\n model_filepath (str): filepath to save model\n Return:\n None\n\t\"\"\"\n joblib.dump(model, model_filepath)\n \ndef plot_validation_curve(estimator, X, y, title, axes=None, cv=None, n_jobs=None): \n if axes is None:\n _, axes = plt.subplots(1, 1, figsize=(20, 5))\n \n param_range = np.logspace(0.5, 1.5, 5)\n train_scores, test_scores = validation_curve(\n estimator,\n X,\n y,\n param_name='clf__estimator__gamma',\n cv=cv,\n n_jobs=n_jobs,\n param_range=param_range,\n #scoring=\"accuracy\",\n )\n train_scores_mean = np.mean(train_scores, axis=1)\n train_scores_std = np.std(train_scores, axis=1)\n test_scores_mean = np.mean(test_scores, axis=1)\n test_scores_std = np.std(test_scores, axis=1)\n \n plt.title(title)\n plt.xlabel(r\"$\\gamma$\")\n plt.ylabel(\"Score\")\n plt.ylim(0.0, 1.1)\n lw = 2\n plt.semilogx(\n param_range, train_scores_mean, label=\"Training score\", color=\"darkorange\", lw=lw\n )\n plt.fill_between(\n param_range,\n train_scores_mean - train_scores_std,\n train_scores_mean + train_scores_std,\n alpha=0.2,\n color=\"darkorange\",\n lw=lw,\n )\n plt.semilogx(\n param_range, test_scores_mean, label=\"Cross-validation score\", color=\"navy\", lw=lw\n )\n plt.fill_between(\n param_range,\n test_scores_mean - test_scores_std,\n test_scores_mean + test_scores_std,\n alpha=0.2,\n color=\"navy\",\n lw=lw,\n )\n plt.legend(loc=\"best\")\n \n return plt\n \ndef plot_learning_curve(\n estimator,\n title,\n X,\n y,\n axes=None,\n ylim=None,\n cv=None,\n n_jobs=None,\n train_sizes=np.linspace(0.1, 1.0, 5),\n):\n \"\"\"\n Generate 3 plots: the test and training learning curve, the training\n samples vs fit times curve, the fit times vs score curve.\n\n Parameters\n ----------\n estimator : estimator instance\n An estimator instance implementing `fit` and `predict` methods which\n will be cloned for each validation.\n\n title : str\n Title for the chart.\n\n X : array-like of shape (n_samples, n_features)\n Training vector, where ``n_samples`` is the number of samples and\n ``n_features`` is the number of features.\n\n y : array-like of shape (n_samples) or (n_samples, n_features)\n Target relative to ``X`` for classification or regression;\n None for unsupervised learning.\n\n axes : array-like of shape (3,), default=None\n Axes to use for plotting the curves.\n\n ylim : tuple of shape (2,), default=None\n Defines minimum and maximum y-values plotted, e.g. (ymin, ymax).\n\n cv : int, cross-validation generator or an iterable, default=None\n Determines the cross-validation splitting strategy.\n Possible inputs for cv are:\n\n - None, to use the default 5-fold cross-validation,\n - integer, to specify the number of folds.\n - :term:`CV splitter`,\n - An iterable yielding (train, test) splits as arrays of indices.\n\n For integer/None inputs, if ``y`` is binary or multiclass,\n :class:`StratifiedKFold` used. If the estimator is not a classifier\n or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.\n\n Refer :ref:`User Guide ` for the various\n cross-validators that can be used here.\n\n n_jobs : int or None, default=None\n Number of jobs to run in parallel.\n ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\n ``-1`` means using all processors. See :term:`Glossary `\n for more details.\n\n train_sizes : array-like of shape (n_ticks,)\n Relative or absolute numbers of training examples that will be used to\n generate the learning curve. If the ``dtype`` is float, it is regarded\n as a fraction of the maximum size of the training set (that is\n determined by the selected validation method), i.e. it has to be within\n (0, 1]. Otherwise it is interpreted as absolute sizes of the training\n sets. Note that for classification the number of samples usually have\n to be big enough to contain at least one sample from each class.\n (default: np.linspace(0.1, 1.0, 5))\n \"\"\"\n if axes is None:\n _, axes = plt.subplots(1, 3, figsize=(20, 5))\n\n axes[0].set_title(title)\n if ylim is not None:\n axes[0].set_ylim(*ylim)\n axes[0].set_xlabel(\"Training examples\")\n axes[0].set_ylabel(\"Score\")\n\n train_sizes, train_scores, test_scores, fit_times, _ = learning_curve(\n estimator,\n X,\n y,\n cv=cv,\n n_jobs=n_jobs,\n train_sizes=train_sizes,\n return_times=True,\n )\n train_scores_mean = np.mean(train_scores, axis=1)\n train_scores_std = np.std(train_scores, axis=1)\n test_scores_mean = np.mean(test_scores, axis=1)\n test_scores_std = np.std(test_scores, axis=1)\n fit_times_mean = np.mean(fit_times, axis=1)\n fit_times_std = np.std(fit_times, axis=1)\n\n # Plot learning curve\n axes[0].grid()\n axes[0].fill_between(\n train_sizes,\n train_scores_mean - train_scores_std,\n train_scores_mean + train_scores_std,\n alpha=0.1,\n color=\"r\",\n )\n axes[0].fill_between(\n train_sizes,\n test_scores_mean - test_scores_std,\n test_scores_mean + test_scores_std,\n alpha=0.1,\n color=\"g\",\n )\n axes[0].plot(\n train_sizes, train_scores_mean, \"o-\", color=\"r\", label=\"Training score\"\n )\n axes[0].plot(\n train_sizes, test_scores_mean, \"o-\", color=\"g\", label=\"Cross-validation score\"\n )\n axes[0].legend(loc=\"best\")\n\n # Plot n_samples vs fit_times\n axes[1].grid()\n axes[1].plot(train_sizes, fit_times_mean, \"o-\")\n axes[1].fill_between(\n train_sizes,\n fit_times_mean - fit_times_std,\n fit_times_mean + fit_times_std,\n alpha=0.1,\n )\n axes[1].set_xlabel(\"Training examples\")\n axes[1].set_ylabel(\"fit_times\")\n axes[1].set_title(\"Scalability of the model\")\n\n # Plot fit_time vs score\n axes[2].grid()\n axes[2].plot(fit_times_mean, test_scores_mean, \"o-\")\n axes[2].fill_between(\n fit_times_mean,\n test_scores_mean - test_scores_std,\n test_scores_mean + test_scores_std,\n alpha=0.1,\n )\n axes[2].set_xlabel(\"fit_times\")\n axes[2].set_ylabel(\"Score\")\n axes[2].set_title(\"Performance of the model\")\n\n return plt\n \ndef custom_cv(trainIndices, valIndices):\n yield trainIndices, valIndices\n \n \ndef main():\n if len(sys.argv) == 1:\n # database_filepath, model_filepath = sys.argv[1:]\n database_filepath = 'data/MDRM-lite.db'\n model_filepath = 'DS_model_SVC.pkl'\n #model_filepath = 'Kaggle_SVCLITE05.pkl'\n print('Loading data...\\n DATABASE: {}'.format(database_filepath))\n \n X, y, categories = load_data(database_filepath, noDisaster=False)\n # cv = ShuffleSplit(n_splits=5, test_size=0.33, random_state=42)\n cv = joblib.load('datasplits.pkl') \n for tr, te in cv.split(X): print(\"TRAIN:\", tr, \"TEST:\", te)\n X_train = X[tr]; y_train = y.loc[tr]; X_test = X[te]; y_test = y.loc[te]\n #joblib.dump(cv, 'datasplits.pkl')\n \n #X_train, y_train, X_val, y_val, X_test, y_test, categories = load_OrigData(noDisaster=True)\n # #idx = np.random.choice(len(X_train), size=int(0.75*len(X_train)))\n # #joblib.dump(idx, 'datasplitsOrigLITE075.pkl')\n #idx = joblib.load('datasplitsOrigLITE05.pkl') \n #X_train = X_train[idx]; y_train = y_train.loc[idx]\n #X = pd.concat([X_train,X_val], sort=False); X.reset_index(drop=True, inplace=True)\n #y = pd.concat([y_train,y_val], sort=False); y.reset_index(drop=True, inplace=True)\n #valIndices = y.index[len(y_train):].values.astype(int)\n #cv = custom_cv(valIndices, valIndices)\n \n #X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n if 'related' not in categories: model_filepath = model_filepath[:-4]+'_noRelatedClass.pkl'\n\n #print('Building model...')\n #model = build_model()\n \n model = joblib.load(model_filepath)\n\n #print('Training model {} ...'.format(model_filepath))\n #model.fit(X_train, y_train)\n # model.fit(X_train.to_numpy(), csr_matrix(y_train).todense()) # for CategoricalNB\n\n print('Evaluating model...')\n evaluate_model(model, X_test, y_test, categories)\n\n #print('Saving model...\\n MODEL: {}'.format(model_filepath))\n #save_model(model, model_filepath)\n #print('Trained model saved!') \n \n \n # plot validation curves\n # fig, axes = plt.subplots(1, 1, figsize=(10, 15))\n # title = r\"Validation Curve with (SVC)\"\n # # train_scores, test_scores = validation_curve(\n # # model.estimator,\n # # X,\n # # y,\n # # param_name='clf__estimator__gamma',\n # # cv=cv,\n # # n_jobs=1,\n # # param_range=1,\n # # scoring=\"accuracy\",\n # # )\n # plot_validation_curve(\n # model.estimator, title, X, y, axes=axes, cv=cv, n_jobs=1\n # )\n # plt.show()\n \n # plot learning curves\n # plt.close()\n #fig, axes = plt.subplots(3, 1, figsize=(10, 15))\n #title = r\"Learning Curves (SVM, RBF kernel)\"\n #plot_learning_curve(\n # model.estimator, title, X, y, axes=axes[:], ylim=(0, 1.01), cv=cv, n_jobs=1\n #)\n #plt.show()\n else:\n print('Please provide the filepath of the disaster messages database '\\\n 'as the first argument and the filepath of the pickle file to '\\\n 'save the model to as the second argument. \\n\\nExample: python '\\\n 'train_classifier.py ../data/DisasterResponse.db classifier.pkl')\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"IslandsLab/NLP-Disaster","sub_path":"DisasterResponseApp/model/train-multiclassifier.py","file_name":"train-multiclassifier.py","file_ext":"py","file_size_in_byte":15680,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"42173326302","text":"# ----------------------------------------\n# Written by Yude Wang\n# ----------------------------------------\nfrom __future__ import print_function, division\nimport os\nimport torch\nimport cv2\nimport multiprocessing\nimport pandas as pd\nimport numpy as np\nfrom skimage import io\nfrom PIL import Image\nfrom torch.utils.data import Dataset\nfrom datasets.transform import *\nfrom utils.imutils import *\nfrom utils.registry import DATASETS\nfrom datasets.BaseDataset import BaseDataset\n\n@DATASETS.register_module\nclass VOCDataset(BaseDataset):\n\tdef __init__(self, cfg, period, transform='none'):\n\t\tsuper(VOCDataset, self).__init__(cfg, period, transform)\n\t\tself.dataset_name = 'VOC%d'%cfg.DATA_YEAR\n\t\tself.root_dir = os.path.join(cfg.ROOT_DIR,'data','VOCdevkit')\n\t\tself.dataset_dir = os.path.join(self.root_dir,self.dataset_name)\n\t\tself.rst_dir = os.path.join(self.root_dir,'results',self.dataset_name,'Segmentation')\n\t\tself.eval_dir = os.path.join(self.root_dir,'eval_result',self.dataset_name,'Segmentation')\n\t\tself.img_dir = os.path.join(self.dataset_dir, 'JPEGImages')\n\t\tself.ann_dir = os.path.join(self.dataset_dir, 'Annotations')\n\t\tself.seg_dir = os.path.join(self.dataset_dir, 'SegmentationClass')\n\t\tself.set_dir = os.path.join(self.dataset_dir, 'ImageSets', 'Segmentation')\n\t\tif cfg.DATA_PSEUDO_GT:\n\t\t\tself.pseudo_gt_dir = cfg.DATA_PSEUDO_GT\n\t\telse:\n\t\t\tself.pseudo_gt_dir = os.path.join(self.root_dir,'pseudo_gt',self.dataset_name,'Segmentation')\n\n\t\tfile_name = None\n\t\tif cfg.DATA_AUG and 'train' in self.period:\n\t\t\tfile_name = self.set_dir+'/'+period+'aug.txt'\n\t\telse:\n\t\t\tfile_name = self.set_dir+'/'+period+'.txt'\n\t\tdf = pd.read_csv(file_name, names=['filename'])\n\t\tself.name_list = df['filename'].values\n\t\tif self.dataset_name == 'VOC2012':\n\t\t\tself.categories = ['aeroplane','bicycle','bird','boat','bottle','bus','car','cat','chair','cow',\n\t\t\t\t\t\t\t 'diningtable','dog','horse','motorbike','person','pottedplant','sheep','sofa','train','tvmonitor']\n\t\t\tself.coco2voc = [[0],[5],[2],[16],[9],[44],[6],[3],[17],[62],\n\t\t\t\t\t\t\t [21],[67],[18],[19],[4],[1],[64],[20],[63],[7],[72]]\n\n\t\t\tself.num_categories = len(self.categories)+1\n\t\t\tself.cmap = self.__colormap(len(self.categories)+1)\n\n\tdef __len__(self):\n\t\treturn len(self.name_list)\n\n\tdef load_name(self, idx):\n\t\tname = self.name_list[idx]\n\t\treturn name\n\n\tdef load_image(self, idx):\n\t\tname = self.name_list[idx]\n\t\timg_file = self.img_dir + '/' + name + '.jpg'\n\t\timage = cv2.imread(img_file)\n\t\timage_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n\t\treturn image_rgb\n\n\tdef load_segmentation(self, idx):\n\t\tname = self.name_list[idx]\n\t\tseg_file = self.seg_dir + '/' + name + '.png'\n\t\tsegmentation = np.array(Image.open(seg_file))\n\t\treturn segmentation\n\n\tdef load_pseudo_segmentation(self, idx):\n\t\tname = self.name_list[idx]\n\t\tseg_file = self.pseudo_gt_dir + '/' + name + '.png'\n\t\tsegmentation = np.array(Image.open(seg_file))\n\t\treturn segmentation\n\n\tdef __colormap(self, N):\n\t\t\"\"\"Get the map from label index to color\n\n\t\tArgs:\n\t\t\tN: number of class\n\n\t\t\treturn: a Nx3 matrix\n\n\t\t\"\"\"\n\t\tcmap = np.zeros((N, 3), dtype = np.uint8)\n\n\t\tdef uint82bin(n, count=8):\n\t\t\t\"\"\"returns the binary of integer n, count refers to amount of bits\"\"\"\n\t\t\treturn ''.join([str((n >> y) & 1) for y in range(count-1, -1, -1)])\n\n\t\tfor i in range(N):\n\t\t\tr = 0\n\t\t\tg = 0\n\t\t\tb = 0\n\t\t\tidx = i\n\t\t\tfor j in range(7):\n\t\t\t\tstr_id = uint82bin(idx)\n\t\t\t\tr = r ^ ( np.uint8(str_id[-1]) << (7-j))\n\t\t\t\tg = g ^ ( np.uint8(str_id[-2]) << (7-j))\n\t\t\t\tb = b ^ ( np.uint8(str_id[-3]) << (7-j))\n\t\t\t\tidx = idx >> 3\n\t\t\tcmap[i, 0] = r\n\t\t\tcmap[i, 1] = g\n\t\t\tcmap[i, 2] = b\n\t\treturn cmap\n\n\tdef load_ranked_namelist(self):\n\t\tdf = self.read_rank_result()\n\t\tself.name_list = df['filename'].values\n\n\tdef label2colormap(self, label):\n\t\tm = label.astype(np.uint8)\n\t\tr,c = m.shape\n\t\tcmap = np.zeros((r,c,3), dtype=np.uint8)\n\t\tcmap[:,:,0] = (m&1)<<7 | (m&8)<<3\n\t\tcmap[:,:,1] = (m&2)<<6 | (m&16)<<2\n\t\tcmap[:,:,2] = (m&4)<<5\n\t\tcmap[m==255] = [255,255,255]\n\t\treturn cmap\n\t\n\tdef save_result(self, result_list, model_id):\n\t\t\"\"\"Save test results\n\n\t\tArgs:\n\t\t\tresult_list(list of dict): [{'name':name1, 'predict':predict_seg1},{...},...]\n\n\t\t\"\"\"\n\t\tfolder_path = os.path.join(self.rst_dir,'%s_%s'%(model_id,self.period))\n\t\tif not os.path.exists(folder_path):\n\t\t\tos.makedirs(folder_path)\n\t\t\t\n\t\tfor sample in result_list:\n\t\t\tfile_path = os.path.join(folder_path, '%s.png'%sample['name'])\n\t\t\tcv2.imwrite(file_path, sample['predict'])\n\n\tdef save_pseudo_gt(self, result_list, folder_path=None):\n\t\t\"\"\"Save pseudo gt\n\n\t\tArgs:\n\t\t\tresult_list(list of dict): [{'name':name1, 'predict':predict_seg1},{...},...]\n\n\t\t\"\"\"\n\t\tfolder_path = self.pseudo_gt_dir if folder_path is None else folder_path\n\t\tif not os.path.exists(folder_path):\n\t\t\tos.makedirs(folder_path)\n\t\tfor sample in result_list:\n\t\t\tfile_path = os.path.join(folder_path, '%s.png'%(sample['name']))\n\t\t\tcv2.imwrite(file_path, sample['predict'])\n\n\tdef do_python_eval(self, model_id):\n\t\tpredict_folder = os.path.join(self.rst_dir,'%s_%s'%(model_id,self.period))\n\t\tgt_folder = self.seg_dir\n\t\tTP = []\n\t\tP = []\n\t\tT = []\n\t\tfor i in range(self.num_categories):\n\t\t\tTP.append(multiprocessing.Value('i', 0, lock=True))\n\t\t\tP.append(multiprocessing.Value('i', 0, lock=True))\n\t\t\tT.append(multiprocessing.Value('i', 0, lock=True))\n\t\t\n\t\tdef compare(start,step,TP,P,T):\n\t\t\tfor idx in range(start,len(self.name_list),step):\n\t\t\t\tname = self.name_list[idx]\n\t\t\t\tpredict_file = os.path.join(predict_folder,'%s.png'%name)\n\t\t\t\tgt_file = os.path.join(gt_folder,'%s.png'%name)\n\t\t\t\tpredict = np.array(Image.open(predict_file))\n\t\t\t\tgt = np.array(Image.open(gt_file))\n\t\t\t\tcal = gt<255\n\t\t\t\tmask = (predict==gt) * cal\n\t\t \n\t\t\t\tfor i in range(self.num_categories):\n\t\t\t\t\tP[i].acquire()\n\t\t\t\t\tP[i].value += np.sum((predict==i)*cal)\n\t\t\t\t\tP[i].release()\n\t\t\t\t\tT[i].acquire()\n\t\t\t\t\tT[i].value += np.sum((gt==i)*cal)\n\t\t\t\t\tT[i].release()\n\t\t\t\t\tTP[i].acquire()\n\t\t\t\t\tTP[i].value += np.sum((gt==i)*mask)\n\t\t\t\t\tTP[i].release()\n\t\tp_list = []\n\t\tfor i in range(8):\n\t\t\tp = multiprocessing.Process(target=compare, args=(i,8,TP,P,T))\n\t\t\tp.start()\n\t\t\tp_list.append(p)\n\t\tfor p in p_list:\n\t\t\tp.join()\n\t\tIoU = []\n\t\tfor i in range(self.num_categories):\n\t\t\tIoU.append(TP[i].value/(T[i].value+P[i].value-TP[i].value+1e-10))\n\t\tloglist = {}\n\t\tfor i in range(self.num_categories):\n\t\t\tif i == 0:\n\t\t\t\tprint('%11s:%7.3f%%'%('background',IoU[i]*100),end='\\t')\n\t\t\t\tloglist['background'] = IoU[i] * 100 \n\t\t\telse:\n\t\t\t\tif i%2 != 1:\n\t\t\t\t\tprint('%11s:%7.3f%%'%(self.categories[i-1],IoU[i]*100),end='\\t')\n\t\t\t\telse:\n\t\t\t\t\tprint('%11s:%7.3f%%'%(self.categories[i-1],IoU[i]*100))\n\t\t\t\tloglist[self.categories[i-1]] = IoU[i] * 100\n\t\t\t\t\t\n\t\tmiou = np.mean(np.array(IoU))\n\t\tprint('\\n======================================================')\n\t\tprint('%11s:%7.3f%%'%('mIoU',miou*100))\n\t\tloglist['mIoU'] = miou * 100\n\t\treturn loglist\n\n\n@DATASETS.register_module\nclass SemiWeak_VOCDataset(VOCDataset):\n\tdef __init__(self, cfg, period, transform='none', split_idx=None, puresemi=False):\n\t\tsuper(SemiWeak_VOCDataset, self).__init__(cfg, period, transform)\n\t\tif split_idx:\n\t\t\tself.split_idx = split_idx\n\t\telif cfg.DATA_SPLIT is not None:\n\t\t\tself.split_idx = cfg.DATA_SPLIT\n\t\t\t#print(self.split_idx)\n\t\telse:\n\t\t\tself.split_idx = len(self.seg_name_list)\n\t\tif period == 'train':\n\t\t\tif puresemi:\n\t\t\t\tfile_name = os.path.join(self.set_dir,f'train_aug_labeled_1-{10581//self.split_idx+1}.txt')\n\t\t\telse:\n\t\t\t\tfile_name = self.set_dir+'/'+period+'.txt'\n\t\t\tdf = pd.read_csv(file_name, names=['filename'])\n\t\t\tself.seg_name_list = df['filename'].values\n\t\t\t#print(len(self.seg_name_list))\n\n\t\t\tlist_all = self.seg_name_list.copy()\n\t\t\tfor name in self.name_list:\n\t\t\t\tif name not in self.seg_name_list: \n\t\t\t\t\tlist_all = np.append(list_all, name)\n\t\t\tself.name_list = list_all\n\t\tif self.cfg.DATA_RANDOMCOPYPASTE>0:\n\t\t\tself.singleclass_filter()\n\t\n\tdef singleclass_filter(self):\n\t\tself.clsidxgroup = {}\n\t\tself.singlecls = []\n\t\tfor i in range(self.num_categories):\n\t\t\tself.clsidxgroup[i] = []\n\t\tfor idx in range(len(self.name_list)):\n\t\t\tsegmentation = self.load_segmentation(idx)\n\t\t\tunique = np.unique(segmentation)\n\t\t\tunique = unique[:-1] if 255 in unique else unique\n\t\t\tunique = unique[1:] if 0 in unique else unique\n\t\t\tif len(unique)==1:\n\t\t\t\tself.clsidxgroup[unique[0]].append(idx)\n\t\t\t\tself.singlecls.append(True)\n\t\t\telse:\n\t\t\t\tself.singlecls.append(False)\n\t\t\t\n\n\tdef __getitem__(self, idx):\n\t\tsample = self.__sample_generate__(idx, self.split_idx)\n\t\tif 'segmentation' in sample.keys():\n\t\t\tsample['mask'] = sample['segmentation'] < self.num_categories\n\t\t\tif idx >= self.split_idx:\n\t\t\t\tmask_numpy = sample['mask']\n\t\t\t\tsample['mask'] = np.zeros(mask_numpy.shape)\n\t\t\tt = sample['segmentation'].copy()\n\t\t\tt[t >= self.num_categories] = 0\n\t\t\tsample['segmentation_onehot']=onehot(t,self.num_categories)\n\t\treturn self.totensor(sample)\n\n\tdef __sample_generate__(self, idx, split_idx=0):\n\t\tname = self.load_name(idx)\n\t\timage = self.load_image(idx)\n\t\tr,c,_ = image.shape\n\t\tsample = {'image': image, 'name': name, 'row': r, 'col': c, 'image_orig': image}\n\n\t\tif 'test' in self.period:\n\t\t\treturn self.__transform__(sample)\n\t\telif self.cfg.DATA_PSEUDO_GT and idx>=split_idx and 'train' in self.period:\n\t\t\tsegmentation = self.load_pseudo_segmentation(idx)\n\t\telse:\n\t\t\tsegmentation = self.load_segmentation(idx)\n\t\tsample['segmentation'] = segmentation\n\t\tt = sample['segmentation'].copy()\n\t\tt[t >= self.num_categories] = 0\n\t\tsample['category'] = seg2cls(t,self.num_categories)\n\t\tsample['category_copypaste'] = np.zeros(sample['category'].shape)\n\n\t\tsample = self.__transform__(sample)\n\t\treturn sample\n","repo_name":"YudeWang/Learning-Pseudo-Label","sub_path":"lib/datasets/VOCDataset.py","file_name":"VOCDataset.py","file_ext":"py","file_size_in_byte":9481,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"50"} +{"seq_id":"39087468510","text":"import osxphotos\n\n# uses the osxphotos package to query the PHotos library on OSX (Mac)\n# https://pypi.org/project/osxphotos/\n#\n# To Install:\n#\n# pip install osxphotos\n\n\ndef interrogateLibrary():\n print('keywords found:')\n print(photosdb.keywords)\n print('persons found:')\n print(photosdb.persons)\n print('albumes found:')\n print(photosdb.albums)\n\n\ndef getFirstVideo():\n videos = photosdb.photos(movies=True)\n firstVideo = videos[0]\n\n if firstVideo is None:\n return None\n\n print('First video found is:' + firstVideo.filename)\n print(f'Shot at lat: {firstVideo._latitude} lng: {firstVideo._longitude}')\n\n\ndef main():\n interrogateLibrary()\n\n getFirstVideo()\n\n\nphotosdb = osxphotos.PhotosDB()\nif __name__ == \"__main__\":\n main()\n","repo_name":"JasonGloverNZ/VidFrameGrabber","sub_path":"query_osx_photos.py","file_name":"query_osx_photos.py","file_ext":"py","file_size_in_byte":776,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"32316300118","text":"import datetime\nimport time\nimport pytz\n \n\n'''Time Formatting'''\nIST_TIMEZONE = pytz.timezone('Asia/Kolkata')\ndmY = \"%d-%m-%Y\"\nYmd = '%Y-%m-%d'\nIMp = \"%I:%M %p\"\nHMS = \"%H:%M:%S\"\nYmdHMS = \"%Y-%m-%d %H:%M:%S\"\ndmYHMS = \"%d-%m-%Y %H:%M:%S\"\nYmdTHMSf = \"%Y-%m-%dT%H:%M:%S.%f\"\nYmdHMSf = \"%Y-%m-%d %H:%M:%S.%f\"\nYmdTHMSfz = \"%Y-%m-%dT%H:%M:%S.%f%z\"\n\nclass TimeFormatException(Exception):\n \"\"\"\n UserDefined exception to hadle time format error\n\n \"\"\"\n def __int__(self):\n super().__init__(\"TimeFormatError...Time should be in HH:MM:SS format\")\n\n\n\ndef generate_current_date():\n\n timezone=pytz.timezone(\"Asia/Kolkata\")\n\n now_=datetime.datetime.now().astimezone(timezone)\n\n return now_\n\ndef update_availabilty(availabilty,current,days_int):\n \n now_=current\n dates_arr=[]\n for i in range(7):\n if now_.weekday() in days_int:\n availabilty[\"days\"][now_.weekday()][\"available\"]=True\n dates_arr.append(now_.strftime(\"%m/%d/%Y\"))\n availabilty[\"days\"][now_.weekday()][\"date\"]=now_.strftime(\"%m/%d/%Y\")\n \n now_=now_+datetime.timedelta(days=1)\n \n availabilty[\"days_arr\"]=days_int\n availabilty[\"dates_arr\"]=dates_arr\n \n return availabilty\n\ndef return_time_type(isotime):\n\n return datetime.time.fromisoformat(isotime)\n\n \ndef calculate_time_slots(start_time,end_time,duration,availabilty,time_slots=None):\n if time_slots:\n \n time_slots_arr=list(time_slots.keys())\n dates_arr=availabilty[\"dates_arr\"]\n\n final_=[]\n new_=[]\n old_=[]\n\n for i in dates_arr:\n if i in time_slots_arr:\n final_.append(i)\n old_.append(i)\n time_slots_arr.remove(i)\n else:\n final_.append(i)\n new_.append(i)\n \n \n slots_={}\n \n morning_={}\n afternoon_={}\n evening_={}\n slot_availabilty_={\n \"available\":True,\n \"count\":0\n }\n start_=datetime.date(1,1,1)\n end_=datetime.date(1,1,1)\n\n start_=datetime.datetime.combine(start_,start_time)\n end_=datetime.datetime.combine(end_,end_time)\n\n twelve_=datetime.datetime.combine(datetime.date(1,1,1),datetime.time(12,0,0))\n five_=datetime.datetime.combine(datetime.date(1,1,1),datetime.time(17,0,0))\n while start_ 0\n\n while self.n_sample < self.batch_size:\n self.tensors = [t.repeat(2, *([1] * (len(t.shape) - 1))) for t in self.tensors]\n self.n_sample *= 2\n logger.info('Tensors are repeated, new sizes:{}'.format([t.shape for t in self.tensors]))\n\n self._s_ix = 0 # index of sample that will be fetched as the first sample in next_batch\n self._order = torch.zeros(self.n_sample, dtype=torch.long, device=self.device) # order of samples fetched in an epoch\n self.reset_batch_order()\n\n def __len__(self):\n return math.ceil(self.n_sample / self.batch_size)\n\n def __iter__(self):\n return self\n\n def _check_new_epoch(self):\n if self.allow_smaller:\n # check whether there is no not-fetched sample left\n return self._s_ix >= self.n_sample\n else:\n # check whether number of remaining not-fetched samples less than the batch size\n return self.n_sample - self._s_ix < self.batch_size\n\n def reset_batch_order(self):\n self._s_ix = 0\n if self.sampling_weights is not None:\n torch.multinomial(self.sampling_weights, self.n_sample, replacement=True, out=self._order)\n elif self.shuffle:\n torch.randperm(self.n_sample, out=self._order)\n else:\n torch.arange(self.n_sample, out=self._order)\n \n def __next__(self):\n new_epoch = self._check_new_epoch()\n if new_epoch:\n self.reset_batch_order()\n if not self.continuous:\n raise StopIteration\n\n inds = self._order[self._s_ix : self._s_ix + self.batch_size]\n self._s_ix += self.batch_size\n batch = [t[inds] for t in self.tensors]\n return batch\n \n\ndef compute_sampling_weights(labels):\n classes = np.unique(labels)\n assert classes.max() == (classes.shape[0] - 1)\n n_samples_per_class = np.array([len(np.where(labels == c)[0]) for c in classes])\n class_weights = 1. / n_samples_per_class\n sample_weights = np.array([class_weights[l] for l in labels])\n sample_weights = torch.from_numpy(sample_weights).float()\n return sample_weights\n","repo_name":"mbsariyildiz/gmn-zsl","sub_path":"src/data/iterator.py","file_name":"iterator.py","file_ext":"py","file_size_in_byte":3111,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"50"} +{"seq_id":"24896677480","text":"#!/usr/bin/env python3\n\nimport sys\nfrom utils import timing\nimport string\n\nfilename = sys.argv[1]\nwith open(filename, 'r') as fin:\n lines = [line.strip() for line in fin.readlines()]\n\ndef get_priority(letter):\n return string.ascii_letters.index(letter) + 1\n\n\n@timing\ndef main():\n part1_score = 0\n part2_score = 0\n\n # part 1\n for line in lines:\n first, second = line[:len(line)//2], line[len(line)//2:]\n part1_score += get_priority(\n list(\n set(first).intersection(set(second))\n )[0]\n )\n\n print('part1 score', part1_score)\n\n # part 2\n groups = list(zip(*(iter(lines),) * 3))\n\n for a,b,c in groups:\n part2_score += get_priority(\n list(\n set(a).intersection(set(b)).intersection(set(c))\n )[0]\n )\n\n print('part2 score', part2_score)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"kishvanchee/advent-of-code","sub_path":"2022/python/day03.py","file_name":"day03.py","file_ext":"py","file_size_in_byte":911,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"3361449510","text":"items = [\"Mohamed\", \"Ahmed\", \"Ali\",\"Gaber\",\"Fathy\",\"Omar\",\"Sayed\",\"Hassan\",\"Raouf\"]\r\nprint (\"List items are: \" , items )\r\nprint (\"please type the name you want to search for: \")\r\nele=input ()\r\n\r\n# You can switch between 2 methods of linear search, change the switch value to select method 1 or 2\r\nswitch =2\r\nif switch ==1:\r\n loc = -1\r\n for i in range (len(items)-1):\r\n if (ele == items[i]):\r\n loc=i\r\n if (loc>=0):\r\n print(ele,\"found in location [\",loc,\"]\")\r\n else:\r\n print(ele,\"not found\")\r\n\r\n \r\nif switch==2:\r\n if ele in items:\r\n loc = items.index(ele)\r\n print(ele,\"is found in location [\",loc,\"]\")\r\n else:\r\n print(\"Sorry,\",ele,\"is not found!\")\r\n\r\ninput(\"Program has completed\")\r\n","repo_name":"aboudeif/Search-and-Sort-C","sub_path":"Linear Search.py","file_name":"Linear Search.py","file_ext":"py","file_size_in_byte":753,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"36034931906","text":"alphabet = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']\n\ndef caesar(message, shift_amount, cipher_direction):\n end_text = \"\"\n if cipher_direction == \"decode\":\n shift_amount *= -1\n for letter in message:\n if letter in alphabet:\n position = alphabet.index(letter)\n new_position = position + shift_amount\n new_message = alphabet[new_position]\n end_text += new_message\n else:\n end_text += letter\n print(f\"Your {cipher_direction}d message is {end_text}\")\n \n \nfrom art import logo\nprint(logo)\nend_program = False\nwhile not end_program:\n direction = input(\"Enter 'decode to decrypt and 'encode' to encrypt.\\n\")\n text = input(\"Enter text message:\\n\")\n shift = int(input(\"Enter shift number:\\n\"))\n shift = shift % 26\n caesar(message=text, shift_amount=shift, cipher_direction=direction)\n restart = input(\"Try again(yes/no)\\n\")\n if restart == \"no\":\n end_program = True\n print(\"GoodBye!\")\n","repo_name":"noobsixt9/30-days-of-python","sub_path":"Day8/Caesar-Cipher/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1151,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"4516839475","text":"\"\"\" helpers for importing and managing program modules\n\"\"\"\n\nimport importlib\nfrom elstruct import par\nfrom elstruct import pclass\n\n\n# Functions to import and call the appropriate writer function\ndef call_module_function(prog, function, *args, **kwargs):\n \"\"\" call the module implementation of a given function\n\n :param prog: the program\n :type prog: str\n :param function_template: a function with the desired signature\n :type function_template: function\n \"\"\"\n\n def _rename_prog(prog):\n \"\"\" Rename a program if number does not match module name \"\"\"\n if prog in ('molpro2021', 'molpro2021_mppx'):\n prog = 'molpro2015'\n elif prog == 'gaussian16':\n prog = 'gaussian09'\n return prog\n\n new_name = _rename_prog(prog)\n assert new_name in pclass.values(par.Program)\n assert new_name in program_modules_with_function(function)\n\n name = f'_{_rename_prog(prog)}'\n module = importlib.import_module(f'elstruct.writer.{name:s}')\n writer = getattr(module, 'write_input')\n\n return writer(function, *args, **kwargs)\n\n\ndef program_modules_with_function(function):\n \"\"\"\n :param function: a function with the desired signature\n :type function: function\n \"\"\"\n\n progs = []\n for prog in pclass.values(par.Program):\n if function in WRITER_MODULE_DCT[prog]:\n progs.append(prog)\n\n return progs\n\n\n# Information on what writers have been implemented\nclass Job():\n \"\"\" Names of electronic structure jobs to ne written\n \"\"\"\n ENERGY = 'energy'\n GRADIENT = 'gradient'\n HESSIAN = 'hessian'\n VPT2 = 'vpt2'\n IRC = 'irc'\n MOLPROP = 'molecular_properties'\n OPTIMIZATION = 'optimization'\n\n\n# Dictionaries that dictate what writer/reader functionality\nWRITER_MODULE_DCT = {\n par.Program.CFOUR2: (\n Job.ENERGY, Job.GRADIENT, Job.HESSIAN, Job.OPTIMIZATION),\n par.Program.GAUSSIAN09: (\n Job.ENERGY, Job.GRADIENT, Job.HESSIAN, Job.OPTIMIZATION,\n Job.MOLPROP, Job.IRC, Job.VPT2),\n par.Program.GAUSSIAN16: (\n Job.ENERGY, Job.GRADIENT, Job.HESSIAN, Job.OPTIMIZATION,\n Job.MOLPROP, Job.IRC, Job.VPT2),\n par.Program.MOLPRO2015: (\n Job.ENERGY, Job.GRADIENT, Job.HESSIAN, Job.OPTIMIZATION,\n Job.MOLPROP, Job.IRC, Job.VPT2),\n par.Program.MOLPRO2021: (\n Job.ENERGY, Job.GRADIENT, Job.HESSIAN, Job.OPTIMIZATION,\n Job.MOLPROP, Job.IRC, Job.VPT2),\n par.Program.MRCC2018: (\n Job.ENERGY, Job.HESSIAN, Job.OPTIMIZATION),\n par.Program.NWCHEM6: (),\n # par.Program.NWCHEM6: (\n # Job.ENERGY, Job.OPTIMIZATION),\n par.Program.ORCA4: (\n Job.ENERGY, Job.GRADIENT, Job.HESSIAN, Job.OPTIMIZATION),\n par.Program.PSI4: (\n Job.ENERGY, Job.GRADIENT, Job.HESSIAN, Job.OPTIMIZATION,\n Job.MOLPROP, Job.IRC),\n par.Program.QCHEM5: (\n Job.ENERGY, Job.GRADIENT, Job.HESSIAN, Job.OPTIMIZATION\n )\n}\n","repo_name":"Auto-Mech/autoio","sub_path":"autoio-interfaces/elstruct/writer/program_modules.py","file_name":"program_modules.py","file_ext":"py","file_size_in_byte":2940,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"6261226544","text":"#! /usr/bin/env python\nimport dislin\n\nnproj = 14\n\ncl1 = ['Cylindrical Equidistant', \n 'Mercator',\n 'Cylindrical Equal-Area',\n 'Hammer (Elliptical)',\n 'Aitoff (Elliptical)',\n 'Winkel (Elliptical)',\n 'Sanson (Elliptical)',\n 'Conical Equidistant',\n 'Conical Equal-Area',\n 'Conical Conformal',\n 'Azimuthal Equidistant',\n 'Azimuthal Equal-Area',\n 'Azimuthal Stereographic',\n 'Azimuthal Orthgraphic']\n\ncl2 = ['CYLI', 'MERC', 'EQUA', 'HAMM', 'AITO', 'WINK',\n 'SANS', 'CONI', 'ALBE', 'CONF', 'AZIM', 'LAMB',\n 'STER', 'ORTH']\n\nid_lis = 0\nid_draw = 0\n\ndef myplot (id):\n xa = -180. \n xe = 180.\n xor = -180.\n xstp = 60.\n\n ya = -90. \n ye = 90.\n yor = -90.\n ystp = 30.\n\n isel = dislin.gwglis (id_lis)\n dislin.setxid (id_draw, 'widget')\n dislin.metafl ('xwin')\n dislin.disini ()\n dislin.erase ()\n dislin.complx ()\n\n if (isel >=4 and isel <= 7): \n dislin.noclip ()\n elif (isel == 2):\n ya = -85\n ye = 85\n yor = -60\n elif (isel >= 8 and isel <= 10):\n ya = 0\n ye = 90\n yor = 0\n\n dislin.labdig (-1, 'xy')\n dislin.name ('Longitude', 'x')\n dislin.name ('Latitude', 'y')\n dislin.projct (cl2[isel-1])\n dislin.titlin (cl1[isel-1] + 'Projection', 3)\n dislin.htitle (50)\n dislin.grafmp (xa, xe, xor, xstp, ya, ye, yor, ystp)\n dislin.title ()\n dislin.gridmp (1, 1)\n dislin.color ('green')\n dislin.world ()\n dislin.unit (0)\n dislin.disfin ()\n\n\nclis = cl1[0]\nfor i in range (1, nproj):\n clis = dislin.itmcat (clis, cl1[i])\n\ndislin.swgtit ('DISLIN Map Plot')\nip = dislin.wgini ('hori')\ndislin.swgwth (-15)\nip1 = dislin.wgbas (ip, 'vert')\ndislin.swgwth (-50)\nip2 = dislin.wgbas (ip, 'vert')\n\ndislin.swgdrw (2100./2970.)\nid = dislin.wglab (ip1, 'Projection:')\nid_lis = dislin.wglis (ip1, clis, 1)\n\nid_but = dislin.wgpbut (ip1, 'Plot')\ndislin.swgcbk (id_but, myplot)\n\nid_quit = dislin.wgquit (ip1)\n\nid = dislin.wglab (ip2, 'DISLIN Draw Widget:')\nid_draw = dislin.wgdraw (ip2)\ndislin.wgfin ()\n\n\n\n\n\n","repo_name":"KikeM/Laminar-Boundary-Layer","sub_path":"src/IO/dislin-11.0/python/map.py","file_name":"map.py","file_ext":"py","file_size_in_byte":2064,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"28768443009","text":"import pandas as pd\nimport pickle\n\nINP_FILE = '../data/Metlife_7th Feb.xlsx'\nOUT_FILE = '../data/Metlife_3files.md'\nPICKLE_DUMP_FILE = '../data/Metlife_3files.pkl'\n\n\ndef filter_df(df, min_length, max_length=None, num_samples=None):\n df = df[df['Message'] == df['Message']]\n df = df[df['Message'].str.len() > min_length]\n if max_length is not None:\n df = df[df['Message'].str.len() < max_length]\n if num_samples is not None:\n df = df.sample(n=num_samples)\n return df\n\n\nglobal_df = pickle.load(open(PICKLE_DUMP_FILE, 'rb'))\n\n\ndef prepare(df, out_file):\n intent_vs_msg = {}\n\n for i, row in df.iterrows():\n msg = row['Message'].strip()\n msg = msg\n intent = row['Validation']\n if intent not in intent_vs_msg:\n intent_vs_msg[intent] = []\n if msg in intent_vs_msg[intent]:\n continue\n intent_vs_msg[intent].append(msg)\n\n md = ''\n for intent in intent_vs_msg.keys():\n md += '## intent:%s\\n' % intent\n for msg in intent_vs_msg[intent]:\n md += '- %s\\n' % msg\n md += '\\n'\n\n f = open(out_file, 'w')\n f.write(md)\n f.close()\n\n\nN = global_df.shape[0]\niterations = [10, 20, 40, 80, 160, 320, 640, 1280, 2560, 5120, N]\nfor count in iterations:\n df = global_df.sample(n=count)\n print('Preparing file for %d' % count)\n prepare(df, out_file='../data/experiment/Metlife_3files_%d.md' % count)\npass\n","repo_name":"nomanahmedsheikh/rasa_nlu","sub_path":"scripts/prepare_traindata_multiple.py","file_name":"prepare_traindata_multiple.py","file_ext":"py","file_size_in_byte":1431,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"34068966874","text":"import pandas as pd\nimport numpy as np\nimport math\n\nfrom scipy.io import loadmat\n\nmedian_filter_window_size = 8\nsequence_length = 100\nsubjects = range(1, 8)\niterations = range(1, 6)\nactivities = ['step', 'rest', 'squat']\ncolumn_names = ['accel_x'] + [f'accel_x.{i}' for i in range(1, 100)] +\\\n ['accel_y'] + [f'accel_y.{i}' for i in range(1, 100)] +\\\n ['accel_z'] + [f'accel_z.{i}' for i in range(1, 100)] +\\\n ['ppg'] + [f'ppg.{i}' for i in range(1, 100)] +\\\n ['activity', 'subject']\n\n\ndef manage_ppgs(activity, subject, iteration):\n folder_name = f'S{subject}'\n filename = f'{activity}{iteration}_ppg.mat'\n f = loadmat(f'PPG_ACC_dataset/{folder_name}/{filename}')\n ppgs = f['PPG'][:, 1]\n # print(ppgs.shape)\n\n max_chunk = math.floor(len(ppgs) / 800) * 800\n ppgs = ppgs[0:max_chunk]\n\n ppg_with_median = np.concatenate([np.median(ppgs[i * median_filter_window_size: (i + 1) * median_filter_window_size])\n for i in range(math.floor(len(ppgs) / median_filter_window_size))], axis=None)\n\n number_of_sequences = math.floor(len(ppg_with_median) / sequence_length)\n split_ppg_with_median = np.split(ppg_with_median[0:number_of_sequences * sequence_length], number_of_sequences)\n stacked_split_ppg_with_median = np.stack(split_ppg_with_median, axis=0)\n\n return stacked_split_ppg_with_median\n\ndef manage_xyz(activity, subject, iteration):\n # activity = 'rest'\n # subject = 1\n # iteration = 1\n folder_name = f'S{subject}'\n filename = f'{activity}{iteration}_acc.mat'\n f = loadmat(f'PPG_ACC_dataset/{folder_name}/{filename}')\n xyz = f['ACC'][:, 1:4]\n # print(xyz.shape)\n parsed = []\n for i in range(3):\n v = xyz[:, i]\n max_chunk = math.floor(len(v) / 800) * 800\n # print(max_chunk, len(v))\n v = v[0:max_chunk]\n # print(v[0:8])\n v_with_median = np.concatenate(\n [np.median(v[i * median_filter_window_size: (i + 1) * median_filter_window_size])\n for i in range(math.floor(len(v) / median_filter_window_size))], axis=None)\n number_of_sequences = math.floor(len(v_with_median) / sequence_length)\n split_v_with_median = np.split(v_with_median[0:number_of_sequences * sequence_length], number_of_sequences)\n stacked_split_v_with_median = np.stack(split_v_with_median, axis=0)\n parsed.append(stacked_split_v_with_median)\n\n concat_xyz = np.concatenate(parsed, axis=1)\n # print(concat_xyz)\n # quit()\n return concat_xyz\n\n\nrows = []\nfor subject_index, subject in enumerate(subjects):\n for iteration in iterations:\n for activity_index, activity in enumerate(activities):\n print(subject, activity, iteration)\n xyzs = manage_xyz(activity, subject, iteration)\n ppgs = manage_ppgs(activity, subject, iteration)\n activity_label = (activity_index + 1) * np.ones((ppgs.shape[0], 1))\n subject_label = (subject_index + 1) * np.ones((ppgs.shape[0], 1))\n print(xyzs.shape)\n print(ppgs.shape)\n batch_rows = np.concatenate([xyzs, ppgs, activity_label, subject_label], axis=1)\n rows.append(batch_rows)\n\noutput = np.concatenate(rows, axis=0)\nprint(output.shape)\ndf = pd.DataFrame(output, columns=column_names)\n# df.to_csv('test_ppg.csv', header=False, index=False)\n\n\n# First fix any first sequences that are nans\nfor col in ['accel_x', 'accel_y', 'accel_z', 'ppg']:\n df[col] = df[col].fillna(df[col + '.1'])\n\n# Now fix any other occurrences with the preceding value\nfor i in range(1, len(df.columns)):\n df[df.columns[i]] = df[df.columns[i]].fillna(df[df.columns[i - 1]])\n\n\nactivity_label = df['activity']#.apply(lambda x: activity_label_mapping[x]).values\n# df = df.drop(columns=['activity', 'subject'])\n\n# Manage imbalanced data\ncounts = np.bincount(activity_label)\n\nactivity_label_1 = df[df['activity'] == 1]\nactivity_label_2 = df[df['activity'] == 2]\nactivity_label_3 = df[df['activity'] == 3]\n\nundersampled_2_index = np.random.choice(activity_label_2.shape[0], size=max(len(activity_label_1), len(activity_label_3)), replace=False)\nundersampled_2 = activity_label_2.iloc[undersampled_2_index]\n\noutput_df = pd.concat([activity_label_1, undersampled_2, activity_label_3], axis=0)\noutput_df.to_csv('accel_x_y_z+ppgv3_removed_nulls_balanced.csv', index=False)\n\n\n# df.to_csv('accel_x_y_z+ppgv3_removed_nulls_test.csv', index=False)\n","repo_name":"paper-code-anon/dp-gan","sub_path":"PPG/parse_ppg_data.py","file_name":"parse_ppg_data.py","file_ext":"py","file_size_in_byte":4443,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"13714406684","text":"import libtcod\n\n\nclass FOV(object):\n def __init__(self, tiles):\n self.t = libtcod\n self.fov_map = self.init_map(tiles)\n self.algo_dict = {\n 'BASIC': self.t.FOV_BASIC,\n 'DIAMOND': self.t.FOV_DIAMOND,\n 'SHADOW': self.t.FOV_SHADOW\n }\n self.algorithm = self.algo_dict['BASIC']\n self.light_walls = True\n self.torch_radius = 100\n\n def init_map(self, tiles):\n fov_map = self.t.map_new(len(tiles), len(tiles[0]))\n for x, row in enumerate(tiles):\n for y, tile in enumerate(row):\n self.t.map_set_properties(fov_map,\n x, y,\n not tile.block_sight,\n not tile.blocked)\n return fov_map\n\n def recompute(self, x, y):\n self.t.map_compute_fov(self.fov_map, x, y,\n self.torch_radius, self.light_walls, self.algorithm)\n\n def is_in_fov(self, x, y):\n return self.t.map_is_in_fov(self.fov_map, x, y)\n","repo_name":"ShaneDrury/roguelike","sub_path":"roguelike/core/fov.py","file_name":"fov.py","file_ext":"py","file_size_in_byte":1084,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"9923405818","text":"#!/usr/bin/env python3\nif(__package__==None or __package__==\"\"):\n import Orbits\nelse:\n from . import Orbits\nclass TwoBodyChannel:\n def __init__(self,J=None,P=None,Z=None,orbits=None,e2max=None):\n self.J = J\n self.P = P\n self.Z = Z\n self.orbits = orbits\n self.e2max = e2max\n self.orbit1_index = []\n self.orbit2_index = []\n self.phase_from_indices = {}\n self.index_from_indices = {}\n self.number_states = 0\n if( self.J != None and self.P != None and self.Z != None and orbits != None ):\n self._set_two_body_channel()\n return\n def _set_two_body_channel(self):\n orbs = self.orbits\n if(self.e2max==None): self.e2max = 2*orbs.emax\n import itertools\n for oa, ob in itertools.combinations_with_replacement( orbs.orbits, 2 ):\n ia = orbs.get_orbit_index_from_orbit( oa )\n ib = orbs.get_orbit_index_from_orbit( ob )\n if( ia == ib and self.J%2==1 ): continue\n if( oa.e + ob.e > self.e2max ): continue\n if( (oa.z + ob.z) != 2*self.Z ): continue\n if( (-1)**(oa.l + ob.l) != self.P ): continue\n if( self._triag( oa.j, ob.j, 2*self.J ) ): continue\n self.orbit1_index.append( ia )\n self.orbit2_index.append( ib )\n idx = len( self.orbit1_index )-1\n self.index_from_indices[(ia,ib)] = idx\n self.index_from_indices[(ib,ia)] = idx\n self.phase_from_indices[(ia,ib)] = 1\n self.phase_from_indices[(ib,ia)] = -(-1)**( (oa.j+ob.j)//2 - self.J )\n self.number_states = len( self.orbit1_index )\n def get_number_states(self):\n return self.number_states\n def get_indices(self,idx):\n return self.orbit1_index[idx], self.orbit2_index[idx]\n def get_orbits(self,idx):\n ia, ib = self.get_indices(idx)\n return self.orbits.get_orbit(ia), self.orbits.get_orbit(ib)\n def get_JPZ(self):\n return self.J, self.P, self.Z\n def _triag(self,J1,J2,J3):\n b = True\n if(abs(J1-J2) <= J3 <= J1+J2): b = False\n return b\n\nclass TwoBodySpace:\n def __init__(self,orbits=None,e2max=None):\n self.orbits = orbits\n self.e2max = e2max\n self.index_from_JPZ = {}\n self.channels = []\n self.number_channels = 0\n if( self.orbits != None ):\n if( self.e2max == None ): self.e2max = 2*self.orbits.emax\n for J in range(self.e2max+2):\n for P in [1,-1]:\n for Z in [-1,0,1]:\n channel = TwoBodyChannel(J=J,P=P,Z=Z,orbits=self.orbits,e2max=e2max)\n if( channel.get_number_states() == 0): continue\n self.channels.append( channel )\n idx = len(self.channels) - 1\n self.index_from_JPZ[(J,P,Z)] = idx\n self.number_channels = len(self.channels)\n def get_number_channels(self):\n return self.number_channels\n def get_index(self,*JPZ):\n return self.index_from_JPZ[JPZ]\n def get_channel(self,idx):\n return self.channels[idx]\n def get_channel_from_JPZ(self,*JPZ):\n return self.get_channel( self.get_index(*JPZ) )\n def print_channels(self):\n print(\" Two-body channels list \")\n print(\" J,par, Z, # of states\")\n for channel in self.channels:\n J,P,Z = channel.get_JPZ()\n print(\"{:3d},{:3d},{:3d},{:12d}\".format(J,P,Z,channel.get_number_states()))\n\ndef main():\n orbs = Orbits.Orbits()\n orbs.set_orbits(emax=6)\n two = TwoBodySpace(orbits=orbs)\n two.print_channels()\nif(__name__==\"__main__\"):\n main()\n","repo_name":"Takayuki-Miyagi/mylib_python","sub_path":"Nucl/TwoBodySpace.py","file_name":"TwoBodySpace.py","file_ext":"py","file_size_in_byte":3709,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"39897527233","text":"#coding:utf-8\r\n\r\nimport psutil\r\n\r\n'''\r\n获取电脑后台正在运行的Process信息\r\n'''\r\n\r\n#定义进程列表\r\nProcessIds=psutil.pids()\r\n#获取进程的ID列表\r\ndef getProcessIdList():\r\n return ProcessIds\r\ndef getgetProcessNameList():\r\n ProcessNames=[]\r\n for id in ProcessIds:\r\n process=psutil.Process(id)\r\n proName=process.name()\r\n ProcessNames.append(proName)\r\n return ProcessNames\r\n\r\n","repo_name":"GIS90/python_base_use","sub_path":"WinNagois/processInfo.py","file_name":"processInfo.py","file_ext":"py","file_size_in_byte":426,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"19161769751","text":"import mxnet as mx\nfrom mxnet import gluon\nimport gluoncv as gcv\nfrom mxnet.gluon import nn\nimport numpy as np\nfrom feature import FPNFeatureExpander\nfrom mxnet import autograd\nfrom mxboard import SummaryWriter\n\n\nclass East(nn.HybridBlock):\n\n def __init__(self, base_model, outputs, text_scale=512, ctx=mx.cpu(), pretrained_base=True, **kwargs):\n super(East, self).__init__()\n self.text_scale = text_scale\n weight_init = mx.init.Xavier(factor_type=\"in\", magnitude=2.34)\n with self.name_scope():\n self.features = FPNFeatureExpander(network=base_model, outputs=outputs,pretrained=pretrained_base, ctx=ctx, **kwargs)\n\n self.score_branch = nn.Conv2D(1, 1, activation='sigmoid')\n self.geo_branch = nn.Conv2D(4, 1, activation='sigmoid')\n self.theta_branch = nn.Conv2D(1, 1, activation='sigmoid')\n\n def hybrid_forward(self, F, x, **kwargs):\n #x = F.Cast(x, dtype='float16')\n x = self.features(x)\n #x = F.Cast(x, dtype='float32')\n score_map = self.score_branch(x)\n geo_map = self.geo_branch(x) * self.text_scale\n\n angle_map = (self.theta_branch(x) - 0.5) * np.pi / 2.\n geometry_map = F.Concat(geo_map, angle_map, dim=1)\n\n return score_map, geometry_map\n\n\ndef get_east_resnet50(**kwargs):\n net = East(base_model='resnet50_v1d',\n outputs=['pool0_fwd','layers2_bottleneckv1b0__plus0','layers3_bottleneckv1b1__plus0' ,'layers4_relu8_fwd'], **kwargs) #layers4_bottleneckv1b2__plus0\n #outputs=['layers1_relu8_fwd', 'layers2_relu11_fwd', 'layers3_relu17_fwd', 'layers4_relu8_fwd'], **kwargs)\n\n return net\n\n\ndef get_east_mobilenet(**kwargs):\n net = East(base_model='mobilenetv2_1.0',\n outputs=['features_linearbottleneck3_relu60_relu6', 'features_linearbottleneck6_relu60_relu6',\n 'features_linearbottleneck9_relu60_relu6', 'features_linearbottleneck16_relu60_relu6'], **kwargs)\n return net\n\n\n_models = {\n 'resnet50': get_east_resnet50,\n 'mobilenet': get_east_mobilenet,\n}\n\n\ndef get_model(name, **kwargs):\n name = name.lower()\n if name not in _models:\n err_str = '\"%s\" is not among the following model list:\\n\\t' % (name)\n err_str += '%s' % ('\\n\\t'.join(sorted(_models.keys())))\n raise ValueError(err_str)\n net = _models[name](**kwargs)\n return net\n\n\nif __name__ == '__main__':\n net = get_model('resnet50', pretrained_base=True)\n net.hybridize()\n net.initialize()\n with autograd.train_mode():\n x = mx.nd.array([np.random.normal(size=(3, 512, 512))])\n net(mx.nd.random.uniform(low=0, high=1, shape=(1, 3, 512, 512)))\n with SummaryWriter(logdir='./logs') as sw:\n sw.add_graph(net)\n","repo_name":"zzmcdc/east","sub_path":"network.py","file_name":"network.py","file_ext":"py","file_size_in_byte":2613,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"39592580681","text":"# *===================================*\n# -*- coding: utf-8 -*-\n# * Time : 2019-06-27 12:37\n# * Author : zhangsf\n# *===================================*\nfrom flask import Flask, request, render_template, redirect, url_for\nfrom werkzeug.utils import secure_filename\nimport os\nfrom flask import send_from_directory\n\napp = Flask(__name__)\n\nUPLOAD_FOLDER = 'upload'\nALLOWED_EXTENSIONS = set(['txt', 'pdf', 'png', 'jpg', 'jpeg', 'gif'])\napp.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER\n\n\n# 判断上传的文件是否是允许的后缀\ndef allowed_file(filename):\n return \".\" in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS\n\n\n@app.route(\"/upload\", methods=['GET', 'POST'])\ndef upload():\n if request.method == 'GET': # 请求方式是get\n return render_template('upload.html') # 返回模板\n else:\n if \"file\" not in request.files:\n return redirect(request.url)\n\n file = request.files.get('file') # 获取文件\n\n if file.filename == '':\n return redirect(request.url)\n\n if file and allowed_file(file.filename):\n filename = secure_filename(file.filename) # 用这个函数确定文件名称是否是安全 （注意：中文不能识别）\n file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename)) # 保存文件\n return redirect(url_for('show',\n filename=filename))\n\n\n# 展示图片\n@app.route('/show/')\ndef show(filename):\n # send_from_directory可以从目录加载文件\n return send_from_directory(app.config['UPLOAD_FOLDER'],\n filename)\n\n\nif __name__ == '__main__':\n # 0.0.0.0代表任何能代表这台机器的地址都可以访问\n app.run(host='0.0.0.0', port=5006) # 运行程序\n\n\n","repo_name":"zhangvalue/flask","sub_path":"upload.py","file_name":"upload.py","file_ext":"py","file_size_in_byte":1804,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"52"} +{"seq_id":"72930038886","text":"import pickle, requests\n\nlink = 'http://www.pythonchallenge.com/pc/def/banner.p'\nbody = requests.get(link)\n\ndata = pickle.loads(body.content)\nfor row in data:\n for item in row:\n print(item[0]*item[1], end='')\n print()","repo_name":"oestej/python_challenge","sub_path":"05.py","file_name":"05.py","file_ext":"py","file_size_in_byte":222,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"8395397047","text":"import random\nimport sys\n# Define the location data\nlocations = [\n\t{\n\t\t\"name\": \"A7\",\n\t\t\t\t\"desc\": \"ตึก 7\",\n\t\t\t\t\"lat\": 17.191058760990806,\n\t\t\t\t\"lng\": 104.0894708420397,\n\t},\n\t{\n\t\t\"name\": \"A9\",\n\t\t\"desc\": \"ตึก 9\",\n\t\t\"lat\": 17.190634027635916,\n\t\t\t\t\"lng\": 104.09040683236034\n\t},\n\t{\n\t\t\"name\": \"A6\",\n\t\t\"desc\": \"ตึก 6\",\n\t\t\t\t\"lat\": 17.190970159860075,\n\t\t\t\t\"lng\": 104.08848902422882\n\t},\n\t{\n\t\t\"name\": \"C4\",\n\t\t\"desc\": \"แยกเนเซี่ยม\",\n\t\t\t\t\"lat\": 17.189400286942945,\n\t\t\t\t\"lng\": 104.09174171837972\n\t},\n\t{\n\t\t\"name\": \"C5\",\n\t\t\"desc\": \"ทางสามแยกทางเข้าครุศาสตร์\",\n\t\t\t\t\"lat\": 17.188738469975437,\n\t\t\t\t\"lng\": 104.09137406371342\n\t},\n\t{\n\t\t\"name\": \"C1\",\n\t\t\"desc\": \"หน้าอาคาร 1 ทางเข้าอาคารครุศาสตร์\",\n\t\t\t\t\"lat\": 17.189578289590823,\n\t\t\t\t\"lng\": 104.09041195449454\n\t}\n\n]\n\n\ndef ACO(locations, req_start, req_goal):\n\t# Define the distance matrix\n\tdistances = []\n\tfor i in range(len(locations)):\n\t\trow = []\n\t\tfor j in range(len(locations)):\n\t\t\tlat1, lng1 = locations[i][\"lat\"], locations[i][\"lng\"]\n\t\t\tlat2, lng2 = locations[j][\"lat\"], locations[j][\"lng\"]\n\t\t\tdistance = ((lat1-lat2)**2 + (lng1-lng2)**2)**0.5 * 111.12\n\t\t\trow.append(distance)\n\t\tdistances.append(row)\n\n\t# Initialize the pheromone matrix with an initial value\n\tpheromone_initial = 0.1\n\tpheromones = [[pheromone_initial for j in range(\n\t\tlen(locations))] for i in range(len(locations))]\n\n\t# Set the parameters for the ACO algorithm\n\tnum_ants = 10\n\talpha = 1\n\tbeta = 2\n\tevaporation_rate = 0.1\n\tq = 1\n\tnum_iterations = 50\n\n\t# Define the start and goal states\n\tstart_state = locations.index(\n\t\tnext(location for location in locations if location[\"name\"] == req_start))\n\tgoal_state = locations.index(\n\t\tnext(location for location in locations if location[\"name\"] == req_goal))\n\n\t# Run the ACO algorithm\n\tshortest_path = None\n\tshortest_distance = float(\"inf\")\n\tfor iteration in range(num_iterations):\n\t\tfor ant in range(num_ants):\n\t\t\t# Initialize the ant's path\n\t\t\tpath = [start_state]\n\t\t\tvisited = set(path)\n\n\t\t\t# Build the ant's path\n\t\t\twhile len(visited) < len(locations):\n\t\t\t\t# Choose the next location based on the pheromone and distance values\n\t\t\t\tcurrent_location = path[-1]\n\t\t\t\tp = [0] * len(locations)\n\t\t\t\tfor j in range(len(locations)):\n\t\t\t\t\tif j not in visited:\n\t\t\t\t\t\tp[j] = pheromones[current_location][j]**alpha * \\\n\t\t\t\t\t\t\t(1/distances[current_location][j])**beta\n\t\t\t\tnext_location = random.choices(\n\t\t\t\t\trange(len(locations)), weights=p)[0]\n\n\t\t\t\t# Add the next location to the ant's path\n\t\t\t\tpath.append(next_location)\n\t\t\t\tvisited.add(next_location)\n\n\t\t\t# Update the pheromone matrix based on the ant's path\n\t\t\tdistance = sum(distances[path[i]][path[i+1]]\n\t\t\t\t\t\t for i in range(len(path)-1))\n\t\t\tif distance < shortest_distance and goal_state in path:\n\t\t\t\tshortest_path = path\n\t\t\t\tshortest_distance = distance\n\t\t\tfor i in range(len(path)-1):\n\t\t\t\tpheromones[path[i]][path[i+1]\n\t\t\t\t\t\t\t\t\t] = (1-evaporation_rate)*pheromones[path[i]][path[i+1]] + q/distance\n\t\t# Evaporate the pheromone matrix\n\t\tfor i in range(len(locations)):\n\t\t\tfor j in range(len(locations)):\n\t\t\t\tpheromones[i][j] *= (1-evaporation_rate)\n\n\t# Print the shortest path and distance in kilometers\n\tstart_name = locations[start_state][\"name\"]\n\tgoal_name = locations[goal_state][\"name\"]\n\tshortest_path_names = [locations[i][\"name\"] for i in shortest_path]\n\t# shortest_path_desc = [locations[i][\"desc\"].encode('utf-8') for i in shortest_path]\n\n\tfor i in shortest_path:\n\t\tmy_desc = locations[i][\"desc\"]\n\t\tsys.stdout.buffer.write(my_desc.encode('utf-8'))\n\t\tprint(\",\")\n\t# my_desc = locations[0]['desc']\n\t# sys.stdout.buffer.write(my_desc.encode('utf-8'))\n\tprint(f\"Shortest path from {start_name} to {goal_name}: {shortest_path_names}\")\n\t# print(f\"Shortest path from {start_name} to {goal_name}: {shortest_path_desc}\")\n\tprint(f\"Shortest distance: {shortest_distance:.2f} km\")\n\n\nmy_req_start = \"A7\"\nmy_req_goal = \"C5\"\n\nACO(locations, \"A7\", \"C5\")\n","repo_name":"billowdev/flask-api-aco-shortest-path","sub_path":"src/utils/aco/aco.py","file_name":"aco.py","file_ext":"py","file_size_in_byte":3991,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"37015695091","text":"#Embedded file name: e:\\jenkins\\workspace\\client_SERENITY\\branches\\release\\SERENITY\\carbon\\common\\script\\util\\componentCommon.py\r\n\r\n\r\ndef UnpackStringToTuple(string, conversionFunc = None):\r\n elementList = eval(string)\r\n if conversionFunc is not None:\r\n elementList = [ conversionFunc(element) for element in elementList ]\r\n return tuple(elementList)\r\n\r\n\r\nexports = {'util.UnpackStringToTuple': UnpackStringToTuple}\r\n","repo_name":"connoryang/dec-eve-serenity","sub_path":"client/carbon/common/script/util/componentCommon.py","file_name":"componentCommon.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"8171580977","text":"#!/usr/bin/env python3.6\n# -*- coding: utf-8 -*-\nimport argparse\nimport logging\nimport os, sys\nimport shutil, tempfile\nimport re\nfrom enum import Enum\nfrom subprocess import Popen, PIPE\n\n# Dependencies:\n# - p8tool (from picotool repo) must be in PATH\n# - luamin must have been installed locally with `npm update` or `pico-boots/setup.sh`\n\n# This script minifies the __lua__ section of a cartridge {game}.p8:\n# 1. It uses p8tool listlua A.p8 to quickly extract the __lua__ code into {game}.lua\n# 2. Convert remaining bits of pico8 lua (generated by p8tool) into clean lua\n# 3. It applies luamin to {game}.lua and outputs to {game}_min.lua\n# 4. It reads the header (before __lua__) of {game}.p8 and copies it into {game}_min.p8\n# 5. It appends {game}_min.lua's content to {game}_min.p8\n# 6. It finishes reading {game}.p8's remaining sections and appends them into {game}_min.p8\n# 7. It replaces {game}.p8 with {game}_min.p8\n\nMINIFY_SCRIPT_RELATIVE_PATH = \"npm/node_modules/.bin/luamin\"\n\nscript_dir_path = os.path.dirname(os.path.realpath(__file__))\nminify_script_path = os.path.join(script_dir_path, MINIFY_SCRIPT_RELATIVE_PATH)\n\nLUA_HEADER = b\"__lua__\\n\"\n# Note that this pattern captures 1. condition 2. result of a \"one-line if\" if it is,\n# but that it also matches a normal if-then, requiring a check before using the pattern.\n# This pattern may not be exhaustive as the user may put extra brackets but still use if-then\n# if issues arive, add a negative check to make sure the line doesn't end with \"then\" or even \"\\\"\n# In practice, a developer using pico-boots should write clean lua, and only picotool\n# should add a short if statement for the require bridging code.\nPICO8_ONE_LINE_IF_PATTERN = re.compile(r\"if \\(([^)]*)\\) (.*)\")\n\n\nclass Phase(Enum):\n CARTRIDGE_HEADER = 1 # copying header, from \"pico-8 cartridge...\" to \"__lua__\"\n LUA_SECTION = 2 # found \"__lua__\", still copy the 2 author/version comment lines then appending minified lua all at once\n LUA_CATCHUP = 3 # skipping the unused unminified lua until we reach the other sections\n OTHER_SECTIONS = 4 # copying the last sections\n\n\ndef minify_lua_in_p8(cartridge_filepath, use_aggressive_minification):\n \"\"\"\n Minifies the __lua__ section of a p8 cartridge, using luamin.\n\n \"\"\"\n logging.debug(f\"Minifying lua in cartridge {cartridge_filepath}...\")\n\n root, ext = os.path.splitext(cartridge_filepath)\n if not ext.endswith(\".p8\"):\n logging.error(f\"Cartridge filepath '{cartridge_filepath}' does not end with '.p8'\")\n sys.exit(1)\n\n min_cartridge_filepath = f\"{root}_min.p8\"\n lua_filepath = f\"{root}.lua\"\n min_lua_filepath = f\"{root}_min.lua\"\n\n # Step 1: extract lua code into separate file\n with open(lua_filepath, 'w') as lua_file:\n extract_lua(cartridge_filepath, lua_file)\n\n # Step 2: clean lua code in this file in-place\n with open(lua_filepath, 'r') as lua_file:\n # create temporary file object (we still need to open it with mode to get file descriptor)\n temp_file_object, temp_filepath = tempfile.mkstemp()\n original_char_count = sum(len(line) for line in lua_file)\n print(f\"Original lua code has {original_char_count} characters\")\n lua_file.seek(0)\n clean_lua(lua_file, os.fdopen(temp_file_object, 'w'))\n # replace original lua code with clean code\n os.remove(lua_filepath)\n shutil.move(temp_filepath, lua_filepath)\n\n # Step 3: apply luamin to generate minified code in a different file\n with open(min_lua_filepath, 'w+') as min_lua_file:\n minify_lua(lua_filepath, min_lua_file, use_aggressive_minification)\n min_lua_file.seek(0)\n min_char_count = sum(len(line) for line in min_lua_file)\n print(f\"Minified lua code to {min_char_count} characters\")\n if min_char_count > 65536:\n logging.error(f\"Maximum character count of 65536 has been exceeded, cartridge would be truncated in PICO-8, so exit with failure.\")\n sys.exit(1)\n\n # Step 4-6: inject minified lua code into target cartridge\n phase = Phase.CARTRIDGE_HEADER\n with open(cartridge_filepath, 'r') as source_file, \\\n open(min_cartridge_filepath, 'w') as target_file, \\\n open(min_lua_filepath, 'r') as min_lua_file:\n inject_minified_lua_in_p8(source_file, target_file, min_lua_file)\n\n # Step 7: replace original p8 with minified p8, clean up intermediate files\n os.remove(cartridge_filepath)\n os.remove(lua_filepath)\n os.remove(min_lua_filepath)\n shutil.move(min_cartridge_filepath, cartridge_filepath)\n\n\ndef extract_lua(source_filepath, lua_file):\n \"\"\"\n Extract lua from .p8 cartridge at source_filepath (string) to lua_file (file descriptor: write)\n\n \"\"\"\n # p8tool listrawlua bug (https://github.com/dansanderson/picotool/issues/59)\n # was fixed, so we prefer it to listlua as it is almost instant compared to listlua\n # which takes ~1s to parse the game .p8\n\n # However, note that it outputs an extra newline after *each* line, which will be stripped during minification most of the time\n # but will stay in [[multi-line strings]]. So we *must* skip every other line (preserve odd lines) using e.g. awk\n # https://superuser.com/questions/101756/show-only-odd-lines-with-cat\n\n # Usually a check_call(stdout=min_lua_file) (and no stderr) is enough,\n # as it throws CalledProcessError on error by itself, but in this case, due to output stream sync issues\n # (luamin error shown before __main__ print at the bottom of this script),\n # we prefer Popen + PIPE + communicate() + check stderrdata\n # For awk we just use a '|' in shell mode, a bit easier than calling Popen a second time with stdin = stdout of first process\n (_stdoutdata, stderrdata) = Popen([f\"p8tool listrawlua \\\"{source_filepath}\\\" | awk 'NR % 2 == 1'\"], shell=True, stdout=lua_file, stderr=PIPE).communicate()\n if stderrdata:\n logging.error(f\"p8tool listrawlua failed with:\\n\\n{stderrdata.decode()}\")\n sys.exit(1)\n\n\ndef clean_lua(lua_file, clean_lua_file):\n \"\"\"\n Convert PICO-8 specific lines from to lua_file (file descriptor: read)\n to native Lua in clean_lua_file (file descriptor: write)\n\n \"\"\"\n for line in lua_file:\n # we simplify things a lot thanks to our assumptions on the generated code\n # we know that the only pico8 one-line if will be generated for the require function\n # and have the pattern \"if (condition) [result]\" without \"then\",\n # and there are no edge cases like embedded conditions or continuing line with \"\\\"\n if line.startswith(\"if (\") and \"then\" not in line:\n # convert to \"if [condition] then [result] end\"\n clean_lua_file.write(PICO8_ONE_LINE_IF_PATTERN.sub(\"if \\\\1 then \\\\2 end\", line))\n else:\n clean_lua_file.write(line)\n\n\n\ndef minify_lua(clean_lua_filepath, min_lua_file, use_aggressive_minification=False):\n \"\"\"\n Minify lua from clean_lua_filepath (string)\n and send output to min_lua_file (file descriptor: write)\n\n Use option:\n -f to pass filepath\n -n to use newline separator\n\n For aggressive minification only, use option:\n -mk to minify member names and table key strings (should be done together as some members will be defined\n directly inside table, others defined and accessed with dot syntax)\n\n \"\"\"\n options = \"-fn\"\n if use_aggressive_minification:\n options += \"mk\"\n\n # see extract_lua for reason to use Popen\n (_stdoutdata, stderrdata) = Popen([minify_script_path, options, clean_lua_filepath], stdout=min_lua_file, stderr=PIPE).communicate()\n if stderrdata:\n logging.error(f\"Minify script failed with:\\n\\n{stderrdata.decode()}\")\n sys.exit(1)\n\n\ndef inject_minified_lua_in_p8(source_file, target_file, min_lua_file):\n \"\"\"\n Inject minified lua from min_lua_file (file descriptor: read)\n into a copy of source_file (file descriptor: read)\n producing target_file (file descriptor: write)\n\n \"\"\"\n phase = Phase.CARTRIDGE_HEADER\n for line in source_file:\n if phase is Phase.CARTRIDGE_HEADER:\n # Step 4: copy header (also copy the \"__lua__\" line just after)\n target_file.write(line)\n if line == \"__lua__\\n\":\n # enter lua section\n phase = Phase.LUA_SECTION\n\n elif phase is Phase.LUA_SECTION:\n # Step 5: copy minified lua\n target_file.writelines(min_lua_file.readlines())\n target_file.write(\"\\n\") # newline required before other sections\n phase = Phase.LUA_CATCHUP\n\n elif phase is Phase.LUA_CATCHUP:\n # skip all lines until __gfx__\n if line == \"__gfx__\\n\":\n # copy the __gfx__ line itself\n target_file.write(line)\n phase = Phase.OTHER_SECTIONS\n\n else: # phase is Phase.CARTRIDGE_HEADER\n # Step 6: copy remaining sections\n target_file.write(line)\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='Minify lua code in cartridge.')\n parser.add_argument('path', type=str, help='path containing cartridge file to minify')\n parser.add_argument('--aggressive-minify', action='store_true', help=\"use aggressive minification (minify member names and table key strings)\")\n args = parser.parse_args()\n\n logging.basicConfig(level=logging.INFO)\n logging.info(f\"Minifying lua code in {args.path} with aggressive minification: {'ON' if args.aggressive_minify else 'OFF'}...\")\n\n minify_lua_in_p8(args.path, args.aggressive_minify)\n\n logging.info(f\"Minified lua code in {args.path}\")\n","repo_name":"hsandt/pico-boots","sub_path":"scripts/minify.py","file_name":"minify.py","file_ext":"py","file_size_in_byte":9678,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"52"} +{"seq_id":"7809854082","text":"from notificaciones.models import Notificacion\nfrom rest_framework import permissions\nfrom django.shortcuts import render\nfrom rest_framework.generics import CreateAPIView\nfrom api.exceptions import *\nfrom .serializers import *\n# from api.logger import log\nimport logging\nlog = logging.getLogger('django')\nfrom preregistro.models import *\nfrom rest_framework.views import APIView\nfrom api.Paginacion import Paginacion\nfrom rest_framework.response import Response\n\n\n\n# Create your views here.\n\n\n# ----------------------------------------------------------------------------------Chat\nclass ChatCreateView(CreateAPIView):\n serializer_class = MensajeSerializer\n permission_classes = (permissions.AllowAny,)\n\n def post(self, request, *args, **kwargs):\n serializer = MensajeSerializer(data=request.data)\n if serializer.is_valid():\n destinatario = self.request.data.get('destinatario')\n # remitente = self.request.data.get('remitente')\n # print(f'--->destinatario: {destinatario}')\n Conversacion.objects.filter(destinatario=destinatario).delete()\n nombre = getNombreSesion(request,destinatario)\n Conversacion.objects.create(destinatario=destinatario,nombre=nombre)\n Notificacion.objects.create(titulo='Chat',mensaje='Tiene un nuevo mensaje',destinatario=destinatario,remitente=0)\n \n return self.create(request, *args, **kwargs)\n log.error(f'--->>>campos incorrectos: {serializer.errors}')\n raise CamposIncorrectos(serializer.errors)\n\n\ndef getNombreSesion(request,destinatario):\n nombre = request.session.get(\"nombre\", None)\n if nombre is None:\n datosMedico = Medico.objects.filter(numRegistro=destinatario).values_list('nombre','apPaterno','apMaterno')\n nombre = str(datosMedico[0][0] + ' ' + datosMedico[0][1] + ' ' + datosMedico[0][2])\n request.session[\"nombre\"] = nombre\n\n return nombre\n\n\nclass ChatListEndPoint(APIView):\n \"\"\"\n ?size=3&page=1&orderby=id&direc=asc\n size -- es el numero de registros a traer\n page -- el numero de pagina a traer\n orderby -- campo opr el cual se ordenaran los registros a traer\n direc -- si es ascendente(asc) o descencende (vacio)\n \"\"\"\n permission_classes = (permissions.AllowAny,)\n\n def get(self, request, remitente, destinatario):\n queryset = Mensaje.objects.all().filter(remitente=remitente, destinatario=destinatario)\n size = self.request.query_params.get('size', None)\n direc = self.request.query_params.get('direc', None)\n orderby = self.request.query_params.get('orderby', None)\n page = self.request.query_params.get('page', None)\n\n paginacion = Paginacion(queryset, MensajeSerializer, size, direc, orderby, page)\n serializer = paginacion.paginar()\n\n respuesta = {\n \"totalElements\": paginacion.totalElements,\n \"totalPages\": paginacion.totalPages,\n \"sort\": paginacion.orderby,\n \"direction\": paginacion.direc,\n \"size\": paginacion.size,\n \"content\": serializer.data\n }\n return Response(respuesta)\n\n\n\nclass ConversacionListEndPoint(APIView):\n \"\"\"\n ?size=3&page=1&orderby=id&direc=asc\n size -- es el numero de registros a traer\n page -- el numero de pagina a traer\n orderby -- campo opr el cual se ordenaran los registros a traer\n direc -- si es ascendente(asc) o descencende (vacio)\n \"\"\"\n permission_classes = (permissions.AllowAny,)\n\n def get(self, request):\n queryset = Conversacion.objects.all()\n size = self.request.query_params.get('size', None)\n direc = self.request.query_params.get('direc', None)\n orderby = self.request.query_params.get('orderby', None)\n page = self.request.query_params.get('page', None)\n\n paginacion = Paginacion(queryset, ConversacionSerializer, size, direc, orderby, page)\n serializer = paginacion.paginar()\n\n respuesta = {\n \"totalElements\": paginacion.totalElements,\n \"totalPages\": paginacion.totalPages,\n \"sort\": paginacion.orderby,\n \"direction\": paginacion.direc,\n \"size\": paginacion.size,\n \"content\": serializer.data\n }\n return Response(respuesta)\n \n \n \nclass MedicoChatListEndPoint(APIView):\n \"\"\"\n ?size=3&page=1&orderby=id&direc=asc\n size -- es el numero de registros a traer\n page -- el numero de pagina a traer\n orderby -- campo opr el cual se ordenaran los registros a traer\n direc -- si es ascendente(asc) o descencende (vacio)\n \"\"\"\n permission_classes = (permissions.AllowAny,)\n\n def get(self, request):\n queryset = Medico.objects.all().filter(aceptado=True)\n size = self.request.query_params.get('size', None)\n direc = self.request.query_params.get('direc', None)\n orderby = self.request.query_params.get('orderby', None)\n page = self.request.query_params.get('page', None)\n\n paginacion = Paginacion(queryset, MedicoChatListSerializer, size, direc, orderby, page)\n serializer = paginacion.paginar()\n\n respuesta = {\n \"totalElements\": paginacion.totalElements,\n \"totalPages\": paginacion.totalPages,\n \"sort\": paginacion.orderby,\n \"direction\": paginacion.direc,\n \"size\": paginacion.size,\n \"content\": serializer.data\n }\n return Response(respuesta)","repo_name":"gbrlQuirozMB/cmcperBack","sub_path":"chat/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5479,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"72930165286","text":"#!/usr/bin/env python3\n\"\"\"\nAuthor : t24 \nDate : 8/10/2021\nPurpose:\n\"\"\"\n\nfrom itertools import islice, count\nfrom math import sqrt\n\ndef is_prime(x):\n \"\"\"\n Returns true or false if input is a prime number\n :param x: integer\n :return: TRUE or FALSE\n \"\"\"\n if x < 2:\n return False\n for i in range(2, int(sqrt(x)) + 1):\n if x % i == 0:\n return False\n return True\n\n\n# --------------------------------------------------\ndef main():\n \"\"\"Make your noise here\"\"\"\n # Task: Create a list of the first 1000 prime numbers\n # thousand_primes = islice((x for x in count() if is_prime(x)), 1000)\n # print(list(thousand_primes))\n\n # Use the built-ins any, all\n print(any([False, False, True]))\n print(all([False, False, True]))\n cities = ['London', 'Madrid', 'New York', 'Ogden']\n # Task: Check all members in the collection have Uppercase for the first letter\n # for index in len(cities):\n # if cities[index][0].isupper():\n #\n # print(cities[0][0].isupper())\n print(f'The cities list is {all(city == city.title() for city in cities)} to go')\n\n\n # Use built-in zip\n sunday = [12, 14, 15, 15, 17, 21, 22, 22, 23, 22, 20, 18]\n monday = [13, 14, 14, 16, 20, 21, 22, 22, 21, 19, 18, 16]\n tuesday = [12, 13, 14, 16, 20, 20, 21, 20, 19, 16, 14, 12]\n\n # Task: Find the min, max, and average\n # for item in zip(sunday, monday):\n for sun, mon in zip(sunday, monday):\n print(f'The average = {(sun/mon)/2.0}')\n print(sun == mon)\n\n for temp in zip(sunday, monday, tuesday):\n print(f'minimum = {min(temp)}, maximum = {max(temp)}, average = {sum(temp)/len(temp)}')\n print(f'minimum = {min(temp):4.1f}, maximum = {max(temp):4.1f}, average = {sum(temp) / len(temp):4.1f}')\n # print(f'minimum = {:4.1f}, maximum = {:4.1f}, average = {:4.1f}'.format(min(temp), max(temp), ...))\n\n# --------------------------------------------------\nif __name__ == '__main__':\n main()\n","repo_name":"obriencody/IntermediatePython","sub_path":"Day2/my_itertools.py","file_name":"my_itertools.py","file_ext":"py","file_size_in_byte":2005,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"22613187790","text":"# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, val=0, left=None, right=None):\n# self.val = val\n# self.left = left\n# self.right = right\nclass Solution:\n def findFrequentTreeSum(self, root: Optional[TreeNode]) -> List[int]:\n mapping = {}\n res = []\n def post_order(node):\n nonlocal mapping\n\n if not node:\n return 0\n\n left_sum = post_order(node.left)\n right_sum = post_order(node.right)\n total = node.val + left_sum + right_sum\n\n mapping[total] = mapping.get(total, 0) + 1\n\n return total\n \n post_order(root)\n\n # Capture max frequent of the map\n max_freq = 0\n for value in mapping.values():\n if value > max_freq:\n max_freq = value\n \n # Find the most frequent from the subtree sum.\n # If there is a tie, return all the values with the highest frequency in any order.\n for key, value in mapping.items():\n if value == max_freq:\n res.append(key)\n\n return res\n ","repo_name":"abdifatahmohamad/Coding-Interview-Solutions","sub_path":"leetcode_solutions/0508-most-frequent-subtree-sum/0508-most-frequent-subtree-sum.py","file_name":"0508-most-frequent-subtree-sum.py","file_ext":"py","file_size_in_byte":1161,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"4488839169","text":"#!/usr/bin/python\n#\n# Block header comment\n#\n#\nimport sys, imp, atexit\nsys.path.append(\"/home/courses/cs3214/software/pexpect-dpty/\");\nimport pexpect, shellio, signal, time, os, re, proc_check\n\n#Ensure the shell process is terminated\ndef force_shell_termination(shell_process):\n\tc.close(force=True)\n\n#pulling in the regular expression and other definitions\ndefinitions_scriptname = sys.argv[1]\ndef_module = imp.load_source('', definitions_scriptname)\nlogfile = None\nif hasattr(def_module, 'logfile'):\n logfile = def_module.logfile\n\n#spawn an instance of the shell\nc = pexpect.spawn(def_module.shell, drainpty=True, logfile=logfile)\natexit.register(force_shell_termination, shell_process=c)\n\nc.timeout = 5\n\n#echo | grep | grep\n#2nd grep will return 'much pipe', and not 'much wow', because of the first grep\nc.sendline(\"echo -e \\\"wow\\nso pipe\\nvery echo\\nmuch pipe\\nwow\\nmuch wow\\\" | grep pipe | grep much\")\nassert c.expect_exact(\"much pipe\") == 0, \"didn't multi-pipe to grep correctly\"\n\nshellio.success()\n","repo_name":"tbporter/trash","sub_path":"esh/src/eshtests/advanced/multi_pipe_test.py","file_name":"multi_pipe_test.py","file_ext":"py","file_size_in_byte":1008,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"30831627187","text":"from scipy.sparse import isspmatrix_csc, isspmatrix_csr\nfrom scipy.sparse import csc_matrix, identity\n\nimport numpy as onp\n\nfrom sksparse.cholmod import analyze, cholesky\nfrom sksparse.cholmod import CholmodNotPositiveDefiniteError as NotPosDefError\n\nfrom optimism.JaxConfig import *\n\n\nclass SparseCholesky:\n \n def factorize(self): \n\n print('Assembling preconditioner', 0)\n stiffnessTryStep = 0\n self.A = self.new_stiffness_func(stiffnessTryStep)\n \n # doing the analyze every time for now, even if we know the sparsity does not change\n # we can improve this later if we are inclined\n assert isspmatrix_csc(self.A), \\\n \"Preconditioner matrix is not in a valid sparse format\"\n self.Precond = analyze(self.A, mode='supernodal',\n ordering_method='nesdis')\n\n attempt = 0\n maxAttempts = 10\n while attempt < maxAttempts:\n try:\n print('Factorizing preconditioner')\n self.Precond.cholesky_inplace(self.A)\n except NotPosDefError:\n attempt += 1\n print('Cholesky failed, assembling preconditioner', attempt)\n # we are assuming that the sparsity does not change here\n self.A = self.new_stiffness_func(attempt)\n else:\n break\n \n if attempt == maxAttempts:\n print(\"Cholesky failed too many times, using identity preconditioner\")\n self.A = identity(self.A.shape[0], format='csc')\n self.Precond.cholesky_inplace(self.A)\n\n \n def update(self, new_stiffness_func):\n self.new_stiffness_func = new_stiffness_func\n self.factorize()\n\n \n def apply(self, b):\n if type(b) == type(np.array([])):\n b = onp.array(b)\n return self.Precond(b)\n\n \n def apply_transpose(self, b):\n return self.apply(b)\n \n \n def multiply_by_approximate(self, x):\n return self.A.dot(x)\n \n \n def multiply_by_transpose(self, x):\n return self.A.T.dot(x)\n\n\n def check_stability(self, x, p):\n A = self.stiffness_func(x, p)\n try:\n self.Precond.cholesky(A)\n print(\"Jacobian is stable.\")\n except NotPosDefError as e:\n print(e)\n print(\"Jacobian is unstable.\")\n\n\n def get_diagonal_stiffness(self):\n return self.A.diagonal()\n\n \n def __matmul__(self, b):\n return self.A.dot(b)\n\n","repo_name":"btalami/optimism","sub_path":"optimism/SparseCholesky.py","file_name":"SparseCholesky.py","file_ext":"py","file_size_in_byte":2526,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"52"} +{"seq_id":"27752935729","text":"from enum import Enum\nimport os\nimport re\nimport typing\nimport time\nimport gzip\nfrom threading import Thread\nfrom commonfun import *\n\n# 使用eval()函数创建对象实例\n# 使用getattr()获取属性值,获取方法并调用\n# 使用setattr()设置属性值\n\nclass SearchStatus(Enum):\n NOSTART=1\n START = 2\n STARTING = 3\n FINISH =4\n \nclass logBase():\n def __init__(self) -> None:\n self.resCallFun = None\n self.currentSearchLine = None\n self.searchStatusChangeFun = None\n self.sendMsgFun = None\n self.searchStatus = SearchStatus.NOSTART\n self.logThread = None\n self.stop = False\n self.tempIndex = -1\n self.keyWordSeries = {}\n self.clear()\n\n def clear(self):\n self.filePaths = []\n self.lineContents = []\n\n def load(self,filePaths):\n self.filePaths.extend(filePaths)\n filePathIndex = 0\n for filePath in self.filePaths:\n assert isinstance(filePath,str)\n self.loadLogFile(filePath)\n filePathIndex = filePathIndex + 1\n self.currentSearchLineChanged(filePathIndex,len(self.filePaths))\n \n\n def searchContentChanged(self,dateStr,value,content,series):\n if self.resCallFun != None:\n self.resCallFun(dateStr,value,content,series)\n else:\n print(dateStr,\" \",value,\" \",content)\n\n def sendMsg(self,msg):\n if self.sendMsgFun != None:\n self.sendMsgFun(msg)\n\n def searchStatusChange(self,status):\n self.searchStatus = status\n if self.searchStatusChangeFun != None:\n self.searchStatusChangeFun(self.searchStatus)\n\n def currentSearchLineChanged(self,lineIndex,count):\n if self.currentSearchLine!= None:\n lineIndex = int(lineIndex / count *100)\n if self.tempIndex != lineIndex:\n self.tempIndex = lineIndex\n self.currentSearchLine(self,lineIndex) \n else:\n pass\n\n @typing.overload\n def loadLogFile(self,filePath):\n self.lineContents.extend(readFileLines(filePath))\n return True\n\n def getLoglineCount(self):\n return len(self.lineContents)\n \n def setKeyWords(self,keyWordSeries):\n self.keyWordSeries = keyWordSeries\n\n def startSearchThread(self):\n self.searchStatusChange(SearchStatus.NOSTART)\n if self.getLoglineCount() == 0 or len(self.keyWordSeries) == 0: return\n self.logThread = Thread(target=self.searchkeyWords)\n self.logThread.setDaemon(True)\n self.logThread.start()\n\n def searchkeyWords(self):\n self.startAnalyze()\n\n # @typing.overload\n def startAnalyze(self):\n self.searchStatusChange(SearchStatus.START)\n lineContentIndex = 0\n keyConentCount = {}\n keyValueConentCount = {}\n searchCount = 0\n isFirstPrint = True\n for keyWord in self.keyWordSeries:\n keyConentCount[keyWord] = 0\n keyValueConentCount[keyWord] = 0\n for lineContent in self.lineContents:\n if self.stop : return\n assert isinstance(lineContent,str)\n for keyWord,series in self.keyWordSeries.items():\n if re.search(keyWord,lineContent,re.A):\n keyConentCount[keyWord] = keyConentCount[keyWord] +1\n searchCount = searchCount + 1\n spaceContent = lineContent.split(\" \")\n dateStr = spaceContent[1] +\" \"+spaceContent[2]\n contents = re.findall(e_i,lineContent,re.A)\n ishasValue = False\n for contentIndex in range(len(contents)):\n if \"value\" in contents[contentIndex]:\n contentNextIndex = contentIndex + 1\n value = contents[contentNextIndex]\n self.searchContentChanged(dateStr,value,lineContent,series)\n keyValueConentCount[keyWord] = keyValueConentCount[keyWord] +1\n ishasValue = True\n\n if not ishasValue:\n if isFirstPrint:\n isFirstPrint = False\n self.sendMsg(f'不符合规则的解析规则:')\n self.sendMsg(f'{lineContent}')\n lineContentIndex = lineContentIndex+1\n self.currentSearchLineChanged(lineContentIndex,self.getLoglineCount())\n self.searchStatusChange(SearchStatus.FINISH)\n for keyWord in self.keyWordSeries:\n self.sendMsg(f'搜索 {keyWord} 共 {keyConentCount[keyWord]}')\n self.sendMsg(f'显示 {keyWord} 共 {keyValueConentCount[keyWord]}')\n self.sendMsg(f\"搜索完成,共 {searchCount}\\n\")\n\nclass dltLogBase(logBase):\n def __init__(self) -> None:\n super().__init__()\n self.dltExe = None\n\n def setDltExe(self,dltExe):\n self.dltExe = dltExe\n\n @staticmethod\n def gunZip(filePath):\n extracted_file_path = filePath.replace('.gz','')\n try:\n # 打开gz文件\n with gzip.open(filePath, 'rb') as gz_file:\n # 读取解压后的文件内容\n extracted_data = gz_file.read()\n\n # 将解压后的数据写入新文件\n with open(extracted_file_path, 'wb') as extracted_file:\n extracted_file.write(extracted_data)\n\n return extracted_file_path\n except IOError as e:\n print(f\"解压失败：{e}\")\n return None\n \n def getConverTxt(self,filePath):\n fileName = os.path.basename(filePath)\n fileName = fileName.replace('.gz','')\n fileName = fileName.replace('.dlt','.txt')\n fileDir = os.path.dirname(filePath)+\"/\"\n logFile = fileDir + fileName\n # print(logFile)\n if os.path.exists(logFile):\n return logFile\n return None\n\n def dltToTxt(self,logPath,filePath,logFile):\n filePath = os.path.basename(filePath)\n logFile = os.path.basename(logFile)\n os.system(f'cd {logPath} && {self.dltExe} -c {filePath} {logFile}')\n\n def loadLogFile(self,filePath):\n assert isinstance(filePath,str)\n logFileDir = os.path.dirname(filePath)\n logFile = self.getConverTxt(filePath)\n if logFile == None and len(self.dltExe) != 0:\n if getSuffix(filePath) == '.gz':\n filePath = dltLogBase.gunZip(filePath)\n if filePath == None:\n return False\n logFile = filePath.replace('.dlt','.txt')\n self.dltToTxt(logFileDir,filePath,logFile)\n self.lineContents.extend(readFileLines(logFile))\n return True\n\nif __name__ == '__main__':\n logObj = dltLogBase()\n logObj.setDltExe(\"D:/Soft/DltViewerSDK/dlt-viewer.exe\")\n logObj.load([r\"C:/Users/chengxiong.zhu/Downloads/log分析/2023-09-24/ BGS-62018 385DAuserDF04镁佳1553左右手机端打开远程智能泊车CdcRemVideoPwrUpReq发送上高压请求退出远程智能泊车后座舱高压CdcRemVideoPwrUpReq未置为无请求没有下高压解锁座舱高压置为无请求/log_003460_20230922-153505.dlt\"])\n # logObj.startSearch(\"HUD_CDC_5C5\")\n while 1 :\n time.sleep(10)","repo_name":"zcx01/Achievement","sub_path":"pythonscript/chartAnalyze/loganalyze/baseAnalyze.py","file_name":"baseAnalyze.py","file_ext":"py","file_size_in_byte":7290,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"8851223107","text":"\"\"\"A.I. Giger model.\"\"\"\n\nimport tensorflow as tf\n\ndef make_discriminator(candidate):\n k = 4\n s0, s1 = 2, 1\n n_filters = 64\n\n with tf.variable_scope('discriminator'):\n layer = convolve(candidate, n_filters, k, s0, 'c1', 'same', norm=False)\n layer = convolve(layer, n_filters * 2, k, s0, 'c2', 'same')\n layer = convolve(layer, n_filters * 4, k, s0, 'c3', 'same')\n layer = convolve(layer, n_filters * 8, k, s1, 'c4', 'same')\n layer = convolve(layer, 1, k, s1, 'c5', 'same', norm=False, relu=False)\n\n return layer\n\n\ndef make_generator(input_layer):\n k0, k1 = 7, 3 # Kernel sizes.\n s0, s1 = 1, 2 # Strides.\n n_filters = 32\n\n with tf.variable_scope('generator'):\n layer = pad(input_layer, k1)\n layer = convolve(layer, n_filters, k0, s0, 'c1', 'valid')\n layer = convolve(layer, n_filters * 2, k1, s1, 'c2', 'same')\n layer = convolve(layer, n_filters * 4, k1, s1, 'c3', 'same')\n\n layer = make_resnet_blocks(layer, n_filters * 4, 6)\n\n layer = deconvolve(layer, n_filters * 2, k1, s1, 'c4')\n layer = deconvolve(layer, n_filters, k1, s1, 'c5')\n layer = pad(layer, k1)\n layer = convolve(layer, 3, k0, s0, 'c6', 'same', False)\n\n layer = tf.nn.tanh(layer)\n\n return layer\n\n\ndef make_resnet_blocks(layer, n_filters, n_blocks):\n for i in range(n_blocks):\n layer = make_resnet_block(layer, n_filters, 'res%d' % i)\n return layer\n\n\ndef make_resnet_block(input_layer, n_filters, name):\n def conv(layer, name, relu=True):\n return convolve(layer, n_filters, 3, 1, name, 'valid', relu)\n\n with tf.variable_scope(name):\n layer = pad(input_layer, 1)\n layer = conv(layer, 'c1')\n layer = pad(layer, 1)\n layer = conv(layer, 'c2', False)\n return tf.nn.leaky_relu(layer + input_layer)\n\n\ndef pad(layer, pad):\n padding = [[0, 0], [pad, pad], [pad, pad], [0, 0]]\n return tf.pad(layer, padding, 'REFLECT')\n\n\ndef convolve(layer, n_filters, size, stride, name, \n padding='valid', norm=True, relu=True):\n with tf.variable_scope(name):\n layer = tf.layers.conv2d(\n layer, n_filters, size, stride, padding=padding,\n activation=tf.nn.leaky_relu if relu else None,\n kernel_initializer=tf.truncated_normal_initializer(stddev=.2))\n\n # Note: this applies norm after relu which differs from the orig paper.\n if norm:\n layer = normalize(layer)\n\n return layer\n\n\ndef deconvolve(layer, n_filters, size, stride, name, norm=True):\n with tf.variable_scope(name):\n layer = tf.layers.conv2d_transpose(\n layer, n_filters, (size, size), (stride, stride), 'same', \n kernel_initializer=tf.truncated_normal_initializer(stddev=.2),\n activation=tf.nn.leaky_relu)\n\n # Note: this applies norm after relu which differs from the orig paper.\n if norm:\n layer = normalize(layer)\n\n return layer\n\n\ndef normalize(layer):\n with tf.variable_scope('instance_norm'):\n epsilon = 1e-5\n mean, var = tf.nn.moments(layer, [1, 2], keep_dims=True)\n n_filters = layer.get_shape()[-1]\n scale = tf.get_variable(\n 'scale', [n_filters],\n initializer=tf.truncated_normal_initializer(mean=1., stddev=.02))\n offset = tf.get_variable(\n 'offset', [n_filters], initializer=tf.constant_initializer(0.))\n\n return scale * tf.div(layer - mean, tf.sqrt(var + epsilon)) + offset\n","repo_name":"redbitshift/aigiger","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":3524,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"15857626520","text":"from tifffile import TiffFile, TiffWriter, TiffTag\nimport cv2\nimport numpy as np\nimport tifffile as tif\n\nfrom finders import Finder\nfrom model.location import Location\n\n\nclass ImageManager:\n \"\"\"\n Loads image and finds runs a finder in the image data and allows you to save the results.\n \"\"\"\n\n def __init__(self, filepath: str):\n \"\"\"\n initialses Mammal Finder\n\n filepath - filepath of the image to process\n \"\"\"\n self.filetype: str = filepath[len(filepath) - 3:].upper()\n self.tags = None\n self.locations: [Location] = None\n self.intermediaryImage = None\n self.outlined = None\n if self.filetype == 'TIF':\n print('found tif')\n with TiffFile(filepath) as tif:\n # fileInfo(tif)\n self.tags = metadataGeoTags(tif)\n self.image = tif.asarray()\n elif self.filetype == 'PNG' or self.filetype == 'JPG':\n print('found png')\n self.image = cv2.imread(filepath, cv2.IMREAD_UNCHANGED)\n else:\n print('invalid file type:', self.filetype)\n\n def singleLayerFind(self, method: Finder, layer: int = 0):\n \"\"\"\n find the the mammel in the Image\n\n method - the finder to find the mammal\n layer - the layer or sample of the image to use.\n \"\"\"\n prepared_image = None\n if len(self.image.shape) > 2: # atleast 3 dimesions in array e.g 100,100,5\n if self.image.shape[0] > self.image.shape[2]: # one image array with multi samples e.g 100,100,5\n if self.image.shape[2] > layer: # check sample that exists\n prepared_image = self.image[:, :, layer]\n else:\n print('sample:', layer, ' out of bounds:', self.image.shape[2],\n 'from the following multi sample image', self.image.shape)\n return\n elif self.image.shape[0] < self.image.shape[2]: # image with more than one layer e.g 5,100,100\n if self.image.shape[0] > layer: # check layer exisits\n prepared_image = self.image[layer]\n else:\n print('layer:', layer, ' out of bounds:', self.image.shape[0],\n 'from the following multi layer image', self.image.shape)\n return\n else:\n print('Unrecognised dimesnsions:', self.image.shape)\n elif len(self.image.shape) == 2: # basic 2 dimesional array\n prepared_image = self.image\n else:\n print('invalid dimensions in image', self.image.shape)\n return\n\n if prepared_image is None:\n print('something went wrong')\n\n self.locations, self.intermediaryImage = method.findInImage(prepared_image)\n return self.locations, self.intermediaryImage\n\n def combineToSingleLayer(self,layers=[1.]):\n prepared_image = None\n if self.image.shape[0] < len(layers) or self.image.shape[2] < len(\n layers): # check number of layers provided is less than or equal to the number of layers in image\n print(\"too many layers given\")\n return -1\n count = 0.\n for l in layers:\n count = count + l\n if 1. < count < 0.9999:\n print(\"layers do not sum to one:\", layers, \"sum:\", count)\n return -1\n for c, l in enumerate(layers):\n if self.image.shape[0] > self.image.shape[2]:\n if prepared_image is None:\n prepared_image = l * self.image[:, :, c]\n else:\n prepared_image = prepared_image + l * self.image[:, :, c]\n else:\n if prepared_image is None:\n prepared_image = l * self.image[c]\n else:\n prepared_image = prepared_image + l * self.image[c]\n return prepared_image\n\n def combinedSingleLayerFind(self, method: Finder, layers=[1.]):\n prepared_image = None\n if len(self.image.shape) > 2: # check number of dimensions in image\n prepared_image = self.combineToSingleLayer(layers)\n if prepared_image == -1:\n return\n else:\n prepared_image = self.image\n\n self.locations, self.intermediaryImage = method.findInImage(prepared_image)\n return self.locations, self.intermediaryImage\n\n def multiLayerFind(self, method: Finder):\n variance = 10\n if len(self.image.shape) > 2:\n results = []\n for i in range(0, min(self.image.shape[0], self.image.shape[2])):\n if self.image.shape[0] > self.image.shape[2]:\n prepared_image = self.image[:, :, i]\n else:\n prepared_image = self.image[i]\n locations, self.intermediaryImage = method.findInImage(\n prepared_image) # only store last intermediaryImage\n print(locations)\n for l in locations:\n found_pair = False\n for r in results:\n if r.coords[0] + variance > l.coords[0] > r.coords[0] - variance and r.coords[1] + variance > \\\n l.coords[1] > r.coords[1] - variance:\n r.detected = r.detected + 1\n found_pair = True\n break\n if not found_pair:\n results.append(l)\n\n self.locations = filter(lambda x: x.detected > 1, results)\n else:\n self.locations, self.intermediaryImage = method.findInImage(self.image)\n\n return self.locations, self.intermediaryImage\n\n def outline_mammal(self, baseImage: str = 'blank', padding=30):\n \"\"\"\n based on coordinates provided outline the sheep in the image\n\n baseImage - one of {'blank','original','rgb'}\n padding - default 30px padding around a mammal when outlined\n\n returns - numpy array of base image with rectangles highlighting the mammals found mammels.\n\n \"\"\"\n if self.locations is None:\n print('no locations yet please run find first')\n return\n\n if baseImage == 'blank':\n outlined = np.zeros(self.intermediaryImage.shape)\n elif baseImage == 'original':\n outlined = self.image\n elif baseImage == 'rgb':\n if len(self.image.shape) > 2 and self.image.shape[0] > self.image.shape[2] >= 3:\n r = self.image[:, :, 0]\n g = self.image[:, :, 1]\n b = self.image[:, :, 2]\n outlined = cv2.merge((r,g,b))\n else:\n print('probably already rgb')\n return\n else:\n print('not a valid baseImage type:', baseImage, '. one of {\\'blank\\',\\'original\\'}')\n return\n\n if outlined is None:\n print(\"errror\")\n return\n print(outlined.shape)\n\n for location in self.locations:\n center = location.coords\n size = location.size\n if size is None:\n size = (25, 25)\n\n outlined = cv2.rectangle(\n outlined,\n (center[1] - round(size[1] / 2) - padding, center[0] - round(size[0] / 2) - padding),\n (center[1] + round(size[1] / 2) + padding, center[0] + round(size[0] / 2) + padding),\n 255,\n 3\n )\n self.outlined = outlined\n return outlined\n\n def saveIntermidiary(self, filepath: str):\n \"\"\"\n saves the intermidiary file to system\n\n filepath - the location to save the image to.\n \"\"\"\n if self.intermediaryImage is None:\n print('No intermidiary image, try run find first')\n return\n self.save(filepath, self.intermediaryImage)\n\n def saveOutlined(self, filepath: str):\n \"\"\"\n saves the outlined sheep image file to system\n\n filepath - the location to save the image to.\n \"\"\"\n\n if self.outlined is None:\n print('No intermidiary image, try run find first')\n return\n self.save(filepath, self.outlined)\n\n def save(self, filepath: str, image):\n if image is None:\n print('No image to save')\n if filepath is None:\n print('No file specified')\n\n saveType = filepath[len(filepath) - 3:].upper()\n if saveType == 'TIF':\n with TiffWriter(filepath, bigtiff=True) as tifw:\n if self.tags is None:\n print('btw there\\'s no tif tags being saved to this image')\n tifw.save(image)\n else:\n tifw.save(image, extratags=self.tags)\n elif saveType == 'PNG' or saveType == 'JPG':\n cv2.imwrite(filepath, image)\n print('saved', filepath)\n else:\n print('file type not handled:', saveType,\n '. try one of {\\'.tif\\',\\'.png\\',\\'.jpg\\'} at end of the filepath')\n\n def extract_sheep(self, folder: str):\n size = 10\n if self.locations is None:\n print(\"No locations\")\n return\n for location in self.locations:\n sheep = self.image[location.coords[0] - int(location.size[0]) - size:location.coords[0] + size,\n location.coords[1] - int(location.size[1]) - size:location.coords[1] + size\n , :]\n\n tif.imwrite(folder + \"/sheep_\" + str(location.coords) + \".tif\", sheep, photometric='rgb')\n\n\ndef fileInfo(tif: TiffFile):\n \"\"\"\n prints out useful tiff info\n \"\"\"\n print(tif.flags)\n print(tif.geotiff_metadata)\n for page in tif.pages:\n print(page.tags)\n print(page.geotiff_tags)\n print(page.shape)\n print(page.dtype)\n print(page.flags)\n\n\ndef metadataGeoTags(tif: TiffFile):\n \"\"\"\n extracts the useful geo tags from the tiff file\n \"\"\"\n geoTag: TiffTag = tif.pages[0].tags.get('GeoKeyDirectoryTag')\n if geoTag is not None:\n g: TiffTag = tif.pages[0].tags.get(34737)\n g2: TiffTag = tif.pages[0].tags.get(34736)\n g3: TiffTag = tif.pages[0].tags.get(33922)\n g4: TiffTag = tif.pages[0].tags.get(33550)\n\n tags = [(geoTag.code, 'H', geoTag.count, geoTag.value),\n (g.code, 's', g.count, g.value),\n (g2.code, 'd', g2.count, g2.value),\n (g3.code, 'd', g3.count, g3.value),\n (g4.code, 'd', g4.count, g4.value)]\n return tags\n else:\n print('no geo tags in file')\n","repo_name":"IdrisTheDragon/wherearemysheep","sub_path":"imageManager.py","file_name":"imageManager.py","file_ext":"py","file_size_in_byte":10706,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"4010818187","text":"'''\nby yzh 2022.2.13\n'''\n# 导入依赖\nimport random\nfrom utils.torch_utils import select_device, load_classifier, time_sync\nfrom utils.general import (\n check_img_size, non_max_suppression, apply_classifier, scale_coords,\n xyxy2xywh, strip_optimizer, set_logging)\nfrom utils.datasets import LoadStreams, LoadImages, letterbox\nfrom models.experimental import attempt_load\nimport torch.backends.cudnn as cudnn\nimport torch\n\nimport pyrealsense2 as rs\nimport math\nimport yaml\nimport argparse\nimport os\nimport time\nimport numpy as np\nimport sys\n\nimport cv2\n# PyTorch\n# YoloV5-PyTorch\n\npipeline = rs.pipeline() # 定义流程pipeline\nconfig = rs.config() # 定义配置config\nconfig.enable_stream(rs.stream.depth, 848, 480, rs.format.z16, 30)\nconfig.enable_stream(rs.stream.color, 848, 480, rs.format.bgr8, 30)\nprofile = pipeline.start(config) # 流程开始\nalign_to = rs.stream.color # 与color流对齐\nalign = rs.align(align_to)\n\n\ndef get_aligned_images():\n frames = pipeline.wait_for_frames() # 等待获取图像帧\n aligned_frames = align.process(frames) # 获取对齐帧\n aligned_depth_frame = aligned_frames.get_depth_frame() # 获取对齐帧中的depth帧\n color_frame = aligned_frames.get_color_frame() # 获取对齐帧中的color帧\n\n ############### 相机参数的获取 #######################\n intr = color_frame.profile.as_video_stream_profile().intrinsics # 获取相机内参\n depth_intrin = aligned_depth_frame.profile.as_video_stream_profile(\n ).intrinsics # 获取深度参数（像素坐标系转相机坐标系会用到）\n '''camera_parameters = {'fx': intr.fx, 'fy': intr.fy,\n 'ppx': intr.ppx, 'ppy': intr.ppy,\n 'height': intr.height, 'width': intr.width,\n 'depth_scale': profile.get_device().first_depth_sensor().get_depth_scale()\n }'''\n\n # 保存内参到本地\n # with open('./intrinsics.json', 'w') as fp:\n #json.dump(camera_parameters, fp)\n #######################################################\n\n depth_image = np.asanyarray(aligned_depth_frame.get_data()) # 深度图（默认16位）\n depth_image_8bit = cv2.convertScaleAbs(depth_image, alpha=0.03) # 深度图（8位）\n depth_image_3d = np.dstack(\n (depth_image_8bit, depth_image_8bit, depth_image_8bit)) # 3通道深度图\n color_image = np.asanyarray(color_frame.get_data()) # RGB图\n\n # 返回相机内参、深度参数、彩色图、深度图、齐帧中的depth帧\n return intr, depth_intrin, color_image, depth_image, aligned_depth_frame\n\n\nclass YoloV5:\n def __init__(self, yolov5_yaml_path='config/yolov5s.yaml'):\n '''初始化'''\n # 载入配置文件\n with open(yolov5_yaml_path, 'r', encoding='utf-8') as f:\n self.yolov5 = yaml.load(f.read(), Loader=yaml.SafeLoader)\n # 随机生成每个类别的颜色\n self.colors = [[np.random.randint(0, 255) for _ in range(\n 3)] for class_id in range(self.yolov5['class_num'])]\n # 模型初始化\n self.init_model()\n\n @torch.no_grad()\n def init_model(self):\n '''模型初始化'''\n # 设置日志输出\n set_logging()\n # 选择计算设备\n device = select_device(self.yolov5['device'])\n # 如果是GPU则使用半精度浮点数 F16\n is_half = device.type != 'cpu'\n # 载入模型\n model = attempt_load(\n self.yolov5['weight'], map_location=device) # 载入全精度浮点数的模型\n input_size = check_img_size(\n self.yolov5['input_size'], s=model.stride.max()) # 检查模型的尺寸\n if is_half:\n model.half() # 将模型转换为半精度\n # 设置BenchMark，加速固定图像的尺寸的推理\n cudnn.benchmark = True # set True to speed up constant image size inference\n # 图像缓冲区初始化\n img_torch = torch.zeros(\n (1, 3, self.yolov5['input_size'], self.yolov5['input_size']), device=device) # init img\n # 创建模型\n # run once\n _ = model(img_torch.half()\n if is_half else img) if device.type != 'cpu' else None\n self.is_half = is_half # 是否开启半精度\n self.device = device # 计算设备\n self.model = model # Yolov5模型\n self.img_torch = img_torch # 图像缓冲区\n\n def preprocessing(self, img):\n '''图像预处理'''\n # 图像缩放\n # 注: auto一定要设置为False -> 图像的宽高不同\n img_resize = letterbox(img, new_shape=(\n self.yolov5['input_size'], self.yolov5['input_size']), auto=False)[0]\n # print(\"img resize shape: {}\".format(img_resize.shape))\n # 增加一个维度\n img_arr = np.stack([img_resize], 0)\n # 图像转换 (Convert) BGR格式转换为RGB\n # 转换为 bs x 3 x 416 x\n # 0(图像i), 1(row行), 2(列), 3(RGB三通道)\n # ---> 0, 3, 1, 2\n # BGR to RGB, to bsx3x416x416\n img_arr = img_arr[:, :, :, ::-1].transpose(0, 3, 1, 2)\n # 数值归一化\n # img_arr = img_arr.astype(np.float32) / 255.0\n # 将数组在内存的存放地址变成连续的(一维)， 行优先\n # 将一个内存不连续存储的数组转换为内存连续存储的数组，使得运行速度更快\n # https://zhuanlan.zhihu.com/p/59767914\n img_arr = np.ascontiguousarray(img_arr)\n return img_arr\n\n @torch.no_grad()\n def detect(self, img, canvas=None, view_img=True):\n '''模型预测'''\n # 图像预处理\n img_resize = self.preprocessing(img) # 图像缩放\n self.img_torch = torch.from_numpy(img_resize).to(self.device) # 图像格式转换\n self.img_torch = self.img_torch.half(\n ) if self.is_half else self.img_torch.float() # 格式转换 uint8-> 浮点数\n self.img_torch /= 255.0 # 图像归一化\n if self.img_torch.ndimension() == 3:\n self.img_torch = self.img_torch.unsqueeze(0)\n # 模型推理\n t1 = time_sync()\n pred = self.model(self.img_torch, augment=False)[0]\n # pred = self.model_trt(self.img_torch, augment=False)[0]\n # NMS 非极大值抑制\n pred = non_max_suppression(pred, self.yolov5['threshold']['confidence'],\n self.yolov5['threshold']['iou'], classes=None, agnostic=False)\n t2 = time_sync()\n # print(\"推理时间: inference period = {}\".format(t2 - t1))\n # 获取检测结果\n det = pred[0]\n gain_whwh = torch.tensor(img.shape)[[1, 0, 1, 0]] # [w, h, w, h]\n\n if view_img and canvas is None:\n canvas = np.copy(img)\n xyxy_list = []\n conf_list = []\n class_id_list = []\n if det is not None and len(det):\n # 画面中存在目标对象\n # 将坐标信息恢复到原始图像的尺寸\n det[:, :4] = scale_coords(\n img_resize.shape[2:], det[:, :4], img.shape).round()\n for *xyxy, conf, class_id in reversed(det):\n class_id = int(class_id)\n xyxy_list.append(xyxy)\n conf_list.append(conf)\n class_id_list.append(class_id)\n if view_img:\n # 绘制矩形框与标签\n label = '%s %.2f' % (\n self.yolov5['class_name'][class_id], conf)\n self.plot_one_box(\n xyxy, canvas, label=label, color=self.colors[class_id], line_thickness=3)\n return canvas, class_id_list, xyxy_list, conf_list\n\n def plot_one_box(self, x, img, color=None, label=None, line_thickness=None):\n ''''绘制矩形框+标签'''\n tl = line_thickness or round(\n 0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness\n color = color or [random.randint(0, 255) for _ in range(3)]\n c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))\n cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)\n if label:\n tf = max(tl - 1, 1) # font thickness\n t_size = cv2.getTextSize(\n label, 0, fontScale=tl / 3, thickness=tf)[0]\n c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3\n cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled\n cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3,\n [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)\n\n\nif __name__ == '__main__':\n print(\"[INFO] YoloV5目标检测-程序启动\")\n print(\"[INFO] 开始YoloV5模型加载\")\n # YOLOV5模型配置文件(YAML格式)的路径 yolov5_yaml_path\n model = YoloV5(yolov5_yaml_path='config/yolov5s.yaml')\n print(\"[INFO] 完成YoloV5模型加载\")\n\n try:\n while True:\n # Wait for a coherent pair of frames: depth and color\n intr, depth_intrin, color_image, depth_image, aligned_depth_frame = get_aligned_images() # 获取对齐的图像与相机内参\n if not depth_image.any() or not color_image.any():\n continue\n # Convert images to numpy arrays\n # Apply colormap on depth image (image must be converted to 8-bit per pixel first)\n depth_colormap = cv2.applyColorMap(cv2.convertScaleAbs(\n depth_image, alpha=0.03), cv2.COLORMAP_JET)\n # Stack both images horizontally\n images = np.hstack((color_image, depth_colormap))\n \n # Show images\n\n t_start = time.time() # 开始计时\n # YoloV5 目标检测\n canvas, class_id_list, xyxy_list, conf_list = model.detect(\n color_image)\n\n t_end = time.time() # 结束计时\\\n #canvas = np.hstack((canvas, depth_colormap))\n #print(class_id_list)\n\n camera_xyz_list=[]\n if xyxy_list:\n for i in range(len(xyxy_list)):\n ux = int((xyxy_list[i][0]+xyxy_list[i][2])/2) # 计算像素坐标系的x\n uy = int((xyxy_list[i][1]+xyxy_list[i][3])/2) # 计算像素坐标系的y\n dis = aligned_depth_frame.get_distance(ux, uy)\n camera_xyz = rs.rs2_deproject_pixel_to_point(\n depth_intrin, (ux, uy), dis) # 计算相机坐标系的xyz\n camera_xyz = np.round(np.array(camera_xyz), 3) # 转成3位小数\n camera_xyz = camera_xyz.tolist()\n cv2.circle(canvas, (ux,uy), 4, (255, 255, 255), 5)#标出中心点\n cv2.putText(canvas, str(camera_xyz), (ux+20, uy+10), 0, 1,\n [225, 255, 255], thickness=2, lineType=cv2.LINE_AA)#标出坐标\n camera_xyz_list.append(camera_xyz)\n #print(camera_xyz_list)\n\n # 添加fps显示\n fps = int(1.0 / (t_end - t_start))\n cv2.putText(canvas, text=\"FPS: {}\".format(fps), org=(50, 50),\n fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, thickness=2,\n lineType=cv2.LINE_AA, color=(0, 0, 0))\n cv2.namedWindow('detection', flags=cv2.WINDOW_NORMAL |\n cv2.WINDOW_KEEPRATIO | cv2.WINDOW_GUI_EXPANDED)\n cv2.imshow('detection', canvas)\n key = cv2.waitKey(1)\n # Press esc or 'q' to close the image window\n if key & 0xFF == ord('q') or key == 27:\n cv2.destroyAllWindows()\n break\n finally:\n # Stop streaming\n pipeline.stop()\n","repo_name":"Thinkin99/yolov5_d435i_detection","sub_path":"rstest.py","file_name":"rstest.py","file_ext":"py","file_size_in_byte":11712,"program_lang":"python","lang":"en","doc_type":"code","stars":110,"dataset":"github-code","pt":"52"} +{"seq_id":"22026805354","text":"\"\"\"initial\n\nRevision ID: a07e50720472\nRevises: \nCreate Date: 2023-05-22 16:36:13.721089\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'a07e50720472'\ndown_revision = None\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade() -> None:\n # ### commands auto generated by Alembic - please adjust! ###\n op.create_table('admin',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('username', sa.String(length=150), nullable=True),\n sa.Column('password', sa.String(length=150), nullable=True),\n sa.PrimaryKeyConstraint('id')\n )\n op.create_table('audio',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('chat_id', sa.BigInteger(), nullable=True),\n sa.Column('text', sa.Text(), nullable=True),\n sa.Column('distination', sa.String(length=150), nullable=True),\n sa.PrimaryKeyConstraint('id')\n )\n op.create_table('channel',\n sa.Column('chat_id', sa.BigInteger(), autoincrement=False, nullable=False),\n sa.Column('title', sa.String(length=150), nullable=True),\n sa.PrimaryKeyConstraint('chat_id'),\n sa.UniqueConstraint('chat_id')\n )\n op.create_table('user',\n sa.Column('user_id', sa.BigInteger(), autoincrement=False, nullable=False),\n sa.Column('username', sa.String(length=50), nullable=True),\n sa.Column('first_name', sa.String(length=50), nullable=True),\n sa.PrimaryKeyConstraint('user_id'),\n sa.UniqueConstraint('user_id')\n )\n # ### end Alembic commands ###\n\n\ndef downgrade() -> None:\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_table('user')\n op.drop_table('channel')\n op.drop_table('audio')\n op.drop_table('admin')\n # ### end Alembic commands ###\n","repo_name":"KarimovMurodilla/post-tts","sub_path":"utils/db_api/migrations/versions/a07e50720472_initial.py","file_name":"a07e50720472_initial.py","file_ext":"py","file_size_in_byte":1750,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"3293744889","text":"# mimi project[ \"clock angle\" ] (24 hours clock)\r\ndef clock_angle(h,m):\r\n #To get 24 hours notation(remander)\r\n h = h % 12\r\n #To find the position of the hour's [formula]\r\n hours = (h*360)//12 + (m*360)//(12*60)\r\n #To find the poaition of the minute's [formula]\r\n minutes=(m*360)//(60)\r\n #To find the angle beteweem hours and minutes [formula]\r\n angle=abs(hours-minutes)\r\n if angle > 180:\r\n angle = 360 - angle\r\n return angle\r\nh=int(input(\"Hours: \"))\r\nm=int(input(\"Minutes: \"))\r\nprint(\"Angle: \",clock_angle(h,m))\r\n\r\n","repo_name":"DotiNiranjan/python-project","sub_path":"mini project.py","file_name":"mini project.py","file_ext":"py","file_size_in_byte":583,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"33321173858","text":"from argparse import Namespace\nfrom pathlib import Path\nfrom typing import Any, Dict, List\n\nimport cairo\nimport cv2\nimport numpy as np\nfrom nonebot.adapters.onebot.v11 import Bot, MessageEvent, MessageSegment\nfrom nonebot.params import ShellCommandArgs\nfrom nonebot.rule import ArgumentParser\nfrom PIL import Image, ImageChops, ImageFilter, ImageOps\n\nfrom util import command, imutil, misc\nfrom util.misc import range_int\nfrom util.user_aliases import AvatarGetter, DefaultType\n\n\ndef kernel_average(size: int) -> np.ndarray[Any, Any]:\n return np.full((size, size), 1 / size ** 2)\n\n\nDIR = Path(__file__).resolve().parent\nKERNELS: Dict[str, np.ndarray[Any, Any]] = {\n \"thin\": kernel_average(5),\n \"normal\": kernel_average(7),\n \"semibold\": kernel_average(9),\n \"bold\": kernel_average(11),\n \"black\": kernel_average(13),\n \"emboss\": np.array([\n [1, 1, 1],\n [1, 1, -1],\n [-1, -1, -1],\n ]),\n}\n# 这些选项在原网站不可调\nSHADE_LIGHT = 80\nLIGHT_CUT = 128\n\n\ndef make_mask(\n im: Image.Image,\n pencil: Image.Image,\n kernel: str = \"normal\",\n dark_cut: int = 118, # 对应原网站线迹轻重\n shade_limit: int = 108, # 对应原网站调子数量\n denoise: bool = True # 对应原网站降噪\n) -> Image.Image:\n shade = im.point(lambda v: 0 if v > shade_limit else 255, \"L\")\n shade = shade.filter(ImageFilter.BoxBlur(3))\n shade = ImageChops.multiply(shade, ImageChops.invert(pencil))\n shade = ImageChops.multiply(shade, Image.new(\"L\", shade.size, SHADE_LIGHT))\n\n if denoise:\n im = im.filter(ImageFilter.Kernel((3, 3), [1] * 9, 9))\n\n # 因为PIL只支持3x3和5x5的卷积核，NumPy的卷积是一维的，要用OpenCV\n im1 = Image.fromarray(cv2.filter2D(np.array(im), -1, KERNELS[kernel]))\n im = ImageChops.subtract(im, im1, 1, 128)\n\n scale = (255 - LIGHT_CUT - dark_cut) / 255\n im = ImageChops.subtract(im, Image.new(\"L\", im.size, dark_cut), scale)\n\n return ImageChops.lighter(ImageChops.invert(im), shade)\n\n\ndef make_gradient(width: int, height: int) -> Image.Image:\n with cairo.ImageSurface(cairo.FORMAT_RGB24, width, height) as surface:\n cr = cairo.Context(surface)\n gradient = cairo.LinearGradient(0, 0, width, height)\n gradient.add_color_stop_rgb(0.0, 0.984313725490196, 0.7294117647058823, 0.18823529411764706)\n gradient.add_color_stop_rgb(0.4, 0.9882352941176471, 0.4470588235294118, 0.20784313725490197)\n gradient.add_color_stop_rgb(0.6, 0.9882352941176471, 0.20784313725490197, 0.3058823529411765)\n gradient.add_color_stop_rgb(0.7, 0.8117647058823529, 0.21176470588235294, 0.8745098039215686)\n gradient.add_color_stop_rgb(0.8, 0.21568627450980393, 0.7098039215686275, 0.8509803921568627)\n gradient.add_color_stop_rgb(1.0, 0.24313725490196078, 0.7137254901960784, 0.8549019607843137)\n cr.set_source(gradient)\n cr.rectangle(0, 0, width, height)\n cr.fill()\n return imutil.from_cairo(surface)\n\n\nparser = ArgumentParser(add_help=False)\nparser.add_argument(\"target\", nargs=\"?\", default=\"\", metavar=\"目标\", help=(\n \"可使用@、QQ号、昵称、群名片或图片链接（可传入动图）\"\n))\nparser.add_argument(\"--style\", \"-s\", choices=list(KERNELS), default=\"normal\", help=(\n \"线条风格，可用: thin (精细)、normal (一般)、semibold (稍粗)、bold (超粗)、black (极粗)、\"\n \"emboss (浮雕)，默认: normal\"\n))\nparser.add_argument(\"--edge\", \"-e\", type=range_int(80, 126), default=118, metavar=\"强度\", help=(\n \"边缘强度，为 [80, 126] 之间的整数，默认: 118\"\n))\nparser.add_argument(\"--shade\", \"-a\", type=range_int(20, 200), default=108, metavar=\"强度\", help=(\n \"暗部强度，为 [20, 200] 之间的整数，默认: 108\"\n))\nparser.add_argument(\"--no-denoise\", \"-D\", action=\"store_false\", dest=\"denoise\", help=\"不进行降噪\")\ngroup = parser.add_mutually_exclusive_group()\ngroup.add_argument(\n \"--webp\", \"-w\", action=\"store_const\", dest=\"format\", const=\"webp\", default=\"gif\",\n help=\"使用WebP而非GIF格式（如果传入动图）\"\n)\ngroup.add_argument(\n \"--png\", \"--apng\", \"-p\", action=\"store_const\", dest=\"format\", const=\"png\",\n help=\"使用APNG而非GIF格式（如果传入动图）\"\n)\nparser.epilog = \"特别感谢: https://lab.magiconch.com/one-last-image/\"\nmatcher = (\n command.CommandBuilder(\"meme_pic.louvre\", \"卢浮宫\")\n .category(\"meme_pic\")\n .brief(\"[动]\")\n .shell(parser)\n .build()\n)\n@matcher.handle()\nasync def handler(bot: Bot, event: MessageEvent, args: Namespace = ShellCommandArgs()) -> None:\n async with AvatarGetter(bot, event) as g:\n target_task = g(args.target, DefaultType.TARGET, raw=True)\n\n def make() -> MessageSegment:\n target, _ = target_task.result()\n pencil = Image.open(DIR / \"pencil.jpg\").convert(\"L\")\n pencil = ImageOps.fit(pencil, target.size, imutil.scale_resample())\n gradient = make_gradient(target.width, target.height)\n frames: List[Image.Image] = []\n for raw in imutil.frames(target):\n l, a = raw.convert(\"LA\").split()\n frame = Image.new(\"L\", l.size, 255)\n frame.paste(l, mask=a)\n mask = make_mask(frame, pencil, args.style, args.edge, args.shade, args.denoise)\n frame = Image.new(\"RGB\", l.size, (255, 255, 255))\n frame.paste(gradient, mask=mask)\n frames.append(frame)\n return imutil.to_segment(frames, target, afmt=args.format)\n\n await matcher.finish(await misc.to_thread(make))\n","repo_name":"su226/IdhagnBot","sub_path":"plugins/meme_pic/louvre/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":5318,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"52"} +{"seq_id":"36417826453","text":"import unittest\n\nimport errno\nimport mock\nfrom exporters.records.base_record import BaseRecord\nfrom exporters.writers import SFTPWriter\nfrom exporters.writers.base_writer import InconsistentWriteState\n\nfrom .utils import meta\n\n\nclass SFTPWriterTest(unittest.TestCase):\n\n def get_batch(self):\n data = [\n {'name': 'Roberto', 'birthday': '12/05/1987'},\n {'name': 'Claudia', 'birthday': '21/12/1985'},\n ]\n return [BaseRecord(d) for d in data]\n\n def get_writer_config(self):\n return {\n 'name': 'exporters.writers.sftp_writer.SFTPWriter',\n 'options': {\n 'sftp_user': 'user',\n 'sftp_password': 'password',\n 'filebase': 'test/',\n 'host': 'sftp.example.com',\n }\n }\n\n def test_create(self):\n options = self.get_writer_config()\n writer = SFTPWriter(options, meta())\n self.assertEquals(22, writer.read_option('port'))\n writer.close()\n\n @mock.patch('pysftp.Connection')\n def test_check_writer_consistency(self, mock_sftp):\n\n items_to_write = self.get_batch()\n options = self.get_writer_config()\n options['options']['check_consistency'] = True\n mock_sftp.return_value.__enter__.return_value.stat.return_value.st_size = 999\n\n # when:\n try:\n writer = SFTPWriter(options, meta())\n writer.write_batch(items_to_write)\n writer.flush()\n finally:\n writer.close()\n\n with self.assertRaisesRegexp(InconsistentWriteState, 'Wrong size for file'):\n writer.finish_writing()\n\n exception = IOError()\n exception.errno = errno.ENOENT\n mock_sftp.return_value.__enter__.return_value.stat.side_effect = exception\n with self.assertRaisesRegexp(InconsistentWriteState, 'is not present at destination'):\n writer.finish_writing()\n","repo_name":"scrapinghub/exporters","sub_path":"tests/test_writers_sftp.py","file_name":"test_writers_sftp.py","file_ext":"py","file_size_in_byte":1933,"program_lang":"python","lang":"en","doc_type":"code","stars":40,"dataset":"github-code","pt":"52"} +{"seq_id":"43759482052","text":"# coding: utf-8\n\"\"\"\nhttps://soulmachine.gitbooks.io/algorithm-essentials/content/cpp/bfs/word-ladder-ii.html\nGiven two words (start and end), and a dictionary, find all shortest transformation sequence(s) from start to end, such that:\nOnly one letter can be changed at a time\nEach intermediate word must exist in the dictionary\nFor example, Given:\nstart = \"hit\"\nend = \"cog\"\ndict = [\"hot\",\"dot\",\"dog\",\"lot\",\"log\"]\nReturn\n[\n [\"hit\",\"hot\",\"dot\",\"dog\",\"cog\"],\n [\"hit\",\"hot\",\"lot\",\"log\",\"cog\"]\n]\nNote:\nAll words have the same length.\nAll words contain only lowercase alphabetic characters.\n\"\"\"\n\ndef get_one_change(word, dict_, visted):\n ret = []\n for word_ in dict_:\n if word_ in visted:\n continue\n common = sum(i==j for i, j in zip(word_, word))\n if common == len(word) - 1:\n ret.append(word_)\n return ret\n\n\ndef get_ladder(start, end, dict_):\n visited = set()\n\n def _ladder(words, paths):\n for i in words:\n visited.add(i)\n\n ret = []\n for word, path in zip(words, paths):\n if word == end:\n ret.append(path)\n continue\n if ret:\n return ret\n\n next_words = []\n next_paths = []\n for word, path in zip(words, paths):\n starts = get_one_change(word, dict_, visited)\n for next_ in starts:\n next_words.append(next_)\n next_paths.append(path + [next_])\n\n if next_words:\n return _ladder(next_words, next_paths)\n\n return []\n\n return _ladder([start], [[start]])\n\n\ndef test():\n start = \"hit\"\n end = \"cog\"\n dict_ = [\"hit\", \"hot\", \"dot\", \"dog\", \"lot\", \"log\", \"cog\"]\n return_ = [\n [\"hit\", \"hot\", \"dot\", \"dog\", \"cog\"],\n [\"hit\", \"hot\", \"lot\", \"log\", \"cog\"]\n ]\n print(get_ladder(start, end, dict_))\n assert get_ladder(start, end, dict_) == return_\n\n\nif __name__ == '__main__':\n \"\"\"\n python -m mixleet.201908.WordLadderII test\n \"\"\"\n from fire import Fire\n Fire()\n\n","repo_name":"spiritdjy/MixLeetCode","sub_path":"mixleet/leecode/WordLadderII.py","file_name":"WordLadderII.py","file_ext":"py","file_size_in_byte":2042,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"23439671284","text":"# GUI.py\r\nimport pygame\r\nimport time\r\npygame.font.init()\r\nimport sys\r\nimport grid_solver\r\nimport numpy as np\r\nimport random\r\n\r\nclass Grid:\r\n # Graphical representation of a grid\r\n \r\n def __init__(self, size, width, height, win):\r\n self.size = size\r\n self.board = [[size*i + j + 1 for j in range(size)] for i in range(size)]\r\n self.board[size-1][size-1] = 0\r\n self.rows = size\r\n self.cols = size\r\n self.cubes = [[Cube(self.board[i][j], size, i, j, width, height) for j in range(size)] for i in range(size)]\r\n self.width = width\r\n self.height = height\r\n self.selected = None\r\n self.win = win\r\n self.update_grid()\r\n\r\n def update_grid(self):\r\n for i in range(self.cols):\r\n for j in range(self.rows):\r\n self.cubes[i][j].value = self.board[i][j]\r\n\r\n def find_neighbour_gap(self):\r\n # If the empty tile is a neighbour of the selected tile, \r\n # returns the coordinates of the empty tile. Otherwise returns None.\r\n \r\n row, col = self.selected\r\n neighbours = [(row-1, col), (row, col-1), (row+1, col), (row, col+1)]\r\n for (a,b) in neighbours:\r\n if 0 <= a < len(self.cubes) and 0 <= b < len(self.cubes[row]):\r\n if self.cubes[a][b].value == 0:\r\n return (a,b)\r\n return None\r\n\r\n def move(self):\r\n # Slides the selected tile into the gap if possible.\r\n # Otherwise does nothing.\r\n \r\n row, col = self.selected\r\n target = self.find_neighbour_gap()\r\n if target:\r\n self.board[target[0]][target[1]] = self.board[row][col]\r\n self.board[row][col] = 0\r\n self.update_grid()\r\n return True\r\n return False\r\n\r\n def draw(self):\r\n # Draw grid lines\r\n gap = self.width / self.size\r\n for i in range(self.rows + 1):\r\n thick = 1\r\n pygame.draw.line(self.win, (0, 0, 0), (0, i * gap), (self.width, i * gap), thick)\r\n pygame.draw.line(self.win, (0, 0, 0), (i * gap, 0), (i * gap, self.height), thick)\r\n\r\n for i in range(self.rows):\r\n for j in range(self.cols):\r\n self.cubes[i][j].draw(self.win)\r\n\r\n def shuffle(self):\r\n valid = False\r\n while not valid:\r\n number_list = list(range(0, self.size * self.size, 1))\r\n random.shuffle(number_list)\r\n if solvable(number_list):\r\n valid = True\r\n\r\n new_grid = np.array(number_list).reshape(self.rows, self.cols)\r\n for i in range(self.rows):\r\n for j in range(self.cols):\r\n self.board[i][j] = new_grid[i][j]\r\n self.update_grid()\r\n\r\n def select(self, row, col):\r\n # Reset\r\n for i in range(self.rows):\r\n for j in range(self.cols):\r\n self.cubes[i][j].selected = False\r\n\r\n self.cubes[row][col].selected = True\r\n self.selected = (row,col)\r\n\r\n def clear(self):\r\n row, col = self.selected\r\n if self.cubes[row][col].value == 0:\r\n self.cubes[row][col].set_temp(0)\r\n\r\n def click(self, pos):\r\n if pos[0] < self.width and pos[1] < self.height:\r\n gap = self.width / self.size\r\n x = pos[0] // gap\r\n y= pos[1] // gap\r\n return (int(y), int(x))\r\n else:\r\n return None\r\n\r\n def change_state(self, new_state):\r\n for i in range(self.rows):\r\n for j in range(self.cols):\r\n if self.board[i][j] != new_state[self.size*i + j] and self.board[i][j] != 0:\r\n self.select(i,j)\r\n self.move()\r\n\r\n\r\nclass Cube:\r\n\r\n def __init__(self, value, grid_size, row, col, width, height):\r\n self.grid_size = grid_size\r\n self.value = value\r\n self.temp = 0\r\n self.row = row\r\n self.col = col\r\n self.width = width\r\n self.height = height\r\n self.selected = False\r\n\r\n def draw(self, win):\r\n fnt = pygame.font.SysFont(\"comicsans\", 40)\r\n\r\n gap = self.width / self.grid_size\r\n x = self.col * gap\r\n y = self.row * gap\r\n\r\n if self.temp != 0 and self.value == 0:\r\n text = fnt.render(str(self.temp), 1, (128, 128, 128))\r\n win.blit(text, (x + 5, y + 5))\r\n elif not (self.value == 0):\r\n text = fnt.render(str(self.value), 1, (0, 0, 0))\r\n win.blit(text, (x + (gap / 2 - text.get_width() / 2), y + (gap / 2 - text.get_height() / 2)))\r\n\r\n if self.selected:\r\n pygame.draw.rect(win, (255, 0, 0), (x, y, gap, gap), self.grid_size)\r\n\r\ndef redraw_window(win, board, time, steps = None):\r\n win.fill((255, 255, 255))\r\n # Draw time\r\n fnt = pygame.font.SysFont(\"comicsans\", 40)\r\n text = fnt.render(\"Time: \" + format_time(time), 1, (0, 0, 0))\r\n win.blit(text, (540 - 160, 560))\r\n # Draw steps\r\n if steps:\r\n text = fnt.render(\"Steps: \" + str(steps), 1, (255, 0, 0))\r\n win.blit(text, (20, 560))\r\n # Draw instructions\r\n fnt = pygame.font.SysFont(\"comicsans\", 20)\r\n text = fnt.render(\"S - Shuffle\" , 1, (0, 0, 0))\r\n win.blit(text, (540 - 320, 560-15))\r\n text = fnt.render(\"LMB - Slide\", 1, (0, 0, 0))\r\n win.blit(text, (540 - 320, 560))\r\n text = fnt.render(\"Space - Solve\", 1, (0, 0, 0))\r\n win.blit(text, (540 - 320, 560+15))\r\n # Draw grid and board\r\n board.draw()\r\n\r\ndef format_time(secs):\r\n sec = secs%60\r\n minute = secs//60\r\n hour = minute//60\r\n mat = \" \" + str(minute) + \":\" + str(sec)\r\n return mat\r\n\r\ndef solvable(order):\r\n count = 0\r\n for i in range(len(order)):\r\n for j in range(i+1, len(order)):\r\n if order[j] and order[i] > order[j]:\r\n count += 1\r\n return count % 2 == 0\r\n\r\ndef main():\r\n size = int(input(\"Enter grid size: \"))\r\n goal_state = [[size * i + j + 1 for j in range(size)] for i in range(size)]\r\n goal_state[size - 1][size - 1] = 0\r\n win = pygame.display.set_mode((540, 600))\r\n pygame.display.set_caption(\"Sliding Puzzle\")\r\n board = Grid(size, 540, 540, win)\r\n key = None\r\n run = True\r\n steps = 0\r\n start = time.time()\r\n\r\n while run:\r\n play_time = round(time.time() - start)\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n pygame.quit()\r\n sys.exit()\r\n\r\n if event.type == pygame.KEYDOWN:\r\n if event.key == pygame.K_SPACE:\r\n solver = grid_solver.GridSolver(np.array(board.board), np.array(goal_state))\r\n for node in reversed(solver.solve_grid()):\r\n steps += 1\r\n board.change_state(node.curr_state)\r\n redraw_window(win, board, play_time, steps)\r\n pygame.display.update()\r\n time.sleep(0.25)\r\n steps = 0\r\n if event.key == pygame.K_s:\r\n board.shuffle()\r\n key = None\r\n\r\n if event.type == pygame.MOUSEBUTTONDOWN:\r\n pos = pygame.mouse.get_pos()\r\n clicked = board.click(pos)\r\n if clicked:\r\n board.select(clicked[0], clicked[1])\r\n key = None\r\n board.move()\r\n\r\n redraw_window(win, board, play_time)\r\n pygame.display.update()\r\n\r\nmain()\r\npygame.quit()\r\n","repo_name":"leonkosarev/N-Puzzle","sub_path":"src/GUI.py","file_name":"GUI.py","file_ext":"py","file_size_in_byte":7536,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"73306036965","text":"from utils import *\n\nimport dgl.function as fn\nimport math\n\n\nclass GCNLayer(nn.Module):\n\tdef __init__(self, in_feats, out_feats, activation=F.relu, dropout=0.1, bias=True):\n\t\tsuper(GCNLayer, self).__init__()\n\t\tself.weight = nn.Parameter(torch.Tensor(in_feats, out_feats)).cuda()\n\t\tif bias:\n\t\t\tself.bias = nn.Parameter(torch.Tensor(out_feats)).cuda()\n\t\telse:\n\t\t\tself.bias = None\n\t\tself.activation = activation\n\t\tif dropout:\n\t\t\tself.dropout = nn.Dropout(p=dropout)\n\t\telse:\n\t\t\tself.dropout = 0.\n\t\tself.reset_parameters()\n\n\tdef reset_parameters(self):\n\t\tstdv = 1. / math.sqrt(self.weight.size(1))\n\t\tself.weight.data.uniform_(-stdv, stdv)\n\t\tif self.bias is not None:\n\t\t\tself.bias.data.uniform_(-stdv, stdv)\n\n\tdef forward(self, g, h):\n\t\tif self.dropout:\n\t\t\th = self.dropout(h)\n\t\th = torch.mm(h, self.weight.cuda())\n\t\tg.ndata['h'] = h\n\t\tg.update_all(fn.copy_src(src='h', out='m'), fn.sum(msg='m', out='h'))\n\t\th = g.ndata.pop('h')\n\t\tif self.bias is not None:\n\t\t\th = h + self.bias\n\t\tif self.activation:\n\t\t\th = self.activation(h)\n\t\treturn h\n\n\nclass GCN(nn.Module):\n\tdef __init__(self, in_dim, hidden_dim, n_desc, n_classes):\n\t\tsuper(GCN, self).__init__()\n\t\tself.layers = nn.ModuleList()\n\t\tself.layers.append(GCNLayer(in_dim, hidden_dim[0], F.relu, 0.))\n\t\tfor i, hid in enumerate(hidden_dim[:-1]):\n\t\t\tself.layers.append(GCNLayer(hid, hidden_dim[i + 1], F.relu, 0.2))\n\n\t\tself.lin1 = nn.Linear(hidden_dim[-1] + n_desc, 1000)\n\t\tself.lin2 = nn.Linear(1000, 500)\n\t\tself.lin3 = nn.Linear(500, 100)\n\t\tself.lin4 = nn.Linear(100, 20)\n\t\tself.drop = nn.Dropout(0.2)\n\t\tself.classify = nn.Linear(20, n_classes)\n\n\tdef forward(self, g, features, descriptors):\n\t\t# GCN\n\t\th = features\n\t\tfor layer in self.layers:\n\t\t\th = layer(g, h)\n\t\tg.ndata['h'] = h\n\t\tfeats = dgl.mean_nodes(g, 'h')\n\n\t\t# Concat (GCN_feat, descriptors)\n\t\th = torch.cat((feats, descriptors), 1)\n\n\t\t# Classify\n\t\th = self.drop(F.relu(self.lin1(h)))\n\t\th = self.drop(F.relu(self.lin2(h)))\n\t\th = self.drop(F.relu(self.lin3(h)))\n\t\th = self.drop(F.relu(self.lin4(h)))\n\t\treturn self.classify(h), feats\n\n\nclass GCN_des(nn.Module):\n\tdef __init__(self, in_dim, hidden_dim, n_desc, n_classes):\n\t\tsuper(GCN_des, self).__init__()\n\t\tself.layers = nn.ModuleList()\n\t\tself.layers.append(GCNLayer(in_dim, hidden_dim[0], F.relu, 0.))\n\t\tfor i, hid in enumerate(hidden_dim[:-1]):\n\t\t\tself.layers.append(GCNLayer(hid, hidden_dim[i + 1], F.relu, 0.2))\n\n\t\tself.lin1 = nn.Linear(hidden_dim[-1], 10)\n\t\tself.lin2 = nn.Linear(10, 5)\n\t\tself.drop = nn.Dropout(0.2)\n\t\tself.classify = nn.Linear(5, n_classes)\n\n\tdef forward(self, g, features, descriptors):\n\t\t# GCN\n\t\th = features\n\t\tfor layer in self.layers:\n\t\t\th = layer(g, h)\n\t\tg.ndata['h'] = h\n\t\tfeats = dgl.mean_nodes(g, 'h')\n\n\t\t# Concat (GCN_feat, descriptors)\n\t\t# h = torch.cat((h, descriptors), 1)\n\n\t\t# Classify\n\t\th = self.drop(F.relu(self.lin1(feats)))\n\t\th = self.drop(F.relu(self.lin2(h)))\n\t\treturn self.classify(h), feats","repo_name":"anandrajasekar18/covid_drug_design","sub_path":"task1/gcn_model/gcn_model.py","file_name":"gcn_model.py","file_ext":"py","file_size_in_byte":2883,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"31505160242","text":"import pytest\n\nimport pennylane as qml\nfrom pennylane.ops import Hamiltonian, Prod, SProd, Sum\nfrom pennylane.pauli.pauli_arithmetic import PauliSentence\n\n\nclass TestDotSum:\n \"\"\"Unittests for the dot function when ``pauli=False``.\"\"\"\n\n def test_dot_returns_sum(self):\n \"\"\"Test that the dot function returns a Sum operator when ``pauli=False``.\"\"\"\n c = [1.0, 2.0, 3.0]\n o = [qml.PauliX(0), qml.PauliY(1), qml.PauliZ(2)]\n S = qml.dot(coeffs=c, ops=o)\n assert isinstance(S, Sum)\n for summand, coeff in zip(S.operands, c):\n if coeff == 1:\n assert isinstance(summand, qml.PauliX)\n else:\n assert isinstance(summand, SProd)\n assert summand.scalar == coeff\n\n def test_dot_returns_sprod(self):\n \"\"\"Test that the dot function returns a SProd operator when only one operator is input.\"\"\"\n O = qml.dot(coeffs=[2.0], ops=[qml.PauliX(0)])\n assert isinstance(O, SProd)\n assert O.scalar == 2\n\n def test_cast_tensor_to_prod(self):\n \"\"\"Test that `dot` casts all `Tensor` objects to `Prod`.\"\"\"\n result = qml.dot(\n coeffs=[1, 1, 1],\n ops=[\n qml.PauliX(0) @ qml.PauliY(0),\n qml.PauliX(0) @ qml.PauliY(0),\n qml.PauliX(0) @ qml.PauliY(0),\n ],\n )\n assert isinstance(result, Sum)\n for op in result:\n assert isinstance(op, Prod)\n\n def test_dot_groups_coeffs(self):\n \"\"\"Test that the `dot` function groups the coefficients.\"\"\"\n result = qml.dot(coeffs=[4, 4, 4], ops=[qml.PauliX(0), qml.PauliX(1), qml.PauliX(2)])\n assert isinstance(result, SProd)\n assert result.scalar == 4\n assert isinstance(result.base, Sum)\n assert len(result.base) == 3\n for op in result.base:\n assert isinstance(op, qml.PauliX)\n\n def test_dot_groups_coeffs_with_different_sign(self):\n \"\"\"test that the `dot` function groups coefficients with different signs.\"\"\"\n cs = [4, -4, 4]\n result = qml.dot(coeffs=cs, ops=[qml.PauliX(0), qml.PauliX(1), qml.PauliX(2)])\n assert isinstance(result, SProd)\n assert result.scalar == 4\n for op, c in zip(result.base, cs):\n if c == -4:\n assert isinstance(op, SProd)\n assert op.scalar == -1\n else:\n assert isinstance(op, qml.PauliX)\n\n def test_dot_different_number_of_coeffs_and_ops(self):\n \"\"\"Test that a ValueError is raised when the number of coefficients and operators does\n not match.\"\"\"\n with pytest.raises(\n ValueError,\n match=\"Number of coefficients and operators does not match\",\n ):\n qml.dot([1.0], [qml.PauliX(0), qml.PauliY(1)])\n\n def test_dot_empty_coeffs_or_ops(self):\n \"\"\"Test that a ValueError is raised when the number of coefficients and operators does\n not match.\"\"\"\n with pytest.raises(\n ValueError,\n match=\"Cannot compute the dot product of an empty sequence\",\n ):\n qml.dot([], [])\n\n @pytest.mark.autograd\n @pytest.mark.parametrize(\"dtype\", (float, complex))\n def test_dot_autograd(self, dtype):\n \"\"\"Test the dot function with the autograd interface.\"\"\"\n c = qml.numpy.array([1.0, 2.0, 3.0], dtype=dtype)\n o = [qml.PauliX(0), qml.PauliY(1), qml.PauliZ(2)]\n op_sum = qml.dot(c, o)\n op_sum_2 = Sum(\n qml.PauliX(0),\n SProd(qml.numpy.array(2.0, dtype=dtype), qml.PauliY(1)),\n SProd(qml.numpy.array(3.0, dtype=dtype), qml.PauliZ(2)),\n )\n assert qml.equal(op_sum, op_sum_2)\n\n @pytest.mark.tf\n @pytest.mark.parametrize(\"dtype\", (\"float64\", \"complex128\"))\n def test_dot_tf(self, dtype):\n \"\"\"Test the dot function with the tensorflow interface.\"\"\"\n import tensorflow as tf\n\n c = tf.constant([1.0, 2.0, 3.0], dtype=getattr(tf, dtype))\n o = [qml.PauliX(0), qml.PauliY(1), qml.PauliZ(2)]\n op_sum = qml.dot(c, o)\n op_sum_2 = Sum(\n qml.PauliX(0),\n SProd(tf.constant(2.0, dtype=getattr(tf, dtype)), qml.PauliY(1)),\n SProd(tf.constant(3.0, dtype=getattr(tf, dtype)), qml.PauliZ(2)),\n )\n assert qml.equal(op_sum, op_sum_2)\n\n @pytest.mark.torch\n @pytest.mark.parametrize(\"dtype\", (\"float64\", \"complex128\"))\n def test_dot_torch(self, dtype):\n \"\"\"Test the dot function with the torch interface.\"\"\"\n import torch\n\n c = torch.tensor([1.0, 2.0, 3.0], dtype=getattr(torch, dtype))\n o = [qml.PauliX(0), qml.PauliY(1), qml.PauliZ(2)]\n op_sum = qml.dot(c, o)\n op_sum_2 = Sum(\n qml.PauliX(0),\n SProd(torch.tensor(2.0, dtype=getattr(torch, dtype)), qml.PauliY(1)),\n SProd(torch.tensor(3.0, dtype=getattr(torch, dtype)), qml.PauliZ(2)),\n )\n assert qml.equal(op_sum, op_sum_2)\n\n @pytest.mark.jax\n @pytest.mark.parametrize(\"dtype\", (float, complex))\n def test_dot_jax(self, dtype):\n \"\"\"Test the dot function with the torch interface.\"\"\"\n import jax\n\n c = jax.numpy.array([1.0, 2.0, 3.0], dtype=dtype)\n o = [qml.PauliX(0), qml.PauliY(1), qml.PauliZ(2)]\n op_sum = qml.dot(c, o)\n op_sum_2 = Sum(\n qml.PauliX(0),\n SProd(jax.numpy.array(2.0, dtype=dtype), qml.PauliY(1)),\n SProd(jax.numpy.array(3.0, dtype=dtype), qml.PauliZ(2)),\n )\n assert qml.equal(op_sum, op_sum_2)\n\n\ncoeffs = [0.12345, 1.2345, 12.345, 123.45, 1234.5, 12345]\nops = [\n qml.PauliX(0),\n qml.PauliY(1),\n qml.PauliZ(2),\n qml.PauliX(3),\n qml.PauliY(4),\n qml.PauliZ(5),\n]\n\n\nclass TestDotPauliSentence:\n \"\"\"Unittest for the dot function when ``pauli=True``\"\"\"\n\n def test_dot_returns_pauli_sentence(self):\n \"\"\"Test that the dot function returns a PauliSentence class.\"\"\"\n ps = qml.dot(coeffs, ops, pauli=True)\n assert isinstance(ps, PauliSentence)\n\n def test_coeffs_and_ops(self):\n \"\"\"Test that the coefficients and operators of the returned PauliSentence are correct.\"\"\"\n ps = qml.dot(coeffs, ops, pauli=True)\n h = ps.hamiltonian()\n assert qml.math.allequal(h.coeffs, coeffs)\n assert all(qml.equal(op1, op2) for op1, op2 in zip(h.ops, ops))\n\n def test_dot_simplifies_linear_combination(self):\n \"\"\"Test that the dot function groups equal pauli words.\"\"\"\n ps = qml.dot(\n coeffs=[0.12, 1.2, 12], ops=[qml.PauliX(0), qml.PauliX(0), qml.PauliX(0)], pauli=True\n )\n assert len(ps) == 1\n h = ps.hamiltonian()\n assert len(h.ops) == 1\n assert qml.equal(h.ops[0], qml.PauliX(0))\n\n def test_dot_returns_hamiltonian_simplified(self):\n \"\"\"Test that hamiltonian computed from the PauliSentence created by the dot function is equal\n to the simplified hamiltonian.\"\"\"\n ps = qml.dot(coeffs, ops, pauli=True)\n h_ps = ps.hamiltonian()\n h = Hamiltonian(coeffs, ops)\n h.simplify()\n assert qml.equal(h_ps, h)\n\n @pytest.mark.autograd\n def test_dot_autograd(self):\n \"\"\"Test the dot function with the autograd interface.\"\"\"\n c = qml.numpy.array([1.0, 2.0, 3.0])\n o = [qml.PauliX(0), qml.PauliY(1), qml.PauliZ(2)]\n ps = qml.dot(c, o, pauli=True)\n\n ps_2 = qml.pauli.PauliSentence(\n {\n qml.pauli.PauliWord({0: \"X\"}): 1.0,\n qml.pauli.PauliWord({1: \"Y\"}): 2.0,\n qml.pauli.PauliWord({2: \"Z\"}): 3.0,\n }\n )\n assert ps == ps_2\n\n @pytest.mark.tf\n def test_dot_tf(self):\n \"\"\"Test the dot function with the tensorflow interface.\"\"\"\n import tensorflow as tf\n\n c = tf.constant([1.0, 2.0, 3.0])\n o = [qml.PauliX(0), qml.PauliY(1), qml.PauliZ(2)]\n ps = qml.dot(c, o, pauli=True)\n\n ps_2 = qml.pauli.PauliSentence(\n {\n qml.pauli.PauliWord({0: \"X\"}): tf.constant(1.0),\n qml.pauli.PauliWord({1: \"Y\"}): tf.constant(2.0),\n qml.pauli.PauliWord({2: \"Z\"}): tf.constant(3.0),\n }\n )\n assert ps == ps_2\n\n @pytest.mark.torch\n def test_dot_torch(self):\n \"\"\"Test the dot function with the torch interface.\"\"\"\n import torch\n\n c = torch.tensor([1.0, 2.0, 3.0])\n o = [qml.PauliX(0), qml.PauliY(1), qml.PauliZ(2)]\n ps = qml.dot(c, o, pauli=True)\n\n ps_2 = qml.pauli.PauliSentence(\n {\n qml.pauli.PauliWord({0: \"X\"}): torch.tensor(1.0),\n qml.pauli.PauliWord({1: \"Y\"}): torch.tensor(2.0),\n qml.pauli.PauliWord({2: \"Z\"}): torch.tensor(3.0),\n }\n )\n assert ps == ps_2\n\n @pytest.mark.jax\n def test_dot_jax(self):\n \"\"\"Test the dot function with the torch interface.\"\"\"\n import jax\n\n c = jax.numpy.array([1.0, 2.0, 3.0])\n o = [qml.PauliX(0), qml.PauliY(1), qml.PauliZ(2)]\n ps = qml.dot(c, o, pauli=True)\n\n ps_2 = qml.pauli.PauliSentence(\n {\n qml.pauli.PauliWord({0: \"X\"}): jax.numpy.array(1.0),\n qml.pauli.PauliWord({1: \"Y\"}): jax.numpy.array(2.0),\n qml.pauli.PauliWord({2: \"Z\"}): jax.numpy.array(3.0),\n }\n )\n assert ps == ps_2\n","repo_name":"PennyLaneAI/pennylane","sub_path":"tests/ops/functions/test_dot.py","file_name":"test_dot.py","file_ext":"py","file_size_in_byte":9449,"program_lang":"python","lang":"en","doc_type":"code","stars":1965,"dataset":"github-code","pt":"52"} +{"seq_id":"69898620325","text":"from plotly.graph_objs import Pie, Figure\nfrom plotly.offline import iplot\n\ntry:\n from pandas.core.base import DataError\nexcept ImportError:\n try:\n from pandas.core.groupby import DataError\n except ImportError:\n from pandas.errors import DataError\n\nimport autovizwidget.utils.configuration as conf\nfrom .graphbase import GraphBase\n\n\nclass PieGraph(GraphBase):\n @staticmethod\n def render(df, encoding, output):\n if encoding.x is None:\n with output:\n print(\"\\n\\n\\nPlease select an X axis.\")\n return\n\n try:\n values, labels = PieGraph._get_x_values_labels(df, encoding)\n except TypeError:\n with output:\n print(\n \"\\n\\n\\nCannot group by X selection because of its type: '{}'. Please select another column.\".format(\n df[encoding.x].dtype\n )\n )\n return\n except (ValueError, DataError):\n with output:\n print(\n \"\\n\\n\\nCannot group by X selection. Please select another column.\".format(\n df[encoding.x].dtype\n )\n )\n if df.size == 0:\n print(\"\\n\\n\\nCannot display a pie graph for an empty data set.\")\n return\n\n max_slices_pie_graph = conf.max_slices_pie_graph()\n with output:\n # There's performance issues with a large amount of slices.\n # 1500 rows crash the browser.\n # 500 rows take ~15 s.\n # 100 rows is almost automatic.\n if len(values) > max_slices_pie_graph:\n print(\n \"There's {} values in your pie graph, which would render the graph unresponsive.\\n\"\n \"Please select another X with at most {} possible values.\".format(\n len(values), max_slices_pie_graph\n )\n )\n else:\n data = [Pie(values=values, labels=labels)]\n\n fig = Figure(data=data)\n iplot(fig, show_link=False)\n\n @staticmethod\n def display_logarithmic_x_axis():\n return False\n\n @staticmethod\n def display_logarithmic_y_axis():\n return False\n\n @staticmethod\n def _get_x_values_labels(df, encoding):\n if encoding.y is None:\n series = df.groupby([encoding.x]).size()\n values = series.values.tolist()\n labels = series.index.tolist()\n else:\n labels, values = GraphBase._get_x_y_values(df, encoding)\n return values, labels\n","repo_name":"jupyter-incubator/sparkmagic","sub_path":"autovizwidget/autovizwidget/plotlygraphs/piegraph.py","file_name":"piegraph.py","file_ext":"py","file_size_in_byte":2676,"program_lang":"python","lang":"en","doc_type":"code","stars":1265,"dataset":"github-code","pt":"52"} +{"seq_id":"31758369940","text":"#import test.py : the code added here\nimport os\nimport sys\nfrom os import walk\n# print python ver\nglobal isPython3\nglobal pyVer\nglobal myPath\nmyPath=os.path.dirname(os.path.realpath(__file__))\nmyPath=myPath+'/'\nprint('running from',myPath)\npyVer=sys.version_info[0]\nif int(pyVer)==3:\n isPython3=True\nelse:\n isPython3=False\n input=raw_input\n \n# Welcome Message\nprint('Welcome to python repo script')\nprint('You can get some help by typing help :)')\n# print('If you are done with script please type exit and did not close the terminal window')\nprint('Thank You!')\nglobal isLineage\nisLineage=os.path.isfile('manifests/snippets/lineage.xml')\nif isLineage==True:\n print('Note : you are syncing LineageOS sources :)')\ncmd1=''\ncmd=''\n# add command in here by putting ,'commnad here'\ncommands=['repo info','python ver','help','exit']\n# add command comment in here by putting ,'comment here'\ncommands_commints=['Get downloaded files informations.','This command show the python version should run this script.','Display this :).','Exit and save data']\n\n# Functions\ndef getrepoinfo(isPrinted,wasDown):\n global count\n f=open(\"manifests/default.xml\",'r')\n lst=list()\n i=0\n # Make a list of the directorys from manifest.xml\n for lin in f:\n isstart=lin.startswith(' ')\n if cmd=='':\n print(\"please type a command. for help use command help \")\n elif cmd!='':\n found=0\n for i in commands:\n if cmd==i:\n found=1\n if cmd=='repo info':\n getrepoinfo(True,lastdown)\n if cmd=='python ver':\n print(\"Python\"+str(pyVer))\n if cmd=='help':\n gethelp()\n if cmd=='exit':\n print('exiting...')\n break\n if found==0:\n print(cmd,\"not found\")\n if cmd=='exit':\n savedata()\n break\n \n \n","repo_name":"HomamAlamier/android_repo_helper","sub_path":"repo_command.py","file_name":"repo_command.py","file_ext":"py","file_size_in_byte":4588,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"20601750875","text":"import logging\nfrom datetime import datetime\nimport time\nimport pytz\n\nlogging.basicConfig(filename='output.log', level=logging.INFO)\nlogger = logging.getLogger()\n\n\ndef wait(timeout=120):\n def decorator(f):\n def wrapper(*args, **kwargs):\n logger.info(f'{timestamp()} - sleeping for {timeout} seconds before calling {f.__name__} ...')\n time.sleep(timeout)\n # value_if_true if condition else value_if_false\n logger.info(\n f'{timestamp()} - running {f.__name__} {\"with\" if args or kwargs else \"\"} {args if args else \"\"} {kwargs if kwargs else \"\"}')\n return f(*args, **kwargs)\n\n return wrapper\n\n return decorator\n\n\ndef timestamp():\n timezone = pytz.timezone('Europe/Berlin')\n now = timezone.localize(datetime.now())\n return now.strftime(\"%d-%m-%Y %H:%M:%S\")\n\n\nif __name__ == '__main__':\n foo(\"fed\")\n","repo_name":"JBartscher/BA_Datengrundlage","sub_path":"util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":896,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"37385758393","text":"n, m = map(int, input().split())\nmat = [list(map(int, input().split())) for _ in range(n) ]\n\ndef idx_to_ij(idx):\n return idx//m, idx%m\n\ndef check():\n # return area of safe region\n stack = []\n visited = set()\n num_wall = 0\n\n for i in range(n):\n for j in range(m):\n if mat[i][j] == 1:\n num_wall += 1\n elif mat[i][j] == 2:\n stack.append((i,j))\n visited.add((i,j))\n\n while stack:\n i,j = stack.pop()\n for di, dj in ((1,0), (0,1), (-1,0), (0,-1)):\n i2, j2 = i+di, j+dj\n if not (0<=i2= width else x2\r\n y1 = 0 if y1 < 0 else y1\r\n y2 = height-1 if y2 >= height else y2\r\n count = (x2-x1)*(y2-y1)\r\n _sum = integral_image[y2*width+x2] - \\\r\n integral_image[y1*width+x2] - \\\r\n integral_image[y2*width+x1] + \\\r\n integral_image[y1*width+x1]\r\n threshold_mask[i, j] = _sum*(1.0-t)//count\r\n return threshold_mask\r\n\r\n# There is an implementation in cv2, but it returns image, not mask, so\r\n# the slower one is used now.\r\n def __threshold_admean(image, window_size=11, c=0):\r\n shp = np.shape(image)\r\n threshold_mask = np.zeros(shp, dtype=np.uint8)\r\n for x in range(shp[0]):\r\n for y in range(shp[1]):\r\n windowed_image = calculateWindow(image, [x, y], window_size)\r\n threshold_mask[x, y] = np.mean(windowed_image) - c\r\n return threshold_mask\r\n\r\n __BIN_METHODS_FUNC = {'mean': np.mean,\r\n 'median': np.median,\r\n 'otsu': skf.threshold_otsu,\r\n 'triangle': skf.threshold_triangle,\r\n 'li': skf.threshold_li,\r\n 'isodata': skf.threshold_isodata,\r\n 'yen': skf.threshold_yen,\r\n 'niblack': skf.threshold_niblack,\r\n 'sauvola': skf.threshold_sauvola,\r\n 'bradley': __threshold_bradley,\r\n 'ad_mean': __threshold_admean,\r\n 'ad_gaussian': __threshold_admean,\r\n 'custom': __threshold_custom}\r\n\r\n if not(bin_method in BIN_METHODS):\r\n raise ValueError(\"method '{}' is not found.\".format(bin_method))\r\n return np.uint8(image)\r\n else:\r\n bin_func = __BIN_METHODS_FUNC[bin_method]\r\n try:\r\n bin_func_args = dict(zip(getargspec(bin_func)[0][1:],\r\n getargspec(bin_func)[3]))\r\n threshold = bin_func(image, **chooseArgs(bin_func_args, kwargs))\r\n except TypeError:\r\n threshold = bin_func(image)\r\n return np.uint8(image > threshold)\r\n\r\n\r\ndef quantize(input_image, color_depth=8):\r\n \"\"\"\\n Returns image with constant number of color depth.\r\n\r\n Parameters\r\n ----------\r\n imput_image : ndarray (with positive only elements)\r\n color_depth : int, output color depth (from 2 to 8 bytes, default = 8)\r\n\r\n Returns\r\n -------\r\n output_image : ndarray (dtype=np.uint8)\r\n \"\"\"\r\n input_image = input_image.astype(float)\r\n output_image = input_image/input_image.max()*(2**color_depth-1)\r\n return output_image.astype(np.uint8)\r\n\r\n\r\n# Remove dummy_sizing\r\ndef cfPreprocessing(input_images, field_color=0, **kwargs):\r\n \"\"\"\\n Returns image, preprocessed for synthes of correlation filter or\r\n for correlation pattern recognition. The main idea is to fimd max size of\r\n all images and CF and place all images on equal square fields with sizes\r\n 2^n.\r\n\r\n Parameters\r\n ----------\r\n input_images : list of ndarray\r\n field_color : int or 'mean', default=0\r\n Default field color is black. It can be a number from black to white,\r\n or 'mean' of input_image.\r\n **kwargs\r\n We use it to pass CF to the function if preprocessing is used for CPR\r\n and size of CF can be bigger then max size of test images.\r\n\r\n Returns\r\n -------\r\n output_images : list of ndarray\r\n \"\"\"\r\n dummy_sizing = False\r\n try:\r\n corr_filter = kwargs['corr_filter']\r\n max_size = np.max(np.shape(corr_filter))\r\n except KeyError:\r\n max_size = 0\r\n for set_of_images in input_images:\r\n for image in set_of_images:\r\n size = np.max(np.shape(image))\r\n if size > max_size:\r\n max_size = size\r\n sizes = [2**num for num in range(17)]\r\n max_size = min(num for num in sizes if num >= max_size)\r\n\r\n# It used for some tests, don't remove it now, until it can be useful or\r\n# some better way of testing will be used.\r\n if dummy_sizing:\r\n max_size *= 2\r\n\r\n output_images = input_images\r\n for index, set_of_images in enumerate(input_images):\r\n output_images[index] = square(set_of_images, max_size, field_color)\r\n\r\n return output_images\r\n\r\n\r\ndef square(input_images, field_size, field_color=0, centered=False):\r\n \"\"\"\\n Returns images placed on square fields.\r\n\r\n Parameters\r\n ----------\r\n input_image : list of ndarray\r\n field_size : int\r\n input_image is placed on field (field_size, field_size).\r\n Should be 2**n.\r\n field_color : int or 'mean', default=0\r\n Default field color is black. It can be a number from black to white,\r\n or 'mean' of input_image.\r\n centered : bool, default=False\r\n If True, place image to the center of field.\r\n\r\n Returns\r\n -------\r\n output_images : list of ndarray\r\n \"\"\"\r\n output_images = []\r\n for input_image in input_images:\r\n\r\n if field_color is 'mean':\r\n field_color = np.mean(input_image)\r\n output_image = np.ones((field_size, field_size))*field_color\r\n [size1, size2] = np.shape(input_image)\r\n if field_size < max([size1, size2]):\r\n raise ValueError(\"field size is less then input image size.\")\r\n if centered:\r\n x1 = int(field_size//2-size1//2)\r\n x2 = int(field_size//2+size1//2) - (size1 % 2)\r\n y1 = int(field_size//2-size2//2)\r\n y2 = int(field_size//2+size2//2) - (size2 % 2)\r\n output_image[x1:x2, y1:y2] = input_image\r\n else:\r\n output_image[:size1, :size2] = input_image\r\n output_images.append(cv.equalizeHist(output_image.astype(np.uint8)))\r\n return output_images\r\n\r\n\r\ndef cfProcessing(raw_image, processing_method, **kwargs):\r\n \"\"\"\\n This function turns raw_image of CF into image can be used for\r\n SLM output or simulation. Now it includes quantization, binarizartion and\r\n phase addition.\r\n In progress: phase output, phase-on-amplitude, noises.\r\n\r\n Parameters\r\n ----------\r\n raw_image : ndarray\r\n processing_method : int, str or list\r\n If int, should be range from 2 to 8. Use for basic amplitude images.\r\n If str, shoul be in BIN_METHODS.\r\n If list, first element should be int or str like above, second one\r\n should be in phase.PHASE_SHAPES.\r\n **kwargs\r\n Can be used for arguments of thresholding or phase surface.\r\n\r\n Returns\r\n -------\r\n processed_image : ndarray\r\n \"\"\"\r\n from phase import phase_surface\r\n if type(processing_method) is int:\r\n processed_image = quantize(raw_image, processing_method)\r\n elif type(processing_method) is str:\r\n preproc_image = quantize(raw_image, 8)\r\n processed_image = binarize(preproc_image, processing_method, **kwargs)\r\n elif type(processing_method) is list:\r\n try:\r\n processing_method_0 = int(processing_method[0])\r\n except ValueError:\r\n processing_method_0 = processing_method[0]\r\n if type(processing_method_0) is int:\r\n processed_image = quantize(raw_image, processing_method_0)\r\n else:\r\n processed_image = quantize(raw_image, 8)\r\n processed_image = binarize(processed_image, processing_method_0,\r\n **kwargs)\r\n phase = phase_surface(np.shape(processed_image), processing_method[1])\r\n processed_image = processed_image * np.exp(1j*phase)\r\n else:\r\n print(\"Error! Processed method not recognised.\")\r\n processed_image = raw_image\r\n return processed_image\r\n","repo_name":"onion-nikolay/pattern-recognition-basics","sub_path":"image_processing.py","file_name":"image_processing.py","file_ext":"py","file_size_in_byte":11517,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"9169791557","text":"from django.urls import path, re_path\n\nfrom . import views\n\napp_name = \"questions\"\nurlpatterns = [\n path(\"\", views.QuestionListView.as_view(), name=\"questions\"),\n path(\"search_question/\", views.SearchQuestionListView.as_view(), name=\"search_question\"),\n path(\"ask_question/\", views.AskQuestionView.as_view(), name=\"ask_question\"),\n path(\"tag/\", views.SearchQuestionByTagListView.as_view(), name=\"search_question_by_tag\"),\n path(\"question/\", views.QuestionDetailView.as_view(), name=\"question\"),\n path(\"question//add_answer\", views.AddAnswerView.as_view(), name=\"add_answer\"),\n path(\"create_tag\", views.CreateTagView.as_view(), name=\"create_tag\"),\n path(\"question//mark_answer_as_correct/\",\n views.MarkCorrectAnswerView.as_view(),\n name=\"mark_answer_as_correct\"),\n re_path(r'^question/(?P\\d+)/action/(?P[+-]1)',\n views.QuestionActionView.as_view(),\n name=\"question_action\"),\n re_path(r'^answer/(?P\\d+)/action/(?P[+-]1)',\n views.AnswerActionView.as_view(),\n name=\"answer_action\")\n]\n","repo_name":"DalerBakhriev/otus_homeworks","sub_path":"hw_week_7/hasker/hasker/questions/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1188,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"1613296510","text":"import stripe\nfrom flask import Flask, render_template, request, jsonify, redirect, url_for\nfrom sqlalchemy import Column, Integer, String\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm.session import sessionmaker\n\n\nfrom local import SECRET_KEY, PUBLISHABLE_KEY\n\n\napp = Flask(__name__, template_folder=\"templates\", static_folder=\"static\")\nstripe_keys = {\n 'secret_key': SECRET_KEY,\n 'publishable_key': PUBLISHABLE_KEY\n}\nstripe.api_key = stripe_keys['secret_key']\n\nBase = declarative_base()\n\n\nclass Affiliate(Base):\n __tablename__ = 'affiliates'\n name = Column(String(250), nullable=False)\n email = Column(String(250), nullable=False, primary_key=True)\n address = Column(String(500), nullable=False)\n code = Column(String(8), nullable=False, unique=True)\n count = Column(Integer, default=0)\n\n\nengine = create_engine('sqlite:///vitesse.db')\ntry:\n Base.metadata.create_all(engine)\nexcept:\n pass\nDBSession = sessionmaker(bind=engine)\nsession = DBSession()\n\n\n@app.route('/')\ndef index():\n return render_template('index.html', key=stripe_keys['publishable_key'])\n\n\n@app.route('/thank-you/')\ndef thank_you(order_number):\n return render_template('thank_you.html', order_number=order_number)\n\n\n@app.route('/charge', methods=['POST'])\ndef charge():\n amount = 62900\n shipping = {\n 'address': {\n 'line1': request.form['shipping_address_line1'],\n 'city': request.form['shipping_address_city'],\n 'state': request.form['shipping_address_state'],\n 'postal_code': request.form['shipping_address_zip'],\n 'country': request.form['shipping_address_country'],\n },\n 'name': request.form['shipping_name'],\n }\n customer = stripe.Customer.create(\n email=request.form['card[name]'],\n card=request.form['id'],\n shipping=shipping,\n )\n charge = stripe.Charge.create(\n customer=customer.id,\n amount=amount,\n currency='usd',\n description='Qty 1: Vitesse Electric Longboard'\n )\n\n code = request.form.get('code')\n if code:\n a = session.query(Affiliate).filter_by(code=code).first()\n if a:\n a.count += 1\n session.add(a)\n session.commit()\n\n return jsonify(order_number=charge.id)\n\n\n@app.route('/affiliates')\ndef affiliates():\n affiliates = session.query(Affiliate).order_by('count desc').order_by('name').all()\n return render_template('affiliate.html', affiliates=affiliates)\n\n\n@app.route('/affiliates/create', methods=['POST'])\ndef affiliate_make():\n if request.form.get('password') == \"JesusIsLord\":\n data = {field: request.form[field]\n for field in ('name', 'email', 'address', 'code')}\n a = Affiliate(**data)\n session.add(a)\n try:\n session.commit()\n except Exception as e:\n session.rollback()\n return str(e)\n return redirect(url_for('affiliates'))\n","repo_name":"exit99/longboardsite","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3037,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"29479162532","text":"#opdracht 1 kilometer converter\n\"\"\"\ndef vraag():\n A = int(input(\"Aantal kilometers:\"))\n B = A*0.6214\n print (\"Aantal miles:\" ,B)\n\nvraag()\n\"\"\"\n\n#opdracht 17 prime Numbers\n\"\"\"\ndef is_prime():\n A = int(input(\"Getal:\"))\n if A > 1:\n for i in range(2,A):\n if (A % i) == 0:\n print(A, \"is geen prime nummer\")\n break\n else:\n print(A, \"is een prime nummer\")\n else:\n print(A, \"is geen prime nummer\")\n\n\nis_prime()\n\"\"\"\n\n#opdracht 21 rock, paper, scissors game\n\ndef game():\n import random\n A = str(random.randint(1,3))\n print(A)\n if A == 1:\n #rock = A\n print(\"rock\")\n elif A == 2:\n #paper = A\n print(\"paper\")\n elif A == 3:\n #scissors = A\n print(\"scissors\")\ngame()\n","repo_name":"Valerie280499/Leerjaar-1-Blok-1","sub_path":"Afvinkopdrachten/Afvinkopdracht6/Kilometer Converter.py","file_name":"Kilometer Converter.py","file_ext":"py","file_size_in_byte":809,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"28924990245","text":"#coding = utf8\nimport sys\nsys.path.append('../../../alg/basic')\nimport str_util\nfrom pgmpy.models import BayesianModel\nfrom pgmpy.factors.discrete import TabularCPD\nfrom pgmpy.inference import VariableElimination\nimport json\nfrom pgmpy.readwrite import BIFReader, BIFWriter, XMLBIFReader, XMLBIFWriter, ProbModelXML, UAIReader, UAIWriter\n\ndef age_id(n):\n age_parts = [1, 10, 20, 40, 60]\n for i in range(len(age_parts)):\n if n < age_parts[i]:\n return i\n return len(age_parts)\n\nclass Diagnosis:\n def __init__(self):\n self.disease_part = {}\n self.disease_rate = {}\n for line in open('disease_intro.json').readlines():\n js = json.loads(line.rstrip())['struct'] \n self.disease_rate[js['id']] = js['rate']\n self.disease_part[js['id']] = js['department']\n\n self.fea_map = str_util.read_kv_file('models/feature.id')\n self.disease_id = {}\n self.disease_name = {}\n self.ds_rind = {}\n for line in open('disease_symptom.json').readlines():\n js = json.loads(line.rstrip())\n id = js['id']\n if not id in self.disease_rate:\n continue\n self.disease_name[id] = js['name']\n self.disease_id[js['name']] = id\n for s, w in js['symptoms'].items():\n if not s in self.ds_rind:\n self.ds_rind[s] = {} \n self.ds_rind[s][id] = self.disease_rate[id]\n\n print('init succeed ... type symptoms ...', file = sys.stderr)\n\n def extract_self_explain(self, req):\n fea_list = []\n #todo: 利用ac和同义词表，解析出更多表述的症状特征\n for word in req['cont']['req_text'].split(' '):\n sym = 'S_' + word\n if sym in self.fea_map:\n fea_list.append((sym, 0))\n\n #todo: 抽取部位特征\n\n return fea_list\n\n def extract_interactive(self, req_list):\n #todo: 利用机器询问和用户回答，解析出新特征\n return []\n\n def get_observed_info(self, req_list):\n observed_info = {}\n for fea, val in self.extract_self_explain(req_list[-1]):\n observed_info[self.fea_map[fea]] = val\n for fea, val in self.extract_interactive(req_list):\n observed_info[self.fea_map[fea]] = val\n \n observed_info['SEX'] = req_list[-1]['user']['sex']\n observed_info['AGE'] = age_id(req_list[-1]['user']['age'])\n\n return observed_info \n\n def run(self, input_json):\n if len(input_json) == 0:\n return ''\n\n req_list = json.loads(input_json, encoding = 'utf-8')\n if len(req_list) == 0:\n return ''\n \n candidates = {} \n sym_list = req_list[0]['cont']['req_text'].split(' ')\n for sym in sym_list:\n if sym in self.disease_id:\n id = self.disease_id[sym]\n if not id in candidates:\n candidates[id] = 0\n candidates[id] += 1 \n if sym in self.ds_rind:\n for id, rate in self.ds_rind[sym].items():\n if not id in candidates:\n candidates[id] = 0\n candidates[id] += rate \n ids = sorted(candidates.items(), key=lambda d:d[1], reverse=True)[:20]\n print('candidates generated', file = sys.stderr)\n \n \n observed_info = self.get_observed_info(req_list)\n print(observed_info)\n\n dw = {}\n deps = {}\n for i, r in ids:\n if r >= 1:\n for d in self.disease_part[i]:\n if not d in deps:\n deps[d] = 0\n deps[d] += 1\n\n try:\n model = BIFReader('models/model.bif.%s' % i).get_model()\n except:\n continue\n infer = VariableElimination(model)\n key = self.fea_map['D_' + self.disease_name[i]]\n \n observed_info_valid = {}\n for k, v in observed_info.items():\n if k in model.nodes():\n observed_info_valid[k] = v\n score = infer.query(variables = [key], evidence = observed_info_valid)[key].values[0]\n dw[self.disease_name[i]] = score\n #print('%s: %s = %.8f' % (i, self.disease_name[i], score)) \n\n for d in self.disease_part[i]:\n if not d in deps:\n deps[d] = 0\n deps[d] += score \n \n if len(deps) == 0:\n print('unknown symptoms...', file = sys.stderr)\n return ''\n \n for name, score in sorted(dw.items(), key=lambda d:d[1], reverse=True)[:10]:\n print('%s\\t%.8f' % (name, score), file = sys.stderr)\n \n dep_list = sorted(deps.items(), key=lambda d:d[1], reverse=True)\n print('department: ', file = sys.stderr)\n for dep, wei in dep_list[:3]:\n print('%s\\t%.4f' % (dep, wei), file = sys.stderr)\n req_list[-1]['cont']['res_text'] += '%s ' % dep\n req_list[-1]['cont']['req_wei'] = 100 * dep_list[0][1] \n return req_list[-1]['cont']['res_text'] \n\nif __name__ == '__main__':\n d = Diagnosis()\n while 1:\n try:\n buf = input().rstrip()\n except:\n break\n req = {'user': {'sex': 1, 'age': 30}, 'cont': {'req_text': buf, 'req_type': 0, 'res_text': ''}}\n req_list = [req]\n for part in buf.split(' '):\n if part.startswith('age='):\n req['user']['age'] = int(part[len('age='):])\n if part.startswith('sex='):\n req['user']['sex'] = int(part[len('sex='):])\n response = d.run(json.dumps(req_list, ensure_ascii = False))\n print(req_list)\n print(response)\n","repo_name":"lixingjian/project","sub_path":"app/hospital_guide_robot/bayesian/experiment/predict_disease.py","file_name":"predict_disease.py","file_ext":"py","file_size_in_byte":5840,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"52"} +{"seq_id":"24583553515","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nimport os\nos.environ['KMP_DUPLICATE_LIB_OK']='True'\n\nnp.random.seed(1337)\nX = np.linspace(-1,1,200)\nnp.random.shuffle(X)\nN = np.random.normal(0,0.5,(200,)) ##噪声\nY=0.5 * X + 2 + N\n#plt.scatter(X,Y)\n#plt.show()\n\nx_train,y_train=X[:160],Y[:160]\n\nx_test,y_test = X[160:],Y[160:]\nmodel = Sequential()\nmodel.add(Dense(input_dim=1,units=1))\n\nmodel.compile(loss='mse',optimizer='sgd')\n\nprint(\"traning ....................\")\nfor step in range(301):\n cost = model.train_on_batch(x_train,y_train)\n if cost % 100 == 0 :\n print('\\n train cost:',cost)\n\n\nprint(\"\\ntest ........\")\ncost = model.evaluate(x_test,y_test,batch_size=40)\nprint('\\ntest cost :',cost)\n\nW,b = model.layers[0].get_weights()\nprint('Weight=',W,'\\n biases=',b)\ny_pred= model.predict(X)\nplt.plot(X,y_pred)\nplt.show()\n\n\n","repo_name":"wwdz540/AI-TEST","sub_path":"keras_test.py","file_name":"keras_test.py","file_ext":"py","file_size_in_byte":911,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"69854876324","text":"__author__ = 'Christoph Jansen, HTW Berlin'\n\nimport copy\n\nclass TokenOrPOS:\n def __init__(self, value: str, isToken: bool):\n self.value = value\n self.isToken = isToken\n\nclass Collocations:\n def __init__(self, check_sequence: [TokenOrPOS], k: int, origin_token: str, replace_token: str):\n self.check_sequence = check_sequence\n self.k = k\n self.origin_token = origin_token\n self.replace_token = replace_token\n\n def apply(self, tokens: [str], pos_tags: [str], index: int) -> str:\n len_tokens = len(tokens)\n if tokens[index] == self.origin_token:\n idx = index + self.k\n if idx < 0:\n return tokens[index]\n found_sequence = True\n for j in range(len(self.check_sequence)):\n if idx == index:\n idx += 1\n if idx >= len_tokens:\n found_sequence = False\n break\n\n check_item = self.check_sequence[j]\n if check_item.isToken:\n if tokens[idx] != check_item.value:\n found_sequence = False\n break\n else:\n if pos_tags[idx] != check_item.value:\n found_sequence = False\n break\n\n idx += 1\n\n if found_sequence:\n return self.replace_token\n return tokens[index]\n\n def _sequences_equal(self, sequence_a, sequence_b):\n if len(sequence_a) != len(sequence_b):\n return False\n for a, b in zip(sequence_a, sequence_b):\n if (a.isToken and not b.isToken) or (not a.isToken and b.isToken):\n return False\n if a.value != b.value:\n return False\n return True\n\n def equals(self, rule) -> bool:\n if self.k == rule.k and self.origin_token == rule.origin_token and self.replace_token == rule.replace_token and self._sequences_equal(self.check_sequence, rule.check_sequence):\n return True\n return False\n\n def __str__(self):\n fill_symbol = '<>'\n result = []\n copy = list(self.check_sequence)\n\n start = min(self.k, 0)\n while True:\n if start == 0:\n result.append(self.origin_token)\n elif not copy:\n if start > 0:\n break\n else:\n result.append(fill_symbol)\n elif start < self.k:\n result.append(fill_symbol)\n else:\n result.append(copy[0].value)\n del copy[0]\n\n start += 1\n\n result_string = ' '.join(result)\n return result_string\n\n def hash(self):\n return 'collo%s%s%s%s' %(str(self), str(self.k), self.origin_token, self.replace_token)\n\nclass CoOccurrences:\n def __init__(self, check_token: str, window: int,\n origin_token: str, replace_token: str):\n self.check_token = check_token # Feature\n self.window = window # Fenstergroesse k\n self.origin_token = origin_token # Quellwort\n self.replace_token = replace_token # Zielwort\n\n def apply(self, tokens: [str], pos_tags: [str], index: int) -> str:\n len_tokens = len(tokens)\n\n if tokens[index] == self.origin_token:\n start = max(index - self.window, 0)\n end = min(index + self.window, len_tokens)\n for j in range(start, end):\n if tokens[j] == self.check_token:\n return self.replace_token\n return tokens[index]\n\n def equals(self, rule) -> bool:\n if self.window == rule.window and self.check_token == rule.check_token and self.origin_token == rule.origin_token and self.replace_token == rule.replace_token:\n return True\n return False\n\n def __str__(self):\n return '\"%s\" in window -%d to %d' % (self.check_token, self.window, self.window)\n\n def hash(self):\n return 'coocc%s%s%s%s' %(str(self.window), self.check_token, self.origin_token, self.replace_token)\n\nclass SimpleBaselinePrediction:\n def __init__(self, origin_token: str, replace_token: str):\n self.origin_token = origin_token\n self.replace_token = replace_token\n\n def apply(self, tokens: [str], pos_tags: [str], index: int) -> [str]:\n result_tokens = list(tokens)\n if tokens[index] == self.origin_token:\n return self.replace_token\n return tokens[index]\n\n def __str__(self):\n return '%s_to_%s' % (self.origin_token, self.replace_token)\n\nclass IdentityBaselinePrediction:\n def __init__(self):\n self.origin_token = ''\n self.replace_token = ''\n\n def apply(self, tokens: [str], pos: [str], index: int) -> str:\n return tokens[index]\n\n def __str__(self):\n return 'identity'\n\ndef generate_collocations(tokens: [str], pos:[str], index:int, intend_token: str, predict_token, confusion_set: (str, str)):\n window = 3\n max_sequence_length = 2\n len_tokens = len(tokens)\n result = []\n\n origin_token = predict_token\n if intend_token != origin_token:\n replace_token = confusion_set[0]\n if origin_token == confusion_set[0]:\n replace_token = confusion_set[1]\n start = max(index - window, 0)\n end = min(index + window, len_tokens)\n for sequence_length in range(1, max_sequence_length + 1):\n sequences = _gen_collocation_sequences(tokens, pos, start, end, index, sequence_length)\n for sequence in sequences:\n rule = Collocations(sequence[0], sequence[1], origin_token, replace_token)\n result.append(rule)\n return result\n\ndef _gen_collocation_sequences(tokens: [str], pos: [str], start: int, end: int, i: int, sequence_length: int) -> [([TokenOrPOS], int)]:\n sequences = []\n len_tokens = len(tokens)\n for x in range(start, end):\n can_create_sequence = True\n index = x\n item_sequence = []\n for j in range(1, sequence_length + 1):\n if index == i:\n index += 1\n if index >= len_tokens:\n can_create_sequence = False\n break\n item = (tokens[index], pos[index])\n item_sequence.append(item)\n\n index += 1\n if can_create_sequence:\n k = x - i\n sequences += _gen_collocation_sequences_at_k(item_sequence, k)\n return sequences\n\n\ndef _gen_collocation_sequences_at_k(item_sequence: [(str, str)], k: int) -> [([TokenOrPOS], int)]:\n item = item_sequence[0]\n del item_sequence[0]\n sequences = [[TokenOrPOS(item[0], True)],[TokenOrPOS(item[1], False)]]\n for item in item_sequence:\n new_sequences = []\n for sequence in sequences:\n new_sequence_token = copy.copy(sequence)\n new_sequence_token.append(TokenOrPOS(item[0], True))\n new_sequence_pos = copy.copy(sequence)\n new_sequence_pos.append(TokenOrPOS(item[1], False))\n new_sequences.append(new_sequence_token)\n new_sequences.append(new_sequence_pos)\n sequences = new_sequences\n result = []\n for sequence in sequences:\n #print(len(sequence))\n result_sequence = (sequence, k)\n result.append(result_sequence)\n return result\n\ndef generate_cooccurences(tokens: [str], pos:[str], index:int, intend_token: str, predict_token, confusion_set: (str, str)):\n window = 2\n len_tokens = len(tokens)\n result = []\n\n origin_token = predict_token\n if intend_token != origin_token:\n start = max(index - window, 0)\n end = min(index + window, len_tokens)\n for j in range(start, end):\n check_token = tokens[j]\n if check_token != confusion_set[0] and check_token != confusion_set[1]:\n replace_token = confusion_set[0]\n if origin_token == replace_token:\n replace_token = confusion_set[1]\n rule = CoOccurrences(check_token, window, origin_token, replace_token)\n result.append(rule)\n return result\n\ndef remove_rule_duplicates(rules) -> None:\n start = 0\n end = len(rules)\n while start < end:\n current_rule = rules[start]\n new_start = start + 1\n while new_start < end:\n if current_rule.equals(rules[new_start]):\n del rules[new_start]\n end -= 1\n else:\n new_start += 1\n start += 1","repo_name":"Gnork/confusion-words","sub_path":"transformation_based_rule_learning/rule_templates.py","file_name":"rule_templates.py","file_ext":"py","file_size_in_byte":8572,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"44899331887","text":"# Implementation of a simple MLP network with one hidden layer. Tested on the iris data set.\n# Requires: numpy, sklearn>=0.18.1, tensorflow>=1.0\n\n# NOTE: In order to make the code simple, we rewrite x * W_1 + b_1 = x' * W_1'\n# where x' = [x | 1] and W_1' is the matrix W_1 appended with a new row with elements b_1's.\n# Similarly, for h * W_2 + b_2\n\nimport tensorflow as tf\nimport numpy as np\nfrom sklearn import datasets\nfrom sklearn.model_selection import train_test_split\n\nRANDOM_SEED = 42\ntf.set_random_seed(RANDOM_SEED)\n\n\ndef init_weights(shape):\n \"\"\" Weight initialization \"\"\"\n weights = tf.random_normal(shape, stddev=0.1)\n return tf.Variable(weights)\n\n\ndef forwardprop(X, w_1, w_2):\n \"\"\"\n Forward-propagation.\n IMPORTANT: yhat is not softmax since TensorFlow's softmax_cross_entropy_with_logits() does that internally.\n \"\"\"\n h = tf.nn.sigmoid(tf.matmul(X, w_1)) # The \\sigma function\n yhat = tf.matmul(h, w_2) # The \\varphi function\n return yhat\n\n\ndef get_iris_data():\n \"\"\" Read the iris data set and split them into training and test sets \"\"\"\n iris = datasets.load_iris()\n data = iris[\"data\"]\n target = iris[\"target\"]\n\n # Prepend the column of 1s for bias\n N, M = data.shape\n all_X = np.ones((N, M + 1))\n all_X[:, 1:] = data\n\n # Convert into one-hot vectors\n num_labels = len(np.unique(target))\n all_Y = np.eye(num_labels)[target] # One liner trick!\n return train_test_split(all_X, all_Y, test_size=0.33, random_state=RANDOM_SEED)\n\n\ndef main():\n\n train_X, test_X, train_y, test_y = get_iris_data()\n\n print('vendo os dados da base > ')\n print('train x = ', len(train_X), ' <> ', train_X)\n print('train y = ', len(train_y), ' <> ', train_y)\n\n print('train x = ', len(test_X), ' <> ', test_X)\n print('test y = ', len(test_y), ' <> ', test_y)\n\n\n # Layer's sizes\n x_size = train_X.shape[1] # Number of input nodes: 4 features and 1 bias\n h_size = 256 # Number of hidden nodes # 256\n y_size = train_y.shape[1] # Number of outcomes (3 iris flowers)\n\n # Symbols\n X = tf.placeholder(\"float\", shape=[None, x_size])\n y = tf.placeholder(\"float\", shape=[None, y_size])\n\n # Weight initializations\n w_1 = init_weights((x_size, h_size))\n w_2 = init_weights((h_size, y_size))\n\n # Forward propagation\n yhat = forwardprop(X, w_1, w_2)\n predict = tf.argmax(yhat, axis=1)\n\n # Backward propagation\n cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=yhat))\n updates = tf.train.GradientDescentOptimizer(0.01).minimize(cost)\n\n # Run SGD\n sess = tf.Session()\n init = tf.global_variables_initializer()\n sess.run(init)\n\n for epoch in range(100):\n # Train with each example\n for i in range(len(train_X)):\n sess.run(updates, feed_dict={X: train_X[i: i + 1], y: train_y[i: i + 1]})\n\n train_accuracy = np.mean(np.argmax(train_y, axis=1) == sess.run(predict, feed_dict={X: train_X, y: train_y}))\n\n #test_accuracy = np.mean(np.argmax(test_y, axis=1) == sess.run(predict, feed_dict={X: test_X, y: test_y}))\n\n #print(\"Epoch = %d, train accuracy = %.2f%%, test accuracy = %.2f%%\" %(epoch + 1, (100. * train_accuracy), (100. * test_accuracy)))\n print(\"Epoch = %d, train accuracy = %.2f%%\" %(epoch + 1, (100. * train_accuracy)))\n\n sess.close()\n\n\ndef main_char():\n from data import loading_data_train\n\n matrix = loading_data_train()\n\n train_X = []\n train_y = []\n test_X = [0, 1, 3]\n test_y = [0, 1, 2]\n\n vetor_x = []\n\n for i in matrix:\n lis_tmp = []\n try:\n value = float((i[1]))\n train_y.append(int(i[0]))\n lis_tmp.append(value)\n train_X.append(lis_tmp)\n vetor_x.append((train_y, train_X))\n except:\n value = float((i[1]))\n lis_tmp.append(value)\n train_X.append(lis_tmp)\n train_y.append(int(ord(i[0])))\n\n\n\n print('vendo os dados da base > ')\n\n print('train x = ', len(train_X), ' <> ', train_X)\n print('train y = ', len(train_y), ' <> ', train_y)\n\n print('test x = ', len(test_X), ' <> ', test_X)\n print('test y = ', len(test_y), ' <> ', test_y)\n\n def func_quatra():\n import numpy as np\n import tensorflow as tf\n\n # Declare list of features, we only have one real-valued feature\n def model(features, labels, mode):\n # Build a linear model and predict values\n W = tf.get_variable(\"W\", [1], dtype=tf.float64)\n b = tf.get_variable(\"b\", [1], dtype=tf.float64)\n y = W * features['x'] + b\n # Loss sub-graph\n loss = tf.reduce_sum(tf.square(y - labels))\n # Training sub-graph\n global_step = tf.train.get_global_step()\n optimizer = tf.train.GradientDescentOptimizer(0.01)\n train = tf.group(optimizer.minimize(loss),\n tf.assign_add(global_step, 1))\n # ModelFnOps connects subgraphs we built to the\n # appropriate functionality.\n return tf.contrib.learn.ModelFnOps(\n mode=mode, predictions=y,\n loss=loss,\n train_op=train)\n\n\n\n\n\n\nif __name__ == '__main__':\n main_char()","repo_name":"Marcos001/SimpleNeuralNetwok","sub_path":"tensorflow/b_simple_mlp_tensorflow.py","file_name":"b_simple_mlp_tensorflow.py","file_ext":"py","file_size_in_byte":5309,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"20332183455","text":"from cached_property import cached_property\nimport logging\n\nclass BlockCallbackMixin():\n '''\n This mixin gives you free expiry of cached properties upon a new block. \n Also lets the class implement new_block_callback()\n to implement custom behavior.\n\n Implementing class is responsible for implementing\n _block_callback_watcher.register_target(self)\n and\n _block_callback_watcher.unregister_target(self)\n '''\n def new_block_callback(self):\n '''\n Override this callback in your dapp to perform actions \n upon new block\n '''\n pass\n\n\n def _new_block_callback(self):\n '''\n This is the private version of new_block_callback.\n It expires the caches of any cached_properties.\n '''\n self._expire_cached_properties()\n self.new_block_callback()\n\n def _expire_cached_properties(self):\n objects = self.__class__.__dict__.values()\n c_props = [x for x in objects if x.__class__ == cached_property]\n if len(c_props) == 0:\n return\n\n logging.info(\"{} attempting to expire {} properties\".format(\n str(self.__class__),\n str(len(c_props))\n ))\n\n for prop in c_props:\n try:\n del self.__dict__[prop.func.__name__]\n logging.info(\"{} expired {}\".format(\n str(self.__class__),\n prop.func.__name__\n ))\n except KeyError as e:\n logging.info(\"no need to expire {}\".format(str(e)))\n\n\n","repo_name":"kayagoban/shadowlands","sub_path":"shadowlands/block_callback_mixin.py","file_name":"block_callback_mixin.py","file_ext":"py","file_size_in_byte":1565,"program_lang":"python","lang":"en","doc_type":"code","stars":142,"dataset":"github-code","pt":"52"} +{"seq_id":"70317187685","text":"\"\"\"\nProject Name: People Sim\nFile Name: Buildings.py\nAuthor: Lex Hall\nLast Updated: 11-13-2018\nPython Version: 3.6\nPygame Version: 1.9.3\n\"\"\"\n\nimport pygame\nfrom random import *\nimport Constants as const\n\nclass VillageBuilding(object):\n def __init__(self, originXGrid, originYGrid):\n self.originXGrid = originXGrid\n self.originYGrid = originYGrid\n self.originXActual = self.originXGrid * const.PIXELSIZE - 5\n self.originYActual = self.originYGrid * const.PIXELSIZE - 5\n self.gridWidth = 10\n self.gridHeight = 10\n self.rect = pygame.Rect(self.originXActual, self.originYActual,\n self.gridWidth * const.PIXELSIZE, self.gridHeight * const.PIXELSIZE)\n self.buildingProgress = 0\n self.isComplete = False\n self.requiredWood = 50\n self.currentWood = 0\n self.buildLocationCount = -1\n\n def update(self, mapController):\n self.buildingProgress = (self.currentWood * 100) / self.requiredWood\n if self.buildingProgress >= 100:\n self.isComplete = True\n\n def draw(self, gameMap):\n if not self.isComplete:\n pygame.draw.rect(gameMap, const.BROWN, self.rect, 3)\n pygame.draw.rect(gameMap, const.BROWN, (self.originXActual, self.originYActual + 100 - self.buildingProgress,\n self.gridWidth * const.PIXELSIZE, self.buildingProgress))\n else:\n pygame.draw.rect(gameMap, const.DARKBROWN, self.rect)\n\n def getBuildLocation(self):\n return(self.originXGrid + 5, self.originYGrid + 5)\n\n def inputWood(self):\n self.currentWood += 1\n\n\nclass FarmHouse(VillageBuilding):\n\n def update(self, mapController):\n VillageBuilding.update(self, mapController)\n if self.isComplete:\n if randint(0,10000) == 1:\n newFood = self.getBuildLocation()\n if randint(0,1) == 0:\n newFoodX = newFood[0] + randint(5,20)\n else:\n newFoodX = newFood[0] - randint(5,20)\n if randint(0,1) == 0:\n newFoodY = newFood[1] + randint(5,20)\n else:\n newFoodY = newFood[1] - randint(5,20)\n mapController.spawnFood(newFoodX, newFoodY)\n\n def draw(self, gameMap):\n if not self.isComplete:\n pygame.draw.rect(gameMap, const.YELLOW, self.rect, 3)\n pygame.draw.rect(gameMap, const.YELLOW, (self.originXActual, self.originYActual + 100 - self.buildingProgress,\n self.gridWidth * const.PIXELSIZE, self.buildingProgress))\n else:\n pygame.draw.rect(gameMap, const.DARKYELLOW, self.rect)","repo_name":"AnubisAbydos/People-Sim","sub_path":"People Sim/Buildings.py","file_name":"Buildings.py","file_ext":"py","file_size_in_byte":2766,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"38264775068","text":"from setuptools import setup, find_packages\n\nwith open('requirements.txt') as f:\n REQUIREMENTS = f.readlines()\n\nsetup(\n name='famgz_utils',\n version='0.1',\n license='MIT',\n author=\"famgz\",\n author_email='famgz@proton.me',\n packages=['famgz_utils'],\n package_dir={'famgz_utils': 'src/famgz_utils'},\n package_data={'famgz_utils': ['mouse_movements/*.json']},\n include_package_data=True,\n url='https://github.com/famgz/famgz_utils',\n install_requires=REQUIREMENTS\n)\n","repo_name":"famgz/famgz-utils","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":499,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"24872521304","text":"import forms\nimport tools\nfrom app import app\nfrom flask import flash\nfrom flask import session\nfrom flask import redirect\nfrom flask import render_template\nfrom flask import request\nfrom flask import url_for\nfrom read_data_from_db import apiReports\n\n\ndef update_session():\n session['data'] = request.form.to_dict()\n\n\ndef general_report_site(form, statuses, plan_id):\n update_session()\n return render_template('general_reports_site.html',\n title='test',\n form=form,\n statuses=statuses,\n plan_id=plan_id)\n\n\ndef read_session_form():\n dt = tools.cast_to_date(session['data']['dt'])\n form = forms.navigationForm(instances=session['data']['instances'],\n country=session['data']['country'],\n periodType=session['data']['periodType'],\n dt=dt)\n return form\n\n\ndef initilize_forms_variables(data):\n form = forms.navigationForm()\n\n form.instances.choices = tools.get_instances()\n form.periodType.choices = tools.get_period_types()\n form.country.choices = tools.get_countries()\n return form\n\n\n@app.route('/', methods=['GET', 'POST'])\ndef instance_type():\n session['data'] = request.form.to_dict()\n\n form = initilize_forms_variables(request.form.to_dict())\n if form.validate_on_submit():\n country = session['data'].get('country')\n current_date = session['data'].get('dt')\n period_type = session['data'].get('periodType')\n instance_type = session['data'].get('instances')\n\n report_api = apiReports(country=country,\n report_name='empty',\n date=current_date,\n period_type=period_type,\n instance_type=instance_type\n )\n\n reports_status = report_api.get_reports_statuses()\n plan_id = report_api.get_default_plan_id()\n return general_report_site(form, reports_status, plan_id)\n\n return render_template('base.html',\n title='inital site',\n form=form)\n\n\n@app.route('/reports/', methods=['POST', 'GET'])\ndef reports(report_name):\n\n form = read_session_form()\n country = request.args.get('country')\n current_date = request.args.get('dt')\n period_type = request.args.get('periodType')\n instance_type = request.args.get('instances')\n plan_id = int(request.args.get('plan_id'))\n\n report_api = apiReports(country=country,\n report_name=report_name,\n date=current_date,\n period_type=period_type,\n instance_type=instance_type,\n plan_id=plan_id)\n\n reports_status = report_api.get_reports_statuses()\n plan_id = 1\n if form.validate_on_submit():\n return general_report_site(form, reports_status, plan_id)\n\n try:\n data = report_api.create_report()\n\n except ValueError as e:\n return render_template('error_site.html',\n title='error',\n form=form,\n statuses=reports_status,\n url_params=request.args,\n error_message=e,\n plan_id=plan_id\n )\n\n platforms = report_api.find_plans_id('on')\n\n return render_template('basic_report.html',\n name=report_name,\n form=form,\n plan_id=plan_id,\n statuses=reports_status,\n platforms=platforms,\n url_params=request.args,\n data=data\n )","repo_name":"gonti89-old/degree_project","sub_path":"interface/app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3959,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"32193966929","text":"#!/usr/bin/env python\n\nimport xml.etree.ElementTree as ET\nimport build_common\nimport os\n\n# This class handles iterating over a set of Visual Studio build configurations\n# for one or more Visual Studio project solutions. For example:\n# for project in [ world_editor.sln, particle_editor.sln ]:\n# for config in [ \"Editor_Hybrid\", \"Editor_Debug\", \"Editor_Release\" ]\n# # generate (project, config)\nclass ProjectConfigIterator( object ):\n\n\tdef __init__( self, projects, configs, vsVersion ):\n\n\t\tif len( projects ) == 0:\n\t\t\traise ValueError( \"Invalid project list\" )\n\n\t\tif len( configs ) == 0:\n\t\t\traise ValueError( \"Invalid config list\" )\n\n\t\tself.projects = projects\n\t\tself.configs = configs\n\n\t\tself.vsVersion = vsVersion\n\n\t\tself.currProjIndex = 0\n\t\tself.currConfIndex = 0\n\n\n\tdef __iter__( self ):\n\t\treturn self\n\n\n\tdef _incCounters( self ):\n\t\tself.currConfIndex += 1\n\n\n\tdef next( self ):\n\t\tisEndOfProjects = self.currProjIndex >= len( self.projects )\n\t\tisEndOfConfigs = self.currConfIndex >= len( self.configs )\n\n\t\tif isEndOfProjects and isEndOfConfigs:\n\t\t\traise StopIteration\n\n\t\tif isEndOfConfigs:\n\t\t\tself.currProjIndex += 1\n\t\t\tself.currConfIndex = 0\n\n\t\t\tif self.currProjIndex >= len( self.projects ):\n\t\t\t\traise StopIteration\n\n\t\t# Perform a replacement on the project string with the Visual Studio\n\t\t# version (if required)\n\t\tprojectString = self.projects[ self.currProjIndex ].replace( \"%(vsVersion)s\", self.vsVersion )\n\t\tretData = (projectString, self.configs[ self.currConfIndex ])\n\n\t\tself._incCounters()\n\n\t\treturn retData\n\n###############################\n# Main module accessor methods\ndef generateProjectConfig( vsVersion, buildType, xmlElement ):\n\t\n\tif xmlElement is None:\n\t\treturn None\n\t\t\n\tisContinuousBuild = build_common.isContinuousBuild( buildType )\n\tpathList = []\n\tconfigList = []\n\titemPaths = xmlElement.find( \"paths\" )\n\tif itemPaths != None:\n\t\tpathList = [ os.path.normpath( path.text.strip() ) for path in itemPaths.findall( \"path\" ) ]\n\t\t\n\tconfigs = xmlElement.find( \"build_configs\" )\n\tif configs != None:\n\t\tif isContinuousBuild:\n\t\t\tfor config in configs.findall( \"config\" ):\n\t\t\t\tif 'continuous_build' in config.attrib and \\\n\t\t\t\t\tconfig.attrib['continuous_build'] == \"True\":\n\t\t\t\t\tconfigList.append( config.text.strip() )\n\t\telse:\n\t\t\tconfigList = [ config.text.strip() for config in configs.findall( \"config\" ) ]\n\n\treturn ProjectConfigIterator( pathList, configList, vsVersion )\n\t\t\t\n\t\ndef getProjects( vsVersion, buildType, projectConfigPath, tag = None ):\n\tprojectList = []\n\t\n\tdoc = ET.parse( projectConfigPath )\n\troot = doc.getroot()\n\tchildrenList = root.getchildren()\n\tfor item in childrenList:\n\t\tif tag != None:\n\t\t\tif tag == item.tag:\n\t\t\t\tprojectList.append( generateProjectConfig( vsVersion, buildType, item ) )\n\t\t\t\tbreak\n\t\telse:\n\t\t\tprojectList.append( generateProjectConfig( vsVersion, buildType, item ) )\n\t\t\n\treturn projectList\n\n# project_manager.py\n","repo_name":"v2v3v4/BigWorld-Engine-14.4.1","sub_path":"programming/bigworld/build/bw_internal/scripts/project_manager.py","file_name":"project_manager.py","file_ext":"py","file_size_in_byte":2884,"program_lang":"python","lang":"en","doc_type":"code","stars":79,"dataset":"github-code","pt":"52"} +{"seq_id":"33041112579","text":"\"\"\"\nStarts a Websocket server, with 3 datasets:\n MNIST\n eICU mortality classification\n eICU length of stay regression\n\nEach server keeps a subset of these dataset and has a teparate test set too.\n\"\"\"\nimport logging\n\nimport syft as sy\nfrom syft.workers import WebsocketServerWorker\n\nimport torch\nimport argparse\nfrom torchvision import datasets\nfrom torchvision import transforms\nfrom sklearn.preprocessing import RobustScaler\nimport numpy as np\nimport pandas as pd\n\n\ndef get_mnist_dataset(keep_labels, training=True):\n \"\"\"\n Sets up MNIST dataset for training or testing.\n \"\"\"\n mnist_dataset = datasets.MNIST(\n root=\"./data\",\n train=training,\n download=True,\n transform=transforms.Compose(\n [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]\n ),\n )\n\n # create mnist training\n indices = np.isin(mnist_dataset.targets, keep_labels).astype(\"uint8\")\n logger.info(\"number of true indices: %s\", indices.sum())\n selected_data = (\n torch.masked_select(\n mnist_dataset.data.transpose(0, 2),\n torch.tensor(indices)\n ).view(28, 28, -1).transpose(2, 0)\n )\n logger.info(\"after selection: %s\", selected_data.shape)\n selected_targets = torch.masked_select(\n mnist_dataset.targets,\n torch.tensor(indices)\n )\n\n return sy.BaseDataset(\n data=selected_data,\n targets=selected_targets,\n transform=mnist_dataset.transform\n )\n\n\ndef get_eicu_dataset(hospitalid, outcome):\n \"\"\"\n Sets up the eICU dataset for training or testing.\n \"\"\"\n df_x = pd.read_csv('x.csv')\n df_y = pd.read_csv('y.csv')\n\n # delete rows where the outcome is missing\n to_keep = ~(pd.isnull(df_y).sum(axis=1) > 0)\n df_x = df_x[to_keep]\n df_y = df_y[to_keep]\n\n # restrict x and y to the required hospital or test set\n to_keep = df_x.hospitalid.values == hospitalid\n df_x.drop('hospitalid', axis=1, inplace=True)\n df_x = df_x[to_keep]\n scaler = RobustScaler(quantile_range=(10.0, 90.0))\n x = scaler.fit_transform(df_x.values)\n y = df_y[outcome][to_keep].values\n\n return sy.BaseDataset(\n data=torch.from_numpy(x.astype('float32')),\n targets=torch.from_numpy(y.astype('float32'))\n )\n\n\ndef start_websocket_server_worker(id, host, port, hook, verbose, keep_labels=None):\n \"\"\"\n Helper function for spinning up a websocket server and setting up the local\n datasets: MNIST, eICU for classification and for regression.\n \"\"\"\n\n server = WebsocketServerWorker(\n id=id,\n host=host,\n port=port,\n hook=hook,\n verbose=verbose\n )\n\n # add mnist train & test\n server.add_dataset(\n get_mnist_dataset(keep_labels, training=True),\n key='mnist_train'\n )\n server.add_dataset(\n get_mnist_dataset(list(range(10)), training=False),\n key='mnist_test'\n )\n\n # add eicu train & test for classification\n id2hospitalid = {\n 'h1': 1,\n 'h2': 2,\n 'h3': 3,\n }\n server.add_dataset(\n get_eicu_dataset(hospitalid=id2hospitalid[id], outcome='hosp_mort'),\n key='eicu_class_train'\n )\n server.add_dataset(\n get_eicu_dataset(hospitalid=4, outcome='hosp_mort'),\n key='eicu_class_test'\n )\n\n # add eicu train & test for regression\n server.add_dataset(\n get_eicu_dataset(hospitalid=id2hospitalid[id], outcome='icu_los_hours'),\n key='eicu_reg_train'\n )\n server.add_dataset(\n get_eicu_dataset(hospitalid=4, outcome='icu_los_hours'),\n key='eicu_reg_test'\n )\n\n server.start()\n return server\n\n\nif __name__ == \"__main__\":\n # Logging setup\n logger = logging.getLogger(\"run_websocket_server\")\n FORMAT = (\"%(asctime)s %(levelname)s %(filename)s(l:%(lineno)d, p:%(process)d) \"\n \"- %(message)s\")\n logging.basicConfig(format=FORMAT)\n logger.setLevel(level=logging.DEBUG)\n\n # Parse args\n parser = argparse.ArgumentParser(description=\"Run websocket server worker.\")\n parser.add_argument(\n \"--port\",\n \"-p\",\n type=int,\n help=\"port number of the websocket server worker, e.g. --port 8777\",\n )\n parser.add_argument(\n \"--host\", type=str,\n default=\"localhost\",\n help=\"host for the connection\"\n )\n parser.add_argument(\n \"--id\",\n type=str,\n help=\"name (id) of the websocket server worker, e.g. --id hospital1\"\n )\n parser.add_argument(\n \"--verbose\",\n \"-v\",\n action=\"store_true\",\n help=\"if set, websocket server worker will be started in verbose mode\",\n )\n\n args = parser.parse_args()\n\n # define which hospital gets which mnist examples for training\n mnist_keep_labels = {\n \"h1\": [0, 1, 2, 3],\n \"h2\": [4, 5, 6],\n \"h3\": [7, 8, 9],\n }\n\n # Hook and start server\n hook = sy.TorchHook(torch)\n server = start_websocket_server_worker(\n id=args.id,\n host=args.host,\n port=args.port,\n hook=hook,\n verbose=args.verbose,\n keep_labels=mnist_keep_labels[args.id]\n )\n","repo_name":"danielhomola/federated_ml_platform","sub_path":"src/neoglia/workers/websocket_start.py","file_name":"websocket_start.py","file_ext":"py","file_size_in_byte":5144,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"52"} +{"seq_id":"8297631693","text":"#!/usr/bin/env python3\n\nimport argparse\nimport os.path\nimport sys\n\nimport numpy as np\nimport cv2\n\n# https://stackoverflow.com/a/11541450\n\n\ndef main(filename, outfile):\n img = cv2.imread(filename, cv2.IMREAD_COLOR)\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n edges = cv2.Canny(gray, 50, 150, apertureSize=3)\n lines = cv2.HoughLinesP(image=edges, rho=1, theta=np.pi / 180,\n threshold=100, lines=np.array([]),\n minLineLength=100, maxLineGap=80)\n lines = lines.reshape(lines.shape[0], lines.shape[2])\n vertical_lines = lines[np.abs(lines[:,2] - lines[:, 0]) < 5,:]\n horizontal_lines = lines[np.abs(lines[:,3] - lines[:, 1]) < 5,:]\n vertical_lines = vertical_lines[np.argsort(vertical_lines[:,0])]\n horizontal_lines = horizontal_lines[np.argsort(horizontal_lines[:,1])]\n\n x = vertical_lines[0, 0]\n y = horizontal_lines[0, 1]\n width = vertical_lines[8, 0] - x\n height = horizontal_lines[8, 1] - y\n print(x, y, width, height)\n\n cv2.imshow('edges', edges)\n cv2.imshow('img', img)\n while True:\n if cv2.waitKey(0) == ord('q'):\n return\n\n cv2.destroyAllWindows()\n\nif __name__ == \"__main__\":\n def is_valid_file(parser, arg):\n if not os.path.exists(arg):\n parser.error(\"The file %s does not exist!\" % arg)\n else:\n return arg\n\n parser = argparse.ArgumentParser(\n description='Read a chess board from an image into FEN notation.')\n parser.add_argument(\"-i\", dest=\"filename\", required=True,\n help=\"Input image with chess board.\", metavar=\"\",\n type=lambda x: is_valid_file(parser, x))\n parser.add_argument(\"outfile\", nargs=\"?\", type=argparse.FileType(\"w\"),\n default=sys.stdout)\n args = parser.parse_args()\n main(args.filename, args.outfile)\n","repo_name":"ebensh/chess_cv","sub_path":"read_board.py","file_name":"read_board.py","file_ext":"py","file_size_in_byte":1804,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"26201335662","text":"\"\"\"Provide utility function for the LivingPark notebook for paper replication.\"\"\"\nimport datetime\nimport glob\nimport math\nimport os.path\nimport pkgutil\nimport subprocess\nimport sys\nimport warnings\nfrom pprint import pprint\n\nimport nilearn.plotting as nplt\nimport numpy as np\nimport pandas as pd\nimport ppmi_downloader\nimport pytz # type: ignore\nfrom boutiques.descriptor2func import function as descriptor2func\nfrom dateutil.parser import parse # type: ignore\nfrom dateutil.relativedelta import relativedelta # type: ignore\nfrom IPython.display import HTML\nfrom IPython.display import Image as ImageDisplay\nfrom matplotlib import axes\nfrom matplotlib import pyplot as plt\nfrom PIL import Image\n\n\nclass LivingParkUtils:\n \"\"\"Contain functions to be reused across LivingPark notebooks.\"\"\"\n\n def __init__(\n self,\n data_cache_path: str = \".cache\",\n ) -> None:\n \"\"\"Initialize a LivingPark notebook.\n\n Parameters\n ----------\n data_cache_path: str, default \".cache\"\n Local path where to store the dataset cache.\n Keep default value unless you know what you're doing.\n \"\"\"\n self.data_cache_path = data_cache_path\n self.study_files_dir = os.path.abspath(os.path.join(\"inputs\", \"study_files\"))\n\n self.setup_notebook_cache()\n os.makedirs(self.study_files_dir, exist_ok=True)\n\n def setup_notebook_cache(self) -> None:\n \"\"\"Create, install, and update the cache directory, if needed.\n\n Notes\n -----\n Aggregate the inputs and outputs into a single dataset by creating symlinks.\n \"\"\"\n for x in (\"\", \"inputs\", \"outputs\"):\n os.makedirs(os.path.join(self.data_cache_path, x), exist_ok=True)\n\n # Make or update links to cache\n for x in [\"inputs\", \"outputs\"]:\n if os.path.islink(x):\n print(f\"removing link {x}\")\n os.remove(x)\n elif os.path.exists(x):\n raise Exception(f\"Directory {x} exists and is not a symlink.\")\n else:\n print(f\"{x} doesnt exist\")\n os.symlink(os.path.join(self.data_cache_path, x), x)\n\n def notebook_init(self) -> HTML:\n \"\"\"Initialize a paper replication notebook.\n\n It ignores cell warnings, install dependencies, show execution time, and create\n a toggle button for displaying/hiding code cells.\n\n Returns\n -------\n HTML\n An HTML button to hide/show code cells in the notebooks.\n \"\"\"\n warnings.filterwarnings(\"ignore\")\n\n print(\"Installing notebook dependencies (see log in install.log)... \")\n with open(\"install.log\", \"wb\") as fout:\n subprocess.check_call(\n [\n sys.executable,\n \"-m\",\n \"pip\",\n \"install\",\n \"-U\",\n \"-r\",\n \"requirements.txt\",\n ],\n stdout=fout,\n stderr=fout,\n )\n\n now = datetime.datetime.now(pytz.utc).strftime(\"%Y-%m-%d %H:%M:%S %Z %z\")\n print(f\"This notebook was run on {now}\")\n\n return HTML(\n filename=os.path.join(\n os.path.dirname(os.path.abspath(__file__)),\n \"toggle_button.html\",\n )\n )\n\n def download_ppmi_metadata(\n self,\n required_files: list,\n force: bool = False,\n headless: bool = True,\n timeout: int = 600,\n ) -> None:\n \"\"\"Download PPMI required study files, if not available.\n\n Parameters\n ----------\n required_files : list\n Required PPMI study files (cvs files) supported by ppmi_downloader.\n force : bool, optional\n headless : bool, default True\n If True, prevent broswer window to open during download.\n timeout : int, default 600\n Number of second before the download times out.\n\n Raises\n ------\n Exception:\n If failure occurs during download.\n \"\"\"\n if force:\n missing_files = required_files\n else:\n missing_files = [\n x\n for x in required_files\n if not os.path.exists(os.path.join(self.study_files_dir, x))\n ]\n\n if len(missing_files) > 0:\n pprint(f\"Downloading files: {missing_files}\")\n try:\n ppmi = ppmi_downloader.PPMIDownloader()\n ppmi.download_metadata(\n missing_files,\n destination_dir=self.study_files_dir,\n headless=headless,\n timeout=timeout,\n )\n except Exception as e:\n print(\"Download failed!\")\n raise (e)\n\n print(\"Download completed!\")\n\n else:\n print(\"Download skipped: No missing files!\")\n\n # def __install_datalad_cache(self) -> None:\n # \"\"\"Install the DataLad dataset.\n\n # Notes\n # -----\n # Requires a functional ssh connection to `self.ssh_username`@`self.host`.\n # Located at `self.host_dir`/`self.notebook_name`/`self.data_cache_path`.\n # \"\"\"\n # if os.path.exists(self.data_cache_path):\n # # noqa: TODO check if path is a valid DataLad dataset without doing d.status because it's too long.\n # d = datalad.api.Dataset(self.data_cache_path)\n # d.update(how=\"merge\")\n # else:\n # datalad.api.install(\n # source=(\n # f\"{self.ssh_username}@{self.ssh_host}:\"\n # f\"{self.ssh_host_dir}/{self.notebook_name}\"\n # ),\n # path=self.data_cache_path,\n # )\n\n def clean_protocol_description(self, desc: str) -> str:\n \"\"\"Create valid protocol description for file names (as done by PPMI).\n\n Parameters\n ----------\n str\n Protocol description. Example: \"MPRAGE GRAPPA\"\n \"\"\"\n return (\n desc.replace(\" \", \"_\").replace(\"(\", \"_\").replace(\")\", \"_\").replace(\"/\", \"_\")\n )\n\n def find_nifti_file_in_cache(\n self,\n subject_id: str,\n event_id: str,\n protocol_description: str,\n base_dir: str = \"inputs\",\n ) -> str | None:\n \"\"\"Return cached nifti files, if any.\n\n Search for nifti file matching `subject_id`, `event_id` and\n `protocol_description` in the cache directory.\n If not found, search for nifti file matching `subject_id` and `event_id` only,\n and return it if a single file is found.\n\n Parameters\n ----------\n subject_id: str\n Subject ID\n event_id: str\n Event ID. Example: BL\n protocol_description: str\n Protocol description. Example: \"MPRAGE GRAPPA\"\n base_dir: str, default \"inputs\"\n TODO Describe this. Not sure what it is exactly.\n\n Returns\n -------\n str or None\n File name matching the `subject_id`, `event_id`, and if possible\n `protocol_description`. None if no matching file is found.\n \"\"\"\n expression = os.path.join(\n self.data_cache_path,\n base_dir,\n f\"sub-{subject_id}\",\n f\"ses-{event_id}\",\n \"anat\",\n f\"PPMI_*{self.clean_protocol_description(protocol_description)}*.nii\",\n )\n files = glob.glob(expression)\n assert len(files) <= 1, f\"More than 1 Nifti file matched by {expression}\"\n if len(files) == 1:\n return files[0]\n # print(\n # \"Warning: no nifti file found for: \"\n # f\"{(subject_id, event_id, protocol_description)} with strict glob \"\n # \"expression. Trying with lenient glob expression.\"\n # )\n expression = os.path.join(\n self.data_cache_path,\n base_dir,\n f\"sub-{subject_id}\",\n f\"ses-{event_id}\",\n \"anat\",\n \"PPMI_*.nii\",\n )\n files = glob.glob(expression)\n assert len(files) <= 1, f\"More than 1 Nifti file matched by {expression}\"\n if len(files) == 1:\n return files[0]\n # print(\n # f\"Warning: no nifti file found for: \"\n # f\"{(subject_id, event_id, protocol_description)} \"\n # \"with lenient expression, returning None\"\n # )\n return None\n\n def disease_duration(self) -> pd.DataFrame:\n \"\"\"Return a DataFrame containing disease durations.\n\n Returns\n -------\n pd.DataFrame\n DataFrame containing disease durations for each (patient,event) pair found\n in \"MDS_UPDRS_Part_III.csv\".\n \"\"\"\n # Download required files\n self.download_ppmi_metadata(\n [\"MDS_UPDRS_Part_III.csv\", \"PD_Diagnosis_History.csv\"]\n )\n\n pddxdt = pd.read_csv(\n os.path.join(self.study_files_dir, \"PD_Diagnosis_History.csv\")\n )[[\"PATNO\", \"EVENT_ID\", \"PDDXDT\"]]\n pddxdt = pddxdt[(pddxdt[\"EVENT_ID\"] == \"SC\") & pddxdt[\"PDDXDT\"].notna()]\n pdxdur = pd.read_csv(\n os.path.join(self.study_files_dir, \"MDS_UPDRS_Part_III.csv\"),\n low_memory=False,\n )[[\"PATNO\", \"EVENT_ID\", \"INFODT\"]]\n\n PDDXDT_map = dict(zip(pddxdt[\"PATNO\"].values, pddxdt[\"PDDXDT\"].values))\n pdxdur[\"PDDXDT\"] = pdxdur[\"PATNO\"].map(PDDXDT_map)\n\n pdxdur[\"PDXDUR\"] = pdxdur.apply(\n lambda row: relativedelta(parse(row[\"INFODT\"]), parse(row[\"PDDXDT\"])).months\n if row[\"PDDXDT\"] is not np.nan\n else np.nan,\n axis=1,\n )\n pdxdur.drop(labels=[\"INFODT\", \"PDDXDT\"], inplace=True, axis=1)\n\n return pdxdur\n\n def moca2mmse(self, moca_score: int) -> int:\n \"\"\"Return a MMSE score given a MoCA score.\n\n Parameters\n ----------\n moca_score: int\n MoCA score\n\n Returns\n -------\n int\n MMSE score corresponding to the MoCA score\n Conversion made using Table 2 in\n https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4371590\n\n \"\"\"\n mapping = {\n 1: 6,\n 2: 9,\n 3: 11,\n 4: 12,\n 5: 13,\n 6: 14,\n 7: 15,\n 8: 15,\n 9: 16,\n 10: 17,\n 11: 18,\n 12: 18,\n 13: 19,\n 14: 20,\n 15: 21,\n 16: 22,\n 17: 22,\n 18: 23,\n 19: 24,\n 20: 25,\n 21: 26,\n 22: 26,\n 23: 27,\n 24: 28,\n 25: 28,\n 26: 29,\n 27: 29,\n 28: 30,\n 29: 30,\n 30: 30,\n }\n\n try:\n if math.isnan(moca_score):\n return np.nan\n else:\n return mapping[moca_score]\n except Exception as e:\n print(e)\n return moca_score\n\n def reformat_plot_labels(self, dist: pd.Series, ax: axes.Axes, freq: int) -> None:\n \"\"\"Reformat tick locations and labels of the x-axis on a plot.\n\n Parameters\n ----------\n dist: pd.Series\n Series representing the number of elements\n for each distinct values of a column\n ax: axes.Axes\n Matplotlib's Axes class to access figure\n elements and set the coordinate system\n freq: int\n interval between labels\n\n Returns\n -------\n None\n \"\"\"\n ax.set_xticklabels([x.removesuffix(\".0\") for x in dist.index.astype(str)])\n for label in ax.xaxis.get_ticklabels():\n try:\n if int(label.get_text()) % freq != 0:\n label.set_visible(False)\n except Exception:\n pass\n\n def download_missing_nifti_files(\n self, cohort: pd.DataFrame, link_in_outputs=False\n ) -> None:\n \"\"\"Download missing nifti files required by cohort.\n\n For each subject in cohort, look for T1-weighted nifti image file in\n notebook cache. Download all the missing files from PPMI, move them\n to notebook cache (inputs directory), and add their names to cohort.\n\n Parameters\n ----------\n cohort: pd.DataFrame\n A Pandas DataFrame containing columns PATNO (PPMI patient id), EVENT_ID\n (MRI visit, for instance 'V06'), and Description (for instance\n 'MPRAGE GRAPPA'). Can be built from the file produced by\n 'MRI metadata.ipynb'. A column 'File name' will be added to the DataFrame\n if not already present. This column\n will contain the paths of the T1-weighted nifti files associated with the\n patient, MRI visit, and protocol description.\n\n link_in_outputs: bool\n If True, create symbolic links to input nifti files in\n outputs/pre-processing. Useful for processing tools that\n write next to input files, such as SPM.\n\n Returns\n -------\n None\n \"\"\"\n # Find nifti file names in cohort\n cohort[\"File name\"] = cohort.apply(\n lambda x: self.find_nifti_file_in_cache(\n x[\"PATNO\"], x[\"EVENT_ID\"], x[\"Description\"]\n ),\n axis=1,\n )\n print(\n f\"Number of available subjects: {len(cohort[cohort['File name'].notna()])}\"\n )\n print(f\"Number of missing subjects: {len(cohort[cohort['File name'].isna()])}\")\n\n # Download missing file names\n try:\n ppmi_dl = ppmi_downloader.PPMIDownloader()\n missing_subject_ids = cohort[cohort[\"File name\"].isna()][\"PATNO\"]\n print(f\"Downloading image data of {len(missing_subject_ids)} subjects\")\n ppmi_dl.download_imaging_data(\n missing_subject_ids,\n type=\"nifti\",\n timeout=120 * len(missing_subject_ids),\n headless=False,\n )\n except Exception as e:\n print(\"Download failed!\")\n raise (e)\n\n # Find cohort file names among downloaded files\n results_path = \"outputs\"\n ppmi_fd = ppmi_downloader.PPMINiftiFileFinder()\n for _, row in cohort.iterrows():\n if row[\"File name\"] is None:\n filename = ppmi_fd.find_nifti(\n row[\"PATNO\"], row[\"EVENT_ID\"], row[\"Description\"]\n )\n if filename is None:\n print(\n \"Not found: \"\n + f\"{row['PATNO'], row['EVENT_ID'], row['Description']}\"\n )\n else: # copy file to dataset\n dest_dir = os.path.join(\n \"inputs\",\n f'sub-{row[\"PATNO\"]}',\n f'ses-{row[\"EVENT_ID\"]}',\n \"anat\",\n )\n os.makedirs(dest_dir, exist_ok=True)\n dest_file = os.path.join(dest_dir, os.path.basename(filename))\n os.rename(filename, dest_file)\n row[\"File name\"] = dest_file\n\n # Update file names in cohort\n cohort[\"File name\"] = cohort.apply(\n lambda x: self.find_nifti_file_in_cache(\n x[\"PATNO\"], x[\"EVENT_ID\"], x[\"Description\"]\n ),\n axis=1,\n )\n\n # Create symlinks to inputs if necessary\n if link_in_outputs:\n for file_name in cohort[\"File name\"].values:\n dest_dir = os.path.dirname(file_name).replace(\n os.path.join(self.data_cache_path, \"inputs\"),\n os.path.join(results_path, \"pre_processing\"),\n )\n dest_file = os.path.join(\n dest_dir,\n os.path.basename(file_name.replace(self.data_cache_path, \"\")),\n )\n if not os.path.exists(dest_file):\n # print(dest_dir, file_name, dest_file)\n os.makedirs(dest_dir, exist_ok=True)\n os.symlink(\n os.path.relpath(os.path.abspath(file_name), start=dest_file),\n dest_file,\n )\n\n def cohort_id(self, cohort: pd.DataFrame) -> str:\n \"\"\"Return a unique id for the cohort.\n\n The id is built as the hash of the sorted list of patient ids in the cohort.\n Since cohort_ids may be used to create file names, negative signs ('-')\n are replaced with underscore characters ('_') since SPM crashes on file names\n containing negative signs. Therefore, the cohort id is a string that cannot\n be cast to an integer.\n\n Parameters\n ----------\n cohort: pd.DataFrame\n A Pandas DataFrame with a column named 'PATNO'.\n\n Return\n ------\n string\n A string containing the unique id of the cohort.\n \"\"\"\n return str(hash(tuple(sorted(cohort[\"PATNO\"])))).replace(\"-\", \"_\")\n\n def write_spm_batch_files(\n self,\n template_job_filename: str,\n replaced_keys: dict,\n executable_job_file_name: str,\n ) -> None:\n \"\"\"Write SPM batch files from a template by replacing placeholder keys in it.\n\n Open the SPM batch file in template_job_filename, search and replace keys found\n in replaced_keys, and write the result in two files, a \"batch\" file and a \"job\"\n file. Output file names are built from job_file_name. Job file names must end\n with '_job.m'\n\n Parameters\n ----------\n template_job_filename: str\n File name of template SPM job. Contains placeholder keys to be replaced to\n create an executable batch. No format is specified for the keys, make sure\n that they are uniquely identified in the template file!\n\n replaced_keys: dict\n Dictionary containing keys to be replaced by values in the template job\n file. Example: {'[IMAGE]': 'inputs/sub-1234/ses-1/anat/image.nii'}. Make\n sure that the keys are present in the template job file name!\n\n executable_job_file_name: str\n File name where to write the executable job file. Must end in '_job.m'.\n An SPM batch file calling this job file will also be written with a\n '_batch.m' ending.\n\n Return\n ------\n None\n \"\"\"\n\n def replace_keys(string, replace_keys):\n for k in replace_keys:\n string = string.replace(k, replace_keys[k])\n return string\n\n # Read template file\n with open(template_job_filename) as f:\n content = f.read()\n\n assert template_job_filename.endswith(\"_job.m\")\n assert executable_job_file_name.endswith(\"_job.m\")\n\n with open(executable_job_file_name, \"w\") as f:\n f.write(replace_keys(content, replaced_keys))\n\n print(f\"Job batch file written in {os.path.basename(executable_job_file_name)}\")\n\n # Batch file\n content_batch = pkgutil.get_data(\n __name__, os.path.join(\"templates\", \"call_batch.m\")\n )\n assert content_batch is not None, \"Cannot read batch template file.\"\n content_batch_str = content_batch.decode(\"utf-8\")\n tempfile_name_batch = executable_job_file_name.replace(\"_job\", \"_batch\")\n\n with open(tempfile_name_batch, \"w\") as f:\n job_dir = os.path.dirname(os.path.abspath(executable_job_file_name))\n f.write(\n replace_keys(\n content_batch_str,\n {\n \"[BATCH]\": f\"addpath('{job_dir}')\"\n + os.linesep\n + os.path.basename(executable_job_file_name.replace(\".m\", \"\"))\n },\n )\n )\n\n print(f\"Batch file written in {os.path.basename(tempfile_name_batch)}\")\n\n def run_spm_batch_file(\n self,\n executable_job_file_name: str,\n boutiques_descriptor: str = \"zenodo.6881412\",\n force: bool = False,\n ) -> None:\n \"\"\"Run an SPM batch file using Boutiques.\n\n Requires Docker or Singularity container engines (Singularity untested yet in\n this context). Download the Boutiques descriptor from Zenodo or use the local\n file passed as argument. Download the Docker container, create a Boutiques\n invocation and run it. Write logs in log file created from\n executable_job_file_name (example: pre_processing_1234.log). If log file\n already exists, skip execution unless force is set to True.\n\n Parameters\n ----------\n executable_job_file_name: str\n An SPM job file ready to be executed.\n Example: 'code/batches/pre_processing_1234_job.m'.\n See self.write_spm_batch_files for a possible way to create such a file.\n\n\n boutiques_descriptor: str\n A Boutiques descriptor in the form of a Zenodo id, local file name, or\n JSON string. Don't modify the default value unless you know what you are\n doing.\n\n force: bool\n Force execution even if log file already exists for this execution.\n Default: False.\n\n Return\n ------\n boutiques.ExecutionOutput\n Boutiques execution output object containing exit code and various logs.\n \"\"\"\n log_dir = os.path.join(\"outputs\", \"logs\")\n os.makedirs(log_dir, exist_ok=True)\n\n log_file_name = os.path.abspath(\n os.path.join(\n log_dir,\n os.path.basename(executable_job_file_name.replace(\"_job.m\", \".log\")),\n )\n )\n spm_batch_file = executable_job_file_name.replace(\"_job\", \"_batch\")\n\n if not force:\n if os.path.exists(log_file_name):\n print(\n f\"Log file {os.path.basename(log_file_name)} exists, \"\n + \"skipping batch execution (remove file or use force=True \"\n + \"to force execution)\"\n )\n return\n else:\n print(\n f\"Log file {os.path.basename(log_file_name)} does not exist, \"\n + \"running batch\"\n )\n\n # Initialize Boutiques Python function for descriptor\n spm_batch = descriptor2func(boutiques_descriptor)\n\n output = spm_batch(\n \"launch\",\n \"-s\",\n \"-u\",\n spm_batch_file=spm_batch_file,\n log_file_name=log_file_name,\n )\n\n assert (\n output.exit_code == 0\n ), f\"Execution error, inspect output object for logs: {output}\"\n\n print(\"Execution was successful.\")\n\n return output\n\n def smwc_scan(\n self,\n tissue_class: int,\n patno: int,\n visit: str,\n pre_processing_dir: str = \"pre_processing\",\n ) -> str:\n \"\"\"Find the SPM tissue class file of patient at visit with given protocol.\n\n Scans the outputs directory for an SPM tissue class file obtained from nifti\n file of patient at visit using protocol description. To find a tissue file,\n matches glob expression\n outputs/{pre_processing_dir}sub-{patno}/ses-{visit}/anat/smwc{tissue_class}PPMI*.nii\n Returns an error if more than one file is found that matches this expression.\n\n Paramters\n ---------\n tissue_class: int\n 1 (grey matter) or 2 (white matter)\n\n patno: int\n PPMI patient identifier\n\n visit: str\n PPMI visit name. Example: 'V04'.\n\n pre_processing_dir: str\n Directory in 'outputs' where pre-processing results are stored.\n\n Return\n ------\n str: path of a tissue class file\n \"\"\"\n if tissue_class not in (1, 2):\n raise Exception(f\"Unrecognized tissue class: {tissue_class}\")\n dirname = os.path.join(\"outputs\", pre_processing_dir)\n expression = (\n f\"{dirname}/sub-{patno}/ses-{visit}/anat/smwc{tissue_class}PPMI*.nii\"\n )\n files = glob.glob(expression)\n assert (\n len(files) == 1\n ), f\"Zero or more than 1 files were matched by expression: {expression}\"\n return os.path.abspath(files[0])\n\n def export_spm_segmentations(\n self,\n cohort: pd.DataFrame,\n folder: str,\n cut_coords: tuple = (-28, -7, 17),\n force: bool = False,\n extension: str = \"png\",\n ) -> None:\n \"\"\"Export segmentation images as 2D image files.\n\n Meant to be used for quality control. make_gifs can assemble these images\n in an animate gif.\n\n Parameters\n ----------\n cohort: pd.DataFrame\n LivingPark cohort to export. Must have a column called 'File name'.\n\n folder: str\n Folder where to export the segmentation images.\n\n cut_coords: tuple\n Passed to Nilearn viewer. The MNI coordinates of the cutting plane.\n\n force: bool\n If True, force export to existing folder. Removes all the files in folder\n before writing new ones.\n\n extension: str\n Image file extension supported by Matplotlib. Example: 'png'.\n \"\"\"\n alpha = 0.5\n\n if (not force) and os.path.exists(folder):\n print(\n f\"Folder {folder} already exists, skipping image export \"\n + \" (remove folder or use --force to force).\"\n )\n return\n\n if os.path.exists(folder): # force is True\n print(f\"Folder {folder} already exists, removing its content\")\n for f in glob.glob(os.path.join(folder, \"*\")):\n os.remove(f)\n\n os.makedirs(folder, exist_ok=True)\n\n for i in range(len(cohort)):\n\n input_file = cohort[\"File name\"].values[i]\n subj_id = cohort[\"PATNO\"].values[i]\n\n output_file_name = input_file.replace(\n os.path.join(self.data_cache_path, \"inputs\"),\n os.path.join(\"outputs\", \"pre_processing\"),\n )\n output_file_c1 = output_file_name.replace(\"PPMI\", \"smwc1PPMI\")\n output_file_c2 = output_file_name.replace(\"PPMI\", \"smwc2PPMI\")\n\n fig = plt.figure()\n display = nplt.plot_anat(\n cut_coords=list(cut_coords), figure=fig, title=f\"#{i}/{len(cohort)}\"\n )\n display.add_overlay(output_file_c1, cmap=\"Reds\", threshold=0.1, alpha=alpha)\n display.add_overlay(\n output_file_c2, cmap=\"Blues\", threshold=0.1, alpha=alpha\n )\n\n os.makedirs(folder, exist_ok=True)\n plt.savefig(\n os.path.join(\n folder, f\"qc_{self.cohort_id(cohort)}_{subj_id}.{extension}\"\n )\n )\n plt.close(fig) # so as to not display the figure\n\n def make_gif(self, frame_folder: str, output_name: str = \"animation.gif\") -> None:\n \"\"\"Make gifs from a set of images located in the same folder.\n\n Parameters\n ----------\n frame_folder : str\n Folder where frames are stored. Frames must be in a format supported by PIL.\n\n output_name : str\n Base name of the gif file. Will be written in frame_folder.\n \"\"\"\n frames = [\n Image.open(image)\n for image in glob.glob(os.path.join(f\"{frame_folder}\", \"*.png\"))\n ]\n frame_one = frames[0]\n frame_one.save(\n os.path.join(frame_folder, output_name),\n format=\"GIF\",\n append_images=frames,\n save_all=True,\n duration=1000,\n loop=0,\n )\n print(f\"Wrote {os.path.join(frame_folder, output_name)}\")\n\n def qc_spm_segmentations(self, cohort) -> None:\n \"\"\"Display a gif file with SPM segmentation results from the cohort.\n\n Parameters\n ----------\n cohort: pd.DataFrame\n LivingPark cohort to QC. Must have a column called 'File name'.\n \"\"\"\n qc_dir = f\"qc_{self.cohort_id(cohort)}\"\n\n self.export_spm_segmentations(cohort, qc_dir)\n self.make_gif(qc_dir)\n return ImageDisplay(url=os.path.join(qc_dir, \"animation.gif\"))\n","repo_name":"chelsieng/livingpark-utils","sub_path":"livingpark_utils/livingpark_utils.py","file_name":"livingpark_utils.py","file_ext":"py","file_size_in_byte":28559,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"52"} +{"seq_id":"31483597288","text":"from fastapi import APIRouter, Response, UploadFile, File, Form\nfrom json import dumps\n\nfrom app import main, config\nfrom app.s3 import upload_fileobj\nfrom app.logger import logger\n\nrouter = APIRouter()\n\n@router.post(\"/\", response_class=Response)\ndef object_image_input(image: UploadFile = File(...), # ... = required\n image_name: str = Form(...),\n time: int = Form(...)): # epoch (seconds)\n\n # Upload image to S3\n upload_fileobj(image.file, image_name, config.S3_BUCKET_OBJECT_IMAGE)\n image_s3_uri = f\"s3://{config.S3_BUCKET_OBJECT_IMAGE}/{image_name}\"\n logger.info(f\"Image is uploaded to {image_s3_uri}\")\n\n # Send data to Kafka\n message = {'image_path': image_s3_uri}\n main.kafka_producer.send(config.KAFKA_TOPIC_OBJECT_IMAGE,\n value=dumps(message).encode(encoding='UTF-8'))\n logger.info(f\"Message is published to Kafka: {dumps(message)}\")","repo_name":"senior-project-spai/image-input-api","sub_path":"app/app/routes/object.py","file_name":"object.py","file_ext":"py","file_size_in_byte":934,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"21071007921","text":"# 递归删除所给目录及其子目录下所有不在编程语言列表中的文件\nimport os\n\n# 编程语言及其对应的文件后缀\nextensions = {\n 'C++': ['.cpp', '.cc', '.cxx', '.c++', '.h', '.hpp'],\n 'C/C++ Header': ['.h', '.hh', '.hpp', '.hxx'],\n 'Java': ['.java', '.class', '.jar'],\n 'JavaScript': ['.js', '.jsx'],\n 'Rust': ['.rs'],\n 'Python': ['.py', '.pyc', '.pyo', '.pyd', '.pyw'],\n 'Objective-C++': ['.mm'],\n 'C': ['.c'],\n 'HTML': ['.html', '.htm'],\n 'TypeScript': ['.ts', '.tsx'],\n 'Assembly': ['.asm', '.s'],\n 'Markdown': ['.md'],\n 'IDL': ['.idl'],\n 'diff': ['.diff', '.patch'],\n 'Mojo': ['.mojom'],\n 'Objective-C': ['.m'],\n 'Bazel': ['.bazel', '.bzl'],\n 'C#': ['.cs'],\n 'Protocol Buffers': ['.proto'],\n 'CSS': ['.css'],\n 'YAML': ['.yaml', '.yml'],\n 'JSON5': ['.json5'],\n 'Windows Module Definition': ['.def'],\n 'Bourne Shell': ['.sh'],\n 'reStructuredText': ['.rst'],\n 'Perl': ['.pl', '.pm'],\n 'CMake': ['.cmake', '.ctest'],\n 'm4': ['.m4'],\n 'PHP': ['.php', '.php3', '.php4', '.php5', '.phtml'],\n 'make': ['.mk', '.make'],\n 'Starlark': ['.bzl'],\n 'Swift': ['.swift'],\n 'TOML': ['.toml'],\n 'DTD': ['.dtd'],\n 'Jinja Template': ['.jinja', '.j2'],\n 'Bourne Again Shell': ['.bash'],\n 'Vuejs Component': ['.vue'],\n 'Maven': ['.pom', '.mvn'],\n 'Groovy': ['.groovy', '.gradle'],\n 'Pascal': ['.pas', '.p'],\n 'TeX': ['.tex', '.sty', '.cls', '.dtx', '.ins', '.ltx'],\n 'SQL': ['.sql'],\n 'Ruby': ['.rb'],\n 'Windows Resource File': ['.rc'],\n 'DOS Batch': ['.bat', '.cmd'],\n 'WiX source': ['.wixproj'],\n 'Meson': ['.meson', '.wrap'],\n 'MSBuild script': ['.csproj', '.vbproj', '.vcxproj', '.proj'],\n 'Jupyter Notebook': ['.ipynb'],\n 'Lisp': ['.lisp', '.lsp', '.cl', '.fasl'],\n 'Kotlin': ['.kt', '.kts'],\n 'yacc': ['.y', '.yy'],\n 'XSD': ['.xsd'],\n 'Go': ['.go'],\n 'vim script': ['.vim'],\n 'awk': ['.awk'],\n 'Gradle': ['.gradle'],\n 'INI': ['.ini'],\n 'CoffeeScript': ['.coffee'],\n 'ANTLR Grammar': ['.g4'],\n 'Dart': ['.dart'],\n 'PowerShell': ['.ps1', '.psm1', '.psd1'],\n}\n\n# 打平 extensions 字典，将所有文件扩展名放入一个集合中\nallowed_extensions = {ext for exts in extensions.values() for ext in exts}\n\ndef delete_unwanted_files(dir_path):\n for root, dirs, files in os.walk(dir_path, topdown=False):\n for name in files:\n _, ext = os.path.splitext(name)\n if ext not in allowed_extensions:\n full_path = os.path.join(root, name)\n os.remove(full_path)\n print(f'Deleted file: {full_path}')\n\n# 请替换为你想要处理的目录\ndirectory_path = '/Users/m1/github/chromium-main'\ndelete_unwanted_files(directory_path)","repo_name":"visnix/chromium-clean","sub_path":"r-delete-file.py","file_name":"r-delete-file.py","file_ext":"py","file_size_in_byte":2796,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"34057870795","text":"from __future__ import division, absolute_import\n\nfrom datetime import datetime\nfrom dateutil.tz import UTC\nfrom functools import wraps\nimport itertools\nimport random\n\nimport six\nimport click\n\n# Use numpy if available for fast decoding\ntry:\n import numpy as np\n _numpy = True\nexcept ImportError: # pragma: no cover\n _numpy = False\n\n__all__ = ['evaluate_ising', 'uniform_iterator', 'uniform_get',\n 'default_text_input', 'click_info_switch', 'datetime_to_timestamp']\n\n\ndef evaluate_ising(linear, quad, state):\n \"\"\"Calculate the energy of a state given the Hamiltonian.\n\n Args:\n linear: Linear Hamiltonian terms.\n quad: Quadratic Hamiltonian terms.\n state: Vector of spins describing the system state.\n\n Returns:\n Energy of the state evaluated by the given energy function.\n \"\"\"\n\n # If we were given a numpy array cast to list\n if _numpy and isinstance(state, np.ndarray):\n return evaluate_ising(linear, quad, state.tolist())\n\n # Accumulate the linear and quadratic values\n energy = 0.0\n for index, value in uniform_iterator(linear):\n energy += state[index] * value\n for (index_a, index_b), value in six.iteritems(quad):\n energy += value * state[index_a] * state[index_b]\n return energy\n\n\ndef active_qubits(linear, quadratic):\n \"\"\"Calculate a set of all active qubits. Qubit is \"active\" if it has\n bias or coupling attached.\n\n Args:\n linear (dict[variable, bias]/list[variable, bias]):\n Linear terms of the model.\n\n quadratic (dict[(variable, variable), bias]):\n Quadratic terms of the model.\n\n Returns:\n set:\n Active qubits' indices.\n \"\"\"\n\n active = {idx for idx,bias in uniform_iterator(linear)}\n for edge, _ in six.iteritems(quadratic):\n active.update(edge)\n return active\n\n\ndef generate_valid_random_problem(solver):\n \"\"\"Generates an Ising problem formulation valid for a particular solver,\n using all qubits and all couplings.\"\"\"\n\n h_range = solver.properties['h_range']\n j_range = solver.properties['j_range']\n\n lin = {qubit: random.uniform(*h_range) for qubit in solver.nodes}\n quad = {edge: random.uniform(*j_range) for edge in solver.undirected_edges}\n\n return lin, quad\n\n\ndef uniform_iterator(sequence):\n \"\"\"Uniform (key, value) iteration on a `dict`,\n or (idx, value) on a `list`.\"\"\"\n\n if isinstance(sequence, dict):\n return six.iteritems(sequence)\n else:\n return enumerate(sequence)\n\n\ndef uniform_get(sequence, index, default=None):\n \"\"\"Uniform `dict`/`list` item getter, where `index` is interpreted as a key\n for maps and as numeric index for lists.\"\"\"\n\n if isinstance(sequence, dict):\n return sequence.get(index, default)\n else:\n return sequence[index] if index < len(sequence) else default\n\n\ndef strip_head(sequence, values):\n \"\"\"Strips elements of `values` from the beginning of `sequence`.\"\"\"\n values = set(values)\n return list(itertools.dropwhile(lambda x: x in values, sequence))\n\n\ndef strip_tail(sequence, values):\n \"\"\"Strip `values` from the end of `sequence`.\"\"\"\n return list(reversed(list(strip_head(reversed(sequence), values))))\n\n\ndef default_text_input(prompt, default=None, optional=True):\n if default:\n prompt = \"{} [{}]: \".format(prompt, default)\n else:\n if optional:\n prompt = \"{} [skip]: \".format(prompt)\n else:\n prompt = \"{}: \".format(prompt)\n\n line = ''\n while not line:\n line = six.moves.input(prompt)\n if not line:\n line = default\n if not line:\n if optional:\n break\n click.echo(\"Input required, please try again.\")\n return line\n\n\ndef click_info_switch(f):\n \"\"\"Decorator to create eager Click info switch option, as described in:\n http://click.pocoo.org/6/options/#callbacks-and-eager-options.\n\n Takes a no-argument function and abstracts the boilerplate required by\n Click (value checking, exit on done).\n\n Example:\n\n @click.option('--my-option', is_flag=True, callback=my_option,\n expose_value=False, is_eager=True)\n def test():\n pass\n\n @click_info_switch\n def my_option()\n click.echo('some info related to my switch')\n \"\"\"\n\n @wraps(f)\n def wrapped(ctx, param, value):\n if not value or ctx.resilient_parsing:\n return\n f()\n ctx.exit()\n return wrapped\n\n\ndef datetime_to_timestamp(dt):\n \"\"\"Convert timezone-aware `datetime` to POSIX timestamp and\n return seconds since UNIX epoch.\n\n Note: similar to `datetime.timestamp()` in Python 3.3+.\n \"\"\"\n\n epoch = datetime.utcfromtimestamp(0).replace(tzinfo=UTC)\n return (dt - epoch).total_seconds()\n\n\ndef strtrunc(s, maxlen=60):\n s = str(s)\n return s[:(maxlen-3)]+'...' if len(s) > maxlen else s\n","repo_name":"Elmistrana/dwave-cloud-client","sub_path":"dwave/cloud/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4922,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"52"} +{"seq_id":"34078422217","text":"from scipy.linalg import svdvals\r\nfrom numpy import linalg\r\nimport numpy as ny\r\nfrom pylab import *\r\nimport matplotlib.pyplot as plt \r\nfrom matplotlib import cm \r\nimport matplotlib.animation as animation \r\nfrom math import *\r\nimport sys\r\nfrom tkinter import *\r\nimport subprocess as sp\r\nfrom easygui import *\r\nimport os\r\nfrom pseudoAbscisseRayon import *\r\n\r\n\r\n\r\n#détermine la portion du plan complexe sur laquelle appliquer l'algorithme GRID\r\ndef limite(M,eps):\r\n\r\n\tn = len(M)\r\n\tborne_X = [0,0]\r\n\tborne_Y = [0,0]\r\n\tx = 0\r\n\ty = 0\r\n\tr = 0\r\n\tcercle = ((x,y),r)\r\n\tliste_cercle = []\r\n\tliste_c = []\r\n\tfor i in range(n):\r\n\t\tx = M[i][i].real\r\n\t\ty = M[i][i].imag\r\n\t\tr = 0\r\n\t\tfor j in range(n):\r\n\t\t\tif(i != j):\r\n\t\t\t\tr = r+norm(M[i][j])\r\n\t\tcercle = ((x,y),r)\r\n\t\tliste_c.append(cercle)\r\n\t\tr = r+sqrt(n)*eps\r\n\t\tcercle = ((x,y),r)\r\n\t\tliste_cercle.append(cercle)\r\n\t\tif(borne_X[0] > x-r):\r\n\t\t\tborne_X[0] = x-r\r\n\t\tif(borne_X[1] < x+r):\r\n\t\t\tborne_X[1] = x+r\r\n\t\tif(borne_Y[0] > y-r):\r\n\t\t\tborne_Y[0] = y-r\r\n\t\tif(borne_Y[1] < y+r):\r\n\t\t\tborne_Y[1] = y+r\r\n\treturn (borne_X,borne_Y,liste_cercle,liste_c)\r\n\r\n\r\n\r\ndef grid1(M,list_eps,pas,b1,b2):\r\n\t\"\"\"\r\n\r\n\tpremière implémentation de l'algorithme Grid, calcul le pseudospectre de la matrice M\r\n\tpour les valeurs de epsilon contenues dans list_eps(par ordre croissant en faisant pas*pas SVD. \r\n\taffiche le pseudo absicce si b1 = True et le pseudo rayon si b2= True\r\n\t\r\n\trenvoie les coordonnées de la fenêtre matplotlib au moment de sa destruction (utile pour le zoom)\r\n\r\n\t\"\"\"\r\n\r\n\t#on définit un rectangle via les disques de Gerschgorin\r\n\tborne_X,borne_Y,liste_cercle,liste_c = limite(M,list_eps[len(list_eps)-1])\r\n\ti = complex(0,1)\r\n\tn = len(M)\r\n\t#on maille le plan\r\n\tx = ny.linspace(borne_X[0],borne_X[1],pas)\r\n\ty = ny.linspace(borne_Y[0],borne_Y[1],pas)\r\n\tsigmin = zeros((pas,pas))\r\n\tlist_sig = []\r\n\r\n\t#gestion de la barre de chargement\r\n\tproc = 0\r\n\twindow = Tk()\r\n\twindow.title(\"processing\")\r\n\twindow.minsize(480,160)\r\n\twindow.maxsize(480,160)\r\n\t#window.configure(bg = 'green')\r\n\tlabel_title1 = Label(window,text= \"Calcul en cours :\",font ='Times')\r\n\tlabel_title1.pack(expand = YES)\r\n\tlabel_subtitle = Label(window,text= \"0%\",font ='Times')\r\n\tlabel_subtitle.pack(expand = YES)\r\n\tcan = Canvas(window)\r\n\tcan.pack(expand=YES)\r\n\tcan.create_rectangle(89,39,291,61,outline=\"black\")\r\n\tc1 = can.create_rectangle(90,40,90,60,outline=\"lime green\",fill=\"lime green\")\r\n\r\n\tfor l in range(pas):\r\n\t\tfor j in range(pas):\r\n\t\t\tlist_sig = svdvals((x[l]+y[j]*i)*eye(n)-M)\r\n\t\t\tsigmin[j][l] = list_sig[len(list_sig)-1]\r\n\t\t\tproc=proc+1\r\n\t\t\tlabel_subtitle.config(text=str(round(proc/(pas*pas)*100,1))+\"%\",font ='Times')\r\n\t\t\tc2 = can.create_rectangle(90,40,90+2*int(proc/(pas*pas)*100),60,outline=\"lime green\",fill=\"lime green\")\r\n\t\t\tcan.delete(c1)\r\n\t\t\tc1 = c2\r\n\t\t\tcan.update()\r\n\t\t\twindow.update()\r\n\twindow.destroy()\r\n\r\n\t#norm = \tcm.colors.Normalize(vmax = list_eps[len(list_eps)-1],vmin = list_eps[0])\r\n \t\r\n \t#affichage du pseudospectre\r\n\tif(len(list_eps)>1):\r\n\t\tnorm = \tcm.colors.BoundaryNorm(boundaries = list_eps,ncolors = 256)\r\n\t\tcontour(x,y,sigmin,levels = list_eps,cmap = 'cool',norm = norm) #Set1 tab10 tab20\r\n\t\tplt.colorbar()\r\n\telse : \r\n\t\tcontour(x,y,sigmin,list_eps,cmap = 'cool') #Set1 tab10 tab20\t\tplt.colorbar()\r\n\tval_p,vecteurs=linalg.eig(M)\r\n\tfig = plt.gcf()\r\n\tax = fig.gca()\r\n\tfig.patch.set_facecolor('#c0c0c0')\r\n\tax.patch.set_facecolor('#9d9d9d')\r\n\r\n\t#affichage du pseudo abcisse\r\n\tif(b1):\r\n\t\tfor eps in list_eps:\r\n\t\t\tp = abscissae(M,eps,axi = ax)\r\n\t\t\t#plt.plot(p.real,p.imag,\"b:.\")\r\n\t\t\tp = radii(M,eps,axi = ax)\r\n\t\t\t#plt.plot(p.real,p.imag,\"r:.\")\r\n\t#affichage du pseudo rayon\r\n\tif(b2):\r\n\r\n\t\tfor i in range (len(liste_cercle)):\r\n\t\t\tif(b1):\r\n\t\t\t\tco,r1 = liste_cercle[i]\r\n\t\t\t\tax.add_artist(plt.Circle(co,radius =r1,color ='k',fill=False,label=\"d2\"))\r\n\t\t\tif(b2):\r\n\t\t\t\tco,r1 = liste_c[i]\r\n\t\t\t\tax.add_artist(plt.Circle(co,ls = \"--\",radius =r1,color ='b',fill=False,label=\"d1\"))\r\n\t\t\t\t\r\n\tfor i in range(len(val_p)):\r\n\t\tplt.plot(val_p[i].real,val_p[i].imag,'k:.',)\r\n\ttitle(\"Pseudospectre GRID\")\r\n\taxis('equal')\r\n\tplt.show()\r\n\t\r\n\treturn list(ax.axis())\r\n\r\n\t\r\n\r\n\r\ndef gridbis(M,list_eps,pas,b1,b2,x_min,x_max,y_min,y_max):\r\n\t\"\"\"\r\n\tfonction de zoom, fonctionnement similaire à grid1.\r\n\t\"\"\"\r\n\tx = ny.linspace(x_min,x_max,pas)\r\n\ty = ny.linspace(y_min,y_max,pas)\r\n\ti = complex(0,1)\r\n\tn = len(M)\r\n\tsigmin = zeros((pas,pas))\r\n\tlist_sig = []\r\n\tproc = 0\r\n\twindow = Tk()\r\n\twindow.title(\"processing\")\r\n\twindow.minsize(480,160)\r\n\twindow.maxsize(480,160)\r\n\tlabel_title1 = Label(window,text= \"Calcul en cours :\")\r\n\tlabel_title1.pack(expand = YES)\r\n\tlabel_subtitle = Label(window,text= \"0%\",font = 'Times')\r\n\tlabel_subtitle.pack(expand = YES)\r\n\tcan = Canvas(window)\r\n\tcan.pack(expand=YES)\r\n\tcan.create_rectangle(89,39,291,61,outline=\"black\")\r\n\tc1 = can.create_rectangle(90,40,90,60,outline=\"lime green\",fill=\"lime green\")\r\n\tfor l in range(pas):\r\n\t\tfor j in range(pas):\r\n\t\t\tlist_sig = svdvals((x[l]+y[j]*i)*eye(n)-M)\r\n\t\t\tsigmin[j][l] = list_sig[len(list_sig)-1]\r\n\t\t\tproc=proc+1\r\n\t\t\tlabel_subtitle.config(text=str(round(proc/(pas*pas)*100,1))+\"%\",font ='Times')\r\n\t\t\tc2 = can.create_rectangle(90,40,90+2*int(proc/(pas*pas)*100),60,outline=\"lime green\",fill=\"lime green\")\r\n\t\t\tcan.delete(c1)\r\n\t\t\tc1 = c2\r\n\t\t\tcan.update()\r\n\t\t\twindow.update()\r\n\twindow.destroy()\r\n\t\r\n\r\n\r\n\tif(len(list_eps)>1):\r\n\t\tnorm = \tcm.colors.BoundaryNorm(boundaries = list_eps,ncolors = 256)\r\n\t\tcontour(x,y,sigmin,list_eps,cmap = 'cool',norm = norm) #Set1 tab10 tab20\r\n\t\tplt.colorbar()\r\n\telse : \r\n\t\tcontour(x,y,sigmin,list_eps,cmap = 'cool') \r\n\r\n\tval_p,vecteurs=linalg.eig(M)\r\n\tfig = plt.gcf()\r\n\tax = fig.gca()\r\n\tfig.patch.set_facecolor('#c0c0c0')\r\n\tax.patch.set_facecolor('#9d9d9d')\r\n\r\n\tif(b1):\r\n\t\tfor eps in list_eps:\r\n\t\t\tp = abscissae(M,eps,axi = ax)\r\n\t\t\t#plt.plot(p.real,p.imag,\"b:.\")\r\n\t\t\tp = radii(M,eps,axi = ax)\r\n\t\t\t#plt.plot(p.real,p.imag,\"r:.\")\r\n\t\r\n\tif(b2):\r\n\t\tfor i in range (len(liste_cercle)):\r\n\t\t\tif(b1):\r\n\t\t\t\tco,r1 = liste_cercle[i]\r\n\t\t\t\tax.add_artist(plt.Circle(co,radius =r1,color ='k',fill=False,label=\"d2\"))\r\n\t\t\tif(b2):\r\n\t\t\t\tco,r1 = liste_c[i]\r\n\t\t\t\tax.add_artist(plt.Circle(co,ls = \"--\",radius =r1,color ='b',fill=False,label=\"d1\"))\r\n\t\t\t\t\r\n\tfor i in range(len(val_p)):\r\n\t\tif(val_p[i].real>=x_min and val_p[i].real<=x_max and val_p[i].imag<= y_max and val_p[i].imag>=y_min):\r\n\t\t\tplt.plot(val_p[i].real,val_p[i].imag,'k:.',)\r\n\ttitle(\"Pseudospectre GRID\")\r\n\taxis('equal')\r\n\r\n\tplt.show()\r\n\t#plt.savefig(\"fig0\",format = \"png\")\r\n\treturn list(ax.axis())\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"Paxheim/Projet-Pseudospectre","sub_path":"grid.py","file_name":"grid.py","file_ext":"py","file_size_in_byte":6438,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"12491691564","text":"#!/usr/bin/python3\n\nfrom .context import src\n\nimport unittest\n\nclass SampleBadTest(unittest.TestCase):\n '''Given sample is a bit corrupted.'''\n\n def test_chop_into_raw_entries(self):\n '''first line does not have dialog'''\n replacee = \"\\ufeff1\\n00:00:00,000 --> 00:00:02,350\\n\\n\\n2\\n00:00:02,350 --> 00:00:06,369\\nMALE SPEAKER: Thank you for\\ncoming to Edmond Lau's talk.\\n\\n3\\n00:00:06,370 --> 00:00:10,790\\nHe will be talking about how to\\nbe a more effective engineer.\\n\\n\\n\"\n\n expected = ['\\ufeff1\\n00:00:00,000 --> 00:00:02,350',\n \"2\\n00:00:02,350 --> 00:00:06,369\\nMALE SPEAKER: Thank you for\\ncoming to Edmond Lau's talk.\",\n '3\\n00:00:06,370 --> 00:00:10,790\\nHe will be talking about how to\\nbe a more effective engineer.']\n\n actual = src.chop_into_raw_entries(replacee)\n\n self.assertEqual(actual, expected)\n\ndef load_file(filename):\n with open(filename, 'r') as fp:\n content = fp.read()\n return content\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"JiniousChoi/srt-to-script","sub_path":"tests/test_sample_bad.py","file_name":"test_sample_bad.py","file_ext":"py","file_size_in_byte":1052,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"28381596691","text":"\nfrom __future__ import absolute_import, unicode_literals\nimport os\nfrom celery import Celery\nfrom datetime import timedelta\nfrom celery.schedules import crontab\n\nfrom django.conf import settings\n\n# set the default Django settings module for the 'celery' program.\nos.environ.setdefault('DJANGO_SETTINGS_MODULE', 'docker.settings')\n\napp = Celery('docker', broker='amqp://admin:1234@rabbit:5672//')\n\n# v4.0 이상 일 경우\napp.config_from_object('django.conf:settings', namespace='CELERY')\n# v3.1 일 경우\n# app.config_from_object('django.conf:settings')\n\n# v4.0 이상 일 경우\napp.autodiscover_tasks()\n# v3.1 일 경우\n# app.autodiscover_tasks(lambda: settings.INSTALLED_APPS)\n\napp.conf.update(\n CELERY_TASK_SERIALIZER='json',\n CELERY_ACCEPT_CONTENT=['json'], # Ignore other content\n CELERY_RESULT_SERIALIZER='json',\n CELERY_TIMEZONE='Asia/Seoul',\n CELERY_ENABLE_UTC=False,\n CELERYBEAT_SCHEDULE = {\n 'say_hello': {\n \"task\": \"app.tasks.print_hello\",\n 'schedule': crontab(minute=0, hour=5),\n 'args': ()\n },\n }\n)","repo_name":"khs9628/Docker-Django","sub_path":"docker_celery_rabbitMQ/docker/celery.py","file_name":"celery.py","file_ext":"py","file_size_in_byte":1085,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"36347711584","text":"# cording: utf-8\n\nimport os\nfrom flask import Flask, request, redirect, render_template, flash\nfrom werkzeug.utils import secure_filename\nfrom tensorflow.keras.models import Sequential, load_model\nfrom tensorflow.keras.preprocessing import image\nimport numpy as np\nfrom tensorflow import keras\nimport cv2\nfrom cv2 import THRESH_BINARY\nimport math\n\n\nclasses = [\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\"]\nimage_size = 28\n\nUPLOAD_FOLDER = \"uploads\"\n\n# 一次的に切り出した画像を保存するフォルダ\nNUM_FOLDER = 'trim-num-file' # プログラムの最後でフォルダ内のファイルをすべて消去します\n\n\n\nALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg', 'gif'])\n\napp = Flask(__name__)\napp.debug = False\ndef allowed_file(filename):\n return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS\n\n\n\n\n\nmodel = load_model('./model.h5', compile=False)#学習済みモデルをロード\n\n@app.route('/', methods=['GET', 'POST'])\ndef upload_file():\n if request.method == 'POST':\n if 'file' not in request.files:\n flash('ファイルがありません')\n return redirect(request.url)\n file = request.files['file']\n if file.filename == '':\n flash('ファイルがありません')\n return redirect(request.url)\n if file and allowed_file(file.filename):\n filename = secure_filename(file.filename)\n file.save(os.path.join(UPLOAD_FOLDER, filename))\n filepath = os.path.join(UPLOAD_FOLDER, filename) \n \n \n # ここから画像を切り出して予測する関数\n\n #　画像サイズが大きすぎる場合は小さくする\n def size_smaller(img):\n if (img.shape[0] > 4000 or img.shape[1] > 3000):\n img = cv2.resize(img, (img.shape[1] // 3, img.shape[0] // 3))\n elif (img.shape[0] > 2000 or img.shape[1] > 1000):\n img = cv2.resize(img, (img.shape[1] // 2, img.shape[0] // 2)) \n img_size = img.shape[0]\n \n return img, img_size\n\n\n #　背景が白ければ黒に反転する\n def white_to_black(img): \n yl = np.mean(img[0:img.shape[0], 0:img.shape[1] // 20])\n yr = np.mean(img[0:img.shape[0], img.shape[1] - img.shape[1] // 20:img.shape[1]])\n xt = np.mean(img[0:img.shape[0] // 20, 0:img.shape[1]])\n xb = np.mean(img[img.shape[0] - img.shape[0] // 20:img.shape[0], 0:img.shape[1]])\n if (yl > 140 and yl <= 255 or yr > 140 and yr <= 255 \n or xt > 140 and xt <= 255 or xb > 140 and xb <= 255):\n img = cv2.bitwise_not(img)\n \n return img\n\n\n #　背景がノイズの入っていない白か黒か判断する\n def noise_detect(img):\n m = []\n yl = np.mean(img[0:img.shape[0], 0:img.shape[1] // 20])\n yr = np.mean(img[0:img.shape[0], img.shape[1] - img.shape[1] // 20:img.shape[1]])\n xt = np.mean(img[0:img.shape[0] // 20, 0:img.shape[1]])\n xb = np.mean(img[img.shape[0] - img.shape[0] // 20:img.shape[0], 0:img.shape[1]])\n yl_min = np.min(img[0:img.shape[0], 0:img.shape[1] // 20])\n yr_min = np.min(img[0:img.shape[0], img.shape[1] - img.shape[1] // 20:img.shape[1]])\n xt_min = np.min(img[0:img.shape[0] // 20, 0:img.shape[1]])\n xb_min = np.min(img[img.shape[0] - img.shape[0] // 20:img.shape[0], 0:img.shape[1]])\n yl_max = np.max(img[0:img.shape[0], 0:img.shape[1] // 20])\n yr_max = np.max(img[0:img.shape[0], img.shape[1] - img.shape[1] // 20:img.shape[1]])\n xt_max = np.max(img[0:img.shape[0] // 20, 0:img.shape[1]])\n xb_max = np.max(img[img.shape[0] - img.shape[0] // 20:img.shape[0], 0:img.shape[1]])\n m_min = [yl_min, yr_min, xt_min, xb_min]\n m_max = [yl_max, yr_max, xt_max, xb_max]\n if (yl == 255 and yr == 255 and xt == 255 and xb == 255):\n screen = \"w\"\n d_lebel = 255\n d_min = np.min(m_min)\n d_max = np.max(img)\n elif (yl == 0 and yr == 0 and xt == 0 and xb == 0):\n screen = \"b\"\n d_lebel = 0\n d_min = np.min(m_min)\n d_max = np.max(img)\n else:\n screen = \"c\"\n d_lebel = (yl + yr + xt + xb) // 4\n d_min = np.min(m_min)\n d_max = np.max(img)\n \n return img, screen, d_lebel, d_min, d_max, xb_min\n\n\n #　画像にフレーム状のマスクをかけて主に四隅の影によるノイズを除去する\n def mask(img, screen): \n if screen == 'w': \n return img\n elif screen =='b':\n #　画像の横サイズを変数に格納\n x_shape = img.shape[1] \n #　画像の縦サイズを変数に格納\n y_shape = img.shape[0] \n #　画像横サイズの１５％を変数に格納しマスクの位置調節で使用\n bold_x = img.shape[1] // 15\n #　画像縦サイズの１５％を変数に格納しマスクの位置調節で使用\n bold_y = img.shape[0] // 15\n #　画像横サイズの1/3を変数に格納しマスクの幅調整で使用\n divided_3 = img.shape[1] // 3\n #　画像縦サイズの1/2を変数に格納\n h = img.shape[0] // 2\n #　画像横サイズの1/2を変数に格納\n w = img.shape[1] // 2\n #　画像の横幅の半分を円の半径とする\n r = img.shape[1] // 2 \n #　時間短縮の為のステップ数を設定\n step = 15 \n #　右下のマスク \n for i in range(0, 90, step):\n x = int(math.cos(math.radians(i)) * r)\n y = int(math.sin(math.radians(i)) * r)\n img[y + h + bold_y:, x + w - bold_x:] = 0 \n #　左下のマスク\n for i in range(90, 180, step):\n x = int(math.cos(math.radians(i)) * r)\n y = int(math.sin(math.radians(i)) * r)\n img[y + h + bold_y:, :x + w + bold_x] = 0\n #　左上のマスク\n for i in range(180, 270, step):\n x = int(math.cos(math.radians(i)) * r)\n y = int(math.sin(math.radians(i)) * r)\n img[:y + w + bold_x, :x + w + bold_x] = 0 \n #　右上のマスク\n for i in range(270, 360, step):\n x = int(math.cos(math.radians(i)) * r)\n y = int(math.sin(math.radians(i)) * r)\n img[:y + w + bold_x, x + w - bold_x:] = 0 \n #　フレームの継ぎ目のマスク\n for i in range(0, bold_x, step):\n img[divided_3:img.shape[0] - divided_3, 0:i] = 0\n img[divided_3:img.shape[0], img.shape[1] - bold_x:] = 0\n \n return img\n\n\n elif screen == 'c':\n #　画像の横サイズを変数に格納\n x_shape = img.shape[1] \n #　画像の縦サイズを変数に格納\n y_shape = img.shape[0] \n #　画像横サイズの１５％を変数に格納しマスクの位置調節で使用\n bold_x = img.shape[1] // 15\n #　画像縦サイズの１５％を変数に格納しマスクの位置調節で使用\n bold_y = img.shape[0] // 15\n #　画像横サイズの1/3を変数に格納しマスクの幅調整で使用\n divided_3 = img.shape[1] // 3\n #　画像縦サイズの1/2を変数に格納\n h = img.shape[0] // 2\n #　画像横サイズの1/2を変数に格納\n w = img.shape[1] // 2\n #　画像の横幅の半分を円の半径とする\n r = img.shape[1] // 2 \n #　背景が白かったら黒にする\n img = white_to_black(img)\n #　時間短縮の為のステップ数を設定\n step = 15 \n #　右下のマスク \n for i in range(0, 90, step):\n x = int(math.cos(math.radians(i)) * r)\n y = int(math.sin(math.radians(i)) * r)\n img[y + h + bold_y:, x + w - bold_x:] = 0 \n #　左下のマスク\n for i in range(90, 180, step):\n x = int(math.cos(math.radians(i)) * r)\n y = int(math.sin(math.radians(i)) * r)\n img[y + h + bold_y:, :x + w + bold_x] = 0\n #　左上のマスク\n for i in range(180, 270, step):\n x = int(math.cos(math.radians(i)) * r)\n y = int(math.sin(math.radians(i)) * r)\n img[:y + w + bold_x, :x + w + bold_x] = 0 \n #　右上のマスク\n for i in range(270, 360, step):\n x = int(math.cos(math.radians(i)) * r)\n y = int(math.sin(math.radians(i)) * r)\n img[:y + w + bold_x, x + w - bold_x:] = 0 \n #　フレームの継ぎ目のマスク\n for i in range(0, bold_x, step):\n img[divided_3:img.shape[0] - divided_3, 0:i] = 0\n img[divided_3:img.shape[0], img.shape[1] - bold_x:] = 0\n \n return img\n\n\n #　キャンバスの暗さに合わせて値を引き陰によるノイズを目立たなくする \n def smoothing_darkness(img, d_lebel):\n d = int(abs(d_lebel * 0.76))#　影カット閾値\n \n if img.shape[0] != 28:\n #　白の背景がもし暗かったら明るく修正する、キャンバスの大きさに合わせて判断する範囲を調整する \n if(np.mean(img[0:img.shape[0], 0:img.shape[0] // 12]) > 200 and \n np.mean(img[0:img.shape[0], 0:img.shape[0] // 12]) < 240):\n img = img - d \n elif(np.mean(img[0:img.shape[0], img.shape[1] - img.shape[0] // 12:img.shape[1]]) > 200 and \n np.mean(img[0:img.shape[0], img.shape[1] - img.shape[0] // 12:img.shape[1]]) < 240):\n img = img - d\n elif(np.mean(img[0:img.shape[0]//12,0:img.shape[1]])>200 and \n np.mean(img[0:img.shape[0] // 12, 0:img.shape[1]]) < 240):\n img = img - d\n elif(np.mean(img[img.shape[0] - img.shape[0] // 12:img.shape[0], 0:img.shape[1]]) > 200 and \n np.mean(img[img.shape[0] - img.shape[0] // 12:img.shape[0], 0:img.shape[1]]) < 240):\n img = img - d\n elif(np.mean(img[0:img.shape[0], 0:img.shape[0] // 12]) > 180 and \n np.mean(img[0:img.shape[0], 0:img.shape[0] // 12]) <= 200):\n img = img - d\n elif(np.mean(img[0:img.shape[0], img.shape[1] - img.shape[0] // 12:img.shape[1]]) > 180 and \n np.mean(img[0:img.shape[0], img.shape[1] - img.shape[0] // 12:img.shape[1]]) <= 200):\n img = img - d\n elif(np.mean(img[0:img.shape[0] // 12, 0:img.shape[1]]) > 180 and \n np.mean(img[0:img.shape[0] // 12, 0:img.shape[1]]) <= 200):\n img = img - d\n elif(np.mean(img[img.shape[0] - img.shape[0] // 12:img.shape[0], 0:img.shape[1]]) > 180 and \n np.mean(img[img.shape[0] - img.shape[0] // 12:img.shape[0], 0:img.shape[1]]) <= 200):\n img = img - d\n\n return img \n\n\n # 膨張 \n def dilate_img(img, screen):\n # 背景が白で膨張をすると文字が見えなくなる\n # screenはノイズのない背景が白のデータの場合\"w\",黒の場合\"b\"を返す\n # 画面が暗い場合は\"c\"を返す \n # ８近傍フィルタ\n filt = np.array([[1,1,1],\n [1,0,1],\n [1,1,1]], np.uint8)\n if screen == \"w\": # 背景が白ならば反転して２回膨張処理を行う\n img = cv2.bitwise_not(img) \n img = cv2.dilate(img, filt)\n img = cv2.dilate(img, filt)\n screen = 'b'\n elif screen == 'b':\n img = cv2.dilate(img, filt)\n img = cv2.dilate(img, filt)\n else: # 反転無しで２回膨張処理を行う\n #img = cv2.bitwise_not(img)\n img = cv2.dilate(img, filt)\n img = cv2.dilate(img, filt) \n screen = 'b'\n return img, screen\n\n\n def erode_img(img, screen):\n # ノイズのない背景が白のデータの場合\"w\",黒の場合\"b\"を返す関数で判定 \n # ８近傍フィルタ\n filt = np.array([[1,1,1],\n [1,0,1],\n [1,1,1]], np.uint8)\n if screen == \"w\": # 背景が白ならば反転して２回収縮処理を行う\n img = cv2.bitwise_not(img) \n img = cv2.erode(img, filt) \n screen = 'b'\n elif screen == 'b':\n img = cv2.erode(img, filt)\n else: # 反転無しで２回収縮処理を行う\n img = cv2.erode(img, filt)\n screen = 'b'\n\n return img, screen\n\n\n # 輪郭を抽出する\n def f_contours(img): \n \n #　背景が白い場合画像の枠が輪郭としてカウントされるので処理する\n img = white_to_black(img)\n \n # グレースケール\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n \n # ２値化\n _, th_img = cv2.threshold(gray, 130, 255, THRESH_BINARY)\n \n # 輪郭を抽出する\n contours, _ = cv2.findContours(th_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n \n # 輪郭のx,y,w,hを格納するリストを作成\n \n n = [] # 輪郭の位置\n p = [] #\n t = 0 #\n thresh = 0.021 #0.028～0.021ノイズカット閾値\n\n # 輪郭を格納する\n for i in contours: \n x, y, w, h = cv2.boundingRect(i)\n \n # 縦横比が１０倍以上は削除する（ノイズカット）\n if (w // h > 0.1 and h // w < 10 or w // h < 10 and h // w > 0.1): \n # 画像サイズの2.1％以下の輪郭は削除する（ノイズカット）\n if (w > img.shape[1] * thresh and h > img.shape[0] * thresh): \n n.append([x, y, w, h])\n \n # 抽出した輪郭を左上から順番に並べる\n num_location = sorted(n, key=lambda x:(x[1] // (img.shape[1] // 3), x[0]))\n \n # 外接矩形を描画した画像と座標を返す \n return img, num_location\n\n\n # 切り出した画像を一旦ファイルに保存して認識した結果をリストに保存する\n def create_num_file(img, num_location, screen):\n \n # 切り出し他画像の一時保存ディレクトリ名\n dir_path = './' + NUM_FOLDER + '/' \n \n # 切り出す数字の余白設定\n if img.shape[0] <= 28: # 画像サイズが(28,28)以下は余白無し\n m1, m2, m3, m4 = 0, 0, 0, 0\n elif img.shape[0] <= 50: # 画像サイズが(50,50)以下は余白1\n m1, m2, m3, m4 = 1, 1, 1, 1\n else: # 画像サイズが大きい場合の余白は10\n m1, m2, m3, m4 = 10, 10, 10, 10\n \n # 画像をファイルに保存する\n if img.shape[0] <= 28 or img.shape[1] <= 28: # (28,28)の場合はそのままファイルに保存する \n cv2.imwrite(os.path.join(NUM_FOLDER, \"num0\" + \".png\"), img)\n else: \n # 画像サイズが大きければ数字の数だけ画像を切り出してファイルを保存する \n for i in range(0, len(num_location)):\n x, y, w, h = num_location[i] \n \n # 画像を正方形に切り取る\n n1, n2 = abs((h - w) // 2), abs((h - w) // 2)\n\n # エラーにならない為の処理 \n if y - m1 < 0: # 切り取る幅が画像の上をオーバー\n m1 = 0\n if y + h + m2 > img.shape[0]: # 切り取る幅が画像���下をオーバー \n m2 = 0\n if x - n1 - m3 < 0: # 切り取る幅が画像の左端をオーバー\n m3 = 0 \n n1 = 0\n if x + w + n2 + m4 > img.shape[1]: # 切り取る幅が画像の右端をオーバー \n m4 = 0\n n2 = 0\n \n # 画像を余白を付けて切り出す\n trim_img = img[y - m1:y + h + m2, x - n1 - m3:x + w + n2 + m4]\n\n # 切り出された画像の大きさに応じて線を太くする\n if h > img.shape[0] * 0.9 and w > img.shape[1] * 0.9:\n #　大きい画像は２度関数を使用\n trim_img, screen = dilate_img(trim_img, screen) \n trim_img, screen = dilate_img(trim_img, screen)\n # 切り出した画像をそれぞれファイルに保存する\n cv2.imwrite(os.path.join(NUM_FOLDER, 'num' + str(i) + '.png'), trim_img)\n elif h > img.shape[0] * 0.5 and w > img.shape[1] * 0.5:\n # 元画像の半分程度の画像は１度関数を使用\n trim_img, screen = dilate_img(trim_img, screen)\n # 切り出した画像をそれぞれファイルに保存する\n cv2.imwrite(os.path.join(NUM_FOLDER, 'num' + str(i) + '.png'), trim_img)\n else: \n # 切り出した画像をそれぞれファイルに保存する\n cv2.imwrite(os.path.join(NUM_FOLDER, 'num' + str(i) + '.png'), trim_img)\n return dir_path \n\n\n def from_paint(img, screen):\n # グレースケール化\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n # matplotlibで表示できるように加工するペイントで作ったPNGはこれが必要\n gray1 = cv2.cvtColor(gray, cv2.COLOR_BGR2RGB)\n # ２値化\n print(img.shape)\n _, th_img = cv2.threshold(gray1, 130, 255, THRESH_BINARY)\n # ２度膨張処理を行う\n if (img.shape[0] <= 50 or img.shape[1] <= 50):\n d_img = th_img\n else:\n d_img, screen = dilate_img(th_img, screen) \n # ２度膨張処理を行う\n if (img.shape[0] <= 500 or img.shape[1] <= 500):\n d1_img = d_img\n else:\n d1_img, screen = dilate_img(d_img, screen)\n # ２度膨張処理を行う\n if (img.shape[0] <= 100 or img.shape[1] <= 100):\n d2_img = d1_img\n else:\n d2_img, screen = dilate_img(d1_img, screen)\n # 輪郭を抽出する\n c_img, num_location = f_contours(d2_img) \n # 画像を切り出してファイルに保存する\n dir_path = create_num_file(c_img, num_location, screen) \n #print('＊＊＊＊paint＊＊＊＊')\n\n return dir_path\n\n\n def from_camera(img, screen, d_min, d_max, xb_min): \n # 四隅のノイズを除去する\n m_img = mask(img, screen)\n # グレースケール化\n gray = cv2.cvtColor(m_img, cv2.COLOR_BGR2GRAY)\n # matplotlibで表示できるように加工するペイントで作った画像はこれが必要\n p_img = cv2.cvtColor(gray, cv2.COLOR_BGR2RGB)\n # ２値化\n _, th_img = cv2.threshold(p_img, 110, 255, THRESH_BINARY)#130 薄い画像は２０\n # ２度膨張処理を行う\n if (img.shape[0] <= 160 or img.shape[1] <= 160):\n d_img = th_img\n else:\n d_img, screen = dilate_img(th_img, screen)\n # 四隅のノイズを除去する\n if (img.shape[0] > 1000 and img.shape[1] >1000):\n m1_img = mask(d_img, screen)\n else:\n m1_img = d_img\n # 輪郭を抽出する\n c_img, num_location = f_contours(m1_img)\n # 画像を切り出してファイルに保存する\n dir_path = create_num_file(c_img, num_location, screen)\n #print('＊＊＊＊camera＊＊＊＊')\n\n return dir_path\n\n\n def from_scaner(img, screen):\n # 四隅のノイズを除去する\n m_img = mask(img, screen)\n # グレースケール化\n gray = cv2.cvtColor(m_img, cv2.COLOR_BGR2GRAY)\n # matplotlibで表示できるように加工するペイントで作ったPNGはこれが必要\n p_img = cv2.cvtColor(gray, cv2.COLOR_BGR2RGB)\n # ２値化\n _, th_img = cv2.threshold(p_img, 130, 255, THRESH_BINARY)\n # ２度膨張処理を行う\n if (img.shape[0] <= 50 or img.shape[1] <= 50):\n d_img = th_img\n else:\n d_img, screen = dilate_img(th_img, screen)\n # 収縮処理を行う\n if (img.shape[0] <= 100 or img.shape[1] <= 100):\n e_img = d_img\n else:\n e_img, screen = erode_img(d_img, screen)\n # 四隅のノイズを除去する\n m1_img = mask(e_img, screen)\n # ２度膨張処理を行う\n if (img.shape[0] <= 100 or img.shape[1] <= 100):\n d1_img = m1_img\n else:\n d1_img, screen = dilate_img(m1_img, screen)\n # 輪郭を抽出する\n c_img, num_location = f_contours(d1_img) \n # 画像を切り出してファイルに保存する\n dir_path = create_num_file(c_img, num_location, screen) \n #print('＊＊＊＊scaner＊＊＊＊')\n\n return dir_path\n\n\n #　ここから画像を受け取り切り出して予測する\n\n # 画像を読み込む\n img = cv2.imread('./' + filepath)\n\n #　画像をNumPy配列に変換\n img = np.array(img)\n\n # 処理速度向上のために画像サイズを小さくする\n _ ,img_size = size_smaller(img)\n\n # 主にカメラやスキャナー用\n img, screen, d_lebel, d_min, d_max, xb_min = noise_detect(img)\n\n # 暗い部分のある画像をスムーズにする\n lite_img = smoothing_darkness(img, d_lebel)\n\n #　ペイント画像、カメラ画像、スキャナで処理を分ける \n if img_size != 28 and d_lebel == 0 or d_lebel == 255:\n # ペイントからの画像を処理\n dir_path = from_paint(lite_img, screen)\n elif (img_size != 28 and d_lebel > 160 and d_lebel <190 or d_lebel >3 and d_lebel < 30):\n # カメラから画像を処理\n dir_path = from_camera(lite_img, screen, d_min, d_max, xb_min)\n elif (img_size != 28 and d_lebel >= 190 and d_lebel <255):\n # スキャナーからの画像を処理 \n dir_path = from_scaner(lite_img, screen) \n elif img_size == 28: #(28,28)サイズの画像は複数文字認識無し\n # 輪郭を抽出する\n _, num_location = f_contours(lite_img) \n # 画像を切り出してファイルに保存する\n dir_path = create_num_file(lite_img, num_location, screen)\n\n\n p = [] # 認識結果を格納するリスト\n q = 0 # 切り出して書き込んだファイル数をカウントする\n\n # 指定のディレクトリ内のファイルを読み込み数字を認識する\n for i in os.listdir(dir_path)[1:]:\n # 切り取った画像を読み込みむ\n img = cv2.imread(dir_path + i)\n # グレースケール化\n img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n #(28,28)にリサイズ \n img = cv2.resize(img,(image_size,image_size))\n img = image.img_to_array(img)\n data = np.array([img]) \n \n # 変換したデータをモデルに渡して予測する\n result = model.predict(data)[0] \n \n # リストに認識結果を格納する\n if np.max(result) < 0.5: # 認識結果が0.5より小さい場合は(*)を表示する\n p.append('(*)')\n elif np.max(result) >= 0.5: # 認識結果が0.5以上の場合は認識結果を格納する\n predicted = result.argmax()\n p.append(classes[predicted]) \n q = q + 1 \n pred_answer = \"これは \" + ''.join(p) + \" です\"\n print(str(pred_answer))\n p=[] # リストをリセットする \n\n # dir_pathフォルダ内の切り出した数字ファイルを削除します\n for i in range(q): #書き込んだファイル分だけ消去する\n # dir_path内の一番初めのファイルを削除 \n os.remove(dir_path + os.listdir(dir_path)[1]) # gitkeepファイルを入れたのでインデックスは１\n # アップロードフォルダ内のファイルを削除する\n os.remove('./' + UPLOAD_FOLDER + '/' + os.listdir(UPLOAD_FOLDER)[1])\n return render_template(\"index.html\",answer=pred_answer)\n\n return render_template(\"index.html\",answer=\"\")\n\n\nif __name__ == \"__main__\":\n port = int(os.environ.get('PORT', 8080))\n app.run(host ='0.0.0.0',port = port)\n\n\n\n\n\n\n \n \n \n","repo_name":"tsudatch/flask-mnist-a4","sub_path":"mnist.py","file_name":"mnist.py","file_ext":"py","file_size_in_byte":26705,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"546598496","text":"# Write a function that takes an unsigned integer and returns the number of '1' bits it has (also known as the Hamming weight).\n\n\n# Take the int n and find the remainder when divided by 2. If remainder is 1, then 1 exists in the last digit\n# Bit shift n to the right by 1 and repeat operation until n is empty\n\nclass Solution:\n def hammingWeight(self, n: int) -> int:\n res = 0\n \n while n: \n res += n % 2\n n = n >> 1\n \n return res","repo_name":"tieonster/leetcode","sub_path":"Bit Manipulation/numberOf1Bits.py","file_name":"numberOf1Bits.py","file_ext":"py","file_size_in_byte":493,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"69897277597","text":"\"\"\"Support for HWKettle.\"\"\"\nimport logging\nfrom .const import *\nfrom homeassistant.core import HomeAssistant\nfrom homeassistant.config_entries import ConfigEntry\nfrom homeassistant.const import *\nimport homeassistant.helpers.event as ev\nfrom homeassistant.helpers.dispatcher import async_dispatcher_send\nfrom homeassistant.helpers.entity import DeviceInfo\nfrom datetime import timedelta\nfrom .kettle_connection import KettleConnection\nimport json\n\n_LOGGER = logging.getLogger(__name__)\n\nPLATFORMS = [\n Platform.WATER_HEATER,\n Platform.SWITCH,\n Platform.NUMBER,\n Platform.SENSOR,\n]\n\n\nasync def async_setup_entry(hass: HomeAssistant, entry: ConfigEntry):\n \"\"\"Set up HWKettle integration from a config entry.\"\"\"\n if DOMAIN not in hass.data: hass.data[DOMAIN] = {}\n if entry.entry_id not in hass.data: hass.data[DOMAIN][entry.entry_id] = {}\n\n email = entry.data[CONF_EMAIL]\n password = entry.data[CONF_PASSWORD]\n kettle_id = entry.data[CONF_KETTLE_ID]\n\n kettle = KettleConnection(\n hass=hass,\n username=email,\n password=password,\n kettle_id=entry.data[CONF_KETTLE_ID],\n )\n await kettle.start_listening()\n\n hass.data[DOMAIN][entry.entry_id][DATA_CONNECTION] = kettle\n hass.data[DOMAIN][DATA_DEVICE_INFO] = lambda: device_info(entry)\n\n for component in PLATFORMS:\n hass.async_create_task(\n hass.config_entries.async_forward_entry_setup(entry, component)\n )\n\n return True\n\n\ndef device_info(entry):\n return DeviceInfo(\n name=(FRIENDLY_NAME + \" \" + entry.data.get(CONF_FRIENDLY_NAME, \"\")).strip(),\n manufacturer=MANUFACTORER,\n model=entry.data.get(CONF_KETTLE_ID, None),\n sw_version=\"?\",\n identifiers={\n (DOMAIN, entry.data[CONF_KETTLE_ID])\n },\n connections={\n (\"kettle_id\", entry.data[CONF_KETTLE_ID])\n }\n )\n\n","repo_name":"lesleyxyz/hass-homewizard-kettle","sub_path":"custom_components/homewizard_kitchen_kettle/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1889,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"16095067502","text":"import cgi\nimport re\nimport sys\nimport os\n\nKEYWORDS = {\n 'class',\n 'constructor',\n 'function',\n 'method',\n 'field',\n 'static',\n 'var',\n 'int',\n 'char',\n 'boolean',\n 'void',\n 'true',\n 'false',\n 'null',\n 'this',\n 'let',\n 'do',\n 'if',\n 'else',\n 'while',\n 'return',\n}\n\nSYMBOLS = {\n '{', '}', '(', ')', '[', ']', '.',\n ',', ';', '+', '-', '*', '/', '&', '|',\n '<', '>', '=', '~'\n}\n\nKEYWORD = 'keyword'\nSYMBOL = 'symbol'\nIDENTIFIER = 'identifier'\nINT_CONST = 'integerConstant'\nSTRING_CONST = 'stringConstant'\n\nTOKEN_TYPES = (\n KEYWORD,\n SYMBOL,\n IDENTIFIER,\n INT_CONST,\n STRING_CONST\n)\n\n\nREGEX_MAPPING = {\n KEYWORD: '(' + r'|'.join(KEYWORDS) + ')\\\\W',\n SYMBOL: r'([&)\\]+\\*\\-,\\/<.}([{;~=|>])',\n INT_CONST: r'(\\d+)',\n STRING_CONST: r'(\".*?\")',\n IDENTIFIER: r'([a-zA-Z_]+[a-zA-Z_\\d]*)'\n}\n\n\ndef remove_comments(line):\n start_from = 0\n while start_from != -1:\n comment = line.find('//', start_from)\n if comment == -1:\n return line\n # terminate if there is an even\n # number of quotes before comment\n if line[:comment].count('\"') % 2 == 0:\n start_from = -1\n else:\n start_from = comment + 1\n\n return line[:comment].rstrip()\n\n\nclass JackTokenizer(object):\n cleaned_input = None\n\n def __init__(self, f):\n # read whole input into a stream\n input_stream = ''.join(\n remove_comments(line)\n for line in f\n ).replace('\\n', '').replace('\\r', '')\n # get rid of the block comments\n regex = r'(\\/\\*.*?\\*\\/)'\n input_stream += '/**/'\n spans = [m.span() for m in re.finditer(regex, input_stream)]\n cleaned_input = ''\n last_hi = 0\n for lo, hi in spans:\n cleaned_input += input_stream[last_hi:lo]\n last_hi = hi\n\n self.cleaned_input = cleaned_input.lstrip()\n\n def match_and_extract(self, token_type, advance=True):\n regex = REGEX_MAPPING[token_type]\n match = re.match(\n regex,\n self.cleaned_input\n )\n if match:\n word = match.groups()[0]\n token = cgi.escape(word).strip('\"')\n if advance:\n self.cleaned_input = self.cleaned_input.replace(\n word, '', 1\n ).lstrip()\n return (token, token_type)\n\n def _next(self, advance=True):\n for token_type in TOKEN_TYPES:\n match = self.match_and_extract(token_type, advance)\n if match:\n return match\n raise RuntimeError\n\n def next(self):\n if not self.cleaned_input:\n raise StopIteration\n\n return self._next()\n\n def peek_next(self):\n return self._next(advance=False)\n\n\nclass ParseTree(object):\n \"\"\"\n Class to store write-only Parse tree as an XML-formatted string\n \"\"\"\n TAB_SIZE = 2\n representation = None\n level = 0\n\n def __init__(self):\n self.representation = ''\n self.level = 0\n\n def append_tag_with_text(self, tag, text):\n self.representation += self.indentation\n self.representation += '<%s> %s \\n' % (tag, text, tag)\n\n def open_tag(self, tag):\n self.representation += self.indentation\n self.representation += '<%s>\\n' % tag\n self.level += 1\n\n def close_tag(self, tag):\n self.level -= 1\n self.representation += self.indentation\n self.representation += '\\n' % tag\n\n @property\n def indentation(self):\n assert self.level >= 0\n return (self.level * self.TAB_SIZE) * ' '\n\n def __str__(self):\n return self.representation\n\n def __repr__(self):\n return self.representation\n \n\nclass CompilationEngine(object):\n tokenizer = None\n\n token = None\n token_type = None\n parse_tree = None\n\n def __init__(self, f):\n self.tokenizer = JackTokenizer(f)\n self.token, self.token_type = next(self.tokenizer)\n self.parse_tree = ParseTree()\n # assign tree methods to current class\n # to avoid self.parse_tree boilerplate\n self.append_tag_with_text = self.parse_tree.append_tag_with_text\n self.open_tag = self.parse_tree.open_tag\n self.close_tag = self.parse_tree.close_tag\n self.compile_class()\n\n def eat_and_append_token(self, token_type, token):\n self.append_tag_with_text(\n token_type,\n token\n )\n self.eat(token_type, token)\n\n def compile_class(self):\n self.open_tag('class')\n self.eat_and_append_token(\n KEYWORD,\n 'class'\n )\n\n self.eat_and_append_token(\n IDENTIFIER,\n self.token\n )\n\n self.eat_and_append_token(\n SYMBOL,\n '{'\n )\n while self.token in ('static', 'field'):\n self.compile_class_var_dec()\n\n while self.token in ('function', 'method', 'constructor'):\n self.compile_subroutine()\n\n self.eat_and_append_token(SYMBOL, '}')\n self.close_tag('class')\n\n def compile_class_var_dec(self):\n self.open_tag('classVarDec')\n self.eat_and_append_token(\n self.token_type,\n self.token\n )\n self.eat_and_append_token(\n self.token_type,\n self.token\n )\n # identifier\n self.eat_and_append_token(\n IDENTIFIER,\n self.token\n )\n\n while self.token == ',':\n self.eat_and_append_token(\n SYMBOL,\n ','\n )\n\n self.eat_and_append_token(\n IDENTIFIER,\n self.token\n )\n\n self.eat_and_append_token(\n SYMBOL,\n ';'\n )\n self.close_tag('classVarDec')\n\n def compile_subroutine(self):\n self.open_tag('subroutineDec')\n self.eat_and_append_token(\n self.token_type,\n self.token\n )\n # return type\n self.eat_and_append_token(\n self.token_type,\n self.token\n )\n\n self.eat_and_append_token(\n IDENTIFIER,\n self.token\n )\n\n self.eat_and_append_token(\n SYMBOL,\n '('\n )\n self.compile_parameter_list()\n self.eat_and_append_token(\n SYMBOL,\n ')'\n )\n\n self.open_tag('subroutineBody')\n\n self.eat_and_append_token(\n SYMBOL,\n '{'\n )\n\n while self.token == 'var':\n self.compile_var_dec()\n\n self.compile_statements()\n\n self.eat_and_append_token(\n SYMBOL,\n '}'\n )\n self.close_tag('subroutineBody')\n self.close_tag('subroutineDec')\n\n def compile_parameter_list(self):\n self.open_tag('parameterList')\n while self.token_type in (IDENTIFIER, KEYWORD):\n self.eat_and_append_token(\n self.token_type, self.token\n )\n self.eat_and_append_token(IDENTIFIER, self.token)\n if self.token == ',':\n self.eat_and_append_token(SYMBOL, ',')\n\n self.close_tag('parameterList')\n\n def compile_var_dec(self):\n self.open_tag('varDec')\n self.eat_and_append_token(\n KEYWORD,\n 'var'\n )\n\n self.eat_and_append_token(\n self.token_type,\n self.token\n )\n\n self.eat_and_append_token(\n IDENTIFIER,\n self.token\n )\n while self.token == ',':\n self.eat_and_append_token(\n SYMBOL,\n ','\n )\n\n self.eat_and_append_token(\n IDENTIFIER,\n self.token\n )\n\n self.eat_and_append_token(\n SYMBOL, ';'\n )\n self.close_tag('varDec')\n\n def compile_statements(self):\n self.open_tag('statements')\n statement_map = {\n 'let': self.compile_let,\n 'if': self.compile_if,\n 'while': self.compile_while,\n 'do': self.compile_do,\n 'return': self.compile_return\n }\n while self.token in statement_map:\n statement_map[self.token]()\n\n self.close_tag('statements')\n\n def compile_subroutine_call(self):\n self.eat_and_append_token(\n IDENTIFIER,\n self.token\n )\n # . or (\n self.eat_and_append_token(\n SYMBOL,\n self.token\n )\n if self.token_type == IDENTIFIER:\n self.eat_and_append_token(\n IDENTIFIER,\n self.token\n )\n self.eat_and_append_token(\n SYMBOL, '('\n )\n self.compile_expression_list()\n self.eat_and_append_token(\n SYMBOL, ')'\n )\n\n def compile_do(self):\n self.open_tag('doStatement')\n\n self.eat_and_append_token(\n KEYWORD, 'do'\n )\n self.compile_subroutine_call()\n self.eat_and_append_token(SYMBOL, ';')\n self.close_tag('doStatement')\n\n def compile_let(self):\n self.open_tag('letStatement')\n\n self.eat_and_append_token(\n KEYWORD,\n 'let'\n )\n\n self.eat_and_append_token(\n IDENTIFIER,\n self.token\n )\n\n if self.token == '[':\n self.eat_and_append_token(\n SYMBOL, '['\n )\n self.compile_expression()\n self.eat_and_append_token(\n SYMBOL, ']'\n )\n\n self.eat_and_append_token(\n SYMBOL,\n '='\n )\n self.compile_expression()\n\n self.eat_and_append_token(\n SYMBOL,\n ';'\n )\n\n self.close_tag('letStatement')\n\n def compile_while(self):\n self.open_tag('whileStatement')\n self.eat_and_append_token(KEYWORD, 'while')\n self.eat_and_append_token(SYMBOL, '(')\n self.compile_expression()\n self.eat_and_append_token(SYMBOL, ')')\n self.eat_and_append_token(SYMBOL, '{')\n self.compile_statements()\n self.eat_and_append_token(SYMBOL, '}')\n self.close_tag('whileStatement')\n\n def compile_return(self):\n self.open_tag('returnStatement')\n self.eat_and_append_token(KEYWORD, 'return')\n if self.token != ';':\n self.compile_expression()\n self.eat_and_append_token(SYMBOL, ';')\n self.close_tag('returnStatement')\n\n def compile_if(self):\n self.open_tag('ifStatement')\n self.eat_and_append_token(KEYWORD, 'if')\n self.eat_and_append_token(SYMBOL, '(')\n self.compile_expression()\n self.eat_and_append_token(SYMBOL, ')')\n\n self.eat_and_append_token(SYMBOL, '{')\n self.compile_statements()\n self.eat_and_append_token(SYMBOL, '}')\n\n if self.token == 'else':\n self.eat_and_append_token(KEYWORD, 'else')\n self.eat_and_append_token(SYMBOL, '{')\n self.compile_statements()\n self.eat_and_append_token(SYMBOL, '}')\n\n self.close_tag('ifStatement')\n\n def compile_expression(self):\n self.open_tag('expression')\n\n self.compile_term()\n operators = map(cgi.escape, (\n '+',\n '-',\n '*',\n '/',\n '&',\n '|',\n '<',\n '>',\n '='\n ))\n while self.token in operators:\n self.eat_and_append_token(\n SYMBOL,\n self.token\n )\n self.compile_term()\n\n self.close_tag('expression')\n\n def compile_term(self):\n self.open_tag('term')\n\n ll_one_types = (\n INT_CONST,\n STRING_CONST\n )\n ll_one_constants = (\n 'true',\n 'false',\n 'null',\n 'this'\n )\n unary_operators = ('-', '~',)\n\n if self.token_type in ll_one_types or self.token in ll_one_constants:\n self.eat_and_append_token(\n self.token_type,\n self.token\n )\n elif self.token in unary_operators:\n self.eat_and_append_token(\n SYMBOL, self.token\n )\n self.compile_term()\n elif self.token_type == IDENTIFIER: # LL(2)\n token, _ = self.tokenizer.peek_next()\n if token == '[':\n self.eat_and_append_token(\n IDENTIFIER, self.token\n )\n self.eat_and_append_token(\n SYMBOL, '['\n )\n self.compile_expression()\n self.eat_and_append_token(\n SYMBOL, ']'\n )\n elif token in ('.', '('):\n self.compile_subroutine_call()\n else:\n self.eat_and_append_token(\n IDENTIFIER, self.token\n )\n elif self.token == '(':\n self.eat_and_append_token(\n SYMBOL, '('\n )\n self.compile_expression()\n self.eat_and_append_token(\n SYMBOL, ')'\n )\n else:\n raise NotImplementedError\n self.close_tag('term')\n\n def compile_expression_list(self):\n self.open_tag('expressionList')\n while self.token != ')':\n self.compile_expression()\n if self.token == ',':\n self.eat_and_append_token(\n SYMBOL,\n ','\n )\n self.close_tag('expressionList')\n\n def eat(self, token_type, token):\n if self.token != token:\n raise RuntimeError(\n 'Unexpected token '\n 'self.token: %s, argument token: %s' % (self.token, token)\n )\n if self.token_type != token_type:\n raise RuntimeError(\n 'Unexpected token_type '\n 'self.token_type: %s, argument token_type: %s' % (\n self.token_type, token_type\n )\n )\n try:\n self.token, self.token_type = next(self.tokenizer)\n except StopIteration:\n self.token = self.token_type = None\n\n\nfile_list = []\npath = sys.argv[1]\nis_dir = os.path.isdir(path)\nif not is_dir:\n file_list.append(path)\nelse:\n if not path.endswith('/'):\n path = path + '/'\n for filename in os.listdir(path):\n if filename.endswith('.jack'):\n path_to_file = os.path.join(path, filename)\n file_list.append(path_to_file)\n\n\nfor filename in file_list:\n with open(filename.replace('.jack', '.xml'), 'w') as f:\n current_file = open(filename)\n compiled = CompilationEngine(current_file)\n f.write(str(compiled.parse_tree))\n current_file.close()\n","repo_name":"isaevpd/nand2tetris","sub_path":"project_10/JackAnalyzer.py","file_name":"JackAnalyzer.py","file_ext":"py","file_size_in_byte":14997,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"2812350452","text":"from flask import Flask, jsonify, request\nfrom uuid import uuid4\n\n# Create a Flask web application\napp = Flask(__name__)\n\n# Create a unique node ID\nnode_id = str(uuid4()).replace('-', '')\nfrom blockchain import Blockchain\n# Import the Blockchain class from the blockchain module\n\n# Create a Blockchain instance\nblockchain = Blockchain()\n\n# Define a route for mining a new block\n@app.route('/mine', methods=['GET'])\ndef mine():\n last_block = blockchain.last_block()\n last_proof = last_block['proof']\n proof = blockchain.proof_of_work(last_proof)\n\n# Create a new transaction for rewarding the miner (sender='0' represents the system)\n blockchain.new_transaction(\n sender='0',\n receiver=node_id,\n amount=1,\n )\n\n # Calculate the previous hash for the new block\n previous_hash = blockchain.hash(last_block)\n # Create a new block and add it to the blockchain\n block = blockchain.new_block(proof, previous_hash)\n\n response = {\n 'message': \"New Block formed\",\n 'index': block['index'],\n 'transactions': block['transactions'],\n 'proof': block['proof'],\n 'previous_hash': block['previous_hash'],\n }\n\n # Return the response as JSON with an HTTP status code 200 (OK)\n return jsonify(response), 200\n\n# Define a route for creating a new transaction\n@app.route('/transactions/new', methods=['POST'])\ndef new_transaction():\n # Get the transaction data from the request\n values = request.get_json()\n\n # Check if the required fields (sender, receiver, amount) are present\n required = ['sender', 'receiver', 'amount']\n if not all(k in values for k in required):\n return 'Missing values', 400\n\n index = blockchain.new_transaction(values['sender'], values['receiver'], values['amount'])\n response = {'message': f'Transaction will be added to Block {index}'}\n return jsonify(response), 201\n\n@app.route('/chain', methods=['GET'])\ndef full_chain():\n # Get the current state of the blockchain and its length\n response = {\n 'chain': blockchain.chain,\n 'length': len(blockchain.chain),\n }\n return jsonify(response), 200\n\n\n# Start the Flask app on host '0.0.0.0' (accessible from any IP) and port 5000\nif __name__ == \"__main__\":\n app.run(host='0.0.0.0', port=5000)\n\n","repo_name":"codeabuu/Simple_Blockchain","sub_path":"BC_Api.py","file_name":"BC_Api.py","file_ext":"py","file_size_in_byte":2292,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"70394468635","text":"from typing import Sequence, Type, Union\nfrom unittest.mock import MagicMock\n\nimport pytest\n\nimport omnicite.exceptions as omnicite_exceptions\nfrom omnicite.sources.base_source import BaseSource\n\n\n@pytest.mark.parametrize(\n (\"test_required_fields\", \"test_key_fields\"),\n (\n (\n (\n \"test\",\n (\n \"test1\",\n \"test2\",\n ),\n ),\n (\"test1\",),\n ),\n ),\n)\ndef test_check_field_existence_and_exclusivity(\n test_required_fields: Sequence[Union[str, Sequence[str]]],\n test_key_fields: Sequence[str],\n):\n BaseSource.check_field_existence_and_exclusivity(test_required_fields, test_key_fields)\n\n\n@pytest.mark.parametrize(\n (\"test_required_fields\", \"test_key_fields\", \"exception_string\"),\n (\n (\n (\n \"test\",\n (\n \"test1\",\n \"test2\",\n ),\n ),\n (),\n \"No field found\",\n ),\n (\n (\n \"test\",\n (\n \"test1\",\n \"test2\",\n ),\n ),\n (\"test\",),\n \"No field found\",\n ),\n (\n (\n \"test\",\n (\n \"test1\",\n \"test2\",\n ),\n ),\n (\n \"test\",\n \"test1\",\n \"test2\",\n ),\n \"Multiple exclusive fields\",\n ),\n ),\n)\ndef test_check_field_existence_and_exclusivity(\n test_required_fields: Sequence[Union[str, Sequence[str]]],\n test_key_fields: Sequence[str],\n exception_string: str,\n):\n with pytest.raises(omnicite_exceptions.OmniCiteSourceFieldError) as excinfo:\n BaseSource.check_field_existence_and_exclusivity(test_required_fields, test_key_fields)\n assert exception_string in str(excinfo.value)\n\n\n@pytest.mark.parametrize(\n (\"test_required_fields\", \"test_key_fields\", \"expected\"),\n (\n ((), (), ()),\n (\n (\"test1\",),\n (\"test1\",),\n (\"test1\",),\n ),\n (\n (\n \"test1\",\n (\"test2\", \"test3\"),\n ),\n (\n \"test1\",\n \"test3\",\n ),\n (\"test1\", \"test3\"),\n ),\n (\n (\n \"test1\",\n (\"test2\", \"test3\"),\n ),\n (\n \"test3\",\n \"test1\",\n ),\n (\"test1\", \"test3\"),\n ),\n ),\n)\ndef test_get_all_filled_required_fields(\n test_required_fields: Sequence[Union[str, Sequence[str]]],\n test_key_fields: Sequence[str],\n expected: Sequence[str],\n):\n result = BaseSource.get_all_filled_required_fields(test_required_fields, test_key_fields)\n assert len(result) == len(expected)\n assert all([r == expected[i] for i, r in enumerate(result)])\n\n\n@pytest.mark.parametrize(\n (\n \"test_optional_fields\",\n \"test_key_fields\",\n \"expected\",\n ),\n (\n ((), (), ()),\n (\n (\n \"option1\",\n \"option2\",\n ),\n (\n \"option1\",\n \"option2\",\n ),\n (\n \"option1\",\n \"option2\",\n ),\n ),\n (\n (\n \"option1\",\n \"option2\",\n ),\n (\n \"option2\",\n \"option1\",\n ),\n (\n \"option1\",\n \"option2\",\n ),\n ),\n (\n (\n \"option1\",\n \"option2\",\n (\"subopt1\", \"subopt2\"),\n ),\n (\n \"option2\",\n \"subopt2\",\n \"option1\",\n ),\n (\"option1\", \"option2\", \"subopt2\"),\n ),\n ),\n)\ndef test_get_all_filled_optional_fields(\n test_optional_fields: Sequence[Union[str, Sequence[str]]],\n test_key_fields: Sequence[str],\n expected: Sequence[str],\n):\n result = BaseSource.get_all_filled_optional_fields(test_optional_fields, test_key_fields)\n assert len(result) == len(expected)\n assert all([r == expected[i] for i, r in enumerate(result)])\n","repo_name":"Serene-Arc/omnicite","sub_path":"tests/sources/test_base_source.py","file_name":"test_base_source.py","file_ext":"py","file_size_in_byte":4391,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"426339398","text":"from __future__ import absolute_import, unicode_literals\n\nfrom celery import shared_task, states\nfrom celery.exceptions import Ignore\nfrom celery_progress.backend import ProgressRecorder\n\nimport urllib.request\nimport json\nimport datetime\nimport io\nimport chess.pgn\n\n\nMAX_DEPTH = 4\n\n\ndef get_archive_links(username):\n link = f\"https://api.chess.com/pub/player/{username}/games/archives\"\n\n try:\n with urllib.request.urlopen(link) as url:\n data = json.loads(url.read().decode())\n return data[\"archives\"]\n except urllib.error.HTTPError as exception:\n if exception.code == 404:\n raise Exception(\"This username was not found on chess.com\")\n else:\n raise Exception(\"Something went wrong, plese try later\")\n except Exception as exception:\n raise Exception(\"Something went wrong, plese try later\")\n\n\ndef download_games(archive_links):\n try:\n for archive_link in archive_links:\n year, month = archive_link.split('/')[-2:]\n period = f\"{datetime.datetime.strptime(month, '%m').strftime('%b')} {year}\"\n with urllib.request.urlopen(archive_link) as url:\n data = json.loads(url.read().decode())\n yield period, data['games']\n except Exception as exception:\n raise Exception(\"Something went wrong, plese try later\")\n\n\ndef update_game_tree(tree, game, result):\n current_node = tree\n \n for i, move in enumerate(game.mainline_moves()):\n if i > MAX_DEPTH: break\n move = str(move)\n if move in current_node['next_moves']:\n current_node['next_moves'][move]['w'] += 1 if result == +1 else 0\n current_node['next_moves'][move]['l'] += 1 if result == -1 else 0\n current_node['next_moves'][move]['d'] += 1 if result == 0 else 0\n else:\n current_node['next_moves'][move] = {\n 'w': 1 if result == +1 else 0,\n 'l': 1 if result == -1 else 0,\n 'd': 1 if result == 0 else 0,\n 'next_moves': {}\n }\n current_node = current_node['next_moves'][move]\n\n\n@shared_task(bind=True)\ndef generate_game_trees(self, username):\n progress_recorder = ProgressRecorder(self)\n\n tree_white = {'next_moves': {}}\n tree_black = {'next_moves': {}}\n result = {\n 'white': tree_white,\n 'black': tree_black,\n 'error': None\n }\n\n try:\n archive_links = get_archive_links(username)\n for i, (period, games_data) in enumerate(download_games(archive_links)):\n for game_data in games_data:\n game = chess.pgn.read_game(io.StringIO(game_data['pgn']))\n if game_data['white']['username'].lower() == username:\n if game_data['white']['result'] == 'win':\n # I won as white\n update_game_tree(tree_white, game, +1)\n elif game_data['black']['result'] == 'win':\n # I lost as white\n update_game_tree(tree_white, game, -1)\n else:\n # I drew as white\n update_game_tree(tree_white, game, 0)\n else:\n if game_data['black']['result'] == 'win':\n # I won as black\n update_game_tree(tree_black, game, +1)\n elif game_data['white']['result'] == 'win':\n # I lost as black\n update_game_tree(tree_black, game, -1)\n else:\n # I drew as black\n update_game_tree(tree_black, game, 0)\n progress_recorder.set_progress(i + 1, len(archive_links), description=f\"Downloading games from {period}\")\n return result\n except Exception as exception:\n result['error'] = str(exception)\n return result","repo_name":"220usamaahmed/ChesscomGameExplorerBackend","sub_path":"api/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":3937,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"37444871234","text":"from googleplaces import GooglePlaces, types, lang\r\nimport requests\r\nimport json\r\nlat= input(\"input a latitute: \")\r\nlng= input(\"input a longitude: \")\r\nAPI_KEY = 'AIzaSyA13MoccnqSpa-p4ZWvfU96Za9VI8dFp2U'\r\ngoogle_places = GooglePlaces(API_KEY)\r\nf = open(\"hospitals.json\", 'w')\r\nquery_result = google_places.nearby_search(lat_lng ={'lat': lat, 'lng': lng}, radius = 5000, types =[types.TYPE_HOSPITAL])\r\nif query_result.has_attributions:\r\n print (query_result.html_attributions)\r\nf.write('hospitals = \\'[')\r\nfor place in query_result.places:\r\n f.write('{\"name\": \"' + place.name.replace(\"'\", \"\") + '\", \"Latitude\": ' +\n str(place.geo_location['lat']) + ', \"Longitude\": ' + str(place.geo_location['lng']) + '}, ')\nf.write('{}]\\';')\r\nf.close()\r\n","repo_name":"matthewyoungbar/Disaster-Guide","sub_path":"get_hospitals.py","file_name":"get_hospitals.py","file_ext":"py","file_size_in_byte":754,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"12362434256","text":"#!/usr/bin/env python\r\n# -*- coding: utf-8-*- \r\n#pyinstaller specification file for windows one exe packing\r\n#use it like :\r\n# python c:\\pyinstaller\\PyInstaller.py main.spec\r\n#and put upx in path for compression\r\n\r\nPYINSTALLER_DIR = 'C:\\\\pyinstaller'\r\n#****************************************PANDA3D******************************************************\r\nPANDA_DIR = 'C:\\\\Panda3D-1.8.1'\r\nimport sys, os, glob\r\n# some C/C++ runtime libraries, machine without these .dlls will crash Panda3D\r\nruntimeLibraries=glob.glob(os.path.join(PANDA_DIR, 'bin/*.manifest'))+\\\r\n glob.glob(os.path.join(PANDA_DIR, 'bin/vcredist*.exe'))\r\nP3DruntimeLibraries=[]\r\nfor r in runtimeLibraries:\r\n P3DruntimeLibraries.append( (r[r.rfind('\\\\')+1:],r,'DATA') )\r\n\r\nP3DmissingDLLs = []\r\nfor d in os.listdir(os.path.join(PANDA_DIR, 'bin/')):\r\n if d[-4:] != '.dll': continue\r\n P3DmissingDLLs.append( (d,os.path.join(PANDA_DIR,'bin/%s' %d),'BINARY') )\r\n\r\n#*******************************************************************************************************\r\n\r\n\r\na = Analysis(['main.py'],\r\n pathex=[PYINSTALLER_DIR])\r\npyz = PYZ(a.pure)\r\nexe = EXE(pyz,\r\n Tree(os.path.join(PANDA_DIR, 'etc'), prefix='etc'),\r\n Tree(os.path.join('models'), prefix='models'),\r\n Tree(os.path.join('fonts'), prefix='fonts'),\r\n Tree(os.path.join('images'), prefix='images'),\r\n a.scripts,\r\n a.binaries+P3DmissingDLLs,\r\n name='main.exe',\r\n debug=False,\r\n strip=None,\r\n upx=True,\r\n console=True,\r\n icon='icon.ico' )\r\n","repo_name":"webplate/astrini","sub_path":"main.spec","file_name":"main.spec","file_ext":"spec","file_size_in_byte":1606,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"2340546086","text":"from shapely.geometry import Polygon\nimport osmnx as ox\nimport matplotlib.pyplot as plt\n\nfrom estaty.engine.vector.clip import clip_dataframe_by_polygon\n\n\nclass AdministrativeBoundaries:\n \"\"\" Load polygon of administrative boundaries for desired city\n\n :param city_name: name of the city for analysis\n \"\"\"\n\n def __init__(self, city_name: str):\n self.use_only_buildings = False\n if 'building' in city_name:\n city_name = city_name.split('_building')[0]\n self.use_only_buildings = True\n\n self.city_name = city_name\n\n def get_city_polygon(self) -> Polygon:\n \"\"\" Query data from OSM and return geometry of obtained polygon \"\"\"\n gdf = ox.geocode_to_gdf({'city': self.city_name})\n city_polygon = gdf.geometry.iloc[0]\n if self.use_only_buildings is False:\n return city_polygon\n\n # Add only buildings\n buildings_tags = {'building': ['apartments', 'barracks', 'bungalow',\n 'cabin', 'detached', 'dormitory', 'farm',\n 'ger', 'hotel', 'house', 'houseboat',\n 'residential', 'semidetached_house',\n 'static_caravan', 'stilt_house',\n 'terrace', 'tree_house', 'commercial',\n 'industrial', 'kiosk', 'office', 'retail',\n 'supermarket', 'warehouse']}\n bbox_info = ox.geometries_from_polygon(polygon=city_polygon,\n tags=buildings_tags)\n bbox_info = clip_dataframe_by_polygon(bbox_info, city_polygon)\n\n # Perform polygons merging into single geometry\n bbox_info['new_column'] = 0\n bbox_info = bbox_info.dissolve(by='new_column')\n return bbox_info.geometry.iloc[0]\n","repo_name":"red5ai/estaty","sub_path":"estaty/experiment/administrative.py","file_name":"administrative.py","file_ext":"py","file_size_in_byte":1906,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"50"} +{"seq_id":"10427675892","text":"import numpy as np\nfrom sklearn.cluster import MeanShift\nfrom ipywidgets import interact, FloatSlider, fixed\nimport matplotlib.pyplot as plt\nfrom matplotlib.figure import figaspect\nfrom matplotlib.patches import Circle, Patch\nimport seaborn as sns\n\nresolution = 200\n\n\ndef plot(bandwidth, df):\n x = df.values\n model = MeanShift(bandwidth=bandwidth, n_jobs=-1)\n model.fit(x)\n plt.figure(figsize=figaspect(1))\n ax = plt.axes()\n ax.set(aspect='equal')\n df.plot.scatter(df.columns[0], df.columns[1], c='black', s=10, ax=ax)\n xlim, ylim = ax.get_xlim(), ax.get_ylim()\n side = max(xlim[1] - xlim[0], ylim[1] - ylim[0])\n x_center, y_center = sum(xlim) / 2, sum(ylim) / 2\n xx, yy = np.meshgrid(\n np.linspace(x_center - side / 2, x_center + side / 2, resolution),\n np.linspace(y_center - side / 2, y_center + side / 2, resolution))\n grid = np.c_[xx.ravel(), yy.ravel()]\n pred = model.predict(grid)\n palette = sns.color_palette()\n ax.scatter(\n xx,\n yy,\n c=[palette[i] for i in pred],\n marker='.',\n alpha=0.2,\n edgecolors='none')\n centers = model.cluster_centers_\n ax.scatter(\n centers[:, 0],\n centers[:, 1],\n marker='x',\n s=100,\n c='blue',\n label='クラスタ中心')\n for center in centers:\n ax.add_patch(\n Circle(\n xy=center,\n radius=bandwidth,\n fill=False,\n edgecolor='black',\n linestyle='--'))\n handles, labels = ax.get_legend_handles_labels()\n handles.extend([Patch(color=palette[i]) for i in range(len(centers))])\n labels.extend([f'クラスタ{i + 1}' for i in range(len(centers))])\n ax.legend(handles, labels)\n ax.set(\n xlim=(xx.min(), xx.max()),\n ylim=(yy.min(), yy.max()),\n xticks=(),\n yticks=())\n plt.show()\n\n\ndef show(df):\n bandwidth = FloatSlider(\n value=0.5,\n min=0.3,\n max=0.9,\n step=0.1,\n description='距離',\n readout_format='.1f',\n continuous_update=False)\n interact(plot, bandwidth=bandwidth, df=fixed(df))\n","repo_name":"ScenesK/data-science-lecture","sub_path":"workspace/beginner/my_functions/mean_shift/visualization.py","file_name":"visualization.py","file_ext":"py","file_size_in_byte":2163,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"13619753634","text":"import cPickle as pickle\nimport gc\nimport sys\nfrom neurogenesis.util import Logger\n\nclass SimulationRun():\n def __init__(self):\n self.hash = \"\"\n self.config = []\n self.last_executed = 0\n self.path = \"\"\n self.executable_path = \"\"\n self.results = {}\n self.result_vectors = {} # {('module', 'name'): [(event, simSec, value), ...], ...}\n self.parameters = {}\n self.execute_binary = \"\"\n self.last_exit_code = -1\n self.last_executed_on_rank = -1\n self.last_run_at = 0\n self.config_name = \"\"\n\nclass Simulation():\n def __init__(self):\n self.simulation_runs = {}\n self.name = \"\"\n self.last_executed = \"\"\n self.total_duration = -1\n self.total_non_zero_exit_codes = -1\n self.inet_commit = \"\"\n self.notes = \"\"\n self.result_dir = \"\"\n\ndef serialize_sim_data(simName, simulations):\n Logger.info(\"Writing simulation data to file: %s\" % (simName))\n file = open(simName, \"wb+\")\n gc.disable()\n pickle.dump(simulations, file, protocol=pickle.HIGHEST_PROTOCOL)\n gc.enable()\n file.close()\n\n\ndef deserialize_sim_data(path):\n Logger.info(\"Reading simulation data from file: %s\" % (path))\n file = open(path, \"rb\")\n gc.disable()\n sim_data = pickle.load(file)\n gc.enable()\n file.close()\n if isinstance(sim_data, dict):\n Logger.error(\"It seems that your .meta.pickle file has been created with an old version of distsim. Please rerun your simulation!\")\n sys.exit(-1)\n return sim_data\n","repo_name":"juliusf/Neurogenesis","sub_path":"neurogenesis/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":1602,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"50"} +{"seq_id":"74775243356","text":"import logging\nimport math\n\nfrom random import randint\nfrom six import string_types\nfrom time import sleep\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.DEBUG)\n\nVERSION = '1.1.1'\n\ntlp_map = {\n 'red': 1,\n 'amber': 2,\n 'green': 3,\n 'white': 4\n}\n\n\nclass ThreatQObject(object):\n \"\"\"\n Object to encapsulate all object types in ThreatQ\n \"\"\"\n\n # Not including Files/Attachments because we can't bulk upload\n object_list = [\n 'indicators', 'adversaries', 'events', 'malware',\n 'campaign', 'course_of_action', 'exploit_target',\n 'identities', 'attack_pattern', 'exploit_target',\n 'instrusion_set', 'report', 'ttp', 'vulnerability',\n 'tool', 'stix_pattern'\n ]\n\n def __init__(self, tq, api_name):\n self.tq = tq\n self.api_name = api_name\n\n # Object metadata\n self.oid = None\n self.value = ''\n self.name = ''\n self.title = ''\n self.description = ''\n self.attributes = []\n self.published_at = None\n self.happened_at = None\n self.comments = []\n self.tlp = None\n self.status_id = None\n self.status = None\n self.type_id = None\n self.type = None\n self.score = None\n self.sources = []\n\n # Related objects\n self.relationships = {}\n self.metadata = {}\n\n def _get_base_endpoint_name(self):\n return self.api_name\n\n def _get_api_endpoint(self):\n return '/api/' + self.api_name + '/' + str(self.oid)\n\n def _id(self):\n \"\"\"\n Get the ID of this object within its namespace\n \"\"\"\n\n return self.oid\n\n def _set_id(self, value):\n \"\"\"\n Set the ID value\n \"\"\"\n\n self.oid = value\n return self\n\n def set_value(self, value):\n \"\"\"\n Set the value for the custom object\n \"\"\"\n\n # Fix any user errors ;)\n if self.api_name == 'adversaries':\n self.name = value\n elif self.api_name == 'events':\n self.title = value\n else:\n self.value = value\n\n def set_type(self, type_value):\n \"\"\"\n Set the type for the object\n \"\"\"\n\n if isinstance(type_value, int):\n self.type_id = type_value\n else:\n self.type = type_value\n\n def set_status(self, status):\n \"\"\"\n Set the status for the object\n \"\"\"\n\n if isinstance(status, int):\n self.status_id = status\n else:\n self.status = status\n\n def set_TLP(self, tlp):\n \"\"\"\n Set the tlp for the custom object\n \"\"\"\n\n self.tlp = tlp\n\n def set_name(self, name):\n \"\"\"\n Set the name for the custom object\n \"\"\"\n\n self.name = name\n\n def set_title(self, title):\n \"\"\"\n Set the title for the custom object\n \"\"\"\n\n self.title = title\n\n def add_source(self, *args, **kwargs):\n \"\"\"\n Handler for adding sources via old vs. new method\n \"\"\"\n\n if len(args) == 1 and isinstance(args[0], string_types):\n self._add_source_quick(args[0], tlp_id=kwargs.get('tlp_id'), tlp=kwargs.get('tlp'))\n elif len(args) == 1 and isinstance(args[0], ThreatQSource):\n self._add_source_object(args[0])\n elif len(args) == 1 and isinstance(args[0], list):\n for i in args[0]:\n self.add_source(i)\n\n def _add_source_quick(self, name, tlp_id=None, tlp=None):\n \"\"\"\n Add a source to the object\n \"\"\"\n\n if not name:\n return\n\n src = ThreatQSource(name, tlp=tlp_id or tlp)\n self._add_source_object(src)\n\n def _add_source_object(self, source):\n \"\"\"\n Add the source object to the list\n \"\"\"\n\n if not source or not source.name:\n return\n\n self.sources.append(source)\n\n def add_metadata(self, name, value):\n \"\"\"\n Add any extra info to the object for tracking\n \"\"\"\n\n self.metadata[name] = value\n\n def relate_object(self, *args):\n \"\"\"\n Relates an object based on the ID\n \"\"\"\n\n if len(args) == 2 and args[0] and args[1]:\n self._relate_object_deprecated(args[0], args[1])\n elif len(args) == 1 and args[0]:\n self._relate_object(args[0])\n\n def _relate_object_deprecated(self, api_name, obj):\n \"\"\"\n Relates an object to another the \"old\" way\n \"\"\"\n\n if api_name not in self.relationships:\n self.relationships[api_name] = []\n\n # Don't relate if duplicate\n if any(item.oid == obj.oid for item in self.relationships[api_name] if item.oid is not None):\n return\n\n self.relationships[api_name].append(obj)\n\n def _relate_object(self, obj):\n \"\"\"\n Relates an object to another the \"new\" way\n \"\"\"\n\n if obj.api_name not in self.relationships:\n self.relationships[obj.api_name] = []\n\n # Don't relate if duplicate\n if any(item.oid == obj.oid for item in self.relationships[obj.api_name] if item.oid is not None):\n return\n\n self.relationships[obj.api_name].append(obj)\n\n def fill_from_api_response(self, api_response, sources=[], attr_sources=[]):\n \"\"\"\n Fill ourselves in based on an API response\n \"\"\"\n\n # Load basic data\n self.api_name = api_response.get('api_name', self.api_name)\n self.oid = api_response.get('id')\n self.value = api_response.get('value', '')\n self.name = api_response.get('name', '')\n self.title = api_response.get('title', '')\n self.description = api_response.get('description', '')\n self.happened_at = api_response.get('happened_at')\n self.type_id = api_response.get('type_id')\n if 'type' in api_response:\n if isinstance(api_response['type'], dict):\n self.type = api_response.get('type', {}).get('name')\n elif isinstance(api_response['type'], string_types):\n self.type = api_response['type']\n elif isinstance(api_response['type'], int):\n self.type_id = api_response['type']\n self.status_id = api_response.get('status_id')\n if 'status' in api_response:\n if isinstance(api_response['status'], dict):\n self.status = api_response.get('status', {}).get('name')\n elif isinstance(api_response['status'], string_types):\n self.status = api_response['status']\n elif isinstance(api_response['status'], int):\n self.status_id = api_response['status']\n\n # Load score\n if self.api_name == \"indicators\" and \"score\" in api_response:\n if isinstance(api_response['score'], dict):\n self.score = api_response['score'].get('manual_score')\n if self.score is None:\n self.score = api_response['score'].get('generated_score')\n else:\n self.score = api_response['score']\n\n self.score = math.floor(float(self.score))\n\n # Load relationships\n for item in self.object_list:\n if item in api_response:\n # Make sure we have a place to store the relationship\n if item not in self.relationships:\n self.relationships[item] = []\n\n # Turn all dictionaries into Threat objects\n for rel in api_response.get(item, []):\n obj = ThreatQObject(self.tq, item)\n obj.fill_from_api_response(rel)\n self.relationships[item].append(obj)\n\n # Load soures\n for item in api_response.get('sources', []):\n self.add_source(item['name'], tlp_id=item.get('tlp_id'), tlp=item.get('tlp'))\n for item in sources:\n if isinstance(item, dict):\n self.add_source(item['name'], tlp_id=item.get('tlp_id'), tlp=item.get('tlp'))\n else:\n self.add_source(item)\n\n # Load attributes\n for item in api_response.get('attributes', []):\n if not item['name'] or not item['value']: # You wouldn't think this would get hit, but it can\n continue\n\n attr_src = item.get('sources', [])\n\n # Append custom attribute sources\n if attr_sources:\n for src in attr_sources:\n if isinstance(item, dict):\n attr_src.append(src)\n else:\n attr_src.append({'name': src})\n\n self.add_attribute(item['name'], item['value'], sources=attr_sources, tlp=item.get('tlp'))\n\n # Load comments\n self.comments = api_response.get('comments', [])\n\n return self\n\n @staticmethod\n def bulk_upload(tq, objects, show_debug=True, ignored_fields=[]):\n \"\"\"\n Bulk upload a list of ThreatObjects\n \"\"\"\n\n if not objects:\n return []\n\n # Load the obejcts\n data = [obj._to_dict(ignore=ignored_fields) for obj in objects if obj]\n output = []\n i = 0\n batch = 500\n\n # Create batches to upload\n while i < len(data):\n delay = randint(1, 3)\n if show_debug:\n logger.debug('Bulk uploading [{}] entries {} - {}'.format(objects[0].api_name, i, i + batch))\n\n try:\n # Upload the objects\n res = tq.post('/api/{}/consume'.format(objects[0].api_name), data=data[i:i + batch])\n res = [] if not res else res.get('data', [])\n output.extend(res)\n\n # Load in the ID from the upload\n for item in res:\n for obj in objects:\n if (\n obj.name and obj.name == item.get('name') or\n obj.value and obj.value == item.get('value') or\n obj.title and obj.title == item.get('title')\n ):\n obj._set_id(item.get('id'))\n break\n except Exception:\n logger.error('Failed to upload entries {} - {}. Continuing...'.format(i, i + batch))\n\n sleep(delay)\n i += batch\n\n return output\n\n def _to_dict(self, ignore=[], for_api=True):\n \"\"\"\n Serialize this object to a representation suitable for upload to ThreatQ\n \"\"\"\n\n output = {}\n\n if not self.value and not self.name and not self.title:\n raise ValueError('Threat Object has no value or name!')\n\n # The default fields\n if not for_api:\n output['api_name'] = self.api_name\n if self.value:\n output['value'] = self.value\n if self.name:\n output['name'] = self.name\n if self.title:\n output['title'] = self.title\n if self.description and 'description' not in ignore:\n # Need this case because of the techdebt in the API\n if self.api_name == 'adversaries':\n output['description'] = [{'value': self.description[:65500]}]\n else:\n output['description'] = self.description[:65500] # Max MariaDB TEXT length\n\n if self.oid and 'id' not in ignore:\n output['id'] = self.oid\n if self.comments and 'comments' not in ignore:\n output['comments'] = self.comments\n if self.attributes and 'attributes' not in ignore:\n self.attributes = ThreatQAttribute.merge_attributes(self.attributes)\n output['attributes'] = [\n attr.to_dict() for attr in self.attributes if attr and isinstance(attr, ThreatQAttribute)]\n if self.tlp and self.tlp in tlp_map and 'tlp' not in ignore:\n output['tlp_id'] = tlp_map.get(self.tlp)\n if self.status and 'status' not in ignore:\n output['status'] = {'name': self.status}\n if self.status_id and 'status' not in ignore and 'status_id' not in ignore:\n output['status_id'] = self.status_id\n if self.type and 'type' not in ignore:\n output['type'] = {'name': self.type}\n if self.type_id and 'type' not in ignore and 'type_id' not in ignore:\n output['type_id'] = self.type_id\n if self.sources and 'sources' not in ignore:\n self.sources = ThreatQSource.merge_sources(self.sources) # Merge the sources by hierarchy\n output['sources'] = [\n src.to_dict() for src in self.sources if src and isinstance(src, ThreatQSource)]\n if self.happened_at and 'happened_at' not in ignore:\n output['happened_at'] = self.happened_at\n if self.api_name == \"indicators\" and self.score is not None and 'score' not in ignore and not for_api:\n output['score'] = self.score\n\n # Add relationships\n if 'relationships' not in ignore:\n for k, v in self.relationships.items():\n output[k] = []\n for item in v:\n # Only add if an ID is available\n if isinstance(item, dict) and 'id' in item:\n output[k].append({'id': item['id']})\n elif isinstance(item, ThreatQObject) and item.oid:\n output[k].append({'id': item.oid} if for_api else item._to_dict())\n\n if self.published_at and 'published_at' not in ignore:\n output['published_at'] = self.published_at\n\n return output\n\n def add_comment(self, value):\n \"\"\"\n Add a comment to a custom object\n \"\"\"\n\n if not value:\n raise Exception('Cannot add a comment to a Threat Object without a value!')\n\n self.comments.append({'value': value})\n\n def get_comments(self):\n \"\"\"\n Gets comments for a custom object\n \"\"\"\n\n if not self.oid:\n raise Exception('Cannot get comments for a Threat Object without an ID!')\n\n p = {'with': 'sources'}\n res = self.tq.get(self._get_api_endpoint() + '/comments', params=p)\n self.comments = res.get('data')\n\n return self.comments\n\n def add_attribute(self, *args, **kwargs):\n if len(args) == 1:\n self._add_attribute_object(args[0])\n elif len(args) == 2:\n self._add_attribute_quick(*args, **kwargs)\n\n return self\n\n def _add_attribute_quick(self, key, value, sources=None, tlp=None):\n \"\"\"\n Add an attribute to the Threat Object\n \"\"\"\n\n if not key or not value:\n return\n\n if isinstance(value, bool):\n value = 'Yes' if value else 'No'\n\n attr = ThreatQAttribute(key, value, sources=sources, tlp=tlp)\n self._add_attribute_object(attr)\n\n def _add_attribute_object(self, attribute):\n \"\"\"\n Adds a ThreatQ Attribute object as an attribute\n \"\"\"\n\n if isinstance(attribute, list):\n for i in attribute:\n if isinstance(i, ThreatQAttribute):\n self.add_attribute(i)\n elif isinstance(attribute, ThreatQAttribute) and attribute.name and attribute.value:\n self.attributes.append(attribute)\n\n def get_attributes(self):\n \"\"\"\n Get attributes associated with this object\n\n :raises: :py:class:`~threatqsdk.exceptions.NotCreatedError` if\n the object has yet to be created\n \"\"\"\n\n if not self.oid:\n raise Exception('Cannot get attributes of a Threat Object without an ID!')\n\n endpoint = self._get_api_endpoint() + '/attributes'\n results = self.tq.get(endpoint, withp='attribute')\n if 'data' not in results:\n return []\n\n self.attributes = results['data']\n return self.attributes\n\n def _get_api_suffix(self, obj_type):\n return obj_type._get_base_endpoint_name()\n\n def get_related_objects(self, obj_type):\n \"\"\"\n Get related objects\n \"\"\"\n\n if not self.oid:\n raise Exception('Cannot get related objects of the Threat Object without an ID!')\n\n suffix = self._get_api_suffix(obj_type)\n if obj_type == self.__class__ and suffix == 'adversaries':\n return []\n endpoint = self._get_api_endpoint() + '/' + suffix\n results = self.tq.get(endpoint)\n if 'data' not in results:\n return []\n\n tr = []\n for obj in results['data']:\n inst = obj_type(self.tq)\n inst.fill_from_api_response(obj)\n tr.append(inst)\n\n return tr\n\n def _object_url(self, oid):\n \"\"\"\n Get a link to the identity suitable for presentation to an\n end user\n\n :param int iid: Identity ID\n \"\"\"\n\n if not self.oid:\n raise Exception('Cannot get Threat Object URL without an ID!')\n\n base = self.tq.threatq_host + '/{}/'.format(self.add_attribute)\n return base + str(oid) + '/details'\n\n def upload(self):\n \"\"\"\n Upload object to ThreatQ\n \"\"\"\n\n ThreatQObject.bulk_upload(self.tq, [self])\n\n def find(self, withp=''):\n \"\"\"\n Finds an object by its value\n \"\"\"\n\n params = {}\n if self.value:\n params['value'] = self.value\n elif self.name:\n params['name'] = self.name\n elif self.title:\n params['title'] = self.title\n if ',' in params['title']:\n params['title'] = params['title'].split(',')[0] + '%'\n if withp:\n params['with'] = withp\n\n res = self.tq.get('/api/{}'.format(self.api_name), params=params)\n if res and res.get('data') and res['data']:\n self.fill_from_api_response(res['data'][0])\n\n return self\n\n def add_tag(self, tag_name):\n self.add_tags([tag_name])\n\n def upload_tags(self, tags):\n\n if not self.oid:\n raise Exception('Cannot add tag to a Threat Object without an ID!')\n\n data = []\n if isinstance(tags, list):\n for tag in tags:\n if isinstance(tag, string_types):\n data.append({'name': tag})\n elif isinstance(tag, dict) and 'name' in tag:\n data.append(tag)\n\n if data:\n self.tq.post('/api/{}/{}/tags'.format(self.api_name, self.oid), data=data)\n\n @staticmethod\n def parse_tlp(tlp):\n \"\"\"\n Parse a generic TLP string/int into a valid ThreatQ one\n \"\"\"\n\n if tlp and isinstance(tlp, string_types) and tlp in tlp_map.keys():\n return tlp_map[tlp]\n elif tlp and isinstance(tlp, int) and tlp in tlp_map.values():\n return tlp\n\n\nclass ThreatQSource(object):\n\n def __init__(self, name, tlp=None):\n \"\"\"\n An encapsulation of a ThreatQ source\n \"\"\"\n\n self.name = name\n self.tlp = ThreatQObject.parse_tlp(tlp)\n\n @staticmethod\n def make_source_list(sources):\n \"\"\"\n Parses sources from an \"any\" variable\n \"\"\"\n\n new_sources = []\n if isinstance(sources, string_types):\n for src in sources.split(','): # Support comma separated sources\n new_sources.append(ThreatQSource(src))\n elif isinstance(sources, list):\n for src in sources:\n new_sources.extend(ThreatQSource.make_source_list(src))\n elif isinstance(sources, dict) and 'name' in sources:\n tlp = sources.get('tlp', sources.get('tlp_id'))\n new_sources.append(ThreatQSource(sources['name'], tlp=tlp))\n elif isinstance(sources, ThreatQSource) and sources.name:\n new_sources.append(sources)\n\n return new_sources\n\n @staticmethod\n def merge_sources(source_list):\n \"\"\"\n Merge sources with the same name together.\n This is so we can apply TLPs by hierarchy.\n \"\"\"\n\n new_sources = []\n for i in source_list:\n\n # Find a match\n found = None\n for j in new_sources:\n if i.name == j.name:\n found = j\n break\n\n # If there is no match, add the source\n if not found:\n new_sources.append(i)\n continue\n\n # If there is a match, compare based on TLP hierarchy\n # If the TLP is more \"secret\", remove old source and apply new one\n if (i.tlp and not j.tlp) or (i.tlp and j.tlp and i.tlp < j.tlp):\n new_sources.remove(j)\n new_sources.append(i)\n\n # Set the source list to the merged source list\n return new_sources\n\n def to_dict(self):\n output = {'name': self.name}\n if self.tlp:\n output['tlp_id'] = self.tlp\n\n return output\n\n\nclass ThreatQAttribute(object):\n\n def __init__(self, name, value, sources=None, tlp=None):\n \"\"\"\n An encapsulation of a ThreatQ attribute\n\n Parameters:\n - name (str): The name of the attribute\n - value (str): The value of the attribute\n - sources (any): Sources for the attribute\n - tlp (str,int): Default TLP for the attribute\n \"\"\"\n\n if sources is None:\n sources = []\n self.name = name\n self.value = value\n self.sources = ThreatQSource.make_source_list(sources)\n self.tlp = ThreatQObject.parse_tlp(tlp)\n\n @staticmethod\n def merge_attributes(attribute_list):\n \"\"\"\n Merge sources with the same name together.\n This is so we can apply TLPs by hierarchy.\n \"\"\"\n\n new_attrs = []\n for i in attribute_list:\n # Find a match\n found = None\n for j in range(len(new_attrs)):\n if i.name == new_attrs[j].name and i.value == new_attrs[j].value:\n found = j\n break\n\n # If there is no match, add the attribute\n if not found:\n new_attrs.append(i)\n continue\n\n # If there is a match, merge the sources\n new_attrs[j].sources.extend(i.sources)\n\n # Set the source list to the merged source list\n return new_attrs\n\n def add_source(self, source, tlp=None):\n \"\"\"\n Adds a source to the list\n \"\"\"\n\n if not source:\n return\n\n if isinstance(source, ThreatQSource):\n self.sources.append(source)\n elif isinstance(source, dict) and 'name' in source:\n if tlp:\n source['tlp'] = tlp\n self.sources.extend(ThreatQSource.make_source_list(source))\n elif isinstance(source, string_types):\n for i in source.split(','):\n self.sources.append(ThreatQSource(i, tlp=tlp))\n elif isinstance(source, list):\n for i in source:\n self.add_source(i, tlp=tlp)\n\n def to_dict(self):\n output = {'name': self.name, 'value': self.value}\n if isinstance(output['value'], bool):\n output['value'] = 'Yes' if output['value'] else 'No'\n\n if self.sources:\n self.sources = ThreatQSource.merge_sources(self.sources) # Merge the sources by hierarchy\n output['sources'] = [src.to_dict() for src in self.sources if src]\n if self.tlp:\n output['tlp_id'] = self.tlp\n\n return output\n","repo_name":"phantomcyber/phantom-apps","sub_path":"Apps/phthreatquotient/threatqsdk/bulk_object.py","file_name":"bulk_object.py","file_ext":"py","file_size_in_byte":23514,"program_lang":"python","lang":"en","doc_type":"code","stars":81,"dataset":"github-code","pt":"50"} +{"seq_id":"31294877451","text":"import pygame\r\nfrom pygame.sprite import Sprite\r\n\r\nclass Bullet(Sprite):\r\n def __init__(self, rlm_game, direction = 'up'):\r\n super().__init__()\r\n self.screen = rlm_game.screen\r\n self.settings = rlm_game.settings\r\n self.direction = direction\r\n \r\n if self.direction == 'up':\r\n self.image = pygame.image.load('images/magic_bolt.png')\r\n elif self.direction == 'down':\r\n self.image = pygame.transform.rotate(pygame.image.load('images/magic_bolt.png'), 180)\r\n elif self.direction == 'left':\r\n self.image = pygame.transform.rotate(pygame.image.load('images/magic_bolt.png'), 90)\r\n elif self.direction == 'right':\r\n self.image = pygame.transform.rotate(pygame.image.load('images/magic_bolt.png'), -90)\r\n \r\n self.rect = self.image.get_rect()\r\n \r\n self.rect.midtop = rlm_game.playerCharacter.rect.midtop\r\n \r\n self.y = float(self.rect.y)\r\n self.x = float(self.rect.x)\r\n\r\n def update(self):\r\n if self.direction == 'up':\r\n self.y -= self.settings.bullet_speed\r\n self.rect.y = self.y\r\n elif self.direction == 'down':\r\n self.y += self.settings.bullet_speed\r\n self.rect.y = self.y\r\n elif self.direction == 'left':\r\n self.x -= self.settings.bullet_speed\r\n self.rect.x = self.x\r\n elif self.direction == 'right':\r\n self.x += self.settings.bullet_speed\r\n self.rect.x = self.x\r\n\r\n def draw_bullet(self):\r\n self.screen.blit(self.image, self.rect)","repo_name":"starlis13/CIT228","sub_path":"forest_rogue/bullet.py","file_name":"bullet.py","file_ext":"py","file_size_in_byte":1616,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"26216625628","text":"\"\"\"The main node for the guidance system that is repsonsible for handling\nthe guidance logic in NAIAD. Currently, the node can only request a guiding\nsession from Athena, but in the future, it will be more intelligent and\ntell the navigation module when a new reference position is needed.\n\nAuthor: Johannes Deivard 2021-11\n\"\"\"\nimport rclpy\nfrom baseclasses.tridentstates import GotoWaypointStatus, NaiadGuidanceSystemState\nfrom rclpy.executors import MultiThreadedExecutor\nfrom rclpy.node import Node\nfrom rclpy.action import ActionClient, ActionServer\n\nfrom geometry_msgs.msg import Pose, Point, Quaternion # https://github.com/ros2/common_interfaces/blob/master/geometry_msgs/msg/Pose.msg\nfrom std_srvs.srv import Trigger # https://github.com/ros2/common_interfaces/blob/master/std_srvs/srv/Trigger.srv\nfrom std_msgs.msg import String\nfrom trident_msgs.action import StartMission, GotoWaypoint\nfrom trident_msgs.msg import Waypoint, WaypointAction, Mission\nfrom trident_msgs.srv import GuidanceRequest, GetGoalPose\n\n\nclass GuidanceSystemNode(Node):\n \"\"\"Node for the guidance system in Athena.\n \"\"\"\n def __init__(self, node_name):\n super().__init__(node_name)\n self._guidance_system_state = NaiadGuidanceSystemState.IDLE\n # (Not really used at the moment)\n self._goto_waypoint_status = None\n # Reference position received from the guidance request response\n self._reference_position = None\n # Timer with callback that sends a guidance stop request\n self._stop_guidance_timer = None\n\n\n # Clients\n # -------\n self._guidance_request_client = self.create_client(\n GuidanceRequest,\n 'guidance_system/guidance/request',\n )\n self._guidance_stop_client = self.create_client(\n Trigger,\n 'guidance_system/guidance/stop',\n )\n\n\n # Servers\n # -------\n\n\n # Publishers\n # ----------\n self._guidance_system_state_publisher = self.create_publisher(\n String,\n 'guidance_system/state',\n 1\n )\n self._guidance_system_reference_position_publisher = self.create_publisher(\n Point,\n 'guidance_system/reference_position',\n 1\n )\n\n # Subscriptions\n # -------------\n # (NOTE: This isn't really needed in this node)\n self._goto_waypoint_status_subscriber = self.create_subscription(\n String,\n 'navigation/waypoint/go/status',\n self._goto_waypoint_status_subscriber_callback,\n 1\n )\n\n self.get_logger().info(\"Created Guidance System node.\")\n\n def request_guidance(self, guidance_duration):\n \"\"\"Sends a guidance request to Athena and starts a timer that sends a cancel request\n once the guidance duration has been reached.\n \"\"\"\n while not self._guidance_request_client.wait_for_service(timeout_sec=1.0):\n self.get_logger().info('Guidance request service not available, waiting again...')\n request = GuidanceRequest()\n request.duration = int(guidance_duration)\n self.update_state(NaiadGuidanceSystemState.AWAITING_GUIDANCE)\n # Send the request\n response = self._guidance_request_client.call(request)\n # Create a rate object\n rate = self.create_rate(0.1) # 0.1 Hz\n # Keep trying to get a guidance request until it is accepted by the server\n while(not response.accepted):\n self.get_logger().info(f\"Guidance request rejected: {response.message}\")\n response = self._guidance_request_client.call(request)\n rate.sleep()\n \n self.get_logger().info(f\"Guidance request accepted: {response.message}. Reference position: {response.reference_position}\")\n self.update_state(NaiadGuidanceSystemState.BEING_GUIDED)\n self.update_reference_position(response.reference_position)\n # Check if the guidance duration is greater than 0\n if guidance_duration > 0:\n # Start the guidance stop timer\n self._stop_guidance_timer = self.create_timer(guidance_duration, self._stop_guidance_timer_callback)\n\n def _stop_guidance_timer_callback(self):\n \"\"\"Callback for the stop_guidance_timer that is started when a guidance request\n with guidance_duration > 0 is accepted.\n \"\"\"\n self._stop_guidance_timer.cancel()\n self._stop_guidance_timer.destroy()\n self.stop_guidance()\n\n def stop_guidance(self):\n \"\"\"Sends a stop guidance request to the guidance server.\n \"\"\"\n response = self._guidance_stop_client.call()\n if not response.success:\n self.get_logger().info(f\"Failed to stop the guidance session, trying again in 10 seconds. Message: {response.message}\")\n # If the request failed, try again in 10 seconds\n rate = self.create_rate(0.1) # 0.1 Hz\n rate.sleep()\n response = self._guidance_stop_client.call()\n\n if response.success:\n self.get_logger().info(f\"Successfully stopped the guidance session. Message: {response.message}\")\n else:\n self.get_logger().info(f\"Second attempt to stop the guidance session failed. Message: {response.message}\")\n\n self.update_state(NaiadGuidanceSystemState.IDLE)\n\n return response.success\n\n def update_reference_position(self, new_ref_pos):\n \"\"\"Updates the reference position with the received reference position\n and publishes the reference position on the reference_position topic\n so that nodes that are using it will stay up to date.\n\n Args:\n new_ref_pos (Point): The new reference position.\n \"\"\"\n # Check if the new_ref_pos is a Point\n if not isinstance(new_ref_pos, Point):\n # If not, convert it to a point\n p = Point()\n p.x, p.y, p.z = new_ref_pos[0], new_ref_pos[1], new_ref_pos[2]\n new_ref_pos = p\n # Update the reference position\n self._reference_position = new_ref_pos\n # Publish the new position\n self._guidance_system_reference_position_publisher.publish(new_ref_pos)\n\n def update_state(self, new_state):\n \"\"\"Updates the state of the node and publishes the new state to the guidance_system/state topic.\n\n Args:\n new_state (GuidanceSystemState): The new state.\n \"\"\"\n self._guidance_system_state = new_state\n state_msg = String()\n state_msg.data = str(new_state)\n self._guidance_system_state_publisher.publish(state_msg)\n\n # CALLBACKS\n # ---------\n def _goto_waypoint_status_subscriber_callback(self, msg):\n \"\"\"Callback that handles the GotoWaypoint status messages sent by the Navigation node.\n NOTE: It might be better to make this a Getter service instead of constantly reading and\n updating the status.\n \"\"\"\n self.get_logger().info(f\"Read GotoWaypoint status update: {msg.data}. Updating state in guidance system.\")\n self._goto_waypoint_status = GotoWaypointStatus[msg.data]\n\n\ndef main(args=None):\n rclpy.init(args=args)\n guidance_system_node = GuidanceSystemNode(\"guidance_system\")\n executor = MultiThreadedExecutor()\n rclpy.spin(guidance_system_node, executor)\n rclpy.shutdown()\n\n\nif __name__==\"__main__\":\n main()\n","repo_name":"kpister/prompt-linter","sub_path":"data/scraping/repos/MDU-C2~TRIDENT-2021/trident_software~naiad_ws~src~naiad_guidance_system~naiad_guidance_system~guidancesystemmain.py","file_name":"trident_software~naiad_ws~src~naiad_guidance_system~naiad_guidance_system~guidancesystemmain.py","file_ext":"py","file_size_in_byte":7436,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"7671222212","text":"numeros = []\r\nfor i in range(0, 20, 1):\r\n x = int(input('Digite um numero: '))\r\n if(i == 0):\r\n constante = int(input('Digite a constante multiplicativa: '))\r\n x= x* constante\r\n numeros.append(x) \r\n\r\n\r\n \r\nprint('Os numeros digitados multiplicados pela constante: ')\r\n\r\n# Exibir de forma crescente\r\nfor i in range(0, 20, 1):\r\n print(numeros[i])","repo_name":"GustavoSantanaSampaio/Programacao","sub_path":"Python/Ex35.py","file_name":"Ex35.py","file_ext":"py","file_size_in_byte":364,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"1531708781","text":"from threading import Event, Thread\r\nimport time\r\n\r\nimport pyautogui\r\nfrom system_hotkey import SystemHotkey\r\nimport yaml\r\n\r\n\r\nclass Util:\r\n\r\n @staticmethod\r\n def get_config() -> dict:\r\n dict = {}\r\n with open(\"config/base.yml\", \"r\", encoding=\"utf-8\") as f:\r\n content = yaml.load(f, Loader=yaml.FullLoader)\r\n return content\r\n\r\n\r\nclass Caiji(Thread):\r\n\r\n def __init__(self, *args, **kwargs):\r\n super().__init__(*args, **kwargs)\r\n self.config = Util.get_config()\r\n self.__flag = Event()\r\n self.__flag.clear()\r\n self.__running = Event()\r\n self.__running.set()\r\n self.hk = SystemHotkey()\r\n self.hk.register((\"control\", \"1\"), callback=self.pause_resume)\r\n self.hk2 = SystemHotkey()\r\n self.hk2.register((\"control\", \"3\"), callback=self.stop)\r\n\r\n def task(self):\r\n print(time.time())\r\n pyautogui.press(str(self.config.get(\"key\")))\r\n\r\n def run(self) -> None:\r\n while True:\r\n self.__flag.wait()\r\n self.task()\r\n if not self.__running.isSet():\r\n break\r\n time.sleep(self.config.get(\"sleep\"))\r\n\r\n def pause_resume(self, *args, **kwargs):\r\n if self.__flag.isSet():\r\n self.pause()\r\n else:\r\n self.resume()\r\n\r\n def begin(self, *args, **kwargs):\r\n print(\"开始了\")\r\n self.__flag.set()\r\n\r\n def pause(self, *args, **kwargs):\r\n print(\"暂停了\")\r\n self.__flag.clear()\r\n\r\n def resume(self, *args, **kwargs):\r\n print(\"恢复了\")\r\n self.__flag.set()\r\n\r\n def stop(self, *args, **kwargs):\r\n print(\"停止了\")\r\n self.__flag.set()\r\n self.__running.clear()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n caiji = Caiji()\r\n caiji.start()\r\n caiji.join()\r\n print(\"main end\")","repo_name":"chengyijun/tool","sub_path":"wow/caiji.py","file_name":"caiji.py","file_ext":"py","file_size_in_byte":1854,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"50"} +{"seq_id":"74778592154","text":"\r\nimport sys\r\nimport os\r\nimport pysam\r\nimport inspect\r\nimport re\r\nimport pickle\r\nimport gzip\r\n\r\n# pysam is for reading sam, bam files.\r\n\r\n# in the bash script, \r\n# data = /mnt/c/data...\r\n# result = /mnt/c/results\r\n# we call python script out with\r\n# python script.py $data/fast.fa $results/fastres.fa\r\n# so $data.. will be the first argument -> sys.argv.[1], assigned to variable infasta\r\n\r\n\r\ndef main(genomefasta,inbam,inJunc):\r\n # input genome fasta file, return genome dictionary \r\n print(\"Read genome fasta\")\r\n gen_dict = readGenome(genomefasta)\r\n #print(gen_dict[\"SA11_S5\"]) \r\n picklefile = os.path.join(os.path.split(os.path.abspath(inbam))[0], os.path.split(os.path.splitext(os.path.abspath(inbam))[0])[1]) #creating a pcl file in the same directory as inbam, and with the same name as inbam file. First part = path, second path = name\r\n try: \r\n interLRI_list, intraLRI_listdr, SRI_list,exp_name,bam_cnt,junc_cnt,totread_bp, totread_ap, per_inter, per_intra, per_SRI = loadData(picklefile)\r\n except:\r\n print(\"Read bam file\")\r\n SRI_list, intraLRI_listdr,bam_cnt, exp_name = readBam(inbam,gen_dict)\r\n #print(\"SRI_list\", SRI_list[:20])\r\n #print(\"intraLRI_listdr\", intraLRI_listdr[:20])\r\n #print(bam_list[:1][0:])\r\n\r\n print(\"Read chimeric junction file\")\r\n interLRI_list, intraLRI_listdr,SRI_list,junc_cnt = readChimJunc(inJunc,intraLRI_listdr,SRI_list)\r\n\r\n print(\"classification summary\")\r\n totread_bp, totread_ap, per_inter, per_intra, per_SRI = summary(bam_cnt, junc_cnt, interLRI_list, intraLRI_listdr, SRI_list)\r\n\r\n saveData((interLRI_list, intraLRI_listdr, SRI_list,exp_name,bam_cnt,junc_cnt,totread_bp, totread_ap, per_inter, per_intra, per_SRI), picklefile)\r\n\r\n\r\n print(f\"For {exp_name} '\\n'total read before processing: {totread_bp} '\\n'total interactions after processing: {totread_ap} '\\n'% of inter-LRI: {per_inter} '\\n'% of intra-LRI: {per_intra}'\\n'% of SRI: {per_SRI}\")\r\n\r\n #print(f\"interlist {interLRI_list[:10]}'\\n'intralist {intraLRI_listdr[:10]}'\\n' SRI {SRI_list[:10]}\")\r\n\r\n #print(\"seq_name\",seq_name[0:5])\r\n #print(\"chim_name\",chim_name[0:5])\r\n \r\n #print(\"Find equal names\")\r\n #s = findrep(seq_name,chim_name)\r\n #print(\"s\",s)\r\n \r\n #print(type(chim_name),chim_name[0:5],type(seq_name),seq_name[0:5])\r\n #for counting the number of chimeric CIGAR\r\n #interLRI_list, intraLRI_listdr, cnt = readChimJunc(inJunc,intraLRI_listdr)\r\n #print(\"cnt\",cnt)\r\n #print(chimCIGAR)\r\n #print(\"interLRI_list\", interLRI_list)\r\n #print(\"intraLRI_listdr\", intraLRI_listdr)\r\n\r\n \r\ndef readGenome(genomefasta):\r\n # load genome fasta file, and return a dictionary\r\n with open(genomefasta, \"r\") as genfasta:\r\n gen_dict = dict()\r\n seg_name = \"\"\r\n for i,line in enumerate(genfasta):\r\n # this command convert the name \">SA11_S5 WT LC178570.1\" into \"'>SA11_s5', 'WT', LC..'\", i.e. cut at space\r\n # \">SA11_S3 WT 65433667.7\\n\"\r\n line = line.strip()\r\n # \">SA11_S3 WT 65433667.7\"\r\n if i % 2 == 0:\r\n # this command removes the > symbol in front of genome segment name (from [1:])\r\n # \">SA11_S3 WT 65433667.7\"\r\n line_list = line.split()\r\n # [\">SA11_S3\" \"WT\" \"65433667.7\"]\r\n seg_name_arrow = line_list[0]\r\n # \">SA11_S3\"\r\n seg_name = seg_name_arrow[1:]\r\n # \"SA11_S3\"\r\n else:\r\n gen_dict[seg_name] = line\r\n #seg_name is a temporary variable which holds name from the previous line\r\n return gen_dict\r\n \r\ndef readBam(inbam,gen_dict):\r\n exp_name = os.path.split(os.path.splitext(os.path.abspath(inbam))[0])[1]\r\n with pysam.AlignmentFile(inbam,\"rb\") as ib:\r\n SRI_list = []\r\n intraLRI_listdr = []\r\n seq_name = []\r\n rname = \"\"\r\n comp = ()\r\n CIGAR = \"\"\r\n bam_cnt = 0\r\n for it,line in enumerate(ib):\r\n #seq_name.append(line.query_name)\r\n bam_cnt += 1\r\n rname = line.reference_name\r\n i = int(line.reference_start) # starting position as integer 0-index\r\n i,j,k,l = readCIGAR(i,line.cigarstring)\r\n if k == 0 and l == 0:\r\n comp = (rname,i,j)\r\n SRI_list.append(comp)\r\n #print(\"bam sequence\",line.query_alignment_sequence)\r\n #print(\"indexed sequence\", comp, gen_dict[rname][i:j])\r\n elif k-j <20:\r\n comp = (rname, i,l)\r\n SRI_list.append(comp)\r\n elif j-i >= 20 and l-k >= 20:\r\n comp = (rname, i, j, k, l)\r\n intraLRI_listdr.append(comp)\r\n elif j-i >= 20 and l-k < 20:\r\n comp = (rname, i,j)\r\n SRI_list.append(comp)\r\n elif j-i < 20 and l-k >= 20:\r\n comp = (rname, k,l)\r\n SRI_list.append(comp)\r\n #print(\"bam\",bam_cnt)\r\n return SRI_list, intraLRI_listdr, bam_cnt, exp_name\r\n\r\ndef readChimJunc(inJunc, intraLRI_listdr,SRI_list):\r\n with open(inJunc, \"r\") as ij:\r\n interLRI_list = []\r\n #chim_name =[]\r\n rname1 = \"\"\r\n rname2 = \"\"\r\n comp = ()\r\n comp2 = ()\r\n comp3 = ()\r\n CIGAR1 = \"\"\r\n CIGAR2= \"\"\r\n cnt = 0\r\n junc_cnt = 0\r\n for it,line in enumerate(ij):\r\n junc_cnt += 1\r\n line = line.split()\r\n if line[0] == \"#\":\r\n continue\r\n rname1 = line[0]\r\n rname2 = line[3]\r\n #chim_name.append(line[9])\r\n rstart1 = int(line[10])\r\n rstart2 = int(line[12])\r\n CIGAR1 = line[11]\r\n CIGAR2 = line[13]\r\n ai,aj,ak,al = readCIGAR(rstart1,CIGAR1)\r\n bi,bj,bk,bl = readCIGAR(rstart2,CIGAR2)\r\n #print (rname1, CIGAR1,rstart1)\r\n if rname1 == rname2:#intraLRI & SRI\r\n if (ak,al) != (0,0) and (bk,bl) != (0,0):\r\n pass\r\n elif (ak,al) != (0,0):\r\n if ak-aj > 20: #gap >20\r\n if aj-ai >= 20 and al-ak >=20: #both match >20\r\n comp = (rname1, ai, aj, bi, bj)\r\n comp2 = (rname1, ak, al, bi,bj)\r\n comp3 = (rname1, ai, aj, ak, al)\r\n intraLRI_listdr.append(comp)\r\n intraLRI_listdr.append(comp2)\r\n intraLRI_listdr.append(comp3)\r\n elif aj-ai <20:\r\n comp = (rname1, ak,al,bi,bj)\r\n intraLRI_listdr.append(comp)\r\n elif al-ak <20:\r\n comp = (rname1,ai,aj,bi,bj)\r\n intraLRI_listdr.append(comp)\r\n else:\r\n comp = (rname1,ai,al,bi,bj)\r\n intraLRI_listdr.append(comp)\r\n SRI_list.append(rname1, ai,al)\r\n elif (bk,bl) != (0,0):\r\n if bk-bj > 20:\r\n if bj-bi >= 20 and bl-bk >=20:\r\n comp = (rname1, bi, bj, ai, aj)\r\n comp2 = (rname1, bk, bl, ai,aj)\r\n comp3 = (rname1, bi, bj, bk, bl)\r\n intraLRI_listdr.append(comp)\r\n intraLRI_listdr.append(comp2)\r\n intraLRI_listdr.append(comp3)\r\n elif bj-bi <20:\r\n comp = (rname1, bk,bl,ai,aj)\r\n intraLRI_listdr.append(comp)\r\n elif bl-bk <20:\r\n comp = (rname1,bi,bj,ai,aj)\r\n intraLRI_listdr.append(comp)\r\n else:\r\n comp = (rname1,bi,bl,ai,aj)\r\n intraLRI_listdr.append(comp)\r\n SRI_list.append(rname1, bi,bl)\r\n else:\r\n intraLRI_listdr.append((rname1,ai,aj,bi,bj))\r\n else: #interLRI\r\n if (ak,al) != (0,0) and (bk,bl) != (0,0):\r\n pass\r\n elif (ak,al) != (0,0):\r\n comp3 = (rname2, bi, bj)\r\n if ak-aj > 20 :\r\n if aj-ai >= 20 and al-ak >= 20:\r\n comp = (rname1, ai, aj)\r\n comp2 = (rname1,ak, al)\r\n intraLRI_listdr.append((rname1,ai,aj,ak,al))\r\n interLRI_list.append((comp,comp3))\r\n interLRI_list.append((comp2,comp3))\r\n elif aj-ai <20:\r\n comp = (rname1, ak,al)\r\n interLRI_list.append((comp,comp3))\r\n elif al-ak <20:\r\n comp = (rname1,ai,aj)\r\n interLRI_list.append((comp,comp3))\r\n else: \r\n comp = (rname1,ai,al)\r\n interLRI_list.append((comp,comp3))\r\n SRI_list.append(comp)\r\n elif (bk,bl) != (0,0):\r\n comp3 = (rname1, ai, aj)\r\n if bk-bj > 20 :\r\n if bj-bi >= 20 and bl-bk >= 20:\r\n comp = (rname2, bi, bj)\r\n comp2 = (rname2,bk, bl)\r\n intraLRI_listdr.append((rname2,bi,bj,bk,bl))\r\n interLRI_list.append((comp,comp3))\r\n interLRI_list.append((comp2,comp3))\r\n elif bj-bi <20:\r\n comp = (rname2, bk,bl)\r\n interLRI_list.append((comp,comp3))\r\n elif bl-bk <20:\r\n comp = (rname2,bi,bj)\r\n interLRI_list.append((comp,comp3))\r\n else: \r\n comp = (rname2,bi,bl)\r\n interLRI_list.append((comp,comp3))\r\n SRI_list.append(comp)\r\n else:\r\n comp = (rname1,ai,aj)\r\n comp3 = (rname2,bi,bj)\r\n interLRI_list.append((comp,comp3))\r\n #print(\"ju\",junc_cnt)\r\n return interLRI_list, intraLRI_listdr, SRI_list, junc_cnt\r\n\r\ndef summary(bam_cnt,junc_cnt, interLRI_list, intraLRI_listdr, SRI_list):\r\n totread_bp = int(bam_cnt)+int(junc_cnt)\r\n leninter = len(interLRI_list)\r\n lenintra = len(intraLRI_listdr)\r\n lenSRI = len(SRI_list)\r\n totread_ap = leninter + lenintra+ lenSRI\r\n per_inter = leninter*100/totread_ap\r\n per_intra = lenintra*100/totread_ap\r\n per_SRI = lenSRI*100/totread_ap\r\n return totread_bp, totread_ap, per_inter, per_intra, per_SRI\r\n\r\n\r\ndef findrep(seq_name,chim_name):\r\n seq_name.sort()\r\n chim_name.sort()\r\n c = 0\r\n s = 0\r\n l = len(seq_name)\r\n for k,name in enumerate(seq_name):\r\n print(f\"{k} of {l}\", end=\"\\r\")\r\n for i in range(c,len(chim_name)):\r\n if name < chim_name[i]:\r\n continue\r\n elif name == chim_name[i]:\r\n s += 1\r\n else:\r\n c = i\r\n break\r\n print(\"\")\r\n return s\r\n# no overlap found between bam and chimJunction\r\n\r\n\r\ndef readCIGAR(i,CIGAR): \r\n ## read CIGAR string\r\n # M = match, S = soft clipping, D = deletion, N = intron (longer deletion), I = insertion\r\n reCig = re.compile(r\"([0-9]+)(M|S|D|N|I)\")\r\n j, k, l = i, 0, 0\r\n for cig in reCig.finditer(CIGAR):\r\n val,key = int(cig.group(1)), cig.group(2)\r\n #if key == \"S\" and i == j: i += val; j += val\r\n if key in \"MD\" and k == 0: j += val\r\n elif key in \"MD\" and k != 0: l += val\r\n elif key == \"N\" and k == 0: k = j + val; l = j + val\r\n return i,j,k,l\r\n\r\ndef loadData(fname): \r\n ## load data with pickle\r\n with open(f\"{fname}.pcl\", \"r+b\") as pcl_in:\r\n pcl_data = pickle.load(pcl_in)\r\n return pcl_data\r\n\r\ndef saveData(pcl_data, fname):\r\n ## save data with pickle\r\n with open(f\"{fname}.pcl\", \"w+b\") as pcl_out:\r\n pickle.dump(pcl_data, pcl_out , protocol=4)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n genomefasta = sys.argv[1]\r\n inbam = sys.argv[2]\r\n inJunc = sys.argv[3]\r\n #print(infastq)\r\n #exit()\r\n main(genomefasta, inbam, inJunc)\r\n\r\nexit \r\n#some_dictionary[\"new key\"] = 700\r\n\r\n#line.strip() #for removing characters at the start and end","repo_name":"AlexisTseng/SPLASH-analysis","sub_path":"ana_draft_2.py","file_name":"ana_draft_2.py","file_ext":"py","file_size_in_byte":12708,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"12357576119","text":"import random\r\nnumber=random.randint(1,10)\r\n\r\nplayer_name= input(\"Hey! What is your name?\")\r\nnumber_of_guesses=0\r\ninput(\"Ok!\" +player_name+\" I will think of a number between 1 to 10. You try and guess the number in 5 tries\")\r\n\r\nwhile number_of_guesses<6:\r\n guess=int(input())\r\n number_of_guesses+=1\r\n if guessnumber:\r\n print(\"Your guess is too high.\") \r\n if guess== number:\r\n break \r\n\r\nif guess==number:\r\n print(\"You guessed the number in\",number_of_guesses, \"tries\")\r\n\r\nelse:\r\n print(\"You could not guess the number.The number is \",number,\" Better luck next time!\") \r\n","repo_name":"AnargaNair/Guess-the-number","sub_path":"GuessTheNumber.py","file_name":"GuessTheNumber.py","file_ext":"py","file_size_in_byte":673,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"34469182478","text":"import socket\nimport subprocess\nimport platform\nimport time\nimport sys\n\nimport google.protobuf\n\nfrom . import messages_pb2\n\nclass Connection():\n \"\"\"Automatically starts the binary and creates a socket connection to it.\n\n When started with the default arguments, will start the binary on an open\n port and connect to it.\n\n If start_binary is set to False, the binary will\n not be automatically started, and connection will instead be made to the\n given address and port.\n\n Once connection has been made, the req member will be a protobuf Request\n class as defined in messages.proto. This member can be edited to set the\n message fields for the next request.\n\n The send_request() method will send the current request to the binary.\n \"\"\"\n\n def __init__(self, address=\"localhost\", port=None, start_binary=True, binary_path=\"main\"):\n self.req = messages_pb2.Request()\n\n # Get a free port number\n if port is None:\n tcp = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n tcp.bind((\"\", 0))\n _, port = tcp.getsockname()\n tcp.close()\n\n # Start the binary\n if start_binary:\n try:\n if platform.system() == \"Windows\":\n subprocess.Popen([binary_path, \"-p\", str(port)],\n stdout=subprocess.DEVNULL)\n else:\n subprocess.Popen([binary_path, \"-p\", str(port)],\n stdout=subprocess.DEVNULL)\n except OSError:\n print(\"Starting the binary failed\")\n sys.exit()\n\n # Attempt connecting until it succeeds\n for _ in range(10):\n try:\n self.s = socket.create_connection((address, port))\n break\n except ConnectionRefusedError:\n time.sleep(0.1)\n continue\n\n # Set TCP_NODELAY to prevent delays when sending short messages\n self.s.setsockopt(socket.SOL_TCP, socket.TCP_NODELAY, 1)\n\n def send_request(self):\n \"\"\"Send the Request message stored in this.req and resets\n it to default values.\n Returns the Response object received from the binary,\n or False if decoding the incoming message failed.\n \"\"\"\n # Serialize message\n serialized = self.req.SerializeToString()\n\n # Reset the message to default values\n self.req = messages_pb2.Request()\n\n # Send message length\n msg_len = len(serialized)\n sent = self.s.send(msg_len.to_bytes(4, \"big\"))\n\n # Send message content\n total_sent = 0\n while total_sent < msg_len:\n sent = self.s.send(serialized[total_sent:])\n total_sent += sent\n\n # Receive message length\n data = b\"\"\n while len(data) < 4:\n received = self.s.recv(4 - len(data))\n if len(received) == 0:\n raise ConnectionResetError(\"Connection was closed\")\n data += received\n msg_len = int.from_bytes(data, \"big\")\n\n # Receive a Response message\n data = b\"\"\n while len(data) < msg_len:\n received = self.s.recv(msg_len - len(data))\n if len(received) == 0:\n raise ConnectionResetError(\"Connection was closed\")\n data += received\n\n # Try to parse the response and return it\n try:\n resp_msg = messages_pb2.Response()\n resp_msg.ParseFromString(data)\n return resp_msg\n\n # Return False if decoding fails\n except google.protobuf.message.DecodeError as e:\n print(\"DecodeError in reponse: {}\".format(e))\n return False\n","repo_name":"joonaspu/video-game-behavioural-cloning","sub_path":"video_game_env/connection.py","file_name":"connection.py","file_ext":"py","file_size_in_byte":3739,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"50"} +{"seq_id":"34094013515","text":"import sys, math, time, random\nimport numpy as np\nimport numpy.random as nr\nimport matplotlib.pyplot as plt\nfrom vcopt import vcopt\n\nnode = [\n [23,39],[ 8,44],[34,36],[12,30],[42,37],[ 6,35],[ 1,15],[12,25],\n [ 4,39],[13,42],[23,13],[ 7,39],[11, 5],[ 6,44],[28,45],[20, 7],\n [ 3,16],[ 4,19],[ 3,39],[ 0, 2],[19,21],[ 3,43],[ 8,34],[20,39],\n [ 2,50],[20,26],[16,36],[24,30],[ 9,40],[ 5,22],[30,35],[ 2, 0],\n [21,36],[22,28],[ 3,33],[11,36],[14,34]\n]\n\n#1TSPの評価関数\ndef tsp_1_score(param):\n\n #idが0のノードからスタートし0に戻ってくるように、先頭と末尾に0を追加\n param_full = np.hstack((0, param, 0))\n\n return np.sum(((node_x[param_full][:-1] - node_x[param_full][1:])**2 + (node_y[param_full][:-1] - node_y[param_full][1:])**2)**0.5)\n\n#2TSPの評価関数\ndef tsp_2_score(param):\n #idが0のノードを中心に2つにわけ、それぞれ0に戻ってくるように、末尾に0を追加\n param_1 = np.hstack((param[:np.where(param==0)[0][0] + 1][::-1],0))\n param_2 = np.hstack((param[np.where(param==0)[0][0]:],0))\n\n #評価\n score_1 = np.sum(((node_x[param_1][:-1] - node_x[param_1][1:])**2 + (node_y[param_1][:-1] - node_y[param_1][1:])**2)**0.5)\n score_2 = np.sum(((node_x[param_2][:-1] - node_x[param_2][1:])**2 + (node_y[param_2][:-1] - node_y[param_2][1:])**2)**0.5)\n\n #距離が長い方を返す\n return max(score_1, score_2)\n\ndef get_route_length(route):\n length = 0\n for i in range(len(route)-1):\n length += math.sqrt((node[route[i]][0]-node[route[i+1]][0])**2 + (node[route[i]][1]-node[route[i+1]][1])**2)\n return length\n\n\nnode_x = np.array([0]*len(node))\nnode_y = np.array([0]*len(node))\nfor i in range(len(node)):\n node_x[i] = node[i][0]\n node_y[i] = node[i][1]\n\nprint(\"\\n-------1 TSP-------\")\n\nprint(\"\\n--2 Opt solve (1 TSP)--\")\nstart_time = time.time()\n#道順を作成\nparam = np.arange(1,len(node))\n#2-opt法で最適化\nparam, length = vcopt().opt2(param, tsp_1_score, 0.0, seed=1)\nend_time = time.time()\nprint(\"Min Length : \"+str(length)+\"\\ntime : \"+str(end_time-start_time))\nprint(\"Route : \" + str(np.hstack((0,param,0))))\n\nprint(\"\\n--GA solve (1 TSP)--\")\nstart_time = time.time()\n#パラメータの範囲を定義\nparam_range = np.arange(1,len(node))\n#GAで最適化\nparam, length = vcopt().tspGA(param_range, tsp_1_score, 0.0, seed=1)\nend_time = time.time()\nprint(\"Min Length : \"+str(length)+\"\\ntime : \"+str(end_time-start_time))\nprint(\"Route : \" + str(np.hstack((0,param,0))))\n\nprint(\"\\n-------2 TSP-------\")\n\nprint(\"\\n--2 Opt solve (2 TSP)--\")\nstart_time = time.time()\n#道順を作成\nparam = np.arange(len(node))\n#2-opt法で最適化\nparam, max_length = vcopt().opt2(param, tsp_2_score, 0.0, seed=1)\nend_time = time.time()\n\nroute_1 = np.hstack((param[:np.where(param==0)[0][0] + 1][::-1],0))\nroute_2 = np.hstack((param[np.where(param==0)[0][0]:],0))\nroute_1_len = get_route_length(route_1)\nroute_2_len = get_route_length(route_2)\nprint(\"Max Length : \"+str(max_length)+\"\\ntime : \"+str(end_time-start_time))\nprint(\"\\nRoute 1 : \" + str(route_1) + \"\\nRoute 1 Length : \" + str(route_1_len))\nprint(\"\\nRoute 2 : \" + str(route_2) + \"\\nRoute 2 Length : \" + str(route_2_len))\n\nprint(\"\\n--GA solve (2 TSP)--\")\nstart_time = time.time()\n#パラメータの範囲を定義\nparam_range = np.arange(len(node))\n#GAで最適化\nparam, max_length = vcopt().tspGA(param_range, tsp_2_score, 0.0, seed=1)\nend_time = time.time()\n\nroute_1 = np.hstack((param[:np.where(param==0)[0][0] + 1][::-1],0))\nroute_2 = np.hstack((param[np.where(param==0)[0][0]:],0))\nroute_1_len = get_route_length(route_1)\nroute_2_len = get_route_length(route_2)\nprint(\"Max Length : \"+str(max_length)+\"\\ntime : \"+str(end_time-start_time))\nprint(\"\\nRoute 1 : \" + str(route_1) + \"\\nRoute 1 Length : \" + str(route_1_len))\nprint(\"\\nRoute 2 : \" + str(route_2) + \"\\nRoute 2 Length : \" + str(route_2_len))","repo_name":"UnknownSP/ProgrammingExercise","sub_path":"巡回セールスマン問題/2人の場合/2TSP.py","file_name":"2TSP.py","file_ext":"py","file_size_in_byte":3886,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"6324511293","text":"import demistomock as demisto\n\n\nclass TestParseWordDoc:\n @staticmethod\n def mock_results(mocker):\n mocker.patch.object(demisto, \"results\")\n\n @staticmethod\n def mock_context(mocker, args_value=None):\n if not args_value:\n args_value = {\n \"entryID\": \"entry_id\",\n }\n mocker.patch.object(demisto, \"args\", return_value=args_value)\n\n @staticmethod\n def mock_file_path(mocker, path, name):\n mocker.patch.object(demisto, \"getFilePath\", return_value={\n \"path\": path,\n \"name\": name\n })\n\n @staticmethod\n def mock_demisto(mocker, args_value=None, file_obj=None):\n TestParseWordDoc.mock_results(mocker)\n TestParseWordDoc.mock_context(mocker, args_value)\n if file_obj:\n TestParseWordDoc.mock_file_path(mocker, **file_obj)\n\n @staticmethod\n def get_demisto_results():\n return demisto.results.call_args[0][0]\n\n @staticmethod\n def create_file_object(file_path):\n return {\n \"path\": file_path,\n \"name\": file_path.split(\"/\")[-1]\n }\n\n def test_parse_word_doc(self, mocker):\n \"\"\"\n Given:\n - A docx file\n\n When:\n - Run the ParseWordDoc script\n\n Then:\n - Verify that the docx file has now returned as .txt\n\n \"\"\"\n from ParseWordDoc import main\n self.mock_demisto(mocker, file_obj=self.create_file_object(\"./test_data/file-sample.docx\"))\n main()\n result = self.get_demisto_results()\n assert result.get('File') == 'file-sample.txt'\n\n\ndef test_extract_urls_xml_with_hyperlink():\n \"\"\"\n Given:\n - A docx file with hyperlink\n\n When:\n - Run the extract_urls_xml method\n\n Then:\n - Verify that the method extracting the url from the document\n\n \"\"\"\n from ParseWordDoc import extract_urls_xml\n urls = extract_urls_xml('./test_data/file-sample2.docx')\n assert urls == ['https://typora.io']\n\n\ndef test_extract_urls_xml_without_hyperlink():\n \"\"\"\n Given:\n - A docx file without hyperlink\n\n When:\n - Run the extract_urls_xml method\n\n Then:\n - Verify that the method extracting none urls from the document\n\n \"\"\"\n from ParseWordDoc import extract_urls_xml\n urls = extract_urls_xml('./test_data/file-sample.docx')\n assert urls == []\n\n\ndef test_extract_urls_docx_without_hyperlink():\n \"\"\"\n Given:\n - A docx file without hyperlink\n\n When:\n - Run the extract_urls_docx method\n\n Then:\n - Verify that the method extracting none urls from the document\n\n \"\"\"\n from docx import Document\n from ParseWordDoc import extract_urls_docx\n document = Document('./test_data/file-sample2.docx')\n urls = extract_urls_docx(document)\n assert urls == []\n\n\ndef test_extract_urls_docx_with_hyperlinks():\n \"\"\"\n Given:\n - A docx file with hyperlinks\n\n When:\n - Run the extract_urls_docx method\n\n Then:\n - Verify that the method extracting the urls from the document\n\n \"\"\"\n from docx import Document\n from ParseWordDoc import extract_urls_docx\n document = Document('./test_data/MS-DOCX-190319.docx')\n urls = extract_urls_docx(document)\n assert 'https://go.microsoft.com/fwlink/?LinkId=90607' in urls\n","repo_name":"demisto/content","sub_path":"Packs/CommonScripts/Scripts/ParseWordDoc/ParseWordDoc_test.py","file_name":"ParseWordDoc_test.py","file_ext":"py","file_size_in_byte":3330,"program_lang":"python","lang":"en","doc_type":"code","stars":1023,"dataset":"github-code","pt":"50"} +{"seq_id":"25730669015","text":"def calculate(n, s):\n global daily, monthly, quaterly, table, res\n\n if n >= 13:\n res = min(s, res)\n else:\n calculate(n + 1, s + daily * table[n])\n calculate(n + 1, s + monthly)\n calculate(n + 3, s + quaterly)\n\n\nfor t in range(int(input())):\n daily, monthly, quaterly, yearly = map(int, input().split())\n table = [0] + list(map(int, input().split()))\n\n res = yearly\n calculate(1, 0)\n\n print(f'#{t + 1} {res}')\n","repo_name":"myeonghan-nim/algorithm-solution-swea","sub_path":"sample/1952.py","file_name":"1952.py","file_ext":"py","file_size_in_byte":461,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"11165203953","text":"import socket\nbuffer_size=1024\ns=socket.socket(socket.AF_INET,socket.SOCK_STREAM)\n\nip_port=('127.0.0.1',1234)\ns.connect(ip_port)\nprint(\"client address:\" ,s.getsockname())\nprint(\"server address:\",s.getpeername())\nmessage=\"Hello I'm the client\".encode('utf-8')\n\ns.send(message)\nmessage=s.recv(buffer_size).decode('utf-8')\nprint(\"received message:\",message)\ns.close()","repo_name":"DrOuissem/useful_programs","sub_path":"code_python2/chapter5_networking/1_video_tcp/tcp_client.py","file_name":"tcp_client.py","file_ext":"py","file_size_in_byte":364,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"34082726313","text":"# https://www.hackerrank.com/challenges/html-parser-part-1/\n\nfrom html.parser import HTMLParser\n\nclass MyHTMLParser(HTMLParser):\n def handle_starttag(self, tag, attrs):\n print('Start :', tag)\n for attr in attrs:\n print ('->',' > '.join(map(str, attr)))\n def handle_endtag(self, tag):\n print('End :', tag)\n def handle_startendtag(self, tag, attrs):\n print('Empty :', tag)\n for attr in attrs:\n print('->',' > '.join(map(str,attr)))\n\nhtml = \"\"\nfor i in range(int(input())):\n html += input()\n\n\nparser = MyHTMLParser()\nparser.feed(html)\nparser.close()","repo_name":"johnjullies/Algorithms","sub_path":"hackerrank/python/HTML Parser - Part 1.py","file_name":"HTML Parser - Part 1.py","file_ext":"py","file_size_in_byte":625,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"50"} +{"seq_id":"71651256154","text":"from collections import deque\ndef solution(maps):\n N, M = len(maps), len(maps[0])\n visit = [[0 for i in range(M)] for i in range(N)]\n Q = deque()\n Q.append([0,0])\n visit[0][0] = 1\n dx, dy = [1,0,-1,0], [0,1,0,-1]\n\n while Q:\n x, y = Q.popleft()\n for i in range(4):\n nx, ny = x + dx[i], y + dy[i]\n if nx < 0 or nx >= N or ny < 0 or ny >= M: continue\n if maps[nx][ny] == 0 or visit[nx][ny]: continue\n Q.append([nx, ny])\n visit[nx][ny] = visit[x][y] + 1\n\n\n return visit[-1][-1] if visit[-1][-1] else -1","repo_name":"Jungdomo/PCT","sub_path":"2/1844/문정민.py","file_name":"문정민.py","file_ext":"py","file_size_in_byte":591,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"40187514540","text":"import FWCore.ParameterSet.Config as cms\n\nprocess = cms.Process(\"EDMtoMEConvert\")\n\nprocess.load(\"FWCore.MessageLogger.MessageLogger_cfi\")\nprocess.MessageLogger.cerr.FwkReport.reportEvery = 2000\n\nprocess.load('Configuration.StandardSequences.EDMtoMEAtJobEnd_cff')\nprocess.load(\"DQMServices.Components.DQMEnvironment_cfi\")\nprocess.load(\"HLTriggerOffline.Higgs.HLTHiggsPostVal_cff\")\n#process.load(\"HLTriggerOffline.Higgs.HLTHiggsQualityTester_cfi\")\n\n\nprocess.maxEvents = cms.untracked.PSet(\n input = cms.untracked.int32(-1)\n)\n\nprocess.source = cms.Source(\"PoolSource\",\n fileNames = cms.untracked.vstring('file:hltHiggsValidator.root')\n)\n\nprocess.postprocessor_path = cms.Path(\n process.HLTHiggsPostVal\n # process.hltHiggsPostProcessors #*\n # process.hltHiggsQualityTester\n)\n\nprocess.edmtome_path = cms.Path(process.EDMtoME)\nprocess.dqmsave_path = cms.Path(process.DQMSaver)\n\nprocess.schedule = cms.Schedule(process.edmtome_path,\n process.postprocessor_path,\n process.dqmsave_path)\n\n","repo_name":"cms-sw/cmssw","sub_path":"HLTriggerOffline/Higgs/test/hltHiggsPostProcessor_cfg.py","file_name":"hltHiggsPostProcessor_cfg.py","file_ext":"py","file_size_in_byte":1069,"program_lang":"python","lang":"en","doc_type":"code","stars":985,"dataset":"github-code","pt":"50"} +{"seq_id":"25883999021","text":"from rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom rest_framework import status\nfrom . import models, serializers\nfrom honugram.users import models as user_models\nfrom honugram.users import serializers as user_serializers\nfrom honugram.notifications import views as notification_views\n\n# viewing all image in database.\n# this practic test. service dev is not adjust to it.\n\nclass ListAllImages(APIView):\n\n def get(self, request, format=None):\n\n print(request.scheme)\n print(request.body)\n\n all_images = models.Image.objects.all()\n\n serializer = serializers.ImageSerializer(all_images, many=True)\n\n return Response(data=serializer.data)\n\nlist_all_images_view = ListAllImages.as_view()\n\nclass ListAllComments(APIView):\n\n def get(self, request, format=None):\n\n all_comments = models.Comment.objects.all()\n\n serializer = serializers.CommentSerializer(all_comments, many=True)\n\n return Response(data=serializer.data)\n\nlist_all_comments_view = ListAllComments.as_view()\n\n\nclass ListAllLikes(APIView):\n\n def get(self, request, format=None):\n\n all_likes = models.Like.objects.all()\n\n print(request.user.website)\n\n serializer = serializers.LikeSerializer(all_likes, many=True)\n\n return Response(data=serializer.data)\n\nlist_all_likes_view = ListAllLikes.as_view()\n\nclass Images(APIView):\n def get(self, request, format=None):\n\n user = request.user\n\n following_users = user.following.all()\n followers_users = user.followers.all()\n\n print(following_users)\n print(followers_users)\n\n image_list = []\n\n for following_user in following_users:\n\n user_image = following_user.images.all()[:2]\n\n for image in user_image:\n image_list.append(image)\n \n my_images = models.Image.objects.all()[:2]\n\n for image in my_images:\n image_list.append(image)\n \n sorted_image = sorted(image_list, key=lambda image: image.created_at , reverse=True)\n print(sorted_image)\n\n serializer = serializers.ImageSerializer(sorted_image, many=True, context={\"request\": request})\n\n return Response(serializer.data)\n\n def put(self, request, format=None):\n\n user = request.user \n\n serializer = serializers.InputImageSerializer(data=request.data)\n\n if serializer.is_valid():\n\n serializer.save(creator=user)\n\n return Response(data=serializer.data, status=status.HTTP_201_CREATED)\n\n else:\n return Response(data=serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n class Meta():\n ordering = ['-created_at']\n\n# this function changed ramda experssion ( ex> 70 lines)\n# def get_key(image):\n# return image.created_at\n\nimages_view = Images.as_view()\n\nclass LikeImage(APIView):\n def get(self, request, image_id, format=None):\n\n user = request.user\n\n likes = models.Like.objects.filter(image__id=image_id)\n\n like_creator_ids = likes.values('creator_id')\n\n users = user_models.User.objects.filter(id__in=like_creator_ids)\n print(likes.values('creator_id'))\n print(users)\n\n serializer = user_serializers.ListUserSerializer(users, many=True, context={\"request\": request}) \n\n return Response(data=serializer.data, status=status.HTTP_200_OK)\n \n\n def post(self, request, image_id, format=None):\n \n user = request.user\n\n try:\n found_image = models.Image.objects.get(id=image_id)\n except models.Image.DoesNotExist:\n return Response(status=status.HTTP_404_NOT_FOUND) \n\n try:\n preexisting_like = models.Like.objects.get(\n creator=user,\n image=found_image,\n )\n\n return Response(status=status.HTTP_304_NOT_MODIFIED)\n\n except models.Like.DoesNotExist:\n\n new_like = models.Like.objects.create(\n creator=user,\n image=found_image,\n )\n\n new_like.save()\n\n notification_views.create_notification(\n user, found_image.creator, 'like', found_image)\n \n return Response(status=status.HTTP_201_CREATED)\n\nlike_image_view = LikeImage.as_view()\n\nclass UnlikeImage(APIView):\n def delete(self, request, image_id, format=None):\n \n user = request.user\n\n try:\n found_image = models.Image.objects.get(id=image_id)\n except models.Image.DoesNotExist:\n return Response(status=status.HTTP_404_NOT_FOUND) \n\n try:\n preexisting_like = models.Like.objects.get(\n creator=user,\n image=found_image,\n )\n\n preexisting_like.delete()\n\n return Response(status=status.HTTP_204_NO_CONTENT)\n\n except models.Like.DoesNotExist:\n \n return Response(status=status.HTTP_304_NOT_MODIFIED)\n\nunlike_image_view = UnlikeImage.as_view()\n\nclass CommentOnImage(APIView):\n\n def post(self, request, image_id, format=None):\n\n user = request.user\n\n try:\n found_image = models.Image.objects.get(id=image_id)\n except models.Image.DoesNotExist:\n return Response(status=status.HTTP_404_NOT_FOUND) \n \n serializer = serializers.CommentSerializer(data=request.data)\n\n if serializer.is_valid() :\n\n serializer.save(creator=user, image=found_image)\n\n notification_views.create_notification(\n user, found_image.creator, 'comment', found_image, serializer.data['message']\n )\n\n return Response(data=serializer.data, status=status.HTTP_201_CREATED)\n else:\n return Response(data=serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\ncomment_on_image_view = CommentOnImage.as_view()\n\nclass Comment(APIView):\n def delete(self, request, comment_id, format=None):\n\n user = request.user\n\n try:\n found_comment = models.Comment.objects.get(id=comment_id, creator=user\n )\n found_comment.delete()\n return Response(status=status.HTTP_204_NO_CONTENT)\n except models.Comment.DoesNotExist:\n return Response(status=status.HTTP_404_NOT_FOUND)\n\ncomment_view = Comment.as_view()\n\nclass Search(APIView):\n\n def get(self, request, format=None):\n\n hashtags = request.query_params.get('hashtags', None)\n\n if hashtags is not None:\n\n hashtags = hashtags.split(\",\")\n\n images = models.Image.objects.filter(\n tags__name__in=hashtags).distinct()\n\n serializer = serializers.ImageSerializer(\n images, many=True, context={'request': request})\n\n print(serializer.data)\n\n return Response(data=serializer.data, status=status.HTTP_200_OK)\n\n else:\n\n images = models.Image.objects.all()[:20]\n serializer = serializers.ImageSerializer(\n images, many=True, context={'request': request})\n return Response(data=serializer.data, status=status.HTTP_200_OK)\n\nsearch_view = Search.as_view()\n\nclass ModerateComment(APIView):\n def delete(self, request, image_id, comment_id, format=None):\n\n user = request.user\n\n try:\n comment_to_delete = models.Comment.obejects.get(\n id=comment_id, image__id=image_id, image__creator=user)\n\n comment_to_delete.delete()\n\n except models.Comment.DoesNotExist:\n return Response(status=status.HTTP_404_NOT_FOUND)\n\n return Response(status=status.HTTP_204_NO_CONTENT)\n \n\nmoderate_comment_view = ModerateComment.as_view()\n\nclass ImageDetail(APIView):\n\n def find_own_image(self, image_id, user):\n try:\n image = models.Image.objects.get(id=image_id, creator=user)\n return image\n except models.Image.DoesNotExist:\n return None\n\n def get(self, request, image_id, format=None):\n\n user = request.user\n\n try:\n image = models.Image.objects.get(id=image_id)\n except models.Image.DoesNotExist:\n print(\"not found imag!!!!\")\n return Response(status=status.HTTP_404_NOT_FOUND)\n\n serializer = serializers.ImageSerializer(image)\n\n return Response(data=serializer.data, status=status.HTTP_200_OK)\n\n def put(self, request, image_id, format=None):\n\n user = request.user \n\n image = self.find_own_image(image_id, user)\n\n if image is None:\n return Response(status=status.HTTP_400_BAD_REQUEST)\n\n serializer = serializers.InputImageSerializer(image, data=request.data, partial=True)\n\n if serializer.is_valid():\n\n serializer.save(creator=user)\n\n return Response(data=serializer.data, status=status.HTTP_204_NO_CONTENT)\n \n else: \n\n return Response(data=serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n def delete(self, request, image_id, format=None):\n user = request.user \n\n image = self.find_own_image(image_id, user)\n \n if image is None:\n return Response(status=status.HTTP_400_BAD_REQUEST)\n\n image.delete()\n\n return Response(status=status.HTTP_204_NO_CONTENT)\n \nimage_detail_view = ImageDetail.as_view()","repo_name":"honu1/honugram","sub_path":"honugram/images/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":9421,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"3319883627","text":"# radix sort\n\nclass LLQueue:\n\n class Node:\n def __init__(self, data=None, next=None, prev=None) -> None:\n self.data = data if data != None else None\n self.next = next if next != None else None\n self.prev = prev if prev != None else None\n\n def __init__(self, items=None) -> None:\n # dummy nodes\n self.head = self.Node()\n self.tail = self.Node(prev=self.head)\n self.head.next = self.tail\n\n if items != None:\n for e in items:\n self.enQueue(e)\n\n def enQueue(self, data):\n # append ต่อท้าย\n rear = self.tail.prev\n rear.next = self.Node(data, self.tail, rear)\n self.tail.prev = rear.next\n \n\n def deQueue(self):\n if self.isEmpty():\n return print(\"Queue is Empty\")\n else:\n dq = self.head.next\n self.head.next = dq.next\n dq.next.prev = dq.prev\n return dq.data\n\n def isEmpty(self):\n return self.head.next == self.tail\n\n def peek(self):\n return self.head.next.data\n\n def __len__(self):\n if self.isEmpty():\n return 0\n else:\n t = self.head.next\n size = 0\n while t.next != self.tail:\n size += 1\n return size\n\n def __str__(self) -> str:\n if self.isEmpty():\n return ''\n else:\n t = self.head.next\n s = []\n while t != self.tail:\n s.append(str(t.data))\n t = t.next\n return ' -> '.join(s)\n\n def str_reverse(self) -> str:\n s = self.__str__().split(' -> ')\n s = s[::-1]\n return ' -> '.join(s)\n\ndef get_digit(value, digit):\n # 0 -> หลักหน่วย\n for i in range(digit):\n value = int(value)\n value //= 10\n\n return str(int(value) % 10)\n\ndef splitPosNeg(arr):\n pos = []\n neg = []\n for e in arr:\n # e = int(e)\n if int(e) < 0:\n neg.append(e)\n else:\n pos.append(e)\n return pos, neg\n\ndef is_mixedsign(arr):\n pos = 0\n neg = 0\n for i in arr:\n # i = int(i)\n if int(i) < 0:\n neg += 1\n else:\n pos += 1\n if pos > 0 and neg > 0:\n return True\n \n return False\n\ndef getMaxDigit(arr):\n max = int(arr[0])\n for i in arr:\n i = int(i)\n if i < 0:\n i *= -1\n\n if i > max:\n max = i\n\n return str(max)\n\ndef radix_sort(arr, isDisplay=False):\n q = LLQueue(arr)\n \n max_digits = getMaxDigit(arr)\n \n qq = [\n LLQueue(), LLQueue(), LLQueue(), LLQueue(), LLQueue(),\n LLQueue(), LLQueue(), LLQueue(), LLQueue(), LLQueue()\n ]\n \n # print(\"------------------------------------------------------------\")\n for i in range(len(str(max_digits))):\n \n while not q.isEmpty():\n if int(q.peek()) < 0:\n cur_digit = get_digit(str(q.peek()[1:]), i)\n else:\n cur_digit = get_digit(str(q.peek()), i)\n qq[int(cur_digit)].enQueue(str(q.deQueue()))\n # if isDisplay == True:\n # print(\"Round : \" + str(i))\n \n for j in range(10):\n # if isDisplay == True:\n # print(f\"{j} : {qq[j]}\")\n \n while not qq[j].isEmpty():\n q.enQueue(str(qq[j].deQueue()))\n \n # if isDisplay == True:\n # print(\"------------------------------------------------------------\")\n return q\n\nif __name__ == '__main__':\n inp = input('Enter input : ').split()\n \n print(f\"{len(getMaxDigit(inp))} Time(s)\")\n print(\"Before Radix Sort :\", \" -> \".join(inp))\n \n if is_mixedsign(inp):\n pos, neg = splitPosNeg(inp)\n qpos = radix_sort(pos)\n qneg = radix_sort(neg)\n \n # print(' '.join(qneg.items), ' '.join(qpos.items))\n radix_sort(inp, True)\n print(\"After Radix Sort :\", qneg.str_reverse(),\" ->\", qpos)\n else: \n print(\"After Radix Sort :\", radix_sort(inp))\n \n \n \n ","repo_name":"erumtw/oods-in-practice","sub_path":"5_LinkedList/ch5_item5.py","file_name":"ch5_item5.py","file_ext":"py","file_size_in_byte":4174,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"27331625130","text":"from behave.model import Feature, Scenario\n\nfrom feature_tests.domain.steps.context import Context\nfrom feature_tests.feature_test_helpers import TestMode\n\n\ndef before_feature(context: Context, feature: Feature):\n # At present we only run domain tests in 'local' mode\n test_mode = TestMode.parse(config=context.config)\n if test_mode is not TestMode.LOCAL:\n feature.mark_skipped()\n\n\ndef before_scenario(context: Context, scenario: Scenario):\n context.questionnaires = {}\n context.ods_organisations = {}\n context.product_teams = {}\n context.events = []\n context.error = None\n context.result = None\n context.subject = None\n","repo_name":"NHSDigital/connecting-party-manager","sub_path":"feature_tests/domain/environment.py","file_name":"environment.py","file_ext":"py","file_size_in_byte":657,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"50"} +{"seq_id":"15996957818","text":"#! /usr/bin/python\n\n'''\nGiven two binary trees, write a function to check if they are equal or not.\n\nTwo binary trees are considered equal if they are structurally identical and the nodes have the same value.\n'''\n\n\nclass Solution:\n # @param p, a tree node\n # @param q, a tree node\n # @return a boolean\n def isSameTree(self, p, q):\n if not p or not q:\n return p == q\n else:\n return p.val == q.val and self.isSameTree(p.left, q.left) and self.isSameTree(p.right, q.right)\n\n def isSameTreeNonRecursive(self, p, q):\n nodes_in_tree = list()\n nodes_in_tree.append((p, q))\n while len(nodes_in_tree) > 0:\n node1, node2 = nodes_in_tree.pop(0)\n if not node1 and not node2:\n continue\n elif not node1 or not node2:\n return False\n elif node1.val != node2.val:\n return False\n else:\n nodes_in_tree.append((node1.left, node2.left))\n nodes_in_tree.append((node1.right, node2.right))\n return True\n","repo_name":"shub0/algorithm-data-structure","sub_path":"python/same_tree.py","file_name":"same_tree.py","file_ext":"py","file_size_in_byte":1088,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"33094309911","text":"# DELETE\n# cities = ['Pune', 'Mumbai', 'Nagpur', 'Nashik', 'Kolhapur']\n# print(cities)\n# # del cities[2]\n# # del cities[3:]\n# # del cities[:]\n# # del cities\n# print(cities)\n\n# ADD\n# booking_ratings = [8, 8, 9, 7, 9]\n# print(booking_ratings)\n# booking_ratings.append(7)\n# print(booking_ratings)\n# booking_ratings.insert(1, 10)\n# print(booking_ratings)\n\n# TRAVERSE\n# cities = ['Pune', 'Mumbai', 'Nagpur', 'Nashik', 'Kolhapur']\n# # for city in cities:\n# # print(cities.index(city), ':', city)\n#\n# for index in range(len(cities)):\n# print(index, ':', cities[index])\n\nspendings = [90.50, 45, 35.50, 67.50, 12]\ntotal = 0.0\nfor spending in spendings:\n total += spending\nprint('Total: ', total)\n","repo_name":"saurabh-kumbhar/python-course-practice","sub_path":"PythonEntryLevel/module4-insert-delete-traverse-lists.py","file_name":"module4-insert-delete-traverse-lists.py","file_ext":"py","file_size_in_byte":698,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"20149922425","text":"def fibo (x):\r\n if x==1:\r\n return 0\r\n if x==2:\r\n return 1\r\n return fibo(x-1)+fibo(x-2)\r\n\r\n\r\ndef fibo2 (x):\r\n result=0\r\n n1=0\r\n n2=1\r\n print('0',end=' => ')\r\n for i in range(n-1):\r\n result = n1 + n2\r\n n2=n1\r\n n1=result\r\n if i==n-2:\r\n print(result)\r\n else:\r\n print(result,end=' => ')\r\n return result\r\n\r\nn = int(input('Quantos termos você deseja: '))\r\n(fibo2(n))\r\n\r\n\r\n","repo_name":"brpadilha/exercicioscursoemvideo","sub_path":"Desafios/fibonacci function.py","file_name":"fibonacci function.py","file_ext":"py","file_size_in_byte":464,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"50"} +{"seq_id":"28479039445","text":"from netgen.geom2d import SplineGeometry\nfrom ngsolve import (Mesh, L2, GridFunction, CoefficientFunction,\n exp, x, y, TaskManager)\nfrom ngstents import TentSlab\nfrom ngstents.conslaw import Burgers\n\n\ndef test_burgers2D():\n order = 2\n dt = 0.025\n c = 16\n tend = dt\n method = \"edge\"\n\n geom = SplineGeometry()\n geom.AddRectangle((0, 0), (1, 1), bc=1)\n mesh = Mesh(geom.GenerateMesh(maxh=0.2))\n ts = TentSlab(mesh, method)\n ts.SetMaxWavespeed(c)\n success = ts.PitchTents(dt)\n try:\n assert success is True\n except AssertionError as e:\n msg = \"Slab could not be pitched\"\n e.args += (\"Failed to pitch slab\", msg)\n raise\n cf = CoefficientFunction(exp(-50*((x-0.3)*(x-0.3)+(y-0.3)*(y-0.3))))\n\n V = L2(mesh, order=order)\n u = GridFunction(V, \"u\")\n burg = Burgers(u, ts)\n burg.SetTentSolver(\"SARK\", substeps=order*order)\n burg.SetInitial(cf)\n\n t = 0\n with TaskManager():\n while t < tend - dt/2:\n burg.Propagate()\n t += dt\n","repo_name":"jayggg/ngstents","sub_path":"tests/test_burgers_2D.py","file_name":"test_burgers_2D.py","file_ext":"py","file_size_in_byte":1052,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"52"} +{"seq_id":"8628464028","text":"import os\nimport csv\n\n#Set path for csv file\ncsvpath = os.path.join('.', 'Resources', 'election_data.csv')\n\n#Declare variables and empty lists\nvoter_ids = []\ncounties = []\ncandidates = []\nunique_candidates = []\nvotes_per_candidate = []\ntotal_votes = 0\n\n\n#Open csv in read mode\nwith open(csvpath, newline='') as csvfile:\n #Store contents of election_data in the variable csvreader\n csvreader = csv.reader(csvfile, delimiter=\",\")\n\n #skip the header lables\n header = next(csvreader)\n\n #Iterate through each row and append \n for row in csvreader:\n voter_ids.append(row[0])\n counties.append(row[1])\n candidates.append(row[2])\n total_votes += 1\n\n\n #Calculate the total numbr of votes each candidate received and store in list\n for candidate in set(candidates):\n unique_candidates.append(candidate)\n votes = candidates.count(candidate)\n votes_per_candidate.append(votes)\n unique_candidates.sort()\n\n #Calculate the percentage of votes each candidate received\n percentages = [(votes / total_votes) * 100 for votes in votes_per_candidate]\n\n #Find the candidate with the most votes to find the winner\n winner_index = votes_per_candidate.index(max(votes_per_candidate))\n winner = unique_candidates[winner_index]\n\n#Print the results\nprint(\"Election Results\")\nprint(\"-------------------------\")\nprint(f\"Total Votes: {total_votes}\")\nprint(\"-------------------------\")\nfor i in range(len(unique_candidates)):\n print(f\"{unique_candidates[i]}: {percentages[i]:.3f}% ({votes_per_candidate[i]})\")\nprint(\"-------------------------\")\nprint(f\"Winner: {winner}\")\nprint(\"-------------------------\")\n\n\n#export results to a text file\nelection_file = os.path.join('analysis', 'election_summary.txt')\nwith open(election_file, \"w\") as file:\n\n#Write to file\n file.write(\"Election Results\\n\")\n file.write(\"-------------------------\\n\")\n file.write(f\"Total Votes: {total_votes}\\n\")\n file.write(\"-------------------------\\n\")\n for i in range(len(unique_candidates)):\n file.write(f\"{unique_candidates[i]}: {percentages[i]:.3f}% ({votes_per_candidate[i]})\\n\") \n file.write(\"-------------------------\\n\")\n file.write(f\"Winner: {winner}\\n\")\n file.write(\"-------------------------\\n\")","repo_name":"jepowers65/python-challenge","sub_path":"PyPoll/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2281,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"27426611916","text":"def solution(gems):\n length = len(set(gems))\n dic = {gems[0]:1}\n start,end = 0,0\n result = [0,len(gems)-1]\n while start < len(gems) and end dict:\n url = 'https://api.superjob.ru/2.0/vacancies'\n all_salaries = []\n for page in count():\n params.update(page=page)\n page_response = requests.get(url=url, headers=headers, params=params)\n page_response.raise_for_status()\n decoded_page_response = page_response.json()\n vacancies = decoded_page_response['objects']\n for vacancy in vacancies:\n all_salaries.append(predict_rub_salary_sj(vacancy))\n if not decoded_page_response['more']:\n break\n return {\n 'vacancies_found': decoded_page_response['total'],\n 'vacancies_processed': len(all_salaries),\n 'average_salary': calculate_average(all_salaries)\n }\n\n\ndef set_sj_parameters(language: str, town: str) -> tuple[dict, dict]:\n params = {'town': town, 'keyword': f'{language} разработчик', 'count': 100}\n headers = {'X-Api-App-Id': os.getenv('SJ_API_KEY')}\n return params, headers\n\n\ndef main():\n load_dotenv()\n sj_vacancies = {}\n keywords, town = parse_arguments()\n for language in keywords:\n params, headers = set_sj_parameters(language, town)\n sj_vacancies[language] = fetch_all_salaries_sj(params, headers)\n print(make_table(sj_vacancies, title='SuperJob Analytics'))\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"triple-s-rank/developers_salary_app","sub_path":"sj_statistics.py","file_name":"sj_statistics.py","file_ext":"py","file_size_in_byte":1609,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"15083721590","text":"import sys\nfrom typing import Optional, List, Any\n\nimport pygame\n\nfrom pythongame.core.common import Millis, SceneTransition, AbstractScene\nfrom pythongame.core.game_data import ENTITY_SPRITE_INITIALIZERS, \\\n UI_ICON_SPRITE_PATHS, PORTRAIT_ICON_SPRITE_PATHS\nfrom pythongame.core.game_state import GameState\nfrom pythongame.core.sound_player import init_sound_player\nfrom pythongame.core.view.game_world_view import GameWorldView\nfrom pythongame.core.view.image_loading import load_images_by_sprite, \\\n load_images_by_ui_sprite, load_images_by_portrait_sprite\nfrom pythongame.core.world_behavior import AbstractWorldBehavior\nfrom pythongame.player_file import SaveFileHandler\nfrom pythongame.register_game_data import register_all_game_data\nfrom pythongame.scene_challenge_complete_screen.scene_challenge_complete_screen import ChallengeCompleteScreenScene\nfrom pythongame.scene_creating_world.scene_creating_world import CreatingWorldScene, InitFlags\nfrom pythongame.scene_main_menu.scene_main_menu import MainMenuScene\nfrom pythongame.scene_main_menu.view_main_menu import MainMenuView\nfrom pythongame.scene_picking_hero.scene_picking_hero import PickingHeroScene\nfrom pythongame.scene_picking_hero.view_picking_hero import PickingHeroView\nfrom pythongame.scene_starting_program.scene_starting_program import CommandlineFlags, StartingProgramScene\nfrom pythongame.scene_victory_screen.scene_victory_screen import VictoryScreenScene\nfrom pythongame.scenes_game.game_engine import GameEngine\nfrom pythongame.scenes_game.game_ui_view import GameUiView, UI_ICON_SIZE, PORTRAIT_ICON_SIZE, UI_ICON_BIG_SIZE\nfrom pythongame.scenes_game.scene_playing import PlayingScene\n\nABILITY_KEY_LABELS = [\"Q\", \"W\", \"E\", \"R\", \"T\"]\nSCREEN_SIZE = (800, 600) # If this is not a supported resolution, performance takes a big hit\nCAMERA_SIZE = (800, 430)\n\nregister_all_game_data()\n\n\nclass Main:\n def __init__(self, map_file_name: Optional[str], chosen_hero_id: Optional[str], hero_start_level: Optional[int],\n start_money: Optional[int]):\n\n cmd_flags = CommandlineFlags(map_file_name, chosen_hero_id, hero_start_level, start_money)\n\n pygame.init()\n\n print(\"Available display modes: \" + str(pygame.display.list_modes()))\n\n self.fullscreen = False # TODO\n self.pygame_screen = self.setup_screen()\n images_by_sprite = load_images_by_sprite(ENTITY_SPRITE_INITIALIZERS)\n images_by_ui_sprite = load_images_by_ui_sprite(UI_ICON_SPRITE_PATHS, UI_ICON_SIZE)\n big_images_by_ui_sprite = load_images_by_ui_sprite(UI_ICON_SPRITE_PATHS, UI_ICON_BIG_SIZE)\n self.images_by_portrait_sprite = load_images_by_portrait_sprite(PORTRAIT_ICON_SPRITE_PATHS, PORTRAIT_ICON_SIZE)\n self.world_view = GameWorldView(self.pygame_screen, CAMERA_SIZE, SCREEN_SIZE, images_by_sprite)\n self.ui_view = GameUiView(\n self.pygame_screen, CAMERA_SIZE, SCREEN_SIZE, images_by_ui_sprite,\n big_images_by_ui_sprite, self.images_by_portrait_sprite, ABILITY_KEY_LABELS)\n self.save_file_handler = SaveFileHandler()\n init_sound_player()\n self.clock = pygame.time.Clock()\n\n self.scene: AbstractScene = StartingProgramScene(\n self.main_menu_scene, self.creating_world_scene, self.picking_hero_scene, cmd_flags, self.save_file_handler)\n\n def main_loop(self):\n while True:\n self.clock.tick()\n time_passed = Millis(self.clock.get_time())\n fps_string = str(int(self.clock.get_fps()))\n self.ui_view.update_fps_string(fps_string)\n\n input_events: List[Any] = pygame.event.get()\n for event in input_events:\n if event.type == pygame.QUIT:\n self.quit_game()\n elif event.type == pygame.KEYDOWN and event.key == pygame.K_ESCAPE and self.fullscreen:\n self.toggle_fullscreen()\n self.ui_view.on_fullscreen_changed(self.fullscreen)\n\n transition: Optional[SceneTransition] = self.scene.handle_user_input(input_events)\n if transition:\n self.change_scene(transition)\n continue\n\n transition: Optional[SceneTransition] = self.scene.run_one_frame(time_passed)\n if transition:\n self.change_scene(transition)\n continue\n\n self.scene.render()\n pygame.display.update()\n\n def toggle_fullscreen(self):\n self.fullscreen = not self.fullscreen\n self.pygame_screen = self.setup_screen()\n\n def setup_screen(self):\n flags = pygame.DOUBLEBUF\n if self.fullscreen:\n flags = flags | pygame.FULLSCREEN | pygame.HWSURFACE\n return pygame.display.set_mode(SCREEN_SIZE, flags)\n\n @staticmethod\n def quit_game():\n pygame.quit()\n sys.exit()\n\n def change_scene(self, scene_transition: SceneTransition):\n self.scene = scene_transition.scene\n self.scene.on_enter()\n\n def main_menu_scene(self, flags: InitFlags):\n view = MainMenuView(self.pygame_screen, self.images_by_portrait_sprite)\n return MainMenuScene(\n self.save_file_handler, self.picking_hero_scene, self.creating_world_scene, flags, view)\n\n def creating_world_scene(self, flags: InitFlags):\n return CreatingWorldScene(self.playing_scene, self.picking_hero_scene, self.challenge_complete_scene,\n self.victory_screen_scene, CAMERA_SIZE, self.ui_view, flags)\n\n def picking_hero_scene(self, init_flags: InitFlags):\n view = PickingHeroView(self.pygame_screen, self.images_by_portrait_sprite)\n return PickingHeroScene(self.creating_world_scene, view, init_flags)\n\n def playing_scene(\n self, game_state: GameState, game_engine: GameEngine, world_behavior: AbstractWorldBehavior,\n ui_view: GameUiView, new_hero_was_created: bool, character_file: Optional[str],\n total_time_played_on_character: Millis):\n return PlayingScene(\n self.world_view, game_state, game_engine, world_behavior, ui_view, new_hero_was_created,\n character_file, self.save_file_handler, total_time_played_on_character, self.toggle_fullscreen)\n\n def challenge_complete_scene(self, total_time_played: Millis):\n return ChallengeCompleteScreenScene(self.pygame_screen, total_time_played)\n\n def victory_screen_scene(self):\n return VictoryScreenScene(self.pygame_screen)\n\n\ndef start(map_file_name: Optional[str], chosen_hero_id: Optional[str], hero_start_level: Optional[int],\n start_money: Optional[int]):\n main = Main(map_file_name, chosen_hero_id, hero_start_level, start_money)\n main.main_loop()\n","repo_name":"risooonho/python-2d-game","sub_path":"pythongame/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6722,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"52"} +{"seq_id":"26177991842","text":"import base64\nimport json\nimport os\nimport time\nimport random\n\nimport jwt\nfrom django.conf import settings\nfrom django.conf.global_settings import MEDIA_URL\nfrom django.http import HttpResponse, JsonResponse\nfrom django.shortcuts import render\n\n# Create your views here.\nfrom data.models import Teacher, Image, Course, Grade\n\nimport hashlib\ndef register(request):\n #判断前端的请求方式\n if request.method ==\"POST\":\n #print(request.body)\n #数据格式是json字节串：b'{\"teacheraccount\":\"\",\"teacherpswd1\":\"\",\"teacherpswd2\":\"\",\"teacheremail\":\"\",\"phone\":\"\"}'\n # request.post 只能获取post表单提交数据\n # request.body 能获取除表单提交的其他数据\n teacher_data = request.body\n if not teacher_data:\n result = {\"code\":10101,\"error\":\"Please give me a data\"}\n return JsonResponse(result)\n #获得json数据\n json_obj = json.loads(teacher_data)\n #检查数据\n teacher_account = json_obj.get(\"teacheraccount\")\n teacherpswd1 = json_obj.get(\"teacherpswd1\")\n teacherpswd2 = json_obj.get(\"teacherpswd2\")\n teacheremail = json_obj.get(\"teacheremail\")\n teacherphone = json_obj.get(\"phone\")\n if not teacher_account:\n result = {\"code\":10102,\"error\":\"请输入账号\"}\n return JsonResponse(result)\n if not teacherpswd1:\n result = {\"code\":10103,\"error\":\"请输入密码\"}\n return JsonResponse(result)\n if not teacherpswd2:\n result = {\"code\": 10103, \"error\":\"请输入密码\"}\n return JsonResponse(result)\n if teacherpswd1 != teacherpswd2:\n result ={\"code\":10106,\"error\":\"两次密码输入不一致\"}\n return JsonResponse(result)\n if not teacheremail:\n result = {\"code\": 10104, \"error\":\"请输入邮箱\"}\n return JsonResponse(result)\n if not teacherphone :\n result = {\"code\": 10105, \"error\":\"请输入手机号\"}\n return JsonResponse(result)\n #检查老师账号是否可用\n old_teacher = Teacher.objects.filter(teacher_account=teacher_account)\n if old_teacher:\n result = {\"code\":10107,\"error\":\"该用户名已经存在!\"}\n return JsonResponse(result)\n #密码加密\n m = hashlib.md5()\n m.update(teacherpswd2.encode())\n #创建用户:\n try:\n Teacher.objects.create(teacher_account=teacher_account,teacher_password=m.hexdigest(),\n teacher_email=teacheremail,teacher_phone=teacherphone)\n except Exception as e :\n result ={\"code\":10108,\"error\":\"该用户已存在!!!\"}\n return JsonResponse(result)\n #发送token\n token = make_token(teacher_account)\n result = {\"code\":200,\"teacher_account\":teacher_account,\"data\":{\"token\":token.decode()}}\n return JsonResponse(result)\n#定义一个制作token的函数\ndef make_token(teacher_account,exp=3600*24):\n now = time.time()\n #key\n key = settings.TOKEN_KEY\n #官方jwt\n return jwt.encode({\"teacher_account\":teacher_account,\"exp\":now+exp},key,algorithm=\"HS256\")\n\n#老师登录\ndef login(request):\n if request.method == \"POST\":\n #获取除post方式之外的其他的数据\n teacher_data = request.body\n #检查数据\n if not teacher_data:\n result = {\"code\":10109,\"error\":\"Please gave me data\"}\n return JsonResponse(result)\n #获取json数据\n json_obj = json.loads(teacher_data)\n teacher_account = json_obj.get(\"teacher_account\")\n teacher_password = json_obj.get(\"pwd\")\n #检查数据\n if not teacher_account:\n result = {\"code\":10110,\"error\":\"请填写账号\"}\n return JsonResponse(result)\n if not teacher_password:\n result ={\"code\":10111,\"error\":\"请填写密码\"}\n return JsonResponse(result)\n #从数据库查询该用户名是否存在\n teacher = Teacher.objects.filter(teacher_account=teacher_account)\n if not teacher:\n result = {\"code\":10112,\"error\":\"账号或者密码错误\"}\n return JsonResponse(result)\n #前端的发送的密码进行加密\n m = hashlib.md5()\n m.update(teacher_password.encode())\n #账号存在 检查密码是否正确\n if m.hexdigest() != teacher[0].teacher_password:\n result = {\"code\":10113,\"error\":\"账号或者密码错误\"}\n return JsonResponse(result)\n #发送token\n token = make_token(teacher_account)\n result = {\"code\": 200,\"teacher_account\":teacher_account,\"data\":{\"token\":token.decode()}}\n return JsonResponse(result)\n if request.method != \"POST\":\n result = {\"code\": 10114, \"error\": \"PLease use POST request\"}\n return JsonResponse(result)\n#验证登录装饰器\ndef login_check1(fn):\n def swarp(request,*args,**kwargs):\n # 校验token\n teacher_data = request.body\n json_obj = json.loads(teacher_data)\n teacher_token = json_obj.get(\"teacher_token\")\n if not teacher_token:\n result = {'code': 10115, 'error': 'Please login'}\n print('logging check no token')\n return JsonResponse(result)\n try:\n res = jwt.decode(teacher_token, settings.TOKEN_KEY, algorithms='HS256')\n # print(res)\n except Exception as e:\n print('---jwt error is %s'%(e))\n result = {'code': 10116, 'error': 'Please login'}\n return JsonResponse(result)\n teacher_account = res['teacher_account']\n try:\n teacher = Teacher.objects.get(teacher_account=teacher_account)\n except Exception as e:\n result = {\"code\":10118,\"error\":\"该用户未曾登录或者注册\"}\n return JsonResponse(result)\n request.teacher = teacher\n request.teacher_account = teacher_account\n return fn(request,*args,**kwargs)\n return swarp\n#保存老师信息\n@login_check1\ndef save(request):\n if request.method ==\"POST\":\n teacher_account = request.teacher_account\n teacher_data = request.body\n #解析前端json串\n json_obj = json.loads(teacher_data)\n # print(json_obj)\n teacher = Teacher.objects.get(teacher_account=teacher_account)\n teacher.teacher_name = json_obj.get(\"name\")\n teacher.teacher_sex = json_obj.get(\"sex\")\n teacher_bir = json_obj.get(\"bir\")\n year = teacher_bir[:4]\n month = teacher_bir[4:6]\n data_list = [year,month,\"01\"]\n teacher.teacher_age1 = (\"-\").join(data_list)\n print(teacher.teacher_age1)\n teacher.teacher_address = json_obj.get(\"address\")\n school_name = json_obj.get(\"school_name\")\n print(school_name)\n cate = json_obj.get(\"cate\")\n teacher.teacher_education = school_name + \" \"+cate\n teacher.teacher_address_now = json_obj.get('address')\n teacher.teacher_certificate =json_obj.get(\"certificate\")\n teacher.teacher_teaching_area =json_obj.get(\"teaching_area\")\n teacher.teacher_teaching_way =json_obj.get(\"teaching_way\")\n teacher.teacher_self_assessment = json_obj.get(\"self_assessment\")\n teacher.teacher_tutor_type = json_obj.get(\"tutor_type\")\n available_subjects = json_obj.get(\"available_subjects\")\n available_grade = json_obj.get(\"available_grad\")\n #查看该老师是否重复存入了相同的科目和年级\n try:\n teacher_course = teacher.fk_teacher_course_id.get(course_name=available_subjects)\n teacher_grade = teacher.fk_teacher_grade_id.get(grade_name = available_grade)\n except Exception as e:\n print(e)\n # 老师创建了一门课程 和一个年级\n try:\n course = Course.objects.create(course_name=available_subjects)\n except Exception as e:\n print(e)\n result = {\"code\": 10228, \"error\": \"该科目已经存在!!\"}\n return JsonResponse(result)\n # 给科目增加一个老师\n course.teacher_set.add(teacher)\n try:\n grade = Grade.objects.create(grade_name=available_grade)\n except Exception as e:\n print(e)\n result = {\"code\": 10229, \"error\": \"该年级已经存在!!\"}\n return JsonResponse(result)\n # 给科目增加\n grade.teacher_set.add(teacher)\n # 给科目增加对应年级\n course.grade_set.add(grade)\n else:\n #判断课程和年级是否已经关联\n if teacher_grade.fk_course_id.course_name == available_subjects:\n result = {\"code\":10330,\"error\":\"课程和年级已经绑定\"}\n return JsonResponse(result)\n #对身份证号加密\n cardId = json_obj.get(\"id\")\n m = hashlib.md5()\n m.update(cardId.encode())\n teacher.teacher_cardID = m.hexdigest()\n #对数据进行更新\n try:\n teacher.save()\n except Exception as e:\n print(e)\n result = {\"code\":10119,\"error\":\"请检查数据\"}\n return JsonResponse(result)\n result = {\"code\":200,\"data\":\"保存成功\"}\n return JsonResponse(result)\n\n# 发表老师成果\n@login_check1\ndef send_result(request):\n if request.method == \"POST\":\n #获取已经登录老师的对象\n teacher = request.teacher\n teacher_data = request.body\n #检查数据\n if not teacher_data:\n result = {\"code\":10120,\"error\":\"请输入数据!!\"}\n return JsonResponse(result)\n json_obj = json.loads(teacher_data)\n customerMessage = json_obj.get(\"customerMessage\")\n print(customerMessage)\n if not customerMessage:\n result = {\"code\": 10121, \"error\": \"请输入数据!!\"}\n return JsonResponse(result)\n teacher.teacher_achievement = customerMessage\n #对数据进行更新\n try:\n teacher.save()\n except Exception as e:\n print(e)\n result = {\"code\":10122,\"error\":\"请检查数据\"}\n return JsonResponse(result)\n result = {\"code\": 200, \"data\":\"展示成功\"}\n return JsonResponse(result)\n\ndef sort(request):\n if request.method == \"POST\":\n\n result = {\"code\":200,\"data\":\"成功\"}\n return JsonResponse(result)\ndef login_check(request):\n if request.method == \"POST\":\n teacher_data = request.body\n json_obj = json.loads(teacher_data)\n teacher_token = json_obj.get(\"teacher_token\")\n if not teacher_token:\n result = {'code': 10115, 'error': 'Please login'}\n print('logging check no token')\n return JsonResponse(result)\n try:\n res = jwt.decode(teacher_token, settings.TOKEN_KEY, algorithms='HS256')\n # print(res)\n except Exception as e:\n print('---jwt error is %s' % (e))\n result = {'code': 10116, 'error': 'Please login'}\n return JsonResponse(result)\n teacher_account = res['teacher_account']\n try:\n teacher = Teacher.objects.get(teacher_account=teacher_account)\n except Exception as e:\n result = {\"code\": 10118, \"error\": \"该用户未曾登录或者注册\"}\n return JsonResponse(result)\n result = {\"code\":200,\"data\":\"验证成功\"}\n return JsonResponse(result)\ndef updateimg(request):\n if request.method ==\"POST\":\n file_img = request.FILES['img']\n file_name = \"images/\"+ file_img.name\n is_img = file_img.name.split('.')[-1]\n if is_img not in ('jpeg', 'jpg', 'png', 'gif', 'tmp'):\n result = {\"code\": 10223, \"error\": \"图片格式不对!\"}\n return JsonResponse(result)\n teacher_token = request.POST.get(\"teacher_token\")\n #获得老师的账号\n res = jwt.decode(teacher_token,key=settings.TOKEN_KEY,algorithms=\"HS256\")\n teacher_account = res[\"teacher_account\"]\n teacher = Teacher.objects.get(teacher_account=teacher_account)\n filename = os.path.join(settings.MEDIA_ROOT, file_img.name)\n with open(filename, 'wb') as f:\n data = file_img.file.read()\n f.write(data)\n #查询照片\n try:\n img = Image.objects.create(fk_teacher_id=teacher,image_url=file_name)\n except Exception as e:\n print(e)\n img = Image.objects.get(fk_teacher_id=teacher)\n img.image_url = file_name\n img.save()\n result = {\"code\":10224,\"error\":\"该老师已存图片\",\"data\":file_name}\n return JsonResponse(result)\n\n result = {\"code\":200,\"data\":file_name}\n return JsonResponse(result)\n\n#获取老师基本信息\n@login_check1\ndef get_base_info(request):\n if request.method == \"POST\":\n #获得老师对象\n teacher = request.teacher\n teacher_name = teacher.teacher_name\n if not teacher_name:\n result = {\"code\":10300,\"error\":\"该老师还未填写详细信息,请先填写详细信息\"}\n return JsonResponse(result)\n teacher_name = teacher.teacher_name[0]\n #反向一对一查询数据\n try:\n teacher_img = teacher.image.image_url\n except Exception as e:\n print(e)\n result = {\"code\":10225,\"error\":\"该老师还未存证件照\"}\n return JsonResponse(result)\n result = {\"code\":200,\"data\":{\"img_url\":str(teacher_img),\"teacher_name\":teacher_name}}\n return JsonResponse(result)\n#获取老师的数据\ndef getinfo(request):\n if request.method ==\"GET\":\n teacher_account = request.GET.get(\"teacher_account\")\n #查询老师的信息\n teacher_data = Teacher.objects.filter(teacher_account=teacher_account)\n teacher_name = teacher_data[0].teacher_name[0]\n teacher_sex = teacher_data[0].teacher_sex\n if teacher_sex == \"1\":\n teacher_sex = \"男\"\n else:\n teacher_sex = \"女\"\n #出生日期\n teacher_age1 = teacher_data[0].teacher_age1\n #地址\n teacher_address = teacher_data[0].teacher_address\n #目前地址\n teacher_address_now = teacher_data[0].teacher_address_now\n #学历\n teacher_education = teacher_data[0].teacher_education\n #联系电话\n teacher_phone = teacher_data[0].teacher_phone\n #成果\n teacher_achievement = teacher_data[0].teacher_achievement\n #身份\n teacher_tutor_type = teacher_data[0].teacher_tutor_type\n #证书\n teacher_certificate = teacher_data[0].teacher_certificate\n #授课区域\n teacher_teaching_area = teacher_data[0].teacher_teaching_area\n #辅导方式\n teacher_teaching_way = teacher_data[0].teacher_teaching_way\n #自我评价\n teacher_self_assessment = teacher_data[0].teacher_self_assessment\n # 反向一对一查询数据\n try:\n teacher = Teacher.objects.get(teacher_account=teacher_account)\n teacher_img = teacher.image.image_url\n except Exception as e:\n print(e)\n result = {\"code\": 10226, \"error\": \"该老师还未存证件照\"}\n return JsonResponse(result)\n #通过课程查询老师 和年级\n teacher_course = (\",\").join([teacher_course.course_name for teacher_course in teacher.fk_teacher_course_id.all()])\n teacher_grade = (\",\").join([teacher_grade.grade_name for teacher_grade in teacher.fk_teacher_grade_id.all()])\n data = {\"teacher_name\":teacher_name,\"teacher_sex\":teacher_sex,\"teacher_age1\":teacher_age1,\n \"teacher_address\":teacher_address,\"teacher_education\":teacher_education[0:-2],\n \"teacher_cate\":teacher_education[-2:],\"teacher_phone\":teacher_phone,\n \"teacher_achievement\":teacher_achievement,\"teacher_img\":str(teacher_img),\n \"teacher_tutor_type\":teacher_tutor_type,\"teacher_address_now\":teacher_address_now,\n \"teacher_course\":teacher_course,\"teacher_grade\":teacher_grade,\"teacher_certificate\":teacher_certificate,\n \"teacher_teaching_area\":teacher_teaching_area,\"teacher_teaching_way\":teacher_teaching_way,\n \"teacher_self_assessment\":teacher_self_assessment}\n result = {\"code\":200,\"data\":data}\n return JsonResponse(result)\n\n\ndef search(request):\n #查询老师数据库前十二条信息发给前段 示例如下\n if request.method == \"GET\":\n data = []\n\n #根据最新时间\n teachers = Teacher.objects.order_by(\"-teacher_createtime\")\n\n for teacher in teachers:\n #判断看是否有图片存入\n try:\n img_url = teacher.image.image_url\n except Exception as e:\n print(e)\n #如果有错误就将退出循环\n continue\n else:\n dict = {\"id\":\"1000\"+str(teacher.id),\"teacher_account\":teacher.teacher_account,\"teacher_name\":teacher.teacher_name,\"img_url\":str(img_url),\n \"teacher_type\":teacher.teacher_tutor_type,\"teacher_info\":teacher.teacher_self_assessment}\n data.append(dict)\n continue\n result = {\"code\":200,\"data\":data}\n return JsonResponse(result)\n","repo_name":"facethesun/Nowubai_website","sub_path":"nowubai_website/teacher/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":17504,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"73640576166","text":"import logging\nfrom typing import Any, Dict, List, Optional\nfrom django.core.exceptions import ValidationError\nfrom django.db import transaction\nfrom django.utils.dateparse import parse_date\nfrom django.utils.timezone import now\nfrom django.utils.translation import gettext as _\nfrom jutil.admin import admin_log\nfrom jutil.format import choices_label\nfrom jutil.parse import parse_datetime\nfrom jutil.xml import xml_to_dict\nfrom jsanctions.helpers import get_country_iso2_code\nfrom jsanctions.models import (\n SanctionsListFile,\n SanctionEntity,\n NameAlias,\n Remark,\n Address,\n Identification,\n SanctionListObject,\n SubjectType,\n)\n\nlogger = logging.getLogger(__name__)\n\nUN_LIST_TYPE = \"UN\"\n\nUN_XML_ARRAY_TAGS = [\n \"VALUE\",\n \"INDIVIDUAL\",\n \"INDIVIDUAL_ALIAS\",\n \"INDIVIDUAL_ADDRESS\",\n \"INDIVIDUAL_DATE_OF_BIRTH\",\n \"INDIVIDUAL_PLACE_OF_BIRTH\",\n \"INDIVIDUAL_DOCUMENT\",\n \"ENTITY\",\n \"ENTITY_ALIAS\",\n \"ENTITY_ADDRESS\",\n]\n\nUN_NAME_FIELDS = [\"FIRST_NAME\", \"SECOND_NAME\", \"THIRD_NAME\", \"FOURTH_NAME\", \"FIFTH_NAME\", \"SIXTH_NAME\"]\n\n\ndef load_un_sanction_list_as_dict(filename: str) -> Dict[str, Any]:\n with open(filename, \"rb\") as fp:\n data: Dict[str, Any] = xml_to_dict(fp.read(), array_tags=UN_XML_ARRAY_TAGS)\n return data\n\n\ndef parse_un_data_id(data: Dict[str, Any]) -> int:\n uid = data.get(\"DATAID\")\n if uid is None:\n raise ValidationError(_(\"DATAID missing\"))\n return int(uid)\n\n\ndef create_un_alias(se: SanctionEntity, **kwargs) -> Optional[NameAlias]:\n names = []\n for k in UN_NAME_FIELDS:\n if k in kwargs and kwargs[k]:\n names.append(kwargs[k])\n if not names:\n logger.warning(\"No names: %s\", kwargs)\n return None\n\n alias = NameAlias(sanction=se, logical_id=parse_un_data_id(kwargs))\n alias.title = kwargs.get(\"TITLE\") or \"\"\n alias.last_name = names.pop() or \"\"\n alias.first_name = \" \".join(names).strip()\n alias.full_clean()\n alias.save()\n return alias\n\n\ndef create_un_comments(se: SanctionEntity, **kwargs) -> List[Remark]:\n out: List[Remark] = []\n for n in range(1, 10):\n k = \"COMMENTS{}\".format(n)\n if k in kwargs and kwargs[k]:\n obj_out = Remark(container=se, text=kwargs.get(k) or \"\") # type: ignore\n obj_out.full_clean()\n obj_out.save()\n out.append(obj_out)\n else:\n break\n return out\n\n\ndef create_un_note(obj: SanctionListObject, note: Any):\n if note:\n remark = Remark(container=obj, text=str(note))\n remark.full_clean()\n remark.save()\n\n\ndef create_un_address(se: SanctionEntity, **kwargs) -> Address:\n # {'STATE_PROVINCE', 'NOTE', 'COUNTRY', 'STREET', 'CITY', 'ZIP_CODE'}\n address = Address(sanction=se)\n address.region = kwargs.get(\"STATE_PROVINCE\") or \"\"\n address.city = kwargs.get(\"CITY\") or \"\"\n address.zip_code = kwargs.get(\"ZIP_CODE\") or \"\"\n address.country_description = kwargs.get(\"COUNTRY\") or \"\"\n address.street = kwargs.get(\"STREET\") or \"\"\n for k, v in kwargs.items():\n if hasattr(address, k):\n setattr(address, k, v)\n address.full_clean()\n address.save()\n create_un_note(address, kwargs.get(\"NOTE\"))\n return address\n\n\ndef create_un_document(se: SanctionEntity, **kwargs) -> Identification:\n # {'DATE_OF_ISSUE', 'NUMBER', 'NOTE', 'ISSUING_COUNTRY', 'CITY_OF_ISSUE', 'COUNTRY_OF_ISSUE',\n # 'TYPE_OF_DOCUMENT', 'TYPE_OF_DOCUMENT2'}\n id_obj = Identification(sanction=se)\n id_obj.identification_type_description = kwargs.get(\"TYPE_OF_DOCUMENT\") or kwargs.get(\"TYPE_OF_DOCUMENT2\") or \"\"\n id_obj.issue_date = parse_date(str(kwargs.get(\"DATE_OF_ISSUE\"))) if kwargs.get(\"DATE_OF_ISSUE\") else None # type: ignore\n id_obj.latin_number = kwargs.get(\"NUMBER\") or \"\"\n id_obj.issued_by = \"{} {} {}\".format(kwargs.get(\"CITY_OF_ISSUE\") or \"\", kwargs.get(\"COUNTRY_OF_ISSUE\") or \"\", kwargs.get(\"ISSUING_COUNTRY\") or \"\").strip()\n id_obj.country_description = kwargs.get(\"COUNTRY_OF_ISSUE\") or kwargs.get(\"ISSUING_COUNTRY\") or \"\"\n id_obj.full_clean()\n id_obj.save()\n create_un_note(id_obj, kwargs.get(\"NOTE\"))\n return id_obj\n\n\ndef set_un_members( # noqa\n se: SanctionEntity,\n data: Dict[str, Any],\n verbose: bool = False,\n padding: int = 0,\n):\n # DATAID\n se.logical_id = parse_un_data_id(data)\n\n # FIRST_NAME, ...\n create_un_alias(se, **data)\n\n # COMMENTSx\n create_un_comments(se, **data)\n\n # INVIDUAL_ADDRESS / ENTITY_ADDRESS\n address_list = data.get(\"INVIDUAL_ADDRESS\", []) or data.get(\"ENTITY_ADDRESS\", [])\n addresses: List[Address] = []\n if address_list:\n for e_data in address_list:\n if e_data:\n addresses.append(create_un_address(se, **e_data))\n\n # try to fill address information from UN list name\n if not addresses:\n un_list_type = data.get(\"UN_LIST_TYPE\")\n if un_list_type:\n country_code = get_country_iso2_code(un_list_type)\n if country_code:\n create_un_address(se, country_description=un_list_type, country_code=country_code)\n\n # INDIVIDUAL_DOCUMENT\n docs = data.get(\"INDIVIDUAL_DOCUMENT\")\n if docs:\n for e_data in docs:\n if e_data:\n create_un_document(se, **e_data)\n\n se.full_clean()\n se.save()\n if verbose:\n logger.debug(\"%sSaved %s\", padding * \" \", se)\n\n\ndef import_un_sanctions(source: SanctionsListFile, verbose: bool = False):\n data = load_un_sanction_list_as_dict(source.full_path)\n generation_date_str = data.get(\"@dateGenerated\") or data.get(\"@generationDate\")\n if not generation_date_str:\n raise Exception(\"Generation date missing\")\n source.generation_date = parse_datetime(generation_date_str).date()\n\n enterprise, created = SubjectType.objects.get_or_create(classification_code=SubjectType.ENTERPRISE)\n assert isinstance(enterprise, SubjectType)\n if created or not enterprise.code:\n enterprise.code = choices_label(SubjectType.CLASSIFICATION_CODES, enterprise.classification_code)\n enterprise.save()\n person, created = SubjectType.objects.get_or_create(classification_code=SubjectType.PERSON)\n assert isinstance(person, SubjectType)\n if created or not person.code:\n person.code = choices_label(SubjectType.CLASSIFICATION_CODES, person.classification_code)\n person.save()\n\n t0 = now()\n individuals_list = data.get(\"INDIVIDUALS\", {}).get(\"INDIVIDUAL\")\n for se_data in individuals_list:\n assert isinstance(se_data, dict)\n if verbose:\n logger.debug(\" sdnEntry uid %s\", se_data.get(\"uid\"))\n with transaction.atomic():\n se = SanctionEntity.objects.create(source=source, data=se_data, subject_type=person)\n set_un_members(se, se_data, verbose=verbose, padding=4)\n\n entities_list = data.get(\"ENTITIES\", {}).get(\"ENTITY\")\n for se_data in entities_list:\n assert isinstance(se_data, dict)\n if verbose:\n logger.debug(\" sdnEntry uid %s\", se_data.get(\"uid\"))\n with transaction.atomic():\n se = SanctionEntity.objects.create(source=source, data=se_data, subject_type=enterprise)\n set_un_members(se, se_data, verbose=verbose, padding=4)\n\n source.imported = now()\n source.save()\n msg = \"Imported {} sanction entities and {} individuals from {} in {}\".format(\n len(entities_list), len(individuals_list), source.full_path, source.imported - t0\n )\n logger.info(msg)\n admin_log([source], msg)\n","repo_name":"kajala/django-jsanctions","sub_path":"jsanctions/un.py","file_name":"un.py","file_ext":"py","file_size_in_byte":7540,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"52"} +{"seq_id":"14546340380","text":"r'''This module provides `pyparsing` grammar for Tripoli-4 output listings.\n\n.. role :: parsing_var(literal)\n.. |keff| replace:: k\\ :sub:`eff`\n.. |kij| replace:: k\\ :sub:`ij`\n.. _pyparsing: https://pythonhosted.org/pyparsing/\n\nDocumentation on the ``pyparsing`` package can be found at `pyparsing`_.\n\nTransformation from ``pyparsing.ParseResults`` to more standard python objects,\nincluding :obj:`numpy` arrays, is done with :mod:`~.transform`, calling\n:mod:`~valjean.eponine.tripoli4.common`.\n\nGeneralitites\n-------------\n\n* This parser only parses the result part of the listing (selection done in\n :mod:`~valjean.eponine.tripoli4.scan`).\n* It takes into account all responses in ``qualtrip`` database up to Tripoli-4,\n version 10.2.\n* If a response is not taken into account parsing will fail:\n\n * with a big, ugly message ending by location of the end of successful\n parsing in the result string (possible to print it) → normally where starts\n your new response\n * it seems to end normally, but did not in reality. One of the best checks in\n that case is to test if the ``endflag`` in\n :mod:`~valjean.eponine.tripoli4.scan` was read in the parser, usually not.\n Then the new response probably have to be added.\n\n* A general parser is proposed for use in the file, but other parsers can be\n built from the partial parsers written here\n* Numbers are automatically converted to :obj:`numpy` numbers (possibility to\n choose the dtype used for numbers)\n* Keywords and most of the variables used to build parsers are private\n\n\nOrganisation\n------------\n\nThis module is divided in 3 parts:\n\nkeywords:\n list of all keywords used to parse the listings, this part is\n important as these keywords trigger the parsing\nparsers:\n parsers for each part of the listing (introduction, mesh,\n spectra, general responses, |keff|, etc.)\ngeneral parser:\n parser to parse the full listing, taking into accout all\n current response (present in V&V)\n\nKeywords are in most of the cases used as flags and suppressed when data are\nstored.\n\nA first structure is designed when building the parsers results as lists and\ndictionaries in the ``pyparsing.ParseResults``. Then `parse actions` are used\nto standard python or :obj:`numpy` objects. These `parse actions`, called with\n``pyparsing.ParserElement.setParseAction``, can be found in :mod:`~.transform`.\n\nMain parsers blocks\n```````````````````\nThe main parsers blocks are defined at the end of the module, named\n:parsing_var:`mygram` and :parsing_var:`response`. The default parser is\n:parsing_var:`mygram`.\n\nTypically, each result block in the listing should start by the `intro` block,\nparsed by :parsing_var:`intro`, and end with at least one `runtime` block,\nparsed by :parsing_var:`runtime`. This parts follows the\n:mod:`~valjean.eponine.tripoli4.scan`: :obj:`str` starting by\n``'RESULTS ARE GIVEN'`` and ending with ``'simulation time'``,\n``'exploitation time'`` or ``'elapsed time'``.\n\nBetween these blocks can be found the data blocks. The major ones are:\n\n* one or more responses, driven by the keyword ``'RESPONSE FUNCTION'``,\n* the editions of IFP adjoint criticality,\n* the \"default\" |keff| block, in most of the cases at the end of the listing,\n* the *contributing particles block*, mainly in first pass listings,\n* the perturbation block,\n* an optional additional `runtime` block.\n\nMain data blocks are described below (results taken into account, main\nfeatures).\n\nResponse block, parser :parsing_var:`response`\n``````````````````````````````````````````````\nThe core of the listings is the list of responses, including all the required\nscores. This big block is constructed as a :obj:`list` of :obj:`dict`, each\none representing a response (key ``'list_responses'`` in the final result).\n\nResponse are constructed as:\n\n* response introduction containing its definition, parsed by\n :parsing_var:`respintro`:\n\n * a description of the response parsed by :parsing_var:`respdesc` including:\n\n * ``'RESPONSE FUNCTION'`` keyword as mandatory (afaik)\n * ``'RESPONSE NAME'``, ``'SCORE NAME'`` and ``'ENERGY DECOUPAGE NAME'``\n that are present in most of the cases\n\n * more characteristics of the response, parsed by :parsing_var:`respcarac`,\n like:\n\n * considered particle (``'PARTICULE'`` in the listing)\n * nucleus on which the reaction happens (if ``'RESPONSE FUNCTION'`` is a\n ``'REACTION'``)\n * temperature\n * compostion of the volumes considered\n * concentration\n * reaction considered (usually given as codes)\n * others like DPA type, required arguments, mode, filters, etc.\n\n* responses themselves, using parser :parsing_var:`responseblock`, are various:\n\n * responses including *score* description, all included in the\n :parsing_var:`scoreblock` parser. More than one can be present, they are\n grouped in the :parsing_var:`listscoreblock` parser.\n :parsing_var:`scoreblock` parser contains:\n\n * score description (parser :parsing_var:`scoredesc`) contains the score\n mode (``'TRACK'``, ``'SURF'`` or ``'COLL'``) and the score zone\n (currently taken into account: mesh, results cumulated on all geometry or\n on all sources, Volume, Volume Sum, Frontier, Frontier Sum, Point, Cells\n and Maille)\n\n * results block, where at least one of these results can be found, are by\n parsed by the following parsers:\n\n * :parsing_var:`spectrumblock`: spectrum\n * :parsing_var:`meshblock`: mesh\n * :parsing_var:`vovspectrumblock`: spectrum with variance of variance\n * :parsing_var:`entropy`: entropy results (Boltzmann and Shannon\n entropies)\n * :parsing_var:`medfile`: location of optional MED file\n * :parsing_var:`genericscoreblock`: default result integrated over energy\n * :parsing_var:`uncertblock`: uncertainty results\n * :parsing_var:`uncertintegblock`: uncertainties on integrated results\n over energy\n * :parsing_var:`gbblock`: Green bands results\n\n * |keff| presented as a generic response, possibly transformed in\n :obj:`numpy.matrix` (parser :parsing_var:`keffblock`)\n * |kij| results: matrix, eigenvalues, eigenvectors (parser\n :parsing_var:`kijres`)\n * |kij| sources (parser :parsing_var:`kijsources`)\n * Adjoint related results (parser :parsing_var:`adjointres`): scores ordered\n by precursors and families, by perturbation index, by cycle length or\n sensitivities (this last case is represented in a 3 dimensions\n :obj:`numpy.ndarray`, incident energy, energy (\"leaving neutron\"),\n direction cosine (µ)). For the moment this is only for IFP method , in\n close future also for Wielandt method\n * default result integrated over energy where no scoring mode and zone are\n precised (parser :parsing_var:`defintegratedres`)\n * perturbation results (parser :parsing_var:`perturbation`)\n\n\nOther parsers\n`````````````\nVarious other blocks can appear in the Tripoli-4 listing, located at the same\nlevel as the response block. These parsers and the associated dictionary key\n(same level as ``'list_responses'``) are:\n\n* :parsing_var:`ifpadjointcriticality`: edition of IFP adjoint criticality, key\n ``'ifp_adjoint_crit_edition'``;\n* :parsing_var:`autokeffblock`: \"default\" |keff| block, containing for example\n the best estimation of |keff| using variable number of discarded batches, key\n ``'keff_auto'``;\n* :parsing_var:`contribpartblock`: *contributing particles block*, key\n ``'contributing_particles'``\n* :parsing_var:`perturbation`: perturbation results, containing a description\n of the calculation of the perturbation followed by the perturbation result\n presented like a usual response (spectrum, mesh, etc. depending on required\n score), key ``'perturbation'``\n* :parsing_var:`runtime`: simulation, exploitation or elapsed time.\n\n\n.. todo::\n\n Adjoint results: for the moment only IFP is really parsed. Grammar has\n already more or less adapted to welcome Wielandt method that will have the\n same kind of outputs (renaming as adjoint_res for example). No key is set\n for the moment to specify the method, it can be obtained from the response\n function itself. Adjoint criticality editions are only done for IFP, this\n may change when the same will be available for Wielandt. Some renaming can\n also be needed.\n\n'''\n\nfrom pyparsing import (Word, Keyword, White, alphas, alphanums,\n Suppress, Optional, LineEnd, CaselessKeyword,\n Group, OneOrMore, ZeroOrMore, Forward, originalTextFor,\n tokenMap, delimitedList, printables, replaceWith)\nfrom pyparsing import pyparsing_common as pyparscom\nfrom . import transform as trans\nfrom .transform import compose2\nfrom .dump import dump_in_logger\nfrom ... import LOGGER\n\n_fnums = pyparscom.fnumber.setParseAction(tokenMap(trans.common.FTYPE))\n_inums = pyparscom.number.setParseAction(tokenMap(trans.common.ITYPE))\n\n###################################\n# KEYWORDS #\n###################################\n\n# General keywords\n_integratedres_kw = Keyword(\"INTEGRATED RESULTS\")\n_numbatchsused_kw = (Keyword(\"number of\")\n + (Keyword(\"batches\") | Keyword(\"batch\"))\n + Optional(Keyword(\"used\")))\n_numbatchs1stdiscarded_kw = Keyword(\"number of first discarded batches\")\n_notconverged_kw = (Keyword(\"NOT YET CONVERGED\")\n | CaselessKeyword(\"Not converged\"))\n_unknown_kw = Keyword(\"unknown\")\n_unavailable_kw = Keyword(\"unavailable\")\n_units_kw = Keyword(\"Units:\")\n_warning_kw = CaselessKeyword(\"Warning\")\n_endtable = LineEnd() + LineEnd()\n\n# Introduction keywords\n_sourceintensity_kw = Keyword(\"RESULTS ARE GIVEN FOR SOURCE INTENSITY\")\n_meanweightleakage_kw = Keyword(\"Mean weight leakage\")\n_meanweightleakagein_kw = Keyword(\"Mean weight leakage inside\")\n_edbatchnum_kw = Keyword(\"Edition after batch number\")\n_meanweightrestartpart_kw = Keyword(\"Mean weight of restarted particles :\")\n\n# End of edition keywords\n_simulationtime_kw = Keyword(\"simulation time (s)\")\n_exploitationtime_kw = Keyword(\"exploitation time (s)\")\n_elapsedtime_kw = Keyword(\"elapsed time (s)\")\n_rdmgenerator_kw = Keyword(\"Type and parameters of random generator \"\n \"at the end of simulation\")\n# _rdmgenerator_kw = Keyword(\"Type and parameters of random generator\" +\n# (\"at the end of simulation\"|\"after batch\"))\n_normalcompletion_kw = Keyword(\"NORMAL COMPLETION\")\n\n# Response description keywords\n_respfunction_kw = Keyword(\"RESPONSE FUNCTION\")\n_respname_kw = Keyword(\"RESPONSE NAME\")\n_scorename_kw = Keyword(\"SCORE NAME\")\n_energysplitname_kw = Keyword(\"ENERGY DECOUPAGE NAME\")\n_nusplitname_kw = Keyword(\"DECOUPAGE NAME\")\n_respfiltered_kw = Keyword(\"RESPONSE FILTERED BY\")\n_respfiltcompos_kw = Keyword(\"COMPOSITIONS\")\n_particule_kw = Keyword(\"PARTICULE\")\n_incparticle_kw = Keyword(\"INCIDENT PARTICULE\")\n_noise_equation_kw = Keyword(\"NOISE EQUATION\")\n_reactiononnucl_kw = Keyword(\"reaction on nucleus\")\n_temperature_kw = Keyword(\"temperature\")\n_composition_kw = Keyword(\"composition\")\n_concentration_kw = Keyword(\"concentration\")\n_reaction_kw = Keyword(\"reaction consists in\")\n_required_kw = Keyword(\"REQUIRED\")\n_dpatype_kw = Keyword(\"DPA TYPE:\")\n_mode_kw = Keyword(\"MODE :\")\n_inducedbyint_kw = Keyword(\"INDUCED BY INTERACTION :\")\n_fxptcontrib_kw = Keyword(\"FXPT CONTRIBUTION\")\n_spectrumresp_kw = Keyword(\"SPECTRUM\")\n_filters_kw = Keyword(\"Score filtered by volume\")\n\n# Scoring description\n_scoremode_kw = Keyword(\"scoring mode\")\n_scorezone_kw = Keyword(\"scoring zone\")\n_scoremesh_kw = (Keyword(\"Results on a mesh\")\n .setParseAction(replaceWith('Mesh')))\n_scoremeshinfobins_kw = Keyword(\"(x,y,z)\")\n_scoreallgeom_kw = (Keyword(\"Results cumulated on all geometry\")\n .setParseAction(replaceWith('All geometry')))\n_scoreallsources_kw = (Keyword(\"Results cumulated on all sources\")\n .setParseAction(replaceWith('All sources')))\n_scorevol_kw = Keyword(\"Volume\")\n_scorevolvol_kw = Keyword(\"num of volume\")\n_scorevolumeunit_kw = Keyword(\"Volume in\")\n_scorevolumeint_kw = (Keyword(\"The result is integrated over the volume\")\n | Keyword(\"The result is integrated in volume\")\n ).setParseAction(replaceWith('volume integrated'))\n_scorevolumenotint_kw = (\n Keyword(\"The volume has been provided by the user \"\n \"(the user requested a score per unit volume)\")\n | Keyword(\"The volume has been calculated by Tripoli-4 \"\n \"or provided by the user\")).setParseAction(\n replaceWith('per unit volume'))\n_scorevolsum_kw = Keyword(\"Volume Sum\")\n_scorevolsumvol_kw = Keyword(\"num of volumes\")\n_scorevolumesum_kw = Keyword(\"Total volume in cm3\")\n_scorevolumecell_kw = Keyword(\"Cell volume in cm3\")\n_scoresurf_kw = Keyword(\"Frontier\")\n_scoresurfvol_kw = Keyword(\"volumes\")\n_scoresurface_kw = Keyword(\"Surface in cm2\")\n_scoresurfaceint_kw = Keyword(\"The surface area has been provided by the user \"\n \"(the user requested a score per unit area)\")\n_scoresurfacenotint_kw = Keyword(\"The result is integrated over the surface\")\n_scoresurfsum_kw = Keyword(\"Frontier Sum\")\n_scoresurfsumfront_kw = Keyword(\"num of frontiers\")\n_scoresurfacesum_kw = Keyword(\"Total surface in cm2\")\n_scorepoint_kw = Keyword(\"Point\")\n_scorecell_kw = Keyword(\"Cells\")\n_scorecelldet_kw = Keyword(\"(numvol,depth,imaille,jmaille,kmaille...)\")\n_scoremaille_kw = Keyword(\"Maille\")\n_scoremaillevol_kw = Keyword(\"num of volume\")\n_scoremailledepth_kw = Keyword(\"depth of lattice\")\n_scoremaillecell_kw = Keyword(\"num of cell\")\n\n# Correspondence table (volumes and their names)\n_corresptable_kw = Keyword(\"Correspondence table between volumes \"\n \"ids and names :\")\n_vol_kw = CaselessKeyword(\"Volume\")\n\n# KEFF keywords\n_notcvg_exp_kw = Keyword(\"(invalid keff domain)\")\n_fullcomb_kw = Keyword(\"full combined estimator\")\n_bestresdiscbatchs_kw = Keyword(\"best results are obtained with discarding\")\n_correlations_kw = Group(Keyword(\"estimators\")\n + Keyword(\"correlations\")\n + Keyword(\"combined values\")\n + Keyword(\"combined sigma%\"))\n_estimator_kw = Keyword(\"ESTIMATOR\")\n_equivkeff_kw = Keyword(\"Equivalent Keff:\")\n_warn_combkeff_kw = (\n Keyword(\"One of the Keffectives is null and should not be\")\n + Keyword(\"Combined Keffectives will not be edited\"))\n_warn_fixsourcekeff_kw = (\n Keyword(\"In FIXED_SOURCES_CRITICITY mode, the keff result\")\n + Keyword(\"is actually an overall multiplication factor \"\n \"(cf User's Guide)\"))\n\n# Time steps\n_timestepnum_kw = Keyword(\"TIME STEP NUMBER\")\n_timestepmin_kw = Keyword(\"time min. =\")\n_timestepmax_kw = Keyword(\"time max. =\")\n\n# Angular zones\n_muangzone_kw = Keyword(\"MU ANGULAR ZONE :\")\n_mumin_kw = Keyword(\"mu min. =\")\n_mumax_kw = Keyword(\"mu max. =\")\n_phiangzone_kw = Keyword(\"PHI ANGULAR ZONE :\")\n_phimin_kw = Keyword(\"phi min. =\")\n_phimax_kw = Keyword(\"phi max. =\")\n\n# Spectrum keywords\n_spectrum_kw = Keyword(\"SPECTRUM RESULTS\")\n_spgroupwunit_kw = Keyword(\"group (MeV)\")\n_spgroup_kw = Keyword(\"group\")\n_spscovlethargy_kw = Keyword(\"score/lethargy\")\n_spvov_kw = Keyword(\"vov\")\n_nuspectrum_kw = Keyword(\"NU RESULTS\")\n_nusprange_kw = Keyword(\"range\")\n_zaspectrum_kw = Keyword(\"ZA RESULTS\")\n_zaspid_kw = Keyword(\"(Z,A)\")\n_spscore_kw = Keyword(\"score\")\n_spsigma_kw = Keyword(\"sigma_%\")\n\n# Mesh keywords\n_energyrange_kw = Keyword(\"Energy range\")\n\n# MED files\n_creationmedfile_kw = Keyword(\"Creating MED output file\")\n_creationfile_kw = Keyword(\"# Creating output file\")\n_medmeshid_kw = Keyword(\"MED mesh id\")\n\n# Entropy\n_boltzmannentropy_kw = Keyword(\"Boltzmann Entropy of sources =\")\n_shannonentropy_kw = Keyword(\"Shannon Entropy of sources =\")\n\n# Scores ordered by nuclei and precursor families\n_nucleiorder_kw = Keyword(\"Scores for nuclei contributions are ordered \"\n \"according to the user list:\")\n_familiesorder_kw = Keyword(\"Scores are ordered from family i = 1 to i = MAX:\")\n_nucleifamilyorder_kw = Keyword(\"Scores are ordered \"\n \"by nuclei and by families:\")\n_nucleus_kw = Keyword(\"Nucleus :\")\n# Perturbation order\n_perturborder_kw = Keyword(\"Scores are ordered by perturbation index:\")\n# Sensitivities\n_sensitivitytypeorder_kw = Keyword(\"Scores are ordered by type\")\n_sensitivityindexorder_kw = Keyword(\"and index:\")\n_sensitivity_kw = Keyword(\"SENSITIVITY :\")\n_sensitivityreactionrate_kw = Keyword(\"REACTION_RATE_RATIO :\")\n_sensitivity_energyint_kw = Keyword(\"Energy integrated S\")\n_sensitivity_incenergy_kw = Keyword(\"Incident energy interval in MeV:\")\n_sensitivity_dircos_kw = Keyword(\"Direction cosine interval:\")\n\n# Variance of variance\n_vovstar_kw = Keyword(\"variance of variance* :\")\n_sensibtomaxval_kw = Keyword(\"sensibility to maximum value:\")\n_vov_kw = Keyword(\"variance of variance :\")\n\n# Greenbands\n_gbspectrumstep_kw = Keyword(\"* SOURCE SPECTRUM STEP NUMBER :\")\n_gbenergymin_kw = Keyword(\"source energy min. =\")\n_gbenergymax_kw = Keyword(\"source energy max. =\")\n_gbsourcenum_kw = Keyword(\"SOURCE NUMBER :\")\n_gbsourcetab_kw = Keyword(\"SOURCE TABULATION :\")\n\n# KIJ matrix\n_kijlefteigenval_kw = Keyword(\"left_eigenvalues called\")\n_kijmkeff_kw = Keyword(\"kij-keff =\")\n_kijdomratio_kw = Keyword(\"dominant ratio =\")\n_kijeigenval_kw = Keyword(\"eigenvalues (re, im)\")\n_kijeigenvec_kw = Keyword(\"eigenvectors\")\n_kijeigenvecnotprint_kw = Keyword(\"KIJ eigenvectors not printed, \"\n \"increase maximum dump size if needed\")\n_kijmatrix_kw = Keyword(\"KIJ_MATRIX :\")\n_kijmatrixnotprint_kw = Keyword(\"KIJ matrix not printed, \"\n \"increase maximum dump size if needed\")\n\n# KIJ sources\n_kijsources_kw = Keyword(\"SOURCES VECTOR :\")\n_kijsourcesorder_kw = Keyword(\"Sources are ordered following\")\n\n# KIJ keff\n_kijfissilevol_kw = Keyword(\"number of fissile volumes :\")\n_kijlistfissilevol_kw = Keyword(\"list of fissile volume numbers : \")\n_kijbatchs_kw = Keyword(\"number of last batches kept :\")\n_kijkeffevid_kw = (Keyword(\"EIGENVECTOR :\") + Keyword(\"index\")\n + Keyword(\"source rate\"))\n_kijkeffmat_kw = Keyword(\"K-IJ MATRIX :\")\n_kijkeffstddevmat_kw = Keyword(\"STANDARD DEVIATION MATRIX :\")\n_kijkeffsensibilitymat_kw = Keyword(\"SENSIBILITY MATRIX :\")\n\n# Adjoint results (IFP for the moment)\n# if IFP is changed to Wielandt in _cvgstat_kw when using Wielandt method this\n# is the way to get the method name\n# convergence statistics\n_cvgstat_kw = Keyword(\"Scores for IFP convergence statistics are ordered \"\n \"from cycle length L = 1 to L = MAX:\")\n# IFP adjoint criticality edition\n_ifpadjcriticality_kw = Keyword(\"IFP_ADJOINT_CRITICALITY EDITION\")\n_ifpadjcyclelength_kw = Keyword(\"IFP CYCLE LENGTH =\")\n_ifpadjnormalizedres_kw = Keyword(\"RESULTS ARE NORMALIZED\")\n_ifpadjvol_kw = Keyword(\"Vol\")\n_ifpadjminmax_kw = Keyword(\"(min | max)\")\n_ifpadjscore_kw = Keyword(\"score [a.u.]\")\n\n# Results on spherical harmonics\n_shr_nb_space_bins_kw = Keyword('Number of space bin :')\n_shr_nb_u_space_bins_kw = Keyword('Number of space bin in u dimension:')\n_shr_nb_v_space_bins_kw = Keyword('Number of space bin in v dimension:')\n_shr_nb_w_space_bins_kw = Keyword('Number of space bin in w dimension:')\n_shr_nb_energy_bins_kw = Keyword('Number of energetic bins :')\n_shr_nb_inc_energy_bins_kw = Keyword('Number of incident energy bins :')\n_shr_lmax_kw = Keyword('Maximum L number of moments :')\n_shr_score_name_kw = Keyword('Score :')\n_shr_space_kw = Keyword('Space bin :')\n_shr_energy_kw = Keyword('Energy range (in MeV):')\n_shr_incident_energy_kw = Keyword('Outgoing data for incident energy '\n 'bin boundaries :')\n_shr_l_kw = Keyword('l')\n_shr_m_kw = Keyword('m')\n_shr_score_kw = Keyword('Score')\n_shr_sigma_kw = Keyword('Sigma (%)')\n\n# Perturbations\n_perturbation_kw = Keyword(\"================== Perturbation result edition \"\n \"======================\")\n_perturank_kw = Keyword(\"Perturbation rank =\")\n_pertumethod_kw = Keyword(\"Method :\")\n_pertuorder_kw = Keyword(\"Order:\")\n_pertutype_kw = Keyword(\"Perturbation de type\")\n_pertucompo_kw = Keyword(\"Composition :\")\n\n# Uncertainties results (linked to perturbations ?)\n_uncertres_kw = Keyword(\"UNCERTAINTY RESULTS\")\n_uncertgp_kw = Keyword(\"group (Mev)\")\n_uncertsig2_kw = Keyword(\"sigma2(means)\")\n_uncertmean_kw = Keyword(\"mean(sigma_n2)\")\n_uncertsig_kw = Keyword(\"sigma(sigma_n2)\")\n_uncertfisher_kw = Keyword(\"fisher test\")\n_uncertintegres_kw = Keyword(\"UNCERTAINTY ON ENERGY INTEGRATED RESULTS\")\n\n# Creation jdds\n_nbcontribpart_kw = Keyword(\"NUMBER OF CONTRIBUTING PARTICLES\")\n_endcontribpart_kw = Keyword(\"--- end of CONTRIBUTING PARTICLES ---\")\n\n# Symbol lines\n_star_line = Suppress(Word('*'))\n_equal_line = Suppress(Word('='))\n_minus_line = Suppress(White() + Word('-'))\n\n\n################################\n# PARSERS #\n################################\n\n# Introduction parser\n_sourceintensity = (Suppress(_sourceintensity_kw + ':')\n + (_fnums | _unavailable_kw)\n ('source_intensity'))\n# unkown -> string not in a list, while list of float per default\n_meanweightleakvals = Group(_fnums('score')\n + Suppress(\"sigma =\") + _fnums('sigma')\n + Suppress('sigma% =') + _fnums('sigma%'))\n_meanweightleak = (Suppress(_meanweightleakage_kw)\n + (Suppress('=') + _meanweightleakvals('mean_weight_leak')\n | Suppress(':') + _unknown_kw('mean_weight_leak')))\n_meanweightleakin = (\n Suppress(_meanweightleakagein_kw)\n + (Suppress('=') + _meanweightleakvals('mean_weight_leak_inside')\n | Suppress(':') + _unknown_kw('mean_weight_leak_inside')))\n_edbatchnum = Suppress(_edbatchnum_kw + ':') + _inums('edition_batch_number')\n\n_meanweightrestartpart = (Suppress(_meanweightrestartpart_kw)\n + _fnums('mean_weight_restart_particle'))\n_introelts = (_meanweightleak | _meanweightleakin | _edbatchnum\n | _meanweightrestartpart)\nintro = Group(_sourceintensity + _star_line + OneOrMore(_introelts))('intro')\n\n# Conclusion parser\n_packet_length_warning = Suppress(\n \"* packet length is\" + _inums\n + \"(check documentation for conventions about discard and batches)\")\n_simutime = Suppress(_simulationtime_kw + ':') + _fnums('simulation_time')\n_exploitime = (Suppress(_exploitationtime_kw + ':')\n + _fnums('exploitation_time'))\n_elapsedtime = Suppress(_elapsedtime_kw + ':') + _fnums('elapsed_time')\nruntime = (Optional(_packet_length_warning)\n + Group(_simutime | _elapsedtime | _exploitime)('conclu'))\n\n# Response parser\n# Description of the response\n_respfunc = (Suppress(_respfunction_kw + ':')\n + OneOrMore(Word(printables), stopOn=LineEnd())\n .setParseAction(' '.join)('response_function'))\n# warning: order matters hier, LineEnd has to be before Optional(Word)\n_respname = (Suppress(_respname_kw + ':')\n + (Suppress(LineEnd())\n | Optional(Word(printables)('response_name'))))\n_scorename = (Suppress(_scorename_kw + \":\")\n + Optional(Word(printables)('score_name')))\n_energysplit = (Suppress(_energysplitname_kw + ':')\n + Word(printables)('energy_split_name'))\n_nusplit = (Suppress(_nusplitname_kw + ':')\n + Word(printables)('nu_split_name'))\nrespdesc = (_respfunc + Optional(_respname) + Optional(_scorename)\n + Optional(_energysplit | _nusplit))\n\n_respfilter = (Suppress(_respfiltered_kw) + _inums('nb_filtered_compos')\n + Suppress(_respfiltcompos_kw + ':')\n + Group(OneOrMore(Word(printables), stopOn=LineEnd()))\n .setParseAction(trans.convert_list_to_tuple)\n ('filtered_compositions'))\n\n_particle = (Suppress(_particule_kw + ':')\n + OneOrMore(Word(alphas+','), stopOn=LineEnd())\n .setParseAction(' '.join)('particle'))\n_incparticle = (Suppress(_incparticle_kw + ':')\n + Word(alphas)('incident_particle'))\n_noise_equation = (Suppress(_noise_equation_kw + ':')\n + OneOrMore(Word(alphas), stopOn=LineEnd())\n .setParseAction(' '.join)('noise_equation'))\n# response characteristics written in lower case\n_reactiononnucl = (Suppress(_reactiononnucl_kw + ':')\n + Word(printables)('reaction_on_nucleus'))\n_temperature = Suppress(_temperature_kw + ':') + _fnums('temperature')\n_composition = (Suppress(_composition_kw + ':')\n + Word(printables)('composition'))\n_concentration = Suppress(_concentration_kw + ':') + _fnums('concentration')\n_reaction = (Suppress(_reaction_kw)\n + ((Suppress(Word(alphas) + ':')\n + Group(delimitedList(_inums, delim='+')))\n | OneOrMore(Word(alphas), stopOn=LineEnd())\n .setParseAction(' '.join))\n .setParseAction(trans.convert_list_to_tuple)('reaction'))\n\n\n# Goal: when more than one reaction are required, keep characteristics grouped\n# by particle, reaction, etc.\ndef _next_compos(toks):\n if toks.getName() == 'reaction_on_nucleus':\n detail = _temperature | _composition | _concentration | _reaction\n elif toks.getName() == 'temperature':\n detail = _reactiononnucl | _composition | _concentration | _reaction\n elif toks.getName() == 'composition':\n detail = _reactiononnucl | _temperature | _concentration | _reaction\n elif toks.getName() == 'concentration':\n detail = _reactiononnucl | _temperature | _composition | _reaction\n elif toks.getName() == 'reaction':\n detail = _reactiononnucl | _temperature | _composition | _concentration\n else:\n LOGGER.warning(\"Not a foreseen result name, please check, keeping all\")\n detail = (_reactiononnucl | _temperature | _composition\n | _concentration | _reaction)\n _otherdetails << OneOrMore(detail) # pylint: disable=W0106\n\n\n_compodetails = Forward()\n_otherdetails = Forward()\n_compoptions = (_reactiononnucl\n | _temperature\n | _composition\n | _concentration\n | _reaction).setParseAction(_next_compos)\n_compodetails << Group(_compoptions + _otherdetails) # pylint: disable=W0106\n_nuclflags = (OneOrMore(_compodetails).setParseAction(trans.lod_to_dot)\n ('compos_details'))\n\n\n# other response characteristics\n_dpatype = (Suppress(_dpatype_kw)\n + OneOrMore(Word(alphas+'-,'), stopOn=LineEnd())\n .setParseAction(' '.join)('dpa_type'))\n_required = (Suppress(_required_kw)\n + OneOrMore(Word(alphanums+'():'), stopOn=LineEnd())\n .setParseAction(' '.join)('required'))\n_mode = Suppress(_mode_kw) + Word(alphas)('mode')\n_inducedbyint = (Suppress(_inducedbyint_kw)\n + Group(OneOrMore(_inums))\n .setParseAction(trans.convert_list_to_tuple)\n ('induced_by_interation'))\n_notinducedbyint = (Suppress(\"NOT\" + _inducedbyint_kw)\n + Group(OneOrMore(_inums))\n .setParseAction(trans.convert_list_to_tuple)\n ('NOT_induced_by_interation'))\n_fxptcontrib = (OneOrMore(Word(alphas+'()'),\n stopOn=_fxptcontrib_kw).setParseAction(' '.join)\n ('fxpt_contribution')\n + Suppress(_fxptcontrib_kw))\n_spectrumresp = Suppress(_spectrumresp_kw + ':') + Word(alphas)('spectrum')\n_filters = (Suppress(_filters_kw + ':')\n + Group(OneOrMore(_inums))\n .setParseAction(trans.convert_list_to_tuple)\n ('filtered_volumes')\n + Optional(Suppress('With') + Word(alphas+'_')('filter_type')))\n\n\nrespcarac = (_respfilter\n | _particle\n | _incparticle\n | _noise_equation\n | _nuclflags\n | _filters\n | _required\n | _dpatype\n | _mode\n | _inducedbyint\n | _notinducedbyint\n | _fxptcontrib\n | _spectrumresp)\n\nrespintro = respdesc + ZeroOrMore(respcarac)\n\n# Responses themselves\n# Score description (not needed for KEFF)\nscoremode = Suppress(_scoremode_kw + ':') + Word(alphas+'_')('scoring_mode')\n# scoring zones\n_score_mesh_unit = (Suppress(\"(in\")\n + Word(alphanums+'.^-+')('unit')\n + Suppress(')'))\n_score_meshinfo_cell_unit = (Suppress(_scoremeshinfobins_kw + '[')\n + _fnums + Word(alphanums+' .^-+')\n + Suppress(']'))('cell_unit')\n_score_meshinfo_volume = (Suppress(_vol_kw + '[')\n + _fnums + Word(alphanums+'.^-+')\n + Suppress(']'))('vol_unit')\n_score_meshcols = Suppress(Word(alphas)\n + Optional(_scoremeshinfobins_kw)\n + Word(alphas)\n + Word(alphas+'() '))\n_score_meshinfounit_cols = (Suppress(Word(alphas))\n + _score_meshinfo_cell_unit\n + _score_meshinfo_volume\n + Suppress(Word(alphas) + Word(alphas+'() ')))\n_score_mesh = (Optional(Suppress(_scorevolumeunit_kw)\n + Word(alphanums+' .^-+')('scoring_zone_volsurf_unit')\n + Suppress(':')\n + _fnums('scoring_zone_volsurf'))\n + Optional(Suppress(_scorevolumecell_kw + ':')\n + _fnums('scoring_zone_cellvol'))\n + _scoremesh_kw('scoring_zone_type') + Suppress(':')\n + (_score_meshinfounit_cols | _score_meshcols)\n + Optional(_score_mesh_unit))\n_score_allgeom = _scoreallgeom_kw('scoring_zone_type')\n_score_allsources = _scoreallsources_kw('scoring_zone_type')\n_score_vol = (_scorevol_kw('scoring_zone_type')\n + Suppress(_scorevolvol_kw + ':')\n + (_inums | Word(printables))('scoring_zone_id')\n + Suppress(_scorevolumeunit_kw)\n + Word(alphanums+' .^-+')('scoring_zone_volsurf_unit')\n + Suppress(':')\n + _fnums('scoring_zone_volsurf')\n + Optional(_scorevolumeint_kw | _scorevolumenotint_kw)\n ('scoring_zone_details'))\n_score_vol_sum = ((_scorevolsum_kw('scoring_zone_type')\n + Suppress(_scorevolsumvol_kw + ':')\n + Group(delimitedList(_inums, delim='+'))('scoring_zone_id')\n + Suppress(_scorevolumesum_kw + ':')\n + _fnums('scoring_zone_volsurf')))\n_score_surf = (_scoresurf_kw('scoring_zone_type')\n + Suppress(_scoresurfvol_kw + ':')\n + Group(delimitedList(_inums\n | Word(printables, excludeChars=',')))\n ('scoring_zone_id')\n + Optional(Suppress(_scoresurface_kw + ':')\n + _fnums('scoring_zone_volsurf'))\n + Optional(Suppress(_scoresurfaceint_kw\n | _scoresurfacenotint_kw)))\n_score_surf_sum = (\n _scoresurfsum_kw('scoring_zone_type')\n + Suppress(_scoresurfsumfront_kw + ':')\n + delimitedList(Group(Suppress('(')\n + delimitedList(Word(printables, excludeChars=',()')\n | _inums)\n + Suppress(')')), delim='+')('scoring_zone_id')\n + Suppress(_scoresurfacesum_kw + ':')\n + _fnums('scoring_zone_volsurf'))\n_score_point = (_scorepoint_kw('scoring_zone_type')\n + Suppress(':')\n + Group(_fnums + Suppress(',')\n + _fnums + Suppress(',')\n + _fnums)('scoring_zone_id'))\n_cellelt = (Group(Suppress('(') + _inums\n + Suppress(',') + _inums\n + OneOrMore(\n Group(Suppress(',') + _inums\n + Suppress(',') + _inums\n + Suppress(',') + _inums))\n + Suppress(')')))\n_score_cell = (_scorecell_kw('scoring_zone_type')\n + Suppress(_scorecelldet_kw)\n + Group(_cellelt\n + ZeroOrMore(Suppress('+') + _cellelt))\n ('scoring_zone_id'))\n_maillelt = (Suppress(_scoremaillevol_kw + ':') + _inums\n + Suppress(_scoremailledepth_kw + ':') + _inums\n + Suppress(_scoremaillecell_kw + ':')\n + OneOrMore(\n Group(Suppress('(') + _inums\n + Suppress(',') + _inums\n + Suppress(',') + _inums + Suppress(')'))))\n_score_maille = (_scoremaille_kw('scoring_zone_type')\n + Group(_maillelt)('scoring_zone_id'))\nscorezone = (Suppress(_scorezone_kw+':')\n + (_score_mesh\n | _score_allgeom\n | _score_allsources\n | _score_vol\n | _score_surf\n | _score_surf_sum\n | _score_vol_sum\n | _score_point\n | _score_cell\n | _score_maille\n | LineEnd()))\n# scoring description = scoring mode + scoring zone\nscoredesc = scoremode + scorezone\n\n# Correspondence table (volumes ids and names)\ncorresptable = (Suppress(_corresptable_kw)\n + OneOrMore(Group(\n Suppress(_vol_kw + ':')\n + _inums('volume_id')\n + Suppress(Keyword('is :'))\n + Word(printables)('volume_name')), stopOn=_endtable)\n ('correspondence_table'))\n\n\n# RESPONSE RESULTS\n\n\ndef _set_no_unit_case(toks):\n '''Deal with the \"not unit\" case'''\n if len(toks) == 1:\n return {'uscore': '', 'usigma': toks[0]}\n LOGGER.warning(\"more than one unit, please check: %s\", toks)\n return None\n\n\ndef _rm_blanks(toks):\n '''Remove leading and trailing spaces (not the ones inside the string)'''\n return toks[0].strip()\n\n\n# Default integrated result\n_numdiscbatch = (Suppress(_numbatchs1stdiscarded_kw + ':')\n + _inums('discarded_batches'))\n_numusedbatch = Suppress(_numbatchsused_kw + ':') + _inums('used_batches')\n_integratedres_name = _integratedres_kw | Word(alphas) + _integratedres_kw\n_integratedres = _fnums('score') + _fnums('sigma')\n_unitsres = (Suppress(_units_kw)\n + (Word('%').setParseAction(_set_no_unit_case)\n | (Word(alphanums+'.^-+ ').setParseAction(_rm_blanks)('uscore')\n + Word('%')('usigma'))))\n# rejection in vov and sensibility cases\n_rejection = (Suppress('[')\n + OneOrMore(Word(alphanums+'<>.+-')).setParseAction(' '.join)\n ('rejection')\n + Suppress(']'))\n_vovnostar = Suppress(_vov_kw) + Group(_fnums('score'))('vov_res')\n_vovstar = Group(\n Suppress(_vovstar_kw) + _fnums('score')\n + Suppress('[') + _fnums('sigma') + Suppress(']')\n + Optional(_rejection))('vovstar_res')\n_sensibtomaxval = Group(\n Suppress(_sensibtomaxval_kw) + _fnums('max_val')\n + Optional(_rejection))('sensibility_res')\n_vov = (_vovnostar | _vovstar + _sensibtomaxval)\n# best result\nbestres = (Group(Suppress(_bestresdiscbatchs_kw) + _inums('discarded_batches')\n + Suppress(\"batches\")\n + _minus_line\n + _numusedbatch + _fnums('score') + _fnums('sigma'))\n ('best_result_res'))\n\n\nintegratedres = (Group(Optional(Suppress(_integratedres_name))\n + Optional(_numdiscbatch)\n + ((_numusedbatch + _integratedres)\n | _notconverged_kw('not_converged')))\n ('integrated_res')\n + Optional(_vov))\n\ngenericscoreblock = (Group(Optional(Suppress(_integratedres_name))\n + ((_numusedbatch\n + _integratedres\n + Optional(_unitsres))\n | _notconverged_kw('not_converged')))\n ('generic_res').setParseAction(trans.group_to_dict))\n\n\n# Time steps\n_timestep = Group(Suppress(_timestepnum_kw + ':') + _inums\n + _minus_line\n + Suppress(_timestepmin_kw) + _fnums\n + Suppress(_timestepmax_kw) + _fnums)('time_step')\n\n\n# Angular zones\n_muangzone = Group(Suppress(_muangzone_kw) + _inums\n + _minus_line\n + Suppress(_mumin_kw) + _fnums\n + Suppress(_mumax_kw) + _fnums)('mu_angle_zone')\n_phiangzone = Group(Suppress(_phiangzone_kw) + _inums\n + _minus_line\n + Suppress(_phimin_kw) + _fnums\n + Suppress(_phimax_kw) + _fnums)('phi_angle_zone')\n\n\n# Spectrum\n_spectrumunits = Group(Suppress(_units_kw)\n + Word(alphanums+'.^-+%') * 4)('units')\n_spectrumbin = _fnums + Suppress('-') + _fnums\n_spectrumcols = Suppress((_spgroupwunit_kw | _spgroup_kw)\n + _spscore_kw\n + _spsigma_kw\n + _spscovlethargy_kw)\n_spectrumvals = (Group(_spectrumbin + _fnums + _fnums + _fnums)\n .setFailAction(trans.fail_spectrum))\n_spectrum = (Suppress(_spectrum_kw)\n + _numdiscbatch\n + _spectrumcols\n + Optional(_spectrumunits)\n + OneOrMore(_spectrumvals, stopOn=_endtable)('spectrum_vals'))\nspectrumblock = (Group(OneOrMore\n (Group(OneOrMore(_timestep | _muangzone | _phiangzone)\n + _spectrum\n + Optional(integratedres)))\n | Group(_spectrum))('spectrum_res'))\n\n\n# Spectrum with vov\n# no unit for vov (not even a space) -> use spectrum ones with 4 fields\n_vovspectrumcols = Suppress((_spgroupwunit_kw | _spgroup_kw)\n + _spscore_kw + _spsigma_kw\n + _spscovlethargy_kw + _spvov_kw)\n_vovspectrumbin = _fnums + Suppress('-') + _fnums\n_vovspectrumvals = Group(_vovspectrumbin + _fnums + _fnums + _fnums + _fnums)\n_vovspectrum = (Suppress(_spectrum_kw)\n + _numdiscbatch\n + _vovspectrumcols\n + Optional(_spectrumunits)\n + OneOrMore(_vovspectrumvals, stopOn=_endtable)\n ('spectrum_vals'))\nvovspectrumblock = Group(Group(_vovspectrum))('vov_spectrum_res')\n\n\n# Nu spectrum\n_nuspectrumunits = Group(Suppress(_units_kw)\n + Word(alphanums+'.^-+%') * 3)('units')\n_nuspectrumcols = Suppress(_nusprange_kw + _spscore_kw + _spsigma_kw)\n_nuspectrumvals = (Group(_spectrumbin + _fnums + _fnums)\n .setFailAction(trans.fail_spectrum))\n_nuspectrum = (Suppress(_nuspectrum_kw)\n + _numdiscbatch\n + _nuspectrumcols\n + Optional(_nuspectrumunits)\n + OneOrMore(_nuspectrumvals, stopOn=_endtable)('spectrum_vals'))\nnuspectrumblock = Group(Group(_nuspectrum + integratedres))('nu_spectrum_res')\n\n\n# ZA spectrum\n_zaspectrumunits = Group(Suppress(_units_kw)\n + Word(alphanums+'.^-+%') * 2)('units')\n_zaspectrumcols = Suppress(_zaspid_kw + _spscore_kw + _spsigma_kw)\n_zaspectrumbin = (Suppress('(') + _inums + Suppress(',')\n + _inums + Suppress(')'))\n_zaspectrumvals = (Group(_zaspectrumbin + _fnums + _fnums)\n .setFailAction(trans.fail_spectrum))\n_zaspectrum = (Suppress(_zaspectrum_kw)\n + _numdiscbatch\n + _zaspectrumcols\n + Optional(_zaspectrumunits)\n + OneOrMore(_zaspectrumvals, stopOn=_endtable)('spectrum_vals'))\nzaspectrumblock = Group(Group(_zaspectrum + integratedres))('za_spectrum_res')\n\n\ndef _printtoks(toks):\n print(toks)\n\n\n# Entropy\n_boltzmannentropy = (Suppress(_boltzmannentropy_kw)\n + _fnums('boltzmann_entropy_res'))\n_shannonentropy = Suppress(_shannonentropy_kw) + _fnums('shannon_entropy_res')\nentropy = _boltzmannentropy + _shannonentropy\n\n\n# Mesh\n_mesh_energyrange = (Group(Suppress(_energyrange_kw + \"(in\") + Word(alphas)\n + Suppress('):') + _fnums + Suppress('-') + _fnums)\n ('mesh_energyrange'))\n_mesh_energyintegrated = ((Suppress(_integratedres_name) + Suppress(':'))\n ('mesh_energyintegrated'))\n_mesh_energyline = _mesh_energyrange | _mesh_energyintegrated\n_meshres = Group(\n _mesh_energyline\n + Group(originalTextFor(\n OneOrMore(Word(printables), stopOn=_endtable)))('mesh_vals')\n + Optional(Group(entropy)('entropy')))\nmeshblock = Group(OneOrMore(Group(_timestep\n + Optional(_score_mesh_unit)\n + Group(OneOrMore(_meshres))('meshes')\n + Optional(integratedres)))\n | Group(Group(OneOrMore(_meshres))('meshes')))('mesh_res')\n\n\n# KIJ matrix\n# before keff as KIJ ESTIMATOR used to evaluate KEFF...\n# definition de kijdim en variable globale ne fonctionne pas car creee avant\n# solution : utiliser forward pour le redefinir !\n\n\ndef _set_kijdim(toks):\n _kijdim = len(toks)\n _kijeigenvec << Group(_fnums * _kijdim) # pylint: disable=W0106\n _kijmatrix << Group(_fnums * _kijdim) # pylint: disable=W0106\n\n\n_kijsum = Group(Optional(Suppress(_kijlefteigenval_kw))\n + Suppress(_kijmkeff_kw) + _fnums\n + Suppress(_kijdomratio_kw) + _fnums)('kijmkeff_res')\n_kijeigenval = Group(_fnums + _fnums)\n_kijeigenvec = Forward()\n_kijmatrix = Forward()\n_kijeigenvaltab = (Suppress(_kijeigenval_kw)\n + (OneOrMore(_kijeigenval).setParseAction(_set_kijdim))\n ('kij_eigenval'))\n_kijeigenvectab = ((Suppress(_kijeigenvec_kw) + (OneOrMore(_kijeigenvec)))\n | _kijeigenvecnotprint_kw)('kij_eigenvec')\n_kijmatrixtab = (Suppress(_kijmatrix_kw)\n + ((OneOrMore(_kijmatrix)) | _kijmatrixnotprint_kw)\n ('kij_matrix'))\nkijres = (Group(Suppress(_integratedres_name)\n + _numusedbatch\n + _kijsum\n + _kijeigenvaltab\n + _kijeigenvectab\n + _kijmatrixtab).setParseAction(trans.convert_kij_result)\n ('kij_res'))\n\n\n# KIJ SOURCES\n_kijsourcesorder = (Suppress(_kijsourcesorder_kw)\n + Word(alphas)('kij_sources_order') + Suppress(':'))\n_kijsourcesval = Group(OneOrMore(_fnums))('kij_sources_vals')\nkijsources = (Group(Suppress(_integratedres_name)\n + _numusedbatch\n + Suppress(_kijsources_kw)\n + Optional(_kijsourcesorder)\n + _kijsourcesval)\n .setParseAction(trans.convert_kij_sources)('kij_sources'))\n\n\n# KIJ estimator for keff\n\ndef _define_kij_dim(toks):\n LOGGER.debug(\"KIJ dimension: %d\", len(toks))\n _kijdim = len(toks)\n _identifier = (Group(Suppress('(')\n + _inums + Suppress(',')\n + _inums + Suppress(',')\n + _inums + Suppress(')'))\n | _inums)\n _idline << Group(_identifier * _kijdim) # pylint: disable=W0106\n _matline << Group(_identifier # pylint: disable=W0106\n + Suppress('|')\n + (_fnums + Suppress('|')) * _kijdim)\n\n\ndef _set_kij_vols(toks):\n _nbvols = int(toks[0])\n _kijlistvol << Group(_inums * _nbvols) # pylint: disable=W0106\n\n\n_kijkeffbeg = Word(alphas)('estimator') + Suppress(_estimator_kw) + _minus_line\n_kijlistvol = Forward()\n_kijfissilevol = ((Suppress(_kijfissilevol_kw) + _inums('nb_fissile_vols'))\n .setParseAction(_set_kij_vols)\n + Suppress(_kijlistfissilevol_kw)\n + _kijlistvol('list_fissile_vols'))\n_kijkeffintro = (Optional(_kijfissilevol)\n + Suppress(_kijbatchs_kw) + _inums('batchs_kept')\n + Suppress(_kijmkeff_kw) + _fnums('kij_mkeff'))\n\n_idline = Forward()\n_matline = Forward()\n_kijkeffev = OneOrMore(Group(_inums + _fnums)).setParseAction(_define_kij_dim)\n_kijkeffevtab = Group(Suppress(_kijkeffevid_kw) + _kijkeffev)('kij_leigenvec')\n_defmatrix = _idline + _minus_line + OneOrMore(_matline + _minus_line)\n_kijkeffmatrix = (Group(Suppress(_kijkeffmat_kw) + _defmatrix)\n ('kij_matrix'))\n_kijkeffstdmatrix = (Group(Suppress(_kijkeffstddevmat_kw) + _defmatrix)\n ('kij_stddev_matrix'))\n_kijkeffsensibmatrix = (Group(Suppress(_kijkeffsensibilitymat_kw) + _defmatrix)\n ('kij_sensibility_matrix'))\n\n_kijkeffblock = (Group(_kijkeffbeg\n + _kijkeffintro\n + _kijkeffevtab\n + _kijkeffmatrix\n + _kijkeffstdmatrix\n + _kijkeffsensibmatrix)\n .setParseAction(trans.convert_kij_keff))\n\n\n# Keff as generic response\n_keffres = Group(Word(alphas) + _fnums + _fnums)\n_keffresblock = Group(OneOrMore(_keffres))('res_per_estimator')\n_correlationdesc = Suppress(_correlations_kw)\n_correlationestim = Group(Word(alphas) + Suppress('<->') + Word(alphas))\n_correlationvals = OneOrMore(_fnums\n | _notconverged_kw)\n_correlation = Group(_correlationestim + _correlationvals)\n_correlationblock = (Group(_correlationdesc + OneOrMore(_correlation))\n ('correlation_mat'))\n_fullcombestimation = (Group(Suppress(_fullcomb_kw)\n + ((_fnums + _fnums)\n | (_notconverged_kw\n + Suppress(Optional(_notcvg_exp_kw)))))\n ('full_comb_estimation'))\n_autokeffres = ((_keffresblock + _correlationblock + _fullcombestimation)\n | _notconverged_kw('not_converged'))\n_warnkeff = (Suppress(_warning_kw)\n + _warn_combkeff_kw.setParseAction(' '.join)('warning'))\nkeffblock = Group(Suppress(_integratedres_name)\n + _numusedbatch\n + (_autokeffres | _warnkeff)\n ).setParseAction(trans.convert_keff)('keff_res')\n\n\n# Keff as historical response\n_bestresestim = (OneOrMore(Word(alphas), stopOn=_estimator_kw)\n .setParseAction(' '.join)('keff_estimator')\n + Suppress(_estimator_kw))\n_bestresdiscbatch = (Suppress(_bestresdiscbatchs_kw)\n + _inums('best_disc_batchs')\n + Suppress(\"batches\"))\n_bestkeff = (Suppress(Keyword(\"keff\") + '=') + _fnums('keff')\n + Suppress(Keyword(\"sigma\") + '=') + _fnums('sigma')\n + Suppress(Keyword(\"sigma%\") + '=') + _fnums('sigma%'))\n_equivkeff = Suppress(_equivkeff_kw) + _fnums('equivalent_keff')\n_bestkeffpestim = (Group(_notconverged_kw('not_converged')\n | Group(Optional(_bestresdiscbatch)\n + _numusedbatch\n + _bestkeff\n + Optional(_equivkeff))('keff_auto'))\n ('results'))\n_bestreskeff = Group(_bestresestim + _minus_line + _bestkeffpestim)\n_warnfixedsources = Group(Suppress(_warning_kw) + _minus_line\n + _warn_fixsourcekeff_kw('warning'))\n_bestresblock = OneOrMore(_bestreskeff, stopOn=\"KIJ\")\nautokeffblock = Group(Group(Optional(_warnfixedsources)\n + _bestresblock\n + Optional(_kijkeffblock))\n .setParseAction(trans.convert_keff_auto))('keff_auto')\n\n\n# MED files\nmedfile = (Suppress((_creationmedfile_kw | _creationfile_kw) + ':')\n + Word(alphanums+'/_.')('med_file')\n + Suppress(_medmeshid_kw + Word(alphanums+'_.')))\n\n\n# Greenbands exploitation\n_gbspectrumstep = Suppress(_gbspectrumstep_kw) + _inums\n_gbenergymin = Suppress(_gbenergymin_kw) + _fnums\n_gbenergymax = Suppress(_gbenergymax_kw) + _fnums\n_gbstepdesc = Group(_gbspectrumstep + _minus_line\n + _gbenergymin + _gbenergymax)('gb_step_desc')\n_gbtabulation = (Suppress(_gbsourcetab_kw)\n + Group(Suppress('u =') + _inums\n + Suppress(', v =') + _inums\n + Suppress(', w =') + _inums))\n_gbsource = Group(Suppress(_gbsourcenum_kw)\n + _inums\n + (_minus_line | (_gbtabulation + _minus_line)))('gb_source')\n_gbrespersource = Group(_gbsource + spectrumblock)\n_gbstep = Group(_gbstepdesc + Group(OneOrMore(_gbrespersource))('gb_step_res'))\ngbblock = Group(OneOrMore(_gbstep))('green_bands_res')\n\n\n# Scores ordered by nuclei and precursor families, IFP outputs\n_generic_score = Suppress(':') + _fnums + _fnums\n# Nuclei order alone\n_scorepernucleus = Group(Word(alphanums) + _generic_score)\n_nucleiorder = (Suppress(_nucleiorder_kw)\n + ZeroOrMore(_scorepernucleus)('score_per_nucleus'))\n# Families order alone\n_scoreperfamily = Group(Suppress(\"i =\") + _inums + _generic_score)\n_familyorder = (Suppress(_familiesorder_kw)\n + ZeroOrMore(_scoreperfamily)('score_per_family'))\n# Nuclei and families order\n_nucleusid = Suppress(_nucleus_kw) + Word(alphanums) + Suppress('.')\n_nucleusfam = Group(_nucleusid + _familyorder)\n_nuclfamorder = (Suppress(_nucleifamilyorder_kw)\n + ZeroOrMore(_nucleusfam)('score_per_nucleus_family'))\n# Perturbation index order\n_scoreperpertuind = Group(Suppress(\"i =\") + _inums + _generic_score)\n_perturborder = (Suppress(_perturborder_kw)\n + ZeroOrMore(_scoreperpertuind)('score_per_perturbation'))\n# Convergence statistics\n_cvgline = Group(Suppress(\"L =\") + _inums + _generic_score)\n_cvgstat = (Suppress(_cvgstat_kw)\n + ZeroOrMore(_cvgline)('score_per_length'))\n# results from adjoint calculation\nadjointres = (Group(Group(Suppress(_integratedres_name)\n + _numusedbatch\n + Group(_nuclfamorder\n | _nucleiorder\n | _familyorder\n | _perturborder\n | _cvgstat)('adj_res')\n + Optional(_unitsres)\n ).setParseAction(trans.convert_generic_adjoint))\n )('adjoint')\n# Convergence statistics\n_kingcritline = Group(Suppress(\"CRITICALITY SOURCE\") + _fnums + _fnums)\n_kingtimestepline = Group(Suppress(\"END OF TIME STEP \") + Suppress(_inums)\n + _fnums + _fnums)\n_kingstat = Group(_kingcritline\n + ZeroOrMore(_kingtimestepline))('kin_generic_res')\nkingres = (Group(Suppress(_integratedres_name)\n + _numusedbatch\n + _kingstat\n + Optional(_unitsres)('units')\n ).setParseAction(trans.convert_generic_kinetic)\n )('kinetic_generic')\n# sensitivities\n_sensitivityorder = (Suppress(_sensitivitytypeorder_kw)\n + OneOrMore(Word(alphas + '_,()'),\n stopOn=_sensitivityindexorder_kw)\n .setParseAction(' '.join)\n + Suppress(_sensitivityindexorder_kw))\n_sensitivityreactionrate_ratio = Group(Suppress(_sensitivityreactionrate_kw)\n + _fnums('score') + _fnums('sigma'))\n_sensitivity_type = (OneOrMore(Word(alphas.upper()), stopOn=_sensitivity_kw)\n .setParseAction(' '.join)\n + Suppress(_sensitivity_kw))('typeI')\n_sensitivity_index = Group(\n Suppress(\"i =\") + _inums('sensitivity_index') + Suppress(';')\n + Suppress(\"NUCLEUS :\") + Word(alphanums + '_')('sensitivity_nucleus')\n + Suppress(',') + Suppress(\"TYPE :\")\n + Word(alphanums.upper() + '_() ')('sensitivity_reaction'))\n_sensitivity_dircos = Group(Suppress(_sensitivity_dircos_kw)\n + _fnums*2)('direction_cosine')\n_sensitivity_energyinc = Group(Suppress(_sensitivity_incenergy_kw)\n + _fnums*2)('energy_incident')\n_sensitivity_cols = (Keyword(\"E min\") + Keyword(\"E max\")\n + Keyword(\"S(E)\") + Keyword(\"sigma\"))\n_sensitivity_vals = Group(_fnums*4)\n_sensitivity_energyint = Group(Suppress(_sensitivity_energyint_kw)\n + _integratedres)\n_sensitivity_res = Group(_sensitivity_index('charac')\n + Group(OneOrMore(Group(\n ZeroOrMore(_sensitivity_dircos\n | _sensitivity_energyinc)\n + Suppress(_sensitivity_cols)\n + OneOrMore(_sensitivity_vals)('values'))))\n ('vals')\n + _sensitivity_energyint('energy_integrated'))\n_sensitivity = (Suppress(_sensitivityorder)\n + ZeroOrMore(Group(_sensitivityreactionrate_ratio\n ('reaction_rate')))\n + (OneOrMore(Group(_sensitivity_type('sensitivity_type')\n + OneOrMore(_sensitivity_res)('res')))))\nsensitivityres = Group(Group(Optional(Suppress(_integratedres_name))\n + _numusedbatch\n + Group(_sensitivity)('sensit_res')\n + Optional(_unitsres)('units'))\n .setParseAction(trans.convert_sensitivities)\n )('sensitivity')\n\n\ndef _rename_norm_kw():\n '''Transform RESULTS ARE NORMALIZED keyword in int (lighter)'''\n return 1\n\n\ndef _define_ifp_adj_table_dim(toks):\n '''Define the format of the IFP adjoint criticality table result:\n coordinates (Vol or space coordinates) are followed by energy.\n '''\n _tabdim = len(toks)\n if \"Vol\" not in toks[0]:\n _ifpadjbinval << _fnums * 2 * _tabdim # pylint: disable=W0104\n else:\n # in Vol case: int to identify volume, then E (min | max)\n _ifpadjbinval << _inums + _fnums * 2 # pylint: disable=W0104\n\n\n# IFP adjoint criticality edition\n_adjcrit_ed_intro = _star_line + Suppress(_ifpadjcriticality_kw) + _star_line\n_ifpadjcrit_intro = Group(Word(alphas+'_')('ifp_response')\n + _scorename\n + Suppress(_ifpadjcyclelength_kw)\n + _inums('ifp_cycle_length')\n + (Optional(_ifpadjnormalizedres_kw\n .setParseAction(_rename_norm_kw))\n ('normalized'))\n + _star_line)\n_ifpadjbinval = Forward()\n_ifpadjcoordinate = Word(alphas) + Suppress(_ifpadjminmax_kw)\n_ifpadjcoordinates = ((Optional(_ifpadjvol_kw) + OneOrMore(_ifpadjcoordinate))\n .setParseAction(_define_ifp_adj_table_dim))\n_ifpadjcolumns = _ifpadjcoordinates + _ifpadjscore_kw + _spsigma_kw\n_ifpadjline = Group(_ifpadjbinval + _fnums + _fnums)\n_ifpadjvalues = OneOrMore(_ifpadjline)\n_adjcritblock = Group(_ifpadjcrit_intro('ifp_adjoint_criticality_intro')\n + _ifpadjcolumns('columns')\n + _ifpadjvalues('values')\n + _star_line)\nifpadjointcriticality = (Group((_adjcrit_ed_intro + OneOrMore(_adjcritblock))\n .setParseAction(trans.convert_ifp_adj_crit_ed))\n ('ifp_adjoint_crit_edition'))\n\n# Spherical harmonics results\n_shr_bins = Group(Suppress(_shr_nb_space_bins_kw) + _inums('nb_sbins')\n + Suppress(_shr_nb_u_space_bins_kw) + _inums('nb_ubins')\n + Suppress(_shr_nb_v_space_bins_kw) + _inums('nb_vbins')\n + Suppress(_shr_nb_w_space_bins_kw) + _inums('nb_wbins')\n + Suppress(_shr_nb_energy_bins_kw) + _inums('nb_ebins')\n + Suppress(_shr_nb_inc_energy_bins_kw) + _inums('nb_iebins')\n + Suppress(_shr_lmax_kw) + _inums('lmax'))('nb_bins')\n_shrscore = (Suppress(_shr_score_name_kw)\n + Group(OneOrMore(Word(printables), stopOn=LineEnd())\n .setParseAction(' '.join))('score_name'))\n_shrspace = Suppress(_shr_space_kw) + Group(_inums + _inums + _inums)('space')\n_shrenergy = Suppress(_shr_energy_kw) + Group(_fnums + _fnums)('energy')\n_shrincenergy = (Suppress(_shr_incident_energy_kw)\n + Group(_fnums + _fnums)('incident_energy'))\n_shrcols = Suppress(_shr_l_kw + _shr_m_kw + _shr_score_kw + _shr_sigma_kw)\n_shrvals = Group(_inums + _inums + _fnums + _fnums)\n_shrtable = _shrenergy + _shrcols + OneOrMore(_shrvals)('values')\n_shr_ietable = Optional(_shrincenergy) + OneOrMore(Group(_shrtable))('vpie')\n_shr_spacetable = _shrspace + OneOrMore(Group(_shr_ietable))('vpspace')\n_shr_scoretable = _shrscore + OneOrMore(Group(_shr_spacetable))('score')\nshrblock = Group(_shr_bins\n + Group(OneOrMore(Group(_shr_scoretable)))('res')\n + Suppress(_star_line)\n ).setParseAction(trans.convert_shr)('spherical_harmonics')\n\n# Perturbations\n_perturank = Suppress(_perturank_kw) + _inums('perturbation_rank')\n_pertumethod = (Suppress(_pertumethod_kw)\n + OneOrMore(Word(alphas), stopOn=LineEnd())\n .setParseAction(' '.join)('perturbation_method'))\n_pertuorder = Suppress(_pertuorder_kw) + _inums('perturbation_order')\n_pertutype = Suppress(_pertutype_kw) + Word(alphas)('perturbation_type')\n_pertucompo = (Suppress(_pertucompo_kw)\n + Word(alphanums+'_')('perturbation_composition'))\npertu_desc = (Group(Suppress(_perturbation_kw)\n + _perturank\n + _pertumethod\n + Optional(_pertuorder)\n + _pertutype\n + _pertucompo)('perturbation_desc'))\n\n# Uncertainties (linked to perturbations ?)\n_uncertcols = Suppress(_uncertgp_kw + _uncertsig2_kw\n + _uncertmean_kw + _uncertsig_kw + _uncertfisher_kw)\n_uncertbin = _fnums + Suppress('-') + _fnums\n_uncertvals = Group(_uncertbin + _fnums * 4)\n_uncertspectrum = (Suppress(_uncertres_kw)\n + _numdiscbatch\n + _uncertcols\n + _minus_line\n + OneOrMore(_uncertvals, stopOn=_endtable)\n ('spectrum_vals'))\nuncertblock = Group(OneOrMore\n (Group(OneOrMore(_timestep | _muangzone | _phiangzone)\n + _uncertspectrum))\n | Group(_uncertspectrum))('uncert_spectrum_res')\n\n_uncertintegres = (_fnums('sigma2(means)')\n + _fnums('mean(sigma_n2)')\n + _fnums('sigma(sigma_n2)')\n + _fnums('fisher test'))\n_uncertintegfullres = _numusedbatch + _uncertintegres\nuncertintegblock = Group(Suppress(_uncertintegres_kw)\n + _numdiscbatch\n + _uncertintegfullres)('uncert_integrated_res')\n\n\n# Creation jdds\n_nbpartline = Group(Suppress(\"FILE\") + _inums\n + Suppress(':') + _inums\n + Suppress(\"particles\"))\ncontribpartblock = (Group(Suppress(_nbcontribpart_kw)\n + _minus_line\n + OneOrMore(_nbpartline)\n + Suppress(_endcontribpart_kw))\n ('contributing_particles'))\n\n\n# Score block\nscoreblock = (Group(scoredesc + (OneOrMore(vovspectrumblock\n | spectrumblock\n | nuspectrumblock\n | zaspectrumblock\n | meshblock\n | medfile\n | integratedres\n | bestres\n | uncertblock\n | uncertintegblock\n | gbblock\n | corresptable)))\n .setParseAction(trans.convert_score))\n\nlistscoreblock = (Group(OneOrMore(scoreblock)\n .setParseAction(trans.index_elements('score_index')))\n ('score_res'))\n\n# Response block\nresponseblock = Group(keffblock\n | kijres\n | kijsources\n | adjointres\n | kingres\n | sensitivityres\n | genericscoreblock\n | shrblock\n | listscoreblock)('results')\n\nresponse = (Group(_star_line\n + respintro\n + _star_line\n + responseblock)\n .setParseAction(trans.finalize_response_dict))\n\nlistresponses = Group(OneOrMore(response).setParseAction(\n compose2(trans.extract_all_metadata,\n trans.index_elements('response_index'))))('list_responses')\n\nperturbation = (OneOrMore(Group(pertu_desc + listresponses)\n .setParseAction(trans.propagate_all_metadata))\n .setParseAction(trans.index_elements('perturbation_index'))\n ('perturbation'))\n\n\n################################\n# DEBUG PARSER #\n################################\n\n# debug grammar, to be used with parse_debug (only for parsing development)\nt4debug_gram = (OneOrMore((intro\n + OneOrMore(listresponses | ifpadjointcriticality\n | autokeffblock | perturbation\n | Suppress(contribpartblock)\n | OneOrMore(runtime)))\n .setParseAction(trans.to_final_dict))\n .setParseAction(dump_in_logger)\n | intro + OneOrMore(runtime)).setFailAction(trans.fail_parsing)\n\n\n################################\n# GENERAL PARSER #\n################################\n\nt4gram = (OneOrMore((intro\n + ZeroOrMore(listresponses | ifpadjointcriticality\n | autokeffblock | perturbation\n | Suppress(contribpartblock))\n + runtime)\n .setParseAction(trans.to_final_dict))\n .setParseAction(dump_in_logger)\n ).setFailAction(trans.fail_parsing)\n","repo_name":"valjean-framework/valjean","sub_path":"valjean/eponine/tripoli4/grammar.py","file_name":"grammar.py","file_ext":"py","file_size_in_byte":62240,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"9061887765","text":"print(\"Welcome to Candies Vending Machine\\n\")\r\n\r\nx=int(input(\"How many candies do you want? \"))\r\nc=1\r\ntotal=5\r\nwhile x>total :\r\n print(\"current stock =\",total)\r\n print(\"do you want to continue with \",total, \"candies?\")\r\n z=input()\r\n if z=='y' or z=='Y':\r\n while total >= 1:\r\n print(c, \",CANDIES \", end=\"\")\r\n total -= 1\r\n c += 1\r\n else:\r\n print(\"\\nTotal = \",c-1,\"Candies.\")\r\n break\r\n if z == 'n' or z == 'N':\r\n print(\"No Candies for you\")\r\n break\r\n else:\r\n print(\"Invalid Input\")\r\n\r\nelse:\r\n while x >= 1:\r\n print(c, \",CANDIES \", end=\"\")\r\n x -= 1\r\n c += 1\r\n else:\r\n print(\"\\nTotal = \",c-1,\"Candies.\")\r\n","repo_name":"suliman-abdalla/1strepo","sub_path":"Vending Machine.py","file_name":"Vending Machine.py","file_ext":"py","file_size_in_byte":742,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"74602872804","text":"import json\nfrom time import time\n\n\nclass IOUnit:\n @staticmethod\n def get_name_from_topic(topic):\n return topic.split(\"/\")[1]\n\n @staticmethod\n def find():\n return \"home/+/info/status\"\n\n def __init__(self, name, mqtt):\n self.name = name\n self.mqtt = mqtt\n self.info = dict()\n self.io_state = dict()\n self.error_log = dict()\n self.info_change_cb = []\n self.io_change_cb = []\n\n def get_name(self):\n return self.name\n\n def get_type(self):\n return self.__class__.__name__\n\n def start_update(self):\n self.mqtt.subscribe(\"home/{}/info/+\".format(self.name), self._mqtt_update_info)\n self.mqtt.subscribe(\"commands/home/{}/+/output\".format(self.name), self._mqtt_update_io_request)\n self.mqtt.subscribe(\"home/{}/+/output\".format(self.name), self._mqtt_update_io_state)\n\n def set_io(self, io, state=True):\n data = {\"commands/home/{}/{}/output\".format(self.name, io): \"on\" if state else \"off\"}\n self.mqtt.publish(data)\n\n def set_info_change_callback(self, callback, property):\n self.info_change_cb.append((callback, property))\n\n def set_io_change_callback(self, callback):\n self.io_change_cb.append(callback)\n\n def _mqtt_update_info(self, topic, payload):\n prop = topic.split(\"/\")[-1]\n self.info[prop] = payload\n\n for tup in self.info_change_cb:\n if tup[1] == prop or tup[1] == \"*\":\n (tup[0])()\n\n def _mqtt_update_io_state(self, topic, payload):\n io = topic.split(\"/\")[-2]\n\n try:\n res = json.loads(payload)\n except ValueError:\n raise RuntimeError(\"Malformated json from node {}: {}\".format(self.name, payload))\n\n try:\n if res[\"status\"].lower() == \"ok\":\n value = res[\"value\"]\n else:\n s = \"Received a failed set-io command on io '{}' from node '{}'\".format(io, self.name)\n self.error_log[time()] = s\n print(s)\n except KeyError:\n raise RuntimeError(\"Missing key in IOUnit output result: {}\".format(payload))\n\n self._set_io_state(io, value)\n\n def _mqtt_update_io_request(self, topic, payload):\n io = topic.split(\"/\")[-2]\n self._set_io_state(io, payload, synced=False)\n\n '''\n Set requested value if synced = 0,\n actual value of synced = True\n '''\n def _set_io_state(self, io, value, synced=True):\n field = \"actual\" if synced else \"requested\"\n\n if not hasattr(self.io_state, io):\n self.io_state[io] = dict()\n\n if value == \"on\":\n self.io_state[io][field] = True\n elif value == \"off\":\n self.io_state[io][field] = False\n else:\n raise RuntimeError(\"Got unknown IO state '{}' from IOUnit {}\".format(value, io))\n\n for cb in self.io_change_cb:\n cb()\n","repo_name":"daniel-falk/home-automation-central","sub_path":"controll-panel/devices/device/IOUnit.py","file_name":"IOUnit.py","file_ext":"py","file_size_in_byte":3203,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"2051761039","text":"from django.shortcuts import render\nfrom .models import Site\nfrom .forms import SiteForm\n\n# Create your views here.\n\ndef ajouter_site(request):\n if request.method == \"POST\":\n form = SiteForm(request.POST)\n if form.is_valid():\n site = form.save(commit=False)\n site.save()\n form = SiteForm()\n return render(request, 'site_web/ajouter_siteh.tml', {'form': form}) \n else:\n form = SiteForm()\n return render(request, 'site_web/ajouter_site.html', {'form': form})","repo_name":"cberdaguer/table_numerique","sub_path":"Table-Tactile/table_numerique/site_web/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":530,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"70600676965","text":"'''\nCreated on 15 de ago de 2016\n\n@author: M146545\n'''\nfrom __future__ import print_function\nfrom polyNomial import Poly\nimport random\n\nimport string\nimport matplotlib.pyplot as plt\n#from mpl_toolkits.mplot3d import Axes3D\n\n\nclass Bug(object):\n '''\n classdocs\n '''\n def __init__(self, seed):\n self.seed = seed\n \n def y_move(self, max_x=100, plot=False):\n #d = random.randint(1,26)\n #exp = random.randint(1,100)\n d = 21\n exp = 71\n vec = []\n x=[]\n y=[]\n for k in range(100):\n values = [random.random() for i in range(d)]\n #values = [float(k/100) for i in range(d)]\n #x.append(values[0])\n #y.append(values[1])\n \n k = Poly(d=d, exp=exp)\n r, f = k.built_func(*values)\n vec.append([values[0]*max_x, r if r <= max_x else max_x]) \n #vec.append([values[0], values[1], k.built_func(*values)])\n print (vec)\n if plot:\n for i in vec:\n x.append(i[0])\n y.append(i[1])\n #z.append(i[2])\n \n xlist = x\n ylist = y\n \n plt.axis([0, max_x, 0, max_x])\n plt.plot(xlist, ylist)\n plt.show()\n\na = Bug(42)\na.y_move(plot=True)\n ","repo_name":"TRBaldim/Studies","sub_path":"genetic/bug.py","file_name":"bug.py","file_ext":"py","file_size_in_byte":1313,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"34455191691","text":"from functools import partial\nfrom typing import Any, Optional\n\nfrom torchvision.prototype.transforms import ImageNetEval\nfrom torchvision.transforms.functional import InterpolationMode\n\nfrom ...models.mobilenetv2 import MobileNetV2\nfrom ._api import WeightsEnum, Weights\nfrom ._meta import _IMAGENET_CATEGORIES\nfrom ._utils import handle_legacy_interface, _ovewrite_named_param\n\n\n__all__ = [\"MobileNetV2\", \"MobileNet_V2_Weights\", \"mobilenet_v2\"]\n\n\nclass MobileNet_V2_Weights(WeightsEnum):\n ImageNet1K_V1 = Weights(\n url=\"https://download.pytorch.org/models/mobilenet_v2-b0353104.pth\",\n transforms=partial(ImageNetEval, crop_size=224),\n meta={\n \"task\": \"image_classification\",\n \"architecture\": \"MobileNetV2\",\n \"publication_year\": 2018,\n \"num_params\": 3504872,\n \"size\": (224, 224),\n \"categories\": _IMAGENET_CATEGORIES,\n \"interpolation\": InterpolationMode.BILINEAR,\n \"recipe\": \"https://github.com/pytorch/vision/tree/main/references/classification#mobilenetv2\",\n \"acc@1\": 71.878,\n \"acc@5\": 90.286,\n },\n )\n default = ImageNet1K_V1\n\n\n@handle_legacy_interface(weights=(\"pretrained\", MobileNet_V2_Weights.ImageNet1K_V1))\ndef mobilenet_v2(\n *, weights: Optional[MobileNet_V2_Weights] = None, progress: bool = True, **kwargs: Any\n) -> MobileNetV2:\n weights = MobileNet_V2_Weights.verify(weights)\n\n if weights is not None:\n _ovewrite_named_param(kwargs, \"num_classes\", len(weights.meta[\"categories\"]))\n\n model = MobileNetV2(**kwargs)\n\n if weights is not None:\n model.load_state_dict(weights.get_state_dict(progress=progress))\n\n return model\n","repo_name":"isLinXu/DL_Frame_Models","sub_path":"Pytorch_vision/torchvision/prototype/models/mobilenetv2.py","file_name":"mobilenetv2.py","file_ext":"py","file_size_in_byte":1711,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"52"} +{"seq_id":"17311630701","text":"from mininet.topo import Topo\nimport logging\nimport os\n\nfrom mininet.net import Mininet\nfrom mininet.node import CPULimitedHost\nfrom mininet.link import TCLink\nfrom mininet.cli import CLI\n\nlogging.basicConfig(filename='./fattree.log', level=logging.DEBUG)\nlogger = logging.getLogger(__name__)\n\nclass FatTree( Topo ):\n \n CoreSwitchList = []\n AggSwitchList = []\n EdgeSwitchList = []\n HostList = []\n \n def __init__( self, k):\n \" Create Fat Tree topo.\"\n self.pod = k\n self.iCoreLayerSwitch = (k/2)**2\n self.iAggLayerSwitch = k*k/2\n self.iEdgeLayerSwitch = k*k/2\n self.density = k/2\n self.iHost = self.iEdgeLayerSwitch * self.density\n \n logger.debug(\"Core Layer Switch: \", self.iCoreLayerSwitch)\n logger.debug(\"Agg Layer Switch: \", self.iAggLayerSwitch)\n \n self.bw_c2a = 0.2\n self.bw_a2e = 0.1\n self.bw_h2a = 0.05\n\n # Init Topo\n Topo.__init__(self)\n \n self.createTopo()\n logger.debug(\"Finished topology creation!\")\n\n self.createLink( bw_c2a=self.bw_c2a, \n bw_a2e=self.bw_a2e, \n bw_h2a=self.bw_h2a)\n logger.debug(\"Finished adding links!\")\n\n # self.set_ovs_protocol_13()\n # logger.debug(\"OF is set to version 1.3!\") \n \n def createTopo(self):\n self.createCoreLayerSwitch(self.iCoreLayerSwitch)\n self.createAggLayerSwitch(self.iAggLayerSwitch)\n self.createEdgeLayerSwitch(self.iEdgeLayerSwitch)\n self.createHost(self.iHost)\n\n \"\"\"\n Create Switch and Host\n \"\"\"\n\n def _addSwitch(self, number, level, switch_list):\n for x in xrange(1, number+1):\n PREFIX = str(level) + \"00\"\n if x >= int(10):\n PREFIX = str(level) + \"0\"\n switch_list.append(self.addSwitch('s' + PREFIX + str(x), stp=True, failMode='standalone'))\n\n def createCoreLayerSwitch(self, NUMBER):\n logger.debug(\"Create Core Layer\")\n self._addSwitch(NUMBER, 1, self.CoreSwitchList)\n\n def createAggLayerSwitch(self, NUMBER):\n logger.debug(\"Create Agg Layer\")\n self._addSwitch(NUMBER, 2, self.AggSwitchList)\n\n def createEdgeLayerSwitch(self, NUMBER):\n logger.debug(\"Create Edge Layer\")\n self._addSwitch(NUMBER, 3, self.EdgeSwitchList)\n\n def createHost(self, NUMBER):\n logger.debug(\"Create Host\")\n\n for x in xrange(1, NUMBER+1):\n PREFIX = \"h00\"\n if x >= int(10):\n PREFIX = \"h0\"\n elif x >= int(100):\n PREFIX = \"h\"\n\n host_ip = \"10.1.%s.0\" % x\n self.HostList.append(self.addHost(PREFIX + str(x), ip=host_ip))\n\n \"\"\"\n Add Link\n \"\"\"\n def createLink(self, bw_c2a=0.2, bw_a2e=0.1, bw_h2a=0.5):\n logger.debug(\"Add link Core to Agg.\")\n end = self.pod/2\n for x in xrange(0, self.iAggLayerSwitch, end):\n for i in xrange(0, end):\n for j in xrange(0, end):\n linkopts = dict(bw=bw_c2a) \n self.addLink(\n self.CoreSwitchList[i*end+j],\n self.AggSwitchList[x+i],\n **linkopts)\n\n logger.debug(\"Add link Agg to Edge.\")\n for x in xrange(0, self.iAggLayerSwitch, end):\n for i in xrange(0, end):\n for j in xrange(0, end):\n linkopts = dict(bw=bw_a2e) \n self.addLink(\n self.AggSwitchList[x+i], self.EdgeSwitchList[x+j],\n **linkopts)\n\n logger.debug(\"Add link Edge to Host.\")\n for x in xrange(0, self.iEdgeLayerSwitch):\n for i in xrange(0, self.density):\n linkopts = dict(bw=bw_h2a) \n self.addLink(\n self.EdgeSwitchList[x],\n self.HostList[self.density * x + i],\n **linkopts)\n\n'''\ndef main():\n topo = FatTree(4)\n\n net = Mininet(topo=topo, host=CPULimitedHost, link = TCLink)\n net.start()\n CLI(net)\n net.stop()\n\nif __name__ == \"__main__\":\n main()\n'''\n\n","repo_name":"ShivamNegi/MPTCP","sub_path":"fattree.py","file_name":"fattree.py","file_ext":"py","file_size_in_byte":4153,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"73810198564","text":"import numpy as np\nfrom gym.envs.robotics import rotations, robot_env, utils\nimport math\nimport mujoco_py\nimport os\nimport xml.etree.ElementTree as et\nimport gym\nfrom gym import error, spaces\nfrom gym.utils import seeding\nimport copy\nimport env.robot.gym_utils as utils # Modified some gym utils to incorporate multiple bodies in mocap\nimport cv2\n\nimport time\nimport matplotlib.pyplot as plt\nimport torch\n\nDEFAULT_SIZE = 500\n\ndef get_full_asset_path(relative_path):\n return os.path.join(os.path.dirname(__file__), 'assets', relative_path)\n\nclass BaseEnv(robot_env.RobotEnv):\n \"\"\"Superclass for all robot environments.\n \"\"\"\n def __init__(\n\n self, model_path, cameras, n_substeps=20, gripper_rotation=[0,1,0,0], \n has_object=False, image_size=84, reset_free=False, distance_threshold=0.01, action_penalty=0,\n observation_type='state+image', reward_type='dense', reward_bonus=True, use_xyz=False, action_scale=0.05, render=False\n ):\n \"\"\"Initializes a new robot environment.\n Args:\n model_path (string): path to the environments XML file\n cameras (int): Camera/s to be used. Description in src/arguments.py\n n_substeps (int): number of substeps the simulation runs on every call to step\n gripper_rotation (array): fixed rotation of the end effector, expressed as a quaternion\n has_object (boolean): whether or not the environment has an object\n image_size (int): size of image observations, if applicable\n reset_free (boolean): whether the arm configuration is reset after each episode\n distance_threshold (float): the threshold after which a goal is considered achieved\n action_penalty (float): scalar multiplier that penalizes high magnitude actions\n observation_type ('image' or 'state+image'): the observation type\n reward_type ('sparse' or 'dense'): the reward type, i.e. sparse or dense\n reward_bonus (boolean): whether bonuses should be given for subgoals (only for dense rewards)\n use_xyz (boolean): whether movement is in 3d (xyz) or 2d (xy)\n\t\t\taction_scale (float): coefficient that scales scale position change\n \"\"\"\n self.xml_dir = '/'.join(model_path.split('/')[:-1])\n self.reference_xml = et.parse(model_path)\n self.root = self.reference_xml.getroot()\n self.n_substeps = n_substeps\n self.gripper_rotation = np.array(gripper_rotation, dtype=np.float32)\n self.has_object = has_object\n self.distance_threshold = distance_threshold\n self.action_penalty = action_penalty\n self.observation_type = observation_type\n self.reward_type = reward_type\n self.image_size = image_size\n self.reset_free = reset_free\n self.reward_bonus = reward_bonus\n self.use_xyz = use_xyz\n self.action_scale = action_scale\n self.closed_angle = 0\n\n # Robot workspace configurations\n self.center_of_table = np.array([1.655, 0.3, 0.53625])\n self.default_z_offset = 0.04\n self.max_z = 1.0\n self.min_z = 0.6\n self.state_dim = 4 if use_xyz else 3\n\n self.state_space = gym.spaces.Box(low=-np.inf, high=np.inf, shape=(self.state_dim,), dtype=np.float32)\n self.state_space_shape = self.state_space.shape\n\n self.render_for_human = render\n self.cameras = cameras\n\n super(BaseEnv, self).__init__(\n model_path=model_path, n_substeps=n_substeps, n_actions=4,\n initial_qpos={})\n \n def goal_distance(self, goal1, goal2, use_xyz):\n assert goal1.shape == goal2.shape\n goal1 = np.around(goal1, 4)\n goal2 = np.around(goal2, 4)\n return np.linalg.norm(goal1 - goal2, axis=-1)\n\n # GoalEnv methods\n # ----------------------------\n def compute_reward(self, achieved_goal, goal, info):\n raise NotImplementedError('Reward signal has not been implemented for this task!')\n\n # Gripper helper\n # ----------------------------\n def _gripper_sync(self):\n self.sim.data.qpos[10] = 0.1\n\n self.sim.data.qpos[12] = -0.5\n\n # print(self.sim.data.qpos)\n # print('shape', self.sim.data.qpos.shape)\n \n def _gripper_consistent(self, angle):\n return 0\n \n # RobotEnv methods\n # ----------------------------\n def _step_callback(self):\n self.sim.forward()\n\n def _limit_gripper(self, gripper_pos, pos_ctrl):\n return pos_ctrl\n\n\n def _set_action(self, action):\n assert action.shape == (4,)\n\n action = action.copy() # ensure that we don't change the action outside of this scope\n pos_ctrl, gripper_ctrl = action[:3], action[3]\n # print('pos', pos_ctrl)\n # print('gripper', gripper_ctrl)\n self._pos_ctrl_magnitude = np.linalg.norm(pos_ctrl)\n\n # make sure gripper does not leave workspace\n gripper_pos = self.sim.data.get_site_xpos('grasp')\n pos_ctrl = self._limit_gripper(gripper_pos, pos_ctrl)\n\n pos_ctrl *= self.action_scale # limit maximum change in position\n gripper_ctrl = np.array([gripper_ctrl, gripper_ctrl])\n\n assert gripper_ctrl.shape == (2,)\n action = np.concatenate([pos_ctrl, self.gripper_rotation, gripper_ctrl])\n\n # Apply action to simulation.\n utils.ctrl_set_action(self.sim, action)\n utils.mocap_set_action(self.sim, action)\n\n\n def end_effector_pos(self):\n raise NotImplementedError('_get_achieved_goal has not been implemented for this task!')\n\n def _get_robot_state_obs(self):\n dt = self.sim.nsubsteps * self.sim.model.opt.timestep\n eef_velp = self.sim.data.get_site_xvelp('grasp') * dt\n\n x = time.process_time()\n #print(x)\n sin = math.sin(x)\n self.sim.data.qvel[13:16] = np.array([ 0, -0.2, 0]) # -0.2, 0.14*sin\n self.sim.data.qvel[-3:] = np.random.randn(3) * 0\n # print('qvel', self.sim.data.qvel)\n # print('qvel_shape', self.sim.data.qvel.shape)\n\n eef_pos = self.sim.data.get_site_xpos('grasp')\n gripper_angle = self.sim.data.get_joint_qpos('right_outer_knuckle_joint')\n\n return np.concatenate([\n eef_pos, np.array([gripper_angle]) #eef_velp later add\n ], axis=0).astype(np.float32)\n\n def _get_image_obs(self):\n return self.render_obs(mode='rgb_array', width=self.image_size, height=self.image_size)\n\n def _get_obs(self):\n end_effector_position = self.end_effector_pos()\n obs = self._get_image_obs()\n state_ = self._get_robot_state_obs()\n\n return {\n 'observation': obs,\n 'achieved_goal': end_effector_position,\n 'desired_goal': self.goal,\n 'state': state_\n }\n\n\n def _render_callback(self):\n self.sim.forward()\n\n def _reset_sim(self):\n # Reset intial gripper position\n if not self.reset_free:\n self.sim.set_state(self.initial_state)\n self._sample_initial_pos()\n\n if self.has_object:\n self._sample_object_pos()\n\n self.sim.forward()\n return True\n\n def _sample_object_pos(self):\n raise NotImplementedError('_sample_object_pos has not been implemented for this task!')\n\n def _sample_goal(self, goal=None):\n assert goal is not None, 'must configure goal in task-specific class'\n self._pos_ctrl_magnitude = 0 # do not penalize at start of episode\n return goal\n\n def _sample_initial_pos(self, gripper_target=None):\n assert gripper_target is not None, 'must configure gripper in task-specific class'\n gripper_target[2] += 0.17 \n self.sim.data.set_mocap_pos('robot0:mocap2', gripper_target)\n self.sim.data.set_mocap_quat('robot0:mocap2', self.gripper_rotation)\n self.sim.data.set_joint_qpos('right_outer_knuckle_joint', self.closed_angle)\n self._gripper_sync()\n for _ in range(10):\n self.sim.step()\n self.initial_gripper_xpos = self.sim.data.get_site_xpos('grasp').copy()\n self.init_finger_xpos = (self.sim.data.get_body_xpos('right_hand') + self.sim.data.get_body_xpos('left_hand'))/2\n\n def _is_success(self, achieved_goal, desired_goal):\n\n object_qpos = self.sim.data.get_joint_qpos('Object:joint')\n object_lift = object_qpos[2]\n self.grasp_lift = object_lift > 0.55\n\n if self.grasp_lift:\n d = self.goal_distance(achieved_goal, desired_goal, self.use_xyz)\n return (d < self.distance_threshold).astype(np.float32)\n\n def _env_setup(self, initial_qpos):\n for name, value in initial_qpos.items():\n self.sim.data.set_joint_qpos(name, value)\n utils.reset_mocap_welds(self.sim)\n self.sim.forward()\n\n # Sample initial position of gripper\n self._sample_initial_pos()\n \n # Extract information for sampling goals\n self.table_xpos = self.sim.data.body_xpos[self.sim.model.body_name2id('table')]\n\n def step(self, action):\n self.goal = self._sample_goal().copy()\n\n return super(BaseEnv, self).step(action)\n\n def render_video(self, mode=None, width=1080, height=1080, camera_id=None):\n data = []\n img = self.sim.render(width, height, camera_name='third_person', depth=False)[::-1, :, :]\n data.append(img)\n return np.asarray(data)\n\n def render(self, mode='human', width=500, height=500, depth=False, camera_id=0):\n return super(BaseEnv, self).render(mode, width, height)\n","repo_name":"JangSeongwon/Moving-object-grasping-with-single-active-view-camera","sub_path":"src/env/robot/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":9530,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"9113328464","text":"import utilities\n\nfrom .direction import Direction\nfrom .actions import Actions\n\n\nclass FeatureExtractor:\n def get_features(self, state, action):\n \"\"\"\n Returns a dictionary from features to counts. Usually, the count\n will just be 1.0 for indicator functions.\n \"\"\"\n utilities.raise_not_defined()\n\n\nclass IdentityExtractor(FeatureExtractor):\n def get_features(self, state, action):\n feats = utilities.Counter()\n feats[(state, action)] = 1.0\n return feats\n\n\nclass CoordinateExtractor(FeatureExtractor):\n def get_features(self, state, action):\n feats = utilities.Counter()\n feats[state] = 1.0\n feats['x=%d' % state[0]] = 1.0\n feats['y=%d' % state[0]] = 1.0\n feats['action=%s' % action] = 1.0\n return feats\n\n\ndef closest_food(pos, food, walls):\n \"\"\"\n Find the closest reachable food pellet.\n \"\"\"\n fringe = [(pos[0], pos[1], 0)]\n expanded = set()\n\n while fringe:\n pos_x, pos_y, dist = fringe.pop(0)\n\n if (pos_x, pos_y) in expanded:\n continue\n\n expanded.add((pos_x, pos_y))\n\n # If the agent finds a food dot at this location, then exit.\n if food[pos_x][pos_y]:\n return dist\n\n # Otherwise spread out from this location to its neighbors.\n neighbors = Actions.get_legal_neighbors((pos_x, pos_y), walls)\n\n for nbr_x, nbr_y in neighbors:\n fringe.append((nbr_x, nbr_y, dist+1))\n\n # No food was found.\n return 0.0 # WAS NONE\n\n\ndef smallest_food_path(pos, food, walls):\n \"\"\"\n Determine how many food pellets are left on the current path (up to 5).\n \"\"\"\n fringe = [(pos[0], pos[1], 0)]\n expanded = set()\n\n while fringe:\n pos_x, pos_y, dist = fringe.pop(0)\n expanded.add((pos_x, pos_y))\n\n # Spread out from the current location to its neighbors.\n neighbors = Actions.get_legal_neighbors((pos_x, pos_y), walls)\n\n count_with_food = 0\n\n for nbr_x, nbr_y in neighbors:\n if (nbr_x, nbr_y) not in expanded and food[nbr_x][nbr_y]:\n fringe.append((nbr_x, nbr_y, dist+1))\n count_with_food += 1\n\n if count_with_food == 0 and food[pos_x][pos_y] or dist == 5: # dist > 0 ?\n return dist\n\n # No food was found.\n return 5\n\n\ndef closest_capsule(pos, capsules, walls):\n \"\"\"\n Find the closest capsule (max distance 5).\n \"\"\"\n fringe = [(pos[0], pos[1], 0)]\n expanded = set()\n\n while fringe:\n pos_x, pos_y, dist = fringe.pop(0)\n\n if (pos_x, pos_y) in expanded:\n continue\n\n expanded.add((pos_x, pos_y))\n\n # If the agent finds a capsule at this location, then exit.\n if (pos_x, pos_y) in capsules or dist == 5:\n return dist\n\n # Otherwise spread out from this location to its neighbors.\n neighbors = Actions.get_legal_neighbors((pos_x, pos_y), walls)\n\n for nbr_x, nbr_y in neighbors:\n fringe.append((nbr_x, nbr_y, dist+1))\n\n # No capsule was found.\n return 0.0\n\n\ndef closest_ghost(pos, ghosts, walls):\n \"\"\"\n Find the minimum distance to ghosts.\n \"\"\"\n fringe = [(pos[0], pos[1], 0)]\n expanded = set()\n\n while fringe:\n pos_x, pos_y, dist = fringe.pop(0)\n\n if (pos_x, pos_y) in expanded:\n continue\n\n expanded.add((pos_x, pos_y))\n\n # If the agent finds a ghost at this location, then exit.\n for ghost in ghosts:\n g_x, g_y = ghost.get_position()\n\n if (int(g_x), int(g_y)) == (pos_x, pos_y):\n return ghost, dist\n\n # Otherwise spread out from this location to its neighbors.\n neighbors = Actions.get_legal_neighbors((pos_x, pos_y), walls)\n\n for nbr_x, nbr_y in neighbors:\n fringe.append((nbr_x, nbr_y, dist+1))\n\n # No ghost was found.\n return None, dist\n\n\nclass TacticExtractor(FeatureExtractor):\n \"\"\"\n Returns features for a pacman agent that is largely being tactical\n in terms of how it learns the game.\n\n - whether food will be eaten\n - how far away the next food is\n - whether a ghost collision is imminent\n - whether a ghost is one step away\n \"\"\"\n def get_features(self, state, action):\n # Extract the grid of food, wall locations, and the ghost\n # locations.\n food = state.get_food()\n walls = state.get_walls()\n ghosts = state.get_ghost_positions()\n\n features = utilities.Counter()\n\n features[\"bias\"] = 1.0\n\n # Compute the location of pacman after he takes the action.\n x, y = state.get_pacman_position()\n dx, dy = Actions.direction_to_vector(action)\n next_x, next_y = int(x + dx), int(y + dy)\n\n # Count the number of ghosts that are one step away.\n features[\"#-of-ghosts-1-step-away\"] = sum((next_x, next_y) in Actions.get_legal_neighbors(g, walls) for g in ghosts)\n\n # If there is no danger of ghosts then add the food feature.\n if not features[\"#-of-ghosts-1-step-away\"] and food[next_x][next_y]:\n features[\"eats-food\"] = 1.0\n\n dist = closest_food((next_x, next_y), food, walls)\n\n if dist is not None:\n # Make the distance a number less than one otherwise the update\n # will diverge wildly.\n features[\"closest-food\"] = float(dist) / (walls.width * walls.height)\n\n features.divide_all(10.0)\n\n return features\n\n\nclass StrategyExtractor(FeatureExtractor):\n \"\"\"\n Returns features for a pacman agent that considers more aspects of\n the game in terms of a strategy to win with the highest possible\n score.\n \"\"\"\n def get_features(self, state, action):\n # Extract the grid of food, power pellets, wall locations, and\n # ghost states.\n food = state.get_food()\n capsules = state.get_capsules()\n walls = state.get_walls()\n ghosts = state.get_ghost_states()\n scared_ghosts = [ghost for ghost in ghosts if ghost.scared_timer > 0]\n not_scared_ghosts = [ghost for ghost in ghosts if ghost.scared_timer == 0]\n\n features = utilities.Counter()\n\n # features[\"bias\"] = 0.5\n\n # Compute the location of pacman after he takes the action.\n x, y = state.get_pacman_position()\n dx, dy = Actions.direction_to_vector(action)\n next_x, next_y = int(x + dx), int(y + dy)\n\n # Count the number of ghosts that are one step away.\n features[\"#-of-ghosts-1-step-away\"] = 0.0\n\n if not_scared_ghosts and closest_ghost((next_x, next_y), not_scared_ghosts, walls)[1] <= 2:\n features[\"#-of-ghosts-1-step-away\"] = 1.0\n\n # Eat a scared ghost (continue in \"closest scared ghost\")\n features[\"eats-scared-ghost\"] = 0.0\n\n # Ghost spawn location (continue in \"closest scared ghost\")\n # features[\"at-ghost-spawn-location\"] = 0.0\n # start position state.data.agent_states[index].start.get_position()\n\n # Look for the closest scared ghost.\n features[\"closest-scared-ghost\"] = 0.0\n\n if scared_ghosts and not features[\"#-of-ghosts-1-step-away\"]:\n closest, dist = closest_ghost((next_x, next_y), scared_ghosts, walls)\n\n if not features[\"#-of-ghosts-1-step-away\"] and dist <= 1.0:\n features[\"eats-scared-ghost\"] = 0.5\n\n if (closest.scared_timer / 2.0 > dist) and closest.scared_timer >= 2:\n features[\"closest-scared-ghost\"] = (closest.scared_timer / 2.0 - dist) / 50.0\n\n # if closest.start.get_position() == (next_x, next_y) and features[\"eats-scared-ghost\"]:\n # features[\"at-ghost-spawn-location\"] = 1.0\n\n should_chase = features[\"closest-scared-ghost\"] > 0.0\n\n # If there is no danger of ghosts then add the food feature.\n features[\"eats-food\"] = 0.0\n\n if not features[\"#-of-ghosts-1-step-away\"] and food[next_x][next_y]:\n features[\"eats-food\"] = 0.1\n\n # Eat small path (< 5) of food before if there is a choice.\n features[\"eat-small-path-food\"] = 0.0\n\n if features[\"eats-food\"]:\n features[\"eat-small-path-food\"] = (5.0 - smallest_food_path((next_x, next_y), food, walls)) / 50.0\n\n dist = closest_food((next_x, next_y), food, walls)\n features[\"closest-food\"] = 0.0\n\n if dist is not None:\n # Make the distance a number less than 1 otherwise the update\n # will diverge wildly.\n features[\"closest-food\"] = float(dist) / (walls.width * walls.height)\n\n # Consider capsules.\n features[\"capsule-nearby\"] = 0.0\n\n if not features[\"#-of-ghosts-1-step-away\"] and capsules:\n features[\"capsule-nearby\"] = (5 - closest_capsule((next_x, next_y), capsules, walls)) / 10.0\n\n # Stopped\n # features[\"stopped\"] = 1.0 if action == 'Stop' else 0.0\n\n # Decrement all other features if pacman is chasing a ghost.\n if should_chase:\n features[\"eats-food\"] /= 10.0\n features[\"closest-food\"] /= 10.0\n features[\"capsule-nearby\"] /= 10.0\n features[\"eat-small-path-food\"] /= 10.0\n\n features.divide_all(10.0)\n\n return features\n","repo_name":"zachnyman/pacumen","sub_path":"game/feature_extractors.py","file_name":"feature_extractors.py","file_ext":"py","file_size_in_byte":9243,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"17326078779","text":"\"\"\"Set up Navigation, ACL & Logos\"\"\"\n\nimport mara_pipelines\nimport mara_data_explorer\nimport flask\nimport mara_acl\nimport mara_acl.users\nimport mara_app\nimport mara_app.layout\nimport mara_db\nimport mara_page.acl\nfrom mara_app import monkey_patch\nfrom mara_page import acl\nfrom mara_page import navigation\n\nfrom app.ui import start_page\n\nblueprint = flask.Blueprint('ui', __name__, url_prefix='/ui', static_folder='static')\n\n\ndef MARA_FLASK_BLUEPRINTS():\n return [start_page.blueprint, blueprint]\n\n\n# replace logo and favicon\nmonkey_patch.patch(mara_app.config.favicon_url)(lambda: flask.url_for('ui.static', filename='favicon.ico'))\nmonkey_patch.patch(mara_app.config.logo_url)(lambda: flask.url_for('ui.static', filename='logo.png'))\n\n\n# add custom css\n@monkey_patch.wrap(mara_app.layout.css_files)\ndef css_files(original_function, response):\n files = original_function(response)\n files.append(flask.url_for('ui.static', filename='styles.css'))\n return files\n\n\n# define protected ACL resources\n@monkey_patch.patch(mara_acl.config.resources)\ndef acl_resources():\n return [acl.AclResource(name='Documentation',\n children=[mara_pipelines.MARA_ACL_RESOURCES().get('Pipelines'),\n mara_db.MARA_ACL_RESOURCES().get('DB Schema')]),\n acl.AclResource(name='Data',\n children=mara_data_explorer.MARA_ACL_RESOURCES().values()),\n acl.AclResource(name='Admin',\n children=[mara_app.MARA_ACL_RESOURCES().get('Configuration'),\n mara_acl.MARA_ACL_RESOURCES().get('Acl')])]\n\n\n# activate ACL\nmonkey_patch.patch(mara_page.acl.current_user_email)(mara_acl.users.current_user_email)\nmonkey_patch.patch(mara_page.acl.current_user_has_permissions)(mara_acl.permissions.current_user_has_permissions)\nmonkey_patch.patch(mara_page.acl.user_has_permissions)(mara_acl.permissions.user_has_permissions)\n\nmonkey_patch.patch(mara_acl.config.whitelisted_uris)(lambda: ['/mara-app/navigation-bar'])\n\n\n# navigation bar (other navigation entries will be automatically added)\n@monkey_patch.patch(mara_app.config.navigation_root)\ndef navigation_root() -> navigation.NavigationEntry:\n return navigation.NavigationEntry(label='Root', children=[\n mara_pipelines.MARA_NAVIGATION_ENTRIES().get('Pipelines'),\n mara_data_explorer.MARA_NAVIGATION_ENTRIES().get('Explore'),\n mara_db.MARA_NAVIGATION_ENTRIES().get('DB Schema'),\n navigation.NavigationEntry(\n 'Settings', icon='cog', description='ACL & Configuration', rank=100,\n children=[mara_app.MARA_NAVIGATION_ENTRIES().get('Package Configs'),\n mara_acl.MARA_NAVIGATION_ENTRIES().get('Acl')])])\n","repo_name":"mara/mara-example-project-2","sub_path":"app/ui/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2765,"program_lang":"python","lang":"en","doc_type":"code","stars":174,"dataset":"github-code","pt":"52"} +{"seq_id":"614217057","text":"from flask import Flask, render_template, jsonify, request, redirect, url_for\nfrom dotenv import load_dotenv\nfrom flask_sqlalchemy import SQLAlchemy\nfrom dataclasses import dataclass\nfrom datetime import datetime\nfrom config import Config\nfrom forms import TaskForm\n\nload_dotenv('./.flaskenv')\n\n# Cria a instância do aplicativo Flask e carrega as configurações do objeto Config\napp = Flask(__name__)\napp.config.from_object(Config)\n\n# Cria uma instância do SQLAlchemy para manipular o banco de dados\ndb = SQLAlchemy(app)\n\n# Define a classe de modelo para a tabela \"tasks\" usando DataClass e SQLAlchemy\n@dataclass\nclass Task(db.Model):\n id: int\n title: str\n date: datetime\n completed: bool\n\n id = db.Column(db.Integer(), primary_key=True)\n title = db.Column(db.String(140))\n date = db.Column(db.DateTime(), default=datetime.now())\n completed = db.Column(db.Boolean(), default=False)\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n def __repr__(self):\n return f''\n\n# Define a rota da página inicial\n@app.route('/')\ndef index():\n tasks = Task.query.all()\n # Retorna uma resposta JSON se a solicitação vier de uma solicitação Ajax\n if request.headers.get('X-Requested-With') == 'XMLHttpRequest':\n return jsonify(tasks)\n # Retorna a página HTML padrão com as tarefas existentes\n return render_template('index.html')\n\n# Define a rota para criar uma nova tarefa\n@app.route('/create', methods=['POST'])\ndef create_task():\n # Obtém o input do usuário enviado como dados JSON\n user_input = request.get_json()\n\n # Valida o formulário com base no esquema definido em \"TaskForm\"\n form = TaskForm(data=user_input)\n # Se o formulário for válido, cria uma nova tarefa e adiciona ao banco de dados\n if form.validate():\n task = Task(title=form.title.data)\n print(task)\n db.session.add(task)\n db.session.commit()\n # Retorna a nova tarefa criada como uma resposta JSON\n return jsonify(task)\n\n # Se houver um erro no formulário, redireciona para a página inicial\n print('error',user_input)\n return redirect(url_for('index'))\n\n# Define a rota para excluir uma tarefa\n@app.route('/delete', methods=['POST'])\ndef delete_task():\n # Obtém o ID da tarefa enviada como dados JSON\n task_id = request.get_json().get('id')\n \n # Obtém a tarefa com o ID especificado do banco de dados e a exclui\n task = Task.query.filter_by(id=task_id).first()\n db.session.delete(task)\n db.session.commit()\n\n # Retorna uma resposta JSON de sucesso\n return jsonify({'result':'okay'}),200\n\n# Define a rota para marcar uma tarefa como concluída\n@app.route('/complete',methods=['POST'])\ndef complete_task():\n # Obtém o ID da tarefa enviada como dados JSON\n task_id = request.get_json().get('id')\n task = Task.query.filter_by(id=task_id).first()\n\n # Obtém a tarefa com o ID especificado do banco de dados e marca como concluída\n task.completed = True\n db.session.add(task)\n db.session.commit()\n\n return jsonify({'result':'okay'}),200\n\n\n@app.route('/getTasks')\ndef get_tasks():\n tasks = Task.query.all()\n return jsonify(tasks)\n \n \nif __name__ == '__main__':\n app.run()\n","repo_name":"LimeHawk/flask-vue-todo","sub_path":"app/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3302,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"33919612689","text":"import json\nimport os\n\nfrom django.http import response\nfrom .models import CarDealer, DealerReview\nfrom requests.auth import HTTPBasicAuth\nimport requests\nfrom ibm_watson import NaturalLanguageUnderstandingV1\nfrom ibm_cloud_sdk_core.authenticators import IAMAuthenticator\nfrom ibm_watson.natural_language_understanding_v1 import (\n Features,\n EntitiesOptions,\n KeywordsOptions,\n SentimentOptions,\n)\n\n\n# Create a `get_request` to make HTTP GET requests\n# e.g., response = requests.get(url, params=params, headers={'Content-Type': 'application/json'},\n# auth=HTTPBasicAuth('apikey', api_key))\ndef get_request(url, apikey, **kwargs):\n print(\"GET from {} \".format(url))\n try:\n # Call get method of requests library with URL and parameters\n if apikey:\n response = requests.get(\n url,\n params=kwargs,\n headers={\"Content-Type\": \"application/json\"},\n auth=HTTPBasicAuth(\"apikey\", apikey),\n )\n else:\n response = requests.get(\n url, params=kwargs, headers={\"Content-Type\": \"application/json\"}\n )\n\n except:\n # If any error occurs\n print(\"Network exception occurred\")\n status_code = response.status_code\n print(\"With status {} \".format(status_code))\n json_data = json.loads(response.text)\n return json_data\n\n\n# Create a `post_request` to make HTTP POST requests\n# e.g., response = requests.post(url, params=kwargs, json=payload)\ndef post_request(url, payload, **kwargs):\n print(\"GET from {} \".format(url))\n try:\n # Call get method of requests library with URL and parameters\n response = requests.post(\n url,\n params=kwargs,\n headers={\"Content-Type\": \"application/json\"},\n json=payload,\n )\n except:\n # If any error occurs\n print(\"Network exception occurred\")\n status_code = response.status_code\n print(\"With status {} \".format(status_code))\n json_data = json.loads(response.text)\n return json_data\n\n\n# Create a get_dealers_from_cf method to get dealers from a cloud function\n# def get_dealers_from_cf(url, **kwargs):\n# - Call get_request() with specified arguments\n# - Parse JSON results into a CarDealer object list\ndef get_dealers_from_cf(url, **kwargs):\n results = []\n # Call get_request with a URL parameter\n json_result = get_request(url, 0)\n if json_result:\n # Get the row list in JSON as dealers\n dealers = json_result[\"res\"][\"rows\"] # For each dealer object\n for dealer in dealers:\n # Get its content in `doc` object\n\n dealer_doc = dealer[\"doc\"]\n # Create a CarDealer object with values in `doc` object\n dealer_obj = CarDealer(\n address=dealer_doc[\"address\"],\n city=dealer_doc[\"city\"],\n full_name=dealer_doc[\"full_name\"],\n id=dealer_doc[\"id\"],\n lat=dealer_doc[\"lat\"],\n long=dealer_doc[\"long\"],\n short_name=dealer_doc[\"short_name\"],\n st=dealer_doc[\"st\"],\n zip=dealer_doc[\"zip\"],\n )\n results.append(dealer_obj)\n\n return results\n\n\n# Create a get_dealer_reviews_from_cf method to get reviews by dealer id from a cloud function\n# def get_dealer_by_id_from_cf(url, dealerId):\n# - Call get_request() with specified arguments\n# - Parse JSON results into a DealerView object list\n\n\ndef get_dealer_by_id_from_cf(url, dealer_id):\n # Call get_request with a URL parameter\n json_result = get_request(url + f\"?id={int(dealer_id)}\", 0)\n if json_result:\n # Get the row list in JSON as dealers\n dealer = json_result[\"res\"]\n # Get its content in `doc` object\n\n dealer_doc = dealer[\"docs\"][0]\n # Create a CarDealer object with values in `doc` object\n dealer_obj = CarDealer(\n address=dealer_doc[\"address\"],\n city=dealer_doc[\"city\"],\n full_name=dealer_doc[\"full_name\"],\n id=dealer_doc[\"id\"],\n lat=dealer_doc[\"lat\"],\n long=dealer_doc[\"long\"],\n short_name=dealer_doc[\"short_name\"],\n st=dealer_doc[\"st\"],\n zip=dealer_doc[\"zip\"],\n )\n\n return dealer_obj\n\n\ndef get_dealer_reviews_from_cf(url, dealerId):\n results = []\n # call get_request using the url param and dealerId\n json_result = get_request(url + f\"?dealerId={dealerId}\", 0)\n if json_result:\n # test result\n reviews = json_result[\"reviews\"] # For each dealer object\n for review in reviews:\n\n # Create a CarDealer object with values in `doc` object\n try:\n review_obj = DealerReview(\n dealership=review[\"dealership\"],\n sentiment=analyze_review_sentiments(review[\"review\"]),\n review=review[\"review\"],\n name=review[\"name\"],\n id=review[\"id\"],\n purchase=review[\"purchase\"],\n car_year=review[\"car_year\"],\n purchase_date=review[\"purchase_date\"],\n car_model=review[\"car_model\"],\n car_make=review[\"car_make\"],\n )\n results.append(review_obj)\n except:\n review_obj = DealerReview(\n dealership=review[\"dealership\"],\n sentiment=analyze_review_sentiments(review[\"review\"]),\n review=review[\"review\"],\n name=review[\"name\"],\n id=review[\"id\"],\n purchase=review[\"purchase\"],\n car_year=None,\n purchase_date=None,\n car_model=None,\n car_make=None,\n )\n results.append(review_obj)\n\n return results\n\n\n# Create an `analyze_review_sentiments` method to call Watson NLU and analyze text\n# def analyze_review_sentiments(text):\n# - Call get_request() with specified arguments\n# - Get the returned sentiment label such as Positive or Negative\ndef analyze_review_sentiments(text):\n authenticator = IAMAuthenticator(os.environ[\"NLU_API_KEY\"])\n natural_language_understanding = NaturalLanguageUnderstandingV1(\n version=\"2021-03-25\", authenticator=authenticator\n )\n\n natural_language_understanding.set_service_url(\n \"https://api.us-south.natural-language-understanding.watson.cloud.ibm.com/instances/d6547bbc-cfb0-4db4-b9a9-269cd97f02f0\"\n )\n\n response = natural_language_understanding.analyze(\n language=\"en\", text=text, features=Features(sentiment=SentimentOptions())\n ).get_result()\n\n result = response[\"sentiment\"][\"document\"][\"label\"]\n return result\n","repo_name":"shifterbit/agfzb-CloudAppDevelopment_Capstone","sub_path":"server/djangoapp/restapis.py","file_name":"restapis.py","file_ext":"py","file_size_in_byte":6857,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"52"} +{"seq_id":"16172044483","text":"import h5py\nimport pandas as pd\nfrom PIL import Image\nimport numpy as np\nimport os\n\nf = h5py.File('train/digitStruct.mat', 'r')\nbboxs = f['digitStruct/bbox']\nnames = f['digitStruct/name']\n\n\ndef get_img_boxes(f, annotations, idx=0):\n ann = {key: [] for key in ['height', 'left', 'top', 'width', 'label']}\n meta = {}\n box = f[bboxs[idx][0]]\n name = f[names[idx][0]]\n for key in box.keys():\n if box[key].shape[0] == 1:\n ann[key].append(float(box[key][0][0]))\n else:\n for i in range(box[key].shape[0]):\n ann[key].append(float(f[box[key][i][0]][()].item()))\n\n file_name = ''.join([chr(v) for v in name])\n img = Image.open('./train/' + file_name)\n meta['filename'] = os.path.splitext(file_name)[0]\n meta['width'] = img.width\n meta['height'] = img.height\n obj_count = 0\n for left, top, width, height, label in zip(ann['left'], ann['top'],\n ann['width'], ann['height'],\n ann['label']):\n meta['x0'] = left\n meta['y0'] = top\n meta['x1'] = left + width\n meta['y1'] = top + height\n meta['label'] = int(label)\n annotations = annotations.append(pd.DataFrame(meta, index=[0]))\n\n return annotations\n\n\nannotations = pd.DataFrame(\n columns=[\"filename\", \"width\", \"height\", \"x0\", \"y0\", \"x1\", \"y1\", \"label\"])\n\nfor i in range(0, 10):\n annotations = get_img_boxes(f, annotations, i)\nannotations.to_csv(\"train/train_ann.csv\", index=False)","repo_name":"sweiichen/digits-detection","sub_path":"generate_csv_data.py","file_name":"generate_csv_data.py","file_ext":"py","file_size_in_byte":1544,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"13134332388","text":"import os\nfrom xml.dom.minidom import parse as xml_parser\n\nimport bs4\n\nfrom common.config import config\nfrom common.misc import fields as f\n\n\ndef return_xml_new_lines(sentence):\n cleaned = ''\n\n for word in sentence.split(\" \"):\n if word != '':\n cleaned += word.strip() + ' '\n\n return cleaned\n\n\ndef recursive_node_value(root):\n if len(root.childNodes) == 0:\n if root.nodeValue and root.nodeType == root.TEXT_NODE and root.nodeValue.strip() != '':\n return return_xml_new_lines(root.nodeValue)\n else:\n # ignore empty non text nodes\n return \"\"\n\n contents = ''\n\n for child in root.childNodes:\n if child.nodeType == child.ELEMENT_NODE and child.tagName == config.TEI_ITALICS_NODE_TAG:\n contents += '{value}'.format(value=recursive_node_value(child))\n else:\n contents += recursive_node_value(child)\n\n return contents\n\n\ndef parse(filename):\n \"\"\"\n\n \"\"\"\n tei_xml = xml_parser(filename)\n\n citations = list()\n\n for i, elem in enumerate(tei_xml.getElementsByTagName(config.TEI_FOOTNOTE_TAG)):\n contents = ''\n for child_node in elem.childNodes:\n # ignore empty nodes\n if child_node.nodeType == child_node.TEXT_NODE and child_node.nodeValue.strip() == '':\n continue\n\n assert child_node.nodeType == child_node.TEXT_NODE or child_node.nodeType == child_node.ELEMENT_NODE\n contents += recursive_node_value(child_node)\n\n citations.append(contents)\n\n return citations\n\n\ndef update(file, references):\n \"\"\"\n Create a new updated xml, containing information for all the bibliographic references.\n As per TEI standards, all bibliographic references are added under `teiHeader`\n\n https://www.tei-c.org/release/doc/tei-p5-doc/en/html/CO.html#COBICOL\n \"\"\"\n with open(file, \"r\") as fd:\n soup = bs4.BeautifulSoup(fd, \"xml\")\n # x3ml will fail if the following is present\n soup.find('TEI').attrs.clear()\n tei_header = soup.find(\"teiHeader\")\n\n listBibl = tei_header.find(\"listBibl\")\n if not listBibl:\n listBibl = soup.new_tag(\"listBibl\")\n\n for ref in references:\n\n biblStruct = soup.new_tag(\"biblStruct\")\n if ref[f.URI]:\n biblStruct['uri'] = ref[f.URI]\n \n analytic = soup.new_tag(\"analytic\")\n\n for author in ref[f.AUTHORS]:\n author_tag = soup.new_tag(\"author\")\n author_tag['level'] = 'a' # `a` for analytic\n author_tag.string = author\n analytic.append(author_tag)\n\n title_tag = soup.new_tag(\"title\")\n title_tag['level'] = 'a'\n title_tag.string = ref[f.TITLE]\n analytic.append(title_tag)\n\n if ref[f.LANG]:\n lang_tag = soup.new_tag('textLang')\n lang_tag.string = ref[f.LANG]\n analytic.append(lang_tag)\n\n biblStruct.append(analytic)\n\n monogr = soup.new_tag('monogr')\n\n if ref[f.PUBLISHER]:\n mono_title_tag = soup.new_tag(\"title\")\n mono_title_tag['level'] = 'm'\n mono_title_tag.string = ref[f.PUBLISHER]\n monogr.append(mono_title_tag)\n\n imprint_tag = soup.new_tag(\"imprint\")\n\n if ref[f.YEAR]:\n date_tag = soup.new_tag(\"date\")\n date_tag.string = ref[f.YEAR]\n imprint_tag.append(date_tag)\n\n monogr.append(imprint_tag)\n\n if ref[f.PAGES]:\n biblScope_tag = soup.new_tag(\"biblScope\")\n biblScope_tag.string = ref[f.PAGES]\n monogr.append(biblScope_tag)\n\n for editor in (ref[f.RESPONSIBILITY] or []):\n editor_tag = soup.new_tag(\"editor\")\n editor_tag.string = editor\n monogr.append(editor_tag)\n\n if ref[f.ISBN]:\n isbn_tag = soup.new_tag('idno')\n isbn_tag['type'] = 'ISBN'\n isbn_tag.string = ref[f.ISBN]\n monogr.append(isbn_tag)\n\n if ref[f.OCLC]:\n oclc_tag = soup.new_tag('idno')\n oclc_tag['type'] = 'OCLC'\n oclc_tag.string = ref[f.OCLC]\n monogr.append(oclc_tag)\n\n biblStruct.append(monogr)\n listBibl.append(biblStruct)\n tei_header.append(listBibl)\n\n filename = os.path.basename(file)\n inter_dirs = file.split(os.sep)\n\n outpath = os.path.join(config.WORKSPACE, config.OUTPUT_FOLDER, inter_dirs[-3], inter_dirs[-2])\n os.makedirs(outpath, exist_ok=True)\n\n outfile = os.path.join(outpath, filename)\n with open(file=outfile, mode=\"w\", encoding=\"utf-8\") as fd:\n fd.write(soup.prettify())\n\n return outfile\n","repo_name":"biblhertz/referency","sub_path":"common/tei/tei.py","file_name":"tei.py","file_ext":"py","file_size_in_byte":4878,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"41752204048","text":"from datetime import datetime\nfrom sources.effects.base import MatrixPixelEffect\nfrom sources.helpers.color_rgb import ColorRGB\n\n\nclass WatchEffect(MatrixPixelEffect):\n def __init__(self, width, height, frame_delay=0.3):\n MatrixPixelEffect.__init__(self, width, height, 1, frame_delay)\n self.base_color = ColorRGB.random()\n self.new_base_color = ColorRGB.random()\n self.color = self.base_color\n self.color_conversion_progress = 0\n self.last_time = ''\n self.sprites = []\n self.sprites.append([\n [1, 1, 1],\n [1, 0, 1],\n [1, 0, 1],\n [1, 0, 1],\n [1, 0, 1],\n [1, 0, 1],\n [1, 1, 1]])\n self.sprites.append([\n [0, 0, 1],\n [0, 0, 1],\n [0, 0, 1],\n [0, 0, 1],\n [0, 0, 1],\n [0, 0, 1],\n [0, 0, 1]])\n self.sprites.append([\n [1, 1, 1],\n [0, 0, 1],\n [0, 0, 1],\n [1, 1, 1],\n [1, 0, 0],\n [1, 0, 0],\n [1, 1, 1]])\n self.sprites.append([\n [1, 1, 1],\n [0, 0, 1],\n [0, 0, 1],\n [1, 1, 1],\n [0, 0, 1],\n [0, 0, 1],\n [1, 1, 1]])\n self.sprites.append([\n [1, 0, 1],\n [1, 0, 1],\n [1, 0, 1],\n [1, 1, 1],\n [0, 0, 1],\n [0, 0, 1],\n [0, 0, 1]])\n self.sprites.append([\n [1, 1, 1],\n [1, 0, 0],\n [1, 0, 0],\n [1, 1, 1],\n [0, 0, 1],\n [0, 0, 1],\n [1, 1, 1]])\n self.sprites.append([\n [1, 1, 1],\n [1, 0, 0],\n [1, 0, 0],\n [1, 1, 1],\n [1, 0, 1],\n [1, 0, 1],\n [1, 1, 1]])\n self.sprites.append([\n [1, 1, 1],\n [0, 0, 1],\n [0, 0, 1],\n [0, 1, 0],\n [0, 1, 0],\n [1, 0, 0],\n [1, 0, 0]])\n self.sprites.append([\n [1, 1, 1],\n [1, 0, 1],\n [1, 0, 1],\n [1, 1, 1],\n [1, 0, 1],\n [1, 0, 1],\n [1, 1, 1]])\n self.sprites.append([\n [1, 1, 1],\n [1, 0, 1],\n [1, 0, 1],\n [1, 1, 1],\n [0, 0, 1],\n [0, 0, 1],\n [1, 1, 1]])\n self.sprites.append([\n [0, 0, 0],\n [0, 0, 0],\n [0, 1, 0],\n [0, 0, 0],\n [0, 1, 0],\n [0, 0, 0],\n [0, 0, 0]])\n\n def play_frame(self, drawer):\n t = datetime.now().strftime(\"%H:%M:%S\")\n if self.last_time == t:\n return\n\n self.last_time = t\n\n self.color_conversion_progress = self.color_conversion_progress + 0.01\n\n if self.color_conversion_progress > 1:\n self.base_color = self.new_base_color\n self.new_base_color = ColorRGB.random()\n self.color_conversion_progress = 0\n\n self.color = self.base_color.convert(self.new_base_color, self.color_conversion_progress)\n\n sec = datetime.now().second\n i = 0\n drawer.clear(False)\n print(t)\n\n for s in t:\n if s == ':':\n if sec % 2 == 0:\n self.print_symbol(drawer, 10, i)\n else:\n self.print_symbol(drawer, int(s), i)\n i = i + 1\n\n drawer.show()\n\n def print_symbol(self, drawer, symbol_index, position):\n x = 4 * position\n sprite = self.sprites[symbol_index]\n for sx in range(3):\n for sy in range(7):\n pixel = super(WatchEffect, self).translateToMatrixColumns(x + sx + 2, sy + 1)\n if sprite[sy][sx] == 1:\n drawer.set_color(pixel, self.color)\n","repo_name":"alxcp/raspberryPi-ws28x-drawer","sub_path":"sources/effects/watch.py","file_name":"watch.py","file_ext":"py","file_size_in_byte":3896,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"12726751835","text":"import os\nimport subprocess\nimport sys\n\nPROTOS = (\n 'perfetto/config/chrome/chrome_config.proto',\n 'perfetto/config/data_source_config.proto',\n 'perfetto/config/inode_file/inode_file_config.proto',\n 'perfetto/config/process_stats/process_stats_config.proto',\n 'perfetto/config/data_source_descriptor.proto',\n 'perfetto/config/ftrace/ftrace_config.proto',\n 'perfetto/config/trace_config.proto',\n 'perfetto/config/test_config.proto',\n 'perfetto/common/commit_data_request.proto',\n)\n\nHEADER_PATH = 'include/perfetto/tracing/core'\nCPP_PATH = 'src/tracing/core'\nINCLUDE_PATH = 'perfetto/tracing/core'\n\n\ndef run(cmd):\n print('\\nRunning ' + ' '.join(cmd))\n subprocess.check_call(cmd)\n\n\ndef main():\n if not os.path.exists('.gn'):\n print('This script must be executed from the perfetto root directory')\n return 1\n if len(sys.argv) < 2:\n print('Usage: %s out/xxx' % sys.argv[0])\n return 1\n out_dir = sys.argv[1]\n clang_format = ['clang-format', '-i', '--sort-includes']\n tool = os.path.join(out_dir, 'proto_to_cpp')\n if not os.path.exists(tool):\n print('Could not find %s, run ninja -C %s proto_to_cpp' % (tool, out_dir))\n for proto in PROTOS:\n run([tool, proto] + [HEADER_PATH, CPP_PATH, INCLUDE_PATH])\n fname = os.path.basename(proto).replace('.proto', '')\n run(clang_format + [os.path.join(HEADER_PATH, fname + '.h')])\n run(clang_format + [os.path.join(CPP_PATH, fname + '.cc')])\n\n\nif __name__ == '__main__':\n sys.exit(main())\n","repo_name":"kiwibrowser/src","sub_path":"third_party/perfetto/tools/gen_tracing_cpp_headers_from_protos.py","file_name":"gen_tracing_cpp_headers_from_protos.py","file_ext":"py","file_size_in_byte":1466,"program_lang":"python","lang":"en","doc_type":"code","stars":2475,"dataset":"github-code","pt":"52"} +{"seq_id":"19610165765","text":"#!/usr/bin/env python3\n\n# 1 2 3\n# 4 5 6\n# 7 8 9\n\nkeypads = {\n 1 : { 'U' : 1, 'D': 4, 'L': 1, 'R' : 2 },\n 2 : { 'U' : 2, 'D': 5, 'L': 1, 'R' : 3 },\n 3 : { 'U' : 3, 'D': 6, 'L': 2, 'R' : 3 },\n 4 : { 'U' : 1, 'D': 7, 'L': 4, 'R' : 5 },\n 5 : { 'U' : 2, 'D': 8, 'L': 4, 'R' : 6 },\n 6 : { 'U' : 3, 'D': 9, 'L': 5, 'R' : 6 },\n 7 : { 'U' : 4, 'D': 7, 'L': 7, 'R' : 8 },\n 8 : { 'U' : 5, 'D': 8, 'L': 7, 'R' : 9 },\n 9 : { 'U' : 6, 'D': 9, 'L': 8, 'R' : 9 }\n}\n\nstart_key = 5\nnext_key = 0\ncombination = []\n\n# Need to be logged in to Advent of Code for the link below to work\n# response = requests.get(\"https://adventofcode.com/2016/day/2/input\")\n\nwith open(r'./keypad-moves.txt', 'r') as fp:\n for line in fp:\n moves = list(line.rstrip())\n total_moves = len(moves)\n count = 0\n for move in moves:\n start_key = keypads[start_key][move]\n count += 1\n if count == total_moves:\n combination.append(start_key)\n\nprint(combination)\n","repo_name":"almostNow/peopledatalabs","sub_path":"peopledatalabs.py","file_name":"peopledatalabs.py","file_ext":"py","file_size_in_byte":1025,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"10112806117","text":"from torch.utils.data import Dataset\nfrom glob import glob\nimport numpy as np\nimport os\n\n\nclass SpeechDataset(Dataset):\n def __init__(self, feature_dir, features_to_use):\n super().__init__()\n self.metadata = self.generate_metadata(feature_dir, features_to_use)\n self.features_to_use = features_to_use\n\n def generate_metadata(self, feature_dir, features):\n\n first_feature = features[0]\n\n feat_path_list = glob(os.path.join(feature_dir, first_feature, '*.npy'))\n basename_list = [os.path.basename(fp) for fp in feat_path_list]\n\n metadata = []\n\n for basename in basename_list:\n meta = {}\n meta['speaker'] = basename.split('_')[0]\n for feat in features:\n meta[feat] = os.path.join(feature_dir, feat, basename)\n metadata.append(meta)\n\n print(f\"Loaded total {len(metadata)} features - What kind of features: {features}\")\n return metadata\n\n def __len__(self):\n return len(self.metadata)\n\n def __getitem__(self, index):\n data = {\n 'speaker': self.metadata[index]['speaker']\n }\n for feat in self.features_to_use:\n data[feat] = np.load(self.metadata[index][feat], allow_pickle=True).item()[feat]\n return data\n","repo_name":"prairie-schooner/wav2vec-vc","sub_path":"dataload/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":1297,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"74931896485","text":"\"\"\"\nmatch one list with another, make current list have same structure\nwith the former.\n\"\"\"\nfrom collections import Iterable\n\ndef _reshape(shaper, recursive_types):\n fn_lst = [_reshape(e, recursive_types) if e.__class__ in recursive_types else next for e in shaper]\n ret_cls = shaper.__class__\n def apply(itor):\n return ret_cls(fn(itor) for fn in fn_lst)\n return apply\n\ndef reshape(shaper, recursive_types=(list, tuple, set)):\n _apply = _reshape(shaper, set(recursive_types))\n def apply(seq):\n return _apply(iter(seq))\n return apply\n\n\nshaper = [(1, 2, 3, 6), {2, 3, 6}, [2, [2]]]\nprint(shaper)\n# [(1, 2, 3, 6), {2, 3, 6}, [2, [2]]]\n\nflatten = lambda nested: list(filter(lambda _: _, \n (lambda _: ((yield from flatten(e)) if isinstance(e, Iterable) else (yield e) for e in _))(nested)))\nlst = flatten(shaper)\nprint(lst)\n# [1, 2, 3, 6, 2, 3, 6, 2, 2]\n\nreshaper = reshape(shaper)\n\nprint(reshaper(lst))","repo_name":"SimonCqk/Python_Toys","sub_path":"utility/list_match.py","file_name":"list_match.py","file_ext":"py","file_size_in_byte":969,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"72292230884","text":"'''\n Crie um programa que leia um número real qualquer e mostre na tela a sua parte inteira.\n'''\nfrom math import trunc\n\nnumero = float(input('Digite um valor:\\n'))\nprint('O valor digitado foi {} e sua parte inteira é {}'.format(numero, trunc(numero)))\n\n#sem usar biblioteca math\nnumero = float(input('Digite um valor:\\n'))\nprint('O valor digitado foi {} e sua parte inteira é {}'.format(numero, int(numero)))","repo_name":"audreyemmely/linguagemPython","sub_path":"fundamentos/ex009.py","file_name":"ex009.py","file_ext":"py","file_size_in_byte":412,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"42425836719","text":"#!/usr/bin/python3\n\nADDRESS = \"\";\nPORT = 1337;\n\nimport http.server as hs;\nfrom modules.Tunnel import tunnel;\n#from modules.Keylogger import logger\nimport json;\n\nclass CustomRequestHandler(hs.BaseHTTPRequestHandler):\n\t\n\tdata={\"user\":\"\"};\n\t\n\tdef do_GET(self):\n\t\tprint(\"\\n\\n\");\n\t\tself.send_response(200);\n\t\tself.send_header(\"Content-Type\",\"text/JSON\");\n\t\tself.send_header(\"Content-Encoding\",\"ASCII\");\n\t\tself.end_headers();\n\n\t\t\n\t\t\n\t\tif self.data[\"user\"] != self.client_address:\n\t\t\tself.data[\"user\"] = self.client_address;\n\t\t\n\t\tdata = self.rfile.read(int(self.headers[\"content-length\"]));\n\t\tdata = str(data).strip(\"b'\").strip(\"'\"); #Ask Jacques why we strip\n\t\tdata = json.loads(data);\n\t\t\n\t\tres = self.execute(data);\n\t\t\n\t\t#self.wfile.write(b\"GET-ed Successfully!\");\n\t\tself.wfile.write(res);\n\t\treturn;\n\n\tdef execute(self,params):\n\t\tprint(\"GOT \",params);\n\t\t\n\t\t#default values\n\t\taction=None;\n\t\tvalue=None;\n\t\textras=None;\n\t\t\n\t\ttry:\n\t\t\taction = params[\"action\"];\n\t\t\tvalue = params[\"value\"];\n\t\t\textras = params[\"extras\"]\n\t\texcept KeyError:\n\t\t\tpass;\n\t\t\n\t\tret = None; #To be returned at end\n\t\t\n\t\tif action == \"tunnel\":\n\t\t\tif \"tunnel_obj\" not in self.data.keys():\n\t\t\t\tself.data[\"tunnel_obj\"] = tunnel.Tunnel();\n\t\t\ttunnel_obj = self.data[\"tunnel_obj\"]; #retrieve tunnel object\n\t\t\turl = value;\n\t\t\tif params[\"sub_action\"] == \"get\":\n\t\t\t\tret = tunnel_obj.get(url);\n\t\t\telif params[\"sub_action\"] == \"post\":\n\t\t\t\tret = tunnel_obj.post(url);\n\t\t\telif params[\"sub_action\"] == \"session\":\n\t\t\t\tret = tunnel_obj.session(url);\n\t\t\telif params[\"sub_action\"] == \"login session\":\n\t\t\t\tret = ret = tunnel_obj.login_session(url,extras);\n\t\t\tself.data[\"tunnel_obj\"] = tunnel_obj; #store tunnel object\n\t\telif action == \"keylogger\":\n\t\t\tif \"logger_obj\" not in self.data.keys():\n\t\t\t\tself.data[\"logger_obj\"] = logger.LoggerThread();\n\t\t\tlogger_obj = self.data[\"logger_obj\"]; #retrieve tunnel object\n\t\t\tsub_act = params[\"sub_action\"];\n\t\t\tif sub_act == \"start\":\n\t\t\t\tret = logger_obj.start();\n\t\t\telif sub_act == \"stop\":\n\t\t\t\tret = logger_obj.stop();\n\t\t\telif sub_act == \"get\":\n\t\t\t\tret = logger_obj.get_file();\n\t\t\tself.data[\"logger_obj\"] = logger_obj;\n\t\treturn(ret);\n\nif __name__ == \"__main__\":\n\tserver_addr = (ADDRESS,PORT);\n\trequest_handler = CustomRequestHandler;\n\thttp_daemon = hs.HTTPServer(server_addr,request_handler);\n\n\ttry:\n\t\tprint(\"Starting server on port \"+str(PORT));\n\t\thttp_daemon.serve_forever();\n\texcept KeyboardInterrupt:\n\t\tprint(\"\\n\\nKilling Server...\");\n\t\texit();\n","repo_name":"ashu20071/backdoor-shell","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":2429,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"9476276799","text":"# -*- coding:utf8 -*-\r\n\r\n# https://leetcode.com/problems/best-time-to-buy-and-sell-stock-ii/\r\n\r\n# pos_open = False\r\n# for i in range(1,len(prices)):\r\n# if prices[i-1]prices[i] and pos_open:\r\n# profit += prices[i-1] - prices[buy_i]\r\n# pos_open = False\r\n \r\n# if pos_open and i == len(prices)-1:\r\n# profit += prices[i] - prices[buy_i]\r\n# pos_open = False\r\n\r\nclass Solution:\r\n def maxProfit(self, prices) -> int:\r\n if len(prices) < 2:\r\n return 0\r\n brise = False\r\n buy_i = 0\r\n cnt = 0\r\n for i in range(1, len(prices)):\r\n \r\n if prices[i-1]prices[i] and brise:\r\n cnt += prices[i-1] - prices[buy_i]\r\n brise = False\r\n \r\n if brise and i == len(prices)-1:\r\n cnt += prices[i] - prices[buy_i]\r\n brise = False\r\n\r\n\r\n return cnt\r\n\r\nif __name__ == \"__main__\":\r\n sol = Solution()\r\n print(sol.maxProfit([7,6,4,3,1]))\r\n print(sol.maxProfit([7,1,5,3,6,4]))\r\n","repo_name":"liuiuge/LeetCodeSummary","sub_path":"122.BestTimetoBuyandSellStockII.py","file_name":"122.BestTimetoBuyandSellStockII.py","file_ext":"py","file_size_in_byte":1363,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"4968329071","text":"\"\"\"Summary: Reservation Model\n\nA reservation model used to convert a reservation document into a reservation object.\n\"\"\"\nfrom app.aws.aws_cloudfront_client import create_cloudfront_url\nfrom app.models.history.metadata.reservation_metadata import ReservationMetadata\n\n\nclass Reservation:\n \"\"\"\n A class to represent a reservation model\n\n Attributes:\n ----------\n customer_id : str\n Reservation's customer id\n datetime : datetime\n Reservation's datetime\n description : str\n Reservation's description\n _id : str\n Reservation's id\n image_path : str\n Reservation's image path\n image_url : str\n Reservation's image url\n metadata : dict\n Reservation's metadata document\n price : str\n Reservation's price\n service_provider_id : str\n Reservation's service provider id\n service_provider_type: str\n Reservation's service provider type\n subtitle : str\n Reservation's subtitle\n title : str\n Reservation's title\n \"\"\"\n\n def __init__(self, reservation_document: dict) -> None:\n self.customer_id = str(reservation_document['customer_id'])\n self.datetime = reservation_document['datetime']\n self.description = reservation_document['description']\n self._id = str(reservation_document['_id'])\n self.image_path = reservation_document['image_path']\n self.image_url = create_cloudfront_url(reservation_document['image_path'])\n self.metadata = ReservationMetadata(reservation_document['metadata']).__dict__\n self.price = reservation_document['price']\n self.service_provider_id = str(reservation_document['service_provider_id'])\n self.service_provider_type = str(reservation_document['service_provider_type'])\n self.subtitle = reservation_document['subtitle']\n self.title = reservation_document['title']\n\n def database_dict(self) -> dict:\n \"\"\"\n :return: A dictionary representation of the reservation object (without _id)\n \"\"\"\n return {\n 'customer_id': self.customer_id,\n 'datetime': self.datetime,\n 'description': self.description,\n 'image_path': self.image_path,\n 'image_url': self.image_url,\n 'metadata': self.metadata,\n 'price': self.price,\n 'service_provider_id': self.service_provider_id,\n 'service_provider_type': self.service_provider_type,\n 'subtitle': self.subtitle,\n 'title': self.title\n }\n","repo_name":"KhaledChehabeddine/sweep_backend_flask_legacy","sub_path":"app/models/history/reservation.py","file_name":"reservation.py","file_ext":"py","file_size_in_byte":2556,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"15614996770","text":"\nfrom selenium import webdriver\nfrom selenium.webdriver.common.action_chains import ActionChains\nfrom selenium.webdriver.common.keys import Keys\nimport time\nimport requests\n\ndef getnamepage(name,b):\n b.get('http://image.baidu.com/')\n search_box=b.find_element_by_id('kw')\n search_box.send_keys(name)\n search_box.send_keys(Keys.ENTER)\n time.sleep(3)\n\ndef download(num,b):\n #选取大尺寸\n ele=b.find_element_by_id('sizeFilter')\n ActionChains(b).move_to_element(ele).perform()\n time.sleep(0.2)\n ele4=b.find_element_by_xpath('//*[@id=\"imgid\"]/div[1]/ul/li[5]')\n ActionChains(b).move_to_element(ele4).perform()\n time.sleep(0.2)\n ele4.click()\n time.sleep(0.2)\n\n\n #打开第5张图片，在此界面中点击左右切换图片\n ele1=b.find_element_by_xpath('//*[@id=\"imgid\"]/div/ul/li[5]')\n ele1.click()\n b.switch_to.window(b.window_handles[1])#很重要的一步，切换窗口，否则页面找不到元素,python shell里面是b.switch_to_window\n x=1\n for i in range(1,num+1):\n #ele3=b.find_element_by_xpath('/html/body/div[1]/div[2]/div/span[2]/span')\n #ele3.click()\n #time.sleep(3)#为保险起见，设置一个睡眠和爬取的时间差\n ele2=b.find_element_by_xpath('//*[@id=\"currentImg\"]')\n img=ele2.get_attribute('src')#获取当前图片的url链接\n #下载img\n r=requests.get(img)\n if r.status_code==200:\n path='./img/%d.jpg'%x\n print('正在爬取 '+img)\n with open(path,'wb') as f:\n f.write(r.content)\n time.sleep(0.8)\n f.close()\n print('爬取成功')\n x+=1\n ele3=b.find_element_by_xpath('/html/body/div[1]/div[2]/div/span[2]')\n ele3.click()\n #time.sleep(3)\n #跳到下一张\n else:\n ele3=b.find_element_by_xpath('/html/body/div[1]/div[2]/div/span[2]/span')\n ele3.click()\n time.sleep(1)\n continue\n\n\n\n\ndef craw_baidupic(keyword,num=5):\n b = webdriver.Chrome()\n name = keyword#定义要搜索的内容\n getnamepage(name,b)\n download(num,b)\n b.close()\n # 关闭之前的页面\n pages = b.window_handles\n for handle in pages:\n b.switch_to.window(handle)\n b.close()\n","repo_name":"OmegaDING/AutoGenPopularVideo","sub_path":"crawl_baidupic.py","file_name":"crawl_baidupic.py","file_ext":"py","file_size_in_byte":2328,"program_lang":"python","lang":"en","doc_type":"code","stars":37,"dataset":"github-code","pt":"52"} +{"seq_id":"17262336535","text":"import json\r\nimport os\r\nfrom zeep.exceptions import Fault\r\nimport traceback\r\nimport sys\r\nsys.path.append(\"../\")\r\nfrom ciscoaxl import axl\r\nfrom common.baseFunctions import *\r\nfrom common.importLogic import updateConfigs\r\n\r\n\r\n## Reading import Logic Sheet\r\nimportLogicFile = f\"../common/importLogic.json\"\r\ndynamicLogicJson = json.load(open(importLogicFile))\r\n\r\n\r\n### Read Source and Destination Input JSON's\r\nsourceJsonFile=f\"../inputs/sourceCluster.json\"\r\ndestinationJsonFile=f\"../inputs/destinationCluster.json\"\r\n#sourceClusterInputJson = \r\n#destinationClusterInputJson = \r\n\r\n## Create Source AXL Object\r\n# ucm_axl_object_source = axl(\r\n# username=,\r\n# password=,\r\n# cucm=,\r\n# cucm_version=,\r\n# )\r\n\r\n## Create Destination AXL Object\r\n# ucm_axl_object_dest = axl(\r\n# username=,\r\n# password=,\r\n# cucm=,\r\n# cucm_version=,\r\n# )\r\n\r\ndataFilterFile=f\"getDataFilter.json\" ## output of generate config patterns\r\nconfigExportPath=f\"./ConfigExports\"\r\n\r\n# ## Sites to import from config Folder\r\n# Sites = destinationClusterInputJson[\"configsFolders\"]\r\n\r\n\r\ndef generate_config_patterns():\r\n\r\n # ## Define an empty dictionary object to store all sites data\r\n # allSitesData = {}\r\n # ## Generate the datafilter JSON Content\r\n # for site in sourceClusterInputJson['siteCode']:\r\n # siteSpecificdataFilterDict = {\r\n # \"CSSList\": [\r\n # f\"{site}_CSS\"\r\n # ]\r\n # }\r\n\r\n # allSitesData[site] = siteSpecificdataFilterDict\r\n\r\n # # Serializing json\r\n # dataFilterDict_JSONobject = json.dumps(allSitesData, indent=4)\r\n # jsonFile = open(dataFilterFile, \"w\")\r\n # jsonFile.write(dataFilterDict_JSONobject)\r\n\r\n # ## Close JSON File\r\n # jsonFile.close()\r\n\r\n return\r\n\r\n\r\ndef SiteDataExport(directory, siteDataFilterContent):\r\n\r\n CSSList = (\r\n siteDataFilterContent[\"CSSList\"]\r\n if \"CSSList\" in siteDataFilterContent.keys()\r\n else []\r\n )\r\n\r\n #Partition List\r\n PartitionList = []\r\n\r\n\r\n # CSS\r\n allCSS = []\r\n\r\n for css in CSSList:\r\n cssFound = ucm_source.get_calling_search_space(name=css)\r\n ## Extract Parition from CSS\r\n\r\n # Write Results\r\n write_results(directory, allCSS, \"css\")\r\n\r\n # Partitions\r\n allPartitions = []\r\n\r\n for partition in set(PartitionList):\r\n partitionFound = ucm_source.get_partition(name=partition)\r\n \r\n\r\n # Write Results\r\n write_results(directory, allPartitions, \"partition\")\r\n\r\n return True\r\n\r\n\r\ndef export_CSS_Partition():\r\n\r\n # Read the dataFilter JSON: This JSON is created in step 1\r\n dataFilterContent = json.load(open(dataFilterFile))\r\n for siteCode, siteData in dataFilterContent.items():\r\n configDirectory = f\"{configExportPath}/{siteCode}\"\r\n if not os.path.exists(configDirectory):\r\n os.makedirs(configDirectory)\r\n logPrint(f\"Files will be saved in '{configDirectory}' directory\")\r\n logPrint(f\"Fetching data for Site: {siteCode}\")\r\n\r\n try:\r\n SiteDataExport(configDirectory, siteData)\r\n except Exception as siteExe:\r\n logPrint(f\"Error Occured while exporting configs: {siteExe}\")\r\n traceback.print_exc()\r\n exit()\r\n\r\n else:\r\n logPrint(f\"Export Completed for Site: {siteCode}. Proceeding..\")\r\n\r\n\r\n\r\ndef import_CSS_Partition():\r\n ## iterate over each site folder and push the configurations\r\n for site in sourceClusterInputJson['configsFolders']:\r\n configDirectory = f\"{configExportPath}/{site}\"\r\n logPrint(f\"Reading Configs from Directory: {configDirectory}. Proceeding...\")\r\n if os.path.exists(configDirectory):\r\n logPrint(\r\n f\"Starting Import for Site: {site} on CUCM: {destinationClusterInputJson['cucm']}\"\r\n )\r\n try:\r\n userAccept = input(\"Do you want to proceed (Y/n)?\")\r\n except KeyError:\r\n print(\"Invalid input. Existing..\")\r\n exit()\r\n else:\r\n if userAccept == \"Y\":\r\n try:\r\n pass\r\n #updateConfigs(configDirectory, ucm_destination)\r\n except Exception as importExe:\r\n logPrint(f\"Error Occured while Importing: {importExe}\")\r\n traceback.print_exc()\r\n exit()\r\n else:\r\n logPrint(f\"Data Import completed for site: {site}. Proceeding...\")\r\n else:\r\n logPrint(f\"invalid response '{userAccept}' received. Exiting...\")\r\n exit()\r\n\r\n\r\n# Step 1\r\ngenerate_config_patterns()\r\n\r\n# Step 2\r\nexport_CSS_Partition()\r\n\r\n# Step 3\r\nimport_CSS_Partition()","repo_name":"CXC-PS-Collab/UCM-Data-Migration","sub_path":"devNet-Workbooks/lab1/lab1-handOn.py","file_name":"lab1-handOn.py","file_ext":"py","file_size_in_byte":4786,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"42745266059","text":"import re\n\nvehicle_reg_plates_list = ['а798ар177', 'Т552ес35', 'о225КО161', 'а552kе139'\n 'з234ап99']\n\n\n\ndef validate_vehicle_reg_plates(text):\n \"\"\"Выводит отдельно номер и регион, если транспортный номер корректный\"\"\"\n look_for = r'([а-я])(\\d{3})([а-я]{2})(\\d+)'\n try:\n number = re.match(look_for, text.lower()).group(2)\n region = re.match(look_for, text.lower()).group(4)\n return number, region\n except:\n return print('Введите не корректный транспортный номер')\n\n\nprint(validate_vehicle_reg_plates('а234ап99'))\n","repo_name":"foggyVas/netology_pyda","sub_path":"part1/hw6/Ex1.py","file_name":"Ex1.py","file_ext":"py","file_size_in_byte":693,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"15845650640","text":"from typing import Text\nfrom rpi_ws281x import *\nfrom patterns import *\nfrom d2patterns import *\nfrom activeSettings import ActiveSettings\n\nimport zmq\n\n# LED strip configuration:\nLED_COUNT = 100 # 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\n\n \n# Main program logic follows:\nif __name__ == '__main__':\n\n context = zmq.Context()\n socket = context.socket(zmq.PULL)\n socket.bind(\"tcp://*:5555\")\n\n \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)\n # Intialize the library (must be called once before other functions).\n strip.begin()\n \n print ('Press Ctrl-C to quit.')\n \n settings = ActiveSettings()\n offset = 0\n patterns =[\n SetColor(),\n ColorWipe(),\n TheaterChase(),\n Rainbow(),\n RainbowCycle(),\n TheaterChaseRainbow(),\n Vline(),\n Hline(),\n ScrollText(),\n ]\n for p in patterns:\n p.setup(strip,settings)\n\n try:\n while True:\n offset = offset + 1 if offset < 100 else 0\n try:\n #print(\"getting msg\")\n message = socket.recv_string(flags=zmq.NOBLOCK)\n print(message)\n settings.parse_update(message)\n except zmq.ZMQError as e:\n #print(\"no msg\",e)\n pass\n if settings.toggleenable == 1:\n if settings.mode == -1:\n settings.mode = settings.prevmode\n else:\n settings.prevmode = settings.mode\n settings.mode = -1\n settings.toggleenable = 0\n\n if -1 <= settings.mode <= len(patterns)-2:\n patterns[settings.mode+1].step()\n \n except KeyboardInterrupt:\n patterns[0].step()\n","repo_name":"IdrisTheDragon/ledpi","sub_path":"newworker.py","file_name":"newworker.py","file_ext":"py","file_size_in_byte":2400,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"5460217091","text":"# dictonary 만들기\ndata_str = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'\ncnt=0\ndata_dic={}\nfor data in data_str:\n data_dic[data]=cnt\n cnt+=1\n\nnumbers = int(input())\nanswer=[]\nresult=''\nfor number in range(numbers): #받은 횟수만큼 돌기\n get = input() #한줄 입력받기\n result=''\n temp='' #4글자 합칠 곳\n cnt=0\n for i in get:\n become_bin = str(bin(data_dic[i]))[2:] #0b를 제외한 2진수 문자화\n while len(become_bin) < 6:\n become_bin = '0' + become_bin #6자리가 아닌 2진수 6자리로\n\n temp+=become_bin #4개의 2진수화된 글자를 합치기\n \n if cnt%4 == 3:\n first_piece = chr(int('0b'+temp[:8],2))\n second_piece = chr(int('0b'+temp[8:16],2))\n third_piece = chr(int('0b'+temp[16:],2))\n result+= first_piece + second_piece + third_piece\n temp=''\n cnt+=1\n answer.append(result)\n\ncnt=1\nfor i in answer:\n print(f\"#{cnt} {i}\") \n cnt+=1","repo_name":"vxda7/end-to-end","sub_path":"Problem/base64.py","file_name":"base64.py","file_ext":"py","file_size_in_byte":1108,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"14383980044","text":"# coding=gbk\n'''\n将一个给定字符串根据给定的行数，以从上往下、从左到右进行 Z 字形排列。\n'''\n\nclass Solution:\n def convert(self, s, numrows):\n if numrows < 2:return s\n res = ['' for _ in range(numrows)]\n i, flag = 0, -1\n for c in s:\n res[i] += c\n if i == 0 or i == numrows-1:flag = -flag\n i += flag\n return ''.join(res)\ns = Solution()\na = s.convert('LEETCODEISHIRING', 4)\nprint(a)","repo_name":"xiawq1/leetcode-c-","sub_path":"leetcode/z.py","file_name":"z.py","file_ext":"py","file_size_in_byte":449,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"52"} +{"seq_id":"18128793522","text":"# In the name of Allah\n\nfrom Crypto.PublicKey import RSA\nfrom Crypto.PublicKey.RSA import RsaKey\nfrom Crypto.Cipher import PKCS1_v1_5\nfrom Crypto.Random import new as Random\nfrom base64 import b64encode\nfrom base64 import b64decode, urlsafe_b64decode\nfrom threading import Lock\nimport pickle\nimport json\nimport typing as T\n\n\nclass Cipher:\n def __init__(self, data_dict: T.Optional[dict] = None):\n self.__lock = Lock()\n self.__generated = False\n if data_dict is not None:\n self.__tag = data_dict['tag']\n if 'private_key' in data_dict:\n # print(f'creating cipher@{self.__tag} (private)')\n self.__key = RSA.import_key(data_dict['private_key'], data_dict['public_key'])#, data_dict['private_key'])\n else:\n # print(f'creating cipher@{self.__tag} (public)')\n self.__key = RSA.import_key(data_dict['public_key'])#, data_dict['private_key'])\n self.__generated = True\n else:\n self.__tag = None\n self.__key = None\n\n def generate_key(self, key_length: int, tag: str = ''):\n with self.__lock:\n if self.__generated:\n raise Exception('Already generated')\n assert key_length in [1024, 2048, 4096]\n self.__tag = tag\n rng = Random().read\n self.__key = RSA.generate(key_length, rng)\n self.__generated = True\n return\n \n @property\n def has_private(self) -> bool:\n return self.key.has_private()\n\n @property\n def generated(self) -> bool:\n return self.__generated\n\n @property\n def tag(self) -> str:\n return self.__tag\n\n @property\n def key(self) -> RsaKey:\n if self.__key is None:\n raise Exception('Please first call the generate_key function')\n return self.__key\n\n def encrypt(self, data: str, perform_b64: bool = False):\n if perform_b64:\n plaintext = b64encode(data.encode())\n else:\n plaintext = data.encode()\n rsa_encryption_cipher = PKCS1_v1_5.new(self.key)\n ciphertext = rsa_encryption_cipher.encrypt(plaintext)\n return b64encode(ciphertext).decode()\n\n def decrypt(self, data: str, perform_b64: bool = False):\n if not self.key.has_private():\n raise Exception('This cipher is only used for encryption')\n rsa_decryption_cipher = PKCS1_v1_5.new(self.key)\n # ciphertext = b64decode(data.encode())\n ciphertext = urlsafe_b64decode(data.encode())\n plaintext = rsa_decryption_cipher.decrypt(ciphertext, 16)\n if perform_b64:\n return b64decode(plaintext).decode()\n else:\n return plaintext.decode()\n\n @property\n def public_key(self) -> bytes:\n return self.key.public_key().export_key()\n\n def write_public_key(self, path: str):\n with open(path, 'wb') as file:\n file.write(self.public_key)\n\n @property\n def private_key(self) -> bytes:\n if not self.key.has_private():\n raise Exception('This cipher is only used for encryption')\n return self.key.export_key()\n\n @property\n def __dict__(self) -> dict:\n k = self.key\n if self.has_private:\n return {\n 'public_key': self.public_key,\n 'private_key': self.private_key,\n 'tag': self.tag\n }\n else:\n return {\n 'public_key': self.public_key,\n 'tag': self.tag\n }\n\n def write(self, path: str, format: str = 'json'):\n \"\"\"type can be either 'json' or 'bin' (pickled)\"\"\"\n if not self.__generated:\n raise Exception('Can not write a non-generated key')\n if format == 'json':\n with open(path, 'w') as file:\n json.dump(vars(self), file, indent=' ', ensure_ascii=False)\n elif format == 'bin':\n with open(path, 'wb') as file:\n pickle.dump(vars(self), file)\n else:\n raise Exception('Right now only bin and json are accepted')\n","repo_name":"ADanayi/iotis_app","sub_path":"python/_classes/__Cipher.py","file_name":"__Cipher.py","file_ext":"py","file_size_in_byte":4152,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"28000690111","text":"import math\nimport random\nfrom datetime import date\n\ndef basic_python():\n\n pi = math.pi\n print(pi)\n\n random_choice = random.choice(['apple', 'pear', 'banana'])\n print(random_choice)\n\n now = date.today()\n birthday = date(1999, 8, 20)\n age = now - birthday\n print(age.days)\n\n\ndef arithmetic_calculation():\n\n a = 21\n b = 10\n c = 0\n\n c = a + b\n print(\"c 的值为：\", c)\n\n c = a - b\n print(\"c 的值为：\", c)\n\n c = a * b\n print(\"c 的值为：\", c)\n\n c = a / b\n print(\"c 的值为：\", c)\n\n c = a % b # 取余数\n print(\"c 的值为：\", c)\n\n # 修改变量 a 、b 、c\n a = 2\n b = 3\n c = a ** b\n print(\"c 的值为：\", c)\n\n a = 10\n b = 5\n c = a // b # 取整\n print(\"c 的值为：\", c)\n\ndef comparison_calculation():\n a = 21\n b = 10\n c = 0\n\n if a == b:\n print(\"a 等于 b\")\n else:\n print(\"a 不等于 b\")\n\n if a != b:\n print(\"a 不等于 b\")\n else:\n print(\"a 等于 b\")\n\n if a < b:\n print(\"a 小于 b\")\n else:\n print(\"a 大于等于 b\")\n\n if a > b:\n print(\"a 大于 b\")\n else:\n print(\"a 小于等于 b\")\n\n # 修改变量 a 和 b 的值\n a = 5\n b = 20\n if a <= b:\n print(\"a 小于等于 b\")\n else:\n print(\"a 大于 b\")\n\n if b >= a:\n print(\"b 大于等于 a\")\n else:\n print(\"b 小于 a\")\n\ndef logic_calculation(a, b):\n\n\n if a and b:\n print(\"变量 a 和 b 都为 true\")\n else:\n print(\"变量 a 和 b 有一个不为 true\")\n\n if a or b:\n print(\"变量 a 和 b 都为 true，或其中一个变量为 true\")\n else:\n print(\"变量 a 和 b 都不为 true\")\n\n if not (a and b):\n print(\"变量 a 和 b 都为 false，或其中一个变量为 false\")\n else:\n print(\"变量 a 和 b 都为 true\")\n\ndef assignment_calculation(a, b, c):\n\n\n c = a + b\n print(\"c 的值为：\", c)\n\n c += a\n print(\"c 的值为：\", c)\n\n c *= a\n print(\"c 的值为：\", c)\n\n c /= a\n print(\"c 的值为：\", c)\n\n c = 2\n c %= a\n print(\"c 的值为：\", c)\n\n c **= a\n print(\"c 的值为：\", c)\n\n c //= a\n print(\"c 的值为：\", c)\n\n\ndef if_statement():\n\n num = 9\n if num >= 0 and num <= 10: # 判断值是否在0~10之间\n print('hello')\n\n num = 10\n if num < 0 or num > 10: # 判断值是否在小于0或大于10\n print('hello')\n else:\n print('undefine')\n\n num = 8\n # 判断值是否在0~5或者10~15之间\n if (num >= 0 and num <= 5) or (num >= 10 and num <= 15):\n print('hello')\n else:\n print('undefine')\n\ndef do_statement():\n\n count = 0\n while count < 5:\n print(count, \" is less than 5\")\n count = count + 1\n else:\n print(count, \" is not less than 5\")\n\n fruits = ['banana', 'apple', 'mango']\n for index in range(len(fruits)):\n print('当前水果 :', fruits[index])\n print(\"Good bye!\")\n\ndef other_statement():\n\n for letter in 'Python':\n if letter == 'h':\n break\n print('当前字母 :', letter)\n\n for letter in 'Python':\n if letter == 'h':\n continue\n print('当前字母 :', letter)\n\n # 输出 Python 的每个字母\n for letter in 'Python':\n if letter == 'h':\n pass\n print('这是 pass 块')\n print('当前字母 :', letter)\n\n print(\"Good bye!\")\n\nif __name__ == '__main__':\n\n comparison_calculation()\n arithmetic_calculation()\n comparison_calculation()\n logic_calculation(True, False)\n assignment_calculation(21, 10, 0)\n if_statement()\n do_statement()\n other_statement()","repo_name":"AbnerCui/Python_Lectures","sub_path":"topic1_introduction_of_Python/introductionOfPython.py","file_name":"introductionOfPython.py","file_ext":"py","file_size_in_byte":3711,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"52"} +{"seq_id":"4574795429","text":"import io,os, sys\n\n#input = io.BytesIO(os.read(0,os.fstat(0).st_size)).readline\ninput = sys.stdin.readline\n\ndef solve():\n n, c, d = map(int, input().split())\n a = [int(i) for i in input().split()]\n a.sort(reverse = True)\n\n low, high = -1, d + 1\n while low < high:\n mid = (low+high+1) >> 1\n total = 0\n for i in range(d):\n id = i % (mid+1)\n if id < n:\n total += a[id]\n if total >= c:\n low = mid\n else:\n high = mid - 1\n if low == d+1:\n return 'Infinity'\n if low == -1:\n return 'Impossible'\n return str(low)\n \n\nfor _ in range(int(input())):\n sys.stdout.write(solve()+'\\n')","repo_name":"C0derTang/mixed-cp-solutions","sub_path":"CF/CF 1760F Quests.py","file_name":"CF 1760F Quests.py","file_ext":"py","file_size_in_byte":706,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"52"} +{"seq_id":"10894818618","text":"import vsketch\nfrom shapely import geometry, affinity\nimport numpy as np\n\nimport sys, os\nsys.path.append(os.path.join(os.path.abspath(__file__), \"../../\"))\nimport utils \n\nclass Day06TradeStylesSketch(vsketch.SketchClass):\n \n rand_x = vsketch.Param(75)\n rand_y = vsketch.Param(55)\n points = vsketch.Param(3)\n\n line_step = vsketch.Param(0.5)\n def draw(self, vsk: vsketch.Vsketch) -> None:\n vsk.size(\"205x130mm\", landscape=False)\n vsk.scale(\"mm\")\n widthmm = utils.css_to_mm(vsk.width)\n\n line_coords = []\n line_count = widthmm / self.line_step\n\n for y in range(int(line_count)):\n y = vsk.map(y, 0, line_count , 0, widthmm)\n line_coords.append(((- widthmm/2,y - widthmm/2),(widthmm/2,y - widthmm/2)))\n \n lines = geometry.MultiLineString(line_coords)\n\n first_pos = (vsk.random(self.rand_x), vsk.random(self.rand_y) )\n prev_pos = first_pos\n\n for i in range(self.points):\n \n if i == self.points - 1:\n new_pos = first_pos\n else:\n sign = (-1, 1)[i % 2]\n new_pos = (vsk.random(self.rand_x) * sign , vsk.random(self.rand_y) * 2 - self.rand_y )\n\n triangle = geometry.Polygon([(0,0), new_pos, prev_pos])\n\n # I don't know why this angle finding works, took a lot of trial and error\n # I need to get better at trigonometry I guess :()\n angle = utils.find_angle((np.array(prev_pos) - np.array(new_pos), np.array(new_pos) - np.array(prev_pos))[prev_pos[1] < new_pos[1]], (0,0), (1,0))\n translated_lines = affinity.translate(lines, 0, vsk.random(self.line_step))\n rotated_lines = affinity.rotate(translated_lines, angle, use_radians=True)\n\n vsk.geometry(rotated_lines.intersection(triangle))\n\n prev_pos = new_pos\n\n\n def finalize(self, vsk: vsketch.Vsketch) -> None:\n vsk.vpype(\"rotate 180 linemerge linesimplify reloop linesort gwrite last.gcode\")\n\n\nif __name__ == \"__main__\":\n Day06TradeStylesSketch.display()\n","repo_name":"hapiel/Genuary-2022","sub_path":"day06_trade_styles/sketch_day06_trade_styles.py","file_name":"sketch_day06_trade_styles.py","file_ext":"py","file_size_in_byte":2092,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"52"}