None:","title":""},{"docid":"2ca6a59ceac654e02f312b1642565d3a","score":"0.6176496","text":"def load_dataset(self, paths, files):\n patch_size = (self.patch_size, self.patch_size, 1)\n for file in files:\n # Load images in (H, W, C) format.\n img_s = np.expand_dims(io.imread(os.path.join(paths['data'], file)), -1)\n img_c = np.expand_dims(io.imread(os.path.join(paths['label'], file)), -1)\n\n # Convert to range [0, 1] in float32\n img_s = (img_s / 255.).astype('float32')\n img_c = (img_c / 255.).astype('float32')\n\n if self.augment_data:\n img_c, img_s = data_augmentation(img_c), data_augmentation(img_s)\n else:\n img_c, img_s = [img_c], [img_s]\n\n for c, s in zip(img_c, img_s):\n c_patches = create_patches(c, patch_size, step=self.patch_size)\n s_patches = create_patches(s, patch_size, step=self.patch_size)\n\n for data, label in zip(s_patches, c_patches):\n data = torch.from_numpy(data.transpose((2, 0, 1)))\n label = torch.from_numpy(label.transpose((2, 0, 1)))\n self.dataset['data'].append(data)\n self.dataset['label'].append(label)","title":""},{"docid":"f8f87b9ed8fe95f15734b58ae277bda3","score":"0.6173269","text":"def _load_data(self):\n load_data = self.load_reader.vals4slice(de_load, self.start, data_max_date, step=1)\n date_range = pd.date_range(self.start, data_max_date, freq='1H')\n self.load_data = pd.DataFrame(data={'load': load_data}, index=date_range)\n self.ex_data = pd.DataFrame(data=[], index=date_range)\n \n if self.exog is None:\n return\n for var_name in self.exog:\n self._get_exog(var_name)","title":""},{"docid":"9397379b816d245aa27748f2de562a12","score":"0.6172759","text":"def collect_data(self):\n if self.debug:\n print \"Collecting point cloud data\"\n try:\n os.mkdir(self.base_dir)\n os.chdir(self.base_dir)\n except OSError:\n os.chdir(self.base_dir)\n os.system(\"rm *.pcd\")\n os.system(\"kill $(pgrep XnSensorServer)\") #this is needed because the process from previous records does not die\n command = \"pcl_openni_recorder\"\n self.run_command(command,35)\n os.chdir(\"../\")","title":""},{"docid":"c8b7358b58d12fe79a0c6a7c2d1d9fdf","score":"0.61490434","text":"def load_data(self):\n for year in self.years:\n self.__load_data(year)","title":""},{"docid":"3f758d195a3596f5b040e73ffd03923a","score":"0.6148267","text":"def load_all_pose_data(self):\n for scene_name in self.scene_generator():\n self.get_pose_data(scene_name)","title":""},{"docid":"409309487d17755b452786a5f25b1310","score":"0.61253846","text":"def loadChirpAndDispersion(self):\n filename = fnmatch.filter(os.listdir(self.directory), '*.dat')\n fullname = os.path.join(self.directory, filename[0])\n self.inputFilenames['chirp'] = fullname\n\n numPoints = int(os.path.getsize(fullname) / 4 / 4)\n mmap = np.fromfile(fullname, dtype=np.int32, count=numPoints, offset=0)\n weight = np.fromfile(fullname, dtype=np.float32, count=numPoints, offset=numPoints * 4)\n\n self.chirp = mmap + 1 - weight\n self.chirp = self.chirp / np.max(self.chirp)\n self.dispersion = np.fromfile(fullname, dtype=np.float32, count=numPoints * 2,\n offset=numPoints * 4 * 2)\n logging.info('Mapping and Disperion data loaded from: {}'.format(filename[0]))","title":""},{"docid":"12fe92128ec11f218857d5c0591e2bc6","score":"0.6099259","text":"def load_data(self, path):\n self.train_labels = np.empty(0)\n self.test_labels = np.empty(0)\n self.validation_labels = np.empty(0)\n self.train_images = np.empty((0, self.resolution, self.resolution))\n self.validation_images = np.empty((0, self.resolution, self.resolution))\n self.test_images = np.empty((0, self.resolution, self.resolution))\n lst1=[]\n lst2=[]\n for dirs in os.listdir(path):\n for label in os.listdir(os.path.join(path, dirs)):\n label_path = os.path.join(path, dirs, label)\n for file in os.listdir(label_path):\n image=Image.open(os.path.join(label_path,file)).convert('F')\n lst1.append(np.array(image))\n lst2.append(label)\n if dirs == \"train\":\n self.train_images=np.array(lst1)\n self.train_labels=np.array(lst2)\n lst1.clear()\n lst2.clear()\n if dirs == \"test\":\n self.test_images=np.array(lst1)\n self.test_labels=np.array(lst2)\n lst1.clear()\n lst2.clear()\n if dirs == \"validation\":\n self.validation_images=np.array(lst1)\n self.validation_labels=np.array(lst2)\n lst1.clear()\n lst2.clear()\n print(\"LOADED\")\n return self.train_labels, self.validation_labels, self.test_labels, self.train_images, self.validation_images, self.test_images","title":""},{"docid":"6fda13e6294154ad6b7f9ddc33763fe9","score":"0.60990393","text":"def __init__(self):\n try:\n path2013 = os.path.join(os.path.curdir,\n 'data\\\\VicRoadsAccidents\\\\2013',\n '2013.shp')\n path2014 = os.path.join(os.path.curdir,\n 'data\\\\VicRoadsAccidents\\\\2014',\n '2014.shp')\n path2015 = os.path.join(os.path.curdir,\n 'data\\\\VicRoadsAccidents\\\\2015',\n '2015.shp')\n path2016 = os.path.join(os.path.curdir,\n 'data\\\\VicRoadsAccidents\\\\2016',\n '2016.shp')\n path2017 = os.path.join(os.path.curdir,\n 'data\\\\VicRoadsAccidents\\\\2017',\n '2017.shp')\n path2018 = os.path.join(os.path.curdir,\n 'data\\\\VicRoadsAccidents\\\\2018',\n '2018.shp')\n\n self.gpd2013 = gpd.read_file(path2013)\n self.gpd2014 = gpd.read_file(path2014)\n self.gpd2015 = gpd.read_file(path2015)\n self.gpd2016 = gpd.read_file(path2016)\n self.gpd2017 = gpd.read_file(path2017)\n self.gpd2018 = gpd.read_file(path2018)\n\n self.pd2013 = pd.DataFrame(self.gpd2013)\n self.pd2014 = pd.DataFrame(self.gpd2014)\n self.pd2015 = pd.DataFrame(self.gpd2015)\n self.pd2016 = pd.DataFrame(self.gpd2016)\n self.pd2017 = pd.DataFrame(self.gpd2017)\n self.pd2018 = pd.DataFrame(self.gpd2018)\n except OSError:\n print('Please check the file path')","title":""},{"docid":"ec4cb58f77f812354d501bbdb4cfa6b4","score":"0.6094891","text":"def load(self):\n self.load_features()\n self.load_images()","title":""},{"docid":"d2af12682b4e348494f1b3f277cdd2bb","score":"0.60718197","text":"def load_data(self):\n self.data = io.imread(self.filename)","title":""},{"docid":"d0d2bcc41c3fe6694b3a39959444147b","score":"0.606857","text":"def read_data(self):\n\n # Read file\n try:\n with open(self.path, newline='') as datafile:\n reader = csv.reader(datafile, delimiter=',')\n for row in reader:\n if len(row) == 5:\n if float(row[4]) < 2 and float(row[4]) >= self.limit:\n self.atoms.append(row[0])\n self.x.append(float(row[1]))\n self.y.append(float(row[2]))\n self.z.append(float(row[3]))\n self.t.append(float(row[4]))\n elif len(row) == 4:\n self.atoms.append(row[0])\n self.x.append(float(row[1]))\n self.y.append(float(row[2]))\n self.z.append(float(row[3]))\n else:\n print(\"Unknown amount of columns in xyz file (\", len(row), \")\")\n except FileNotFoundError:\n print(\"Coordinate file not found. Check the file path.\")\n\n # Convert to numpy arrays\n self.x = np.array(self.x)\n self.y = np.array(self.y)\n self.z = np.array(self.z)\n self.t = np.array(self.t)\n\n # Write xyz file\n if self.write == True:\n Data.write_to_file(self)\n print(\"VMD-compatible xyz file written.\")","title":""},{"docid":"0e56d25a3a91fc1313d20a607ff9763b","score":"0.60555005","text":"def load_data(self):\n # Step 0: Load transformed data if it exists.\n if Path(self.transformed_data_path).is_file(): # found transformed data\n self.pd_data = load_pickle(self.transformed_data_path)\n # TODO: Here we should also load used label, numerical, and categorical column names, meaning they should be\n # saved. This allows:\n # 1) using data without prior knowledge of the mentioned class variables.\n # 2) rationalizing once we know something, we have the option to recover them without remembering them\n # 3) so no need to rely on any \"self\" variables, i.e., our saved data is self-sufficient\n # dump() the class instance as a YAML config document\n self.hash_sizes = [self.pd_data[categ_name].nunique() for categ_name in self.categ_names]\n return\n\n # Step 1: Load label, numerical, and categorical features\n label_ll = [list() for _ in range(self.label_num)] # list of label data\n numer_ll = [list() for _ in range(self.numer_num)] # list of each numerical feature's data\n categ_ll = [list() for _ in range(self.categ_num)] # list of each categorical feature's data\n # Define defaultdict where key=\"feature index\" & val=\"defaultdict\" where key=\"category\" & val=\"count\".\n categ_count_dict = defaultdict(lambda: defaultdict(int))\n\n with open(self.dataset_path, 'r') as ld:\n for line in ld:\n # Using zero \"0\" as substitute for missing values in:\n # 1) Numerical feature may corrupt data because 0 is numerical.\n # 2) Categorical feature will not corrupt data because the string \"0\" is a new category.\n # TODO: Need a principled way to determine missing values for numerical features.\n line = [v if v != \"\" else \"0\" for v in line.strip('\\n').split('\\t')]\n\n for i, j in enumerate(self.label_indices): # record label feature data\n label_ll[i].append(int(line[j])) # typecast from string to save memory\n for i, j in enumerate(self.numer_indices): # record numerical feature data\n numer_ll[i].append(int(line[j])) # typecast from string to save memory\n for i, j in enumerate(self.categ_indices): # record categorical feature data\n categ_ll[i].append(line[j]) # cannot typecast string data\n categ_count_dict[i][line[j]] += 1 # count category occurrences\n\n # Step 2: Create dictionary for indexing categories.\n if Path(self.fit_dictionary_path).is_file():\n categ_index_dict = load_pickle(self.fit_dictionary_path)\n else:\n # Define defaultdict where key=\"feature index\" & val=\"defaultdict\" where key=\"category\" & val=\"index\".\n # TODO: Implement as a parent-class function\n categ_index_dict = defaultdict(lambda: defaultdict(int))\n\n # TODO: Rename variables, e.g., feat_index > i\n for feat_index, categ_dict in categ_count_dict.items():\n categ_index = 0\n for categ, count in categ_dict.items():\n if count >= self.categ_filter: # index filtered categories at a later stage\n categ_index_dict[feat_index][categ] = categ_index\n categ_index += 1\n del categ_count_dict # release memory\n gc.collect()\n # Save fit dictionary.\n save_pickle(self.fit_dictionary_path, dict(categ_index_dict)) # cannot pickle defaultdict using lambda\n\n # Step 3: Index categories.\n for i, categ_l in enumerate(categ_ll):\n for j, c in enumerate(categ_l):\n if c in categ_index_dict[i]:\n categ_ll[i][j] = categ_index_dict[i][c] # index in-place\n else: # index filtered categories as the end index\n categ_ll[i][j] = len(categ_index_dict[i])\n del categ_index_dict # release memory\n gc.collect()\n\n # Step 4: Format data and obtain hash statistics.\n array_data = np.concatenate((np.asarray(label_ll).T, np.asarray(numer_ll).T, np.asarray(categ_ll).T), axis=1)\n del label_ll, numer_ll, categ_ll # release memory\n gc.collect()\n\n self.pd_data = pd.DataFrame(array_data, columns=self.used_columns_names)\n self.hash_sizes = [self.pd_data[categ_name].nunique() for categ_name in self.categ_names]\n del array_data # release memory\n gc.collect()\n\n for col_name, dtype in self.dtype_dict.items():\n self.pd_data[col_name] = self.pd_data[col_name].astype(dtype)\n # Save transformed data.\n save_pickle(self.transformed_data_path, self.pd_data)","title":""},{"docid":"4327df5c665a0aaa9de860156f1b3c80","score":"0.60475284","text":"def __init__(self, filenames: list):\n self.x_dim = None\n self.y_dim = None\n self.all_records_num = None\n self.label_data = dict()\n self.index_data = []\n self.full_data_x = None\n self.full_data_y = None\n has_data = False\n for file_id, filename in enumerate(filenames):\n if isinstance(filename, (tuple, list)):\n if len(filename) >= 3:\n name_x, name_y, label = filename\n label = str(label)\n elif len(filename) == 2:\n name_x, name_y = filename\n label = None\n else:\n raise Exception(\"Incomplete data.\")\n dat_x = np.genfromtxt(name_x, delimiter=\",\")\n dat_y = np.genfromtxt(name_y, delimiter=\",\")\n if dat_y.ndim == 1:\n dat_y = dat_y[:, np.newaxis]\n if dat_x.shape[0] != dat_y.shape[0]:\n raise Exception(\"In file<{0}>, X and Y has different records count.\".format(file_id))\n if not has_data:\n self.x_dim = dat_x.shape[1:]\n self.y_dim = dat_y.shape[1:]\n self.all_records_num = dat_x.shape[0]\n self.full_data_x = dat_x\n self.full_data_y = dat_y\n else:\n if dat_x.shape[1:] != self.x_dim or dat_y.shape[1:] != self.y_dim:\n raise Exception(\"In file<{0}>, X or Y data dim doesn't match with other files.\".format(file_id))\n self.all_records_num += dat_x.shape[0]\n self.full_data_x = np.vstack(self.full_data_x, dat_x)\n self.full_data_y = np.vstack(self.full_data_y, dat_y)\n\n cur_data_dict = {\"X\": dat_x, \"Y\": dat_y}\n self.index_data.append(cur_data_dict)\n if label is not None:\n self.label_data[label] = cur_data_dict\n else:\n raise Exception(\"<{0}>, filename type not supported.\".format(file_id))","title":""},{"docid":"9b51362af4fa34926dd3979d119cd9ce","score":"0.6043146","text":"def load_data(self):","title":""},{"docid":"9b51362af4fa34926dd3979d119cd9ce","score":"0.6043146","text":"def load_data(self):","title":""},{"docid":"0f0290827a6671068bb58a46dc4cc4e6","score":"0.6038656","text":"def load_data(self):\n images = list()\n labels = list()\n emotion_index_map = dict()\n label_directories = [dir for dir in os.listdir(self.datapath) if not dir.startswith('.')]\n for label_directory in label_directories:\n if self.target_emotion_map:\n if label_directory not in self.target_emotion_map.keys(): continue\n self._add_new_label_to_map(label_directory, emotion_index_map)\n label_directory_path = self.datapath + '/' + label_directory\n\n if self.time_delay:\n self._load_series_for_single_emotion_directory(images, label_directory, label_directory_path, labels)\n else:\n image_files = [image_file for image_file in os.listdir(label_directory_path) if not image_file.startswith('.')]\n self._load_images_from_directory_to_array(image_files, images, label_directory, label_directory_path, labels)\n\n vectorized_labels = self._vectorize_labels(emotion_index_map, labels)\n self._check_data_not_empty(images)\n return self._load_dataset(np.array(images), np.array(vectorized_labels), emotion_index_map)","title":""},{"docid":"47bb46b10c300853ff86ab0058fdc3a6","score":"0.6032076","text":"def load_data(self):\n if not exists(self.path + self.file_name):\n if not exists(self.path + self.name + \".zip\"):\n #### Download ####\n url = f'http://85.215.86.232/tig/data/{self.name}.zip'\n r = requests.get(url, allow_redirects=True, stream=True)\n\n chunkSize = 1024\n pbar = tqdm(r.iter_content(chunk_size=chunkSize), unit=\"B\",\n total=int(r.headers['Content-Length']),\n desc=f'Download {self.name}',\n unit_scale=True, unit_divisor=1024)\n\n Path(self.path).mkdir(parents=True, exist_ok=True)\n with open(self.path + self.name + \".zip\", 'wb') as f:\n for chunk in pbar:\n if chunk: # filter out keep-alive new chunks\n pbar.update(len(chunk))\n f.write(chunk)\n log.info(\"Data set donwloaded! (%s%s.zip)\", self.path, self.name)\n else:\n log.info(\"Data exist already! (%s%s.zip)\", self.path, self.name)\n\n if not exists(self.path + self.name + '.npz'):\n #### Extract ####\n with ZipFile(file=self.path + self.name + '.zip') as zip_file:\n # Loop over each file\n for member in tqdm(iterable=zip_file.namelist(), total=len(zip_file.namelist()),\n desc=f'Extract {self.name} '):\n zip_file.extract(member, path=self.path)\n else:\n log.info(\"Data exist extracted! (%s%s.zip)\", self.path, self.name)\n\n if not exists(self.path + 'kinetics_class_dict.json'):\n #### Download ####\n url = 'http://85.215.86.232/tig/data/kinetics_class_dict.json'\n r = requests.get(url, allow_redirects=True, stream=True)\n\n pbar = tqdm(unit=\"B\", total=int(r.headers['Content-Length']) // 10**6,\n desc=f'Download {self.name} ')\n chunkSize = 1024\n\n Path(self.path).mkdir(parents=True, exist_ok=True)\n with open(self.path + 'kinetics_class_dict.json', 'wb') as f:\n for chunk in r.iter_content(chunk_size=chunkSize):\n if chunk: # filter out keep-alive new chunks\n pbar.update(len(chunk) // 10**6)\n f.write(chunk)\n log.info(\"Class dictionary donwloaded! (%skinetics_class_dict.json)\", self.path)\n else:\n log.info(\"Class dictionary already exist! (%skinetics_class_dict.json)\", self.path)\n\n log.info(\"Load data...\")\n data = np.load(self.path + self.file_name, allow_pickle=True, mmap_mode=\"r\")\n if self.lim is not None:\n self.x = np.asarray(list(data[\"x\"]))[:self.lim]\n self.y = np.asarray(list(data[\"y\"]))[:self.lim]\n else:\n self.x = np.asarray(list(data[\"x\"]))\n self.y = np.asarray(list(data[\"y\"]))\n\n le = preprocessing.LabelEncoder()\n self.y = le.fit_transform(self.y)\n\n with open(self.path + 'kinetics_class_dict.json', \"rb\") as f:\n self.classes = json.load(f)","title":""},{"docid":"991303587063de38b4bacacbcd2f72bd","score":"0.60309863","text":"def get_local_data(self):\n try:\n h5 = h5py.File(os.path.join(self.directory, \"trajectory.h5\"), mode=\"r\")\n self.timepoints = [key for key in h5[\"concentration\"][\"co2\"].keys()]\n self.timepoints.sort(key=int)\n self.dims = list(h5[\"concentration\"][\"co2\"][\"0\"].shape)\n self.nutrients = list(h5[\"concentration\"].keys())\n self.collect_positions(h5)\n self.get_nutrient_grid(h5)\n h5.close()\n except:\n print(\"Missing HDF5 file\")\n\n self.biomass = pd.read_csv(\n os.path.join(self.directory, \"Results\", \"biomass.csv\"),\n usecols=[0, 1, 2],\n delimiter=\"\\t\",\n )\n self.ntypes = pd.read_csv(\n os.path.join(self.directory, \"Results\", \"ntypes.csv\"),\n usecols=[0, 1, 2],\n delimiter=\"\\t\",\n )\n self.avg_con = pd.read_csv(\n os.path.join(self.directory, \"Results\", \"avg_concentration.csv\"),\n usecols=[0, 2, 3, 4],\n delimiter=\"\\t\",\n names=[\"Time\", \"O2\", \"Sucrose\", \"CO2\"],\n skiprows=1,\n )\n f = open(os.path.join(self.directory, \"metadata.json\"), \"r\")\n self.metadata = json.load(f)\n f.close()\n if \"IPTG\" in self.metadata:\n self.IPTG = self.metadata[\"IPTG\"]\n self.sucRatio = self.metadata[\"IPTG\"]\n else:\n self.IPTG = self.metadata[\"SucRatio\"]\n # TODO replace sucRatio with IPTG\n self.sucRatio = self.metadata[\"SucRatio\"]\n self.convert_units_avg_con()\n self.convert_units_biomass()","title":""},{"docid":"13d42c6533bdbd8ff51bd30770859732","score":"0.60293514","text":"def load_data(self):\n t0 = time.time()\n if not all([self.current_time_start, self.current_time_end]):\n raise Exception\n\n self.reset_data()\n\n # Load files within time range\n self.current_data['mit'] = self.get_data_within_time_range('mit', self.current_time_start, self.current_time_end)\n self.current_data['co2'] = self.get_data_within_time_range('co2', self.current_time_start, self.current_time_end)\n\n # Reset index\n self.current_data['mit'] = self.current_data['mit'].reset_index(drop=True)\n self.current_data['co2'] = self.current_data['co2'].reset_index(drop=True)\n\n # print('Load data')\n # print('mit', len(self.current_data['mit']))\n # print('co2', len(self.current_data['co2']))\n # print('Loaded in: {}'.format(time.time()-t0))","title":""},{"docid":"5451f31a15b9d4eadcc7eb2095883fe7","score":"0.6023418","text":"def prepare_loaded_data(self, file_name):\n #utworzenie bufora\n buff = Buffer()\n data = CsvReader.read_from_file(file_name, 1)\n #zmiana czasu\n data = DataOperation.change_time_relative(data)\n #zmiana wysokosci na metry\n data = DataOperation.change_altitude_cm_m(data)\n #stworzenie zapisywacza\n saver = FileSaver(\"saved_data/dane.txt\")\n\n #kazda linijke z pliku csv buforujemy osobno\n for d in data:\n buff.set_data(d)\n buffered_data = buff.get_data()\n #sprawdzamy czy kazda linijka jest poprawnie zapisana\n if InputValidator.input_val(buffered_data):\n #zapisujemy kazda linijke do nowego pliku\n saver.save_data(buffered_data)\n\n #odczyt danych z pliku csv i wizualizacja\n r_data = CsvReader.read_from_file(saver.get_file_name())\n #tworzymy wizualizator, drugi parametr do interwal czasowy\n self.visualizer = PlotCreator(r_data, 1)\n self.data_loaded = True\n print \"Dane zaladowane\"","title":""},{"docid":"84718ca56bf4cb30a69fa8a52a034f5a","score":"0.6016522","text":"def _loaddata(self):\n _header = self._header\n elemlist = self.elemlist\n frame = self.frame\n \n def nans(numitems):\n return [np.nan for i in xrange(numitems)]\n\n _elemid_lookup = dict(zip(_header.name, _header.id))\n \n # Data gets unpacked to a temporary dict tmpdata before building\n # Element objects\n # \n # We will unpack 1d elements to an array object and then\n # change them to 2d ndarrays after the data is loaded.\n #\n # Elements with more than 1 dimension will get unpacked\n # as lists of arrays and transformed to ndarrays after\n # the data is loaded\n #\n # The array.array objects are typed unlike regular lists.\n # This improves memory usage. The array.array objects also\n # have highly efficient appending unlike ndarrays\n \n # initialize temporary dict\n tmpdata = {} \n for name, typ, varrate, numvalues in \\\n zip(_header.name, _header.type, \n _header.varrateflag, _header.numvalues):\n if elemlist is None or any(fnmatch(name, wc) for wc in elemlist):\n if not varrate and numvalues == 1:\n tmpdata[name] = array(typ)\n else:\n tmpdata[name] = []\n\n # Intialize Frame arrays for CSSDC measures\n for name in tmpdata.keys():\n if _header.rate[_elemid_lookup[name]] != 1:\n tmpdata[name+'_Frames'] = array('i')\n\n fid = open(self.info.filename,'rb')\n read = fid.read # dots make things slow...\n read(self.cursor) # move to where the data begins\n\n # The daq files have a frame for every sample. Each frame\n # contains the variables states at the time the frame was\n # saved. \n #\n # we want to create arrays that can be indexed to decide\n # whether the variable needs stored. This way we don't\n # have to look through the elemlist for every variable on\n # every frame\n if elemlist is None: # elemlist empty \n mask = [True for i in xrange(len(_header.name))]\n else:\n mask = []\n for name in _header.name:\n mask.append(any(fnmatch(name, wc) for wc in elemlist))\n\n bombed = False\n # Python quirk no. 372837462: while 1 is faster than while True\n while 1:\n try:\n frame.code.append(unpack('i',read(4))[0])\n if frame.code[-1] == -2:\n break\n \n frame.frame.append(unpack('i', read(4))[0])\n frame.count.append(unpack('i', read(4))[0])\n\n # xrange (2.7, range in 3) is faster than range\n for j in xrange(frame.count[-1]):\n i = unpack('i', read(4))[0]\n\n if _header.varrateflag[i]:\n numitems = unpack('i', read(4))[0]\n else:\n numitems = _header.numvalues[i]\n \n size = _header.bytes[i]\n \n if mask[i]:\n name = _header.name[i]\n \n if numitems == 1:\n typ = _header.type[i]\n tmpdata[name].append(unpack(typ,read(size))[0])\n \n else: # numitems > 1\n typ = _header.nptype[i]\n tmpdata[name].append(fromfile(fid,typ,numitems))\n \n if _header.rate[i] != 1:\n tmpdata[name+'_Frames'].append(frame.frame[-1])\n \n else: # we don't need to read this element\n read(numitems*size) # seek is slow...\n # reduce calls to read\n except:\n msg = 'Failed loading file on frame %i.'%frame.frame[-1]\n msg += ' (stopped reading file)'\n warnings.warn(msg, RuntimeWarning)\n bombed = True\n break\n\n fid.close()\n\n # We made it through the daq file.\n # Now it is time to do some bookkeeping.\n frame.frame = np.array(frame.frame)\n self.f0 = f0 = frame.frame[0]\n self.fend = fend = frame.frame[-1]\n\n # If we bombed unpacking a frame we need to make sure\n # everything is aligned before we intialize Elements\n if bombed:\n # we will strip off the last frame just to make sure\n # everything is kosher\n n = min(len(frame.code),len(frame.frame),len(frame.count))-1\n frame.code = frame.code[:n]\n frame.frame = frame.frame[:n]\n frame.count = frame.count[:n]\n \n for name in tmpdata:\n if name.endswith('_Frames'):\n continue\n\n val = tmpdata[name]\n i = _elemid_lookup[name]\n if _header.rate[i] != 1:\n # CSSDC measures should always be okay. If they have 1\n # value they use read, if they have multiple values they\n # use fromfile. In either case if the unpacking fails\n # nothing will get appended to tmpdata and it will exit\n # before appending to the cooresponding _Frames array.\n #\n # At least that is what my mental interpreter thinks.\n # we will assert just to be on the safe side.\n assert len(tmpdata[name]) == len(tmpdata[name+'_Frames'])\n \n else:\n tmpdata[name] = np.array(tmpdata[name][:n], ndmin=2)\n \n # gets transposed in next for loop if numvalues > 1\n if _header.numvalues[i] > 1:\n assert tmpdata[name].shape[0] == frame.frame.shape[0]\n else:\n assert tmpdata[name].shape[1] == frame.frame.shape[0]\n\n # cast as Element objects\n # 'varrateflag' variables remain lists of lists\n #\n # There are obvious more compact ways to write this but I'm\n # paranoid about reference counting and garbage collection not\n # functioning properly\n for name, i, rate in zip(_header.name, _header.id, _header.rate):\n if elemlist is not None:\n if not any(fnmatch(name, wc) for wc in elemlist):\n continue\n \n # transpose Element with more than 1 row \n if _header.numvalues[i] > 1:\n tmpdata[name] = np.array(tmpdata[name]).transpose()\n \n if rate != 1:\n self[name] = \\\n Element(tmpdata[name],\n tmpdata[name+'_Frames'],\n rate=_header.rate[i],\n name=_header.name[i],\n dtype=_header.type[i],\n varrateflag=_header.varrateflag[i],\n elemid=_header.id[i],\n units=_header.units[i])\n \n del tmpdata[name+'_Frames']\n else:\n self[name] = \\\n Element(tmpdata[name],\n frame.frame[:],\n rate=_header.rate[i],\n name=_header.name[i],\n dtype=_header.type[i],\n varrateflag=_header.varrateflag[i],\n elemid=_header.id[i],\n units=_header.units[i])\n\n # delete tmpdata arrays as we go to save memory\n del tmpdata[name]\n \n del _header, self._header\n self._header = None","title":""},{"docid":"6125bdb53d5aed8bcb4eb296a74625df","score":"0.6012088","text":"def loaddata(self, x_path, y_path):\n # load x-series and y-series data\n df_x = pd.read_csv(x_path, dtype={'id': str}, parse_dates=['ds'], date_parser=self.dateparse)\n df_y = pd.read_csv(y_path, dtype={'id': str}, parse_dates=['ds'], date_parser=self.dateparse)\n self.df_x = df_x[['id', 'ds', 'x']]\n self.df_y = df_y[['id', 'ds', 'y']]\n self.lg.logger.info(\"load data: {} | {}\".format(os.path.basename(x_path), os.path.basename(y_path)))","title":""},{"docid":"cde7e3dc7a92ff340201336cab894c99","score":"0.59944457","text":"def load_data(self):\n if not os.path.isdir(self.path):\n print(\"Data for these parameters not generated!!!\")\n print(\"Generating data, this can take a while...\")\n generate_data(self.glove_dimension, self.max_words, self.full)\n print(\"Generating data done!\")\n self.embedding_matrix = np.load(os.path.join(self.path, \"embedding_matrix.npy\"))\n self.X_train = np.load(os.path.join(self.path, \"X_train.npy\"))\n self.Y_train = np.load(os.path.join(self.path, \"Y_train.npy\"))\n self.X_test = np.load(os.path.join(self.path, \"X_test.npy\"))","title":""},{"docid":"c03747fd92b1d9c97efa0443bf7addd5","score":"0.5991191","text":"def _load_data(self):\n cols = [list() for _ in range(len(self.columns_names))]\n user2index_dict = dict() # needs renumber since CustomerID ranges from 1 to 2649429, with gaps\n\n for fp in self.dataset_path: # TODO: deal with list of paths\n\n with open(fp, 'r') as f:\n for line in f.readlines():\n if ':' in line:\n movie = int(line.strip(\":\\n\"))-1 # -1 because the sequential MovieIDs starts from 1\n else:\n user, rating = [int(v) for v in line.strip().split(',')[:2]]\n cols[1].append(movie)\n cols[2].append(rating)\n\n if user in user2index_dict.keys():\n cols[0].append(user2index_dict[user])\n else:\n cols[0].append(len(user2index_dict.keys())) # number users from 0\n user2index_dict[user] = len(user2index_dict.keys())\n\n # TODO: load date as well, later keep only selected data via self.used_columns_names\n self.pd_data = pd.DataFrame(dict(zip(self.columns_names, cols)))\n\n for col_name, dtype in self.dtype_dict.items():\n self.pd_data[col_name] = self.pd_data[col_name].astype(dtype)\n\n self.pd_data = self.pd_data[self.used_columns_names]\n self.user_num = self.pd_data[\"user_id\"].nunique()\n self.item_num = self.pd_data[\"item_id\"].nunique()","title":""},{"docid":"ed5b989f5a48d68b557c919be221d493","score":"0.59870875","text":"def _read_files(self):\n data = []\n print(\"Leyendo los archivos...\",end=\" \")\n init = time.time()\n\n for ii in range(self.ndata):\n # Leo las coordenadas y el numero atomico\n file_name = self.path_C + str(ii+1) + \".xyz\"\n atomic_number, positions, how_many = self._read_xyz(file_name)\n\n # Leo Densidades, Energia XC, type gaussians y Nuc\n file_name = self.path_P + str(ii+1) + \".dat\"\n Exc, Pmat_fit, gtype, Nuc = self._read_rhoE(file_name)\n\n # Guardo los datos en un solo diccionario\n single_data = {\n \"targets\": Exc,\n \"atomic_number\": atomic_number,\n \"how_many\": how_many,\n \"Pmat_fit\": Pmat_fit,\n \"gtype\": gtype, \n \"Nuc\": Nuc,\n }\n\n if len(positions) != 0:\n single_data[\"positions\"] = positions\n \n # Guardo los datos de una molecula\n data.append(single_data)\n \n fin = time.time()\n print(str(np.round(fin-init,2))+\" s.\")\n\n return data","title":""},{"docid":"2db3e6105fac9e46d4cbe6c60d3e1593","score":"0.5980057","text":"def __init__(self, data_dir):\n print('Loading original MNIST data from', data_dir)\n self.data = []\n for name, key in [('train', 'train'), ('test', 't10k')]:\n images = os.path.join(data_dir, '%s-images.idx3-ubyte' % key)\n labels = os.path.join(data_dir, '%s-labels.idx1-ubyte' % key)\n if not os.path.isfile(images) or not os.path.isfile(labels):\n print('Warning: Missing %s data' % name)\n else:\n self.data.append((name, images, labels))","title":""},{"docid":"78fde79e39a1dde2c51dc5cdb21288eb","score":"0.5975555","text":"def load_data(self):\n dset = self.config.dataset\n dset.load()\n found = 0\n for i,cband in enumerate(dset.dmap):\n emin, emax, event_type = cband.emin(), cband.emax(), cband.event_class()\n if event_type == -2147483648: event_type=0 # previous bug in FITS setup\n try:\n band = self.find(emin, event_type)\n found +=1\n except Exception:\n continue\n band.load_data(cband)\n band.data_index = i # useful to look up associated index\n if found!=len(self):\n print ('{}: Loaded subset of bands {} instead of {}'.format( self.__repr__(), found, len(self)))\n self[:]= self[:found] \n self.config.emax=self[-1].emax\n self.has_data = True","title":""},{"docid":"d90c82b56133369719db443f99ec3ac8","score":"0.59707594","text":"def load_all_pose_data(self):\n raise NotImplementedError('subclass must implement this method')","title":""},{"docid":"7b07a36e9ebbe87d7bd6c2689315702e","score":"0.5941635","text":"def loadBasicData(self):\n train = pd.read_csv(os.path.join(self.path, 'train.csv'))\n \n s = LabelEncoder().fit(train.species) \n self.classes = list(s.classes_) \n classes_labels = s.transform(train.species)\n train = train.drop(['species'], axis=1)\n\n if self.pca:\n trainX = train.drop(['id'], axis=1)\n pca = PCA(n_components=0.9 ,svd_solver='full')\n pca.fit(trainX)\n trainX=pca.transform(trainX)\n train_df=pd.DataFrame.from_records(trainX)\n train_df.insert(loc=0, column='id', value=train['id'])\n train=train_df\n\n \n for train_index, test_index in StratifiedShuffleSplit(n_splits=1, test_size=self.nb_test_data, random_state=self.r).split(train, classes_labels): \n X_train, X_test = train.values[train_index], train.values[test_index] \n self.t_train, self.t_test = classes_labels[train_index], classes_labels[test_index]\n\n self.id_img_train = list(np.int_( X_train[:,0]))\n self.id_img_test = list(np.int_( X_test[:,0])) \n self.X_data_train = np.delete(X_train, 0, 1)\n self.X_data_test = np.delete(X_test, 0, 1)","title":""},{"docid":"8af6c89f57159a47b73d405ef97c77ca","score":"0.5937247","text":"def load(self):\n\n meta_data = []\n if len(os.listdir(self.processed_data_dir)) != 0:\n\n print(\"Loading Saved Data from Disk.......\")\n\n train_dir = os.path.join(self.processed_data_dir, 'train')\n test_dir = os.path.join(self.processed_data_dir, 'test')\n\n dataset = DiskDataset(data_dir=self.processed_data_dir)\n train = DiskDataset(data_dir=train_dir)\n test = DiskDataset(data_dir=test_dir)\n\n meta_data = pickle.load(open(os.path.join(self.processed_data_dir, 'meta.pkl'), 'rb'))\n max_atom = meta_data[0]\n\n print(\"Transforming Data.\")\n if not self.transformer_types:\n if self.transformer_types == 'normalization_y':\n self.transformers += [\n NormalizationTransformer(transform_y=True, dataset=dataset)\n ]\n elif self.transformer_types == 'normalization_w':\n self.transformers += [\n NormalizationTransformer(transform_w=True, dataset=dataset)\n ]\n elif self.transformer_types == 'balancing_w':\n self.transformers += [\n BalancingTransformer(transform_w=True, dataset=dataset)\n ]\n elif self.transformer_types == 'balancing_y':\n self.transformers += [\n BalancingTransformer(transform_y=True, dataset=dataset)\n ]\n else:\n ValueError(\"Transformer type Not defined!{}\".format(self.transformer_types))\n\n else:\n print(\"Loading and Featurizing Data.......\")\n # loader = dc.data.CSVLoader(\n # tasks=self.tasks, smiles_field=self.smiles_field, featurizer=self.Featurizer)\n dataset = self.featurize(shard_size=2048)\n\n print(\"Transforming Data.\")\n if not self.transformer_types:\n if self.transformer_types == 'normalization_y':\n self.transformers += [\n NormalizationTransformer(transform_y=True, dataset=dataset)\n ]\n elif self.transformer_types == 'normalization_w':\n self.transformers += [\n NormalizationTransformer(transform_w=True, dataset=dataset)\n ]\n elif self.transformer_types == 'balancing_w':\n self.transformers += [\n BalancingTransformer(transform_w=True, dataset=dataset)\n ]\n elif self.transformer_types == 'balancing_y':\n self.transformers += [\n BalancingTransformer(transform_y=True, dataset=dataset)\n ]\n else:\n ValueError(\"Transformer type Not defined!{}\".format(self.transformer_types))\n\n if len(self.transformers) > 0:\n for transformer in self.transformers:\n # pass dataset through maybe more than one transformer\n dataset = transformer.transform(dataset)\n\n \"\"\"max_atom is the max atom of molecule in all_dataset \"\"\"\n max_atom = self.find_max_atom(dataset)\n meta_data.append(max_atom)\n meta_data.extend(self.tasks)\n with open(os.path.join(self.processed_data_dir, 'meta.pkl'), 'wb') as f:\n pickle.dump(meta_data, f)\n\n \"\"\"\n Split Dataset\n \"\"\"\n print(\"Splitting Date to Train/Validation/Testing\")\n splitters = {\n 'index': IndexSplitter(),\n 'random': RandomSplitter(),\n 'scaffold': ScaffoldSplitter()\n }\n\n if self.splitter not in splitters:\n raise ValueError(\"Splitter not defined!\")\n else:\n splitter = splitters[self.splitter]\n\n # create processed dirs as train, valid, test\n train_dir = os.path.join(self.processed_data_dir, 'train')\n test_dir = os.path.join(self.processed_data_dir, 'test')\n\n print(\"Saving Data at %s...\", self.processed_data_dir)\n train, test = splitter.train_test_split(\n dataset,\n train_dir=train_dir,\n test_dir=test_dir,\n frac_train=self.split_frac[0],\n )\n\n return self.tasks, (train, test), self.transformers, max_atom","title":""},{"docid":"6758cc52f44c8b357ed3d7891c0b848e","score":"0.59339476","text":"def load_data_set_graphs():\n\n asc_graphs_array = []\n desc_graphs_array = []\n flat_graphs_array = []\n soy_graphs_array = []\n\n for asc_file in os.listdir(\"samples/ASC/graphs/\"):\n if asc_file.endswith(\".png\"):\n img_data = cv2.imread(\"samples/ASC/graphs/\" + asc_file, 0)\n asc_graphs_array.append(img_data)\n\n for desc_file in os.listdir(\"samples/DESC/graphs/\"):\n if desc_file.endswith(\".png\"):\n img_data = cv2.imread(\"samples/DESC/graphs/\" + desc_file, 0)\n desc_graphs_array.append(img_data)\n\n for flat_file in os.listdir(\"samples/FLAT/graphs/\"):\n if flat_file.endswith(\".png\"):\n img_data = cv2.imread(\"samples/FLAT/graphs/\" + flat_file, 0)\n flat_graphs_array.append(img_data)\n\n for soy_file in os.listdir(\"samples/SOY/graphs/\"):\n if soy_file.endswith(\".png\"):\n img_data = cv2.imread(\"samples/SOY/graphs/\" + soy_file, 0)\n soy_graphs_array.append(img_data)\n\n\n return asc_graphs_array, desc_graphs_array, flat_graphs_array, soy_graphs_array","title":""},{"docid":"0c162860e049cdcd6df07e931a7de844","score":"0.59312016","text":"def _post_file_load_processing(self):\n # When loading files with just point arrays, create and\n # set the polydata vertices\n if self.n_points > 0 and self.n_cells == 0:\n verts = self._make_vertex_cells(self.n_points)\n self.verts = CellArray(verts, self.n_points, deep=False)","title":""},{"docid":"bd3c8e433d4911cb2c46e6da8803b5d0","score":"0.59306145","text":"def build_data_loader(self):\n # Load dataset\n dataset = build_dataset(self.cfg)","title":""},{"docid":"ea737d3461bc2ff86e2d0f6854012207","score":"0.5930364","text":"def load_list(self):\n file_names = ['cube1.nc', 'cube2.nc']\n file_names = [os.path.join(self.data_dir, f) for f in file_names]\n\n iris.load(file_names)","title":""},{"docid":"717cdc4b17306d16580d1a7e88e55217","score":"0.59258443","text":"def load_data(data_dir):\n\n ### YOUR CODE HERE\n\n files = os.listdir(data_dir)\n\n x_train = np.zeros(shape=[0,32,32,3], dtype=float)\n y_train = np.asarray([], dtype=int)\n\n x_test = np.zeros(shape=[0,32,32,3], dtype=float)\n y_test = np.asarray([], dtype=int)\n\n def unpickle(file):\n with open(file, 'rb') as fo:\n dict = pickle.load(fo, encoding='bytes')\n return dict\n\n def get_xy_batch(cur_file_path):\n current_set = unpickle(cur_file_path)\n x_temp = np.array(current_set[b'data'], dtype=float) / 255.0\n x_temp = x_temp.reshape([-1, 3, 32, 32])\n x_temp = x_temp.transpose([0, 2, 3, 1])\n y_temp = np.array(current_set[b'labels'])\n\n return x_temp, y_temp\n\n for file in files:\n cur_file_path = os.path.join(data_dir, file)\n if 'data_batch' in file:\n x_temp, y_temp = get_xy_batch(cur_file_path)\n x_train = np.concatenate((x_train, x_temp), axis=0)\n y_train = np.concatenate((y_train, y_temp), axis=0)\n\n elif 'test_batch' in file:\n x_temp, y_temp = get_xy_batch(cur_file_path)\n x_test = np.concatenate((x_test, x_temp))\n y_test = np.concatenate((y_test, y_temp))\n\n ### END CODE HERE\n\n return x_train, y_train, x_test, y_test","title":""},{"docid":"c3948cb7e1ae2894bb35ef23a00fc2da","score":"0.59197044","text":"def _load_data(self):\n raise NotImplementedError","title":""},{"docid":"e62a9581844ef2e78c9d81499739e8b0","score":"0.5912595","text":"def load_data(filepath,fnames,jinds,iinds,varname,num_cores=20,dim4D=True, sum_over_depth=False, depth_lim=13, model_data=False, remove_clim=False,dt=1, depth_lim0=0):\n # create temp files to host the shared memory variables\n folder1 = tempfile.mkdtemp()\n folder2 = tempfile.mkdtemp()\n path1 = os.path.join(folder1, 'dum0.mmap')\n path2 = os.path.join(folder2, 'dum1.mmap')\n if dim4D: # incase the files have more than one timestep in each file\n vshape=(len(fnames),366,len(jinds))\n var_par=np.memmap(path1, dtype=float, shape=vshape, mode='w+')\n else: # incase there is only one timestep in a file\n vshape=(len(fnames),len(jinds))\n var_par=np.memmap(path1, dtype=float, shape=vshape, mode='w+')\n # nts will keep track of number of days in a year\n nts=np.memmap(path2, dtype=float, shape=(len(fnames)), mode='w+')\n fnames2=np.memmap(path2, dtype='U'+str(len(fnames[0])+1), shape=(len(fnames)), mode='w+')\n fnames2[:]=fnames #np.asarray(fnames[:])\n # launch the parallel reading\n Parallel(n_jobs=num_cores)(delayed(read_files)(j,nts,jinds,iinds,filepath,fnames2,var_par,varname,sum_over_depth, depth_lim, depth_lim0, model_data=model_data) for j,fname in enumerate(fnames))\n if dim4D:\n print('removing climatology')\n var_clim=np.nanmean(var_par,0)\n if remove_clim:\n print('removing climatology') \n # smooth the daily climatology with monthly filter, as the climatology will be still noisy at daily scales\n var_clim=np.concatenate([var_clim[-120//dt:,],var_clim,var_clim[:120//dt,]],axis=0)\n b,a=signal.butter(3,2./(30/dt))\n jnonan=np.where(np.isfinite(np.sum(var_clim,0)))\n var_clim[:,jnonan]=signal.filtfilt(b,a,var_clim[:,jnonan],axis=0)\n var_clim=var_clim[120//dt:120//dt+366//dt,]\n #\n # this is the on off switch for removing the climatology\n var_clim=var_clim*int(remove_clim) \n var=var_par[0,:int(nts[0]),:]-var_clim[:int(nts[0]),:]\n # concatenate the data - note that here nts is used to strip down the 366th day when it's not a leap year\n # and include the 366th day when it is a leap year\n for j in range(1,len(fnames)):\n print(j)\n var=np.concatenate([var,var_par[j,:int(nts[j]),:]-var_clim[:int(nts[j]),:]],axis=0)\n #\n else:\n # if only one timestep per file\n var=np.asarray(var_par)\n var[np.where(var==0)]=np.nan\n if remove_clim:\n print('removing climatology')\n year0=datetime.date(int(fnames[0][-20:-16]),int(fnames[0][-16:-14]),int(fnames[0][-14:-12])).isocalendar()[0]\n year1=datetime.date(int(fnames[-1][-20:-16]),int(fnames[-1][-16:-14]),int(fnames[-1][-14:-12])).isocalendar()[0]\n var2=np.ones((year1-year0+1,int(np.ceil(366./dt)),var.shape[1]))*np.nan\n #\n for j, fname in enumerate(fnames):\n year = int(fname[-20:-16])\n month = int(fname[-16:-14])\n day = int(fname[-14:-12])\n c,c1 = datetime.date(year,month,day).isocalendar()[:2]\n c = c-year0\n c1 = c1-1\n var2[c,c1,:] = var[j,:]\n #\n var_clim=np.nanmean(var2,0)\n ind=np.where(np.nansum(var2,-1)[0,:]>0)[0]\n var=var2[0,ind,:]-var_clim[ind,:]\n for j in range(1,var2.shape[0]):\n ind=np.where(np.nansum(var2,-1)[j,:]>0)[0]\n var=np.concatenate([var,var2[j,ind,:]-var_clim[ind,:]],axis=0)\n else:\n var_clim=None\n # \n print('close files')\n #\n try:\n shutil.rmtree(folder1)\n except OSError:\n pass\n try:\n shutil.rmtree(folder2)\n except OSError:\n pass\n #\n return var, var_clim","title":""},{"docid":"11798ccc67e02663e8357fea9199550d","score":"0.5912269","text":"def load_data(data_dir):\n\n\t### YOUR CODE HERE\n\tfnames = os.listdir(data_dir)\n\tx_train = np.array([[]]).reshape(0,3072)\n\ty_train = np.array([])\n\tfor fn in fnames:\n\t\tif fn.endswith(\"html\") or fn.endswith(\"meta\"):\n\t\t\tcontinue\n\t\twith open(os.path.join(data_dir,fn), 'rb') as fo:\n\t\t\tds = pickle.load(fo, encoding='bytes')\n\t\t\txtemp = np.array(ds[b'data']) #.reshape(10000,3,32,32).transpose(0,2,3,1).astype(np.uint8)\n\t\t\tytemp = np.array(ds[b'labels'])\n\n\t\tif fn.startswith(\"test\"):\n\t\t\tx_test = xtemp\n\t\t\ty_test = ytemp\n\n\t\tif fn.startswith(\"data\"):\n\t\t\tx_train = np.concatenate((xtemp,x_train), axis=0)\n\t\t\ty_train = np.concatenate((ytemp,y_train), axis=0)\n\t### END CODE HERE\n\n\treturn x_train, y_train, x_test, y_test","title":""},{"docid":"eef24da965880b538b996ae68c599f22","score":"0.5905727","text":"def load_data(self):\n self.idl.load_data()\n self.legacy.load_data()","title":""},{"docid":"07618452435f299bce27fd188640865f","score":"0.58980036","text":"def load_data(self):\n\n # Now done at beginning to make sure it only does this one per target\n if glob.glob(self.params['path']+'%d_*' % self.target) != []:\n # Load light curve\n if not os.path.exists(self.params['path']+'%d_LC.txt' % self.target):\n if self.verbose:\n print('Error: %s%d_LC.txt not found' % (self.params['path'], self.target))\n return False\n else:\n self.get_file(self.params['path'] + '%d_LC.txt' % self.target)\n self.time = np.copy(self.x)\n self.flux = np.copy(self.y)\n self.cadence = int(np.nanmedian(np.diff(self.time)*24.0*60.0*60.0))\n if self.cadence/60.0 < 10.0:\n self.short_cadence = True\n else:\n self.short_cadence = False\n self.nyquist = 10**6/(2.0*self.cadence)\n if self.verbose:\n print('# LIGHT CURVE: %d lines of data read' % len(self.time))\n if self.short_cadence:\n self.fitbg['smooth_ps'] = 2.5\n # Load power spectrum\n if not os.path.exists(self.params['path'] + '%d_PS.txt' % self.target):\n if self.verbose:\n print('Error: %s%d_PS.txt not found' % (self.params['path'], self.target))\n return False\n else:\n self.get_file(self.params['path'] + '%d_PS.txt' % self.target)\n self.frequency = np.copy(self.x)\n self.power = np.copy(self.y)\n if self.keplercorr:\n self.remove_artefact(self.frequency, self.power)\n self.power = np.copy(self.y)\n if self.verbose:\n print('## Removing Kepler artefacts ##')\n if self.verbose:\n print('# POWER SPECTRUM: %d lines of data read' % len(self.frequency))\n self.oversample = int(round((1./((max(self.time)-min(self.time))*0.0864))/(self.frequency[1]-self.frequency[0])))\n self.resolution = (self.frequency[1]-self.frequency[0])*self.oversample\n\n if self.verbose:\n print('-------------------------------------------------')\n print('Target: %d' % self.target)\n if self.oversample == 1:\n print('critically sampled')\n else:\n print('oversampled by a factor of %d' % self.oversample)\n print('time series cadence: %d seconds' % self.cadence)\n print('power spectrum resolution: %.6f muHz' % self.resolution)\n print('-------------------------------------------------')\n # Create critically sampled PS\n if self.oversample != 1:\n self.freq = np.copy(self.frequency)\n self.pow = np.copy(self.power)\n self.frequency = np.array(self.frequency[self.oversample-1::self.oversample])\n self.power = np.array(self.power[self.oversample-1::self.oversample])\n else:\n self.freq = np.copy(self.frequency)\n self.pow = np.copy(self.power)\n self.frequency = np.copy(self.frequency)\n self.power = np.copy(self.power)\n if hasattr(self, 'findex'):\n if self.findex['do']:\n # Make a mask using the given frequency bounds for the find excess routine\n mask = np.ones_like(self.freq, dtype=bool)\n if self.params[self.target]['lowerx'] is not None:\n mask *= np.ma.getmask(np.ma.masked_greater_equal(self.freq, self.params[self.target]['lowerx']))\n else:\n mask *= np.ma.getmask(np.ma.masked_greater_equal(self.freq, self.findex['lower']))\n if self.params[self.target]['upperx'] is not None:\n mask *= np.ma.getmask(np.ma.masked_less_equal(self.freq, self.params[self.target]['upperx']))\n else:\n mask *= np.ma.getmask(np.ma.masked_less_equal(self.freq, self.findex['upper']))\n self.freq = self.freq[mask]\n self.pow = self.pow[mask]\n return True\n else:\n print('Error: data not found for target %d' % self.target)\n return False","title":""},{"docid":"ea527761adf30721dfc4bd47d21a6514","score":"0.58884585","text":"def load_data():\n def un_pickle(file):\n with open(file, 'rb') as fo:\n return_dict = pickle.load(fo, encoding='latin1')\n return return_dict\n xTr = np.array([], dtype=np.float64).reshape(0, 3072)\n yTr = np.array([], dtype=np.int64).reshape(0)\n for i in range(1, 6):\n dataDict = un_pickle('cifar-10-batches-py/data_batch_'+str(i))\n xTr = np.concatenate((xTr, dataDict['data']), axis=0)\n yTr = np.concatenate((yTr, dataDict['labels']), axis=0)\n xTe = un_pickle('cifar-10-batches-py/test_batch')['data'].astype(np.float64)\n yTe = np.array(un_pickle('cifar-10-batches-py/test_batch')['labels'], dtype=np.int64)\n return (xTr, yTr), (xTe, yTe)","title":""},{"docid":"8773ef4bdc17ea39aae9abc827e17b25","score":"0.5883034","text":"def load_data(args, data_dir=None):\n df_all = pd.DataFrame(columns=['center', 'left', 'right', 'steering', 'throttle', 'reverse', 'speed'])\n\n if data_dir:\n for part in data_dir:\n part = os.path.join(args.data_dir, part)\n for subdir, dirs, files in os.walk(part):\n for file in files:\n if file == 'driving_log.csv':\n logging.info(\"Loading : \" + os.path.join(subdir, file))\n try:\n if dirs[0] != 'IMG':\n logging.info(\"Missing IMG directory in \" + subdir)\n break\n except IndexError:\n logging.info(\"No directories!\")\n break\n df_new = pd.read_csv(os.path.join(os.getcwd(), os.path.join(subdir, file)),\n names=['center', 'left', 'right',\n 'steering', 'throttle', 'reverse', 'speed'])\n df_all = df_all.append(df_new)\n\n else:\n for subdir, dirs, files in os.walk(args.data_dir):\n for file in files:\n if file == 'driving_log.csv':\n logging.info(\"Loading : \" + os.path.join(subdir, file))\n try:\n if dirs[0] != 'IMG':\n logging.info(\"Missing IMG directory in \" + subdir)\n break\n except IndexError:\n logging.info(\"No directories!\")\n break\n df_new = pd.read_csv(os.path.join(os.getcwd(), os.path.join(subdir, file)),\n names=['center', 'left', 'right',\n 'steering', 'throttle', 'reverse', 'speed'])\n df_all = df_all.append(df_new)\n\n # Checking for invalid data\n if df_all.isnull().any().any():\n logging.info('Corrupted data')\n return -1\n else:\n logging.info('Data not corrupted')\n\n images_all = df_all[['center', 'left', 'right']].values\n labels = df_all[['steering', 'speed']].values\n\n x_data, y_data = divide_images(images_all, labels)\n\n return x_data, y_data","title":""},{"docid":"0af3f302a0ef5c610eacd197a1ede5b3","score":"0.5882122","text":"def dataArr(filename):\r\n #Open the file\r\n f=h5py.File(filename,'r')\r\n \r\n #Initialize the data arrays\r\n cdata=[]\r\n idxset=[]\r\n vertices=[]\r\n \r\n #Open groups in the file\r\n for group in f.keys():\r\n# print('Group- '+group)\r\n \r\n #Get the group\r\n currGroup=f[group]\r\n \r\n #Open keys in the group\r\n for key in currGroup.keys():\r\n# print('Key- '+key)\r\n \r\n #Append the data to the respective arrays\r\n if key=='cdata(Complex)':\r\n cdataGroup=currGroup[key]\r\n \r\n imag=[]\r\n real=[]\r\n #Open the keys in cdata\r\n for subkey in cdataGroup.keys():\r\n# print('Subkey- '+subkey)\r\n \r\n #Get the real and imaginary parts of the array\r\n if subkey=='Imag':\r\n imag=cdataGroup[subkey][()]\r\n elif subkey=='Real':\r\n real=cdataGroup[subkey][()]\r\n \r\n #Convert lists to numpy arrays\r\n imag=np.array(imag)\r\n real=np.array(real)\r\n #Get the cdata value\r\n cdata=real+1j*imag\r\n \r\n elif key=='idxset':\r\n idxset=currGroup[key][()]\r\n elif key=='vertices':\r\n vertices=currGroup[key][()]\r\n \r\n #Remove the unwanted component from the vertices\r\n #Vertices are stored in a 3 element array containing (x,y,z) but we only\r\n #need 2 to make flat plots, therefore we need to remove one based on the\r\n #plane on which the data is being plotted\r\n xVals=[]\r\n yVals=[]\r\n newVertices=[]\r\n for vertex in vertices:\r\n xVals.append(vertex[0])\r\n yVals.append(vertex[2])\r\n newVertices.append([vertex[0],vertex[1]])\r\n vertices=newVertices\r\n \r\n #Convert to numpy arrays\r\n cdata=np.array(cdata)\r\n xVals=np.array(xVals)\r\n yVals=np.array(yVals)\r\n \r\n #Close the file\r\n f.close()\r\n \r\n return cdata, xVals, yVals","title":""},{"docid":"3b87d24f5dbcf7555daea8fc8a209185","score":"0.588185","text":"def load_data(self):\n num_actual = len(list(CROPS_DIRNAME.glob('*.jpg')))\n num_target = len(self.iam_dataset.line_regions_by_id)\n if num_actual < num_target - 2: # There are a couple of instances that could not be cropped\n self.process_iam_paragraphs()\n\n self.x, self.y, self.ids = self.load_iam_paragraphs()\n self.train_ind, self.test_ind = get_random_split(self.x.shape[0])\n ids_train, ids_test = self.train_ind, self.test_ind\n self.x_train, self.y_train = self.x[ids_train], self.y[ids_train]\n self.x_test, self.y_test = self.x[ids_test], self.y[ids_test]","title":""},{"docid":"f7893cd93900c09766e0a131fcbc6b56","score":"0.58773375","text":"def init_data():\n f, _ = my_bokeh_utils.string_to_function_parser(f_input.value, ['x', 'y'])\n contour_f.compute_contour_data(f)\n g, _ = my_bokeh_utils.string_to_function_parser(g_input.value, ['x', 'y'])\n contour_g.compute_contour_data(g, isovalue=[0])\n interactor.update_to_user_view()","title":""},{"docid":"3f47ca393031185b349c408a4e6a20b9","score":"0.58720464","text":"def load_data(self) -> None:\n raise NotImplementedError","title":""},{"docid":"9dacafb09f8e025e7eb6be0b536b4ce6","score":"0.5870313","text":"def load_raw_data(self):\n for i in self.dictionary_np_to_ar[self.n_pivot]:\n file_name = (self.folder + '/result' + str(i) + '.txt')\n f = open(file_name)\n data = format_data(f, self.n_pivot)\n self.raw_data[i] = data","title":""},{"docid":"1c198f9f0b9b98de591a2b5a6ea9c494","score":"0.58690315","text":"def get_data(self, contour_path, image_path, index):\n images = []\n contours = []\n # handle `/` missing\n if contour_path[-1] != '/':\n contour_path += '/'\n if image_path[-1] != '/':\n image_path += '/'\n # get contour file\n contour_file = self.get_contour_file(contour_path)\n # Find contour data\n contour_data = pydicom.read_file(contour_path + '/' + contour_file)\n # Get ROI names\n print(get_roi_names(contour_data))\n # get slice orders\n ordered_slices = self.slice_order(image_path)\n print(ordered_slices[:5])\n # get contour dict\n contour_dict = self.get_contour_dict(contour_file, contour_path, image_path, index)\n\n for k, v in ordered_slices:\n # get data from contour dict\n if k in contour_dict:\n images.append(contour_dict[k][0])\n contours.append(contour_dict[k][1])\n # get data from dicom.read_file\n else:\n fpaths = [image_path + f for f in os.listdir(image_path) if '.dcm' in f]\n for fpath in fpaths:\n img = pydicom.read_file(fpath)\n scan_ID = img.SOPInstanceUID\n if k == scan_ID:\n print(\"Got From Dicom\")\n img_arr = img.pixel_array\n contour_arr = np.zeros_like(img_arr)\n images.append(img_arr)\n contours.append(contour_arr)\n break\n else:\n print(\"Wrong File ID.\")\n\n return np.array(images), np.array(contours)","title":""},{"docid":"0fb2505786c0c41d16e47e5e63141e57","score":"0.5867914","text":"def load_data_files(label, data_files):\n data_x = np.empty((0, NUM_FEATURES))\n data_y = np.empty((0))\n\n for filename in data_files:\n try:\n # data = np.loadtxt(BytesIO(zipped_dataset.read(filename)))\n data = np.loadtxt(filename)\n print('... file {0}'.format(filename))\n x, y = process_dataset_file(data, label)\n data_x = np.vstack((data_x, x))\n data_y = np.concatenate([data_y, y])\n # print(\"Data's shape yet: \", data_x.shape())\n except KeyError:\n print('ERROR: Did not find {0} in zip file'.format(filename))\n return data_x, data_y","title":""},{"docid":"3ef1ea225fe60f8614de107081cbb54f","score":"0.586448","text":"def load_dataset(self):\n\n\t\t# Paths to the training and testing data for STL-10 dataset.\n\t\tpath = './dataset/stl-10'\n\t\ttrain_data_path = os.path.join(path, 'train_X.bin')\n\t\ttrain_label_path = os.path.join(path, 'train_y.bin')\n\t\ttest_data_path = os.path.join(path, 'test_X.bin')\n\t\ttest_label_path = os.path.join(path, 'test_y.bin')\n\n\t\t# Read the training data images and thier labels from the disk.\n\t\tself.train_data = self.read_images(train_data_path)\n\t\tself.train_labels = self.read_labels(train_label_path)\n\n\t\t# Read the test data images and thier labels from the disk.\n\t\tself.test_data = self.read_images(test_data_path)\n\t\tself.test_labels = self.read_labels(test_label_path)\n\n\t\treturn","title":""},{"docid":"a5d23d3fd8c1c48eff2b95e0bed5ce17","score":"0.58631","text":"def __load_data(self, dirname):\n #dirname = \"cifar-10-batches-py\"\n origin = \"http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\"\n path = get_file(dirname, origin=origin, untar=True)\n\n nb_train_samples = 50000\n\n X_train = np.zeros((nb_train_samples, 3, 32, 32), dtype=\"uint8\")\n y_train = np.zeros((nb_train_samples,), dtype=\"uint8\")\n\n for i in range(1, 6):\n fpath = os.path.join(path, 'data_batch_' + str(i))\n data, labels = load_batch(fpath)\n X_train[(i - 1) * 10000: i * 10000, :, :, :] = data\n y_train[(i - 1) * 10000: i * 10000] = labels\n\n fpath = os.path.join(path, 'test_batch')\n X_test, y_test = load_batch(fpath)\n\n y_train = np.reshape(y_train, (len(y_train), 1))\n y_test = np.reshape(y_test, (len(y_test), 1))\n\n if K.image_data_format() == 'channels_last':\n X_train = X_train.transpose(0, 2, 3, 1)\n X_test = X_test.transpose(0, 2, 3, 1)\n\n return (X_train, y_train), (X_test, y_test)","title":""},{"docid":"da0d95df0c52bc86033c5de18b70304c","score":"0.58627766","text":"def get_data(download=True, unzip=True, load=True, loadupto=6000):\n\n from os import path, makedirs, getcwd, listdir\n from zipfile import ZipFile\n\n filename = 'cocodataset'\n zdirname = path.join(getcwd(), filename + '.zip')\n fdirname = path.join(getcwd(), filename)\n\n # Download data\n if download is True:\n if path.exists(zdirname):\n print('Ignored download: Cannot download file that already exists!')\n else:\n url = 'http://images.cocodataset.org/zips/val2017.zip'\n filename = 'cocodataset'\n urlretrieve(url, zdirname)\n print('* File downloaded')\n\n # Unzip data\n if unzip is True:\n if path.exists(fdirname):\n print('Ignored unzip: Cannot unzip file that already exists!')\n else:\n dirname = path.join(getcwd(), filename)\n makedirs(dirname, exist_ok=True)\n with ZipFile(filename + '.zip', 'r') as zipfile:\n zipfile.extractall(fdirname)\n print('* File unzipped')\n\n # Load data\n if load is True:\n images = []\n shapes = []\n i = 1\n for fdir in listdir(fdirname):\n for file in listdir(path.join(fdirname, fdir)):\n fpath = path.join(*[fdirname, fdir, file])\n image = Image.open(fpath)\n image = np.array(image) # Convert to numpy array\n shape = np.array(image.shape)\n if len(shape) < 3: # Skip black and white images\n continue\n shapes.append(shape)\n images.append(image)\n if i >= loadupto:\n break\n i += 1\n\n # Trim all images to a the smallest size\n shapes = np.array(shapes)\n min_shape = np.amin(shapes, axis=0)\n min_shape = (124, 124, 3)\n data_imgs = np.array([image[: min_shape[0], : min_shape[1], : min_shape[2]] for image in images])\n print('* Loaded {0} images to numpy array'.format(i))\n\n return data_imgs","title":""},{"docid":"3373dd382cf4dd644d2d7cc0f2ddd59b","score":"0.5853483","text":"def load_data(self, fname):\n self.hyperspec_data = np.zeros((10,10,34))#np.random.rand(10,10,34)\n self.display_image =np.zeros((10,10,34))# np.random.rand(10,10)\n self.spec_x_array = 3*np.arange(34)","title":""},{"docid":"da8773f2f198f47b40ba8eebbbb85961","score":"0.58465314","text":"def _load_base_data(self, scp_fname, labels_fnames, labels_dtype):\n args = self.args\n if is_incremental(scp_fname):\n scps = io.read_lines(scp_fname)\n else:\n scps = [scp_fname]\n data_gens = []\n for scp in scps:\n data_gens.append(DataGenerator(\n MultiLabelTemporalData.from_kaldi,\n scp=scp, alipdfs=labels_fnames, num_pdfs=args.num_classes,\n context=args.context, padding='replicate',\n utt_feats_dict=self.ivectors, labels_dtype=labels_dtype\n ))\n return data_gens","title":""},{"docid":"a4f3047a8fc11c017ed2ba0e252397d7","score":"0.5832999","text":"def _load_data(self, train_dicts=None, dev_dicts=None, test_dicts=None):\n logger.info(\"\\nLoading data into the data silo ...\"\n \"{}\".format(TRACTOR_SMALL))\n\n # dev data\n dev_file = self.processor.data_dir / self.processor.dev_filename\n logger.info(\"Loading dev set from: {}\".format(dev_file))\n self.data[\"dev\"], self.tensor_names = self._get_dataset(dev_file)\n\n # skip the test data\n self.data[\"test\"] = None\n\n if self.caching:\n self._save_dataset_to_cache()\n\n # derive stats and meta data\n self._calculate_statistics()\n\n self._initialize_data_loaders()","title":""},{"docid":"31579b0a33b87140f506abf0c66d3daa","score":"0.58303064","text":"def loadData(tempdir=None, nsamples=100, npoints=100000):\n\n\t# def loadDataHelper(data):\n\t# \treturn data\n\n\tdata = np.zeros((0, npoints))\n\n\tif not tempdir:\n\t\ttempdir = TemporaryDirectory().name\n\t\ttry:\n\t\t\tif platform == \"win32\":\n\t\t\t\tsystem(\"wsl -r -N -c -np -P %s https://physionet.org/files/ptbdb/1.0.0/\" % tempdir)\n\t\t\tif platform == \"darwin\":\n\t\t\t\tsystem(\"wget -r -N -c -np -P %s https://physionet.org/files/ptbdb/1.0.0/\" % tempdir)\n\t\t\telse:\n\t\t\t\tprint(\"I don't know what platform you have... Tell Ethan on Slack.\")\n\t\t\t\treturn None, None\n\t\texcept Exception as e:\n\t\t\tprint(\"[%s]:[%s] %s\" % __name, __func__, e)\n\t\t\tprint(\"Make sure if you are on Windows that you have wsl install. If you are on Mac, please install wget.\")\n\t\t\treturn None, None\n\n\tfor dataloc in getDataFiles(tempdir):\n\t\tif data.shape[0] == nsamples:\n\t\t\tbreak\n\t\trec = rdrecord(dataloc, channels=[0])\n\t\tx = rec.p_signal\n\t\tif x is None:\n\t\t\tx = rec.d_signal\n\t\tif x is None:\n\t\t\tcontinue\n\t\tif len(x) < npoints:\n\t\t\tcontinue\n\t\tx_t = np.transpose(x)\n\t\tdata = np.vstack([data, x_t[:, 0:npoints]])\n\n\treturn data, tempdir","title":""},{"docid":"b38ce6813b44b62c416fd97ac6ace511","score":"0.58130115","text":"def _load_data():\n import Oger as og\n import csv\n now = datetime.now()\n now -= timedelta(microseconds=now.microsecond)\n periods = 500\n o2 = og.datasets.mackey_glass(sample_len=periods,\n n_samples=1,\n seed=1)[0][0].flatten()\n o2 = _minmax_scale(o2, 96, 99)\n timestamp = pd.date_range(now, periods=periods, freq='500L')\n temperature = og.datasets.mackey_glass(sample_len=periods,\n n_samples=1,\n seed=3)[0][0].flatten()\n temperature = _minmax_scale(temperature, 36, 37.5)\n air_flow = og.datasets.mackey_glass(sample_len=periods,\n n_samples=1,\n seed=5)[0][0].flatten()\n air_flow = _minmax_scale(air_flow, 0, 100)\n heart_rate = og.datasets.mackey_glass(sample_len=periods,\n n_samples=1,\n seed=7)[0][0].flatten()\n heart_rate = _minmax_scale(heart_rate, 60, 70)\n data = zip(*[o2, temperature, air_flow, heart_rate])\n data = pd.DataFrame(data, columns=['o2', 'temperature',\n 'air_flow', 'heart_rate'],\n index=timestamp)\n data = data.resample('250L', fill_method='pad')\n last_row = data.xs(data.index[-1])\n last_row.name = data.index[-1] + timedelta(seconds=0.25)\n data = data.append(last_row)\n acc_magn = og.datasets.mackey_glass(sample_len=2 * periods,\n n_samples=1,\n seed=11)[0][0].flatten()\n acc_magn = _minmax_scale(acc_magn, 0, 5)\n data['acc_magn'] = acc_magn\n data = data.resample('10L', fill_method='pad')\n with open('ecg_v1.csv', 'rb') as f:\n reader = csv.reader(f)\n reader.next()\n ecg_v1 = [float(r[0]) for r in reader]\n last_rows = data.iloc[-24:]\n last_rows['index'] = last_rows.index + timedelta(seconds=0.01)\n last_rows.set_index('index', inplace=True)\n data = data.append(last_rows)\n data['ecg_v1'] = ecg_v1\n return data","title":""},{"docid":"fc74a33af253554a3113c55ae006da61","score":"0.58085763","text":"def _get_all_datas(self) -> None:\n\n # init\n self.all_list = {\"train\": [], \"unknown\": [], \"known\": []}\n self.classes = {}\n\n path = Path(self.path)\n\n # directories in [image_path]\n dirs = [d for d in path.glob(\"*\") if d.is_dir()]\n\n # all extensions / all sub directories\n for label_idx, _dir in enumerate(dirs):\n xs = []\n\n for ext in self.extensions:\n tmp = [\n Data(x.as_posix(), label_idx, _dir.name)\n for x in _dir.glob(f\"*.{ext}\")\n if x.is_file()\n ]\n xs.extend(tmp)\n\n # adjust to limit size\n if self.limit_size is not None:\n random.shuffle(xs)\n xs = xs[: self.limit_size]\n\n # split dataset\n train, test = train_test_split(xs, test_size=self.test_size, shuffle=True)\n\n self.all_list[\"train\"].extend(train)\n self.all_list[\"unknown\"].extend(test)\n self.all_list[\"known\"].extend(random.sample(train, len(test)))\n\n self.classes[label_idx] = _dir.name\n\n self.train_size = len(self.all_list[\"train\"])\n self.unknown_size = len(self.all_list[\"unknown\"])\n self.known_size = len(self.all_list[\"known\"])\n\n self.all_size = self.train_size + self.unknown_size","title":""},{"docid":"ceb46d7fcff3d74e2c7dcbbed3fa9794","score":"0.57918715","text":"def load(self, root):\n files = os.listdir(root)\n data_x = []\n data_y = []\n data_traj_x = []\n data_traj_y = []\n seq_continuity = []\n traj_continuity = []\n for i in files:\n # Load file\n tmp = self.read_file(os.path.join(root,i))\n # Remove time-stamp if need be\n tmp = self.remove_ts(tmp)\n # split the input and targets\n tmp_x, tmp_y = self.split_input_output(tmp)\n # generate trajectories\n traj_x, traj_y = self.trajectory_generator(tmp_x, tmp_y)\n # generates sequences for training\n tmp_x, tmp_y, seq_idx = self.sequence_generator(tmp_x, tmp_y)\n # append for concatenation\n data_traj_x.append(traj_x)\n data_traj_y.append(traj_y)\n data_x.append(tmp_x)\n data_y.append(tmp_y)\n seq_continuity.append(seq_idx)\n numpy_traj_x = np.concatenate((data_traj_x), axis=0)\n numpy_traj_y = np.concatenate((data_traj_y), axis=0)\n numpy_data_x = np.concatenate((data_x), axis=0)\n numpy_data_y = np.concatenate((data_y), axis=0)\n numpy_seq_c = np.concatenate((seq_continuity), axis=0)\n return numpy_data_x, numpy_data_y, numpy_traj_x, numpy_traj_y, numpy_seq_c","title":""},{"docid":"3792f2d543748635903f34aa4f80cd07","score":"0.57902795","text":"def populate_data(self):\r\n print('FINISH: Load drone data')\r\n self.tree.delete(*self.tree.get_children())\r\n for drone in self.drones.list():\r\n values = (drone.id, drone.name, drone.class_type, drone.rescue, drone.operator)\r\n self.tree.insert('', 'end', values=values)","title":""},{"docid":"181ef563a94caa35e8eccb2f9ca977f1","score":"0.57892513","text":"def get_data(self, year, month, day, run):\n labrad.cd(year, month, day, run, kind='images')\n images = _dv.dir()[1]\n for image in images:\n _dv.open(image)\n data = list(_dv.get())\n self.d['images'].append(data)\n self.d['sums'].append(sum(data))\n self.d['sterr'].append(_sem(data))\n self._get_params()","title":""},{"docid":"a9f43efef1f6026ca5ca21e7287c7ee1","score":"0.5788692","text":"def load_data(self):\r\n\r\n X, Y = np.empty((self.num_instances, self.img_size, self.img_size, self.num_variables)), \\\r\n np.empty((self.num_instances, self.num_variables))\r\n print(X.shape)\r\n\r\n # Initialize PAA transformer\r\n paa = PiecewiseAggregateApproximation(window_size=None, output_size=self.img_size, overlapping=False)\r\n rp = RecurrencePlot()\r\n\r\n # For all instance\r\n start = time.time()\r\n for idx, row in enumerate(self.data.iterrows()):\r\n for i in range(self.num_variables):\r\n # Get current variable's series\r\n # Apply linear interpolation on missing values\r\n s = row[1][i].interpolate(limit_direction='both').to_numpy()[:self.ts_length]\r\n # Apply PAA and RP\r\n X[idx, :, :, i] = rp.transform(paa.transform(np.expand_dims(s[:-1], axis=0)))[0]\r\n Y[idx, i] = s[-1]\r\n end = time.time()\r\n print(f\"Data loaded in {end - start} seconds\")\r\n\r\n return X, Y","title":""},{"docid":"98e27adf8a16a4a577cd1dcd9faa7043","score":"0.57796514","text":"def _load_data(self):\n m = ROW_END - ROW_START\n n = COL_END - COL_START\n t = self.data.time.size\n\n low_res = []\n for c in LOW_RES_CHANNELS:\n channel_name = f\"C{c:02}\"\n if channel_name in self.data:\n x = self.data[channel_name].data\n else:\n x = np.zeros((t, m, n), dtype=np.float32)\n x[:] = np.nan\n low_res.append(x)\n low_res = np.stack(low_res, axis=1)\n\n med_res = []\n for c in MED_RES_CHANNELS:\n channel_name = f\"C{c:02}\"\n if channel_name in self.data:\n x = self.data[channel_name].data\n else:\n x = np.zeros((t, 2 * m, 2 * n), dtype=np.float32)\n x[:] = np.nan\n med_res.append(x)\n med_res = np.stack(med_res, axis=1)\n\n hi_res = []\n for c in HI_RES_CHANNELS:\n channel_name = f\"C{c:02}\"\n if channel_name in self.data:\n x = self.data[channel_name].data\n else:\n x = np.zeros((t, 4 * m, 4 * n), dtype=np.float32)\n x[:] = np.nan\n hi_res.append(x)\n hi_res = np.stack(hi_res, axis=1)\n\n invalid = np.any(np.all(np.isnan(low_res), axis=1), axis=(-2, -1))\n invalid *= np.any(np.all(np.isnan(med_res), axis=1), axis=(-2, -1))\n invalid *= np.any(np.all(np.isnan(hi_res), axis=1), axis=(-2, -1))\n valid = ~invalid\n low_res = low_res[valid]\n med_res = med_res[valid]\n hi_res = hi_res[valid]\n\n low_res = self.normalizer[0](low_res)\n med_res = self.normalizer[1](med_res)\n hi_res = self.normalizer[2](hi_res)\n\n self.x = (torch.tensor(low_res), torch.tensor(med_res), torch.tensor(hi_res))","title":""},{"docid":"aae977be42c250a4b0c917ff5e27310a","score":"0.5768356","text":"def load(self, root):\n files = os.listdir(root)\n data_x = []\n data_y = []\n data_traj_x = []\n data_traj_y = []\n for i in files:\n # Load file\n tmp = self.read_file(os.path.join(root,i))\n # Remove time-stamp if need be\n tmp = self.remove_ts(tmp)\n # split the input and targets\n tmp_x, tmp_y = self.split_input_output(tmp)\n # generate trajectories\n traj_x, traj_y = self.trajectory_generator(tmp_x, tmp_y)\n # generates sequences for training\n tmp_x, tmp_y = self.sequence_generator(tmp_x, tmp_y)\n # append for concatenation\n data_traj_x.append(traj_x)\n data_traj_y.append(traj_y)\n data_x.append(tmp_x)\n data_y.append(tmp_y)\n # concatenates as a numpy array\n numpy_traj_x = np.concatenate((data_traj_x), axis=0)\n numpy_traj_y = np.concatenate((data_traj_y), axis=0)\n numpy_data_x = np.concatenate((data_x), axis=0)\n numpy_data_y = np.concatenate((data_y), axis=0)\n return numpy_data_x, numpy_data_y, numpy_traj_x, numpy_traj_y","title":""},{"docid":"3bb1a5c5ce86aaad64587bf638dbe90d","score":"0.5767288","text":"def __get_local_data__(self):\n if self.param.data_name in ['djia30', 'webtraffic', 'netflow', 'clockerr']:\n self.raw_data_array['data'] = np.load(os.path.join(self.param.data_path, self.param.data_name, 'series.npy'))\n self.raw_data_array['label'] = np.load(os.path.join(self.param.data_path, self.param.data_name, 'events.npy'))\n\n else:\n raise Exception('no data')","title":""},{"docid":"df6cce4bd2439a921e7ddae19dc9c14d","score":"0.5759458","text":"def dataArr(filename):\r\n #Open the file\r\n f=h5py.File(filename,'r')\r\n \r\n #Initialize the data arrays\r\n cdata=[]\r\n idxset=[]\r\n vertices=[]\r\n \r\n #Open groups in the file\r\n for group in f.keys():\r\n# print('Group- '+group)\r\n \r\n #Get the group\r\n currGroup=f[group]\r\n \r\n #Open keys in the group\r\n for key in currGroup.keys():\r\n# print('Key- '+key)\r\n \r\n #Append the data to the respective arrays\r\n if key=='cdata(Complex)':\r\n cdataGroup=currGroup[key]\r\n \r\n imag=[]\r\n real=[]\r\n #Open the keys in cdata\r\n for subkey in cdataGroup.keys():\r\n# print('Subkey- '+subkey)\r\n \r\n #Get the real and imaginary parts of the array\r\n if subkey=='Imag':\r\n imag=cdataGroup[subkey][()]\r\n elif subkey=='Real':\r\n real=cdataGroup[subkey][()]\r\n \r\n #Convert lists to numpy arrays\r\n imag=np.array(imag)\r\n real=np.array(real)\r\n #Get the cdata value\r\n cdata=real+1j*imag\r\n \r\n elif key=='idxset':\r\n idxset=currGroup[key][()]\r\n elif key=='vertices':\r\n vertices=currGroup[key][()]\r\n \r\n #Remove the z component from the vertices\r\n xVals=[]\r\n yVals=[]\r\n newVertices=[]\r\n for vertex in vertices:\r\n xVals.append(vertex[0])\r\n yVals.append(vertex[1])\r\n newVertices.append([vertex[0],vertex[1]])\r\n vertices=newVertices\r\n \r\n #Convert to numpy arrays\r\n cdata=np.array(cdata)\r\n xVals=np.array(xVals)\r\n yVals=np.array(yVals)\r\n \r\n #Close the file\r\n f.close()\r\n \r\n return cdata, xVals, yVals","title":""},{"docid":"71fdd269f8f8d31cf0393b344b97ff10","score":"0.5757709","text":"def load_data(files,varname,returndata,x,y):\n for i,f in enumerate(files):\n data=swim_io.read_nc(f,varname).data\n returndata[:,y[i],x[i]]=data","title":""},{"docid":"591a196cd3c2413a4db125b1642c64f0","score":"0.5750041","text":"def _data_generation_(self, list_IDs_temp):\n # Initialization\n X = np.empty((self.batch_size, *self.dim))\n # Generate data\n for i, ID in enumerate(list_IDs_temp):\n # Load sample\n try:\n x = np.load(self.data_directory + str(ID) + self.npy)\n try:\n # pre-processing\n X[i,] = self._preprocess_sample_(x)\n\n except (ValueError, KeyError):\n warnings.warn('Shape or key-error {} sample'.format(ID))\n\n except FileNotFoundError:\n warnings.warn(ID, ' not found')\n\n return X, X","title":""},{"docid":"0a4f22eb4f75dbd8eb1e0ad9c96a6bf5","score":"0.57446283","text":"def __load_data(self, delimiter, semantic_mapping):\n # List that contains all the frames of the datasets. Each dataset is a\n # list of frames of shape (num_peds, (frameID, pedID, x and y))\n self.__frames = []\n self.__navigation_map = []\n self.__top_left = []\n self.__semantic_map = []\n self.__homography_matrix = []\n semantic_map_labeled = {}\n homography_map = {}\n\n # Load and add the one hot encoding to the semantic maps\n for i, smap in enumerate(self.__semantic):\n # Load the semantic map\n semantic_map = np.load(smap)\n homography = np.loadtxt(self.__homography[i], delimiter=delimiter)\n filename = os.path.splitext(os.path.basename(smap))[0]\n semantic_map_labeled[filename] = semantic_map\n homography_map[filename] = homography\n\n for i, dataset_path in enumerate(self.__datasets):\n # Load the dataset. Each line is formed by frameID, pedID, x, y\n dataset = np.loadtxt(dataset_path, delimiter=delimiter)\n # Get the frames in dataset\n num_frames = np.unique(dataset[:, 0])\n # Initialize the array of frames for the current dataset\n frames_dataset = []\n # Load the navigation map\n navigation_map = np.load(self.__navigation[i])\n\n # Image has padding so we add padding to the top_left point.\n top_left = [\n np.floor(min(dataset[:, 2]) - self.neighborood_size / 2),\n np.ceil(max(dataset[:, 3]) + self.neighborood_size / 2),\n ]\n\n # For each frame add to frames_dataset the pedestrian that appears\n # in the current frame\n for frame in num_frames:\n # Get the pedestrians\n frame = dataset[dataset[:, 0] == frame, :]\n frames_dataset.append(frame)\n\n self.__frames.append(frames_dataset)\n self.__navigation_map.append(navigation_map)\n self.__top_left.append(top_left)\n self.__semantic_map.append(semantic_map_labeled[semantic_mapping[i]])\n self.__homography_matrix.append(homography_map[semantic_mapping[i]])","title":""},{"docid":"6a470c6efad897bdd2e6e5f87684732d","score":"0.5743957","text":"def load_data(self):\n pass # stub","title":""},{"docid":"6a470c6efad897bdd2e6e5f87684732d","score":"0.5743957","text":"def load_data(self):\n pass # stub","title":""},{"docid":"ac10a8e42dc6d1ecbc7a45164a65708c","score":"0.57406956","text":"def load_data():\n # Clear all of the data structures\n log.info(\"Clearing data structures for full resync\")\n eps_by_host.clear()\n all_groups.clear()\n ips_by_endpointid.clear()\n\n result = client.read('/calico', recursive=True)\n\n # Iterate over all the leaves that we get back. For each leave we get the full path,\n # so we parse that to determine whether to process the key as network or endpoint API data.\n # The goal of this iteration is to get the data into a simple Python data structure,\n # as opposed to the slightly complicated etcd datastructure.\n for res in result.leaves:\n log.debug(\"Processing key %s\", res.key)\n keyparts = res.key.split(\"/\")\n\n try:\n if keyparts[2] == \"network\":\n log.debug(\"Network\")\n process_network_data(res, keyparts)\n elif keyparts[4] == \"workload\":\n log.debug(\"Endpoint\")\n process_endpoint_data(res, keyparts)\n else:\n log.debug(\"Ignoring key %s\", res.key)\n continue\n except IndexError:\n log.debug(\"Ignoring key %s\", res.key)\n continue\n log.info(\"Finished reading data. Database contains %s hosts and %s groups\",\n len(eps_by_host), len(all_groups))","title":""},{"docid":"fd1016a1313c54680eb3daf9417d6a11","score":"0.57384074","text":"def load_dataset(self):\n # for mnist data use the function\n if self.dataset_name == 'mnist':\n self.data_X, self.data_y = load_mnist(self.y_dim, train=True) # load_mnist() imported from utils.py\n self.data_v=self.data_X[:,:,self.v_col] # extract columns of each image\n self.c_dim = self.data_X[0].shape[-1] # self.data_X[0] is first image, .shape[-1] is the dim of each element of the image\n self.v_dim = self.data_v.shape[2]\n print(\"Dim v: \",self.data_v.shape)\n else:\n self.data = glob(os.path.join(\"./data\", self.dataset_name, self.input_fname_pattern))\n imreadImg = imread(self.data[0])\n\n if len(imreadImg.shape) >= 3:\n # check if image is a non-grayscale image by checking channel number\n self.c_dim = imread(self.data[0]).shape[-1]\n else:\n self.c_dim = 1\n\n self.grayscale = (self.c_dim == 1)","title":""},{"docid":"603b66c39825f716ed070c9341ff4bc6","score":"0.5734043","text":"def _load(dir, samples, points, obsolete, debug=False):\n # Initialize storage arrays\n pd_r = N.zeros((samples, points))\n ref_r = N.zeros((samples, points))\n v_r = N.zeros((samples, points))\n i_r = N.zeros((samples, points))\n pd_s = N.zeros((samples, points))\n ref_s = N.zeros((samples, points))\n v_s = N.zeros((samples, points))\n i_s = N.zeros((samples, points))\n \n # Loop over all acquired files\n for i in range(samples):\n if obsolete:\n reference = N.loadtxt(path.join(dir, 'Reference%g.dat' % i),\n delimiter='\\t', skiprows=2)\n signal = N.loadtxt(path.join(dir, 'Signal%g.dat' % i),\n delimiter='\\t', skiprows=2)\n else:\n reference = N.loadtxt(path.join(dir, 'Reference%g.dat' % i),\n delimiter='\\t', usecols=(1,2,3,4))\n signal = N.loadtxt(path.join(dir, 'Signal%g.dat' % i),\n delimiter='\\t', usecols=(1,2,3,4))\n\n pd_r[i,:] = reference[:,0]\n ref_r[i,:] = reference[:,1]\n v_r[i,:] = reference[:,2]\n i_r[i,:] = reference[:,3]\n \n pd_s[i,:] = signal[:,0]\n ref_s[i,:] = signal[:,1]\n v_s[i,:] = signal[:,2]\n i_s[i,:] = signal[:,3]\n \n return (pd_s, pd_r), (ref_s, ref_r)","title":""},{"docid":"0a643e91249dc3c7e6751300e2ecfc35","score":"0.57268494","text":"def load_data(self):\n\n\t\tr = requests.get(RankDrugRev.data_url, verify = False)\n\t\tz = zipfile.ZipFile(io.BytesIO(r.content))\n\t\tdf_train = pd.read_csv(io.BytesIO(z.read(\"drugsComTrain_raw.tsv\")),sep = '\\t', parse_dates = [5])\n\t\tdf_train.rename(columns={\"Unnamed: 0\": \"id\"}, inplace = True)\n\n\t\tdf_test = pd.read_csv(io.BytesIO(z.read(\"drugsComTest_raw.tsv\")),sep = '\\t', parse_dates = [5])\n\t\tdf_test.rename(columns={\"Unnamed: 0\": \"id\"}, inplace = True)\n\t\n\t\tself.raw_data_train = df_train\n\t\tself.raw_data_test = df_test\n\n\t\tself._raw_data_loaded = True","title":""},{"docid":"22fca985869efdbbaf4b86257661055e","score":"0.57258135","text":"def load_data(self):\n\t\tmodels_name = os.path.join(self.data_dir,'Public_Objects.p')\n\t\tmodels_file = open(models_name,'rb')\n\t\tself.models = pickle.load(models_file)\n\t\tmodels_file.close()\n\n\t\tsne_name = os.path.join(self.data_dir,'Public_Novas_Phase.p')\n\t\tsne_file = open(sne_name,'rb')\n\t\tself.novas = pickle.load(sne_file)\n\t\tsne_file.close()","title":""},{"docid":"b102e355a9a66c48e7dd854605171b23","score":"0.57233614","text":"def load_data(self, DATA_DIR):\r\n # class files\r\n self.class_idxtonum = {}\r\n self.class_numtoidx = {}\r\n self.img_clsnum = {}\r\n self.model_clsnum = {}\r\n img_cls_path = DATA_DIR+'/Image_%s.cla' % self.mode.capitalize()\r\n model_cls_path = DATA_DIR+'/Model.cla'\r\n self.read_class_file(img_cls_path, 'img')\r\n self.read_class_file(model_cls_path, 'model')\r\n\r\n # img filenames\r\n self.IMG_DIR = os.path.join(DATA_DIR, 'IMAGES')\r\n class_keys = [x for x in self.class_numtoidx.keys()]\r\n self.img_filenames = []\r\n for key in class_keys:\r\n img_dir = os.path.join(self.IMG_DIR, self.class_numtoidx[key], self.mode.lower())\r\n filenames = [os.path.join(img_dir, x+'.png') for x in self.class_imgs[key]]\r\n filenames = sorted(filenames[:])\r\n if self.shuffle:\r\n shuffle(filenames)\r\n self.img_filenames.append(filenames)\r\n\r\n# # model filenames\r\n# self.MODEL_DIR = os.path.join(DATA_DIR, 'TARGET_MODELS/models')\r\n# self.model_filenames = glob.glob(self.MODEL_DIR+'/*.off') # or png\r\n\r\n # view filenames\r\n self.VIEW_DIR = os.path.join(DATA_DIR, 'VIEWS') #_GRAY_BLACK')\r\n self.view_len = 0\r\n self.view_filenames = {}\r\n for key in class_keys:\r\n view_dir = os.path.join(self.VIEW_DIR, self.class_numtoidx[key])\r\n filenames = glob.glob(view_dir+'/*.png')\r\n filenames = sorted(filenames[:])\r\n if self.shuffle:\r\n shuffle(filenames)\r\n model_name = self.class_numtoidx[key]\r\n self.view_filenames[model_name] = []\r\n self.view_len += self.view_num\r\n step = 1 #len(filenames)//self.view_num\r\n for idx in range(len(filenames)): #self.view_num):#filename in filenames[0:self.view_num]: # maybe view_num == len(filenames)\r\n self.view_filenames[model_name].append(filenames[min(idx*step, len(filenames)-1)].split('/')[-1].rstrip('.png'))\r\n# self.view_len += 1\r\n\r\n # for calculating epoch..\r\n# self.whole_batch_size = self.img_len if (self.train_mode == 'img' or self.mode == 'test') else self.model_len\r\n\r\n self.whole_batch_size = self.model_len if self.train_mode == 'view' else self.img_len","title":""},{"docid":"d7983f22dbaeb857a4600fc7f005c6dc","score":"0.57144225","text":"def load_data(self):\n if hasattr(self, 'xtr'):\n return # has already been run\n\n with h5py.File('./data/Input/train/images_training.h5','r') as H:\n self.xtr = np.copy(H['datatrain'])\n\n with h5py.File('./data/Input/train/labels_training.h5','r') as H:\n self.ytr = np.copy(H['labeltrain'])\n\n with h5py.File('./data/Input/test/labels_testing_2000.h5', 'r') as H:\n self.yte = np.copy(H['labeltest'])\n\n with h5py.File('./data/Input/test/images_testing.h5', 'r') as H:\n self.xte = np.copy(H['datatest'])[:len(self.yte)]","title":""},{"docid":"ea5fef430d34d95494c0e9b0b32fb410","score":"0.5712898","text":"def create_dataset(): \n # load the temperature data from years 1999-2009\n # don't include the 29. feb\n # use the maximum from the 4 values per day (maxtemp)\n years = list(range(1999, 2009))\n longitude = -79.63 + 180\n latitude = 43.68\n grib_folder = '/media/isa/VIS1/temperature/'\n maxtemp_data = np.empty((365, len(years)))\n\n # load the data\n for year_it in range(len(years)):\n f = open(grib_folder + str(years[year_it]) + '.grib')\n time_it = 0\n day_it = 0\n day_data = np.empty((4))\n \n while 1:\n gid = codes_grib_new_from_file(f)\n if (gid is None):\n break\n # check for feb 29\n dataDate = str(codes_get(gid, 'dataDate')).zfill(8)\n if (dataDate[4:6] == '02' and dataDate[6:8] == '29'):\n codes_release(gid)\n print ('skipping ' + dataDate)\n continue\n\n nearest = codes_grib_find_nearest(gid, latitude, longitude)[0]\n \n if (nearest.value == codes_get_double(gid, 'missingValue')):\n raise Warning('missing value!')\n \n day_data[time_it] = nearest.value\n \n time_it = time_it + 1\n if time_it == 4:\n maxtemp_data[day_it, year_it] = day_data.max()\n day_it = day_it + 1\n time_it = 0\n \n codes_release(gid)\n \n f.close()\n \n min_data, ptp_data = maxtemp_data.min(), maxtemp_data.ptp()\n maxtemp_data = (maxtemp_data - min_data) / ptp_data\n \n # x: [year0, year1, ...., year9]\n # y: the next day, ie year9 + 1 day\n # get x data: x is all samples but the last (it wouldn't have an corresponding y)\n data_x = maxtemp_data[:-1, :]\n # get y data: y is the next day\n data_y = np.empty((364))\n for d in range(364):\n data_y[d] = maxtemp_data[d + 1, -1]\n\n train_x, test_x, train_y, test_y = train_test_split(data_x, data_y, test_size=0.2)\n \n dataset = AttrDict()\n dataset.train_x = train_x\n dataset.test_x = test_x\n dataset.train_y = train_y\n dataset.test_y = test_y\n dataset.min_data = min_data\n dataset.ptp_data = ptp_data\n return dataset","title":""},{"docid":"03a48649c1008eec000154e0fa10ab03","score":"0.571136","text":"def extractImageData(self): \n if len(self.id_img_train) == 0 or len(self.id_img_test) == 0 :\n self.loadBasicData()\n\n if len(self.X_img_train) == 0:\n self.X_img_train=self.extractImagesCaracteristics(self.id_img_train).to_numpy()\n\n if len(self.X_img_test) == 0:\n self.X_img_test=self.extractImagesCaracteristics(self.id_img_test).to_numpy()","title":""},{"docid":"05efaa37c305e833a3dbc863a3c9785f","score":"0.5711356","text":"def readAllData(self):\n\n # Read basic data parameters (number of energy groups, assemblies, axial nodes, etc.)\n self.read1D()\n\n # Read the hex ordering map between DIF3D \"four color\" nodal and DIF3D GEODST\n # Also read index pointers to incoming partial currents on outer reactor surface\n # (these don't belong to any assembly)\n # Incoming partial currents are non-zero due to flux extrapolation\n ng = self.fc[\"ngroup\"] # number of energy groups\n imax = self.fc[\n \"ninti\"\n ] # number of triangular mesh cells in \"i\" direction (rhombus or rectangle cells)\n jmax = self.fc[\n \"nintj\"\n ] # number of triangular mesh cells in \"j\" direction (rhombus or rectangle cells)\n zmax = self.fc[\n \"nintk\"\n ] # number of axial nodes (same for each assembly in DIF3D)\n\n self.triangleFluxes = numpy.zeros((imax, jmax, zmax, ng))\n\n for g in range(ng): # loop through energy groups\n\n gEff = self.getEnergyGroupIndex(g)\n\n for z in range(zmax): # loop through axial nodes\n self.triangleFluxes[\n :, :, z, gEff\n ] = self.readTriangleFluxes() # read fluxes on this i-j plane\n\n self.f.close()","title":""},{"docid":"622e5f7b457db2100d5619d8206ba1fc","score":"0.5710531","text":"def loadNext(self, files, c):\n\n # If I have already retrieved all data, return None\n if self.counters[c] == len(files):\n self.counters[c] += 1\n return None\n # Next time, it will start over\n elif self.counters[c] > len(files):\n self.counters[c] = 0\n\n target = files[self.counters[c]]\n study, timepoint, subject = target.split(\"/\")[-4:-1]\n id_ = study + \"_\" + timepoint + \"_\" + subject\n\n # This if controls that the behavior is different when the container\n # used when loadInMemory is not empty.\n # TODO:\n # - Put in a single container all data loaded.\n # - Read X, process Y, pass to the child class.\n # - Create child class to get X,Y,ids and return everything processed.\n try:\n X_train = self.X_container[id_][\"in_volume\"]\n Y_train = self.Y_container[id_][\"out_segmentation\"]\n except:\n # Read the actual data\n X_train = nib.load(target+\"scan.nii.gz\").get_data()\n\n X_train = np.moveaxis(X_train, -1, 0)\n X_train = np.expand_dims(X_train, axis=0)\n\n if c == 1:\n ext = \"_lesion\"\n else:\n ext = self.ext\n if os.path.isfile(target+\"scan\"+ext+\".nii.gz\"):\n Y_train = nib.load(target+\"scan\"+ext+\".nii.gz\").get_data()\n #Y_train = np.expand_dims(Y_train, -1)\n Y_train = np.stack([1.0*(Y_train==j) for j in range(2)], axis=0)\n else:\n Y_train = np.ones([2] + list(X_train.shape[2:]))\n Y_train[1,:,:,:] = 0\n #print(Y_train.shape)\n\n #Y_train = np.moveaxis(Y_train, -1, 0)\n Y_train = np.expand_dims(Y_train, 0)\n\n self.counters[c] += 1\n\n # The ID must be a list, so that I can later iterate over it\n #return X_train, Y_train, [id_]\n return X_train, Y_train, [id_]","title":""},{"docid":"68d324729d695a57de8de489891b6847","score":"0.5709875","text":"def readSampledGEOS(self):\n col = 'aer_Nv'\n if self.verbose: \n print 'opening file',self.LBinFile.replace('%col',col)\n nc = Dataset(self.LBinFile.replace('%col',col))\n\n print 'File opened'\n\n for sds in self.SDS_AER:\n sds_ = sds\n if sds in ncALIAS:\n sds_ = ncALIAS[sds]\n if self.verbose: \n print 'Reading ',sds_ \n var = nc.variables[sds_][:]\n self.__dict__[sds] = var\n if self.verbose: \n print 'Finished Reading ',sds_\n\n nc.close()\n\n if self.verbose: \n print 'opening file',self.LCinFile\n nc = Dataset(self.LCinFile)\n\n for sds in self.SDS_CLD:\n sds_ = sds\n if sds in ncALIAS:\n sds_ = ncALIAS[sds]\n var = nc.variables[sds_][:]\n self.__dict__[sds] = var\n if self.verbose: \n print 'Finished Reading',sds_\n\n nc.close()","title":""}],"string":"[\n {\n \"docid\": \"05efec025234f1f56eeac96532b96fa5\",\n \"score\": \"0.73711884\",\n \"text\": \"def load_dataset(self, contour): \\n self.dataset = []\\n for patient_id in self.link_dict.keys():\\n self.dataset += self.get_patient_data(patient_id, contour)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8ebb063f524c146e945c3c6c4ceea62\",\n \"score\": \"0.70130897\",\n \"text\": \"def loadData(self):\\n for entry in self._dataToLoad:\\n self._loadFile( entry, self.data, False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"087dbd3f3dda6a974036acd1b4fa6a24\",\n \"score\": \"0.66223025\",\n \"text\": \"def loadData(self):\\n for ptype,UIname,dec_factor in list(zip(self.ptypes,self.UInames,self.dec_factors)):\\n print(\\\"Loading ptype %s\\\"%ptype)\\n snapdict = openSnapshot(\\n self.snapdir,\\n self.snapnum,\\n int(ptype[-1]), ## ptype should be PartType4,etc...\\n keys_to_extract = ['Coordinates']+self.returnKeys\\n )\\n\\n tracked_names,tracked_arrays,tracked_filter_flags,tracked_colormap_flags = [],[],[],[]\\n for returnKey,filterFlag,colormapFlag,doMag,doLog in list(zip(\\n self.returnKeys,self.filterFlags,self.colormapFlags,self.doMags,self.doLogs)):\\n if returnKey in snapdict:\\n arr = snapdict[returnKey]\\n if doLog:\\n arr = np.log10(arr)\\n returnKey = 'log10%s'%returnKey\\n elif doMag:\\n arr = np.linalg.norm(arr,axis=1)\\n returnKey = 'mag%s'%returnKey\\n\\n tracked_names += [returnKey]\\n tracked_filter_flags += [filterFlag]\\n tracked_colormap_flags += [colormapFlag]\\n tracked_arrays+= [arr]\\n \\n self.particleGroups += [ParticleGroup(\\n UIname,\\n snapdict['Coordinates'],\\n tracked_names = tracked_names,\\n tracked_arrays = tracked_arrays,\\n decimation_factor = dec_factor,\\n tracked_filter_flags = tracked_filter_flags,\\n tracked_colormap_flags = tracked_colormap_flags\\n )]\\n\\n ## save the filenames that were opened (so you can re-open them yourself in that order)\\n self.particleGroups[-1].filenames_opened = snapdict['fnames']\\n\\n ## add this particle group to the reader's options file\\n self.options.addToOptions(self.particleGroups[-1])\\n\\n return self.particleGroups\",\n \"title\": \"\"\n },\n {\n \"docid\": \"071745d074b8ddc1f39b55c08be53c80\",\n \"score\": \"0.6580832\",\n \"text\": \"def load_data(self):\\n print \\\"Combining all data\\\"\\n\\n # Set up the output WCS object\\n output_wcs = WCS(naxis=2)\\n output_wcs.wcs.crpix = [self.output_naxis1 / 2.,\\n self.output_naxis2 / 2.]\\n output_wcs.wcs.crval = [self.output_center_ra, self.output_center_dec]\\n output_wcs.wcs.cdelt = np.array([-self.output_pixel_scale,\\n self.output_pixel_scale]) / 3600.\\n output_wcs.wcs.ctype = ['RA---TAN', 'DEC--TAN']\\n self.output_wcs = output_wcs\\n\\n # Load all of the data.\\n # Note that we convert all of our ra and dec values into x and y\\n # coordinates in arcseconds relative to (0., 0.). We flip the x-axis so\\n # that our coordinates increase from left to right (or East to West).\\n # TODO: We use double the memory that we need to currently. This should\\n # be optimized to query all of the files for how many points they have,\\n # and then load them one by one into preallocated arrays.\\n self.ref_vals = np.hstack([i.vals for i in self.ref_images])\\n self.ref_errs = np.hstack([i.errs for i in self.ref_images])\\n self.ref_psfs = np.hstack([i.psfs for i in self.ref_images])\\n self.ref_xs = np.hstack([i.ras for i in self.ref_images])\\n self.ref_xs *= -np.cos(self.output_center_dec * np.pi / 180.) * 3600.\\n self.ref_ys = np.hstack([i.decs for i in self.ref_images])\\n self.ref_ys *= 3600.\\n\\n self.new_vals = np.hstack([i.vals for i in self.new_images])\\n self.new_errs = np.hstack([i.errs for i in self.new_images])\\n self.new_psfs = np.hstack([i.psfs for i in self.new_images])\\n self.new_xs = np.hstack([i.ras for i in self.new_images])\\n self.new_xs *= -np.cos(self.output_center_dec * np.pi / 180.) * 3600.\\n self.new_ys = np.hstack([i.decs for i in self.new_images])\\n self.new_ys *= 3600.\\n\\n # Generate KDTrees.\\n # TODO: Try to use a single KDTree for both? Profile this, and if the\\n # KDTree is limiting then that might be a good idea.\\n print \\\"Generating KDTrees\\\"\\n self.ref_kdtree = cKDTree(zip(self.ref_xs, self.ref_ys))\\n self.new_kdtree = cKDTree(zip(self.new_xs, self.new_ys))\\n\\n # Load the bright objects\\n self.bright_obj_xs = (-np.cos(self.output_center_dec * np.pi / 180.) *\\n 3600. * self.bright_obj_ras)\\n self.bright_obj_ys = self.bright_obj_decs * 3600.\\n bright_obj_fluxes = [self._calc_value(x, y, radius=0.3, is_star=True)\\n for x, y in zip(self.bright_obj_xs,\\n self.bright_obj_ys)]\\n self.bright_obj_fluxes = np.array(bright_obj_fluxes)\\n\\n print \\\"Done loading data\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"927ac16f42cfa6fb9e640d2776802daa\",\n \"score\": \"0.6545536\",\n \"text\": \"def loadData(self):\\n self.getXrfDetector()\\n self.getXrdDetector()\\n self.getXrdDetectorMask()\\n self.getBeamMonitor()\\n self.getScanGeometry()\\n with h5py.File(self.dataPath, 'r') as h5In:\\n for scan in self.scans:\\n self.dataUrls.append(h5In.get('%s/measurement/%s' % (scan, self.xrddetector), getlink=True).path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06e06c108fdd6b805f7289d95958192a\",\n \"score\": \"0.6476002\",\n \"text\": \"def load_all_data(self):\\n images = []\\n measurements = []\\n lines = self.__read_csv__(self.csv_file_path)\\n for line in lines:\\n file_name = line[0][0].split('/')[-1]\\n imageFolder = line[0][0].split('/')[-2]\\n csv_split_path = self.csv_file_path.split('/')\\n # path format '../data/IMG/filename'\\n center_image_path = csv_split_path[0] + '/' + csv_split_path[1] + '/' + imageFolder + '/' + file_name\\n image = cv2.imread(center_image_path)\\n images.append(image)\\n measurement = float(line[3])\\n measurements.append(measurement)\\n\\n X_train = np.array(images, dtype=float)\\n y_train = np.array(measurements, dtype=float)\\n\\n return X_train, y_train\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15f210a9643fdd72cef75f1d784f1ee3\",\n \"score\": \"0.6451589\",\n \"text\": \"def load_data(self):\\n try:\\n import trimesh\\n except ModuleNotFoundError:\\n print(\\\"module trimesh not found. install using 'pip install trimesh' command\\\")\\n\\n if self.datadir is None:\\n\\n self.__load_modelnet()\\n\\n else:\\n\\n self.__load_custom_datafiles(self.datadir)\\n\\n data_voxels = []\\n\\n for file in tqdm(self.data_files, desc=\\\"rendering data\\\"):\\n\\n mesh = trimesh.load(file)\\n voxel = mesh.voxelized(0.5)\\n (x, y, z) = map(float, voxel.shape)\\n zoom_fac = (self.side_length / x, self.side_length / y, self.side_length / z)\\n voxel = ndimage.zoom(voxel.matrix, zoom_fac, order=1, mode=\\\"nearest\\\")\\n data_voxels.append(voxel)\\n\\n data_voxels = np.array(data_voxels)\\n data_voxels = data_voxels.reshape(\\n (-1, self.side_length, self.side_length, self.side_length, 1)\\n )\\n return data_voxels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d2b2959bab306a7bab55c7bbf760f57\",\n \"score\": \"0.64324296\",\n \"text\": \"def create_data_points(dicom_dir, contour_dir, link_file):\\n if not os.path.isdir(dicom_dir):\\n logger.error('Dicom directory not found at: ' + dicom_dir)\\n return\\n\\n if not os.path.isdir(contour_dir):\\n logger.error('Contour directory not found at: ' + contour_dir)\\n return\\n\\n if not os.path.isfile(link_file):\\n logger.error('Link file not found at: ' + link_file)\\n return\\n\\n result = []\\n\\n link_data = pd.read_csv(link_file)\\n for index, row in link_data.iterrows():\\n\\n current_dicom_dir = os.path.join(dicom_dir, row['patient_id'])\\n if not os.path.isdir(current_dicom_dir):\\n logger.info('Skipping the data point, No dicom dir found at:' + current_dicom_dir)\\n\\n current_contour_dir = os.path.join(os.path.join(contour_dir, row['original_id']), 'i-contours')\\n # print single_contour_dir\\n if not os.path.isdir(current_contour_dir):\\n logger.info('Skipping the data point, No contour dir found at: ' + current_contour_dir)\\n\\n for file_name in os.listdir(current_dicom_dir):\\n if not file_name.endswith('.dcm'):\\n continue\\n\\n dcm_file_number = file_name.replace('.dcm', '')\\n contour_file_name = 'IM-0001-' + dcm_file_number.zfill(4) + '-icontour-manual.txt'\\n\\n full_dicom_file_path = os.path.join(current_dicom_dir, file_name)\\n full_contour_file_path = os.path.join(current_contour_dir, contour_file_name)\\n\\n if not os.path.isfile(full_contour_file_path):\\n logger.info('no contour file found against: ' + full_dicom_file_path)\\n continue\\n\\n result.append(DataPoint(full_dicom_file_path, full_contour_file_path))\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3912007c4d6599c6abcc0ca015a0c8f\",\n \"score\": \"0.64228433\",\n \"text\": \"def run(self):\\n\\n self.set_extents()\\n self.load_basin_shapefiles()\\n self.load_dem()\\n self.load_vegetation()\\n self.create_netcdf()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"293e5c67f88a124de2fc0c833223c501\",\n \"score\": \"0.63532233\",\n \"text\": \"def load(self):\\n self.data = GEX.parse(self.path).data_df\\n\\n ##Dealing with cmapPy data type instability:\\n rows = list(map(lambda x: x[2:-1], list(self.data.index)))\\n self.data.index = rows\\n columns = list(map(lambda x: x[2:-1], list(self.data)))\\n self.data.columns = columns\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e64994bdf00f0946aaac87dbcbb7a095\",\n \"score\": \"0.62415665\",\n \"text\": \"def getData(self, roi_w, roi_h):\\n file_manager = FileHandler()\\n files_and_points = file_manager.pointsDictionary() # complete list of images and points\\n\\n first_img_name = next(iter(files_and_points.keys())) # first image name\\n\\n # getting the current parameters of the first image in the dictionary red from the path stored.\\n # lines, samples, bands = get_parameters(files_and_points[first_img_name][0][4])\\n lines, samples, bands = get_parameters(file_manager.get_img_path(first_img_name))\\n\\n cnt_imgs = -1\\n\\n if (roi_w is 0) or (roi_h is 0):\\n full_image = True\\n n_full_imgs = len(files_and_points)\\n dataset = np.zeros([n_full_imgs, lines, samples, bands])\\n dataset_ann = np.zeros([n_full_imgs, 1])\\n printProgressBar(0, n_full_imgs, prefix='Progress:', suffix='Complete', length=50)\\n else:\\n full_image = False\\n n_points = len(FileHandler.listSelectedPoints())\\n dataset = np.zeros([n_points, roi_w, roi_h, bands])\\n dataset_ann = np.zeros([n_points, 1])\\n\\n # iteration over each image which has associated N selected points in data\\n for img_name, data_points in files_and_points.items():\\n # print(img_name, \\\" \\\", data)\\n\\n # checking specific parameters of the current image\\n params = get_parameters(file_manager.get_img_path(img_name))\\n if params is not None:\\n # retrieving data from parameters\\n lines, samples, bands = params\\n\\n img_layers = np.zeros([lines, samples, bands])\\n\\n # reading all the wavelengths\\n for i in range(0, bands):\\n file_path_complete = file_manager.get_img_path(img_name) # with datacube extension\\n img_layers[:, :, i] = ReadBilFile(file_path_complete, i + 1)\\n\\n if full_image:\\n cnt_imgs = cnt_imgs + 1\\n dataset[cnt_imgs] = img_layers\\n for point in data_points:\\n # checking if there is at least one ROI with \\\"Anomaly\\\" annotation\\n dataset_ann[cnt_imgs] = True if point[2] == p_anomaly else None\\n\\n else:\\n for point in data_points:\\n cnt_imgs = cnt_imgs + 1\\n # lines : number of row of the image\\n # samples: number of columns of the image\\n x1, y1, x2, y2 = DatasetGenerator.check_limits(int(point[0]), int(point[1]),\\n roi_w, roi_h, samples, lines)\\n dataset[cnt_imgs] = img_layers[y1:y2, x1:x2, :]\\n dataset_ann[cnt_imgs] = True if point[2] == p_anomaly else False\\n\\n # Progress report\\n if full_image:\\n printProgressBar(cnt_imgs + 1, n_full_imgs, prefix='Progress:', suffix='Complete', length=50)\\n # print(cnt_imgs)\\n\\n # print(dataset.shape)\\n # print(dataset.max())\\n\\n with h5py.File(local_path_out + 'datacube_dataset.hdf5', 'w') as f:\\n # datacube_images optimized for 2 bytes integer\\n f.create_dataset('datacube_images', data=dataset, dtype='i2', compression=\\\"gzip\\\", compression_opts=9)\\n f.create_dataset('datacube_annotations', data=dataset_ann, dtype='i1', compression=\\\"gzip\\\",\\n compression_opts=9)\\n\\n # TODO raise an exception if there is any problem with the file or the number of bands read\\n\\n # return img_layers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7313cf85a315d125a2dd59570c290a1\",\n \"score\": \"0.62341446\",\n \"text\": \"def setData():\\n global x_data\\n global y_data\\n global fCoefficients\\n global fPrimeCoefficients\\n x_path = join('..','Data','CrimeXData.bin')\\n y_path = join('..','Data','CrimeYData.bin')\\n c_path = join('..','Data','Coefficients.bin')\\n cp_path = join('..','Data','FPrimeCoefficients.bin')\\n with open(c_path,'rb') as file:\\n fCoefficients = pickle.load(file)\\n with open(cp_path,'rb') as file:\\n fPrimeCoefficients = pickle.load(file)\\n with open(x_path,'rb') as file:\\n x_data = pickle.load(file)\\n with open(y_path,'rb') as file:\\n y_data = pickle.load(file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b94873e5a20516992d978f08d05a7277\",\n \"score\": \"0.6206832\",\n \"text\": \"def load_all_calib_data():\\n\\n return\\n\\n #DANGEROUS below here!!!\\n calib_directories = glob.glob('/Volumes/HD3/strat_calib_data/*')\\n\\n for d in calib_directories:\\n calib_id = int(d.split('/')[-1])\\n filename = os.path.join(d, 'calib.csv')\\n if os.path.exists(filename):\\n print \\\"Importing %d/calib.csv\\\" % calib_id\\n load_calib_data(filename, calib_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb1d1acdad0a8622852d85981bf3ca7a\",\n \"score\": \"0.61924255\",\n \"text\": \"def importDataset(self, verbose = False):\\n # Get a list of the various txt files\\n listOfPositionFiles = [] # actual cartesian position\\n listOfForceFiles = [] # sensor forces\\n listOfInputFiles = [] # f_ref \\\"forces\\\"\\n\\n full_positions = np.empty((1,3))\\n full_forces = np.empty((1,3))\\n full_inputs = np.empty((1,3))\\n\\n for (dirpath, dirnames, filenames) in os.walk(self.base_path):\\n dirnames.sort()\\n\\n for txtname in fnmatch.filter(filenames, 'positions.txt'):\\n listOfPositionFiles.append(os.path.join(dirpath, txtname))\\n\\n for txtname in fnmatch.filter(filenames, 'forces.txt'):\\n listOfForceFiles.append(os.path.join(dirpath, txtname))\\n\\n for txtname in fnmatch.filter(filenames, 'f_ref.txt'):\\n listOfInputFiles.append(os.path.join(dirpath, txtname))\\n\\n print(\\\"Retrieved %d position files, %d force files and %d input files from base directory '%s' \\\"\\n %(len(listOfPositionFiles), len(listOfForceFiles), len(listOfInputFiles), self.base_path))\\n\\n # Read the appropriate columns and parse them into np arrays\\n for file_idx, filename in enumerate(listOfForceFiles):\\n time, seq, f_x, f_y, f_z, t_x, t_y, t_z = np.loadtxt(filename, usecols = (0, 1, 4, 5, 6, 7, 8, 9), skiprows = 1, delimiter = ',', unpack = True)\\n f_x = f_x.reshape(-1,1)\\n f_y = f_y.reshape(-1,1)\\n f_z = f_z.reshape(-1,1)\\n forces = np.concatenate((f_x, f_y, f_z), axis = 1)\\n self.force_data.update({file_idx : forces})\\n full_forces = np.append(full_forces, forces, axis = 0)\\n\\n full_forces = full_forces[1:,] # remove the first \\\"trash\\\" line that was created with np.empty\\n\\n for file_idx, filename in enumerate(listOfPositionFiles):\\n time, seq, p_x, p_y, p_z = np.loadtxt(filename, usecols = (0, 1, 4, 5, 6), skiprows = 1, delimiter = ',', unpack = True)\\n p_x = p_x.reshape(-1,1)\\n p_y = p_y.reshape(-1,1)\\n p_z = p_z.reshape(-1,1)\\n positions = np.concatenate((p_x, p_y, p_z), axis = 1)\\n self.position_data.update({file_idx : positions})\\n full_positions = np.append(full_positions, positions, axis = 0)\\n\\n full_positions = full_positions[1:,] # remove the first \\\"trash\\\" line that was created with np.empty\\n\\n for file_idx, filename in enumerate(listOfInputFiles):\\n time, seq, f_ref_x, f_ref_y, f_ref_z = np.loadtxt(filename, usecols = (0, 1, 4, 5, 6), skiprows = 1, delimiter = ',', unpack = True)\\n f_ref_x = f_ref_x.reshape(-1,1)\\n f_ref_y = f_ref_y.reshape(-1,1)\\n f_ref_z = f_ref_z.reshape(-1,1)\\n inputs = np.concatenate((f_ref_x, f_ref_y, f_ref_z), axis = 1)\\n self.input_data.update({file_idx : inputs})\\n full_inputs = np.append(full_inputs, inputs, axis = 0)\\n\\n full_inputs = full_inputs[1:,] # remove the first \\\"trash\\\" line that was created with np.empty\\n\\n faulty_files = 0\\n for i in range(len(self.position_data)):\\n if len(self.position_data[i]) == len(self.force_data[i]) and len(self.force_data[i]) == len(self.input_data[i]):\\n if verbose:\\n print(i, self.position_data[i].shape, self.force_data[i].shape, self.input_data[i].shape)\\n else:\\n faulty_files += 1\\n print(\\\"In dataset %s there is a size mismatch. Pos : %d F_s %d F_ref %d \\\" %(listOfPositionFiles[i], self.position_data[i].shape[0], self.force_data[i].shape[0], self.input_data[i].shape[0]))\\n print(\\\"%d files need to be fixed\\\" %faulty_files)\\n full = np.concatenate((full_positions, full_forces, full_inputs), axis = 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7346c908d80bf8039426e209159cf460\",\n \"score\": \"0.61921275\",\n \"text\": \"def load_data(self):\\n\\t data_samples, labels, htsoft = extract_imagedata(normalization=0)\\n\\t aug_data = data_samples.reshape((data_samples.shape[0], data_samples.shape[1], data_samples.shape[2], 1))\\n\\t total_batches = np.floor(data_samples.shape[0]/self.config.batch_size)\\n\\t num_train = np.floor(self.config.frac_train*total_batches) * self.config.batch_size\\n\\t num_train = int(num_train)\\n\\t self.X_train = aug_data[:num_train, :, :, :]\\n\\t self.Y_train = labels[:num_train, :]\\n\\t self.htsoft_train = htsoft[:, :num_train]\\n\\t self.X_test = aug_data[num_train:int(total_batches*self.config.batch_size), :, :, :]\\n\\t self.htsoft_test = htsoft[:, num_train:int(total_batches*self.config.batch_size)]\\n\\t self.Y_test = labels[num_train:int(total_batches*self.config.batch_size) , :]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"497da0735cf8d790b0965a3b96e4b397\",\n \"score\": \"0.61912876\",\n \"text\": \"def _load_data(self, data):\\n if not isinstance(data, list):\\n self.z = [data.T] # creates a list of length 1\\n else:\\n n = len(data)\\n for snip in range(n):\\n if snip == 0:\\n self.z = [data[snip].T]\\n else:\\n self.z.append(data[snip].T) # time is a function of the columns, internally\\n\\n self.N = self.z[0].shape[0] # will be 8, the number of features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2432e8a2f8405d4035d7b809c7e4043f\",\n \"score\": \"0.6178262\",\n \"text\": \"def load_data(self) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ca6a59ceac654e02f312b1642565d3a\",\n \"score\": \"0.6176496\",\n \"text\": \"def load_dataset(self, paths, files):\\n patch_size = (self.patch_size, self.patch_size, 1)\\n for file in files:\\n # Load images in (H, W, C) format.\\n img_s = np.expand_dims(io.imread(os.path.join(paths['data'], file)), -1)\\n img_c = np.expand_dims(io.imread(os.path.join(paths['label'], file)), -1)\\n\\n # Convert to range [0, 1] in float32\\n img_s = (img_s / 255.).astype('float32')\\n img_c = (img_c / 255.).astype('float32')\\n\\n if self.augment_data:\\n img_c, img_s = data_augmentation(img_c), data_augmentation(img_s)\\n else:\\n img_c, img_s = [img_c], [img_s]\\n\\n for c, s in zip(img_c, img_s):\\n c_patches = create_patches(c, patch_size, step=self.patch_size)\\n s_patches = create_patches(s, patch_size, step=self.patch_size)\\n\\n for data, label in zip(s_patches, c_patches):\\n data = torch.from_numpy(data.transpose((2, 0, 1)))\\n label = torch.from_numpy(label.transpose((2, 0, 1)))\\n self.dataset['data'].append(data)\\n self.dataset['label'].append(label)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8f87b9ed8fe95f15734b58ae277bda3\",\n \"score\": \"0.6173269\",\n \"text\": \"def _load_data(self):\\n load_data = self.load_reader.vals4slice(de_load, self.start, data_max_date, step=1)\\n date_range = pd.date_range(self.start, data_max_date, freq='1H')\\n self.load_data = pd.DataFrame(data={'load': load_data}, index=date_range)\\n self.ex_data = pd.DataFrame(data=[], index=date_range)\\n \\n if self.exog is None:\\n return\\n for var_name in self.exog:\\n self._get_exog(var_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9397379b816d245aa27748f2de562a12\",\n \"score\": \"0.6172759\",\n \"text\": \"def collect_data(self):\\n if self.debug:\\n print \\\"Collecting point cloud data\\\"\\n try:\\n os.mkdir(self.base_dir)\\n os.chdir(self.base_dir)\\n except OSError:\\n os.chdir(self.base_dir)\\n os.system(\\\"rm *.pcd\\\")\\n os.system(\\\"kill $(pgrep XnSensorServer)\\\") #this is needed because the process from previous records does not die\\n command = \\\"pcl_openni_recorder\\\"\\n self.run_command(command,35)\\n os.chdir(\\\"../\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8b7358b58d12fe79a0c6a7c2d1d9fdf\",\n \"score\": \"0.61490434\",\n \"text\": \"def load_data(self):\\n for year in self.years:\\n self.__load_data(year)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f758d195a3596f5b040e73ffd03923a\",\n \"score\": \"0.6148267\",\n \"text\": \"def load_all_pose_data(self):\\n for scene_name in self.scene_generator():\\n self.get_pose_data(scene_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"409309487d17755b452786a5f25b1310\",\n \"score\": \"0.61253846\",\n \"text\": \"def loadChirpAndDispersion(self):\\n filename = fnmatch.filter(os.listdir(self.directory), '*.dat')\\n fullname = os.path.join(self.directory, filename[0])\\n self.inputFilenames['chirp'] = fullname\\n\\n numPoints = int(os.path.getsize(fullname) / 4 / 4)\\n mmap = np.fromfile(fullname, dtype=np.int32, count=numPoints, offset=0)\\n weight = np.fromfile(fullname, dtype=np.float32, count=numPoints, offset=numPoints * 4)\\n\\n self.chirp = mmap + 1 - weight\\n self.chirp = self.chirp / np.max(self.chirp)\\n self.dispersion = np.fromfile(fullname, dtype=np.float32, count=numPoints * 2,\\n offset=numPoints * 4 * 2)\\n logging.info('Mapping and Disperion data loaded from: {}'.format(filename[0]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12fe92128ec11f218857d5c0591e2bc6\",\n \"score\": \"0.6099259\",\n \"text\": \"def load_data(self, path):\\n self.train_labels = np.empty(0)\\n self.test_labels = np.empty(0)\\n self.validation_labels = np.empty(0)\\n self.train_images = np.empty((0, self.resolution, self.resolution))\\n self.validation_images = np.empty((0, self.resolution, self.resolution))\\n self.test_images = np.empty((0, self.resolution, self.resolution))\\n lst1=[]\\n lst2=[]\\n for dirs in os.listdir(path):\\n for label in os.listdir(os.path.join(path, dirs)):\\n label_path = os.path.join(path, dirs, label)\\n for file in os.listdir(label_path):\\n image=Image.open(os.path.join(label_path,file)).convert('F')\\n lst1.append(np.array(image))\\n lst2.append(label)\\n if dirs == \\\"train\\\":\\n self.train_images=np.array(lst1)\\n self.train_labels=np.array(lst2)\\n lst1.clear()\\n lst2.clear()\\n if dirs == \\\"test\\\":\\n self.test_images=np.array(lst1)\\n self.test_labels=np.array(lst2)\\n lst1.clear()\\n lst2.clear()\\n if dirs == \\\"validation\\\":\\n self.validation_images=np.array(lst1)\\n self.validation_labels=np.array(lst2)\\n lst1.clear()\\n lst2.clear()\\n print(\\\"LOADED\\\")\\n return self.train_labels, self.validation_labels, self.test_labels, self.train_images, self.validation_images, self.test_images\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fda13e6294154ad6b7f9ddc33763fe9\",\n \"score\": \"0.60990393\",\n \"text\": \"def __init__(self):\\n try:\\n path2013 = os.path.join(os.path.curdir,\\n 'data\\\\\\\\VicRoadsAccidents\\\\\\\\2013',\\n '2013.shp')\\n path2014 = os.path.join(os.path.curdir,\\n 'data\\\\\\\\VicRoadsAccidents\\\\\\\\2014',\\n '2014.shp')\\n path2015 = os.path.join(os.path.curdir,\\n 'data\\\\\\\\VicRoadsAccidents\\\\\\\\2015',\\n '2015.shp')\\n path2016 = os.path.join(os.path.curdir,\\n 'data\\\\\\\\VicRoadsAccidents\\\\\\\\2016',\\n '2016.shp')\\n path2017 = os.path.join(os.path.curdir,\\n 'data\\\\\\\\VicRoadsAccidents\\\\\\\\2017',\\n '2017.shp')\\n path2018 = os.path.join(os.path.curdir,\\n 'data\\\\\\\\VicRoadsAccidents\\\\\\\\2018',\\n '2018.shp')\\n\\n self.gpd2013 = gpd.read_file(path2013)\\n self.gpd2014 = gpd.read_file(path2014)\\n self.gpd2015 = gpd.read_file(path2015)\\n self.gpd2016 = gpd.read_file(path2016)\\n self.gpd2017 = gpd.read_file(path2017)\\n self.gpd2018 = gpd.read_file(path2018)\\n\\n self.pd2013 = pd.DataFrame(self.gpd2013)\\n self.pd2014 = pd.DataFrame(self.gpd2014)\\n self.pd2015 = pd.DataFrame(self.gpd2015)\\n self.pd2016 = pd.DataFrame(self.gpd2016)\\n self.pd2017 = pd.DataFrame(self.gpd2017)\\n self.pd2018 = pd.DataFrame(self.gpd2018)\\n except OSError:\\n print('Please check the file path')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec4cb58f77f812354d501bbdb4cfa6b4\",\n \"score\": \"0.6094891\",\n \"text\": \"def load(self):\\n self.load_features()\\n self.load_images()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2af12682b4e348494f1b3f277cdd2bb\",\n \"score\": \"0.60718197\",\n \"text\": \"def load_data(self):\\n self.data = io.imread(self.filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0d2bcc41c3fe6694b3a39959444147b\",\n \"score\": \"0.606857\",\n \"text\": \"def read_data(self):\\n\\n # Read file\\n try:\\n with open(self.path, newline='') as datafile:\\n reader = csv.reader(datafile, delimiter=',')\\n for row in reader:\\n if len(row) == 5:\\n if float(row[4]) < 2 and float(row[4]) >= self.limit:\\n self.atoms.append(row[0])\\n self.x.append(float(row[1]))\\n self.y.append(float(row[2]))\\n self.z.append(float(row[3]))\\n self.t.append(float(row[4]))\\n elif len(row) == 4:\\n self.atoms.append(row[0])\\n self.x.append(float(row[1]))\\n self.y.append(float(row[2]))\\n self.z.append(float(row[3]))\\n else:\\n print(\\\"Unknown amount of columns in xyz file (\\\", len(row), \\\")\\\")\\n except FileNotFoundError:\\n print(\\\"Coordinate file not found. Check the file path.\\\")\\n\\n # Convert to numpy arrays\\n self.x = np.array(self.x)\\n self.y = np.array(self.y)\\n self.z = np.array(self.z)\\n self.t = np.array(self.t)\\n\\n # Write xyz file\\n if self.write == True:\\n Data.write_to_file(self)\\n print(\\\"VMD-compatible xyz file written.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e56d25a3a91fc1313d20a607ff9763b\",\n \"score\": \"0.60555005\",\n \"text\": \"def load_data(self):\\n # Step 0: Load transformed data if it exists.\\n if Path(self.transformed_data_path).is_file(): # found transformed data\\n self.pd_data = load_pickle(self.transformed_data_path)\\n # TODO: Here we should also load used label, numerical, and categorical column names, meaning they should be\\n # saved. This allows:\\n # 1) using data without prior knowledge of the mentioned class variables.\\n # 2) rationalizing once we know something, we have the option to recover them without remembering them\\n # 3) so no need to rely on any \\\"self\\\" variables, i.e., our saved data is self-sufficient\\n # dump() the class instance as a YAML config document\\n self.hash_sizes = [self.pd_data[categ_name].nunique() for categ_name in self.categ_names]\\n return\\n\\n # Step 1: Load label, numerical, and categorical features\\n label_ll = [list() for _ in range(self.label_num)] # list of label data\\n numer_ll = [list() for _ in range(self.numer_num)] # list of each numerical feature's data\\n categ_ll = [list() for _ in range(self.categ_num)] # list of each categorical feature's data\\n # Define defaultdict where key=\\\"feature index\\\" & val=\\\"defaultdict\\\" where key=\\\"category\\\" & val=\\\"count\\\".\\n categ_count_dict = defaultdict(lambda: defaultdict(int))\\n\\n with open(self.dataset_path, 'r') as ld:\\n for line in ld:\\n # Using zero \\\"0\\\" as substitute for missing values in:\\n # 1) Numerical feature may corrupt data because 0 is numerical.\\n # 2) Categorical feature will not corrupt data because the string \\\"0\\\" is a new category.\\n # TODO: Need a principled way to determine missing values for numerical features.\\n line = [v if v != \\\"\\\" else \\\"0\\\" for v in line.strip('\\\\n').split('\\\\t')]\\n\\n for i, j in enumerate(self.label_indices): # record label feature data\\n label_ll[i].append(int(line[j])) # typecast from string to save memory\\n for i, j in enumerate(self.numer_indices): # record numerical feature data\\n numer_ll[i].append(int(line[j])) # typecast from string to save memory\\n for i, j in enumerate(self.categ_indices): # record categorical feature data\\n categ_ll[i].append(line[j]) # cannot typecast string data\\n categ_count_dict[i][line[j]] += 1 # count category occurrences\\n\\n # Step 2: Create dictionary for indexing categories.\\n if Path(self.fit_dictionary_path).is_file():\\n categ_index_dict = load_pickle(self.fit_dictionary_path)\\n else:\\n # Define defaultdict where key=\\\"feature index\\\" & val=\\\"defaultdict\\\" where key=\\\"category\\\" & val=\\\"index\\\".\\n # TODO: Implement as a parent-class function\\n categ_index_dict = defaultdict(lambda: defaultdict(int))\\n\\n # TODO: Rename variables, e.g., feat_index > i\\n for feat_index, categ_dict in categ_count_dict.items():\\n categ_index = 0\\n for categ, count in categ_dict.items():\\n if count >= self.categ_filter: # index filtered categories at a later stage\\n categ_index_dict[feat_index][categ] = categ_index\\n categ_index += 1\\n del categ_count_dict # release memory\\n gc.collect()\\n # Save fit dictionary.\\n save_pickle(self.fit_dictionary_path, dict(categ_index_dict)) # cannot pickle defaultdict using lambda\\n\\n # Step 3: Index categories.\\n for i, categ_l in enumerate(categ_ll):\\n for j, c in enumerate(categ_l):\\n if c in categ_index_dict[i]:\\n categ_ll[i][j] = categ_index_dict[i][c] # index in-place\\n else: # index filtered categories as the end index\\n categ_ll[i][j] = len(categ_index_dict[i])\\n del categ_index_dict # release memory\\n gc.collect()\\n\\n # Step 4: Format data and obtain hash statistics.\\n array_data = np.concatenate((np.asarray(label_ll).T, np.asarray(numer_ll).T, np.asarray(categ_ll).T), axis=1)\\n del label_ll, numer_ll, categ_ll # release memory\\n gc.collect()\\n\\n self.pd_data = pd.DataFrame(array_data, columns=self.used_columns_names)\\n self.hash_sizes = [self.pd_data[categ_name].nunique() for categ_name in self.categ_names]\\n del array_data # release memory\\n gc.collect()\\n\\n for col_name, dtype in self.dtype_dict.items():\\n self.pd_data[col_name] = self.pd_data[col_name].astype(dtype)\\n # Save transformed data.\\n save_pickle(self.transformed_data_path, self.pd_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4327df5c665a0aaa9de860156f1b3c80\",\n \"score\": \"0.60475284\",\n \"text\": \"def __init__(self, filenames: list):\\n self.x_dim = None\\n self.y_dim = None\\n self.all_records_num = None\\n self.label_data = dict()\\n self.index_data = []\\n self.full_data_x = None\\n self.full_data_y = None\\n has_data = False\\n for file_id, filename in enumerate(filenames):\\n if isinstance(filename, (tuple, list)):\\n if len(filename) >= 3:\\n name_x, name_y, label = filename\\n label = str(label)\\n elif len(filename) == 2:\\n name_x, name_y = filename\\n label = None\\n else:\\n raise Exception(\\\"Incomplete data.\\\")\\n dat_x = np.genfromtxt(name_x, delimiter=\\\",\\\")\\n dat_y = np.genfromtxt(name_y, delimiter=\\\",\\\")\\n if dat_y.ndim == 1:\\n dat_y = dat_y[:, np.newaxis]\\n if dat_x.shape[0] != dat_y.shape[0]:\\n raise Exception(\\\"In file<{0}>, X and Y has different records count.\\\".format(file_id))\\n if not has_data:\\n self.x_dim = dat_x.shape[1:]\\n self.y_dim = dat_y.shape[1:]\\n self.all_records_num = dat_x.shape[0]\\n self.full_data_x = dat_x\\n self.full_data_y = dat_y\\n else:\\n if dat_x.shape[1:] != self.x_dim or dat_y.shape[1:] != self.y_dim:\\n raise Exception(\\\"In file<{0}>, X or Y data dim doesn't match with other files.\\\".format(file_id))\\n self.all_records_num += dat_x.shape[0]\\n self.full_data_x = np.vstack(self.full_data_x, dat_x)\\n self.full_data_y = np.vstack(self.full_data_y, dat_y)\\n\\n cur_data_dict = {\\\"X\\\": dat_x, \\\"Y\\\": dat_y}\\n self.index_data.append(cur_data_dict)\\n if label is not None:\\n self.label_data[label] = cur_data_dict\\n else:\\n raise Exception(\\\"<{0}>, filename type not supported.\\\".format(file_id))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b51362af4fa34926dd3979d119cd9ce\",\n \"score\": \"0.6043146\",\n \"text\": \"def load_data(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b51362af4fa34926dd3979d119cd9ce\",\n \"score\": \"0.6043146\",\n \"text\": \"def load_data(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f0290827a6671068bb58a46dc4cc4e6\",\n \"score\": \"0.6038656\",\n \"text\": \"def load_data(self):\\n images = list()\\n labels = list()\\n emotion_index_map = dict()\\n label_directories = [dir for dir in os.listdir(self.datapath) if not dir.startswith('.')]\\n for label_directory in label_directories:\\n if self.target_emotion_map:\\n if label_directory not in self.target_emotion_map.keys(): continue\\n self._add_new_label_to_map(label_directory, emotion_index_map)\\n label_directory_path = self.datapath + '/' + label_directory\\n\\n if self.time_delay:\\n self._load_series_for_single_emotion_directory(images, label_directory, label_directory_path, labels)\\n else:\\n image_files = [image_file for image_file in os.listdir(label_directory_path) if not image_file.startswith('.')]\\n self._load_images_from_directory_to_array(image_files, images, label_directory, label_directory_path, labels)\\n\\n vectorized_labels = self._vectorize_labels(emotion_index_map, labels)\\n self._check_data_not_empty(images)\\n return self._load_dataset(np.array(images), np.array(vectorized_labels), emotion_index_map)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47bb46b10c300853ff86ab0058fdc3a6\",\n \"score\": \"0.6032076\",\n \"text\": \"def load_data(self):\\n if not exists(self.path + self.file_name):\\n if not exists(self.path + self.name + \\\".zip\\\"):\\n #### Download ####\\n url = f'http://85.215.86.232/tig/data/{self.name}.zip'\\n r = requests.get(url, allow_redirects=True, stream=True)\\n\\n chunkSize = 1024\\n pbar = tqdm(r.iter_content(chunk_size=chunkSize), unit=\\\"B\\\",\\n total=int(r.headers['Content-Length']),\\n desc=f'Download {self.name}',\\n unit_scale=True, unit_divisor=1024)\\n\\n Path(self.path).mkdir(parents=True, exist_ok=True)\\n with open(self.path + self.name + \\\".zip\\\", 'wb') as f:\\n for chunk in pbar:\\n if chunk: # filter out keep-alive new chunks\\n pbar.update(len(chunk))\\n f.write(chunk)\\n log.info(\\\"Data set donwloaded! (%s%s.zip)\\\", self.path, self.name)\\n else:\\n log.info(\\\"Data exist already! (%s%s.zip)\\\", self.path, self.name)\\n\\n if not exists(self.path + self.name + '.npz'):\\n #### Extract ####\\n with ZipFile(file=self.path + self.name + '.zip') as zip_file:\\n # Loop over each file\\n for member in tqdm(iterable=zip_file.namelist(), total=len(zip_file.namelist()),\\n desc=f'Extract {self.name} '):\\n zip_file.extract(member, path=self.path)\\n else:\\n log.info(\\\"Data exist extracted! (%s%s.zip)\\\", self.path, self.name)\\n\\n if not exists(self.path + 'kinetics_class_dict.json'):\\n #### Download ####\\n url = 'http://85.215.86.232/tig/data/kinetics_class_dict.json'\\n r = requests.get(url, allow_redirects=True, stream=True)\\n\\n pbar = tqdm(unit=\\\"B\\\", total=int(r.headers['Content-Length']) // 10**6,\\n desc=f'Download {self.name} ')\\n chunkSize = 1024\\n\\n Path(self.path).mkdir(parents=True, exist_ok=True)\\n with open(self.path + 'kinetics_class_dict.json', 'wb') as f:\\n for chunk in r.iter_content(chunk_size=chunkSize):\\n if chunk: # filter out keep-alive new chunks\\n pbar.update(len(chunk) // 10**6)\\n f.write(chunk)\\n log.info(\\\"Class dictionary donwloaded! (%skinetics_class_dict.json)\\\", self.path)\\n else:\\n log.info(\\\"Class dictionary already exist! (%skinetics_class_dict.json)\\\", self.path)\\n\\n log.info(\\\"Load data...\\\")\\n data = np.load(self.path + self.file_name, allow_pickle=True, mmap_mode=\\\"r\\\")\\n if self.lim is not None:\\n self.x = np.asarray(list(data[\\\"x\\\"]))[:self.lim]\\n self.y = np.asarray(list(data[\\\"y\\\"]))[:self.lim]\\n else:\\n self.x = np.asarray(list(data[\\\"x\\\"]))\\n self.y = np.asarray(list(data[\\\"y\\\"]))\\n\\n le = preprocessing.LabelEncoder()\\n self.y = le.fit_transform(self.y)\\n\\n with open(self.path + 'kinetics_class_dict.json', \\\"rb\\\") as f:\\n self.classes = json.load(f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"991303587063de38b4bacacbcd2f72bd\",\n \"score\": \"0.60309863\",\n \"text\": \"def get_local_data(self):\\n try:\\n h5 = h5py.File(os.path.join(self.directory, \\\"trajectory.h5\\\"), mode=\\\"r\\\")\\n self.timepoints = [key for key in h5[\\\"concentration\\\"][\\\"co2\\\"].keys()]\\n self.timepoints.sort(key=int)\\n self.dims = list(h5[\\\"concentration\\\"][\\\"co2\\\"][\\\"0\\\"].shape)\\n self.nutrients = list(h5[\\\"concentration\\\"].keys())\\n self.collect_positions(h5)\\n self.get_nutrient_grid(h5)\\n h5.close()\\n except:\\n print(\\\"Missing HDF5 file\\\")\\n\\n self.biomass = pd.read_csv(\\n os.path.join(self.directory, \\\"Results\\\", \\\"biomass.csv\\\"),\\n usecols=[0, 1, 2],\\n delimiter=\\\"\\\\t\\\",\\n )\\n self.ntypes = pd.read_csv(\\n os.path.join(self.directory, \\\"Results\\\", \\\"ntypes.csv\\\"),\\n usecols=[0, 1, 2],\\n delimiter=\\\"\\\\t\\\",\\n )\\n self.avg_con = pd.read_csv(\\n os.path.join(self.directory, \\\"Results\\\", \\\"avg_concentration.csv\\\"),\\n usecols=[0, 2, 3, 4],\\n delimiter=\\\"\\\\t\\\",\\n names=[\\\"Time\\\", \\\"O2\\\", \\\"Sucrose\\\", \\\"CO2\\\"],\\n skiprows=1,\\n )\\n f = open(os.path.join(self.directory, \\\"metadata.json\\\"), \\\"r\\\")\\n self.metadata = json.load(f)\\n f.close()\\n if \\\"IPTG\\\" in self.metadata:\\n self.IPTG = self.metadata[\\\"IPTG\\\"]\\n self.sucRatio = self.metadata[\\\"IPTG\\\"]\\n else:\\n self.IPTG = self.metadata[\\\"SucRatio\\\"]\\n # TODO replace sucRatio with IPTG\\n self.sucRatio = self.metadata[\\\"SucRatio\\\"]\\n self.convert_units_avg_con()\\n self.convert_units_biomass()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13d42c6533bdbd8ff51bd30770859732\",\n \"score\": \"0.60293514\",\n \"text\": \"def load_data(self):\\n t0 = time.time()\\n if not all([self.current_time_start, self.current_time_end]):\\n raise Exception\\n\\n self.reset_data()\\n\\n # Load files within time range\\n self.current_data['mit'] = self.get_data_within_time_range('mit', self.current_time_start, self.current_time_end)\\n self.current_data['co2'] = self.get_data_within_time_range('co2', self.current_time_start, self.current_time_end)\\n\\n # Reset index\\n self.current_data['mit'] = self.current_data['mit'].reset_index(drop=True)\\n self.current_data['co2'] = self.current_data['co2'].reset_index(drop=True)\\n\\n # print('Load data')\\n # print('mit', len(self.current_data['mit']))\\n # print('co2', len(self.current_data['co2']))\\n # print('Loaded in: {}'.format(time.time()-t0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5451f31a15b9d4eadcc7eb2095883fe7\",\n \"score\": \"0.6023418\",\n \"text\": \"def prepare_loaded_data(self, file_name):\\n #utworzenie bufora\\n buff = Buffer()\\n data = CsvReader.read_from_file(file_name, 1)\\n #zmiana czasu\\n data = DataOperation.change_time_relative(data)\\n #zmiana wysokosci na metry\\n data = DataOperation.change_altitude_cm_m(data)\\n #stworzenie zapisywacza\\n saver = FileSaver(\\\"saved_data/dane.txt\\\")\\n\\n #kazda linijke z pliku csv buforujemy osobno\\n for d in data:\\n buff.set_data(d)\\n buffered_data = buff.get_data()\\n #sprawdzamy czy kazda linijka jest poprawnie zapisana\\n if InputValidator.input_val(buffered_data):\\n #zapisujemy kazda linijke do nowego pliku\\n saver.save_data(buffered_data)\\n\\n #odczyt danych z pliku csv i wizualizacja\\n r_data = CsvReader.read_from_file(saver.get_file_name())\\n #tworzymy wizualizator, drugi parametr do interwal czasowy\\n self.visualizer = PlotCreator(r_data, 1)\\n self.data_loaded = True\\n print \\\"Dane zaladowane\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84718ca56bf4cb30a69fa8a52a034f5a\",\n \"score\": \"0.6016522\",\n \"text\": \"def _loaddata(self):\\n _header = self._header\\n elemlist = self.elemlist\\n frame = self.frame\\n \\n def nans(numitems):\\n return [np.nan for i in xrange(numitems)]\\n\\n _elemid_lookup = dict(zip(_header.name, _header.id))\\n \\n # Data gets unpacked to a temporary dict tmpdata before building\\n # Element objects\\n # \\n # We will unpack 1d elements to an array object and then\\n # change them to 2d ndarrays after the data is loaded.\\n #\\n # Elements with more than 1 dimension will get unpacked\\n # as lists of arrays and transformed to ndarrays after\\n # the data is loaded\\n #\\n # The array.array objects are typed unlike regular lists.\\n # This improves memory usage. The array.array objects also\\n # have highly efficient appending unlike ndarrays\\n \\n # initialize temporary dict\\n tmpdata = {} \\n for name, typ, varrate, numvalues in \\\\\\n zip(_header.name, _header.type, \\n _header.varrateflag, _header.numvalues):\\n if elemlist is None or any(fnmatch(name, wc) for wc in elemlist):\\n if not varrate and numvalues == 1:\\n tmpdata[name] = array(typ)\\n else:\\n tmpdata[name] = []\\n\\n # Intialize Frame arrays for CSSDC measures\\n for name in tmpdata.keys():\\n if _header.rate[_elemid_lookup[name]] != 1:\\n tmpdata[name+'_Frames'] = array('i')\\n\\n fid = open(self.info.filename,'rb')\\n read = fid.read # dots make things slow...\\n read(self.cursor) # move to where the data begins\\n\\n # The daq files have a frame for every sample. Each frame\\n # contains the variables states at the time the frame was\\n # saved. \\n #\\n # we want to create arrays that can be indexed to decide\\n # whether the variable needs stored. This way we don't\\n # have to look through the elemlist for every variable on\\n # every frame\\n if elemlist is None: # elemlist empty \\n mask = [True for i in xrange(len(_header.name))]\\n else:\\n mask = []\\n for name in _header.name:\\n mask.append(any(fnmatch(name, wc) for wc in elemlist))\\n\\n bombed = False\\n # Python quirk no. 372837462: while 1 is faster than while True\\n while 1:\\n try:\\n frame.code.append(unpack('i',read(4))[0])\\n if frame.code[-1] == -2:\\n break\\n \\n frame.frame.append(unpack('i', read(4))[0])\\n frame.count.append(unpack('i', read(4))[0])\\n\\n # xrange (2.7, range in 3) is faster than range\\n for j in xrange(frame.count[-1]):\\n i = unpack('i', read(4))[0]\\n\\n if _header.varrateflag[i]:\\n numitems = unpack('i', read(4))[0]\\n else:\\n numitems = _header.numvalues[i]\\n \\n size = _header.bytes[i]\\n \\n if mask[i]:\\n name = _header.name[i]\\n \\n if numitems == 1:\\n typ = _header.type[i]\\n tmpdata[name].append(unpack(typ,read(size))[0])\\n \\n else: # numitems > 1\\n typ = _header.nptype[i]\\n tmpdata[name].append(fromfile(fid,typ,numitems))\\n \\n if _header.rate[i] != 1:\\n tmpdata[name+'_Frames'].append(frame.frame[-1])\\n \\n else: # we don't need to read this element\\n read(numitems*size) # seek is slow...\\n # reduce calls to read\\n except:\\n msg = 'Failed loading file on frame %i.'%frame.frame[-1]\\n msg += ' (stopped reading file)'\\n warnings.warn(msg, RuntimeWarning)\\n bombed = True\\n break\\n\\n fid.close()\\n\\n # We made it through the daq file.\\n # Now it is time to do some bookkeeping.\\n frame.frame = np.array(frame.frame)\\n self.f0 = f0 = frame.frame[0]\\n self.fend = fend = frame.frame[-1]\\n\\n # If we bombed unpacking a frame we need to make sure\\n # everything is aligned before we intialize Elements\\n if bombed:\\n # we will strip off the last frame just to make sure\\n # everything is kosher\\n n = min(len(frame.code),len(frame.frame),len(frame.count))-1\\n frame.code = frame.code[:n]\\n frame.frame = frame.frame[:n]\\n frame.count = frame.count[:n]\\n \\n for name in tmpdata:\\n if name.endswith('_Frames'):\\n continue\\n\\n val = tmpdata[name]\\n i = _elemid_lookup[name]\\n if _header.rate[i] != 1:\\n # CSSDC measures should always be okay. If they have 1\\n # value they use read, if they have multiple values they\\n # use fromfile. In either case if the unpacking fails\\n # nothing will get appended to tmpdata and it will exit\\n # before appending to the cooresponding _Frames array.\\n #\\n # At least that is what my mental interpreter thinks.\\n # we will assert just to be on the safe side.\\n assert len(tmpdata[name]) == len(tmpdata[name+'_Frames'])\\n \\n else:\\n tmpdata[name] = np.array(tmpdata[name][:n], ndmin=2)\\n \\n # gets transposed in next for loop if numvalues > 1\\n if _header.numvalues[i] > 1:\\n assert tmpdata[name].shape[0] == frame.frame.shape[0]\\n else:\\n assert tmpdata[name].shape[1] == frame.frame.shape[0]\\n\\n # cast as Element objects\\n # 'varrateflag' variables remain lists of lists\\n #\\n # There are obvious more compact ways to write this but I'm\\n # paranoid about reference counting and garbage collection not\\n # functioning properly\\n for name, i, rate in zip(_header.name, _header.id, _header.rate):\\n if elemlist is not None:\\n if not any(fnmatch(name, wc) for wc in elemlist):\\n continue\\n \\n # transpose Element with more than 1 row \\n if _header.numvalues[i] > 1:\\n tmpdata[name] = np.array(tmpdata[name]).transpose()\\n \\n if rate != 1:\\n self[name] = \\\\\\n Element(tmpdata[name],\\n tmpdata[name+'_Frames'],\\n rate=_header.rate[i],\\n name=_header.name[i],\\n dtype=_header.type[i],\\n varrateflag=_header.varrateflag[i],\\n elemid=_header.id[i],\\n units=_header.units[i])\\n \\n del tmpdata[name+'_Frames']\\n else:\\n self[name] = \\\\\\n Element(tmpdata[name],\\n frame.frame[:],\\n rate=_header.rate[i],\\n name=_header.name[i],\\n dtype=_header.type[i],\\n varrateflag=_header.varrateflag[i],\\n elemid=_header.id[i],\\n units=_header.units[i])\\n\\n # delete tmpdata arrays as we go to save memory\\n del tmpdata[name]\\n \\n del _header, self._header\\n self._header = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6125bdb53d5aed8bcb4eb296a74625df\",\n \"score\": \"0.6012088\",\n \"text\": \"def loaddata(self, x_path, y_path):\\n # load x-series and y-series data\\n df_x = pd.read_csv(x_path, dtype={'id': str}, parse_dates=['ds'], date_parser=self.dateparse)\\n df_y = pd.read_csv(y_path, dtype={'id': str}, parse_dates=['ds'], date_parser=self.dateparse)\\n self.df_x = df_x[['id', 'ds', 'x']]\\n self.df_y = df_y[['id', 'ds', 'y']]\\n self.lg.logger.info(\\\"load data: {} | {}\\\".format(os.path.basename(x_path), os.path.basename(y_path)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cde7e3dc7a92ff340201336cab894c99\",\n \"score\": \"0.59944457\",\n \"text\": \"def load_data(self):\\n if not os.path.isdir(self.path):\\n print(\\\"Data for these parameters not generated!!!\\\")\\n print(\\\"Generating data, this can take a while...\\\")\\n generate_data(self.glove_dimension, self.max_words, self.full)\\n print(\\\"Generating data done!\\\")\\n self.embedding_matrix = np.load(os.path.join(self.path, \\\"embedding_matrix.npy\\\"))\\n self.X_train = np.load(os.path.join(self.path, \\\"X_train.npy\\\"))\\n self.Y_train = np.load(os.path.join(self.path, \\\"Y_train.npy\\\"))\\n self.X_test = np.load(os.path.join(self.path, \\\"X_test.npy\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c03747fd92b1d9c97efa0443bf7addd5\",\n \"score\": \"0.5991191\",\n \"text\": \"def _load_data(self):\\n cols = [list() for _ in range(len(self.columns_names))]\\n user2index_dict = dict() # needs renumber since CustomerID ranges from 1 to 2649429, with gaps\\n\\n for fp in self.dataset_path: # TODO: deal with list of paths\\n\\n with open(fp, 'r') as f:\\n for line in f.readlines():\\n if ':' in line:\\n movie = int(line.strip(\\\":\\\\n\\\"))-1 # -1 because the sequential MovieIDs starts from 1\\n else:\\n user, rating = [int(v) for v in line.strip().split(',')[:2]]\\n cols[1].append(movie)\\n cols[2].append(rating)\\n\\n if user in user2index_dict.keys():\\n cols[0].append(user2index_dict[user])\\n else:\\n cols[0].append(len(user2index_dict.keys())) # number users from 0\\n user2index_dict[user] = len(user2index_dict.keys())\\n\\n # TODO: load date as well, later keep only selected data via self.used_columns_names\\n self.pd_data = pd.DataFrame(dict(zip(self.columns_names, cols)))\\n\\n for col_name, dtype in self.dtype_dict.items():\\n self.pd_data[col_name] = self.pd_data[col_name].astype(dtype)\\n\\n self.pd_data = self.pd_data[self.used_columns_names]\\n self.user_num = self.pd_data[\\\"user_id\\\"].nunique()\\n self.item_num = self.pd_data[\\\"item_id\\\"].nunique()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed5b989f5a48d68b557c919be221d493\",\n \"score\": \"0.59870875\",\n \"text\": \"def _read_files(self):\\n data = []\\n print(\\\"Leyendo los archivos...\\\",end=\\\" \\\")\\n init = time.time()\\n\\n for ii in range(self.ndata):\\n # Leo las coordenadas y el numero atomico\\n file_name = self.path_C + str(ii+1) + \\\".xyz\\\"\\n atomic_number, positions, how_many = self._read_xyz(file_name)\\n\\n # Leo Densidades, Energia XC, type gaussians y Nuc\\n file_name = self.path_P + str(ii+1) + \\\".dat\\\"\\n Exc, Pmat_fit, gtype, Nuc = self._read_rhoE(file_name)\\n\\n # Guardo los datos en un solo diccionario\\n single_data = {\\n \\\"targets\\\": Exc,\\n \\\"atomic_number\\\": atomic_number,\\n \\\"how_many\\\": how_many,\\n \\\"Pmat_fit\\\": Pmat_fit,\\n \\\"gtype\\\": gtype, \\n \\\"Nuc\\\": Nuc,\\n }\\n\\n if len(positions) != 0:\\n single_data[\\\"positions\\\"] = positions\\n \\n # Guardo los datos de una molecula\\n data.append(single_data)\\n \\n fin = time.time()\\n print(str(np.round(fin-init,2))+\\\" s.\\\")\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2db3e6105fac9e46d4cbe6c60d3e1593\",\n \"score\": \"0.5980057\",\n \"text\": \"def __init__(self, data_dir):\\n print('Loading original MNIST data from', data_dir)\\n self.data = []\\n for name, key in [('train', 'train'), ('test', 't10k')]:\\n images = os.path.join(data_dir, '%s-images.idx3-ubyte' % key)\\n labels = os.path.join(data_dir, '%s-labels.idx1-ubyte' % key)\\n if not os.path.isfile(images) or not os.path.isfile(labels):\\n print('Warning: Missing %s data' % name)\\n else:\\n self.data.append((name, images, labels))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78fde79e39a1dde2c51dc5cdb21288eb\",\n \"score\": \"0.5975555\",\n \"text\": \"def load_data(self):\\n dset = self.config.dataset\\n dset.load()\\n found = 0\\n for i,cband in enumerate(dset.dmap):\\n emin, emax, event_type = cband.emin(), cband.emax(), cband.event_class()\\n if event_type == -2147483648: event_type=0 # previous bug in FITS setup\\n try:\\n band = self.find(emin, event_type)\\n found +=1\\n except Exception:\\n continue\\n band.load_data(cband)\\n band.data_index = i # useful to look up associated index\\n if found!=len(self):\\n print ('{}: Loaded subset of bands {} instead of {}'.format( self.__repr__(), found, len(self)))\\n self[:]= self[:found] \\n self.config.emax=self[-1].emax\\n self.has_data = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d90c82b56133369719db443f99ec3ac8\",\n \"score\": \"0.59707594\",\n \"text\": \"def load_all_pose_data(self):\\n raise NotImplementedError('subclass must implement this method')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b07a36e9ebbe87d7bd6c2689315702e\",\n \"score\": \"0.5941635\",\n \"text\": \"def loadBasicData(self):\\n train = pd.read_csv(os.path.join(self.path, 'train.csv'))\\n \\n s = LabelEncoder().fit(train.species) \\n self.classes = list(s.classes_) \\n classes_labels = s.transform(train.species)\\n train = train.drop(['species'], axis=1)\\n\\n if self.pca:\\n trainX = train.drop(['id'], axis=1)\\n pca = PCA(n_components=0.9 ,svd_solver='full')\\n pca.fit(trainX)\\n trainX=pca.transform(trainX)\\n train_df=pd.DataFrame.from_records(trainX)\\n train_df.insert(loc=0, column='id', value=train['id'])\\n train=train_df\\n\\n \\n for train_index, test_index in StratifiedShuffleSplit(n_splits=1, test_size=self.nb_test_data, random_state=self.r).split(train, classes_labels): \\n X_train, X_test = train.values[train_index], train.values[test_index] \\n self.t_train, self.t_test = classes_labels[train_index], classes_labels[test_index]\\n\\n self.id_img_train = list(np.int_( X_train[:,0]))\\n self.id_img_test = list(np.int_( X_test[:,0])) \\n self.X_data_train = np.delete(X_train, 0, 1)\\n self.X_data_test = np.delete(X_test, 0, 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8af6c89f57159a47b73d405ef97c77ca\",\n \"score\": \"0.5937247\",\n \"text\": \"def load(self):\\n\\n meta_data = []\\n if len(os.listdir(self.processed_data_dir)) != 0:\\n\\n print(\\\"Loading Saved Data from Disk.......\\\")\\n\\n train_dir = os.path.join(self.processed_data_dir, 'train')\\n test_dir = os.path.join(self.processed_data_dir, 'test')\\n\\n dataset = DiskDataset(data_dir=self.processed_data_dir)\\n train = DiskDataset(data_dir=train_dir)\\n test = DiskDataset(data_dir=test_dir)\\n\\n meta_data = pickle.load(open(os.path.join(self.processed_data_dir, 'meta.pkl'), 'rb'))\\n max_atom = meta_data[0]\\n\\n print(\\\"Transforming Data.\\\")\\n if not self.transformer_types:\\n if self.transformer_types == 'normalization_y':\\n self.transformers += [\\n NormalizationTransformer(transform_y=True, dataset=dataset)\\n ]\\n elif self.transformer_types == 'normalization_w':\\n self.transformers += [\\n NormalizationTransformer(transform_w=True, dataset=dataset)\\n ]\\n elif self.transformer_types == 'balancing_w':\\n self.transformers += [\\n BalancingTransformer(transform_w=True, dataset=dataset)\\n ]\\n elif self.transformer_types == 'balancing_y':\\n self.transformers += [\\n BalancingTransformer(transform_y=True, dataset=dataset)\\n ]\\n else:\\n ValueError(\\\"Transformer type Not defined!{}\\\".format(self.transformer_types))\\n\\n else:\\n print(\\\"Loading and Featurizing Data.......\\\")\\n # loader = dc.data.CSVLoader(\\n # tasks=self.tasks, smiles_field=self.smiles_field, featurizer=self.Featurizer)\\n dataset = self.featurize(shard_size=2048)\\n\\n print(\\\"Transforming Data.\\\")\\n if not self.transformer_types:\\n if self.transformer_types == 'normalization_y':\\n self.transformers += [\\n NormalizationTransformer(transform_y=True, dataset=dataset)\\n ]\\n elif self.transformer_types == 'normalization_w':\\n self.transformers += [\\n NormalizationTransformer(transform_w=True, dataset=dataset)\\n ]\\n elif self.transformer_types == 'balancing_w':\\n self.transformers += [\\n BalancingTransformer(transform_w=True, dataset=dataset)\\n ]\\n elif self.transformer_types == 'balancing_y':\\n self.transformers += [\\n BalancingTransformer(transform_y=True, dataset=dataset)\\n ]\\n else:\\n ValueError(\\\"Transformer type Not defined!{}\\\".format(self.transformer_types))\\n\\n if len(self.transformers) > 0:\\n for transformer in self.transformers:\\n # pass dataset through maybe more than one transformer\\n dataset = transformer.transform(dataset)\\n\\n \\\"\\\"\\\"max_atom is the max atom of molecule in all_dataset \\\"\\\"\\\"\\n max_atom = self.find_max_atom(dataset)\\n meta_data.append(max_atom)\\n meta_data.extend(self.tasks)\\n with open(os.path.join(self.processed_data_dir, 'meta.pkl'), 'wb') as f:\\n pickle.dump(meta_data, f)\\n\\n \\\"\\\"\\\"\\n Split Dataset\\n \\\"\\\"\\\"\\n print(\\\"Splitting Date to Train/Validation/Testing\\\")\\n splitters = {\\n 'index': IndexSplitter(),\\n 'random': RandomSplitter(),\\n 'scaffold': ScaffoldSplitter()\\n }\\n\\n if self.splitter not in splitters:\\n raise ValueError(\\\"Splitter not defined!\\\")\\n else:\\n splitter = splitters[self.splitter]\\n\\n # create processed dirs as train, valid, test\\n train_dir = os.path.join(self.processed_data_dir, 'train')\\n test_dir = os.path.join(self.processed_data_dir, 'test')\\n\\n print(\\\"Saving Data at %s...\\\", self.processed_data_dir)\\n train, test = splitter.train_test_split(\\n dataset,\\n train_dir=train_dir,\\n test_dir=test_dir,\\n frac_train=self.split_frac[0],\\n )\\n\\n return self.tasks, (train, test), self.transformers, max_atom\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6758cc52f44c8b357ed3d7891c0b848e\",\n \"score\": \"0.59339476\",\n \"text\": \"def load_data_set_graphs():\\n\\n asc_graphs_array = []\\n desc_graphs_array = []\\n flat_graphs_array = []\\n soy_graphs_array = []\\n\\n for asc_file in os.listdir(\\\"samples/ASC/graphs/\\\"):\\n if asc_file.endswith(\\\".png\\\"):\\n img_data = cv2.imread(\\\"samples/ASC/graphs/\\\" + asc_file, 0)\\n asc_graphs_array.append(img_data)\\n\\n for desc_file in os.listdir(\\\"samples/DESC/graphs/\\\"):\\n if desc_file.endswith(\\\".png\\\"):\\n img_data = cv2.imread(\\\"samples/DESC/graphs/\\\" + desc_file, 0)\\n desc_graphs_array.append(img_data)\\n\\n for flat_file in os.listdir(\\\"samples/FLAT/graphs/\\\"):\\n if flat_file.endswith(\\\".png\\\"):\\n img_data = cv2.imread(\\\"samples/FLAT/graphs/\\\" + flat_file, 0)\\n flat_graphs_array.append(img_data)\\n\\n for soy_file in os.listdir(\\\"samples/SOY/graphs/\\\"):\\n if soy_file.endswith(\\\".png\\\"):\\n img_data = cv2.imread(\\\"samples/SOY/graphs/\\\" + soy_file, 0)\\n soy_graphs_array.append(img_data)\\n\\n\\n return asc_graphs_array, desc_graphs_array, flat_graphs_array, soy_graphs_array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c162860e049cdcd6df07e931a7de844\",\n \"score\": \"0.59312016\",\n \"text\": \"def _post_file_load_processing(self):\\n # When loading files with just point arrays, create and\\n # set the polydata vertices\\n if self.n_points > 0 and self.n_cells == 0:\\n verts = self._make_vertex_cells(self.n_points)\\n self.verts = CellArray(verts, self.n_points, deep=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd3c8e433d4911cb2c46e6da8803b5d0\",\n \"score\": \"0.59306145\",\n \"text\": \"def build_data_loader(self):\\n # Load dataset\\n dataset = build_dataset(self.cfg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea737d3461bc2ff86e2d0f6854012207\",\n \"score\": \"0.5930364\",\n \"text\": \"def load_list(self):\\n file_names = ['cube1.nc', 'cube2.nc']\\n file_names = [os.path.join(self.data_dir, f) for f in file_names]\\n\\n iris.load(file_names)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"717cdc4b17306d16580d1a7e88e55217\",\n \"score\": \"0.59258443\",\n \"text\": \"def load_data(data_dir):\\n\\n ### YOUR CODE HERE\\n\\n files = os.listdir(data_dir)\\n\\n x_train = np.zeros(shape=[0,32,32,3], dtype=float)\\n y_train = np.asarray([], dtype=int)\\n\\n x_test = np.zeros(shape=[0,32,32,3], dtype=float)\\n y_test = np.asarray([], dtype=int)\\n\\n def unpickle(file):\\n with open(file, 'rb') as fo:\\n dict = pickle.load(fo, encoding='bytes')\\n return dict\\n\\n def get_xy_batch(cur_file_path):\\n current_set = unpickle(cur_file_path)\\n x_temp = np.array(current_set[b'data'], dtype=float) / 255.0\\n x_temp = x_temp.reshape([-1, 3, 32, 32])\\n x_temp = x_temp.transpose([0, 2, 3, 1])\\n y_temp = np.array(current_set[b'labels'])\\n\\n return x_temp, y_temp\\n\\n for file in files:\\n cur_file_path = os.path.join(data_dir, file)\\n if 'data_batch' in file:\\n x_temp, y_temp = get_xy_batch(cur_file_path)\\n x_train = np.concatenate((x_train, x_temp), axis=0)\\n y_train = np.concatenate((y_train, y_temp), axis=0)\\n\\n elif 'test_batch' in file:\\n x_temp, y_temp = get_xy_batch(cur_file_path)\\n x_test = np.concatenate((x_test, x_temp))\\n y_test = np.concatenate((y_test, y_temp))\\n\\n ### END CODE HERE\\n\\n return x_train, y_train, x_test, y_test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3948cb7e1ae2894bb35ef23a00fc2da\",\n \"score\": \"0.59197044\",\n \"text\": \"def _load_data(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e62a9581844ef2e78c9d81499739e8b0\",\n \"score\": \"0.5912595\",\n \"text\": \"def load_data(filepath,fnames,jinds,iinds,varname,num_cores=20,dim4D=True, sum_over_depth=False, depth_lim=13, model_data=False, remove_clim=False,dt=1, depth_lim0=0):\\n # create temp files to host the shared memory variables\\n folder1 = tempfile.mkdtemp()\\n folder2 = tempfile.mkdtemp()\\n path1 = os.path.join(folder1, 'dum0.mmap')\\n path2 = os.path.join(folder2, 'dum1.mmap')\\n if dim4D: # incase the files have more than one timestep in each file\\n vshape=(len(fnames),366,len(jinds))\\n var_par=np.memmap(path1, dtype=float, shape=vshape, mode='w+')\\n else: # incase there is only one timestep in a file\\n vshape=(len(fnames),len(jinds))\\n var_par=np.memmap(path1, dtype=float, shape=vshape, mode='w+')\\n # nts will keep track of number of days in a year\\n nts=np.memmap(path2, dtype=float, shape=(len(fnames)), mode='w+')\\n fnames2=np.memmap(path2, dtype='U'+str(len(fnames[0])+1), shape=(len(fnames)), mode='w+')\\n fnames2[:]=fnames #np.asarray(fnames[:])\\n # launch the parallel reading\\n Parallel(n_jobs=num_cores)(delayed(read_files)(j,nts,jinds,iinds,filepath,fnames2,var_par,varname,sum_over_depth, depth_lim, depth_lim0, model_data=model_data) for j,fname in enumerate(fnames))\\n if dim4D:\\n print('removing climatology')\\n var_clim=np.nanmean(var_par,0)\\n if remove_clim:\\n print('removing climatology') \\n # smooth the daily climatology with monthly filter, as the climatology will be still noisy at daily scales\\n var_clim=np.concatenate([var_clim[-120//dt:,],var_clim,var_clim[:120//dt,]],axis=0)\\n b,a=signal.butter(3,2./(30/dt))\\n jnonan=np.where(np.isfinite(np.sum(var_clim,0)))\\n var_clim[:,jnonan]=signal.filtfilt(b,a,var_clim[:,jnonan],axis=0)\\n var_clim=var_clim[120//dt:120//dt+366//dt,]\\n #\\n # this is the on off switch for removing the climatology\\n var_clim=var_clim*int(remove_clim) \\n var=var_par[0,:int(nts[0]),:]-var_clim[:int(nts[0]),:]\\n # concatenate the data - note that here nts is used to strip down the 366th day when it's not a leap year\\n # and include the 366th day when it is a leap year\\n for j in range(1,len(fnames)):\\n print(j)\\n var=np.concatenate([var,var_par[j,:int(nts[j]),:]-var_clim[:int(nts[j]),:]],axis=0)\\n #\\n else:\\n # if only one timestep per file\\n var=np.asarray(var_par)\\n var[np.where(var==0)]=np.nan\\n if remove_clim:\\n print('removing climatology')\\n year0=datetime.date(int(fnames[0][-20:-16]),int(fnames[0][-16:-14]),int(fnames[0][-14:-12])).isocalendar()[0]\\n year1=datetime.date(int(fnames[-1][-20:-16]),int(fnames[-1][-16:-14]),int(fnames[-1][-14:-12])).isocalendar()[0]\\n var2=np.ones((year1-year0+1,int(np.ceil(366./dt)),var.shape[1]))*np.nan\\n #\\n for j, fname in enumerate(fnames):\\n year = int(fname[-20:-16])\\n month = int(fname[-16:-14])\\n day = int(fname[-14:-12])\\n c,c1 = datetime.date(year,month,day).isocalendar()[:2]\\n c = c-year0\\n c1 = c1-1\\n var2[c,c1,:] = var[j,:]\\n #\\n var_clim=np.nanmean(var2,0)\\n ind=np.where(np.nansum(var2,-1)[0,:]>0)[0]\\n var=var2[0,ind,:]-var_clim[ind,:]\\n for j in range(1,var2.shape[0]):\\n ind=np.where(np.nansum(var2,-1)[j,:]>0)[0]\\n var=np.concatenate([var,var2[j,ind,:]-var_clim[ind,:]],axis=0)\\n else:\\n var_clim=None\\n # \\n print('close files')\\n #\\n try:\\n shutil.rmtree(folder1)\\n except OSError:\\n pass\\n try:\\n shutil.rmtree(folder2)\\n except OSError:\\n pass\\n #\\n return var, var_clim\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11798ccc67e02663e8357fea9199550d\",\n \"score\": \"0.5912269\",\n \"text\": \"def load_data(data_dir):\\n\\n\\t### YOUR CODE HERE\\n\\tfnames = os.listdir(data_dir)\\n\\tx_train = np.array([[]]).reshape(0,3072)\\n\\ty_train = np.array([])\\n\\tfor fn in fnames:\\n\\t\\tif fn.endswith(\\\"html\\\") or fn.endswith(\\\"meta\\\"):\\n\\t\\t\\tcontinue\\n\\t\\twith open(os.path.join(data_dir,fn), 'rb') as fo:\\n\\t\\t\\tds = pickle.load(fo, encoding='bytes')\\n\\t\\t\\txtemp = np.array(ds[b'data']) #.reshape(10000,3,32,32).transpose(0,2,3,1).astype(np.uint8)\\n\\t\\t\\tytemp = np.array(ds[b'labels'])\\n\\n\\t\\tif fn.startswith(\\\"test\\\"):\\n\\t\\t\\tx_test = xtemp\\n\\t\\t\\ty_test = ytemp\\n\\n\\t\\tif fn.startswith(\\\"data\\\"):\\n\\t\\t\\tx_train = np.concatenate((xtemp,x_train), axis=0)\\n\\t\\t\\ty_train = np.concatenate((ytemp,y_train), axis=0)\\n\\t### END CODE HERE\\n\\n\\treturn x_train, y_train, x_test, y_test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eef24da965880b538b996ae68c599f22\",\n \"score\": \"0.5905727\",\n \"text\": \"def load_data(self):\\n self.idl.load_data()\\n self.legacy.load_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07618452435f299bce27fd188640865f\",\n \"score\": \"0.58980036\",\n \"text\": \"def load_data(self):\\n\\n # Now done at beginning to make sure it only does this one per target\\n if glob.glob(self.params['path']+'%d_*' % self.target) != []:\\n # Load light curve\\n if not os.path.exists(self.params['path']+'%d_LC.txt' % self.target):\\n if self.verbose:\\n print('Error: %s%d_LC.txt not found' % (self.params['path'], self.target))\\n return False\\n else:\\n self.get_file(self.params['path'] + '%d_LC.txt' % self.target)\\n self.time = np.copy(self.x)\\n self.flux = np.copy(self.y)\\n self.cadence = int(np.nanmedian(np.diff(self.time)*24.0*60.0*60.0))\\n if self.cadence/60.0 < 10.0:\\n self.short_cadence = True\\n else:\\n self.short_cadence = False\\n self.nyquist = 10**6/(2.0*self.cadence)\\n if self.verbose:\\n print('# LIGHT CURVE: %d lines of data read' % len(self.time))\\n if self.short_cadence:\\n self.fitbg['smooth_ps'] = 2.5\\n # Load power spectrum\\n if not os.path.exists(self.params['path'] + '%d_PS.txt' % self.target):\\n if self.verbose:\\n print('Error: %s%d_PS.txt not found' % (self.params['path'], self.target))\\n return False\\n else:\\n self.get_file(self.params['path'] + '%d_PS.txt' % self.target)\\n self.frequency = np.copy(self.x)\\n self.power = np.copy(self.y)\\n if self.keplercorr:\\n self.remove_artefact(self.frequency, self.power)\\n self.power = np.copy(self.y)\\n if self.verbose:\\n print('## Removing Kepler artefacts ##')\\n if self.verbose:\\n print('# POWER SPECTRUM: %d lines of data read' % len(self.frequency))\\n self.oversample = int(round((1./((max(self.time)-min(self.time))*0.0864))/(self.frequency[1]-self.frequency[0])))\\n self.resolution = (self.frequency[1]-self.frequency[0])*self.oversample\\n\\n if self.verbose:\\n print('-------------------------------------------------')\\n print('Target: %d' % self.target)\\n if self.oversample == 1:\\n print('critically sampled')\\n else:\\n print('oversampled by a factor of %d' % self.oversample)\\n print('time series cadence: %d seconds' % self.cadence)\\n print('power spectrum resolution: %.6f muHz' % self.resolution)\\n print('-------------------------------------------------')\\n # Create critically sampled PS\\n if self.oversample != 1:\\n self.freq = np.copy(self.frequency)\\n self.pow = np.copy(self.power)\\n self.frequency = np.array(self.frequency[self.oversample-1::self.oversample])\\n self.power = np.array(self.power[self.oversample-1::self.oversample])\\n else:\\n self.freq = np.copy(self.frequency)\\n self.pow = np.copy(self.power)\\n self.frequency = np.copy(self.frequency)\\n self.power = np.copy(self.power)\\n if hasattr(self, 'findex'):\\n if self.findex['do']:\\n # Make a mask using the given frequency bounds for the find excess routine\\n mask = np.ones_like(self.freq, dtype=bool)\\n if self.params[self.target]['lowerx'] is not None:\\n mask *= np.ma.getmask(np.ma.masked_greater_equal(self.freq, self.params[self.target]['lowerx']))\\n else:\\n mask *= np.ma.getmask(np.ma.masked_greater_equal(self.freq, self.findex['lower']))\\n if self.params[self.target]['upperx'] is not None:\\n mask *= np.ma.getmask(np.ma.masked_less_equal(self.freq, self.params[self.target]['upperx']))\\n else:\\n mask *= np.ma.getmask(np.ma.masked_less_equal(self.freq, self.findex['upper']))\\n self.freq = self.freq[mask]\\n self.pow = self.pow[mask]\\n return True\\n else:\\n print('Error: data not found for target %d' % self.target)\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea527761adf30721dfc4bd47d21a6514\",\n \"score\": \"0.58884585\",\n \"text\": \"def load_data():\\n def un_pickle(file):\\n with open(file, 'rb') as fo:\\n return_dict = pickle.load(fo, encoding='latin1')\\n return return_dict\\n xTr = np.array([], dtype=np.float64).reshape(0, 3072)\\n yTr = np.array([], dtype=np.int64).reshape(0)\\n for i in range(1, 6):\\n dataDict = un_pickle('cifar-10-batches-py/data_batch_'+str(i))\\n xTr = np.concatenate((xTr, dataDict['data']), axis=0)\\n yTr = np.concatenate((yTr, dataDict['labels']), axis=0)\\n xTe = un_pickle('cifar-10-batches-py/test_batch')['data'].astype(np.float64)\\n yTe = np.array(un_pickle('cifar-10-batches-py/test_batch')['labels'], dtype=np.int64)\\n return (xTr, yTr), (xTe, yTe)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8773ef4bdc17ea39aae9abc827e17b25\",\n \"score\": \"0.5883034\",\n \"text\": \"def load_data(args, data_dir=None):\\n df_all = pd.DataFrame(columns=['center', 'left', 'right', 'steering', 'throttle', 'reverse', 'speed'])\\n\\n if data_dir:\\n for part in data_dir:\\n part = os.path.join(args.data_dir, part)\\n for subdir, dirs, files in os.walk(part):\\n for file in files:\\n if file == 'driving_log.csv':\\n logging.info(\\\"Loading : \\\" + os.path.join(subdir, file))\\n try:\\n if dirs[0] != 'IMG':\\n logging.info(\\\"Missing IMG directory in \\\" + subdir)\\n break\\n except IndexError:\\n logging.info(\\\"No directories!\\\")\\n break\\n df_new = pd.read_csv(os.path.join(os.getcwd(), os.path.join(subdir, file)),\\n names=['center', 'left', 'right',\\n 'steering', 'throttle', 'reverse', 'speed'])\\n df_all = df_all.append(df_new)\\n\\n else:\\n for subdir, dirs, files in os.walk(args.data_dir):\\n for file in files:\\n if file == 'driving_log.csv':\\n logging.info(\\\"Loading : \\\" + os.path.join(subdir, file))\\n try:\\n if dirs[0] != 'IMG':\\n logging.info(\\\"Missing IMG directory in \\\" + subdir)\\n break\\n except IndexError:\\n logging.info(\\\"No directories!\\\")\\n break\\n df_new = pd.read_csv(os.path.join(os.getcwd(), os.path.join(subdir, file)),\\n names=['center', 'left', 'right',\\n 'steering', 'throttle', 'reverse', 'speed'])\\n df_all = df_all.append(df_new)\\n\\n # Checking for invalid data\\n if df_all.isnull().any().any():\\n logging.info('Corrupted data')\\n return -1\\n else:\\n logging.info('Data not corrupted')\\n\\n images_all = df_all[['center', 'left', 'right']].values\\n labels = df_all[['steering', 'speed']].values\\n\\n x_data, y_data = divide_images(images_all, labels)\\n\\n return x_data, y_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0af3f302a0ef5c610eacd197a1ede5b3\",\n \"score\": \"0.5882122\",\n \"text\": \"def dataArr(filename):\\r\\n #Open the file\\r\\n f=h5py.File(filename,'r')\\r\\n \\r\\n #Initialize the data arrays\\r\\n cdata=[]\\r\\n idxset=[]\\r\\n vertices=[]\\r\\n \\r\\n #Open groups in the file\\r\\n for group in f.keys():\\r\\n# print('Group- '+group)\\r\\n \\r\\n #Get the group\\r\\n currGroup=f[group]\\r\\n \\r\\n #Open keys in the group\\r\\n for key in currGroup.keys():\\r\\n# print('Key- '+key)\\r\\n \\r\\n #Append the data to the respective arrays\\r\\n if key=='cdata(Complex)':\\r\\n cdataGroup=currGroup[key]\\r\\n \\r\\n imag=[]\\r\\n real=[]\\r\\n #Open the keys in cdata\\r\\n for subkey in cdataGroup.keys():\\r\\n# print('Subkey- '+subkey)\\r\\n \\r\\n #Get the real and imaginary parts of the array\\r\\n if subkey=='Imag':\\r\\n imag=cdataGroup[subkey][()]\\r\\n elif subkey=='Real':\\r\\n real=cdataGroup[subkey][()]\\r\\n \\r\\n #Convert lists to numpy arrays\\r\\n imag=np.array(imag)\\r\\n real=np.array(real)\\r\\n #Get the cdata value\\r\\n cdata=real+1j*imag\\r\\n \\r\\n elif key=='idxset':\\r\\n idxset=currGroup[key][()]\\r\\n elif key=='vertices':\\r\\n vertices=currGroup[key][()]\\r\\n \\r\\n #Remove the unwanted component from the vertices\\r\\n #Vertices are stored in a 3 element array containing (x,y,z) but we only\\r\\n #need 2 to make flat plots, therefore we need to remove one based on the\\r\\n #plane on which the data is being plotted\\r\\n xVals=[]\\r\\n yVals=[]\\r\\n newVertices=[]\\r\\n for vertex in vertices:\\r\\n xVals.append(vertex[0])\\r\\n yVals.append(vertex[2])\\r\\n newVertices.append([vertex[0],vertex[1]])\\r\\n vertices=newVertices\\r\\n \\r\\n #Convert to numpy arrays\\r\\n cdata=np.array(cdata)\\r\\n xVals=np.array(xVals)\\r\\n yVals=np.array(yVals)\\r\\n \\r\\n #Close the file\\r\\n f.close()\\r\\n \\r\\n return cdata, xVals, yVals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b87d24f5dbcf7555daea8fc8a209185\",\n \"score\": \"0.588185\",\n \"text\": \"def load_data(self):\\n num_actual = len(list(CROPS_DIRNAME.glob('*.jpg')))\\n num_target = len(self.iam_dataset.line_regions_by_id)\\n if num_actual < num_target - 2: # There are a couple of instances that could not be cropped\\n self.process_iam_paragraphs()\\n\\n self.x, self.y, self.ids = self.load_iam_paragraphs()\\n self.train_ind, self.test_ind = get_random_split(self.x.shape[0])\\n ids_train, ids_test = self.train_ind, self.test_ind\\n self.x_train, self.y_train = self.x[ids_train], self.y[ids_train]\\n self.x_test, self.y_test = self.x[ids_test], self.y[ids_test]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7893cd93900c09766e0a131fcbc6b56\",\n \"score\": \"0.58773375\",\n \"text\": \"def init_data():\\n f, _ = my_bokeh_utils.string_to_function_parser(f_input.value, ['x', 'y'])\\n contour_f.compute_contour_data(f)\\n g, _ = my_bokeh_utils.string_to_function_parser(g_input.value, ['x', 'y'])\\n contour_g.compute_contour_data(g, isovalue=[0])\\n interactor.update_to_user_view()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f47ca393031185b349c408a4e6a20b9\",\n \"score\": \"0.58720464\",\n \"text\": \"def load_data(self) -> None:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dacafb09f8e025e7eb6be0b536b4ce6\",\n \"score\": \"0.5870313\",\n \"text\": \"def load_raw_data(self):\\n for i in self.dictionary_np_to_ar[self.n_pivot]:\\n file_name = (self.folder + '/result' + str(i) + '.txt')\\n f = open(file_name)\\n data = format_data(f, self.n_pivot)\\n self.raw_data[i] = data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c198f9f0b9b98de591a2b5a6ea9c494\",\n \"score\": \"0.58690315\",\n \"text\": \"def get_data(self, contour_path, image_path, index):\\n images = []\\n contours = []\\n # handle `/` missing\\n if contour_path[-1] != '/':\\n contour_path += '/'\\n if image_path[-1] != '/':\\n image_path += '/'\\n # get contour file\\n contour_file = self.get_contour_file(contour_path)\\n # Find contour data\\n contour_data = pydicom.read_file(contour_path + '/' + contour_file)\\n # Get ROI names\\n print(get_roi_names(contour_data))\\n # get slice orders\\n ordered_slices = self.slice_order(image_path)\\n print(ordered_slices[:5])\\n # get contour dict\\n contour_dict = self.get_contour_dict(contour_file, contour_path, image_path, index)\\n\\n for k, v in ordered_slices:\\n # get data from contour dict\\n if k in contour_dict:\\n images.append(contour_dict[k][0])\\n contours.append(contour_dict[k][1])\\n # get data from dicom.read_file\\n else:\\n fpaths = [image_path + f for f in os.listdir(image_path) if '.dcm' in f]\\n for fpath in fpaths:\\n img = pydicom.read_file(fpath)\\n scan_ID = img.SOPInstanceUID\\n if k == scan_ID:\\n print(\\\"Got From Dicom\\\")\\n img_arr = img.pixel_array\\n contour_arr = np.zeros_like(img_arr)\\n images.append(img_arr)\\n contours.append(contour_arr)\\n break\\n else:\\n print(\\\"Wrong File ID.\\\")\\n\\n return np.array(images), np.array(contours)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fb2505786c0c41d16e47e5e63141e57\",\n \"score\": \"0.5867914\",\n \"text\": \"def load_data_files(label, data_files):\\n data_x = np.empty((0, NUM_FEATURES))\\n data_y = np.empty((0))\\n\\n for filename in data_files:\\n try:\\n # data = np.loadtxt(BytesIO(zipped_dataset.read(filename)))\\n data = np.loadtxt(filename)\\n print('... file {0}'.format(filename))\\n x, y = process_dataset_file(data, label)\\n data_x = np.vstack((data_x, x))\\n data_y = np.concatenate([data_y, y])\\n # print(\\\"Data's shape yet: \\\", data_x.shape())\\n except KeyError:\\n print('ERROR: Did not find {0} in zip file'.format(filename))\\n return data_x, data_y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ef1ea225fe60f8614de107081cbb54f\",\n \"score\": \"0.586448\",\n \"text\": \"def load_dataset(self):\\n\\n\\t\\t# Paths to the training and testing data for STL-10 dataset.\\n\\t\\tpath = './dataset/stl-10'\\n\\t\\ttrain_data_path = os.path.join(path, 'train_X.bin')\\n\\t\\ttrain_label_path = os.path.join(path, 'train_y.bin')\\n\\t\\ttest_data_path = os.path.join(path, 'test_X.bin')\\n\\t\\ttest_label_path = os.path.join(path, 'test_y.bin')\\n\\n\\t\\t# Read the training data images and thier labels from the disk.\\n\\t\\tself.train_data = self.read_images(train_data_path)\\n\\t\\tself.train_labels = self.read_labels(train_label_path)\\n\\n\\t\\t# Read the test data images and thier labels from the disk.\\n\\t\\tself.test_data = self.read_images(test_data_path)\\n\\t\\tself.test_labels = self.read_labels(test_label_path)\\n\\n\\t\\treturn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5d23d3fd8c1c48eff2b95e0bed5ce17\",\n \"score\": \"0.58631\",\n \"text\": \"def __load_data(self, dirname):\\n #dirname = \\\"cifar-10-batches-py\\\"\\n origin = \\\"http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\\\"\\n path = get_file(dirname, origin=origin, untar=True)\\n\\n nb_train_samples = 50000\\n\\n X_train = np.zeros((nb_train_samples, 3, 32, 32), dtype=\\\"uint8\\\")\\n y_train = np.zeros((nb_train_samples,), dtype=\\\"uint8\\\")\\n\\n for i in range(1, 6):\\n fpath = os.path.join(path, 'data_batch_' + str(i))\\n data, labels = load_batch(fpath)\\n X_train[(i - 1) * 10000: i * 10000, :, :, :] = data\\n y_train[(i - 1) * 10000: i * 10000] = labels\\n\\n fpath = os.path.join(path, 'test_batch')\\n X_test, y_test = load_batch(fpath)\\n\\n y_train = np.reshape(y_train, (len(y_train), 1))\\n y_test = np.reshape(y_test, (len(y_test), 1))\\n\\n if K.image_data_format() == 'channels_last':\\n X_train = X_train.transpose(0, 2, 3, 1)\\n X_test = X_test.transpose(0, 2, 3, 1)\\n\\n return (X_train, y_train), (X_test, y_test)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da0d95df0c52bc86033c5de18b70304c\",\n \"score\": \"0.58627766\",\n \"text\": \"def get_data(download=True, unzip=True, load=True, loadupto=6000):\\n\\n from os import path, makedirs, getcwd, listdir\\n from zipfile import ZipFile\\n\\n filename = 'cocodataset'\\n zdirname = path.join(getcwd(), filename + '.zip')\\n fdirname = path.join(getcwd(), filename)\\n\\n # Download data\\n if download is True:\\n if path.exists(zdirname):\\n print('Ignored download: Cannot download file that already exists!')\\n else:\\n url = 'http://images.cocodataset.org/zips/val2017.zip'\\n filename = 'cocodataset'\\n urlretrieve(url, zdirname)\\n print('* File downloaded')\\n\\n # Unzip data\\n if unzip is True:\\n if path.exists(fdirname):\\n print('Ignored unzip: Cannot unzip file that already exists!')\\n else:\\n dirname = path.join(getcwd(), filename)\\n makedirs(dirname, exist_ok=True)\\n with ZipFile(filename + '.zip', 'r') as zipfile:\\n zipfile.extractall(fdirname)\\n print('* File unzipped')\\n\\n # Load data\\n if load is True:\\n images = []\\n shapes = []\\n i = 1\\n for fdir in listdir(fdirname):\\n for file in listdir(path.join(fdirname, fdir)):\\n fpath = path.join(*[fdirname, fdir, file])\\n image = Image.open(fpath)\\n image = np.array(image) # Convert to numpy array\\n shape = np.array(image.shape)\\n if len(shape) < 3: # Skip black and white images\\n continue\\n shapes.append(shape)\\n images.append(image)\\n if i >= loadupto:\\n break\\n i += 1\\n\\n # Trim all images to a the smallest size\\n shapes = np.array(shapes)\\n min_shape = np.amin(shapes, axis=0)\\n min_shape = (124, 124, 3)\\n data_imgs = np.array([image[: min_shape[0], : min_shape[1], : min_shape[2]] for image in images])\\n print('* Loaded {0} images to numpy array'.format(i))\\n\\n return data_imgs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3373dd382cf4dd644d2d7cc0f2ddd59b\",\n \"score\": \"0.5853483\",\n \"text\": \"def load_data(self, fname):\\n self.hyperspec_data = np.zeros((10,10,34))#np.random.rand(10,10,34)\\n self.display_image =np.zeros((10,10,34))# np.random.rand(10,10)\\n self.spec_x_array = 3*np.arange(34)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da8773f2f198f47b40ba8eebbbb85961\",\n \"score\": \"0.58465314\",\n \"text\": \"def _load_base_data(self, scp_fname, labels_fnames, labels_dtype):\\n args = self.args\\n if is_incremental(scp_fname):\\n scps = io.read_lines(scp_fname)\\n else:\\n scps = [scp_fname]\\n data_gens = []\\n for scp in scps:\\n data_gens.append(DataGenerator(\\n MultiLabelTemporalData.from_kaldi,\\n scp=scp, alipdfs=labels_fnames, num_pdfs=args.num_classes,\\n context=args.context, padding='replicate',\\n utt_feats_dict=self.ivectors, labels_dtype=labels_dtype\\n ))\\n return data_gens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4f3047a8fc11c017ed2ba0e252397d7\",\n \"score\": \"0.5832999\",\n \"text\": \"def _load_data(self, train_dicts=None, dev_dicts=None, test_dicts=None):\\n logger.info(\\\"\\\\nLoading data into the data silo ...\\\"\\n \\\"{}\\\".format(TRACTOR_SMALL))\\n\\n # dev data\\n dev_file = self.processor.data_dir / self.processor.dev_filename\\n logger.info(\\\"Loading dev set from: {}\\\".format(dev_file))\\n self.data[\\\"dev\\\"], self.tensor_names = self._get_dataset(dev_file)\\n\\n # skip the test data\\n self.data[\\\"test\\\"] = None\\n\\n if self.caching:\\n self._save_dataset_to_cache()\\n\\n # derive stats and meta data\\n self._calculate_statistics()\\n\\n self._initialize_data_loaders()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31579b0a33b87140f506abf0c66d3daa\",\n \"score\": \"0.58303064\",\n \"text\": \"def loadData(tempdir=None, nsamples=100, npoints=100000):\\n\\n\\t# def loadDataHelper(data):\\n\\t# \\treturn data\\n\\n\\tdata = np.zeros((0, npoints))\\n\\n\\tif not tempdir:\\n\\t\\ttempdir = TemporaryDirectory().name\\n\\t\\ttry:\\n\\t\\t\\tif platform == \\\"win32\\\":\\n\\t\\t\\t\\tsystem(\\\"wsl -r -N -c -np -P %s https://physionet.org/files/ptbdb/1.0.0/\\\" % tempdir)\\n\\t\\t\\tif platform == \\\"darwin\\\":\\n\\t\\t\\t\\tsystem(\\\"wget -r -N -c -np -P %s https://physionet.org/files/ptbdb/1.0.0/\\\" % tempdir)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tprint(\\\"I don't know what platform you have... Tell Ethan on Slack.\\\")\\n\\t\\t\\t\\treturn None, None\\n\\t\\texcept Exception as e:\\n\\t\\t\\tprint(\\\"[%s]:[%s] %s\\\" % __name, __func__, e)\\n\\t\\t\\tprint(\\\"Make sure if you are on Windows that you have wsl install. If you are on Mac, please install wget.\\\")\\n\\t\\t\\treturn None, None\\n\\n\\tfor dataloc in getDataFiles(tempdir):\\n\\t\\tif data.shape[0] == nsamples:\\n\\t\\t\\tbreak\\n\\t\\trec = rdrecord(dataloc, channels=[0])\\n\\t\\tx = rec.p_signal\\n\\t\\tif x is None:\\n\\t\\t\\tx = rec.d_signal\\n\\t\\tif x is None:\\n\\t\\t\\tcontinue\\n\\t\\tif len(x) < npoints:\\n\\t\\t\\tcontinue\\n\\t\\tx_t = np.transpose(x)\\n\\t\\tdata = np.vstack([data, x_t[:, 0:npoints]])\\n\\n\\treturn data, tempdir\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b38ce6813b44b62c416fd97ac6ace511\",\n \"score\": \"0.58130115\",\n \"text\": \"def _load_data():\\n import Oger as og\\n import csv\\n now = datetime.now()\\n now -= timedelta(microseconds=now.microsecond)\\n periods = 500\\n o2 = og.datasets.mackey_glass(sample_len=periods,\\n n_samples=1,\\n seed=1)[0][0].flatten()\\n o2 = _minmax_scale(o2, 96, 99)\\n timestamp = pd.date_range(now, periods=periods, freq='500L')\\n temperature = og.datasets.mackey_glass(sample_len=periods,\\n n_samples=1,\\n seed=3)[0][0].flatten()\\n temperature = _minmax_scale(temperature, 36, 37.5)\\n air_flow = og.datasets.mackey_glass(sample_len=periods,\\n n_samples=1,\\n seed=5)[0][0].flatten()\\n air_flow = _minmax_scale(air_flow, 0, 100)\\n heart_rate = og.datasets.mackey_glass(sample_len=periods,\\n n_samples=1,\\n seed=7)[0][0].flatten()\\n heart_rate = _minmax_scale(heart_rate, 60, 70)\\n data = zip(*[o2, temperature, air_flow, heart_rate])\\n data = pd.DataFrame(data, columns=['o2', 'temperature',\\n 'air_flow', 'heart_rate'],\\n index=timestamp)\\n data = data.resample('250L', fill_method='pad')\\n last_row = data.xs(data.index[-1])\\n last_row.name = data.index[-1] + timedelta(seconds=0.25)\\n data = data.append(last_row)\\n acc_magn = og.datasets.mackey_glass(sample_len=2 * periods,\\n n_samples=1,\\n seed=11)[0][0].flatten()\\n acc_magn = _minmax_scale(acc_magn, 0, 5)\\n data['acc_magn'] = acc_magn\\n data = data.resample('10L', fill_method='pad')\\n with open('ecg_v1.csv', 'rb') as f:\\n reader = csv.reader(f)\\n reader.next()\\n ecg_v1 = [float(r[0]) for r in reader]\\n last_rows = data.iloc[-24:]\\n last_rows['index'] = last_rows.index + timedelta(seconds=0.01)\\n last_rows.set_index('index', inplace=True)\\n data = data.append(last_rows)\\n data['ecg_v1'] = ecg_v1\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc74a33af253554a3113c55ae006da61\",\n \"score\": \"0.58085763\",\n \"text\": \"def _get_all_datas(self) -> None:\\n\\n # init\\n self.all_list = {\\\"train\\\": [], \\\"unknown\\\": [], \\\"known\\\": []}\\n self.classes = {}\\n\\n path = Path(self.path)\\n\\n # directories in [image_path]\\n dirs = [d for d in path.glob(\\\"*\\\") if d.is_dir()]\\n\\n # all extensions / all sub directories\\n for label_idx, _dir in enumerate(dirs):\\n xs = []\\n\\n for ext in self.extensions:\\n tmp = [\\n Data(x.as_posix(), label_idx, _dir.name)\\n for x in _dir.glob(f\\\"*.{ext}\\\")\\n if x.is_file()\\n ]\\n xs.extend(tmp)\\n\\n # adjust to limit size\\n if self.limit_size is not None:\\n random.shuffle(xs)\\n xs = xs[: self.limit_size]\\n\\n # split dataset\\n train, test = train_test_split(xs, test_size=self.test_size, shuffle=True)\\n\\n self.all_list[\\\"train\\\"].extend(train)\\n self.all_list[\\\"unknown\\\"].extend(test)\\n self.all_list[\\\"known\\\"].extend(random.sample(train, len(test)))\\n\\n self.classes[label_idx] = _dir.name\\n\\n self.train_size = len(self.all_list[\\\"train\\\"])\\n self.unknown_size = len(self.all_list[\\\"unknown\\\"])\\n self.known_size = len(self.all_list[\\\"known\\\"])\\n\\n self.all_size = self.train_size + self.unknown_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ceb46d7fcff3d74e2c7dcbbed3fa9794\",\n \"score\": \"0.57918715\",\n \"text\": \"def load(self, root):\\n files = os.listdir(root)\\n data_x = []\\n data_y = []\\n data_traj_x = []\\n data_traj_y = []\\n seq_continuity = []\\n traj_continuity = []\\n for i in files:\\n # Load file\\n tmp = self.read_file(os.path.join(root,i))\\n # Remove time-stamp if need be\\n tmp = self.remove_ts(tmp)\\n # split the input and targets\\n tmp_x, tmp_y = self.split_input_output(tmp)\\n # generate trajectories\\n traj_x, traj_y = self.trajectory_generator(tmp_x, tmp_y)\\n # generates sequences for training\\n tmp_x, tmp_y, seq_idx = self.sequence_generator(tmp_x, tmp_y)\\n # append for concatenation\\n data_traj_x.append(traj_x)\\n data_traj_y.append(traj_y)\\n data_x.append(tmp_x)\\n data_y.append(tmp_y)\\n seq_continuity.append(seq_idx)\\n numpy_traj_x = np.concatenate((data_traj_x), axis=0)\\n numpy_traj_y = np.concatenate((data_traj_y), axis=0)\\n numpy_data_x = np.concatenate((data_x), axis=0)\\n numpy_data_y = np.concatenate((data_y), axis=0)\\n numpy_seq_c = np.concatenate((seq_continuity), axis=0)\\n return numpy_data_x, numpy_data_y, numpy_traj_x, numpy_traj_y, numpy_seq_c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3792f2d543748635903f34aa4f80cd07\",\n \"score\": \"0.57902795\",\n \"text\": \"def populate_data(self):\\r\\n print('FINISH: Load drone data')\\r\\n self.tree.delete(*self.tree.get_children())\\r\\n for drone in self.drones.list():\\r\\n values = (drone.id, drone.name, drone.class_type, drone.rescue, drone.operator)\\r\\n self.tree.insert('', 'end', values=values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"181ef563a94caa35e8eccb2f9ca977f1\",\n \"score\": \"0.57892513\",\n \"text\": \"def get_data(self, year, month, day, run):\\n labrad.cd(year, month, day, run, kind='images')\\n images = _dv.dir()[1]\\n for image in images:\\n _dv.open(image)\\n data = list(_dv.get())\\n self.d['images'].append(data)\\n self.d['sums'].append(sum(data))\\n self.d['sterr'].append(_sem(data))\\n self._get_params()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9f43efef1f6026ca5ca21e7287c7ee1\",\n \"score\": \"0.5788692\",\n \"text\": \"def load_data(self):\\r\\n\\r\\n X, Y = np.empty((self.num_instances, self.img_size, self.img_size, self.num_variables)), \\\\\\r\\n np.empty((self.num_instances, self.num_variables))\\r\\n print(X.shape)\\r\\n\\r\\n # Initialize PAA transformer\\r\\n paa = PiecewiseAggregateApproximation(window_size=None, output_size=self.img_size, overlapping=False)\\r\\n rp = RecurrencePlot()\\r\\n\\r\\n # For all instance\\r\\n start = time.time()\\r\\n for idx, row in enumerate(self.data.iterrows()):\\r\\n for i in range(self.num_variables):\\r\\n # Get current variable's series\\r\\n # Apply linear interpolation on missing values\\r\\n s = row[1][i].interpolate(limit_direction='both').to_numpy()[:self.ts_length]\\r\\n # Apply PAA and RP\\r\\n X[idx, :, :, i] = rp.transform(paa.transform(np.expand_dims(s[:-1], axis=0)))[0]\\r\\n Y[idx, i] = s[-1]\\r\\n end = time.time()\\r\\n print(f\\\"Data loaded in {end - start} seconds\\\")\\r\\n\\r\\n return X, Y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98e27adf8a16a4a577cd1dcd9faa7043\",\n \"score\": \"0.57796514\",\n \"text\": \"def _load_data(self):\\n m = ROW_END - ROW_START\\n n = COL_END - COL_START\\n t = self.data.time.size\\n\\n low_res = []\\n for c in LOW_RES_CHANNELS:\\n channel_name = f\\\"C{c:02}\\\"\\n if channel_name in self.data:\\n x = self.data[channel_name].data\\n else:\\n x = np.zeros((t, m, n), dtype=np.float32)\\n x[:] = np.nan\\n low_res.append(x)\\n low_res = np.stack(low_res, axis=1)\\n\\n med_res = []\\n for c in MED_RES_CHANNELS:\\n channel_name = f\\\"C{c:02}\\\"\\n if channel_name in self.data:\\n x = self.data[channel_name].data\\n else:\\n x = np.zeros((t, 2 * m, 2 * n), dtype=np.float32)\\n x[:] = np.nan\\n med_res.append(x)\\n med_res = np.stack(med_res, axis=1)\\n\\n hi_res = []\\n for c in HI_RES_CHANNELS:\\n channel_name = f\\\"C{c:02}\\\"\\n if channel_name in self.data:\\n x = self.data[channel_name].data\\n else:\\n x = np.zeros((t, 4 * m, 4 * n), dtype=np.float32)\\n x[:] = np.nan\\n hi_res.append(x)\\n hi_res = np.stack(hi_res, axis=1)\\n\\n invalid = np.any(np.all(np.isnan(low_res), axis=1), axis=(-2, -1))\\n invalid *= np.any(np.all(np.isnan(med_res), axis=1), axis=(-2, -1))\\n invalid *= np.any(np.all(np.isnan(hi_res), axis=1), axis=(-2, -1))\\n valid = ~invalid\\n low_res = low_res[valid]\\n med_res = med_res[valid]\\n hi_res = hi_res[valid]\\n\\n low_res = self.normalizer[0](low_res)\\n med_res = self.normalizer[1](med_res)\\n hi_res = self.normalizer[2](hi_res)\\n\\n self.x = (torch.tensor(low_res), torch.tensor(med_res), torch.tensor(hi_res))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aae977be42c250a4b0c917ff5e27310a\",\n \"score\": \"0.5768356\",\n \"text\": \"def load(self, root):\\n files = os.listdir(root)\\n data_x = []\\n data_y = []\\n data_traj_x = []\\n data_traj_y = []\\n for i in files:\\n # Load file\\n tmp = self.read_file(os.path.join(root,i))\\n # Remove time-stamp if need be\\n tmp = self.remove_ts(tmp)\\n # split the input and targets\\n tmp_x, tmp_y = self.split_input_output(tmp)\\n # generate trajectories\\n traj_x, traj_y = self.trajectory_generator(tmp_x, tmp_y)\\n # generates sequences for training\\n tmp_x, tmp_y = self.sequence_generator(tmp_x, tmp_y)\\n # append for concatenation\\n data_traj_x.append(traj_x)\\n data_traj_y.append(traj_y)\\n data_x.append(tmp_x)\\n data_y.append(tmp_y)\\n # concatenates as a numpy array\\n numpy_traj_x = np.concatenate((data_traj_x), axis=0)\\n numpy_traj_y = np.concatenate((data_traj_y), axis=0)\\n numpy_data_x = np.concatenate((data_x), axis=0)\\n numpy_data_y = np.concatenate((data_y), axis=0)\\n return numpy_data_x, numpy_data_y, numpy_traj_x, numpy_traj_y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bb1a5c5ce86aaad64587bf638dbe90d\",\n \"score\": \"0.5767288\",\n \"text\": \"def __get_local_data__(self):\\n if self.param.data_name in ['djia30', 'webtraffic', 'netflow', 'clockerr']:\\n self.raw_data_array['data'] = np.load(os.path.join(self.param.data_path, self.param.data_name, 'series.npy'))\\n self.raw_data_array['label'] = np.load(os.path.join(self.param.data_path, self.param.data_name, 'events.npy'))\\n\\n else:\\n raise Exception('no data')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df6cce4bd2439a921e7ddae19dc9c14d\",\n \"score\": \"0.5759458\",\n \"text\": \"def dataArr(filename):\\r\\n #Open the file\\r\\n f=h5py.File(filename,'r')\\r\\n \\r\\n #Initialize the data arrays\\r\\n cdata=[]\\r\\n idxset=[]\\r\\n vertices=[]\\r\\n \\r\\n #Open groups in the file\\r\\n for group in f.keys():\\r\\n# print('Group- '+group)\\r\\n \\r\\n #Get the group\\r\\n currGroup=f[group]\\r\\n \\r\\n #Open keys in the group\\r\\n for key in currGroup.keys():\\r\\n# print('Key- '+key)\\r\\n \\r\\n #Append the data to the respective arrays\\r\\n if key=='cdata(Complex)':\\r\\n cdataGroup=currGroup[key]\\r\\n \\r\\n imag=[]\\r\\n real=[]\\r\\n #Open the keys in cdata\\r\\n for subkey in cdataGroup.keys():\\r\\n# print('Subkey- '+subkey)\\r\\n \\r\\n #Get the real and imaginary parts of the array\\r\\n if subkey=='Imag':\\r\\n imag=cdataGroup[subkey][()]\\r\\n elif subkey=='Real':\\r\\n real=cdataGroup[subkey][()]\\r\\n \\r\\n #Convert lists to numpy arrays\\r\\n imag=np.array(imag)\\r\\n real=np.array(real)\\r\\n #Get the cdata value\\r\\n cdata=real+1j*imag\\r\\n \\r\\n elif key=='idxset':\\r\\n idxset=currGroup[key][()]\\r\\n elif key=='vertices':\\r\\n vertices=currGroup[key][()]\\r\\n \\r\\n #Remove the z component from the vertices\\r\\n xVals=[]\\r\\n yVals=[]\\r\\n newVertices=[]\\r\\n for vertex in vertices:\\r\\n xVals.append(vertex[0])\\r\\n yVals.append(vertex[1])\\r\\n newVertices.append([vertex[0],vertex[1]])\\r\\n vertices=newVertices\\r\\n \\r\\n #Convert to numpy arrays\\r\\n cdata=np.array(cdata)\\r\\n xVals=np.array(xVals)\\r\\n yVals=np.array(yVals)\\r\\n \\r\\n #Close the file\\r\\n f.close()\\r\\n \\r\\n return cdata, xVals, yVals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71fdd269f8f8d31cf0393b344b97ff10\",\n \"score\": \"0.5757709\",\n \"text\": \"def load_data(files,varname,returndata,x,y):\\n for i,f in enumerate(files):\\n data=swim_io.read_nc(f,varname).data\\n returndata[:,y[i],x[i]]=data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"591a196cd3c2413a4db125b1642c64f0\",\n \"score\": \"0.5750041\",\n \"text\": \"def _data_generation_(self, list_IDs_temp):\\n # Initialization\\n X = np.empty((self.batch_size, *self.dim))\\n # Generate data\\n for i, ID in enumerate(list_IDs_temp):\\n # Load sample\\n try:\\n x = np.load(self.data_directory + str(ID) + self.npy)\\n try:\\n # pre-processing\\n X[i,] = self._preprocess_sample_(x)\\n\\n except (ValueError, KeyError):\\n warnings.warn('Shape or key-error {} sample'.format(ID))\\n\\n except FileNotFoundError:\\n warnings.warn(ID, ' not found')\\n\\n return X, X\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a4f22eb4f75dbd8eb1e0ad9c96a6bf5\",\n \"score\": \"0.57446283\",\n \"text\": \"def __load_data(self, delimiter, semantic_mapping):\\n # List that contains all the frames of the datasets. Each dataset is a\\n # list of frames of shape (num_peds, (frameID, pedID, x and y))\\n self.__frames = []\\n self.__navigation_map = []\\n self.__top_left = []\\n self.__semantic_map = []\\n self.__homography_matrix = []\\n semantic_map_labeled = {}\\n homography_map = {}\\n\\n # Load and add the one hot encoding to the semantic maps\\n for i, smap in enumerate(self.__semantic):\\n # Load the semantic map\\n semantic_map = np.load(smap)\\n homography = np.loadtxt(self.__homography[i], delimiter=delimiter)\\n filename = os.path.splitext(os.path.basename(smap))[0]\\n semantic_map_labeled[filename] = semantic_map\\n homography_map[filename] = homography\\n\\n for i, dataset_path in enumerate(self.__datasets):\\n # Load the dataset. Each line is formed by frameID, pedID, x, y\\n dataset = np.loadtxt(dataset_path, delimiter=delimiter)\\n # Get the frames in dataset\\n num_frames = np.unique(dataset[:, 0])\\n # Initialize the array of frames for the current dataset\\n frames_dataset = []\\n # Load the navigation map\\n navigation_map = np.load(self.__navigation[i])\\n\\n # Image has padding so we add padding to the top_left point.\\n top_left = [\\n np.floor(min(dataset[:, 2]) - self.neighborood_size / 2),\\n np.ceil(max(dataset[:, 3]) + self.neighborood_size / 2),\\n ]\\n\\n # For each frame add to frames_dataset the pedestrian that appears\\n # in the current frame\\n for frame in num_frames:\\n # Get the pedestrians\\n frame = dataset[dataset[:, 0] == frame, :]\\n frames_dataset.append(frame)\\n\\n self.__frames.append(frames_dataset)\\n self.__navigation_map.append(navigation_map)\\n self.__top_left.append(top_left)\\n self.__semantic_map.append(semantic_map_labeled[semantic_mapping[i]])\\n self.__homography_matrix.append(homography_map[semantic_mapping[i]])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a470c6efad897bdd2e6e5f87684732d\",\n \"score\": \"0.5743957\",\n \"text\": \"def load_data(self):\\n pass # stub\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a470c6efad897bdd2e6e5f87684732d\",\n \"score\": \"0.5743957\",\n \"text\": \"def load_data(self):\\n pass # stub\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac10a8e42dc6d1ecbc7a45164a65708c\",\n \"score\": \"0.57406956\",\n \"text\": \"def load_data():\\n # Clear all of the data structures\\n log.info(\\\"Clearing data structures for full resync\\\")\\n eps_by_host.clear()\\n all_groups.clear()\\n ips_by_endpointid.clear()\\n\\n result = client.read('/calico', recursive=True)\\n\\n # Iterate over all the leaves that we get back. For each leave we get the full path,\\n # so we parse that to determine whether to process the key as network or endpoint API data.\\n # The goal of this iteration is to get the data into a simple Python data structure,\\n # as opposed to the slightly complicated etcd datastructure.\\n for res in result.leaves:\\n log.debug(\\\"Processing key %s\\\", res.key)\\n keyparts = res.key.split(\\\"/\\\")\\n\\n try:\\n if keyparts[2] == \\\"network\\\":\\n log.debug(\\\"Network\\\")\\n process_network_data(res, keyparts)\\n elif keyparts[4] == \\\"workload\\\":\\n log.debug(\\\"Endpoint\\\")\\n process_endpoint_data(res, keyparts)\\n else:\\n log.debug(\\\"Ignoring key %s\\\", res.key)\\n continue\\n except IndexError:\\n log.debug(\\\"Ignoring key %s\\\", res.key)\\n continue\\n log.info(\\\"Finished reading data. Database contains %s hosts and %s groups\\\",\\n len(eps_by_host), len(all_groups))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd1016a1313c54680eb3daf9417d6a11\",\n \"score\": \"0.57384074\",\n \"text\": \"def load_dataset(self):\\n # for mnist data use the function\\n if self.dataset_name == 'mnist':\\n self.data_X, self.data_y = load_mnist(self.y_dim, train=True) # load_mnist() imported from utils.py\\n self.data_v=self.data_X[:,:,self.v_col] # extract columns of each image\\n self.c_dim = self.data_X[0].shape[-1] # self.data_X[0] is first image, .shape[-1] is the dim of each element of the image\\n self.v_dim = self.data_v.shape[2]\\n print(\\\"Dim v: \\\",self.data_v.shape)\\n else:\\n self.data = glob(os.path.join(\\\"./data\\\", self.dataset_name, self.input_fname_pattern))\\n imreadImg = imread(self.data[0])\\n\\n if len(imreadImg.shape) >= 3:\\n # check if image is a non-grayscale image by checking channel number\\n self.c_dim = imread(self.data[0]).shape[-1]\\n else:\\n self.c_dim = 1\\n\\n self.grayscale = (self.c_dim == 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"603b66c39825f716ed070c9341ff4bc6\",\n \"score\": \"0.5734043\",\n \"text\": \"def _load(dir, samples, points, obsolete, debug=False):\\n # Initialize storage arrays\\n pd_r = N.zeros((samples, points))\\n ref_r = N.zeros((samples, points))\\n v_r = N.zeros((samples, points))\\n i_r = N.zeros((samples, points))\\n pd_s = N.zeros((samples, points))\\n ref_s = N.zeros((samples, points))\\n v_s = N.zeros((samples, points))\\n i_s = N.zeros((samples, points))\\n \\n # Loop over all acquired files\\n for i in range(samples):\\n if obsolete:\\n reference = N.loadtxt(path.join(dir, 'Reference%g.dat' % i),\\n delimiter='\\\\t', skiprows=2)\\n signal = N.loadtxt(path.join(dir, 'Signal%g.dat' % i),\\n delimiter='\\\\t', skiprows=2)\\n else:\\n reference = N.loadtxt(path.join(dir, 'Reference%g.dat' % i),\\n delimiter='\\\\t', usecols=(1,2,3,4))\\n signal = N.loadtxt(path.join(dir, 'Signal%g.dat' % i),\\n delimiter='\\\\t', usecols=(1,2,3,4))\\n\\n pd_r[i,:] = reference[:,0]\\n ref_r[i,:] = reference[:,1]\\n v_r[i,:] = reference[:,2]\\n i_r[i,:] = reference[:,3]\\n \\n pd_s[i,:] = signal[:,0]\\n ref_s[i,:] = signal[:,1]\\n v_s[i,:] = signal[:,2]\\n i_s[i,:] = signal[:,3]\\n \\n return (pd_s, pd_r), (ref_s, ref_r)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a643e91249dc3c7e6751300e2ecfc35\",\n \"score\": \"0.57268494\",\n \"text\": \"def load_data(self):\\n\\n\\t\\tr = requests.get(RankDrugRev.data_url, verify = False)\\n\\t\\tz = zipfile.ZipFile(io.BytesIO(r.content))\\n\\t\\tdf_train = pd.read_csv(io.BytesIO(z.read(\\\"drugsComTrain_raw.tsv\\\")),sep = '\\\\t', parse_dates = [5])\\n\\t\\tdf_train.rename(columns={\\\"Unnamed: 0\\\": \\\"id\\\"}, inplace = True)\\n\\n\\t\\tdf_test = pd.read_csv(io.BytesIO(z.read(\\\"drugsComTest_raw.tsv\\\")),sep = '\\\\t', parse_dates = [5])\\n\\t\\tdf_test.rename(columns={\\\"Unnamed: 0\\\": \\\"id\\\"}, inplace = True)\\n\\t\\n\\t\\tself.raw_data_train = df_train\\n\\t\\tself.raw_data_test = df_test\\n\\n\\t\\tself._raw_data_loaded = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22fca985869efdbbaf4b86257661055e\",\n \"score\": \"0.57258135\",\n \"text\": \"def load_data(self):\\n\\t\\tmodels_name = os.path.join(self.data_dir,'Public_Objects.p')\\n\\t\\tmodels_file = open(models_name,'rb')\\n\\t\\tself.models = pickle.load(models_file)\\n\\t\\tmodels_file.close()\\n\\n\\t\\tsne_name = os.path.join(self.data_dir,'Public_Novas_Phase.p')\\n\\t\\tsne_file = open(sne_name,'rb')\\n\\t\\tself.novas = pickle.load(sne_file)\\n\\t\\tsne_file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b102e355a9a66c48e7dd854605171b23\",\n \"score\": \"0.57233614\",\n \"text\": \"def load_data(self, DATA_DIR):\\r\\n # class files\\r\\n self.class_idxtonum = {}\\r\\n self.class_numtoidx = {}\\r\\n self.img_clsnum = {}\\r\\n self.model_clsnum = {}\\r\\n img_cls_path = DATA_DIR+'/Image_%s.cla' % self.mode.capitalize()\\r\\n model_cls_path = DATA_DIR+'/Model.cla'\\r\\n self.read_class_file(img_cls_path, 'img')\\r\\n self.read_class_file(model_cls_path, 'model')\\r\\n\\r\\n # img filenames\\r\\n self.IMG_DIR = os.path.join(DATA_DIR, 'IMAGES')\\r\\n class_keys = [x for x in self.class_numtoidx.keys()]\\r\\n self.img_filenames = []\\r\\n for key in class_keys:\\r\\n img_dir = os.path.join(self.IMG_DIR, self.class_numtoidx[key], self.mode.lower())\\r\\n filenames = [os.path.join(img_dir, x+'.png') for x in self.class_imgs[key]]\\r\\n filenames = sorted(filenames[:])\\r\\n if self.shuffle:\\r\\n shuffle(filenames)\\r\\n self.img_filenames.append(filenames)\\r\\n\\r\\n# # model filenames\\r\\n# self.MODEL_DIR = os.path.join(DATA_DIR, 'TARGET_MODELS/models')\\r\\n# self.model_filenames = glob.glob(self.MODEL_DIR+'/*.off') # or png\\r\\n\\r\\n # view filenames\\r\\n self.VIEW_DIR = os.path.join(DATA_DIR, 'VIEWS') #_GRAY_BLACK')\\r\\n self.view_len = 0\\r\\n self.view_filenames = {}\\r\\n for key in class_keys:\\r\\n view_dir = os.path.join(self.VIEW_DIR, self.class_numtoidx[key])\\r\\n filenames = glob.glob(view_dir+'/*.png')\\r\\n filenames = sorted(filenames[:])\\r\\n if self.shuffle:\\r\\n shuffle(filenames)\\r\\n model_name = self.class_numtoidx[key]\\r\\n self.view_filenames[model_name] = []\\r\\n self.view_len += self.view_num\\r\\n step = 1 #len(filenames)//self.view_num\\r\\n for idx in range(len(filenames)): #self.view_num):#filename in filenames[0:self.view_num]: # maybe view_num == len(filenames)\\r\\n self.view_filenames[model_name].append(filenames[min(idx*step, len(filenames)-1)].split('/')[-1].rstrip('.png'))\\r\\n# self.view_len += 1\\r\\n\\r\\n # for calculating epoch..\\r\\n# self.whole_batch_size = self.img_len if (self.train_mode == 'img' or self.mode == 'test') else self.model_len\\r\\n\\r\\n self.whole_batch_size = self.model_len if self.train_mode == 'view' else self.img_len\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7983f22dbaeb857a4600fc7f005c6dc\",\n \"score\": \"0.57144225\",\n \"text\": \"def load_data(self):\\n if hasattr(self, 'xtr'):\\n return # has already been run\\n\\n with h5py.File('./data/Input/train/images_training.h5','r') as H:\\n self.xtr = np.copy(H['datatrain'])\\n\\n with h5py.File('./data/Input/train/labels_training.h5','r') as H:\\n self.ytr = np.copy(H['labeltrain'])\\n\\n with h5py.File('./data/Input/test/labels_testing_2000.h5', 'r') as H:\\n self.yte = np.copy(H['labeltest'])\\n\\n with h5py.File('./data/Input/test/images_testing.h5', 'r') as H:\\n self.xte = np.copy(H['datatest'])[:len(self.yte)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea5fef430d34d95494c0e9b0b32fb410\",\n \"score\": \"0.5712898\",\n \"text\": \"def create_dataset(): \\n # load the temperature data from years 1999-2009\\n # don't include the 29. feb\\n # use the maximum from the 4 values per day (maxtemp)\\n years = list(range(1999, 2009))\\n longitude = -79.63 + 180\\n latitude = 43.68\\n grib_folder = '/media/isa/VIS1/temperature/'\\n maxtemp_data = np.empty((365, len(years)))\\n\\n # load the data\\n for year_it in range(len(years)):\\n f = open(grib_folder + str(years[year_it]) + '.grib')\\n time_it = 0\\n day_it = 0\\n day_data = np.empty((4))\\n \\n while 1:\\n gid = codes_grib_new_from_file(f)\\n if (gid is None):\\n break\\n # check for feb 29\\n dataDate = str(codes_get(gid, 'dataDate')).zfill(8)\\n if (dataDate[4:6] == '02' and dataDate[6:8] == '29'):\\n codes_release(gid)\\n print ('skipping ' + dataDate)\\n continue\\n\\n nearest = codes_grib_find_nearest(gid, latitude, longitude)[0]\\n \\n if (nearest.value == codes_get_double(gid, 'missingValue')):\\n raise Warning('missing value!')\\n \\n day_data[time_it] = nearest.value\\n \\n time_it = time_it + 1\\n if time_it == 4:\\n maxtemp_data[day_it, year_it] = day_data.max()\\n day_it = day_it + 1\\n time_it = 0\\n \\n codes_release(gid)\\n \\n f.close()\\n \\n min_data, ptp_data = maxtemp_data.min(), maxtemp_data.ptp()\\n maxtemp_data = (maxtemp_data - min_data) / ptp_data\\n \\n # x: [year0, year1, ...., year9]\\n # y: the next day, ie year9 + 1 day\\n # get x data: x is all samples but the last (it wouldn't have an corresponding y)\\n data_x = maxtemp_data[:-1, :]\\n # get y data: y is the next day\\n data_y = np.empty((364))\\n for d in range(364):\\n data_y[d] = maxtemp_data[d + 1, -1]\\n\\n train_x, test_x, train_y, test_y = train_test_split(data_x, data_y, test_size=0.2)\\n \\n dataset = AttrDict()\\n dataset.train_x = train_x\\n dataset.test_x = test_x\\n dataset.train_y = train_y\\n dataset.test_y = test_y\\n dataset.min_data = min_data\\n dataset.ptp_data = ptp_data\\n return dataset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03a48649c1008eec000154e0fa10ab03\",\n \"score\": \"0.571136\",\n \"text\": \"def extractImageData(self): \\n if len(self.id_img_train) == 0 or len(self.id_img_test) == 0 :\\n self.loadBasicData()\\n\\n if len(self.X_img_train) == 0:\\n self.X_img_train=self.extractImagesCaracteristics(self.id_img_train).to_numpy()\\n\\n if len(self.X_img_test) == 0:\\n self.X_img_test=self.extractImagesCaracteristics(self.id_img_test).to_numpy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05efaa37c305e833a3dbc863a3c9785f\",\n \"score\": \"0.5711356\",\n \"text\": \"def readAllData(self):\\n\\n # Read basic data parameters (number of energy groups, assemblies, axial nodes, etc.)\\n self.read1D()\\n\\n # Read the hex ordering map between DIF3D \\\"four color\\\" nodal and DIF3D GEODST\\n # Also read index pointers to incoming partial currents on outer reactor surface\\n # (these don't belong to any assembly)\\n # Incoming partial currents are non-zero due to flux extrapolation\\n ng = self.fc[\\\"ngroup\\\"] # number of energy groups\\n imax = self.fc[\\n \\\"ninti\\\"\\n ] # number of triangular mesh cells in \\\"i\\\" direction (rhombus or rectangle cells)\\n jmax = self.fc[\\n \\\"nintj\\\"\\n ] # number of triangular mesh cells in \\\"j\\\" direction (rhombus or rectangle cells)\\n zmax = self.fc[\\n \\\"nintk\\\"\\n ] # number of axial nodes (same for each assembly in DIF3D)\\n\\n self.triangleFluxes = numpy.zeros((imax, jmax, zmax, ng))\\n\\n for g in range(ng): # loop through energy groups\\n\\n gEff = self.getEnergyGroupIndex(g)\\n\\n for z in range(zmax): # loop through axial nodes\\n self.triangleFluxes[\\n :, :, z, gEff\\n ] = self.readTriangleFluxes() # read fluxes on this i-j plane\\n\\n self.f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"622e5f7b457db2100d5619d8206ba1fc\",\n \"score\": \"0.5710531\",\n \"text\": \"def loadNext(self, files, c):\\n\\n # If I have already retrieved all data, return None\\n if self.counters[c] == len(files):\\n self.counters[c] += 1\\n return None\\n # Next time, it will start over\\n elif self.counters[c] > len(files):\\n self.counters[c] = 0\\n\\n target = files[self.counters[c]]\\n study, timepoint, subject = target.split(\\\"/\\\")[-4:-1]\\n id_ = study + \\\"_\\\" + timepoint + \\\"_\\\" + subject\\n\\n # This if controls that the behavior is different when the container\\n # used when loadInMemory is not empty.\\n # TODO:\\n # - Put in a single container all data loaded.\\n # - Read X, process Y, pass to the child class.\\n # - Create child class to get X,Y,ids and return everything processed.\\n try:\\n X_train = self.X_container[id_][\\\"in_volume\\\"]\\n Y_train = self.Y_container[id_][\\\"out_segmentation\\\"]\\n except:\\n # Read the actual data\\n X_train = nib.load(target+\\\"scan.nii.gz\\\").get_data()\\n\\n X_train = np.moveaxis(X_train, -1, 0)\\n X_train = np.expand_dims(X_train, axis=0)\\n\\n if c == 1:\\n ext = \\\"_lesion\\\"\\n else:\\n ext = self.ext\\n if os.path.isfile(target+\\\"scan\\\"+ext+\\\".nii.gz\\\"):\\n Y_train = nib.load(target+\\\"scan\\\"+ext+\\\".nii.gz\\\").get_data()\\n #Y_train = np.expand_dims(Y_train, -1)\\n Y_train = np.stack([1.0*(Y_train==j) for j in range(2)], axis=0)\\n else:\\n Y_train = np.ones([2] + list(X_train.shape[2:]))\\n Y_train[1,:,:,:] = 0\\n #print(Y_train.shape)\\n\\n #Y_train = np.moveaxis(Y_train, -1, 0)\\n Y_train = np.expand_dims(Y_train, 0)\\n\\n self.counters[c] += 1\\n\\n # The ID must be a list, so that I can later iterate over it\\n #return X_train, Y_train, [id_]\\n return X_train, Y_train, [id_]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68d324729d695a57de8de489891b6847\",\n \"score\": \"0.5709875\",\n \"text\": \"def readSampledGEOS(self):\\n col = 'aer_Nv'\\n if self.verbose: \\n print 'opening file',self.LBinFile.replace('%col',col)\\n nc = Dataset(self.LBinFile.replace('%col',col))\\n\\n print 'File opened'\\n\\n for sds in self.SDS_AER:\\n sds_ = sds\\n if sds in ncALIAS:\\n sds_ = ncALIAS[sds]\\n if self.verbose: \\n print 'Reading ',sds_ \\n var = nc.variables[sds_][:]\\n self.__dict__[sds] = var\\n if self.verbose: \\n print 'Finished Reading ',sds_\\n\\n nc.close()\\n\\n if self.verbose: \\n print 'opening file',self.LCinFile\\n nc = Dataset(self.LCinFile)\\n\\n for sds in self.SDS_CLD:\\n sds_ = sds\\n if sds in ncALIAS:\\n sds_ = ncALIAS[sds]\\n var = nc.variables[sds_][:]\\n self.__dict__[sds] = var\\n if self.verbose: \\n print 'Finished Reading',sds_\\n\\n nc.close()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994179,"cells":{"query_id":{"kind":"string","value":"b0863bb99a634fc50175ddef28794392"},"query":{"kind":"string","value":"Draw a correlation heatmap and return the frame."},"positive_passages":{"kind":"list like","value":[{"docid":"e8041ceb481120c7ee9fd76c08fa2068","score":"0.69290173","text":"def get_correlation(frame, outdir=None):\n\n corr = frame.corr()\n\n if outdir != None:\n corr.to_csv(outdir/'correlation.csv')\n plt.figure()\n sns.heatmap(corr, cmap=plt.cm.RdYlBu_r)\n plt.title('Correlation Heatmap')\n plt.tight_layout()\n plt.savefig(str(outdir/'correlation.png'))\n plt.close()\n\n return corr","title":""}],"string":"[\n {\n \"docid\": \"e8041ceb481120c7ee9fd76c08fa2068\",\n \"score\": \"0.69290173\",\n \"text\": \"def get_correlation(frame, outdir=None):\\n\\n corr = frame.corr()\\n\\n if outdir != None:\\n corr.to_csv(outdir/'correlation.csv')\\n plt.figure()\\n sns.heatmap(corr, cmap=plt.cm.RdYlBu_r)\\n plt.title('Correlation Heatmap')\\n plt.tight_layout()\\n plt.savefig(str(outdir/'correlation.png'))\\n plt.close()\\n\\n return corr\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"c32d7300ec62ae5ea0e58e19b2099924","score":"0.74469304","text":"def correlation_heatmap(df):\n _, ax = plt.subplots(figsize=(14, 12))\n colormap = sns.diverging_palette(220, 10, as_cmap=True)\n\n _ = sns.heatmap(df.corr().round(3),\n cmap=colormap,\n square=True,\n cbar_kws={'shrink': .9},\n ax=ax,\n annot=True,\n linewidths=0.1,\n vmax=1.0,\n linecolor='white',\n annot_kws={'fontsize': 12})\n\n plt.title(\"Feature's Pearson Correlation\", y=1.05, size=15)\n return","title":""},{"docid":"2d2e09723c8aa1d2a583a7334e57a4d8","score":"0.72475976","text":"def plot_correlation_matrix(df):\n corr = df.corr()\n mask = np.zeros_like(corr, dtype=np.bool)\n mask[np.triu_indices_from(mask)] = True\n f, ax = plt.subplots(figsize=(12,8))\n cmap = sns.color_palette('coolwarm')\n sns.heatmap(corr, mask=mask, cmap=cmap, center=0, square=True, linewidths=.5,\n yticklabels=True, cbar_kws={'shrink':.5})\n plt.title('Correlation Matrix')\n plt.xticks(rotation=90, fontsize=7)\n plt.yticks(rotation=0, fontsize=7)\n plt.tight_layout()\n plt.show()","title":""},{"docid":"fbd5ee95bc4598540e8173a90735cd22","score":"0.7207547","text":"def plot_corr(data) :\n \n corr = data.corr()\n mask = np.zeros_like(corr, dtype=bool)\n mask[np.triu_indices_from(mask)] = True\n palette = sns.diverging_palette(20, 220, n=256)\n \n fig = plt.figure(figsize=(16,16))\n sns.heatmap(corr,mask=mask, cmap=palette,annot=True)\n plt.tight_layout()","title":""},{"docid":"3c42b518eaf55ce0de7c963bcdc4233f","score":"0.7150536","text":"def correlation_heatmap(df, *, font_size=12, ax=None):\n if ax is None:\n plt.gca()\n\n # Correlation matrix via Pandas (numeric data only)\n corr_matrix = df.corr()\n # Generate a mask for the upper triangle\n mask = np.zeros_like(corr_matrix, dtype=np.bool)\n mask[np.triu_indices_from(mask)] = True\n\n # Store heatmap from mask\n heat_plot = sns.heatmap(corr_matrix, mask=mask,\n cmap='RdBu_r', cbar_kws={\"shrink\": .6},\n annot=True, annot_kws={\"size\": font_size},\n vmax=1, vmin=-1, linewidths=.5,\n square=True, ax=ax)\n fig = heat_plot.figure\n return fig","title":""},{"docid":"1980211836a385133a288765bb593a8e","score":"0.7121204","text":"def plot_corr_matrix(corr):\n fig, ax = plt.subplots()\n mask = np.zeros_like(corr, dtype=np.bool)\n mask[np.triu_indices_from(mask, k=1)] = True\n sns.heatmap(corr, vmin=-1, vmax=1, mask=mask,\n cmap=sns.diverging_palette(220, 10, as_cmap=True),\n linewidths=0.5, cbar=True, square=True, ax=ax)\n ax.set_title('Correlation Matrix')\n fig.tight_layout()\n return fig","title":""},{"docid":"5fb4b269fb7616a4495f82e9b6c1735c","score":"0.7109663","text":"def plot_correlations(df):\n m_cor = df.corr()\n fig, ax = plt.subplots(figsize=(11, 9))\n # Add diverging colormap from red to blue\n cmap = sns.diverging_palette(10, 180, center='dark', as_cmap=True)\n sns.heatmap(m_cor,\n xticklabels=m_cor.columns, yticklabels=m_cor.columns,\n vmin=-1, vmax=1, cmap=cmap, square=True, cbar_kws={\"shrink\": .5}, ax=ax)\n plt.tight_layout()","title":""},{"docid":"80e9ba64d1e48755b8ee468db4f4e038","score":"0.70773","text":"def plot_corelation():\n sns.set(color_codes=False, font_scale=1)\n df_cor = self.__df[[\"Close\", \"Adj Close\",\"Volume\",\"EMA5\",\"SMA20\",\"TSI\"]]\n corr = df_cor.corr()\n\n # Generate a mask for the upper triangle\n mask = np.triu(np.ones_like(corr, dtype=bool))\n\n # Set up the matplotlib figure\n f, ax = plt.subplots(figsize=(11, 9))\n\n # Generate a custom diverging colormap\n cmap = sns.diverging_palette(230, 20, as_cmap=True)\n\n # Draw the heatmap with the mask and correct aspect ratio\n sns.heatmap(corr, mask=mask,cmap= 'coolwarm', vmax=.3, center=0,annot=False,\n square=True, linewidths=.5, cbar_kws={\"shrink\": .5})","title":""},{"docid":"4acee719983c88fe0c2a67cab4c4c556","score":"0.70678544","text":"def hof_correlation_plot(self, save=False):\n plt.figure('Correlation Heatmap', figsize=(16, 14),\n facecolor='white', edgecolor='black')\n rows, cols = (1, 1)\n ax0 = plt.subplot2grid((rows, cols), (0, 0))\n\n correlation = self.stats_fame.corr()\n cut = 0.5\n color_mask = (correlation[(correlation > -cut) & (correlation < cut)]\n .fillna(0)\n .astype(bool))\n correlation[color_mask] = 0\n\n cmap = mplcol.LinearSegmentedColormap.from_list(\n 'blue_white_blue', ['indianred'] + ['white'] * 3 + ['C0'])\n\n sns.heatmap(correlation, center=0,\n cmap=cmap,\n cbar_kws={'orientation': 'vertical'},\n linecolor='lightgray', linewidths=0.1, vmin=-1, vmax=1,\n ax=ax0)\n\n cbar = ax0.collections[0].colorbar\n cbar.set_ticks(np.arange(-1, 1.5, 0.5).tolist())\n cbar.ax.tick_params(labelsize=size['label'])\n cbar.outline.set_linewidth(1)\n cbar.outline.set_edgecolor('lightgray')\n\n ax0.set_title('Statistics Correlation (0.5 Threshold)',\n fontsize=size['title'])\n ax0.set_xticklabels(ax0.xaxis.get_majorticklabels(),\n fontsize=size['label'], rotation=90)\n ax0.set_yticklabels(ax0.yaxis.get_majorticklabels(),\n fontsize=size['label'], rotation=0)\n\n super_title = plt.suptitle('Hall of Fame Players',\n fontsize=size['super_title'],\n x=0.03, y=0.93)\n\n save_fig('hof_correlation', save, super_title)","title":""},{"docid":"a97f49b29bf28021f9c7aece2580bb79","score":"0.7011578","text":"def plot_correlation_heatmap(df, fname=None, figsize=(10, 10)):\n corr = df.corr()\n fig = plt.figure(figsize=figsize)\n sns.heatmap(corr\n , xticklabels=corr.columns.values\n , yticklabels=corr.columns.values\n , cmap='bwr'\n , annot=True)\n\n if fname:\n fig.savefig(fname\n , bbox_inches='tight')","title":""},{"docid":"a717b6f2ec1b256873c2060c5ace4e92","score":"0.7002763","text":"def correlations(df, cols):\r\n plt.figure(figsize=(16, 12))\r\n imgs = sns.heatmap(df[cols].corr(), cmap=\"YlGnBu\", annot=True, fmt='.2f', vmin=0)\r\n plt.savefig(\"correlation.png\")\r\n return imgs","title":""},{"docid":"3eec0831f01e6a204299fafc4ea2bb55","score":"0.69915277","text":"def correlation_image(the_data, labels=None, cmap=0):\n colormap = [COLORMAP, COLORMAPGP][cmap]\n binned_data = np.digitize(the_data, np.linspace(-1, 1, len(colormap)))\n color = np.array(colormap)[np.rot90(binned_data, 2)-1].flatten().tolist()\n\n # Adjust the transparency value\n orig = np.rot90(np.abs(the_data), 2)\n alpha = orig + 0.3\n alpha[orig >= 0.25] = 0.8\n alpha[orig >= 0.6] = 1.0\n alpha = alpha.flatten().tolist()\n\n orientation = np.pi / 3\n if labels is None:\n labels = list(map(lambda x: str(x), range(the_data.shape[0])))\n orientation = 0.0\n\n xname, yname = list(zip(*product(labels, labels)))\n\n data = dict(xname=list(reversed(xname)), yname=list(reversed(yname)),\n colors=color, alphas=alpha,\n count=list(reversed(the_data.round(2).flatten())))\n\n # Create the figure\n p = figure(x_axis_location=\"above\", tools=\"hover,save\",\n y_range=list(reversed(labels)), x_range=labels,\n tooltips = [('Names', '@yname, @xname'), ('Correlation', '@count')])\n\n p.plot_width = 800\n p.plot_height = 800\n p.grid.grid_line_color = None\n p.axis.axis_line_color = None\n p.axis.major_tick_line_color = None\n p.axis.major_label_text_font_size = \"5pt\"\n p.axis.major_label_standoff = 0\n p.xaxis.major_label_orientation = orientation\n\n p.rect('xname', 'yname', 0.9, 0.9, source=data,\n color='colors', line_color=None, alpha='alphas',\n hover_line_color='black', hover_color='colors')\n\n return row(p, colormap_legend(cmap))","title":""},{"docid":"1be1335bd5c7443dece7a476480725ad","score":"0.69528145","text":"def correlation_plot(x, h):\n corr = np.corrcoef(x.T)\n fig, ax = plt.subplots(figsize=(10, 10))\n \n sns.heatmap(corr, cmap=sns.diverging_palette(200, 10, n=200), center=0,\n square=True, linewidths=.2, vmin=-1, vmax=1, cbar_kws={\"shrink\": .5}, ax=ax)\n ax.set_xticks(np.arange(len(h)) + 0.5)\n ax.set_yticks(np.arange(len(h)) + 0.5)\n \n ax.set_xticklabels(h, rotation=90);\n ax.set_yticklabels(h, rotation=0);\n ax.set_ylim(-1, 31);","title":""},{"docid":"9b4613e357d195c9bc0cc38b65e7b437","score":"0.6837894","text":"def plot_corr(df, sort=False, **kwargs):\r\n # TODO: sns.clustermap has a metric kwarg that may be interesting to test out and implement.\r\n # TODO: Bug, if plt.show() is used after this function two windows will open, one with\r\n # the desired heatmap and one empty.\r\n # Create the corrmat or clustermap in correlation.py insted?\r\n sns.set(context=\"paper\", font=\"monospace\")\r\n save_figure = kwargs.get(\"save_figure\", False)\r\n file_name = kwargs.get(\"file_name\", \"tmp_heatmap.png\")\r\n corr_method = kwargs.get(\"corr_method\", \"pearson\")\r\n abs_values = kwargs.get(\"abs_values\", False)\r\n return_corrmat = kwargs.get(\"return_corrmat\", False)\r\n\r\n corrmat = df.corr(method=corr_method)\r\n if abs_values:\r\n corrmat = corrmat.abs()\r\n if (sort):\r\n sort_method = kwargs.get(\"sort_method\", \"average\")\r\n metric = kwargs.get(\"metric\", \"euclidian\")\r\n cg = sns.clustermap(corrmat, method=sort_method, metric=metric,\r\n cmap=plt.cm.inferno, linewidths=.5)\r\n #Rotate x and y tick labels so long texts will fitt next to eachother.\r\n plt.setp(cg.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)\r\n plt.setp(cg.ax_heatmap.xaxis.get_majorticklabels(), rotation=90)\r\n if save_figure:\r\n cg.savefig(file_name)\r\n else:\r\n f, sns_ax = plt.subplots(figsize=(12, 10))\r\n hmap = sns.heatmap(corrmat, ax=sns_ax, vmax=.8, linewidths=.5,\r\n square=True, cmap=plt.cm.inferno)\r\n\r\n colnames = corrmat.columns.values\r\n for i, colname in enumerate(colnames):\r\n if i and colname != colnames[i - 1]:\r\n ax.axhline(len(colnames) - i, c=\"black\")\r\n ax.axvline(i, c=\"black\")\r\n\r\n f.tight_layout()\r\n\r\n if return_corrmat:\r\n if sort: # Sort the columns and rows in the clustered fashion.\r\n corrmat = corrmat[cg.dendrogram_col.reordered_ind]\r\n corrmat = corrmat.reindex(corrmat.columns.values)\r\n return cg, corrmat\r\n else:\r\n if sort:\r\n return cg\r\n else:\r\n return f","title":""},{"docid":"ac8aa4f4699da706637c2a55fc11753a","score":"0.68348444","text":"def plot_correlation(df: pd.DataFrame, only_save=True, dpi=300):\n # Compute the correlation matrix\n corr_all = df.corr()\n\n # Generate a mask for the upper triangle\n mask = np.zeros_like(corr_all, dtype=np.bool)\n mask[np.triu_indices_from(mask)] = True\n\n # Set up the matplotlib figure\n f, ax = plt.subplots(figsize=(7, 7))\n\n # Draw the heatmap with the mask and correct aspect ratio\n sns.heatmap(corr_all, mask=mask, square=True, linewidths=0.5, ax=ax, cmap=\"BuPu\")\n plt.title(\"Correlation Matrix Heatmap\")\n if only_save:\n plt.savefig(\"img/correlation_heatmap.png\", dpi=dpi)\n plt.close()\n else:\n plt.show()","title":""},{"docid":"c21e06600ef92cf0003d31f3cd02a21b","score":"0.6806044","text":"def _heatmap(data_df, output_path):\n\n plt.figure(figsize=(20, 10))\n plot = sns.heatmap(data_df.corr(), annot=False,\n cmap='Dark2_r', linewidths=2)\n plot.figure.savefig(output_path)\n plt.clf()","title":""},{"docid":"3b442ea219339fbd3fe7e543ac7b1311","score":"0.67628884","text":"def correlation_plot(data,output_path=None): \n \n corrmat = data.corr()\n fig, ax = plt.subplots(figsize=(15,15))\n sns.heatmap(corrmat,cmap=\"RdBu_r\",linewidths=.5,annot=True)\n if output_path is not None:\n output = os.path.join(output_path,'Corr_plot'+'.png')\n plt.savefig(output)\n print('Image saved at',str(output))","title":""},{"docid":"bd9959d269cb351507c99c829aa99ddc","score":"0.67297167","text":"def corelationMatrix():\n try:\n corr_matrix = obj_Data.df.corr()\n fig = px.imshow(corr_matrix, title= \"Corelation Matrix\")\n return fig\n\n except:\n logging.exception('Something went wrong with Corelation Matrix plot')","title":""},{"docid":"987f0a4f6db076988d2886aa91e368e9","score":"0.6723992","text":"def show_correlation_plot(self, correlations, data):\n\n plt.figure(figsize=(10, 8))\n ax = sns.heatmap(correlations,\n xticklabels=correlations.columns.values,\n yticklabels=correlations.columns.values, annot=True)\n\n plt.setp(ax.get_xticklabels(), rotation=45, horizontalalignment=\"right\",\n rotation_mode=\"anchor\")\n\n plt.tight_layout()\n plt.autoscale()\n plt.savefig(\"../data/04_analytics/correlation_plot.png\")\n plt.show()","title":""},{"docid":"9e7b55ec4cda6f5711cce6980be37a9a","score":"0.6681251","text":"def heatmap_full(df):\r\n sns.set_style('whitegrid')\r\n plt.subplots(figsize = (14,8))\r\n # Generate mask for upper triangle\r\n m1 = np.zeros_like(df.corr(), dtype=np.bool) \r\n m1[np.triu_indices_from(m1)] = True\r\n sns.set(font_scale=0.8)\r\n sns.heatmap(df.corr(), cmap=sns.diverging_palette(20, 220, n=200), \r\n mask=m1, center=0, square=True, annot=True)\r\n plt.title(\"All features heatmap\", fontsize=15)","title":""},{"docid":"5e1897d41791c864b803f7db55a85636","score":"0.6593312","text":"def generate_heatmap(dataset, title):\n corr = dataset.corr()\n sns.heatmap(corr, xticklabels=corr.columns,\n yticklabels=corr.columns)\n\n plt.title(title)\n plt.show()","title":""},{"docid":"f6614f9ae02808b5b43f83645563d60c","score":"0.65319544","text":"def draw_corrmatrix(self, mode='stock'):\n fig, ax = plt.subplots(dpi=200, figsize=(6, 6))\n\n if mode == 'stock':\n im = ax.imshow(self.corrmatrix, vmin=-1, vmax=1)\n fig.colorbar(im, fraction=0.046, pad=0.04)\n ax.set_xlabel('Companies')\n ax.set_ylabel('Companies')\n return fig\n elif mode == 'category':\n im = ax.imshow(self.categ_corrmatrix, vmin=0, vmax=1)\n ax.set_xticks(range(len(self.categories)))\n ax.set_yticks(range(len(self.categories)))\n ax.set_xticklabels(self.categories, rotation=45,\n horizontalalignment='right', fontsize=8)\n ax.set_yticklabels(self.categories, fontsize=8)\n # Annotate grid\n for i in range(len(self.categories)):\n for j in range(len(self.categories)):\n ax.text(j, i, round(self.categ_corrmatrix[i, j], 2),\n ha=\"center\", va=\"center\", color=\"k\", fontsize=8)\n fig.colorbar(im, fraction=0.046, pad=0.04)\n return fig","title":""},{"docid":"15434dd38172978aaf79f42bb83d51a8","score":"0.6420136","text":"def plot_correlation_matrix(list_df: List[pd.DataFrame], names: List[str]) -> None:\n corr = compute_correlation_matrix(list_df)\n\n corrMatrix = pd.DataFrame(corr, columns=names, index=names)\n if len(list_df) <= 4:\n plt.subplots(figsize=(15, 10))\n else:\n plt.subplots(figsize=(20, 10))\n\n sns.heatmap(corrMatrix, annot=True, fmt='g')\n plt.title('Correlation matrix')\n\n plt.show()","title":""},{"docid":"13f0918b2812b18ee9763eec2ec5f3c6","score":"0.64004743","text":"def plot_single_mode_correlation(self, data, output_dir, addition):\n correlations = data[self.features].corr()\n fig = plt.figure(figsize=(10, 10))\n ax = fig.add_subplot(111)\n cax = ax.matshow(correlations, vmin=-1, vmax=1, cmap='viridis')\n ticks = np.arange(0, len(self.features), 1)\n plt.rc('axes', labelsize=8)\n ax.set_xticks(ticks)\n ax.set_yticks(ticks)\n ax.set_xticklabels(self.features, rotation=-90)\n ax.set_yticklabels(self.features)\n fig.colorbar(cax)\n fig.tight_layout()\n plot_out = os.path.join(\n output_dir, str(addition) + '_correlations.png')\n plt.savefig(plot_out, bbox_inches='tight')\n plt.close('all')","title":""},{"docid":"4f61a76440ef496f0781b42d77a9b28e","score":"0.6372198","text":"def plot_cor_matrix(data):\n correlation_matrix = data.corr() # creating correlation_matrix\n # print(correlation_matrix)\n dataplot = sb.heatmap(correlation_matrix) # creating a heat map of the correlation_matrix\n mp.show() # showing the correlation_matrix\n sorted_mat = correlation_matrix.unstack().sort_values()\n # Retain upper triangular values of correlation matrix and\n # make Lower triangular values Null\n upper_corr_mat = correlation_matrix.where(np.triu(np.ones(correlation_matrix.shape), k=1).astype(bool))\n # Convert to 1-D series and drop Null values\n unique_corr_pairs = upper_corr_mat.unstack().dropna()\n # Sort correlation pairs\n sorted_mat1 = unique_corr_pairs.sort_values()\n print(sorted_mat1)\n return sorted_mat1","title":""},{"docid":"47cdce25f8ae6ee0f7f78ace0cb91f10","score":"0.63189405","text":"def plot_correlations(matrix):\n\n pass","title":""},{"docid":"e0ea48564c7b6d0c9b52fdcb54a0a49c","score":"0.62723863","text":"def correlation_matrix(self, data, col_y, corr_value = 0.95, corr_value_w_targhet = 0.95, plot_matr = 'yes'):\n corr = data.corr().abs()\n\n if plot_matr == 'yes':\n ds().nuova_fig(7, height =8, width =10)\n ds().titoli(titolo=\"Correlation matrix\")\n sns.heatmap(corr[(corr >= 0.5)], cmap='viridis', vmax=1.0, vmin=-1.0, linewidths=0.1, annot=True, annot_kws={\"size\": 8}, square=True, linecolor=\"black\");\n ds().aggiusta_la_finestra()\n st.pyplot()\n\n corr_with_target = corr[col_y]#correlation with the target\n relevant_feature_with_target = corr_with_target[corr_with_target < corr_value_w_targhet].sort_values(ascending = False).reset_index()\n\n upper = corr.where(np.triu(np.ones(corr.shape), k=1).astype(np.bool))#select upper triangle of correlation matrix\n correlation_between_parameters = [column for column in upper.columns if any(upper[column] > corr_value)]\n return relevant_feature_with_target, correlation_between_parameters","title":""},{"docid":"780ae438bd821d468612b7ea31eba739","score":"0.6259866","text":"def _heatmap_plot(self,heatmap,title):\n fig, ax = plt.subplots(figsize=(self.plot_scale,self.plot_scale*(self.y_size/self.x_size)))\n plt.title(title)\n plt.xlabel(\"X\")\n plt.ylabel(\"Y\")\n im = plt.imshow(heatmap.T, extent=[0, plotcnf[\"scrsize\"][\"x\"], 0, plotcnf[\"scrsize\"][\"y\"]], origin='lower')\n plt.gca().invert_yaxis()\n divider = axes_grid1.make_axes_locatable(ax)\n cax = divider.append_axes('right', size='5%', pad=0.05)\n fig.colorbar(im, cax=cax)\n\n return fig","title":""},{"docid":"5d9df5f5b0aa285fff9c5470be3c7c0e","score":"0.6212737","text":"def correlation(x_list, y_list, title):\n fig = plt.figure()\n ax1 = fig.add_subplot(111)\n ax1.xcorr(x_list, y_list, usevlines=True, maxlags=75, normed=True, lw=2) #\n ax1.grid(True)\n #ax1.axhline(0, color='black', lw=2)\n save(title)\n plt.close(fig)","title":""},{"docid":"bdab770813d42d3832d82e22a17e6a95","score":"0.6206435","text":"def show_correlation(df, size=10):\n corr = df.corr()\n fig, ax = plt.subplots(figsize=(size, size))\n cax = ax.matshow(corr)\n fig.colorbar(cax)\n plt.xticks(range(len(corr.columns)), corr.columns, rotation = 90)\n plt.yticks(range(len(corr.columns)), corr.columns)\n plt.show()","title":""},{"docid":"500c14209ca15688f2c48360230e7734","score":"0.6198036","text":"def correlations(data, **kwds):\n\t# simply call df.corr() to get a table of\n\t# correlation values if you do not need\n\t# the fancy plotting\n\tcorrmat = data.corr(**kwds)\n\t\n\tfig, ax1 = plt.subplots(ncols=1, figsize=(6,5))\n\t\n\topts = {'cmap': plt.get_cmap(\"RdBu\"),\n\t\t'vmin': -1, 'vmax': +1}\n\theatmap1 = ax1.pcolor(corrmat, **opts)\n\tplt.colorbar(heatmap1, ax=ax1)\n\n\tax1.set_title(\"Correlations\")\n\t\n\tlabels = corrmat.columns.values\n\tfor ax in (ax1,):\n\t\t# shift location of ticks to center of the bins\n\t\tax.set_xticks(np.arange(len(labels))+0.5, minor=False)\n\t\tax.set_yticks(np.arange(len(labels))+0.5, minor=False)\n\t\tax.set_xticklabels(labels, minor=False, ha='right', rotation=70)\n\t\tax.set_yticklabels(labels, minor=False)\n\t\n\t#plt.tight_layout()\n\n\t#fig.savefig(file)","title":""},{"docid":"f6ab2cc003006260da744341f353c219","score":"0.61778855","text":"def correlation_heatmap(file: str, options: list):\n if \"/protein_table\" in options:\n with st.beta_expander(\"Correlation heatmap\"):\n df = cached_file(file)\n\n cols = [_ for _ in df.columns if \"LFQ\" in _]\n if len(cols) == 0:\n cols = df.columns\n\n if multiple_file_check(cols):\n df = np.log(df[cols])\n corr = df.corr()\n\n fig = make_subplots(rows=1, cols=1)\n fig.add_trace(\n trace=go.Heatmap(\n z=corr.values,\n x=corr.index.values,\n y=corr.columns.values,\n colorscale=\"Greys\",\n )\n )\n fig.update_layout(height=600, width=600)\n st.write(fig)","title":""},{"docid":"b7ad3c53a74a5723e9b1bde0a6e8f842","score":"0.61768585","text":"def plot_corr(df, size=10):\n set_my_plt_style()\n corr = df.corr()\n # Re-order the rows and columns using clustering\n d = sch.distance.pdist(corr)\n L = sch.linkage(d, method='ward')\n ind = sch.fcluster(L, 0.5 * d.max(), 'distance')\n columns = [df.columns.tolist()[i] for i in list((np.argsort(ind)))]\n corr = corr.reindex(columns, axis=1)\n corr = corr.reindex(columns, axis=0)\n\n # Plot the correlation matrix\n fig, ax = plt.subplots(figsize=(size, size))\n cax = ax.matshow(corr, cmap=Curl_5_r.mpl_colormap, vmin=-1, vmax=1)\n plt.xticks(range(len(corr.columns)), corr.columns, rotation=90)\n plt.yticks(range(len(corr.columns)), corr.columns)\n\n # Add the colorbar legend\n cbar = fig.colorbar(cax, ticks=[-1, -0.5, 0, 0.5, 1], aspect=10, shrink=.8)\n # plt.show()\n return fig","title":""},{"docid":"80160f95563b86613f3d04c55bacef14","score":"0.61421454","text":"def correlation_plot(self, target = None, top_N = 10, ascending = False):\r\n corrmat = self.df.corr()\r\n\r\n if target is None:\r\n plt.figure(figsize=(12,9))\r\n sns.heatmap(corrmat, vmax=.8, square=True)\r\n else:\r\n plt.figure(figsize=(10,8))\r\n sns.heatmap(corrmat[[target]].sort_values(by=[target],ascending = ascending)[:top_N],\r\n vmin=-1,\r\n cmap='coolwarm',\r\n annot=True)","title":""},{"docid":"7fea3cb690ab7105e2783934526ff559","score":"0.6124578","text":"def correlation_matrix(\n corr_mat: np.ndarray,\n labels: Union[List[str], np.ndarray],\n figure_path: pathlib.Path,\n) -> None:\n # rounding for test in CI to match reference\n fig, ax = plt.subplots(\n figsize=(round(5 + len(labels) / 1.6, 1), round(3 + len(labels) / 1.6, 1)),\n dpi=100,\n )\n im = ax.imshow(corr_mat, vmin=-1, vmax=1, cmap=\"RdBu\")\n\n ax.set_xticks(np.arange(len(labels)))\n ax.set_yticks(np.arange(len(labels)))\n ax.set_xticklabels(labels)\n ax.set_yticklabels(labels)\n for tick in ax.get_xticklabels():\n tick.set_rotation(45)\n tick.set_horizontalalignment(\"right\")\n\n fig.colorbar(im, ax=ax)\n ax.set_aspect(\"auto\") # to get colorbar aligned with matrix\n fig.tight_layout()\n\n # add correlation as text\n for (j, i), corr in np.ndenumerate(corr_mat):\n text_color = \"white\" if abs(corr_mat[j, i]) > 0.75 else \"black\"\n if abs(corr) > 0.005:\n ax.text(i, j, f\"{corr:.2f}\", ha=\"center\", va=\"center\", color=text_color)\n\n figure_path.parent.mkdir(parents=True, exist_ok=True)\n log.debug(f\"saving figure as {figure_path}\")\n fig.savefig(figure_path)\n plt.close(fig)","title":""},{"docid":"78ef41f2a3aaadf36f546c855f935ef1","score":"0.61020917","text":"def fit(self, X, y=None):\n\n self.correlation = X.corr()\n# print(self.corr)\n\n # Generate a mask for the upper triangle\n self.mask = np.zeros_like(self.correlation, dtype=np.bool)\n self.mask[np.triu_indices_from(self.mask)] = True\n\n # Set up the matplotlib figure\n f, ax = plt.subplots(figsize=(11, 9))\n\n # Generate a custom diverging colormap\n self.cmap = sns.diverging_palette(\n h_neg=self.h_neg,\n h_pos=self.h_pos,\n as_cmap=self.as_cmap\n )\n\n # Draw the heatmap with the mask and correct aspect ratio\n sns.heatmap(\n self.correlation,\n mask=self.mask,\n cmap=self.cmap,\n vmax=.3,\n center=0,\n square=True,\n linewidths=.5,\n cbar_kws={\"shrink\": .5}\n )\n\n plt.show()\n\n return self","title":""},{"docid":"2f101aed6225f5361cf064f0e2fc7061","score":"0.59934545","text":"def cor_heatmap_plot(self, heatmap_name, heatmap_val):\n\n # Split the data into SE and PE\n pconfig = {\n 'title': 'Pearson correlation',\n 'xlab': True,\n }\n self.add_section(\n description='Pearson correlation between log<sub>2</sub> normalised CPM values are calculated and clustered.',\n plot=heatmap.plot(heatmap_val, heatmap_name, pconfig=pconfig)\n )","title":""},{"docid":"6f7a423fcdc41296fc42ccd4cec0c07c","score":"0.59932196","text":"def correlations(DataFrame):\n # copy df in\n df = DataFrame.copy()\n\n # get the correlations\n corrs = df.corr()\n\n # get only the correlations for the target variable\n corrs_target = corrs['fare_amount'].sort_values(ascending = True)\n\n # corrs['fare_amount'].plot.bar(color = 'b') annot = True, vmin = -1, vmax = 1,\n\n # plot the total heat map\n # plt.figure(figsize = (12, 12))\n # sns.heatmap(corrs, annot = True, fmt = '.3f')\n # plt.show()\n\n # barplot of the correlations between fare amount and features\n corrs_target.plot(kind=\"barh\", color = 'b')\n # sns.barplot(x = corrs_target.index.values, y = corrs_target.values)\n plt.show()\n\n\n return None","title":""},{"docid":"c40f7e4e094c6ce9373cefb6a67ab15c","score":"0.59882206","text":"def create_corrolation_plot(data):\n corr = data.corr().reset_index().melt(id_vars=\"index\")\n corr.columns = [\"Variable 1\", \"Variable 2\", \"corr_values\"]\n corr[\"Correlation\"] = corr.corr_values.round(3)\n \n # Base chart \n cht = alt.Chart(corr).encode(\n x=\"Variable 1:N\", y=\"Variable 2:N\"\n ).properties(title=\"Correlation Plot\", width=700, height=700)\n \n # Text overlay \n txt = cht.mark_text().encode(\n text=\"Correlation\", \n color=alt.condition(\n alt.datum.Correlation > 0.25, \n if_true=alt.value('white'),\n if_false=alt.value('black')\n ))\n \n # Colored boxes \n rct = cht.mark_rect().encode(\n color=alt.Color(\"corr_values:Q\", scale=alt.Scale(scheme=\"purples\"),\n legend=None),\n tooltip=[\"Variable 1\", \"Variable 2\", \"Correlation\"]\n )\n cht = rct + txt\n cht = cht.configure_axis(\n ticks=False, title=None, labelPadding=10, labelFontSize=14\n ).configure_title(\n fontSize=20, anchor=\"middle\", dy=-20\n )\n return cht","title":""},{"docid":"d1877b88b00abc491abf85e5c57b7ec4","score":"0.59726924","text":"def heatmap(self, stats, ax=None, **kwargs):","title":""},{"docid":"6e0d49d3fd2e51844a2d787e2daaa04e","score":"0.5960367","text":"def render_heatmap(\n data: pd.DataFrame,\n ax_hm: plt.Axes = None,\n cmap: colors.Colormap = None,\n norm: colors.Normalize = colors.Normalize(),\n annotate: bool = True,\n annotation_valfmt: str = '{x:.0f}',\n add_sep_colorbar: bool = False,\n ax_cb: plt.Axes = None,\n colorbar_label: str = None,\n use_index_labels: bool = False,\n xlabel: str = None,\n ylabel: str = None,\n fig_canvas_title: str = None,\n fig_size: tuple = (8, 6),\n manipulate_ticks: bool = False,\n tick_label_prec: int = 3,\n xtick_label_prec: int = None,\n ytick_label_prec: int = None\n) -> (plt.Figure, plt.Figure):\n if isinstance(data, pd.DataFrame):\n if not isinstance(data.index, NumericIndex):\n raise pyrado.TypeErr(given=data.index, expected_type=NumericIndex)\n if not isinstance(data.columns, NumericIndex):\n raise pyrado.TypeErr(given=data.columns, expected_type=NumericIndex)\n # Extract the data\n x = data.columns\n y = data.index\n else:\n raise pyrado.TypeErr(given=data, expected_type=pd.DataFrame)\n\n # Create axes if not provided\n if ax_hm is None:\n fig_hm, ax_hm = plt.subplots(1, figsize=fig_size)\n else:\n fig_hm = ax_hm.figure\n\n if fig_canvas_title is not None:\n fig_hm.canvas.set_window_title(fig_canvas_title)\n\n # Create the image\n img = ax_hm.imshow(data, cmap=cmap, norm=norm, aspect=(x.max()-x.min())/(y.max()-y.min()), origin='lower',\n extent=[x.min(), x.max(), y.min(), y.max()]) # former: aspect='auto'\n\n # Set axes limits\n ax_hm.set_xlim(x.min(), x.max())\n ax_hm.set_ylim(y.min(), y.max())\n\n # Annotate the heat map\n if annotate:\n _annotate_heatmap(img, valfmt=annotation_valfmt)\n\n # Prepare the ticks\n if manipulate_ticks:\n _setup_index_axis(ax_hm.xaxis, x, use_index_labels,\n xtick_label_prec if xtick_label_prec is not None else tick_label_prec)\n _setup_index_axis(ax_hm.yaxis, y, use_index_labels,\n ytick_label_prec if ytick_label_prec is not None else tick_label_prec)\n\n ax_hm.stale = True # to cause redraw\n\n # Set the labels\n if xlabel is not None:\n ax_hm.set_xlabel(xlabel)\n if ylabel is not None:\n ax_hm.set_ylabel(ylabel)\n\n # Add color bar if requested\n if add_sep_colorbar:\n # Draw a new figure and re-plot the color bar there\n if ax_cb is None:\n fig_cb, ax_cb = plt.subplots(1, figsize=fig_size)\n else:\n fig_cb = plt.gcf()\n\n if colorbar_label is not None:\n colorbar.ColorbarBase(ax_cb, cmap=cmap, norm=norm, label=colorbar_label)\n else:\n colorbar.ColorbarBase(ax_cb, cmap=cmap, norm=norm)\n # if colorbar_label is not None:\n # fig_cb.colorbar(img, ax=ax_cb, label=colorbar_label) # plt.colorbar works, too\n # else:\n # fig_cb.colorbar(img, ax=ax_cb) # plt.colorbar works, too\n #\n # # Only show the color bar\n # ax_cb.remove()\n return fig_hm, fig_cb\n\n else:\n return fig_hm, None","title":""},{"docid":"4a04fc16e30a5c0bfc7882688af5bcdb","score":"0.59285873","text":"def correlations(self):\n fontProperties = {'family':'sans-serif'}\n opts = {'cmap': plt.get_cmap(\"bwr\"), 'vmin': -1, 'vmax': +1}\n\n for c,target in enumerate(self.targets):\n\n saveAs = \"{0}/correlations_{1}_{2}\".format(self.output_dir,target.name,self.date)\n\n allkeys = target.df.keys()\n keys = []\n for key in allkeys:\n if key!='target': keys.append(key)\n t_ = target.df[keys]\n corrmat = t_.corr()\n\n # Save correlation matrix to CSV file\n corrmat.to_csv(\"{0}.csv\".format(saveAs))\n\n # Use matplotlib directly\n fig,ax = plt.subplots()\n\n heatmap1 = ax.pcolor(corrmat, **opts)\n cbar = plt.colorbar(heatmap1, ax=ax)\n\n cbar.ax.set_yticklabels( [i.get_text().strip('$') for i in cbar.ax.get_yticklabels()], **fontProperties )\n\n labels = corrmat.columns.values\n labels = [self.variable_labels[i].label for i in labels]\n\n # shift location of ticks to center of the bins\n ax.set_xticks(np.arange(len(labels))+0.5, minor=False)\n ax.set_xticklabels(labels, fontProperties, fontsize=14, minor=False, ha='right', rotation=70)\n\n ax.set_yticks(np.arange(len(labels))+0.5, minor=False)\n ax.set_yticklabels(labels, fontProperties, fontsize=14, minor=False)\n\n ## CMS/COM Energy Label + Signal name\n cms_stamp = hpl.CMSStamp(self.CMSlabelStatus)\n cms_stamp.coords = [0.02,1.00]\n cms_stamp.fontsize = 16\n cms_stamp.va = 'bottom'\n ax.text(0.02,1.00,cms_stamp.text,fontsize=cms_stamp.fontsize,\n ha=cms_stamp.ha,va=cms_stamp.va,transform=ax.transAxes)\n\n energy_stamp = hpl.EnergyStamp()\n energy_stamp.ha = 'right'\n energy_stamp.coords = [0.99,1.00]\n energy_stamp.fontsize = 16\n energy_stamp.va = 'bottom'\n ax.text(energy_stamp.coords[0],energy_stamp.coords[1],energy_stamp.text, \n fontsize=energy_stamp.fontsize,ha=energy_stamp.ha, va=energy_stamp.va, transform=ax.transAxes)\n\n ax.text(0.03,0.93,target.label,fontsize=16,ha='left',va='top',transform=ax.transAxes)\n\n\n plt.savefig(\"{0}.{1}\".format(saveAs,self.image_format),\n format=self.image_format,dpi=300,bbox_inches='tight')\n plt.close()\n\n return","title":""},{"docid":"8e23ff82c707b850587fc9ad88046878","score":"0.59212583","text":"def plot_correlations(X, ids, weights=None, classes=None, round_threshold=0.0, targetdir=None, colorbar = False):\n N = X.shape[0]\n\n if classes is None:\n classes = np.zeros(N)\n num_classes = int(1)\n else:\n num_classes = len(np.unique(classes))\n\n figs = {}\n axs = {}\n \n for i in range(num_classes):\n\n label = f'all' if (num_classes == 1) else f'class_{i}'\n\n # Compute correlation matrix\n w = weights[classes==i] if weights is not None else None\n C = statstools.correlation_matrix(X=X[classes==i,:], weights=w)\n C[np.abs(C) < round_threshold] = np.nan\n C *= 100\n \n # Compute suitable figsize\n size = np.ceil(C.shape[0] / 3)\n \n # Plot it\n figs[label], axs[label] = plt.subplots(1,1, figsize=(size,size))\n\n axs[label].imshow(C)\n axs[label] = annotate_heatmap(X = C, ax = axs[label], xlabels = ids,\n ylabels = ids, decimals = 0, x_rot = 90, y_rot = 0, color = \"w\")\n axs[label].set_title(f'{label}: linear correlation $\\\\in$ [-100,100]', fontsize=10)\n \n if colorbar:\n cb = plt.colorbar()\n\n if targetdir is not None:\n fname = targetdir + f'{label}-correlation-matrix.pdf'\n print(__name__ + f'.plot_correlations: Save: \"{fname}\"')\n plt.savefig(fname=fname, pad_inches=0.2, bbox_inches='tight')\n\n return figs, axs","title":""},{"docid":"847edc9560fd80bef55c0a7c1f4b1881","score":"0.59083694","text":"def plot_corr(self, values=True):\n plot_corr(self.data, values)","title":""},{"docid":"7268ab825f669e8bc80a4361408d6432","score":"0.5851477","text":"def correlation_training(self):\n self.x=pd.DataFrame(self.x)\n dfcorr=self.x.corr()\n Tools.table(dfcorr,'.3f','fancy_grid','Correlation Matrix on Training Data', 60)\n c=corrplot.Corrplot(dfcorr)\n c.plot()\n plt.suptitle(\"Pair-Correlation Matrix on Training Data\")\n return plt.show()","title":""},{"docid":"2b206ccc0170817ac6a3efe9d9e0b5c4","score":"0.5848216","text":"def correlation_matrix(data: pd.DataFrame, vmin: int = -1) -> str:\n fig_cor, axes_cor = plt.subplots()\n cmap_name = config[\"plot\"][\"correlation\"][\"cmap\"].get(str)\n cmap_bad = config[\"plot\"][\"correlation\"][\"bad\"].get(str)\n\n cmap = plt.get_cmap(cmap_name)\n if vmin == 0:\n cmap = get_cmap_half(cmap)\n cmap.set_bad(cmap_bad)\n\n labels = data.columns\n matrix_image = axes_cor.imshow(\n data, vmin=vmin, vmax=1, interpolation=\"nearest\", cmap=cmap\n )\n plt.colorbar(matrix_image)\n\n if data.isnull().values.any():\n legend_elements = [Patch(facecolor=cmap(np.nan), label=\"invalid\\ncoefficient\")]\n\n plt.legend(\n handles=legend_elements, loc=\"upper right\", handleheight=2.5,\n )\n\n axes_cor.set_xticks(np.arange(0, data.shape[0], float(data.shape[0]) / len(labels)))\n axes_cor.set_yticks(np.arange(0, data.shape[1], float(data.shape[1]) / len(labels)))\n\n font_size = get_correlation_font_size(len(labels))\n axes_cor.set_xticklabels(labels, rotation=90, fontsize=font_size)\n axes_cor.set_yticklabels(labels, fontsize=font_size)\n plt.subplots_adjust(bottom=0.2)\n\n return plot_360_n0sc0pe(plt)","title":""},{"docid":"b3ec3b9394dd40e556f65007ba31ed14","score":"0.5841142","text":"def spearman_corr_heatmap(portfolio, data_source='price_data',\n attribute='changePercent', absolute=True):\n matrix = analysis.spearman_corr_coeffs(portfolio, data_source, attribute)\n\n mask = np.zeros_like(matrix)\n mask[np.triu_indices_from(mask)] = True\n ax = sns.heatmap(matrix,\n annot=True,\n mask=mask,\n cmap='bwr', vmin=-1, vmax=1)\n ax.set_title(\n f'Portfolio {attribute.capitalize()} Spearman Correlation Heat Map')\n\n return ax","title":""},{"docid":"ab03d167a158bff55fd14d9e9672bbf5","score":"0.5834672","text":"def heatmap_carnivore(self):\n x = self.sim.animal_distribution\n carn = x.pivot('Row', 'Col', 'Carnivore').values\n plot = self.ax4.imshow(carn, vmax=self.cmax_animals['Carnivore'])\n self.ax4.set_title('Carnivore density map', y=-0.3)","title":""},{"docid":"cad15024d8c46b1c9fb7e3701a804e83","score":"0.5812108","text":"def _test_heatmap():\n # Hit the drum at time t = 0\n drum = Drum()\n drum.hit((0.8, 0, 0), 1)\n\n # Calculate drum values\n t = 0\n points = drum.points\n values = drum.value(0)\n \n # Polar heatmap\n data = griddata(points, values, (drum.grid_r, drum.grid_theta), method='cubic', fill_value=0)\n ax1 = plt.subplot(projection=\"polar\")\n ax1.pcolormesh(drum.thetas, drum.rs, data.T)\n plt.show()","title":""},{"docid":"164442cce44300142d9dd045f4e76f83","score":"0.5804257","text":"def corr_matrix(cls, config, df, path, title, method, footnote=None):\n\t\t#timestamp\n\t\t_t0 = datetime.datetime.now()\n\t\t_f = debug(message='t', source=\"timestamp\")\n\t\tconsole('running bokeh corr_matrix()', 'blue')\n\n\t\t# math\n\t\timport bisect\n\t\tfrom math import pi\n\t\tfrom numpy import arange\n\t\tfrom itertools import chain\n\t\tfrom collections import OrderedDict\n\t\t\n\t\t# bokeh\n\t\tfrom bokeh.models import ColorBar, LinearColorMapper, TapTool, HoverTool, Range1d, ColumnDataSource\n\t\tfrom bokeh.models.callbacks import CustomJS\n\t\tfrom bokeh.plotting import reset_output, figure\n\t\tfrom bokeh.embed import components\n\n\t\t#create color palette\n\t\tmatplotlib.use('Agg')\n\t\timport matplotlib.colors\n\t\tfrom matplotlib import cm as mpl_cmap\n\t\t\n\t\t#p-values\n\t\tif method == \"pearson\":\n\t\t\tfrom scipy.stats import pearsonr as cf\n\t\telif method == \"spearman\":\n\t\t\tfrom scipy.stats import spearmanr as cf\n\t\t\n\t\t#calculate p-values correlation coefficent\n\t\tdef get_pvalue(df):\n\t\t\tdf = df.dropna()._get_numeric_data()\n\t\t\tdfcols = pd.DataFrame(columns=df.columns)\n\t\t\tpvalues = dfcols.transpose().join(dfcols, how='outer')\n\t\t\tfor row in df.columns:\n\t\t\t\tfor column in df.columns:\n\t\t\t\t\tpvalues[row][column] = round(cf(df[row], df[column])[1], 4)\n\t\t\treturn pvalues\n\n\t\t#Gets bounds for quads with n features\n\t\tdef get_bounds(n):\n\t\t\tbottom = list(chain.from_iterable(\n\t\t\t\t[[ii]*nlabels for ii in range(nlabels)]))\n\t\t\ttop = list(chain.from_iterable(\n\t\t\t\t[[ii+1]*nlabels for ii in range(nlabels)]))\n\t\t\tleft = list(chain.from_iterable(\n\t\t\t\t[list(range(nlabels)) for ii in range(nlabels)]))\n\t\t\tright = list(chain.from_iterable(\n\t\t\t\t[list(range(1, nlabels+1)) for ii in range(nlabels)]))\n\t\t\tcorr_items = list(chain.from_iterable(\n\t\t\t\t[[ii+1]*nlabels for ii in range(nlabels)]))\n\t\t\treturn top, bottom, left, right, corr_items\n\t\t\n\t\t#Aligns color values from palette with the correlation coefficient and p-values\n\t\tdef get_colors_corr(corr_array, p_array, colors):\n\t\t\tc_corr = arange(-1, 1, 1/(len(colors)/2))\n\t\t\tcorr_color = []\n\t\t\tp_color = []\n\t\t\tcorr_list = []\n\t\t\tp_list = []\n\t\t\tfactor_list = []\n\t\t\titr = 0\n\t\t\tfor corr, pvalue in zip(corr_array,p_array):\n\t\t\t\tind = bisect.bisect_left(c_corr, corr)\n\t\t\t\t#colors\n\t\t\t\t##corr_color\n\t\t\t\tif (itr)%(nlabels+1)==0:\n\t\t\t\t\tcorr_color.append('#022f62')\n\t\t\t\telse:\n\t\t\t\t\tcorr_color.append(colors[ind-1])\n\t\t\t\t##pcolor\n\t\t\t\tif pvalue<=0.05:\n\t\t\t\t\tp_color.append(\"#F44336\")\n\t\t\t\telse:\n\t\t\t\t\tp_color.append(colors[ind-1])\n\t\t\t\t#append corr and pvalues\n\t\t\t\tcorr_list.append(corr)\n\t\t\t\tp_list.append(pvalue)\n\t\t\t\tfactor_list.append(['[%s, %s]'%(l_labels[itr][0],l_labels[itr][1])])\n\t\t\t\titr = itr + 1\n\t\t\treturn corr_color, corr_list, p_color, p_list, factor_list\n\t\t\n\t\t#create seaborn plots for each possible correlation combinations\n\t\tdef get_corr_plot(df, x, y):\n\t\t\timportlib.reload(plt); importlib.reload(sns)\n\n\t\t\t#----parameters\n\t\t\t#figure\n\t\t\tfig, ax = plt.subplots(1, 1, figsize=(10,10))\n\t\t\t#bounds\n\t\t\tplt.subplots_adjust(top=0.95, bottom=0.085, left=0.125, right=0.975, hspace=0.2, wspace=0.2)\n\t\t\t\n\t\t\t#----title\n\t\t\ttitle = \"x=%s, y=%s\"%(x,y)\n\t\t\t\n\t\t\t#----set style\n\t\t\t#pal = sns.color_palette(\"Set1\", n_colors=2, desat=.5)\n\t\t\tsns.set(style=config['style']['seaborn'], font=\"Helvetica\", font_scale=1.1)\n\t\t\tsns.despine(offset=10, trim=True)\n\t\t\t\n\t\t\t#----plot\n\t\t\t#if grouping #g = sns.lmplot(x, y, hue=groupby, data=df, palette=pal).set_title(title)\n\t\t\tsns.regplot(x, y, data=df, scatter=True, fit_reg=True, ci=95).set_title(title)\n\t\t\t\n\t\t\t#----add rho, p-value to plot\n\t\t\t#get values\n\t\t\tcorr_r = cf(df[x],df[y])\n\t\t\tcorr_t = \"r = %.2f, p < %.2f\"%(corr_r[0],corr_r[1])\n\t\t\t#place text 90% of max-x and 90% of max-y\n\t\t\tfig.text(0.85, 0.05, corr_t, size=16, horizontalalignment='center',\n\t\t\t\t\tverticalalignment='center', transform=ax.transAxes)\n\n\t\t\t#----save\n\t\t\t#check if path exists\n\t\t\tfile = \"%s.png\"%(title)\n\t\t\tpath = config['path']['output'] + \"/analysis/html/img/corr/\"\n\t\t\tif not os.path.exists(path):\n\t\t\t\tos.makedirs(path)\n\t\t\t#save\n\t\t\tfig.savefig(path + file, dpi=300)\n\n\t\t#reset\n\t\treset_output()\n\t\t\n\t\t#get color map\n\t\tRdYlBu = mpl_cmap.get_cmap('RdBu', 32)\n\t\tcolors_np = np.vstack(RdYlBu(np.linspace(0, 1, 32)))\n\t\tcmap = matplotlib.colors.ListedColormap(colors_np, name='RedBlue')\n\t\tcolors = [matplotlib.colors.rgb2hex(cmap(x/32)[:3]) for x in range(32)]\n\n\t\t# calculate correlation coefficients\n\t\t#coeff\n\t\tcorr_coeff = df.corr(method=method)\n\t\t#p-value\n\t\tp_value = get_pvalue(df)\n\n\t\t# get list and number of variables\n\t\tlabels = df.columns\n\t\tnlabels = len(corr_coeff)\n\n\t\t#create list of labels\n\t\t##x\n\t\tl_labels_x = labels.tolist() * nlabels\n\t\t##y\n\t\tl_labels_y = labels.tolist() * nlabels\n\t\t###split into chunks\n\t\tl_labels_y_split = np.hsplit(np.array(np.array_split(l_labels_y, nlabels)), nlabels)\n\t\tl_labels_y = np.concatenate(l_labels_y_split).ravel().tolist()\n\t\t##combine\n\t\tl_labels = list(zip(l_labels_x, l_labels_y))\n\n\t\t#create coeff label coordinates\n\t\tnum = np.arange(0, nlabels, 0.5).tolist()\n\t\tnum = [x for x in num if x not in np.arange(0, nlabels).tolist()]\n\t\t##x\n\t\tnum_x = [(x - 0.025) for x in num]\n\t\tnum_x = [x for x in num_x if x not in np.arange(0, nlabels).tolist()] * nlabels\n\t\t##y\n\t\tnum_y = [(x + .125) for x in num]\n\t\tnum_y = [y for y in num_y if y not in np.arange(0, nlabels).tolist()] * nlabels\n\n\t\t###split into chunks\n\t\tnum_y_split = np.hsplit(np.array(np.array_split(num_y, nlabels)), nlabels)\n\t\tnum_y = np.concatenate(num_y_split).ravel().tolist()\n\t\t##combine\n\t\t#num_labels = list(zip(num_x, num_y))\n\n\t\t#plot\n\t\ttools=\"box_select,save,reset\"\n\t\tcm = figure(tools=tools, plot_width=1200, plot_height=1200, x_range=(0, nlabels), y_range=(0, nlabels), \n\t\t\t\t\toutput_backend=\"webgl\")\n\n\t\t#hover\n\t\thover = HoverTool()\n\t\thover.tooltips = [\n\t\t\t(\"factor\", \"@factor\"),\n\t\t\t(\"rho\", \"@coeff_num\"),\n\t\t\t(\"p\", \"@p_num\"),\n\t\t]\n\t\t#cm.tools.append(hover)\n\t\tcm.hover.line_policy = \"nearest\"\n\n\t\t#legend\n\t\t#cm.toolbar.logo = None\n\n\t\t#grid\n\t\tcm.xgrid.grid_line_color = None\n\t\tcm.ygrid.grid_line_color = None\n\t\tcm.xaxis.major_label_orientation = pi/4\n\t\tcm.yaxis.major_label_orientation = pi/4\n\n\t\t#turn off scientific notation\n\t\tcm.left[0].formatter.use_scientific = False\n\n\t\t# prepare squares for plot\n\t\ttop, bottom, left, right, corr_items = get_bounds(nlabels)\n\t\tcolor_list,corr,p_color,pvalues,factor = get_colors_corr(corr_coeff.values.flatten(),p_value.values.flatten(),colors)\n\n\t\t#prepare corr_coeff label for plot\n\t\tcoeff_color = [\"#444444\"] * (nlabels * nlabels)\n\t\tcoeff_color = ['#f9f9f9' if cr >= np.float64(0.74) else cof for cof, cr in zip(coeff_color, corr)]\n\t\tcoeff_num = [\"%.2f\"%(x) for x in corr]\n\n\t\t#prepare pvalue for plot\n\t\tp_color = [\"#444444\"] * (nlabels * nlabels)\n\t\tp_color = ['#F44336' if p <= np.float64(0.05) else cof for pcol, cof, p in zip(p_color, coeff_color, pvalues)]\n\t\tp_num = [\"(%.2f)\"%(y) for y in pvalues]\n\n\t\t#data source\n\t\tsource = ColumnDataSource(data=dict(left=left, right=right, top=top, bottom=bottom,\n\t\t\t\t\t\t\t\t\t\t\tx=num_x, y=num_y, coeff_num=coeff_num, coeff_color=coeff_color,\n\t\t\t\t\t\t\t\t\t\t\tp_num=p_num, factor=factor, square_color=color_list, p_color=p_color))\n\t\t#create squares\n\t\tcm.quad(top='top', bottom='bottom', left='left', right='right', \n\t\t\t\tline_color='white', color='square_color', source=source)\n\n\t\t#create text\n\t\t#correlation coefficient\n\t\tcm.text(x='x', y='y', source=source, text_color=\"coeff_color\", text='coeff_num', \n\t\t\t\ttext_font_style='normal', text_font_size = '1.5em', text_align='center',\n\t\t\t\ty_offset=-5, x_offset=1, text_line_height=1)\n\t\t#p-value\n\t\tcm.text(x='x', y='y', source=source, text_color=\"p_color\", text='p_num', \n\t\t\t\ttext_font_style='normal', text_font_size = '1.0em', text_align='center',\n\t\t\t\ty_offset=20, x_offset=1, text_line_height=1)\n\n\t\t#callback\n\t\tlink = CustomJS(args = dict(source = source), code=\"\"\"\n\t\t\tconsole.log(\"second\")\n\t\t\tobj = cb_obj\n\t\t\ts = source\n\t\t\tdata = source.data\n\t\t\tattr = source.attributes\n\t\t\td = attr.data\n\n\t\t\t//selected item\n\t\t\tid = source.selected['1d'].indices\n\t\t\titem = d.factor[id][0].replace(/[[\\]]/g,'').match(/(\".*?\")|(\\S+)/g)\n\t\t\tconsole.log('id: ' + id + ', item: ' + item)\n\t\t\t\n\t\t\t//url\n\t\t\tpath = window.location.href\n\t\t\tcurrent = path.split(\"/\").slice(0,-1).join(\"/\")\n\t\t\turl = current + '/reg.html?x='+ item[0] + 'y=' + item[1]\n\t\t\twindow.open(url)\n\t\t\t//window.open(url, 'newwindow', config='height=720, width=1280')\n\t\t\"\"\")\n\t\tcm.add_tools(TapTool(callback=link)) \n\n\t\t#reverse y-axis\n\t\tcm.y_range = Range1d(nlabels, 0)\n\t\t#p.y_range = Range1d(monitorSize[1], 0)\n\n\t\t# Set ticks with labels\n\t\tticks = [tick+0.5 for tick in list(range(nlabels))]\n\t\ttick_dict = OrderedDict([[tick, labels[ii]] for ii, tick in enumerate(ticks)])\n\n\t\t# Create the correct number of ticks for each axis\n\t\tcm.xaxis.ticker = ticks\n\t\tcm.yaxis.ticker = ticks\n\n\t\t# Override the labels\n\t\tcm.xaxis.major_label_overrides = tick_dict\n\t\tcm.yaxis.major_label_overrides = tick_dict\n\n\t\t#color bar\n\t\tmapper = LinearColorMapper(\n\t\t\tpalette=colors,\n\t\t\tlow=-1, high=1\n\t\t)\n\t\tcolor_bar = ColorBar(color_mapper=mapper, location=(0, 0))\n\t\tcm.add_layout(color_bar, 'right')\n\n\t\t#create link for each comparison\n\t\tall_columns = [[x,y] for x in list(labels) for y in list(labels)]\n\t\tfor idx, clm in enumerate(all_columns):\n\t\t\tget_corr_plot(df=df, x=clm[0], y=clm[1])\n\n\t\t#get html\n\t\tscript, div = components(cm)\n\t\t\n\t\t##convert seperate plots and div to single string\n\t\tplots = (''.join(map(str, [div, '\\n', script])))\n\n\t\t#create html\n\t\thtml(cls, config, path=path, plots=plots, source='bokeh', title=title, footnote=footnote)\n\n\t\t#reset\n\t\treset_output()\n\n\t\t#--------finished\n\t\t#timestamp\n\t\tconsole('%s finished in %s msec'%(_f,((datetime.datetime.now()-_t0).total_seconds()*1000)), 'blue')\n\t\treturn cm","title":""},{"docid":"579772c39464fd6b0bc295f06539f021","score":"0.57749116","text":"def draw_heatmap(df, figsize=(15, 6), cmap='YlOrBr', ylabel='', xlabel='', title=''):\n _, ax = plt.subplots(figsize=figsize)\n sns.heatmap(df, cmap=cmap, annot=False)\n ax.set_ylabel(ylabel, fontsize=15)\n ax.set_xlabel(xlabel, fontsize=15)\n ax.set_title(title, fontsize=20, weight='bold')\n plt.show()","title":""},{"docid":"8868f3cd3d27717bebb45feec1b00367","score":"0.57121176","text":"def plot_corrcoef_raftscope(raftsfits, ROIrows, ROIcols, xylabels=None, title='', norm=True):\n datadir, dataname = os.path.split(raftsfits[0])\n dataname = os.path.splitext(dataname)[0]\n\n a = corrcoef_raftscope(raftsfits, ROIrows, ROIcols, norm)\n fig, ax = plt.subplots(figsize=(10, 8))\n if norm:\n cax = ax.imshow(a, cmap=plt.get_cmap('jet'), norm=mplcol.Normalize(vmax=1, clip=True), interpolation='nearest')\n else:\n cax = ax.imshow(a, cmap=plt.get_cmap('jet'), norm=mplcol.Normalize(vmax=20000, clip=True), interpolation='nearest')\n if norm:\n titlestr = \"Correlation for %s\"\n else:\n titlestr = \"Covariances for %s\"\n if title:\n ax.set_title(titlestr % title)\n else:\n ax.set_title(titlestr % dataname)\n ax.set_xticks(np.arange(0, 16*len(raftsfits), 16))\n ax.set_yticks(np.arange(0, 16*len(raftsfits), 16))\n if xylabels:\n ax.set_xticklabels(xylabels)\n ax.set_yticklabels(xylabels)\n cbar = fig.colorbar(cax, orientation='vertical')\n\n plt.savefig(os.path.join(datadir, \"corrscope-%s.png\" % dataname))\n plt.show()","title":""},{"docid":"f75ad74a90af8d32921297c0d07628ab","score":"0.57056934","text":"def plot_corr(path):\n data, cols, rows = read_csv(path)\n\n plt.figure().canvas.set_window_title('Figure - Correlation (' + path + ')')\n for i in range(1, cols): plt.plot(data[0], data[i])\n \n plt.title('Correlation')\n plt.xlabel('Time (ns)')\n plt.ylabel('Correlation Coefficient')\n plt.gca().yaxis.grid(color='gray', linestyle='dashed')\n plt.show()","title":""},{"docid":"0e43f09b6c7e5d4fe9dc3f6925a14d0a","score":"0.57038325","text":"def plot_correlation_matrices(chr_list):\n for ch in chr_list:\n ss_ch = BlockMatrix.read('gs://nbaya/sumstats_corr/'+variant_set+'_ss_correlation_chr{}.bm/'.format(ch))\n gt_ch = BlockMatrix.read('gs://nbaya/sumstats_corr/'+variant_set+'_gt_correlation_chr{}.bm/'.format(ch))\n M_max = int(1e4) #max number of variants to be taken from the block matrices (suggested: 2e4)\n M = ss_ch.shape[0] #dimension of block matrix\n# for idx in range(int(M/M_max)+1): #index of which disjoint window we are looking at in the block matrix\n for idx in range(0,int(M/M_max)+1): #index of which disjoint window we are looking at in the block matrix\n M0 = M_max*(idx) #start variant index for block matrix filtering\n M1 = min(M_max*(idx+1),M) #stop variant index for block matrix filtering\n ss_np = ss_ch[M0:M1,M0:M1].to_numpy()\n gt_np = gt_ch[M0:M1,M0:M1].to_numpy()\n print('\\nStarting variant window: ['+str(M0)+','+str(M1)+']')\n w = int(5e3) #window width of variants for correlation matrix (suggested: 2e3)\n for i in range(int((M1-M0-1)/w)+1):\n w0 = w*i #start variant index for window of correlation matrix\n w1 = min(w*(i+1),M1-M0) #stop variant index for window of correlation matrix\n full = (ss_np[w0:w1,w0:w1]+\n gt_np[w0:w1,w0:w1].T)\n np.fill_diagonal(full,1)\n fig,ax = plt.subplots()\n ax.imshow(full,cmap='bwr')\n ax.plot([0, w],[0, w],'k--',alpha=0.5,lw=2)\n plt.xlim([0,w])\n plt.ylim([w,0])\n ax.text(w*0.83,w*0.1,\"SS\",fontsize=60,alpha=0.5)\n ax.text(w*0.02,w*0.97,\"GT\",fontsize=60,alpha=0.5)\n plt.title('chr'+str(ch)+' '+variant_set+' variants ('+str(M0+w0)+'-'+str(M0+w1)+')')\n fig=plt.gcf()\n fig.set_size_inches(10,10)\n path=('gs://nbaya/sumstats_corr/plots/chr'+str(ch)+'_'+variant_set+\n '_'+str(M0+w0).zfill(len(str(M)))+'-'+str(M0+w1).zfill(len(str(M)))+'.png')\n with hl.hadoop_open(path, 'wb') as f: \n fig.savefig(f,dpi=600)\n plt.close()\n print('\\nFinished variant window: ['+str(M0)+','+str(M1)+']')","title":""},{"docid":"dcc767bfe38719b5b3949d492864ed78","score":"0.5673527","text":"def constructHeatMap(data, show=False):\n plt.figure(figsize=(20, 10))\n sns.heatmap(data, cbar=False)\n if show:\n plt.show()","title":""},{"docid":"0c79f04921fcf87404536d46e6198bbd","score":"0.56536555","text":"def heatmap_focused(df, target, n):\r\n n = n # Number of variables\r\n features = df.corr().nlargest(n, target)[target].index\r\n hm_data = np.corrcoef(df[features].values.T)\r\n sns.set(font_scale=0.8)\r\n sns.heatmap(hm_data, cbar=True, cmap=sns.diverging_palette(20, 220, n=200), \r\n annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, \r\n yticklabels=features.values, xticklabels=features.values).set_title(\"Main features heatmap\")","title":""},{"docid":"20319472cf1b536362aed44fa5ae9394","score":"0.562109","text":"def draw_heatmap(*args, **kwargs):\n data = kwargs.pop('data')\n d = data.pivot(index=args[1], columns=args[0], values=args[2])\n sns.heatmap(d, **kwargs)","title":""},{"docid":"593b87cdbd87f8d52b2d3fa009a1e457","score":"0.561807","text":"def heatmap(data, row_labels, col_labels, ax=None,\n cbar_kw={}, cbarlabel=\"\", **kwargs):\n\n if not ax:\n ax = plt.gca()\n\n # Plot the heatmap\n im = ax.imshow(data, **kwargs)\n\n # Create colorbar\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\n # cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\"bottom\")\n\n # We want to show all ticks...\n ax.set_xticks(np.arange(data.shape[1]))\n ax.set_yticks(np.arange(data.shape[0]))\n # ... and label them with the respective list entries.\n ax.set_xticklabels(col_labels, fontproperties=prop)\n ax.set_yticklabels(row_labels, fontproperties=prop)\n\n # Let the horizontal axes labeling appear on top.\n ax.tick_params(top=True, bottom=False,\n labeltop=True, labelbottom=False)\n\n # Rotate the tick labels and set their alignment.\n # plt.setp(ax.get_xticklabels(), rotation=-30, ha=\"right\",\n # rotation_mode=\"anchor\")\n\n # Turn spines off and create white grid.\n for edge, spine in ax.spines.items():\n spine.set_visible(False)\n\n ax.set_xticks(np.arange(data.shape[1] + 1) - .5, minor=True)\n ax.set_yticks(np.arange(data.shape[0] + 1) - .5, minor=True)\n ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=3)\n ax.tick_params(which=\"minor\", bottom=False, left=False)\n\n return im, cbar","title":""},{"docid":"6c1529a1fd1e65523056787b4f1956fa","score":"0.5616676","text":"def autocorr_raster_plot(ax, trange, spks=None, cor=None, **kwargs):\r\n if cor is None:\r\n if spks is None:\r\n raise IOError('Need to give cor or spks')\r\n cor = cc_func(np.array(spks), np.array(spks),\r\n trange=trange, keep_zero=False)\r\n\r\n ys = [np.zeros_like(v) + i for i, v in enumerate(cor)]\r\n d = dict(color='k', alpha=.1, s=1)\r\n d.update(kwargs)\r\n ax.scatter(flaten_list(cor) * 1000, flaten_list(ys), **d)\r\n ax.set_ylim(0, len(cor))\r\n ax.set_xlim(*np.array(trange) * 1000)\r\n return cor, ys","title":""},{"docid":"401d989e2180c21973bc18f01de0c90d","score":"0.5598288","text":"def heatmap(data, row_labels, col_labels, ax=None, cbar_kw={}, cbarlabel=\"\", **kwargs):\n\n if not ax:\n ax = plt.gca()\n\n # Plot the heatmap\n im = ax.imshow(data, **kwargs)\n\n # Create colorbar\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\"bottom\")\n\n # We want to show all ticks...\n ax.set_xticks(np.arange(data.shape[1]))\n ax.set_yticks(np.arange(data.shape[0]))\n # ... and label them with the respective list entries.\n ax.set_xticklabels(col_labels)\n ax.set_yticklabels(row_labels)\n\n # Let the horizontal axes labeling appear on top.\n ax.tick_params(top=True, bottom=False, labeltop=True, labelbottom=False)\n\n # Rotate the tick labels and set their alignment.\n plt.setp(ax.get_xticklabels(), rotation=-45, ha=\"right\", rotation_mode=\"anchor\")\n\n # Turn spines off and create white grid.\n for edge, spine in ax.spines.items():\n spine.set_visible(False)\n\n ax.set_xticks(np.arange(data.shape[1] + 1) - 0.5, minor=True)\n ax.set_yticks(np.arange(data.shape[0] + 1) - 0.5, minor=True)\n ax.grid(which=\"minor\", color=\"w\", linestyle=\"-\", linewidth=3)\n ax.tick_params(which=\"minor\", bottom=False, left=False)\n\n return im, cbar","title":""},{"docid":"bb7220f2c95686978d74ac3290bf0900","score":"0.5581191","text":"def heatmap(data, row_labels, col_labels, ax=None,\n cbar_kw={}, cbarlabel=\"\", **kwargs):\n\n if not ax:\n ax = plt.gca()\n\n # Plot the heatmap\n im = ax.imshow(data, **kwargs)\n\n # Create colorbar\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\n cbar.ax.set_ylabel(cbarlabel, rotation=00, va=\"bottom\")\n\n # We want to show all ticks...\n ax.set_xticks(np.arange(data.shape[1]))\n ax.set_yticks(np.arange(data.shape[0]))\n # ... and label them with the respective list entries.\n ax.set_xticklabels(col_labels)\n ax.set_yticklabels(row_labels)\n\n # Let the horizontal axes labeling appear on top.\n ax.tick_params(top=True, bottom=False,\n labeltop=True, labelbottom=False)\n\n # Rotate the tick labels and set their alignment.\n plt.setp(ax.get_xticklabels(), rotation=00, ha=\"right\",\n rotation_mode=\"anchor\")\n\n # Turn spines off and create white grid.\n for edge, spine in ax.spines.items():\n spine.set_visible(False)\n\n ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\n ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\n ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=3)\n ax.tick_params(which=\"minor\", bottom=False, left=False)\n\n return im, cbar","title":""},{"docid":"24b36632f737b0977ff0c8b357697087","score":"0.5579378","text":"def heatmap(data, row_labels, col_labels, ax=None,\n cbar_kw={}, cbarlabel=\"\", **kwargs):\n\n if not ax:\n ax = plt.gca()\n\n # Plot the heatmap\n im = ax.imshow(data, **kwargs)\n\n # Create colorbar\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\"bottom\")\n\n # We want to show all ticks...\n ax.set_xticks(np.arange(data.shape[1]))\n ax.set_yticks(np.arange(data.shape[0]))\n # ... and label them with the respective list entries.\n ax.set_xticklabels(col_labels)\n ax.set_yticklabels(row_labels)\n\n # Let the horizontal axes labeling appear on top.\n ax.tick_params(top=True, bottom=False,\n labeltop=True, labelbottom=False)\n\n # Rotate the tick labels and set their alignment.\n plt.setp(ax.get_xticklabels(), rotation=-30, ha=\"right\",\n rotation_mode=\"anchor\")\n\n # Turn spines off and create white grid.\n for edge, spine in ax.spines.items():\n spine.set_visible(False)\n\n ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\n ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\n ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=3)\n ax.tick_params(which=\"minor\", bottom=False, left=False)\n\n return im, cbar","title":""},{"docid":"24b36632f737b0977ff0c8b357697087","score":"0.5579378","text":"def heatmap(data, row_labels, col_labels, ax=None,\n cbar_kw={}, cbarlabel=\"\", **kwargs):\n\n if not ax:\n ax = plt.gca()\n\n # Plot the heatmap\n im = ax.imshow(data, **kwargs)\n\n # Create colorbar\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\"bottom\")\n\n # We want to show all ticks...\n ax.set_xticks(np.arange(data.shape[1]))\n ax.set_yticks(np.arange(data.shape[0]))\n # ... and label them with the respective list entries.\n ax.set_xticklabels(col_labels)\n ax.set_yticklabels(row_labels)\n\n # Let the horizontal axes labeling appear on top.\n ax.tick_params(top=True, bottom=False,\n labeltop=True, labelbottom=False)\n\n # Rotate the tick labels and set their alignment.\n plt.setp(ax.get_xticklabels(), rotation=-30, ha=\"right\",\n rotation_mode=\"anchor\")\n\n # Turn spines off and create white grid.\n for edge, spine in ax.spines.items():\n spine.set_visible(False)\n\n ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\n ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\n ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=3)\n ax.tick_params(which=\"minor\", bottom=False, left=False)\n\n return im, cbar","title":""},{"docid":"66b878769a4292f453d388fc4af2f68d","score":"0.5573627","text":"def heatmap(data, row_labels, col_labels, ax=None,\n cbar_kw={}, cbarlabel=\"\", **kwargs):\n\n if not ax:\n ax = plt.gca()\n\n # Plot the heatmap\n im = ax.imshow(data, **kwargs)\n\n # Create colorbar\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\"bottom\")\n\n # We want to show all ticks...\n ax.set_xticks(np.arange(data.shape[1]))\n ax.set_yticks(np.arange(data.shape[0]))\n # ... and label them with the respective list entries.\n ax.set_xticklabels(col_labels)\n ax.set_yticklabels(row_labels)\n\n # Let the horizontal axes labeling appear on top.\n ax.tick_params(top=True, bottom=False,\n labeltop=True, labelbottom=False)\n\n # Rotate the tick labels and set their alignment.\n plt.setp(ax.get_xticklabels(), rotation=-30, ha=\"right\",\n rotation_mode=\"anchor\")\n\n # Turn spines off and create white grid.\n for edge, spine in ax.spines.items():\n spine.set_visible(False)\n\n ax.set_xticks(np.arange(data.shape[1] + 1) - .5, minor=True)\n ax.set_yticks(np.arange(data.shape[0] + 1) - .5, minor=True)\n ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=3)\n ax.tick_params(which=\"minor\", bottom=False, left=False)\n\n return im, cbar","title":""},{"docid":"12b61a7c8106098b976abfd82b7465de","score":"0.55722404","text":"def topic_correlation(self, topic_matrix):\r\n data = pd.DataFrame(data=topic_matrix)\r\n corr = data.corr()\r\n fig, ax = plt.subplots(figsize=(16, 8), sharey='all', dpi=160)\r\n cax = ax.matshow(corr, cmap='coolwarm', vmin=-1, vmax=1)\r\n fig.colorbar(cax)\r\n ticks = np.arange(0, len(data.columns), 1)\r\n ax.set_xticks(ticks)\r\n plt.xticks(rotation=90)\r\n ax.set_yticks(ticks)\r\n ax.set_xticklabels(data.columns)\r\n ax.set_yticklabels(data.columns)\r\n\r\n fig.suptitle('Topic Correlation')\r\n\r\n if self.report:\r\n self.pdf.savefig(fig)\r\n\r\n if self._display:\r\n plt.show()\r\n else:\r\n plt.close(fig)","title":""},{"docid":"728576c6989f4605462873087d0cc38d","score":"0.55645734","text":"def heatmap(data, row_labels, col_labels, ax=None,\n cbar_kw={}, cbarlabel=\"\",lab_size=4, **kwargs):\n\n if not ax:\n ax = plt.gca()\n\n # Plot the heatmap\n im = ax.imshow(data, **kwargs)\n\n # Create colorbar\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\"bottom\",size=lab_size)\n cbar.ax.tick_params(labelsize=lab_size)\n\n # We want to show all ticks...\n ax.set_xticks(numpy.arange(data.shape[1]))\n ax.set_yticks(numpy.arange(data.shape[0]))\n # ... and label them with the respective list entries.\n ax.set_xticklabels(col_labels)\n ax.set_yticklabels(row_labels)\n\n # Let the horizontal axes labeling appear on top.\n ax.tick_params(top=True, bottom=False,\n labeltop=True, labelbottom=False,labelsize=lab_size)\n\n # Rotate the tick labels and set their alignment.\n plt.setp(ax.get_xticklabels(), rotation=-90, ha=\"right\",va='center',\n rotation_mode=\"anchor\", fontsize=lab_size)\n plt.setp(ax.get_yticklabels(), fontsize=lab_size)\n\n # Turn spines off and create white grid.\n #for edge, spine in ax.spines.items():\n # spine.set_visible(False)\n\n ax.set_xticks(numpy.arange(data.shape[1]+1)-.5, minor=True)\n ax.set_yticks(numpy.arange(data.shape[0]+1)-.5, minor=True)\n ax.grid(which=\"minor\", color=\"k\", linestyle='-', linewidth=0.1)\n ax.tick_params(which=\"minor\", bottom=False, left=False)\n\n return im, cbar","title":""},{"docid":"ac5f741909215516197da6e8262f172e","score":"0.55473465","text":"def corr_plot(\n data, features=None, method=\"pearson\", plot_width=500, plot_height=400\n):\n # Defining numeric_features list\n numeric_features = [\n \"int16\",\n \"int32\",\n \"int64\",\n \"float16\",\n \"float32\",\n \"float64\",\n ]\n\n # Cheking user's inputs\n\n # Tests whether input data is of pd.DataFrame type\n if not isinstance(data, pd.DataFrame):\n raise TypeError(\"Please pass in a Pandas DataFrame for `data`\")\n\n # Tests whether input features is of the type list\n if features is not None:\n if not isinstance(features, list):\n raise TypeError(\"Please pass in a list for `features`\")\n\n # Tests whether input features has at least two features\n if features is not None:\n if len(features) < 2:\n raise ValueError(\"At least two features should be selected\")\n\n # Tests whether input method is of the type str\n if not isinstance(method, str):\n raise TypeError(\"Please pass in a str for `method`\")\n\n # Tests whether input method is one of the 3 available options\n if method not in (\"pearson\", \"spearman\", \"kendall\"):\n raise Exception(\n \"Please pick a correlation method: 'pearson', 'spearman' or\"\n \" 'kendall'\"\n )\n\n # Tests whether input plot width and height are of the type int\n if (not isinstance(plot_width, int)) or (not isinstance(plot_height, int)):\n raise TypeError(\"Both plot_width and plot_height must be integers\")\n\n # Subsetting the data dataframe\n if features is None:\n if data.select_dtypes(include=numeric_features).shape[1] < 2:\n raise ValueError(\n \"Dataframe should have at least two numerical features\"\n )\n data = data.select_dtypes(include=numeric_features)\n else:\n if data[features].select_dtypes(np.number).shape[1] < 2:\n raise ValueError(\n \"Dataframe should have at least two numerical features\"\n )\n data = data[features].select_dtypes(include=np.number)\n # Creating corr_df dataframe\n corr_df = data.corr(method).stack().reset_index(name=\"corr\")\n corr_df.loc[corr_df[\"corr\"] == 1, \"corr\"] = 0\n corr_df[\"abs\"] = corr_df[\"corr\"].abs()\n\n # Correlation plot\n corr_plot = (\n alt.Chart(\n corr_df, title=f\"{method} Correlations Plot for Numerical Features\"\n )\n .mark_circle()\n .encode(\n x=alt.X(\"level_0\", title=\"Numerical Features\"),\n y=alt.Y(\"level_1\", title=\"Numerical Features\"),\n size=alt.Size(\"abs\", title=\"Correlation Size\"),\n color=alt.Color(\n \"corr\",\n title=\"Correlation\",\n scale=alt.Scale(scheme=\"blueorange\"),\n ),\n tooltip=alt.Tooltip(\"corr\"),\n )\n .properties(width=plot_width, height=plot_height)\n )\n\n return corr_plot","title":""},{"docid":"6e0dd156668d0d817225fdfba31479aa","score":"0.55360407","text":"def plot_correlation_with_lag(\n self,\n lag: Union[int, List[int]],\n cols: Optional[List[Any]] = None,\n ax: Optional[mpl.axes.Axes] = None,\n mode: str = \"ins\",\n ) -> pd.DataFrame:\n df = self._get_df(cols=cols, mode=mode)\n # Calculate correlation.\n corr_df = csigna.correlate_with_lag(df, lag=lag)\n cplott.plot_heatmap(corr_df, ax=ax)\n return corr_df","title":""},{"docid":"8b096de850614fe846d29c9923804b1f","score":"0.55310494","text":"def plotRawCorrelations(figure, gridSystem, plotRow, seqA, seqB):\n\n # Calculate the correlations:\n valid = np.correlate(seqA, seqB, 'valid')\n same = np.correlate(seqA, seqB, 'same')\n full = np.correlate(seqA, seqB, 'full')\n\n # Plot the correlations:\n ax = figure.add_subplot(gridSystem[plotRow, :])\n ax.plot(valid, 'ro', rasterized=RASTERIZE_PLOTS)\n ax.set_title('Correlation: Valid')\n plotRow += 1\n\n ax = figure.add_subplot(gridSystem[plotRow, :])\n ax.plot(same, 'ro', rasterized=RASTERIZE_PLOTS)\n ax.set_title('Correlation: Same')\n plotRow += 1\n\n ax = figure.add_subplot(gridSystem[plotRow, :])\n ax.plot(full, 'ro', rasterized=RASTERIZE_PLOTS)\n ax.set_title('Correlation: Full')\n plotRow += 1\n\n return plotRow","title":""},{"docid":"3f8bd423f555d556a8b408eec4ec3d48","score":"0.5519529","text":"def draw_heatmap(**kwargs):\n data = kwargs.pop('data')\n data = data.pivot(columns='Plant Name', index='Objective', values='Sobol Index')\n return sns.heatmap(data, **kwargs)","title":""},{"docid":"3075e0ca41c3c6d8ee6df1639fc4cfa3","score":"0.5497765","text":"def heatmap(data, ax=None, cbar_kw={}, cbarlabel=\"\", **kwargs):\n plt.figure(figsize=(12, 6))\n\n if not ax:\n ax = plt.gca()\n\n # Plot the heatmap\n im = ax.imshow(data, **kwargs, aspect=\"auto\")\n\n # Create colorbar\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\"bottom\")\n\n # We want to show all ticks...\n ax.set_xticks(np.arange(data.shape[1]))\n ax.set_yticks(np.arange(data.shape[0]))\n\n # Let the horizontal axes labeling appear on top.\n ax.tick_params(top=False, bottom=True,\n labeltop=False, labelbottom=True)\n\n # Turn spines off and create white grid.\n for edge, spine in ax.spines.items():\n spine.set_visible(False)\n\n ax.set_xticks(np.arange(data.shape[1] + 1) - .5, minor=True)\n ax.set_yticks(np.arange(data.shape[0] + 1) - .5, minor=True)\n ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=3)\n ax.tick_params(which=\"minor\", bottom=False, left=False)\n\n return im, cbar","title":""},{"docid":"f89109d7d954090e5c2262d533c05113","score":"0.54860026","text":"def heatmap(data, row_labels, col_labels, ax=None, cbar_kw={}, cbarlabel=\"\", **kwargs):\n\n if not ax:\n ax = plt.gca()\n\n # Plot the heatmap\n im = ax.imshow(data, **kwargs)\n\n # We want to show all ticks...\n ax.set_xticks(np.arange(data.shape[1]))\n ax.set_yticks(np.arange(data.shape[0]))\n # ... and label them with the respective list entries.\n ax.set_xticklabels(col_labels)\n ax.set_yticklabels(row_labels)\n\n # Let the horizontal axes labeling appear on top.\n ax.tick_params(top=True, bottom=False, labeltop=True, labelbottom=False)\n\n # Rotate the tick labels and set their alignment.\n plt.setp(ax.get_xticklabels(), rotation=-30, ha=\"right\", rotation_mode=\"anchor\")\n\n # Turn spines off and create white grid.\n for edge, spine in ax.spines.items():\n spine.set_visible(False)\n\n ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\n ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\n ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=3)\n ax.tick_params(which=\"minor\", bottom=False, left=False)\n\n return im","title":""},{"docid":"291efd02e5b6f3e191f197a73a2fd052","score":"0.54723936","text":"def plot_frame_cells(self, frame, coll_df, color_column, c_min= 0., c_max= 1., n_ticks= 5, figsize= (6, 10), polygon_lw= .1, color_map= cm.afmhot, title= ''):\n plt.figure(figsize= figsize)\n plt.title(title, fontsize= 25)\n self.show_image(frame)\n plt.gca().autoscale_view()\n plt.gca().set_aspect('equal')\n colors= color_map((coll_df[color_column].values-c_min)/(c_max - c_min)) \n coll= mc.PolyCollection(coll_df['plot_vertices'].values, lw= polygon_lw)\n coll.set(facecolors= colors)\n plt.gca().add_collection(coll)\n plt.xlim(0, 900)\n plt.ylim(300, 1800)\n plt.gca().invert_yaxis()\n plt.axis('off')\n divider= make_axes_locatable(plt.gca())\n cax= divider.append_axes('right', size= '5%', pad= 0.05)\n mm= cm.ScalarMappable(cmap= color_map)\n mm.set_array(colors)\n cbar= plt.colorbar(mm, cax= cax, cmap= color_map, ticks= np.linspace(0, 1, n_ticks + 1))\n cbar.ax.set_yticklabels(np.linspace(c_min, c_max, n_ticks + 1))","title":""},{"docid":"edf60c3e152234bdfbc49baf11f3c073","score":"0.54602236","text":"def heatmap_creator(given_filter):\n\tdraw_heatmap(given_filter)","title":""},{"docid":"e02de2f57e56d65687b232bee974c09c","score":"0.54509777","text":"def correlation(x, y, xlabel='x', ylabel='y'):\n \n plt.title(xlabel + ', ' + ylabel, fontsize = 24, fontweight = 'bold')\n plt.xlabel(xlabel)\n plt.ylabel(ylabel)\n plt.scatter(x, y);\n \n plt.show()\n \n # statistical tests\n pearson_coef, pearson_p = sp.stats.pearsonr(x, y)\n spearman_coef, spearman_p = sp.stats.spearmanr(x, y)\n \n correl_test_results = pd.DataFrame()\n correl_test_results.loc['Pearson', 'Coefficient'] = pearson_coef\n correl_test_results.loc['Pearson', 'p-value'] = pearson_p\n correl_test_results.loc['Spearman', 'Coefficient'] = spearman_coef\n correl_test_results.loc['Spearman', 'p-value'] = spearman_p\n\n IPython.display.display(correl_test_results)","title":""},{"docid":"5a0a1b19054966840d1f84c908c69190","score":"0.5446537","text":"def plot_heatmap(df_to_plot):\n # create figure and axis\n fig, ax = plt.subplots(figsize=(20, 20))\n\n # Generate a custom diverging colormap\n cmap = sns.diverging_palette(50, 10, as_cmap=True)\n\n # Draw the heat map with the mask and correct aspect ratio\n sns.heatmap(df_to_plot,\n cmap=cmap,\n vmax=1,\n center=0,\n square=True,\n linewidths=.5,\n cbar_kws={\"shrink\": .5},\n annot=True,\n ax=ax)\n\n plt.show()","title":""},{"docid":"18cdd250fd811795e003c09336e25843","score":"0.54337734","text":"def hist_fx_correlations_physical_plot(year: str, interval: str) -> None:\n\n function_name: str = \\\n hist_fx_correlations_physical_plot.__name__\n hist_data_tools_matrices_physical \\\n .hist_function_header_print_plot(function_name, year, kind='correlations')\n\n try:\n\n periods: int\n n_cols: int\n n_rows: int\n if interval == 'week':\n periods = 52\n n_cols = 13\n n_rows = 4\n elif interval == 'month':\n periods = 12\n n_cols = 4\n n_rows = 3\n elif interval == 'quarter':\n periods = 4\n n_cols = 2\n n_rows = 2\n else:\n periods = 1\n n_cols = 1\n n_rows = 1\n\n figure: plt.figure = plt.figure(figsize=(16, 9))\n cbar_ax: plt.axes = figure.add_axes([0.91, 0.3, 0.03, 0.4])\n\n for per in range(1, periods + 1):\n\n if per < 10:\n per_str: str = f'0{per}'\n else:\n per_str = f'{per}'\n\n # Load data\n corr: pd.DataFrame = pickle.load(open(\n f'../../hist_data/matrices_physical_{year}/hist_fx_matrices'\n + f'_physical_data/hist_fx_corr_physical_data_{year}_int'\n + f'_{interval}_{per_str}.pickle', 'rb'))\n\n ax_sub = plt.subplot(n_rows, n_cols, per)\n\n if interval in ('week', 'month'):\n sns.heatmap(corr, ax=ax_sub, cbar=per == 1,\n cbar_ax=None if (per-1) else cbar_ax,\n vmin=-1, vmax=1)\n\n else:\n sns.heatmap(corr, annot=True, ax=ax_sub, cbar=per == 1,\n cbar_ax=None if (per-1) else cbar_ax,\n vmin=-1, vmax=1)\n\n if interval == 'week':\n ax_sub.tick_params(axis='x', bottom=False, labelbottom=False)\n ax_sub.tick_params(axis='y', left=False, labelleft=False)\n\n plt.yticks(rotation=45)\n plt.xticks(rotation=45)\n\n figure.tight_layout(rect=[0, 0, .9, 1])\n\n # Plotting\n hist_data_tools_matrices_physical \\\n .hist_save_plot(function_name, figure, year, interval)\n\n plt.close()\n del corr\n del figure\n gc.collect()\n\n except FileNotFoundError as error:\n print('No data')\n print(error)\n print()","title":""},{"docid":"da4d1664a826caa3cb0b75c48ccfab88","score":"0.54255104","text":"def corr_plot(self, parameters: list = None,\n inherit: plt = None,\n figsize: list = None,\n title: str = None,\n annot: bool = True,\n show: bool = True):\n\n # Check if y is list or str\n if not isinstance(parameters, list):\n self._raise_plot_format_error([\"parameters\"], \"list or None\")\n\n # previous configuration of plot\n plt = self._plot_prev_config(inherit, figsize, \"seaborn-dark\")\n fig, ax = plt.subplots()\n\n data = self[parameters].corr()\n score = data.values\n col = data.columns\n length = len(col)\n im = ax.imshow(score, cmap='rocket_r')\n ax.xaxis.set_ticks_position('top')\n ax.set_xticks(np.arange(length))\n ax.set_yticks(np.arange(length))\n ax.set_xticklabels(col)\n ax.set_yticklabels(col)\n fig.colorbar(im, pad=0.03)\n\n # the annotation part\n if annot:\n for i in range(length):\n for j in range(length):\n if score[i, j] > 0.4:\n color = \"w\"\n else:\n color = \"black\"\n ax.text(j, i, round(score[i, j], 2),\n ha=\"center\", va=\"center\", color=color)\n\n # return for advanced adjustment\n return self._plot_post_config(plt, '', title, '', '', show)","title":""},{"docid":"cf991a923d449e7b829a72da3d5b5f87","score":"0.5412111","text":"def plot_correlaton_matrix(self, mask_autocorrelations: bool = False, **kwargs):\n max_ma_index = self.mini_arrays.max() + 1\n all_mas = np.arange(max_ma_index)\n matrix = np.full([max_ma_index, max_ma_index], np.nan, \"complex\")\n ma1, ma2 = np.tril_indices(self.mini_arrays.size, 0)\n for ma in all_mas:\n if ma not in self.mini_arrays:\n ma1[ma1 >= ma] += 1\n ma2[ma2 >= ma] += 1\n\n mask = None\n if mask_autocorrelations:\n mask = ma1 != ma2 # cross_correlation mask\n matrix[ma2[mask], ma1[mask]] = np.mean(self.value, axis=(0, 1))[mask]\n\n fig = plt.figure(figsize=kwargs.get(\"figsize\", (10, 10)))\n ax = fig.add_subplot(111)\n ax.set_aspect(\"equal\")\n\n data = np.absolute(matrix)\n if kwargs.get(\"decibel\", True):\n data = 10*np.log10(data)\n\n im = ax.pcolormesh(\n all_mas,\n all_mas,\n data,\n shading=\"nearest\",\n cmap=kwargs.get(\"cmap\", \"YlGnBu\"),\n vmin=kwargs.get(\"vmin\", np.nanmin(data)),\n vmax=kwargs.get(\"vmax\", np.nanmax(data))\n )\n ax.set_xticks(all_mas[::2])\n ax.set_yticks(all_mas[::2])\n ax.grid(alpha=0.5)\n\n divider = make_axes_locatable(ax)\n cax = divider.append_axes(\"right\", size=\"5%\", pad=0.3)\n cbar = fig.colorbar(im, cax=cax)\n cbar.set_label(kwargs.get(\"colorbar_label\", \"dB\" if kwargs.get(\"decibel\", True) else \"Amp\"))\n \n # Axis abels\n ax.set_xlabel(f\"Mini-Array index\")\n ax.set_ylabel(f\"Mini-Array index\")\n\n # Title\n ax.set_title(kwargs.get(\"title\", \"\"))\n\n # Save or show the figure\n figname = kwargs.get(\"figname\", \"\")\n if figname != \"\":\n plt.savefig(\n figname,\n dpi=300,\n bbox_inches=\"tight\",\n transparent=True\n )\n log.info(f\"Figure '{figname}' saved.\")\n else:\n plt.show()\n plt.close(\"all\")","title":""},{"docid":"13ad289c23981fce522b01a2d2ac978b","score":"0.5408755","text":"def correlationSpectroscopyInteractive(dataset, target, mode='SHY', correlationMethod='Pearson'):\n\tif mode.lower() == 'shy':\n\t\tcolour = _vcorrcoef(dataset.intensityData[dataset.sampleMask, :], target, method=correlationMethod)\n\n\tmagnitude = numpy.mean(dataset.intensityData[dataset.sampleMask, :],axis=0)\n\t\n\tplot = plotyShadedLineplot(dataset.featureMetadata.loc[dataset.featureMask, 'ppm'], magnitude[dataset.featureMask], colour[dataset.featureMask])\n\n\tif 'ppm' in dataset.featureMetadata.columns:\n\t\txaxis = 'reversed'\n\telse:\n\t\txaxis = 'auto'\n\t\n\tlayout = go.Layout(\n\t\ttitle=None,\n\t\tlegend=dict(\n\t\t\torientation=\"h\"),\n\t\thovermode = \"closest\",\n\t\tyaxis = dict(\n\t\t\tshowticklabels=False,\n\t\t\thoverformat = '.2f'\n\t\t),\n\t\txaxis=dict(\n\t\t\tautorange=xaxis, \n\t\t\ttitle='PPM',\n\t\t\thoverformat = '.2f'\n\t\t)\n\t)\n\n\tfigure = go.Figure(data=plot, layout=layout)\n\t\n\treturn figure","title":""},{"docid":"4dee24c7c99015d58c4a736411182f6a","score":"0.5406638","text":"def plot_correlation(data, couplings, name, type_to_idx, subset=[0,1,2,3,4,5,11], \n filename='correlation_matrix.pdf', linkage=None):\n subset = np.asarray(subset, dtype=int)\n name = np.asarray(name)\n\n x = extract_weighted_subset(data, couplings, type_to_idx, subset=subset, metric='rmse')\n corr = np.corrcoef(x)\n\n if linkage not in (None, 'single', 'complete', 'average', 'weighted', 'centroid', 'median', 'ward'):\n print(\"Unknown linkage\", linkage)\n raise SystemExit\n\n # Unsorted plots\n if linkage is None:\n # Plot heatmap\n sns.heatmap((corr), square=True, linewidths=.25, cbar_kws={\"shrink\": .5},\n cmap=sns.diverging_palette(220, 10, as_cmap=True),\n yticklabels=name[subset], xticklabels=subset+1,\n center=0.5, vmax=1, vmin=0)\n # Sorted from clustering\n else:\n d = scipy.cluster.hierarchy.distance.pdist(x)#, 'cityblock')\n L = scipy.cluster.hierarchy.linkage(d, method=linkage, optimal_ordering=True)\n\n sns.clustermap((corr), square=True, linewidths=.25, cbar_kws={\"shrink\": .5},\n cmap = sns.diverging_palette(220, 10, as_cmap=True),\n yticklabels=name[subset], xticklabels=subset+1,\n center=0.5, vmax=1, vmin=0, row_linkage=L, col_linkage=L)\n #plt.xticks(rotation=-45)\n\n plt.yticks(rotation=0)\n\n plt.savefig(filename, bbox_inches = \"tight\")\n plt.clf()","title":""},{"docid":"d493a9cbe7a1d28b6d58cbd4fe7aa6e2","score":"0.5405102","text":"def annotated_heatmap(data, row_labels, column_labels, subplot=None, colorbar_kw={}, colorbar_label=\"\",\n title=None, xlabel=None, ylabel=None,\n horizontal_ticks_angle=0, **kwargs):\n\n if not subplot:\n subplot = plt.gca()\n\n # Plot the heatmap\n image = subplot.imshow(data, **kwargs)\n\n # Create colorbar\n colorbar = subplot.figure.colorbar(image, ax=subplot, **colorbar_kw)\n colorbar.ax.set_ylabel(colorbar_label, rotation=-90, va=\"bottom\")\n\n # We want to show all ticks...\n subplot.set_xticks(np.arange(data.shape[1]))\n subplot.set_yticks(np.arange(data.shape[0]))\n # ... and label them with the respective list entries.\n subplot.set_xticklabels(column_labels)\n subplot.set_yticklabels(row_labels)\n\n # Let the horizontal axes labeling appear on top.\n #subplot.tick_params(top=True, bottom=False, labeltop=True, labelbottom=False)\n\n # Rotate the tick labels and set their alignment.\n plt.setp(subplot.get_xticklabels(), rotation=horizontal_ticks_angle, ha=\"right\",\n rotation_mode=\"anchor\")\n\n # Turn spines off and create white grid.\n for edge, spine in subplot.spines.items():\n spine.set_visible(False)\n\n subplot.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\n subplot.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\n subplot.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=3)\n subplot.tick_params(which=\"minor\", bottom=False, left=False)\n\n if title is not None:\n plt.title(title)\n if xlabel is not None:\n plt.xlabel(xlabel)\n if ylabel is not None:\n plt.ylabel(ylabel)\n\n return image, colorbar","title":""},{"docid":"f0e8cf54ff312d9d2181174df8919ddf","score":"0.53944826","text":"def heatmap(data, row_labels, col_labels, ax=None,\n cbar_kw={}, cbarlabel=\"\", **kwargs):\n\n mpl.rcParams.update({'font.size':24, 'lines.linewidth':3, 'lines.markersize':15, 'font.family':'Times New Roman'}) # avoid type 3 (i.e. bitmap) fonts in figures\n mpl.rcParams['ps.useafm'] = True\n mpl.rcParams['pdf.use14corefonts'] = True\n mpl.rcParams['text.usetex'] = True\n\n if not ax:\n ax = plt.gca()\n\n # handle nans\n current_cmap = mpl.cm.get_cmap()\n current_cmap.set_bad(color='w')\n\n # Plot the heatmap\n im = ax.imshow(data, **kwargs)\n\n # Create colorbar\n# cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\n# cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\"bottom\")\n\n # We want to show all ticks...\n ax.set_xticks(np.arange(data.shape[1]))\n ax.set_yticks(np.arange(data.shape[0]))\n # ... and label them with the respective list entries.\n ax.set_xticklabels(col_labels)\n ax.set_yticklabels(row_labels)\n\n # Let the horizontal axes labeling appear on top.\n # ax.tick_params(top=True, bottom=False,\n # labeltop=True, labelbottom=False)\n\n # Rotate the tick labels and set their alignment.\n# plt.setp(ax.get_xticklabels(), rotation=-30, ha=\"right\",\n# rotation_mode=\"anchor\")\n\n # Turn spines off and create white grid.\n for _, spine in ax.spines.items():\n spine.set_visible(False)\n\n ax.set_xticks(np.arange(data.shape[1] + 1) - .5, minor=True)\n ax.set_yticks(np.arange(data.shape[0] + 1) - .5, minor=True)\n ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=3)\n ax.tick_params(which=\"minor\", bottom=False, left=False)\n\n ax.invert_yaxis()\n\n ax.set_xlabel('$p_{1}$', labelpad=15)\n ax.set_ylabel('$p_{2}$', labelpad=15)\n\n return im # , cbar","title":""},{"docid":"0bcbdb562ea536bf02f683b7f87355e5","score":"0.5375192","text":"def graph_heatmap():\n\n\tpath=\"RESULTS/Graficar_HeatMap\"\n\tpath2=\"RESULTS/Graficos\"\n\tif os.path.isdir(path2) == True:\n\t\tpass\n\telse:\n\t\tos.mkdir(path2)\n\n\tfor file1 in os.listdir(path):\n\t\tout=path2+ \"/\" + file1[:-9] + \"_heatmap.jpg\"\n\t\tinput_file1=open(path + \"/\" + file1, \"r\")\n\t\tz=list()\n\t\tfor line1 in input_file1:\n\t\t\tcolumns=line1.split(\"\\t\")\n\t\t\tz.append(float(columns[2]))\n\t\t\n\t\tcoverage=np.linspace(0,100,11)\n\t\tidentity=np.linspace(0,100,11)\n\t\tcantidad=np.array(z).reshape(11,11)\n\t\tz_min, z_max = np.abs(z).min(), np.abs(z).max()\n\n\t\tfig, ax = plt.subplots()\n\t\tlevels=MaxNLocator(nbins=10).tick_values(z_min, z_max)\n\t\tc=ax.contourf(coverage, identity, cantidad, levels=levels, \n\t\tcmap='GnBu', vmin=z_min, vmax=z_max)\n\t\ttitle='Nº de subjects para cada intervalo de \\ncobertura e identidad'\n\t\tax.set_title(title + \" del query \" + file1[:-9])\n\t\tax.axis([coverage.min(),coverage.max(), \n\t\t\t\tidentity.min(), identity.max()])\n\t\tfig.colorbar(c, ax=ax)\n\t\tplt.ylabel('Porcentaje de cobertura')\n\t\tplt.xlabel('Porcentaje de identidad')\n\t\tplt.savefig(out)","title":""},{"docid":"f227e23b676adde8bdf857cea492b9b0","score":"0.53670585","text":"def construct_heatmap( datamatrix, genes, tissues, heatmap_file ):\n\t\n\tmy_vmax = 100\n\t\n\tprint \"number of genes for heatmap construction: \" + str( len( genes ) )\n\tdf = DataFrame( datamatrix, index=genes[::-1], columns=tissues).round( 0 )\n\t\n\tfig, ax = plt.subplots( )\n\t\n\tsns.heatmap( df, vmin=0, vmax= my_vmax, ax=ax, linewidths=0.3, annot=True, annot_kws={'fontsize':3}, cbar=False, fmt=\"g\", cmap='YlGnBu' )\t#binary\t#cmap='YlGnBu' = 1\n\t\n\tfor idx, gene in enumerate( genes ):\n\t\tax.text( -2.65, idx+0.8, gene, fontsize=3 )\n\t\n\tfor idx, tissue in enumerate( tissues ):\n\t\tax.text( idx+0.4, len( genes )+0.5, \"20\"+tissue[-2:] + \"-\" + tissue[2:4] + \"-\" + tissue[:2], rotation=90, fontsize=3 )\n\t\n\tax.set_yticklabels( [], rotation=0, fontsize=2 )\n\tax.set_xticklabels( [] , rotation=90, fontsize=3 )\n\t\n\tax.spines['bottom'].set_visible(False)\n\tax.spines['left'].set_visible(False)\n\t\n\tax.axes.get_yaxis().set_visible(False)\n\tax.axes.get_xaxis().set_visible(False)\n\t\n\tplt.yticks( rotation=0 )\n\tplt.subplots_adjust( left=0.135, right=0.99, top=0.99, bottom=0.0575, wspace=0.2 )\n\t\n\tplt.savefig( heatmap_file, dpi=900 )\n\tplt.savefig( heatmap_file.replace( \".pdf\", \".svg\" ) )","title":""},{"docid":"489be190ef5475545b27b6f9460bd009","score":"0.5357623","text":"def vis_corr_photometry(self):\n # Look at photometry after subtracting expected magnitude\n # (i.e. subtract an approximate phase curve, but the ZTF predicted values do have errors sometimes)\n fig = plt.figure(figsize=(8, 6))\n for f in self.filterlist:\n o = self.lcobs.query('fid == @f')\n plt.errorbar(o.jd - self.lcobs.jd.iloc[0], o.magcorr, yerr=o.sigmamag, color=filtercolors[f],\n marker='.', linestyle='')\n plt.xticks(rotation=90)\n plt.xlabel('delta JD', fontsize='x-large')\n plt.ylabel('MagCorr (mag_obs - pred)', fontsize='x-large')\n plt.gca().invert_yaxis()\n plt.title(self.name)\n plt.grid(True, alpha=0.3)\n label = self._make_figurelabel()\n plt.figtext(0.15, 0.8, label)\n return fig","title":""},{"docid":"dc9afac1b7f9bb81a270e60a2a8a57a2","score":"0.53570837","text":"def _corr_grid(corr: NDArray[float]) -> NDArray[float]:\n xs = np.arange(-corr.shape[0] / 2, corr.shape[0] / 2) + 0.5\n ys = np.arange(-corr.shape[1] / 2, corr.shape[1] / 2) + 0.5\n return np.meshgrid(xs, ys)","title":""},{"docid":"c766738dc491739308744a5cf8d38ce6","score":"0.53538287","text":"def plotHeatmap(T, Y, ax = None):\n if ax is None:\n ax = plt.gca()\n cax1 = ax.imshow(Y, aspect='auto', interpolation='none', origin='lower',extent=[0,T[-1],len(Y),0],vmax=2, vmin=-2)\n ax.set_xlabel('Time (s)')\n ax.set_ylabel(\"Neuron\")\n return cax1","title":""},{"docid":"c8f5f4b7e6b421696a0592aee556a47c","score":"0.53533655","text":"def plot_selectivity_heatmap(corrs, title, res_dir):\n\n # Plot each layer.\n for layer_name, lcorrs in corrs.groupby(level='layer'):\n\n # Create heatmap.\n lcorrs.index = lcorrs.index.droplevel()\n lcorrs = lcorrs.T\n wfig, hfig = np.sqrt(lcorrs.shape[1]), np.sqrt(lcorrs.shape[0])\n fig = plt.figure(figsize=(wfig, hfig))\n ax = fig.add_subplot(111)\n sns.heatmap(lcorrs, linewidths=.5, center=0, xticklabels=2, ax=ax)\n\n # Set labels.\n plt.yticks(rotation=0)\n ax.set_xlabel('Unit')\n ax.set_ylabel(corrs.columns.name)\n plt.title('%s in %s' % (title, layer_name))\n\n # Save plot.\n fname = '{}/{}.png'.format(res_dir, layer_name)\n utils.save_fig(fname, fig, close=True)","title":""},{"docid":"0fccb04e486f7c59ed3cec2313dbdaf0","score":"0.53528035","text":"def plot(Xt, sample=0, colorscale='greys', origin='upper',\n plotly_params=None):\n return plot_heatmap(\n Xt[sample] * 1, colorscale=colorscale, origin=origin,\n title=f\"Binarization of image {sample}\",\n plotly_params=plotly_params\n )","title":""},{"docid":"271f1b9cd4dc9c6df67cbf573e0b11f9","score":"0.53442097","text":"def update_heatmap_carn(self):\n x = self.sim.animal_distribution\n carn = x.pivot('Row', 'Col', 'Carnivore').values\n self.ax4.imshow(carn, vmax=self.cmax_animals['Carnivore'])","title":""},{"docid":"46b7aa1b1dd19ff27daa2d7c5b7c33fa","score":"0.53392583","text":"def correlation_matrix(self):\n pass # implementation can be omitted","title":""},{"docid":"ae50d63584b53b20167c5f7d05e6ca66","score":"0.53233975","text":"def draw_heat_map(center, df, radius = 16):\n\n from ipyleaflet import Map,Marker,CircleMarker,Heatmap\n from ipywidgets import HTML\n from ipyleaflet import Popup\n\n m = Map(center = center, zoom = 11)\n\n heatmap = Heatmap(\n locations=[(df.loc[i, \"lat\"], df.loc[i,\"lng\"]) for i in df.index],\n radius= radius\n )\n\n m.add_layer(heatmap);\n\n return display(m)","title":""},{"docid":"1d0a410c59ade904d9b454048013fbdb","score":"0.531939","text":"def plot_cmap(matrix_values, figsize_w, figsize_h, filename):\n if figsize_w is not None and figsize_h is not None:\n plt.figure(figsize=(figsize_w,figsize_h))\n else:\n plt.figure()\n cmap = sns.diverging_palette(240, 10, sep=20, as_cmap=True)\n sns.heatmap(matrix_values, annot=True, fmt=\".2f\", cmap=cmap, vmin=-1, vmax=1)\n plt.savefig(filename)\n plt.show()\n return cmap","title":""},{"docid":"262d8f1bf862cf176026079568b12242","score":"0.53168684","text":"def heatmap(data, key1, key2, values='value', xlabel=\"\", ylabel=\"\", log=False,\n carryover=False, draw_bars=True, cmap='CMRmap', disable_axes=True, **kwargs):\n\n def do_disable_ticks(ax):\n ax.xaxis.set_major_locator(plt.NullLocator())\n ax.yaxis.set_major_locator(plt.NullLocator())\n\n def do_disable_axes(ax):\n ax.xaxis.set_visible(False)\n ax.yaxis.set_visible(False)\n\n #cmap = \"YlGnBu\"\n bar_col = \"silver\"\n\n\n # create a 2 x 3 subplot field\n # TODO: disable option\n if draw_bars:\n fig = plt.figure(frameon=False)\n gs = gridspec.GridSpec(2, 3, width_ratios=[5, 1, 1], height_ratios = [1, 5], figure=fig)\n ax_center = plt.subplot(gs[1,0], frameon=False)\n ax_top = plt.subplot(gs[0,0], frameon=False, sharex=ax_center)\n ax_right = plt.subplot(gs[1,1], frameon=False)\n ax_right2 = plt.subplot(gs[1,2], frameon=False, sharex=ax_center)\n else:\n fig = plt.figure()\n gs = gridspec.GridSpec(2, 1, height_ratios=[9, 1], width_ratios = [1], figure=fig)\n ax_center = plt.subplot(gs[0,0], frameon=False)\n ax_right2 = plt.subplot(gs[1,0], frameon=False)\n\n # prepare data\n matrix = data.pivot(key1, key2, values=values)\n if carryover:\n matrix = matrix.fillna(method='ffill')\n else:\n matrix = matrix.fillna(0)\n\n if draw_bars:\n top = data.groupby(key1)[values].sum()\n right = data.groupby(key2)[values].sum()[::-1]\n\n # plot\n hm_data = matrix.T\n hm_data = hm_data.sort_index(ascending=False)\n if log:\n norm = SymLogNorm(vmin=hm_data.min().min(), vmax=hm_data.max().max(),\n linthresh=1)\n hm = sns.heatmap(hm_data, ax=ax_center, cbar=False, cmap=cmap,\n norm=norm, linewidths=0, **kwargs)\n else:\n hm = sns.heatmap(hm_data, ax=ax_center, cbar=False, cmap=cmap, linewidths=0, **kwargs)\n # previously used imshow, didn't work as nicely\n ## ax_center.imshow(matrix.T, aspect='auto')\n if draw_bars:\n top.plot.bar(ax=ax_top, color=bar_col, align='edge', log=log)\n #ax_top.set_yscale('symlog')\n right.plot.barh(ax=ax_right, color=bar_col, align='edge', log=log)\n #ax_top.set_yscale('symlog')\n plt.colorbar(ax_center.get_children()[0], ax=ax_right2, orientation='vertical')\n\n # naming\n ax_center.set_xlabel(xlabel)\n ax_center.set_ylabel(ylabel)\n\n # remove axes and ticks where possible\n do_disable_axes(ax_right2)\n do_disable_axes(ax_top)\n do_disable_axes(ax_right)\n do_disable_ticks(ax_center)\n\n # remove spaces\n fig.tight_layout(pad = 0)\n fig.patch.set_visible(False)\n return fig, [ax_top, ax_right, ax_right2, ax_center], {key1:top, key2:right}\n else:\n plt.colorbar(ax_center.get_children()[0], ax=ax_right2, orientation='horizontal', fraction=1, aspect=40)\n fig.subplots_adjust(hspace=0)\n # naming\n ax_center.set_xlabel(xlabel)\n ax_center.set_ylabel(ylabel)\n\n # remove axes and ticks where possible\n if disable_axes:\n do_disable_axes(ax_right2)\n #do_disable_ticks(ax_center)\n\n fig.tight_layout(pad = 0)\n #fig.patch.set_visible(False)\n return fig, [ax_center], None","title":""},{"docid":"78970ea2e2fdef31ce6027be88bf855e","score":"0.531118","text":"def heatmap(data, row_labels, col_labels, ax=None,\r\n cbar_kw={}, cbarlabel=\"\", title = \"Default\", x_title=\" \",y_title=\" \",saveFile = None, **kwargs):\r\n\r\n #plt.clf()\r\n if not ax:\r\n ax = plt.gca()\r\n\r\n # Plot the heatmap\r\n im = ax.imshow(data, **kwargs)\r\n\r\n # Create colorbar\r\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw) # pad=0.1\r\n #cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\"bottom\")\r\n\r\n # We want to show all ticks...\r\n ax.set_xticks(np.arange(data.shape[1]))\r\n ax.set_yticks(np.arange(data.shape[0]))\r\n # ... and label them with the respective list entries.\r\n ax.set_xticklabels(col_labels)\r\n ax.set_yticklabels(row_labels)\r\n\r\n # Let the horizontal axes labeling appear on bottom\r\n ax.tick_params(top=False, bottom=True,\r\n labeltop=False, labelbottom=True)\r\n\r\n # Rotate the tick labels and set their alignment.\r\n plt.setp(ax.get_xticklabels(), rotation=30, ha=\"right\",\r\n rotation_mode=\"anchor\")\r\n\r\n # Turn spines off and create white grid.\r\n for edge, spine in ax.spines.items():\r\n spine.set_visible(False)\r\n\r\n ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\r\n ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\r\n ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=3)\r\n ax.tick_params(which=\"minor\", bottom=False, left=False)\r\n \r\n # plt.title(title)\r\n # plt.xlabel(x_title)\r\n # plt.ylabel(y_title)\r\n ax.set_xlabel(x_title,fontsize=12)\r\n ax.set_ylabel(y_title,fontsize=12) # 10\r\n ax.set_title(title,fontsize=12) #10, pad=-10)\r\n \r\n \r\n if saveFile != None:\r\n plt.savefig(saveFile + '.png', bbox_inches='tight') \r\n\r\n return im, cbar","title":""},{"docid":"42a0fa4fb075b58e6f7d0a4e36a260d0","score":"0.5309405","text":"def plot_autocorr_grid(self, data):\n if data.ndim == 1:\n self.plot_autocorr(data)\n else:\n nax = data.shape[0]\n if nax > self.max_grid_plot:\n print 'Too many plots requested.'\n return\n\n self._plot_grid(data, self.plot_autocorr)","title":""},{"docid":"8af29cf3f086318471dd29d1df06c178","score":"0.53038985","text":"def plotDiffMatHeatmap(\n fa,\n fb,\n fo,\n start=0,\n end=-1,\n r=5000,\n cut=0,\n mcut=-1,\n na=\"\",\n nb=\"\",\n log=False,\n vmin=None,\n vmax=None,\n):\n labela, chroma, xya, tota = getData(fa, cut, mcut,start,end)\n labelb, chromb, xyb, totb = getData(fb, cut, mcut,start,end)\n if chroma != chromb:\n print(\"ERROR! %s and %s are not the same target chromosome, return.\" %\n (fa, fb))\n return\n if start == 0:\n start = min(np.min(xya), np.min(xyb))\n if end == -1:\n end = max(np.max(xya), np.max(xyb))\n if na == \"\":\n na = labela\n if nb == \"\":\n nb = labelb\n mata = getObsMat(xya, start, end, r)\n matb = getObsMat(xyb, start, end, r)\n sf = tota / totb\n mata = mata / sf\n if log:\n mat = np.log2((mata + 1) / (matb + 1))\n label = \"log2( %s/%s )\" % (na, nb)\n else:\n mat = mata - matb\n label = \"%s-%s\" % (na, nb)\n\n hights = 4\n hr = [6, 0.1]\n fig = pylab.figure(figsize=(4, hights))\n gs = mpl.gridspec.GridSpec(len(hr),\n 1,\n height_ratios=hr,\n top=0.95,\n bottom=0.05,\n left=0.1,\n right=0.9,\n wspace=0.05)\n pylab.suptitle(\"%s-%s, %s:%s-%s\" % (na, nb, chroma[0], start, end),\n fontsize=8)\n cmap = sns.color_palette(\"RdBu_r\", 11).as_hex()\n cmap[int(len(cmap) / 2)] = \"#FFFFFF\"\n cmap = ListedColormap(cmap)\n ax = fig.add_subplot(gs[-2])\n cax = fig.add_subplot(gs[-1])\n sns.set(font_scale=0.5)\n ax = sns.heatmap(mat,\n xticklabels=False,\n yticklabels=False,\n linewidths=0.0,\n square=True,\n cmap=cmap,\n ax=ax,\n center=0,\n vmin=vmin,\n vmax=vmax,\n cbar_ax=cax,\n cbar_kws={\n 'label': label,\n 'orientation': 'horizontal',\n \"shrink\": 0.5,\n \"fraction\": 0.2,\n \"anchor\": (0.0, 1.0)\n })\n cax.tick_params(labelsize=4)\n #draw the box\n ax.axvline(x=ax.get_xlim()[0], color=\"k\", linewidth=2)\n ax.axvline(x=ax.get_xlim()[1], color=\"k\", linewidth=2)\n ax.axhline(y=ax.get_ylim()[0], color=\"k\", linewidth=2)\n ax.axhline(y=ax.get_ylim()[1], color=\"k\", linewidth=2)\n pylab.savefig(fo + \"_compareMatrix.pdf\")","title":""},{"docid":"8be07a705d11792466c562aaf5b72a6e","score":"0.5281724","text":"def my_draw(self):\n \n fig = Figure()\n canvas = FigureCanvas(fig)\n ax = fig.gca()\n \n ax.imshow(self.image_playground)\n \n rect = patches.Rectangle((self.agent_window[0],self.agent_window[1]),self.agent_window[2]-self.agent_window[0],self.agent_window[3]-self.agent_window[1],linewidth=1,edgecolor='r',facecolor='none')\n ax.add_patch(rect)\n\n for target in [self.targets[0]]:\n rect2 = patches.Rectangle((target[0],target[1]),target[2]-target[0],target[3]-target[1],linewidth=1,edgecolor='b',facecolor='none')\n ax.add_patch(rect2)\n \n canvas.draw() \n \n width, height = fig.get_size_inches() * fig.get_dpi()\n\n return np.fromstring(canvas.tostring_rgb(), dtype='uint8').reshape(int(height), int(width), 3)","title":""},{"docid":"746eba819ec15a7b501c6fecaaf8b21c","score":"0.52727544","text":"def cmap_show(self):\r\n\r\n ##SHOW THE AVAILABLE COLOUR MAPS GRAPHICALLY\r\n\r\n ##CREATE A NEW FIGURE AND CLEAR IT\r\n\r\n fig=figure(3)\r\n clf()\r\n \r\n ##CREATE AN ARRAY THAT VARIES FROM ZERO TO ONE IN THE X AXIS,\r\n ##AND IS CONSTANT IN THE Y AXIS\r\n\r\n a=outer(ones(10),arange(0,1,0.01))\r\n\r\n i=0\r\n ##FOR EACH COLOUR \r\n for col in self.cmaps:\r\n subplot(len(self.cmaps),1,i)\r\n\r\n ##THIS PLOTS A BAND STRETCHING OVER THE WHOLE COLOUR MAP\r\n\r\n plot=imshow(a,aspect='auto',cmap=get_cmap(col),origin=\"lower\")\r\n\r\n ##NO TICKMARKS\r\n plot.axes.get_xaxis().set_ticks([])\r\n plot.axes.get_yaxis().set_ticks([])\r\n\r\n ##AND LABELS IT WITH THE COLOUR NAME\r\n annotate(col, (0,1),color='white')\r\n i=i+1\r\n ##AND RESET THE FIGURE NUMBER\r\n fig=figure(1)\r\n\r\n if(matplotlib.get_backend() != 'macosx'):\r\n show()","title":""},{"docid":"22b6312f1a1f80c1bc8e7d4cde2cf88d","score":"0.5267274","text":"def plotXCorrel(data1,data2,units,title,filename):\n\n confid_int_a = 2.0/(math.sqrt(len(data1)))\n confid_int_b = -2.0/(math.sqrt(len(data1)))\n\n plt.xcorr(data1,data2,maxlags=None)\n plt.ylabel('Cross-correlation [-1,1]')\n plt.xlabel('Lag ('+units+')')\n plt.axhline(y = confid_int_a,ls='dashed')\n plt.axhline(y = confid_int_b,ls='dashed')\n plt.title(title)\n savefig(filename)\n plt.close()\n return","title":""}],"string":"[\n {\n \"docid\": \"c32d7300ec62ae5ea0e58e19b2099924\",\n \"score\": \"0.74469304\",\n \"text\": \"def correlation_heatmap(df):\\n _, ax = plt.subplots(figsize=(14, 12))\\n colormap = sns.diverging_palette(220, 10, as_cmap=True)\\n\\n _ = sns.heatmap(df.corr().round(3),\\n cmap=colormap,\\n square=True,\\n cbar_kws={'shrink': .9},\\n ax=ax,\\n annot=True,\\n linewidths=0.1,\\n vmax=1.0,\\n linecolor='white',\\n annot_kws={'fontsize': 12})\\n\\n plt.title(\\\"Feature's Pearson Correlation\\\", y=1.05, size=15)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d2e09723c8aa1d2a583a7334e57a4d8\",\n \"score\": \"0.72475976\",\n \"text\": \"def plot_correlation_matrix(df):\\n corr = df.corr()\\n mask = np.zeros_like(corr, dtype=np.bool)\\n mask[np.triu_indices_from(mask)] = True\\n f, ax = plt.subplots(figsize=(12,8))\\n cmap = sns.color_palette('coolwarm')\\n sns.heatmap(corr, mask=mask, cmap=cmap, center=0, square=True, linewidths=.5,\\n yticklabels=True, cbar_kws={'shrink':.5})\\n plt.title('Correlation Matrix')\\n plt.xticks(rotation=90, fontsize=7)\\n plt.yticks(rotation=0, fontsize=7)\\n plt.tight_layout()\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbd5ee95bc4598540e8173a90735cd22\",\n \"score\": \"0.7207547\",\n \"text\": \"def plot_corr(data) :\\n \\n corr = data.corr()\\n mask = np.zeros_like(corr, dtype=bool)\\n mask[np.triu_indices_from(mask)] = True\\n palette = sns.diverging_palette(20, 220, n=256)\\n \\n fig = plt.figure(figsize=(16,16))\\n sns.heatmap(corr,mask=mask, cmap=palette,annot=True)\\n plt.tight_layout()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c42b518eaf55ce0de7c963bcdc4233f\",\n \"score\": \"0.7150536\",\n \"text\": \"def correlation_heatmap(df, *, font_size=12, ax=None):\\n if ax is None:\\n plt.gca()\\n\\n # Correlation matrix via Pandas (numeric data only)\\n corr_matrix = df.corr()\\n # Generate a mask for the upper triangle\\n mask = np.zeros_like(corr_matrix, dtype=np.bool)\\n mask[np.triu_indices_from(mask)] = True\\n\\n # Store heatmap from mask\\n heat_plot = sns.heatmap(corr_matrix, mask=mask,\\n cmap='RdBu_r', cbar_kws={\\\"shrink\\\": .6},\\n annot=True, annot_kws={\\\"size\\\": font_size},\\n vmax=1, vmin=-1, linewidths=.5,\\n square=True, ax=ax)\\n fig = heat_plot.figure\\n return fig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1980211836a385133a288765bb593a8e\",\n \"score\": \"0.7121204\",\n \"text\": \"def plot_corr_matrix(corr):\\n fig, ax = plt.subplots()\\n mask = np.zeros_like(corr, dtype=np.bool)\\n mask[np.triu_indices_from(mask, k=1)] = True\\n sns.heatmap(corr, vmin=-1, vmax=1, mask=mask,\\n cmap=sns.diverging_palette(220, 10, as_cmap=True),\\n linewidths=0.5, cbar=True, square=True, ax=ax)\\n ax.set_title('Correlation Matrix')\\n fig.tight_layout()\\n return fig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fb4b269fb7616a4495f82e9b6c1735c\",\n \"score\": \"0.7109663\",\n \"text\": \"def plot_correlations(df):\\n m_cor = df.corr()\\n fig, ax = plt.subplots(figsize=(11, 9))\\n # Add diverging colormap from red to blue\\n cmap = sns.diverging_palette(10, 180, center='dark', as_cmap=True)\\n sns.heatmap(m_cor,\\n xticklabels=m_cor.columns, yticklabels=m_cor.columns,\\n vmin=-1, vmax=1, cmap=cmap, square=True, cbar_kws={\\\"shrink\\\": .5}, ax=ax)\\n plt.tight_layout()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80e9ba64d1e48755b8ee468db4f4e038\",\n \"score\": \"0.70773\",\n \"text\": \"def plot_corelation():\\n sns.set(color_codes=False, font_scale=1)\\n df_cor = self.__df[[\\\"Close\\\", \\\"Adj Close\\\",\\\"Volume\\\",\\\"EMA5\\\",\\\"SMA20\\\",\\\"TSI\\\"]]\\n corr = df_cor.corr()\\n\\n # Generate a mask for the upper triangle\\n mask = np.triu(np.ones_like(corr, dtype=bool))\\n\\n # Set up the matplotlib figure\\n f, ax = plt.subplots(figsize=(11, 9))\\n\\n # Generate a custom diverging colormap\\n cmap = sns.diverging_palette(230, 20, as_cmap=True)\\n\\n # Draw the heatmap with the mask and correct aspect ratio\\n sns.heatmap(corr, mask=mask,cmap= 'coolwarm', vmax=.3, center=0,annot=False,\\n square=True, linewidths=.5, cbar_kws={\\\"shrink\\\": .5})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4acee719983c88fe0c2a67cab4c4c556\",\n \"score\": \"0.70678544\",\n \"text\": \"def hof_correlation_plot(self, save=False):\\n plt.figure('Correlation Heatmap', figsize=(16, 14),\\n facecolor='white', edgecolor='black')\\n rows, cols = (1, 1)\\n ax0 = plt.subplot2grid((rows, cols), (0, 0))\\n\\n correlation = self.stats_fame.corr()\\n cut = 0.5\\n color_mask = (correlation[(correlation > -cut) & (correlation < cut)]\\n .fillna(0)\\n .astype(bool))\\n correlation[color_mask] = 0\\n\\n cmap = mplcol.LinearSegmentedColormap.from_list(\\n 'blue_white_blue', ['indianred'] + ['white'] * 3 + ['C0'])\\n\\n sns.heatmap(correlation, center=0,\\n cmap=cmap,\\n cbar_kws={'orientation': 'vertical'},\\n linecolor='lightgray', linewidths=0.1, vmin=-1, vmax=1,\\n ax=ax0)\\n\\n cbar = ax0.collections[0].colorbar\\n cbar.set_ticks(np.arange(-1, 1.5, 0.5).tolist())\\n cbar.ax.tick_params(labelsize=size['label'])\\n cbar.outline.set_linewidth(1)\\n cbar.outline.set_edgecolor('lightgray')\\n\\n ax0.set_title('Statistics Correlation (0.5 Threshold)',\\n fontsize=size['title'])\\n ax0.set_xticklabels(ax0.xaxis.get_majorticklabels(),\\n fontsize=size['label'], rotation=90)\\n ax0.set_yticklabels(ax0.yaxis.get_majorticklabels(),\\n fontsize=size['label'], rotation=0)\\n\\n super_title = plt.suptitle('Hall of Fame Players',\\n fontsize=size['super_title'],\\n x=0.03, y=0.93)\\n\\n save_fig('hof_correlation', save, super_title)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a97f49b29bf28021f9c7aece2580bb79\",\n \"score\": \"0.7011578\",\n \"text\": \"def plot_correlation_heatmap(df, fname=None, figsize=(10, 10)):\\n corr = df.corr()\\n fig = plt.figure(figsize=figsize)\\n sns.heatmap(corr\\n , xticklabels=corr.columns.values\\n , yticklabels=corr.columns.values\\n , cmap='bwr'\\n , annot=True)\\n\\n if fname:\\n fig.savefig(fname\\n , bbox_inches='tight')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a717b6f2ec1b256873c2060c5ace4e92\",\n \"score\": \"0.7002763\",\n \"text\": \"def correlations(df, cols):\\r\\n plt.figure(figsize=(16, 12))\\r\\n imgs = sns.heatmap(df[cols].corr(), cmap=\\\"YlGnBu\\\", annot=True, fmt='.2f', vmin=0)\\r\\n plt.savefig(\\\"correlation.png\\\")\\r\\n return imgs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3eec0831f01e6a204299fafc4ea2bb55\",\n \"score\": \"0.69915277\",\n \"text\": \"def correlation_image(the_data, labels=None, cmap=0):\\n colormap = [COLORMAP, COLORMAPGP][cmap]\\n binned_data = np.digitize(the_data, np.linspace(-1, 1, len(colormap)))\\n color = np.array(colormap)[np.rot90(binned_data, 2)-1].flatten().tolist()\\n\\n # Adjust the transparency value\\n orig = np.rot90(np.abs(the_data), 2)\\n alpha = orig + 0.3\\n alpha[orig >= 0.25] = 0.8\\n alpha[orig >= 0.6] = 1.0\\n alpha = alpha.flatten().tolist()\\n\\n orientation = np.pi / 3\\n if labels is None:\\n labels = list(map(lambda x: str(x), range(the_data.shape[0])))\\n orientation = 0.0\\n\\n xname, yname = list(zip(*product(labels, labels)))\\n\\n data = dict(xname=list(reversed(xname)), yname=list(reversed(yname)),\\n colors=color, alphas=alpha,\\n count=list(reversed(the_data.round(2).flatten())))\\n\\n # Create the figure\\n p = figure(x_axis_location=\\\"above\\\", tools=\\\"hover,save\\\",\\n y_range=list(reversed(labels)), x_range=labels,\\n tooltips = [('Names', '@yname, @xname'), ('Correlation', '@count')])\\n\\n p.plot_width = 800\\n p.plot_height = 800\\n p.grid.grid_line_color = None\\n p.axis.axis_line_color = None\\n p.axis.major_tick_line_color = None\\n p.axis.major_label_text_font_size = \\\"5pt\\\"\\n p.axis.major_label_standoff = 0\\n p.xaxis.major_label_orientation = orientation\\n\\n p.rect('xname', 'yname', 0.9, 0.9, source=data,\\n color='colors', line_color=None, alpha='alphas',\\n hover_line_color='black', hover_color='colors')\\n\\n return row(p, colormap_legend(cmap))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1be1335bd5c7443dece7a476480725ad\",\n \"score\": \"0.69528145\",\n \"text\": \"def correlation_plot(x, h):\\n corr = np.corrcoef(x.T)\\n fig, ax = plt.subplots(figsize=(10, 10))\\n \\n sns.heatmap(corr, cmap=sns.diverging_palette(200, 10, n=200), center=0,\\n square=True, linewidths=.2, vmin=-1, vmax=1, cbar_kws={\\\"shrink\\\": .5}, ax=ax)\\n ax.set_xticks(np.arange(len(h)) + 0.5)\\n ax.set_yticks(np.arange(len(h)) + 0.5)\\n \\n ax.set_xticklabels(h, rotation=90);\\n ax.set_yticklabels(h, rotation=0);\\n ax.set_ylim(-1, 31);\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b4613e357d195c9bc0cc38b65e7b437\",\n \"score\": \"0.6837894\",\n \"text\": \"def plot_corr(df, sort=False, **kwargs):\\r\\n # TODO: sns.clustermap has a metric kwarg that may be interesting to test out and implement.\\r\\n # TODO: Bug, if plt.show() is used after this function two windows will open, one with\\r\\n # the desired heatmap and one empty.\\r\\n # Create the corrmat or clustermap in correlation.py insted?\\r\\n sns.set(context=\\\"paper\\\", font=\\\"monospace\\\")\\r\\n save_figure = kwargs.get(\\\"save_figure\\\", False)\\r\\n file_name = kwargs.get(\\\"file_name\\\", \\\"tmp_heatmap.png\\\")\\r\\n corr_method = kwargs.get(\\\"corr_method\\\", \\\"pearson\\\")\\r\\n abs_values = kwargs.get(\\\"abs_values\\\", False)\\r\\n return_corrmat = kwargs.get(\\\"return_corrmat\\\", False)\\r\\n\\r\\n corrmat = df.corr(method=corr_method)\\r\\n if abs_values:\\r\\n corrmat = corrmat.abs()\\r\\n if (sort):\\r\\n sort_method = kwargs.get(\\\"sort_method\\\", \\\"average\\\")\\r\\n metric = kwargs.get(\\\"metric\\\", \\\"euclidian\\\")\\r\\n cg = sns.clustermap(corrmat, method=sort_method, metric=metric,\\r\\n cmap=plt.cm.inferno, linewidths=.5)\\r\\n #Rotate x and y tick labels so long texts will fitt next to eachother.\\r\\n plt.setp(cg.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)\\r\\n plt.setp(cg.ax_heatmap.xaxis.get_majorticklabels(), rotation=90)\\r\\n if save_figure:\\r\\n cg.savefig(file_name)\\r\\n else:\\r\\n f, sns_ax = plt.subplots(figsize=(12, 10))\\r\\n hmap = sns.heatmap(corrmat, ax=sns_ax, vmax=.8, linewidths=.5,\\r\\n square=True, cmap=plt.cm.inferno)\\r\\n\\r\\n colnames = corrmat.columns.values\\r\\n for i, colname in enumerate(colnames):\\r\\n if i and colname != colnames[i - 1]:\\r\\n ax.axhline(len(colnames) - i, c=\\\"black\\\")\\r\\n ax.axvline(i, c=\\\"black\\\")\\r\\n\\r\\n f.tight_layout()\\r\\n\\r\\n if return_corrmat:\\r\\n if sort: # Sort the columns and rows in the clustered fashion.\\r\\n corrmat = corrmat[cg.dendrogram_col.reordered_ind]\\r\\n corrmat = corrmat.reindex(corrmat.columns.values)\\r\\n return cg, corrmat\\r\\n else:\\r\\n if sort:\\r\\n return cg\\r\\n else:\\r\\n return f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac8aa4f4699da706637c2a55fc11753a\",\n \"score\": \"0.68348444\",\n \"text\": \"def plot_correlation(df: pd.DataFrame, only_save=True, dpi=300):\\n # Compute the correlation matrix\\n corr_all = df.corr()\\n\\n # Generate a mask for the upper triangle\\n mask = np.zeros_like(corr_all, dtype=np.bool)\\n mask[np.triu_indices_from(mask)] = True\\n\\n # Set up the matplotlib figure\\n f, ax = plt.subplots(figsize=(7, 7))\\n\\n # Draw the heatmap with the mask and correct aspect ratio\\n sns.heatmap(corr_all, mask=mask, square=True, linewidths=0.5, ax=ax, cmap=\\\"BuPu\\\")\\n plt.title(\\\"Correlation Matrix Heatmap\\\")\\n if only_save:\\n plt.savefig(\\\"img/correlation_heatmap.png\\\", dpi=dpi)\\n plt.close()\\n else:\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c21e06600ef92cf0003d31f3cd02a21b\",\n \"score\": \"0.6806044\",\n \"text\": \"def _heatmap(data_df, output_path):\\n\\n plt.figure(figsize=(20, 10))\\n plot = sns.heatmap(data_df.corr(), annot=False,\\n cmap='Dark2_r', linewidths=2)\\n plot.figure.savefig(output_path)\\n plt.clf()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b442ea219339fbd3fe7e543ac7b1311\",\n \"score\": \"0.67628884\",\n \"text\": \"def correlation_plot(data,output_path=None): \\n \\n corrmat = data.corr()\\n fig, ax = plt.subplots(figsize=(15,15))\\n sns.heatmap(corrmat,cmap=\\\"RdBu_r\\\",linewidths=.5,annot=True)\\n if output_path is not None:\\n output = os.path.join(output_path,'Corr_plot'+'.png')\\n plt.savefig(output)\\n print('Image saved at',str(output))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd9959d269cb351507c99c829aa99ddc\",\n \"score\": \"0.67297167\",\n \"text\": \"def corelationMatrix():\\n try:\\n corr_matrix = obj_Data.df.corr()\\n fig = px.imshow(corr_matrix, title= \\\"Corelation Matrix\\\")\\n return fig\\n\\n except:\\n logging.exception('Something went wrong with Corelation Matrix plot')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"987f0a4f6db076988d2886aa91e368e9\",\n \"score\": \"0.6723992\",\n \"text\": \"def show_correlation_plot(self, correlations, data):\\n\\n plt.figure(figsize=(10, 8))\\n ax = sns.heatmap(correlations,\\n xticklabels=correlations.columns.values,\\n yticklabels=correlations.columns.values, annot=True)\\n\\n plt.setp(ax.get_xticklabels(), rotation=45, horizontalalignment=\\\"right\\\",\\n rotation_mode=\\\"anchor\\\")\\n\\n plt.tight_layout()\\n plt.autoscale()\\n plt.savefig(\\\"../data/04_analytics/correlation_plot.png\\\")\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e7b55ec4cda6f5711cce6980be37a9a\",\n \"score\": \"0.6681251\",\n \"text\": \"def heatmap_full(df):\\r\\n sns.set_style('whitegrid')\\r\\n plt.subplots(figsize = (14,8))\\r\\n # Generate mask for upper triangle\\r\\n m1 = np.zeros_like(df.corr(), dtype=np.bool) \\r\\n m1[np.triu_indices_from(m1)] = True\\r\\n sns.set(font_scale=0.8)\\r\\n sns.heatmap(df.corr(), cmap=sns.diverging_palette(20, 220, n=200), \\r\\n mask=m1, center=0, square=True, annot=True)\\r\\n plt.title(\\\"All features heatmap\\\", fontsize=15)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e1897d41791c864b803f7db55a85636\",\n \"score\": \"0.6593312\",\n \"text\": \"def generate_heatmap(dataset, title):\\n corr = dataset.corr()\\n sns.heatmap(corr, xticklabels=corr.columns,\\n yticklabels=corr.columns)\\n\\n plt.title(title)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6614f9ae02808b5b43f83645563d60c\",\n \"score\": \"0.65319544\",\n \"text\": \"def draw_corrmatrix(self, mode='stock'):\\n fig, ax = plt.subplots(dpi=200, figsize=(6, 6))\\n\\n if mode == 'stock':\\n im = ax.imshow(self.corrmatrix, vmin=-1, vmax=1)\\n fig.colorbar(im, fraction=0.046, pad=0.04)\\n ax.set_xlabel('Companies')\\n ax.set_ylabel('Companies')\\n return fig\\n elif mode == 'category':\\n im = ax.imshow(self.categ_corrmatrix, vmin=0, vmax=1)\\n ax.set_xticks(range(len(self.categories)))\\n ax.set_yticks(range(len(self.categories)))\\n ax.set_xticklabels(self.categories, rotation=45,\\n horizontalalignment='right', fontsize=8)\\n ax.set_yticklabels(self.categories, fontsize=8)\\n # Annotate grid\\n for i in range(len(self.categories)):\\n for j in range(len(self.categories)):\\n ax.text(j, i, round(self.categ_corrmatrix[i, j], 2),\\n ha=\\\"center\\\", va=\\\"center\\\", color=\\\"k\\\", fontsize=8)\\n fig.colorbar(im, fraction=0.046, pad=0.04)\\n return fig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15434dd38172978aaf79f42bb83d51a8\",\n \"score\": \"0.6420136\",\n \"text\": \"def plot_correlation_matrix(list_df: List[pd.DataFrame], names: List[str]) -> None:\\n corr = compute_correlation_matrix(list_df)\\n\\n corrMatrix = pd.DataFrame(corr, columns=names, index=names)\\n if len(list_df) <= 4:\\n plt.subplots(figsize=(15, 10))\\n else:\\n plt.subplots(figsize=(20, 10))\\n\\n sns.heatmap(corrMatrix, annot=True, fmt='g')\\n plt.title('Correlation matrix')\\n\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13f0918b2812b18ee9763eec2ec5f3c6\",\n \"score\": \"0.64004743\",\n \"text\": \"def plot_single_mode_correlation(self, data, output_dir, addition):\\n correlations = data[self.features].corr()\\n fig = plt.figure(figsize=(10, 10))\\n ax = fig.add_subplot(111)\\n cax = ax.matshow(correlations, vmin=-1, vmax=1, cmap='viridis')\\n ticks = np.arange(0, len(self.features), 1)\\n plt.rc('axes', labelsize=8)\\n ax.set_xticks(ticks)\\n ax.set_yticks(ticks)\\n ax.set_xticklabels(self.features, rotation=-90)\\n ax.set_yticklabels(self.features)\\n fig.colorbar(cax)\\n fig.tight_layout()\\n plot_out = os.path.join(\\n output_dir, str(addition) + '_correlations.png')\\n plt.savefig(plot_out, bbox_inches='tight')\\n plt.close('all')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f61a76440ef496f0781b42d77a9b28e\",\n \"score\": \"0.6372198\",\n \"text\": \"def plot_cor_matrix(data):\\n correlation_matrix = data.corr() # creating correlation_matrix\\n # print(correlation_matrix)\\n dataplot = sb.heatmap(correlation_matrix) # creating a heat map of the correlation_matrix\\n mp.show() # showing the correlation_matrix\\n sorted_mat = correlation_matrix.unstack().sort_values()\\n # Retain upper triangular values of correlation matrix and\\n # make Lower triangular values Null\\n upper_corr_mat = correlation_matrix.where(np.triu(np.ones(correlation_matrix.shape), k=1).astype(bool))\\n # Convert to 1-D series and drop Null values\\n unique_corr_pairs = upper_corr_mat.unstack().dropna()\\n # Sort correlation pairs\\n sorted_mat1 = unique_corr_pairs.sort_values()\\n print(sorted_mat1)\\n return sorted_mat1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47cdce25f8ae6ee0f7f78ace0cb91f10\",\n \"score\": \"0.63189405\",\n \"text\": \"def plot_correlations(matrix):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0ea48564c7b6d0c9b52fdcb54a0a49c\",\n \"score\": \"0.62723863\",\n \"text\": \"def correlation_matrix(self, data, col_y, corr_value = 0.95, corr_value_w_targhet = 0.95, plot_matr = 'yes'):\\n corr = data.corr().abs()\\n\\n if plot_matr == 'yes':\\n ds().nuova_fig(7, height =8, width =10)\\n ds().titoli(titolo=\\\"Correlation matrix\\\")\\n sns.heatmap(corr[(corr >= 0.5)], cmap='viridis', vmax=1.0, vmin=-1.0, linewidths=0.1, annot=True, annot_kws={\\\"size\\\": 8}, square=True, linecolor=\\\"black\\\");\\n ds().aggiusta_la_finestra()\\n st.pyplot()\\n\\n corr_with_target = corr[col_y]#correlation with the target\\n relevant_feature_with_target = corr_with_target[corr_with_target < corr_value_w_targhet].sort_values(ascending = False).reset_index()\\n\\n upper = corr.where(np.triu(np.ones(corr.shape), k=1).astype(np.bool))#select upper triangle of correlation matrix\\n correlation_between_parameters = [column for column in upper.columns if any(upper[column] > corr_value)]\\n return relevant_feature_with_target, correlation_between_parameters\",\n \"title\": \"\"\n },\n {\n \"docid\": \"780ae438bd821d468612b7ea31eba739\",\n \"score\": \"0.6259866\",\n \"text\": \"def _heatmap_plot(self,heatmap,title):\\n fig, ax = plt.subplots(figsize=(self.plot_scale,self.plot_scale*(self.y_size/self.x_size)))\\n plt.title(title)\\n plt.xlabel(\\\"X\\\")\\n plt.ylabel(\\\"Y\\\")\\n im = plt.imshow(heatmap.T, extent=[0, plotcnf[\\\"scrsize\\\"][\\\"x\\\"], 0, plotcnf[\\\"scrsize\\\"][\\\"y\\\"]], origin='lower')\\n plt.gca().invert_yaxis()\\n divider = axes_grid1.make_axes_locatable(ax)\\n cax = divider.append_axes('right', size='5%', pad=0.05)\\n fig.colorbar(im, cax=cax)\\n\\n return fig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d9df5f5b0aa285fff9c5470be3c7c0e\",\n \"score\": \"0.6212737\",\n \"text\": \"def correlation(x_list, y_list, title):\\n fig = plt.figure()\\n ax1 = fig.add_subplot(111)\\n ax1.xcorr(x_list, y_list, usevlines=True, maxlags=75, normed=True, lw=2) #\\n ax1.grid(True)\\n #ax1.axhline(0, color='black', lw=2)\\n save(title)\\n plt.close(fig)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdab770813d42d3832d82e22a17e6a95\",\n \"score\": \"0.6206435\",\n \"text\": \"def show_correlation(df, size=10):\\n corr = df.corr()\\n fig, ax = plt.subplots(figsize=(size, size))\\n cax = ax.matshow(corr)\\n fig.colorbar(cax)\\n plt.xticks(range(len(corr.columns)), corr.columns, rotation = 90)\\n plt.yticks(range(len(corr.columns)), corr.columns)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"500c14209ca15688f2c48360230e7734\",\n \"score\": \"0.6198036\",\n \"text\": \"def correlations(data, **kwds):\\n\\t# simply call df.corr() to get a table of\\n\\t# correlation values if you do not need\\n\\t# the fancy plotting\\n\\tcorrmat = data.corr(**kwds)\\n\\t\\n\\tfig, ax1 = plt.subplots(ncols=1, figsize=(6,5))\\n\\t\\n\\topts = {'cmap': plt.get_cmap(\\\"RdBu\\\"),\\n\\t\\t'vmin': -1, 'vmax': +1}\\n\\theatmap1 = ax1.pcolor(corrmat, **opts)\\n\\tplt.colorbar(heatmap1, ax=ax1)\\n\\n\\tax1.set_title(\\\"Correlations\\\")\\n\\t\\n\\tlabels = corrmat.columns.values\\n\\tfor ax in (ax1,):\\n\\t\\t# shift location of ticks to center of the bins\\n\\t\\tax.set_xticks(np.arange(len(labels))+0.5, minor=False)\\n\\t\\tax.set_yticks(np.arange(len(labels))+0.5, minor=False)\\n\\t\\tax.set_xticklabels(labels, minor=False, ha='right', rotation=70)\\n\\t\\tax.set_yticklabels(labels, minor=False)\\n\\t\\n\\t#plt.tight_layout()\\n\\n\\t#fig.savefig(file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6ab2cc003006260da744341f353c219\",\n \"score\": \"0.61778855\",\n \"text\": \"def correlation_heatmap(file: str, options: list):\\n if \\\"/protein_table\\\" in options:\\n with st.beta_expander(\\\"Correlation heatmap\\\"):\\n df = cached_file(file)\\n\\n cols = [_ for _ in df.columns if \\\"LFQ\\\" in _]\\n if len(cols) == 0:\\n cols = df.columns\\n\\n if multiple_file_check(cols):\\n df = np.log(df[cols])\\n corr = df.corr()\\n\\n fig = make_subplots(rows=1, cols=1)\\n fig.add_trace(\\n trace=go.Heatmap(\\n z=corr.values,\\n x=corr.index.values,\\n y=corr.columns.values,\\n colorscale=\\\"Greys\\\",\\n )\\n )\\n fig.update_layout(height=600, width=600)\\n st.write(fig)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7ad3c53a74a5723e9b1bde0a6e8f842\",\n \"score\": \"0.61768585\",\n \"text\": \"def plot_corr(df, size=10):\\n set_my_plt_style()\\n corr = df.corr()\\n # Re-order the rows and columns using clustering\\n d = sch.distance.pdist(corr)\\n L = sch.linkage(d, method='ward')\\n ind = sch.fcluster(L, 0.5 * d.max(), 'distance')\\n columns = [df.columns.tolist()[i] for i in list((np.argsort(ind)))]\\n corr = corr.reindex(columns, axis=1)\\n corr = corr.reindex(columns, axis=0)\\n\\n # Plot the correlation matrix\\n fig, ax = plt.subplots(figsize=(size, size))\\n cax = ax.matshow(corr, cmap=Curl_5_r.mpl_colormap, vmin=-1, vmax=1)\\n plt.xticks(range(len(corr.columns)), corr.columns, rotation=90)\\n plt.yticks(range(len(corr.columns)), corr.columns)\\n\\n # Add the colorbar legend\\n cbar = fig.colorbar(cax, ticks=[-1, -0.5, 0, 0.5, 1], aspect=10, shrink=.8)\\n # plt.show()\\n return fig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80160f95563b86613f3d04c55bacef14\",\n \"score\": \"0.61421454\",\n \"text\": \"def correlation_plot(self, target = None, top_N = 10, ascending = False):\\r\\n corrmat = self.df.corr()\\r\\n\\r\\n if target is None:\\r\\n plt.figure(figsize=(12,9))\\r\\n sns.heatmap(corrmat, vmax=.8, square=True)\\r\\n else:\\r\\n plt.figure(figsize=(10,8))\\r\\n sns.heatmap(corrmat[[target]].sort_values(by=[target],ascending = ascending)[:top_N],\\r\\n vmin=-1,\\r\\n cmap='coolwarm',\\r\\n annot=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fea3cb690ab7105e2783934526ff559\",\n \"score\": \"0.6124578\",\n \"text\": \"def correlation_matrix(\\n corr_mat: np.ndarray,\\n labels: Union[List[str], np.ndarray],\\n figure_path: pathlib.Path,\\n) -> None:\\n # rounding for test in CI to match reference\\n fig, ax = plt.subplots(\\n figsize=(round(5 + len(labels) / 1.6, 1), round(3 + len(labels) / 1.6, 1)),\\n dpi=100,\\n )\\n im = ax.imshow(corr_mat, vmin=-1, vmax=1, cmap=\\\"RdBu\\\")\\n\\n ax.set_xticks(np.arange(len(labels)))\\n ax.set_yticks(np.arange(len(labels)))\\n ax.set_xticklabels(labels)\\n ax.set_yticklabels(labels)\\n for tick in ax.get_xticklabels():\\n tick.set_rotation(45)\\n tick.set_horizontalalignment(\\\"right\\\")\\n\\n fig.colorbar(im, ax=ax)\\n ax.set_aspect(\\\"auto\\\") # to get colorbar aligned with matrix\\n fig.tight_layout()\\n\\n # add correlation as text\\n for (j, i), corr in np.ndenumerate(corr_mat):\\n text_color = \\\"white\\\" if abs(corr_mat[j, i]) > 0.75 else \\\"black\\\"\\n if abs(corr) > 0.005:\\n ax.text(i, j, f\\\"{corr:.2f}\\\", ha=\\\"center\\\", va=\\\"center\\\", color=text_color)\\n\\n figure_path.parent.mkdir(parents=True, exist_ok=True)\\n log.debug(f\\\"saving figure as {figure_path}\\\")\\n fig.savefig(figure_path)\\n plt.close(fig)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78ef41f2a3aaadf36f546c855f935ef1\",\n \"score\": \"0.61020917\",\n \"text\": \"def fit(self, X, y=None):\\n\\n self.correlation = X.corr()\\n# print(self.corr)\\n\\n # Generate a mask for the upper triangle\\n self.mask = np.zeros_like(self.correlation, dtype=np.bool)\\n self.mask[np.triu_indices_from(self.mask)] = True\\n\\n # Set up the matplotlib figure\\n f, ax = plt.subplots(figsize=(11, 9))\\n\\n # Generate a custom diverging colormap\\n self.cmap = sns.diverging_palette(\\n h_neg=self.h_neg,\\n h_pos=self.h_pos,\\n as_cmap=self.as_cmap\\n )\\n\\n # Draw the heatmap with the mask and correct aspect ratio\\n sns.heatmap(\\n self.correlation,\\n mask=self.mask,\\n cmap=self.cmap,\\n vmax=.3,\\n center=0,\\n square=True,\\n linewidths=.5,\\n cbar_kws={\\\"shrink\\\": .5}\\n )\\n\\n plt.show()\\n\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f101aed6225f5361cf064f0e2fc7061\",\n \"score\": \"0.59934545\",\n \"text\": \"def cor_heatmap_plot(self, heatmap_name, heatmap_val):\\n\\n # Split the data into SE and PE\\n pconfig = {\\n 'title': 'Pearson correlation',\\n 'xlab': True,\\n }\\n self.add_section(\\n description='Pearson correlation between log<sub>2</sub> normalised CPM values are calculated and clustered.',\\n plot=heatmap.plot(heatmap_val, heatmap_name, pconfig=pconfig)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f7a423fcdc41296fc42ccd4cec0c07c\",\n \"score\": \"0.59932196\",\n \"text\": \"def correlations(DataFrame):\\n # copy df in\\n df = DataFrame.copy()\\n\\n # get the correlations\\n corrs = df.corr()\\n\\n # get only the correlations for the target variable\\n corrs_target = corrs['fare_amount'].sort_values(ascending = True)\\n\\n # corrs['fare_amount'].plot.bar(color = 'b') annot = True, vmin = -1, vmax = 1,\\n\\n # plot the total heat map\\n # plt.figure(figsize = (12, 12))\\n # sns.heatmap(corrs, annot = True, fmt = '.3f')\\n # plt.show()\\n\\n # barplot of the correlations between fare amount and features\\n corrs_target.plot(kind=\\\"barh\\\", color = 'b')\\n # sns.barplot(x = corrs_target.index.values, y = corrs_target.values)\\n plt.show()\\n\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c40f7e4e094c6ce9373cefb6a67ab15c\",\n \"score\": \"0.59882206\",\n \"text\": \"def create_corrolation_plot(data):\\n corr = data.corr().reset_index().melt(id_vars=\\\"index\\\")\\n corr.columns = [\\\"Variable 1\\\", \\\"Variable 2\\\", \\\"corr_values\\\"]\\n corr[\\\"Correlation\\\"] = corr.corr_values.round(3)\\n \\n # Base chart \\n cht = alt.Chart(corr).encode(\\n x=\\\"Variable 1:N\\\", y=\\\"Variable 2:N\\\"\\n ).properties(title=\\\"Correlation Plot\\\", width=700, height=700)\\n \\n # Text overlay \\n txt = cht.mark_text().encode(\\n text=\\\"Correlation\\\", \\n color=alt.condition(\\n alt.datum.Correlation > 0.25, \\n if_true=alt.value('white'),\\n if_false=alt.value('black')\\n ))\\n \\n # Colored boxes \\n rct = cht.mark_rect().encode(\\n color=alt.Color(\\\"corr_values:Q\\\", scale=alt.Scale(scheme=\\\"purples\\\"),\\n legend=None),\\n tooltip=[\\\"Variable 1\\\", \\\"Variable 2\\\", \\\"Correlation\\\"]\\n )\\n cht = rct + txt\\n cht = cht.configure_axis(\\n ticks=False, title=None, labelPadding=10, labelFontSize=14\\n ).configure_title(\\n fontSize=20, anchor=\\\"middle\\\", dy=-20\\n )\\n return cht\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1877b88b00abc491abf85e5c57b7ec4\",\n \"score\": \"0.59726924\",\n \"text\": \"def heatmap(self, stats, ax=None, **kwargs):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e0d49d3fd2e51844a2d787e2daaa04e\",\n \"score\": \"0.5960367\",\n \"text\": \"def render_heatmap(\\n data: pd.DataFrame,\\n ax_hm: plt.Axes = None,\\n cmap: colors.Colormap = None,\\n norm: colors.Normalize = colors.Normalize(),\\n annotate: bool = True,\\n annotation_valfmt: str = '{x:.0f}',\\n add_sep_colorbar: bool = False,\\n ax_cb: plt.Axes = None,\\n colorbar_label: str = None,\\n use_index_labels: bool = False,\\n xlabel: str = None,\\n ylabel: str = None,\\n fig_canvas_title: str = None,\\n fig_size: tuple = (8, 6),\\n manipulate_ticks: bool = False,\\n tick_label_prec: int = 3,\\n xtick_label_prec: int = None,\\n ytick_label_prec: int = None\\n) -> (plt.Figure, plt.Figure):\\n if isinstance(data, pd.DataFrame):\\n if not isinstance(data.index, NumericIndex):\\n raise pyrado.TypeErr(given=data.index, expected_type=NumericIndex)\\n if not isinstance(data.columns, NumericIndex):\\n raise pyrado.TypeErr(given=data.columns, expected_type=NumericIndex)\\n # Extract the data\\n x = data.columns\\n y = data.index\\n else:\\n raise pyrado.TypeErr(given=data, expected_type=pd.DataFrame)\\n\\n # Create axes if not provided\\n if ax_hm is None:\\n fig_hm, ax_hm = plt.subplots(1, figsize=fig_size)\\n else:\\n fig_hm = ax_hm.figure\\n\\n if fig_canvas_title is not None:\\n fig_hm.canvas.set_window_title(fig_canvas_title)\\n\\n # Create the image\\n img = ax_hm.imshow(data, cmap=cmap, norm=norm, aspect=(x.max()-x.min())/(y.max()-y.min()), origin='lower',\\n extent=[x.min(), x.max(), y.min(), y.max()]) # former: aspect='auto'\\n\\n # Set axes limits\\n ax_hm.set_xlim(x.min(), x.max())\\n ax_hm.set_ylim(y.min(), y.max())\\n\\n # Annotate the heat map\\n if annotate:\\n _annotate_heatmap(img, valfmt=annotation_valfmt)\\n\\n # Prepare the ticks\\n if manipulate_ticks:\\n _setup_index_axis(ax_hm.xaxis, x, use_index_labels,\\n xtick_label_prec if xtick_label_prec is not None else tick_label_prec)\\n _setup_index_axis(ax_hm.yaxis, y, use_index_labels,\\n ytick_label_prec if ytick_label_prec is not None else tick_label_prec)\\n\\n ax_hm.stale = True # to cause redraw\\n\\n # Set the labels\\n if xlabel is not None:\\n ax_hm.set_xlabel(xlabel)\\n if ylabel is not None:\\n ax_hm.set_ylabel(ylabel)\\n\\n # Add color bar if requested\\n if add_sep_colorbar:\\n # Draw a new figure and re-plot the color bar there\\n if ax_cb is None:\\n fig_cb, ax_cb = plt.subplots(1, figsize=fig_size)\\n else:\\n fig_cb = plt.gcf()\\n\\n if colorbar_label is not None:\\n colorbar.ColorbarBase(ax_cb, cmap=cmap, norm=norm, label=colorbar_label)\\n else:\\n colorbar.ColorbarBase(ax_cb, cmap=cmap, norm=norm)\\n # if colorbar_label is not None:\\n # fig_cb.colorbar(img, ax=ax_cb, label=colorbar_label) # plt.colorbar works, too\\n # else:\\n # fig_cb.colorbar(img, ax=ax_cb) # plt.colorbar works, too\\n #\\n # # Only show the color bar\\n # ax_cb.remove()\\n return fig_hm, fig_cb\\n\\n else:\\n return fig_hm, None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a04fc16e30a5c0bfc7882688af5bcdb\",\n \"score\": \"0.59285873\",\n \"text\": \"def correlations(self):\\n fontProperties = {'family':'sans-serif'}\\n opts = {'cmap': plt.get_cmap(\\\"bwr\\\"), 'vmin': -1, 'vmax': +1}\\n\\n for c,target in enumerate(self.targets):\\n\\n saveAs = \\\"{0}/correlations_{1}_{2}\\\".format(self.output_dir,target.name,self.date)\\n\\n allkeys = target.df.keys()\\n keys = []\\n for key in allkeys:\\n if key!='target': keys.append(key)\\n t_ = target.df[keys]\\n corrmat = t_.corr()\\n\\n # Save correlation matrix to CSV file\\n corrmat.to_csv(\\\"{0}.csv\\\".format(saveAs))\\n\\n # Use matplotlib directly\\n fig,ax = plt.subplots()\\n\\n heatmap1 = ax.pcolor(corrmat, **opts)\\n cbar = plt.colorbar(heatmap1, ax=ax)\\n\\n cbar.ax.set_yticklabels( [i.get_text().strip('$') for i in cbar.ax.get_yticklabels()], **fontProperties )\\n\\n labels = corrmat.columns.values\\n labels = [self.variable_labels[i].label for i in labels]\\n\\n # shift location of ticks to center of the bins\\n ax.set_xticks(np.arange(len(labels))+0.5, minor=False)\\n ax.set_xticklabels(labels, fontProperties, fontsize=14, minor=False, ha='right', rotation=70)\\n\\n ax.set_yticks(np.arange(len(labels))+0.5, minor=False)\\n ax.set_yticklabels(labels, fontProperties, fontsize=14, minor=False)\\n\\n ## CMS/COM Energy Label + Signal name\\n cms_stamp = hpl.CMSStamp(self.CMSlabelStatus)\\n cms_stamp.coords = [0.02,1.00]\\n cms_stamp.fontsize = 16\\n cms_stamp.va = 'bottom'\\n ax.text(0.02,1.00,cms_stamp.text,fontsize=cms_stamp.fontsize,\\n ha=cms_stamp.ha,va=cms_stamp.va,transform=ax.transAxes)\\n\\n energy_stamp = hpl.EnergyStamp()\\n energy_stamp.ha = 'right'\\n energy_stamp.coords = [0.99,1.00]\\n energy_stamp.fontsize = 16\\n energy_stamp.va = 'bottom'\\n ax.text(energy_stamp.coords[0],energy_stamp.coords[1],energy_stamp.text, \\n fontsize=energy_stamp.fontsize,ha=energy_stamp.ha, va=energy_stamp.va, transform=ax.transAxes)\\n\\n ax.text(0.03,0.93,target.label,fontsize=16,ha='left',va='top',transform=ax.transAxes)\\n\\n\\n plt.savefig(\\\"{0}.{1}\\\".format(saveAs,self.image_format),\\n format=self.image_format,dpi=300,bbox_inches='tight')\\n plt.close()\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e23ff82c707b850587fc9ad88046878\",\n \"score\": \"0.59212583\",\n \"text\": \"def plot_correlations(X, ids, weights=None, classes=None, round_threshold=0.0, targetdir=None, colorbar = False):\\n N = X.shape[0]\\n\\n if classes is None:\\n classes = np.zeros(N)\\n num_classes = int(1)\\n else:\\n num_classes = len(np.unique(classes))\\n\\n figs = {}\\n axs = {}\\n \\n for i in range(num_classes):\\n\\n label = f'all' if (num_classes == 1) else f'class_{i}'\\n\\n # Compute correlation matrix\\n w = weights[classes==i] if weights is not None else None\\n C = statstools.correlation_matrix(X=X[classes==i,:], weights=w)\\n C[np.abs(C) < round_threshold] = np.nan\\n C *= 100\\n \\n # Compute suitable figsize\\n size = np.ceil(C.shape[0] / 3)\\n \\n # Plot it\\n figs[label], axs[label] = plt.subplots(1,1, figsize=(size,size))\\n\\n axs[label].imshow(C)\\n axs[label] = annotate_heatmap(X = C, ax = axs[label], xlabels = ids,\\n ylabels = ids, decimals = 0, x_rot = 90, y_rot = 0, color = \\\"w\\\")\\n axs[label].set_title(f'{label}: linear correlation $\\\\\\\\in$ [-100,100]', fontsize=10)\\n \\n if colorbar:\\n cb = plt.colorbar()\\n\\n if targetdir is not None:\\n fname = targetdir + f'{label}-correlation-matrix.pdf'\\n print(__name__ + f'.plot_correlations: Save: \\\"{fname}\\\"')\\n plt.savefig(fname=fname, pad_inches=0.2, bbox_inches='tight')\\n\\n return figs, axs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"847edc9560fd80bef55c0a7c1f4b1881\",\n \"score\": \"0.59083694\",\n \"text\": \"def plot_corr(self, values=True):\\n plot_corr(self.data, values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7268ab825f669e8bc80a4361408d6432\",\n \"score\": \"0.5851477\",\n \"text\": \"def correlation_training(self):\\n self.x=pd.DataFrame(self.x)\\n dfcorr=self.x.corr()\\n Tools.table(dfcorr,'.3f','fancy_grid','Correlation Matrix on Training Data', 60)\\n c=corrplot.Corrplot(dfcorr)\\n c.plot()\\n plt.suptitle(\\\"Pair-Correlation Matrix on Training Data\\\")\\n return plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b206ccc0170817ac6a3efe9d9e0b5c4\",\n \"score\": \"0.5848216\",\n \"text\": \"def correlation_matrix(data: pd.DataFrame, vmin: int = -1) -> str:\\n fig_cor, axes_cor = plt.subplots()\\n cmap_name = config[\\\"plot\\\"][\\\"correlation\\\"][\\\"cmap\\\"].get(str)\\n cmap_bad = config[\\\"plot\\\"][\\\"correlation\\\"][\\\"bad\\\"].get(str)\\n\\n cmap = plt.get_cmap(cmap_name)\\n if vmin == 0:\\n cmap = get_cmap_half(cmap)\\n cmap.set_bad(cmap_bad)\\n\\n labels = data.columns\\n matrix_image = axes_cor.imshow(\\n data, vmin=vmin, vmax=1, interpolation=\\\"nearest\\\", cmap=cmap\\n )\\n plt.colorbar(matrix_image)\\n\\n if data.isnull().values.any():\\n legend_elements = [Patch(facecolor=cmap(np.nan), label=\\\"invalid\\\\ncoefficient\\\")]\\n\\n plt.legend(\\n handles=legend_elements, loc=\\\"upper right\\\", handleheight=2.5,\\n )\\n\\n axes_cor.set_xticks(np.arange(0, data.shape[0], float(data.shape[0]) / len(labels)))\\n axes_cor.set_yticks(np.arange(0, data.shape[1], float(data.shape[1]) / len(labels)))\\n\\n font_size = get_correlation_font_size(len(labels))\\n axes_cor.set_xticklabels(labels, rotation=90, fontsize=font_size)\\n axes_cor.set_yticklabels(labels, fontsize=font_size)\\n plt.subplots_adjust(bottom=0.2)\\n\\n return plot_360_n0sc0pe(plt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3ec3b9394dd40e556f65007ba31ed14\",\n \"score\": \"0.5841142\",\n \"text\": \"def spearman_corr_heatmap(portfolio, data_source='price_data',\\n attribute='changePercent', absolute=True):\\n matrix = analysis.spearman_corr_coeffs(portfolio, data_source, attribute)\\n\\n mask = np.zeros_like(matrix)\\n mask[np.triu_indices_from(mask)] = True\\n ax = sns.heatmap(matrix,\\n annot=True,\\n mask=mask,\\n cmap='bwr', vmin=-1, vmax=1)\\n ax.set_title(\\n f'Portfolio {attribute.capitalize()} Spearman Correlation Heat Map')\\n\\n return ax\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab03d167a158bff55fd14d9e9672bbf5\",\n \"score\": \"0.5834672\",\n \"text\": \"def heatmap_carnivore(self):\\n x = self.sim.animal_distribution\\n carn = x.pivot('Row', 'Col', 'Carnivore').values\\n plot = self.ax4.imshow(carn, vmax=self.cmax_animals['Carnivore'])\\n self.ax4.set_title('Carnivore density map', y=-0.3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cad15024d8c46b1c9fb7e3701a804e83\",\n \"score\": \"0.5812108\",\n \"text\": \"def _test_heatmap():\\n # Hit the drum at time t = 0\\n drum = Drum()\\n drum.hit((0.8, 0, 0), 1)\\n\\n # Calculate drum values\\n t = 0\\n points = drum.points\\n values = drum.value(0)\\n \\n # Polar heatmap\\n data = griddata(points, values, (drum.grid_r, drum.grid_theta), method='cubic', fill_value=0)\\n ax1 = plt.subplot(projection=\\\"polar\\\")\\n ax1.pcolormesh(drum.thetas, drum.rs, data.T)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"164442cce44300142d9dd045f4e76f83\",\n \"score\": \"0.5804257\",\n \"text\": \"def corr_matrix(cls, config, df, path, title, method, footnote=None):\\n\\t\\t#timestamp\\n\\t\\t_t0 = datetime.datetime.now()\\n\\t\\t_f = debug(message='t', source=\\\"timestamp\\\")\\n\\t\\tconsole('running bokeh corr_matrix()', 'blue')\\n\\n\\t\\t# math\\n\\t\\timport bisect\\n\\t\\tfrom math import pi\\n\\t\\tfrom numpy import arange\\n\\t\\tfrom itertools import chain\\n\\t\\tfrom collections import OrderedDict\\n\\t\\t\\n\\t\\t# bokeh\\n\\t\\tfrom bokeh.models import ColorBar, LinearColorMapper, TapTool, HoverTool, Range1d, ColumnDataSource\\n\\t\\tfrom bokeh.models.callbacks import CustomJS\\n\\t\\tfrom bokeh.plotting import reset_output, figure\\n\\t\\tfrom bokeh.embed import components\\n\\n\\t\\t#create color palette\\n\\t\\tmatplotlib.use('Agg')\\n\\t\\timport matplotlib.colors\\n\\t\\tfrom matplotlib import cm as mpl_cmap\\n\\t\\t\\n\\t\\t#p-values\\n\\t\\tif method == \\\"pearson\\\":\\n\\t\\t\\tfrom scipy.stats import pearsonr as cf\\n\\t\\telif method == \\\"spearman\\\":\\n\\t\\t\\tfrom scipy.stats import spearmanr as cf\\n\\t\\t\\n\\t\\t#calculate p-values correlation coefficent\\n\\t\\tdef get_pvalue(df):\\n\\t\\t\\tdf = df.dropna()._get_numeric_data()\\n\\t\\t\\tdfcols = pd.DataFrame(columns=df.columns)\\n\\t\\t\\tpvalues = dfcols.transpose().join(dfcols, how='outer')\\n\\t\\t\\tfor row in df.columns:\\n\\t\\t\\t\\tfor column in df.columns:\\n\\t\\t\\t\\t\\tpvalues[row][column] = round(cf(df[row], df[column])[1], 4)\\n\\t\\t\\treturn pvalues\\n\\n\\t\\t#Gets bounds for quads with n features\\n\\t\\tdef get_bounds(n):\\n\\t\\t\\tbottom = list(chain.from_iterable(\\n\\t\\t\\t\\t[[ii]*nlabels for ii in range(nlabels)]))\\n\\t\\t\\ttop = list(chain.from_iterable(\\n\\t\\t\\t\\t[[ii+1]*nlabels for ii in range(nlabels)]))\\n\\t\\t\\tleft = list(chain.from_iterable(\\n\\t\\t\\t\\t[list(range(nlabels)) for ii in range(nlabels)]))\\n\\t\\t\\tright = list(chain.from_iterable(\\n\\t\\t\\t\\t[list(range(1, nlabels+1)) for ii in range(nlabels)]))\\n\\t\\t\\tcorr_items = list(chain.from_iterable(\\n\\t\\t\\t\\t[[ii+1]*nlabels for ii in range(nlabels)]))\\n\\t\\t\\treturn top, bottom, left, right, corr_items\\n\\t\\t\\n\\t\\t#Aligns color values from palette with the correlation coefficient and p-values\\n\\t\\tdef get_colors_corr(corr_array, p_array, colors):\\n\\t\\t\\tc_corr = arange(-1, 1, 1/(len(colors)/2))\\n\\t\\t\\tcorr_color = []\\n\\t\\t\\tp_color = []\\n\\t\\t\\tcorr_list = []\\n\\t\\t\\tp_list = []\\n\\t\\t\\tfactor_list = []\\n\\t\\t\\titr = 0\\n\\t\\t\\tfor corr, pvalue in zip(corr_array,p_array):\\n\\t\\t\\t\\tind = bisect.bisect_left(c_corr, corr)\\n\\t\\t\\t\\t#colors\\n\\t\\t\\t\\t##corr_color\\n\\t\\t\\t\\tif (itr)%(nlabels+1)==0:\\n\\t\\t\\t\\t\\tcorr_color.append('#022f62')\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tcorr_color.append(colors[ind-1])\\n\\t\\t\\t\\t##pcolor\\n\\t\\t\\t\\tif pvalue<=0.05:\\n\\t\\t\\t\\t\\tp_color.append(\\\"#F44336\\\")\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tp_color.append(colors[ind-1])\\n\\t\\t\\t\\t#append corr and pvalues\\n\\t\\t\\t\\tcorr_list.append(corr)\\n\\t\\t\\t\\tp_list.append(pvalue)\\n\\t\\t\\t\\tfactor_list.append(['[%s, %s]'%(l_labels[itr][0],l_labels[itr][1])])\\n\\t\\t\\t\\titr = itr + 1\\n\\t\\t\\treturn corr_color, corr_list, p_color, p_list, factor_list\\n\\t\\t\\n\\t\\t#create seaborn plots for each possible correlation combinations\\n\\t\\tdef get_corr_plot(df, x, y):\\n\\t\\t\\timportlib.reload(plt); importlib.reload(sns)\\n\\n\\t\\t\\t#----parameters\\n\\t\\t\\t#figure\\n\\t\\t\\tfig, ax = plt.subplots(1, 1, figsize=(10,10))\\n\\t\\t\\t#bounds\\n\\t\\t\\tplt.subplots_adjust(top=0.95, bottom=0.085, left=0.125, right=0.975, hspace=0.2, wspace=0.2)\\n\\t\\t\\t\\n\\t\\t\\t#----title\\n\\t\\t\\ttitle = \\\"x=%s, y=%s\\\"%(x,y)\\n\\t\\t\\t\\n\\t\\t\\t#----set style\\n\\t\\t\\t#pal = sns.color_palette(\\\"Set1\\\", n_colors=2, desat=.5)\\n\\t\\t\\tsns.set(style=config['style']['seaborn'], font=\\\"Helvetica\\\", font_scale=1.1)\\n\\t\\t\\tsns.despine(offset=10, trim=True)\\n\\t\\t\\t\\n\\t\\t\\t#----plot\\n\\t\\t\\t#if grouping #g = sns.lmplot(x, y, hue=groupby, data=df, palette=pal).set_title(title)\\n\\t\\t\\tsns.regplot(x, y, data=df, scatter=True, fit_reg=True, ci=95).set_title(title)\\n\\t\\t\\t\\n\\t\\t\\t#----add rho, p-value to plot\\n\\t\\t\\t#get values\\n\\t\\t\\tcorr_r = cf(df[x],df[y])\\n\\t\\t\\tcorr_t = \\\"r = %.2f, p < %.2f\\\"%(corr_r[0],corr_r[1])\\n\\t\\t\\t#place text 90% of max-x and 90% of max-y\\n\\t\\t\\tfig.text(0.85, 0.05, corr_t, size=16, horizontalalignment='center',\\n\\t\\t\\t\\t\\tverticalalignment='center', transform=ax.transAxes)\\n\\n\\t\\t\\t#----save\\n\\t\\t\\t#check if path exists\\n\\t\\t\\tfile = \\\"%s.png\\\"%(title)\\n\\t\\t\\tpath = config['path']['output'] + \\\"/analysis/html/img/corr/\\\"\\n\\t\\t\\tif not os.path.exists(path):\\n\\t\\t\\t\\tos.makedirs(path)\\n\\t\\t\\t#save\\n\\t\\t\\tfig.savefig(path + file, dpi=300)\\n\\n\\t\\t#reset\\n\\t\\treset_output()\\n\\t\\t\\n\\t\\t#get color map\\n\\t\\tRdYlBu = mpl_cmap.get_cmap('RdBu', 32)\\n\\t\\tcolors_np = np.vstack(RdYlBu(np.linspace(0, 1, 32)))\\n\\t\\tcmap = matplotlib.colors.ListedColormap(colors_np, name='RedBlue')\\n\\t\\tcolors = [matplotlib.colors.rgb2hex(cmap(x/32)[:3]) for x in range(32)]\\n\\n\\t\\t# calculate correlation coefficients\\n\\t\\t#coeff\\n\\t\\tcorr_coeff = df.corr(method=method)\\n\\t\\t#p-value\\n\\t\\tp_value = get_pvalue(df)\\n\\n\\t\\t# get list and number of variables\\n\\t\\tlabels = df.columns\\n\\t\\tnlabels = len(corr_coeff)\\n\\n\\t\\t#create list of labels\\n\\t\\t##x\\n\\t\\tl_labels_x = labels.tolist() * nlabels\\n\\t\\t##y\\n\\t\\tl_labels_y = labels.tolist() * nlabels\\n\\t\\t###split into chunks\\n\\t\\tl_labels_y_split = np.hsplit(np.array(np.array_split(l_labels_y, nlabels)), nlabels)\\n\\t\\tl_labels_y = np.concatenate(l_labels_y_split).ravel().tolist()\\n\\t\\t##combine\\n\\t\\tl_labels = list(zip(l_labels_x, l_labels_y))\\n\\n\\t\\t#create coeff label coordinates\\n\\t\\tnum = np.arange(0, nlabels, 0.5).tolist()\\n\\t\\tnum = [x for x in num if x not in np.arange(0, nlabels).tolist()]\\n\\t\\t##x\\n\\t\\tnum_x = [(x - 0.025) for x in num]\\n\\t\\tnum_x = [x for x in num_x if x not in np.arange(0, nlabels).tolist()] * nlabels\\n\\t\\t##y\\n\\t\\tnum_y = [(x + .125) for x in num]\\n\\t\\tnum_y = [y for y in num_y if y not in np.arange(0, nlabels).tolist()] * nlabels\\n\\n\\t\\t###split into chunks\\n\\t\\tnum_y_split = np.hsplit(np.array(np.array_split(num_y, nlabels)), nlabels)\\n\\t\\tnum_y = np.concatenate(num_y_split).ravel().tolist()\\n\\t\\t##combine\\n\\t\\t#num_labels = list(zip(num_x, num_y))\\n\\n\\t\\t#plot\\n\\t\\ttools=\\\"box_select,save,reset\\\"\\n\\t\\tcm = figure(tools=tools, plot_width=1200, plot_height=1200, x_range=(0, nlabels), y_range=(0, nlabels), \\n\\t\\t\\t\\t\\toutput_backend=\\\"webgl\\\")\\n\\n\\t\\t#hover\\n\\t\\thover = HoverTool()\\n\\t\\thover.tooltips = [\\n\\t\\t\\t(\\\"factor\\\", \\\"@factor\\\"),\\n\\t\\t\\t(\\\"rho\\\", \\\"@coeff_num\\\"),\\n\\t\\t\\t(\\\"p\\\", \\\"@p_num\\\"),\\n\\t\\t]\\n\\t\\t#cm.tools.append(hover)\\n\\t\\tcm.hover.line_policy = \\\"nearest\\\"\\n\\n\\t\\t#legend\\n\\t\\t#cm.toolbar.logo = None\\n\\n\\t\\t#grid\\n\\t\\tcm.xgrid.grid_line_color = None\\n\\t\\tcm.ygrid.grid_line_color = None\\n\\t\\tcm.xaxis.major_label_orientation = pi/4\\n\\t\\tcm.yaxis.major_label_orientation = pi/4\\n\\n\\t\\t#turn off scientific notation\\n\\t\\tcm.left[0].formatter.use_scientific = False\\n\\n\\t\\t# prepare squares for plot\\n\\t\\ttop, bottom, left, right, corr_items = get_bounds(nlabels)\\n\\t\\tcolor_list,corr,p_color,pvalues,factor = get_colors_corr(corr_coeff.values.flatten(),p_value.values.flatten(),colors)\\n\\n\\t\\t#prepare corr_coeff label for plot\\n\\t\\tcoeff_color = [\\\"#444444\\\"] * (nlabels * nlabels)\\n\\t\\tcoeff_color = ['#f9f9f9' if cr >= np.float64(0.74) else cof for cof, cr in zip(coeff_color, corr)]\\n\\t\\tcoeff_num = [\\\"%.2f\\\"%(x) for x in corr]\\n\\n\\t\\t#prepare pvalue for plot\\n\\t\\tp_color = [\\\"#444444\\\"] * (nlabels * nlabels)\\n\\t\\tp_color = ['#F44336' if p <= np.float64(0.05) else cof for pcol, cof, p in zip(p_color, coeff_color, pvalues)]\\n\\t\\tp_num = [\\\"(%.2f)\\\"%(y) for y in pvalues]\\n\\n\\t\\t#data source\\n\\t\\tsource = ColumnDataSource(data=dict(left=left, right=right, top=top, bottom=bottom,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tx=num_x, y=num_y, coeff_num=coeff_num, coeff_color=coeff_color,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tp_num=p_num, factor=factor, square_color=color_list, p_color=p_color))\\n\\t\\t#create squares\\n\\t\\tcm.quad(top='top', bottom='bottom', left='left', right='right', \\n\\t\\t\\t\\tline_color='white', color='square_color', source=source)\\n\\n\\t\\t#create text\\n\\t\\t#correlation coefficient\\n\\t\\tcm.text(x='x', y='y', source=source, text_color=\\\"coeff_color\\\", text='coeff_num', \\n\\t\\t\\t\\ttext_font_style='normal', text_font_size = '1.5em', text_align='center',\\n\\t\\t\\t\\ty_offset=-5, x_offset=1, text_line_height=1)\\n\\t\\t#p-value\\n\\t\\tcm.text(x='x', y='y', source=source, text_color=\\\"p_color\\\", text='p_num', \\n\\t\\t\\t\\ttext_font_style='normal', text_font_size = '1.0em', text_align='center',\\n\\t\\t\\t\\ty_offset=20, x_offset=1, text_line_height=1)\\n\\n\\t\\t#callback\\n\\t\\tlink = CustomJS(args = dict(source = source), code=\\\"\\\"\\\"\\n\\t\\t\\tconsole.log(\\\"second\\\")\\n\\t\\t\\tobj = cb_obj\\n\\t\\t\\ts = source\\n\\t\\t\\tdata = source.data\\n\\t\\t\\tattr = source.attributes\\n\\t\\t\\td = attr.data\\n\\n\\t\\t\\t//selected item\\n\\t\\t\\tid = source.selected['1d'].indices\\n\\t\\t\\titem = d.factor[id][0].replace(/[[\\\\]]/g,'').match(/(\\\".*?\\\")|(\\\\S+)/g)\\n\\t\\t\\tconsole.log('id: ' + id + ', item: ' + item)\\n\\t\\t\\t\\n\\t\\t\\t//url\\n\\t\\t\\tpath = window.location.href\\n\\t\\t\\tcurrent = path.split(\\\"/\\\").slice(0,-1).join(\\\"/\\\")\\n\\t\\t\\turl = current + '/reg.html?x='+ item[0] + 'y=' + item[1]\\n\\t\\t\\twindow.open(url)\\n\\t\\t\\t//window.open(url, 'newwindow', config='height=720, width=1280')\\n\\t\\t\\\"\\\"\\\")\\n\\t\\tcm.add_tools(TapTool(callback=link)) \\n\\n\\t\\t#reverse y-axis\\n\\t\\tcm.y_range = Range1d(nlabels, 0)\\n\\t\\t#p.y_range = Range1d(monitorSize[1], 0)\\n\\n\\t\\t# Set ticks with labels\\n\\t\\tticks = [tick+0.5 for tick in list(range(nlabels))]\\n\\t\\ttick_dict = OrderedDict([[tick, labels[ii]] for ii, tick in enumerate(ticks)])\\n\\n\\t\\t# Create the correct number of ticks for each axis\\n\\t\\tcm.xaxis.ticker = ticks\\n\\t\\tcm.yaxis.ticker = ticks\\n\\n\\t\\t# Override the labels\\n\\t\\tcm.xaxis.major_label_overrides = tick_dict\\n\\t\\tcm.yaxis.major_label_overrides = tick_dict\\n\\n\\t\\t#color bar\\n\\t\\tmapper = LinearColorMapper(\\n\\t\\t\\tpalette=colors,\\n\\t\\t\\tlow=-1, high=1\\n\\t\\t)\\n\\t\\tcolor_bar = ColorBar(color_mapper=mapper, location=(0, 0))\\n\\t\\tcm.add_layout(color_bar, 'right')\\n\\n\\t\\t#create link for each comparison\\n\\t\\tall_columns = [[x,y] for x in list(labels) for y in list(labels)]\\n\\t\\tfor idx, clm in enumerate(all_columns):\\n\\t\\t\\tget_corr_plot(df=df, x=clm[0], y=clm[1])\\n\\n\\t\\t#get html\\n\\t\\tscript, div = components(cm)\\n\\t\\t\\n\\t\\t##convert seperate plots and div to single string\\n\\t\\tplots = (''.join(map(str, [div, '\\\\n', script])))\\n\\n\\t\\t#create html\\n\\t\\thtml(cls, config, path=path, plots=plots, source='bokeh', title=title, footnote=footnote)\\n\\n\\t\\t#reset\\n\\t\\treset_output()\\n\\n\\t\\t#--------finished\\n\\t\\t#timestamp\\n\\t\\tconsole('%s finished in %s msec'%(_f,((datetime.datetime.now()-_t0).total_seconds()*1000)), 'blue')\\n\\t\\treturn cm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"579772c39464fd6b0bc295f06539f021\",\n \"score\": \"0.57749116\",\n \"text\": \"def draw_heatmap(df, figsize=(15, 6), cmap='YlOrBr', ylabel='', xlabel='', title=''):\\n _, ax = plt.subplots(figsize=figsize)\\n sns.heatmap(df, cmap=cmap, annot=False)\\n ax.set_ylabel(ylabel, fontsize=15)\\n ax.set_xlabel(xlabel, fontsize=15)\\n ax.set_title(title, fontsize=20, weight='bold')\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8868f3cd3d27717bebb45feec1b00367\",\n \"score\": \"0.57121176\",\n \"text\": \"def plot_corrcoef_raftscope(raftsfits, ROIrows, ROIcols, xylabels=None, title='', norm=True):\\n datadir, dataname = os.path.split(raftsfits[0])\\n dataname = os.path.splitext(dataname)[0]\\n\\n a = corrcoef_raftscope(raftsfits, ROIrows, ROIcols, norm)\\n fig, ax = plt.subplots(figsize=(10, 8))\\n if norm:\\n cax = ax.imshow(a, cmap=plt.get_cmap('jet'), norm=mplcol.Normalize(vmax=1, clip=True), interpolation='nearest')\\n else:\\n cax = ax.imshow(a, cmap=plt.get_cmap('jet'), norm=mplcol.Normalize(vmax=20000, clip=True), interpolation='nearest')\\n if norm:\\n titlestr = \\\"Correlation for %s\\\"\\n else:\\n titlestr = \\\"Covariances for %s\\\"\\n if title:\\n ax.set_title(titlestr % title)\\n else:\\n ax.set_title(titlestr % dataname)\\n ax.set_xticks(np.arange(0, 16*len(raftsfits), 16))\\n ax.set_yticks(np.arange(0, 16*len(raftsfits), 16))\\n if xylabels:\\n ax.set_xticklabels(xylabels)\\n ax.set_yticklabels(xylabels)\\n cbar = fig.colorbar(cax, orientation='vertical')\\n\\n plt.savefig(os.path.join(datadir, \\\"corrscope-%s.png\\\" % dataname))\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f75ad74a90af8d32921297c0d07628ab\",\n \"score\": \"0.57056934\",\n \"text\": \"def plot_corr(path):\\n data, cols, rows = read_csv(path)\\n\\n plt.figure().canvas.set_window_title('Figure - Correlation (' + path + ')')\\n for i in range(1, cols): plt.plot(data[0], data[i])\\n \\n plt.title('Correlation')\\n plt.xlabel('Time (ns)')\\n plt.ylabel('Correlation Coefficient')\\n plt.gca().yaxis.grid(color='gray', linestyle='dashed')\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e43f09b6c7e5d4fe9dc3f6925a14d0a\",\n \"score\": \"0.57038325\",\n \"text\": \"def plot_correlation_matrices(chr_list):\\n for ch in chr_list:\\n ss_ch = BlockMatrix.read('gs://nbaya/sumstats_corr/'+variant_set+'_ss_correlation_chr{}.bm/'.format(ch))\\n gt_ch = BlockMatrix.read('gs://nbaya/sumstats_corr/'+variant_set+'_gt_correlation_chr{}.bm/'.format(ch))\\n M_max = int(1e4) #max number of variants to be taken from the block matrices (suggested: 2e4)\\n M = ss_ch.shape[0] #dimension of block matrix\\n# for idx in range(int(M/M_max)+1): #index of which disjoint window we are looking at in the block matrix\\n for idx in range(0,int(M/M_max)+1): #index of which disjoint window we are looking at in the block matrix\\n M0 = M_max*(idx) #start variant index for block matrix filtering\\n M1 = min(M_max*(idx+1),M) #stop variant index for block matrix filtering\\n ss_np = ss_ch[M0:M1,M0:M1].to_numpy()\\n gt_np = gt_ch[M0:M1,M0:M1].to_numpy()\\n print('\\\\nStarting variant window: ['+str(M0)+','+str(M1)+']')\\n w = int(5e3) #window width of variants for correlation matrix (suggested: 2e3)\\n for i in range(int((M1-M0-1)/w)+1):\\n w0 = w*i #start variant index for window of correlation matrix\\n w1 = min(w*(i+1),M1-M0) #stop variant index for window of correlation matrix\\n full = (ss_np[w0:w1,w0:w1]+\\n gt_np[w0:w1,w0:w1].T)\\n np.fill_diagonal(full,1)\\n fig,ax = plt.subplots()\\n ax.imshow(full,cmap='bwr')\\n ax.plot([0, w],[0, w],'k--',alpha=0.5,lw=2)\\n plt.xlim([0,w])\\n plt.ylim([w,0])\\n ax.text(w*0.83,w*0.1,\\\"SS\\\",fontsize=60,alpha=0.5)\\n ax.text(w*0.02,w*0.97,\\\"GT\\\",fontsize=60,alpha=0.5)\\n plt.title('chr'+str(ch)+' '+variant_set+' variants ('+str(M0+w0)+'-'+str(M0+w1)+')')\\n fig=plt.gcf()\\n fig.set_size_inches(10,10)\\n path=('gs://nbaya/sumstats_corr/plots/chr'+str(ch)+'_'+variant_set+\\n '_'+str(M0+w0).zfill(len(str(M)))+'-'+str(M0+w1).zfill(len(str(M)))+'.png')\\n with hl.hadoop_open(path, 'wb') as f: \\n fig.savefig(f,dpi=600)\\n plt.close()\\n print('\\\\nFinished variant window: ['+str(M0)+','+str(M1)+']')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcc767bfe38719b5b3949d492864ed78\",\n \"score\": \"0.5673527\",\n \"text\": \"def constructHeatMap(data, show=False):\\n plt.figure(figsize=(20, 10))\\n sns.heatmap(data, cbar=False)\\n if show:\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c79f04921fcf87404536d46e6198bbd\",\n \"score\": \"0.56536555\",\n \"text\": \"def heatmap_focused(df, target, n):\\r\\n n = n # Number of variables\\r\\n features = df.corr().nlargest(n, target)[target].index\\r\\n hm_data = np.corrcoef(df[features].values.T)\\r\\n sns.set(font_scale=0.8)\\r\\n sns.heatmap(hm_data, cbar=True, cmap=sns.diverging_palette(20, 220, n=200), \\r\\n annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, \\r\\n yticklabels=features.values, xticklabels=features.values).set_title(\\\"Main features heatmap\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20319472cf1b536362aed44fa5ae9394\",\n \"score\": \"0.562109\",\n \"text\": \"def draw_heatmap(*args, **kwargs):\\n data = kwargs.pop('data')\\n d = data.pivot(index=args[1], columns=args[0], values=args[2])\\n sns.heatmap(d, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"593b87cdbd87f8d52b2d3fa009a1e457\",\n \"score\": \"0.561807\",\n \"text\": \"def heatmap(data, row_labels, col_labels, ax=None,\\n cbar_kw={}, cbarlabel=\\\"\\\", **kwargs):\\n\\n if not ax:\\n ax = plt.gca()\\n\\n # Plot the heatmap\\n im = ax.imshow(data, **kwargs)\\n\\n # Create colorbar\\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\\n # cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\\\"bottom\\\")\\n\\n # We want to show all ticks...\\n ax.set_xticks(np.arange(data.shape[1]))\\n ax.set_yticks(np.arange(data.shape[0]))\\n # ... and label them with the respective list entries.\\n ax.set_xticklabels(col_labels, fontproperties=prop)\\n ax.set_yticklabels(row_labels, fontproperties=prop)\\n\\n # Let the horizontal axes labeling appear on top.\\n ax.tick_params(top=True, bottom=False,\\n labeltop=True, labelbottom=False)\\n\\n # Rotate the tick labels and set their alignment.\\n # plt.setp(ax.get_xticklabels(), rotation=-30, ha=\\\"right\\\",\\n # rotation_mode=\\\"anchor\\\")\\n\\n # Turn spines off and create white grid.\\n for edge, spine in ax.spines.items():\\n spine.set_visible(False)\\n\\n ax.set_xticks(np.arange(data.shape[1] + 1) - .5, minor=True)\\n ax.set_yticks(np.arange(data.shape[0] + 1) - .5, minor=True)\\n ax.grid(which=\\\"minor\\\", color=\\\"w\\\", linestyle='-', linewidth=3)\\n ax.tick_params(which=\\\"minor\\\", bottom=False, left=False)\\n\\n return im, cbar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c1529a1fd1e65523056787b4f1956fa\",\n \"score\": \"0.5616676\",\n \"text\": \"def autocorr_raster_plot(ax, trange, spks=None, cor=None, **kwargs):\\r\\n if cor is None:\\r\\n if spks is None:\\r\\n raise IOError('Need to give cor or spks')\\r\\n cor = cc_func(np.array(spks), np.array(spks),\\r\\n trange=trange, keep_zero=False)\\r\\n\\r\\n ys = [np.zeros_like(v) + i for i, v in enumerate(cor)]\\r\\n d = dict(color='k', alpha=.1, s=1)\\r\\n d.update(kwargs)\\r\\n ax.scatter(flaten_list(cor) * 1000, flaten_list(ys), **d)\\r\\n ax.set_ylim(0, len(cor))\\r\\n ax.set_xlim(*np.array(trange) * 1000)\\r\\n return cor, ys\",\n \"title\": \"\"\n },\n {\n \"docid\": \"401d989e2180c21973bc18f01de0c90d\",\n \"score\": \"0.5598288\",\n \"text\": \"def heatmap(data, row_labels, col_labels, ax=None, cbar_kw={}, cbarlabel=\\\"\\\", **kwargs):\\n\\n if not ax:\\n ax = plt.gca()\\n\\n # Plot the heatmap\\n im = ax.imshow(data, **kwargs)\\n\\n # Create colorbar\\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\\\"bottom\\\")\\n\\n # We want to show all ticks...\\n ax.set_xticks(np.arange(data.shape[1]))\\n ax.set_yticks(np.arange(data.shape[0]))\\n # ... and label them with the respective list entries.\\n ax.set_xticklabels(col_labels)\\n ax.set_yticklabels(row_labels)\\n\\n # Let the horizontal axes labeling appear on top.\\n ax.tick_params(top=True, bottom=False, labeltop=True, labelbottom=False)\\n\\n # Rotate the tick labels and set their alignment.\\n plt.setp(ax.get_xticklabels(), rotation=-45, ha=\\\"right\\\", rotation_mode=\\\"anchor\\\")\\n\\n # Turn spines off and create white grid.\\n for edge, spine in ax.spines.items():\\n spine.set_visible(False)\\n\\n ax.set_xticks(np.arange(data.shape[1] + 1) - 0.5, minor=True)\\n ax.set_yticks(np.arange(data.shape[0] + 1) - 0.5, minor=True)\\n ax.grid(which=\\\"minor\\\", color=\\\"w\\\", linestyle=\\\"-\\\", linewidth=3)\\n ax.tick_params(which=\\\"minor\\\", bottom=False, left=False)\\n\\n return im, cbar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb7220f2c95686978d74ac3290bf0900\",\n \"score\": \"0.5581191\",\n \"text\": \"def heatmap(data, row_labels, col_labels, ax=None,\\n cbar_kw={}, cbarlabel=\\\"\\\", **kwargs):\\n\\n if not ax:\\n ax = plt.gca()\\n\\n # Plot the heatmap\\n im = ax.imshow(data, **kwargs)\\n\\n # Create colorbar\\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\\n cbar.ax.set_ylabel(cbarlabel, rotation=00, va=\\\"bottom\\\")\\n\\n # We want to show all ticks...\\n ax.set_xticks(np.arange(data.shape[1]))\\n ax.set_yticks(np.arange(data.shape[0]))\\n # ... and label them with the respective list entries.\\n ax.set_xticklabels(col_labels)\\n ax.set_yticklabels(row_labels)\\n\\n # Let the horizontal axes labeling appear on top.\\n ax.tick_params(top=True, bottom=False,\\n labeltop=True, labelbottom=False)\\n\\n # Rotate the tick labels and set their alignment.\\n plt.setp(ax.get_xticklabels(), rotation=00, ha=\\\"right\\\",\\n rotation_mode=\\\"anchor\\\")\\n\\n # Turn spines off and create white grid.\\n for edge, spine in ax.spines.items():\\n spine.set_visible(False)\\n\\n ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\\n ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\\n ax.grid(which=\\\"minor\\\", color=\\\"w\\\", linestyle='-', linewidth=3)\\n ax.tick_params(which=\\\"minor\\\", bottom=False, left=False)\\n\\n return im, cbar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24b36632f737b0977ff0c8b357697087\",\n \"score\": \"0.5579378\",\n \"text\": \"def heatmap(data, row_labels, col_labels, ax=None,\\n cbar_kw={}, cbarlabel=\\\"\\\", **kwargs):\\n\\n if not ax:\\n ax = plt.gca()\\n\\n # Plot the heatmap\\n im = ax.imshow(data, **kwargs)\\n\\n # Create colorbar\\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\\\"bottom\\\")\\n\\n # We want to show all ticks...\\n ax.set_xticks(np.arange(data.shape[1]))\\n ax.set_yticks(np.arange(data.shape[0]))\\n # ... and label them with the respective list entries.\\n ax.set_xticklabels(col_labels)\\n ax.set_yticklabels(row_labels)\\n\\n # Let the horizontal axes labeling appear on top.\\n ax.tick_params(top=True, bottom=False,\\n labeltop=True, labelbottom=False)\\n\\n # Rotate the tick labels and set their alignment.\\n plt.setp(ax.get_xticklabels(), rotation=-30, ha=\\\"right\\\",\\n rotation_mode=\\\"anchor\\\")\\n\\n # Turn spines off and create white grid.\\n for edge, spine in ax.spines.items():\\n spine.set_visible(False)\\n\\n ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\\n ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\\n ax.grid(which=\\\"minor\\\", color=\\\"w\\\", linestyle='-', linewidth=3)\\n ax.tick_params(which=\\\"minor\\\", bottom=False, left=False)\\n\\n return im, cbar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24b36632f737b0977ff0c8b357697087\",\n \"score\": \"0.5579378\",\n \"text\": \"def heatmap(data, row_labels, col_labels, ax=None,\\n cbar_kw={}, cbarlabel=\\\"\\\", **kwargs):\\n\\n if not ax:\\n ax = plt.gca()\\n\\n # Plot the heatmap\\n im = ax.imshow(data, **kwargs)\\n\\n # Create colorbar\\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\\\"bottom\\\")\\n\\n # We want to show all ticks...\\n ax.set_xticks(np.arange(data.shape[1]))\\n ax.set_yticks(np.arange(data.shape[0]))\\n # ... and label them with the respective list entries.\\n ax.set_xticklabels(col_labels)\\n ax.set_yticklabels(row_labels)\\n\\n # Let the horizontal axes labeling appear on top.\\n ax.tick_params(top=True, bottom=False,\\n labeltop=True, labelbottom=False)\\n\\n # Rotate the tick labels and set their alignment.\\n plt.setp(ax.get_xticklabels(), rotation=-30, ha=\\\"right\\\",\\n rotation_mode=\\\"anchor\\\")\\n\\n # Turn spines off and create white grid.\\n for edge, spine in ax.spines.items():\\n spine.set_visible(False)\\n\\n ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\\n ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\\n ax.grid(which=\\\"minor\\\", color=\\\"w\\\", linestyle='-', linewidth=3)\\n ax.tick_params(which=\\\"minor\\\", bottom=False, left=False)\\n\\n return im, cbar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66b878769a4292f453d388fc4af2f68d\",\n \"score\": \"0.5573627\",\n \"text\": \"def heatmap(data, row_labels, col_labels, ax=None,\\n cbar_kw={}, cbarlabel=\\\"\\\", **kwargs):\\n\\n if not ax:\\n ax = plt.gca()\\n\\n # Plot the heatmap\\n im = ax.imshow(data, **kwargs)\\n\\n # Create colorbar\\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\\\"bottom\\\")\\n\\n # We want to show all ticks...\\n ax.set_xticks(np.arange(data.shape[1]))\\n ax.set_yticks(np.arange(data.shape[0]))\\n # ... and label them with the respective list entries.\\n ax.set_xticklabels(col_labels)\\n ax.set_yticklabels(row_labels)\\n\\n # Let the horizontal axes labeling appear on top.\\n ax.tick_params(top=True, bottom=False,\\n labeltop=True, labelbottom=False)\\n\\n # Rotate the tick labels and set their alignment.\\n plt.setp(ax.get_xticklabels(), rotation=-30, ha=\\\"right\\\",\\n rotation_mode=\\\"anchor\\\")\\n\\n # Turn spines off and create white grid.\\n for edge, spine in ax.spines.items():\\n spine.set_visible(False)\\n\\n ax.set_xticks(np.arange(data.shape[1] + 1) - .5, minor=True)\\n ax.set_yticks(np.arange(data.shape[0] + 1) - .5, minor=True)\\n ax.grid(which=\\\"minor\\\", color=\\\"w\\\", linestyle='-', linewidth=3)\\n ax.tick_params(which=\\\"minor\\\", bottom=False, left=False)\\n\\n return im, cbar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12b61a7c8106098b976abfd82b7465de\",\n \"score\": \"0.55722404\",\n \"text\": \"def topic_correlation(self, topic_matrix):\\r\\n data = pd.DataFrame(data=topic_matrix)\\r\\n corr = data.corr()\\r\\n fig, ax = plt.subplots(figsize=(16, 8), sharey='all', dpi=160)\\r\\n cax = ax.matshow(corr, cmap='coolwarm', vmin=-1, vmax=1)\\r\\n fig.colorbar(cax)\\r\\n ticks = np.arange(0, len(data.columns), 1)\\r\\n ax.set_xticks(ticks)\\r\\n plt.xticks(rotation=90)\\r\\n ax.set_yticks(ticks)\\r\\n ax.set_xticklabels(data.columns)\\r\\n ax.set_yticklabels(data.columns)\\r\\n\\r\\n fig.suptitle('Topic Correlation')\\r\\n\\r\\n if self.report:\\r\\n self.pdf.savefig(fig)\\r\\n\\r\\n if self._display:\\r\\n plt.show()\\r\\n else:\\r\\n plt.close(fig)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"728576c6989f4605462873087d0cc38d\",\n \"score\": \"0.55645734\",\n \"text\": \"def heatmap(data, row_labels, col_labels, ax=None,\\n cbar_kw={}, cbarlabel=\\\"\\\",lab_size=4, **kwargs):\\n\\n if not ax:\\n ax = plt.gca()\\n\\n # Plot the heatmap\\n im = ax.imshow(data, **kwargs)\\n\\n # Create colorbar\\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\\\"bottom\\\",size=lab_size)\\n cbar.ax.tick_params(labelsize=lab_size)\\n\\n # We want to show all ticks...\\n ax.set_xticks(numpy.arange(data.shape[1]))\\n ax.set_yticks(numpy.arange(data.shape[0]))\\n # ... and label them with the respective list entries.\\n ax.set_xticklabels(col_labels)\\n ax.set_yticklabels(row_labels)\\n\\n # Let the horizontal axes labeling appear on top.\\n ax.tick_params(top=True, bottom=False,\\n labeltop=True, labelbottom=False,labelsize=lab_size)\\n\\n # Rotate the tick labels and set their alignment.\\n plt.setp(ax.get_xticklabels(), rotation=-90, ha=\\\"right\\\",va='center',\\n rotation_mode=\\\"anchor\\\", fontsize=lab_size)\\n plt.setp(ax.get_yticklabels(), fontsize=lab_size)\\n\\n # Turn spines off and create white grid.\\n #for edge, spine in ax.spines.items():\\n # spine.set_visible(False)\\n\\n ax.set_xticks(numpy.arange(data.shape[1]+1)-.5, minor=True)\\n ax.set_yticks(numpy.arange(data.shape[0]+1)-.5, minor=True)\\n ax.grid(which=\\\"minor\\\", color=\\\"k\\\", linestyle='-', linewidth=0.1)\\n ax.tick_params(which=\\\"minor\\\", bottom=False, left=False)\\n\\n return im, cbar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac5f741909215516197da6e8262f172e\",\n \"score\": \"0.55473465\",\n \"text\": \"def corr_plot(\\n data, features=None, method=\\\"pearson\\\", plot_width=500, plot_height=400\\n):\\n # Defining numeric_features list\\n numeric_features = [\\n \\\"int16\\\",\\n \\\"int32\\\",\\n \\\"int64\\\",\\n \\\"float16\\\",\\n \\\"float32\\\",\\n \\\"float64\\\",\\n ]\\n\\n # Cheking user's inputs\\n\\n # Tests whether input data is of pd.DataFrame type\\n if not isinstance(data, pd.DataFrame):\\n raise TypeError(\\\"Please pass in a Pandas DataFrame for `data`\\\")\\n\\n # Tests whether input features is of the type list\\n if features is not None:\\n if not isinstance(features, list):\\n raise TypeError(\\\"Please pass in a list for `features`\\\")\\n\\n # Tests whether input features has at least two features\\n if features is not None:\\n if len(features) < 2:\\n raise ValueError(\\\"At least two features should be selected\\\")\\n\\n # Tests whether input method is of the type str\\n if not isinstance(method, str):\\n raise TypeError(\\\"Please pass in a str for `method`\\\")\\n\\n # Tests whether input method is one of the 3 available options\\n if method not in (\\\"pearson\\\", \\\"spearman\\\", \\\"kendall\\\"):\\n raise Exception(\\n \\\"Please pick a correlation method: 'pearson', 'spearman' or\\\"\\n \\\" 'kendall'\\\"\\n )\\n\\n # Tests whether input plot width and height are of the type int\\n if (not isinstance(plot_width, int)) or (not isinstance(plot_height, int)):\\n raise TypeError(\\\"Both plot_width and plot_height must be integers\\\")\\n\\n # Subsetting the data dataframe\\n if features is None:\\n if data.select_dtypes(include=numeric_features).shape[1] < 2:\\n raise ValueError(\\n \\\"Dataframe should have at least two numerical features\\\"\\n )\\n data = data.select_dtypes(include=numeric_features)\\n else:\\n if data[features].select_dtypes(np.number).shape[1] < 2:\\n raise ValueError(\\n \\\"Dataframe should have at least two numerical features\\\"\\n )\\n data = data[features].select_dtypes(include=np.number)\\n # Creating corr_df dataframe\\n corr_df = data.corr(method).stack().reset_index(name=\\\"corr\\\")\\n corr_df.loc[corr_df[\\\"corr\\\"] == 1, \\\"corr\\\"] = 0\\n corr_df[\\\"abs\\\"] = corr_df[\\\"corr\\\"].abs()\\n\\n # Correlation plot\\n corr_plot = (\\n alt.Chart(\\n corr_df, title=f\\\"{method} Correlations Plot for Numerical Features\\\"\\n )\\n .mark_circle()\\n .encode(\\n x=alt.X(\\\"level_0\\\", title=\\\"Numerical Features\\\"),\\n y=alt.Y(\\\"level_1\\\", title=\\\"Numerical Features\\\"),\\n size=alt.Size(\\\"abs\\\", title=\\\"Correlation Size\\\"),\\n color=alt.Color(\\n \\\"corr\\\",\\n title=\\\"Correlation\\\",\\n scale=alt.Scale(scheme=\\\"blueorange\\\"),\\n ),\\n tooltip=alt.Tooltip(\\\"corr\\\"),\\n )\\n .properties(width=plot_width, height=plot_height)\\n )\\n\\n return corr_plot\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e0dd156668d0d817225fdfba31479aa\",\n \"score\": \"0.55360407\",\n \"text\": \"def plot_correlation_with_lag(\\n self,\\n lag: Union[int, List[int]],\\n cols: Optional[List[Any]] = None,\\n ax: Optional[mpl.axes.Axes] = None,\\n mode: str = \\\"ins\\\",\\n ) -> pd.DataFrame:\\n df = self._get_df(cols=cols, mode=mode)\\n # Calculate correlation.\\n corr_df = csigna.correlate_with_lag(df, lag=lag)\\n cplott.plot_heatmap(corr_df, ax=ax)\\n return corr_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b096de850614fe846d29c9923804b1f\",\n \"score\": \"0.55310494\",\n \"text\": \"def plotRawCorrelations(figure, gridSystem, plotRow, seqA, seqB):\\n\\n # Calculate the correlations:\\n valid = np.correlate(seqA, seqB, 'valid')\\n same = np.correlate(seqA, seqB, 'same')\\n full = np.correlate(seqA, seqB, 'full')\\n\\n # Plot the correlations:\\n ax = figure.add_subplot(gridSystem[plotRow, :])\\n ax.plot(valid, 'ro', rasterized=RASTERIZE_PLOTS)\\n ax.set_title('Correlation: Valid')\\n plotRow += 1\\n\\n ax = figure.add_subplot(gridSystem[plotRow, :])\\n ax.plot(same, 'ro', rasterized=RASTERIZE_PLOTS)\\n ax.set_title('Correlation: Same')\\n plotRow += 1\\n\\n ax = figure.add_subplot(gridSystem[plotRow, :])\\n ax.plot(full, 'ro', rasterized=RASTERIZE_PLOTS)\\n ax.set_title('Correlation: Full')\\n plotRow += 1\\n\\n return plotRow\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f8bd423f555d556a8b408eec4ec3d48\",\n \"score\": \"0.5519529\",\n \"text\": \"def draw_heatmap(**kwargs):\\n data = kwargs.pop('data')\\n data = data.pivot(columns='Plant Name', index='Objective', values='Sobol Index')\\n return sns.heatmap(data, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3075e0ca41c3c6d8ee6df1639fc4cfa3\",\n \"score\": \"0.5497765\",\n \"text\": \"def heatmap(data, ax=None, cbar_kw={}, cbarlabel=\\\"\\\", **kwargs):\\n plt.figure(figsize=(12, 6))\\n\\n if not ax:\\n ax = plt.gca()\\n\\n # Plot the heatmap\\n im = ax.imshow(data, **kwargs, aspect=\\\"auto\\\")\\n\\n # Create colorbar\\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\\\"bottom\\\")\\n\\n # We want to show all ticks...\\n ax.set_xticks(np.arange(data.shape[1]))\\n ax.set_yticks(np.arange(data.shape[0]))\\n\\n # Let the horizontal axes labeling appear on top.\\n ax.tick_params(top=False, bottom=True,\\n labeltop=False, labelbottom=True)\\n\\n # Turn spines off and create white grid.\\n for edge, spine in ax.spines.items():\\n spine.set_visible(False)\\n\\n ax.set_xticks(np.arange(data.shape[1] + 1) - .5, minor=True)\\n ax.set_yticks(np.arange(data.shape[0] + 1) - .5, minor=True)\\n ax.grid(which=\\\"minor\\\", color=\\\"w\\\", linestyle='-', linewidth=3)\\n ax.tick_params(which=\\\"minor\\\", bottom=False, left=False)\\n\\n return im, cbar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f89109d7d954090e5c2262d533c05113\",\n \"score\": \"0.54860026\",\n \"text\": \"def heatmap(data, row_labels, col_labels, ax=None, cbar_kw={}, cbarlabel=\\\"\\\", **kwargs):\\n\\n if not ax:\\n ax = plt.gca()\\n\\n # Plot the heatmap\\n im = ax.imshow(data, **kwargs)\\n\\n # We want to show all ticks...\\n ax.set_xticks(np.arange(data.shape[1]))\\n ax.set_yticks(np.arange(data.shape[0]))\\n # ... and label them with the respective list entries.\\n ax.set_xticklabels(col_labels)\\n ax.set_yticklabels(row_labels)\\n\\n # Let the horizontal axes labeling appear on top.\\n ax.tick_params(top=True, bottom=False, labeltop=True, labelbottom=False)\\n\\n # Rotate the tick labels and set their alignment.\\n plt.setp(ax.get_xticklabels(), rotation=-30, ha=\\\"right\\\", rotation_mode=\\\"anchor\\\")\\n\\n # Turn spines off and create white grid.\\n for edge, spine in ax.spines.items():\\n spine.set_visible(False)\\n\\n ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\\n ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\\n ax.grid(which=\\\"minor\\\", color=\\\"w\\\", linestyle='-', linewidth=3)\\n ax.tick_params(which=\\\"minor\\\", bottom=False, left=False)\\n\\n return im\",\n \"title\": \"\"\n },\n {\n \"docid\": \"291efd02e5b6f3e191f197a73a2fd052\",\n \"score\": \"0.54723936\",\n \"text\": \"def plot_frame_cells(self, frame, coll_df, color_column, c_min= 0., c_max= 1., n_ticks= 5, figsize= (6, 10), polygon_lw= .1, color_map= cm.afmhot, title= ''):\\n plt.figure(figsize= figsize)\\n plt.title(title, fontsize= 25)\\n self.show_image(frame)\\n plt.gca().autoscale_view()\\n plt.gca().set_aspect('equal')\\n colors= color_map((coll_df[color_column].values-c_min)/(c_max - c_min)) \\n coll= mc.PolyCollection(coll_df['plot_vertices'].values, lw= polygon_lw)\\n coll.set(facecolors= colors)\\n plt.gca().add_collection(coll)\\n plt.xlim(0, 900)\\n plt.ylim(300, 1800)\\n plt.gca().invert_yaxis()\\n plt.axis('off')\\n divider= make_axes_locatable(plt.gca())\\n cax= divider.append_axes('right', size= '5%', pad= 0.05)\\n mm= cm.ScalarMappable(cmap= color_map)\\n mm.set_array(colors)\\n cbar= plt.colorbar(mm, cax= cax, cmap= color_map, ticks= np.linspace(0, 1, n_ticks + 1))\\n cbar.ax.set_yticklabels(np.linspace(c_min, c_max, n_ticks + 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edf60c3e152234bdfbc49baf11f3c073\",\n \"score\": \"0.54602236\",\n \"text\": \"def heatmap_creator(given_filter):\\n\\tdraw_heatmap(given_filter)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e02de2f57e56d65687b232bee974c09c\",\n \"score\": \"0.54509777\",\n \"text\": \"def correlation(x, y, xlabel='x', ylabel='y'):\\n \\n plt.title(xlabel + ', ' + ylabel, fontsize = 24, fontweight = 'bold')\\n plt.xlabel(xlabel)\\n plt.ylabel(ylabel)\\n plt.scatter(x, y);\\n \\n plt.show()\\n \\n # statistical tests\\n pearson_coef, pearson_p = sp.stats.pearsonr(x, y)\\n spearman_coef, spearman_p = sp.stats.spearmanr(x, y)\\n \\n correl_test_results = pd.DataFrame()\\n correl_test_results.loc['Pearson', 'Coefficient'] = pearson_coef\\n correl_test_results.loc['Pearson', 'p-value'] = pearson_p\\n correl_test_results.loc['Spearman', 'Coefficient'] = spearman_coef\\n correl_test_results.loc['Spearman', 'p-value'] = spearman_p\\n\\n IPython.display.display(correl_test_results)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a0a1b19054966840d1f84c908c69190\",\n \"score\": \"0.5446537\",\n \"text\": \"def plot_heatmap(df_to_plot):\\n # create figure and axis\\n fig, ax = plt.subplots(figsize=(20, 20))\\n\\n # Generate a custom diverging colormap\\n cmap = sns.diverging_palette(50, 10, as_cmap=True)\\n\\n # Draw the heat map with the mask and correct aspect ratio\\n sns.heatmap(df_to_plot,\\n cmap=cmap,\\n vmax=1,\\n center=0,\\n square=True,\\n linewidths=.5,\\n cbar_kws={\\\"shrink\\\": .5},\\n annot=True,\\n ax=ax)\\n\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18cdd250fd811795e003c09336e25843\",\n \"score\": \"0.54337734\",\n \"text\": \"def hist_fx_correlations_physical_plot(year: str, interval: str) -> None:\\n\\n function_name: str = \\\\\\n hist_fx_correlations_physical_plot.__name__\\n hist_data_tools_matrices_physical \\\\\\n .hist_function_header_print_plot(function_name, year, kind='correlations')\\n\\n try:\\n\\n periods: int\\n n_cols: int\\n n_rows: int\\n if interval == 'week':\\n periods = 52\\n n_cols = 13\\n n_rows = 4\\n elif interval == 'month':\\n periods = 12\\n n_cols = 4\\n n_rows = 3\\n elif interval == 'quarter':\\n periods = 4\\n n_cols = 2\\n n_rows = 2\\n else:\\n periods = 1\\n n_cols = 1\\n n_rows = 1\\n\\n figure: plt.figure = plt.figure(figsize=(16, 9))\\n cbar_ax: plt.axes = figure.add_axes([0.91, 0.3, 0.03, 0.4])\\n\\n for per in range(1, periods + 1):\\n\\n if per < 10:\\n per_str: str = f'0{per}'\\n else:\\n per_str = f'{per}'\\n\\n # Load data\\n corr: pd.DataFrame = pickle.load(open(\\n f'../../hist_data/matrices_physical_{year}/hist_fx_matrices'\\n + f'_physical_data/hist_fx_corr_physical_data_{year}_int'\\n + f'_{interval}_{per_str}.pickle', 'rb'))\\n\\n ax_sub = plt.subplot(n_rows, n_cols, per)\\n\\n if interval in ('week', 'month'):\\n sns.heatmap(corr, ax=ax_sub, cbar=per == 1,\\n cbar_ax=None if (per-1) else cbar_ax,\\n vmin=-1, vmax=1)\\n\\n else:\\n sns.heatmap(corr, annot=True, ax=ax_sub, cbar=per == 1,\\n cbar_ax=None if (per-1) else cbar_ax,\\n vmin=-1, vmax=1)\\n\\n if interval == 'week':\\n ax_sub.tick_params(axis='x', bottom=False, labelbottom=False)\\n ax_sub.tick_params(axis='y', left=False, labelleft=False)\\n\\n plt.yticks(rotation=45)\\n plt.xticks(rotation=45)\\n\\n figure.tight_layout(rect=[0, 0, .9, 1])\\n\\n # Plotting\\n hist_data_tools_matrices_physical \\\\\\n .hist_save_plot(function_name, figure, year, interval)\\n\\n plt.close()\\n del corr\\n del figure\\n gc.collect()\\n\\n except FileNotFoundError as error:\\n print('No data')\\n print(error)\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da4d1664a826caa3cb0b75c48ccfab88\",\n \"score\": \"0.54255104\",\n \"text\": \"def corr_plot(self, parameters: list = None,\\n inherit: plt = None,\\n figsize: list = None,\\n title: str = None,\\n annot: bool = True,\\n show: bool = True):\\n\\n # Check if y is list or str\\n if not isinstance(parameters, list):\\n self._raise_plot_format_error([\\\"parameters\\\"], \\\"list or None\\\")\\n\\n # previous configuration of plot\\n plt = self._plot_prev_config(inherit, figsize, \\\"seaborn-dark\\\")\\n fig, ax = plt.subplots()\\n\\n data = self[parameters].corr()\\n score = data.values\\n col = data.columns\\n length = len(col)\\n im = ax.imshow(score, cmap='rocket_r')\\n ax.xaxis.set_ticks_position('top')\\n ax.set_xticks(np.arange(length))\\n ax.set_yticks(np.arange(length))\\n ax.set_xticklabels(col)\\n ax.set_yticklabels(col)\\n fig.colorbar(im, pad=0.03)\\n\\n # the annotation part\\n if annot:\\n for i in range(length):\\n for j in range(length):\\n if score[i, j] > 0.4:\\n color = \\\"w\\\"\\n else:\\n color = \\\"black\\\"\\n ax.text(j, i, round(score[i, j], 2),\\n ha=\\\"center\\\", va=\\\"center\\\", color=color)\\n\\n # return for advanced adjustment\\n return self._plot_post_config(plt, '', title, '', '', show)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf991a923d449e7b829a72da3d5b5f87\",\n \"score\": \"0.5412111\",\n \"text\": \"def plot_correlaton_matrix(self, mask_autocorrelations: bool = False, **kwargs):\\n max_ma_index = self.mini_arrays.max() + 1\\n all_mas = np.arange(max_ma_index)\\n matrix = np.full([max_ma_index, max_ma_index], np.nan, \\\"complex\\\")\\n ma1, ma2 = np.tril_indices(self.mini_arrays.size, 0)\\n for ma in all_mas:\\n if ma not in self.mini_arrays:\\n ma1[ma1 >= ma] += 1\\n ma2[ma2 >= ma] += 1\\n\\n mask = None\\n if mask_autocorrelations:\\n mask = ma1 != ma2 # cross_correlation mask\\n matrix[ma2[mask], ma1[mask]] = np.mean(self.value, axis=(0, 1))[mask]\\n\\n fig = plt.figure(figsize=kwargs.get(\\\"figsize\\\", (10, 10)))\\n ax = fig.add_subplot(111)\\n ax.set_aspect(\\\"equal\\\")\\n\\n data = np.absolute(matrix)\\n if kwargs.get(\\\"decibel\\\", True):\\n data = 10*np.log10(data)\\n\\n im = ax.pcolormesh(\\n all_mas,\\n all_mas,\\n data,\\n shading=\\\"nearest\\\",\\n cmap=kwargs.get(\\\"cmap\\\", \\\"YlGnBu\\\"),\\n vmin=kwargs.get(\\\"vmin\\\", np.nanmin(data)),\\n vmax=kwargs.get(\\\"vmax\\\", np.nanmax(data))\\n )\\n ax.set_xticks(all_mas[::2])\\n ax.set_yticks(all_mas[::2])\\n ax.grid(alpha=0.5)\\n\\n divider = make_axes_locatable(ax)\\n cax = divider.append_axes(\\\"right\\\", size=\\\"5%\\\", pad=0.3)\\n cbar = fig.colorbar(im, cax=cax)\\n cbar.set_label(kwargs.get(\\\"colorbar_label\\\", \\\"dB\\\" if kwargs.get(\\\"decibel\\\", True) else \\\"Amp\\\"))\\n \\n # Axis abels\\n ax.set_xlabel(f\\\"Mini-Array index\\\")\\n ax.set_ylabel(f\\\"Mini-Array index\\\")\\n\\n # Title\\n ax.set_title(kwargs.get(\\\"title\\\", \\\"\\\"))\\n\\n # Save or show the figure\\n figname = kwargs.get(\\\"figname\\\", \\\"\\\")\\n if figname != \\\"\\\":\\n plt.savefig(\\n figname,\\n dpi=300,\\n bbox_inches=\\\"tight\\\",\\n transparent=True\\n )\\n log.info(f\\\"Figure '{figname}' saved.\\\")\\n else:\\n plt.show()\\n plt.close(\\\"all\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13ad289c23981fce522b01a2d2ac978b\",\n \"score\": \"0.5408755\",\n \"text\": \"def correlationSpectroscopyInteractive(dataset, target, mode='SHY', correlationMethod='Pearson'):\\n\\tif mode.lower() == 'shy':\\n\\t\\tcolour = _vcorrcoef(dataset.intensityData[dataset.sampleMask, :], target, method=correlationMethod)\\n\\n\\tmagnitude = numpy.mean(dataset.intensityData[dataset.sampleMask, :],axis=0)\\n\\t\\n\\tplot = plotyShadedLineplot(dataset.featureMetadata.loc[dataset.featureMask, 'ppm'], magnitude[dataset.featureMask], colour[dataset.featureMask])\\n\\n\\tif 'ppm' in dataset.featureMetadata.columns:\\n\\t\\txaxis = 'reversed'\\n\\telse:\\n\\t\\txaxis = 'auto'\\n\\t\\n\\tlayout = go.Layout(\\n\\t\\ttitle=None,\\n\\t\\tlegend=dict(\\n\\t\\t\\torientation=\\\"h\\\"),\\n\\t\\thovermode = \\\"closest\\\",\\n\\t\\tyaxis = dict(\\n\\t\\t\\tshowticklabels=False,\\n\\t\\t\\thoverformat = '.2f'\\n\\t\\t),\\n\\t\\txaxis=dict(\\n\\t\\t\\tautorange=xaxis, \\n\\t\\t\\ttitle='PPM',\\n\\t\\t\\thoverformat = '.2f'\\n\\t\\t)\\n\\t)\\n\\n\\tfigure = go.Figure(data=plot, layout=layout)\\n\\t\\n\\treturn figure\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dee24c7c99015d58c4a736411182f6a\",\n \"score\": \"0.5406638\",\n \"text\": \"def plot_correlation(data, couplings, name, type_to_idx, subset=[0,1,2,3,4,5,11], \\n filename='correlation_matrix.pdf', linkage=None):\\n subset = np.asarray(subset, dtype=int)\\n name = np.asarray(name)\\n\\n x = extract_weighted_subset(data, couplings, type_to_idx, subset=subset, metric='rmse')\\n corr = np.corrcoef(x)\\n\\n if linkage not in (None, 'single', 'complete', 'average', 'weighted', 'centroid', 'median', 'ward'):\\n print(\\\"Unknown linkage\\\", linkage)\\n raise SystemExit\\n\\n # Unsorted plots\\n if linkage is None:\\n # Plot heatmap\\n sns.heatmap((corr), square=True, linewidths=.25, cbar_kws={\\\"shrink\\\": .5},\\n cmap=sns.diverging_palette(220, 10, as_cmap=True),\\n yticklabels=name[subset], xticklabels=subset+1,\\n center=0.5, vmax=1, vmin=0)\\n # Sorted from clustering\\n else:\\n d = scipy.cluster.hierarchy.distance.pdist(x)#, 'cityblock')\\n L = scipy.cluster.hierarchy.linkage(d, method=linkage, optimal_ordering=True)\\n\\n sns.clustermap((corr), square=True, linewidths=.25, cbar_kws={\\\"shrink\\\": .5},\\n cmap = sns.diverging_palette(220, 10, as_cmap=True),\\n yticklabels=name[subset], xticklabels=subset+1,\\n center=0.5, vmax=1, vmin=0, row_linkage=L, col_linkage=L)\\n #plt.xticks(rotation=-45)\\n\\n plt.yticks(rotation=0)\\n\\n plt.savefig(filename, bbox_inches = \\\"tight\\\")\\n plt.clf()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d493a9cbe7a1d28b6d58cbd4fe7aa6e2\",\n \"score\": \"0.5405102\",\n \"text\": \"def annotated_heatmap(data, row_labels, column_labels, subplot=None, colorbar_kw={}, colorbar_label=\\\"\\\",\\n title=None, xlabel=None, ylabel=None,\\n horizontal_ticks_angle=0, **kwargs):\\n\\n if not subplot:\\n subplot = plt.gca()\\n\\n # Plot the heatmap\\n image = subplot.imshow(data, **kwargs)\\n\\n # Create colorbar\\n colorbar = subplot.figure.colorbar(image, ax=subplot, **colorbar_kw)\\n colorbar.ax.set_ylabel(colorbar_label, rotation=-90, va=\\\"bottom\\\")\\n\\n # We want to show all ticks...\\n subplot.set_xticks(np.arange(data.shape[1]))\\n subplot.set_yticks(np.arange(data.shape[0]))\\n # ... and label them with the respective list entries.\\n subplot.set_xticklabels(column_labels)\\n subplot.set_yticklabels(row_labels)\\n\\n # Let the horizontal axes labeling appear on top.\\n #subplot.tick_params(top=True, bottom=False, labeltop=True, labelbottom=False)\\n\\n # Rotate the tick labels and set their alignment.\\n plt.setp(subplot.get_xticklabels(), rotation=horizontal_ticks_angle, ha=\\\"right\\\",\\n rotation_mode=\\\"anchor\\\")\\n\\n # Turn spines off and create white grid.\\n for edge, spine in subplot.spines.items():\\n spine.set_visible(False)\\n\\n subplot.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\\n subplot.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\\n subplot.grid(which=\\\"minor\\\", color=\\\"w\\\", linestyle='-', linewidth=3)\\n subplot.tick_params(which=\\\"minor\\\", bottom=False, left=False)\\n\\n if title is not None:\\n plt.title(title)\\n if xlabel is not None:\\n plt.xlabel(xlabel)\\n if ylabel is not None:\\n plt.ylabel(ylabel)\\n\\n return image, colorbar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0e8cf54ff312d9d2181174df8919ddf\",\n \"score\": \"0.53944826\",\n \"text\": \"def heatmap(data, row_labels, col_labels, ax=None,\\n cbar_kw={}, cbarlabel=\\\"\\\", **kwargs):\\n\\n mpl.rcParams.update({'font.size':24, 'lines.linewidth':3, 'lines.markersize':15, 'font.family':'Times New Roman'}) # avoid type 3 (i.e. bitmap) fonts in figures\\n mpl.rcParams['ps.useafm'] = True\\n mpl.rcParams['pdf.use14corefonts'] = True\\n mpl.rcParams['text.usetex'] = True\\n\\n if not ax:\\n ax = plt.gca()\\n\\n # handle nans\\n current_cmap = mpl.cm.get_cmap()\\n current_cmap.set_bad(color='w')\\n\\n # Plot the heatmap\\n im = ax.imshow(data, **kwargs)\\n\\n # Create colorbar\\n# cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\\n# cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\\\"bottom\\\")\\n\\n # We want to show all ticks...\\n ax.set_xticks(np.arange(data.shape[1]))\\n ax.set_yticks(np.arange(data.shape[0]))\\n # ... and label them with the respective list entries.\\n ax.set_xticklabels(col_labels)\\n ax.set_yticklabels(row_labels)\\n\\n # Let the horizontal axes labeling appear on top.\\n # ax.tick_params(top=True, bottom=False,\\n # labeltop=True, labelbottom=False)\\n\\n # Rotate the tick labels and set their alignment.\\n# plt.setp(ax.get_xticklabels(), rotation=-30, ha=\\\"right\\\",\\n# rotation_mode=\\\"anchor\\\")\\n\\n # Turn spines off and create white grid.\\n for _, spine in ax.spines.items():\\n spine.set_visible(False)\\n\\n ax.set_xticks(np.arange(data.shape[1] + 1) - .5, minor=True)\\n ax.set_yticks(np.arange(data.shape[0] + 1) - .5, minor=True)\\n ax.grid(which=\\\"minor\\\", color=\\\"w\\\", linestyle='-', linewidth=3)\\n ax.tick_params(which=\\\"minor\\\", bottom=False, left=False)\\n\\n ax.invert_yaxis()\\n\\n ax.set_xlabel('$p_{1}$', labelpad=15)\\n ax.set_ylabel('$p_{2}$', labelpad=15)\\n\\n return im # , cbar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bcbdb562ea536bf02f683b7f87355e5\",\n \"score\": \"0.5375192\",\n \"text\": \"def graph_heatmap():\\n\\n\\tpath=\\\"RESULTS/Graficar_HeatMap\\\"\\n\\tpath2=\\\"RESULTS/Graficos\\\"\\n\\tif os.path.isdir(path2) == True:\\n\\t\\tpass\\n\\telse:\\n\\t\\tos.mkdir(path2)\\n\\n\\tfor file1 in os.listdir(path):\\n\\t\\tout=path2+ \\\"/\\\" + file1[:-9] + \\\"_heatmap.jpg\\\"\\n\\t\\tinput_file1=open(path + \\\"/\\\" + file1, \\\"r\\\")\\n\\t\\tz=list()\\n\\t\\tfor line1 in input_file1:\\n\\t\\t\\tcolumns=line1.split(\\\"\\\\t\\\")\\n\\t\\t\\tz.append(float(columns[2]))\\n\\t\\t\\n\\t\\tcoverage=np.linspace(0,100,11)\\n\\t\\tidentity=np.linspace(0,100,11)\\n\\t\\tcantidad=np.array(z).reshape(11,11)\\n\\t\\tz_min, z_max = np.abs(z).min(), np.abs(z).max()\\n\\n\\t\\tfig, ax = plt.subplots()\\n\\t\\tlevels=MaxNLocator(nbins=10).tick_values(z_min, z_max)\\n\\t\\tc=ax.contourf(coverage, identity, cantidad, levels=levels, \\n\\t\\tcmap='GnBu', vmin=z_min, vmax=z_max)\\n\\t\\ttitle='Nº de subjects para cada intervalo de \\\\ncobertura e identidad'\\n\\t\\tax.set_title(title + \\\" del query \\\" + file1[:-9])\\n\\t\\tax.axis([coverage.min(),coverage.max(), \\n\\t\\t\\t\\tidentity.min(), identity.max()])\\n\\t\\tfig.colorbar(c, ax=ax)\\n\\t\\tplt.ylabel('Porcentaje de cobertura')\\n\\t\\tplt.xlabel('Porcentaje de identidad')\\n\\t\\tplt.savefig(out)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f227e23b676adde8bdf857cea492b9b0\",\n \"score\": \"0.53670585\",\n \"text\": \"def construct_heatmap( datamatrix, genes, tissues, heatmap_file ):\\n\\t\\n\\tmy_vmax = 100\\n\\t\\n\\tprint \\\"number of genes for heatmap construction: \\\" + str( len( genes ) )\\n\\tdf = DataFrame( datamatrix, index=genes[::-1], columns=tissues).round( 0 )\\n\\t\\n\\tfig, ax = plt.subplots( )\\n\\t\\n\\tsns.heatmap( df, vmin=0, vmax= my_vmax, ax=ax, linewidths=0.3, annot=True, annot_kws={'fontsize':3}, cbar=False, fmt=\\\"g\\\", cmap='YlGnBu' )\\t#binary\\t#cmap='YlGnBu' = 1\\n\\t\\n\\tfor idx, gene in enumerate( genes ):\\n\\t\\tax.text( -2.65, idx+0.8, gene, fontsize=3 )\\n\\t\\n\\tfor idx, tissue in enumerate( tissues ):\\n\\t\\tax.text( idx+0.4, len( genes )+0.5, \\\"20\\\"+tissue[-2:] + \\\"-\\\" + tissue[2:4] + \\\"-\\\" + tissue[:2], rotation=90, fontsize=3 )\\n\\t\\n\\tax.set_yticklabels( [], rotation=0, fontsize=2 )\\n\\tax.set_xticklabels( [] , rotation=90, fontsize=3 )\\n\\t\\n\\tax.spines['bottom'].set_visible(False)\\n\\tax.spines['left'].set_visible(False)\\n\\t\\n\\tax.axes.get_yaxis().set_visible(False)\\n\\tax.axes.get_xaxis().set_visible(False)\\n\\t\\n\\tplt.yticks( rotation=0 )\\n\\tplt.subplots_adjust( left=0.135, right=0.99, top=0.99, bottom=0.0575, wspace=0.2 )\\n\\t\\n\\tplt.savefig( heatmap_file, dpi=900 )\\n\\tplt.savefig( heatmap_file.replace( \\\".pdf\\\", \\\".svg\\\" ) )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"489be190ef5475545b27b6f9460bd009\",\n \"score\": \"0.5357623\",\n \"text\": \"def vis_corr_photometry(self):\\n # Look at photometry after subtracting expected magnitude\\n # (i.e. subtract an approximate phase curve, but the ZTF predicted values do have errors sometimes)\\n fig = plt.figure(figsize=(8, 6))\\n for f in self.filterlist:\\n o = self.lcobs.query('fid == @f')\\n plt.errorbar(o.jd - self.lcobs.jd.iloc[0], o.magcorr, yerr=o.sigmamag, color=filtercolors[f],\\n marker='.', linestyle='')\\n plt.xticks(rotation=90)\\n plt.xlabel('delta JD', fontsize='x-large')\\n plt.ylabel('MagCorr (mag_obs - pred)', fontsize='x-large')\\n plt.gca().invert_yaxis()\\n plt.title(self.name)\\n plt.grid(True, alpha=0.3)\\n label = self._make_figurelabel()\\n plt.figtext(0.15, 0.8, label)\\n return fig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc9afac1b7f9bb81a270e60a2a8a57a2\",\n \"score\": \"0.53570837\",\n \"text\": \"def _corr_grid(corr: NDArray[float]) -> NDArray[float]:\\n xs = np.arange(-corr.shape[0] / 2, corr.shape[0] / 2) + 0.5\\n ys = np.arange(-corr.shape[1] / 2, corr.shape[1] / 2) + 0.5\\n return np.meshgrid(xs, ys)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c766738dc491739308744a5cf8d38ce6\",\n \"score\": \"0.53538287\",\n \"text\": \"def plotHeatmap(T, Y, ax = None):\\n if ax is None:\\n ax = plt.gca()\\n cax1 = ax.imshow(Y, aspect='auto', interpolation='none', origin='lower',extent=[0,T[-1],len(Y),0],vmax=2, vmin=-2)\\n ax.set_xlabel('Time (s)')\\n ax.set_ylabel(\\\"Neuron\\\")\\n return cax1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8f5f4b7e6b421696a0592aee556a47c\",\n \"score\": \"0.53533655\",\n \"text\": \"def plot_selectivity_heatmap(corrs, title, res_dir):\\n\\n # Plot each layer.\\n for layer_name, lcorrs in corrs.groupby(level='layer'):\\n\\n # Create heatmap.\\n lcorrs.index = lcorrs.index.droplevel()\\n lcorrs = lcorrs.T\\n wfig, hfig = np.sqrt(lcorrs.shape[1]), np.sqrt(lcorrs.shape[0])\\n fig = plt.figure(figsize=(wfig, hfig))\\n ax = fig.add_subplot(111)\\n sns.heatmap(lcorrs, linewidths=.5, center=0, xticklabels=2, ax=ax)\\n\\n # Set labels.\\n plt.yticks(rotation=0)\\n ax.set_xlabel('Unit')\\n ax.set_ylabel(corrs.columns.name)\\n plt.title('%s in %s' % (title, layer_name))\\n\\n # Save plot.\\n fname = '{}/{}.png'.format(res_dir, layer_name)\\n utils.save_fig(fname, fig, close=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fccb04e486f7c59ed3cec2313dbdaf0\",\n \"score\": \"0.53528035\",\n \"text\": \"def plot(Xt, sample=0, colorscale='greys', origin='upper',\\n plotly_params=None):\\n return plot_heatmap(\\n Xt[sample] * 1, colorscale=colorscale, origin=origin,\\n title=f\\\"Binarization of image {sample}\\\",\\n plotly_params=plotly_params\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"271f1b9cd4dc9c6df67cbf573e0b11f9\",\n \"score\": \"0.53442097\",\n \"text\": \"def update_heatmap_carn(self):\\n x = self.sim.animal_distribution\\n carn = x.pivot('Row', 'Col', 'Carnivore').values\\n self.ax4.imshow(carn, vmax=self.cmax_animals['Carnivore'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46b7aa1b1dd19ff27daa2d7c5b7c33fa\",\n \"score\": \"0.53392583\",\n \"text\": \"def correlation_matrix(self):\\n pass # implementation can be omitted\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae50d63584b53b20167c5f7d05e6ca66\",\n \"score\": \"0.53233975\",\n \"text\": \"def draw_heat_map(center, df, radius = 16):\\n\\n from ipyleaflet import Map,Marker,CircleMarker,Heatmap\\n from ipywidgets import HTML\\n from ipyleaflet import Popup\\n\\n m = Map(center = center, zoom = 11)\\n\\n heatmap = Heatmap(\\n locations=[(df.loc[i, \\\"lat\\\"], df.loc[i,\\\"lng\\\"]) for i in df.index],\\n radius= radius\\n )\\n\\n m.add_layer(heatmap);\\n\\n return display(m)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d0a410c59ade904d9b454048013fbdb\",\n \"score\": \"0.531939\",\n \"text\": \"def plot_cmap(matrix_values, figsize_w, figsize_h, filename):\\n if figsize_w is not None and figsize_h is not None:\\n plt.figure(figsize=(figsize_w,figsize_h))\\n else:\\n plt.figure()\\n cmap = sns.diverging_palette(240, 10, sep=20, as_cmap=True)\\n sns.heatmap(matrix_values, annot=True, fmt=\\\".2f\\\", cmap=cmap, vmin=-1, vmax=1)\\n plt.savefig(filename)\\n plt.show()\\n return cmap\",\n \"title\": \"\"\n },\n {\n \"docid\": \"262d8f1bf862cf176026079568b12242\",\n \"score\": \"0.53168684\",\n \"text\": \"def heatmap(data, key1, key2, values='value', xlabel=\\\"\\\", ylabel=\\\"\\\", log=False,\\n carryover=False, draw_bars=True, cmap='CMRmap', disable_axes=True, **kwargs):\\n\\n def do_disable_ticks(ax):\\n ax.xaxis.set_major_locator(plt.NullLocator())\\n ax.yaxis.set_major_locator(plt.NullLocator())\\n\\n def do_disable_axes(ax):\\n ax.xaxis.set_visible(False)\\n ax.yaxis.set_visible(False)\\n\\n #cmap = \\\"YlGnBu\\\"\\n bar_col = \\\"silver\\\"\\n\\n\\n # create a 2 x 3 subplot field\\n # TODO: disable option\\n if draw_bars:\\n fig = plt.figure(frameon=False)\\n gs = gridspec.GridSpec(2, 3, width_ratios=[5, 1, 1], height_ratios = [1, 5], figure=fig)\\n ax_center = plt.subplot(gs[1,0], frameon=False)\\n ax_top = plt.subplot(gs[0,0], frameon=False, sharex=ax_center)\\n ax_right = plt.subplot(gs[1,1], frameon=False)\\n ax_right2 = plt.subplot(gs[1,2], frameon=False, sharex=ax_center)\\n else:\\n fig = plt.figure()\\n gs = gridspec.GridSpec(2, 1, height_ratios=[9, 1], width_ratios = [1], figure=fig)\\n ax_center = plt.subplot(gs[0,0], frameon=False)\\n ax_right2 = plt.subplot(gs[1,0], frameon=False)\\n\\n # prepare data\\n matrix = data.pivot(key1, key2, values=values)\\n if carryover:\\n matrix = matrix.fillna(method='ffill')\\n else:\\n matrix = matrix.fillna(0)\\n\\n if draw_bars:\\n top = data.groupby(key1)[values].sum()\\n right = data.groupby(key2)[values].sum()[::-1]\\n\\n # plot\\n hm_data = matrix.T\\n hm_data = hm_data.sort_index(ascending=False)\\n if log:\\n norm = SymLogNorm(vmin=hm_data.min().min(), vmax=hm_data.max().max(),\\n linthresh=1)\\n hm = sns.heatmap(hm_data, ax=ax_center, cbar=False, cmap=cmap,\\n norm=norm, linewidths=0, **kwargs)\\n else:\\n hm = sns.heatmap(hm_data, ax=ax_center, cbar=False, cmap=cmap, linewidths=0, **kwargs)\\n # previously used imshow, didn't work as nicely\\n ## ax_center.imshow(matrix.T, aspect='auto')\\n if draw_bars:\\n top.plot.bar(ax=ax_top, color=bar_col, align='edge', log=log)\\n #ax_top.set_yscale('symlog')\\n right.plot.barh(ax=ax_right, color=bar_col, align='edge', log=log)\\n #ax_top.set_yscale('symlog')\\n plt.colorbar(ax_center.get_children()[0], ax=ax_right2, orientation='vertical')\\n\\n # naming\\n ax_center.set_xlabel(xlabel)\\n ax_center.set_ylabel(ylabel)\\n\\n # remove axes and ticks where possible\\n do_disable_axes(ax_right2)\\n do_disable_axes(ax_top)\\n do_disable_axes(ax_right)\\n do_disable_ticks(ax_center)\\n\\n # remove spaces\\n fig.tight_layout(pad = 0)\\n fig.patch.set_visible(False)\\n return fig, [ax_top, ax_right, ax_right2, ax_center], {key1:top, key2:right}\\n else:\\n plt.colorbar(ax_center.get_children()[0], ax=ax_right2, orientation='horizontal', fraction=1, aspect=40)\\n fig.subplots_adjust(hspace=0)\\n # naming\\n ax_center.set_xlabel(xlabel)\\n ax_center.set_ylabel(ylabel)\\n\\n # remove axes and ticks where possible\\n if disable_axes:\\n do_disable_axes(ax_right2)\\n #do_disable_ticks(ax_center)\\n\\n fig.tight_layout(pad = 0)\\n #fig.patch.set_visible(False)\\n return fig, [ax_center], None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78970ea2e2fdef31ce6027be88bf855e\",\n \"score\": \"0.531118\",\n \"text\": \"def heatmap(data, row_labels, col_labels, ax=None,\\r\\n cbar_kw={}, cbarlabel=\\\"\\\", title = \\\"Default\\\", x_title=\\\" \\\",y_title=\\\" \\\",saveFile = None, **kwargs):\\r\\n\\r\\n #plt.clf()\\r\\n if not ax:\\r\\n ax = plt.gca()\\r\\n\\r\\n # Plot the heatmap\\r\\n im = ax.imshow(data, **kwargs)\\r\\n\\r\\n # Create colorbar\\r\\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw) # pad=0.1\\r\\n #cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\\\"bottom\\\")\\r\\n\\r\\n # We want to show all ticks...\\r\\n ax.set_xticks(np.arange(data.shape[1]))\\r\\n ax.set_yticks(np.arange(data.shape[0]))\\r\\n # ... and label them with the respective list entries.\\r\\n ax.set_xticklabels(col_labels)\\r\\n ax.set_yticklabels(row_labels)\\r\\n\\r\\n # Let the horizontal axes labeling appear on bottom\\r\\n ax.tick_params(top=False, bottom=True,\\r\\n labeltop=False, labelbottom=True)\\r\\n\\r\\n # Rotate the tick labels and set their alignment.\\r\\n plt.setp(ax.get_xticklabels(), rotation=30, ha=\\\"right\\\",\\r\\n rotation_mode=\\\"anchor\\\")\\r\\n\\r\\n # Turn spines off and create white grid.\\r\\n for edge, spine in ax.spines.items():\\r\\n spine.set_visible(False)\\r\\n\\r\\n ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\\r\\n ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\\r\\n ax.grid(which=\\\"minor\\\", color=\\\"w\\\", linestyle='-', linewidth=3)\\r\\n ax.tick_params(which=\\\"minor\\\", bottom=False, left=False)\\r\\n \\r\\n # plt.title(title)\\r\\n # plt.xlabel(x_title)\\r\\n # plt.ylabel(y_title)\\r\\n ax.set_xlabel(x_title,fontsize=12)\\r\\n ax.set_ylabel(y_title,fontsize=12) # 10\\r\\n ax.set_title(title,fontsize=12) #10, pad=-10)\\r\\n \\r\\n \\r\\n if saveFile != None:\\r\\n plt.savefig(saveFile + '.png', bbox_inches='tight') \\r\\n\\r\\n return im, cbar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42a0fa4fb075b58e6f7d0a4e36a260d0\",\n \"score\": \"0.5309405\",\n \"text\": \"def plot_autocorr_grid(self, data):\\n if data.ndim == 1:\\n self.plot_autocorr(data)\\n else:\\n nax = data.shape[0]\\n if nax > self.max_grid_plot:\\n print 'Too many plots requested.'\\n return\\n\\n self._plot_grid(data, self.plot_autocorr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8af29cf3f086318471dd29d1df06c178\",\n \"score\": \"0.53038985\",\n \"text\": \"def plotDiffMatHeatmap(\\n fa,\\n fb,\\n fo,\\n start=0,\\n end=-1,\\n r=5000,\\n cut=0,\\n mcut=-1,\\n na=\\\"\\\",\\n nb=\\\"\\\",\\n log=False,\\n vmin=None,\\n vmax=None,\\n):\\n labela, chroma, xya, tota = getData(fa, cut, mcut,start,end)\\n labelb, chromb, xyb, totb = getData(fb, cut, mcut,start,end)\\n if chroma != chromb:\\n print(\\\"ERROR! %s and %s are not the same target chromosome, return.\\\" %\\n (fa, fb))\\n return\\n if start == 0:\\n start = min(np.min(xya), np.min(xyb))\\n if end == -1:\\n end = max(np.max(xya), np.max(xyb))\\n if na == \\\"\\\":\\n na = labela\\n if nb == \\\"\\\":\\n nb = labelb\\n mata = getObsMat(xya, start, end, r)\\n matb = getObsMat(xyb, start, end, r)\\n sf = tota / totb\\n mata = mata / sf\\n if log:\\n mat = np.log2((mata + 1) / (matb + 1))\\n label = \\\"log2( %s/%s )\\\" % (na, nb)\\n else:\\n mat = mata - matb\\n label = \\\"%s-%s\\\" % (na, nb)\\n\\n hights = 4\\n hr = [6, 0.1]\\n fig = pylab.figure(figsize=(4, hights))\\n gs = mpl.gridspec.GridSpec(len(hr),\\n 1,\\n height_ratios=hr,\\n top=0.95,\\n bottom=0.05,\\n left=0.1,\\n right=0.9,\\n wspace=0.05)\\n pylab.suptitle(\\\"%s-%s, %s:%s-%s\\\" % (na, nb, chroma[0], start, end),\\n fontsize=8)\\n cmap = sns.color_palette(\\\"RdBu_r\\\", 11).as_hex()\\n cmap[int(len(cmap) / 2)] = \\\"#FFFFFF\\\"\\n cmap = ListedColormap(cmap)\\n ax = fig.add_subplot(gs[-2])\\n cax = fig.add_subplot(gs[-1])\\n sns.set(font_scale=0.5)\\n ax = sns.heatmap(mat,\\n xticklabels=False,\\n yticklabels=False,\\n linewidths=0.0,\\n square=True,\\n cmap=cmap,\\n ax=ax,\\n center=0,\\n vmin=vmin,\\n vmax=vmax,\\n cbar_ax=cax,\\n cbar_kws={\\n 'label': label,\\n 'orientation': 'horizontal',\\n \\\"shrink\\\": 0.5,\\n \\\"fraction\\\": 0.2,\\n \\\"anchor\\\": (0.0, 1.0)\\n })\\n cax.tick_params(labelsize=4)\\n #draw the box\\n ax.axvline(x=ax.get_xlim()[0], color=\\\"k\\\", linewidth=2)\\n ax.axvline(x=ax.get_xlim()[1], color=\\\"k\\\", linewidth=2)\\n ax.axhline(y=ax.get_ylim()[0], color=\\\"k\\\", linewidth=2)\\n ax.axhline(y=ax.get_ylim()[1], color=\\\"k\\\", linewidth=2)\\n pylab.savefig(fo + \\\"_compareMatrix.pdf\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8be07a705d11792466c562aaf5b72a6e\",\n \"score\": \"0.5281724\",\n \"text\": \"def my_draw(self):\\n \\n fig = Figure()\\n canvas = FigureCanvas(fig)\\n ax = fig.gca()\\n \\n ax.imshow(self.image_playground)\\n \\n rect = patches.Rectangle((self.agent_window[0],self.agent_window[1]),self.agent_window[2]-self.agent_window[0],self.agent_window[3]-self.agent_window[1],linewidth=1,edgecolor='r',facecolor='none')\\n ax.add_patch(rect)\\n\\n for target in [self.targets[0]]:\\n rect2 = patches.Rectangle((target[0],target[1]),target[2]-target[0],target[3]-target[1],linewidth=1,edgecolor='b',facecolor='none')\\n ax.add_patch(rect2)\\n \\n canvas.draw() \\n \\n width, height = fig.get_size_inches() * fig.get_dpi()\\n\\n return np.fromstring(canvas.tostring_rgb(), dtype='uint8').reshape(int(height), int(width), 3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"746eba819ec15a7b501c6fecaaf8b21c\",\n \"score\": \"0.52727544\",\n \"text\": \"def cmap_show(self):\\r\\n\\r\\n ##SHOW THE AVAILABLE COLOUR MAPS GRAPHICALLY\\r\\n\\r\\n ##CREATE A NEW FIGURE AND CLEAR IT\\r\\n\\r\\n fig=figure(3)\\r\\n clf()\\r\\n \\r\\n ##CREATE AN ARRAY THAT VARIES FROM ZERO TO ONE IN THE X AXIS,\\r\\n ##AND IS CONSTANT IN THE Y AXIS\\r\\n\\r\\n a=outer(ones(10),arange(0,1,0.01))\\r\\n\\r\\n i=0\\r\\n ##FOR EACH COLOUR \\r\\n for col in self.cmaps:\\r\\n subplot(len(self.cmaps),1,i)\\r\\n\\r\\n ##THIS PLOTS A BAND STRETCHING OVER THE WHOLE COLOUR MAP\\r\\n\\r\\n plot=imshow(a,aspect='auto',cmap=get_cmap(col),origin=\\\"lower\\\")\\r\\n\\r\\n ##NO TICKMARKS\\r\\n plot.axes.get_xaxis().set_ticks([])\\r\\n plot.axes.get_yaxis().set_ticks([])\\r\\n\\r\\n ##AND LABELS IT WITH THE COLOUR NAME\\r\\n annotate(col, (0,1),color='white')\\r\\n i=i+1\\r\\n ##AND RESET THE FIGURE NUMBER\\r\\n fig=figure(1)\\r\\n\\r\\n if(matplotlib.get_backend() != 'macosx'):\\r\\n show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22b6312f1a1f80c1bc8e7d4cde2cf88d\",\n \"score\": \"0.5267274\",\n \"text\": \"def plotXCorrel(data1,data2,units,title,filename):\\n\\n confid_int_a = 2.0/(math.sqrt(len(data1)))\\n confid_int_b = -2.0/(math.sqrt(len(data1)))\\n\\n plt.xcorr(data1,data2,maxlags=None)\\n plt.ylabel('Cross-correlation [-1,1]')\\n plt.xlabel('Lag ('+units+')')\\n plt.axhline(y = confid_int_a,ls='dashed')\\n plt.axhline(y = confid_int_b,ls='dashed')\\n plt.title(title)\\n savefig(filename)\\n plt.close()\\n return\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994180,"cells":{"query_id":{"kind":"string","value":"a6f121548d76e6dc98afa53d020aa3df"},"query":{"kind":"string","value":"Check if this task failed."},"positive_passages":{"kind":"list like","value":[{"docid":"dd5c1773e64de9e555e6c0c34cc41a45","score":"0.7249489","text":"def failed(self) -> bool:\n return self.completed is not None and not self.completed","title":""}],"string":"[\n {\n \"docid\": \"dd5c1773e64de9e555e6c0c34cc41a45\",\n \"score\": \"0.7249489\",\n \"text\": \"def failed(self) -> bool:\\n return self.completed is not None and not self.completed\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"6af889faa0e6ed8836e8b78b10e2a744","score":"0.77773494","text":"def task_is_failure(task):\n\n if task and task.state == 'FAILURE':\n return True\n return False","title":""},{"docid":"c14544e02e3f0b540cbf63fd7c879100","score":"0.76552993","text":"def failed(self) -> bool:\n return self.__failed","title":""},{"docid":"7e19d29a37d92d798d37f12f89e45dd7","score":"0.75677633","text":"def is_failed(self) -> bool:\n return self.execution_status == 'FAILED'","title":""},{"docid":"1c8540d632463b524993ba06741e75ec","score":"0.73092407","text":"def failed(self):\n return bool(self._exception)","title":""},{"docid":"fc523f80feb64eeff3b0c05a4104144f","score":"0.72882026","text":"def failed(self) -> bool:\n if self.status in RUNNING_STATUSES:\n self.refresh()\n return self.status in FAILED_STATUSES","title":""},{"docid":"5beb2e4731aa3174bc0d8337c10df921","score":"0.7171653","text":"def _was_failed( self ):\n # I think it also makes sense to have a single failed flag, but note that any\n # lanes with status .done are still to be regarded as good. Ie. the interpretation\n # of this flag is that any 'started' lane is reallY a 'failed' lane.\n return self._exists( 'pipeline/failed' )","title":""},{"docid":"d70a38943bed04322014fdff81e2e59f","score":"0.7117338","text":"def failed(self):\n return self._failed","title":""},{"docid":"4a597cdc5ddcd738679ecd820493c8d2","score":"0.70847005","text":"def is_failure(self):\n return sum(1 for f in self.failures if f[\"message\"] or f[\"output\"]) > 0","title":""},{"docid":"58d8be084c356d5759034698d40a8d5e","score":"0.70551085","text":"def is_failure(self, result):\n return len(result) == 0","title":""},{"docid":"ae90a5373830f51699944957e8397245","score":"0.6971109","text":"def hasFailed(self,identifier):\n if identifier in self._threads:\n return self._threads[identifier].failed()\n return False","title":""},{"docid":"e9e0401d9c198ca76249e16ba92137a7","score":"0.69340616","text":"def failed(self):\n return self._failed","title":""},{"docid":"66ffa7d50127a6b83fa727d18a767d93","score":"0.6869511","text":"def has_failed(self):\n currenttime = time.time()\n return self.EnlapsedTime() > self.timeout","title":""},{"docid":"af4d52a14262385e2a92ff7453e0dcb2","score":"0.68377984","text":"def is_failed(self) -> bool:\n return isinstance(self, Failed)","title":""},{"docid":"3a9b72bee06fd05c11d8b7b1672b037a","score":"0.68356586","text":"def failure(self):\n return bool(self.errors or self.warnings)","title":""},{"docid":"5aebdd214892064ec5063a0a09b0d9b1","score":"0.67788523","text":"def fail ( self ):\n return bool ( self.sync_status & SYNC_FAIL )","title":""},{"docid":"f8f02663b6294599556cbd50ceff61ae","score":"0.67611015","text":"def is_failed(self, *, sync: bool = False) -> bool:\n\n if sync:\n self.sync()\n\n return self.state.lower() == defaults.states.FAILED","title":""},{"docid":"8a2e82ce0dd074ca76b5caf70a589896","score":"0.66841114","text":"def alert_on_failure(self):\n return True","title":""},{"docid":"d5808585b585c9d4db266a1dd3114fbf","score":"0.6633913","text":"def junit_is_failure(self):\n return sum(1 for f in self.failures if f[\"message\"] or f[\"output\"]) > 0","title":""},{"docid":"1fd2183650df3dd9710abead250e998d","score":"0.6626132","text":"def fail(self):\n self.failed = True\n self.finish()","title":""},{"docid":"656904b37e73ed9eff220f99b26bb8d9","score":"0.66234434","text":"def __process_failed_task(self):\n while self.run:\n # Block for one second\n try:\n task = self.qerr.get(True, 1)\n except Queue.Empty:\n continue\n\n source = os.path.join(self.in_dir, task[\"file\"])\n if not os.path.isfile(source):\n return False\n\n if self.save_failed:\n destination = os.path.join(self.err_dir, task[\"file\"])\n os.rename(source, destination)\n else:\n os.remove(source)","title":""},{"docid":"7edabf11fde765ddf67ca6760fd651c1","score":"0.6612919","text":"def has_error(self) -> bool:\n return self.execution_output.status.code != 200","title":""},{"docid":"e623becec9a85b1de0549b0253a82fe4","score":"0.66118795","text":"def failed(self):\n ...","title":""},{"docid":"1754990def035604ee90a05856042e54","score":"0.6586701","text":"def fail(self) -> None:\n self.success = False","title":""},{"docid":"edad59cd68541c16c51cc8f9d7a2ea34","score":"0.6578857","text":"def check_on_run(self, task_runner: Type[TaskRunner]) -> None:\n if not task_runner.is_ok(self.run_id) and self.get_state() not in ['SYSTEM_ERROR', 'EXECUTOR_ERROR', 'COMPLETE', 'CANCELED']:\n logger.error('Failing run %s because the task to run its leader crashed', self.run_id)\n self.state_machine.send_system_error()","title":""},{"docid":"9d461de487271f519d2d9c5f3f85651f","score":"0.6560773","text":"def test_failure(self):\n self.assertEqual(Status.FAILURE, self.run_task('exit 1'))\n self.assertEqual(Status.FAILURE, self.run_task('asd78sad7ftaoq'))","title":""},{"docid":"02329e22dd09ce4a89e849beb84e32f4","score":"0.65443164","text":"def analysis_failed(self) -> bool:\n return not self.analysis_completed and self.analysis_started","title":""},{"docid":"7f22b1b421fac358ed6ca3306743549e","score":"0.65239406","text":"def failed(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"failed\")","title":""},{"docid":"7f22b1b421fac358ed6ca3306743549e","score":"0.65239406","text":"def failed(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"failed\")","title":""},{"docid":"af48c5d99a676c4331387765151477d7","score":"0.6493763","text":"def has_failed(self):\r\n for action in self.actions:\r\n if action.status in ('nok', 'cancelled'):\r\n return True\r\n return False","title":""},{"docid":"e18569432063a661ef18c8fa40f4cbc5","score":"0.64920485","text":"def _test_run_with_failure(self, task_class, expected_message):\n task_entry = self._create_input_entry()\n self.define_option_problem(PROBLEM_URL_NAME)\n with pytest.raises(TestTaskFailure):\n self._run_task_with_mock_celery(task_class, task_entry.id, task_entry.task_id, expected_message)\n # compare with entry in table:\n entry = InstructorTask.objects.get(id=task_entry.id)\n assert entry.task_state == FAILURE\n output = json.loads(entry.task_output)\n assert output['exception'] == 'TestTaskFailure'\n assert output['message'] == expected_message","title":""},{"docid":"edc94fb1be407a9cbdc69c16ed9afeb4","score":"0.64901346","text":"def task_failed(self, task):\n self._queue.discard_result(task.id, self.serialize(TASK_EXPIRED))","title":""},{"docid":"dd68979529c5a9d97db6dc8491208012","score":"0.6419343","text":"def check_fail(cmd, **kwargs):\n ret = run(cmd, **kwargs)\n assert ret.returncode > 0\n return ret","title":""},{"docid":"4ef3c7e18f6985928c1dc36acb710dd1","score":"0.64110875","text":"def check_failed(return_code):\n if return_code != 0:\n global _failed\n _failed = True","title":""},{"docid":"80a150e0b8418005db42ec765f08fade","score":"0.6391514","text":"def test_wasSuccessfulFalseAfterFailures(self):\n try:\n self.fail(\"foo\")\n except self.failureException:\n self.result.addFailure(self.test, sys.exc_info())\n self.assertEquals(False, self.result.wasSuccessful())","title":""},{"docid":"ad724086b71046669299c8977f29e0a4","score":"0.6380098","text":"def fail_task_not_found():\n return cc_html.fail_with_error_stay_logged_in(\"Task not found.\")","title":""},{"docid":"53301e9db78067ce02605573d0aacbd1","score":"0.63572764","text":"def ErrorHasHappenedInJob(self) -> bool:","title":""},{"docid":"a71ddc8bce0dc4669728ac812db66872","score":"0.63400525","text":"def junit_is_error(self):\n return sum(1 for e in self.errors if e[\"message\"] or e[\"output\"]) > 0","title":""},{"docid":"766955bb69517e9dc80035c8bc6d9d18","score":"0.62683487","text":"def _create_execution_has_failed_tests_flag():\n return multiprocessing.Manager().Value('error', False)","title":""},{"docid":"0ea9cb625f2b2b889622982a87e9006e","score":"0.62444496","text":"def allow_failure(self) -> Optional[bool]:\n return pulumi.get(self, \"allow_failure\")","title":""},{"docid":"18f259ee2b1a7e2c9a419dea2b201ac7","score":"0.61810863","text":"def failure(self) -> _ErrorType:","title":""},{"docid":"7c12f7a0082387d1c022a0fc24e2688f","score":"0.6173469","text":"def getFailureState(self, task):\n return self._failureTaskMap.get(task, None)","title":""},{"docid":"ea54cbc55f0798af83efe6f3e0a21260","score":"0.61614394","text":"def has_error(self):\n\n return self._err is not None","title":""},{"docid":"cffbacd9dc2b6d46b0b58f92f1f86432","score":"0.6136735","text":"def failed(cls, status: str) -> bool:\n return status == V1Statuses.FAILED or status == V1Statuses.UPSTREAM_FAILED","title":""},{"docid":"a923426e753e783e8d80ccf9421c537b","score":"0.61306083","text":"def isServerFailed(self):\n isFailed = False\n with open(self.logPath) as f:\n for line in f:\n if line.lower().find(\"error\") >= 0:\n isFailed = True\n break\n return isFailed","title":""},{"docid":"9b9bb48dbdafaa9edda3738c300f7a62","score":"0.61281186","text":"def lastStepErrorOk(self):\n return True","title":""},{"docid":"6f71d64d874abf1e904612c24efc3f21","score":"0.6114285","text":"def check_cached_task_failures(task_name, task_uid):\n cache_key = f\"{task_uid}-task-attempts\"\n task_attempts = cache.get_or_set(cache_key, 0)\n task_attempts += 1\n cache.set(cache_key, task_attempts)\n if task_attempts > settings.MAX_TASK_ATTEMPTS:\n raise FailedException(task_name=task_name)","title":""},{"docid":"dd85a184380a03a710a7e6577b92ad0f","score":"0.6103362","text":"def testFailed(self, test):\n pass","title":""},{"docid":"3ab71d7f49ac818c93b575480c5f5d96","score":"0.6100009","text":"def failed(self):\n self.drive.debug(debug_modules=True) #Prints some debugging info\n self.next_state('finish')","title":""},{"docid":"c92c16d8f6842771ead98ffdbdb26aeb","score":"0.60963756","text":"def success(self):\n return not self.failure","title":""},{"docid":"4bf622094eceebe2bacf95bf168162cf","score":"0.6074788","text":"def test_fail(self):\r\n # Grrr, we seem to get stdout here, not stderr.\r\n self._count_eq('FAIL: ', 1)","title":""},{"docid":"26ec531d19342eb225baf3cdc7107d9a","score":"0.60615087","text":"def was_successful(self) -> bool:","title":""},{"docid":"74cc636bdecde54ebf7ecdaa8c7710e1","score":"0.604986","text":"def check_actual():\n if get_actual() is None:\n print('No actual task. Use \"task --create <task>\" to create one.')\n return False\n\n if not os.path.exists(get_task_path(get_actual()['task'])):\n print('Actual task points to invalid directory \"'\n + get_actual()['task'] +\n '\". Please set correct task with task -a <Folder/Task>.')\n return False\n\n return True","title":""},{"docid":"82e8077d97025afcd0ca3139da17a391","score":"0.6027442","text":"def check_task_complete(self):\n complete = True\n error_message = 'The following errors are preventing saving: '\n if len(self.window.username_textbox.text()) > 0:\n complete *= True\n else:\n complete *= False\n error_message += 'username is blank, '\n if self.window.consent_checkbox.isChecked():\n complete *= True\n else:\n complete *= False\n error_message += 'consent was not provided, '\n if self.window.age_spinbox.value() > 17:\n complete *= True\n else:\n complete *= False\n error_message += 'must be an adult (18+) to participate, '\n if str(self.window.edu_combobox.currentText()) != '':\n complete *= True\n else:\n complete *= False\n error_message += 'education level was not provided, '\n print(self.window.edu_combobox.currentText())\n if str(self.window.gender_combobox.currentText()) != '':\n complete *= True\n else:\n complete *= False\n error_message += 'gender was not provided, '\n print(self.window.gender_combobox.currentText())\n trial = self.ff_urn_draw_count + self.random_urn_draw_count\n trial_delta_req = trial - self.req_trials\n trial_delta_max = trial - self.max_trials\n print(self.req_trials, self.max_trials, trial)\n if trial == self.req_trials:\n complete *= True\n elif trial < self.req_trials:\n error_message += (str(abs(trial_delta_req)) +\n 'too few marble draws executed, ')\n elif trial > self.max_trials:\n error_message += (str(abs(trial_delta_max)) +\n 'too many marble draws executed (restart the app), ') \n error_message = error_message[:-2] + '.'\n return (complete, error_message)","title":""},{"docid":"f83a72df38791eab503e97c0b75d5f05","score":"0.6026283","text":"def _test_run_with_long_error_msg(self, task_class):\n task_entry = self._create_input_entry()\n self.define_option_problem(PROBLEM_URL_NAME)\n expected_message = \"x\" * 1500\n with pytest.raises(TestTaskFailure):\n self._run_task_with_mock_celery(task_class, task_entry.id, task_entry.task_id, expected_message)\n # compare with entry in table:\n entry = InstructorTask.objects.get(id=task_entry.id)\n assert entry.task_state == FAILURE\n assert 1023 > len(entry.task_output)\n output = json.loads(entry.task_output)\n assert output['exception'] == 'TestTaskFailure'\n assert output['message'] == (expected_message[:(len(output['message']) - 3)] + '...')\n assert 'traceback' not in output","title":""},{"docid":"8983d062cb66c268463de0224f5b5fb3","score":"0.6020274","text":"def success(self):\n return self.errors == 0","title":""},{"docid":"8c2c33df072fc44ede86a2e4d2f96b3a","score":"0.6020236","text":"def lastStepErrorOk(self):\n self.computeErrorEstimate()\n return True","title":""},{"docid":"8719df88b00ff5b35c1c9f4391600727","score":"0.60163635","text":"def is_error(self):\n return sum(1 for e in self.errors if e[\"message\"] or e[\"output\"]) > 0","title":""},{"docid":"af80ed16679122971fa4857cbfe9181f","score":"0.60003185","text":"def failed(self, failed):\n\n self._failed = failed","title":""},{"docid":"d133efadfdea7384789e9af51706a9e1","score":"0.5986603","text":"def failed_tests(self, *args):\n return self.__failing_unittests","title":""},{"docid":"a26979e7e83329b9cf7e2d657d2e858f","score":"0.5974275","text":"def is_failed(behave_data):\n return \"failed\" in map(lambda i: i['status'], behave_data)","title":""},{"docid":"a1db33606ef4f133ef43703d56165424","score":"0.59515023","text":"def __call__(self):\n for task in self.tasks:\n task()\n if not self.dry_run and task.returncode != 0:\n self.failed_task = task\n break\n if self.failed_task is not None and not self.ignore_errors:\n raise JobRuntimeError","title":""},{"docid":"a1db33606ef4f133ef43703d56165424","score":"0.59515023","text":"def __call__(self):\n for task in self.tasks:\n task()\n if not self.dry_run and task.returncode != 0:\n self.failed_task = task\n break\n if self.failed_task is not None and not self.ignore_errors:\n raise JobRuntimeError","title":""},{"docid":"80aac3eff5010422861e6f08efa07872","score":"0.5950959","text":"def pass_fail(self, result):\n if result == 'PASS':\n return 1\n if result == 'FAIL':\n return 0\n else:\n return None","title":""},{"docid":"10650502661f3fc2c48fd1affdf5e808","score":"0.59481573","text":"def _can_override_failed(self, job: dict) -> Tuple[bool, str]:\n try:\n container_reason = job[\"attempts\"][-1][\"container\"][\"reason\"]\n except (KeyError, IndexError):\n container_reason = \"\"\n\n if DOCKER_INSPECT_ERROR in container_reason:\n redun_job = self.pending_batch_jobs[job[\"jobId\"]]\n assert redun_job.task\n if redun_job.task.script:\n # Script tasks will report their status in a status file.\n status_file = File(\n aws_utils.get_job_scratch_file(\n self.s3_scratch_prefix, redun_job, aws_utils.S3_SCRATCH_STATUS\n )\n )\n if status_file.exists():\n return status_file.read().strip() == \"ok\", container_reason\n else:\n # Non-script tasks only create an output file if it is successful.\n output_file = File(\n aws_utils.get_job_scratch_file(\n self.s3_scratch_prefix, redun_job, aws_utils.S3_SCRATCH_OUTPUT\n )\n )\n return output_file.exists(), container_reason\n\n return False, container_reason","title":""},{"docid":"49a270fef06b767611dfdb709b83e77e","score":"0.59427625","text":"def check_result(self):\n for json_path in [self.task_json, self.asset_json,\n self.tips_json]:\n if not os.path.exists(json_path):\n msg = \"Json file is not generated: {0}\".format(json_path)\n return False, msg\n return True, None","title":""},{"docid":"dabdcf46cb02cec23db880fb0340704d","score":"0.59305775","text":"def on_failure(self, f):\n self.connect_signal(task_failure, f)\n return f","title":""},{"docid":"ae2cce7b5fe83240814497e7c048c95b","score":"0.5928106","text":"def validate(self, task, tasks):","title":""},{"docid":"86c4d8b991abd7fbc9e1b22ccd21d8c0","score":"0.59032226","text":"def _test_run_with_short_error_msg(self, task_class):\n task_entry = self._create_input_entry()\n self.define_option_problem(PROBLEM_URL_NAME)\n expected_message = \"x\" * 900\n with pytest.raises(TestTaskFailure):\n self._run_task_with_mock_celery(task_class, task_entry.id, task_entry.task_id, expected_message)\n # compare with entry in table:\n entry = InstructorTask.objects.get(id=task_entry.id)\n assert entry.task_state == FAILURE\n assert 1023 > len(entry.task_output)\n output = json.loads(entry.task_output)\n assert output['exception'] == 'TestTaskFailure'\n assert output['message'] == expected_message\n assert output['traceback'][(- 3):] == '...'","title":""},{"docid":"2b51d7a1612b80ba4244c1b2c0c6aa76","score":"0.58968526","text":"def return_work_failed(self, results, worker_key):\n pass","title":""},{"docid":"3ed8639cdf87da9e291aa27ae715dca9","score":"0.58961254","text":"def failure_reason(self):\n return self.__failure_reason","title":""},{"docid":"22ba3a4627a235aec5a1957960bf8d2b","score":"0.5888151","text":"def test_invalid_feed_error_message(self):\n task = self.run_task(gen_repo(importer_config={'feed': utils.uuid4()}))\n\n with self.subTest(comment='check task error description'):\n tokens = ['scheme', 'must', 'be', 'http', 'https', 'file']\n self.assertTrue(\n all(\n [\n token\n in task['error']['description'].lower()\n for token in tokens\n ]\n )\n )","title":""},{"docid":"6312ee3d349848cb0bfff0409433f936","score":"0.58830255","text":"def errorExists(self):\n\t\ttry: return self.error != None\n\t\texcept AttributeError: return False","title":""},{"docid":"37c678212eda8ee2e351011dbc2dd8b3","score":"0.5874994","text":"def test_run_raises(self):\n with pytest.raises(AssertionError):\n self.task.run(\"something-else\")","title":""},{"docid":"1a9bc03c1dcc461b0d27f5083acdfd8d","score":"0.5874528","text":"def wait_until_fails(self, command: str) -> str:\n output = \"\"\n\n def check_failure(_: Any) -> bool:\n nonlocal output\n status, output = self.execute(command)\n return status != 0\n\n with self.nested(\"waiting for failure: {}\".format(command)):\n retry(check_failure)\n return output","title":""},{"docid":"b8d6a6a0359d6ebb0ff5b814e857379e","score":"0.5871044","text":"def is_auth_failure(self):\r\n return self.auth_failure","title":""},{"docid":"16c75b2e5f2335661ec6c21d0bb6fad8","score":"0.5853189","text":"def test_handle_failed_db_update_task(self):\n\n # Get database update task\n when = now()\n self.assertFalse(self.system_task_mgr._is_db_update_completed)\n task = self.system_task_mgr.get_tasks_to_schedule(when)[0]\n self.assertTrue(task.id.startswith(DB_UPDATE_TASK_ID_PREFIX))\n task_1_id = task.id\n\n # Fail task after running and get different task next time\n task.agent_id = self.agent_id\n self.task_mgr.launch_tasks([task], now())\n update = job_test_utils.create_task_status_update(task.id, task.agent_id, TaskStatusUpdate.RUNNING, now())\n self.task_mgr.handle_task_update(update)\n self.system_task_mgr.handle_task_update(update)\n update = job_test_utils.create_task_status_update(task.id, task.agent_id, TaskStatusUpdate.FAILED, now())\n self.task_mgr.handle_task_update(update)\n self.system_task_mgr.handle_task_update(update)\n\n # No new database update right away\n tasks = self.system_task_mgr.get_tasks_to_schedule(when + datetime.timedelta(seconds=5))\n self.assertListEqual([], tasks)\n self.assertFalse(self.system_task_mgr._is_db_update_completed)\n\n # After error threshold, we should get new database update task\n new_time = when + SystemTaskManager.DATABASE_UPDATE_ERR_THRESHOLD + datetime.timedelta(seconds=5)\n task = self.system_task_mgr.get_tasks_to_schedule(new_time)[0]\n self.assertNotEqual(task.id, task_1_id)\n self.assertTrue(task.id.startswith(DB_UPDATE_TASK_ID_PREFIX))\n self.assertFalse(self.system_task_mgr._is_db_update_completed)","title":""},{"docid":"1ca706a87eb8d39e63eba3db3f8b1d61","score":"0.584821","text":"def fail(self, tup_id):\n pass","title":""},{"docid":"c96ffcfc6083d9b6bd02b7df05a3bb8c","score":"0.58464134","text":"def to_failed(self):\n self.status = FAILED","title":""},{"docid":"d676ac1272b0abe3f74d61fea035cea4","score":"0.58377147","text":"def set_fail(self, e, batch):\n print(\"FAIL\", e, batch)\n self.failed = True","title":""},{"docid":"1899e8d662047656c2825bd8fdfeb3c9","score":"0.58320653","text":"def __failure(self):\n self._failure_count += 1\n if self._failure_count >= self._failure_threshold:\n self.open()","title":""},{"docid":"05c3080134c510665562180944dc2b07","score":"0.58244705","text":"def has_error(self):\n self.get_info()\n\n if 'status' not in self.info:\n return False\n if 'hasError' not in self.info['status']:\n return False\n\n return self.info['status']['hasError']","title":""},{"docid":"e4b995ce0e7a52d7fd9fe54576e4e3ed","score":"0.58240205","text":"def _assert_remote_failure( self, role ):\n self._ssh( role, 'true' )\n try:\n self._ssh( role, 'false' )\n self.fail( )\n except SystemExit as e:\n self.assertEqual( e.code, 1 )","title":""},{"docid":"8c2c2ffa74393384cde1772d3c118997","score":"0.5823604","text":"async def check_failure(ctx: Context, e: errors.CheckFailure) -> None:\n\n bot_missing_errors = (\n errors.BotMissingPermissions,\n errors.BotMissingRole,\n errors.BotMissingAnyRole,\n )\n\n if isinstance(e, bot_missing_errors):\n ctx.bot.stats.incr(\"errors.bot_permission_error\")\n await ctx.send(\n \"Sorry, it looks like I don't have the permissions or roles I need to do that.\"\n )\n elif isinstance(e, errors.NoPrivateMessage):\n await ctx.send(e)","title":""},{"docid":"1be4799a29146d58bd769492904265d2","score":"0.58189017","text":"def check_failures(self) -> None:\n # If the dtypes do not exist return\n if self.dtypes is None:\n return\n # If the upload_failed path does not exist return\n if not os.path.isdir(f\"STORE/upload_failed/{self.name}/{self.stream_type}/\"):\n return\n # Get the list of failed uploads\n failed_list = os.listdir(f\"STORE/upload_failed/{self.name}/{self.stream_type}/\")\n # If there are no failed uploads return\n if len(failed_list) == 0:\n return\n logger = get_logger()\n logger.info((f\"uploading {len(failed_list)} failed upload files found in \"\n \"STORE/upload_failed/{self.name}/{self.stream_type}/\")\n )\n # Compose all the failed uploads into a single DataFrame\n df = self.static_compose_df_from_dir(path=f\"STORE/upload_failed/{self.name}/{self.stream_type}/\",\n fmt=\"parquet\")\n # If the DataFrame is None return\n if df is None:\n return\n # Attempt to case the DataFrame to its expected types\n try:\n df = df.astype(self.dtypes)\n except Exception as e:\n logger.exception(e)\n return\n if self.is_store_instance:\n self.stream(df)\n else:\n self.q.put(df)","title":""},{"docid":"a048bc7fdbec4a4db61d9a07c4e9b9bc","score":"0.5809967","text":"def verify(self):\r\n errors = []\r\n for task in self._tasks:\r\n try:\r\n task.verify()\r\n except AssertionError, e:\r\n error = str(e)\r\n if not error:\r\n raise RuntimeError(\"Empty error message from %r\" % task)\r\n errors.append(error)\r\n if errors:\r\n message = [str(self.path)]\r\n for error in errors:\r\n lines = error.splitlines()\r\n message.append(\"- \" + lines.pop(0))\r\n message.extend([\" \" + line for line in lines])\r\n raise AssertionError(os.linesep.join(message))","title":""},{"docid":"a048bc7fdbec4a4db61d9a07c4e9b9bc","score":"0.5809967","text":"def verify(self):\r\n errors = []\r\n for task in self._tasks:\r\n try:\r\n task.verify()\r\n except AssertionError, e:\r\n error = str(e)\r\n if not error:\r\n raise RuntimeError(\"Empty error message from %r\" % task)\r\n errors.append(error)\r\n if errors:\r\n message = [str(self.path)]\r\n for error in errors:\r\n lines = error.splitlines()\r\n message.append(\"- \" + lines.pop(0))\r\n message.extend([\" \" + line for line in lines])\r\n raise AssertionError(os.linesep.join(message))","title":""},{"docid":"a048bc7fdbec4a4db61d9a07c4e9b9bc","score":"0.5809967","text":"def verify(self):\r\n errors = []\r\n for task in self._tasks:\r\n try:\r\n task.verify()\r\n except AssertionError, e:\r\n error = str(e)\r\n if not error:\r\n raise RuntimeError(\"Empty error message from %r\" % task)\r\n errors.append(error)\r\n if errors:\r\n message = [str(self.path)]\r\n for error in errors:\r\n lines = error.splitlines()\r\n message.append(\"- \" + lines.pop(0))\r\n message.extend([\" \" + line for line in lines])\r\n raise AssertionError(os.linesep.join(message))","title":""},{"docid":"e697a4c69b7cbc701fb132b2b2e95e5b","score":"0.5808749","text":"def set_test_failure_tryjob_result(self):\n self._set_failure_type('TEST_FAILURE')","title":""},{"docid":"be25f632ad9d1db01fefa79cc68bfbbc","score":"0.5806069","text":"def was_successful(self):\n return False","title":""},{"docid":"5fbe07e0c89dea38ba860d2b178932a5","score":"0.58057576","text":"async def test_failed_tests(self):\n self.set_source_parameter(\"test_result\", [\"failed\"])\n response = await self.collect(get_request_text=self.JUNIT_XML)\n self.assert_measurement(\n response,\n value=\"1\",\n total=\"5\",\n entities=[dict(key=\"tc3\", name=\"tc3\", class_name=\"cn\", test_result=\"failed\")],\n )","title":""},{"docid":"42ddfa5af8d99d7c862caa7a0b29ba54","score":"0.5797261","text":"def assertInstanceFailed(self, instance_name, exc_type):\n # type: (Text, type) -> None\n self.assertInstanceStatus(instance_name, TaskInstance.STATUS_FAILED)\n\n self.assertEqual(\n self.manager.storage[instance_name]\n .metadata[TaskInstance.META_EXCEPTION_TYPE],\n\n '{module}.{name}'.format(\n module = exc_type.__module__,\n name = exc_type.__name__,\n ),\n )","title":""},{"docid":"c2a09ddc2fd7c85dc1dee510dbf74327","score":"0.5795169","text":"def _failure(self):\n return []","title":""},{"docid":"cf815204a163b4a1e63de0cf8259616d","score":"0.5795137","text":"def failed(self, error_message):\n self.start_now = False\n self.status = self.STATUS.ERROR\n self.error_message = error_message","title":""},{"docid":"2aef2ed837325698e5b93c7f6358c487","score":"0.5793467","text":"def test_failed_jobs(self):\n assert self.failed in self.all_statuses, 'A core assumption in the Batch Executor has changed. ' \\\n 'What happened to the list of statuses or the running state?'\n running_job = BatchJob('CCC', self.failed)\n self.assertEqual(running_job.get_job_state(), State.FAILED)","title":""},{"docid":"acd7cdb5bc0a386169f64121a78fef07","score":"0.5791061","text":"def timed_out(self):\n elapsed = time.time() - self.start_time\n if elapsed >= self.task_manager_timeout:\n logger.warning(f\"{self.prefix} manager has run for {elapsed} which is greater than TASK_MANAGER_TIMEOUT of {settings.TASK_MANAGER_TIMEOUT}.\")\n return True\n return False","title":""},{"docid":"421e03bf72b382467e9d90870db04523","score":"0.5784614","text":"def failure_reason(self):\n return self._failure_reason","title":""},{"docid":"0f193af9e19e9d3f2d26ab4a35d4940a","score":"0.5783912","text":"def check_ok (result):\n\treturn not result is None and (not \"due to errors\" in result)","title":""},{"docid":"56934ccec973b433634b6d6b9bfae2fa","score":"0.57652456","text":"def pass_fail(self, success):\n return \"PASS\" if success else \"FAIL\"","title":""},{"docid":"2fca8beb6d41b456d3322880cf35ee08","score":"0.5754738","text":"def handle_fail():\n fail = True\n while not task_queue.empty():\n try:\n task_queue.get(False)\n except Empty:\n continue\n task_queue.task_done()","title":""},{"docid":"99b186a000b9ed8c925842b54a61d362","score":"0.5750136","text":"def on_batch_job_failure(cls):\n # TODO(sll): Alert the site admin via email.\n logging.error('Job %s failed.' % cls.__name__)\n job_status = cls._register_end_of_batch_job_and_return_status()\n if job_status == job_models.CONTINUOUS_COMPUTATION_STATUS_CODE_RUNNING:\n cls._kickoff_batch_job_after_previous_one_ends()","title":""}],"string":"[\n {\n \"docid\": \"6af889faa0e6ed8836e8b78b10e2a744\",\n \"score\": \"0.77773494\",\n \"text\": \"def task_is_failure(task):\\n\\n if task and task.state == 'FAILURE':\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c14544e02e3f0b540cbf63fd7c879100\",\n \"score\": \"0.76552993\",\n \"text\": \"def failed(self) -> bool:\\n return self.__failed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e19d29a37d92d798d37f12f89e45dd7\",\n \"score\": \"0.75677633\",\n \"text\": \"def is_failed(self) -> bool:\\n return self.execution_status == 'FAILED'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c8540d632463b524993ba06741e75ec\",\n \"score\": \"0.73092407\",\n \"text\": \"def failed(self):\\n return bool(self._exception)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc523f80feb64eeff3b0c05a4104144f\",\n \"score\": \"0.72882026\",\n \"text\": \"def failed(self) -> bool:\\n if self.status in RUNNING_STATUSES:\\n self.refresh()\\n return self.status in FAILED_STATUSES\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5beb2e4731aa3174bc0d8337c10df921\",\n \"score\": \"0.7171653\",\n \"text\": \"def _was_failed( self ):\\n # I think it also makes sense to have a single failed flag, but note that any\\n # lanes with status .done are still to be regarded as good. Ie. the interpretation\\n # of this flag is that any 'started' lane is reallY a 'failed' lane.\\n return self._exists( 'pipeline/failed' )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d70a38943bed04322014fdff81e2e59f\",\n \"score\": \"0.7117338\",\n \"text\": \"def failed(self):\\n return self._failed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a597cdc5ddcd738679ecd820493c8d2\",\n \"score\": \"0.70847005\",\n \"text\": \"def is_failure(self):\\n return sum(1 for f in self.failures if f[\\\"message\\\"] or f[\\\"output\\\"]) > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58d8be084c356d5759034698d40a8d5e\",\n \"score\": \"0.70551085\",\n \"text\": \"def is_failure(self, result):\\n return len(result) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae90a5373830f51699944957e8397245\",\n \"score\": \"0.6971109\",\n \"text\": \"def hasFailed(self,identifier):\\n if identifier in self._threads:\\n return self._threads[identifier].failed()\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9e0401d9c198ca76249e16ba92137a7\",\n \"score\": \"0.69340616\",\n \"text\": \"def failed(self):\\n return self._failed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66ffa7d50127a6b83fa727d18a767d93\",\n \"score\": \"0.6869511\",\n \"text\": \"def has_failed(self):\\n currenttime = time.time()\\n return self.EnlapsedTime() > self.timeout\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af4d52a14262385e2a92ff7453e0dcb2\",\n \"score\": \"0.68377984\",\n \"text\": \"def is_failed(self) -> bool:\\n return isinstance(self, Failed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a9b72bee06fd05c11d8b7b1672b037a\",\n \"score\": \"0.68356586\",\n \"text\": \"def failure(self):\\n return bool(self.errors or self.warnings)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5aebdd214892064ec5063a0a09b0d9b1\",\n \"score\": \"0.67788523\",\n \"text\": \"def fail ( self ):\\n return bool ( self.sync_status & SYNC_FAIL )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8f02663b6294599556cbd50ceff61ae\",\n \"score\": \"0.67611015\",\n \"text\": \"def is_failed(self, *, sync: bool = False) -> bool:\\n\\n if sync:\\n self.sync()\\n\\n return self.state.lower() == defaults.states.FAILED\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a2e82ce0dd074ca76b5caf70a589896\",\n \"score\": \"0.66841114\",\n \"text\": \"def alert_on_failure(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5808585b585c9d4db266a1dd3114fbf\",\n \"score\": \"0.6633913\",\n \"text\": \"def junit_is_failure(self):\\n return sum(1 for f in self.failures if f[\\\"message\\\"] or f[\\\"output\\\"]) > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fd2183650df3dd9710abead250e998d\",\n \"score\": \"0.6626132\",\n \"text\": \"def fail(self):\\n self.failed = True\\n self.finish()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"656904b37e73ed9eff220f99b26bb8d9\",\n \"score\": \"0.66234434\",\n \"text\": \"def __process_failed_task(self):\\n while self.run:\\n # Block for one second\\n try:\\n task = self.qerr.get(True, 1)\\n except Queue.Empty:\\n continue\\n\\n source = os.path.join(self.in_dir, task[\\\"file\\\"])\\n if not os.path.isfile(source):\\n return False\\n\\n if self.save_failed:\\n destination = os.path.join(self.err_dir, task[\\\"file\\\"])\\n os.rename(source, destination)\\n else:\\n os.remove(source)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7edabf11fde765ddf67ca6760fd651c1\",\n \"score\": \"0.6612919\",\n \"text\": \"def has_error(self) -> bool:\\n return self.execution_output.status.code != 200\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e623becec9a85b1de0549b0253a82fe4\",\n \"score\": \"0.66118795\",\n \"text\": \"def failed(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1754990def035604ee90a05856042e54\",\n \"score\": \"0.6586701\",\n \"text\": \"def fail(self) -> None:\\n self.success = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edad59cd68541c16c51cc8f9d7a2ea34\",\n \"score\": \"0.6578857\",\n \"text\": \"def check_on_run(self, task_runner: Type[TaskRunner]) -> None:\\n if not task_runner.is_ok(self.run_id) and self.get_state() not in ['SYSTEM_ERROR', 'EXECUTOR_ERROR', 'COMPLETE', 'CANCELED']:\\n logger.error('Failing run %s because the task to run its leader crashed', self.run_id)\\n self.state_machine.send_system_error()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d461de487271f519d2d9c5f3f85651f\",\n \"score\": \"0.6560773\",\n \"text\": \"def test_failure(self):\\n self.assertEqual(Status.FAILURE, self.run_task('exit 1'))\\n self.assertEqual(Status.FAILURE, self.run_task('asd78sad7ftaoq'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02329e22dd09ce4a89e849beb84e32f4\",\n \"score\": \"0.65443164\",\n \"text\": \"def analysis_failed(self) -> bool:\\n return not self.analysis_completed and self.analysis_started\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f22b1b421fac358ed6ca3306743549e\",\n \"score\": \"0.65239406\",\n \"text\": \"def failed(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"failed\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f22b1b421fac358ed6ca3306743549e\",\n \"score\": \"0.65239406\",\n \"text\": \"def failed(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"failed\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af48c5d99a676c4331387765151477d7\",\n \"score\": \"0.6493763\",\n \"text\": \"def has_failed(self):\\r\\n for action in self.actions:\\r\\n if action.status in ('nok', 'cancelled'):\\r\\n return True\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e18569432063a661ef18c8fa40f4cbc5\",\n \"score\": \"0.64920485\",\n \"text\": \"def _test_run_with_failure(self, task_class, expected_message):\\n task_entry = self._create_input_entry()\\n self.define_option_problem(PROBLEM_URL_NAME)\\n with pytest.raises(TestTaskFailure):\\n self._run_task_with_mock_celery(task_class, task_entry.id, task_entry.task_id, expected_message)\\n # compare with entry in table:\\n entry = InstructorTask.objects.get(id=task_entry.id)\\n assert entry.task_state == FAILURE\\n output = json.loads(entry.task_output)\\n assert output['exception'] == 'TestTaskFailure'\\n assert output['message'] == expected_message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edc94fb1be407a9cbdc69c16ed9afeb4\",\n \"score\": \"0.64901346\",\n \"text\": \"def task_failed(self, task):\\n self._queue.discard_result(task.id, self.serialize(TASK_EXPIRED))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd68979529c5a9d97db6dc8491208012\",\n \"score\": \"0.6419343\",\n \"text\": \"def check_fail(cmd, **kwargs):\\n ret = run(cmd, **kwargs)\\n assert ret.returncode > 0\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ef3c7e18f6985928c1dc36acb710dd1\",\n \"score\": \"0.64110875\",\n \"text\": \"def check_failed(return_code):\\n if return_code != 0:\\n global _failed\\n _failed = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80a150e0b8418005db42ec765f08fade\",\n \"score\": \"0.6391514\",\n \"text\": \"def test_wasSuccessfulFalseAfterFailures(self):\\n try:\\n self.fail(\\\"foo\\\")\\n except self.failureException:\\n self.result.addFailure(self.test, sys.exc_info())\\n self.assertEquals(False, self.result.wasSuccessful())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad724086b71046669299c8977f29e0a4\",\n \"score\": \"0.6380098\",\n \"text\": \"def fail_task_not_found():\\n return cc_html.fail_with_error_stay_logged_in(\\\"Task not found.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53301e9db78067ce02605573d0aacbd1\",\n \"score\": \"0.63572764\",\n \"text\": \"def ErrorHasHappenedInJob(self) -> bool:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a71ddc8bce0dc4669728ac812db66872\",\n \"score\": \"0.63400525\",\n \"text\": \"def junit_is_error(self):\\n return sum(1 for e in self.errors if e[\\\"message\\\"] or e[\\\"output\\\"]) > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"766955bb69517e9dc80035c8bc6d9d18\",\n \"score\": \"0.62683487\",\n \"text\": \"def _create_execution_has_failed_tests_flag():\\n return multiprocessing.Manager().Value('error', False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ea9cb625f2b2b889622982a87e9006e\",\n \"score\": \"0.62444496\",\n \"text\": \"def allow_failure(self) -> Optional[bool]:\\n return pulumi.get(self, \\\"allow_failure\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18f259ee2b1a7e2c9a419dea2b201ac7\",\n \"score\": \"0.61810863\",\n \"text\": \"def failure(self) -> _ErrorType:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c12f7a0082387d1c022a0fc24e2688f\",\n \"score\": \"0.6173469\",\n \"text\": \"def getFailureState(self, task):\\n return self._failureTaskMap.get(task, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea54cbc55f0798af83efe6f3e0a21260\",\n \"score\": \"0.61614394\",\n \"text\": \"def has_error(self):\\n\\n return self._err is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cffbacd9dc2b6d46b0b58f92f1f86432\",\n \"score\": \"0.6136735\",\n \"text\": \"def failed(cls, status: str) -> bool:\\n return status == V1Statuses.FAILED or status == V1Statuses.UPSTREAM_FAILED\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a923426e753e783e8d80ccf9421c537b\",\n \"score\": \"0.61306083\",\n \"text\": \"def isServerFailed(self):\\n isFailed = False\\n with open(self.logPath) as f:\\n for line in f:\\n if line.lower().find(\\\"error\\\") >= 0:\\n isFailed = True\\n break\\n return isFailed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b9bb48dbdafaa9edda3738c300f7a62\",\n \"score\": \"0.61281186\",\n \"text\": \"def lastStepErrorOk(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f71d64d874abf1e904612c24efc3f21\",\n \"score\": \"0.6114285\",\n \"text\": \"def check_cached_task_failures(task_name, task_uid):\\n cache_key = f\\\"{task_uid}-task-attempts\\\"\\n task_attempts = cache.get_or_set(cache_key, 0)\\n task_attempts += 1\\n cache.set(cache_key, task_attempts)\\n if task_attempts > settings.MAX_TASK_ATTEMPTS:\\n raise FailedException(task_name=task_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd85a184380a03a710a7e6577b92ad0f\",\n \"score\": \"0.6103362\",\n \"text\": \"def testFailed(self, test):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ab71d7f49ac818c93b575480c5f5d96\",\n \"score\": \"0.6100009\",\n \"text\": \"def failed(self):\\n self.drive.debug(debug_modules=True) #Prints some debugging info\\n self.next_state('finish')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c92c16d8f6842771ead98ffdbdb26aeb\",\n \"score\": \"0.60963756\",\n \"text\": \"def success(self):\\n return not self.failure\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bf622094eceebe2bacf95bf168162cf\",\n \"score\": \"0.6074788\",\n \"text\": \"def test_fail(self):\\r\\n # Grrr, we seem to get stdout here, not stderr.\\r\\n self._count_eq('FAIL: ', 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26ec531d19342eb225baf3cdc7107d9a\",\n \"score\": \"0.60615087\",\n \"text\": \"def was_successful(self) -> bool:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74cc636bdecde54ebf7ecdaa8c7710e1\",\n \"score\": \"0.604986\",\n \"text\": \"def check_actual():\\n if get_actual() is None:\\n print('No actual task. Use \\\"task --create <task>\\\" to create one.')\\n return False\\n\\n if not os.path.exists(get_task_path(get_actual()['task'])):\\n print('Actual task points to invalid directory \\\"'\\n + get_actual()['task'] +\\n '\\\". Please set correct task with task -a <Folder/Task>.')\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82e8077d97025afcd0ca3139da17a391\",\n \"score\": \"0.6027442\",\n \"text\": \"def check_task_complete(self):\\n complete = True\\n error_message = 'The following errors are preventing saving: '\\n if len(self.window.username_textbox.text()) > 0:\\n complete *= True\\n else:\\n complete *= False\\n error_message += 'username is blank, '\\n if self.window.consent_checkbox.isChecked():\\n complete *= True\\n else:\\n complete *= False\\n error_message += 'consent was not provided, '\\n if self.window.age_spinbox.value() > 17:\\n complete *= True\\n else:\\n complete *= False\\n error_message += 'must be an adult (18+) to participate, '\\n if str(self.window.edu_combobox.currentText()) != '':\\n complete *= True\\n else:\\n complete *= False\\n error_message += 'education level was not provided, '\\n print(self.window.edu_combobox.currentText())\\n if str(self.window.gender_combobox.currentText()) != '':\\n complete *= True\\n else:\\n complete *= False\\n error_message += 'gender was not provided, '\\n print(self.window.gender_combobox.currentText())\\n trial = self.ff_urn_draw_count + self.random_urn_draw_count\\n trial_delta_req = trial - self.req_trials\\n trial_delta_max = trial - self.max_trials\\n print(self.req_trials, self.max_trials, trial)\\n if trial == self.req_trials:\\n complete *= True\\n elif trial < self.req_trials:\\n error_message += (str(abs(trial_delta_req)) +\\n 'too few marble draws executed, ')\\n elif trial > self.max_trials:\\n error_message += (str(abs(trial_delta_max)) +\\n 'too many marble draws executed (restart the app), ') \\n error_message = error_message[:-2] + '.'\\n return (complete, error_message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f83a72df38791eab503e97c0b75d5f05\",\n \"score\": \"0.6026283\",\n \"text\": \"def _test_run_with_long_error_msg(self, task_class):\\n task_entry = self._create_input_entry()\\n self.define_option_problem(PROBLEM_URL_NAME)\\n expected_message = \\\"x\\\" * 1500\\n with pytest.raises(TestTaskFailure):\\n self._run_task_with_mock_celery(task_class, task_entry.id, task_entry.task_id, expected_message)\\n # compare with entry in table:\\n entry = InstructorTask.objects.get(id=task_entry.id)\\n assert entry.task_state == FAILURE\\n assert 1023 > len(entry.task_output)\\n output = json.loads(entry.task_output)\\n assert output['exception'] == 'TestTaskFailure'\\n assert output['message'] == (expected_message[:(len(output['message']) - 3)] + '...')\\n assert 'traceback' not in output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8983d062cb66c268463de0224f5b5fb3\",\n \"score\": \"0.6020274\",\n \"text\": \"def success(self):\\n return self.errors == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c2c33df072fc44ede86a2e4d2f96b3a\",\n \"score\": \"0.6020236\",\n \"text\": \"def lastStepErrorOk(self):\\n self.computeErrorEstimate()\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8719df88b00ff5b35c1c9f4391600727\",\n \"score\": \"0.60163635\",\n \"text\": \"def is_error(self):\\n return sum(1 for e in self.errors if e[\\\"message\\\"] or e[\\\"output\\\"]) > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af80ed16679122971fa4857cbfe9181f\",\n \"score\": \"0.60003185\",\n \"text\": \"def failed(self, failed):\\n\\n self._failed = failed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d133efadfdea7384789e9af51706a9e1\",\n \"score\": \"0.5986603\",\n \"text\": \"def failed_tests(self, *args):\\n return self.__failing_unittests\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a26979e7e83329b9cf7e2d657d2e858f\",\n \"score\": \"0.5974275\",\n \"text\": \"def is_failed(behave_data):\\n return \\\"failed\\\" in map(lambda i: i['status'], behave_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1db33606ef4f133ef43703d56165424\",\n \"score\": \"0.59515023\",\n \"text\": \"def __call__(self):\\n for task in self.tasks:\\n task()\\n if not self.dry_run and task.returncode != 0:\\n self.failed_task = task\\n break\\n if self.failed_task is not None and not self.ignore_errors:\\n raise JobRuntimeError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1db33606ef4f133ef43703d56165424\",\n \"score\": \"0.59515023\",\n \"text\": \"def __call__(self):\\n for task in self.tasks:\\n task()\\n if not self.dry_run and task.returncode != 0:\\n self.failed_task = task\\n break\\n if self.failed_task is not None and not self.ignore_errors:\\n raise JobRuntimeError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80aac3eff5010422861e6f08efa07872\",\n \"score\": \"0.5950959\",\n \"text\": \"def pass_fail(self, result):\\n if result == 'PASS':\\n return 1\\n if result == 'FAIL':\\n return 0\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10650502661f3fc2c48fd1affdf5e808\",\n \"score\": \"0.59481573\",\n \"text\": \"def _can_override_failed(self, job: dict) -> Tuple[bool, str]:\\n try:\\n container_reason = job[\\\"attempts\\\"][-1][\\\"container\\\"][\\\"reason\\\"]\\n except (KeyError, IndexError):\\n container_reason = \\\"\\\"\\n\\n if DOCKER_INSPECT_ERROR in container_reason:\\n redun_job = self.pending_batch_jobs[job[\\\"jobId\\\"]]\\n assert redun_job.task\\n if redun_job.task.script:\\n # Script tasks will report their status in a status file.\\n status_file = File(\\n aws_utils.get_job_scratch_file(\\n self.s3_scratch_prefix, redun_job, aws_utils.S3_SCRATCH_STATUS\\n )\\n )\\n if status_file.exists():\\n return status_file.read().strip() == \\\"ok\\\", container_reason\\n else:\\n # Non-script tasks only create an output file if it is successful.\\n output_file = File(\\n aws_utils.get_job_scratch_file(\\n self.s3_scratch_prefix, redun_job, aws_utils.S3_SCRATCH_OUTPUT\\n )\\n )\\n return output_file.exists(), container_reason\\n\\n return False, container_reason\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49a270fef06b767611dfdb709b83e77e\",\n \"score\": \"0.59427625\",\n \"text\": \"def check_result(self):\\n for json_path in [self.task_json, self.asset_json,\\n self.tips_json]:\\n if not os.path.exists(json_path):\\n msg = \\\"Json file is not generated: {0}\\\".format(json_path)\\n return False, msg\\n return True, None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dabdcf46cb02cec23db880fb0340704d\",\n \"score\": \"0.59305775\",\n \"text\": \"def on_failure(self, f):\\n self.connect_signal(task_failure, f)\\n return f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae2cce7b5fe83240814497e7c048c95b\",\n \"score\": \"0.5928106\",\n \"text\": \"def validate(self, task, tasks):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86c4d8b991abd7fbc9e1b22ccd21d8c0\",\n \"score\": \"0.59032226\",\n \"text\": \"def _test_run_with_short_error_msg(self, task_class):\\n task_entry = self._create_input_entry()\\n self.define_option_problem(PROBLEM_URL_NAME)\\n expected_message = \\\"x\\\" * 900\\n with pytest.raises(TestTaskFailure):\\n self._run_task_with_mock_celery(task_class, task_entry.id, task_entry.task_id, expected_message)\\n # compare with entry in table:\\n entry = InstructorTask.objects.get(id=task_entry.id)\\n assert entry.task_state == FAILURE\\n assert 1023 > len(entry.task_output)\\n output = json.loads(entry.task_output)\\n assert output['exception'] == 'TestTaskFailure'\\n assert output['message'] == expected_message\\n assert output['traceback'][(- 3):] == '...'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b51d7a1612b80ba4244c1b2c0c6aa76\",\n \"score\": \"0.58968526\",\n \"text\": \"def return_work_failed(self, results, worker_key):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ed8639cdf87da9e291aa27ae715dca9\",\n \"score\": \"0.58961254\",\n \"text\": \"def failure_reason(self):\\n return self.__failure_reason\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22ba3a4627a235aec5a1957960bf8d2b\",\n \"score\": \"0.5888151\",\n \"text\": \"def test_invalid_feed_error_message(self):\\n task = self.run_task(gen_repo(importer_config={'feed': utils.uuid4()}))\\n\\n with self.subTest(comment='check task error description'):\\n tokens = ['scheme', 'must', 'be', 'http', 'https', 'file']\\n self.assertTrue(\\n all(\\n [\\n token\\n in task['error']['description'].lower()\\n for token in tokens\\n ]\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6312ee3d349848cb0bfff0409433f936\",\n \"score\": \"0.58830255\",\n \"text\": \"def errorExists(self):\\n\\t\\ttry: return self.error != None\\n\\t\\texcept AttributeError: return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37c678212eda8ee2e351011dbc2dd8b3\",\n \"score\": \"0.5874994\",\n \"text\": \"def test_run_raises(self):\\n with pytest.raises(AssertionError):\\n self.task.run(\\\"something-else\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a9bc03c1dcc461b0d27f5083acdfd8d\",\n \"score\": \"0.5874528\",\n \"text\": \"def wait_until_fails(self, command: str) -> str:\\n output = \\\"\\\"\\n\\n def check_failure(_: Any) -> bool:\\n nonlocal output\\n status, output = self.execute(command)\\n return status != 0\\n\\n with self.nested(\\\"waiting for failure: {}\\\".format(command)):\\n retry(check_failure)\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8d6a6a0359d6ebb0ff5b814e857379e\",\n \"score\": \"0.5871044\",\n \"text\": \"def is_auth_failure(self):\\r\\n return self.auth_failure\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16c75b2e5f2335661ec6c21d0bb6fad8\",\n \"score\": \"0.5853189\",\n \"text\": \"def test_handle_failed_db_update_task(self):\\n\\n # Get database update task\\n when = now()\\n self.assertFalse(self.system_task_mgr._is_db_update_completed)\\n task = self.system_task_mgr.get_tasks_to_schedule(when)[0]\\n self.assertTrue(task.id.startswith(DB_UPDATE_TASK_ID_PREFIX))\\n task_1_id = task.id\\n\\n # Fail task after running and get different task next time\\n task.agent_id = self.agent_id\\n self.task_mgr.launch_tasks([task], now())\\n update = job_test_utils.create_task_status_update(task.id, task.agent_id, TaskStatusUpdate.RUNNING, now())\\n self.task_mgr.handle_task_update(update)\\n self.system_task_mgr.handle_task_update(update)\\n update = job_test_utils.create_task_status_update(task.id, task.agent_id, TaskStatusUpdate.FAILED, now())\\n self.task_mgr.handle_task_update(update)\\n self.system_task_mgr.handle_task_update(update)\\n\\n # No new database update right away\\n tasks = self.system_task_mgr.get_tasks_to_schedule(when + datetime.timedelta(seconds=5))\\n self.assertListEqual([], tasks)\\n self.assertFalse(self.system_task_mgr._is_db_update_completed)\\n\\n # After error threshold, we should get new database update task\\n new_time = when + SystemTaskManager.DATABASE_UPDATE_ERR_THRESHOLD + datetime.timedelta(seconds=5)\\n task = self.system_task_mgr.get_tasks_to_schedule(new_time)[0]\\n self.assertNotEqual(task.id, task_1_id)\\n self.assertTrue(task.id.startswith(DB_UPDATE_TASK_ID_PREFIX))\\n self.assertFalse(self.system_task_mgr._is_db_update_completed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ca706a87eb8d39e63eba3db3f8b1d61\",\n \"score\": \"0.584821\",\n \"text\": \"def fail(self, tup_id):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c96ffcfc6083d9b6bd02b7df05a3bb8c\",\n \"score\": \"0.58464134\",\n \"text\": \"def to_failed(self):\\n self.status = FAILED\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d676ac1272b0abe3f74d61fea035cea4\",\n \"score\": \"0.58377147\",\n \"text\": \"def set_fail(self, e, batch):\\n print(\\\"FAIL\\\", e, batch)\\n self.failed = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1899e8d662047656c2825bd8fdfeb3c9\",\n \"score\": \"0.58320653\",\n \"text\": \"def __failure(self):\\n self._failure_count += 1\\n if self._failure_count >= self._failure_threshold:\\n self.open()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05c3080134c510665562180944dc2b07\",\n \"score\": \"0.58244705\",\n \"text\": \"def has_error(self):\\n self.get_info()\\n\\n if 'status' not in self.info:\\n return False\\n if 'hasError' not in self.info['status']:\\n return False\\n\\n return self.info['status']['hasError']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4b995ce0e7a52d7fd9fe54576e4e3ed\",\n \"score\": \"0.58240205\",\n \"text\": \"def _assert_remote_failure( self, role ):\\n self._ssh( role, 'true' )\\n try:\\n self._ssh( role, 'false' )\\n self.fail( )\\n except SystemExit as e:\\n self.assertEqual( e.code, 1 )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c2c2ffa74393384cde1772d3c118997\",\n \"score\": \"0.5823604\",\n \"text\": \"async def check_failure(ctx: Context, e: errors.CheckFailure) -> None:\\n\\n bot_missing_errors = (\\n errors.BotMissingPermissions,\\n errors.BotMissingRole,\\n errors.BotMissingAnyRole,\\n )\\n\\n if isinstance(e, bot_missing_errors):\\n ctx.bot.stats.incr(\\\"errors.bot_permission_error\\\")\\n await ctx.send(\\n \\\"Sorry, it looks like I don't have the permissions or roles I need to do that.\\\"\\n )\\n elif isinstance(e, errors.NoPrivateMessage):\\n await ctx.send(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1be4799a29146d58bd769492904265d2\",\n \"score\": \"0.58189017\",\n \"text\": \"def check_failures(self) -> None:\\n # If the dtypes do not exist return\\n if self.dtypes is None:\\n return\\n # If the upload_failed path does not exist return\\n if not os.path.isdir(f\\\"STORE/upload_failed/{self.name}/{self.stream_type}/\\\"):\\n return\\n # Get the list of failed uploads\\n failed_list = os.listdir(f\\\"STORE/upload_failed/{self.name}/{self.stream_type}/\\\")\\n # If there are no failed uploads return\\n if len(failed_list) == 0:\\n return\\n logger = get_logger()\\n logger.info((f\\\"uploading {len(failed_list)} failed upload files found in \\\"\\n \\\"STORE/upload_failed/{self.name}/{self.stream_type}/\\\")\\n )\\n # Compose all the failed uploads into a single DataFrame\\n df = self.static_compose_df_from_dir(path=f\\\"STORE/upload_failed/{self.name}/{self.stream_type}/\\\",\\n fmt=\\\"parquet\\\")\\n # If the DataFrame is None return\\n if df is None:\\n return\\n # Attempt to case the DataFrame to its expected types\\n try:\\n df = df.astype(self.dtypes)\\n except Exception as e:\\n logger.exception(e)\\n return\\n if self.is_store_instance:\\n self.stream(df)\\n else:\\n self.q.put(df)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a048bc7fdbec4a4db61d9a07c4e9b9bc\",\n \"score\": \"0.5809967\",\n \"text\": \"def verify(self):\\r\\n errors = []\\r\\n for task in self._tasks:\\r\\n try:\\r\\n task.verify()\\r\\n except AssertionError, e:\\r\\n error = str(e)\\r\\n if not error:\\r\\n raise RuntimeError(\\\"Empty error message from %r\\\" % task)\\r\\n errors.append(error)\\r\\n if errors:\\r\\n message = [str(self.path)]\\r\\n for error in errors:\\r\\n lines = error.splitlines()\\r\\n message.append(\\\"- \\\" + lines.pop(0))\\r\\n message.extend([\\\" \\\" + line for line in lines])\\r\\n raise AssertionError(os.linesep.join(message))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a048bc7fdbec4a4db61d9a07c4e9b9bc\",\n \"score\": \"0.5809967\",\n \"text\": \"def verify(self):\\r\\n errors = []\\r\\n for task in self._tasks:\\r\\n try:\\r\\n task.verify()\\r\\n except AssertionError, e:\\r\\n error = str(e)\\r\\n if not error:\\r\\n raise RuntimeError(\\\"Empty error message from %r\\\" % task)\\r\\n errors.append(error)\\r\\n if errors:\\r\\n message = [str(self.path)]\\r\\n for error in errors:\\r\\n lines = error.splitlines()\\r\\n message.append(\\\"- \\\" + lines.pop(0))\\r\\n message.extend([\\\" \\\" + line for line in lines])\\r\\n raise AssertionError(os.linesep.join(message))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a048bc7fdbec4a4db61d9a07c4e9b9bc\",\n \"score\": \"0.5809967\",\n \"text\": \"def verify(self):\\r\\n errors = []\\r\\n for task in self._tasks:\\r\\n try:\\r\\n task.verify()\\r\\n except AssertionError, e:\\r\\n error = str(e)\\r\\n if not error:\\r\\n raise RuntimeError(\\\"Empty error message from %r\\\" % task)\\r\\n errors.append(error)\\r\\n if errors:\\r\\n message = [str(self.path)]\\r\\n for error in errors:\\r\\n lines = error.splitlines()\\r\\n message.append(\\\"- \\\" + lines.pop(0))\\r\\n message.extend([\\\" \\\" + line for line in lines])\\r\\n raise AssertionError(os.linesep.join(message))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e697a4c69b7cbc701fb132b2b2e95e5b\",\n \"score\": \"0.5808749\",\n \"text\": \"def set_test_failure_tryjob_result(self):\\n self._set_failure_type('TEST_FAILURE')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be25f632ad9d1db01fefa79cc68bfbbc\",\n \"score\": \"0.5806069\",\n \"text\": \"def was_successful(self):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fbe07e0c89dea38ba860d2b178932a5\",\n \"score\": \"0.58057576\",\n \"text\": \"async def test_failed_tests(self):\\n self.set_source_parameter(\\\"test_result\\\", [\\\"failed\\\"])\\n response = await self.collect(get_request_text=self.JUNIT_XML)\\n self.assert_measurement(\\n response,\\n value=\\\"1\\\",\\n total=\\\"5\\\",\\n entities=[dict(key=\\\"tc3\\\", name=\\\"tc3\\\", class_name=\\\"cn\\\", test_result=\\\"failed\\\")],\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42ddfa5af8d99d7c862caa7a0b29ba54\",\n \"score\": \"0.5797261\",\n \"text\": \"def assertInstanceFailed(self, instance_name, exc_type):\\n # type: (Text, type) -> None\\n self.assertInstanceStatus(instance_name, TaskInstance.STATUS_FAILED)\\n\\n self.assertEqual(\\n self.manager.storage[instance_name]\\n .metadata[TaskInstance.META_EXCEPTION_TYPE],\\n\\n '{module}.{name}'.format(\\n module = exc_type.__module__,\\n name = exc_type.__name__,\\n ),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2a09ddc2fd7c85dc1dee510dbf74327\",\n \"score\": \"0.5795169\",\n \"text\": \"def _failure(self):\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf815204a163b4a1e63de0cf8259616d\",\n \"score\": \"0.5795137\",\n \"text\": \"def failed(self, error_message):\\n self.start_now = False\\n self.status = self.STATUS.ERROR\\n self.error_message = error_message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2aef2ed837325698e5b93c7f6358c487\",\n \"score\": \"0.5793467\",\n \"text\": \"def test_failed_jobs(self):\\n assert self.failed in self.all_statuses, 'A core assumption in the Batch Executor has changed. ' \\\\\\n 'What happened to the list of statuses or the running state?'\\n running_job = BatchJob('CCC', self.failed)\\n self.assertEqual(running_job.get_job_state(), State.FAILED)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acd7cdb5bc0a386169f64121a78fef07\",\n \"score\": \"0.5791061\",\n \"text\": \"def timed_out(self):\\n elapsed = time.time() - self.start_time\\n if elapsed >= self.task_manager_timeout:\\n logger.warning(f\\\"{self.prefix} manager has run for {elapsed} which is greater than TASK_MANAGER_TIMEOUT of {settings.TASK_MANAGER_TIMEOUT}.\\\")\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"421e03bf72b382467e9d90870db04523\",\n \"score\": \"0.5784614\",\n \"text\": \"def failure_reason(self):\\n return self._failure_reason\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f193af9e19e9d3f2d26ab4a35d4940a\",\n \"score\": \"0.5783912\",\n \"text\": \"def check_ok (result):\\n\\treturn not result is None and (not \\\"due to errors\\\" in result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56934ccec973b433634b6d6b9bfae2fa\",\n \"score\": \"0.57652456\",\n \"text\": \"def pass_fail(self, success):\\n return \\\"PASS\\\" if success else \\\"FAIL\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fca8beb6d41b456d3322880cf35ee08\",\n \"score\": \"0.5754738\",\n \"text\": \"def handle_fail():\\n fail = True\\n while not task_queue.empty():\\n try:\\n task_queue.get(False)\\n except Empty:\\n continue\\n task_queue.task_done()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99b186a000b9ed8c925842b54a61d362\",\n \"score\": \"0.5750136\",\n \"text\": \"def on_batch_job_failure(cls):\\n # TODO(sll): Alert the site admin via email.\\n logging.error('Job %s failed.' % cls.__name__)\\n job_status = cls._register_end_of_batch_job_and_return_status()\\n if job_status == job_models.CONTINUOUS_COMPUTATION_STATUS_CODE_RUNNING:\\n cls._kickoff_batch_job_after_previous_one_ends()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994181,"cells":{"query_id":{"kind":"string","value":"1f741524474d86566b65cfc9c0d6fb19"},"query":{"kind":"string","value":"Gets the mapping of pickleable python attribute name and values."},"positive_passages":{"kind":"list like","value":[{"docid":"00f6279d2da02e8235168a49f842043f","score":"0.5921677","text":"def _get_python_attributes(self):\r\n python_attrs = dict()\r\n for key, value in vars(self).iteritems():\r\n if key.startswith('_'):\r\n continue\r\n if isinstance(value, hou.Node):\r\n continue\r\n python_attrs[key] = value\r\n return python_attrs","title":""}],"string":"[\n {\n \"docid\": \"00f6279d2da02e8235168a49f842043f\",\n \"score\": \"0.5921677\",\n \"text\": \"def _get_python_attributes(self):\\r\\n python_attrs = dict()\\r\\n for key, value in vars(self).iteritems():\\r\\n if key.startswith('_'):\\r\\n continue\\r\\n if isinstance(value, hou.Node):\\r\\n continue\\r\\n python_attrs[key] = value\\r\\n return python_attrs\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"26012275e08f96726959da3317a3d693","score":"0.67514306","text":"def attributes(self) -> t.Dict[str, str]:","title":""},{"docid":"9449c140f2f9e78549c886a816759fab","score":"0.6637769","text":"def get_attributes(self) -> Mapping[str, typing.Any]:\n return MappingProxyType(self._get_attributes())","title":""},{"docid":"6c806a0459284571b04f0623a17eab0b","score":"0.658317","text":"def attr_info(self) -> typing.Mapping[str, AttributeInfo]:\n return MappingProxyType(\n {\n attr_name: AttributeInfo(self.bind[attr_name].attrs)\n for attr_name in self.list_attributes()\n }\n )","title":""},{"docid":"51c007b7108c51d14690d61111aa92af","score":"0.6560819","text":"def ldap_attribute_map(cls) -> Dict[str, str]:\n return {k: v.ldap_key for (k, v) in vars(cls).items()\n if isinstance(v, Attribute)}","title":""},{"docid":"91f34724c159ef254b9c63175b743c12","score":"0.6513589","text":"def get_stored_dict(self):\n return {x: getattr(self, x)() for x in self._stored_attrs}","title":""},{"docid":"5f123556f6ed6c30dcf09f5bc3ecedbb","score":"0.6438715","text":"def _getAttrMap(self):\r\n if not getattr(self, 'attrMap'):\r\n self.attrMap = {}\r\n for (key, value) in self.attrs:\r\n self.attrMap[key] = value \r\n return self.attrMap","title":""},{"docid":"d807f7f2d164507698d46ae77018957f","score":"0.6434606","text":"def attrs(self) -> typing.Mapping[str, DatasetAttribute]:\n return self._mapper.view()","title":""},{"docid":"d9a7890e12a11d59d799df0a8358f28c","score":"0.6351924","text":"def _get_attributes(self) -> typing.Dict[str, typing.Any]:\n raise NotImplementedError()","title":""},{"docid":"50850f487491251292d53f748f62098f","score":"0.6341671","text":"def get_attributes(cls) -> Dict[str, Attribute[Any]]:\n attributes = OrderedDict()\n for name, value in vars(cls).items(): # noqa: WPS421\n if isinstance(value, Attribute):\n attributes[name] = value\n return attributes","title":""},{"docid":"240330e66cae1561a4870b80a79cfa19","score":"0.6235785","text":"def as_dict(self):\n return {attr : getattr(self, attr) for attr in self.attrs}","title":""},{"docid":"384a9e218a9e61959ef8d6a04243a626","score":"0.621797","text":"def get_attribute_data(self):\n ret = {}\n\n for attribute in Attribute.objects.all().prefetch_related(\"value_choices\"):\n deserialized_value = None\n\n if attribute.value_type == Attribute.TYPE_GEOMETRY:\n geometry = ProjectAttributeMultipolygonGeometry.objects.filter(\n attribute=attribute, project=self\n ).first()\n if not geometry:\n continue\n deserialized_value = geometry.geometry\n elif attribute.value_type in [Attribute.TYPE_IMAGE, Attribute.TYPE_FILE]:\n try:\n deserialized_value = ProjectAttributeFile.objects.get(\n attribute=attribute, project=self\n ).file\n except ProjectAttributeFile.DoesNotExist:\n deserialized_value = None\n elif attribute.identifier in self.attribute_data:\n deserialized_value = attribute.deserialize_value(\n self.attribute_data[attribute.identifier]\n )\n\n ret[attribute.identifier] = deserialized_value\n return ret","title":""},{"docid":"0782d6881d4de0ed8829253ed48c3b33","score":"0.6196533","text":"def as_dict(self):\n d = {}\n for a in self.InstAttr | self.MetaAttr:\n d[a] = getattr(self, a)\n return d","title":""},{"docid":"e2621f47a85e4dcfc50d02e03270dde9","score":"0.6196316","text":"def get_attrs(self) -> Mapping:\n return self.dict(include=set(self._attributes))","title":""},{"docid":"44f6e08da925aecf2752ed9833816054","score":"0.6184532","text":"def attributes(self):\n if self._attributes is None:\n self._attributes = {}\n rows = db.model_query(db.Attribute).filter_by(\n uuid=self.uuid)\n for row in rows:\n self._attributes.setdefault(row.name, []).append(row.value)\n return self._attributes","title":""},{"docid":"58f50a06749369b5711039460e0d6635","score":"0.6173073","text":"def attr(self) -> google.protobuf.internal.containers.MessageMap[builtins.str, tensorflow.core.framework.attr_value_pb2.AttrValue]:","title":""},{"docid":"fc754400d72b4344b0313d0f3e59f64a","score":"0.61281246","text":"def get_attributes(self):\n\n return {\n 'compound': self.compound,\n 'properties': self.properties,\n 'T_range': self.T_range,\n 'n_points': self.n_points,\n 'steps': self.steps,\n 'swap_freq': self.swap_freq,\n 'biasing_factor': self.biasing_factor}","title":""},{"docid":"b8e6a56ff173911dc3cb05977a57c309","score":"0.61229056","text":"def to_map(self, key_attribute: str):\n map_ = {}\n for item in self.list:\n key = getattr(item, key_attribute, None)\n if key:\n map_[key] = item\n\n return map_","title":""},{"docid":"93987efe254a21c65b206ae12652e158","score":"0.60838","text":"def stored_attributes(self):\n result = []\n result.append('key')\n result.extend(self.get_attribute_names_defined_in_store())\n return result","title":""},{"docid":"14a0e8be1762011f1b6ed47fb86c0815","score":"0.60629773","text":"def attributes_dict(self):\n h = {}\n for k in self.attributes.keys():\n h[k] = getattr(self, k)\n for k in self.lists.keys():\n h[k] = getattr(self, k)\n for k in self.references.keys():\n h[k] = getattr(self, k)\n return h","title":""},{"docid":"5e7f238902bddae9e0a6c2e102987f16","score":"0.60626084","text":"def identifiers(self) -> typing.Mapping[str, str]: # pylint: disable=function-redefined\n return {\n attr_name: getattr(self, attr_name)\n for attr_name in self.info.get(\"identifiers\", self.info.get(\"params\", []))\n if attr_name in self.bind\n }","title":""},{"docid":"6e430e6d15d5c8496767b28ee1628aec","score":"0.6055799","text":"def json(self):\n\t\treturn {p: self.__getattribute__(p) for p in self.__slots__}","title":""},{"docid":"4d873a5043d6c4950f24b7bcf22ec799","score":"0.6011129","text":"def get_attributes(self):\n attributes = OrderedDict()\n attributes['init'] = self._init\n attributes['lb'] = self._lb\n attributes['ub'] = self._ub\n return attributes","title":""},{"docid":"2d3aaf7228394033604e05de8516fba2","score":"0.5997634","text":"def attributes(cls):\n return dict(cls._attributes)","title":""},{"docid":"e3c04fd050657d7178ee9b8a5175d903","score":"0.5995523","text":"def as_dict(self) -> t.Dict[str, t.Any]:\n\n return {k: getattr(self, k, None) for k in self.fields_map.keys()}","title":""},{"docid":"591760bf7f597bc9d95481341038581f","score":"0.59728444","text":"def get_all_attributes(self):\r\n return self.__key + self.__value","title":""},{"docid":"ec2d2bc25bb81d039de2bdcd699964a8","score":"0.5952853","text":"def __dict__(self):\n return dict([(field, getattr(self, \"%s\" %field))\n for field in self.__class__.DICT_FIELDS])","title":""},{"docid":"2e67f823017cc019158ce7329e2e3549","score":"0.59505326","text":"def extract_mapping(self):\n return {\n self.type_name: self.fields\n }","title":""},{"docid":"d7687de833741aede5959dc91989de6a","score":"0.59490645","text":"def items(self) -> Dict:\n\n return {key: getattr(self.attribute, key)\n for key in dir(self.attribute)\n if not key.startswith(\"_\")}","title":""},{"docid":"5e43861f026c6e427469ec8a1cb21132","score":"0.58994234","text":"def _attrs(self):\n return self._dets.keys()","title":""},{"docid":"9f5845575e7c910eb9966d7fc1d4594c","score":"0.5897431","text":"def state_attributes(self):\r\n return {self.id: self.value}","title":""},{"docid":"030d2b2ab611343f46ef360862047723","score":"0.5890485","text":"def map_attribute(self, attribute, value):\n return {}","title":""},{"docid":"030d2b2ab611343f46ef360862047723","score":"0.5890485","text":"def map_attribute(self, attribute, value):\n return {}","title":""},{"docid":"2ddc0886368a97497ca1d68b13aced34","score":"0.5881178","text":"def values(self):\r\n return [getattr(self, k) for k in self.keys()]","title":""},{"docid":"47828414495de3388024e5b9e62e2749","score":"0.58804315","text":"def get_attributes(self):\n for _, an_object in self.objects_data.items():\n return list(an_object[\"attributes\"].keys())","title":""},{"docid":"298839d297eec87c3650fd888371e046","score":"0.5874971","text":"def toDict(self: object) -> dict:\n\n return {key: self.__getattribute__(key) for key in self.__slots__}","title":""},{"docid":"8ae795368b438b54ac8a029ffb2eda8b","score":"0.5861259","text":"def attr_dict(self):\n \n base_keys = set(Info().__dict__.keys())\n self_keys = set(self.__dict__.keys())\n res = {}\n for key in self_keys.difference(base_keys):\n val = self.__dict__[key]\n try:\n res[key] = val.attr_dict()\n except AttributeError:\n res[key] = val\n \n return(res)","title":""},{"docid":"93e3c04639db2c8f05e599a26dd74f29","score":"0.5848899","text":"def _getAttributeNames(self, *args, **kwargs):\n return map(_namify, self.keys())","title":""},{"docid":"9fe19048c6efa3c780c5bdf2d5756718","score":"0.5836906","text":"def get_attributes (self):\n return (self.attr)","title":""},{"docid":"959be0b60913afda19e98ad6e61ef0e7","score":"0.583006","text":"def list_attributes(self) -> List[str]:\n return list(self.attrs.keys())","title":""},{"docid":"75562bbfa0bda5b3d65674b043095101","score":"0.5819719","text":"def get_attribute_names_defined_in_store(self):\n pass","title":""},{"docid":"398bbeeb0936e837a978a4cbbb050f29","score":"0.58195925","text":"def list_attrs(self):\n return self._attributes.keys()","title":""},{"docid":"2f9c60ec85dfd737ea7bd15c88bdc963","score":"0.5804851","text":"def instance_attributes(self):\n return self.infer().instance_attributes()","title":""},{"docid":"15b23b9a59f1aa39d8b5cdf3351eb748","score":"0.5787162","text":"def asdict(self):\n return attr.asdict(self)","title":""},{"docid":"b9ab4be0fc865e071d657d9b0590d25c","score":"0.57617927","text":"def _values(self):\n return [self._node.getAttribute(k) for k in self._node.attributes.keys()]","title":""},{"docid":"9fe6c903dfa6da3c050e5c0ca91e954f","score":"0.576031","text":"def maps(self) -> Tuple[Dict[str, str], Dict[str, str], Dict[str, float]]:\n sources = {}\n targets = {}\n weights = {}\n for e in self.edges.data():\n label = e[2][self.attr_label]\n sources[label] = e[0]\n targets[label] = e[1]\n weights[label] = e[2][self.attr]\n return sources, targets, weights","title":""},{"docid":"271b84d1b89b665b117b3c1aeaa9d2f4","score":"0.5759584","text":"def attrs(self):\n return self.__dict__.keys()","title":""},{"docid":"2afcb181b0b2f4467a484a5e9318a8b6","score":"0.57474965","text":"def object_as_dict(obj):\n return {c.key: getattr(obj, c.key)\n for c in inspect(obj).mapper.column_attrs}","title":""},{"docid":"de3600a662a8244107357fd0e4a7c10e","score":"0.57365453","text":"def toDict(self):\n dict = {}\n for attr, value in self.__dict__.items():\n corrected_name = attr[1:] # remove first underscore\n dict[corrected_name] = value.__str__()\n return dict","title":""},{"docid":"deb684948980db8e30bb8a211de71c62","score":"0.5725433","text":"def to_dictionary(self):\n my_attri = [\"id\", \"width\", \"height\", \"x\", \"y\"]\n val_attr = 0\n dic_ret = {}\n\n for i in range(len(my_attri)):\n val_attri = getattr(self, my_attri[i])\n dic_ret[my_attri[i]] = val_attri\n\n return dic_ret","title":""},{"docid":"0ef553a53de21530358f21fe6ad3f8c6","score":"0.572354","text":"def to_dict(self):\n return dict((str(f.field.name), unicode(f.value)) \\\n for f in self.attributes.values() \\\n if f.value is not None)","title":""},{"docid":"69dd37a171feb4284d1814e5c530b7ea","score":"0.5721035","text":"def attributes(cls):\n return tuple(cls.__dict__.get(\"__fields__\", {}).keys())","title":""},{"docid":"9b19ebf24ac628e7e26724394ed4bdf7","score":"0.57191956","text":"def _get_field_data(self):\n return {field_name: getattr(self, field_name) for field_name in self._meta.fields.keys()}","title":""},{"docid":"6e69870c5e38483a59f2ec49041f27bf","score":"0.56885177","text":"def get_var_mapping_dict(self):\r\n # type: () -> dict\r\n return self._map_variable_dict","title":""},{"docid":"bf15a63f9551f3d51c2115c5f13c2fce","score":"0.5674434","text":"def as_dict(self) -> dict:\n\n return {k: getattr(self, k) for k in list(vars(self).keys())}","title":""},{"docid":"1b5fe9fc43051c434a353077a6a49c5e","score":"0.5659958","text":"def get_dict(self) -> dict:\n d = {}\n for attribute, value in self.__dict__.items():\n d[attribute] = value\n\n return d","title":""},{"docid":"987a817a109f495a131799d19da19932","score":"0.5657744","text":"def get_attributes(self):\n attribute_map = {\n 'urn:oid:0.9.2342.19200300.100.1.1': 'uwnetid',\n 'urn:oid:1.3.6.1.4.1.5923.1.1.1.1': 'affiliations',\n 'urn:oid:1.3.6.1.4.1.5923.1.1.1.6': 'eppn',\n 'urn:oid:1.3.6.1.4.1.5923.1.1.1.9': 'scopedAffiliations'\n }\n return {attribute_map.get(key, key): value\n for key, value in super().get_attributes().items()}","title":""},{"docid":"5722c78f1ead7f56a65af45c2b06be68","score":"0.56549287","text":"def attrs(self):\n return self._obj.coords[GEO_MAP_COORD].attrs","title":""},{"docid":"7cec7998715a91132817b5febdc96b30","score":"0.5651105","text":"def names(self) -> List[str]:\n\n return list(self.attribute.keys())","title":""},{"docid":"3d0c81c5c2e8cfef739ee8c3cc1be3fe","score":"0.5648379","text":"def to_map(self):\n obj = {}\n self._add_defined_attribs(obj, self._yaml_map().keys())\n return obj","title":""},{"docid":"344060bddb57a44a7fd8f7c75452ef1e","score":"0.56431675","text":"def __getstate__(self):\n return {k: getattr(self, k) for k in self.__slots__}","title":""},{"docid":"bbe61037919116b6e4948e869f9cc449","score":"0.5637252","text":"def test_can_pickle_attributedictionary(self):\n d = AttributeDictionary(hello=\"world\")\n self.assertEqual(d['hello'],\"world\")\n pickled = pickle.dumps(d)\n unpickled = pickle.loads(pickled)\n self.assertTrue(isinstance(unpickled,AttributeDictionary))\n self.assertEqual(unpickled['hello'],\"world\")\n self.assertEqual(unpickled.hello,\"world\")","title":""},{"docid":"7ce099564c4fc3741529be96145f64b9","score":"0.56315374","text":"def export(self) -> Dict[str, Any]:\n values: Dict[str, Any] = {}\n for attribute, value in self._values.items():\n if isinstance(value, Exportable):\n exported = value.export()\n if exported is not None:\n values[attribute.name] = exported\n elif value is not None:\n values[attribute.name] = value\n values.update(self._extra)\n return values","title":""},{"docid":"e02f0b7d7d6a52e5fc177e67a52fc39a","score":"0.5627427","text":"def get_attribute_values(obj):\n return [value for key, value in obj.__dict__.items() if not key.startswith(\"__\")]","title":""},{"docid":"5ef69c9015a3c5ac1bccb6f8bc51cc23","score":"0.56218684","text":"def to_dict(self) -> dict:\n return {field.name: getattr(self, field.name) for field in fields(self)}","title":""},{"docid":"d2cb52f25592e8cd4359329262b6cd8b","score":"0.5614424","text":"def column_name_mapping_dict(self):\n return self.__column_name_mapping_dict.output","title":""},{"docid":"5c3cc5cedadc6a455c1a5c56b1256b4a","score":"0.5609309","text":"def trainable_variables(self):\n # Override this because reflection seems not to work.\n return dict((k, v)\n for k, v in self.keyed_variables.items()\n if self.task_attribute_name in k)","title":""},{"docid":"4a5a99650bd5c76ae50c62e0572146de","score":"0.55903196","text":"def __serialize__(self):\n return {k: v for k, v in self.items()}","title":""},{"docid":"bbe18e7d5659d0151cba64ecd1ee1e78","score":"0.55876184","text":"def get_custom_attributes(self):\n return self[\"custom_attributes\"]","title":""},{"docid":"3a253684180363c3e9e1735eec27a157","score":"0.55866903","text":"def dataclass_quick_asdict(obj) -> Dict[str, Any]:\n d = {f.name: getattr(obj, f.name) for f in dataclasses.fields(obj)}\n return d","title":""},{"docid":"301259dfdfdde15a152609404a29f6bc","score":"0.5578841","text":"def _items(self):\n return [(k, self._node.getAttribute(k)) for k in self._node.attributes.keys()]","title":""},{"docid":"b38f12b4c7e8b0dbf2418ff3bf162e25","score":"0.55710065","text":"def __getstate__(self):\n attrs = {}\n if hasattr(self, \"__dict__\"):\n attrs.update(self.__dict__)\n slots = []\n c = self.__class__\n while c is not None:\n if hasattr(c, \"__slots__\"):\n slots.extend(c.__slots__)\n c = c.__base__\n for name in slots:\n if hasattr(self, name):\n attrs[name] = getattr(self, name)\n return attrs","title":""},{"docid":"5467039058f1d3f85303e1e421f5a9fe","score":"0.5557673","text":"def as_dict(self) -> dict[str, Any]:\n attrs = vars(self)\n return {key: attrs[key] for key in attrs if not key.startswith(\"_\")}","title":""},{"docid":"1ed1d7153b8ca75c13bad8c387279a5b","score":"0.5557543","text":"def to_json(self):\n return dict((attr, getattr(self, attr))\n for attr in self.__attributes__)","title":""},{"docid":"1ed1d7153b8ca75c13bad8c387279a5b","score":"0.5557543","text":"def to_json(self):\n return dict((attr, getattr(self, attr))\n for attr in self.__attributes__)","title":""},{"docid":"6c4468d356ce3eb8dfe201c3e4172cb8","score":"0.5555928","text":"def attributes(self, alias=None):\n cls = _inheritor(_Attribute, self.ID)\n return OrderedDict(\n (name, cls(name, source=(alias or self._name)))\n for name in self._schema.columns(self._name)['name']\n )","title":""},{"docid":"fc1e2dc2a004c0684443c61c4e145f76","score":"0.5554138","text":"def attr_names(self):\n l = self.attrs.keys()\n l.sort()\n return l","title":""},{"docid":"fc4f8ef97a55a55ba41c35249067996c","score":"0.55302197","text":"def getAttributes(self):\n\t\treturn self._attributes","title":""},{"docid":"6d7966d57dd19ac870eecdfc77aa6ec6","score":"0.5521307","text":"def _toDict(self):\n return {\n 'name': self._name,\n 'colors': self.getColormapLUT(),\n 'vmin': self._vmin,\n 'vmax': self._vmax,\n 'autoscale': self.isAutoscale(),\n 'normalization': self.getNormalization(),\n 'autoscaleMode': self.getAutoscaleMode(),\n }","title":""},{"docid":"1bddb2dc34b25fbd8d554df54702165f","score":"0.55179185","text":"def state_attributes(self):\r\n data = {}\r\n data[self.id] = self.value\r\n data[self.name + \"_modes\"] = self.values\r\n return data","title":""},{"docid":"ac0f804e8d418b1e279c333a79c0afbc","score":"0.5505109","text":"def attrs(self):\n assert isinstance(self._attrs, list)\n return dict(self._attrs)","title":""},{"docid":"8d17b4cc12250e5d40fb7622b18f2b26","score":"0.5500228","text":"def keys_python(self) -> Iterable[str]:\n yield from dejsonify_attribute_names(self.keys())","title":""},{"docid":"8d17b4cc12250e5d40fb7622b18f2b26","score":"0.5500228","text":"def keys_python(self) -> Iterable[str]:\n yield from dejsonify_attribute_names(self.keys())","title":""},{"docid":"a1714d49568ad2509605777f2d840c17","score":"0.5483594","text":"def get_mapping(self):","title":""},{"docid":"2f3ef00fe73d775c04f2097a37bd030f","score":"0.5481292","text":"def to_dict(self):\n return {'name': self.name, 'value': self.value}","title":""},{"docid":"0951a32461a4ec6f999cf54c848e1917","score":"0.5478671","text":"def signable_dict(self):\n\n return attr.asdict(self)","title":""},{"docid":"99e8834a52ddb61940511f8e103d2bd9","score":"0.54667675","text":"def get(self):\n\n d = {}\n for i in self.__list:\n d[i[0]] = getattr(self, i[0])\n return d","title":""},{"docid":"2af288108c5712db6c82ed4c05ed859b","score":"0.54587424","text":"def get_class_attributes(self):\n return {\n \"base\": self.base,\n \"internal_type\": self.internal_type,\n \"precision_fractional\": self.precision_fractional,\n }","title":""},{"docid":"7478e09b4e4c96346c16bea888693b7c","score":"0.5455201","text":"def __getstate__(self):\n state = {}\n for entry in self.__slots__:\n state[entry] = getattr(self, entry)\n\n return state","title":""},{"docid":"8cb735772f7fdb94941f55277d366878","score":"0.54495835","text":"def as_dict(self):\n \n return {c.name: getattr(self, c.name) for c in self.__table__.columns}","title":""},{"docid":"881a27352016a5006e9f74b08f318ea3","score":"0.5440619","text":"def __getstate__(self):\n state={}\n for attrname in self._expected_attrs:\n state[attrname]=getattr(self,attrname)\n return state","title":""},{"docid":"a460d55adbdd2d9f78bec303b04bd69e","score":"0.54390496","text":"def to_dict(self) -> dict:\n mapper = self.__mapper__\n attribute_keys = mapper.columns.keys() + mapper.relationships.keys()\n\n return {k: getattr(self, k, None) for k in attribute_keys}","title":""},{"docid":"562184d8d770035fe5317be12d692f6f","score":"0.54379374","text":"def to_dict(self):\n data = {\n \"type\": self.__class__.__name__\n }\n\n for key in self.__class__.DATA_KEYS:\n data[key] = getattr(self, key)\n\n return data","title":""},{"docid":"e6fed61bb57d9d74027ac4eeeb32c8dd","score":"0.5436603","text":"def get_mbean_attributes(self):\n _method_name = 'get_mbean_attributes'\n _logger.entering(class_name=self.__class__.__name__, method_name=_method_name)\n map_to_list = list()\n attributes = self.__get_mbean_info_map()\n if len(attributes) > 0:\n map_to_list = [attribute for attribute in attributes.iterkeys()]\n map_to_list.sort()\n _logger.exiting(class_name=self.__class__.__name__, method_name=_method_name, result=len(map_to_list))\n return map_to_list","title":""},{"docid":"b8e6ee57d60a5d384d854a9efa4c3737","score":"0.54333323","text":"def get_attributes(self):\n return self.attribs","title":""},{"docid":"f5c8b3ae8d1dba96db6d379d02303085","score":"0.54287755","text":"def as_dict(self):\n\n return {c.name: getattr(self, c.name) for c in self.__table__.columns}","title":""},{"docid":"68b5cd9a262481536074ecc991200e11","score":"0.5409635","text":"def AttrDict(slots = tuple()):\n return _get_attr_dict_factory(slots)()","title":""},{"docid":"052f974cb84d7a51d6880cc433f9db05","score":"0.54028714","text":"def getAttrsAsDict(self, attrs):\n\t\treturn dict((k.split(\":\")[-1], v) for k, v in attrs.items())","title":""},{"docid":"05d26097faa72a8a6f61f8f847431166","score":"0.5399544","text":"def _get_type_attribute_dict(self, type_name: str) -> Dict[str, Dict]:\n return self._get_type_info(type_name)[constants.ATTR_INFO_KEY]","title":""},{"docid":"34138c00d46b665c0405daf20ea86678","score":"0.53984386","text":"def get_meta_attribute_info(self):\n return self.__meta_attribute_info","title":""},{"docid":"f502343df565934f8a988965cd93c912","score":"0.53967124","text":"def get_label_map(self):\n trainable_classes = self.read_trainable_classes()\n label_map, reverse_label_map = {}, {}\n index = 0\n\n for key, cls_name in trainable_classes.items():\n label_map[key] = index\n reverse_label_map[index] = key\n index += 1\n\n return label_map, reverse_label_map","title":""}],"string":"[\n {\n \"docid\": \"26012275e08f96726959da3317a3d693\",\n \"score\": \"0.67514306\",\n \"text\": \"def attributes(self) -> t.Dict[str, str]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9449c140f2f9e78549c886a816759fab\",\n \"score\": \"0.6637769\",\n \"text\": \"def get_attributes(self) -> Mapping[str, typing.Any]:\\n return MappingProxyType(self._get_attributes())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c806a0459284571b04f0623a17eab0b\",\n \"score\": \"0.658317\",\n \"text\": \"def attr_info(self) -> typing.Mapping[str, AttributeInfo]:\\n return MappingProxyType(\\n {\\n attr_name: AttributeInfo(self.bind[attr_name].attrs)\\n for attr_name in self.list_attributes()\\n }\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51c007b7108c51d14690d61111aa92af\",\n \"score\": \"0.6560819\",\n \"text\": \"def ldap_attribute_map(cls) -> Dict[str, str]:\\n return {k: v.ldap_key for (k, v) in vars(cls).items()\\n if isinstance(v, Attribute)}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91f34724c159ef254b9c63175b743c12\",\n \"score\": \"0.6513589\",\n \"text\": \"def get_stored_dict(self):\\n return {x: getattr(self, x)() for x in self._stored_attrs}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f123556f6ed6c30dcf09f5bc3ecedbb\",\n \"score\": \"0.6438715\",\n \"text\": \"def _getAttrMap(self):\\r\\n if not getattr(self, 'attrMap'):\\r\\n self.attrMap = {}\\r\\n for (key, value) in self.attrs:\\r\\n self.attrMap[key] = value \\r\\n return self.attrMap\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d807f7f2d164507698d46ae77018957f\",\n \"score\": \"0.6434606\",\n \"text\": \"def attrs(self) -> typing.Mapping[str, DatasetAttribute]:\\n return self._mapper.view()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9a7890e12a11d59d799df0a8358f28c\",\n \"score\": \"0.6351924\",\n \"text\": \"def _get_attributes(self) -> typing.Dict[str, typing.Any]:\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50850f487491251292d53f748f62098f\",\n \"score\": \"0.6341671\",\n \"text\": \"def get_attributes(cls) -> Dict[str, Attribute[Any]]:\\n attributes = OrderedDict()\\n for name, value in vars(cls).items(): # noqa: WPS421\\n if isinstance(value, Attribute):\\n attributes[name] = value\\n return attributes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"240330e66cae1561a4870b80a79cfa19\",\n \"score\": \"0.6235785\",\n \"text\": \"def as_dict(self):\\n return {attr : getattr(self, attr) for attr in self.attrs}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"384a9e218a9e61959ef8d6a04243a626\",\n \"score\": \"0.621797\",\n \"text\": \"def get_attribute_data(self):\\n ret = {}\\n\\n for attribute in Attribute.objects.all().prefetch_related(\\\"value_choices\\\"):\\n deserialized_value = None\\n\\n if attribute.value_type == Attribute.TYPE_GEOMETRY:\\n geometry = ProjectAttributeMultipolygonGeometry.objects.filter(\\n attribute=attribute, project=self\\n ).first()\\n if not geometry:\\n continue\\n deserialized_value = geometry.geometry\\n elif attribute.value_type in [Attribute.TYPE_IMAGE, Attribute.TYPE_FILE]:\\n try:\\n deserialized_value = ProjectAttributeFile.objects.get(\\n attribute=attribute, project=self\\n ).file\\n except ProjectAttributeFile.DoesNotExist:\\n deserialized_value = None\\n elif attribute.identifier in self.attribute_data:\\n deserialized_value = attribute.deserialize_value(\\n self.attribute_data[attribute.identifier]\\n )\\n\\n ret[attribute.identifier] = deserialized_value\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0782d6881d4de0ed8829253ed48c3b33\",\n \"score\": \"0.6196533\",\n \"text\": \"def as_dict(self):\\n d = {}\\n for a in self.InstAttr | self.MetaAttr:\\n d[a] = getattr(self, a)\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2621f47a85e4dcfc50d02e03270dde9\",\n \"score\": \"0.6196316\",\n \"text\": \"def get_attrs(self) -> Mapping:\\n return self.dict(include=set(self._attributes))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44f6e08da925aecf2752ed9833816054\",\n \"score\": \"0.6184532\",\n \"text\": \"def attributes(self):\\n if self._attributes is None:\\n self._attributes = {}\\n rows = db.model_query(db.Attribute).filter_by(\\n uuid=self.uuid)\\n for row in rows:\\n self._attributes.setdefault(row.name, []).append(row.value)\\n return self._attributes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58f50a06749369b5711039460e0d6635\",\n \"score\": \"0.6173073\",\n \"text\": \"def attr(self) -> google.protobuf.internal.containers.MessageMap[builtins.str, tensorflow.core.framework.attr_value_pb2.AttrValue]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc754400d72b4344b0313d0f3e59f64a\",\n \"score\": \"0.61281246\",\n \"text\": \"def get_attributes(self):\\n\\n return {\\n 'compound': self.compound,\\n 'properties': self.properties,\\n 'T_range': self.T_range,\\n 'n_points': self.n_points,\\n 'steps': self.steps,\\n 'swap_freq': self.swap_freq,\\n 'biasing_factor': self.biasing_factor}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8e6a56ff173911dc3cb05977a57c309\",\n \"score\": \"0.61229056\",\n \"text\": \"def to_map(self, key_attribute: str):\\n map_ = {}\\n for item in self.list:\\n key = getattr(item, key_attribute, None)\\n if key:\\n map_[key] = item\\n\\n return map_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93987efe254a21c65b206ae12652e158\",\n \"score\": \"0.60838\",\n \"text\": \"def stored_attributes(self):\\n result = []\\n result.append('key')\\n result.extend(self.get_attribute_names_defined_in_store())\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14a0e8be1762011f1b6ed47fb86c0815\",\n \"score\": \"0.60629773\",\n \"text\": \"def attributes_dict(self):\\n h = {}\\n for k in self.attributes.keys():\\n h[k] = getattr(self, k)\\n for k in self.lists.keys():\\n h[k] = getattr(self, k)\\n for k in self.references.keys():\\n h[k] = getattr(self, k)\\n return h\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e7f238902bddae9e0a6c2e102987f16\",\n \"score\": \"0.60626084\",\n \"text\": \"def identifiers(self) -> typing.Mapping[str, str]: # pylint: disable=function-redefined\\n return {\\n attr_name: getattr(self, attr_name)\\n for attr_name in self.info.get(\\\"identifiers\\\", self.info.get(\\\"params\\\", []))\\n if attr_name in self.bind\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e430e6d15d5c8496767b28ee1628aec\",\n \"score\": \"0.6055799\",\n \"text\": \"def json(self):\\n\\t\\treturn {p: self.__getattribute__(p) for p in self.__slots__}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d873a5043d6c4950f24b7bcf22ec799\",\n \"score\": \"0.6011129\",\n \"text\": \"def get_attributes(self):\\n attributes = OrderedDict()\\n attributes['init'] = self._init\\n attributes['lb'] = self._lb\\n attributes['ub'] = self._ub\\n return attributes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d3aaf7228394033604e05de8516fba2\",\n \"score\": \"0.5997634\",\n \"text\": \"def attributes(cls):\\n return dict(cls._attributes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3c04fd050657d7178ee9b8a5175d903\",\n \"score\": \"0.5995523\",\n \"text\": \"def as_dict(self) -> t.Dict[str, t.Any]:\\n\\n return {k: getattr(self, k, None) for k in self.fields_map.keys()}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"591760bf7f597bc9d95481341038581f\",\n \"score\": \"0.59728444\",\n \"text\": \"def get_all_attributes(self):\\r\\n return self.__key + self.__value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec2d2bc25bb81d039de2bdcd699964a8\",\n \"score\": \"0.5952853\",\n \"text\": \"def __dict__(self):\\n return dict([(field, getattr(self, \\\"%s\\\" %field))\\n for field in self.__class__.DICT_FIELDS])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e67f823017cc019158ce7329e2e3549\",\n \"score\": \"0.59505326\",\n \"text\": \"def extract_mapping(self):\\n return {\\n self.type_name: self.fields\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7687de833741aede5959dc91989de6a\",\n \"score\": \"0.59490645\",\n \"text\": \"def items(self) -> Dict:\\n\\n return {key: getattr(self.attribute, key)\\n for key in dir(self.attribute)\\n if not key.startswith(\\\"_\\\")}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e43861f026c6e427469ec8a1cb21132\",\n \"score\": \"0.58994234\",\n \"text\": \"def _attrs(self):\\n return self._dets.keys()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f5845575e7c910eb9966d7fc1d4594c\",\n \"score\": \"0.5897431\",\n \"text\": \"def state_attributes(self):\\r\\n return {self.id: self.value}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"030d2b2ab611343f46ef360862047723\",\n \"score\": \"0.5890485\",\n \"text\": \"def map_attribute(self, attribute, value):\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"030d2b2ab611343f46ef360862047723\",\n \"score\": \"0.5890485\",\n \"text\": \"def map_attribute(self, attribute, value):\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ddc0886368a97497ca1d68b13aced34\",\n \"score\": \"0.5881178\",\n \"text\": \"def values(self):\\r\\n return [getattr(self, k) for k in self.keys()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47828414495de3388024e5b9e62e2749\",\n \"score\": \"0.58804315\",\n \"text\": \"def get_attributes(self):\\n for _, an_object in self.objects_data.items():\\n return list(an_object[\\\"attributes\\\"].keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"298839d297eec87c3650fd888371e046\",\n \"score\": \"0.5874971\",\n \"text\": \"def toDict(self: object) -> dict:\\n\\n return {key: self.__getattribute__(key) for key in self.__slots__}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ae795368b438b54ac8a029ffb2eda8b\",\n \"score\": \"0.5861259\",\n \"text\": \"def attr_dict(self):\\n \\n base_keys = set(Info().__dict__.keys())\\n self_keys = set(self.__dict__.keys())\\n res = {}\\n for key in self_keys.difference(base_keys):\\n val = self.__dict__[key]\\n try:\\n res[key] = val.attr_dict()\\n except AttributeError:\\n res[key] = val\\n \\n return(res)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93e3c04639db2c8f05e599a26dd74f29\",\n \"score\": \"0.5848899\",\n \"text\": \"def _getAttributeNames(self, *args, **kwargs):\\n return map(_namify, self.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fe19048c6efa3c780c5bdf2d5756718\",\n \"score\": \"0.5836906\",\n \"text\": \"def get_attributes (self):\\n return (self.attr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"959be0b60913afda19e98ad6e61ef0e7\",\n \"score\": \"0.583006\",\n \"text\": \"def list_attributes(self) -> List[str]:\\n return list(self.attrs.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75562bbfa0bda5b3d65674b043095101\",\n \"score\": \"0.5819719\",\n \"text\": \"def get_attribute_names_defined_in_store(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"398bbeeb0936e837a978a4cbbb050f29\",\n \"score\": \"0.58195925\",\n \"text\": \"def list_attrs(self):\\n return self._attributes.keys()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f9c60ec85dfd737ea7bd15c88bdc963\",\n \"score\": \"0.5804851\",\n \"text\": \"def instance_attributes(self):\\n return self.infer().instance_attributes()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15b23b9a59f1aa39d8b5cdf3351eb748\",\n \"score\": \"0.5787162\",\n \"text\": \"def asdict(self):\\n return attr.asdict(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9ab4be0fc865e071d657d9b0590d25c\",\n \"score\": \"0.57617927\",\n \"text\": \"def _values(self):\\n return [self._node.getAttribute(k) for k in self._node.attributes.keys()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fe6c903dfa6da3c050e5c0ca91e954f\",\n \"score\": \"0.576031\",\n \"text\": \"def maps(self) -> Tuple[Dict[str, str], Dict[str, str], Dict[str, float]]:\\n sources = {}\\n targets = {}\\n weights = {}\\n for e in self.edges.data():\\n label = e[2][self.attr_label]\\n sources[label] = e[0]\\n targets[label] = e[1]\\n weights[label] = e[2][self.attr]\\n return sources, targets, weights\",\n \"title\": \"\"\n },\n {\n \"docid\": \"271b84d1b89b665b117b3c1aeaa9d2f4\",\n \"score\": \"0.5759584\",\n \"text\": \"def attrs(self):\\n return self.__dict__.keys()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2afcb181b0b2f4467a484a5e9318a8b6\",\n \"score\": \"0.57474965\",\n \"text\": \"def object_as_dict(obj):\\n return {c.key: getattr(obj, c.key)\\n for c in inspect(obj).mapper.column_attrs}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de3600a662a8244107357fd0e4a7c10e\",\n \"score\": \"0.57365453\",\n \"text\": \"def toDict(self):\\n dict = {}\\n for attr, value in self.__dict__.items():\\n corrected_name = attr[1:] # remove first underscore\\n dict[corrected_name] = value.__str__()\\n return dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"deb684948980db8e30bb8a211de71c62\",\n \"score\": \"0.5725433\",\n \"text\": \"def to_dictionary(self):\\n my_attri = [\\\"id\\\", \\\"width\\\", \\\"height\\\", \\\"x\\\", \\\"y\\\"]\\n val_attr = 0\\n dic_ret = {}\\n\\n for i in range(len(my_attri)):\\n val_attri = getattr(self, my_attri[i])\\n dic_ret[my_attri[i]] = val_attri\\n\\n return dic_ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ef553a53de21530358f21fe6ad3f8c6\",\n \"score\": \"0.572354\",\n \"text\": \"def to_dict(self):\\n return dict((str(f.field.name), unicode(f.value)) \\\\\\n for f in self.attributes.values() \\\\\\n if f.value is not None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69dd37a171feb4284d1814e5c530b7ea\",\n \"score\": \"0.5721035\",\n \"text\": \"def attributes(cls):\\n return tuple(cls.__dict__.get(\\\"__fields__\\\", {}).keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b19ebf24ac628e7e26724394ed4bdf7\",\n \"score\": \"0.57191956\",\n \"text\": \"def _get_field_data(self):\\n return {field_name: getattr(self, field_name) for field_name in self._meta.fields.keys()}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e69870c5e38483a59f2ec49041f27bf\",\n \"score\": \"0.56885177\",\n \"text\": \"def get_var_mapping_dict(self):\\r\\n # type: () -> dict\\r\\n return self._map_variable_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf15a63f9551f3d51c2115c5f13c2fce\",\n \"score\": \"0.5674434\",\n \"text\": \"def as_dict(self) -> dict:\\n\\n return {k: getattr(self, k) for k in list(vars(self).keys())}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b5fe9fc43051c434a353077a6a49c5e\",\n \"score\": \"0.5659958\",\n \"text\": \"def get_dict(self) -> dict:\\n d = {}\\n for attribute, value in self.__dict__.items():\\n d[attribute] = value\\n\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"987a817a109f495a131799d19da19932\",\n \"score\": \"0.5657744\",\n \"text\": \"def get_attributes(self):\\n attribute_map = {\\n 'urn:oid:0.9.2342.19200300.100.1.1': 'uwnetid',\\n 'urn:oid:1.3.6.1.4.1.5923.1.1.1.1': 'affiliations',\\n 'urn:oid:1.3.6.1.4.1.5923.1.1.1.6': 'eppn',\\n 'urn:oid:1.3.6.1.4.1.5923.1.1.1.9': 'scopedAffiliations'\\n }\\n return {attribute_map.get(key, key): value\\n for key, value in super().get_attributes().items()}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5722c78f1ead7f56a65af45c2b06be68\",\n \"score\": \"0.56549287\",\n \"text\": \"def attrs(self):\\n return self._obj.coords[GEO_MAP_COORD].attrs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cec7998715a91132817b5febdc96b30\",\n \"score\": \"0.5651105\",\n \"text\": \"def names(self) -> List[str]:\\n\\n return list(self.attribute.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d0c81c5c2e8cfef739ee8c3cc1be3fe\",\n \"score\": \"0.5648379\",\n \"text\": \"def to_map(self):\\n obj = {}\\n self._add_defined_attribs(obj, self._yaml_map().keys())\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"344060bddb57a44a7fd8f7c75452ef1e\",\n \"score\": \"0.56431675\",\n \"text\": \"def __getstate__(self):\\n return {k: getattr(self, k) for k in self.__slots__}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbe61037919116b6e4948e869f9cc449\",\n \"score\": \"0.5637252\",\n \"text\": \"def test_can_pickle_attributedictionary(self):\\n d = AttributeDictionary(hello=\\\"world\\\")\\n self.assertEqual(d['hello'],\\\"world\\\")\\n pickled = pickle.dumps(d)\\n unpickled = pickle.loads(pickled)\\n self.assertTrue(isinstance(unpickled,AttributeDictionary))\\n self.assertEqual(unpickled['hello'],\\\"world\\\")\\n self.assertEqual(unpickled.hello,\\\"world\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ce099564c4fc3741529be96145f64b9\",\n \"score\": \"0.56315374\",\n \"text\": \"def export(self) -> Dict[str, Any]:\\n values: Dict[str, Any] = {}\\n for attribute, value in self._values.items():\\n if isinstance(value, Exportable):\\n exported = value.export()\\n if exported is not None:\\n values[attribute.name] = exported\\n elif value is not None:\\n values[attribute.name] = value\\n values.update(self._extra)\\n return values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e02f0b7d7d6a52e5fc177e67a52fc39a\",\n \"score\": \"0.5627427\",\n \"text\": \"def get_attribute_values(obj):\\n return [value for key, value in obj.__dict__.items() if not key.startswith(\\\"__\\\")]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ef69c9015a3c5ac1bccb6f8bc51cc23\",\n \"score\": \"0.56218684\",\n \"text\": \"def to_dict(self) -> dict:\\n return {field.name: getattr(self, field.name) for field in fields(self)}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2cb52f25592e8cd4359329262b6cd8b\",\n \"score\": \"0.5614424\",\n \"text\": \"def column_name_mapping_dict(self):\\n return self.__column_name_mapping_dict.output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c3cc5cedadc6a455c1a5c56b1256b4a\",\n \"score\": \"0.5609309\",\n \"text\": \"def trainable_variables(self):\\n # Override this because reflection seems not to work.\\n return dict((k, v)\\n for k, v in self.keyed_variables.items()\\n if self.task_attribute_name in k)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a5a99650bd5c76ae50c62e0572146de\",\n \"score\": \"0.55903196\",\n \"text\": \"def __serialize__(self):\\n return {k: v for k, v in self.items()}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbe18e7d5659d0151cba64ecd1ee1e78\",\n \"score\": \"0.55876184\",\n \"text\": \"def get_custom_attributes(self):\\n return self[\\\"custom_attributes\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a253684180363c3e9e1735eec27a157\",\n \"score\": \"0.55866903\",\n \"text\": \"def dataclass_quick_asdict(obj) -> Dict[str, Any]:\\n d = {f.name: getattr(obj, f.name) for f in dataclasses.fields(obj)}\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"301259dfdfdde15a152609404a29f6bc\",\n \"score\": \"0.5578841\",\n \"text\": \"def _items(self):\\n return [(k, self._node.getAttribute(k)) for k in self._node.attributes.keys()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b38f12b4c7e8b0dbf2418ff3bf162e25\",\n \"score\": \"0.55710065\",\n \"text\": \"def __getstate__(self):\\n attrs = {}\\n if hasattr(self, \\\"__dict__\\\"):\\n attrs.update(self.__dict__)\\n slots = []\\n c = self.__class__\\n while c is not None:\\n if hasattr(c, \\\"__slots__\\\"):\\n slots.extend(c.__slots__)\\n c = c.__base__\\n for name in slots:\\n if hasattr(self, name):\\n attrs[name] = getattr(self, name)\\n return attrs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5467039058f1d3f85303e1e421f5a9fe\",\n \"score\": \"0.5557673\",\n \"text\": \"def as_dict(self) -> dict[str, Any]:\\n attrs = vars(self)\\n return {key: attrs[key] for key in attrs if not key.startswith(\\\"_\\\")}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ed1d7153b8ca75c13bad8c387279a5b\",\n \"score\": \"0.5557543\",\n \"text\": \"def to_json(self):\\n return dict((attr, getattr(self, attr))\\n for attr in self.__attributes__)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ed1d7153b8ca75c13bad8c387279a5b\",\n \"score\": \"0.5557543\",\n \"text\": \"def to_json(self):\\n return dict((attr, getattr(self, attr))\\n for attr in self.__attributes__)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c4468d356ce3eb8dfe201c3e4172cb8\",\n \"score\": \"0.5555928\",\n \"text\": \"def attributes(self, alias=None):\\n cls = _inheritor(_Attribute, self.ID)\\n return OrderedDict(\\n (name, cls(name, source=(alias or self._name)))\\n for name in self._schema.columns(self._name)['name']\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc1e2dc2a004c0684443c61c4e145f76\",\n \"score\": \"0.5554138\",\n \"text\": \"def attr_names(self):\\n l = self.attrs.keys()\\n l.sort()\\n return l\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc4f8ef97a55a55ba41c35249067996c\",\n \"score\": \"0.55302197\",\n \"text\": \"def getAttributes(self):\\n\\t\\treturn self._attributes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d7966d57dd19ac870eecdfc77aa6ec6\",\n \"score\": \"0.5521307\",\n \"text\": \"def _toDict(self):\\n return {\\n 'name': self._name,\\n 'colors': self.getColormapLUT(),\\n 'vmin': self._vmin,\\n 'vmax': self._vmax,\\n 'autoscale': self.isAutoscale(),\\n 'normalization': self.getNormalization(),\\n 'autoscaleMode': self.getAutoscaleMode(),\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bddb2dc34b25fbd8d554df54702165f\",\n \"score\": \"0.55179185\",\n \"text\": \"def state_attributes(self):\\r\\n data = {}\\r\\n data[self.id] = self.value\\r\\n data[self.name + \\\"_modes\\\"] = self.values\\r\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac0f804e8d418b1e279c333a79c0afbc\",\n \"score\": \"0.5505109\",\n \"text\": \"def attrs(self):\\n assert isinstance(self._attrs, list)\\n return dict(self._attrs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d17b4cc12250e5d40fb7622b18f2b26\",\n \"score\": \"0.5500228\",\n \"text\": \"def keys_python(self) -> Iterable[str]:\\n yield from dejsonify_attribute_names(self.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d17b4cc12250e5d40fb7622b18f2b26\",\n \"score\": \"0.5500228\",\n \"text\": \"def keys_python(self) -> Iterable[str]:\\n yield from dejsonify_attribute_names(self.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1714d49568ad2509605777f2d840c17\",\n \"score\": \"0.5483594\",\n \"text\": \"def get_mapping(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f3ef00fe73d775c04f2097a37bd030f\",\n \"score\": \"0.5481292\",\n \"text\": \"def to_dict(self):\\n return {'name': self.name, 'value': self.value}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0951a32461a4ec6f999cf54c848e1917\",\n \"score\": \"0.5478671\",\n \"text\": \"def signable_dict(self):\\n\\n return attr.asdict(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99e8834a52ddb61940511f8e103d2bd9\",\n \"score\": \"0.54667675\",\n \"text\": \"def get(self):\\n\\n d = {}\\n for i in self.__list:\\n d[i[0]] = getattr(self, i[0])\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2af288108c5712db6c82ed4c05ed859b\",\n \"score\": \"0.54587424\",\n \"text\": \"def get_class_attributes(self):\\n return {\\n \\\"base\\\": self.base,\\n \\\"internal_type\\\": self.internal_type,\\n \\\"precision_fractional\\\": self.precision_fractional,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7478e09b4e4c96346c16bea888693b7c\",\n \"score\": \"0.5455201\",\n \"text\": \"def __getstate__(self):\\n state = {}\\n for entry in self.__slots__:\\n state[entry] = getattr(self, entry)\\n\\n return state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cb735772f7fdb94941f55277d366878\",\n \"score\": \"0.54495835\",\n \"text\": \"def as_dict(self):\\n \\n return {c.name: getattr(self, c.name) for c in self.__table__.columns}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"881a27352016a5006e9f74b08f318ea3\",\n \"score\": \"0.5440619\",\n \"text\": \"def __getstate__(self):\\n state={}\\n for attrname in self._expected_attrs:\\n state[attrname]=getattr(self,attrname)\\n return state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a460d55adbdd2d9f78bec303b04bd69e\",\n \"score\": \"0.54390496\",\n \"text\": \"def to_dict(self) -> dict:\\n mapper = self.__mapper__\\n attribute_keys = mapper.columns.keys() + mapper.relationships.keys()\\n\\n return {k: getattr(self, k, None) for k in attribute_keys}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"562184d8d770035fe5317be12d692f6f\",\n \"score\": \"0.54379374\",\n \"text\": \"def to_dict(self):\\n data = {\\n \\\"type\\\": self.__class__.__name__\\n }\\n\\n for key in self.__class__.DATA_KEYS:\\n data[key] = getattr(self, key)\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6fed61bb57d9d74027ac4eeeb32c8dd\",\n \"score\": \"0.5436603\",\n \"text\": \"def get_mbean_attributes(self):\\n _method_name = 'get_mbean_attributes'\\n _logger.entering(class_name=self.__class__.__name__, method_name=_method_name)\\n map_to_list = list()\\n attributes = self.__get_mbean_info_map()\\n if len(attributes) > 0:\\n map_to_list = [attribute for attribute in attributes.iterkeys()]\\n map_to_list.sort()\\n _logger.exiting(class_name=self.__class__.__name__, method_name=_method_name, result=len(map_to_list))\\n return map_to_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8e6ee57d60a5d384d854a9efa4c3737\",\n \"score\": \"0.54333323\",\n \"text\": \"def get_attributes(self):\\n return self.attribs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5c8b3ae8d1dba96db6d379d02303085\",\n \"score\": \"0.54287755\",\n \"text\": \"def as_dict(self):\\n\\n return {c.name: getattr(self, c.name) for c in self.__table__.columns}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68b5cd9a262481536074ecc991200e11\",\n \"score\": \"0.5409635\",\n \"text\": \"def AttrDict(slots = tuple()):\\n return _get_attr_dict_factory(slots)()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"052f974cb84d7a51d6880cc433f9db05\",\n \"score\": \"0.54028714\",\n \"text\": \"def getAttrsAsDict(self, attrs):\\n\\t\\treturn dict((k.split(\\\":\\\")[-1], v) for k, v in attrs.items())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05d26097faa72a8a6f61f8f847431166\",\n \"score\": \"0.5399544\",\n \"text\": \"def _get_type_attribute_dict(self, type_name: str) -> Dict[str, Dict]:\\n return self._get_type_info(type_name)[constants.ATTR_INFO_KEY]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34138c00d46b665c0405daf20ea86678\",\n \"score\": \"0.53984386\",\n \"text\": \"def get_meta_attribute_info(self):\\n return self.__meta_attribute_info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f502343df565934f8a988965cd93c912\",\n \"score\": \"0.53967124\",\n \"text\": \"def get_label_map(self):\\n trainable_classes = self.read_trainable_classes()\\n label_map, reverse_label_map = {}, {}\\n index = 0\\n\\n for key, cls_name in trainable_classes.items():\\n label_map[key] = index\\n reverse_label_map[index] = key\\n index += 1\\n\\n return label_map, reverse_label_map\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994182,"cells":{"query_id":{"kind":"string","value":"daf973153a7da1e68c0c9d42349d0b2c"},"query":{"kind":"string","value":"Function to calculate the conditional priors p(xp|xeff,q) on a set of {xp,xeff,q} posterior samples. INPUTS"},"positive_passages":{"kind":"list like","value":[{"docid":"f87f1caa5f85a8a02591eeabab79177f","score":"0.5392014","text":"def joint_prior_from_isotropic_spins(q,aMax,xeffs,xps,ndraws=10000,bw_method='scott'):\n\n # Convert to arrays for safety\n xeffs = np.reshape(xeffs,-1)\n xps = np.reshape(xps,-1)\n \n # Compute marginal prior on xeff, conditional prior on xp, and multiply to get joint prior!\n p_chi_eff = chi_effective_prior_from_isotropic_spins(q,aMax,xeffs)\n p_chi_p_given_chi_eff = np.array([chi_p_prior_given_chi_eff_q(q,aMax,xeffs[i],xps[i],ndraws,bw_method) for i in range(len(xeffs))])\n joint_p_chi_p_chi_eff = p_chi_eff*p_chi_p_given_chi_eff\n\n return joint_p_chi_p_chi_eff","title":""}],"string":"[\n {\n \"docid\": \"f87f1caa5f85a8a02591eeabab79177f\",\n \"score\": \"0.5392014\",\n \"text\": \"def joint_prior_from_isotropic_spins(q,aMax,xeffs,xps,ndraws=10000,bw_method='scott'):\\n\\n # Convert to arrays for safety\\n xeffs = np.reshape(xeffs,-1)\\n xps = np.reshape(xps,-1)\\n \\n # Compute marginal prior on xeff, conditional prior on xp, and multiply to get joint prior!\\n p_chi_eff = chi_effective_prior_from_isotropic_spins(q,aMax,xeffs)\\n p_chi_p_given_chi_eff = np.array([chi_p_prior_given_chi_eff_q(q,aMax,xeffs[i],xps[i],ndraws,bw_method) for i in range(len(xeffs))])\\n joint_p_chi_p_chi_eff = p_chi_eff*p_chi_p_given_chi_eff\\n\\n return joint_p_chi_p_chi_eff\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"3df82a3c097cc9531b6d612c43603726","score":"0.6071597","text":"def chi_p_prior_given_chi_eff_q(q,aMax,xeff,xp,ndraws=10000,bw_method='scott'):\n\n # Draw random spin magnitudes.\n # Note that, given a fixed chi_eff, a1 can be no larger than (1+q)*chi_eff,\n # and a2 can be no larger than (1+q)*chi_eff/q\n a1 = np.random.random(ndraws)*aMax\n a2 = np.random.random(ndraws)*aMax\n\n # Draw random tilts for spin 2\n cost2 = 2.*np.random.random(ndraws)-1.\n\n # Finally, given our conditional value for chi_eff, we can solve for cost1\n # Note, though, that we still must require that the implied value of cost1 be *physical*\n cost1 = (xeff*(1.+q) - q*a2*cost2)/a1 \n\n # While any cost1 values remain unphysical, redraw a1, a2, and cost2, and recompute\n # Repeat as necessary\n while np.any(cost1<-1) or np.any(cost1>1): \n to_replace = np.where((cost1<-1) | (cost1>1))[0] \n a1[to_replace] = np.random.random(to_replace.size)*aMax\n a2[to_replace] = np.random.random(to_replace.size)*aMax\n cost2[to_replace] = 2.*np.random.random(to_replace.size)-1. \n cost1 = (xeff*(1.+q) - q*a2*cost2)/a1 \n \n # Compute precessing spins and corresponding weights, build KDE\n # See `Joint-ChiEff-ChiP-Prior.ipynb` for a discussion of these weights\n Xp_draws = chi_p_from_components(a1,a2,cost1,cost2,q)\n jacobian_weights = (1.+q)/a1\n prior_kde = gaussian_kde(Xp_draws,weights=jacobian_weights,bw_method=bw_method)\n\n # Compute maximum chi_p\n if (1.+q)*np.abs(xeff)/q<aMax:\n max_Xp = aMax\n else:\n max_Xp = np.sqrt(aMax**2 - ((1.+q)*np.abs(xeff)-q)**2.)\n\n # Set up a grid slightly inside (0,max chi_p) and evaluate KDE\n reference_grid = np.linspace(0.05*max_Xp,0.95*max_Xp,50)\n reference_vals = prior_kde(reference_grid)\n\n # Manually prepend/append zeros at the boundaries\n reference_grid = np.concatenate([[0],reference_grid,[max_Xp]])\n reference_vals = np.concatenate([[0],reference_vals,[0]])\n norm_constant = np.trapz(reference_vals,reference_grid)\n\n # Interpolate!\n p_chi_p = np.interp(xp,reference_grid,reference_vals/norm_constant)\n return p_chi_p","title":""},{"docid":"61ec7af6b4c3bc911ad127608246f670","score":"0.6038365","text":"def P(self,x):\n indx = np.arange(len(x))\n ps = self.pX[x,indx] #probability of xi for each i\n joint = ps.prod()\n pi = np.true_divide(joint,ps) # will be nan for elements for which ps is 0 -(should never happen if sampling)\n for j in np.where(np.isnan(pi))[0]:\n pi[j] = np.prod(ps[indx != j]) \n pij = np.vstack((pi,pi))\n pij[1-x,indx] = 0\n pij = pij.reshape((len(x)*2,)) #flatten first N-1 will be px=0,2nd px=1\n pobserve = joint# the probability of x given do()\n result = np.hstack((pij,pobserve))\n return result","title":""},{"docid":"ee8e5436eadc7abc54b3589b8a0c9c10","score":"0.5970335","text":"def ppf(self, q, *args, **kwargs):\n return self.scipy_distribution.ppf(q, *args, **kwargs)","title":""},{"docid":"3fb61e35f7c700c13f41535bc1474682","score":"0.5799747","text":"def evaluate_p_all(self, p_in):\n return np.exp(self.evaluate_logp_all(p_in))","title":""},{"docid":"853f2e1b6db1dcf8b3a9c0bac2ad768b","score":"0.57089645","text":"def bigauss(x, p):\n return (x < p[1]).choose([\n p[0] * exp(-twolntwo * (x - p[1])**2 / (p[2] * (1 + p[3])**2)) + p[4],\n p[0] * exp(-twolntwo * (x - p[1])**2 / (p[2] * (1 - p[3])**2)) + p[4]\n ])","title":""},{"docid":"cae99ff6f62dc61786fbcff772f08977","score":"0.5703504","text":"def compute_pvalue(expression, thresh, alpha=0.05):\n\n #count the cases\n #ndown = [1 if exp_i <= thresh_i else 0 for exp_i in zip(expression, thresh_i)]\n ndown = expression[expression <= - thresh].shape[0]\n nup = expression[expression >= thresh].shape[0]\n nneutral = expression.shape[0] - ndown - nup\n\n #get the one with most peptide\n ns = np.array([nup, ndown, nneutral])\n max_idx = np.argmax(ns)\n\n\n if max_idx == 0:\n direction = \"up\"\n\n elif max_idx == 1:\n direction = \"down\"\n\n else:\n direction = \"neutral\"\n\n #n number of trails for binomial\n ntrials = len(expression)\n #k success\n ksuccess = ns[max_idx]\n x = np.arange(ksuccess, ntrials + 1)\n\n #compute the pvalue as number of cases as extreme (and higher)\n #as the one observed\n pvalue = np.sum(stat.binom.pmf(x, ntrials, alpha))\n\n res_vec = pd.Series([pvalue, direction, ntrials, ksuccess])\n res_vec.index([\"pvalue\", \"direction\", \"ntrials\", \"ksuccess\"])\n return(res_vec)","title":""},{"docid":"001751d13783817a650eb4e3fd46ec9f","score":"0.5665702","text":"def chi_p_prior_from_isotropic_spins(q,aMax,xs):\n\n # Ensure that `xs` is an array and take absolute value\n xs = np.reshape(xs,-1)\n\n # Set up various piecewise cases\n pdfs = np.zeros(xs.size)\n caseA = xs<q*aMax*(3.+4.*q)/(4.+3.*q)\n caseB = (xs>=q*aMax*(3.+4.*q)/(4.+3.*q))*(xs<aMax)\n\n # Select relevant effective spins\n x_A = xs[caseA]\n x_B = xs[caseB]\n\n pdfs[caseA] = (1./(aMax**2*q))*((4.+3.*q)/(3.+4.*q))*(\n np.arccos((4.+3.*q)*x_A/((3.+4.*q)*q*aMax))*(\n aMax\n - np.sqrt(aMax**2-x_A**2)\n + x_A*np.arccos(x_A/aMax)\n )\n + np.arccos(x_A/aMax)*(\n aMax*q*(3.+4.*q)/(4.+3.*q)\n - np.sqrt(aMax**2*q**2*((3.+4.*q)/(4.+3.*q))**2 - x_A**2)\n + x_A*np.arccos((4.+3.*q)*x_A/((3.+4.*q)*aMax*q))\n )\n )\n \n pdfs[caseB] = (1./aMax)*np.arccos(x_B/aMax)\n\n return pdfs","title":""},{"docid":"5cfeaae4232a479df9325334fc6e2ea9","score":"0.56629515","text":"def exbayesdices():\n hypos = [4,6,8,12,20]\n def dlike(d,h):\n if (d>h): return 0.\n return 1./(1.*h)\n ys = [1./len(hypos)]*len(hypos)\n zs = zip(hypos,ys)\n xfun = psutils.Funxy_xdiscrete(zs)\n pb = psbayes.PBayes(xfun,hypos,dlike)\n datas = [6,6,8,7,7,5,4]\n pb.evidence(datas)\n print 'posterior ',zip(hypos,pb.posterior(hypos))\n return","title":""},{"docid":"4fcb96a0e8e07a29bc2939206f43548c","score":"0.56107455","text":"def calculate_probability(x1=0, x2=0, x3=0, x4=0):\n\n\tprobability = 1.0\n\t\n\t# Do not edit any code outside the edit region\n\t# Edit region starts here\n\t#########################\n\t# Your code goes here\n\tif 'p_table' not in calculate_probability.__dict__:\n\t\tp_table = -np.ones([4,4,4,4])\n\tif p_table[x1,x2,x3,x4] != -1:\n\t\treturn p_table[x1,x2,x3,x4]\n\tpara = [x1,x2,x3,x4]\n\tif 0 not in para:\n\t\tp_table[x1,x2,x3,x4] = p_x1[x1-1]*p_x2_given_x1[x1-1, x2-1]*p_x3_given_x2[x2-1,x3-1]*p_x4_given_x3[x3-1, x4-1]\n\t\treturn p_table[x1,x2,x3,x4]\n\telse:\n\t\tfor i,x in enumerate(para):\n\t\t\tif x==0:\n\t\t\t\ttemp_para = para[:]\n\t\t\t\ttemp_p = 0.0\n\t\t\t\tfor j in range(1,4):\n\t\t\t\t\ttemp_para[i] = j\n\t\t\t\t\ttemp_p += calculate_probability(temp_para[0],temp_para[1],temp_para[2],temp_para[3])\n\t\t\t\tp_table[x1,x2,x3,x4] = temp_p\n\t\t\t\treturn temp_p\n\t#########################\n\t# Edit region ends here\n\n\treturn probability","title":""},{"docid":"ee0a181ae490e18644d4fd216bf30b1b","score":"0.56059885","text":"def calc_prior(p,fitfunction, params):\n \n plike = 0.\n \n if fitfunction.__name__ == 'greybody':\n # param ordering is [amp,Tdust,L0,beta]\n # setting priors as uniform\n if p[params.index('amp')] < 0: plike -= np.inf\n if (p[params.index('Td')]<15) or (p[params.index('Td')]>300): plike -= np.inf\n if (p[params.index('L0')]<50) or (p[params.index('L0')]>500): plike -= np.inf\n if (p[params.index('beta')]<0.) or (p[params.index('beta')]>4.): plike -= np.inf\n return plike\n elif fitfunction.func_name == 'greybody_powerlaw':\n # param ordering is[amp,Tdust,L0,beta,alpha,z]\n if p[0] < 0: plike -= np.inf\n if (p[1]<15) or (p[1]>300): plike -= np.inf\n if (p[2]<50e-6) or (p[2]>500e-6): plike -= np.inf\n if (p[3]<0.) or (p[3]>4.): plike -= np.inf\n if (p[4]<0.25) or (p[4]>3.): plike -= np.inf\n if (p[5]<0.) or (p[5]>10.): plike -= np.inf\n return plike","title":""},{"docid":"8a144c8c81f4c20e003d8410e02767fc","score":"0.5593349","text":"def posterior(x, n, p1, p2):\n if not isinstance(n, int) or n < 1:\n raise ValueError(\"n must be a positive integer\")\n if type(x) != int or x < 0:\n err = \"x must be an integer that is greater than or equal to 0\"\n raise ValueError(err)\n if x > n:\n raise ValueError(\"x cannot be greater than n\")\n if not isinstance(P, np.ndarray) or len(P.shape) != 1:\n raise TypeError(\"P must be a 1D numpy.ndarray\")\n if not isinstance(p1, float) or p1 < 0 or p1 > 1:\n raise TypeError(\"p1 must be a float in the range [0, 1]\")\n if not isinstance(p2, float) or p2 < 0 or p2 > 1:\n raise TypeError(\"p2 must be a float in the range [0, 1]\")\n if p2 <= p1:\n raise ValueError(\"p2 must be greater than p1\")\n return p1","title":""},{"docid":"9a9d60666907e4e3c0b5a7ba1824cb44","score":"0.55737007","text":"def x2p(X = Math.array([]), tol = 1e-5, perplexity = 30.0):\n\n\t# Initialize some variables\n\tprint \"Computing pairwise distances...\"\n\t(n, d) = X.shape;\n\tsum_X = Math.sum(Math.square(X), 1);\n\tD = Math.add(Math.add(-2 * Math.dot(X, X.T), sum_X).T, sum_X);\n\tP = Math.zeros((n, n));\n\tbeta = Math.ones((n, 1));\n\tlogU = Math.log(perplexity);\n\n\t# Loop over all datapoints\n\tfor i in range(n):\n\n\t\t# Print progress\n\t\tif i % 500 == 0:\n\t\t\tprint \"Computing P-values for point \", i, \" of \", n, \"...\"\n\n\t\t# Compute the Gaussian kernel and entropy for the current precision\n\t\tbetamin = -Math.inf;\n\t\tbetamax = Math.inf;\n\t\tDi = D[i, Math.concatenate((Math.r_[0:i], Math.r_[i+1:n]))];\n\t\t(H, thisP) = Hbeta(Di, beta[i]);\n\n\t\t# Evaluate whether the perplexity is within tolerance\n\t\tHdiff = H - logU;\n\t\ttries = 0;\n\t\twhile Math.abs(Hdiff) > tol and tries < 50:\n\n\t\t\t# If not, increase or decrease precision\n\t\t\tif Hdiff > 0:\n\t\t\t\tbetamin = beta[i].copy();\n\t\t\t\tif betamax == Math.inf or betamax == -Math.inf:\n\t\t\t\t\tbeta[i] = beta[i] * 2;\n\t\t\t\telse:\n\t\t\t\t\tbeta[i] = (beta[i] + betamax) / 2;\n\t\t\telse:\n\t\t\t\tbetamax = beta[i].copy();\n\t\t\t\tif betamin == Math.inf or betamin == -Math.inf:\n\t\t\t\t\tbeta[i] = beta[i] / 2;\n\t\t\t\telse:\n\t\t\t\t\tbeta[i] = (beta[i] + betamin) / 2;\n\n\t\t\t# Recompute the values\n\t\t\t(H, thisP) = Hbeta(Di, beta[i]);\n\t\t\tHdiff = H - logU;\n\t\t\ttries = tries + 1;\n\n\t\t# Set the final row of P\n\t\tP[i, Math.concatenate((Math.r_[0:i], Math.r_[i+1:n]))] = thisP;\n\n\t# Return final P-matrix\n\tprint \"Mean value of sigma: \", Math.mean(Math.sqrt(1 / beta));\n\treturn P;","title":""},{"docid":"57f50fff7694e62384e28b9cdbbfd7de","score":"0.5565294","text":"def x2p(X = Math.array([]), tol = 1e-5, perplexity = 30.0):\n\n\t# Initialize some variables\n\tprint (\"Computing pairwise distances...\")\n\t(n, d) = X.shape;\n\tsum_X = Math.sum(Math.square(X), 1);\n\tD = Math.add(Math.add(-2 * Math.dot(X, X.T), sum_X).T, sum_X);\n\tP = Math.zeros((n, n));\n\tbeta = Math.ones((n, 1));\n\tlogU = Math.log(perplexity);\n\n\t# Loop over all datapoints\n\tfor i in range(n):\n\n\t\t# Print progress\n\t\tif i % 500 == 0:\n\t\t\tprint (\"Computing P-values for point \", i, \" of \", n, \"...\")\n\n\t\t# Compute the Gaussian kernel and entropy for the current precision\n\t\tbetamin = -Math.inf;\n\t\tbetamax = Math.inf;\n\t\tDi = D[i, Math.concatenate((Math.r_[0:i], Math.r_[i+1:n]))];\n\t\t(H, thisP) = Hbeta(Di, beta[i]);\n\n\t\t# Evaluate whether the perplexity is within tolerance\n\t\tHdiff = H - logU;\n\t\ttries = 0;\n\t\twhile Math.abs(Hdiff) > tol and tries < 50:\n\n\t\t\t# If not, increase or decrease precision\n\t\t\tif Hdiff > 0:\n\t\t\t\tbetamin = beta[i].copy();\n\t\t\t\tif betamax == Math.inf or betamax == -Math.inf:\n\t\t\t\t\tbeta[i] = beta[i] * 2;\n\t\t\t\telse:\n\t\t\t\t\tbeta[i] = (beta[i] + betamax) / 2;\n\t\t\telse:\n\t\t\t\tbetamax = beta[i].copy();\n\t\t\t\tif betamin == Math.inf or betamin == -Math.inf:\n\t\t\t\t\tbeta[i] = beta[i] / 2;\n\t\t\t\telse:\n\t\t\t\t\tbeta[i] = (beta[i] + betamin) / 2;\n\n\t\t\t# Recompute the values\n\t\t\t(H, thisP) = Hbeta(Di, beta[i]);\n\t\t\tHdiff = H - logU;\n\t\t\ttries = tries + 1;\n\n\t\t# Set the final row of P\n\t\tP[i, Math.concatenate((Math.r_[0:i], Math.r_[i+1:n]))] = thisP;\n\n\t# Return final P-matrix\n\tprint(\"Mean value of sigma: \", Math.mean(Math.sqrt(1 / beta)))\n\treturn P;","title":""},{"docid":"ca4f15080d0c717db14905503a17c1ac","score":"0.55517834","text":"def _set_priors(self):\n bool_arr = np.array([self.MAP_uniform_prior, self.MAP_use_log_Teff_log_g_prior])\n n_bool = np.sum(bool_arr * 1)\n if n_bool != 1:\n logger.error('_set_priors: set only one of \"MAP_uniform_prior\" or \"MAP_use_log_Teff_log_g_prior\" to True:')\n sys.exit(1)\n\n work_y = self.get('MAP_work_frequencies')\n m, K = work_y.shape\n\n if self.MAP_uniform_prior:\n prior = old_div(np.ones(m), float(m))\n elif self.MAP_use_log_Teff_log_g_prior:\n # get observed log_Teff and log_g together with their errors from the star\n if self.log_Teff_err_lower == 0 or self.log_Teff_err_upper == 0:\n logger.error('_set_priors: set log_Teff_err_lower and log_Teff_err_upper first')\n sys.exit(1)\n\n obs_log_Teff = self.log_Teff\n obs_log_Teff_err = np.max([ self.log_Teff_err_lower, self.log_Teff_err_upper ])\n obs_log_g = self.log_g\n obs_log_g_err= np.max([ self.log_g_err_lower, self.log_g_err_upper ])\n if not all([ obs_log_Teff != 0, obs_log_Teff_err != 0, \n obs_log_g != 0, obs_log_g_err != 0 ]):\n logger.error('_set_priors: Specify the log_Teff, log_g and their errors properly')\n sys.exit(1)\n\n lrn_log_Teff = self.learning_log_Teff[:]\n lrn_log_g = self.learning_log_g[:]\n\n prior_log_Teff = utils.gaussian(x=lrn_log_Teff, mu=obs_log_Teff, sigma=obs_log_Teff_err)\n prior_log_g = utils.gaussian(x=lrn_log_g, mu=obs_log_g, sigma=obs_log_g_err)\n\n prior = prior_log_Teff * prior_log_g\n\n if utils.has_nan(prior):\n logger.error('_set_priors: NaN detected')\n sys.exit(1)\n\n prior = utils.substitute_inf(prior)\n ln_prior = np.log(prior)\n\n self.set('MAP_prior', prior)\n self.set('MAP_ln_prior', ln_prior)","title":""},{"docid":"9bd3465cf64e2a2dae0f90b2c2bb63b1","score":"0.5542093","text":"def probit(x):\n eps = np.finfo(float).eps\n result = 0.5 + 0.5 * pt.erf(x / pt.sqrt(2))\n result = pt.switch(pt.eq(result, 0), eps, result)\n result = pt.switch(pt.eq(result, 1), 1 - eps, result)\n\n return result","title":""},{"docid":"4eb157e75fd97440ef81c2e98c077cbf","score":"0.55243176","text":"def exbayesexpo():\n tgs = []\n NS = [10,100,1000,10000]\n RS = [(0.1,3.),(0.4,1.6),(0.8,1.2),(0.94,1.06)]\n for i in range(len(NS)):\n N = NS[i]\n u0,uf = RS[i]\n fu = pspdf.pdf_uniform(u0,uf)\n fe = pspdf.pdf_exponential(1.)\n hypos = fu.rvariables()\n dlike = pslib.exponential\n pb = psbayes.PBayes(fu,hypos,dlike)\n data = fe.random(N)\n x0,xf = fe.domain()\n pb.evidence(data)\n xs = pb.hypothesis()\n zs = zip(xs,pb.posterior(xs))\n xpf = pspdf.pdf_points(zs)\n print ' N points ',N\n print ' average ',xpf.average(),' rms ',xpf.rms()\n print ' contaiment ',xpf.inverse_comulative(0.5-0.341),xpf.inverse_comulative(0.5+0.341) \n tg = psplot.tgraph(zs,title='posterior_N'+str(N))\n h = psplot.thisto(data)\n tgs.append((h,tg))\n return tgs","title":""},{"docid":"085cc3c6e04ab13f78f32bcc5daafc90","score":"0.5499824","text":"def prob(x, xi):\n return sum([1 for x0 in x if x0 == xi])/len(x)","title":""},{"docid":"4bf07dd22601f20def3de8502b68014f","score":"0.5489826","text":"def x2p(X=np.array([]), tol=1e-5, perplexity=30.0):\n\n # Initialize some variables\n print \"Computing pairwise distances...\"\n (n, d) = X.shape\n sum_X = np.sum(np.square(X), 1)\n D = np.add(np.add(-2 * np.dot(X, X.T), sum_X).T, sum_X)\n P = np.zeros((n, n))\n beta = np.ones((n, 1))\n logU = np.log(perplexity)\n\n # Loop over all datapoints\n for i in range(n):\n\n # Print progress\n if i % 500 == 0:\n print \"Computing P-values for point \", i, \" of \", n, \"...\"\n\n # Compute the Gaussian kernel and entropy for the current precision\n betamin = -np.inf\n betamax = np.inf\n Di = D[i, np.concatenate((np.r_[0:i], np.r_[i+1:n]))]\n (H, thisP) = Hbeta(Di, beta[i])\n\n # Evaluate whether the perplexity is within tolerance\n Hdiff = H - logU\n tries = 0\n while np.abs(Hdiff) > tol and tries < 50:\n\n # If not, increase or decrease precision\n if Hdiff > 0:\n betamin = beta[i].copy()\n if betamax == np.inf or betamax == -np.inf:\n beta[i] = beta[i] * 2\n else:\n beta[i] = (beta[i] + betamax) / 2\n else:\n betamax = beta[i].copy()\n if betamin == np.inf or betamin == -np.inf:\n beta[i] = beta[i] / 2\n else:\n beta[i] = (beta[i] + betamin) / 2\n\n # Recompute the values\n (H, thisP) = Hbeta(Di, beta[i])\n Hdiff = H - logU\n tries = tries + 1\n\n # Set the final row of P\n P[i, np.concatenate((np.r_[0:i], np.r_[i+1:n]))] = thisP\n\n # Return final P-matrix\n print \"Mean value of sigma: \", np.mean(np.sqrt(1 / beta))\n return P","title":""},{"docid":"230700129186c1eb55e343bcc422e9a8","score":"0.5466392","text":"def _ppf(self, p, betaL, betaH, mL, mH):\n\n def inttail(beta, m):\n return m / beta / (m - 1) * np.exp(-0.5 * beta * beta)\n\n def intcore(betaL, betaH):\n return _norm_pdf_C * (_norm_cdf(betaH) - _norm_cdf(-betaL))\n\n def hightail(p, betaL, betaH, mL, mH):\n CL = inttail(betaL, mL)\n CH = inttail(betaH, mH)\n C = CL + CH\n N = 1 / (C + intcore(betaL, betaH))\n eb2H = np.exp(-0.5 * betaH * betaH)\n return -(\n mH / betaH\n - betaH\n - ((mH - 1) * (mH / betaH) ** (-mH) / eb2H * (1 - p) / N)\n ** (1 / (1 - mH))\n )\n\n def lowtail(p, betaL, betaH, mL, mH):\n CL = inttail(betaL, mL)\n CH = inttail(betaH, mH)\n C = CL + CH\n N = 1 / (C + intcore(betaL, betaH))\n eb2L = np.exp(-0.5 * betaL * betaL)\n return (\n mL / betaL\n - betaL\n - ((mL - 1) * (mL / betaL) ** (-mL) / eb2L * p / N) ** (1 / (1 - mL))\n )\n\n def core(p, betaL, betaH, mL, mH):\n CL = inttail(betaL, mL)\n CH = inttail(betaH, mH)\n C = CL + CH\n N = 1 / (C + intcore(betaL, betaH))\n return _norm_ppf(_norm_cdf(-betaL) + (1 / _norm_pdf_C) * (p / N - CL))\n\n def ppf_greater(p, betaL, betaH, mL, mH):\n N = 1.0 / (inttail(betaL, mL) + intcore(betaL, betaH) + inttail(betaH, mH))\n pbetaH = 1 - (N * (mH / betaH) * np.exp(-0.5 * betaH * betaH) / (mH - 1))\n return _lazywhere(\n p > pbetaH, (p, betaL, betaH, mL, mH), f=hightail, f2=core\n )\n\n N = 1.0 / (inttail(betaL, mL) + intcore(betaL, betaH) + inttail(betaH, mH))\n pbetaL = N * (mL / betaL) * np.exp(-0.5 * betaL * betaL) / (mL - 1)\n return _lazywhere(\n p < pbetaL, (p, betaL, betaH, mL, mH), f=lowtail, f2=ppf_greater\n )","title":""},{"docid":"3e211be2e3b6314ced1a5d6c3e4d89e8","score":"0.5454346","text":"def peval(x, p):\n A,B,C,D = p\n return logistic4(x, A, B, C, D)","title":""},{"docid":"595bfeaf3fb9b98b5c8136490cdb474c","score":"0.5447155","text":"def peval(x, p):\r\n A,B,C,D = p\r\n return logistic4(x, A, B, C, D)","title":""},{"docid":"4f1e337e48961b7bed9f1afe705ea28c","score":"0.54432005","text":"def pr(self, source):\n\n H_0 = self.PROB - self.EPSILON\n H_1 = self.PROB + self.EPSILON\n beta = self.ALPHA\n a = np.log(beta / (1 - self.ALPHA))\n b = np.log((1 - beta) / self.ALPHA)\n k = 0\n\n w_sum = 0.0\n w_sum_true = 0.0\n s = Sampler()\n s.create(source)\n li = s.take(10)\n count = 0.0\n for i in li:\n if i >= 0.5:\n count = count + 1\n count = count / len(li)\n num_samples = 0\n while (num_samples < self.INITIAL_SAMPLE_SIZE):\n if li[num_samples] > 0.5:\n k = k + 1\n w_sum_true = w_sum_true + count\n w_sum = w_sum + count\n num_samples += 1\n test = None\n while (self.NUMM_SAMPLES <= self.MAX_SAMPLES):\n log_likelihood = w_sum_true * \\\n np.log(H_1 / H_0) + (w_sum - w_sum_true) * np.log((1 - H_1) / (1 - H_0))\n\n if (log_likelihood >= b):\n test = True\n elif (log_likelihood <= a):\n test = False\n else:\n i = 0\n while (i < self.SAMPLE_SIZE_STEP):\n count = 0.0\n li = s.Take(10)\n for it in li:\n if it >= 0.5:\n count = count + 1\n count = count / len(li)\n\n if li[i] > 0.5:\n k = k + 1\n w_sum_true = w_sum_true + count\n w_sum = w_sum + count\n i = i + 1\n self.NUMM_SAMPLES += self.SAMPLE_SIZE_STEP\n test = False\n\n return test","title":""},{"docid":"c1e6fcfd98cafcf44dc3228ab4d0f08a","score":"0.5438436","text":"def x2p(X=np.array([]), tol=1e-5, perplexity=30.0):\n\n # Initialize some variables\n print(\"Computing pairwise distances...\")\n (n, d) = X.shape\n sum_X = np.sum(np.square(X), 1)\n D = np.add(np.add(-2 * np.dot(X, X.T), sum_X).T, sum_X)\n P = np.zeros((n, n))\n beta = np.ones((n, 1))\n logU = np.log(perplexity)\n\n # Loop over all datapoints\n for i in range(n):\n\n # Print progress\n if i % 500 == 0:\n print(\"Computing P-values for point %d of %d...\" % (i, n))\n\n # Compute the Gaussian kernel and entropy for the current precision\n betamin = -np.inf\n betamax = np.inf\n Di = D[i, np.concatenate((np.r_[0:i], np.r_[i+1:n]))]\n (H, thisP) = Hbeta(Di, beta[i])\n\n # Evaluate whether the perplexity is within tolerance\n Hdiff = H - logU\n tries = 0\n while np.abs(Hdiff) > tol and tries < 50:\n\n # If not, increase or decrease precision\n if Hdiff > 0:\n betamin = beta[i].copy()\n if betamax == np.inf or betamax == -np.inf:\n beta[i] = beta[i] * 2.\n else:\n beta[i] = (beta[i] + betamax) / 2.\n else:\n betamax = beta[i].copy()\n if betamin == np.inf or betamin == -np.inf:\n beta[i] = beta[i] / 2.\n else:\n beta[i] = (beta[i] + betamin) / 2.\n\n # Recompute the values\n (H, thisP) = Hbeta(Di, beta[i])\n Hdiff = H - logU\n tries += 1\n\n # Set the final row of P\n P[i, np.concatenate((np.r_[0:i], np.r_[i+1:n]))] = thisP\n\n # Return final P-matrix\n print(\"Mean value of sigma: %f\" % np.mean(np.sqrt(1 / beta)))\n return P","title":""},{"docid":"94cfd45cec2e7d2819e9a69135b9d4f6","score":"0.54333556","text":"def test_correct_conditional_prob_h(self):\n pass","title":""},{"docid":"f75311b537c5e377c6b9205d54e1e34d","score":"0.54235107","text":"def piecewise_p(x, val_a=25, val_b=300, val_c=100):\n if x > 8:\n p = val_a\n elif x > 6:\n p = val_b\n elif x > 4:\n p = val_c\n elif x > 2:\n p = val_a\n else:\n p = val_c\n\n return p","title":""},{"docid":"de21e17695d6f2c468fbb851ce196cff","score":"0.54168653","text":"def qftProbability(N,list_Primes,k):\n \n summation = 0\n for prime in list_Primes:\n summation += cmath.exp(2*math.pi*1j*prime*k/N)\n return abs(summation)**2","title":""},{"docid":"640586056718454210659fda2ed61040","score":"0.5415285","text":"def _pmf(self, x):\n if (x is True) or (x == 1):\n return self.proba_is_true\n\n if (x is False) or (x == 0):\n return 1 - self.proba_is_true\n\n return 0.","title":""},{"docid":"80129b59897d8105f4bac314de4e112d","score":"0.5411826","text":"def multiple_comparisons(p, n):\n if p > 1e-6: #if p is large and n small, calculate directly\n return 1 - (1-p)**n\n else: \n return one_minus_exp(-n * p)","title":""},{"docid":"3a06cc42d6d2c9ffb274226b2aa2853b","score":"0.54018855","text":"def qftPeaks(N,list_Primes, pi_N, t1):\n \n weights_tuple = []\n weights_tuple_normalized = []\n for k in range(0,N):\n if k % 5000 == 0:\n t2 = time.time()\n print k, \"([{:.3f} s])\".format(t2 - t1)\n t1 = t2\n sys.stdout.flush()\n weight = qftProbability(N, list_Primes, k)\n weights_tuple.append([k,weight])\n weights_tuple_normalized.append([k, weight/ float(N*pi_N)]) \n return weights_tuple, weights_tuple_normalized, t1","title":""},{"docid":"92bfe324bc5894234197e13ee2cf0d28","score":"0.53984207","text":"def _calc_p(self,factors):\n logp = self.calc_logp(factors)\n return np.exp(logp)","title":""},{"docid":"f40044b227b0311340c0d895247eaca5","score":"0.5397628","text":"def x_prob(x_sample, a):\n x_sample = np.array(x_sample)\n Pax = 1 #initialize P(a|x)\n for _x in x_sample:\n N = np.sqrt(np.log(a)/2*np.pi) #Normalization factor\n Pxa = N * a**(-_x**2 / 2) #P(x|a)\n Pa = 1 #P(a) is independent of 'a'\n Pax = Pax * Pxa * Pa \n return Pax","title":""},{"docid":"ed21da6e1c09a4a45fd098433327459b","score":"0.5389835","text":"def pesq_example() -> tuple:\n from torchmetrics.audio.pesq import PerceptualEvaluationSpeechQuality\n\n p = lambda: torch.randn(8000)\n t = lambda: torch.randn(8000)\n\n # plot single value\n metric = PerceptualEvaluationSpeechQuality(8000, \"nb\")\n metric.update(p(), t())\n fig, ax = metric.plot()\n\n # plot multiple values\n metric = PerceptualEvaluationSpeechQuality(16000, \"wb\")\n vals = [metric(p(), t()) for _ in range(10)]\n fig, ax = metric.plot(vals)\n\n return fig, ax","title":""},{"docid":"3747db3821c4500c863dd6559b15905a","score":"0.5384205","text":"def computeQ(data):\n Q = 0\n # Start with the expectation of the sum of priors over all tasks\n Q += (data.probZ * np.log(data.priorZ)).sum()\n\n # the expectation of the sum of posteriors over all tasks\n ab = np.dot(np.array([np.exp(data.beta)]).T, np.array([data.alpha]))\n\n # logSigma = - np.log(1 + np.exp(-ab))\n logSigma = logsigmoid(ab) # logP\n idxna = np.isnan(logSigma)\n if np.any(idxna):\n logger.warning('an invalid value was assigned to np.log [computeQ]')\n logSigma[idxna] = ab[idxna] # For large negative x, -log(1 + exp(-x)) = x\n\n # logOneMinusSigma = - np.log(1 + np.exp(ab))\n logOneMinusSigma = logsigmoid(-ab) - np.log(float(data.numClasses - 1)) # log((1-P)/(K-1))\n idxna = np.isnan(logOneMinusSigma)\n if np.any(idxna):\n logger.warning('an invalid value was assigned to np.log [computeQ]')\n logOneMinusSigma[idxna] = -ab[idxna] # For large positive x, -log(1 + exp(x)) = x\n\n for k in range(data.numClasses):\n delta = (data.labels == k + 1)\n Q += (data.probZ[:, k] * logSigma.T).T[delta].sum()\n oneMinusDelta = (data.labels != k + 1) & (data.labels != 0) # label == 0 -> no response\n Q += (data.probZ[:, k] * logOneMinusSigma.T).T[oneMinusDelta].sum()\n\n # Add Gaussian (standard normal) prior for alpha\n Q += np.log(sp.stats.norm.pdf(data.alpha - data.priorAlpha)).sum()\n\n # Add Gaussian (standard normal) prior for beta\n Q += np.log(sp.stats.norm.pdf(data.beta - data.priorBeta)).sum()\n\n if debug:\n logger.debug('a[0]={} a[1]={} a[2]={} b[0]={}'.format(data.alpha[0], data.alpha[1],\n data.alpha[2], data.beta[0]))\n logger.debug('Q={}'.format(Q))\n if np.isnan(Q):\n return -np.inf\n return Q","title":""},{"docid":"b0467d3c7e727051f737479890812320","score":"0.5382626","text":"def psi_prior(psi):\n a = 2\n b = 40\n return get_list_val(scipy.stats.beta.pdf(psi, a, b))","title":""},{"docid":"c13605bd1524c4498af355c65a00e00a","score":"0.53797555","text":"def x_prime_log_prior(self, x_prime):\n log_p = np.zeros(x_prime.size)\n for r in self.values():\n log_p += r.x_prime_log_prior(x_prime)\n return log_p","title":""},{"docid":"acff8e79ed8c5513ce0b551c459e8389","score":"0.5374655","text":"def ppf(self, q):\n f = lambda x: self.cdf(x) - q\n return sp.optimize.newton(func=f, x0=self.mean(), fprime=self.pdf)","title":""},{"docid":"fce89d7e326eeabfd0fa30e078a0ece4","score":"0.53650934","text":"def prob_early(pmf):\n return prob_bin(pmf, 0, 37)","title":""},{"docid":"bf8e673281fb8ce252f20a1a671a2c71","score":"0.53595436","text":"def test_correct_p_values(self):\n exp = [0.003, 0.006, 0.003]\n obs = self.mc._correct_p_values([0.001, 0.002, 0.001])\n self.assertFloatEqual(obs, exp)","title":""},{"docid":"c87a94eb6c938fc60bf7f168f355ff6c","score":"0.53517747","text":"def multinomial_predict(X, prior_0, prior_1, p_label_0, p_label_1):\n a = prior_0\n b = prior_1\n # print X.shape, p_label_1.shape, prior_1\n p_0 = np.exp(prior_0 + np.dot(X, p_label_0))\n p_1 = np.exp(prior_1 + np.dot(X, p_label_1))\n p_spam = [ p_1[i]/(p_0[i] + p_1[i]) if(p_0[i] + p_1[i]) > 0 else 0 for i in range(len(p_0))]\n\n return np.uint8(p_1 > p_0), np.array(p_spam)","title":""},{"docid":"25b87bc33128b4ddb663f70b558b0ae9","score":"0.5351664","text":"def project_probs(P):\n values = np.random.normal(1, 0.5, P)\n values[values<0] = 0\n project_probs = values / np.sum(values)\n return values, project_probs","title":""},{"docid":"940308d045ac04799b242c98638d5af8","score":"0.5348281","text":"def Prox(x, s, mu, p, q):\n \n X = smat(x[0:-2])\n S = smat(s[0:-2])\n \n xl = min(np.linalg.eigvals(X))\n sl = min(np.linalg.eigvals(S))\n \n if xl <= 0 or sl <= 0 or x[-2] <= 0 or x[-1] <= 0 or s[-2] <= 0 or s[-1] <= 0:\n return float('inf')\n \n D = NTScaling(X, S)\n d = np.sqrt(x[-2:] / s[-2:])\n \n V = Rotate(S, D) / np.sqrt(mu)\n [evals, evecs] = np.linalg.eigh(V)\n phi0 = Phi(evals, p, q)\n phi1 = Phi(d * s[-2:] / np.sqrt(mu), p, q)\n \n return phi0 + phi1","title":""},{"docid":"a15b9856de81c6ffb33c754dfae57b57","score":"0.5343132","text":"def _p(self, x: np.ndarray) -> np.ndarray:\n p = np.zeros((self.n_samples, self.n_samples))\n for i, j in combinations(range(self.n_samples), 2):\n if i != j:\n pij = self._pij(x, i, j)\n pji = self._pij(x, j, i)\n # Ensure that all the pairwise probabilities sum to 1.\n pair_value = (pij + pji) / (2 * self.n_samples)\n p[i, j] = pair_value\n p[j, i] = pair_value\n return p","title":""},{"docid":"70f1640a7a112a41903fb998eaa2fda1","score":"0.53339154","text":"def calcProb(tp):\n lamb = float(tp[0])\n thre = float(tp[1])\n maxi = thre*10000.\n# fPoi = ROOT.TF1(\"fPoi\", \"TMath::Poisson(x, [0])\", 0, maxi)\n fPoiI = ROOT.TF1(\"fPoi\", \"TMath::PoissonI(x, [0])\", 0, maxi)\n# fPoi.SetParameter(0, lamb)\n fPoiI.SetParameter(0, lamb)\n# return [fPoi.Integral(thre, maxi)/fPoi.Integral(0, maxi), fPoiI.Integral(thre, maxi)/fPoiI.Integral(0, maxi)]\n print lamb, maxi, thre\n return fPoiI.Integral(thre, maxi)/fPoiI.Integral(0, maxi)","title":""},{"docid":"b4d048495ef14d746bfbf4d72da3f1a0","score":"0.5322259","text":"def default_priors(x, y, model_function):\n priors = [_x0_prior(x), _dx_prior(x), _A_prior(y)]\n if 'multi' in model_function:\n priors += [_x0_prior(x), _dx_prior(x), _A_prior(y)]\n elif 'thick' in model_function:\n priors += [_tau_prior()]\n elif 'hermite' in model_function:\n priors += [_h3_prior(), _h4_prior()]\n if '_cont' in model_function:\n priors += [_cont_prior(y)]\n return priors","title":""},{"docid":"bba2424f5ac051f842ff3e6698b63d22","score":"0.531893","text":"def chi_effective_prior_from_isotropic_spins(q,aMax,xs):\n\n # Ensure that `xs` is an array and take absolute value\n xs = np.reshape(np.abs(xs),-1)\n\n # Set up various piecewise cases\n pdfs = np.ones(xs.size,dtype=complex)*(-1.)\n caseZ = (xs==0)\n caseA = (xs>0)*(xs<aMax*(1.-q)/(1.+q))*(xs<q*aMax/(1.+q))\n caseB = (xs<aMax*(1.-q)/(1.+q))*(xs>q*aMax/(1.+q))\n caseC = (xs>aMax*(1.-q)/(1.+q))*(xs<q*aMax/(1.+q))\n caseD = (xs>aMax*(1.-q)/(1.+q))*(xs<aMax/(1.+q))*(xs>=q*aMax/(1.+q))\n caseE = (xs>aMax*(1.-q)/(1.+q))*(xs>aMax/(1.+q))*(xs<aMax)\n caseF = (xs>=aMax)\n\n # Select relevant effective spins\n x_A = xs[caseA]\n x_B = xs[caseB]\n x_C = xs[caseC]\n x_D = xs[caseD]\n x_E = xs[caseE]\n\n pdfs[caseZ] = (1.+q)/(2.*aMax)*(2.-np.log(q))\n\n pdfs[caseA] = (1.+q)/(4.*q*aMax**2)*(\n q*aMax*(4.+2.*np.log(aMax) - np.log(q**2*aMax**2 - (1.+q)**2*x_A**2))\n - 2.*(1.+q)*x_A*np.arctanh((1.+q)*x_A/(q*aMax))\n + (1.+q)*x_A*(Di(-q*aMax/((1.+q)*x_A)) - Di(q*aMax/((1.+q)*x_A)))\n )\n\n pdfs[caseB] = (1.+q)/(4.*q*aMax**2)*(\n 4.*q*aMax\n + 2.*q*aMax*np.log(aMax)\n - 2.*(1.+q)*x_B*np.arctanh(q*aMax/((1.+q)*x_B))\n - q*aMax*np.log((1.+q)**2*x_B**2 - q**2*aMax**2)\n + (1.+q)*x_B*(Di(-q*aMax/((1.+q)*x_B)) - Di(q*aMax/((1.+q)*x_B)))\n )\n\n pdfs[caseC] = (1.+q)/(4.*q*aMax**2)*(\n 2.*(1.+q)*(aMax-x_C)\n - (1.+q)*x_C*np.log(aMax)**2.\n + (aMax + (1.+q)*x_C*np.log((1.+q)*x_C))*np.log(q*aMax/(aMax-(1.+q)*x_C))\n - (1.+q)*x_C*np.log(aMax)*(2. + np.log(q) - np.log(aMax-(1.+q)*x_C))\n + q*aMax*np.log(aMax/(q*aMax-(1.+q)*x_C))\n + (1.+q)*x_C*np.log((aMax-(1.+q)*x_C)*(q*aMax-(1.+q)*x_C)/q)\n + (1.+q)*x_C*(Di(1.-aMax/((1.+q)*x_C)) - Di(q*aMax/((1.+q)*x_C)))\n )\n\n pdfs[caseD] = (1.+q)/(4.*q*aMax**2)*(\n -x_D*np.log(aMax)**2\n + 2.*(1.+q)*(aMax-x_D)\n + q*aMax*np.log(aMax/((1.+q)*x_D-q*aMax))\n + aMax*np.log(q*aMax/(aMax-(1.+q)*x_D))\n - x_D*np.log(aMax)*(2.*(1.+q) - np.log((1.+q)*x_D) - q*np.log((1.+q)*x_D/aMax))\n + (1.+q)*x_D*np.log((-q*aMax+(1.+q)*x_D)*(aMax-(1.+q)*x_D)/q)\n + (1.+q)*x_D*np.log(aMax/((1.+q)*x_D))*np.log((aMax-(1.+q)*x_D)/q)\n + (1.+q)*x_D*(Di(1.-aMax/((1.+q)*x_D)) - Di(q*aMax/((1.+q)*x_D)))\n )\n\n pdfs[caseE] = (1.+q)/(4.*q*aMax**2)*(\n 2.*(1.+q)*(aMax-x_E)\n - (1.+q)*x_E*np.log(aMax)**2\n + np.log(aMax)*(\n aMax\n -2.*(1.+q)*x_E\n -(1.+q)*x_E*np.log(q/((1.+q)*x_E-aMax))\n )\n - aMax*np.log(((1.+q)*x_E-aMax)/q)\n + (1.+q)*x_E*np.log(((1.+q)*x_E-aMax)*((1.+q)*x_E-q*aMax)/q)\n + (1.+q)*x_E*np.log((1.+q)*x_E)*np.log(q*aMax/((1.+q)*x_E-aMax))\n - q*aMax*np.log(((1.+q)*x_E-q*aMax)/aMax)\n + (1.+q)*x_E*(Di(1.-aMax/((1.+q)*x_E)) - Di(q*aMax/((1.+q)*x_E)))\n )\n\n pdfs[caseF] = 0.\n\n # Deal with spins on the boundary between cases\n if np.any(pdfs==-1):\n boundary = (pdfs==-1)\n pdfs[boundary] = 0.5*(chi_effective_prior_from_isotropic_spins(q,aMax,xs[boundary]+1e-6)\\\n + chi_effective_prior_from_isotropic_spins(q,aMax,xs[boundary]-1e-6))\n\n return np.real(pdfs)","title":""},{"docid":"32391e786e38d197735f19c6c3c35b75","score":"0.5317258","text":"def AdditiveEquiprobabilities():\r\n pass","title":""},{"docid":"11d7c537bd574a1460ee81d6db268927","score":"0.5313194","text":"def prob59():","title":""},{"docid":"4745082d1b07736369556a66635d0f6e","score":"0.53005433","text":"def get_p(features):\r\n # Compute the raw value\r\n raw_prediction = intercept + coefficients_broadcast.value.dot(features)\r\n # Bound the raw value between 20 and -20\r\n raw_prediction = min(20, max(-20, raw_prediction))\r\n # Return the probability\r\n probability = 1.0 / (1 + exp(- raw_prediction))\r\n return probability","title":""},{"docid":"ce6e143ed471a178aa534f1b2099b409","score":"0.5298077","text":"def test_correct_conditional_prob_v(self):\n pass","title":""},{"docid":"7a527689900d1a0bb7454209c4af3a70","score":"0.5294685","text":"def ppf(x, mu=0, sigma=1):\n return mu - sigma * math.sqrt(2) * erfcinv(2 * x)","title":""},{"docid":"29b591350716fe9fcac754b32d4aed89","score":"0.5281783","text":"def f_per_particle(self,m, alpha, X, y, P):\n total_features = X.shape[1]\n # Get the subset of the features from the binary mask\n if np.count_nonzero(m) == 0:\n X_subset = X\n else:\n X_subset = X[:,m==1]\n\n ratio_selected_features = X_subset.shape[1]/total_features\n \n #Particle fittness error/loss computed using cross validation\n fitness_error = make_scorer(self.__objective_fcn, ratio_selected_features=ratio_selected_features, P=P, alpha=alpha)\n scores = cross_val_score(self.regressor, X_subset, y, cv=10, scoring=fitness_error) #does first 4 steps of k-fold CV\n particle_fitness_err_mean = scores.mean()\n\n #Stdev \n particle_fitness_err_stdev = np.std(scores)\n\n return (particle_fitness_err_mean, particle_fitness_err_stdev)","title":""},{"docid":"0f4f7314a053c38a83d7fa54e418dd59","score":"0.52714187","text":"def perplexity(self, data):\n test_tokens = self._preprocess(data, self.n)\n N = len(test_tokens)\n\n probs = 0.0\n for word in test_tokens:\n probs -= self.get_single_probability(word, log=True)\n\n return math.exp(probs/N)","title":""},{"docid":"98c6bfcb4315d422571af70bcb76e4f6","score":"0.5263221","text":"def sample(preds, temperature=1.0):\n preds = np.asarray(preds).astype('float64')\n preds = np.log(preds) / temperature\n exp_preds = np.exp(preds)\n preds = exp_preds / np.sum(exp_preds)\n probas = np.random.multinomial(1, preds, 1)\n return probas","title":""},{"docid":"e9027b01ff967e5b02c69efaa4c8cd86","score":"0.5261927","text":"def ppf(self, p):\n d = self.loc[p.index, p.columns]\n icdf_arr = d.mu + d.sigma * np.sqrt(2) * erfinv(2 * p.values - 1)\n return pd.DataFrame(icdf_arr, index=p.index, columns=p.columns)","title":""},{"docid":"c57344492bc9af651a3ecbfc95d995f9","score":"0.5257148","text":"def evaluate_p(self, X, best_p, p_range=(0, 1), sample_density=100):\n return np.mean((best_p - self.estimate_best_p(X, p_range, sample_density)) ** 2)","title":""},{"docid":"18203532f60f34602f01f74f01c3ce76","score":"0.52518934","text":"def extract_priors(options):\n # Initialize empty dictionary to store priors \n options['priors_definitions'] = dict()\n options['priors_params'] = dict()\n\n if len(options['priors']) < 4:\n raise ValueError('''Please provide list of four arguments for \n priors. Less than four arguments provided.''')\n elif len(options['priors']) > 4:\n raise ValueError('''Please provide list of four arguments for \n priors. More than four arguments provided.''')\n elif len(options['priors']) == 4: \n NoneType = type(None)\n # Check whether arguments in list are of type integer or float\n for i in range(0,4):\n if isinstance(options['priors'][i], (str, NoneType)) is False:\n raise ValueError('''User Error: Please provide numerical values or NoneType \n for priors of parameters of the model.''')\n \n if options['priors'][i] is not None:\n if i == 0:\n # Extract prior for alpha parameter\n options['priors_definitions']['scale'] = \" \".join(re.findall(\"[a-zA-Z]+\", \n options['priors'][0]))\n \n alpha_prior_values = re.findall(r'[-+]?\\d*\\.\\d+|\\d+', options['priors'][0])\n options['priors_params']['scale'] = [np.float(x) for x in alpha_prior_values]\n elif i == 1:\n # Extract prior for beta parameter\n options['priors_definitions']['slope'] = \" \".join(re.findall(\"[a-zA-Z]+\", \n options['priors'][1]))\n \n beta_prior_values = re.findall(r'[-+]?\\d*\\.\\d+|\\d+', options['priors'][1])\n options['priors_params']['slope'] = [np.float(x) for x in beta_prior_values] \n elif i == 2:\n # Extract prior for gamma parameter\n options['priors_definitions']['gamma'] = \" \".join(re.findall(\"[a-zA-Z]+\", \n options['priors'][2])) \n gamma_prior_values = re.findall(r'[-+]?\\d*\\.\\d+|\\d+', options['priors'][2])\n options['priors_params']['gamma'] = [np.float(x) for x in gamma_prior_values]\n elif i == 3:\n # Extract prior for lambda parameter\n options['priors_definitions']['lambda'] = \" \".join(re.findall(\"[a-zA-Z]+\", \n options['priors'][3]))\n \n lambda_prior_values = re.findall(r'[-+]?\\d*\\.\\d+|\\d+', options['priors'][3])\n options['priors_params']['lambda'] = [np.float(x) for x in lambda_prior_values]\n \n return options","title":""},{"docid":"992b75ebef63d1911731e82c10726dc9","score":"0.52492476","text":"def ppf(self, x, range):\n min, max = range\n pmin = min ** (-self.power)\n pmax = max ** (-self.power)\n scaled = pmin - x*(pmin - pmax)\n return scaled ** (-1 / self.power)","title":""},{"docid":"696b90a8ad7327e37c2ae29814e7a865","score":"0.52484584","text":"def upP(self,q,p, *args) :\n\t\treturn array( [ p[1]**2 * self.Rp(q[0]) / (self.R(q[0])**3) , 0] )","title":""},{"docid":"2894f5144b19a0f0973502e54fdde66c","score":"0.5244034","text":"def evaluate_p(self, p_in):\n return np.exp(self.evaluate_logp(p_in))","title":""},{"docid":"795b66a6542544630181011a59623c96","score":"0.5243482","text":"def predict_proba(self, X):\n values = self.decision_function(X)\n likelihood = np.exp(values - values.min(axis=1)[:, np.newaxis])\n # compute posterior probabilities\n return likelihood / likelihood.sum(axis=1)[:, np.newaxis]","title":""},{"docid":"91861f70e05e530c827be2a6260c8124","score":"0.5236747","text":"def test_guess_priors():\n kernel = Exponentiation(\n ConstantKernel(constant_value_bounds=\"fixed\") * Matern()\n + WhiteKernel()\n + CompoundKernel([RBF(), Matern()]),\n 2.0,\n )\n\n priors = guess_priors(kernel)\n\n assert len(priors) == 4\n expected = [\n -1.737085713764618,\n -4.107091211892862,\n -1.737085713764618,\n -1.737085713764618,\n ]\n for p, v in zip(priors, expected):\n assert_almost_equal(p(0.0), v)","title":""},{"docid":"177a646c01a4ff035d425e2bf266daf5","score":"0.5236103","text":"def prior(z,m,info='hdfn',nt=6,ninterp=0,x=None,y=None): \n if info=='none' or info=='flat': return\n #We estimate the priors at m_step intervals\n #and keep them in a dictionary, and then\n #interpolate them for other values\n m_step=0.1\n accuracy=str(len(str(int(1./m_step)))-1)#number of decimals kept\n\n exec(\"from desc_bpz.prior_{} import *\".format(info), globals())\n global prior_dict\n try:\n len(prior_dict)\n except NameError:\n prior_dict={}\n\n #The dictionary keys are values of the \n #magnitud quantized to mstep mags\n #The values of the dictionary are the corresponding\n #prior probabilities.They are only calculated once \n #and kept in the dictionary for future\n #use if needed. \n forma='%.'+accuracy+'f'\n m_dict=forma %m \n if m_dict not in prior_dict or info=='lensing': #if lensing, the magnitude alone is not enough\n if info!='lensing':\n prior_dict[m_dict]=function(z,float(m_dict),nt)\n else:\n prior_dict[m_dict]=function(z,float(m_dict),nt,x,y) \n if ninterp:\n pp_i=prior_dict[m_dict]\n nz=pp_i.shape[0]\n nt=pp_i.shape[1]\n nti=nt+(nt-1)*int(ninterp)\n tipos=arange(nt)*1.\n itipos=arange(nti)*1./(1.+float(ninterp))\n buffer=zeros((nz,nti))*1.\n for iz in range(nz):\n buffer[iz,:]=match_resol(tipos,pp_i[iz,:],itipos)\n prior_dict[m_dict]=buffer\n return prior_dict[m_dict]","title":""},{"docid":"078a73db8a4de3e09e29f2467dfb6e1b","score":"0.5231932","text":"def ppf(self, q):\n\n if np.any(q < 0.) or np.any(q > 1.):\n msg = 'Provided quantiles are out of allowed range of [0, 1]: {!r}'\n raise ValueError(msg.format(q))\n\n # ---------\n # Exercise:\n # ---------\n # Angle PDf | part c) (exercises/angle_pdf.py)\n #\n\n # define N\n N = self.k/(2 * (1 - np.exp(- np.pi * self.k)))\n\n # make value array\n values = np.zeros_like(q)\n\n # in case q is just one value\n if np.isscalar(q):\n if q == 0.:\n values = -np.pi\n elif q == 0.5:\n values = 0.\n elif q == 1.:\n values = np.pi\n elif q > 0 and q < 0.5:\n values = np.log(self.k * q / N + np.exp(- np.pi * self.k)) /self.k\n elif q > 0.5 and q < 1:\n values = - np.log(1 - (self.k/N) * (q - 1/2))/self.k\n \n else:\n # special cases first\n values[q == 0.0] = -np.pi\n values[q == 0.5] = 0.\n values[q == 1.0] = np.pi\n\n mask_lower = np.logical_and(q > 0, q < 0.5) # formula for lower percentage\n values[mask_lower] = np.log(self.k * q[mask_lower] / N + np.exp(- np.pi * self.k)) /self.k\n\n mask_higher = np.logical_and(q > 0.5, q < 1.) # formula for higher percentage\n values[mask_higher] = - np.log(1 - (self.k/N) * (q[mask_higher] - 1/2))/self.k\n\n return values","title":""},{"docid":"dbc049ede849ba998d043a10b91cc05a","score":"0.5231932","text":"def x_prime(x):\r\n r1 = np.random.uniform(0, 1)\r\n xprime = x + (0.5 - r1) * min([3, 10**5 - x, x + 10**5])\r\n \r\n return xprime","title":""},{"docid":"dd43580827e54111be148c42500113fc","score":"0.5221587","text":"def call_post_prob(self, val, post_cutoff):\n number_state = len(self.prior_state)\n prob = []\n for i in range(0, number_state):\n gauss_pmf = self.get_gauss_pmf_cdf(\n val, self.mu_state[i], self.sigma_state[i]\n )[0]\n prob.append(gauss_pmf * self.prior_state[i])\n sum_prob = float(sum(prob))\n post_prob = [float(a) / sum_prob for a in prob]\n max_prob = max(post_prob)\n if max_prob >= post_cutoff:\n return post_prob.index(max_prob)\n else:\n return None","title":""},{"docid":"9d30b7ff4e1b862cc98f37cef75fd122","score":"0.5219188","text":"def posteriors(likelihoods, priors):\n #Check that there is a prior for each likelihood\n if len(likelihoods) != len(priors):\n raise ValueError, \"Lists not equal lengths.\"\n #Posterior probability is defined as prior * likelihood\n return [l * p for l, p in zip(likelihoods, priors)]","title":""},{"docid":"bea50441112c0cd9bb0ba347e550899b","score":"0.5215778","text":"def _ex_set(p):\n k = GF(p)\n res = [ k(0), k(1), k(2), k(4) ]\n R = k['X']\n f = R([1,-3,1]) #(X**2 - 3*X+1)\n ro = f.roots()\n for a in ro:\n if a[0] not in res:\n res.append(a[0])\n return res","title":""},{"docid":"ce8023d4eb64fdad7925f147727ce5db","score":"0.52146107","text":"def prime_net(self, primer):\n x = np.zeros((self.vocab_size, 1))\n x[self.char_to_ix[primer[0]]] = 1\n\n for i in range(len(primer)):\n self.h = np.tanh(np.dot(self.Wxh, x) + np.dot(self.Whh, self.h) + self.bh)\n y = np.dot(self.Why, self.h) + self.by\n p = np.exp(y) / np.sum(np.exp(y)) # Probabilities\n ix = self.char_to_ix[primer[i]]\n x = np.zeros((self.vocab_size, 1))\n x[ix] = 1\n return x","title":""},{"docid":"769735c92a1ff5f88a73c1d640aa4793","score":"0.52032566","text":"def chisq_prob(data=None, model=None):\n chisq = np.sum(np.square(data - model) / model)\n p_value = np.exp(-0.5 * chisq)\n\n return p_value, chisq","title":""},{"docid":"857773373d58102c233dcee263b77c6d","score":"0.5193262","text":"def log_prior(self, x):\n # Check if the values are in the priors bounds, will return -inf if not\n log_p = np.log(self.in_bounds(x), dtype=\"float\")\n # Uniform on x\n log_p -= np.log(self.bounds[\"x\"][1] - self.bounds[\"x\"][0])\n # Gaussian on y\n log_p += norm(scale=5).logpdf(x[\"y\"])\n return log_p","title":""},{"docid":"85fefad4488eb215edc2f1120ee6f7d1","score":"0.518961","text":"def pred_evaluation(prepare_data, data, iterator):\n recall = 0.0\n mrr = 0.0\n evalutation_point_count = 0\n # pred_res = []\n # att = []\n\n for _, valid_index in iterator:\n x, mask, y = prepare_data([data[0][t] for t in valid_index],\n np.array(data[1])[valid_index])\n preds = sess.run(output_probs,feed_dict={input_items: x,target_items : y,input_item_mask :mask,keep_prob_1:1.0,keep_prob_2:1.0})\n # weights = f_weight(x, mask)\n targets = y\n ranks = (preds.T > np.diag(preds.T[targets])).sum(axis=0) + 1\n rank_ok = (ranks <= 20)\n\n # pred_res += list(rank_ok)\n recall += rank_ok.sum()\n mrr += (1.0 / ranks[rank_ok]).sum()\n evalutation_point_count += len(ranks)\n # att.append(weights)\n recall = numpy_floatX(recall) / evalutation_point_count\n mrr = numpy_floatX(mrr) / evalutation_point_count\n eval_score = (recall, mrr)\n return eval_score","title":""},{"docid":"80bfcecff56948735b3b960991527f7a","score":"0.5183773","text":"def prior(hypercube):\n\n fitting_par = [0.0] * nfit\n for i, x in enumerate(hypercube):\n fitting_par[i] = PC_priors.UniformPrior(parameters_minmax[i,0], parameters_minmax[i,1])(x)\n\n return fitting_par","title":""},{"docid":"035b4f9267cc79352c932415a871d77e","score":"0.5176513","text":"def get_conditional(self, x, Px, y, Py, Pxy, yo, Pyo):\n\n # Add measurement noise\n Py1 = Py + Pyo\n # Kalman gain\n print(Pxy)\n K = Pxy@np.linalg.inv(Py)\n print(\"K\", K)\n \n x_prime = x + K@(yo - y)\n Px_prime = Px - K@Py1@K.T\n\n return x_prime, Px_prime","title":""},{"docid":"421bf6edd0b56b25a75b427e109d58fa","score":"0.51719713","text":"def predictedRating(x, P, Q):\n #### TO DO\n p = P[x[0]-1]\n q = Q[x[1]-1]\n # q_t = q.T\n # q_t = q.transpose()\n predicted = np.matmul(p, q)\n l = predicted\n # print(l)\n # print(x[2])\n return (x[0],x[1],x[2],l)","title":""},{"docid":"4c723c6b76f134210c91be7d64e09724","score":"0.5169575","text":"def check_preqs(preqs, metrics) -> List[CheckedPreq]:\n\n checked_preqs = []\n\n def add_preq(key: str, msg: str, error: bool, ctx: dict):\n checked_preqs.append(CheckedPreq(key=key, msg=msg, error=error, ctx=ctx))\n\n for pkey, rng_str in preqs.items():\n ctx = metrics.get(\"ctx\", {})\n mval = metrics.get(pkey, None)\n if mval is None:\n add_preq(\n key=pkey, error=True, ctx=ctx, msg=\"expected to be measured, but wasn't\"\n )\n continue\n\n try:\n rng = Range(rng_str)\n except InvalidRangeError as ex:\n add_preq(\n key=pkey, error=True, ctx=ctx, msg=f'invalid range \"{rng_str}\": {ex}'\n )\n continue\n\n if not rng.contains(mval):\n add_preq(\n key=pkey,\n error=True,\n ctx=ctx,\n msg=f\"{rng.format_val(mval)} in {rng} failed\",\n )\n continue\n\n add_preq(\n key=pkey,\n error=False,\n ctx=ctx,\n msg=f\"{rng.format_val(mval)} in {rng} satisfied\",\n )\n\n return checked_preqs","title":""},{"docid":"87afda02ac70f0a95b5cdee2014fed08","score":"0.5167855","text":"def get_pdfvals(xarr, qarr, pdfset):\n lhapdf_pdf = lhapdf.mkPDF(pdfset)\n res = {}\n for x, q in zip(xarr, qarr):\n dict_update(res, lhapdf_pdf.xfxQ2(x, q))\n return res","title":""},{"docid":"2a680069624b98fb0b0577dac28c4ee4","score":"0.5161852","text":"def posterior(x, n, P, Pr):\n if not isinstance(n, int) or (n <= 0):\n raise ValueError('n must be a positive integer')\n if not isinstance(x, int) or (x < 0):\n err = 'x must be an integer that is greater than or equal to 0'\n raise ValueError(err)\n if x > n:\n raise ValueError('x cannot be greater than n')\n if not isinstance(P, np.ndarray) or len(P.shape) != 1:\n raise TypeError('P must be a 1D numpy.ndarray')\n if np.any(P > 1) or np.any(P < 0):\n raise ValueError('All values in P must be in the range [0, 1]')\n if not isinstance(Pr, np.ndarray) or (P.shape != Pr.shape):\n err = 'Pr must be a numpy.ndarray with the same shape as P'\n raise TypeError(err)\n if np.any(Pr > 1) or np.any(Pr < 0):\n raise ValueError('All values in Pr must be in the range [0, 1]')\n\n E = np.sum(Pr)\n if not np.isclose(E, 1):\n raise ValueError('Pr must sum to 1')\n\n fact = (np.math.factorial(n)) / \\\n (np.math.factorial(x) * np.math.factorial(n - x))\n fact *= (np.power(P, x)) * (np.power((1 - P), (n - x)))\n inter = fact * Pr\n marg = np.sum(inter)\n return inter / marg","title":""},{"docid":"891faa0c1aa22d38a02ef73613460574","score":"0.5161048","text":"def true_posterior_prob(self, thetas):\n return self.true_posterior.pdf(thetas)","title":""},{"docid":"8cf1d78f0c1b7487c7d1eb1a71f0b5c8","score":"0.51605046","text":"def prob54():","title":""},{"docid":"ae1791d42735332d54a9f8186b1bd15a","score":"0.51601565","text":"def posterior(x, n, p1, p2):\n if not isinstance(n, int) or n <= 0:\n raise ValueError('n must be a positive integer')\n if not isinstance(x, int) or x < 0:\n raise ValueError(\n 'x must be an integer that is greater than or equal to 0')\n if x > n:\n raise ValueError('x cannot be greater than n')\n if not isinstance(p1, float) or (p1 > 1) or (p1 < 0):\n raise ValueError('p1 must be a float in the range [0, 1]')\n if not isinstance(p2, float) or (p2 > 1) or (p2 < 0):\n raise ValueError('p2 must be a float in the range [0, 1]')\n if p2 <= p1:\n raise ValueError('p2 must be greater than p1')\n\n P1 = special.btdtr(x+1, n-x+1, p1)\n P2 = special.btdtr(x+1, n-x+1, p2)\n\n return P2-P1","title":""},{"docid":"01227567adb8dc53729f3fc9fd65b0ed","score":"0.5159808","text":"def logprior(p):\n res = [_.logpdf(__) for _, __ in zip(priorList, p)]\n res = sum(res)\n if not np.isfinite(res):\n res = BADV\n return res","title":""},{"docid":"80c8b271fa9d62c7166fd51f0e1740e5","score":"0.51560634","text":"def generate_preconditioner(self, f_all):\n hessian = []\n a = f_all.min.hess\n b = f_all.real.hess\n c = f_all.max.hess\n size = len(f_all.min.hess)\n for i in range(size):\n hessian.append([])\n for j in range(size):\n gmax = max(a[i,j],b[i,j],c[i,j])\n gmin = min(a[i,j],b[i,j],c[i,j])\n gmid = (gmin+gmax)*0.5\n hessian[i].append(gmid)\n hessian = np.asarray(hessian)\n return hessian","title":""},{"docid":"7f853c8793702b1737badab05d415ae1","score":"0.5148506","text":"def classify(X,mu,sigma,Pi):\n N,D = X.shape\n K = len(Pi)\n return np.array([argmax([Pi[k]*norm_pdf_multivariate(X[n],mu[k],sigma[k]) for k in range(K)]) for n in range(N)])","title":""},{"docid":"84828a4b289040f92710686d1cb2d5ec","score":"0.5144026","text":"def ProcessPQ(joints, marg, feature_length):\n #variable defining the heap\n pq = []\n\n for i in range(feature_length):\n\tfor j in range(i+1, feature_length):\n\t I = 0\n\t for x_u, p_x_u in marg[i].iteritems():\n\t\tfor x_v, p_x_v in marg[j].iteritems():\n\t\t if (x_u, x_v) in joints[(i, j)]:\n\t\t\tp_x_uv = joints[(i, j)][(x_u, x_v)]\n\t\t\tI += p_x_uv * (math.log(p_x_uv, 2) - math.log(p_x_u, 2) - math.log(p_x_v, 2))\n\t heapq.heappush(pq, (-I, i, j))\n \n return pq","title":""},{"docid":"1778c91b326f790f0743f46d3aa0baf3","score":"0.51428276","text":"def get_q_probs(self):\n return F.softmax(self.q_logits, dim=0)","title":""},{"docid":"36326af82d9a74250499eb2300a1bdf4","score":"0.51424074","text":"def ApproximatePrecessingFisherMatrixElementFactor(P, p1,p2,m,s,psd,phase_deriv_cache=None,omit_geometric_factor=False,break_out_beta=False,**kwargs):\n\n # Create basic infrastructure for IP. This requires VERY DENSE sampling...try to avoid\n # hF0 = lalsimutils.complex_hoff(P)\n # IP = lalsimutils.CreateCompatibleComplexIP(hF0,psd=psd,**kwargs)\n # dPsi2F_func = interp1d(fvals,dPsi2F, fill_value=0, bounds_error=False)\n # dalphaF_func= interp\n\n# print \" called on \", p1,p2, m, s\n\n # This geometric stuff is all in common. I should make one call to generate Sh once and for all\n beta_0 = P.extract_param('beta')\n thetaJN_0 = P.extract_param('thetaJN')\n mc_s = P.extract_param('mc')/lal.MSUN_SI *lalsimutils.MsunInSec\n d_s = P.extract_param('dist')/lal.C_SI # distance in seconds\n\n if omit_geometric_factor:\n geometric_factor=1\n else:\n geometric_factor = np.abs(lal.SpinWeightedSphericalHarmonic(thetaJN_0,0,-2,2,int(m)) * lal.WignerDMatrix(2,m,s,0,beta_0,0))**2\n if np.abs(geometric_factor) < 1e-5:\n return 0,0 # saves lots of time\n\n # Create phase derivatives. Interpolate onto grid? OR use lower-density sampling\n if phase_deriv_cache:\n if not (p1 in phase_deriv_cache.keys()):\n phase_deriv_cache[p1] = PhaseDerivativeSeries(P,p1)\n else:\n fvals,fmax_safe, dPsi2F,dPhiF, dalphaF,dgammaF = phase_deriv_cache[p1] \n if not (p2 in phase_deriv_cache.keys()):\n phase_deriv_cache[p2] = PhaseDerivativeSeries(P,p2)\n else:\n fvals,fmax_safe, dPsi2F_2,dPhiF_2, dalphaF_2,dgammaF_2 =phase_deriv_cache[p2] \n else:\n fvals,fmax_safe, dPsi2F,dPhiF, dalphaF,dgammaF = PhaseDerivativeSeries(P,p1)\n fvals,fmax_safe, dPsi2F_2,dPhiF_2, dalphaF_2,dgammaF_2 = PhaseDerivativeSeries(P,p2)\n\n dropme = np.logical_or(fvals <P.fmin, fvals>0.98*fmax_safe)\n\n Shvals = np.array(map(psd, np.abs(fvals)))\n Shvals[np.isnan(Shvals)]=float('inf')\n phase_weights = 4* (np.pi*mc_s*mc_s)**2/(3*d_s*d_s) *np.power((np.pi*mc_s*np.maximum(np.abs(fvals),P.fmin/2)),-7./3.)/Shvals # hopefully one-sided. Note the P.fmin removes nans \n phase_weights[np.isnan(phase_weights)]=0\n phase_weights[dropme] =0\n\n dalphaF[dropme] = 0\n dPsi2F[dropme] = 0\n dgammaF[dropme]=0\n\n dalphaF_2[dropme] = 0\n dPsi2F_2[dropme] = 0\n dgammaF_2[dropme]=0\n\n rhoms2 = np.sum(phase_weights*P.deltaF)*geometric_factor\n\n if (beta_0) < 1e-2: # Pathological, cannot calculate alpha or gamma\n ret_weights = P.deltaF*phase_weights*geometric_factor*( dPsi2F)*(dPsi2F_2)\n ret = np.sum(ret_weights)\n else:\n if not break_out_beta:\n ret_weights = P.deltaF*phase_weights*geometric_factor*( dPsi2F- 2 *dgammaF + m*s*dalphaF)*(dPsi2F_2 - 2*dgammaF_2+m*s*dalphaF_2)\n ret = np.sum(ret_weights)\n else:\n # Warming: this uses the explicit assumption \\gamma - -alpha cos beta\n # Warning: you probably never want this unless geometric_factor=1\n if not omit_geometric_factor:\n print(\" You are extracting a breakdown of the fisher matrix versus beta, but are not fixing the geometric factor...are you sure?\")\n ret_00 = np.sum(P.deltaF*phase_weights*geometric_factor*( dPsi2F)*(dPsi2F_2 ))\n ret_01 = np.sum(P.deltaF*phase_weights*geometric_factor*( dPsi2F)*(dalphaF_2 ))\n ret_10 = np.sum(P.deltaF*phase_weights*geometric_factor*( dalphaF)*(dPsi2F_2 ))\n ret_11 = np.sum(P.deltaF*phase_weights*geometric_factor*( dalphaF)*(dalphaF_2))\n print(\" -- submatrix \", p1,p2, ret_00, ret_01, ret_10, ret_11)\n return rhoms2,{\"00\":ret_00, \"01\":ret_01, \"10\": ret_10, \"11\": ret_11}\n\n# print \" Internal fisher element\", geometric_factor, rhoms2, ret\n # plt.plot(fvals,ret_weights)\n # plt.ylim(0,np.max(ret_weights))\n # plt.show()\n \n return rhoms2, ret","title":""},{"docid":"d5a77645e4f56495b85092cd33e91714","score":"0.51353604","text":"def prob():\n nx = 2 #number of variables [x1, x2]\n np = 2 #number of parameters [p1, p2]\n neq = 0 #number of equality constraints\n niq = 2 #number of inequality constraints\n name = \"Problem 1\"\n return nx, np, neq, niq, name","title":""},{"docid":"aad40a8d315c31681e6f22f707229fbc","score":"0.512335","text":"def parzen_estimation(x_samples, point_x, h):\n k_n = 0\n for row in x_samples:\n x_i = (point_x - row[:, np.newaxis]) / (h)\n for row in x_i:\n if np.abs(row) > (1 / 2):\n break\n else:\n k_n += 1\n return h, (k_n / len(x_samples)) / (h ** point_x.shape[1])","title":""},{"docid":"19dd1e86d0dbd8a04ab880ab92b564bc","score":"0.5118249","text":"def gen_p(a, b, x1, x2):\n x = np.arange(x1, x2, 0.1)\n p = a*x*(b - x)\n return np.stack((x, p))","title":""},{"docid":"3101507d4ff26c6124c0aaf2f717ce74","score":"0.5116654","text":"def _evaluate_values(props, h, P):\r\n output = []\r\n for i in range(len(h)):\r\n tmp = [h[i], P[i]]\r\n if 611.23 <= P[i] <= 100e6 and 1000. <= h[i] <= 4500e3:\r\n region = _region_ph(P[i], h[i])\r\n if region in [1, 2, 3]:\r\n for prop in props:\r\n if prop != 's':\r\n tmp.append(_eval(spl_objs[prop], h[i]*scaling['h'], P[i]*scaling['P']) / scaling[prop])\r\n else:\r\n if region == 1:\r\n tmp.append(0)\r\n elif region == 2:\r\n tmp.append(1)\r\n elif region == 3:\r\n tmp.append(-1)\r\n elif region == 4:\r\n hl, hv = _hl_p(P[i]), _hv_p(P[i])\r\n vtmp, ltmp = [hv], [hl]\r\n if hl == hv: x = 1.\r\n else: x = (h[i] - hl) / (hv - hl)\r\n Tl, Tv = _eval(spl_objs['T'], hl*scaling['h'], P[i]*scaling['P']) / scaling['T'], _eval(spl_objs['T'], hv*scaling['h'], P[i]*scaling['P']) / scaling['T']\r\n dl, dv = _eval(spl_objs['d'], hl * scaling['h'], P[i] * scaling['P']) / scaling['d'], _eval(spl_objs['d'], hv * scaling['h'], P[i] *scaling['P']) / scaling['d']\r\n vl, vv = _eval(spl_objs['v'], hl * scaling['h'], P[i] * scaling['P']) / scaling['v'], _eval(spl_objs['v'], hv * scaling['h'], P[i] *scaling['P']) / scaling['v']\r\n T = Tl + x * (Tv - Tl)\r\n d = dl * dv / (dv + x * (dl - dv))\r\n for prop in props:\r\n if prop == 'T':\r\n tmp.append(T)\r\n ltmp.append(Tl)\r\n vtmp.append(Tv)\r\n elif prop == 'd':\r\n tmp.append(d)\r\n ltmp.append(dl)\r\n vtmp.append(dv)\r\n elif prop == 'v':\r\n tmp.append(1./_visc(d,T))\r\n ltmp.append(vl)\r\n vtmp.append(vv)\r\n else: tmp.append(x)\r\n tmp.append(vtmp)\r\n tmp.append(ltmp)\r\n else:\r\n for prop in props:\r\n tmp.append(numpy.nan)\r\n else:\r\n for prop in props:\r\n tmp.append(numpy.nan)\r\n output.append(tmp)\r\n return output","title":""},{"docid":"270d3da4d2f6d9601e522c79eb4401d6","score":"0.5115687","text":"def p_value(self):\n h = np.hstack(self.hist)\n f = np.hstack(self.fit_function(x=self.between, **self.coeff))\n b = h >= 5\n h = h[b]\n f = f[b]\n return chisquare(h, f, self.n_coeff).pvalue","title":""},{"docid":"76e59bca3d25f8aa10435ef3f2563f6b","score":"0.5115008","text":"def e_step(gaussians, prior, data, posterior=None):\n if posterior==None: \n posterior = Probmap(prior)\n\n mask = prior.mask()\n for tissue in tissues: \n posterior[tissue][mask] = prior[tissue][mask] * gaussians[tissue].pdf(data)\n \n posterior.normalize()\n return posterior","title":""},{"docid":"40a84fbd98b825679ccbec70b5ef4d66","score":"0.5112961","text":"def pred_evaluation(self, f_pred_prob, prepare_data, data, iterator):\n recall = 0.0\n mrr = 0.0\n evalutation_point_count = 0\n # pred_res = []\n # att = []\n \n for _, valid_index in iterator:\n x, mask, y = prepare_data([data[0][t] for t in valid_index],\n np.array(data[1])[valid_index])\n preds = f_pred_prob(x, mask)\n # weights = f_weight(x, mask)\n targets = y\n ranks = (preds.T > np.diag(preds.T[targets])).sum(axis=0) + 1\n rank_ok = (ranks <= 20)\n # pred_res += list(rank_ok)\n recall += rank_ok.sum()\n mrr += (1.0 / ranks[rank_ok]).sum()\n evalutation_point_count += len(ranks)\n # att.append(weights)\n \n recall = self.numpy_floatX(recall) / evalutation_point_count\n mrr = self.numpy_floatX(mrr) / evalutation_point_count\n eval_score = (recall, mrr)\n \n # ff = open('/storage/lijing/mydataset/res_attention_correct.pkl', 'wb')\n # pickle.dump(pred_res, ff)\n # ff.close()\n # ff2 = open('/storage/lijing/mydataset/attention_weights.pkl', 'wb')\n # pickle.dump(att, ff2)\n # ff2.close()\n \n return eval_score","title":""},{"docid":"3b9f3320362fd87a531c8692771d3057","score":"0.5109721","text":"def get_predicted_probabilities(self, clf, test_x):\n if hasattr(clf, \"predict_proba\"):\n predicted_prob = clf.predict_proba(test_x)[:, 1]\n else:\n prob = clf.decision_function(test_x)\n predicted_prob = (prob - prob.min()) / (prob.max() - prob.min())\n\n return predicted_prob","title":""},{"docid":"ab6d15cb14b8773dbf255bd4ace75318","score":"0.5099645","text":"def conditional_prob(self):\n return self.opt_exp","title":""},{"docid":"3fb695102ffd54256a04f0b7220ae188","score":"0.509872","text":"def test_select_proportional_to_weight(self):\n th_rng = RandomStreams(12345)\n\n p = tensor.fmatrix()\n n = tensor.iscalar()\n m = th_rng.multinomial_wo_replacement(pvals=p, n=n)\n\n f = function([p, n], m, allow_input_downcast=True)\n\n n_elements = 100\n n_selected = 10\n mean_rtol = 0.0005\n numpy.random.seed(12345)\n pvals = numpy.random.randint(1, 100, (1, n_elements)).astype(config.floatX)\n pvals /= pvals.sum(1)\n avg_pvals = numpy.zeros((n_elements,), dtype=config.floatX)\n\n for rep in range(10000):\n res = f(pvals, n_selected)\n res = numpy.squeeze(res)\n avg_pvals[res] += 1\n avg_pvals /= avg_pvals.sum()\n avg_diff = numpy.mean(abs(avg_pvals - pvals))\n assert avg_diff < mean_rtol","title":""},{"docid":"893a9590b9282da583be185f837c91a2","score":"0.5097317","text":"def test_h_p(self):\n qs = QState(qubit_num=1).h(0).p(0, phase=0.25)\n actual = qs.amp\n expect = np.array([0.70710678, 0.5+0.5j])\n ans = equal_vectors(actual, expect)\n self.assertEqual(ans,True)","title":""},{"docid":"9cbbda24fd499cba2d2c0851518153d0","score":"0.5097116","text":"def probability_improvement(x):\n x_scaled = self.scaler.transform(x.reshape(1, -1))\n too_close = np.array([True if np.linalg.norm(\n x_scaled[0][no_discrete] - p[no_discrete], -1) < 0.02\n else False for p in self.points]).any()\n if too_close:\n return np.inf\n\n pred, sigma = self.pred_sigma(x)\n pred = sign * pred\n std_dev = np.sqrt(sigma)\n pi = norm.cdf((target - pred) / std_dev)\n\n return - pi","title":""},{"docid":"ea8cd74ca5ca4d4f64ebffb40709cf69","score":"0.5093838","text":"def setup():\n Q = pick_numbers()\n target = np.random.randint(1,1000)\n Q.sort()\n return Q, target","title":""},{"docid":"ec20ddd7428fa09c1e8bc289e92f5bbb","score":"0.5093456","text":"def posterior(x, n, p1, p2):\n if type(n) is not int or n < 1:\n raise ValueError(\"n must be a positive integer\")\n if type(x) is not int or x < 0:\n err = \"x must be an integer that is greater than or equal to 0\"\n raise ValueError(err)\n if x > n:\n raise ValueError(\"x cannot be greater than n\")\n if type(p1) is not float or p1 < 0 or p1 > 1:\n raise ValueError(\"p1 must be a float in the range [0, 1]\")\n if type(p2) is not float or p2 < 0 or p2 > 1:\n raise ValueError(\"p2 must be a float in the range [0, 1]\")\n if p2 <= p1:\n raise ValueError(\"p2 must be greater than p1\")\n\n f1 = x + 1\n f2 = n - x + 1\n alfa = special.btdtr(f1, f2, p2)\n beta = special.btdtr(f1, f2, p1)\n return alfa - beta","title":""}],"string":"[\n {\n \"docid\": \"3df82a3c097cc9531b6d612c43603726\",\n \"score\": \"0.6071597\",\n \"text\": \"def chi_p_prior_given_chi_eff_q(q,aMax,xeff,xp,ndraws=10000,bw_method='scott'):\\n\\n # Draw random spin magnitudes.\\n # Note that, given a fixed chi_eff, a1 can be no larger than (1+q)*chi_eff,\\n # and a2 can be no larger than (1+q)*chi_eff/q\\n a1 = np.random.random(ndraws)*aMax\\n a2 = np.random.random(ndraws)*aMax\\n\\n # Draw random tilts for spin 2\\n cost2 = 2.*np.random.random(ndraws)-1.\\n\\n # Finally, given our conditional value for chi_eff, we can solve for cost1\\n # Note, though, that we still must require that the implied value of cost1 be *physical*\\n cost1 = (xeff*(1.+q) - q*a2*cost2)/a1 \\n\\n # While any cost1 values remain unphysical, redraw a1, a2, and cost2, and recompute\\n # Repeat as necessary\\n while np.any(cost1<-1) or np.any(cost1>1): \\n to_replace = np.where((cost1<-1) | (cost1>1))[0] \\n a1[to_replace] = np.random.random(to_replace.size)*aMax\\n a2[to_replace] = np.random.random(to_replace.size)*aMax\\n cost2[to_replace] = 2.*np.random.random(to_replace.size)-1. \\n cost1 = (xeff*(1.+q) - q*a2*cost2)/a1 \\n \\n # Compute precessing spins and corresponding weights, build KDE\\n # See `Joint-ChiEff-ChiP-Prior.ipynb` for a discussion of these weights\\n Xp_draws = chi_p_from_components(a1,a2,cost1,cost2,q)\\n jacobian_weights = (1.+q)/a1\\n prior_kde = gaussian_kde(Xp_draws,weights=jacobian_weights,bw_method=bw_method)\\n\\n # Compute maximum chi_p\\n if (1.+q)*np.abs(xeff)/q<aMax:\\n max_Xp = aMax\\n else:\\n max_Xp = np.sqrt(aMax**2 - ((1.+q)*np.abs(xeff)-q)**2.)\\n\\n # Set up a grid slightly inside (0,max chi_p) and evaluate KDE\\n reference_grid = np.linspace(0.05*max_Xp,0.95*max_Xp,50)\\n reference_vals = prior_kde(reference_grid)\\n\\n # Manually prepend/append zeros at the boundaries\\n reference_grid = np.concatenate([[0],reference_grid,[max_Xp]])\\n reference_vals = np.concatenate([[0],reference_vals,[0]])\\n norm_constant = np.trapz(reference_vals,reference_grid)\\n\\n # Interpolate!\\n p_chi_p = np.interp(xp,reference_grid,reference_vals/norm_constant)\\n return p_chi_p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61ec7af6b4c3bc911ad127608246f670\",\n \"score\": \"0.6038365\",\n \"text\": \"def P(self,x):\\n indx = np.arange(len(x))\\n ps = self.pX[x,indx] #probability of xi for each i\\n joint = ps.prod()\\n pi = np.true_divide(joint,ps) # will be nan for elements for which ps is 0 -(should never happen if sampling)\\n for j in np.where(np.isnan(pi))[0]:\\n pi[j] = np.prod(ps[indx != j]) \\n pij = np.vstack((pi,pi))\\n pij[1-x,indx] = 0\\n pij = pij.reshape((len(x)*2,)) #flatten first N-1 will be px=0,2nd px=1\\n pobserve = joint# the probability of x given do()\\n result = np.hstack((pij,pobserve))\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee8e5436eadc7abc54b3589b8a0c9c10\",\n \"score\": \"0.5970335\",\n \"text\": \"def ppf(self, q, *args, **kwargs):\\n return self.scipy_distribution.ppf(q, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fb61e35f7c700c13f41535bc1474682\",\n \"score\": \"0.5799747\",\n \"text\": \"def evaluate_p_all(self, p_in):\\n return np.exp(self.evaluate_logp_all(p_in))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"853f2e1b6db1dcf8b3a9c0bac2ad768b\",\n \"score\": \"0.57089645\",\n \"text\": \"def bigauss(x, p):\\n return (x < p[1]).choose([\\n p[0] * exp(-twolntwo * (x - p[1])**2 / (p[2] * (1 + p[3])**2)) + p[4],\\n p[0] * exp(-twolntwo * (x - p[1])**2 / (p[2] * (1 - p[3])**2)) + p[4]\\n ])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cae99ff6f62dc61786fbcff772f08977\",\n \"score\": \"0.5703504\",\n \"text\": \"def compute_pvalue(expression, thresh, alpha=0.05):\\n\\n #count the cases\\n #ndown = [1 if exp_i <= thresh_i else 0 for exp_i in zip(expression, thresh_i)]\\n ndown = expression[expression <= - thresh].shape[0]\\n nup = expression[expression >= thresh].shape[0]\\n nneutral = expression.shape[0] - ndown - nup\\n\\n #get the one with most peptide\\n ns = np.array([nup, ndown, nneutral])\\n max_idx = np.argmax(ns)\\n\\n\\n if max_idx == 0:\\n direction = \\\"up\\\"\\n\\n elif max_idx == 1:\\n direction = \\\"down\\\"\\n\\n else:\\n direction = \\\"neutral\\\"\\n\\n #n number of trails for binomial\\n ntrials = len(expression)\\n #k success\\n ksuccess = ns[max_idx]\\n x = np.arange(ksuccess, ntrials + 1)\\n\\n #compute the pvalue as number of cases as extreme (and higher)\\n #as the one observed\\n pvalue = np.sum(stat.binom.pmf(x, ntrials, alpha))\\n\\n res_vec = pd.Series([pvalue, direction, ntrials, ksuccess])\\n res_vec.index([\\\"pvalue\\\", \\\"direction\\\", \\\"ntrials\\\", \\\"ksuccess\\\"])\\n return(res_vec)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"001751d13783817a650eb4e3fd46ec9f\",\n \"score\": \"0.5665702\",\n \"text\": \"def chi_p_prior_from_isotropic_spins(q,aMax,xs):\\n\\n # Ensure that `xs` is an array and take absolute value\\n xs = np.reshape(xs,-1)\\n\\n # Set up various piecewise cases\\n pdfs = np.zeros(xs.size)\\n caseA = xs<q*aMax*(3.+4.*q)/(4.+3.*q)\\n caseB = (xs>=q*aMax*(3.+4.*q)/(4.+3.*q))*(xs<aMax)\\n\\n # Select relevant effective spins\\n x_A = xs[caseA]\\n x_B = xs[caseB]\\n\\n pdfs[caseA] = (1./(aMax**2*q))*((4.+3.*q)/(3.+4.*q))*(\\n np.arccos((4.+3.*q)*x_A/((3.+4.*q)*q*aMax))*(\\n aMax\\n - np.sqrt(aMax**2-x_A**2)\\n + x_A*np.arccos(x_A/aMax)\\n )\\n + np.arccos(x_A/aMax)*(\\n aMax*q*(3.+4.*q)/(4.+3.*q)\\n - np.sqrt(aMax**2*q**2*((3.+4.*q)/(4.+3.*q))**2 - x_A**2)\\n + x_A*np.arccos((4.+3.*q)*x_A/((3.+4.*q)*aMax*q))\\n )\\n )\\n \\n pdfs[caseB] = (1./aMax)*np.arccos(x_B/aMax)\\n\\n return pdfs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cfeaae4232a479df9325334fc6e2ea9\",\n \"score\": \"0.56629515\",\n \"text\": \"def exbayesdices():\\n hypos = [4,6,8,12,20]\\n def dlike(d,h):\\n if (d>h): return 0.\\n return 1./(1.*h)\\n ys = [1./len(hypos)]*len(hypos)\\n zs = zip(hypos,ys)\\n xfun = psutils.Funxy_xdiscrete(zs)\\n pb = psbayes.PBayes(xfun,hypos,dlike)\\n datas = [6,6,8,7,7,5,4]\\n pb.evidence(datas)\\n print 'posterior ',zip(hypos,pb.posterior(hypos))\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fcb96a0e8e07a29bc2939206f43548c\",\n \"score\": \"0.56107455\",\n \"text\": \"def calculate_probability(x1=0, x2=0, x3=0, x4=0):\\n\\n\\tprobability = 1.0\\n\\t\\n\\t# Do not edit any code outside the edit region\\n\\t# Edit region starts here\\n\\t#########################\\n\\t# Your code goes here\\n\\tif 'p_table' not in calculate_probability.__dict__:\\n\\t\\tp_table = -np.ones([4,4,4,4])\\n\\tif p_table[x1,x2,x3,x4] != -1:\\n\\t\\treturn p_table[x1,x2,x3,x4]\\n\\tpara = [x1,x2,x3,x4]\\n\\tif 0 not in para:\\n\\t\\tp_table[x1,x2,x3,x4] = p_x1[x1-1]*p_x2_given_x1[x1-1, x2-1]*p_x3_given_x2[x2-1,x3-1]*p_x4_given_x3[x3-1, x4-1]\\n\\t\\treturn p_table[x1,x2,x3,x4]\\n\\telse:\\n\\t\\tfor i,x in enumerate(para):\\n\\t\\t\\tif x==0:\\n\\t\\t\\t\\ttemp_para = para[:]\\n\\t\\t\\t\\ttemp_p = 0.0\\n\\t\\t\\t\\tfor j in range(1,4):\\n\\t\\t\\t\\t\\ttemp_para[i] = j\\n\\t\\t\\t\\t\\ttemp_p += calculate_probability(temp_para[0],temp_para[1],temp_para[2],temp_para[3])\\n\\t\\t\\t\\tp_table[x1,x2,x3,x4] = temp_p\\n\\t\\t\\t\\treturn temp_p\\n\\t#########################\\n\\t# Edit region ends here\\n\\n\\treturn probability\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee0a181ae490e18644d4fd216bf30b1b\",\n \"score\": \"0.56059885\",\n \"text\": \"def calc_prior(p,fitfunction, params):\\n \\n plike = 0.\\n \\n if fitfunction.__name__ == 'greybody':\\n # param ordering is [amp,Tdust,L0,beta]\\n # setting priors as uniform\\n if p[params.index('amp')] < 0: plike -= np.inf\\n if (p[params.index('Td')]<15) or (p[params.index('Td')]>300): plike -= np.inf\\n if (p[params.index('L0')]<50) or (p[params.index('L0')]>500): plike -= np.inf\\n if (p[params.index('beta')]<0.) or (p[params.index('beta')]>4.): plike -= np.inf\\n return plike\\n elif fitfunction.func_name == 'greybody_powerlaw':\\n # param ordering is[amp,Tdust,L0,beta,alpha,z]\\n if p[0] < 0: plike -= np.inf\\n if (p[1]<15) or (p[1]>300): plike -= np.inf\\n if (p[2]<50e-6) or (p[2]>500e-6): plike -= np.inf\\n if (p[3]<0.) or (p[3]>4.): plike -= np.inf\\n if (p[4]<0.25) or (p[4]>3.): plike -= np.inf\\n if (p[5]<0.) or (p[5]>10.): plike -= np.inf\\n return plike\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a144c8c81f4c20e003d8410e02767fc\",\n \"score\": \"0.5593349\",\n \"text\": \"def posterior(x, n, p1, p2):\\n if not isinstance(n, int) or n < 1:\\n raise ValueError(\\\"n must be a positive integer\\\")\\n if type(x) != int or x < 0:\\n err = \\\"x must be an integer that is greater than or equal to 0\\\"\\n raise ValueError(err)\\n if x > n:\\n raise ValueError(\\\"x cannot be greater than n\\\")\\n if not isinstance(P, np.ndarray) or len(P.shape) != 1:\\n raise TypeError(\\\"P must be a 1D numpy.ndarray\\\")\\n if not isinstance(p1, float) or p1 < 0 or p1 > 1:\\n raise TypeError(\\\"p1 must be a float in the range [0, 1]\\\")\\n if not isinstance(p2, float) or p2 < 0 or p2 > 1:\\n raise TypeError(\\\"p2 must be a float in the range [0, 1]\\\")\\n if p2 <= p1:\\n raise ValueError(\\\"p2 must be greater than p1\\\")\\n return p1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a9d60666907e4e3c0b5a7ba1824cb44\",\n \"score\": \"0.55737007\",\n \"text\": \"def x2p(X = Math.array([]), tol = 1e-5, perplexity = 30.0):\\n\\n\\t# Initialize some variables\\n\\tprint \\\"Computing pairwise distances...\\\"\\n\\t(n, d) = X.shape;\\n\\tsum_X = Math.sum(Math.square(X), 1);\\n\\tD = Math.add(Math.add(-2 * Math.dot(X, X.T), sum_X).T, sum_X);\\n\\tP = Math.zeros((n, n));\\n\\tbeta = Math.ones((n, 1));\\n\\tlogU = Math.log(perplexity);\\n\\n\\t# Loop over all datapoints\\n\\tfor i in range(n):\\n\\n\\t\\t# Print progress\\n\\t\\tif i % 500 == 0:\\n\\t\\t\\tprint \\\"Computing P-values for point \\\", i, \\\" of \\\", n, \\\"...\\\"\\n\\n\\t\\t# Compute the Gaussian kernel and entropy for the current precision\\n\\t\\tbetamin = -Math.inf;\\n\\t\\tbetamax = Math.inf;\\n\\t\\tDi = D[i, Math.concatenate((Math.r_[0:i], Math.r_[i+1:n]))];\\n\\t\\t(H, thisP) = Hbeta(Di, beta[i]);\\n\\n\\t\\t# Evaluate whether the perplexity is within tolerance\\n\\t\\tHdiff = H - logU;\\n\\t\\ttries = 0;\\n\\t\\twhile Math.abs(Hdiff) > tol and tries < 50:\\n\\n\\t\\t\\t# If not, increase or decrease precision\\n\\t\\t\\tif Hdiff > 0:\\n\\t\\t\\t\\tbetamin = beta[i].copy();\\n\\t\\t\\t\\tif betamax == Math.inf or betamax == -Math.inf:\\n\\t\\t\\t\\t\\tbeta[i] = beta[i] * 2;\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tbeta[i] = (beta[i] + betamax) / 2;\\n\\t\\t\\telse:\\n\\t\\t\\t\\tbetamax = beta[i].copy();\\n\\t\\t\\t\\tif betamin == Math.inf or betamin == -Math.inf:\\n\\t\\t\\t\\t\\tbeta[i] = beta[i] / 2;\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tbeta[i] = (beta[i] + betamin) / 2;\\n\\n\\t\\t\\t# Recompute the values\\n\\t\\t\\t(H, thisP) = Hbeta(Di, beta[i]);\\n\\t\\t\\tHdiff = H - logU;\\n\\t\\t\\ttries = tries + 1;\\n\\n\\t\\t# Set the final row of P\\n\\t\\tP[i, Math.concatenate((Math.r_[0:i], Math.r_[i+1:n]))] = thisP;\\n\\n\\t# Return final P-matrix\\n\\tprint \\\"Mean value of sigma: \\\", Math.mean(Math.sqrt(1 / beta));\\n\\treturn P;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57f50fff7694e62384e28b9cdbbfd7de\",\n \"score\": \"0.5565294\",\n \"text\": \"def x2p(X = Math.array([]), tol = 1e-5, perplexity = 30.0):\\n\\n\\t# Initialize some variables\\n\\tprint (\\\"Computing pairwise distances...\\\")\\n\\t(n, d) = X.shape;\\n\\tsum_X = Math.sum(Math.square(X), 1);\\n\\tD = Math.add(Math.add(-2 * Math.dot(X, X.T), sum_X).T, sum_X);\\n\\tP = Math.zeros((n, n));\\n\\tbeta = Math.ones((n, 1));\\n\\tlogU = Math.log(perplexity);\\n\\n\\t# Loop over all datapoints\\n\\tfor i in range(n):\\n\\n\\t\\t# Print progress\\n\\t\\tif i % 500 == 0:\\n\\t\\t\\tprint (\\\"Computing P-values for point \\\", i, \\\" of \\\", n, \\\"...\\\")\\n\\n\\t\\t# Compute the Gaussian kernel and entropy for the current precision\\n\\t\\tbetamin = -Math.inf;\\n\\t\\tbetamax = Math.inf;\\n\\t\\tDi = D[i, Math.concatenate((Math.r_[0:i], Math.r_[i+1:n]))];\\n\\t\\t(H, thisP) = Hbeta(Di, beta[i]);\\n\\n\\t\\t# Evaluate whether the perplexity is within tolerance\\n\\t\\tHdiff = H - logU;\\n\\t\\ttries = 0;\\n\\t\\twhile Math.abs(Hdiff) > tol and tries < 50:\\n\\n\\t\\t\\t# If not, increase or decrease precision\\n\\t\\t\\tif Hdiff > 0:\\n\\t\\t\\t\\tbetamin = beta[i].copy();\\n\\t\\t\\t\\tif betamax == Math.inf or betamax == -Math.inf:\\n\\t\\t\\t\\t\\tbeta[i] = beta[i] * 2;\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tbeta[i] = (beta[i] + betamax) / 2;\\n\\t\\t\\telse:\\n\\t\\t\\t\\tbetamax = beta[i].copy();\\n\\t\\t\\t\\tif betamin == Math.inf or betamin == -Math.inf:\\n\\t\\t\\t\\t\\tbeta[i] = beta[i] / 2;\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tbeta[i] = (beta[i] + betamin) / 2;\\n\\n\\t\\t\\t# Recompute the values\\n\\t\\t\\t(H, thisP) = Hbeta(Di, beta[i]);\\n\\t\\t\\tHdiff = H - logU;\\n\\t\\t\\ttries = tries + 1;\\n\\n\\t\\t# Set the final row of P\\n\\t\\tP[i, Math.concatenate((Math.r_[0:i], Math.r_[i+1:n]))] = thisP;\\n\\n\\t# Return final P-matrix\\n\\tprint(\\\"Mean value of sigma: \\\", Math.mean(Math.sqrt(1 / beta)))\\n\\treturn P;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca4f15080d0c717db14905503a17c1ac\",\n \"score\": \"0.55517834\",\n \"text\": \"def _set_priors(self):\\n bool_arr = np.array([self.MAP_uniform_prior, self.MAP_use_log_Teff_log_g_prior])\\n n_bool = np.sum(bool_arr * 1)\\n if n_bool != 1:\\n logger.error('_set_priors: set only one of \\\"MAP_uniform_prior\\\" or \\\"MAP_use_log_Teff_log_g_prior\\\" to True:')\\n sys.exit(1)\\n\\n work_y = self.get('MAP_work_frequencies')\\n m, K = work_y.shape\\n\\n if self.MAP_uniform_prior:\\n prior = old_div(np.ones(m), float(m))\\n elif self.MAP_use_log_Teff_log_g_prior:\\n # get observed log_Teff and log_g together with their errors from the star\\n if self.log_Teff_err_lower == 0 or self.log_Teff_err_upper == 0:\\n logger.error('_set_priors: set log_Teff_err_lower and log_Teff_err_upper first')\\n sys.exit(1)\\n\\n obs_log_Teff = self.log_Teff\\n obs_log_Teff_err = np.max([ self.log_Teff_err_lower, self.log_Teff_err_upper ])\\n obs_log_g = self.log_g\\n obs_log_g_err= np.max([ self.log_g_err_lower, self.log_g_err_upper ])\\n if not all([ obs_log_Teff != 0, obs_log_Teff_err != 0, \\n obs_log_g != 0, obs_log_g_err != 0 ]):\\n logger.error('_set_priors: Specify the log_Teff, log_g and their errors properly')\\n sys.exit(1)\\n\\n lrn_log_Teff = self.learning_log_Teff[:]\\n lrn_log_g = self.learning_log_g[:]\\n\\n prior_log_Teff = utils.gaussian(x=lrn_log_Teff, mu=obs_log_Teff, sigma=obs_log_Teff_err)\\n prior_log_g = utils.gaussian(x=lrn_log_g, mu=obs_log_g, sigma=obs_log_g_err)\\n\\n prior = prior_log_Teff * prior_log_g\\n\\n if utils.has_nan(prior):\\n logger.error('_set_priors: NaN detected')\\n sys.exit(1)\\n\\n prior = utils.substitute_inf(prior)\\n ln_prior = np.log(prior)\\n\\n self.set('MAP_prior', prior)\\n self.set('MAP_ln_prior', ln_prior)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bd3465cf64e2a2dae0f90b2c2bb63b1\",\n \"score\": \"0.5542093\",\n \"text\": \"def probit(x):\\n eps = np.finfo(float).eps\\n result = 0.5 + 0.5 * pt.erf(x / pt.sqrt(2))\\n result = pt.switch(pt.eq(result, 0), eps, result)\\n result = pt.switch(pt.eq(result, 1), 1 - eps, result)\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4eb157e75fd97440ef81c2e98c077cbf\",\n \"score\": \"0.55243176\",\n \"text\": \"def exbayesexpo():\\n tgs = []\\n NS = [10,100,1000,10000]\\n RS = [(0.1,3.),(0.4,1.6),(0.8,1.2),(0.94,1.06)]\\n for i in range(len(NS)):\\n N = NS[i]\\n u0,uf = RS[i]\\n fu = pspdf.pdf_uniform(u0,uf)\\n fe = pspdf.pdf_exponential(1.)\\n hypos = fu.rvariables()\\n dlike = pslib.exponential\\n pb = psbayes.PBayes(fu,hypos,dlike)\\n data = fe.random(N)\\n x0,xf = fe.domain()\\n pb.evidence(data)\\n xs = pb.hypothesis()\\n zs = zip(xs,pb.posterior(xs))\\n xpf = pspdf.pdf_points(zs)\\n print ' N points ',N\\n print ' average ',xpf.average(),' rms ',xpf.rms()\\n print ' contaiment ',xpf.inverse_comulative(0.5-0.341),xpf.inverse_comulative(0.5+0.341) \\n tg = psplot.tgraph(zs,title='posterior_N'+str(N))\\n h = psplot.thisto(data)\\n tgs.append((h,tg))\\n return tgs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"085cc3c6e04ab13f78f32bcc5daafc90\",\n \"score\": \"0.5499824\",\n \"text\": \"def prob(x, xi):\\n return sum([1 for x0 in x if x0 == xi])/len(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bf07dd22601f20def3de8502b68014f\",\n \"score\": \"0.5489826\",\n \"text\": \"def x2p(X=np.array([]), tol=1e-5, perplexity=30.0):\\n\\n # Initialize some variables\\n print \\\"Computing pairwise distances...\\\"\\n (n, d) = X.shape\\n sum_X = np.sum(np.square(X), 1)\\n D = np.add(np.add(-2 * np.dot(X, X.T), sum_X).T, sum_X)\\n P = np.zeros((n, n))\\n beta = np.ones((n, 1))\\n logU = np.log(perplexity)\\n\\n # Loop over all datapoints\\n for i in range(n):\\n\\n # Print progress\\n if i % 500 == 0:\\n print \\\"Computing P-values for point \\\", i, \\\" of \\\", n, \\\"...\\\"\\n\\n # Compute the Gaussian kernel and entropy for the current precision\\n betamin = -np.inf\\n betamax = np.inf\\n Di = D[i, np.concatenate((np.r_[0:i], np.r_[i+1:n]))]\\n (H, thisP) = Hbeta(Di, beta[i])\\n\\n # Evaluate whether the perplexity is within tolerance\\n Hdiff = H - logU\\n tries = 0\\n while np.abs(Hdiff) > tol and tries < 50:\\n\\n # If not, increase or decrease precision\\n if Hdiff > 0:\\n betamin = beta[i].copy()\\n if betamax == np.inf or betamax == -np.inf:\\n beta[i] = beta[i] * 2\\n else:\\n beta[i] = (beta[i] + betamax) / 2\\n else:\\n betamax = beta[i].copy()\\n if betamin == np.inf or betamin == -np.inf:\\n beta[i] = beta[i] / 2\\n else:\\n beta[i] = (beta[i] + betamin) / 2\\n\\n # Recompute the values\\n (H, thisP) = Hbeta(Di, beta[i])\\n Hdiff = H - logU\\n tries = tries + 1\\n\\n # Set the final row of P\\n P[i, np.concatenate((np.r_[0:i], np.r_[i+1:n]))] = thisP\\n\\n # Return final P-matrix\\n print \\\"Mean value of sigma: \\\", np.mean(np.sqrt(1 / beta))\\n return P\",\n \"title\": \"\"\n },\n {\n \"docid\": \"230700129186c1eb55e343bcc422e9a8\",\n \"score\": \"0.5466392\",\n \"text\": \"def _ppf(self, p, betaL, betaH, mL, mH):\\n\\n def inttail(beta, m):\\n return m / beta / (m - 1) * np.exp(-0.5 * beta * beta)\\n\\n def intcore(betaL, betaH):\\n return _norm_pdf_C * (_norm_cdf(betaH) - _norm_cdf(-betaL))\\n\\n def hightail(p, betaL, betaH, mL, mH):\\n CL = inttail(betaL, mL)\\n CH = inttail(betaH, mH)\\n C = CL + CH\\n N = 1 / (C + intcore(betaL, betaH))\\n eb2H = np.exp(-0.5 * betaH * betaH)\\n return -(\\n mH / betaH\\n - betaH\\n - ((mH - 1) * (mH / betaH) ** (-mH) / eb2H * (1 - p) / N)\\n ** (1 / (1 - mH))\\n )\\n\\n def lowtail(p, betaL, betaH, mL, mH):\\n CL = inttail(betaL, mL)\\n CH = inttail(betaH, mH)\\n C = CL + CH\\n N = 1 / (C + intcore(betaL, betaH))\\n eb2L = np.exp(-0.5 * betaL * betaL)\\n return (\\n mL / betaL\\n - betaL\\n - ((mL - 1) * (mL / betaL) ** (-mL) / eb2L * p / N) ** (1 / (1 - mL))\\n )\\n\\n def core(p, betaL, betaH, mL, mH):\\n CL = inttail(betaL, mL)\\n CH = inttail(betaH, mH)\\n C = CL + CH\\n N = 1 / (C + intcore(betaL, betaH))\\n return _norm_ppf(_norm_cdf(-betaL) + (1 / _norm_pdf_C) * (p / N - CL))\\n\\n def ppf_greater(p, betaL, betaH, mL, mH):\\n N = 1.0 / (inttail(betaL, mL) + intcore(betaL, betaH) + inttail(betaH, mH))\\n pbetaH = 1 - (N * (mH / betaH) * np.exp(-0.5 * betaH * betaH) / (mH - 1))\\n return _lazywhere(\\n p > pbetaH, (p, betaL, betaH, mL, mH), f=hightail, f2=core\\n )\\n\\n N = 1.0 / (inttail(betaL, mL) + intcore(betaL, betaH) + inttail(betaH, mH))\\n pbetaL = N * (mL / betaL) * np.exp(-0.5 * betaL * betaL) / (mL - 1)\\n return _lazywhere(\\n p < pbetaL, (p, betaL, betaH, mL, mH), f=lowtail, f2=ppf_greater\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e211be2e3b6314ced1a5d6c3e4d89e8\",\n \"score\": \"0.5454346\",\n \"text\": \"def peval(x, p):\\n A,B,C,D = p\\n return logistic4(x, A, B, C, D)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"595bfeaf3fb9b98b5c8136490cdb474c\",\n \"score\": \"0.5447155\",\n \"text\": \"def peval(x, p):\\r\\n A,B,C,D = p\\r\\n return logistic4(x, A, B, C, D)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f1e337e48961b7bed9f1afe705ea28c\",\n \"score\": \"0.54432005\",\n \"text\": \"def pr(self, source):\\n\\n H_0 = self.PROB - self.EPSILON\\n H_1 = self.PROB + self.EPSILON\\n beta = self.ALPHA\\n a = np.log(beta / (1 - self.ALPHA))\\n b = np.log((1 - beta) / self.ALPHA)\\n k = 0\\n\\n w_sum = 0.0\\n w_sum_true = 0.0\\n s = Sampler()\\n s.create(source)\\n li = s.take(10)\\n count = 0.0\\n for i in li:\\n if i >= 0.5:\\n count = count + 1\\n count = count / len(li)\\n num_samples = 0\\n while (num_samples < self.INITIAL_SAMPLE_SIZE):\\n if li[num_samples] > 0.5:\\n k = k + 1\\n w_sum_true = w_sum_true + count\\n w_sum = w_sum + count\\n num_samples += 1\\n test = None\\n while (self.NUMM_SAMPLES <= self.MAX_SAMPLES):\\n log_likelihood = w_sum_true * \\\\\\n np.log(H_1 / H_0) + (w_sum - w_sum_true) * np.log((1 - H_1) / (1 - H_0))\\n\\n if (log_likelihood >= b):\\n test = True\\n elif (log_likelihood <= a):\\n test = False\\n else:\\n i = 0\\n while (i < self.SAMPLE_SIZE_STEP):\\n count = 0.0\\n li = s.Take(10)\\n for it in li:\\n if it >= 0.5:\\n count = count + 1\\n count = count / len(li)\\n\\n if li[i] > 0.5:\\n k = k + 1\\n w_sum_true = w_sum_true + count\\n w_sum = w_sum + count\\n i = i + 1\\n self.NUMM_SAMPLES += self.SAMPLE_SIZE_STEP\\n test = False\\n\\n return test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1e6fcfd98cafcf44dc3228ab4d0f08a\",\n \"score\": \"0.5438436\",\n \"text\": \"def x2p(X=np.array([]), tol=1e-5, perplexity=30.0):\\n\\n # Initialize some variables\\n print(\\\"Computing pairwise distances...\\\")\\n (n, d) = X.shape\\n sum_X = np.sum(np.square(X), 1)\\n D = np.add(np.add(-2 * np.dot(X, X.T), sum_X).T, sum_X)\\n P = np.zeros((n, n))\\n beta = np.ones((n, 1))\\n logU = np.log(perplexity)\\n\\n # Loop over all datapoints\\n for i in range(n):\\n\\n # Print progress\\n if i % 500 == 0:\\n print(\\\"Computing P-values for point %d of %d...\\\" % (i, n))\\n\\n # Compute the Gaussian kernel and entropy for the current precision\\n betamin = -np.inf\\n betamax = np.inf\\n Di = D[i, np.concatenate((np.r_[0:i], np.r_[i+1:n]))]\\n (H, thisP) = Hbeta(Di, beta[i])\\n\\n # Evaluate whether the perplexity is within tolerance\\n Hdiff = H - logU\\n tries = 0\\n while np.abs(Hdiff) > tol and tries < 50:\\n\\n # If not, increase or decrease precision\\n if Hdiff > 0:\\n betamin = beta[i].copy()\\n if betamax == np.inf or betamax == -np.inf:\\n beta[i] = beta[i] * 2.\\n else:\\n beta[i] = (beta[i] + betamax) / 2.\\n else:\\n betamax = beta[i].copy()\\n if betamin == np.inf or betamin == -np.inf:\\n beta[i] = beta[i] / 2.\\n else:\\n beta[i] = (beta[i] + betamin) / 2.\\n\\n # Recompute the values\\n (H, thisP) = Hbeta(Di, beta[i])\\n Hdiff = H - logU\\n tries += 1\\n\\n # Set the final row of P\\n P[i, np.concatenate((np.r_[0:i], np.r_[i+1:n]))] = thisP\\n\\n # Return final P-matrix\\n print(\\\"Mean value of sigma: %f\\\" % np.mean(np.sqrt(1 / beta)))\\n return P\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94cfd45cec2e7d2819e9a69135b9d4f6\",\n \"score\": \"0.54333556\",\n \"text\": \"def test_correct_conditional_prob_h(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f75311b537c5e377c6b9205d54e1e34d\",\n \"score\": \"0.54235107\",\n \"text\": \"def piecewise_p(x, val_a=25, val_b=300, val_c=100):\\n if x > 8:\\n p = val_a\\n elif x > 6:\\n p = val_b\\n elif x > 4:\\n p = val_c\\n elif x > 2:\\n p = val_a\\n else:\\n p = val_c\\n\\n return p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de21e17695d6f2c468fbb851ce196cff\",\n \"score\": \"0.54168653\",\n \"text\": \"def qftProbability(N,list_Primes,k):\\n \\n summation = 0\\n for prime in list_Primes:\\n summation += cmath.exp(2*math.pi*1j*prime*k/N)\\n return abs(summation)**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"640586056718454210659fda2ed61040\",\n \"score\": \"0.5415285\",\n \"text\": \"def _pmf(self, x):\\n if (x is True) or (x == 1):\\n return self.proba_is_true\\n\\n if (x is False) or (x == 0):\\n return 1 - self.proba_is_true\\n\\n return 0.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80129b59897d8105f4bac314de4e112d\",\n \"score\": \"0.5411826\",\n \"text\": \"def multiple_comparisons(p, n):\\n if p > 1e-6: #if p is large and n small, calculate directly\\n return 1 - (1-p)**n\\n else: \\n return one_minus_exp(-n * p)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a06cc42d6d2c9ffb274226b2aa2853b\",\n \"score\": \"0.54018855\",\n \"text\": \"def qftPeaks(N,list_Primes, pi_N, t1):\\n \\n weights_tuple = []\\n weights_tuple_normalized = []\\n for k in range(0,N):\\n if k % 5000 == 0:\\n t2 = time.time()\\n print k, \\\"([{:.3f} s])\\\".format(t2 - t1)\\n t1 = t2\\n sys.stdout.flush()\\n weight = qftProbability(N, list_Primes, k)\\n weights_tuple.append([k,weight])\\n weights_tuple_normalized.append([k, weight/ float(N*pi_N)]) \\n return weights_tuple, weights_tuple_normalized, t1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92bfe324bc5894234197e13ee2cf0d28\",\n \"score\": \"0.53984207\",\n \"text\": \"def _calc_p(self,factors):\\n logp = self.calc_logp(factors)\\n return np.exp(logp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f40044b227b0311340c0d895247eaca5\",\n \"score\": \"0.5397628\",\n \"text\": \"def x_prob(x_sample, a):\\n x_sample = np.array(x_sample)\\n Pax = 1 #initialize P(a|x)\\n for _x in x_sample:\\n N = np.sqrt(np.log(a)/2*np.pi) #Normalization factor\\n Pxa = N * a**(-_x**2 / 2) #P(x|a)\\n Pa = 1 #P(a) is independent of 'a'\\n Pax = Pax * Pxa * Pa \\n return Pax\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed21da6e1c09a4a45fd098433327459b\",\n \"score\": \"0.5389835\",\n \"text\": \"def pesq_example() -> tuple:\\n from torchmetrics.audio.pesq import PerceptualEvaluationSpeechQuality\\n\\n p = lambda: torch.randn(8000)\\n t = lambda: torch.randn(8000)\\n\\n # plot single value\\n metric = PerceptualEvaluationSpeechQuality(8000, \\\"nb\\\")\\n metric.update(p(), t())\\n fig, ax = metric.plot()\\n\\n # plot multiple values\\n metric = PerceptualEvaluationSpeechQuality(16000, \\\"wb\\\")\\n vals = [metric(p(), t()) for _ in range(10)]\\n fig, ax = metric.plot(vals)\\n\\n return fig, ax\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3747db3821c4500c863dd6559b15905a\",\n \"score\": \"0.5384205\",\n \"text\": \"def computeQ(data):\\n Q = 0\\n # Start with the expectation of the sum of priors over all tasks\\n Q += (data.probZ * np.log(data.priorZ)).sum()\\n\\n # the expectation of the sum of posteriors over all tasks\\n ab = np.dot(np.array([np.exp(data.beta)]).T, np.array([data.alpha]))\\n\\n # logSigma = - np.log(1 + np.exp(-ab))\\n logSigma = logsigmoid(ab) # logP\\n idxna = np.isnan(logSigma)\\n if np.any(idxna):\\n logger.warning('an invalid value was assigned to np.log [computeQ]')\\n logSigma[idxna] = ab[idxna] # For large negative x, -log(1 + exp(-x)) = x\\n\\n # logOneMinusSigma = - np.log(1 + np.exp(ab))\\n logOneMinusSigma = logsigmoid(-ab) - np.log(float(data.numClasses - 1)) # log((1-P)/(K-1))\\n idxna = np.isnan(logOneMinusSigma)\\n if np.any(idxna):\\n logger.warning('an invalid value was assigned to np.log [computeQ]')\\n logOneMinusSigma[idxna] = -ab[idxna] # For large positive x, -log(1 + exp(x)) = x\\n\\n for k in range(data.numClasses):\\n delta = (data.labels == k + 1)\\n Q += (data.probZ[:, k] * logSigma.T).T[delta].sum()\\n oneMinusDelta = (data.labels != k + 1) & (data.labels != 0) # label == 0 -> no response\\n Q += (data.probZ[:, k] * logOneMinusSigma.T).T[oneMinusDelta].sum()\\n\\n # Add Gaussian (standard normal) prior for alpha\\n Q += np.log(sp.stats.norm.pdf(data.alpha - data.priorAlpha)).sum()\\n\\n # Add Gaussian (standard normal) prior for beta\\n Q += np.log(sp.stats.norm.pdf(data.beta - data.priorBeta)).sum()\\n\\n if debug:\\n logger.debug('a[0]={} a[1]={} a[2]={} b[0]={}'.format(data.alpha[0], data.alpha[1],\\n data.alpha[2], data.beta[0]))\\n logger.debug('Q={}'.format(Q))\\n if np.isnan(Q):\\n return -np.inf\\n return Q\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0467d3c7e727051f737479890812320\",\n \"score\": \"0.5382626\",\n \"text\": \"def psi_prior(psi):\\n a = 2\\n b = 40\\n return get_list_val(scipy.stats.beta.pdf(psi, a, b))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c13605bd1524c4498af355c65a00e00a\",\n \"score\": \"0.53797555\",\n \"text\": \"def x_prime_log_prior(self, x_prime):\\n log_p = np.zeros(x_prime.size)\\n for r in self.values():\\n log_p += r.x_prime_log_prior(x_prime)\\n return log_p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acff8e79ed8c5513ce0b551c459e8389\",\n \"score\": \"0.5374655\",\n \"text\": \"def ppf(self, q):\\n f = lambda x: self.cdf(x) - q\\n return sp.optimize.newton(func=f, x0=self.mean(), fprime=self.pdf)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fce89d7e326eeabfd0fa30e078a0ece4\",\n \"score\": \"0.53650934\",\n \"text\": \"def prob_early(pmf):\\n return prob_bin(pmf, 0, 37)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf8e673281fb8ce252f20a1a671a2c71\",\n \"score\": \"0.53595436\",\n \"text\": \"def test_correct_p_values(self):\\n exp = [0.003, 0.006, 0.003]\\n obs = self.mc._correct_p_values([0.001, 0.002, 0.001])\\n self.assertFloatEqual(obs, exp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c87a94eb6c938fc60bf7f168f355ff6c\",\n \"score\": \"0.53517747\",\n \"text\": \"def multinomial_predict(X, prior_0, prior_1, p_label_0, p_label_1):\\n a = prior_0\\n b = prior_1\\n # print X.shape, p_label_1.shape, prior_1\\n p_0 = np.exp(prior_0 + np.dot(X, p_label_0))\\n p_1 = np.exp(prior_1 + np.dot(X, p_label_1))\\n p_spam = [ p_1[i]/(p_0[i] + p_1[i]) if(p_0[i] + p_1[i]) > 0 else 0 for i in range(len(p_0))]\\n\\n return np.uint8(p_1 > p_0), np.array(p_spam)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25b87bc33128b4ddb663f70b558b0ae9\",\n \"score\": \"0.5351664\",\n \"text\": \"def project_probs(P):\\n values = np.random.normal(1, 0.5, P)\\n values[values<0] = 0\\n project_probs = values / np.sum(values)\\n return values, project_probs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"940308d045ac04799b242c98638d5af8\",\n \"score\": \"0.5348281\",\n \"text\": \"def Prox(x, s, mu, p, q):\\n \\n X = smat(x[0:-2])\\n S = smat(s[0:-2])\\n \\n xl = min(np.linalg.eigvals(X))\\n sl = min(np.linalg.eigvals(S))\\n \\n if xl <= 0 or sl <= 0 or x[-2] <= 0 or x[-1] <= 0 or s[-2] <= 0 or s[-1] <= 0:\\n return float('inf')\\n \\n D = NTScaling(X, S)\\n d = np.sqrt(x[-2:] / s[-2:])\\n \\n V = Rotate(S, D) / np.sqrt(mu)\\n [evals, evecs] = np.linalg.eigh(V)\\n phi0 = Phi(evals, p, q)\\n phi1 = Phi(d * s[-2:] / np.sqrt(mu), p, q)\\n \\n return phi0 + phi1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a15b9856de81c6ffb33c754dfae57b57\",\n \"score\": \"0.5343132\",\n \"text\": \"def _p(self, x: np.ndarray) -> np.ndarray:\\n p = np.zeros((self.n_samples, self.n_samples))\\n for i, j in combinations(range(self.n_samples), 2):\\n if i != j:\\n pij = self._pij(x, i, j)\\n pji = self._pij(x, j, i)\\n # Ensure that all the pairwise probabilities sum to 1.\\n pair_value = (pij + pji) / (2 * self.n_samples)\\n p[i, j] = pair_value\\n p[j, i] = pair_value\\n return p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70f1640a7a112a41903fb998eaa2fda1\",\n \"score\": \"0.53339154\",\n \"text\": \"def calcProb(tp):\\n lamb = float(tp[0])\\n thre = float(tp[1])\\n maxi = thre*10000.\\n# fPoi = ROOT.TF1(\\\"fPoi\\\", \\\"TMath::Poisson(x, [0])\\\", 0, maxi)\\n fPoiI = ROOT.TF1(\\\"fPoi\\\", \\\"TMath::PoissonI(x, [0])\\\", 0, maxi)\\n# fPoi.SetParameter(0, lamb)\\n fPoiI.SetParameter(0, lamb)\\n# return [fPoi.Integral(thre, maxi)/fPoi.Integral(0, maxi), fPoiI.Integral(thre, maxi)/fPoiI.Integral(0, maxi)]\\n print lamb, maxi, thre\\n return fPoiI.Integral(thre, maxi)/fPoiI.Integral(0, maxi)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4d048495ef14d746bfbf4d72da3f1a0\",\n \"score\": \"0.5322259\",\n \"text\": \"def default_priors(x, y, model_function):\\n priors = [_x0_prior(x), _dx_prior(x), _A_prior(y)]\\n if 'multi' in model_function:\\n priors += [_x0_prior(x), _dx_prior(x), _A_prior(y)]\\n elif 'thick' in model_function:\\n priors += [_tau_prior()]\\n elif 'hermite' in model_function:\\n priors += [_h3_prior(), _h4_prior()]\\n if '_cont' in model_function:\\n priors += [_cont_prior(y)]\\n return priors\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bba2424f5ac051f842ff3e6698b63d22\",\n \"score\": \"0.531893\",\n \"text\": \"def chi_effective_prior_from_isotropic_spins(q,aMax,xs):\\n\\n # Ensure that `xs` is an array and take absolute value\\n xs = np.reshape(np.abs(xs),-1)\\n\\n # Set up various piecewise cases\\n pdfs = np.ones(xs.size,dtype=complex)*(-1.)\\n caseZ = (xs==0)\\n caseA = (xs>0)*(xs<aMax*(1.-q)/(1.+q))*(xs<q*aMax/(1.+q))\\n caseB = (xs<aMax*(1.-q)/(1.+q))*(xs>q*aMax/(1.+q))\\n caseC = (xs>aMax*(1.-q)/(1.+q))*(xs<q*aMax/(1.+q))\\n caseD = (xs>aMax*(1.-q)/(1.+q))*(xs<aMax/(1.+q))*(xs>=q*aMax/(1.+q))\\n caseE = (xs>aMax*(1.-q)/(1.+q))*(xs>aMax/(1.+q))*(xs<aMax)\\n caseF = (xs>=aMax)\\n\\n # Select relevant effective spins\\n x_A = xs[caseA]\\n x_B = xs[caseB]\\n x_C = xs[caseC]\\n x_D = xs[caseD]\\n x_E = xs[caseE]\\n\\n pdfs[caseZ] = (1.+q)/(2.*aMax)*(2.-np.log(q))\\n\\n pdfs[caseA] = (1.+q)/(4.*q*aMax**2)*(\\n q*aMax*(4.+2.*np.log(aMax) - np.log(q**2*aMax**2 - (1.+q)**2*x_A**2))\\n - 2.*(1.+q)*x_A*np.arctanh((1.+q)*x_A/(q*aMax))\\n + (1.+q)*x_A*(Di(-q*aMax/((1.+q)*x_A)) - Di(q*aMax/((1.+q)*x_A)))\\n )\\n\\n pdfs[caseB] = (1.+q)/(4.*q*aMax**2)*(\\n 4.*q*aMax\\n + 2.*q*aMax*np.log(aMax)\\n - 2.*(1.+q)*x_B*np.arctanh(q*aMax/((1.+q)*x_B))\\n - q*aMax*np.log((1.+q)**2*x_B**2 - q**2*aMax**2)\\n + (1.+q)*x_B*(Di(-q*aMax/((1.+q)*x_B)) - Di(q*aMax/((1.+q)*x_B)))\\n )\\n\\n pdfs[caseC] = (1.+q)/(4.*q*aMax**2)*(\\n 2.*(1.+q)*(aMax-x_C)\\n - (1.+q)*x_C*np.log(aMax)**2.\\n + (aMax + (1.+q)*x_C*np.log((1.+q)*x_C))*np.log(q*aMax/(aMax-(1.+q)*x_C))\\n - (1.+q)*x_C*np.log(aMax)*(2. + np.log(q) - np.log(aMax-(1.+q)*x_C))\\n + q*aMax*np.log(aMax/(q*aMax-(1.+q)*x_C))\\n + (1.+q)*x_C*np.log((aMax-(1.+q)*x_C)*(q*aMax-(1.+q)*x_C)/q)\\n + (1.+q)*x_C*(Di(1.-aMax/((1.+q)*x_C)) - Di(q*aMax/((1.+q)*x_C)))\\n )\\n\\n pdfs[caseD] = (1.+q)/(4.*q*aMax**2)*(\\n -x_D*np.log(aMax)**2\\n + 2.*(1.+q)*(aMax-x_D)\\n + q*aMax*np.log(aMax/((1.+q)*x_D-q*aMax))\\n + aMax*np.log(q*aMax/(aMax-(1.+q)*x_D))\\n - x_D*np.log(aMax)*(2.*(1.+q) - np.log((1.+q)*x_D) - q*np.log((1.+q)*x_D/aMax))\\n + (1.+q)*x_D*np.log((-q*aMax+(1.+q)*x_D)*(aMax-(1.+q)*x_D)/q)\\n + (1.+q)*x_D*np.log(aMax/((1.+q)*x_D))*np.log((aMax-(1.+q)*x_D)/q)\\n + (1.+q)*x_D*(Di(1.-aMax/((1.+q)*x_D)) - Di(q*aMax/((1.+q)*x_D)))\\n )\\n\\n pdfs[caseE] = (1.+q)/(4.*q*aMax**2)*(\\n 2.*(1.+q)*(aMax-x_E)\\n - (1.+q)*x_E*np.log(aMax)**2\\n + np.log(aMax)*(\\n aMax\\n -2.*(1.+q)*x_E\\n -(1.+q)*x_E*np.log(q/((1.+q)*x_E-aMax))\\n )\\n - aMax*np.log(((1.+q)*x_E-aMax)/q)\\n + (1.+q)*x_E*np.log(((1.+q)*x_E-aMax)*((1.+q)*x_E-q*aMax)/q)\\n + (1.+q)*x_E*np.log((1.+q)*x_E)*np.log(q*aMax/((1.+q)*x_E-aMax))\\n - q*aMax*np.log(((1.+q)*x_E-q*aMax)/aMax)\\n + (1.+q)*x_E*(Di(1.-aMax/((1.+q)*x_E)) - Di(q*aMax/((1.+q)*x_E)))\\n )\\n\\n pdfs[caseF] = 0.\\n\\n # Deal with spins on the boundary between cases\\n if np.any(pdfs==-1):\\n boundary = (pdfs==-1)\\n pdfs[boundary] = 0.5*(chi_effective_prior_from_isotropic_spins(q,aMax,xs[boundary]+1e-6)\\\\\\n + chi_effective_prior_from_isotropic_spins(q,aMax,xs[boundary]-1e-6))\\n\\n return np.real(pdfs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32391e786e38d197735f19c6c3c35b75\",\n \"score\": \"0.5317258\",\n \"text\": \"def AdditiveEquiprobabilities():\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11d7c537bd574a1460ee81d6db268927\",\n \"score\": \"0.5313194\",\n \"text\": \"def prob59():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4745082d1b07736369556a66635d0f6e\",\n \"score\": \"0.53005433\",\n \"text\": \"def get_p(features):\\r\\n # Compute the raw value\\r\\n raw_prediction = intercept + coefficients_broadcast.value.dot(features)\\r\\n # Bound the raw value between 20 and -20\\r\\n raw_prediction = min(20, max(-20, raw_prediction))\\r\\n # Return the probability\\r\\n probability = 1.0 / (1 + exp(- raw_prediction))\\r\\n return probability\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce6e143ed471a178aa534f1b2099b409\",\n \"score\": \"0.5298077\",\n \"text\": \"def test_correct_conditional_prob_v(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a527689900d1a0bb7454209c4af3a70\",\n \"score\": \"0.5294685\",\n \"text\": \"def ppf(x, mu=0, sigma=1):\\n return mu - sigma * math.sqrt(2) * erfcinv(2 * x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29b591350716fe9fcac754b32d4aed89\",\n \"score\": \"0.5281783\",\n \"text\": \"def f_per_particle(self,m, alpha, X, y, P):\\n total_features = X.shape[1]\\n # Get the subset of the features from the binary mask\\n if np.count_nonzero(m) == 0:\\n X_subset = X\\n else:\\n X_subset = X[:,m==1]\\n\\n ratio_selected_features = X_subset.shape[1]/total_features\\n \\n #Particle fittness error/loss computed using cross validation\\n fitness_error = make_scorer(self.__objective_fcn, ratio_selected_features=ratio_selected_features, P=P, alpha=alpha)\\n scores = cross_val_score(self.regressor, X_subset, y, cv=10, scoring=fitness_error) #does first 4 steps of k-fold CV\\n particle_fitness_err_mean = scores.mean()\\n\\n #Stdev \\n particle_fitness_err_stdev = np.std(scores)\\n\\n return (particle_fitness_err_mean, particle_fitness_err_stdev)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f4f7314a053c38a83d7fa54e418dd59\",\n \"score\": \"0.52714187\",\n \"text\": \"def perplexity(self, data):\\n test_tokens = self._preprocess(data, self.n)\\n N = len(test_tokens)\\n\\n probs = 0.0\\n for word in test_tokens:\\n probs -= self.get_single_probability(word, log=True)\\n\\n return math.exp(probs/N)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98c6bfcb4315d422571af70bcb76e4f6\",\n \"score\": \"0.5263221\",\n \"text\": \"def sample(preds, temperature=1.0):\\n preds = np.asarray(preds).astype('float64')\\n preds = np.log(preds) / temperature\\n exp_preds = np.exp(preds)\\n preds = exp_preds / np.sum(exp_preds)\\n probas = np.random.multinomial(1, preds, 1)\\n return probas\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9027b01ff967e5b02c69efaa4c8cd86\",\n \"score\": \"0.5261927\",\n \"text\": \"def ppf(self, p):\\n d = self.loc[p.index, p.columns]\\n icdf_arr = d.mu + d.sigma * np.sqrt(2) * erfinv(2 * p.values - 1)\\n return pd.DataFrame(icdf_arr, index=p.index, columns=p.columns)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c57344492bc9af651a3ecbfc95d995f9\",\n \"score\": \"0.5257148\",\n \"text\": \"def evaluate_p(self, X, best_p, p_range=(0, 1), sample_density=100):\\n return np.mean((best_p - self.estimate_best_p(X, p_range, sample_density)) ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18203532f60f34602f01f74f01c3ce76\",\n \"score\": \"0.52518934\",\n \"text\": \"def extract_priors(options):\\n # Initialize empty dictionary to store priors \\n options['priors_definitions'] = dict()\\n options['priors_params'] = dict()\\n\\n if len(options['priors']) < 4:\\n raise ValueError('''Please provide list of four arguments for \\n priors. Less than four arguments provided.''')\\n elif len(options['priors']) > 4:\\n raise ValueError('''Please provide list of four arguments for \\n priors. More than four arguments provided.''')\\n elif len(options['priors']) == 4: \\n NoneType = type(None)\\n # Check whether arguments in list are of type integer or float\\n for i in range(0,4):\\n if isinstance(options['priors'][i], (str, NoneType)) is False:\\n raise ValueError('''User Error: Please provide numerical values or NoneType \\n for priors of parameters of the model.''')\\n \\n if options['priors'][i] is not None:\\n if i == 0:\\n # Extract prior for alpha parameter\\n options['priors_definitions']['scale'] = \\\" \\\".join(re.findall(\\\"[a-zA-Z]+\\\", \\n options['priors'][0]))\\n \\n alpha_prior_values = re.findall(r'[-+]?\\\\d*\\\\.\\\\d+|\\\\d+', options['priors'][0])\\n options['priors_params']['scale'] = [np.float(x) for x in alpha_prior_values]\\n elif i == 1:\\n # Extract prior for beta parameter\\n options['priors_definitions']['slope'] = \\\" \\\".join(re.findall(\\\"[a-zA-Z]+\\\", \\n options['priors'][1]))\\n \\n beta_prior_values = re.findall(r'[-+]?\\\\d*\\\\.\\\\d+|\\\\d+', options['priors'][1])\\n options['priors_params']['slope'] = [np.float(x) for x in beta_prior_values] \\n elif i == 2:\\n # Extract prior for gamma parameter\\n options['priors_definitions']['gamma'] = \\\" \\\".join(re.findall(\\\"[a-zA-Z]+\\\", \\n options['priors'][2])) \\n gamma_prior_values = re.findall(r'[-+]?\\\\d*\\\\.\\\\d+|\\\\d+', options['priors'][2])\\n options['priors_params']['gamma'] = [np.float(x) for x in gamma_prior_values]\\n elif i == 3:\\n # Extract prior for lambda parameter\\n options['priors_definitions']['lambda'] = \\\" \\\".join(re.findall(\\\"[a-zA-Z]+\\\", \\n options['priors'][3]))\\n \\n lambda_prior_values = re.findall(r'[-+]?\\\\d*\\\\.\\\\d+|\\\\d+', options['priors'][3])\\n options['priors_params']['lambda'] = [np.float(x) for x in lambda_prior_values]\\n \\n return options\",\n \"title\": \"\"\n },\n {\n \"docid\": \"992b75ebef63d1911731e82c10726dc9\",\n \"score\": \"0.52492476\",\n \"text\": \"def ppf(self, x, range):\\n min, max = range\\n pmin = min ** (-self.power)\\n pmax = max ** (-self.power)\\n scaled = pmin - x*(pmin - pmax)\\n return scaled ** (-1 / self.power)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"696b90a8ad7327e37c2ae29814e7a865\",\n \"score\": \"0.52484584\",\n \"text\": \"def upP(self,q,p, *args) :\\n\\t\\treturn array( [ p[1]**2 * self.Rp(q[0]) / (self.R(q[0])**3) , 0] )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2894f5144b19a0f0973502e54fdde66c\",\n \"score\": \"0.5244034\",\n \"text\": \"def evaluate_p(self, p_in):\\n return np.exp(self.evaluate_logp(p_in))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"795b66a6542544630181011a59623c96\",\n \"score\": \"0.5243482\",\n \"text\": \"def predict_proba(self, X):\\n values = self.decision_function(X)\\n likelihood = np.exp(values - values.min(axis=1)[:, np.newaxis])\\n # compute posterior probabilities\\n return likelihood / likelihood.sum(axis=1)[:, np.newaxis]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91861f70e05e530c827be2a6260c8124\",\n \"score\": \"0.5236747\",\n \"text\": \"def test_guess_priors():\\n kernel = Exponentiation(\\n ConstantKernel(constant_value_bounds=\\\"fixed\\\") * Matern()\\n + WhiteKernel()\\n + CompoundKernel([RBF(), Matern()]),\\n 2.0,\\n )\\n\\n priors = guess_priors(kernel)\\n\\n assert len(priors) == 4\\n expected = [\\n -1.737085713764618,\\n -4.107091211892862,\\n -1.737085713764618,\\n -1.737085713764618,\\n ]\\n for p, v in zip(priors, expected):\\n assert_almost_equal(p(0.0), v)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"177a646c01a4ff035d425e2bf266daf5\",\n \"score\": \"0.5236103\",\n \"text\": \"def prior(z,m,info='hdfn',nt=6,ninterp=0,x=None,y=None): \\n if info=='none' or info=='flat': return\\n #We estimate the priors at m_step intervals\\n #and keep them in a dictionary, and then\\n #interpolate them for other values\\n m_step=0.1\\n accuracy=str(len(str(int(1./m_step)))-1)#number of decimals kept\\n\\n exec(\\\"from desc_bpz.prior_{} import *\\\".format(info), globals())\\n global prior_dict\\n try:\\n len(prior_dict)\\n except NameError:\\n prior_dict={}\\n\\n #The dictionary keys are values of the \\n #magnitud quantized to mstep mags\\n #The values of the dictionary are the corresponding\\n #prior probabilities.They are only calculated once \\n #and kept in the dictionary for future\\n #use if needed. \\n forma='%.'+accuracy+'f'\\n m_dict=forma %m \\n if m_dict not in prior_dict or info=='lensing': #if lensing, the magnitude alone is not enough\\n if info!='lensing':\\n prior_dict[m_dict]=function(z,float(m_dict),nt)\\n else:\\n prior_dict[m_dict]=function(z,float(m_dict),nt,x,y) \\n if ninterp:\\n pp_i=prior_dict[m_dict]\\n nz=pp_i.shape[0]\\n nt=pp_i.shape[1]\\n nti=nt+(nt-1)*int(ninterp)\\n tipos=arange(nt)*1.\\n itipos=arange(nti)*1./(1.+float(ninterp))\\n buffer=zeros((nz,nti))*1.\\n for iz in range(nz):\\n buffer[iz,:]=match_resol(tipos,pp_i[iz,:],itipos)\\n prior_dict[m_dict]=buffer\\n return prior_dict[m_dict]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"078a73db8a4de3e09e29f2467dfb6e1b\",\n \"score\": \"0.5231932\",\n \"text\": \"def ppf(self, q):\\n\\n if np.any(q < 0.) or np.any(q > 1.):\\n msg = 'Provided quantiles are out of allowed range of [0, 1]: {!r}'\\n raise ValueError(msg.format(q))\\n\\n # ---------\\n # Exercise:\\n # ---------\\n # Angle PDf | part c) (exercises/angle_pdf.py)\\n #\\n\\n # define N\\n N = self.k/(2 * (1 - np.exp(- np.pi * self.k)))\\n\\n # make value array\\n values = np.zeros_like(q)\\n\\n # in case q is just one value\\n if np.isscalar(q):\\n if q == 0.:\\n values = -np.pi\\n elif q == 0.5:\\n values = 0.\\n elif q == 1.:\\n values = np.pi\\n elif q > 0 and q < 0.5:\\n values = np.log(self.k * q / N + np.exp(- np.pi * self.k)) /self.k\\n elif q > 0.5 and q < 1:\\n values = - np.log(1 - (self.k/N) * (q - 1/2))/self.k\\n \\n else:\\n # special cases first\\n values[q == 0.0] = -np.pi\\n values[q == 0.5] = 0.\\n values[q == 1.0] = np.pi\\n\\n mask_lower = np.logical_and(q > 0, q < 0.5) # formula for lower percentage\\n values[mask_lower] = np.log(self.k * q[mask_lower] / N + np.exp(- np.pi * self.k)) /self.k\\n\\n mask_higher = np.logical_and(q > 0.5, q < 1.) # formula for higher percentage\\n values[mask_higher] = - np.log(1 - (self.k/N) * (q[mask_higher] - 1/2))/self.k\\n\\n return values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbc049ede849ba998d043a10b91cc05a\",\n \"score\": \"0.5231932\",\n \"text\": \"def x_prime(x):\\r\\n r1 = np.random.uniform(0, 1)\\r\\n xprime = x + (0.5 - r1) * min([3, 10**5 - x, x + 10**5])\\r\\n \\r\\n return xprime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd43580827e54111be148c42500113fc\",\n \"score\": \"0.5221587\",\n \"text\": \"def call_post_prob(self, val, post_cutoff):\\n number_state = len(self.prior_state)\\n prob = []\\n for i in range(0, number_state):\\n gauss_pmf = self.get_gauss_pmf_cdf(\\n val, self.mu_state[i], self.sigma_state[i]\\n )[0]\\n prob.append(gauss_pmf * self.prior_state[i])\\n sum_prob = float(sum(prob))\\n post_prob = [float(a) / sum_prob for a in prob]\\n max_prob = max(post_prob)\\n if max_prob >= post_cutoff:\\n return post_prob.index(max_prob)\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d30b7ff4e1b862cc98f37cef75fd122\",\n \"score\": \"0.5219188\",\n \"text\": \"def posteriors(likelihoods, priors):\\n #Check that there is a prior for each likelihood\\n if len(likelihoods) != len(priors):\\n raise ValueError, \\\"Lists not equal lengths.\\\"\\n #Posterior probability is defined as prior * likelihood\\n return [l * p for l, p in zip(likelihoods, priors)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bea50441112c0cd9bb0ba347e550899b\",\n \"score\": \"0.5215778\",\n \"text\": \"def _ex_set(p):\\n k = GF(p)\\n res = [ k(0), k(1), k(2), k(4) ]\\n R = k['X']\\n f = R([1,-3,1]) #(X**2 - 3*X+1)\\n ro = f.roots()\\n for a in ro:\\n if a[0] not in res:\\n res.append(a[0])\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce8023d4eb64fdad7925f147727ce5db\",\n \"score\": \"0.52146107\",\n \"text\": \"def prime_net(self, primer):\\n x = np.zeros((self.vocab_size, 1))\\n x[self.char_to_ix[primer[0]]] = 1\\n\\n for i in range(len(primer)):\\n self.h = np.tanh(np.dot(self.Wxh, x) + np.dot(self.Whh, self.h) + self.bh)\\n y = np.dot(self.Why, self.h) + self.by\\n p = np.exp(y) / np.sum(np.exp(y)) # Probabilities\\n ix = self.char_to_ix[primer[i]]\\n x = np.zeros((self.vocab_size, 1))\\n x[ix] = 1\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"769735c92a1ff5f88a73c1d640aa4793\",\n \"score\": \"0.52032566\",\n \"text\": \"def chisq_prob(data=None, model=None):\\n chisq = np.sum(np.square(data - model) / model)\\n p_value = np.exp(-0.5 * chisq)\\n\\n return p_value, chisq\",\n \"title\": \"\"\n },\n {\n \"docid\": \"857773373d58102c233dcee263b77c6d\",\n \"score\": \"0.5193262\",\n \"text\": \"def log_prior(self, x):\\n # Check if the values are in the priors bounds, will return -inf if not\\n log_p = np.log(self.in_bounds(x), dtype=\\\"float\\\")\\n # Uniform on x\\n log_p -= np.log(self.bounds[\\\"x\\\"][1] - self.bounds[\\\"x\\\"][0])\\n # Gaussian on y\\n log_p += norm(scale=5).logpdf(x[\\\"y\\\"])\\n return log_p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85fefad4488eb215edc2f1120ee6f7d1\",\n \"score\": \"0.518961\",\n \"text\": \"def pred_evaluation(prepare_data, data, iterator):\\n recall = 0.0\\n mrr = 0.0\\n evalutation_point_count = 0\\n # pred_res = []\\n # att = []\\n\\n for _, valid_index in iterator:\\n x, mask, y = prepare_data([data[0][t] for t in valid_index],\\n np.array(data[1])[valid_index])\\n preds = sess.run(output_probs,feed_dict={input_items: x,target_items : y,input_item_mask :mask,keep_prob_1:1.0,keep_prob_2:1.0})\\n # weights = f_weight(x, mask)\\n targets = y\\n ranks = (preds.T > np.diag(preds.T[targets])).sum(axis=0) + 1\\n rank_ok = (ranks <= 20)\\n\\n # pred_res += list(rank_ok)\\n recall += rank_ok.sum()\\n mrr += (1.0 / ranks[rank_ok]).sum()\\n evalutation_point_count += len(ranks)\\n # att.append(weights)\\n recall = numpy_floatX(recall) / evalutation_point_count\\n mrr = numpy_floatX(mrr) / evalutation_point_count\\n eval_score = (recall, mrr)\\n return eval_score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80bfcecff56948735b3b960991527f7a\",\n \"score\": \"0.5183773\",\n \"text\": \"def prior(hypercube):\\n\\n fitting_par = [0.0] * nfit\\n for i, x in enumerate(hypercube):\\n fitting_par[i] = PC_priors.UniformPrior(parameters_minmax[i,0], parameters_minmax[i,1])(x)\\n\\n return fitting_par\",\n \"title\": \"\"\n },\n {\n \"docid\": \"035b4f9267cc79352c932415a871d77e\",\n \"score\": \"0.5176513\",\n \"text\": \"def get_conditional(self, x, Px, y, Py, Pxy, yo, Pyo):\\n\\n # Add measurement noise\\n Py1 = Py + Pyo\\n # Kalman gain\\n print(Pxy)\\n K = Pxy@np.linalg.inv(Py)\\n print(\\\"K\\\", K)\\n \\n x_prime = x + K@(yo - y)\\n Px_prime = Px - K@Py1@K.T\\n\\n return x_prime, Px_prime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"421bf6edd0b56b25a75b427e109d58fa\",\n \"score\": \"0.51719713\",\n \"text\": \"def predictedRating(x, P, Q):\\n #### TO DO\\n p = P[x[0]-1]\\n q = Q[x[1]-1]\\n # q_t = q.T\\n # q_t = q.transpose()\\n predicted = np.matmul(p, q)\\n l = predicted\\n # print(l)\\n # print(x[2])\\n return (x[0],x[1],x[2],l)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c723c6b76f134210c91be7d64e09724\",\n \"score\": \"0.5169575\",\n \"text\": \"def check_preqs(preqs, metrics) -> List[CheckedPreq]:\\n\\n checked_preqs = []\\n\\n def add_preq(key: str, msg: str, error: bool, ctx: dict):\\n checked_preqs.append(CheckedPreq(key=key, msg=msg, error=error, ctx=ctx))\\n\\n for pkey, rng_str in preqs.items():\\n ctx = metrics.get(\\\"ctx\\\", {})\\n mval = metrics.get(pkey, None)\\n if mval is None:\\n add_preq(\\n key=pkey, error=True, ctx=ctx, msg=\\\"expected to be measured, but wasn't\\\"\\n )\\n continue\\n\\n try:\\n rng = Range(rng_str)\\n except InvalidRangeError as ex:\\n add_preq(\\n key=pkey, error=True, ctx=ctx, msg=f'invalid range \\\"{rng_str}\\\": {ex}'\\n )\\n continue\\n\\n if not rng.contains(mval):\\n add_preq(\\n key=pkey,\\n error=True,\\n ctx=ctx,\\n msg=f\\\"{rng.format_val(mval)} in {rng} failed\\\",\\n )\\n continue\\n\\n add_preq(\\n key=pkey,\\n error=False,\\n ctx=ctx,\\n msg=f\\\"{rng.format_val(mval)} in {rng} satisfied\\\",\\n )\\n\\n return checked_preqs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87afda02ac70f0a95b5cdee2014fed08\",\n \"score\": \"0.5167855\",\n \"text\": \"def get_pdfvals(xarr, qarr, pdfset):\\n lhapdf_pdf = lhapdf.mkPDF(pdfset)\\n res = {}\\n for x, q in zip(xarr, qarr):\\n dict_update(res, lhapdf_pdf.xfxQ2(x, q))\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a680069624b98fb0b0577dac28c4ee4\",\n \"score\": \"0.5161852\",\n \"text\": \"def posterior(x, n, P, Pr):\\n if not isinstance(n, int) or (n <= 0):\\n raise ValueError('n must be a positive integer')\\n if not isinstance(x, int) or (x < 0):\\n err = 'x must be an integer that is greater than or equal to 0'\\n raise ValueError(err)\\n if x > n:\\n raise ValueError('x cannot be greater than n')\\n if not isinstance(P, np.ndarray) or len(P.shape) != 1:\\n raise TypeError('P must be a 1D numpy.ndarray')\\n if np.any(P > 1) or np.any(P < 0):\\n raise ValueError('All values in P must be in the range [0, 1]')\\n if not isinstance(Pr, np.ndarray) or (P.shape != Pr.shape):\\n err = 'Pr must be a numpy.ndarray with the same shape as P'\\n raise TypeError(err)\\n if np.any(Pr > 1) or np.any(Pr < 0):\\n raise ValueError('All values in Pr must be in the range [0, 1]')\\n\\n E = np.sum(Pr)\\n if not np.isclose(E, 1):\\n raise ValueError('Pr must sum to 1')\\n\\n fact = (np.math.factorial(n)) / \\\\\\n (np.math.factorial(x) * np.math.factorial(n - x))\\n fact *= (np.power(P, x)) * (np.power((1 - P), (n - x)))\\n inter = fact * Pr\\n marg = np.sum(inter)\\n return inter / marg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"891faa0c1aa22d38a02ef73613460574\",\n \"score\": \"0.5161048\",\n \"text\": \"def true_posterior_prob(self, thetas):\\n return self.true_posterior.pdf(thetas)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cf1d78f0c1b7487c7d1eb1a71f0b5c8\",\n \"score\": \"0.51605046\",\n \"text\": \"def prob54():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae1791d42735332d54a9f8186b1bd15a\",\n \"score\": \"0.51601565\",\n \"text\": \"def posterior(x, n, p1, p2):\\n if not isinstance(n, int) or n <= 0:\\n raise ValueError('n must be a positive integer')\\n if not isinstance(x, int) or x < 0:\\n raise ValueError(\\n 'x must be an integer that is greater than or equal to 0')\\n if x > n:\\n raise ValueError('x cannot be greater than n')\\n if not isinstance(p1, float) or (p1 > 1) or (p1 < 0):\\n raise ValueError('p1 must be a float in the range [0, 1]')\\n if not isinstance(p2, float) or (p2 > 1) or (p2 < 0):\\n raise ValueError('p2 must be a float in the range [0, 1]')\\n if p2 <= p1:\\n raise ValueError('p2 must be greater than p1')\\n\\n P1 = special.btdtr(x+1, n-x+1, p1)\\n P2 = special.btdtr(x+1, n-x+1, p2)\\n\\n return P2-P1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01227567adb8dc53729f3fc9fd65b0ed\",\n \"score\": \"0.5159808\",\n \"text\": \"def logprior(p):\\n res = [_.logpdf(__) for _, __ in zip(priorList, p)]\\n res = sum(res)\\n if not np.isfinite(res):\\n res = BADV\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80c8b271fa9d62c7166fd51f0e1740e5\",\n \"score\": \"0.51560634\",\n \"text\": \"def generate_preconditioner(self, f_all):\\n hessian = []\\n a = f_all.min.hess\\n b = f_all.real.hess\\n c = f_all.max.hess\\n size = len(f_all.min.hess)\\n for i in range(size):\\n hessian.append([])\\n for j in range(size):\\n gmax = max(a[i,j],b[i,j],c[i,j])\\n gmin = min(a[i,j],b[i,j],c[i,j])\\n gmid = (gmin+gmax)*0.5\\n hessian[i].append(gmid)\\n hessian = np.asarray(hessian)\\n return hessian\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f853c8793702b1737badab05d415ae1\",\n \"score\": \"0.5148506\",\n \"text\": \"def classify(X,mu,sigma,Pi):\\n N,D = X.shape\\n K = len(Pi)\\n return np.array([argmax([Pi[k]*norm_pdf_multivariate(X[n],mu[k],sigma[k]) for k in range(K)]) for n in range(N)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84828a4b289040f92710686d1cb2d5ec\",\n \"score\": \"0.5144026\",\n \"text\": \"def ProcessPQ(joints, marg, feature_length):\\n #variable defining the heap\\n pq = []\\n\\n for i in range(feature_length):\\n\\tfor j in range(i+1, feature_length):\\n\\t I = 0\\n\\t for x_u, p_x_u in marg[i].iteritems():\\n\\t\\tfor x_v, p_x_v in marg[j].iteritems():\\n\\t\\t if (x_u, x_v) in joints[(i, j)]:\\n\\t\\t\\tp_x_uv = joints[(i, j)][(x_u, x_v)]\\n\\t\\t\\tI += p_x_uv * (math.log(p_x_uv, 2) - math.log(p_x_u, 2) - math.log(p_x_v, 2))\\n\\t heapq.heappush(pq, (-I, i, j))\\n \\n return pq\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1778c91b326f790f0743f46d3aa0baf3\",\n \"score\": \"0.51428276\",\n \"text\": \"def get_q_probs(self):\\n return F.softmax(self.q_logits, dim=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36326af82d9a74250499eb2300a1bdf4\",\n \"score\": \"0.51424074\",\n \"text\": \"def ApproximatePrecessingFisherMatrixElementFactor(P, p1,p2,m,s,psd,phase_deriv_cache=None,omit_geometric_factor=False,break_out_beta=False,**kwargs):\\n\\n # Create basic infrastructure for IP. This requires VERY DENSE sampling...try to avoid\\n # hF0 = lalsimutils.complex_hoff(P)\\n # IP = lalsimutils.CreateCompatibleComplexIP(hF0,psd=psd,**kwargs)\\n # dPsi2F_func = interp1d(fvals,dPsi2F, fill_value=0, bounds_error=False)\\n # dalphaF_func= interp\\n\\n# print \\\" called on \\\", p1,p2, m, s\\n\\n # This geometric stuff is all in common. I should make one call to generate Sh once and for all\\n beta_0 = P.extract_param('beta')\\n thetaJN_0 = P.extract_param('thetaJN')\\n mc_s = P.extract_param('mc')/lal.MSUN_SI *lalsimutils.MsunInSec\\n d_s = P.extract_param('dist')/lal.C_SI # distance in seconds\\n\\n if omit_geometric_factor:\\n geometric_factor=1\\n else:\\n geometric_factor = np.abs(lal.SpinWeightedSphericalHarmonic(thetaJN_0,0,-2,2,int(m)) * lal.WignerDMatrix(2,m,s,0,beta_0,0))**2\\n if np.abs(geometric_factor) < 1e-5:\\n return 0,0 # saves lots of time\\n\\n # Create phase derivatives. Interpolate onto grid? OR use lower-density sampling\\n if phase_deriv_cache:\\n if not (p1 in phase_deriv_cache.keys()):\\n phase_deriv_cache[p1] = PhaseDerivativeSeries(P,p1)\\n else:\\n fvals,fmax_safe, dPsi2F,dPhiF, dalphaF,dgammaF = phase_deriv_cache[p1] \\n if not (p2 in phase_deriv_cache.keys()):\\n phase_deriv_cache[p2] = PhaseDerivativeSeries(P,p2)\\n else:\\n fvals,fmax_safe, dPsi2F_2,dPhiF_2, dalphaF_2,dgammaF_2 =phase_deriv_cache[p2] \\n else:\\n fvals,fmax_safe, dPsi2F,dPhiF, dalphaF,dgammaF = PhaseDerivativeSeries(P,p1)\\n fvals,fmax_safe, dPsi2F_2,dPhiF_2, dalphaF_2,dgammaF_2 = PhaseDerivativeSeries(P,p2)\\n\\n dropme = np.logical_or(fvals <P.fmin, fvals>0.98*fmax_safe)\\n\\n Shvals = np.array(map(psd, np.abs(fvals)))\\n Shvals[np.isnan(Shvals)]=float('inf')\\n phase_weights = 4* (np.pi*mc_s*mc_s)**2/(3*d_s*d_s) *np.power((np.pi*mc_s*np.maximum(np.abs(fvals),P.fmin/2)),-7./3.)/Shvals # hopefully one-sided. Note the P.fmin removes nans \\n phase_weights[np.isnan(phase_weights)]=0\\n phase_weights[dropme] =0\\n\\n dalphaF[dropme] = 0\\n dPsi2F[dropme] = 0\\n dgammaF[dropme]=0\\n\\n dalphaF_2[dropme] = 0\\n dPsi2F_2[dropme] = 0\\n dgammaF_2[dropme]=0\\n\\n rhoms2 = np.sum(phase_weights*P.deltaF)*geometric_factor\\n\\n if (beta_0) < 1e-2: # Pathological, cannot calculate alpha or gamma\\n ret_weights = P.deltaF*phase_weights*geometric_factor*( dPsi2F)*(dPsi2F_2)\\n ret = np.sum(ret_weights)\\n else:\\n if not break_out_beta:\\n ret_weights = P.deltaF*phase_weights*geometric_factor*( dPsi2F- 2 *dgammaF + m*s*dalphaF)*(dPsi2F_2 - 2*dgammaF_2+m*s*dalphaF_2)\\n ret = np.sum(ret_weights)\\n else:\\n # Warming: this uses the explicit assumption \\\\gamma - -alpha cos beta\\n # Warning: you probably never want this unless geometric_factor=1\\n if not omit_geometric_factor:\\n print(\\\" You are extracting a breakdown of the fisher matrix versus beta, but are not fixing the geometric factor...are you sure?\\\")\\n ret_00 = np.sum(P.deltaF*phase_weights*geometric_factor*( dPsi2F)*(dPsi2F_2 ))\\n ret_01 = np.sum(P.deltaF*phase_weights*geometric_factor*( dPsi2F)*(dalphaF_2 ))\\n ret_10 = np.sum(P.deltaF*phase_weights*geometric_factor*( dalphaF)*(dPsi2F_2 ))\\n ret_11 = np.sum(P.deltaF*phase_weights*geometric_factor*( dalphaF)*(dalphaF_2))\\n print(\\\" -- submatrix \\\", p1,p2, ret_00, ret_01, ret_10, ret_11)\\n return rhoms2,{\\\"00\\\":ret_00, \\\"01\\\":ret_01, \\\"10\\\": ret_10, \\\"11\\\": ret_11}\\n\\n# print \\\" Internal fisher element\\\", geometric_factor, rhoms2, ret\\n # plt.plot(fvals,ret_weights)\\n # plt.ylim(0,np.max(ret_weights))\\n # plt.show()\\n \\n return rhoms2, ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5a77645e4f56495b85092cd33e91714\",\n \"score\": \"0.51353604\",\n \"text\": \"def prob():\\n nx = 2 #number of variables [x1, x2]\\n np = 2 #number of parameters [p1, p2]\\n neq = 0 #number of equality constraints\\n niq = 2 #number of inequality constraints\\n name = \\\"Problem 1\\\"\\n return nx, np, neq, niq, name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aad40a8d315c31681e6f22f707229fbc\",\n \"score\": \"0.512335\",\n \"text\": \"def parzen_estimation(x_samples, point_x, h):\\n k_n = 0\\n for row in x_samples:\\n x_i = (point_x - row[:, np.newaxis]) / (h)\\n for row in x_i:\\n if np.abs(row) > (1 / 2):\\n break\\n else:\\n k_n += 1\\n return h, (k_n / len(x_samples)) / (h ** point_x.shape[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19dd1e86d0dbd8a04ab880ab92b564bc\",\n \"score\": \"0.5118249\",\n \"text\": \"def gen_p(a, b, x1, x2):\\n x = np.arange(x1, x2, 0.1)\\n p = a*x*(b - x)\\n return np.stack((x, p))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3101507d4ff26c6124c0aaf2f717ce74\",\n \"score\": \"0.5116654\",\n \"text\": \"def _evaluate_values(props, h, P):\\r\\n output = []\\r\\n for i in range(len(h)):\\r\\n tmp = [h[i], P[i]]\\r\\n if 611.23 <= P[i] <= 100e6 and 1000. <= h[i] <= 4500e3:\\r\\n region = _region_ph(P[i], h[i])\\r\\n if region in [1, 2, 3]:\\r\\n for prop in props:\\r\\n if prop != 's':\\r\\n tmp.append(_eval(spl_objs[prop], h[i]*scaling['h'], P[i]*scaling['P']) / scaling[prop])\\r\\n else:\\r\\n if region == 1:\\r\\n tmp.append(0)\\r\\n elif region == 2:\\r\\n tmp.append(1)\\r\\n elif region == 3:\\r\\n tmp.append(-1)\\r\\n elif region == 4:\\r\\n hl, hv = _hl_p(P[i]), _hv_p(P[i])\\r\\n vtmp, ltmp = [hv], [hl]\\r\\n if hl == hv: x = 1.\\r\\n else: x = (h[i] - hl) / (hv - hl)\\r\\n Tl, Tv = _eval(spl_objs['T'], hl*scaling['h'], P[i]*scaling['P']) / scaling['T'], _eval(spl_objs['T'], hv*scaling['h'], P[i]*scaling['P']) / scaling['T']\\r\\n dl, dv = _eval(spl_objs['d'], hl * scaling['h'], P[i] * scaling['P']) / scaling['d'], _eval(spl_objs['d'], hv * scaling['h'], P[i] *scaling['P']) / scaling['d']\\r\\n vl, vv = _eval(spl_objs['v'], hl * scaling['h'], P[i] * scaling['P']) / scaling['v'], _eval(spl_objs['v'], hv * scaling['h'], P[i] *scaling['P']) / scaling['v']\\r\\n T = Tl + x * (Tv - Tl)\\r\\n d = dl * dv / (dv + x * (dl - dv))\\r\\n for prop in props:\\r\\n if prop == 'T':\\r\\n tmp.append(T)\\r\\n ltmp.append(Tl)\\r\\n vtmp.append(Tv)\\r\\n elif prop == 'd':\\r\\n tmp.append(d)\\r\\n ltmp.append(dl)\\r\\n vtmp.append(dv)\\r\\n elif prop == 'v':\\r\\n tmp.append(1./_visc(d,T))\\r\\n ltmp.append(vl)\\r\\n vtmp.append(vv)\\r\\n else: tmp.append(x)\\r\\n tmp.append(vtmp)\\r\\n tmp.append(ltmp)\\r\\n else:\\r\\n for prop in props:\\r\\n tmp.append(numpy.nan)\\r\\n else:\\r\\n for prop in props:\\r\\n tmp.append(numpy.nan)\\r\\n output.append(tmp)\\r\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"270d3da4d2f6d9601e522c79eb4401d6\",\n \"score\": \"0.5115687\",\n \"text\": \"def p_value(self):\\n h = np.hstack(self.hist)\\n f = np.hstack(self.fit_function(x=self.between, **self.coeff))\\n b = h >= 5\\n h = h[b]\\n f = f[b]\\n return chisquare(h, f, self.n_coeff).pvalue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76e59bca3d25f8aa10435ef3f2563f6b\",\n \"score\": \"0.5115008\",\n \"text\": \"def e_step(gaussians, prior, data, posterior=None):\\n if posterior==None: \\n posterior = Probmap(prior)\\n\\n mask = prior.mask()\\n for tissue in tissues: \\n posterior[tissue][mask] = prior[tissue][mask] * gaussians[tissue].pdf(data)\\n \\n posterior.normalize()\\n return posterior\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40a84fbd98b825679ccbec70b5ef4d66\",\n \"score\": \"0.5112961\",\n \"text\": \"def pred_evaluation(self, f_pred_prob, prepare_data, data, iterator):\\n recall = 0.0\\n mrr = 0.0\\n evalutation_point_count = 0\\n # pred_res = []\\n # att = []\\n \\n for _, valid_index in iterator:\\n x, mask, y = prepare_data([data[0][t] for t in valid_index],\\n np.array(data[1])[valid_index])\\n preds = f_pred_prob(x, mask)\\n # weights = f_weight(x, mask)\\n targets = y\\n ranks = (preds.T > np.diag(preds.T[targets])).sum(axis=0) + 1\\n rank_ok = (ranks <= 20)\\n # pred_res += list(rank_ok)\\n recall += rank_ok.sum()\\n mrr += (1.0 / ranks[rank_ok]).sum()\\n evalutation_point_count += len(ranks)\\n # att.append(weights)\\n \\n recall = self.numpy_floatX(recall) / evalutation_point_count\\n mrr = self.numpy_floatX(mrr) / evalutation_point_count\\n eval_score = (recall, mrr)\\n \\n # ff = open('/storage/lijing/mydataset/res_attention_correct.pkl', 'wb')\\n # pickle.dump(pred_res, ff)\\n # ff.close()\\n # ff2 = open('/storage/lijing/mydataset/attention_weights.pkl', 'wb')\\n # pickle.dump(att, ff2)\\n # ff2.close()\\n \\n return eval_score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b9f3320362fd87a531c8692771d3057\",\n \"score\": \"0.5109721\",\n \"text\": \"def get_predicted_probabilities(self, clf, test_x):\\n if hasattr(clf, \\\"predict_proba\\\"):\\n predicted_prob = clf.predict_proba(test_x)[:, 1]\\n else:\\n prob = clf.decision_function(test_x)\\n predicted_prob = (prob - prob.min()) / (prob.max() - prob.min())\\n\\n return predicted_prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab6d15cb14b8773dbf255bd4ace75318\",\n \"score\": \"0.5099645\",\n \"text\": \"def conditional_prob(self):\\n return self.opt_exp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fb695102ffd54256a04f0b7220ae188\",\n \"score\": \"0.509872\",\n \"text\": \"def test_select_proportional_to_weight(self):\\n th_rng = RandomStreams(12345)\\n\\n p = tensor.fmatrix()\\n n = tensor.iscalar()\\n m = th_rng.multinomial_wo_replacement(pvals=p, n=n)\\n\\n f = function([p, n], m, allow_input_downcast=True)\\n\\n n_elements = 100\\n n_selected = 10\\n mean_rtol = 0.0005\\n numpy.random.seed(12345)\\n pvals = numpy.random.randint(1, 100, (1, n_elements)).astype(config.floatX)\\n pvals /= pvals.sum(1)\\n avg_pvals = numpy.zeros((n_elements,), dtype=config.floatX)\\n\\n for rep in range(10000):\\n res = f(pvals, n_selected)\\n res = numpy.squeeze(res)\\n avg_pvals[res] += 1\\n avg_pvals /= avg_pvals.sum()\\n avg_diff = numpy.mean(abs(avg_pvals - pvals))\\n assert avg_diff < mean_rtol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"893a9590b9282da583be185f837c91a2\",\n \"score\": \"0.5097317\",\n \"text\": \"def test_h_p(self):\\n qs = QState(qubit_num=1).h(0).p(0, phase=0.25)\\n actual = qs.amp\\n expect = np.array([0.70710678, 0.5+0.5j])\\n ans = equal_vectors(actual, expect)\\n self.assertEqual(ans,True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cbbda24fd499cba2d2c0851518153d0\",\n \"score\": \"0.5097116\",\n \"text\": \"def probability_improvement(x):\\n x_scaled = self.scaler.transform(x.reshape(1, -1))\\n too_close = np.array([True if np.linalg.norm(\\n x_scaled[0][no_discrete] - p[no_discrete], -1) < 0.02\\n else False for p in self.points]).any()\\n if too_close:\\n return np.inf\\n\\n pred, sigma = self.pred_sigma(x)\\n pred = sign * pred\\n std_dev = np.sqrt(sigma)\\n pi = norm.cdf((target - pred) / std_dev)\\n\\n return - pi\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea8cd74ca5ca4d4f64ebffb40709cf69\",\n \"score\": \"0.5093838\",\n \"text\": \"def setup():\\n Q = pick_numbers()\\n target = np.random.randint(1,1000)\\n Q.sort()\\n return Q, target\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec20ddd7428fa09c1e8bc289e92f5bbb\",\n \"score\": \"0.5093456\",\n \"text\": \"def posterior(x, n, p1, p2):\\n if type(n) is not int or n < 1:\\n raise ValueError(\\\"n must be a positive integer\\\")\\n if type(x) is not int or x < 0:\\n err = \\\"x must be an integer that is greater than or equal to 0\\\"\\n raise ValueError(err)\\n if x > n:\\n raise ValueError(\\\"x cannot be greater than n\\\")\\n if type(p1) is not float or p1 < 0 or p1 > 1:\\n raise ValueError(\\\"p1 must be a float in the range [0, 1]\\\")\\n if type(p2) is not float or p2 < 0 or p2 > 1:\\n raise ValueError(\\\"p2 must be a float in the range [0, 1]\\\")\\n if p2 <= p1:\\n raise ValueError(\\\"p2 must be greater than p1\\\")\\n\\n f1 = x + 1\\n f2 = n - x + 1\\n alfa = special.btdtr(f1, f2, p2)\\n beta = special.btdtr(f1, f2, p1)\\n return alfa - beta\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994183,"cells":{"query_id":{"kind":"string","value":"fa3504efd3ea644eaeb652cc41290809"},"query":{"kind":"string","value":"Returns the energy (in keV) associated with a given Xray line. By example, if xray_line = 'Mn_Ka' this function returns 5.8987"},"positive_passages":{"kind":"list like","value":[{"docid":"ddb0535509d1c85afca40d5ae14511c0","score":"0.8575026","text":"def _get_energy_xray_line(xray_line):\n element, line = _get_element_and_line(xray_line)\n return elements_db[element]['Atomic_properties']['Xray_lines'][\n line]['energy (keV)']","title":""}],"string":"[\n {\n \"docid\": \"ddb0535509d1c85afca40d5ae14511c0\",\n \"score\": \"0.8575026\",\n \"text\": \"def _get_energy_xray_line(xray_line):\\n element, line = _get_element_and_line(xray_line)\\n return elements_db[element]['Atomic_properties']['Xray_lines'][\\n line]['energy (keV)']\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"d3e919af76ff67321b75206d1b772a8f","score":"0.7023946","text":"def line_energy(element,line):\n z = elementDB[element][\"Z\"]\n if not isinstance(line,LinePair):\n line = _lookupxlsubline(line)\n return xraylib.LineEnergy(z,line.subline)","title":""},{"docid":"d9a8e10e1068b90fd3237243e4aedec8","score":"0.631008","text":"def energy(x):\n th1, th1d, th2, th2d = x.T\n # Potential\n V = -(M1+M2)*L1*G*np.cos(th1) - M2*L2*G*np.cos(th2)\n # Kinetic\n T = 0.5*M1* (L1*th1d)**2 + \\\n 0.5*M2*((L1*th1d)**2 + (L2*th2d)**2 + \\\n 2*L1*L2*th1d*th2d*np.cos(th1-th2))\n # Sum\n return T + V","title":""},{"docid":"249b5de5d7e34b2e58c010c1d08d776a","score":"0.62636083","text":"def get_energy(input_line):\n start_extract = input_line.find('[')\n stop_extract = input_line.find(']')\n if stop_extract == -1 or start_extract == -1:\n return 0\n data = input_line[start_extract+1:stop_extract]\n values = data.split(', ')\n \n energy = int(values[2]) * (2**24) + int(values[3]) * (2**16) + int(values[4]) * (2**8) + int(values[5])\n energy = float(energy)/3\n regex = re.compile('.*source: (\\d+).*')\n matches = regex.match(input_line)\n if matches == None:\n return 0\n groups = matches.groups()\n if groups == None:\n return 0\n src = groups[0]\n \n return (src, energy,)","title":""},{"docid":"230e63090ac4c1c6597d03582614d0b9","score":"0.5991744","text":"def calc_xrfdetector_efficiency(exptdesc,line_energy):\n \n if isinstance(line_energy, (list, tuple, np.ndarray)):\n trans_curve=np.ones_like(line_energy)\n else:\n trans_curve=1.0\n if isinstance(exptdesc, dict):\n try:\n detector = exptdesc[\"Acquisition_instrument\"][\"XRF\"][\"Detector\"]\n if(\"Attenuators\" in detector):\n adict = detector[\"Attenuators\"]\n for attenuator in adict.keys():\n thickness = adict[attenuator][\"thickness\"]\n composition = adict[attenuator][\"composition\"]\n if(composition in exptdesc[\"Materials\"]):\n density = exptdesc[\"Materials\"][composition][\"Density\"]\n else:\n density = exptdesc[\"Elements\"][composition][\"Density\"]\n # outgoing angle correction...\n # The thickness may be x microns but if x-rays aren't \n # normal to it (on average) there will be some correction \n \n mass_thickness = density*thickness\n cross_section=calcTotalMassAttenuationCoefficients(composition, \n 1.0 ,line_energy,exptdesc[\"Materials\"],\n exptdesc[\"Elements\"])\n if(attenuator!='sensor'):\n trans_curve = trans_curve * \\\n np.exp(-mass_thickness*cross_section)\n else:\n trans_curve = trans_curve * \\\n (1.0-np.exp(-mass_thickness*cross_section))\n return trans_curve\n \n except KeyError:\n print \"Main expt keys not present...Materials,Elements,\\\n Acquisition_instrument/XRF...returning ones \"\n return trans_curve\n \n else:\n return trans_curve","title":""},{"docid":"3d366c0ac1fbab7de59df4be85c5b5cc","score":"0.59856224","text":"def hartree2ev(x):\n return x * HARTREE_TO_EV","title":""},{"docid":"4fbdd2abbc7f5f9c0113a832256cd23f","score":"0.58669424","text":"def energy(self):\n return self.spectral_axis.to(u.eV, u.spectral())","title":""},{"docid":"0c88723ee48867a4418567684e63eb41","score":"0.58260596","text":"def compute_mean_teagerkaiser_energy(x):\n import numpy as np\n\n if isinstance(x, list):\n x = np.asarray(x)\n\n tk_energy = np.mean((x**2)[1:-1] - x[2:] * x[:-2])\n\n return tk_energy","title":""},{"docid":"0aeff2fb2d91bc16781a18743bc8ffff","score":"0.5727968","text":"def energy(self):\n return 0.5 * self.mass * (self.rtd1._x ** 2 + self.rtd1._y ** 2)","title":""},{"docid":"6145ab5563bbb35c2584081627b57633","score":"0.5714193","text":"def Energy( self, x):\n lp = 0.0 #Log posterior\n ca = x[0]\n ce = x[1]\n indx = 2\n for i, md in enumerate(self.mdlist):\n ################### a e s\n lp += md.LogPost( x[indx], x[indx+1], x[indx+2:(indx+md.n)])\n ei = x[indx+1]\n ##Hyper prior for E_i\n lp += (ca-1)*log(ei)-ei*(ca/ce)\n indx = indx+md.n\n ### The normalization constant in each hyper prior for E_i\n lp += self.L*(ca*log(ca) - ca*log(ce) - gammaln(ca))\n ### HERE THE HYPER-HYPER PRIOR FOR E ~ U(0,10) (constant), AND A ~exp(b)\n lp += self.Cte - ca/self.b\n return -lp #Return energy","title":""},{"docid":"a929d2c18f112957e00659f102899084","score":"0.57131916","text":"def predict_local_energy(self, x_t: AtomicEnvironment) -> float:\n\n k_v = self.en_kern_vec(x_t)\n pred_mean = np.matmul(k_v, self.alpha)\n\n return pred_mean","title":""},{"docid":"f2eb89b4ba309f6e75bb52021c4e3b80","score":"0.56488097","text":"def kernel(self):\n self.e, self.xy = self.__kernel__()\n self.e = self.e*nist.HARTREE2EV\n print('Excited State energies (eV) \\n', self.e)\n return self.e, self.xy","title":""},{"docid":"0af4197e3b87ab0f224d2194a0f4c25c","score":"0.5636421","text":"def energy_elec(ks, dm_kpts=None, h1e_kpts=None, vhf=None):\n if h1e_kpts is None: h1e_kpts = ks.get_hcore(ks.cell, ks.kpts)\n if dm_kpts is None: dm_kpts = ks.make_rdm1()\n if vhf is None or getattr(vhf, 'ecoul', None) is None:\n vhf = ks.get_veff(ks.cell, dm_kpts)\n\n weight = getattr(ks.kpts, \"weights_ibz\",\n np.array([1.0/len(h1e_kpts),]*len(h1e_kpts)))\n e1 = np.einsum('k,kij,kji', weight, h1e_kpts, dm_kpts)\n tot_e = e1 + vhf.ecoul + vhf.exc + vhf.E_U\n ks.scf_summary['e1'] = e1.real\n ks.scf_summary['coul'] = vhf.ecoul.real\n ks.scf_summary['exc'] = vhf.exc.real\n ks.scf_summary['E_U'] = vhf.E_U.real\n logger.debug(ks, 'E1 = %s Ecoul = %s Exc = %s EU = %s', e1, vhf.ecoul,\n vhf.exc, vhf.E_U)\n return tot_e.real, vhf.ecoul + vhf.exc + vhf.E_U","title":""},{"docid":"780f53e2340efefa9c06ea52937c8ee9","score":"0.5621941","text":"def energy(self):\n return 0.5 * self.mass * (self.vx ** 2 + self.vy ** 2)","title":""},{"docid":"87ab10286dfb8e74bfb985a0d44912a0","score":"0.5618177","text":"def get_energy(self, x):\n # Get the checkpoints on either side of x, search by distance\n cp2_i = next(itertools.dropwhile(lambda elem: elem[1][1] < x, enumerate(self.valid_checkpoints)), [-1])[0]\n\n # Return muon energy before track begins, return 0 beyond track\n if cp2_i == 0:\n return self.checkpoints[0][0]\n if cp2_i < 0:\n return 0\n\n cp1_i = cp2_i - 1\n cp1 = self.valid_checkpoints[cp1_i]\n cp2 = self.valid_checkpoints[cp2_i]\n\n if x == cp1[1]:\n return cp1[0]\n if x == cp2[1]:\n return cp2[0]\n\n # Get the loss rate and losses between the checkpoints\n loss_rate = self.loss_rates[cp1_i]\n losses_begin, losses_end = self.loss_ranges[cp1_i]\n\n # Get the sum of losses between x and the checkpoint before x\n stoch_loss_since_cp1 = 0\n if losses_begin != losses_end:\n i_loss_before_x = next(itertools.dropwhile(lambda loss: loss[1][1] <= x, enumerate(self.losses[losses_begin:losses_end])), [losses_end-losses_begin])[0] - 1\n if i_loss_before_x >= 0:\n stoch_loss_since_cp1 = self.losses[losses_begin+i_loss_before_x][3]\n\n # (E at last cp) - (stoch losses since last cp) - (loss rate * distance from last cp)\n energy = cp1[0] - stoch_loss_since_cp1 - (x - cp1[1]) * loss_rate\n\n return energy","title":""},{"docid":"0d099d01fda756a2547cabc3e87f6baf","score":"0.5570865","text":"def _E(self, x, vx):\n # note that v and x dot are not the same: v includes the y direction!\n return self.m*(self.g*self._h(x)+0.5*(1+self._dhdx(x)**2)*vx**2)","title":""},{"docid":"744d705282f7ed4eb7bcc960b8aadacb","score":"0.551524","text":"def get_energy(lines):\n for line in reversed(lines):\n if 'CCSD(T) energy' in line:\n ccsdpt_energy = line.split()[2]\n if 'CCSD energy' in line:\n ccsd_energy = line.split()[2]\n if 'Total MP2 energy' in line:\n mp2_energy = line.split()[4]\n if 'E(SCF)=' in line:\n hf_energy = line.split()[1]\n\n return ccsdpt_energy, ccsd_energy, mp2_energy, hf_energy","title":""},{"docid":"bae59b2be22580445f449c5fc7cb1c47","score":"0.54818887","text":"def EnergyVdot(x):\n # P1, P2, z1, z2, L, hp, Km, epsilon, rho, nu, g, boo = constants\n # f, Vdot, Re = variables \n P1 = constants[\"P1\"]\n rho = constants[\"rho\"]\n g = constants[\"g\"]\n z1 = constants[\"z1\"]\n hp = constants[\"hp\"]\n P2 = constants[\"P2\"]\n z2 = constants[\"z2\"]\n Km = constants[\"Km\"]\n L = constants[\"L\"]\n D = constants[\"D\"]\n return np.emath.sqrt((P1/(rho*g)+z1+hp-P2/(rho*g)-z2)/(x[0]*L/D+Km)*(math.pi**2*g*D**4)/8)","title":""},{"docid":"f383575560fa3c78cbcfe444acdb0628","score":"0.54405713","text":"def energy(self, density):\n return 0.0","title":""},{"docid":"783ce31ec43e455953bf4226d9231e2d","score":"0.54314387","text":"def GeV_to_km(x):\n return x*2*10**(-19)","title":""},{"docid":"25d996aa7391af62afae6038be951a5c","score":"0.5415439","text":"def _get_element_and_line(xray_line):\n lim = xray_line.find('_')\n if lim == -1:\n raise ValueError(\"Invalid xray-line: %\" % xray_line)\n return xray_line[:lim], xray_line[lim + 1:]","title":""},{"docid":"74dd33850c1aeadf22839f49da117dff","score":"0.54123163","text":"def line_search() -> float:\n prev_alpha = HW1.alpha0\n alpha = HW1.alpha0 + 1\n phi_alpha = HW1.phi(HW1.alpha0) # This is overwritten inside the loop\n\n phi_al_0 = HW1.phi(HW1.alpha0)\n d_phi_al_0 = HW1.d_phi(HW1.alpha0)\n for i in range(1, HW1.max_line_search_attempts + 1):\n prev_phi_alpha = phi_alpha\n\n phi_alpha = HW1.phi(alpha)\n if phi_alpha > phi_al_0 + HW1.C1 * alpha * d_phi_al_0 \\\n or (i > 1 and phi_alpha >= prev_phi_alpha):\n return HW1.zoom(prev_alpha, alpha)\n\n d_phi_alpha = HW1.d_phi(alpha)\n if abs(d_phi_alpha) <= -HW1.C2 * d_phi_al_0:\n return alpha\n if d_phi_alpha >= 0:\n return HW1.zoom(alpha, prev_alpha)\n\n prev_alpha = alpha\n alpha *= 2\n assert False","title":""},{"docid":"a0f798ca5e05e7c74626a2c220fa3bdb","score":"0.53941935","text":"def energy_func(self, x, atomlist, I):\n # fetch options from command line\n (options,args) = parser.parse_args(self.getEnergies)\n (scf_options,args) = parser.parse_args(self.dftb2.runSCC)\n options.update(scf_options)\n atpos = XYZ.vector2atomlist(x, atomlist)\n self.setGeometry(atpos)\n self.getEnergies(**options)\n return self.Omega[I]","title":""},{"docid":"9a7ee2850ccdbf9309260252dcd06cd2","score":"0.5378639","text":"def getLowGrayLevelEmphasisFeatureValue(self):\n pg = self.coefficients['pg']\n ivector = self.coefficients['ivector']\n Nz = self.coefficients['Nz']\n\n lgle = numpy.sum(pg / (ivector[None, :] ** 2), 1) / Nz\n return lgle","title":""},{"docid":"5a4e732fe2d260bdde8e998fc21d8a0e","score":"0.5353623","text":"def energy_expr(self):\n\n pass","title":""},{"docid":"e51b5cc9ef9b564030253a5bf2a3f284","score":"0.5352645","text":"def energy(frame):\n return np.sum(np.double(frame) ** 2)","title":""},{"docid":"695fd3140c32005cff142943ab29dc9e","score":"0.53491306","text":"def xray_transmission(path_length_m, energy_kev, material='air stp'):\n\t\t\n\tcoefficients = mass_attenuation_coefficicent(energy_kev, material=material)\n\ttransmission = np.exp(-coefficients * density_cgs.get(material) * path_length_m * 100.0)\n\t\n\treturn transmission","title":""},{"docid":"091ed37518044feb362d5c7914b3033e","score":"0.5335636","text":"def getHexEnergy(c, *args ):\n volume = args[0]\n qe = args[1]\n qe.structure.lattice.a = np.sqrt(volume/c)\n qe.structure.lattice.c = c\n qe.structure.saveStructureToPWSCF()\n qe.structure.lattice.printBase()\n# a = np.sqrt(volume/c)\n# geometry = ['Al 0.000000000 0.0000000000000000 0.000000000',\n# 'B 0.500000000 0.2886751345948129 '+str(c/a/2.),\n# 'B 0.000000000 0.5773502691896257 '+str(c/a/2.)]\n# varnameValue(task.pwscfInput, 'celldm(1)', a)\n# varnameValue(task.pwscfInput, 'celldm(2)', a)\n# varnameValue(task.pwscfInput, 'celldm(3)', c/a)\n# atomic_positions(task.pwscfInput, geometry)\n \n qe.pwscfLauncher()\n return qe.getTotalEnergy()[0]","title":""},{"docid":"a46d2c63da3e52b485b98cc38dab5715","score":"0.53311014","text":"def cktelement_energymeter(self):\n result = ctypes.c_char_p(self.dssObj.CktElementS(ctypes.c_int32(4), ctypes.c_int32(0)))\n return result.value.decode('ascii')","title":""},{"docid":"a508b2e8923c18b681ea588068f2ebbd","score":"0.5327071","text":"def get_x_at(y, line):\n x1, y1, x2, y2 = [float(a) for a in line]\n \n \n # Vertical line case, return one of the x's\n if (x1 == x2):\n return int(x1)\n \n # Calculate slope\n m = (y2 - y1) / (x2 - x1)\n \n # Check if line is horizontal before the next division\n if (m == 0):\n return y\n \n # Calculate the intercept, and x value given y.\n c = y2 - m * x2\n return int((y - c) / m)","title":""},{"docid":"57e2cc25be55a8bc20fddeca5920f19a","score":"0.5326914","text":"def get_energies(filename):\n\n f = open(filename, \"r\")\n lines = f.readlines()\n f.close()\n\n energies = dict()\n\n for line in lines:\n tokens = line.split()\n\n xyz_name = tokens[0]\n hof = float(tokens[1]) - float(tokens[2])\n\n energies[xyz_name] = hof\n\n return energies","title":""},{"docid":"87407792ea402d4b32fa2cadbe9fa48b","score":"0.5324538","text":"def edge_energy(element,shell):\n\n z = elementDB[element][\"Z\"]\n xlshell = _lookupxlshell(shell)\n return xraylib.EdgeEnergy(z,xlshell.shell)","title":""},{"docid":"ed7697fb727721c0fdc8e5f5a58a99aa","score":"0.5304294","text":"def energy(self):\n\n e = self.compute_performance()\n e *= 10000\n\n return e","title":""},{"docid":"f80b055988dd1e1372c29b913f8ee165","score":"0.5301904","text":"def get_k_energies_and_positions(eltname, spectrum):\n try:\n energy_kalpha = emission[eltname]['ka1']\n energy_kbeta = emission[eltname]['kb']\n except KeyError:\n raise KeyError(\"element identifier not found: \" + eltname)\n\n n_kalpha = np.argmax(spectrum)\n offset = n_kalpha + 20\n n_kbeta = np.argmax(spectrum[offset:]) + offset\n\n return energy_kalpha, energy_kbeta, n_kalpha, n_kbeta","title":""},{"docid":"bcc22878b7c3dfac40f453330cf26a93","score":"0.53016156","text":"def energy(x, lmbd=1.0, ptype='log'):\n\n if ptype == 'quad':\n return lmbd * np.sum(x**2)\n elif ptype == 'log':\n return np.log(np.sqrt(np.sum(x**2, axis=1))) / lmbd\n else:\n raise RuntimeError(\"Invalid potential\")","title":""},{"docid":"5c6e3cf5873288460a3bb018ee90daa3","score":"0.5269032","text":"def GetKineticEnergy(self):\n return 0.5 * np.dot(self.momentum, self.momentum) / self.mass","title":""},{"docid":"eb0f47c87a4131fd7f06a6929d7a40f6","score":"0.52613586","text":"def find_energy(self):\n kin = .5*self.mu*self.v**2\n pot = -self.G(self.m_r*self.m_p)/(np.linalg.norm(self.r))\n self.E = kin + pot","title":""},{"docid":"3683760246a7f6367671927ef5eee039","score":"0.52603227","text":"def energy(self):\n return (self.n + 0.5) * self.omega","title":""},{"docid":"da1cf6712b59aa381190feefb33d95b1","score":"0.52373976","text":"def park1_euc(x):\n max_val = 25.5872304\n x1 = x[0]\n x2 = x[1]\n x3 = x[2]\n x4 = x[3]\n ret1 = (x1/2) * (np.sqrt(1 + (x2 + x3**2)*x4/(x1**2 + 0.00001)) - 1)\n ret2 = (x1 + 3*x4) * np.exp(1 + np.sin(x3))\n return min(ret1 + ret2, max_val)","title":""},{"docid":"9d6cc8752b683dbe4896b692bd00cd26","score":"0.522561","text":"def avg_line_x(self, extremity):\n # I want to use this regardless of current page state\n if isinstance(self.children[0], Area):\n lines = []\n for area in self.children:\n lines.extend(area.children)\n if isinstance(self.children[0], Line):\n lines = self.children\n if extremity == 'START':\n x = [l.children[0].box.x0 for l in lines]\n if extremity == 'END':\n x = [l.children[-1].box.x1 for l in lines]\n return np.median(x)","title":""},{"docid":"c2ac3257abf0b8c34cab22a67826923a","score":"0.5211104","text":"def x(self):\n return self._kml['x']","title":""},{"docid":"181d6c5c54c4f5867437ec0a3991ba47","score":"0.52106774","text":"def en_kern_vec(self, x: AtomicEnvironment):\n\n ds = [1, 2, 3]\n size = len(self.training_data) * 3\n k_v = np.zeros(size, )\n\n for m_index in range(size):\n x_2 = self.training_data[int(math.floor(m_index / 3))]\n d_2 = ds[m_index % 3]\n k_v[m_index] = self.energy_force_kernel(x_2, x, d_2,\n self.hyps, self.cutoffs)\n\n return k_v","title":""},{"docid":"8fdec80d7cc61ce4934a9647c922e3fd","score":"0.5195022","text":"def line(x):\n \t\treturn b * x + A","title":""},{"docid":"01622c7e86dedf84575cfd26a43e7bfd","score":"0.51923674","text":"def energy(self):\n return self.mass * self.speed() * self.speed()","title":""},{"docid":"5ecd2daafb3944e5e343ac2728c70b56","score":"0.5192153","text":"def kinenergy(p,m=0.511):\n ene = energy(p,m)\n te = ene-m\n return te","title":""},{"docid":"8bc65518fa2c641ee0c4d46d40153001","score":"0.5190681","text":"def h_value(cls, line, m):\n for x1, y1, x2, y2 in line:\n assert (x1 != x2)\n return y1 + (m - x1) * (y2 - y1) / (x2 - x1)","title":""},{"docid":"acfc2b86a8de2c784518956aade51cfb","score":"0.5189933","text":"def getSmallDependenceLowGrayLevelEmphasisFeatureValue(self):\n ivector = self.coefficients['ivector']\n jvector = self.coefficients['jvector']\n Nz = self.coefficients['Nz']\n\n sdlgle = numpy.sum(self.P_gldm / ((ivector[None, :, None] ** 2) * (jvector[None, None, :] ** 2)), (1, 2)) / Nz\n return sdlgle","title":""},{"docid":"5ad7399bd8479c8b83c7efbb8fe901a4","score":"0.51886326","text":"def calcStrainEnergy(u_el, K_el) -> float:\n return u_el.T @ K_el @ u_el","title":""},{"docid":"d7448919359568982c71e5f4396a61f9","score":"0.5180682","text":"def calc_escape_peak_ratios(lineEnergy,detectorelement='Si'):\n \n if(detectorelement=='Si'):\n #\n # For Si the K peak is 95% of the transition\n # and the photoionization to total cross section is ~ 95% \n # Si escape peak is typically only 0.2-1% (bigger at lower energies) \n #\n jump = xraylib.JumpFactor(14,xraylib.K_SHELL)\n fluy = xraylib.FluorYield(14,xraylib.K_SHELL)\n corr = fluy*(jump-1.0)/jump\n corr_photo = \\\n xraylib.CS_Photo(14,lineEnergy)/xraylib.CS_Total(14,lineEnergy)\n corr_trans = xraylib.RadRate(14,xraylib.KA_LINE)\\\n +xraylib.RadRate(14,xraylib.KB_LINE)\n mu_si= xraylib.CS_Total(14,lineEnergy)\n mu_internal = xraylib.CS_Total(14,1.73998)\n r = mu_internal/mu_si\n eta = corr_trans*corr_photo*corr*0.5*(1.0-r*log(1.0+1.0/r))\n ratio = eta/(1.0-eta)\n #\n # escape peak sigma should be narrower than the main peak.\n #\n return ratio\n else:\n # \n # Ge detector...\n # Ge has a large escape peak ratio ~ 5-15% and a Ka and kb component\n #\n if(lineEnergy < 11.5):\n return 0.0,0.0\n jump = xraylib.JumpFactor(32,xraylib.K_SHELL)\n fluy = xraylib.FluorYield(32,xraylib.K_SHELL)\n corr = fluy*(jump-1.0)/jump\n corr_photo = \\\n xraylib.CS_Photo(32,lineEnergy)/xraylib.CS_Total(32,lineEnergy)\n corr_trans_ka = xraylib.RadRate(32,xraylib.KA_LINE)\n corr_trans_kb =xraylib.RadRate(32,xraylib.KB_LINE)\n mu_ge= xraylib.CS_Total(32,lineEnergy)#\n # one for the Ka and one for the Kb peak...\n mu_internal_ka = \\\n xraylib.CS_Total(32,xraylib.LineEnergy(32,xraylib.KA_LINE))\n r_ka = mu_internal_ka/mu_ge\n eta_ka = corr_trans_ka*corr_photo*corr*0.5*(1.0-r_ka*log(1.0+1.0/r_ka))\n ratio_ka = eta_ka/(1.0-eta_ka)\n\n mu_internal_kb = \\\n xraylib.CS_Total(32,xraylib.LineEnergy(32,xraylib.KB_LINE))\n r_kb = mu_internal_kb/mu_ge\n eta_kb = corr_trans_kb*corr_photo*corr*0.5*(1.0-r_kb*log(1.0+1.0/r_kb))\n ratio_kb = eta_kb/(1.0-eta_kb)\n\n return ratio_ka,ratio_kb","title":""},{"docid":"708e58948affff4c743dce881075879b","score":"0.51802236","text":"def _get_xray_lines_family(xray_line):\n return xray_line[:xray_line.find('_') + 2]","title":""},{"docid":"b9b4a790fa79b335348334c5074fe23f","score":"0.51762104","text":"def erfc(x: float) -> float:\n ...","title":""},{"docid":"af10012bdd8d8507b0e2d9347f27fe0f","score":"0.51752347","text":"def _energy_func(self, x, atomlist, which_en):\n atpos = XYZ.vector2atomlist(x, atomlist)\n self.setGeometry(atpos)\n self.getEnergy()\n # UGLY: getEnergies() is called twice, first inside getEnergy() and then here again\n if which_en == \"Ebs\":\n en = self.getEnergies()[0] # band structure gradient\n elif which_en == \"Ecoulomb\":\n en = self.getEnergies()[1]\n elif which_en == \"Eelec\":\n en = self.getEnergies()[2]\n elif which_en == \"Enuc\":\n en = self.getEnergies()[3]\n elif which_en == \"Etot\": \n en = self.getEnergies()[4]\n return en","title":""},{"docid":"59f5f691ded5df8ef79f97d7e092ea64","score":"0.5165331","text":"def hx(x):\n\n return (x[0]**2 + x[2]**2)**.5","title":""},{"docid":"e597909c75f0ed61d1781da82df4a407","score":"0.5157375","text":"def get_energy(pardict):\n return orm.Float(pardict['EtotRyd'] * 13.605693122994)","title":""},{"docid":"7a6f1dd86c431306fee8e72f7bea80aa","score":"0.5155683","text":"def _compute_sp_energies(self, path, **kwds):\n xtbcalc = XtbCalculator(\n charge=self._charge, spin=self._spin, opt=False\n ) # Add ekstra kwds such as solvent\n path_sp_energies = []\n for path_point in path:\n sp_results = xtbcalc(\n self._atmoic_symbols, path_point, namespace=\"sp_calc\"\n )\n path_sp_energies.append(sp_results['energy'][\"elec_energy\"])\n return np.asarray(path_sp_energies)","title":""},{"docid":"e21a0575165008019852a2f8e3e478c3","score":"0.5149577","text":"def getHighGrayLevelEmphasisFeatureValue(self):\n pg = self.coefficients['pg']\n ivector = self.coefficients['ivector']\n Nz = self.coefficients['Nz']\n\n hgle = numpy.sum(pg * (ivector[None, :] ** 2), 1) / Nz\n return hgle","title":""},{"docid":"8bb544ff327ca2db4a1f32f055babb8c","score":"0.5148058","text":"def get_energy(self,uav_mass):\n if self.energy is None:\n self.energy = self.get_length3d() * uav_mass * 9.81 * self.height\n return self.energy","title":""},{"docid":"8c3bb71ca6be7407c48724c6d04a8867","score":"0.5134457","text":"def v_value(cls, line, m):\n for x1, y1, x2, y2 in line:\n assert (y1 != y2)\n return x1 + (m - y1) * (x2 - x1) / (y2 - y1)","title":""},{"docid":"9ebf17dd64556fae6f6616c0566d0ea2","score":"0.5124515","text":"def get_total_energy(self):\n results = self._node.xpath( \"energy/i[@name='e_fr_energy']\")\n if results:\n return float(results[0].text)\n else:\n raise LookupError('Value not found')","title":""},{"docid":"e94a8614d2b0025dfa9f51ff34629041","score":"0.50993466","text":"def get_energy(file_path):\n\n f = open(file_path, 'r')\n for line in f:\n if 'SCF Done' in line:\n l = line.split()\n f.close()\n return float(l[4])","title":""},{"docid":"fcffc26826a7e25f978040a334bf6472","score":"0.5095197","text":"def getX0(R):\n print(\"Enter the entries of x0 in a single line ({} values separated by spaces) : \".format(R))\n # User input of entries in a\n # single line separated by space\n entries = list(map(float, input().split()))\n vector = np.array(entries).reshape(R, 1)\n print(\"You entered:\\n {}\".format(vector))\n return vector","title":""},{"docid":"76ddac358658422df912475ae8fe4189","score":"0.50913167","text":"def energy(density, coeff=1.0):\n\n # Imports\n from numpy import array, sum\n \n # Assign density list to an numpy array\n density = array(density)\n\n # Check density\n if density.ndim != 1:\n raise ValueError(\"Density should be an a *1-dimensional* array.\")\n\n if any(density < 0):\n raise ValueError(\"Density should be an array of *positive integers*.\")\n\n if density.dtype.kind != 'i' and len(density) > 0:\n raise TypeError(\"Density should be an array of *integers*.\")\n\n # Main code in return\n return coeff * sum(density * (density - 1)) / 2","title":""},{"docid":"27c1ed1e2e2b1ddc682ee0e3b332f005","score":"0.5081118","text":"def erfcx(x):\n return exp(x**2)*(1-erf(x))","title":""},{"docid":"d3a8f3017830179280ee379caedbbc58","score":"0.5079506","text":"def get_atomization_energy(name):\n assert name in extra or name in g2\n d = data[name]\n e = d['enthalpy']\n z = d['ZPE']\n dh = d['thermal correction']\n ae = -e + z + dh\n for a in string2symbols(d['symbols']):\n h = data[a]['enthalpy']\n dh = data[a]['thermal correction']\n ae += h - dh\n return ae","title":""},{"docid":"64c91b6a6c7c6436dcf9265b7a5f2564","score":"0.5079131","text":"def get_energy(phi_variable):\n return (- _MU_0 * msi * h * cos(phi_variable) * sign\n + k * (sin(phi_variable - theta)**2)\n - j_n * _MU_0 * msi * msi_n * cos(phi_variable - phi_n)\n - j_p * _MU_0 * msi * msi_p * cos(phi_variable - phi_p))","title":""},{"docid":"dc1a92599297742f27dc7418b012ce96","score":"0.50788057","text":"def evaluate_epsilon(L_disk, M_BH, eta, R_tilde):\n M_8 = (M_BH / (1e8 * M_sun)).to_value(\"\")\n L_Edd = 1.26 * 1e46 * M_8 << u.Unit(\"erg s-1\")\n l_Edd = (L_disk / L_Edd).to_value(\"\")\n xi = np.power(l_Edd / (M_8 * eta), 1 / 4)\n return 2.7 * 1e-4 * xi * np.power(R_tilde, -3 / 4)","title":""},{"docid":"4e57345c39206f9f3c311003ca75ebbe","score":"0.50734854","text":"def kinetic_energy_history(self):\n return (self.velocity_history**2).sum(axis=-1) * self.eff_m / 2","title":""},{"docid":"e570fb41815681874e39ff228bbcf245","score":"0.5069858","text":"def getDispersionEnergy(self):\n if hasattr(self, \"dispersion\"):\n return self.dispersion.getEnergy(self.atomlist)\n else:\n return 0.0","title":""},{"docid":"396485a769689e33fac251c4c0341283","score":"0.5060783","text":"def K_iso(self):\n return numpy.trace(self.K)/3.0","title":""},{"docid":"851f598b746e688de445ab93d4f4c38f","score":"0.5051419","text":"def park2_euc(x):\n max_val = 5.925698\n x1 = x[0]\n x2 = x[1]\n x3 = x[2]\n x4 = x[3]\n ret = (2.0/3.0) * np.exp(x1 + x2) - x4*np.sin(x3) + x3\n return min(ret, max_val)","title":""},{"docid":"9cc237897c05fab6510d2dbbd0ce288f","score":"0.5048679","text":"def E1(x):\n A0=-0.57721566\n A1= 0.99999193\n A2=-0.24991055\n A3= 0.05519968\n A4=-0.00976004\n A5= 0.00107857\n B1=8.5733287401\n B2=18.059016973\n B3=8.6347608925\n B4=0.2677737343\n C1=9.5733223454\n C2=25.6329561486\n C3=21.0996530827\n C4=3.9584969228\n \n x2=x**2\n x3=x**3\n x4=x**4\n x5=x**5\n ep1A=-jnp.log(x)+A0+A1*x+A2*x2+A3*x3+A4*x4+A5*x5\n ep1B=jnp.exp(-x)/x*\\\n (x4+B1*x3+B2*x2+B3*x+B4)/\\\n (x4+C1*x3+C2*x2+C3*x+C4)\n ep=jnp.where(x<=1.0, ep1A, ep1B)\n return ep","title":""},{"docid":"ac47782cd2a6c901371efedec2f888ba","score":"0.5040625","text":"def erfint(x):\n return x * erf(x) - 1.0 / np.sqrt(np.pi) * (1.0 - np.exp(-x**2))","title":""},{"docid":"c3ab5ca24e3f959332754db830950b6c","score":"0.50399417","text":"def getLargeDependenceLowGrayLevelEmphasisFeatureValue(self):\n ivector = self.coefficients['ivector']\n jvector = self.coefficients['jvector']\n Nz = self.coefficients['Nz']\n\n ldlgle = numpy.sum(self.P_gldm * (jvector[None, None, :] ** 2) / (ivector[None, :, None] ** 2), (1, 2)) / Nz\n return ldlgle","title":""},{"docid":"52a216846ebc86322b40c3cb2f413253","score":"0.50382006","text":"def energy(data):\n N = len(data)\n KE = 0.5*sum([data[i][2]**2+data[i][3]**2 for i in range(N)])\n PE = sum(sum([[-1.0/(d(data[i][0:2],data[j][0:2])) for i in range(j)] for j in range(N)]))\n return KE+PE","title":""},{"docid":"e3e16aee7ab3c3cac9599f2e30c14dab","score":"0.5036348","text":"def findElasticLinePosition(spectrum,tofRange=(0,5000)):\n peak = FindHighestPeak(spectrum,tofRange=tofRange,dx=dt) #find the strongest peak in the TOF range\n #Fit the line to a Gaussian. Initial guess from output from FindHighestPeak\n try:\n centre = peak['centre']; sigma = peak['width']/2.35 #From FWHM to standard deviation\n except TypeError:\n print 'could not find peak for spectrum index ',spectrum.getDetector(0).getID()\n raise\n startX = centre-sigma; endX = centre+sigma #set the fitting boundaries\n funcStr = 'name=Gaussian, Height=%f, PeakCentre=%f, Sigma=%f'%(peak['height'], centre, sigma)\n gfit = Fit(funcStr, spectrum.getName(), StartX=startX, EndX=endX, CreateOutput='1')\n #Retrieve the optimized fitting parameters\n fittedParams = { 'Height':0, 'PeakCentre':0, 'Sigma':0}\n for iRow in range( gfit[3].rowCount() - 1):\n row = gfit[3].row( iRow )\n fittedParams[ row['Name'] ] = row['Value']\n return fittedParams['PeakCentre'], fittedParams['Height'],fittedParams['Sigma']","title":""},{"docid":"744c8f8af57f48f78b88d253f92e53e6","score":"0.5031463","text":"def read_energy(self):\n with open(self.label + '/deMon.out', 'r') as f:\n text = f.read().upper()\n\n lines = iter(text.split('\\n'))\n\n for line in lines:\n if line.startswith(' TOTAL ENERGY ='):\n self.results['energy'] = float(line.split()[-1]) * Hartree\n break\n else:\n raise RuntimeError","title":""},{"docid":"b8e92bfba2b26c039a6450e89b3c4a39","score":"0.5015484","text":"def KE(c, x, m, h, v, u):\n xc, mc, vc, hc = x[c], m[c], v[c], h[c]\n\n ## velocity w.r.t. com velocity of clump\n v_well = vc - np.average(vc, weights=mc,axis=0)\n vSqr = np.sum(v_well**2,axis=1)\n return (mc*(vSqr/2 + u[c])).sum()","title":""},{"docid":"22f441446e03d9a78a2703fc679308ae","score":"0.50140053","text":"def energy(data):\n energy = np.sum(data ** 2) / len(data)\n return energy","title":""},{"docid":"996fade952f2cfd18f6a095dacedc1f3","score":"0.5008575","text":"def resolution(self, energies_in_kev):\n return 0.6 * np.sqrt(energies_in_kev)","title":""},{"docid":"9ced5e96cc232e3f1461bb0ef56c9f49","score":"0.5008167","text":"def resolution(self, energies_in_kev):\n return 0.7 * np.sqrt(energies_in_kev)","title":""},{"docid":"fd0e87e8999fb994258b664b71da30e8","score":"0.5004573","text":"def nm2eV(E_nm):\n E_freq = c / E_nm * 1e9\n E_J = E_freq * h\n E_eV = E_J / Cb\n return E_eV","title":""},{"docid":"fce146f15db02bb71d70b434576caf04","score":"0.4991882","text":"def energy_density(self):\n self._H_func.vector()[:] = self.compute_field()\n nodal_E = df.assemble(self._nodal_E).array() * self.unit_length ** self.dim\n return nodal_E / self._nodal_volumes","title":""},{"docid":"3862d0ad180f7dc045f45f6aa1dffff7","score":"0.4978468","text":"def er(self,wl):\n n,k = self.n(wl)\n return (n + 1j*k)**2","title":""},{"docid":"107b2466be284dd9e402ee6fab74a328","score":"0.49767685","text":"def getMeanIonizationEnergy_eV(atomicNumber):\n\n if atomicNumber <= 13.0:\n Value = 11.5*atomicNumber\n else:\n if math.pow(atomicNumber, 0.19) > 0.0:\n Value = 9.76*atomicNumber + 58.8/math.pow(atomicNumber, 0.19)\n else:\n Value = 0.0\n\n return Value","title":""},{"docid":"5c5ec7d63ab2da699c9c7e7533cd2607","score":"0.4974487","text":"def kinetic_energy(m:'in KG', v:'in M/S')->'Joules':\n \n return 1/2*m*v**2","title":""},{"docid":"7ac77bb5db19c83f47e76da7234b8cbc","score":"0.49684364","text":"def K(self):\n return numpy.array(self.magres_isc['K'])","title":""},{"docid":"22e9670fe9442422558a698ca1be0a8e","score":"0.49683946","text":"def x(self):\n return self['x']","title":""},{"docid":"a4421409adb026e24061aa5a65412a6b","score":"0.49642023","text":"def kinetic_energy(self):\n # Apply atom mask\n momenta = self.momenta * self.atom_masks\n\n kinetic_energy = 0.5 * torch.sum(\n torch.sum(momenta ** 2, 3) / self.masses[..., 0], 2\n )\n return kinetic_energy.detach()","title":""},{"docid":"400daf6e04a1562afeb86363f1940283","score":"0.49579078","text":"def _traget_energy_function(self, x, lambda_value: float = 0.0):\n x = np.asarray([x.reshape(-1, 3)]) * unit.angstrom\n force_energy = self.calculate_force(x, lambda_value)\n F_flat = -np.array(\n force_energy.force.value_in_unit(\n unit.kilojoule_per_mole / unit.angstrom\n ).flatten(),\n dtype=np.float64,\n )\n self.memory_of_energy.append(force_energy.energy)\n self.memory_of_restrain_contribution.append(\n force_energy.restraint_energy_contribution\n )\n return (force_energy.energy.value_in_unit(unit.kilojoule_per_mole), F_flat)","title":""},{"docid":"8bc064a549a2b0375131e991e795b456","score":"0.49577746","text":"def error(line,data):\n \n err = np.sum((data[:,1]-line[0]*data[:,0]-line[1])**2)\n \n return err","title":""},{"docid":"0b69cb0aa26e21b885b5c0c78d7db3cf","score":"0.49555","text":"def K(x_0, x):\n\tret = 1/np.sqrt(2*np.pi)*np.exp(-(x_0*np.ones(x.shape[0]) - x)**2/2)\n\treturn ret","title":""},{"docid":"2773cef6ad4a90ea883a665c4dd79f1b","score":"0.49485254","text":"def get_ee_energy_density(self):\n if self.ee_energy_density is None:\n self.ee_energy_density = self.get_ha_energy_density()\\\n + self.get_fx_energy_density()\n return self.ee_energy_density","title":""},{"docid":"2773cef6ad4a90ea883a665c4dd79f1b","score":"0.49485254","text":"def get_ee_energy_density(self):\n if self.ee_energy_density is None:\n self.ee_energy_density = self.get_ha_energy_density()\\\n + self.get_fx_energy_density()\n return self.ee_energy_density","title":""},{"docid":"2d0a98bff6f9cb46f2269173ab447b31","score":"0.49450573","text":"def ev2kcalmol(x):\n return x * EV_TO_KCALMOL","title":""},{"docid":"c5d6dec265d058233ebc3b00e459e1ca","score":"0.49439126","text":"def kinetic_energy(self):\n return 0.5*self.mass*self.vel**2","title":""},{"docid":"47fd66c4b04d3a28cc53496792f49ed8","score":"0.49437764","text":"def energy(self, n):\n mass = 1.0\n return (n**2 * self.hbar**2 * np.pi**2) / (2.0 * mass * self.length**2)","title":""},{"docid":"94071f200353e2e54c2a5411f2438cb2","score":"0.4940596","text":"def HAtomEnergy(n,Z=1):\n E = c.m_e * np.power(c.c,2) / 2. * np.power(c.alpha,2) * np.power(Z,2)/np.power(n,2)\n E = -E.to(u.eV)\n return E","title":""},{"docid":"08ea87a0ec4a4bbb3ae472f2234221b2","score":"0.49387676","text":"def get_xray_lines_near_energy(energy, width=0.2, only_lines=None):\n only_lines = _parse_only_lines(only_lines)\n valid_lines = []\n E_min, E_max = energy - width / 2., energy + width / 2.\n for element, el_props in elements_db.items():\n # Not all elements in the DB have the keys, so catch KeyErrors\n try:\n lines = el_props['Atomic_properties']['Xray_lines']\n except KeyError:\n continue\n for line, l_props in lines.items():\n if only_lines and line not in only_lines:\n continue\n line_energy = l_props['energy (keV)']\n if E_min <= line_energy <= E_max:\n # Store line in Element_Line format, and energy difference\n valid_lines.append((element + \"_\" + line,\n np.abs(line_energy - energy)))\n # Sort by energy difference, but return only the line names\n return [line for line, _ in sorted(valid_lines, key=lambda x: x[1])]","title":""},{"docid":"2a4722231918385ffa66baf81a4eb22e","score":"0.493853","text":"def energia(m: float, c: float) -> float:\r\n \"\"\"Luis Angel Vealazquez Jimenez\"\"\"\r\n \"\"\"GITI7092e\"\"\"\r\n opera = float\r\n opera = m * (c * c)\r\n return opera","title":""},{"docid":"881d977d09904d4d5ab4a676f4f4bd82","score":"0.49368802","text":"def getLargeDependenceHighGrayLevelEmphasisFeatureValue(self):\n ivector = self.coefficients['ivector']\n jvector = self.coefficients['jvector']\n Nz = self.coefficients['Nz']\n\n ldhgle = numpy.sum(self.P_gldm * ((jvector[None, None, :] ** 2) * (ivector[None, :, None] ** 2)), (1, 2)) / Nz\n return ldhgle","title":""},{"docid":"af7947bea3234122379adbc551d15221","score":"0.49359745","text":"def energy(self):\n return self.discrete_op.energy","title":""}],"string":"[\n {\n \"docid\": \"d3e919af76ff67321b75206d1b772a8f\",\n \"score\": \"0.7023946\",\n \"text\": \"def line_energy(element,line):\\n z = elementDB[element][\\\"Z\\\"]\\n if not isinstance(line,LinePair):\\n line = _lookupxlsubline(line)\\n return xraylib.LineEnergy(z,line.subline)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9a8e10e1068b90fd3237243e4aedec8\",\n \"score\": \"0.631008\",\n \"text\": \"def energy(x):\\n th1, th1d, th2, th2d = x.T\\n # Potential\\n V = -(M1+M2)*L1*G*np.cos(th1) - M2*L2*G*np.cos(th2)\\n # Kinetic\\n T = 0.5*M1* (L1*th1d)**2 + \\\\\\n 0.5*M2*((L1*th1d)**2 + (L2*th2d)**2 + \\\\\\n 2*L1*L2*th1d*th2d*np.cos(th1-th2))\\n # Sum\\n return T + V\",\n \"title\": \"\"\n },\n {\n \"docid\": \"249b5de5d7e34b2e58c010c1d08d776a\",\n \"score\": \"0.62636083\",\n \"text\": \"def get_energy(input_line):\\n start_extract = input_line.find('[')\\n stop_extract = input_line.find(']')\\n if stop_extract == -1 or start_extract == -1:\\n return 0\\n data = input_line[start_extract+1:stop_extract]\\n values = data.split(', ')\\n \\n energy = int(values[2]) * (2**24) + int(values[3]) * (2**16) + int(values[4]) * (2**8) + int(values[5])\\n energy = float(energy)/3\\n regex = re.compile('.*source: (\\\\d+).*')\\n matches = regex.match(input_line)\\n if matches == None:\\n return 0\\n groups = matches.groups()\\n if groups == None:\\n return 0\\n src = groups[0]\\n \\n return (src, energy,)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"230e63090ac4c1c6597d03582614d0b9\",\n \"score\": \"0.5991744\",\n \"text\": \"def calc_xrfdetector_efficiency(exptdesc,line_energy):\\n \\n if isinstance(line_energy, (list, tuple, np.ndarray)):\\n trans_curve=np.ones_like(line_energy)\\n else:\\n trans_curve=1.0\\n if isinstance(exptdesc, dict):\\n try:\\n detector = exptdesc[\\\"Acquisition_instrument\\\"][\\\"XRF\\\"][\\\"Detector\\\"]\\n if(\\\"Attenuators\\\" in detector):\\n adict = detector[\\\"Attenuators\\\"]\\n for attenuator in adict.keys():\\n thickness = adict[attenuator][\\\"thickness\\\"]\\n composition = adict[attenuator][\\\"composition\\\"]\\n if(composition in exptdesc[\\\"Materials\\\"]):\\n density = exptdesc[\\\"Materials\\\"][composition][\\\"Density\\\"]\\n else:\\n density = exptdesc[\\\"Elements\\\"][composition][\\\"Density\\\"]\\n # outgoing angle correction...\\n # The thickness may be x microns but if x-rays aren't \\n # normal to it (on average) there will be some correction \\n \\n mass_thickness = density*thickness\\n cross_section=calcTotalMassAttenuationCoefficients(composition, \\n 1.0 ,line_energy,exptdesc[\\\"Materials\\\"],\\n exptdesc[\\\"Elements\\\"])\\n if(attenuator!='sensor'):\\n trans_curve = trans_curve * \\\\\\n np.exp(-mass_thickness*cross_section)\\n else:\\n trans_curve = trans_curve * \\\\\\n (1.0-np.exp(-mass_thickness*cross_section))\\n return trans_curve\\n \\n except KeyError:\\n print \\\"Main expt keys not present...Materials,Elements,\\\\\\n Acquisition_instrument/XRF...returning ones \\\"\\n return trans_curve\\n \\n else:\\n return trans_curve\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d366c0ac1fbab7de59df4be85c5b5cc\",\n \"score\": \"0.59856224\",\n \"text\": \"def hartree2ev(x):\\n return x * HARTREE_TO_EV\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fbdd2abbc7f5f9c0113a832256cd23f\",\n \"score\": \"0.58669424\",\n \"text\": \"def energy(self):\\n return self.spectral_axis.to(u.eV, u.spectral())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c88723ee48867a4418567684e63eb41\",\n \"score\": \"0.58260596\",\n \"text\": \"def compute_mean_teagerkaiser_energy(x):\\n import numpy as np\\n\\n if isinstance(x, list):\\n x = np.asarray(x)\\n\\n tk_energy = np.mean((x**2)[1:-1] - x[2:] * x[:-2])\\n\\n return tk_energy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0aeff2fb2d91bc16781a18743bc8ffff\",\n \"score\": \"0.5727968\",\n \"text\": \"def energy(self):\\n return 0.5 * self.mass * (self.rtd1._x ** 2 + self.rtd1._y ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6145ab5563bbb35c2584081627b57633\",\n \"score\": \"0.5714193\",\n \"text\": \"def Energy( self, x):\\n lp = 0.0 #Log posterior\\n ca = x[0]\\n ce = x[1]\\n indx = 2\\n for i, md in enumerate(self.mdlist):\\n ################### a e s\\n lp += md.LogPost( x[indx], x[indx+1], x[indx+2:(indx+md.n)])\\n ei = x[indx+1]\\n ##Hyper prior for E_i\\n lp += (ca-1)*log(ei)-ei*(ca/ce)\\n indx = indx+md.n\\n ### The normalization constant in each hyper prior for E_i\\n lp += self.L*(ca*log(ca) - ca*log(ce) - gammaln(ca))\\n ### HERE THE HYPER-HYPER PRIOR FOR E ~ U(0,10) (constant), AND A ~exp(b)\\n lp += self.Cte - ca/self.b\\n return -lp #Return energy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a929d2c18f112957e00659f102899084\",\n \"score\": \"0.57131916\",\n \"text\": \"def predict_local_energy(self, x_t: AtomicEnvironment) -> float:\\n\\n k_v = self.en_kern_vec(x_t)\\n pred_mean = np.matmul(k_v, self.alpha)\\n\\n return pred_mean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2eb89b4ba309f6e75bb52021c4e3b80\",\n \"score\": \"0.56488097\",\n \"text\": \"def kernel(self):\\n self.e, self.xy = self.__kernel__()\\n self.e = self.e*nist.HARTREE2EV\\n print('Excited State energies (eV) \\\\n', self.e)\\n return self.e, self.xy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0af4197e3b87ab0f224d2194a0f4c25c\",\n \"score\": \"0.5636421\",\n \"text\": \"def energy_elec(ks, dm_kpts=None, h1e_kpts=None, vhf=None):\\n if h1e_kpts is None: h1e_kpts = ks.get_hcore(ks.cell, ks.kpts)\\n if dm_kpts is None: dm_kpts = ks.make_rdm1()\\n if vhf is None or getattr(vhf, 'ecoul', None) is None:\\n vhf = ks.get_veff(ks.cell, dm_kpts)\\n\\n weight = getattr(ks.kpts, \\\"weights_ibz\\\",\\n np.array([1.0/len(h1e_kpts),]*len(h1e_kpts)))\\n e1 = np.einsum('k,kij,kji', weight, h1e_kpts, dm_kpts)\\n tot_e = e1 + vhf.ecoul + vhf.exc + vhf.E_U\\n ks.scf_summary['e1'] = e1.real\\n ks.scf_summary['coul'] = vhf.ecoul.real\\n ks.scf_summary['exc'] = vhf.exc.real\\n ks.scf_summary['E_U'] = vhf.E_U.real\\n logger.debug(ks, 'E1 = %s Ecoul = %s Exc = %s EU = %s', e1, vhf.ecoul,\\n vhf.exc, vhf.E_U)\\n return tot_e.real, vhf.ecoul + vhf.exc + vhf.E_U\",\n \"title\": \"\"\n },\n {\n \"docid\": \"780f53e2340efefa9c06ea52937c8ee9\",\n \"score\": \"0.5621941\",\n \"text\": \"def energy(self):\\n return 0.5 * self.mass * (self.vx ** 2 + self.vy ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87ab10286dfb8e74bfb985a0d44912a0\",\n \"score\": \"0.5618177\",\n \"text\": \"def get_energy(self, x):\\n # Get the checkpoints on either side of x, search by distance\\n cp2_i = next(itertools.dropwhile(lambda elem: elem[1][1] < x, enumerate(self.valid_checkpoints)), [-1])[0]\\n\\n # Return muon energy before track begins, return 0 beyond track\\n if cp2_i == 0:\\n return self.checkpoints[0][0]\\n if cp2_i < 0:\\n return 0\\n\\n cp1_i = cp2_i - 1\\n cp1 = self.valid_checkpoints[cp1_i]\\n cp2 = self.valid_checkpoints[cp2_i]\\n\\n if x == cp1[1]:\\n return cp1[0]\\n if x == cp2[1]:\\n return cp2[0]\\n\\n # Get the loss rate and losses between the checkpoints\\n loss_rate = self.loss_rates[cp1_i]\\n losses_begin, losses_end = self.loss_ranges[cp1_i]\\n\\n # Get the sum of losses between x and the checkpoint before x\\n stoch_loss_since_cp1 = 0\\n if losses_begin != losses_end:\\n i_loss_before_x = next(itertools.dropwhile(lambda loss: loss[1][1] <= x, enumerate(self.losses[losses_begin:losses_end])), [losses_end-losses_begin])[0] - 1\\n if i_loss_before_x >= 0:\\n stoch_loss_since_cp1 = self.losses[losses_begin+i_loss_before_x][3]\\n\\n # (E at last cp) - (stoch losses since last cp) - (loss rate * distance from last cp)\\n energy = cp1[0] - stoch_loss_since_cp1 - (x - cp1[1]) * loss_rate\\n\\n return energy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d099d01fda756a2547cabc3e87f6baf\",\n \"score\": \"0.5570865\",\n \"text\": \"def _E(self, x, vx):\\n # note that v and x dot are not the same: v includes the y direction!\\n return self.m*(self.g*self._h(x)+0.5*(1+self._dhdx(x)**2)*vx**2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"744d705282f7ed4eb7bcc960b8aadacb\",\n \"score\": \"0.551524\",\n \"text\": \"def get_energy(lines):\\n for line in reversed(lines):\\n if 'CCSD(T) energy' in line:\\n ccsdpt_energy = line.split()[2]\\n if 'CCSD energy' in line:\\n ccsd_energy = line.split()[2]\\n if 'Total MP2 energy' in line:\\n mp2_energy = line.split()[4]\\n if 'E(SCF)=' in line:\\n hf_energy = line.split()[1]\\n\\n return ccsdpt_energy, ccsd_energy, mp2_energy, hf_energy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bae59b2be22580445f449c5fc7cb1c47\",\n \"score\": \"0.54818887\",\n \"text\": \"def EnergyVdot(x):\\n # P1, P2, z1, z2, L, hp, Km, epsilon, rho, nu, g, boo = constants\\n # f, Vdot, Re = variables \\n P1 = constants[\\\"P1\\\"]\\n rho = constants[\\\"rho\\\"]\\n g = constants[\\\"g\\\"]\\n z1 = constants[\\\"z1\\\"]\\n hp = constants[\\\"hp\\\"]\\n P2 = constants[\\\"P2\\\"]\\n z2 = constants[\\\"z2\\\"]\\n Km = constants[\\\"Km\\\"]\\n L = constants[\\\"L\\\"]\\n D = constants[\\\"D\\\"]\\n return np.emath.sqrt((P1/(rho*g)+z1+hp-P2/(rho*g)-z2)/(x[0]*L/D+Km)*(math.pi**2*g*D**4)/8)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f383575560fa3c78cbcfe444acdb0628\",\n \"score\": \"0.54405713\",\n \"text\": \"def energy(self, density):\\n return 0.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"783ce31ec43e455953bf4226d9231e2d\",\n \"score\": \"0.54314387\",\n \"text\": \"def GeV_to_km(x):\\n return x*2*10**(-19)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25d996aa7391af62afae6038be951a5c\",\n \"score\": \"0.5415439\",\n \"text\": \"def _get_element_and_line(xray_line):\\n lim = xray_line.find('_')\\n if lim == -1:\\n raise ValueError(\\\"Invalid xray-line: %\\\" % xray_line)\\n return xray_line[:lim], xray_line[lim + 1:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74dd33850c1aeadf22839f49da117dff\",\n \"score\": \"0.54123163\",\n \"text\": \"def line_search() -> float:\\n prev_alpha = HW1.alpha0\\n alpha = HW1.alpha0 + 1\\n phi_alpha = HW1.phi(HW1.alpha0) # This is overwritten inside the loop\\n\\n phi_al_0 = HW1.phi(HW1.alpha0)\\n d_phi_al_0 = HW1.d_phi(HW1.alpha0)\\n for i in range(1, HW1.max_line_search_attempts + 1):\\n prev_phi_alpha = phi_alpha\\n\\n phi_alpha = HW1.phi(alpha)\\n if phi_alpha > phi_al_0 + HW1.C1 * alpha * d_phi_al_0 \\\\\\n or (i > 1 and phi_alpha >= prev_phi_alpha):\\n return HW1.zoom(prev_alpha, alpha)\\n\\n d_phi_alpha = HW1.d_phi(alpha)\\n if abs(d_phi_alpha) <= -HW1.C2 * d_phi_al_0:\\n return alpha\\n if d_phi_alpha >= 0:\\n return HW1.zoom(alpha, prev_alpha)\\n\\n prev_alpha = alpha\\n alpha *= 2\\n assert False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0f798ca5e05e7c74626a2c220fa3bdb\",\n \"score\": \"0.53941935\",\n \"text\": \"def energy_func(self, x, atomlist, I):\\n # fetch options from command line\\n (options,args) = parser.parse_args(self.getEnergies)\\n (scf_options,args) = parser.parse_args(self.dftb2.runSCC)\\n options.update(scf_options)\\n atpos = XYZ.vector2atomlist(x, atomlist)\\n self.setGeometry(atpos)\\n self.getEnergies(**options)\\n return self.Omega[I]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a7ee2850ccdbf9309260252dcd06cd2\",\n \"score\": \"0.5378639\",\n \"text\": \"def getLowGrayLevelEmphasisFeatureValue(self):\\n pg = self.coefficients['pg']\\n ivector = self.coefficients['ivector']\\n Nz = self.coefficients['Nz']\\n\\n lgle = numpy.sum(pg / (ivector[None, :] ** 2), 1) / Nz\\n return lgle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a4e732fe2d260bdde8e998fc21d8a0e\",\n \"score\": \"0.5353623\",\n \"text\": \"def energy_expr(self):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e51b5cc9ef9b564030253a5bf2a3f284\",\n \"score\": \"0.5352645\",\n \"text\": \"def energy(frame):\\n return np.sum(np.double(frame) ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"695fd3140c32005cff142943ab29dc9e\",\n \"score\": \"0.53491306\",\n \"text\": \"def xray_transmission(path_length_m, energy_kev, material='air stp'):\\n\\t\\t\\n\\tcoefficients = mass_attenuation_coefficicent(energy_kev, material=material)\\n\\ttransmission = np.exp(-coefficients * density_cgs.get(material) * path_length_m * 100.0)\\n\\t\\n\\treturn transmission\",\n \"title\": \"\"\n },\n {\n \"docid\": \"091ed37518044feb362d5c7914b3033e\",\n \"score\": \"0.5335636\",\n \"text\": \"def getHexEnergy(c, *args ):\\n volume = args[0]\\n qe = args[1]\\n qe.structure.lattice.a = np.sqrt(volume/c)\\n qe.structure.lattice.c = c\\n qe.structure.saveStructureToPWSCF()\\n qe.structure.lattice.printBase()\\n# a = np.sqrt(volume/c)\\n# geometry = ['Al 0.000000000 0.0000000000000000 0.000000000',\\n# 'B 0.500000000 0.2886751345948129 '+str(c/a/2.),\\n# 'B 0.000000000 0.5773502691896257 '+str(c/a/2.)]\\n# varnameValue(task.pwscfInput, 'celldm(1)', a)\\n# varnameValue(task.pwscfInput, 'celldm(2)', a)\\n# varnameValue(task.pwscfInput, 'celldm(3)', c/a)\\n# atomic_positions(task.pwscfInput, geometry)\\n \\n qe.pwscfLauncher()\\n return qe.getTotalEnergy()[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a46d2c63da3e52b485b98cc38dab5715\",\n \"score\": \"0.53311014\",\n \"text\": \"def cktelement_energymeter(self):\\n result = ctypes.c_char_p(self.dssObj.CktElementS(ctypes.c_int32(4), ctypes.c_int32(0)))\\n return result.value.decode('ascii')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a508b2e8923c18b681ea588068f2ebbd\",\n \"score\": \"0.5327071\",\n \"text\": \"def get_x_at(y, line):\\n x1, y1, x2, y2 = [float(a) for a in line]\\n \\n \\n # Vertical line case, return one of the x's\\n if (x1 == x2):\\n return int(x1)\\n \\n # Calculate slope\\n m = (y2 - y1) / (x2 - x1)\\n \\n # Check if line is horizontal before the next division\\n if (m == 0):\\n return y\\n \\n # Calculate the intercept, and x value given y.\\n c = y2 - m * x2\\n return int((y - c) / m)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57e2cc25be55a8bc20fddeca5920f19a\",\n \"score\": \"0.5326914\",\n \"text\": \"def get_energies(filename):\\n\\n f = open(filename, \\\"r\\\")\\n lines = f.readlines()\\n f.close()\\n\\n energies = dict()\\n\\n for line in lines:\\n tokens = line.split()\\n\\n xyz_name = tokens[0]\\n hof = float(tokens[1]) - float(tokens[2])\\n\\n energies[xyz_name] = hof\\n\\n return energies\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87407792ea402d4b32fa2cadbe9fa48b\",\n \"score\": \"0.5324538\",\n \"text\": \"def edge_energy(element,shell):\\n\\n z = elementDB[element][\\\"Z\\\"]\\n xlshell = _lookupxlshell(shell)\\n return xraylib.EdgeEnergy(z,xlshell.shell)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed7697fb727721c0fdc8e5f5a58a99aa\",\n \"score\": \"0.5304294\",\n \"text\": \"def energy(self):\\n\\n e = self.compute_performance()\\n e *= 10000\\n\\n return e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f80b055988dd1e1372c29b913f8ee165\",\n \"score\": \"0.5301904\",\n \"text\": \"def get_k_energies_and_positions(eltname, spectrum):\\n try:\\n energy_kalpha = emission[eltname]['ka1']\\n energy_kbeta = emission[eltname]['kb']\\n except KeyError:\\n raise KeyError(\\\"element identifier not found: \\\" + eltname)\\n\\n n_kalpha = np.argmax(spectrum)\\n offset = n_kalpha + 20\\n n_kbeta = np.argmax(spectrum[offset:]) + offset\\n\\n return energy_kalpha, energy_kbeta, n_kalpha, n_kbeta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcc22878b7c3dfac40f453330cf26a93\",\n \"score\": \"0.53016156\",\n \"text\": \"def energy(x, lmbd=1.0, ptype='log'):\\n\\n if ptype == 'quad':\\n return lmbd * np.sum(x**2)\\n elif ptype == 'log':\\n return np.log(np.sqrt(np.sum(x**2, axis=1))) / lmbd\\n else:\\n raise RuntimeError(\\\"Invalid potential\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c6e3cf5873288460a3bb018ee90daa3\",\n \"score\": \"0.5269032\",\n \"text\": \"def GetKineticEnergy(self):\\n return 0.5 * np.dot(self.momentum, self.momentum) / self.mass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb0f47c87a4131fd7f06a6929d7a40f6\",\n \"score\": \"0.52613586\",\n \"text\": \"def find_energy(self):\\n kin = .5*self.mu*self.v**2\\n pot = -self.G(self.m_r*self.m_p)/(np.linalg.norm(self.r))\\n self.E = kin + pot\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3683760246a7f6367671927ef5eee039\",\n \"score\": \"0.52603227\",\n \"text\": \"def energy(self):\\n return (self.n + 0.5) * self.omega\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da1cf6712b59aa381190feefb33d95b1\",\n \"score\": \"0.52373976\",\n \"text\": \"def park1_euc(x):\\n max_val = 25.5872304\\n x1 = x[0]\\n x2 = x[1]\\n x3 = x[2]\\n x4 = x[3]\\n ret1 = (x1/2) * (np.sqrt(1 + (x2 + x3**2)*x4/(x1**2 + 0.00001)) - 1)\\n ret2 = (x1 + 3*x4) * np.exp(1 + np.sin(x3))\\n return min(ret1 + ret2, max_val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d6cc8752b683dbe4896b692bd00cd26\",\n \"score\": \"0.522561\",\n \"text\": \"def avg_line_x(self, extremity):\\n # I want to use this regardless of current page state\\n if isinstance(self.children[0], Area):\\n lines = []\\n for area in self.children:\\n lines.extend(area.children)\\n if isinstance(self.children[0], Line):\\n lines = self.children\\n if extremity == 'START':\\n x = [l.children[0].box.x0 for l in lines]\\n if extremity == 'END':\\n x = [l.children[-1].box.x1 for l in lines]\\n return np.median(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2ac3257abf0b8c34cab22a67826923a\",\n \"score\": \"0.5211104\",\n \"text\": \"def x(self):\\n return self._kml['x']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"181d6c5c54c4f5867437ec0a3991ba47\",\n \"score\": \"0.52106774\",\n \"text\": \"def en_kern_vec(self, x: AtomicEnvironment):\\n\\n ds = [1, 2, 3]\\n size = len(self.training_data) * 3\\n k_v = np.zeros(size, )\\n\\n for m_index in range(size):\\n x_2 = self.training_data[int(math.floor(m_index / 3))]\\n d_2 = ds[m_index % 3]\\n k_v[m_index] = self.energy_force_kernel(x_2, x, d_2,\\n self.hyps, self.cutoffs)\\n\\n return k_v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fdec80d7cc61ce4934a9647c922e3fd\",\n \"score\": \"0.5195022\",\n \"text\": \"def line(x):\\n \\t\\treturn b * x + A\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01622c7e86dedf84575cfd26a43e7bfd\",\n \"score\": \"0.51923674\",\n \"text\": \"def energy(self):\\n return self.mass * self.speed() * self.speed()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ecd2daafb3944e5e343ac2728c70b56\",\n \"score\": \"0.5192153\",\n \"text\": \"def kinenergy(p,m=0.511):\\n ene = energy(p,m)\\n te = ene-m\\n return te\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bc65518fa2c641ee0c4d46d40153001\",\n \"score\": \"0.5190681\",\n \"text\": \"def h_value(cls, line, m):\\n for x1, y1, x2, y2 in line:\\n assert (x1 != x2)\\n return y1 + (m - x1) * (y2 - y1) / (x2 - x1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acfc2b86a8de2c784518956aade51cfb\",\n \"score\": \"0.5189933\",\n \"text\": \"def getSmallDependenceLowGrayLevelEmphasisFeatureValue(self):\\n ivector = self.coefficients['ivector']\\n jvector = self.coefficients['jvector']\\n Nz = self.coefficients['Nz']\\n\\n sdlgle = numpy.sum(self.P_gldm / ((ivector[None, :, None] ** 2) * (jvector[None, None, :] ** 2)), (1, 2)) / Nz\\n return sdlgle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ad7399bd8479c8b83c7efbb8fe901a4\",\n \"score\": \"0.51886326\",\n \"text\": \"def calcStrainEnergy(u_el, K_el) -> float:\\n return u_el.T @ K_el @ u_el\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7448919359568982c71e5f4396a61f9\",\n \"score\": \"0.5180682\",\n \"text\": \"def calc_escape_peak_ratios(lineEnergy,detectorelement='Si'):\\n \\n if(detectorelement=='Si'):\\n #\\n # For Si the K peak is 95% of the transition\\n # and the photoionization to total cross section is ~ 95% \\n # Si escape peak is typically only 0.2-1% (bigger at lower energies) \\n #\\n jump = xraylib.JumpFactor(14,xraylib.K_SHELL)\\n fluy = xraylib.FluorYield(14,xraylib.K_SHELL)\\n corr = fluy*(jump-1.0)/jump\\n corr_photo = \\\\\\n xraylib.CS_Photo(14,lineEnergy)/xraylib.CS_Total(14,lineEnergy)\\n corr_trans = xraylib.RadRate(14,xraylib.KA_LINE)\\\\\\n +xraylib.RadRate(14,xraylib.KB_LINE)\\n mu_si= xraylib.CS_Total(14,lineEnergy)\\n mu_internal = xraylib.CS_Total(14,1.73998)\\n r = mu_internal/mu_si\\n eta = corr_trans*corr_photo*corr*0.5*(1.0-r*log(1.0+1.0/r))\\n ratio = eta/(1.0-eta)\\n #\\n # escape peak sigma should be narrower than the main peak.\\n #\\n return ratio\\n else:\\n # \\n # Ge detector...\\n # Ge has a large escape peak ratio ~ 5-15% and a Ka and kb component\\n #\\n if(lineEnergy < 11.5):\\n return 0.0,0.0\\n jump = xraylib.JumpFactor(32,xraylib.K_SHELL)\\n fluy = xraylib.FluorYield(32,xraylib.K_SHELL)\\n corr = fluy*(jump-1.0)/jump\\n corr_photo = \\\\\\n xraylib.CS_Photo(32,lineEnergy)/xraylib.CS_Total(32,lineEnergy)\\n corr_trans_ka = xraylib.RadRate(32,xraylib.KA_LINE)\\n corr_trans_kb =xraylib.RadRate(32,xraylib.KB_LINE)\\n mu_ge= xraylib.CS_Total(32,lineEnergy)#\\n # one for the Ka and one for the Kb peak...\\n mu_internal_ka = \\\\\\n xraylib.CS_Total(32,xraylib.LineEnergy(32,xraylib.KA_LINE))\\n r_ka = mu_internal_ka/mu_ge\\n eta_ka = corr_trans_ka*corr_photo*corr*0.5*(1.0-r_ka*log(1.0+1.0/r_ka))\\n ratio_ka = eta_ka/(1.0-eta_ka)\\n\\n mu_internal_kb = \\\\\\n xraylib.CS_Total(32,xraylib.LineEnergy(32,xraylib.KB_LINE))\\n r_kb = mu_internal_kb/mu_ge\\n eta_kb = corr_trans_kb*corr_photo*corr*0.5*(1.0-r_kb*log(1.0+1.0/r_kb))\\n ratio_kb = eta_kb/(1.0-eta_kb)\\n\\n return ratio_ka,ratio_kb\",\n \"title\": \"\"\n },\n {\n \"docid\": \"708e58948affff4c743dce881075879b\",\n \"score\": \"0.51802236\",\n \"text\": \"def _get_xray_lines_family(xray_line):\\n return xray_line[:xray_line.find('_') + 2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9b4a790fa79b335348334c5074fe23f\",\n \"score\": \"0.51762104\",\n \"text\": \"def erfc(x: float) -> float:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af10012bdd8d8507b0e2d9347f27fe0f\",\n \"score\": \"0.51752347\",\n \"text\": \"def _energy_func(self, x, atomlist, which_en):\\n atpos = XYZ.vector2atomlist(x, atomlist)\\n self.setGeometry(atpos)\\n self.getEnergy()\\n # UGLY: getEnergies() is called twice, first inside getEnergy() and then here again\\n if which_en == \\\"Ebs\\\":\\n en = self.getEnergies()[0] # band structure gradient\\n elif which_en == \\\"Ecoulomb\\\":\\n en = self.getEnergies()[1]\\n elif which_en == \\\"Eelec\\\":\\n en = self.getEnergies()[2]\\n elif which_en == \\\"Enuc\\\":\\n en = self.getEnergies()[3]\\n elif which_en == \\\"Etot\\\": \\n en = self.getEnergies()[4]\\n return en\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59f5f691ded5df8ef79f97d7e092ea64\",\n \"score\": \"0.5165331\",\n \"text\": \"def hx(x):\\n\\n return (x[0]**2 + x[2]**2)**.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e597909c75f0ed61d1781da82df4a407\",\n \"score\": \"0.5157375\",\n \"text\": \"def get_energy(pardict):\\n return orm.Float(pardict['EtotRyd'] * 13.605693122994)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a6f1dd86c431306fee8e72f7bea80aa\",\n \"score\": \"0.5155683\",\n \"text\": \"def _compute_sp_energies(self, path, **kwds):\\n xtbcalc = XtbCalculator(\\n charge=self._charge, spin=self._spin, opt=False\\n ) # Add ekstra kwds such as solvent\\n path_sp_energies = []\\n for path_point in path:\\n sp_results = xtbcalc(\\n self._atmoic_symbols, path_point, namespace=\\\"sp_calc\\\"\\n )\\n path_sp_energies.append(sp_results['energy'][\\\"elec_energy\\\"])\\n return np.asarray(path_sp_energies)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e21a0575165008019852a2f8e3e478c3\",\n \"score\": \"0.5149577\",\n \"text\": \"def getHighGrayLevelEmphasisFeatureValue(self):\\n pg = self.coefficients['pg']\\n ivector = self.coefficients['ivector']\\n Nz = self.coefficients['Nz']\\n\\n hgle = numpy.sum(pg * (ivector[None, :] ** 2), 1) / Nz\\n return hgle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bb544ff327ca2db4a1f32f055babb8c\",\n \"score\": \"0.5148058\",\n \"text\": \"def get_energy(self,uav_mass):\\n if self.energy is None:\\n self.energy = self.get_length3d() * uav_mass * 9.81 * self.height\\n return self.energy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c3bb71ca6be7407c48724c6d04a8867\",\n \"score\": \"0.5134457\",\n \"text\": \"def v_value(cls, line, m):\\n for x1, y1, x2, y2 in line:\\n assert (y1 != y2)\\n return x1 + (m - y1) * (x2 - x1) / (y2 - y1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ebf17dd64556fae6f6616c0566d0ea2\",\n \"score\": \"0.5124515\",\n \"text\": \"def get_total_energy(self):\\n results = self._node.xpath( \\\"energy/i[@name='e_fr_energy']\\\")\\n if results:\\n return float(results[0].text)\\n else:\\n raise LookupError('Value not found')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e94a8614d2b0025dfa9f51ff34629041\",\n \"score\": \"0.50993466\",\n \"text\": \"def get_energy(file_path):\\n\\n f = open(file_path, 'r')\\n for line in f:\\n if 'SCF Done' in line:\\n l = line.split()\\n f.close()\\n return float(l[4])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcffc26826a7e25f978040a334bf6472\",\n \"score\": \"0.5095197\",\n \"text\": \"def getX0(R):\\n print(\\\"Enter the entries of x0 in a single line ({} values separated by spaces) : \\\".format(R))\\n # User input of entries in a\\n # single line separated by space\\n entries = list(map(float, input().split()))\\n vector = np.array(entries).reshape(R, 1)\\n print(\\\"You entered:\\\\n {}\\\".format(vector))\\n return vector\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76ddac358658422df912475ae8fe4189\",\n \"score\": \"0.50913167\",\n \"text\": \"def energy(density, coeff=1.0):\\n\\n # Imports\\n from numpy import array, sum\\n \\n # Assign density list to an numpy array\\n density = array(density)\\n\\n # Check density\\n if density.ndim != 1:\\n raise ValueError(\\\"Density should be an a *1-dimensional* array.\\\")\\n\\n if any(density < 0):\\n raise ValueError(\\\"Density should be an array of *positive integers*.\\\")\\n\\n if density.dtype.kind != 'i' and len(density) > 0:\\n raise TypeError(\\\"Density should be an array of *integers*.\\\")\\n\\n # Main code in return\\n return coeff * sum(density * (density - 1)) / 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27c1ed1e2e2b1ddc682ee0e3b332f005\",\n \"score\": \"0.5081118\",\n \"text\": \"def erfcx(x):\\n return exp(x**2)*(1-erf(x))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3a8f3017830179280ee379caedbbc58\",\n \"score\": \"0.5079506\",\n \"text\": \"def get_atomization_energy(name):\\n assert name in extra or name in g2\\n d = data[name]\\n e = d['enthalpy']\\n z = d['ZPE']\\n dh = d['thermal correction']\\n ae = -e + z + dh\\n for a in string2symbols(d['symbols']):\\n h = data[a]['enthalpy']\\n dh = data[a]['thermal correction']\\n ae += h - dh\\n return ae\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64c91b6a6c7c6436dcf9265b7a5f2564\",\n \"score\": \"0.5079131\",\n \"text\": \"def get_energy(phi_variable):\\n return (- _MU_0 * msi * h * cos(phi_variable) * sign\\n + k * (sin(phi_variable - theta)**2)\\n - j_n * _MU_0 * msi * msi_n * cos(phi_variable - phi_n)\\n - j_p * _MU_0 * msi * msi_p * cos(phi_variable - phi_p))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc1a92599297742f27dc7418b012ce96\",\n \"score\": \"0.50788057\",\n \"text\": \"def evaluate_epsilon(L_disk, M_BH, eta, R_tilde):\\n M_8 = (M_BH / (1e8 * M_sun)).to_value(\\\"\\\")\\n L_Edd = 1.26 * 1e46 * M_8 << u.Unit(\\\"erg s-1\\\")\\n l_Edd = (L_disk / L_Edd).to_value(\\\"\\\")\\n xi = np.power(l_Edd / (M_8 * eta), 1 / 4)\\n return 2.7 * 1e-4 * xi * np.power(R_tilde, -3 / 4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e57345c39206f9f3c311003ca75ebbe\",\n \"score\": \"0.50734854\",\n \"text\": \"def kinetic_energy_history(self):\\n return (self.velocity_history**2).sum(axis=-1) * self.eff_m / 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e570fb41815681874e39ff228bbcf245\",\n \"score\": \"0.5069858\",\n \"text\": \"def getDispersionEnergy(self):\\n if hasattr(self, \\\"dispersion\\\"):\\n return self.dispersion.getEnergy(self.atomlist)\\n else:\\n return 0.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"396485a769689e33fac251c4c0341283\",\n \"score\": \"0.5060783\",\n \"text\": \"def K_iso(self):\\n return numpy.trace(self.K)/3.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"851f598b746e688de445ab93d4f4c38f\",\n \"score\": \"0.5051419\",\n \"text\": \"def park2_euc(x):\\n max_val = 5.925698\\n x1 = x[0]\\n x2 = x[1]\\n x3 = x[2]\\n x4 = x[3]\\n ret = (2.0/3.0) * np.exp(x1 + x2) - x4*np.sin(x3) + x3\\n return min(ret, max_val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cc237897c05fab6510d2dbbd0ce288f\",\n \"score\": \"0.5048679\",\n \"text\": \"def E1(x):\\n A0=-0.57721566\\n A1= 0.99999193\\n A2=-0.24991055\\n A3= 0.05519968\\n A4=-0.00976004\\n A5= 0.00107857\\n B1=8.5733287401\\n B2=18.059016973\\n B3=8.6347608925\\n B4=0.2677737343\\n C1=9.5733223454\\n C2=25.6329561486\\n C3=21.0996530827\\n C4=3.9584969228\\n \\n x2=x**2\\n x3=x**3\\n x4=x**4\\n x5=x**5\\n ep1A=-jnp.log(x)+A0+A1*x+A2*x2+A3*x3+A4*x4+A5*x5\\n ep1B=jnp.exp(-x)/x*\\\\\\n (x4+B1*x3+B2*x2+B3*x+B4)/\\\\\\n (x4+C1*x3+C2*x2+C3*x+C4)\\n ep=jnp.where(x<=1.0, ep1A, ep1B)\\n return ep\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac47782cd2a6c901371efedec2f888ba\",\n \"score\": \"0.5040625\",\n \"text\": \"def erfint(x):\\n return x * erf(x) - 1.0 / np.sqrt(np.pi) * (1.0 - np.exp(-x**2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3ab5ca24e3f959332754db830950b6c\",\n \"score\": \"0.50399417\",\n \"text\": \"def getLargeDependenceLowGrayLevelEmphasisFeatureValue(self):\\n ivector = self.coefficients['ivector']\\n jvector = self.coefficients['jvector']\\n Nz = self.coefficients['Nz']\\n\\n ldlgle = numpy.sum(self.P_gldm * (jvector[None, None, :] ** 2) / (ivector[None, :, None] ** 2), (1, 2)) / Nz\\n return ldlgle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52a216846ebc86322b40c3cb2f413253\",\n \"score\": \"0.50382006\",\n \"text\": \"def energy(data):\\n N = len(data)\\n KE = 0.5*sum([data[i][2]**2+data[i][3]**2 for i in range(N)])\\n PE = sum(sum([[-1.0/(d(data[i][0:2],data[j][0:2])) for i in range(j)] for j in range(N)]))\\n return KE+PE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3e16aee7ab3c3cac9599f2e30c14dab\",\n \"score\": \"0.5036348\",\n \"text\": \"def findElasticLinePosition(spectrum,tofRange=(0,5000)):\\n peak = FindHighestPeak(spectrum,tofRange=tofRange,dx=dt) #find the strongest peak in the TOF range\\n #Fit the line to a Gaussian. Initial guess from output from FindHighestPeak\\n try:\\n centre = peak['centre']; sigma = peak['width']/2.35 #From FWHM to standard deviation\\n except TypeError:\\n print 'could not find peak for spectrum index ',spectrum.getDetector(0).getID()\\n raise\\n startX = centre-sigma; endX = centre+sigma #set the fitting boundaries\\n funcStr = 'name=Gaussian, Height=%f, PeakCentre=%f, Sigma=%f'%(peak['height'], centre, sigma)\\n gfit = Fit(funcStr, spectrum.getName(), StartX=startX, EndX=endX, CreateOutput='1')\\n #Retrieve the optimized fitting parameters\\n fittedParams = { 'Height':0, 'PeakCentre':0, 'Sigma':0}\\n for iRow in range( gfit[3].rowCount() - 1):\\n row = gfit[3].row( iRow )\\n fittedParams[ row['Name'] ] = row['Value']\\n return fittedParams['PeakCentre'], fittedParams['Height'],fittedParams['Sigma']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"744c8f8af57f48f78b88d253f92e53e6\",\n \"score\": \"0.5031463\",\n \"text\": \"def read_energy(self):\\n with open(self.label + '/deMon.out', 'r') as f:\\n text = f.read().upper()\\n\\n lines = iter(text.split('\\\\n'))\\n\\n for line in lines:\\n if line.startswith(' TOTAL ENERGY ='):\\n self.results['energy'] = float(line.split()[-1]) * Hartree\\n break\\n else:\\n raise RuntimeError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8e92bfba2b26c039a6450e89b3c4a39\",\n \"score\": \"0.5015484\",\n \"text\": \"def KE(c, x, m, h, v, u):\\n xc, mc, vc, hc = x[c], m[c], v[c], h[c]\\n\\n ## velocity w.r.t. com velocity of clump\\n v_well = vc - np.average(vc, weights=mc,axis=0)\\n vSqr = np.sum(v_well**2,axis=1)\\n return (mc*(vSqr/2 + u[c])).sum()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22f441446e03d9a78a2703fc679308ae\",\n \"score\": \"0.50140053\",\n \"text\": \"def energy(data):\\n energy = np.sum(data ** 2) / len(data)\\n return energy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"996fade952f2cfd18f6a095dacedc1f3\",\n \"score\": \"0.5008575\",\n \"text\": \"def resolution(self, energies_in_kev):\\n return 0.6 * np.sqrt(energies_in_kev)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ced5e96cc232e3f1461bb0ef56c9f49\",\n \"score\": \"0.5008167\",\n \"text\": \"def resolution(self, energies_in_kev):\\n return 0.7 * np.sqrt(energies_in_kev)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd0e87e8999fb994258b664b71da30e8\",\n \"score\": \"0.5004573\",\n \"text\": \"def nm2eV(E_nm):\\n E_freq = c / E_nm * 1e9\\n E_J = E_freq * h\\n E_eV = E_J / Cb\\n return E_eV\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fce146f15db02bb71d70b434576caf04\",\n \"score\": \"0.4991882\",\n \"text\": \"def energy_density(self):\\n self._H_func.vector()[:] = self.compute_field()\\n nodal_E = df.assemble(self._nodal_E).array() * self.unit_length ** self.dim\\n return nodal_E / self._nodal_volumes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3862d0ad180f7dc045f45f6aa1dffff7\",\n \"score\": \"0.4978468\",\n \"text\": \"def er(self,wl):\\n n,k = self.n(wl)\\n return (n + 1j*k)**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"107b2466be284dd9e402ee6fab74a328\",\n \"score\": \"0.49767685\",\n \"text\": \"def getMeanIonizationEnergy_eV(atomicNumber):\\n\\n if atomicNumber <= 13.0:\\n Value = 11.5*atomicNumber\\n else:\\n if math.pow(atomicNumber, 0.19) > 0.0:\\n Value = 9.76*atomicNumber + 58.8/math.pow(atomicNumber, 0.19)\\n else:\\n Value = 0.0\\n\\n return Value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c5ec7d63ab2da699c9c7e7533cd2607\",\n \"score\": \"0.4974487\",\n \"text\": \"def kinetic_energy(m:'in KG', v:'in M/S')->'Joules':\\n \\n return 1/2*m*v**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ac77bb5db19c83f47e76da7234b8cbc\",\n \"score\": \"0.49684364\",\n \"text\": \"def K(self):\\n return numpy.array(self.magres_isc['K'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22e9670fe9442422558a698ca1be0a8e\",\n \"score\": \"0.49683946\",\n \"text\": \"def x(self):\\n return self['x']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4421409adb026e24061aa5a65412a6b\",\n \"score\": \"0.49642023\",\n \"text\": \"def kinetic_energy(self):\\n # Apply atom mask\\n momenta = self.momenta * self.atom_masks\\n\\n kinetic_energy = 0.5 * torch.sum(\\n torch.sum(momenta ** 2, 3) / self.masses[..., 0], 2\\n )\\n return kinetic_energy.detach()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"400daf6e04a1562afeb86363f1940283\",\n \"score\": \"0.49579078\",\n \"text\": \"def _traget_energy_function(self, x, lambda_value: float = 0.0):\\n x = np.asarray([x.reshape(-1, 3)]) * unit.angstrom\\n force_energy = self.calculate_force(x, lambda_value)\\n F_flat = -np.array(\\n force_energy.force.value_in_unit(\\n unit.kilojoule_per_mole / unit.angstrom\\n ).flatten(),\\n dtype=np.float64,\\n )\\n self.memory_of_energy.append(force_energy.energy)\\n self.memory_of_restrain_contribution.append(\\n force_energy.restraint_energy_contribution\\n )\\n return (force_energy.energy.value_in_unit(unit.kilojoule_per_mole), F_flat)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bc064a549a2b0375131e991e795b456\",\n \"score\": \"0.49577746\",\n \"text\": \"def error(line,data):\\n \\n err = np.sum((data[:,1]-line[0]*data[:,0]-line[1])**2)\\n \\n return err\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b69cb0aa26e21b885b5c0c78d7db3cf\",\n \"score\": \"0.49555\",\n \"text\": \"def K(x_0, x):\\n\\tret = 1/np.sqrt(2*np.pi)*np.exp(-(x_0*np.ones(x.shape[0]) - x)**2/2)\\n\\treturn ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2773cef6ad4a90ea883a665c4dd79f1b\",\n \"score\": \"0.49485254\",\n \"text\": \"def get_ee_energy_density(self):\\n if self.ee_energy_density is None:\\n self.ee_energy_density = self.get_ha_energy_density()\\\\\\n + self.get_fx_energy_density()\\n return self.ee_energy_density\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2773cef6ad4a90ea883a665c4dd79f1b\",\n \"score\": \"0.49485254\",\n \"text\": \"def get_ee_energy_density(self):\\n if self.ee_energy_density is None:\\n self.ee_energy_density = self.get_ha_energy_density()\\\\\\n + self.get_fx_energy_density()\\n return self.ee_energy_density\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d0a98bff6f9cb46f2269173ab447b31\",\n \"score\": \"0.49450573\",\n \"text\": \"def ev2kcalmol(x):\\n return x * EV_TO_KCALMOL\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5d6dec265d058233ebc3b00e459e1ca\",\n \"score\": \"0.49439126\",\n \"text\": \"def kinetic_energy(self):\\n return 0.5*self.mass*self.vel**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47fd66c4b04d3a28cc53496792f49ed8\",\n \"score\": \"0.49437764\",\n \"text\": \"def energy(self, n):\\n mass = 1.0\\n return (n**2 * self.hbar**2 * np.pi**2) / (2.0 * mass * self.length**2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94071f200353e2e54c2a5411f2438cb2\",\n \"score\": \"0.4940596\",\n \"text\": \"def HAtomEnergy(n,Z=1):\\n E = c.m_e * np.power(c.c,2) / 2. * np.power(c.alpha,2) * np.power(Z,2)/np.power(n,2)\\n E = -E.to(u.eV)\\n return E\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08ea87a0ec4a4bbb3ae472f2234221b2\",\n \"score\": \"0.49387676\",\n \"text\": \"def get_xray_lines_near_energy(energy, width=0.2, only_lines=None):\\n only_lines = _parse_only_lines(only_lines)\\n valid_lines = []\\n E_min, E_max = energy - width / 2., energy + width / 2.\\n for element, el_props in elements_db.items():\\n # Not all elements in the DB have the keys, so catch KeyErrors\\n try:\\n lines = el_props['Atomic_properties']['Xray_lines']\\n except KeyError:\\n continue\\n for line, l_props in lines.items():\\n if only_lines and line not in only_lines:\\n continue\\n line_energy = l_props['energy (keV)']\\n if E_min <= line_energy <= E_max:\\n # Store line in Element_Line format, and energy difference\\n valid_lines.append((element + \\\"_\\\" + line,\\n np.abs(line_energy - energy)))\\n # Sort by energy difference, but return only the line names\\n return [line for line, _ in sorted(valid_lines, key=lambda x: x[1])]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a4722231918385ffa66baf81a4eb22e\",\n \"score\": \"0.493853\",\n \"text\": \"def energia(m: float, c: float) -> float:\\r\\n \\\"\\\"\\\"Luis Angel Vealazquez Jimenez\\\"\\\"\\\"\\r\\n \\\"\\\"\\\"GITI7092e\\\"\\\"\\\"\\r\\n opera = float\\r\\n opera = m * (c * c)\\r\\n return opera\",\n \"title\": \"\"\n },\n {\n \"docid\": \"881d977d09904d4d5ab4a676f4f4bd82\",\n \"score\": \"0.49368802\",\n \"text\": \"def getLargeDependenceHighGrayLevelEmphasisFeatureValue(self):\\n ivector = self.coefficients['ivector']\\n jvector = self.coefficients['jvector']\\n Nz = self.coefficients['Nz']\\n\\n ldhgle = numpy.sum(self.P_gldm * ((jvector[None, None, :] ** 2) * (ivector[None, :, None] ** 2)), (1, 2)) / Nz\\n return ldhgle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af7947bea3234122379adbc551d15221\",\n \"score\": \"0.49359745\",\n \"text\": \"def energy(self):\\n return self.discrete_op.energy\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994184,"cells":{"query_id":{"kind":"string","value":"03e7c53a79ac094226a73f5970f9b23b"},"query":{"kind":"string","value":"Utility function to get the timeout value used in requests"},"positive_passages":{"kind":"list like","value":[{"docid":"c8799e09a6258eab949006f89eb0cde2","score":"0.81352955","text":"def _timeout(self, timeout):\n if timeout is None:\n return self._request_timeout\n return timeout","title":""}],"string":"[\n {\n \"docid\": \"c8799e09a6258eab949006f89eb0cde2\",\n \"score\": \"0.81352955\",\n \"text\": \"def _timeout(self, timeout):\\n if timeout is None:\\n return self._request_timeout\\n return timeout\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"22dfdd2cc0a669d48d7d0ff56ef6578b","score":"0.8522555","text":"def gettimeout(self): # real signature unknown; restored from __doc__\n return timeout","title":""},{"docid":"ddc80f25e3f51af1150befad01524a0a","score":"0.8129886","text":"def get_timeout(self):\n return self._get_timeout()","title":""},{"docid":"1119d2363cc3ffbc1e0f0d770186e5c3","score":"0.8098538","text":"def get_timeout(self):\n return self.timeout","title":""},{"docid":"bd86d1cf5da2fd3a2aa0b6f4c3e312e5","score":"0.80850226","text":"def request_timeout(self) -> Optional[int]:\n return pulumi.get(self, \"request_timeout\")","title":""},{"docid":"a22d53d02edfa2f14a3eea72c6778cff","score":"0.8067339","text":"def getdefaulttimeout(): # real signature unknown; restored from __doc__\n return timeout","title":""},{"docid":"f95eb10bb25e6061d2ca929427113219","score":"0.8016301","text":"def timeout(self):\n return self.data.get('timeout')","title":""},{"docid":"d0010cdcd065dddc301a53e3df0ace5f","score":"0.80120677","text":"def get_timeout(self):\n return self.socket.get_timeout()","title":""},{"docid":"30dca7cfab1ade3067967ed2a8a87d99","score":"0.8003331","text":"def timeout(self) -> int:\n return pulumi.get(self, \"timeout\")","title":""},{"docid":"30dca7cfab1ade3067967ed2a8a87d99","score":"0.8003331","text":"def timeout(self) -> int:\n return pulumi.get(self, \"timeout\")","title":""},{"docid":"30dca7cfab1ade3067967ed2a8a87d99","score":"0.8003331","text":"def timeout(self) -> int:\n return pulumi.get(self, \"timeout\")","title":""},{"docid":"30dca7cfab1ade3067967ed2a8a87d99","score":"0.8003331","text":"def timeout(self) -> int:\n return pulumi.get(self, \"timeout\")","title":""},{"docid":"bfea1d13ea2fab18e3a48c3b4212e970","score":"0.7936762","text":"def get_timeout(self):\n return None","title":""},{"docid":"60df2e1c11d4cfcabb259e52272a5d41","score":"0.7925852","text":"def get_timeout(self):\n return self._socket.gettimeout()","title":""},{"docid":"d5c4c64becb812e0f2316b73a1f821a1","score":"0.78546286","text":"def get_timeout(self):\n return self.config['timeout']","title":""},{"docid":"78b8f9d8b8edb2eeaf879bd4a9a2b7d8","score":"0.7758228","text":"def timeout(self):\n return self.__timeout","title":""},{"docid":"d4bd040bf4ced14f3c63b2422ec51cba","score":"0.7730424","text":"def timeout(self):\n return self._timeout","title":""},{"docid":"c1a995d0f7fe96cdbb30917e0fd17750","score":"0.77082044","text":"def timeout(self) -> Optional[int]:\n return pulumi.get(self, \"timeout\")","title":""},{"docid":"c1a995d0f7fe96cdbb30917e0fd17750","score":"0.7707487","text":"def timeout(self) -> Optional[int]:\n return pulumi.get(self, \"timeout\")","title":""},{"docid":"c1a995d0f7fe96cdbb30917e0fd17750","score":"0.7707487","text":"def timeout(self) -> Optional[int]:\n return pulumi.get(self, \"timeout\")","title":""},{"docid":"c1a995d0f7fe96cdbb30917e0fd17750","score":"0.7707487","text":"def timeout(self) -> Optional[int]:\n return pulumi.get(self, \"timeout\")","title":""},{"docid":"c1a995d0f7fe96cdbb30917e0fd17750","score":"0.7707487","text":"def timeout(self) -> Optional[int]:\n return pulumi.get(self, \"timeout\")","title":""},{"docid":"c1a995d0f7fe96cdbb30917e0fd17750","score":"0.7707487","text":"def timeout(self) -> Optional[int]:\n return pulumi.get(self, \"timeout\")","title":""},{"docid":"c1a995d0f7fe96cdbb30917e0fd17750","score":"0.7707487","text":"def timeout(self) -> Optional[int]:\n return pulumi.get(self, \"timeout\")","title":""},{"docid":"c1a995d0f7fe96cdbb30917e0fd17750","score":"0.7707487","text":"def timeout(self) -> Optional[int]:\n return pulumi.get(self, \"timeout\")","title":""},{"docid":"ed9b29587579e035132d51ede9994bdf","score":"0.7676719","text":"def get_timeout(self, *args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"b1e99e96586cce422430d9bbbbe4f3af","score":"0.76634705","text":"def get_timeout(self):\n val = self['tpsjob'][0]\n try:\n timeout = int(float(val) * 60)\n except Exception, msg:\n raise Exception(\"%s\\nException : %s\" % (val, msg))\n return timeout","title":""},{"docid":"a60e240c79ce9973eead7df0496940e5","score":"0.76230234","text":"def timeout(self) -> str:\n return self._timeout","title":""},{"docid":"133b41c200138a815d843897b7e0a74d","score":"0.7581552","text":"def timeout(self) -> typing.Optional[aws_cdk.core.Duration]:\n return self._values.get('timeout')","title":""},{"docid":"550347e0896e08e727c104d34357e587","score":"0.7537911","text":"def get_timeout(self):\n return self.instr.timeout","title":""},{"docid":"550347e0896e08e727c104d34357e587","score":"0.7537911","text":"def get_timeout(self):\n return self.instr.timeout","title":""},{"docid":"ba32ed04593993a5b0e5918e92d9001a","score":"0.7509191","text":"def get_timeout():\n try: # gipc-based implementation\n from gevent import Timeout\n return Timeout(TIMEOUT_IN_SEC), Timeout\n except ImportError:\n from Queue import Empty\n timeout = TIMEOUT_IN_SEC\n return timeout, Empty","title":""},{"docid":"b8ff97fdc87fc444d169b339baef574c","score":"0.74755865","text":"def _get_wait_time(self):\n return self._MAX_REQUEST_WAIT_TIME","title":""},{"docid":"c50f0f1a2d3e98275015c8bc94d56557","score":"0.7439643","text":"def getTimeout(self) -> float:\n with self._queueMutex:\n return self._timeout / 1e6","title":""},{"docid":"534ef1aad77e96af17aea932f3270672","score":"0.74292386","text":"def timeout_seconds(self) -> Optional[float]:\n return pulumi.get(self, \"timeout_seconds\")","title":""},{"docid":"19e2e1bf2177988660dd61775c08f368","score":"0.74152976","text":"def timeout_secs(self) -> \"int\":\n return self._attrs.get(\"timeoutSecs\")","title":""},{"docid":"19e2e1bf2177988660dd61775c08f368","score":"0.74152976","text":"def timeout_secs(self) -> \"int\":\n return self._attrs.get(\"timeoutSecs\")","title":""},{"docid":"b34ebc9f91dbe04ff095f95ede9948b0","score":"0.73758566","text":"def timeout(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"timeout\")","title":""},{"docid":"b34ebc9f91dbe04ff095f95ede9948b0","score":"0.73758566","text":"def timeout(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"timeout\")","title":""},{"docid":"b34ebc9f91dbe04ff095f95ede9948b0","score":"0.73758566","text":"def timeout(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"timeout\")","title":""},{"docid":"53ede45f707d021030648513452aa2e1","score":"0.7372905","text":"def get_timeout(title):\n route_dict = get_route_dict(title)\n timeout = route_dict['timeout']\n if not timeout:\n timeout = config['timeout']\n return timeout","title":""},{"docid":"f5753d551fb684b74bc02a065fa53d27","score":"0.7327633","text":"def timeout_in_seconds(self) -> Optional[pulumi.Input[float]]:\n return pulumi.get(self, \"timeout_in_seconds\")","title":""},{"docid":"00371a9ff9cc08b4395c2295fd787fcd","score":"0.732669","text":"def timeout(self) -> pulumi.Output[Optional[int]]:\n return pulumi.get(self, \"timeout\")","title":""},{"docid":"f7671fb9089dbd1133eb39f180814cec","score":"0.72947246","text":"def _get_timeout(self):\n scheduler = Scheduler.get_scheduler()\n self.logger.info(f\"Using scheduler interface {scheduler}\")\n timeout = scheduler.get_remaining_seconds()\n self.logger.info(f\"Obtained timeout from scheduler {timeout}\")\n return timeout","title":""},{"docid":"bf264350ead57a512efd0c526ea8b5fc","score":"0.72519463","text":"def timeout_seconds(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"timeout_seconds\")","title":""},{"docid":"bf264350ead57a512efd0c526ea8b5fc","score":"0.72519463","text":"def timeout_seconds(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"timeout_seconds\")","title":""},{"docid":"bf264350ead57a512efd0c526ea8b5fc","score":"0.72519463","text":"def timeout_seconds(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"timeout_seconds\")","title":""},{"docid":"bf264350ead57a512efd0c526ea8b5fc","score":"0.72519463","text":"def timeout_seconds(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"timeout_seconds\")","title":""},{"docid":"bf264350ead57a512efd0c526ea8b5fc","score":"0.72519463","text":"def timeout_seconds(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"timeout_seconds\")","title":""},{"docid":"bf264350ead57a512efd0c526ea8b5fc","score":"0.72519463","text":"def timeout_seconds(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"timeout_seconds\")","title":""},{"docid":"2879606dc03d572e59250b84df348ccc","score":"0.7251322","text":"def timeout(self):\n return current_app.config[\"IIIF_CACHE_TIME\"]","title":""},{"docid":"4bc028165f9c56811afc5f7cc417f97c","score":"0.72327924","text":"def get_sanity_test_timeout_sec(self):\n self.sanity_test_timeout_sec","title":""},{"docid":"9eddb382370d827c05a371b2c04414ed","score":"0.7215822","text":"def timeout(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"timeout\")","title":""},{"docid":"9eddb382370d827c05a371b2c04414ed","score":"0.7215822","text":"def timeout(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"timeout\")","title":""},{"docid":"8fed0ac0d90dc174f669c3e76d08149a","score":"0.71421504","text":"def poll_timeout(self):\n return self._poll_timeout","title":""},{"docid":"ae7af5505e1c252789c65ca4e3de2bd9","score":"0.7110401","text":"def get_timeout(self):\r\n a = self.get_attributes('VisibilityTimeout')\r\n return int(a['VisibilityTimeout'])","title":""},{"docid":"b2c57189c55628b28b8c8a502e72a76a","score":"0.70609283","text":"def get_client_timeout():\n if os.getenv(\"ARKOUDA_CLIENT_TIMEOUT\"):\n return int(os.getenv(\"ARKOUDA_CLIENT_TIMEOUT\"))\n return None","title":""},{"docid":"1596f25e6d03ff3da85dd17f57aa5454","score":"0.7036144","text":"def zGetTimeout(self):\n return self.conversation.GetDDETimeout()","title":""},{"docid":"1c0f37a59ab956832980d827b9cb79c3","score":"0.70185834","text":"def getTimeOut(self, filesize):\n\n timeout_max = 6*3600 # 6 hours\n timeout_min = self.timeout #5*60 # 5 mins\n\n timeout = timeout_min + int(filesize/0.4e6) # approx < 0.4 Mb/sec\n\n return max(timeout, timeout_max)","title":""},{"docid":"8e1fb691ff38dbc1f82daf38dbe751e3","score":"0.70138425","text":"def discoverabletimeout(self):\n return self.adapter_property.Get(self.ADAPTER_IFACE,\n 'DiscoverableTimeout')","title":""},{"docid":"023379605b054fa87fd74bc58b4516a6","score":"0.6989225","text":"def idle_timeout(self) -> str:\n return pulumi.get(self, \"idle_timeout\")","title":""},{"docid":"2e267f59c5156d07d1a7b6b6ee9ae1a6","score":"0.69802403","text":"def test_get_timeout():\n (read, connect) = AWSClient.get_timeout(None)\n assert read == 60 and connect == 10\n (read, connect) = AWSClient.get_timeout(\"100\")\n assert read == 100 and connect == 10\n (read, connect) = AWSClient.get_timeout(\"200,2\")\n assert read == 200 and connect == 2\n (read, connect) = AWSClient.get_timeout(60)\n assert read == 60 and connect == 10\n (read, connect) = AWSClient.get_timeout(u\"60, 10\") # testing for unicode variable\n assert read == 60 and connect == 10","title":""},{"docid":"e9374ad6bac74e2eec806a7c27a6cb0e","score":"0.69736826","text":"def getConnectTimeout():\n return 10000","title":""},{"docid":"a34221e0ec8445cbd4646f74287a07aa","score":"0.6961935","text":"def session_timeout(self):\n return self._session_timeout","title":""},{"docid":"1e8489b081a101b9b902b550c7b9b34d","score":"0.6946508","text":"def dtp_timeout(self):\n return self.pi.dtp_timeout","title":""},{"docid":"8fdeb393b27b4c2ddbbc424fc3196100","score":"0.69389987","text":"def getReadTimeout():\n return 60000","title":""},{"docid":"9dbd56cfcf0144ee0b680ed77051bdbb","score":"0.69289154","text":"def serverless_connection_timeout_in_seconds(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"serverless_connection_timeout_in_seconds\")","title":""},{"docid":"9dbd56cfcf0144ee0b680ed77051bdbb","score":"0.69289154","text":"def serverless_connection_timeout_in_seconds(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"serverless_connection_timeout_in_seconds\")","title":""},{"docid":"4abeaecc948304b6d5c27eb8eda3edc0","score":"0.69252473","text":"def per_try_timeout(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"per_try_timeout\")","title":""},{"docid":"b1adc6ac79d89d6add8f77c9a810fb2e","score":"0.6906411","text":"def wlan_timeout():\n return json.dumps(dict(\n timeout=rfid_handler.get_wlan_time_left() if rfid_handler else 0\n ))","title":""},{"docid":"a6575be5c28be32c3ec636cb27ae8e7c","score":"0.6884021","text":"def dtp_timeout(self):\n return self.factory.dtp_timeout","title":""},{"docid":"1e5220b09480fb9a635d9eb8104a5633","score":"0.6882846","text":"def timeout(self):\n return self._get_conf('timeout', DEFAULT_WORKER_TIMEOUT)","title":""},{"docid":"a1786311b352952226494368afcac4d5","score":"0.6865132","text":"def serverless_connection_timeout_in_seconds(self) -> pulumi.Output[Optional[int]]:\n return pulumi.get(self, \"serverless_connection_timeout_in_seconds\")","title":""},{"docid":"6ae49abaab331b208a46422c4f5f854a","score":"0.6850533","text":"def running_timeout(self) -> str:\n return pulumi.get(self, \"running_timeout\")","title":""},{"docid":"928b0fb9d40b8a72a0c930fce49e069c","score":"0.68328464","text":"def msg_timer_timeout(self):\n return self._msg_timer_timeout","title":""},{"docid":"81a57f40f8ae05b1a9548192354d7908","score":"0.68312556","text":"def pool_timeout(self):\n return self._pool_timeout","title":""},{"docid":"2c88ac3aa2f0c023394229258ffbd4ec","score":"0.682287","text":"def get_web_session_timeout(self):\n url = self.base_url + \"/web_session_timeout\"\n response = self.ctx.session.get(url)\n response.raise_for_status()\n return response.json()","title":""},{"docid":"3a6aa9ee782768cebd22ce8408ac2d7e","score":"0.6786987","text":"async def _get_timeouts(self):\n return {\"\": self._timeout or self._TIMEOUT}","title":""},{"docid":"3f23a65b2504e03583fd0bef62127206","score":"0.6757808","text":"def connection_idle_timeout(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"connection_idle_timeout\")","title":""},{"docid":"3f23a65b2504e03583fd0bef62127206","score":"0.6757808","text":"def connection_idle_timeout(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"connection_idle_timeout\")","title":""},{"docid":"ba4636aea33f24a22fbfbef1522834c7","score":"0.6757743","text":"def get_testability_timeout(self: \"SeleniumTestability\") -> str:\n return secs_to_timestr(self.timeout)","title":""},{"docid":"9679178ae33a57a6ab709943c11b07ab","score":"0.67545974","text":"def connection_idle_timeout(self) -> pulumi.Output[int]:\n return pulumi.get(self, \"connection_idle_timeout\")","title":""},{"docid":"f93ec0b0c378b53bf2673b63f3823ec3","score":"0.6744489","text":"def getPingTimeout(self):\n\n return self._pingTimeout","title":""},{"docid":"789ed5bae039ffeada114808340a6e79","score":"0.6741402","text":"def serial_timeout(self):\n return self._serial_timeout","title":""},{"docid":"92ba9083406e37404fcb24c38c3ebe88","score":"0.6738017","text":"def readTimeout(self) -> float:\n\n return self._device.timeout","title":""},{"docid":"f8e6de2504500dfc7fefc9bf775814ec","score":"0.6725182","text":"def graceful_timeout(self) -> str:\n return self._graceful_timeout","title":""},{"docid":"b992db6230b18e26f845bf3d9aed4235","score":"0.671494","text":"def soft_timeout(self):\n return self._soft_timeout","title":""},{"docid":"10972bf2ba7b2f6c23c55704198381aa","score":"0.6698095","text":"def timeout_fn():\n return evaluated_last_ckpt","title":""},{"docid":"72a3de76dc4803476fca8283062b6f44","score":"0.6671137","text":"def idle_client_timeout(self) -> pulumi.Output[int]:\n return pulumi.get(self, \"idle_client_timeout\")","title":""},{"docid":"bfe43ad728a919ed4f4814a64cb0a398","score":"0.66660804","text":"def get_timeout(value):\n\n maximum_dbus_timeout_ms = 1073741823\n\n # Ensure the input str is not a float\n if isinstance(value, float):\n raise StratisCliEnvironmentError(\n \"The timeout value provided is a float; it should be an integer.\"\n )\n\n try:\n timeout_int = int(value)\n\n except ValueError:\n raise StratisCliEnvironmentError(\n \"The timeout value provided is not an integer.\"\n )\n\n # Ensure the integer is not too small\n if timeout_int < -1:\n raise StratisCliEnvironmentError(\n \"The timeout value provided is smaller than the smallest acceptable value, -1.\"\n )\n\n # Ensure the integer is not too large\n if timeout_int > maximum_dbus_timeout_ms:\n raise StratisCliEnvironmentError(\n \"The timeout value provided exceeds the largest acceptable value, %s.\"\n % maximum_dbus_timeout_ms\n )\n\n # Convert from milliseconds to seconds\n return timeout_int / 1000","title":""},{"docid":"716203da652647470a631274320af539","score":"0.6642015","text":"def ping_timeout(self):\n return self.settings.get( # type:ignore[attr-defined]\n \"ws_ping_timeout\", max(3 * self.ping_interval, WS_PING_INTERVAL)\n )","title":""},{"docid":"a145822d5517f64b87471f071a44795e","score":"0.6641549","text":"def get_max_response_time(self):\n \n return self.max_response_time","title":""},{"docid":"b3681f3fda2633748cb8fc951fbd26b6","score":"0.6629686","text":"def initialization_timeout(self) -> int:\n return pulumi.get(self, \"initialization_timeout\")","title":""},{"docid":"9307181b6cc7ddfae6d3569b7c4012f0","score":"0.6587432","text":"def idle_client_timeout(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"idle_client_timeout\")","title":""},{"docid":"9307181b6cc7ddfae6d3569b7c4012f0","score":"0.6587432","text":"def idle_client_timeout(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"idle_client_timeout\")","title":""},{"docid":"b415ef151d775e6694ab0faf13dc6f91","score":"0.6584212","text":"def _timeoutCall(self):\n return self._TimeoutMixin__timeoutCall","title":""},{"docid":"b415ef151d775e6694ab0faf13dc6f91","score":"0.6584212","text":"def _timeoutCall(self):\n return self._TimeoutMixin__timeoutCall","title":""},{"docid":"0f942e39065687c986163a69bd579ebb","score":"0.65440726","text":"def _current_wait_time(self):\n connection_timeout = self.connection_timeout or DEFAULT_TIMEOUT\n if self.reconnect_strategy is None:\n return self.connection_timeout\n elif self.reconnect_strategy == 'exp_backoff':\n r = random.uniform(1.0, 2.0)\n return min(r * connection_timeout * math.pow(2.0, self.unsuccessful_connects),\n self.upper_backoff_bound)\n elif self.reconnect_strategy == 'constant':\n return connection_timeout\n else:\n raise ValueError('Reconnect strategy must be None, \"exp_backoff\", or \"constant\"')","title":""},{"docid":"c6264ddd4306aab3b77a03f9b12543f0","score":"0.65381277","text":"def dpd_timeout_seconds(self) -> Optional[int]:\n return pulumi.get(self, \"dpd_timeout_seconds\")","title":""},{"docid":"4385912d98d66562da295c168d2dd713","score":"0.65243006","text":"def ldap_connection_timeout(self):\n return self._ldap_connection_timeout","title":""},{"docid":"87137de1e01cf3950df00abeb951d47b","score":"0.650621","text":"def default_command_timeout(self):\n return self._timeout","title":""}],"string":"[\n {\n \"docid\": \"22dfdd2cc0a669d48d7d0ff56ef6578b\",\n \"score\": \"0.8522555\",\n \"text\": \"def gettimeout(self): # real signature unknown; restored from __doc__\\n return timeout\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddc80f25e3f51af1150befad01524a0a\",\n \"score\": \"0.8129886\",\n \"text\": \"def get_timeout(self):\\n return self._get_timeout()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1119d2363cc3ffbc1e0f0d770186e5c3\",\n \"score\": \"0.8098538\",\n \"text\": \"def get_timeout(self):\\n return self.timeout\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd86d1cf5da2fd3a2aa0b6f4c3e312e5\",\n \"score\": \"0.80850226\",\n \"text\": \"def request_timeout(self) -> Optional[int]:\\n return pulumi.get(self, \\\"request_timeout\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a22d53d02edfa2f14a3eea72c6778cff\",\n \"score\": \"0.8067339\",\n \"text\": \"def getdefaulttimeout(): # real signature unknown; restored from __doc__\\n return timeout\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f95eb10bb25e6061d2ca929427113219\",\n \"score\": \"0.8016301\",\n \"text\": \"def timeout(self):\\n return self.data.get('timeout')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0010cdcd065dddc301a53e3df0ace5f\",\n \"score\": \"0.80120677\",\n \"text\": \"def get_timeout(self):\\n return self.socket.get_timeout()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30dca7cfab1ade3067967ed2a8a87d99\",\n \"score\": \"0.8003331\",\n \"text\": \"def timeout(self) -> int:\\n return pulumi.get(self, \\\"timeout\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30dca7cfab1ade3067967ed2a8a87d99\",\n \"score\": \"0.8003331\",\n \"text\": \"def timeout(self) -> int:\\n return pulumi.get(self, \\\"timeout\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30dca7cfab1ade3067967ed2a8a87d99\",\n \"score\": \"0.8003331\",\n \"text\": \"def timeout(self) -> int:\\n return pulumi.get(self, \\\"timeout\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30dca7cfab1ade3067967ed2a8a87d99\",\n \"score\": \"0.8003331\",\n \"text\": \"def timeout(self) -> int:\\n return pulumi.get(self, \\\"timeout\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfea1d13ea2fab18e3a48c3b4212e970\",\n \"score\": \"0.7936762\",\n \"text\": \"def get_timeout(self):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60df2e1c11d4cfcabb259e52272a5d41\",\n \"score\": \"0.7925852\",\n \"text\": \"def get_timeout(self):\\n return self._socket.gettimeout()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5c4c64becb812e0f2316b73a1f821a1\",\n \"score\": \"0.78546286\",\n \"text\": \"def get_timeout(self):\\n return self.config['timeout']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78b8f9d8b8edb2eeaf879bd4a9a2b7d8\",\n \"score\": \"0.7758228\",\n \"text\": \"def timeout(self):\\n return self.__timeout\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4bd040bf4ced14f3c63b2422ec51cba\",\n \"score\": \"0.7730424\",\n \"text\": \"def timeout(self):\\n return self._timeout\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1a995d0f7fe96cdbb30917e0fd17750\",\n \"score\": \"0.77082044\",\n \"text\": \"def timeout(self) -> Optional[int]:\\n return pulumi.get(self, \\\"timeout\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1a995d0f7fe96cdbb30917e0fd17750\",\n \"score\": \"0.7707487\",\n \"text\": \"def timeout(self) -> Optional[int]:\\n return pulumi.get(self, \\\"timeout\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1a995d0f7fe96cdbb30917e0fd17750\",\n \"score\": \"0.7707487\",\n \"text\": \"def timeout(self) -> Optional[int]:\\n return pulumi.get(self, \\\"timeout\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1a995d0f7fe96cdbb30917e0fd17750\",\n \"score\": \"0.7707487\",\n \"text\": \"def timeout(self) -> Optional[int]:\\n return pulumi.get(self, \\\"timeout\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1a995d0f7fe96cdbb30917e0fd17750\",\n \"score\": \"0.7707487\",\n \"text\": \"def timeout(self) -> Optional[int]:\\n return pulumi.get(self, \\\"timeout\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1a995d0f7fe96cdbb30917e0fd17750\",\n \"score\": \"0.7707487\",\n \"text\": \"def timeout(self) -> Optional[int]:\\n return pulumi.get(self, \\\"timeout\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1a995d0f7fe96cdbb30917e0fd17750\",\n \"score\": \"0.7707487\",\n \"text\": \"def timeout(self) -> Optional[int]:\\n return pulumi.get(self, \\\"timeout\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1a995d0f7fe96cdbb30917e0fd17750\",\n \"score\": \"0.7707487\",\n \"text\": \"def timeout(self) -> Optional[int]:\\n return pulumi.get(self, \\\"timeout\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed9b29587579e035132d51ede9994bdf\",\n \"score\": \"0.7676719\",\n \"text\": \"def get_timeout(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1e99e96586cce422430d9bbbbe4f3af\",\n \"score\": \"0.76634705\",\n \"text\": \"def get_timeout(self):\\n val = self['tpsjob'][0]\\n try:\\n timeout = int(float(val) * 60)\\n except Exception, msg:\\n raise Exception(\\\"%s\\\\nException : %s\\\" % (val, msg))\\n return timeout\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a60e240c79ce9973eead7df0496940e5\",\n \"score\": \"0.76230234\",\n \"text\": \"def timeout(self) -> str:\\n return self._timeout\",\n \"title\": \"\"\n },\n {\n \"docid\": \"133b41c200138a815d843897b7e0a74d\",\n \"score\": \"0.7581552\",\n \"text\": \"def timeout(self) -> typing.Optional[aws_cdk.core.Duration]:\\n return self._values.get('timeout')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"550347e0896e08e727c104d34357e587\",\n \"score\": \"0.7537911\",\n \"text\": \"def get_timeout(self):\\n return self.instr.timeout\",\n \"title\": \"\"\n },\n {\n \"docid\": \"550347e0896e08e727c104d34357e587\",\n \"score\": \"0.7537911\",\n \"text\": \"def get_timeout(self):\\n return self.instr.timeout\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba32ed04593993a5b0e5918e92d9001a\",\n \"score\": \"0.7509191\",\n \"text\": \"def get_timeout():\\n try: # gipc-based implementation\\n from gevent import Timeout\\n return Timeout(TIMEOUT_IN_SEC), Timeout\\n except ImportError:\\n from Queue import Empty\\n timeout = TIMEOUT_IN_SEC\\n return timeout, Empty\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8ff97fdc87fc444d169b339baef574c\",\n \"score\": \"0.74755865\",\n \"text\": \"def _get_wait_time(self):\\n return self._MAX_REQUEST_WAIT_TIME\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c50f0f1a2d3e98275015c8bc94d56557\",\n \"score\": \"0.7439643\",\n \"text\": \"def getTimeout(self) -> float:\\n with self._queueMutex:\\n return self._timeout / 1e6\",\n \"title\": \"\"\n },\n {\n \"docid\": \"534ef1aad77e96af17aea932f3270672\",\n \"score\": \"0.74292386\",\n \"text\": \"def timeout_seconds(self) -> Optional[float]:\\n return pulumi.get(self, \\\"timeout_seconds\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19e2e1bf2177988660dd61775c08f368\",\n \"score\": \"0.74152976\",\n \"text\": \"def timeout_secs(self) -> \\\"int\\\":\\n return self._attrs.get(\\\"timeoutSecs\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19e2e1bf2177988660dd61775c08f368\",\n \"score\": \"0.74152976\",\n \"text\": \"def timeout_secs(self) -> \\\"int\\\":\\n return self._attrs.get(\\\"timeoutSecs\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b34ebc9f91dbe04ff095f95ede9948b0\",\n \"score\": \"0.73758566\",\n \"text\": \"def timeout(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"timeout\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b34ebc9f91dbe04ff095f95ede9948b0\",\n \"score\": \"0.73758566\",\n \"text\": \"def timeout(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"timeout\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b34ebc9f91dbe04ff095f95ede9948b0\",\n \"score\": \"0.73758566\",\n \"text\": \"def timeout(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"timeout\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53ede45f707d021030648513452aa2e1\",\n \"score\": \"0.7372905\",\n \"text\": \"def get_timeout(title):\\n route_dict = get_route_dict(title)\\n timeout = route_dict['timeout']\\n if not timeout:\\n timeout = config['timeout']\\n return timeout\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5753d551fb684b74bc02a065fa53d27\",\n \"score\": \"0.7327633\",\n \"text\": \"def timeout_in_seconds(self) -> Optional[pulumi.Input[float]]:\\n return pulumi.get(self, \\\"timeout_in_seconds\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00371a9ff9cc08b4395c2295fd787fcd\",\n \"score\": \"0.732669\",\n \"text\": \"def timeout(self) -> pulumi.Output[Optional[int]]:\\n return pulumi.get(self, \\\"timeout\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7671fb9089dbd1133eb39f180814cec\",\n \"score\": \"0.72947246\",\n \"text\": \"def _get_timeout(self):\\n scheduler = Scheduler.get_scheduler()\\n self.logger.info(f\\\"Using scheduler interface {scheduler}\\\")\\n timeout = scheduler.get_remaining_seconds()\\n self.logger.info(f\\\"Obtained timeout from scheduler {timeout}\\\")\\n return timeout\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf264350ead57a512efd0c526ea8b5fc\",\n \"score\": \"0.72519463\",\n \"text\": \"def timeout_seconds(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"timeout_seconds\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf264350ead57a512efd0c526ea8b5fc\",\n \"score\": \"0.72519463\",\n \"text\": \"def timeout_seconds(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"timeout_seconds\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf264350ead57a512efd0c526ea8b5fc\",\n \"score\": \"0.72519463\",\n \"text\": \"def timeout_seconds(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"timeout_seconds\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf264350ead57a512efd0c526ea8b5fc\",\n \"score\": \"0.72519463\",\n \"text\": \"def timeout_seconds(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"timeout_seconds\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf264350ead57a512efd0c526ea8b5fc\",\n \"score\": \"0.72519463\",\n \"text\": \"def timeout_seconds(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"timeout_seconds\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf264350ead57a512efd0c526ea8b5fc\",\n \"score\": \"0.72519463\",\n \"text\": \"def timeout_seconds(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"timeout_seconds\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2879606dc03d572e59250b84df348ccc\",\n \"score\": \"0.7251322\",\n \"text\": \"def timeout(self):\\n return current_app.config[\\\"IIIF_CACHE_TIME\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bc028165f9c56811afc5f7cc417f97c\",\n \"score\": \"0.72327924\",\n \"text\": \"def get_sanity_test_timeout_sec(self):\\n self.sanity_test_timeout_sec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9eddb382370d827c05a371b2c04414ed\",\n \"score\": \"0.7215822\",\n \"text\": \"def timeout(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"timeout\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9eddb382370d827c05a371b2c04414ed\",\n \"score\": \"0.7215822\",\n \"text\": \"def timeout(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"timeout\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fed0ac0d90dc174f669c3e76d08149a\",\n \"score\": \"0.71421504\",\n \"text\": \"def poll_timeout(self):\\n return self._poll_timeout\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae7af5505e1c252789c65ca4e3de2bd9\",\n \"score\": \"0.7110401\",\n \"text\": \"def get_timeout(self):\\r\\n a = self.get_attributes('VisibilityTimeout')\\r\\n return int(a['VisibilityTimeout'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2c57189c55628b28b8c8a502e72a76a\",\n \"score\": \"0.70609283\",\n \"text\": \"def get_client_timeout():\\n if os.getenv(\\\"ARKOUDA_CLIENT_TIMEOUT\\\"):\\n return int(os.getenv(\\\"ARKOUDA_CLIENT_TIMEOUT\\\"))\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1596f25e6d03ff3da85dd17f57aa5454\",\n \"score\": \"0.7036144\",\n \"text\": \"def zGetTimeout(self):\\n return self.conversation.GetDDETimeout()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c0f37a59ab956832980d827b9cb79c3\",\n \"score\": \"0.70185834\",\n \"text\": \"def getTimeOut(self, filesize):\\n\\n timeout_max = 6*3600 # 6 hours\\n timeout_min = self.timeout #5*60 # 5 mins\\n\\n timeout = timeout_min + int(filesize/0.4e6) # approx < 0.4 Mb/sec\\n\\n return max(timeout, timeout_max)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e1fb691ff38dbc1f82daf38dbe751e3\",\n \"score\": \"0.70138425\",\n \"text\": \"def discoverabletimeout(self):\\n return self.adapter_property.Get(self.ADAPTER_IFACE,\\n 'DiscoverableTimeout')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"023379605b054fa87fd74bc58b4516a6\",\n \"score\": \"0.6989225\",\n \"text\": \"def idle_timeout(self) -> str:\\n return pulumi.get(self, \\\"idle_timeout\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e267f59c5156d07d1a7b6b6ee9ae1a6\",\n \"score\": \"0.69802403\",\n \"text\": \"def test_get_timeout():\\n (read, connect) = AWSClient.get_timeout(None)\\n assert read == 60 and connect == 10\\n (read, connect) = AWSClient.get_timeout(\\\"100\\\")\\n assert read == 100 and connect == 10\\n (read, connect) = AWSClient.get_timeout(\\\"200,2\\\")\\n assert read == 200 and connect == 2\\n (read, connect) = AWSClient.get_timeout(60)\\n assert read == 60 and connect == 10\\n (read, connect) = AWSClient.get_timeout(u\\\"60, 10\\\") # testing for unicode variable\\n assert read == 60 and connect == 10\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9374ad6bac74e2eec806a7c27a6cb0e\",\n \"score\": \"0.69736826\",\n \"text\": \"def getConnectTimeout():\\n return 10000\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a34221e0ec8445cbd4646f74287a07aa\",\n \"score\": \"0.6961935\",\n \"text\": \"def session_timeout(self):\\n return self._session_timeout\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e8489b081a101b9b902b550c7b9b34d\",\n \"score\": \"0.6946508\",\n \"text\": \"def dtp_timeout(self):\\n return self.pi.dtp_timeout\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fdeb393b27b4c2ddbbc424fc3196100\",\n \"score\": \"0.69389987\",\n \"text\": \"def getReadTimeout():\\n return 60000\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dbd56cfcf0144ee0b680ed77051bdbb\",\n \"score\": \"0.69289154\",\n \"text\": \"def serverless_connection_timeout_in_seconds(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"serverless_connection_timeout_in_seconds\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dbd56cfcf0144ee0b680ed77051bdbb\",\n \"score\": \"0.69289154\",\n \"text\": \"def serverless_connection_timeout_in_seconds(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"serverless_connection_timeout_in_seconds\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4abeaecc948304b6d5c27eb8eda3edc0\",\n \"score\": \"0.69252473\",\n \"text\": \"def per_try_timeout(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"per_try_timeout\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1adc6ac79d89d6add8f77c9a810fb2e\",\n \"score\": \"0.6906411\",\n \"text\": \"def wlan_timeout():\\n return json.dumps(dict(\\n timeout=rfid_handler.get_wlan_time_left() if rfid_handler else 0\\n ))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6575be5c28be32c3ec636cb27ae8e7c\",\n \"score\": \"0.6884021\",\n \"text\": \"def dtp_timeout(self):\\n return self.factory.dtp_timeout\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e5220b09480fb9a635d9eb8104a5633\",\n \"score\": \"0.6882846\",\n \"text\": \"def timeout(self):\\n return self._get_conf('timeout', DEFAULT_WORKER_TIMEOUT)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1786311b352952226494368afcac4d5\",\n \"score\": \"0.6865132\",\n \"text\": \"def serverless_connection_timeout_in_seconds(self) -> pulumi.Output[Optional[int]]:\\n return pulumi.get(self, \\\"serverless_connection_timeout_in_seconds\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ae49abaab331b208a46422c4f5f854a\",\n \"score\": \"0.6850533\",\n \"text\": \"def running_timeout(self) -> str:\\n return pulumi.get(self, \\\"running_timeout\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"928b0fb9d40b8a72a0c930fce49e069c\",\n \"score\": \"0.68328464\",\n \"text\": \"def msg_timer_timeout(self):\\n return self._msg_timer_timeout\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81a57f40f8ae05b1a9548192354d7908\",\n \"score\": \"0.68312556\",\n \"text\": \"def pool_timeout(self):\\n return self._pool_timeout\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c88ac3aa2f0c023394229258ffbd4ec\",\n \"score\": \"0.682287\",\n \"text\": \"def get_web_session_timeout(self):\\n url = self.base_url + \\\"/web_session_timeout\\\"\\n response = self.ctx.session.get(url)\\n response.raise_for_status()\\n return response.json()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a6aa9ee782768cebd22ce8408ac2d7e\",\n \"score\": \"0.6786987\",\n \"text\": \"async def _get_timeouts(self):\\n return {\\\"\\\": self._timeout or self._TIMEOUT}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f23a65b2504e03583fd0bef62127206\",\n \"score\": \"0.6757808\",\n \"text\": \"def connection_idle_timeout(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"connection_idle_timeout\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f23a65b2504e03583fd0bef62127206\",\n \"score\": \"0.6757808\",\n \"text\": \"def connection_idle_timeout(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"connection_idle_timeout\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba4636aea33f24a22fbfbef1522834c7\",\n \"score\": \"0.6757743\",\n \"text\": \"def get_testability_timeout(self: \\\"SeleniumTestability\\\") -> str:\\n return secs_to_timestr(self.timeout)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9679178ae33a57a6ab709943c11b07ab\",\n \"score\": \"0.67545974\",\n \"text\": \"def connection_idle_timeout(self) -> pulumi.Output[int]:\\n return pulumi.get(self, \\\"connection_idle_timeout\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f93ec0b0c378b53bf2673b63f3823ec3\",\n \"score\": \"0.6744489\",\n \"text\": \"def getPingTimeout(self):\\n\\n return self._pingTimeout\",\n \"title\": \"\"\n },\n {\n \"docid\": \"789ed5bae039ffeada114808340a6e79\",\n \"score\": \"0.6741402\",\n \"text\": \"def serial_timeout(self):\\n return self._serial_timeout\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92ba9083406e37404fcb24c38c3ebe88\",\n \"score\": \"0.6738017\",\n \"text\": \"def readTimeout(self) -> float:\\n\\n return self._device.timeout\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8e6de2504500dfc7fefc9bf775814ec\",\n \"score\": \"0.6725182\",\n \"text\": \"def graceful_timeout(self) -> str:\\n return self._graceful_timeout\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b992db6230b18e26f845bf3d9aed4235\",\n \"score\": \"0.671494\",\n \"text\": \"def soft_timeout(self):\\n return self._soft_timeout\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10972bf2ba7b2f6c23c55704198381aa\",\n \"score\": \"0.6698095\",\n \"text\": \"def timeout_fn():\\n return evaluated_last_ckpt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72a3de76dc4803476fca8283062b6f44\",\n \"score\": \"0.6671137\",\n \"text\": \"def idle_client_timeout(self) -> pulumi.Output[int]:\\n return pulumi.get(self, \\\"idle_client_timeout\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfe43ad728a919ed4f4814a64cb0a398\",\n \"score\": \"0.66660804\",\n \"text\": \"def get_timeout(value):\\n\\n maximum_dbus_timeout_ms = 1073741823\\n\\n # Ensure the input str is not a float\\n if isinstance(value, float):\\n raise StratisCliEnvironmentError(\\n \\\"The timeout value provided is a float; it should be an integer.\\\"\\n )\\n\\n try:\\n timeout_int = int(value)\\n\\n except ValueError:\\n raise StratisCliEnvironmentError(\\n \\\"The timeout value provided is not an integer.\\\"\\n )\\n\\n # Ensure the integer is not too small\\n if timeout_int < -1:\\n raise StratisCliEnvironmentError(\\n \\\"The timeout value provided is smaller than the smallest acceptable value, -1.\\\"\\n )\\n\\n # Ensure the integer is not too large\\n if timeout_int > maximum_dbus_timeout_ms:\\n raise StratisCliEnvironmentError(\\n \\\"The timeout value provided exceeds the largest acceptable value, %s.\\\"\\n % maximum_dbus_timeout_ms\\n )\\n\\n # Convert from milliseconds to seconds\\n return timeout_int / 1000\",\n \"title\": \"\"\n },\n {\n \"docid\": \"716203da652647470a631274320af539\",\n \"score\": \"0.6642015\",\n \"text\": \"def ping_timeout(self):\\n return self.settings.get( # type:ignore[attr-defined]\\n \\\"ws_ping_timeout\\\", max(3 * self.ping_interval, WS_PING_INTERVAL)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a145822d5517f64b87471f071a44795e\",\n \"score\": \"0.6641549\",\n \"text\": \"def get_max_response_time(self):\\n \\n return self.max_response_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3681f3fda2633748cb8fc951fbd26b6\",\n \"score\": \"0.6629686\",\n \"text\": \"def initialization_timeout(self) -> int:\\n return pulumi.get(self, \\\"initialization_timeout\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9307181b6cc7ddfae6d3569b7c4012f0\",\n \"score\": \"0.6587432\",\n \"text\": \"def idle_client_timeout(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"idle_client_timeout\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9307181b6cc7ddfae6d3569b7c4012f0\",\n \"score\": \"0.6587432\",\n \"text\": \"def idle_client_timeout(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"idle_client_timeout\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b415ef151d775e6694ab0faf13dc6f91\",\n \"score\": \"0.6584212\",\n \"text\": \"def _timeoutCall(self):\\n return self._TimeoutMixin__timeoutCall\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b415ef151d775e6694ab0faf13dc6f91\",\n \"score\": \"0.6584212\",\n \"text\": \"def _timeoutCall(self):\\n return self._TimeoutMixin__timeoutCall\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f942e39065687c986163a69bd579ebb\",\n \"score\": \"0.65440726\",\n \"text\": \"def _current_wait_time(self):\\n connection_timeout = self.connection_timeout or DEFAULT_TIMEOUT\\n if self.reconnect_strategy is None:\\n return self.connection_timeout\\n elif self.reconnect_strategy == 'exp_backoff':\\n r = random.uniform(1.0, 2.0)\\n return min(r * connection_timeout * math.pow(2.0, self.unsuccessful_connects),\\n self.upper_backoff_bound)\\n elif self.reconnect_strategy == 'constant':\\n return connection_timeout\\n else:\\n raise ValueError('Reconnect strategy must be None, \\\"exp_backoff\\\", or \\\"constant\\\"')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6264ddd4306aab3b77a03f9b12543f0\",\n \"score\": \"0.65381277\",\n \"text\": \"def dpd_timeout_seconds(self) -> Optional[int]:\\n return pulumi.get(self, \\\"dpd_timeout_seconds\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4385912d98d66562da295c168d2dd713\",\n \"score\": \"0.65243006\",\n \"text\": \"def ldap_connection_timeout(self):\\n return self._ldap_connection_timeout\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87137de1e01cf3950df00abeb951d47b\",\n \"score\": \"0.650621\",\n \"text\": \"def default_command_timeout(self):\\n return self._timeout\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994185,"cells":{"query_id":{"kind":"string","value":"291fe1dfc69deae35dbac946b041f543"},"query":{"kind":"string","value":"Actually set the volume via `amixer`."},"positive_passages":{"kind":"list like","value":[{"docid":"6339ec27cb24476ee916f2cb7eda5b98","score":"0.8421663","text":"def set_volume(self):\n subprocess.call([\"amixer\", \"set\", \"PCM\", \"--\", \"%d%%\" % (self.volume,)])","title":""}],"string":"[\n {\n \"docid\": \"6339ec27cb24476ee916f2cb7eda5b98\",\n \"score\": \"0.8421663\",\n \"text\": \"def set_volume(self):\\n subprocess.call([\\\"amixer\\\", \\\"set\\\", \\\"PCM\\\", \\\"--\\\", \\\"%d%%\\\" % (self.volume,)])\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"008be544c722e9ddde9fbf00f2f58a24","score":"0.7889102","text":"def set_volume(self, volume):\r\n if self.is_active:\r\n if 0 < volume < 1:\r\n volume = int(volume*100)\r\n self.sp.volume(volume)","title":""},{"docid":"2fba4be3c921afd787309e29022ba900","score":"0.78266275","text":"def set_volume(vol):\n command = \"osascript -e 'set volume output volume {}'\".format(vol)\n return run_command(command)","title":""},{"docid":"17a92497da1a931189edc4069e41768c","score":"0.7718421","text":"def set_volume(channel, volume):\n\n call(\"set_volume\", channel, volume)","title":""},{"docid":"85020a794ef428b5ab62258eacc87d11","score":"0.7597933","text":"def volume(self, volume):\n self._volume = volume","title":""},{"docid":"8aed53508e501e3fd414dde3dcbe6629","score":"0.7555571","text":"def setVolume(self, track, volume):\n self.processor.setVolume(track, volume)","title":""},{"docid":"a77eebb4be0eddcd4e09b5ca45cc7062","score":"0.7488555","text":"def set_volume(self, volume, which='_all_'):\n pass","title":""},{"docid":"e3e22004b19cd0d4f7438497a2ecb4e9","score":"0.74149466","text":"def volume(self, volume):\n\n self._volume = volume","title":""},{"docid":"e3e22004b19cd0d4f7438497a2ecb4e9","score":"0.74149466","text":"def volume(self, volume):\n\n self._volume = volume","title":""},{"docid":"e3e22004b19cd0d4f7438497a2ecb4e9","score":"0.74149466","text":"def volume(self, volume):\n\n self._volume = volume","title":""},{"docid":"e3e22004b19cd0d4f7438497a2ecb4e9","score":"0.74149466","text":"def volume(self, volume):\n\n self._volume = volume","title":""},{"docid":"331c0562044e5871037ef7abff63d860","score":"0.7388495","text":"def set_volume(self, volume):\n self.volume = volume\n self.player.set_property('volume', self.volume)","title":""},{"docid":"aaa23d6bd0d9e1d4fbea8ef0d192b06c","score":"0.73611593","text":"def set_volume(self, volume):\n self.sound.set_volume(volume)","title":""},{"docid":"1892cc989506c486bde4dcdb6cd38617","score":"0.7300104","text":"def volume(self,volume:float):\n mixer.music.set_volume(volume)","title":""},{"docid":"f437a287534568b916f341fe0193cc86","score":"0.726341","text":"def setVolume(self, volume=0.7):\n\t\tself.volume=volume\n\t\tpygame.mixer.music.set_volume(self.volume)","title":""},{"docid":"0917e7983072f31f0f2e06f757f9f44b","score":"0.72532773","text":"async def set_volume(self, zone: int, volume: int) -> None:\n await self._protocol.send(_format_set_volume(zone, volume))","title":""},{"docid":"ab687f26011a091f0256e8fbe37e925c","score":"0.72436464","text":"def set_volume(self, new_volume):\n self._post_request('core.mixer.set_volume', volume=new_volume)","title":""},{"docid":"437f3324009aa974d91360deb07db72e","score":"0.7240415","text":"def set_volume(self, val):\n \n self.__volume = val\n \n pygame.mixer.music.set_volume(val)","title":""},{"docid":"f7f8d3972edcb24ae20584ec66e20c15","score":"0.7206662","text":"def set_volume(self, zone: int, volume: int) -> None:\n self._process_request(_format_set_volume(zone, volume))","title":""},{"docid":"23bd999770657977f801d19005bd7193","score":"0.71519357","text":"def set_volume(v):\n if type(v) != float:\n if type(v) == int:\n v = float(v)\n else:\n raise TypeError('Has to be a number')\n\n if not 0 <= v <= 1:\n raise ValueError('Has to be between 0 or 1')\n \n musicapp.setVolume_(v)","title":""},{"docid":"2f72bd6fb5082e5440830c4cc1430eed","score":"0.714975","text":"def volume(self, value):\n value = min(1, max(0, value))\n logger.info('set volume %s', value)\n mixer.music.set_volume(value)","title":""},{"docid":"af71d2c1d6d88671527c74be25b90ab7","score":"0.7133459","text":"def set_volume(self, volume):\n if self.musicpresent==True:\n pygame.mixer.music.set_volume(volume)\n else:\n self.soundeffect.set_volume(volume)","title":""},{"docid":"968e85692da9767843e4df9ed1007676","score":"0.7110554","text":"def set_volume(self, volume):\n svc_type = 'urn:schemas-upnp-org:service:RenderingControl:1'\n self._send_cmd('SetVolume', service_type=svc_type, Channel='Master',\n DesiredVolume=volume)\n return None","title":""},{"docid":"192c37b439bfb454e6b1acc2aa458b8a","score":"0.7057902","text":"def onSetVolume(self, event):\r\n self.currentVolume = self.volumeCOP.GetValue()\r\n self.mediaPlayer.SetVolume(float(self.currentVolume / 100))","title":""},{"docid":"5adf6481a26f38c66b9071d315a18c52","score":"0.70216686","text":"def set_volume_level(self, volume):\n if 'volume' in self._status:\n self._client.setvol(int(volume * 100))","title":""},{"docid":"c55a020175a712a9c94196a7106a5c43","score":"0.70117956","text":"def set_volume_level(self, volume: int) -> None:\n self._spotify.volume(int(volume * 100))","title":""},{"docid":"6781af140174c4d98bdf2d9fa04506d0","score":"0.70045114","text":"def set_master_volume(self, val):\n vol = self.calc_vol_for_mixer(val)\n\n self.main.log.write(log.MESSAGE, \"[ALSA] setting mixer volume to \"\n \"%d (=%d)...\" % (val, vol))\n alsaaudio.Mixer(self.mixer).setvolume(vol)","title":""},{"docid":"303f51146a9c7f9a75da086662673041","score":"0.69950217","text":"def set_volume(channel, volume):\n return fmod.FSOUND_SetVolume(channel, volume)","title":""},{"docid":"aa8e2798de841504538c20aba77833c7","score":"0.69819754","text":"def set_amp_volume(self, val):\n vol = self.calc_vol_for_mixer(val)\n\n self.main.log.write(log.MESSAGE, \"[ALSA] setting amp volume to \"\n \"%d (=%d)...\" % (val, vol))\n alsaaudio.Mixer(self.amp).setvolume(vol)","title":""},{"docid":"da49e0abd5a7a01fb83d652c832b2adf","score":"0.69744354","text":"def set_aspirate_volume(self, probe_num, volume, block = True):\n self.wait_for_buffered()\n device_id = self.syringe[probe_num]['device_id']\n probe_letter = \"L\"\n if self.syringe[probe_num]['side'] is 'right':\n probe_letter = \"R\"\n suffix = ''\n if not volume % 1 > 0:\n suffix = '.0'\n if (volume % 1) == (self.get_syringe_pump_status(probe_num)[1] % 1):\n return\n self.buffered(('A%s' % (probe_letter)) + str(volume) + suffix, \n device_id)\n self.syringe[probe_num]['next_operation'] = -volume\n while self.get_syringe_pump_status(probe_num)[0] != 'H':\n self.sleep(.05, '[aspirate block]')","title":""},{"docid":"76be7b622a86ee14e8b5289fbc91fb5a","score":"0.6914168","text":"def cmd_increase_volume(self):\n pass","title":""},{"docid":"fa50de71da3ecf0675de15e0a2314d35","score":"0.68965644","text":"def setVolume(volume=50):\n if volume > 100:\n volume = 100\n elif volume < 0:\n volume = 0\n tkSnack.audio.play_gain(volume)","title":""},{"docid":"9c5065ef38807e4bafc9ca749bf97a43","score":"0.68597615","text":"def set_volume_value(self, index, value):\n self._volumes[index] = value","title":""},{"docid":"b677af321513591268b75dbfc92685ea","score":"0.6856387","text":"def SetVol(self, e):\n \n vol = self.sldr_vol.GetValue()\n print 'cur vol val:', vol\n vol /= 100.0\n print 'volume to be set:', vol\n self.mediaPlayer.SetVolume(vol)","title":""},{"docid":"2332a50c1717014305494b8df427aacf","score":"0.67961496","text":"async def volume(self, ctx, volume: int):\r\n\r\n if ctx.voice_client is None:\r\n return await ctx.send(\"Not connected to a voice channel.\")\r\n\r\n ctx.voice_client.source.volume = volume / 100\r\n await ctx.send(\"Changed volume to {}%\".format(volume))","title":""},{"docid":"a0819431df8a60fef963ea11e9ba4792","score":"0.6790397","text":"async def volume(self, ctx, volume: int):\n\n if ctx.voice_client is None:\n return await ctx.send(\"Not connected to a voice channel.\")\n\n ctx.voice_client.source.volume = volume / 100\n await ctx.send(\"Changed volume to {}%\".format(volume))","title":""},{"docid":"f7ca074a8cabee2c635233fd8e6b1bd6","score":"0.6781557","text":"def volume(object,val):\n if object[1]==1:\n mixer.music.set_volume(0.0)\n elif object[1]==0:\n mixer.music.set_volume(1.0)","title":""},{"docid":"2a639ecc2bbb0d011498f30beaa28106","score":"0.6757883","text":"def __init__(self):\n self.volume = 70 # in percent\n self.set_volume()","title":""},{"docid":"43d80db5c4c3e13cdec95e29bf1fd7ef","score":"0.67351687","text":"def set_volume(self):\n self.town_theme.set_volume(0.5)\n self.fireball.set_volume(1)\n self.monster_kill.set_volume(0.75)","title":""},{"docid":"237ff89a06fbcd5aa86639f053f78645","score":"0.67323464","text":"def root_volume(self, root_volume):\n self._root_volume = root_volume","title":""},{"docid":"e1b4f58ee196e30815c6bcd8cd6b5de2","score":"0.67320853","text":"def set_volume(self, volume_perc):\n res = requests.get(Song.SET_VOLUME.format(self.room.ip_address, volume_perc))\n return res.json()","title":""},{"docid":"e1b4f58ee196e30815c6bcd8cd6b5de2","score":"0.67320853","text":"def set_volume(self, volume_perc):\n res = requests.get(Song.SET_VOLUME.format(self.room.ip_address, volume_perc))\n return res.json()","title":""},{"docid":"278a8b26706357ea83fac3e2e5e3a672","score":"0.66813874","text":"def OnSetVolume(self):\r\n volume = self.volume_var.get()\r\n # vlc.MediaPlayer.audio_set_volume returns 0 if success, -1 otherwise\r\n if volume > 100:\r\n volume = 100\r\n if self.player.audio_set_volume(volume) == -1:\r\n self.errorDialog(\"Failed to set volume\")","title":""},{"docid":"1b9f9e0261882c66993e5e5bc97a1fed","score":"0.66727656","text":"def updateVolume(val):\n\n global VOLUME\n\n VOLUME = SLIDERS[0][\"volume\"].val","title":""},{"docid":"3cfa2f27415b1561afbd1c314f25e7af","score":"0.6633869","text":"def change_volume_control(self, available):\n self.device.volume_control(available)","title":""},{"docid":"3bec790d112ba64dd8e045a9b131ce8f","score":"0.66276306","text":"def set_music_volume(self, value):\n pygame.mixer.music.set_volume(value / 100)","title":""},{"docid":"cce0893cf7c11424100e1a66b406ae13","score":"0.6608898","text":"async def volume(self, ctx, value : int):\n global player\n player.volume = value / 100\n await self.bot.say('Set the volume to {:.0%}'.format(player.volume))","title":""},{"docid":"33fe494f6e947056f8803193af7b8fde","score":"0.6606079","text":"def bcp_set_volume(self, track, value):\n if track == 'master':\n self.sound.set_volume(value)\n\n #if track in self.sound.tracks:\n #self.sound.tracks[track]\n\n # todo add per-track volume support to sound system","title":""},{"docid":"f1163fea68ab6b1720e21f210e02faf9","score":"0.66035086","text":"async def _set_music_master_volume(self, request, volume):\n await self.music_manager.set_master_volume(self.discord_context, request, volume)","title":""},{"docid":"9515ff4d06c5e76fdc18aaba69dd3309","score":"0.6602625","text":"def on_volume(self, event):\n volume = event.GetPosition()\n self.video.SetVolume(volume / 100.0)\n ev.ConfigUpdate(params={u'volume': volume})","title":""},{"docid":"337668e4cf78c272dcee082cb0d03a51","score":"0.6550764","text":"async def async_set_volume_level(self, volume):\n self._player.set_volume(volume * 100)","title":""},{"docid":"f61cef59c11318f098cac9869676c193","score":"0.65102637","text":"def create_volume(self, volume):\n pass","title":""},{"docid":"74891d7725ca1d812d84cc3f0a003851","score":"0.65026903","text":"def volumes(self, volumes):\n\n self._volumes = volumes","title":""},{"docid":"d8132297e80518b6be8322392f5f8fdd","score":"0.64582324","text":"async def volume(self, ctx, volume: int=None):\n\n\t\tplayer = music.get_player(guild_id=ctx.guild.id)\n\t\tif player is None: return await ctx.send(\"I am not in a Voice Channel.\")\n\t\tplayer.change_volume(float(volume/100))\n\t\tawait ctx.send(\"Changed volume to {}%\".format(volume))","title":""},{"docid":"2c87324a037bc00e0629b78185d89c94","score":"0.6456995","text":"def volume(self, value: float) -> None:\n self.call_service(\n \"media_player/volume_set\",\n entity_id=self.args[\"speaker\"],\n volume_level=value,\n )","title":""},{"docid":"0df1bd34b39c630920b574d4c7c8b917","score":"0.64529353","text":"def set_secondary_volume(channel, volume, delay):\n\n call(\"set_secondary_volume\", channel, volume, delay)","title":""},{"docid":"85479b69ba79bce6a77af38762a8b6df","score":"0.6432605","text":"async def volume(self, ctx: Context, *, volume=None):\n \n try:\n volume = int(volume)\n if not ctx.voice_state.is_playing:\n await ctx.send(embed=NOTHING_PLAYING_ERROR)\n\n elif volume < 0 or volume > 100:\n await ctx.send(embed=INVALID_VOLUME_ERROR(volume))\n\n else:\n ctx.voice_state.volume = volume / 100\n await ctx.send('Volume of the player set to {}%'.format(volume))\n except ValueError:\n await ctx.send(embed=get_error_message(\n \"That's an invalid volume :eyes:\"\n ))","title":""},{"docid":"6b4232ed23fcc7a55eb05e1914c609bf","score":"0.63723564","text":"def volume_unit(self, volume_unit):\n\n self._volume_unit = volume_unit","title":""},{"docid":"f1eea926e74578a4e22b8cc362dee63c","score":"0.63495463","text":"async def set_default_volume(self, volume):\n return await self._post('audio/volume', dict(Volume=min(max(volume, 0), 100)))","title":""},{"docid":"2eabf48ebacb6b00224923db482e7c6f","score":"0.6322727","text":"def set_all_volume(self) -> None:\n for speaker in self.speakers:\n speaker.volume = speaker.default_volume","title":""},{"docid":"7c2d3fbd5717935cc27eb46cc65d99b2","score":"0.62947226","text":"def volume(self, volume=False):\n if volume:\n action = '\"urn:schemas-upnp-org:service:RenderingControl:1#SetVolume\"'\n\n body = ('<u:SetVolume xmlns:u=\"urn:schemas-upnp-org:service:'\n 'RenderingControl:1\"><InstanceID>0</InstanceID><Channel>Master'\n '</Channel><DesiredVolume>' + repr(volume) +\n '</DesiredVolume></u:SetVolume>')\n\n response = self.__send_command(SoCo.RENDERING_ENDPOINT, action, body)\n\n if (response == ('<s:Envelope xmlns:s=\"http://schemas.xmlsoap.org/soap/'\n 'envelope/\" s:encodingStyle=\"http://schemas.xmlsoap.org'\n '/soap/encoding/\"><s:Body><u:SetVolumeResponse '\n 'xmlns:u=\"urn:schemas-upnp-org:service:RenderingControl'\n ':1\"></u:SetVolumeResponse></s:Body></s:Envelope>')):\n return True\n else:\n return self.__parse_error(response)\n else:\n action = '\"urn:schemas-upnp-org:service:RenderingControl:1#GetVolume\"'\n\n body = ('<u:GetVolume xmlns:u=\"urn:schemas-upnp-org:service:'\n 'RenderingControl:1\"><InstanceID>0</InstanceID><Channel>Master'\n '</Channel></u:GetVolume>')\n\n response = self.__send_command(SoCo.RENDERING_ENDPOINT, action, body)\n\n dom = XML.fromstring(response)\n\n volume = dom.findtext('.//CurrentVolume')\n\n return int(volume)","title":""},{"docid":"aea56d71cc6a5e159b46c3d8b2914d96","score":"0.62866116","text":"def restore_volume(self):","title":""},{"docid":"b7f9c2b27296e6b4ca0dab36b8a0d22d","score":"0.625767","text":"def set_dispense_volume(self, probe_num, volume):\n self.wait_for_buffered()\n device_id = self.syringe[probe_num]['device_id']\n probe_letter = \"L\"\n if self.syringe[probe_num]['side'] is 'right':\n probe_letter = \"R\"\n suffix = ''\n if not volume % 1 > 0:\n suffix = '.0' \n if (volume % 1) == (self.get_syringe_pump_status(probe_num)[1] % 1):\n return\n self.buffered(('D%s' % (probe_letter)) + str(volume) + suffix, \n device_id)\n self.syringe[probe_num]['next_operation'] = volume\n while self.get_syringe_pump_status(probe_num)[0] != 'H':\n self.sleep(.05, '[aspirate block]')","title":""},{"docid":"81aafd4fe39b1ad7d617b0ff5f53f4d5","score":"0.6251954","text":"async def vol(self, ctx, volume: int):\n player: DefaultPlayer = self.bot.lavalink.player_manager.get(ctx.guild.id)\n if not player.is_connected:\n # We can't disconnect, if we're not connected.\n return await ctx.send('Not connected.')\n\n await player.set_volume(volume)\n dt = datetime.now()\n embed = discord.Embed(color=discord.Color.blurple(), datetime=dt)\n embed.title = 'Player Volume Changed'\n embed.description = \"Changed volume to {}%\".format(volume)\n return await ctx.send(embed=embed)","title":""},{"docid":"b90d45ce72bc960a33b933918e8f47c0","score":"0.62444043","text":"def setmusic():\n G_CHNL.set_volume(PLYR_VOL_DICT[PLYR][0] * theVolume())\n D_CHNL.set_volume(PLYR_VOL_DICT[PLYR][1] * theVolume())\n G_CHNL.play(G_DICT[glob.LEVEL], -1)\n D_CHNL.play(D_DICT[glob.LEVEL], -1)","title":""},{"docid":"b46ec3fb7c41f9cdee20d1eff76676a0","score":"0.6229608","text":"def set_master_volume(volume):\n return fmod.FSOUND_SetSFXMasterVolume(volume)","title":""},{"docid":"c5476788472d8e624c50784345e26a5b","score":"0.6171493","text":"async def async_set_volume_level(self, volume):\n await self._device.async_set_volume_level(volume)","title":""},{"docid":"ac028782f0894bfec440a1f1b169791d","score":"0.6147204","text":"def setRoomVolume(name_udn, volume):\n returndata = {}\n returndata[\"success\"] = False\n room = __getSingleRoom(name_udn)\n if room != None:\n room.volume = volume\n returndata[\"success\"] = True\n return json.dumps(returndata)","title":""},{"docid":"4dd0bf8d5a3df2e408d10fcf7f5bad9b","score":"0.61338377","text":"async def volume(self, ctx, vol: float):\n if vol > 50.0:\n embed = discord.Embed(title=\"It is unsafe to go that high!\", colour=RED)\n else:\n voice = get(self.bot.voice_clients, guild=ctx.guild)\n voice.source = discord.PCMVolumeTransformer(voice.source)\n voice.source.volume = vol / 10\n embed = discord.Embed(title=\"Volume changed\", description=f\"New volume: {vol}%\")\n embed.set_author(name=ctx.author.nick if ctx.author.nick else ctx.author.name, icon_url=ctx.author.avatar_url)\n await ctx.message.delete()\n await ctx.send(embed=embed)","title":""},{"docid":"7094b3da35fd791acb4f448b5241796f","score":"0.61272246","text":"async def async_set_volume_level(self, volume):\n await self._volume_entity.async_set_volume_level(self.hass, volume)","title":""},{"docid":"af89e802748b0e022f424dbac07e7b01","score":"0.6107115","text":"def volume(self):\n raise NotImplementedError","title":""},{"docid":"af89e802748b0e022f424dbac07e7b01","score":"0.6107115","text":"def volume(self):\n raise NotImplementedError","title":""},{"docid":"9c2ea392b82d9e94a41c5e3448546ca6","score":"0.6106013","text":"def mute_volume(self, mute):\n if 'volume' in self._status:\n if mute:\n self._muted_volume = self.volume_level\n self.set_volume_level(0)\n else:\n self.set_volume_level(self._muted_volume)\n self._muted = mute","title":""},{"docid":"4cddbc8d1c32fb8c8903ceb48fa89ae5","score":"0.61047566","text":"def volume_name(self, volume_name):\n\n self._volume_name = volume_name","title":""},{"docid":"cbdabc8893d1c7660446ec58e3385c69","score":"0.60872823","text":"def SetSpeechVolume(self, vol):\n try:\n fcn = '[SetSpeechVolume(\"{}\")]'.format(vol)\n self._get_request(fcn)\n except Exception as e:\n print(e)\n print(\"Exception in SetSpeechVolume function\")\n return","title":""},{"docid":"04c4da02639838f123245ec4572cdd28","score":"0.6070909","text":"def attach_volume(self, context, volume, instance_uuid, host_name,\n mountpoint):\n pass","title":""},{"docid":"79cfdbd86ed4d126ddf59d9d530d42f6","score":"0.6068145","text":"def updateVolume(self, newVolume):\n boundedVolume = self.restrictToBounds(newVolume)\n if(boundedVolume != self.volume):\n output = self.amixer(\"set 'PCM' unmute {}%\".format(boundedVolume))\n self.synchronize(output)","title":""},{"docid":"c2a1f50781e785e6e8db4d59bab16b42","score":"0.60619235","text":"def volumes(self, volumes):\n\n self._volumes = volumes","title":""},{"docid":"53076d4d33b3a1c6ffa6fd68ccfff8b3","score":"0.6044846","text":"def set_velocity(self, velocity):\n self.volume = min(127, velocity)","title":""},{"docid":"78bc764e3cea0e437905106c27cc6dcd","score":"0.60245776","text":"async def volume(self, ctx, value: int):\r\n if value > 100:\r\n value = 100\r\n elif value < 10:\r\n value = 10\r\n\r\n state = self.get_voice_state(ctx.message.server)\r\n if state.is_playing():\r\n player = state.player\r\n player.volume = value / 100\r\n await self.bot.say('Set the volume to {:.0%}'.format(player.volume))","title":""},{"docid":"4f0f64197800b34dd7cfd1fc2aef59e8","score":"0.6007267","text":"def volume_controller(level):\n sys.stdout.write(\"\\r\")\n if operating_system == 'Darwin':\n level = round((8 * level) / 100)\n os.system(f'osascript -e \"set Volume {level}\"')\n elif operating_system == 'Windows':\n if 'SetVol.exe' not in current_dir:\n # Courtesy: https://rlatour.com/setvol/\n sys.stdout.write(\"\\rPLEASE WAIT::Downloading volume controller for Windows\")\n os.system(\"\"\"curl https://thevickypedia.com/Jarvis/SetVol.exe --output SetVol.exe --silent\"\"\")\n sys.stdout.write(\"\\r\")\n os.system(f'SetVol.exe {level}')","title":""},{"docid":"441c831fb84ad0c2a71f4b5ccdb48f01","score":"0.6002478","text":"def modify_volume_attribute(self, VolumeId: str, AutoEnableIO: Dict = None, DryRun: bool = None):\n pass","title":""},{"docid":"991bf53113384ae994a1e0dcc17d3fd6","score":"0.6002317","text":"def _set_volume(self, station: RadioStation):\n frequency_distance = abs(self.current_freq - station.frequency)\n amp = self._get_media_amplification(frequency_distance)\n\n if amp >= 20:\n # Fully tuned into station\n self.noise_player.stop()\n else:\n if not self.noise_player.is_playing():\n self.noise_player.play()\n if amp <= -20:\n # Fully outside station bounds\n self.music_player.stop()\n else:\n if not self.music_player.is_playing():\n self.music_player.play()\n time_offset = self._get_station_offset(station)\n self.music_player.get_media_player().set_time(int(time_offset))\n\n # Set the volume using the equalizer\n self.equalizer.set_preamp(amp)\n self.music_player.get_media_player().set_equalizer(self.equalizer)\n self.equalizer.set_preamp(-amp)\n self.noise_player.get_media_player().set_equalizer(self.equalizer)","title":""},{"docid":"3219c0567c403878cb33bd7dc154c723","score":"0.5995478","text":"def send_channel_volume(self, value=127, ch=None, delay=0):\n self.send_control_change(CHANNEL_VOLUME, value, ch=ch, delay=delay)","title":""},{"docid":"2f5915bd3ccf1e06372a8f64fa5ec5f4","score":"0.59922343","text":"def volume_calculation(self, volume_calculation):\n\n self._volume_calculation = volume_calculation","title":""},{"docid":"19c38d2aafdcff923d965d06dc2d3ec5","score":"0.59802496","text":"def setZoneVolume(name_udn, volume):\n returndata = {}\n returndata[\"success\"] = False\n zone = __getSingleZone(name_udn)\n if zone != None:\n zone.volume = volume\n returndata[\"success\"] = True\n return json.dumps(returndata)","title":""},{"docid":"91c48851e27d497e0c388a9d54822a71","score":"0.59186894","text":"def ComAudioOptionsAddVolume(builder, volume):\n return AddVolume(builder, volume)","title":""},{"docid":"cf38def09b3503faf8f7c6401ae32777","score":"0.5916988","text":"def rename_volume_setup(self):\n\t\t\n\t\t#\n\t\t#---------------------- Select Volume To Rename -----------------------\n\t\t#\n\t\t\n\t\tpass","title":""},{"docid":"cbe3611beeedae2ae25862345e89e08c","score":"0.5911226","text":"def volume_up(self):\n if 'volume' in self._status:\n current_volume = int(self._status['volume'])\n\n if current_volume <= 100:\n self._client.setvol(current_volume + 5)","title":""},{"docid":"ce39694f7124d8b5a86cfc41d61aa02a","score":"0.58853155","text":"def initMusic():\n FX_CHNL.set_volume(theVolume())\n FX_2_CHNL.set_volume(theVolume())\n setmusic()","title":""},{"docid":"4735132ab78985bbad5d182c0b98fd84","score":"0.5880529","text":"def lower_volume(self):","title":""},{"docid":"fac1d3667f536934365a545bbc9b1462","score":"0.58379817","text":"def __init__(self, volume, width, height):\n\n self.volume = volume\n self.width = width\n self.height = height","title":""},{"docid":"fac1d3667f536934365a545bbc9b1462","score":"0.58379817","text":"def __init__(self, volume, width, height):\n\n self.volume = volume\n self.width = width\n self.height = height","title":""},{"docid":"6c9101cabb595c87d56bbbe7128d635b","score":"0.58368075","text":"def get_volume():\n command = \"osascript -e 'set ovol to output volume of\"\\\n \"(get volume settings)'\"\n return run_command(command)","title":""},{"docid":"fa909285a16236759025b62760ae1681","score":"0.5830253","text":"def set_write_volume(self, level=0, mute=False):\n if mute:\n return bgapi_wrapper('uuid_audio {uuid} start write mute'.format(\n uuid=self.uuid))\n elif level == 0:\n return bgapi_wrapper('uuid_audio {uuid} stop'.format(\n uuid=self.uuid))\n else:\n return bgapi_wrapper('uuid_audio {uuid} start write level '\n '{level}'.format(uuid=self.uuid, level=level))","title":""},{"docid":"540d5b062f94fb3aa1213c1762175036","score":"0.5813251","text":"def correct_volume(self, vol, writing):\n return vol","title":""},{"docid":"25b21c7a95431f25f4dc9676bcbeb4fe","score":"0.5796543","text":"def updateVolumeBar(self):\n self.mixer = alsaaudio.Mixer()\n volumes = self.mixer.getvolume()\n mutes = self.mixer.getmute()\n\n # update on changes and prolong living time of self.\n if self.masterVol != volumes[0]:\n self.masterVol = volumes[0]\n self.prolongLiving()\n\n if self.masterMute != mutes[0]:\n self.masterMute = mutes[0]\n self.prolongLiving()\n\n if(self.masterMute == 1):\n self.volumeBar.set_fraction(0)\n self.label.set_markup(\"<span foreground='white' size='small'>0</span>\")\n else:\n self.volumeBar.set_fraction(self.masterVol/100)\n if(self.masterVol == 100):\n self.label.set_markup(\"<span foreground='white' size='xx-small'>\" + str(self.masterVol) + \"</span>\")\n else:\n self.label.set_markup(\"<span foreground='white' size='small'>\" + str(self.masterVol) + \"</span>\")\n\n\n\n return True","title":""},{"docid":"5482abdccaf69a01a0496a5247bf678b","score":"0.5790783","text":"def attach_volume(self, context, connection_info, instance, mountpoint,\n disk_bus=None, device_type=None, encryption=None):\n raise NotImplementedError()","title":""},{"docid":"9b4e9da98553ce20abd7346232161a30","score":"0.5775059","text":"def run(self,inputVolume,outputVolume):\n return True","title":""},{"docid":"71819554ac626229b9628f327dd49ac2","score":"0.5772074","text":"def __call__(self, name, vol=None, hdr=None):\n _Volume.__call__(self, name, vol=vol, hdr=hdr)\n self._bgd.set_volume(self._vol, self._hdr)\n self._grid_transform()\n self._update()","title":""},{"docid":"67f82b6a2a87e267c703b5ce72876925","score":"0.5764509","text":"def increase(self):\n if self.volume == 0:\n self.updateVolume(VOLUME_MIN)\n else:\n self.updateVolume(self.volume + VOLUME_INCREMENT)","title":""}],"string":"[\n {\n \"docid\": \"008be544c722e9ddde9fbf00f2f58a24\",\n \"score\": \"0.7889102\",\n \"text\": \"def set_volume(self, volume):\\r\\n if self.is_active:\\r\\n if 0 < volume < 1:\\r\\n volume = int(volume*100)\\r\\n self.sp.volume(volume)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fba4be3c921afd787309e29022ba900\",\n \"score\": \"0.78266275\",\n \"text\": \"def set_volume(vol):\\n command = \\\"osascript -e 'set volume output volume {}'\\\".format(vol)\\n return run_command(command)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17a92497da1a931189edc4069e41768c\",\n \"score\": \"0.7718421\",\n \"text\": \"def set_volume(channel, volume):\\n\\n call(\\\"set_volume\\\", channel, volume)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85020a794ef428b5ab62258eacc87d11\",\n \"score\": \"0.7597933\",\n \"text\": \"def volume(self, volume):\\n self._volume = volume\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8aed53508e501e3fd414dde3dcbe6629\",\n \"score\": \"0.7555571\",\n \"text\": \"def setVolume(self, track, volume):\\n self.processor.setVolume(track, volume)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a77eebb4be0eddcd4e09b5ca45cc7062\",\n \"score\": \"0.7488555\",\n \"text\": \"def set_volume(self, volume, which='_all_'):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3e22004b19cd0d4f7438497a2ecb4e9\",\n \"score\": \"0.74149466\",\n \"text\": \"def volume(self, volume):\\n\\n self._volume = volume\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3e22004b19cd0d4f7438497a2ecb4e9\",\n \"score\": \"0.74149466\",\n \"text\": \"def volume(self, volume):\\n\\n self._volume = volume\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3e22004b19cd0d4f7438497a2ecb4e9\",\n \"score\": \"0.74149466\",\n \"text\": \"def volume(self, volume):\\n\\n self._volume = volume\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3e22004b19cd0d4f7438497a2ecb4e9\",\n \"score\": \"0.74149466\",\n \"text\": \"def volume(self, volume):\\n\\n self._volume = volume\",\n \"title\": \"\"\n },\n {\n \"docid\": \"331c0562044e5871037ef7abff63d860\",\n \"score\": \"0.7388495\",\n \"text\": \"def set_volume(self, volume):\\n self.volume = volume\\n self.player.set_property('volume', self.volume)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaa23d6bd0d9e1d4fbea8ef0d192b06c\",\n \"score\": \"0.73611593\",\n \"text\": \"def set_volume(self, volume):\\n self.sound.set_volume(volume)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1892cc989506c486bde4dcdb6cd38617\",\n \"score\": \"0.7300104\",\n \"text\": \"def volume(self,volume:float):\\n mixer.music.set_volume(volume)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f437a287534568b916f341fe0193cc86\",\n \"score\": \"0.726341\",\n \"text\": \"def setVolume(self, volume=0.7):\\n\\t\\tself.volume=volume\\n\\t\\tpygame.mixer.music.set_volume(self.volume)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0917e7983072f31f0f2e06f757f9f44b\",\n \"score\": \"0.72532773\",\n \"text\": \"async def set_volume(self, zone: int, volume: int) -> None:\\n await self._protocol.send(_format_set_volume(zone, volume))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab687f26011a091f0256e8fbe37e925c\",\n \"score\": \"0.72436464\",\n \"text\": \"def set_volume(self, new_volume):\\n self._post_request('core.mixer.set_volume', volume=new_volume)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"437f3324009aa974d91360deb07db72e\",\n \"score\": \"0.7240415\",\n \"text\": \"def set_volume(self, val):\\n \\n self.__volume = val\\n \\n pygame.mixer.music.set_volume(val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7f8d3972edcb24ae20584ec66e20c15\",\n \"score\": \"0.7206662\",\n \"text\": \"def set_volume(self, zone: int, volume: int) -> None:\\n self._process_request(_format_set_volume(zone, volume))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23bd999770657977f801d19005bd7193\",\n \"score\": \"0.71519357\",\n \"text\": \"def set_volume(v):\\n if type(v) != float:\\n if type(v) == int:\\n v = float(v)\\n else:\\n raise TypeError('Has to be a number')\\n\\n if not 0 <= v <= 1:\\n raise ValueError('Has to be between 0 or 1')\\n \\n musicapp.setVolume_(v)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f72bd6fb5082e5440830c4cc1430eed\",\n \"score\": \"0.714975\",\n \"text\": \"def volume(self, value):\\n value = min(1, max(0, value))\\n logger.info('set volume %s', value)\\n mixer.music.set_volume(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af71d2c1d6d88671527c74be25b90ab7\",\n \"score\": \"0.7133459\",\n \"text\": \"def set_volume(self, volume):\\n if self.musicpresent==True:\\n pygame.mixer.music.set_volume(volume)\\n else:\\n self.soundeffect.set_volume(volume)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"968e85692da9767843e4df9ed1007676\",\n \"score\": \"0.7110554\",\n \"text\": \"def set_volume(self, volume):\\n svc_type = 'urn:schemas-upnp-org:service:RenderingControl:1'\\n self._send_cmd('SetVolume', service_type=svc_type, Channel='Master',\\n DesiredVolume=volume)\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"192c37b439bfb454e6b1acc2aa458b8a\",\n \"score\": \"0.7057902\",\n \"text\": \"def onSetVolume(self, event):\\r\\n self.currentVolume = self.volumeCOP.GetValue()\\r\\n self.mediaPlayer.SetVolume(float(self.currentVolume / 100))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5adf6481a26f38c66b9071d315a18c52\",\n \"score\": \"0.70216686\",\n \"text\": \"def set_volume_level(self, volume):\\n if 'volume' in self._status:\\n self._client.setvol(int(volume * 100))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c55a020175a712a9c94196a7106a5c43\",\n \"score\": \"0.70117956\",\n \"text\": \"def set_volume_level(self, volume: int) -> None:\\n self._spotify.volume(int(volume * 100))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6781af140174c4d98bdf2d9fa04506d0\",\n \"score\": \"0.70045114\",\n \"text\": \"def set_master_volume(self, val):\\n vol = self.calc_vol_for_mixer(val)\\n\\n self.main.log.write(log.MESSAGE, \\\"[ALSA] setting mixer volume to \\\"\\n \\\"%d (=%d)...\\\" % (val, vol))\\n alsaaudio.Mixer(self.mixer).setvolume(vol)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"303f51146a9c7f9a75da086662673041\",\n \"score\": \"0.69950217\",\n \"text\": \"def set_volume(channel, volume):\\n return fmod.FSOUND_SetVolume(channel, volume)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa8e2798de841504538c20aba77833c7\",\n \"score\": \"0.69819754\",\n \"text\": \"def set_amp_volume(self, val):\\n vol = self.calc_vol_for_mixer(val)\\n\\n self.main.log.write(log.MESSAGE, \\\"[ALSA] setting amp volume to \\\"\\n \\\"%d (=%d)...\\\" % (val, vol))\\n alsaaudio.Mixer(self.amp).setvolume(vol)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da49e0abd5a7a01fb83d652c832b2adf\",\n \"score\": \"0.69744354\",\n \"text\": \"def set_aspirate_volume(self, probe_num, volume, block = True):\\n self.wait_for_buffered()\\n device_id = self.syringe[probe_num]['device_id']\\n probe_letter = \\\"L\\\"\\n if self.syringe[probe_num]['side'] is 'right':\\n probe_letter = \\\"R\\\"\\n suffix = ''\\n if not volume % 1 > 0:\\n suffix = '.0'\\n if (volume % 1) == (self.get_syringe_pump_status(probe_num)[1] % 1):\\n return\\n self.buffered(('A%s' % (probe_letter)) + str(volume) + suffix, \\n device_id)\\n self.syringe[probe_num]['next_operation'] = -volume\\n while self.get_syringe_pump_status(probe_num)[0] != 'H':\\n self.sleep(.05, '[aspirate block]')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76be7b622a86ee14e8b5289fbc91fb5a\",\n \"score\": \"0.6914168\",\n \"text\": \"def cmd_increase_volume(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa50de71da3ecf0675de15e0a2314d35\",\n \"score\": \"0.68965644\",\n \"text\": \"def setVolume(volume=50):\\n if volume > 100:\\n volume = 100\\n elif volume < 0:\\n volume = 0\\n tkSnack.audio.play_gain(volume)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c5065ef38807e4bafc9ca749bf97a43\",\n \"score\": \"0.68597615\",\n \"text\": \"def set_volume_value(self, index, value):\\n self._volumes[index] = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b677af321513591268b75dbfc92685ea\",\n \"score\": \"0.6856387\",\n \"text\": \"def SetVol(self, e):\\n \\n vol = self.sldr_vol.GetValue()\\n print 'cur vol val:', vol\\n vol /= 100.0\\n print 'volume to be set:', vol\\n self.mediaPlayer.SetVolume(vol)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2332a50c1717014305494b8df427aacf\",\n \"score\": \"0.67961496\",\n \"text\": \"async def volume(self, ctx, volume: int):\\r\\n\\r\\n if ctx.voice_client is None:\\r\\n return await ctx.send(\\\"Not connected to a voice channel.\\\")\\r\\n\\r\\n ctx.voice_client.source.volume = volume / 100\\r\\n await ctx.send(\\\"Changed volume to {}%\\\".format(volume))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0819431df8a60fef963ea11e9ba4792\",\n \"score\": \"0.6790397\",\n \"text\": \"async def volume(self, ctx, volume: int):\\n\\n if ctx.voice_client is None:\\n return await ctx.send(\\\"Not connected to a voice channel.\\\")\\n\\n ctx.voice_client.source.volume = volume / 100\\n await ctx.send(\\\"Changed volume to {}%\\\".format(volume))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7ca074a8cabee2c635233fd8e6b1bd6\",\n \"score\": \"0.6781557\",\n \"text\": \"def volume(object,val):\\n if object[1]==1:\\n mixer.music.set_volume(0.0)\\n elif object[1]==0:\\n mixer.music.set_volume(1.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a639ecc2bbb0d011498f30beaa28106\",\n \"score\": \"0.6757883\",\n \"text\": \"def __init__(self):\\n self.volume = 70 # in percent\\n self.set_volume()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43d80db5c4c3e13cdec95e29bf1fd7ef\",\n \"score\": \"0.67351687\",\n \"text\": \"def set_volume(self):\\n self.town_theme.set_volume(0.5)\\n self.fireball.set_volume(1)\\n self.monster_kill.set_volume(0.75)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"237ff89a06fbcd5aa86639f053f78645\",\n \"score\": \"0.67323464\",\n \"text\": \"def root_volume(self, root_volume):\\n self._root_volume = root_volume\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1b4f58ee196e30815c6bcd8cd6b5de2\",\n \"score\": \"0.67320853\",\n \"text\": \"def set_volume(self, volume_perc):\\n res = requests.get(Song.SET_VOLUME.format(self.room.ip_address, volume_perc))\\n return res.json()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1b4f58ee196e30815c6bcd8cd6b5de2\",\n \"score\": \"0.67320853\",\n \"text\": \"def set_volume(self, volume_perc):\\n res = requests.get(Song.SET_VOLUME.format(self.room.ip_address, volume_perc))\\n return res.json()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"278a8b26706357ea83fac3e2e5e3a672\",\n \"score\": \"0.66813874\",\n \"text\": \"def OnSetVolume(self):\\r\\n volume = self.volume_var.get()\\r\\n # vlc.MediaPlayer.audio_set_volume returns 0 if success, -1 otherwise\\r\\n if volume > 100:\\r\\n volume = 100\\r\\n if self.player.audio_set_volume(volume) == -1:\\r\\n self.errorDialog(\\\"Failed to set volume\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b9f9e0261882c66993e5e5bc97a1fed\",\n \"score\": \"0.66727656\",\n \"text\": \"def updateVolume(val):\\n\\n global VOLUME\\n\\n VOLUME = SLIDERS[0][\\\"volume\\\"].val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cfa2f27415b1561afbd1c314f25e7af\",\n \"score\": \"0.6633869\",\n \"text\": \"def change_volume_control(self, available):\\n self.device.volume_control(available)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bec790d112ba64dd8e045a9b131ce8f\",\n \"score\": \"0.66276306\",\n \"text\": \"def set_music_volume(self, value):\\n pygame.mixer.music.set_volume(value / 100)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cce0893cf7c11424100e1a66b406ae13\",\n \"score\": \"0.6608898\",\n \"text\": \"async def volume(self, ctx, value : int):\\n global player\\n player.volume = value / 100\\n await self.bot.say('Set the volume to {:.0%}'.format(player.volume))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33fe494f6e947056f8803193af7b8fde\",\n \"score\": \"0.6606079\",\n \"text\": \"def bcp_set_volume(self, track, value):\\n if track == 'master':\\n self.sound.set_volume(value)\\n\\n #if track in self.sound.tracks:\\n #self.sound.tracks[track]\\n\\n # todo add per-track volume support to sound system\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1163fea68ab6b1720e21f210e02faf9\",\n \"score\": \"0.66035086\",\n \"text\": \"async def _set_music_master_volume(self, request, volume):\\n await self.music_manager.set_master_volume(self.discord_context, request, volume)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9515ff4d06c5e76fdc18aaba69dd3309\",\n \"score\": \"0.6602625\",\n \"text\": \"def on_volume(self, event):\\n volume = event.GetPosition()\\n self.video.SetVolume(volume / 100.0)\\n ev.ConfigUpdate(params={u'volume': volume})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"337668e4cf78c272dcee082cb0d03a51\",\n \"score\": \"0.6550764\",\n \"text\": \"async def async_set_volume_level(self, volume):\\n self._player.set_volume(volume * 100)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f61cef59c11318f098cac9869676c193\",\n \"score\": \"0.65102637\",\n \"text\": \"def create_volume(self, volume):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74891d7725ca1d812d84cc3f0a003851\",\n \"score\": \"0.65026903\",\n \"text\": \"def volumes(self, volumes):\\n\\n self._volumes = volumes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8132297e80518b6be8322392f5f8fdd\",\n \"score\": \"0.64582324\",\n \"text\": \"async def volume(self, ctx, volume: int=None):\\n\\n\\t\\tplayer = music.get_player(guild_id=ctx.guild.id)\\n\\t\\tif player is None: return await ctx.send(\\\"I am not in a Voice Channel.\\\")\\n\\t\\tplayer.change_volume(float(volume/100))\\n\\t\\tawait ctx.send(\\\"Changed volume to {}%\\\".format(volume))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c87324a037bc00e0629b78185d89c94\",\n \"score\": \"0.6456995\",\n \"text\": \"def volume(self, value: float) -> None:\\n self.call_service(\\n \\\"media_player/volume_set\\\",\\n entity_id=self.args[\\\"speaker\\\"],\\n volume_level=value,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0df1bd34b39c630920b574d4c7c8b917\",\n \"score\": \"0.64529353\",\n \"text\": \"def set_secondary_volume(channel, volume, delay):\\n\\n call(\\\"set_secondary_volume\\\", channel, volume, delay)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85479b69ba79bce6a77af38762a8b6df\",\n \"score\": \"0.6432605\",\n \"text\": \"async def volume(self, ctx: Context, *, volume=None):\\n \\n try:\\n volume = int(volume)\\n if not ctx.voice_state.is_playing:\\n await ctx.send(embed=NOTHING_PLAYING_ERROR)\\n\\n elif volume < 0 or volume > 100:\\n await ctx.send(embed=INVALID_VOLUME_ERROR(volume))\\n\\n else:\\n ctx.voice_state.volume = volume / 100\\n await ctx.send('Volume of the player set to {}%'.format(volume))\\n except ValueError:\\n await ctx.send(embed=get_error_message(\\n \\\"That's an invalid volume :eyes:\\\"\\n ))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b4232ed23fcc7a55eb05e1914c609bf\",\n \"score\": \"0.63723564\",\n \"text\": \"def volume_unit(self, volume_unit):\\n\\n self._volume_unit = volume_unit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1eea926e74578a4e22b8cc362dee63c\",\n \"score\": \"0.63495463\",\n \"text\": \"async def set_default_volume(self, volume):\\n return await self._post('audio/volume', dict(Volume=min(max(volume, 0), 100)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2eabf48ebacb6b00224923db482e7c6f\",\n \"score\": \"0.6322727\",\n \"text\": \"def set_all_volume(self) -> None:\\n for speaker in self.speakers:\\n speaker.volume = speaker.default_volume\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c2d3fbd5717935cc27eb46cc65d99b2\",\n \"score\": \"0.62947226\",\n \"text\": \"def volume(self, volume=False):\\n if volume:\\n action = '\\\"urn:schemas-upnp-org:service:RenderingControl:1#SetVolume\\\"'\\n\\n body = ('<u:SetVolume xmlns:u=\\\"urn:schemas-upnp-org:service:'\\n 'RenderingControl:1\\\"><InstanceID>0</InstanceID><Channel>Master'\\n '</Channel><DesiredVolume>' + repr(volume) +\\n '</DesiredVolume></u:SetVolume>')\\n\\n response = self.__send_command(SoCo.RENDERING_ENDPOINT, action, body)\\n\\n if (response == ('<s:Envelope xmlns:s=\\\"http://schemas.xmlsoap.org/soap/'\\n 'envelope/\\\" s:encodingStyle=\\\"http://schemas.xmlsoap.org'\\n '/soap/encoding/\\\"><s:Body><u:SetVolumeResponse '\\n 'xmlns:u=\\\"urn:schemas-upnp-org:service:RenderingControl'\\n ':1\\\"></u:SetVolumeResponse></s:Body></s:Envelope>')):\\n return True\\n else:\\n return self.__parse_error(response)\\n else:\\n action = '\\\"urn:schemas-upnp-org:service:RenderingControl:1#GetVolume\\\"'\\n\\n body = ('<u:GetVolume xmlns:u=\\\"urn:schemas-upnp-org:service:'\\n 'RenderingControl:1\\\"><InstanceID>0</InstanceID><Channel>Master'\\n '</Channel></u:GetVolume>')\\n\\n response = self.__send_command(SoCo.RENDERING_ENDPOINT, action, body)\\n\\n dom = XML.fromstring(response)\\n\\n volume = dom.findtext('.//CurrentVolume')\\n\\n return int(volume)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aea56d71cc6a5e159b46c3d8b2914d96\",\n \"score\": \"0.62866116\",\n \"text\": \"def restore_volume(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7f9c2b27296e6b4ca0dab36b8a0d22d\",\n \"score\": \"0.625767\",\n \"text\": \"def set_dispense_volume(self, probe_num, volume):\\n self.wait_for_buffered()\\n device_id = self.syringe[probe_num]['device_id']\\n probe_letter = \\\"L\\\"\\n if self.syringe[probe_num]['side'] is 'right':\\n probe_letter = \\\"R\\\"\\n suffix = ''\\n if not volume % 1 > 0:\\n suffix = '.0' \\n if (volume % 1) == (self.get_syringe_pump_status(probe_num)[1] % 1):\\n return\\n self.buffered(('D%s' % (probe_letter)) + str(volume) + suffix, \\n device_id)\\n self.syringe[probe_num]['next_operation'] = volume\\n while self.get_syringe_pump_status(probe_num)[0] != 'H':\\n self.sleep(.05, '[aspirate block]')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81aafd4fe39b1ad7d617b0ff5f53f4d5\",\n \"score\": \"0.6251954\",\n \"text\": \"async def vol(self, ctx, volume: int):\\n player: DefaultPlayer = self.bot.lavalink.player_manager.get(ctx.guild.id)\\n if not player.is_connected:\\n # We can't disconnect, if we're not connected.\\n return await ctx.send('Not connected.')\\n\\n await player.set_volume(volume)\\n dt = datetime.now()\\n embed = discord.Embed(color=discord.Color.blurple(), datetime=dt)\\n embed.title = 'Player Volume Changed'\\n embed.description = \\\"Changed volume to {}%\\\".format(volume)\\n return await ctx.send(embed=embed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b90d45ce72bc960a33b933918e8f47c0\",\n \"score\": \"0.62444043\",\n \"text\": \"def setmusic():\\n G_CHNL.set_volume(PLYR_VOL_DICT[PLYR][0] * theVolume())\\n D_CHNL.set_volume(PLYR_VOL_DICT[PLYR][1] * theVolume())\\n G_CHNL.play(G_DICT[glob.LEVEL], -1)\\n D_CHNL.play(D_DICT[glob.LEVEL], -1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b46ec3fb7c41f9cdee20d1eff76676a0\",\n \"score\": \"0.6229608\",\n \"text\": \"def set_master_volume(volume):\\n return fmod.FSOUND_SetSFXMasterVolume(volume)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5476788472d8e624c50784345e26a5b\",\n \"score\": \"0.6171493\",\n \"text\": \"async def async_set_volume_level(self, volume):\\n await self._device.async_set_volume_level(volume)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac028782f0894bfec440a1f1b169791d\",\n \"score\": \"0.6147204\",\n \"text\": \"def setRoomVolume(name_udn, volume):\\n returndata = {}\\n returndata[\\\"success\\\"] = False\\n room = __getSingleRoom(name_udn)\\n if room != None:\\n room.volume = volume\\n returndata[\\\"success\\\"] = True\\n return json.dumps(returndata)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dd0bf8d5a3df2e408d10fcf7f5bad9b\",\n \"score\": \"0.61338377\",\n \"text\": \"async def volume(self, ctx, vol: float):\\n if vol > 50.0:\\n embed = discord.Embed(title=\\\"It is unsafe to go that high!\\\", colour=RED)\\n else:\\n voice = get(self.bot.voice_clients, guild=ctx.guild)\\n voice.source = discord.PCMVolumeTransformer(voice.source)\\n voice.source.volume = vol / 10\\n embed = discord.Embed(title=\\\"Volume changed\\\", description=f\\\"New volume: {vol}%\\\")\\n embed.set_author(name=ctx.author.nick if ctx.author.nick else ctx.author.name, icon_url=ctx.author.avatar_url)\\n await ctx.message.delete()\\n await ctx.send(embed=embed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7094b3da35fd791acb4f448b5241796f\",\n \"score\": \"0.61272246\",\n \"text\": \"async def async_set_volume_level(self, volume):\\n await self._volume_entity.async_set_volume_level(self.hass, volume)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af89e802748b0e022f424dbac07e7b01\",\n \"score\": \"0.6107115\",\n \"text\": \"def volume(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af89e802748b0e022f424dbac07e7b01\",\n \"score\": \"0.6107115\",\n \"text\": \"def volume(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c2ea392b82d9e94a41c5e3448546ca6\",\n \"score\": \"0.6106013\",\n \"text\": \"def mute_volume(self, mute):\\n if 'volume' in self._status:\\n if mute:\\n self._muted_volume = self.volume_level\\n self.set_volume_level(0)\\n else:\\n self.set_volume_level(self._muted_volume)\\n self._muted = mute\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cddbc8d1c32fb8c8903ceb48fa89ae5\",\n \"score\": \"0.61047566\",\n \"text\": \"def volume_name(self, volume_name):\\n\\n self._volume_name = volume_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbdabc8893d1c7660446ec58e3385c69\",\n \"score\": \"0.60872823\",\n \"text\": \"def SetSpeechVolume(self, vol):\\n try:\\n fcn = '[SetSpeechVolume(\\\"{}\\\")]'.format(vol)\\n self._get_request(fcn)\\n except Exception as e:\\n print(e)\\n print(\\\"Exception in SetSpeechVolume function\\\")\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04c4da02639838f123245ec4572cdd28\",\n \"score\": \"0.6070909\",\n \"text\": \"def attach_volume(self, context, volume, instance_uuid, host_name,\\n mountpoint):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79cfdbd86ed4d126ddf59d9d530d42f6\",\n \"score\": \"0.6068145\",\n \"text\": \"def updateVolume(self, newVolume):\\n boundedVolume = self.restrictToBounds(newVolume)\\n if(boundedVolume != self.volume):\\n output = self.amixer(\\\"set 'PCM' unmute {}%\\\".format(boundedVolume))\\n self.synchronize(output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2a1f50781e785e6e8db4d59bab16b42\",\n \"score\": \"0.60619235\",\n \"text\": \"def volumes(self, volumes):\\n\\n self._volumes = volumes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53076d4d33b3a1c6ffa6fd68ccfff8b3\",\n \"score\": \"0.6044846\",\n \"text\": \"def set_velocity(self, velocity):\\n self.volume = min(127, velocity)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78bc764e3cea0e437905106c27cc6dcd\",\n \"score\": \"0.60245776\",\n \"text\": \"async def volume(self, ctx, value: int):\\r\\n if value > 100:\\r\\n value = 100\\r\\n elif value < 10:\\r\\n value = 10\\r\\n\\r\\n state = self.get_voice_state(ctx.message.server)\\r\\n if state.is_playing():\\r\\n player = state.player\\r\\n player.volume = value / 100\\r\\n await self.bot.say('Set the volume to {:.0%}'.format(player.volume))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f0f64197800b34dd7cfd1fc2aef59e8\",\n \"score\": \"0.6007267\",\n \"text\": \"def volume_controller(level):\\n sys.stdout.write(\\\"\\\\r\\\")\\n if operating_system == 'Darwin':\\n level = round((8 * level) / 100)\\n os.system(f'osascript -e \\\"set Volume {level}\\\"')\\n elif operating_system == 'Windows':\\n if 'SetVol.exe' not in current_dir:\\n # Courtesy: https://rlatour.com/setvol/\\n sys.stdout.write(\\\"\\\\rPLEASE WAIT::Downloading volume controller for Windows\\\")\\n os.system(\\\"\\\"\\\"curl https://thevickypedia.com/Jarvis/SetVol.exe --output SetVol.exe --silent\\\"\\\"\\\")\\n sys.stdout.write(\\\"\\\\r\\\")\\n os.system(f'SetVol.exe {level}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"441c831fb84ad0c2a71f4b5ccdb48f01\",\n \"score\": \"0.6002478\",\n \"text\": \"def modify_volume_attribute(self, VolumeId: str, AutoEnableIO: Dict = None, DryRun: bool = None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"991bf53113384ae994a1e0dcc17d3fd6\",\n \"score\": \"0.6002317\",\n \"text\": \"def _set_volume(self, station: RadioStation):\\n frequency_distance = abs(self.current_freq - station.frequency)\\n amp = self._get_media_amplification(frequency_distance)\\n\\n if amp >= 20:\\n # Fully tuned into station\\n self.noise_player.stop()\\n else:\\n if not self.noise_player.is_playing():\\n self.noise_player.play()\\n if amp <= -20:\\n # Fully outside station bounds\\n self.music_player.stop()\\n else:\\n if not self.music_player.is_playing():\\n self.music_player.play()\\n time_offset = self._get_station_offset(station)\\n self.music_player.get_media_player().set_time(int(time_offset))\\n\\n # Set the volume using the equalizer\\n self.equalizer.set_preamp(amp)\\n self.music_player.get_media_player().set_equalizer(self.equalizer)\\n self.equalizer.set_preamp(-amp)\\n self.noise_player.get_media_player().set_equalizer(self.equalizer)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3219c0567c403878cb33bd7dc154c723\",\n \"score\": \"0.5995478\",\n \"text\": \"def send_channel_volume(self, value=127, ch=None, delay=0):\\n self.send_control_change(CHANNEL_VOLUME, value, ch=ch, delay=delay)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f5915bd3ccf1e06372a8f64fa5ec5f4\",\n \"score\": \"0.59922343\",\n \"text\": \"def volume_calculation(self, volume_calculation):\\n\\n self._volume_calculation = volume_calculation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19c38d2aafdcff923d965d06dc2d3ec5\",\n \"score\": \"0.59802496\",\n \"text\": \"def setZoneVolume(name_udn, volume):\\n returndata = {}\\n returndata[\\\"success\\\"] = False\\n zone = __getSingleZone(name_udn)\\n if zone != None:\\n zone.volume = volume\\n returndata[\\\"success\\\"] = True\\n return json.dumps(returndata)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91c48851e27d497e0c388a9d54822a71\",\n \"score\": \"0.59186894\",\n \"text\": \"def ComAudioOptionsAddVolume(builder, volume):\\n return AddVolume(builder, volume)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf38def09b3503faf8f7c6401ae32777\",\n \"score\": \"0.5916988\",\n \"text\": \"def rename_volume_setup(self):\\n\\t\\t\\n\\t\\t#\\n\\t\\t#---------------------- Select Volume To Rename -----------------------\\n\\t\\t#\\n\\t\\t\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbe3611beeedae2ae25862345e89e08c\",\n \"score\": \"0.5911226\",\n \"text\": \"def volume_up(self):\\n if 'volume' in self._status:\\n current_volume = int(self._status['volume'])\\n\\n if current_volume <= 100:\\n self._client.setvol(current_volume + 5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce39694f7124d8b5a86cfc41d61aa02a\",\n \"score\": \"0.58853155\",\n \"text\": \"def initMusic():\\n FX_CHNL.set_volume(theVolume())\\n FX_2_CHNL.set_volume(theVolume())\\n setmusic()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4735132ab78985bbad5d182c0b98fd84\",\n \"score\": \"0.5880529\",\n \"text\": \"def lower_volume(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fac1d3667f536934365a545bbc9b1462\",\n \"score\": \"0.58379817\",\n \"text\": \"def __init__(self, volume, width, height):\\n\\n self.volume = volume\\n self.width = width\\n self.height = height\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fac1d3667f536934365a545bbc9b1462\",\n \"score\": \"0.58379817\",\n \"text\": \"def __init__(self, volume, width, height):\\n\\n self.volume = volume\\n self.width = width\\n self.height = height\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c9101cabb595c87d56bbbe7128d635b\",\n \"score\": \"0.58368075\",\n \"text\": \"def get_volume():\\n command = \\\"osascript -e 'set ovol to output volume of\\\"\\\\\\n \\\"(get volume settings)'\\\"\\n return run_command(command)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa909285a16236759025b62760ae1681\",\n \"score\": \"0.5830253\",\n \"text\": \"def set_write_volume(self, level=0, mute=False):\\n if mute:\\n return bgapi_wrapper('uuid_audio {uuid} start write mute'.format(\\n uuid=self.uuid))\\n elif level == 0:\\n return bgapi_wrapper('uuid_audio {uuid} stop'.format(\\n uuid=self.uuid))\\n else:\\n return bgapi_wrapper('uuid_audio {uuid} start write level '\\n '{level}'.format(uuid=self.uuid, level=level))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"540d5b062f94fb3aa1213c1762175036\",\n \"score\": \"0.5813251\",\n \"text\": \"def correct_volume(self, vol, writing):\\n return vol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25b21c7a95431f25f4dc9676bcbeb4fe\",\n \"score\": \"0.5796543\",\n \"text\": \"def updateVolumeBar(self):\\n self.mixer = alsaaudio.Mixer()\\n volumes = self.mixer.getvolume()\\n mutes = self.mixer.getmute()\\n\\n # update on changes and prolong living time of self.\\n if self.masterVol != volumes[0]:\\n self.masterVol = volumes[0]\\n self.prolongLiving()\\n\\n if self.masterMute != mutes[0]:\\n self.masterMute = mutes[0]\\n self.prolongLiving()\\n\\n if(self.masterMute == 1):\\n self.volumeBar.set_fraction(0)\\n self.label.set_markup(\\\"<span foreground='white' size='small'>0</span>\\\")\\n else:\\n self.volumeBar.set_fraction(self.masterVol/100)\\n if(self.masterVol == 100):\\n self.label.set_markup(\\\"<span foreground='white' size='xx-small'>\\\" + str(self.masterVol) + \\\"</span>\\\")\\n else:\\n self.label.set_markup(\\\"<span foreground='white' size='small'>\\\" + str(self.masterVol) + \\\"</span>\\\")\\n\\n\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5482abdccaf69a01a0496a5247bf678b\",\n \"score\": \"0.5790783\",\n \"text\": \"def attach_volume(self, context, connection_info, instance, mountpoint,\\n disk_bus=None, device_type=None, encryption=None):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b4e9da98553ce20abd7346232161a30\",\n \"score\": \"0.5775059\",\n \"text\": \"def run(self,inputVolume,outputVolume):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71819554ac626229b9628f327dd49ac2\",\n \"score\": \"0.5772074\",\n \"text\": \"def __call__(self, name, vol=None, hdr=None):\\n _Volume.__call__(self, name, vol=vol, hdr=hdr)\\n self._bgd.set_volume(self._vol, self._hdr)\\n self._grid_transform()\\n self._update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67f82b6a2a87e267c703b5ce72876925\",\n \"score\": \"0.5764509\",\n \"text\": \"def increase(self):\\n if self.volume == 0:\\n self.updateVolume(VOLUME_MIN)\\n else:\\n self.updateVolume(self.volume + VOLUME_INCREMENT)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994186,"cells":{"query_id":{"kind":"string","value":"a3dff4a0b5a3b63b7a71b29519ee5309"},"query":{"kind":"string","value":"Moves duplicate files to a separate directory, while preserving the subdirectory structure in there."},"positive_passages":{"kind":"list like","value":[{"docid":"320ae484e2d52eae10f58cdc2fc7bd2b","score":"0.7337616","text":"def __call__(self, directory_to_move_duplicates_to=None):\n if directory_to_move_duplicates_to is None:\n directory_to_move_duplicates_to = os.path.join(self.directory, \"duplicates\")\n directory_to_move_duplicates_to = os.path.join(os.path.abspath(directory_to_move_duplicates_to), \"\")\n # ^ ensures correct formatting\n\n classes = self.get_equivalence_classes()\n for equiv_class in classes:\n best_file = self.get_best_file_from_class(equiv_class)\n for file in equiv_class:\n if file == best_file:\n continue\n dest = file.replace(self.directory, directory_to_move_duplicates_to)\n os.makedirs(os.path.dirname(dest), exist_ok=True)\n shutil.move(file, dest)","title":""}],"string":"[\n {\n \"docid\": \"320ae484e2d52eae10f58cdc2fc7bd2b\",\n \"score\": \"0.7337616\",\n \"text\": \"def __call__(self, directory_to_move_duplicates_to=None):\\n if directory_to_move_duplicates_to is None:\\n directory_to_move_duplicates_to = os.path.join(self.directory, \\\"duplicates\\\")\\n directory_to_move_duplicates_to = os.path.join(os.path.abspath(directory_to_move_duplicates_to), \\\"\\\")\\n # ^ ensures correct formatting\\n\\n classes = self.get_equivalence_classes()\\n for equiv_class in classes:\\n best_file = self.get_best_file_from_class(equiv_class)\\n for file in equiv_class:\\n if file == best_file:\\n continue\\n dest = file.replace(self.directory, directory_to_move_duplicates_to)\\n os.makedirs(os.path.dirname(dest), exist_ok=True)\\n shutil.move(file, dest)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"b996a5c6073bbe3944cfbade4eb38d7a","score":"0.65459585","text":"def scan_and_remove_duplicate(from_dir, debug=False):\n all_files = defaultdict(list)\n for root, dirs, files in os.walk(from_dir):\n for file in files:\n full_path = os.path.join(root, file)\n md5 = hashlib.md5(file_as_bytes(open(full_path, 'rb'))).hexdigest()\n all_files[md5].append(full_path)\n for v in all_files.values():\n duplicated_files = v[1:]\n if duplicated_files:\n for f in duplicated_files:\n print('Remove diplicated file: {} (original: {})'.format(f, v[0]))\n if not debug:\n os.remove(f)","title":""},{"docid":"7ac9be7e6ee8bd07dc43eb76e37893c9","score":"0.6354505","text":"def restore_dublicates(path: str):\n old_path = os.path.join(path, 'dublicates')\n files = os.listdir(old_path)\n for p in files:\n if p.endswith('jpg'):\n try:\n shutil.move(os.path.join(old_path, p), os.path.join(path, p.split('jpg_')[1]))\n except:\n pass\n if len(os.listdir(old_path)) == 0:\n shutil.rmtree(old_path)","title":""},{"docid":"aea4690d0b925107ee06c5696fa0caee","score":"0.6176697","text":"def find_duplicates(path):\n # find groups of files consisting of more than 1 item (suspected duplicates)\n bgroups = []\n dir_contents = {}\n for dirname, dirnames, filenames in os.walk(path):\n groups = group_files(dirname, filenames)\n for group in groups.items():\n if len(group[1]) > 1:\n bgroups.append((dirname, group))\n dir_contents[dirname] = (dirnames, sorted(groups.keys()))\n\n # check suspected duplicate files\n duplicates = []\n for dirname, group in bgroups:\n files = sorted(group[1], key=lambda p: p[1], reverse=True)\n while len(files) > 1:\n champ = files[0]\n suspects = files[1:]\n f1 = os.path.join(dirname, champ[0])\n unconfirmed = []\n for suspect in suspects:\n f2 = os.path.join(dirname, suspect[0])\n if filecmp.cmp(f1, f2, shallow=False):\n duplicates.append(f2)\n print '\"{}\" is a duplicate of \"{}\" in \"{}\"'.format(suspect[0], champ[0], dirname)\n else:\n unconfirmed.append(suspect)\n\n files = unconfirmed\n\n for item in dir_contents.items():\n parent = item[0]\n dirs = item[1][0]\n files = item[1][1]\n for d1 in range(len(dirs)-1):\n for d2 in range(d1+1, len(dirs)):\n dir1 = os.path.join(parent, dirs[d1])\n dir2 = os.path.join(parent, dirs[d2])\n if compare_dirs(dir_contents, dir1, dir2):\n if os.stat(dir1).st_mtime > os.stat(dir2).st_mtime:\n master = dirs[d1]\n dup = dirs[d2]\n dup_fullpath = dir2\n else:\n master = dirs[d2]\n dup = dirs[d1]\n dup_fullpath = dir1\n\n if dup_fullpath not in duplicates:\n duplicates.append(dup_fullpath)\n print 'Directory \"{}\" is a duplicate of \"{}\" in \"{}\"'.format(dup, master, parent)\n\n return duplicates","title":""},{"docid":"91d411182d5fda79c8085e5692040b8b","score":"0.6167216","text":"def extract_from_directories():\n directories = glob.glob(\"*/\")\n for directory in directories:\n files_to_extract = get_music_in_directory(directory)\n for file in files_to_extract:\n shutil.move(file, os.getcwd())\n os.rmdir(directory)","title":""},{"docid":"6a7b23b33982645b72acd806c2e67ef2","score":"0.59987","text":"def move_tree(self, src: pm.path, dst: pm.path):\n self._log_command(f\"\"\"Recursively move files from \\\"{src}\\\" to \\\"{dst}\\\"\"\"\")\n self.copy_tree(src, dst)\n self.remove_tree(src)","title":""},{"docid":"15c68ef7178a48b5350e02f69a5a91ab","score":"0.59847116","text":"def move_files_to_subdirectory(file_list, working_directory, subdirectory):\n # create subdirectory, if it does not exist\n if not os.path.exists(working_directory + os.sep + subdirectory):\n os.makedirs(working_directory + os.sep + subdirectory)\n\n # move files\n for file in file_list:\n os.rename(working_directory + os.sep + file, working_directory + os.sep + subdirectory + os.sep + file)","title":""},{"docid":"021533fb2f7bbb6c9384b53e9771437b","score":"0.5971916","text":"def move_files(abs_dirname):\n\n files = [os.path.join(abs_dirname, f) for f in os.listdir(abs_dirname)]\n\n i = 0\n curr_subdir = None\n\n for f in files:\n # create new subdir if necessary\n if i % N == 0:\n subdir_name = os.path.join(abs_dirname, '{0:03d}'.format(i / N + 1))\n os.mkdir(subdir_name)\n curr_subdir = subdir_name\n\n # move file to current dir\n f_base = os.path.basename(f)\n shutil.move(f, os.path.join(subdir_name, f_base))\n i += 1","title":""},{"docid":"6f7ee5c037340cf43113a88964e5db64","score":"0.59539324","text":"def replicate_all_files(src_dir, copy_file, use_gitignore=True, debugging=False):\n spec = get_pathspec(src_dir, use_gitignore)\n for filename in pathspec.util.iter_tree(src_dir):\n if not spec.match_file(filename):\n copy_file(os.path.join(src_dir, filename))","title":""},{"docid":"95dae4519c2739bc862bbfb601d30fcb","score":"0.5923724","text":"def moveResultsTo(self, dir, trimCommonPaths = False):\n allChanges = set()\n self._scanChanges(self._dir, allChanges)\n\n prefix = self._dir\n if trimCommonPaths:\n prefix = None\n for c in allChanges:\n if prefix is None:\n prefix = c\n else:\n while not c.startswith(prefix):\n prefix = os.path.dirname(prefix)\n\n if prefix is None:\n prefix = self._dir\n elif not os.path.isdir(prefix):\n # Only one file remains, the common path is its folder\n prefix = os.path.dirname(prefix)\n\n allErrors = []\n for c in allChanges:\n # Add 1 to len(prefix) so that we don't include the preceding slash,\n # which would make os.path.join treat it as an absolute\n target = os.path.join(dir, os.path.relpath(c, prefix))\n safeMake(os.path.dirname(target))\n nRetries = 4\n for _retry in range(nRetries):\n try:\n os.rename(c, target)\n except OSError as e:\n if e.errno == 16 and _retry != nRetries - 1:\n # Device busy, will retry\n pass\n elif e.errno == 2:\n # 2 - No such file; was temporary\n break\n else:\n allErrors.append(\"{0}: {1}\".format(c, ''.join(\n traceback.format_exception_only(\n *sys.exc_info()[:2]))))\n break\n else:\n break\n\n # Will retry\n time.sleep(1.)\n\n if allErrors:\n raise Exception(\"Errors during file move:\\n\\n{0}\".format(\n '\\n\\n'.join(allErrors)))","title":""},{"docid":"65f87e6299d2fc20993347197d05c0ac","score":"0.58844805","text":"def move(src_path, dst_path, src_root=None):\n root_ext = os.path.splitext(dst_path)\n i = 0\n while os.path.isfile(dst_path):\n # Recursively avoid the collision\n i += 1\n dst_path = root_ext[0] + \" ({})\".format(i) + root_ext[1]\n # Move file, make directories if needed\n os.makedirs(os.path.dirname(dst_path), exist_ok=True)\n shutil.move(src_path, dst_path)\n # Delete directory if necessary (recursively)\n directory = os.path.dirname(src_path)\n while (src_root is not None and src_path.startswith(src_root) and directory != src_root):\n delete_directory_if_empty_or_hidden(directory)\n directory = os.path.dirname(directory)","title":""},{"docid":"52771a455b04ecfb99cfedd413a7f5af","score":"0.5881753","text":"def rsync_replacement(src: str, dst: str) -> None:\n if not os.path.exists(dst):\n os.makedirs(dst)\n for item in os.listdir(src):\n s = os.path.join(src, item)\n d = os.path.join(dst, item)\n if os.path.isdir(s):\n rsync_replacement(s, d)\n else:\n shutil.copy2(s, d)","title":""},{"docid":"e85f61418c77edaddb953ae5c446fa83","score":"0.5869827","text":"def put_originals_in_subdirectory(file_paths):\n directory, file_name, extension = split_file_path(file_paths[0])\n\n destination_directory = os.path.join(directory, 'originals')\n if not os.path.exists(destination_directory):\n os.mkdir(destination_directory)\n\n for file_path in file_paths:\n os.rename(\n src=file_path,\n dst=os.path.join(destination_directory, os.path.basename(file_path))\n )","title":""},{"docid":"be6ad133f0e79fff064882ad7af311f8","score":"0.58319145","text":"def move_files(dir_name):\n file_ls = glob.glob('*dat') \n plot_ls = glob.glob('*png')\n file_ls += plot_ls\n os.makedirs(dir_name)\n for file in file_ls:\n shutil.move(str(file), dir_name)","title":""},{"docid":"6a8678a748f1e58b1d315cddd85ca690","score":"0.5827099","text":"def _directory_processor(srcdir, new_suffix=\"part_\"):\n weeks = []\n srcdir = srcdir.rstrip(\"/\")\n for f in os.listdir(srcdir):\n # for example, stub_00_01.md\n w = f.split(\"_\")[1]\n # gives w as 00\n if w not in weeks:\n weeks.append(w)\n for w in weeks:\n newdir = f\"{srcdir}/{new_suffix}{w}\"\n # for example, testdir/week_00\n if os.path.isdir(newdir):\n print(f\"{newdir} already exists...\")\n else:\n os.makedirs(newdir)\n for f in os.listdir(srcdir):\n if f.endswith(\".md\"):\n # For example stub_00_01.md\n w = f.split(\"_\")[1]\n # for example w as 00\n os.rename(f\"{srcdir}/{f}\", f\"{srcdir}/{new_suffix}{w}/{f}\")\n # so testdir/stub_00_01.md becomes testdir/week_00/stub_00_01.md","title":""},{"docid":"9ee90884dc4e19739f4f328838543136","score":"0.57634467","text":"def test_dir_to_child(self):\n root = tempfile.mkdtemp(dir=tmpdir)\n src = os.path.join(root, 'subdir', 'folder')\n src2 = os.path.join(root, 'subdir', 'folder', 'file.txt')\n dst = os.path.join(root, 'subdir', 'folder', 'subfolder')\n util.fs.mkdir(src)\n util.fs.save(src2, DUMMY_BYTES)\n with self.assertRaises(util.fs.FSMoveInsideError):\n util.fs.move(src, dst)\n\n # verify no unexpected content change\n self.assertEqual(\n glob_files(src),\n {os.path.join(src, ''), src2},\n )","title":""},{"docid":"220eeecdd4fb57a2455d1c2be0aa7962","score":"0.57633305","text":"def _copy_into_tempdir(self, old):\n extraction_tempdir = tempfile.mkdtemp()\n old.extractall(extraction_tempdir)\n old_filesdir = os.path.join(extraction_tempdir, self.basename)\n for item in os.listdir(old_filesdir):\n shutil.move(os.path.join(old_filesdir, item), self.tempdir)\n shutil.rmtree(extraction_tempdir)","title":""},{"docid":"8f5a5ee3ebb174f04d4012f84daa6433","score":"0.57532245","text":"def handleFolder(foo, dirName, names):\n for name in names:\n path = os.path.join(dirName, name)\n # move all files to target folder\n if os.path.isfile(path):\n shutil.move(path, outFolder)","title":""},{"docid":"16adf5eb74570eb7c08b5f0a731d0775","score":"0.57263696","text":"def _copy_template_dir(self):\n for (dirpath, dirnames, filenames) in os.walk(self.template_dir):\n for filename in filenames:\n if filename in FILES_TO_IGNORE:\n continue\n path = os.path.join(dirpath, filename)\n self.zip_file.write(path, os.path.relpath(path, self.template_dir))","title":""},{"docid":"098a0433691ec1708e51503fba6114e7","score":"0.5724657","text":"def safe_move(file_path, out_dir):\n name = os.path.basename(file_path)\n if not os.path.exists(os.path.join(out_dir, name)):\n shutil.move(file_path, os.path.join(out_dir, name))\n else:\n base, extension = os.path.splitext(name)\n i = 1\n while os.path.exists(os.path.join(out_dir, f'{base}_{i}{extension}')):\n i += 1\n shutil.move(file_path, os.path.join(out_dir, f'{base}_{i}{extension}'))","title":""},{"docid":"87d27d2d4da39de449db1f72c2b16c4a","score":"0.5722395","text":"def squash_directory(directory_path):\n file_list = []\n for file in os.listdir(directory_path):\n file_path = os.path.join(directory_path, file)\n if file.startswith(\"squash\"):\n logger.info(\"Directory (%s) already squashed.\", directory_path)\n return\n elif os.path.isfile(file_path):\n file_list.append(file_path)\n\n with open(\n os.path.join(directory_path, \"squash_\" + str(uuid.uuid1())) + \".csv\",\n \"w\",\n ) as new_file:\n for file in file_list:\n logger.info(\"getting content of: %s\", file)\n with open(file, \"r\") as reader:\n new_file.write(reader.read())\n\n for file in file_list:\n logger.info(\"removing file: %s\", file)\n os.remove(file)\n\n logger.info(\"successfully squashed: %s\", directory_path)","title":""},{"docid":"29480d16126ec1092de7aaf1c9b92f8b","score":"0.5713722","text":"def newFolderByExtension(folder_path):\n new_folder_path = folder_path + \"_sorted\"\n print(\"new_folder_path:\", new_folder_path)\n\n for root, dirs, files in os.walk(folder_path):\n for file in files:\n print(file)\n print(root)\n print(dirs)\n file_ext = splitext(file)[1].strip(\".\")\n if file_ext == \"\":\n file_ext = \"NoExtension\"\n sub_folder = join(new_folder_path,file_ext)\n if not exists(sub_folder):\n print(\"new sub fodler:\", sub_folder)\n makedirs(sub_folder)\n if not exists(sub_folder):\n print(\"not created:\",sub_folder)\n copyfile(file,join(sub_folder,file))","title":""},{"docid":"917c26372b8c92f66f610cbfd796d788","score":"0.5692911","text":"def recursive_overwrite(src, dest, ignore=None):\n if os.path.isdir(src):\n if not os.path.isdir(dest):\n os.makedirs(dest)\n files = os.listdir(src)\n if ignore is not None:\n ignored = ignore(src, files)\n else:\n ignored = set()\n for f in files:\n if f not in ignored:\n recursive_overwrite(os.path.join(src, f),\n os.path.join(dest, f),\n ignore)\n else:\n try:\n shutil.copyfile(src, dest)\n except Exception as e:\n print(e)","title":""},{"docid":"9f1946139ca7c06167d88cdf2f2eb6c6","score":"0.56908065","text":"def mergefolders(root_src_dir, root_dst_dir, replace=False):\n for src_dir, dirs, files in os.walk(root_src_dir):\n dst_dir = src_dir.replace(root_src_dir, root_dst_dir, 1)\n if not os.path.exists(dst_dir):\n os.makedirs(dst_dir)\n for file_ in files:\n src_file = os.path.join(src_dir, file_)\n dst_file = os.path.join(dst_dir, file_)\n if os.path.exists(dst_file) and replace:\n os.remove(dst_file)\n shutil.copy(src_file, dst_dir)\n else:\n if not os.path.exists(dst_file):\n shutil.copy(src_file, dst_dir)","title":""},{"docid":"829e156532efbe54409d891b416d23dd","score":"0.5683604","text":"def test_dir_to_child_self(self):\n root = tempfile.mkdtemp(dir=tmpdir)\n src = os.path.join(root, 'subdir', 'folder')\n src2 = os.path.join(root, 'subdir', 'folder', 'file.txt')\n dst = os.path.join(root, 'subdir', 'folder')\n util.fs.mkdir(src)\n util.fs.save(src2, DUMMY_BYTES)\n with self.assertRaises(util.fs.FSMoveInsideError):\n util.fs.move(src, dst)\n\n # verify no unexpected content change\n self.assertEqual(\n glob_files(src),\n {os.path.join(src, ''), src2},\n )","title":""},{"docid":"0ecefdbfbcfd057eecfc6a9454e58289","score":"0.56791705","text":"def _overwriteExistingFiles(self):\n\n\t\tshutil.rmtree(self.output_folder)\n\t\tfiletools.checkDir(self.output_folder)","title":""},{"docid":"3842a01b9961fcd693b2d5d0c8af25cb","score":"0.5654301","text":"def clean(self):\n\n dest = self._make_extraction_dir()\n p_join = os.path.join\n for name in self.names:\n try:\n shutil.move(p_join(self.root, name), p_join(dest, name))\n except IOError as e:\n if self.partial:\n pass\n else:\n raise e","title":""},{"docid":"d87c946e5e8b1fabcdb3c3682bec33e9","score":"0.56516594","text":"def move_files(source_directory, destination_directory):\n \n files_in_source_dir = [f for f in listdir(source_directory) if isfile(join(source_directory, f))]\n \n for file in files_in_source_dir:\n shutil.copy(join(source_directory, file), destination_directory)","title":""},{"docid":"4663709dcbfa036b5965b4d8e5e29623","score":"0.5651226","text":"def clean_up(name):\n if os.path.isdir(name):\n shutil.rmtree(name)\n\n make_destination_dir(name)","title":""},{"docid":"976e62572ab1520701b2d44a0ef4ba42","score":"0.56466764","text":"def shutilDirectory(inPath, outPath, connection_info):\n files = os.listdir(inPath)\n filePaths = [os.path.join(inPath, i) for i in files]\n uniqueName = os.path.split(inPath)[1]\n for i in filePaths:\n tf = Insitu(i, connection_info)\n tf.parse()\n startDate = datetime.datetime.strftime(tf.keyDict[\"tstart\"], \"%Y%m%d\")\n endDate = datetime.datetime.strftime(tf.keyDict[\"tend\"], \"%Y%m%d\")\n tfNewName = uniqueName + \"_\" + startDate + \"_\" + endDate\n tfNewPath = os.path.join(outPath,tfNewName)\n shutil.copy(i, tfNewPath)","title":""},{"docid":"952e320d7a106b1ea931b4770710b809","score":"0.56431067","text":"def loop_through_copy_files_to_one_dir(looped_dir, target_dir, include_link=False):\n if not os.path.isdir(looped_dir):\n raise Exception(\"looped_dir: a directory.\")\n if not os.path.isdir(target_dir):\n raise Exception(\"target_dir: a directory.\")\n for thing in os.listdir(looped_dir):\n thing = os.path.join(looped_dir, thing)\n if os.path.isdir(thing):\n loop_through_copy_files_to_one_dir(thing, target_dir)\n elif os.path.isfile(thing):\n shutil.move(thing, os.path.join(target_dir, parent_dir_and_name(thing)[1]))\n elif include_link:\n shutil.move(thing, os.path.join(target_dir, parent_dir_and_name(thing)[1]))\n return","title":""},{"docid":"6e520c8a6da76f6ab4e09f4a1d80da94","score":"0.5641401","text":"def findDup(parentFolder):\n dups = {}\n for root, subDirs, fileList in os.walk(parentFolder):\n print(f'Scanning {root}...')\n for filename in fileList:\n # Get the path to the file\n path = os.path.join(root, filename)\n # Get the hash with 'hashfile' function\n fileHash = hashfile(path)\n if fileHash in dups:\n dups[fileHash].append(path)\n else:\n dups[fileHash] = [path]\n return dups","title":""},{"docid":"62b3c1e68389a94cd9c555b78cb71f3c","score":"0.5640435","text":"def _create_destination_dir_and_move_image_files(self, destination):\n nii_rel_path = self.assembly_nifti_rel_path if destination == 'assembly_bids' else self.trashbin_nifti_rel_path\n json_rel_path = re.sub(r\"\\.nii(\\.gz)?$\", '.json', nii_rel_path) if self.json_path else None\n bval_rel_path = re.sub(r\"\\.nii(\\.gz)?$\", '.bval', nii_rel_path) if self.bval_path else None\n bvec_rel_path = re.sub(r\"\\.nii(\\.gz)?$\", '.bvec', nii_rel_path) if self.bvec_path else None\n\n absolute_dir_path = os.path.join(self.data_dir, os.path.dirname(nii_rel_path))\n self.create_dir(absolute_dir_path)\n\n file_type_to_move_list = [\n {\n 'original_file_path': self.nifti_path,\n 'new_file_path': os.path.join(self.data_dir, nii_rel_path)\n }\n ]\n if self.json_path:\n file_type_to_move_list.append(\n {\n 'original_file_path': self.json_path,\n 'new_file_path': os.path.join(self.data_dir, json_rel_path)\n }\n )\n if self.bval_path:\n file_type_to_move_list.append(\n {\n 'original_file_path': self.bval_path,\n 'new_file_path': os.path.join(self.data_dir, bval_rel_path)\n }\n )\n if self.bvec_path:\n file_type_to_move_list.append(\n {\n 'original_file_path': self.bvec_path,\n 'new_file_path': os.path.join(self.data_dir, bvec_rel_path)\n }\n )\n\n for file_dict in file_type_to_move_list:\n original_file_path = file_dict['original_file_path']\n new_file_path = file_dict['new_file_path']\n\n message = f\"Moving file {original_file_path} to {new_file_path}\"\n self.log_info(message, is_error='N', is_verbose='Y')\n self.move_file(original_file_path, new_file_path)\n\n if destination == 'assembly_bids':\n self.json_file_dict['file_blake2b_hash'] = self.nifti_blake2\n if self.json_path:\n self.json_file_dict['bids_json_file'] = json_rel_path\n self.json_file_dict['bids_json_file_blake2b_hash'] = self.json_blake2\n if self.bval_path:\n self.json_file_dict['check_bval_filename'] = bval_rel_path\n self.json_file_dict['check_bval_filename_blake2b_hash'] = self.bval_blake2\n if self.bvec_path:\n self.json_file_dict['check_bvec_filename'] = bvec_rel_path\n self.json_file_dict['check_bvec_filename_blake2b_hash'] = self.bvec_blake2","title":""},{"docid":"7df815c4afff2afe7b72c60738bd6e0f","score":"0.5626348","text":"def copy_dir(source_dir, dest_dir):\n for root, _, files in walk(source_dir):\n src_root = Path(root)\n dest_root = Path(dest_dir, src_root.relative_to(source_dir))\n Path(dest_root).mkdir(parents=True, exist_ok=True)\n for f in files:\n src_file = Path(src_root, f)\n dest_file = Path(\n dest_root, f.replace(\".testjava\", \".java\").replace(\".testkt\", \".kt\")\n )\n dest_file.write_text(src_file.read_text())","title":""},{"docid":"c409b7973b50750f996d13c72243f17e","score":"0.5621315","text":"def _copy_files(context, files):\n for file_task in files:\n if not file_task.dst:\n file_task.dst = file_task.src\n if os.path.isdir(file_task.src):\n context.remove_tree(file_task.dst)\n context.copytree_to(file_task.src, file_task.dst)\n else:\n context.copy_to(file_task.src, file_task.dst)","title":""},{"docid":"d59091e33010f403fbfc1145ee5c36d4","score":"0.55967206","text":"def move_files(path):\n moved = 0\n for f in os.listdir(path):\n prefix = path + '/'\n current_dir = prefix + f\n for key in extensions.keys():\n if f.endswith(extensions[key]):\n try:\n os.rename(current_dir, prefix + key + '/' + f)\n except OSError:\n for i in range(10000):\n try:\n os.rename(current_dir, prefix + key + '/' + 'copy_' + str(i) + f)\n except:\n continue\n break\n moved += 1\n return moved","title":""},{"docid":"3a38d1741e87af55c27da21178e65f9c","score":"0.5596366","text":"def copy_files(source_dir, dest_dir):\n for files in os.scandir(source_dir):\n filename = source_dir + files.name\n copy2(filename, dest_dir)","title":""},{"docid":"d05f1a869b8c3bdf36344278c2ac5715","score":"0.55857086","text":"def _cleanup_filled_directory(\n self, symbol, dest_node, src_entries, copy_source\n ):\n\n cvs_paths = src_entries.keys()\n cvs_paths.sort()\n for cvs_path in cvs_paths:\n if isinstance(cvs_path, CVSDirectory):\n # Path is a CVSDirectory:\n try:\n dest_subnode = dest_node[cvs_path]\n except KeyError:\n # Path doesn't exist yet; it has to be created:\n dest_node = self._fill_directory(\n symbol, None, src_entries[cvs_path], None\n ).parent_mirror_dir\n else:\n # Path already exists, but might have to be cleaned up:\n dest_node = self._fill_directory(\n symbol, dest_subnode, src_entries[cvs_path], copy_source\n ).parent_mirror_dir\n else:\n # Path is a CVSFile:\n self._fill_file(\n symbol, cvs_path in dest_node, src_entries[cvs_path], copy_source\n )\n # Reread dest_node since the call to _fill_file() might have\n # made it writable:\n dest_node = self._mirror.get_current_path(\n dest_node.cvs_path, dest_node.lod\n )\n\n return dest_node","title":""},{"docid":"0292f6a0ebb0b4bbe8d28a95817c2e2e","score":"0.5579431","text":"def file_arrangement(abs_dir_path):\n if not os.path.isdir(abs_dir_path):\n return\n suffix_type = []\n sep = os.path.sep\n suffix_format = re.compile('(.*)\\.(.*)') # name in group(1) and suffix in group(2)\n # using to split extension also can use os.path.splitext(path)\n for root, dirs, files in os.walk(abs_dir_path):\n dir_name = os.path.basename(root) # current directory name\n for file_name in files:\n curr_path = root + sep + file_name\n mapping = suffix_format.search(file_name)\n if mapping is None:\n new_path_name = abs_dir_path + sep + dir_name + '_' + file_name\n shutil.copy(curr_path, new_path_name)\n continue\n name = mapping.group(1)\n suffix = mapping.group(2)\n if suffix not in suffix_type:\n suffix_type.append(suffix)\n os.makedirs(abs_dir_path + sep + suffix)\n new_path_name = abs_dir_path + sep + suffix + sep + dir_name + '_' + file_name\n # that is put in to abs_dir_path\\suffix\\dir_name_filename\n shutil.copy(curr_path, new_path_name)","title":""},{"docid":"8da16925dc8b72df454aed9b0800de6e","score":"0.5579215","text":"def copy_tree_over(src, dest):\n # From https://stackoverflow.com/questions/9160227/dir-util-copy-tree-fails-after-shutil-rmtree/28055993 : \n # If you copy folder, then remove it, then copy again it will fail, because it caches all the created dirs. \n # To workaround you can clear _path_created before copy:\n distutils.dir_util._path_created = {}\n distutils.dir_util.copy_tree(src, dest)","title":""},{"docid":"2616a910f42bc65ffa285978c5dd41f1","score":"0.55754566","text":"def copytree_preserve_existing(os_service, src, dst):\n oss = os_service\n helper = Helpers()\n helper.copy_directory_tree(oss, src, dst)\n\n file_it = FileIterator(os_service, src)\n for file_sub_path in file_it:\n source_file_path = os.path.join(src, file_sub_path)\n dest_file_path = os.path.join(dst, file_sub_path)\n if not oss.exists(dest_file_path):\n oss.copyfile(source_file_path, dest_file_path)","title":""},{"docid":"aabe8267416d2bc6588b7369860bb5c1","score":"0.5570711","text":"def copy_dirs(self, dst, subs='', src_sub='', dst_sub=''):\n # Join paths (if specified)\n src = self.src\n if subs:\n src = os.path.join(src, subs)\n dst = os.path.join(dst, subs)\n if dst_sub:\n dst = os.path.join(dst, dst_sub)\n if src_sub:\n src = os.path.join(src, src_sub)\n print('\\nComparing...\\n')\n print('\\nSource: %s' % src)\n print('Destination: %s\\n' % dst)\n for src_root, src_dirs, src_files in os.walk(src, topdown=True):\n\n dst_root = os.path.join(dst, os.path.relpath(src_root, src))\n dirs = filecmp.dircmp(src_root, dst_root)\n\n # Find old files and delete them from destination\n for item in dirs.right_only:\n try:\n print('Removing ' + item)\n except UnicodeEncodeError:\n # Prevents the program from stopping in the event of an\n # awkward file name\n print('Removing file (Unicode error)')\n dst_path = os.path.join(dst_root, item)\n if os.path.isdir(dst_path):\n shutil.rmtree(dst_path)\n else:\n os.remove(dst_path)\n\n # Find new files and add them to destination\n for item in dirs.left_only:\n try:\n print('Adding ' + item)\n except UnicodeEncodeError:\n # Prevents the program from stopping in the event of an\n # awkward file name\n print('Adding file (Unicode error)')\n src_path = os.path.join(src_root, item)\n if os.path.isdir(src_path):\n shutil.copytree(src_path, os.path.join(dst_root, item))\n else:\n shutil.copy2(src_path, os.path.join(dst_root, item))\n\n # Once clearing and adding has completed, update existing files\n print('\\nUpdating: ')\n if plat != 'win32':\n os.system(\n \"\"\"rsync -r -u -v --links \"{0}\"/* \"{1}\" \"\"\".format(src, dst))\n else:\n os.system(\"\"\"xcopy /I /E /Y /D \"{0}\" \"{1}\" \"\"\".format(src, dst))","title":""},{"docid":"f6ba29e8b3369f5c134f8674fb030863","score":"0.55684304","text":"def move(sfile, source_dir, directory):\n sbase = osp.splitext(osp.split(sfile)[1])[0]\n already_here = any(sbase in dfile for dfile in dest_files)\n if not already_here:\n shutil.copy(osp.join(source_dir, sfile), directory)\n logger.info(\"Copied {} into pylinac directory\".format(sfile))","title":""},{"docid":"7c11a41bc54a09cc18d12eb2e559434c","score":"0.55585176","text":"def moveResultsToMain(self):\n # Delete input file\n os.remove(self.subDir+self.simName+'\\\\'+self.simName+'.inp')\n # Move files\n allFiles = os.listdir(self.subDir+self.simName+'\\\\')\n for fName in allFiles:\n os.rename(self.subDir+self.simName+'\\\\'+fName, self.subDir+fName)\n # Delete (empty) simulation folder\n os.rmdir(self.subDir+self.simName)\n # Brief pause\n time.sleep(5)","title":""},{"docid":"d1ba59211445527a8926e400decf0aa8","score":"0.55563086","text":"def make_files_match(from_dir, to_dir, do_commits = True, verbose=False, delete_missing_from = False, \n do_delete_files = True):\n\n # if from dir doesn't exist, delete to dir\n # This is used by pull_step.\n if (not os.path.exists(from_dir)):\n if delete_missing_from and os.path.exists(to_dir):\n shutil.rmtree(to_dir)\n return\n\n\n # if to_dir doesn't exist, create it - we are probably pushing\n # for the first time - Issue #2\n if (not os.path.exists(to_dir)):\n try:\n os.makedirs(to_dir, 0755)\n except:\n pass\n\n # What files is git managing for us in each directory?\n if not os.path.exists(from_dir):\n from_files = [ ]\n else:\n from_files = git.list_files(from_dir)\n to_files = git.list_files(to_dir)\n\n from_files_set = set(from_files)\n to_files_set = set(to_files)\n\n # We need to remove \"by hand\" those files which are no longer meant to\n # be in the \"to\" directory, so work out which files that is\n deleted_files = to_files_set - from_files_set\n\n if verbose:\n print 'Making files the same'\n print '====================='\n print 'From', from_dir\n print 'To ', to_dir\n if (len(from_files) > 0):\n max_len = max(len(n) for n in from_files)\n else:\n max_len = 1\n for name in sorted(from_files_set | to_files_set):\n if name in from_files_set and name in to_files_set:\n print '%-*s %s'%(max_len, name, name)\n elif name in from_files_set:\n print '%-*s'%(max_len, name)\n else:\n print '%-*s %s'%(max_len, ' ', name)\n print '====================='\n\n # If we copy over everything from the \"from\" directory to the \"to\"\n # directory, and \"git add\" them all, then that will cope with changes to\n # existing files, and also add in any new files. We then need to remember\n # to \"git rm\" any files that are meant to have gone away, at which point we\n # can commit all the changes.\n #\n # We use rsync for our copy because it will handle things like:\n #\n # rsync -a --relative four/jim four/bob <weld_root>/.weld/bases/project124\n #\n # and put \"jim\" and \"bob\" into <weld_root>/.weld/bases/project124/four\n if os.path.exists(from_dir):\n cmd = ['rsync', '-a', '--relative']\n cmd += from_files\n cmd += [to_dir]\n run_silently(cmd, cwd=from_dir, verbose=verbose)\n\n if len(from_files) >0:\n git.add(to_dir, from_files, verbose=verbose)\n if do_commits:\n git.commit_using_message(to_dir, \"Add files from %s\"%from_dir, verbose=verbose)\n\n if deleted_files and do_delete_files:\n git.rm(to_dir, list(deleted_files))\n if do_commits: \n git.commit_using_message(to_dir, \"Delete files no longer in %s\"%from_dir)","title":""},{"docid":"3f2aa5c46b28e9b21b8c5c50bc334787","score":"0.5544597","text":"def move_files(files, destdir):\n ensure_dir(destdir)\n for f in files:\n if f.startswith(destdir):\n continue\n subprocess.call([\"mv\", \"-f\", f, destdir])","title":""},{"docid":"71603c45a0219c0bf7e5e23c80132c6e","score":"0.5533806","text":"def RelocateNonRepoFiles(all_files):\n tempDir = os.path.join(u'scripts', u'temp')\n rm(tempDir)\n os.makedirs(tempDir)\n\n non_repo = GetNonRepoFiles(all_files)\n if non_repo:\n lprint(\" Relocating non-repository files:\")\n for path in non_repo:\n lprint(\" \", path)\n src = os.path.join(mopy, path)\n dst = os.path.join(tempDir, path)\n if os.path.isdir(src):\n shutil.move(src, dst)\n elif os.path.isfile(src):\n dirname = os.path.dirname(dst)\n if not os.path.exists(dirname):\n os.makedirs(dirname)\n shutil.move(src, dst)","title":""},{"docid":"9864431a8adcaca6fb82d9e420bd838f","score":"0.5531388","text":"def prep_tree_for_tar(repodir, subdir, outdir, dstname):\n src = os.path.join(repodir, subdir)\n if not os.path.exists(src):\n sys.exit(\"%s: No such file or directory\" % src)\n\n dst = os.path.join(outdir, dstname)\n if os.path.exists(dst) and \\\n (os.path.samefile(src, dst) or\n os.path.samefile(os.path.dirname(src), dst)):\n sys.exit(\"%s: src and dst refer to same file\" % src)\n\n #shutil.copytree(src, dst, symlinks=True)\n gitlock = os.path.join(src, '.git/index.lock')\n count = 0\n while os.path.exists(gitlock):\n time.sleep(5)\n count = count + 1\n if count > 100:\n break\n safe_run(['cp', '-a', src, dst], cwd=os.getcwd(),interactive=sys.stdout.isatty())\n\n return dst","title":""},{"docid":"84e8d8b04e948e1c30aa4bc7df405edf","score":"0.5518604","text":"def move_files(src, dst, group):\n print(\"Moving!\")\n print(group)\n for fname in group:\n print(src+ '/' + fname)\n os.rename(src + '/' + fname, dst + '/' + fname)","title":""},{"docid":"0ed4254726c1bfa74a01b65d24e97c5f","score":"0.5513786","text":"def gc_mvall( scratchdir ):\n sdirs = glob.glob(scratchdir)\n for scratchdir in sdirs:\n gcdir = scratchdir.replace( '/scratch/', '/scratch/_gc/' )\n print \"scratchdir=\",scratchdir\n print \"gcdir =\",gcdir\n if os.path.isdir(scratchdir):\n if os.path.isdir(gcdir):\n print \"WARNING, gcdir %s already exists,\\n will not move from scratchdir %s\"%\\\n (gcdir,scratchdir)\n else:\n shutil.move( scratchdir, gcdir )\n else:\n #raise Exception(\"source directory %s doesn't exist\"%scratchdir)\n print \"WARNING\", \"source directory %s doesn't exist\"%scratchdir\n print \"Nothing will be moved from scratch to scratch/_gs.\"\n if not os.path.isdir(glob.glob(gcdir)[0]):\n raise Exception(\"gcdir %s doesn't exist\"%gcdir)","title":""},{"docid":"7b53a4ac23d2d6b8c876aaafab56bfdf","score":"0.5511875","text":"def _copy_dir_recursive(src_rel, dst_rel):\n src = os.path.expanduser(src_rel)\n dst = os.path.expanduser(dst_rel)\n try:\n shutil.copytree(src, dst)\n logger.info('Files copied from %s to %s', src, dst)\n except OSError as exc:\n if exc.errno == errno.ENOTDIR:\n shutil.copy(src, dst)\n logger.info('Files copied from %s to %s', src, dst)\n else:\n raise","title":""},{"docid":"2bb807e1ca46d9cdfbf6ba7dd8928e8c","score":"0.5511476","text":"def movefiles(sub, newid, cid):\n # construct full CISC ID (with sub-)\n cid_full = \"sub-\" + cid\n for root, dirs, files in os.walk(sub):\n for f in files:\n # Only continue with the file if it's NOT a stupid annoying mac file\n if \"DS_Store\" not in f:\n # Construct full path\n fullf = os.path.join(root, f)\n # Create new path by replacing sub-CISCID with sub- ADIEID\n newf = fullf.replace(cid_full, newid)\n # Check that directories already exist - should've been created in F3\n if not os.path.isdir(os.path.split(newf)[0]):\n print(\n \"WARNING: '{}' does NOT exists\".format(\n os.path.split(newf)[0]\n )\n )\n # If directory does exist, proceed with move and rename\n else:\n print(\"Attempting to move\", fullf, \"to\", newf, \"\\n\")\n shutil.move(fullf, newf)\n elif \"DS_Store\" in f:\n continue","title":""},{"docid":"d2f051c566763bc0cec954eb9772a164","score":"0.5510668","text":"def remove_entries(dest):\n for entry in os.listdir(dest):\n fullpath = os.path.join(dest, entry)\n if os.path.isfile(fullpath):\n os.remove(fullpath)\n else:\n shutil.rmtree(fullpath)","title":""},{"docid":"775041c355bd41c2c34db388a53e54ab","score":"0.5499051","text":"def rsync_files(src_file: Union[str, PathLike], dest_file: Union[str, PathLike]) -> None:\n copy2(src_file, dest_file)\n src_folder = dirname(src_file)\n if not src_folder:\n src_folder = '.'\n copystat(src_folder, dirname(dest_file))","title":""},{"docid":"e5ae744e8068cab08b1d8555209a7d1a","score":"0.5497786","text":"def upload_tree(self, src, dst, ignore=None):\n names = os.listdir(src)\n if ignore is not None:\n ignored_names = ignore(src, names)\n else:\n ignored_names = set()\n\n try:\n dst = dst.replace('\\\\', '/')\n self.conn.mkd(dst)\n except error_perm:\n pass\n\n errors = []\n for name in names:\n if name in ignored_names:\n continue\n src_name = os.path.join(src, name)\n dst_name = os.path.join(dst, name)\n try:\n if os.path.islink(src_name):\n pass\n elif os.path.isdir(src_name):\n self.upload_tree(src_name, dst_name, ignore)\n else:\n # Will raise a SpecialFileError for unsupported file types\n self.put(src_name, dst_name)\n except Exception as why:\n errors.append((src_name, dst_name, str(why)))\n\n return dst","title":""},{"docid":"0382cbf8d9c1895f0fb360781da2f9b3","score":"0.54899615","text":"def single_folder_2_planet_order_hierarchy(input_dir, target_dir):\r\n for root, dirs, files in os.walk(os.path.normpath(input_dir), topdown=False):\r\n for file_name in files:\r\n # This selects only the actual image *.tif file\r\n if \"AnalyticMS_clip\" in file_name and file_name.endswith(\".tif\"):\r\n logging.info(\"Found: {}\".format(file_name))\r\n file_name_split = file_name.split(\"_\")\r\n # The date_id in the ISO format YYYYMMDD becomes a folder\r\n date_id = file_name_split[0]\r\n date_dir = os.path.join(target_dir, date_id)\r\n logging.info(\"Writing date directory as: {}\".format(date_dir))\r\n if not os.path.isdir(date_dir):\r\n os.mkdir(date_dir)\r\n # The img_id is formatted as YYYYMMDD_id_sat\r\n img_id = \"_\".join(file_name_split[0:3])\r\n logging.info(\"Writing image folder as: {}\".format(img_id))\r\n img_dir = os.path.join(date_dir, img_id)\r\n if not os.path.isdir(img_dir):\r\n os.mkdir(img_dir)\r\n logging.info(\"Writing to image directory: {}\".format(img_dir))\r\n # loop for copying all metadata files as well with the same img_id\r\n for file_name2 in files:\r\n if img_id in file_name2:\r\n source_dir = os.path.join(input_dir, file_name2)\r\n shutil.copy2(source_dir, img_dir) \r\n return 0","title":""},{"docid":"6ea009c9c62d1858746e62f28c2d19d3","score":"0.5485185","text":"def copydir(self, src, dst, replace=True):\n for file in os.listdir(src):\n if os.path.isfile(os.path.join(src, file)):\n if os.path.exists(os.path.join(dst, file)) and not replace:\n self.logger.debug(\"> Skipped file \\\"\" + os.path.join(src, file) + \"\\\": Already exists.\")\n elif os.path.exists(os.path.join(dst, file)):\n os.unlink(os.path.join(dst, file))\n shutil.copy2(os.path.join(src, file), dst)\n self.logger.debug(\"> Replaced file \\\"\" + os.path.join(src, file) + \"\\\".\")\n else:\n shutil.copy2(os.path.join(src, file), dst)\n self.logger.debug(\"> Copied file \\\"\" + os.path.join(src, file) + \"\\\".\")\n elif os.path.isdir(os.path.join(dst, file)):\n self.copydir(os.path.join(src, file), os.path.join(dst, file))\n else:\n os.makedirs(os.path.join(dst, file))\n self.copydir(os.path.join(src, file), os.path.join(dst, file))","title":""},{"docid":"f8909620f8f6b691a824f7e780eca88b","score":"0.54825467","text":"def cleanup_project_dir(self):\n contents = os.listdir(self.project.root)\n num = 0\n for name in contents:\n if name.lower().endswith(\".asc\"):\n os.rename(self.project.root + name, self.project.data_dir + name)\n num += 1\n\n if num > 0:\n messagebox.showinfo(\n \"New Project\",\n \"{} ASC files were moved to {}\".format(num, self.project.data_dir),\n )","title":""},{"docid":"cf82e570a7ed52e0b94b1afdee663101","score":"0.5452133","text":"def copy_directory(source, dest):\n for root, dirs, files in os.walk(source):\n if not os.path.isdir(root):\n os.makedirs(root)\n\n for file in files:\n rel_path = root.replace(source, '').lstrip(os.sep)\n dest_path = os.path.join(dest, rel_path)\n\n if not os.path.isdir(dest_path):\n os.makedirs(dest_path)\n if(dirs and files):\n shutil.copyfile(os.path.join(root, file),\n os.path.join(dest_path, file))","title":""},{"docid":"255b76c568aa4b73ed9b4a2bf2e5bd91","score":"0.5451473","text":"def find_duplicates(data_dir):\n duplicates = []\n im_hashes = dict()\n\n def append_stuff(directory, im_hashes):\n \"\"\"\n Return a list of absolute paths to all duplicate files in directory.\n \"\"\"\n print(directory)\n for file_name in os.listdir(directory):\n file_name = f'{directory}/{file_name}'\n if os.path.isfile(file_name):\n with open(file_name, 'rb') as file:\n file_hash = hashlib.md5(file.read()).hexdigest()\n try:\n temp = im_hashes[file_hash]\n del temp\n duplicates.append(file_name)\n print(file_name.split('/')[-1])\n except KeyError:\n im_hashes[file_hash] = file_name\n else:\n append_stuff(file_name, im_hashes)\n append_stuff(data_dir, im_hashes)\n return duplicates","title":""},{"docid":"c5ec29f44525327d2df19fe422b88f50","score":"0.544854","text":"def clean_files(self) -> None:\n api_dir = os.path.join(self._src_dir, \"_api\")\n\n shutil.rmtree(api_dir, ignore_errors=True)\n shutil.rmtree(self._out_dir, ignore_errors=True)\n os.makedirs(api_dir, exist_ok=True)\n os.makedirs(self._out_dir, exist_ok=True)\n\n print(f\"Recreated content of the {shlex.quote(self._out_dir)} and {shlex.quote(api_dir)} folders\")","title":""},{"docid":"49097edb46a99cc8a7e7e9d1e4a52354","score":"0.5430227","text":"def moveFilesToTestDirectory(directory):\n amount_of_files = getAmountOffFilesInDirectory(directory)\n amount_of_files_moved = amount_of_files * 0.7 # Gets finds which number is 70% of the files\n files_counted = 0 # Counts the amount of files\n for file_name in os.listdir(directory):\n # Once loop reaches past the 70% mark it begins to move training files to the test file directory\n if files_counted >= amount_of_files_moved:\n class_name = directory.split('/')[1] # Gets name of the class I.E. Digimon or Pokemon\n shutil.move(directory + '/' + file_name, 'test/' + class_name + '/' + file_name) # Moves files\n files_counted += 1","title":""},{"docid":"5ceff1e84e3fb9b5dfa67b280823a45d","score":"0.5419111","text":"def move(src, dst):\n\n try:\n os.rename(src, dst)\n except OSError:\n if os.path.isdir(src):\n if destinsrc(src, dst):\n raise Error, \"Cannot move a directory '%s' into itself '%s'.\" % (src, dst)\n copytree(src, dst, symlinks=True)\n rmtree(src)\n else:\n copy2(src,dst)\n os.unlink(src)","title":""},{"docid":"5a77427c2dd2ada459d01903eb2ed1e9","score":"0.54185","text":"def move_files(original_fold, data_fold, data_filename):\n with open(data_filename) as f:\n for line in f.readlines():\n vals = line.split('/')\n dest_fold = os.path.join(data_fold, vals[0])\n if not os.path.exists(dest_fold):\n os.mkdir(dest_fold)\n shutil.move(os.path.join(original_fold, line[:-1]), os.path.join(data_fold, line[:-1]))","title":""},{"docid":"87f3d2e56e9d512ce978f5d4d2a731ab","score":"0.5409844","text":"def merge_multi_folder_2_single_folder(input_dir, merged_dir):\r\n for root, dirs, files in os.walk((os.path.normpath(input_dir)), topdown=False):\r\n for file_name in files:\r\n logging.info(\"Found: {}\".format(file_name))\r\n source_dir = os.path.join(root, file_name)\r\n shutil.copy2(source_dir, merged_dir)\r\n logging.info(\"Wrote: {} to merged directory\".format(file_name))\r\n return 0","title":""},{"docid":"acf82960079d6018c53bfa1d8c2198d1","score":"0.54056674","text":"def extract_step(self):\n super(EB_OpenFOAM, self).extract_step()\n # make sure that the expected subdir is really there after extracting\n # if not, the build scripts (e.g., the etc/bashrc being sourced) will likely fail\n openfoam_installdir = os.path.join(self.installdir, self.openfoamdir)\n if not os.path.exists(openfoam_installdir):\n self.log.warning(\"Creating expected directory %s, and moving everything there\" % openfoam_installdir)\n try:\n contents_installdir = os.listdir(self.installdir)\n source = os.path.join(self.installdir, contents_installdir[0])\n # it's one directory but has a wrong name\n if len(contents_installdir) == 1 and os.path.isdir(source):\n target = os.path.join(self.installdir, self.openfoamdir)\n self.log.debug(\"Renaming %s to %s\", source, target)\n os.rename(source, target)\n else:\n mkdir(openfoam_installdir)\n for fil in contents_installdir:\n if fil != self.openfoamdir:\n source = os.path.join(self.installdir, fil)\n target = os.path.join(openfoam_installdir, fil)\n self.log.debug(\"Moving %s to %s\", source, target)\n shutil.move(source, target)\n os.chdir(openfoam_installdir)\n except OSError as err:\n raise EasyBuildError(\"Failed to move all files to %s: %s\", openfoam_installdir, err)","title":""},{"docid":"088f78b7fe91603e6456e91e534cab66","score":"0.53969646","text":"def file_move_dir(mv_input_img_address, mv_output_img_address):\n single_char_list = glob.glob(os.path.join(mv_input_img_address, \"*\"))\n last_name_of_output_file = int(((os.listdir(mv_output_img_address))[-1]).split(sep='.')[0])\n\n for img_index in single_char_list:\n # input_image = DOT_IMG_ADDRESS + str(img_index) + \".jpg\"\n # mv_input_image = os.path.join(DOT_INPUT_IMG_ADDRESS, \"{}.png\".format(str(img_index).zfill(6)))\n mv_raw_img = cv2.imread(img_index)\n DEBUG = False\n if DEBUG:\n cv2.imshow('mv_raw_img', mv_raw_img)\n cv2.waitKey()\n img_output_address = os.path.join(mv_output_img_address,\n \"{}.png\".format(str(last_name_of_output_file + 1).zfill(6)))\n cv2.imwrite(img_output_address, mv_raw_img)\n last_name_of_output_file += 1\n for img_index in single_char_list:\n # input_image = DOT_IMG_ADDRESS + str(img_index) + \".jpg\"\n # mv_input_image = os.path.join(DOT_INPUT_IMG_ADDRESS, \"{}.png\".format(str(img_index).zfill(6)))\n # if os.path.exists(mv_input_image):\n os.remove(img_index)\n # print(os.path.split(img_index)[:-1])\n os.removedirs(mv_input_img_address)","title":""},{"docid":"b5d27487fcea039f1f673e4983aa6a0e","score":"0.539501","text":"def move_songs():\n\n OLD_DIR = os.path.expanduser(\"~/.playx\")\n NEW_DIR = os.path.expanduser(\"~/.playx/songs\")\n\n if not os.path.isdir(NEW_DIR):\n os.makedirs(NEW_DIR)\n\n for file in os.listdir(OLD_DIR):\n if file.endswith(\"mp3\"):\n move(os.path.join(OLD_DIR, file), os.path.join(NEW_DIR, file))","title":""},{"docid":"b975b474aa96c27d12f7da2e3b88a698","score":"0.5394871","text":"def move_files(self, photo_paths, move_to):\n\n update_folders = []\n moved = 0\n for fullpath in photo_paths:\n photo_info = self.database_exists(fullpath)\n if photo_info:\n new_path = os.path.join(photo_info[2], move_to)\n try:\n if not os.path.isdir(new_path):\n os.makedirs(new_path)\n except:\n self.popup_message(text='Error: Could Not Create Folder', title='Error')\n break\n photo_path = os.path.join(photo_info[2], photo_info[0])\n current_folder, current_file = os.path.split(photo_path)\n new_photo_path = os.path.join(new_path, current_file)\n new_fullpath = os.path.join(move_to, current_file)\n backup_path = photo_info[10]\n if os.path.exists(backup_path):\n new_backup_path = os.path.join(new_path, '.originals')\n new_backup_file = os.path.join(new_backup_path, current_file)\n try:\n os.makedirs(new_backup_path)\n os.rename(backup_path, new_backup_file)\n except:\n self.popup_message(text='Error: Could Not Move Backup File', title='Error')\n break\n if not os.path.exists(new_backup_file):\n self.popup_message(text='Error: Could Not Move Backup File', title='Error')\n break\n photo_info[10] = new_backup_file\n if os.path.exists(photo_path):\n try:\n os.rename(photo_path, new_photo_path)\n except:\n self.popup_message(text='Error: Could Not Move File', title='Error')\n break\n if not os.path.exists(new_photo_path):\n self.popup_message(text='Error: Could Not Move File', title='Error')\n break\n\n self.database_item_update(photo_info)\n self.database_item_rename(fullpath, new_fullpath, move_to)\n update_folders.append(photo_info[1])\n moved = moved + 1\n if moved:\n self.message(\"Moved \"+str(moved)+\" files.\")\n update_folders.append(move_to)\n self.update_photoinfo(folders=update_folders)","title":""},{"docid":"77d140b223afe752df41227c612548c3","score":"0.5384689","text":"def clear_destination_dir (self):\n \n for path, dirs, files in os.walk(self.DESTINATION_DIR, topdown=False):\n for file in files:\n os.remove(os.path.join(path, file))","title":""},{"docid":"20bb9ad0107696dbead529ef90f28c08","score":"0.53845114","text":"def _copy_new_files(directory, config):\n def move(sfile, source_dir, directory):\n \"\"\"The function to move files from the source to the destination\"\"\"\n sbase = osp.splitext(osp.split(sfile)[1])[0]\n already_here = any(sbase in dfile for dfile in dest_files)\n if not already_here:\n shutil.copy(osp.join(source_dir, sfile), directory)\n logger.info(\"Copied {} into pylinac directory\".format(sfile))\n\n # return if no sources are configured or are not real directories\n source_undefined = config['general']['sources'] is None\n if source_undefined:\n return\n sources_are_dirs = all(osp.isdir(source) for source in config['general']['sources'])\n if not sources_are_dirs:\n return\n\n # move new files into destination directory\n dest_files = os.listdir(directory)\n with concurrent.futures.ThreadPoolExecutor(4) as exec:\n for source_dir in config['general']['sources']:\n logger.info(\"Querying new files from {}\".format(source_dir))\n source_files = os.listdir(source_dir)\n time.sleep(0.5)\n if config['general']['rolling-window-days'] > 0:\n window_cutoff = datetime.datetime.timestamp(datetime.datetime.now() - datetime.timedelta(days=config['general']['rolling-window-days']))\n source_files = [f for f in source_files if osp.getmtime(osp.join(source_dir, f)) > window_cutoff]\n for file in source_files:\n exec.submit(move, file, source_dir, directory)","title":""},{"docid":"197d2ca14c339db2efd242892850f953","score":"0.53807014","text":"def copy_over(temp_dir, paths, artefact=\"model\", dir=\"models\"):\n\n for path in paths:\n print(\"Importing %s %s\" % (artefact, str(Path(path).resolve().relative_to(Path(temp_dir).resolve()))))\n dest_path = Path(dir, Path(path).name)\n if dest_path.exists():\n print(\"Skipping `%s` as it already exists\" % str(dest_path))\n else:\n shutil.copytree(path, str(dest_path))","title":""},{"docid":"090bc18a430f0cf4d076f6288befabde","score":"0.53780043","text":"def copy_folder(self, src, dst):\n\n src_list, dst_list = [], []\n for folder, _, files in os.walk(src):\n rfolder = folder.replace(src, dst)\n command = \"mkdir -p {0}\".format(rfolder)\n self.ssh.exec_command(command)\n src_list.extend([os.path.join(folder, f) for f in files if not f.endswith(\".pyc\")])\n dst_list.extend([os.path.join(rfolder, f) for f in files if not f.endswith(\".pyc\")])\n\n self.ssh.put_file(src_list, dst_list)","title":""},{"docid":"45a12b318adc73b9d513335ae5987dea","score":"0.53752345","text":"def move_clean(images, destination):\n\tfor image in images:\n\t\tshutil.move(image, destination)","title":""},{"docid":"4c88a28034af4d2c2c4cb3db93e89e0a","score":"0.5372583","text":"def rename_directories(self):\r\n\r\n folder_classes = os.listdir(PATH_TO_DATA)\r\n\r\n # Append [class, image] of dataset as entry of new array\r\n for directory in folder_classes:\r\n new_directory = directory[:2]\r\n new_directory = new_directory.strip(\"_\")\r\n new_directory = int(new_directory) - 1\r\n new_directory = str(new_directory) + \"_\" + directory[3:]\r\n os.rename(os.path.join(PATH_TO_DATA, directory), os.path.join(PATH_TO_DATA, new_directory))","title":""},{"docid":"13ff59f56d42bc8e0aa08f79e1ae7490","score":"0.53669804","text":"def remove_duplicates(directory_path):\n print(f'Indexing images...')\n images = glob.glob(os.path.join(directory_path, '*.jpg'))\n set_img = set()\n for image in images:\n img = tuple(average_image(cv.imread(image)))\n if img not in set_img:\n set_img.add(img)\n else:\n os.remove(image)\n print(f'Removing {image} ...')","title":""},{"docid":"a1587752159c3e1440ae80f86c6a44df","score":"0.53663343","text":"def upsert_parent_dirs(filepath):\n comps = [c for c in filepath.split('/') if c]\n comps.pop()\n\n if filepath.startswith('/'):\n path = '/'\n else:\n path = ''\n\n for dir in comps:\n path += (dir + '/')\n\n if not os.path.exists(path):\n try:\n os.mkdir(path)\n except:\n pass","title":""},{"docid":"f9a592076341ed2ea6a1f930b2dbe58d","score":"0.5356832","text":"def move_output_files():\n # Create output folder if it doesn't exist\n if not os.path.exists(OUTPUT_FILES_FOLDER):\n os.makedirs(OUTPUT_FILES_FOLDER)\n\n # Move the map files to the Output folder\n os.rename(os.path.join(PROGRAM_PATH, \"map_WHITE.png\"), os.path.join(OUTPUT_FILES_FOLDER, \"map_WHITE.png\"))\n os.rename(os.path.join(PROGRAM_PATH, \"map_BROWN.png\"), os.path.join(OUTPUT_FILES_FOLDER, \"map_BROWN.png\"))\n os.rename(os.path.join(PROGRAM_PATH, \"map_RED.png\"), os.path.join(OUTPUT_FILES_FOLDER, \"map_RED.png\"))\n os.rename(os.path.join(PROGRAM_PATH, \"map_SCORE-O.png\"), os.path.join(OUTPUT_FILES_FOLDER, \"map_SCORE-O.png\"))","title":""},{"docid":"4840d38a6c95750734d2063d6fe86043","score":"0.5349183","text":"def move_files(scan_dir, filepaths):\n for f in filepaths:\n _, ext = os.path.splitext(f)\n name = os.path.basename(f)\n new_path = os.path.join(scan_dir, ext[1:], name)\n os.rename(f, new_path)","title":""},{"docid":"4f53f6129526bd6d97c1856bc6d2bda5","score":"0.53444535","text":"def __move_up(self):\n for filename in self._selected:\n index = self._files.index(filename)\n files = OrderedSet()\n if index == 0:\n return\n\n if index > 1:\n files = self._files[:index - 1]\n files = list(files) + [self._files[index], self._files[index - 1]] + self._files[index + 1:]\n\n self._files = files\n self.__update_tree()","title":""},{"docid":"cbb582de6847d80d3215489706a6a0ba","score":"0.53393376","text":"def create_output_directories(self):\r\n check_or_create_dir(self.dst_path)\r\n\r\n if self.medias:\r\n check_or_create_dir(join(self.dst_path, self.settings[\"GALLERY_THUMB_DIR\"]))\r\n\r\n if self.medias and self.settings[\"KEEP_ORIG\"]:\r\n self.orig_path = join(self.dst_path, self.settings[\"orig_dir\"])\r\n check_or_create_dir(self.orig_path)","title":""},{"docid":"d19c19bd38282419100b378a25f51e13","score":"0.5339199","text":"def resave_all_archives(self,new_dirpath):\n\t\tif self.zippath!=None:\n\t\t\told_dirpath,f=os.path.split(self.zippath)\n\t\telse : old_dirpath=None\n\t\tassert os.path.abspath(new_dirpath)!=os.path.abspath(old_dirpath)\n\t\tfor arch_name in self.list_archives:\n\t\t\tif not os.path.exists(os.path.join(new_dirpath,TMP_FILE_MARK+arch_name)):\n\t\t\t\tos.mkdir(os.path.join(new_dirpath,TMP_FILE_MARK+arch_name))\n\t\t\t\n\t\t\tfor file in os.listdir(os.path.join(old_dirpath,TMP_FILE_MARK+arch_name)):\n\t\t\t\tfilepath=os.path.join(old_dirpath,TMP_FILE_MARK+arch_name,file)\n\t\t\t\tshutil.copy(filepath,os.path.join(new_dirpath,TMP_FILE_MARK+arch_name))","title":""},{"docid":"09737768c435f6d46e6e85affe62bba4","score":"0.5338812","text":"def shuffle_folders(folder):\n for s in os.listdir(folder):\n if os.path.isfile(folder + '/' + s):\n shuffle_file(folder, s)\n print 'finish shuffling folder: %s' % s","title":""},{"docid":"dfc00f904f6ca172850ab2304c7826e7","score":"0.53345555","text":"def recorrer(carpeta):\r\n\tlista = os.listdir(carpeta)\r\n\t#print (lista)\r\n\tfor elemento in lista:\r\n\t\tnuevo = os.path.join(carpeta,os.path.basename(elemento))\r\n\t\tif os.path.isdir(nuevo):\r\n\t\t\trecorrer(nuevo)\t\t\t\r\n\t\telse:\r\n\t\t\tdestino = os.path.join(os.getcwd(),pathDestino)\r\n\t\t\tprint(\"hashing \", nuevo)\r\n\t\t\tappendhash(genhash(nuevo).upper(), nuevo, filehash)","title":""},{"docid":"89395491c8df71babf3a9bea570db48e","score":"0.5331348","text":"def move_malicious(abs_dirname, ratio):\n train_malicious = 'train/malicious/'\n shutil.rmtree(train_malicious)\n os.makedirs(train_malicious)\n val_malicious = 'validation/malicious/'\n shutil.rmtree(val_malicious)\n os.makedirs(val_malicious)\n files = [os.path.join(abs_dirname, f) for f in os.listdir(abs_dirname)]\n for f in files[0:ratio]:\n # create new subdir if necessary\n #subdir_name = os.path.join(abs_dirname, '{0:03d}'.format(i / N + 1))\n #os.mkdir('train/' + file_type)\n #curr_subdir = subdir_name\n\n # move file to current dir\n f_base = os.path.basename(f)\n\n copyfile(f, train_malicious + f_base)\n\n for f in files[ratio:]:\n # create new subdir if necessary\n #subdir_name = os.path.join(abs_dirname, '{0:03d}'.format(i / N + 1))\n #os.mkdir('validation/' + file_type )\n f_base = os.path.basename(f)\n\n copyfile(f, val_malicious + f_base)\n #copyfile(f, 'validation/malicious/' + f_base)","title":""},{"docid":"7c4fd08a297e91d04592b7414fe28635","score":"0.53109795","text":"def dir_structure(image_dir):\n image_dir_files = os.listdir(image_dir)\n fake_dir = '/input'\n assert len(image_dir_files) != 0, f\"Image directory {image_dir_files} is empty.\"\n if os.path.exists(image_dir+fake_dir):\n if os.path.isdir(image_dir+fake_dir):\n print('Image directory structure test: OK.')\n else:\n print(\"Rearranging image dir structure, this might take a while...\")\n os.mkdir(image_dir+fake_dir)\n for i in image_dir_files:\n src = os.path.join(image_dir, i)\n dst = os.path.join(image_dir+fake_dir, i)\n move(src, dst)\n print(\"Done. Directory structure is now OK.\")","title":""},{"docid":"5aa8f40c3fd01c5825432c96448de9cb","score":"0.53109616","text":"def copying_files(self):\n to_copy_folders = os.listdir(os.getcwd() + '/to_copy/')\n\n for folder in to_copy_folders:\n\n to_copy_files = os.listdir(os.getcwd() + '/to_copy/' + folder)\n for files in to_copy_files:\n if not files.startswith('.'):\n if not os.path.exists(self.app_location + '/' + folder + '/' + files):\n if not folder.startswith('.'):\n shutil.copyfile(os.getcwd() + '/to_copy/' + folder + '/' + files, self.app_location + '/' + folder + '/' + files)","title":""},{"docid":"663274598bed082be9e720a68a287238","score":"0.5310453","text":"def move(src, dst, ignore=None, logger=None):\n\n src = Path(src)\n dst = Path(dst)\n print('src,dst',src.absolute(),dst.absolute())\n if not (src.is_dir() and dst.is_dir()):\n raise NotADirectoryError(f\"src: {src} and dst: {dst} must be directories\")\n\n # check if src and dst exist\n # read errors are already skipped by os.walk\n # and are directories\n # add logger to handle_exception\n for rpath, dirs, files in os.walk(src, topdown=False, onerror=handle_exception):\n for fi in files:\n # path to src \n fi_src = Path(rpath).absolute() / fi\n #if not fi_src.exists():\n # raise Exception(\"HELLOOO\", src, rpath, fi, fi_src)\n dir_dst = (dst / Path(rpath).relative_to(src)).absolute()\n fi_dst = dir_dst / fi\n print(\"dir dst\", dir_dst, dir_dst.exists())\n print(\"fi_src, fi dst\", fi_src, fi_dst)\n \n try:\n dir_dst.mkdir(exist_ok=True)\n os.rename(fi_src, fi_dst)\n # if dst is different device then copy and remove\n # TODO: use error code instead of broad OSError\n # log this activity\n except OSError:\n try:\n scopy(fi_src, fi_dst, follow_symlinks=False)\n set_owner_mode_xattr(fi_src, fi_dst)\n os.unlink(fi_src)\n # catch all exceptions\n except Exception as ex:\n handle_exception(ex, fi_src, fi_dst, logger=logger)\n except Exception as ex:\n handle_exception(ex, fi_src, fi_dst, logger)\n # set dst dir properties and remove src\n for di in dirs:\n # source dir path\n dir_src = Path(rpath).absolute() / di\n # check if dir exists? it must when os.walk if not top down\n dir_dst = dst / Path(rpath).relative_to(src) / di\n # to ensure not deleteting directories when not ignored\n if ignore and ignore(dir_dst):\n continue\n try:\n # handle if dst exists?\n set_owner_mode_xattr(dir_src, dir_dst)\n os.rmdir(dir_src)\n # if dir is not empty and other exceptions\n except Exception as ex:\n handle_exception(ex)\n # set permissions and delete","title":""},{"docid":"bc3a97f9025bdb7ccf7941c7f00c3ea8","score":"0.53066707","text":"def move(old, new):\n if os.path.exists(new):\n count = 1\n root, ext = os.path.splitext(new)\n while True:\n new = \"%s_%i%s\" % (root, count, ext)\n if os.path.exists(new):\n count += 1\n else:\n break\n os.rename(old, new)\n else:\n os.rename(old, new)","title":""},{"docid":"675bee6d62cdeeb13bb7009764e9bd4a","score":"0.53064406","text":"def copytree_replace_existing(os_service, src, dst):\n oss = os_service\n helper = Helpers()\n helper.copy_directory_tree(oss, src, dst)\n\n file_it = FileIterator(oss, src)\n for file_sub_path in file_it:\n source_file_path = os.path.join(src, file_sub_path)\n dest_file_path = os.path.join(dst, file_sub_path)\n oss.copyfile(source_file_path, dest_file_path)","title":""},{"docid":"89497c3cb44718886a567f7b0d0a7bba","score":"0.53050506","text":"def scan_directory(dir_):\n for root, dirs, fname in os.walk(dir_):\n for f in fname:\n try:\n path_ = os.path.join(dir_, f)\n date_ = get_date_pfx(f)\n if valid_format(date_):\n new = get_datedir(date_, dir_)\n if not os.path.exists(new):\n os.makedirs(new)\n if os.path.exists(path_):\n os.rename(path_, name_new_file(new, f))\n except Exception as ex:\n print str(ex)\n continue","title":""},{"docid":"96b6b58eeb478bf211cb42b22f56bbb2","score":"0.5304466","text":"def copyDirectoryContents(srcDir: RelFilePathT, destDir: RelFilePathT) -> None:\n filePathList = os.listdir(srcDir)\n for filePath in filePathList:\n absFilePath = osp.join(srcDir, filePath)\n subp.run(f\"cp -r {absFilePath} {destDir}\", shell=True)","title":""},{"docid":"b462e79ceca0a566939f2d11feff446f","score":"0.5302334","text":"def main():\n print(\"Starting directory is: {}\".format(os.getcwd()))\n\n # Change to desired directory\n os.chdir('FilesToSort')\n\n # Print a list of all files in current directory\n print(\"Files in {}:\\n{}\\n\".format(os.getcwd(), os.listdir('.')))\n\n #breakpoint()\n\n # Make a new directory\n # The next time you run this, it will crash if the directory exists\n\n # Getting the extensions available:\n extension_list = []\n extension_exist = True\n\n for filename in os.listdir('.'):\n\n if filename == \".DS_Store\":\n continue\n\n extension = os.path.splitext(filename)[1]\n\n if len(extension_list) == 0:\n extension_list.append(extension)\n\n for each in extension_list:\n\n if extension == each:\n extension_exist = True\n break\n else:\n extension_exist = False\n\n if extension_exist == False:\n extension_list.append(extension)\n\n # Create extension dirs:\n extension_dict = dict()\n\n for each in extension_list:\n\n if each == \"\":\n continue\n\n key_to_add = input(\"Add {}\".format(each))\n\n if not (key_to_add in extension_dict):\n extension_dict[key_to_add] = []\n extension_dict[key_to_add].append(each)\n else:\n extension_dict[key_to_add].append(each)\n\n print(extension_dict)\n #breakpoint()\n\n for key in extension_dict.keys():\n if key != \"\":\n try:\n os.mkdir(key)\n except:\n os.rmdir(key)\n os.mkdir(key)\n\n # Loop through each file in the (current) directory\n for filename in os.listdir('.'):\n\n if filename == \".DS_Store\":\n continue\n\n extension = os.path.splitext(filename)[1]\n dir_to_move = \"\"\n # Ignore directories, just process files\n if os.path.isdir(filename):\n continue\n\n #find the dir to move:\n for key, value in extension_dict.items():\n if extension in extension_dict[key]:\n dir_to_move = key\n\n shutil.copy(filename, dir_to_move + '/' + filename)","title":""},{"docid":"573a721b444eb8267b2484828b521252","score":"0.52874583","text":"def test_move_files(self):\n # Initialize key variables\n source_filenames = {}\n target_filenames = {}\n\n #################################################\n # Test with invalid source directory\n #################################################\n\n invalid_path = '/tmp/{}.{}'.format(\n self.random_string,\n self.random_string)\n\n with self.assertRaises(SystemExit):\n general.move_files(invalid_path, '/tmp')\n\n #################################################\n # Test with invalid destination directory\n #################################################\n\n invalid_path = '/tmp/{}.{}'.format(\n self.random_string,\n self.random_string)\n\n with self.assertRaises(SystemExit):\n general.move_files('/tmp', invalid_path)\n\n #################################################\n # Test with valid directory\n #################################################\n\n # Create a source directory\n source_dir = '/tmp/{}.1'.format(self.random_string)\n if os.path.exists(source_dir) is False:\n os.makedirs(source_dir)\n\n # Create a target directory\n target_dir = '/tmp/{}.2'.format(self.random_string)\n if os.path.exists(target_dir) is False:\n os.makedirs(target_dir)\n\n # Place files in the directory\n for count in range(0, 4):\n filename = ''.join([random.choice(\n string.ascii_letters + string.digits) for n in range(15)])\n source_filenames[count] = '{}/{}'.format(source_dir, filename)\n target_filenames[count] = '{}/{}'.format(target_dir, filename)\n open(source_filenames[count], 'a').close()\n\n # Check files in directory\n self.assertEqual(os.path.isfile(source_filenames[count]), True)\n\n # Delete files in directory\n general.move_files(source_dir, target_dir)\n\n # Check that files are not in source_dir\n for filename in source_filenames.values():\n self.assertEqual(os.path.isfile(filename), False)\n\n # Check that files are in in target_dir\n for filename in target_filenames.values():\n self.assertEqual(os.path.isfile(filename), True)\n\n # Delete directory\n shutil.rmtree(source_dir)\n\n # Delete directory\n shutil.rmtree(target_dir)","title":""},{"docid":"7f3b270e73e784c64bdd46fb0d8466b1","score":"0.5283628","text":"def reorg_files(raw_dir):\n os.chdir(raw_dir)\n\n if not Path('People.csv').is_file():\n unzip_dir = raw_dir / 'baseballdatabank-master' / 'core'\n\n # move the unzipped csv files to the current working directory\n for root, dirs, files in os.walk(unzip_dir):\n for file in files:\n shutil.move(root + '/' + file, '.')\n\n # rm the extract directory\n shutil.rmtree('baseballdatabank-master')\n\n msg = '\\n'.join(os.listdir('.'))\n logger.info(f'{raw_dir} contents:\\n {msg}')","title":""},{"docid":"8b1abbb6e682699d0ec361b1555bf918","score":"0.52821255","text":"def mv2scratch( filename, dirpath ):\n scpath = dirpath.replace('/scratch/_gc/','/scratch/',1)\n oldpath = os.path.join(dirpath,filename)\n #newpath = os.path.join(scpath,filename)\n print \"moving from\",oldpath,\"\\nto\",scpath\n if not os.path.isdir(scpath):\n os.makedirs(scpath)\n shutil.move( oldpath, scpath )\n listgood( filename )","title":""},{"docid":"6635cfecfb52ed40319d0404c2099d8e","score":"0.5281678","text":"def convert_and_move(filename, date, atmos_cor):\n # TODO: Something is wrong with this function, getting false positives\n # for files already existing, so the conversion process is aborted\n # when it shouldn't be\n\n logger.debug('Starting conversion process for {}'.format(filename))\n tqdm.tqdm.write('Starting conversion process for {}'.format(filename))\n\n # Get tile id using re, break it out into zone num, tile ids, MGRS format\n tile = get_utm_tile(filename)\n\n if not tile:\n tile_zone = 'none'\n tile_band = 'none'\n tile_squareID = 'none'\n else:\n tile_zone = tile['zone']\n tile_band = tile['band']\n tile_squareID = tile['square']\n # check if converted directory already exists\n destination_dir = \"S2_{}_{}_{}_{}\".format(tile_zone,\n tile_band,\n tile_squareID,\n date.strftime('%Y%m%d_%H%M'))\n\n # Check if the simplified directory already exists\n if Path.exists(Path(FINAL_DIR, destination_dir)):\n logger.debug('Final result directory already exists, checking file'\n 'extension of first file to see if it matches the current'\n 'atmospheric correction value')\n\n file_list_iter = os.scandir(Path(FINAL_DIR, destination_dir))\n\n\n for file_name in file_list_iter:\n if file_name.name[-7:-5] == str(atmos_cor):\n logger.debug('That resolution of atmos correction already'\n 'exists..., aborting the conversion...')\n logger.debug('Checking if the .zip still exists...')\n\n if not (Path(FINAL_DIR, destination_dir + '.zip').exists()):\n zip_directory(destination_dir)\n\n return \"already exists\"\n\n if not file_name.name[-7:-5] in ['10','20','60'] and atmos_cor == 0:\n logger.debug('Uncorrected files already exist, aborting...')\n\n logger.debug('Checking if the .zip still exists...')\n\n if not (Path(FINAL_DIR, destination_dir + '.zip').exists()):\n zip_directory(destination_dir)\n\n return \"already exists\"\n\n logger.info('Final result directory does not exist. Beginning conversion')\n logger.debug('atmos_cor: {}'.format(atmos_cor))\n\n if atmos_cor in [10, 20, 60]:\n # Convert filename to L2A with .SAFE suffix\n file_string = filename.replace('L1C', 'L2A')\n else:\n file_string = filename\n\n file_string += '.SAFE'\n\n # Make sure the temporary data folder we are converting exists\n if not os.path.isdir(os.path.join(BUNDLE_DIR, TEMP_DIR, file_string)):\n logger.warning('Expected data directory to be converted does not exist'\n ' exiting...')\n tqdm.tqdm.write('Expected data directory to convert does not exist, stopping...')\n\n return 'failed'\n\n granule_dir = Path('temp', file_string, 'GRANULE')\n\n # Iterate over each granule, if there are multiple\n for dir in granule_dir.iterdir():\n\n logger.debug(str(dir))\n if atmos_cor in [10, 20, 60]:\n convert_dir = Path(dir, 'IMG_DATA', \"R{}m\".format(atmos_cor))\n else:\n convert_dir = Path(dir, 'IMG_DATA',)\n\n logger.debug(convert_dir)\n file_list = convert_dir.glob('*.jp2')\n\n # For the actual img data, use the convert_jp2 function\n for file in file_list:\n logger.debug(file)\n dest_path = Path(FINAL_DIR, destination_dir)\n try:\n conv.convert_jp2_to_tif(file,\n destination_dir,\n atmos_cor,\n dest_path)\n except Exception as e:\n logger.error('Something went wrong with the conversion')\n logger.error(str(e))\n return 'failure'\n\n # Copy and rename the metadata file in the GRANULE\n shutil.copy2(str(Path(dir, 'MTD_TL.xml')),\n str(Path(FINAL_DIR,\n destination_dir,\n destination_dir + \"_metadata.xml\")))\n\n logger.info('converted jp2 to tif successfully, zipping result')\n zip_directory(destination_dir)\n\n return 'success'","title":""},{"docid":"e985ef77f29f78ddf4b26a0879bc8e75","score":"0.5277273","text":"def setup_destination_directory(self):\n\n new_subpath = self.dir_to_import[len(self.top_of_repo):]\n\n destination_directory = os.path.join(self.destination_directory, new_subpath)\n\n if not os.path.exists(destination_directory):\n os.makedirs(destination_directory)\n\n _log.info('Tests will be imported into: %s', destination_directory)","title":""},{"docid":"946be042ff7540732aababf327aa6f5f","score":"0.527297","text":"def copy_files(src_folder: str, dest_folder: str) -> None:\n shutil.copytree(src_folder, dest_folder, dirs_exist_ok=True)","title":""},{"docid":"48f71ed72d55817281cee9ad5801f8da","score":"0.526326","text":"def fix_directories(input_dir_path, output_dir_path):\n if not input_dir_path:\n input_dir_path = output_dir_path\n input_dir_path = os.path.realpath(input_dir_path)\n output_dir_path = os.path.realpath(output_dir_path)\n return input_dir_path, output_dir_path","title":""},{"docid":"a6c922808be1ebe1c983b95050534aa5","score":"0.5263","text":"def clean_up_files(output_dir):\n\t\n\tpass\n\treturn","title":""}],"string":"[\n {\n \"docid\": \"b996a5c6073bbe3944cfbade4eb38d7a\",\n \"score\": \"0.65459585\",\n \"text\": \"def scan_and_remove_duplicate(from_dir, debug=False):\\n all_files = defaultdict(list)\\n for root, dirs, files in os.walk(from_dir):\\n for file in files:\\n full_path = os.path.join(root, file)\\n md5 = hashlib.md5(file_as_bytes(open(full_path, 'rb'))).hexdigest()\\n all_files[md5].append(full_path)\\n for v in all_files.values():\\n duplicated_files = v[1:]\\n if duplicated_files:\\n for f in duplicated_files:\\n print('Remove diplicated file: {} (original: {})'.format(f, v[0]))\\n if not debug:\\n os.remove(f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ac9be7e6ee8bd07dc43eb76e37893c9\",\n \"score\": \"0.6354505\",\n \"text\": \"def restore_dublicates(path: str):\\n old_path = os.path.join(path, 'dublicates')\\n files = os.listdir(old_path)\\n for p in files:\\n if p.endswith('jpg'):\\n try:\\n shutil.move(os.path.join(old_path, p), os.path.join(path, p.split('jpg_')[1]))\\n except:\\n pass\\n if len(os.listdir(old_path)) == 0:\\n shutil.rmtree(old_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aea4690d0b925107ee06c5696fa0caee\",\n \"score\": \"0.6176697\",\n \"text\": \"def find_duplicates(path):\\n # find groups of files consisting of more than 1 item (suspected duplicates)\\n bgroups = []\\n dir_contents = {}\\n for dirname, dirnames, filenames in os.walk(path):\\n groups = group_files(dirname, filenames)\\n for group in groups.items():\\n if len(group[1]) > 1:\\n bgroups.append((dirname, group))\\n dir_contents[dirname] = (dirnames, sorted(groups.keys()))\\n\\n # check suspected duplicate files\\n duplicates = []\\n for dirname, group in bgroups:\\n files = sorted(group[1], key=lambda p: p[1], reverse=True)\\n while len(files) > 1:\\n champ = files[0]\\n suspects = files[1:]\\n f1 = os.path.join(dirname, champ[0])\\n unconfirmed = []\\n for suspect in suspects:\\n f2 = os.path.join(dirname, suspect[0])\\n if filecmp.cmp(f1, f2, shallow=False):\\n duplicates.append(f2)\\n print '\\\"{}\\\" is a duplicate of \\\"{}\\\" in \\\"{}\\\"'.format(suspect[0], champ[0], dirname)\\n else:\\n unconfirmed.append(suspect)\\n\\n files = unconfirmed\\n\\n for item in dir_contents.items():\\n parent = item[0]\\n dirs = item[1][0]\\n files = item[1][1]\\n for d1 in range(len(dirs)-1):\\n for d2 in range(d1+1, len(dirs)):\\n dir1 = os.path.join(parent, dirs[d1])\\n dir2 = os.path.join(parent, dirs[d2])\\n if compare_dirs(dir_contents, dir1, dir2):\\n if os.stat(dir1).st_mtime > os.stat(dir2).st_mtime:\\n master = dirs[d1]\\n dup = dirs[d2]\\n dup_fullpath = dir2\\n else:\\n master = dirs[d2]\\n dup = dirs[d1]\\n dup_fullpath = dir1\\n\\n if dup_fullpath not in duplicates:\\n duplicates.append(dup_fullpath)\\n print 'Directory \\\"{}\\\" is a duplicate of \\\"{}\\\" in \\\"{}\\\"'.format(dup, master, parent)\\n\\n return duplicates\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91d411182d5fda79c8085e5692040b8b\",\n \"score\": \"0.6167216\",\n \"text\": \"def extract_from_directories():\\n directories = glob.glob(\\\"*/\\\")\\n for directory in directories:\\n files_to_extract = get_music_in_directory(directory)\\n for file in files_to_extract:\\n shutil.move(file, os.getcwd())\\n os.rmdir(directory)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a7b23b33982645b72acd806c2e67ef2\",\n \"score\": \"0.59987\",\n \"text\": \"def move_tree(self, src: pm.path, dst: pm.path):\\n self._log_command(f\\\"\\\"\\\"Recursively move files from \\\\\\\"{src}\\\\\\\" to \\\\\\\"{dst}\\\\\\\"\\\"\\\"\\\")\\n self.copy_tree(src, dst)\\n self.remove_tree(src)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15c68ef7178a48b5350e02f69a5a91ab\",\n \"score\": \"0.59847116\",\n \"text\": \"def move_files_to_subdirectory(file_list, working_directory, subdirectory):\\n # create subdirectory, if it does not exist\\n if not os.path.exists(working_directory + os.sep + subdirectory):\\n os.makedirs(working_directory + os.sep + subdirectory)\\n\\n # move files\\n for file in file_list:\\n os.rename(working_directory + os.sep + file, working_directory + os.sep + subdirectory + os.sep + file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"021533fb2f7bbb6c9384b53e9771437b\",\n \"score\": \"0.5971916\",\n \"text\": \"def move_files(abs_dirname):\\n\\n files = [os.path.join(abs_dirname, f) for f in os.listdir(abs_dirname)]\\n\\n i = 0\\n curr_subdir = None\\n\\n for f in files:\\n # create new subdir if necessary\\n if i % N == 0:\\n subdir_name = os.path.join(abs_dirname, '{0:03d}'.format(i / N + 1))\\n os.mkdir(subdir_name)\\n curr_subdir = subdir_name\\n\\n # move file to current dir\\n f_base = os.path.basename(f)\\n shutil.move(f, os.path.join(subdir_name, f_base))\\n i += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f7ee5c037340cf43113a88964e5db64\",\n \"score\": \"0.59539324\",\n \"text\": \"def replicate_all_files(src_dir, copy_file, use_gitignore=True, debugging=False):\\n spec = get_pathspec(src_dir, use_gitignore)\\n for filename in pathspec.util.iter_tree(src_dir):\\n if not spec.match_file(filename):\\n copy_file(os.path.join(src_dir, filename))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95dae4519c2739bc862bbfb601d30fcb\",\n \"score\": \"0.5923724\",\n \"text\": \"def moveResultsTo(self, dir, trimCommonPaths = False):\\n allChanges = set()\\n self._scanChanges(self._dir, allChanges)\\n\\n prefix = self._dir\\n if trimCommonPaths:\\n prefix = None\\n for c in allChanges:\\n if prefix is None:\\n prefix = c\\n else:\\n while not c.startswith(prefix):\\n prefix = os.path.dirname(prefix)\\n\\n if prefix is None:\\n prefix = self._dir\\n elif not os.path.isdir(prefix):\\n # Only one file remains, the common path is its folder\\n prefix = os.path.dirname(prefix)\\n\\n allErrors = []\\n for c in allChanges:\\n # Add 1 to len(prefix) so that we don't include the preceding slash,\\n # which would make os.path.join treat it as an absolute\\n target = os.path.join(dir, os.path.relpath(c, prefix))\\n safeMake(os.path.dirname(target))\\n nRetries = 4\\n for _retry in range(nRetries):\\n try:\\n os.rename(c, target)\\n except OSError as e:\\n if e.errno == 16 and _retry != nRetries - 1:\\n # Device busy, will retry\\n pass\\n elif e.errno == 2:\\n # 2 - No such file; was temporary\\n break\\n else:\\n allErrors.append(\\\"{0}: {1}\\\".format(c, ''.join(\\n traceback.format_exception_only(\\n *sys.exc_info()[:2]))))\\n break\\n else:\\n break\\n\\n # Will retry\\n time.sleep(1.)\\n\\n if allErrors:\\n raise Exception(\\\"Errors during file move:\\\\n\\\\n{0}\\\".format(\\n '\\\\n\\\\n'.join(allErrors)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65f87e6299d2fc20993347197d05c0ac\",\n \"score\": \"0.58844805\",\n \"text\": \"def move(src_path, dst_path, src_root=None):\\n root_ext = os.path.splitext(dst_path)\\n i = 0\\n while os.path.isfile(dst_path):\\n # Recursively avoid the collision\\n i += 1\\n dst_path = root_ext[0] + \\\" ({})\\\".format(i) + root_ext[1]\\n # Move file, make directories if needed\\n os.makedirs(os.path.dirname(dst_path), exist_ok=True)\\n shutil.move(src_path, dst_path)\\n # Delete directory if necessary (recursively)\\n directory = os.path.dirname(src_path)\\n while (src_root is not None and src_path.startswith(src_root) and directory != src_root):\\n delete_directory_if_empty_or_hidden(directory)\\n directory = os.path.dirname(directory)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52771a455b04ecfb99cfedd413a7f5af\",\n \"score\": \"0.5881753\",\n \"text\": \"def rsync_replacement(src: str, dst: str) -> None:\\n if not os.path.exists(dst):\\n os.makedirs(dst)\\n for item in os.listdir(src):\\n s = os.path.join(src, item)\\n d = os.path.join(dst, item)\\n if os.path.isdir(s):\\n rsync_replacement(s, d)\\n else:\\n shutil.copy2(s, d)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e85f61418c77edaddb953ae5c446fa83\",\n \"score\": \"0.5869827\",\n \"text\": \"def put_originals_in_subdirectory(file_paths):\\n directory, file_name, extension = split_file_path(file_paths[0])\\n\\n destination_directory = os.path.join(directory, 'originals')\\n if not os.path.exists(destination_directory):\\n os.mkdir(destination_directory)\\n\\n for file_path in file_paths:\\n os.rename(\\n src=file_path,\\n dst=os.path.join(destination_directory, os.path.basename(file_path))\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be6ad133f0e79fff064882ad7af311f8\",\n \"score\": \"0.58319145\",\n \"text\": \"def move_files(dir_name):\\n file_ls = glob.glob('*dat') \\n plot_ls = glob.glob('*png')\\n file_ls += plot_ls\\n os.makedirs(dir_name)\\n for file in file_ls:\\n shutil.move(str(file), dir_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a8678a748f1e58b1d315cddd85ca690\",\n \"score\": \"0.5827099\",\n \"text\": \"def _directory_processor(srcdir, new_suffix=\\\"part_\\\"):\\n weeks = []\\n srcdir = srcdir.rstrip(\\\"/\\\")\\n for f in os.listdir(srcdir):\\n # for example, stub_00_01.md\\n w = f.split(\\\"_\\\")[1]\\n # gives w as 00\\n if w not in weeks:\\n weeks.append(w)\\n for w in weeks:\\n newdir = f\\\"{srcdir}/{new_suffix}{w}\\\"\\n # for example, testdir/week_00\\n if os.path.isdir(newdir):\\n print(f\\\"{newdir} already exists...\\\")\\n else:\\n os.makedirs(newdir)\\n for f in os.listdir(srcdir):\\n if f.endswith(\\\".md\\\"):\\n # For example stub_00_01.md\\n w = f.split(\\\"_\\\")[1]\\n # for example w as 00\\n os.rename(f\\\"{srcdir}/{f}\\\", f\\\"{srcdir}/{new_suffix}{w}/{f}\\\")\\n # so testdir/stub_00_01.md becomes testdir/week_00/stub_00_01.md\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ee90884dc4e19739f4f328838543136\",\n \"score\": \"0.57634467\",\n \"text\": \"def test_dir_to_child(self):\\n root = tempfile.mkdtemp(dir=tmpdir)\\n src = os.path.join(root, 'subdir', 'folder')\\n src2 = os.path.join(root, 'subdir', 'folder', 'file.txt')\\n dst = os.path.join(root, 'subdir', 'folder', 'subfolder')\\n util.fs.mkdir(src)\\n util.fs.save(src2, DUMMY_BYTES)\\n with self.assertRaises(util.fs.FSMoveInsideError):\\n util.fs.move(src, dst)\\n\\n # verify no unexpected content change\\n self.assertEqual(\\n glob_files(src),\\n {os.path.join(src, ''), src2},\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"220eeecdd4fb57a2455d1c2be0aa7962\",\n \"score\": \"0.57633305\",\n \"text\": \"def _copy_into_tempdir(self, old):\\n extraction_tempdir = tempfile.mkdtemp()\\n old.extractall(extraction_tempdir)\\n old_filesdir = os.path.join(extraction_tempdir, self.basename)\\n for item in os.listdir(old_filesdir):\\n shutil.move(os.path.join(old_filesdir, item), self.tempdir)\\n shutil.rmtree(extraction_tempdir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f5a5ee3ebb174f04d4012f84daa6433\",\n \"score\": \"0.57532245\",\n \"text\": \"def handleFolder(foo, dirName, names):\\n for name in names:\\n path = os.path.join(dirName, name)\\n # move all files to target folder\\n if os.path.isfile(path):\\n shutil.move(path, outFolder)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16adf5eb74570eb7c08b5f0a731d0775\",\n \"score\": \"0.57263696\",\n \"text\": \"def _copy_template_dir(self):\\n for (dirpath, dirnames, filenames) in os.walk(self.template_dir):\\n for filename in filenames:\\n if filename in FILES_TO_IGNORE:\\n continue\\n path = os.path.join(dirpath, filename)\\n self.zip_file.write(path, os.path.relpath(path, self.template_dir))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"098a0433691ec1708e51503fba6114e7\",\n \"score\": \"0.5724657\",\n \"text\": \"def safe_move(file_path, out_dir):\\n name = os.path.basename(file_path)\\n if not os.path.exists(os.path.join(out_dir, name)):\\n shutil.move(file_path, os.path.join(out_dir, name))\\n else:\\n base, extension = os.path.splitext(name)\\n i = 1\\n while os.path.exists(os.path.join(out_dir, f'{base}_{i}{extension}')):\\n i += 1\\n shutil.move(file_path, os.path.join(out_dir, f'{base}_{i}{extension}'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87d27d2d4da39de449db1f72c2b16c4a\",\n \"score\": \"0.5722395\",\n \"text\": \"def squash_directory(directory_path):\\n file_list = []\\n for file in os.listdir(directory_path):\\n file_path = os.path.join(directory_path, file)\\n if file.startswith(\\\"squash\\\"):\\n logger.info(\\\"Directory (%s) already squashed.\\\", directory_path)\\n return\\n elif os.path.isfile(file_path):\\n file_list.append(file_path)\\n\\n with open(\\n os.path.join(directory_path, \\\"squash_\\\" + str(uuid.uuid1())) + \\\".csv\\\",\\n \\\"w\\\",\\n ) as new_file:\\n for file in file_list:\\n logger.info(\\\"getting content of: %s\\\", file)\\n with open(file, \\\"r\\\") as reader:\\n new_file.write(reader.read())\\n\\n for file in file_list:\\n logger.info(\\\"removing file: %s\\\", file)\\n os.remove(file)\\n\\n logger.info(\\\"successfully squashed: %s\\\", directory_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29480d16126ec1092de7aaf1c9b92f8b\",\n \"score\": \"0.5713722\",\n \"text\": \"def newFolderByExtension(folder_path):\\n new_folder_path = folder_path + \\\"_sorted\\\"\\n print(\\\"new_folder_path:\\\", new_folder_path)\\n\\n for root, dirs, files in os.walk(folder_path):\\n for file in files:\\n print(file)\\n print(root)\\n print(dirs)\\n file_ext = splitext(file)[1].strip(\\\".\\\")\\n if file_ext == \\\"\\\":\\n file_ext = \\\"NoExtension\\\"\\n sub_folder = join(new_folder_path,file_ext)\\n if not exists(sub_folder):\\n print(\\\"new sub fodler:\\\", sub_folder)\\n makedirs(sub_folder)\\n if not exists(sub_folder):\\n print(\\\"not created:\\\",sub_folder)\\n copyfile(file,join(sub_folder,file))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"917c26372b8c92f66f610cbfd796d788\",\n \"score\": \"0.5692911\",\n \"text\": \"def recursive_overwrite(src, dest, ignore=None):\\n if os.path.isdir(src):\\n if not os.path.isdir(dest):\\n os.makedirs(dest)\\n files = os.listdir(src)\\n if ignore is not None:\\n ignored = ignore(src, files)\\n else:\\n ignored = set()\\n for f in files:\\n if f not in ignored:\\n recursive_overwrite(os.path.join(src, f),\\n os.path.join(dest, f),\\n ignore)\\n else:\\n try:\\n shutil.copyfile(src, dest)\\n except Exception as e:\\n print(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f1946139ca7c06167d88cdf2f2eb6c6\",\n \"score\": \"0.56908065\",\n \"text\": \"def mergefolders(root_src_dir, root_dst_dir, replace=False):\\n for src_dir, dirs, files in os.walk(root_src_dir):\\n dst_dir = src_dir.replace(root_src_dir, root_dst_dir, 1)\\n if not os.path.exists(dst_dir):\\n os.makedirs(dst_dir)\\n for file_ in files:\\n src_file = os.path.join(src_dir, file_)\\n dst_file = os.path.join(dst_dir, file_)\\n if os.path.exists(dst_file) and replace:\\n os.remove(dst_file)\\n shutil.copy(src_file, dst_dir)\\n else:\\n if not os.path.exists(dst_file):\\n shutil.copy(src_file, dst_dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"829e156532efbe54409d891b416d23dd\",\n \"score\": \"0.5683604\",\n \"text\": \"def test_dir_to_child_self(self):\\n root = tempfile.mkdtemp(dir=tmpdir)\\n src = os.path.join(root, 'subdir', 'folder')\\n src2 = os.path.join(root, 'subdir', 'folder', 'file.txt')\\n dst = os.path.join(root, 'subdir', 'folder')\\n util.fs.mkdir(src)\\n util.fs.save(src2, DUMMY_BYTES)\\n with self.assertRaises(util.fs.FSMoveInsideError):\\n util.fs.move(src, dst)\\n\\n # verify no unexpected content change\\n self.assertEqual(\\n glob_files(src),\\n {os.path.join(src, ''), src2},\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ecefdbfbcfd057eecfc6a9454e58289\",\n \"score\": \"0.56791705\",\n \"text\": \"def _overwriteExistingFiles(self):\\n\\n\\t\\tshutil.rmtree(self.output_folder)\\n\\t\\tfiletools.checkDir(self.output_folder)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3842a01b9961fcd693b2d5d0c8af25cb\",\n \"score\": \"0.5654301\",\n \"text\": \"def clean(self):\\n\\n dest = self._make_extraction_dir()\\n p_join = os.path.join\\n for name in self.names:\\n try:\\n shutil.move(p_join(self.root, name), p_join(dest, name))\\n except IOError as e:\\n if self.partial:\\n pass\\n else:\\n raise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d87c946e5e8b1fabcdb3c3682bec33e9\",\n \"score\": \"0.56516594\",\n \"text\": \"def move_files(source_directory, destination_directory):\\n \\n files_in_source_dir = [f for f in listdir(source_directory) if isfile(join(source_directory, f))]\\n \\n for file in files_in_source_dir:\\n shutil.copy(join(source_directory, file), destination_directory)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4663709dcbfa036b5965b4d8e5e29623\",\n \"score\": \"0.5651226\",\n \"text\": \"def clean_up(name):\\n if os.path.isdir(name):\\n shutil.rmtree(name)\\n\\n make_destination_dir(name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"976e62572ab1520701b2d44a0ef4ba42\",\n \"score\": \"0.56466764\",\n \"text\": \"def shutilDirectory(inPath, outPath, connection_info):\\n files = os.listdir(inPath)\\n filePaths = [os.path.join(inPath, i) for i in files]\\n uniqueName = os.path.split(inPath)[1]\\n for i in filePaths:\\n tf = Insitu(i, connection_info)\\n tf.parse()\\n startDate = datetime.datetime.strftime(tf.keyDict[\\\"tstart\\\"], \\\"%Y%m%d\\\")\\n endDate = datetime.datetime.strftime(tf.keyDict[\\\"tend\\\"], \\\"%Y%m%d\\\")\\n tfNewName = uniqueName + \\\"_\\\" + startDate + \\\"_\\\" + endDate\\n tfNewPath = os.path.join(outPath,tfNewName)\\n shutil.copy(i, tfNewPath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"952e320d7a106b1ea931b4770710b809\",\n \"score\": \"0.56431067\",\n \"text\": \"def loop_through_copy_files_to_one_dir(looped_dir, target_dir, include_link=False):\\n if not os.path.isdir(looped_dir):\\n raise Exception(\\\"looped_dir: a directory.\\\")\\n if not os.path.isdir(target_dir):\\n raise Exception(\\\"target_dir: a directory.\\\")\\n for thing in os.listdir(looped_dir):\\n thing = os.path.join(looped_dir, thing)\\n if os.path.isdir(thing):\\n loop_through_copy_files_to_one_dir(thing, target_dir)\\n elif os.path.isfile(thing):\\n shutil.move(thing, os.path.join(target_dir, parent_dir_and_name(thing)[1]))\\n elif include_link:\\n shutil.move(thing, os.path.join(target_dir, parent_dir_and_name(thing)[1]))\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e520c8a6da76f6ab4e09f4a1d80da94\",\n \"score\": \"0.5641401\",\n \"text\": \"def findDup(parentFolder):\\n dups = {}\\n for root, subDirs, fileList in os.walk(parentFolder):\\n print(f'Scanning {root}...')\\n for filename in fileList:\\n # Get the path to the file\\n path = os.path.join(root, filename)\\n # Get the hash with 'hashfile' function\\n fileHash = hashfile(path)\\n if fileHash in dups:\\n dups[fileHash].append(path)\\n else:\\n dups[fileHash] = [path]\\n return dups\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62b3c1e68389a94cd9c555b78cb71f3c\",\n \"score\": \"0.5640435\",\n \"text\": \"def _create_destination_dir_and_move_image_files(self, destination):\\n nii_rel_path = self.assembly_nifti_rel_path if destination == 'assembly_bids' else self.trashbin_nifti_rel_path\\n json_rel_path = re.sub(r\\\"\\\\.nii(\\\\.gz)?$\\\", '.json', nii_rel_path) if self.json_path else None\\n bval_rel_path = re.sub(r\\\"\\\\.nii(\\\\.gz)?$\\\", '.bval', nii_rel_path) if self.bval_path else None\\n bvec_rel_path = re.sub(r\\\"\\\\.nii(\\\\.gz)?$\\\", '.bvec', nii_rel_path) if self.bvec_path else None\\n\\n absolute_dir_path = os.path.join(self.data_dir, os.path.dirname(nii_rel_path))\\n self.create_dir(absolute_dir_path)\\n\\n file_type_to_move_list = [\\n {\\n 'original_file_path': self.nifti_path,\\n 'new_file_path': os.path.join(self.data_dir, nii_rel_path)\\n }\\n ]\\n if self.json_path:\\n file_type_to_move_list.append(\\n {\\n 'original_file_path': self.json_path,\\n 'new_file_path': os.path.join(self.data_dir, json_rel_path)\\n }\\n )\\n if self.bval_path:\\n file_type_to_move_list.append(\\n {\\n 'original_file_path': self.bval_path,\\n 'new_file_path': os.path.join(self.data_dir, bval_rel_path)\\n }\\n )\\n if self.bvec_path:\\n file_type_to_move_list.append(\\n {\\n 'original_file_path': self.bvec_path,\\n 'new_file_path': os.path.join(self.data_dir, bvec_rel_path)\\n }\\n )\\n\\n for file_dict in file_type_to_move_list:\\n original_file_path = file_dict['original_file_path']\\n new_file_path = file_dict['new_file_path']\\n\\n message = f\\\"Moving file {original_file_path} to {new_file_path}\\\"\\n self.log_info(message, is_error='N', is_verbose='Y')\\n self.move_file(original_file_path, new_file_path)\\n\\n if destination == 'assembly_bids':\\n self.json_file_dict['file_blake2b_hash'] = self.nifti_blake2\\n if self.json_path:\\n self.json_file_dict['bids_json_file'] = json_rel_path\\n self.json_file_dict['bids_json_file_blake2b_hash'] = self.json_blake2\\n if self.bval_path:\\n self.json_file_dict['check_bval_filename'] = bval_rel_path\\n self.json_file_dict['check_bval_filename_blake2b_hash'] = self.bval_blake2\\n if self.bvec_path:\\n self.json_file_dict['check_bvec_filename'] = bvec_rel_path\\n self.json_file_dict['check_bvec_filename_blake2b_hash'] = self.bvec_blake2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7df815c4afff2afe7b72c60738bd6e0f\",\n \"score\": \"0.5626348\",\n \"text\": \"def copy_dir(source_dir, dest_dir):\\n for root, _, files in walk(source_dir):\\n src_root = Path(root)\\n dest_root = Path(dest_dir, src_root.relative_to(source_dir))\\n Path(dest_root).mkdir(parents=True, exist_ok=True)\\n for f in files:\\n src_file = Path(src_root, f)\\n dest_file = Path(\\n dest_root, f.replace(\\\".testjava\\\", \\\".java\\\").replace(\\\".testkt\\\", \\\".kt\\\")\\n )\\n dest_file.write_text(src_file.read_text())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c409b7973b50750f996d13c72243f17e\",\n \"score\": \"0.5621315\",\n \"text\": \"def _copy_files(context, files):\\n for file_task in files:\\n if not file_task.dst:\\n file_task.dst = file_task.src\\n if os.path.isdir(file_task.src):\\n context.remove_tree(file_task.dst)\\n context.copytree_to(file_task.src, file_task.dst)\\n else:\\n context.copy_to(file_task.src, file_task.dst)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d59091e33010f403fbfc1145ee5c36d4\",\n \"score\": \"0.55967206\",\n \"text\": \"def move_files(path):\\n moved = 0\\n for f in os.listdir(path):\\n prefix = path + '/'\\n current_dir = prefix + f\\n for key in extensions.keys():\\n if f.endswith(extensions[key]):\\n try:\\n os.rename(current_dir, prefix + key + '/' + f)\\n except OSError:\\n for i in range(10000):\\n try:\\n os.rename(current_dir, prefix + key + '/' + 'copy_' + str(i) + f)\\n except:\\n continue\\n break\\n moved += 1\\n return moved\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a38d1741e87af55c27da21178e65f9c\",\n \"score\": \"0.5596366\",\n \"text\": \"def copy_files(source_dir, dest_dir):\\n for files in os.scandir(source_dir):\\n filename = source_dir + files.name\\n copy2(filename, dest_dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d05f1a869b8c3bdf36344278c2ac5715\",\n \"score\": \"0.55857086\",\n \"text\": \"def _cleanup_filled_directory(\\n self, symbol, dest_node, src_entries, copy_source\\n ):\\n\\n cvs_paths = src_entries.keys()\\n cvs_paths.sort()\\n for cvs_path in cvs_paths:\\n if isinstance(cvs_path, CVSDirectory):\\n # Path is a CVSDirectory:\\n try:\\n dest_subnode = dest_node[cvs_path]\\n except KeyError:\\n # Path doesn't exist yet; it has to be created:\\n dest_node = self._fill_directory(\\n symbol, None, src_entries[cvs_path], None\\n ).parent_mirror_dir\\n else:\\n # Path already exists, but might have to be cleaned up:\\n dest_node = self._fill_directory(\\n symbol, dest_subnode, src_entries[cvs_path], copy_source\\n ).parent_mirror_dir\\n else:\\n # Path is a CVSFile:\\n self._fill_file(\\n symbol, cvs_path in dest_node, src_entries[cvs_path], copy_source\\n )\\n # Reread dest_node since the call to _fill_file() might have\\n # made it writable:\\n dest_node = self._mirror.get_current_path(\\n dest_node.cvs_path, dest_node.lod\\n )\\n\\n return dest_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0292f6a0ebb0b4bbe8d28a95817c2e2e\",\n \"score\": \"0.5579431\",\n \"text\": \"def file_arrangement(abs_dir_path):\\n if not os.path.isdir(abs_dir_path):\\n return\\n suffix_type = []\\n sep = os.path.sep\\n suffix_format = re.compile('(.*)\\\\.(.*)') # name in group(1) and suffix in group(2)\\n # using to split extension also can use os.path.splitext(path)\\n for root, dirs, files in os.walk(abs_dir_path):\\n dir_name = os.path.basename(root) # current directory name\\n for file_name in files:\\n curr_path = root + sep + file_name\\n mapping = suffix_format.search(file_name)\\n if mapping is None:\\n new_path_name = abs_dir_path + sep + dir_name + '_' + file_name\\n shutil.copy(curr_path, new_path_name)\\n continue\\n name = mapping.group(1)\\n suffix = mapping.group(2)\\n if suffix not in suffix_type:\\n suffix_type.append(suffix)\\n os.makedirs(abs_dir_path + sep + suffix)\\n new_path_name = abs_dir_path + sep + suffix + sep + dir_name + '_' + file_name\\n # that is put in to abs_dir_path\\\\suffix\\\\dir_name_filename\\n shutil.copy(curr_path, new_path_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8da16925dc8b72df454aed9b0800de6e\",\n \"score\": \"0.5579215\",\n \"text\": \"def copy_tree_over(src, dest):\\n # From https://stackoverflow.com/questions/9160227/dir-util-copy-tree-fails-after-shutil-rmtree/28055993 : \\n # If you copy folder, then remove it, then copy again it will fail, because it caches all the created dirs. \\n # To workaround you can clear _path_created before copy:\\n distutils.dir_util._path_created = {}\\n distutils.dir_util.copy_tree(src, dest)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2616a910f42bc65ffa285978c5dd41f1\",\n \"score\": \"0.55754566\",\n \"text\": \"def copytree_preserve_existing(os_service, src, dst):\\n oss = os_service\\n helper = Helpers()\\n helper.copy_directory_tree(oss, src, dst)\\n\\n file_it = FileIterator(os_service, src)\\n for file_sub_path in file_it:\\n source_file_path = os.path.join(src, file_sub_path)\\n dest_file_path = os.path.join(dst, file_sub_path)\\n if not oss.exists(dest_file_path):\\n oss.copyfile(source_file_path, dest_file_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aabe8267416d2bc6588b7369860bb5c1\",\n \"score\": \"0.5570711\",\n \"text\": \"def copy_dirs(self, dst, subs='', src_sub='', dst_sub=''):\\n # Join paths (if specified)\\n src = self.src\\n if subs:\\n src = os.path.join(src, subs)\\n dst = os.path.join(dst, subs)\\n if dst_sub:\\n dst = os.path.join(dst, dst_sub)\\n if src_sub:\\n src = os.path.join(src, src_sub)\\n print('\\\\nComparing...\\\\n')\\n print('\\\\nSource: %s' % src)\\n print('Destination: %s\\\\n' % dst)\\n for src_root, src_dirs, src_files in os.walk(src, topdown=True):\\n\\n dst_root = os.path.join(dst, os.path.relpath(src_root, src))\\n dirs = filecmp.dircmp(src_root, dst_root)\\n\\n # Find old files and delete them from destination\\n for item in dirs.right_only:\\n try:\\n print('Removing ' + item)\\n except UnicodeEncodeError:\\n # Prevents the program from stopping in the event of an\\n # awkward file name\\n print('Removing file (Unicode error)')\\n dst_path = os.path.join(dst_root, item)\\n if os.path.isdir(dst_path):\\n shutil.rmtree(dst_path)\\n else:\\n os.remove(dst_path)\\n\\n # Find new files and add them to destination\\n for item in dirs.left_only:\\n try:\\n print('Adding ' + item)\\n except UnicodeEncodeError:\\n # Prevents the program from stopping in the event of an\\n # awkward file name\\n print('Adding file (Unicode error)')\\n src_path = os.path.join(src_root, item)\\n if os.path.isdir(src_path):\\n shutil.copytree(src_path, os.path.join(dst_root, item))\\n else:\\n shutil.copy2(src_path, os.path.join(dst_root, item))\\n\\n # Once clearing and adding has completed, update existing files\\n print('\\\\nUpdating: ')\\n if plat != 'win32':\\n os.system(\\n \\\"\\\"\\\"rsync -r -u -v --links \\\"{0}\\\"/* \\\"{1}\\\" \\\"\\\"\\\".format(src, dst))\\n else:\\n os.system(\\\"\\\"\\\"xcopy /I /E /Y /D \\\"{0}\\\" \\\"{1}\\\" \\\"\\\"\\\".format(src, dst))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6ba29e8b3369f5c134f8674fb030863\",\n \"score\": \"0.55684304\",\n \"text\": \"def move(sfile, source_dir, directory):\\n sbase = osp.splitext(osp.split(sfile)[1])[0]\\n already_here = any(sbase in dfile for dfile in dest_files)\\n if not already_here:\\n shutil.copy(osp.join(source_dir, sfile), directory)\\n logger.info(\\\"Copied {} into pylinac directory\\\".format(sfile))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c11a41bc54a09cc18d12eb2e559434c\",\n \"score\": \"0.55585176\",\n \"text\": \"def moveResultsToMain(self):\\n # Delete input file\\n os.remove(self.subDir+self.simName+'\\\\\\\\'+self.simName+'.inp')\\n # Move files\\n allFiles = os.listdir(self.subDir+self.simName+'\\\\\\\\')\\n for fName in allFiles:\\n os.rename(self.subDir+self.simName+'\\\\\\\\'+fName, self.subDir+fName)\\n # Delete (empty) simulation folder\\n os.rmdir(self.subDir+self.simName)\\n # Brief pause\\n time.sleep(5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1ba59211445527a8926e400decf0aa8\",\n \"score\": \"0.55563086\",\n \"text\": \"def make_files_match(from_dir, to_dir, do_commits = True, verbose=False, delete_missing_from = False, \\n do_delete_files = True):\\n\\n # if from dir doesn't exist, delete to dir\\n # This is used by pull_step.\\n if (not os.path.exists(from_dir)):\\n if delete_missing_from and os.path.exists(to_dir):\\n shutil.rmtree(to_dir)\\n return\\n\\n\\n # if to_dir doesn't exist, create it - we are probably pushing\\n # for the first time - Issue #2\\n if (not os.path.exists(to_dir)):\\n try:\\n os.makedirs(to_dir, 0755)\\n except:\\n pass\\n\\n # What files is git managing for us in each directory?\\n if not os.path.exists(from_dir):\\n from_files = [ ]\\n else:\\n from_files = git.list_files(from_dir)\\n to_files = git.list_files(to_dir)\\n\\n from_files_set = set(from_files)\\n to_files_set = set(to_files)\\n\\n # We need to remove \\\"by hand\\\" those files which are no longer meant to\\n # be in the \\\"to\\\" directory, so work out which files that is\\n deleted_files = to_files_set - from_files_set\\n\\n if verbose:\\n print 'Making files the same'\\n print '====================='\\n print 'From', from_dir\\n print 'To ', to_dir\\n if (len(from_files) > 0):\\n max_len = max(len(n) for n in from_files)\\n else:\\n max_len = 1\\n for name in sorted(from_files_set | to_files_set):\\n if name in from_files_set and name in to_files_set:\\n print '%-*s %s'%(max_len, name, name)\\n elif name in from_files_set:\\n print '%-*s'%(max_len, name)\\n else:\\n print '%-*s %s'%(max_len, ' ', name)\\n print '====================='\\n\\n # If we copy over everything from the \\\"from\\\" directory to the \\\"to\\\"\\n # directory, and \\\"git add\\\" them all, then that will cope with changes to\\n # existing files, and also add in any new files. We then need to remember\\n # to \\\"git rm\\\" any files that are meant to have gone away, at which point we\\n # can commit all the changes.\\n #\\n # We use rsync for our copy because it will handle things like:\\n #\\n # rsync -a --relative four/jim four/bob <weld_root>/.weld/bases/project124\\n #\\n # and put \\\"jim\\\" and \\\"bob\\\" into <weld_root>/.weld/bases/project124/four\\n if os.path.exists(from_dir):\\n cmd = ['rsync', '-a', '--relative']\\n cmd += from_files\\n cmd += [to_dir]\\n run_silently(cmd, cwd=from_dir, verbose=verbose)\\n\\n if len(from_files) >0:\\n git.add(to_dir, from_files, verbose=verbose)\\n if do_commits:\\n git.commit_using_message(to_dir, \\\"Add files from %s\\\"%from_dir, verbose=verbose)\\n\\n if deleted_files and do_delete_files:\\n git.rm(to_dir, list(deleted_files))\\n if do_commits: \\n git.commit_using_message(to_dir, \\\"Delete files no longer in %s\\\"%from_dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f2aa5c46b28e9b21b8c5c50bc334787\",\n \"score\": \"0.5544597\",\n \"text\": \"def move_files(files, destdir):\\n ensure_dir(destdir)\\n for f in files:\\n if f.startswith(destdir):\\n continue\\n subprocess.call([\\\"mv\\\", \\\"-f\\\", f, destdir])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71603c45a0219c0bf7e5e23c80132c6e\",\n \"score\": \"0.5533806\",\n \"text\": \"def RelocateNonRepoFiles(all_files):\\n tempDir = os.path.join(u'scripts', u'temp')\\n rm(tempDir)\\n os.makedirs(tempDir)\\n\\n non_repo = GetNonRepoFiles(all_files)\\n if non_repo:\\n lprint(\\\" Relocating non-repository files:\\\")\\n for path in non_repo:\\n lprint(\\\" \\\", path)\\n src = os.path.join(mopy, path)\\n dst = os.path.join(tempDir, path)\\n if os.path.isdir(src):\\n shutil.move(src, dst)\\n elif os.path.isfile(src):\\n dirname = os.path.dirname(dst)\\n if not os.path.exists(dirname):\\n os.makedirs(dirname)\\n shutil.move(src, dst)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9864431a8adcaca6fb82d9e420bd838f\",\n \"score\": \"0.5531388\",\n \"text\": \"def prep_tree_for_tar(repodir, subdir, outdir, dstname):\\n src = os.path.join(repodir, subdir)\\n if not os.path.exists(src):\\n sys.exit(\\\"%s: No such file or directory\\\" % src)\\n\\n dst = os.path.join(outdir, dstname)\\n if os.path.exists(dst) and \\\\\\n (os.path.samefile(src, dst) or\\n os.path.samefile(os.path.dirname(src), dst)):\\n sys.exit(\\\"%s: src and dst refer to same file\\\" % src)\\n\\n #shutil.copytree(src, dst, symlinks=True)\\n gitlock = os.path.join(src, '.git/index.lock')\\n count = 0\\n while os.path.exists(gitlock):\\n time.sleep(5)\\n count = count + 1\\n if count > 100:\\n break\\n safe_run(['cp', '-a', src, dst], cwd=os.getcwd(),interactive=sys.stdout.isatty())\\n\\n return dst\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84e8d8b04e948e1c30aa4bc7df405edf\",\n \"score\": \"0.5518604\",\n \"text\": \"def move_files(src, dst, group):\\n print(\\\"Moving!\\\")\\n print(group)\\n for fname in group:\\n print(src+ '/' + fname)\\n os.rename(src + '/' + fname, dst + '/' + fname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ed4254726c1bfa74a01b65d24e97c5f\",\n \"score\": \"0.5513786\",\n \"text\": \"def gc_mvall( scratchdir ):\\n sdirs = glob.glob(scratchdir)\\n for scratchdir in sdirs:\\n gcdir = scratchdir.replace( '/scratch/', '/scratch/_gc/' )\\n print \\\"scratchdir=\\\",scratchdir\\n print \\\"gcdir =\\\",gcdir\\n if os.path.isdir(scratchdir):\\n if os.path.isdir(gcdir):\\n print \\\"WARNING, gcdir %s already exists,\\\\n will not move from scratchdir %s\\\"%\\\\\\n (gcdir,scratchdir)\\n else:\\n shutil.move( scratchdir, gcdir )\\n else:\\n #raise Exception(\\\"source directory %s doesn't exist\\\"%scratchdir)\\n print \\\"WARNING\\\", \\\"source directory %s doesn't exist\\\"%scratchdir\\n print \\\"Nothing will be moved from scratch to scratch/_gs.\\\"\\n if not os.path.isdir(glob.glob(gcdir)[0]):\\n raise Exception(\\\"gcdir %s doesn't exist\\\"%gcdir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b53a4ac23d2d6b8c876aaafab56bfdf\",\n \"score\": \"0.5511875\",\n \"text\": \"def _copy_dir_recursive(src_rel, dst_rel):\\n src = os.path.expanduser(src_rel)\\n dst = os.path.expanduser(dst_rel)\\n try:\\n shutil.copytree(src, dst)\\n logger.info('Files copied from %s to %s', src, dst)\\n except OSError as exc:\\n if exc.errno == errno.ENOTDIR:\\n shutil.copy(src, dst)\\n logger.info('Files copied from %s to %s', src, dst)\\n else:\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bb807e1ca46d9cdfbf6ba7dd8928e8c\",\n \"score\": \"0.5511476\",\n \"text\": \"def movefiles(sub, newid, cid):\\n # construct full CISC ID (with sub-)\\n cid_full = \\\"sub-\\\" + cid\\n for root, dirs, files in os.walk(sub):\\n for f in files:\\n # Only continue with the file if it's NOT a stupid annoying mac file\\n if \\\"DS_Store\\\" not in f:\\n # Construct full path\\n fullf = os.path.join(root, f)\\n # Create new path by replacing sub-CISCID with sub- ADIEID\\n newf = fullf.replace(cid_full, newid)\\n # Check that directories already exist - should've been created in F3\\n if not os.path.isdir(os.path.split(newf)[0]):\\n print(\\n \\\"WARNING: '{}' does NOT exists\\\".format(\\n os.path.split(newf)[0]\\n )\\n )\\n # If directory does exist, proceed with move and rename\\n else:\\n print(\\\"Attempting to move\\\", fullf, \\\"to\\\", newf, \\\"\\\\n\\\")\\n shutil.move(fullf, newf)\\n elif \\\"DS_Store\\\" in f:\\n continue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2f051c566763bc0cec954eb9772a164\",\n \"score\": \"0.5510668\",\n \"text\": \"def remove_entries(dest):\\n for entry in os.listdir(dest):\\n fullpath = os.path.join(dest, entry)\\n if os.path.isfile(fullpath):\\n os.remove(fullpath)\\n else:\\n shutil.rmtree(fullpath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"775041c355bd41c2c34db388a53e54ab\",\n \"score\": \"0.5499051\",\n \"text\": \"def rsync_files(src_file: Union[str, PathLike], dest_file: Union[str, PathLike]) -> None:\\n copy2(src_file, dest_file)\\n src_folder = dirname(src_file)\\n if not src_folder:\\n src_folder = '.'\\n copystat(src_folder, dirname(dest_file))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5ae744e8068cab08b1d8555209a7d1a\",\n \"score\": \"0.5497786\",\n \"text\": \"def upload_tree(self, src, dst, ignore=None):\\n names = os.listdir(src)\\n if ignore is not None:\\n ignored_names = ignore(src, names)\\n else:\\n ignored_names = set()\\n\\n try:\\n dst = dst.replace('\\\\\\\\', '/')\\n self.conn.mkd(dst)\\n except error_perm:\\n pass\\n\\n errors = []\\n for name in names:\\n if name in ignored_names:\\n continue\\n src_name = os.path.join(src, name)\\n dst_name = os.path.join(dst, name)\\n try:\\n if os.path.islink(src_name):\\n pass\\n elif os.path.isdir(src_name):\\n self.upload_tree(src_name, dst_name, ignore)\\n else:\\n # Will raise a SpecialFileError for unsupported file types\\n self.put(src_name, dst_name)\\n except Exception as why:\\n errors.append((src_name, dst_name, str(why)))\\n\\n return dst\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0382cbf8d9c1895f0fb360781da2f9b3\",\n \"score\": \"0.54899615\",\n \"text\": \"def single_folder_2_planet_order_hierarchy(input_dir, target_dir):\\r\\n for root, dirs, files in os.walk(os.path.normpath(input_dir), topdown=False):\\r\\n for file_name in files:\\r\\n # This selects only the actual image *.tif file\\r\\n if \\\"AnalyticMS_clip\\\" in file_name and file_name.endswith(\\\".tif\\\"):\\r\\n logging.info(\\\"Found: {}\\\".format(file_name))\\r\\n file_name_split = file_name.split(\\\"_\\\")\\r\\n # The date_id in the ISO format YYYYMMDD becomes a folder\\r\\n date_id = file_name_split[0]\\r\\n date_dir = os.path.join(target_dir, date_id)\\r\\n logging.info(\\\"Writing date directory as: {}\\\".format(date_dir))\\r\\n if not os.path.isdir(date_dir):\\r\\n os.mkdir(date_dir)\\r\\n # The img_id is formatted as YYYYMMDD_id_sat\\r\\n img_id = \\\"_\\\".join(file_name_split[0:3])\\r\\n logging.info(\\\"Writing image folder as: {}\\\".format(img_id))\\r\\n img_dir = os.path.join(date_dir, img_id)\\r\\n if not os.path.isdir(img_dir):\\r\\n os.mkdir(img_dir)\\r\\n logging.info(\\\"Writing to image directory: {}\\\".format(img_dir))\\r\\n # loop for copying all metadata files as well with the same img_id\\r\\n for file_name2 in files:\\r\\n if img_id in file_name2:\\r\\n source_dir = os.path.join(input_dir, file_name2)\\r\\n shutil.copy2(source_dir, img_dir) \\r\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ea009c9c62d1858746e62f28c2d19d3\",\n \"score\": \"0.5485185\",\n \"text\": \"def copydir(self, src, dst, replace=True):\\n for file in os.listdir(src):\\n if os.path.isfile(os.path.join(src, file)):\\n if os.path.exists(os.path.join(dst, file)) and not replace:\\n self.logger.debug(\\\"> Skipped file \\\\\\\"\\\" + os.path.join(src, file) + \\\"\\\\\\\": Already exists.\\\")\\n elif os.path.exists(os.path.join(dst, file)):\\n os.unlink(os.path.join(dst, file))\\n shutil.copy2(os.path.join(src, file), dst)\\n self.logger.debug(\\\"> Replaced file \\\\\\\"\\\" + os.path.join(src, file) + \\\"\\\\\\\".\\\")\\n else:\\n shutil.copy2(os.path.join(src, file), dst)\\n self.logger.debug(\\\"> Copied file \\\\\\\"\\\" + os.path.join(src, file) + \\\"\\\\\\\".\\\")\\n elif os.path.isdir(os.path.join(dst, file)):\\n self.copydir(os.path.join(src, file), os.path.join(dst, file))\\n else:\\n os.makedirs(os.path.join(dst, file))\\n self.copydir(os.path.join(src, file), os.path.join(dst, file))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8909620f8f6b691a824f7e780eca88b\",\n \"score\": \"0.54825467\",\n \"text\": \"def cleanup_project_dir(self):\\n contents = os.listdir(self.project.root)\\n num = 0\\n for name in contents:\\n if name.lower().endswith(\\\".asc\\\"):\\n os.rename(self.project.root + name, self.project.data_dir + name)\\n num += 1\\n\\n if num > 0:\\n messagebox.showinfo(\\n \\\"New Project\\\",\\n \\\"{} ASC files were moved to {}\\\".format(num, self.project.data_dir),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf82e570a7ed52e0b94b1afdee663101\",\n \"score\": \"0.5452133\",\n \"text\": \"def copy_directory(source, dest):\\n for root, dirs, files in os.walk(source):\\n if not os.path.isdir(root):\\n os.makedirs(root)\\n\\n for file in files:\\n rel_path = root.replace(source, '').lstrip(os.sep)\\n dest_path = os.path.join(dest, rel_path)\\n\\n if not os.path.isdir(dest_path):\\n os.makedirs(dest_path)\\n if(dirs and files):\\n shutil.copyfile(os.path.join(root, file),\\n os.path.join(dest_path, file))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"255b76c568aa4b73ed9b4a2bf2e5bd91\",\n \"score\": \"0.5451473\",\n \"text\": \"def find_duplicates(data_dir):\\n duplicates = []\\n im_hashes = dict()\\n\\n def append_stuff(directory, im_hashes):\\n \\\"\\\"\\\"\\n Return a list of absolute paths to all duplicate files in directory.\\n \\\"\\\"\\\"\\n print(directory)\\n for file_name in os.listdir(directory):\\n file_name = f'{directory}/{file_name}'\\n if os.path.isfile(file_name):\\n with open(file_name, 'rb') as file:\\n file_hash = hashlib.md5(file.read()).hexdigest()\\n try:\\n temp = im_hashes[file_hash]\\n del temp\\n duplicates.append(file_name)\\n print(file_name.split('/')[-1])\\n except KeyError:\\n im_hashes[file_hash] = file_name\\n else:\\n append_stuff(file_name, im_hashes)\\n append_stuff(data_dir, im_hashes)\\n return duplicates\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5ec29f44525327d2df19fe422b88f50\",\n \"score\": \"0.544854\",\n \"text\": \"def clean_files(self) -> None:\\n api_dir = os.path.join(self._src_dir, \\\"_api\\\")\\n\\n shutil.rmtree(api_dir, ignore_errors=True)\\n shutil.rmtree(self._out_dir, ignore_errors=True)\\n os.makedirs(api_dir, exist_ok=True)\\n os.makedirs(self._out_dir, exist_ok=True)\\n\\n print(f\\\"Recreated content of the {shlex.quote(self._out_dir)} and {shlex.quote(api_dir)} folders\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49097edb46a99cc8a7e7e9d1e4a52354\",\n \"score\": \"0.5430227\",\n \"text\": \"def moveFilesToTestDirectory(directory):\\n amount_of_files = getAmountOffFilesInDirectory(directory)\\n amount_of_files_moved = amount_of_files * 0.7 # Gets finds which number is 70% of the files\\n files_counted = 0 # Counts the amount of files\\n for file_name in os.listdir(directory):\\n # Once loop reaches past the 70% mark it begins to move training files to the test file directory\\n if files_counted >= amount_of_files_moved:\\n class_name = directory.split('/')[1] # Gets name of the class I.E. Digimon or Pokemon\\n shutil.move(directory + '/' + file_name, 'test/' + class_name + '/' + file_name) # Moves files\\n files_counted += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ceff1e84e3fb9b5dfa67b280823a45d\",\n \"score\": \"0.5419111\",\n \"text\": \"def move(src, dst):\\n\\n try:\\n os.rename(src, dst)\\n except OSError:\\n if os.path.isdir(src):\\n if destinsrc(src, dst):\\n raise Error, \\\"Cannot move a directory '%s' into itself '%s'.\\\" % (src, dst)\\n copytree(src, dst, symlinks=True)\\n rmtree(src)\\n else:\\n copy2(src,dst)\\n os.unlink(src)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a77427c2dd2ada459d01903eb2ed1e9\",\n \"score\": \"0.54185\",\n \"text\": \"def move_files(original_fold, data_fold, data_filename):\\n with open(data_filename) as f:\\n for line in f.readlines():\\n vals = line.split('/')\\n dest_fold = os.path.join(data_fold, vals[0])\\n if not os.path.exists(dest_fold):\\n os.mkdir(dest_fold)\\n shutil.move(os.path.join(original_fold, line[:-1]), os.path.join(data_fold, line[:-1]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87f3d2e56e9d512ce978f5d4d2a731ab\",\n \"score\": \"0.5409844\",\n \"text\": \"def merge_multi_folder_2_single_folder(input_dir, merged_dir):\\r\\n for root, dirs, files in os.walk((os.path.normpath(input_dir)), topdown=False):\\r\\n for file_name in files:\\r\\n logging.info(\\\"Found: {}\\\".format(file_name))\\r\\n source_dir = os.path.join(root, file_name)\\r\\n shutil.copy2(source_dir, merged_dir)\\r\\n logging.info(\\\"Wrote: {} to merged directory\\\".format(file_name))\\r\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acf82960079d6018c53bfa1d8c2198d1\",\n \"score\": \"0.54056674\",\n \"text\": \"def extract_step(self):\\n super(EB_OpenFOAM, self).extract_step()\\n # make sure that the expected subdir is really there after extracting\\n # if not, the build scripts (e.g., the etc/bashrc being sourced) will likely fail\\n openfoam_installdir = os.path.join(self.installdir, self.openfoamdir)\\n if not os.path.exists(openfoam_installdir):\\n self.log.warning(\\\"Creating expected directory %s, and moving everything there\\\" % openfoam_installdir)\\n try:\\n contents_installdir = os.listdir(self.installdir)\\n source = os.path.join(self.installdir, contents_installdir[0])\\n # it's one directory but has a wrong name\\n if len(contents_installdir) == 1 and os.path.isdir(source):\\n target = os.path.join(self.installdir, self.openfoamdir)\\n self.log.debug(\\\"Renaming %s to %s\\\", source, target)\\n os.rename(source, target)\\n else:\\n mkdir(openfoam_installdir)\\n for fil in contents_installdir:\\n if fil != self.openfoamdir:\\n source = os.path.join(self.installdir, fil)\\n target = os.path.join(openfoam_installdir, fil)\\n self.log.debug(\\\"Moving %s to %s\\\", source, target)\\n shutil.move(source, target)\\n os.chdir(openfoam_installdir)\\n except OSError as err:\\n raise EasyBuildError(\\\"Failed to move all files to %s: %s\\\", openfoam_installdir, err)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"088f78b7fe91603e6456e91e534cab66\",\n \"score\": \"0.53969646\",\n \"text\": \"def file_move_dir(mv_input_img_address, mv_output_img_address):\\n single_char_list = glob.glob(os.path.join(mv_input_img_address, \\\"*\\\"))\\n last_name_of_output_file = int(((os.listdir(mv_output_img_address))[-1]).split(sep='.')[0])\\n\\n for img_index in single_char_list:\\n # input_image = DOT_IMG_ADDRESS + str(img_index) + \\\".jpg\\\"\\n # mv_input_image = os.path.join(DOT_INPUT_IMG_ADDRESS, \\\"{}.png\\\".format(str(img_index).zfill(6)))\\n mv_raw_img = cv2.imread(img_index)\\n DEBUG = False\\n if DEBUG:\\n cv2.imshow('mv_raw_img', mv_raw_img)\\n cv2.waitKey()\\n img_output_address = os.path.join(mv_output_img_address,\\n \\\"{}.png\\\".format(str(last_name_of_output_file + 1).zfill(6)))\\n cv2.imwrite(img_output_address, mv_raw_img)\\n last_name_of_output_file += 1\\n for img_index in single_char_list:\\n # input_image = DOT_IMG_ADDRESS + str(img_index) + \\\".jpg\\\"\\n # mv_input_image = os.path.join(DOT_INPUT_IMG_ADDRESS, \\\"{}.png\\\".format(str(img_index).zfill(6)))\\n # if os.path.exists(mv_input_image):\\n os.remove(img_index)\\n # print(os.path.split(img_index)[:-1])\\n os.removedirs(mv_input_img_address)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5d27487fcea039f1f673e4983aa6a0e\",\n \"score\": \"0.539501\",\n \"text\": \"def move_songs():\\n\\n OLD_DIR = os.path.expanduser(\\\"~/.playx\\\")\\n NEW_DIR = os.path.expanduser(\\\"~/.playx/songs\\\")\\n\\n if not os.path.isdir(NEW_DIR):\\n os.makedirs(NEW_DIR)\\n\\n for file in os.listdir(OLD_DIR):\\n if file.endswith(\\\"mp3\\\"):\\n move(os.path.join(OLD_DIR, file), os.path.join(NEW_DIR, file))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b975b474aa96c27d12f7da2e3b88a698\",\n \"score\": \"0.5394871\",\n \"text\": \"def move_files(self, photo_paths, move_to):\\n\\n update_folders = []\\n moved = 0\\n for fullpath in photo_paths:\\n photo_info = self.database_exists(fullpath)\\n if photo_info:\\n new_path = os.path.join(photo_info[2], move_to)\\n try:\\n if not os.path.isdir(new_path):\\n os.makedirs(new_path)\\n except:\\n self.popup_message(text='Error: Could Not Create Folder', title='Error')\\n break\\n photo_path = os.path.join(photo_info[2], photo_info[0])\\n current_folder, current_file = os.path.split(photo_path)\\n new_photo_path = os.path.join(new_path, current_file)\\n new_fullpath = os.path.join(move_to, current_file)\\n backup_path = photo_info[10]\\n if os.path.exists(backup_path):\\n new_backup_path = os.path.join(new_path, '.originals')\\n new_backup_file = os.path.join(new_backup_path, current_file)\\n try:\\n os.makedirs(new_backup_path)\\n os.rename(backup_path, new_backup_file)\\n except:\\n self.popup_message(text='Error: Could Not Move Backup File', title='Error')\\n break\\n if not os.path.exists(new_backup_file):\\n self.popup_message(text='Error: Could Not Move Backup File', title='Error')\\n break\\n photo_info[10] = new_backup_file\\n if os.path.exists(photo_path):\\n try:\\n os.rename(photo_path, new_photo_path)\\n except:\\n self.popup_message(text='Error: Could Not Move File', title='Error')\\n break\\n if not os.path.exists(new_photo_path):\\n self.popup_message(text='Error: Could Not Move File', title='Error')\\n break\\n\\n self.database_item_update(photo_info)\\n self.database_item_rename(fullpath, new_fullpath, move_to)\\n update_folders.append(photo_info[1])\\n moved = moved + 1\\n if moved:\\n self.message(\\\"Moved \\\"+str(moved)+\\\" files.\\\")\\n update_folders.append(move_to)\\n self.update_photoinfo(folders=update_folders)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77d140b223afe752df41227c612548c3\",\n \"score\": \"0.5384689\",\n \"text\": \"def clear_destination_dir (self):\\n \\n for path, dirs, files in os.walk(self.DESTINATION_DIR, topdown=False):\\n for file in files:\\n os.remove(os.path.join(path, file))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20bb9ad0107696dbead529ef90f28c08\",\n \"score\": \"0.53845114\",\n \"text\": \"def _copy_new_files(directory, config):\\n def move(sfile, source_dir, directory):\\n \\\"\\\"\\\"The function to move files from the source to the destination\\\"\\\"\\\"\\n sbase = osp.splitext(osp.split(sfile)[1])[0]\\n already_here = any(sbase in dfile for dfile in dest_files)\\n if not already_here:\\n shutil.copy(osp.join(source_dir, sfile), directory)\\n logger.info(\\\"Copied {} into pylinac directory\\\".format(sfile))\\n\\n # return if no sources are configured or are not real directories\\n source_undefined = config['general']['sources'] is None\\n if source_undefined:\\n return\\n sources_are_dirs = all(osp.isdir(source) for source in config['general']['sources'])\\n if not sources_are_dirs:\\n return\\n\\n # move new files into destination directory\\n dest_files = os.listdir(directory)\\n with concurrent.futures.ThreadPoolExecutor(4) as exec:\\n for source_dir in config['general']['sources']:\\n logger.info(\\\"Querying new files from {}\\\".format(source_dir))\\n source_files = os.listdir(source_dir)\\n time.sleep(0.5)\\n if config['general']['rolling-window-days'] > 0:\\n window_cutoff = datetime.datetime.timestamp(datetime.datetime.now() - datetime.timedelta(days=config['general']['rolling-window-days']))\\n source_files = [f for f in source_files if osp.getmtime(osp.join(source_dir, f)) > window_cutoff]\\n for file in source_files:\\n exec.submit(move, file, source_dir, directory)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"197d2ca14c339db2efd242892850f953\",\n \"score\": \"0.53807014\",\n \"text\": \"def copy_over(temp_dir, paths, artefact=\\\"model\\\", dir=\\\"models\\\"):\\n\\n for path in paths:\\n print(\\\"Importing %s %s\\\" % (artefact, str(Path(path).resolve().relative_to(Path(temp_dir).resolve()))))\\n dest_path = Path(dir, Path(path).name)\\n if dest_path.exists():\\n print(\\\"Skipping `%s` as it already exists\\\" % str(dest_path))\\n else:\\n shutil.copytree(path, str(dest_path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"090bc18a430f0cf4d076f6288befabde\",\n \"score\": \"0.53780043\",\n \"text\": \"def copy_folder(self, src, dst):\\n\\n src_list, dst_list = [], []\\n for folder, _, files in os.walk(src):\\n rfolder = folder.replace(src, dst)\\n command = \\\"mkdir -p {0}\\\".format(rfolder)\\n self.ssh.exec_command(command)\\n src_list.extend([os.path.join(folder, f) for f in files if not f.endswith(\\\".pyc\\\")])\\n dst_list.extend([os.path.join(rfolder, f) for f in files if not f.endswith(\\\".pyc\\\")])\\n\\n self.ssh.put_file(src_list, dst_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45a12b318adc73b9d513335ae5987dea\",\n \"score\": \"0.53752345\",\n \"text\": \"def move_clean(images, destination):\\n\\tfor image in images:\\n\\t\\tshutil.move(image, destination)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c88a28034af4d2c2c4cb3db93e89e0a\",\n \"score\": \"0.5372583\",\n \"text\": \"def rename_directories(self):\\r\\n\\r\\n folder_classes = os.listdir(PATH_TO_DATA)\\r\\n\\r\\n # Append [class, image] of dataset as entry of new array\\r\\n for directory in folder_classes:\\r\\n new_directory = directory[:2]\\r\\n new_directory = new_directory.strip(\\\"_\\\")\\r\\n new_directory = int(new_directory) - 1\\r\\n new_directory = str(new_directory) + \\\"_\\\" + directory[3:]\\r\\n os.rename(os.path.join(PATH_TO_DATA, directory), os.path.join(PATH_TO_DATA, new_directory))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13ff59f56d42bc8e0aa08f79e1ae7490\",\n \"score\": \"0.53669804\",\n \"text\": \"def remove_duplicates(directory_path):\\n print(f'Indexing images...')\\n images = glob.glob(os.path.join(directory_path, '*.jpg'))\\n set_img = set()\\n for image in images:\\n img = tuple(average_image(cv.imread(image)))\\n if img not in set_img:\\n set_img.add(img)\\n else:\\n os.remove(image)\\n print(f'Removing {image} ...')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1587752159c3e1440ae80f86c6a44df\",\n \"score\": \"0.53663343\",\n \"text\": \"def upsert_parent_dirs(filepath):\\n comps = [c for c in filepath.split('/') if c]\\n comps.pop()\\n\\n if filepath.startswith('/'):\\n path = '/'\\n else:\\n path = ''\\n\\n for dir in comps:\\n path += (dir + '/')\\n\\n if not os.path.exists(path):\\n try:\\n os.mkdir(path)\\n except:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9a592076341ed2ea6a1f930b2dbe58d\",\n \"score\": \"0.5356832\",\n \"text\": \"def move_output_files():\\n # Create output folder if it doesn't exist\\n if not os.path.exists(OUTPUT_FILES_FOLDER):\\n os.makedirs(OUTPUT_FILES_FOLDER)\\n\\n # Move the map files to the Output folder\\n os.rename(os.path.join(PROGRAM_PATH, \\\"map_WHITE.png\\\"), os.path.join(OUTPUT_FILES_FOLDER, \\\"map_WHITE.png\\\"))\\n os.rename(os.path.join(PROGRAM_PATH, \\\"map_BROWN.png\\\"), os.path.join(OUTPUT_FILES_FOLDER, \\\"map_BROWN.png\\\"))\\n os.rename(os.path.join(PROGRAM_PATH, \\\"map_RED.png\\\"), os.path.join(OUTPUT_FILES_FOLDER, \\\"map_RED.png\\\"))\\n os.rename(os.path.join(PROGRAM_PATH, \\\"map_SCORE-O.png\\\"), os.path.join(OUTPUT_FILES_FOLDER, \\\"map_SCORE-O.png\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4840d38a6c95750734d2063d6fe86043\",\n \"score\": \"0.5349183\",\n \"text\": \"def move_files(scan_dir, filepaths):\\n for f in filepaths:\\n _, ext = os.path.splitext(f)\\n name = os.path.basename(f)\\n new_path = os.path.join(scan_dir, ext[1:], name)\\n os.rename(f, new_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f53f6129526bd6d97c1856bc6d2bda5\",\n \"score\": \"0.53444535\",\n \"text\": \"def __move_up(self):\\n for filename in self._selected:\\n index = self._files.index(filename)\\n files = OrderedSet()\\n if index == 0:\\n return\\n\\n if index > 1:\\n files = self._files[:index - 1]\\n files = list(files) + [self._files[index], self._files[index - 1]] + self._files[index + 1:]\\n\\n self._files = files\\n self.__update_tree()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbb582de6847d80d3215489706a6a0ba\",\n \"score\": \"0.53393376\",\n \"text\": \"def create_output_directories(self):\\r\\n check_or_create_dir(self.dst_path)\\r\\n\\r\\n if self.medias:\\r\\n check_or_create_dir(join(self.dst_path, self.settings[\\\"GALLERY_THUMB_DIR\\\"]))\\r\\n\\r\\n if self.medias and self.settings[\\\"KEEP_ORIG\\\"]:\\r\\n self.orig_path = join(self.dst_path, self.settings[\\\"orig_dir\\\"])\\r\\n check_or_create_dir(self.orig_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d19c19bd38282419100b378a25f51e13\",\n \"score\": \"0.5339199\",\n \"text\": \"def resave_all_archives(self,new_dirpath):\\n\\t\\tif self.zippath!=None:\\n\\t\\t\\told_dirpath,f=os.path.split(self.zippath)\\n\\t\\telse : old_dirpath=None\\n\\t\\tassert os.path.abspath(new_dirpath)!=os.path.abspath(old_dirpath)\\n\\t\\tfor arch_name in self.list_archives:\\n\\t\\t\\tif not os.path.exists(os.path.join(new_dirpath,TMP_FILE_MARK+arch_name)):\\n\\t\\t\\t\\tos.mkdir(os.path.join(new_dirpath,TMP_FILE_MARK+arch_name))\\n\\t\\t\\t\\n\\t\\t\\tfor file in os.listdir(os.path.join(old_dirpath,TMP_FILE_MARK+arch_name)):\\n\\t\\t\\t\\tfilepath=os.path.join(old_dirpath,TMP_FILE_MARK+arch_name,file)\\n\\t\\t\\t\\tshutil.copy(filepath,os.path.join(new_dirpath,TMP_FILE_MARK+arch_name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09737768c435f6d46e6e85affe62bba4\",\n \"score\": \"0.5338812\",\n \"text\": \"def shuffle_folders(folder):\\n for s in os.listdir(folder):\\n if os.path.isfile(folder + '/' + s):\\n shuffle_file(folder, s)\\n print 'finish shuffling folder: %s' % s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfc00f904f6ca172850ab2304c7826e7\",\n \"score\": \"0.53345555\",\n \"text\": \"def recorrer(carpeta):\\r\\n\\tlista = os.listdir(carpeta)\\r\\n\\t#print (lista)\\r\\n\\tfor elemento in lista:\\r\\n\\t\\tnuevo = os.path.join(carpeta,os.path.basename(elemento))\\r\\n\\t\\tif os.path.isdir(nuevo):\\r\\n\\t\\t\\trecorrer(nuevo)\\t\\t\\t\\r\\n\\t\\telse:\\r\\n\\t\\t\\tdestino = os.path.join(os.getcwd(),pathDestino)\\r\\n\\t\\t\\tprint(\\\"hashing \\\", nuevo)\\r\\n\\t\\t\\tappendhash(genhash(nuevo).upper(), nuevo, filehash)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89395491c8df71babf3a9bea570db48e\",\n \"score\": \"0.5331348\",\n \"text\": \"def move_malicious(abs_dirname, ratio):\\n train_malicious = 'train/malicious/'\\n shutil.rmtree(train_malicious)\\n os.makedirs(train_malicious)\\n val_malicious = 'validation/malicious/'\\n shutil.rmtree(val_malicious)\\n os.makedirs(val_malicious)\\n files = [os.path.join(abs_dirname, f) for f in os.listdir(abs_dirname)]\\n for f in files[0:ratio]:\\n # create new subdir if necessary\\n #subdir_name = os.path.join(abs_dirname, '{0:03d}'.format(i / N + 1))\\n #os.mkdir('train/' + file_type)\\n #curr_subdir = subdir_name\\n\\n # move file to current dir\\n f_base = os.path.basename(f)\\n\\n copyfile(f, train_malicious + f_base)\\n\\n for f in files[ratio:]:\\n # create new subdir if necessary\\n #subdir_name = os.path.join(abs_dirname, '{0:03d}'.format(i / N + 1))\\n #os.mkdir('validation/' + file_type )\\n f_base = os.path.basename(f)\\n\\n copyfile(f, val_malicious + f_base)\\n #copyfile(f, 'validation/malicious/' + f_base)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c4fd08a297e91d04592b7414fe28635\",\n \"score\": \"0.53109795\",\n \"text\": \"def dir_structure(image_dir):\\n image_dir_files = os.listdir(image_dir)\\n fake_dir = '/input'\\n assert len(image_dir_files) != 0, f\\\"Image directory {image_dir_files} is empty.\\\"\\n if os.path.exists(image_dir+fake_dir):\\n if os.path.isdir(image_dir+fake_dir):\\n print('Image directory structure test: OK.')\\n else:\\n print(\\\"Rearranging image dir structure, this might take a while...\\\")\\n os.mkdir(image_dir+fake_dir)\\n for i in image_dir_files:\\n src = os.path.join(image_dir, i)\\n dst = os.path.join(image_dir+fake_dir, i)\\n move(src, dst)\\n print(\\\"Done. Directory structure is now OK.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5aa8f40c3fd01c5825432c96448de9cb\",\n \"score\": \"0.53109616\",\n \"text\": \"def copying_files(self):\\n to_copy_folders = os.listdir(os.getcwd() + '/to_copy/')\\n\\n for folder in to_copy_folders:\\n\\n to_copy_files = os.listdir(os.getcwd() + '/to_copy/' + folder)\\n for files in to_copy_files:\\n if not files.startswith('.'):\\n if not os.path.exists(self.app_location + '/' + folder + '/' + files):\\n if not folder.startswith('.'):\\n shutil.copyfile(os.getcwd() + '/to_copy/' + folder + '/' + files, self.app_location + '/' + folder + '/' + files)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"663274598bed082be9e720a68a287238\",\n \"score\": \"0.5310453\",\n \"text\": \"def move(src, dst, ignore=None, logger=None):\\n\\n src = Path(src)\\n dst = Path(dst)\\n print('src,dst',src.absolute(),dst.absolute())\\n if not (src.is_dir() and dst.is_dir()):\\n raise NotADirectoryError(f\\\"src: {src} and dst: {dst} must be directories\\\")\\n\\n # check if src and dst exist\\n # read errors are already skipped by os.walk\\n # and are directories\\n # add logger to handle_exception\\n for rpath, dirs, files in os.walk(src, topdown=False, onerror=handle_exception):\\n for fi in files:\\n # path to src \\n fi_src = Path(rpath).absolute() / fi\\n #if not fi_src.exists():\\n # raise Exception(\\\"HELLOOO\\\", src, rpath, fi, fi_src)\\n dir_dst = (dst / Path(rpath).relative_to(src)).absolute()\\n fi_dst = dir_dst / fi\\n print(\\\"dir dst\\\", dir_dst, dir_dst.exists())\\n print(\\\"fi_src, fi dst\\\", fi_src, fi_dst)\\n \\n try:\\n dir_dst.mkdir(exist_ok=True)\\n os.rename(fi_src, fi_dst)\\n # if dst is different device then copy and remove\\n # TODO: use error code instead of broad OSError\\n # log this activity\\n except OSError:\\n try:\\n scopy(fi_src, fi_dst, follow_symlinks=False)\\n set_owner_mode_xattr(fi_src, fi_dst)\\n os.unlink(fi_src)\\n # catch all exceptions\\n except Exception as ex:\\n handle_exception(ex, fi_src, fi_dst, logger=logger)\\n except Exception as ex:\\n handle_exception(ex, fi_src, fi_dst, logger)\\n # set dst dir properties and remove src\\n for di in dirs:\\n # source dir path\\n dir_src = Path(rpath).absolute() / di\\n # check if dir exists? it must when os.walk if not top down\\n dir_dst = dst / Path(rpath).relative_to(src) / di\\n # to ensure not deleteting directories when not ignored\\n if ignore and ignore(dir_dst):\\n continue\\n try:\\n # handle if dst exists?\\n set_owner_mode_xattr(dir_src, dir_dst)\\n os.rmdir(dir_src)\\n # if dir is not empty and other exceptions\\n except Exception as ex:\\n handle_exception(ex)\\n # set permissions and delete\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc3a97f9025bdb7ccf7941c7f00c3ea8\",\n \"score\": \"0.53066707\",\n \"text\": \"def move(old, new):\\n if os.path.exists(new):\\n count = 1\\n root, ext = os.path.splitext(new)\\n while True:\\n new = \\\"%s_%i%s\\\" % (root, count, ext)\\n if os.path.exists(new):\\n count += 1\\n else:\\n break\\n os.rename(old, new)\\n else:\\n os.rename(old, new)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"675bee6d62cdeeb13bb7009764e9bd4a\",\n \"score\": \"0.53064406\",\n \"text\": \"def copytree_replace_existing(os_service, src, dst):\\n oss = os_service\\n helper = Helpers()\\n helper.copy_directory_tree(oss, src, dst)\\n\\n file_it = FileIterator(oss, src)\\n for file_sub_path in file_it:\\n source_file_path = os.path.join(src, file_sub_path)\\n dest_file_path = os.path.join(dst, file_sub_path)\\n oss.copyfile(source_file_path, dest_file_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89497c3cb44718886a567f7b0d0a7bba\",\n \"score\": \"0.53050506\",\n \"text\": \"def scan_directory(dir_):\\n for root, dirs, fname in os.walk(dir_):\\n for f in fname:\\n try:\\n path_ = os.path.join(dir_, f)\\n date_ = get_date_pfx(f)\\n if valid_format(date_):\\n new = get_datedir(date_, dir_)\\n if not os.path.exists(new):\\n os.makedirs(new)\\n if os.path.exists(path_):\\n os.rename(path_, name_new_file(new, f))\\n except Exception as ex:\\n print str(ex)\\n continue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96b6b58eeb478bf211cb42b22f56bbb2\",\n \"score\": \"0.5304466\",\n \"text\": \"def copyDirectoryContents(srcDir: RelFilePathT, destDir: RelFilePathT) -> None:\\n filePathList = os.listdir(srcDir)\\n for filePath in filePathList:\\n absFilePath = osp.join(srcDir, filePath)\\n subp.run(f\\\"cp -r {absFilePath} {destDir}\\\", shell=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b462e79ceca0a566939f2d11feff446f\",\n \"score\": \"0.5302334\",\n \"text\": \"def main():\\n print(\\\"Starting directory is: {}\\\".format(os.getcwd()))\\n\\n # Change to desired directory\\n os.chdir('FilesToSort')\\n\\n # Print a list of all files in current directory\\n print(\\\"Files in {}:\\\\n{}\\\\n\\\".format(os.getcwd(), os.listdir('.')))\\n\\n #breakpoint()\\n\\n # Make a new directory\\n # The next time you run this, it will crash if the directory exists\\n\\n # Getting the extensions available:\\n extension_list = []\\n extension_exist = True\\n\\n for filename in os.listdir('.'):\\n\\n if filename == \\\".DS_Store\\\":\\n continue\\n\\n extension = os.path.splitext(filename)[1]\\n\\n if len(extension_list) == 0:\\n extension_list.append(extension)\\n\\n for each in extension_list:\\n\\n if extension == each:\\n extension_exist = True\\n break\\n else:\\n extension_exist = False\\n\\n if extension_exist == False:\\n extension_list.append(extension)\\n\\n # Create extension dirs:\\n extension_dict = dict()\\n\\n for each in extension_list:\\n\\n if each == \\\"\\\":\\n continue\\n\\n key_to_add = input(\\\"Add {}\\\".format(each))\\n\\n if not (key_to_add in extension_dict):\\n extension_dict[key_to_add] = []\\n extension_dict[key_to_add].append(each)\\n else:\\n extension_dict[key_to_add].append(each)\\n\\n print(extension_dict)\\n #breakpoint()\\n\\n for key in extension_dict.keys():\\n if key != \\\"\\\":\\n try:\\n os.mkdir(key)\\n except:\\n os.rmdir(key)\\n os.mkdir(key)\\n\\n # Loop through each file in the (current) directory\\n for filename in os.listdir('.'):\\n\\n if filename == \\\".DS_Store\\\":\\n continue\\n\\n extension = os.path.splitext(filename)[1]\\n dir_to_move = \\\"\\\"\\n # Ignore directories, just process files\\n if os.path.isdir(filename):\\n continue\\n\\n #find the dir to move:\\n for key, value in extension_dict.items():\\n if extension in extension_dict[key]:\\n dir_to_move = key\\n\\n shutil.copy(filename, dir_to_move + '/' + filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"573a721b444eb8267b2484828b521252\",\n \"score\": \"0.52874583\",\n \"text\": \"def test_move_files(self):\\n # Initialize key variables\\n source_filenames = {}\\n target_filenames = {}\\n\\n #################################################\\n # Test with invalid source directory\\n #################################################\\n\\n invalid_path = '/tmp/{}.{}'.format(\\n self.random_string,\\n self.random_string)\\n\\n with self.assertRaises(SystemExit):\\n general.move_files(invalid_path, '/tmp')\\n\\n #################################################\\n # Test with invalid destination directory\\n #################################################\\n\\n invalid_path = '/tmp/{}.{}'.format(\\n self.random_string,\\n self.random_string)\\n\\n with self.assertRaises(SystemExit):\\n general.move_files('/tmp', invalid_path)\\n\\n #################################################\\n # Test with valid directory\\n #################################################\\n\\n # Create a source directory\\n source_dir = '/tmp/{}.1'.format(self.random_string)\\n if os.path.exists(source_dir) is False:\\n os.makedirs(source_dir)\\n\\n # Create a target directory\\n target_dir = '/tmp/{}.2'.format(self.random_string)\\n if os.path.exists(target_dir) is False:\\n os.makedirs(target_dir)\\n\\n # Place files in the directory\\n for count in range(0, 4):\\n filename = ''.join([random.choice(\\n string.ascii_letters + string.digits) for n in range(15)])\\n source_filenames[count] = '{}/{}'.format(source_dir, filename)\\n target_filenames[count] = '{}/{}'.format(target_dir, filename)\\n open(source_filenames[count], 'a').close()\\n\\n # Check files in directory\\n self.assertEqual(os.path.isfile(source_filenames[count]), True)\\n\\n # Delete files in directory\\n general.move_files(source_dir, target_dir)\\n\\n # Check that files are not in source_dir\\n for filename in source_filenames.values():\\n self.assertEqual(os.path.isfile(filename), False)\\n\\n # Check that files are in in target_dir\\n for filename in target_filenames.values():\\n self.assertEqual(os.path.isfile(filename), True)\\n\\n # Delete directory\\n shutil.rmtree(source_dir)\\n\\n # Delete directory\\n shutil.rmtree(target_dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f3b270e73e784c64bdd46fb0d8466b1\",\n \"score\": \"0.5283628\",\n \"text\": \"def reorg_files(raw_dir):\\n os.chdir(raw_dir)\\n\\n if not Path('People.csv').is_file():\\n unzip_dir = raw_dir / 'baseballdatabank-master' / 'core'\\n\\n # move the unzipped csv files to the current working directory\\n for root, dirs, files in os.walk(unzip_dir):\\n for file in files:\\n shutil.move(root + '/' + file, '.')\\n\\n # rm the extract directory\\n shutil.rmtree('baseballdatabank-master')\\n\\n msg = '\\\\n'.join(os.listdir('.'))\\n logger.info(f'{raw_dir} contents:\\\\n {msg}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b1abbb6e682699d0ec361b1555bf918\",\n \"score\": \"0.52821255\",\n \"text\": \"def mv2scratch( filename, dirpath ):\\n scpath = dirpath.replace('/scratch/_gc/','/scratch/',1)\\n oldpath = os.path.join(dirpath,filename)\\n #newpath = os.path.join(scpath,filename)\\n print \\\"moving from\\\",oldpath,\\\"\\\\nto\\\",scpath\\n if not os.path.isdir(scpath):\\n os.makedirs(scpath)\\n shutil.move( oldpath, scpath )\\n listgood( filename )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6635cfecfb52ed40319d0404c2099d8e\",\n \"score\": \"0.5281678\",\n \"text\": \"def convert_and_move(filename, date, atmos_cor):\\n # TODO: Something is wrong with this function, getting false positives\\n # for files already existing, so the conversion process is aborted\\n # when it shouldn't be\\n\\n logger.debug('Starting conversion process for {}'.format(filename))\\n tqdm.tqdm.write('Starting conversion process for {}'.format(filename))\\n\\n # Get tile id using re, break it out into zone num, tile ids, MGRS format\\n tile = get_utm_tile(filename)\\n\\n if not tile:\\n tile_zone = 'none'\\n tile_band = 'none'\\n tile_squareID = 'none'\\n else:\\n tile_zone = tile['zone']\\n tile_band = tile['band']\\n tile_squareID = tile['square']\\n # check if converted directory already exists\\n destination_dir = \\\"S2_{}_{}_{}_{}\\\".format(tile_zone,\\n tile_band,\\n tile_squareID,\\n date.strftime('%Y%m%d_%H%M'))\\n\\n # Check if the simplified directory already exists\\n if Path.exists(Path(FINAL_DIR, destination_dir)):\\n logger.debug('Final result directory already exists, checking file'\\n 'extension of first file to see if it matches the current'\\n 'atmospheric correction value')\\n\\n file_list_iter = os.scandir(Path(FINAL_DIR, destination_dir))\\n\\n\\n for file_name in file_list_iter:\\n if file_name.name[-7:-5] == str(atmos_cor):\\n logger.debug('That resolution of atmos correction already'\\n 'exists..., aborting the conversion...')\\n logger.debug('Checking if the .zip still exists...')\\n\\n if not (Path(FINAL_DIR, destination_dir + '.zip').exists()):\\n zip_directory(destination_dir)\\n\\n return \\\"already exists\\\"\\n\\n if not file_name.name[-7:-5] in ['10','20','60'] and atmos_cor == 0:\\n logger.debug('Uncorrected files already exist, aborting...')\\n\\n logger.debug('Checking if the .zip still exists...')\\n\\n if not (Path(FINAL_DIR, destination_dir + '.zip').exists()):\\n zip_directory(destination_dir)\\n\\n return \\\"already exists\\\"\\n\\n logger.info('Final result directory does not exist. Beginning conversion')\\n logger.debug('atmos_cor: {}'.format(atmos_cor))\\n\\n if atmos_cor in [10, 20, 60]:\\n # Convert filename to L2A with .SAFE suffix\\n file_string = filename.replace('L1C', 'L2A')\\n else:\\n file_string = filename\\n\\n file_string += '.SAFE'\\n\\n # Make sure the temporary data folder we are converting exists\\n if not os.path.isdir(os.path.join(BUNDLE_DIR, TEMP_DIR, file_string)):\\n logger.warning('Expected data directory to be converted does not exist'\\n ' exiting...')\\n tqdm.tqdm.write('Expected data directory to convert does not exist, stopping...')\\n\\n return 'failed'\\n\\n granule_dir = Path('temp', file_string, 'GRANULE')\\n\\n # Iterate over each granule, if there are multiple\\n for dir in granule_dir.iterdir():\\n\\n logger.debug(str(dir))\\n if atmos_cor in [10, 20, 60]:\\n convert_dir = Path(dir, 'IMG_DATA', \\\"R{}m\\\".format(atmos_cor))\\n else:\\n convert_dir = Path(dir, 'IMG_DATA',)\\n\\n logger.debug(convert_dir)\\n file_list = convert_dir.glob('*.jp2')\\n\\n # For the actual img data, use the convert_jp2 function\\n for file in file_list:\\n logger.debug(file)\\n dest_path = Path(FINAL_DIR, destination_dir)\\n try:\\n conv.convert_jp2_to_tif(file,\\n destination_dir,\\n atmos_cor,\\n dest_path)\\n except Exception as e:\\n logger.error('Something went wrong with the conversion')\\n logger.error(str(e))\\n return 'failure'\\n\\n # Copy and rename the metadata file in the GRANULE\\n shutil.copy2(str(Path(dir, 'MTD_TL.xml')),\\n str(Path(FINAL_DIR,\\n destination_dir,\\n destination_dir + \\\"_metadata.xml\\\")))\\n\\n logger.info('converted jp2 to tif successfully, zipping result')\\n zip_directory(destination_dir)\\n\\n return 'success'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e985ef77f29f78ddf4b26a0879bc8e75\",\n \"score\": \"0.5277273\",\n \"text\": \"def setup_destination_directory(self):\\n\\n new_subpath = self.dir_to_import[len(self.top_of_repo):]\\n\\n destination_directory = os.path.join(self.destination_directory, new_subpath)\\n\\n if not os.path.exists(destination_directory):\\n os.makedirs(destination_directory)\\n\\n _log.info('Tests will be imported into: %s', destination_directory)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"946be042ff7540732aababf327aa6f5f\",\n \"score\": \"0.527297\",\n \"text\": \"def copy_files(src_folder: str, dest_folder: str) -> None:\\n shutil.copytree(src_folder, dest_folder, dirs_exist_ok=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48f71ed72d55817281cee9ad5801f8da\",\n \"score\": \"0.526326\",\n \"text\": \"def fix_directories(input_dir_path, output_dir_path):\\n if not input_dir_path:\\n input_dir_path = output_dir_path\\n input_dir_path = os.path.realpath(input_dir_path)\\n output_dir_path = os.path.realpath(output_dir_path)\\n return input_dir_path, output_dir_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6c922808be1ebe1c983b95050534aa5\",\n \"score\": \"0.5263\",\n \"text\": \"def clean_up_files(output_dir):\\n\\t\\n\\tpass\\n\\treturn\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994187,"cells":{"query_id":{"kind":"string","value":"dd403594a26d4ed6b1599a1e49a12d9a"},"query":{"kind":"string","value":"Draws the points onto the GUI canvas"},"positive_passages":{"kind":"list like","value":[{"docid":"3d0fe5e512da5b59f5dc44f3070a7bab","score":"0.6598843","text":"def on_paint(self, event):\n dc = wx.PaintDC(self.panel)\n dc.SetPen(wx.Pen('black', 1))\n dc.SetBrush(wx.Brush('black', 1))\n for point in self.annealing.state.points:\n dc.DrawCircle(point[0], point[1], 2)","title":""}],"string":"[\n {\n \"docid\": \"3d0fe5e512da5b59f5dc44f3070a7bab\",\n \"score\": \"0.6598843\",\n \"text\": \"def on_paint(self, event):\\n dc = wx.PaintDC(self.panel)\\n dc.SetPen(wx.Pen('black', 1))\\n dc.SetBrush(wx.Brush('black', 1))\\n for point in self.annealing.state.points:\\n dc.DrawCircle(point[0], point[1], 2)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ab4edc0d7656931e0deb95b2ce5bd993","score":"0.74049175","text":"def _draw(self):\n if self.colorTable != {}: CT = self.colorTable\n else:\n CT = self._genColorTable()\n self.plotter.clear()\n for p in self.points:\n self.plotter.setColor(CT[p])\n self.plotter.spot(p, self.spotWidth)","title":""},{"docid":"99b08b619d77280ec9c902bc57ae0553","score":"0.72308236","text":"def draw(self):\n self.canvas.draw()","title":""},{"docid":"c9e5dea799389f8e2047272ac01f5e9e","score":"0.7219184","text":"def drawPoint(self, qp):\r\n size = self.size()\r\n\r\n # Loops through every point in the window\r\n for i in range(self.num_xpoints):\r\n for j in range(self.num_ypoints):\r\n\r\n # Evenly distribute the points in both the x and y directions\r\n x_coord = ((size.width() - 100) / 21.0) * (i + 1) + 50\r\n y_coord = ((size.height() - 100) / 21.0) * (j + 1) + 50\r\n\r\n # Colours are determined by colourList\r\n qp.setPen(QPen(self.colourList[i][j], 1, Qt.SolidLine))\r\n qp.setBrush(QBrush(self.colourList[i][j], Qt.SolidPattern))\r\n\r\n # We draw the points as rectangles, and updates their locations\r\n qp.drawRect(x_coord, y_coord, 10, 10)\r\n self.pointLocation[i][j] = (x_coord, y_coord)","title":""},{"docid":"fc54db68b3e8c1662bd6927189be6cd9","score":"0.71158445","text":"def DrawPoint(self, *args, **kw):","title":""},{"docid":"c262c67fa87a29c83822da9ade0ff382","score":"0.7056661","text":"def onPaint(self, event):\r\n self.SetCurrent(self.context)\r\n\r\n # Make sure the canvas has been initialised;\r\n if not self.bInitialised:\r\n self.initialiseGL()\r\n\r\n # OpenGL, in this case, uses a \"double bufferring\" system:\r\n # it displays one buffer whilst drawing to the other.\r\n # Clear the current back buffer\r\n GL.glClear(GL.GL_COLOR_BUFFER_BIT | GL.GL_DEPTH_BUFFER_BIT)\r\n\r\n # Draw the points stored in the vertex array onto the buffer\r\n GL.glDrawArrays(GL.GL_LINE_STRIP, 0, self.numPoints)\r\n\r\n # Now make the back buffer the front buffer\r\n # i.e. this is now displayed on the screen\r\n GL.glFlush()\r\n self.SwapBuffers()","title":""},{"docid":"bde0c050f4d3cba08435fd4920e2665e","score":"0.70482403","text":"def draw(self):\n\n glBegin(GL_POINTS)\n glColor((1, 1, 1))\n glVertex3fv(self.get_point())\n glEnd()","title":""},{"docid":"0cb4e5cde4d1cd8a51168c87213f6ded","score":"0.7002541","text":"def draw(self):\n xlim_max = max([find_max(0, self.graph), 0])\n xlim_min = min([find_min(0, self.graph), 0])\n ylim_max = max([find_max(1, self.graph), 0])\n ylim_min = min([find_min(1, self.graph), 0])\n\n # Add some parameters to make graph in center of picture\n plt.xlim(xlim_min - 1, xlim_max + 2)\n plt.ylim(ylim_min - 1, ylim_max + 2)\n plt.gca().set_aspect('equal', adjustable='box')\n\n # Add list with steps on axes to see coordinates of points\n x_steps = []\n y_steps = []\n for i in range(xlim_min - 1, xlim_max + 2):\n x_steps.append(i)\n for i in range(ylim_min - 1, ylim_max + 2):\n y_steps.append(i)\n\n plt.xticks(x_steps)\n plt.yticks(y_steps)\n\n start_x = 0\n start_y = 0\n for i in range(self.graph.num_rows()):\n x1, y1 = [], []\n for j in range(self.graph.num_cols()):\n if self.graph[(i, j)]:\n x1.append(self.graph[(i, j)][0])\n y1.append(self.graph[(i, j)][1])\n\n plt.plot(x1, y1, 'b', marker='o')\n plt.show()","title":""},{"docid":"4f2b43d544a6200347ba850c5049e246","score":"0.6836888","text":"def draw(self):\n points = [{'pos': list(self.primitive), 'color': self.color, 'name': self.name}]\n self.guids = compas_rhino.draw_points(points, layer=self.layer, clear=False, redraw=False)","title":""},{"docid":"71ec4a486281bf80e3853a9ccf6b6615","score":"0.68199426","text":"def drawPoint(self, x, y):\n self.plotArea.create_oval(self.xToCanvas(x), self.yToCanvas(y), self.xToCanvas(x), self.yToCanvas(y))","title":""},{"docid":"a82bb6a62b328cd0db8a90baafcf0015","score":"0.6798981","text":"def display(self):\n point1, point2 = self.points\n pyxel.line(\n x1=point1.x + self.x,\n y1=point1.y + self.y,\n x2=point2.x + self.x,\n y2=point2.y + self.y,\n col=self.colour,\n )","title":""},{"docid":"8088b60d7bbe481f7a3529b1420da40a","score":"0.679548","text":"def draw(self):","title":""},{"docid":"8088b60d7bbe481f7a3529b1420da40a","score":"0.679548","text":"def draw(self):","title":""},{"docid":"8088b60d7bbe481f7a3529b1420da40a","score":"0.679548","text":"def draw(self):","title":""},{"docid":"8088b60d7bbe481f7a3529b1420da40a","score":"0.679548","text":"def draw(self):","title":""},{"docid":"ed27d331a9844a73940780db25ac219f","score":"0.6770996","text":"def draw(self):\n if (not self._surface):\n self._surface = display.set_mode((self.SIZE, self.SIZE))\n self._surface.fill(self.BACKGROUND_COLOR)\n for point in self.points:\n color = self.NOT_STAR_COLOR\n if (point.star):\n color = self.STAR_COLOR\n draw.circle(self._surface, color, point.position.round.tuple, point.radius, self.BORDER_THICKNESS)\n display.flip()\n for e in event.get():\n if e.type == QUIT:\n quit()\n exit(0)","title":""},{"docid":"d5d6b53e7db2b0e688e08bf38669af21","score":"0.67390406","text":"def draw_raw_points(self):\n # wp_xyz_list = list(self.nx_graph_topology.nodes(data='xyz')\n xp_xyz_list = list()\n for vertex_id in self.nx_graph_topology.nodes():\n xp_xyz_list.append(self.node_id_map_topology[vertex_id].transform.location.x)\n xp_xyz_list.append(self.node_id_map_topology[vertex_id].transform.location.y)\n\n # all_elements1 = xp_xyz_list[::2]\n # p1 = xp_xyz_list[::2]\n # p2 = xp_xyz_list[1::2]\n\n segment_list = list()\n\n for edge in self.nx_graph_topology.edges():\n segment_list.append(edge)\n\n fig, ax = plt.subplots(1, 1)\n ax.scatter(xp_xyz_list[::2], xp_xyz_list[1::2], s=5)\n ax.set_title('Global navi waypoints')\n\n for i in range(len(segment_list)):\n ax.plot([self.node_id_map_topology[segment_list[i][0]].transform.location.x,\n self.node_id_map_topology[segment_list[i][1]].transform.location.x],\n [self.node_id_map_topology[segment_list[i][0]].transform.location.y,\n self.node_id_map_topology[segment_list[i][1]].transform.location.y],\n 'k-',\n lw=1)\n\n def onclick(event):\n print('%s click: button=%d, x=%d, y=%d, xdata=%f, ydata=%f' %\n ('double' if event.dblclick else 'single', event.button,\n event.x, event.y, event.xdata, event.ydata))\n if self.clicks < self.click_max:\n self.clicks += 1\n self.nav_points_map.append((event.xdata, event.ydata))\n\n # return event.xdata, event.ydata\n\n cid = fig.canvas.mpl_connect('button_press_event', onclick)\n\n plt.show()\n # wp_xyz_list = list(self.nx_graph_topology.nodes())","title":""},{"docid":"835162cc6ec678130acb4549fc546d02","score":"0.6735644","text":"def display(self):\n\n for point1, point2 in zip(self.points, self.points[1:] + [self.points[0]]):\n pyxel.line(\n x1=point1.x + self.x,\n y1=point1.y + self.y,\n x2=point2.x + self.x,\n y2=point2.y + self.y,\n col=self.colour,\n )\n\n if self.accelerating:\n self.display_acceleration()","title":""},{"docid":"aab025ba212b74433fa00ac1d62d1700","score":"0.67278993","text":"def draw(self):\n pass","title":""},{"docid":"aab025ba212b74433fa00ac1d62d1700","score":"0.67278993","text":"def draw(self):\n pass","title":""},{"docid":"aab025ba212b74433fa00ac1d62d1700","score":"0.67278993","text":"def draw(self):\n pass","title":""},{"docid":"aab025ba212b74433fa00ac1d62d1700","score":"0.67278993","text":"def draw(self):\n pass","title":""},{"docid":"aab025ba212b74433fa00ac1d62d1700","score":"0.67278993","text":"def draw(self):\n pass","title":""},{"docid":"cb1d6b2102c44e483c249400d5919ad3","score":"0.6715999","text":"def draw(self):\n ...","title":""},{"docid":"33f4281a21d1a02b232c2b8863ac85c2","score":"0.6613458","text":"def draw(self):\n\t\tpass","title":""},{"docid":"be473ae37043ea89cc5c9545242228ef","score":"0.65960264","text":"def draw(self) -> None:\n pass","title":""},{"docid":"268810559c00cd7c910337cb3cb15745","score":"0.65592295","text":"def draw(self, **kwargs):\n pass","title":""},{"docid":"1054bb296adbfe9ace7edb2581942013","score":"0.6534617","text":"def draw(self, win, center):","title":""},{"docid":"d85526c6016741641619de885617786b","score":"0.65293276","text":"def DrawLines(self, points, xoffset=0, yoffset=0):","title":""},{"docid":"1ce46b0f9e34733de8e52d4f71577ada","score":"0.6499565","text":"def plot(self, pr):\n x, y = self.transform(pr)\n self.gfx.drawPoint(self.tr.X(x), self.tr.Y(y))","title":""},{"docid":"98da1f37efdd481191de1335bcd1cf79","score":"0.64826924","text":"def _draw(self):\n pass","title":""},{"docid":"9809598353cdbae9d3ae7426cb029b30","score":"0.6477086","text":"def draw(self):\n fig, ax = plt.subplots(nrows=1, ncols=1)\n ax.plot(self.x, self.y, linestyle='', marker='s')\n line = list()\n if self.m is not None and self.b is not None:\n for i in self.x:\n line.append(self.m * i + self.b)\n ax.plot(self.x, line, color='red')\n plt.show()","title":""},{"docid":"dc397592cec484bfb48738feadc3d36c","score":"0.6470176","text":"def draw(self, *args, **kwargs):\n pass","title":""},{"docid":"fc31efc4a783dc12aeca2d1f74a98b5b","score":"0.6464472","text":"def draw(self):\n self.prepare()\n self.show()","title":""},{"docid":"ade6194f48ec419ba1139664290dda0e","score":"0.645895","text":"def paint(self):\r\n if self.ready:\r\n self.canvas.delete(tkinter.ALL)\r\n self.visit(self.tree.root)\r\n \r\n if self.newRegion:\r\n self.canvas.create_rectangle(self.newRegion.x_min, self.toTk(self.newRegion.y_min),\r\n self.newRegion.x_max, self.toTk(self.newRegion.y_max), \r\n outline='Black', dash=(2, 4))\r\n \r\n if self.selectedRegion:\r\n self.canvas.create_rectangle(self.selectedRegion.x_min, self.toTk(self.selectedRegion.y_min),\r\n self.selectedRegion.x_max, self.toTk(self.selectedRegion.y_max), \r\n outline='Red', dash=(2, 4))\r\n else:\r\n self.label = tkinter.Label(self.w, width=100, height = 40, text=\"Click To Add Points\")\r\n self.label.bind(\"<Button-1>\", self.prepare)\r\n self.label.pack()\r\n self.ready = True","title":""},{"docid":"352d0d2f8a3bdc7ab4f3518cee23db5d","score":"0.64500105","text":"def update_canvas(code, point = Point(0,0)):\n fig = pylab.figure(figsize=[4, 4], dpi=100)\n ax = fig.gca()\n\n if code == \"np\": # new point\n ax.plot(points_x, points_y, 'ro')\n\n if code == \"dp\": # draw path\n ax.plot(points_x, points_y, 'ro')\n x, y = curve_to_arrays(point, 1000, 0.001)\n ax.plot(x, y, 'r--')\n\n canvas = agg.FigureCanvasAgg(fig)\n canvas.draw()\n renderer = canvas.get_renderer()\n raw_data = renderer.tostring_rgb()\n\n size = canvas.get_width_height()\n\n surf = pygame.image.fromstring(raw_data, size, \"RGB\")\n screen.blit(surf, (0, 0))\n pygame.display.flip()\n\n return ax","title":""},{"docid":"d3811d976b0b791cf6be9488d36be261","score":"0.6440813","text":"def draw(self, output=None):","title":""},{"docid":"6583158c485f0a678c827ef842da6ed0","score":"0.6435612","text":"def _draw(self):\r\n pass","title":""},{"docid":"070971094cde182e950222e0f92d6d98","score":"0.6429595","text":"def start_draw(event):\r\n pos_x.append(event.xdata)\r\n pos_y.append(event.ydata)\r\n \r\n \"\"\"keshidane khotoot va kamel kardane shekl\"\"\"\r\n \r\n if len(pos_x)>1:\r\n plt.scatter(pos_x, pos_y, color =\"red\")\r\n plt.draw()\r\n if abs(event.xdata-pos_x[0]) > 0.05*pos_x[0] or \\\r\n abs(event.ydata-pos_y[0]) > 0.05*pos_y[0]:\r\n plt.plot(pos_x, pos_y, color = \"dodgerblue\")\r\n \r\n else:\r\n plt.scatter(pos_x[0], pos_y[0], color =\"red\")\r\n plt.draw()\r\n plt.plot([pos_x[-2], pos_x[0]], [pos_y[-2], pos_y[0]], \r\n color = \"dodgerblue\") \r\n fig.canvas.mpl_disconnect(button)\r\n pos_x.pop()\r\n pos_y.pop()\r\n pos_x.append(pos_x[0])\r\n pos_y.append(pos_y[0])\r\n plt.fill(pos_x, pos_y, color = \"cyan\") \r\n\r\n else : \r\n plt.scatter(pos_x[0], pos_y[0], color =\"red\")\r\n plt.draw()\r\n print([event.xdata, event.ydata])","title":""},{"docid":"5eda2fc38ed3bd4888cc9ab5db5a8b82","score":"0.6411792","text":"def draw(self, setlim=True):\n\n # Don't plot if variable full of Nan\n if np.count_nonzero(~np.isnan(self.x)) == 0 or \\\n np.count_nonzero(~np.isnan(self.y)) == 0: return\n\n self.canvas.mpl_connect('motion_notify_event', self.ShowPosition)\n\n self.x = ma.masked_invalid(self.x)\n self.y = ma.masked_invalid(self.y)\n if setlim:\n self.xlim = [np.min(self.x), np.max(self.x)]\n self.ylim = [np.min(self.y), np.max(self.y)]\n title = self.title\n plotCurv(self, x=self.x, y=self.y, title=title, xlabel=self.xlabel,\n ylabel=self.ylabel, xlim=self.xlim, ylim=self.ylim)\n self.canvas.draw()\n self.canvas.Refresh()\n self.Show()","title":""},{"docid":"0c4fa495be0a85dd1516384cfe48a274","score":"0.64081395","text":"def _draw(self):\n\n self.lgnd_crvs = []\n self.lgnd_pnts = []\n self.lgnd_lbls = []\n\n for ii,ll in enumerate(self.lgnd):\n if False == self._reverse:\n yy = len(self.lgnd)-ii-1\n else:\n yy = ii\n add_curve( [0.25,1], [yy,yy], self.lgnd[ii] )\n self.lgnd_crvs.append( self._get_current_object_name(\"Curve\") )\n set_curve(\"symbol.style=none\")\n pnt = self._get_point_properties( self.lgnd[ii].symbol ) \n add_point( 0.625, yy, pnt )\n self.lgnd_pnts.append( self._get_current_object_name(\"Point\") )\n add_label( 1.25, yy, str(ii), \"valign=0.5\" )\n self.lgnd_lbls.append( self._get_current_object_name(\"Label\") )","title":""},{"docid":"5bfcd1996c4999315e0efede413b8d06","score":"0.64076024","text":"def showPoints(self, context):\n for point in self.points:\n point.show(context)","title":""},{"docid":"c3435ae8fff08384eb1fc4a0a0240309","score":"0.64066726","text":"def update_canvas(self):\n velocity_data.append(self.current_velocity)\n altitude_data.append(self.current_altitude)\n sub_velocity.clear()\n sub_altitude.clear()\n sub_velocity.plot(velocity_data)\n sub_altitude.plot(altitude_data)\n self.canvas_left.draw()\n self.canvas_right.draw()\n if self.update_plot:\n self.frame_upper_middle.after(100, self.update_canvas)","title":""},{"docid":"21b719e687b52b31446ddcbaa82982a6","score":"0.6400952","text":"def draw(self, canvas): \n self.draw_self(canvas) \n self.draw_children(canvas)","title":""},{"docid":"f852c2a6cdb09e67c2c41e5f408d0bef","score":"0.63838935","text":"def draw(self):\n self.image.draw()\n self.text.draw()","title":""},{"docid":"161c0e174ba79e0ebd9f718bf8ea59fa","score":"0.63780683","text":"def ButtonPress(self, event):\n self.clicked = True\n self.update_plot()\n self.newx = event.x\n self.newy = event.y\n self.canvas.draw_line(self.newx, self.newy)\n self.canvas.index(self.newx)\n self.create_ppdata()\n self.update_label()","title":""},{"docid":"bcdc49685711a9176d4e572f31fd71d1","score":"0.63757294","text":"def paintPlug(self, canvas, x, y):\n pass","title":""},{"docid":"66a12b3760e508b89672a24b93ba155f","score":"0.6375435","text":"def plot_points(self):\n self.operator._set_monitor_plot_points(self.plot_points_spinbox.value())\n self.plot_points_spinbox.setValue(self.operator.properties['monitor']['plot_points'])\n set_spinbox_stepsize(self.plot_points_spinbox)","title":""},{"docid":"078df1510309d220eaef839c875ba6da","score":"0.6368254","text":"def DrawXYZ(self):\r\n self.xyPanel.Refresh()","title":""},{"docid":"9065c246c1f3087d6f1b5fb53725f2d5","score":"0.6358699","text":"def draw(self):\n raise NotImplementedError(\"draw() needs to be implemented\")","title":""},{"docid":"df8c3e2fa8b51b2cbd13381be4400c8b","score":"0.6354665","text":"def draw(points, **scatter_params):\n if scatter_params is None:\n scatter_params = {label: 'Data points'}\n X = points[:,0] / points[:,2]\n Y = points[:,1] / points[:,2]\n\n # Plot the points\n plt.scatter(X, Y, **scatter_params)","title":""},{"docid":"19052fea20ee3399147bde6ea5459129","score":"0.63519806","text":"def update_plot(self):\n\n self.fig.canvas.draw()","title":""},{"docid":"19052fea20ee3399147bde6ea5459129","score":"0.63519806","text":"def update_plot(self):\n\n self.fig.canvas.draw()","title":""},{"docid":"19052fea20ee3399147bde6ea5459129","score":"0.63519806","text":"def update_plot(self):\n\n self.fig.canvas.draw()","title":""},{"docid":"19052fea20ee3399147bde6ea5459129","score":"0.63519806","text":"def update_plot(self):\n\n self.fig.canvas.draw()","title":""},{"docid":"19052fea20ee3399147bde6ea5459129","score":"0.63519806","text":"def update_plot(self):\n\n self.fig.canvas.draw()","title":""},{"docid":"19052fea20ee3399147bde6ea5459129","score":"0.63519806","text":"def update_plot(self):\n\n self.fig.canvas.draw()","title":""},{"docid":"757daf6c0f7046c81698f02deb847f6e","score":"0.6343511","text":"def draw(self, context):\n pass","title":""},{"docid":"757daf6c0f7046c81698f02deb847f6e","score":"0.6343511","text":"def draw(self, context):\n pass","title":""},{"docid":"c21f2e007aac4aded15febc7bb46fae4","score":"0.6339141","text":"def draw(self):\n pg.draw.circle(self.surface, self.color, self.xy, self.rect_radius)","title":""},{"docid":"995653a4c3eada74acf8f08629fdc553","score":"0.6324916","text":"def render(self):\r\n plt.draw() #Draw and display the plot in new window\r\n plt.show()","title":""},{"docid":"c050d90f80f2c14dfd8e6830eae7c6da","score":"0.6316409","text":"def draw_canvas(self):\n fig = plt.figure()\n ax = fig.add_subplot(111)\n maxtag = np.amax(self.lattice)\n # rect = np.ma.masked_equal(self.lattice, 0)\n rect = np.copy(self.lattice)\n rect[rect == 0] = -1\n palette = plt.cm.gray\n palette.set_over('b', 1.0)\n palette.set_under('w', 1.0)\n palette.set_bad('w', 1.0)\n for i, tag in enumerate(list(self.ptag)):\n rect[rect == tag] = maxtag + i + 2\n # ax.matshow(rect, cmap=plt.cm.jet)\n ax.matshow(rect, cmap=palette,\n norm=colors.Normalize(vmin=1, vmax=maxtag + 1, clip=False))\n ax.set_xticklabels([])\n ax.set_yticklabels([])\n plt.show()","title":""},{"docid":"7456fb950a8eeb0506fbba06672076c5","score":"0.6276118","text":"def paintEvent(self, event):\r\n # Initializes the Painter object\r\n qp = QPainter()\r\n qp.begin(self)\r\n\r\n self.drawSpace(qp) # Draws the box for the points\r\n self.drawPoint(qp) # Draw the points\r\n self.drawTheCircle(qp) # Draws the circles if there are any\r\n qp.end()","title":""},{"docid":"b110fe784790956532cd53f254d13057","score":"0.62706745","text":"def points(self, points):\n self.cpoints(points)","title":""},{"docid":"f90e1bc9431039b7e16da0590efa9ee0","score":"0.6267569","text":"def draw_callback(self, *args):\n #print \"callback data %s\" % data_tuple\n print \"callback args %s\" % args\n data = args[0]\n tool = data[0]\n print tool\n if (tool == 'clear'):\n self.backend.clear()\n if (tool == 'line'):\n #print \"start_x=%d, start_y=%d, end_x=%d, end_y=%d\" % (data[1].x(), data[1].y(), data[2].x(), data[2].y())\n self.backend.line((data[1].x(), data[1].y()), (data[2].x(), data[2].y()))\n if (tool == 'circle'):\n origo = (data[1].x(), data[1].y())\n r = int(round(helpers().point_distance(origo, (data[1].x(), data[1].y()))))\n self.backend.circle(r, origo)","title":""},{"docid":"b62b03fcc367215a7258ca8d8e79d261","score":"0.6247265","text":"def Draw(self):\r\n pass","title":""},{"docid":"ccf3ca4d4b419ebb57b7cd36db2747dd","score":"0.6245673","text":"def refresh_plot(self):\n self.view.canvas.draw()","title":""},{"docid":"9f8eb339cc032364b9f25c9a52e327e4","score":"0.6222222","text":"def drawPoints(self, pointPen, roundCoordinates=1):\n pointPen.beginPath()\n for (x, y), segmentType, args, kwargs in self.contour:\n # identifiers are not needed here\n # but the could cause errors, so remove them\n kwargs[\"identifier\"] = None\n pointPen.addPoint((x, y), segmentType, *args, **kwargs)\n pointPen.endPath()","title":""},{"docid":"5b083cca31ca184dd4c924406b372dcc","score":"0.62093407","text":"def update_xy_plot():\n\n\tlines.set_xdata(self.current_frame.x)\n\tlines.set_ydata(self.current_frame.y)\n\tfigure.canvas.draw()","title":""},{"docid":"babaf50d7133a36211550ee5235b72d9","score":"0.62000763","text":"def draw(self, backend):\n # Drawn by world\n pass","title":""},{"docid":"72cce35f85a4d52d1b7dd112e8134cda","score":"0.6190761","text":"def draw(self):\n\t\ttry:\n\t\t\tself.canvas.draw()\n\t\texcept Exception as e:\n\t\t\twarnings.warn(\"Error while drawing matplotlib figure: \\n\" + str(e))\n\t\tself._repaint_signal.signal.emit(\"emitting\")","title":""},{"docid":"b06b71e63824937258a37680935ddabc","score":"0.6189219","text":"def draw(self, position):\n # Getting value at position\n vector = self.value(position)\n x = vector[0]\n y = vector[1]\n # Using self.ped to draw vector\n self.ped.goto(position[0], position[1])\n self.ped.pendown()\n self.ped.goto(position[0]+x, position[1]+y)\n self.ped.penup()","title":""},{"docid":"6dbe24102f50864754172e4508a5a3a2","score":"0.6179086","text":"def draw(self, window):\n window.blit(self.image, (self.x, self.y))","title":""},{"docid":"99d6091d9dd0c26c1f197ca88dc8b68c","score":"0.6175171","text":"def display(self, canvas, x, y, width, height):\n # Do we need this?\n pass","title":""},{"docid":"bc46e144c200bad26ab72357e17f16cf","score":"0.6169833","text":"def draw(self, shape):","title":""},{"docid":"f471475ddc961393cd14236ca728b190","score":"0.61688405","text":"def draw():\n pass","title":""},{"docid":"a3974fd9d3dab3a4f36c43adf99f4de0","score":"0.61666214","text":"def draw_gui(self):\r\n self.draw_button_view()\r\n # self.draw_tree_widget()\r\n pass","title":""},{"docid":"c71cd077f04629119cbadfc5aa5a3201","score":"0.6166349","text":"def updatecanvas(self):\r\n # reseting canvas (add this before calling the draw shapes)\r\n self.canvas.delete('all')\r\n\r\n self.draw_shape(True, 37, 42)\r\n self.draw_shape(True, 43, 48)\r\n self.draw_shape(False, 1, 17)\r\n self.draw_shape(True, 18, 22)\r\n self.draw_shape(True, 23, 27)\r\n self.draw_shape(True, 37, 42)\r\n self.draw_shape(False, 28, 31)\r\n self.draw_shape(True, 32, 36)\r\n self.draw_shape(True, 49, 60)\r\n self.draw_shape(True, 61, 68)\r\n\r\n self.canvas.update()","title":""},{"docid":"d06f3ed18c3670c6ec37f887e4e619de","score":"0.61662865","text":"def show_points(self):\r\n self.screen.blit(self.player_points_image, self.player_points_rect)\r\n self.screen.blit(self.ai_points_image, self.ai_points_rect)","title":""},{"docid":"82f11dcca2e67e07d1c0bd788dc947b7","score":"0.61629325","text":"def draw_from_points(self,cv_image, points):\n for (x, y, w, h), n in points:\n cv.Rectangle(cv_image, (x, y), (x + w, y + h), 255)\n return cv_image","title":""},{"docid":"9337bff3cc7b9225c9f76e21437676ad","score":"0.61591405","text":"def show_curves_callback(self):\n self.plotter.update()\n self.fig_canvas.draw()","title":""},{"docid":"865f8f1ed1b82ad509ba0292a459553c","score":"0.6152894","text":"def draw(self):\n\t\tself.graphic.blit(self.viewport.x + self.offset_x, self.viewport.y + self.offset_y)","title":""},{"docid":"d126640838abb8f840a9e58262d5e38d","score":"0.61479783","text":"def draw(self):\n self.batch.draw()","title":""},{"docid":"0cd7d739302137da2c84c061bddb1596","score":"0.614265","text":"def draw(self):\n self.canvas.coords(self.hunger_bar, 150 * self.id + 50, 50, 150 * self.id + 50 + 100 * self.hunger / self.max_value, 100)\n self.canvas.coords(self.stamina_bar, 150 * self.id + 50, 150, 150 * self.id + 50 + 100 * self.stamina / self.max_value, 200)\n self.hunger_text.set(\"Hunger: %s / %s \"%(self.hunger, self.max_value))\n self.stamina_text.set(\"Stamina: %s / %s \"%(self.stamina, self.max_value))\n self.food_text.set(\"Remaining food: %s\"%self.food)\n preg = bool(self.until_birth)\n self.birth_text.set(\"Is \" + \"not\" * (not preg) + \" pregnant.\" + preg * (\" Time till giving birth: %s\"%self.until_birth))","title":""},{"docid":"4b1b5245d2b005b8370871ba90be65d5","score":"0.613991","text":"def draw(self):\n arcade.create_rectangle_filled(\n center_x=self.x,\n center_y=self.y,\n width=self.width,\n height=self.height,\n color=arcade.color.WHITE,\n ).draw()\n\n if self.turning_point:\n self.turning_point.draw()","title":""},{"docid":"3cf9eac49b0db4f49ec81cef5ec4f14d","score":"0.61281675","text":"def draw(self,window):\n\t\tself.main.draw(window) \n\t\tself.outer.draw(window)\n\t\tself.text.draw(window)\n\t\tself.window = window","title":""},{"docid":"27ece5f215feb2407de9febd926b7cfb","score":"0.61274135","text":"def draw(self, screen):\n pg.draw.circle(screen,\n self.color,\n (int(self.x), int(self.y)),\n self.size,\n self.thickness\n )","title":""},{"docid":"4eadb654d940f11afb7778f68d855cf8","score":"0.6120313","text":"def draw(self, canvas):\n for node in self.graph.nodes():\n self.graph.node[node]['object'].draw(canvas)\n\n self.set_linewidth()\n\n for edge in self.graph.edges():\n self.graph[edge[0]][edge[1]]['object'].draw(canvas)","title":""},{"docid":"d4213f10c55aa9c454633a2ed1b155d6","score":"0.6111755","text":"def draw(self, window):\n self.rect.draw(window)\n self.text.draw(window)","title":""},{"docid":"20583142a8556330d4a240087b8882fc","score":"0.6111176","text":"def draw(self):\r\n #draw a dot at the flying object center point, dot is blue and 5px\r\n arcade.draw_point(self.center.x, self.center.y, arcade.color.BLUE_SAPPHIRE, 5)","title":""},{"docid":"7a903fe00d10aa75f620d2979dec7dc6","score":"0.6111115","text":"def draw_circle(self):","title":""},{"docid":"e5901c5cbff840c396b94954aaaeeae6","score":"0.6105898","text":"def update_gps(self, idx):\n if self.gps_data is not None:\n self.gps_plot.set_xdata(self.gps_data[0][:idx])\n self.gps_plot.set_ydata(self.gps_data[1][:idx])\n self.canvas.draw()\n else:\n pass","title":""},{"docid":"4a718d76d0065b8edc33d35bad45850b","score":"0.6104651","text":"def draw(self):\n\n #drawing the screen\n self.screen.fill((0,51,102))\n\n #drawing the walls\n self.model.walls.draw(self.screen)\n\n #drawing the cat\n self.model.cat.playerrepresentation.draw(self.screen)\n \n #drawing the circles\n for circle in self.model.allcircles:\n circle.draw(self.screen)","title":""},{"docid":"a9cfc95b8758d271873a87e041ffee23","score":"0.6095452","text":"def draw(self):\n self.vertex_list.draw(self.mode)","title":""},{"docid":"952bc52cb245e6afe73922aebd1daa77","score":"0.60936254","text":"def draw(self, screen):\n pg.draw.polygon(screen, self.color, self.vertexes)","title":""},{"docid":"85f13af7a7db9af601389edb08919372","score":"0.60898936","text":"def DrawSpline(self, points):","title":""},{"docid":"6f7247ed9eb09adbd813c190cd950975","score":"0.6089498","text":"def ButtonMotion(self, event):\n if self.clicked == True: \n self.update_plot()\n self.newx = event.x\n self.newy = event.y\n self.canvas.draw_line(self.newx, self.newy)\n self.canvas.index(self.newx)#for the black line\n self.create_ppdata()\n self.update_label()\n else:\n return","title":""},{"docid":"dcc1e446c80b2b50b02e7f8d805bdaa0","score":"0.60818344","text":"def draw(self, canvas):\n\t\tcanvas.create_polygon([pixelFromPosition(vertex) for vertex in self.transformedVertices], **self.properties)","title":""},{"docid":"41a827aca9d45e1536df2eb960985010","score":"0.6079189","text":"def draw(self, win):\n pygame.draw.circle(win, self.color, (self.x, self.y), self.radius)","title":""},{"docid":"b15382ff7e5413c58f939ddcb600f9cc","score":"0.6074624","text":"def plot(self):","title":""},{"docid":"2420cbdf43383fcb21537d6b12e5d0d2","score":"0.6072332","text":"def figure(self, points):\n edges = len(points)-1 # number of edges\n for edge in range(edges):\n self.line(points[edge], points[edge+1])","title":""}],"string":"[\n {\n \"docid\": \"ab4edc0d7656931e0deb95b2ce5bd993\",\n \"score\": \"0.74049175\",\n \"text\": \"def _draw(self):\\n if self.colorTable != {}: CT = self.colorTable\\n else:\\n CT = self._genColorTable()\\n self.plotter.clear()\\n for p in self.points:\\n self.plotter.setColor(CT[p])\\n self.plotter.spot(p, self.spotWidth)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99b08b619d77280ec9c902bc57ae0553\",\n \"score\": \"0.72308236\",\n \"text\": \"def draw(self):\\n self.canvas.draw()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9e5dea799389f8e2047272ac01f5e9e\",\n \"score\": \"0.7219184\",\n \"text\": \"def drawPoint(self, qp):\\r\\n size = self.size()\\r\\n\\r\\n # Loops through every point in the window\\r\\n for i in range(self.num_xpoints):\\r\\n for j in range(self.num_ypoints):\\r\\n\\r\\n # Evenly distribute the points in both the x and y directions\\r\\n x_coord = ((size.width() - 100) / 21.0) * (i + 1) + 50\\r\\n y_coord = ((size.height() - 100) / 21.0) * (j + 1) + 50\\r\\n\\r\\n # Colours are determined by colourList\\r\\n qp.setPen(QPen(self.colourList[i][j], 1, Qt.SolidLine))\\r\\n qp.setBrush(QBrush(self.colourList[i][j], Qt.SolidPattern))\\r\\n\\r\\n # We draw the points as rectangles, and updates their locations\\r\\n qp.drawRect(x_coord, y_coord, 10, 10)\\r\\n self.pointLocation[i][j] = (x_coord, y_coord)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc54db68b3e8c1662bd6927189be6cd9\",\n \"score\": \"0.71158445\",\n \"text\": \"def DrawPoint(self, *args, **kw):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c262c67fa87a29c83822da9ade0ff382\",\n \"score\": \"0.7056661\",\n \"text\": \"def onPaint(self, event):\\r\\n self.SetCurrent(self.context)\\r\\n\\r\\n # Make sure the canvas has been initialised;\\r\\n if not self.bInitialised:\\r\\n self.initialiseGL()\\r\\n\\r\\n # OpenGL, in this case, uses a \\\"double bufferring\\\" system:\\r\\n # it displays one buffer whilst drawing to the other.\\r\\n # Clear the current back buffer\\r\\n GL.glClear(GL.GL_COLOR_BUFFER_BIT | GL.GL_DEPTH_BUFFER_BIT)\\r\\n\\r\\n # Draw the points stored in the vertex array onto the buffer\\r\\n GL.glDrawArrays(GL.GL_LINE_STRIP, 0, self.numPoints)\\r\\n\\r\\n # Now make the back buffer the front buffer\\r\\n # i.e. this is now displayed on the screen\\r\\n GL.glFlush()\\r\\n self.SwapBuffers()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bde0c050f4d3cba08435fd4920e2665e\",\n \"score\": \"0.70482403\",\n \"text\": \"def draw(self):\\n\\n glBegin(GL_POINTS)\\n glColor((1, 1, 1))\\n glVertex3fv(self.get_point())\\n glEnd()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cb4e5cde4d1cd8a51168c87213f6ded\",\n \"score\": \"0.7002541\",\n \"text\": \"def draw(self):\\n xlim_max = max([find_max(0, self.graph), 0])\\n xlim_min = min([find_min(0, self.graph), 0])\\n ylim_max = max([find_max(1, self.graph), 0])\\n ylim_min = min([find_min(1, self.graph), 0])\\n\\n # Add some parameters to make graph in center of picture\\n plt.xlim(xlim_min - 1, xlim_max + 2)\\n plt.ylim(ylim_min - 1, ylim_max + 2)\\n plt.gca().set_aspect('equal', adjustable='box')\\n\\n # Add list with steps on axes to see coordinates of points\\n x_steps = []\\n y_steps = []\\n for i in range(xlim_min - 1, xlim_max + 2):\\n x_steps.append(i)\\n for i in range(ylim_min - 1, ylim_max + 2):\\n y_steps.append(i)\\n\\n plt.xticks(x_steps)\\n plt.yticks(y_steps)\\n\\n start_x = 0\\n start_y = 0\\n for i in range(self.graph.num_rows()):\\n x1, y1 = [], []\\n for j in range(self.graph.num_cols()):\\n if self.graph[(i, j)]:\\n x1.append(self.graph[(i, j)][0])\\n y1.append(self.graph[(i, j)][1])\\n\\n plt.plot(x1, y1, 'b', marker='o')\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f2b43d544a6200347ba850c5049e246\",\n \"score\": \"0.6836888\",\n \"text\": \"def draw(self):\\n points = [{'pos': list(self.primitive), 'color': self.color, 'name': self.name}]\\n self.guids = compas_rhino.draw_points(points, layer=self.layer, clear=False, redraw=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71ec4a486281bf80e3853a9ccf6b6615\",\n \"score\": \"0.68199426\",\n \"text\": \"def drawPoint(self, x, y):\\n self.plotArea.create_oval(self.xToCanvas(x), self.yToCanvas(y), self.xToCanvas(x), self.yToCanvas(y))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a82bb6a62b328cd0db8a90baafcf0015\",\n \"score\": \"0.6798981\",\n \"text\": \"def display(self):\\n point1, point2 = self.points\\n pyxel.line(\\n x1=point1.x + self.x,\\n y1=point1.y + self.y,\\n x2=point2.x + self.x,\\n y2=point2.y + self.y,\\n col=self.colour,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8088b60d7bbe481f7a3529b1420da40a\",\n \"score\": \"0.679548\",\n \"text\": \"def draw(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8088b60d7bbe481f7a3529b1420da40a\",\n \"score\": \"0.679548\",\n \"text\": \"def draw(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8088b60d7bbe481f7a3529b1420da40a\",\n \"score\": \"0.679548\",\n \"text\": \"def draw(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8088b60d7bbe481f7a3529b1420da40a\",\n \"score\": \"0.679548\",\n \"text\": \"def draw(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed27d331a9844a73940780db25ac219f\",\n \"score\": \"0.6770996\",\n \"text\": \"def draw(self):\\n if (not self._surface):\\n self._surface = display.set_mode((self.SIZE, self.SIZE))\\n self._surface.fill(self.BACKGROUND_COLOR)\\n for point in self.points:\\n color = self.NOT_STAR_COLOR\\n if (point.star):\\n color = self.STAR_COLOR\\n draw.circle(self._surface, color, point.position.round.tuple, point.radius, self.BORDER_THICKNESS)\\n display.flip()\\n for e in event.get():\\n if e.type == QUIT:\\n quit()\\n exit(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5d6b53e7db2b0e688e08bf38669af21\",\n \"score\": \"0.67390406\",\n \"text\": \"def draw_raw_points(self):\\n # wp_xyz_list = list(self.nx_graph_topology.nodes(data='xyz')\\n xp_xyz_list = list()\\n for vertex_id in self.nx_graph_topology.nodes():\\n xp_xyz_list.append(self.node_id_map_topology[vertex_id].transform.location.x)\\n xp_xyz_list.append(self.node_id_map_topology[vertex_id].transform.location.y)\\n\\n # all_elements1 = xp_xyz_list[::2]\\n # p1 = xp_xyz_list[::2]\\n # p2 = xp_xyz_list[1::2]\\n\\n segment_list = list()\\n\\n for edge in self.nx_graph_topology.edges():\\n segment_list.append(edge)\\n\\n fig, ax = plt.subplots(1, 1)\\n ax.scatter(xp_xyz_list[::2], xp_xyz_list[1::2], s=5)\\n ax.set_title('Global navi waypoints')\\n\\n for i in range(len(segment_list)):\\n ax.plot([self.node_id_map_topology[segment_list[i][0]].transform.location.x,\\n self.node_id_map_topology[segment_list[i][1]].transform.location.x],\\n [self.node_id_map_topology[segment_list[i][0]].transform.location.y,\\n self.node_id_map_topology[segment_list[i][1]].transform.location.y],\\n 'k-',\\n lw=1)\\n\\n def onclick(event):\\n print('%s click: button=%d, x=%d, y=%d, xdata=%f, ydata=%f' %\\n ('double' if event.dblclick else 'single', event.button,\\n event.x, event.y, event.xdata, event.ydata))\\n if self.clicks < self.click_max:\\n self.clicks += 1\\n self.nav_points_map.append((event.xdata, event.ydata))\\n\\n # return event.xdata, event.ydata\\n\\n cid = fig.canvas.mpl_connect('button_press_event', onclick)\\n\\n plt.show()\\n # wp_xyz_list = list(self.nx_graph_topology.nodes())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"835162cc6ec678130acb4549fc546d02\",\n \"score\": \"0.6735644\",\n \"text\": \"def display(self):\\n\\n for point1, point2 in zip(self.points, self.points[1:] + [self.points[0]]):\\n pyxel.line(\\n x1=point1.x + self.x,\\n y1=point1.y + self.y,\\n x2=point2.x + self.x,\\n y2=point2.y + self.y,\\n col=self.colour,\\n )\\n\\n if self.accelerating:\\n self.display_acceleration()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aab025ba212b74433fa00ac1d62d1700\",\n \"score\": \"0.67278993\",\n \"text\": \"def draw(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aab025ba212b74433fa00ac1d62d1700\",\n \"score\": \"0.67278993\",\n \"text\": \"def draw(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aab025ba212b74433fa00ac1d62d1700\",\n \"score\": \"0.67278993\",\n \"text\": \"def draw(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aab025ba212b74433fa00ac1d62d1700\",\n \"score\": \"0.67278993\",\n \"text\": \"def draw(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aab025ba212b74433fa00ac1d62d1700\",\n \"score\": \"0.67278993\",\n \"text\": \"def draw(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb1d6b2102c44e483c249400d5919ad3\",\n \"score\": \"0.6715999\",\n \"text\": \"def draw(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33f4281a21d1a02b232c2b8863ac85c2\",\n \"score\": \"0.6613458\",\n \"text\": \"def draw(self):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be473ae37043ea89cc5c9545242228ef\",\n \"score\": \"0.65960264\",\n \"text\": \"def draw(self) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"268810559c00cd7c910337cb3cb15745\",\n \"score\": \"0.65592295\",\n \"text\": \"def draw(self, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1054bb296adbfe9ace7edb2581942013\",\n \"score\": \"0.6534617\",\n \"text\": \"def draw(self, win, center):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d85526c6016741641619de885617786b\",\n \"score\": \"0.65293276\",\n \"text\": \"def DrawLines(self, points, xoffset=0, yoffset=0):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ce46b0f9e34733de8e52d4f71577ada\",\n \"score\": \"0.6499565\",\n \"text\": \"def plot(self, pr):\\n x, y = self.transform(pr)\\n self.gfx.drawPoint(self.tr.X(x), self.tr.Y(y))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98da1f37efdd481191de1335bcd1cf79\",\n \"score\": \"0.64826924\",\n \"text\": \"def _draw(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9809598353cdbae9d3ae7426cb029b30\",\n \"score\": \"0.6477086\",\n \"text\": \"def draw(self):\\n fig, ax = plt.subplots(nrows=1, ncols=1)\\n ax.plot(self.x, self.y, linestyle='', marker='s')\\n line = list()\\n if self.m is not None and self.b is not None:\\n for i in self.x:\\n line.append(self.m * i + self.b)\\n ax.plot(self.x, line, color='red')\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc397592cec484bfb48738feadc3d36c\",\n \"score\": \"0.6470176\",\n \"text\": \"def draw(self, *args, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc31efc4a783dc12aeca2d1f74a98b5b\",\n \"score\": \"0.6464472\",\n \"text\": \"def draw(self):\\n self.prepare()\\n self.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ade6194f48ec419ba1139664290dda0e\",\n \"score\": \"0.645895\",\n \"text\": \"def paint(self):\\r\\n if self.ready:\\r\\n self.canvas.delete(tkinter.ALL)\\r\\n self.visit(self.tree.root)\\r\\n \\r\\n if self.newRegion:\\r\\n self.canvas.create_rectangle(self.newRegion.x_min, self.toTk(self.newRegion.y_min),\\r\\n self.newRegion.x_max, self.toTk(self.newRegion.y_max), \\r\\n outline='Black', dash=(2, 4))\\r\\n \\r\\n if self.selectedRegion:\\r\\n self.canvas.create_rectangle(self.selectedRegion.x_min, self.toTk(self.selectedRegion.y_min),\\r\\n self.selectedRegion.x_max, self.toTk(self.selectedRegion.y_max), \\r\\n outline='Red', dash=(2, 4))\\r\\n else:\\r\\n self.label = tkinter.Label(self.w, width=100, height = 40, text=\\\"Click To Add Points\\\")\\r\\n self.label.bind(\\\"<Button-1>\\\", self.prepare)\\r\\n self.label.pack()\\r\\n self.ready = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"352d0d2f8a3bdc7ab4f3518cee23db5d\",\n \"score\": \"0.64500105\",\n \"text\": \"def update_canvas(code, point = Point(0,0)):\\n fig = pylab.figure(figsize=[4, 4], dpi=100)\\n ax = fig.gca()\\n\\n if code == \\\"np\\\": # new point\\n ax.plot(points_x, points_y, 'ro')\\n\\n if code == \\\"dp\\\": # draw path\\n ax.plot(points_x, points_y, 'ro')\\n x, y = curve_to_arrays(point, 1000, 0.001)\\n ax.plot(x, y, 'r--')\\n\\n canvas = agg.FigureCanvasAgg(fig)\\n canvas.draw()\\n renderer = canvas.get_renderer()\\n raw_data = renderer.tostring_rgb()\\n\\n size = canvas.get_width_height()\\n\\n surf = pygame.image.fromstring(raw_data, size, \\\"RGB\\\")\\n screen.blit(surf, (0, 0))\\n pygame.display.flip()\\n\\n return ax\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3811d976b0b791cf6be9488d36be261\",\n \"score\": \"0.6440813\",\n \"text\": \"def draw(self, output=None):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6583158c485f0a678c827ef842da6ed0\",\n \"score\": \"0.6435612\",\n \"text\": \"def _draw(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"070971094cde182e950222e0f92d6d98\",\n \"score\": \"0.6429595\",\n \"text\": \"def start_draw(event):\\r\\n pos_x.append(event.xdata)\\r\\n pos_y.append(event.ydata)\\r\\n \\r\\n \\\"\\\"\\\"keshidane khotoot va kamel kardane shekl\\\"\\\"\\\"\\r\\n \\r\\n if len(pos_x)>1:\\r\\n plt.scatter(pos_x, pos_y, color =\\\"red\\\")\\r\\n plt.draw()\\r\\n if abs(event.xdata-pos_x[0]) > 0.05*pos_x[0] or \\\\\\r\\n abs(event.ydata-pos_y[0]) > 0.05*pos_y[0]:\\r\\n plt.plot(pos_x, pos_y, color = \\\"dodgerblue\\\")\\r\\n \\r\\n else:\\r\\n plt.scatter(pos_x[0], pos_y[0], color =\\\"red\\\")\\r\\n plt.draw()\\r\\n plt.plot([pos_x[-2], pos_x[0]], [pos_y[-2], pos_y[0]], \\r\\n color = \\\"dodgerblue\\\") \\r\\n fig.canvas.mpl_disconnect(button)\\r\\n pos_x.pop()\\r\\n pos_y.pop()\\r\\n pos_x.append(pos_x[0])\\r\\n pos_y.append(pos_y[0])\\r\\n plt.fill(pos_x, pos_y, color = \\\"cyan\\\") \\r\\n\\r\\n else : \\r\\n plt.scatter(pos_x[0], pos_y[0], color =\\\"red\\\")\\r\\n plt.draw()\\r\\n print([event.xdata, event.ydata])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5eda2fc38ed3bd4888cc9ab5db5a8b82\",\n \"score\": \"0.6411792\",\n \"text\": \"def draw(self, setlim=True):\\n\\n # Don't plot if variable full of Nan\\n if np.count_nonzero(~np.isnan(self.x)) == 0 or \\\\\\n np.count_nonzero(~np.isnan(self.y)) == 0: return\\n\\n self.canvas.mpl_connect('motion_notify_event', self.ShowPosition)\\n\\n self.x = ma.masked_invalid(self.x)\\n self.y = ma.masked_invalid(self.y)\\n if setlim:\\n self.xlim = [np.min(self.x), np.max(self.x)]\\n self.ylim = [np.min(self.y), np.max(self.y)]\\n title = self.title\\n plotCurv(self, x=self.x, y=self.y, title=title, xlabel=self.xlabel,\\n ylabel=self.ylabel, xlim=self.xlim, ylim=self.ylim)\\n self.canvas.draw()\\n self.canvas.Refresh()\\n self.Show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c4fa495be0a85dd1516384cfe48a274\",\n \"score\": \"0.64081395\",\n \"text\": \"def _draw(self):\\n\\n self.lgnd_crvs = []\\n self.lgnd_pnts = []\\n self.lgnd_lbls = []\\n\\n for ii,ll in enumerate(self.lgnd):\\n if False == self._reverse:\\n yy = len(self.lgnd)-ii-1\\n else:\\n yy = ii\\n add_curve( [0.25,1], [yy,yy], self.lgnd[ii] )\\n self.lgnd_crvs.append( self._get_current_object_name(\\\"Curve\\\") )\\n set_curve(\\\"symbol.style=none\\\")\\n pnt = self._get_point_properties( self.lgnd[ii].symbol ) \\n add_point( 0.625, yy, pnt )\\n self.lgnd_pnts.append( self._get_current_object_name(\\\"Point\\\") )\\n add_label( 1.25, yy, str(ii), \\\"valign=0.5\\\" )\\n self.lgnd_lbls.append( self._get_current_object_name(\\\"Label\\\") )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bfcd1996c4999315e0efede413b8d06\",\n \"score\": \"0.64076024\",\n \"text\": \"def showPoints(self, context):\\n for point in self.points:\\n point.show(context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3435ae8fff08384eb1fc4a0a0240309\",\n \"score\": \"0.64066726\",\n \"text\": \"def update_canvas(self):\\n velocity_data.append(self.current_velocity)\\n altitude_data.append(self.current_altitude)\\n sub_velocity.clear()\\n sub_altitude.clear()\\n sub_velocity.plot(velocity_data)\\n sub_altitude.plot(altitude_data)\\n self.canvas_left.draw()\\n self.canvas_right.draw()\\n if self.update_plot:\\n self.frame_upper_middle.after(100, self.update_canvas)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21b719e687b52b31446ddcbaa82982a6\",\n \"score\": \"0.6400952\",\n \"text\": \"def draw(self, canvas): \\n self.draw_self(canvas) \\n self.draw_children(canvas)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f852c2a6cdb09e67c2c41e5f408d0bef\",\n \"score\": \"0.63838935\",\n \"text\": \"def draw(self):\\n self.image.draw()\\n self.text.draw()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"161c0e174ba79e0ebd9f718bf8ea59fa\",\n \"score\": \"0.63780683\",\n \"text\": \"def ButtonPress(self, event):\\n self.clicked = True\\n self.update_plot()\\n self.newx = event.x\\n self.newy = event.y\\n self.canvas.draw_line(self.newx, self.newy)\\n self.canvas.index(self.newx)\\n self.create_ppdata()\\n self.update_label()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcdc49685711a9176d4e572f31fd71d1\",\n \"score\": \"0.63757294\",\n \"text\": \"def paintPlug(self, canvas, x, y):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66a12b3760e508b89672a24b93ba155f\",\n \"score\": \"0.6375435\",\n \"text\": \"def plot_points(self):\\n self.operator._set_monitor_plot_points(self.plot_points_spinbox.value())\\n self.plot_points_spinbox.setValue(self.operator.properties['monitor']['plot_points'])\\n set_spinbox_stepsize(self.plot_points_spinbox)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"078df1510309d220eaef839c875ba6da\",\n \"score\": \"0.6368254\",\n \"text\": \"def DrawXYZ(self):\\r\\n self.xyPanel.Refresh()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9065c246c1f3087d6f1b5fb53725f2d5\",\n \"score\": \"0.6358699\",\n \"text\": \"def draw(self):\\n raise NotImplementedError(\\\"draw() needs to be implemented\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df8c3e2fa8b51b2cbd13381be4400c8b\",\n \"score\": \"0.6354665\",\n \"text\": \"def draw(points, **scatter_params):\\n if scatter_params is None:\\n scatter_params = {label: 'Data points'}\\n X = points[:,0] / points[:,2]\\n Y = points[:,1] / points[:,2]\\n\\n # Plot the points\\n plt.scatter(X, Y, **scatter_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19052fea20ee3399147bde6ea5459129\",\n \"score\": \"0.63519806\",\n \"text\": \"def update_plot(self):\\n\\n self.fig.canvas.draw()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19052fea20ee3399147bde6ea5459129\",\n \"score\": \"0.63519806\",\n \"text\": \"def update_plot(self):\\n\\n self.fig.canvas.draw()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19052fea20ee3399147bde6ea5459129\",\n \"score\": \"0.63519806\",\n \"text\": \"def update_plot(self):\\n\\n self.fig.canvas.draw()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19052fea20ee3399147bde6ea5459129\",\n \"score\": \"0.63519806\",\n \"text\": \"def update_plot(self):\\n\\n self.fig.canvas.draw()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19052fea20ee3399147bde6ea5459129\",\n \"score\": \"0.63519806\",\n \"text\": \"def update_plot(self):\\n\\n self.fig.canvas.draw()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19052fea20ee3399147bde6ea5459129\",\n \"score\": \"0.63519806\",\n \"text\": \"def update_plot(self):\\n\\n self.fig.canvas.draw()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"757daf6c0f7046c81698f02deb847f6e\",\n \"score\": \"0.6343511\",\n \"text\": \"def draw(self, context):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"757daf6c0f7046c81698f02deb847f6e\",\n \"score\": \"0.6343511\",\n \"text\": \"def draw(self, context):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c21f2e007aac4aded15febc7bb46fae4\",\n \"score\": \"0.6339141\",\n \"text\": \"def draw(self):\\n pg.draw.circle(self.surface, self.color, self.xy, self.rect_radius)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"995653a4c3eada74acf8f08629fdc553\",\n \"score\": \"0.6324916\",\n \"text\": \"def render(self):\\r\\n plt.draw() #Draw and display the plot in new window\\r\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c050d90f80f2c14dfd8e6830eae7c6da\",\n \"score\": \"0.6316409\",\n \"text\": \"def draw_canvas(self):\\n fig = plt.figure()\\n ax = fig.add_subplot(111)\\n maxtag = np.amax(self.lattice)\\n # rect = np.ma.masked_equal(self.lattice, 0)\\n rect = np.copy(self.lattice)\\n rect[rect == 0] = -1\\n palette = plt.cm.gray\\n palette.set_over('b', 1.0)\\n palette.set_under('w', 1.0)\\n palette.set_bad('w', 1.0)\\n for i, tag in enumerate(list(self.ptag)):\\n rect[rect == tag] = maxtag + i + 2\\n # ax.matshow(rect, cmap=plt.cm.jet)\\n ax.matshow(rect, cmap=palette,\\n norm=colors.Normalize(vmin=1, vmax=maxtag + 1, clip=False))\\n ax.set_xticklabels([])\\n ax.set_yticklabels([])\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7456fb950a8eeb0506fbba06672076c5\",\n \"score\": \"0.6276118\",\n \"text\": \"def paintEvent(self, event):\\r\\n # Initializes the Painter object\\r\\n qp = QPainter()\\r\\n qp.begin(self)\\r\\n\\r\\n self.drawSpace(qp) # Draws the box for the points\\r\\n self.drawPoint(qp) # Draw the points\\r\\n self.drawTheCircle(qp) # Draws the circles if there are any\\r\\n qp.end()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b110fe784790956532cd53f254d13057\",\n \"score\": \"0.62706745\",\n \"text\": \"def points(self, points):\\n self.cpoints(points)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f90e1bc9431039b7e16da0590efa9ee0\",\n \"score\": \"0.6267569\",\n \"text\": \"def draw_callback(self, *args):\\n #print \\\"callback data %s\\\" % data_tuple\\n print \\\"callback args %s\\\" % args\\n data = args[0]\\n tool = data[0]\\n print tool\\n if (tool == 'clear'):\\n self.backend.clear()\\n if (tool == 'line'):\\n #print \\\"start_x=%d, start_y=%d, end_x=%d, end_y=%d\\\" % (data[1].x(), data[1].y(), data[2].x(), data[2].y())\\n self.backend.line((data[1].x(), data[1].y()), (data[2].x(), data[2].y()))\\n if (tool == 'circle'):\\n origo = (data[1].x(), data[1].y())\\n r = int(round(helpers().point_distance(origo, (data[1].x(), data[1].y()))))\\n self.backend.circle(r, origo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b62b03fcc367215a7258ca8d8e79d261\",\n \"score\": \"0.6247265\",\n \"text\": \"def Draw(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccf3ca4d4b419ebb57b7cd36db2747dd\",\n \"score\": \"0.6245673\",\n \"text\": \"def refresh_plot(self):\\n self.view.canvas.draw()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f8eb339cc032364b9f25c9a52e327e4\",\n \"score\": \"0.6222222\",\n \"text\": \"def drawPoints(self, pointPen, roundCoordinates=1):\\n pointPen.beginPath()\\n for (x, y), segmentType, args, kwargs in self.contour:\\n # identifiers are not needed here\\n # but the could cause errors, so remove them\\n kwargs[\\\"identifier\\\"] = None\\n pointPen.addPoint((x, y), segmentType, *args, **kwargs)\\n pointPen.endPath()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b083cca31ca184dd4c924406b372dcc\",\n \"score\": \"0.62093407\",\n \"text\": \"def update_xy_plot():\\n\\n\\tlines.set_xdata(self.current_frame.x)\\n\\tlines.set_ydata(self.current_frame.y)\\n\\tfigure.canvas.draw()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"babaf50d7133a36211550ee5235b72d9\",\n \"score\": \"0.62000763\",\n \"text\": \"def draw(self, backend):\\n # Drawn by world\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72cce35f85a4d52d1b7dd112e8134cda\",\n \"score\": \"0.6190761\",\n \"text\": \"def draw(self):\\n\\t\\ttry:\\n\\t\\t\\tself.canvas.draw()\\n\\t\\texcept Exception as e:\\n\\t\\t\\twarnings.warn(\\\"Error while drawing matplotlib figure: \\\\n\\\" + str(e))\\n\\t\\tself._repaint_signal.signal.emit(\\\"emitting\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b06b71e63824937258a37680935ddabc\",\n \"score\": \"0.6189219\",\n \"text\": \"def draw(self, position):\\n # Getting value at position\\n vector = self.value(position)\\n x = vector[0]\\n y = vector[1]\\n # Using self.ped to draw vector\\n self.ped.goto(position[0], position[1])\\n self.ped.pendown()\\n self.ped.goto(position[0]+x, position[1]+y)\\n self.ped.penup()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6dbe24102f50864754172e4508a5a3a2\",\n \"score\": \"0.6179086\",\n \"text\": \"def draw(self, window):\\n window.blit(self.image, (self.x, self.y))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99d6091d9dd0c26c1f197ca88dc8b68c\",\n \"score\": \"0.6175171\",\n \"text\": \"def display(self, canvas, x, y, width, height):\\n # Do we need this?\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc46e144c200bad26ab72357e17f16cf\",\n \"score\": \"0.6169833\",\n \"text\": \"def draw(self, shape):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f471475ddc961393cd14236ca728b190\",\n \"score\": \"0.61688405\",\n \"text\": \"def draw():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3974fd9d3dab3a4f36c43adf99f4de0\",\n \"score\": \"0.61666214\",\n \"text\": \"def draw_gui(self):\\r\\n self.draw_button_view()\\r\\n # self.draw_tree_widget()\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c71cd077f04629119cbadfc5aa5a3201\",\n \"score\": \"0.6166349\",\n \"text\": \"def updatecanvas(self):\\r\\n # reseting canvas (add this before calling the draw shapes)\\r\\n self.canvas.delete('all')\\r\\n\\r\\n self.draw_shape(True, 37, 42)\\r\\n self.draw_shape(True, 43, 48)\\r\\n self.draw_shape(False, 1, 17)\\r\\n self.draw_shape(True, 18, 22)\\r\\n self.draw_shape(True, 23, 27)\\r\\n self.draw_shape(True, 37, 42)\\r\\n self.draw_shape(False, 28, 31)\\r\\n self.draw_shape(True, 32, 36)\\r\\n self.draw_shape(True, 49, 60)\\r\\n self.draw_shape(True, 61, 68)\\r\\n\\r\\n self.canvas.update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d06f3ed18c3670c6ec37f887e4e619de\",\n \"score\": \"0.61662865\",\n \"text\": \"def show_points(self):\\r\\n self.screen.blit(self.player_points_image, self.player_points_rect)\\r\\n self.screen.blit(self.ai_points_image, self.ai_points_rect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82f11dcca2e67e07d1c0bd788dc947b7\",\n \"score\": \"0.61629325\",\n \"text\": \"def draw_from_points(self,cv_image, points):\\n for (x, y, w, h), n in points:\\n cv.Rectangle(cv_image, (x, y), (x + w, y + h), 255)\\n return cv_image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9337bff3cc7b9225c9f76e21437676ad\",\n \"score\": \"0.61591405\",\n \"text\": \"def show_curves_callback(self):\\n self.plotter.update()\\n self.fig_canvas.draw()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"865f8f1ed1b82ad509ba0292a459553c\",\n \"score\": \"0.6152894\",\n \"text\": \"def draw(self):\\n\\t\\tself.graphic.blit(self.viewport.x + self.offset_x, self.viewport.y + self.offset_y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d126640838abb8f840a9e58262d5e38d\",\n \"score\": \"0.61479783\",\n \"text\": \"def draw(self):\\n self.batch.draw()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cd7d739302137da2c84c061bddb1596\",\n \"score\": \"0.614265\",\n \"text\": \"def draw(self):\\n self.canvas.coords(self.hunger_bar, 150 * self.id + 50, 50, 150 * self.id + 50 + 100 * self.hunger / self.max_value, 100)\\n self.canvas.coords(self.stamina_bar, 150 * self.id + 50, 150, 150 * self.id + 50 + 100 * self.stamina / self.max_value, 200)\\n self.hunger_text.set(\\\"Hunger: %s / %s \\\"%(self.hunger, self.max_value))\\n self.stamina_text.set(\\\"Stamina: %s / %s \\\"%(self.stamina, self.max_value))\\n self.food_text.set(\\\"Remaining food: %s\\\"%self.food)\\n preg = bool(self.until_birth)\\n self.birth_text.set(\\\"Is \\\" + \\\"not\\\" * (not preg) + \\\" pregnant.\\\" + preg * (\\\" Time till giving birth: %s\\\"%self.until_birth))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b1b5245d2b005b8370871ba90be65d5\",\n \"score\": \"0.613991\",\n \"text\": \"def draw(self):\\n arcade.create_rectangle_filled(\\n center_x=self.x,\\n center_y=self.y,\\n width=self.width,\\n height=self.height,\\n color=arcade.color.WHITE,\\n ).draw()\\n\\n if self.turning_point:\\n self.turning_point.draw()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cf9eac49b0db4f49ec81cef5ec4f14d\",\n \"score\": \"0.61281675\",\n \"text\": \"def draw(self,window):\\n\\t\\tself.main.draw(window) \\n\\t\\tself.outer.draw(window)\\n\\t\\tself.text.draw(window)\\n\\t\\tself.window = window\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27ece5f215feb2407de9febd926b7cfb\",\n \"score\": \"0.61274135\",\n \"text\": \"def draw(self, screen):\\n pg.draw.circle(screen,\\n self.color,\\n (int(self.x), int(self.y)),\\n self.size,\\n self.thickness\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4eadb654d940f11afb7778f68d855cf8\",\n \"score\": \"0.6120313\",\n \"text\": \"def draw(self, canvas):\\n for node in self.graph.nodes():\\n self.graph.node[node]['object'].draw(canvas)\\n\\n self.set_linewidth()\\n\\n for edge in self.graph.edges():\\n self.graph[edge[0]][edge[1]]['object'].draw(canvas)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4213f10c55aa9c454633a2ed1b155d6\",\n \"score\": \"0.6111755\",\n \"text\": \"def draw(self, window):\\n self.rect.draw(window)\\n self.text.draw(window)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20583142a8556330d4a240087b8882fc\",\n \"score\": \"0.6111176\",\n \"text\": \"def draw(self):\\r\\n #draw a dot at the flying object center point, dot is blue and 5px\\r\\n arcade.draw_point(self.center.x, self.center.y, arcade.color.BLUE_SAPPHIRE, 5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a903fe00d10aa75f620d2979dec7dc6\",\n \"score\": \"0.6111115\",\n \"text\": \"def draw_circle(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5901c5cbff840c396b94954aaaeeae6\",\n \"score\": \"0.6105898\",\n \"text\": \"def update_gps(self, idx):\\n if self.gps_data is not None:\\n self.gps_plot.set_xdata(self.gps_data[0][:idx])\\n self.gps_plot.set_ydata(self.gps_data[1][:idx])\\n self.canvas.draw()\\n else:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a718d76d0065b8edc33d35bad45850b\",\n \"score\": \"0.6104651\",\n \"text\": \"def draw(self):\\n\\n #drawing the screen\\n self.screen.fill((0,51,102))\\n\\n #drawing the walls\\n self.model.walls.draw(self.screen)\\n\\n #drawing the cat\\n self.model.cat.playerrepresentation.draw(self.screen)\\n \\n #drawing the circles\\n for circle in self.model.allcircles:\\n circle.draw(self.screen)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9cfc95b8758d271873a87e041ffee23\",\n \"score\": \"0.6095452\",\n \"text\": \"def draw(self):\\n self.vertex_list.draw(self.mode)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"952bc52cb245e6afe73922aebd1daa77\",\n \"score\": \"0.60936254\",\n \"text\": \"def draw(self, screen):\\n pg.draw.polygon(screen, self.color, self.vertexes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85f13af7a7db9af601389edb08919372\",\n \"score\": \"0.60898936\",\n \"text\": \"def DrawSpline(self, points):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f7247ed9eb09adbd813c190cd950975\",\n \"score\": \"0.6089498\",\n \"text\": \"def ButtonMotion(self, event):\\n if self.clicked == True: \\n self.update_plot()\\n self.newx = event.x\\n self.newy = event.y\\n self.canvas.draw_line(self.newx, self.newy)\\n self.canvas.index(self.newx)#for the black line\\n self.create_ppdata()\\n self.update_label()\\n else:\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcc1e446c80b2b50b02e7f8d805bdaa0\",\n \"score\": \"0.60818344\",\n \"text\": \"def draw(self, canvas):\\n\\t\\tcanvas.create_polygon([pixelFromPosition(vertex) for vertex in self.transformedVertices], **self.properties)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41a827aca9d45e1536df2eb960985010\",\n \"score\": \"0.6079189\",\n \"text\": \"def draw(self, win):\\n pygame.draw.circle(win, self.color, (self.x, self.y), self.radius)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b15382ff7e5413c58f939ddcb600f9cc\",\n \"score\": \"0.6074624\",\n \"text\": \"def plot(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2420cbdf43383fcb21537d6b12e5d0d2\",\n \"score\": \"0.6072332\",\n \"text\": \"def figure(self, points):\\n edges = len(points)-1 # number of edges\\n for edge in range(edges):\\n self.line(points[edge], points[edge+1])\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994188,"cells":{"query_id":{"kind":"string","value":"768e00ec8ddd94a666938045bfd89fe0"},"query":{"kind":"string","value":"Matches the NDC to db"},"positive_passages":{"kind":"list like","value":[{"docid":"671597d49d82516294ea16dbefe37f43","score":"0.58281344","text":"def match_ndc(ndc):\n\n def find_recent_db():\n \"\"\" returns most recent db csv file. Gives error pop up if no db file found \"\"\"\n files = glob.glob('ndcdb-*.csv')\n if len(files) == 0:\n error_msg = \"No NDC database file found (prefixed with ndcdb-[date].csv). This means that either \" \\\n \"the file doesn't exist or was renamed to something else. Please generate that file\"\n popupmsg(error_msg)\n print(error_msg)\n else:\n pattern = re.compile(r\"^ndcdb-(\\d{4}-\\d{2}-\\d{2}).csv$\")\n dates_list = []\n for file in files:\n str_date = pattern.match(file).groups()[0]\n date = datetime.strptime(str_date, '%Y-%m-%d')\n dates_list.append(date)\n\n formatted_date = datetime.strftime(max(dates_list).date(), '%Y-%m-%d')\n\n recent_db_file = 'ndcdb-' + formatted_date + '.csv'\n lastupdated_label[\"text\"] = \"Using data last updated on: \" + formatted_date\n return recent_db_file\n\n recent_file = find_recent_db()\n df = pd.read_csv(recent_file)\n print('Using db from this file: ', recent_file)\n\n # print(\"First entry of ndc package code no hyphen: \", df['NDCPACKAGECODE_nohyphen'][0], \", and type: \", type(df['NDCPACKAGECODE_nohyphen'][0]))\n # print(\"First entry of ALL_NDC: \", df['ALL_NDC'][0], \", and type: \", type(df['ALL_NDC'][0]))\n\n # below will be if ndc in df['NDCPACKAGECODE_nohyphen'].values: -- if ndc is found in a list of NDCs with no hyphen\n\n # 012037086010010821TXZ811R4ZKNC\t1723022810MT005\n # if df['ALL_NDC'].apply(lambda x: ndc in x).sum() == 1:\n # str_to_list = lambda x: x.strip('][').split(', ')\n if df['ALL_NDC_NO_HYPHEN'].apply(lambda x: ndc in x).any():\n index = df.loc[df['ALL_NDC_NO_HYPHEN'].apply(lambda x: ndc in x)].index[0]\n\n # assign \"raw_ndc\" with hyphens by looking through column with list of all ndcs under a package\n ndc_list = str_to_list(df['ALL_NDC'][index])\n for check_ndc in ndc_list:\n if ndc == remove_hyphen(check_ndc):\n raw_ndc = check_ndc\n\n pack_desc = df['PACKAGEDESCRIPTION'][index]\n brand = df['PROPRIETARYNAME'][index]\n generic = df['NONPROPRIETARYNAME'][index]\n dosage_form = df['DOSAGEFORMNAME'][index]\n route = df['ROUTENAME'][index]\n mfg = df['LABELERNAME'][index]\n strength = df['ACTIVE_NUMERATOR_STRENGTH'][index]\n str_units = df['ACTIVE_INGRED_UNIT'][index]\n pharm_class = df['PHARM_CLASSES'][index]\n\n def eleven_dig(ten_digit):\n \"\"\" changes 10 digit ndc to 11 digit ndc adding the zero to the appropriate place \"\"\"\n lst = ten_digit.split(\"-\")\n lst[0] = lst[0].zfill(5)\n lst[1] = lst[1].zfill(4)\n lst[2] = lst[2].zfill(2)\n return ''.join(lst)\n\n ten_dig = eleven_dig(raw_ndc)\n return True, raw_ndc, ten_dig, pack_desc, brand, generic, dosage_form, route, mfg, strength, \\\n str_units, pharm_class\n else:\n error_msg = \"NDC doesn't exist in database? If drug is new or unofficial (or maybe OTC? not sure if those are\" \\\n \" in there.., may require updating database?\"\n print(error_msg)\n popupmsg(error_msg)","title":""}],"string":"[\n {\n \"docid\": \"671597d49d82516294ea16dbefe37f43\",\n \"score\": \"0.58281344\",\n \"text\": \"def match_ndc(ndc):\\n\\n def find_recent_db():\\n \\\"\\\"\\\" returns most recent db csv file. Gives error pop up if no db file found \\\"\\\"\\\"\\n files = glob.glob('ndcdb-*.csv')\\n if len(files) == 0:\\n error_msg = \\\"No NDC database file found (prefixed with ndcdb-[date].csv). This means that either \\\" \\\\\\n \\\"the file doesn't exist or was renamed to something else. Please generate that file\\\"\\n popupmsg(error_msg)\\n print(error_msg)\\n else:\\n pattern = re.compile(r\\\"^ndcdb-(\\\\d{4}-\\\\d{2}-\\\\d{2}).csv$\\\")\\n dates_list = []\\n for file in files:\\n str_date = pattern.match(file).groups()[0]\\n date = datetime.strptime(str_date, '%Y-%m-%d')\\n dates_list.append(date)\\n\\n formatted_date = datetime.strftime(max(dates_list).date(), '%Y-%m-%d')\\n\\n recent_db_file = 'ndcdb-' + formatted_date + '.csv'\\n lastupdated_label[\\\"text\\\"] = \\\"Using data last updated on: \\\" + formatted_date\\n return recent_db_file\\n\\n recent_file = find_recent_db()\\n df = pd.read_csv(recent_file)\\n print('Using db from this file: ', recent_file)\\n\\n # print(\\\"First entry of ndc package code no hyphen: \\\", df['NDCPACKAGECODE_nohyphen'][0], \\\", and type: \\\", type(df['NDCPACKAGECODE_nohyphen'][0]))\\n # print(\\\"First entry of ALL_NDC: \\\", df['ALL_NDC'][0], \\\", and type: \\\", type(df['ALL_NDC'][0]))\\n\\n # below will be if ndc in df['NDCPACKAGECODE_nohyphen'].values: -- if ndc is found in a list of NDCs with no hyphen\\n\\n # 012037086010010821TXZ811R4ZKNC\\t1723022810MT005\\n # if df['ALL_NDC'].apply(lambda x: ndc in x).sum() == 1:\\n # str_to_list = lambda x: x.strip('][').split(', ')\\n if df['ALL_NDC_NO_HYPHEN'].apply(lambda x: ndc in x).any():\\n index = df.loc[df['ALL_NDC_NO_HYPHEN'].apply(lambda x: ndc in x)].index[0]\\n\\n # assign \\\"raw_ndc\\\" with hyphens by looking through column with list of all ndcs under a package\\n ndc_list = str_to_list(df['ALL_NDC'][index])\\n for check_ndc in ndc_list:\\n if ndc == remove_hyphen(check_ndc):\\n raw_ndc = check_ndc\\n\\n pack_desc = df['PACKAGEDESCRIPTION'][index]\\n brand = df['PROPRIETARYNAME'][index]\\n generic = df['NONPROPRIETARYNAME'][index]\\n dosage_form = df['DOSAGEFORMNAME'][index]\\n route = df['ROUTENAME'][index]\\n mfg = df['LABELERNAME'][index]\\n strength = df['ACTIVE_NUMERATOR_STRENGTH'][index]\\n str_units = df['ACTIVE_INGRED_UNIT'][index]\\n pharm_class = df['PHARM_CLASSES'][index]\\n\\n def eleven_dig(ten_digit):\\n \\\"\\\"\\\" changes 10 digit ndc to 11 digit ndc adding the zero to the appropriate place \\\"\\\"\\\"\\n lst = ten_digit.split(\\\"-\\\")\\n lst[0] = lst[0].zfill(5)\\n lst[1] = lst[1].zfill(4)\\n lst[2] = lst[2].zfill(2)\\n return ''.join(lst)\\n\\n ten_dig = eleven_dig(raw_ndc)\\n return True, raw_ndc, ten_dig, pack_desc, brand, generic, dosage_form, route, mfg, strength, \\\\\\n str_units, pharm_class\\n else:\\n error_msg = \\\"NDC doesn't exist in database? If drug is new or unofficial (or maybe OTC? not sure if those are\\\" \\\\\\n \\\" in there.., may require updating database?\\\"\\n print(error_msg)\\n popupmsg(error_msg)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"e0c3d258c19d4d7fa03901c154ec7bfa","score":"0.5899773","text":"def inspect_db(self):","title":""},{"docid":"4c75ae12209357a13cd26ecb99d48a79","score":"0.56975514","text":"def getdb(self):\n if self.cl._db is not None:\n return self.cl._db\n return discretezoo.DEFAULT_DB","title":""},{"docid":"2d7600e1e532f9e6b12b0494dbf3a5dd","score":"0.5669286","text":"def ReadNDCDatabase(SearchText, SearchType, SearchSize):\n\n NDCRecords = []\n NDCEnhancedArray = []\n\n\n if SearchType==\"ndc\":\n SearchText = SearchText.upper()\n SearchText = SearchText.strip()\n SearchError = False\n CleanNDC = SearchText.replace(\"-\", \"\")\n if len(CleanNDC) == 9:\n qNDC = NDCLookup.query(NDCLookup.ninedigitndc == CleanNDC)\n else:\n qNDC = NDCLookup.query(NDCLookup.ndc == CleanNDC)\n\n elif SearchType == \"name\":\n SearchText = SearchText.upper()\n SearchText = SearchText.strip()\n SearchError = len(SearchText) == 0\n qNDC = NDCLookup.query(NDCLookup.proprietaryname == SearchText)\n\n elif SearchType == \"ingredient\":\n QueryString = IngredientQuery(SearchText)\n logging.info(\"Executing Ingredient Search: %s\", QueryString)\n SearchError = len(QueryString) == 0 \n qNDC= NDCLookup.gql(QueryString)\n\n elif SearchType == \"image\":\n SearchText = SearchText.upper()\n SearchText = SearchText.strip()\n QueryString = NDC9ArrayQuery(SearchText)\n logging.info(\"Executing NDC9 Search: %s\", QueryString)\n SearchError = len(QueryString) == 0\n qNDC= NDCLookup.gql(QueryString)\n\n elif SearchType == \"active\":\n SearchText = SearchText.upper()\n SearchText = SearchText.strip()\n SearchError = len(SearchText) == 0\n qNDC = NDCLookup.query(NDCLookup.substancename == SearchText)\n \n else:\n SearchError = True\n\n if not(SearchError) : NDCRecords = qNDC.fetch(limit=int(SearchSize))\n\n for NDCRecord in NDCRecords:\n NDCRecord.ndc = NDCTenDigitFormat(NDCRecord.ndc, NDCRecord.format)\n NDCRecord.proprietaryname = NDCRecord.proprietaryname.title()\n NDCRecord.nonproprietaryname = NDCRecord.nonproprietaryname.title()\n NDCRecord.packagedescription = NDCRecord.packagedescription.title()\n\n substancelist = []\n\n for substance in NDCRecord.substancelist:\n substancelist.append(substance.title())\n\n NDCRecord.substancelist = substancelist\n\n\n TemplateValues = {\"NDCRecordArray\": NDCRecords,\n \"SearchText\": SearchText,\n \"SearchType\": SearchType,\n \"SearchSize\": SearchSize}\n\n return TemplateValues","title":""},{"docid":"808d9a25b42b6f3d15a009c3bbb5e49e","score":"0.5438229","text":"def getMainDbDN(self):\n \n \n if self.conn.search(search_base=\"cn=config\", search_scope=LEVEL,\n search_filter=\"(olcDbDirectory=/opt/gluu/data/main_db)\",\n attributes='*'):\n if self.conn.response:\n return self.conn.response[0]['dn']","title":""},{"docid":"faa84fb710c5caa6a1b2549f07ff4aea","score":"0.53561217","text":"def db_for_read(self, model, **hints):\n return random.choice(['replica_1'])","title":""},{"docid":"e00622aa8d0d49fff6a6a11e28c41937","score":"0.5321135","text":"def db(self) -> ConfigNodePropertyString:\n return self._db","title":""},{"docid":"abef234fddbac44609b1aafb0e2cce6d","score":"0.53086776","text":"def get_db():\n if not hasattr(g, 'mongo_db'):\n g.mongo_db = pymongo.MongoClient('db')\n return g.mongo_db.filterforge","title":""},{"docid":"6099d13b58ec8776840d902d254ade31","score":"0.5238932","text":"def get_db():\n pass","title":""},{"docid":"ef33563c491377b11678fe20f4dfc96f","score":"0.5220712","text":"def db_for_read(self, model, **hints):\n if model._meta.app_label == 'fblog':\n return 'fblog_db'\n return None","title":""},{"docid":"389799e6ff7064b6ebf88447c09f4e1e","score":"0.52157825","text":"def _db_exist(self, source):\n\t\tpass","title":""},{"docid":"b72f57ab10518ae657f2cbf0f2e1abe7","score":"0.51940084","text":"def dispatch_database():\n pass","title":""},{"docid":"75c9dd6cbc580743d1a9d43792f6ae33","score":"0.51888806","text":"def db(self):\r\n return self.__db","title":""},{"docid":"fd2ed4dd2b09b95e6f22bfbd3c33c0b1","score":"0.5169626","text":"def get_db_info(db_coll_dict,config_path):\n ip,username,password=load_mongo_account(config_path)\n client=MongoClient(ip,username=username,password=password)\n detail_dict={}\n for db in db_coll_dict.keys():\n meta_client=client[db][db+\".meta\"]\n detail_dict[db]={}\n\n detail_dict[db]['episodes']=[i for i in client[db].list_collection_names() if \".meta\" not in i and \".files\" not in i and \".chunks\" not in i and \".vis\" not in i and \".scans\" not in i ]\n\n # Add task description\n detail_dict[db]['task_description']=get_task_description(meta_client)\n\n # Add class and id info for entities\n detail_dict[db]['entities']={}\n class_info=get_all_class(meta_client)\n for each_object in class_info:\n obj_id=each_object['id']\n obj_class=each_object['class']\n if obj_class not in detail_dict[db]['entities'].keys():\n detail_dict[db]['entities'][obj_class]=[obj_id]\n else:\n detail_dict[db]['entities'][obj_class].append(obj_id)\n \n # Add skel entities\n detail_dict[db]['skels']={}\n bone_info=get_bone(meta_client)\n for each_bone in bone_info:\n bone=each_bone['bone']\n skel=each_bone['skel']\n if skel not in detail_dict[db]['skels'].keys():\n detail_dict[db]['skels'][skel]=[bone]\n else:\n detail_dict[db]['skels'][skel].append(bone)\n\n # Add camera view\n detail_dict[db]['camera_views']=get_camera_view(meta_client)\n # pprint.pprint(detail_dict)\n \n return detail_dict","title":""},{"docid":"cea77859580f584522b8b98ba0892c08","score":"0.5169591","text":"def legacy_db_configured(self):\n if (\n self.kv.get(\"db_host\")\n and self.kv.get(\"db_port\")\n and self.kv.get(\"db_db\")\n and self.kv.get(\"db_user\")\n and self.kv.get(\"db_pass\")\n ):\n return True\n return False","title":""},{"docid":"acb9ce2f999a8235e840fd94d2128e30","score":"0.5157214","text":"def dbc_db(self):\n return self.__dbc_db","title":""},{"docid":"c986b9db2d7cd8d6e5c55fb2b65759b5","score":"0.5132187","text":"def db(self):\n return self._conf['db']","title":""},{"docid":"59c589aa63afdf12dde81088a8648e97","score":"0.51253587","text":"def get_db_state():\n print \"\\nreference implementation of get_db_state\\n\"\n return None","title":""},{"docid":"f8b6921814cd96eaf5c4e4836d56d0d0","score":"0.5109895","text":"def CGD(cls):\n cls.Namespace(\"http://www.candidagenome.org/cgi-bin/locus.pl?dbid=\")","title":""},{"docid":"30d7f1379c3f7f0f80343538209cc154","score":"0.5109482","text":"def check_mongodb_state(config_path):\n ip,username,password=load_mongo_account(config_path)\n client=pymongo.MongoClient(ip,username=username,password=password,serverSelectionTimeoutMS=100)\n try:\n print(client.list_database_names())\n return True\n except Exception as e:\n return False","title":""},{"docid":"5a52a415663f3ab4721fe938d3b92013","score":"0.5103297","text":"def test_ngen_with_known_db(runner):\n result = runner.invoke(cli.main, ['list', '-d' , '~/.keepassxc/worth.kdbx'])\n assert result.exit_code == 0","title":""},{"docid":"5fc530f67300c30c79dc8432d0146653","score":"0.5096329","text":"def db_for_read(self, model, **hints):\n if model._meta.app_label == 'auth':\n return 'auth_db'\n return None","title":""},{"docid":"0db5d66430443a9b54386c3076b54d8b","score":"0.5089351","text":"def __init__(self,db):\n self.db = db","title":""},{"docid":"d1d22064ce85a9d9e5421703f72a124a","score":"0.5073341","text":"def create_db_from_dict(self, ref_dict, database = 'smart_meter'):\n\n\n\n\t\tprint(\"Database {} has been created from the dictionary\".format(database))","title":""},{"docid":"e7129aa5fee58918ea3e721d4b5062e8","score":"0.50717753","text":"def __init__(self, db):\n self._db = db","title":""},{"docid":"e7129aa5fee58918ea3e721d4b5062e8","score":"0.50717753","text":"def __init__(self, db):\n self._db = db","title":""},{"docid":"6e422fbc834c5ce8795ad8afb0331382","score":"0.50672495","text":"def _get_database_id(self, db_name):\n for expert_database in Database.objects.all():\n if re.match(expert_database.label, db_name, re.IGNORECASE):\n return expert_database.id\n return None","title":""},{"docid":"be694c1d9a541835fe2b57be9e5d36ea","score":"0.506357","text":"def _init_db(self):\n raise NotImplementedError(\"Use subclasses of Database.\")","title":""},{"docid":"14377ae4a1264e70fb40459e55696168","score":"0.5059923","text":"def checkAccesslogDB(self):\n \n return self.conn.search(search_base='cn=config',\n search_filter='(olcSuffix=cn=accesslog)',\n search_scope=SUBTREE, attributes=[\"*\"])","title":""},{"docid":"f884bbe9255899e5ccad50d1aa7da105","score":"0.50476664","text":"def db():\n pass","title":""},{"docid":"4ec067f043854af130dc68d6310bb290","score":"0.5025044","text":"def db_for_read(self, model, **hints):\n if model._meta.app_label == 'mapdata':\n return 'msemap_db'\n return None","title":""},{"docid":"782deb8c0adabe9849cfdf86e50738ea","score":"0.501365","text":"def db_show(self, name) -> bool:\n found = False\n dbs = self._index_local_databases()\n\n for db in dbs:\n if db == name:\n found = True;\n\n updated = datetime.datetime.fromtimestamp(dbs[db]['last modified']).strftime('%Y-%m-%d %H:%M:%S')\n checked = datetime.datetime.fromtimestamp(dbs[db]['last checked']).strftime('%Y-%m-%d %H:%M:%S')\n self.logger.info(f\"Database: {db}\")\n if dbs[db]['last modified'] == 0:\n self.logger.info(\" last modified: not downloaded\")\n else:\n self.logger.info(f\" last modified: {updated}\")\n if dbs[db]['last checked'] == 0:\n self.logger.info(\" last checked: n/a\")\n else:\n self.logger.info(f\" last checked: {checked}\")\n self.logger.info(f\" url: {dbs[db]['url']}\")\n if db.endswith(\".cvd\"):\n self.logger.info(f\" local version: {dbs[db]['local version']}\")\n if len(dbs[db]['CDIFFs']) > 0:\n self.logger.info(f\" CDIFFs: \\n{json.dumps(dbs[db]['CDIFFs'], indent=4)}\")\n return True\n\n if not found:\n self.logger.error(f\"No such database: {name}\")\n return found","title":""},{"docid":"bebd245ae8ad61090e8d6a3ec3a0f552","score":"0.4993931","text":"def test_003_dbtype(self) -> None:\n self.assertEqual(self.ds.dbtype, \"dynamo\")","title":""},{"docid":"e536412c7386152d4bae645d367cb220","score":"0.4969846","text":"def db():\n raise NotImplementedError()","title":""},{"docid":"1061a95e9c40d3e60d39cc5f11b0033a","score":"0.49446747","text":"def get_db():\n if not hasattr(g, 'db_con'):\n g.db_con = connect_db()\n return g.db_con","title":""},{"docid":"3bca35078fe59913957c9f19e658127a","score":"0.49268067","text":"def set_candb(self, db):\n self.candb = db","title":""},{"docid":"9bcdaf6f50aeeb2c3ed60f75fc79ce09","score":"0.49224553","text":"def db(self):\n# log.info('fetching request.db')\n return DBSession","title":""},{"docid":"aec98e1603bb3a5d602c25f3edde63ad","score":"0.4914233","text":"def mogo_db_info(self) -> str:\n return self.config['database']['mongo']['information']","title":""},{"docid":"cf985bf73df44a6316c55391fcc69e3e","score":"0.4912167","text":"def db_variables():\n\n return (\"azurehack.n8sw8.gcp.mongodb.net\",\"backend\",\"azure2020\",\"admin\",\"azurehack\")","title":""},{"docid":"a465304cf06a63ebd646ac049c4a6702","score":"0.490552","text":"def dbformat(self):\n\n pass","title":""},{"docid":"7ebea65531837f781815f8a5d7cdeae1","score":"0.48924413","text":"def getDbCount(self):\n\t\tc= self.ds.value(\"dbCounter\",0).toInt()[0]\n\t\treturn c","title":""},{"docid":"c8a44427758ee3855d217be560bc8631","score":"0.4883515","text":"def db_for_write(self, model, **hints):\n if model._meta.app_label == 'fblog':\n return 'fblog_db'\n return None","title":""},{"docid":"3e2aff155f9b1b697a164fdacc3ab9f6","score":"0.4879035","text":"def get_vesync_database_info_from_nacos(self) -> dict:\n nacos_client = nacos.NacosClient(self.nacos_server.host, namespace=self.stage_namespace_id)\n self.set_nacos_client_debug(nacos_client)\n configs = nacos_client.get_config(settings.VESYNC_DATABASE_DATA_ID,\n settings.VESYNC_DATABASE_GROUP,\n no_snapshot=True)\n logger.debug(f\"configs (data id: {settings.VESYNC_DATABASE_DATA_ID}, group: {settings.VESYNC_DATABASE_GROUP}): \"\n f\"{configs}\")\n configs = common.load_properties_from_string(configs)\n database_info = {\n \"host\": configs[settings.KEY_TO_VESYNC_DATABASE_HOST],\n \"port\": int(configs[settings.KEY_TO_VESYNC_DATABASE_PORT]),\n \"user\": configs[settings.KEY_TO_VESYNC_DATABASE_USER],\n \"password\": configs[settings.KEY_TO_VESYNC_DATABASE_PASSWORD],\n \"database\": configs[settings.KEY_TO_VESYNC_DATABASE_NAME],\n \"charset\": \"utf8\",\n \"cursorclass\": pymysql.cursors.DictCursor\n }\n logger.info(f\"database info used to connect: {database_info}\")\n return database_info","title":""},{"docid":"ee111f5320870999c82485c6eb76104f","score":"0.48773244","text":"def internalDB_process(self, *args, **kwargs):\n d_ret = self.dcm.internalDB_process(*args, **kwargs)\n return d_ret","title":""},{"docid":"300a2074558c1aa272dc718eae3183ec","score":"0.48739982","text":"def preferred_dc(self):\n return self._preferred_dc","title":""},{"docid":"a779cf9d9baf7039fa83e9cf136e8ffe","score":"0.4869784","text":"def _get_db(cls):\r\n return get_db(cls._meta.get(\"db_alias\", DEFAULT_CONNECTION_NAME ))","title":""},{"docid":"d5c97ce33139911dad3ffd7a0a2253c7","score":"0.48628843","text":"def __init__(self, db) -> None:\n self._db = db","title":""},{"docid":"214f1e1b15658dd10a2111c5317b104e","score":"0.48577946","text":"def filter_databases(rc):\n dbs = rc.databases\n public_only = rc._get(\"public_only\", False)\n if public_only:\n dbs = [db for db in dbs if db[\"public\"]]\n dbname = rc._get(\"db\")\n if dbname is not None:\n dbs = [db for db in dbs if db[\"name\"] == dbname]\n elif len(dbs) == 1:\n rc.db = dbs[0][\"name\"]\n rc.databases = dbs","title":""},{"docid":"7079b600de1b490e5a7b5d5d76912821","score":"0.485489","text":"def _initdb(self, db=None):\n if self._db is not None:\n return\n if db is None:\n self._db = discretezoo.DEFAULT_DB\n else:\n self._db = db","title":""},{"docid":"d05dfa6c864d6c3db4bc0ae5b5620b9e","score":"0.48525262","text":"def load(self, db):\n\t\tpass","title":""},{"docid":"b10a78718803ce0ec49b89b80a3f64c4","score":"0.48498744","text":"def db(self) -> Any:\n return self._db","title":""},{"docid":"a688e70742fe7fb640e8b7e42eed47fc","score":"0.48465395","text":"def get_master_mongo_conn(env='dev'):\n if env == 'prod':\n MONGO_URI = 'mongodb://chefd_prod:chefd_prod@ds135459-a0.mlab.com:35459/'\n MONGO_URI += 'prod_chefd_1_gb?replicaSet=rs-ds135459'\n else:\n MONGO_URI = 'mongodb://staging_chefd:staging_chefd@ds141098.mlab.com:41098/'\n MONGO_URI += 'staging-chefd-replica'\n client = pymongo.MongoClient(MONGO_URI)\n db = client.get_default_database()\n return db","title":""},{"docid":"c1a9648f2d860cb95a6f5dd2859d1061","score":"0.48406836","text":"def db(self, db: ConfigNodePropertyString):\n\n self._db = db","title":""},{"docid":"7c40713744c3a79de6b7d1dc95549911","score":"0.4830416","text":"def _get_db():\n global db_conn\n\n if not db_conn:\n db_conn = _connect_db(DSN)\n return db_conn","title":""},{"docid":"45adfa4a8a41cc5ced4962b86c5acad3","score":"0.4827729","text":"def db_for_read(self, model, **hints):\n if model._meta.db_table in self.call_center_tables:\n return 'call_center'\n return None","title":""},{"docid":"6bf6b0932ec62e7aa0d6b98cf3ea7b1a","score":"0.48261854","text":"def get_database_for(self, id):\n url=\"https://app.crepc.sk/oai\"\n params={\"verb\":\"GetRecord\",\n \"metadataPrefix\":\"xml-crepc2\",\n \"identifier\":\"oai:crepc.sk:database/\"+id\n }\n r = requests.get(url = url, params = params)\n return r.content.decode(\"utf-8\")","title":""},{"docid":"31a7f676bab2ed16a7cf35919b03e346","score":"0.4823183","text":"def _get_nsd_catalog(self):\n return self.__nsd_catalog","title":""},{"docid":"e2178508a7adc4d9478519f598f8e8e0","score":"0.48205787","text":"def before_request():\n g.db = connect_db(app.config['ACCOUNT'], app.config['DBNAME'])\n #Entry.set_db(g.db)","title":""},{"docid":"1049a65383a146966505e8e8bcc12bd6","score":"0.48180845","text":"def connect_db():\n pass","title":""},{"docid":"6c25bd4fb73f28e625785e71da259f68","score":"0.4813999","text":"def db(self) -> Database[DBRecord]:\n raise NotImplementedError","title":""},{"docid":"31240513e1360995e5ebc17db19072c7","score":"0.4811872","text":"def db_for_read(self, model, **hints):\n if model._meta.app_label == 'travel_buddy':\n return 'travel_buddy_db'\n return None","title":""},{"docid":"5e7792b1126518cc230e39697c64c0ad","score":"0.48093012","text":"def test_db(config_parser):\n mongo_host = config_parser.get('mongo','db-host')\n mongo_db = config_parser.get('mongo','db-name')\n mongo_port = config_parser.getint('mongo','db-port')\n mongo_user = config_parser.get('mongo','username')\n mongo_pwd = config_parser.get('mongo','password')\n mongo_auth = config_parser.get('mongo','auth-db')\n client = MongoClient(mongo_host)\n db = client[mongo_db]\n db.authenticate(mongo_user,mongo_pwd,source=mongo_auth)\n logger.debug(\"Successfully connected to %s\"%db)\n return db","title":""},{"docid":"bbabd11b6efa7da267c65435dd760e60","score":"0.4806655","text":"def get_uri_db_tny(self,id_db_tny):\n \n if id_db_tny:\n if id_db_tny in [\"softrans_sb\",\"softlog\"]:\n db = self.get_session.query(self.stringconexoes_model).filter(self.stringconexoes_model.banco_dados==id_db_tny).first()\n else:\n db = self.get_session.query(self.stringconexoes_model).filter(self.stringconexoes_model.banco_dados==\"softlog_\" + id_db_tny).first()\n \n if db is not None:\n return db.db_uri_db_pg()\n\n if id_db_tny:\n db = self.get_session.query(self.stringconexoes_model).filter(self.stringconexoes_model.id_string_conexao==id_db_tny).first()\n else:\n return None\n\n return db.db_uri_db_pg()","title":""},{"docid":"ae7dc1377dd58edfea824a5393775a31","score":"0.4789945","text":"def db_for_write(self, model, **hints):\n if model._meta.app_label == 'auth':\n return 'auth_db'\n return None","title":""},{"docid":"f5cd05e589f912f524c8410fc99add35","score":"0.47840783","text":"def __init__(self, db_con_str=\"dbname=catalogdb\"):\n self.conn = psycopg2.connect(db_con_str)","title":""},{"docid":"8776bb80d80432ccdeee4801baba1114","score":"0.47730455","text":"def get_db():\n if not hasattr(g, 'db_conn'):\n g.db_conn = db_connect()\n return g.db_conn","title":""},{"docid":"adf227e79ef42ab8883ed455430fb593","score":"0.4772139","text":"def _get_nsd(self):\n return self.__nsd","title":""},{"docid":"76847efaba7e5a8392258df064fcac5b","score":"0.47715753","text":"def get_database(bases_list, database_name, desired_key):\n\tthere = False\n\tfor base in bases_list:\n#\t\tprint base.name\n#\t\tprint database_name\n\t\tif str(base.name) == database_name:\n\t\t\td = base\n\t\t\tthere = True\n\tif not there:\n#\t\tprint 'not there'\n\t\td = Database(parent = desired_key)\n\t\td.name = database_name\n\t\td.content = db.Text('')\n\treturn d","title":""},{"docid":"5b1703b356641fa0f1b0d8fdf2659578","score":"0.47707888","text":"def db_for_read(self, model, **hints):\n if model._meta.app_label in DATABASE_DICT:\n return DATABASE_DICT[model._meta.app_label]\n return None","title":""},{"docid":"db556679b20f0c272a5b0281b319a053","score":"0.47697708","text":"def test_db_table(self):\n\n\t\tself.assertEqual(self.record._meta.db_table,\"reviews\")","title":""},{"docid":"249e6e635ff6c09ab7eec0bc32fde578","score":"0.47613108","text":"def get_connection(**kwargs):\n try:\n logging.debug(\"Connecting to mapd db...\")\n con = pymapd.connect(\n user=kwargs[\"db_user\"],\n password=kwargs[\"db_passwd\"],\n host=kwargs[\"db_server\"],\n port=kwargs[\"db_port\"],\n dbname=kwargs[\"db_name\"],\n )\n logging.info(\"Succesfully connected to mapd db\")\n return con\n except (pymapd.exceptions.OperationalError, pymapd.exceptions.Error):\n logging.exception(\"Error connecting to database.\")\n return False","title":""},{"docid":"b24b310e971351286e27d62cd9fbf645","score":"0.47595018","text":"def dc(self):\n pass\n # TODO Client disconnection","title":""},{"docid":"26de2e1fb255c2a5a43aef5fa1145655","score":"0.4756963","text":"def testDetermineDatabaseType(self):\n test_extractor = catalog_extractor.EseDbCatalogExtractor()\n\n database_type = test_extractor._DetermineDatabaseType([\n 'SystemIndex_0A', 'SystemIndex_Gthr'])\n self.assertEqual(database_type, 'search')","title":""},{"docid":"b5470e0f8db47cbfa374940c7064db0c","score":"0.47476617","text":"def accesslogDBEntry(self, replicator_dn, \n log_dir=\"/opt/gluu/data/accesslog\"):\n\n attributes = {'objectClass': ['olcDatabaseConfig', 'olcMdbConfig'],\n 'olcDatabase': '{2}mdb',\n 'olcDbDirectory': log_dir,\n 'OlcDbMaxSize': 1073741824,\n 'olcSuffix': 'cn=accesslog',\n 'olcRootDN': 'cn=admin, cn=accesslog',\n 'olcRootPW': ldap_encode(self.passwd),\n 'olcDbIndex': ['default eq', 'objectClass,entryCSN,entryUUID,reqEnd,reqResult,reqStart,reqDN'],\n 'olcLimits': 'dn.exact=\"{0}\" time.soft=unlimited time.hard=unlimited size.soft=unlimited size.hard=unlimited'.format(replicator_dn),\n\n }\n #check if accesslogdb entry is allread exists. If not exists, create it.\n if not self.checkAccesslogDBEntry():\n return self.conn.add('olcDatabase={2}mdb,cn=config',\n attributes=attributes)","title":""},{"docid":"17eb67d6b93aec15e1ad14cdb6106aad","score":"0.47474527","text":"def configureDBSnapshot(self):\n tag = { \"DDDB\": self.getProp('DDDBtag')\n , \"LHCBCOND\": self.getProp('CondDBtag')\n , \"SIMCOND\" : self.getProp('CondDBtag')\n , \"ONLINE\" : 'fake'\n }\n\n # https://savannah.cern.ch/bugs/?94454#comment12\n from Configurables import MagneticFieldSvc\n MagneticFieldSvc().UseSetCurrent = True\n\n from Configurables import CondDB\n cdb = CondDB()\n cdb.Tags = tag\n cdb.setProp('IgnoreHeartBeat', True)\n self.setOtherProps( cdb, [ 'UseDBSnapshot',\n 'DBSnapshotDirectory',\n 'PartitionName' ])\n\n # So, here is the problem: we don't want to run the risk that\n # the CondDB() configurable (which configures /after/ us)\n # overwrites our conditions. Yet, we don't want to miss the\n # default conditions (e.g. velo stepper motor, magnetic field)\n # either. if we add our conditions to its\n # RunChangeHandlerConditions list, then we a) need to fix the\n # path and b) don't know what happens for conditions that\n # appear twice, because we don't control the ordering of the\n # list. So, the hack is:\n # - don't set 'EnableRunChangeHandler'\n # - copy what is hidden behind that flag in CondDB()._configureDBSnapshot\n # - do the test of the RunChangeHandler configuration ourselves:\n cdb.setProp('EnableRunChangeHandler', False)\n from Configurables import RunChangeHandlerSvc\n rch = RunChangeHandlerSvc()\n ApplicationMgr().ExtSvc.append(rch)\n baseloc = self.getProp( \"DBSnapshotDirectory\" )\n rch.Conditions = dict( (c,'/'.join([baseloc,f])) for f,cs in cdb.getProp(\"RunChangeHandlerConditions\").iteritems() for c in cs )\n \n #path = self.getProp('DBSnapshotDirectory') + \"/..\"*4 + \"/group/online/AligWork/current/\"\n allconds = {\n 'Velo' : [\n 'Conditions/Alignment/Velo/VeloSystem',\n 'Conditions/Alignment/Velo/VeloRight',\n 'Conditions/Alignment/Velo/VeloLeft']\n + ['Conditions/Alignment/Velo/Module%02d'%i for i in range(0, 42)] \n + ['Conditions/Alignment/Velo/Detector%02d-%02d' % (i, (1 + i / 2) % 2) for i in range(0, 42)],\n 'IT' : []\n + [ 'Conditions/Alignment/IT/ITSystem' ]\n + [ 'Conditions/Alignment/IT/ITT%d' % i for i in range(1,4) ]\n + [ 'Conditions/Alignment/IT/ITT%d%sBox' % (i,b) for i in range(1,4) for b in ['Top','Bottom','ASide','CSide' ] ]\n + [ 'Conditions/Alignment/IT/ITT%d%sLayer%s' % (i,b,l) for i in range(1,4) for b in ['Top','Bottom','ASide','CSide' ] for l in ['X1','U','V','X2' ] ]\n + [ 'Conditions/Alignment/IT/ITT%d%sLayer%sLadder%d' % (i,b,l,a) for i in range(1,4) for b in ['Top','Bottom','ASide','CSide' ] for l in ['X1','U','V','X2' ] for a in range(1,8) ],\n # + [ 'Conditions/Alignment/IT/ITT%d%sLayer%sLadder%dSector' % (i,b,l,a) for i in range(1,4) for b in ['Top','Bottom','ASide','CSide' ] for l in ['X1','U','V','X2' ] for a in range(1,8) ]\n # + [ 'Conditions/Alignment/IT/ITT%d%sLayer%sLadder%dSector_Sensor1' % (i,b,l,a) for i in range(1,4) for b in ['Top','Bottom','ASide','CSide' ] for l in ['X1','U','V','X2' ] for a in range(1,8) ]\n # + [ 'Conditions/Alignment/IT/ITT%d%sLayer%sLadder%dSector_Sensor2' % (i,b,l,a) for i in range(1,4) for b in ['ASide','CSide' ] for l in ['X1','U','V','X2' ] for a in range(1,8) ] ,\n 'OT' : []\n + [ 'Conditions/Alignment/OT/OTSystem' ]\n + [ 'Conditions/Alignment/OT/T%d' %i for i in range(1,4) ]\n + [ 'Conditions/Alignment/OT/T%d%s' % (i,l) for i in range(1,4) for l in ['X1','U','V','X2' ] ]\n + [ 'Conditions/Alignment/OT/T%d%sQ%d' % (i,l,q) for i in range(1,4) for l in ['X1','U','V','X2' ] for q in range(0,4) ]\n + [ 'Conditions/Alignment/OT/T%d%sQ%dM%d' % (i,l,q,m) for i in range(1,4) for l in ['X1','U','V','X2' ] for q in range(0,4) for m in range(1,10) ],\n 'TT' : []\n + [ 'Conditions/Alignment/TT/TTSystem' ]\n + [ 'Conditions/Alignment/TT/TT%s' % i for i in ['a','b' ] ]\n + [ 'Conditions/Alignment/TT/TT%sLayer' % (l) for l in ['aX','aU','bV','bX' ] ]\n + [ 'Conditions/Alignment/TT/TT%sLayerR%dModule%d%s' % (l,r,m,w) for w in ['T','B'] for l in ['aX','aU','bV','bX'] for r in range(1,4) for m in range(1,4)]\n + [ 'Conditions/Alignment/TT/TT%sLayerR%dModule%d%s' % (l,r,m,w) for w in ['T','B'] for l in ['bV','bX'] for r in range(1,4) for m in range(4,6)]\n + [ 'Conditions/Alignment/TT/TT%sLayerR%dModule%d%s' % (l,r,m,w) for w in ['T','B'] for l in ['aX','aU','bV','bX'] for r in [1,3] for m in range(6,7)]\n + [ 'Conditions/Alignment/TT/TT%sLayerR%dModule%d%s' % (l,r,m,w) for w in ['T','B'] for l in ['aX','aU'] for r in [1,3] for m in range(4,6)],\n 'Muon' : []\n + ['Conditions/Alignment/Muon/MuonSystem']\n + [ 'Conditions/Alignment/Muon/M%sStation' % i for i in range(1,6) ]\n + [ 'Conditions/Alignment/Muon/M%sASide' % i for i in range(1,6) ]\n + [ 'Conditions/Alignment/Muon/M%sCSide' % i for i in range(1,6) ]\n }\n\n ## This is a bit dirty, since we're supposed to control TAlignment. We\n ## know that this is set from top level, so let's give it a try anyway\n ta = TAlignment()\n sdToWrite = set(ta.getProp(\"WriteCondSubDetList\"))\n pat = self.getProp(\"OnlineAligWorkDir\") + \"/xml/%s.xml\" \n conditionmap = dict((pat % sd, f) for (sd, f) in allconds.iteritems() if sd in sdToWrite)\n \n # add to the existing map\n rch.Conditions = dict(rch.Conditions.items() + dict( (c,f) for f,cs in conditionmap.iteritems() for c in cs ).items() )\n\n from Configurables import MagneticFieldSvc\n MagneticFieldSvc().UseSetCurrent = True","title":""},{"docid":"157a6f85df20d249486b84e76c7046f1","score":"0.47464782","text":"def mongo_list_dbs():\n client = mongo_connect()\n for dbname in client.list_database_names():\n if dbname in ['admin', 'local']:\n continue\n db = client[dbname]\n gpup = db['gpu_procs'].find_one()\n if gpup is not None:\n gpup = gpup['gpu_procs']\n print(f\"DB: {dbname}\\n GPU proc count: {gpup}\")\n for colname in db.list_collection_names():\n col = db[colname]\n print(f\" Col: {colname}, count: {col.count()}\")\n client.close()","title":""},{"docid":"c1c546ff3533607a7c08e2103a8e3296","score":"0.47407824","text":"def get_kdb():\n return current_app.extensions['kdb'].connection","title":""},{"docid":"2be622382abe530d362506be550627f9","score":"0.47388348","text":"def checkAccesslogDBEntry(self):\n \n return self.conn.search(search_base='cn=config',\n search_filter='(olcSuffix=cn=accesslog)',\n search_scope=SUBTREE, attributes=[\"*\"])","title":""},{"docid":"07435a72d7afb184ec87d740dcaa0776","score":"0.47269005","text":"def get_graph_db(self):\n return self.get_variable(\"general\", \"graph_db\")","title":""},{"docid":"6a7daa68cfeb920c088725c5d078cf2c","score":"0.47193885","text":"def __init__(self, dsn, dsnDict, console):\n super(MySQLdbStorage, self).__init__(dsn, dsnDict, console)","title":""},{"docid":"17388a380ba9911fda750bbbcd80851e","score":"0.47184587","text":"def __init__(self):\n self.db = database.db_connection()","title":""},{"docid":"397399c38248591d8b2403a3d550fa74","score":"0.47183996","text":"def exist_care(mongoconn,db_name,carer_id,cared_id):\n factor = {'carer_id':carer_id,'cared_id':cared_id}\n res = mongoconn[db_name]['care_record'].find_one(factor)\n if res is not None:\n return res['_id']\n return None","title":""},{"docid":"22ea32a1bbe0a4a2789cd49b063ec39e","score":"0.47158706","text":"def allow_syncdb(self, db, model):\n\n if db in DATABASE_DICT.values():\n return DATABASE_DICT.get(model._meta.app_label) == db\n elif model._meta.app_label in DATABASE_DICT:\n return False\n return None","title":""},{"docid":"efcd17c7e244e8ec6c9d39fa2d9120a0","score":"0.47150096","text":"def database(self):\n return self.info_query(\"SELECT DATABASE()\")[0]","title":""},{"docid":"5894d4b1c01c5091c9cb8d691e58dcd9","score":"0.47148642","text":"def get_db():\n if not hasattr(g, 'mongo'):\n g.mongo = mongo # Flask-Mongo connection\n return g.mongo.db","title":""},{"docid":"aed0cd135cb4c801742f2e69a2f69c51","score":"0.47099882","text":"def _db_read_nonprimary(self, cur=None):\n return False","title":""},{"docid":"123972ebf820beee8b10c334341d80ce","score":"0.4706701","text":"def ready(self, dbname):\r\n return dbname in self","title":""},{"docid":"c60a20f7558c1cc5b1e8c76a71b50a9e","score":"0.47008118","text":"def get_custom_db(firmware_version, _db):\r\n if _db:\r\n if firmware_version in _db:\r\n return _db[firmware_version]\r\n return None","title":""},{"docid":"9e94f29ce5612aafc0907a72795eaea6","score":"0.4698479","text":"def _configure_nve_db(self, vni, device_id, mcast_group, host_id):\n host_connections = self._get_switch_info(host_id)\n for switch_ip, intf_type, nexus_port in host_connections:\n nxos_db.add_nexusnve_binding(vni, switch_ip, device_id,\n mcast_group)","title":""},{"docid":"76312512dc2ded0f5ad963b73f24aaad","score":"0.4696835","text":"def __init__(self):\n self._db_params = self._configuration_database()","title":""},{"docid":"9c8bb710b4a166859cc2182dcc78304d","score":"0.46957707","text":"def get_db(server_id):\n DATABASE = \"DATABASE\" + str(server_id)\n print(DATABASE)\n \"\"\"The internal LocalStack that holds AppContext instances...application context binds an application object implicitly to the current thread\"\"\"\n tracktop = _app_ctx_stack.top\n \"\"\"If the database connection is not initiated then initiate connection. We are using sqllite3 for the database connection.\"\"\"\n \"\"\"detect_types in connect makes the sqlite3 module parse the declared type for each column it returns.\"\"\"\n if not hasattr(tracktop, 'track_db0') and server_id == 0:\n tracktop.track_db0 = sqlite3.connect(app.config[DATABASE], detect_types=sqlite3.PARSE_DECLTYPES)\n tracktop.track_db0.row_factory = sqlite3.Row\n if not hasattr(tracktop, 'track_db1') and server_id == 1:\n tracktop.track_db1 = sqlite3.connect(app.config[DATABASE], detect_types=sqlite3.PARSE_DECLTYPES)\n tracktop.track_db1.row_factory = sqlite3.Row\n if not hasattr(tracktop, 'track_db2') and server_id == 2:\n tracktop.track_db2 = sqlite3.connect(app.config[DATABASE], detect_types=sqlite3.PARSE_DECLTYPES)\n tracktop.track_db2.row_factory = sqlite3.Row\n \"\"\" check server id return the track database accordingly\"\"\"\n if server_id == 0:\n return tracktop.track_db0\n elif server_id == 1:\n return tracktop.track_db1\n else:\n return tracktop.track_db2","title":""},{"docid":"27e1ffbd14a947894b7936437351383b","score":"0.4693095","text":"def test_get_db():\n db_test = User._get_db() # Get pymongo.database.Database instance\n assert isinstance(db_test, Database)","title":""},{"docid":"7364862c404d1c3dc4e933539b7d50ea","score":"0.468888","text":"def get_db():\n if not hasattr(g, 'db'):\n g.db = connect_db()\n return g.db","title":""},{"docid":"305a8e9dcd23d67e92986ad73c269c1e","score":"0.46874708","text":"def get_mongo_conn():\n MONGO_URI = 'mongodb://saran:Saran1!@ds113736.mlab.com:13736/ingredientmaster'\n client = pymongo.MongoClient(MONGO_URI)\n db = client.get_database('ingredientmaster')\n return db","title":""},{"docid":"f4a815af9118297f580ce6106690b9df","score":"0.46834752","text":"def test_ngen_with_unknown_db(runner):\n result = runner.invoke(cli.main, ['list', 'fake.'])\n assert result.exit_code == 2","title":""},{"docid":"37d90dc6bdb91cbc0cf53e1ed35489fd","score":"0.46785578","text":"def db_for_read(self, model, **hints):\r\n if model._meta.app_label == 'virvo':\r\n return 'virvo'\r\n return None","title":""},{"docid":"9c227f6c0dcc3bdd23a06f9623fb35d2","score":"0.4678507","text":"def test_db_host_get(self):\r\n\r\n db_path = os.path.join(self.res_dir, 'regular.sqlite')\r\n db_obj = db.DataBaseHost.get(db_path)\r\n\r\n self.assertEqual(db.DataBaseHost.get(db_path), db_obj)\r\n\r\n db_obj.close()","title":""},{"docid":"c5f02e637e32b8ccef59b854bba5f422","score":"0.46758056","text":"def connections(self):\n tag, has_dimension = {}, False\n try:\n self.es_tag = pd.read_sql(\"SELECT * FROM es_connection\", con).to_dict('results')[-1]\n dimensions = pd.read_sql(\"SELECT * FROM data_connection\", con).to_dict('results')[0]\n if dimensions['dimension'] not in ['None', None]:\n has_dimension = True\n except Exception as e:\n print(e)\n return has_dimension","title":""},{"docid":"4bdf84a3c72f6ab54c9d1a3335be458a","score":"0.4670276","text":"def load_db(self, cursor, connection):","title":""},{"docid":"0560115b04f42f82e10f0b7c43c687eb","score":"0.46691918","text":"def _lookup_cddb(self):\n import DiscID, CDDB\n if self.cddevice: dev = DiscID.open(self.cddevice)\n else: dev = DiscID.open()\n cdid = DiscID.disc_id(dev)\n tracks = cdid[1]\n (status, info) = CDDB.query(cdid)\n if status == 200:\n (status, info) = CDDB.read(info['category'], \\\n info['disc_id'])\n elif status == 210 or status == 211:\n (status, info) = CDDB.read(info[0]['category'], \\\n info[0]['disc_id'])\n else:\n return None\n\n ret = cdinfo()\n for key in info.keys():\n if key.startswith('TTITLE'):\n n = int(re.findall(\"TTITLE([0-9]+)\", key)[0])\n ret.titles[n] = info[key].encode('ascii', errors='replace')\n elif key == 'DTITLE':\n (artist, album) = info[key].split('/')\n ret.artist = artist.strip()\n ret.album = album.strip()\n elif key == 'DYEAR':\n ret.releasedate = info[key]\n wx.LogMessage(\"cddb succeeded\")\n return ret","title":""},{"docid":"cc3e31a4747600f576f65d8a3a1a9855","score":"0.46684363","text":"def db_for_write(self, model, **hints):\n if model._meta.app_label == 'mapdata':\n return 'msemap_db'\n return None","title":""}],"string":"[\n {\n \"docid\": \"e0c3d258c19d4d7fa03901c154ec7bfa\",\n \"score\": \"0.5899773\",\n \"text\": \"def inspect_db(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c75ae12209357a13cd26ecb99d48a79\",\n \"score\": \"0.56975514\",\n \"text\": \"def getdb(self):\\n if self.cl._db is not None:\\n return self.cl._db\\n return discretezoo.DEFAULT_DB\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d7600e1e532f9e6b12b0494dbf3a5dd\",\n \"score\": \"0.5669286\",\n \"text\": \"def ReadNDCDatabase(SearchText, SearchType, SearchSize):\\n\\n NDCRecords = []\\n NDCEnhancedArray = []\\n\\n\\n if SearchType==\\\"ndc\\\":\\n SearchText = SearchText.upper()\\n SearchText = SearchText.strip()\\n SearchError = False\\n CleanNDC = SearchText.replace(\\\"-\\\", \\\"\\\")\\n if len(CleanNDC) == 9:\\n qNDC = NDCLookup.query(NDCLookup.ninedigitndc == CleanNDC)\\n else:\\n qNDC = NDCLookup.query(NDCLookup.ndc == CleanNDC)\\n\\n elif SearchType == \\\"name\\\":\\n SearchText = SearchText.upper()\\n SearchText = SearchText.strip()\\n SearchError = len(SearchText) == 0\\n qNDC = NDCLookup.query(NDCLookup.proprietaryname == SearchText)\\n\\n elif SearchType == \\\"ingredient\\\":\\n QueryString = IngredientQuery(SearchText)\\n logging.info(\\\"Executing Ingredient Search: %s\\\", QueryString)\\n SearchError = len(QueryString) == 0 \\n qNDC= NDCLookup.gql(QueryString)\\n\\n elif SearchType == \\\"image\\\":\\n SearchText = SearchText.upper()\\n SearchText = SearchText.strip()\\n QueryString = NDC9ArrayQuery(SearchText)\\n logging.info(\\\"Executing NDC9 Search: %s\\\", QueryString)\\n SearchError = len(QueryString) == 0\\n qNDC= NDCLookup.gql(QueryString)\\n\\n elif SearchType == \\\"active\\\":\\n SearchText = SearchText.upper()\\n SearchText = SearchText.strip()\\n SearchError = len(SearchText) == 0\\n qNDC = NDCLookup.query(NDCLookup.substancename == SearchText)\\n \\n else:\\n SearchError = True\\n\\n if not(SearchError) : NDCRecords = qNDC.fetch(limit=int(SearchSize))\\n\\n for NDCRecord in NDCRecords:\\n NDCRecord.ndc = NDCTenDigitFormat(NDCRecord.ndc, NDCRecord.format)\\n NDCRecord.proprietaryname = NDCRecord.proprietaryname.title()\\n NDCRecord.nonproprietaryname = NDCRecord.nonproprietaryname.title()\\n NDCRecord.packagedescription = NDCRecord.packagedescription.title()\\n\\n substancelist = []\\n\\n for substance in NDCRecord.substancelist:\\n substancelist.append(substance.title())\\n\\n NDCRecord.substancelist = substancelist\\n\\n\\n TemplateValues = {\\\"NDCRecordArray\\\": NDCRecords,\\n \\\"SearchText\\\": SearchText,\\n \\\"SearchType\\\": SearchType,\\n \\\"SearchSize\\\": SearchSize}\\n\\n return TemplateValues\",\n \"title\": \"\"\n },\n {\n \"docid\": \"808d9a25b42b6f3d15a009c3bbb5e49e\",\n \"score\": \"0.5438229\",\n \"text\": \"def getMainDbDN(self):\\n \\n \\n if self.conn.search(search_base=\\\"cn=config\\\", search_scope=LEVEL,\\n search_filter=\\\"(olcDbDirectory=/opt/gluu/data/main_db)\\\",\\n attributes='*'):\\n if self.conn.response:\\n return self.conn.response[0]['dn']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"faa84fb710c5caa6a1b2549f07ff4aea\",\n \"score\": \"0.53561217\",\n \"text\": \"def db_for_read(self, model, **hints):\\n return random.choice(['replica_1'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e00622aa8d0d49fff6a6a11e28c41937\",\n \"score\": \"0.5321135\",\n \"text\": \"def db(self) -> ConfigNodePropertyString:\\n return self._db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abef234fddbac44609b1aafb0e2cce6d\",\n \"score\": \"0.53086776\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'mongo_db'):\\n g.mongo_db = pymongo.MongoClient('db')\\n return g.mongo_db.filterforge\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6099d13b58ec8776840d902d254ade31\",\n \"score\": \"0.5238932\",\n \"text\": \"def get_db():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef33563c491377b11678fe20f4dfc96f\",\n \"score\": \"0.5220712\",\n \"text\": \"def db_for_read(self, model, **hints):\\n if model._meta.app_label == 'fblog':\\n return 'fblog_db'\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"389799e6ff7064b6ebf88447c09f4e1e\",\n \"score\": \"0.52157825\",\n \"text\": \"def _db_exist(self, source):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b72f57ab10518ae657f2cbf0f2e1abe7\",\n \"score\": \"0.51940084\",\n \"text\": \"def dispatch_database():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75c9dd6cbc580743d1a9d43792f6ae33\",\n \"score\": \"0.51888806\",\n \"text\": \"def db(self):\\r\\n return self.__db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd2ed4dd2b09b95e6f22bfbd3c33c0b1\",\n \"score\": \"0.5169626\",\n \"text\": \"def get_db_info(db_coll_dict,config_path):\\n ip,username,password=load_mongo_account(config_path)\\n client=MongoClient(ip,username=username,password=password)\\n detail_dict={}\\n for db in db_coll_dict.keys():\\n meta_client=client[db][db+\\\".meta\\\"]\\n detail_dict[db]={}\\n\\n detail_dict[db]['episodes']=[i for i in client[db].list_collection_names() if \\\".meta\\\" not in i and \\\".files\\\" not in i and \\\".chunks\\\" not in i and \\\".vis\\\" not in i and \\\".scans\\\" not in i ]\\n\\n # Add task description\\n detail_dict[db]['task_description']=get_task_description(meta_client)\\n\\n # Add class and id info for entities\\n detail_dict[db]['entities']={}\\n class_info=get_all_class(meta_client)\\n for each_object in class_info:\\n obj_id=each_object['id']\\n obj_class=each_object['class']\\n if obj_class not in detail_dict[db]['entities'].keys():\\n detail_dict[db]['entities'][obj_class]=[obj_id]\\n else:\\n detail_dict[db]['entities'][obj_class].append(obj_id)\\n \\n # Add skel entities\\n detail_dict[db]['skels']={}\\n bone_info=get_bone(meta_client)\\n for each_bone in bone_info:\\n bone=each_bone['bone']\\n skel=each_bone['skel']\\n if skel not in detail_dict[db]['skels'].keys():\\n detail_dict[db]['skels'][skel]=[bone]\\n else:\\n detail_dict[db]['skels'][skel].append(bone)\\n\\n # Add camera view\\n detail_dict[db]['camera_views']=get_camera_view(meta_client)\\n # pprint.pprint(detail_dict)\\n \\n return detail_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cea77859580f584522b8b98ba0892c08\",\n \"score\": \"0.5169591\",\n \"text\": \"def legacy_db_configured(self):\\n if (\\n self.kv.get(\\\"db_host\\\")\\n and self.kv.get(\\\"db_port\\\")\\n and self.kv.get(\\\"db_db\\\")\\n and self.kv.get(\\\"db_user\\\")\\n and self.kv.get(\\\"db_pass\\\")\\n ):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acb9ce2f999a8235e840fd94d2128e30\",\n \"score\": \"0.5157214\",\n \"text\": \"def dbc_db(self):\\n return self.__dbc_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c986b9db2d7cd8d6e5c55fb2b65759b5\",\n \"score\": \"0.5132187\",\n \"text\": \"def db(self):\\n return self._conf['db']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59c589aa63afdf12dde81088a8648e97\",\n \"score\": \"0.51253587\",\n \"text\": \"def get_db_state():\\n print \\\"\\\\nreference implementation of get_db_state\\\\n\\\"\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8b6921814cd96eaf5c4e4836d56d0d0\",\n \"score\": \"0.5109895\",\n \"text\": \"def CGD(cls):\\n cls.Namespace(\\\"http://www.candidagenome.org/cgi-bin/locus.pl?dbid=\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30d7f1379c3f7f0f80343538209cc154\",\n \"score\": \"0.5109482\",\n \"text\": \"def check_mongodb_state(config_path):\\n ip,username,password=load_mongo_account(config_path)\\n client=pymongo.MongoClient(ip,username=username,password=password,serverSelectionTimeoutMS=100)\\n try:\\n print(client.list_database_names())\\n return True\\n except Exception as e:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a52a415663f3ab4721fe938d3b92013\",\n \"score\": \"0.5103297\",\n \"text\": \"def test_ngen_with_known_db(runner):\\n result = runner.invoke(cli.main, ['list', '-d' , '~/.keepassxc/worth.kdbx'])\\n assert result.exit_code == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fc530f67300c30c79dc8432d0146653\",\n \"score\": \"0.5096329\",\n \"text\": \"def db_for_read(self, model, **hints):\\n if model._meta.app_label == 'auth':\\n return 'auth_db'\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0db5d66430443a9b54386c3076b54d8b\",\n \"score\": \"0.5089351\",\n \"text\": \"def __init__(self,db):\\n self.db = db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1d22064ce85a9d9e5421703f72a124a\",\n \"score\": \"0.5073341\",\n \"text\": \"def create_db_from_dict(self, ref_dict, database = 'smart_meter'):\\n\\n\\n\\n\\t\\tprint(\\\"Database {} has been created from the dictionary\\\".format(database))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7129aa5fee58918ea3e721d4b5062e8\",\n \"score\": \"0.50717753\",\n \"text\": \"def __init__(self, db):\\n self._db = db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7129aa5fee58918ea3e721d4b5062e8\",\n \"score\": \"0.50717753\",\n \"text\": \"def __init__(self, db):\\n self._db = db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e422fbc834c5ce8795ad8afb0331382\",\n \"score\": \"0.50672495\",\n \"text\": \"def _get_database_id(self, db_name):\\n for expert_database in Database.objects.all():\\n if re.match(expert_database.label, db_name, re.IGNORECASE):\\n return expert_database.id\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be694c1d9a541835fe2b57be9e5d36ea\",\n \"score\": \"0.506357\",\n \"text\": \"def _init_db(self):\\n raise NotImplementedError(\\\"Use subclasses of Database.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14377ae4a1264e70fb40459e55696168\",\n \"score\": \"0.5059923\",\n \"text\": \"def checkAccesslogDB(self):\\n \\n return self.conn.search(search_base='cn=config',\\n search_filter='(olcSuffix=cn=accesslog)',\\n search_scope=SUBTREE, attributes=[\\\"*\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f884bbe9255899e5ccad50d1aa7da105\",\n \"score\": \"0.50476664\",\n \"text\": \"def db():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ec067f043854af130dc68d6310bb290\",\n \"score\": \"0.5025044\",\n \"text\": \"def db_for_read(self, model, **hints):\\n if model._meta.app_label == 'mapdata':\\n return 'msemap_db'\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"782deb8c0adabe9849cfdf86e50738ea\",\n \"score\": \"0.501365\",\n \"text\": \"def db_show(self, name) -> bool:\\n found = False\\n dbs = self._index_local_databases()\\n\\n for db in dbs:\\n if db == name:\\n found = True;\\n\\n updated = datetime.datetime.fromtimestamp(dbs[db]['last modified']).strftime('%Y-%m-%d %H:%M:%S')\\n checked = datetime.datetime.fromtimestamp(dbs[db]['last checked']).strftime('%Y-%m-%d %H:%M:%S')\\n self.logger.info(f\\\"Database: {db}\\\")\\n if dbs[db]['last modified'] == 0:\\n self.logger.info(\\\" last modified: not downloaded\\\")\\n else:\\n self.logger.info(f\\\" last modified: {updated}\\\")\\n if dbs[db]['last checked'] == 0:\\n self.logger.info(\\\" last checked: n/a\\\")\\n else:\\n self.logger.info(f\\\" last checked: {checked}\\\")\\n self.logger.info(f\\\" url: {dbs[db]['url']}\\\")\\n if db.endswith(\\\".cvd\\\"):\\n self.logger.info(f\\\" local version: {dbs[db]['local version']}\\\")\\n if len(dbs[db]['CDIFFs']) > 0:\\n self.logger.info(f\\\" CDIFFs: \\\\n{json.dumps(dbs[db]['CDIFFs'], indent=4)}\\\")\\n return True\\n\\n if not found:\\n self.logger.error(f\\\"No such database: {name}\\\")\\n return found\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bebd245ae8ad61090e8d6a3ec3a0f552\",\n \"score\": \"0.4993931\",\n \"text\": \"def test_003_dbtype(self) -> None:\\n self.assertEqual(self.ds.dbtype, \\\"dynamo\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e536412c7386152d4bae645d367cb220\",\n \"score\": \"0.4969846\",\n \"text\": \"def db():\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1061a95e9c40d3e60d39cc5f11b0033a\",\n \"score\": \"0.49446747\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'db_con'):\\n g.db_con = connect_db()\\n return g.db_con\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bca35078fe59913957c9f19e658127a\",\n \"score\": \"0.49268067\",\n \"text\": \"def set_candb(self, db):\\n self.candb = db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bcdaf6f50aeeb2c3ed60f75fc79ce09\",\n \"score\": \"0.49224553\",\n \"text\": \"def db(self):\\n# log.info('fetching request.db')\\n return DBSession\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aec98e1603bb3a5d602c25f3edde63ad\",\n \"score\": \"0.4914233\",\n \"text\": \"def mogo_db_info(self) -> str:\\n return self.config['database']['mongo']['information']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf985bf73df44a6316c55391fcc69e3e\",\n \"score\": \"0.4912167\",\n \"text\": \"def db_variables():\\n\\n return (\\\"azurehack.n8sw8.gcp.mongodb.net\\\",\\\"backend\\\",\\\"azure2020\\\",\\\"admin\\\",\\\"azurehack\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a465304cf06a63ebd646ac049c4a6702\",\n \"score\": \"0.490552\",\n \"text\": \"def dbformat(self):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ebea65531837f781815f8a5d7cdeae1\",\n \"score\": \"0.48924413\",\n \"text\": \"def getDbCount(self):\\n\\t\\tc= self.ds.value(\\\"dbCounter\\\",0).toInt()[0]\\n\\t\\treturn c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8a44427758ee3855d217be560bc8631\",\n \"score\": \"0.4883515\",\n \"text\": \"def db_for_write(self, model, **hints):\\n if model._meta.app_label == 'fblog':\\n return 'fblog_db'\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e2aff155f9b1b697a164fdacc3ab9f6\",\n \"score\": \"0.4879035\",\n \"text\": \"def get_vesync_database_info_from_nacos(self) -> dict:\\n nacos_client = nacos.NacosClient(self.nacos_server.host, namespace=self.stage_namespace_id)\\n self.set_nacos_client_debug(nacos_client)\\n configs = nacos_client.get_config(settings.VESYNC_DATABASE_DATA_ID,\\n settings.VESYNC_DATABASE_GROUP,\\n no_snapshot=True)\\n logger.debug(f\\\"configs (data id: {settings.VESYNC_DATABASE_DATA_ID}, group: {settings.VESYNC_DATABASE_GROUP}): \\\"\\n f\\\"{configs}\\\")\\n configs = common.load_properties_from_string(configs)\\n database_info = {\\n \\\"host\\\": configs[settings.KEY_TO_VESYNC_DATABASE_HOST],\\n \\\"port\\\": int(configs[settings.KEY_TO_VESYNC_DATABASE_PORT]),\\n \\\"user\\\": configs[settings.KEY_TO_VESYNC_DATABASE_USER],\\n \\\"password\\\": configs[settings.KEY_TO_VESYNC_DATABASE_PASSWORD],\\n \\\"database\\\": configs[settings.KEY_TO_VESYNC_DATABASE_NAME],\\n \\\"charset\\\": \\\"utf8\\\",\\n \\\"cursorclass\\\": pymysql.cursors.DictCursor\\n }\\n logger.info(f\\\"database info used to connect: {database_info}\\\")\\n return database_info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee111f5320870999c82485c6eb76104f\",\n \"score\": \"0.48773244\",\n \"text\": \"def internalDB_process(self, *args, **kwargs):\\n d_ret = self.dcm.internalDB_process(*args, **kwargs)\\n return d_ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"300a2074558c1aa272dc718eae3183ec\",\n \"score\": \"0.48739982\",\n \"text\": \"def preferred_dc(self):\\n return self._preferred_dc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a779cf9d9baf7039fa83e9cf136e8ffe\",\n \"score\": \"0.4869784\",\n \"text\": \"def _get_db(cls):\\r\\n return get_db(cls._meta.get(\\\"db_alias\\\", DEFAULT_CONNECTION_NAME ))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5c97ce33139911dad3ffd7a0a2253c7\",\n \"score\": \"0.48628843\",\n \"text\": \"def __init__(self, db) -> None:\\n self._db = db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"214f1e1b15658dd10a2111c5317b104e\",\n \"score\": \"0.48577946\",\n \"text\": \"def filter_databases(rc):\\n dbs = rc.databases\\n public_only = rc._get(\\\"public_only\\\", False)\\n if public_only:\\n dbs = [db for db in dbs if db[\\\"public\\\"]]\\n dbname = rc._get(\\\"db\\\")\\n if dbname is not None:\\n dbs = [db for db in dbs if db[\\\"name\\\"] == dbname]\\n elif len(dbs) == 1:\\n rc.db = dbs[0][\\\"name\\\"]\\n rc.databases = dbs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7079b600de1b490e5a7b5d5d76912821\",\n \"score\": \"0.485489\",\n \"text\": \"def _initdb(self, db=None):\\n if self._db is not None:\\n return\\n if db is None:\\n self._db = discretezoo.DEFAULT_DB\\n else:\\n self._db = db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d05dfa6c864d6c3db4bc0ae5b5620b9e\",\n \"score\": \"0.48525262\",\n \"text\": \"def load(self, db):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b10a78718803ce0ec49b89b80a3f64c4\",\n \"score\": \"0.48498744\",\n \"text\": \"def db(self) -> Any:\\n return self._db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a688e70742fe7fb640e8b7e42eed47fc\",\n \"score\": \"0.48465395\",\n \"text\": \"def get_master_mongo_conn(env='dev'):\\n if env == 'prod':\\n MONGO_URI = 'mongodb://chefd_prod:chefd_prod@ds135459-a0.mlab.com:35459/'\\n MONGO_URI += 'prod_chefd_1_gb?replicaSet=rs-ds135459'\\n else:\\n MONGO_URI = 'mongodb://staging_chefd:staging_chefd@ds141098.mlab.com:41098/'\\n MONGO_URI += 'staging-chefd-replica'\\n client = pymongo.MongoClient(MONGO_URI)\\n db = client.get_default_database()\\n return db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1a9648f2d860cb95a6f5dd2859d1061\",\n \"score\": \"0.48406836\",\n \"text\": \"def db(self, db: ConfigNodePropertyString):\\n\\n self._db = db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c40713744c3a79de6b7d1dc95549911\",\n \"score\": \"0.4830416\",\n \"text\": \"def _get_db():\\n global db_conn\\n\\n if not db_conn:\\n db_conn = _connect_db(DSN)\\n return db_conn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45adfa4a8a41cc5ced4962b86c5acad3\",\n \"score\": \"0.4827729\",\n \"text\": \"def db_for_read(self, model, **hints):\\n if model._meta.db_table in self.call_center_tables:\\n return 'call_center'\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bf6b0932ec62e7aa0d6b98cf3ea7b1a\",\n \"score\": \"0.48261854\",\n \"text\": \"def get_database_for(self, id):\\n url=\\\"https://app.crepc.sk/oai\\\"\\n params={\\\"verb\\\":\\\"GetRecord\\\",\\n \\\"metadataPrefix\\\":\\\"xml-crepc2\\\",\\n \\\"identifier\\\":\\\"oai:crepc.sk:database/\\\"+id\\n }\\n r = requests.get(url = url, params = params)\\n return r.content.decode(\\\"utf-8\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31a7f676bab2ed16a7cf35919b03e346\",\n \"score\": \"0.4823183\",\n \"text\": \"def _get_nsd_catalog(self):\\n return self.__nsd_catalog\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2178508a7adc4d9478519f598f8e8e0\",\n \"score\": \"0.48205787\",\n \"text\": \"def before_request():\\n g.db = connect_db(app.config['ACCOUNT'], app.config['DBNAME'])\\n #Entry.set_db(g.db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1049a65383a146966505e8e8bcc12bd6\",\n \"score\": \"0.48180845\",\n \"text\": \"def connect_db():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c25bd4fb73f28e625785e71da259f68\",\n \"score\": \"0.4813999\",\n \"text\": \"def db(self) -> Database[DBRecord]:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31240513e1360995e5ebc17db19072c7\",\n \"score\": \"0.4811872\",\n \"text\": \"def db_for_read(self, model, **hints):\\n if model._meta.app_label == 'travel_buddy':\\n return 'travel_buddy_db'\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e7792b1126518cc230e39697c64c0ad\",\n \"score\": \"0.48093012\",\n \"text\": \"def test_db(config_parser):\\n mongo_host = config_parser.get('mongo','db-host')\\n mongo_db = config_parser.get('mongo','db-name')\\n mongo_port = config_parser.getint('mongo','db-port')\\n mongo_user = config_parser.get('mongo','username')\\n mongo_pwd = config_parser.get('mongo','password')\\n mongo_auth = config_parser.get('mongo','auth-db')\\n client = MongoClient(mongo_host)\\n db = client[mongo_db]\\n db.authenticate(mongo_user,mongo_pwd,source=mongo_auth)\\n logger.debug(\\\"Successfully connected to %s\\\"%db)\\n return db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbabd11b6efa7da267c65435dd760e60\",\n \"score\": \"0.4806655\",\n \"text\": \"def get_uri_db_tny(self,id_db_tny):\\n \\n if id_db_tny:\\n if id_db_tny in [\\\"softrans_sb\\\",\\\"softlog\\\"]:\\n db = self.get_session.query(self.stringconexoes_model).filter(self.stringconexoes_model.banco_dados==id_db_tny).first()\\n else:\\n db = self.get_session.query(self.stringconexoes_model).filter(self.stringconexoes_model.banco_dados==\\\"softlog_\\\" + id_db_tny).first()\\n \\n if db is not None:\\n return db.db_uri_db_pg()\\n\\n if id_db_tny:\\n db = self.get_session.query(self.stringconexoes_model).filter(self.stringconexoes_model.id_string_conexao==id_db_tny).first()\\n else:\\n return None\\n\\n return db.db_uri_db_pg()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae7dc1377dd58edfea824a5393775a31\",\n \"score\": \"0.4789945\",\n \"text\": \"def db_for_write(self, model, **hints):\\n if model._meta.app_label == 'auth':\\n return 'auth_db'\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5cd05e589f912f524c8410fc99add35\",\n \"score\": \"0.47840783\",\n \"text\": \"def __init__(self, db_con_str=\\\"dbname=catalogdb\\\"):\\n self.conn = psycopg2.connect(db_con_str)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8776bb80d80432ccdeee4801baba1114\",\n \"score\": \"0.47730455\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'db_conn'):\\n g.db_conn = db_connect()\\n return g.db_conn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adf227e79ef42ab8883ed455430fb593\",\n \"score\": \"0.4772139\",\n \"text\": \"def _get_nsd(self):\\n return self.__nsd\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76847efaba7e5a8392258df064fcac5b\",\n \"score\": \"0.47715753\",\n \"text\": \"def get_database(bases_list, database_name, desired_key):\\n\\tthere = False\\n\\tfor base in bases_list:\\n#\\t\\tprint base.name\\n#\\t\\tprint database_name\\n\\t\\tif str(base.name) == database_name:\\n\\t\\t\\td = base\\n\\t\\t\\tthere = True\\n\\tif not there:\\n#\\t\\tprint 'not there'\\n\\t\\td = Database(parent = desired_key)\\n\\t\\td.name = database_name\\n\\t\\td.content = db.Text('')\\n\\treturn d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b1703b356641fa0f1b0d8fdf2659578\",\n \"score\": \"0.47707888\",\n \"text\": \"def db_for_read(self, model, **hints):\\n if model._meta.app_label in DATABASE_DICT:\\n return DATABASE_DICT[model._meta.app_label]\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db556679b20f0c272a5b0281b319a053\",\n \"score\": \"0.47697708\",\n \"text\": \"def test_db_table(self):\\n\\n\\t\\tself.assertEqual(self.record._meta.db_table,\\\"reviews\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"249e6e635ff6c09ab7eec0bc32fde578\",\n \"score\": \"0.47613108\",\n \"text\": \"def get_connection(**kwargs):\\n try:\\n logging.debug(\\\"Connecting to mapd db...\\\")\\n con = pymapd.connect(\\n user=kwargs[\\\"db_user\\\"],\\n password=kwargs[\\\"db_passwd\\\"],\\n host=kwargs[\\\"db_server\\\"],\\n port=kwargs[\\\"db_port\\\"],\\n dbname=kwargs[\\\"db_name\\\"],\\n )\\n logging.info(\\\"Succesfully connected to mapd db\\\")\\n return con\\n except (pymapd.exceptions.OperationalError, pymapd.exceptions.Error):\\n logging.exception(\\\"Error connecting to database.\\\")\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b24b310e971351286e27d62cd9fbf645\",\n \"score\": \"0.47595018\",\n \"text\": \"def dc(self):\\n pass\\n # TODO Client disconnection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26de2e1fb255c2a5a43aef5fa1145655\",\n \"score\": \"0.4756963\",\n \"text\": \"def testDetermineDatabaseType(self):\\n test_extractor = catalog_extractor.EseDbCatalogExtractor()\\n\\n database_type = test_extractor._DetermineDatabaseType([\\n 'SystemIndex_0A', 'SystemIndex_Gthr'])\\n self.assertEqual(database_type, 'search')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5470e0f8db47cbfa374940c7064db0c\",\n \"score\": \"0.47476617\",\n \"text\": \"def accesslogDBEntry(self, replicator_dn, \\n log_dir=\\\"/opt/gluu/data/accesslog\\\"):\\n\\n attributes = {'objectClass': ['olcDatabaseConfig', 'olcMdbConfig'],\\n 'olcDatabase': '{2}mdb',\\n 'olcDbDirectory': log_dir,\\n 'OlcDbMaxSize': 1073741824,\\n 'olcSuffix': 'cn=accesslog',\\n 'olcRootDN': 'cn=admin, cn=accesslog',\\n 'olcRootPW': ldap_encode(self.passwd),\\n 'olcDbIndex': ['default eq', 'objectClass,entryCSN,entryUUID,reqEnd,reqResult,reqStart,reqDN'],\\n 'olcLimits': 'dn.exact=\\\"{0}\\\" time.soft=unlimited time.hard=unlimited size.soft=unlimited size.hard=unlimited'.format(replicator_dn),\\n\\n }\\n #check if accesslogdb entry is allread exists. If not exists, create it.\\n if not self.checkAccesslogDBEntry():\\n return self.conn.add('olcDatabase={2}mdb,cn=config',\\n attributes=attributes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17eb67d6b93aec15e1ad14cdb6106aad\",\n \"score\": \"0.47474527\",\n \"text\": \"def configureDBSnapshot(self):\\n tag = { \\\"DDDB\\\": self.getProp('DDDBtag')\\n , \\\"LHCBCOND\\\": self.getProp('CondDBtag')\\n , \\\"SIMCOND\\\" : self.getProp('CondDBtag')\\n , \\\"ONLINE\\\" : 'fake'\\n }\\n\\n # https://savannah.cern.ch/bugs/?94454#comment12\\n from Configurables import MagneticFieldSvc\\n MagneticFieldSvc().UseSetCurrent = True\\n\\n from Configurables import CondDB\\n cdb = CondDB()\\n cdb.Tags = tag\\n cdb.setProp('IgnoreHeartBeat', True)\\n self.setOtherProps( cdb, [ 'UseDBSnapshot',\\n 'DBSnapshotDirectory',\\n 'PartitionName' ])\\n\\n # So, here is the problem: we don't want to run the risk that\\n # the CondDB() configurable (which configures /after/ us)\\n # overwrites our conditions. Yet, we don't want to miss the\\n # default conditions (e.g. velo stepper motor, magnetic field)\\n # either. if we add our conditions to its\\n # RunChangeHandlerConditions list, then we a) need to fix the\\n # path and b) don't know what happens for conditions that\\n # appear twice, because we don't control the ordering of the\\n # list. So, the hack is:\\n # - don't set 'EnableRunChangeHandler'\\n # - copy what is hidden behind that flag in CondDB()._configureDBSnapshot\\n # - do the test of the RunChangeHandler configuration ourselves:\\n cdb.setProp('EnableRunChangeHandler', False)\\n from Configurables import RunChangeHandlerSvc\\n rch = RunChangeHandlerSvc()\\n ApplicationMgr().ExtSvc.append(rch)\\n baseloc = self.getProp( \\\"DBSnapshotDirectory\\\" )\\n rch.Conditions = dict( (c,'/'.join([baseloc,f])) for f,cs in cdb.getProp(\\\"RunChangeHandlerConditions\\\").iteritems() for c in cs )\\n \\n #path = self.getProp('DBSnapshotDirectory') + \\\"/..\\\"*4 + \\\"/group/online/AligWork/current/\\\"\\n allconds = {\\n 'Velo' : [\\n 'Conditions/Alignment/Velo/VeloSystem',\\n 'Conditions/Alignment/Velo/VeloRight',\\n 'Conditions/Alignment/Velo/VeloLeft']\\n + ['Conditions/Alignment/Velo/Module%02d'%i for i in range(0, 42)] \\n + ['Conditions/Alignment/Velo/Detector%02d-%02d' % (i, (1 + i / 2) % 2) for i in range(0, 42)],\\n 'IT' : []\\n + [ 'Conditions/Alignment/IT/ITSystem' ]\\n + [ 'Conditions/Alignment/IT/ITT%d' % i for i in range(1,4) ]\\n + [ 'Conditions/Alignment/IT/ITT%d%sBox' % (i,b) for i in range(1,4) for b in ['Top','Bottom','ASide','CSide' ] ]\\n + [ 'Conditions/Alignment/IT/ITT%d%sLayer%s' % (i,b,l) for i in range(1,4) for b in ['Top','Bottom','ASide','CSide' ] for l in ['X1','U','V','X2' ] ]\\n + [ 'Conditions/Alignment/IT/ITT%d%sLayer%sLadder%d' % (i,b,l,a) for i in range(1,4) for b in ['Top','Bottom','ASide','CSide' ] for l in ['X1','U','V','X2' ] for a in range(1,8) ],\\n # + [ 'Conditions/Alignment/IT/ITT%d%sLayer%sLadder%dSector' % (i,b,l,a) for i in range(1,4) for b in ['Top','Bottom','ASide','CSide' ] for l in ['X1','U','V','X2' ] for a in range(1,8) ]\\n # + [ 'Conditions/Alignment/IT/ITT%d%sLayer%sLadder%dSector_Sensor1' % (i,b,l,a) for i in range(1,4) for b in ['Top','Bottom','ASide','CSide' ] for l in ['X1','U','V','X2' ] for a in range(1,8) ]\\n # + [ 'Conditions/Alignment/IT/ITT%d%sLayer%sLadder%dSector_Sensor2' % (i,b,l,a) for i in range(1,4) for b in ['ASide','CSide' ] for l in ['X1','U','V','X2' ] for a in range(1,8) ] ,\\n 'OT' : []\\n + [ 'Conditions/Alignment/OT/OTSystem' ]\\n + [ 'Conditions/Alignment/OT/T%d' %i for i in range(1,4) ]\\n + [ 'Conditions/Alignment/OT/T%d%s' % (i,l) for i in range(1,4) for l in ['X1','U','V','X2' ] ]\\n + [ 'Conditions/Alignment/OT/T%d%sQ%d' % (i,l,q) for i in range(1,4) for l in ['X1','U','V','X2' ] for q in range(0,4) ]\\n + [ 'Conditions/Alignment/OT/T%d%sQ%dM%d' % (i,l,q,m) for i in range(1,4) for l in ['X1','U','V','X2' ] for q in range(0,4) for m in range(1,10) ],\\n 'TT' : []\\n + [ 'Conditions/Alignment/TT/TTSystem' ]\\n + [ 'Conditions/Alignment/TT/TT%s' % i for i in ['a','b' ] ]\\n + [ 'Conditions/Alignment/TT/TT%sLayer' % (l) for l in ['aX','aU','bV','bX' ] ]\\n + [ 'Conditions/Alignment/TT/TT%sLayerR%dModule%d%s' % (l,r,m,w) for w in ['T','B'] for l in ['aX','aU','bV','bX'] for r in range(1,4) for m in range(1,4)]\\n + [ 'Conditions/Alignment/TT/TT%sLayerR%dModule%d%s' % (l,r,m,w) for w in ['T','B'] for l in ['bV','bX'] for r in range(1,4) for m in range(4,6)]\\n + [ 'Conditions/Alignment/TT/TT%sLayerR%dModule%d%s' % (l,r,m,w) for w in ['T','B'] for l in ['aX','aU','bV','bX'] for r in [1,3] for m in range(6,7)]\\n + [ 'Conditions/Alignment/TT/TT%sLayerR%dModule%d%s' % (l,r,m,w) for w in ['T','B'] for l in ['aX','aU'] for r in [1,3] for m in range(4,6)],\\n 'Muon' : []\\n + ['Conditions/Alignment/Muon/MuonSystem']\\n + [ 'Conditions/Alignment/Muon/M%sStation' % i for i in range(1,6) ]\\n + [ 'Conditions/Alignment/Muon/M%sASide' % i for i in range(1,6) ]\\n + [ 'Conditions/Alignment/Muon/M%sCSide' % i for i in range(1,6) ]\\n }\\n\\n ## This is a bit dirty, since we're supposed to control TAlignment. We\\n ## know that this is set from top level, so let's give it a try anyway\\n ta = TAlignment()\\n sdToWrite = set(ta.getProp(\\\"WriteCondSubDetList\\\"))\\n pat = self.getProp(\\\"OnlineAligWorkDir\\\") + \\\"/xml/%s.xml\\\" \\n conditionmap = dict((pat % sd, f) for (sd, f) in allconds.iteritems() if sd in sdToWrite)\\n \\n # add to the existing map\\n rch.Conditions = dict(rch.Conditions.items() + dict( (c,f) for f,cs in conditionmap.iteritems() for c in cs ).items() )\\n\\n from Configurables import MagneticFieldSvc\\n MagneticFieldSvc().UseSetCurrent = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"157a6f85df20d249486b84e76c7046f1\",\n \"score\": \"0.47464782\",\n \"text\": \"def mongo_list_dbs():\\n client = mongo_connect()\\n for dbname in client.list_database_names():\\n if dbname in ['admin', 'local']:\\n continue\\n db = client[dbname]\\n gpup = db['gpu_procs'].find_one()\\n if gpup is not None:\\n gpup = gpup['gpu_procs']\\n print(f\\\"DB: {dbname}\\\\n GPU proc count: {gpup}\\\")\\n for colname in db.list_collection_names():\\n col = db[colname]\\n print(f\\\" Col: {colname}, count: {col.count()}\\\")\\n client.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1c546ff3533607a7c08e2103a8e3296\",\n \"score\": \"0.47407824\",\n \"text\": \"def get_kdb():\\n return current_app.extensions['kdb'].connection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2be622382abe530d362506be550627f9\",\n \"score\": \"0.47388348\",\n \"text\": \"def checkAccesslogDBEntry(self):\\n \\n return self.conn.search(search_base='cn=config',\\n search_filter='(olcSuffix=cn=accesslog)',\\n search_scope=SUBTREE, attributes=[\\\"*\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07435a72d7afb184ec87d740dcaa0776\",\n \"score\": \"0.47269005\",\n \"text\": \"def get_graph_db(self):\\n return self.get_variable(\\\"general\\\", \\\"graph_db\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a7daa68cfeb920c088725c5d078cf2c\",\n \"score\": \"0.47193885\",\n \"text\": \"def __init__(self, dsn, dsnDict, console):\\n super(MySQLdbStorage, self).__init__(dsn, dsnDict, console)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17388a380ba9911fda750bbbcd80851e\",\n \"score\": \"0.47184587\",\n \"text\": \"def __init__(self):\\n self.db = database.db_connection()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"397399c38248591d8b2403a3d550fa74\",\n \"score\": \"0.47183996\",\n \"text\": \"def exist_care(mongoconn,db_name,carer_id,cared_id):\\n factor = {'carer_id':carer_id,'cared_id':cared_id}\\n res = mongoconn[db_name]['care_record'].find_one(factor)\\n if res is not None:\\n return res['_id']\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22ea32a1bbe0a4a2789cd49b063ec39e\",\n \"score\": \"0.47158706\",\n \"text\": \"def allow_syncdb(self, db, model):\\n\\n if db in DATABASE_DICT.values():\\n return DATABASE_DICT.get(model._meta.app_label) == db\\n elif model._meta.app_label in DATABASE_DICT:\\n return False\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efcd17c7e244e8ec6c9d39fa2d9120a0\",\n \"score\": \"0.47150096\",\n \"text\": \"def database(self):\\n return self.info_query(\\\"SELECT DATABASE()\\\")[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5894d4b1c01c5091c9cb8d691e58dcd9\",\n \"score\": \"0.47148642\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'mongo'):\\n g.mongo = mongo # Flask-Mongo connection\\n return g.mongo.db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aed0cd135cb4c801742f2e69a2f69c51\",\n \"score\": \"0.47099882\",\n \"text\": \"def _db_read_nonprimary(self, cur=None):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"123972ebf820beee8b10c334341d80ce\",\n \"score\": \"0.4706701\",\n \"text\": \"def ready(self, dbname):\\r\\n return dbname in self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c60a20f7558c1cc5b1e8c76a71b50a9e\",\n \"score\": \"0.47008118\",\n \"text\": \"def get_custom_db(firmware_version, _db):\\r\\n if _db:\\r\\n if firmware_version in _db:\\r\\n return _db[firmware_version]\\r\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e94f29ce5612aafc0907a72795eaea6\",\n \"score\": \"0.4698479\",\n \"text\": \"def _configure_nve_db(self, vni, device_id, mcast_group, host_id):\\n host_connections = self._get_switch_info(host_id)\\n for switch_ip, intf_type, nexus_port in host_connections:\\n nxos_db.add_nexusnve_binding(vni, switch_ip, device_id,\\n mcast_group)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76312512dc2ded0f5ad963b73f24aaad\",\n \"score\": \"0.4696835\",\n \"text\": \"def __init__(self):\\n self._db_params = self._configuration_database()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c8bb710b4a166859cc2182dcc78304d\",\n \"score\": \"0.46957707\",\n \"text\": \"def get_db(server_id):\\n DATABASE = \\\"DATABASE\\\" + str(server_id)\\n print(DATABASE)\\n \\\"\\\"\\\"The internal LocalStack that holds AppContext instances...application context binds an application object implicitly to the current thread\\\"\\\"\\\"\\n tracktop = _app_ctx_stack.top\\n \\\"\\\"\\\"If the database connection is not initiated then initiate connection. We are using sqllite3 for the database connection.\\\"\\\"\\\"\\n \\\"\\\"\\\"detect_types in connect makes the sqlite3 module parse the declared type for each column it returns.\\\"\\\"\\\"\\n if not hasattr(tracktop, 'track_db0') and server_id == 0:\\n tracktop.track_db0 = sqlite3.connect(app.config[DATABASE], detect_types=sqlite3.PARSE_DECLTYPES)\\n tracktop.track_db0.row_factory = sqlite3.Row\\n if not hasattr(tracktop, 'track_db1') and server_id == 1:\\n tracktop.track_db1 = sqlite3.connect(app.config[DATABASE], detect_types=sqlite3.PARSE_DECLTYPES)\\n tracktop.track_db1.row_factory = sqlite3.Row\\n if not hasattr(tracktop, 'track_db2') and server_id == 2:\\n tracktop.track_db2 = sqlite3.connect(app.config[DATABASE], detect_types=sqlite3.PARSE_DECLTYPES)\\n tracktop.track_db2.row_factory = sqlite3.Row\\n \\\"\\\"\\\" check server id return the track database accordingly\\\"\\\"\\\"\\n if server_id == 0:\\n return tracktop.track_db0\\n elif server_id == 1:\\n return tracktop.track_db1\\n else:\\n return tracktop.track_db2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27e1ffbd14a947894b7936437351383b\",\n \"score\": \"0.4693095\",\n \"text\": \"def test_get_db():\\n db_test = User._get_db() # Get pymongo.database.Database instance\\n assert isinstance(db_test, Database)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7364862c404d1c3dc4e933539b7d50ea\",\n \"score\": \"0.468888\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'db'):\\n g.db = connect_db()\\n return g.db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"305a8e9dcd23d67e92986ad73c269c1e\",\n \"score\": \"0.46874708\",\n \"text\": \"def get_mongo_conn():\\n MONGO_URI = 'mongodb://saran:Saran1!@ds113736.mlab.com:13736/ingredientmaster'\\n client = pymongo.MongoClient(MONGO_URI)\\n db = client.get_database('ingredientmaster')\\n return db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4a815af9118297f580ce6106690b9df\",\n \"score\": \"0.46834752\",\n \"text\": \"def test_ngen_with_unknown_db(runner):\\n result = runner.invoke(cli.main, ['list', 'fake.'])\\n assert result.exit_code == 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37d90dc6bdb91cbc0cf53e1ed35489fd\",\n \"score\": \"0.46785578\",\n \"text\": \"def db_for_read(self, model, **hints):\\r\\n if model._meta.app_label == 'virvo':\\r\\n return 'virvo'\\r\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c227f6c0dcc3bdd23a06f9623fb35d2\",\n \"score\": \"0.4678507\",\n \"text\": \"def test_db_host_get(self):\\r\\n\\r\\n db_path = os.path.join(self.res_dir, 'regular.sqlite')\\r\\n db_obj = db.DataBaseHost.get(db_path)\\r\\n\\r\\n self.assertEqual(db.DataBaseHost.get(db_path), db_obj)\\r\\n\\r\\n db_obj.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5f02e637e32b8ccef59b854bba5f422\",\n \"score\": \"0.46758056\",\n \"text\": \"def connections(self):\\n tag, has_dimension = {}, False\\n try:\\n self.es_tag = pd.read_sql(\\\"SELECT * FROM es_connection\\\", con).to_dict('results')[-1]\\n dimensions = pd.read_sql(\\\"SELECT * FROM data_connection\\\", con).to_dict('results')[0]\\n if dimensions['dimension'] not in ['None', None]:\\n has_dimension = True\\n except Exception as e:\\n print(e)\\n return has_dimension\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bdf84a3c72f6ab54c9d1a3335be458a\",\n \"score\": \"0.4670276\",\n \"text\": \"def load_db(self, cursor, connection):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0560115b04f42f82e10f0b7c43c687eb\",\n \"score\": \"0.46691918\",\n \"text\": \"def _lookup_cddb(self):\\n import DiscID, CDDB\\n if self.cddevice: dev = DiscID.open(self.cddevice)\\n else: dev = DiscID.open()\\n cdid = DiscID.disc_id(dev)\\n tracks = cdid[1]\\n (status, info) = CDDB.query(cdid)\\n if status == 200:\\n (status, info) = CDDB.read(info['category'], \\\\\\n info['disc_id'])\\n elif status == 210 or status == 211:\\n (status, info) = CDDB.read(info[0]['category'], \\\\\\n info[0]['disc_id'])\\n else:\\n return None\\n\\n ret = cdinfo()\\n for key in info.keys():\\n if key.startswith('TTITLE'):\\n n = int(re.findall(\\\"TTITLE([0-9]+)\\\", key)[0])\\n ret.titles[n] = info[key].encode('ascii', errors='replace')\\n elif key == 'DTITLE':\\n (artist, album) = info[key].split('/')\\n ret.artist = artist.strip()\\n ret.album = album.strip()\\n elif key == 'DYEAR':\\n ret.releasedate = info[key]\\n wx.LogMessage(\\\"cddb succeeded\\\")\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc3e31a4747600f576f65d8a3a1a9855\",\n \"score\": \"0.46684363\",\n \"text\": \"def db_for_write(self, model, **hints):\\n if model._meta.app_label == 'mapdata':\\n return 'msemap_db'\\n return None\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994189,"cells":{"query_id":{"kind":"string","value":"c6ccb384e1ab09d3b8f9ca491cfa70c2"},"query":{"kind":"string","value":"Fetch and format documents screen. Content is supplied from self.DATA_INDEX. App handles formatting and display"},"positive_passages":{"kind":"list like","value":[{"docid":"933d4bea7adbf38994604d04c98390c4","score":"0.6130742","text":"def setup_document(self, *args, **kwargs):\n doc_dict = self.DATA_INDEX.current_doc\n doc_idx, group_len = self.DATA_INDEX.current_index\n self.current_document_title = doc_dict['Job_Title']\n self.current_document_topics = self.format_topic_header(doc_dict)\n self.current_document_text = self.format_doc_text(doc_dict)\n self.current_document_idx = doc_idx\n self.current_document_idx_max = group_len\n Clock.schedule_once(self.highlight_doc_label, 0.5)","title":""}],"string":"[\n {\n \"docid\": \"933d4bea7adbf38994604d04c98390c4\",\n \"score\": \"0.6130742\",\n \"text\": \"def setup_document(self, *args, **kwargs):\\n doc_dict = self.DATA_INDEX.current_doc\\n doc_idx, group_len = self.DATA_INDEX.current_index\\n self.current_document_title = doc_dict['Job_Title']\\n self.current_document_topics = self.format_topic_header(doc_dict)\\n self.current_document_text = self.format_doc_text(doc_dict)\\n self.current_document_idx = doc_idx\\n self.current_document_idx_max = group_len\\n Clock.schedule_once(self.highlight_doc_label, 0.5)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"792b5fddc5dab6657fbe27fdc6166895","score":"0.63426816","text":"def process_data(self):\n\n # Drivers License\n if self.Data.DriversLicence is not None:\n doc = Document.DriversLicense(None, self.Data.DriversLicence, self.photoImage)\n self.Documents.append(doc)\n # PASSPORT\n if self.Data.Passport is not None:\n doc = Document.Passport(None, self.Data.Passport, self.photoImage)\n self.Documents.append(doc)\n # Badge\n if self.Data.Badge is not None:\n doc = Document.Badge(None, self.Data.Badge, self.photoImage)\n self.Documents.append(doc)\n # INSURANCE\n if self.Data.Insurance is not None:\n doc = Document.Insurance(None, self.Data.Insurance, self.photoImage)\n self.Documents.append(doc)\n # CLAIM\n if self.Data.Claim is not None:\n doc = Document.InsuranceClaim(None, self.Data.Claim, self.photoImage)\n self.Documents.append(doc)\n # CONVERSATION\n if self.Data.Conversation is not None:\n self.Conversation = self.Data.Conversation.ListOfText","title":""},{"docid":"b8c7ff139f5f401b50255ef3eff805fb","score":"0.6150794","text":"def fetch(self):\n next_batch_first_index = int(self.request.get('documentPointer', 0))\n self.request.response.setHeader('X-Theme-Disabled', 'True')\n return self.render_batch(next_batch_first_index)","title":""},{"docid":"68b44b639903a0e3b0fe38b033a24ac9","score":"0.612292","text":"def document_retrieval(logger):\n # override args\n document_retrieval_main(args.database, 7, args.infile, args.outfile, args.path_wiki_titles, True, True)","title":""},{"docid":"c6342b18bcf0c51508a3c96256c1f27a","score":"0.5973333","text":"def document_detail(request, id):\n\n document = SavedData.objects.get(row_id=id)\n abstract_list = []\n for paragraph in document.abstracts.split('\\n'):\n abstract_list.append(paragraph)\n\n return render(request, 'gui/document_detail.html', {\n 'document': document, 'abstracts': abstract_list})","title":""},{"docid":"ca3d20faa9020d8bba14297de7585234","score":"0.58931184","text":"def documents(res):\n keys = ['description', 'source', 'usage']\n global gresults\n desc_translations = gresults[res]['desc_sentence_code']\n translation = {k:v for k, v in gresults[res].items() if 'segmentation' in k or 'sentence' in k or 'translation' in k}\n idiom = {k:v for k, v in gresults[res].items() if k not in translation.keys()}\n idiomtitle = idiom['name']\n for term in idiom:\n if type(idiom[term]) is AttrList:\n s = \"\\n\"\n for item in idiom[term]:\n s += item + \",\\n \"\n idiom[term] = s\n # fetch the movie from the elasticsearch index using its id\n chengyu = Idiom.get(id=res, index='idioms_search')\n chengyudic = chengyu.to_dict()\n index = 0\n description_dict = dict()\n for key, value in chengyudic.items():\n if 'translation' in key:\n translation[key] = value\n else:\n idiom[key] = value\n '''\n if key == 'description':\n print(value)\n for v in value:\n description_dict[str(index)] = v\n index += 1\n idiom = {k: v for k, v in idiom.items() if k not in translation.keys()}\n \n print(description_dict)\n '''\n # idiom['runtime'] = str(chengyudic['runtime']) + \" min\" if filmdic['runtime'] > -1 else \"Unknown\"\n return render_template('page_targetArticle.html', idiom=idiom, name=idiomtitle, keys=keys, translation=translation)","title":""},{"docid":"31243f106593f9d8f7ff6d186de115f1","score":"0.5892145","text":"def documentsProcessing(self, personalData: PersonalData = PersonalData.all):\n\n pass","title":""},{"docid":"90dbae986b4555469cf978a47ec2ba05","score":"0.5855406","text":"async def docs(self, ctx: commands.Context, *args: Optional[str]) -> None:\n\n data = db_parser.search_for_docs(\" \".join(args))\n if not data:\n return await ctx.send(f\"{ctx.author.mention} No Results Found!\")\n\n topic, descp, argums, returs, link = data\n embed = discord.Embed(title=topic, color=discord.Color.random())\n embed.add_field(name=\"Description\",\n value=f\"```\\n{descp}```\", inline=False)\n embed.add_field(name=\"Arguments\",\n value=f\"```python\\n{argums}```\", inline=False)\n embed.add_field(\n name=\"Returns\", value=f\"```python\\n{returs}```\", inline=False)\n embed.add_field(name=\"Link\", value=link, inline=False)\n await ctx.send(ctx.author.mention, embed=embed)","title":""},{"docid":"03030ef76c56000a4b45898c9270674f","score":"0.5854476","text":"def database(request):\n\n #entries = Entry.objects.all().order_by('-rating') #Hier dus normalized?\n documents = SavedData.objects.all().order_by('-normalized_number_of_mentions')\n\n return render(request, 'gui/database.html', {'database': documents})\n # return render(request, 'interface/database.html')","title":""},{"docid":"3bffabe261d821e4e8e5b5e13a955b3b","score":"0.584822","text":"def index(self):\n self.log.info('Fetching document ids that match the criteria...')\n doc_ids = self._get_doc_ids()\n\n self.log.info('Found documents: %d' % len(doc_ids))\n\n with ThreadPoolExecutor(max_workers=self.annotation_indexer_config.threads) as executor:\n executor.map(self._process_document, doc_ids)","title":""},{"docid":"8b2676797ad1ab06ccc8a81e6732dc76","score":"0.58295107","text":"def doc_text_datablock():\n global api_doc_\n\n space_data = bpy.context.space_data\n\n try:\n doc_text = bpy.data.texts['api_doc_']\n space_data.text = doc_text\n doc_text.clear()\n except:\n bpy.data.texts.new(name='api_doc_')\n doc_text = bpy.data.texts['api_doc_']\n space_data.text = doc_text\n\n doc_text.write(text=api_doc_)\n return {'FINISHED'}","title":""},{"docid":"874a90a3891dce0a220aa375504aa169","score":"0.58218527","text":"def index(self):\r\n self.subsection = 'Contents'\r\n return self.get_flatpage()","title":""},{"docid":"874a90a3891dce0a220aa375504aa169","score":"0.58218527","text":"def index(self):\r\n self.subsection = 'Contents'\r\n return self.get_flatpage()","title":""},{"docid":"874a90a3891dce0a220aa375504aa169","score":"0.58218527","text":"def index(self):\r\n self.subsection = 'Contents'\r\n return self.get_flatpage()","title":""},{"docid":"9dce775017cf295e9f2413558e9cad1f","score":"0.5771713","text":"def index_documents(self, data):\n\t\tbulk(self.client, actions=data)","title":""},{"docid":"dcda86bb55f1f1e6b89451750c227636","score":"0.5751059","text":"def index():\n page = request.args.get('page', 1)\n documents = documentcloud.search(DEFAULT_SEARCH, page=page)\n return render_template('index.html', documents=documents)","title":""},{"docid":"ec0baf7230eaa3ae2ecdb86646a898a9","score":"0.57493937","text":"def _get_doc_details(self, dbx):\n result, response = dbx.paper_docs_download(\n self.paper_id, ExportFormat.markdown)\n if response.status_code == 200:\n response.close()\n else:\n raise dropbox.exceptions.APIError\n return (result.title, result.revision)","title":""},{"docid":"48f1c5ea49b6322a076aad4bc8c21fb8","score":"0.5695987","text":"def documents():\n documents = Document.query.all() # return a list with all values\n return render_template('documents.html', name=current_user.username, role=current_user.role_code, \n documents=documents) # name parameter send to html the value of the current logged_in user","title":""},{"docid":"70afe7b7e7bfcae26bff735a82ac405e","score":"0.56497496","text":"def doc_list():\n user = g.current_user\n email = user.email\n\n #Find all the user's documents\n docs = Page.objects(email=email, resource__page_number=1)\n docs = docs.order_by('resource__title','resource__author')\n \n if not docs:\n msg = \"Couldn't find user's documents\"\n response = resource_not_found(msg)\n return response\n \n #Collect document names into a response\n works = []\n for doc in docs:\n work = {\n \"title\": doc.resource.title,\n \"author\": doc.resource.author,\n \"language\": doc.resource.language\n }\n works.append(work)\n \n response = jsonify({\"works\": works})\n return response, HTTPStatus.OK.value","title":""},{"docid":"97f83c15711b740a070a3cb4a3c51f70","score":"0.56411767","text":"def index(self):\n return self.draw(self.requests())","title":""},{"docid":"f0aa97ce39fa4d6ad81a893df92822ae","score":"0.5613384","text":"def documents(self):\n for index in range(self.count()):\n yield self.widget(index)","title":""},{"docid":"f0aa97ce39fa4d6ad81a893df92822ae","score":"0.5613384","text":"def documents(self):\n for index in range(self.count()):\n yield self.widget(index)","title":""},{"docid":"c65d04f9277270ff16897b1152a078ca","score":"0.5607856","text":"def readDocs(self):\n DataPathList = glob.glob(self.pathDocs+'*.html')\n DataPathList.sort()\n\n self.docs = []\n \n h = 0\n for docPath in DataPathList:\n f = codecs.open(docPath, 'r')\n text = get_text(f.read())\n\n self.docs.append({\n 'id': str(h),\n 'text': self.removePonctuation(re.split('\\s|\\n',text))\n })\n\n print(re.split('\\s|\\n',text))\n # print(re.split('\\s|, |\\*|\\n',text))\n\n h += 1","title":""},{"docid":"ac96580a00be97566b2eef2311e380e8","score":"0.5547341","text":"def main():\n creds = None\n # The file token.json stores the user's access and refresh tokens, and is\n # created automatically when the authorization flow completes for the first\n # time.\n if os.path.exists('token.json'):\n creds = Credentials.from_authorized_user_file('token.json', SCOPES)\n # If there are no (valid) credentials available, let the user log in.\n if not creds or not creds.valid:\n if creds and creds.expired and creds.refresh_token:\n creds.refresh(Request())\n else:\n flow = InstalledAppFlow.from_client_secrets_file(\n 'credentials.json', SCOPES)\n creds = flow.run_local_server(port=8000)\n # Save the credentials for the next run\n with open('token.json', 'w') as token:\n token.write(creds.to_json())\n\n service = build('docs', 'v1', credentials=creds)\n\n # Retrieve the documents contents from the Docs service.\n document = service.documents().get(documentId=DOCUMENT_ID).execute()\n\n document_data = [\n {\n 'text': \"Document created by MayankDoc\",\n 'type': \"watermark\",\n },\n {\n 'text': \"Growth Roadmap Review: 10:30am-11:30am\",\n 'type': \"event\",\n },\n { \n 'text': \"Growth Roadmap Review\",\n 'type': \"h1\",\n },\n {\n 'text': \"Agenda\",\n 'type': \"h2\",\n },\n {\n 'text': \"Roadmap Study Hall - 15 minutes\",\n 'type': \"p\",\n },\n {\n 'text': \"Roadmap Discussion - 20 minutes\",\n 'type': \"p\",\n },\n {\n 'text': \"Backlog Review - 20 minutes\",\n 'type': \"p\",\n },\n {\n 'text': \"Follow Ups\",\n 'type': \"h2\",\n },\n {\n 'text': \"\",\n 'type': \"check\",\n },\n {\n 'text': \"Roadmap\",\n 'type': \"h2\",\n },\n {\n 'text': \"Channels\",\n 'type': \"p\",\n },\n {\n 'text': \"Rose Liu\",\n 'type': \"request\",\n },\n {\n 'text': \"👍\",\n 'type': 'emoji',\n 'data': ['Mayank Jain', 'Yee Chen', 'Mike Jiao'],\n },\n {\n 'text': \"Core Growth\",\n 'type': \"p\",\n },\n {\n 'text': \"Mike Jiao\",\n 'type': \"request\",\n },\n {\n 'text': \"👍\",\n 'type': 'emoji',\n 'data': [],\n },\n {\n 'text': \"International\",\n 'type': \"p\",\n },\n {\n 'text': \"Jason Lee\",\n 'type': \"request\",\n },\n {\n 'text': \"👍\",\n 'type': 'emoji',\n 'data': [],\n },\n {\n 'text': \"SEO\",\n 'type': \"p\",\n },\n {\n 'text': \"Chuck Kao\",\n 'type': \"request\",\n },\n {\n 'text': \"👍\",\n 'type': 'emoji',\n 'data': [],\n },\n {\n 'text': \"Key Discussion\",\n 'type': \"h2\",\n },\n {\n 'text': \"Can we do anything to accelerate subreddit notifications?\",\n 'type': \"discussion\",\n },\n {\n 'text': \"Action Items\",\n 'type': \"h2\",\n },\n {\n 'text': \"\",\n 'type': \"check\",\n },\n {\n 'text': \"Signoff\",\n 'type': \"h2\",\n },\n {\n 'text': \"KD Bhulani\",\n 'type': \"signoff\",\n },\n {\n 'text': \"Vee Sahgal\",\n 'type': \"signoff\",\n },\n {\n 'text': \"Yee Chen\",\n 'type': \"signoff\",\n }\n ]\n\n requests = []\n\n for item in document_data:\n docs_item = translate_to_doc(item)\n requests += docs_item\n\n print(requests[::-1])\n\n result = service.documents().batchUpdate(\n documentId=DOCUMENT_ID, body={'requests': requests[::-1]}).execute()\n\n print('The title of the document is: {}'.format(document.get('title')))","title":""},{"docid":"3e99ad10bc90a3e0e0437d86dc2bdd01","score":"0.5540074","text":"def documents(self):\n logging.info(\"Extracting docs from [%s]\", self._wikinews_archive)\n file_list = glob.glob(self._wikinews_archive)\n assert file_list, self._wikinews_archive\n for archive in file_list:\n with bz2file.open(archive, \"rt\", encoding=\"utf-8\", errors=\"strict\") as xf:\n # One line of json as produced by wikiextractor.\n for line in xf:\n record = json.loads(line)\n\n # Extract page title.\n title = html.unescape(record[\"title\"])\n curid = record[\"id\"] # pageid, e.g. 73052\n revid = record[\"revid\"] # revision, e.g. 730271\n url = record[\"url\"] # e.g. https://de.wikinews.org/wiki?curid=73052\n logging.debug(\"Got title: %s\", title)\n\n # Skip pages that don't have text.\n wiki_doc = record.get(\"text\")\n if not wiki_doc:\n self._counter[\"no text\"] += 1\n self._filtered_no_text.append(url)\n logging.debug(\"Skip: no text element\")\n continue\n\n # Apply manual fixes.\n wiki_doc = _apply_wiki_overrides(wiki_doc, url)\n\n # Create internal document identifier.\n docid = f\"{self._language}-{curid}\"\n logging.debug(\"Found (%s): %s\", docid, title)\n self._counter[\"found\"] += 1\n\n yield docid, title, url, curid, revid, wiki_doc","title":""},{"docid":"7345e6b67d4c52977714a913deed0b8b","score":"0.55391085","text":"def render_batch(self, first):\n items = self.items.get_batch(first, self.batchsize)\n if items:\n return self.previews_template(\n previews=map(self.items.get_infos_for, items))\n else:\n # We have to return an empty string if we have no more documents\n # to render. Otherwise plone.protect will log a error-warning:\n # WARNING plone.protect error parsing dom, failure to add csrf\n # token to response.\n return ''","title":""},{"docid":"27c60046b0ad130539897a9c11716c5f","score":"0.5526614","text":"def fetch(self):\n self.request.response.setHeader('X-Theme-Disabled', 'True')\n # The HTML stripped in order to have empty response content when\n # there are no tags at all, so that diazo does not try to\n # parse it.\n if int(self.request.get('documentPointer', 0)) >= self.number_of_documents():\n # We have to return an empty string if we have no more documents\n # to render. Otherwise plone.protect will log a error-warning:\n # WARNING plone.protect error parsing dom, failure to add csrf\n # token to response\n return ''\n return self.previews_template().strip()","title":""},{"docid":"84eadb4d5c6c853026c26c372024a1c0","score":"0.5504719","text":"def gener_doc(dataset):\n # check or create doc folder\n folder = get_dataset_folder(dataset.dataset_id) + '/docs'\n if not os.path.exists(folder):\n os.makedirs(folder)\n os.makedirs(folder + '/_build')\n os.makedirs(folder + '/_static')\n os.makedirs(folder + '/_templates')\n\n # generate conf.py\n render('conf.txt', folder + '/conf.py', dataset=dataset)\n render('make.bat', folder + '/make.bat', dataset=dataset)\n render('makefile.txt', folder + '/Makefile', dataset=dataset)\n\n # generate index\n render('index.rst', folder + '/index.rst', dataset=dataset)\n\n # dataset data and features\n search = get_search_rounds(dataset.dataset_id)\n if len(search) > 0:\n best = get_best_models(dataset.dataset_id)\n best_pp = get_best_pp(dataset.dataset_id)\n # separate models (level 0) from ensembles (level 1)\n best1 = [b for b in best if b['level'] == 1]\n best2 = [b for b in best if b['level'] == 2]\n print(len(best1), len(best2))\n print(best1[:2])\n render('dataset.rst', folder + '/dataset.rst', dataset=dataset, best1=best1, best2=best2, best_pp=best_pp,\n n_searches1=len(search[search.level == 1]),\n n_searches2=len(search[search.level == 2]))\n\n # then for the best rounds\n N_ROUNDS = 5\n for round_id in list([b['round_id'] for b in best1[:N_ROUNDS]]) + list([b['round_id'] for b in best2[:N_ROUNDS]]):\n round = search[search.round_id == int(round_id)].to_dict(orient='records')[0]\n pipeline = [s for s in round['pipeline'] if s[0] not in ['NO-SCALE', 'PASS']]\n params = get_round_params(search, round_id)\n features = get_feature_importance(dataset.dataset_id, round_id)\n render('round.rst', folder + '/round_%s.rst' % round_id, dataset=dataset, round=round,\n pipeline=pipeline, features=features, params=params, cols=params.keys())\n else:\n # return render_template('dataset.html', dataset=dataset, n_searches1=0)\n render('dataset.rst', folder + '/dataset.rst', dataset=dataset, n_searches1=0)\n\n # then generate html and pdf with make\n if sys.platform == 'linux':\n subprocess.call(['sh', '../scripts/gen_doc.sh', os.path.abspath(get_dataset_folder(dataset.dataset_id)+'/docs')])\n else:\n os.system('call ../scripts/gen_doc ' + os.path.abspath(get_dataset_folder(dataset.dataset_id)+'/docs'))\n\n # generate zip file of the html site\n with zipfile.ZipFile(get_dataset_folder(dataset.dataset_id) + '/doc.zip', 'w') as z:\n root = get_dataset_folder(dataset.dataset_id) + '/docs/_build/html/'\n for dir in ['', '_static/', '_images/', '_sources/']:\n for f in glob.glob(root + dir + '*.*'):\n z.write(f, dataset.dataset_id + '/' + dir + os.path.basename(f))","title":""},{"docid":"47c1681b45562e77bd47a79588dbfb7e","score":"0.5497769","text":"def get_results():\n documents = business_layer.BusinessLayer().get_results(request.args)\n return render_template('titles.html', documents=documents)","title":""},{"docid":"85b2a3a202118eceb26728f5dabeb650","score":"0.549089","text":"def get_data():\n return [\n\t\t{\n\t\t\t\"label\": _(\"OCR Read\"),\n\t\t\t\"items\": [\n\t\t\t\t{\n\t\t\t\t\t\"type\": \"doctype\",\n\t\t\t\t\t\"name\": \"OCR Read\",\n\t\t\t\t\t\"description\": _(\"OCR Read\"),\n\t\t\t\t}\n\t\t\t\t]\n\t\t},\n\t\t{\n\t\t\t\"label\": _(\"OCR Import\"),\n\t\t\t\"items\": [\n\t\t\t\t{\n\t\t\t\t\t\"type\": \"doctype\",\n\t\t\t\t\t\"name\": \"OCR Import\",\n\t\t\t\t\t\"description\": _(\"OCR Import\"),\n\t\t\t\t}\n\t\t\t\t]\n\t\t}\n ]","title":""},{"docid":"f1c92d9525875014e93a9664c3d37d4f","score":"0.54637945","text":"def load_document(self):\n pass","title":""},{"docid":"03108348e8effdc7a8f959723fc822fa","score":"0.54457164","text":"def documentations():\n return render_template('documentations.html')","title":""},{"docid":"95528f562106ea181314684df03a5059","score":"0.54443765","text":"def get_all_docs(self):\n request = urllib2.Request(self.url_address +'_all_docs')\n request.get_method = lambda : 'GET'\n request.add_header('Content-Type', 'text/plain')\n url = self.opener.open(request)\n return json.loads(url.read())","title":""},{"docid":"d1e12668f86567450fb00a986101e681","score":"0.54393756","text":"def get_data(self):\n\n self.code_names, self.categories = self.app.get_data()\n cur = self.app.conn.cursor()\n sql = \"select owner from code_image union select owner from code_text union select owner from code_av\"\n cur.execute(sql)\n result = cur.fetchall()\n self.coders = [\"\"]\n for row in result:\n self.coders.append(row[0])\n cur.execute('select id, length(fulltext) from source where (mediapath is Null or substr(mediapath,1,5)=\"docs:\")')\n self.file_summaries = cur.fetchall()","title":""},{"docid":"78ebb6d8137ed79dddce842297ec7844","score":"0.5438988","text":"def pull( request ) :\n\n log = logging.getLogger(__name__)\n\n log.debug( 'pulls the database content for an entry' )\n\n idx = request.GET.get('id', 'None')\n\n if idx == 'None' : return JsonResponse({'error_flag':True})\n\n obj = models.Element.objects.get( id=int(idx) )\n document_objects = models.Document.objects.filter(element=obj)\n\n context = { 'id' : idx,\n 'name': obj.name,\n 'description' : obj.description,\n 'category' : obj.category.id,\n 'document' : list(document_objects.values_list( 'id',\n 'type__name',\n 'author__username',\n 'link',\n 'date')),\n 'error_flag' : False}\n\n print context\n return JsonResponse(context)","title":""},{"docid":"f0224254bb37161210cb0afaaa062fe1","score":"0.54367024","text":"def _process_document(self, src_doc_id):\n\n self.log.info('Processing document with id: ' + src_doc_id)\n doc = self.annotation_indexer_config.source_indexer.get_doc(src_doc_id)\n\n\n # check whether there is document content to process\n if (isinstance(doc, dict) and (self.annotation_indexer_config.source_text_field not in doc.keys() or doc[self.annotation_indexer_config.source_text_field] is None)) or \\\n len(doc[self.annotation_indexer_config.source_text_field]) < self.MIN_TEXT_LEN:\n self.log.info('- skipping: no content')\n return\n \n try:\n # check whether the document has been already processed\n if self.annotation_indexer_config.skip_doc_check and self._document_already_processed(doc):\n self.log.info('doc id : ' + str(src_doc_id) + ' - skipping: document already processed')\n return\n \n # get the text\n doc_text = doc[self.annotation_indexer_config.source_text_field]\n \n # query the NLP service and retrieve back the annotations\n self.log.info('- querying the NLP service')\n \n nlp_response = self.annotation_indexer_config.nlp_service.query(text=doc_text)\n\n self.log.info(\"Finished processing NLP for document with id: \" + src_doc_id)\n\n if \"result\" in nlp_response.keys():\n result = nlp_response[\"result\"]\n\n if 'annotations' not in result.keys() or result['annotations'] is None or result is None:\n self.log.error(\" - no annotations available in the NLP result payload\")\n return \n\n if 'entities' not in result[\"annotations\"].keys() or result[\"annotations\"][\"entities\"] is None :\n self.log.error(\" - no annotation entities available in the NLP result payload\")\n return\n\n result = result['annotations']['entities']\n \n elif \"entities\" in nlp_response.keys():\n # Entities are present alone only when using GATE-NLP MODE ENDPOINT\n if nlp_response[\"entities\"] is not None:\n result = nlp_response[\"entities\"]\n else:\n self.log.error(\" - no annotation entities available in the NLP result payload\")\n return\n\n elif \"result\" not in nlp_response.keys() or \"entities\" not in nlp_response.keys():\n self.log.error(\" - no result payload returned from NLP service\")\n return\n\n if self.annotation_indexer_config.nlp_service.use_bulk_indexing:\n self._index_annotations_bulk(result, doc, src_doc_id)\n else:\n self._index_annotations(result, doc, src_doc_id)\n \n except Exception as e:\n self.log.error(repr(e))","title":""},{"docid":"76433654d516e453aadfb4b412e834bb","score":"0.54329705","text":"def docs(self, request):\n return self.doc.generate()","title":""},{"docid":"43e19798b1d61d4d1834ae1278fbee6f","score":"0.54181135","text":"def _fetch_docs(self, user, slice_data=True, format_docs=True, *args, **kwargs):\n query = self._construct_query(*args, **kwargs)\n docs = self.model.objects.find_by_user(user, **query)\n if slice_data:\n start = request.args.get('start', 0)\n limit = request.args.get('limit', DEFAULT_LIMIT)\n docs = docs[start:start + limit]\n if format_docs:\n docs = self._format_multiple_docs(docs)\n return docs","title":""},{"docid":"3ab3ca959c96b98d815a2ebe5a523678","score":"0.5410954","text":"def document(document_id):\n return render_template('document.html', document_id=document_id)","title":""},{"docid":"c3ff7964c2c4336a5e0d2b3036465aa0","score":"0.5407521","text":"def _ListAllDocuments(self):\n feed = self.gd_client.GetDocumentListFeed()\n self._PrintFeed(feed)","title":""},{"docid":"f704914d586e2c74825f087aa4672fce","score":"0.5399946","text":"def action_show_documents(self):\n ids = []\n approver = self.mapped('approver_ids').filtered(\n lambda approver: approver.user_id == self.env.user\n )\n print(approver, '==============approver')\n print(approver.approve_attachment_id,'===========approve_attachment_id')\n print(approver.refused_attachment_id,'===========refused_attachment_id')\n print(approver.cancel_attachment_id,'===========cancel_attachment_id')\n\n if approver.approve_attachment_id:\n ids.append(approver.approve_attachment_id.id)\n if approver.refused_attachment_id:\n ids.append(approver.refused_attachment_id.id)\n if approver.cancel_attachment_id:\n ids.append(approver.cancel_attachment_id.id)\n print(ids, '==============ids')\n return {\n 'name': _('Documents'),\n 'type': 'ir.actions.act_window',\n 'view_mode': 'tree,form',\n 'view_id': False,\n 'res_model': 'ir.attachment',\n 'domain': [('id', 'in', ids)]\n }","title":""},{"docid":"3eb126e1f1e4160d610ca9b6f059d1c9","score":"0.5393111","text":"def parse_docs_from_page(self, page_url: str, page_text: str) -> Iterable[Document]:\n\n # parse html response\n page2 = requests.get(page_url)\n soup2 = bs4.BeautifulSoup(page2.content, 'html.parser')\n doc_num = []\n pdf = []\n doc_title = []\n datelist = []\n doctype = []\n parsed_docs = []\n webpart2 = soup2.find(\"div\", {\"class\": \"skin-pane2 col-md-8\"})\n meta = webpart2.find_all(\"div\")\n for row in meta:\n if ((remove_html_tags((str(row))).isspace()) or not remove_html_tags((str(row)))):\n continue\n for cell in row.find_all('p'):\n # print(cell.find(\"strong\"))\n words = ''\n links = cell.find_all(\"a\")\n link_list = list(links)\n # print(links)\n nums = []\n pdf_links = [link['href'] for link in link_list if \"pdf\" in link['href'] or \"aspx\" in link['href']]\n if not pdf_links:\n continue\n pdf.append(str(pdf_links[0]))\n # print(pdf_links)\n words = remove_html_tags((str(cell.find(\"strong\")))).encode('ascii', 'ignore').decode('ascii').lstrip(\n \" \").rstrip(\" \")\n words = \" \".join(words.split())\n # print(words)\n # else:\n # print(cell)\n doc_num.append(words)\n pdf.append(str(pdf_links[0]))\n title = [text for text in cell.find_all(text=True) if text.parent.name != \"strong\"]\n doc_title.append(title)\n pub_links = []\n [pub_links.append(x) for x in pdf if x not in pub_links]\n document_name = []\n [document_name.append(x) for x in doc_num if x not in document_name]\n document_type = []\n [document_type.append(' '.join(x.split()[0:2])) for x in document_name]\n document_number = []\n [document_number.append(' '.join(x.split()[2:])) for x in document_name]\n document_title = []\n [document_title.append(x) for x in doc_title if x not in document_title]\n document_title = [item for sublist in document_title for item in sublist]\n document_title = [str(item).encode('ascii', 'ignore').decode('ascii').lstrip(\" \").rstrip(\" \") for item in\n document_title]\n final = list(itertools.zip_longest(document_type, document_number, document_title, pub_links))\n final = [list(x) for x in final]\n for item in final:\n doc_name = item[0]+' '+item[1]\n if (item[2] is None):\n doc_title=\"\"\n else:\n doc_title = item[2]\n doc_num = item[1]\n doc_type = item[0]\n publication_date = \"N/A\"\n if item[3].startswith(\"https\"):\n cac_login_required=True\n url = item[3]\n url = url.replace(\" \",\"%20\")\n else:\n cac_login_required=False\n url = \"https://www.usar.army.mil\"+item[3]\n url = url.replace(\" \",\"%20\")\n pdf_di = DownloadableItem(doc_type='pdf', web_url=url)\n version_hash_fields = {\n \"item_currency\": str(url).split('/')[-1], # version metadata found on pdf links\n \"document_title\": doc_title.strip(),\n \"document_number\": doc_num.strip()\n }\n if (str(doc_type).startswith(\"USAR\") == False):\n doc_title = doc_name\n doc_num = \"\"\n doc_type = \"USAR Doc\"\n version_hash_fields = {\n \"item_currency\": str(url).split('/')[-1], # version metadata found on pdf links\n \"document_title\": doc_title.strip(),\n \"document_number\": doc_num.strip()}\n\n doc = Document(\n doc_name=doc_name.strip(),\n doc_title=re.sub('\\\\\"', '', doc_title),\n doc_num=doc_num.strip(),\n doc_type=doc_type.strip(),\n publication_date=publication_date,\n cac_login_required=cac_login_required,\n crawler_used=\"Army_Reserve\",\n source_page_url=page_url.strip(),\n version_hash_raw_data=version_hash_fields,\n downloadable_items=[pdf_di]\n )\n\n parsed_docs.append(doc)\n return parsed_docs","title":""},{"docid":"b1df67cc8547c85426ee5fc1cb079396","score":"0.53898543","text":"def do_GET(self):\n\n try:\n uri = None\n query_string = None\n if self.path and self.path[0] == '/':\n uri, sep, query_string = self.path[1:].partition('?')\n \n tab = self.server.window.get_tab_from_location(Gio.File.new_for_uri(uri))\n if tab:\n buf = tab.get_view().get_buffer()\n md_text = buf.get_text(buf.get_start_iter(), buf.get_end_iter(), False)\n try:\n md_html = markdown.markdown(md_text, self.server.markdown_extensions)\n except NameError:\n self.wfile.write(\n \"Python-Markdown not installed. Please install \"\n \"before using this plugin. For up-to-date code, \"\n \"install from https://github.com/waylan/Python-Markdown\")\n return\n self.wfile.write(\"<head>\")\n self.wfile.write(get_style(self.css_path.format(self.general_css)))\n highlight_css = None\n if self.server.settings:\n highlight_css = get_style(self.css_path.format(\n self.server.settings.get_string('code-theme')))\n if not highlight_css:\n highlight_css = get_style(\n self.css_path.format(self.default_highlight_css))\n if query_string: # Override code-theme if provided in query\n params = urlparse.parse_qs(query_string)\n for css_name in params.get('style', []):\n highlight_css = get_style(self.css_path.format(css_name))\n if highlight_css:\n break\n self.wfile.write(highlight_css)\n self.wfile.write(\"</head>\")\n self.wfile.write(md_html)\n else:\n self.wfile.write(\"Could not match file {0}.\".format(uri))\n except Exception as err:\n self.wfile.write(\"Exception occurred while rendering: \" + str(err))","title":""},{"docid":"1d346dc4722e86adf58cf71fd840de9b","score":"0.53839433","text":"def view(self, files):","title":""},{"docid":"185eba95491f7916c9d7f81310623e34","score":"0.5372585","text":"def GetDocument(self, *args, **kwargs):\n pass","title":""},{"docid":"2e9660cfa662d730db8123ba754f479e","score":"0.53709745","text":"def loadDocument(self, filename):\n \n #for index in range(self.mdiArea.count()):\n \n # document = self.mdiArea.widget(index)\n # if document:\n # if filename == document.filename:\n # self.mdiArea.setCurrentIndex(index)\n # document.reload()\n # return\n # Else load from file and create new document tab.\n\n self.statusBar.showMessage(self.tr(\"Loading...\"), 2500)\n\n document = Document(filename, self)\n index = self.mdiArea.addDocument(document)\n #self.mdiArea.setCurrentIndex(index)\n index.show()\n\n # After loading a conenction file, it is possible to refresh the current module.\n self.refreshAct.setEnabled(True)\n self.statusBar.showMessage(self.tr(\"Successfully loaded file\"), 2500)\n\n # Enable close action\n #self.closeAct.setEnabled(self.mdiArea.count())","title":""},{"docid":"9c638ea0d31fcdba25e946363a273ecb","score":"0.53628904","text":"def onOpen(self):\n \n if self.document_type == 1:\n\n my_list=[]\n #self.actionUpdate.triggered.connect(self.onEdit)\n fileName = \"VAS\"\n the_doc = VAS_view(self)\n self.count = self.count +1 \n the_doc.fileName = fileName + str(self.count)\n sub = self.mdiArea.addSubWindow(the_doc)\n sub.show()\n \n #self.loadDocument(fileName)\n\n if self.document_type == 2:\n fileName = \"VCCP\"\n the_doc = VCCP_view(self)\n self.count = self.count + 1 \n the_doc.fileName = fileName + str(self.count) \n sub = self.mdiArea.addSubWindow(the_doc)\n sub.show()\n #self.loadDocument(fileName)\n\n if self.document_type == 3:\n fileName = 'VCRH'\n the_doc = VCRH_Edit(self)\n self.count = self.count + 1 \n the_doc.fileName = fileName + str(self.count) \n sub = self.mdiArea.addSubWindow(the_doc)\n sub.show()\n\n if self.document_type == 4:\n fileName = 'VCRLD'\n the_doc = VCRLD_Edit(self)\n self.count = self.count + 1 \n the_doc.fileName = fileName + str(self.count) \n sub = self.mdiArea.addSubWindow(the_doc)\n sub.show()\n\n if self.document_type == 5:\n fileName = 'VProj'\n the_doc = VProj_Edit(self)\n self.count = self.count + 1 \n the_doc.fileName = fileName + str(self.count) \n sub = self.mdiArea.addSubWindow(the_doc)\n sub.show()\n\n if self.document_type == 6:\n fileName = 'EditLayers'\n the_doc = RPE_Edit(self)\n self.count = self.count + 1 \n the_doc.fileName = fileName + str(self.count) \n sub = self.mdiArea.addSubWindow(the_doc)\n sub.show()\n\n if self.document_type == 7:\n fileName = 'Split_Section'\n the_doc = splitSections(self)\n self.count = self.count+1\n the_doc.fileName = fileName + str(self.count) \n sub = self.mdiArea.addSubWindow(the_doc)\n sub.show()\n\n if self.document_type == 8:\n fileName = 'Tweak_Section'\n the_doc = tweakSections(self)\n self.count = self.count+1\n the_doc.fileName = fileName + str(self.count) \n sub = self.mdiArea.addSubWindow(the_doc)\n sub.show()\n\n if self.document_type == 9:\n fileName = 'Move_Section'\n the_doc = moveSections(self)\n self.count = self.count+1\n the_doc.fileName = fileName + str(self.count) \n sub = self.mdiArea.addSubWindow(the_doc)\n sub.show()\n\n if self.document_type == 10:\n fileName = 'Copy_Section'\n the_doc = copySections(self)\n self.count = self.count+1\n the_doc.fileName = fileName + str(self.count) \n sub = self.mdiArea.addSubWindow(the_doc)\n sub.show()","title":""},{"docid":"f505aba27a6032eef6309e79387e2be2","score":"0.53608304","text":"def index():\n return render_template('records.html')","title":""},{"docid":"681341909be97b3e0051c95525b9b5ec","score":"0.535427","text":"def make_docs_data_view(api_info):\n return get_schema_view(\n api_info,\n generator_class=ApiSchemaGenerator,\n public=True,\n permission_classes=(permissions.AllowAny,),\n ).without_ui(cache_timeout=get_docs_cache_timeout())","title":""},{"docid":"5e4fb43f1b9662e7a24eadd19b6df66d","score":"0.53498733","text":"def processUserRequest(documentList):\n queryWordList = getQueryWordListFromUser()\n numOfDocs = int(input('How many documents to retrieve?\\n'))\n return displayResults(queryWordList,documentList,numOfDocs)","title":""},{"docid":"3a6d7a5f4072b977e9aaea891d0c1f29","score":"0.5340088","text":"def index (request):\n try:\n a = Article.objects.get (title=\"HT Wiki\")\n return preview (request, a.id)\n except ObjectDoesNotExist:\n return preview (request, -1)","title":""},{"docid":"4b98a2dae2b656389fd5c05f3e8611fb","score":"0.5321335","text":"def make_index(self, doc_name, protocol, number_ini, number_fin):\n temp = string.ascii_uppercase\n upper = temp.replace(\"N\", \"NÑ\")\n\n document = word.Documento(doc_name)\n\n for letter in upper:\n data = self.database.get_table_by_letter(protocol, letter,\n number_ini, number_fin)\n if data:\n page = []\n for doc in data:\n number = doc[0]\n body = doc[1]\n if doc[2] == \"\":\n body += \". \"\n else:\n body += \"; \"\n body += doc[2]\n body += \". \"\n body += doc[3]\n body += \".\"\n page.append([number, body])\n document.write_page(letter, page)","title":""},{"docid":"aa1a2aad44cc8df79f8891cfaebb4069","score":"0.5319376","text":"def IndexReader(self, dir):\n output_details_review_name = \"details_review_output.pkl\"\n output_index_file_name =\"dictionary_index.pkl\"\n self.dictionary_reviews = save_obj(dir+\"//\"+output_details_review_name)\n self.dictionary_text = save_obj(dir+\"//\"+output_index_file_name)","title":""},{"docid":"6b692e1dec4c083a6b11fdcbe76f652e","score":"0.5308543","text":"def documents(self):\n catalog = self.context.portal_catalog\n query = {'isWorkingCopy': 0,\n 'path': {'depth': 1,\n 'query': '/'.join(self.context.getPhysicalPath())},\n 'portal_type': ['opengever.document.document',\n 'ftw.mail.mail']}\n documents = catalog(query)[:10]\n\n document_list = [{\n 'Title': document.Title,\n 'getURL': document.getURL,\n 'alt': document.document_date and\n document.document_date.strftime('%d.%m.%Y') or '',\n 'css_class': get_css_class(document),\n 'portal_type': document.portal_type,\n } for document in documents]\n\n return document_list","title":""},{"docid":"c94e5011fe4810ba5178b7a261cd0d67","score":"0.53033286","text":"def get(self, request):\n\n records = Document.objects.all()\n uploaded_files = []\n for record in records:\n uploaded_files.append(\n {\n \"id\": record.id,\n \"file_name\": str(record.document).split(\"/\")[1],\n \"uploaded_at\": record.uploaded_at,\n }\n )\n response = render(\n request,\n \"data_enrichment/list_view.html\",\n {\n \"uploaded_files\": uploaded_files,\n },\n )\n return response","title":""},{"docid":"5cfcdecf1ac8caba4681a4525a2726b0","score":"0.5300169","text":"def user_documents(request, userid):\n logger.debug('reading ' + userid + '\\'s documents.')\n documents, remains = Document.objects.recent(userid)\n context = {}\n context['documents'] = documents\n context['hasMoreDocuments'] = remains > 0\n context['page'] = 'my'\n return render(request, 'document/mydocuments.html', context)","title":""},{"docid":"e738d6ffcdbdbad1bd3f5425ddb61587","score":"0.52995944","text":"def load_main_page(self, domain_filter=None):\n\n unsorted_docs = {}\n req = requests.get(IHE_TF_URL)\n if req.status_code == 200:\n soup = BeautifulSoup(req.text, \"html5lib\")\n links = list(filter(lambda x: x.get(\"href\"), soup.find_all(\"a\")))\n pdf_list = list(filter(lambda x: x.get(\"href\").endswith(\".pdf\"), links))\n\n print(\"Get information about documents\")\n for link in pdf_list:\n docinfo = self.get_infos(link.text, link.get(\"href\"), domain_filter)\n unsorted_docs[docinfo[\"filename\"]] = docinfo\n print(f\"\\n{len(unsorted_docs)} documents found in IHE website.\")\n self.classify(unsorted_docs)\n for key, value in self.doc.items():\n print(f\"{key}: {len(value)} documents\")","title":""},{"docid":"6d3951aadb6120d656de97b219489998","score":"0.52897406","text":"def displayJournalEntries(self):","title":""},{"docid":"625d6c91d1ff2c5ce00d0aafae9b9905","score":"0.52878875","text":"def _index(self, content: Content):\n pass","title":""},{"docid":"23e88f145fa38e464ddd8b649d6ed1f1","score":"0.5277039","text":"def doc_show(context, request):\n context = ContextWrapper(context)\n stage = context.stage\n name, version = context.project, context.version\n doc_info = get_doc_info(context, request)\n version_links = []\n latest_doc_info = get_doc_info(context, request, version='latest', check_content=False)\n if latest_doc_info['doc_version'] != doc_info['doc_version']:\n version_links.append(dict(\n title=\"Latest documentation\",\n url=request.route_url(\n \"docviewroot\", user=stage.user.name, index=stage.index,\n project=name, version='latest', relpath=\"index.html\")))\n try:\n stable_doc_info = get_doc_info(context, request, version='stable', check_content=False)\n if stable_doc_info['doc_version'] not in (doc_info['doc_version'], latest_doc_info['doc_version']):\n version_links.append(dict(\n title=\"Stable documentation\",\n url=request.route_url(\n \"docviewroot\", user=stage.user.name, index=stage.index,\n project=name, version='stable', relpath=\"index.html\")))\n except (HTTPFound, HTTPNotFound):\n pass\n return dict(\n title=\"%s-%s Documentation\" % (name, version),\n base_url=request.route_url(\n \"docroot\", user=stage.user.name, index=stage.index,\n project=name, version=version, relpath=''),\n baseview_url=request.route_url(\n \"docviewroot\", user=stage.user.name, index=stage.index,\n project=name, version=version, relpath=''),\n url=request.route_url(\n \"docroot\", user=stage.user.name, index=stage.index,\n project=name, version=version,\n relpath=doc_info['relpath'], _query=request.query_string),\n version_mismatch=doc_info['version_mismatch'],\n version_links=version_links,\n doc_version=doc_info['doc_version'])","title":""},{"docid":"97afbf74ad4bf28f958d7300588eda06","score":"0.52533275","text":"def docs():\n adapters = sorted(AdapterBase.registry.keys())\n\n return render_template(\"docs.html\", adapters=adapters)","title":""},{"docid":"5907cd4e7c91e248687f3841964a5c52","score":"0.5252057","text":"def getAllDocs(idNum):\n \n # connect to database\n conn, cursor = getConnectionAndCursor()\n\n # build SQL\n sql = \"\"\"\n SELECT *\n FROM documents\n WHERE class_num = %s\n \"\"\"\n\n # execute the query\n parameters = (int(idNum), )\n cursor.execute(sql,parameters)\n\n # get the data from the database:\n data = cursor.fetchall()\n\n # clean up\n conn.close()\n cursor.close()\n \n return data","title":""},{"docid":"c16991b2bfbea818783936950dadf8d9","score":"0.52500963","text":"def get_document_details():\n document = business_layer.BusinessLayer().get_details(request.args.get('id'))\n return render_template('details.html', details=document)","title":""},{"docid":"16a62ad6994d86f26bf0eb55568402ea","score":"0.5246594","text":"def document_access(paper, document):\n path = os.path.join(\"documents\", files[str(document)])\n app.logger.debug(path)\n content = open(path, \"rb\").read()\n headers = {\n \"X-TODO\": \"Last-Modified\",\n \"Content-Length\": len(content)\n }\n return Response(\n headers=headers,\n content_type=model[\"documents\"][document][\"mime_type\"],\n response=content)","title":""},{"docid":"7d2eaae4b4d35c32b6922fde40ae8125","score":"0.52339095","text":"def documents_processor(urls, job_id, client_id):\n global workfiles\n workfiles = []\n for url in urls:\n try:\n result = api_call_manager(add_api_key(url))\n process_results(result)\n except:\n logger.error('Error - URL processing failed')\n result = json.loads(json.dumps({\"job_id\" : job_id, \"type\": \"docs\", \"data\" : workfiles, \"client_id\" : str(client_id), \"version\" : version}))\n return result","title":""},{"docid":"ad05b1f9d088c6670d450085741f1bc2","score":"0.5233528","text":"def view_content(self, lines=None):\n\n if lines is None:\n text = self.storage.page_text(self.title)\n else:\n text = ''.join(lines)\n return self.highlight(text, mime=self.mime)","title":""},{"docid":"b0c4c04445b1595c4bed67f098267834","score":"0.52280146","text":"def store_new_docs_in_db(self):\n self.status = self.model_status.PROCESSING\n self.docs_to_process = self.search_docs_in_processed_docs()\n self.length_of_docs_to_process = len(self.docs_to_process)\n if self.length_of_docs_to_process <= 0:\n return json.dumps({'status':'ERROR','user_prompt':'There are no documents to process'}) \n for doc in list(self.docs_to_process):\n self.doc_in_process = doc\n self.current_process = 'Getting paragraphs and footnotes from {}.'.format(self.doc_in_process)\n paragraphs_from_doc_status = self.get_paragraphs_from_doc(doc)\n if paragraphs_from_doc_status['status'] == 'ok':\n name_of_table, paragraphs, footnotes = paragraphs_from_doc_status['data']\n else:\n os.remove(config.TEXTS_PATH + \"/\" + self.doc_in_process)\n for doc in self.processed_docs:\n rest, ext = doc.split(\".\")\n table_name = \"[\" + rest + \"_\" + ext + \"]\"\n erase_table_and_words_for_doc(table_name)\n os.remove(config.TEXTS_PATH + \"/\" + doc)\n return paragraphs_from_doc_status\n self.current_process = 'Storing paragraphs and footnotes in database.'\n self.create_table_for_doc(name_of_table, paragraphs, footnotes)\n self.current_process = 'Storing words in database'\n self.fill_words_db(doc)\n self.processed_docs.append(doc)\n self.docs_to_process.remove(doc)\n self.status = self.model_status.DONE\n \n return {'status':'ok', 'user_prompt':''}","title":""},{"docid":"4d2f64c27211418e1a53196938305a0c","score":"0.5225968","text":"def action_show_all_documents(self):\n ids = []\n approvers = self.mapped('approver_ids')\n for approver in approvers:\n if approver.approve_attachment_id:\n ids.append(approver.approve_attachment_id.id)\n if approver.refused_attachment_id:\n ids.append(approver.refused_attachment_id.id)\n if approver.cancel_attachment_id:\n ids.append(approver.cancel_attachment_id.id)\n return {\n 'name': _('Documents'),\n 'type': 'ir.actions.act_window',\n 'view_mode': 'tree,form',\n 'view_id': False,\n 'res_model': 'ir.attachment',\n 'domain': [('id', 'in', ids)]\n }","title":""},{"docid":"a456bf4bb206ca072ee10f8c8b7c6d1b","score":"0.5222932","text":"def get_docs(args):\n DbSession = get_db_session()\n numbers = sort_preserve_order(args.number) # Remove duplicate arguments\n docs = []\n dne = []\n if args.is_also:\n for number in numbers:\n std = query_std(DbSession, number)\n if std is None:\n dne.append(\"STD {} does not exist.\".format(number))\n else:\n aliases = query_std_is_also(DbSession, number)\n docs.extend(aliases)\n else:\n for number in numbers:\n rfc = query_std(DbSession, number)\n if rfc is not None:\n docs.append(rfc)\n else:\n dne.append(\"STD {} does not exist.\".format(number))\n\n # Display found documents\n show_docs(sort_preserve_order(docs), args.editor, args.pager)\n # Display messages about nonexistent documents\n for msg in dne:\n print(msg)\n\n # Exit successfully\n sys.exit(0)","title":""},{"docid":"aa2c17de9ea403aec14be4f8d500c66a","score":"0.5220909","text":"def index(content, override_body=None):","title":""},{"docid":"6ee0aa568c6e76e3297047e4db12632f","score":"0.52024424","text":"def index(self):\n return self.render_default()","title":""},{"docid":"d41eed24f367099fe6149ae00bce8bd7","score":"0.5197253","text":"def index(self):\n first_three_books_reviews = self.models['Review'].get_first_three_books_reviews(3)\n book_ids = [r['bookId'] for r in first_three_books_reviews]\n all_books = self.models['Book'].get_all_books(book_ids)\n return self.load_view('/books/index.html', first_three_books_reviews = first_three_books_reviews, all_books = all_books)","title":""},{"docid":"57116bc7bc637b6894fd387e07727313","score":"0.5196881","text":"def documentation_view(request): \r\n context = {\r\n 'docs':docs,\r\n } \r\n return render_to_response('document.html', context, RequestContext(request))","title":""},{"docid":"5c41b7441bcdbc92921a10db0d6ce1d5","score":"0.51937556","text":"def frontpage_content(self, primary=False):\r\n return \"<div><h2>Module %s</h2><p>Handles %s documents</p></div>\" % (module_dir, rdf_type)","title":""},{"docid":"f3c680fa83d37d450bb0e6394583f945","score":"0.5187936","text":"def display_file(self, path, pages=None):\n\n self.master.title(\"tkDocViewer: \" + path)\n self.doc_viewer.display_file(path, pages)","title":""},{"docid":"290e58446456587dab96a229d5ded81b","score":"0.5182599","text":"def get(self):\n result_set, total = self.execute_query()\n key_values = self.get_key_values(result_set)\n keys = key_values.keys()\n keys.sort()\n\n headers = []\n for key in keys:\n sample_value = key_values[key][0]\n headers.append({\n 'name': key,\n 'type': DataType.get(sample_value).name(),\n })\n\n entities = []\n edit_path = self.base_path() + DatastoreEditHandler.PATH\n for entity in result_set:\n attributes = []\n for key in keys:\n if entity.has_key(key):\n raw_value = entity[key]\n value = DataType.get(raw_value).format(raw_value)\n short_value = DataType.get(raw_value).short_format(raw_value)\n else:\n value = ''\n short_value = ''\n attributes.append({\n 'name': key,\n 'value': value,\n 'short_value': short_value,\n })\n entities.append({\n 'key': str(entity.key()),\n 'key_name': entity.key().name(),\n 'key_id': entity.key().id(),\n 'shortened_key': str(entity.key())[:8] + '...',\n 'attributes': attributes,\n 'edit_uri': edit_path + '?key=' + str(entity.key()) + '&kind=' + urllib.quote(self.request.get('kind')) + '&next=' + urllib.quote(self.request.uri),\n })\n\n start = self.start()\n num = self.num()\n max_pager_links = 8\n current_page = start / num\n num_pages = int(math.ceil(total * 1.0 / num))\n page_start = max(math.floor(current_page - max_pager_links / 2), 0)\n page_end = min(page_start + max_pager_links, num_pages)\n\n pages = []\n for page in range(page_start + 1, page_end + 1):\n pages.append({\n 'number': page,\n 'start': (page - 1) * num,\n })\n current_page += 1\n\n in_production = self.in_production()\n if in_production:\n kinds = None\n else:\n kinds = self.get_kinds()\n\n values = {\n 'request': self.request,\n 'in_production': in_production,\n 'kinds': kinds,\n 'kind': self.request.get('kind'),\n 'order': self.request.get('order'),\n 'headers': headers,\n 'entities': entities,\n 'message': self.request.get('msg'),\n 'pages': pages,\n 'current_page': current_page,\n 'num': num,\n 'next_start': -1,\n 'prev_start': -1,\n 'start': start,\n 'total': total,\n 'start_base_url': self.filter_url(['kind', 'order', 'order_type',\n 'num']),\n 'order_base_url': self.filter_url(['kind', 'num']),\n }\n if current_page > 1:\n values['prev_start'] = int((current_page - 2) * num)\n if current_page < num_pages:\n values['next_start'] = int(current_page * num)\n\n self.generate('datastore.html', values)","title":""},{"docid":"df88b7f54247f7b2ad9e0e6f667c6c66","score":"0.5179713","text":"def viewDocument(self, format, path):\n self.ui.tabWidget.setStyleSheet('QTabWidget::pane {background: none;}')\n\n # Read document\n fh = open(path, 'r')\n data = fh.read()\n fh.close()\n\n # Create a new editor and configure with current settings\n frame = QWidget(self.ui.tabWidget)\n label = self.formatLabels[format]\n if format == self.handler.HTML:\n editor = PyQt4.QtWebKit.QWebView(frame)\n editor.setHtml(data)\n else:\n editor = LinedEditor(parent=frame)\n font = QFont(self.settings.value('appearance/previewFontFamily').toString(),\n self.settings.value('appearance/previewFontSize').toInt()[0])\n editor.setFont(font)\n editor.setPlainText(data)\n if format == self.handler.XML:\n if not os.access(path, os.W_OK):\n # Read only!\n editor.setReadOnly(True)\n label += ' (Read-only)'\n lineNumbers = self.settings.value('appearance/previewLineNumbersXml').toBool()\n self.xmlEditor = editor\n # Create callback\n self.connect(editor, SIGNAL('textChanged()'), self.xmlChanged)\n self.xml_modified = False\n else:\n lineNumbers = self.settings.value('appearance/previewLineNumbersText').toBool()\n editor.setReadOnly(True)\n editor.enableLineNumbers = lineNumbers\n self.textEditors.append(editor)\n\n # Add editor to the tab\n layout = QVBoxLayout()\n layout.setContentsMargins(1, 1, 1, 1)\n layout.addWidget(editor)\n frame.setLayout(layout) \n self.ui.tabWidget.addTab(frame, label)","title":""},{"docid":"63d757188b53e65d2dcbde844c8b866c","score":"0.5179206","text":"def store_documents():\n pass","title":""},{"docid":"0ff044d4a0845f53af0f6a85e6e42384","score":"0.5170479","text":"def index(self):\n files = self.get_files()\n for file in files:\n self.process_file(file)\n return None","title":""},{"docid":"d0bb0f547efd37ffcc71138e51d64260","score":"0.51645446","text":"def retrieve_new_documents(self, limit=100):\n today = dt.datetime.combine(dt.date.today(), dt.time.min)\n\n entry_path = etree.XPath(\"//div[@class = 'SearchResult']\")\n date_path = etree.XPath(\n \"\"\"\n .//dl/dd[preceding-sibling::dt[contains(text(), 'Date') or\n contains(text(), 'Datum')]]/text()\n \"\"\")\n doc_path = etree.XPath(\n \"\"\"\n .//ul[contains(@class, 'SearchResultDoc')]/li\n /a[contains(@href, 'PDF') or contains(@href, 'HTML')]/@href\n \"\"\")\n title_path = etree.XPath(\".//h2/a[@class = 'title']/text()\")\n detail_path = etree.XPath(\".//h2/a[@class = 'title']/@href\")\n\n timestamp = int(round(time.time() * 1000))\n url_tmpl = (\"https://eur-lex.europa.eu/search.html?lang=de&qid=\"\n f\"{timestamp}&type=quick&scope=EURLEX&sortOneOrder=desc\"\n \"&sortOne=DD&locale=de&page={}\")\n\n has_unseen_documents = True\n doc_count = 0\n page = 1\n\n while (doc_count < limit) and has_unseen_documents:\n search_url = url_tmpl.format(page)\n logging.info(f\"Crawling page '{search_url}' (page {page})\")\n res = _retry_connection(search_url, \"get\")\n html_string = res.content\n tree = html.fromstring(html_string)\n\n for entry in entry_path(tree):\n if not isinstance(entry, list):\n entry = [entry]\n\n date_string = _flat_map(date_path, entry)[0]\n match = re.search(r\"(\\d+\\/\\d+\\/\\d+)\", date_string)\n\n doc_date = dt.datetime.min\n if match:\n doc_date = dt.datetime.strptime(match[1], \"%d/%m/%Y\")\n if len(_flat_map(doc_path, entry)) == 0:\n continue\n link = _make_resource_path(_flat_map(doc_path, entry)[0],\n \"https://eur-lex.europa.eu\")\n detail = _make_resource_path(_flat_map(detail_path, entry)[0],\n \"https://eur-lex.europa.eu\")\n title = _flat_map(title_path, entry)[0]\n\n doc = {\"url\": link, \"detail_url\": detail, \"date\": doc_date,\n \"title\": title, \"crawl_date\": today}\n\n logging.debug(f\"Process Document: {link} - {doc_date.date()}\")\n\n num_docs = self.collection.count_documents({\"url\": link})\n\n if num_docs > 0:\n logging.debug(f\"Document was crawled before: '{link}'\")\n # check whether this document had a date before the crawl\n # date, if not, break.\n duplicate_doc = self.collection.find_one({\"url\": link})\n\n if duplicate_doc[\"date\"] >= duplicate_doc[\"crawl_date\"]:\n logging.debug(\"Document date lies in the future.\"\n \" Continue...\")\n continue\n\n logging.debug(\"Break!\")\n has_unseen_documents = False\n break\n\n logging.debug(f\"Found new document: {link}.\")\n res = self.collection.insert_one(doc)\n doc_count += 1\n page += 1\n logging.info(f\"Found {doc_count} new or potentially modified docs.\")\n return doc_count","title":""},{"docid":"f0216e63e586ed1464e2115ef9ac2772","score":"0.5163617","text":"def listDoc(self, dbName, reverse=False, startKey=0, count=-1):\r\n # Responses: {u'rows': [{u'_rev': -1825937535, u'_id': u'mydoc'}],\r\n # u'view': u'_all_docs'}, 404 Object Not Found\r\n uri = \"/%s/_all_docs\" % (dbName,)\r\n args = {}\r\n if reverse:\r\n args[\"reverse\"] = \"true\"\r\n if startKey > 0:\r\n args[\"startkey\"] = int(startKey)\r\n if count >= 0:\r\n args[\"count\"] = int(count)\r\n if args:\r\n uri += \"?%s\" % (urlencode(args),)\r\n return self.get(uri\r\n ).addCallback(self.parseResult)","title":""},{"docid":"7e9d1fe5f139ac6329ad057af3865af4","score":"0.515995","text":"def index(self):\r\n data = []\r\n fn = PAGES / \"index.html\"\r\n if sys.version < '3':\r\n f_in = io.TextWrapper(fn.open(), encoding='utf-8')\r\n else:\r\n f_in = fn.open(encoding='utf-8')\r\n with f_in:\r\n for line in f_in:\r\n data.append(line)\r\n return data","title":""},{"docid":"b00248b7a2adc46987290de0e7f3d459","score":"0.5154607","text":"def enrich_documents(self, limit=100):\n entry_path = etree.XPath(\"//dl[contains(@class, 'NMetadata')]/dd\")\n key_path = etree.XPath(\"normalize-space(./preceding-sibling::dt[1])\")\n value_path = etree.XPath(\"\"\"\n normalize-space(\n string-join(\n ./text() | .//*[self::span[@lang] or\n self::a[not(child::span)] or\n self::i[not(child::span)]]/text(), \"#\"\n )\n )\n \"\"\")\n\n success_count = 0\n cursor = (self.collection.find({\"metadata\": {\"$exists\": False}})\n # newest first, and limit by limit\n .sort([(\"date\", -1)]).limit(limit))\n # extract additional metadata\n for index, document in enumerate(cursor):\n logging.info(f\"Processing document number {index}...\")\n\n res = _retry_connection(document[\"detail_url\"], \"get\")\n\n tree = html.fromstring(res.content)\n\n metadata = {}\n for idx, entry in enumerate(entry_path(tree)):\n key = key_path(entry).strip(\" .:,;!?-_#\")\n val = value_path(entry).strip(\" .:,;!?-_#\").split(\"#\")\n if len(key) == 0 or key in metadata:\n key += str(idx)\n if len(val) == 0:\n continue\n metadata[key] = val\n\n document[\"metadata\"] = metadata\n # update document in the database\n result = self.collection.update_one({\"_id\": document[\"_id\"]},\n {\"$set\": document})\n success_count += result.modified_count\n return success_count","title":""},{"docid":"91d2f8fa0d4a9322b56547b6dd8b95ae","score":"0.5149818","text":"def open(request, doc):\n userid = request.session['userid']\n member = Member.objects.get(user_id=userid)\n context = {}\n context['document'] = createDummyDocument(member, 1)[0]\n return render(request, 'document/document.html', context)","title":""},{"docid":"670f1d784df2703a85f152d3389fa018","score":"0.51390713","text":"def update(self):\n\n row = self.parent.get_row_by_file(self.filepath)\n\n # update results\n self.display_results = [\n self.filepath,\n self.parent_folder,\n self.filename,\n self.inspection.get_display('timecode_start'),\n self.inspection.get_display('content_start_timecode'),\n self.inspection.get_display('framecount'),\n self.inspection.get_display('full_duration'),\n self.inspection.get_display('content_duration'),\n self.inspection.get_display('slate'),\n self.inspection.get_display('black_at_tail'),\n self.inspection.get_display('slate_key_number'),\n self.inspection.get_display('op48_audio'),\n self.inspection.get_display('op59_audio'),\n self.inspection.get_display('audio_peak'),\n self.inspection.get_display('resolution'),\n self.inspection.get_display('fps'),\n self.inspection.get_display('video_bitrate'),\n self.inspection.get_display('video_codec'),\n self.inspection.get_display('audio_codec'),\n self.inspection.get_display('audio_bitrate'),\n self.inspection.get_display('audio_sample_rate'),\n self.inspection.get_display('slate_date'),\n self.inspection.get_display('slate_aspect'),\n self.inspection.get_display('slate_duration'),\n ]\n\n for col, v in enumerate(self.display_results):\n self.parent.dataview.SetValue(v, row, col)","title":""},{"docid":"14edc14e7de3f9c80bca3f1efda45568","score":"0.51333195","text":"def open_document(self):\n\n # Read the DXF file\n document = readfile(self.dxf_path)\n modelspace = document.modelspace()\n # Convert DXF to pyleecan objects\n self.line_list = dxf_to_pyleecan_list(modelspace)\n # Display\n self.selected_list = [False for line in self.line_list]\n self.update_graph()","title":""},{"docid":"b319ba6a16ba660df2d32315964fbda6","score":"0.513093","text":"def __build_document(self):\n with open('consolidated_nyt.tsv') as r:\n self.documents = r.read().splitlines()\n self.documents = preprocess_documents(self.documents)\n self.number_of_documents = len(self.documents)\n self.vocabulary = corpora.Dictionary(self.documents)\n self.vocabulary_size = len(self.vocabulary)\n print(\"Number of documents:\" + str(len(self.documents)))\n print(\"Vocabulary size:\" + str(self.vocabulary_size))","title":""},{"docid":"0f3be98afefdaec1996913b2b1e625b2","score":"0.5124302","text":"def main():\n\tstc.html(HTML_BANNER)\n\t#menu = [\"Home\",\"About\"]\n\tst.markdown(\n f\"\"\"\n<style>\n .reportview-container .main .block-container{{\n max-width: {1500}px;\n padding-top: {1}rem;\n padding-right: {1}rem;\n padding-left: {1}rem;\n padding-bottom: {1}rem;\n }}\n \n</style>\n\"\"\",\n unsafe_allow_html=True,\n )\n\t\n\n\n\tchoice = \"Home\"#st.sidebar.selectbox(\"Menu\",menu)\n\n\tif choice == 'Home':\n\t\tst.subheader(\"Text Analysis\")\n\t\traw_text = st.text_area('Enter Text Here')\n\t\t\n\n\t\tif ( len(raw_text) > 2 & st.button(\"Analyze\")):\n\t\t\tcol1,col2,col3 = st.beta_columns(3)\n\t\t\tprocess_text = nfx.remove_stopwords(raw_text)\n\t\t\twith col1:\n\n\n\t\t\t\twith st.beta_expander(\"Preview Tagged Text\"):\n\t\t\t\t\ttagged_docx = generate_tags(raw_text)\n\t\t\t\t\tprocessed_tag_docx = mytag_visualizer(tagged_docx)\n\t\t\t\t\tstc.html(processed_tag_docx,scrolling=True)\n\n\t\t\t\twith st.beta_expander(\"Plot Mendelhall Curve\"):\n\t\t\t\t\tplot_mendelhall_curve_2(raw_text)\n\n\n\t\t\t\twith st.beta_expander(\"NER\"):\n\t\t\t\t\t\n\t\t\t\t\t# st.write(most_common_tokens)\n\t\t\t\t\tdocx = nlp(raw_text)\n\t\t\t\t\thtml = displacy.render(docx,style=\"ent\")\n\t\t\t\t\thtml = html.replace(\"\\n\\n\",\"\\n\")\n\t\t\t\t\tst.write(HTML_WRAPPER.format(html),unsafe_allow_html=True)\n\t\t\t\t\t#spacy_streamlit.visualize_ner(docx, labels=nlp.get_pipe(\"ner\").labels)\n\n\t\t\twith col2:\n\t\t\t\twith st.beta_expander('Process Text'):\n\t\t\t\t\tst.write(process_text)\n\n\t\t\t\twith st.beta_expander(\"Most Common Words\"):\n\t\t\t\t\t#st.write(raw_text)\n\t\t\t\t\tplot_most_common_tokens(process_text)\n\n\t\t\t\twith st.beta_expander(\"Plot Wordcloud\"):\n\t\t\t\t\tst.info(\"word Cloud\")\n\t\t\t\t\tplot_wordcloud(process_text)\n\n\t\t\t\t\n\t\t\t\n\t\t\twith col3:\n\t\t\t\twith st.beta_expander(\"Tokenizer\"):\n\t\t\t\t\tdocx = nlp(raw_text)\n\t\t\t\t\tspacy_streamlit.visualize_tokens(docx, attrs=[\"text\", \"pos_\", \"dep_\", \"ent_type_\"])\n\n\t\t\t\twith st.beta_expander(\"Sentiment Analysis\"):\n\t\t\t\t\tsentiment(raw_text)\n\n\t\t\t\twith st.beta_expander(\"Summarizer\"):\n\t\t\t\t\tsummarizer(raw_text)\n\n\n\n\t\telif (len(raw_text) == 1 ):\n\t\t\tst.warning(\"Insufficient Text, Minimum must be more than 1\") \n\t\t\t\n\n\n\t\t\t\n\n\n\t\t\n\n\telif choice == \"About\":\n\t\tst.subheader(\"Text Analysis NLP App\")","title":""},{"docid":"4e46ba637e33b4d42ba461a7a021670d","score":"0.51233447","text":"def docs():","title":""},{"docid":"140c0ea39a5918f4999d57e96cd8b79f","score":"0.511046","text":"def index():\n g.lcursor.execute('SELECT * FROM articles ORDER BY id DESC LIMIT 3')\n return render_template('main_index.html',\n articles_data=g.lcursor.fetchall())","title":""},{"docid":"acfe4e54b84c3e3f952b6d19592528b1","score":"0.51098657","text":"def home_page():\n files = list_conversions()\n return render_template('index.html', files=files)","title":""},{"docid":"99549e25377f27ae27a4a43017c5b58a","score":"0.51065785","text":"def render_qweb_doc(self, docids, data=None):\n\t\t# If the report is using a custom model to render its html, we must use it. otherwise, fallback on the generic html rendering.\n\t\treport_model_name = 'report.%s' % self.report_name\n\t\treport_model = self.env.get(report_model_name)\n\t\t\n\t\tif report_model is not None:\n\t\t\tdata = report_model.make_ppt(data)\n\t\telse:\n\t\t\tdocs = self.env[self.model].browse(docids)\n\t\t\tdata = {\n\t\t\t\t'doc_ids': docids,\n\t\t\t\t'doc_model': self.model,\n\t\t\t\t'docs': docs,\n\t\t\t}\n\t\t\treturn docs.doc5()\n\t\treturn data","title":""},{"docid":"4ca339f1e3c9784ae4fb07e533a3ee6e","score":"0.51034266","text":"def loadDocs(dataFormat, path=None, ignoreEntities=[]):\n assert dataFormat == 'bioc'\n if dataFormat == 'bioc':\n assert not path is None\n docs = loadDataFromBioC(path, ignoreEntities=ignoreEntities)\n assert isinstance(docs, list)\n for doc in docs:\n assert isinstance(doc, kindred.Document)\n return docs","title":""},{"docid":"f39fe1d8d6a2f93052194cafed4f2419","score":"0.50999314","text":"def _get_documents(self, url):\n\n documents = []\n with urllib.request.urlopen(url) as response:\n response = response.read().decode('utf-8')\n\n for doc_raw in json.loads(response)['data']:\n\n timestamp = doc_raw['created_utc']\n datetime_utc = datetime.utcfromtimestamp(timestamp)\n datetime_est = datetime_utc.replace(tzinfo=timezone.utc).astimezone(tz=None)\n date_str = datetime_est.strftime('%Y-%m-%d')\n\n if 'permalink' in doc_raw:\n url = f'https://www.reddit.com{doc_raw[\"permalink\"]}'\n else:\n url = 'n/a'\n\n documents.append({\n 'date': date_str,\n 'author': doc_raw['author'],\n 'subreddit': doc_raw['subreddit'],\n 'score': doc_raw['score'],\n 'url': url,\n 'text': html.unescape(doc_raw['body']),\n })\n\n print(len(documents))\n\n return documents","title":""},{"docid":"89e1e6aea0291a0961f22be40eb07e78","score":"0.50967455","text":"async def frame_data_doc(self, interaction: discord.Interaction):\n link = ('https://docs.google.com/spreadsheets/d/'\n '19UtK7xG2c-ehxdlhCFKMpM4_IHSG-EXFgXLJaunE79I')\n patch = requests.get(url='https://rivals.academy/library/pomme/data.json').json()['patch']\n embed = discord.Embed(\n url=link,\n title=f'Rivals of Aether Academy Frame Data - Updated for {patch}',\n description='Data extracted manually in-game and from dev-mode files by SNC. '\n 'Extra information provided by Menace13 and Youngblood. ')\n embed.set_thumbnail(url='https://i.imgur.com/nMS0QPT.png')\n await interaction.response.send_message(content=link, embed=embed)","title":""},{"docid":"be10cd8180521b550baf725a9dbc21d8","score":"0.5095544","text":"def app_documents(id):\n documents = Document.query.filter_by(application_id=id) # return a list with all values\n return render_template('app_documents.html', name=current_user.username, role=current_user.role_code,\n documents=documents) # name parameter send to html the value of the current logged_in user","title":""},{"docid":"e9b63e19004eb1170330d7bfdb4a8b8c","score":"0.5094836","text":"def get_index():\n\n # version of the data contained in the dataset description\n db = current.db\n qry = (db.published_datasets.id == db.dataset_files.dataset_id)\n val = dataset_query_to_json(qry, \n fields = [('published_datasets', 'publication_date'), \n ('published_datasets', 'zenodo_concept_id'), \n ('published_datasets', 'zenodo_record_id'), \n ('published_datasets', 'dataset_access'), \n ('published_datasets', 'dataset_embargo'), \n ('published_datasets', 'dataset_title'), \n ('published_datasets', 'most_recent'), \n ('dataset_files', 'checksum'),\n ('dataset_files', 'filename'),\n ('dataset_files', 'filesize')])\n \n # repackage the db output into a single dictionary per file\n entries = val['entries'].as_list()\n [r['published_datasets'].update(r.pop('dataset_files')) for r in entries]\n val['entries'] = [r['published_datasets'] for r in entries]\n \n # Find the hashes\n index_hash = hashlib.md5(json(val).encode('utf-8')).hexdigest()\n \n # Use the file hash of the static gazetteer geojson\n gazetteer_file = os.path.join(current.request.folder, 'static', 'files', 'gis', 'gazetteer.geojson')\n with open(gazetteer_file) as f:\n gazetteer_hash = hashlib.md5(f.read().encode('utf-8')).hexdigest()\n\n # Use the file hash of the static locations alias csv\n location_aliases_file = os.path.join(current.request.folder, 'static', 'files', 'gis', 'location_aliases.csv')\n with open(location_aliases_file) as f:\n location_aliases_hash = hashlib.md5(f.read().encode('utf-8')).hexdigest()\n \n return dict(hashes=dict(index=index_hash, \n gazetteer=gazetteer_hash,\n location_aliases=location_aliases_hash),\n index=val)\n\n \n \n # return the dictionary - this will be json serialised by the API when it is returned\n return val","title":""},{"docid":"5d0d755a343f7cf1ec9c811977d6f28d","score":"0.5094192","text":"def __call__(self, **kwargs):\n resp = get_docs(self._r_session,\n self.url,\n self.design_doc.encoder,\n **kwargs)\n return response_to_json_dict(resp)","title":""},{"docid":"d3890f22cd8cf051498cbe2771247b1f","score":"0.5093888","text":"def document(self):\n return publish_doctree(self.body)","title":""},{"docid":"9a91470aa975607e47481880d12436b1","score":"0.5089864","text":"def get(self):\n res = self.plag_dao.get_docs(page=int(request.args.get(\"page\", 1)),\n per_page=int(request.args.get(\"per_page\", 10)), all='all' in request.args)\n docs_info = dict(data=[d.to_dict() for d in res['data']], count=res['count'])\n print(docs_info)\n return Response(data=docs_info)","title":""},{"docid":"39f2c6a73418a355cd0021c7fde50a91","score":"0.50828326","text":"def getWikidataContent(self):","title":""}],"string":"[\n {\n \"docid\": \"792b5fddc5dab6657fbe27fdc6166895\",\n \"score\": \"0.63426816\",\n \"text\": \"def process_data(self):\\n\\n # Drivers License\\n if self.Data.DriversLicence is not None:\\n doc = Document.DriversLicense(None, self.Data.DriversLicence, self.photoImage)\\n self.Documents.append(doc)\\n # PASSPORT\\n if self.Data.Passport is not None:\\n doc = Document.Passport(None, self.Data.Passport, self.photoImage)\\n self.Documents.append(doc)\\n # Badge\\n if self.Data.Badge is not None:\\n doc = Document.Badge(None, self.Data.Badge, self.photoImage)\\n self.Documents.append(doc)\\n # INSURANCE\\n if self.Data.Insurance is not None:\\n doc = Document.Insurance(None, self.Data.Insurance, self.photoImage)\\n self.Documents.append(doc)\\n # CLAIM\\n if self.Data.Claim is not None:\\n doc = Document.InsuranceClaim(None, self.Data.Claim, self.photoImage)\\n self.Documents.append(doc)\\n # CONVERSATION\\n if self.Data.Conversation is not None:\\n self.Conversation = self.Data.Conversation.ListOfText\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8c7ff139f5f401b50255ef3eff805fb\",\n \"score\": \"0.6150794\",\n \"text\": \"def fetch(self):\\n next_batch_first_index = int(self.request.get('documentPointer', 0))\\n self.request.response.setHeader('X-Theme-Disabled', 'True')\\n return self.render_batch(next_batch_first_index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68b44b639903a0e3b0fe38b033a24ac9\",\n \"score\": \"0.612292\",\n \"text\": \"def document_retrieval(logger):\\n # override args\\n document_retrieval_main(args.database, 7, args.infile, args.outfile, args.path_wiki_titles, True, True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6342b18bcf0c51508a3c96256c1f27a\",\n \"score\": \"0.5973333\",\n \"text\": \"def document_detail(request, id):\\n\\n document = SavedData.objects.get(row_id=id)\\n abstract_list = []\\n for paragraph in document.abstracts.split('\\\\n'):\\n abstract_list.append(paragraph)\\n\\n return render(request, 'gui/document_detail.html', {\\n 'document': document, 'abstracts': abstract_list})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca3d20faa9020d8bba14297de7585234\",\n \"score\": \"0.58931184\",\n \"text\": \"def documents(res):\\n keys = ['description', 'source', 'usage']\\n global gresults\\n desc_translations = gresults[res]['desc_sentence_code']\\n translation = {k:v for k, v in gresults[res].items() if 'segmentation' in k or 'sentence' in k or 'translation' in k}\\n idiom = {k:v for k, v in gresults[res].items() if k not in translation.keys()}\\n idiomtitle = idiom['name']\\n for term in idiom:\\n if type(idiom[term]) is AttrList:\\n s = \\\"\\\\n\\\"\\n for item in idiom[term]:\\n s += item + \\\",\\\\n \\\"\\n idiom[term] = s\\n # fetch the movie from the elasticsearch index using its id\\n chengyu = Idiom.get(id=res, index='idioms_search')\\n chengyudic = chengyu.to_dict()\\n index = 0\\n description_dict = dict()\\n for key, value in chengyudic.items():\\n if 'translation' in key:\\n translation[key] = value\\n else:\\n idiom[key] = value\\n '''\\n if key == 'description':\\n print(value)\\n for v in value:\\n description_dict[str(index)] = v\\n index += 1\\n idiom = {k: v for k, v in idiom.items() if k not in translation.keys()}\\n \\n print(description_dict)\\n '''\\n # idiom['runtime'] = str(chengyudic['runtime']) + \\\" min\\\" if filmdic['runtime'] > -1 else \\\"Unknown\\\"\\n return render_template('page_targetArticle.html', idiom=idiom, name=idiomtitle, keys=keys, translation=translation)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31243f106593f9d8f7ff6d186de115f1\",\n \"score\": \"0.5892145\",\n \"text\": \"def documentsProcessing(self, personalData: PersonalData = PersonalData.all):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90dbae986b4555469cf978a47ec2ba05\",\n \"score\": \"0.5855406\",\n \"text\": \"async def docs(self, ctx: commands.Context, *args: Optional[str]) -> None:\\n\\n data = db_parser.search_for_docs(\\\" \\\".join(args))\\n if not data:\\n return await ctx.send(f\\\"{ctx.author.mention} No Results Found!\\\")\\n\\n topic, descp, argums, returs, link = data\\n embed = discord.Embed(title=topic, color=discord.Color.random())\\n embed.add_field(name=\\\"Description\\\",\\n value=f\\\"```\\\\n{descp}```\\\", inline=False)\\n embed.add_field(name=\\\"Arguments\\\",\\n value=f\\\"```python\\\\n{argums}```\\\", inline=False)\\n embed.add_field(\\n name=\\\"Returns\\\", value=f\\\"```python\\\\n{returs}```\\\", inline=False)\\n embed.add_field(name=\\\"Link\\\", value=link, inline=False)\\n await ctx.send(ctx.author.mention, embed=embed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03030ef76c56000a4b45898c9270674f\",\n \"score\": \"0.5854476\",\n \"text\": \"def database(request):\\n\\n #entries = Entry.objects.all().order_by('-rating') #Hier dus normalized?\\n documents = SavedData.objects.all().order_by('-normalized_number_of_mentions')\\n\\n return render(request, 'gui/database.html', {'database': documents})\\n # return render(request, 'interface/database.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bffabe261d821e4e8e5b5e13a955b3b\",\n \"score\": \"0.584822\",\n \"text\": \"def index(self):\\n self.log.info('Fetching document ids that match the criteria...')\\n doc_ids = self._get_doc_ids()\\n\\n self.log.info('Found documents: %d' % len(doc_ids))\\n\\n with ThreadPoolExecutor(max_workers=self.annotation_indexer_config.threads) as executor:\\n executor.map(self._process_document, doc_ids)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b2676797ad1ab06ccc8a81e6732dc76\",\n \"score\": \"0.58295107\",\n \"text\": \"def doc_text_datablock():\\n global api_doc_\\n\\n space_data = bpy.context.space_data\\n\\n try:\\n doc_text = bpy.data.texts['api_doc_']\\n space_data.text = doc_text\\n doc_text.clear()\\n except:\\n bpy.data.texts.new(name='api_doc_')\\n doc_text = bpy.data.texts['api_doc_']\\n space_data.text = doc_text\\n\\n doc_text.write(text=api_doc_)\\n return {'FINISHED'}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"874a90a3891dce0a220aa375504aa169\",\n \"score\": \"0.58218527\",\n \"text\": \"def index(self):\\r\\n self.subsection = 'Contents'\\r\\n return self.get_flatpage()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"874a90a3891dce0a220aa375504aa169\",\n \"score\": \"0.58218527\",\n \"text\": \"def index(self):\\r\\n self.subsection = 'Contents'\\r\\n return self.get_flatpage()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"874a90a3891dce0a220aa375504aa169\",\n \"score\": \"0.58218527\",\n \"text\": \"def index(self):\\r\\n self.subsection = 'Contents'\\r\\n return self.get_flatpage()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dce775017cf295e9f2413558e9cad1f\",\n \"score\": \"0.5771713\",\n \"text\": \"def index_documents(self, data):\\n\\t\\tbulk(self.client, actions=data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcda86bb55f1f1e6b89451750c227636\",\n \"score\": \"0.5751059\",\n \"text\": \"def index():\\n page = request.args.get('page', 1)\\n documents = documentcloud.search(DEFAULT_SEARCH, page=page)\\n return render_template('index.html', documents=documents)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec0baf7230eaa3ae2ecdb86646a898a9\",\n \"score\": \"0.57493937\",\n \"text\": \"def _get_doc_details(self, dbx):\\n result, response = dbx.paper_docs_download(\\n self.paper_id, ExportFormat.markdown)\\n if response.status_code == 200:\\n response.close()\\n else:\\n raise dropbox.exceptions.APIError\\n return (result.title, result.revision)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48f1c5ea49b6322a076aad4bc8c21fb8\",\n \"score\": \"0.5695987\",\n \"text\": \"def documents():\\n documents = Document.query.all() # return a list with all values\\n return render_template('documents.html', name=current_user.username, role=current_user.role_code, \\n documents=documents) # name parameter send to html the value of the current logged_in user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70afe7b7e7bfcae26bff735a82ac405e\",\n \"score\": \"0.56497496\",\n \"text\": \"def doc_list():\\n user = g.current_user\\n email = user.email\\n\\n #Find all the user's documents\\n docs = Page.objects(email=email, resource__page_number=1)\\n docs = docs.order_by('resource__title','resource__author')\\n \\n if not docs:\\n msg = \\\"Couldn't find user's documents\\\"\\n response = resource_not_found(msg)\\n return response\\n \\n #Collect document names into a response\\n works = []\\n for doc in docs:\\n work = {\\n \\\"title\\\": doc.resource.title,\\n \\\"author\\\": doc.resource.author,\\n \\\"language\\\": doc.resource.language\\n }\\n works.append(work)\\n \\n response = jsonify({\\\"works\\\": works})\\n return response, HTTPStatus.OK.value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97f83c15711b740a070a3cb4a3c51f70\",\n \"score\": \"0.56411767\",\n \"text\": \"def index(self):\\n return self.draw(self.requests())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0aa97ce39fa4d6ad81a893df92822ae\",\n \"score\": \"0.5613384\",\n \"text\": \"def documents(self):\\n for index in range(self.count()):\\n yield self.widget(index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0aa97ce39fa4d6ad81a893df92822ae\",\n \"score\": \"0.5613384\",\n \"text\": \"def documents(self):\\n for index in range(self.count()):\\n yield self.widget(index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c65d04f9277270ff16897b1152a078ca\",\n \"score\": \"0.5607856\",\n \"text\": \"def readDocs(self):\\n DataPathList = glob.glob(self.pathDocs+'*.html')\\n DataPathList.sort()\\n\\n self.docs = []\\n \\n h = 0\\n for docPath in DataPathList:\\n f = codecs.open(docPath, 'r')\\n text = get_text(f.read())\\n\\n self.docs.append({\\n 'id': str(h),\\n 'text': self.removePonctuation(re.split('\\\\s|\\\\n',text))\\n })\\n\\n print(re.split('\\\\s|\\\\n',text))\\n # print(re.split('\\\\s|, |\\\\*|\\\\n',text))\\n\\n h += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac96580a00be97566b2eef2311e380e8\",\n \"score\": \"0.5547341\",\n \"text\": \"def main():\\n creds = None\\n # The file token.json stores the user's access and refresh tokens, and is\\n # created automatically when the authorization flow completes for the first\\n # time.\\n if os.path.exists('token.json'):\\n creds = Credentials.from_authorized_user_file('token.json', SCOPES)\\n # If there are no (valid) credentials available, let the user log in.\\n if not creds or not creds.valid:\\n if creds and creds.expired and creds.refresh_token:\\n creds.refresh(Request())\\n else:\\n flow = InstalledAppFlow.from_client_secrets_file(\\n 'credentials.json', SCOPES)\\n creds = flow.run_local_server(port=8000)\\n # Save the credentials for the next run\\n with open('token.json', 'w') as token:\\n token.write(creds.to_json())\\n\\n service = build('docs', 'v1', credentials=creds)\\n\\n # Retrieve the documents contents from the Docs service.\\n document = service.documents().get(documentId=DOCUMENT_ID).execute()\\n\\n document_data = [\\n {\\n 'text': \\\"Document created by MayankDoc\\\",\\n 'type': \\\"watermark\\\",\\n },\\n {\\n 'text': \\\"Growth Roadmap Review: 10:30am-11:30am\\\",\\n 'type': \\\"event\\\",\\n },\\n { \\n 'text': \\\"Growth Roadmap Review\\\",\\n 'type': \\\"h1\\\",\\n },\\n {\\n 'text': \\\"Agenda\\\",\\n 'type': \\\"h2\\\",\\n },\\n {\\n 'text': \\\"Roadmap Study Hall - 15 minutes\\\",\\n 'type': \\\"p\\\",\\n },\\n {\\n 'text': \\\"Roadmap Discussion - 20 minutes\\\",\\n 'type': \\\"p\\\",\\n },\\n {\\n 'text': \\\"Backlog Review - 20 minutes\\\",\\n 'type': \\\"p\\\",\\n },\\n {\\n 'text': \\\"Follow Ups\\\",\\n 'type': \\\"h2\\\",\\n },\\n {\\n 'text': \\\"\\\",\\n 'type': \\\"check\\\",\\n },\\n {\\n 'text': \\\"Roadmap\\\",\\n 'type': \\\"h2\\\",\\n },\\n {\\n 'text': \\\"Channels\\\",\\n 'type': \\\"p\\\",\\n },\\n {\\n 'text': \\\"Rose Liu\\\",\\n 'type': \\\"request\\\",\\n },\\n {\\n 'text': \\\"👍\\\",\\n 'type': 'emoji',\\n 'data': ['Mayank Jain', 'Yee Chen', 'Mike Jiao'],\\n },\\n {\\n 'text': \\\"Core Growth\\\",\\n 'type': \\\"p\\\",\\n },\\n {\\n 'text': \\\"Mike Jiao\\\",\\n 'type': \\\"request\\\",\\n },\\n {\\n 'text': \\\"👍\\\",\\n 'type': 'emoji',\\n 'data': [],\\n },\\n {\\n 'text': \\\"International\\\",\\n 'type': \\\"p\\\",\\n },\\n {\\n 'text': \\\"Jason Lee\\\",\\n 'type': \\\"request\\\",\\n },\\n {\\n 'text': \\\"👍\\\",\\n 'type': 'emoji',\\n 'data': [],\\n },\\n {\\n 'text': \\\"SEO\\\",\\n 'type': \\\"p\\\",\\n },\\n {\\n 'text': \\\"Chuck Kao\\\",\\n 'type': \\\"request\\\",\\n },\\n {\\n 'text': \\\"👍\\\",\\n 'type': 'emoji',\\n 'data': [],\\n },\\n {\\n 'text': \\\"Key Discussion\\\",\\n 'type': \\\"h2\\\",\\n },\\n {\\n 'text': \\\"Can we do anything to accelerate subreddit notifications?\\\",\\n 'type': \\\"discussion\\\",\\n },\\n {\\n 'text': \\\"Action Items\\\",\\n 'type': \\\"h2\\\",\\n },\\n {\\n 'text': \\\"\\\",\\n 'type': \\\"check\\\",\\n },\\n {\\n 'text': \\\"Signoff\\\",\\n 'type': \\\"h2\\\",\\n },\\n {\\n 'text': \\\"KD Bhulani\\\",\\n 'type': \\\"signoff\\\",\\n },\\n {\\n 'text': \\\"Vee Sahgal\\\",\\n 'type': \\\"signoff\\\",\\n },\\n {\\n 'text': \\\"Yee Chen\\\",\\n 'type': \\\"signoff\\\",\\n }\\n ]\\n\\n requests = []\\n\\n for item in document_data:\\n docs_item = translate_to_doc(item)\\n requests += docs_item\\n\\n print(requests[::-1])\\n\\n result = service.documents().batchUpdate(\\n documentId=DOCUMENT_ID, body={'requests': requests[::-1]}).execute()\\n\\n print('The title of the document is: {}'.format(document.get('title')))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e99ad10bc90a3e0e0437d86dc2bdd01\",\n \"score\": \"0.5540074\",\n \"text\": \"def documents(self):\\n logging.info(\\\"Extracting docs from [%s]\\\", self._wikinews_archive)\\n file_list = glob.glob(self._wikinews_archive)\\n assert file_list, self._wikinews_archive\\n for archive in file_list:\\n with bz2file.open(archive, \\\"rt\\\", encoding=\\\"utf-8\\\", errors=\\\"strict\\\") as xf:\\n # One line of json as produced by wikiextractor.\\n for line in xf:\\n record = json.loads(line)\\n\\n # Extract page title.\\n title = html.unescape(record[\\\"title\\\"])\\n curid = record[\\\"id\\\"] # pageid, e.g. 73052\\n revid = record[\\\"revid\\\"] # revision, e.g. 730271\\n url = record[\\\"url\\\"] # e.g. https://de.wikinews.org/wiki?curid=73052\\n logging.debug(\\\"Got title: %s\\\", title)\\n\\n # Skip pages that don't have text.\\n wiki_doc = record.get(\\\"text\\\")\\n if not wiki_doc:\\n self._counter[\\\"no text\\\"] += 1\\n self._filtered_no_text.append(url)\\n logging.debug(\\\"Skip: no text element\\\")\\n continue\\n\\n # Apply manual fixes.\\n wiki_doc = _apply_wiki_overrides(wiki_doc, url)\\n\\n # Create internal document identifier.\\n docid = f\\\"{self._language}-{curid}\\\"\\n logging.debug(\\\"Found (%s): %s\\\", docid, title)\\n self._counter[\\\"found\\\"] += 1\\n\\n yield docid, title, url, curid, revid, wiki_doc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7345e6b67d4c52977714a913deed0b8b\",\n \"score\": \"0.55391085\",\n \"text\": \"def render_batch(self, first):\\n items = self.items.get_batch(first, self.batchsize)\\n if items:\\n return self.previews_template(\\n previews=map(self.items.get_infos_for, items))\\n else:\\n # We have to return an empty string if we have no more documents\\n # to render. Otherwise plone.protect will log a error-warning:\\n # WARNING plone.protect error parsing dom, failure to add csrf\\n # token to response.\\n return ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27c60046b0ad130539897a9c11716c5f\",\n \"score\": \"0.5526614\",\n \"text\": \"def fetch(self):\\n self.request.response.setHeader('X-Theme-Disabled', 'True')\\n # The HTML stripped in order to have empty response content when\\n # there are no tags at all, so that diazo does not try to\\n # parse it.\\n if int(self.request.get('documentPointer', 0)) >= self.number_of_documents():\\n # We have to return an empty string if we have no more documents\\n # to render. Otherwise plone.protect will log a error-warning:\\n # WARNING plone.protect error parsing dom, failure to add csrf\\n # token to response\\n return ''\\n return self.previews_template().strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84eadb4d5c6c853026c26c372024a1c0\",\n \"score\": \"0.5504719\",\n \"text\": \"def gener_doc(dataset):\\n # check or create doc folder\\n folder = get_dataset_folder(dataset.dataset_id) + '/docs'\\n if not os.path.exists(folder):\\n os.makedirs(folder)\\n os.makedirs(folder + '/_build')\\n os.makedirs(folder + '/_static')\\n os.makedirs(folder + '/_templates')\\n\\n # generate conf.py\\n render('conf.txt', folder + '/conf.py', dataset=dataset)\\n render('make.bat', folder + '/make.bat', dataset=dataset)\\n render('makefile.txt', folder + '/Makefile', dataset=dataset)\\n\\n # generate index\\n render('index.rst', folder + '/index.rst', dataset=dataset)\\n\\n # dataset data and features\\n search = get_search_rounds(dataset.dataset_id)\\n if len(search) > 0:\\n best = get_best_models(dataset.dataset_id)\\n best_pp = get_best_pp(dataset.dataset_id)\\n # separate models (level 0) from ensembles (level 1)\\n best1 = [b for b in best if b['level'] == 1]\\n best2 = [b for b in best if b['level'] == 2]\\n print(len(best1), len(best2))\\n print(best1[:2])\\n render('dataset.rst', folder + '/dataset.rst', dataset=dataset, best1=best1, best2=best2, best_pp=best_pp,\\n n_searches1=len(search[search.level == 1]),\\n n_searches2=len(search[search.level == 2]))\\n\\n # then for the best rounds\\n N_ROUNDS = 5\\n for round_id in list([b['round_id'] for b in best1[:N_ROUNDS]]) + list([b['round_id'] for b in best2[:N_ROUNDS]]):\\n round = search[search.round_id == int(round_id)].to_dict(orient='records')[0]\\n pipeline = [s for s in round['pipeline'] if s[0] not in ['NO-SCALE', 'PASS']]\\n params = get_round_params(search, round_id)\\n features = get_feature_importance(dataset.dataset_id, round_id)\\n render('round.rst', folder + '/round_%s.rst' % round_id, dataset=dataset, round=round,\\n pipeline=pipeline, features=features, params=params, cols=params.keys())\\n else:\\n # return render_template('dataset.html', dataset=dataset, n_searches1=0)\\n render('dataset.rst', folder + '/dataset.rst', dataset=dataset, n_searches1=0)\\n\\n # then generate html and pdf with make\\n if sys.platform == 'linux':\\n subprocess.call(['sh', '../scripts/gen_doc.sh', os.path.abspath(get_dataset_folder(dataset.dataset_id)+'/docs')])\\n else:\\n os.system('call ../scripts/gen_doc ' + os.path.abspath(get_dataset_folder(dataset.dataset_id)+'/docs'))\\n\\n # generate zip file of the html site\\n with zipfile.ZipFile(get_dataset_folder(dataset.dataset_id) + '/doc.zip', 'w') as z:\\n root = get_dataset_folder(dataset.dataset_id) + '/docs/_build/html/'\\n for dir in ['', '_static/', '_images/', '_sources/']:\\n for f in glob.glob(root + dir + '*.*'):\\n z.write(f, dataset.dataset_id + '/' + dir + os.path.basename(f))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47c1681b45562e77bd47a79588dbfb7e\",\n \"score\": \"0.5497769\",\n \"text\": \"def get_results():\\n documents = business_layer.BusinessLayer().get_results(request.args)\\n return render_template('titles.html', documents=documents)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85b2a3a202118eceb26728f5dabeb650\",\n \"score\": \"0.549089\",\n \"text\": \"def get_data():\\n return [\\n\\t\\t{\\n\\t\\t\\t\\\"label\\\": _(\\\"OCR Read\\\"),\\n\\t\\t\\t\\\"items\\\": [\\n\\t\\t\\t\\t{\\n\\t\\t\\t\\t\\t\\\"type\\\": \\\"doctype\\\",\\n\\t\\t\\t\\t\\t\\\"name\\\": \\\"OCR Read\\\",\\n\\t\\t\\t\\t\\t\\\"description\\\": _(\\\"OCR Read\\\"),\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\t]\\n\\t\\t},\\n\\t\\t{\\n\\t\\t\\t\\\"label\\\": _(\\\"OCR Import\\\"),\\n\\t\\t\\t\\\"items\\\": [\\n\\t\\t\\t\\t{\\n\\t\\t\\t\\t\\t\\\"type\\\": \\\"doctype\\\",\\n\\t\\t\\t\\t\\t\\\"name\\\": \\\"OCR Import\\\",\\n\\t\\t\\t\\t\\t\\\"description\\\": _(\\\"OCR Import\\\"),\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\t]\\n\\t\\t}\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1c92d9525875014e93a9664c3d37d4f\",\n \"score\": \"0.54637945\",\n \"text\": \"def load_document(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03108348e8effdc7a8f959723fc822fa\",\n \"score\": \"0.54457164\",\n \"text\": \"def documentations():\\n return render_template('documentations.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95528f562106ea181314684df03a5059\",\n \"score\": \"0.54443765\",\n \"text\": \"def get_all_docs(self):\\n request = urllib2.Request(self.url_address +'_all_docs')\\n request.get_method = lambda : 'GET'\\n request.add_header('Content-Type', 'text/plain')\\n url = self.opener.open(request)\\n return json.loads(url.read())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1e12668f86567450fb00a986101e681\",\n \"score\": \"0.54393756\",\n \"text\": \"def get_data(self):\\n\\n self.code_names, self.categories = self.app.get_data()\\n cur = self.app.conn.cursor()\\n sql = \\\"select owner from code_image union select owner from code_text union select owner from code_av\\\"\\n cur.execute(sql)\\n result = cur.fetchall()\\n self.coders = [\\\"\\\"]\\n for row in result:\\n self.coders.append(row[0])\\n cur.execute('select id, length(fulltext) from source where (mediapath is Null or substr(mediapath,1,5)=\\\"docs:\\\")')\\n self.file_summaries = cur.fetchall()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78ebb6d8137ed79dddce842297ec7844\",\n \"score\": \"0.5438988\",\n \"text\": \"def pull( request ) :\\n\\n log = logging.getLogger(__name__)\\n\\n log.debug( 'pulls the database content for an entry' )\\n\\n idx = request.GET.get('id', 'None')\\n\\n if idx == 'None' : return JsonResponse({'error_flag':True})\\n\\n obj = models.Element.objects.get( id=int(idx) )\\n document_objects = models.Document.objects.filter(element=obj)\\n\\n context = { 'id' : idx,\\n 'name': obj.name,\\n 'description' : obj.description,\\n 'category' : obj.category.id,\\n 'document' : list(document_objects.values_list( 'id',\\n 'type__name',\\n 'author__username',\\n 'link',\\n 'date')),\\n 'error_flag' : False}\\n\\n print context\\n return JsonResponse(context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0224254bb37161210cb0afaaa062fe1\",\n \"score\": \"0.54367024\",\n \"text\": \"def _process_document(self, src_doc_id):\\n\\n self.log.info('Processing document with id: ' + src_doc_id)\\n doc = self.annotation_indexer_config.source_indexer.get_doc(src_doc_id)\\n\\n\\n # check whether there is document content to process\\n if (isinstance(doc, dict) and (self.annotation_indexer_config.source_text_field not in doc.keys() or doc[self.annotation_indexer_config.source_text_field] is None)) or \\\\\\n len(doc[self.annotation_indexer_config.source_text_field]) < self.MIN_TEXT_LEN:\\n self.log.info('- skipping: no content')\\n return\\n \\n try:\\n # check whether the document has been already processed\\n if self.annotation_indexer_config.skip_doc_check and self._document_already_processed(doc):\\n self.log.info('doc id : ' + str(src_doc_id) + ' - skipping: document already processed')\\n return\\n \\n # get the text\\n doc_text = doc[self.annotation_indexer_config.source_text_field]\\n \\n # query the NLP service and retrieve back the annotations\\n self.log.info('- querying the NLP service')\\n \\n nlp_response = self.annotation_indexer_config.nlp_service.query(text=doc_text)\\n\\n self.log.info(\\\"Finished processing NLP for document with id: \\\" + src_doc_id)\\n\\n if \\\"result\\\" in nlp_response.keys():\\n result = nlp_response[\\\"result\\\"]\\n\\n if 'annotations' not in result.keys() or result['annotations'] is None or result is None:\\n self.log.error(\\\" - no annotations available in the NLP result payload\\\")\\n return \\n\\n if 'entities' not in result[\\\"annotations\\\"].keys() or result[\\\"annotations\\\"][\\\"entities\\\"] is None :\\n self.log.error(\\\" - no annotation entities available in the NLP result payload\\\")\\n return\\n\\n result = result['annotations']['entities']\\n \\n elif \\\"entities\\\" in nlp_response.keys():\\n # Entities are present alone only when using GATE-NLP MODE ENDPOINT\\n if nlp_response[\\\"entities\\\"] is not None:\\n result = nlp_response[\\\"entities\\\"]\\n else:\\n self.log.error(\\\" - no annotation entities available in the NLP result payload\\\")\\n return\\n\\n elif \\\"result\\\" not in nlp_response.keys() or \\\"entities\\\" not in nlp_response.keys():\\n self.log.error(\\\" - no result payload returned from NLP service\\\")\\n return\\n\\n if self.annotation_indexer_config.nlp_service.use_bulk_indexing:\\n self._index_annotations_bulk(result, doc, src_doc_id)\\n else:\\n self._index_annotations(result, doc, src_doc_id)\\n \\n except Exception as e:\\n self.log.error(repr(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76433654d516e453aadfb4b412e834bb\",\n \"score\": \"0.54329705\",\n \"text\": \"def docs(self, request):\\n return self.doc.generate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43e19798b1d61d4d1834ae1278fbee6f\",\n \"score\": \"0.54181135\",\n \"text\": \"def _fetch_docs(self, user, slice_data=True, format_docs=True, *args, **kwargs):\\n query = self._construct_query(*args, **kwargs)\\n docs = self.model.objects.find_by_user(user, **query)\\n if slice_data:\\n start = request.args.get('start', 0)\\n limit = request.args.get('limit', DEFAULT_LIMIT)\\n docs = docs[start:start + limit]\\n if format_docs:\\n docs = self._format_multiple_docs(docs)\\n return docs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ab3ca959c96b98d815a2ebe5a523678\",\n \"score\": \"0.5410954\",\n \"text\": \"def document(document_id):\\n return render_template('document.html', document_id=document_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3ff7964c2c4336a5e0d2b3036465aa0\",\n \"score\": \"0.5407521\",\n \"text\": \"def _ListAllDocuments(self):\\n feed = self.gd_client.GetDocumentListFeed()\\n self._PrintFeed(feed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f704914d586e2c74825f087aa4672fce\",\n \"score\": \"0.5399946\",\n \"text\": \"def action_show_documents(self):\\n ids = []\\n approver = self.mapped('approver_ids').filtered(\\n lambda approver: approver.user_id == self.env.user\\n )\\n print(approver, '==============approver')\\n print(approver.approve_attachment_id,'===========approve_attachment_id')\\n print(approver.refused_attachment_id,'===========refused_attachment_id')\\n print(approver.cancel_attachment_id,'===========cancel_attachment_id')\\n\\n if approver.approve_attachment_id:\\n ids.append(approver.approve_attachment_id.id)\\n if approver.refused_attachment_id:\\n ids.append(approver.refused_attachment_id.id)\\n if approver.cancel_attachment_id:\\n ids.append(approver.cancel_attachment_id.id)\\n print(ids, '==============ids')\\n return {\\n 'name': _('Documents'),\\n 'type': 'ir.actions.act_window',\\n 'view_mode': 'tree,form',\\n 'view_id': False,\\n 'res_model': 'ir.attachment',\\n 'domain': [('id', 'in', ids)]\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3eb126e1f1e4160d610ca9b6f059d1c9\",\n \"score\": \"0.5393111\",\n \"text\": \"def parse_docs_from_page(self, page_url: str, page_text: str) -> Iterable[Document]:\\n\\n # parse html response\\n page2 = requests.get(page_url)\\n soup2 = bs4.BeautifulSoup(page2.content, 'html.parser')\\n doc_num = []\\n pdf = []\\n doc_title = []\\n datelist = []\\n doctype = []\\n parsed_docs = []\\n webpart2 = soup2.find(\\\"div\\\", {\\\"class\\\": \\\"skin-pane2 col-md-8\\\"})\\n meta = webpart2.find_all(\\\"div\\\")\\n for row in meta:\\n if ((remove_html_tags((str(row))).isspace()) or not remove_html_tags((str(row)))):\\n continue\\n for cell in row.find_all('p'):\\n # print(cell.find(\\\"strong\\\"))\\n words = ''\\n links = cell.find_all(\\\"a\\\")\\n link_list = list(links)\\n # print(links)\\n nums = []\\n pdf_links = [link['href'] for link in link_list if \\\"pdf\\\" in link['href'] or \\\"aspx\\\" in link['href']]\\n if not pdf_links:\\n continue\\n pdf.append(str(pdf_links[0]))\\n # print(pdf_links)\\n words = remove_html_tags((str(cell.find(\\\"strong\\\")))).encode('ascii', 'ignore').decode('ascii').lstrip(\\n \\\" \\\").rstrip(\\\" \\\")\\n words = \\\" \\\".join(words.split())\\n # print(words)\\n # else:\\n # print(cell)\\n doc_num.append(words)\\n pdf.append(str(pdf_links[0]))\\n title = [text for text in cell.find_all(text=True) if text.parent.name != \\\"strong\\\"]\\n doc_title.append(title)\\n pub_links = []\\n [pub_links.append(x) for x in pdf if x not in pub_links]\\n document_name = []\\n [document_name.append(x) for x in doc_num if x not in document_name]\\n document_type = []\\n [document_type.append(' '.join(x.split()[0:2])) for x in document_name]\\n document_number = []\\n [document_number.append(' '.join(x.split()[2:])) for x in document_name]\\n document_title = []\\n [document_title.append(x) for x in doc_title if x not in document_title]\\n document_title = [item for sublist in document_title for item in sublist]\\n document_title = [str(item).encode('ascii', 'ignore').decode('ascii').lstrip(\\\" \\\").rstrip(\\\" \\\") for item in\\n document_title]\\n final = list(itertools.zip_longest(document_type, document_number, document_title, pub_links))\\n final = [list(x) for x in final]\\n for item in final:\\n doc_name = item[0]+' '+item[1]\\n if (item[2] is None):\\n doc_title=\\\"\\\"\\n else:\\n doc_title = item[2]\\n doc_num = item[1]\\n doc_type = item[0]\\n publication_date = \\\"N/A\\\"\\n if item[3].startswith(\\\"https\\\"):\\n cac_login_required=True\\n url = item[3]\\n url = url.replace(\\\" \\\",\\\"%20\\\")\\n else:\\n cac_login_required=False\\n url = \\\"https://www.usar.army.mil\\\"+item[3]\\n url = url.replace(\\\" \\\",\\\"%20\\\")\\n pdf_di = DownloadableItem(doc_type='pdf', web_url=url)\\n version_hash_fields = {\\n \\\"item_currency\\\": str(url).split('/')[-1], # version metadata found on pdf links\\n \\\"document_title\\\": doc_title.strip(),\\n \\\"document_number\\\": doc_num.strip()\\n }\\n if (str(doc_type).startswith(\\\"USAR\\\") == False):\\n doc_title = doc_name\\n doc_num = \\\"\\\"\\n doc_type = \\\"USAR Doc\\\"\\n version_hash_fields = {\\n \\\"item_currency\\\": str(url).split('/')[-1], # version metadata found on pdf links\\n \\\"document_title\\\": doc_title.strip(),\\n \\\"document_number\\\": doc_num.strip()}\\n\\n doc = Document(\\n doc_name=doc_name.strip(),\\n doc_title=re.sub('\\\\\\\\\\\"', '', doc_title),\\n doc_num=doc_num.strip(),\\n doc_type=doc_type.strip(),\\n publication_date=publication_date,\\n cac_login_required=cac_login_required,\\n crawler_used=\\\"Army_Reserve\\\",\\n source_page_url=page_url.strip(),\\n version_hash_raw_data=version_hash_fields,\\n downloadable_items=[pdf_di]\\n )\\n\\n parsed_docs.append(doc)\\n return parsed_docs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1df67cc8547c85426ee5fc1cb079396\",\n \"score\": \"0.53898543\",\n \"text\": \"def do_GET(self):\\n\\n try:\\n uri = None\\n query_string = None\\n if self.path and self.path[0] == '/':\\n uri, sep, query_string = self.path[1:].partition('?')\\n \\n tab = self.server.window.get_tab_from_location(Gio.File.new_for_uri(uri))\\n if tab:\\n buf = tab.get_view().get_buffer()\\n md_text = buf.get_text(buf.get_start_iter(), buf.get_end_iter(), False)\\n try:\\n md_html = markdown.markdown(md_text, self.server.markdown_extensions)\\n except NameError:\\n self.wfile.write(\\n \\\"Python-Markdown not installed. Please install \\\"\\n \\\"before using this plugin. For up-to-date code, \\\"\\n \\\"install from https://github.com/waylan/Python-Markdown\\\")\\n return\\n self.wfile.write(\\\"<head>\\\")\\n self.wfile.write(get_style(self.css_path.format(self.general_css)))\\n highlight_css = None\\n if self.server.settings:\\n highlight_css = get_style(self.css_path.format(\\n self.server.settings.get_string('code-theme')))\\n if not highlight_css:\\n highlight_css = get_style(\\n self.css_path.format(self.default_highlight_css))\\n if query_string: # Override code-theme if provided in query\\n params = urlparse.parse_qs(query_string)\\n for css_name in params.get('style', []):\\n highlight_css = get_style(self.css_path.format(css_name))\\n if highlight_css:\\n break\\n self.wfile.write(highlight_css)\\n self.wfile.write(\\\"</head>\\\")\\n self.wfile.write(md_html)\\n else:\\n self.wfile.write(\\\"Could not match file {0}.\\\".format(uri))\\n except Exception as err:\\n self.wfile.write(\\\"Exception occurred while rendering: \\\" + str(err))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d346dc4722e86adf58cf71fd840de9b\",\n \"score\": \"0.53839433\",\n \"text\": \"def view(self, files):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"185eba95491f7916c9d7f81310623e34\",\n \"score\": \"0.5372585\",\n \"text\": \"def GetDocument(self, *args, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e9660cfa662d730db8123ba754f479e\",\n \"score\": \"0.53709745\",\n \"text\": \"def loadDocument(self, filename):\\n \\n #for index in range(self.mdiArea.count()):\\n \\n # document = self.mdiArea.widget(index)\\n # if document:\\n # if filename == document.filename:\\n # self.mdiArea.setCurrentIndex(index)\\n # document.reload()\\n # return\\n # Else load from file and create new document tab.\\n\\n self.statusBar.showMessage(self.tr(\\\"Loading...\\\"), 2500)\\n\\n document = Document(filename, self)\\n index = self.mdiArea.addDocument(document)\\n #self.mdiArea.setCurrentIndex(index)\\n index.show()\\n\\n # After loading a conenction file, it is possible to refresh the current module.\\n self.refreshAct.setEnabled(True)\\n self.statusBar.showMessage(self.tr(\\\"Successfully loaded file\\\"), 2500)\\n\\n # Enable close action\\n #self.closeAct.setEnabled(self.mdiArea.count())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c638ea0d31fcdba25e946363a273ecb\",\n \"score\": \"0.53628904\",\n \"text\": \"def onOpen(self):\\n \\n if self.document_type == 1:\\n\\n my_list=[]\\n #self.actionUpdate.triggered.connect(self.onEdit)\\n fileName = \\\"VAS\\\"\\n the_doc = VAS_view(self)\\n self.count = self.count +1 \\n the_doc.fileName = fileName + str(self.count)\\n sub = self.mdiArea.addSubWindow(the_doc)\\n sub.show()\\n \\n #self.loadDocument(fileName)\\n\\n if self.document_type == 2:\\n fileName = \\\"VCCP\\\"\\n the_doc = VCCP_view(self)\\n self.count = self.count + 1 \\n the_doc.fileName = fileName + str(self.count) \\n sub = self.mdiArea.addSubWindow(the_doc)\\n sub.show()\\n #self.loadDocument(fileName)\\n\\n if self.document_type == 3:\\n fileName = 'VCRH'\\n the_doc = VCRH_Edit(self)\\n self.count = self.count + 1 \\n the_doc.fileName = fileName + str(self.count) \\n sub = self.mdiArea.addSubWindow(the_doc)\\n sub.show()\\n\\n if self.document_type == 4:\\n fileName = 'VCRLD'\\n the_doc = VCRLD_Edit(self)\\n self.count = self.count + 1 \\n the_doc.fileName = fileName + str(self.count) \\n sub = self.mdiArea.addSubWindow(the_doc)\\n sub.show()\\n\\n if self.document_type == 5:\\n fileName = 'VProj'\\n the_doc = VProj_Edit(self)\\n self.count = self.count + 1 \\n the_doc.fileName = fileName + str(self.count) \\n sub = self.mdiArea.addSubWindow(the_doc)\\n sub.show()\\n\\n if self.document_type == 6:\\n fileName = 'EditLayers'\\n the_doc = RPE_Edit(self)\\n self.count = self.count + 1 \\n the_doc.fileName = fileName + str(self.count) \\n sub = self.mdiArea.addSubWindow(the_doc)\\n sub.show()\\n\\n if self.document_type == 7:\\n fileName = 'Split_Section'\\n the_doc = splitSections(self)\\n self.count = self.count+1\\n the_doc.fileName = fileName + str(self.count) \\n sub = self.mdiArea.addSubWindow(the_doc)\\n sub.show()\\n\\n if self.document_type == 8:\\n fileName = 'Tweak_Section'\\n the_doc = tweakSections(self)\\n self.count = self.count+1\\n the_doc.fileName = fileName + str(self.count) \\n sub = self.mdiArea.addSubWindow(the_doc)\\n sub.show()\\n\\n if self.document_type == 9:\\n fileName = 'Move_Section'\\n the_doc = moveSections(self)\\n self.count = self.count+1\\n the_doc.fileName = fileName + str(self.count) \\n sub = self.mdiArea.addSubWindow(the_doc)\\n sub.show()\\n\\n if self.document_type == 10:\\n fileName = 'Copy_Section'\\n the_doc = copySections(self)\\n self.count = self.count+1\\n the_doc.fileName = fileName + str(self.count) \\n sub = self.mdiArea.addSubWindow(the_doc)\\n sub.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f505aba27a6032eef6309e79387e2be2\",\n \"score\": \"0.53608304\",\n \"text\": \"def index():\\n return render_template('records.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"681341909be97b3e0051c95525b9b5ec\",\n \"score\": \"0.535427\",\n \"text\": \"def make_docs_data_view(api_info):\\n return get_schema_view(\\n api_info,\\n generator_class=ApiSchemaGenerator,\\n public=True,\\n permission_classes=(permissions.AllowAny,),\\n ).without_ui(cache_timeout=get_docs_cache_timeout())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e4fb43f1b9662e7a24eadd19b6df66d\",\n \"score\": \"0.53498733\",\n \"text\": \"def processUserRequest(documentList):\\n queryWordList = getQueryWordListFromUser()\\n numOfDocs = int(input('How many documents to retrieve?\\\\n'))\\n return displayResults(queryWordList,documentList,numOfDocs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a6d7a5f4072b977e9aaea891d0c1f29\",\n \"score\": \"0.5340088\",\n \"text\": \"def index (request):\\n try:\\n a = Article.objects.get (title=\\\"HT Wiki\\\")\\n return preview (request, a.id)\\n except ObjectDoesNotExist:\\n return preview (request, -1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b98a2dae2b656389fd5c05f3e8611fb\",\n \"score\": \"0.5321335\",\n \"text\": \"def make_index(self, doc_name, protocol, number_ini, number_fin):\\n temp = string.ascii_uppercase\\n upper = temp.replace(\\\"N\\\", \\\"NÑ\\\")\\n\\n document = word.Documento(doc_name)\\n\\n for letter in upper:\\n data = self.database.get_table_by_letter(protocol, letter,\\n number_ini, number_fin)\\n if data:\\n page = []\\n for doc in data:\\n number = doc[0]\\n body = doc[1]\\n if doc[2] == \\\"\\\":\\n body += \\\". \\\"\\n else:\\n body += \\\"; \\\"\\n body += doc[2]\\n body += \\\". \\\"\\n body += doc[3]\\n body += \\\".\\\"\\n page.append([number, body])\\n document.write_page(letter, page)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa1a2aad44cc8df79f8891cfaebb4069\",\n \"score\": \"0.5319376\",\n \"text\": \"def IndexReader(self, dir):\\n output_details_review_name = \\\"details_review_output.pkl\\\"\\n output_index_file_name =\\\"dictionary_index.pkl\\\"\\n self.dictionary_reviews = save_obj(dir+\\\"//\\\"+output_details_review_name)\\n self.dictionary_text = save_obj(dir+\\\"//\\\"+output_index_file_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b692e1dec4c083a6b11fdcbe76f652e\",\n \"score\": \"0.5308543\",\n \"text\": \"def documents(self):\\n catalog = self.context.portal_catalog\\n query = {'isWorkingCopy': 0,\\n 'path': {'depth': 1,\\n 'query': '/'.join(self.context.getPhysicalPath())},\\n 'portal_type': ['opengever.document.document',\\n 'ftw.mail.mail']}\\n documents = catalog(query)[:10]\\n\\n document_list = [{\\n 'Title': document.Title,\\n 'getURL': document.getURL,\\n 'alt': document.document_date and\\n document.document_date.strftime('%d.%m.%Y') or '',\\n 'css_class': get_css_class(document),\\n 'portal_type': document.portal_type,\\n } for document in documents]\\n\\n return document_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c94e5011fe4810ba5178b7a261cd0d67\",\n \"score\": \"0.53033286\",\n \"text\": \"def get(self, request):\\n\\n records = Document.objects.all()\\n uploaded_files = []\\n for record in records:\\n uploaded_files.append(\\n {\\n \\\"id\\\": record.id,\\n \\\"file_name\\\": str(record.document).split(\\\"/\\\")[1],\\n \\\"uploaded_at\\\": record.uploaded_at,\\n }\\n )\\n response = render(\\n request,\\n \\\"data_enrichment/list_view.html\\\",\\n {\\n \\\"uploaded_files\\\": uploaded_files,\\n },\\n )\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cfcdecf1ac8caba4681a4525a2726b0\",\n \"score\": \"0.5300169\",\n \"text\": \"def user_documents(request, userid):\\n logger.debug('reading ' + userid + '\\\\'s documents.')\\n documents, remains = Document.objects.recent(userid)\\n context = {}\\n context['documents'] = documents\\n context['hasMoreDocuments'] = remains > 0\\n context['page'] = 'my'\\n return render(request, 'document/mydocuments.html', context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e738d6ffcdbdbad1bd3f5425ddb61587\",\n \"score\": \"0.52995944\",\n \"text\": \"def load_main_page(self, domain_filter=None):\\n\\n unsorted_docs = {}\\n req = requests.get(IHE_TF_URL)\\n if req.status_code == 200:\\n soup = BeautifulSoup(req.text, \\\"html5lib\\\")\\n links = list(filter(lambda x: x.get(\\\"href\\\"), soup.find_all(\\\"a\\\")))\\n pdf_list = list(filter(lambda x: x.get(\\\"href\\\").endswith(\\\".pdf\\\"), links))\\n\\n print(\\\"Get information about documents\\\")\\n for link in pdf_list:\\n docinfo = self.get_infos(link.text, link.get(\\\"href\\\"), domain_filter)\\n unsorted_docs[docinfo[\\\"filename\\\"]] = docinfo\\n print(f\\\"\\\\n{len(unsorted_docs)} documents found in IHE website.\\\")\\n self.classify(unsorted_docs)\\n for key, value in self.doc.items():\\n print(f\\\"{key}: {len(value)} documents\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d3951aadb6120d656de97b219489998\",\n \"score\": \"0.52897406\",\n \"text\": \"def displayJournalEntries(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"625d6c91d1ff2c5ce00d0aafae9b9905\",\n \"score\": \"0.52878875\",\n \"text\": \"def _index(self, content: Content):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23e88f145fa38e464ddd8b649d6ed1f1\",\n \"score\": \"0.5277039\",\n \"text\": \"def doc_show(context, request):\\n context = ContextWrapper(context)\\n stage = context.stage\\n name, version = context.project, context.version\\n doc_info = get_doc_info(context, request)\\n version_links = []\\n latest_doc_info = get_doc_info(context, request, version='latest', check_content=False)\\n if latest_doc_info['doc_version'] != doc_info['doc_version']:\\n version_links.append(dict(\\n title=\\\"Latest documentation\\\",\\n url=request.route_url(\\n \\\"docviewroot\\\", user=stage.user.name, index=stage.index,\\n project=name, version='latest', relpath=\\\"index.html\\\")))\\n try:\\n stable_doc_info = get_doc_info(context, request, version='stable', check_content=False)\\n if stable_doc_info['doc_version'] not in (doc_info['doc_version'], latest_doc_info['doc_version']):\\n version_links.append(dict(\\n title=\\\"Stable documentation\\\",\\n url=request.route_url(\\n \\\"docviewroot\\\", user=stage.user.name, index=stage.index,\\n project=name, version='stable', relpath=\\\"index.html\\\")))\\n except (HTTPFound, HTTPNotFound):\\n pass\\n return dict(\\n title=\\\"%s-%s Documentation\\\" % (name, version),\\n base_url=request.route_url(\\n \\\"docroot\\\", user=stage.user.name, index=stage.index,\\n project=name, version=version, relpath=''),\\n baseview_url=request.route_url(\\n \\\"docviewroot\\\", user=stage.user.name, index=stage.index,\\n project=name, version=version, relpath=''),\\n url=request.route_url(\\n \\\"docroot\\\", user=stage.user.name, index=stage.index,\\n project=name, version=version,\\n relpath=doc_info['relpath'], _query=request.query_string),\\n version_mismatch=doc_info['version_mismatch'],\\n version_links=version_links,\\n doc_version=doc_info['doc_version'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97afbf74ad4bf28f958d7300588eda06\",\n \"score\": \"0.52533275\",\n \"text\": \"def docs():\\n adapters = sorted(AdapterBase.registry.keys())\\n\\n return render_template(\\\"docs.html\\\", adapters=adapters)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5907cd4e7c91e248687f3841964a5c52\",\n \"score\": \"0.5252057\",\n \"text\": \"def getAllDocs(idNum):\\n \\n # connect to database\\n conn, cursor = getConnectionAndCursor()\\n\\n # build SQL\\n sql = \\\"\\\"\\\"\\n SELECT *\\n FROM documents\\n WHERE class_num = %s\\n \\\"\\\"\\\"\\n\\n # execute the query\\n parameters = (int(idNum), )\\n cursor.execute(sql,parameters)\\n\\n # get the data from the database:\\n data = cursor.fetchall()\\n\\n # clean up\\n conn.close()\\n cursor.close()\\n \\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c16991b2bfbea818783936950dadf8d9\",\n \"score\": \"0.52500963\",\n \"text\": \"def get_document_details():\\n document = business_layer.BusinessLayer().get_details(request.args.get('id'))\\n return render_template('details.html', details=document)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16a62ad6994d86f26bf0eb55568402ea\",\n \"score\": \"0.5246594\",\n \"text\": \"def document_access(paper, document):\\n path = os.path.join(\\\"documents\\\", files[str(document)])\\n app.logger.debug(path)\\n content = open(path, \\\"rb\\\").read()\\n headers = {\\n \\\"X-TODO\\\": \\\"Last-Modified\\\",\\n \\\"Content-Length\\\": len(content)\\n }\\n return Response(\\n headers=headers,\\n content_type=model[\\\"documents\\\"][document][\\\"mime_type\\\"],\\n response=content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d2eaae4b4d35c32b6922fde40ae8125\",\n \"score\": \"0.52339095\",\n \"text\": \"def documents_processor(urls, job_id, client_id):\\n global workfiles\\n workfiles = []\\n for url in urls:\\n try:\\n result = api_call_manager(add_api_key(url))\\n process_results(result)\\n except:\\n logger.error('Error - URL processing failed')\\n result = json.loads(json.dumps({\\\"job_id\\\" : job_id, \\\"type\\\": \\\"docs\\\", \\\"data\\\" : workfiles, \\\"client_id\\\" : str(client_id), \\\"version\\\" : version}))\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad05b1f9d088c6670d450085741f1bc2\",\n \"score\": \"0.5233528\",\n \"text\": \"def view_content(self, lines=None):\\n\\n if lines is None:\\n text = self.storage.page_text(self.title)\\n else:\\n text = ''.join(lines)\\n return self.highlight(text, mime=self.mime)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0c4c04445b1595c4bed67f098267834\",\n \"score\": \"0.52280146\",\n \"text\": \"def store_new_docs_in_db(self):\\n self.status = self.model_status.PROCESSING\\n self.docs_to_process = self.search_docs_in_processed_docs()\\n self.length_of_docs_to_process = len(self.docs_to_process)\\n if self.length_of_docs_to_process <= 0:\\n return json.dumps({'status':'ERROR','user_prompt':'There are no documents to process'}) \\n for doc in list(self.docs_to_process):\\n self.doc_in_process = doc\\n self.current_process = 'Getting paragraphs and footnotes from {}.'.format(self.doc_in_process)\\n paragraphs_from_doc_status = self.get_paragraphs_from_doc(doc)\\n if paragraphs_from_doc_status['status'] == 'ok':\\n name_of_table, paragraphs, footnotes = paragraphs_from_doc_status['data']\\n else:\\n os.remove(config.TEXTS_PATH + \\\"/\\\" + self.doc_in_process)\\n for doc in self.processed_docs:\\n rest, ext = doc.split(\\\".\\\")\\n table_name = \\\"[\\\" + rest + \\\"_\\\" + ext + \\\"]\\\"\\n erase_table_and_words_for_doc(table_name)\\n os.remove(config.TEXTS_PATH + \\\"/\\\" + doc)\\n return paragraphs_from_doc_status\\n self.current_process = 'Storing paragraphs and footnotes in database.'\\n self.create_table_for_doc(name_of_table, paragraphs, footnotes)\\n self.current_process = 'Storing words in database'\\n self.fill_words_db(doc)\\n self.processed_docs.append(doc)\\n self.docs_to_process.remove(doc)\\n self.status = self.model_status.DONE\\n \\n return {'status':'ok', 'user_prompt':''}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d2f64c27211418e1a53196938305a0c\",\n \"score\": \"0.5225968\",\n \"text\": \"def action_show_all_documents(self):\\n ids = []\\n approvers = self.mapped('approver_ids')\\n for approver in approvers:\\n if approver.approve_attachment_id:\\n ids.append(approver.approve_attachment_id.id)\\n if approver.refused_attachment_id:\\n ids.append(approver.refused_attachment_id.id)\\n if approver.cancel_attachment_id:\\n ids.append(approver.cancel_attachment_id.id)\\n return {\\n 'name': _('Documents'),\\n 'type': 'ir.actions.act_window',\\n 'view_mode': 'tree,form',\\n 'view_id': False,\\n 'res_model': 'ir.attachment',\\n 'domain': [('id', 'in', ids)]\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a456bf4bb206ca072ee10f8c8b7c6d1b\",\n \"score\": \"0.5222932\",\n \"text\": \"def get_docs(args):\\n DbSession = get_db_session()\\n numbers = sort_preserve_order(args.number) # Remove duplicate arguments\\n docs = []\\n dne = []\\n if args.is_also:\\n for number in numbers:\\n std = query_std(DbSession, number)\\n if std is None:\\n dne.append(\\\"STD {} does not exist.\\\".format(number))\\n else:\\n aliases = query_std_is_also(DbSession, number)\\n docs.extend(aliases)\\n else:\\n for number in numbers:\\n rfc = query_std(DbSession, number)\\n if rfc is not None:\\n docs.append(rfc)\\n else:\\n dne.append(\\\"STD {} does not exist.\\\".format(number))\\n\\n # Display found documents\\n show_docs(sort_preserve_order(docs), args.editor, args.pager)\\n # Display messages about nonexistent documents\\n for msg in dne:\\n print(msg)\\n\\n # Exit successfully\\n sys.exit(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa2c17de9ea403aec14be4f8d500c66a\",\n \"score\": \"0.5220909\",\n \"text\": \"def index(content, override_body=None):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ee0aa568c6e76e3297047e4db12632f\",\n \"score\": \"0.52024424\",\n \"text\": \"def index(self):\\n return self.render_default()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d41eed24f367099fe6149ae00bce8bd7\",\n \"score\": \"0.5197253\",\n \"text\": \"def index(self):\\n first_three_books_reviews = self.models['Review'].get_first_three_books_reviews(3)\\n book_ids = [r['bookId'] for r in first_three_books_reviews]\\n all_books = self.models['Book'].get_all_books(book_ids)\\n return self.load_view('/books/index.html', first_three_books_reviews = first_three_books_reviews, all_books = all_books)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57116bc7bc637b6894fd387e07727313\",\n \"score\": \"0.5196881\",\n \"text\": \"def documentation_view(request): \\r\\n context = {\\r\\n 'docs':docs,\\r\\n } \\r\\n return render_to_response('document.html', context, RequestContext(request))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c41b7441bcdbc92921a10db0d6ce1d5\",\n \"score\": \"0.51937556\",\n \"text\": \"def frontpage_content(self, primary=False):\\r\\n return \\\"<div><h2>Module %s</h2><p>Handles %s documents</p></div>\\\" % (module_dir, rdf_type)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3c680fa83d37d450bb0e6394583f945\",\n \"score\": \"0.5187936\",\n \"text\": \"def display_file(self, path, pages=None):\\n\\n self.master.title(\\\"tkDocViewer: \\\" + path)\\n self.doc_viewer.display_file(path, pages)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"290e58446456587dab96a229d5ded81b\",\n \"score\": \"0.5182599\",\n \"text\": \"def get(self):\\n result_set, total = self.execute_query()\\n key_values = self.get_key_values(result_set)\\n keys = key_values.keys()\\n keys.sort()\\n\\n headers = []\\n for key in keys:\\n sample_value = key_values[key][0]\\n headers.append({\\n 'name': key,\\n 'type': DataType.get(sample_value).name(),\\n })\\n\\n entities = []\\n edit_path = self.base_path() + DatastoreEditHandler.PATH\\n for entity in result_set:\\n attributes = []\\n for key in keys:\\n if entity.has_key(key):\\n raw_value = entity[key]\\n value = DataType.get(raw_value).format(raw_value)\\n short_value = DataType.get(raw_value).short_format(raw_value)\\n else:\\n value = ''\\n short_value = ''\\n attributes.append({\\n 'name': key,\\n 'value': value,\\n 'short_value': short_value,\\n })\\n entities.append({\\n 'key': str(entity.key()),\\n 'key_name': entity.key().name(),\\n 'key_id': entity.key().id(),\\n 'shortened_key': str(entity.key())[:8] + '...',\\n 'attributes': attributes,\\n 'edit_uri': edit_path + '?key=' + str(entity.key()) + '&kind=' + urllib.quote(self.request.get('kind')) + '&next=' + urllib.quote(self.request.uri),\\n })\\n\\n start = self.start()\\n num = self.num()\\n max_pager_links = 8\\n current_page = start / num\\n num_pages = int(math.ceil(total * 1.0 / num))\\n page_start = max(math.floor(current_page - max_pager_links / 2), 0)\\n page_end = min(page_start + max_pager_links, num_pages)\\n\\n pages = []\\n for page in range(page_start + 1, page_end + 1):\\n pages.append({\\n 'number': page,\\n 'start': (page - 1) * num,\\n })\\n current_page += 1\\n\\n in_production = self.in_production()\\n if in_production:\\n kinds = None\\n else:\\n kinds = self.get_kinds()\\n\\n values = {\\n 'request': self.request,\\n 'in_production': in_production,\\n 'kinds': kinds,\\n 'kind': self.request.get('kind'),\\n 'order': self.request.get('order'),\\n 'headers': headers,\\n 'entities': entities,\\n 'message': self.request.get('msg'),\\n 'pages': pages,\\n 'current_page': current_page,\\n 'num': num,\\n 'next_start': -1,\\n 'prev_start': -1,\\n 'start': start,\\n 'total': total,\\n 'start_base_url': self.filter_url(['kind', 'order', 'order_type',\\n 'num']),\\n 'order_base_url': self.filter_url(['kind', 'num']),\\n }\\n if current_page > 1:\\n values['prev_start'] = int((current_page - 2) * num)\\n if current_page < num_pages:\\n values['next_start'] = int(current_page * num)\\n\\n self.generate('datastore.html', values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df88b7f54247f7b2ad9e0e6f667c6c66\",\n \"score\": \"0.5179713\",\n \"text\": \"def viewDocument(self, format, path):\\n self.ui.tabWidget.setStyleSheet('QTabWidget::pane {background: none;}')\\n\\n # Read document\\n fh = open(path, 'r')\\n data = fh.read()\\n fh.close()\\n\\n # Create a new editor and configure with current settings\\n frame = QWidget(self.ui.tabWidget)\\n label = self.formatLabels[format]\\n if format == self.handler.HTML:\\n editor = PyQt4.QtWebKit.QWebView(frame)\\n editor.setHtml(data)\\n else:\\n editor = LinedEditor(parent=frame)\\n font = QFont(self.settings.value('appearance/previewFontFamily').toString(),\\n self.settings.value('appearance/previewFontSize').toInt()[0])\\n editor.setFont(font)\\n editor.setPlainText(data)\\n if format == self.handler.XML:\\n if not os.access(path, os.W_OK):\\n # Read only!\\n editor.setReadOnly(True)\\n label += ' (Read-only)'\\n lineNumbers = self.settings.value('appearance/previewLineNumbersXml').toBool()\\n self.xmlEditor = editor\\n # Create callback\\n self.connect(editor, SIGNAL('textChanged()'), self.xmlChanged)\\n self.xml_modified = False\\n else:\\n lineNumbers = self.settings.value('appearance/previewLineNumbersText').toBool()\\n editor.setReadOnly(True)\\n editor.enableLineNumbers = lineNumbers\\n self.textEditors.append(editor)\\n\\n # Add editor to the tab\\n layout = QVBoxLayout()\\n layout.setContentsMargins(1, 1, 1, 1)\\n layout.addWidget(editor)\\n frame.setLayout(layout) \\n self.ui.tabWidget.addTab(frame, label)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63d757188b53e65d2dcbde844c8b866c\",\n \"score\": \"0.5179206\",\n \"text\": \"def store_documents():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ff044d4a0845f53af0f6a85e6e42384\",\n \"score\": \"0.5170479\",\n \"text\": \"def index(self):\\n files = self.get_files()\\n for file in files:\\n self.process_file(file)\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0bb0f547efd37ffcc71138e51d64260\",\n \"score\": \"0.51645446\",\n \"text\": \"def retrieve_new_documents(self, limit=100):\\n today = dt.datetime.combine(dt.date.today(), dt.time.min)\\n\\n entry_path = etree.XPath(\\\"//div[@class = 'SearchResult']\\\")\\n date_path = etree.XPath(\\n \\\"\\\"\\\"\\n .//dl/dd[preceding-sibling::dt[contains(text(), 'Date') or\\n contains(text(), 'Datum')]]/text()\\n \\\"\\\"\\\")\\n doc_path = etree.XPath(\\n \\\"\\\"\\\"\\n .//ul[contains(@class, 'SearchResultDoc')]/li\\n /a[contains(@href, 'PDF') or contains(@href, 'HTML')]/@href\\n \\\"\\\"\\\")\\n title_path = etree.XPath(\\\".//h2/a[@class = 'title']/text()\\\")\\n detail_path = etree.XPath(\\\".//h2/a[@class = 'title']/@href\\\")\\n\\n timestamp = int(round(time.time() * 1000))\\n url_tmpl = (\\\"https://eur-lex.europa.eu/search.html?lang=de&qid=\\\"\\n f\\\"{timestamp}&type=quick&scope=EURLEX&sortOneOrder=desc\\\"\\n \\\"&sortOne=DD&locale=de&page={}\\\")\\n\\n has_unseen_documents = True\\n doc_count = 0\\n page = 1\\n\\n while (doc_count < limit) and has_unseen_documents:\\n search_url = url_tmpl.format(page)\\n logging.info(f\\\"Crawling page '{search_url}' (page {page})\\\")\\n res = _retry_connection(search_url, \\\"get\\\")\\n html_string = res.content\\n tree = html.fromstring(html_string)\\n\\n for entry in entry_path(tree):\\n if not isinstance(entry, list):\\n entry = [entry]\\n\\n date_string = _flat_map(date_path, entry)[0]\\n match = re.search(r\\\"(\\\\d+\\\\/\\\\d+\\\\/\\\\d+)\\\", date_string)\\n\\n doc_date = dt.datetime.min\\n if match:\\n doc_date = dt.datetime.strptime(match[1], \\\"%d/%m/%Y\\\")\\n if len(_flat_map(doc_path, entry)) == 0:\\n continue\\n link = _make_resource_path(_flat_map(doc_path, entry)[0],\\n \\\"https://eur-lex.europa.eu\\\")\\n detail = _make_resource_path(_flat_map(detail_path, entry)[0],\\n \\\"https://eur-lex.europa.eu\\\")\\n title = _flat_map(title_path, entry)[0]\\n\\n doc = {\\\"url\\\": link, \\\"detail_url\\\": detail, \\\"date\\\": doc_date,\\n \\\"title\\\": title, \\\"crawl_date\\\": today}\\n\\n logging.debug(f\\\"Process Document: {link} - {doc_date.date()}\\\")\\n\\n num_docs = self.collection.count_documents({\\\"url\\\": link})\\n\\n if num_docs > 0:\\n logging.debug(f\\\"Document was crawled before: '{link}'\\\")\\n # check whether this document had a date before the crawl\\n # date, if not, break.\\n duplicate_doc = self.collection.find_one({\\\"url\\\": link})\\n\\n if duplicate_doc[\\\"date\\\"] >= duplicate_doc[\\\"crawl_date\\\"]:\\n logging.debug(\\\"Document date lies in the future.\\\"\\n \\\" Continue...\\\")\\n continue\\n\\n logging.debug(\\\"Break!\\\")\\n has_unseen_documents = False\\n break\\n\\n logging.debug(f\\\"Found new document: {link}.\\\")\\n res = self.collection.insert_one(doc)\\n doc_count += 1\\n page += 1\\n logging.info(f\\\"Found {doc_count} new or potentially modified docs.\\\")\\n return doc_count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0216e63e586ed1464e2115ef9ac2772\",\n \"score\": \"0.5163617\",\n \"text\": \"def listDoc(self, dbName, reverse=False, startKey=0, count=-1):\\r\\n # Responses: {u'rows': [{u'_rev': -1825937535, u'_id': u'mydoc'}],\\r\\n # u'view': u'_all_docs'}, 404 Object Not Found\\r\\n uri = \\\"/%s/_all_docs\\\" % (dbName,)\\r\\n args = {}\\r\\n if reverse:\\r\\n args[\\\"reverse\\\"] = \\\"true\\\"\\r\\n if startKey > 0:\\r\\n args[\\\"startkey\\\"] = int(startKey)\\r\\n if count >= 0:\\r\\n args[\\\"count\\\"] = int(count)\\r\\n if args:\\r\\n uri += \\\"?%s\\\" % (urlencode(args),)\\r\\n return self.get(uri\\r\\n ).addCallback(self.parseResult)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e9d1fe5f139ac6329ad057af3865af4\",\n \"score\": \"0.515995\",\n \"text\": \"def index(self):\\r\\n data = []\\r\\n fn = PAGES / \\\"index.html\\\"\\r\\n if sys.version < '3':\\r\\n f_in = io.TextWrapper(fn.open(), encoding='utf-8')\\r\\n else:\\r\\n f_in = fn.open(encoding='utf-8')\\r\\n with f_in:\\r\\n for line in f_in:\\r\\n data.append(line)\\r\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b00248b7a2adc46987290de0e7f3d459\",\n \"score\": \"0.5154607\",\n \"text\": \"def enrich_documents(self, limit=100):\\n entry_path = etree.XPath(\\\"//dl[contains(@class, 'NMetadata')]/dd\\\")\\n key_path = etree.XPath(\\\"normalize-space(./preceding-sibling::dt[1])\\\")\\n value_path = etree.XPath(\\\"\\\"\\\"\\n normalize-space(\\n string-join(\\n ./text() | .//*[self::span[@lang] or\\n self::a[not(child::span)] or\\n self::i[not(child::span)]]/text(), \\\"#\\\"\\n )\\n )\\n \\\"\\\"\\\")\\n\\n success_count = 0\\n cursor = (self.collection.find({\\\"metadata\\\": {\\\"$exists\\\": False}})\\n # newest first, and limit by limit\\n .sort([(\\\"date\\\", -1)]).limit(limit))\\n # extract additional metadata\\n for index, document in enumerate(cursor):\\n logging.info(f\\\"Processing document number {index}...\\\")\\n\\n res = _retry_connection(document[\\\"detail_url\\\"], \\\"get\\\")\\n\\n tree = html.fromstring(res.content)\\n\\n metadata = {}\\n for idx, entry in enumerate(entry_path(tree)):\\n key = key_path(entry).strip(\\\" .:,;!?-_#\\\")\\n val = value_path(entry).strip(\\\" .:,;!?-_#\\\").split(\\\"#\\\")\\n if len(key) == 0 or key in metadata:\\n key += str(idx)\\n if len(val) == 0:\\n continue\\n metadata[key] = val\\n\\n document[\\\"metadata\\\"] = metadata\\n # update document in the database\\n result = self.collection.update_one({\\\"_id\\\": document[\\\"_id\\\"]},\\n {\\\"$set\\\": document})\\n success_count += result.modified_count\\n return success_count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91d2f8fa0d4a9322b56547b6dd8b95ae\",\n \"score\": \"0.5149818\",\n \"text\": \"def open(request, doc):\\n userid = request.session['userid']\\n member = Member.objects.get(user_id=userid)\\n context = {}\\n context['document'] = createDummyDocument(member, 1)[0]\\n return render(request, 'document/document.html', context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"670f1d784df2703a85f152d3389fa018\",\n \"score\": \"0.51390713\",\n \"text\": \"def update(self):\\n\\n row = self.parent.get_row_by_file(self.filepath)\\n\\n # update results\\n self.display_results = [\\n self.filepath,\\n self.parent_folder,\\n self.filename,\\n self.inspection.get_display('timecode_start'),\\n self.inspection.get_display('content_start_timecode'),\\n self.inspection.get_display('framecount'),\\n self.inspection.get_display('full_duration'),\\n self.inspection.get_display('content_duration'),\\n self.inspection.get_display('slate'),\\n self.inspection.get_display('black_at_tail'),\\n self.inspection.get_display('slate_key_number'),\\n self.inspection.get_display('op48_audio'),\\n self.inspection.get_display('op59_audio'),\\n self.inspection.get_display('audio_peak'),\\n self.inspection.get_display('resolution'),\\n self.inspection.get_display('fps'),\\n self.inspection.get_display('video_bitrate'),\\n self.inspection.get_display('video_codec'),\\n self.inspection.get_display('audio_codec'),\\n self.inspection.get_display('audio_bitrate'),\\n self.inspection.get_display('audio_sample_rate'),\\n self.inspection.get_display('slate_date'),\\n self.inspection.get_display('slate_aspect'),\\n self.inspection.get_display('slate_duration'),\\n ]\\n\\n for col, v in enumerate(self.display_results):\\n self.parent.dataview.SetValue(v, row, col)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14edc14e7de3f9c80bca3f1efda45568\",\n \"score\": \"0.51333195\",\n \"text\": \"def open_document(self):\\n\\n # Read the DXF file\\n document = readfile(self.dxf_path)\\n modelspace = document.modelspace()\\n # Convert DXF to pyleecan objects\\n self.line_list = dxf_to_pyleecan_list(modelspace)\\n # Display\\n self.selected_list = [False for line in self.line_list]\\n self.update_graph()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b319ba6a16ba660df2d32315964fbda6\",\n \"score\": \"0.513093\",\n \"text\": \"def __build_document(self):\\n with open('consolidated_nyt.tsv') as r:\\n self.documents = r.read().splitlines()\\n self.documents = preprocess_documents(self.documents)\\n self.number_of_documents = len(self.documents)\\n self.vocabulary = corpora.Dictionary(self.documents)\\n self.vocabulary_size = len(self.vocabulary)\\n print(\\\"Number of documents:\\\" + str(len(self.documents)))\\n print(\\\"Vocabulary size:\\\" + str(self.vocabulary_size))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f3be98afefdaec1996913b2b1e625b2\",\n \"score\": \"0.5124302\",\n \"text\": \"def main():\\n\\tstc.html(HTML_BANNER)\\n\\t#menu = [\\\"Home\\\",\\\"About\\\"]\\n\\tst.markdown(\\n f\\\"\\\"\\\"\\n<style>\\n .reportview-container .main .block-container{{\\n max-width: {1500}px;\\n padding-top: {1}rem;\\n padding-right: {1}rem;\\n padding-left: {1}rem;\\n padding-bottom: {1}rem;\\n }}\\n \\n</style>\\n\\\"\\\"\\\",\\n unsafe_allow_html=True,\\n )\\n\\t\\n\\n\\n\\tchoice = \\\"Home\\\"#st.sidebar.selectbox(\\\"Menu\\\",menu)\\n\\n\\tif choice == 'Home':\\n\\t\\tst.subheader(\\\"Text Analysis\\\")\\n\\t\\traw_text = st.text_area('Enter Text Here')\\n\\t\\t\\n\\n\\t\\tif ( len(raw_text) > 2 & st.button(\\\"Analyze\\\")):\\n\\t\\t\\tcol1,col2,col3 = st.beta_columns(3)\\n\\t\\t\\tprocess_text = nfx.remove_stopwords(raw_text)\\n\\t\\t\\twith col1:\\n\\n\\n\\t\\t\\t\\twith st.beta_expander(\\\"Preview Tagged Text\\\"):\\n\\t\\t\\t\\t\\ttagged_docx = generate_tags(raw_text)\\n\\t\\t\\t\\t\\tprocessed_tag_docx = mytag_visualizer(tagged_docx)\\n\\t\\t\\t\\t\\tstc.html(processed_tag_docx,scrolling=True)\\n\\n\\t\\t\\t\\twith st.beta_expander(\\\"Plot Mendelhall Curve\\\"):\\n\\t\\t\\t\\t\\tplot_mendelhall_curve_2(raw_text)\\n\\n\\n\\t\\t\\t\\twith st.beta_expander(\\\"NER\\\"):\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t# st.write(most_common_tokens)\\n\\t\\t\\t\\t\\tdocx = nlp(raw_text)\\n\\t\\t\\t\\t\\thtml = displacy.render(docx,style=\\\"ent\\\")\\n\\t\\t\\t\\t\\thtml = html.replace(\\\"\\\\n\\\\n\\\",\\\"\\\\n\\\")\\n\\t\\t\\t\\t\\tst.write(HTML_WRAPPER.format(html),unsafe_allow_html=True)\\n\\t\\t\\t\\t\\t#spacy_streamlit.visualize_ner(docx, labels=nlp.get_pipe(\\\"ner\\\").labels)\\n\\n\\t\\t\\twith col2:\\n\\t\\t\\t\\twith st.beta_expander('Process Text'):\\n\\t\\t\\t\\t\\tst.write(process_text)\\n\\n\\t\\t\\t\\twith st.beta_expander(\\\"Most Common Words\\\"):\\n\\t\\t\\t\\t\\t#st.write(raw_text)\\n\\t\\t\\t\\t\\tplot_most_common_tokens(process_text)\\n\\n\\t\\t\\t\\twith st.beta_expander(\\\"Plot Wordcloud\\\"):\\n\\t\\t\\t\\t\\tst.info(\\\"word Cloud\\\")\\n\\t\\t\\t\\t\\tplot_wordcloud(process_text)\\n\\n\\t\\t\\t\\t\\n\\t\\t\\t\\n\\t\\t\\twith col3:\\n\\t\\t\\t\\twith st.beta_expander(\\\"Tokenizer\\\"):\\n\\t\\t\\t\\t\\tdocx = nlp(raw_text)\\n\\t\\t\\t\\t\\tspacy_streamlit.visualize_tokens(docx, attrs=[\\\"text\\\", \\\"pos_\\\", \\\"dep_\\\", \\\"ent_type_\\\"])\\n\\n\\t\\t\\t\\twith st.beta_expander(\\\"Sentiment Analysis\\\"):\\n\\t\\t\\t\\t\\tsentiment(raw_text)\\n\\n\\t\\t\\t\\twith st.beta_expander(\\\"Summarizer\\\"):\\n\\t\\t\\t\\t\\tsummarizer(raw_text)\\n\\n\\n\\n\\t\\telif (len(raw_text) == 1 ):\\n\\t\\t\\tst.warning(\\\"Insufficient Text, Minimum must be more than 1\\\") \\n\\t\\t\\t\\n\\n\\n\\t\\t\\t\\n\\n\\n\\t\\t\\n\\n\\telif choice == \\\"About\\\":\\n\\t\\tst.subheader(\\\"Text Analysis NLP App\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e46ba637e33b4d42ba461a7a021670d\",\n \"score\": \"0.51233447\",\n \"text\": \"def docs():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"140c0ea39a5918f4999d57e96cd8b79f\",\n \"score\": \"0.511046\",\n \"text\": \"def index():\\n g.lcursor.execute('SELECT * FROM articles ORDER BY id DESC LIMIT 3')\\n return render_template('main_index.html',\\n articles_data=g.lcursor.fetchall())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acfe4e54b84c3e3f952b6d19592528b1\",\n \"score\": \"0.51098657\",\n \"text\": \"def home_page():\\n files = list_conversions()\\n return render_template('index.html', files=files)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99549e25377f27ae27a4a43017c5b58a\",\n \"score\": \"0.51065785\",\n \"text\": \"def render_qweb_doc(self, docids, data=None):\\n\\t\\t# If the report is using a custom model to render its html, we must use it. otherwise, fallback on the generic html rendering.\\n\\t\\treport_model_name = 'report.%s' % self.report_name\\n\\t\\treport_model = self.env.get(report_model_name)\\n\\t\\t\\n\\t\\tif report_model is not None:\\n\\t\\t\\tdata = report_model.make_ppt(data)\\n\\t\\telse:\\n\\t\\t\\tdocs = self.env[self.model].browse(docids)\\n\\t\\t\\tdata = {\\n\\t\\t\\t\\t'doc_ids': docids,\\n\\t\\t\\t\\t'doc_model': self.model,\\n\\t\\t\\t\\t'docs': docs,\\n\\t\\t\\t}\\n\\t\\t\\treturn docs.doc5()\\n\\t\\treturn data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ca339f1e3c9784ae4fb07e533a3ee6e\",\n \"score\": \"0.51034266\",\n \"text\": \"def loadDocs(dataFormat, path=None, ignoreEntities=[]):\\n assert dataFormat == 'bioc'\\n if dataFormat == 'bioc':\\n assert not path is None\\n docs = loadDataFromBioC(path, ignoreEntities=ignoreEntities)\\n assert isinstance(docs, list)\\n for doc in docs:\\n assert isinstance(doc, kindred.Document)\\n return docs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f39fe1d8d6a2f93052194cafed4f2419\",\n \"score\": \"0.50999314\",\n \"text\": \"def _get_documents(self, url):\\n\\n documents = []\\n with urllib.request.urlopen(url) as response:\\n response = response.read().decode('utf-8')\\n\\n for doc_raw in json.loads(response)['data']:\\n\\n timestamp = doc_raw['created_utc']\\n datetime_utc = datetime.utcfromtimestamp(timestamp)\\n datetime_est = datetime_utc.replace(tzinfo=timezone.utc).astimezone(tz=None)\\n date_str = datetime_est.strftime('%Y-%m-%d')\\n\\n if 'permalink' in doc_raw:\\n url = f'https://www.reddit.com{doc_raw[\\\"permalink\\\"]}'\\n else:\\n url = 'n/a'\\n\\n documents.append({\\n 'date': date_str,\\n 'author': doc_raw['author'],\\n 'subreddit': doc_raw['subreddit'],\\n 'score': doc_raw['score'],\\n 'url': url,\\n 'text': html.unescape(doc_raw['body']),\\n })\\n\\n print(len(documents))\\n\\n return documents\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89e1e6aea0291a0961f22be40eb07e78\",\n \"score\": \"0.50967455\",\n \"text\": \"async def frame_data_doc(self, interaction: discord.Interaction):\\n link = ('https://docs.google.com/spreadsheets/d/'\\n '19UtK7xG2c-ehxdlhCFKMpM4_IHSG-EXFgXLJaunE79I')\\n patch = requests.get(url='https://rivals.academy/library/pomme/data.json').json()['patch']\\n embed = discord.Embed(\\n url=link,\\n title=f'Rivals of Aether Academy Frame Data - Updated for {patch}',\\n description='Data extracted manually in-game and from dev-mode files by SNC. '\\n 'Extra information provided by Menace13 and Youngblood. ')\\n embed.set_thumbnail(url='https://i.imgur.com/nMS0QPT.png')\\n await interaction.response.send_message(content=link, embed=embed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be10cd8180521b550baf725a9dbc21d8\",\n \"score\": \"0.5095544\",\n \"text\": \"def app_documents(id):\\n documents = Document.query.filter_by(application_id=id) # return a list with all values\\n return render_template('app_documents.html', name=current_user.username, role=current_user.role_code,\\n documents=documents) # name parameter send to html the value of the current logged_in user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9b63e19004eb1170330d7bfdb4a8b8c\",\n \"score\": \"0.5094836\",\n \"text\": \"def get_index():\\n\\n # version of the data contained in the dataset description\\n db = current.db\\n qry = (db.published_datasets.id == db.dataset_files.dataset_id)\\n val = dataset_query_to_json(qry, \\n fields = [('published_datasets', 'publication_date'), \\n ('published_datasets', 'zenodo_concept_id'), \\n ('published_datasets', 'zenodo_record_id'), \\n ('published_datasets', 'dataset_access'), \\n ('published_datasets', 'dataset_embargo'), \\n ('published_datasets', 'dataset_title'), \\n ('published_datasets', 'most_recent'), \\n ('dataset_files', 'checksum'),\\n ('dataset_files', 'filename'),\\n ('dataset_files', 'filesize')])\\n \\n # repackage the db output into a single dictionary per file\\n entries = val['entries'].as_list()\\n [r['published_datasets'].update(r.pop('dataset_files')) for r in entries]\\n val['entries'] = [r['published_datasets'] for r in entries]\\n \\n # Find the hashes\\n index_hash = hashlib.md5(json(val).encode('utf-8')).hexdigest()\\n \\n # Use the file hash of the static gazetteer geojson\\n gazetteer_file = os.path.join(current.request.folder, 'static', 'files', 'gis', 'gazetteer.geojson')\\n with open(gazetteer_file) as f:\\n gazetteer_hash = hashlib.md5(f.read().encode('utf-8')).hexdigest()\\n\\n # Use the file hash of the static locations alias csv\\n location_aliases_file = os.path.join(current.request.folder, 'static', 'files', 'gis', 'location_aliases.csv')\\n with open(location_aliases_file) as f:\\n location_aliases_hash = hashlib.md5(f.read().encode('utf-8')).hexdigest()\\n \\n return dict(hashes=dict(index=index_hash, \\n gazetteer=gazetteer_hash,\\n location_aliases=location_aliases_hash),\\n index=val)\\n\\n \\n \\n # return the dictionary - this will be json serialised by the API when it is returned\\n return val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d0d755a343f7cf1ec9c811977d6f28d\",\n \"score\": \"0.5094192\",\n \"text\": \"def __call__(self, **kwargs):\\n resp = get_docs(self._r_session,\\n self.url,\\n self.design_doc.encoder,\\n **kwargs)\\n return response_to_json_dict(resp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3890f22cd8cf051498cbe2771247b1f\",\n \"score\": \"0.5093888\",\n \"text\": \"def document(self):\\n return publish_doctree(self.body)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a91470aa975607e47481880d12436b1\",\n \"score\": \"0.5089864\",\n \"text\": \"def get(self):\\n res = self.plag_dao.get_docs(page=int(request.args.get(\\\"page\\\", 1)),\\n per_page=int(request.args.get(\\\"per_page\\\", 10)), all='all' in request.args)\\n docs_info = dict(data=[d.to_dict() for d in res['data']], count=res['count'])\\n print(docs_info)\\n return Response(data=docs_info)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39f2c6a73418a355cd0021c7fde50a91\",\n \"score\": \"0.50828326\",\n \"text\": \"def getWikidataContent(self):\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994190,"cells":{"query_id":{"kind":"string","value":"8abadc08d2224205c0322496613c7174"},"query":{"kind":"string","value":"Returns followers/following, number of posts"},"positive_passages":{"kind":"list like","value":[{"docid":"268b1f4295be22b013434acc84c45951","score":"0.0","text":"def get_instagram_stats(self, username):\n user = self.get_user(username)\n user_info = self.get_user_profile(user[\"id\"])\n return user_info[\"data\"][\"counts\"]","title":""}],"string":"[\n {\n \"docid\": \"268b1f4295be22b013434acc84c45951\",\n \"score\": \"0.0\",\n \"text\": \"def get_instagram_stats(self, username):\\n user = self.get_user(username)\\n user_info = self.get_user_profile(user[\\\"id\\\"])\\n return user_info[\\\"data\\\"][\\\"counts\\\"]\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"0c002c2cc4835dc347d3d1959dbbd10a","score":"0.7136349","text":"def followers_count(self):\n user_type = ContentType.objects.get_for_model(self)\n return UserInterest.objects\\\n .filter(interest_type='follow', content_type__pk=user_type.id, object_id=self.id)\\\n .count()","title":""},{"docid":"57f18077da40654c6db6a29b111e57c2","score":"0.7008957","text":"def follow_index(request):\n post_list = Post.objects.filter(author__following__user = request.user).select_related(\"author\", \"group\")\n page_number = request.GET.get('page', 1)\n paginator = Paginator(post_list, 4) \n page = paginator.get_page(page_number)\n post_count = page.object_list.count()\n context = { \n \"page\": page,\n \"post_count\": post_count,\n }\n return render(request, \"follow.html\", context)","title":""},{"docid":"a4a172ad8ac928b5a1c706b29bef0da5","score":"0.6932286","text":"def follower_count(self):\n return self.followers.count()","title":""},{"docid":"4409acd21253b05c46d62a8d0844666e","score":"0.688722","text":"def getNumFollowers(self):\n\n return getTwitterDump(self, 'u')[u'followers_count']","title":""},{"docid":"52810e0d79662222a11f7f0bdc18da74","score":"0.6844079","text":"def followed_posts(self):\n # the join operation creates a temporary table which merges the post and followers table data\n # the join is merged based on the condition passed as an argument ie the conditions to filter user's followed posts\n followed = Post.query.join(followers, (followers.c.followed_id == Post.user_id)).filter(followers.c.follower_id==self.id) \n\n # the user will also want to see her personal posts in the blog posts. \n # To do that the user's posts are queried and are then merged with the other posts already queried using the union operation\n # then the posts are sorted based on recency \n own = Post.query.filter_by(user_id = self.id)\n return followed.union(own).order_by(Post.timestamp.desc()) # Post.timestamp.desc() sorts the posts by time on a descending order.\n # Due to this, the most recent post will be rendered first","title":""},{"docid":"10c70740fbab0709787211d81a0c031a","score":"0.67538553","text":"def followed_posts(self):\n # this method returns a query object and NOT the result, similar to 'lazy' = 'dynamic' in relationship\n # this is good practice since the caller can tach on additional queries\n return Post.query.join(followers,\n (followers.c.followed_id == Post.user_id)).filter(\n followers.c.follower_id == self.id).order_by(Post.timestamp.desc())","title":""},{"docid":"d8579337648252898ac46ef9aa59083d","score":"0.67458844","text":"def follow_counts(request,userobj):\n\n following_count = Follow.objects.filter(follower = userobj).count()\n follower_count = Follow.objects.filter(following = userobj).count()\n\n\n if request.user.username == userobj.username:\n \n follow_count = {\"following_count\":following_count,\"follower_count\":follower_count}\n return follow_count\n \n return follower_count,following_count","title":""},{"docid":"a0134e84dbde29970c1f5e99f27636b0","score":"0.66715217","text":"def get_follower_count(tweet):\n if is_original_format(tweet):\n return tweet[\"user\"][\"followers_count\"]\n else:\n return tweet[\"actor\"][\"followersCount\"]","title":""},{"docid":"0417c32dd66b1a7c39f46dacc8fd0a9c","score":"0.66394466","text":"def num_pages(following_count):\n \n n = following_count / settings.TWITTER_FRIENDS_PAGE_LENGTH\n if following_count % settings.TWITTER_FRIENDS_PAGE_LENGTH > 0:\n n += 1\n return n","title":""},{"docid":"2abdb9fad9e9acf57df9624247dbfceb","score":"0.6569586","text":"def get_following_count(tweet):\n if is_original_format(tweet):\n return tweet[\"user\"][\"friends_count\"]\n else:\n return tweet[\"actor\"][\"friendsCount\"]","title":""},{"docid":"87b311ad9f5e84b54be3a9ac75a6ef4c","score":"0.65661263","text":"def alcance(followers):\r\n pot = 0\r\n for follower in followers:\r\n pot += follower[\"followers_count\"]\r\n return pot + len(followers)","title":""},{"docid":"e19f209e9f3d0753ca3e8317148d76b6","score":"0.6421191","text":"def follow_index(request):\n following_posts = Post.objects.filter(author__following__user=request.user)\n paginator = Paginator(following_posts, settings.PER_PAGE_INDEX)\n page_number = request.GET.get(\"page\")\n page = paginator.get_page(page_number)\n return render(\n request,\n \"follow.html\",\n {\"page\": page, \"paginator\": paginator},\n )","title":""},{"docid":"f162726dd7f206979c6886feda0854b0","score":"0.63893497","text":"def GetNumberOfPosts(self):\n\n return len(self.all_posts)","title":""},{"docid":"a04c7b699ca24cd4e8b58cdf5f0359ae","score":"0.63271505","text":"def followers(self):\n url = '{0}/followers'.format(self.get_url())\n return http.Request('GET', url), parsers.parse_json","title":""},{"docid":"aaf747f85eefdcc4cc743ec59d47dcf4","score":"0.6303726","text":"def getFollowerCount(connection, userID):\n\n\t\tvariables = [userID]\n\n\t\treturn TableTools.getCount(connection, UsersTableTools._FOLLOWS_TABLE,\n\t\t\t\"flwee = :1\", variables)","title":""},{"docid":"fa8de2714618d6a5ebf4b34661719621","score":"0.62701845","text":"def following_posts(request):\n if request.user.is_authenticated:\n\n userobj = util.get_user_obj_by_userId(request.user.id)\n posts = paginate(request,get_all_posts_of_user_network(request,userobj))\n post_liked_ids = get_myliked_post(request).values_list(\"id\",flat= True)\n return render (request, \"network/following.html\",{\n \"allposts\": posts,\n \"post_liked_ids\":post_liked_ids,\n\n })\n else:\n\n return HttpResponseRedirect(reverse(\"network:login\"))","title":""},{"docid":"0d929c65aa503c6bc5deaaca6117f26c","score":"0.62489426","text":"def followers(github, user, pager):\n github.followers(user, pager)","title":""},{"docid":"7221e8e8810ecfb407ac6f89380f4667","score":"0.6238051","text":"def get_followers(profile: str, session) -> Optional[float]:\n url = f'https://www.instagram.com/{profile}'\n r = session.get(url)\n if r.ok:\n instagram = BeautifulSoup(r.text, features=\"lxml\")\n scripts = instagram.select('script[type=\"text/javascript\"]')\n json_s = scripts[3]\n json_s = json_s.text\n json_s = json_s[len('window._sharedData = '):-1]\n json_s = json.loads(json_s)\n entry_data = json_s['entry_data']\n profile_page = entry_data['ProfilePage'][0]\n graph_ql = profile_page['graphql']\n user = graph_ql['user']\n user['edge_owner_to_timeline_media'] = None\n with open(f'user_information_{profile}.json', 'w') as f:\n f.write(json.dumps(user, indent=4, sort_keys=True))\n follow_count = user['edge_followed_by']\n final: float = float(follow_count['count'])\n print(f'Finished for {profile} - Followers {final}')\n return final\n print(f\"Error. It wasn't possible to get the following count\")\n return 0","title":""},{"docid":"042a8dd59b121bc4f51586643dc6d45a","score":"0.6230698","text":"def get_followed_user_count( self, user ):\n count = self.filter( user=user, content_type=ContentType.objects.get_for_model( User ) ).count()\n return count","title":""},{"docid":"5d1901c1c4a8d090323a621c7f4b7237","score":"0.6223728","text":"def followers(user):\n return {\"followers\": Follow.objects.followers(user)}","title":""},{"docid":"5f17e16bbd25f3fc6d7e46546f625866","score":"0.6220345","text":"def follower_ids_count(self):\n return len(self._follower_ids)","title":""},{"docid":"6fd8cc0b9e997787fb48ab365964cd55","score":"0.6214273","text":"def user_posts_statistics(user, days_ago, reverse=None):\n if reverse is None:\n reverse = False\n\n start_time = datetime.now() - timedelta(days=days_ago)\n\n hit_query = Hit.objects.filter(post__publisher__id=user.id, \n created__date__gte=start_time).values(created_date=F(\"created__date\")) \\\n .annotate(number=Count(\"pk\")).order_by(\"-created_date\")\n\n like_query = Like.objects.filter(post__publisher__id=user.id, \n created__date__gte=start_time, status=True).values(created_date=F(\"updated__date\")) \\\n .annotate(number=Count(\"pk\")).order_by(\"-created_date\")\n\n download_query = Download.objects.filter(post__publisher__id=user.id, \n created__date__gte=start_time).values(created_date=F(\"created__date\")) \\\n .annotate(number=Count(\"pk\")).order_by(\"-created_date\")\n\n created_dates = hit_query.values(\"created_date\").union(\n like_query.values(\"created_date\"), \n download_query.values(\"created_date\")\n ).values_list(\"created_date\").order_by(\"-created_date\")\n\n hits = list(map(lambda date: hit_query.get(created_date=date[0])[\"number\"]\n if hit_query.filter(created_date=date[0]).exists() else 0, created_dates.iterator()))\n\n likes = list(map(lambda date: like_query.get(created_date=date[0])[\"number\"]\n if like_query.filter(created_date=date[0]).exists() else 0, created_dates.iterator()))\n\n downloads = list(map(lambda date: download_query.get(created_date=date[0])[\"number\"]\n if download_query.filter(created_date=date[0]).exists() else 0, created_dates.iterator()))\n\n dates = list(map(lambda date_: date.fromgregorian(date=date_[0])\n .strftime(\"%Y/%m/%d\"), created_dates.iterator()))\n\n if reverse:\n hits, likes, downloads, dates = hits[::-1], likes[::-1], downloads[::-1], dates[::-1]\n\n return {\n \"dates\": dates,\n \"hits\": hits,\n \"likes\": likes,\n \"downloads\": downloads\n }","title":""},{"docid":"8da1157043230ddf65c26a374a4acb34","score":"0.6208782","text":"def add_total_followers(G, d):\n for influencer in d:\n d[influencer]['num_follow'] = len(G.in_edges(influencer))\n return d","title":""},{"docid":"8692d716cd1c411fec554de892e82767","score":"0.61743456","text":"def fetch_posts(self):\n requests = []\n friends = self.person.friends\n for friend in friends:\n requests.append({\n 'id': friend.fb_id,\n 'request': str(friend.fb_id) + '/posts'\n })\n progress = {\n 'from': 20, 'to': 50,\n 'description': 'Collecting your and your friends posts'\n }\n self.store_api_response('posts', requests, progress)","title":""},{"docid":"9157a0b04516d3f46926c4d97b3dad68","score":"0.61446565","text":"def get_nb_posts(self):\n i = 0\n try:\n messages = self.html.find(class_=\"pagination\").text.split()\n while True:\n try:\n # if messages[i] == 'messages' or messages[i] == 'message':\n # check if 'message' of 'messages'\n if (re.match(r\"message[s]?\", messages[i]) and\n messages[i - 1] != \"premier\"):\n self.messages = int(messages[i - 1])\n break\n else:\n i += 1\n except Exception:\n # print(e)\n print(\"Error in Topic get_nb_post\")\n self.messages = 1\n break\n except AttributeError:\n self.messages = 1\n return self.messages","title":""},{"docid":"b984f96b7e4d3f77a1acbd91875c91a0","score":"0.6084396","text":"def get_post_count(self):\n return self.get_number_with_id(self.post_id)","title":""},{"docid":"2af5695213583d10708481fb2fe46146","score":"0.60823214","text":"def get_followers(user):\n res = urllib2.urlopen(\"https://api.github.com/users/\" + user)\n soup = json.load(res)\n\n logger = logging.getLogger('github api')\n logger.info('printing','followers')\n\n print \"followers:\", soup['followers']\n print \"following:\", soup['following']","title":""},{"docid":"1201b7acc5e3141fe467ea3e9ee92026","score":"0.6074673","text":"def getFollowingCount(connection, userID):\n\n\t\tvariables = [userID]\n\n\t\treturn TableTools.getCount(connection, UsersTableTools._FOLLOWS_TABLE,\n\t\t\t\"flwer = :1\", variables)","title":""},{"docid":"c8bcdc6525e647236f9dd75882856369","score":"0.60707694","text":"def get_followers(username, count=5000):\n follower_usernames = get_follower_usernames(username, count=count)\n return get_users_for_usernames(follower_usernames)","title":""},{"docid":"c213733bae05eaea82f6a983e3c636d5","score":"0.6023095","text":"def get_top_posts(\n L: Instaloader,\n context: CallbackContext,\n chat_id: int,\n messages: dict,\n profile_url: str,\n top_n=10,\n lookback_posts=100,\n) -> tuple:\n try:\n username = re.findall(\n r\"(?:(?:http|https):\\/\\/)?(?:www.)?(?:instagram.com|instagr.am)\\/([A-Za-z0-9-_.]+)\",\n profile_url,\n )[0]\n profile = Profile.from_username(L.context, username)\n\n n = 0\n posts_photos = []\n posts_videos = []\n\n for post in profile.get_posts():\n if n > lookback_posts:\n break\n\n link = f\"https://www.instagram.com/p/{post.shortcode}\"\n\n if post.is_video:\n posts_videos.append(\n {\"Post URL\": link, \"Views\": post.video_view_count,}\n )\n else:\n posts_photos.append(\n {\"Post URL\": link, \"Likes\": post.likes,}\n )\n n = n + 1\n\n if len(posts_photos) == 0 and len(posts_videos) == 0:\n raise ValueError(\"Profile is closed.\")\n\n if len(posts_photos) > 0:\n df_photos = (\n pd.DataFrame(posts_photos)\n .sort_values(\"Likes\", ascending=False)\n .head(n=top_n)\n .set_index(\"Post URL\")\n )\n df_photos[\"Likes\"] = df_photos[\"Likes\"].apply(\n lambda x: f\"{thousands_sep(x)} likes\"\n )\n\n top_photos = \"Top posts by likes:\\n\" + tabulate(df_photos, tablefmt=\"plain\")\n\n context.bot.send_message(\n chat_id=chat_id, text=top_photos, disable_web_page_preview=True\n )\n\n if len(posts_videos) > 0:\n df_videos = (\n pd.DataFrame(posts_videos)\n .sort_values(\"Views\", ascending=False)\n .head(n=top_n)\n .set_index(\"Post URL\")\n )\n df_videos[\"Views\"] = df_videos[\"Views\"].apply(\n lambda x: f\"{thousands_sep(x)} views\"\n )\n\n top_videos = \"Top videos by views:\\n\" + tabulate(\n df_videos, tablefmt=\"plain\"\n )\n context.bot.send_message(\n chat_id=chat_id, text=top_videos, disable_web_page_preview=True\n )\n\n result = True\n traceback = \"Success\"\n\n except Exception as e:\n context.bot.send_message(chat_id=chat_id, text=messages[\"error\"])\n result = False\n traceback = str(e)\n\n return result, traceback","title":""},{"docid":"1b1692a0cb74128d3f824d1f8f4c6711","score":"0.6010295","text":"def followers(self):\n if not self._username:\n raise Error('microblogging.API instance must be authenticated.')\n url = self.get_url('/statuses/followers.json')\n json = self._fetch_url(url)\n data = simplejson.loads(json)\n return [User.get_from_json(x) for x in data]","title":""},{"docid":"d8bcc9ebb01a69b902db975d40a894f0","score":"0.59414184","text":"def print_post_list(list_of_users):\n print(f'users by their posts')\n for i in range(len(list_of_users)):\n print(f'user_id = {list_of_users[i].id}, total_posts = {list_of_users[i].number_of_posts}, '\n f'list of user_posts ={list_of_users[i].post_per_likes_list}')","title":""},{"docid":"f943d9feec2817d7e23853cbed99c54b","score":"0.5936655","text":"def get_following(self, username, num_followers, print_progress=False):\n friend_list = []\n count = 0\n for user in limit_handled(Cursor(self.api.friends, screen_name=username).items(num_followers)):\n cur_following = Format.format_username(user.screen_name)\n friend_list.append(cur_following)\n if print_progress:\n print('get_follower', count, cur_following)\n count += 1\n return friend_list","title":""},{"docid":"95d95c24bb2a8201592f321a156ff9db","score":"0.59198076","text":"def get_people(self) -> Dict[str, int]:\r\n users = dict()\r\n\r\n for post in self.ps.search_submissions(after=self.start, subreddit=self.sub,\r\n filter=['author']):\r\n if post.author in users:\r\n users[post.author] += 1\r\n else:\r\n users[post.author] = 1\r\n\r\n for post in self.ps.search_comments(after=self.start, subreddit=self.sub,\r\n filter=['author']):\r\n if post.author in users:\r\n users[post.author] += 1\r\n else:\r\n users[post.author] = 1\r\n\r\n return users","title":""},{"docid":"467ac85d518b06d9286de6db84732b6c","score":"0.590841","text":"def list(self, request, **kwargs):\n user = request.query_params.get('user')\n if self.queryset.filter(me=user).count() == 0:\n return Response({'status': 'no following list'})\n else:\n users_followers = Following.objects.get(me=user)\n data = {\"id\": users_followers.id, \"following\": users_followers.usernames}\n return Response(data)","title":""},{"docid":"a64caf46f408cc13ed0e8e71353e1a1a","score":"0.58928674","text":"def count(self):\n return query.get_total_post_count(self.base_url, self.params)","title":""},{"docid":"1b74eb6d00389a35daa0a7a7c551639a","score":"0.58847","text":"def getNewsFeed(self, userId: int) -> List[int]:\n feed = []\n for p in self.post[::-1]:\n if self.post_user[p] in self.follows[userId] or self.post_user[\n p] == userId:\n feed.append(p)\n if len(feed) == 10:\n break\n\n return feed","title":""},{"docid":"b0286bc6d43f25979ef090b8b02b8878","score":"0.5870798","text":"def max_followers_among_all_platforms(self):\n plats = self.platforms()\n max_num_followers = 0\n for p in plats:\n if p.num_followers > max_num_followers:\n max_num_followers = p.num_followers\n return max_num_followers","title":""},{"docid":"98932cd379b29a0c154bb9b2ed28e482","score":"0.5852411","text":"def test_get_followers_list(self):\n self.api.get_followers_list()\n self.api.get_followers_list(screen_name='twitter')","title":""},{"docid":"fa89954078e93d8789337f2c5403e06a","score":"0.5850981","text":"def ListFollowers(self):\n try:\n i = 1\n follower_list = []\n while (True):\n param = {\"access_token\":self.personal_access_token, \"per_page\": 1, \"page\": i}\n r = requests.get(self.url_address+\"user/followers\", params = param)\n if (r.status_code!=200):\n raise ClientError(r.status_code)\n if (r.text == '[]'):\n break\n else:\n follower = json.loads(r.text[1:-1])\n follower_dict = {\"login\": follower[\"login\"], \"url\": follower[\"url\"]} \n follower_list.append(follower_dict)\n i = i+1 \n return json.dumps(follower_list)\n except ClientError as err:\n print(err.status)\n print(err.message)\n print(err.status, err.message)\n print(err)","title":""},{"docid":"8950fc59a4207e0dd9cb78d5721acc91","score":"0.58497524","text":"def show_tweets_high_followed_users(output_file, follower_num):\r\n pass","title":""},{"docid":"af87a02ff7dba95abf478afa73cea272","score":"0.5844426","text":"def get_all_count(self):\n html_text = self.get_html()\n soup = BeautifulSoup(html_text, \"lxml\")\n count_of_post = soup.find(id=self.post_id).find(\"span\", {\"class\": \"number\"}).string\n count_of_theme = soup.find(id=self.theme_id).find(\"span\", {\"class\": \"number\"}).string\n count = int(count_of_post) + int(count_of_theme)\n return count","title":""},{"docid":"7602aa226400f56d1587b2f2a8b2ce4e","score":"0.5817576","text":"def test_get_followers_ids(self):\n self.api.get_followers_ids()\n self.api.get_followers_ids(screen_name='twitter')","title":""},{"docid":"60e573d9894df3cc431486d569a76ef5","score":"0.58127546","text":"def get_post_count(tags):\n\n page = 1\n\n while True:\n resp = get_page(tags, page)\n\n if len(resp) == 0:\n earliest_empty_page = page\n break\n else:\n latest_populated_page = page\n\n page *= 2\n\n while (earliest_empty_page - latest_populated_page) > 1:\n page = int((latest_populated_page + earliest_empty_page) / 2)\n resp = get_page(tags, page)\n\n if len(resp) == 0:\n earliest_empty_page = page\n else:\n latest_populated_page = page\n\n return (\n (latest_populated_page - 1) * LIMIT +\n len(get_page(tags, latest_populated_page))\n )","title":""},{"docid":"295d36aef624de05188846e671059461","score":"0.5810799","text":"def total_likes(self):\n return self.likes.count()\n #return int(3)","title":""},{"docid":"d6b9eb9f30ab39daf22ab5c7b799dd9e","score":"0.5809124","text":"def get(self, request, *args, **kwargs):\n username = kwargs.get(\"username\")\n follower = self.get_object(username)\n followers = follower.get_following()\n return Response(self.serializer_class(followers, many=True).data)","title":""},{"docid":"37528a2fe66a7367091762bb2796ba7f","score":"0.58006597","text":"def get_following(self, username):\n user = self.get_user(username)\n user_id = user['id']\n params = {'access_token': os.getenv('INSTAGRAM_ACCESS_TOKEN'), 'count': 100}\n url = self.api_url + 'users/' + user_id + '/follows?'\n response = requests.get(url, params=params).json()\n if response['meta']['code'] != 200:\n raise MediaMissingException('Unable to access account!')\n return response['data']","title":""},{"docid":"fd515e8aa767c3bbf5e11c39a5684106","score":"0.5780504","text":"def get_followers(self, username):\n user = self.get_user(username)\n user_id = user['id']\n params = {'access_token': os.getenv('INSTAGRAM_ACCESS_TOKEN'), 'count': 100}\n url = self.api_url + 'users/' + user_id + '/followed-by?'\n response = requests.get(url, params=params).json()\n if response['meta']['code'] != 200:\n raise MediaMissingException('Unable to access account!')\n return response['data']","title":""},{"docid":"2dbb94b03eccac94bac3aa105fbcce19","score":"0.5778688","text":"def get_follower_requests():\n return get_all_requests()","title":""},{"docid":"99f63e215e3e7e1ca3dd9c57524ae9a9","score":"0.57725054","text":"def following(user):\n return {\"following\": Follow.objects.following(user)}","title":""},{"docid":"2a67e84eff2c07b94c4a04ab793cc1a5","score":"0.57575667","text":"def calculate_num_likes(self, posts=None):\n\n if self.platform_name in [\"Custom\", \"Blogspot\", \"Wordpress\"]:\n return 0\n else:\n if posts:\n return posts.aggregate(Sum('engagement_media_numlikes'))['engagement_media_numlikes__sum']\n else:\n return self.posts_set.aggregate(Sum('engagement_media_numlikes'))['engagement_media_numlikes__sum']","title":""},{"docid":"381ba006c45ee484b621729316336a9b","score":"0.57518095","text":"def followers(user):\n try:\n return Relationship.objects.get_followers_for_user(user)\n except AttributeError:\n pass","title":""},{"docid":"bcb28bc1d1d8b4e1bf96776af2e41edf","score":"0.57358927","text":"def unfollower_ids_count(self):\n return len(self.unfollower_ids)","title":""},{"docid":"f45f17ece7f7ddce2c57b89d7d12eb19","score":"0.5726824","text":"def get(self, request, username):\n try:\n the_user = User.objects.get(username=username)\n except User.DoesNotExist:\n raise UserNotFound\n to_check = Profile.objects.get(user_id=the_user.id)\n my_follows = to_check.w_following.all()\n serializer = self.get_serializer(my_follows, many=True)\n return Response({\"followers\": serializer.data, \"count\":\n len(serializer.data)},\n status=status.HTTP_200_OK)","title":""},{"docid":"fcdd9ca1dfeecfbc75619dea8b54894f","score":"0.5719411","text":"def calls_authors(*args):\n calls_posts = CallsPost.objects.published()\n authors = User.objects.filter(callsposts__in=calls_posts)\n return list(authors.annotate(post_count=Count(\"callsposts\")))","title":""},{"docid":"36530e41b1fd674e4fbc6d3bfdf6b903","score":"0.5716276","text":"def notifications_count(request):\n if 'user' not in request or request.user.is_anonymous:\n count = 0\n else:\n count = FeedEntry.objects.filter(\n user=request.user, is_read=False, category='notification',\n ).count()\n return {\n 'notifications_count': count,\n }","title":""},{"docid":"625c7311f470d221efebd9f79dac725c","score":"0.5714881","text":"def get_follower_usernames(username, count=5000):\n return _get_friend_or_follower_usernames(FOLLOWERS, username, count)","title":""},{"docid":"ad64a9be4a1bb81d069794bbcffad9d8","score":"0.5711323","text":"def list_private(self, request, *args, **kwargs):\n posts = []\n followees = [uf.user for uf in UserFollower.objects.filter(follower=self.request.user)]\n [posts.extend(Post.objects.filter(author=user)) for user in followees]\n posts.sort(key=lambda post: post.date)\n return Response(serializers.PostSerializer(posts, many=True, context={'request': request}).data)","title":""},{"docid":"0746d9e27c6c1e43d15f6fc7d4746d08","score":"0.57094544","text":"def get(self, request, *args, **kwargs):\n username = kwargs.get(\"username\")\n creator = self.get_object(username)\n followers = creator.get_followers()\n return Response(self.serializer_class(followers, many=True).data)","title":""},{"docid":"f59dbf4ca1658e486a7ae96312337ef8","score":"0.57048434","text":"def getNewsFeed(self, userId: int) -> List[int]:\n q = [(idx, tweet) for idx, tweet in self.users[userId]['tweets']]\n heapq.heapify(q)\n for followeeId in self.users[userId]['followed']:\n for idx, tweet in self.users[followeeId]['tweets']:\n if (idx, tweet) not in q:\n heapq.heappush(q, (idx, tweet))\n r = []\n while q and len(r) < self.numTweets:\n r.append(heapq.heappop(q)[1])\n return r","title":""},{"docid":"aba357fadf166ade093f8eeb1441d9a7","score":"0.570118","text":"def get_followers(api,user):\n return api.followers(user)","title":""},{"docid":"011d099a3d9d87fb2fbbf0fc5d1fccbf","score":"0.56876034","text":"def nr_user_stories(self) -> Tuple[int, List[str]]:\n raise NotImplementedError","title":""},{"docid":"f61ce95420fde73aaade126299f50c5b","score":"0.568267","text":"def get_followers(self, username, num_followers, print_progress=False):\n follower_list = []\n count = 1\n for follower in limit_handled(Cursor(self.api.followers, screen_name=username).items(num_followers)):\n cur_follower = Format.format_username(follower.screen_name)\n follower_list.append(cur_follower)\n if print_progress:\n print('get_follower', count, cur_follower)\n count += 1\n return follower_list","title":""},{"docid":"d9671557405c13d64fa38a0aa11f141b","score":"0.5650465","text":"def testGetAllFollowedReturnsNextPageID(self):\n time = datetime.utcfromtimestamp(1336604400)\n objectID = ObjectAPI(self.user).create(u'about')\n TagValueAPI(self.user).set({objectID: {u'username/follows': None}})\n comments = SecureCommentAPI(self.user)\n for i in range(26):\n comments.create(u'Comment', u'username', about=[u'about'],\n when=time - timedelta(minutes=i),\n url='http://example.com/comment')\n self.store.commit()\n\n result = yield self.invoke('getAllFollowed', username='username')\n result = loads(result)['result']\n self.assertEqual(1336602960, result['nextPageID'])\n self.assertEqual(25, len(result['comments']))","title":""},{"docid":"132fc38061f936f8643c51b56c5bff9f","score":"0.56404454","text":"def get_followers(self):\n return User.find({'following': {'$in': self.categories}})","title":""},{"docid":"f17e39e0406b0a8b491a922c61defc75","score":"0.56342286","text":"def getCount():\n return twitterdb.statuses.count()","title":""},{"docid":"ab9d5d916c8246a582ee76550dd95890","score":"0.56341815","text":"def get_follows(self):\n follows = []\n if self._status == DataLocations.CACHED:\n follows = self.__dict__.get('follows')\n else:\n # Get the users from the feed.\n # TODO: Only works for local users.\n items = []\n if self._status == DataLocations.LOCAL:\n location = SettingsManager.get_user(self.user_id)['follows_location']\n item_els = self._get_attr_el(location, self.get_follows.binding, '//channel/item')\n follows = [_recursive_dict(item)[1] for item in item_els]\n\n follows = [User(remote_url=item['user_link']) for item in follows]\n cache_users(follows)\n return follows","title":""},{"docid":"882476cbf914044b07373dd0f087d88b","score":"0.5633867","text":"def number_of_messages(idea):\n posts = Post.objects(ideas=str(idea.id))\n return posts","title":""},{"docid":"77d9a22a559125d6de82ec702e8a4d71","score":"0.5618872","text":"def followed(self):\n return self.filter('followed')","title":""},{"docid":"145cecf9a92b5f257316a03685b37652","score":"0.56128067","text":"def show_followers(request, username):\n\n try:\n user = User.objects.get(username__iexact=username)\n except User.DoesNotExist:\n raise Http404\n\n following = user.followers.following()\n\n data = {\n 'users' : following,\n 'user_profile': user\n }\n\n return render(\n request,\n templates['PROFILE_FOLLOWERS'],\n data,\n )","title":""},{"docid":"214748cf295686f0568e174560438eed","score":"0.5603917","text":"def get_followers(self, url: str) -> Dict:\n\n user_data = self.get_user_info(url=url, target=\"followers\")\n\n query_url = f\"{self.BASE_URL}{self.GRAPHQL_QUERY}\"\n after = \"\"\n\n user_followers = {\n \"count\": user_data.followed_by,\n \"usernames\": list(),\n \"followers\": list(),\n }\n while True:\n params = {\n \"query_hash\": self.followers_query_hash,\n \"id\": user_data.user_id,\n \"include_reel\": False,\n \"fetch_mutual\": False,\n \"first\": 50,\n \"after\": after or \"\",\n }\n data = self._make_request(url=query_url, params=params)[\"data\"][\"user\"][\"edge_followed_by\"]\n self._extract_usernames(users=data[\"edges\"], result=user_followers[\"usernames\"])\n after = self._has_next_page(data)\n if after is None:\n break\n\n self._extract_users_by_usernames(usernames=user_followers[\"usernames\"], result=user_followers[\"followers\"])\n logging.info(msg=f'User {url} followers. Count: {len(user_followers[\"followers\"])}')\n\n return user_followers","title":""},{"docid":"ba83893bb81cab812f42904d87260ae6","score":"0.5595626","text":"def followers(self):\n return(\n User.select().join(\n Relationship, on=Relationship.from_user\n ).where(\n Relationship.to_user == self\n )\n )","title":""},{"docid":"ba83893bb81cab812f42904d87260ae6","score":"0.5595626","text":"def followers(self):\n return(\n User.select().join(\n Relationship, on=Relationship.from_user\n ).where(\n Relationship.to_user == self\n )\n )","title":""},{"docid":"63c6c199c1bda619e5a21b11d48a83d3","score":"0.559146","text":"def get_usertofollow_row_count(self):\n\n return self._session.query(UserToFollow).count()","title":""},{"docid":"db185a484f5db98a4a3f7c4264cadbc2","score":"0.5589288","text":"def followers(self):\n\n return (\n User.select().join(\n Relationship, on=Relationship.from_user\n ).where(\n Relationship.to_user == self # el usuario que inicio sesion\n )\n )","title":""},{"docid":"669c2541f5d7e873b2ca2164e9c1a7db","score":"0.55855876","text":"def find_post_stats(post_data):\n num_posts = len(post_data)\n\n # calculate stats about post creator\n creator_id = post_data[0]['poster_id']\n num_creator_posts = 0\n for post in post_data:\n if post['poster_id'] == creator_id:\n num_creator_posts += 1\n\n num_posters = len({post['poster_id'] for post in post_data})\n\n # calculate stats for reaching certain post milestones\n topic_created_date = datetime.datetime.fromisoformat(post_data[0]['post_date'])\n post_25_delta = None\n post_50_delta = None\n if len(post_data) >= 25:\n post_25_date = datetime.datetime.fromisoformat(post_data[24]['post_date'])\n post_25_delta = str(post_25_date - topic_created_date)\n if len(post_data) >= 50:\n post_50_date = datetime.datetime.fromisoformat(post_data[49]['post_date'])\n post_50_delta = str(post_50_date - topic_created_date)\n\n post_stats = {'num_posts': num_posts,\n 'num_posters': num_posters,\n 'num_creator_posts': num_creator_posts,\n 'post_25_delta': post_25_delta,\n 'post_50_delta': post_50_delta}\n\n return post_stats","title":""},{"docid":"e0874024d66ad14d809ce00b8e6bf058","score":"0.5580903","text":"def followers_view(request, username, *args, **kwargs):\n\n try:\n user = User.objects.get(username=username)\n except:\n return Response({\n 'details': 'Username not found'\n }, status=status.HTTP_404_NOT_FOUND)\n\n users_id_who_follow = [user.id for user in Profile.objects.get(user=user).follower.all()]\n followers = Profile.objects.filter(user__id__in=users_id_who_follow)\n serializer = ProfileSerializers(followers, context={'request':request}, many=True)\n\n return Response(serializer.data, status=200)","title":""},{"docid":"1359561a775053646f65616ffc8093e7","score":"0.5562729","text":"def get_following(se, proxy: dict) -> dict:\n try:\n with se.get(_ROOT_URL + 'bookmark.php',\n params={'type': 'user'},\n headers={'User-Agent': random.choice(misc.USER_AGENT)},\n proxies=proxy,\n timeout=5) as fo_res:\n fo_html = BeautifulSoup(fo_res.text, 'lxml')\n fo_node = fo_html.find_all('div', class_='userdata')\n if not fo_node:\n raise exception.ResponseError('Cannot fetch following info.')\n\n fo_info = {ele.a['data-user_id']: ele.a['data-user_name'] for ele in fo_node}\n return fo_info\n except requests.Timeout:\n raise requests.Timeout('Timeout during getting following info.')\n except exception.ResponseError:\n raise","title":""},{"docid":"cc0548d9839411a76bca1c3e789344a4","score":"0.5547523","text":"def download_followers_list(self, user_id, username):\n followers = []\n end_cursor = ''\n has_next_page = True\n\n self.session.headers['Referer']= f'https://www.instagram.com/{username}/followers'\n\n first_page_url = f'https://www.instagram.com/graphql/query/?query_hash=c76146de99bb02f6415203be841dd25a&' \\\n f'variables=%7B%22id%22%3A%22{user_id}%22%2C%22include_reel%22%3Atrue%2C%22' \\\n f'fetch_mutual%22%3Atrue%2C%22first%22%3A24%7D'\n\n res = self.session.get(first_page_url, headers=self.session.headers)\n self.session.headers['X-CSRFToken'] = res.cookies['csrftoken']\n\n print('getting followers ... ', end='')\n while has_next_page:\n try:\n has_next_page = bool(\n res.json()['data']['user']['edge_followed_by']['page_info']['has_next_page'])\n except KeyError as kerr:\n pass\n try:\n for node in res.json()['data']['user']['edge_followed_by']['edges']:\n followers.append(\n (node['node']['id'], node['node']['username']))\n except Exception as err:\n print()\n\n if has_next_page:\n end_cursor = res.json()[\n 'data']['user']['edge_followed_by']['page_info']['end_cursor'].replace('==', '')\n next_page = f'https://www.instagram.com/graphql/query/?query_hash=c76146de99bb02f6415203be841dd25a' \\\n f'&variables=%7B%22id%22%3A%22{user_id}%22%2C%22' \\\n f'include_reel%22%3Atrue%2C%22fetch_mutual%22%3Afalse%2C%22first%22%3A14%2C%22' \\\n f'after%22%3A%22{end_cursor}%3D%3D%22%7D'\n\n # time.sleep(randint(2,5)) # delay, prevents instagram ban\n res = self.session.get(next_page, headers=self.session.headers)\n self.session.headers['X-CSRFToken'] = res.cookies['csrftoken']\n\n else:\n print(colored('DONE', 'green'))\n\n return followers","title":""},{"docid":"b4addb4162b8cdc0401c0452c3b309e8","score":"0.55358255","text":"def followers(self):\n if self._followers is None:\n data = yield 'users/{user}/followers'.format(user=self.username)\n self._followers = []\n for user in data:\n user_data = user.pop('user')\n date = user.pop('followed_at')\n self._followers.append(User(followed_at=date, **user_data))\n yield self._followers","title":""},{"docid":"352c5074447cb46e7d80de8be1e2005a","score":"0.55306405","text":"def getNumLikes(self):\n try:\n friendCount = getFacebookDump(self)[u'likes']\n \n except KeyError:\n dump = json.loads(urllib.urlopen(\\\n \"https://graph.facebook.com/fql?q=SELECT%20friend_count%20FROM%20user%20WHERE%20uid=\"\\\n +self.getFacebook()).readline())\n friendCount = dump[u'data'][0][u'friend_count']\n \n return friendCount","title":""},{"docid":"eb8b5c88090b5e5e4d755a20588c602d","score":"0.55284023","text":"def __init__(self):\n self.post = []\n self.follows = defaultdict(list)\n self.post_user = {}","title":""},{"docid":"b293dcfd2204a7f9da73df19445c12d1","score":"0.55264217","text":"def getNewsFeed(self, userId):\n heap = []\n if userId in self.tweets:\n for i in self.tweets[userId]:\n heapq.heappush(heap, i)\n if len(heap) > 10:\n heapq.heappop(heap)\n if userId in self.followers:\n for i in self.followers[userId]:\n for j in self.tweets[i]:\n heapq.heappush(heap, j)\n if len(heap) > 10:\n heapq.heappop(heap)\n if not heap:\n return []\n res = []\n while heap:\n res.append(heapq.heappop(heap)[1])\n return res[::-1]","title":""},{"docid":"844e66f02114db0e34be5aa7e9bf8077","score":"0.5514456","text":"def followers_ids(auth, **params):\n\n maxitems = params.pop(\"maxitems\", 0)\n if maxitems > 0:\n return cursor_iter(followers_ids, maxitems, auth, params)\n\n endpoint = \"https://api.twitter.com/1.1/followers/ids.json\"\n\n params.setdefault(\"count\", 5000)\n\n data, status_code, error_code = rest_call(endpoint, auth, params)\n try:\n next_cursor = data[\"next_cursor\"]\n count = len(data[\"ids\"])\n except KeyError:\n next_cursor, count = 0, 0\n\n meta = {\n \"code\": status_code,\n \"error_code\": error_code,\n \"next_cursor\": next_cursor,\n \"count\": count,\n }\n\n return data, meta","title":""},{"docid":"7232970fe9a42719d88994dd51de94e4","score":"0.5514358","text":"def _get_followers(self, direction=\"follower\", last_user=\"\", what=\"blog\", limit=100):\n if not self.hive.is_connected():\n raise OfflineHasNoRPCException(\"No RPC available in offline mode!\")\n followers_list = []\n limit_reached = True\n cnt = 0\n while limit_reached:\n self.hive.rpc.set_next_node_on_empty_reply(False)\n if self.hive.rpc.get_use_appbase():\n query = {'account': self.name, 'start': last_user, 'type': what, 'limit': limit}\n if direction == \"follower\":\n followers = self.hive.rpc.get_followers(query, api='follow')\n if isinstance(followers, dict) and 'followers' in followers:\n followers = followers['followers']\n elif direction == \"following\":\n followers = self.hive.rpc.get_following(query, api='follow')\n if isinstance(followers, dict) and 'following' in followers:\n followers = followers['following']\n else:\n if direction == \"follower\":\n followers = self.hive.rpc.get_followers(self.name, last_user, what, limit, api='follow')\n elif direction == \"following\":\n followers = self.hive.rpc.get_following(self.name, last_user, what, limit, api='follow')\n if cnt == 0:\n followers_list = followers\n elif followers is not None and len(followers) > 1:\n followers_list += followers[1:]\n if followers is not None and len(followers) >= limit:\n last_user = followers[-1][direction]\n limit_reached = True\n cnt += 1\n else:\n limit_reached = False\n\n return followers_list","title":""},{"docid":"e0532a410bafc34c6637d8ab6c0ef76e","score":"0.55119175","text":"def getFollowers(channel):\n\tapi = getApi()\n\ttry:\n\t\tusers = api.GetFollowers()\n\t\tf = [u.screen_name for u in users]\n\t\tsendToChannel(channel, string.join(f).replace(' ',', '))\n\texcept:\n\t\tsendToChannel(channel, \"Hurr\")","title":""},{"docid":"f5debfb84f14c3a93558b9e269a55770","score":"0.55074036","text":"def getNewsFeed(self, userId):\n feed = self.feeds[userId][:]\n for user in self.follows[userId]:\n feed += self.feeds[user]\n feed = [x[0] for x in heapq.nlargest(10,feed,key=lambda x:x[1])]\n # feed = sorted(feed, key=lambda x: x[1], reverse=True)\n # feed = [x[0] for x in feed[:10]]\n return feed","title":""},{"docid":"d74da5d08c78207d51782fec0e20990a","score":"0.550385","text":"def get_liker_frequencies(posts_likers):\n\n liker_counter = Counter()\n\n for post_id, post_likers in posts_likers.items():\n for post_liker in post_likers:\n liker_pk = str(post_liker['pk'])\n liker_counter.update({liker_pk: 1})\n\n\n return liker_counter","title":""},{"docid":"d4980c63c0459b480ff2714d5176002e","score":"0.55018276","text":"def get_followed_by_user(self, url: str) -> Dict:\n\n user_data = self.get_user_info(url=url, target=\"followed_by_user\")\n\n query_url = f\"{self.BASE_URL}{self.GRAPHQL_QUERY}\"\n after = \"\"\n\n user_follow = {\n \"count\": user_data.follow,\n \"usernames\": list(),\n \"followed\": list(),\n }\n while True:\n params = {\n \"query_hash\": self.followed_by_user_query_hash,\n \"id\": user_data.user_id,\n \"first\": 50,\n \"after\": after if after else \"\",\n }\n\n data = self._make_request(url=query_url, params=params)[\"data\"][\"user\"][\"edge_follow\"]\n self._extract_usernames(users=data[\"edges\"], result=user_follow[\"usernames\"])\n\n after = self._has_next_page(data)\n if after is None:\n break\n\n self._extract_users_by_usernames(usernames=user_follow[\"usernames\"], result=user_follow[\"followed\"])\n logging.info(msg=f'Followed by user {url}. Count: {len(user_follow[\"followed\"])}')\n\n return user_follow","title":""},{"docid":"760843f7236d4fde0fed15c4a5f57843","score":"0.55013126","text":"def getNewsFeed(self, userId: int) -> List[int]:\n if userId not in self.user:\n return list()\n else:\n # 取出自己最近10条推文\n ans = self.user[userId].tweet[-10:][::-1]\n # 取出自己followee\n for followeeId in self.user[userId].followee:\n if followeeId in self.user:\n # follow人的最近十条推文\n opt = self.user[followeeId].tweet[-10:][::-1]\n i, j, combined = 0, 0, list()\n while i < len(ans) and j < len(opt):\n if self.tweetTime[ans[i]] > self.tweetTime[opt[j]]:\n combined.append(ans[i])\n i += 1\n else:\n combined.append(opt[j])\n j += 1\n combined.extend(ans[i:])\n combined.extend(opt[j:])\n ans = combined[:10]\n return ans","title":""},{"docid":"1e1208668d51f4e5fe3d773c12aa8f58","score":"0.5499597","text":"def calculate_num_shares(self, posts=None):\n if posts is None:\n posts = self.posts_set.all()\n\n if self.platform_name == \"Facebook\":\n return posts.aggregate(Sum('engagement_media_numfbshares'))['engagement_media_numfbshares__sum']\n if self.platform_name == \"Pinterest\":\n return posts.aggregate(Sum('engagement_media_numrepins'))['engagement_media_numrepins__sum']\n if self.platform_name == \"Twitter\":\n return posts.aggregate(Sum('engagement_media_numshares'))['engagement_media_numshares__sum']\n return 0","title":""},{"docid":"e958d7308828ebb79fe9a6b80ce80185","score":"0.54984397","text":"def getNewsFeed(self, userId: 'int') -> 'List[int]':\n # extract the user\n # go through each user's followers\n # add their tweets to an array --> array of arrays\n # put most recent tweet from each array into max heap\n # removing from max heap ten times\n\n user = self.users[userId]\n collected_tweets = []\n result = []\n\n for followerId in user.following:\n follower = self.users[followerId]\n collected_tweets.append(follower.tweets)\n\n maxheap = Heap(\"max\")\n\n for index in range(len(collected_tweets)):\n current_tweets = collected_tweets[index]\n last_tweet = current_tweets[-1]\n maxheap.insert([last_tweet.timestamp, last_tweet.tweetId, index])\n\n count_in_newsfeed = 10\n\n while (maxheap.size() > 0 and count_in_newsfeed > 0):\n timestamp, tweetId, user = maxheap.remove_peak()\n result.append(tweetId)\n if len(collected_tweets[user]) > 0:\n last_tweet = collected_tweets[user].pop()\n maxheap.insert([last_tweet.timestamp, last_tweet.tweetId, user])\n count_in_newsfeed -= 1","title":""},{"docid":"4a4b8c032d3fb1a9e7855d7c16c1fc8e","score":"0.5485443","text":"def get_all_posts(access_token, owner_id, count=100, offset=0):\n \n all_posts = []\n while True:\n time.sleep(random.random())\n wall = getjson(\"https://api.vk.com/method/wall.get\", {\n \"owner_id\" : owner_id,\n \"count\": count,\n \"access_token\": access_token,\n \"offset\": offset,\n \"v\": '5.131'\n })\n count_posts = wall['response']['count']\n posts = wall['response']['items']\n\n all_posts.extend(posts)\n\n if len(all_posts) >= count_posts:\n break\n else:\n offset += 100\n\n return all_posts, count_posts","title":""},{"docid":"2753e129586ed28c5b7fb8188ea9d109","score":"0.54846305","text":"def users_i_follow(self):\n return _get_session().query(Connection).filter_by(following=self).all()","title":""},{"docid":"05bac14dc609d63cba5e319271545e1e","score":"0.5480283","text":"def users_following_me(self):\n return _get_session().query(Connection).filter_by(followed=self).all()","title":""},{"docid":"5a7aefe392d87e5f10ea033e32968599","score":"0.5470377","text":"def users_count(self):\n return self._data.get('related_field_counts', {}).get('users', 0)","title":""},{"docid":"3e52dbeec36cd2adbbcd2be7fab37680","score":"0.5470254","text":"def get_users_followed(self, username):\n path = f'/api/user/{username}/following'\n return self._r.get(path, stream=True, fmt=NDJSON,\n converter=models.User.convert)","title":""},{"docid":"ef873da48e20e6c7152301d0d7246cd8","score":"0.5465221","text":"def new_follower_ids_count(self):\n return len(self.new_follower_ids)","title":""},{"docid":"d9a736f544d5aec0cd05df518afae463","score":"0.5462012","text":"def getNewsFeed(self, user):\n import heapq\n h, tweets = [], self.tweets\n people = self.followee.get(user, set()) | set([user])\n for person in people:\n if person in tweets and tweets[person]:\n time, tweet = tweets[person][-1]\n h.append((time, tweet, person, len(tweets[person]) - 1))\n heapq.heapify(h)\n news = []\n for _ in range(10):\n if h:\n time, tweet, person, idx = heapq.heappop(h)\n news.append(tweet)\n if idx:\n new_time, new_tweet = tweets[person][idx - 1]\n heapq.heappush(h, (new_time, new_tweet, person, idx - 1))\n return news","title":""},{"docid":"72a4a548da874fcfe29c6586c7e80fc7","score":"0.546016","text":"def get_users_following(self, username):\n path = f'/api/user/{username}/followers'\n return self._r.get(path, stream=True, fmt=NDJSON,\n converter=models.User.convert)","title":""},{"docid":"b7235885f72e0d9dd8dc0e28fda14a3f","score":"0.5451477","text":"def get_unfollowers_number(message, user_id, username):\n doesnt_follow_back = model.get_unfollowers(user_id, username)\n bot.edit_message_reply_markup(chat_id=message.chat.id,\n message_id=message.message_id,\n reply_markup=None)\n\n keyboard = types.InlineKeyboardMarkup()\n\n if doesnt_follow_back.__len__() > 0:\n keyboard.add(types.InlineKeyboardButton(text=f'{emojize(\" :heavy_plus_sign:\", use_aliases=True)}gimme list',\n callback_data=f'list_unfollowing_id:{user_id}_page:0'),\n types.InlineKeyboardButton(text=f'{emojize(\" :pencil2:\", use_aliases=True)}another one',\n callback_data=f'get_instagram_username'))\n else:\n keyboard.add(types.InlineKeyboardButton(text=f'{emojize(\" :pencil2:\", use_aliases=True)}another one', callback_data=f'get_instagram_username'))\n\n bot.send_message(chat_id=message.chat.id,\n text=f'{emojize(\" :pig_nose:\", use_aliases=True)}doesnt follow u back: {doesnt_follow_back.__len__()} users',\n reply_markup=keyboard)","title":""}],"string":"[\n {\n \"docid\": \"0c002c2cc4835dc347d3d1959dbbd10a\",\n \"score\": \"0.7136349\",\n \"text\": \"def followers_count(self):\\n user_type = ContentType.objects.get_for_model(self)\\n return UserInterest.objects\\\\\\n .filter(interest_type='follow', content_type__pk=user_type.id, object_id=self.id)\\\\\\n .count()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57f18077da40654c6db6a29b111e57c2\",\n \"score\": \"0.7008957\",\n \"text\": \"def follow_index(request):\\n post_list = Post.objects.filter(author__following__user = request.user).select_related(\\\"author\\\", \\\"group\\\")\\n page_number = request.GET.get('page', 1)\\n paginator = Paginator(post_list, 4) \\n page = paginator.get_page(page_number)\\n post_count = page.object_list.count()\\n context = { \\n \\\"page\\\": page,\\n \\\"post_count\\\": post_count,\\n }\\n return render(request, \\\"follow.html\\\", context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4a172ad8ac928b5a1c706b29bef0da5\",\n \"score\": \"0.6932286\",\n \"text\": \"def follower_count(self):\\n return self.followers.count()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4409acd21253b05c46d62a8d0844666e\",\n \"score\": \"0.688722\",\n \"text\": \"def getNumFollowers(self):\\n\\n return getTwitterDump(self, 'u')[u'followers_count']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52810e0d79662222a11f7f0bdc18da74\",\n \"score\": \"0.6844079\",\n \"text\": \"def followed_posts(self):\\n # the join operation creates a temporary table which merges the post and followers table data\\n # the join is merged based on the condition passed as an argument ie the conditions to filter user's followed posts\\n followed = Post.query.join(followers, (followers.c.followed_id == Post.user_id)).filter(followers.c.follower_id==self.id) \\n\\n # the user will also want to see her personal posts in the blog posts. \\n # To do that the user's posts are queried and are then merged with the other posts already queried using the union operation\\n # then the posts are sorted based on recency \\n own = Post.query.filter_by(user_id = self.id)\\n return followed.union(own).order_by(Post.timestamp.desc()) # Post.timestamp.desc() sorts the posts by time on a descending order.\\n # Due to this, the most recent post will be rendered first\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10c70740fbab0709787211d81a0c031a\",\n \"score\": \"0.67538553\",\n \"text\": \"def followed_posts(self):\\n # this method returns a query object and NOT the result, similar to 'lazy' = 'dynamic' in relationship\\n # this is good practice since the caller can tach on additional queries\\n return Post.query.join(followers,\\n (followers.c.followed_id == Post.user_id)).filter(\\n followers.c.follower_id == self.id).order_by(Post.timestamp.desc())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8579337648252898ac46ef9aa59083d\",\n \"score\": \"0.67458844\",\n \"text\": \"def follow_counts(request,userobj):\\n\\n following_count = Follow.objects.filter(follower = userobj).count()\\n follower_count = Follow.objects.filter(following = userobj).count()\\n\\n\\n if request.user.username == userobj.username:\\n \\n follow_count = {\\\"following_count\\\":following_count,\\\"follower_count\\\":follower_count}\\n return follow_count\\n \\n return follower_count,following_count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0134e84dbde29970c1f5e99f27636b0\",\n \"score\": \"0.66715217\",\n \"text\": \"def get_follower_count(tweet):\\n if is_original_format(tweet):\\n return tweet[\\\"user\\\"][\\\"followers_count\\\"]\\n else:\\n return tweet[\\\"actor\\\"][\\\"followersCount\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0417c32dd66b1a7c39f46dacc8fd0a9c\",\n \"score\": \"0.66394466\",\n \"text\": \"def num_pages(following_count):\\n \\n n = following_count / settings.TWITTER_FRIENDS_PAGE_LENGTH\\n if following_count % settings.TWITTER_FRIENDS_PAGE_LENGTH > 0:\\n n += 1\\n return n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2abdb9fad9e9acf57df9624247dbfceb\",\n \"score\": \"0.6569586\",\n \"text\": \"def get_following_count(tweet):\\n if is_original_format(tweet):\\n return tweet[\\\"user\\\"][\\\"friends_count\\\"]\\n else:\\n return tweet[\\\"actor\\\"][\\\"friendsCount\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87b311ad9f5e84b54be3a9ac75a6ef4c\",\n \"score\": \"0.65661263\",\n \"text\": \"def alcance(followers):\\r\\n pot = 0\\r\\n for follower in followers:\\r\\n pot += follower[\\\"followers_count\\\"]\\r\\n return pot + len(followers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e19f209e9f3d0753ca3e8317148d76b6\",\n \"score\": \"0.6421191\",\n \"text\": \"def follow_index(request):\\n following_posts = Post.objects.filter(author__following__user=request.user)\\n paginator = Paginator(following_posts, settings.PER_PAGE_INDEX)\\n page_number = request.GET.get(\\\"page\\\")\\n page = paginator.get_page(page_number)\\n return render(\\n request,\\n \\\"follow.html\\\",\\n {\\\"page\\\": page, \\\"paginator\\\": paginator},\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f162726dd7f206979c6886feda0854b0\",\n \"score\": \"0.63893497\",\n \"text\": \"def GetNumberOfPosts(self):\\n\\n return len(self.all_posts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a04c7b699ca24cd4e8b58cdf5f0359ae\",\n \"score\": \"0.63271505\",\n \"text\": \"def followers(self):\\n url = '{0}/followers'.format(self.get_url())\\n return http.Request('GET', url), parsers.parse_json\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaf747f85eefdcc4cc743ec59d47dcf4\",\n \"score\": \"0.6303726\",\n \"text\": \"def getFollowerCount(connection, userID):\\n\\n\\t\\tvariables = [userID]\\n\\n\\t\\treturn TableTools.getCount(connection, UsersTableTools._FOLLOWS_TABLE,\\n\\t\\t\\t\\\"flwee = :1\\\", variables)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa8de2714618d6a5ebf4b34661719621\",\n \"score\": \"0.62701845\",\n \"text\": \"def following_posts(request):\\n if request.user.is_authenticated:\\n\\n userobj = util.get_user_obj_by_userId(request.user.id)\\n posts = paginate(request,get_all_posts_of_user_network(request,userobj))\\n post_liked_ids = get_myliked_post(request).values_list(\\\"id\\\",flat= True)\\n return render (request, \\\"network/following.html\\\",{\\n \\\"allposts\\\": posts,\\n \\\"post_liked_ids\\\":post_liked_ids,\\n\\n })\\n else:\\n\\n return HttpResponseRedirect(reverse(\\\"network:login\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d929c65aa503c6bc5deaaca6117f26c\",\n \"score\": \"0.62489426\",\n \"text\": \"def followers(github, user, pager):\\n github.followers(user, pager)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7221e8e8810ecfb407ac6f89380f4667\",\n \"score\": \"0.6238051\",\n \"text\": \"def get_followers(profile: str, session) -> Optional[float]:\\n url = f'https://www.instagram.com/{profile}'\\n r = session.get(url)\\n if r.ok:\\n instagram = BeautifulSoup(r.text, features=\\\"lxml\\\")\\n scripts = instagram.select('script[type=\\\"text/javascript\\\"]')\\n json_s = scripts[3]\\n json_s = json_s.text\\n json_s = json_s[len('window._sharedData = '):-1]\\n json_s = json.loads(json_s)\\n entry_data = json_s['entry_data']\\n profile_page = entry_data['ProfilePage'][0]\\n graph_ql = profile_page['graphql']\\n user = graph_ql['user']\\n user['edge_owner_to_timeline_media'] = None\\n with open(f'user_information_{profile}.json', 'w') as f:\\n f.write(json.dumps(user, indent=4, sort_keys=True))\\n follow_count = user['edge_followed_by']\\n final: float = float(follow_count['count'])\\n print(f'Finished for {profile} - Followers {final}')\\n return final\\n print(f\\\"Error. It wasn't possible to get the following count\\\")\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"042a8dd59b121bc4f51586643dc6d45a\",\n \"score\": \"0.6230698\",\n \"text\": \"def get_followed_user_count( self, user ):\\n count = self.filter( user=user, content_type=ContentType.objects.get_for_model( User ) ).count()\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d1901c1c4a8d090323a621c7f4b7237\",\n \"score\": \"0.6223728\",\n \"text\": \"def followers(user):\\n return {\\\"followers\\\": Follow.objects.followers(user)}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f17e16bbd25f3fc6d7e46546f625866\",\n \"score\": \"0.6220345\",\n \"text\": \"def follower_ids_count(self):\\n return len(self._follower_ids)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fd8cc0b9e997787fb48ab365964cd55\",\n \"score\": \"0.6214273\",\n \"text\": \"def user_posts_statistics(user, days_ago, reverse=None):\\n if reverse is None:\\n reverse = False\\n\\n start_time = datetime.now() - timedelta(days=days_ago)\\n\\n hit_query = Hit.objects.filter(post__publisher__id=user.id, \\n created__date__gte=start_time).values(created_date=F(\\\"created__date\\\")) \\\\\\n .annotate(number=Count(\\\"pk\\\")).order_by(\\\"-created_date\\\")\\n\\n like_query = Like.objects.filter(post__publisher__id=user.id, \\n created__date__gte=start_time, status=True).values(created_date=F(\\\"updated__date\\\")) \\\\\\n .annotate(number=Count(\\\"pk\\\")).order_by(\\\"-created_date\\\")\\n\\n download_query = Download.objects.filter(post__publisher__id=user.id, \\n created__date__gte=start_time).values(created_date=F(\\\"created__date\\\")) \\\\\\n .annotate(number=Count(\\\"pk\\\")).order_by(\\\"-created_date\\\")\\n\\n created_dates = hit_query.values(\\\"created_date\\\").union(\\n like_query.values(\\\"created_date\\\"), \\n download_query.values(\\\"created_date\\\")\\n ).values_list(\\\"created_date\\\").order_by(\\\"-created_date\\\")\\n\\n hits = list(map(lambda date: hit_query.get(created_date=date[0])[\\\"number\\\"]\\n if hit_query.filter(created_date=date[0]).exists() else 0, created_dates.iterator()))\\n\\n likes = list(map(lambda date: like_query.get(created_date=date[0])[\\\"number\\\"]\\n if like_query.filter(created_date=date[0]).exists() else 0, created_dates.iterator()))\\n\\n downloads = list(map(lambda date: download_query.get(created_date=date[0])[\\\"number\\\"]\\n if download_query.filter(created_date=date[0]).exists() else 0, created_dates.iterator()))\\n\\n dates = list(map(lambda date_: date.fromgregorian(date=date_[0])\\n .strftime(\\\"%Y/%m/%d\\\"), created_dates.iterator()))\\n\\n if reverse:\\n hits, likes, downloads, dates = hits[::-1], likes[::-1], downloads[::-1], dates[::-1]\\n\\n return {\\n \\\"dates\\\": dates,\\n \\\"hits\\\": hits,\\n \\\"likes\\\": likes,\\n \\\"downloads\\\": downloads\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8da1157043230ddf65c26a374a4acb34\",\n \"score\": \"0.6208782\",\n \"text\": \"def add_total_followers(G, d):\\n for influencer in d:\\n d[influencer]['num_follow'] = len(G.in_edges(influencer))\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8692d716cd1c411fec554de892e82767\",\n \"score\": \"0.61743456\",\n \"text\": \"def fetch_posts(self):\\n requests = []\\n friends = self.person.friends\\n for friend in friends:\\n requests.append({\\n 'id': friend.fb_id,\\n 'request': str(friend.fb_id) + '/posts'\\n })\\n progress = {\\n 'from': 20, 'to': 50,\\n 'description': 'Collecting your and your friends posts'\\n }\\n self.store_api_response('posts', requests, progress)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9157a0b04516d3f46926c4d97b3dad68\",\n \"score\": \"0.61446565\",\n \"text\": \"def get_nb_posts(self):\\n i = 0\\n try:\\n messages = self.html.find(class_=\\\"pagination\\\").text.split()\\n while True:\\n try:\\n # if messages[i] == 'messages' or messages[i] == 'message':\\n # check if 'message' of 'messages'\\n if (re.match(r\\\"message[s]?\\\", messages[i]) and\\n messages[i - 1] != \\\"premier\\\"):\\n self.messages = int(messages[i - 1])\\n break\\n else:\\n i += 1\\n except Exception:\\n # print(e)\\n print(\\\"Error in Topic get_nb_post\\\")\\n self.messages = 1\\n break\\n except AttributeError:\\n self.messages = 1\\n return self.messages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b984f96b7e4d3f77a1acbd91875c91a0\",\n \"score\": \"0.6084396\",\n \"text\": \"def get_post_count(self):\\n return self.get_number_with_id(self.post_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2af5695213583d10708481fb2fe46146\",\n \"score\": \"0.60823214\",\n \"text\": \"def get_followers(user):\\n res = urllib2.urlopen(\\\"https://api.github.com/users/\\\" + user)\\n soup = json.load(res)\\n\\n logger = logging.getLogger('github api')\\n logger.info('printing','followers')\\n\\n print \\\"followers:\\\", soup['followers']\\n print \\\"following:\\\", soup['following']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1201b7acc5e3141fe467ea3e9ee92026\",\n \"score\": \"0.6074673\",\n \"text\": \"def getFollowingCount(connection, userID):\\n\\n\\t\\tvariables = [userID]\\n\\n\\t\\treturn TableTools.getCount(connection, UsersTableTools._FOLLOWS_TABLE,\\n\\t\\t\\t\\\"flwer = :1\\\", variables)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8bcdc6525e647236f9dd75882856369\",\n \"score\": \"0.60707694\",\n \"text\": \"def get_followers(username, count=5000):\\n follower_usernames = get_follower_usernames(username, count=count)\\n return get_users_for_usernames(follower_usernames)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c213733bae05eaea82f6a983e3c636d5\",\n \"score\": \"0.6023095\",\n \"text\": \"def get_top_posts(\\n L: Instaloader,\\n context: CallbackContext,\\n chat_id: int,\\n messages: dict,\\n profile_url: str,\\n top_n=10,\\n lookback_posts=100,\\n) -> tuple:\\n try:\\n username = re.findall(\\n r\\\"(?:(?:http|https):\\\\/\\\\/)?(?:www.)?(?:instagram.com|instagr.am)\\\\/([A-Za-z0-9-_.]+)\\\",\\n profile_url,\\n )[0]\\n profile = Profile.from_username(L.context, username)\\n\\n n = 0\\n posts_photos = []\\n posts_videos = []\\n\\n for post in profile.get_posts():\\n if n > lookback_posts:\\n break\\n\\n link = f\\\"https://www.instagram.com/p/{post.shortcode}\\\"\\n\\n if post.is_video:\\n posts_videos.append(\\n {\\\"Post URL\\\": link, \\\"Views\\\": post.video_view_count,}\\n )\\n else:\\n posts_photos.append(\\n {\\\"Post URL\\\": link, \\\"Likes\\\": post.likes,}\\n )\\n n = n + 1\\n\\n if len(posts_photos) == 0 and len(posts_videos) == 0:\\n raise ValueError(\\\"Profile is closed.\\\")\\n\\n if len(posts_photos) > 0:\\n df_photos = (\\n pd.DataFrame(posts_photos)\\n .sort_values(\\\"Likes\\\", ascending=False)\\n .head(n=top_n)\\n .set_index(\\\"Post URL\\\")\\n )\\n df_photos[\\\"Likes\\\"] = df_photos[\\\"Likes\\\"].apply(\\n lambda x: f\\\"{thousands_sep(x)} likes\\\"\\n )\\n\\n top_photos = \\\"Top posts by likes:\\\\n\\\" + tabulate(df_photos, tablefmt=\\\"plain\\\")\\n\\n context.bot.send_message(\\n chat_id=chat_id, text=top_photos, disable_web_page_preview=True\\n )\\n\\n if len(posts_videos) > 0:\\n df_videos = (\\n pd.DataFrame(posts_videos)\\n .sort_values(\\\"Views\\\", ascending=False)\\n .head(n=top_n)\\n .set_index(\\\"Post URL\\\")\\n )\\n df_videos[\\\"Views\\\"] = df_videos[\\\"Views\\\"].apply(\\n lambda x: f\\\"{thousands_sep(x)} views\\\"\\n )\\n\\n top_videos = \\\"Top videos by views:\\\\n\\\" + tabulate(\\n df_videos, tablefmt=\\\"plain\\\"\\n )\\n context.bot.send_message(\\n chat_id=chat_id, text=top_videos, disable_web_page_preview=True\\n )\\n\\n result = True\\n traceback = \\\"Success\\\"\\n\\n except Exception as e:\\n context.bot.send_message(chat_id=chat_id, text=messages[\\\"error\\\"])\\n result = False\\n traceback = str(e)\\n\\n return result, traceback\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b1692a0cb74128d3f824d1f8f4c6711\",\n \"score\": \"0.6010295\",\n \"text\": \"def followers(self):\\n if not self._username:\\n raise Error('microblogging.API instance must be authenticated.')\\n url = self.get_url('/statuses/followers.json')\\n json = self._fetch_url(url)\\n data = simplejson.loads(json)\\n return [User.get_from_json(x) for x in data]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8bcc9ebb01a69b902db975d40a894f0\",\n \"score\": \"0.59414184\",\n \"text\": \"def print_post_list(list_of_users):\\n print(f'users by their posts')\\n for i in range(len(list_of_users)):\\n print(f'user_id = {list_of_users[i].id}, total_posts = {list_of_users[i].number_of_posts}, '\\n f'list of user_posts ={list_of_users[i].post_per_likes_list}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f943d9feec2817d7e23853cbed99c54b\",\n \"score\": \"0.5936655\",\n \"text\": \"def get_following(self, username, num_followers, print_progress=False):\\n friend_list = []\\n count = 0\\n for user in limit_handled(Cursor(self.api.friends, screen_name=username).items(num_followers)):\\n cur_following = Format.format_username(user.screen_name)\\n friend_list.append(cur_following)\\n if print_progress:\\n print('get_follower', count, cur_following)\\n count += 1\\n return friend_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95d95c24bb2a8201592f321a156ff9db\",\n \"score\": \"0.59198076\",\n \"text\": \"def get_people(self) -> Dict[str, int]:\\r\\n users = dict()\\r\\n\\r\\n for post in self.ps.search_submissions(after=self.start, subreddit=self.sub,\\r\\n filter=['author']):\\r\\n if post.author in users:\\r\\n users[post.author] += 1\\r\\n else:\\r\\n users[post.author] = 1\\r\\n\\r\\n for post in self.ps.search_comments(after=self.start, subreddit=self.sub,\\r\\n filter=['author']):\\r\\n if post.author in users:\\r\\n users[post.author] += 1\\r\\n else:\\r\\n users[post.author] = 1\\r\\n\\r\\n return users\",\n \"title\": \"\"\n },\n {\n \"docid\": \"467ac85d518b06d9286de6db84732b6c\",\n \"score\": \"0.590841\",\n \"text\": \"def list(self, request, **kwargs):\\n user = request.query_params.get('user')\\n if self.queryset.filter(me=user).count() == 0:\\n return Response({'status': 'no following list'})\\n else:\\n users_followers = Following.objects.get(me=user)\\n data = {\\\"id\\\": users_followers.id, \\\"following\\\": users_followers.usernames}\\n return Response(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a64caf46f408cc13ed0e8e71353e1a1a\",\n \"score\": \"0.58928674\",\n \"text\": \"def count(self):\\n return query.get_total_post_count(self.base_url, self.params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b74eb6d00389a35daa0a7a7c551639a\",\n \"score\": \"0.58847\",\n \"text\": \"def getNewsFeed(self, userId: int) -> List[int]:\\n feed = []\\n for p in self.post[::-1]:\\n if self.post_user[p] in self.follows[userId] or self.post_user[\\n p] == userId:\\n feed.append(p)\\n if len(feed) == 10:\\n break\\n\\n return feed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0286bc6d43f25979ef090b8b02b8878\",\n \"score\": \"0.5870798\",\n \"text\": \"def max_followers_among_all_platforms(self):\\n plats = self.platforms()\\n max_num_followers = 0\\n for p in plats:\\n if p.num_followers > max_num_followers:\\n max_num_followers = p.num_followers\\n return max_num_followers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98932cd379b29a0c154bb9b2ed28e482\",\n \"score\": \"0.5852411\",\n \"text\": \"def test_get_followers_list(self):\\n self.api.get_followers_list()\\n self.api.get_followers_list(screen_name='twitter')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa89954078e93d8789337f2c5403e06a\",\n \"score\": \"0.5850981\",\n \"text\": \"def ListFollowers(self):\\n try:\\n i = 1\\n follower_list = []\\n while (True):\\n param = {\\\"access_token\\\":self.personal_access_token, \\\"per_page\\\": 1, \\\"page\\\": i}\\n r = requests.get(self.url_address+\\\"user/followers\\\", params = param)\\n if (r.status_code!=200):\\n raise ClientError(r.status_code)\\n if (r.text == '[]'):\\n break\\n else:\\n follower = json.loads(r.text[1:-1])\\n follower_dict = {\\\"login\\\": follower[\\\"login\\\"], \\\"url\\\": follower[\\\"url\\\"]} \\n follower_list.append(follower_dict)\\n i = i+1 \\n return json.dumps(follower_list)\\n except ClientError as err:\\n print(err.status)\\n print(err.message)\\n print(err.status, err.message)\\n print(err)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8950fc59a4207e0dd9cb78d5721acc91\",\n \"score\": \"0.58497524\",\n \"text\": \"def show_tweets_high_followed_users(output_file, follower_num):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af87a02ff7dba95abf478afa73cea272\",\n \"score\": \"0.5844426\",\n \"text\": \"def get_all_count(self):\\n html_text = self.get_html()\\n soup = BeautifulSoup(html_text, \\\"lxml\\\")\\n count_of_post = soup.find(id=self.post_id).find(\\\"span\\\", {\\\"class\\\": \\\"number\\\"}).string\\n count_of_theme = soup.find(id=self.theme_id).find(\\\"span\\\", {\\\"class\\\": \\\"number\\\"}).string\\n count = int(count_of_post) + int(count_of_theme)\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7602aa226400f56d1587b2f2a8b2ce4e\",\n \"score\": \"0.5817576\",\n \"text\": \"def test_get_followers_ids(self):\\n self.api.get_followers_ids()\\n self.api.get_followers_ids(screen_name='twitter')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60e573d9894df3cc431486d569a76ef5\",\n \"score\": \"0.58127546\",\n \"text\": \"def get_post_count(tags):\\n\\n page = 1\\n\\n while True:\\n resp = get_page(tags, page)\\n\\n if len(resp) == 0:\\n earliest_empty_page = page\\n break\\n else:\\n latest_populated_page = page\\n\\n page *= 2\\n\\n while (earliest_empty_page - latest_populated_page) > 1:\\n page = int((latest_populated_page + earliest_empty_page) / 2)\\n resp = get_page(tags, page)\\n\\n if len(resp) == 0:\\n earliest_empty_page = page\\n else:\\n latest_populated_page = page\\n\\n return (\\n (latest_populated_page - 1) * LIMIT +\\n len(get_page(tags, latest_populated_page))\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"295d36aef624de05188846e671059461\",\n \"score\": \"0.5810799\",\n \"text\": \"def total_likes(self):\\n return self.likes.count()\\n #return int(3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6b9eb9f30ab39daf22ab5c7b799dd9e\",\n \"score\": \"0.5809124\",\n \"text\": \"def get(self, request, *args, **kwargs):\\n username = kwargs.get(\\\"username\\\")\\n follower = self.get_object(username)\\n followers = follower.get_following()\\n return Response(self.serializer_class(followers, many=True).data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37528a2fe66a7367091762bb2796ba7f\",\n \"score\": \"0.58006597\",\n \"text\": \"def get_following(self, username):\\n user = self.get_user(username)\\n user_id = user['id']\\n params = {'access_token': os.getenv('INSTAGRAM_ACCESS_TOKEN'), 'count': 100}\\n url = self.api_url + 'users/' + user_id + '/follows?'\\n response = requests.get(url, params=params).json()\\n if response['meta']['code'] != 200:\\n raise MediaMissingException('Unable to access account!')\\n return response['data']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd515e8aa767c3bbf5e11c39a5684106\",\n \"score\": \"0.5780504\",\n \"text\": \"def get_followers(self, username):\\n user = self.get_user(username)\\n user_id = user['id']\\n params = {'access_token': os.getenv('INSTAGRAM_ACCESS_TOKEN'), 'count': 100}\\n url = self.api_url + 'users/' + user_id + '/followed-by?'\\n response = requests.get(url, params=params).json()\\n if response['meta']['code'] != 200:\\n raise MediaMissingException('Unable to access account!')\\n return response['data']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dbb94b03eccac94bac3aa105fbcce19\",\n \"score\": \"0.5778688\",\n \"text\": \"def get_follower_requests():\\n return get_all_requests()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99f63e215e3e7e1ca3dd9c57524ae9a9\",\n \"score\": \"0.57725054\",\n \"text\": \"def following(user):\\n return {\\\"following\\\": Follow.objects.following(user)}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a67e84eff2c07b94c4a04ab793cc1a5\",\n \"score\": \"0.57575667\",\n \"text\": \"def calculate_num_likes(self, posts=None):\\n\\n if self.platform_name in [\\\"Custom\\\", \\\"Blogspot\\\", \\\"Wordpress\\\"]:\\n return 0\\n else:\\n if posts:\\n return posts.aggregate(Sum('engagement_media_numlikes'))['engagement_media_numlikes__sum']\\n else:\\n return self.posts_set.aggregate(Sum('engagement_media_numlikes'))['engagement_media_numlikes__sum']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"381ba006c45ee484b621729316336a9b\",\n \"score\": \"0.57518095\",\n \"text\": \"def followers(user):\\n try:\\n return Relationship.objects.get_followers_for_user(user)\\n except AttributeError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcb28bc1d1d8b4e1bf96776af2e41edf\",\n \"score\": \"0.57358927\",\n \"text\": \"def unfollower_ids_count(self):\\n return len(self.unfollower_ids)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f45f17ece7f7ddce2c57b89d7d12eb19\",\n \"score\": \"0.5726824\",\n \"text\": \"def get(self, request, username):\\n try:\\n the_user = User.objects.get(username=username)\\n except User.DoesNotExist:\\n raise UserNotFound\\n to_check = Profile.objects.get(user_id=the_user.id)\\n my_follows = to_check.w_following.all()\\n serializer = self.get_serializer(my_follows, many=True)\\n return Response({\\\"followers\\\": serializer.data, \\\"count\\\":\\n len(serializer.data)},\\n status=status.HTTP_200_OK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcdd9ca1dfeecfbc75619dea8b54894f\",\n \"score\": \"0.5719411\",\n \"text\": \"def calls_authors(*args):\\n calls_posts = CallsPost.objects.published()\\n authors = User.objects.filter(callsposts__in=calls_posts)\\n return list(authors.annotate(post_count=Count(\\\"callsposts\\\")))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36530e41b1fd674e4fbc6d3bfdf6b903\",\n \"score\": \"0.5716276\",\n \"text\": \"def notifications_count(request):\\n if 'user' not in request or request.user.is_anonymous:\\n count = 0\\n else:\\n count = FeedEntry.objects.filter(\\n user=request.user, is_read=False, category='notification',\\n ).count()\\n return {\\n 'notifications_count': count,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"625c7311f470d221efebd9f79dac725c\",\n \"score\": \"0.5714881\",\n \"text\": \"def get_follower_usernames(username, count=5000):\\n return _get_friend_or_follower_usernames(FOLLOWERS, username, count)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad64a9be4a1bb81d069794bbcffad9d8\",\n \"score\": \"0.5711323\",\n \"text\": \"def list_private(self, request, *args, **kwargs):\\n posts = []\\n followees = [uf.user for uf in UserFollower.objects.filter(follower=self.request.user)]\\n [posts.extend(Post.objects.filter(author=user)) for user in followees]\\n posts.sort(key=lambda post: post.date)\\n return Response(serializers.PostSerializer(posts, many=True, context={'request': request}).data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0746d9e27c6c1e43d15f6fc7d4746d08\",\n \"score\": \"0.57094544\",\n \"text\": \"def get(self, request, *args, **kwargs):\\n username = kwargs.get(\\\"username\\\")\\n creator = self.get_object(username)\\n followers = creator.get_followers()\\n return Response(self.serializer_class(followers, many=True).data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f59dbf4ca1658e486a7ae96312337ef8\",\n \"score\": \"0.57048434\",\n \"text\": \"def getNewsFeed(self, userId: int) -> List[int]:\\n q = [(idx, tweet) for idx, tweet in self.users[userId]['tweets']]\\n heapq.heapify(q)\\n for followeeId in self.users[userId]['followed']:\\n for idx, tweet in self.users[followeeId]['tweets']:\\n if (idx, tweet) not in q:\\n heapq.heappush(q, (idx, tweet))\\n r = []\\n while q and len(r) < self.numTweets:\\n r.append(heapq.heappop(q)[1])\\n return r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aba357fadf166ade093f8eeb1441d9a7\",\n \"score\": \"0.570118\",\n \"text\": \"def get_followers(api,user):\\n return api.followers(user)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"011d099a3d9d87fb2fbbf0fc5d1fccbf\",\n \"score\": \"0.56876034\",\n \"text\": \"def nr_user_stories(self) -> Tuple[int, List[str]]:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f61ce95420fde73aaade126299f50c5b\",\n \"score\": \"0.568267\",\n \"text\": \"def get_followers(self, username, num_followers, print_progress=False):\\n follower_list = []\\n count = 1\\n for follower in limit_handled(Cursor(self.api.followers, screen_name=username).items(num_followers)):\\n cur_follower = Format.format_username(follower.screen_name)\\n follower_list.append(cur_follower)\\n if print_progress:\\n print('get_follower', count, cur_follower)\\n count += 1\\n return follower_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9671557405c13d64fa38a0aa11f141b\",\n \"score\": \"0.5650465\",\n \"text\": \"def testGetAllFollowedReturnsNextPageID(self):\\n time = datetime.utcfromtimestamp(1336604400)\\n objectID = ObjectAPI(self.user).create(u'about')\\n TagValueAPI(self.user).set({objectID: {u'username/follows': None}})\\n comments = SecureCommentAPI(self.user)\\n for i in range(26):\\n comments.create(u'Comment', u'username', about=[u'about'],\\n when=time - timedelta(minutes=i),\\n url='http://example.com/comment')\\n self.store.commit()\\n\\n result = yield self.invoke('getAllFollowed', username='username')\\n result = loads(result)['result']\\n self.assertEqual(1336602960, result['nextPageID'])\\n self.assertEqual(25, len(result['comments']))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"132fc38061f936f8643c51b56c5bff9f\",\n \"score\": \"0.56404454\",\n \"text\": \"def get_followers(self):\\n return User.find({'following': {'$in': self.categories}})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f17e39e0406b0a8b491a922c61defc75\",\n \"score\": \"0.56342286\",\n \"text\": \"def getCount():\\n return twitterdb.statuses.count()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab9d5d916c8246a582ee76550dd95890\",\n \"score\": \"0.56341815\",\n \"text\": \"def get_follows(self):\\n follows = []\\n if self._status == DataLocations.CACHED:\\n follows = self.__dict__.get('follows')\\n else:\\n # Get the users from the feed.\\n # TODO: Only works for local users.\\n items = []\\n if self._status == DataLocations.LOCAL:\\n location = SettingsManager.get_user(self.user_id)['follows_location']\\n item_els = self._get_attr_el(location, self.get_follows.binding, '//channel/item')\\n follows = [_recursive_dict(item)[1] for item in item_els]\\n\\n follows = [User(remote_url=item['user_link']) for item in follows]\\n cache_users(follows)\\n return follows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"882476cbf914044b07373dd0f087d88b\",\n \"score\": \"0.5633867\",\n \"text\": \"def number_of_messages(idea):\\n posts = Post.objects(ideas=str(idea.id))\\n return posts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77d9a22a559125d6de82ec702e8a4d71\",\n \"score\": \"0.5618872\",\n \"text\": \"def followed(self):\\n return self.filter('followed')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"145cecf9a92b5f257316a03685b37652\",\n \"score\": \"0.56128067\",\n \"text\": \"def show_followers(request, username):\\n\\n try:\\n user = User.objects.get(username__iexact=username)\\n except User.DoesNotExist:\\n raise Http404\\n\\n following = user.followers.following()\\n\\n data = {\\n 'users' : following,\\n 'user_profile': user\\n }\\n\\n return render(\\n request,\\n templates['PROFILE_FOLLOWERS'],\\n data,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"214748cf295686f0568e174560438eed\",\n \"score\": \"0.5603917\",\n \"text\": \"def get_followers(self, url: str) -> Dict:\\n\\n user_data = self.get_user_info(url=url, target=\\\"followers\\\")\\n\\n query_url = f\\\"{self.BASE_URL}{self.GRAPHQL_QUERY}\\\"\\n after = \\\"\\\"\\n\\n user_followers = {\\n \\\"count\\\": user_data.followed_by,\\n \\\"usernames\\\": list(),\\n \\\"followers\\\": list(),\\n }\\n while True:\\n params = {\\n \\\"query_hash\\\": self.followers_query_hash,\\n \\\"id\\\": user_data.user_id,\\n \\\"include_reel\\\": False,\\n \\\"fetch_mutual\\\": False,\\n \\\"first\\\": 50,\\n \\\"after\\\": after or \\\"\\\",\\n }\\n data = self._make_request(url=query_url, params=params)[\\\"data\\\"][\\\"user\\\"][\\\"edge_followed_by\\\"]\\n self._extract_usernames(users=data[\\\"edges\\\"], result=user_followers[\\\"usernames\\\"])\\n after = self._has_next_page(data)\\n if after is None:\\n break\\n\\n self._extract_users_by_usernames(usernames=user_followers[\\\"usernames\\\"], result=user_followers[\\\"followers\\\"])\\n logging.info(msg=f'User {url} followers. Count: {len(user_followers[\\\"followers\\\"])}')\\n\\n return user_followers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba83893bb81cab812f42904d87260ae6\",\n \"score\": \"0.5595626\",\n \"text\": \"def followers(self):\\n return(\\n User.select().join(\\n Relationship, on=Relationship.from_user\\n ).where(\\n Relationship.to_user == self\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba83893bb81cab812f42904d87260ae6\",\n \"score\": \"0.5595626\",\n \"text\": \"def followers(self):\\n return(\\n User.select().join(\\n Relationship, on=Relationship.from_user\\n ).where(\\n Relationship.to_user == self\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63c6c199c1bda619e5a21b11d48a83d3\",\n \"score\": \"0.559146\",\n \"text\": \"def get_usertofollow_row_count(self):\\n\\n return self._session.query(UserToFollow).count()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db185a484f5db98a4a3f7c4264cadbc2\",\n \"score\": \"0.5589288\",\n \"text\": \"def followers(self):\\n\\n return (\\n User.select().join(\\n Relationship, on=Relationship.from_user\\n ).where(\\n Relationship.to_user == self # el usuario que inicio sesion\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"669c2541f5d7e873b2ca2164e9c1a7db\",\n \"score\": \"0.55855876\",\n \"text\": \"def find_post_stats(post_data):\\n num_posts = len(post_data)\\n\\n # calculate stats about post creator\\n creator_id = post_data[0]['poster_id']\\n num_creator_posts = 0\\n for post in post_data:\\n if post['poster_id'] == creator_id:\\n num_creator_posts += 1\\n\\n num_posters = len({post['poster_id'] for post in post_data})\\n\\n # calculate stats for reaching certain post milestones\\n topic_created_date = datetime.datetime.fromisoformat(post_data[0]['post_date'])\\n post_25_delta = None\\n post_50_delta = None\\n if len(post_data) >= 25:\\n post_25_date = datetime.datetime.fromisoformat(post_data[24]['post_date'])\\n post_25_delta = str(post_25_date - topic_created_date)\\n if len(post_data) >= 50:\\n post_50_date = datetime.datetime.fromisoformat(post_data[49]['post_date'])\\n post_50_delta = str(post_50_date - topic_created_date)\\n\\n post_stats = {'num_posts': num_posts,\\n 'num_posters': num_posters,\\n 'num_creator_posts': num_creator_posts,\\n 'post_25_delta': post_25_delta,\\n 'post_50_delta': post_50_delta}\\n\\n return post_stats\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0874024d66ad14d809ce00b8e6bf058\",\n \"score\": \"0.5580903\",\n \"text\": \"def followers_view(request, username, *args, **kwargs):\\n\\n try:\\n user = User.objects.get(username=username)\\n except:\\n return Response({\\n 'details': 'Username not found'\\n }, status=status.HTTP_404_NOT_FOUND)\\n\\n users_id_who_follow = [user.id for user in Profile.objects.get(user=user).follower.all()]\\n followers = Profile.objects.filter(user__id__in=users_id_who_follow)\\n serializer = ProfileSerializers(followers, context={'request':request}, many=True)\\n\\n return Response(serializer.data, status=200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1359561a775053646f65616ffc8093e7\",\n \"score\": \"0.5562729\",\n \"text\": \"def get_following(se, proxy: dict) -> dict:\\n try:\\n with se.get(_ROOT_URL + 'bookmark.php',\\n params={'type': 'user'},\\n headers={'User-Agent': random.choice(misc.USER_AGENT)},\\n proxies=proxy,\\n timeout=5) as fo_res:\\n fo_html = BeautifulSoup(fo_res.text, 'lxml')\\n fo_node = fo_html.find_all('div', class_='userdata')\\n if not fo_node:\\n raise exception.ResponseError('Cannot fetch following info.')\\n\\n fo_info = {ele.a['data-user_id']: ele.a['data-user_name'] for ele in fo_node}\\n return fo_info\\n except requests.Timeout:\\n raise requests.Timeout('Timeout during getting following info.')\\n except exception.ResponseError:\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc0548d9839411a76bca1c3e789344a4\",\n \"score\": \"0.5547523\",\n \"text\": \"def download_followers_list(self, user_id, username):\\n followers = []\\n end_cursor = ''\\n has_next_page = True\\n\\n self.session.headers['Referer']= f'https://www.instagram.com/{username}/followers'\\n\\n first_page_url = f'https://www.instagram.com/graphql/query/?query_hash=c76146de99bb02f6415203be841dd25a&' \\\\\\n f'variables=%7B%22id%22%3A%22{user_id}%22%2C%22include_reel%22%3Atrue%2C%22' \\\\\\n f'fetch_mutual%22%3Atrue%2C%22first%22%3A24%7D'\\n\\n res = self.session.get(first_page_url, headers=self.session.headers)\\n self.session.headers['X-CSRFToken'] = res.cookies['csrftoken']\\n\\n print('getting followers ... ', end='')\\n while has_next_page:\\n try:\\n has_next_page = bool(\\n res.json()['data']['user']['edge_followed_by']['page_info']['has_next_page'])\\n except KeyError as kerr:\\n pass\\n try:\\n for node in res.json()['data']['user']['edge_followed_by']['edges']:\\n followers.append(\\n (node['node']['id'], node['node']['username']))\\n except Exception as err:\\n print()\\n\\n if has_next_page:\\n end_cursor = res.json()[\\n 'data']['user']['edge_followed_by']['page_info']['end_cursor'].replace('==', '')\\n next_page = f'https://www.instagram.com/graphql/query/?query_hash=c76146de99bb02f6415203be841dd25a' \\\\\\n f'&variables=%7B%22id%22%3A%22{user_id}%22%2C%22' \\\\\\n f'include_reel%22%3Atrue%2C%22fetch_mutual%22%3Afalse%2C%22first%22%3A14%2C%22' \\\\\\n f'after%22%3A%22{end_cursor}%3D%3D%22%7D'\\n\\n # time.sleep(randint(2,5)) # delay, prevents instagram ban\\n res = self.session.get(next_page, headers=self.session.headers)\\n self.session.headers['X-CSRFToken'] = res.cookies['csrftoken']\\n\\n else:\\n print(colored('DONE', 'green'))\\n\\n return followers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4addb4162b8cdc0401c0452c3b309e8\",\n \"score\": \"0.55358255\",\n \"text\": \"def followers(self):\\n if self._followers is None:\\n data = yield 'users/{user}/followers'.format(user=self.username)\\n self._followers = []\\n for user in data:\\n user_data = user.pop('user')\\n date = user.pop('followed_at')\\n self._followers.append(User(followed_at=date, **user_data))\\n yield self._followers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"352c5074447cb46e7d80de8be1e2005a\",\n \"score\": \"0.55306405\",\n \"text\": \"def getNumLikes(self):\\n try:\\n friendCount = getFacebookDump(self)[u'likes']\\n \\n except KeyError:\\n dump = json.loads(urllib.urlopen(\\\\\\n \\\"https://graph.facebook.com/fql?q=SELECT%20friend_count%20FROM%20user%20WHERE%20uid=\\\"\\\\\\n +self.getFacebook()).readline())\\n friendCount = dump[u'data'][0][u'friend_count']\\n \\n return friendCount\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb8b5c88090b5e5e4d755a20588c602d\",\n \"score\": \"0.55284023\",\n \"text\": \"def __init__(self):\\n self.post = []\\n self.follows = defaultdict(list)\\n self.post_user = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b293dcfd2204a7f9da73df19445c12d1\",\n \"score\": \"0.55264217\",\n \"text\": \"def getNewsFeed(self, userId):\\n heap = []\\n if userId in self.tweets:\\n for i in self.tweets[userId]:\\n heapq.heappush(heap, i)\\n if len(heap) > 10:\\n heapq.heappop(heap)\\n if userId in self.followers:\\n for i in self.followers[userId]:\\n for j in self.tweets[i]:\\n heapq.heappush(heap, j)\\n if len(heap) > 10:\\n heapq.heappop(heap)\\n if not heap:\\n return []\\n res = []\\n while heap:\\n res.append(heapq.heappop(heap)[1])\\n return res[::-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"844e66f02114db0e34be5aa7e9bf8077\",\n \"score\": \"0.5514456\",\n \"text\": \"def followers_ids(auth, **params):\\n\\n maxitems = params.pop(\\\"maxitems\\\", 0)\\n if maxitems > 0:\\n return cursor_iter(followers_ids, maxitems, auth, params)\\n\\n endpoint = \\\"https://api.twitter.com/1.1/followers/ids.json\\\"\\n\\n params.setdefault(\\\"count\\\", 5000)\\n\\n data, status_code, error_code = rest_call(endpoint, auth, params)\\n try:\\n next_cursor = data[\\\"next_cursor\\\"]\\n count = len(data[\\\"ids\\\"])\\n except KeyError:\\n next_cursor, count = 0, 0\\n\\n meta = {\\n \\\"code\\\": status_code,\\n \\\"error_code\\\": error_code,\\n \\\"next_cursor\\\": next_cursor,\\n \\\"count\\\": count,\\n }\\n\\n return data, meta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7232970fe9a42719d88994dd51de94e4\",\n \"score\": \"0.5514358\",\n \"text\": \"def _get_followers(self, direction=\\\"follower\\\", last_user=\\\"\\\", what=\\\"blog\\\", limit=100):\\n if not self.hive.is_connected():\\n raise OfflineHasNoRPCException(\\\"No RPC available in offline mode!\\\")\\n followers_list = []\\n limit_reached = True\\n cnt = 0\\n while limit_reached:\\n self.hive.rpc.set_next_node_on_empty_reply(False)\\n if self.hive.rpc.get_use_appbase():\\n query = {'account': self.name, 'start': last_user, 'type': what, 'limit': limit}\\n if direction == \\\"follower\\\":\\n followers = self.hive.rpc.get_followers(query, api='follow')\\n if isinstance(followers, dict) and 'followers' in followers:\\n followers = followers['followers']\\n elif direction == \\\"following\\\":\\n followers = self.hive.rpc.get_following(query, api='follow')\\n if isinstance(followers, dict) and 'following' in followers:\\n followers = followers['following']\\n else:\\n if direction == \\\"follower\\\":\\n followers = self.hive.rpc.get_followers(self.name, last_user, what, limit, api='follow')\\n elif direction == \\\"following\\\":\\n followers = self.hive.rpc.get_following(self.name, last_user, what, limit, api='follow')\\n if cnt == 0:\\n followers_list = followers\\n elif followers is not None and len(followers) > 1:\\n followers_list += followers[1:]\\n if followers is not None and len(followers) >= limit:\\n last_user = followers[-1][direction]\\n limit_reached = True\\n cnt += 1\\n else:\\n limit_reached = False\\n\\n return followers_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0532a410bafc34c6637d8ab6c0ef76e\",\n \"score\": \"0.55119175\",\n \"text\": \"def getFollowers(channel):\\n\\tapi = getApi()\\n\\ttry:\\n\\t\\tusers = api.GetFollowers()\\n\\t\\tf = [u.screen_name for u in users]\\n\\t\\tsendToChannel(channel, string.join(f).replace(' ',', '))\\n\\texcept:\\n\\t\\tsendToChannel(channel, \\\"Hurr\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5debfb84f14c3a93558b9e269a55770\",\n \"score\": \"0.55074036\",\n \"text\": \"def getNewsFeed(self, userId):\\n feed = self.feeds[userId][:]\\n for user in self.follows[userId]:\\n feed += self.feeds[user]\\n feed = [x[0] for x in heapq.nlargest(10,feed,key=lambda x:x[1])]\\n # feed = sorted(feed, key=lambda x: x[1], reverse=True)\\n # feed = [x[0] for x in feed[:10]]\\n return feed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d74da5d08c78207d51782fec0e20990a\",\n \"score\": \"0.550385\",\n \"text\": \"def get_liker_frequencies(posts_likers):\\n\\n liker_counter = Counter()\\n\\n for post_id, post_likers in posts_likers.items():\\n for post_liker in post_likers:\\n liker_pk = str(post_liker['pk'])\\n liker_counter.update({liker_pk: 1})\\n\\n\\n return liker_counter\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4980c63c0459b480ff2714d5176002e\",\n \"score\": \"0.55018276\",\n \"text\": \"def get_followed_by_user(self, url: str) -> Dict:\\n\\n user_data = self.get_user_info(url=url, target=\\\"followed_by_user\\\")\\n\\n query_url = f\\\"{self.BASE_URL}{self.GRAPHQL_QUERY}\\\"\\n after = \\\"\\\"\\n\\n user_follow = {\\n \\\"count\\\": user_data.follow,\\n \\\"usernames\\\": list(),\\n \\\"followed\\\": list(),\\n }\\n while True:\\n params = {\\n \\\"query_hash\\\": self.followed_by_user_query_hash,\\n \\\"id\\\": user_data.user_id,\\n \\\"first\\\": 50,\\n \\\"after\\\": after if after else \\\"\\\",\\n }\\n\\n data = self._make_request(url=query_url, params=params)[\\\"data\\\"][\\\"user\\\"][\\\"edge_follow\\\"]\\n self._extract_usernames(users=data[\\\"edges\\\"], result=user_follow[\\\"usernames\\\"])\\n\\n after = self._has_next_page(data)\\n if after is None:\\n break\\n\\n self._extract_users_by_usernames(usernames=user_follow[\\\"usernames\\\"], result=user_follow[\\\"followed\\\"])\\n logging.info(msg=f'Followed by user {url}. Count: {len(user_follow[\\\"followed\\\"])}')\\n\\n return user_follow\",\n \"title\": \"\"\n },\n {\n \"docid\": \"760843f7236d4fde0fed15c4a5f57843\",\n \"score\": \"0.55013126\",\n \"text\": \"def getNewsFeed(self, userId: int) -> List[int]:\\n if userId not in self.user:\\n return list()\\n else:\\n # 取出自己最近10条推文\\n ans = self.user[userId].tweet[-10:][::-1]\\n # 取出自己followee\\n for followeeId in self.user[userId].followee:\\n if followeeId in self.user:\\n # follow人的最近十条推文\\n opt = self.user[followeeId].tweet[-10:][::-1]\\n i, j, combined = 0, 0, list()\\n while i < len(ans) and j < len(opt):\\n if self.tweetTime[ans[i]] > self.tweetTime[opt[j]]:\\n combined.append(ans[i])\\n i += 1\\n else:\\n combined.append(opt[j])\\n j += 1\\n combined.extend(ans[i:])\\n combined.extend(opt[j:])\\n ans = combined[:10]\\n return ans\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e1208668d51f4e5fe3d773c12aa8f58\",\n \"score\": \"0.5499597\",\n \"text\": \"def calculate_num_shares(self, posts=None):\\n if posts is None:\\n posts = self.posts_set.all()\\n\\n if self.platform_name == \\\"Facebook\\\":\\n return posts.aggregate(Sum('engagement_media_numfbshares'))['engagement_media_numfbshares__sum']\\n if self.platform_name == \\\"Pinterest\\\":\\n return posts.aggregate(Sum('engagement_media_numrepins'))['engagement_media_numrepins__sum']\\n if self.platform_name == \\\"Twitter\\\":\\n return posts.aggregate(Sum('engagement_media_numshares'))['engagement_media_numshares__sum']\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e958d7308828ebb79fe9a6b80ce80185\",\n \"score\": \"0.54984397\",\n \"text\": \"def getNewsFeed(self, userId: 'int') -> 'List[int]':\\n # extract the user\\n # go through each user's followers\\n # add their tweets to an array --> array of arrays\\n # put most recent tweet from each array into max heap\\n # removing from max heap ten times\\n\\n user = self.users[userId]\\n collected_tweets = []\\n result = []\\n\\n for followerId in user.following:\\n follower = self.users[followerId]\\n collected_tweets.append(follower.tweets)\\n\\n maxheap = Heap(\\\"max\\\")\\n\\n for index in range(len(collected_tweets)):\\n current_tweets = collected_tweets[index]\\n last_tweet = current_tweets[-1]\\n maxheap.insert([last_tweet.timestamp, last_tweet.tweetId, index])\\n\\n count_in_newsfeed = 10\\n\\n while (maxheap.size() > 0 and count_in_newsfeed > 0):\\n timestamp, tweetId, user = maxheap.remove_peak()\\n result.append(tweetId)\\n if len(collected_tweets[user]) > 0:\\n last_tweet = collected_tweets[user].pop()\\n maxheap.insert([last_tweet.timestamp, last_tweet.tweetId, user])\\n count_in_newsfeed -= 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a4b8c032d3fb1a9e7855d7c16c1fc8e\",\n \"score\": \"0.5485443\",\n \"text\": \"def get_all_posts(access_token, owner_id, count=100, offset=0):\\n \\n all_posts = []\\n while True:\\n time.sleep(random.random())\\n wall = getjson(\\\"https://api.vk.com/method/wall.get\\\", {\\n \\\"owner_id\\\" : owner_id,\\n \\\"count\\\": count,\\n \\\"access_token\\\": access_token,\\n \\\"offset\\\": offset,\\n \\\"v\\\": '5.131'\\n })\\n count_posts = wall['response']['count']\\n posts = wall['response']['items']\\n\\n all_posts.extend(posts)\\n\\n if len(all_posts) >= count_posts:\\n break\\n else:\\n offset += 100\\n\\n return all_posts, count_posts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2753e129586ed28c5b7fb8188ea9d109\",\n \"score\": \"0.54846305\",\n \"text\": \"def users_i_follow(self):\\n return _get_session().query(Connection).filter_by(following=self).all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05bac14dc609d63cba5e319271545e1e\",\n \"score\": \"0.5480283\",\n \"text\": \"def users_following_me(self):\\n return _get_session().query(Connection).filter_by(followed=self).all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a7aefe392d87e5f10ea033e32968599\",\n \"score\": \"0.5470377\",\n \"text\": \"def users_count(self):\\n return self._data.get('related_field_counts', {}).get('users', 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e52dbeec36cd2adbbcd2be7fab37680\",\n \"score\": \"0.5470254\",\n \"text\": \"def get_users_followed(self, username):\\n path = f'/api/user/{username}/following'\\n return self._r.get(path, stream=True, fmt=NDJSON,\\n converter=models.User.convert)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef873da48e20e6c7152301d0d7246cd8\",\n \"score\": \"0.5465221\",\n \"text\": \"def new_follower_ids_count(self):\\n return len(self.new_follower_ids)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9a736f544d5aec0cd05df518afae463\",\n \"score\": \"0.5462012\",\n \"text\": \"def getNewsFeed(self, user):\\n import heapq\\n h, tweets = [], self.tweets\\n people = self.followee.get(user, set()) | set([user])\\n for person in people:\\n if person in tweets and tweets[person]:\\n time, tweet = tweets[person][-1]\\n h.append((time, tweet, person, len(tweets[person]) - 1))\\n heapq.heapify(h)\\n news = []\\n for _ in range(10):\\n if h:\\n time, tweet, person, idx = heapq.heappop(h)\\n news.append(tweet)\\n if idx:\\n new_time, new_tweet = tweets[person][idx - 1]\\n heapq.heappush(h, (new_time, new_tweet, person, idx - 1))\\n return news\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72a4a548da874fcfe29c6586c7e80fc7\",\n \"score\": \"0.546016\",\n \"text\": \"def get_users_following(self, username):\\n path = f'/api/user/{username}/followers'\\n return self._r.get(path, stream=True, fmt=NDJSON,\\n converter=models.User.convert)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7235885f72e0d9dd8dc0e28fda14a3f\",\n \"score\": \"0.5451477\",\n \"text\": \"def get_unfollowers_number(message, user_id, username):\\n doesnt_follow_back = model.get_unfollowers(user_id, username)\\n bot.edit_message_reply_markup(chat_id=message.chat.id,\\n message_id=message.message_id,\\n reply_markup=None)\\n\\n keyboard = types.InlineKeyboardMarkup()\\n\\n if doesnt_follow_back.__len__() > 0:\\n keyboard.add(types.InlineKeyboardButton(text=f'{emojize(\\\" :heavy_plus_sign:\\\", use_aliases=True)}gimme list',\\n callback_data=f'list_unfollowing_id:{user_id}_page:0'),\\n types.InlineKeyboardButton(text=f'{emojize(\\\" :pencil2:\\\", use_aliases=True)}another one',\\n callback_data=f'get_instagram_username'))\\n else:\\n keyboard.add(types.InlineKeyboardButton(text=f'{emojize(\\\" :pencil2:\\\", use_aliases=True)}another one', callback_data=f'get_instagram_username'))\\n\\n bot.send_message(chat_id=message.chat.id,\\n text=f'{emojize(\\\" :pig_nose:\\\", use_aliases=True)}doesnt follow u back: {doesnt_follow_back.__len__()} users',\\n reply_markup=keyboard)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994191,"cells":{"query_id":{"kind":"string","value":"53bf878325c3da25c2bdf2b255812f70"},"query":{"kind":"string","value":"Wraps up the different methods to get the complex trajectories in experiment"},"positive_passages":{"kind":"list like","value":[{"docid":"2cd396a60027d05589a8224e8825f982","score":"0.6717981","text":"def get_complex_trajectories(experiment):\n raco_before,raco_after=experiment.trajectory_racomodation()\n \n list_of_raccomodated = []\n for i,(frame,label,number) in enumerate(raco_after):\n index = raccomodate_after_to_before(frame,label,number,raco_before,experiment)\n if index!=-1:\n list_of_raccomodated.append((i,index))\n \n list_of_racc_disposable = list_of_raccomodated[:]\n total_merged_trajectories = [] #List of complex trajectories\n for i in range(len(list_of_racc_disposable)):\n comp_traj = []\n if i>=len(list_of_racc_disposable):\n break\n assemble_complex_trajectories(comp_traj,i,list_of_racc_disposable,raco_before,raco_after,experiment)\n total_merged_trajectories.append(comp_traj)\n return total_merged_trajectories","title":""}],"string":"[\n {\n \"docid\": \"2cd396a60027d05589a8224e8825f982\",\n \"score\": \"0.6717981\",\n \"text\": \"def get_complex_trajectories(experiment):\\n raco_before,raco_after=experiment.trajectory_racomodation()\\n \\n list_of_raccomodated = []\\n for i,(frame,label,number) in enumerate(raco_after):\\n index = raccomodate_after_to_before(frame,label,number,raco_before,experiment)\\n if index!=-1:\\n list_of_raccomodated.append((i,index))\\n \\n list_of_racc_disposable = list_of_raccomodated[:]\\n total_merged_trajectories = [] #List of complex trajectories\\n for i in range(len(list_of_racc_disposable)):\\n comp_traj = []\\n if i>=len(list_of_racc_disposable):\\n break\\n assemble_complex_trajectories(comp_traj,i,list_of_racc_disposable,raco_before,raco_after,experiment)\\n total_merged_trajectories.append(comp_traj)\\n return total_merged_trajectories\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"704c0d232adc815ba1223712ec930fbb","score":"0.5725358","text":"def test_step_methods_in_each_level(self):\n _, model, _ = mv_simple()\n _, model_coarse, _ = mv_simple_coarse()\n _, model_very_coarse, _ = mv_simple_very_coarse()\n with model:\n initial_point = model.initial_point\n initial_point_size = sum(initial_point[n.name].size for n in model.value_vars)\n s = np.ones(initial_point_size) + 2.0\n sampler = MLDA(\n coarse_models=[model_very_coarse, model_coarse],\n base_S=s,\n base_sampler=\"Metropolis\",\n )\n assert isinstance(sampler.step_method_below, MLDA)\n assert isinstance(sampler.step_method_below.step_method_below, Metropolis)\n assert np.all(sampler.step_method_below.step_method_below.proposal_dist.s == s)\n\n sampler = MLDA(coarse_models=[model_very_coarse, model_coarse], base_S=s)\n assert isinstance(sampler.step_method_below, MLDA)\n assert isinstance(sampler.step_method_below.step_method_below, DEMetropolisZ)\n assert np.all(sampler.step_method_below.step_method_below.proposal_dist.s == s)","title":""},{"docid":"a86620ffdc3a1c24164d2818c31cf511","score":"0.56778145","text":"def show_complex_trajectory(comp_traj,experiment,wait=50):\n path_im = experiment.path\n path_body = experiment.body_path\n path_arm = experiment.arm_path\n previous = 'trajectory'\n \n width = width_monitor\n height = height_monitor\n cv2.namedWindow(\"Trajectory\", cv2.WINDOW_NORMAL)\n cv2.resizeWindow('Trajectory', width, height)\n for i,elt in enumerate(comp_traj):\n if type(elt)==tuple:\n #raccomodation\n if i>0:\n if type(comp_traj[i-1])==tuple:\n previous='tuple'\n else:\n previous='trajectory'\n \n if previous=='trajectory':\n #looks after\n stop_frame = 239\n if i!=len(comp_traj)-1:\n index_next1,index_next_2,_ = comp_traj[i+1]\n next_traj = experiment.trajectories[index_next1][index_next_2]\n stop_frame = next_traj.beginning\n \n last_frame = comp_traj[i-1].end\n next_frame = last_frame+1\n label = elt[2]\n while next_frame<stop_frame and experiment.arm_tracker.next_cell(next_frame,label)!=-1:\n \n img = m.open_frame(path_im,next_frame+1)\n arms = m.open_frame(path_arm,next_frame+1)\n mask = (arms==(label+1)).astype(np.uint8)*255\n label = experiment.arm_tracker.next_cell(next_frame,label)\n next_frame+=1\n overlaid = m.cv_overlay_mask2image(mask,img,\"red\")\n cv2.imshow(\"Trajectory\",overlaid)\n cv2.waitKey(wait)\n else:\n cells_list = elt.cells \n for cell in cells_list:\n frame_number = cell.frame_number+1\n img = m.open_frame(path_im,frame_number)\n body = m.open_frame(path_body,frame_number)\n arms = m.open_frame(path_arm,frame_number)\n mask = (body==(cell.body+1)).astype(np.uint8)*255\n mask_arms = np.zeros(mask.shape,dtype=np.uint8)\n for arm in cell.arms:\n mask_arms+=(arms==(arm+1)).astype(np.uint8)*255\n overlaid = m.cv_overlay_mask2image(mask,img,\"green\")\n overlaid = m.cv_overlay_mask2image(mask_arms,overlaid,\"red\")\n cv2.imshow(\"Trajectory\",overlaid)\n \n cv2.waitKey(wait)\n \n #cv2.destroyAllWindows()","title":""},{"docid":"7f92352eedb7758a9aec30927601bd34","score":"0.5551739","text":"def test_nested_cv():\n _experiment(3, 3)","title":""},{"docid":"dfc53a0493bb5da14f0ea8943c6e4aa5","score":"0.5491269","text":"def sample_trajectories(self, env):\n# return self.model.predict(self.x_test)\n return self.i*2","title":""},{"docid":"5c20a7742907d598a43254ed2e2ea301","score":"0.5468829","text":"def assemble_complex_trajectories(complex_trajectory,z,list_of_raccom,raco_before,raco_after,experiment):\n l,k = list_of_raccom.pop(z)\n traj1 = experiment.trajectories[raco_before[k][0]][raco_before[k][1]]\n if len(complex_trajectory)==0:\n traj0 = experiment.trajectories[raco_after[l][0]][raco_after[l][1]]\n complex_trajectory.extend([traj0,raco_after[l],raco_before[k],traj1])\n else:\n complex_trajectory.extend([raco_after[l],raco_before[k],traj1])\n \n for l_prime, (i1,i2,label) in enumerate(raco_after):\n #Find if there is a racomodation after\n if traj1==experiment.trajectories[i1][i2]:\n #Find if this raccomoadtion can itself be raccomodated\n candidates = [i for i,(x,y) in enumerate(list_of_raccom) if x==l_prime]\n if len(candidates)==1:\n assemble_complex_trajectories(complex_trajectory,candidates[0],list_of_raccom,raco_before,raco_after,experiment)\n else:\n complex_trajectory.append((i1,i2,label))\n return\n return","title":""},{"docid":"759175b20f27fa8aaeaaa570473ca19d","score":"0.54281145","text":"def show(self,experiment,wait=50):\n path_im = experiment.path\n path_body = experiment.body_path\n path_arm = experiment.arm_path\n previous = 'trajectory'\n \n monitor = get_monitors()[0]\n width = monitor.width\n height = monitor.height\n cv2.namedWindow(\"Complex Trajectory\", cv2.WINDOW_NORMAL)\n cv2.resizeWindow('Complex Trajectory', width, height)\n for i,elt in enumerate(self):\n if type(elt)==tuple:\n #raccomodation\n if i>0:\n if type(self[i-1])==tuple:\n previous='tuple'\n else:\n previous='trajectory'\n \n if previous=='trajectory':\n #looks after\n stop_frame = 239\n if i!=len(self)-1:\n index_next1,index_next_2,_ = self[i+1]\n next_traj = experiment.trajectories[index_next1][index_next_2]\n stop_frame = next_traj.beginning\n \n last_frame = self[i-1].end\n next_frame = last_frame+1\n label = elt[2]\n while next_frame<stop_frame and experiment.arm_tracker.next_cell(next_frame,label)!=-1:\n \n img = m.open_frame(path_im,next_frame+1)\n arms = m.open_frame(path_arm,next_frame+1)\n mask = (arms==(label+1)).astype(np.uint8)*255\n label = experiment.arm_tracker.next_cell(next_frame,label)\n next_frame+=1\n overlaid = m.cv_overlay_mask2image(mask,img,\"red\")\n cv2.imshow(\"Complex Trajectory\",overlaid)\n cv2.waitKey(wait)\n else:\n cells_list = elt.cells \n for cell in cells_list:\n frame_number = cell.frame_number+1\n img = m.open_frame(path_im,frame_number)\n body = m.open_frame(path_body,frame_number)\n arms = m.open_frame(path_arm,frame_number)\n mask = (body==(cell.body+1)).astype(np.uint8)*255\n mask_arms = np.zeros(mask.shape,dtype=np.uint8)\n for arm in cell.arms:\n mask_arms+=(arms==(arm+1)).astype(np.uint8)*255\n overlaid = m.cv_overlay_mask2image(mask,img,\"green\")\n overlaid = m.cv_overlay_mask2image(mask_arms,overlaid,\"red\")\n cv2.imshow(\"Complex Trajectory\",overlaid)\n \n cv2.waitKey(wait)\n \n cv2.destroyWindow(\"Complex Trajectory\")","title":""},{"docid":"cb410d6b976f3fc8447c3d0238c2da1c","score":"0.5422803","text":"def new_method():\n channels = [\n 'RADAR_FRONT',\n 'RADAR_FRONT_LEFT',\n 'RADAR_FRONT_RIGHT',\n 'RADAR_BACK_LEFT',\n 'RADAR_BACK_RIGHT',\n ]\n samples = scene_in_sample_data\n scene_points = list()\n for sample in samples:\n x, y, z, vx_comp, vy_comp, pointclouds = list(), list(), list(), list(), list(), list()\n ego_pose_coords = []\n for channel in channels:\n pc = sample[channel]['radar_point_cloud']\n radar_token = sample['data'][channel]\n current_radar = nusc.get('sample_data', radar_token)\n ego_pose = nusc.get('ego_pose', current_radar['ego_pose_token'])\n calibrated_sensor = nusc.get('calibrated_sensor', current_radar['calibrated_sensor_token'])\n sensor_to_car = transform_matrix(calibrated_sensor['translation'],\n Quaternion(calibrated_sensor['rotation'], inverse=False))\n car_to_world = transform_matrix(ego_pose['translation'], Quaternion(ego_pose['rotation'], inverse=False))\n\n sensor_to_world = car_to_world @ sensor_to_car\n\n pc.transform(sensor_to_world)\n\n pointclouds.append(pc)\n\n ego_pose_coords = ego_pose['translation']\n\n # combine radar\n\n for i in range(pc.points.shape[1]):\n x.append(pc.points[0][i])\n y.append(pc.points[1][i])\n z.append(pc.points[2][i]) # redundant?\n vx_comp.append(pc.points[7][i])\n vy_comp.append(pc.points[8][i])\n scene_points.append([\n np.asarray(x),\n np.asarray(y),\n np.asarray(z),\n np.asarray(vx_comp),\n np.asarray(vy_comp),\n np.asarray(pointclouds),\n np.asarray(ego_pose_coords)\n ])\n\n return np.asarray(scene_points, dtype=object)","title":""},{"docid":"c30310d1c2bb8c61ced40beae38996a3","score":"0.5397106","text":"def executeTrajectory(self):","title":""},{"docid":"97e1f2612efb18295f7d4a45e811a8dd","score":"0.5390741","text":"def test_trajectories():\n trajectory_true_out = np.load(\"trajectory_particle_2642532_out.npy\")\n trajectory_true_in = np.load(\"trajectory_particle_12599035.npy\")\n\n def trajectories_test():\n p = parameters.InitialConditionsParameters(\n initial_snapshot=\"/Users/lls/Documents/CODE/Nina-Simulations/double/ICs_z99_256_L50_gadget3.dat\",\n final_snapshot=\"/Users/lls/Documents/CODE/Nina-Simulations/double/snapshot_104\",\n min_halo_number=0, max_halo_number=400,\n ids_type='all', num_particles=None)\n\n trajectory_test = trajectories.Trajectories(init_parameters=p, particles=[12599035, 2642532], num_of_filters=20,\n num_particles=1)\n trajectory_in = trajectory_test.delta_in\n trajectory_out = trajectory_test.delta_out\n return trajectory_in, trajectory_out\n\n trajectory_test_in, trajectory_test_out = trajectories_test()\n\n assert np.allclose(trajectory_true_in, trajectory_test_in)\n assert np.allclose(trajectory_true_out, trajectory_test_out)","title":""},{"docid":"e58452ebce45030b9745ea519ed6e550","score":"0.5386184","text":"def __init__(self, wl, n0, n1, n2, theta0, d, ninterface0, ninterface1):\n self.n0 = n0 # Refractive index of first medium, either glass of air\n self.n1 = n1 # Refractive index of intermediate medium.\n self.n2 = n2 # Refractive index of last medium usually h2o or d2o\n # A helping list of indeces\n self.n = (n0, n1, n2)\n # Incidencea angle of promary beam\n self.theta0 = theta0 * np.pi/180 # transforms theta1 to rad\n # Calculate angles with snellius. This doesnt work with the complex\n # ns, atleas it doesnt give the samre sult if I take real part\n # of self.sintheta or self.costheta\n #self.theta1 = np.arcsin(self.n0.real/self.n1.real*np.sin(self.theta0))\n #self.theta2 = np.arcsin(self.n1.real/self.n2.real*np.sin(self.theta1))\n # Because n_j is complex we use costheta or sintheta during\n # calculations. costheta and sintheta can be complex numbers.\n self.sintheta0 = np.sin(self.theta0)\n self.sintheta1 = self.calc_sintheta(1)\n self.sintheta2 = self.calc_sintheta(2)\n self.sintheta = (self.sintheta0, self.sintheta1, self.sintheta2)\n self.costheta0 = np.cos(self.theta0)\n self.costheta1 = self.calc_costheta(1)\n self.costheta2 = self.calc_costheta(2)\n # Helpful list of angles\n #self.theta = (self.theta0, self.theta1, self.theta2)\n self.costheta = (self.costheta0, self.costheta1, self.costheta2)\n # Array of wavelengths\n self.wl = wl\n # Thickness of intermediate material\n self.d = d\n # Phase difference due to thickness of intermediate material\n self.beta = 2 * np.pi/self.wl * self.n1 * self.d * self.costheta1\n\n # This is not fully correct because I would need a self.theta1 for VIS\n # and one for IR but I dont see why this is the case?\n self.delta = (2*np.pi*self.n2*d/self.wl) * \\\n (1/self.costheta1-(self.sintheta1/self.costheta1))\n\n # Has todo with where sfg is generated\n self.ninterface0 = ninterface0\n self.ninterface1 = ninterface1","title":""},{"docid":"bc798bb23c69175a349b089f135a7802","score":"0.53129655","text":"def generate_subtr_examples():\n generate_generic_examples('subtr', subtr_example)","title":""},{"docid":"977cc38d953458966edef71a38876c26","score":"0.5290277","text":"def __init__(self, traj, robot):\n # type: (orpy.RaveTrajectory, orpy.Robot) -> None\n super(RaveTrajectoryWrapper, self).__init__()\n self.traj = traj #: init\n self.spec = traj.GetConfigurationSpecification()\n self._dof = robot.GetActiveDOF()\n\n self._interpolation = self.spec.GetGroupFromName('joint').interpolation\n if self._interpolation not in ['quadratic', 'cubic']:\n raise ValueError(\n \"This class only handles trajectories with quadratic or cubic interpolation\"\n )\n self._duration = traj.GetDuration()\n all_waypoints = traj.GetWaypoints(0, traj.GetNumWaypoints()).reshape(\n traj.GetNumWaypoints(), -1)\n valid_wp_indices = [0]\n self.ss_waypoints = [0.0]\n for i in range(1, traj.GetNumWaypoints()):\n dt = self.spec.ExtractDeltaTime(all_waypoints[i])\n if dt > 1e-5: # If delta is too small, skip it.\n valid_wp_indices.append(i)\n self.ss_waypoints.append(self.ss_waypoints[-1] + dt)\n\n self.n_waypoints = len(valid_wp_indices)\n self.ss_waypoints = np.array(self.ss_waypoints)\n self.s_start = self.ss_waypoints[0]\n self.s_end = self.ss_waypoints[-1]\n\n self.waypoints = np.array([\n self.spec.ExtractJointValues(all_waypoints[i], robot,\n robot.GetActiveDOFIndices())\n for i in valid_wp_indices\n ])\n self.waypoints_d = np.array([\n self.spec.ExtractJointValues(all_waypoints[i], robot,\n robot.GetActiveDOFIndices(), 1)\n for i in valid_wp_indices\n ])\n\n # Degenerate case: there is only one waypoint.\n if self.n_waypoints == 1:\n pp_coeffs = np.zeros((1, 1, self.dof))\n for idof in range(self.dof):\n pp_coeffs[0, 0, idof] = self.waypoints[0, idof]\n # A constant function\n self.ppoly = PPoly(pp_coeffs, [0, 1])\n\n elif self._interpolation == \"quadratic\":\n self.waypoints_dd = []\n for i in range(self.n_waypoints - 1):\n qdd = ((self.waypoints_d[i + 1] - self.waypoints_d[i]) /\n (self.ss_waypoints[i + 1] - self.ss_waypoints[i]))\n self.waypoints_dd.append(qdd)\n self.waypoints_dd = np.array(self.waypoints_dd)\n\n # Fill the coefficient matrix for scipy.PPoly class\n pp_coeffs = np.zeros((3, self.n_waypoints - 1, self.dof))\n for idof in range(self.dof):\n for iseg in range(self.n_waypoints - 1):\n pp_coeffs[:, iseg, idof] = [\n self.waypoints_dd[iseg, idof] / 2,\n self.waypoints_d[iseg, idof],\n self.waypoints[iseg, idof]\n ]\n self.ppoly = PPoly(pp_coeffs, self.ss_waypoints)\n\n elif self._interpolation == \"cubic\":\n self.waypoints_dd = np.array([\n self.spec.ExtractJointValues(all_waypoints[i], robot,\n robot.GetActiveDOFIndices(), 2)\n for i in valid_wp_indices\n ])\n self.waypoints_ddd = []\n for i in range(self.n_waypoints - 1):\n qddd = ((self.waypoints_dd[i + 1] - self.waypoints_dd[i]) /\n (self.ss_waypoints[i + 1] - self.ss_waypoints[i]))\n self.waypoints_ddd.append(qddd)\n self.waypoints_ddd = np.array(self.waypoints_ddd)\n\n # Fill the coefficient matrix for scipy.PPoly class\n pp_coeffs = np.zeros((4, self.n_waypoints - 1, self.dof))\n for idof in range(self.dof):\n for iseg in range(self.n_waypoints - 1):\n pp_coeffs[:, iseg, idof] = [\n self.waypoints_ddd[iseg, idof] / 6,\n self.waypoints_dd[iseg, idof] / 2,\n self.waypoints_d[iseg, idof],\n self.waypoints[iseg, idof]\n ]\n self.ppoly = PPoly(pp_coeffs, self.ss_waypoints)\n\n self.ppoly_d = self.ppoly.derivative()\n self.ppoly_dd = self.ppoly.derivative(2)","title":""},{"docid":"6d89fb770fdfd9c339b4264c793de4f4","score":"0.5281159","text":"def compute_ros_trajectory(self):\n raise NotImplementedError","title":""},{"docid":"d7cd7c00e710a94945a313344c062efb","score":"0.5252293","text":"def test_composition(self):\n light = RTAPhase(\n prop_wload=PeriodicWload(\n duty_cycle_pct=10,\n duration=1.0,\n period=10e-3,\n )\n )\n\n ramp = DutyCycleSweepPhase(\n start=10,\n stop=90,\n step=20,\n period=50e-3,\n duration=1,\n duration_of='step',\n )\n\n heavy = RTAPhase(\n prop_wload=PeriodicWload(\n duty_cycle_pct=90,\n duration=0.1,\n period=100e-3,\n )\n )\n\n profile = {\"test\": light + ramp + heavy}\n\n exp_phases = [\n # Light phase:\n {\n \"loop\": 100,\n \"run\": 1000,\n \"timer\": {\n \"period\": 10000,\n \"ref\": \"unique\"\n }\n },\n # Ramp phases:\n {\n \"loop\": 20,\n \"run\": 5000,\n \"timer\": {\n \"period\": 50000,\n \"ref\": \"unique\"\n }\n },\n {\n \"loop\": 20,\n \"run\": 15000,\n \"timer\": {\n \"period\": 50000,\n \"ref\": \"unique\"\n }\n },\n {\n \"loop\": 20,\n \"run\": 25000,\n \"timer\": {\n \"period\": 50000,\n \"ref\": \"unique\"\n }\n },\n {\n \"loop\": 20,\n \"run\": 35000,\n \"timer\": {\n \"period\": 50000,\n \"ref\": \"unique\"\n }\n },\n {\n \"loop\": 20,\n \"run\": 45000,\n \"timer\": {\n \"period\": 50000,\n \"ref\": \"unique\"\n }\n },\n # Heavy phase:\n {\n \"loop\": 1,\n \"run\": 90000,\n \"timer\": {\n \"period\": 100000,\n \"ref\": \"unique\"\n }\n }]\n\n self._do_test(profile, exp_phases)","title":""},{"docid":"6e7e19ac68f4d29ed7cdaab31d4fdcd4","score":"0.5246129","text":"def get_trajectory(self) -> list:\n pass","title":""},{"docid":"253cf464c7523299d4a210659fc3915f","score":"0.5242545","text":"def supercomplex(self):\n return self.supercomplex_","title":""},{"docid":"caa34b02e2411e07924cf17b8e01da4e","score":"0.5225824","text":"def generate_trajectories(self, duration=0.5, time_step=0.01, y0=None):\n\n return np.stack([\n primitive.generate_trajectory(duration=duration, time_step=time_step, y0=y0)\n for primitive in self.primitives\n ])","title":""},{"docid":"c9e63a54e404aec6c1f4d9a627303990","score":"0.52136993","text":"def dti_tracking_analysis():","title":""},{"docid":"d8bf7156c305480e8c912938511e85c9","score":"0.52131766","text":"def Stepper(object):\n\n def __init__(self, flow, parameters):\n super(Stepper, self).__init__()\n self.flow = flow\n\n def r_next(self, r, w, v, order=1):\n \"\"\" Get the next particle position r, given the current location, the velocity difference w and the fluid velocity v.\n\n We use the scheme from Daitsche (2013; http://arxiv.org/pdf/1210.2576.pdf) which is third-order accurate in the timestep\n\n Parameters:\n r - the current position of the particle\n w - the current velocity mismatch of the particle\n v - the current velocity of the fluid flow at r\n\n Returns:\n the new position for the particle\n \"\"\"\n pass\n\n def w_next(self, r, v, order=1):\n \"\"\" Get the next particle velocity difference, w, given the current location, the velocity of the fluid flow at \n \"\"\"\n pass\n\n def velocity_kernel(self, x, t):\n \"\"\" Return the velocity part of the the Maxey-Riley equations \n \n The velocity kernel is:\n\n \\[\n G(t) = (R-1)\\frac{du}{dt} - Rw\\cdot \\nabla u - \\frac{R}{S}w\n \\]\n \"\"\"\n pass\n\n def history_kernel(self, x, t):\n \"\"\" Return the history part of the Maxey-Riley equations\n \n The history kernel is:\n\n \\[\n H(t) = -R \\sqrt{\\frac{3}{\\pi S}} \\int_{t_0}^{t+\\delta t} \\frac{w(\\tau)}{\\sqrt{t-\\tau}}d\\tau\n \\]\n\n (see Daitsche, 2013).\n \"\"\"\n pass","title":""},{"docid":"4daec651d3acee79b9e97901d977779e","score":"0.5195512","text":"def generateRealizations(self):","title":""},{"docid":"5f9113969a9668b2d2f8e8ae706ae6b9","score":"0.51625925","text":"def experiment_main():\n\n\tgenerator_specs = {\"original_dim\": original_dim, \"intermediate_dim\": 8, \"latent_dim\": latent_dim, \"epochs\": 100, \"dropout\": 0.2,\\\n\t\t\t\t\t\t\"experiment\": \"German\", \"feature_names\": features}\n\n\tprint ('---------------------')\n\tprint ('Training adversarial models....')\n\tprint ('---------------------')\n\n\t# Adversarial models\n\tadv_models = dict()\n\tadv_models[\"Perturbation\"] = Adversarial_IME_Model(racist_model_f(), innocuous_model_psi()).train(xtrain, ytrain,\\\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfeature_names=features, perturbation_multiplier=1)\n\tadv_models[\"DropoutVAE\"] = Adversarial_IME_Model(racist_model_f(), innocuous_model_psi(), generator = \"DropoutVAE\", generator_specs = generator_specs).\\\n \t\t\t\ttrain(xtrain, ytrain, feature_names=features, dummy_idcs=dummy_idcs, integer_idcs=integer_attributes, perturbation_multiplier=1)\n\tadv_models[\"ForestFill\"] = Adversarial_IME_Model(racist_model_f(), innocuous_model_psi(), generator = \"Forest\", generator_specs = generator_specs).\\\n \t\t\t\ttrain(xtrain, ytrain, feature_names=features, dummy_idcs=dummy_idcs, integer_idcs=integer_attributes, perturbation_multiplier=1)\n\n\tfor adversarial in [\"Perturbation\", \"DropoutVAE\", \"ForestFill\"]:\n\t\tadv_model = adv_models[adversarial]\n\n\t\tprint ('---------------------')\n\t\tprint (f'Training explainers with adversarial {adversarial}....')\n\t\tprint ('---------------------')\n\n\t\t# Explainers\n\t\tadv_kernel_explainers = dict()\n\t\tadv_kernel_explainers[\"Perturbation\"] = shap.SamplingExplainer(adv_model.predict, xtrain)\n\t\tadv_kernel_explainers[\"DropoutVAE\"] = shap.SamplingExplainer(adv_model.predict, xtrain, generator=\"DropoutVAE\", generator_specs=generator_specs,\\\n\t\t\t\t\t\t\t\tdummy_idcs=dummy_idcs, integer_idcs=integer_attributes, instance_multiplier = 1000)\n\t\tadv_kernel_explainers[\"ForestFill\"] = shap.SamplingExplainer(adv_model.predict, xtrain, generator=\"Forest\", generator_specs=generator_specs,\\\n\t\t\t\t\t\t\t\tdummy_idcs=dummy_idcs, integer_idcs=integer_attributes)\n\n\t\tfor explainer in [\"Perturbation\", \"DropoutVAE\", \"ForestFill\"]:\n\t\t\tadv_kernel_explainer = adv_kernel_explainers[explainer]\n\t\t\texplanations = adv_kernel_explainer.shap_values(xtest, fill_data=True, data_location=\"...\\Data/german_forest_ime.csv\", distribution_size=1000)\n\n\t\t\t# format for display\n\t\t\tformatted_explanations = []\n\t\t\tfor exp in explanations:\n\t\t\t\tformatted_explanations.append([(features[i], exp[i]) for i in range(len(exp))])\n\n\t\t\tprint (f\"IME Ranks and Pct Occurances one unrelated feature, adversarial: {adversarial}, explainer: {explainer}:\")\n\t\t\tsummary = experiment_summary(formatted_explanations, features)\n\t\t\tprint (summary)\n\t\t\tprint (\"Fidelity:\",round(adv_model.fidelity(xtest),2))\n\n\t\t\tfile_name = f\"../Results/GermanIme/germanImeSummary_adversarial_{adversarial}_explainer_{explainer}.csv\"\n\t\t\twith open(file_name, \"w\") as output:\n\t\t\t\tw = csv.writer(output)\n\t\t\t\tfor key, val in summary.items():\n\t\t\t\t\tw.writerow([key] + [pair for pair in val])","title":""},{"docid":"09b511b3c4be9e61639555ab960e0717","score":"0.5159934","text":"def test():\n\treturn [\"vice.toolkit.interpolation.interp_scheme_1d\",\n\t\t[\n\t\t\ttest_initialize(),\n\t\t\ttest_attributes(),\n\t\t\ttest_call(),\n\t\t\ttest_getitem()\n\t\t]\n\t]","title":""},{"docid":"66c454a7889a057059967dba8b7a1dd5","score":"0.5155187","text":"def __complex__(self):\n return complex(self.re,self.im)","title":""},{"docid":"7a244ff8995f32ce73bd7ef6363d75dc","score":"0.5128895","text":"def getTestCasesForTestPlan(self):","title":""},{"docid":"99f45c51764bf81394b64b56a6b5765d","score":"0.5119969","text":"def __init__(self, *args, **kwargs):\n# verbose = True\n verbose = False\n\n pigasus.__init__(self, *args, **kwargs)\n\n # ...\n # non homogeneous Dirichlet Boundary conditions\n # ...\n try:\n self.bc_dirichlet = self.testcase['bc_dirichlet']\n self.Dirichlet = True\n except:\n self.bc_dirichlet = {}\n self.Dirichlet = False\n # ...\n\n # ...\n # neumann Boundary conditions\n # ...\n try:\n self.bc_neumann = self.testcase['bc_neumann']\n self.Neumann = True\n except:\n self.bc_neumann = {}\n self.Neumann = False\n # ...\n\n # ...\n try:\n func_mass = self.testcase['b']\n self.withMass = True\n except:\n self.withMass = False\n # ...\n\n # ...\n try:\n func_adv = self.testcase['v']\n self.withAdvection = True\n except:\n self.withAdvection = False\n # ...\n\n # ...\n try:\n func_tadv = self.testcase['w']\n try:\n func_dw = self.testcase['dw']\n except:\n print(\"Warning: You should give the (div w) term. Pigasus is not yet smart enough!\")\n # ...\n if self.dim == 1:\n def func_dw(x):\n eps = 1.e-3\n return [(w(x+eps)[0]-w(x)[0])/eps]\n if self.dim == 2:\n def func_dw(x,y):\n eps = 1.e-3\n return [ (w(x+eps,y)[0]-w(x,y)[0])/eps \\\n + (w(x,y+eps)[1]-w(x,y)[1])/eps]\n if self.dim == 3:\n def func_dw(x,y,z):\n eps = 1.e-3\n return [ (w(x+eps,y,z)[0]-w(x,y,z)[0])/eps \\\n + (w(x,y+eps,z)[1]-w(x,y,z)[1])/eps \\\n + (w(x,y,z+eps)[2]-w(x,y,z)[2])/eps]\n # ...\n self.withTAdvection = True\n self.withMass = True\n except:\n self.withTAdvection = False\n # ...\n\n # ...\n try:\n func_stiff = self.testcase['A']\n self.withStiffness = True\n except:\n self.withStiffness = False\n # ...\n\n # ...\n try:\n func_D2 = self.testcase['D2']\n self.withD2 = True\n except:\n self.withD2 = False\n # ...\n\n # ...\n self.withMetric = False\n try:\n Metric = self.testcase['metric']\n if Metric is not None:\n self.withMetric = True\n except:\n pass\n # ...\n\n # ...\n withAllDirichlet = False\n try:\n if self.testcase['AllDirichlet'] is not None:\n withAllDirichlet = self.testcase['AllDirichlet']\n except:\n pass\n # ...\n\n # ...\n try:\n solverInfo = self.testcase['solverInfo']\n if solverInfo is not None:\n self.solverInfo = {}\n except:\n self.solverInfo = None\n # ...\n\n # ... set geometry\n if self.geometry is None:\n try:\n V = kwargs['V']\n self.geometry = V.geometry\n except:\n raise(\"Unable to find a geometry for the current basicPDE\")\n # ...\n\n self.withAllDirichlet = withAllDirichlet\n\n # ...\n list_DirFaces = []\n for i in range(0, self.geometry.npatchs):\n list_DirFaces.append([])\n if withAllDirichlet:\n list_extFaces = self.geometry.external_faces\n for extFaces in list_extFaces:\n patch_id = extFaces[0]\n face_id = extFaces[1]\n list_DirFaces[patch_id].append(face_id)\n else:\n try:\n list_DirFaces = self.testcase['Dirichlet']\n except:\n pass\n # ...\n\n self.list_DirFaces = list_DirFaces\n\n # ...\n self.UseDuplicateFaces = False\n list_DuplicatedFaces = []\n list_DuplicataFaces = []\n list_DuplicatedFacesPeriodic = []\n try:\n list_connectivity = self.testcase['connectivity']\n except:\n list_connectivity = self.geometry.connectivity\n\n for dict_con in list_connectivity:\n list_DuplicatedFaces.append(dict_con['original'])\n list_DuplicataFaces.append(dict_con['clone'])\n try:\n list_DuplicatedFacesPeriodic.append(dict_con['periodic'])\n except:\n list_DuplicatedFacesPeriodic.append(False)\n\n if len(list_DuplicatedFaces) > 0:\n self.UseDuplicateFaces = True\n # ...\n\n self.list_DuplicataFaces = list_DuplicataFaces\n self.list_DuplicatedFaces = list_DuplicatedFaces\n self.list_DuplicatedFacesPeriodic = list_DuplicatedFacesPeriodic\n\n # ...\n self.meanConstraint = False\n nExtFaces = len(self.geometry.external_faces)\n nBCNeumann = len(self.bc_neumann)\n nDuplicatedFaces = len(self.list_DuplicataFaces)\n if (nExtFaces == nBCNeumann) \\\n or (nExtFaces == nDuplicatedFaces) \\\n or (nExtFaces == nBCNeumann+nDuplicatedFaces) \\\n or (nExtFaces == 0):\n self.meanConstraint = True\n# print (\" self.meanConstraint \", self.meanConstraint)\n # ...\n\n #-----------------------------------\n nrb = self.geometry[0]\n list_n = nrb.shape\n list_p = nrb.degree\n\n # ...\n try:\n n_gauss = kwargs['n_gauss']\n lpi_ordergl = n_gauss\n except:\n lpi_ordergl = list_p\n\n _system = matrix()\n _slv = solver(matrix=_system, solverInfo=self.solverInfo)\n #-----------------------------------\n\n #-----------------------------------\n if self.dim == 1:\n func_zero = lambda x : [ 0. ]\n func_one = lambda x : [ 1. ]\n if self.dim == 2:\n func_zero = lambda x,y : [ 0. ]\n func_one = lambda x,y : [ 1. ]\n if self.dim == 3:\n func_zero = lambda x,y,z : [ 0. ]\n func_one = lambda x,y,z : [ 1. ]\n #-----------------------------------\n\n # ...\n try:\n V = kwargs['V']\n except:\n #-----------------------------------\n # space declaration\n #-----------------------------------\n list_DirFaces_V = list_DirFaces\n if self.Dirichlet:\n list_DirFaces_V = []\n for i in range(0,self.geometry.npatchs):\n list_DirFaces_V.append([])\n list_extFaces = self.geometry.external_faces\n for extFaces in list_extFaces:\n patch_id = extFaces[0]\n face_id = extFaces[1]\n list_DirFaces_V[patch_id].append(face_id)\n self.list_DirFaces_V = list_DirFaces_V\n\n if verbose:\n print(\"* DirFaces_V \", self.list_DirFaces_V)\n print(\"* DuplicatedFaces \", list_DuplicatedFaces)\n print(\"* DuplicataFaces \", list_DuplicataFaces)\n\n# print(\"* DuplicatedFaces \", list_DuplicatedFaces)\n# print(\"* DuplicataFaces \", list_DuplicataFaces)\n# print(\"* DuplicatedFacesPeriodic \", list_DuplicatedFacesPeriodic)\n\n V = space(geometry=self.geometry)\n V.dirichlet(faces=list_DirFaces_V)\n if self.UseDuplicateFaces:\n V.duplicate( faces_base = list_DuplicatedFaces \\\n , faces = list_DuplicataFaces \\\n , isPeriodic = list_DuplicatedFacesPeriodic)\n V.set_boundary_conditions()\n if self.withMetric:\n V.create_grids(type=\"legendre\", k=lpi_ordergl, metric=Metric)\n else:\n V.create_grids(type=\"legendre\", k=lpi_ordergl)\n #-----------------------------------\n # ...\n\n # ...\n try:\n GrV = kwargs['GrV'] # [V,V] graph\n except:\n #-----------------------------------\n # graph declaration\n #-----------------------------------\n GrV = graph(spaces=[V,V])\n #-----------------------------------\n # ...\n\n # ...\n #-----------------------------------\n # Matrix declaration\n #-----------------------------------\n Matrix_V = matrix(graph=GrV)\n MatProj_V = matrix(graph=GrV)\n #-----------------------------------\n # ...\n\n # ...\n if self.Dirichlet:\n try:\n W = kwargs['W']\n except:\n #-----------------------------------\n # space declaration\n #-----------------------------------\n W = space(geometry=self.geometry)\n W.dirichlet(faces=[[]] * self.geometry.npatchs)\n if self.UseDuplicateFaces:\n W.duplicate( faces_base = list_DuplicatedFaces \\\n , faces = list_DuplicataFaces )\n W.set_boundary_conditions()\n# W.create_grids(type=\"legendre\", k=lpi_ordergl)\n W.grids = V.grids\n #-----------------------------------\n # ...\n\n # ...\n if self.Dirichlet:\n try:\n GrVW = kwargs['GrVW']\n except:\n #-----------------------------------\n # graph declaration\n #-----------------------------------\n GrVW = graph(spaces=[V,W])\n #-----------------------------------\n # ...\n\n # ...\n if self.Dirichlet:\n #-----------------------------------\n # Matrix declaration\n #-----------------------------------\n Matrix_VW = matrix(graph=GrVW)\n #-----------------------------------\n # ...\n\n # ...\n try:\n func_u = self.testcase['u']\n except:\n func_u = func_zero\n\n try:\n U_V = kwargs['U_V']\n except:\n U_V = field(space=V, func = func_u)\n\n if self.Dirichlet:\n try:\n U_W = kwargs['U_W']\n except:\n U_W = field(space=W, func = func_u)\n # ...\n\n # ...\n try:\n func_f = self.testcase['f']\n except:\n func_f = func_zero\n\n try:\n F_V = kwargs['F_V']\n except:\n F_V = field(space=V, func = func_f)\n\n if self.Dirichlet:\n func_w = func_one\n try:\n G_W = kwargs['G_W']\n except:\n G_W = field(space=W, func = func_w)\n # ...\n\n # ... Temp field\n T_V = field(space=V, func = func_zero)\n # ...\n\n # ...\n if self.Dirichlet:\n try:\n U_W = kwargs['U_W']\n except:\n U_W = field(space=W, func = func_u)\n # ...\n\n # ...\n try:\n Projector_V = kwargs['Projector_V']\n except:\n if V.dim == 1:\n func_one = lambda x : [ 1. ]\n if V.dim == 2:\n func_one = lambda x,y : [ 1. ]\n if V.dim == 3:\n func_one = lambda x,y,z : [ 1. ]\n Projector_V = oper(spaces=[V, V], type=MASS , func=func_one)\n Projector_V.addto(MatProj_V)\n # ...\n\n # ...\n try:\n trial = kwargs['trial']\n except:\n trial = V\n # ...\n\n # ...\n try:\n M_V = kwargs['M_V']\n except:\n if self.withMass:\n M_V = oper(spaces=[V, trial], type=MASS , func=func_mass)\n M_V.addto(Matrix_V)\n # ...\n if self.withTAdvection:\n Mdw_V = oper(spaces=[V, trial], type=MASS , func=func_dw)\n Mdw_V.addto(Matrix_V)\n # ...\n\n # TODO what to do with the trial space for VW?\n try:\n M_VW = kwargs['M_VW']\n except:\n if self.withMass and self.Dirichlet:\n M_VW = oper(spaces=[V, W], type=MASS , func=func_mass)\n M_VW.addto(Matrix_VW)\n # ...\n\n # ...\n try:\n A_V = kwargs['A_V']\n except:\n if self.withAdvection:\n A_V = oper(spaces=[V, trial], type=ADVECTION , func=func_adv)\n A_V.addto(Matrix_V)\n\n try:\n A_VW = kwargs['A_VW']\n except:\n if self.withAdvection and self.Dirichlet:\n A_VW = oper(spaces=[V, W], type=ADVECTION , func=func_adv)\n A_VW.addto(Matrix_VW)\n # ...\n\n # ...\n try:\n At_V = kwargs['At_V']\n except:\n if self.withTAdvection:\n At_V = oper(spaces=[V, trial], type=ADVECTION , func=func_tadv, transpose=True)\n At_V.addto(Matrix_V)\n\n try:\n At_VW = kwargs['At_VW']\n except:\n if self.withTAdvection and self.Dirichlet:\n At_VW = oper(spaces=[V, W], type=ADVECTION , func=func_tadv, transpose=True)\n At_VW.addto(Matrix_VW)\n # ...\n\n # ...\n try:\n S_V = kwargs['S_V']\n except:\n if self.withStiffness:\n S_V = oper(spaces=[V, trial], type=STIFFNESS , func=func_stiff)\n S_V.addto(Matrix_V)\n\n try:\n S_VW = kwargs['S_VW']\n except:\n if self.withStiffness and self.Dirichlet:\n S_VW = oper(spaces=[V, W], type=STIFFNESS , func=func_stiff)\n S_VW.addto(Matrix_VW)\n # ...\n\n # ...\n try:\n B_V = kwargs['B_V']\n except:\n if self.withD2:\n B_V = oper(spaces=[V, trial], type=SECOND_DERIV, func=func_D2)\n B_V.addto(Matrix_V)\n\n try:\n B_VW = kwargs['B_VW']\n except:\n if self.withD2 and self.Dirichlet:\n B_VW = oper(spaces=[V, W], type=SECOND_DERIV, func=func_D2)\n B_VW.addto(Matrix_VW)\n # ...\n\n # ...\n self.list_G_V_BC_faces = []\n try:\n V_BC = kwargs['V_BC']\n except:\n if self.Neumann:\n list_V_BC = []\n list_G_V_BC = []\n #-----------------------------------\n # boundary space declaration\n #-----------------------------------\n for key, func_g in self.bc_neumann.items():\n patch_id = int(key[0])\n face_id = int(key[1])\n\n self.list_G_V_BC_faces.append([patch_id,face_id])\n\n nrb = self.geometry[patch_id]\n\n axis, side = face_to_bc(face_id)\n\n nrb_bnd = nrb.extract_face(axis, side)\n geo = cad_geometry()\n geo.append(nrb_bnd)\n\n lpi_ordergl = nrb_bnd.degree\n\n # ...\n V_BC = space(geometry=geo)\n V_BC.dirichlet(faces=[[]])\n V_BC.set_boundary_conditions()\n V_BC.create_grids(type=\"legendre\", k=lpi_ordergl)\n # ...\n\n # ...\n g = boundary_function(self.geometry\\\n , patch_id\\\n , face_id\\\n , func_g)\n G_V_BC = field(space=V_BC, pfunc=g)\n # ...\n\n # ...\n list_V_BC.append(V_BC)\n list_G_V_BC.append(G_V_BC)\n # ...\n #-----------------------------------\n # ...\n\n # ...\n try:\n Mean_V = kwargs['Mean_V']\n except:\n if self.meanConstraint:\n if self.dim == 1:\n func_one = lambda x : [ 1. ]\n if self.dim == 2:\n func_one = lambda x,y : [ 1. ]\n if self.dim == 3:\n func_one = lambda x,y,z : [ 1. ]\n Mean_V = field(space=V, func = func_one)\n # ...\n\n # ...\n try:\n N_U = kwargs['N_U']\n except:\n #-----------------------------------\n # NORM\n #-----------------------------------\n if self.Dirichlet:\n N_U = norm(field=U_W, type=NORM_L2, exact=func_u)\n else:\n N_U = norm(field=U_V, type=NORM_L2, exact=func_u)\n #-----------------------------------\n # ...\n\n #-----------------------------------\n # Save access for data\n #-----------------------------------\n self._slv = _slv\n self._system = _system\n self.Matrix_V = Matrix_V\n self.MatProj_V = MatProj_V\n self.GrV = GrV\n\n self.spaces = []\n self.norms = []\n self.fields = []\n self.graphs = [self.GrV]\n self.matrices = [self.Matrix_V]\n\n self.V = V\n self.spaces += [self.V]\n\n if self.withMetric:\n self.Metric = Metric\n\n if self.Dirichlet:\n self.W = W\n self.GrVW = GrVW\n self.spaces += [self.W]\n self.graphs += [self.GrVW]\n\n self.N_U = N_U\n self.norms += [self.N_U]\n\n # ... rhs\n self.F_V = F_V\n self.fields += [self.F_V]\n # ... L2 projector\n self.Projector_V = Projector_V\n\n if self.Dirichlet:\n self.G_W = G_W\n # insteed fo assembling this field, we will rise it from the\n # boundary\n# self.fields += [self.G_W]\n # ...\n\n # ... unknown\n self.U_V = U_V\n self.fields += [self.U_V]\n if self.Dirichlet:\n self.U_W = U_W\n# self.fields += [self.U_W]\n # ...\n\n # ... Temp field\n self.T_V = T_V\n # ...\n\n # ... Mass operator\n if self.withMass:\n self.M_V = M_V\n if self.withTAdvection:\n self.Mdw_V = Mdw_V\n if self.Dirichlet:\n self.M_VW = M_VW\n # ...\n\n # ... Advection operator\n if self.withAdvection:\n self.A_V = A_V\n if self.Dirichlet:\n self.A_VW = A_VW\n # ...\n\n # ... Transpose Advection operator\n if self.withTAdvection:\n self.At_V = At_V\n if self.Dirichlet:\n self.At_VW = At_VW\n # ...\n\n # ... Stiffness operator\n if self.withStiffness:\n self.S_V = S_V\n if self.Dirichlet:\n self.S_VW = S_VW\n # ...\n\n # ... Second Derivative operator\n if self.withD2:\n self.B_V = B_V\n if self.Dirichlet:\n self.B_VW = B_VW\n # ...\n\n # ...\n if self.Dirichlet:\n self.Matrix_VW = Matrix_VW\n self.matrices += [self.Matrix_VW]\n # ...\n\n # ...\n if self.Neumann:\n self.list_V_BC = list_V_BC\n for S in list_V_BC:\n self.spaces+= [S]\n\n self.list_G_V_BC= list_G_V_BC\n for G in list_G_V_BC:\n self.fields+= [G]\n # ...\n\n # ... Mean Constraint in the case of Pure Neumann and Periodic bc\n if self.meanConstraint:\n self.Mean_V = Mean_V\n self.fields += [self.Mean_V]\n # ...\n\n self.forceAssembly = False\n self.Assembled = False\n\n #-----------------------------------","title":""},{"docid":"616dcff6eec4e549342268ce1cfb980b","score":"0.5114144","text":"def train(self):","title":""},{"docid":"616dcff6eec4e549342268ce1cfb980b","score":"0.5114144","text":"def train(self):","title":""},{"docid":"286828bd63348181bfb65a5621cb59ee","score":"0.50948656","text":"def sample_trajectory(pi, model, env, horizon=150, rolloutSize=50, render=False):\n if render:\n env.setRender(True)\n else:\n env.setRender(False)\n\n ac = env.action_space.sample() # not used, just so we have the datatype\n new = True # marks if we're on first timestep of an episode\n ob = env.reset()\n cF = env.getContactForce()\n num_options = pi.num_options\n cur_ep_ret = 0 # return in current episode\n cur_ep_len = 0 # len of current episode\n ep_rets = [] # returns of completed episodes in this segment\n ep_lens = [] # lengths of ...\n batch_size = int(horizon * rolloutSize)\n # Initialise history of arrays\n obs = np.array([ob for _ in range(batch_size)])\n cFs = np.array([cF for _ in range(batch_size)])\n rews = np.zeros(batch_size, 'float32')\n news = np.zeros(batch_size, 'int32')\n opts = np.zeros(batch_size, 'int32')\n activated_options = np.zeros((batch_size, num_options), 'float32')\n\n acs = np.array([ac for _ in range(batch_size)])\n model.currentMode = env.mode\n option, active_options_t = pi.get_option(ob)\n\n opt_duration = [[] for _ in range(num_options)]\n sample_index = 0\n curr_opt_duration = 0\n\n success = 0\n successFlag = False\n while sample_index < batch_size:\n ac = pi.act(True, ob, option)\n if math.isnan(ac[0]) or math.isnan(ac[1]):\n #print(\"Resolving NAN !! \")\n continue\n obs[sample_index] = ob\n news[sample_index] = new\n opts[sample_index] = option\n acs[sample_index] = ac\n activated_options[sample_index] = active_options_t\n\n # Step in the environment\n ob, rew, new, _ = env.step(ac)\n rews[sample_index] = rew\n cFs[sample_index] = env.getContactForce()\n #print(cFs[sample_index])\n curr_opt_duration += 1\n # check if current option is about to end in this state\n nbeta = pi.get_tpred(ob)\n tprob = nbeta[option]\n\n if render:\n env.render()\n\n # Check for termination\n if tprob >= pi.term_prob:\n opt_duration[option].append(curr_opt_duration)\n curr_opt_duration = 0.\n model.currentMode = model.getNextMode(ob)\n option, active_options_t = pi.get_option(ob)\n\n cur_ep_ret += rew\n cur_ep_len += 1\n\n dist = env.getGoalDist()\n if np.linalg.norm(dist) < 0.005 and not successFlag:\n success = success + 1\n successFlag = True\n\n sample_index += 1\n\n if new or (sample_index > 0 and sample_index % horizon == 0):\n render = False\n env.setRender(False)\n opt_duration[option].append(curr_opt_duration)\n curr_opt_duration = 0.\n ep_rets.append(cur_ep_ret)\n ep_lens.append(cur_ep_len)\n cur_ep_ret = 0\n cur_ep_len = 0\n ob = env.reset()\n model.currentMode = env.mode\n option, active_options_t = pi.get_option(ob)\n successFlag = False\n new = True\n\n env.close()\n print(\"Selected options\")\n for o in range(0, num_options):\n print(\"Option: \", o, \" - \", sum(opt_duration[o]))\n\n print(\"\\n Maximum Reward this iteration: \", max(ep_rets), \" \\n\")\n rollouts = {\"ob\": obs, \"rew\": rews, \"new\": news, \"ac\": acs, \"opts\": opts, \"ep_rets\": ep_rets, \"ep_lens\": ep_lens, \"opt_dur\": opt_duration, \"activated_options\": activated_options, \"success\": success, \"contactF\": cFs}\n\n return rollouts","title":""},{"docid":"efc20144a6ca92706653364c876660c1","score":"0.5094545","text":"def _calculate_trajectories(self):\n print \"calculate initial trajectories\"\n trajectories = []\n for data in self.model_data:\n trajectories.append(pyBalloon.pyb_traj.calc_movements(data,\n self.loc0, BALLOON))\n pyBalloon.pyb_io.save_kml(PARAMETERS['kml_file'], trajectories)","title":""},{"docid":"a97d17f00cd4b3dc18f749efca4b0005","score":"0.50894016","text":"def sample_trajectories(num_traj, num_steps, env, controller, scaler, transformer, scaler2, transformer2, show_visual=False, first_run=False, normalize_inputs=True, normalize_outputs=True):\n return_vec = np.array([])\n reward_dict = defaultdict(list)\n for traj_num in range(num_traj):\n ob = env.reset()\n obs, next_obs, acs, rewards, returns, reward_to_go = [], [], [], [], [], []\n steps = 0\n ret = 0\n while True:\n if(show_visual or traj_num==0):\n env.render()\n obs.append(ob)\n if(first_run):\n ac = env.action_space.sample() \n if ac[0]<0:\n ac[0] = 0\n else:\n inputs = np.expand_dims(ob.astype(np.float32),0) \n inputs = inputs + 1e-3*np.random.standard_normal(inputs.shape[1:])\n # normalize inputs\n if(normalize_inputs):\n inputs = transformer.transform(inputs)\n inputs = scaler.transform(inputs)\n ac = controller(inputs)[0]\n ac = np.array(ac)\n acs.append(ac)\n #if ac[0]<0:\n # ac[0] = 0\n if(np.any(np.isnan(ac))):\n print('nan error')\n pdb.set_trace()\n if(not isinstance(env.action_space, gym.spaces.Discrete)):\n ac = np.minimum(ac,env.action_space.high)\n ac = np.maximum(ac,env.action_space.low)\n action = ac\n if(normalize_outputs):\n action = scaler2.inverse_transform(action)\n action = transform2.inverse_transform(action)\n ob, reward, done, info = env.step(action)\n\n reward = -1e1*ob[0]**2 # custom: penalize deviation from x=0\n reward = -1e1*ob[2]**2 # custom: penalize deviation from x=0\n reward += -1e1*ob[4]**2\n reward += -1e1*ob[5]**2\n #reward += -1e1*(ob[1]-0.1)**2\n reward += 100 # reward staying in episode\n next_obs.append(ob)\n rewards.append(reward)\n ret += reward\n returns.append(ret)\n steps += 1\n\n if done or steps>num_steps:\n print(\"Episode {} finished after {} timesteps\".format(traj_num, steps))\n break\n\n # backwards pass to calculate reward-to-go\n reward_to_go = np.full(len(rewards),np.nan)\n discount = 0.2\n reward_to_go[-1] = rewards[-1]\n for i in range(2, reward_to_go.shape[0]+1):\n reward_to_go[-(i)] = rewards[-(i)] + discount*reward_to_go[-(i-1)] \n for idx in range(len(reward_to_go)):\n reward_dict[str(idx)].append(reward_to_go[idx])\n\n print('run return: {}'.format(returns[-1]))\n if(traj_num==0):\n trajectories = {\"observations\" : np.array(obs),\n \"next_observations\": np.array(next_obs),\n \"rewards\" : np.array(rewards),\n \"actions\" : np.array(acs),\n \"returns\" : np.array(returns),\n \"reward_to_go\" : np.array(reward_to_go),\n \"step\" : np.arange(steps)}\n else:\n traj = {\"observations\" : np.array(obs),\n \"next_observations\": np.array(next_obs),\n \"rewards\" : np.array(rewards),\n \"actions\" : np.array(acs),\n \"returns\" : np.array(returns),\n \"reward_to_go\" : np.array(reward_to_go),\n \"step\" : np.arange(steps)}\n for k in traj:\n trajectories[k] = np.append(trajectories[k],traj[k],axis=0)\n return_vec = np.append(return_vec, returns[-1])\n\n for k in reward_dict:\n reward_dict[k] = np.array(reward_dict[k])\n return trajectories, return_vec, reward_dict","title":""},{"docid":"67c5d1d409843ce6b3eaa8aa377943ae","score":"0.50839204","text":"def __init__(self, N_X: int, N_Y: int, \n t: float, u: float, mu: float=None, t_ij=None):\n self.N_X = N_X \n self.N_Y = N_Y \n\n self.t = t\n self.t_ij = t_ij or np.ones((N_Y, N_X), dtype='complex128')\n self.u = u \n self.mu = mu \n\n self.lattice = [[ { \n 'i': i, \n 'j': j, \n 'c-up': CreationOperator('up', self.N_Y*i+j, self.N_Y*self.N_X), \n 'c-down': CreationOperator('down', self.N_Y*i+j, self.N_Y*self.N_X), \n 'a-up': AnnihilationOperator('up', self.N_Y*i+j, self.N_Y*self.N_X), \n 'a-down': AnnihilationOperator('down', self.N_Y*i+j, self.N_Y*\n self.N_X) \n } for j in range(self.N_X) ] for i in range(self.N_Y) ]\n\n self.Hs = [ \n # Get horizontal, even hopping terms\n self._sum_pair_operators(lambda i, j: 1-j%2, lambda i, j: (i, j+1)), \n # Get horizontal, odd hopping terms \n self._sum_pair_operators(lambda i, j: j%2, lambda i, j: (i, j+1)), \n # Get vertical, even hopping terms \n self._sum_pair_operators(lambda i, j: 1-i%2, lambda i, j: (i+1, j)), \n # Get vertical, odd hopping terms \n self._sum_pair_operators(lambda i, j: i%2, lambda i, j: (i+1, j)), \n # Get interacting U term \n self._sum_interacting_term()\n ]\n\n self.H = sum(self.Hs)","title":""},{"docid":"88ab7bd815e9a08a39e490f748853eec","score":"0.5069024","text":"def functional_test():\n print(\"Expected results: \", grids[1]['expected'])\n print(\"output: \", spiral(grids[1]['grid']))\n print()\n print(\"Expected results: \", grids[2]['expected'])\n print(\"output: \", spiral(grids[2]['grid']))\n print()\n print(\"Expected results: \", grids[3]['expected'])\n print(\"output: \", spiral(grids[3]['grid']))","title":""},{"docid":"0874ff421a9b282c9f05c2fd41d80939","score":"0.5066498","text":"def test_artefact_kernik(self):\n\n simple_protocol = protocols.VoltageClampProtocol([\n protocols.VoltageClampStep(voltage=-80, duration=1000),\n protocols.VoltageClampStep(voltage=-40, duration=1000)])\n\n fig, axs = plt.subplots(4, 1, sharex=True, figsize=(12, 8))\n\n for i, alpha in enumerate([0, .4, .7, .9]):\n if i == 0:\n baseline_model = kernik.KernikModel()\n else:\n baseline_model = kernik.KernikModel(is_exp_artefact=True, exp_artefact_params={'alpha': alpha})\n tr = baseline_model.generate_response(simple_protocol,\n is_no_ion_selective=False)\n if i == 0:\n axs[0].plot(tr.t, tr.command_voltages, 'k')\n axs[0].plot(tr.t, tr.y)\n axs[1].plot(tr.t, tr.current_response_info.get_current_summed())\n axs[2].plot(tr.t, tr.current_response_info.get_current('I_Na'), label=f'alpha={alpha}')\n if i != 0:\n axs[3].plot(tr.t, tr.current_response_info.get_current('I_seal_leak'), label=f'alpha={alpha}')\n\n axs[2].legend()\n plt.show()\n\n #plt.plot(baseline_model.t, baseline_model.y[0, :])\n #plt.plot(baseline_model.t, baseline_model.y[26, :])\n #plt.show()\n\n plt.show()","title":""},{"docid":"6ccce2a8a4026e23be6189b40a3e273e","score":"0.50654083","text":"def test_18(self):\n d = {'EBstep': -1,\n 'L1item': 'L1_J100',\n 'chainCounter': 727,\n 'chainName': '2hypochain',\n 'chainParts': [{'L1item': '',\n 'TLA': '1i2c500m700TLA',\n 'addInfo': [],\n 'bConfig': ['split'],\n 'bMatching': [],\n 'bTag': 'bmv2c2060',\n 'bTracking': '',\n 'calib': 'em',\n 'chainPartName': 'j175_bmv2c2060_split',\n 'cleaning': 'noCleaning',\n 'dataScouting': '',\n 'dataType': 'tc',\n 'etaRange': '0eta320',\n 'extra': '',\n 'jetCalib': 'subjes',\n 'multiplicity': '1',\n 'recoAlg': 'a4',\n 'scan': 'FS',\n 'signature': 'Jet',\n 'threshold': '0',\n 'topo': [],\n 'trigType': 'j'},\n {'L1item': '',\n 'TLA': '',\n 'addInfo': [],\n 'bConfig': ['split'],\n 'bMatching': [],\n 'bTag': 'bmv2c2050',\n 'bTracking': '',\n 'calib': 'em',\n 'chainPartName': 'j50_bmv2c2050_split',\n 'cleaning': 'noCleaning',\n 'dataScouting': '',\n 'dataType': 'tc',\n 'etaRange': '0eta320',\n 'extra': '',\n 'jetCalib': 'subjes',\n 'multiplicity': '1',\n 'recoAlg': 'a4',\n 'scan': 'FS',\n 'signature': 'Jet',\n 'threshold': '50',\n 'topo': [],\n 'trigType': 'j'}],\n 'groups': ['RATE:MultiBJet', 'BW:Bjet'],\n 'signature': 'Jet',\n 'signatures': '',\n 'stream': ['Main'],\n 'topo': [],\n 'topoStartFrom': False,\n 'topoThreshold': None}\n \n generateHLTChainDef(d)","title":""},{"docid":"ff148eab0bf0cb7c69f4b5eea743b2ad","score":"0.5046788","text":"def test_experiments_multiaxisgonio():\n # beam along +z\n gonio_1 = GoniometerFactory.from_dict(\n {\n \"axes\": [\n [\n 1.0 / sqrt(2.0),\n 0.0,\n -1.0 / sqrt(2.0),\n ],\n [1.0, 0.0, 0.0],\n ],\n \"angles\": [0.0, 0.0],\n \"names\": [\"GON_PHI\", \"GON_OMEGA\"],\n \"scan_axis\": 1,\n }\n )\n gonio_2 = GoniometerFactory.from_dict(\n {\n \"axes\": [\n [\n 1.0 / sqrt(2.0),\n 0.0,\n -1.0 / sqrt(2.0),\n ],\n [1.0, 0.0, 0.0],\n ],\n \"angles\": [0.0, 0.0],\n \"names\": [\"GON_PHI\", \"GON_OMEGA\"],\n \"scan_axis\": 0,\n }\n )\n\n experiments = ExperimentList()\n for g in [gonio_1, gonio_2]:\n experiments.append(\n Experiment(\n beam=Beam(s0=(0.0, 0.0, 2.0)),\n goniometer=g,\n scan=Scan(image_range=[1, 90], oscillation=[0.0, 1.0]),\n )\n )\n\n return experiments","title":""},{"docid":"10d342c92727e48097935a8cd2fdc489","score":"0.50422406","text":"def exercise():\n\n uc = uctbx.unit_cell( \"96 74 77 90 113 90\")\n xs = crystal.symmetry(uc, \"C2\")\n trlw = reticular_twin_laws( xs, max_index=2, max_delta=1.5 )\n #trlw.show()\n m = [2, 1, 0,0, 1, 0,0, 0, 1]\n for ii, jj in zip( trlw.derived_laws[0].m, m):\n assert(ii==jj)\n\n tl = [-1, 0, 0, 0, -1, 0,-1, 0, 1]\n dl = trlw.derived_laws[0].twin_laws[0]\n for ii, jj in zip(tl,dl):\n assert(ii==jj)\n\n uc = uctbx.unit_cell( \"10.079 10.079 48.409 90 90 120\" )\n xs = crystal.symmetry( uc, \"R32\")\n rtl = reticular_twin_laws( xs , max_index=8, max_delta=1.5)\n for tli in rtl.derived_laws:\n for ttl in tli.twin_laws:\n assert(ttl.determinant()==1) #check that these twin laws have det equal to 1","title":""},{"docid":"ca50f2ac29602900038b4ea1f4aa14d0","score":"0.5036874","text":"def test_inner_cv():\n _experiment(3, None)","title":""},{"docid":"dc1e604b16631f9455fa03d2099f0cef","score":"0.50362664","text":"def GenSinRoutineTraj(self, seq, ax, amplitudes, period, j_0):\n\n traj = JointTrajectory()\n traj.header.seq = seq\n traj.header.frame_id = 'base_link'\n traj.joint_names = ['x','y','z','dummy','dummy1','yaw','joint1','joint2','joint3','joint4','joint5']\n traj.header.stamp = rospy.get_rostime()\n \n Hz = 100 # Number of traj points per second as required by the controller\n T = float(period) # casting to avoid possible error that could be \n w = 2*math.pi*(1/T) # \n small_pause = 1.0 # Pause between fowards and backwards movements on same degree of freedom\n big_pause = 1.0 # Pause between movements ivolving different degrees of freedom\n points_no = int((T + small_pause + big_pause)*Hz) #\n t_s = np.linspace(0, period+small_pause+big_pause, points_no) #\n\n last_t = t_s[-1]\n count = 0.0\n\n for axis in ax:\n #v = [] # Used for debugging\n #s = [] # Used for debugging\n #a = [] # Used for debugging\n \n k = ax.index(axis)\n A = float(amplitudes[k])\n\n if not(axis == 'x' or axis == 'y' or axis == 'z' or axis == 'yaw' or axis == 'man_yaw', axis == 'man_pitch', axis == 'man_pitch_base'):\n continue\n\n for i in range(len(t_s)):\n tmp_point = JointTrajectoryPoint()\n\n if (t_s[i] >= T/2.0 and t_s[i] <= (T/2.0 + small_pause)): # Setting values for the points of desired trajectory\n s_s = 2.0*A/w \n v_s = 0.0 # which include position, speed and acceleration\n a_s = 0.0 # \n # \n elif t_s[i] >= (T + small_pause): # \n s_s = 0.0 # \n v_s = 0.0 # \n a_s = 0.0 # \n # \n elif t_s[i] < T/2.0: #\n v_s = A * math.sin( w*t_s[i] ) # \n s_s =-A/w * math.cos( w*t_s[i] ) + A/w # \n a_s = A*w * math.cos( w*t_s[i] ) # \n #\n elif t_s[i] > (T/2.0 + small_pause) and t_s[i] < (T + small_pause): # \n v_s = A * math.sin( w*(t_s[i] - small_pause)) # \n s_s =-A/w * math.cos( w*(t_s[i] - small_pause)) + A/w # \n a_s = A*w * math.cos( w*(t_s[i] - small_pause)) #\n \n if axis == 'x':\n # x y z dummy dummy1 yaw joint1 joint2 joint3 joint4 joint5 \n tmp_point.positions = [j_0[0]+s_s, j_0[1] , j_0[2] , j_0[3], j_0[4], j_0[5] , j_0[6], j_0[7], j_0[8], j_0[9], j_0[10]]\n tmp_point.velocities = [v_s , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ]\n tmp_point.accelerations = [a_s , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ]\n elif axis == 'y':\n # x y z dummy dummy1 yaw joint1 joint2 joint3 joint4 joint5 \n tmp_point.positions = [j_0[0] , s_s+j_0[1], j_0[2] , j_0[3], j_0[4], j_0[5] , j_0[6], j_0[7], j_0[8], j_0[9], j_0[10]]\n tmp_point.velocities = [0.0 , v_s , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ]\n tmp_point.accelerations = [0.0 , a_s , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ]\n \n elif axis == 'z':\n # x y z dummy dummy1 yaw joint1 joint2 joint3 joint4 joint5 \n tmp_point.positions = [j_0[0] , j_0[1] , s_s+j_0[2], j_0[3], j_0[4], j_0[5] , j_0[6], j_0[7], j_0[8], j_0[9], j_0[10]]\n tmp_point.velocities = [0.0 , 0.0 , v_s , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ]\n tmp_point.accelerations = [0.0 , 0.0 , a_s , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ]\n\n elif axis == 'yaw':\n # x y z dummy dummy1 yaw joint1 joint2 joint3 joint4 joint5 \n tmp_point.positions = [j_0[0] , j_0[1] , j_0[2] , j_0[3], j_0[4], s_s+j_0[5], j_0[6], j_0[7], j_0[8], j_0[9], j_0[10]]\n tmp_point.velocities = [0.0 , 0.0 , 0.0 , 0.0 , 0.0 , v_s , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ]\n tmp_point.accelerations = [0.0 , 0.0 , 0.0 , 0.0 , 0.0 , a_s , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ]\n\n elif axis == 'man_yaw':\n # x y z dummy dummy1 yaw joint1 joint2 joint3 joint4 joint5 \n tmp_point.positions = [j_0[0] , j_0[1] , j_0[2] , j_0[3], j_0[4], j_0[5], j_0[6], j_0[7], s_s+j_0[8], j_0[9], j_0[10]]\n tmp_point.velocities = [0.0 , 0.0 , 0.0 , 0.0 , 0.0 , v_s , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ]\n tmp_point.accelerations = [0.0 , 0.0 , 0.0 , 0.0 , 0.0 , a_s , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ]\n\n elif axis == 'man_pitch':\n # x y z dummy dummy1 yaw joint1 joint2 joint3 joint4 joint5 \n tmp_point.positions = [j_0[0] , j_0[1] , j_0[2] , j_0[3], j_0[4], j_0[5], j_0[6], s_s+j_0[7], j_0[8], j_0[9], j_0[10]]\n tmp_point.velocities = [0.0 , 0.0 , 0.0 , 0.0 , 0.0 , v_s , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ]\n tmp_point.accelerations = [0.0 , 0.0 , 0.0 , 0.0 , 0.0 , a_s , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ]\n\n elif axis == 'man_pitch_base':\n # x y z dummy dummy1 yaw joint1 joint2 joint3 joint4 joint5 \n tmp_point.positions = [j_0[0] , j_0[1] , j_0[2] , j_0[3], j_0[4], j_0[5], s_s+j_0[6], j_0[7], j_0[8], j_0[9], j_0[10]]\n tmp_point.velocities = [0.0 , 0.0 , 0.0 , 0.0 , 0.0 , v_s , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ]\n tmp_point.accelerations = [0.0 , 0.0 , 0.0 , 0.0 , 0.0 , a_s , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ]\n \n tmp_point.time_from_start.secs = int( t_s[i] + last_t * count)\n tmp_point.time_from_start.nsecs = int((t_s[i] + last_t * count)*1000000000 - int(t_s[i] + last_t * count)*1000000000)\n traj.points.append(tmp_point)\n \n #print(\"t_s :\", t_s[i]) # Used for debugging\n #print(\"last_t :\", last_t) # Used for debugging\n #print(\"count :\", count) # Used for debugging\n #print(\"secs :\", tmp_point.time_from_start.secs) # Used for debugging\n #print(\"nsecs :\", tmp_point.time_from_start.nsecs) # Used for debugging\n\n \n #v.append(v_s) # Used for debugging\n #s.append(s_s) # Used for debugging\n #a.append(a_s) # Used for debugging\n #print(t_s[i]) # Used for debugging \n\n count = count + 1.0 \n\n #plt.figure(int(count)) # Used for debugging\n #plt.plot(t_s, s, t_s, v, t_s, a) # Used for debugging\n #plt.axis('equal') # Used for debugging\n #plt.show() # Used for debugging\n\n return traj","title":""},{"docid":"508eea8d2dc041e3f191ff3955cdbfa0","score":"0.5035858","text":"def evaluate(self, edict):\n # can we run SupervisedLearning.evaluate? Should this be an evaluateLocal?\n ## set up the results dict with the correct dimensionality\n ### actually, let's wait for the first sample to come in.\n self.raiseADebug('Evaluating interpolated ROM ...')\n results = None\n ## TODO set up right for ND??\n forcedMax = self._maxCycles if self._maxCycles is not None else np.inf\n numMacro = min(len(self._macroSteps), forcedMax)\n macroIndexValues = []\n for m, (macroStep, model) in enumerate(sorted(self._macroSteps.items(), key=lambda x: x[0])):\n if m + 1 > numMacro:\n break\n # m is an index of the macro step, in order of the macro values (e.g. in order of years)\n # macroStep is the actual macro step value (e.g. the year)\n # model is the ClusterROM instance for this macro step\n macroIndexValues.append(macroStep)\n self.raiseADebug(f' ... evaluating macro step \"{macroStep}\" ({m+1} / {numMacro})')\n subResult = model.evaluate(edict) # TODO same input for all macro steps? True for ARMA at least...\n indexMap = subResult.get('_indexMap', {})\n # if not set up yet, then frame results structure\n if results is None:\n results = {}\n finalIndexMap = indexMap # in case every rlz doesn't use same order, which would be lame\n pivotID = model._templateROM.pivotParameterID\n indices = set([pivotID, self._macroParameter])\n for indexes in finalIndexMap.values():\n indices.update(set(indexes))\n #pivotVals = subResult[pivotID]\n #numPivot = len(pivotVals)\n for target, values in subResult.items():\n # if an index, just set the values now # FIXME assuming always the same!\n ## FIXME thing is, they're not always the same, we're clustering, so sometimes there's diff num days!\n ## TODO for now, we simply require using a classifier that always has the same number of entries.\n if target in [pivotID, '_indexMap'] or target in indices:\n results[target] = values\n else:\n # TODO there's a strange behavior here where we have nested numpy arrays instead of\n # proper matrices sometimes; maybe it has to be this way for unequal clusters\n # As a result, we use the object dtype, onto which we can place a whole numpy array.\n results[target] = np.zeros([numMacro] + list(values.shape), dtype=object)\n # END setting up results structure, if needed\n # FIXME reshape in case indexMap is not the same as finalIndexMap?\n for target, values in subResult.items():\n if target in [pivotID, '_indexMap'] or target in indices:# indexMap:\n continue\n indexer = tuple([m] + [None]*len(values.shape))\n try:\n results[target][indexer] = values\n except ValueError:\n self.raiseAnError(RuntimeError, 'The shape of the histories along the pivot parameter is not consistent! Try using a clustering classifier that always returns the same number of clusters.')\n results['_indexMap'] = {} #finalIndexMap\n for target, vals in results.items():\n if target not in indices and target not in ['_indexMap']: # TODO get a list of meta vars?\n default = [] if vals.size == 1 else [pivotID]\n results['_indexMap'][target] = [self._macroParameter] + list(finalIndexMap.get(target, default))\n results[self._macroParameter] = macroIndexValues\n return results","title":""},{"docid":"0f136b73f4404ddd880e683bd259c82f","score":"0.50345784","text":"def __iter__(self):\n return iter(self.trajs)","title":""},{"docid":"89db482fc26b10feea8c0fe58467adeb","score":"0.50141114","text":"def classify_complex_trajectory(traj,experiment):\n show_complex_trajectory(complex_trajectories[i],experiment,50)\n possible_answers = ['r','w','t','m','q','e']\n inp = ''\n while(not inp in possible_answers):\n inp= raw_input(\"\"\"how would you classify this image: ramified, withdrawal, transitional,\n motile, error (r/w/t/m/e)? Press q to see the sequence again\\n\"\"\")\n \n if inp=='q':\n inp = classify_complex_trajectory(traj,experiment)\n return inp","title":""},{"docid":"5673b36d312fc9fba76f347d0fd28b86","score":"0.50132704","text":"def implementation(self):","title":""},{"docid":"84f4f2177b68931bc77087ed9eab420a","score":"0.5013004","text":"def simulate(self) -> None:","title":""},{"docid":"a280af291eb7ee3a4be49f4da39da9a8","score":"0.5007999","text":"def compute_rave_trajectory(self, robot):\n\n traj = orpy.RaveCreateTrajectory(robot.GetEnv(), \"\")\n spec = robot.GetActiveConfigurationSpecification('cubic')\n spec.AddDerivativeGroups(1, False)\n spec.AddDerivativeGroups(2, True)\n\n traj.Init(spec)\n deltas = [0]\n for i in range(len(self.ss_waypoints) - 1):\n deltas.append(self.ss_waypoints[i + 1] - self.ss_waypoints[i])\n if len(self.ss_waypoints) == 1:\n q = self.eval(0)\n qd = self.evald(0)\n qdd = self.evaldd(0)\n traj.Insert(traj.GetNumWaypoints(),\n list(q) + list(qd) + list(qdd) + [0])\n else:\n qs = self.eval(self.ss_waypoints)\n qds = self.evald(self.ss_waypoints)\n qdds = self.evaldd(self.ss_waypoints)\n for (q, qd, qdd, dt) in zip(qs, qds, qdds, deltas):\n traj.Insert(traj.GetNumWaypoints(),\n q.tolist() + qd.tolist() + qdd.tolist() + [dt])\n return traj","title":""},{"docid":"e49acd5e9611c77f759d081ab6c31e20","score":"0.49908498","text":"def runPRINCIPLE(self):\n\n # index of stations with good obs\n tymes = np.arange(self.ntyme)[self.nobs>0]\n\n self.I = []\n self.Q = []\n self.U = []\n self.reflectance = []\n self.surf_reflectance = []\n self.ROT = []\n for t in tymes: \n tau = self.tau[t][:,:,self.iGood[t]]\n ssa = self.ssa[t][:,:,self.iGood[t]]\n pmom = self.pmom[t][:,:,self.iGood[t],:,:]\n pe = self.pe[t][:,self.iGood[t]]\n ze = self.ze[t][:,self.iGood[t]]\n te = self.te[t][:,self.iGood[t]]\n\n # Initiate output arrays\n nlev = tau.shape[0]\n self.I_ = np.ones([self.nstations,self.npp])*MISSING\n self.Q_ = np.ones([self.nstations,self.npp])*MISSING\n self.U_ = np.ones([self.nstations,self.npp])*MISSING\n self.reflectance_ = np.ones([self.nstations,self.npp])*MISSING\n self.surf_reflectance_ = np.ones([self.nstations,self.npp])*MISSING\n self.ROT_ = np.ones([self.nstations,nlev])*MISSING\n \n # Get VLIDORT wrapper name from dictionary\n vlidortWrapper = WrapperFuncs[self.albedoType]\n\n\n # do principle plane\n for pi, pp in enumerate(self.pp_angles): \n # Solar Geometry\n sza = self.SZA[t][self.iGood[t]]\n saa = self.SAA[t][self.iGood[t]]\n\n #viewing geometry\n vza = sza - pp \n\n raa = np.zeros(saa.shape)\n\n # if pointing towards the sun\n iGood = vza >= 0\n raa[iGood] = 180.0\n\n #if poinitng away from the sun\n iGood = vza < 0\n raa[iGood] = 0.0\n\n # make all vza's positive\n vza[iGood] = np.abs(vza[iGood])\n\n # Limit viewing angles to less than 80\n # also limits to pointing above the horizon\n iGood = vza < 80\n nobs = np.sum(iGood)\n\n if nobs >0:\n # run VLIDORT \n # get args list for each surface model\n if self.albedoType == 'MODIS_BRDF':\n kernel_wt = self.kernel_wt[t][:,:,self.iGood[t]]\n param = self.RTLSparam[t][:,:,self.iGood[t]] \n \n args = [self.channel,tau[:,:,iGood], ssa[:,:,iGood], pmom[:,:,iGood,:,:], \n pe[:,iGood], ze[:,iGood], te[:,iGood], \n kernel_wt, param, \n sza[iGood], raa[iGood], vza[iGood], \n MISSING,\n self.verbose]\n\n # Call VLIDORT wrapper function\n I, reflectance, ROT, surf_reflectance, Q, U, rc = vlidortWrapper(*args) \n \n elif self.albedoType == 'MODIS_BRDF_BPDF':\n # For albedo\n kernel_wt = self.kernel_wt[t][:,:,self.iGood[t]]\n RTLSparam = self.RTLSparam[t][:,:,self.iGood[t]] \n RTLSparam = np.append(RTLSparam,np.zeros([1,1,self.nobs[t]]),axis=0) \n\n # For BPDF\n BPDFparam = self.BPDFparam[t][:,:,self.iGood[t]]\n\n # Loop through one by one\n # Some land covers do not have polarization (i.e. urban)\n I = np.zeros([nobs,1])\n Q = np.zeros([nobs,1])\n U = np.zeros([nobs,1])\n reflectance = np.zeros([nobs,1])\n surf_reflectance = np.zeros([nobs,1])\n ROT = np.zeros([nlev,nobs,1])\n\n index = np.arange(self.nobs[t])\n index = index[iGood]\n\n for pindex,p in enumerate(index):\n if BPDFparam[2,0,p] == MISSING: \n args = [self.channel,\n tau[:,:,p:p+1], \n ssa[:,:,p:p+1], \n pmom[:,:,p:p+1,:,:], \n pe[:,p:p+1], \n ze[:,p:p+1], \n te[:,p:p+1], \n kernel_wt[:,:,p:p+1], \n RTLSparam[:,:,p:p+1], \n sza[p:p+1], \n raa[p:p+1], \n vza[p:p+1], \n MISSING,\n self.verbose]\n\n BRDFvlidortWrapper = WrapperFuncs['MODIS_BRDF']\n # Call VLIDORT wrapper function\n I_, reflectance_, ROT_, surf_reflectance_, Q_, U_, rc = BRDFvlidortWrapper(*args) \n\n else:\n args = [self.channel,\n tau[:,:,p:p+1], \n ssa[:,:,p:p+1], \n pmom[:,:,p:p+1,:,:], \n pe[:,p:p+1], \n ze[:,p:p+1], \n te[:,p:p+1], \n kernel_wt[:,:,p:p+1], \n RTLSparam[:,:,p:p+1], \n BPDFparam[:,:,p:p+1],\n sza[p:p+1], \n raa[p:p+1], \n vza[p:p+1], \n MISSING,\n self.verbose]\n\n # Call VLIDORT wrapper function\n I_, reflectance_, ROT_, surf_reflectance_, Q_, U_, rc = vlidortWrapper(*args)\n \n I[pindex:pindex+1,:] = I_\n Q[pindex:pindex+1,:] = Q_\n U[pindex:pindex+1,:] = U_\n reflectance[pindex:pindex+1,:] = reflectance_\n surf_reflectance[pindex:pindex+1,:] = surf_reflectance_\n ROT[:,pindex:pindex+1,:] = ROT_\n \n elif self.albedoType == 'LAMBERTIAN':\n albedo = self.albedo[t][self.iGood[t],:]\n \n args = [self.channel,tau[:,:,iGood], ssa[:,:,iGood], pmom[:,:,iGood,:,:], \n pe[:,iGood], ze[:,iGood], te[:,iGood], \n albedo[iGood,:], \n sza[iGood], raa[iGood], vza[iGood], \n MISSING,\n self.verbose]\n\n # Call VLIDORT wrapper function\n I, reflectance, ROT, Q, U, rc = vlidortWrapper(*args) \n surf_reflectance = albedo[iGood,:]\n\n # Store values in initialized arrays\n II = np.arange(self.nstations)\n II = II[self.iGood[t]]\n II = II[iGood]\n if pi == 0:\n self.ROT_[II,:] = np.squeeze(ROT).T\n\n self.I_[II,pi] = np.squeeze(I)\n self.reflectance_[II,pi] = np.squeeze(reflectance)\n self.surf_reflectance_[II,pi] = np.squeeze(surf_reflectance)\n self.Q_[II,pi] = np.squeeze(Q)\n self.U_[II,pi] = np.squeeze(U) \n\n #Store full principle plane for writing later\n self.I.append(self.I_)\n self.Q.append(self.Q_)\n self.U.append(self.U_)\n self.reflectance.append(self.reflectance_)\n self.surf_reflectance.append(self.surf_reflectance_)\n self.ROT.append(self.ROT_)\n\n self.writeNCpp()","title":""},{"docid":"061ad9c6ed7dc12b344760d7b2a0bb66","score":"0.49899322","text":"def _create_objectives(self):\n # Define structures for each contrast.\n self.structures = [self._using_conditions(contrast_sld)\n for contrast_sld in self.contrast_slds]\n\n # Iterate over each measured structure.\n self.objectives = []\n for i, structure in enumerate(self.structures):\n # Define the model.\n model = refnx.reflect.ReflectModel(structure,\n scale=self.scale,\n bkg=self.bkgs[i],\n dq=self.dq)\n # Load the measured data.\n filename = '{}.dat'.format(self.labels[i])\n file_path = os.path.join(self.data_path, filename)\n data = refnx.dataset.ReflectDataset(file_path)\n\n # Combine model and data into an objective that can be fitted.\n self.objectives.append(refnx.analysis.Objective(model, data))","title":""},{"docid":"77fc64bf0c6514e53061580d515d36c9","score":"0.49797496","text":"def run(self):\n trajectories=self.get_option('trajectories',required=True).split(',')\n failure=False\n self.start_pdf()\n reference_plotted=dict()\n for traj in trajectories:\n for runmode in self._filter_runmodes(section=traj):\n for n in range(len(self._get_columns(traj,runmode))):\n self.figureLegendRight(ylabel='value '+str(n+1), title=self.test, n=n)\n\n data,timeArray,data_label=self._get_data(section=traj,runmode=runmode,n=n)\n reference,reference_label=self._get_reference(section=traj,runmode=runmode,n=n)\n if (reference_label,n) not in reference_plotted :\n self.plot(timeArray,reference(timeArray),label=reference_label)\n reference_plotted[(reference_label,n)]=True\n self.plot(timeArray,data(timeArray),label=data_label)\n logging.debug(\"Evaluating {0}, value number {1}.\".format(data_label,n+1))\n eps=self._get_eps(runmode, traj, n)\n if not self._regression(reference,data,timeArray,eps):\n logging.debug(\"====== FAILED ======\")\n failure=True\n self.close_pdf()\n if failure:\n sys.exit(-1)","title":""},{"docid":"19e9c18eddd096275be7fffd3fd6ba82","score":"0.4978773","text":"def get_tdr_dynamical(self):\n \n nkpt = self.eigr2d.nkpt\n nband = self.eigr2d.nband\n natom = self.eigr2d.natom\n ntemp = self.ntemp\n \n self.tdr = zeros((ntemp, nkpt, nband), dtype=complex)\n \n bose = self.ddb.get_bose(self.temperatures)\n \n fan_term = zeros((ntemp, nkpt, nband), dtype=complex)\n ddw_term = zeros((ntemp, nkpt, nband), dtype=complex)\n fan_add = zeros((ntemp, nkpt, nband),dtype=complex)\n ddw_add = zeros((ntemp, nkpt, nband),dtype=complex)\n \n # Sternheimer contribution\n \n # nmode, nkpt, nband\n fan_stern, ddw_stern = self.get_fan_ddw_sternheimer()\n \n fan_term = einsum('ijk,il->ljk', fan_stern, 2*bose+1.0)\n ddw_term = einsum('ijk,il->ljk', ddw_stern, 2*bose+1.0)\n \n # Active space contribution\n fan_num, ddw_num = self.get_fan_ddw_active()\n \n # jband\n occ = self.get_occ_nospin()\n \n delta_E_ddw = (einsum('ij,k->ijk', self.eig0.EIG[0,:,:].real, ones(nband)) -\n einsum('ij,k->ikj', self.eig0.EIG[0,:,:].real, ones(nband)) -\n einsum('ij,k->ijk', ones((nkpt, nband)), (2*occ-1)) * self.smearing * 1j)\n \n # ntemp,ikpt,iband,jband\n tmp = einsum('ijkl,lm->mijk', ddw_num, 2*bose+1.0)\n ddw_add = einsum('ijkl,jkl->ijk', tmp, 1.0 / delta_E_ddw)\n \n # ikpt,iband,jband\n delta_E = (einsum('ij,k->ijk', self.eig0.EIG[0,:,:].real, ones(nband)) -\n einsum('ij,k->ikj', self.eigq.EIG[0,:,:].real, ones(nband)) -\n einsum('ij,k->ijk', ones((nkpt,nband)), (2*occ-1)) * self.smearing * 1j)\n \n omega = self.ddb.omega[:].real # imode\n \n # imode,ntemp,jband\n num1 = (einsum('ij,k->ijk', bose, ones(nband)) + 1.0 -\n einsum('ij,k->ijk', ones((3*natom, ntemp)), occ))\n \n # ikpt,iband,jband,imode\n deno1 = (einsum('ijk,l->ijkl', delta_E,ones(3*natom)) -\n einsum('ijk,l->ijkl', ones((nkpt, nband, nband)), omega))\n \n # (imode,ntemp,jband)/(ikpt,iband,jband,imode) ==> imode,ntemp,jband,ikpt,iband\n invdeno1 = np.real(deno1) / (np.real(deno1) ** 2 + np.imag(deno1) ** 2)\n div1 = einsum('ijk,lmki->ijklm', num1, invdeno1)\n #div1 = einsum('ijk,lmki->ijklm', num1, 1.0 / deno1)\n \n # imode,ntemp,jband\n num2 = (einsum('ij,k->ijk', bose, ones(nband)) +\n einsum('ij,k->ijk', ones((3*natom, ntemp)), occ))\n \n # ikpt,iband,jband,imode\n deno2 = (einsum('ijk,l->ijkl', delta_E, ones(3*natom)) +\n einsum('ijk,l->ijkl', ones((nkpt, nband, nband)), omega))\n \n # (imode,ntemp,jband)/(ikpt,iband,jband,imode) ==> imode,ntemp,jband,ikpt,iband\n invdeno2 = np.real(deno2) / (np.real(deno2) ** 2 + np.imag(deno2) ** 2)\n div2 = einsum('ijk,lmki->ijklm', num2, invdeno2)\n #div2 = einsum('ijk,lmki->ijklm', num2, 1.0 / deno2)\n \n # ikpt,iband,jband,imode\n fan_add = einsum('ijkl,lmkij->mij', fan_num, div1 + div2)\n \n\n fan_term += fan_add\n ddw_term += ddw_add\n \n self.tdr = (fan_term - ddw_term) * self.wtq\n \n self.tdr = self.eig0.make_average(self.tdr)\n \n # nkpt, nband, ntemp\n self.tdr = np.einsum('kij->ijk', self.tdr)\n \n return self.tdr","title":""},{"docid":"d2aad0814721edf48e74e73d7dd7da52","score":"0.49773735","text":"def analyse( self ) :\n\n TracksLocation = 'Rec/Track/Best'\n \n tracks = self.get( TracksLocation )\n\n print '# tarcks ', tracks.size()\n\n ## get the relation table\n table = self.get( 'Relations/Rec/Track/Default' )\n\n print '# #tracks/links : ', tracks.size() , table.relations().size() \n \n t2mc = table\n mc2t = table.inverse()\n\n for t in tracks :\n\n ## get all related MC-partiles: \n mcps = table.relations ( t )\n if not mcps.empty() :\n ## print number of related Mc-particles, ID of the first one and track key \n mcp = mcps[0]._to() \n print ' #links ' , mcps.size() , mcp.pname() , mcp.key() , t.key () \n\n \n # get all MC-particles \n mcparticles = self.mcselect('all', MCALL )\n\n for mcp in mcparticles :\n\n trks = mc2t.relations ( mcp )\n if not trks.empty() :\n # print number of tracks, partile name and track key \n trk = trks[0]._to()\n print ' #tracks ', trks.size() , mcp.pname() , mcp.key() , trk.key(), trks[0].weight() \n \n \n ## \n self.setFilterPassed( True ) \n return SUCCESS","title":""},{"docid":"cc0e135fbaacf294c97e7094588d965f","score":"0.49733922","text":"def breakdown(self,verbose=0):\n children = []\n for tensorcontraction in self.list:\n if (len(tensorcontraction.tensors) == 1):\n label = 0\n else:\n label = len(tensorcontraction.tensors) - 2\n newchild = tensorcontraction.breakdown(label,self.excitationtensortypes(),verbose)\n children.append(newchild)\n result = OperationTree(NoOperation(),[],children)\n return result","title":""},{"docid":"7f10184c98d5e7e18184629f30fb040f","score":"0.49541593","text":"def dynamic_programming_on_trellis(self, instance, run_forward_alg=True):\n\t\t#TODO:Initialize trellis and backtrace pointers \n\t\ttrellis = numpy.zeros((len(instance.data),self.label_codebook.size()))\n\t\tbacktrace_pointers = numpy.zeros((len(instance.data),self.label_codebook.size()))\n\t\t#TODO:Traverse through the trellis here\n\t\t#observation_prob_vector = self.emission_matrix[self.feature_codebook.get_index(instance.data[0][0]),:]*self.emission_matrix[self.feature_codebook.get_index(instance.data[0][1]),:]\n\t\tobservation_prob_vector = self.emission_matrix[self.feature_codebook.get_index(instance.data[0][1]),:]\n\t\tif run_forward_alg == True:\n\t\t\t#the first element of the alpha values\n\t\t\ttrellis[0]=self.init_pro*observation_prob_vector\n\t\t\tfor i in range(1,len(instance.data)):\n\t\t\t\t#observation_prob_vector = self.emission_matrix[self.feature_codebook.get_index(intance.data[i][0]),:]*self.emission_matrix[self.feature_codebook.get_index(intance.data[i][1]),:]\n\t\t\t\tobservation_prob_vector = self.emission_matrix[self.feature_codebook.get_index(intance.data[i][0]),:]\n\t\t\t\ttrellis[i]=(trellis[i-1]*self.transition_matrix).sum(1)*observation_prob_vector\n\t\t\treturn trellis\n\t\telse:\n\t\t\ttrellis[0]=self.init_pro*observation_prob_vector\n\t\t\t#backtrace_pointers[0] = ('START','START','START')\n\t\t\tfor i in range(1,len(instance.data)):\n\t\t\t\t#observation_prob_vector = self.emission_matrix[self.feature_codebook.get_index(instance.data[i][0]),:]*self.emission_matrix[self.feature_codebook.get_index(instance.data[i][1]),:]\n\t\t\t\tobservation_prob_vector = self.emission_matrix[self.feature_codebook.get_index(instance.data[i][1]),:]\n\t\t\t\t#here is an important place,we have to select the max then multiply the emission probability\n\t\t\t\t#if _DEBUG==True:\n\t\t\t\t\t#import pdb\n\t\t\t\t\t#pdb.set_trace()\n\t\t\t\talpha_list = trellis[i-1]*self.transition_matrix\n\t\t\t\ttrellis[i]=(alpha_list).max(1)*observation_prob_vector\n\t\t\t\tbacktrace_pointers[i]=numpy.argmax(alpha_list,1)\t\t\n\n\t\t\treturn (trellis, backtrace_pointers)","title":""},{"docid":"19e1b898e87a48da62bd73c4a827a195","score":"0.49519736","text":"def process_experiment((path, object_timeline)):\n print('Processing', path)\n bag = open_bag(path)\n if bag is None:\n return None\n object_stats = ObjectStats()\n time_taken_processor = processors.TimeTaken(object_timeline)\n camera_movement_processor = processors.CameraMovementTime(object_timeline)\n marker_movement_processor = processors.MarkerMovementTime(object_timeline)\n grasp_count_processor = processors.GraspCount(object_timeline)\n for topic, message, time in bag.read_messages(topics=EXPERIMENT_TOPICS):\n model = message_factory.model(message)\n if model is None:\n continue\n time_taken_processor.update(topic, model, time)\n camera_movement_processor.update(topic, model, time)\n marker_movement_processor.update(topic, model, time)\n grasp_count_processor.update(topic, model, time)\n bag.close()\n time_taken_processor.update_last()\n object_stats.update(time_taken_processor.object_stats())\n object_stats.update(camera_movement_processor.object_stats())\n object_stats.update(marker_movement_processor.object_stats())\n object_stats.update(grasp_count_processor.object_stats())\n for obj, stats in object_stats.items():\n stats['other_time'] = (\n stats['time_taken'] - stats['camera_movement_time'] -\n stats['marker_movement_time']\n )\n\n timeline = build_timeline(\n marker_movement_processor.timeline(),\n camera_movement_processor.timeline())\n \n return object_stats, timeline","title":""},{"docid":"55692e654c8f39497dddcf62d8b3eb54","score":"0.49491498","text":"def getIntelligence():","title":""},{"docid":"a3d88562b59168f20fe776a30f24c0d6","score":"0.49472186","text":"def __init__(self, A, y, lamb=0.5, complex=True):\n \n self.A = A\n self.y = y\n self.lamb = lamb\n self.m, self.n = A.shape\n if len(y.shape) > 1:\n self.p = y.shape[1]\n else:\n self.p = 1\n \n \n #Run som sanity checks\n assert(isinstance(A,np.ndarray))\n assert(isinstance(y,np.ndarray))\n assert(self.m == self.y.shape[0])\n assert(self.lamb > 0)\n if not complex:\n assert(not np.iscomplexobj(A))\n assert(not np.iscomplexobj(y))\n \n\n if complex:\n self.A_backward = self._Hermitian(self.A)\n else:\n self.A_backward = self.A.T\n \n #get M as A^H*A which is used more than once \n M = self.A_backward.dot(self.A)\n \n self.alpha = 1/np.linalg.norm(A,ord = 2)**2 \n\n \n #Compute matricies involved in gradient step in CISTA\n self.Phi = np.eye(self.n) - self.alpha*M\n self.phi_y = self.alpha*self.A_backward.dot(self.y)","title":""},{"docid":"4560f63789dc6a53a43dfcd25c3e63bf","score":"0.49415022","text":"def test_access_to_internal_models(self):\n est = LinearIntentToTreatDRIV(LinearRegression(), LogisticRegression(C=1000), WeightedLasso(),\n featurizer=PolynomialFeatures(degree=2, include_bias=False))\n Y = np.array([1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 2, 2])\n T = np.array([1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 2, 2])\n Z = np.array([1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2])\n X = np.array([1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6]).reshape(-1, 1)\n est.fit(Y, T, Z=Z, X=X)\n assert isinstance(est.original_featurizer, PolynomialFeatures)\n assert isinstance(est.featurizer, Pipeline)\n assert isinstance(est.model_final, StatsModelsLinearRegression)\n for mdl in est.models_Y_X:\n assert isinstance(mdl, LinearRegression)\n for mdl in est.models_T_XZ:\n assert isinstance(mdl, LogisticRegression)\n np.testing.assert_array_equal(est.cate_feature_names(['A']), ['A', 'A^2'])\n np.testing.assert_array_equal(est.cate_feature_names(), ['x0', 'x0^2'])\n\n est = LinearIntentToTreatDRIV(LinearRegression(), LogisticRegression(C=1000), WeightedLasso(),\n featurizer=None)\n est.fit(Y, T, Z=Z, X=X)\n assert est.original_featurizer is None\n assert isinstance(est.featurizer, FunctionTransformer)\n assert isinstance(est.model_final, StatsModelsLinearRegression)\n for mdl in est.models_Y_X:\n assert isinstance(mdl, LinearRegression)\n for mdl in est.models_T_XZ:\n assert isinstance(mdl, LogisticRegression)\n np.testing.assert_array_equal(est.cate_feature_names(['A']), ['A'])","title":""},{"docid":"98645f0392d6c8979e13ba7cd1a09848","score":"0.49414748","text":"def qft(qubits):\n\n def qft_function(amplitudes):\n\n new_amplitudes = []\n num_points = len(amplitudes)\n\n # for each element in the transformed vector\n for i in range(0, num_points):\n\n summation = complex(0, 0)\n\n # for each element in the input vector\n for j in range(0, num_points):\n\n angle = (2.0 * pi * i * j)/num_points\n\n summation += amplitudes[j] * cmath.exp(-1j * angle)\n\n new_amplitudes.append(summation)\n\n print(new_amplitudes)\n\n return new_amplitudes\n\n qubits.manipulate(qft_function)","title":""},{"docid":"fabe0a185e8ec4c6e753e358242616f8","score":"0.4939888","text":"def __init__(self, trajectories, returns, config, iter_ids, iter_ius, k):\n self.trajectories = trajectories\n self.returns = returns\n self.config = config\n self.iter_ids = iter_ids\n self.iter_ius = iter_ius\n self.k = k","title":""},{"docid":"349eb70503aa9b4ac811f391bf06fe71","score":"0.49392518","text":"def build_experiments(self):\n\n # We set the camera\n # This single RGB camera is used on every experiment\n\n camera = Camera('CameraRGB')\n camera.set(FOV=90)\n camera.set_image_size(800, 600)\n camera.set_position(1.44, 0.0, 1.2)\n camera.set_rotation(0, 0, 0)\n\n if self._city_name == 'Town01':\n poses_tasks = self._poses_town01()\n vehicles_tasks = [0, 20, 100]\n pedestrians_tasks = [0, 50, 250]\n else:\n poses_tasks = self._poses_town02()\n vehicles_tasks = [0, 15, 70]\n pedestrians_tasks = [0, 50, 150]\n\n experiments_vector = []\n\n for weather in self.weathers:\n\n for iteration in range(len(poses_tasks)):\n poses = poses_tasks[iteration]\n vehicles = vehicles_tasks[iteration]\n pedestrians = pedestrians_tasks[iteration]\n\n conditions = CarlaSettings()\n conditions.set(\n SendNonPlayerAgentsInfo=True,\n NumberOfVehicles=vehicles,\n NumberOfPedestrians=pedestrians,\n WeatherId=weather\n )\n\n conditions.set(DisableTwoWheeledVehicles=True)\n # Add all the cameras that were set for this experiments\n\n conditions.add_sensor(camera)\n\n experiment = Experiment()\n experiment.set(\n Conditions=conditions,\n Poses=poses,\n Task=iteration,\n Repetitions=1\n )\n experiments_vector.append(experiment)\n\n return experiments_vector","title":""},{"docid":"e0aa1640ea5bf7eabc230edc85f9edc0","score":"0.49386477","text":"def train(self):\r\n raise NotImplementedError","title":""},{"docid":"617b4d809a821c260ff3d6995a9531cb","score":"0.49346885","text":"def sample_trajectories(self, itr, env, info_name=None, render=False):\n timesteps_this_batch = 0\n paths = []\n total_rew = 0\n total_info, avg_info = 0, 0\n while True:\n animate_this_episode = (len(paths)==0 and (itr % 1 == 0))\n path = self.sample_trajectory(env, animate_this_episode)\n paths.append(path)\n timesteps_this_batch += len(path[\"reward\"])\n total_rew += np.sum(path[\"reward\"])\n if info_name is not None:\n info_dict_list = path['info']\n for dict in info_dict_list:\n info = dict[info_name]\n total_info += info\n if timesteps_this_batch > self.min_timesteps_per_batch:\n break\n avg_info = total_info / len(paths)\n avg_rew = total_rew / len(paths)\n return paths, timesteps_this_batch, avg_rew, avg_info","title":""},{"docid":"355db437f33cc925d1670ac165563e56","score":"0.4933888","text":"def algebra_generators(self):","title":""},{"docid":"23308677ea588bda9020f4ef18c9ee6b","score":"0.49320087","text":"def test_traj(ic, tend=20):\n ode_sys.set(ics=ic, tdata=[0,tend])\n traj = ode_sys.compute('test')\n pts = traj.sample()\n plt.plot(pts['phi'], pts['nu'], 'k:', lw=1)\n plt.plot(pts['phi'][0], pts['nu'][0], 'ko')","title":""},{"docid":"5701eee23f66440b5e41da09c8c9db38","score":"0.49281594","text":"def integrations(self):\n raise NotImplementedError(\"TODO: Not implemented yet\")","title":""},{"docid":"a57d4f949642b92eec617547c7bc2a98","score":"0.49259016","text":"def test_trajectory_logicnetwork(self):\n net = LogicNetwork([((1, 2), {'01', '10'}),\n ((0, 2), {'01', '10', '11'}),\n ((0, 1), {'11'})])\n state = [0, 1, 0]\n got = trajectory(net, state, 3)\n self.assertEqual([[0, 1, 0], [1, 0, 0], [0, 1, 0], [1, 0, 0]], got)\n self.assertEqual([0, 1, 0], state)\n\n got = list(trajectory(net, state, 3, encode=True))\n self.assertEqual([2, 1, 2, 1], got)","title":""},{"docid":"eb4dc0a3bb75fcdd20c206ab8ac77386","score":"0.492501","text":"def bode_data_analysis(source, port, real_world, debug):\n\n exp_id = input('EXPRERIMENT ID : ')\n experiment = Experiment.from_id(exp_id, real_world.data_folder)\n n_experiments = len(experiment.data['entree_sinus']['measures'])\n amplitudes = []\n phases = []\n freqs = []\n \n # fig, axes = pl.subplots(n_experiments)\n\n for i in range(n_experiments):\n pos_measures = np.array(\n list(map(float, experiment.data['entree_sinus']['measures'][i]['pos_measures']))\n )[30:]\n speed_measures = np.array(\n list(map(float, experiment.data['entree_sinus']['measures'][i]['speed_measures']))\n )[30:]\n speed_orders = np.array(\n list(map(float, experiment.data['entree_sinus']['measures'][i]['speed_orders']))\n )[30:]\n times = np.array(\n list(map(float, experiment.data['entree_sinus']['measures'][i]['times']))\n )[30:]\n order_freq = float(experiment.data['entree_sinus']['measures'][i]['order_freq'])\n order_amplitude = float(experiment.data['entree_sinus']['measures'][i]['order_amplitude'])\n\n pos_spectrum = np.fft.rfft(pos_measures)\n speed_spectrum = np.fft.rfft(speed_measures)\n freq_range = np.fft.rfftfreq(times.size, sample_time(times))\n\n amplitudes.append(amplitude(\n times,\n speed_measures, \n 1/order_freq\n ))\n # phases.append(phase(\n # times,\n # speed_measures,\n # speed_orders,\n # 1/order_freq\n # ))\n freqs.append(order_freq)\n\n freqs = np.array(freqs)\n amplitudes = np.array(amplitudes)/(order_amplitude/real_world.pwm_incs_ratio) # convert in pwm\n #phases = np.array(phases)\n\n j = complex(0, 1)\n\n def func(f, K, xi, f0):\n return np.abs(K/(1 + (2*xi)*j*(f/f0) - (f/f0)**2))\n \n (K, xi, f0), pcov = curve_fit(func, freqs, amplitudes, bounds=([0, 0.6, 1], [np.inf, np.inf, 10]))\n print(K, xi, f0)\n print(np.sqrt(np.diag(pcov)))\n\n #pl.subplot('211')\n pl.title('Gain en vitesse de la corbeille en fonction de la fréquence')\n pl.semilogx(\n freqs,\n 20*np.log10(amplitudes)\n )\n pl.semilogx(\n np.geomspace(freqs[0], freqs[-1], 500),\n 20*np.log10([func(freq, K, xi, f0) for freq in np.geomspace(freqs[0], freqs[-1], 500)])\n )\n pl.legend(['Expérience', 'Régression'])\n \n pl.xlabel('Fréquence (Hz)')\n pl.ylabel('Gain (dB)')\n pl.grid(True, color='0.7', linestyle='-', which='both', axis='both')\n\n precision = 3\n text = ' K = {} \\n Xi = {}\\n F0 = {}'.format(\n round(K, precision), round(xi, precision), round(f0, precision)\n )\n\n pl.text(\n 2*10**(-1), -20, \n text,\n color='white',\n fontsize=12,\n bbox=dict(facecolor='black', edgecolor='black', pad=10.0)\n )\n\n # pl.subplot('212')\n # pl.title('Phase en vitesse de la corbeille en fonction de la fréquence')\n # pl.semilogx(\n # freqs,\n # phases*(180/pi)\n # )\n pl.show()","title":""},{"docid":"f131c45b99c92ffec02790385d53d316","score":"0.49191204","text":"def generate(s):\n dic={\n \"INPUT\": [\"V0\", \"P0\", \"Q0\"],\n\n\n \"V0\": {\n \"u\": \"V1\",\n \"type\": \"PREDICT\",\n \"final_fc\": False,\n },\n\n \"P0\": {\n \"u\": \"P1\",\n \"type\": \"PREDICT\",\n \"final_fc\": True,\n \"bias\": True\n\n },\n\n \"Q0\": {\n \"u\":\"P2\",\n \"type\": \"PREDICT\",\n \"final_fc\": True,\n \"units\":6,\n \"classify\": True\n\n },\n\n \"SETTINGS\": s.__dict__\n }\n\n\n\n #Visual path\n tau=s.tau_start\n VISUAL_UNITS=s.VISUAL_UNITS\n for i in range(1,s.N_V_LAYERS+1):\n dic[\"V%d\"%i] =dict(\n d=\"V%d\"%(i-1),\n #l=\"P%d\"%i,\n u=\"V%d\"%(i+1),\n type=\"MSTRNN\",\n units=VISUAL_UNITS,\n filter=s.FILTER,\n tau=tau\n )\n tau += s.tau_delta\n VISUAL_UNITS += s.VISUAL_UNITS_DELTA\n\n\n #Proprioceptive path\n tau=s.tau_start\n for i in range(1, s.N_P_LAYERS + 1):\n dic[\"P%d\" % i] = dict(\n d=\"P%d\" % (i - 1),\n #l=\"Q%d\" % i,\n u=\"P%d\" % (i + 1),\n type=\"CTRNN\",\n units=s.PROP_UNITS,\n tau=tau\n )\n tau+=s.tau_delta\n\n #Control path\n tau=s.tau_start\n for i in range(1, s.N_Q_LAYERS + 1):\n dic[\"Q%d\" % i] = dict(\n d=\"Q%d\" % (i - 1),\n #l=\"P%d\" % i,\n u=\"Q%d\" % (i + 1),\n type=\"CTRNN\",\n units=s.Q_UNITS,\n tau=tau\n )\n tau+=s.tau_delta\n\n if \"P%d\"%i in dic: dic[\"P%d\"%i].pop(\"u\")\n if \"Q%d\"%i in dic: dic[\"Q%d\"%i].pop(\"u\")\n if \"V%d\"%i in dic: dic[\"V%d\"%i].pop(\"u\")\n\n #Override default tau\n if s.TAU_LIST != []:\n for i,tau in enumerate(s.TAU_LIST):\n dic['V%d'%i]['tau'] = tau\n\n #Override default filter\n if s.FILTER_LIST and s.FILTER_FOR_LIST!=s.FILTER:\n for l in (s.FILTER_LIST):\n dic['V%d'%l]['filter'] = s.FILTER_FOR_LIST\n\n\n #Delete input and links if a channel isnt present\n if s.N_V_LAYERS==0:\n dic['INPUT'] = [input for input in dic['INPUT'] if input != 'V0']\n dic.pop('V0')\n\n if s.N_P_LAYERS==0:\n dic['INPUT'] = [input for input in dic['INPUT'] if input != 'P0']\n dic.pop('P0')\n\n\n if s.N_Q_LAYERS==0:\n dic['INPUT'] = [input for input in dic['INPUT'] if input != 'Q0']\n dic.pop('Q0')\n\n\n LATERAL = 1\n if LATERAL:\n for i in range(0, s.N_V_LAYERS+1):\n dic['V%d'%i]['l'] = ['P%d'%i]\n dic['P%d'%i]['l'] = ['V%d'%i]\n\n\n\n print(dic)\n with open('net.json', 'w') as outfile:\n json.dump(dic, outfile)","title":""},{"docid":"800816e338e316b6a33f14ac08f888b1","score":"0.4918915","text":"def sub_directions(self,t,j):\n raise NotImplementedError","title":""},{"docid":"59f25435a4876a46376a3c02d23316cc","score":"0.49185294","text":"def test_get_elements_from_classification(self):\n pass","title":""},{"docid":"7079f739fbc7bcac6c1f56fac213aacb","score":"0.4916937","text":"def test_Part3PETCT(self,enableScreenshotsFlag=0,screenshotScaleFactor=1):\r\n logic = RSNAQuantTutorialLogic()\r\n logic.enableScreenshots = enableScreenshotsFlag\r\n logic.screenshotScaleFactor = screenshotScaleFactor\r\n\r\n self.delayDisplay(\"Starting the test\")\r\n\r\n #\r\n # first, get some data\r\n #\r\n import SampleData\r\n extractPath = SampleData.downloadFromURL(\r\n fileNames='dataset3_PETCT.zip',\r\n uris='http://slicer.kitware.com/midas3/download?items=124185',\r\n checksums='SHA256:11e81af3462076f4ca371b632e03ed435240042915c2daf07f80059b3f78f88d')[0]\r\n\r\n self.delayDisplay(\"Loading PET_CT_pre-treatment.mrb\")\r\n preTreatmentPath = extractPath + '/PET_CT_pre-treatment.mrb'\r\n slicer.util.loadScene(preTreatmentPath)\r\n logic.takeScreenshot('PETCT-LoadedPre','Loaded pre-treatement scene',-1)\r\n\r\n try:\r\n mainWindow = slicer.util.mainWindow()\r\n layoutManager = slicer.app.layoutManager()\r\n threeDView = layoutManager.threeDWidget(0).threeDView()\r\n redWidget = layoutManager.sliceWidget('Red')\r\n redController = redWidget.sliceController()\r\n greenWidget = layoutManager.sliceWidget('Green')\r\n greenController = greenWidget.sliceController()\r\n yellowWidget = layoutManager.sliceWidget('Yellow')\r\n yellowController = yellowWidget.sliceController()\r\n viewNode = threeDView.mrmlViewNode()\r\n cameras = slicer.util.getNodes('vtkMRMLCameraNode*')\r\n for cameraNode in cameras.values():\r\n if cameraNode.GetActiveTag() == viewNode.GetID():\r\n break\r\n\r\n threeDView.resetFocalPoint()\r\n slicer.util.clickAndDrag(threeDView,button='Right')\r\n redWidget.sliceController().setSliceVisible(True)\r\n yellowWidget.sliceController().setSliceVisible(True)\r\n logic.takeScreenshot('PETCT-ConfigureView','Configure View',-1)\r\n\r\n mainWindow.moduleSelector().selectModule('Volumes')\r\n compositNode = redWidget.mrmlSliceCompositeNode()\r\n compositNode.SetForegroundOpacity(0.2)\r\n logic.takeScreenshot('PETCT-ShowVolumes','Show Volumes with lesion',-1)\r\n\r\n compositNode.SetForegroundOpacity(0.5)\r\n logic.takeScreenshot('PETCT-CTOpacity','CT1 volume opacity to 0.5',-1)\r\n\r\n yellowWidget.sliceController().setSliceVisible(False)\r\n greenWidget.sliceController().setSliceVisible(True)\r\n logic.takeScreenshot('PETCT-ShowSlices','Show axial and sagittal slices',-1)\r\n\r\n self.delayDisplay('SUV Computation')\r\n if not hasattr(slicer.modules, 'petstandarduptakevaluecomputation'):\r\n self.delayDisplay(\"PET SUV Computation not available, skipping the test.\")\r\n return\r\n\r\n slicer.util.selectModule('PETStandardUptakeValueComputation')\r\n\r\n parameters = {\r\n \"PETDICOMPath\": extractPath + '/' + 'PET1',\r\n \"PETVolume\": slicer.util.getNode('PET1'),\r\n \"VOIVolume\": slicer.util.getNode('PET1-label'),\r\n }\r\n\r\n suvComputation = slicer.modules.petstandarduptakevaluecomputation\r\n self.CLINode1 = None\r\n self.CLINode1 = slicer.cli.runSync(suvComputation, self.CLINode1, parameters, delete_temporary_files=False)\r\n\r\n # close the scene\r\n slicer.mrmlScene.Clear(0)\r\n\r\n self.delayDisplay(\"Loading PET_CT_post-treatment.mrb\")\r\n postTreatmentPath = extractPath + '/PET_CT_post-treatment.mrb'\r\n slicer.util.loadScene(postTreatmentPath)\r\n logic.takeScreenshot('PETCT-LoadedPost','Loaded post-treatement scene',-1)\r\n\r\n compositNode.SetForegroundOpacity(0.5)\r\n logic.takeScreenshot('PETCT-CT2Opacity','CT2 volume opacity to 0.5',-1)\r\n\r\n redController.setSliceOffsetValue(-165.01)\r\n logic.takeScreenshot('PETCT-LarynxUptake','Mild uptake in the larynx and pharynx',-1)\r\n\r\n redController.setSliceOffsetValue(-106.15)\r\n logic.takeScreenshot('PETCT-TumorUptake','No uptake in the area of the primary tumor',-1)\r\n\r\n self.delayDisplay('Test passed!')\r\n except Exception as e:\r\n import traceback\r\n traceback.print_exc()\r\n self.delayDisplay('Test caused exception!\\n' + str(e))","title":""},{"docid":"abf87eb7bcf1cff737f68a8e40ae2b94","score":"0.4903885","text":"def compute_all_trajectories(self):\n self.trajectories = []\n for frame_nb in range(self.n_frames-1):\n trajectory_list = self.compute_trajectories_in_frame(frame_nb)\n self.trajectories.append(trajectory_list)","title":""},{"docid":"fda00ef9a9cff3608ad21bc174f550f0","score":"0.49032158","text":"def experiment1():\n\tagent1 = epsilon_greedy_agent(10, epsilon = 0, initial_value = 0)\n\tagent2 = epsilon_greedy_agent(10, epsilon = 0.01, initial_value = 0)\n\tagent3 = epsilon_greedy_agent(10, epsilon = 0.1, initial_value = 0)\n\tagent4 = epsilon_greedy_agent(10, epsilon = 0.5, initial_value = 0)\n\ttb = bandit_problem_test_bed(10)\n\ttb.test(agent1,time_steps = 1000, runs = 2000)\n\ttb.test(agent2,time_steps = 1000, runs = 2000)\n\ttb.test(agent3,time_steps = 1000, runs = 2000)\n\ttb.test(agent4,time_steps = 1000, runs = 2000)\n\ttb.show_figures()","title":""},{"docid":"b1a0fe3ed7786f9aae852d81d57400d9","score":"0.48963913","text":"def main():\n # movement_experiment()\n # degree_experiment()\n # test_go_inches()\n # test_spin_degree()\n # turn_degree_experiment()\n # test_turn_degrees()\n # test_polygon()\n # test_calibrate()\n # test_raise_and_close()\n # test_move_arm_to_position()\n big_test()","title":""},{"docid":"af9b69510f32fa63acd591f49b99f68f","score":"0.48952454","text":"def __init__(self, spectrum):\n TRTHeader = spectrum.find('./TRTHeaderedClass')\n hardware_header = TRTHeader.find(\n \"./ClassInstance[@Type='TRTSpectrumHardwareHeader']\")\n detector_header = TRTHeader.find(\n \"./ClassInstance[@Type='TRTDetectorHeader']\")\n esma_header = TRTHeader.find(\n \"./ClassInstance[@Type='TRTESMAHeader']\")\n # what TRT means?\n # ESMA could stand for Electron Scanning Microscope Analysis\n spectrum_header = spectrum.find(\n \"./ClassInstance[@Type='TRTSpectrumHeader']\")\n xrf_header = TRTHeader.find(\"./ClassInstance[@Type='TRTXrfHeader']\")\n\n # map stuff from harware xml branch:\n self.hardware_metadata = dictionarize(hardware_header)\n self.amplification = self.hardware_metadata['Amplification'] # USED\n\n # map stuff from detector xml branch\n self.detector_metadata = dictionarize(detector_header)\n self.detector_type = self.detector_metadata['Type'] # USED\n\n # decode silly hidden detector layer info:\n det_l_str = self.detector_metadata['DetLayers']\n dec_det_l_str = codecs.decode(det_l_str.encode('ascii'), 'base64')\n mini_xml = ET.fromstring(unzip_block(dec_det_l_str))\n self.detector_metadata['DetLayers'] = {} # Overwrite with dict\n for i in list(mini_xml):\n self.detector_metadata['DetLayers'][i.tag] = dict(i.attrib)\n\n # map stuff from esma xml branch:\n if esma_header:\n self.esma_metadata = dictionarize(esma_header)\n if xrf_header:\n xrf_header_dict = dictionarize(xrf_header)\n self.esma_metadata = {\n 'PrimaryEnergy':xrf_header_dict['Voltage'],\n 'ElevationAngle':xrf_header_dict['ExcitationAngle']\n }\n # USED:\n self.hv = self.esma_metadata['PrimaryEnergy']\n self.elev_angle = self.esma_metadata['ElevationAngle']\n date_time = gen_iso_date_time(spectrum_header)\n if date_time is not None:\n self.date, self.time = date_time\n\n self.spectrum_metadata = dictionarize(spectrum_header)\n self.offset = self.spectrum_metadata['CalibAbs']\n self.scale = self.spectrum_metadata['CalibLin']\n\n # main data:\n self.data = np.fromstring(spectrum.find('./Channels').text,\n dtype='Q', sep=\",\")","title":""},{"docid":"e00d1a81081a1e05af508f39b58c36f0","score":"0.48899361","text":"def explore(self) -> None:\n raise NotImplementedError","title":""},{"docid":"1bda39ff39926510df4c8eb4468d4fed","score":"0.48849887","text":"def train(self):\r\n pass","title":""},{"docid":"1bda39ff39926510df4c8eb4468d4fed","score":"0.48849887","text":"def train(self):\r\n pass","title":""},{"docid":"063a15a2747790b6266a42900d9e3ca2","score":"0.48812932","text":"def extractFeatures(self):\n self.features = [] #Not a good idea\n self.features.append(self.subEx())\n self.features.append(self.subLen())\n self.features.append(self.subLang())\n self.features.append(self.subCod())\n self.features.append(self.subPunct())\n\n self.features.append(self.recCnt())\n self.features.extend(self.recParts())\n self.features.append(self.recGeoVec())\n self.features.append(self.recTimeDelta())\n self.features.extend(self.recWith())\n self.features.extend(self.recTZs())\n\n self.features.append(self.dateBucket())\n self.features.append(self.dateTZ())\n self.features.append(self.dateRecDelta())\n\n self.features.append(self.fromEx())\n self.features.append(self.fromLen())\n self.features.append(self.fromExAlias())\n self.features.append(self.fromDomain())\n self.features.append(self.fromDomainFQN())\n self.features.append(self.fromErrorsToDom())\n self.features.append(self.fromReplyToDom())\n #self.features.append(self.fromExDomain())\n\n self.features.append(self.toCnt())\n self.features.append(self.toDomain())\n self.features.append(self.toMsgidDom())\n #self.features.append(self.toExDomain())\n self.features.append(self.toFromDom())\n self.features.append(self.msgidDomain())\n self.features.append(self.msgidFromDom())\n\n self.features.append(self.attachCnt())\n self.features.extend(self.extractAttFeatures())\n\n self.features.extend(self.cType())\n self.features.extend(self.cTypeActual())\n\n self.features.append(self.partCnt())\n self.features.extend(self.partFeatures())\n self.features.append(self.partStruct())\n\n self.features.append(self.bodyLang())\n self.features.append(self.bodyLength())\n\n self.features.extend(self.xMailerTokens())\n\n self.features.extend(self.multipleFeatures())\n self.features.extend(self.genFeatures())\n\n self.features.append(self.replyToErrorsToDom())\n return self.features","title":""},{"docid":"3b93e0a669ba5c95516188b6a1ad94c2","score":"0.48805553","text":"def run(model1):\r\n\tstep = 0\r\n\tlt=0\r\n\tz=0\r\n\tflag=0\r\n\r\n\ttls_data = [\r\n\t\t['yrrr', 'Grrr'],\r\n\t\t['ryrr', 'rGrr'],\r\n\t\t['rryr', 'rrGr'],\r\n\t\t['rrry', 'rrrG']\r\n\t] \r\n\t\r\n\tprint(\"simulation started ..............\")\r\n\ttraci.trafficlight.setPhase(\"0\", 6)\r\n\twhile traci.simulation.getMinExpectedNumber() > 0:\r\n\t\ttraci.simulationStep()\r\n\t\t#sim.work()\r\n\r\n\t\t#For normal algorithm without model\r\n\t\t#For getting number of vehicle/lane and find maximum\r\n\t\tL1 = int(get_vehicle_numbers(\"1i_0\"))\r\n\t\tL2 = int(get_vehicle_numbers(\"2i_0\"))\r\n\t\tL3 = int(get_vehicle_numbers(\"3i_0\"))\r\n\t\tL4 = int(get_vehicle_numbers(\"4i_0\"))\r\n\r\n\t\ttotal_l = L1 + L2 + L3 + L4\r\n\r\n\t\tx = [L1, L2, L3 ,L4]\r\n\t\tx = np.asarray(x).astype('float32')\r\n\t\tdata_dict2= {\r\n\t\t\t'L1':L1,\r\n\t\t\t'L2':L2,\r\n\t\t\t'L3':L3,\r\n\t\t\t'L4':L4,\r\n\t\t}\r\n\t\t\r\n\t\tdata_dict = {\r\n\t\t\t\"1i_0\":L1,\r\n\t\t\t\"2i_0\":L2,\r\n\t\t\t\"3i_0\":L3,\r\n\t\t\t\"4i_0\":L4,\r\n\t\t}\r\n\r\n\t\tlm = max(data_dict.items(), key=operator.itemgetter(1))[0]\r\n\t\tlm_val = max(data_dict.items(), key=operator.itemgetter(1))[1]\r\n\t\t#print(lm_val)\r\n\r\n\t\tif lm == '1i_0':\r\n\t\t\ti = 3\r\n\t\telif lm == '2i_0':\r\n\t\t\ti = 1\r\n\t\telif lm == '3i_0':\r\n\t\t\ti = 2\r\n\t\telif lm == '4i_0':\r\n\t\t\ti = 0\r\n\r\n\t\tmet1 = metrics.Accuracy()\r\n\t\tmet2 = metrics.Accuracy()\r\n\r\n\t\tif flag == 0:\r\n\t\t\tprint(\"in if\")\r\n\t\t\tk_pred = model1.predict_one(data_dict2)\r\n\t\t\tprint(k_pred)\r\n\r\n\t\t\tif k_pred != None:\r\n\t\t\t\tyellow_select = tls_data[ypred][0]\r\n\t\t\t\tgreen_select = tls_data[ypred][1]\r\n\t\t\t\tprint(yellow_select, green_select)\r\n\r\n\t\t\t\tphaseDuration(junction=\"0\", phase_state=yellow_select, phase_time=6)\r\n\t\t\t\tphaseDuration(junction=\"0\", phase_state=green_select, phase_time=30)\r\n\t\t\t\tflag = 30\r\n\t\t\t\r\n\t\telse:\r\n\t\t\tflag -= 1\r\n\t\t\t\r\n\t\t#For ARDUINO :::\r\n\t\tph = str(traci.trafficlight.getPhase(\"0\"))\r\n\t\tvalue = write_read(ph)\r\n\t\tstep += 1\r\n\ttraci.close()\r\n\tsys.stdout.flush()","title":""},{"docid":"db6cdd6d5533ddb681060d297062426d","score":"0.4880063","text":"def __call__(self, solution):\n raise NotImplementedError","title":""},{"docid":"443b091bef3a93fe7690ef1fe8c5cdb1","score":"0.48790157","text":"def _112_complex_1_3(self):\n asyncio.gather(self.__complex(stage=[1, 2, 3]))","title":""},{"docid":"0646ad35b0db78e8565f460dafc73a71","score":"0.48549443","text":"def _do_lc(self):\n\t\t########## LC step #######\n\t\t## Basic LC step, combined. Store all the results in the instance.\n\t\t(self.axComb, self.bxComb, self.ktComb, self.kt_unfitComb,\n\t\t self.UComb, self.XComb, self.VComb, self.lnmxAdjustedComb, self.lnmxComb) = lcInfer(\n\t\t\tself.nmxComb, ageCutoff=self.ageCutoff, lifeTableParams=self.lifeTableParams, flattenBx=self.flattenBx) \n\n\t\t## Basic LC step, combined. Store all the results in the instance.\n\t\t(self.axFem, self.bxFem, self.ktFem, self.kt_unfitFem,\n\t\t self.UFem, self.XFem, self.VFem, self.lnmxAdjustedFem, self.lnmxFem) = lcInfer(\n\t\t\tself.nmxFem, ageCutoff=self.ageCutoff, lifeTableParams=self.lifeTableParams, flattenBx=self.flattenBx) \n\t\t\n\t\t## Basic LC step, combined. Store all the results in the instance.\n\t\t(self.axMale, self.bxMale, self.ktMale, self.kt_unfitMale,\n\t\t self.UMale, self.XMale, self.VMale, self.lnmxAdjustedMale, self.lnmxMale) = lcInfer(\n\t\t\tself.nmxMale, ageCutoff=self.ageCutoff, lifeTableParams=self.lifeTableParams, flattenBx=self.flattenBx) \n\t\t\t\n\t\tjumpoffAxFem = N.log(self.nmxFem[-1,0:self.ageCutoffIndex].ravel())\n\t\tjumpoffAxMale = N.log(self.nmxMale[-1,0:self.ageCutoffIndex].ravel())\n\n\t\t## Derive current year stuff from LC inference results (kt etc).\n\t\t## Note use of list comprehensions.\n\t\tself.e0sFromEmpiricalNmxComb = N.array([LcUtil.lifeTable(nmxp=nmxRow, **self.lifeTableParams)\n\t\t\t\t\t\t\t\t\t\t\t\tfor nmxRow in self.nmxComb])\n\t\tself.e0sFromEmpiricalNmxMale = N.array([LcUtil.lifeTable(nmxp=nmxRow, **self.lifeTableParams)\n\t\t\t\t\t\t\t\t\t\t\t\tfor nmxRow in self.nmxMale])\n\t\tself.e0sFromEmpiricalNmxFem = N.array([LcUtil.lifeTable(nmxp=nmxRow, **self.lifeTableParams)\n\t\t\t\t\t\t\t\t\t\t\t\tfor nmxRow in self.nmxFem])\n\t\tself.nmxsFromKtCurrentComb = project_nmx(kt=self.ktComb, bx=self.bxComb, ax=self.axComb,\n\t\t\t\t\t\t\t\t\t\t\t ageCutoff=self.ageCutoff)\n\t\tself.nmxsFromKtCurrentMale = project_nmx(kt=self.ktComb, bx=self.bxComb, ax=self.axMale,\n\t\t\t\t\t\t\t\t\t\t\t ageCutoff=self.ageCutoff)\n\t\tself.nmxsFromKtCurrentFem = project_nmx(kt=self.ktComb, bx=self.bxComb, ax=self.axFem,\n\t\t\t\t\t\t\t\t\t\t\t ageCutoff=self.ageCutoff)\n\t\tself.nmxsFromKtCurrent_unfitComb = project_nmx(kt=self.kt_unfitComb, bx=self.bxComb, ax=self.axComb,\n\t\t\t\t\t\t\t\t\t\t\t\t ageCutoff=self.ageCutoff) \n\t\tself.nmxsFromKtCurrent_unfitMale = project_nmx(kt=self.kt_unfitComb, bx=self.bxComb, ax=self.axMale,\n\t\t\t\t\t\t\t\t\t\t\t\t ageCutoff=self.ageCutoff) \n\t\tself.nmxsFromKtCurrent_unfitFem = project_nmx(kt=self.kt_unfitComb, bx=self.bxComb, ax=self.axFem,\n\t\t\t\t\t\t\t\t\t\t\t\t ageCutoff=self.ageCutoff) \n\t\tself.e0sFromKtCurrentComb = N.array([LcUtil.lifeTable(nmxp=nmxRow, **self.lifeTableParams)\n\t\t\t\t\t\t\t\t\t\t for nmxRow in self.nmxsFromKtCurrentComb])\n\t\tself.e0sFromKtCurrentMale = N.array([LcUtil.lifeTable(nmxp=nmxRow, **self.lifeTableParams)\n\t\t\t\t\t\t\t\t\t\t for nmxRow in self.nmxsFromKtCurrentMale])\n\t\tself.e0sFromKtCurrentFem = N.array([LcUtil.lifeTable(nmxp=nmxRow, **self.lifeTableParams)\n\t\t\t\t\t\t\t\t\t\t for nmxRow in self.nmxsFromKtCurrentFem])\n\t\tself.e0sFromKtCurrent_unfitComb = N.array([LcUtil.lifeTable(nmxp=nmxRow, **self.lifeTableParams)\n\t\t\t\t\t\t\t\t\t\t for nmxRow in self.nmxsFromKtCurrent_unfitComb])\n\t\tself.e0sFromKtCurrent_unfitMale = N.array([LcUtil.lifeTable(nmxp=nmxRow, **self.lifeTableParams)\n\t\t\t\t\t\t\t\t\t\t for nmxRow in self.nmxsFromKtCurrent_unfitMale])\n\t\tself.e0sFromKtCurrent_unfitFem = N.array([LcUtil.lifeTable(nmxp=nmxRow, **self.lifeTableParams)\n\t\t\t\t\t\t\t\t\t\t for nmxRow in self.nmxsFromKtCurrent_unfitFem])\n\t\tassert len(self.e0sFromKtCurrentComb) >= 1, \\\n\t\t\t AssertionError(\"self.e0sFromKtCurrentComb: %s\" % self.e0sFromKtCurrentComb)\n\t\tassert type(self.e0sFromKtCurrentComb) == N.ndarray, \\\n\t\t\t AssertionError(\"Weird type: %s\" % type(self.e0sFromKtCurrentComb))\n\n\t\t######## Simulation ##############\n\t\t## Get random walk parameters\n\t\tself.diffedKt = S.diff(self.ktComb)\n\t\tself.drift = S.average(self.diffedKt)\n\t\tself.stdErrorEq = S.std(self.diffedKt)\n\t\tself.stdErrorCoeff = self.stdErrorEq/S.sqrt(len(self.diffedKt))\n\t\t\n\t\t## Multi run simulation with above paramenters\n\t\tself.kt_simul = sim_kt(SEC=self.stdErrorCoeff, SEE=self.stdErrorEq, drift=self.drift,\n\t\t\t\t\t\t\t numRuns=self.numRuns, stepsForward=self.stepsForward, sortflag=False)\n\t\tself.nmx_projectedFem = project_nmx(kt=self.kt_simul, bx=self.bxComb, ax=jumpoffAxFem,\n\t\t\t\t\t\t\t\t\t\t ageCutoff=self.ageCutoff)\n\t\tself.nmx_projectedMale = project_nmx(kt=self.kt_simul, bx=self.bxComb, ax=jumpoffAxMale,\n\t\t\t\t\t\t\t\t\t\t ageCutoff=self.ageCutoff)\n\t\tself.nmx_projected_stochastic_median_final_F = project_nmx(kt=N.median(N.sort(self.kt_simul,0)),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t bx=self.bxComb, ax=jumpoffAxFem, ageCutoff=self.ageCutoff)\n\t\tself.nmx_projected_stochastic_median_final_M = project_nmx(kt=N.median(N.sort(self.kt_simul,0)),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t bx=self.bxComb, ax=jumpoffAxMale, ageCutoff=self.ageCutoff)\n\n\t\tself.e0s_projectedFem = lots_e0s(self.percentileIndices, self.nmx_projectedFem, self.lifeTableParams)\n\t\tself.e0s_projectedMale = lots_e0s(self.percentileIndices, self.nmx_projectedMale, self.lifeTableParams)\n\t\t\n\t\t## Derive analytic mean and x% forecast interval of kt\n\t\tx = S.arange(0.0, float(self.stepsForward+1))\n\t\tself.KtStdError = ((x*(self.stdErrorEq**2.0)) +\n\t\t\t\t\t\t (x*self.stdErrorCoeff)**2.0)**.5 # kt.stderr <- ( (x*see^2) + (x*sec)^2 )^.5\n\t\ttArray = N.array([self.drift] * (self.stepsForward+1))\n\t\ttArray[0] = 0.0\n\t\tself.meanKtProjected = N.cumsum(tArray)\n\t\tself.upperKtProjected = self.meanKtProjected + (self.zscore * self.KtStdError)\n\t\tself.lowerKtProjected = self.meanKtProjected - (self.zscore * self.KtStdError)\n\n\t\tself.nmx_projectedMedian_F = project_nmx(kt=self.meanKtProjected, bx=self.bxComb, ax=jumpoffAxFem,\n\t\t\t\t\t\t\t\t\t\t\t\t ageCutoff=self.ageCutoff)\n\t\tself.nmx_projectedMedian_M = project_nmx(kt=self.meanKtProjected, bx=self.bxComb, ax=jumpoffAxMale,\n\t\t\t\t\t\t\t\t\t\t\t\t ageCutoff=self.ageCutoff)\n\t\tself.nmx_projected_median_final_F = self.nmx_projectedMedian_F[-1,:]\n\t\tself.nmx_projected_median_final_M = self.nmx_projectedMedian_M[-1,:]\n\n\t\t\n\t\t## Derive the projected e0s, F\n\t\tself.upperE0ProjectedFem = LcUtil.multiKt2e0(self.upperKtProjected, ax=jumpoffAxFem, bx=self.bxComb,\n\t\t\t\t\t\t\t\t\t\t\t\t lifeTableParams=self.lifeTableParams)\n\t\tself.lowerE0ProjectedFem = LcUtil.multiKt2e0(self.lowerKtProjected, ax=jumpoffAxFem, bx=self.bxComb,\n\t\t\t\t\t\t\t\t\t\t\t\t lifeTableParams=self.lifeTableParams)\n\t\tself.meanE0ProjectedFem = LcUtil.multiKt2e0(self.meanKtProjected, ax=jumpoffAxFem, bx=self.bxComb,\n\t\t\t\t\t\t\t\t\t\t\t\t lifeTableParams=self.lifeTableParams)\n\t\t## Derive the projected e0s, M\n\t\tself.upperE0ProjectedMale = LcUtil.multiKt2e0(self.upperKtProjected, ax=jumpoffAxMale, bx=self.bxComb,\n\t\t\t\t\t\t\t\t\t\t\t\t lifeTableParams=self.lifeTableParams)\n\t\tself.lowerE0ProjectedMale = LcUtil.multiKt2e0(self.lowerKtProjected, ax=jumpoffAxMale, bx=self.bxComb,\n\t\t\t\t\t\t\t\t\t\t\t\t lifeTableParams=self.lifeTableParams)\n\t\tself.meanE0ProjectedMale = LcUtil.multiKt2e0(self.meanKtProjected, ax=jumpoffAxMale, bx=self.bxComb,\n\t\t\t\t\t\t\t\t\t\t\t\t lifeTableParams=self.lifeTableParams)\n\t\t\n\t\treturn\t\t\t\t\t\t\t# Don't return anything useful","title":""},{"docid":"433e817aad6d5f6ae055547a3bdf57cc","score":"0.48529863","text":"def get_discrete_loads(self):\n # input values derived\n x1, x2, x3, xa, xa1, xa2, theta, d1, d3, E, G, P, la, step = self.get_discrete_input()\n Q_l, Mqz_l, ddq_x2, ddq_xa2, ddq_x1, ddq_x3 = self.get_distributed_loads_aero()\n dsch, dsca_y, dsca_z, Izz, Iyy, J, z = self.get_geometry()\n\n # function\n cte_v = -1 / (E * Izz) # cte in v deflection formula\n cte_w = -1 / (E * Iyy) # cte in w deflection formula\n cte_T = 1 / (G * J) # cte in torsion equation\n\n # order variables matrix\n # F_z1 , F_z2, F_z3, F_a, F_y1, F_y2, F_y3, c1, c2, c3, c4, c5\n left_column = np.array([\n [la - x1, la - x2, la - x3, np.cos(theta) * (la - xa1), 0, 0, 0, 0, 0, 0, 0, 0],\n # Moment around y equation at la\n [0, 0, 0, np.sin(theta) * (la - xa1), la - x1, la - x2, la - x3, 0, 0, 0, 0, 0],\n # Moment around z equation at la\n [-dsch, dsch, -dsch, dsca_y * np.cos(theta) + dsca_z * np.sin(theta), 0, 0, 0, 0, 0, 0, 0, 0],\n # Torque aka Moment around x at la\n [1, 1, 1, np.cos(theta), 0, 0, 0, 0, 0, 0, 0, 0], # force/shear z\n [0, 0, 0, np.sin(theta), 1, 1, 1, 0, 0, 0, 0, 0], # force/shear y\n [0, 0, 0, cte_v / 6 * np.sin(theta) * (x2 - xa1) ** 3, cte_v / 6 * (x2 - x1) ** 3, 0, 0, x2, 1, 0, 0, 0],\n # v deflection at x2\n [0, 0, 0, cte_v / 6 * np.sin(theta) * (xa2 - xa1) ** 3, cte_v / 6 * (xa2 - x1) ** 3,\n cte_v / 6 * (xa2 - x2) ** 3, 0, xa2, 1, 0, 0, 0], # v deflection at xa2\n [cte_w / 6 * (x2 - x1) ** 3, 0, 0, cte_w / 6 * np.cos(theta) * (x2 - xa1) ** 3, 0, 0, 0, 0, 0, x2, 1, 0],\n # w deflection at x2\n [0, 0, 0, 0, 0, 0, 0, x1, 1, 0, 0, (z - x1)], # v + theta(z-x) deflection at x1\n [-cte_T * dsch * (x3 - x1) * (z - x3), cte_T * dsch * (x3 - x2) * (z - x3), 0,\n cte_v / 6 * np.sin(theta) * (x3 - xa1) ** 3 + cte_T * (dsca_y * np.cos(theta) + dsca_z * np.sin(theta)) * (\n z - x3),\n cte_v / 6 * (x3 - x1) ** 3, cte_v / 6 * (x3 - x2) ** 3, 0, x3, 1, 0, 0, (z - x3)],\n # v + theta(z-x) deflection at x3\n [0, 0, 0, 0, 0, 0, 0, 0, 0, x1, 1, (z - x1)], # w + theta(z-x) deflection at x1\n [cte_w / 6 * (x3 - x1) ** 3 - cte_T * dsch * (x3 - x1) * (z - x3),\n cte_w / 6 * (x3 - x2) ** 3 + cte_T * dsch * (x3 - x2) * (z - x3), 0,\n cte_v / 6 * np.cos(theta) * (x3 - xa1) ** 3 + cte_T * (dsca_y * np.cos(theta) + dsca_z * np.sin(theta)) * (\n z - x3),\n 0, 0, 0, 0, 0, x3, 1, (z - x3)] # w + theta(z-x) deflection at x3\n ])\n\n right_column = np.array([\n [P * np.cos(theta) * (la - xa2)], # Moment around y equation at la\n [P * np.sin(theta) * (la - xa2) + Mqz_l], # Moment around z equation at la\n [P * (dsca_y * np.cos(theta) + dsca_z * np.sin(theta))], # Torque aka Moment around x at la\n [P * np.cos(theta)], # force/shear z\n [P * np.sin(theta) + Q_l], # force/shear y\n [cte_v * ddq_x2], # v deflection at x2 qq at x2\n [cte_v * ddq_xa2], # v deflection at xa2 qq at x2\n [0], # w deflection at x2\n [d1 * np.sin(theta) + cte_v * ddq_x1], # v deflection at x1 qq at x1\n [d3 * np.sin(theta) + cte_v * (1 / 6 * P * np.sin(theta) * (x3 - xa2) ** 3 + ddq_x3)\n + (cte_T * (P * (dsca_y * np.cos(theta) + dsca_z * np.sin(theta)) * (x3 - x1))) * (z - x3)],\n # v deflection at x3 qqt qq at x3\n [d1 * np.cos(theta)], # w deflection at x1\n [d3 * np.cos(theta) + cte_w / 6 * P * np.cos(theta) * (x3 - xa2) ** 3\n + (cte_T * (P * (dsca_y * np.cos(theta) + dsca_z * np.sin(theta)) * (x3 - x1))) * (z - x3)]\n # w deflection at x3\n ])\n\n F_z1, F_z2, F_z3, F_a, F_y1, F_y2, F_y3, c1, c2, c3, c4, c5 = np.linalg.solve(left_column, right_column)\n\n return F_z1, F_z2, F_z3, F_a, F_y1, F_y2, F_y3, c1, c2, c3, c4, c5","title":""},{"docid":"23c07d3e9f8ba2e638eeee3801fe36bc","score":"0.48499912","text":"def test_analysis_get(self):\n pass","title":""},{"docid":"d3af1717b4ff9b5929126f12402d4027","score":"0.4847316","text":"def extraction(self):\r\n #TODO - \r\n listtags = []\r\n resultvalues = []\r\n modname = self.extfile\r\n methodlist = []\r\n #Load each extractor code\r\n #Case 1: Extractor = External library with methods and standard parameters - inputfile\r\n #Import external library\r\n mod = __import__(modname)\r\n dirlist = dir(mod)\r\n for met in dirlist:\r\n if \"__\" not in met and met not in ['re','os']:\r\n methodlist.append(met)\r\n for method in methodlist:\r\n result = \"\"\r\n #Load each desired method from external module\r\n loading = getattr(mod, method)\r\n #Get the result from method invocation using infile as standard parameter\r\n result = loading(self, self.infile)\r\n #Test if result is a dictionary\r\n if type(result) == dict:\r\n #Adds each subtuple to the tags and results list\r\n tags = result.keys()\r\n tags.sort()\r\n #Add the new tags\r\n listtags.extend(tags)\r\n #Add the new values\r\n for tag in tags:\r\n if (result[tag]) == str:\r\n resultvalues.append(result[tag])\r\n else:\r\n resultvalues.append(str(result[tag]))\r\n else:\r\n #Converts unknown format to string\r\n result = str(result)\r\n \r\n #Adds method name to listtags and result to resultvalues\r\n listtags.append(method)\r\n resultvalues.append(result)\r\n #Case 2: Extractor = Legacy code to be executed by shell\r\n #TODO\r\n #Case 3: Extractor = python class\r\n #TODO \r\n #Standard extraction output\r\n \r\n# begin-vitor\r\n if self.extfile == \"PA_Readseq\":\r\n \tlisttags.append(\"PHYLIP\")\r\n \tfilename = self.infile.split(\"/\")[len(self.infile.split(\"/\"))-1]\r\n \tresultvalues.append(filename + \".phylip\")\r\n elif self.extfile == \"PA_Mafft\":\r\n \tlisttags.append(\"MAFFT_FILE\")\r\n \tfilename = self.infile.split(\"/\")[len(self.infile.split(\"/\"))-1]\r\n \tresultvalues.append(filename + \".mafft\")\r\n elif self.extfile == \"PA_Modelgenerator\":\r\n \tlisttags.append(\"MG\")\r\n \tfilename = self.infile.split(\"/\")[len(self.infile.split(\"/\"))-1]\r\n \tresultvalues.append(filename + \".mg.modelFromMG.txt\")\r\n elif self.extfile == \"PA_Raxml\":\r\n \tlisttags.append(\"RAXML\")\r\n \tfilename = self.infile.split(\"/\")[len(self.infile.split(\"/\"))-1]\r\n \tresultvalues.append(\"RAxML_bipartitions.\" + filename + \".phylip_tree3\")\r\n# end-vitor\r\n \t\r\n \t\r\n outlist = self.buildOutputFile(tags=listtags, provdata = resultvalues)\r\n output = open(self.outfile, \"w\")\r\n for line in outlist:\r\n output.write(line)","title":""},{"docid":"924973242f8b1dabba767053790ea5f9","score":"0.48471954","text":"def get_trajectories(env, policy_model, n_episode, episode_length):\n paths = []\n epoch_return = 0\n episode_count = 0\n for i_episode in range(n_episode):\n episode_return = 0\n episode_count += 1\n obs_list = []\n log_prob_list = []\n action_list = []\n reward_list = []\n mask_list = []\n observation = env.reset()\n\n for t in range(episode_length):\n # env.render()\n obs_list.append(observation)\n obs_tensor = torch.Tensor(observation).view(1,-1)\n\n mu, log_sigma = policy_model(obs_tensor)\n\n normal_dist = Normal(mu,log_sigma.exp())\n\n action = normal_dist.sample()\n action_list.append(action.reshape(-1))\n\n ## the log prob here is the log prob of taking the set of actions, so we take sum of log\n ## you can also take product of exp of log to get same results\n log_prob = normal_dist.log_prob(action)\n log_prob = torch.sum(log_prob)\n log_prob_list.append(log_prob)\n\n ## make sure action is within env's specifics\n action = torch.clamp(action, -1, 1).reshape(-1)\n observation, reward, done, info = env.step(action.data.numpy())\n\n if done:\n mask_list.append(0) ## used in calculating advantage\n else:\n mask_list.append(1)\n\n episode_return += reward\n reward_list.append(reward)\n if done:\n break\n epoch_return += episode_return\n ## now we have finished one episode, we now assign reward (all the data points in\n ## the same trajectory have the same reward)\n path = {'obs':obs_list,'mask':mask_list, 'log_probs':log_prob_list, 'rewards':reward_list,'actions':action_list,'episode_return':episode_return}\n paths.append(path)\n return paths, epoch_return/n_episode\n #REturns the paths and","title":""},{"docid":"5d5e3f93067c12da57d20071e09ebdc0","score":"0.48467577","text":"def trajectory(self):\n return Trajectory.fit(self.xyz)","title":""},{"docid":"7429f4cde8403c409ab98f94ca80a05c","score":"0.48438308","text":"def __call__(self, observation: Mapping[str, np.ndarray],\n **kwargs) -> np.ndarray:\n\n # Prepares observation for the neural-network.\n observation[\"overhead_features\"] = observation[\n \"bird_view_camera_cityscapes\"]\n for attr in observation:\n if not isinstance(observation[attr], np.ndarray):\n observation[attr] = np.atleast_1d(observation[attr])\n observation[attr] = observation[attr][None, ...].astype(np.float32)\n\n # Makes `goal` 2D.\n observation[\"goal\"] = observation[\"goal\"][..., :2]\n # Convert image to CHW.\n observation[\"lidar\"] = np.transpose(observation[\"lidar\"], (0, 3, 1, 2))\n # Processes observations for the `ImitativeModel`.\n observation = {\n key: torch.from_numpy(tensor).to(self._device) # pylint: disable=no-member\n for (key, tensor) in observation.items()\n }\n observation = self._model.transform(observation)\n\n # Queries model.\n plan = self._model(num_steps=kwargs.get(\"num_steps\", 20),\n epsilon=kwargs.get(\"epsilon\", 1.0),\n lr=kwargs.get(\"lr\", 5e-2),\n **observation).detach().cpu().numpy()[0] # [T, 2]\n\n # TODO(filangel): clean API.\n # Interpolates plan.\n player_future_length = 40\n increments = player_future_length // plan.shape[0]\n time_index = list(range(0, player_future_length, increments)) # [T]\n plan_interp = scipy.interpolate.interp1d(x=time_index, y=plan, axis=0)\n xy = plan_interp(np.arange(0, time_index[-1]))\n\n # Appends z dimension.\n z = np.zeros(shape=(xy.shape[0], 1))\n return np.c_[xy, z]","title":""},{"docid":"7aa47e88009d625d48219a36b3cd0153","score":"0.48420858","text":"def AddToTrajectory(self,sample):\n #Replacing the action with the hierarchical action.\n nestedMethodSample = [sample[0]] + [sample[5]] + sample[2:5] +sample[6:]\n self.nestedMethod.AddToTrajectory(nestedMethodSample)","title":""},{"docid":"48ecf4a4f59196f16eded5c607b3a066","score":"0.48416775","text":"def test_interface():\n import pKaTool.pKa_calc\n X=pKaTool.pKa_calc.Monte_Carlo_Mult_CPP()\n\n X.intrinsic_pKa={':0001:ASP':[0.0,4.0,5.0]}\n X.charged_state={':0001:ASP':[0,1,1]}\n X.acid_base={':0001:ASP':-1}\n X.intene_mult={':0001:ASP':{':0001:ASP':[[0,0,0],[0,0,0],[0,0,0]]}}\n X._calc_pKas(0.0,10.0,0.5)\n return","title":""},{"docid":"ca29e47b3ba298f8a68f04964b6826ab","score":"0.4839466","text":"def construct_traj_set(self, planning_problem_list):\n trajectories = []\n cnt = 1\n total_path_len = 0\n # Add code here to create a traj set #\n for planning_problem in planning_problem_list: \n print(cnt)\n cnt+=1 \n traj,traj_dist = planning_problem.get_traj(trajectories)\n total_path_len += traj_dist\n if(len(traj)==0):\n print(\"TRAJS NOT FOUND, BREAK\")\n return [], 0, False\n trajectories.append(traj)\n \n\n return trajectories, total_path_len, True","title":""},{"docid":"75c716746377eb39778f331cc9fd2da2","score":"0.4837887","text":"def perform_exploration():\n model = un.NeuronModel(file=\"tabak.py\", name=\"tabak\")\n\n original_g_BKs = np.linspace(0, 1, nr_points_exploration)\n g_BKs = scale_conductance(original_g_BKs)\n\n # g_SK\n original_g_SKs = np.linspace(1, 3, nr_points_exploration)\n g_SKs = scale_conductance(original_g_SKs)\n\n event_durations_SK = np.zeros((len(original_g_BKs), len(original_g_SKs)))\n for i, g_BK in enumerate(tqdm(g_BKs, desc=\"Varying g_BK\")):\n for j, g_SK in enumerate(tqdm(g_SKs, desc=\"Varying g_SK\")):\n time, voltage, info = model.run(g_BK=g_BK,\n g_SK=g_SK,\n discard=discard,\n noise_amplitude=noise_amplitude,\n simulation_time=simulation_time)\n\n tmp_duration = np.mean(duration(time, voltage))\n\n if np.isnan(tmp_duration):\n tmp_duration = -1\n\n event_durations_SK[i, j] = tmp_duration\n\n\n original_g_Ks = np.linspace(1.5, 4.5, nr_points_exploration)\n g_Ks = scale_conductance(original_g_Ks)\n\n event_durations_K = np.zeros((len(original_g_BKs), len(original_g_Ks)))\n for i, g_BK in enumerate(tqdm(g_BKs, desc=\"Varying g_BK\")):\n for j, g_K in enumerate(tqdm(g_Ks, desc=\"Varying g_K\")):\n time, voltage, info = model.run(g_BK=g_BK,\n g_K=g_K,\n discard=discard,\n noise_amplitude=noise_amplitude,\n simulation_time=simulation_time)\n\n\n tmp_duration = np.mean(duration(time, voltage))\n\n if np.isnan(tmp_duration):\n tmp_duration = -1\n\n event_durations_K[i, j] = tmp_duration\n\n np.save(os.path.join(data_folder, \"original_g_BKs\"), original_g_BKs)\n np.save(os.path.join(data_folder, \"original_g_SKs\"), original_g_SKs)\n np.save(os.path.join(data_folder, \"original_g_Ks\"), original_g_Ks)\n np.save(os.path.join(data_folder, \"event_durations_SK\"), event_durations_SK)\n np.save(os.path.join(data_folder, \"event_durations_K\"), event_durations_K)\n\n return original_g_BKs, original_g_SKs, original_g_Ks, event_durations_SK, event_durations_K","title":""},{"docid":"0f7b2398fd8c47565c574bd8990d20f7","score":"0.48374778","text":"def test2():\n print('test2 ............................................................',\n \"\"\"\n To understand and to confirm the correctnes of the SIMULINAC plots,\n this test checks the influence of the twiss-alpha parameter using a \n thin lens FODO.\n \"\"\")\n # initial conditions for twiss parameters\n beta = 1. # [m]\n alfa = -0.5\n epsi = 1.e-6 # emittance [m*rad]\n # other initials\n NC=56 # NC cells\n T = 25. # kin. energy [MeV]\n T=T*1.e-3 # [GeV] kin.energy \n betakin=M.sqrt(1.-(1+T/E0)**-2) # beta kinetic\n E=E0+T # [GeV] energy\n k=0.2998*Bg/(betakin*E) # [1/m^2]\n L=0.596 # distance between quads [m]\n Ql=0.04 # full quad length [m]\n f=1./(k*Ql) # ff = -fd = f\n mfodo1 = Mq(-2*f,Ql/2.) # 1/2 qd\n mfodo2 = mmult(mfodo1,Md(L/2.))\n mfodo3 = mmult(mfodo2,Mq(2*f,Ql/2.)) # 1/2 qf\n m11 = mfodo3[0][0]\n m12 = mfodo3[0][1]\n m21 = mfodo3[1][0]\n m22 = mfodo3[1][1]\n mfodor = np.array([[m22,m12],[m21,m11]]) # reverse\n \n mfodo = mmult(mfodo3,mfodor)\n trace = abs(mfodo.trace())\n \n mfodo2 = Mfodo(2*f,L/2.) # Wiedemann\n my_debug('matrix probe: mfodo-mfodo2 must be zero matrix')\n zero = mfodo-mfodo2\n my_debug(f'{abs(zero[0][0]):.5f} {abs(zero[0][1]):.5f}')\n my_debug(f'{abs(zero[1][0]):.5f} {abs(zero[1][1]):.5f}')\n \n # now in slices\n slices = 20\n ld = L/slices\n lq = Ql/slices\n f = 1./(k*lq)\n md = Md(ld/2) # element\n mqf = Mq(+2*f,lq/2) # element\n mqd = Mq(-2*f,lq/2) # element\n twd = twmatrix(md) # twiss-matrix\n twqf = twmatrix(mqf) # twiss-matrix\n twqd = twmatrix(mqd) # twiss-matrix\n lattice = [] # element list\n twlattice = [] # twiss matrix list\n pos = [0.] # position list\n s = 0. # postion (abzsisse)\n mfodo5 = np.array([[1.,0.],[0.,1.]])# unit matrix\n for m in range(NC):\n mfodo6 = mfodo5.dot(mfodo2) # chain Wiedemann cells\n mfodo5 = mfodo6\n for n in range(1,slices+1): # chain slices\n s += lq\n pos.append(s)\n lattice.append(mqd)\n twlattice.append(twqd)\n for n in range(1,slices+1):\n s += ld\n pos.append(s)\n lattice.append(md)\n twlattice.append(twd)\n for n in range(1,slices+1):\n s += lq\n pos.append(s)\n lattice.append(mqf)\n twlattice.append(twqf)\n for n in range(1,slices+1):\n s += lq\n pos.append(s)\n lattice.append(mqf)\n twlattice.append(twqf)\n for n in range(1,slices+1):\n s += ld\n pos.append(s)\n lattice.append(md)\n twlattice.append(twd)\n for n in range(1,slices+1):\n s += lq\n pos.append(s)\n lattice.append(mqd)\n twlattice.append(twqd)\n # full lattice matrix\n mfodo3 = np.array([[1.,0.],[0.,1.]])\n for node in lattice:\n mfodo4 = mfodo3.dot(node)\n mfodo3 = mfodo4\n my_debug('lattice probe: mfodo3-mfodo5 must be zero matrix')\n zero = mfodo3-mfodo5\n my_debug(f'{abs(zero[0][0]):.5f} {abs(zero[0][1]):.5f}')\n my_debug(f'{abs(zero[1][0]):.5f} {abs(zero[1][1]):.5f}')\n \n twx = Twiss(beta,alfa,epsi)\n # C track\n track_point_C = np.array(twx.y1())\n # S track\n track_point_S = np.array(twx.y4())\n\n points_c = [track_point_C]\n points_s = [track_point_S]\n for node in lattice:\n C_point = node.dot(track_point_C)\n points_c.append(C_point)\n track_point_C = C_point\n S_point = node.dot(track_point_S)\n points_s.append(S_point)\n track_point_S = S_point\n\n # enveloppe \n beta0, alfa0, gamma0 , epsi0 = twx()\n twiss_point = np.array([beta0,alfa0,gamma0]) # twiss track\n twpoints = [twiss_point]\n for bmx in twlattice:\n twissp = bmx.dot(twiss_point)\n twpoints.append(twissp)\n twiss_point = twissp\n\n # trajectories\n c_trk = [v[0] for v in points_c]\n s_trk = [v[0] for v in points_s]\n # enveloppe (beta*emittance)^(1/2)\n sgx = [M.sqrt(v[0]*epsi0) for v in twpoints]\n \n plt.figure('x & sgx')\n ax1 = plt.subplot(111)\n ax1.set_title(F'alpha = {alfa:2.1f}')\n ax1.plot(pos,c_trk, label='C')\n ax1.plot(pos,s_trk, label='S')\n ax1.plot(pos,sgx, label='env')\n ax1.legend(loc='lower right',fontsize='small')\n plt.show()\n return","title":""},{"docid":"1d71f0d0082f3527649c54dc841786fe","score":"0.4835147","text":"def testTransformation3():\n\n\n\n i = 0\n while i < 40:\n i = i + 1\n\n N = 20\n model_points = np.ones((3, N))\n\n variance = 1/40 * i\n var = np.array([0.1, 0.1, variance])\n C = eye(3,3) #np.outer(var, var)\n # C = eye(3, 3)\n C[0,1] = variance\n C[1,0] = variance\n C[2,1] = variance\n C[1,2] = variance\n C[0,2] = variance/10\n C[2,0] = variance/10\n\n model_points = np.random.multivariate_normal([0,0,0], C, 15) * 200\n model_points = model_points.T\n\n planar, ratio = IsPlanar(model_points)\n\n # flat = False\n # while not flat:\n # # Camera A\n # model_points = ((2 * np.random.rand(3, 20)) - 1) * 500\n # # model_points = np.random.normal(0, 20, (3, 100))\n #\n # planar, ratio = IsPlanar(model_points)\n #\n # flat = ratio > 0.8\n\n # Ground truth transformation parameters\n # x y z\n R_params = [23, -12, 4]\n t_params = [44, -102, 12]\n transform_parms = R_params + t_params\n transfomed_points, R, t = transform(transform_parms, model_points, False, 0, 5)\n\n # fx fy cx cy k0 k1\n project_params = [100, 100, 50, 50, 0, 0]\n image_points = projective_transform(project_params, transfomed_points)\n\n # Seed Params\n # seed = np.zeros(12) # transform_parms + (np.random.normal(0, 11, 6))\n # seed = seed + [0,0,0,0,0,0,10, 10, 5, 5, 0, 0]\n\n noiseval = 5 #i * 1\n transformNoise = transform_parms + np.random.normal(0, noiseval, 6)\n projectNoise = project_params[1:] + np.random.normal(0, noiseval, 5)\n seed = np.concatenate([projectNoise, transformNoise])\n # seed = seed + (np.random.normal(0, 0.1, 11))\n\n\n # Run LMA\n out = LMA.LM(seed, (model_points, image_points),\n projective_error_function,\n lambda_multiplier=2, kmax=2000, eps=0.1)\n\n\n print(\"\\n\\nRMS ERR: \\t{}\".format(out[0]))\n print(\"Eigen Ratio: \\t{}, {}\".format(ratio, variance))\n print(\"Noise Val: {}\".format(noiseval))\n print(\"Projection: \\t{}\".format(out[1][0:5]))\n print(\"Angle: \\t{}\".format(out[1][5:8]))\n print(\"Translation: \\t{}\".format(out[1][8:11]))\n print(\"Reason: \\t{}\".format(out[2]))\n\n\n\n print(model_points)","title":""},{"docid":"b44b817af2255dbfa9e1f6b8a61ba589","score":"0.48300886","text":"def get_trajectory_estimates(self, policy: Controller):\n\n state_mean = self.start_mean\n state_cov = self.start_cov\n\n # container required plotting later on\n state_means_container = []\n state_covs_container = []\n action_means_container = []\n action_covs_container = []\n\n state_means_container.append(state_mean)\n state_covs_container.append(state_cov)\n\n for t in range(self.args.horizon):\n state_next_mean, state_next_cov, action_mean, action_cov = self.rollout(policy, state_mean, state_cov)\n\n state_means_container.append(state_next_mean)\n state_covs_container.append(state_next_cov)\n action_means_container.append(action_mean)\n action_covs_container.append(action_cov)\n\n state_mean = state_next_mean\n state_cov = state_next_cov\n\n return np.array(state_means_container), np.array(state_covs_container), np.array(action_means_container),\\\n np.array(action_covs_container)","title":""},{"docid":"3c9ce2a511927b6d35c65c5446485c90","score":"0.4827862","text":"def _generate_trajectory(self):\n transitions = []\n state = self.env.reset()\n for i in range(HORIZON):\n # if i < HORIZON / 2:\n # action = [0.1, 0.1]\n # else:\n action = self._expert_control(state, i)\n next_state, cost, done, _ = self.env.step(action)\n transitions.append([state, action, cost, next_state, done])\n state = next_state\n assert done, \"Did not reach the goal set on task completion.\"\n V = self.env.values()\n for i, t in enumerate(transitions):\n t.append(V[i])\n # self.env.plot_trajectory()\n return transitions","title":""},{"docid":"3d05ab063f0369f231d396df2abd3cc5","score":"0.4827535","text":"def algorithms(dxm_state):","title":""},{"docid":"0dfd9a4b6b2f19ab1b792bbfe54dd1a2","score":"0.4825209","text":"def gather(self):","title":""},{"docid":"4feb76db7004c1ae9d6f30bdbb820c68","score":"0.48250794","text":"def calcOneStructure(loopInfo): \n # Generate initial structure and minimize.\n #===========================================================================\n # Generate initial structure by randomizing torsion angles.\n if args.unfold == 'yes':\n import monteCarlo\n monteCarlo.randomizeTorsions(dyn)\n\n # Then set torsion angles from restraints (this shortens high T dynamics).\n protocol.fixupCovalentGeom(maxIters=100, useVDW=1)\n import torsionTools\n if DIHE:\n import torsionTools\n torsionTools.setTorsionsFromTable(DIHE)\n\n # Initialize parameters\n InitialParams(rampedParams) # Parameters for SA.\n InitialParams(highTempParams1) # Reset some rampedParams.\n\n if args.resetCenter == 'yes':\n setCenter(immx_com, Zpos) # Translate selected center of mass to IMMx Zpos.\n\n # Torsion angle minimization.\n #==========================================================================\n protocol.initMinimize(dyn,\n potList=pots,\n numSteps=100,\n printInterval=50)\n dyn.run()\n\n # High temperature dynamics.\n #===========================================================================\n # Start with REPEL to remove clashes then phase out\n if repelStart == 'yes':\n # High temperature dynamics stage 1.\n protocol.initDynamics(dyn,\n potList=pots, # potential terms to use.\n bathTemp=ini_temp, # set bath temperature.\n initVelocities=1, # uniform initial velocities.\n finalTime=30, # run for finalTime or\n numSteps=3001, # numSteps * 0.001, whichever is less.\n printInterval=100) # printing rate in steps.\n dyn.setETolerance(ini_temp/100) # used to det. stepsize, dflt [temp/1000].\n dyn.run()\n \n # High temperature dynamics stage 2.\n InitialParams(highTempParams2)\n if args.resetCenter == 'yes':\n setCenter(immx_com, Zpos) # translate selected center of mass to IMMx Zpos.\n protocol.initDynamics(dyn,\n potList=pots, # potential terms to use.\n bathTemp=ini_temp, # set bath temperature.\n initVelocities=1, # uniform initial velocities.\n finalTime=30, # run for finalTime or\n numSteps=3001, # numSteps * 0.001, whichever is less.\n printInterval=100) # printing rate in steps.\n dyn.setETolerance(ini_temp/100) # used to det. stepsize, dflt [temp/1000].\n dyn.run()\n\n # High temperature dynamics stage 3.\n InitialParams(highTempParams)\n if args.resetCenter == 'yes':\n setCenter(immx_com, Zpos) # translate selected center of mass to IMMx Zpos.\n protocol.initDynamics(dyn,\n potList=pots, # potential terms to use.\n bathTemp=ini_temp, # set bath temperature.\n initVelocities=1, # uniform initial velocities.\n numSteps=highTempSteps, # numSteps * 0.001, whichever is less.\n finalTime=highTempSteps/100,\t # run for finalTime or\n printInterval=100) # printing rate in steps.\n dyn.setETolerance(ini_temp/100) # used to det. stepsize, dflt [temp/1000].\n dyn.run()\n\n # Initialize integrator and loop for simulated annealing and run.\n #===========================================================================\n # Dynamics for annealing.\n if args.resetCenter == 'yes':\n setCenter(immx_com, Zpos) # translate selected center of mass to IMMx Zpos.\n protocol.initDynamics(dyn,\n potList=pots,\n finalTime=0.4, # run for finalTime or\n numSteps=annealSteps, # numSteps*0.001, whichever is less.\n printInterval=100) \n\n # Set up cooling loop and run.\n from simulationTools import AnnealIVM\n AnnealIVM(initTemp=ini_temp,\n finalTemp=fin_temp,\n tempStep=step_temp,\n ivm=dyn,\n rampedParams=rampedParams\n ).run()\n\n # Run Ez-Potential to position Z-axis\n #===========================================================================\n if args.ezPot:\n from xplor import select\n m = IVM(xplor.simulation)\n protocol.initMinimize(m,numSteps=10)\n m.setVerbose(m.verbose() | m.printNodeDef)\n m.setStepType(\"MinimizeCG\")\n m.setNumSteps(10)\n m.setDEpred(1)\n m.setETolerance(1e-7)\n m.setPrintInterval(1)\n groupList=m.groupList()\n groupList.append(select(args.ezPot))\n m.setGroupList(groupList)\n m.setHingeList([('translate', select(args.ezPot)),])\n m.potList().removeAll()\n m.potList().add(Ezt)\n m.run()\n\n if args.relax == 'yes':\n InitialParams(relaxParams)\n protocol.initDynamics(dyn,\n potList=pots, # potential terms to use.\n bathTemp=args.relaxTemp, # set bath temperature.\n initVelocities=1, # uniform initial velocities.\n finalTime=30, # run for finalTime or\n numSteps=args.relaxSteps, # numSteps * 0.001, whichever is less.\n printInterval=100) # printing rate in steps.\n dyn.setETolerance(args.relaxTemp/100) # used to det. stepsize, dflt [temp/1000].\n dyn.run()\n \n # Final minimization.\n #===========================================================================\n # Torsion angle minimization.\n protocol.initMinimize(dyn,\n numSteps=500, # dflt [500 steps]\n potList=pots,\n printInterval=50)\n dyn.run()\n\n # Final Cartesian minimization.\n protocol.initMinimize(minc,\n numSteps=500, # dflt [500 steps]\n potList=pots,\n dEPred=10)\n minc.run()\n\n # Recenter coordinates in XY plane.\n setCenterXY() # translate protein coordinates to XY center.\n \n # Do analysis and write structure when this routine is finished.\n pass","title":""},{"docid":"bae36d63197e84f10f88daf33608947d","score":"0.4824436","text":"def test_tdp(self):\n \n expstring = \"\"\"\n 0 4 4 -3.96678714134e-19 0.0\n 25 4 4 0.0123395314303 1.10216297674e-16\n 50 4 4 0.0521821949151 4.41386645353e-16\n 75 4 4 0.0375170438965 3.03228186587e-16\n 0 3 3 -2.15151658263e-18 0.0\n 25 3 3 0.0542304209663 3.27264685377e-16\n 50 3 3 0.133087497663 5.56816047005e-16\n 75 3 3 0.114486463162 5.08756535778e-16\n 0 0 5 -0.855304990279 0.0\n 25 0 5 0.338783123139 0.205218959392\n 50 0 5 -0.0404678523217 -0.0413353312194\n 75 0 5 -0.0747762812157 0.00493941699829\n \"\"\"\n \n with energy_units(\"1/cm\"):\n mol1 = Molecule([0.0, 12000.0])\n mod1 = Mode(300.0)\n mol1.add_Mode(mod1)\n mod1.set_nmax(0, 1)\n mod1.set_nmax(1, 8)\n mod1.set_HR(1,0.2)\n \n mol1.set_dipole((0,1), [1.0, 0.0, 0.0])\n \n time = TimeAxis(0.0, 100, 1.0)\n params = dict(ftype=\"OverdampedBrownian\",\n cortime=30.0, reorg=20, T=300,\n matsubara=30)\n \n cfce = CorrelationFunction(time, params=params)\n \n params = dict(ftype=\"OverdampedBrownian\",\n cortime=30.0, reorg=50, T=300,\n matsubara=30)\n \n cfc1 = CorrelationFunction(time, params=params)\n \n mol1.set_mode_environment(0, 0, corfunc=cfce)\n mol1.set_mode_environment(0, 1, corfunc=cfce)\n mol1.set_transition_environment((0,1), cfc1)\n \n rhoi = mol1.get_excited_density_matrix(condition=\"delta\")\n \n prop = mol1.get_ReducedDensityMatrixPropagator(time,\n relaxation_theory=\"stR\",\n time_dependent=True)\n HH = mol1.get_Hamiltonian()\n #rhoi.data[:,:] = 0.0\n with eigenbasis_of(HH):\n rhoi.data[0,4] = 1.0\n rhoi.data[0,3] = 1.0\n \n rhot1 = prop.propagate(rhoi, Nref=1)\n\n HH.set_rwa([0,1]) \n \n rhot2 = prop.propagate(rhoi, Nref=1)\n \n rhot2.convert_from_RWA(HH)\n \n #\n # checking that rotating wave approximation is the same as standard \n #\n #numpy.testing.assert_allclose(rhot1.data, rhot2.data, rtol=8.0e-2)\n \n \n _show_plot_ = False\n if _show_plot_:\n import matplotlib.pyplot as plt\n with eigenbasis_of(HH):\n for ii in range(HH.dim):\n plt.plot(time.data, numpy.real(rhot1.data[:,ii,ii]))\n plt.plot(time.data, numpy.real(rhot2.data[:,ii,ii]),\"--\")\n \n plt.show()\n \n _create_data_ = False\n if _create_data_:\n \n elements = [(4,4), (3,3), (0,5)]\n for el in elements:\n for tt in range(0, 100, 25):\n print(tt, el[0], el[1],\n numpy.real(rhot2.data[tt,el[0],el[1]]),\n numpy.imag(rhot2.data[tt,el[0],el[1]]))\n \n # \n # compare to precalculated data\n #\n expected = {}\n il = 0\n for line in expstring.splitlines():\n rel = line.strip()\n if len(rel) > 0:\n nmbrs = rel.split(\" \")\n tmi = int(nmbrs[0])\n i1 = int(nmbrs[1])\n i2 = int(nmbrs[2])\n re = float(nmbrs[3])\n im = float(nmbrs[4])\n expected[il] = (tmi, i1, i2, re, im)\n il += 1\n\n _perform_test_ = True\n if _perform_test_:\n for ks in expected:\n dats = expected[ks]\n tt = dats[0]\n i1 = dats[1]\n i2 = dats[2]\n numpy.testing.assert_allclose(dats[3], \n numpy.real(rhot2.data[tt,i1,i2]),\n atol=1.0e-6)\n numpy.testing.assert_allclose(dats[4], \n numpy.imag(rhot2.data[tt,i1,i2]),\n atol=1.0e-6)","title":""},{"docid":"10749cb54c17dc10f1c4429fd97e3a40","score":"0.4823318","text":"def __call__(self):\n raw_s_t, s_t, s_t_tensors = self._raw_s_t, self._s_t, self._s_t_tensors\n a_t_tensors, a_t, q_t, q_vals = self._action_f(s_t_tensors, self._t, raw_s_t)\n raw_s_t_1, raw_r_t, done, _ = self._env.step(a_t)\n r_t = torch.tensor([raw_r_t], dtype=torch.float, device=self._device)\n self._n_trans.append(\n (s_t_tensors, a_t_tensors, r_t, q_t)\n )\n # track funtions\n for track_f in self._track_fs:\n track_f(self._tracker, self._t)\n self._t += 1\n s_t_1, s_t_1_tensors = self._get_s_t_1(raw_s_t_1, done)\n\n done_cond = done or self._env.epi_step > self._max_step_per_epi\n\n # Generate nstep trans result\n if len(self._n_trans) == self._n_steps:\n r_t_n = self._aggr_nsteps_return()\n s_0, a_0, _, __ = self._n_trans.pop(0)\n n_step_trans = [(s_0, a_0, r_t_n, s_t_1_tensors)]\n else:\n n_step_trans = []\n\n ###\n # Handle done or max step epi\n ###\n if self._env.epi_step > self._max_step_per_epi:\n print(\"Episode forcefully terminated\")\n if done_cond:\n if self._eps_config is not None:\n track_keys = [\"reward\", \"step\", \"eps\", \"total_epi\"]\n track_vals = [self._env.epi_reward, self._env.epi_step, self._eps_config.eps_schedule_f(self._t), self._epi]\n else:\n track_keys = [\"reward\", \"step\", \"total_epi\"]\n track_vals = [self._env.epi_reward, self._env.epi_step, self._epi]\n self._tracker.tracks(track_keys, self._t, track_vals)\n self._epi += 1\n # Reste s_t if done or max step\n while len(self._n_trans) > 0:\n r_t_n = self._aggr_nsteps_return()\n s_0, a_0, _, __ = self._n_trans.pop(0)\n n_step_trans.append((s_0, a_0, r_t_n, s_t_1_tensors))\n self._raw_s_t = self._env.reset()\n self._s_t = self._q_net.prep(self._raw_s_t)\n self._s_t_tensors = prep_one_trans_device_tensors(self._s_t, self._device)\n else:\n self._raw_s_t, self._s_t, self._s_t_tensors = raw_s_t_1, s_t_1, s_t_1_tensors \n return n_step_trans, self._t","title":""}],"string":"[\n {\n \"docid\": \"704c0d232adc815ba1223712ec930fbb\",\n \"score\": \"0.5725358\",\n \"text\": \"def test_step_methods_in_each_level(self):\\n _, model, _ = mv_simple()\\n _, model_coarse, _ = mv_simple_coarse()\\n _, model_very_coarse, _ = mv_simple_very_coarse()\\n with model:\\n initial_point = model.initial_point\\n initial_point_size = sum(initial_point[n.name].size for n in model.value_vars)\\n s = np.ones(initial_point_size) + 2.0\\n sampler = MLDA(\\n coarse_models=[model_very_coarse, model_coarse],\\n base_S=s,\\n base_sampler=\\\"Metropolis\\\",\\n )\\n assert isinstance(sampler.step_method_below, MLDA)\\n assert isinstance(sampler.step_method_below.step_method_below, Metropolis)\\n assert np.all(sampler.step_method_below.step_method_below.proposal_dist.s == s)\\n\\n sampler = MLDA(coarse_models=[model_very_coarse, model_coarse], base_S=s)\\n assert isinstance(sampler.step_method_below, MLDA)\\n assert isinstance(sampler.step_method_below.step_method_below, DEMetropolisZ)\\n assert np.all(sampler.step_method_below.step_method_below.proposal_dist.s == s)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a86620ffdc3a1c24164d2818c31cf511\",\n \"score\": \"0.56778145\",\n \"text\": \"def show_complex_trajectory(comp_traj,experiment,wait=50):\\n path_im = experiment.path\\n path_body = experiment.body_path\\n path_arm = experiment.arm_path\\n previous = 'trajectory'\\n \\n width = width_monitor\\n height = height_monitor\\n cv2.namedWindow(\\\"Trajectory\\\", cv2.WINDOW_NORMAL)\\n cv2.resizeWindow('Trajectory', width, height)\\n for i,elt in enumerate(comp_traj):\\n if type(elt)==tuple:\\n #raccomodation\\n if i>0:\\n if type(comp_traj[i-1])==tuple:\\n previous='tuple'\\n else:\\n previous='trajectory'\\n \\n if previous=='trajectory':\\n #looks after\\n stop_frame = 239\\n if i!=len(comp_traj)-1:\\n index_next1,index_next_2,_ = comp_traj[i+1]\\n next_traj = experiment.trajectories[index_next1][index_next_2]\\n stop_frame = next_traj.beginning\\n \\n last_frame = comp_traj[i-1].end\\n next_frame = last_frame+1\\n label = elt[2]\\n while next_frame<stop_frame and experiment.arm_tracker.next_cell(next_frame,label)!=-1:\\n \\n img = m.open_frame(path_im,next_frame+1)\\n arms = m.open_frame(path_arm,next_frame+1)\\n mask = (arms==(label+1)).astype(np.uint8)*255\\n label = experiment.arm_tracker.next_cell(next_frame,label)\\n next_frame+=1\\n overlaid = m.cv_overlay_mask2image(mask,img,\\\"red\\\")\\n cv2.imshow(\\\"Trajectory\\\",overlaid)\\n cv2.waitKey(wait)\\n else:\\n cells_list = elt.cells \\n for cell in cells_list:\\n frame_number = cell.frame_number+1\\n img = m.open_frame(path_im,frame_number)\\n body = m.open_frame(path_body,frame_number)\\n arms = m.open_frame(path_arm,frame_number)\\n mask = (body==(cell.body+1)).astype(np.uint8)*255\\n mask_arms = np.zeros(mask.shape,dtype=np.uint8)\\n for arm in cell.arms:\\n mask_arms+=(arms==(arm+1)).astype(np.uint8)*255\\n overlaid = m.cv_overlay_mask2image(mask,img,\\\"green\\\")\\n overlaid = m.cv_overlay_mask2image(mask_arms,overlaid,\\\"red\\\")\\n cv2.imshow(\\\"Trajectory\\\",overlaid)\\n \\n cv2.waitKey(wait)\\n \\n #cv2.destroyAllWindows()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f92352eedb7758a9aec30927601bd34\",\n \"score\": \"0.5551739\",\n \"text\": \"def test_nested_cv():\\n _experiment(3, 3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfc53a0493bb5da14f0ea8943c6e4aa5\",\n \"score\": \"0.5491269\",\n \"text\": \"def sample_trajectories(self, env):\\n# return self.model.predict(self.x_test)\\n return self.i*2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c20a7742907d598a43254ed2e2ea301\",\n \"score\": \"0.5468829\",\n \"text\": \"def assemble_complex_trajectories(complex_trajectory,z,list_of_raccom,raco_before,raco_after,experiment):\\n l,k = list_of_raccom.pop(z)\\n traj1 = experiment.trajectories[raco_before[k][0]][raco_before[k][1]]\\n if len(complex_trajectory)==0:\\n traj0 = experiment.trajectories[raco_after[l][0]][raco_after[l][1]]\\n complex_trajectory.extend([traj0,raco_after[l],raco_before[k],traj1])\\n else:\\n complex_trajectory.extend([raco_after[l],raco_before[k],traj1])\\n \\n for l_prime, (i1,i2,label) in enumerate(raco_after):\\n #Find if there is a racomodation after\\n if traj1==experiment.trajectories[i1][i2]:\\n #Find if this raccomoadtion can itself be raccomodated\\n candidates = [i for i,(x,y) in enumerate(list_of_raccom) if x==l_prime]\\n if len(candidates)==1:\\n assemble_complex_trajectories(complex_trajectory,candidates[0],list_of_raccom,raco_before,raco_after,experiment)\\n else:\\n complex_trajectory.append((i1,i2,label))\\n return\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"759175b20f27fa8aaeaaa570473ca19d\",\n \"score\": \"0.54281145\",\n \"text\": \"def show(self,experiment,wait=50):\\n path_im = experiment.path\\n path_body = experiment.body_path\\n path_arm = experiment.arm_path\\n previous = 'trajectory'\\n \\n monitor = get_monitors()[0]\\n width = monitor.width\\n height = monitor.height\\n cv2.namedWindow(\\\"Complex Trajectory\\\", cv2.WINDOW_NORMAL)\\n cv2.resizeWindow('Complex Trajectory', width, height)\\n for i,elt in enumerate(self):\\n if type(elt)==tuple:\\n #raccomodation\\n if i>0:\\n if type(self[i-1])==tuple:\\n previous='tuple'\\n else:\\n previous='trajectory'\\n \\n if previous=='trajectory':\\n #looks after\\n stop_frame = 239\\n if i!=len(self)-1:\\n index_next1,index_next_2,_ = self[i+1]\\n next_traj = experiment.trajectories[index_next1][index_next_2]\\n stop_frame = next_traj.beginning\\n \\n last_frame = self[i-1].end\\n next_frame = last_frame+1\\n label = elt[2]\\n while next_frame<stop_frame and experiment.arm_tracker.next_cell(next_frame,label)!=-1:\\n \\n img = m.open_frame(path_im,next_frame+1)\\n arms = m.open_frame(path_arm,next_frame+1)\\n mask = (arms==(label+1)).astype(np.uint8)*255\\n label = experiment.arm_tracker.next_cell(next_frame,label)\\n next_frame+=1\\n overlaid = m.cv_overlay_mask2image(mask,img,\\\"red\\\")\\n cv2.imshow(\\\"Complex Trajectory\\\",overlaid)\\n cv2.waitKey(wait)\\n else:\\n cells_list = elt.cells \\n for cell in cells_list:\\n frame_number = cell.frame_number+1\\n img = m.open_frame(path_im,frame_number)\\n body = m.open_frame(path_body,frame_number)\\n arms = m.open_frame(path_arm,frame_number)\\n mask = (body==(cell.body+1)).astype(np.uint8)*255\\n mask_arms = np.zeros(mask.shape,dtype=np.uint8)\\n for arm in cell.arms:\\n mask_arms+=(arms==(arm+1)).astype(np.uint8)*255\\n overlaid = m.cv_overlay_mask2image(mask,img,\\\"green\\\")\\n overlaid = m.cv_overlay_mask2image(mask_arms,overlaid,\\\"red\\\")\\n cv2.imshow(\\\"Complex Trajectory\\\",overlaid)\\n \\n cv2.waitKey(wait)\\n \\n cv2.destroyWindow(\\\"Complex Trajectory\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb410d6b976f3fc8447c3d0238c2da1c\",\n \"score\": \"0.5422803\",\n \"text\": \"def new_method():\\n channels = [\\n 'RADAR_FRONT',\\n 'RADAR_FRONT_LEFT',\\n 'RADAR_FRONT_RIGHT',\\n 'RADAR_BACK_LEFT',\\n 'RADAR_BACK_RIGHT',\\n ]\\n samples = scene_in_sample_data\\n scene_points = list()\\n for sample in samples:\\n x, y, z, vx_comp, vy_comp, pointclouds = list(), list(), list(), list(), list(), list()\\n ego_pose_coords = []\\n for channel in channels:\\n pc = sample[channel]['radar_point_cloud']\\n radar_token = sample['data'][channel]\\n current_radar = nusc.get('sample_data', radar_token)\\n ego_pose = nusc.get('ego_pose', current_radar['ego_pose_token'])\\n calibrated_sensor = nusc.get('calibrated_sensor', current_radar['calibrated_sensor_token'])\\n sensor_to_car = transform_matrix(calibrated_sensor['translation'],\\n Quaternion(calibrated_sensor['rotation'], inverse=False))\\n car_to_world = transform_matrix(ego_pose['translation'], Quaternion(ego_pose['rotation'], inverse=False))\\n\\n sensor_to_world = car_to_world @ sensor_to_car\\n\\n pc.transform(sensor_to_world)\\n\\n pointclouds.append(pc)\\n\\n ego_pose_coords = ego_pose['translation']\\n\\n # combine radar\\n\\n for i in range(pc.points.shape[1]):\\n x.append(pc.points[0][i])\\n y.append(pc.points[1][i])\\n z.append(pc.points[2][i]) # redundant?\\n vx_comp.append(pc.points[7][i])\\n vy_comp.append(pc.points[8][i])\\n scene_points.append([\\n np.asarray(x),\\n np.asarray(y),\\n np.asarray(z),\\n np.asarray(vx_comp),\\n np.asarray(vy_comp),\\n np.asarray(pointclouds),\\n np.asarray(ego_pose_coords)\\n ])\\n\\n return np.asarray(scene_points, dtype=object)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c30310d1c2bb8c61ced40beae38996a3\",\n \"score\": \"0.5397106\",\n \"text\": \"def executeTrajectory(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97e1f2612efb18295f7d4a45e811a8dd\",\n \"score\": \"0.5390741\",\n \"text\": \"def test_trajectories():\\n trajectory_true_out = np.load(\\\"trajectory_particle_2642532_out.npy\\\")\\n trajectory_true_in = np.load(\\\"trajectory_particle_12599035.npy\\\")\\n\\n def trajectories_test():\\n p = parameters.InitialConditionsParameters(\\n initial_snapshot=\\\"/Users/lls/Documents/CODE/Nina-Simulations/double/ICs_z99_256_L50_gadget3.dat\\\",\\n final_snapshot=\\\"/Users/lls/Documents/CODE/Nina-Simulations/double/snapshot_104\\\",\\n min_halo_number=0, max_halo_number=400,\\n ids_type='all', num_particles=None)\\n\\n trajectory_test = trajectories.Trajectories(init_parameters=p, particles=[12599035, 2642532], num_of_filters=20,\\n num_particles=1)\\n trajectory_in = trajectory_test.delta_in\\n trajectory_out = trajectory_test.delta_out\\n return trajectory_in, trajectory_out\\n\\n trajectory_test_in, trajectory_test_out = trajectories_test()\\n\\n assert np.allclose(trajectory_true_in, trajectory_test_in)\\n assert np.allclose(trajectory_true_out, trajectory_test_out)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e58452ebce45030b9745ea519ed6e550\",\n \"score\": \"0.5386184\",\n \"text\": \"def __init__(self, wl, n0, n1, n2, theta0, d, ninterface0, ninterface1):\\n self.n0 = n0 # Refractive index of first medium, either glass of air\\n self.n1 = n1 # Refractive index of intermediate medium.\\n self.n2 = n2 # Refractive index of last medium usually h2o or d2o\\n # A helping list of indeces\\n self.n = (n0, n1, n2)\\n # Incidencea angle of promary beam\\n self.theta0 = theta0 * np.pi/180 # transforms theta1 to rad\\n # Calculate angles with snellius. This doesnt work with the complex\\n # ns, atleas it doesnt give the samre sult if I take real part\\n # of self.sintheta or self.costheta\\n #self.theta1 = np.arcsin(self.n0.real/self.n1.real*np.sin(self.theta0))\\n #self.theta2 = np.arcsin(self.n1.real/self.n2.real*np.sin(self.theta1))\\n # Because n_j is complex we use costheta or sintheta during\\n # calculations. costheta and sintheta can be complex numbers.\\n self.sintheta0 = np.sin(self.theta0)\\n self.sintheta1 = self.calc_sintheta(1)\\n self.sintheta2 = self.calc_sintheta(2)\\n self.sintheta = (self.sintheta0, self.sintheta1, self.sintheta2)\\n self.costheta0 = np.cos(self.theta0)\\n self.costheta1 = self.calc_costheta(1)\\n self.costheta2 = self.calc_costheta(2)\\n # Helpful list of angles\\n #self.theta = (self.theta0, self.theta1, self.theta2)\\n self.costheta = (self.costheta0, self.costheta1, self.costheta2)\\n # Array of wavelengths\\n self.wl = wl\\n # Thickness of intermediate material\\n self.d = d\\n # Phase difference due to thickness of intermediate material\\n self.beta = 2 * np.pi/self.wl * self.n1 * self.d * self.costheta1\\n\\n # This is not fully correct because I would need a self.theta1 for VIS\\n # and one for IR but I dont see why this is the case?\\n self.delta = (2*np.pi*self.n2*d/self.wl) * \\\\\\n (1/self.costheta1-(self.sintheta1/self.costheta1))\\n\\n # Has todo with where sfg is generated\\n self.ninterface0 = ninterface0\\n self.ninterface1 = ninterface1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc798bb23c69175a349b089f135a7802\",\n \"score\": \"0.53129655\",\n \"text\": \"def generate_subtr_examples():\\n generate_generic_examples('subtr', subtr_example)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"977cc38d953458966edef71a38876c26\",\n \"score\": \"0.5290277\",\n \"text\": \"def __init__(self, traj, robot):\\n # type: (orpy.RaveTrajectory, orpy.Robot) -> None\\n super(RaveTrajectoryWrapper, self).__init__()\\n self.traj = traj #: init\\n self.spec = traj.GetConfigurationSpecification()\\n self._dof = robot.GetActiveDOF()\\n\\n self._interpolation = self.spec.GetGroupFromName('joint').interpolation\\n if self._interpolation not in ['quadratic', 'cubic']:\\n raise ValueError(\\n \\\"This class only handles trajectories with quadratic or cubic interpolation\\\"\\n )\\n self._duration = traj.GetDuration()\\n all_waypoints = traj.GetWaypoints(0, traj.GetNumWaypoints()).reshape(\\n traj.GetNumWaypoints(), -1)\\n valid_wp_indices = [0]\\n self.ss_waypoints = [0.0]\\n for i in range(1, traj.GetNumWaypoints()):\\n dt = self.spec.ExtractDeltaTime(all_waypoints[i])\\n if dt > 1e-5: # If delta is too small, skip it.\\n valid_wp_indices.append(i)\\n self.ss_waypoints.append(self.ss_waypoints[-1] + dt)\\n\\n self.n_waypoints = len(valid_wp_indices)\\n self.ss_waypoints = np.array(self.ss_waypoints)\\n self.s_start = self.ss_waypoints[0]\\n self.s_end = self.ss_waypoints[-1]\\n\\n self.waypoints = np.array([\\n self.spec.ExtractJointValues(all_waypoints[i], robot,\\n robot.GetActiveDOFIndices())\\n for i in valid_wp_indices\\n ])\\n self.waypoints_d = np.array([\\n self.spec.ExtractJointValues(all_waypoints[i], robot,\\n robot.GetActiveDOFIndices(), 1)\\n for i in valid_wp_indices\\n ])\\n\\n # Degenerate case: there is only one waypoint.\\n if self.n_waypoints == 1:\\n pp_coeffs = np.zeros((1, 1, self.dof))\\n for idof in range(self.dof):\\n pp_coeffs[0, 0, idof] = self.waypoints[0, idof]\\n # A constant function\\n self.ppoly = PPoly(pp_coeffs, [0, 1])\\n\\n elif self._interpolation == \\\"quadratic\\\":\\n self.waypoints_dd = []\\n for i in range(self.n_waypoints - 1):\\n qdd = ((self.waypoints_d[i + 1] - self.waypoints_d[i]) /\\n (self.ss_waypoints[i + 1] - self.ss_waypoints[i]))\\n self.waypoints_dd.append(qdd)\\n self.waypoints_dd = np.array(self.waypoints_dd)\\n\\n # Fill the coefficient matrix for scipy.PPoly class\\n pp_coeffs = np.zeros((3, self.n_waypoints - 1, self.dof))\\n for idof in range(self.dof):\\n for iseg in range(self.n_waypoints - 1):\\n pp_coeffs[:, iseg, idof] = [\\n self.waypoints_dd[iseg, idof] / 2,\\n self.waypoints_d[iseg, idof],\\n self.waypoints[iseg, idof]\\n ]\\n self.ppoly = PPoly(pp_coeffs, self.ss_waypoints)\\n\\n elif self._interpolation == \\\"cubic\\\":\\n self.waypoints_dd = np.array([\\n self.spec.ExtractJointValues(all_waypoints[i], robot,\\n robot.GetActiveDOFIndices(), 2)\\n for i in valid_wp_indices\\n ])\\n self.waypoints_ddd = []\\n for i in range(self.n_waypoints - 1):\\n qddd = ((self.waypoints_dd[i + 1] - self.waypoints_dd[i]) /\\n (self.ss_waypoints[i + 1] - self.ss_waypoints[i]))\\n self.waypoints_ddd.append(qddd)\\n self.waypoints_ddd = np.array(self.waypoints_ddd)\\n\\n # Fill the coefficient matrix for scipy.PPoly class\\n pp_coeffs = np.zeros((4, self.n_waypoints - 1, self.dof))\\n for idof in range(self.dof):\\n for iseg in range(self.n_waypoints - 1):\\n pp_coeffs[:, iseg, idof] = [\\n self.waypoints_ddd[iseg, idof] / 6,\\n self.waypoints_dd[iseg, idof] / 2,\\n self.waypoints_d[iseg, idof],\\n self.waypoints[iseg, idof]\\n ]\\n self.ppoly = PPoly(pp_coeffs, self.ss_waypoints)\\n\\n self.ppoly_d = self.ppoly.derivative()\\n self.ppoly_dd = self.ppoly.derivative(2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d89fb770fdfd9c339b4264c793de4f4\",\n \"score\": \"0.5281159\",\n \"text\": \"def compute_ros_trajectory(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7cd7c00e710a94945a313344c062efb\",\n \"score\": \"0.5252293\",\n \"text\": \"def test_composition(self):\\n light = RTAPhase(\\n prop_wload=PeriodicWload(\\n duty_cycle_pct=10,\\n duration=1.0,\\n period=10e-3,\\n )\\n )\\n\\n ramp = DutyCycleSweepPhase(\\n start=10,\\n stop=90,\\n step=20,\\n period=50e-3,\\n duration=1,\\n duration_of='step',\\n )\\n\\n heavy = RTAPhase(\\n prop_wload=PeriodicWload(\\n duty_cycle_pct=90,\\n duration=0.1,\\n period=100e-3,\\n )\\n )\\n\\n profile = {\\\"test\\\": light + ramp + heavy}\\n\\n exp_phases = [\\n # Light phase:\\n {\\n \\\"loop\\\": 100,\\n \\\"run\\\": 1000,\\n \\\"timer\\\": {\\n \\\"period\\\": 10000,\\n \\\"ref\\\": \\\"unique\\\"\\n }\\n },\\n # Ramp phases:\\n {\\n \\\"loop\\\": 20,\\n \\\"run\\\": 5000,\\n \\\"timer\\\": {\\n \\\"period\\\": 50000,\\n \\\"ref\\\": \\\"unique\\\"\\n }\\n },\\n {\\n \\\"loop\\\": 20,\\n \\\"run\\\": 15000,\\n \\\"timer\\\": {\\n \\\"period\\\": 50000,\\n \\\"ref\\\": \\\"unique\\\"\\n }\\n },\\n {\\n \\\"loop\\\": 20,\\n \\\"run\\\": 25000,\\n \\\"timer\\\": {\\n \\\"period\\\": 50000,\\n \\\"ref\\\": \\\"unique\\\"\\n }\\n },\\n {\\n \\\"loop\\\": 20,\\n \\\"run\\\": 35000,\\n \\\"timer\\\": {\\n \\\"period\\\": 50000,\\n \\\"ref\\\": \\\"unique\\\"\\n }\\n },\\n {\\n \\\"loop\\\": 20,\\n \\\"run\\\": 45000,\\n \\\"timer\\\": {\\n \\\"period\\\": 50000,\\n \\\"ref\\\": \\\"unique\\\"\\n }\\n },\\n # Heavy phase:\\n {\\n \\\"loop\\\": 1,\\n \\\"run\\\": 90000,\\n \\\"timer\\\": {\\n \\\"period\\\": 100000,\\n \\\"ref\\\": \\\"unique\\\"\\n }\\n }]\\n\\n self._do_test(profile, exp_phases)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e7e19ac68f4d29ed7cdaab31d4fdcd4\",\n \"score\": \"0.5246129\",\n \"text\": \"def get_trajectory(self) -> list:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"253cf464c7523299d4a210659fc3915f\",\n \"score\": \"0.5242545\",\n \"text\": \"def supercomplex(self):\\n return self.supercomplex_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"caa34b02e2411e07924cf17b8e01da4e\",\n \"score\": \"0.5225824\",\n \"text\": \"def generate_trajectories(self, duration=0.5, time_step=0.01, y0=None):\\n\\n return np.stack([\\n primitive.generate_trajectory(duration=duration, time_step=time_step, y0=y0)\\n for primitive in self.primitives\\n ])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9e63a54e404aec6c1f4d9a627303990\",\n \"score\": \"0.52136993\",\n \"text\": \"def dti_tracking_analysis():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8bf7156c305480e8c912938511e85c9\",\n \"score\": \"0.52131766\",\n \"text\": \"def Stepper(object):\\n\\n def __init__(self, flow, parameters):\\n super(Stepper, self).__init__()\\n self.flow = flow\\n\\n def r_next(self, r, w, v, order=1):\\n \\\"\\\"\\\" Get the next particle position r, given the current location, the velocity difference w and the fluid velocity v.\\n\\n We use the scheme from Daitsche (2013; http://arxiv.org/pdf/1210.2576.pdf) which is third-order accurate in the timestep\\n\\n Parameters:\\n r - the current position of the particle\\n w - the current velocity mismatch of the particle\\n v - the current velocity of the fluid flow at r\\n\\n Returns:\\n the new position for the particle\\n \\\"\\\"\\\"\\n pass\\n\\n def w_next(self, r, v, order=1):\\n \\\"\\\"\\\" Get the next particle velocity difference, w, given the current location, the velocity of the fluid flow at \\n \\\"\\\"\\\"\\n pass\\n\\n def velocity_kernel(self, x, t):\\n \\\"\\\"\\\" Return the velocity part of the the Maxey-Riley equations \\n \\n The velocity kernel is:\\n\\n \\\\[\\n G(t) = (R-1)\\\\frac{du}{dt} - Rw\\\\cdot \\\\nabla u - \\\\frac{R}{S}w\\n \\\\]\\n \\\"\\\"\\\"\\n pass\\n\\n def history_kernel(self, x, t):\\n \\\"\\\"\\\" Return the history part of the Maxey-Riley equations\\n \\n The history kernel is:\\n\\n \\\\[\\n H(t) = -R \\\\sqrt{\\\\frac{3}{\\\\pi S}} \\\\int_{t_0}^{t+\\\\delta t} \\\\frac{w(\\\\tau)}{\\\\sqrt{t-\\\\tau}}d\\\\tau\\n \\\\]\\n\\n (see Daitsche, 2013).\\n \\\"\\\"\\\"\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4daec651d3acee79b9e97901d977779e\",\n \"score\": \"0.5195512\",\n \"text\": \"def generateRealizations(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f9113969a9668b2d2f8e8ae706ae6b9\",\n \"score\": \"0.51625925\",\n \"text\": \"def experiment_main():\\n\\n\\tgenerator_specs = {\\\"original_dim\\\": original_dim, \\\"intermediate_dim\\\": 8, \\\"latent_dim\\\": latent_dim, \\\"epochs\\\": 100, \\\"dropout\\\": 0.2,\\\\\\n\\t\\t\\t\\t\\t\\t\\\"experiment\\\": \\\"German\\\", \\\"feature_names\\\": features}\\n\\n\\tprint ('---------------------')\\n\\tprint ('Training adversarial models....')\\n\\tprint ('---------------------')\\n\\n\\t# Adversarial models\\n\\tadv_models = dict()\\n\\tadv_models[\\\"Perturbation\\\"] = Adversarial_IME_Model(racist_model_f(), innocuous_model_psi()).train(xtrain, ytrain,\\\\\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tfeature_names=features, perturbation_multiplier=1)\\n\\tadv_models[\\\"DropoutVAE\\\"] = Adversarial_IME_Model(racist_model_f(), innocuous_model_psi(), generator = \\\"DropoutVAE\\\", generator_specs = generator_specs).\\\\\\n \\t\\t\\t\\ttrain(xtrain, ytrain, feature_names=features, dummy_idcs=dummy_idcs, integer_idcs=integer_attributes, perturbation_multiplier=1)\\n\\tadv_models[\\\"ForestFill\\\"] = Adversarial_IME_Model(racist_model_f(), innocuous_model_psi(), generator = \\\"Forest\\\", generator_specs = generator_specs).\\\\\\n \\t\\t\\t\\ttrain(xtrain, ytrain, feature_names=features, dummy_idcs=dummy_idcs, integer_idcs=integer_attributes, perturbation_multiplier=1)\\n\\n\\tfor adversarial in [\\\"Perturbation\\\", \\\"DropoutVAE\\\", \\\"ForestFill\\\"]:\\n\\t\\tadv_model = adv_models[adversarial]\\n\\n\\t\\tprint ('---------------------')\\n\\t\\tprint (f'Training explainers with adversarial {adversarial}....')\\n\\t\\tprint ('---------------------')\\n\\n\\t\\t# Explainers\\n\\t\\tadv_kernel_explainers = dict()\\n\\t\\tadv_kernel_explainers[\\\"Perturbation\\\"] = shap.SamplingExplainer(adv_model.predict, xtrain)\\n\\t\\tadv_kernel_explainers[\\\"DropoutVAE\\\"] = shap.SamplingExplainer(adv_model.predict, xtrain, generator=\\\"DropoutVAE\\\", generator_specs=generator_specs,\\\\\\n\\t\\t\\t\\t\\t\\t\\t\\tdummy_idcs=dummy_idcs, integer_idcs=integer_attributes, instance_multiplier = 1000)\\n\\t\\tadv_kernel_explainers[\\\"ForestFill\\\"] = shap.SamplingExplainer(adv_model.predict, xtrain, generator=\\\"Forest\\\", generator_specs=generator_specs,\\\\\\n\\t\\t\\t\\t\\t\\t\\t\\tdummy_idcs=dummy_idcs, integer_idcs=integer_attributes)\\n\\n\\t\\tfor explainer in [\\\"Perturbation\\\", \\\"DropoutVAE\\\", \\\"ForestFill\\\"]:\\n\\t\\t\\tadv_kernel_explainer = adv_kernel_explainers[explainer]\\n\\t\\t\\texplanations = adv_kernel_explainer.shap_values(xtest, fill_data=True, data_location=\\\"...\\\\Data/german_forest_ime.csv\\\", distribution_size=1000)\\n\\n\\t\\t\\t# format for display\\n\\t\\t\\tformatted_explanations = []\\n\\t\\t\\tfor exp in explanations:\\n\\t\\t\\t\\tformatted_explanations.append([(features[i], exp[i]) for i in range(len(exp))])\\n\\n\\t\\t\\tprint (f\\\"IME Ranks and Pct Occurances one unrelated feature, adversarial: {adversarial}, explainer: {explainer}:\\\")\\n\\t\\t\\tsummary = experiment_summary(formatted_explanations, features)\\n\\t\\t\\tprint (summary)\\n\\t\\t\\tprint (\\\"Fidelity:\\\",round(adv_model.fidelity(xtest),2))\\n\\n\\t\\t\\tfile_name = f\\\"../Results/GermanIme/germanImeSummary_adversarial_{adversarial}_explainer_{explainer}.csv\\\"\\n\\t\\t\\twith open(file_name, \\\"w\\\") as output:\\n\\t\\t\\t\\tw = csv.writer(output)\\n\\t\\t\\t\\tfor key, val in summary.items():\\n\\t\\t\\t\\t\\tw.writerow([key] + [pair for pair in val])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09b511b3c4be9e61639555ab960e0717\",\n \"score\": \"0.5159934\",\n \"text\": \"def test():\\n\\treturn [\\\"vice.toolkit.interpolation.interp_scheme_1d\\\",\\n\\t\\t[\\n\\t\\t\\ttest_initialize(),\\n\\t\\t\\ttest_attributes(),\\n\\t\\t\\ttest_call(),\\n\\t\\t\\ttest_getitem()\\n\\t\\t]\\n\\t]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66c454a7889a057059967dba8b7a1dd5\",\n \"score\": \"0.5155187\",\n \"text\": \"def __complex__(self):\\n return complex(self.re,self.im)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a244ff8995f32ce73bd7ef6363d75dc\",\n \"score\": \"0.5128895\",\n \"text\": \"def getTestCasesForTestPlan(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99f45c51764bf81394b64b56a6b5765d\",\n \"score\": \"0.5119969\",\n \"text\": \"def __init__(self, *args, **kwargs):\\n# verbose = True\\n verbose = False\\n\\n pigasus.__init__(self, *args, **kwargs)\\n\\n # ...\\n # non homogeneous Dirichlet Boundary conditions\\n # ...\\n try:\\n self.bc_dirichlet = self.testcase['bc_dirichlet']\\n self.Dirichlet = True\\n except:\\n self.bc_dirichlet = {}\\n self.Dirichlet = False\\n # ...\\n\\n # ...\\n # neumann Boundary conditions\\n # ...\\n try:\\n self.bc_neumann = self.testcase['bc_neumann']\\n self.Neumann = True\\n except:\\n self.bc_neumann = {}\\n self.Neumann = False\\n # ...\\n\\n # ...\\n try:\\n func_mass = self.testcase['b']\\n self.withMass = True\\n except:\\n self.withMass = False\\n # ...\\n\\n # ...\\n try:\\n func_adv = self.testcase['v']\\n self.withAdvection = True\\n except:\\n self.withAdvection = False\\n # ...\\n\\n # ...\\n try:\\n func_tadv = self.testcase['w']\\n try:\\n func_dw = self.testcase['dw']\\n except:\\n print(\\\"Warning: You should give the (div w) term. Pigasus is not yet smart enough!\\\")\\n # ...\\n if self.dim == 1:\\n def func_dw(x):\\n eps = 1.e-3\\n return [(w(x+eps)[0]-w(x)[0])/eps]\\n if self.dim == 2:\\n def func_dw(x,y):\\n eps = 1.e-3\\n return [ (w(x+eps,y)[0]-w(x,y)[0])/eps \\\\\\n + (w(x,y+eps)[1]-w(x,y)[1])/eps]\\n if self.dim == 3:\\n def func_dw(x,y,z):\\n eps = 1.e-3\\n return [ (w(x+eps,y,z)[0]-w(x,y,z)[0])/eps \\\\\\n + (w(x,y+eps,z)[1]-w(x,y,z)[1])/eps \\\\\\n + (w(x,y,z+eps)[2]-w(x,y,z)[2])/eps]\\n # ...\\n self.withTAdvection = True\\n self.withMass = True\\n except:\\n self.withTAdvection = False\\n # ...\\n\\n # ...\\n try:\\n func_stiff = self.testcase['A']\\n self.withStiffness = True\\n except:\\n self.withStiffness = False\\n # ...\\n\\n # ...\\n try:\\n func_D2 = self.testcase['D2']\\n self.withD2 = True\\n except:\\n self.withD2 = False\\n # ...\\n\\n # ...\\n self.withMetric = False\\n try:\\n Metric = self.testcase['metric']\\n if Metric is not None:\\n self.withMetric = True\\n except:\\n pass\\n # ...\\n\\n # ...\\n withAllDirichlet = False\\n try:\\n if self.testcase['AllDirichlet'] is not None:\\n withAllDirichlet = self.testcase['AllDirichlet']\\n except:\\n pass\\n # ...\\n\\n # ...\\n try:\\n solverInfo = self.testcase['solverInfo']\\n if solverInfo is not None:\\n self.solverInfo = {}\\n except:\\n self.solverInfo = None\\n # ...\\n\\n # ... set geometry\\n if self.geometry is None:\\n try:\\n V = kwargs['V']\\n self.geometry = V.geometry\\n except:\\n raise(\\\"Unable to find a geometry for the current basicPDE\\\")\\n # ...\\n\\n self.withAllDirichlet = withAllDirichlet\\n\\n # ...\\n list_DirFaces = []\\n for i in range(0, self.geometry.npatchs):\\n list_DirFaces.append([])\\n if withAllDirichlet:\\n list_extFaces = self.geometry.external_faces\\n for extFaces in list_extFaces:\\n patch_id = extFaces[0]\\n face_id = extFaces[1]\\n list_DirFaces[patch_id].append(face_id)\\n else:\\n try:\\n list_DirFaces = self.testcase['Dirichlet']\\n except:\\n pass\\n # ...\\n\\n self.list_DirFaces = list_DirFaces\\n\\n # ...\\n self.UseDuplicateFaces = False\\n list_DuplicatedFaces = []\\n list_DuplicataFaces = []\\n list_DuplicatedFacesPeriodic = []\\n try:\\n list_connectivity = self.testcase['connectivity']\\n except:\\n list_connectivity = self.geometry.connectivity\\n\\n for dict_con in list_connectivity:\\n list_DuplicatedFaces.append(dict_con['original'])\\n list_DuplicataFaces.append(dict_con['clone'])\\n try:\\n list_DuplicatedFacesPeriodic.append(dict_con['periodic'])\\n except:\\n list_DuplicatedFacesPeriodic.append(False)\\n\\n if len(list_DuplicatedFaces) > 0:\\n self.UseDuplicateFaces = True\\n # ...\\n\\n self.list_DuplicataFaces = list_DuplicataFaces\\n self.list_DuplicatedFaces = list_DuplicatedFaces\\n self.list_DuplicatedFacesPeriodic = list_DuplicatedFacesPeriodic\\n\\n # ...\\n self.meanConstraint = False\\n nExtFaces = len(self.geometry.external_faces)\\n nBCNeumann = len(self.bc_neumann)\\n nDuplicatedFaces = len(self.list_DuplicataFaces)\\n if (nExtFaces == nBCNeumann) \\\\\\n or (nExtFaces == nDuplicatedFaces) \\\\\\n or (nExtFaces == nBCNeumann+nDuplicatedFaces) \\\\\\n or (nExtFaces == 0):\\n self.meanConstraint = True\\n# print (\\\" self.meanConstraint \\\", self.meanConstraint)\\n # ...\\n\\n #-----------------------------------\\n nrb = self.geometry[0]\\n list_n = nrb.shape\\n list_p = nrb.degree\\n\\n # ...\\n try:\\n n_gauss = kwargs['n_gauss']\\n lpi_ordergl = n_gauss\\n except:\\n lpi_ordergl = list_p\\n\\n _system = matrix()\\n _slv = solver(matrix=_system, solverInfo=self.solverInfo)\\n #-----------------------------------\\n\\n #-----------------------------------\\n if self.dim == 1:\\n func_zero = lambda x : [ 0. ]\\n func_one = lambda x : [ 1. ]\\n if self.dim == 2:\\n func_zero = lambda x,y : [ 0. ]\\n func_one = lambda x,y : [ 1. ]\\n if self.dim == 3:\\n func_zero = lambda x,y,z : [ 0. ]\\n func_one = lambda x,y,z : [ 1. ]\\n #-----------------------------------\\n\\n # ...\\n try:\\n V = kwargs['V']\\n except:\\n #-----------------------------------\\n # space declaration\\n #-----------------------------------\\n list_DirFaces_V = list_DirFaces\\n if self.Dirichlet:\\n list_DirFaces_V = []\\n for i in range(0,self.geometry.npatchs):\\n list_DirFaces_V.append([])\\n list_extFaces = self.geometry.external_faces\\n for extFaces in list_extFaces:\\n patch_id = extFaces[0]\\n face_id = extFaces[1]\\n list_DirFaces_V[patch_id].append(face_id)\\n self.list_DirFaces_V = list_DirFaces_V\\n\\n if verbose:\\n print(\\\"* DirFaces_V \\\", self.list_DirFaces_V)\\n print(\\\"* DuplicatedFaces \\\", list_DuplicatedFaces)\\n print(\\\"* DuplicataFaces \\\", list_DuplicataFaces)\\n\\n# print(\\\"* DuplicatedFaces \\\", list_DuplicatedFaces)\\n# print(\\\"* DuplicataFaces \\\", list_DuplicataFaces)\\n# print(\\\"* DuplicatedFacesPeriodic \\\", list_DuplicatedFacesPeriodic)\\n\\n V = space(geometry=self.geometry)\\n V.dirichlet(faces=list_DirFaces_V)\\n if self.UseDuplicateFaces:\\n V.duplicate( faces_base = list_DuplicatedFaces \\\\\\n , faces = list_DuplicataFaces \\\\\\n , isPeriodic = list_DuplicatedFacesPeriodic)\\n V.set_boundary_conditions()\\n if self.withMetric:\\n V.create_grids(type=\\\"legendre\\\", k=lpi_ordergl, metric=Metric)\\n else:\\n V.create_grids(type=\\\"legendre\\\", k=lpi_ordergl)\\n #-----------------------------------\\n # ...\\n\\n # ...\\n try:\\n GrV = kwargs['GrV'] # [V,V] graph\\n except:\\n #-----------------------------------\\n # graph declaration\\n #-----------------------------------\\n GrV = graph(spaces=[V,V])\\n #-----------------------------------\\n # ...\\n\\n # ...\\n #-----------------------------------\\n # Matrix declaration\\n #-----------------------------------\\n Matrix_V = matrix(graph=GrV)\\n MatProj_V = matrix(graph=GrV)\\n #-----------------------------------\\n # ...\\n\\n # ...\\n if self.Dirichlet:\\n try:\\n W = kwargs['W']\\n except:\\n #-----------------------------------\\n # space declaration\\n #-----------------------------------\\n W = space(geometry=self.geometry)\\n W.dirichlet(faces=[[]] * self.geometry.npatchs)\\n if self.UseDuplicateFaces:\\n W.duplicate( faces_base = list_DuplicatedFaces \\\\\\n , faces = list_DuplicataFaces )\\n W.set_boundary_conditions()\\n# W.create_grids(type=\\\"legendre\\\", k=lpi_ordergl)\\n W.grids = V.grids\\n #-----------------------------------\\n # ...\\n\\n # ...\\n if self.Dirichlet:\\n try:\\n GrVW = kwargs['GrVW']\\n except:\\n #-----------------------------------\\n # graph declaration\\n #-----------------------------------\\n GrVW = graph(spaces=[V,W])\\n #-----------------------------------\\n # ...\\n\\n # ...\\n if self.Dirichlet:\\n #-----------------------------------\\n # Matrix declaration\\n #-----------------------------------\\n Matrix_VW = matrix(graph=GrVW)\\n #-----------------------------------\\n # ...\\n\\n # ...\\n try:\\n func_u = self.testcase['u']\\n except:\\n func_u = func_zero\\n\\n try:\\n U_V = kwargs['U_V']\\n except:\\n U_V = field(space=V, func = func_u)\\n\\n if self.Dirichlet:\\n try:\\n U_W = kwargs['U_W']\\n except:\\n U_W = field(space=W, func = func_u)\\n # ...\\n\\n # ...\\n try:\\n func_f = self.testcase['f']\\n except:\\n func_f = func_zero\\n\\n try:\\n F_V = kwargs['F_V']\\n except:\\n F_V = field(space=V, func = func_f)\\n\\n if self.Dirichlet:\\n func_w = func_one\\n try:\\n G_W = kwargs['G_W']\\n except:\\n G_W = field(space=W, func = func_w)\\n # ...\\n\\n # ... Temp field\\n T_V = field(space=V, func = func_zero)\\n # ...\\n\\n # ...\\n if self.Dirichlet:\\n try:\\n U_W = kwargs['U_W']\\n except:\\n U_W = field(space=W, func = func_u)\\n # ...\\n\\n # ...\\n try:\\n Projector_V = kwargs['Projector_V']\\n except:\\n if V.dim == 1:\\n func_one = lambda x : [ 1. ]\\n if V.dim == 2:\\n func_one = lambda x,y : [ 1. ]\\n if V.dim == 3:\\n func_one = lambda x,y,z : [ 1. ]\\n Projector_V = oper(spaces=[V, V], type=MASS , func=func_one)\\n Projector_V.addto(MatProj_V)\\n # ...\\n\\n # ...\\n try:\\n trial = kwargs['trial']\\n except:\\n trial = V\\n # ...\\n\\n # ...\\n try:\\n M_V = kwargs['M_V']\\n except:\\n if self.withMass:\\n M_V = oper(spaces=[V, trial], type=MASS , func=func_mass)\\n M_V.addto(Matrix_V)\\n # ...\\n if self.withTAdvection:\\n Mdw_V = oper(spaces=[V, trial], type=MASS , func=func_dw)\\n Mdw_V.addto(Matrix_V)\\n # ...\\n\\n # TODO what to do with the trial space for VW?\\n try:\\n M_VW = kwargs['M_VW']\\n except:\\n if self.withMass and self.Dirichlet:\\n M_VW = oper(spaces=[V, W], type=MASS , func=func_mass)\\n M_VW.addto(Matrix_VW)\\n # ...\\n\\n # ...\\n try:\\n A_V = kwargs['A_V']\\n except:\\n if self.withAdvection:\\n A_V = oper(spaces=[V, trial], type=ADVECTION , func=func_adv)\\n A_V.addto(Matrix_V)\\n\\n try:\\n A_VW = kwargs['A_VW']\\n except:\\n if self.withAdvection and self.Dirichlet:\\n A_VW = oper(spaces=[V, W], type=ADVECTION , func=func_adv)\\n A_VW.addto(Matrix_VW)\\n # ...\\n\\n # ...\\n try:\\n At_V = kwargs['At_V']\\n except:\\n if self.withTAdvection:\\n At_V = oper(spaces=[V, trial], type=ADVECTION , func=func_tadv, transpose=True)\\n At_V.addto(Matrix_V)\\n\\n try:\\n At_VW = kwargs['At_VW']\\n except:\\n if self.withTAdvection and self.Dirichlet:\\n At_VW = oper(spaces=[V, W], type=ADVECTION , func=func_tadv, transpose=True)\\n At_VW.addto(Matrix_VW)\\n # ...\\n\\n # ...\\n try:\\n S_V = kwargs['S_V']\\n except:\\n if self.withStiffness:\\n S_V = oper(spaces=[V, trial], type=STIFFNESS , func=func_stiff)\\n S_V.addto(Matrix_V)\\n\\n try:\\n S_VW = kwargs['S_VW']\\n except:\\n if self.withStiffness and self.Dirichlet:\\n S_VW = oper(spaces=[V, W], type=STIFFNESS , func=func_stiff)\\n S_VW.addto(Matrix_VW)\\n # ...\\n\\n # ...\\n try:\\n B_V = kwargs['B_V']\\n except:\\n if self.withD2:\\n B_V = oper(spaces=[V, trial], type=SECOND_DERIV, func=func_D2)\\n B_V.addto(Matrix_V)\\n\\n try:\\n B_VW = kwargs['B_VW']\\n except:\\n if self.withD2 and self.Dirichlet:\\n B_VW = oper(spaces=[V, W], type=SECOND_DERIV, func=func_D2)\\n B_VW.addto(Matrix_VW)\\n # ...\\n\\n # ...\\n self.list_G_V_BC_faces = []\\n try:\\n V_BC = kwargs['V_BC']\\n except:\\n if self.Neumann:\\n list_V_BC = []\\n list_G_V_BC = []\\n #-----------------------------------\\n # boundary space declaration\\n #-----------------------------------\\n for key, func_g in self.bc_neumann.items():\\n patch_id = int(key[0])\\n face_id = int(key[1])\\n\\n self.list_G_V_BC_faces.append([patch_id,face_id])\\n\\n nrb = self.geometry[patch_id]\\n\\n axis, side = face_to_bc(face_id)\\n\\n nrb_bnd = nrb.extract_face(axis, side)\\n geo = cad_geometry()\\n geo.append(nrb_bnd)\\n\\n lpi_ordergl = nrb_bnd.degree\\n\\n # ...\\n V_BC = space(geometry=geo)\\n V_BC.dirichlet(faces=[[]])\\n V_BC.set_boundary_conditions()\\n V_BC.create_grids(type=\\\"legendre\\\", k=lpi_ordergl)\\n # ...\\n\\n # ...\\n g = boundary_function(self.geometry\\\\\\n , patch_id\\\\\\n , face_id\\\\\\n , func_g)\\n G_V_BC = field(space=V_BC, pfunc=g)\\n # ...\\n\\n # ...\\n list_V_BC.append(V_BC)\\n list_G_V_BC.append(G_V_BC)\\n # ...\\n #-----------------------------------\\n # ...\\n\\n # ...\\n try:\\n Mean_V = kwargs['Mean_V']\\n except:\\n if self.meanConstraint:\\n if self.dim == 1:\\n func_one = lambda x : [ 1. ]\\n if self.dim == 2:\\n func_one = lambda x,y : [ 1. ]\\n if self.dim == 3:\\n func_one = lambda x,y,z : [ 1. ]\\n Mean_V = field(space=V, func = func_one)\\n # ...\\n\\n # ...\\n try:\\n N_U = kwargs['N_U']\\n except:\\n #-----------------------------------\\n # NORM\\n #-----------------------------------\\n if self.Dirichlet:\\n N_U = norm(field=U_W, type=NORM_L2, exact=func_u)\\n else:\\n N_U = norm(field=U_V, type=NORM_L2, exact=func_u)\\n #-----------------------------------\\n # ...\\n\\n #-----------------------------------\\n # Save access for data\\n #-----------------------------------\\n self._slv = _slv\\n self._system = _system\\n self.Matrix_V = Matrix_V\\n self.MatProj_V = MatProj_V\\n self.GrV = GrV\\n\\n self.spaces = []\\n self.norms = []\\n self.fields = []\\n self.graphs = [self.GrV]\\n self.matrices = [self.Matrix_V]\\n\\n self.V = V\\n self.spaces += [self.V]\\n\\n if self.withMetric:\\n self.Metric = Metric\\n\\n if self.Dirichlet:\\n self.W = W\\n self.GrVW = GrVW\\n self.spaces += [self.W]\\n self.graphs += [self.GrVW]\\n\\n self.N_U = N_U\\n self.norms += [self.N_U]\\n\\n # ... rhs\\n self.F_V = F_V\\n self.fields += [self.F_V]\\n # ... L2 projector\\n self.Projector_V = Projector_V\\n\\n if self.Dirichlet:\\n self.G_W = G_W\\n # insteed fo assembling this field, we will rise it from the\\n # boundary\\n# self.fields += [self.G_W]\\n # ...\\n\\n # ... unknown\\n self.U_V = U_V\\n self.fields += [self.U_V]\\n if self.Dirichlet:\\n self.U_W = U_W\\n# self.fields += [self.U_W]\\n # ...\\n\\n # ... Temp field\\n self.T_V = T_V\\n # ...\\n\\n # ... Mass operator\\n if self.withMass:\\n self.M_V = M_V\\n if self.withTAdvection:\\n self.Mdw_V = Mdw_V\\n if self.Dirichlet:\\n self.M_VW = M_VW\\n # ...\\n\\n # ... Advection operator\\n if self.withAdvection:\\n self.A_V = A_V\\n if self.Dirichlet:\\n self.A_VW = A_VW\\n # ...\\n\\n # ... Transpose Advection operator\\n if self.withTAdvection:\\n self.At_V = At_V\\n if self.Dirichlet:\\n self.At_VW = At_VW\\n # ...\\n\\n # ... Stiffness operator\\n if self.withStiffness:\\n self.S_V = S_V\\n if self.Dirichlet:\\n self.S_VW = S_VW\\n # ...\\n\\n # ... Second Derivative operator\\n if self.withD2:\\n self.B_V = B_V\\n if self.Dirichlet:\\n self.B_VW = B_VW\\n # ...\\n\\n # ...\\n if self.Dirichlet:\\n self.Matrix_VW = Matrix_VW\\n self.matrices += [self.Matrix_VW]\\n # ...\\n\\n # ...\\n if self.Neumann:\\n self.list_V_BC = list_V_BC\\n for S in list_V_BC:\\n self.spaces+= [S]\\n\\n self.list_G_V_BC= list_G_V_BC\\n for G in list_G_V_BC:\\n self.fields+= [G]\\n # ...\\n\\n # ... Mean Constraint in the case of Pure Neumann and Periodic bc\\n if self.meanConstraint:\\n self.Mean_V = Mean_V\\n self.fields += [self.Mean_V]\\n # ...\\n\\n self.forceAssembly = False\\n self.Assembled = False\\n\\n #-----------------------------------\",\n \"title\": \"\"\n },\n {\n \"docid\": \"616dcff6eec4e549342268ce1cfb980b\",\n \"score\": \"0.5114144\",\n \"text\": \"def train(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"616dcff6eec4e549342268ce1cfb980b\",\n \"score\": \"0.5114144\",\n \"text\": \"def train(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"286828bd63348181bfb65a5621cb59ee\",\n \"score\": \"0.50948656\",\n \"text\": \"def sample_trajectory(pi, model, env, horizon=150, rolloutSize=50, render=False):\\n if render:\\n env.setRender(True)\\n else:\\n env.setRender(False)\\n\\n ac = env.action_space.sample() # not used, just so we have the datatype\\n new = True # marks if we're on first timestep of an episode\\n ob = env.reset()\\n cF = env.getContactForce()\\n num_options = pi.num_options\\n cur_ep_ret = 0 # return in current episode\\n cur_ep_len = 0 # len of current episode\\n ep_rets = [] # returns of completed episodes in this segment\\n ep_lens = [] # lengths of ...\\n batch_size = int(horizon * rolloutSize)\\n # Initialise history of arrays\\n obs = np.array([ob for _ in range(batch_size)])\\n cFs = np.array([cF for _ in range(batch_size)])\\n rews = np.zeros(batch_size, 'float32')\\n news = np.zeros(batch_size, 'int32')\\n opts = np.zeros(batch_size, 'int32')\\n activated_options = np.zeros((batch_size, num_options), 'float32')\\n\\n acs = np.array([ac for _ in range(batch_size)])\\n model.currentMode = env.mode\\n option, active_options_t = pi.get_option(ob)\\n\\n opt_duration = [[] for _ in range(num_options)]\\n sample_index = 0\\n curr_opt_duration = 0\\n\\n success = 0\\n successFlag = False\\n while sample_index < batch_size:\\n ac = pi.act(True, ob, option)\\n if math.isnan(ac[0]) or math.isnan(ac[1]):\\n #print(\\\"Resolving NAN !! \\\")\\n continue\\n obs[sample_index] = ob\\n news[sample_index] = new\\n opts[sample_index] = option\\n acs[sample_index] = ac\\n activated_options[sample_index] = active_options_t\\n\\n # Step in the environment\\n ob, rew, new, _ = env.step(ac)\\n rews[sample_index] = rew\\n cFs[sample_index] = env.getContactForce()\\n #print(cFs[sample_index])\\n curr_opt_duration += 1\\n # check if current option is about to end in this state\\n nbeta = pi.get_tpred(ob)\\n tprob = nbeta[option]\\n\\n if render:\\n env.render()\\n\\n # Check for termination\\n if tprob >= pi.term_prob:\\n opt_duration[option].append(curr_opt_duration)\\n curr_opt_duration = 0.\\n model.currentMode = model.getNextMode(ob)\\n option, active_options_t = pi.get_option(ob)\\n\\n cur_ep_ret += rew\\n cur_ep_len += 1\\n\\n dist = env.getGoalDist()\\n if np.linalg.norm(dist) < 0.005 and not successFlag:\\n success = success + 1\\n successFlag = True\\n\\n sample_index += 1\\n\\n if new or (sample_index > 0 and sample_index % horizon == 0):\\n render = False\\n env.setRender(False)\\n opt_duration[option].append(curr_opt_duration)\\n curr_opt_duration = 0.\\n ep_rets.append(cur_ep_ret)\\n ep_lens.append(cur_ep_len)\\n cur_ep_ret = 0\\n cur_ep_len = 0\\n ob = env.reset()\\n model.currentMode = env.mode\\n option, active_options_t = pi.get_option(ob)\\n successFlag = False\\n new = True\\n\\n env.close()\\n print(\\\"Selected options\\\")\\n for o in range(0, num_options):\\n print(\\\"Option: \\\", o, \\\" - \\\", sum(opt_duration[o]))\\n\\n print(\\\"\\\\n Maximum Reward this iteration: \\\", max(ep_rets), \\\" \\\\n\\\")\\n rollouts = {\\\"ob\\\": obs, \\\"rew\\\": rews, \\\"new\\\": news, \\\"ac\\\": acs, \\\"opts\\\": opts, \\\"ep_rets\\\": ep_rets, \\\"ep_lens\\\": ep_lens, \\\"opt_dur\\\": opt_duration, \\\"activated_options\\\": activated_options, \\\"success\\\": success, \\\"contactF\\\": cFs}\\n\\n return rollouts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efc20144a6ca92706653364c876660c1\",\n \"score\": \"0.5094545\",\n \"text\": \"def _calculate_trajectories(self):\\n print \\\"calculate initial trajectories\\\"\\n trajectories = []\\n for data in self.model_data:\\n trajectories.append(pyBalloon.pyb_traj.calc_movements(data,\\n self.loc0, BALLOON))\\n pyBalloon.pyb_io.save_kml(PARAMETERS['kml_file'], trajectories)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a97d17f00cd4b3dc18f749efca4b0005\",\n \"score\": \"0.50894016\",\n \"text\": \"def sample_trajectories(num_traj, num_steps, env, controller, scaler, transformer, scaler2, transformer2, show_visual=False, first_run=False, normalize_inputs=True, normalize_outputs=True):\\n return_vec = np.array([])\\n reward_dict = defaultdict(list)\\n for traj_num in range(num_traj):\\n ob = env.reset()\\n obs, next_obs, acs, rewards, returns, reward_to_go = [], [], [], [], [], []\\n steps = 0\\n ret = 0\\n while True:\\n if(show_visual or traj_num==0):\\n env.render()\\n obs.append(ob)\\n if(first_run):\\n ac = env.action_space.sample() \\n if ac[0]<0:\\n ac[0] = 0\\n else:\\n inputs = np.expand_dims(ob.astype(np.float32),0) \\n inputs = inputs + 1e-3*np.random.standard_normal(inputs.shape[1:])\\n # normalize inputs\\n if(normalize_inputs):\\n inputs = transformer.transform(inputs)\\n inputs = scaler.transform(inputs)\\n ac = controller(inputs)[0]\\n ac = np.array(ac)\\n acs.append(ac)\\n #if ac[0]<0:\\n # ac[0] = 0\\n if(np.any(np.isnan(ac))):\\n print('nan error')\\n pdb.set_trace()\\n if(not isinstance(env.action_space, gym.spaces.Discrete)):\\n ac = np.minimum(ac,env.action_space.high)\\n ac = np.maximum(ac,env.action_space.low)\\n action = ac\\n if(normalize_outputs):\\n action = scaler2.inverse_transform(action)\\n action = transform2.inverse_transform(action)\\n ob, reward, done, info = env.step(action)\\n\\n reward = -1e1*ob[0]**2 # custom: penalize deviation from x=0\\n reward = -1e1*ob[2]**2 # custom: penalize deviation from x=0\\n reward += -1e1*ob[4]**2\\n reward += -1e1*ob[5]**2\\n #reward += -1e1*(ob[1]-0.1)**2\\n reward += 100 # reward staying in episode\\n next_obs.append(ob)\\n rewards.append(reward)\\n ret += reward\\n returns.append(ret)\\n steps += 1\\n\\n if done or steps>num_steps:\\n print(\\\"Episode {} finished after {} timesteps\\\".format(traj_num, steps))\\n break\\n\\n # backwards pass to calculate reward-to-go\\n reward_to_go = np.full(len(rewards),np.nan)\\n discount = 0.2\\n reward_to_go[-1] = rewards[-1]\\n for i in range(2, reward_to_go.shape[0]+1):\\n reward_to_go[-(i)] = rewards[-(i)] + discount*reward_to_go[-(i-1)] \\n for idx in range(len(reward_to_go)):\\n reward_dict[str(idx)].append(reward_to_go[idx])\\n\\n print('run return: {}'.format(returns[-1]))\\n if(traj_num==0):\\n trajectories = {\\\"observations\\\" : np.array(obs),\\n \\\"next_observations\\\": np.array(next_obs),\\n \\\"rewards\\\" : np.array(rewards),\\n \\\"actions\\\" : np.array(acs),\\n \\\"returns\\\" : np.array(returns),\\n \\\"reward_to_go\\\" : np.array(reward_to_go),\\n \\\"step\\\" : np.arange(steps)}\\n else:\\n traj = {\\\"observations\\\" : np.array(obs),\\n \\\"next_observations\\\": np.array(next_obs),\\n \\\"rewards\\\" : np.array(rewards),\\n \\\"actions\\\" : np.array(acs),\\n \\\"returns\\\" : np.array(returns),\\n \\\"reward_to_go\\\" : np.array(reward_to_go),\\n \\\"step\\\" : np.arange(steps)}\\n for k in traj:\\n trajectories[k] = np.append(trajectories[k],traj[k],axis=0)\\n return_vec = np.append(return_vec, returns[-1])\\n\\n for k in reward_dict:\\n reward_dict[k] = np.array(reward_dict[k])\\n return trajectories, return_vec, reward_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67c5d1d409843ce6b3eaa8aa377943ae\",\n \"score\": \"0.50839204\",\n \"text\": \"def __init__(self, N_X: int, N_Y: int, \\n t: float, u: float, mu: float=None, t_ij=None):\\n self.N_X = N_X \\n self.N_Y = N_Y \\n\\n self.t = t\\n self.t_ij = t_ij or np.ones((N_Y, N_X), dtype='complex128')\\n self.u = u \\n self.mu = mu \\n\\n self.lattice = [[ { \\n 'i': i, \\n 'j': j, \\n 'c-up': CreationOperator('up', self.N_Y*i+j, self.N_Y*self.N_X), \\n 'c-down': CreationOperator('down', self.N_Y*i+j, self.N_Y*self.N_X), \\n 'a-up': AnnihilationOperator('up', self.N_Y*i+j, self.N_Y*self.N_X), \\n 'a-down': AnnihilationOperator('down', self.N_Y*i+j, self.N_Y*\\n self.N_X) \\n } for j in range(self.N_X) ] for i in range(self.N_Y) ]\\n\\n self.Hs = [ \\n # Get horizontal, even hopping terms\\n self._sum_pair_operators(lambda i, j: 1-j%2, lambda i, j: (i, j+1)), \\n # Get horizontal, odd hopping terms \\n self._sum_pair_operators(lambda i, j: j%2, lambda i, j: (i, j+1)), \\n # Get vertical, even hopping terms \\n self._sum_pair_operators(lambda i, j: 1-i%2, lambda i, j: (i+1, j)), \\n # Get vertical, odd hopping terms \\n self._sum_pair_operators(lambda i, j: i%2, lambda i, j: (i+1, j)), \\n # Get interacting U term \\n self._sum_interacting_term()\\n ]\\n\\n self.H = sum(self.Hs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88ab7bd815e9a08a39e490f748853eec\",\n \"score\": \"0.5069024\",\n \"text\": \"def functional_test():\\n print(\\\"Expected results: \\\", grids[1]['expected'])\\n print(\\\"output: \\\", spiral(grids[1]['grid']))\\n print()\\n print(\\\"Expected results: \\\", grids[2]['expected'])\\n print(\\\"output: \\\", spiral(grids[2]['grid']))\\n print()\\n print(\\\"Expected results: \\\", grids[3]['expected'])\\n print(\\\"output: \\\", spiral(grids[3]['grid']))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0874ff421a9b282c9f05c2fd41d80939\",\n \"score\": \"0.5066498\",\n \"text\": \"def test_artefact_kernik(self):\\n\\n simple_protocol = protocols.VoltageClampProtocol([\\n protocols.VoltageClampStep(voltage=-80, duration=1000),\\n protocols.VoltageClampStep(voltage=-40, duration=1000)])\\n\\n fig, axs = plt.subplots(4, 1, sharex=True, figsize=(12, 8))\\n\\n for i, alpha in enumerate([0, .4, .7, .9]):\\n if i == 0:\\n baseline_model = kernik.KernikModel()\\n else:\\n baseline_model = kernik.KernikModel(is_exp_artefact=True, exp_artefact_params={'alpha': alpha})\\n tr = baseline_model.generate_response(simple_protocol,\\n is_no_ion_selective=False)\\n if i == 0:\\n axs[0].plot(tr.t, tr.command_voltages, 'k')\\n axs[0].plot(tr.t, tr.y)\\n axs[1].plot(tr.t, tr.current_response_info.get_current_summed())\\n axs[2].plot(tr.t, tr.current_response_info.get_current('I_Na'), label=f'alpha={alpha}')\\n if i != 0:\\n axs[3].plot(tr.t, tr.current_response_info.get_current('I_seal_leak'), label=f'alpha={alpha}')\\n\\n axs[2].legend()\\n plt.show()\\n\\n #plt.plot(baseline_model.t, baseline_model.y[0, :])\\n #plt.plot(baseline_model.t, baseline_model.y[26, :])\\n #plt.show()\\n\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ccce2a8a4026e23be6189b40a3e273e\",\n \"score\": \"0.50654083\",\n \"text\": \"def test_18(self):\\n d = {'EBstep': -1,\\n 'L1item': 'L1_J100',\\n 'chainCounter': 727,\\n 'chainName': '2hypochain',\\n 'chainParts': [{'L1item': '',\\n 'TLA': '1i2c500m700TLA',\\n 'addInfo': [],\\n 'bConfig': ['split'],\\n 'bMatching': [],\\n 'bTag': 'bmv2c2060',\\n 'bTracking': '',\\n 'calib': 'em',\\n 'chainPartName': 'j175_bmv2c2060_split',\\n 'cleaning': 'noCleaning',\\n 'dataScouting': '',\\n 'dataType': 'tc',\\n 'etaRange': '0eta320',\\n 'extra': '',\\n 'jetCalib': 'subjes',\\n 'multiplicity': '1',\\n 'recoAlg': 'a4',\\n 'scan': 'FS',\\n 'signature': 'Jet',\\n 'threshold': '0',\\n 'topo': [],\\n 'trigType': 'j'},\\n {'L1item': '',\\n 'TLA': '',\\n 'addInfo': [],\\n 'bConfig': ['split'],\\n 'bMatching': [],\\n 'bTag': 'bmv2c2050',\\n 'bTracking': '',\\n 'calib': 'em',\\n 'chainPartName': 'j50_bmv2c2050_split',\\n 'cleaning': 'noCleaning',\\n 'dataScouting': '',\\n 'dataType': 'tc',\\n 'etaRange': '0eta320',\\n 'extra': '',\\n 'jetCalib': 'subjes',\\n 'multiplicity': '1',\\n 'recoAlg': 'a4',\\n 'scan': 'FS',\\n 'signature': 'Jet',\\n 'threshold': '50',\\n 'topo': [],\\n 'trigType': 'j'}],\\n 'groups': ['RATE:MultiBJet', 'BW:Bjet'],\\n 'signature': 'Jet',\\n 'signatures': '',\\n 'stream': ['Main'],\\n 'topo': [],\\n 'topoStartFrom': False,\\n 'topoThreshold': None}\\n \\n generateHLTChainDef(d)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff148eab0bf0cb7c69f4b5eea743b2ad\",\n \"score\": \"0.5046788\",\n \"text\": \"def test_experiments_multiaxisgonio():\\n # beam along +z\\n gonio_1 = GoniometerFactory.from_dict(\\n {\\n \\\"axes\\\": [\\n [\\n 1.0 / sqrt(2.0),\\n 0.0,\\n -1.0 / sqrt(2.0),\\n ],\\n [1.0, 0.0, 0.0],\\n ],\\n \\\"angles\\\": [0.0, 0.0],\\n \\\"names\\\": [\\\"GON_PHI\\\", \\\"GON_OMEGA\\\"],\\n \\\"scan_axis\\\": 1,\\n }\\n )\\n gonio_2 = GoniometerFactory.from_dict(\\n {\\n \\\"axes\\\": [\\n [\\n 1.0 / sqrt(2.0),\\n 0.0,\\n -1.0 / sqrt(2.0),\\n ],\\n [1.0, 0.0, 0.0],\\n ],\\n \\\"angles\\\": [0.0, 0.0],\\n \\\"names\\\": [\\\"GON_PHI\\\", \\\"GON_OMEGA\\\"],\\n \\\"scan_axis\\\": 0,\\n }\\n )\\n\\n experiments = ExperimentList()\\n for g in [gonio_1, gonio_2]:\\n experiments.append(\\n Experiment(\\n beam=Beam(s0=(0.0, 0.0, 2.0)),\\n goniometer=g,\\n scan=Scan(image_range=[1, 90], oscillation=[0.0, 1.0]),\\n )\\n )\\n\\n return experiments\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10d342c92727e48097935a8cd2fdc489\",\n \"score\": \"0.50422406\",\n \"text\": \"def exercise():\\n\\n uc = uctbx.unit_cell( \\\"96 74 77 90 113 90\\\")\\n xs = crystal.symmetry(uc, \\\"C2\\\")\\n trlw = reticular_twin_laws( xs, max_index=2, max_delta=1.5 )\\n #trlw.show()\\n m = [2, 1, 0,0, 1, 0,0, 0, 1]\\n for ii, jj in zip( trlw.derived_laws[0].m, m):\\n assert(ii==jj)\\n\\n tl = [-1, 0, 0, 0, -1, 0,-1, 0, 1]\\n dl = trlw.derived_laws[0].twin_laws[0]\\n for ii, jj in zip(tl,dl):\\n assert(ii==jj)\\n\\n uc = uctbx.unit_cell( \\\"10.079 10.079 48.409 90 90 120\\\" )\\n xs = crystal.symmetry( uc, \\\"R32\\\")\\n rtl = reticular_twin_laws( xs , max_index=8, max_delta=1.5)\\n for tli in rtl.derived_laws:\\n for ttl in tli.twin_laws:\\n assert(ttl.determinant()==1) #check that these twin laws have det equal to 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca50f2ac29602900038b4ea1f4aa14d0\",\n \"score\": \"0.5036874\",\n \"text\": \"def test_inner_cv():\\n _experiment(3, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc1e604b16631f9455fa03d2099f0cef\",\n \"score\": \"0.50362664\",\n \"text\": \"def GenSinRoutineTraj(self, seq, ax, amplitudes, period, j_0):\\n\\n traj = JointTrajectory()\\n traj.header.seq = seq\\n traj.header.frame_id = 'base_link'\\n traj.joint_names = ['x','y','z','dummy','dummy1','yaw','joint1','joint2','joint3','joint4','joint5']\\n traj.header.stamp = rospy.get_rostime()\\n \\n Hz = 100 # Number of traj points per second as required by the controller\\n T = float(period) # casting to avoid possible error that could be \\n w = 2*math.pi*(1/T) # \\n small_pause = 1.0 # Pause between fowards and backwards movements on same degree of freedom\\n big_pause = 1.0 # Pause between movements ivolving different degrees of freedom\\n points_no = int((T + small_pause + big_pause)*Hz) #\\n t_s = np.linspace(0, period+small_pause+big_pause, points_no) #\\n\\n last_t = t_s[-1]\\n count = 0.0\\n\\n for axis in ax:\\n #v = [] # Used for debugging\\n #s = [] # Used for debugging\\n #a = [] # Used for debugging\\n \\n k = ax.index(axis)\\n A = float(amplitudes[k])\\n\\n if not(axis == 'x' or axis == 'y' or axis == 'z' or axis == 'yaw' or axis == 'man_yaw', axis == 'man_pitch', axis == 'man_pitch_base'):\\n continue\\n\\n for i in range(len(t_s)):\\n tmp_point = JointTrajectoryPoint()\\n\\n if (t_s[i] >= T/2.0 and t_s[i] <= (T/2.0 + small_pause)): # Setting values for the points of desired trajectory\\n s_s = 2.0*A/w \\n v_s = 0.0 # which include position, speed and acceleration\\n a_s = 0.0 # \\n # \\n elif t_s[i] >= (T + small_pause): # \\n s_s = 0.0 # \\n v_s = 0.0 # \\n a_s = 0.0 # \\n # \\n elif t_s[i] < T/2.0: #\\n v_s = A * math.sin( w*t_s[i] ) # \\n s_s =-A/w * math.cos( w*t_s[i] ) + A/w # \\n a_s = A*w * math.cos( w*t_s[i] ) # \\n #\\n elif t_s[i] > (T/2.0 + small_pause) and t_s[i] < (T + small_pause): # \\n v_s = A * math.sin( w*(t_s[i] - small_pause)) # \\n s_s =-A/w * math.cos( w*(t_s[i] - small_pause)) + A/w # \\n a_s = A*w * math.cos( w*(t_s[i] - small_pause)) #\\n \\n if axis == 'x':\\n # x y z dummy dummy1 yaw joint1 joint2 joint3 joint4 joint5 \\n tmp_point.positions = [j_0[0]+s_s, j_0[1] , j_0[2] , j_0[3], j_0[4], j_0[5] , j_0[6], j_0[7], j_0[8], j_0[9], j_0[10]]\\n tmp_point.velocities = [v_s , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ]\\n tmp_point.accelerations = [a_s , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ]\\n elif axis == 'y':\\n # x y z dummy dummy1 yaw joint1 joint2 joint3 joint4 joint5 \\n tmp_point.positions = [j_0[0] , s_s+j_0[1], j_0[2] , j_0[3], j_0[4], j_0[5] , j_0[6], j_0[7], j_0[8], j_0[9], j_0[10]]\\n tmp_point.velocities = [0.0 , v_s , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ]\\n tmp_point.accelerations = [0.0 , a_s , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ]\\n \\n elif axis == 'z':\\n # x y z dummy dummy1 yaw joint1 joint2 joint3 joint4 joint5 \\n tmp_point.positions = [j_0[0] , j_0[1] , s_s+j_0[2], j_0[3], j_0[4], j_0[5] , j_0[6], j_0[7], j_0[8], j_0[9], j_0[10]]\\n tmp_point.velocities = [0.0 , 0.0 , v_s , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ]\\n tmp_point.accelerations = [0.0 , 0.0 , a_s , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ]\\n\\n elif axis == 'yaw':\\n # x y z dummy dummy1 yaw joint1 joint2 joint3 joint4 joint5 \\n tmp_point.positions = [j_0[0] , j_0[1] , j_0[2] , j_0[3], j_0[4], s_s+j_0[5], j_0[6], j_0[7], j_0[8], j_0[9], j_0[10]]\\n tmp_point.velocities = [0.0 , 0.0 , 0.0 , 0.0 , 0.0 , v_s , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ]\\n tmp_point.accelerations = [0.0 , 0.0 , 0.0 , 0.0 , 0.0 , a_s , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ]\\n\\n elif axis == 'man_yaw':\\n # x y z dummy dummy1 yaw joint1 joint2 joint3 joint4 joint5 \\n tmp_point.positions = [j_0[0] , j_0[1] , j_0[2] , j_0[3], j_0[4], j_0[5], j_0[6], j_0[7], s_s+j_0[8], j_0[9], j_0[10]]\\n tmp_point.velocities = [0.0 , 0.0 , 0.0 , 0.0 , 0.0 , v_s , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ]\\n tmp_point.accelerations = [0.0 , 0.0 , 0.0 , 0.0 , 0.0 , a_s , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ]\\n\\n elif axis == 'man_pitch':\\n # x y z dummy dummy1 yaw joint1 joint2 joint3 joint4 joint5 \\n tmp_point.positions = [j_0[0] , j_0[1] , j_0[2] , j_0[3], j_0[4], j_0[5], j_0[6], s_s+j_0[7], j_0[8], j_0[9], j_0[10]]\\n tmp_point.velocities = [0.0 , 0.0 , 0.0 , 0.0 , 0.0 , v_s , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ]\\n tmp_point.accelerations = [0.0 , 0.0 , 0.0 , 0.0 , 0.0 , a_s , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ]\\n\\n elif axis == 'man_pitch_base':\\n # x y z dummy dummy1 yaw joint1 joint2 joint3 joint4 joint5 \\n tmp_point.positions = [j_0[0] , j_0[1] , j_0[2] , j_0[3], j_0[4], j_0[5], s_s+j_0[6], j_0[7], j_0[8], j_0[9], j_0[10]]\\n tmp_point.velocities = [0.0 , 0.0 , 0.0 , 0.0 , 0.0 , v_s , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ]\\n tmp_point.accelerations = [0.0 , 0.0 , 0.0 , 0.0 , 0.0 , a_s , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ]\\n \\n tmp_point.time_from_start.secs = int( t_s[i] + last_t * count)\\n tmp_point.time_from_start.nsecs = int((t_s[i] + last_t * count)*1000000000 - int(t_s[i] + last_t * count)*1000000000)\\n traj.points.append(tmp_point)\\n \\n #print(\\\"t_s :\\\", t_s[i]) # Used for debugging\\n #print(\\\"last_t :\\\", last_t) # Used for debugging\\n #print(\\\"count :\\\", count) # Used for debugging\\n #print(\\\"secs :\\\", tmp_point.time_from_start.secs) # Used for debugging\\n #print(\\\"nsecs :\\\", tmp_point.time_from_start.nsecs) # Used for debugging\\n\\n \\n #v.append(v_s) # Used for debugging\\n #s.append(s_s) # Used for debugging\\n #a.append(a_s) # Used for debugging\\n #print(t_s[i]) # Used for debugging \\n\\n count = count + 1.0 \\n\\n #plt.figure(int(count)) # Used for debugging\\n #plt.plot(t_s, s, t_s, v, t_s, a) # Used for debugging\\n #plt.axis('equal') # Used for debugging\\n #plt.show() # Used for debugging\\n\\n return traj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"508eea8d2dc041e3f191ff3955cdbfa0\",\n \"score\": \"0.5035858\",\n \"text\": \"def evaluate(self, edict):\\n # can we run SupervisedLearning.evaluate? Should this be an evaluateLocal?\\n ## set up the results dict with the correct dimensionality\\n ### actually, let's wait for the first sample to come in.\\n self.raiseADebug('Evaluating interpolated ROM ...')\\n results = None\\n ## TODO set up right for ND??\\n forcedMax = self._maxCycles if self._maxCycles is not None else np.inf\\n numMacro = min(len(self._macroSteps), forcedMax)\\n macroIndexValues = []\\n for m, (macroStep, model) in enumerate(sorted(self._macroSteps.items(), key=lambda x: x[0])):\\n if m + 1 > numMacro:\\n break\\n # m is an index of the macro step, in order of the macro values (e.g. in order of years)\\n # macroStep is the actual macro step value (e.g. the year)\\n # model is the ClusterROM instance for this macro step\\n macroIndexValues.append(macroStep)\\n self.raiseADebug(f' ... evaluating macro step \\\"{macroStep}\\\" ({m+1} / {numMacro})')\\n subResult = model.evaluate(edict) # TODO same input for all macro steps? True for ARMA at least...\\n indexMap = subResult.get('_indexMap', {})\\n # if not set up yet, then frame results structure\\n if results is None:\\n results = {}\\n finalIndexMap = indexMap # in case every rlz doesn't use same order, which would be lame\\n pivotID = model._templateROM.pivotParameterID\\n indices = set([pivotID, self._macroParameter])\\n for indexes in finalIndexMap.values():\\n indices.update(set(indexes))\\n #pivotVals = subResult[pivotID]\\n #numPivot = len(pivotVals)\\n for target, values in subResult.items():\\n # if an index, just set the values now # FIXME assuming always the same!\\n ## FIXME thing is, they're not always the same, we're clustering, so sometimes there's diff num days!\\n ## TODO for now, we simply require using a classifier that always has the same number of entries.\\n if target in [pivotID, '_indexMap'] or target in indices:\\n results[target] = values\\n else:\\n # TODO there's a strange behavior here where we have nested numpy arrays instead of\\n # proper matrices sometimes; maybe it has to be this way for unequal clusters\\n # As a result, we use the object dtype, onto which we can place a whole numpy array.\\n results[target] = np.zeros([numMacro] + list(values.shape), dtype=object)\\n # END setting up results structure, if needed\\n # FIXME reshape in case indexMap is not the same as finalIndexMap?\\n for target, values in subResult.items():\\n if target in [pivotID, '_indexMap'] or target in indices:# indexMap:\\n continue\\n indexer = tuple([m] + [None]*len(values.shape))\\n try:\\n results[target][indexer] = values\\n except ValueError:\\n self.raiseAnError(RuntimeError, 'The shape of the histories along the pivot parameter is not consistent! Try using a clustering classifier that always returns the same number of clusters.')\\n results['_indexMap'] = {} #finalIndexMap\\n for target, vals in results.items():\\n if target not in indices and target not in ['_indexMap']: # TODO get a list of meta vars?\\n default = [] if vals.size == 1 else [pivotID]\\n results['_indexMap'][target] = [self._macroParameter] + list(finalIndexMap.get(target, default))\\n results[self._macroParameter] = macroIndexValues\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f136b73f4404ddd880e683bd259c82f\",\n \"score\": \"0.50345784\",\n \"text\": \"def __iter__(self):\\n return iter(self.trajs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89db482fc26b10feea8c0fe58467adeb\",\n \"score\": \"0.50141114\",\n \"text\": \"def classify_complex_trajectory(traj,experiment):\\n show_complex_trajectory(complex_trajectories[i],experiment,50)\\n possible_answers = ['r','w','t','m','q','e']\\n inp = ''\\n while(not inp in possible_answers):\\n inp= raw_input(\\\"\\\"\\\"how would you classify this image: ramified, withdrawal, transitional,\\n motile, error (r/w/t/m/e)? Press q to see the sequence again\\\\n\\\"\\\"\\\")\\n \\n if inp=='q':\\n inp = classify_complex_trajectory(traj,experiment)\\n return inp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5673b36d312fc9fba76f347d0fd28b86\",\n \"score\": \"0.50132704\",\n \"text\": \"def implementation(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84f4f2177b68931bc77087ed9eab420a\",\n \"score\": \"0.5013004\",\n \"text\": \"def simulate(self) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a280af291eb7ee3a4be49f4da39da9a8\",\n \"score\": \"0.5007999\",\n \"text\": \"def compute_rave_trajectory(self, robot):\\n\\n traj = orpy.RaveCreateTrajectory(robot.GetEnv(), \\\"\\\")\\n spec = robot.GetActiveConfigurationSpecification('cubic')\\n spec.AddDerivativeGroups(1, False)\\n spec.AddDerivativeGroups(2, True)\\n\\n traj.Init(spec)\\n deltas = [0]\\n for i in range(len(self.ss_waypoints) - 1):\\n deltas.append(self.ss_waypoints[i + 1] - self.ss_waypoints[i])\\n if len(self.ss_waypoints) == 1:\\n q = self.eval(0)\\n qd = self.evald(0)\\n qdd = self.evaldd(0)\\n traj.Insert(traj.GetNumWaypoints(),\\n list(q) + list(qd) + list(qdd) + [0])\\n else:\\n qs = self.eval(self.ss_waypoints)\\n qds = self.evald(self.ss_waypoints)\\n qdds = self.evaldd(self.ss_waypoints)\\n for (q, qd, qdd, dt) in zip(qs, qds, qdds, deltas):\\n traj.Insert(traj.GetNumWaypoints(),\\n q.tolist() + qd.tolist() + qdd.tolist() + [dt])\\n return traj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e49acd5e9611c77f759d081ab6c31e20\",\n \"score\": \"0.49908498\",\n \"text\": \"def runPRINCIPLE(self):\\n\\n # index of stations with good obs\\n tymes = np.arange(self.ntyme)[self.nobs>0]\\n\\n self.I = []\\n self.Q = []\\n self.U = []\\n self.reflectance = []\\n self.surf_reflectance = []\\n self.ROT = []\\n for t in tymes: \\n tau = self.tau[t][:,:,self.iGood[t]]\\n ssa = self.ssa[t][:,:,self.iGood[t]]\\n pmom = self.pmom[t][:,:,self.iGood[t],:,:]\\n pe = self.pe[t][:,self.iGood[t]]\\n ze = self.ze[t][:,self.iGood[t]]\\n te = self.te[t][:,self.iGood[t]]\\n\\n # Initiate output arrays\\n nlev = tau.shape[0]\\n self.I_ = np.ones([self.nstations,self.npp])*MISSING\\n self.Q_ = np.ones([self.nstations,self.npp])*MISSING\\n self.U_ = np.ones([self.nstations,self.npp])*MISSING\\n self.reflectance_ = np.ones([self.nstations,self.npp])*MISSING\\n self.surf_reflectance_ = np.ones([self.nstations,self.npp])*MISSING\\n self.ROT_ = np.ones([self.nstations,nlev])*MISSING\\n \\n # Get VLIDORT wrapper name from dictionary\\n vlidortWrapper = WrapperFuncs[self.albedoType]\\n\\n\\n # do principle plane\\n for pi, pp in enumerate(self.pp_angles): \\n # Solar Geometry\\n sza = self.SZA[t][self.iGood[t]]\\n saa = self.SAA[t][self.iGood[t]]\\n\\n #viewing geometry\\n vza = sza - pp \\n\\n raa = np.zeros(saa.shape)\\n\\n # if pointing towards the sun\\n iGood = vza >= 0\\n raa[iGood] = 180.0\\n\\n #if poinitng away from the sun\\n iGood = vza < 0\\n raa[iGood] = 0.0\\n\\n # make all vza's positive\\n vza[iGood] = np.abs(vza[iGood])\\n\\n # Limit viewing angles to less than 80\\n # also limits to pointing above the horizon\\n iGood = vza < 80\\n nobs = np.sum(iGood)\\n\\n if nobs >0:\\n # run VLIDORT \\n # get args list for each surface model\\n if self.albedoType == 'MODIS_BRDF':\\n kernel_wt = self.kernel_wt[t][:,:,self.iGood[t]]\\n param = self.RTLSparam[t][:,:,self.iGood[t]] \\n \\n args = [self.channel,tau[:,:,iGood], ssa[:,:,iGood], pmom[:,:,iGood,:,:], \\n pe[:,iGood], ze[:,iGood], te[:,iGood], \\n kernel_wt, param, \\n sza[iGood], raa[iGood], vza[iGood], \\n MISSING,\\n self.verbose]\\n\\n # Call VLIDORT wrapper function\\n I, reflectance, ROT, surf_reflectance, Q, U, rc = vlidortWrapper(*args) \\n \\n elif self.albedoType == 'MODIS_BRDF_BPDF':\\n # For albedo\\n kernel_wt = self.kernel_wt[t][:,:,self.iGood[t]]\\n RTLSparam = self.RTLSparam[t][:,:,self.iGood[t]] \\n RTLSparam = np.append(RTLSparam,np.zeros([1,1,self.nobs[t]]),axis=0) \\n\\n # For BPDF\\n BPDFparam = self.BPDFparam[t][:,:,self.iGood[t]]\\n\\n # Loop through one by one\\n # Some land covers do not have polarization (i.e. urban)\\n I = np.zeros([nobs,1])\\n Q = np.zeros([nobs,1])\\n U = np.zeros([nobs,1])\\n reflectance = np.zeros([nobs,1])\\n surf_reflectance = np.zeros([nobs,1])\\n ROT = np.zeros([nlev,nobs,1])\\n\\n index = np.arange(self.nobs[t])\\n index = index[iGood]\\n\\n for pindex,p in enumerate(index):\\n if BPDFparam[2,0,p] == MISSING: \\n args = [self.channel,\\n tau[:,:,p:p+1], \\n ssa[:,:,p:p+1], \\n pmom[:,:,p:p+1,:,:], \\n pe[:,p:p+1], \\n ze[:,p:p+1], \\n te[:,p:p+1], \\n kernel_wt[:,:,p:p+1], \\n RTLSparam[:,:,p:p+1], \\n sza[p:p+1], \\n raa[p:p+1], \\n vza[p:p+1], \\n MISSING,\\n self.verbose]\\n\\n BRDFvlidortWrapper = WrapperFuncs['MODIS_BRDF']\\n # Call VLIDORT wrapper function\\n I_, reflectance_, ROT_, surf_reflectance_, Q_, U_, rc = BRDFvlidortWrapper(*args) \\n\\n else:\\n args = [self.channel,\\n tau[:,:,p:p+1], \\n ssa[:,:,p:p+1], \\n pmom[:,:,p:p+1,:,:], \\n pe[:,p:p+1], \\n ze[:,p:p+1], \\n te[:,p:p+1], \\n kernel_wt[:,:,p:p+1], \\n RTLSparam[:,:,p:p+1], \\n BPDFparam[:,:,p:p+1],\\n sza[p:p+1], \\n raa[p:p+1], \\n vza[p:p+1], \\n MISSING,\\n self.verbose]\\n\\n # Call VLIDORT wrapper function\\n I_, reflectance_, ROT_, surf_reflectance_, Q_, U_, rc = vlidortWrapper(*args)\\n \\n I[pindex:pindex+1,:] = I_\\n Q[pindex:pindex+1,:] = Q_\\n U[pindex:pindex+1,:] = U_\\n reflectance[pindex:pindex+1,:] = reflectance_\\n surf_reflectance[pindex:pindex+1,:] = surf_reflectance_\\n ROT[:,pindex:pindex+1,:] = ROT_\\n \\n elif self.albedoType == 'LAMBERTIAN':\\n albedo = self.albedo[t][self.iGood[t],:]\\n \\n args = [self.channel,tau[:,:,iGood], ssa[:,:,iGood], pmom[:,:,iGood,:,:], \\n pe[:,iGood], ze[:,iGood], te[:,iGood], \\n albedo[iGood,:], \\n sza[iGood], raa[iGood], vza[iGood], \\n MISSING,\\n self.verbose]\\n\\n # Call VLIDORT wrapper function\\n I, reflectance, ROT, Q, U, rc = vlidortWrapper(*args) \\n surf_reflectance = albedo[iGood,:]\\n\\n # Store values in initialized arrays\\n II = np.arange(self.nstations)\\n II = II[self.iGood[t]]\\n II = II[iGood]\\n if pi == 0:\\n self.ROT_[II,:] = np.squeeze(ROT).T\\n\\n self.I_[II,pi] = np.squeeze(I)\\n self.reflectance_[II,pi] = np.squeeze(reflectance)\\n self.surf_reflectance_[II,pi] = np.squeeze(surf_reflectance)\\n self.Q_[II,pi] = np.squeeze(Q)\\n self.U_[II,pi] = np.squeeze(U) \\n\\n #Store full principle plane for writing later\\n self.I.append(self.I_)\\n self.Q.append(self.Q_)\\n self.U.append(self.U_)\\n self.reflectance.append(self.reflectance_)\\n self.surf_reflectance.append(self.surf_reflectance_)\\n self.ROT.append(self.ROT_)\\n\\n self.writeNCpp()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"061ad9c6ed7dc12b344760d7b2a0bb66\",\n \"score\": \"0.49899322\",\n \"text\": \"def _create_objectives(self):\\n # Define structures for each contrast.\\n self.structures = [self._using_conditions(contrast_sld)\\n for contrast_sld in self.contrast_slds]\\n\\n # Iterate over each measured structure.\\n self.objectives = []\\n for i, structure in enumerate(self.structures):\\n # Define the model.\\n model = refnx.reflect.ReflectModel(structure,\\n scale=self.scale,\\n bkg=self.bkgs[i],\\n dq=self.dq)\\n # Load the measured data.\\n filename = '{}.dat'.format(self.labels[i])\\n file_path = os.path.join(self.data_path, filename)\\n data = refnx.dataset.ReflectDataset(file_path)\\n\\n # Combine model and data into an objective that can be fitted.\\n self.objectives.append(refnx.analysis.Objective(model, data))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77fc64bf0c6514e53061580d515d36c9\",\n \"score\": \"0.49797496\",\n \"text\": \"def run(self):\\n trajectories=self.get_option('trajectories',required=True).split(',')\\n failure=False\\n self.start_pdf()\\n reference_plotted=dict()\\n for traj in trajectories:\\n for runmode in self._filter_runmodes(section=traj):\\n for n in range(len(self._get_columns(traj,runmode))):\\n self.figureLegendRight(ylabel='value '+str(n+1), title=self.test, n=n)\\n\\n data,timeArray,data_label=self._get_data(section=traj,runmode=runmode,n=n)\\n reference,reference_label=self._get_reference(section=traj,runmode=runmode,n=n)\\n if (reference_label,n) not in reference_plotted :\\n self.plot(timeArray,reference(timeArray),label=reference_label)\\n reference_plotted[(reference_label,n)]=True\\n self.plot(timeArray,data(timeArray),label=data_label)\\n logging.debug(\\\"Evaluating {0}, value number {1}.\\\".format(data_label,n+1))\\n eps=self._get_eps(runmode, traj, n)\\n if not self._regression(reference,data,timeArray,eps):\\n logging.debug(\\\"====== FAILED ======\\\")\\n failure=True\\n self.close_pdf()\\n if failure:\\n sys.exit(-1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19e9c18eddd096275be7fffd3fd6ba82\",\n \"score\": \"0.4978773\",\n \"text\": \"def get_tdr_dynamical(self):\\n \\n nkpt = self.eigr2d.nkpt\\n nband = self.eigr2d.nband\\n natom = self.eigr2d.natom\\n ntemp = self.ntemp\\n \\n self.tdr = zeros((ntemp, nkpt, nband), dtype=complex)\\n \\n bose = self.ddb.get_bose(self.temperatures)\\n \\n fan_term = zeros((ntemp, nkpt, nband), dtype=complex)\\n ddw_term = zeros((ntemp, nkpt, nband), dtype=complex)\\n fan_add = zeros((ntemp, nkpt, nband),dtype=complex)\\n ddw_add = zeros((ntemp, nkpt, nband),dtype=complex)\\n \\n # Sternheimer contribution\\n \\n # nmode, nkpt, nband\\n fan_stern, ddw_stern = self.get_fan_ddw_sternheimer()\\n \\n fan_term = einsum('ijk,il->ljk', fan_stern, 2*bose+1.0)\\n ddw_term = einsum('ijk,il->ljk', ddw_stern, 2*bose+1.0)\\n \\n # Active space contribution\\n fan_num, ddw_num = self.get_fan_ddw_active()\\n \\n # jband\\n occ = self.get_occ_nospin()\\n \\n delta_E_ddw = (einsum('ij,k->ijk', self.eig0.EIG[0,:,:].real, ones(nband)) -\\n einsum('ij,k->ikj', self.eig0.EIG[0,:,:].real, ones(nband)) -\\n einsum('ij,k->ijk', ones((nkpt, nband)), (2*occ-1)) * self.smearing * 1j)\\n \\n # ntemp,ikpt,iband,jband\\n tmp = einsum('ijkl,lm->mijk', ddw_num, 2*bose+1.0)\\n ddw_add = einsum('ijkl,jkl->ijk', tmp, 1.0 / delta_E_ddw)\\n \\n # ikpt,iband,jband\\n delta_E = (einsum('ij,k->ijk', self.eig0.EIG[0,:,:].real, ones(nband)) -\\n einsum('ij,k->ikj', self.eigq.EIG[0,:,:].real, ones(nband)) -\\n einsum('ij,k->ijk', ones((nkpt,nband)), (2*occ-1)) * self.smearing * 1j)\\n \\n omega = self.ddb.omega[:].real # imode\\n \\n # imode,ntemp,jband\\n num1 = (einsum('ij,k->ijk', bose, ones(nband)) + 1.0 -\\n einsum('ij,k->ijk', ones((3*natom, ntemp)), occ))\\n \\n # ikpt,iband,jband,imode\\n deno1 = (einsum('ijk,l->ijkl', delta_E,ones(3*natom)) -\\n einsum('ijk,l->ijkl', ones((nkpt, nband, nband)), omega))\\n \\n # (imode,ntemp,jband)/(ikpt,iband,jband,imode) ==> imode,ntemp,jband,ikpt,iband\\n invdeno1 = np.real(deno1) / (np.real(deno1) ** 2 + np.imag(deno1) ** 2)\\n div1 = einsum('ijk,lmki->ijklm', num1, invdeno1)\\n #div1 = einsum('ijk,lmki->ijklm', num1, 1.0 / deno1)\\n \\n # imode,ntemp,jband\\n num2 = (einsum('ij,k->ijk', bose, ones(nband)) +\\n einsum('ij,k->ijk', ones((3*natom, ntemp)), occ))\\n \\n # ikpt,iband,jband,imode\\n deno2 = (einsum('ijk,l->ijkl', delta_E, ones(3*natom)) +\\n einsum('ijk,l->ijkl', ones((nkpt, nband, nband)), omega))\\n \\n # (imode,ntemp,jband)/(ikpt,iband,jband,imode) ==> imode,ntemp,jband,ikpt,iband\\n invdeno2 = np.real(deno2) / (np.real(deno2) ** 2 + np.imag(deno2) ** 2)\\n div2 = einsum('ijk,lmki->ijklm', num2, invdeno2)\\n #div2 = einsum('ijk,lmki->ijklm', num2, 1.0 / deno2)\\n \\n # ikpt,iband,jband,imode\\n fan_add = einsum('ijkl,lmkij->mij', fan_num, div1 + div2)\\n \\n\\n fan_term += fan_add\\n ddw_term += ddw_add\\n \\n self.tdr = (fan_term - ddw_term) * self.wtq\\n \\n self.tdr = self.eig0.make_average(self.tdr)\\n \\n # nkpt, nband, ntemp\\n self.tdr = np.einsum('kij->ijk', self.tdr)\\n \\n return self.tdr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2aad0814721edf48e74e73d7dd7da52\",\n \"score\": \"0.49773735\",\n \"text\": \"def analyse( self ) :\\n\\n TracksLocation = 'Rec/Track/Best'\\n \\n tracks = self.get( TracksLocation )\\n\\n print '# tarcks ', tracks.size()\\n\\n ## get the relation table\\n table = self.get( 'Relations/Rec/Track/Default' )\\n\\n print '# #tracks/links : ', tracks.size() , table.relations().size() \\n \\n t2mc = table\\n mc2t = table.inverse()\\n\\n for t in tracks :\\n\\n ## get all related MC-partiles: \\n mcps = table.relations ( t )\\n if not mcps.empty() :\\n ## print number of related Mc-particles, ID of the first one and track key \\n mcp = mcps[0]._to() \\n print ' #links ' , mcps.size() , mcp.pname() , mcp.key() , t.key () \\n\\n \\n # get all MC-particles \\n mcparticles = self.mcselect('all', MCALL )\\n\\n for mcp in mcparticles :\\n\\n trks = mc2t.relations ( mcp )\\n if not trks.empty() :\\n # print number of tracks, partile name and track key \\n trk = trks[0]._to()\\n print ' #tracks ', trks.size() , mcp.pname() , mcp.key() , trk.key(), trks[0].weight() \\n \\n \\n ## \\n self.setFilterPassed( True ) \\n return SUCCESS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc0e135fbaacf294c97e7094588d965f\",\n \"score\": \"0.49733922\",\n \"text\": \"def breakdown(self,verbose=0):\\n children = []\\n for tensorcontraction in self.list:\\n if (len(tensorcontraction.tensors) == 1):\\n label = 0\\n else:\\n label = len(tensorcontraction.tensors) - 2\\n newchild = tensorcontraction.breakdown(label,self.excitationtensortypes(),verbose)\\n children.append(newchild)\\n result = OperationTree(NoOperation(),[],children)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f10184c98d5e7e18184629f30fb040f\",\n \"score\": \"0.49541593\",\n \"text\": \"def dynamic_programming_on_trellis(self, instance, run_forward_alg=True):\\n\\t\\t#TODO:Initialize trellis and backtrace pointers \\n\\t\\ttrellis = numpy.zeros((len(instance.data),self.label_codebook.size()))\\n\\t\\tbacktrace_pointers = numpy.zeros((len(instance.data),self.label_codebook.size()))\\n\\t\\t#TODO:Traverse through the trellis here\\n\\t\\t#observation_prob_vector = self.emission_matrix[self.feature_codebook.get_index(instance.data[0][0]),:]*self.emission_matrix[self.feature_codebook.get_index(instance.data[0][1]),:]\\n\\t\\tobservation_prob_vector = self.emission_matrix[self.feature_codebook.get_index(instance.data[0][1]),:]\\n\\t\\tif run_forward_alg == True:\\n\\t\\t\\t#the first element of the alpha values\\n\\t\\t\\ttrellis[0]=self.init_pro*observation_prob_vector\\n\\t\\t\\tfor i in range(1,len(instance.data)):\\n\\t\\t\\t\\t#observation_prob_vector = self.emission_matrix[self.feature_codebook.get_index(intance.data[i][0]),:]*self.emission_matrix[self.feature_codebook.get_index(intance.data[i][1]),:]\\n\\t\\t\\t\\tobservation_prob_vector = self.emission_matrix[self.feature_codebook.get_index(intance.data[i][0]),:]\\n\\t\\t\\t\\ttrellis[i]=(trellis[i-1]*self.transition_matrix).sum(1)*observation_prob_vector\\n\\t\\t\\treturn trellis\\n\\t\\telse:\\n\\t\\t\\ttrellis[0]=self.init_pro*observation_prob_vector\\n\\t\\t\\t#backtrace_pointers[0] = ('START','START','START')\\n\\t\\t\\tfor i in range(1,len(instance.data)):\\n\\t\\t\\t\\t#observation_prob_vector = self.emission_matrix[self.feature_codebook.get_index(instance.data[i][0]),:]*self.emission_matrix[self.feature_codebook.get_index(instance.data[i][1]),:]\\n\\t\\t\\t\\tobservation_prob_vector = self.emission_matrix[self.feature_codebook.get_index(instance.data[i][1]),:]\\n\\t\\t\\t\\t#here is an important place,we have to select the max then multiply the emission probability\\n\\t\\t\\t\\t#if _DEBUG==True:\\n\\t\\t\\t\\t\\t#import pdb\\n\\t\\t\\t\\t\\t#pdb.set_trace()\\n\\t\\t\\t\\talpha_list = trellis[i-1]*self.transition_matrix\\n\\t\\t\\t\\ttrellis[i]=(alpha_list).max(1)*observation_prob_vector\\n\\t\\t\\t\\tbacktrace_pointers[i]=numpy.argmax(alpha_list,1)\\t\\t\\n\\n\\t\\t\\treturn (trellis, backtrace_pointers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19e1b898e87a48da62bd73c4a827a195\",\n \"score\": \"0.49519736\",\n \"text\": \"def process_experiment((path, object_timeline)):\\n print('Processing', path)\\n bag = open_bag(path)\\n if bag is None:\\n return None\\n object_stats = ObjectStats()\\n time_taken_processor = processors.TimeTaken(object_timeline)\\n camera_movement_processor = processors.CameraMovementTime(object_timeline)\\n marker_movement_processor = processors.MarkerMovementTime(object_timeline)\\n grasp_count_processor = processors.GraspCount(object_timeline)\\n for topic, message, time in bag.read_messages(topics=EXPERIMENT_TOPICS):\\n model = message_factory.model(message)\\n if model is None:\\n continue\\n time_taken_processor.update(topic, model, time)\\n camera_movement_processor.update(topic, model, time)\\n marker_movement_processor.update(topic, model, time)\\n grasp_count_processor.update(topic, model, time)\\n bag.close()\\n time_taken_processor.update_last()\\n object_stats.update(time_taken_processor.object_stats())\\n object_stats.update(camera_movement_processor.object_stats())\\n object_stats.update(marker_movement_processor.object_stats())\\n object_stats.update(grasp_count_processor.object_stats())\\n for obj, stats in object_stats.items():\\n stats['other_time'] = (\\n stats['time_taken'] - stats['camera_movement_time'] -\\n stats['marker_movement_time']\\n )\\n\\n timeline = build_timeline(\\n marker_movement_processor.timeline(),\\n camera_movement_processor.timeline())\\n \\n return object_stats, timeline\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55692e654c8f39497dddcf62d8b3eb54\",\n \"score\": \"0.49491498\",\n \"text\": \"def getIntelligence():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3d88562b59168f20fe776a30f24c0d6\",\n \"score\": \"0.49472186\",\n \"text\": \"def __init__(self, A, y, lamb=0.5, complex=True):\\n \\n self.A = A\\n self.y = y\\n self.lamb = lamb\\n self.m, self.n = A.shape\\n if len(y.shape) > 1:\\n self.p = y.shape[1]\\n else:\\n self.p = 1\\n \\n \\n #Run som sanity checks\\n assert(isinstance(A,np.ndarray))\\n assert(isinstance(y,np.ndarray))\\n assert(self.m == self.y.shape[0])\\n assert(self.lamb > 0)\\n if not complex:\\n assert(not np.iscomplexobj(A))\\n assert(not np.iscomplexobj(y))\\n \\n\\n if complex:\\n self.A_backward = self._Hermitian(self.A)\\n else:\\n self.A_backward = self.A.T\\n \\n #get M as A^H*A which is used more than once \\n M = self.A_backward.dot(self.A)\\n \\n self.alpha = 1/np.linalg.norm(A,ord = 2)**2 \\n\\n \\n #Compute matricies involved in gradient step in CISTA\\n self.Phi = np.eye(self.n) - self.alpha*M\\n self.phi_y = self.alpha*self.A_backward.dot(self.y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4560f63789dc6a53a43dfcd25c3e63bf\",\n \"score\": \"0.49415022\",\n \"text\": \"def test_access_to_internal_models(self):\\n est = LinearIntentToTreatDRIV(LinearRegression(), LogisticRegression(C=1000), WeightedLasso(),\\n featurizer=PolynomialFeatures(degree=2, include_bias=False))\\n Y = np.array([1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 2, 2])\\n T = np.array([1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 2, 2])\\n Z = np.array([1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2])\\n X = np.array([1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6]).reshape(-1, 1)\\n est.fit(Y, T, Z=Z, X=X)\\n assert isinstance(est.original_featurizer, PolynomialFeatures)\\n assert isinstance(est.featurizer, Pipeline)\\n assert isinstance(est.model_final, StatsModelsLinearRegression)\\n for mdl in est.models_Y_X:\\n assert isinstance(mdl, LinearRegression)\\n for mdl in est.models_T_XZ:\\n assert isinstance(mdl, LogisticRegression)\\n np.testing.assert_array_equal(est.cate_feature_names(['A']), ['A', 'A^2'])\\n np.testing.assert_array_equal(est.cate_feature_names(), ['x0', 'x0^2'])\\n\\n est = LinearIntentToTreatDRIV(LinearRegression(), LogisticRegression(C=1000), WeightedLasso(),\\n featurizer=None)\\n est.fit(Y, T, Z=Z, X=X)\\n assert est.original_featurizer is None\\n assert isinstance(est.featurizer, FunctionTransformer)\\n assert isinstance(est.model_final, StatsModelsLinearRegression)\\n for mdl in est.models_Y_X:\\n assert isinstance(mdl, LinearRegression)\\n for mdl in est.models_T_XZ:\\n assert isinstance(mdl, LogisticRegression)\\n np.testing.assert_array_equal(est.cate_feature_names(['A']), ['A'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98645f0392d6c8979e13ba7cd1a09848\",\n \"score\": \"0.49414748\",\n \"text\": \"def qft(qubits):\\n\\n def qft_function(amplitudes):\\n\\n new_amplitudes = []\\n num_points = len(amplitudes)\\n\\n # for each element in the transformed vector\\n for i in range(0, num_points):\\n\\n summation = complex(0, 0)\\n\\n # for each element in the input vector\\n for j in range(0, num_points):\\n\\n angle = (2.0 * pi * i * j)/num_points\\n\\n summation += amplitudes[j] * cmath.exp(-1j * angle)\\n\\n new_amplitudes.append(summation)\\n\\n print(new_amplitudes)\\n\\n return new_amplitudes\\n\\n qubits.manipulate(qft_function)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fabe0a185e8ec4c6e753e358242616f8\",\n \"score\": \"0.4939888\",\n \"text\": \"def __init__(self, trajectories, returns, config, iter_ids, iter_ius, k):\\n self.trajectories = trajectories\\n self.returns = returns\\n self.config = config\\n self.iter_ids = iter_ids\\n self.iter_ius = iter_ius\\n self.k = k\",\n \"title\": \"\"\n },\n {\n \"docid\": \"349eb70503aa9b4ac811f391bf06fe71\",\n \"score\": \"0.49392518\",\n \"text\": \"def build_experiments(self):\\n\\n # We set the camera\\n # This single RGB camera is used on every experiment\\n\\n camera = Camera('CameraRGB')\\n camera.set(FOV=90)\\n camera.set_image_size(800, 600)\\n camera.set_position(1.44, 0.0, 1.2)\\n camera.set_rotation(0, 0, 0)\\n\\n if self._city_name == 'Town01':\\n poses_tasks = self._poses_town01()\\n vehicles_tasks = [0, 20, 100]\\n pedestrians_tasks = [0, 50, 250]\\n else:\\n poses_tasks = self._poses_town02()\\n vehicles_tasks = [0, 15, 70]\\n pedestrians_tasks = [0, 50, 150]\\n\\n experiments_vector = []\\n\\n for weather in self.weathers:\\n\\n for iteration in range(len(poses_tasks)):\\n poses = poses_tasks[iteration]\\n vehicles = vehicles_tasks[iteration]\\n pedestrians = pedestrians_tasks[iteration]\\n\\n conditions = CarlaSettings()\\n conditions.set(\\n SendNonPlayerAgentsInfo=True,\\n NumberOfVehicles=vehicles,\\n NumberOfPedestrians=pedestrians,\\n WeatherId=weather\\n )\\n\\n conditions.set(DisableTwoWheeledVehicles=True)\\n # Add all the cameras that were set for this experiments\\n\\n conditions.add_sensor(camera)\\n\\n experiment = Experiment()\\n experiment.set(\\n Conditions=conditions,\\n Poses=poses,\\n Task=iteration,\\n Repetitions=1\\n )\\n experiments_vector.append(experiment)\\n\\n return experiments_vector\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0aa1640ea5bf7eabc230edc85f9edc0\",\n \"score\": \"0.49386477\",\n \"text\": \"def train(self):\\r\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"617b4d809a821c260ff3d6995a9531cb\",\n \"score\": \"0.49346885\",\n \"text\": \"def sample_trajectories(self, itr, env, info_name=None, render=False):\\n timesteps_this_batch = 0\\n paths = []\\n total_rew = 0\\n total_info, avg_info = 0, 0\\n while True:\\n animate_this_episode = (len(paths)==0 and (itr % 1 == 0))\\n path = self.sample_trajectory(env, animate_this_episode)\\n paths.append(path)\\n timesteps_this_batch += len(path[\\\"reward\\\"])\\n total_rew += np.sum(path[\\\"reward\\\"])\\n if info_name is not None:\\n info_dict_list = path['info']\\n for dict in info_dict_list:\\n info = dict[info_name]\\n total_info += info\\n if timesteps_this_batch > self.min_timesteps_per_batch:\\n break\\n avg_info = total_info / len(paths)\\n avg_rew = total_rew / len(paths)\\n return paths, timesteps_this_batch, avg_rew, avg_info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"355db437f33cc925d1670ac165563e56\",\n \"score\": \"0.4933888\",\n \"text\": \"def algebra_generators(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23308677ea588bda9020f4ef18c9ee6b\",\n \"score\": \"0.49320087\",\n \"text\": \"def test_traj(ic, tend=20):\\n ode_sys.set(ics=ic, tdata=[0,tend])\\n traj = ode_sys.compute('test')\\n pts = traj.sample()\\n plt.plot(pts['phi'], pts['nu'], 'k:', lw=1)\\n plt.plot(pts['phi'][0], pts['nu'][0], 'ko')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5701eee23f66440b5e41da09c8c9db38\",\n \"score\": \"0.49281594\",\n \"text\": \"def integrations(self):\\n raise NotImplementedError(\\\"TODO: Not implemented yet\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a57d4f949642b92eec617547c7bc2a98\",\n \"score\": \"0.49259016\",\n \"text\": \"def test_trajectory_logicnetwork(self):\\n net = LogicNetwork([((1, 2), {'01', '10'}),\\n ((0, 2), {'01', '10', '11'}),\\n ((0, 1), {'11'})])\\n state = [0, 1, 0]\\n got = trajectory(net, state, 3)\\n self.assertEqual([[0, 1, 0], [1, 0, 0], [0, 1, 0], [1, 0, 0]], got)\\n self.assertEqual([0, 1, 0], state)\\n\\n got = list(trajectory(net, state, 3, encode=True))\\n self.assertEqual([2, 1, 2, 1], got)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb4dc0a3bb75fcdd20c206ab8ac77386\",\n \"score\": \"0.492501\",\n \"text\": \"def bode_data_analysis(source, port, real_world, debug):\\n\\n exp_id = input('EXPRERIMENT ID : ')\\n experiment = Experiment.from_id(exp_id, real_world.data_folder)\\n n_experiments = len(experiment.data['entree_sinus']['measures'])\\n amplitudes = []\\n phases = []\\n freqs = []\\n \\n # fig, axes = pl.subplots(n_experiments)\\n\\n for i in range(n_experiments):\\n pos_measures = np.array(\\n list(map(float, experiment.data['entree_sinus']['measures'][i]['pos_measures']))\\n )[30:]\\n speed_measures = np.array(\\n list(map(float, experiment.data['entree_sinus']['measures'][i]['speed_measures']))\\n )[30:]\\n speed_orders = np.array(\\n list(map(float, experiment.data['entree_sinus']['measures'][i]['speed_orders']))\\n )[30:]\\n times = np.array(\\n list(map(float, experiment.data['entree_sinus']['measures'][i]['times']))\\n )[30:]\\n order_freq = float(experiment.data['entree_sinus']['measures'][i]['order_freq'])\\n order_amplitude = float(experiment.data['entree_sinus']['measures'][i]['order_amplitude'])\\n\\n pos_spectrum = np.fft.rfft(pos_measures)\\n speed_spectrum = np.fft.rfft(speed_measures)\\n freq_range = np.fft.rfftfreq(times.size, sample_time(times))\\n\\n amplitudes.append(amplitude(\\n times,\\n speed_measures, \\n 1/order_freq\\n ))\\n # phases.append(phase(\\n # times,\\n # speed_measures,\\n # speed_orders,\\n # 1/order_freq\\n # ))\\n freqs.append(order_freq)\\n\\n freqs = np.array(freqs)\\n amplitudes = np.array(amplitudes)/(order_amplitude/real_world.pwm_incs_ratio) # convert in pwm\\n #phases = np.array(phases)\\n\\n j = complex(0, 1)\\n\\n def func(f, K, xi, f0):\\n return np.abs(K/(1 + (2*xi)*j*(f/f0) - (f/f0)**2))\\n \\n (K, xi, f0), pcov = curve_fit(func, freqs, amplitudes, bounds=([0, 0.6, 1], [np.inf, np.inf, 10]))\\n print(K, xi, f0)\\n print(np.sqrt(np.diag(pcov)))\\n\\n #pl.subplot('211')\\n pl.title('Gain en vitesse de la corbeille en fonction de la fréquence')\\n pl.semilogx(\\n freqs,\\n 20*np.log10(amplitudes)\\n )\\n pl.semilogx(\\n np.geomspace(freqs[0], freqs[-1], 500),\\n 20*np.log10([func(freq, K, xi, f0) for freq in np.geomspace(freqs[0], freqs[-1], 500)])\\n )\\n pl.legend(['Expérience', 'Régression'])\\n \\n pl.xlabel('Fréquence (Hz)')\\n pl.ylabel('Gain (dB)')\\n pl.grid(True, color='0.7', linestyle='-', which='both', axis='both')\\n\\n precision = 3\\n text = ' K = {} \\\\n Xi = {}\\\\n F0 = {}'.format(\\n round(K, precision), round(xi, precision), round(f0, precision)\\n )\\n\\n pl.text(\\n 2*10**(-1), -20, \\n text,\\n color='white',\\n fontsize=12,\\n bbox=dict(facecolor='black', edgecolor='black', pad=10.0)\\n )\\n\\n # pl.subplot('212')\\n # pl.title('Phase en vitesse de la corbeille en fonction de la fréquence')\\n # pl.semilogx(\\n # freqs,\\n # phases*(180/pi)\\n # )\\n pl.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f131c45b99c92ffec02790385d53d316\",\n \"score\": \"0.49191204\",\n \"text\": \"def generate(s):\\n dic={\\n \\\"INPUT\\\": [\\\"V0\\\", \\\"P0\\\", \\\"Q0\\\"],\\n\\n\\n \\\"V0\\\": {\\n \\\"u\\\": \\\"V1\\\",\\n \\\"type\\\": \\\"PREDICT\\\",\\n \\\"final_fc\\\": False,\\n },\\n\\n \\\"P0\\\": {\\n \\\"u\\\": \\\"P1\\\",\\n \\\"type\\\": \\\"PREDICT\\\",\\n \\\"final_fc\\\": True,\\n \\\"bias\\\": True\\n\\n },\\n\\n \\\"Q0\\\": {\\n \\\"u\\\":\\\"P2\\\",\\n \\\"type\\\": \\\"PREDICT\\\",\\n \\\"final_fc\\\": True,\\n \\\"units\\\":6,\\n \\\"classify\\\": True\\n\\n },\\n\\n \\\"SETTINGS\\\": s.__dict__\\n }\\n\\n\\n\\n #Visual path\\n tau=s.tau_start\\n VISUAL_UNITS=s.VISUAL_UNITS\\n for i in range(1,s.N_V_LAYERS+1):\\n dic[\\\"V%d\\\"%i] =dict(\\n d=\\\"V%d\\\"%(i-1),\\n #l=\\\"P%d\\\"%i,\\n u=\\\"V%d\\\"%(i+1),\\n type=\\\"MSTRNN\\\",\\n units=VISUAL_UNITS,\\n filter=s.FILTER,\\n tau=tau\\n )\\n tau += s.tau_delta\\n VISUAL_UNITS += s.VISUAL_UNITS_DELTA\\n\\n\\n #Proprioceptive path\\n tau=s.tau_start\\n for i in range(1, s.N_P_LAYERS + 1):\\n dic[\\\"P%d\\\" % i] = dict(\\n d=\\\"P%d\\\" % (i - 1),\\n #l=\\\"Q%d\\\" % i,\\n u=\\\"P%d\\\" % (i + 1),\\n type=\\\"CTRNN\\\",\\n units=s.PROP_UNITS,\\n tau=tau\\n )\\n tau+=s.tau_delta\\n\\n #Control path\\n tau=s.tau_start\\n for i in range(1, s.N_Q_LAYERS + 1):\\n dic[\\\"Q%d\\\" % i] = dict(\\n d=\\\"Q%d\\\" % (i - 1),\\n #l=\\\"P%d\\\" % i,\\n u=\\\"Q%d\\\" % (i + 1),\\n type=\\\"CTRNN\\\",\\n units=s.Q_UNITS,\\n tau=tau\\n )\\n tau+=s.tau_delta\\n\\n if \\\"P%d\\\"%i in dic: dic[\\\"P%d\\\"%i].pop(\\\"u\\\")\\n if \\\"Q%d\\\"%i in dic: dic[\\\"Q%d\\\"%i].pop(\\\"u\\\")\\n if \\\"V%d\\\"%i in dic: dic[\\\"V%d\\\"%i].pop(\\\"u\\\")\\n\\n #Override default tau\\n if s.TAU_LIST != []:\\n for i,tau in enumerate(s.TAU_LIST):\\n dic['V%d'%i]['tau'] = tau\\n\\n #Override default filter\\n if s.FILTER_LIST and s.FILTER_FOR_LIST!=s.FILTER:\\n for l in (s.FILTER_LIST):\\n dic['V%d'%l]['filter'] = s.FILTER_FOR_LIST\\n\\n\\n #Delete input and links if a channel isnt present\\n if s.N_V_LAYERS==0:\\n dic['INPUT'] = [input for input in dic['INPUT'] if input != 'V0']\\n dic.pop('V0')\\n\\n if s.N_P_LAYERS==0:\\n dic['INPUT'] = [input for input in dic['INPUT'] if input != 'P0']\\n dic.pop('P0')\\n\\n\\n if s.N_Q_LAYERS==0:\\n dic['INPUT'] = [input for input in dic['INPUT'] if input != 'Q0']\\n dic.pop('Q0')\\n\\n\\n LATERAL = 1\\n if LATERAL:\\n for i in range(0, s.N_V_LAYERS+1):\\n dic['V%d'%i]['l'] = ['P%d'%i]\\n dic['P%d'%i]['l'] = ['V%d'%i]\\n\\n\\n\\n print(dic)\\n with open('net.json', 'w') as outfile:\\n json.dump(dic, outfile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"800816e338e316b6a33f14ac08f888b1\",\n \"score\": \"0.4918915\",\n \"text\": \"def sub_directions(self,t,j):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59f25435a4876a46376a3c02d23316cc\",\n \"score\": \"0.49185294\",\n \"text\": \"def test_get_elements_from_classification(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7079f739fbc7bcac6c1f56fac213aacb\",\n \"score\": \"0.4916937\",\n \"text\": \"def test_Part3PETCT(self,enableScreenshotsFlag=0,screenshotScaleFactor=1):\\r\\n logic = RSNAQuantTutorialLogic()\\r\\n logic.enableScreenshots = enableScreenshotsFlag\\r\\n logic.screenshotScaleFactor = screenshotScaleFactor\\r\\n\\r\\n self.delayDisplay(\\\"Starting the test\\\")\\r\\n\\r\\n #\\r\\n # first, get some data\\r\\n #\\r\\n import SampleData\\r\\n extractPath = SampleData.downloadFromURL(\\r\\n fileNames='dataset3_PETCT.zip',\\r\\n uris='http://slicer.kitware.com/midas3/download?items=124185',\\r\\n checksums='SHA256:11e81af3462076f4ca371b632e03ed435240042915c2daf07f80059b3f78f88d')[0]\\r\\n\\r\\n self.delayDisplay(\\\"Loading PET_CT_pre-treatment.mrb\\\")\\r\\n preTreatmentPath = extractPath + '/PET_CT_pre-treatment.mrb'\\r\\n slicer.util.loadScene(preTreatmentPath)\\r\\n logic.takeScreenshot('PETCT-LoadedPre','Loaded pre-treatement scene',-1)\\r\\n\\r\\n try:\\r\\n mainWindow = slicer.util.mainWindow()\\r\\n layoutManager = slicer.app.layoutManager()\\r\\n threeDView = layoutManager.threeDWidget(0).threeDView()\\r\\n redWidget = layoutManager.sliceWidget('Red')\\r\\n redController = redWidget.sliceController()\\r\\n greenWidget = layoutManager.sliceWidget('Green')\\r\\n greenController = greenWidget.sliceController()\\r\\n yellowWidget = layoutManager.sliceWidget('Yellow')\\r\\n yellowController = yellowWidget.sliceController()\\r\\n viewNode = threeDView.mrmlViewNode()\\r\\n cameras = slicer.util.getNodes('vtkMRMLCameraNode*')\\r\\n for cameraNode in cameras.values():\\r\\n if cameraNode.GetActiveTag() == viewNode.GetID():\\r\\n break\\r\\n\\r\\n threeDView.resetFocalPoint()\\r\\n slicer.util.clickAndDrag(threeDView,button='Right')\\r\\n redWidget.sliceController().setSliceVisible(True)\\r\\n yellowWidget.sliceController().setSliceVisible(True)\\r\\n logic.takeScreenshot('PETCT-ConfigureView','Configure View',-1)\\r\\n\\r\\n mainWindow.moduleSelector().selectModule('Volumes')\\r\\n compositNode = redWidget.mrmlSliceCompositeNode()\\r\\n compositNode.SetForegroundOpacity(0.2)\\r\\n logic.takeScreenshot('PETCT-ShowVolumes','Show Volumes with lesion',-1)\\r\\n\\r\\n compositNode.SetForegroundOpacity(0.5)\\r\\n logic.takeScreenshot('PETCT-CTOpacity','CT1 volume opacity to 0.5',-1)\\r\\n\\r\\n yellowWidget.sliceController().setSliceVisible(False)\\r\\n greenWidget.sliceController().setSliceVisible(True)\\r\\n logic.takeScreenshot('PETCT-ShowSlices','Show axial and sagittal slices',-1)\\r\\n\\r\\n self.delayDisplay('SUV Computation')\\r\\n if not hasattr(slicer.modules, 'petstandarduptakevaluecomputation'):\\r\\n self.delayDisplay(\\\"PET SUV Computation not available, skipping the test.\\\")\\r\\n return\\r\\n\\r\\n slicer.util.selectModule('PETStandardUptakeValueComputation')\\r\\n\\r\\n parameters = {\\r\\n \\\"PETDICOMPath\\\": extractPath + '/' + 'PET1',\\r\\n \\\"PETVolume\\\": slicer.util.getNode('PET1'),\\r\\n \\\"VOIVolume\\\": slicer.util.getNode('PET1-label'),\\r\\n }\\r\\n\\r\\n suvComputation = slicer.modules.petstandarduptakevaluecomputation\\r\\n self.CLINode1 = None\\r\\n self.CLINode1 = slicer.cli.runSync(suvComputation, self.CLINode1, parameters, delete_temporary_files=False)\\r\\n\\r\\n # close the scene\\r\\n slicer.mrmlScene.Clear(0)\\r\\n\\r\\n self.delayDisplay(\\\"Loading PET_CT_post-treatment.mrb\\\")\\r\\n postTreatmentPath = extractPath + '/PET_CT_post-treatment.mrb'\\r\\n slicer.util.loadScene(postTreatmentPath)\\r\\n logic.takeScreenshot('PETCT-LoadedPost','Loaded post-treatement scene',-1)\\r\\n\\r\\n compositNode.SetForegroundOpacity(0.5)\\r\\n logic.takeScreenshot('PETCT-CT2Opacity','CT2 volume opacity to 0.5',-1)\\r\\n\\r\\n redController.setSliceOffsetValue(-165.01)\\r\\n logic.takeScreenshot('PETCT-LarynxUptake','Mild uptake in the larynx and pharynx',-1)\\r\\n\\r\\n redController.setSliceOffsetValue(-106.15)\\r\\n logic.takeScreenshot('PETCT-TumorUptake','No uptake in the area of the primary tumor',-1)\\r\\n\\r\\n self.delayDisplay('Test passed!')\\r\\n except Exception as e:\\r\\n import traceback\\r\\n traceback.print_exc()\\r\\n self.delayDisplay('Test caused exception!\\\\n' + str(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abf87eb7bcf1cff737f68a8e40ae2b94\",\n \"score\": \"0.4903885\",\n \"text\": \"def compute_all_trajectories(self):\\n self.trajectories = []\\n for frame_nb in range(self.n_frames-1):\\n trajectory_list = self.compute_trajectories_in_frame(frame_nb)\\n self.trajectories.append(trajectory_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fda00ef9a9cff3608ad21bc174f550f0\",\n \"score\": \"0.49032158\",\n \"text\": \"def experiment1():\\n\\tagent1 = epsilon_greedy_agent(10, epsilon = 0, initial_value = 0)\\n\\tagent2 = epsilon_greedy_agent(10, epsilon = 0.01, initial_value = 0)\\n\\tagent3 = epsilon_greedy_agent(10, epsilon = 0.1, initial_value = 0)\\n\\tagent4 = epsilon_greedy_agent(10, epsilon = 0.5, initial_value = 0)\\n\\ttb = bandit_problem_test_bed(10)\\n\\ttb.test(agent1,time_steps = 1000, runs = 2000)\\n\\ttb.test(agent2,time_steps = 1000, runs = 2000)\\n\\ttb.test(agent3,time_steps = 1000, runs = 2000)\\n\\ttb.test(agent4,time_steps = 1000, runs = 2000)\\n\\ttb.show_figures()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1a0fe3ed7786f9aae852d81d57400d9\",\n \"score\": \"0.48963913\",\n \"text\": \"def main():\\n # movement_experiment()\\n # degree_experiment()\\n # test_go_inches()\\n # test_spin_degree()\\n # turn_degree_experiment()\\n # test_turn_degrees()\\n # test_polygon()\\n # test_calibrate()\\n # test_raise_and_close()\\n # test_move_arm_to_position()\\n big_test()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af9b69510f32fa63acd591f49b99f68f\",\n \"score\": \"0.48952454\",\n \"text\": \"def __init__(self, spectrum):\\n TRTHeader = spectrum.find('./TRTHeaderedClass')\\n hardware_header = TRTHeader.find(\\n \\\"./ClassInstance[@Type='TRTSpectrumHardwareHeader']\\\")\\n detector_header = TRTHeader.find(\\n \\\"./ClassInstance[@Type='TRTDetectorHeader']\\\")\\n esma_header = TRTHeader.find(\\n \\\"./ClassInstance[@Type='TRTESMAHeader']\\\")\\n # what TRT means?\\n # ESMA could stand for Electron Scanning Microscope Analysis\\n spectrum_header = spectrum.find(\\n \\\"./ClassInstance[@Type='TRTSpectrumHeader']\\\")\\n xrf_header = TRTHeader.find(\\\"./ClassInstance[@Type='TRTXrfHeader']\\\")\\n\\n # map stuff from harware xml branch:\\n self.hardware_metadata = dictionarize(hardware_header)\\n self.amplification = self.hardware_metadata['Amplification'] # USED\\n\\n # map stuff from detector xml branch\\n self.detector_metadata = dictionarize(detector_header)\\n self.detector_type = self.detector_metadata['Type'] # USED\\n\\n # decode silly hidden detector layer info:\\n det_l_str = self.detector_metadata['DetLayers']\\n dec_det_l_str = codecs.decode(det_l_str.encode('ascii'), 'base64')\\n mini_xml = ET.fromstring(unzip_block(dec_det_l_str))\\n self.detector_metadata['DetLayers'] = {} # Overwrite with dict\\n for i in list(mini_xml):\\n self.detector_metadata['DetLayers'][i.tag] = dict(i.attrib)\\n\\n # map stuff from esma xml branch:\\n if esma_header:\\n self.esma_metadata = dictionarize(esma_header)\\n if xrf_header:\\n xrf_header_dict = dictionarize(xrf_header)\\n self.esma_metadata = {\\n 'PrimaryEnergy':xrf_header_dict['Voltage'],\\n 'ElevationAngle':xrf_header_dict['ExcitationAngle']\\n }\\n # USED:\\n self.hv = self.esma_metadata['PrimaryEnergy']\\n self.elev_angle = self.esma_metadata['ElevationAngle']\\n date_time = gen_iso_date_time(spectrum_header)\\n if date_time is not None:\\n self.date, self.time = date_time\\n\\n self.spectrum_metadata = dictionarize(spectrum_header)\\n self.offset = self.spectrum_metadata['CalibAbs']\\n self.scale = self.spectrum_metadata['CalibLin']\\n\\n # main data:\\n self.data = np.fromstring(spectrum.find('./Channels').text,\\n dtype='Q', sep=\\\",\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e00d1a81081a1e05af508f39b58c36f0\",\n \"score\": \"0.48899361\",\n \"text\": \"def explore(self) -> None:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bda39ff39926510df4c8eb4468d4fed\",\n \"score\": \"0.48849887\",\n \"text\": \"def train(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bda39ff39926510df4c8eb4468d4fed\",\n \"score\": \"0.48849887\",\n \"text\": \"def train(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"063a15a2747790b6266a42900d9e3ca2\",\n \"score\": \"0.48812932\",\n \"text\": \"def extractFeatures(self):\\n self.features = [] #Not a good idea\\n self.features.append(self.subEx())\\n self.features.append(self.subLen())\\n self.features.append(self.subLang())\\n self.features.append(self.subCod())\\n self.features.append(self.subPunct())\\n\\n self.features.append(self.recCnt())\\n self.features.extend(self.recParts())\\n self.features.append(self.recGeoVec())\\n self.features.append(self.recTimeDelta())\\n self.features.extend(self.recWith())\\n self.features.extend(self.recTZs())\\n\\n self.features.append(self.dateBucket())\\n self.features.append(self.dateTZ())\\n self.features.append(self.dateRecDelta())\\n\\n self.features.append(self.fromEx())\\n self.features.append(self.fromLen())\\n self.features.append(self.fromExAlias())\\n self.features.append(self.fromDomain())\\n self.features.append(self.fromDomainFQN())\\n self.features.append(self.fromErrorsToDom())\\n self.features.append(self.fromReplyToDom())\\n #self.features.append(self.fromExDomain())\\n\\n self.features.append(self.toCnt())\\n self.features.append(self.toDomain())\\n self.features.append(self.toMsgidDom())\\n #self.features.append(self.toExDomain())\\n self.features.append(self.toFromDom())\\n self.features.append(self.msgidDomain())\\n self.features.append(self.msgidFromDom())\\n\\n self.features.append(self.attachCnt())\\n self.features.extend(self.extractAttFeatures())\\n\\n self.features.extend(self.cType())\\n self.features.extend(self.cTypeActual())\\n\\n self.features.append(self.partCnt())\\n self.features.extend(self.partFeatures())\\n self.features.append(self.partStruct())\\n\\n self.features.append(self.bodyLang())\\n self.features.append(self.bodyLength())\\n\\n self.features.extend(self.xMailerTokens())\\n\\n self.features.extend(self.multipleFeatures())\\n self.features.extend(self.genFeatures())\\n\\n self.features.append(self.replyToErrorsToDom())\\n return self.features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b93e0a669ba5c95516188b6a1ad94c2\",\n \"score\": \"0.48805553\",\n \"text\": \"def run(model1):\\r\\n\\tstep = 0\\r\\n\\tlt=0\\r\\n\\tz=0\\r\\n\\tflag=0\\r\\n\\r\\n\\ttls_data = [\\r\\n\\t\\t['yrrr', 'Grrr'],\\r\\n\\t\\t['ryrr', 'rGrr'],\\r\\n\\t\\t['rryr', 'rrGr'],\\r\\n\\t\\t['rrry', 'rrrG']\\r\\n\\t] \\r\\n\\t\\r\\n\\tprint(\\\"simulation started ..............\\\")\\r\\n\\ttraci.trafficlight.setPhase(\\\"0\\\", 6)\\r\\n\\twhile traci.simulation.getMinExpectedNumber() > 0:\\r\\n\\t\\ttraci.simulationStep()\\r\\n\\t\\t#sim.work()\\r\\n\\r\\n\\t\\t#For normal algorithm without model\\r\\n\\t\\t#For getting number of vehicle/lane and find maximum\\r\\n\\t\\tL1 = int(get_vehicle_numbers(\\\"1i_0\\\"))\\r\\n\\t\\tL2 = int(get_vehicle_numbers(\\\"2i_0\\\"))\\r\\n\\t\\tL3 = int(get_vehicle_numbers(\\\"3i_0\\\"))\\r\\n\\t\\tL4 = int(get_vehicle_numbers(\\\"4i_0\\\"))\\r\\n\\r\\n\\t\\ttotal_l = L1 + L2 + L3 + L4\\r\\n\\r\\n\\t\\tx = [L1, L2, L3 ,L4]\\r\\n\\t\\tx = np.asarray(x).astype('float32')\\r\\n\\t\\tdata_dict2= {\\r\\n\\t\\t\\t'L1':L1,\\r\\n\\t\\t\\t'L2':L2,\\r\\n\\t\\t\\t'L3':L3,\\r\\n\\t\\t\\t'L4':L4,\\r\\n\\t\\t}\\r\\n\\t\\t\\r\\n\\t\\tdata_dict = {\\r\\n\\t\\t\\t\\\"1i_0\\\":L1,\\r\\n\\t\\t\\t\\\"2i_0\\\":L2,\\r\\n\\t\\t\\t\\\"3i_0\\\":L3,\\r\\n\\t\\t\\t\\\"4i_0\\\":L4,\\r\\n\\t\\t}\\r\\n\\r\\n\\t\\tlm = max(data_dict.items(), key=operator.itemgetter(1))[0]\\r\\n\\t\\tlm_val = max(data_dict.items(), key=operator.itemgetter(1))[1]\\r\\n\\t\\t#print(lm_val)\\r\\n\\r\\n\\t\\tif lm == '1i_0':\\r\\n\\t\\t\\ti = 3\\r\\n\\t\\telif lm == '2i_0':\\r\\n\\t\\t\\ti = 1\\r\\n\\t\\telif lm == '3i_0':\\r\\n\\t\\t\\ti = 2\\r\\n\\t\\telif lm == '4i_0':\\r\\n\\t\\t\\ti = 0\\r\\n\\r\\n\\t\\tmet1 = metrics.Accuracy()\\r\\n\\t\\tmet2 = metrics.Accuracy()\\r\\n\\r\\n\\t\\tif flag == 0:\\r\\n\\t\\t\\tprint(\\\"in if\\\")\\r\\n\\t\\t\\tk_pred = model1.predict_one(data_dict2)\\r\\n\\t\\t\\tprint(k_pred)\\r\\n\\r\\n\\t\\t\\tif k_pred != None:\\r\\n\\t\\t\\t\\tyellow_select = tls_data[ypred][0]\\r\\n\\t\\t\\t\\tgreen_select = tls_data[ypred][1]\\r\\n\\t\\t\\t\\tprint(yellow_select, green_select)\\r\\n\\r\\n\\t\\t\\t\\tphaseDuration(junction=\\\"0\\\", phase_state=yellow_select, phase_time=6)\\r\\n\\t\\t\\t\\tphaseDuration(junction=\\\"0\\\", phase_state=green_select, phase_time=30)\\r\\n\\t\\t\\t\\tflag = 30\\r\\n\\t\\t\\t\\r\\n\\t\\telse:\\r\\n\\t\\t\\tflag -= 1\\r\\n\\t\\t\\t\\r\\n\\t\\t#For ARDUINO :::\\r\\n\\t\\tph = str(traci.trafficlight.getPhase(\\\"0\\\"))\\r\\n\\t\\tvalue = write_read(ph)\\r\\n\\t\\tstep += 1\\r\\n\\ttraci.close()\\r\\n\\tsys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db6cdd6d5533ddb681060d297062426d\",\n \"score\": \"0.4880063\",\n \"text\": \"def __call__(self, solution):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"443b091bef3a93fe7690ef1fe8c5cdb1\",\n \"score\": \"0.48790157\",\n \"text\": \"def _112_complex_1_3(self):\\n asyncio.gather(self.__complex(stage=[1, 2, 3]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0646ad35b0db78e8565f460dafc73a71\",\n \"score\": \"0.48549443\",\n \"text\": \"def _do_lc(self):\\n\\t\\t########## LC step #######\\n\\t\\t## Basic LC step, combined. Store all the results in the instance.\\n\\t\\t(self.axComb, self.bxComb, self.ktComb, self.kt_unfitComb,\\n\\t\\t self.UComb, self.XComb, self.VComb, self.lnmxAdjustedComb, self.lnmxComb) = lcInfer(\\n\\t\\t\\tself.nmxComb, ageCutoff=self.ageCutoff, lifeTableParams=self.lifeTableParams, flattenBx=self.flattenBx) \\n\\n\\t\\t## Basic LC step, combined. Store all the results in the instance.\\n\\t\\t(self.axFem, self.bxFem, self.ktFem, self.kt_unfitFem,\\n\\t\\t self.UFem, self.XFem, self.VFem, self.lnmxAdjustedFem, self.lnmxFem) = lcInfer(\\n\\t\\t\\tself.nmxFem, ageCutoff=self.ageCutoff, lifeTableParams=self.lifeTableParams, flattenBx=self.flattenBx) \\n\\t\\t\\n\\t\\t## Basic LC step, combined. Store all the results in the instance.\\n\\t\\t(self.axMale, self.bxMale, self.ktMale, self.kt_unfitMale,\\n\\t\\t self.UMale, self.XMale, self.VMale, self.lnmxAdjustedMale, self.lnmxMale) = lcInfer(\\n\\t\\t\\tself.nmxMale, ageCutoff=self.ageCutoff, lifeTableParams=self.lifeTableParams, flattenBx=self.flattenBx) \\n\\t\\t\\t\\n\\t\\tjumpoffAxFem = N.log(self.nmxFem[-1,0:self.ageCutoffIndex].ravel())\\n\\t\\tjumpoffAxMale = N.log(self.nmxMale[-1,0:self.ageCutoffIndex].ravel())\\n\\n\\t\\t## Derive current year stuff from LC inference results (kt etc).\\n\\t\\t## Note use of list comprehensions.\\n\\t\\tself.e0sFromEmpiricalNmxComb = N.array([LcUtil.lifeTable(nmxp=nmxRow, **self.lifeTableParams)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tfor nmxRow in self.nmxComb])\\n\\t\\tself.e0sFromEmpiricalNmxMale = N.array([LcUtil.lifeTable(nmxp=nmxRow, **self.lifeTableParams)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tfor nmxRow in self.nmxMale])\\n\\t\\tself.e0sFromEmpiricalNmxFem = N.array([LcUtil.lifeTable(nmxp=nmxRow, **self.lifeTableParams)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tfor nmxRow in self.nmxFem])\\n\\t\\tself.nmxsFromKtCurrentComb = project_nmx(kt=self.ktComb, bx=self.bxComb, ax=self.axComb,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t ageCutoff=self.ageCutoff)\\n\\t\\tself.nmxsFromKtCurrentMale = project_nmx(kt=self.ktComb, bx=self.bxComb, ax=self.axMale,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t ageCutoff=self.ageCutoff)\\n\\t\\tself.nmxsFromKtCurrentFem = project_nmx(kt=self.ktComb, bx=self.bxComb, ax=self.axFem,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t ageCutoff=self.ageCutoff)\\n\\t\\tself.nmxsFromKtCurrent_unfitComb = project_nmx(kt=self.kt_unfitComb, bx=self.bxComb, ax=self.axComb,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t ageCutoff=self.ageCutoff) \\n\\t\\tself.nmxsFromKtCurrent_unfitMale = project_nmx(kt=self.kt_unfitComb, bx=self.bxComb, ax=self.axMale,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t ageCutoff=self.ageCutoff) \\n\\t\\tself.nmxsFromKtCurrent_unfitFem = project_nmx(kt=self.kt_unfitComb, bx=self.bxComb, ax=self.axFem,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t ageCutoff=self.ageCutoff) \\n\\t\\tself.e0sFromKtCurrentComb = N.array([LcUtil.lifeTable(nmxp=nmxRow, **self.lifeTableParams)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t for nmxRow in self.nmxsFromKtCurrentComb])\\n\\t\\tself.e0sFromKtCurrentMale = N.array([LcUtil.lifeTable(nmxp=nmxRow, **self.lifeTableParams)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t for nmxRow in self.nmxsFromKtCurrentMale])\\n\\t\\tself.e0sFromKtCurrentFem = N.array([LcUtil.lifeTable(nmxp=nmxRow, **self.lifeTableParams)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t for nmxRow in self.nmxsFromKtCurrentFem])\\n\\t\\tself.e0sFromKtCurrent_unfitComb = N.array([LcUtil.lifeTable(nmxp=nmxRow, **self.lifeTableParams)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t for nmxRow in self.nmxsFromKtCurrent_unfitComb])\\n\\t\\tself.e0sFromKtCurrent_unfitMale = N.array([LcUtil.lifeTable(nmxp=nmxRow, **self.lifeTableParams)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t for nmxRow in self.nmxsFromKtCurrent_unfitMale])\\n\\t\\tself.e0sFromKtCurrent_unfitFem = N.array([LcUtil.lifeTable(nmxp=nmxRow, **self.lifeTableParams)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t for nmxRow in self.nmxsFromKtCurrent_unfitFem])\\n\\t\\tassert len(self.e0sFromKtCurrentComb) >= 1, \\\\\\n\\t\\t\\t AssertionError(\\\"self.e0sFromKtCurrentComb: %s\\\" % self.e0sFromKtCurrentComb)\\n\\t\\tassert type(self.e0sFromKtCurrentComb) == N.ndarray, \\\\\\n\\t\\t\\t AssertionError(\\\"Weird type: %s\\\" % type(self.e0sFromKtCurrentComb))\\n\\n\\t\\t######## Simulation ##############\\n\\t\\t## Get random walk parameters\\n\\t\\tself.diffedKt = S.diff(self.ktComb)\\n\\t\\tself.drift = S.average(self.diffedKt)\\n\\t\\tself.stdErrorEq = S.std(self.diffedKt)\\n\\t\\tself.stdErrorCoeff = self.stdErrorEq/S.sqrt(len(self.diffedKt))\\n\\t\\t\\n\\t\\t## Multi run simulation with above paramenters\\n\\t\\tself.kt_simul = sim_kt(SEC=self.stdErrorCoeff, SEE=self.stdErrorEq, drift=self.drift,\\n\\t\\t\\t\\t\\t\\t\\t numRuns=self.numRuns, stepsForward=self.stepsForward, sortflag=False)\\n\\t\\tself.nmx_projectedFem = project_nmx(kt=self.kt_simul, bx=self.bxComb, ax=jumpoffAxFem,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t ageCutoff=self.ageCutoff)\\n\\t\\tself.nmx_projectedMale = project_nmx(kt=self.kt_simul, bx=self.bxComb, ax=jumpoffAxMale,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t ageCutoff=self.ageCutoff)\\n\\t\\tself.nmx_projected_stochastic_median_final_F = project_nmx(kt=N.median(N.sort(self.kt_simul,0)),\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t bx=self.bxComb, ax=jumpoffAxFem, ageCutoff=self.ageCutoff)\\n\\t\\tself.nmx_projected_stochastic_median_final_M = project_nmx(kt=N.median(N.sort(self.kt_simul,0)),\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t bx=self.bxComb, ax=jumpoffAxMale, ageCutoff=self.ageCutoff)\\n\\n\\t\\tself.e0s_projectedFem = lots_e0s(self.percentileIndices, self.nmx_projectedFem, self.lifeTableParams)\\n\\t\\tself.e0s_projectedMale = lots_e0s(self.percentileIndices, self.nmx_projectedMale, self.lifeTableParams)\\n\\t\\t\\n\\t\\t## Derive analytic mean and x% forecast interval of kt\\n\\t\\tx = S.arange(0.0, float(self.stepsForward+1))\\n\\t\\tself.KtStdError = ((x*(self.stdErrorEq**2.0)) +\\n\\t\\t\\t\\t\\t\\t (x*self.stdErrorCoeff)**2.0)**.5 # kt.stderr <- ( (x*see^2) + (x*sec)^2 )^.5\\n\\t\\ttArray = N.array([self.drift] * (self.stepsForward+1))\\n\\t\\ttArray[0] = 0.0\\n\\t\\tself.meanKtProjected = N.cumsum(tArray)\\n\\t\\tself.upperKtProjected = self.meanKtProjected + (self.zscore * self.KtStdError)\\n\\t\\tself.lowerKtProjected = self.meanKtProjected - (self.zscore * self.KtStdError)\\n\\n\\t\\tself.nmx_projectedMedian_F = project_nmx(kt=self.meanKtProjected, bx=self.bxComb, ax=jumpoffAxFem,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t ageCutoff=self.ageCutoff)\\n\\t\\tself.nmx_projectedMedian_M = project_nmx(kt=self.meanKtProjected, bx=self.bxComb, ax=jumpoffAxMale,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t ageCutoff=self.ageCutoff)\\n\\t\\tself.nmx_projected_median_final_F = self.nmx_projectedMedian_F[-1,:]\\n\\t\\tself.nmx_projected_median_final_M = self.nmx_projectedMedian_M[-1,:]\\n\\n\\t\\t\\n\\t\\t## Derive the projected e0s, F\\n\\t\\tself.upperE0ProjectedFem = LcUtil.multiKt2e0(self.upperKtProjected, ax=jumpoffAxFem, bx=self.bxComb,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t lifeTableParams=self.lifeTableParams)\\n\\t\\tself.lowerE0ProjectedFem = LcUtil.multiKt2e0(self.lowerKtProjected, ax=jumpoffAxFem, bx=self.bxComb,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t lifeTableParams=self.lifeTableParams)\\n\\t\\tself.meanE0ProjectedFem = LcUtil.multiKt2e0(self.meanKtProjected, ax=jumpoffAxFem, bx=self.bxComb,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t lifeTableParams=self.lifeTableParams)\\n\\t\\t## Derive the projected e0s, M\\n\\t\\tself.upperE0ProjectedMale = LcUtil.multiKt2e0(self.upperKtProjected, ax=jumpoffAxMale, bx=self.bxComb,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t lifeTableParams=self.lifeTableParams)\\n\\t\\tself.lowerE0ProjectedMale = LcUtil.multiKt2e0(self.lowerKtProjected, ax=jumpoffAxMale, bx=self.bxComb,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t lifeTableParams=self.lifeTableParams)\\n\\t\\tself.meanE0ProjectedMale = LcUtil.multiKt2e0(self.meanKtProjected, ax=jumpoffAxMale, bx=self.bxComb,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t lifeTableParams=self.lifeTableParams)\\n\\t\\t\\n\\t\\treturn\\t\\t\\t\\t\\t\\t\\t# Don't return anything useful\",\n \"title\": \"\"\n },\n {\n \"docid\": \"433e817aad6d5f6ae055547a3bdf57cc\",\n \"score\": \"0.48529863\",\n \"text\": \"def get_discrete_loads(self):\\n # input values derived\\n x1, x2, x3, xa, xa1, xa2, theta, d1, d3, E, G, P, la, step = self.get_discrete_input()\\n Q_l, Mqz_l, ddq_x2, ddq_xa2, ddq_x1, ddq_x3 = self.get_distributed_loads_aero()\\n dsch, dsca_y, dsca_z, Izz, Iyy, J, z = self.get_geometry()\\n\\n # function\\n cte_v = -1 / (E * Izz) # cte in v deflection formula\\n cte_w = -1 / (E * Iyy) # cte in w deflection formula\\n cte_T = 1 / (G * J) # cte in torsion equation\\n\\n # order variables matrix\\n # F_z1 , F_z2, F_z3, F_a, F_y1, F_y2, F_y3, c1, c2, c3, c4, c5\\n left_column = np.array([\\n [la - x1, la - x2, la - x3, np.cos(theta) * (la - xa1), 0, 0, 0, 0, 0, 0, 0, 0],\\n # Moment around y equation at la\\n [0, 0, 0, np.sin(theta) * (la - xa1), la - x1, la - x2, la - x3, 0, 0, 0, 0, 0],\\n # Moment around z equation at la\\n [-dsch, dsch, -dsch, dsca_y * np.cos(theta) + dsca_z * np.sin(theta), 0, 0, 0, 0, 0, 0, 0, 0],\\n # Torque aka Moment around x at la\\n [1, 1, 1, np.cos(theta), 0, 0, 0, 0, 0, 0, 0, 0], # force/shear z\\n [0, 0, 0, np.sin(theta), 1, 1, 1, 0, 0, 0, 0, 0], # force/shear y\\n [0, 0, 0, cte_v / 6 * np.sin(theta) * (x2 - xa1) ** 3, cte_v / 6 * (x2 - x1) ** 3, 0, 0, x2, 1, 0, 0, 0],\\n # v deflection at x2\\n [0, 0, 0, cte_v / 6 * np.sin(theta) * (xa2 - xa1) ** 3, cte_v / 6 * (xa2 - x1) ** 3,\\n cte_v / 6 * (xa2 - x2) ** 3, 0, xa2, 1, 0, 0, 0], # v deflection at xa2\\n [cte_w / 6 * (x2 - x1) ** 3, 0, 0, cte_w / 6 * np.cos(theta) * (x2 - xa1) ** 3, 0, 0, 0, 0, 0, x2, 1, 0],\\n # w deflection at x2\\n [0, 0, 0, 0, 0, 0, 0, x1, 1, 0, 0, (z - x1)], # v + theta(z-x) deflection at x1\\n [-cte_T * dsch * (x3 - x1) * (z - x3), cte_T * dsch * (x3 - x2) * (z - x3), 0,\\n cte_v / 6 * np.sin(theta) * (x3 - xa1) ** 3 + cte_T * (dsca_y * np.cos(theta) + dsca_z * np.sin(theta)) * (\\n z - x3),\\n cte_v / 6 * (x3 - x1) ** 3, cte_v / 6 * (x3 - x2) ** 3, 0, x3, 1, 0, 0, (z - x3)],\\n # v + theta(z-x) deflection at x3\\n [0, 0, 0, 0, 0, 0, 0, 0, 0, x1, 1, (z - x1)], # w + theta(z-x) deflection at x1\\n [cte_w / 6 * (x3 - x1) ** 3 - cte_T * dsch * (x3 - x1) * (z - x3),\\n cte_w / 6 * (x3 - x2) ** 3 + cte_T * dsch * (x3 - x2) * (z - x3), 0,\\n cte_v / 6 * np.cos(theta) * (x3 - xa1) ** 3 + cte_T * (dsca_y * np.cos(theta) + dsca_z * np.sin(theta)) * (\\n z - x3),\\n 0, 0, 0, 0, 0, x3, 1, (z - x3)] # w + theta(z-x) deflection at x3\\n ])\\n\\n right_column = np.array([\\n [P * np.cos(theta) * (la - xa2)], # Moment around y equation at la\\n [P * np.sin(theta) * (la - xa2) + Mqz_l], # Moment around z equation at la\\n [P * (dsca_y * np.cos(theta) + dsca_z * np.sin(theta))], # Torque aka Moment around x at la\\n [P * np.cos(theta)], # force/shear z\\n [P * np.sin(theta) + Q_l], # force/shear y\\n [cte_v * ddq_x2], # v deflection at x2 qq at x2\\n [cte_v * ddq_xa2], # v deflection at xa2 qq at x2\\n [0], # w deflection at x2\\n [d1 * np.sin(theta) + cte_v * ddq_x1], # v deflection at x1 qq at x1\\n [d3 * np.sin(theta) + cte_v * (1 / 6 * P * np.sin(theta) * (x3 - xa2) ** 3 + ddq_x3)\\n + (cte_T * (P * (dsca_y * np.cos(theta) + dsca_z * np.sin(theta)) * (x3 - x1))) * (z - x3)],\\n # v deflection at x3 qqt qq at x3\\n [d1 * np.cos(theta)], # w deflection at x1\\n [d3 * np.cos(theta) + cte_w / 6 * P * np.cos(theta) * (x3 - xa2) ** 3\\n + (cte_T * (P * (dsca_y * np.cos(theta) + dsca_z * np.sin(theta)) * (x3 - x1))) * (z - x3)]\\n # w deflection at x3\\n ])\\n\\n F_z1, F_z2, F_z3, F_a, F_y1, F_y2, F_y3, c1, c2, c3, c4, c5 = np.linalg.solve(left_column, right_column)\\n\\n return F_z1, F_z2, F_z3, F_a, F_y1, F_y2, F_y3, c1, c2, c3, c4, c5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23c07d3e9f8ba2e638eeee3801fe36bc\",\n \"score\": \"0.48499912\",\n \"text\": \"def test_analysis_get(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3af1717b4ff9b5929126f12402d4027\",\n \"score\": \"0.4847316\",\n \"text\": \"def extraction(self):\\r\\n #TODO - \\r\\n listtags = []\\r\\n resultvalues = []\\r\\n modname = self.extfile\\r\\n methodlist = []\\r\\n #Load each extractor code\\r\\n #Case 1: Extractor = External library with methods and standard parameters - inputfile\\r\\n #Import external library\\r\\n mod = __import__(modname)\\r\\n dirlist = dir(mod)\\r\\n for met in dirlist:\\r\\n if \\\"__\\\" not in met and met not in ['re','os']:\\r\\n methodlist.append(met)\\r\\n for method in methodlist:\\r\\n result = \\\"\\\"\\r\\n #Load each desired method from external module\\r\\n loading = getattr(mod, method)\\r\\n #Get the result from method invocation using infile as standard parameter\\r\\n result = loading(self, self.infile)\\r\\n #Test if result is a dictionary\\r\\n if type(result) == dict:\\r\\n #Adds each subtuple to the tags and results list\\r\\n tags = result.keys()\\r\\n tags.sort()\\r\\n #Add the new tags\\r\\n listtags.extend(tags)\\r\\n #Add the new values\\r\\n for tag in tags:\\r\\n if (result[tag]) == str:\\r\\n resultvalues.append(result[tag])\\r\\n else:\\r\\n resultvalues.append(str(result[tag]))\\r\\n else:\\r\\n #Converts unknown format to string\\r\\n result = str(result)\\r\\n \\r\\n #Adds method name to listtags and result to resultvalues\\r\\n listtags.append(method)\\r\\n resultvalues.append(result)\\r\\n #Case 2: Extractor = Legacy code to be executed by shell\\r\\n #TODO\\r\\n #Case 3: Extractor = python class\\r\\n #TODO \\r\\n #Standard extraction output\\r\\n \\r\\n# begin-vitor\\r\\n if self.extfile == \\\"PA_Readseq\\\":\\r\\n \\tlisttags.append(\\\"PHYLIP\\\")\\r\\n \\tfilename = self.infile.split(\\\"/\\\")[len(self.infile.split(\\\"/\\\"))-1]\\r\\n \\tresultvalues.append(filename + \\\".phylip\\\")\\r\\n elif self.extfile == \\\"PA_Mafft\\\":\\r\\n \\tlisttags.append(\\\"MAFFT_FILE\\\")\\r\\n \\tfilename = self.infile.split(\\\"/\\\")[len(self.infile.split(\\\"/\\\"))-1]\\r\\n \\tresultvalues.append(filename + \\\".mafft\\\")\\r\\n elif self.extfile == \\\"PA_Modelgenerator\\\":\\r\\n \\tlisttags.append(\\\"MG\\\")\\r\\n \\tfilename = self.infile.split(\\\"/\\\")[len(self.infile.split(\\\"/\\\"))-1]\\r\\n \\tresultvalues.append(filename + \\\".mg.modelFromMG.txt\\\")\\r\\n elif self.extfile == \\\"PA_Raxml\\\":\\r\\n \\tlisttags.append(\\\"RAXML\\\")\\r\\n \\tfilename = self.infile.split(\\\"/\\\")[len(self.infile.split(\\\"/\\\"))-1]\\r\\n \\tresultvalues.append(\\\"RAxML_bipartitions.\\\" + filename + \\\".phylip_tree3\\\")\\r\\n# end-vitor\\r\\n \\t\\r\\n \\t\\r\\n outlist = self.buildOutputFile(tags=listtags, provdata = resultvalues)\\r\\n output = open(self.outfile, \\\"w\\\")\\r\\n for line in outlist:\\r\\n output.write(line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"924973242f8b1dabba767053790ea5f9\",\n \"score\": \"0.48471954\",\n \"text\": \"def get_trajectories(env, policy_model, n_episode, episode_length):\\n paths = []\\n epoch_return = 0\\n episode_count = 0\\n for i_episode in range(n_episode):\\n episode_return = 0\\n episode_count += 1\\n obs_list = []\\n log_prob_list = []\\n action_list = []\\n reward_list = []\\n mask_list = []\\n observation = env.reset()\\n\\n for t in range(episode_length):\\n # env.render()\\n obs_list.append(observation)\\n obs_tensor = torch.Tensor(observation).view(1,-1)\\n\\n mu, log_sigma = policy_model(obs_tensor)\\n\\n normal_dist = Normal(mu,log_sigma.exp())\\n\\n action = normal_dist.sample()\\n action_list.append(action.reshape(-1))\\n\\n ## the log prob here is the log prob of taking the set of actions, so we take sum of log\\n ## you can also take product of exp of log to get same results\\n log_prob = normal_dist.log_prob(action)\\n log_prob = torch.sum(log_prob)\\n log_prob_list.append(log_prob)\\n\\n ## make sure action is within env's specifics\\n action = torch.clamp(action, -1, 1).reshape(-1)\\n observation, reward, done, info = env.step(action.data.numpy())\\n\\n if done:\\n mask_list.append(0) ## used in calculating advantage\\n else:\\n mask_list.append(1)\\n\\n episode_return += reward\\n reward_list.append(reward)\\n if done:\\n break\\n epoch_return += episode_return\\n ## now we have finished one episode, we now assign reward (all the data points in\\n ## the same trajectory have the same reward)\\n path = {'obs':obs_list,'mask':mask_list, 'log_probs':log_prob_list, 'rewards':reward_list,'actions':action_list,'episode_return':episode_return}\\n paths.append(path)\\n return paths, epoch_return/n_episode\\n #REturns the paths and\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d5e3f93067c12da57d20071e09ebdc0\",\n \"score\": \"0.48467577\",\n \"text\": \"def trajectory(self):\\n return Trajectory.fit(self.xyz)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7429f4cde8403c409ab98f94ca80a05c\",\n \"score\": \"0.48438308\",\n \"text\": \"def __call__(self, observation: Mapping[str, np.ndarray],\\n **kwargs) -> np.ndarray:\\n\\n # Prepares observation for the neural-network.\\n observation[\\\"overhead_features\\\"] = observation[\\n \\\"bird_view_camera_cityscapes\\\"]\\n for attr in observation:\\n if not isinstance(observation[attr], np.ndarray):\\n observation[attr] = np.atleast_1d(observation[attr])\\n observation[attr] = observation[attr][None, ...].astype(np.float32)\\n\\n # Makes `goal` 2D.\\n observation[\\\"goal\\\"] = observation[\\\"goal\\\"][..., :2]\\n # Convert image to CHW.\\n observation[\\\"lidar\\\"] = np.transpose(observation[\\\"lidar\\\"], (0, 3, 1, 2))\\n # Processes observations for the `ImitativeModel`.\\n observation = {\\n key: torch.from_numpy(tensor).to(self._device) # pylint: disable=no-member\\n for (key, tensor) in observation.items()\\n }\\n observation = self._model.transform(observation)\\n\\n # Queries model.\\n plan = self._model(num_steps=kwargs.get(\\\"num_steps\\\", 20),\\n epsilon=kwargs.get(\\\"epsilon\\\", 1.0),\\n lr=kwargs.get(\\\"lr\\\", 5e-2),\\n **observation).detach().cpu().numpy()[0] # [T, 2]\\n\\n # TODO(filangel): clean API.\\n # Interpolates plan.\\n player_future_length = 40\\n increments = player_future_length // plan.shape[0]\\n time_index = list(range(0, player_future_length, increments)) # [T]\\n plan_interp = scipy.interpolate.interp1d(x=time_index, y=plan, axis=0)\\n xy = plan_interp(np.arange(0, time_index[-1]))\\n\\n # Appends z dimension.\\n z = np.zeros(shape=(xy.shape[0], 1))\\n return np.c_[xy, z]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7aa47e88009d625d48219a36b3cd0153\",\n \"score\": \"0.48420858\",\n \"text\": \"def AddToTrajectory(self,sample):\\n #Replacing the action with the hierarchical action.\\n nestedMethodSample = [sample[0]] + [sample[5]] + sample[2:5] +sample[6:]\\n self.nestedMethod.AddToTrajectory(nestedMethodSample)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48ecf4a4f59196f16eded5c607b3a066\",\n \"score\": \"0.48416775\",\n \"text\": \"def test_interface():\\n import pKaTool.pKa_calc\\n X=pKaTool.pKa_calc.Monte_Carlo_Mult_CPP()\\n\\n X.intrinsic_pKa={':0001:ASP':[0.0,4.0,5.0]}\\n X.charged_state={':0001:ASP':[0,1,1]}\\n X.acid_base={':0001:ASP':-1}\\n X.intene_mult={':0001:ASP':{':0001:ASP':[[0,0,0],[0,0,0],[0,0,0]]}}\\n X._calc_pKas(0.0,10.0,0.5)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca29e47b3ba298f8a68f04964b6826ab\",\n \"score\": \"0.4839466\",\n \"text\": \"def construct_traj_set(self, planning_problem_list):\\n trajectories = []\\n cnt = 1\\n total_path_len = 0\\n # Add code here to create a traj set #\\n for planning_problem in planning_problem_list: \\n print(cnt)\\n cnt+=1 \\n traj,traj_dist = planning_problem.get_traj(trajectories)\\n total_path_len += traj_dist\\n if(len(traj)==0):\\n print(\\\"TRAJS NOT FOUND, BREAK\\\")\\n return [], 0, False\\n trajectories.append(traj)\\n \\n\\n return trajectories, total_path_len, True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75c716746377eb39778f331cc9fd2da2\",\n \"score\": \"0.4837887\",\n \"text\": \"def perform_exploration():\\n model = un.NeuronModel(file=\\\"tabak.py\\\", name=\\\"tabak\\\")\\n\\n original_g_BKs = np.linspace(0, 1, nr_points_exploration)\\n g_BKs = scale_conductance(original_g_BKs)\\n\\n # g_SK\\n original_g_SKs = np.linspace(1, 3, nr_points_exploration)\\n g_SKs = scale_conductance(original_g_SKs)\\n\\n event_durations_SK = np.zeros((len(original_g_BKs), len(original_g_SKs)))\\n for i, g_BK in enumerate(tqdm(g_BKs, desc=\\\"Varying g_BK\\\")):\\n for j, g_SK in enumerate(tqdm(g_SKs, desc=\\\"Varying g_SK\\\")):\\n time, voltage, info = model.run(g_BK=g_BK,\\n g_SK=g_SK,\\n discard=discard,\\n noise_amplitude=noise_amplitude,\\n simulation_time=simulation_time)\\n\\n tmp_duration = np.mean(duration(time, voltage))\\n\\n if np.isnan(tmp_duration):\\n tmp_duration = -1\\n\\n event_durations_SK[i, j] = tmp_duration\\n\\n\\n original_g_Ks = np.linspace(1.5, 4.5, nr_points_exploration)\\n g_Ks = scale_conductance(original_g_Ks)\\n\\n event_durations_K = np.zeros((len(original_g_BKs), len(original_g_Ks)))\\n for i, g_BK in enumerate(tqdm(g_BKs, desc=\\\"Varying g_BK\\\")):\\n for j, g_K in enumerate(tqdm(g_Ks, desc=\\\"Varying g_K\\\")):\\n time, voltage, info = model.run(g_BK=g_BK,\\n g_K=g_K,\\n discard=discard,\\n noise_amplitude=noise_amplitude,\\n simulation_time=simulation_time)\\n\\n\\n tmp_duration = np.mean(duration(time, voltage))\\n\\n if np.isnan(tmp_duration):\\n tmp_duration = -1\\n\\n event_durations_K[i, j] = tmp_duration\\n\\n np.save(os.path.join(data_folder, \\\"original_g_BKs\\\"), original_g_BKs)\\n np.save(os.path.join(data_folder, \\\"original_g_SKs\\\"), original_g_SKs)\\n np.save(os.path.join(data_folder, \\\"original_g_Ks\\\"), original_g_Ks)\\n np.save(os.path.join(data_folder, \\\"event_durations_SK\\\"), event_durations_SK)\\n np.save(os.path.join(data_folder, \\\"event_durations_K\\\"), event_durations_K)\\n\\n return original_g_BKs, original_g_SKs, original_g_Ks, event_durations_SK, event_durations_K\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f7b2398fd8c47565c574bd8990d20f7\",\n \"score\": \"0.48374778\",\n \"text\": \"def test2():\\n print('test2 ............................................................',\\n \\\"\\\"\\\"\\n To understand and to confirm the correctnes of the SIMULINAC plots,\\n this test checks the influence of the twiss-alpha parameter using a \\n thin lens FODO.\\n \\\"\\\"\\\")\\n # initial conditions for twiss parameters\\n beta = 1. # [m]\\n alfa = -0.5\\n epsi = 1.e-6 # emittance [m*rad]\\n # other initials\\n NC=56 # NC cells\\n T = 25. # kin. energy [MeV]\\n T=T*1.e-3 # [GeV] kin.energy \\n betakin=M.sqrt(1.-(1+T/E0)**-2) # beta kinetic\\n E=E0+T # [GeV] energy\\n k=0.2998*Bg/(betakin*E) # [1/m^2]\\n L=0.596 # distance between quads [m]\\n Ql=0.04 # full quad length [m]\\n f=1./(k*Ql) # ff = -fd = f\\n mfodo1 = Mq(-2*f,Ql/2.) # 1/2 qd\\n mfodo2 = mmult(mfodo1,Md(L/2.))\\n mfodo3 = mmult(mfodo2,Mq(2*f,Ql/2.)) # 1/2 qf\\n m11 = mfodo3[0][0]\\n m12 = mfodo3[0][1]\\n m21 = mfodo3[1][0]\\n m22 = mfodo3[1][1]\\n mfodor = np.array([[m22,m12],[m21,m11]]) # reverse\\n \\n mfodo = mmult(mfodo3,mfodor)\\n trace = abs(mfodo.trace())\\n \\n mfodo2 = Mfodo(2*f,L/2.) # Wiedemann\\n my_debug('matrix probe: mfodo-mfodo2 must be zero matrix')\\n zero = mfodo-mfodo2\\n my_debug(f'{abs(zero[0][0]):.5f} {abs(zero[0][1]):.5f}')\\n my_debug(f'{abs(zero[1][0]):.5f} {abs(zero[1][1]):.5f}')\\n \\n # now in slices\\n slices = 20\\n ld = L/slices\\n lq = Ql/slices\\n f = 1./(k*lq)\\n md = Md(ld/2) # element\\n mqf = Mq(+2*f,lq/2) # element\\n mqd = Mq(-2*f,lq/2) # element\\n twd = twmatrix(md) # twiss-matrix\\n twqf = twmatrix(mqf) # twiss-matrix\\n twqd = twmatrix(mqd) # twiss-matrix\\n lattice = [] # element list\\n twlattice = [] # twiss matrix list\\n pos = [0.] # position list\\n s = 0. # postion (abzsisse)\\n mfodo5 = np.array([[1.,0.],[0.,1.]])# unit matrix\\n for m in range(NC):\\n mfodo6 = mfodo5.dot(mfodo2) # chain Wiedemann cells\\n mfodo5 = mfodo6\\n for n in range(1,slices+1): # chain slices\\n s += lq\\n pos.append(s)\\n lattice.append(mqd)\\n twlattice.append(twqd)\\n for n in range(1,slices+1):\\n s += ld\\n pos.append(s)\\n lattice.append(md)\\n twlattice.append(twd)\\n for n in range(1,slices+1):\\n s += lq\\n pos.append(s)\\n lattice.append(mqf)\\n twlattice.append(twqf)\\n for n in range(1,slices+1):\\n s += lq\\n pos.append(s)\\n lattice.append(mqf)\\n twlattice.append(twqf)\\n for n in range(1,slices+1):\\n s += ld\\n pos.append(s)\\n lattice.append(md)\\n twlattice.append(twd)\\n for n in range(1,slices+1):\\n s += lq\\n pos.append(s)\\n lattice.append(mqd)\\n twlattice.append(twqd)\\n # full lattice matrix\\n mfodo3 = np.array([[1.,0.],[0.,1.]])\\n for node in lattice:\\n mfodo4 = mfodo3.dot(node)\\n mfodo3 = mfodo4\\n my_debug('lattice probe: mfodo3-mfodo5 must be zero matrix')\\n zero = mfodo3-mfodo5\\n my_debug(f'{abs(zero[0][0]):.5f} {abs(zero[0][1]):.5f}')\\n my_debug(f'{abs(zero[1][0]):.5f} {abs(zero[1][1]):.5f}')\\n \\n twx = Twiss(beta,alfa,epsi)\\n # C track\\n track_point_C = np.array(twx.y1())\\n # S track\\n track_point_S = np.array(twx.y4())\\n\\n points_c = [track_point_C]\\n points_s = [track_point_S]\\n for node in lattice:\\n C_point = node.dot(track_point_C)\\n points_c.append(C_point)\\n track_point_C = C_point\\n S_point = node.dot(track_point_S)\\n points_s.append(S_point)\\n track_point_S = S_point\\n\\n # enveloppe \\n beta0, alfa0, gamma0 , epsi0 = twx()\\n twiss_point = np.array([beta0,alfa0,gamma0]) # twiss track\\n twpoints = [twiss_point]\\n for bmx in twlattice:\\n twissp = bmx.dot(twiss_point)\\n twpoints.append(twissp)\\n twiss_point = twissp\\n\\n # trajectories\\n c_trk = [v[0] for v in points_c]\\n s_trk = [v[0] for v in points_s]\\n # enveloppe (beta*emittance)^(1/2)\\n sgx = [M.sqrt(v[0]*epsi0) for v in twpoints]\\n \\n plt.figure('x & sgx')\\n ax1 = plt.subplot(111)\\n ax1.set_title(F'alpha = {alfa:2.1f}')\\n ax1.plot(pos,c_trk, label='C')\\n ax1.plot(pos,s_trk, label='S')\\n ax1.plot(pos,sgx, label='env')\\n ax1.legend(loc='lower right',fontsize='small')\\n plt.show()\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d71f0d0082f3527649c54dc841786fe\",\n \"score\": \"0.4835147\",\n \"text\": \"def testTransformation3():\\n\\n\\n\\n i = 0\\n while i < 40:\\n i = i + 1\\n\\n N = 20\\n model_points = np.ones((3, N))\\n\\n variance = 1/40 * i\\n var = np.array([0.1, 0.1, variance])\\n C = eye(3,3) #np.outer(var, var)\\n # C = eye(3, 3)\\n C[0,1] = variance\\n C[1,0] = variance\\n C[2,1] = variance\\n C[1,2] = variance\\n C[0,2] = variance/10\\n C[2,0] = variance/10\\n\\n model_points = np.random.multivariate_normal([0,0,0], C, 15) * 200\\n model_points = model_points.T\\n\\n planar, ratio = IsPlanar(model_points)\\n\\n # flat = False\\n # while not flat:\\n # # Camera A\\n # model_points = ((2 * np.random.rand(3, 20)) - 1) * 500\\n # # model_points = np.random.normal(0, 20, (3, 100))\\n #\\n # planar, ratio = IsPlanar(model_points)\\n #\\n # flat = ratio > 0.8\\n\\n # Ground truth transformation parameters\\n # x y z\\n R_params = [23, -12, 4]\\n t_params = [44, -102, 12]\\n transform_parms = R_params + t_params\\n transfomed_points, R, t = transform(transform_parms, model_points, False, 0, 5)\\n\\n # fx fy cx cy k0 k1\\n project_params = [100, 100, 50, 50, 0, 0]\\n image_points = projective_transform(project_params, transfomed_points)\\n\\n # Seed Params\\n # seed = np.zeros(12) # transform_parms + (np.random.normal(0, 11, 6))\\n # seed = seed + [0,0,0,0,0,0,10, 10, 5, 5, 0, 0]\\n\\n noiseval = 5 #i * 1\\n transformNoise = transform_parms + np.random.normal(0, noiseval, 6)\\n projectNoise = project_params[1:] + np.random.normal(0, noiseval, 5)\\n seed = np.concatenate([projectNoise, transformNoise])\\n # seed = seed + (np.random.normal(0, 0.1, 11))\\n\\n\\n # Run LMA\\n out = LMA.LM(seed, (model_points, image_points),\\n projective_error_function,\\n lambda_multiplier=2, kmax=2000, eps=0.1)\\n\\n\\n print(\\\"\\\\n\\\\nRMS ERR: \\\\t{}\\\".format(out[0]))\\n print(\\\"Eigen Ratio: \\\\t{}, {}\\\".format(ratio, variance))\\n print(\\\"Noise Val: {}\\\".format(noiseval))\\n print(\\\"Projection: \\\\t{}\\\".format(out[1][0:5]))\\n print(\\\"Angle: \\\\t{}\\\".format(out[1][5:8]))\\n print(\\\"Translation: \\\\t{}\\\".format(out[1][8:11]))\\n print(\\\"Reason: \\\\t{}\\\".format(out[2]))\\n\\n\\n\\n print(model_points)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b44b817af2255dbfa9e1f6b8a61ba589\",\n \"score\": \"0.48300886\",\n \"text\": \"def get_trajectory_estimates(self, policy: Controller):\\n\\n state_mean = self.start_mean\\n state_cov = self.start_cov\\n\\n # container required plotting later on\\n state_means_container = []\\n state_covs_container = []\\n action_means_container = []\\n action_covs_container = []\\n\\n state_means_container.append(state_mean)\\n state_covs_container.append(state_cov)\\n\\n for t in range(self.args.horizon):\\n state_next_mean, state_next_cov, action_mean, action_cov = self.rollout(policy, state_mean, state_cov)\\n\\n state_means_container.append(state_next_mean)\\n state_covs_container.append(state_next_cov)\\n action_means_container.append(action_mean)\\n action_covs_container.append(action_cov)\\n\\n state_mean = state_next_mean\\n state_cov = state_next_cov\\n\\n return np.array(state_means_container), np.array(state_covs_container), np.array(action_means_container),\\\\\\n np.array(action_covs_container)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c9ce2a511927b6d35c65c5446485c90\",\n \"score\": \"0.4827862\",\n \"text\": \"def _generate_trajectory(self):\\n transitions = []\\n state = self.env.reset()\\n for i in range(HORIZON):\\n # if i < HORIZON / 2:\\n # action = [0.1, 0.1]\\n # else:\\n action = self._expert_control(state, i)\\n next_state, cost, done, _ = self.env.step(action)\\n transitions.append([state, action, cost, next_state, done])\\n state = next_state\\n assert done, \\\"Did not reach the goal set on task completion.\\\"\\n V = self.env.values()\\n for i, t in enumerate(transitions):\\n t.append(V[i])\\n # self.env.plot_trajectory()\\n return transitions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d05ab063f0369f231d396df2abd3cc5\",\n \"score\": \"0.4827535\",\n \"text\": \"def algorithms(dxm_state):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dfd9a4b6b2f19ab1b792bbfe54dd1a2\",\n \"score\": \"0.4825209\",\n \"text\": \"def gather(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4feb76db7004c1ae9d6f30bdbb820c68\",\n \"score\": \"0.48250794\",\n \"text\": \"def calcOneStructure(loopInfo): \\n # Generate initial structure and minimize.\\n #===========================================================================\\n # Generate initial structure by randomizing torsion angles.\\n if args.unfold == 'yes':\\n import monteCarlo\\n monteCarlo.randomizeTorsions(dyn)\\n\\n # Then set torsion angles from restraints (this shortens high T dynamics).\\n protocol.fixupCovalentGeom(maxIters=100, useVDW=1)\\n import torsionTools\\n if DIHE:\\n import torsionTools\\n torsionTools.setTorsionsFromTable(DIHE)\\n\\n # Initialize parameters\\n InitialParams(rampedParams) # Parameters for SA.\\n InitialParams(highTempParams1) # Reset some rampedParams.\\n\\n if args.resetCenter == 'yes':\\n setCenter(immx_com, Zpos) # Translate selected center of mass to IMMx Zpos.\\n\\n # Torsion angle minimization.\\n #==========================================================================\\n protocol.initMinimize(dyn,\\n potList=pots,\\n numSteps=100,\\n printInterval=50)\\n dyn.run()\\n\\n # High temperature dynamics.\\n #===========================================================================\\n # Start with REPEL to remove clashes then phase out\\n if repelStart == 'yes':\\n # High temperature dynamics stage 1.\\n protocol.initDynamics(dyn,\\n potList=pots, # potential terms to use.\\n bathTemp=ini_temp, # set bath temperature.\\n initVelocities=1, # uniform initial velocities.\\n finalTime=30, # run for finalTime or\\n numSteps=3001, # numSteps * 0.001, whichever is less.\\n printInterval=100) # printing rate in steps.\\n dyn.setETolerance(ini_temp/100) # used to det. stepsize, dflt [temp/1000].\\n dyn.run()\\n \\n # High temperature dynamics stage 2.\\n InitialParams(highTempParams2)\\n if args.resetCenter == 'yes':\\n setCenter(immx_com, Zpos) # translate selected center of mass to IMMx Zpos.\\n protocol.initDynamics(dyn,\\n potList=pots, # potential terms to use.\\n bathTemp=ini_temp, # set bath temperature.\\n initVelocities=1, # uniform initial velocities.\\n finalTime=30, # run for finalTime or\\n numSteps=3001, # numSteps * 0.001, whichever is less.\\n printInterval=100) # printing rate in steps.\\n dyn.setETolerance(ini_temp/100) # used to det. stepsize, dflt [temp/1000].\\n dyn.run()\\n\\n # High temperature dynamics stage 3.\\n InitialParams(highTempParams)\\n if args.resetCenter == 'yes':\\n setCenter(immx_com, Zpos) # translate selected center of mass to IMMx Zpos.\\n protocol.initDynamics(dyn,\\n potList=pots, # potential terms to use.\\n bathTemp=ini_temp, # set bath temperature.\\n initVelocities=1, # uniform initial velocities.\\n numSteps=highTempSteps, # numSteps * 0.001, whichever is less.\\n finalTime=highTempSteps/100,\\t # run for finalTime or\\n printInterval=100) # printing rate in steps.\\n dyn.setETolerance(ini_temp/100) # used to det. stepsize, dflt [temp/1000].\\n dyn.run()\\n\\n # Initialize integrator and loop for simulated annealing and run.\\n #===========================================================================\\n # Dynamics for annealing.\\n if args.resetCenter == 'yes':\\n setCenter(immx_com, Zpos) # translate selected center of mass to IMMx Zpos.\\n protocol.initDynamics(dyn,\\n potList=pots,\\n finalTime=0.4, # run for finalTime or\\n numSteps=annealSteps, # numSteps*0.001, whichever is less.\\n printInterval=100) \\n\\n # Set up cooling loop and run.\\n from simulationTools import AnnealIVM\\n AnnealIVM(initTemp=ini_temp,\\n finalTemp=fin_temp,\\n tempStep=step_temp,\\n ivm=dyn,\\n rampedParams=rampedParams\\n ).run()\\n\\n # Run Ez-Potential to position Z-axis\\n #===========================================================================\\n if args.ezPot:\\n from xplor import select\\n m = IVM(xplor.simulation)\\n protocol.initMinimize(m,numSteps=10)\\n m.setVerbose(m.verbose() | m.printNodeDef)\\n m.setStepType(\\\"MinimizeCG\\\")\\n m.setNumSteps(10)\\n m.setDEpred(1)\\n m.setETolerance(1e-7)\\n m.setPrintInterval(1)\\n groupList=m.groupList()\\n groupList.append(select(args.ezPot))\\n m.setGroupList(groupList)\\n m.setHingeList([('translate', select(args.ezPot)),])\\n m.potList().removeAll()\\n m.potList().add(Ezt)\\n m.run()\\n\\n if args.relax == 'yes':\\n InitialParams(relaxParams)\\n protocol.initDynamics(dyn,\\n potList=pots, # potential terms to use.\\n bathTemp=args.relaxTemp, # set bath temperature.\\n initVelocities=1, # uniform initial velocities.\\n finalTime=30, # run for finalTime or\\n numSteps=args.relaxSteps, # numSteps * 0.001, whichever is less.\\n printInterval=100) # printing rate in steps.\\n dyn.setETolerance(args.relaxTemp/100) # used to det. stepsize, dflt [temp/1000].\\n dyn.run()\\n \\n # Final minimization.\\n #===========================================================================\\n # Torsion angle minimization.\\n protocol.initMinimize(dyn,\\n numSteps=500, # dflt [500 steps]\\n potList=pots,\\n printInterval=50)\\n dyn.run()\\n\\n # Final Cartesian minimization.\\n protocol.initMinimize(minc,\\n numSteps=500, # dflt [500 steps]\\n potList=pots,\\n dEPred=10)\\n minc.run()\\n\\n # Recenter coordinates in XY plane.\\n setCenterXY() # translate protein coordinates to XY center.\\n \\n # Do analysis and write structure when this routine is finished.\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bae36d63197e84f10f88daf33608947d\",\n \"score\": \"0.4824436\",\n \"text\": \"def test_tdp(self):\\n \\n expstring = \\\"\\\"\\\"\\n 0 4 4 -3.96678714134e-19 0.0\\n 25 4 4 0.0123395314303 1.10216297674e-16\\n 50 4 4 0.0521821949151 4.41386645353e-16\\n 75 4 4 0.0375170438965 3.03228186587e-16\\n 0 3 3 -2.15151658263e-18 0.0\\n 25 3 3 0.0542304209663 3.27264685377e-16\\n 50 3 3 0.133087497663 5.56816047005e-16\\n 75 3 3 0.114486463162 5.08756535778e-16\\n 0 0 5 -0.855304990279 0.0\\n 25 0 5 0.338783123139 0.205218959392\\n 50 0 5 -0.0404678523217 -0.0413353312194\\n 75 0 5 -0.0747762812157 0.00493941699829\\n \\\"\\\"\\\"\\n \\n with energy_units(\\\"1/cm\\\"):\\n mol1 = Molecule([0.0, 12000.0])\\n mod1 = Mode(300.0)\\n mol1.add_Mode(mod1)\\n mod1.set_nmax(0, 1)\\n mod1.set_nmax(1, 8)\\n mod1.set_HR(1,0.2)\\n \\n mol1.set_dipole((0,1), [1.0, 0.0, 0.0])\\n \\n time = TimeAxis(0.0, 100, 1.0)\\n params = dict(ftype=\\\"OverdampedBrownian\\\",\\n cortime=30.0, reorg=20, T=300,\\n matsubara=30)\\n \\n cfce = CorrelationFunction(time, params=params)\\n \\n params = dict(ftype=\\\"OverdampedBrownian\\\",\\n cortime=30.0, reorg=50, T=300,\\n matsubara=30)\\n \\n cfc1 = CorrelationFunction(time, params=params)\\n \\n mol1.set_mode_environment(0, 0, corfunc=cfce)\\n mol1.set_mode_environment(0, 1, corfunc=cfce)\\n mol1.set_transition_environment((0,1), cfc1)\\n \\n rhoi = mol1.get_excited_density_matrix(condition=\\\"delta\\\")\\n \\n prop = mol1.get_ReducedDensityMatrixPropagator(time,\\n relaxation_theory=\\\"stR\\\",\\n time_dependent=True)\\n HH = mol1.get_Hamiltonian()\\n #rhoi.data[:,:] = 0.0\\n with eigenbasis_of(HH):\\n rhoi.data[0,4] = 1.0\\n rhoi.data[0,3] = 1.0\\n \\n rhot1 = prop.propagate(rhoi, Nref=1)\\n\\n HH.set_rwa([0,1]) \\n \\n rhot2 = prop.propagate(rhoi, Nref=1)\\n \\n rhot2.convert_from_RWA(HH)\\n \\n #\\n # checking that rotating wave approximation is the same as standard \\n #\\n #numpy.testing.assert_allclose(rhot1.data, rhot2.data, rtol=8.0e-2)\\n \\n \\n _show_plot_ = False\\n if _show_plot_:\\n import matplotlib.pyplot as plt\\n with eigenbasis_of(HH):\\n for ii in range(HH.dim):\\n plt.plot(time.data, numpy.real(rhot1.data[:,ii,ii]))\\n plt.plot(time.data, numpy.real(rhot2.data[:,ii,ii]),\\\"--\\\")\\n \\n plt.show()\\n \\n _create_data_ = False\\n if _create_data_:\\n \\n elements = [(4,4), (3,3), (0,5)]\\n for el in elements:\\n for tt in range(0, 100, 25):\\n print(tt, el[0], el[1],\\n numpy.real(rhot2.data[tt,el[0],el[1]]),\\n numpy.imag(rhot2.data[tt,el[0],el[1]]))\\n \\n # \\n # compare to precalculated data\\n #\\n expected = {}\\n il = 0\\n for line in expstring.splitlines():\\n rel = line.strip()\\n if len(rel) > 0:\\n nmbrs = rel.split(\\\" \\\")\\n tmi = int(nmbrs[0])\\n i1 = int(nmbrs[1])\\n i2 = int(nmbrs[2])\\n re = float(nmbrs[3])\\n im = float(nmbrs[4])\\n expected[il] = (tmi, i1, i2, re, im)\\n il += 1\\n\\n _perform_test_ = True\\n if _perform_test_:\\n for ks in expected:\\n dats = expected[ks]\\n tt = dats[0]\\n i1 = dats[1]\\n i2 = dats[2]\\n numpy.testing.assert_allclose(dats[3], \\n numpy.real(rhot2.data[tt,i1,i2]),\\n atol=1.0e-6)\\n numpy.testing.assert_allclose(dats[4], \\n numpy.imag(rhot2.data[tt,i1,i2]),\\n atol=1.0e-6)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10749cb54c17dc10f1c4429fd97e3a40\",\n \"score\": \"0.4823318\",\n \"text\": \"def __call__(self):\\n raw_s_t, s_t, s_t_tensors = self._raw_s_t, self._s_t, self._s_t_tensors\\n a_t_tensors, a_t, q_t, q_vals = self._action_f(s_t_tensors, self._t, raw_s_t)\\n raw_s_t_1, raw_r_t, done, _ = self._env.step(a_t)\\n r_t = torch.tensor([raw_r_t], dtype=torch.float, device=self._device)\\n self._n_trans.append(\\n (s_t_tensors, a_t_tensors, r_t, q_t)\\n )\\n # track funtions\\n for track_f in self._track_fs:\\n track_f(self._tracker, self._t)\\n self._t += 1\\n s_t_1, s_t_1_tensors = self._get_s_t_1(raw_s_t_1, done)\\n\\n done_cond = done or self._env.epi_step > self._max_step_per_epi\\n\\n # Generate nstep trans result\\n if len(self._n_trans) == self._n_steps:\\n r_t_n = self._aggr_nsteps_return()\\n s_0, a_0, _, __ = self._n_trans.pop(0)\\n n_step_trans = [(s_0, a_0, r_t_n, s_t_1_tensors)]\\n else:\\n n_step_trans = []\\n\\n ###\\n # Handle done or max step epi\\n ###\\n if self._env.epi_step > self._max_step_per_epi:\\n print(\\\"Episode forcefully terminated\\\")\\n if done_cond:\\n if self._eps_config is not None:\\n track_keys = [\\\"reward\\\", \\\"step\\\", \\\"eps\\\", \\\"total_epi\\\"]\\n track_vals = [self._env.epi_reward, self._env.epi_step, self._eps_config.eps_schedule_f(self._t), self._epi]\\n else:\\n track_keys = [\\\"reward\\\", \\\"step\\\", \\\"total_epi\\\"]\\n track_vals = [self._env.epi_reward, self._env.epi_step, self._epi]\\n self._tracker.tracks(track_keys, self._t, track_vals)\\n self._epi += 1\\n # Reste s_t if done or max step\\n while len(self._n_trans) > 0:\\n r_t_n = self._aggr_nsteps_return()\\n s_0, a_0, _, __ = self._n_trans.pop(0)\\n n_step_trans.append((s_0, a_0, r_t_n, s_t_1_tensors))\\n self._raw_s_t = self._env.reset()\\n self._s_t = self._q_net.prep(self._raw_s_t)\\n self._s_t_tensors = prep_one_trans_device_tensors(self._s_t, self._device)\\n else:\\n self._raw_s_t, self._s_t, self._s_t_tensors = raw_s_t_1, s_t_1, s_t_1_tensors \\n return n_step_trans, self._t\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994192,"cells":{"query_id":{"kind":"string","value":"c187fe94c06737319ce6a05f9f97bdf6"},"query":{"kind":"string","value":"Decodes the auth token"},"positive_passages":{"kind":"list like","value":[{"docid":"02be4b20298fe9880c620e1a3a35e5a0","score":"0.7484995","text":"def decode_auth(auth_token):\n try:\n payload = jwt.decode(auth_token, app.config.get('SECRET_KEY'))\n return payload['sub']\n except jwt.exceptions.InvalidTokenError:\n return 'Invalid token. Please log in again.'","title":""}],"string":"[\n {\n \"docid\": \"02be4b20298fe9880c620e1a3a35e5a0\",\n \"score\": \"0.7484995\",\n \"text\": \"def decode_auth(auth_token):\\n try:\\n payload = jwt.decode(auth_token, app.config.get('SECRET_KEY'))\\n return payload['sub']\\n except jwt.exceptions.InvalidTokenError:\\n return 'Invalid token. Please log in again.'\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ca7dd14a048ddd0283f1e392a638ab5b","score":"0.7767273","text":"def decode_auth_token(auth_token):\n try:\n pyload= jwt.decode(auth_token, JWT_SECRET_KEY)\n return pyload['id']\n except jwt.ExpiredSignatureError:\n return 'Signature expired. Please log in again.'\n except jwt.InvalidTokenError:\n return 'Invalid token. Please log in again.'","title":""},{"docid":"47f7e3b1d77cde15dded9aca66f6afdb","score":"0.775636","text":"def decode_auth_token(auth_token: Union[bytes, str]\n ) -> Union[ObjectId, str]:\n try:\n payload = jwt.decode(\n jwt=auth_token, key=app.config.get('SECRET_KEY', None)\n )\n return ObjectId(payload['sub'])\n except jwt.ExpiredSignatureError:\n return 'Signature expired. Please login again.'\n except jwt.InvalidTokenError:\n logger.info('Invalid token error.')\n apm.capture_exception()\n return 'Invalid token. Please log in again.'","title":""},{"docid":"4d3b77bfec4b9e0be6e1e46f867ffff1","score":"0.7524331","text":"def decode_auth_token(auth_token):\n try:\n payload = jwt.decode(auth_token, \"webProyect2020\")\n return payload['sub']\n except jwt.ExpiredSignatureError:\n return 'Signature expired. Please log in again.'\n except jwt.InvalidTokenError:\n return 'Invalid token. Please log in again.'","title":""},{"docid":"0c0b1047167d9f3bc66c06efceab5272","score":"0.74842906","text":"def decode_auth_token(auth_token):\n try:\n payload = jwt.decode(auth_token, key)\n is_blacklisted_token = BlacklistToken.check_blacklist(auth_token)\n if is_blacklisted_token:\n return 'Token blacklisted. Please log in again.'\n else:\n return payload['sub']\n except jwt.ExpiredSignatureError:\n return 'Signature expired. Please log in again.'\n except jwt.InvalidTokenError:\n return 'Invalid token. Please log in again.'","title":""},{"docid":"81d159fd9c74fb03ff33b917b1d7ee44","score":"0.74725163","text":"def decode_auth_token(auth_token):\n try:\n payload = jwt.decode(auth_token, app.config['SECRET_KEY'])\n is_active_token = Active_Sessions.check_active_session(auth_token)\n if not is_active_token:\n return 'Token invalid.'\n else:\n return payload['sub'] \n except jwt.ExpiredSignatureError:\n return 'Signature expired. Please log in again.'\n except jwt.InvalidTokenError:\n return 'Invalid token. Please log in again.'","title":""},{"docid":"331f1f08e7f52d2eab8b90d8bf7663c4","score":"0.7468616","text":"def decode_auth_token(auth_token):\n try:\n payload = jwt.decode(auth_token, inject.instance(\"jwt_secret\"))\n return payload['sub']\n except jwt.ExpiredSignatureError:\n raise TokenExpired()\n except jwt.InvalidTokenError:\n raise NoToken()","title":""},{"docid":"441cdb0048645ecd6c30bf2d84924a60","score":"0.74175173","text":"def decode_auth_token(auth_token):\n try:\n payload = jwt.decode(auth_token, key)\n is_blacklisted_token = BlacklistToken.check_blacklist(auth_token)\n if is_blacklisted_token:\n return 'Token blacklisted. Please log in again.'\n else:\n return payload['sub']\n except jwt.ExpiredSignatureError:\n return 'Signature expired. Please log in again.'\n except jwt.InvalidTokenError:\n return 'Invalid token. Please log in again.'","title":""},{"docid":"c90e7539a0bd25eaac5f6cc9067e7d16","score":"0.7406942","text":"def decode_auth_token(auth_token):\n try:\n payload = jwt.decode(auth_token, app.config.get('SECRET_KEY'))\n is_blacklisted_token = BlacklistToken.check_blacklist(auth_token)\n if is_blacklisted_token:\n return 'Token blacklisted. Please log in again.'\n else:\n return payload['sub']\n except jwt.ExpiredSignatureError:\n return 'Signature expired. Please log in again.'\n except jwt.InvalidTokenError:\n return 'Invalid token. Please log in again.'","title":""},{"docid":"ae1fd64c3e51b59691bb8f4001472212","score":"0.7402157","text":"def decode_auth_token(auth_token):\n try:\n payload = jwt.decode(auth_token, current_app.config['SECRET_KEY'])\n return payload['sub']\n except jwt.ExpiredSignatureError:\n return 'Signature expired'\n except jwt.InvalidAlgorithmError:\n return 'Invalid Token'","title":""},{"docid":"36ee1b4527439abbef33d7beeb990a12","score":"0.7401791","text":"def decode_auth_token(auth_token):\n try:\n payload = jwt.decode(auth_token, app.config.get('SECRET_KEY'))\n is_blacklisted_token = ExpiredToken.check_blacklist(auth_token)\n if is_blacklisted_token:\n return 'Token blacklisted. Please log in again.'\n else:\n return payload['sub']\n except jwt.ExpiredSignatureError:\n return 'Signature expired. Please log in again.'\n except jwt.InvalidTokenError:\n return 'Invalid token. Please log in again.'","title":""},{"docid":"feeeb29f0084e44be01e0c7d16c2414d","score":"0.73797643","text":"def decode_auth_token(token):\n try:\n payload = jwt.decode(token, app.config['SECRET_KEY'], algorithms='HS256')\n is_token_blacklisted = BlackListToken.check_blacklist(token)\n if is_token_blacklisted:\n return 'Token was Blacklisted, Please login In'\n return payload['sub']\n except jwt.ExpiredSignatureError:\n return 'Signature expired, Please sign in again'\n except jwt.InvalidTokenError:\n return 'Invalid token. Please sign in again'","title":""},{"docid":"8dbbf3d24afc1306e1b7ff0230d1391e","score":"0.7377921","text":"def decode_auth_token(auth_token):\n try:\n if is_blacklisted(auth_token):\n return 'Token has been blacklisted. Please log in again'\n payload = jwt.decode(auth_token, 'SECRET_KEY', algorithm='HS256')\n session['user_id'] = str(payload.get('sub'))\n return payload['sub']\n except jwt.ExpiredSignatureError:\n return 'Token Signature expired. Please log in again.'\n except jwt.InvalidTokenError:\n return 'Invalid token. Please log in again.'","title":""},{"docid":"3fd711b1b43a7068e4d130e115c921eb","score":"0.7376567","text":"def decode_auth_token(auth_token):\n try:\n payload = jwt.decode(auth_token, flask.current_app.config.get(\n 'SECRET_KEY'))\n is_blacklisted_token = BlacklistToken.check_blacklist(auth_token)\n if is_blacklisted_token:\n return 'Token blacklisted. Please log in again.'\n else:\n return payload['sub']\n except jwt.ExpiredSignatureError:\n return 'Signature expired. Please log in again.'\n except jwt.InvalidTokenError:\n return 'Invalid token. Please log in again.'","title":""},{"docid":"3e010f9e5586b9f2180fbbd689cdea53","score":"0.73390514","text":"def decode_auth_token(auth_token):\n try:\n payload = jwt.decode(auth_token, SECRET_KEY)\n return payload['sub']\n except jwt.ExpiredSignatureError:\n return 'Expired'\n except jwt.InvalidTokenError:\n return None","title":""},{"docid":"9aebaf475fac6a775fa6ee637a427634","score":"0.7335341","text":"def decode_auth_token(token):\n try:\n payload = jwt.decode(token, os.environ['SECRET_KEY'])\n return payload['sub']\n except jwt.ExpiredSignatureError:\n return 'Expired Token'\n except jwt.InvalidTokenError:\n return 'Invalid Token'","title":""},{"docid":"5bf20f71092695aee29b53eb73b39ac7","score":"0.7331229","text":"def decode_auth_token(auth_token):\n try:\n payload = jwt.decode(auth_token, SECRET_KEY)\n return payload['sub']\n except jwt.ExpiredSignatureError:\n return \"Signature expired. Please sign-in again\"\n except jwt.InvalidTokenError:\n return \"Invalid token. Please sign-in again\"","title":""},{"docid":"4a37b4375e19577cacfc89dab6fc03bb","score":"0.73310727","text":"def decode_auth_token(auth_token):\n try:\n payload = jwt.decode(auth_token, app.config.get(\"SECRET_KEY\"))\n is_blacklisted_token = BlacklistToken.check_blacklist(auth_token)\n if is_blacklisted_token:\n return \"Token blacklisted. Please log in again.\"\n else:\n return payload[\"sub\"]\n except jwt.ExpiredSignatureError:\n return \"Signature expired. Please log in again.\"\n except jwt.InvalidTokenError:\n return \"Invalid token. Please log in again.\"","title":""},{"docid":"c5a491ecaafbc13cb06e8b0be9c17f85","score":"0.7321038","text":"def decode_auth_token(token):\n try:\n payload = jwt.decode(token, app.config['SECRET_KEY'], algorithms='HS256')\n is_token_blacklisted = BlacklistedToken.check_blacklist(token)\n if is_token_blacklisted:\n return 'Token was blacklisted, please sign in again'\n return payload['sub']\n except jwt.ExpiredSignatureError:\n return 'Signature expired, Please sign in again'\n except jwt.InvalidSignatureError:\n return 'Signature verification failed'","title":""},{"docid":"5a1e4c82c794381a520349cb3c0f227d","score":"0.7318235","text":"def decode_token(token):\n public_key = config('PUBLIC_KEY')\n try:\n return jwt.decode(token[7:], verify=False)\n except jwt.exceptions.ExpiredSignatureError:\n data = {\n 'status': 'error',\n 'error': 'token_expired',\n 'message': 'Get a new token'\n }\n raise exceptions.AuthenticationFailed(\n data, status.HTTP_401_UNAUTHORIZED)\n except jwt.exceptions.InvalidTokenError:\n data = {\n 'status': 'error',\n 'error': 'Invalid token',\n 'message': 'Ensure you are using a Goauth token'\n }\n raise exceptions.AuthenticationFailed(\n data, status.HTTP_400_BAD_REQUEST)","title":""},{"docid":"b6f5b4559acf507b2a39ced1f3375dc1","score":"0.7254783","text":"def decode(token):\n return jwt.decode(token, TOKEN_SECRET, algorithms=['HS256'])","title":""},{"docid":"916870ecba0337096950d2cc9dadd3a4","score":"0.72450095","text":"def decode_auth_token(token):\n try:\n payload = jwt.decode(token, app.config['SECRET_KEY'], algorithms='HS256')\n return payload['sub']\n except jwt.ExpiredSignatureError:\n return 'Signature expired, Please sign in again'\n except jwt.InvalidTokenError:\n return 'Invalid token. Please sign in again'","title":""},{"docid":"fdac84473a6485769daed2d9da3dcaf8","score":"0.7175359","text":"def decode_token(token_str):\n tokenParts = token_str.split(\".\")\n if len(tokenParts) < 3:\n raise Exception(\"Invalid JWT. Could not split into parts.\")\n padding = \"====\"\n infoStr = tokenParts[1] + padding[0 : len(tokenParts[1]) % 4]\n jsonStr = base64.urlsafe_b64decode(infoStr)\n return json.loads(jsonStr)","title":""},{"docid":"8b001e7d7dcd187851e8040a6f0e7ded","score":"0.7137952","text":"def decode_password_reset_token(auth_token: bytes) -> Union[ObjectId, str]:\n try:\n payload = jwt.decode(\n jwt=auth_token, key=app.config.get('SECRET_KEY', None)\n )\n return ObjectId(payload['sub'])\n except jwt.ExpiredSignatureError:\n return 'Signature expired. Please get a new token.'\n except jwt.InvalidTokenError:\n return 'Invalid token. Please get a new token.'","title":""},{"docid":"3922070d8c43974436c031c85057d7f2","score":"0.71127415","text":"def jwt_decode_token(token):\n return jwt_lib.decode(token, current_app.config['JWT_SECRET_KEY'],\n algorithms=current_app.config['JWT_ALGORITHMS'])","title":""},{"docid":"c139a1bd9babb41202a610a02ac06995","score":"0.7100378","text":"def decode(self, token):\n json_string = base64.b64decode(token.encode('utf-8')).decode('utf-8')\n decoded_token = json.loads(json_string)\n\n # Remove the encoding metadata as it is read since it will no longer\n # be needed.\n encoded_keys = decoded_token.pop('boto_encoded_keys', None)\n if encoded_keys is None:\n return decoded_token\n else:\n return self._decode(decoded_token, encoded_keys)","title":""},{"docid":"e6c8ecccb1d3433cf04a81166b313da9","score":"0.7038034","text":"def decode_token_or_400(auth_token):\n serializer = URLSafeSerializer(current_app.config['SECRET_KEY'])\n try:\n return serializer.loads(auth_token)\n except BadData as ex:\n generic_error_handler(ex)\n abort(400)","title":""},{"docid":"d58167db9decee5c9e9e3abeeec5f669","score":"0.69546914","text":"def jwt_decode_handler(token):\n options = {\n \"verify_exp\": api_settings.JWT_VERIFY_EXPIRATION,\n }\n\n import jwt\n\n return jwt.decode(\n token,\n str(api_settings.JWT_SECRET_KEY),\n str(api_settings.JWT_VERIFY),\n options=options,\n leeway=api_settings.JWT_LEEWAY,\n audience=api_settings.JWT_AUDIENCE,\n issuer=api_settings.JWT_ISSUER,\n algorithms=[api_settings.JWT_ALGORITHM],\n )","title":""},{"docid":"274bd0406d6c4793aab90eb1d32a164c","score":"0.6913792","text":"def decode(self, token: bytes) -> Mapping[str, Any]:\n payload = jwt.decode(\n token,\n key=self.secret,\n options={'verify_exp': False}\n )\n payload['exp'] = datetime.utcfromtimestamp(payload['exp'])\n payload['iat'] = datetime.utcfromtimestamp(payload['iat'])\n return payload","title":""},{"docid":"8d82a205fd149fab00bc6f577e72699e","score":"0.6904669","text":"def decode_authorization_message(EncodedMessage=None):\n pass","title":""},{"docid":"33c8246834a7fca3a3c35ad4cae29e68","score":"0.6841103","text":"def decode_token(token):\n try:\n payload = jwt.decode(token, os.environ.get('SECRET_KEY'))\n return payload['id']\n except jwt.ExpiredSignatureError:\n return 'Token has already Expired, Please login again'\n except jwt.InvalidTokenError:\n return 'Invalid token, please login again'","title":""},{"docid":"318009e033be9b1cb0680229195ba56f","score":"0.6821571","text":"def decode_user_token_id(self, token):\n \"\"\" Method to return the token to its readable state \"\"\"\n decoded = jwt.decode(token, 'mylovelydaughters', algorithms=\"HS256\")\n return decoded['id']","title":""},{"docid":"1cce190851c860e6de0a86c2e60ddd17","score":"0.6805388","text":"def for_value(cls, token_value):\n\n try:\n contents = base64.b64decode(token_value)\n except TypeError:\n # Not proper base64 encoded value.\n logging.info(\"Tried to decode auth token that isn't \" +\n \"base64 encoded\")\n return None\n\n parts = contents.split(\"\\n\")\n if len(parts) != 3:\n # Wrong number of parts / malformed.\n logging.info(\"Tried to decode malformed auth token\")\n return None\n user_id, timestamp, signature = parts\n return cls(user_id, timestamp, signature)","title":""},{"docid":"f8fdb0d6833d11421ec6d20eeccc49a0","score":"0.6546678","text":"def get_token(self):\n response = self.Login(\"aminah\", \"12345\")\n data = json.loads(response.data)\n return data['token']","title":""},{"docid":"d8528c1a3c6ea703c964b24e8b6bb888","score":"0.64676774","text":"async def get_payload_from_token(request: Request):\n http_bearer = HTTPBearer()\n auth_credentials: HTTPAuthorizationCredentials = await http_bearer(request)\n token = auth_credentials.credentials\n payload = jwt.decode(token, options=dict(verify_signature=False))\n realm = Auth.get_realm_from_token(token)\n return {\"realm\": realm, \"username\": payload[\"preferred_username\"]}","title":""},{"docid":"80c553a01ad6db958610c82f3dd46385","score":"0.644588","text":"def loads(self, raw_token):\n try:\n parsed = jwt.decode(\n raw_token, key=self.secret_key, algorithms=[self.algorithm]\n )\n except jwt.ExpiredSignatureError:\n raise tokens.TokenError.expired()\n except jwt.DecodeError: # pragma: no branch\n raise tokens.TokenError.invalid()\n # ideally we never end up here as DecodeError should\n # catch everything else, however since this is the root\n # exception for PyJWT we'll catch it down and\n # and re-raise our own\n except jwt.InvalidTokenError: # pragma: no cover\n raise tokens.TokenError.bad()\n else:\n return tokens.Token(user_id=parsed[\"id\"], operation=parsed[\"op\"])","title":""},{"docid":"57e7b6e67fb24c547c5fb3fb8abec916","score":"0.64371026","text":"def decode_token(token: str) -> Optional[dict]:\n try:\n with open(settings.AUTH_PUBLIC_KEY_PATH, \"rb\") as public_key:\n return jwt.decode(\n jwt=token,\n key=public_key.read(),\n algorithms=[JWT_ALGORITHM],\n audience=settings.AUTH_ACCESS_TOKEN_AUDIENCE,\n )\n except jwt.exceptions.InvalidTokenError:\n return None","title":""},{"docid":"ca76cdc71bf41ab86190bddb0a16e2af","score":"0.64095944","text":"def decode(token, expiring=True):\n try:\n if expiring:\n val = signing.loads(token, max_age=settings.TOKEN_LIFETIME)\n else:\n val = signing.loads(token)\n except Exception as e:\n LOG.info(\"The token seems to be invalid (%s)\" % str(e))\n val = None\n return val","title":""},{"docid":"a78ebff2d9fa3f840751c6ce53eca048","score":"0.629871","text":"def decode(self, token, verify=True):\n try:\n return jwt.decode(token, self.verifying_key, algorithms=[self.algorithm], verify=verify)\n except InvalidTokenError:\n raise TokenBackendError(_('Token is invalid or expired'))","title":""},{"docid":"b51b2c046ad0fcaf606ec3adae616a4f","score":"0.62967557","text":"def deserialize(cls, token):\n s = Serializer(current_app.config['SECRET_KEY'])\n data = s.loads(token)\n return cls.get(data.get('confirm'))","title":""},{"docid":"38011d4eeeaa04ed396275c22f1d6be3","score":"0.626913","text":"def get_auth_token():\n token = g.user.generate_auth_token()\n return jsonify({'token': token.decode('ascii')})","title":""},{"docid":"826cf725f69300f9d14cf849bc6e952b","score":"0.6251695","text":"def get_account_auth_token(self):","title":""},{"docid":"d1e30299b4efa25cadeaea3e749b9658","score":"0.62305206","text":"def info_from_BearerAuth(token: str) -> Optional[TokenInfo]:\n return decode_api_key_v2(token, Const.AUTH_PUBLIC_KEY)","title":""},{"docid":"3674a276006504a5ec07191905183eaf","score":"0.62135303","text":"def verify_token(auth_token):\n try:\n payload = jwt.decode(auth_token, SECRET_KEY, algorithms='HS256')\n return payload['sub']\n except jwt.ExpiredSignatureError:\n raise Exception('Signature expired. Please log in again.')\n except jwt.InvalidTokenError:\n raise Exception('Invalid token. Please log in again.')","title":""},{"docid":"ba076a3f7faad7a05fe8d68965032396","score":"0.6193463","text":"def parse_auth_token(token: str) -> str:\n \n if not token.startswith('Bearer '):\n return None\n \n return token.split('Bearer ')[1]","title":""},{"docid":"6609595c91bc6bbd53036643882a135b","score":"0.6150235","text":"def get_token_auth_cookie():\n\n auth = request.headers.get(\"Authorization\", None)\n if not auth:\n\n abort(401)\n raise AuthError({\"code\": \"authorization_missing\",\n \"description\":\n \"Authorization is expected\"}, 401)\n\n parts = auth.split()\n\n if parts[0].lower() != \"bearer\":\n abort(401)\n raise AuthError({\"code\": \"invalid_header\",\n \"description\":\n \"Authorization header must start with\"\n \" Bearer\"}, 401)\n elif len(parts) == 1:\n abort(401) # No token Sent\n raise AuthError({\"code\": \"invalid_header\",\n \"description\": \"Token not found\"}, 401)\n elif len(parts) > 2:\n abort(401)\n raise AuthError({\"code\": \"invalid_header\",\n \"description\":\n \"Authorization header must be\"\n \" Bearer token\"}, 401)\n\n token = parts[1]\n return token","title":""},{"docid":"166c8ed075dd94b63f251c168f412bb6","score":"0.6149182","text":"def decodeJWTView(request):\n input_jwt = request.POST.get('jwt', None)\n if not input_jwt:\n webapp2.abort(404)\n\n try:\n input_jwt = unicodedata.normalize(NORMAL_FORM, input_jwt).encode(\n 'ascii', 'ignore')\n # Append extra characters to make original string base 64 decodable.\n input_jwt += '=' * (4 - (len(input_jwt) % 4))\n decoded_jwt = base64.urlsafe_b64decode(input_jwt)\n except JSONDecodeError as e:\n result = {'error': True}\n webapp2.abort(404)\n else:\n decoded_jwt = json.loads(decoded_jwt)\n return webapp2.Response(json.dumps(decoded_jwt, indent=8))","title":""},{"docid":"feea660bb72555785886b69abd4ed779","score":"0.6142326","text":"def get_auth_token(self):\n data = [str(self.user_data[\"_id\"]), self.user_data[\"password\"], self.user_data[\"role\"]]\n login_serializer = TimedJSONWebSignatureSerializer(current_app.config[\"SECRET_KEY\"],\n expires_in=current_app.config[\"COOKIE_DURATION\"])\n return login_serializer.dumps(data)","title":""},{"docid":"22645774a81bce45200340d841ee36e0","score":"0.61396885","text":"def get_auth_token(self):\n data = (self.id, sha1(self.password).hexdigest())\n return login_serializer.dumps(data)","title":""},{"docid":"fa26d52faac9a4de2593311af18400db","score":"0.6108767","text":"def jwt_authenticate(request: HttpRequest) -> Dict[str, Any]:\n token = get_raw_token_from_request(request)\n validated_token = tokens.decode_token(token)\n return validated_token","title":""},{"docid":"b192865d55b0f632716c78c553be4515","score":"0.6107343","text":"def decode_token(self, firebase_token):\n scope_name = settings.SCOPE_NAME\n try:\n claims = firebase_auth.verify_id_token(\n firebase_token,\n check_revoked=api_settings.FIREBASE_CHECK_JWT_REVOKED\n )\n if scope_name != '':\n if claims.get(scope_name) is None or claims.get(scope_name) is False:\n raise exceptions.AuthenticationFailed(\n '! Token claim permission denied !'\n )\n return claims\n except ValueError as exc:\n raise exceptions.AuthenticationFailed(\n 'JWT was found to be invalid, or the App’s project ID cannot '\n 'be determined.'\n )\n except firebase_auth.AuthError as exc:\n if exc.code == 'ID_TOKEN_REVOKED':\n raise exceptions.AuthenticationFailed(\n 'Token revoked, inform the user to reauthenticate or '\n 'signOut().'\n )\n else:\n raise exceptions.AuthenticationFailed(\n 'Token is invalid.'\n )","title":""},{"docid":"1174e90d8260a51604fd76772ecb9068","score":"0.61054146","text":"def ulogin_response(self, token, host):\n response = requests.get(\n settings.TOKEN_URL,\n params={\n 'token': token,\n 'host': host\n })\n content = response.content.decode('utf8')\n return json.loads(content)","title":""},{"docid":"f6980f9368bccf84be8096806995bf47","score":"0.6101621","text":"def process_token(encoded_token):\n keyset = requests.get(CERNER_JWKS).json()\n try:\n token = jwt.decode(encoded_token, keyset,\n audience=MY_ORIGIN,\n issuer=TRUSTED_ORIGIN,\n options= {\n 'verify_iat': True,\n 'verify_exp': True,\n }\n )\n\n # OK, this is schenanigans to always \"be\" a test user in the FHIR sandbox no matter\n # what test user you're really using. You won't do this in a real app :) \n token['sub'] = DEMO_USER_FPA\n return token\n\n except (jwt.JWTError, jwt.ExpiredSignatureError, jwt.JWTClaimsError) as e:\n app.logger.info('BCS Token process failure: %s', e)\n abort(403)\n return","title":""},{"docid":"4b0f4e7036171fca63e896703fe2691c","score":"0.60998386","text":"def _verified_token(self,encoded_token: bytes) -> Dict[str,Union[str,int,bool]]:\n # raise an error if secret key doesn't exist\n if not self._secret_key:\n raise RuntimeError(\n \"AUTHJWT_SECRET_KEY must be set when using symmetric algorithm {}\".format(self._algorithm)\n )\n\n try:\n return jwt.decode(\n encoded_token,\n self._secret_key,\n algorithms=self._algorithm\n )\n except jwt.exceptions.ExpiredSignatureError as err:\n raise HTTPException(status_code=422,detail=str(err))\n except jwt.exceptions.DecodeError as err:\n raise HTTPException(status_code=422,detail=str(err))\n except jwt.exceptions.InvalidAlgorithmError as err:\n raise HTTPException(status_code=422,detail=str(err))\n except jwt.exceptions.InvalidKeyError as err:\n raise HTTPException(status_code=422,detail=str(err))\n except jwt.exceptions.InvalidTokenError as err:\n raise HTTPException(status_code=422,detail=str(err))\n except jwt.exceptions.InvalidIssuerError as err:\n raise HTTPException(status_code=422,detail=str(err))\n except jwt.exceptions.InvalidAudienceError as err:\n raise HTTPException(status_code=422,detail=str(err))\n except jwt.exceptions.InvalidIssuedAtError as err:\n raise HTTPException(status_code=422,detail=str(err))\n except jwt.exceptions.InvalidSignatureError as err:\n raise HTTPException(status_code=422,detail=str(err))\n except jwt.exceptions.ImmatureSignatureError as err:\n raise HTTPException(status_code=422,detail=str(err))\n except jwt.exceptions.MissingRequiredClaimError as err:\n raise HTTPException(status_code=422,detail=str(err))","title":""},{"docid":"0926f3050b67f2748f563be6d13c5c0d","score":"0.60977006","text":"def get_token(self):\n payload = self.register_user(self.email, self.password)\n data = json.loads(payload.data.decode())\n payload = self.login_user(self.email, self.password)\n data = json.loads(payload.data.decode())\n return data.get('token')","title":""},{"docid":"db0e612c699517da33c9095dad9c972c","score":"0.6050448","text":"def response_auth(status, message, token, status_code):\n return {'status': status, \n 'message': message, \n 'auth_token': token.decode(\"utf-8\")\n }, status_code","title":""},{"docid":"2a4f6c27b10cb76d7568acec8daf92bc","score":"0.6030416","text":"def access_token(self):\n return json_loads(self.token_json)['access_token']","title":""},{"docid":"ba66b4fc96a14b521a7771550b9bbcf6","score":"0.60283047","text":"def decode_id(token_id: int) -> int:\n decoded_type = token_id >> 128\n return decoded_type","title":""},{"docid":"a485afa395cabb3b0561e97981b4723f","score":"0.601571","text":"def exchange_token():\n return jsonify(dict(token=current_user.token.decode('ascii')))","title":""},{"docid":"54910ec2563080952cd0ff346bf27a27","score":"0.60035264","text":"def decode_authorization_message(self, encoded_message):\r\n params = {\r\n 'EncodedMessage': encoded_message,\r\n }\r\n return self.get_object(\r\n 'DecodeAuthorizationMessage',\r\n params,\r\n DecodeAuthorizationMessage,\r\n verb='POST'\r\n )","title":""},{"docid":"0f6771a9e098f2e0a77fb902079ec614","score":"0.5995791","text":"def get_token(self):\n data = json.dumps({'username': 'User1', 'password': 'p@ssw0rd'})\n response = self.app.post('/auth/login/', data=data,\n content_type='application/json')\n token = json.loads(response.data).get('token')\n return token","title":""},{"docid":"a6145b76fbc1e6e5db879fd5118a1783","score":"0.5989262","text":"def verify_token(token: str) -> Union[Dict[str, Any], None]:\n\n try:\n payload = jwt.decode(token, settings.SECRET_KEY, algorithms=['HS256'])\n except jwt.exceptions.InvalidSignatureError:\n return None\n except jwt.exceptions.ExpiredSignatureError:\n return None\n except jwt.exceptions.DecodeError:\n return None\n\n return payload","title":""},{"docid":"f5410a5a46cb55ece4b6612f1b2852d8","score":"0.5975735","text":"def to_python(self, value):\n try:\n value = fernet.decrypt(force_bytes(value))\n except InvalidToken:\n return value\n else:\n return force_str(value)","title":""},{"docid":"b83fc705882368a0beefb4f4f2f38349","score":"0.5970702","text":"def token(self):\n if not self.auth_token or not self.auth_token.valid:\n self.auth_token = self._fetch_token()\n\n return self.auth_token","title":""},{"docid":"9228b3dd0d3a8510ebb06c87b99ecb9a","score":"0.596466","text":"def handle_token_auth(token_auth):\r\n # print(\"Handle token auth\", token_auth)\r\n token_auth = token_auth[len(\"MSAuth1.0 \"):]\r\n token_auth = token_auth.split(\",\")\r\n if (len(token_auth) != 3):\r\n print('Auth token contains {} segments'.format(len(token_auth)))\r\n return False\r\n\r\n tokens = parse_tokens(token_auth) \r\n #print(tokens)\r\n if (len(tokens) != 3):\r\n print(\"malformed authorization header: header conatins {} values, 3 expected\".format(len(tokens)), )\r\n return False\r\n print(\"Tokens : \", tokens)\r\n #access_token_body = jwt.get_unverified_claims(tokens.get(\"accesstoken\"))\r\n\r\n # print(\"JWT AccessToken Body: \", access_token_body)\r\n if verify_token(tokens.get(\"actortoken\")):\r\n return True\r\n return False","title":""},{"docid":"a565c330876235d7945faa0b3f700665","score":"0.59575313","text":"def decode_base64_authorization_header(\r\n self,\r\n base64_authorization_header: str\r\n ) -> str:\r\n try:\r\n utf_val = base64_authorization_header.encode('utf-8')\r\n decode = b64decode(utf_val).decode('utf-8')\r\n return decode\r\n except (AttributeError, ValueError) as a:\r\n return None","title":""},{"docid":"aeaef4564278658bf73b400174abd849","score":"0.595356","text":"def get_token(self,data):\n data = a2b_hex(data)\n safe = self.encrypt(data)\n safe = b2a_hex(safe)\n return safe","title":""},{"docid":"0f3f3307cc0d5eedabd6c644336894c0","score":"0.5949489","text":"def _dehydrate_token(token):\n session_token = {}\n session_token[\"key\"] = token.key\n session_token[\"secret\"] = token.secret\n return session_token","title":""},{"docid":"897fac6270ce7b7bb9f8bcb467eded0f","score":"0.59484315","text":"def decode_secret_token(secret_token):\r\n secret_token = urllib.parse.unquote(secret_token)\r\n decrypted = hangman.decrypt(secret_token)\r\n return decrypted.split(';')","title":""},{"docid":"0f8d67be792163694859d04b1dc7e0f4","score":"0.592503","text":"def parsed_token(payload: dict):\n token = scitokens.utils.demo.token(payload)\n return scitokens.SciToken.deserialize(token)","title":""},{"docid":"a284cdabeb71571c78362d72d6cff91d","score":"0.5921979","text":"def get_auth_token(self):\n # The data keys should be as short as possible to keep the token short\n data = {\n # The user's id\n 'i': self.id,\n # Time of issue\n 't': time(),\n # Last characters of user's hashed password, makes sure the key is automatically\n # expired if the user changes password\n 'p': self.password_hash[-10:],\n }\n serializer = URLSafeSerializer(current_app.config['SECRET_KEY'])\n return serializer.dumps(data)","title":""},{"docid":"61b106c03a3412979ea4b3bdff057ba0","score":"0.59084606","text":"def get_encoded_auth_token(self):\n auth_token = '{}:{}'.format(self.client_id, self.client_secret).encode('ascii')\n return base64.b64encode(auth_token).decode('ascii')","title":""},{"docid":"64d6cef4aaa613091c02ab9b0b4fee31","score":"0.5905913","text":"def get_auth_token():\n url = 'https://{}/dna/system/api/v1/auth/token'.format(DNAC_URL) # Endpoint URL\n hdr = {'content-type' : 'application/json'} # Define request header\n resp = requests.post(url, auth=HTTPBasicAuth(DNAC_USER, DNAC_PASS), headers=hdr) # Make the POST Request\n token = resp.json()['Token'] # Retrieve the Token\n return token # Create a return statement to send the token back for later use","title":""},{"docid":"987f595cb7d716ccc559d2a3d013a813","score":"0.5903592","text":"def load_token(token):\n\n #The Token itself was generated by User.get_auth_token. So it is up to\n #us to known the format of the token data itself.\n\n #The Token was encrypted using itsdangerous.URLSafeTimedSerializer which\n #allows us to have a max_age on the token itself. When the cookie is stored\n #on the users computer it also has a exipry date, but could be changed by\n #the user, so this feature allows us to enforce the exipry date of the token\n #server side and not rely on the users cookie to exipre.\n max_age = current_app.config[\"COOKIE_DURATION\"].total_seconds()\n\n #Decrypt the Security Token, data = [username, hashpass]\n try:\n login_serializer = URLSafeTimedSerializer(current_app.config[\"SECRET_KEY\"])\n data = login_serializer.loads(token, max_age=max_age)\n except:\n return None\n #Find the User\n user_id = data[0]\n hashed_password = data[1]\n user = User.get(user_id)\n\n #Check Password and return user or None\n if user and hashed_password == user.password:\n return user\n return None","title":""},{"docid":"42908960d2b652161cd088bfc8c8a041","score":"0.5890365","text":"def unpack(self, token, timestamp=None):\n if not token:\n raise KeyError\n\n _jwe_header = _jws_header = None\n\n # Check if it's an encrypted JWT\n darg = {}\n if self.allowed_enc_encs:\n darg[\"enc\"] = self.allowed_enc_encs\n if self.allowed_enc_algs:\n darg[\"alg\"] = self.allowed_enc_algs\n try:\n _decryptor = jwe_factory(token, **darg)\n except (KeyError, HeaderError):\n _decryptor = None\n\n if _decryptor:\n # Yes, try to decode\n _info = self._decrypt(_decryptor, token)\n _jwe_header = _decryptor.jwt.headers\n # Try to find out if the information encrypted was a signed JWT\n try:\n _content_type = _decryptor.jwt.headers[\"cty\"]\n except KeyError:\n _content_type = \"\"\n else:\n _content_type = \"jwt\"\n _info = token\n\n # If I have reason to believe the information I have is a signed JWT\n if _content_type.lower() == \"jwt\":\n # Check that is a signed JWT\n if self.allowed_sign_algs:\n _verifier = jws_factory(_info, alg=self.allowed_sign_algs)\n else:\n _verifier = jws_factory(_info)\n\n if _verifier:\n _info = self._verify(_verifier, _info)\n else:\n raise Exception()\n _jws_header = _verifier.jwt.headers\n else:\n # So, not a signed JWT\n try:\n # A JSON document ?\n _info = json.loads(_info)\n except JSONDecodeError: # Oh, no ! Not JSON\n return _info\n except TypeError:\n try:\n _info = as_unicode(_info)\n _info = json.loads(_info)\n except JSONDecodeError: # Oh, no ! Not JSON\n return _info\n\n # If I know what message class the info should be mapped into\n if self.msg_cls:\n _msg_cls = self.msg_cls\n else:\n try:\n # try to find a issuer specific message class\n _msg_cls = self.iss2msg_cls[_info[\"iss\"]]\n except KeyError:\n _msg_cls = None\n\n timestamp = timestamp or utc_time_sans_frac()\n\n if \"nbf\" in _info:\n nbf = int(_info[\"nbf\"])\n if timestamp < nbf - self.skew:\n raise VerificationError(\"Token not yet valid\")\n\n if \"exp\" in _info:\n exp = int(_info[\"exp\"])\n if timestamp >= exp + self.skew:\n raise VerificationError(\"Token expired\")\n else:\n exp = None\n\n if \"iat\" in _info:\n iat = int(_info[\"iat\"])\n if self.allowed_max_lifetime and exp:\n if abs(exp - iat) > self.allowed_max_lifetime:\n raise VerificationError(\"Token lifetime exceeded\")\n\n if _msg_cls:\n vp_args = {\"skew\": self.skew}\n if self.iss:\n vp_args[\"aud\"] = self.iss\n _info = self.verify_profile(_msg_cls, _info, **vp_args)\n _info.jwe_header = _jwe_header\n _info.jws_header = _jws_header\n return _info\n else:\n return _info","title":""},{"docid":"4fbbd59bdfffa0d3db59501397f82702","score":"0.58806986","text":"def getAuthToken():\n # Effettua la richiest di POST autenticandosi con username e password in basicAuth\n response = requests.post(api, auth=HTTPBasicAuth(USERNAME, PASSWORD), verify=False)\n # Ottiene il token dalla risposta in Json\n token = response.json()['Token']\n\n # Ritorna il token per poterlo riutilizzare\n return token","title":""},{"docid":"9fcba151110d399dfd4c81547569ce20","score":"0.58735234","text":"def decode(self, data):\n pass","title":""},{"docid":"77dccdeaa5a448ab8f706c3acc262b0e","score":"0.58663964","text":"def _decode_jwt(jwt):\n #pubkey = settings.AGAVE_JWT_PUBKEY\n pubkey = 'MIGfMA0GCSqGSIb3DQEBAQUAA4GNADCBiQKBgQCUp/oV1vWc8/TkQSiAvTousMzO\\nM4asB2iltr2QKozni5aVFu818MpOLZIr8LMnTzWllJvvaA5RAAdpbECb+48FjbBe\\n0hseUdN5HpwvnH/DW8ZccGvk53I6Orq7hLCv1ZHtuOCokghz/ATrhyPq+QktMfXn\\nRS4HrKGJTzxaCcU7OQIDAQAB'\n try:\n key_der = b64decode(pubkey)\n key = load_der_public_key(key_der, backend=default_backend())\n except (TypeError, ValueError, UnsupportedAlgorithm):\n logger.exception('Could not load public key.')\n return {}\n\n try:\n decoded = pyjwt.decode(jwt, key, issuer=settings.AGAVE_JWT_ISSUER)\n except pyjwt.exceptions.DecodeError as exc:\n logger.exception('Could not decode JWT. %s', exc)\n return {}\n return decoded","title":""},{"docid":"922732d43df5eae78c5d76bdef7dddb0","score":"0.586006","text":"def _decrypt(self, rj, token):\n if self.iss:\n keys = self.key_jar.get_jwt_decrypt_keys(rj.jwt, aud=self.iss)\n else:\n keys = self.key_jar.get_jwt_decrypt_keys(rj.jwt)\n return rj.decrypt(token, keys=keys)","title":""},{"docid":"e85956ab2619cafdc37045a67c9e90b3","score":"0.5857477","text":"def get_auth(self, auth_token):\n\n data = {\n 'grant_type': 'authorization_code',\n 'code': str(auth_token),\n 'redirect_uri': self.redirect_uri,\n 'client_id': self.client_id,\n 'client_secret': self.client_secret,\n }\n\n post_request = requests.post(self.SPOTIFY_TOKEN_URL, data=data)\n\n response_data = json.loads(post_request.text)\n self._access_token = response_data['access_token']\n self.authorization_header = {\n \"Authorization\": f\"Bearer {self._access_token}\"}\n\n session['access_token'] = response_data['access_token']\n\n return dict(\n access_token=response_data['access_token'],\n refresh_token=response_data['refresh_token'],\n token_type=response_data['token_type'],\n expires_in=response_data['expires_in'],\n )","title":""},{"docid":"f0466580c2d31c475b233e785ca6af28","score":"0.5851844","text":"def _decode(self, token, encoded_keys):\n for key in encoded_keys:\n encoded = self._path_get(token, key)\n decoded = base64.b64decode(encoded.encode('utf-8'))\n self._path_set(token, key, decoded)\n return token","title":""},{"docid":"8b3713b61b1bb445ed0136704cfeb38c","score":"0.5841084","text":"def decrypt(self, rawdata):\n if self.version:\n data = base64.b64decode(rawdata[19:])\n else:\n data = rawdata\n\n data = self.cipher.decrypt(data)\n try:\n return json.loads(data[: data.rfind(b\"}\") + 1])\n except:\n return data","title":""},{"docid":"cab2ff380924a91e4c1031ee4d10118d","score":"0.5833197","text":"def get_raw_token(header):\n parts = header.split()\n\n if len(parts) == 0:\n # Empty AUTHORIZATION header sent\n return None\n\n if str(parts[0]) != \"b'Bearer'\":\n # Assume the header does not contain a JSON web token\n return None\n\n if len(parts) != 2:\n raise exceptions.AuthenticationFailed(\n _('Authorization header must contain two space-delimited values'),\n code='bad_authorization_header',\n )\n\n return parts[1]","title":""},{"docid":"ace98bf7f987be58e2d5ab23653fa3b9","score":"0.58189744","text":"def validate_token(self, token):\n\n try:\n payload = jwt.decode(token, settings.SECRET_KEY, algorithms=['HS256'])\n type = payload['type']\n\n if type != 'email_confirmation':\n raise jwt.PyJWTError\n\n except jwt.ExpiredSignatureError:\n raise serializers.ValidationError('The validation time has expired.')\n\n except jwt.PyJWTError:\n raise serializers.ValidationError('Invalid token.')\n\n else:\n\n self.context['payload'] = payload\n\n return token","title":""},{"docid":"96c18e1043a0cebf0d79423662646aa8","score":"0.58126706","text":"def token():\n return json.dumps({\n \"refresh\": \"eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJ0b2tlbl90eXBlIjoicmVmcmVzaCIsImV4cCI6MTU4Nzk5MjIyNCwianRpIjoiN2EwMzdlNGYxOGFmNDVmOWE1YzcxZDRhYzhiMDg1ODEiLCJ1c2VyX2lkIjoxLCJjYXJyZXJhcyI6WyJBIiwiQiIsIkMiLCJEIiwiRSIsIkYiLCJHIiwiSCIsIkkiLCJKIiwiSjIiLCJLIiwiTCIsIk4yIiwiUCIsIlEiLCJPIiwiUiIsIlMiLCJOIiwiTSIsIlQiLCJBMSIsIkcxIiwiVSIsIlYiLCJWMSIsIlciLCJIMSJdLCJ1c2VybmFtZSI6ImFkbWluIn0.2QgqtOEsSreA-yp43qJsTyno_K6ptyB_WkbPUS1doCQ\",\n \"access\": \"eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJ0b2tlbl90eXBlIjoiYWNjZXNzIiwiZXhwIjoyNTM0OTc1OTk5LCJqdGkiOiJlNTAyNTYzZWE5MmI0MDYwOTczYTAxNDE5ODkxMDU4OSIsInVzZXJfaWQiOjEsImNhcnJlcmFzIjpbIlRFU1QiXSwidXNlcm5hbWUiOiJ0ZXN0In0.1pto6DgaLX_GetMuJNQ8pkL9jMStUDOWqkk8P3Y5PXM\"\n })","title":""},{"docid":"8e347cebff8f423cc8c533a6cb0b04ad","score":"0.57975435","text":"def auth_token(self) -> AuthToken:\n return self._auth_token","title":""},{"docid":"ffc6ad0f1088a20cafc8672ac38be09e","score":"0.57819307","text":"def getToken(response):\n\n resp_dict = json.loads(response.text)\n\n try:\n token = resp_dict[\"token\"]\n except KeyError:\n print('Retrieval unsuccessful.')\n return None\n\n return token","title":""},{"docid":"8c0bf0a189596ae9fb197d7fdf88e721","score":"0.5773634","text":"def auth_token():\n return check_output([\"heroku\", \"auth:token\"]).rstrip().decode(\"utf8\")","title":""},{"docid":"5bac3280023752ae4c25e1bf3eca097d","score":"0.57712096","text":"def authorization_token(self) -> str:\n return pulumi.get(self, \"authorization_token\")","title":""},{"docid":"5b40ec94e3892c0eb7895d3a8f70c5dc","score":"0.5768494","text":"def check_jwt_token(token):\n # jwt.decode will verify the expiration date of the token\n # We won't have the secret so we can't verify the signature, but we should verify everything else\n return decode(token, options={'verify_signature': False})","title":""},{"docid":"9e151914abb69857cca81b80fda98f31","score":"0.57606655","text":"def decrypt(self, token: ByteString) -> str:\n return self.clf.decrypt(token).decode()","title":""},{"docid":"5dcd6b16b67c13c414af2be23217917b","score":"0.5749952","text":"def parse_tokens(token_auth):\r\n tokens = {}\r\n for item in token_auth:\r\n token_type = item.split('=')\r\n if (len(token_type) != 2):\r\n print(\"malformed authorization header: no '=' sign\", )\r\n tokens = {}\r\n break\r\n token_type[1] = token_type[1].replace(\"\\\"\", \"\")\r\n token = token_type[1].split()\r\n # print (token_type[0] , \" : \", token)\r\n if len(token) == 1:\r\n tokens[token_type[0]] = token[0]\r\n if len(token) == 2:\r\n tokens[token_type[0]] = token[1]\r\n return tokens","title":""},{"docid":"19edc6d0d232825d38e4c03a36dc0288","score":"0.5747173","text":"def get_token():\n if g.current_user.is_anonymous() or g.token_used:\n return unauthorized('Invalid credentials')\n return jsonify({'token':g.current_user.generate_auth_token(expiration=3600),\n 'expiration':3600})","title":""},{"docid":"f5b5f971cd4892f93b2f1a6e8c3b352b","score":"0.5743919","text":"def encode_auth_token(self):\n try:\n payload = {\n 'exp': datetime.datetime.utcnow() + datetime.timedelta(\n days=app.config.get('AUTH_TOKEN_EXPIRY_DAYS'),\n seconds=app.config.get('AUTH_TOKEN_EXPIRY_SECONDS')\n ),\n 'iat': datetime.datetime.utcnow(),\n 'sub': self.id\n }\n return jwt.encode(\n payload,\n app.config['SECRET_KEY'],\n algorithm='HS256'\n ).decode('utf-8')\n except Exception as e:\n return e","title":""},{"docid":"b7dcff8707cfdbdac98b8f2463f21710","score":"0.5735489","text":"def verify_token(token):\n try:\n # try to decode the token using our SECRET variable\n payload = jwt.decode(token, current_app.config.get('SECRET_KEY'))\n return payload['sub']\n except jwt.ExpiredSignatureError:\n # the token is expired, return an error string\n return \"Expired token. Please login to get a new token.\"\n except jwt.InvalidTokenError:\n # the token is invalid, return an error string\n return \"Invalid token. Please register or login.\"","title":""},{"docid":"b7dcff8707cfdbdac98b8f2463f21710","score":"0.5735489","text":"def verify_token(token):\n try:\n # try to decode the token using our SECRET variable\n payload = jwt.decode(token, current_app.config.get('SECRET_KEY'))\n return payload['sub']\n except jwt.ExpiredSignatureError:\n # the token is expired, return an error string\n return \"Expired token. Please login to get a new token.\"\n except jwt.InvalidTokenError:\n # the token is invalid, return an error string\n return \"Invalid token. Please register or login.\"","title":""},{"docid":"ce310ff12bb8ebe2a4faa219b6b06bdc","score":"0.5721923","text":"def decode(data: bytes) -> bytes:","title":""},{"docid":"104078b94f2ed76585c006e443b967e3","score":"0.57185364","text":"def decode_base64_authorization_header(self,\n base64_authorization_header: str\n ) -> str:\n if base64_authorization_header is None:\n return None\n if not isinstance(base64_authorization_header, str):\n return None\n try:\n baseEncode = base64_authorization_header.encode('utf-8')\n baseDecode = b64decode(baseEncode)\n decodedValue = baseDecode.decode('utf-8')\n return decodedValue\n except Exception:\n return None","title":""},{"docid":"e2443ce8530f1edbb3628016acb202c2","score":"0.57102567","text":"def decode_token(cls, key: str, token: str, verify: bool = True):\n data = jwt.decode(token, key, algorithms=['HS256'], verify=verify)\n # Enforce conversion to satisfy typing\n data = dict(data)\n return cls(**data)","title":""},{"docid":"364fb98c15541832fb844660d25bad68","score":"0.5709821","text":"def get_token_auth_header():\n\n auth = request.headers.get(\"Authorization\", None)\n if not auth:\n\n abort(401)\n raise AuthError({\"code\": \"authorization_header_missing\",\n \"description\":\n \"Authorization header is expected\"}, 401)\n\n parts = auth.split()\n\n if parts[0].lower() != \"bearer\":\n abort(401)\n raise AuthError({\"code\": \"invalid_header\",\n \"description\":\n \"Authorization header must start with\"\n \" Bearer\"}, 401)\n elif len(parts) == 1:\n abort(401) # No token Sent\n raise AuthError({\"code\": \"invalid_header\",\n \"description\": \"Token not found\"}, 401)\n elif len(parts) > 2:\n abort(401)\n raise AuthError({\"code\": \"invalid_header\",\n \"description\":\n \"Authorization header must be\"\n \" Bearer token\"}, 401)\n\n token = parts[1]\n return token","title":""},{"docid":"037aa45e6203dbe33ff1df33cfae9cc7","score":"0.570254","text":"def _get_id_token_claims_without_verification(bearer_token: str):\n\ttry:\n\t\theader, payload, signature = bearer_token.split(\".\")\n\texcept IndexError:\n\t\tL.warning(\"Cannot parse ID token: Wrong number of '.'.\")\n\t\traise aiohttp.web.HTTPBadRequest()\n\n\ttry:\n\t\tclaims = json.loads(base64.b64decode(payload.encode(\"utf-8\")))\n\texcept binascii.Error:\n\t\tL.warning(\"Cannot parse ID token: Payload is not base 64.\")\n\t\traise aiohttp.web.HTTPBadRequest()\n\texcept json.JSONDecodeError:\n\t\tL.warning(\"Cannot parse ID token: Payload cannot be parsed as JSON.\")\n\t\traise aiohttp.web.HTTPBadRequest()\n\n\treturn claims","title":""}],"string":"[\n {\n \"docid\": \"ca7dd14a048ddd0283f1e392a638ab5b\",\n \"score\": \"0.7767273\",\n \"text\": \"def decode_auth_token(auth_token):\\n try:\\n pyload= jwt.decode(auth_token, JWT_SECRET_KEY)\\n return pyload['id']\\n except jwt.ExpiredSignatureError:\\n return 'Signature expired. Please log in again.'\\n except jwt.InvalidTokenError:\\n return 'Invalid token. Please log in again.'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47f7e3b1d77cde15dded9aca66f6afdb\",\n \"score\": \"0.775636\",\n \"text\": \"def decode_auth_token(auth_token: Union[bytes, str]\\n ) -> Union[ObjectId, str]:\\n try:\\n payload = jwt.decode(\\n jwt=auth_token, key=app.config.get('SECRET_KEY', None)\\n )\\n return ObjectId(payload['sub'])\\n except jwt.ExpiredSignatureError:\\n return 'Signature expired. Please login again.'\\n except jwt.InvalidTokenError:\\n logger.info('Invalid token error.')\\n apm.capture_exception()\\n return 'Invalid token. Please log in again.'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d3b77bfec4b9e0be6e1e46f867ffff1\",\n \"score\": \"0.7524331\",\n \"text\": \"def decode_auth_token(auth_token):\\n try:\\n payload = jwt.decode(auth_token, \\\"webProyect2020\\\")\\n return payload['sub']\\n except jwt.ExpiredSignatureError:\\n return 'Signature expired. Please log in again.'\\n except jwt.InvalidTokenError:\\n return 'Invalid token. Please log in again.'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c0b1047167d9f3bc66c06efceab5272\",\n \"score\": \"0.74842906\",\n \"text\": \"def decode_auth_token(auth_token):\\n try:\\n payload = jwt.decode(auth_token, key)\\n is_blacklisted_token = BlacklistToken.check_blacklist(auth_token)\\n if is_blacklisted_token:\\n return 'Token blacklisted. Please log in again.'\\n else:\\n return payload['sub']\\n except jwt.ExpiredSignatureError:\\n return 'Signature expired. Please log in again.'\\n except jwt.InvalidTokenError:\\n return 'Invalid token. Please log in again.'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81d159fd9c74fb03ff33b917b1d7ee44\",\n \"score\": \"0.74725163\",\n \"text\": \"def decode_auth_token(auth_token):\\n try:\\n payload = jwt.decode(auth_token, app.config['SECRET_KEY'])\\n is_active_token = Active_Sessions.check_active_session(auth_token)\\n if not is_active_token:\\n return 'Token invalid.'\\n else:\\n return payload['sub'] \\n except jwt.ExpiredSignatureError:\\n return 'Signature expired. Please log in again.'\\n except jwt.InvalidTokenError:\\n return 'Invalid token. Please log in again.'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"331f1f08e7f52d2eab8b90d8bf7663c4\",\n \"score\": \"0.7468616\",\n \"text\": \"def decode_auth_token(auth_token):\\n try:\\n payload = jwt.decode(auth_token, inject.instance(\\\"jwt_secret\\\"))\\n return payload['sub']\\n except jwt.ExpiredSignatureError:\\n raise TokenExpired()\\n except jwt.InvalidTokenError:\\n raise NoToken()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"441cdb0048645ecd6c30bf2d84924a60\",\n \"score\": \"0.74175173\",\n \"text\": \"def decode_auth_token(auth_token):\\n try:\\n payload = jwt.decode(auth_token, key)\\n is_blacklisted_token = BlacklistToken.check_blacklist(auth_token)\\n if is_blacklisted_token:\\n return 'Token blacklisted. Please log in again.'\\n else:\\n return payload['sub']\\n except jwt.ExpiredSignatureError:\\n return 'Signature expired. Please log in again.'\\n except jwt.InvalidTokenError:\\n return 'Invalid token. Please log in again.'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c90e7539a0bd25eaac5f6cc9067e7d16\",\n \"score\": \"0.7406942\",\n \"text\": \"def decode_auth_token(auth_token):\\n try:\\n payload = jwt.decode(auth_token, app.config.get('SECRET_KEY'))\\n is_blacklisted_token = BlacklistToken.check_blacklist(auth_token)\\n if is_blacklisted_token:\\n return 'Token blacklisted. Please log in again.'\\n else:\\n return payload['sub']\\n except jwt.ExpiredSignatureError:\\n return 'Signature expired. Please log in again.'\\n except jwt.InvalidTokenError:\\n return 'Invalid token. Please log in again.'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae1fd64c3e51b59691bb8f4001472212\",\n \"score\": \"0.7402157\",\n \"text\": \"def decode_auth_token(auth_token):\\n try:\\n payload = jwt.decode(auth_token, current_app.config['SECRET_KEY'])\\n return payload['sub']\\n except jwt.ExpiredSignatureError:\\n return 'Signature expired'\\n except jwt.InvalidAlgorithmError:\\n return 'Invalid Token'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36ee1b4527439abbef33d7beeb990a12\",\n \"score\": \"0.7401791\",\n \"text\": \"def decode_auth_token(auth_token):\\n try:\\n payload = jwt.decode(auth_token, app.config.get('SECRET_KEY'))\\n is_blacklisted_token = ExpiredToken.check_blacklist(auth_token)\\n if is_blacklisted_token:\\n return 'Token blacklisted. Please log in again.'\\n else:\\n return payload['sub']\\n except jwt.ExpiredSignatureError:\\n return 'Signature expired. Please log in again.'\\n except jwt.InvalidTokenError:\\n return 'Invalid token. Please log in again.'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"feeeb29f0084e44be01e0c7d16c2414d\",\n \"score\": \"0.73797643\",\n \"text\": \"def decode_auth_token(token):\\n try:\\n payload = jwt.decode(token, app.config['SECRET_KEY'], algorithms='HS256')\\n is_token_blacklisted = BlackListToken.check_blacklist(token)\\n if is_token_blacklisted:\\n return 'Token was Blacklisted, Please login In'\\n return payload['sub']\\n except jwt.ExpiredSignatureError:\\n return 'Signature expired, Please sign in again'\\n except jwt.InvalidTokenError:\\n return 'Invalid token. Please sign in again'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dbbf3d24afc1306e1b7ff0230d1391e\",\n \"score\": \"0.7377921\",\n \"text\": \"def decode_auth_token(auth_token):\\n try:\\n if is_blacklisted(auth_token):\\n return 'Token has been blacklisted. Please log in again'\\n payload = jwt.decode(auth_token, 'SECRET_KEY', algorithm='HS256')\\n session['user_id'] = str(payload.get('sub'))\\n return payload['sub']\\n except jwt.ExpiredSignatureError:\\n return 'Token Signature expired. Please log in again.'\\n except jwt.InvalidTokenError:\\n return 'Invalid token. Please log in again.'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fd711b1b43a7068e4d130e115c921eb\",\n \"score\": \"0.7376567\",\n \"text\": \"def decode_auth_token(auth_token):\\n try:\\n payload = jwt.decode(auth_token, flask.current_app.config.get(\\n 'SECRET_KEY'))\\n is_blacklisted_token = BlacklistToken.check_blacklist(auth_token)\\n if is_blacklisted_token:\\n return 'Token blacklisted. Please log in again.'\\n else:\\n return payload['sub']\\n except jwt.ExpiredSignatureError:\\n return 'Signature expired. Please log in again.'\\n except jwt.InvalidTokenError:\\n return 'Invalid token. Please log in again.'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e010f9e5586b9f2180fbbd689cdea53\",\n \"score\": \"0.73390514\",\n \"text\": \"def decode_auth_token(auth_token):\\n try:\\n payload = jwt.decode(auth_token, SECRET_KEY)\\n return payload['sub']\\n except jwt.ExpiredSignatureError:\\n return 'Expired'\\n except jwt.InvalidTokenError:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9aebaf475fac6a775fa6ee637a427634\",\n \"score\": \"0.7335341\",\n \"text\": \"def decode_auth_token(token):\\n try:\\n payload = jwt.decode(token, os.environ['SECRET_KEY'])\\n return payload['sub']\\n except jwt.ExpiredSignatureError:\\n return 'Expired Token'\\n except jwt.InvalidTokenError:\\n return 'Invalid Token'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bf20f71092695aee29b53eb73b39ac7\",\n \"score\": \"0.7331229\",\n \"text\": \"def decode_auth_token(auth_token):\\n try:\\n payload = jwt.decode(auth_token, SECRET_KEY)\\n return payload['sub']\\n except jwt.ExpiredSignatureError:\\n return \\\"Signature expired. Please sign-in again\\\"\\n except jwt.InvalidTokenError:\\n return \\\"Invalid token. Please sign-in again\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a37b4375e19577cacfc89dab6fc03bb\",\n \"score\": \"0.73310727\",\n \"text\": \"def decode_auth_token(auth_token):\\n try:\\n payload = jwt.decode(auth_token, app.config.get(\\\"SECRET_KEY\\\"))\\n is_blacklisted_token = BlacklistToken.check_blacklist(auth_token)\\n if is_blacklisted_token:\\n return \\\"Token blacklisted. Please log in again.\\\"\\n else:\\n return payload[\\\"sub\\\"]\\n except jwt.ExpiredSignatureError:\\n return \\\"Signature expired. Please log in again.\\\"\\n except jwt.InvalidTokenError:\\n return \\\"Invalid token. Please log in again.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5a491ecaafbc13cb06e8b0be9c17f85\",\n \"score\": \"0.7321038\",\n \"text\": \"def decode_auth_token(token):\\n try:\\n payload = jwt.decode(token, app.config['SECRET_KEY'], algorithms='HS256')\\n is_token_blacklisted = BlacklistedToken.check_blacklist(token)\\n if is_token_blacklisted:\\n return 'Token was blacklisted, please sign in again'\\n return payload['sub']\\n except jwt.ExpiredSignatureError:\\n return 'Signature expired, Please sign in again'\\n except jwt.InvalidSignatureError:\\n return 'Signature verification failed'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a1e4c82c794381a520349cb3c0f227d\",\n \"score\": \"0.7318235\",\n \"text\": \"def decode_token(token):\\n public_key = config('PUBLIC_KEY')\\n try:\\n return jwt.decode(token[7:], verify=False)\\n except jwt.exceptions.ExpiredSignatureError:\\n data = {\\n 'status': 'error',\\n 'error': 'token_expired',\\n 'message': 'Get a new token'\\n }\\n raise exceptions.AuthenticationFailed(\\n data, status.HTTP_401_UNAUTHORIZED)\\n except jwt.exceptions.InvalidTokenError:\\n data = {\\n 'status': 'error',\\n 'error': 'Invalid token',\\n 'message': 'Ensure you are using a Goauth token'\\n }\\n raise exceptions.AuthenticationFailed(\\n data, status.HTTP_400_BAD_REQUEST)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6f5b4559acf507b2a39ced1f3375dc1\",\n \"score\": \"0.7254783\",\n \"text\": \"def decode(token):\\n return jwt.decode(token, TOKEN_SECRET, algorithms=['HS256'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"916870ecba0337096950d2cc9dadd3a4\",\n \"score\": \"0.72450095\",\n \"text\": \"def decode_auth_token(token):\\n try:\\n payload = jwt.decode(token, app.config['SECRET_KEY'], algorithms='HS256')\\n return payload['sub']\\n except jwt.ExpiredSignatureError:\\n return 'Signature expired, Please sign in again'\\n except jwt.InvalidTokenError:\\n return 'Invalid token. Please sign in again'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdac84473a6485769daed2d9da3dcaf8\",\n \"score\": \"0.7175359\",\n \"text\": \"def decode_token(token_str):\\n tokenParts = token_str.split(\\\".\\\")\\n if len(tokenParts) < 3:\\n raise Exception(\\\"Invalid JWT. Could not split into parts.\\\")\\n padding = \\\"====\\\"\\n infoStr = tokenParts[1] + padding[0 : len(tokenParts[1]) % 4]\\n jsonStr = base64.urlsafe_b64decode(infoStr)\\n return json.loads(jsonStr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b001e7d7dcd187851e8040a6f0e7ded\",\n \"score\": \"0.7137952\",\n \"text\": \"def decode_password_reset_token(auth_token: bytes) -> Union[ObjectId, str]:\\n try:\\n payload = jwt.decode(\\n jwt=auth_token, key=app.config.get('SECRET_KEY', None)\\n )\\n return ObjectId(payload['sub'])\\n except jwt.ExpiredSignatureError:\\n return 'Signature expired. Please get a new token.'\\n except jwt.InvalidTokenError:\\n return 'Invalid token. Please get a new token.'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3922070d8c43974436c031c85057d7f2\",\n \"score\": \"0.71127415\",\n \"text\": \"def jwt_decode_token(token):\\n return jwt_lib.decode(token, current_app.config['JWT_SECRET_KEY'],\\n algorithms=current_app.config['JWT_ALGORITHMS'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c139a1bd9babb41202a610a02ac06995\",\n \"score\": \"0.7100378\",\n \"text\": \"def decode(self, token):\\n json_string = base64.b64decode(token.encode('utf-8')).decode('utf-8')\\n decoded_token = json.loads(json_string)\\n\\n # Remove the encoding metadata as it is read since it will no longer\\n # be needed.\\n encoded_keys = decoded_token.pop('boto_encoded_keys', None)\\n if encoded_keys is None:\\n return decoded_token\\n else:\\n return self._decode(decoded_token, encoded_keys)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6c8ecccb1d3433cf04a81166b313da9\",\n \"score\": \"0.7038034\",\n \"text\": \"def decode_token_or_400(auth_token):\\n serializer = URLSafeSerializer(current_app.config['SECRET_KEY'])\\n try:\\n return serializer.loads(auth_token)\\n except BadData as ex:\\n generic_error_handler(ex)\\n abort(400)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d58167db9decee5c9e9e3abeeec5f669\",\n \"score\": \"0.69546914\",\n \"text\": \"def jwt_decode_handler(token):\\n options = {\\n \\\"verify_exp\\\": api_settings.JWT_VERIFY_EXPIRATION,\\n }\\n\\n import jwt\\n\\n return jwt.decode(\\n token,\\n str(api_settings.JWT_SECRET_KEY),\\n str(api_settings.JWT_VERIFY),\\n options=options,\\n leeway=api_settings.JWT_LEEWAY,\\n audience=api_settings.JWT_AUDIENCE,\\n issuer=api_settings.JWT_ISSUER,\\n algorithms=[api_settings.JWT_ALGORITHM],\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"274bd0406d6c4793aab90eb1d32a164c\",\n \"score\": \"0.6913792\",\n \"text\": \"def decode(self, token: bytes) -> Mapping[str, Any]:\\n payload = jwt.decode(\\n token,\\n key=self.secret,\\n options={'verify_exp': False}\\n )\\n payload['exp'] = datetime.utcfromtimestamp(payload['exp'])\\n payload['iat'] = datetime.utcfromtimestamp(payload['iat'])\\n return payload\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d82a205fd149fab00bc6f577e72699e\",\n \"score\": \"0.6904669\",\n \"text\": \"def decode_authorization_message(EncodedMessage=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33c8246834a7fca3a3c35ad4cae29e68\",\n \"score\": \"0.6841103\",\n \"text\": \"def decode_token(token):\\n try:\\n payload = jwt.decode(token, os.environ.get('SECRET_KEY'))\\n return payload['id']\\n except jwt.ExpiredSignatureError:\\n return 'Token has already Expired, Please login again'\\n except jwt.InvalidTokenError:\\n return 'Invalid token, please login again'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"318009e033be9b1cb0680229195ba56f\",\n \"score\": \"0.6821571\",\n \"text\": \"def decode_user_token_id(self, token):\\n \\\"\\\"\\\" Method to return the token to its readable state \\\"\\\"\\\"\\n decoded = jwt.decode(token, 'mylovelydaughters', algorithms=\\\"HS256\\\")\\n return decoded['id']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cce190851c860e6de0a86c2e60ddd17\",\n \"score\": \"0.6805388\",\n \"text\": \"def for_value(cls, token_value):\\n\\n try:\\n contents = base64.b64decode(token_value)\\n except TypeError:\\n # Not proper base64 encoded value.\\n logging.info(\\\"Tried to decode auth token that isn't \\\" +\\n \\\"base64 encoded\\\")\\n return None\\n\\n parts = contents.split(\\\"\\\\n\\\")\\n if len(parts) != 3:\\n # Wrong number of parts / malformed.\\n logging.info(\\\"Tried to decode malformed auth token\\\")\\n return None\\n user_id, timestamp, signature = parts\\n return cls(user_id, timestamp, signature)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8fdb0d6833d11421ec6d20eeccc49a0\",\n \"score\": \"0.6546678\",\n \"text\": \"def get_token(self):\\n response = self.Login(\\\"aminah\\\", \\\"12345\\\")\\n data = json.loads(response.data)\\n return data['token']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8528c1a3c6ea703c964b24e8b6bb888\",\n \"score\": \"0.64676774\",\n \"text\": \"async def get_payload_from_token(request: Request):\\n http_bearer = HTTPBearer()\\n auth_credentials: HTTPAuthorizationCredentials = await http_bearer(request)\\n token = auth_credentials.credentials\\n payload = jwt.decode(token, options=dict(verify_signature=False))\\n realm = Auth.get_realm_from_token(token)\\n return {\\\"realm\\\": realm, \\\"username\\\": payload[\\\"preferred_username\\\"]}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80c553a01ad6db958610c82f3dd46385\",\n \"score\": \"0.644588\",\n \"text\": \"def loads(self, raw_token):\\n try:\\n parsed = jwt.decode(\\n raw_token, key=self.secret_key, algorithms=[self.algorithm]\\n )\\n except jwt.ExpiredSignatureError:\\n raise tokens.TokenError.expired()\\n except jwt.DecodeError: # pragma: no branch\\n raise tokens.TokenError.invalid()\\n # ideally we never end up here as DecodeError should\\n # catch everything else, however since this is the root\\n # exception for PyJWT we'll catch it down and\\n # and re-raise our own\\n except jwt.InvalidTokenError: # pragma: no cover\\n raise tokens.TokenError.bad()\\n else:\\n return tokens.Token(user_id=parsed[\\\"id\\\"], operation=parsed[\\\"op\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57e7b6e67fb24c547c5fb3fb8abec916\",\n \"score\": \"0.64371026\",\n \"text\": \"def decode_token(token: str) -> Optional[dict]:\\n try:\\n with open(settings.AUTH_PUBLIC_KEY_PATH, \\\"rb\\\") as public_key:\\n return jwt.decode(\\n jwt=token,\\n key=public_key.read(),\\n algorithms=[JWT_ALGORITHM],\\n audience=settings.AUTH_ACCESS_TOKEN_AUDIENCE,\\n )\\n except jwt.exceptions.InvalidTokenError:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca76cdc71bf41ab86190bddb0a16e2af\",\n \"score\": \"0.64095944\",\n \"text\": \"def decode(token, expiring=True):\\n try:\\n if expiring:\\n val = signing.loads(token, max_age=settings.TOKEN_LIFETIME)\\n else:\\n val = signing.loads(token)\\n except Exception as e:\\n LOG.info(\\\"The token seems to be invalid (%s)\\\" % str(e))\\n val = None\\n return val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a78ebff2d9fa3f840751c6ce53eca048\",\n \"score\": \"0.629871\",\n \"text\": \"def decode(self, token, verify=True):\\n try:\\n return jwt.decode(token, self.verifying_key, algorithms=[self.algorithm], verify=verify)\\n except InvalidTokenError:\\n raise TokenBackendError(_('Token is invalid or expired'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b51b2c046ad0fcaf606ec3adae616a4f\",\n \"score\": \"0.62967557\",\n \"text\": \"def deserialize(cls, token):\\n s = Serializer(current_app.config['SECRET_KEY'])\\n data = s.loads(token)\\n return cls.get(data.get('confirm'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38011d4eeeaa04ed396275c22f1d6be3\",\n \"score\": \"0.626913\",\n \"text\": \"def get_auth_token():\\n token = g.user.generate_auth_token()\\n return jsonify({'token': token.decode('ascii')})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"826cf725f69300f9d14cf849bc6e952b\",\n \"score\": \"0.6251695\",\n \"text\": \"def get_account_auth_token(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1e30299b4efa25cadeaea3e749b9658\",\n \"score\": \"0.62305206\",\n \"text\": \"def info_from_BearerAuth(token: str) -> Optional[TokenInfo]:\\n return decode_api_key_v2(token, Const.AUTH_PUBLIC_KEY)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3674a276006504a5ec07191905183eaf\",\n \"score\": \"0.62135303\",\n \"text\": \"def verify_token(auth_token):\\n try:\\n payload = jwt.decode(auth_token, SECRET_KEY, algorithms='HS256')\\n return payload['sub']\\n except jwt.ExpiredSignatureError:\\n raise Exception('Signature expired. Please log in again.')\\n except jwt.InvalidTokenError:\\n raise Exception('Invalid token. Please log in again.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba076a3f7faad7a05fe8d68965032396\",\n \"score\": \"0.6193463\",\n \"text\": \"def parse_auth_token(token: str) -> str:\\n \\n if not token.startswith('Bearer '):\\n return None\\n \\n return token.split('Bearer ')[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6609595c91bc6bbd53036643882a135b\",\n \"score\": \"0.6150235\",\n \"text\": \"def get_token_auth_cookie():\\n\\n auth = request.headers.get(\\\"Authorization\\\", None)\\n if not auth:\\n\\n abort(401)\\n raise AuthError({\\\"code\\\": \\\"authorization_missing\\\",\\n \\\"description\\\":\\n \\\"Authorization is expected\\\"}, 401)\\n\\n parts = auth.split()\\n\\n if parts[0].lower() != \\\"bearer\\\":\\n abort(401)\\n raise AuthError({\\\"code\\\": \\\"invalid_header\\\",\\n \\\"description\\\":\\n \\\"Authorization header must start with\\\"\\n \\\" Bearer\\\"}, 401)\\n elif len(parts) == 1:\\n abort(401) # No token Sent\\n raise AuthError({\\\"code\\\": \\\"invalid_header\\\",\\n \\\"description\\\": \\\"Token not found\\\"}, 401)\\n elif len(parts) > 2:\\n abort(401)\\n raise AuthError({\\\"code\\\": \\\"invalid_header\\\",\\n \\\"description\\\":\\n \\\"Authorization header must be\\\"\\n \\\" Bearer token\\\"}, 401)\\n\\n token = parts[1]\\n return token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"166c8ed075dd94b63f251c168f412bb6\",\n \"score\": \"0.6149182\",\n \"text\": \"def decodeJWTView(request):\\n input_jwt = request.POST.get('jwt', None)\\n if not input_jwt:\\n webapp2.abort(404)\\n\\n try:\\n input_jwt = unicodedata.normalize(NORMAL_FORM, input_jwt).encode(\\n 'ascii', 'ignore')\\n # Append extra characters to make original string base 64 decodable.\\n input_jwt += '=' * (4 - (len(input_jwt) % 4))\\n decoded_jwt = base64.urlsafe_b64decode(input_jwt)\\n except JSONDecodeError as e:\\n result = {'error': True}\\n webapp2.abort(404)\\n else:\\n decoded_jwt = json.loads(decoded_jwt)\\n return webapp2.Response(json.dumps(decoded_jwt, indent=8))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"feea660bb72555785886b69abd4ed779\",\n \"score\": \"0.6142326\",\n \"text\": \"def get_auth_token(self):\\n data = [str(self.user_data[\\\"_id\\\"]), self.user_data[\\\"password\\\"], self.user_data[\\\"role\\\"]]\\n login_serializer = TimedJSONWebSignatureSerializer(current_app.config[\\\"SECRET_KEY\\\"],\\n expires_in=current_app.config[\\\"COOKIE_DURATION\\\"])\\n return login_serializer.dumps(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22645774a81bce45200340d841ee36e0\",\n \"score\": \"0.61396885\",\n \"text\": \"def get_auth_token(self):\\n data = (self.id, sha1(self.password).hexdigest())\\n return login_serializer.dumps(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa26d52faac9a4de2593311af18400db\",\n \"score\": \"0.6108767\",\n \"text\": \"def jwt_authenticate(request: HttpRequest) -> Dict[str, Any]:\\n token = get_raw_token_from_request(request)\\n validated_token = tokens.decode_token(token)\\n return validated_token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b192865d55b0f632716c78c553be4515\",\n \"score\": \"0.6107343\",\n \"text\": \"def decode_token(self, firebase_token):\\n scope_name = settings.SCOPE_NAME\\n try:\\n claims = firebase_auth.verify_id_token(\\n firebase_token,\\n check_revoked=api_settings.FIREBASE_CHECK_JWT_REVOKED\\n )\\n if scope_name != '':\\n if claims.get(scope_name) is None or claims.get(scope_name) is False:\\n raise exceptions.AuthenticationFailed(\\n '! Token claim permission denied !'\\n )\\n return claims\\n except ValueError as exc:\\n raise exceptions.AuthenticationFailed(\\n 'JWT was found to be invalid, or the App’s project ID cannot '\\n 'be determined.'\\n )\\n except firebase_auth.AuthError as exc:\\n if exc.code == 'ID_TOKEN_REVOKED':\\n raise exceptions.AuthenticationFailed(\\n 'Token revoked, inform the user to reauthenticate or '\\n 'signOut().'\\n )\\n else:\\n raise exceptions.AuthenticationFailed(\\n 'Token is invalid.'\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1174e90d8260a51604fd76772ecb9068\",\n \"score\": \"0.61054146\",\n \"text\": \"def ulogin_response(self, token, host):\\n response = requests.get(\\n settings.TOKEN_URL,\\n params={\\n 'token': token,\\n 'host': host\\n })\\n content = response.content.decode('utf8')\\n return json.loads(content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6980f9368bccf84be8096806995bf47\",\n \"score\": \"0.6101621\",\n \"text\": \"def process_token(encoded_token):\\n keyset = requests.get(CERNER_JWKS).json()\\n try:\\n token = jwt.decode(encoded_token, keyset,\\n audience=MY_ORIGIN,\\n issuer=TRUSTED_ORIGIN,\\n options= {\\n 'verify_iat': True,\\n 'verify_exp': True,\\n }\\n )\\n\\n # OK, this is schenanigans to always \\\"be\\\" a test user in the FHIR sandbox no matter\\n # what test user you're really using. You won't do this in a real app :) \\n token['sub'] = DEMO_USER_FPA\\n return token\\n\\n except (jwt.JWTError, jwt.ExpiredSignatureError, jwt.JWTClaimsError) as e:\\n app.logger.info('BCS Token process failure: %s', e)\\n abort(403)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b0f4e7036171fca63e896703fe2691c\",\n \"score\": \"0.60998386\",\n \"text\": \"def _verified_token(self,encoded_token: bytes) -> Dict[str,Union[str,int,bool]]:\\n # raise an error if secret key doesn't exist\\n if not self._secret_key:\\n raise RuntimeError(\\n \\\"AUTHJWT_SECRET_KEY must be set when using symmetric algorithm {}\\\".format(self._algorithm)\\n )\\n\\n try:\\n return jwt.decode(\\n encoded_token,\\n self._secret_key,\\n algorithms=self._algorithm\\n )\\n except jwt.exceptions.ExpiredSignatureError as err:\\n raise HTTPException(status_code=422,detail=str(err))\\n except jwt.exceptions.DecodeError as err:\\n raise HTTPException(status_code=422,detail=str(err))\\n except jwt.exceptions.InvalidAlgorithmError as err:\\n raise HTTPException(status_code=422,detail=str(err))\\n except jwt.exceptions.InvalidKeyError as err:\\n raise HTTPException(status_code=422,detail=str(err))\\n except jwt.exceptions.InvalidTokenError as err:\\n raise HTTPException(status_code=422,detail=str(err))\\n except jwt.exceptions.InvalidIssuerError as err:\\n raise HTTPException(status_code=422,detail=str(err))\\n except jwt.exceptions.InvalidAudienceError as err:\\n raise HTTPException(status_code=422,detail=str(err))\\n except jwt.exceptions.InvalidIssuedAtError as err:\\n raise HTTPException(status_code=422,detail=str(err))\\n except jwt.exceptions.InvalidSignatureError as err:\\n raise HTTPException(status_code=422,detail=str(err))\\n except jwt.exceptions.ImmatureSignatureError as err:\\n raise HTTPException(status_code=422,detail=str(err))\\n except jwt.exceptions.MissingRequiredClaimError as err:\\n raise HTTPException(status_code=422,detail=str(err))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0926f3050b67f2748f563be6d13c5c0d\",\n \"score\": \"0.60977006\",\n \"text\": \"def get_token(self):\\n payload = self.register_user(self.email, self.password)\\n data = json.loads(payload.data.decode())\\n payload = self.login_user(self.email, self.password)\\n data = json.loads(payload.data.decode())\\n return data.get('token')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db0e612c699517da33c9095dad9c972c\",\n \"score\": \"0.6050448\",\n \"text\": \"def response_auth(status, message, token, status_code):\\n return {'status': status, \\n 'message': message, \\n 'auth_token': token.decode(\\\"utf-8\\\")\\n }, status_code\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a4f6c27b10cb76d7568acec8daf92bc\",\n \"score\": \"0.6030416\",\n \"text\": \"def access_token(self):\\n return json_loads(self.token_json)['access_token']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba66b4fc96a14b521a7771550b9bbcf6\",\n \"score\": \"0.60283047\",\n \"text\": \"def decode_id(token_id: int) -> int:\\n decoded_type = token_id >> 128\\n return decoded_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a485afa395cabb3b0561e97981b4723f\",\n \"score\": \"0.601571\",\n \"text\": \"def exchange_token():\\n return jsonify(dict(token=current_user.token.decode('ascii')))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54910ec2563080952cd0ff346bf27a27\",\n \"score\": \"0.60035264\",\n \"text\": \"def decode_authorization_message(self, encoded_message):\\r\\n params = {\\r\\n 'EncodedMessage': encoded_message,\\r\\n }\\r\\n return self.get_object(\\r\\n 'DecodeAuthorizationMessage',\\r\\n params,\\r\\n DecodeAuthorizationMessage,\\r\\n verb='POST'\\r\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f6771a9e098f2e0a77fb902079ec614\",\n \"score\": \"0.5995791\",\n \"text\": \"def get_token(self):\\n data = json.dumps({'username': 'User1', 'password': 'p@ssw0rd'})\\n response = self.app.post('/auth/login/', data=data,\\n content_type='application/json')\\n token = json.loads(response.data).get('token')\\n return token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6145b76fbc1e6e5db879fd5118a1783\",\n \"score\": \"0.5989262\",\n \"text\": \"def verify_token(token: str) -> Union[Dict[str, Any], None]:\\n\\n try:\\n payload = jwt.decode(token, settings.SECRET_KEY, algorithms=['HS256'])\\n except jwt.exceptions.InvalidSignatureError:\\n return None\\n except jwt.exceptions.ExpiredSignatureError:\\n return None\\n except jwt.exceptions.DecodeError:\\n return None\\n\\n return payload\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5410a5a46cb55ece4b6612f1b2852d8\",\n \"score\": \"0.5975735\",\n \"text\": \"def to_python(self, value):\\n try:\\n value = fernet.decrypt(force_bytes(value))\\n except InvalidToken:\\n return value\\n else:\\n return force_str(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b83fc705882368a0beefb4f4f2f38349\",\n \"score\": \"0.5970702\",\n \"text\": \"def token(self):\\n if not self.auth_token or not self.auth_token.valid:\\n self.auth_token = self._fetch_token()\\n\\n return self.auth_token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9228b3dd0d3a8510ebb06c87b99ecb9a\",\n \"score\": \"0.596466\",\n \"text\": \"def handle_token_auth(token_auth):\\r\\n # print(\\\"Handle token auth\\\", token_auth)\\r\\n token_auth = token_auth[len(\\\"MSAuth1.0 \\\"):]\\r\\n token_auth = token_auth.split(\\\",\\\")\\r\\n if (len(token_auth) != 3):\\r\\n print('Auth token contains {} segments'.format(len(token_auth)))\\r\\n return False\\r\\n\\r\\n tokens = parse_tokens(token_auth) \\r\\n #print(tokens)\\r\\n if (len(tokens) != 3):\\r\\n print(\\\"malformed authorization header: header conatins {} values, 3 expected\\\".format(len(tokens)), )\\r\\n return False\\r\\n print(\\\"Tokens : \\\", tokens)\\r\\n #access_token_body = jwt.get_unverified_claims(tokens.get(\\\"accesstoken\\\"))\\r\\n\\r\\n # print(\\\"JWT AccessToken Body: \\\", access_token_body)\\r\\n if verify_token(tokens.get(\\\"actortoken\\\")):\\r\\n return True\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a565c330876235d7945faa0b3f700665\",\n \"score\": \"0.59575313\",\n \"text\": \"def decode_base64_authorization_header(\\r\\n self,\\r\\n base64_authorization_header: str\\r\\n ) -> str:\\r\\n try:\\r\\n utf_val = base64_authorization_header.encode('utf-8')\\r\\n decode = b64decode(utf_val).decode('utf-8')\\r\\n return decode\\r\\n except (AttributeError, ValueError) as a:\\r\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aeaef4564278658bf73b400174abd849\",\n \"score\": \"0.595356\",\n \"text\": \"def get_token(self,data):\\n data = a2b_hex(data)\\n safe = self.encrypt(data)\\n safe = b2a_hex(safe)\\n return safe\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f3f3307cc0d5eedabd6c644336894c0\",\n \"score\": \"0.5949489\",\n \"text\": \"def _dehydrate_token(token):\\n session_token = {}\\n session_token[\\\"key\\\"] = token.key\\n session_token[\\\"secret\\\"] = token.secret\\n return session_token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"897fac6270ce7b7bb9f8bcb467eded0f\",\n \"score\": \"0.59484315\",\n \"text\": \"def decode_secret_token(secret_token):\\r\\n secret_token = urllib.parse.unquote(secret_token)\\r\\n decrypted = hangman.decrypt(secret_token)\\r\\n return decrypted.split(';')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f8d67be792163694859d04b1dc7e0f4\",\n \"score\": \"0.592503\",\n \"text\": \"def parsed_token(payload: dict):\\n token = scitokens.utils.demo.token(payload)\\n return scitokens.SciToken.deserialize(token)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a284cdabeb71571c78362d72d6cff91d\",\n \"score\": \"0.5921979\",\n \"text\": \"def get_auth_token(self):\\n # The data keys should be as short as possible to keep the token short\\n data = {\\n # The user's id\\n 'i': self.id,\\n # Time of issue\\n 't': time(),\\n # Last characters of user's hashed password, makes sure the key is automatically\\n # expired if the user changes password\\n 'p': self.password_hash[-10:],\\n }\\n serializer = URLSafeSerializer(current_app.config['SECRET_KEY'])\\n return serializer.dumps(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61b106c03a3412979ea4b3bdff057ba0\",\n \"score\": \"0.59084606\",\n \"text\": \"def get_encoded_auth_token(self):\\n auth_token = '{}:{}'.format(self.client_id, self.client_secret).encode('ascii')\\n return base64.b64encode(auth_token).decode('ascii')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64d6cef4aaa613091c02ab9b0b4fee31\",\n \"score\": \"0.5905913\",\n \"text\": \"def get_auth_token():\\n url = 'https://{}/dna/system/api/v1/auth/token'.format(DNAC_URL) # Endpoint URL\\n hdr = {'content-type' : 'application/json'} # Define request header\\n resp = requests.post(url, auth=HTTPBasicAuth(DNAC_USER, DNAC_PASS), headers=hdr) # Make the POST Request\\n token = resp.json()['Token'] # Retrieve the Token\\n return token # Create a return statement to send the token back for later use\",\n \"title\": \"\"\n },\n {\n \"docid\": \"987f595cb7d716ccc559d2a3d013a813\",\n \"score\": \"0.5903592\",\n \"text\": \"def load_token(token):\\n\\n #The Token itself was generated by User.get_auth_token. So it is up to\\n #us to known the format of the token data itself.\\n\\n #The Token was encrypted using itsdangerous.URLSafeTimedSerializer which\\n #allows us to have a max_age on the token itself. When the cookie is stored\\n #on the users computer it also has a exipry date, but could be changed by\\n #the user, so this feature allows us to enforce the exipry date of the token\\n #server side and not rely on the users cookie to exipre.\\n max_age = current_app.config[\\\"COOKIE_DURATION\\\"].total_seconds()\\n\\n #Decrypt the Security Token, data = [username, hashpass]\\n try:\\n login_serializer = URLSafeTimedSerializer(current_app.config[\\\"SECRET_KEY\\\"])\\n data = login_serializer.loads(token, max_age=max_age)\\n except:\\n return None\\n #Find the User\\n user_id = data[0]\\n hashed_password = data[1]\\n user = User.get(user_id)\\n\\n #Check Password and return user or None\\n if user and hashed_password == user.password:\\n return user\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42908960d2b652161cd088bfc8c8a041\",\n \"score\": \"0.5890365\",\n \"text\": \"def unpack(self, token, timestamp=None):\\n if not token:\\n raise KeyError\\n\\n _jwe_header = _jws_header = None\\n\\n # Check if it's an encrypted JWT\\n darg = {}\\n if self.allowed_enc_encs:\\n darg[\\\"enc\\\"] = self.allowed_enc_encs\\n if self.allowed_enc_algs:\\n darg[\\\"alg\\\"] = self.allowed_enc_algs\\n try:\\n _decryptor = jwe_factory(token, **darg)\\n except (KeyError, HeaderError):\\n _decryptor = None\\n\\n if _decryptor:\\n # Yes, try to decode\\n _info = self._decrypt(_decryptor, token)\\n _jwe_header = _decryptor.jwt.headers\\n # Try to find out if the information encrypted was a signed JWT\\n try:\\n _content_type = _decryptor.jwt.headers[\\\"cty\\\"]\\n except KeyError:\\n _content_type = \\\"\\\"\\n else:\\n _content_type = \\\"jwt\\\"\\n _info = token\\n\\n # If I have reason to believe the information I have is a signed JWT\\n if _content_type.lower() == \\\"jwt\\\":\\n # Check that is a signed JWT\\n if self.allowed_sign_algs:\\n _verifier = jws_factory(_info, alg=self.allowed_sign_algs)\\n else:\\n _verifier = jws_factory(_info)\\n\\n if _verifier:\\n _info = self._verify(_verifier, _info)\\n else:\\n raise Exception()\\n _jws_header = _verifier.jwt.headers\\n else:\\n # So, not a signed JWT\\n try:\\n # A JSON document ?\\n _info = json.loads(_info)\\n except JSONDecodeError: # Oh, no ! Not JSON\\n return _info\\n except TypeError:\\n try:\\n _info = as_unicode(_info)\\n _info = json.loads(_info)\\n except JSONDecodeError: # Oh, no ! Not JSON\\n return _info\\n\\n # If I know what message class the info should be mapped into\\n if self.msg_cls:\\n _msg_cls = self.msg_cls\\n else:\\n try:\\n # try to find a issuer specific message class\\n _msg_cls = self.iss2msg_cls[_info[\\\"iss\\\"]]\\n except KeyError:\\n _msg_cls = None\\n\\n timestamp = timestamp or utc_time_sans_frac()\\n\\n if \\\"nbf\\\" in _info:\\n nbf = int(_info[\\\"nbf\\\"])\\n if timestamp < nbf - self.skew:\\n raise VerificationError(\\\"Token not yet valid\\\")\\n\\n if \\\"exp\\\" in _info:\\n exp = int(_info[\\\"exp\\\"])\\n if timestamp >= exp + self.skew:\\n raise VerificationError(\\\"Token expired\\\")\\n else:\\n exp = None\\n\\n if \\\"iat\\\" in _info:\\n iat = int(_info[\\\"iat\\\"])\\n if self.allowed_max_lifetime and exp:\\n if abs(exp - iat) > self.allowed_max_lifetime:\\n raise VerificationError(\\\"Token lifetime exceeded\\\")\\n\\n if _msg_cls:\\n vp_args = {\\\"skew\\\": self.skew}\\n if self.iss:\\n vp_args[\\\"aud\\\"] = self.iss\\n _info = self.verify_profile(_msg_cls, _info, **vp_args)\\n _info.jwe_header = _jwe_header\\n _info.jws_header = _jws_header\\n return _info\\n else:\\n return _info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fbbd59bdfffa0d3db59501397f82702\",\n \"score\": \"0.58806986\",\n \"text\": \"def getAuthToken():\\n # Effettua la richiest di POST autenticandosi con username e password in basicAuth\\n response = requests.post(api, auth=HTTPBasicAuth(USERNAME, PASSWORD), verify=False)\\n # Ottiene il token dalla risposta in Json\\n token = response.json()['Token']\\n\\n # Ritorna il token per poterlo riutilizzare\\n return token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fcba151110d399dfd4c81547569ce20\",\n \"score\": \"0.58735234\",\n \"text\": \"def decode(self, data):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77dccdeaa5a448ab8f706c3acc262b0e\",\n \"score\": \"0.58663964\",\n \"text\": \"def _decode_jwt(jwt):\\n #pubkey = settings.AGAVE_JWT_PUBKEY\\n pubkey = 'MIGfMA0GCSqGSIb3DQEBAQUAA4GNADCBiQKBgQCUp/oV1vWc8/TkQSiAvTousMzO\\\\nM4asB2iltr2QKozni5aVFu818MpOLZIr8LMnTzWllJvvaA5RAAdpbECb+48FjbBe\\\\n0hseUdN5HpwvnH/DW8ZccGvk53I6Orq7hLCv1ZHtuOCokghz/ATrhyPq+QktMfXn\\\\nRS4HrKGJTzxaCcU7OQIDAQAB'\\n try:\\n key_der = b64decode(pubkey)\\n key = load_der_public_key(key_der, backend=default_backend())\\n except (TypeError, ValueError, UnsupportedAlgorithm):\\n logger.exception('Could not load public key.')\\n return {}\\n\\n try:\\n decoded = pyjwt.decode(jwt, key, issuer=settings.AGAVE_JWT_ISSUER)\\n except pyjwt.exceptions.DecodeError as exc:\\n logger.exception('Could not decode JWT. %s', exc)\\n return {}\\n return decoded\",\n \"title\": \"\"\n },\n {\n \"docid\": \"922732d43df5eae78c5d76bdef7dddb0\",\n \"score\": \"0.586006\",\n \"text\": \"def _decrypt(self, rj, token):\\n if self.iss:\\n keys = self.key_jar.get_jwt_decrypt_keys(rj.jwt, aud=self.iss)\\n else:\\n keys = self.key_jar.get_jwt_decrypt_keys(rj.jwt)\\n return rj.decrypt(token, keys=keys)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e85956ab2619cafdc37045a67c9e90b3\",\n \"score\": \"0.5857477\",\n \"text\": \"def get_auth(self, auth_token):\\n\\n data = {\\n 'grant_type': 'authorization_code',\\n 'code': str(auth_token),\\n 'redirect_uri': self.redirect_uri,\\n 'client_id': self.client_id,\\n 'client_secret': self.client_secret,\\n }\\n\\n post_request = requests.post(self.SPOTIFY_TOKEN_URL, data=data)\\n\\n response_data = json.loads(post_request.text)\\n self._access_token = response_data['access_token']\\n self.authorization_header = {\\n \\\"Authorization\\\": f\\\"Bearer {self._access_token}\\\"}\\n\\n session['access_token'] = response_data['access_token']\\n\\n return dict(\\n access_token=response_data['access_token'],\\n refresh_token=response_data['refresh_token'],\\n token_type=response_data['token_type'],\\n expires_in=response_data['expires_in'],\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0466580c2d31c475b233e785ca6af28\",\n \"score\": \"0.5851844\",\n \"text\": \"def _decode(self, token, encoded_keys):\\n for key in encoded_keys:\\n encoded = self._path_get(token, key)\\n decoded = base64.b64decode(encoded.encode('utf-8'))\\n self._path_set(token, key, decoded)\\n return token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b3713b61b1bb445ed0136704cfeb38c\",\n \"score\": \"0.5841084\",\n \"text\": \"def decrypt(self, rawdata):\\n if self.version:\\n data = base64.b64decode(rawdata[19:])\\n else:\\n data = rawdata\\n\\n data = self.cipher.decrypt(data)\\n try:\\n return json.loads(data[: data.rfind(b\\\"}\\\") + 1])\\n except:\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cab2ff380924a91e4c1031ee4d10118d\",\n \"score\": \"0.5833197\",\n \"text\": \"def get_raw_token(header):\\n parts = header.split()\\n\\n if len(parts) == 0:\\n # Empty AUTHORIZATION header sent\\n return None\\n\\n if str(parts[0]) != \\\"b'Bearer'\\\":\\n # Assume the header does not contain a JSON web token\\n return None\\n\\n if len(parts) != 2:\\n raise exceptions.AuthenticationFailed(\\n _('Authorization header must contain two space-delimited values'),\\n code='bad_authorization_header',\\n )\\n\\n return parts[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ace98bf7f987be58e2d5ab23653fa3b9\",\n \"score\": \"0.58189744\",\n \"text\": \"def validate_token(self, token):\\n\\n try:\\n payload = jwt.decode(token, settings.SECRET_KEY, algorithms=['HS256'])\\n type = payload['type']\\n\\n if type != 'email_confirmation':\\n raise jwt.PyJWTError\\n\\n except jwt.ExpiredSignatureError:\\n raise serializers.ValidationError('The validation time has expired.')\\n\\n except jwt.PyJWTError:\\n raise serializers.ValidationError('Invalid token.')\\n\\n else:\\n\\n self.context['payload'] = payload\\n\\n return token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96c18e1043a0cebf0d79423662646aa8\",\n \"score\": \"0.58126706\",\n \"text\": \"def token():\\n return json.dumps({\\n \\\"refresh\\\": \\\"eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJ0b2tlbl90eXBlIjoicmVmcmVzaCIsImV4cCI6MTU4Nzk5MjIyNCwianRpIjoiN2EwMzdlNGYxOGFmNDVmOWE1YzcxZDRhYzhiMDg1ODEiLCJ1c2VyX2lkIjoxLCJjYXJyZXJhcyI6WyJBIiwiQiIsIkMiLCJEIiwiRSIsIkYiLCJHIiwiSCIsIkkiLCJKIiwiSjIiLCJLIiwiTCIsIk4yIiwiUCIsIlEiLCJPIiwiUiIsIlMiLCJOIiwiTSIsIlQiLCJBMSIsIkcxIiwiVSIsIlYiLCJWMSIsIlciLCJIMSJdLCJ1c2VybmFtZSI6ImFkbWluIn0.2QgqtOEsSreA-yp43qJsTyno_K6ptyB_WkbPUS1doCQ\\\",\\n \\\"access\\\": \\\"eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJ0b2tlbl90eXBlIjoiYWNjZXNzIiwiZXhwIjoyNTM0OTc1OTk5LCJqdGkiOiJlNTAyNTYzZWE5MmI0MDYwOTczYTAxNDE5ODkxMDU4OSIsInVzZXJfaWQiOjEsImNhcnJlcmFzIjpbIlRFU1QiXSwidXNlcm5hbWUiOiJ0ZXN0In0.1pto6DgaLX_GetMuJNQ8pkL9jMStUDOWqkk8P3Y5PXM\\\"\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e347cebff8f423cc8c533a6cb0b04ad\",\n \"score\": \"0.57975435\",\n \"text\": \"def auth_token(self) -> AuthToken:\\n return self._auth_token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffc6ad0f1088a20cafc8672ac38be09e\",\n \"score\": \"0.57819307\",\n \"text\": \"def getToken(response):\\n\\n resp_dict = json.loads(response.text)\\n\\n try:\\n token = resp_dict[\\\"token\\\"]\\n except KeyError:\\n print('Retrieval unsuccessful.')\\n return None\\n\\n return token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c0bf0a189596ae9fb197d7fdf88e721\",\n \"score\": \"0.5773634\",\n \"text\": \"def auth_token():\\n return check_output([\\\"heroku\\\", \\\"auth:token\\\"]).rstrip().decode(\\\"utf8\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bac3280023752ae4c25e1bf3eca097d\",\n \"score\": \"0.57712096\",\n \"text\": \"def authorization_token(self) -> str:\\n return pulumi.get(self, \\\"authorization_token\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b40ec94e3892c0eb7895d3a8f70c5dc\",\n \"score\": \"0.5768494\",\n \"text\": \"def check_jwt_token(token):\\n # jwt.decode will verify the expiration date of the token\\n # We won't have the secret so we can't verify the signature, but we should verify everything else\\n return decode(token, options={'verify_signature': False})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e151914abb69857cca81b80fda98f31\",\n \"score\": \"0.57606655\",\n \"text\": \"def decrypt(self, token: ByteString) -> str:\\n return self.clf.decrypt(token).decode()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dcd6b16b67c13c414af2be23217917b\",\n \"score\": \"0.5749952\",\n \"text\": \"def parse_tokens(token_auth):\\r\\n tokens = {}\\r\\n for item in token_auth:\\r\\n token_type = item.split('=')\\r\\n if (len(token_type) != 2):\\r\\n print(\\\"malformed authorization header: no '=' sign\\\", )\\r\\n tokens = {}\\r\\n break\\r\\n token_type[1] = token_type[1].replace(\\\"\\\\\\\"\\\", \\\"\\\")\\r\\n token = token_type[1].split()\\r\\n # print (token_type[0] , \\\" : \\\", token)\\r\\n if len(token) == 1:\\r\\n tokens[token_type[0]] = token[0]\\r\\n if len(token) == 2:\\r\\n tokens[token_type[0]] = token[1]\\r\\n return tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19edc6d0d232825d38e4c03a36dc0288\",\n \"score\": \"0.5747173\",\n \"text\": \"def get_token():\\n if g.current_user.is_anonymous() or g.token_used:\\n return unauthorized('Invalid credentials')\\n return jsonify({'token':g.current_user.generate_auth_token(expiration=3600),\\n 'expiration':3600})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5b5f971cd4892f93b2f1a6e8c3b352b\",\n \"score\": \"0.5743919\",\n \"text\": \"def encode_auth_token(self):\\n try:\\n payload = {\\n 'exp': datetime.datetime.utcnow() + datetime.timedelta(\\n days=app.config.get('AUTH_TOKEN_EXPIRY_DAYS'),\\n seconds=app.config.get('AUTH_TOKEN_EXPIRY_SECONDS')\\n ),\\n 'iat': datetime.datetime.utcnow(),\\n 'sub': self.id\\n }\\n return jwt.encode(\\n payload,\\n app.config['SECRET_KEY'],\\n algorithm='HS256'\\n ).decode('utf-8')\\n except Exception as e:\\n return e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7dcff8707cfdbdac98b8f2463f21710\",\n \"score\": \"0.5735489\",\n \"text\": \"def verify_token(token):\\n try:\\n # try to decode the token using our SECRET variable\\n payload = jwt.decode(token, current_app.config.get('SECRET_KEY'))\\n return payload['sub']\\n except jwt.ExpiredSignatureError:\\n # the token is expired, return an error string\\n return \\\"Expired token. Please login to get a new token.\\\"\\n except jwt.InvalidTokenError:\\n # the token is invalid, return an error string\\n return \\\"Invalid token. Please register or login.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7dcff8707cfdbdac98b8f2463f21710\",\n \"score\": \"0.5735489\",\n \"text\": \"def verify_token(token):\\n try:\\n # try to decode the token using our SECRET variable\\n payload = jwt.decode(token, current_app.config.get('SECRET_KEY'))\\n return payload['sub']\\n except jwt.ExpiredSignatureError:\\n # the token is expired, return an error string\\n return \\\"Expired token. Please login to get a new token.\\\"\\n except jwt.InvalidTokenError:\\n # the token is invalid, return an error string\\n return \\\"Invalid token. Please register or login.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce310ff12bb8ebe2a4faa219b6b06bdc\",\n \"score\": \"0.5721923\",\n \"text\": \"def decode(data: bytes) -> bytes:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"104078b94f2ed76585c006e443b967e3\",\n \"score\": \"0.57185364\",\n \"text\": \"def decode_base64_authorization_header(self,\\n base64_authorization_header: str\\n ) -> str:\\n if base64_authorization_header is None:\\n return None\\n if not isinstance(base64_authorization_header, str):\\n return None\\n try:\\n baseEncode = base64_authorization_header.encode('utf-8')\\n baseDecode = b64decode(baseEncode)\\n decodedValue = baseDecode.decode('utf-8')\\n return decodedValue\\n except Exception:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2443ce8530f1edbb3628016acb202c2\",\n \"score\": \"0.57102567\",\n \"text\": \"def decode_token(cls, key: str, token: str, verify: bool = True):\\n data = jwt.decode(token, key, algorithms=['HS256'], verify=verify)\\n # Enforce conversion to satisfy typing\\n data = dict(data)\\n return cls(**data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"364fb98c15541832fb844660d25bad68\",\n \"score\": \"0.5709821\",\n \"text\": \"def get_token_auth_header():\\n\\n auth = request.headers.get(\\\"Authorization\\\", None)\\n if not auth:\\n\\n abort(401)\\n raise AuthError({\\\"code\\\": \\\"authorization_header_missing\\\",\\n \\\"description\\\":\\n \\\"Authorization header is expected\\\"}, 401)\\n\\n parts = auth.split()\\n\\n if parts[0].lower() != \\\"bearer\\\":\\n abort(401)\\n raise AuthError({\\\"code\\\": \\\"invalid_header\\\",\\n \\\"description\\\":\\n \\\"Authorization header must start with\\\"\\n \\\" Bearer\\\"}, 401)\\n elif len(parts) == 1:\\n abort(401) # No token Sent\\n raise AuthError({\\\"code\\\": \\\"invalid_header\\\",\\n \\\"description\\\": \\\"Token not found\\\"}, 401)\\n elif len(parts) > 2:\\n abort(401)\\n raise AuthError({\\\"code\\\": \\\"invalid_header\\\",\\n \\\"description\\\":\\n \\\"Authorization header must be\\\"\\n \\\" Bearer token\\\"}, 401)\\n\\n token = parts[1]\\n return token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"037aa45e6203dbe33ff1df33cfae9cc7\",\n \"score\": \"0.570254\",\n \"text\": \"def _get_id_token_claims_without_verification(bearer_token: str):\\n\\ttry:\\n\\t\\theader, payload, signature = bearer_token.split(\\\".\\\")\\n\\texcept IndexError:\\n\\t\\tL.warning(\\\"Cannot parse ID token: Wrong number of '.'.\\\")\\n\\t\\traise aiohttp.web.HTTPBadRequest()\\n\\n\\ttry:\\n\\t\\tclaims = json.loads(base64.b64decode(payload.encode(\\\"utf-8\\\")))\\n\\texcept binascii.Error:\\n\\t\\tL.warning(\\\"Cannot parse ID token: Payload is not base 64.\\\")\\n\\t\\traise aiohttp.web.HTTPBadRequest()\\n\\texcept json.JSONDecodeError:\\n\\t\\tL.warning(\\\"Cannot parse ID token: Payload cannot be parsed as JSON.\\\")\\n\\t\\traise aiohttp.web.HTTPBadRequest()\\n\\n\\treturn claims\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994193,"cells":{"query_id":{"kind":"string","value":"68f06fdad826c9bce003a7c53d88e283"},"query":{"kind":"string","value":"Compute inverse CDF for a given value."},"positive_passages":{"kind":"list like","value":[{"docid":"cd4d641d3fb9a0113530e0471fedd092","score":"0.5649419","text":"def at(self, value):\n assert(0 <= value <= 1)\n return bisect.bisect_right(self.cdf, value)","title":""}],"string":"[\n {\n \"docid\": \"cd4d641d3fb9a0113530e0471fedd092\",\n \"score\": \"0.5649419\",\n \"text\": \"def at(self, value):\\n assert(0 <= value <= 1)\\n return bisect.bisect_right(self.cdf, value)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"2b8d83cb66b7de2be6af77838e1cc5db","score":"0.82533187","text":"def inverse_cdf(self, value):\n raise NotImplementedError","title":""},{"docid":"8a47abed2865294dce40758967737770","score":"0.66790795","text":"def inverse_cdf(self, u):\n return norm.ppf(u, loc=self.mean, scale=self.std)","title":""},{"docid":"a9a830e214f7a650caa327b2d1f4ac96","score":"0.66787773","text":"def invCDF_1(y):\n return -np.log(1-y)","title":""},{"docid":"d7022d7be1b09e8ba349c501494e1d34","score":"0.66091365","text":"def cdf(self, value):\n name = self.name + '_cdf'\n\n if not isinstance(value, framework.Variable):\n raise TypeError(\n f\"Expected type of value is Variable, but got {type(value)}\"\n )\n\n value = self._check_values_dtype_in_probs(self.loc, value)\n loc, scale, value = paddle.broadcast_tensors(\n [self.loc, self.scale, value]\n )\n\n return (\n paddle.atan(\n paddle.divide(paddle.subtract(value, loc), scale), name=name\n )\n / np.pi\n + 0.5\n )","title":""},{"docid":"40e91250d64c3c5e9f43ca81a41e391a","score":"0.63846","text":"def get_cdf(self, value):\n raise NotImplementedError","title":""},{"docid":"c6047928f1a0bf01ad3dca31a7b390a9","score":"0.62599695","text":"def cdf(self, x):\n return unif_cdf(self.a, self.b, x)","title":""},{"docid":"f559e6ea45d8d55c8fe7e87557ed27dc","score":"0.625413","text":"def _brentq_cdf(self, value):\n # The decorator expects an instance method, but usually are decorated before being bounded\n bound_cdf = partial(scalarize(GaussianKDE.cumulative_distribution), self)\n\n def f(x):\n return bound_cdf(x) - value\n\n return f","title":""},{"docid":"5c784ab4bc2633b6d4ade664346c1b2e","score":"0.6242908","text":"def cdf(self, x):\n if x < 0:\n return 0\n else:\n cdf = -Exponential.e ** (-self.lambtha * x) + 1\n return cdf","title":""},{"docid":"a76ba5429e3c5a643ad74bc6d634d299","score":"0.61770535","text":"def CDF_1(x):\n return 1 - np.exp(-x)","title":""},{"docid":"6732bc03fa0ae822f4946c92cfbf0485","score":"0.6118077","text":"def cdf(self, x):\n if x < 0:\n return (0)\n else:\n a = 1 - 2.7182818285 ** (-1 * self.lambtha * x)\n return (a)","title":""},{"docid":"57ae1c4403d0b5c307d1aaecba343f5c","score":"0.6087419","text":"def cdf(self, x):\n return tdist_cdf(self.v, x)","title":""},{"docid":"4767c864c9811fbe5a37503a53df38db","score":"0.60097516","text":"def normal_standard_cdf(val):\n return 1/2 * (1 + torch.erf(val/np.sqrt(2)))","title":""},{"docid":"6d33c8243ff8ebfb342a0c95777c3627","score":"0.59783274","text":"def cdf(self, x):\n return chisq_cdf(self.v, x)","title":""},{"docid":"eec7024ebfc0f5fc21efbd5d9cb54fe9","score":"0.59385973","text":"def cdf(self, x):\n return hyper_cdf(self.s, self.f, self.n, x)","title":""},{"docid":"75da5cf5c374d0b2496416fc37f45451","score":"0.59324825","text":"def invlogccdf(self, lq):\n return tdist_invlogccdf(self.v, lq)","title":""},{"docid":"9442406366674371eb915880830b967e","score":"0.59259665","text":"def _f(self, c):\n if c == 0:\n return 0\n return c**(-2)","title":""},{"docid":"7aea6173176a473366ea4878c8688175","score":"0.59132576","text":"def cdf(self, X):\n cdf = stats.norm.cdf(X)\n return cdf","title":""},{"docid":"a039dd5da32dbbbbdb2ae176aea8c767","score":"0.58947","text":"def invlogcdf(self, lq):\n return tdist_invlogcdf(self.v, lq)","title":""},{"docid":"78204983c4ee3949a16da8d44ca7a491","score":"0.58927554","text":"def cdf(self, x):\n return fdist_cdf(self.v1, self.v2, x)","title":""},{"docid":"884b9b999c62aba0bb60ebff57796763","score":"0.58926636","text":"def cdf(self, x):\n return weibull_cdf(self.alpha, self.theta, x)","title":""},{"docid":"336546d3ca733513d6dfc572132079fc","score":"0.5885892","text":"def cdf(self, x):\r\n return self._dist.cdf(x)","title":""},{"docid":"e89852dd709a70a0e7b6939954e757c9","score":"0.5881372","text":"def erfcinv(y):\r\n return -ndtri(0.5*y)/sqrt(2)","title":""},{"docid":"287713aa939d51923fe31093fb032cc7","score":"0.5875282","text":"def inv_exp_cdf(x, rate=1):\n return -np.log(1. - x) / rate","title":""},{"docid":"a31ec526f51ae6064d6cb8b4f7a3593b","score":"0.5861798","text":"def cdf(x, lmbd):\n\treturn 1 - np.exp(-lmbd * x)","title":""},{"docid":"440903ad02fdcfc1b840e26b7e0cb508","score":"0.584286","text":"def make_inv_cdf(cdf):\n inv_cdf = np.zeros((cdf['n_items'] + 1))\n cdf_ = np.int64(cdf['cdf'] * cdf['n_items'])\n\n idx, vals = np.unique(cdf_, return_index=True)\n \n for i in range(len(idx)-1):\n inv_cdf[idx[i]:idx[i+1]] = vals[i] * np.ones(len(inv_cdf[idx[i]:idx[i+1]]))\n inv_cdf[idx[i+1]] = vals[i+1]\n \n return {'inv_cdf': inv_cdf, 'n_items': cdf['n_items']}","title":""},{"docid":"6c8c145a53072816676ae58df79afcd9","score":"0.5829142","text":"def invlogccdf(self, lq):\n return fdist_invlogccdf(self.v1, self.v2, lq)","title":""},{"docid":"4b8b649fefedae2623d47a644e16a7a9","score":"0.58269393","text":"def cdf(x):\n\n y = math.fabs(x)\n\n if y < 7.07106781186547:\n a = (0.0352624965998911, 0.700383064443688,\n 6.37396220353165, 33.912866078383,\n 112.079291497871, 221.213596169931,\n 220.206867912376\n )\n b = (0.0883883476483184, 1.75566716318264,\n 16.064177579207, 86.7807322029461,\n 296.564248779674, 637.333633378831,\n 793.826512519948, 440.413735824752)\n\n aa = (((((a[0] * y + a[1]) * y + a[2]) * y + a[3]) * y + a[4]) * y + a[5]) * y + a[6]\n bb = ((((((b[0] * y + b[1]) * y + b[2]) * y + b[3]) * y + b[4]) * y + b[5]) * y + b[6]) * y + b[7]\n\n n = math.exp(- y * y / 2) * (aa / bb)\n\n elif 7.07106781186547 <= y <= 37:\n c = y + 1 / (y + 2 / (y + 3 / (y + 4 / (y + 0.65))))\n n = math.exp(- y * y / 2) / (2.506628274631 * c)\n\n else: # y > 37\n n = 0\n\n return (1 - n) if x > 0 else n","title":""},{"docid":"4b8d573dc1b8c53581051664196f7bae","score":"0.5817679","text":"def invert_value(self, value):\n return self.mean - (value - self.mean)","title":""},{"docid":"bed10650d8cfe3b58e58a53b3c14db01","score":"0.58132035","text":"def _denormalizeThroughCDF(self, data, params):\n denormed = self.normEngine.cdf(data)\n denormed = self._sampleICDF(denormed, params)\n return denormed","title":""},{"docid":"33853d941a4f7f3c0964f0dc6d636b74","score":"0.5811901","text":"def invlogccdf(self, lq):\n return chisq_invlogccdf(self.v, lq)","title":""},{"docid":"03985ea515768430eca0b720e85ea54b","score":"0.5804701","text":"def compute_cdf(x, y):\n\n\n return CDF","title":""},{"docid":"b982bec9b493bb1759ab5c42e92922f3","score":"0.5798588","text":"def cdf(self, x):\n y = 0.5 * betainc(self.m / 2.0, 0.5, np.sin(np.pi * x) ** 2)\n return np.where(x < 0.5, y, 1 - y)","title":""},{"docid":"dc75942917a4f289bba880f0876455f0","score":"0.5794821","text":"def cdf(self, x):\n expression = (x - self.mean)/(self.stddev * (2 ** (1/2)))\n return (1/2) * (1 + self.get_erf(expression))","title":""},{"docid":"7310adb4afb1906e15dd72242364b3b2","score":"0.5776293","text":"def getCdf(self):\r\n return self.cdf","title":""},{"docid":"4093e060178e3c2a053786f58645f1ec","score":"0.5768438","text":"def get_CLD_inverse(self, normalize=False):\n # Invert it to make a probability -> energy converter\n return self.get_CLD(normalize=normalize).inverse()","title":""},{"docid":"5dc76d08961eaff25614faf1496d16ca","score":"0.57647336","text":"def invlogcdf(self, lq):\n return unif_invlogcdf(self.a, self.b, lq)","title":""},{"docid":"64056fecc104306f530a2146cdc7ed27","score":"0.5759312","text":"def invlogccdf(self, lq):\n return hyper_invlogccdf(self.s, self.f, self.n, lq)","title":""},{"docid":"0d8c4f805e94d51f24219a9c738ae568","score":"0.5752328","text":"def ccdf(self, x):\n return tdist_ccdf(self.v, x)","title":""},{"docid":"2006bc4936193e310540a5ecdf2e6e53","score":"0.57477903","text":"def invlogcdf(self, lq):\n return chisq_invlogcdf(self.v, lq)","title":""},{"docid":"8b685ae1a3bff722919fb8d355a3f05e","score":"0.57215595","text":"def ccdf(self, x):\n return unif_ccdf(self.a, self.b, x)","title":""},{"docid":"411dfbb9b0348e77687e5b890f2f08a0","score":"0.5690793","text":"def invlogcdf(self, lq):\n return hyper_invlogcdf(self.s, self.f, self.n, lq)","title":""},{"docid":"4cb2e08cbc66b6a8b37d5b7e51d3ef4f","score":"0.5682713","text":"def invlogccdf(self, lq):\n return unif_invlogccdf(self.a, self.b, lq)","title":""},{"docid":"e4b4d0162b77d28552207c18782b57cc","score":"0.5679432","text":"def InverseStudentT(dof, probability):\n\n assert 0 <= probability <= 1\n\n if probability == 1:\n return float(\"inf\")\n if probability == 0:\n return float(\"-inf\")\n if probability == 0.5:\n return 0.0\n\n def f(x):\n return StudentTCDF(dof, x)\n\n return findRoot(probability, -10**4, 10**4, f)","title":""},{"docid":"e04bfdda3564323549832b7f46c6ab52","score":"0.56676","text":"def invlogccdf(self, lq):\n return binom_invlogccdf(self.n, self.p, lq)","title":""},{"docid":"d338b18a35c2a142c2fe7f955d882f9c","score":"0.5656897","text":"def invlogcdf(self, lq):\n return fdist_invlogcdf(self.v1, self.v2, lq)","title":""},{"docid":"38c6870130d99ee1241eb97208858f92","score":"0.564027","text":"def normal_cdf(x):\n return quad(norm.pdf, -5, x)[0]","title":""},{"docid":"8c549931d67e20ae6b200c750945ee1a","score":"0.56320333","text":"def cdf(self, x):\n return norm_cdf(self.mu, self.sigma, x)","title":""},{"docid":"17292149269746f37e001b67f7dea9ab","score":"0.56288797","text":"def cdf(self, x):\r\n return self._cdf(x,\r\n np.vectorize(lambda v: (self._dist_samples <= v).mean()))","title":""},{"docid":"e77c8c53c3f250d1437aca3789c77594","score":"0.5614364","text":"def invlogccdf(self, lq):\n return pois_invlogccdf(self.mu, lq)","title":""},{"docid":"98e0f3de5826b0c642c08926b0975ee2","score":"0.5608365","text":"def cdf(value, df, loc, scale, skewness):\n dtype = dtype_util.common_dtype(\n [value, df, loc, scale, skewness], tf.float32)\n numpy_dtype = dtype_util.as_numpy_dtype(dtype)\n\n value, df, loc, scale, skewness = [\n tf.convert_to_tensor(param, dtype=dtype)\n for param in (value, df, loc, scale, skewness)]\n\n one = numpy_dtype(1.)\n two = numpy_dtype(2.)\n\n t = standardize(value, loc=loc, scale=scale, skewness=skewness)\n t_cdf = student_t.stdtr(df, t=t)\n\n squared_skewness = tf.math.square(skewness)\n return tf.math.reciprocal(one + squared_skewness) * tf.where(\n t < 0.,\n two * t_cdf,\n one - squared_skewness + two * squared_skewness * t_cdf)","title":""},{"docid":"eff103fdd6db2b6427f142439a960a96","score":"0.56069505","text":"def inverse(self, value: Union[int, float, np.ndarray, to.Tensor]) -> Union[int, float, np.ndarray, to.Tensor]:\n inverse_fcn = getattr(self._wrapped_env, \"inverse\", None)\n if callable(inverse_fcn):\n return inverse_fcn(value)\n else:\n # Arrived at the inner env, no transformation found\n return value","title":""},{"docid":"79b39ea37a8d29b5f5659439f61d6577","score":"0.5598078","text":"def cdf(self, x):\n return pois_cdf(self.mu, x)","title":""},{"docid":"f4bb407dad7ff71cdfb68d86ec76a493","score":"0.55946785","text":"def cdf(self, x):\n return beta_cdf(self.alpha, self.beta, x)","title":""},{"docid":"7a88609931ee52e232a6a01f47c2df0c","score":"0.55895454","text":"def ccdf(self, x):\n return chisq_ccdf(self.v, x)","title":""},{"docid":"9330d575685a5304b26fa25d45a72356","score":"0.5583527","text":"def inverse(n):\r\n return 1.0 / n","title":""},{"docid":"e259d4341143ee50ecdef6008ac3764e","score":"0.55821663","text":"def cdf(self, x):\n return cauchy_cdf(self.mu, self.sigma, x)","title":""},{"docid":"dcceb3de94dd77028b0fe447669a1e26","score":"0.5571044","text":"def f_distribution_critical_value(f_value, df_numerator, df_denominator, loc=0, scale=1):\n return float(FDistribution.pdf(f_value, df_numerator, df_denominator, loc, scale))","title":""},{"docid":"171db98af1d41539fd1a4753f46559ad","score":"0.5568167","text":"def cdf(self, x):\n return binom_cdf(self.n, self.p, x)","title":""},{"docid":"8d99c36bdc39b58aa16b93ebde53a23e","score":"0.55600554","text":"def invlogccdf(self, lq):\n return geom_invlogccdf(self.p, lq)","title":""},{"docid":"c0771297c20464eb7f8453801d347ceb","score":"0.55588114","text":"def _invf(self, fY):\n Y = fY ** 3\n Y[Y < 0.008856] = (fY[Y < 0.008856] - 4 / 29) * (108 / 841)\n return Y","title":""},{"docid":"94311dbf4c2abbf539cda3777fcff80c","score":"0.55569875","text":"def inverse(self):\n return self.distribution.inverse","title":""},{"docid":"756b2519c94a5e038aa4d582202ac77d","score":"0.55568653","text":"def invlogccdf(self, lq):\n return beta_invlogccdf(self.alpha, self.beta, lq)","title":""},{"docid":"5a7fc7effe3f4c9df630e195259fae4a","score":"0.5555484","text":"def cdf(self, x):\n return exp_cdf(self.theta, x)","title":""},{"docid":"2e886380cbf848f5a05d4ce3d34c35dc","score":"0.55547625","text":"def invlogccdf(self, lq):\n return cauchy_invlogccdf(self.mu, self.sigma, lq)","title":""},{"docid":"68255f0f34bf10f818b16d82e7d274c7","score":"0.5553923","text":"def invcdf(p, loc=0, scale=1):\n with mp.extradps(5):\n p = _validate_p(p)\n loc, scale = _validate_params(loc, scale)\n if p == 0:\n return mp.zero\n if p == 1:\n return mp.inf\n x0, x1 = _find_bracket(lambda x: cdf(x, loc, scale), p, 0, mp.inf)\n if x0 == x1:\n return x0\n try:\n x = mp.findroot(lambda x: cdf(x, loc, scale) - p, x0=(x0, x1),\n solver='secant')\n except Exception:\n x = mp.findroot(lambda x: cdf(x, loc, scale) - p, x0=(x0 + x1)/2,\n solver='newton')\n return x","title":""},{"docid":"667f8b8ceff4ac8b83ae4368d8a18f59","score":"0.5553153","text":"def infomation(val):\n return -val*(math.log(val, 2))","title":""},{"docid":"25dc70568ca80852c1520b3e7cf77507","score":"0.5545433","text":"def cdf(self, x):\n\n if self.shapeParam != 0:\n return 1 - (1 + self.shapeParam * x / self.scaleParam) ** (-1 / self.shapeParam)\n\n else:\n return 1 - np.exp(-x / self.scaleParam)","title":""},{"docid":"2830ebe5c55894d4fd57d100002c4c98","score":"0.5542253","text":"def _cdf(self, value, mean=None, sd=None):\n value = self._check_value(value, 'value')\n value = self.cast(value, self.dtype)\n mean, sd = self._check_param_type(mean, sd)\n sqrt2 = self.sqrt(self.const(2.0, mstype.float32))\n adjusted = (value - mean) / (sd * sqrt2)\n return 0.5 * (1.0 + self.erf(adjusted))","title":""},{"docid":"53efa7306d9de168571cecd08a7344c1","score":"0.55303943","text":"def ccdf(self, x):\n return hyper_ccdf(self.s, self.f, self.n, x)","title":""},{"docid":"e74add657ff9d6fe695b70de154e40df","score":"0.55253017","text":"def inverse(x): \n return x ** 2","title":""},{"docid":"54f8066fd2b6b34c5847cac5df4a3033","score":"0.5516761","text":"def ccdf(self, x):\n return weibull_ccdf(self.alpha, self.theta, x)","title":""},{"docid":"9927443112ea9d6f42099dc2491e86ac","score":"0.55085015","text":"def calculate_inverse_density(cluster):\n inverse_density = cluster['volume'] / cluster['size']\n return inverse_density","title":""},{"docid":"3b0f16e23f36d99c3ecd0ec978883b5c","score":"0.5508163","text":"def cdf(self, x):\n\n a = 1 + self.erf((x - self.mean) / (self.stddev * (2 ** 0.5)))\n return a / 2","title":""},{"docid":"f8229739df569c68d4c64bbaaa40d784","score":"0.55026054","text":"def calculate_cdf(histogram):\n # Get the cumulative sum of the elements\n cdf = histogram.cumsum()\n \n # Normalize the cdf\n normalized_cdf = cdf / float(cdf.max())\n \n return normalized_cdf","title":""},{"docid":"b7c4f45e2fcf6f3ed027952df8369dfe","score":"0.5499215","text":"def cdfXY(inputV):\n # used to do this with statsmodels.distributions.ECDF but this removes that dependency:\n def ecdf(x):\n xs = np.sort(x)\n ys = np.arange(1, len(xs)+1)/float(len(xs))\n return xs, ys\n return ecdf(inputV)","title":""},{"docid":"e0efa01b3014a1bcd80e4d68b5fd78fe","score":"0.54928744","text":"def ccdf(self, x):\n return fdist_ccdf(self.v1, self.v2, x)","title":""},{"docid":"d3b65b38638d33d8687cdb5c64154e64","score":"0.5489231","text":"def inverse_transforme_method(u_value: float, lambda_value: float):\n\n try:\n return -(log(1-u_value)/lambda_value)\n except ValueError:\n return 0","title":""},{"docid":"d746bd0d451daa9b2ff927b4dc2bd0ff","score":"0.5487835","text":"def get_density_func(cdf, var):\r\n\r\n pdf = cdf\r\n\r\n for i in range(len(var)):\r\n pdf = diff(pdf, var[i])\r\n \r\n return pdf","title":""},{"docid":"137176572c6621c9eb7efbfab9407f90","score":"0.54775673","text":"def cdf(self, x):\n return gamma_cdf(self.alpha, self.beta, x)","title":""},{"docid":"7c636de8ec16ca889e5eafb346ee394d","score":"0.54764175","text":"def invlogcdf(self, lq):\n return beta_invlogcdf(self.alpha, self.beta, lq)","title":""},{"docid":"480c72583a2e36f1ce1efd1fa1df3e1f","score":"0.54662704","text":"def cdf(self, x):\n return lognormal_cdf(self.mu, self.sigma, x)","title":""},{"docid":"6f7335ac823d7fc32b0ea476792725d1","score":"0.54565454","text":"def invlogcdf(self, lq):\n return geom_invlogcdf(self.p, lq)","title":""},{"docid":"31ff97ebd8dd1da9593b1c3426f377f4","score":"0.54556006","text":"def cdf(self, x):\n return logis_cdf(self.mu, self.theta, x)","title":""},{"docid":"ddcec04e32ccfa9bc0f30644bec33bbf","score":"0.5453972","text":"def ecdf(x):\n nobs = len(x)\n return arange(1,nobs+1)/float(nobs)","title":""},{"docid":"d117e79033fc7ad85dc4d45d4da58899","score":"0.5453101","text":"def compute_inverse(\n b_func, epsilon, delta\n):\n sol = optimize.root_scalar(\n lambda v: b_func(v, epsilon) - delta, bracket=(0, 5000)\n )\n return sol.root","title":""},{"docid":"f74e6ea083396b4d9964a5d30797d470","score":"0.5446779","text":"def invlogcdf(self, lq):\n return pois_invlogcdf(self.mu, lq)","title":""},{"docid":"31f87e969f8bf886aea8a0bafba815c9","score":"0.54462343","text":"def invlogccdf(self, lq):\n return norm_invlogccdf(self.mu, self.sigma, lq)","title":""},{"docid":"f5b06b53db76a3f48c4de7b6f00be539","score":"0.54456246","text":"def invlogcdf(self, lq):\n return binom_invlogcdf(self.n, self.p, lq)","title":""},{"docid":"d64a82ddb645de1a9f6ed1658d313e55","score":"0.5436837","text":"def invcdf(p, mu=0, sigma=1):\n with mp.extradps(mp.dps):\n p = _validate_p(p)\n mu = mp.mpf(mu)\n sigma = mp.mpf(sigma)\n\n a = mp.erfinv(2*p - 1)\n x = mp.sqrt(2)*sigma*a + mu\n return x","title":""},{"docid":"8c7392df177d14f992faea1019012fb9","score":"0.5434988","text":"def logccdf(self, x):\n return chisq_logccdf(self.v, x)","title":""},{"docid":"6994d24f59877082fbfd2e50ba1dc94b","score":"0.5425989","text":"def invlogccdf(self, lq):\n return exp_invlogccdf(self.theta, lq)","title":""},{"docid":"d45bfed2c1b83a280a680656c44b91d7","score":"0.5424483","text":"def df(x):\n return (2*x - x**2 * cot(x))/sin(x)","title":""},{"docid":"16d33a192cb34ca689df5a7d39724160","score":"0.54218924","text":"def icdf(self, p, mode='toobig'):\n assert 0 <= p <= 1, \"Desired CDF prob must be between 0 and 1 inclusive\"\n\n if mode=='toobig':\n for i in range(self.size):\n x = self.domain[i]\n # if cdf=p, we're done.\n # if cdf>p, that means we just went past a gap, and we're now\n # returning the smallest too-big value.\n if self.cdf(x) >= p:\n return x\n else:\n # This should never happen since p is no more than 1.0, and\n # always the last item in the domain has CDF=1.0.\n assert False, \"Didn't find value with prob at least %s\" % p\n elif mode=='toosmall':\n # go in descending order\n # list(range(5,-1,-1)) => [5, 4, 3, 2, 1, 0]\n for i in range(self.size-1, -1, -1):\n x = self.domain[i]\n if self.cdf(x) <= p:\n return x\n else:\n # The problem now is, the user requested a CDF less then the\n # CDF of the smallest value in the support (and we don't have a\n # separate notion of the domain).\n # Not sure what the right behavior is. The 'toosmall'\n # criterion doesn't make sense here.\n assert False, \"Didn't find value with prob <= %s\" % p\n else:\n assert False, \"bad mode given\"","title":""},{"docid":"654cf3a02a9980f2c5dde1d42467fb3f","score":"0.5415278","text":"def invlogccdf(self, lq):\n return nbinom_invlogccdf(self.r, self.p, lq)","title":""},{"docid":"b01688fe13a2017bee16b385e985ee9e","score":"0.54141307","text":"def invlogccdf(self, lq):\n return gamma_invlogccdf(self.alpha, self.beta, lq)","title":""},{"docid":"763d32cb85924b9ec17d2de3cd1f7ccb","score":"0.5406932","text":"def pdf_to_cdf(pdf):\n cdf = np.cumsum(pdf)\n cdf /= cdf[-1]\n\n return cdf","title":""},{"docid":"1fdbd0c5d2de135c6b52864e27d459ec","score":"0.54047555","text":"def invlogccdf(self, lq):\n return weibull_invlogccdf(self.alpha, self.theta, lq)","title":""},{"docid":"1c5818d5ec689dbac9d65e47071086c1","score":"0.53933424","text":"def cdf(self, x):\r\n raise NotImplementedError","title":""},{"docid":"7c647257663ad44e7275fa66b04cc5f7","score":"0.53895634","text":"def logccdf(self, x):\n return fdist_logccdf(self.v1, self.v2, x)","title":""},{"docid":"fc78717523efa3cc775c3db0352f2ac8","score":"0.53876674","text":"def invlogccdf(self, lq):\n return lognormal_invlogccdf(self.mu, self.sigma, lq)","title":""}],"string":"[\n {\n \"docid\": \"2b8d83cb66b7de2be6af77838e1cc5db\",\n \"score\": \"0.82533187\",\n \"text\": \"def inverse_cdf(self, value):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a47abed2865294dce40758967737770\",\n \"score\": \"0.66790795\",\n \"text\": \"def inverse_cdf(self, u):\\n return norm.ppf(u, loc=self.mean, scale=self.std)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9a830e214f7a650caa327b2d1f4ac96\",\n \"score\": \"0.66787773\",\n \"text\": \"def invCDF_1(y):\\n return -np.log(1-y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7022d7be1b09e8ba349c501494e1d34\",\n \"score\": \"0.66091365\",\n \"text\": \"def cdf(self, value):\\n name = self.name + '_cdf'\\n\\n if not isinstance(value, framework.Variable):\\n raise TypeError(\\n f\\\"Expected type of value is Variable, but got {type(value)}\\\"\\n )\\n\\n value = self._check_values_dtype_in_probs(self.loc, value)\\n loc, scale, value = paddle.broadcast_tensors(\\n [self.loc, self.scale, value]\\n )\\n\\n return (\\n paddle.atan(\\n paddle.divide(paddle.subtract(value, loc), scale), name=name\\n )\\n / np.pi\\n + 0.5\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40e91250d64c3c5e9f43ca81a41e391a\",\n \"score\": \"0.63846\",\n \"text\": \"def get_cdf(self, value):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6047928f1a0bf01ad3dca31a7b390a9\",\n \"score\": \"0.62599695\",\n \"text\": \"def cdf(self, x):\\n return unif_cdf(self.a, self.b, x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f559e6ea45d8d55c8fe7e87557ed27dc\",\n \"score\": \"0.625413\",\n \"text\": \"def _brentq_cdf(self, value):\\n # The decorator expects an instance method, but usually are decorated before being bounded\\n bound_cdf = partial(scalarize(GaussianKDE.cumulative_distribution), self)\\n\\n def f(x):\\n return bound_cdf(x) - value\\n\\n return f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c784ab4bc2633b6d4ade664346c1b2e\",\n \"score\": \"0.6242908\",\n \"text\": \"def cdf(self, x):\\n if x < 0:\\n return 0\\n else:\\n cdf = -Exponential.e ** (-self.lambtha * x) + 1\\n return cdf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a76ba5429e3c5a643ad74bc6d634d299\",\n \"score\": \"0.61770535\",\n \"text\": \"def CDF_1(x):\\n return 1 - np.exp(-x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6732bc03fa0ae822f4946c92cfbf0485\",\n \"score\": \"0.6118077\",\n \"text\": \"def cdf(self, x):\\n if x < 0:\\n return (0)\\n else:\\n a = 1 - 2.7182818285 ** (-1 * self.lambtha * x)\\n return (a)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57ae1c4403d0b5c307d1aaecba343f5c\",\n \"score\": \"0.6087419\",\n \"text\": \"def cdf(self, x):\\n return tdist_cdf(self.v, x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4767c864c9811fbe5a37503a53df38db\",\n \"score\": \"0.60097516\",\n \"text\": \"def normal_standard_cdf(val):\\n return 1/2 * (1 + torch.erf(val/np.sqrt(2)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d33c8243ff8ebfb342a0c95777c3627\",\n \"score\": \"0.59783274\",\n \"text\": \"def cdf(self, x):\\n return chisq_cdf(self.v, x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eec7024ebfc0f5fc21efbd5d9cb54fe9\",\n \"score\": \"0.59385973\",\n \"text\": \"def cdf(self, x):\\n return hyper_cdf(self.s, self.f, self.n, x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75da5cf5c374d0b2496416fc37f45451\",\n \"score\": \"0.59324825\",\n \"text\": \"def invlogccdf(self, lq):\\n return tdist_invlogccdf(self.v, lq)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9442406366674371eb915880830b967e\",\n \"score\": \"0.59259665\",\n \"text\": \"def _f(self, c):\\n if c == 0:\\n return 0\\n return c**(-2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7aea6173176a473366ea4878c8688175\",\n \"score\": \"0.59132576\",\n \"text\": \"def cdf(self, X):\\n cdf = stats.norm.cdf(X)\\n return cdf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a039dd5da32dbbbbdb2ae176aea8c767\",\n \"score\": \"0.58947\",\n \"text\": \"def invlogcdf(self, lq):\\n return tdist_invlogcdf(self.v, lq)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78204983c4ee3949a16da8d44ca7a491\",\n \"score\": \"0.58927554\",\n \"text\": \"def cdf(self, x):\\n return fdist_cdf(self.v1, self.v2, x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"884b9b999c62aba0bb60ebff57796763\",\n \"score\": \"0.58926636\",\n \"text\": \"def cdf(self, x):\\n return weibull_cdf(self.alpha, self.theta, x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"336546d3ca733513d6dfc572132079fc\",\n \"score\": \"0.5885892\",\n \"text\": \"def cdf(self, x):\\r\\n return self._dist.cdf(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e89852dd709a70a0e7b6939954e757c9\",\n \"score\": \"0.5881372\",\n \"text\": \"def erfcinv(y):\\r\\n return -ndtri(0.5*y)/sqrt(2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"287713aa939d51923fe31093fb032cc7\",\n \"score\": \"0.5875282\",\n \"text\": \"def inv_exp_cdf(x, rate=1):\\n return -np.log(1. - x) / rate\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a31ec526f51ae6064d6cb8b4f7a3593b\",\n \"score\": \"0.5861798\",\n \"text\": \"def cdf(x, lmbd):\\n\\treturn 1 - np.exp(-lmbd * x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"440903ad02fdcfc1b840e26b7e0cb508\",\n \"score\": \"0.584286\",\n \"text\": \"def make_inv_cdf(cdf):\\n inv_cdf = np.zeros((cdf['n_items'] + 1))\\n cdf_ = np.int64(cdf['cdf'] * cdf['n_items'])\\n\\n idx, vals = np.unique(cdf_, return_index=True)\\n \\n for i in range(len(idx)-1):\\n inv_cdf[idx[i]:idx[i+1]] = vals[i] * np.ones(len(inv_cdf[idx[i]:idx[i+1]]))\\n inv_cdf[idx[i+1]] = vals[i+1]\\n \\n return {'inv_cdf': inv_cdf, 'n_items': cdf['n_items']}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c8c145a53072816676ae58df79afcd9\",\n \"score\": \"0.5829142\",\n \"text\": \"def invlogccdf(self, lq):\\n return fdist_invlogccdf(self.v1, self.v2, lq)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b8b649fefedae2623d47a644e16a7a9\",\n \"score\": \"0.58269393\",\n \"text\": \"def cdf(x):\\n\\n y = math.fabs(x)\\n\\n if y < 7.07106781186547:\\n a = (0.0352624965998911, 0.700383064443688,\\n 6.37396220353165, 33.912866078383,\\n 112.079291497871, 221.213596169931,\\n 220.206867912376\\n )\\n b = (0.0883883476483184, 1.75566716318264,\\n 16.064177579207, 86.7807322029461,\\n 296.564248779674, 637.333633378831,\\n 793.826512519948, 440.413735824752)\\n\\n aa = (((((a[0] * y + a[1]) * y + a[2]) * y + a[3]) * y + a[4]) * y + a[5]) * y + a[6]\\n bb = ((((((b[0] * y + b[1]) * y + b[2]) * y + b[3]) * y + b[4]) * y + b[5]) * y + b[6]) * y + b[7]\\n\\n n = math.exp(- y * y / 2) * (aa / bb)\\n\\n elif 7.07106781186547 <= y <= 37:\\n c = y + 1 / (y + 2 / (y + 3 / (y + 4 / (y + 0.65))))\\n n = math.exp(- y * y / 2) / (2.506628274631 * c)\\n\\n else: # y > 37\\n n = 0\\n\\n return (1 - n) if x > 0 else n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b8d573dc1b8c53581051664196f7bae\",\n \"score\": \"0.5817679\",\n \"text\": \"def invert_value(self, value):\\n return self.mean - (value - self.mean)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bed10650d8cfe3b58e58a53b3c14db01\",\n \"score\": \"0.58132035\",\n \"text\": \"def _denormalizeThroughCDF(self, data, params):\\n denormed = self.normEngine.cdf(data)\\n denormed = self._sampleICDF(denormed, params)\\n return denormed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33853d941a4f7f3c0964f0dc6d636b74\",\n \"score\": \"0.5811901\",\n \"text\": \"def invlogccdf(self, lq):\\n return chisq_invlogccdf(self.v, lq)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03985ea515768430eca0b720e85ea54b\",\n \"score\": \"0.5804701\",\n \"text\": \"def compute_cdf(x, y):\\n\\n\\n return CDF\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b982bec9b493bb1759ab5c42e92922f3\",\n \"score\": \"0.5798588\",\n \"text\": \"def cdf(self, x):\\n y = 0.5 * betainc(self.m / 2.0, 0.5, np.sin(np.pi * x) ** 2)\\n return np.where(x < 0.5, y, 1 - y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc75942917a4f289bba880f0876455f0\",\n \"score\": \"0.5794821\",\n \"text\": \"def cdf(self, x):\\n expression = (x - self.mean)/(self.stddev * (2 ** (1/2)))\\n return (1/2) * (1 + self.get_erf(expression))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7310adb4afb1906e15dd72242364b3b2\",\n \"score\": \"0.5776293\",\n \"text\": \"def getCdf(self):\\r\\n return self.cdf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4093e060178e3c2a053786f58645f1ec\",\n \"score\": \"0.5768438\",\n \"text\": \"def get_CLD_inverse(self, normalize=False):\\n # Invert it to make a probability -> energy converter\\n return self.get_CLD(normalize=normalize).inverse()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dc76d08961eaff25614faf1496d16ca\",\n \"score\": \"0.57647336\",\n \"text\": \"def invlogcdf(self, lq):\\n return unif_invlogcdf(self.a, self.b, lq)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64056fecc104306f530a2146cdc7ed27\",\n \"score\": \"0.5759312\",\n \"text\": \"def invlogccdf(self, lq):\\n return hyper_invlogccdf(self.s, self.f, self.n, lq)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d8c4f805e94d51f24219a9c738ae568\",\n \"score\": \"0.5752328\",\n \"text\": \"def ccdf(self, x):\\n return tdist_ccdf(self.v, x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2006bc4936193e310540a5ecdf2e6e53\",\n \"score\": \"0.57477903\",\n \"text\": \"def invlogcdf(self, lq):\\n return chisq_invlogcdf(self.v, lq)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b685ae1a3bff722919fb8d355a3f05e\",\n \"score\": \"0.57215595\",\n \"text\": \"def ccdf(self, x):\\n return unif_ccdf(self.a, self.b, x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"411dfbb9b0348e77687e5b890f2f08a0\",\n \"score\": \"0.5690793\",\n \"text\": \"def invlogcdf(self, lq):\\n return hyper_invlogcdf(self.s, self.f, self.n, lq)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cb2e08cbc66b6a8b37d5b7e51d3ef4f\",\n \"score\": \"0.5682713\",\n \"text\": \"def invlogccdf(self, lq):\\n return unif_invlogccdf(self.a, self.b, lq)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4b4d0162b77d28552207c18782b57cc\",\n \"score\": \"0.5679432\",\n \"text\": \"def InverseStudentT(dof, probability):\\n\\n assert 0 <= probability <= 1\\n\\n if probability == 1:\\n return float(\\\"inf\\\")\\n if probability == 0:\\n return float(\\\"-inf\\\")\\n if probability == 0.5:\\n return 0.0\\n\\n def f(x):\\n return StudentTCDF(dof, x)\\n\\n return findRoot(probability, -10**4, 10**4, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e04bfdda3564323549832b7f46c6ab52\",\n \"score\": \"0.56676\",\n \"text\": \"def invlogccdf(self, lq):\\n return binom_invlogccdf(self.n, self.p, lq)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d338b18a35c2a142c2fe7f955d882f9c\",\n \"score\": \"0.5656897\",\n \"text\": \"def invlogcdf(self, lq):\\n return fdist_invlogcdf(self.v1, self.v2, lq)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38c6870130d99ee1241eb97208858f92\",\n \"score\": \"0.564027\",\n \"text\": \"def normal_cdf(x):\\n return quad(norm.pdf, -5, x)[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c549931d67e20ae6b200c750945ee1a\",\n \"score\": \"0.56320333\",\n \"text\": \"def cdf(self, x):\\n return norm_cdf(self.mu, self.sigma, x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17292149269746f37e001b67f7dea9ab\",\n \"score\": \"0.56288797\",\n \"text\": \"def cdf(self, x):\\r\\n return self._cdf(x,\\r\\n np.vectorize(lambda v: (self._dist_samples <= v).mean()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e77c8c53c3f250d1437aca3789c77594\",\n \"score\": \"0.5614364\",\n \"text\": \"def invlogccdf(self, lq):\\n return pois_invlogccdf(self.mu, lq)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98e0f3de5826b0c642c08926b0975ee2\",\n \"score\": \"0.5608365\",\n \"text\": \"def cdf(value, df, loc, scale, skewness):\\n dtype = dtype_util.common_dtype(\\n [value, df, loc, scale, skewness], tf.float32)\\n numpy_dtype = dtype_util.as_numpy_dtype(dtype)\\n\\n value, df, loc, scale, skewness = [\\n tf.convert_to_tensor(param, dtype=dtype)\\n for param in (value, df, loc, scale, skewness)]\\n\\n one = numpy_dtype(1.)\\n two = numpy_dtype(2.)\\n\\n t = standardize(value, loc=loc, scale=scale, skewness=skewness)\\n t_cdf = student_t.stdtr(df, t=t)\\n\\n squared_skewness = tf.math.square(skewness)\\n return tf.math.reciprocal(one + squared_skewness) * tf.where(\\n t < 0.,\\n two * t_cdf,\\n one - squared_skewness + two * squared_skewness * t_cdf)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eff103fdd6db2b6427f142439a960a96\",\n \"score\": \"0.56069505\",\n \"text\": \"def inverse(self, value: Union[int, float, np.ndarray, to.Tensor]) -> Union[int, float, np.ndarray, to.Tensor]:\\n inverse_fcn = getattr(self._wrapped_env, \\\"inverse\\\", None)\\n if callable(inverse_fcn):\\n return inverse_fcn(value)\\n else:\\n # Arrived at the inner env, no transformation found\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79b39ea37a8d29b5f5659439f61d6577\",\n \"score\": \"0.5598078\",\n \"text\": \"def cdf(self, x):\\n return pois_cdf(self.mu, x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4bb407dad7ff71cdfb68d86ec76a493\",\n \"score\": \"0.55946785\",\n \"text\": \"def cdf(self, x):\\n return beta_cdf(self.alpha, self.beta, x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a88609931ee52e232a6a01f47c2df0c\",\n \"score\": \"0.55895454\",\n \"text\": \"def ccdf(self, x):\\n return chisq_ccdf(self.v, x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9330d575685a5304b26fa25d45a72356\",\n \"score\": \"0.5583527\",\n \"text\": \"def inverse(n):\\r\\n return 1.0 / n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e259d4341143ee50ecdef6008ac3764e\",\n \"score\": \"0.55821663\",\n \"text\": \"def cdf(self, x):\\n return cauchy_cdf(self.mu, self.sigma, x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcceb3de94dd77028b0fe447669a1e26\",\n \"score\": \"0.5571044\",\n \"text\": \"def f_distribution_critical_value(f_value, df_numerator, df_denominator, loc=0, scale=1):\\n return float(FDistribution.pdf(f_value, df_numerator, df_denominator, loc, scale))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"171db98af1d41539fd1a4753f46559ad\",\n \"score\": \"0.5568167\",\n \"text\": \"def cdf(self, x):\\n return binom_cdf(self.n, self.p, x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d99c36bdc39b58aa16b93ebde53a23e\",\n \"score\": \"0.55600554\",\n \"text\": \"def invlogccdf(self, lq):\\n return geom_invlogccdf(self.p, lq)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0771297c20464eb7f8453801d347ceb\",\n \"score\": \"0.55588114\",\n \"text\": \"def _invf(self, fY):\\n Y = fY ** 3\\n Y[Y < 0.008856] = (fY[Y < 0.008856] - 4 / 29) * (108 / 841)\\n return Y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94311dbf4c2abbf539cda3777fcff80c\",\n \"score\": \"0.55569875\",\n \"text\": \"def inverse(self):\\n return self.distribution.inverse\",\n \"title\": \"\"\n },\n {\n \"docid\": \"756b2519c94a5e038aa4d582202ac77d\",\n \"score\": \"0.55568653\",\n \"text\": \"def invlogccdf(self, lq):\\n return beta_invlogccdf(self.alpha, self.beta, lq)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a7fc7effe3f4c9df630e195259fae4a\",\n \"score\": \"0.5555484\",\n \"text\": \"def cdf(self, x):\\n return exp_cdf(self.theta, x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e886380cbf848f5a05d4ce3d34c35dc\",\n \"score\": \"0.55547625\",\n \"text\": \"def invlogccdf(self, lq):\\n return cauchy_invlogccdf(self.mu, self.sigma, lq)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68255f0f34bf10f818b16d82e7d274c7\",\n \"score\": \"0.5553923\",\n \"text\": \"def invcdf(p, loc=0, scale=1):\\n with mp.extradps(5):\\n p = _validate_p(p)\\n loc, scale = _validate_params(loc, scale)\\n if p == 0:\\n return mp.zero\\n if p == 1:\\n return mp.inf\\n x0, x1 = _find_bracket(lambda x: cdf(x, loc, scale), p, 0, mp.inf)\\n if x0 == x1:\\n return x0\\n try:\\n x = mp.findroot(lambda x: cdf(x, loc, scale) - p, x0=(x0, x1),\\n solver='secant')\\n except Exception:\\n x = mp.findroot(lambda x: cdf(x, loc, scale) - p, x0=(x0 + x1)/2,\\n solver='newton')\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"667f8b8ceff4ac8b83ae4368d8a18f59\",\n \"score\": \"0.5553153\",\n \"text\": \"def infomation(val):\\n return -val*(math.log(val, 2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25dc70568ca80852c1520b3e7cf77507\",\n \"score\": \"0.5545433\",\n \"text\": \"def cdf(self, x):\\n\\n if self.shapeParam != 0:\\n return 1 - (1 + self.shapeParam * x / self.scaleParam) ** (-1 / self.shapeParam)\\n\\n else:\\n return 1 - np.exp(-x / self.scaleParam)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2830ebe5c55894d4fd57d100002c4c98\",\n \"score\": \"0.5542253\",\n \"text\": \"def _cdf(self, value, mean=None, sd=None):\\n value = self._check_value(value, 'value')\\n value = self.cast(value, self.dtype)\\n mean, sd = self._check_param_type(mean, sd)\\n sqrt2 = self.sqrt(self.const(2.0, mstype.float32))\\n adjusted = (value - mean) / (sd * sqrt2)\\n return 0.5 * (1.0 + self.erf(adjusted))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53efa7306d9de168571cecd08a7344c1\",\n \"score\": \"0.55303943\",\n \"text\": \"def ccdf(self, x):\\n return hyper_ccdf(self.s, self.f, self.n, x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e74add657ff9d6fe695b70de154e40df\",\n \"score\": \"0.55253017\",\n \"text\": \"def inverse(x): \\n return x ** 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54f8066fd2b6b34c5847cac5df4a3033\",\n \"score\": \"0.5516761\",\n \"text\": \"def ccdf(self, x):\\n return weibull_ccdf(self.alpha, self.theta, x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9927443112ea9d6f42099dc2491e86ac\",\n \"score\": \"0.55085015\",\n \"text\": \"def calculate_inverse_density(cluster):\\n inverse_density = cluster['volume'] / cluster['size']\\n return inverse_density\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b0f16e23f36d99c3ecd0ec978883b5c\",\n \"score\": \"0.5508163\",\n \"text\": \"def cdf(self, x):\\n\\n a = 1 + self.erf((x - self.mean) / (self.stddev * (2 ** 0.5)))\\n return a / 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8229739df569c68d4c64bbaaa40d784\",\n \"score\": \"0.55026054\",\n \"text\": \"def calculate_cdf(histogram):\\n # Get the cumulative sum of the elements\\n cdf = histogram.cumsum()\\n \\n # Normalize the cdf\\n normalized_cdf = cdf / float(cdf.max())\\n \\n return normalized_cdf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7c4f45e2fcf6f3ed027952df8369dfe\",\n \"score\": \"0.5499215\",\n \"text\": \"def cdfXY(inputV):\\n # used to do this with statsmodels.distributions.ECDF but this removes that dependency:\\n def ecdf(x):\\n xs = np.sort(x)\\n ys = np.arange(1, len(xs)+1)/float(len(xs))\\n return xs, ys\\n return ecdf(inputV)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0efa01b3014a1bcd80e4d68b5fd78fe\",\n \"score\": \"0.54928744\",\n \"text\": \"def ccdf(self, x):\\n return fdist_ccdf(self.v1, self.v2, x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3b65b38638d33d8687cdb5c64154e64\",\n \"score\": \"0.5489231\",\n \"text\": \"def inverse_transforme_method(u_value: float, lambda_value: float):\\n\\n try:\\n return -(log(1-u_value)/lambda_value)\\n except ValueError:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d746bd0d451daa9b2ff927b4dc2bd0ff\",\n \"score\": \"0.5487835\",\n \"text\": \"def get_density_func(cdf, var):\\r\\n\\r\\n pdf = cdf\\r\\n\\r\\n for i in range(len(var)):\\r\\n pdf = diff(pdf, var[i])\\r\\n \\r\\n return pdf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"137176572c6621c9eb7efbfab9407f90\",\n \"score\": \"0.54775673\",\n \"text\": \"def cdf(self, x):\\n return gamma_cdf(self.alpha, self.beta, x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c636de8ec16ca889e5eafb346ee394d\",\n \"score\": \"0.54764175\",\n \"text\": \"def invlogcdf(self, lq):\\n return beta_invlogcdf(self.alpha, self.beta, lq)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"480c72583a2e36f1ce1efd1fa1df3e1f\",\n \"score\": \"0.54662704\",\n \"text\": \"def cdf(self, x):\\n return lognormal_cdf(self.mu, self.sigma, x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f7335ac823d7fc32b0ea476792725d1\",\n \"score\": \"0.54565454\",\n \"text\": \"def invlogcdf(self, lq):\\n return geom_invlogcdf(self.p, lq)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31ff97ebd8dd1da9593b1c3426f377f4\",\n \"score\": \"0.54556006\",\n \"text\": \"def cdf(self, x):\\n return logis_cdf(self.mu, self.theta, x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddcec04e32ccfa9bc0f30644bec33bbf\",\n \"score\": \"0.5453972\",\n \"text\": \"def ecdf(x):\\n nobs = len(x)\\n return arange(1,nobs+1)/float(nobs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d117e79033fc7ad85dc4d45d4da58899\",\n \"score\": \"0.5453101\",\n \"text\": \"def compute_inverse(\\n b_func, epsilon, delta\\n):\\n sol = optimize.root_scalar(\\n lambda v: b_func(v, epsilon) - delta, bracket=(0, 5000)\\n )\\n return sol.root\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f74e6ea083396b4d9964a5d30797d470\",\n \"score\": \"0.5446779\",\n \"text\": \"def invlogcdf(self, lq):\\n return pois_invlogcdf(self.mu, lq)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31f87e969f8bf886aea8a0bafba815c9\",\n \"score\": \"0.54462343\",\n \"text\": \"def invlogccdf(self, lq):\\n return norm_invlogccdf(self.mu, self.sigma, lq)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5b06b53db76a3f48c4de7b6f00be539\",\n \"score\": \"0.54456246\",\n \"text\": \"def invlogcdf(self, lq):\\n return binom_invlogcdf(self.n, self.p, lq)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d64a82ddb645de1a9f6ed1658d313e55\",\n \"score\": \"0.5436837\",\n \"text\": \"def invcdf(p, mu=0, sigma=1):\\n with mp.extradps(mp.dps):\\n p = _validate_p(p)\\n mu = mp.mpf(mu)\\n sigma = mp.mpf(sigma)\\n\\n a = mp.erfinv(2*p - 1)\\n x = mp.sqrt(2)*sigma*a + mu\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c7392df177d14f992faea1019012fb9\",\n \"score\": \"0.5434988\",\n \"text\": \"def logccdf(self, x):\\n return chisq_logccdf(self.v, x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6994d24f59877082fbfd2e50ba1dc94b\",\n \"score\": \"0.5425989\",\n \"text\": \"def invlogccdf(self, lq):\\n return exp_invlogccdf(self.theta, lq)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d45bfed2c1b83a280a680656c44b91d7\",\n \"score\": \"0.5424483\",\n \"text\": \"def df(x):\\n return (2*x - x**2 * cot(x))/sin(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16d33a192cb34ca689df5a7d39724160\",\n \"score\": \"0.54218924\",\n \"text\": \"def icdf(self, p, mode='toobig'):\\n assert 0 <= p <= 1, \\\"Desired CDF prob must be between 0 and 1 inclusive\\\"\\n\\n if mode=='toobig':\\n for i in range(self.size):\\n x = self.domain[i]\\n # if cdf=p, we're done.\\n # if cdf>p, that means we just went past a gap, and we're now\\n # returning the smallest too-big value.\\n if self.cdf(x) >= p:\\n return x\\n else:\\n # This should never happen since p is no more than 1.0, and\\n # always the last item in the domain has CDF=1.0.\\n assert False, \\\"Didn't find value with prob at least %s\\\" % p\\n elif mode=='toosmall':\\n # go in descending order\\n # list(range(5,-1,-1)) => [5, 4, 3, 2, 1, 0]\\n for i in range(self.size-1, -1, -1):\\n x = self.domain[i]\\n if self.cdf(x) <= p:\\n return x\\n else:\\n # The problem now is, the user requested a CDF less then the\\n # CDF of the smallest value in the support (and we don't have a\\n # separate notion of the domain).\\n # Not sure what the right behavior is. The 'toosmall'\\n # criterion doesn't make sense here.\\n assert False, \\\"Didn't find value with prob <= %s\\\" % p\\n else:\\n assert False, \\\"bad mode given\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"654cf3a02a9980f2c5dde1d42467fb3f\",\n \"score\": \"0.5415278\",\n \"text\": \"def invlogccdf(self, lq):\\n return nbinom_invlogccdf(self.r, self.p, lq)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b01688fe13a2017bee16b385e985ee9e\",\n \"score\": \"0.54141307\",\n \"text\": \"def invlogccdf(self, lq):\\n return gamma_invlogccdf(self.alpha, self.beta, lq)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"763d32cb85924b9ec17d2de3cd1f7ccb\",\n \"score\": \"0.5406932\",\n \"text\": \"def pdf_to_cdf(pdf):\\n cdf = np.cumsum(pdf)\\n cdf /= cdf[-1]\\n\\n return cdf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fdbd0c5d2de135c6b52864e27d459ec\",\n \"score\": \"0.54047555\",\n \"text\": \"def invlogccdf(self, lq):\\n return weibull_invlogccdf(self.alpha, self.theta, lq)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c5818d5ec689dbac9d65e47071086c1\",\n \"score\": \"0.53933424\",\n \"text\": \"def cdf(self, x):\\r\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c647257663ad44e7275fa66b04cc5f7\",\n \"score\": \"0.53895634\",\n \"text\": \"def logccdf(self, x):\\n return fdist_logccdf(self.v1, self.v2, x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc78717523efa3cc775c3db0352f2ac8\",\n \"score\": \"0.53876674\",\n \"text\": \"def invlogccdf(self, lq):\\n return lognormal_invlogccdf(self.mu, self.sigma, lq)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994194,"cells":{"query_id":{"kind":"string","value":"4086f19c350b78f3c7029a5d86742ae7"},"query":{"kind":"string","value":"This class holds all the models for a dataset"},"positive_passages":{"kind":"list like","value":[{"docid":"5f8fa3d84739edffa3cf68ed1dc1e208","score":"0.0","text":"def __init__(self, id):\n self.id = id\n self.models = BTrees.OOBTree.BTree()\n self.constructor_id = -1\n self.dataset_id = -1\n self.group_connection_id = -1","title":""}],"string":"[\n {\n \"docid\": \"5f8fa3d84739edffa3cf68ed1dc1e208\",\n \"score\": \"0.0\",\n \"text\": \"def __init__(self, id):\\n self.id = id\\n self.models = BTrees.OOBTree.BTree()\\n self.constructor_id = -1\\n self.dataset_id = -1\\n self.group_connection_id = -1\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ea662cd6f629aaa29b6be895da859b88","score":"0.7318091","text":"def _get_model_dataset(self):\n\n # Instantiate the model object\n model = getattr(\n importlib.import_module('models.' + self._args.model),\n self._args.model)()\n\n # Instantiate the input object\n dataset = getattr(\n importlib.import_module('inputs.' + self._args.dataset),\n self._args.dataset)()\n return model, dataset","title":""},{"docid":"78607a83b3243243d67fdae05282e466","score":"0.70887065","text":"def trainModel(self, data, attributes, class_label):","title":""},{"docid":"c4f2545c36c8fd09d700b889b736423e","score":"0.67790407","text":"def models(self, models):\n if models is not None:\n models = DatasetModels(models)\n models = models.select(datasets_names=self.name)\n for model in models:\n model._exposure = self.data.exposure\n self._models = models","title":""},{"docid":"63a0615905b22dac2ac78a21faf4a3b0","score":"0.6748353","text":"def get_dataset(self):\n\n # https://developer.nvidia.com/blog/preparing-state-of-the-art-models-for-classification-and-object-detection-with-tlt/\n train_download = not os.path.exists(os.path.join(self.load_path, \"train\"))\n trainval_2012 = datasets.VOCDetection(os.path.join(self.load_path, \"train\"), image_set='trainval',\n transform=transforms.Compose([transforms.ToTensor()]),\n target_transform=None, download=train_download)\n trainval_2007 = datasets.VOCDetection(os.path.join(self.load_path, \"train\"), image_set='trainval',\n year='2007',\n transform=transforms.Compose([transforms.ToTensor()]),\n target_transform=None, download=train_download)\n test_download = not os.path.exists(os.path.join(self.load_path, \"test\"))\n valset = datasets.VOCDetection(os.path.join(self.load_path, \"test\"), image_set='test',\n year='2007',\n transform=transforms.Compose([transforms.ToTensor()]),\n target_transform=None, download=test_download)\n train_loader_2007 = torch.utils.data.DataLoader(trainval_2007, batch_size=1, shuffle=False, num_workers=2)\n train_loader_2012 = torch.utils.data.DataLoader(trainval_2012, batch_size=1, shuffle=False, num_workers=2)\n val_loader = torch.utils.data.DataLoader(valset, batch_size=1, shuffle=False, num_workers=2)\n\n check = 0\n directories = [os.path.join(self.save_path, \"train\"), os.path.join(self.save_path, \"test\")]\n for directory in directories:\n if os.path.exists(directory):\n check += 1\n if not os.path.exists(directory):\n os.makedirs(directory)\n\n if check != len(directories):\n indices_data = {}\n # create folders to save data\n for loader_name, loader in [('train', train_loader_2007),\n ('train', train_loader_2012),\n ('test', val_loader)]:\n for (img, annotation) in tqdm(loader):\n\n #print(annotation)\n # there may be multiple labels, they are concatenated to: 'label1_label2_'\n label = ''\n int_label = []\n\n elems = annotation['annotation']['object']\n # if only 1 label - it is a dictionary, but not list of dictionaries\n # for consistency reasons and to be able to use the loop later\n if not isinstance(elems, list):\n elems = [elems]\n\n # get bboxes, compute object size, add all object sizes and divide by img size (h*w)\n obj_sizes = 0\n num_instances = 0\n\n for elem in elems:\n # every name is in a list\n # there may be multiple instances of the same object\n # those are disregarded for label\n\n if not (bool(int(elem['difficult'][0])) and loader_name == 'test'):\n if not str(self.class_to_idx[elem['name'][0]]) in label:\n label += str(self.class_to_idx[elem['name'][0]]) + '_'\n int_label.append(self.class_to_idx[elem['name'][0]])\n\n num_instances += 1\n # percentage of objects in the image: sum obj_size/img_size\n obj_sizes += (int(elem['bndbox']['xmax'][0]) - int(elem['bndbox']['xmin'][0])) * \\\n (int(elem['bndbox']['ymax'][0]) - int(elem['bndbox']['ymin'][0]))\n obj_sizes /= float(int(annotation['annotation']['size']['width'][0]) *\n int(annotation['annotation']['size']['height'][0]))\n\n img_name = label + '_' + annotation['annotation']['filename'][0]\n\n directory = os.path.join(os.path.join(self.save_path, loader_name), label)\n if not os.path.exists(directory):\n os.makedirs(directory)\n save_image(img, os.path.join(directory, img_name))\n\n indices_data[os.path.join(directory, img_name)] = (int_label,\n obj_sizes, num_instances)\n\n # store img_paths which serves as indices and the labels for further analysis\n indices_data = collections.OrderedDict(sorted(indices_data.items()))\n\n dataframe = pd.DataFrame({'img_paths': list(indices_data.keys()),\n 'labels': np.array(list(indices_data.values()), dtype=object)[:, 0],\n 'obj_sizes': np.array(list(indices_data.values()), dtype=object)[:, 1],\n 'num_instances': np.array(list(indices_data.values()), dtype=object)[:, 2]})\n DatasetMetrics.indices_paths(self.name, dataframe)\n\n train_transform = transforms.Compose([\n # you can add other transformations in this list\n # resize (256 x remaining larger size) and RandomCrop(224)\n # like in https://papers.nips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf\n # https://arxiv.org/pdf/1409.1556.pdf\n transforms.Resize(256), # resize smaller size to 256\n transforms.RandomCrop(self.args.patch_size), # 224\n transforms.ToTensor()\n ])\n\n test_transform = transforms.Compose([\n # you can add other transformations in this list\n # resize (256 x remaining larger size) and RandomCrop(224)\n transforms.Resize(256), # resize smaller size to 256\n transforms.CenterCrop((self.args.patch_size, self.args.patch_size)), # 224\n transforms.ToTensor()\n ])\n\n if self.args.compute_dataset_metrics is True:\n # when computing dataset metrics, an original image should be used\n # - without randomness of RandomCrop\n train_transform = transforms.Compose([\n transforms.ToTensor()\n ])\n test_transform = transforms.Compose([\n transforms.ToTensor()\n ])\n\n # if not already set, set batch-size to 1 for computing the metrics\n # due to different image sizes\n self.args.batch_size = 1\n\n # load the image dataset from folder with indices\n trainset = IndxImageFolder(root = os.path.join(self.save_path, \"train\"), transform=train_transform,\n num_classes=len(self.class_to_idx), multilabel=self.args.multilabel)\n valset = IndxImageFolder(root=os.path.join(self.save_path, \"test\"), transform=test_transform,\n num_classes=len(self.class_to_idx), multilabel=self.args.multilabel)\n\n return trainset, valset","title":""},{"docid":"0a41ce9ccec715c381387cff5211410f","score":"0.66953593","text":"def get_data_model(cls):\n raise NotImplementedError","title":""},{"docid":"0a41ce9ccec715c381387cff5211410f","score":"0.66953593","text":"def get_data_model(cls):\n raise NotImplementedError","title":""},{"docid":"bbdf9b93d03bce8db2031663b7338bfe","score":"0.6614376","text":"def parse_for_model(self):\n print('Pre-processing data for model...')\n if any(self.dataset):\n model_data = self.dataset.copy()\n else:\n raise ValueError('First make dataset then pre-process it for Model')\n self.room_encoder, self.ap_encoder = LabelEncoder(), LabelEncoder()\n model_data.room_id = self.room_encoder.fit_transform(model_data.room_id)\n model_data.ap_id = self.ap_encoder.fit_transform(model_data.ap_id)\n features = model_data.iloc[:, :-1].values\n target = model_data.iloc[:, -1].values\n \"\"\" Splitting data into train, validation and test \"\"\"\n x_train, x_test, y_train, y_test = train_test_split(features, target, test_size=0.1)\n x_train, x_valid, y_train, y_valid = train_test_split(x_train, y_train, test_size=0.1)\n self.model_data = (x_train, x_valid, x_test, y_train, y_valid, y_test)","title":""},{"docid":"9d66fbbf227dfc6ab99b6adc4154fab0","score":"0.6558669","text":"def create_models(self):\n self.__handle_csv()\n self.__export_models()","title":""},{"docid":"c4b1b0f46bd362c5e19c033c27465c81","score":"0.65492105","text":"def createModel(self, dataFile, classifierType):","title":""},{"docid":"f7237f86667dc30cba8ddf3d8bf1b2f2","score":"0.6529856","text":"def model(self):\n raise NotImplementedError","title":""},{"docid":"978af77a2915f0e622eab5eae1c62efa","score":"0.6520754","text":"def fit_storage(self, data):\n models = []\n for epoch in range(self.k):\n sample = self.selector(data) # data with a subset of attributes\n models.append(self.learner(sample))\n model = BaggedModel(data.domain, models)\n model.name = self.name\n return model","title":""},{"docid":"0dc1e7bf48bfccc497a9ba779bf3051d","score":"0.6504034","text":"def __init__(self, datasets: List[IterableDataset]):\n raise NotImplementedError","title":""},{"docid":"06055d9094aca48587e82926dfd159f1","score":"0.6475266","text":"def iter_data():\n for v, q, a in dataset:\n yield (Model(v, ontology), q.split(), a)","title":""},{"docid":"b829e70cc66c36e149238672495316cf","score":"0.64297676","text":"def __init__(self, dataset):\r\n self.dataset = dataset","title":""},{"docid":"ff42f9452ff2ff43fccb37ec127c6f25","score":"0.64160985","text":"def __init__(self, data, models: Iterable[BaseModel], combiner: Combiner):\n assert models, \"The list of models cannot be empty\"\n assert combiner, \"You need to provide a Combiner\"\n super().__init__(data)\n\n self.models: Iterable[BaseModel] = models\n self.combiner: Combiner = combiner\n self.model_weights: List[float] = []","title":""},{"docid":"bf177400dca7c09077803697a591fbcb","score":"0.6382948","text":"def __init__(self, dataset):\n self.dataset = dataset","title":""},{"docid":"1542ec9e58a9ab3a9bdbf240d0a86296","score":"0.6374203","text":"def model(self):","title":""},{"docid":"1542ec9e58a9ab3a9bdbf240d0a86296","score":"0.6374203","text":"def model(self):","title":""},{"docid":"cde3fdf752b361890fb16ed1a03bfdfb","score":"0.63718194","text":"def make_model(self):\n raise NotImplementedError","title":""},{"docid":"e582a024b4c2a2b480a7cd12fe7423d9","score":"0.63537705","text":"def datasets(self):\n \n class CategoryData(DataSet):\n def data(self):\n return (\n ('gray_stuff', dict(id=1, name='gray')),\n ('yellow_stuff', dict(id=2, name='yellow')),\n )\n return [CategoryData]","title":""},{"docid":"0f755d2b043bf15c2afae59e5c3f6173","score":"0.6344792","text":"def get_models():\n models = {}\n models['LR'] = LogisticRegression()\n models['LDA'] = LinearDiscriminantAnalysis()\n models['KNN'] = KNeighborsClassifier()\n models['CART'] = DecisionTreeClassifier()\n models['NB'] = GaussianNB()\n models['SVM'] = SVC()\n return models","title":""},{"docid":"aaf7cceb0afba15f05425acaa63f41fd","score":"0.63406163","text":"def run_models():\n\n ### list to contain all individual model results\n models = []\n\n ### placeholder example\n example_model_results = ['model name', 'model description', 'data name',\n 'data description', 0.0, 0.0]\n models.append(example_model_results)\n\n ### contributors add models below #########################################\n\n\n\n ###########################################################################\n\n return models","title":""},{"docid":"f1934063c18e2a0a1c18c148dec977bc","score":"0.63323915","text":"def train_model(self):\n\n\t\tself.X_train = []\n\t\tself.Y_train = []\n\n\t\tself.X_test = []\n\t\tself.Y_test = []\n\n\t\t#We are currently using a decision tree, however this can be quite modular\n\t\tif self.model == \"none\":\n\t\t\tself.model = DecisionTreeRegressor()\n\n\n\t\tfor rollout in self.rollouts:\n\n\t\t\tif uniform() > 0.2:\n\t\t\t\ttrain = True\n\t\t\telse:\n\t\t\t\ttrain = False\n\n\t\t\tfor datum in rollout[0]:\n\n\t\t\t\tobservations = datum['state']\n\t\t\t\tactions = datum['action']\n\n\t\t\t\tfor i in range(len(actions)):\n\n\t\t\t\t\ta_ = actions[i].get_value()\n\n\t\t\t\t\ts_ = observations[i]\n\n\t\t\t\t\tif train:\n\t\t\t\t\t\tself.X_train.append(s_)\n\t\t\t\t\t\tself.Y_train.append(a_)\n\t\t\t\t\telse:\n\t\t\t\t\t\tself.X_test.append(s_)\n\t\t\t\t\t\tself.Y_test.append(a_)\n\n\t\t#self.standardize()\n\t\tself.model.fit(self.X_train,self.Y_train) \n\n\t\tif self.il_config['save_model']:\n\t\t\tif not os.path.exists(self.file_path+'/model'):\n\t\t\t\tos.makedirs(self.file_path+'/model')\n\t\t\tmodel_file_path = self.file_path+'/model/model_iter_'+str(self.iter_count)\n\t\t\tnp.save(model_file_path,self.model)\n\t\t\t#pickle.dump(self.model,open(model_file_path,'w'))\n\t\t\tself.iter_count += 1","title":""},{"docid":"9c3c85163fb50701d083797cf97f50d6","score":"0.63247085","text":"def create_models(self):\r\n raise NotImplementedError","title":""},{"docid":"bc83fb92976bcfac8275c96158219377","score":"0.6268053","text":"def define_models():\n models = []\n models.append(('LR', LogisticRegression(class_weight='balanced', max_iter=2500)))\n models.append(('RF', RandomForestClassifier(class_weight='balanced',criterion='entropy')))\n models.append(('ET', ExtraTreesClassifier(class_weight='balanced',criterion='entropy')))\n models.append(('XG', xgb.XGBClassifier(class_weight='balanced',criterion='entropy')))\n models.append(('KNN', BalancedBaggingClassifier(base_estimator=KNeighborsClassifier(),\n sampling_strategy='auto',\n replacement=False)))\n models.append(('NB', MultinomialNB()))\n models.append(('MLP','mlp'))\n models.append(('EMB','emb'))\n models.append(('CON','con'))\n return models","title":""},{"docid":"96c3794cfa7ffbf05ac22d738f2e71b9","score":"0.6264579","text":"def models_from_data():\n input_filepath = os.path.join(data_path, \"spring_mass_1D_inputs.txt\")\n output1_filepath = os.path.join(data_path, \"spring_mass_1D_outputs_1.0.txt\")\n output2_filepath = os.path.join(data_path, \"spring_mass_1D_outputs_0.1.txt\")\n output3_filepath = os.path.join(data_path,\n \"spring_mass_1D_outputs_0.01.txt\")\n\n model1 = ModelFromData(input_filepath, output1_filepath, cost=1.)\n model2 = ModelFromData(input_filepath, output2_filepath, cost=4.)\n model3 = ModelFromData(input_filepath, output3_filepath, cost=16.)\n\n return [model1, model2, model3]","title":""},{"docid":"4aaba645fdb0cec6dae95b2e4a0f1fb2","score":"0.62255543","text":"def create_model(self):","title":""},{"docid":"5f09930f19ef6429118e256b7677532b","score":"0.620214","text":"def __init__(self, dataset, env):\n super(EncodingSimple, self).__init__()\n self.ds = dataset\n self.env = env\n self.model = None\n self.nsteps = int(self.ds.numAttr * self.ds.numTuple / self.env['batch_size'])\n self.dataset = self.ds.df.groupby(np.arange(self.ds.numTuple) // env['batch_size'])","title":""},{"docid":"753557d3114d7e7592ff4eda2be697ca","score":"0.6187025","text":"def build_model(self):\n raise NotImplementedError","title":""},{"docid":"66a113b3151bfab1ce7ac2e641a755fd","score":"0.61857843","text":"def get_dsets(self):\n\t\tdata_class = get_dataset(self.name, self.name, self.img_dir, self.LDS_type, self.is_target)\n\n\t\tself.num_classes, train_dataset, val_dataset, test_dataset, self.train_transforms, self.test_transforms = data_class.get_data()\n\n\t\tself.train_dataset = DatasetWithIndicesWrapper(self.name, train_dataset.data, train_dataset.targets, self.train_transforms, self.test_transforms)\n\t\tself.val_dataset = DatasetWithIndicesWrapper(self.name, val_dataset.data, val_dataset.targets, self.test_transforms, self.test_transforms)\n\t\tself.test_dataset = DatasetWithIndicesWrapper(self.name, test_dataset.data, test_dataset.targets, self.test_transforms, self.test_transforms)\n\n\t\treturn self.train_dataset, self.val_dataset, self.test_dataset","title":""},{"docid":"32e73b2d4f8a6d244c3fc8e9b5468357","score":"0.61855143","text":"def TrainModel(self):\n # Initialize the Needed Classifier\n self.classifier = NaiveBayesClassifier.train(self.train_features)\n #self.classifier = MaxentClassifier.train(self.train_features,algorithm=\"iis\")","title":""},{"docid":"8eab8763d224c83136e21721f2cb316b","score":"0.6182225","text":"def get_models(self):\n nb = GaussianNB()\n svc = SVC(C=100, probability=True)\n knn = KNeighborsClassifier(n_neighbors=3)\n lr = LogisticRegression(C=100, random_state=SEED)\n nn = MLPClassifier((80, 10), early_stopping=False, random_state=SEED)\n gb = GradientBoostingClassifier(n_estimators=100, random_state=SEED)\n rf = RandomForestClassifier(n_estimators=10, max_features=3, random_state=SEED)\n\n models = {\n # 'svm': svc,\n 'knn': knn,\n 'naive bayes': nb,\n # 'mlp-nn': nn,\n 'random forest': rf,\n 'gbm': gb,\n 'logistic': lr,\n }\n\n return models","title":""},{"docid":"f5448a0232066ce34452fd732a809ab5","score":"0.61742306","text":"def __init__(self,batch_size: int, epochs: int, dataset, labels, dataset_test, labels_test, model):\n self.batch_size = batch_size\n self.epochs = epochs\n self.dataset = dataset\n self.labels = labels\n self.dataset_test = dataset_test\n self.labels_test = labels_test\n self.model = model\n self.__save_folder = \"savedModels/\"","title":""},{"docid":"a588726c6a87ae362966334b1ecf1a7e","score":"0.6171815","text":"def dataset_setup(self):\n settings = self.settings\n if settings.crowd_dataset == CrowdDataset.ucf_qnrf:\n self.dataset_class = UcfQnrfFullImageDataset\n self.train_dataset = UcfQnrfTransformedDataset(middle_transform=data.RandomHorizontalFlip(),\n seed=settings.labeled_dataset_seed,\n number_of_examples=settings.labeled_dataset_size)\n self.train_dataset_loader = DataLoader(self.train_dataset, batch_size=settings.batch_size,\n pin_memory=self.settings.pin_memory,\n num_workers=settings.number_of_data_workers)\n self.validation_dataset = UcfQnrfTransformedDataset(dataset='test', seed=101)\n elif settings.crowd_dataset == CrowdDataset.shanghai_tech:\n self.dataset_class = ShanghaiTechFullImageDataset\n self.train_dataset = ShanghaiTechTransformedDataset(middle_transform=data.RandomHorizontalFlip(),\n seed=settings.labeled_dataset_seed,\n number_of_examples=settings.labeled_dataset_size,\n map_directory_name=settings.map_directory_name,\n image_patch_size=self.settings.image_patch_size,\n label_patch_size=self.settings.label_patch_size)\n self.train_dataset_loader = DataLoader(self.train_dataset, batch_size=settings.batch_size,\n pin_memory=self.settings.pin_memory,\n num_workers=settings.number_of_data_workers)\n self.validation_dataset = ShanghaiTechTransformedDataset(dataset='test', seed=101,\n map_directory_name=settings.map_directory_name,\n image_patch_size=self.settings.image_patch_size,\n label_patch_size=self.settings.label_patch_size)\n elif settings.crowd_dataset == CrowdDataset.ucf_cc_50:\n seed = 0\n self.dataset_class = UcfCc50FullImageDataset\n self.train_dataset = UcfCc50TransformedDataset(middle_transform=data.RandomHorizontalFlip(),\n seed=seed,\n test_start=settings.labeled_dataset_seed * 10,\n inverse_map=settings.inverse_map,\n map_directory_name=settings.map_directory_name)\n self.train_dataset_loader = DataLoader(self.train_dataset, batch_size=settings.batch_size,\n pin_memory=self.settings.pin_memory,\n num_workers=settings.number_of_data_workers)\n self.validation_dataset = UcfCc50TransformedDataset(dataset='test', seed=seed,\n test_start=settings.labeled_dataset_seed * 10,\n inverse_map=settings.inverse_map,\n map_directory_name=settings.map_directory_name)\n else:\n raise ValueError('{} is not an understood crowd dataset.'.format(settings.crowd_dataset))","title":""},{"docid":"684fc3c7d4134977533b41111ccd1990","score":"0.61668634","text":"def build_model(self):\r\n self.source_images, self.source_labels = self.dataloader.get_model_inputs()\r\n self.target_images, self.target_labels = self.dataloader.get_model_inputs()\r\n\r\n source_model = SimpleModel(self.source_images, self.source_labels, F.output_dim, scope='source_regressor')\r\n target_model = SimpleModel(self.target_images, self.target_labels, F.output_dim, scope='target_regressor')\r\n \r\n self.source_out, _ = source_model.get_model()\r\n self.target_out, _ = target_model.get_model()\r\n\r\n self.get_loss()","title":""},{"docid":"9517f49d612808a2e00ebe8d56c3bf34","score":"0.616244","text":"def loadModel(self, dataFile):","title":""},{"docid":"dbc6e9c2e814b8fe7c46b56dad66f833","score":"0.61484253","text":"def init_datasets(self):\n phases = self.data_dict[\"row_id\"].keys()\n tensor_dict_dict = {\n key: {\n \"features\": self.data_dict[\"features\"][key],\n \"labels\": torch.tensor(self.data_dict[\"labels\"][key], dtype=torch.long),\n \"row_id\": torch.tensor(self.data_dict[\"row_id\"][key], dtype=torch.long),\n }\n for key in phases\n }\n if self.config_dict.get(\"include_group_in_dataset\"):\n for key in phases:\n tensor_dict_dict[key][\"group\"] = torch.as_tensor(\n np.copy(self.data_dict[\"group\"][key]), dtype=torch.long\n )\n if self.config_dict.get(\"weight_var_name\") is not None:\n for key in phases:\n tensor_dict_dict[key][\"weights\"] = torch.as_tensor(\n np.copy(self.data_dict[\"weights\"][key]), dtype=torch.float\n )\n\n dataset_dict = {\n key: ArrayDataset(\n tensor_dict=tensor_dict_dict[key],\n sparse_mode=self.config_dict.get(\"sparse_mode\"),\n )\n for key in phases\n }\n\n return dataset_dict","title":""},{"docid":"2da6dd6b2b574d6bd5ee5854d4228782","score":"0.61387175","text":"def setUp(self):\n\n def rf_model_builder(**model_params):\n rf_params = {\n k: v for (k, v) in model_params.items() if k != 'model_dir'\n }\n model_dir = model_params['model_dir']\n sklearn_model = sklearn.ensemble.RandomForestRegressor(**rf_params)\n return dc.models.SklearnModel(sklearn_model, model_dir)\n\n self.rf_model_builder = rf_model_builder\n self.train_dataset = dc.data.NumpyDataset(X=np.random.rand(50, 5),\n y=np.random.rand(50, 1))\n self.valid_dataset = dc.data.NumpyDataset(X=np.random.rand(20, 5),\n y=np.random.rand(20, 1))","title":""},{"docid":"f8efb4ef3ff6ee80b98640449350d866","score":"0.6137208","text":"def __init__(\n self,\n model: Model,\n dataset: Dataset[BatchType],\n evaluator: Evaluator,\n ) -> None:\n self.model = model\n self.evaluator = evaluator\n self.dataset = dataset","title":""},{"docid":"c91a4df2326338a7e34dca2e97cbc0cf","score":"0.61296207","text":"def make_model(self, data):\n return self.MODEL(**data)","title":""},{"docid":"56716aa7d12063260cc467faa28bca09","score":"0.6127462","text":"def _build_models(self):\n with tf.variable_scope('model'):\n meval = Model(self.hparams, mode='test')\n meval.build()\n self._saver = meval.saver\n\n self.meval = meval","title":""},{"docid":"20966932a11c048d77d6df10cf4d0425","score":"0.612052","text":"def loading_data_set(self):\n # Data loading code\n if self.framework_type == FrameworkType.Classification:\n # TODO\n # change dataset in outer method\n dataset = SimpsonsDataset(cfg=self.config)\n batch_size = 16\n validation_split = .2\n shuffle_dataset = True\n random_seed = 42\n\n # Creating data indices for training and validation splits:\n dataset_size = len(dataset)\n indices = list(range(dataset_size))\n split = int(np.floor(validation_split * dataset_size))\n if shuffle_dataset:\n np.random.seed(random_seed)\n np.random.shuffle(indices)\n train_indices, val_indices = indices[split:], indices[:split]\n\n # Creating PT data samplers and loaders:\n train_sampler = SubsetRandomSampler(train_indices)\n valid_sampler = SubsetRandomSampler(val_indices)\n\n data_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,\n sampler=train_sampler)\n data_loader_eval = torch.utils.data.DataLoader(dataset, batch_size=batch_size,\n sampler=valid_sampler)\n else:\n # TODO\n # change dataset in outer method\n train_dataset = VOCSegmentation(split=DataSplit.Train, cfg=self.config)\n eval_dataset = VOCSegmentation(split=DataSplit.Eval, cfg=self.config)\n data_loader = torch.utils.data.DataLoader(\n train_dataset,\n batch_size=20,\n shuffle=True,\n num_workers=1,\n pin_memory=True)\n data_loader_eval = torch.utils.data.DataLoader(\n eval_dataset, batch_size=1,\n num_workers=1)\n print('Loading data...')\n\n print('Done...')\n return data_loader, data_loader_eval","title":""},{"docid":"95e26ba5f2cae45acfd565e48ac3151e","score":"0.611596","text":"def model(self):\n return self.modeldata","title":""},{"docid":"5c369a134e23790b69260a4ea5e29dbc","score":"0.6112193","text":"def setup_models():\n global classifiers\n\n grega_clf = GregaClassifier(GREGA_MODEL_PATH)\n classifiers.append(grega_clf)\n\n uci_clf = UciClassifier(UCI_MODEL_PATH)\n classifiers.append(uci_clf)","title":""},{"docid":"b7d56da32154c25fac256253749948a5","score":"0.6110365","text":"def init_model(self):\n self.places = tools.keydefaultdict(Place)\n self.users = tools.keydefaultdict(User)\n\n self.predictions = {}","title":""},{"docid":"77024290e71f74a808038d96ff416d2f","score":"0.61031425","text":"def __init__(self, dataset, env):\n super(Encoding, self).__init__()\n self.ds = dataset\n self.env = env\n self.model = None\n self.nsteps = int(self.ds.numAttr * self.ds.numTuple / self.env['batch_size'])\n self.dataset = self.ds.df.groupby(np.arange(self.ds.numTuple) // env['batch_size'])","title":""},{"docid":"4caaec4a00e7405258dcbf9867ef2a5c","score":"0.6095743","text":"def load_datasets(self):\n raise NotImplementedError","title":""},{"docid":"ac98fb551a4ce9e036e46ec5575288cb","score":"0.60925215","text":"def create_model(self, parameters={}):\n if self.model_name == \"DBSCAN\":\n self.model = cluster.DBSCAN()\n elif self.model_name == \"KMeans\":\n self.model = cluster.KMeans()\n elif self.model_name == \"hdbscan\":\n self.model_glo = hdbscan\n self.model = self.model_glo.HDBSCAN()\n\n for key, value in parameters.items():\n setattr(self.model, key, value)\n return","title":""},{"docid":"9a0c5cf1d4c54e24357ce7666a642389","score":"0.6091835","text":"def __init__(self, data, model, config):\n\n self.data = data\n self.model = model\n self.config = config","title":""},{"docid":"d0e5d6c7f3b067aafa93b7c4a3a3591e","score":"0.6085573","text":"def init_models(self):\n pass","title":""},{"docid":"9782b40687ea1edc2c65290c85b04cec","score":"0.60783416","text":"def __init__(self, x_train, x_test, y_train, y_test, y_preds, model_def, fitted_model, original_data):\r\n self.x_train = x_train\r\n self.x_test = x_test\r\n self.y_train = y_train\r\n self.y_test = y_test\r\n self.y_preds = y_preds\r\n self.model_def = model_def\r\n self.fitted_model = fitted_model\r\n self.original_data = original_data","title":""},{"docid":"330d6d1bd7873aad992bbd40c66b13a5","score":"0.6070308","text":"def train_model(self):\n # load manually labeled data\n print('Loading manually labeled data for model training...')\n manual_data = pd.read_csv(os.path.join(os.path.dirname(__file__), config['labeled_data']))\n manual_data['duplicates'] = manual_data['duplicates'].apply(self._format_duplicates)\n manual_data = manual_data[['properties.address.formatted_address', 'properties.name',\n 'lat', 'lng', 'id', 'duplicates']]\n manual_data = gpd.GeoDataFrame(manual_data,\n geometry=gpd.points_from_xy(manual_data['lng'],\n manual_data['lat']))\n\n # process manually labeled data\n print('Processing manually labeled data for model training...')\n train_test_data = self._process_manual_data(manual_data)\n train_test_data = pd.DataFrame(train_test_data, columns=['address_similarity', 'address_str_similarity',\n 'name_similarity', 'label'])\n\n # perform data sampling to balance class distribution\n print('Performing data sampling to balance class distribution...')\n train_datasets, test_data = self._perform_sampling(train_test_data)\n\n # train models\n print('Begin model training...')\n gb_models = self._train(train_datasets, 'GB')\n rf_models = self._train(train_datasets, 'RF')\n xgboost_models = self._train(train_datasets, 'XGB')\n\n # evaluate model performance on hold out set\n print('Perform model evaluation...')\n y_pred_gb = self._predict(gb_models, test_data[['address_similarity', 'address_str_similarity',\n 'name_similarity']])\n y_pred_rf = self._predict(rf_models, test_data[['address_similarity', 'address_str_similarity',\n 'name_similarity']])\n y_pred_xgboost = self._predict(xgboost_models, test_data[['address_similarity', 'address_str_similarity',\n 'name_similarity']])\n self._evaluate(test_data['label'], y_pred_gb, 'Gradient Boosting')\n self._evaluate(test_data['label'], y_pred_rf, 'Random Forest')\n self._evaluate(test_data['label'], y_pred_xgboost, 'XGBoost')\n\n # save trained models locally\n print('Saving trained models locally...')\n if args.best_algorithm == 'GB':\n models = gb_models\n elif args.best_algorithm == 'RF':\n models = rf_models\n elif args.best_algorithm == 'XGB':\n models = xgboost_models\n else:\n raise ValueError('{} is not supported.'.format(args.best_algorithm))\n for i in range(len(models)):\n dump(models[i], os.path.join(os.path.dirname(__file__),\n config['models_directory'] + 'model_{}.joblib'.format(i+1)))","title":""},{"docid":"591c7647a6d517ac685fb0e5d2b3bda4","score":"0.6068656","text":"def __init__(self,model_parameters): \n self.counter = Counter()\n self.corpus = CorpusReader()\n self.nlp = TextProcess()\n self.train_features = []\n self.classifier = None\n self.test_model = []\n self.model_parameters = {}","title":""},{"docid":"5ee5312ed36537bca50e5b39334e1124","score":"0.606777","text":"def __init__(self):\n super(Blender, self).__init__()\n self.models = []\n for m in models_names:\n name = str(m).split()[1]\n model = m().cuda()\n model.eval()\n\n model = nn.DataParallel(model)\n model.load_state_dict(torch.load('models/full_data_{}.pth'.format(name)))\n for p in model.parameters():\n p.requires_grad = False\n self.models.append(model)\n self.weighing = nn.Sequential(\n nn.BatchNorm1d(len(models_names)*17),\n # nn.ReLU(),\n nn.Linear(in_features=len(models_names)*17, out_features=256, bias=False),\n nn.BatchNorm1d(256),\n nn.ReLU(),\n nn.Linear(in_features=256, out_features=17)\n )","title":""},{"docid":"c9b188d25dda6d073ee68db475e46974","score":"0.6066141","text":"def build_model(self):\n ...","title":""},{"docid":"9584d7224b58807ce741f74522817fff","score":"0.60589874","text":"def getModelsData(self):\n return self.models_data","title":""},{"docid":"23a5a982c7f35aa3095efa3e008f3523","score":"0.60569793","text":"def get_dataset(self):\n transform = transforms.Compose([\n # you can add other transformations in this list\n transforms.ToTensor()\n ])\n\n trainset = datasets.CIFAR10('datasets/CIFAR10/train/', train=True, transform=transform,\n target_transform=None, download=True)\n\n valset = datasets.CIFAR10('datasets/CIFAR10/test/', train=False, transform=transform,\n target_transform=None, download=True)\n\n train_loader = torch.utils.data.DataLoader(trainset, batch_size=1, shuffle=False, num_workers=2)\n val_loader = torch.utils.data.DataLoader(valset, batch_size=1, shuffle=False, num_workers=2)\n\n check = 0\n directories = [\"datasets/CIFAR10_indx/train\", \"datasets/CIFAR10_indx/test\"]\n for directory in directories:\n if os.path.exists(directory):\n check += 1\n if not os.path.exists(directory):\n os.makedirs(directory)\n\n if check != len(directories):\n # create folders to save data\n indices_data = {}\n\n indx = 0\n for loader_name, loader in [('train', train_loader), ('test', val_loader)]:\n for (img, label) in loader:\n img_name = str(int(label))+'_'+str(int(indx))+'.jpg'\n\n directory = os.path.join(\"datasets/CIFAR10_indx/\" + loader_name,\n format(int(label), '06d'))\n if not os.path.exists(directory):\n os.makedirs(directory)\n save_image(img, os.path.join(directory, img_name))\n # img_path is the index for the images\n indices_data[os.path.join(directory, img_name)] = int(label)\n indx += 1\n\n # store img_paths which serves as indices and the labels for further analysis\n indices_data = collections.OrderedDict(sorted(indices_data.items()))\n dataframe = pd.DataFrame({'img_paths': list(indices_data.keys()),\n 'labels': list(indices_data.values())})\n DatasetMetrics.indices_paths(self.name, dataframe)\n\n if self.randomize_labels:\n evaluation = DatasetMetrics.load_evaluation_metrics(self.name)\n self.random_labels = corrupt_labels(evaluation['labels'].to_dict(),\n self.num_classes,\n self.corrupt_prob)\n else:\n self.random_labels = None\n\n # load the image dataset from folder with indices\n trainset = IndxImageFolder(root=\"datasets/CIFAR10_indx/train\", transform = transform,\n random_labels=self.random_labels)\n valset = IndxImageFolder(root=\"datasets/CIFAR10_indx/test\", transform=transform,\n random_labels=self.random_labels)\n\n return trainset, valset","title":""},{"docid":"ebd20a286c5098c978a68f56cbec530e","score":"0.6056964","text":"def init_model():","title":""},{"docid":"42750721fcac919b0e53ec669f66fed3","score":"0.6055318","text":"def data_model_class(self) -> DataModel:\n return BaicellsQRTBTrDataModel","title":""},{"docid":"3e6a407a877c9f15b575895edb24acb3","score":"0.60502315","text":"def models(self) -> Model:\n return [\n self.repository.models[step.model_name]\n for step in self.config.ensemble_scheduling.step\n ]","title":""},{"docid":"fb3f32100853d930f7b71ae70c748741","score":"0.6041466","text":"def get_datasets(self):\n d = {\n \"X_train\": self.X_train,\n \"X_test\": self.X_test,\n \"Y_train\": self.Y_train,\n \"Y_test\": self.Y_test\n }\n return d","title":""},{"docid":"380f48cf7489a9e0a784af2ab9f3310c","score":"0.604073","text":"def get_model(name, dataset):\n field_dims = dataset.field_dims\n print('field_dims={}, {}'.format(type(field_dims), field_dims.tolist()))\n\n if name == 'lr':\n return LogisticRegressionModel(field_dims)\n elif name == 'fm':\n return FactorizationMachineModel(field_dims, embed_dim=16)\n elif name == 'hofm':\n return HighOrderFactorizationMachineModel(field_dims, order=3, embed_dim=16)\n elif name == 'ffm':\n return FieldAwareFactorizationMachineModel(field_dims, embed_dim=4)\n elif name == 'fnn':\n return FactorizationSupportedNeuralNetworkModel(field_dims, embed_dim=16, mlp_dims=(16, 16), dropout=0.2)\n elif name == 'wd':\n return WideAndDeepModel(field_dims, embed_dim=16, mlp_dims=(16, 16), dropout=0.2)\n elif name == 'ipnn':\n return ProductNeuralNetworkModel(field_dims, embed_dim=64, mlp_dims=(400, 400, 400), method='inner', dropout=0.2)\n elif name == 'opnn':\n return ProductNeuralNetworkModel(field_dims, embed_dim=16, mlp_dims=(16,), method='outer', dropout=0.2)\n elif name == 'dcn':\n return DeepCrossNetworkModel(field_dims, embed_dim=16, num_layers=3, mlp_dims=(16, 16), dropout=0.2)\n elif name == 'nfm':\n return NeuralFactorizationMachineModel(field_dims, embed_dim=64, mlp_dims=(64,), dropouts=(0.2, 0.2))\n elif name == 'ncf':\n # only supports MovieLens dataset because for other datasets user/item colums are indistinguishable\n assert isinstance(dataset, MovieLens20MDataset) or isinstance(dataset, MovieLens1MDataset)\n return NeuralCollaborativeFiltering(field_dims, embed_dim=16, mlp_dims=(16, 16), dropout=0.2,\n user_field_idx=dataset.user_field_idx,\n item_field_idx=dataset.item_field_idx)\n elif name == 'fnfm':\n return FieldAwareNeuralFactorizationMachineModel(field_dims, embed_dim=64, mlp_dims=(64,32,), dropouts=(0.2, 0.2))\n elif name == 'dfm':\n return DeepFactorizationMachineModel(field_dims, embed_dim=64, mlp_dims=(400, 400, 400), dropout=0.2)\n elif name == 'xdfm':\n return ExtremeDeepFactorizationMachineModel(\n field_dims, embed_dim=16, cross_layer_sizes=(16, 16), split_half=False, mlp_dims=(16, 16), dropout=0.2)\n elif name == 'afm':\n return AttentionalFactorizationMachineModel(field_dims, embed_dim=16, attn_size=16, dropouts=(0.2, 0.2))\n elif name == 'afi':\n return AutomaticFeatureInteractionModel(\n field_dims, embed_dim=16, atten_embed_dim=64, num_heads=2, num_layers=3, mlp_dims=(400, 400), dropouts=(0, 0, 0))\n elif name == 'afn':\n print(\"Model:AFN\")\n return AdaptiveFactorizationNetwork(\n field_dims, embed_dim=64, LNN_dim=1500, mlp_dims=(400, 400, 400), dropouts=(0, 0, 0))\n else:\n raise ValueError('unknown model name: ' + name)","title":""},{"docid":"6aca67178bd2ddeaa4da34ac11fcc372","score":"0.60254836","text":"def provides(self):\n\n # models\n possible_values = [3, 5, 7, 9, 18, 27]\n\n value = [name(n=i, version=1) for i in possible_values]\n\n possible_values.append(111)\n value.extend([name(n=i, version=2) for i in possible_values])\n\n # TODO: automate this\n backends = []\n datasets = self.available_datasets()\n\n from deeprace.models.keras_details import resnet_details as keras_resnet\n if keras_resnet.can_train() != []:\n backends.extend(keras_resnet.can_train())\n\n from deeprace.models.keras_details import tfkeras_resnet_details as tfkeras_resnet\n if tfkeras_resnet.can_train() != []:\n backends.extend(tfkeras_resnet.can_train())\n\n from deeprace.models.tf_details import resnet_details as tf_resnet\n if tf_resnet.can_train() != []:\n backends.extend(tf_resnet.can_train())\n\n # datasets\n\n return value, backends, datasets","title":""},{"docid":"a723b465c8ed5475e830b4986f61abbf","score":"0.60243565","text":"def fit_storage(self, data):\n\n models = []\n X = data.X\n Y, self.num_classes = get_eye_matrix(data.Y)\n\n Fs = np.zeros((X.shape[0], self.num_classes))\n Ps = self.softmax1(Fs)\n\n grads = self.loss(Y, Ps)\n\n for i in range(self.n_estimators):\n model_per_class = []\n for j in range(Y.shape[1]):\n newdata = Orange.data.Table(data.X, grads[:, j])\n model = self.learner(newdata)\n tmp = model(newdata)\n Fs[:, j] += tmp\n Ps = self.softmax1(Fs)\n #grads[:, j] = self.loss(Y[:, j], Ps[:, j])\n grads = self.loss(Y, Ps)\n model_per_class.append(model)\n models.append(model_per_class)\n\n self.models = models\n return self","title":""},{"docid":"e7577867bb39f31392b945092b703672","score":"0.6023276","text":"def __init__(self, saved_model_dir_path: str, saved_dataset_custodian_dir_name_or_path: str = None,\n saved_dc_is_path: bool = False):\n self.saved_model_dir_path = saved_model_dir_path\n print(\"Reading model from\", self.saved_model_dir_path, \"...\")\n # Load model itself:\n self.model: tfkeras.Model = load_model_from_path(self.saved_model_dir_path)\n # Read associated data/information:\n print(\"Model info:\")\n saved_model_dir_path_obj = pathlib.Path(saved_model_dir_path)\n self.model_params = from_json(read_file_content(saved_model_dir_path_obj.parents[0].joinpath(\"params.json\")))\n self.model_scores = from_json(read_file_content(\n saved_model_dir_path_obj.parents[0].joinpath(saved_model_dir_path_obj.name + \"_scores.json\")))\n self.train_threshold = self.model_scores[\"train\"][\"scores\"].get(\"threshold\", None)\n self.validation_threshold = self.model_scores[\"validation\"][\"scores\"].get(\"threshold\", None)\n assert saved_model_dir_path_obj.parents[1].name == \"GridSearch\"\n\n print(\" Parameters:\", self.model_params)\n print(\" Scores:\", self.model_scores)\n print(\" Validation threshold:\", self.validation_threshold)\n\n # Load model's associated dataset custodian since is necessary in both cases:\n model_dc = DatasetCustodian.load(saved_model_dir_path_obj.parents[2], is_path=True)\n\n if saved_dataset_custodian_dir_name_or_path is None:\n # No dataset is given. Use model's dataset:\n self.dataset_custodian: DatasetCustodian = model_dc\n self.model_to_dataset_mapping = None\n else:\n # Dataset is given. Load it:\n dataset_dc: DatasetCustodian = DatasetCustodian.load(saved_dataset_custodian_dir_name_or_path,\n is_path=saved_dc_is_path)\n # Check if mapping is necessary\n if dataset_dc.get_labels() != model_dc.get_labels():\n if len(dataset_dc.get_labels()) == len(model_dc.get_labels()):\n # Len matches and score calculation is possible. However, results may be senseless if labels do not\n # match.\n print(\"WARNING: Model labels\", model_dc.get_labels(), \"do not match dataset labels\",\n dataset_dc.get_labels())\n elif len(dataset_dc.get_labels()) < len(model_dc.get_labels()):\n # Model can distinguish more classes than dataset has. Try to automatically create mapping:\n self.model_to_dataset_mapping = {}\n for model_label in model_dc.get_labels():\n if model_label in dataset_dc.get_labels():\n self.model_to_dataset_mapping[model_dc.label_to_int_mapping[model_label]] \\\n = dataset_dc.label_to_int_mapping[model_label]\n else:\n self.model_to_dataset_mapping[model_dc.label_to_int_mapping[model_label]] \\\n = dataset_dc.label_to_int_mapping[dataset_dc.get_no_bug_label()]\n print(\"WARNING: Model predicts more classes than present in the dataset.\",\n \"Created model-to-dataset mapping, which maps each non-present class to non-vulnerable\",\n \"class:\", self.model_to_dataset_mapping)\n elif len(dataset_dc.get_labels()) > len(model_dc.get_labels()):\n print(\"WARNING: Model predicts less classes than present in the dataset.\")\n\n # Use requested dataset:\n self.dataset_custodian: DatasetCustodian = dataset_dc\n\n self.dataset_custodian_info \\\n = from_json(read_file_content(os.path.join(self.dataset_custodian.prepared_data_dir_path, \"info.json\")))\n print(\"Dataset info:\", self.dataset_custodian_info)\n self.dataset_custodian.print_dataset_statistics()","title":""},{"docid":"ad61dfee683dd4305036ff041f6dc2ac","score":"0.6021987","text":"def _load_model(self):\n # Setup teacher to label samples\n self.model = serial.load(self.teacher_path)","title":""},{"docid":"de3e75865e21e796de903d0d661cd72c","score":"0.6013955","text":"def __init__(self):\n self.new_dataset()","title":""},{"docid":"1ed14c73e18a5f011792314e96f8fb6f","score":"0.6009634","text":"def load_dataset(self):\n rng = np.random.RandomState(seed=self.seed['val'])\n\n #if self.args.sets_are_pre_split == True:\n data_image_paths, index_to_label_name_dict_file, label_to_index = self.load_datapaths()\n dataset_splits = dict()\n for key, value in data_image_paths.items():\n key = self.get_label_from_index(index=key)\n bits = key.split(\"/\")\n set_name = bits[0]\n class_label = bits[1]\n if set_name not in dataset_splits:\n dataset_splits[set_name] = {class_label: value}\n else:\n dataset_splits[set_name][class_label] = value\n# else:\n# data_image_paths, index_to_label_name_dict_file, label_to_index = self.load_datapaths()\n# total_label_types = len(data_image_paths)\n# num_classes_idx = np.arange(len(data_image_paths.keys()), dtype=np.int32)\n# rng.shuffle(num_classes_idx)\n# keys = list(data_image_paths.keys())\n# values = list(data_image_paths.values())\n# new_keys = [keys[idx] for idx in num_classes_idx]\n# new_values = [values[idx] for idx in num_classes_idx]\n# data_image_paths = dict(zip(new_keys, new_values))\n# # data_image_paths = self.shuffle(data_image_paths)\n# x_train_id, x_val_id, x_test_id = int(self.train_val_test_split[0] * total_label_types), \\\n# int(np.sum(self.train_val_test_split[:2]) * total_label_types), \\\n# int(total_label_types)\n# print(x_train_id, x_val_id, x_test_id)\n# x_train_classes = (class_key for class_key in list(data_image_paths.keys())[:x_train_id])\n# x_val_classes = (class_key for class_key in list(data_image_paths.keys())[x_train_id:x_val_id])\n# x_test_classes = (class_key for class_key in list(data_image_paths.keys())[x_val_id:x_test_id])\n# x_train, x_val, x_test = {class_key: data_image_paths[class_key] for class_key in x_train_classes}, \\\n# {class_key: data_image_paths[class_key] for class_key in x_val_classes}, \\\n# {class_key: data_image_paths[class_key] for class_key in x_test_classes},\n# dataset_splits = {\"train\": x_train, \"val\":x_val , \"test\": x_test}\n\n if self.args.load_into_memory is True:\n\n print(\"Loading data into RAM\")\n x_loaded = {\"train\": [], \"val\": [], \"test\": []}\n\n for set_key, set_value in dataset_splits.items():\n print(\"Currently loading into memory the {} set\".format(set_key))\n x_loaded[set_key] = {key: np.zeros(len(value), ) for key, value in set_value.items()}\n # for class_key, class_value in set_value.items():\n with tqdm.tqdm(total=len(set_value)) as pbar_memory_load:\n with concurrent.futures.ProcessPoolExecutor(max_workers=4) as executor:\n # Process the list of files, but split the work across the process pool to use all CPUs!\n for (class_label, class_images_loaded) in executor.map(self.load_parallel_batch, (set_value.items())):\n x_loaded[set_key][class_label] = class_images_loaded\n pbar_memory_load.update(1)\n\n dataset_splits = x_loaded\n self.data_loaded_in_memory = True\n\n return dataset_splits","title":""},{"docid":"8a6478ba0e22a4cdcf9b403d6937c8b5","score":"0.6003628","text":"def _train(self):","title":""},{"docid":"81d526eb8fab08d7d5b6f409bdcdbfe5","score":"0.6001419","text":"def gen_model(self, *args):\n # generate data grid\n yy, xx = np.indices(self.data.shape)\n xdata_tuple = (xx, yy)\n # return model\n return self.model(xdata_tuple, *args)","title":""},{"docid":"c98949e5628394c51a94c88a3b70326b","score":"0.5991787","text":"def __init__(self):\n\n self.train = pd.read_csv(os.path.join(_DATA_DIR, _TRAIN))\n self.test = pd.read_csv(os.path.join(_DATA_DIR, _TEST))","title":""},{"docid":"4ab0b4e47abc225f388ede8178508f18","score":"0.5988505","text":"def model(self) -> Model:\n pass","title":""},{"docid":"8b48dd63b0122ba6c1628e3104db236e","score":"0.5984528","text":"def compile_model(self):\n raise NotImplementedError(\"train method has not been implemented\")","title":""},{"docid":"9cd5b48ad16ce4ae2914321b26650389","score":"0.59714246","text":"def load_model(self):\n raise NotImplementedError","title":""},{"docid":"82b6afc3f0644396e34b431513bfd2e1","score":"0.5965924","text":"def generate_models(self):\n # Get the group of connections from the id\n group_of_connections = __group_of_group_of_connections__.get_group(self.get_group_connection_id())\n\n # For each connection\n for connection in group_of_connections.get_connections():\n # Create its model. Remember that the connection id and the model id is the 4-tuple\n model_id = connection.get_id()\n new_model = Model(model_id)\n # Set the constructor for this model. Each model has a specific way of constructing the states\n #new_model.set_constructor(__modelsconstructors__.get_default_constructor())\n constructor_id = self.get_constructor_id()\n new_model.set_constructor(__modelsconstructors__.get_constructor(constructor_id))\n for flow in connection.get_flows():\n # Try to add the flow\n if not new_model.add_flow(flow):\n self.delete_model_by_id(new_model.get_id())\n # The flows are not ordered. Delete the truckated models\n __groupofgroupofmodels__.delete_group_of_models(self.get_id())\n return False\n self.models[model_id] = new_model","title":""},{"docid":"86485f9351eb528526292ce5a7a6cfaf","score":"0.5961841","text":"def get_dataset(self): \n # Get lists of file paths for training and test sets, respectively.\n train_set_paths, test_set_paths = self._get_file_paths() \n\n # Build appropriate dataframes. \n train_set = self._build_dataframe(train_set_paths)\n test_set = self._build_dataframe(test_set_paths) \n\n return train_set, test_set","title":""},{"docid":"2288cd41a8517323faa2ac3fd9e468c2","score":"0.5959713","text":"def read_model(self):\n f1 = open(self.name + '_' + 'words', 'r')\n f2 = open(self.name + '_' + 'word_lengths', 'r')\n f3 = open(self.name + '_' + 'stems', 'r')\n f4 = open(self.name + '_' + 'sentence_lengths', 'r')\n f5 = open(self.name + '_' + 'word_pair', 'r')\n d_str1 = f1.read() \n d_str2 = f2.read() \n d_str3 = f3.read() \n d_str4 = f4.read() \n d_str5 = f5.read() \n self.words = dict(eval(d_str1))\n self.word_lengths= dict(eval(d_str2))\n self.stems = dict(eval(d_str3))\n self.sentence_lengths = dict(eval(d_str4))\n self.word_pair = dict(eval(d_str5))","title":""},{"docid":"2bb10a539955b78bbda751dcbb22d75a","score":"0.5946806","text":"def __init__(self, model_dir, models_path=settings.MODELS_FOLDER):\n self.path = os.path.join(models_path, model_dir)\n print('------', self.path)\n self.model = self.load_model()\n self.preds = None","title":""},{"docid":"11f4d5018cc0e4d0824bd4ff530f94d0","score":"0.59453404","text":"def get_model(cls):\n if cls.encoder_model_50 is None:\n #cls.model = load_model(os.path.join(model_path, MODEL_NAME))\n #print(cls.model.summary())\n #cls.encoder_model = load_model(os.path.join(model_path, encoder_model-100.hdf5))\n #print(cls.encoder_model.summary())\n #cls.decoder_model = load_model(os.path.join(model_path, decoder_model-100.hdf5))\n #print(cls.decoder_model.summary())\n #cls.model_100 = load_model(os.path.join(model_path, 'model-100.hdf5'))\n #cls.model_50 = load_model(os.path.join(model_path, 'model-50.hdf5'))\n cls.encoder_model_100 = load_model(os.path.join(model_path, 'encoder_model-100.hdf5'))\n cls.encoder_model_50 = load_model(os.path.join(model_path, 'encoder_model-50.hdf5'))\n cls.decoder_model_100 = load_model(os.path.join(model_path, 'decoder_model-100.hdf5'))\n cls.decoder_model_50 = load_model(os.path.join(model_path, 'decoder_model-50.hdf5'))\n \n return cls.encoder_model_50, cls.decoder_model_50, cls.encoder_model_100, cls.decoder_model_100","title":""},{"docid":"183f3a2dba1c5cdf236068a381e82edc","score":"0.59435695","text":"def main() -> None:\n data = download_dataset(DATA_URL)\n features, labels = pre_process_data(data)\n trained_model = train_model(features, labels)\n persist_model(trained_model)","title":""},{"docid":"48ef13d6f5f1bf6f123fdf5a05e49dbb","score":"0.59416366","text":"def _train(self):\n\n raise NotImplementedError","title":""},{"docid":"d0af7575dd2ef883653323abeface3e1","score":"0.5941535","text":"def _add_model_data_methods(self):\n self.inputs = self._model_data.filter_by_attrs(is_result=0)\n self.results = self._model_data.filter_by_attrs(is_result=1)\n self._add_observed_dict(\"model_config\")\n self._add_observed_dict(\"run_config\")\n self._add_observed_dict(\"math\")\n\n self.inputs = self._model_data.filter_by_attrs(is_result=0)\n results = self._model_data.filter_by_attrs(is_result=1)\n if len(results.data_vars) > 0:\n self.results = results\n log_time(\n logger,\n self._timings,\n \"model_data_loaded\",\n comment=\"Model: loaded model_data\",\n )","title":""},{"docid":"722d2d1901e010c3156305a2bc9f4a18","score":"0.5938245","text":"def initialize_models(self):\n pass","title":""},{"docid":"e537c6a984aa8fed70c87d64c05674b8","score":"0.59382355","text":"def load_dataset(self, name=''):\n name = name or (name(self) + '.model')\n return self.loader.load_model(name)","title":""},{"docid":"c9ae5a49b1d47aee4a3700ffc22e53a3","score":"0.5937723","text":"def create_loaders(self):\n self.spam_data.text_to_tensors()\n print('creating dataloaders')\n train_data = TensorDataset(self.spam_data.train_inputs, \n self.spam_data.train_masks, \n self.spam_data.train_labels)\n train_sampler = RandomSampler(train_data)\n self.train_dataloader = DataLoader(train_data, \n sampler=train_sampler, \n batch_size=self.batch_size)\n\n validation_data = TensorDataset(self.spam_data.validation_inputs, \n self.spam_data.validation_masks, \n self.spam_data.validation_labels)\n validation_sampler = SequentialSampler(validation_data)\n self.validation_dataloader = DataLoader(validation_data, \n sampler=validation_sampler, \n batch_size=self.batch_size)\n \n test_data = TensorDataset(self.spam_data.test_inputs, \n self.spam_data.test_masks, \n self.spam_data.test_labels)\n test_sampler = SequentialSampler(test_data)\n self.test_dataloader = DataLoader(test_data, \n sampler=test_sampler, \n batch_size=self.batch_size)\n print('finished creating dataloaders')","title":""},{"docid":"babd17f7c461638ea9e0481d6a4a4611","score":"0.5934158","text":"def begin_local(self):\n\n # Obtain Dataset\n\n sys.path.append(os.getcwd())\n data_module = importlib.import_module(\"data.%s.data\" % self.dataset_name)\n dataset_class = find_dataset_class(data_module)\n\n if not dataset_class:\n return\n\n dataset = dataset_class(name=self.dataset_name, trial=True, **self.kwargs)\n dataset.configure_core_arguments(self.args)\n \n # Obtain the model class\n\n model_module = importlib.import_module(\"models.%s.model\" % self.model_name)\n model_class = find_model_class(model_module)\n\n # Obtain the task class\n \n task_class = None\n\n if self.task_name is not None:\n task_module = importlib.import_module(\"tasks.%s.task\" % self.task_name)\n task_class = find_task_class(task_module)\n\n if task_class is None:\n task = None\n else:\n task = task_class(data=dataset)\n\n if not model_class:\n return\n\n model = model_class(data=dataset, task=task, **self.kwargs)\n model.args = self.args\n model.settings = self.settings\n model.cloudremote = self.cloudremote\n model.configure_core_arguments(self.args)\n\n if not self.cloudremote: # local training\n self.setup_local_training(model)\n else:\n model.trial = Trial(project_name=self.project_name, \n model_name=self.model_name, \n dataset_name=self.dataset_name, \n task_name=self.task_name, \n cloud=self.cloud)\n\n model.sync_trial_metadata()\n\n if not self.cloudremote: \n print(colored('\\n \\033[1m Training\\n', 'blue'))\n\n model.run()","title":""},{"docid":"6a16b5b973ed21825e524b0156154c17","score":"0.59317327","text":"def __init__(self, models):\n self.models = models","title":""},{"docid":"8aca508aa9581be29cbbc872b7941372","score":"0.5930965","text":"def load_model(self):\n pass","title":""},{"docid":"8aca508aa9581be29cbbc872b7941372","score":"0.5930965","text":"def load_model(self):\n pass","title":""},{"docid":"72aa2cd3d79c8913a0cdecddcc5f347d","score":"0.5926102","text":"def __init__(self):\n\n # The name of the models.\n # TODO: Refector to query them from Django\n self.str_force_models = ['Campaign',\n 'Deployment',\n 'Image',\n 'AUVDeployment',\n 'BRUVDeployment',\n 'DOVDeployment',\n 'TVDeployment',\n 'TIDeployment']\n\n for model in self.str_force_models:\n self.stat_fields = {model: 0}\n\n # List of model instances\n self.force_models = [Campaign,\n Deployment,\n Image,\n AUVDeployment,\n BRUVDeployment,\n DOVDeployment,\n TVDeployment,\n TIDeployment]","title":""},{"docid":"d47b7541ac9c62728469b5626d9606ff","score":"0.59258354","text":"def buildModel(self):\n\t\tself.clf = svm.SVC()","title":""},{"docid":"f497f44b1e0f04c9092eebfb91e91f3e","score":"0.59257245","text":"def __init__(self):\n\n self._experiment = Experiment\n self.data = DataContainer\n self.model = ModelContainer\n self.fit = Fit","title":""},{"docid":"43c10a5ef02fc81fa1aed9b36c786567","score":"0.59194636","text":"def make_dataset(self, dir):\n pass","title":""},{"docid":"55d99919e4e8fc81fc5c1119dc5cd030","score":"0.5917552","text":"def __init__(self, model_name):\n download_model(model_name)\n label_map = label_map_util.load_labelmap(PATH_TO_LABELS)\n categories = label_map_util.convert_label_map_to_categories(label_map, \n max_num_classes=NUM_CLASSES, use_display_name=True)\n self.category_index = label_map_util.create_category_index(categories)\n self.load_graph(model_name)","title":""},{"docid":"c77af5b494a196f832e13ab1159d055f","score":"0.59164995","text":"def _train(self, observations, metadata):\n pass","title":""},{"docid":"e77d534d195abee70ee41131076860d4","score":"0.5914514","text":"def create_model(self) -> AmiciModel:","title":""},{"docid":"576fa45543f130a0e5ca3d9b58a30542","score":"0.59135574","text":"def _load(self):\n dataset, info = tfds.load(self.name, with_info=True, as_supervised=True)\n self.input_shape = info.features['image'].shape\n self.output_shape = (info.features['label'].num_classes,)\n self.output_size = self.output_shape[-1]\n self.num_examples = {t: s.num_examples for (t, s) in info.splits.items()}\n self.ds = dict()\n for tag in ['train', 'test']:\n self.ds[tag] = (\n dataset[tag].map(self.convert).map(self.augment).shuffle(10000))","title":""},{"docid":"05ff0fffc116699dc9009d00bce18da2","score":"0.59132826","text":"def get_data_from_model(self):\n model = self.model\n for i in range(model.rowCount()):\n section = model.item(i)\n key1 = str(section.text())\n if key1 in [\"Global attributes\"]:\n # global attributes\n for j in range(section.rowCount()):\n key2 = str(section.child(j, 0).text())\n val2 = str(section.child(j, 1).text())\n self.ds.root[\"Attributes\"][key2] = val2\n elif key1 in [\"Variables\"]:\n for j in range(section.rowCount()):\n variable_section = section.child(j)\n label = variable_section.text()\n for k in range(variable_section.rowCount()):\n key2 = str(variable_section.child(k, 0).text())\n val2 = str(variable_section.child(k, 1).text())\n self.ds.root[\"Variables\"][label][\"Attr\"][key2] = val2\n else:\n # this is a netCDF file with groups\n group_attributes = {}\n variables = {}\n for j in range(section.rowCount()):\n if section.child(j).text() in [\"Group attributes\"]:\n for k in range(section.child(j).rowCount()):\n key = str(section.child(j).child(k, 0).text())\n value = str(section.child(j).child(k, 1).text())\n group_attributes[key] = value\n else:\n label = section.child(j).text()\n group = getattr(self.ds, key1)\n variables[label] = {\"Data\": group[\"Variables\"][label][\"Data\"],\n \"Flag\": group[\"Variables\"][label][\"Flag\"],\n \"Attr\": group[\"Variables\"][label][\"Attr\"]}\n for k in range(section.child(j).rowCount()):\n key = str(section.child(j).child(k, 0).text())\n value = str(section.child(j).child(k, 1).text())\n variables[label][\"Attr\"][key] = value\n # create the group as an attribute in the data structure\n setattr(self.ds, key1, {\"Attributes\": group_attributes,\n \"Variables\": variables})\n #msg = \" Unrecognised object (\" + key1 + \") in netCDF file\"\n #msg += \", skipping ...\"\n #logger.warning(msg)\n return self.ds","title":""},{"docid":"bf6e9ab846d54b0c7d553593fb57c978","score":"0.59122497","text":"def prepDatasets(self):\n self.train_ds = DataGenerator2022(generateForCellPred = False, batch_size = self.batch_size)\n self.val_ds = DataGenerator2022(train=False, generateForCellPred = False, batch_size = self.batch_size)","title":""},{"docid":"8c7ce70028cae2d08538eeba8b99887b","score":"0.590531","text":"def get_dataset(self):\n\n train_transform = transforms.Compose([\n # you can add other transformations in this list\n # resize (256 x remaining larger size) and RandomCrop(224)\n # like in https://papers.nips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf\n # https://arxiv.org/pdf/1409.1556.pdf\n transforms.Resize(256), # resize smaller size to 256\n transforms.RandomCrop(self.args.patch_size), # 224\n transforms.ToTensor()\n ])\n test_transform = transforms.Compose([\n # you can add other transformations in this list\n # resize\n transforms.Resize(256), # resize smaller size to 256\n transforms.CenterCrop((self.args.patch_size, self.args.patch_size)), # 224\n transforms.ToTensor()\n ])\n\n if self.args.compute_dataset_metrics is True:\n\n train_transform = transforms.Compose([\n transforms.Resize((self.args.patch_size, self.args.patch_size)), # resize smaller size to 256\n transforms.ToTensor()\n ])\n test_transform = transforms.Compose([\n transforms.Resize((self.args.patch_size, self.args.patch_size)), # resize smaller size to 256\n transforms.ToTensor()\n ])\n\n '''\n train_transform = transforms.Compose([\n transforms.ToTensor()\n ])\n test_transform = transforms.Compose([\n transforms.ToTensor()\n ])'''\n\n # if not already set, set batch-size to 1 for computing the metrics\n # due to different image sizes\n self.args.batch_size = 1\n self.args.workers = 0\n\n trainset = IndxImageFolder(os.path.join(self.load_path, \"train\"),\n transform=train_transform,\n target_transform=None)\n\n valset = IndxImageFolder(os.path.join(self.load_path, \"val\"),\n transform=test_transform,\n target_transform=None)\n\n # https://pytorch.org/vision/0.8/_modules/torchvision/datasets/imagenet.html#ImageNet\n # https://www.kaggle.com/c/imagenet-object-localization-challenge/data?select=LOC_synset_mapping.txt\n # https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a\n self.class_to_idx = trainset.class_to_idx\n self.idx_to_class = dict((v, k) for k, v in self.class_to_idx.items())\n\n if not os.path.exists(os.path.join('metrics/datasets', self.name) +'.csv'):\n train_loader = torch.utils.data.DataLoader(trainset, batch_size=1, shuffle=False, num_workers=0)\n val_loader = torch.utils.data.DataLoader(valset, batch_size=1, shuffle=False, num_workers=0)\n\n indices_data = {}\n for loader_name, loader in [('train', train_loader), ('test', val_loader)]:\n for (img, label, img_path) in tqdm(loader):\n # since img_path is a tuple, it is converted to a list\n # since batch-size is 1, the first element is taken\n indices_data[list(img_path)[0]] = label.detach().cpu().numpy()[0]\n\n # store img_paths which serves as indices and the labels for further analysis\n indices_data = collections.OrderedDict(sorted(indices_data.items()))\n\n dataframe = pd.DataFrame({'img_paths': list(indices_data.keys()),\n 'labels': list(indices_data.values())})\n DatasetMetrics.indices_paths(self.name, dataframe)\n\n return trainset, valset","title":""},{"docid":"812ce1568c4c0af43cec52fda45df864","score":"0.5903093","text":"def __init__(self, model_name, input_shape=(256, 256, 3)):\n super().__init__(model_name=model_name)\n\n # Number of classes to segment\n # 0 -> not a building\n # 1 -> a building\n self.n_classes = 2\n # Input data shape\n self.input_shape = input_shape\n # File extensions for data to predict\n self.FILE_EXTENSIONS = [\n \"tif\",\n \"tiff\",\n \"png\",\n \"jpg\",\n \"jpeg\"\n ]\n # Files to predict\n self.filenames = list()","title":""}],"string":"[\n {\n \"docid\": \"ea662cd6f629aaa29b6be895da859b88\",\n \"score\": \"0.7318091\",\n \"text\": \"def _get_model_dataset(self):\\n\\n # Instantiate the model object\\n model = getattr(\\n importlib.import_module('models.' + self._args.model),\\n self._args.model)()\\n\\n # Instantiate the input object\\n dataset = getattr(\\n importlib.import_module('inputs.' + self._args.dataset),\\n self._args.dataset)()\\n return model, dataset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78607a83b3243243d67fdae05282e466\",\n \"score\": \"0.70887065\",\n \"text\": \"def trainModel(self, data, attributes, class_label):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4f2545c36c8fd09d700b889b736423e\",\n \"score\": \"0.67790407\",\n \"text\": \"def models(self, models):\\n if models is not None:\\n models = DatasetModels(models)\\n models = models.select(datasets_names=self.name)\\n for model in models:\\n model._exposure = self.data.exposure\\n self._models = models\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63a0615905b22dac2ac78a21faf4a3b0\",\n \"score\": \"0.6748353\",\n \"text\": \"def get_dataset(self):\\n\\n # https://developer.nvidia.com/blog/preparing-state-of-the-art-models-for-classification-and-object-detection-with-tlt/\\n train_download = not os.path.exists(os.path.join(self.load_path, \\\"train\\\"))\\n trainval_2012 = datasets.VOCDetection(os.path.join(self.load_path, \\\"train\\\"), image_set='trainval',\\n transform=transforms.Compose([transforms.ToTensor()]),\\n target_transform=None, download=train_download)\\n trainval_2007 = datasets.VOCDetection(os.path.join(self.load_path, \\\"train\\\"), image_set='trainval',\\n year='2007',\\n transform=transforms.Compose([transforms.ToTensor()]),\\n target_transform=None, download=train_download)\\n test_download = not os.path.exists(os.path.join(self.load_path, \\\"test\\\"))\\n valset = datasets.VOCDetection(os.path.join(self.load_path, \\\"test\\\"), image_set='test',\\n year='2007',\\n transform=transforms.Compose([transforms.ToTensor()]),\\n target_transform=None, download=test_download)\\n train_loader_2007 = torch.utils.data.DataLoader(trainval_2007, batch_size=1, shuffle=False, num_workers=2)\\n train_loader_2012 = torch.utils.data.DataLoader(trainval_2012, batch_size=1, shuffle=False, num_workers=2)\\n val_loader = torch.utils.data.DataLoader(valset, batch_size=1, shuffle=False, num_workers=2)\\n\\n check = 0\\n directories = [os.path.join(self.save_path, \\\"train\\\"), os.path.join(self.save_path, \\\"test\\\")]\\n for directory in directories:\\n if os.path.exists(directory):\\n check += 1\\n if not os.path.exists(directory):\\n os.makedirs(directory)\\n\\n if check != len(directories):\\n indices_data = {}\\n # create folders to save data\\n for loader_name, loader in [('train', train_loader_2007),\\n ('train', train_loader_2012),\\n ('test', val_loader)]:\\n for (img, annotation) in tqdm(loader):\\n\\n #print(annotation)\\n # there may be multiple labels, they are concatenated to: 'label1_label2_'\\n label = ''\\n int_label = []\\n\\n elems = annotation['annotation']['object']\\n # if only 1 label - it is a dictionary, but not list of dictionaries\\n # for consistency reasons and to be able to use the loop later\\n if not isinstance(elems, list):\\n elems = [elems]\\n\\n # get bboxes, compute object size, add all object sizes and divide by img size (h*w)\\n obj_sizes = 0\\n num_instances = 0\\n\\n for elem in elems:\\n # every name is in a list\\n # there may be multiple instances of the same object\\n # those are disregarded for label\\n\\n if not (bool(int(elem['difficult'][0])) and loader_name == 'test'):\\n if not str(self.class_to_idx[elem['name'][0]]) in label:\\n label += str(self.class_to_idx[elem['name'][0]]) + '_'\\n int_label.append(self.class_to_idx[elem['name'][0]])\\n\\n num_instances += 1\\n # percentage of objects in the image: sum obj_size/img_size\\n obj_sizes += (int(elem['bndbox']['xmax'][0]) - int(elem['bndbox']['xmin'][0])) * \\\\\\n (int(elem['bndbox']['ymax'][0]) - int(elem['bndbox']['ymin'][0]))\\n obj_sizes /= float(int(annotation['annotation']['size']['width'][0]) *\\n int(annotation['annotation']['size']['height'][0]))\\n\\n img_name = label + '_' + annotation['annotation']['filename'][0]\\n\\n directory = os.path.join(os.path.join(self.save_path, loader_name), label)\\n if not os.path.exists(directory):\\n os.makedirs(directory)\\n save_image(img, os.path.join(directory, img_name))\\n\\n indices_data[os.path.join(directory, img_name)] = (int_label,\\n obj_sizes, num_instances)\\n\\n # store img_paths which serves as indices and the labels for further analysis\\n indices_data = collections.OrderedDict(sorted(indices_data.items()))\\n\\n dataframe = pd.DataFrame({'img_paths': list(indices_data.keys()),\\n 'labels': np.array(list(indices_data.values()), dtype=object)[:, 0],\\n 'obj_sizes': np.array(list(indices_data.values()), dtype=object)[:, 1],\\n 'num_instances': np.array(list(indices_data.values()), dtype=object)[:, 2]})\\n DatasetMetrics.indices_paths(self.name, dataframe)\\n\\n train_transform = transforms.Compose([\\n # you can add other transformations in this list\\n # resize (256 x remaining larger size) and RandomCrop(224)\\n # like in https://papers.nips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf\\n # https://arxiv.org/pdf/1409.1556.pdf\\n transforms.Resize(256), # resize smaller size to 256\\n transforms.RandomCrop(self.args.patch_size), # 224\\n transforms.ToTensor()\\n ])\\n\\n test_transform = transforms.Compose([\\n # you can add other transformations in this list\\n # resize (256 x remaining larger size) and RandomCrop(224)\\n transforms.Resize(256), # resize smaller size to 256\\n transforms.CenterCrop((self.args.patch_size, self.args.patch_size)), # 224\\n transforms.ToTensor()\\n ])\\n\\n if self.args.compute_dataset_metrics is True:\\n # when computing dataset metrics, an original image should be used\\n # - without randomness of RandomCrop\\n train_transform = transforms.Compose([\\n transforms.ToTensor()\\n ])\\n test_transform = transforms.Compose([\\n transforms.ToTensor()\\n ])\\n\\n # if not already set, set batch-size to 1 for computing the metrics\\n # due to different image sizes\\n self.args.batch_size = 1\\n\\n # load the image dataset from folder with indices\\n trainset = IndxImageFolder(root = os.path.join(self.save_path, \\\"train\\\"), transform=train_transform,\\n num_classes=len(self.class_to_idx), multilabel=self.args.multilabel)\\n valset = IndxImageFolder(root=os.path.join(self.save_path, \\\"test\\\"), transform=test_transform,\\n num_classes=len(self.class_to_idx), multilabel=self.args.multilabel)\\n\\n return trainset, valset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a41ce9ccec715c381387cff5211410f\",\n \"score\": \"0.66953593\",\n \"text\": \"def get_data_model(cls):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a41ce9ccec715c381387cff5211410f\",\n \"score\": \"0.66953593\",\n \"text\": \"def get_data_model(cls):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbdf9b93d03bce8db2031663b7338bfe\",\n \"score\": \"0.6614376\",\n \"text\": \"def parse_for_model(self):\\n print('Pre-processing data for model...')\\n if any(self.dataset):\\n model_data = self.dataset.copy()\\n else:\\n raise ValueError('First make dataset then pre-process it for Model')\\n self.room_encoder, self.ap_encoder = LabelEncoder(), LabelEncoder()\\n model_data.room_id = self.room_encoder.fit_transform(model_data.room_id)\\n model_data.ap_id = self.ap_encoder.fit_transform(model_data.ap_id)\\n features = model_data.iloc[:, :-1].values\\n target = model_data.iloc[:, -1].values\\n \\\"\\\"\\\" Splitting data into train, validation and test \\\"\\\"\\\"\\n x_train, x_test, y_train, y_test = train_test_split(features, target, test_size=0.1)\\n x_train, x_valid, y_train, y_valid = train_test_split(x_train, y_train, test_size=0.1)\\n self.model_data = (x_train, x_valid, x_test, y_train, y_valid, y_test)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d66fbbf227dfc6ab99b6adc4154fab0\",\n \"score\": \"0.6558669\",\n \"text\": \"def create_models(self):\\n self.__handle_csv()\\n self.__export_models()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4b1b0f46bd362c5e19c033c27465c81\",\n \"score\": \"0.65492105\",\n \"text\": \"def createModel(self, dataFile, classifierType):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7237f86667dc30cba8ddf3d8bf1b2f2\",\n \"score\": \"0.6529856\",\n \"text\": \"def model(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"978af77a2915f0e622eab5eae1c62efa\",\n \"score\": \"0.6520754\",\n \"text\": \"def fit_storage(self, data):\\n models = []\\n for epoch in range(self.k):\\n sample = self.selector(data) # data with a subset of attributes\\n models.append(self.learner(sample))\\n model = BaggedModel(data.domain, models)\\n model.name = self.name\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dc1e7bf48bfccc497a9ba779bf3051d\",\n \"score\": \"0.6504034\",\n \"text\": \"def __init__(self, datasets: List[IterableDataset]):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06055d9094aca48587e82926dfd159f1\",\n \"score\": \"0.6475266\",\n \"text\": \"def iter_data():\\n for v, q, a in dataset:\\n yield (Model(v, ontology), q.split(), a)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b829e70cc66c36e149238672495316cf\",\n \"score\": \"0.64297676\",\n \"text\": \"def __init__(self, dataset):\\r\\n self.dataset = dataset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff42f9452ff2ff43fccb37ec127c6f25\",\n \"score\": \"0.64160985\",\n \"text\": \"def __init__(self, data, models: Iterable[BaseModel], combiner: Combiner):\\n assert models, \\\"The list of models cannot be empty\\\"\\n assert combiner, \\\"You need to provide a Combiner\\\"\\n super().__init__(data)\\n\\n self.models: Iterable[BaseModel] = models\\n self.combiner: Combiner = combiner\\n self.model_weights: List[float] = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf177400dca7c09077803697a591fbcb\",\n \"score\": \"0.6382948\",\n \"text\": \"def __init__(self, dataset):\\n self.dataset = dataset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1542ec9e58a9ab3a9bdbf240d0a86296\",\n \"score\": \"0.6374203\",\n \"text\": \"def model(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1542ec9e58a9ab3a9bdbf240d0a86296\",\n \"score\": \"0.6374203\",\n \"text\": \"def model(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cde3fdf752b361890fb16ed1a03bfdfb\",\n \"score\": \"0.63718194\",\n \"text\": \"def make_model(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e582a024b4c2a2b480a7cd12fe7423d9\",\n \"score\": \"0.63537705\",\n \"text\": \"def datasets(self):\\n \\n class CategoryData(DataSet):\\n def data(self):\\n return (\\n ('gray_stuff', dict(id=1, name='gray')),\\n ('yellow_stuff', dict(id=2, name='yellow')),\\n )\\n return [CategoryData]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f755d2b043bf15c2afae59e5c3f6173\",\n \"score\": \"0.6344792\",\n \"text\": \"def get_models():\\n models = {}\\n models['LR'] = LogisticRegression()\\n models['LDA'] = LinearDiscriminantAnalysis()\\n models['KNN'] = KNeighborsClassifier()\\n models['CART'] = DecisionTreeClassifier()\\n models['NB'] = GaussianNB()\\n models['SVM'] = SVC()\\n return models\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaf7cceb0afba15f05425acaa63f41fd\",\n \"score\": \"0.63406163\",\n \"text\": \"def run_models():\\n\\n ### list to contain all individual model results\\n models = []\\n\\n ### placeholder example\\n example_model_results = ['model name', 'model description', 'data name',\\n 'data description', 0.0, 0.0]\\n models.append(example_model_results)\\n\\n ### contributors add models below #########################################\\n\\n\\n\\n ###########################################################################\\n\\n return models\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1934063c18e2a0a1c18c148dec977bc\",\n \"score\": \"0.63323915\",\n \"text\": \"def train_model(self):\\n\\n\\t\\tself.X_train = []\\n\\t\\tself.Y_train = []\\n\\n\\t\\tself.X_test = []\\n\\t\\tself.Y_test = []\\n\\n\\t\\t#We are currently using a decision tree, however this can be quite modular\\n\\t\\tif self.model == \\\"none\\\":\\n\\t\\t\\tself.model = DecisionTreeRegressor()\\n\\n\\n\\t\\tfor rollout in self.rollouts:\\n\\n\\t\\t\\tif uniform() > 0.2:\\n\\t\\t\\t\\ttrain = True\\n\\t\\t\\telse:\\n\\t\\t\\t\\ttrain = False\\n\\n\\t\\t\\tfor datum in rollout[0]:\\n\\n\\t\\t\\t\\tobservations = datum['state']\\n\\t\\t\\t\\tactions = datum['action']\\n\\n\\t\\t\\t\\tfor i in range(len(actions)):\\n\\n\\t\\t\\t\\t\\ta_ = actions[i].get_value()\\n\\n\\t\\t\\t\\t\\ts_ = observations[i]\\n\\n\\t\\t\\t\\t\\tif train:\\n\\t\\t\\t\\t\\t\\tself.X_train.append(s_)\\n\\t\\t\\t\\t\\t\\tself.Y_train.append(a_)\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tself.X_test.append(s_)\\n\\t\\t\\t\\t\\t\\tself.Y_test.append(a_)\\n\\n\\t\\t#self.standardize()\\n\\t\\tself.model.fit(self.X_train,self.Y_train) \\n\\n\\t\\tif self.il_config['save_model']:\\n\\t\\t\\tif not os.path.exists(self.file_path+'/model'):\\n\\t\\t\\t\\tos.makedirs(self.file_path+'/model')\\n\\t\\t\\tmodel_file_path = self.file_path+'/model/model_iter_'+str(self.iter_count)\\n\\t\\t\\tnp.save(model_file_path,self.model)\\n\\t\\t\\t#pickle.dump(self.model,open(model_file_path,'w'))\\n\\t\\t\\tself.iter_count += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c3c85163fb50701d083797cf97f50d6\",\n \"score\": \"0.63247085\",\n \"text\": \"def create_models(self):\\r\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc83fb92976bcfac8275c96158219377\",\n \"score\": \"0.6268053\",\n \"text\": \"def define_models():\\n models = []\\n models.append(('LR', LogisticRegression(class_weight='balanced', max_iter=2500)))\\n models.append(('RF', RandomForestClassifier(class_weight='balanced',criterion='entropy')))\\n models.append(('ET', ExtraTreesClassifier(class_weight='balanced',criterion='entropy')))\\n models.append(('XG', xgb.XGBClassifier(class_weight='balanced',criterion='entropy')))\\n models.append(('KNN', BalancedBaggingClassifier(base_estimator=KNeighborsClassifier(),\\n sampling_strategy='auto',\\n replacement=False)))\\n models.append(('NB', MultinomialNB()))\\n models.append(('MLP','mlp'))\\n models.append(('EMB','emb'))\\n models.append(('CON','con'))\\n return models\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96c3794cfa7ffbf05ac22d738f2e71b9\",\n \"score\": \"0.6264579\",\n \"text\": \"def models_from_data():\\n input_filepath = os.path.join(data_path, \\\"spring_mass_1D_inputs.txt\\\")\\n output1_filepath = os.path.join(data_path, \\\"spring_mass_1D_outputs_1.0.txt\\\")\\n output2_filepath = os.path.join(data_path, \\\"spring_mass_1D_outputs_0.1.txt\\\")\\n output3_filepath = os.path.join(data_path,\\n \\\"spring_mass_1D_outputs_0.01.txt\\\")\\n\\n model1 = ModelFromData(input_filepath, output1_filepath, cost=1.)\\n model2 = ModelFromData(input_filepath, output2_filepath, cost=4.)\\n model3 = ModelFromData(input_filepath, output3_filepath, cost=16.)\\n\\n return [model1, model2, model3]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4aaba645fdb0cec6dae95b2e4a0f1fb2\",\n \"score\": \"0.62255543\",\n \"text\": \"def create_model(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f09930f19ef6429118e256b7677532b\",\n \"score\": \"0.620214\",\n \"text\": \"def __init__(self, dataset, env):\\n super(EncodingSimple, self).__init__()\\n self.ds = dataset\\n self.env = env\\n self.model = None\\n self.nsteps = int(self.ds.numAttr * self.ds.numTuple / self.env['batch_size'])\\n self.dataset = self.ds.df.groupby(np.arange(self.ds.numTuple) // env['batch_size'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"753557d3114d7e7592ff4eda2be697ca\",\n \"score\": \"0.6187025\",\n \"text\": \"def build_model(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66a113b3151bfab1ce7ac2e641a755fd\",\n \"score\": \"0.61857843\",\n \"text\": \"def get_dsets(self):\\n\\t\\tdata_class = get_dataset(self.name, self.name, self.img_dir, self.LDS_type, self.is_target)\\n\\n\\t\\tself.num_classes, train_dataset, val_dataset, test_dataset, self.train_transforms, self.test_transforms = data_class.get_data()\\n\\n\\t\\tself.train_dataset = DatasetWithIndicesWrapper(self.name, train_dataset.data, train_dataset.targets, self.train_transforms, self.test_transforms)\\n\\t\\tself.val_dataset = DatasetWithIndicesWrapper(self.name, val_dataset.data, val_dataset.targets, self.test_transforms, self.test_transforms)\\n\\t\\tself.test_dataset = DatasetWithIndicesWrapper(self.name, test_dataset.data, test_dataset.targets, self.test_transforms, self.test_transforms)\\n\\n\\t\\treturn self.train_dataset, self.val_dataset, self.test_dataset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32e73b2d4f8a6d244c3fc8e9b5468357\",\n \"score\": \"0.61855143\",\n \"text\": \"def TrainModel(self):\\n # Initialize the Needed Classifier\\n self.classifier = NaiveBayesClassifier.train(self.train_features)\\n #self.classifier = MaxentClassifier.train(self.train_features,algorithm=\\\"iis\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8eab8763d224c83136e21721f2cb316b\",\n \"score\": \"0.6182225\",\n \"text\": \"def get_models(self):\\n nb = GaussianNB()\\n svc = SVC(C=100, probability=True)\\n knn = KNeighborsClassifier(n_neighbors=3)\\n lr = LogisticRegression(C=100, random_state=SEED)\\n nn = MLPClassifier((80, 10), early_stopping=False, random_state=SEED)\\n gb = GradientBoostingClassifier(n_estimators=100, random_state=SEED)\\n rf = RandomForestClassifier(n_estimators=10, max_features=3, random_state=SEED)\\n\\n models = {\\n # 'svm': svc,\\n 'knn': knn,\\n 'naive bayes': nb,\\n # 'mlp-nn': nn,\\n 'random forest': rf,\\n 'gbm': gb,\\n 'logistic': lr,\\n }\\n\\n return models\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5448a0232066ce34452fd732a809ab5\",\n \"score\": \"0.61742306\",\n \"text\": \"def __init__(self,batch_size: int, epochs: int, dataset, labels, dataset_test, labels_test, model):\\n self.batch_size = batch_size\\n self.epochs = epochs\\n self.dataset = dataset\\n self.labels = labels\\n self.dataset_test = dataset_test\\n self.labels_test = labels_test\\n self.model = model\\n self.__save_folder = \\\"savedModels/\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a588726c6a87ae362966334b1ecf1a7e\",\n \"score\": \"0.6171815\",\n \"text\": \"def dataset_setup(self):\\n settings = self.settings\\n if settings.crowd_dataset == CrowdDataset.ucf_qnrf:\\n self.dataset_class = UcfQnrfFullImageDataset\\n self.train_dataset = UcfQnrfTransformedDataset(middle_transform=data.RandomHorizontalFlip(),\\n seed=settings.labeled_dataset_seed,\\n number_of_examples=settings.labeled_dataset_size)\\n self.train_dataset_loader = DataLoader(self.train_dataset, batch_size=settings.batch_size,\\n pin_memory=self.settings.pin_memory,\\n num_workers=settings.number_of_data_workers)\\n self.validation_dataset = UcfQnrfTransformedDataset(dataset='test', seed=101)\\n elif settings.crowd_dataset == CrowdDataset.shanghai_tech:\\n self.dataset_class = ShanghaiTechFullImageDataset\\n self.train_dataset = ShanghaiTechTransformedDataset(middle_transform=data.RandomHorizontalFlip(),\\n seed=settings.labeled_dataset_seed,\\n number_of_examples=settings.labeled_dataset_size,\\n map_directory_name=settings.map_directory_name,\\n image_patch_size=self.settings.image_patch_size,\\n label_patch_size=self.settings.label_patch_size)\\n self.train_dataset_loader = DataLoader(self.train_dataset, batch_size=settings.batch_size,\\n pin_memory=self.settings.pin_memory,\\n num_workers=settings.number_of_data_workers)\\n self.validation_dataset = ShanghaiTechTransformedDataset(dataset='test', seed=101,\\n map_directory_name=settings.map_directory_name,\\n image_patch_size=self.settings.image_patch_size,\\n label_patch_size=self.settings.label_patch_size)\\n elif settings.crowd_dataset == CrowdDataset.ucf_cc_50:\\n seed = 0\\n self.dataset_class = UcfCc50FullImageDataset\\n self.train_dataset = UcfCc50TransformedDataset(middle_transform=data.RandomHorizontalFlip(),\\n seed=seed,\\n test_start=settings.labeled_dataset_seed * 10,\\n inverse_map=settings.inverse_map,\\n map_directory_name=settings.map_directory_name)\\n self.train_dataset_loader = DataLoader(self.train_dataset, batch_size=settings.batch_size,\\n pin_memory=self.settings.pin_memory,\\n num_workers=settings.number_of_data_workers)\\n self.validation_dataset = UcfCc50TransformedDataset(dataset='test', seed=seed,\\n test_start=settings.labeled_dataset_seed * 10,\\n inverse_map=settings.inverse_map,\\n map_directory_name=settings.map_directory_name)\\n else:\\n raise ValueError('{} is not an understood crowd dataset.'.format(settings.crowd_dataset))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"684fc3c7d4134977533b41111ccd1990\",\n \"score\": \"0.61668634\",\n \"text\": \"def build_model(self):\\r\\n self.source_images, self.source_labels = self.dataloader.get_model_inputs()\\r\\n self.target_images, self.target_labels = self.dataloader.get_model_inputs()\\r\\n\\r\\n source_model = SimpleModel(self.source_images, self.source_labels, F.output_dim, scope='source_regressor')\\r\\n target_model = SimpleModel(self.target_images, self.target_labels, F.output_dim, scope='target_regressor')\\r\\n \\r\\n self.source_out, _ = source_model.get_model()\\r\\n self.target_out, _ = target_model.get_model()\\r\\n\\r\\n self.get_loss()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9517f49d612808a2e00ebe8d56c3bf34\",\n \"score\": \"0.616244\",\n \"text\": \"def loadModel(self, dataFile):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbc6e9c2e814b8fe7c46b56dad66f833\",\n \"score\": \"0.61484253\",\n \"text\": \"def init_datasets(self):\\n phases = self.data_dict[\\\"row_id\\\"].keys()\\n tensor_dict_dict = {\\n key: {\\n \\\"features\\\": self.data_dict[\\\"features\\\"][key],\\n \\\"labels\\\": torch.tensor(self.data_dict[\\\"labels\\\"][key], dtype=torch.long),\\n \\\"row_id\\\": torch.tensor(self.data_dict[\\\"row_id\\\"][key], dtype=torch.long),\\n }\\n for key in phases\\n }\\n if self.config_dict.get(\\\"include_group_in_dataset\\\"):\\n for key in phases:\\n tensor_dict_dict[key][\\\"group\\\"] = torch.as_tensor(\\n np.copy(self.data_dict[\\\"group\\\"][key]), dtype=torch.long\\n )\\n if self.config_dict.get(\\\"weight_var_name\\\") is not None:\\n for key in phases:\\n tensor_dict_dict[key][\\\"weights\\\"] = torch.as_tensor(\\n np.copy(self.data_dict[\\\"weights\\\"][key]), dtype=torch.float\\n )\\n\\n dataset_dict = {\\n key: ArrayDataset(\\n tensor_dict=tensor_dict_dict[key],\\n sparse_mode=self.config_dict.get(\\\"sparse_mode\\\"),\\n )\\n for key in phases\\n }\\n\\n return dataset_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2da6dd6b2b574d6bd5ee5854d4228782\",\n \"score\": \"0.61387175\",\n \"text\": \"def setUp(self):\\n\\n def rf_model_builder(**model_params):\\n rf_params = {\\n k: v for (k, v) in model_params.items() if k != 'model_dir'\\n }\\n model_dir = model_params['model_dir']\\n sklearn_model = sklearn.ensemble.RandomForestRegressor(**rf_params)\\n return dc.models.SklearnModel(sklearn_model, model_dir)\\n\\n self.rf_model_builder = rf_model_builder\\n self.train_dataset = dc.data.NumpyDataset(X=np.random.rand(50, 5),\\n y=np.random.rand(50, 1))\\n self.valid_dataset = dc.data.NumpyDataset(X=np.random.rand(20, 5),\\n y=np.random.rand(20, 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8efb4ef3ff6ee80b98640449350d866\",\n \"score\": \"0.6137208\",\n \"text\": \"def __init__(\\n self,\\n model: Model,\\n dataset: Dataset[BatchType],\\n evaluator: Evaluator,\\n ) -> None:\\n self.model = model\\n self.evaluator = evaluator\\n self.dataset = dataset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c91a4df2326338a7e34dca2e97cbc0cf\",\n \"score\": \"0.61296207\",\n \"text\": \"def make_model(self, data):\\n return self.MODEL(**data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56716aa7d12063260cc467faa28bca09\",\n \"score\": \"0.6127462\",\n \"text\": \"def _build_models(self):\\n with tf.variable_scope('model'):\\n meval = Model(self.hparams, mode='test')\\n meval.build()\\n self._saver = meval.saver\\n\\n self.meval = meval\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20966932a11c048d77d6df10cf4d0425\",\n \"score\": \"0.612052\",\n \"text\": \"def loading_data_set(self):\\n # Data loading code\\n if self.framework_type == FrameworkType.Classification:\\n # TODO\\n # change dataset in outer method\\n dataset = SimpsonsDataset(cfg=self.config)\\n batch_size = 16\\n validation_split = .2\\n shuffle_dataset = True\\n random_seed = 42\\n\\n # Creating data indices for training and validation splits:\\n dataset_size = len(dataset)\\n indices = list(range(dataset_size))\\n split = int(np.floor(validation_split * dataset_size))\\n if shuffle_dataset:\\n np.random.seed(random_seed)\\n np.random.shuffle(indices)\\n train_indices, val_indices = indices[split:], indices[:split]\\n\\n # Creating PT data samplers and loaders:\\n train_sampler = SubsetRandomSampler(train_indices)\\n valid_sampler = SubsetRandomSampler(val_indices)\\n\\n data_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,\\n sampler=train_sampler)\\n data_loader_eval = torch.utils.data.DataLoader(dataset, batch_size=batch_size,\\n sampler=valid_sampler)\\n else:\\n # TODO\\n # change dataset in outer method\\n train_dataset = VOCSegmentation(split=DataSplit.Train, cfg=self.config)\\n eval_dataset = VOCSegmentation(split=DataSplit.Eval, cfg=self.config)\\n data_loader = torch.utils.data.DataLoader(\\n train_dataset,\\n batch_size=20,\\n shuffle=True,\\n num_workers=1,\\n pin_memory=True)\\n data_loader_eval = torch.utils.data.DataLoader(\\n eval_dataset, batch_size=1,\\n num_workers=1)\\n print('Loading data...')\\n\\n print('Done...')\\n return data_loader, data_loader_eval\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95e26ba5f2cae45acfd565e48ac3151e\",\n \"score\": \"0.611596\",\n \"text\": \"def model(self):\\n return self.modeldata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c369a134e23790b69260a4ea5e29dbc\",\n \"score\": \"0.6112193\",\n \"text\": \"def setup_models():\\n global classifiers\\n\\n grega_clf = GregaClassifier(GREGA_MODEL_PATH)\\n classifiers.append(grega_clf)\\n\\n uci_clf = UciClassifier(UCI_MODEL_PATH)\\n classifiers.append(uci_clf)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7d56da32154c25fac256253749948a5\",\n \"score\": \"0.6110365\",\n \"text\": \"def init_model(self):\\n self.places = tools.keydefaultdict(Place)\\n self.users = tools.keydefaultdict(User)\\n\\n self.predictions = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77024290e71f74a808038d96ff416d2f\",\n \"score\": \"0.61031425\",\n \"text\": \"def __init__(self, dataset, env):\\n super(Encoding, self).__init__()\\n self.ds = dataset\\n self.env = env\\n self.model = None\\n self.nsteps = int(self.ds.numAttr * self.ds.numTuple / self.env['batch_size'])\\n self.dataset = self.ds.df.groupby(np.arange(self.ds.numTuple) // env['batch_size'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4caaec4a00e7405258dcbf9867ef2a5c\",\n \"score\": \"0.6095743\",\n \"text\": \"def load_datasets(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac98fb551a4ce9e036e46ec5575288cb\",\n \"score\": \"0.60925215\",\n \"text\": \"def create_model(self, parameters={}):\\n if self.model_name == \\\"DBSCAN\\\":\\n self.model = cluster.DBSCAN()\\n elif self.model_name == \\\"KMeans\\\":\\n self.model = cluster.KMeans()\\n elif self.model_name == \\\"hdbscan\\\":\\n self.model_glo = hdbscan\\n self.model = self.model_glo.HDBSCAN()\\n\\n for key, value in parameters.items():\\n setattr(self.model, key, value)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a0c5cf1d4c54e24357ce7666a642389\",\n \"score\": \"0.6091835\",\n \"text\": \"def __init__(self, data, model, config):\\n\\n self.data = data\\n self.model = model\\n self.config = config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0e5d6c7f3b067aafa93b7c4a3a3591e\",\n \"score\": \"0.6085573\",\n \"text\": \"def init_models(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9782b40687ea1edc2c65290c85b04cec\",\n \"score\": \"0.60783416\",\n \"text\": \"def __init__(self, x_train, x_test, y_train, y_test, y_preds, model_def, fitted_model, original_data):\\r\\n self.x_train = x_train\\r\\n self.x_test = x_test\\r\\n self.y_train = y_train\\r\\n self.y_test = y_test\\r\\n self.y_preds = y_preds\\r\\n self.model_def = model_def\\r\\n self.fitted_model = fitted_model\\r\\n self.original_data = original_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"330d6d1bd7873aad992bbd40c66b13a5\",\n \"score\": \"0.6070308\",\n \"text\": \"def train_model(self):\\n # load manually labeled data\\n print('Loading manually labeled data for model training...')\\n manual_data = pd.read_csv(os.path.join(os.path.dirname(__file__), config['labeled_data']))\\n manual_data['duplicates'] = manual_data['duplicates'].apply(self._format_duplicates)\\n manual_data = manual_data[['properties.address.formatted_address', 'properties.name',\\n 'lat', 'lng', 'id', 'duplicates']]\\n manual_data = gpd.GeoDataFrame(manual_data,\\n geometry=gpd.points_from_xy(manual_data['lng'],\\n manual_data['lat']))\\n\\n # process manually labeled data\\n print('Processing manually labeled data for model training...')\\n train_test_data = self._process_manual_data(manual_data)\\n train_test_data = pd.DataFrame(train_test_data, columns=['address_similarity', 'address_str_similarity',\\n 'name_similarity', 'label'])\\n\\n # perform data sampling to balance class distribution\\n print('Performing data sampling to balance class distribution...')\\n train_datasets, test_data = self._perform_sampling(train_test_data)\\n\\n # train models\\n print('Begin model training...')\\n gb_models = self._train(train_datasets, 'GB')\\n rf_models = self._train(train_datasets, 'RF')\\n xgboost_models = self._train(train_datasets, 'XGB')\\n\\n # evaluate model performance on hold out set\\n print('Perform model evaluation...')\\n y_pred_gb = self._predict(gb_models, test_data[['address_similarity', 'address_str_similarity',\\n 'name_similarity']])\\n y_pred_rf = self._predict(rf_models, test_data[['address_similarity', 'address_str_similarity',\\n 'name_similarity']])\\n y_pred_xgboost = self._predict(xgboost_models, test_data[['address_similarity', 'address_str_similarity',\\n 'name_similarity']])\\n self._evaluate(test_data['label'], y_pred_gb, 'Gradient Boosting')\\n self._evaluate(test_data['label'], y_pred_rf, 'Random Forest')\\n self._evaluate(test_data['label'], y_pred_xgboost, 'XGBoost')\\n\\n # save trained models locally\\n print('Saving trained models locally...')\\n if args.best_algorithm == 'GB':\\n models = gb_models\\n elif args.best_algorithm == 'RF':\\n models = rf_models\\n elif args.best_algorithm == 'XGB':\\n models = xgboost_models\\n else:\\n raise ValueError('{} is not supported.'.format(args.best_algorithm))\\n for i in range(len(models)):\\n dump(models[i], os.path.join(os.path.dirname(__file__),\\n config['models_directory'] + 'model_{}.joblib'.format(i+1)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"591c7647a6d517ac685fb0e5d2b3bda4\",\n \"score\": \"0.6068656\",\n \"text\": \"def __init__(self,model_parameters): \\n self.counter = Counter()\\n self.corpus = CorpusReader()\\n self.nlp = TextProcess()\\n self.train_features = []\\n self.classifier = None\\n self.test_model = []\\n self.model_parameters = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ee5312ed36537bca50e5b39334e1124\",\n \"score\": \"0.606777\",\n \"text\": \"def __init__(self):\\n super(Blender, self).__init__()\\n self.models = []\\n for m in models_names:\\n name = str(m).split()[1]\\n model = m().cuda()\\n model.eval()\\n\\n model = nn.DataParallel(model)\\n model.load_state_dict(torch.load('models/full_data_{}.pth'.format(name)))\\n for p in model.parameters():\\n p.requires_grad = False\\n self.models.append(model)\\n self.weighing = nn.Sequential(\\n nn.BatchNorm1d(len(models_names)*17),\\n # nn.ReLU(),\\n nn.Linear(in_features=len(models_names)*17, out_features=256, bias=False),\\n nn.BatchNorm1d(256),\\n nn.ReLU(),\\n nn.Linear(in_features=256, out_features=17)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9b188d25dda6d073ee68db475e46974\",\n \"score\": \"0.6066141\",\n \"text\": \"def build_model(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9584d7224b58807ce741f74522817fff\",\n \"score\": \"0.60589874\",\n \"text\": \"def getModelsData(self):\\n return self.models_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23a5a982c7f35aa3095efa3e008f3523\",\n \"score\": \"0.60569793\",\n \"text\": \"def get_dataset(self):\\n transform = transforms.Compose([\\n # you can add other transformations in this list\\n transforms.ToTensor()\\n ])\\n\\n trainset = datasets.CIFAR10('datasets/CIFAR10/train/', train=True, transform=transform,\\n target_transform=None, download=True)\\n\\n valset = datasets.CIFAR10('datasets/CIFAR10/test/', train=False, transform=transform,\\n target_transform=None, download=True)\\n\\n train_loader = torch.utils.data.DataLoader(trainset, batch_size=1, shuffle=False, num_workers=2)\\n val_loader = torch.utils.data.DataLoader(valset, batch_size=1, shuffle=False, num_workers=2)\\n\\n check = 0\\n directories = [\\\"datasets/CIFAR10_indx/train\\\", \\\"datasets/CIFAR10_indx/test\\\"]\\n for directory in directories:\\n if os.path.exists(directory):\\n check += 1\\n if not os.path.exists(directory):\\n os.makedirs(directory)\\n\\n if check != len(directories):\\n # create folders to save data\\n indices_data = {}\\n\\n indx = 0\\n for loader_name, loader in [('train', train_loader), ('test', val_loader)]:\\n for (img, label) in loader:\\n img_name = str(int(label))+'_'+str(int(indx))+'.jpg'\\n\\n directory = os.path.join(\\\"datasets/CIFAR10_indx/\\\" + loader_name,\\n format(int(label), '06d'))\\n if not os.path.exists(directory):\\n os.makedirs(directory)\\n save_image(img, os.path.join(directory, img_name))\\n # img_path is the index for the images\\n indices_data[os.path.join(directory, img_name)] = int(label)\\n indx += 1\\n\\n # store img_paths which serves as indices and the labels for further analysis\\n indices_data = collections.OrderedDict(sorted(indices_data.items()))\\n dataframe = pd.DataFrame({'img_paths': list(indices_data.keys()),\\n 'labels': list(indices_data.values())})\\n DatasetMetrics.indices_paths(self.name, dataframe)\\n\\n if self.randomize_labels:\\n evaluation = DatasetMetrics.load_evaluation_metrics(self.name)\\n self.random_labels = corrupt_labels(evaluation['labels'].to_dict(),\\n self.num_classes,\\n self.corrupt_prob)\\n else:\\n self.random_labels = None\\n\\n # load the image dataset from folder with indices\\n trainset = IndxImageFolder(root=\\\"datasets/CIFAR10_indx/train\\\", transform = transform,\\n random_labels=self.random_labels)\\n valset = IndxImageFolder(root=\\\"datasets/CIFAR10_indx/test\\\", transform=transform,\\n random_labels=self.random_labels)\\n\\n return trainset, valset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebd20a286c5098c978a68f56cbec530e\",\n \"score\": \"0.6056964\",\n \"text\": \"def init_model():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42750721fcac919b0e53ec669f66fed3\",\n \"score\": \"0.6055318\",\n \"text\": \"def data_model_class(self) -> DataModel:\\n return BaicellsQRTBTrDataModel\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e6a407a877c9f15b575895edb24acb3\",\n \"score\": \"0.60502315\",\n \"text\": \"def models(self) -> Model:\\n return [\\n self.repository.models[step.model_name]\\n for step in self.config.ensemble_scheduling.step\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb3f32100853d930f7b71ae70c748741\",\n \"score\": \"0.6041466\",\n \"text\": \"def get_datasets(self):\\n d = {\\n \\\"X_train\\\": self.X_train,\\n \\\"X_test\\\": self.X_test,\\n \\\"Y_train\\\": self.Y_train,\\n \\\"Y_test\\\": self.Y_test\\n }\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"380f48cf7489a9e0a784af2ab9f3310c\",\n \"score\": \"0.604073\",\n \"text\": \"def get_model(name, dataset):\\n field_dims = dataset.field_dims\\n print('field_dims={}, {}'.format(type(field_dims), field_dims.tolist()))\\n\\n if name == 'lr':\\n return LogisticRegressionModel(field_dims)\\n elif name == 'fm':\\n return FactorizationMachineModel(field_dims, embed_dim=16)\\n elif name == 'hofm':\\n return HighOrderFactorizationMachineModel(field_dims, order=3, embed_dim=16)\\n elif name == 'ffm':\\n return FieldAwareFactorizationMachineModel(field_dims, embed_dim=4)\\n elif name == 'fnn':\\n return FactorizationSupportedNeuralNetworkModel(field_dims, embed_dim=16, mlp_dims=(16, 16), dropout=0.2)\\n elif name == 'wd':\\n return WideAndDeepModel(field_dims, embed_dim=16, mlp_dims=(16, 16), dropout=0.2)\\n elif name == 'ipnn':\\n return ProductNeuralNetworkModel(field_dims, embed_dim=64, mlp_dims=(400, 400, 400), method='inner', dropout=0.2)\\n elif name == 'opnn':\\n return ProductNeuralNetworkModel(field_dims, embed_dim=16, mlp_dims=(16,), method='outer', dropout=0.2)\\n elif name == 'dcn':\\n return DeepCrossNetworkModel(field_dims, embed_dim=16, num_layers=3, mlp_dims=(16, 16), dropout=0.2)\\n elif name == 'nfm':\\n return NeuralFactorizationMachineModel(field_dims, embed_dim=64, mlp_dims=(64,), dropouts=(0.2, 0.2))\\n elif name == 'ncf':\\n # only supports MovieLens dataset because for other datasets user/item colums are indistinguishable\\n assert isinstance(dataset, MovieLens20MDataset) or isinstance(dataset, MovieLens1MDataset)\\n return NeuralCollaborativeFiltering(field_dims, embed_dim=16, mlp_dims=(16, 16), dropout=0.2,\\n user_field_idx=dataset.user_field_idx,\\n item_field_idx=dataset.item_field_idx)\\n elif name == 'fnfm':\\n return FieldAwareNeuralFactorizationMachineModel(field_dims, embed_dim=64, mlp_dims=(64,32,), dropouts=(0.2, 0.2))\\n elif name == 'dfm':\\n return DeepFactorizationMachineModel(field_dims, embed_dim=64, mlp_dims=(400, 400, 400), dropout=0.2)\\n elif name == 'xdfm':\\n return ExtremeDeepFactorizationMachineModel(\\n field_dims, embed_dim=16, cross_layer_sizes=(16, 16), split_half=False, mlp_dims=(16, 16), dropout=0.2)\\n elif name == 'afm':\\n return AttentionalFactorizationMachineModel(field_dims, embed_dim=16, attn_size=16, dropouts=(0.2, 0.2))\\n elif name == 'afi':\\n return AutomaticFeatureInteractionModel(\\n field_dims, embed_dim=16, atten_embed_dim=64, num_heads=2, num_layers=3, mlp_dims=(400, 400), dropouts=(0, 0, 0))\\n elif name == 'afn':\\n print(\\\"Model:AFN\\\")\\n return AdaptiveFactorizationNetwork(\\n field_dims, embed_dim=64, LNN_dim=1500, mlp_dims=(400, 400, 400), dropouts=(0, 0, 0))\\n else:\\n raise ValueError('unknown model name: ' + name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6aca67178bd2ddeaa4da34ac11fcc372\",\n \"score\": \"0.60254836\",\n \"text\": \"def provides(self):\\n\\n # models\\n possible_values = [3, 5, 7, 9, 18, 27]\\n\\n value = [name(n=i, version=1) for i in possible_values]\\n\\n possible_values.append(111)\\n value.extend([name(n=i, version=2) for i in possible_values])\\n\\n # TODO: automate this\\n backends = []\\n datasets = self.available_datasets()\\n\\n from deeprace.models.keras_details import resnet_details as keras_resnet\\n if keras_resnet.can_train() != []:\\n backends.extend(keras_resnet.can_train())\\n\\n from deeprace.models.keras_details import tfkeras_resnet_details as tfkeras_resnet\\n if tfkeras_resnet.can_train() != []:\\n backends.extend(tfkeras_resnet.can_train())\\n\\n from deeprace.models.tf_details import resnet_details as tf_resnet\\n if tf_resnet.can_train() != []:\\n backends.extend(tf_resnet.can_train())\\n\\n # datasets\\n\\n return value, backends, datasets\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a723b465c8ed5475e830b4986f61abbf\",\n \"score\": \"0.60243565\",\n \"text\": \"def fit_storage(self, data):\\n\\n models = []\\n X = data.X\\n Y, self.num_classes = get_eye_matrix(data.Y)\\n\\n Fs = np.zeros((X.shape[0], self.num_classes))\\n Ps = self.softmax1(Fs)\\n\\n grads = self.loss(Y, Ps)\\n\\n for i in range(self.n_estimators):\\n model_per_class = []\\n for j in range(Y.shape[1]):\\n newdata = Orange.data.Table(data.X, grads[:, j])\\n model = self.learner(newdata)\\n tmp = model(newdata)\\n Fs[:, j] += tmp\\n Ps = self.softmax1(Fs)\\n #grads[:, j] = self.loss(Y[:, j], Ps[:, j])\\n grads = self.loss(Y, Ps)\\n model_per_class.append(model)\\n models.append(model_per_class)\\n\\n self.models = models\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7577867bb39f31392b945092b703672\",\n \"score\": \"0.6023276\",\n \"text\": \"def __init__(self, saved_model_dir_path: str, saved_dataset_custodian_dir_name_or_path: str = None,\\n saved_dc_is_path: bool = False):\\n self.saved_model_dir_path = saved_model_dir_path\\n print(\\\"Reading model from\\\", self.saved_model_dir_path, \\\"...\\\")\\n # Load model itself:\\n self.model: tfkeras.Model = load_model_from_path(self.saved_model_dir_path)\\n # Read associated data/information:\\n print(\\\"Model info:\\\")\\n saved_model_dir_path_obj = pathlib.Path(saved_model_dir_path)\\n self.model_params = from_json(read_file_content(saved_model_dir_path_obj.parents[0].joinpath(\\\"params.json\\\")))\\n self.model_scores = from_json(read_file_content(\\n saved_model_dir_path_obj.parents[0].joinpath(saved_model_dir_path_obj.name + \\\"_scores.json\\\")))\\n self.train_threshold = self.model_scores[\\\"train\\\"][\\\"scores\\\"].get(\\\"threshold\\\", None)\\n self.validation_threshold = self.model_scores[\\\"validation\\\"][\\\"scores\\\"].get(\\\"threshold\\\", None)\\n assert saved_model_dir_path_obj.parents[1].name == \\\"GridSearch\\\"\\n\\n print(\\\" Parameters:\\\", self.model_params)\\n print(\\\" Scores:\\\", self.model_scores)\\n print(\\\" Validation threshold:\\\", self.validation_threshold)\\n\\n # Load model's associated dataset custodian since is necessary in both cases:\\n model_dc = DatasetCustodian.load(saved_model_dir_path_obj.parents[2], is_path=True)\\n\\n if saved_dataset_custodian_dir_name_or_path is None:\\n # No dataset is given. Use model's dataset:\\n self.dataset_custodian: DatasetCustodian = model_dc\\n self.model_to_dataset_mapping = None\\n else:\\n # Dataset is given. Load it:\\n dataset_dc: DatasetCustodian = DatasetCustodian.load(saved_dataset_custodian_dir_name_or_path,\\n is_path=saved_dc_is_path)\\n # Check if mapping is necessary\\n if dataset_dc.get_labels() != model_dc.get_labels():\\n if len(dataset_dc.get_labels()) == len(model_dc.get_labels()):\\n # Len matches and score calculation is possible. However, results may be senseless if labels do not\\n # match.\\n print(\\\"WARNING: Model labels\\\", model_dc.get_labels(), \\\"do not match dataset labels\\\",\\n dataset_dc.get_labels())\\n elif len(dataset_dc.get_labels()) < len(model_dc.get_labels()):\\n # Model can distinguish more classes than dataset has. Try to automatically create mapping:\\n self.model_to_dataset_mapping = {}\\n for model_label in model_dc.get_labels():\\n if model_label in dataset_dc.get_labels():\\n self.model_to_dataset_mapping[model_dc.label_to_int_mapping[model_label]] \\\\\\n = dataset_dc.label_to_int_mapping[model_label]\\n else:\\n self.model_to_dataset_mapping[model_dc.label_to_int_mapping[model_label]] \\\\\\n = dataset_dc.label_to_int_mapping[dataset_dc.get_no_bug_label()]\\n print(\\\"WARNING: Model predicts more classes than present in the dataset.\\\",\\n \\\"Created model-to-dataset mapping, which maps each non-present class to non-vulnerable\\\",\\n \\\"class:\\\", self.model_to_dataset_mapping)\\n elif len(dataset_dc.get_labels()) > len(model_dc.get_labels()):\\n print(\\\"WARNING: Model predicts less classes than present in the dataset.\\\")\\n\\n # Use requested dataset:\\n self.dataset_custodian: DatasetCustodian = dataset_dc\\n\\n self.dataset_custodian_info \\\\\\n = from_json(read_file_content(os.path.join(self.dataset_custodian.prepared_data_dir_path, \\\"info.json\\\")))\\n print(\\\"Dataset info:\\\", self.dataset_custodian_info)\\n self.dataset_custodian.print_dataset_statistics()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad61dfee683dd4305036ff041f6dc2ac\",\n \"score\": \"0.6021987\",\n \"text\": \"def _load_model(self):\\n # Setup teacher to label samples\\n self.model = serial.load(self.teacher_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de3e75865e21e796de903d0d661cd72c\",\n \"score\": \"0.6013955\",\n \"text\": \"def __init__(self):\\n self.new_dataset()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ed14c73e18a5f011792314e96f8fb6f\",\n \"score\": \"0.6009634\",\n \"text\": \"def load_dataset(self):\\n rng = np.random.RandomState(seed=self.seed['val'])\\n\\n #if self.args.sets_are_pre_split == True:\\n data_image_paths, index_to_label_name_dict_file, label_to_index = self.load_datapaths()\\n dataset_splits = dict()\\n for key, value in data_image_paths.items():\\n key = self.get_label_from_index(index=key)\\n bits = key.split(\\\"/\\\")\\n set_name = bits[0]\\n class_label = bits[1]\\n if set_name not in dataset_splits:\\n dataset_splits[set_name] = {class_label: value}\\n else:\\n dataset_splits[set_name][class_label] = value\\n# else:\\n# data_image_paths, index_to_label_name_dict_file, label_to_index = self.load_datapaths()\\n# total_label_types = len(data_image_paths)\\n# num_classes_idx = np.arange(len(data_image_paths.keys()), dtype=np.int32)\\n# rng.shuffle(num_classes_idx)\\n# keys = list(data_image_paths.keys())\\n# values = list(data_image_paths.values())\\n# new_keys = [keys[idx] for idx in num_classes_idx]\\n# new_values = [values[idx] for idx in num_classes_idx]\\n# data_image_paths = dict(zip(new_keys, new_values))\\n# # data_image_paths = self.shuffle(data_image_paths)\\n# x_train_id, x_val_id, x_test_id = int(self.train_val_test_split[0] * total_label_types), \\\\\\n# int(np.sum(self.train_val_test_split[:2]) * total_label_types), \\\\\\n# int(total_label_types)\\n# print(x_train_id, x_val_id, x_test_id)\\n# x_train_classes = (class_key for class_key in list(data_image_paths.keys())[:x_train_id])\\n# x_val_classes = (class_key for class_key in list(data_image_paths.keys())[x_train_id:x_val_id])\\n# x_test_classes = (class_key for class_key in list(data_image_paths.keys())[x_val_id:x_test_id])\\n# x_train, x_val, x_test = {class_key: data_image_paths[class_key] for class_key in x_train_classes}, \\\\\\n# {class_key: data_image_paths[class_key] for class_key in x_val_classes}, \\\\\\n# {class_key: data_image_paths[class_key] for class_key in x_test_classes},\\n# dataset_splits = {\\\"train\\\": x_train, \\\"val\\\":x_val , \\\"test\\\": x_test}\\n\\n if self.args.load_into_memory is True:\\n\\n print(\\\"Loading data into RAM\\\")\\n x_loaded = {\\\"train\\\": [], \\\"val\\\": [], \\\"test\\\": []}\\n\\n for set_key, set_value in dataset_splits.items():\\n print(\\\"Currently loading into memory the {} set\\\".format(set_key))\\n x_loaded[set_key] = {key: np.zeros(len(value), ) for key, value in set_value.items()}\\n # for class_key, class_value in set_value.items():\\n with tqdm.tqdm(total=len(set_value)) as pbar_memory_load:\\n with concurrent.futures.ProcessPoolExecutor(max_workers=4) as executor:\\n # Process the list of files, but split the work across the process pool to use all CPUs!\\n for (class_label, class_images_loaded) in executor.map(self.load_parallel_batch, (set_value.items())):\\n x_loaded[set_key][class_label] = class_images_loaded\\n pbar_memory_load.update(1)\\n\\n dataset_splits = x_loaded\\n self.data_loaded_in_memory = True\\n\\n return dataset_splits\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a6478ba0e22a4cdcf9b403d6937c8b5\",\n \"score\": \"0.6003628\",\n \"text\": \"def _train(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81d526eb8fab08d7d5b6f409bdcdbfe5\",\n \"score\": \"0.6001419\",\n \"text\": \"def gen_model(self, *args):\\n # generate data grid\\n yy, xx = np.indices(self.data.shape)\\n xdata_tuple = (xx, yy)\\n # return model\\n return self.model(xdata_tuple, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c98949e5628394c51a94c88a3b70326b\",\n \"score\": \"0.5991787\",\n \"text\": \"def __init__(self):\\n\\n self.train = pd.read_csv(os.path.join(_DATA_DIR, _TRAIN))\\n self.test = pd.read_csv(os.path.join(_DATA_DIR, _TEST))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ab0b4e47abc225f388ede8178508f18\",\n \"score\": \"0.5988505\",\n \"text\": \"def model(self) -> Model:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b48dd63b0122ba6c1628e3104db236e\",\n \"score\": \"0.5984528\",\n \"text\": \"def compile_model(self):\\n raise NotImplementedError(\\\"train method has not been implemented\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cd5b48ad16ce4ae2914321b26650389\",\n \"score\": \"0.59714246\",\n \"text\": \"def load_model(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82b6afc3f0644396e34b431513bfd2e1\",\n \"score\": \"0.5965924\",\n \"text\": \"def generate_models(self):\\n # Get the group of connections from the id\\n group_of_connections = __group_of_group_of_connections__.get_group(self.get_group_connection_id())\\n\\n # For each connection\\n for connection in group_of_connections.get_connections():\\n # Create its model. Remember that the connection id and the model id is the 4-tuple\\n model_id = connection.get_id()\\n new_model = Model(model_id)\\n # Set the constructor for this model. Each model has a specific way of constructing the states\\n #new_model.set_constructor(__modelsconstructors__.get_default_constructor())\\n constructor_id = self.get_constructor_id()\\n new_model.set_constructor(__modelsconstructors__.get_constructor(constructor_id))\\n for flow in connection.get_flows():\\n # Try to add the flow\\n if not new_model.add_flow(flow):\\n self.delete_model_by_id(new_model.get_id())\\n # The flows are not ordered. Delete the truckated models\\n __groupofgroupofmodels__.delete_group_of_models(self.get_id())\\n return False\\n self.models[model_id] = new_model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86485f9351eb528526292ce5a7a6cfaf\",\n \"score\": \"0.5961841\",\n \"text\": \"def get_dataset(self): \\n # Get lists of file paths for training and test sets, respectively.\\n train_set_paths, test_set_paths = self._get_file_paths() \\n\\n # Build appropriate dataframes. \\n train_set = self._build_dataframe(train_set_paths)\\n test_set = self._build_dataframe(test_set_paths) \\n\\n return train_set, test_set\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2288cd41a8517323faa2ac3fd9e468c2\",\n \"score\": \"0.5959713\",\n \"text\": \"def read_model(self):\\n f1 = open(self.name + '_' + 'words', 'r')\\n f2 = open(self.name + '_' + 'word_lengths', 'r')\\n f3 = open(self.name + '_' + 'stems', 'r')\\n f4 = open(self.name + '_' + 'sentence_lengths', 'r')\\n f5 = open(self.name + '_' + 'word_pair', 'r')\\n d_str1 = f1.read() \\n d_str2 = f2.read() \\n d_str3 = f3.read() \\n d_str4 = f4.read() \\n d_str5 = f5.read() \\n self.words = dict(eval(d_str1))\\n self.word_lengths= dict(eval(d_str2))\\n self.stems = dict(eval(d_str3))\\n self.sentence_lengths = dict(eval(d_str4))\\n self.word_pair = dict(eval(d_str5))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bb10a539955b78bbda751dcbb22d75a\",\n \"score\": \"0.5946806\",\n \"text\": \"def __init__(self, model_dir, models_path=settings.MODELS_FOLDER):\\n self.path = os.path.join(models_path, model_dir)\\n print('------', self.path)\\n self.model = self.load_model()\\n self.preds = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11f4d5018cc0e4d0824bd4ff530f94d0\",\n \"score\": \"0.59453404\",\n \"text\": \"def get_model(cls):\\n if cls.encoder_model_50 is None:\\n #cls.model = load_model(os.path.join(model_path, MODEL_NAME))\\n #print(cls.model.summary())\\n #cls.encoder_model = load_model(os.path.join(model_path, encoder_model-100.hdf5))\\n #print(cls.encoder_model.summary())\\n #cls.decoder_model = load_model(os.path.join(model_path, decoder_model-100.hdf5))\\n #print(cls.decoder_model.summary())\\n #cls.model_100 = load_model(os.path.join(model_path, 'model-100.hdf5'))\\n #cls.model_50 = load_model(os.path.join(model_path, 'model-50.hdf5'))\\n cls.encoder_model_100 = load_model(os.path.join(model_path, 'encoder_model-100.hdf5'))\\n cls.encoder_model_50 = load_model(os.path.join(model_path, 'encoder_model-50.hdf5'))\\n cls.decoder_model_100 = load_model(os.path.join(model_path, 'decoder_model-100.hdf5'))\\n cls.decoder_model_50 = load_model(os.path.join(model_path, 'decoder_model-50.hdf5'))\\n \\n return cls.encoder_model_50, cls.decoder_model_50, cls.encoder_model_100, cls.decoder_model_100\",\n \"title\": \"\"\n },\n {\n \"docid\": \"183f3a2dba1c5cdf236068a381e82edc\",\n \"score\": \"0.59435695\",\n \"text\": \"def main() -> None:\\n data = download_dataset(DATA_URL)\\n features, labels = pre_process_data(data)\\n trained_model = train_model(features, labels)\\n persist_model(trained_model)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48ef13d6f5f1bf6f123fdf5a05e49dbb\",\n \"score\": \"0.59416366\",\n \"text\": \"def _train(self):\\n\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0af7575dd2ef883653323abeface3e1\",\n \"score\": \"0.5941535\",\n \"text\": \"def _add_model_data_methods(self):\\n self.inputs = self._model_data.filter_by_attrs(is_result=0)\\n self.results = self._model_data.filter_by_attrs(is_result=1)\\n self._add_observed_dict(\\\"model_config\\\")\\n self._add_observed_dict(\\\"run_config\\\")\\n self._add_observed_dict(\\\"math\\\")\\n\\n self.inputs = self._model_data.filter_by_attrs(is_result=0)\\n results = self._model_data.filter_by_attrs(is_result=1)\\n if len(results.data_vars) > 0:\\n self.results = results\\n log_time(\\n logger,\\n self._timings,\\n \\\"model_data_loaded\\\",\\n comment=\\\"Model: loaded model_data\\\",\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"722d2d1901e010c3156305a2bc9f4a18\",\n \"score\": \"0.5938245\",\n \"text\": \"def initialize_models(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e537c6a984aa8fed70c87d64c05674b8\",\n \"score\": \"0.59382355\",\n \"text\": \"def load_dataset(self, name=''):\\n name = name or (name(self) + '.model')\\n return self.loader.load_model(name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9ae5a49b1d47aee4a3700ffc22e53a3\",\n \"score\": \"0.5937723\",\n \"text\": \"def create_loaders(self):\\n self.spam_data.text_to_tensors()\\n print('creating dataloaders')\\n train_data = TensorDataset(self.spam_data.train_inputs, \\n self.spam_data.train_masks, \\n self.spam_data.train_labels)\\n train_sampler = RandomSampler(train_data)\\n self.train_dataloader = DataLoader(train_data, \\n sampler=train_sampler, \\n batch_size=self.batch_size)\\n\\n validation_data = TensorDataset(self.spam_data.validation_inputs, \\n self.spam_data.validation_masks, \\n self.spam_data.validation_labels)\\n validation_sampler = SequentialSampler(validation_data)\\n self.validation_dataloader = DataLoader(validation_data, \\n sampler=validation_sampler, \\n batch_size=self.batch_size)\\n \\n test_data = TensorDataset(self.spam_data.test_inputs, \\n self.spam_data.test_masks, \\n self.spam_data.test_labels)\\n test_sampler = SequentialSampler(test_data)\\n self.test_dataloader = DataLoader(test_data, \\n sampler=test_sampler, \\n batch_size=self.batch_size)\\n print('finished creating dataloaders')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"babd17f7c461638ea9e0481d6a4a4611\",\n \"score\": \"0.5934158\",\n \"text\": \"def begin_local(self):\\n\\n # Obtain Dataset\\n\\n sys.path.append(os.getcwd())\\n data_module = importlib.import_module(\\\"data.%s.data\\\" % self.dataset_name)\\n dataset_class = find_dataset_class(data_module)\\n\\n if not dataset_class:\\n return\\n\\n dataset = dataset_class(name=self.dataset_name, trial=True, **self.kwargs)\\n dataset.configure_core_arguments(self.args)\\n \\n # Obtain the model class\\n\\n model_module = importlib.import_module(\\\"models.%s.model\\\" % self.model_name)\\n model_class = find_model_class(model_module)\\n\\n # Obtain the task class\\n \\n task_class = None\\n\\n if self.task_name is not None:\\n task_module = importlib.import_module(\\\"tasks.%s.task\\\" % self.task_name)\\n task_class = find_task_class(task_module)\\n\\n if task_class is None:\\n task = None\\n else:\\n task = task_class(data=dataset)\\n\\n if not model_class:\\n return\\n\\n model = model_class(data=dataset, task=task, **self.kwargs)\\n model.args = self.args\\n model.settings = self.settings\\n model.cloudremote = self.cloudremote\\n model.configure_core_arguments(self.args)\\n\\n if not self.cloudremote: # local training\\n self.setup_local_training(model)\\n else:\\n model.trial = Trial(project_name=self.project_name, \\n model_name=self.model_name, \\n dataset_name=self.dataset_name, \\n task_name=self.task_name, \\n cloud=self.cloud)\\n\\n model.sync_trial_metadata()\\n\\n if not self.cloudremote: \\n print(colored('\\\\n \\\\033[1m Training\\\\n', 'blue'))\\n\\n model.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a16b5b973ed21825e524b0156154c17\",\n \"score\": \"0.59317327\",\n \"text\": \"def __init__(self, models):\\n self.models = models\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8aca508aa9581be29cbbc872b7941372\",\n \"score\": \"0.5930965\",\n \"text\": \"def load_model(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8aca508aa9581be29cbbc872b7941372\",\n \"score\": \"0.5930965\",\n \"text\": \"def load_model(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72aa2cd3d79c8913a0cdecddcc5f347d\",\n \"score\": \"0.5926102\",\n \"text\": \"def __init__(self):\\n\\n # The name of the models.\\n # TODO: Refector to query them from Django\\n self.str_force_models = ['Campaign',\\n 'Deployment',\\n 'Image',\\n 'AUVDeployment',\\n 'BRUVDeployment',\\n 'DOVDeployment',\\n 'TVDeployment',\\n 'TIDeployment']\\n\\n for model in self.str_force_models:\\n self.stat_fields = {model: 0}\\n\\n # List of model instances\\n self.force_models = [Campaign,\\n Deployment,\\n Image,\\n AUVDeployment,\\n BRUVDeployment,\\n DOVDeployment,\\n TVDeployment,\\n TIDeployment]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d47b7541ac9c62728469b5626d9606ff\",\n \"score\": \"0.59258354\",\n \"text\": \"def buildModel(self):\\n\\t\\tself.clf = svm.SVC()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f497f44b1e0f04c9092eebfb91e91f3e\",\n \"score\": \"0.59257245\",\n \"text\": \"def __init__(self):\\n\\n self._experiment = Experiment\\n self.data = DataContainer\\n self.model = ModelContainer\\n self.fit = Fit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43c10a5ef02fc81fa1aed9b36c786567\",\n \"score\": \"0.59194636\",\n \"text\": \"def make_dataset(self, dir):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55d99919e4e8fc81fc5c1119dc5cd030\",\n \"score\": \"0.5917552\",\n \"text\": \"def __init__(self, model_name):\\n download_model(model_name)\\n label_map = label_map_util.load_labelmap(PATH_TO_LABELS)\\n categories = label_map_util.convert_label_map_to_categories(label_map, \\n max_num_classes=NUM_CLASSES, use_display_name=True)\\n self.category_index = label_map_util.create_category_index(categories)\\n self.load_graph(model_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c77af5b494a196f832e13ab1159d055f\",\n \"score\": \"0.59164995\",\n \"text\": \"def _train(self, observations, metadata):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e77d534d195abee70ee41131076860d4\",\n \"score\": \"0.5914514\",\n \"text\": \"def create_model(self) -> AmiciModel:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"576fa45543f130a0e5ca3d9b58a30542\",\n \"score\": \"0.59135574\",\n \"text\": \"def _load(self):\\n dataset, info = tfds.load(self.name, with_info=True, as_supervised=True)\\n self.input_shape = info.features['image'].shape\\n self.output_shape = (info.features['label'].num_classes,)\\n self.output_size = self.output_shape[-1]\\n self.num_examples = {t: s.num_examples for (t, s) in info.splits.items()}\\n self.ds = dict()\\n for tag in ['train', 'test']:\\n self.ds[tag] = (\\n dataset[tag].map(self.convert).map(self.augment).shuffle(10000))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05ff0fffc116699dc9009d00bce18da2\",\n \"score\": \"0.59132826\",\n \"text\": \"def get_data_from_model(self):\\n model = self.model\\n for i in range(model.rowCount()):\\n section = model.item(i)\\n key1 = str(section.text())\\n if key1 in [\\\"Global attributes\\\"]:\\n # global attributes\\n for j in range(section.rowCount()):\\n key2 = str(section.child(j, 0).text())\\n val2 = str(section.child(j, 1).text())\\n self.ds.root[\\\"Attributes\\\"][key2] = val2\\n elif key1 in [\\\"Variables\\\"]:\\n for j in range(section.rowCount()):\\n variable_section = section.child(j)\\n label = variable_section.text()\\n for k in range(variable_section.rowCount()):\\n key2 = str(variable_section.child(k, 0).text())\\n val2 = str(variable_section.child(k, 1).text())\\n self.ds.root[\\\"Variables\\\"][label][\\\"Attr\\\"][key2] = val2\\n else:\\n # this is a netCDF file with groups\\n group_attributes = {}\\n variables = {}\\n for j in range(section.rowCount()):\\n if section.child(j).text() in [\\\"Group attributes\\\"]:\\n for k in range(section.child(j).rowCount()):\\n key = str(section.child(j).child(k, 0).text())\\n value = str(section.child(j).child(k, 1).text())\\n group_attributes[key] = value\\n else:\\n label = section.child(j).text()\\n group = getattr(self.ds, key1)\\n variables[label] = {\\\"Data\\\": group[\\\"Variables\\\"][label][\\\"Data\\\"],\\n \\\"Flag\\\": group[\\\"Variables\\\"][label][\\\"Flag\\\"],\\n \\\"Attr\\\": group[\\\"Variables\\\"][label][\\\"Attr\\\"]}\\n for k in range(section.child(j).rowCount()):\\n key = str(section.child(j).child(k, 0).text())\\n value = str(section.child(j).child(k, 1).text())\\n variables[label][\\\"Attr\\\"][key] = value\\n # create the group as an attribute in the data structure\\n setattr(self.ds, key1, {\\\"Attributes\\\": group_attributes,\\n \\\"Variables\\\": variables})\\n #msg = \\\" Unrecognised object (\\\" + key1 + \\\") in netCDF file\\\"\\n #msg += \\\", skipping ...\\\"\\n #logger.warning(msg)\\n return self.ds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf6e9ab846d54b0c7d553593fb57c978\",\n \"score\": \"0.59122497\",\n \"text\": \"def prepDatasets(self):\\n self.train_ds = DataGenerator2022(generateForCellPred = False, batch_size = self.batch_size)\\n self.val_ds = DataGenerator2022(train=False, generateForCellPred = False, batch_size = self.batch_size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c7ce70028cae2d08538eeba8b99887b\",\n \"score\": \"0.590531\",\n \"text\": \"def get_dataset(self):\\n\\n train_transform = transforms.Compose([\\n # you can add other transformations in this list\\n # resize (256 x remaining larger size) and RandomCrop(224)\\n # like in https://papers.nips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf\\n # https://arxiv.org/pdf/1409.1556.pdf\\n transforms.Resize(256), # resize smaller size to 256\\n transforms.RandomCrop(self.args.patch_size), # 224\\n transforms.ToTensor()\\n ])\\n test_transform = transforms.Compose([\\n # you can add other transformations in this list\\n # resize\\n transforms.Resize(256), # resize smaller size to 256\\n transforms.CenterCrop((self.args.patch_size, self.args.patch_size)), # 224\\n transforms.ToTensor()\\n ])\\n\\n if self.args.compute_dataset_metrics is True:\\n\\n train_transform = transforms.Compose([\\n transforms.Resize((self.args.patch_size, self.args.patch_size)), # resize smaller size to 256\\n transforms.ToTensor()\\n ])\\n test_transform = transforms.Compose([\\n transforms.Resize((self.args.patch_size, self.args.patch_size)), # resize smaller size to 256\\n transforms.ToTensor()\\n ])\\n\\n '''\\n train_transform = transforms.Compose([\\n transforms.ToTensor()\\n ])\\n test_transform = transforms.Compose([\\n transforms.ToTensor()\\n ])'''\\n\\n # if not already set, set batch-size to 1 for computing the metrics\\n # due to different image sizes\\n self.args.batch_size = 1\\n self.args.workers = 0\\n\\n trainset = IndxImageFolder(os.path.join(self.load_path, \\\"train\\\"),\\n transform=train_transform,\\n target_transform=None)\\n\\n valset = IndxImageFolder(os.path.join(self.load_path, \\\"val\\\"),\\n transform=test_transform,\\n target_transform=None)\\n\\n # https://pytorch.org/vision/0.8/_modules/torchvision/datasets/imagenet.html#ImageNet\\n # https://www.kaggle.com/c/imagenet-object-localization-challenge/data?select=LOC_synset_mapping.txt\\n # https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a\\n self.class_to_idx = trainset.class_to_idx\\n self.idx_to_class = dict((v, k) for k, v in self.class_to_idx.items())\\n\\n if not os.path.exists(os.path.join('metrics/datasets', self.name) +'.csv'):\\n train_loader = torch.utils.data.DataLoader(trainset, batch_size=1, shuffle=False, num_workers=0)\\n val_loader = torch.utils.data.DataLoader(valset, batch_size=1, shuffle=False, num_workers=0)\\n\\n indices_data = {}\\n for loader_name, loader in [('train', train_loader), ('test', val_loader)]:\\n for (img, label, img_path) in tqdm(loader):\\n # since img_path is a tuple, it is converted to a list\\n # since batch-size is 1, the first element is taken\\n indices_data[list(img_path)[0]] = label.detach().cpu().numpy()[0]\\n\\n # store img_paths which serves as indices and the labels for further analysis\\n indices_data = collections.OrderedDict(sorted(indices_data.items()))\\n\\n dataframe = pd.DataFrame({'img_paths': list(indices_data.keys()),\\n 'labels': list(indices_data.values())})\\n DatasetMetrics.indices_paths(self.name, dataframe)\\n\\n return trainset, valset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"812ce1568c4c0af43cec52fda45df864\",\n \"score\": \"0.5903093\",\n \"text\": \"def __init__(self, model_name, input_shape=(256, 256, 3)):\\n super().__init__(model_name=model_name)\\n\\n # Number of classes to segment\\n # 0 -> not a building\\n # 1 -> a building\\n self.n_classes = 2\\n # Input data shape\\n self.input_shape = input_shape\\n # File extensions for data to predict\\n self.FILE_EXTENSIONS = [\\n \\\"tif\\\",\\n \\\"tiff\\\",\\n \\\"png\\\",\\n \\\"jpg\\\",\\n \\\"jpeg\\\"\\n ]\\n # Files to predict\\n self.filenames = list()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994195,"cells":{"query_id":{"kind":"string","value":"80687f3cf13f915b78d9c85561dd33fb"},"query":{"kind":"string","value":"Return readable informal exception description about checksumed exception."},"positive_passages":{"kind":"list like","value":[{"docid":"1747be14a5b639183ce75ef786e8178c","score":"0.0","text":"def __str__(self):\n return 'Invalid {} checksum for the {} file'.format(self.algo, 'original' if self.decrypted else 'encrypted')","title":""}],"string":"[\n {\n \"docid\": \"1747be14a5b639183ce75ef786e8178c\",\n \"score\": \"0.0\",\n \"text\": \"def __str__(self):\\n return 'Invalid {} checksum for the {} file'.format(self.algo, 'original' if self.decrypted else 'encrypted')\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"9271c80dc57fab8ae115327f3df1069a","score":"0.6199383","text":"def exception(self):\r\n buf = traceback.format_exception_only(self.exc_type, self.exc_value)\r\n return ''.join(buf).strip().decode('utf-8', 'replace')","title":""},{"docid":"fb1ae58044d5b91590512fdd3726d074","score":"0.60794216","text":"def format_error_message(self) -> str:\n return f\"{self.exc_type.__name__}: {self.exc_value}\"","title":""},{"docid":"9bc39d440b7bf99d019f5035d908cc88","score":"0.60473853","text":"def __str__(self):\n return 'Database integerity error - %s' % self.reason","title":""},{"docid":"c3c27ba8ab94db5472077b0e0a97113a","score":"0.6006324","text":"def exc_info(self):\r\n return self.exception","title":""},{"docid":"7e63a9a43764baca471f52eec33819bc","score":"0.59391123","text":"def formatException(self, ei):\n return","title":""},{"docid":"d2385ca71f53c336e0e3e76675c70df3","score":"0.5920419","text":"def _exc_info_to_string(self, err, test):\n return err","title":""},{"docid":"9de97764282b29d2261fb095db6482ef","score":"0.58595777","text":"def exc_repr(e):\n return \"{}: {}\".format(type(e).__name__, str(e))","title":""},{"docid":"af4df25dd30e2ad06974aed720020bf1","score":"0.58202285","text":"def standard_exc_info(self):\r\n return self.exc_type, self.exc_value, self.frames[0].tb","title":""},{"docid":"39ced7dcdb0e9a2808e6487f967cdaa2","score":"0.581941","text":"def get_error_message(self):\n return self.open.query(\":SYST:ERR?\")","title":""},{"docid":"754abcd3b97fa9e5b1e4aee96720ad21","score":"0.58062327","text":"def excused(self):\n return self._excused","title":""},{"docid":"ff8c1eb51ff6dbf49bf46a5de491f9b8","score":"0.5754992","text":"def error_str(self):\n return u\"{0:<20} {1}\\n{3:<11} {2}\".format(\n package_helpers.color_output(\n self.get_logging_level() if self.get_logging_level() != constants.VALIDATE_LOG_LEVEL_DEBUG\n else \"PASS\", \n self.get_logging_level()), \n self.description, self.solution, \"\"\n )","title":""},{"docid":"aa70429c940e19a26739526cb42b48fb","score":"0.5741728","text":"def generate_message(self):\n message = \"Oops! Assertion failed\"\n message += self.generate_expression_section()\n message += self.generate_description_section()\n message += self.generate_values_section()\n message += self.generate_where_section()\n return message","title":""},{"docid":"485726a75047be5ee8daef6e226521f8","score":"0.5719327","text":"def exception_detail(\n self, exception_type: type, exception_value: Exception, tb\n ):\n text = \"[{}]: Unhandled WebSocket Error:{}\\n\".format(\n datetime.now().isoformat(), exception_type\n )\n text += \"LastSentText:\\n{}\\n\".format(self._last_sent_text)\n text += \"LastReceivedText:\\n{}\\n\".format(self._last_received_text)\n text += \"Exception trace: \\n\"\n text += \"\".join(\n traceback.format_exception(exception_type, exception_value, tb)\n )\n return text","title":""},{"docid":"0c388fadd9d94e9a5e29cd5a80d0fb8f","score":"0.5694837","text":"def source_message(self):\n return ''.join(traceback.format_exception_only(self._src_exc[0], self._src_exc[1]))","title":""},{"docid":"b3a950b6304c5840564791d4f38847e4","score":"0.5693524","text":"def _get_exception():\n exc_type, exc_value, exc_traceback = sys.exc_info()\n d = traceback.format_exception(exc_type, exc_value, exc_traceback)\n msg = ''.join(d)\n return msg","title":""},{"docid":"8b7fd8fcc1fb84975659f8e63b4be81f","score":"0.567742","text":"def formatException(self, ei):\n exc_tb = ei[2]\n exc_type = ei[0]\n exc_value = ei[1]\n msg = []\n try:\n try:\n # log exception details\n now = datetime.datetime.now().isoformat()\n py_version = getattr(config, 'py_version', 'not found')\n wx_version = getattr(config, 'wx_version', 'not found')\n platform = getattr(config, 'platform', 'not found')\n app_version = getattr(config, 'version', 'not found')\n\n msg.append('An unexpected error occurred!\\n')\n msg.append('\\n')\n msg.append('Exception type: %s\\n' % exc_type)\n msg.append('Exception details: %s\\n' % exc_value)\n if exc_tb:\n msg.append('\\nApplication stack traceback:\\n')\n msg += traceback.format_tb(exc_tb)\n msg.append('\\n')\n msg.append('Date and time: %s\\n' % now)\n msg.append('Python version: %s\\n' % py_version)\n msg.append('wxPython version: %s\\n' % wx_version)\n msg.append('wxWidgets platform: %s\\n' % platform)\n msg.append('wxGlade version: %s\\n' % app_version)\n msg.append('\\n')\n\n except Exception as e:\n # This code should NEVER be executed!\n if config.debugging: raise\n logging.error('An exception has been raised inside the exception handler: %s', e)\n sys.exit(1)\n\n # delete local references of trace backs or part of them to avoid circular references\n finally:\n del ei, exc_tb, exc_type, exc_value\n\n if msg[-1][-1] == \"\\n\":\n msg[-1] = msg[-1][:-1]\n return \"\".join(msg)","title":""},{"docid":"2368e9dffe07585bf094fb92c26606f4","score":"0.5610206","text":"def get_error_msg(self):\n raise NotImplementedError(\"Must be implemented\")","title":""},{"docid":"56ed53ec2a88e5ef0adf74be2c61a747","score":"0.5606838","text":"def rdap_pretty_error_message(exc):\n\n if isinstance(exc, RdapNotFoundError):\n return _(\"This ASN is not assigned by any RIR\")\n if isinstance(exc, RdapInvalidRange):\n return _(\"ASNs in this range are private or reserved\")\n\n return _(\"{}\").format(exc)","title":""},{"docid":"d9673b778f6a2c4f1f1497dd458de087","score":"0.56065124","text":"def getExceptionText():\n (_type,value,tback)=sys.exc_info()\n return \"\".join(traceback.format_exception(_type, value, tback))","title":""},{"docid":"0079174b7c53c79159f216eababccaae","score":"0.5606293","text":"def exconly(self, tryshort: bool = False) -> str:\n lines = format_exception_only(self.type, self.value)\n text = \"\".join(lines)\n text = text.rstrip()\n if tryshort:\n if text.startswith(self._striptext):\n text = text[len(self._striptext) :]\n return text","title":""},{"docid":"813627fe9f21c67dd8aa1ca76abea9ea","score":"0.55978507","text":"def message(self):\n if not self.enabled:\n return None\n return self._lint_message + \\\n \"\\n=======================================\" + \\\n \"\\n%s: Error:%d, Warning:%d \\n\" % \\\n (self._version, self._error_count, self._warning_count)","title":""},{"docid":"996968b070f528c9786a435aeb617980","score":"0.5596806","text":"def err_msg(self):\n return err_msg_template.substitute(\n object_name=self.object_name,\n missing_dependency=self.missing_dependency)","title":""},{"docid":"044f7377c916dadb1344d84f69197c0b","score":"0.5580845","text":"def format_exception(self) -> str:\n return \"\".join(\n traceback.format_exception(self.exc_type, self.exc_value, self.exc_tb)\n )","title":""},{"docid":"675c6c2a6c9600313afaf4ffa87469ae","score":"0.5577244","text":"def format_scan_error(\n self,\n next_file: str,\n this_exception: Exception,\n show_extended_information: bool = False,\n ) -> Optional[str]:\n formatted_error = f\"{type(this_exception).__name__} encountered while scanning '{next_file}':\\n{this_exception}\"\n if show_extended_information:\n current_cause = this_exception.__cause__\n while current_cause:\n formatted_error += (\n f\"\\nCaused by: {type(current_cause).__name__}:\\n {current_cause}\"\n )\n current_cause = current_cause.__cause__\n return formatted_error","title":""},{"docid":"7d79095ca7ef8edee3d27ad2fcd16eae","score":"0.5573219","text":"def error_details(self):\n return self._error_details","title":""},{"docid":"66af77c6e6ddc576247b7a793af9d51a","score":"0.55666035","text":"def get_error_msg( self ):\n try:\n if (self.__dict__['status_info']['status'] != 'ERROR'):\n return None\n return self.__dict__['status_info']['message']\n except KeyError as ex:\n raise GPUdbException( \"Unknown wrapped object; could not find \"\n \" the following key: {}\"\n \"\".format( GPUdbException.stringify_exception( ex ) ) )","title":""},{"docid":"5e062dca3af64d1f8901dc3afde1d4b0","score":"0.5547183","text":"def getErrorMsg(self):\n pass","title":""},{"docid":"d2f0ac9e876a142c372f541edd1c30be","score":"0.5544872","text":"def exc_info(self):\r\n return self.exc_type, self.exc_value, self.frames[0]","title":""},{"docid":"43b5d1f201597d263d40d5c0a28284a9","score":"0.5541972","text":"def error(self) -> str:\r\n if self.state == STATE_ERROR:\r\n mower_attributes = self.__get_mower_attributes()\r\n return ERRORCODES.get(mower_attributes[\"mower\"][\"errorCode\"])\r\n return \"\"","title":""},{"docid":"16f3932874ef158b974ae9da07b501c6","score":"0.5540333","text":"def _exc_str(ex: Exception, color=_exception_color) -> str:\n if color is None:\n color = \"\"\n return_color = \"\"\n else:\n return_color = _message_color\n exception_name = ex.__name__\n use_an = exception_name[0] in 'aeiouAEIOU' and exception_name[0:4] != \"User\"\n article = 'an' if use_an else 'a'\n return f\"{article} {color}{exception_name}{return_color}\"","title":""},{"docid":"e230ea07c65d236ef187a9e0b8dd1a6f","score":"0.55307996","text":"def generate_exception_info(self, exception: Optional[Exception]) -> str:\n\n return \"Exception information: \\n\" \\\n f\"\\tException type: {type(exception)}\\n\" \\\n f\"\\tException text: {str(exception)}\\n\" \\\n f\"\\tTrace: {traceback.format_exc()}\"","title":""},{"docid":"cd99b8a343d94a804dba19495ef9aa29","score":"0.55146784","text":"def formatError(self):\n return \"(Unable to format message)\"","title":""},{"docid":"95ff2699b2c93e142c29b7fc4c5241bd","score":"0.55130976","text":"def message(self):\n return self._error.message","title":""},{"docid":"6927e41cd9eeb27fc4bd2737dbf1a2a4","score":"0.5500051","text":"def get_message(self):\n return self.err","title":""},{"docid":"7e92240e63081dbf82917911dbec8e66","score":"0.5492574","text":"def repr_failure(self, excinfo):\n if excinfo.errisinstance(BadRequest):\n return str(excinfo.value)\n return super().repr_failure(excinfo)","title":""},{"docid":"c498955ed77cd16990a275d1e104e3e3","score":"0.5491929","text":"def exc_info(self):\n return self.exc_type, self.exc_value, self.frames[0]","title":""},{"docid":"0d4268b6cf48deb3c9cdca4822558339","score":"0.549162","text":"def _get_error_message_from_exception(self, e):\n\n try:\n if e.args:\n if len(e.args) > 1:\n error_code = e.args[0]\n error_msg = e.args[1]\n elif len(e.args) == 1:\n error_code = ERR_CODE_MSG\n error_msg = e.args[0]\n else:\n error_code = ERR_CODE_MSG\n error_msg = ERR_MSG_UNAVAILABLE\n except:\n error_code = ERR_CODE_MSG\n error_msg = ERR_MSG_UNAVAILABLE\n\n try:\n error_msg = self._handle_py_ver_compat_for_input_str(error_msg)\n except TypeError:\n error_msg = TYPE_ERR_MSG\n except:\n error_msg = ERR_MSG_UNAVAILABLE\n\n try:\n if error_code in ERR_CODE_MSG:\n error_text = \"Error Message: {0}\".format(error_msg)\n else:\n error_text = \"Error Code: {0}. Error Message: {1}\".format(error_code, error_msg)\n except:\n self.debug_print(PARSE_ERR_MSG)\n error_text = PARSE_ERR_MSG\n\n return error_text","title":""},{"docid":"8c5783b74f281c0b7b0dafa60e5d86c2","score":"0.5491152","text":"def exc_info(cls) -> \"ErrorInfo\":\n return cls(*sys.exc_info())","title":""},{"docid":"36e7a419dff15b48c6549ab6156f006a","score":"0.5482453","text":"def description(self) -> str:\n return \"Invalid value in column '%s'\" % self.column_name","title":""},{"docid":"b7ceca5387c090ca88ca9cf58a0d9760","score":"0.54719484","text":"def explain(self):\n if self is PoolMaintenanceErrorCode.NO_IPC_REQUESTS:\n return (\n \"The pool will return an error on any IPC request that could \"\n \"cause a change in the pool state, for example, a request to \"\n \"rename a filesystem. It will still be able to respond to \"\n \"purely informational requests.\"\n )\n\n if self is PoolMaintenanceErrorCode.NO_POOL_CHANGES:\n return (\n \"The pool is unable to manage itself by reacting to events, \"\n \"such as devicemapper events, that might require it to take \"\n \"any maintenance operations.\"\n )\n\n if self is PoolMaintenanceErrorCode.READ_ONLY: # pragma: no cover\n return \"The pool is in read-only mode.\"\n\n assert False, \"impossible error code reached\" # pragma: no cover","title":""},{"docid":"e4e39ecb1aa20140c9ce355a1cddeb9c","score":"0.54719114","text":"def error_message(self) -> str:\n return self._error_message","title":""},{"docid":"8023a496044b2d547213fcab1178bd5b","score":"0.5469984","text":"def __str__(self):\n return 'Database access error - %s' % self.reason","title":""},{"docid":"933a26dbfd66d5d54e4f45b1cc5a6522","score":"0.5467869","text":"def dead_letter_error_description(self) -> Optional[str]:\n if self._raw_amqp_message.application_properties:\n try:\n return self._raw_amqp_message.application_properties.get( # type: ignore\n PROPERTIES_DEAD_LETTER_ERROR_DESCRIPTION\n ).decode(\"UTF-8\")\n except AttributeError:\n pass\n return None","title":""},{"docid":"aeb3f9109b6101de354ad25f0e10a729","score":"0.5458247","text":"def exception(self):\n if self.end_message:\n return self.end_message.contents.get(EXCEPTION_FIELD, None)","title":""},{"docid":"c8e5f29072e83007eb428eb55d9aeb1b","score":"0.54414946","text":"def error_message(self):\n return self._error_message","title":""},{"docid":"c8e5f29072e83007eb428eb55d9aeb1b","score":"0.54414946","text":"def error_message(self):\n return self._error_message","title":""},{"docid":"307029b4ffaf78a74cd9611448a14c1e","score":"0.54342765","text":"def exc_info(self):\n e = self._exc_info\n if e:\n return (e[0], e[1], load_traceback(e[2]))","title":""},{"docid":"fcf05a090e058a3665fdbf1ad41846d2","score":"0.5431123","text":"def __str__(self):\n return repr(self.error_message)","title":""},{"docid":"648ae861068703e7818e4d29fc1add95","score":"0.54292065","text":"def format(self):\n\n return format_exception(self.type, self.value, self.traceback)","title":""},{"docid":"2ee9ff6fdface7e63576571423b0e76f","score":"0.54117876","text":"def get_error(self):\n return '[%s]' % (self.error)","title":""},{"docid":"ad55f5efb8216dc79e0f7afd7213b6a2","score":"0.54111767","text":"def error(self) -> Optional[str]:\n\n if self.status != \"errored\":\n return None\n\n message = next(\n iter(stage.error for stage in self.stages if stage.status == \"errored\")\n )\n return \"unknown error\" if message is None else message","title":""},{"docid":"b911b986ae96bdf3521e36ba8ea2b799","score":"0.5409309","text":"def _err(exception):\n print(exception)","title":""},{"docid":"1fad75fe980269738fbf18b9cc3f97e9","score":"0.54014397","text":"def __str__(self):\n return \"ParseException: %s\" % self.__msg","title":""},{"docid":"3f5fcdd8e58d8b8518f906f56539ca8e","score":"0.5401238","text":"def exception(self):\n if self._get_hnode().get(\"UNTRANSLATED\"):\n return \"Status: error (ctx needs translation)\"\n codecell = self._get_codecell()\n exception = codecell.exception\n if exception is None and codecell.status == \"Status: OK\":\n gen_moduledict = self._get_ctx().gen_moduledict \n exc2 = gen_moduledict.exception\n if exc2 is not None:\n exception = {\n \"gen_moduledict\": exc2\n } \n return exception","title":""},{"docid":"11152d60d7b2ac87efc54a2b053baced","score":"0.5396628","text":"def __str__(self):\n return self.base_message.format(filename=self.md_file_path) + ERROR_MESSAGE","title":""},{"docid":"d5e191b3622185c632ea5d9d70884698","score":"0.53854686","text":"def _exc_info_to_string(self, err, test):\r\n exctype, value, tb = err\r\n # Skip test runner traceback levels\r\n while tb and self._is_relevant_tb_level(tb):\r\n tb = tb.tb_next\r\n if exctype is test.failureException:\r\n # Skip assert*() traceback levels\r\n length = self._count_relevant_tb_levels(tb)\r\n msgLines = traceback.format_exception(exctype, value, tb, length)\r\n else:\r\n msgLines = traceback.format_exception(exctype, value, tb)\r\n \r\n if self.buffer:\r\n output = sys.stdout.getvalue()\r\n error = sys.stderr.getvalue() \r\n if output:\r\n if not output.endswith('\\n'):\r\n output += '\\n'\r\n msgLines.append(STDOUT_LINE % output)\r\n if error:\r\n if not error.endswith('\\n'):\r\n error += '\\n'\r\n msgLines.append(STDERR_LINE % error)\r\n return ''.join(msgLines)","title":""},{"docid":"84fc9d213200a3866610c9d2b86a9506","score":"0.5379236","text":"def message(self, *args, **kwargs):\n base_msg = super(CoconutInternalException, self).message(*args, **kwargs)\n if \"\\n\" in base_msg:\n return base_msg + \"\\n\" + report_this_text\n else:\n return base_msg + \" \" + report_this_text","title":""},{"docid":"5edcb3e40f224f2fe15690f7efc9922e","score":"0.5375226","text":"def _error_msg(self, e):\n print(textwrap.dedent((\"\"\"\n ERROR! -- An unrecoverable error has occurred.\n\n If you believe the CUE file is correct, please send the input file to\n <%s>, along with the error message below.\n\n ---> %s\n \"\"\" % (__email__,e))), file=sys.stderr)","title":""},{"docid":"27997bd68cbf0fa590fd976b081b88e6","score":"0.537171","text":"def message(self):\n self.process()\n # Check whether the error comes with source information\n if hasattr(self.error, \"line\"):\n args = (\n self.header(),\n self.highlight(),\n self.error_code(),\n self.hint(),\n )\n return \"{}\\n\\n{}\\n\\n{}: {}\".format(*args)\n else:\n return f\"{self.error_code()}: {self.hint()}\"","title":""},{"docid":"db70d4f81e827ef6a85555102acc8c07","score":"0.5336603","text":"def description(self):\n return 'unknown'","title":""},{"docid":"222f2ef4dcf1ac1be9810903cd1a2890","score":"0.5332498","text":"def stringify_exception( ex ):\n if str(ex):\n return str(ex)\n else:\n return repr(ex)","title":""},{"docid":"015abafead869e36246ec25fe54a59c9","score":"0.5312414","text":"def get_desc(self):\n return \"The Developer of this Probe didn't provide a description\"","title":""},{"docid":"3e72030f3d80804cb15de578b957bc65","score":"0.53012925","text":"def traceback(self):\n type, value, tb = sys.exc_info()\n return ''.join(traceback.format_exception(type, value, tb.tb_next))","title":""},{"docid":"31f6f0a7d8c24175f8ebc18f522c5187","score":"0.53003526","text":"def standard_exc_info(self):\n tb = self.frames[0]\n # the frame will be an actual traceback (or transparent proxy) if\n # we are on pypy or a python implementation with support for tproxy\n if type(tb) is not TracebackType:\n tb = tb.tb\n return self.exc_type, self.exc_value, tb","title":""},{"docid":"6c80b1e969a354f1b70bfa2a5a0b1148","score":"0.52959055","text":"def __str__(self):\n ex_msg = \"Feature plugin registration failed:\\n\"\n for ex in self.exceptions or []:\n ex_msg += ex + \"\\n\"\n return ex_msg","title":""},{"docid":"b33c24c19cd83cfdc3d034fe39189ef6","score":"0.5294852","text":"def reason(self) -> str:\n return self.Reason","title":""},{"docid":"33c79ae44030bb9903345c6eee13176d","score":"0.528951","text":"def invalid_reason(self):\n if self.valid():\n return ''\n else:\n if self.name is None:\n return 'No name defined'\n elif self.homepage is None:\n return 'No homepage defined'\n elif self.patterns[0].pattern is None:\n return 'No searchpattern defined'\n else:\n return 'Unknown error'","title":""},{"docid":"4ab777fccc90218fbca6eccfbb89e1fb","score":"0.52856106","text":"def reason(self):\n r = []\n if not self.has_var:\n r.append(u'no_var')\n if not self.has_func:\n r.append(u'no_func')\n for vname in self.bad_vars:\n r.append(u'bad_var ' + vname)\n return u','.join(r)","title":""},{"docid":"ab3fd85d471b1328db02500352f95d4d","score":"0.52804965","text":"def name(self) -> str:\n return \"Multimatic Errors\"","title":""},{"docid":"061e4191a7bbe0e5db73717d9b76c715","score":"0.5279127","text":"def _HandleException(self, e):\n return str(e)","title":""},{"docid":"28d163d18890ceb07c10fdb43f6bcfdd","score":"0.5278666","text":"def error(self) -> str:\n return typing.cast(\n str,\n self._properties.get(\"error\"),\n )","title":""},{"docid":"ca3aab25b4be1851b5a43d1847e834f9","score":"0.52730286","text":"def _exc_info_to_string(self, err, test):\n exctype, value, tb = err\n # Skip test runner traceback levels\n while tb and self._is_relevant_tb_level(tb):\n tb = tb.tb_next\n\n if exctype is test.failureException:\n # Skip assert*() traceback levels\n length = self._count_relevant_tb_levels(tb)\n msgLines = value\n else:\n msgLines = value\n\n if self.buffer:\n output = sys.stdout.getvalue()\n error = sys.stderr.getvalue()\n print error\n if output:\n if not output.endswith('\\n'):\n output += '\\n'\n msgLines.append(STDOUT_LINE % output)\n if error:\n if not error.endswith('\\n'):\n error += '\\n'\n msgLines.append(STDERR_LINE % error)\n return ''.join(map(str, msgLines))","title":""},{"docid":"0ee5f63a1d32974d9cbb4341a5a38e39","score":"0.5270419","text":"async def _get_extract_failure_reason(ex: Exception, data: Dict[str, Any]):\n\n api_message = ''\n\n if 'code' in data and data['code'] != 0:\n api_message += 'Error code {}: '.format(data['code'])\n elif 'error_code' in data:\n api_message += 'Error code {}: '.format(data['error_code'])\n\n if 'msg' in data and data['msg'] and data['msg'] != '':\n api_message += data['msg']\n if 'detailMsg' in data and data['detailMsg'] and data['detailMsg'] != '':\n api_message += '; ' + data['detailMsg']\n else:\n api_message += 'empty or missing results'\n\n return \"{} ({}: {})\".format(api_message, type(ex).__name__, ex)","title":""},{"docid":"f1a97bed5ad6d3e1bba81544981662d6","score":"0.52660227","text":"def __str__(self):\n s = Exception.__str__(self)\n if not self.fname:\n return s\n return \"File {0}, line {1}: {2}\".format(self.fname, self.line, s)","title":""},{"docid":"11817171cd255036e35e5d6f915080c6","score":"0.5261123","text":"def _get_fault_message(self, exception):\n try:\n return exception.failure.errors[0].message\n except AttributeError:\n try:\n return exception.details()\n except AttributeError:\n return None","title":""},{"docid":"879ba82d06f5ec712a50a50b11844ce4","score":"0.5257593","text":"def print_exc_info():\n\n import StringIO, traceback\n \n sio = StringIO.StringIO()\n traceback.print_exc(file=sio) #thread-safe print_exception to string\n sio.seek(0, 0)\n \n return sio.read()","title":""},{"docid":"dc54ec531b6c0dca2f6f42e2e1a3240e","score":"0.5249521","text":"def get_exception_message(self, exception_type, args):\n message = None\n try:\n raise exception_type(*args)\n except exception_type as exc:\n message = str(exc)\n return message","title":""},{"docid":"536660c6e2a6a6c47f2ac7e94de5cdf0","score":"0.5241921","text":"def get_error_msg(self):\n\n status_display = None\n if self.get_status() == self.HIGH_TEMP_ERROR:\n status_display = \"High Temperature (100%cC)\" % self.DEGREE_SIGN\n elif self.get_status() == self.LOW_TEMP_ERROR:\n status_display = \"Low Temperature (-50%cC)\" % self.DEGREE_SIGN\n\n reading_datetime = datetime.datetime.strptime(self.format_datetime_string(), \"%Y/%m/%d %H:%M\")\n reading_display_datetime = reading_datetime.strftime('%Y/%m/%d %H:%M')\n\n reading_seq_num = self.get_sequence_num()\n\n error_msg = \"%s at %s, Sequence: %d\" % (\n status_display, reading_display_datetime, reading_seq_num)\n return error_msg","title":""},{"docid":"c91634e65a58ee887ffabe121442accf","score":"0.52325565","text":"def get_fault_message(self):\n\n pass # pragma: no cover","title":""},{"docid":"23614d013c8f58fe4d1047855159d98a","score":"0.5226735","text":"def message(self) -> str:\n if not self._message:\n raise Exception\n return self._message","title":""},{"docid":"8ac0963e6d21b1b1a1cc240edd0903b3","score":"0.52178687","text":"def __str__(self):\n obj_str = \"Systematic uncertainty: {:s}\".format(self.name)\n return obj_str","title":""},{"docid":"c3c1acb0e2cd5601687c21ffd4ff3b95","score":"0.52152044","text":"def formatEx3(excepInst):\n msg = \"%s:%s\" % (excepInst.__class__.__name__, str(excepInst))\n return msg","title":""},{"docid":"4c883876b1822271bd01b5b8e55efe1d","score":"0.52130306","text":"def __str__(self):\n return (\n \"Critical errors: {CRITICAL}, errors: {ERROR}, warnings: \"\n \"{WARNING}\"\n ).format(**self.logged)","title":""},{"docid":"27c0206bc4d8824ab3b65caf7548f457","score":"0.52104795","text":"def repr_failure(self, excinfo):\n if isinstance(excinfo.value, FunctionalTestException):\n return f\"Functional Test failed {excinfo.value.args}. See below, or see output log file for test {self.name}. \"\n else:\n log.error(f\"unknown failure {excinfo}\")\n print(\"Unknown failure:\", str(excinfo))\n print(excinfo.getrepr())","title":""},{"docid":"713df0d6f7e9e8552aad15657b4e271f","score":"0.5207485","text":"def render_error(self) -> str:\n return f'{str(self.cause) if self.cause else self.msg}'","title":""},{"docid":"c7000531254472b1555ac1039dcdd9ce","score":"0.52051544","text":"def __str__(self):\n \n return self.reason","title":""},{"docid":"737605eb41ed786830dc5b6afd70fc0d","score":"0.5194999","text":"def repr_failure(self, excinfo):\r\n if isinstance(excinfo.value, DocoptTestException):\r\n return \"\\n\".join((\r\n \"usecase execution failed:\",\r\n self.doc.rstrip(),\r\n \"$ %s %s\" % (self.prog, self.argv),\r\n \"result> %s\" % json.dumps(excinfo.value.args[1]),\r\n \"expect> %s\" % json.dumps(self.expect),\r\n ))","title":""},{"docid":"facb76f14223c41a5a6712aa59303fca","score":"0.5192028","text":"def __str__(self):\n return 'Failed to read file \"%s\"' % self.filepath","title":""},{"docid":"04c0a8cfcc3255fdda0c0f8cb0dc989c","score":"0.5187984","text":"def errors_occurred(self):\n return self.logged['ERROR'] + self.logged['CRITICAL']","title":""},{"docid":"a19bdb6596b3edb450a1449d60015861","score":"0.5185789","text":"def exception(self):\n if self._exception is not _NONE:\n return self._exception","title":""},{"docid":"4ddd78eb8f69eddf6da1708ac08f689d","score":"0.5179353","text":"def warning_message(self) -> str:","title":""},{"docid":"4835f7c69a0cf093fec8c61b7458dd5e","score":"0.5175048","text":"def status_reason(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"status_reason\")","title":""},{"docid":"45e38227c8cd8898a4423df6acc44315","score":"0.51723176","text":"def show_err():\n for err in list(sys.exc_info()):\n err_msg = \"[!]%s\" % str(err).strip(\"<>\")\n print(err_msg, file=sys.stderr)","title":""},{"docid":"5fb692314b382cc23514cbc07302541c","score":"0.5169245","text":"def exception(self):\n return self._exc_info[1] if self._exc_info else None","title":""},{"docid":"cff0c1ba2b82b577767f7ed0f638f008","score":"0.5164884","text":"def GetFailureReason(self):\n return self._failure_reason","title":""},{"docid":"9cdc208aa987740317114f71f9a3bd1b","score":"0.5159561","text":"def get_formatted_traceback( self ):\n return self.traceback_msg","title":""},{"docid":"92f7b86a8348577fd5c0b101151cb5dc","score":"0.5154065","text":"def reason(self) -> str:\n return pulumi.get(self, \"reason\")","title":""},{"docid":"92f7b86a8348577fd5c0b101151cb5dc","score":"0.5154065","text":"def reason(self) -> str:\n return pulumi.get(self, \"reason\")","title":""},{"docid":"b8add316c74c628b96fdc7b9c01daae8","score":"0.5152142","text":"def print_exc_plus( ):\r\n msg = \"\"\r\n tb = sys.exc_info( )[2]\r\n while tb.tb_next:\r\n tb = tb.tb_next\r\n stack = [ ]\r\n f = tb.tb_frame\r\n while f:\r\n if r'\\sahm\\\\' in f.f_code.co_filename:\r\n stack.append(f)\r\n f = f.f_back\r\n stack.reverse( )\r\n traceback.print_exc( )\r\n msg += \"\\n\" + \"Locals by frame, innermost last\"\r\n for frame in stack:\r\n msg += \"\\n\"\r\n msg += \"\\n\" + \"Frame %s in %s at line %s\" % (frame.f_code.co_name,\r\n frame.f_code.co_filename,\r\n frame.f_lineno)\r\n msg += \"\\n\"\r\n for key, value in frame.f_locals.items( ):\r\n msg += \"\\t%20s = \" % key\r\n # we must _absolutely_ avoid propagating exceptions, and str(value)\r\n # COULD cause any exception, so we MUST catch any...:\r\n try:\r\n msg += str(value)\r\n except:\r\n msg += \"<ERROR WHILE PRINTING VALUE>\"\r\n \r\n msg += \"\\n\\n\" + ' '.join([str(i) for i in sys.exc_info()[:2]])\r\n \r\n return msg","title":""},{"docid":"e650b85d71c67437113c772e1c3d523c","score":"0.51513445","text":"def exception_info(e):\n except_type, except_class, tb = sys.exc_info()\n error_tuple = (except_type, except_class, traceback.extract_tb(tb))\n return error_tuple","title":""},{"docid":"fa438e4ecdbf0606300fb3df5e741335","score":"0.51506144","text":"def Message(self, granular = False):\r\n if granular:\r\n granular = ''\r\n if len(self.Contents) > 0:\r\n granular = \"The following command line errors were improperly set: \\n{ %s }\" % (',\\n'.join([arg + ' : ' + msg for arg, msg in self.Contents]))\r\n # Include information about invalid arguments being passed if any occurred:\r\n if len(self.InvalidArgs) > 0:\r\n granular += ('\\n' if granular else '') + 'The following arguments are invalid: \\n{ %s }' % (','.join(self.InvalidArgs))\r\n return granular\r\n else:\r\n return CommandLineErrors.__Concise % self.ErrorCount()","title":""}],"string":"[\n {\n \"docid\": \"9271c80dc57fab8ae115327f3df1069a\",\n \"score\": \"0.6199383\",\n \"text\": \"def exception(self):\\r\\n buf = traceback.format_exception_only(self.exc_type, self.exc_value)\\r\\n return ''.join(buf).strip().decode('utf-8', 'replace')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb1ae58044d5b91590512fdd3726d074\",\n \"score\": \"0.60794216\",\n \"text\": \"def format_error_message(self) -> str:\\n return f\\\"{self.exc_type.__name__}: {self.exc_value}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bc39d440b7bf99d019f5035d908cc88\",\n \"score\": \"0.60473853\",\n \"text\": \"def __str__(self):\\n return 'Database integerity error - %s' % self.reason\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3c27ba8ab94db5472077b0e0a97113a\",\n \"score\": \"0.6006324\",\n \"text\": \"def exc_info(self):\\r\\n return self.exception\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e63a9a43764baca471f52eec33819bc\",\n \"score\": \"0.59391123\",\n \"text\": \"def formatException(self, ei):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2385ca71f53c336e0e3e76675c70df3\",\n \"score\": \"0.5920419\",\n \"text\": \"def _exc_info_to_string(self, err, test):\\n return err\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9de97764282b29d2261fb095db6482ef\",\n \"score\": \"0.58595777\",\n \"text\": \"def exc_repr(e):\\n return \\\"{}: {}\\\".format(type(e).__name__, str(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af4df25dd30e2ad06974aed720020bf1\",\n \"score\": \"0.58202285\",\n \"text\": \"def standard_exc_info(self):\\r\\n return self.exc_type, self.exc_value, self.frames[0].tb\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39ced7dcdb0e9a2808e6487f967cdaa2\",\n \"score\": \"0.581941\",\n \"text\": \"def get_error_message(self):\\n return self.open.query(\\\":SYST:ERR?\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"754abcd3b97fa9e5b1e4aee96720ad21\",\n \"score\": \"0.58062327\",\n \"text\": \"def excused(self):\\n return self._excused\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff8c1eb51ff6dbf49bf46a5de491f9b8\",\n \"score\": \"0.5754992\",\n \"text\": \"def error_str(self):\\n return u\\\"{0:<20} {1}\\\\n{3:<11} {2}\\\".format(\\n package_helpers.color_output(\\n self.get_logging_level() if self.get_logging_level() != constants.VALIDATE_LOG_LEVEL_DEBUG\\n else \\\"PASS\\\", \\n self.get_logging_level()), \\n self.description, self.solution, \\\"\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa70429c940e19a26739526cb42b48fb\",\n \"score\": \"0.5741728\",\n \"text\": \"def generate_message(self):\\n message = \\\"Oops! Assertion failed\\\"\\n message += self.generate_expression_section()\\n message += self.generate_description_section()\\n message += self.generate_values_section()\\n message += self.generate_where_section()\\n return message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"485726a75047be5ee8daef6e226521f8\",\n \"score\": \"0.5719327\",\n \"text\": \"def exception_detail(\\n self, exception_type: type, exception_value: Exception, tb\\n ):\\n text = \\\"[{}]: Unhandled WebSocket Error:{}\\\\n\\\".format(\\n datetime.now().isoformat(), exception_type\\n )\\n text += \\\"LastSentText:\\\\n{}\\\\n\\\".format(self._last_sent_text)\\n text += \\\"LastReceivedText:\\\\n{}\\\\n\\\".format(self._last_received_text)\\n text += \\\"Exception trace: \\\\n\\\"\\n text += \\\"\\\".join(\\n traceback.format_exception(exception_type, exception_value, tb)\\n )\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c388fadd9d94e9a5e29cd5a80d0fb8f\",\n \"score\": \"0.5694837\",\n \"text\": \"def source_message(self):\\n return ''.join(traceback.format_exception_only(self._src_exc[0], self._src_exc[1]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3a950b6304c5840564791d4f38847e4\",\n \"score\": \"0.5693524\",\n \"text\": \"def _get_exception():\\n exc_type, exc_value, exc_traceback = sys.exc_info()\\n d = traceback.format_exception(exc_type, exc_value, exc_traceback)\\n msg = ''.join(d)\\n return msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b7fd8fcc1fb84975659f8e63b4be81f\",\n \"score\": \"0.567742\",\n \"text\": \"def formatException(self, ei):\\n exc_tb = ei[2]\\n exc_type = ei[0]\\n exc_value = ei[1]\\n msg = []\\n try:\\n try:\\n # log exception details\\n now = datetime.datetime.now().isoformat()\\n py_version = getattr(config, 'py_version', 'not found')\\n wx_version = getattr(config, 'wx_version', 'not found')\\n platform = getattr(config, 'platform', 'not found')\\n app_version = getattr(config, 'version', 'not found')\\n\\n msg.append('An unexpected error occurred!\\\\n')\\n msg.append('\\\\n')\\n msg.append('Exception type: %s\\\\n' % exc_type)\\n msg.append('Exception details: %s\\\\n' % exc_value)\\n if exc_tb:\\n msg.append('\\\\nApplication stack traceback:\\\\n')\\n msg += traceback.format_tb(exc_tb)\\n msg.append('\\\\n')\\n msg.append('Date and time: %s\\\\n' % now)\\n msg.append('Python version: %s\\\\n' % py_version)\\n msg.append('wxPython version: %s\\\\n' % wx_version)\\n msg.append('wxWidgets platform: %s\\\\n' % platform)\\n msg.append('wxGlade version: %s\\\\n' % app_version)\\n msg.append('\\\\n')\\n\\n except Exception as e:\\n # This code should NEVER be executed!\\n if config.debugging: raise\\n logging.error('An exception has been raised inside the exception handler: %s', e)\\n sys.exit(1)\\n\\n # delete local references of trace backs or part of them to avoid circular references\\n finally:\\n del ei, exc_tb, exc_type, exc_value\\n\\n if msg[-1][-1] == \\\"\\\\n\\\":\\n msg[-1] = msg[-1][:-1]\\n return \\\"\\\".join(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2368e9dffe07585bf094fb92c26606f4\",\n \"score\": \"0.5610206\",\n \"text\": \"def get_error_msg(self):\\n raise NotImplementedError(\\\"Must be implemented\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56ed53ec2a88e5ef0adf74be2c61a747\",\n \"score\": \"0.5606838\",\n \"text\": \"def rdap_pretty_error_message(exc):\\n\\n if isinstance(exc, RdapNotFoundError):\\n return _(\\\"This ASN is not assigned by any RIR\\\")\\n if isinstance(exc, RdapInvalidRange):\\n return _(\\\"ASNs in this range are private or reserved\\\")\\n\\n return _(\\\"{}\\\").format(exc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9673b778f6a2c4f1f1497dd458de087\",\n \"score\": \"0.56065124\",\n \"text\": \"def getExceptionText():\\n (_type,value,tback)=sys.exc_info()\\n return \\\"\\\".join(traceback.format_exception(_type, value, tback))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0079174b7c53c79159f216eababccaae\",\n \"score\": \"0.5606293\",\n \"text\": \"def exconly(self, tryshort: bool = False) -> str:\\n lines = format_exception_only(self.type, self.value)\\n text = \\\"\\\".join(lines)\\n text = text.rstrip()\\n if tryshort:\\n if text.startswith(self._striptext):\\n text = text[len(self._striptext) :]\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"813627fe9f21c67dd8aa1ca76abea9ea\",\n \"score\": \"0.55978507\",\n \"text\": \"def message(self):\\n if not self.enabled:\\n return None\\n return self._lint_message + \\\\\\n \\\"\\\\n=======================================\\\" + \\\\\\n \\\"\\\\n%s: Error:%d, Warning:%d \\\\n\\\" % \\\\\\n (self._version, self._error_count, self._warning_count)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"996968b070f528c9786a435aeb617980\",\n \"score\": \"0.5596806\",\n \"text\": \"def err_msg(self):\\n return err_msg_template.substitute(\\n object_name=self.object_name,\\n missing_dependency=self.missing_dependency)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"044f7377c916dadb1344d84f69197c0b\",\n \"score\": \"0.5580845\",\n \"text\": \"def format_exception(self) -> str:\\n return \\\"\\\".join(\\n traceback.format_exception(self.exc_type, self.exc_value, self.exc_tb)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"675c6c2a6c9600313afaf4ffa87469ae\",\n \"score\": \"0.5577244\",\n \"text\": \"def format_scan_error(\\n self,\\n next_file: str,\\n this_exception: Exception,\\n show_extended_information: bool = False,\\n ) -> Optional[str]:\\n formatted_error = f\\\"{type(this_exception).__name__} encountered while scanning '{next_file}':\\\\n{this_exception}\\\"\\n if show_extended_information:\\n current_cause = this_exception.__cause__\\n while current_cause:\\n formatted_error += (\\n f\\\"\\\\nCaused by: {type(current_cause).__name__}:\\\\n {current_cause}\\\"\\n )\\n current_cause = current_cause.__cause__\\n return formatted_error\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d79095ca7ef8edee3d27ad2fcd16eae\",\n \"score\": \"0.5573219\",\n \"text\": \"def error_details(self):\\n return self._error_details\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66af77c6e6ddc576247b7a793af9d51a\",\n \"score\": \"0.55666035\",\n \"text\": \"def get_error_msg( self ):\\n try:\\n if (self.__dict__['status_info']['status'] != 'ERROR'):\\n return None\\n return self.__dict__['status_info']['message']\\n except KeyError as ex:\\n raise GPUdbException( \\\"Unknown wrapped object; could not find \\\"\\n \\\" the following key: {}\\\"\\n \\\"\\\".format( GPUdbException.stringify_exception( ex ) ) )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e062dca3af64d1f8901dc3afde1d4b0\",\n \"score\": \"0.5547183\",\n \"text\": \"def getErrorMsg(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2f0ac9e876a142c372f541edd1c30be\",\n \"score\": \"0.5544872\",\n \"text\": \"def exc_info(self):\\r\\n return self.exc_type, self.exc_value, self.frames[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43b5d1f201597d263d40d5c0a28284a9\",\n \"score\": \"0.5541972\",\n \"text\": \"def error(self) -> str:\\r\\n if self.state == STATE_ERROR:\\r\\n mower_attributes = self.__get_mower_attributes()\\r\\n return ERRORCODES.get(mower_attributes[\\\"mower\\\"][\\\"errorCode\\\"])\\r\\n return \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16f3932874ef158b974ae9da07b501c6\",\n \"score\": \"0.5540333\",\n \"text\": \"def _exc_str(ex: Exception, color=_exception_color) -> str:\\n if color is None:\\n color = \\\"\\\"\\n return_color = \\\"\\\"\\n else:\\n return_color = _message_color\\n exception_name = ex.__name__\\n use_an = exception_name[0] in 'aeiouAEIOU' and exception_name[0:4] != \\\"User\\\"\\n article = 'an' if use_an else 'a'\\n return f\\\"{article} {color}{exception_name}{return_color}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e230ea07c65d236ef187a9e0b8dd1a6f\",\n \"score\": \"0.55307996\",\n \"text\": \"def generate_exception_info(self, exception: Optional[Exception]) -> str:\\n\\n return \\\"Exception information: \\\\n\\\" \\\\\\n f\\\"\\\\tException type: {type(exception)}\\\\n\\\" \\\\\\n f\\\"\\\\tException text: {str(exception)}\\\\n\\\" \\\\\\n f\\\"\\\\tTrace: {traceback.format_exc()}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd99b8a343d94a804dba19495ef9aa29\",\n \"score\": \"0.55146784\",\n \"text\": \"def formatError(self):\\n return \\\"(Unable to format message)\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95ff2699b2c93e142c29b7fc4c5241bd\",\n \"score\": \"0.55130976\",\n \"text\": \"def message(self):\\n return self._error.message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6927e41cd9eeb27fc4bd2737dbf1a2a4\",\n \"score\": \"0.5500051\",\n \"text\": \"def get_message(self):\\n return self.err\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e92240e63081dbf82917911dbec8e66\",\n \"score\": \"0.5492574\",\n \"text\": \"def repr_failure(self, excinfo):\\n if excinfo.errisinstance(BadRequest):\\n return str(excinfo.value)\\n return super().repr_failure(excinfo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c498955ed77cd16990a275d1e104e3e3\",\n \"score\": \"0.5491929\",\n \"text\": \"def exc_info(self):\\n return self.exc_type, self.exc_value, self.frames[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d4268b6cf48deb3c9cdca4822558339\",\n \"score\": \"0.549162\",\n \"text\": \"def _get_error_message_from_exception(self, e):\\n\\n try:\\n if e.args:\\n if len(e.args) > 1:\\n error_code = e.args[0]\\n error_msg = e.args[1]\\n elif len(e.args) == 1:\\n error_code = ERR_CODE_MSG\\n error_msg = e.args[0]\\n else:\\n error_code = ERR_CODE_MSG\\n error_msg = ERR_MSG_UNAVAILABLE\\n except:\\n error_code = ERR_CODE_MSG\\n error_msg = ERR_MSG_UNAVAILABLE\\n\\n try:\\n error_msg = self._handle_py_ver_compat_for_input_str(error_msg)\\n except TypeError:\\n error_msg = TYPE_ERR_MSG\\n except:\\n error_msg = ERR_MSG_UNAVAILABLE\\n\\n try:\\n if error_code in ERR_CODE_MSG:\\n error_text = \\\"Error Message: {0}\\\".format(error_msg)\\n else:\\n error_text = \\\"Error Code: {0}. Error Message: {1}\\\".format(error_code, error_msg)\\n except:\\n self.debug_print(PARSE_ERR_MSG)\\n error_text = PARSE_ERR_MSG\\n\\n return error_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c5783b74f281c0b7b0dafa60e5d86c2\",\n \"score\": \"0.5491152\",\n \"text\": \"def exc_info(cls) -> \\\"ErrorInfo\\\":\\n return cls(*sys.exc_info())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36e7a419dff15b48c6549ab6156f006a\",\n \"score\": \"0.5482453\",\n \"text\": \"def description(self) -> str:\\n return \\\"Invalid value in column '%s'\\\" % self.column_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7ceca5387c090ca88ca9cf58a0d9760\",\n \"score\": \"0.54719484\",\n \"text\": \"def explain(self):\\n if self is PoolMaintenanceErrorCode.NO_IPC_REQUESTS:\\n return (\\n \\\"The pool will return an error on any IPC request that could \\\"\\n \\\"cause a change in the pool state, for example, a request to \\\"\\n \\\"rename a filesystem. It will still be able to respond to \\\"\\n \\\"purely informational requests.\\\"\\n )\\n\\n if self is PoolMaintenanceErrorCode.NO_POOL_CHANGES:\\n return (\\n \\\"The pool is unable to manage itself by reacting to events, \\\"\\n \\\"such as devicemapper events, that might require it to take \\\"\\n \\\"any maintenance operations.\\\"\\n )\\n\\n if self is PoolMaintenanceErrorCode.READ_ONLY: # pragma: no cover\\n return \\\"The pool is in read-only mode.\\\"\\n\\n assert False, \\\"impossible error code reached\\\" # pragma: no cover\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4e39ecb1aa20140c9ce355a1cddeb9c\",\n \"score\": \"0.54719114\",\n \"text\": \"def error_message(self) -> str:\\n return self._error_message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8023a496044b2d547213fcab1178bd5b\",\n \"score\": \"0.5469984\",\n \"text\": \"def __str__(self):\\n return 'Database access error - %s' % self.reason\",\n \"title\": \"\"\n },\n {\n \"docid\": \"933a26dbfd66d5d54e4f45b1cc5a6522\",\n \"score\": \"0.5467869\",\n \"text\": \"def dead_letter_error_description(self) -> Optional[str]:\\n if self._raw_amqp_message.application_properties:\\n try:\\n return self._raw_amqp_message.application_properties.get( # type: ignore\\n PROPERTIES_DEAD_LETTER_ERROR_DESCRIPTION\\n ).decode(\\\"UTF-8\\\")\\n except AttributeError:\\n pass\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aeb3f9109b6101de354ad25f0e10a729\",\n \"score\": \"0.5458247\",\n \"text\": \"def exception(self):\\n if self.end_message:\\n return self.end_message.contents.get(EXCEPTION_FIELD, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8e5f29072e83007eb428eb55d9aeb1b\",\n \"score\": \"0.54414946\",\n \"text\": \"def error_message(self):\\n return self._error_message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8e5f29072e83007eb428eb55d9aeb1b\",\n \"score\": \"0.54414946\",\n \"text\": \"def error_message(self):\\n return self._error_message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"307029b4ffaf78a74cd9611448a14c1e\",\n \"score\": \"0.54342765\",\n \"text\": \"def exc_info(self):\\n e = self._exc_info\\n if e:\\n return (e[0], e[1], load_traceback(e[2]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcf05a090e058a3665fdbf1ad41846d2\",\n \"score\": \"0.5431123\",\n \"text\": \"def __str__(self):\\n return repr(self.error_message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"648ae861068703e7818e4d29fc1add95\",\n \"score\": \"0.54292065\",\n \"text\": \"def format(self):\\n\\n return format_exception(self.type, self.value, self.traceback)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ee9ff6fdface7e63576571423b0e76f\",\n \"score\": \"0.54117876\",\n \"text\": \"def get_error(self):\\n return '[%s]' % (self.error)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad55f5efb8216dc79e0f7afd7213b6a2\",\n \"score\": \"0.54111767\",\n \"text\": \"def error(self) -> Optional[str]:\\n\\n if self.status != \\\"errored\\\":\\n return None\\n\\n message = next(\\n iter(stage.error for stage in self.stages if stage.status == \\\"errored\\\")\\n )\\n return \\\"unknown error\\\" if message is None else message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b911b986ae96bdf3521e36ba8ea2b799\",\n \"score\": \"0.5409309\",\n \"text\": \"def _err(exception):\\n print(exception)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fad75fe980269738fbf18b9cc3f97e9\",\n \"score\": \"0.54014397\",\n \"text\": \"def __str__(self):\\n return \\\"ParseException: %s\\\" % self.__msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f5fcdd8e58d8b8518f906f56539ca8e\",\n \"score\": \"0.5401238\",\n \"text\": \"def exception(self):\\n if self._get_hnode().get(\\\"UNTRANSLATED\\\"):\\n return \\\"Status: error (ctx needs translation)\\\"\\n codecell = self._get_codecell()\\n exception = codecell.exception\\n if exception is None and codecell.status == \\\"Status: OK\\\":\\n gen_moduledict = self._get_ctx().gen_moduledict \\n exc2 = gen_moduledict.exception\\n if exc2 is not None:\\n exception = {\\n \\\"gen_moduledict\\\": exc2\\n } \\n return exception\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11152d60d7b2ac87efc54a2b053baced\",\n \"score\": \"0.5396628\",\n \"text\": \"def __str__(self):\\n return self.base_message.format(filename=self.md_file_path) + ERROR_MESSAGE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5e191b3622185c632ea5d9d70884698\",\n \"score\": \"0.53854686\",\n \"text\": \"def _exc_info_to_string(self, err, test):\\r\\n exctype, value, tb = err\\r\\n # Skip test runner traceback levels\\r\\n while tb and self._is_relevant_tb_level(tb):\\r\\n tb = tb.tb_next\\r\\n if exctype is test.failureException:\\r\\n # Skip assert*() traceback levels\\r\\n length = self._count_relevant_tb_levels(tb)\\r\\n msgLines = traceback.format_exception(exctype, value, tb, length)\\r\\n else:\\r\\n msgLines = traceback.format_exception(exctype, value, tb)\\r\\n \\r\\n if self.buffer:\\r\\n output = sys.stdout.getvalue()\\r\\n error = sys.stderr.getvalue() \\r\\n if output:\\r\\n if not output.endswith('\\\\n'):\\r\\n output += '\\\\n'\\r\\n msgLines.append(STDOUT_LINE % output)\\r\\n if error:\\r\\n if not error.endswith('\\\\n'):\\r\\n error += '\\\\n'\\r\\n msgLines.append(STDERR_LINE % error)\\r\\n return ''.join(msgLines)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84fc9d213200a3866610c9d2b86a9506\",\n \"score\": \"0.5379236\",\n \"text\": \"def message(self, *args, **kwargs):\\n base_msg = super(CoconutInternalException, self).message(*args, **kwargs)\\n if \\\"\\\\n\\\" in base_msg:\\n return base_msg + \\\"\\\\n\\\" + report_this_text\\n else:\\n return base_msg + \\\" \\\" + report_this_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5edcb3e40f224f2fe15690f7efc9922e\",\n \"score\": \"0.5375226\",\n \"text\": \"def _error_msg(self, e):\\n print(textwrap.dedent((\\\"\\\"\\\"\\n ERROR! -- An unrecoverable error has occurred.\\n\\n If you believe the CUE file is correct, please send the input file to\\n <%s>, along with the error message below.\\n\\n ---> %s\\n \\\"\\\"\\\" % (__email__,e))), file=sys.stderr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27997bd68cbf0fa590fd976b081b88e6\",\n \"score\": \"0.537171\",\n \"text\": \"def message(self):\\n self.process()\\n # Check whether the error comes with source information\\n if hasattr(self.error, \\\"line\\\"):\\n args = (\\n self.header(),\\n self.highlight(),\\n self.error_code(),\\n self.hint(),\\n )\\n return \\\"{}\\\\n\\\\n{}\\\\n\\\\n{}: {}\\\".format(*args)\\n else:\\n return f\\\"{self.error_code()}: {self.hint()}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db70d4f81e827ef6a85555102acc8c07\",\n \"score\": \"0.5336603\",\n \"text\": \"def description(self):\\n return 'unknown'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"222f2ef4dcf1ac1be9810903cd1a2890\",\n \"score\": \"0.5332498\",\n \"text\": \"def stringify_exception( ex ):\\n if str(ex):\\n return str(ex)\\n else:\\n return repr(ex)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"015abafead869e36246ec25fe54a59c9\",\n \"score\": \"0.5312414\",\n \"text\": \"def get_desc(self):\\n return \\\"The Developer of this Probe didn't provide a description\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e72030f3d80804cb15de578b957bc65\",\n \"score\": \"0.53012925\",\n \"text\": \"def traceback(self):\\n type, value, tb = sys.exc_info()\\n return ''.join(traceback.format_exception(type, value, tb.tb_next))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31f6f0a7d8c24175f8ebc18f522c5187\",\n \"score\": \"0.53003526\",\n \"text\": \"def standard_exc_info(self):\\n tb = self.frames[0]\\n # the frame will be an actual traceback (or transparent proxy) if\\n # we are on pypy or a python implementation with support for tproxy\\n if type(tb) is not TracebackType:\\n tb = tb.tb\\n return self.exc_type, self.exc_value, tb\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c80b1e969a354f1b70bfa2a5a0b1148\",\n \"score\": \"0.52959055\",\n \"text\": \"def __str__(self):\\n ex_msg = \\\"Feature plugin registration failed:\\\\n\\\"\\n for ex in self.exceptions or []:\\n ex_msg += ex + \\\"\\\\n\\\"\\n return ex_msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b33c24c19cd83cfdc3d034fe39189ef6\",\n \"score\": \"0.5294852\",\n \"text\": \"def reason(self) -> str:\\n return self.Reason\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33c79ae44030bb9903345c6eee13176d\",\n \"score\": \"0.528951\",\n \"text\": \"def invalid_reason(self):\\n if self.valid():\\n return ''\\n else:\\n if self.name is None:\\n return 'No name defined'\\n elif self.homepage is None:\\n return 'No homepage defined'\\n elif self.patterns[0].pattern is None:\\n return 'No searchpattern defined'\\n else:\\n return 'Unknown error'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ab777fccc90218fbca6eccfbb89e1fb\",\n \"score\": \"0.52856106\",\n \"text\": \"def reason(self):\\n r = []\\n if not self.has_var:\\n r.append(u'no_var')\\n if not self.has_func:\\n r.append(u'no_func')\\n for vname in self.bad_vars:\\n r.append(u'bad_var ' + vname)\\n return u','.join(r)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab3fd85d471b1328db02500352f95d4d\",\n \"score\": \"0.52804965\",\n \"text\": \"def name(self) -> str:\\n return \\\"Multimatic Errors\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"061e4191a7bbe0e5db73717d9b76c715\",\n \"score\": \"0.5279127\",\n \"text\": \"def _HandleException(self, e):\\n return str(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28d163d18890ceb07c10fdb43f6bcfdd\",\n \"score\": \"0.5278666\",\n \"text\": \"def error(self) -> str:\\n return typing.cast(\\n str,\\n self._properties.get(\\\"error\\\"),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca3aab25b4be1851b5a43d1847e834f9\",\n \"score\": \"0.52730286\",\n \"text\": \"def _exc_info_to_string(self, err, test):\\n exctype, value, tb = err\\n # Skip test runner traceback levels\\n while tb and self._is_relevant_tb_level(tb):\\n tb = tb.tb_next\\n\\n if exctype is test.failureException:\\n # Skip assert*() traceback levels\\n length = self._count_relevant_tb_levels(tb)\\n msgLines = value\\n else:\\n msgLines = value\\n\\n if self.buffer:\\n output = sys.stdout.getvalue()\\n error = sys.stderr.getvalue()\\n print error\\n if output:\\n if not output.endswith('\\\\n'):\\n output += '\\\\n'\\n msgLines.append(STDOUT_LINE % output)\\n if error:\\n if not error.endswith('\\\\n'):\\n error += '\\\\n'\\n msgLines.append(STDERR_LINE % error)\\n return ''.join(map(str, msgLines))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ee5f63a1d32974d9cbb4341a5a38e39\",\n \"score\": \"0.5270419\",\n \"text\": \"async def _get_extract_failure_reason(ex: Exception, data: Dict[str, Any]):\\n\\n api_message = ''\\n\\n if 'code' in data and data['code'] != 0:\\n api_message += 'Error code {}: '.format(data['code'])\\n elif 'error_code' in data:\\n api_message += 'Error code {}: '.format(data['error_code'])\\n\\n if 'msg' in data and data['msg'] and data['msg'] != '':\\n api_message += data['msg']\\n if 'detailMsg' in data and data['detailMsg'] and data['detailMsg'] != '':\\n api_message += '; ' + data['detailMsg']\\n else:\\n api_message += 'empty or missing results'\\n\\n return \\\"{} ({}: {})\\\".format(api_message, type(ex).__name__, ex)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1a97bed5ad6d3e1bba81544981662d6\",\n \"score\": \"0.52660227\",\n \"text\": \"def __str__(self):\\n s = Exception.__str__(self)\\n if not self.fname:\\n return s\\n return \\\"File {0}, line {1}: {2}\\\".format(self.fname, self.line, s)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11817171cd255036e35e5d6f915080c6\",\n \"score\": \"0.5261123\",\n \"text\": \"def _get_fault_message(self, exception):\\n try:\\n return exception.failure.errors[0].message\\n except AttributeError:\\n try:\\n return exception.details()\\n except AttributeError:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"879ba82d06f5ec712a50a50b11844ce4\",\n \"score\": \"0.5257593\",\n \"text\": \"def print_exc_info():\\n\\n import StringIO, traceback\\n \\n sio = StringIO.StringIO()\\n traceback.print_exc(file=sio) #thread-safe print_exception to string\\n sio.seek(0, 0)\\n \\n return sio.read()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc54ec531b6c0dca2f6f42e2e1a3240e\",\n \"score\": \"0.5249521\",\n \"text\": \"def get_exception_message(self, exception_type, args):\\n message = None\\n try:\\n raise exception_type(*args)\\n except exception_type as exc:\\n message = str(exc)\\n return message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"536660c6e2a6a6c47f2ac7e94de5cdf0\",\n \"score\": \"0.5241921\",\n \"text\": \"def get_error_msg(self):\\n\\n status_display = None\\n if self.get_status() == self.HIGH_TEMP_ERROR:\\n status_display = \\\"High Temperature (100%cC)\\\" % self.DEGREE_SIGN\\n elif self.get_status() == self.LOW_TEMP_ERROR:\\n status_display = \\\"Low Temperature (-50%cC)\\\" % self.DEGREE_SIGN\\n\\n reading_datetime = datetime.datetime.strptime(self.format_datetime_string(), \\\"%Y/%m/%d %H:%M\\\")\\n reading_display_datetime = reading_datetime.strftime('%Y/%m/%d %H:%M')\\n\\n reading_seq_num = self.get_sequence_num()\\n\\n error_msg = \\\"%s at %s, Sequence: %d\\\" % (\\n status_display, reading_display_datetime, reading_seq_num)\\n return error_msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c91634e65a58ee887ffabe121442accf\",\n \"score\": \"0.52325565\",\n \"text\": \"def get_fault_message(self):\\n\\n pass # pragma: no cover\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23614d013c8f58fe4d1047855159d98a\",\n \"score\": \"0.5226735\",\n \"text\": \"def message(self) -> str:\\n if not self._message:\\n raise Exception\\n return self._message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ac0963e6d21b1b1a1cc240edd0903b3\",\n \"score\": \"0.52178687\",\n \"text\": \"def __str__(self):\\n obj_str = \\\"Systematic uncertainty: {:s}\\\".format(self.name)\\n return obj_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3c1acb0e2cd5601687c21ffd4ff3b95\",\n \"score\": \"0.52152044\",\n \"text\": \"def formatEx3(excepInst):\\n msg = \\\"%s:%s\\\" % (excepInst.__class__.__name__, str(excepInst))\\n return msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c883876b1822271bd01b5b8e55efe1d\",\n \"score\": \"0.52130306\",\n \"text\": \"def __str__(self):\\n return (\\n \\\"Critical errors: {CRITICAL}, errors: {ERROR}, warnings: \\\"\\n \\\"{WARNING}\\\"\\n ).format(**self.logged)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27c0206bc4d8824ab3b65caf7548f457\",\n \"score\": \"0.52104795\",\n \"text\": \"def repr_failure(self, excinfo):\\n if isinstance(excinfo.value, FunctionalTestException):\\n return f\\\"Functional Test failed {excinfo.value.args}. See below, or see output log file for test {self.name}. \\\"\\n else:\\n log.error(f\\\"unknown failure {excinfo}\\\")\\n print(\\\"Unknown failure:\\\", str(excinfo))\\n print(excinfo.getrepr())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"713df0d6f7e9e8552aad15657b4e271f\",\n \"score\": \"0.5207485\",\n \"text\": \"def render_error(self) -> str:\\n return f'{str(self.cause) if self.cause else self.msg}'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7000531254472b1555ac1039dcdd9ce\",\n \"score\": \"0.52051544\",\n \"text\": \"def __str__(self):\\n \\n return self.reason\",\n \"title\": \"\"\n },\n {\n \"docid\": \"737605eb41ed786830dc5b6afd70fc0d\",\n \"score\": \"0.5194999\",\n \"text\": \"def repr_failure(self, excinfo):\\r\\n if isinstance(excinfo.value, DocoptTestException):\\r\\n return \\\"\\\\n\\\".join((\\r\\n \\\"usecase execution failed:\\\",\\r\\n self.doc.rstrip(),\\r\\n \\\"$ %s %s\\\" % (self.prog, self.argv),\\r\\n \\\"result> %s\\\" % json.dumps(excinfo.value.args[1]),\\r\\n \\\"expect> %s\\\" % json.dumps(self.expect),\\r\\n ))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"facb76f14223c41a5a6712aa59303fca\",\n \"score\": \"0.5192028\",\n \"text\": \"def __str__(self):\\n return 'Failed to read file \\\"%s\\\"' % self.filepath\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04c0a8cfcc3255fdda0c0f8cb0dc989c\",\n \"score\": \"0.5187984\",\n \"text\": \"def errors_occurred(self):\\n return self.logged['ERROR'] + self.logged['CRITICAL']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a19bdb6596b3edb450a1449d60015861\",\n \"score\": \"0.5185789\",\n \"text\": \"def exception(self):\\n if self._exception is not _NONE:\\n return self._exception\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ddd78eb8f69eddf6da1708ac08f689d\",\n \"score\": \"0.5179353\",\n \"text\": \"def warning_message(self) -> str:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4835f7c69a0cf093fec8c61b7458dd5e\",\n \"score\": \"0.5175048\",\n \"text\": \"def status_reason(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"status_reason\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45e38227c8cd8898a4423df6acc44315\",\n \"score\": \"0.51723176\",\n \"text\": \"def show_err():\\n for err in list(sys.exc_info()):\\n err_msg = \\\"[!]%s\\\" % str(err).strip(\\\"<>\\\")\\n print(err_msg, file=sys.stderr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fb692314b382cc23514cbc07302541c\",\n \"score\": \"0.5169245\",\n \"text\": \"def exception(self):\\n return self._exc_info[1] if self._exc_info else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cff0c1ba2b82b577767f7ed0f638f008\",\n \"score\": \"0.5164884\",\n \"text\": \"def GetFailureReason(self):\\n return self._failure_reason\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cdc208aa987740317114f71f9a3bd1b\",\n \"score\": \"0.5159561\",\n \"text\": \"def get_formatted_traceback( self ):\\n return self.traceback_msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92f7b86a8348577fd5c0b101151cb5dc\",\n \"score\": \"0.5154065\",\n \"text\": \"def reason(self) -> str:\\n return pulumi.get(self, \\\"reason\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92f7b86a8348577fd5c0b101151cb5dc\",\n \"score\": \"0.5154065\",\n \"text\": \"def reason(self) -> str:\\n return pulumi.get(self, \\\"reason\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8add316c74c628b96fdc7b9c01daae8\",\n \"score\": \"0.5152142\",\n \"text\": \"def print_exc_plus( ):\\r\\n msg = \\\"\\\"\\r\\n tb = sys.exc_info( )[2]\\r\\n while tb.tb_next:\\r\\n tb = tb.tb_next\\r\\n stack = [ ]\\r\\n f = tb.tb_frame\\r\\n while f:\\r\\n if r'\\\\sahm\\\\\\\\' in f.f_code.co_filename:\\r\\n stack.append(f)\\r\\n f = f.f_back\\r\\n stack.reverse( )\\r\\n traceback.print_exc( )\\r\\n msg += \\\"\\\\n\\\" + \\\"Locals by frame, innermost last\\\"\\r\\n for frame in stack:\\r\\n msg += \\\"\\\\n\\\"\\r\\n msg += \\\"\\\\n\\\" + \\\"Frame %s in %s at line %s\\\" % (frame.f_code.co_name,\\r\\n frame.f_code.co_filename,\\r\\n frame.f_lineno)\\r\\n msg += \\\"\\\\n\\\"\\r\\n for key, value in frame.f_locals.items( ):\\r\\n msg += \\\"\\\\t%20s = \\\" % key\\r\\n # we must _absolutely_ avoid propagating exceptions, and str(value)\\r\\n # COULD cause any exception, so we MUST catch any...:\\r\\n try:\\r\\n msg += str(value)\\r\\n except:\\r\\n msg += \\\"<ERROR WHILE PRINTING VALUE>\\\"\\r\\n \\r\\n msg += \\\"\\\\n\\\\n\\\" + ' '.join([str(i) for i in sys.exc_info()[:2]])\\r\\n \\r\\n return msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e650b85d71c67437113c772e1c3d523c\",\n \"score\": \"0.51513445\",\n \"text\": \"def exception_info(e):\\n except_type, except_class, tb = sys.exc_info()\\n error_tuple = (except_type, except_class, traceback.extract_tb(tb))\\n return error_tuple\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa438e4ecdbf0606300fb3df5e741335\",\n \"score\": \"0.51506144\",\n \"text\": \"def Message(self, granular = False):\\r\\n if granular:\\r\\n granular = ''\\r\\n if len(self.Contents) > 0:\\r\\n granular = \\\"The following command line errors were improperly set: \\\\n{ %s }\\\" % (',\\\\n'.join([arg + ' : ' + msg for arg, msg in self.Contents]))\\r\\n # Include information about invalid arguments being passed if any occurred:\\r\\n if len(self.InvalidArgs) > 0:\\r\\n granular += ('\\\\n' if granular else '') + 'The following arguments are invalid: \\\\n{ %s }' % (','.join(self.InvalidArgs))\\r\\n return granular\\r\\n else:\\r\\n return CommandLineErrors.__Concise % self.ErrorCount()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994196,"cells":{"query_id":{"kind":"string","value":"889e0e73799057cf38eacd20067fa5b8"},"query":{"kind":"string","value":"Test case for api_v1_statuses_secrets_get"},"positive_passages":{"kind":"list like","value":[{"docid":"22d22c4deda381211a78dce4bd31ebf8","score":"0.9513433","text":"def test_api_v1_statuses_secrets_get(self):\n pass","title":""}],"string":"[\n {\n \"docid\": \"22d22c4deda381211a78dce4bd31ebf8\",\n \"score\": \"0.9513433\",\n \"text\": \"def test_api_v1_statuses_secrets_get(self):\\n pass\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"db56674662f0780175ab53fac68d5b6a","score":"0.6973698","text":"def test_get_all_secrets(self):\n pass","title":""},{"docid":"233629767b99031fd14f7b095067d9ae","score":"0.66280454","text":"def get_secrets(self):\n secret_url = \"{}/secrets\".format(self.api_base_url)\n try:\n logger.debug(\"Retrieving secrets from API endpoint\")\n resp = self.session.get(secret_url)\n except Exception:\n logger.exception(\"Failed to get secrets. Aborting\")\n failed_exit()\n if resp.status_code != 200:\n logger.error(\"Failed to get secrets. Endpoint returned status code {}.\\nFull response: {}\"\n .format(resp.status_code, resp.text))\n failed_exit()\n return resp.json()","title":""},{"docid":"8f894670456d448c3a024a1174b116c2","score":"0.6238599","text":"def test_read_image_openshift_io_v1_namespaced_secret_list_secrets(self):\n pass","title":""},{"docid":"aee205342d00854a2ba4f0bf785f827a","score":"0.621693","text":"def test_api_v1_statuses_intelligence_get(self):\n pass","title":""},{"docid":"3eba8f92986e4e626847bfb355b7e06f","score":"0.6199641","text":"def test_get_secret(self):\n pass","title":""},{"docid":"d110f96de2d87a59ee9157892746f8e9","score":"0.6166211","text":"def test_api_can_list_secretsanta(self):\n res = self.client.get('/api/santas/', format=\"json\")\n self.assertEqual(res.status_code, status.HTTP_200_OK)","title":""},{"docid":"0201c547c3a1933476fc1ee16763f267","score":"0.6151494","text":"def test_get_status_secrets_error(mocker, gc):\n mocker.patch(f'{import_path}.OAuth2Connector.get_access_token', side_effect=Exception)\n assert gc.get_status().status is False","title":""},{"docid":"d1013b2b802a389b31cbddc0c939bfaa","score":"0.60816294","text":"def available_secrets(self) -> 'outputs.SecretsResponse':\n return pulumi.get(self, \"available_secrets\")","title":""},{"docid":"a8acf5fb7155e9604c10ca89bd8b9f0a","score":"0.6064439","text":"def _fetch_secrets(vault_url, path, token):\n url = _url_joiner(vault_url, 'v1', path)\n resp = requests.get(url, headers=VaultLoader._get_headers(token))\n resp.raise_for_status()\n data = resp.json()\n if data.get('errors'):\n raise VaultException(u'Error fetching Vault secrets from path {}: {}'\n .format(path, data['errors']))\n return data['data']","title":""},{"docid":"7818e3d1e4d1f342e438c241fdc65384","score":"0.6009855","text":"def test_get_statuses_list(self):\r\n response = self.client.get(\r\n path=reverse('api_infrastructure_statuses'), **self.authentication_headers)\r\n self.assertEquals(response.status_code, 200)","title":""},{"docid":"7db67e7331a96b727c9971648d408b17","score":"0.5968243","text":"def get_secret_output(secrets: Optional[pulumi.Input[Sequence[pulumi.InputType['GetSecretSecretArgs']]]] = None,\n opts: Optional[pulumi.InvokeOptions] = None) -> pulumi.Output[GetSecretResult]:\n ...","title":""},{"docid":"21f2a45422c397b0786b51fdaa58269b","score":"0.5908694","text":"def test_service_secret_not_set():\n # pylint: disable=protected-access\n config_instance = config._get()\n secret_string = \"secret string 1\"\n\n stubber = stub.Stubber(config._SECRETS_MANAGER_CLIENT)\n expected_params = {\"SecretId\": config_instance.service_secret_name}\n stubber.add_response(\n \"get_secret_value\", {\"SecretString\": secret_string}, expected_params\n )\n stubber.activate()\n\n service_secret = config_instance.service_secret\n\n stubber.assert_no_pending_responses()\n assert service_secret == secret_string.encode()\n\n service_secret = config_instance.service_secret\n\n assert service_secret == secret_string.encode()","title":""},{"docid":"c180dd98c393001def47d65bdb505bb8","score":"0.59069747","text":"def get_secrets():\n # Handle local credentials.\n if \"AWS_SESSION_TOKEN\" not in os.environ:\n if \"AWS_ROLE_ARN\" in os.environ:\n session = boto3.Session(aws_access_key_id=os.environ['AWS_ACCESS_KEY_ID'], aws_secret_access_key=os.environ['AWS_SECRET_ACCESS_KEY'])\n client = session.client(service_name='sts', region_name='us-west-2')\n creds = client.assume_role(RoleArn=os.environ['AWS_ROLE_ARN'], RoleSessionName='test')['Credentials']\n os.environ['AWS_ACCESS_KEY_ID'] = creds['AccessKeyId']\n os.environ['AWS_SECRET_ACCESS_KEY'] = creds['SecretAccessKey']\n os.environ['AWS_SESSION_TOKEN'] = creds['SessionToken']\n\n else:\n raise ValueError('Missing AWS credentials')\n\n # Create a session using the given creds\n session = boto3.Session(aws_access_key_id=os.environ['AWS_ACCESS_KEY_ID'], aws_secret_access_key=os.environ['AWS_SECRET_ACCESS_KEY'], aws_session_token=os.environ['AWS_SESSION_TOKEN'])\n client = session.client(service_name='secretsmanager', region_name='us-west-2')\n try:\n get_secret_value_response = client.get_secret_value(\n SecretId='drivers/test'\n )\n except Exception as e:\n # For a list of exceptions thrown, see\n # https://docs.aws.amazon.com/secretsmanager/latest/apireference/API_GetSecretValue.html\n raise e\n\n # Decrypts secret using the associated KMS key.\n return json.loads(get_secret_value_response['SecretString'])","title":""},{"docid":"49b34cf4f5ce5348ffd1738e9f41046f","score":"0.57949907","text":"def secrets(self) -> Optional[Sequence['outputs.SecretReferenceResponse']]:\n return pulumi.get(self, \"secrets\")","title":""},{"docid":"5d2a52bc009031cf8bedcbb2999ed56b","score":"0.57918","text":"def list_(client_obj, path):\n result = client_obj.list_secrets(path=path)\n click.echo(\"\\n\".join(result))","title":""},{"docid":"ce66a60a9c64c8018ae1f6f037915292","score":"0.5768433","text":"def test_api_v1_statuses_registry_get(self):\n pass","title":""},{"docid":"44252d058e285f963b65a53e4d16c665","score":"0.5760645","text":"def get_secret(setting, secrets=secrets):\n try:\n return secrets[setting]\n except KeyError:\n print('Secrets not downloaded')","title":""},{"docid":"63fcb48d777104f6fb27beeefc83c881","score":"0.57218325","text":"def get_secrets():\n\n return SECRETS","title":""},{"docid":"8e2fc31ba5970ce10717d522086e8eea","score":"0.57007414","text":"def test_get_access_token_secret(self):\n self.assertEqual(\"Acc_tok_sec\",self._account.get_access_token_secret(),\"Access token secrets don't match\")","title":""},{"docid":"bd89d70762396e7420c2e259d4eedcf1","score":"0.5699439","text":"def _get_secrets(secrets_location):\n with open(secrets_location, 'r') as json_file:\n secrets= json.load(json_file, object_hook=Config.decode_config)\n\n return secrets","title":""},{"docid":"2687f0c3b7193d388309115c3dd31647","score":"0.5694316","text":"def test_get_statuses_list(self):\r\n response = self.client.get(\r\n path=reverse('api_sales_status'), **self.authentication_headers)\r\n self.assertEquals(response.status_code, 200)","title":""},{"docid":"d115c18fd23e86ddfb2d7851d8968167","score":"0.56911546","text":"def secrets_pull(src, environment, secret, type):\n pass","title":""},{"docid":"93f7e6d5ab902b265cde354fd394d4dd","score":"0.5677414","text":"def get_secrets(source):\n # Expand ~ to full path\n secrets_dict = yaml.load(source)\n\n # we need to have at least api_key in secrets\n try:\n secrets = Secrets(secrets_dict)\n except ConfigFieldMissingError as e:\n print('Failed to read values from secrets file: {}'.format(e))\n print('Exiting.')\n sys.exit(1)\n\n return secrets","title":""},{"docid":"3009832fac69294eef937b315c92f498","score":"0.5655301","text":"def test_get_shared_secret(self):\n pass","title":""},{"docid":"64567b7364f9800f1c9e3f7569e5a69e","score":"0.5650154","text":"def get_secret(secrets: Optional[Sequence[pulumi.InputType['GetSecretSecretArgs']]] = None,\n opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetSecretResult:\n __args__ = dict()\n __args__['secrets'] = secrets\n opts = pulumi.InvokeOptions.merge(_utilities.get_invoke_opts_defaults(), opts)\n __ret__ = pulumi.runtime.invoke('aws:kms/getSecret:getSecret', __args__, opts=opts, typ=GetSecretResult).value\n\n return AwaitableGetSecretResult(\n id=pulumi.get(__ret__, 'id'),\n secrets=pulumi.get(__ret__, 'secrets'))","title":""},{"docid":"6f0ccd3666e176b7ea01f424b7d03dbb","score":"0.55011624","text":"def test_api_v1_statuses_serverless_radar_get(self):\n pass","title":""},{"docid":"fd050230796c78b14b7cd0fede2c5e58","score":"0.5452719","text":"def put_config_secrets(\n config_key: str,\n *,\n db: Session = Depends(deps.get_db),\n storage_secrets: Dict[str, str] = Body(...),\n verify: Optional[bool] = True,\n) -> TestStatusMessage:\n logger.info(f\"Finding storage config with key '{config_key}'\")\n\n storage_config = StorageConfig.get_by(db=db, field=\"key\", value=config_key)\n if not storage_config:\n raise HTTPException(\n status_code=HTTP_404_NOT_FOUND,\n detail=f\"No storage configuration with key {config_key}.\",\n )\n\n try:\n secrets_schema = get_schema_for_secrets(\n storage_type=storage_config.type,\n secrets=storage_secrets,\n )\n except KeyError as exc:\n raise HTTPException(\n status_code=HTTP_422_UNPROCESSABLE_ENTITY,\n detail=exc.args[0],\n )\n except ValueError as exc:\n raise HTTPException(\n status_code=HTTP_400_BAD_REQUEST,\n detail=exc.args[0],\n )\n\n logger.info(f\"Updating storage config secrets for config with key '{config_key}'\")\n try:\n storage_config.set_secrets(db=db, storage_secrets=secrets_schema.dict())\n except ValueError as exc:\n raise HTTPException(\n status_code=HTTP_400_BAD_REQUEST,\n detail=exc.args[0],\n )\n\n msg = f\"Secrets updated for StorageConfig with key: {config_key}.\"\n if verify:\n status = secrets_are_valid(secrets_schema, storage_config.type)\n if status:\n logger.info(f\"Storage secrets are valid for config with key '{config_key}'\")\n else:\n logger.warning(\n f\"Storage secrets are invalid for config with key '{config_key}'\"\n )\n\n return TestStatusMessage(\n msg=msg, test_status=\"succeeded\" if status else \"failed\"\n )\n\n return TestStatusMessage(msg=msg, test_status=None)","title":""},{"docid":"690682bef65ab994bfc2461e73e5c817","score":"0.5451394","text":"def test_get_stashes_unauthorized(self):\n with self.client as c:\n \n resp = c.get(\"/api/stashes\")\n \n self.assertEqual(resp.status_code, 401)\n self.assertIn(\"Unauthorized\", str(resp.data))","title":""},{"docid":"b5f2a6719b7447d058f9197b9a113eeb","score":"0.54076934","text":"def test_read_secret_v2_key(self):\n # given path secrets/mysecret generate v2 output\n version = {\n \"v2\": True,\n \"data\": \"secrets/data/mysecret\",\n \"metadata\": \"secrets/metadata/mysecret\",\n \"type\": \"kv\",\n }\n mock_version = MagicMock(return_value=version)\n mock_vault = MagicMock()\n mock_vault.return_value.status_code = 200\n v2_return = {\n \"data\": {\n \"data\": {\"akey\": \"avalue\"},\n \"metadata\": {\n \"created_time\": \"2018-10-23T20:21:55.042755098Z\",\n \"destroyed\": False,\n \"version\": 13,\n \"deletion_time\": \"\",\n },\n }\n }\n\n mock_vault.return_value.json.return_value = v2_return\n with patch.dict(\n vault.__utils__, {\"vault.make_request\": mock_vault}\n ), patch.dict(vault.__utils__, {\"vault.is_v2\": mock_version}):\n vault_return = vault.read_secret(\"/secret/my/secret\", \"akey\")\n\n self.assertEqual(vault_return, \"avalue\")","title":""},{"docid":"41a61d9a53d640fd09ac30a042e188e9","score":"0.5401017","text":"def get_secret(setting, secrets_in=secrets):\n try:\n return secrets_in[setting]\n except KeyError:\n raise ImproperlyConfigured(\"Set the {} setting\".format(setting))","title":""},{"docid":"210ef7886340d0c9d471c528598577f2","score":"0.53979146","text":"def test_read_secret_v2(self):\n # given path secrets/mysecret generate v2 output\n version = {\n \"v2\": True,\n \"data\": \"secrets/data/mysecret\",\n \"metadata\": \"secrets/metadata/mysecret\",\n \"type\": \"kv\",\n }\n mock_version = MagicMock(return_value=version)\n mock_vault = MagicMock()\n mock_vault.return_value.status_code = 200\n v2_return = {\n \"data\": {\n \"data\": {\"akey\": \"avalue\"},\n \"metadata\": {\n \"created_time\": \"2018-10-23T20:21:55.042755098Z\",\n \"destroyed\": False,\n \"version\": 13,\n \"deletion_time\": \"\",\n },\n }\n }\n\n mock_vault.return_value.json.return_value = v2_return\n with patch.dict(\n vault.__utils__, {\"vault.make_request\": mock_vault}\n ), patch.dict(vault.__utils__, {\"vault.is_v2\": mock_version}):\n # Validate metadata returned\n vault_return = vault.read_secret(\"/secret/my/secret\", metadata=True)\n self.assertDictContainsSubset({\"data\": {\"akey\": \"avalue\"}}, vault_return)\n # Validate just data returned\n vault_return = vault.read_secret(\"/secret/my/secret\")\n self.assertDictContainsSubset({\"akey\": \"avalue\"}, vault_return)","title":""},{"docid":"42fb26bc094bdcaf4c24738e30e0f7d0","score":"0.53950924","text":"def list_secrets(client: hvac.Client, path: str) -> Union[dict, None]:\n secret_list = client.list(path=path)\n\n return secret_list.get('data', None)","title":""},{"docid":"94d4fb16cf7cd4fdf2803326695fc826","score":"0.53946084","text":"def _get_secret_dict(arn, stage, token=None):\n # Only do VersionId validation against the stage if a token is passed in\n if token:\n secret = secrets_manager_client.get_secret_value(SecretId=arn, VersionId=token, VersionStage=stage)\n else:\n secret = secrets_manager_client.get_secret_value(SecretId=arn, VersionStage=stage)\n return json.loads(secret['SecretString'])","title":""},{"docid":"271e95aa7210b35fb2626cb442d33a0e","score":"0.539014","text":"def test_get_ticket_statuses_list(self):\r\n response = self.client.get(\r\n path=reverse('api_services_status'), **self.authentication_headers)\r\n self.assertEquals(response.status_code, 200)","title":""},{"docid":"27151fa91a5af259d555e0d0cdaf1d3a","score":"0.53884006","text":"def list_secrets(self, secret_type: SecretType) -> List[V1Secret]:\n secret_list = self._api.list_secret_for_all_namespaces(\n label_selector=f\"{KUBERNETES_TOKEN_TYPE_LABEL}={secret_type.value}\"\n )\n return secret_list.items","title":""},{"docid":"93f6c14a7b385f119aee1922ee539151","score":"0.53876984","text":"def test_get_secret_encoding(self, input_secret_dto):\n secret = input_secret_dto.secret\n key_spec = input_secret_dto.key_spec\n secret_type = input_secret_dto.type\n\n decrypt_mock = self.retrieving_plugin.decrypt\n decrypt_mock.return_value = base64.decodebytes(secret)\n\n secret_model = self.context.secret_model\n secret_model.algorithm = key_spec.alg\n secret_model.bit_length = key_spec.bit_length\n secret_model.mode = key_spec.mode\n\n secret_dto = self.plugin_to_test.get_secret(\n secret_type,\n None, # Secret metadata is not relevant to store_crypto process.\n self.context)\n\n # Verify response.\n self.assertIsInstance(secret_dto, secret_store.SecretDTO)\n self.assertEqual(secret, secret_dto.secret)\n self.assertEqual(secret_type, secret_dto.type)\n self.assertIsInstance(secret_dto.key_spec, secret_store.KeySpec)\n self.assertEqual(\n secret_model.algorithm, secret_dto.key_spec.alg)\n self.assertEqual(\n secret_model.bit_length, secret_dto.key_spec.bit_length)\n self.assertEqual(\n secret_model.mode, secret_dto.key_spec.mode)","title":""},{"docid":"719d6d9d865657d70aebd21657a28729","score":"0.5378437","text":"def test_create_secret_as_audit(self):\n resp = self.behaviors.create_secret_from_config()\n self.assertEqual(resp.status_code, 403)","title":""},{"docid":"475a58beada1c3d9fc3279af922a1053","score":"0.53765696","text":"def get_secret(setting, secrets=secrets):\n try:\n return secrets[setting]\n except KeyError:\n raise Exception(\"Set the {} setting\".format(setting))","title":""},{"docid":"12452c58422fbdb4b6b6087d745e9c47","score":"0.5353421","text":"def test_read_secret_v1_key(self):\n version = {\"v2\": False, \"data\": None, \"metadata\": None, \"type\": None}\n mock_version = MagicMock(return_value=version)\n mock_vault = MagicMock()\n mock_vault.return_value.status_code = 200\n mock_vault.return_value.json.return_value = {\"data\": {\"key\": \"somevalue\"}}\n with patch.dict(\n vault.__utils__, {\"vault.make_request\": mock_vault}\n ), patch.dict(vault.__utils__, {\"vault.is_v2\": mock_version}):\n vault_return = vault.read_secret(\"/secret/my/secret\", \"key\")\n\n self.assertEqual(vault_return, \"somevalue\")","title":""},{"docid":"3e8d04195f978be02bb0cb5be7ea1f30","score":"0.5353245","text":"def secrets():\n return render_template(\"secrets.html\")","title":""},{"docid":"56e36ba933bbc41fa9156ec54ec07148","score":"0.5343","text":"def test_config(client):\n response = client.get(\"/health/config/\")\n assert 200 <= response.status_code < 300\n assert response.json[\"SECRET_KEY\"] == \"<hidden>\"\n assert response.json[\"SENTRY_DSN\"] == \"<hidden>\"","title":""},{"docid":"0c0b2353ddaddaecdf973d885b83a279","score":"0.5338981","text":"def get(client_obj, text, name):\n secret = client_obj.get_secret(path=name)\n if text:\n click.echo(secret)\n return\n\n click.echo(yaml.safe_dump(secret,\n default_flow_style=False,\n explicit_start=True), nl=False)","title":""},{"docid":"40311a0e8e015b158eb33f82caa8d224","score":"0.53214324","text":"def get_secret(setting, secrets=secrets):\n try:\n return secrets[setting]\n except KeyError:\n raise ImproperlyConfigured(\"Set the {} setting\".format(setting))","title":""},{"docid":"0ce051d99b526f140999d88458fe80f7","score":"0.5318574","text":"def get_secrets(bucket, key):\n warnings.warn(\"Use Vault for secrets\", DeprecationWarning)\n global _secrets\n if not _secrets:\n logger.debug('Obtaining secrets from S3 bucket %s, key %s...', bucket, key)\n s3 = get_s3(bucket)\n obj = s3.open_r(key)\n _secrets = json.load(obj)\n else:\n logger.debug('Using pre-fetched secrets...')\n return _secrets","title":""},{"docid":"55f8d296b5a0c89ca355da0708546ab0","score":"0.5313115","text":"def secrets(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['SecretArgs']]]]:\n return pulumi.get(self, \"secrets\")","title":""},{"docid":"1cebbba967213f746ea2e64b7ccfae72","score":"0.5301734","text":"async def test_device_diagnostics_secret_value(\n hass: HomeAssistant,\n client,\n multisensor_6_state,\n integration,\n hass_client: ClientSessionGenerator,\n version_state,\n) -> None:\n\n def _find_ultraviolet_val(data: dict) -> dict:\n \"\"\"Find ultraviolet property value in data.\"\"\"\n return next(\n val\n for val in (\n data[\"values\"]\n if isinstance(data[\"values\"], list)\n else data[\"values\"].values()\n )\n if val[\"commandClass\"] == CommandClass.SENSOR_MULTILEVEL\n and val[\"property\"] == PROPERTY_ULTRAVIOLET\n )\n\n node_state = copy.deepcopy(multisensor_6_state)\n # Force a value to be secret so we can check if it gets redacted\n secret_value = _find_ultraviolet_val(node_state)\n secret_value[\"metadata\"][\"secret\"] = True\n node = Node(client, node_state)\n client.driver.controller.nodes[node.node_id] = node\n client.driver.controller.emit(\"node added\", {\"node\": node})\n await hass.async_block_till_done()\n dev_reg = dr.async_get(hass)\n device = dev_reg.async_get_device(identifiers={get_device_id(client.driver, node)})\n assert device\n\n diagnostics_data = await get_diagnostics_for_device(\n hass, hass_client, integration, device\n )\n test_value = _find_ultraviolet_val(diagnostics_data[\"state\"])\n assert test_value[\"value\"] == REDACTED","title":""},{"docid":"f5d26a9cc3e0c869a585aed2f71730ac","score":"0.5273805","text":"def vm_secrets(self) -> Optional[Sequence['outputs.VaultSecretGroupResponse']]:\n return pulumi.get(self, \"vm_secrets\")","title":""},{"docid":"9ed57b3a306cd6ab8bb80a7ed9255eca","score":"0.5261279","text":"def get_secrets(self):\n credentials = ServicePrincipalCredentials(\n client_id=self._config[\"client_id\"],\n secret=self._config[\"app_secret\"],\n tenant=self._config[\"tenant_id\"],\n )\n client = KeyVaultClient(credentials)\n\n try:\n secrets = client.get_secrets(self._config[\"key_vault_uri\"])\n except KeyVaultErrorException:\n secrets = []\n\n return secrets","title":""},{"docid":"724b6be2d9b850d8949594980ef468ae","score":"0.52607644","text":"def secrets(self) -> pulumi.Input['SasDatastoreSecretsArgs']:\n return pulumi.get(self, \"secrets\")","title":""},{"docid":"c833cf01d48f445f9118ba78cb4ac270","score":"0.5258891","text":"def test_delete_all_secrets(self):\n pass","title":""},{"docid":"e867ae0b990855da1cca591a1daf5b66","score":"0.52417755","text":"def test_get_status_no_secrets(gc, remove_secrets):\n assert gc.get_status().status is False","title":""},{"docid":"16ca306f5db0be0dc3a84640951aff0f","score":"0.52229756","text":"def test_api_v1_statuses_serverless_auto_deploy_get(self):\n pass","title":""},{"docid":"8c17b050f7d158017c5a21457cb78626","score":"0.5211298","text":"async def test_find_pets_by_status(client):\n params = [('status', 'available')]\n headers = { \n 'Accept': 'application/json',\n 'Authorization': 'Bearer special-key',\n }\n response = await client.request(\n method='GET',\n path='/v2/pet/findByStatus',\n headers=headers,\n params=params,\n )\n assert response.status == 200, 'Response body is : ' + (await response.read()).decode('utf-8')","title":""},{"docid":"d0af2f1398b0f3c6a6e12e16b5e1e998","score":"0.5163564","text":"def get(self) -> Optional[Any]:\n if isinstance(prefect.context.get(\"flow\"), prefect.core.flow.Flow):\n raise ValueError(\n \"Secrets should only be retrieved during a Flow run, not while building a Flow.\"\n )\n\n secrets = prefect.context.get(\"secrets\", {})\n try:\n value = secrets[self.name]\n except KeyError:\n if prefect.config.backend != \"cloud\":\n raise ValueError(\n 'Local Secret \"{}\" was not found.'.format(self.name)\n ) from None\n if prefect.context.config.cloud.use_local_secrets is False:\n try:\n result = self.client.graphql(\n \"\"\"\n query($name: String!) {\n secret_value(name: $name)\n }\n \"\"\",\n variables=dict(name=self.name),\n )\n except ClientError as exc:\n if \"No value found for the requested key\" in str(exc):\n raise KeyError(\n f\"The secret {self.name} was not found. Please ensure that it \"\n f\"was set correctly in your tenant: https://docs.prefect.io/\"\n f\"orchestration/concepts/secrets.html\"\n ) from exc\n else:\n raise exc\n # the result object is a Box, so we recursively restore builtin\n # dict/list classes\n result_dict = result.to_dict()\n value = result_dict[\"data\"][\"secret_value\"]\n else:\n raise ValueError(\n 'Local Secret \"{}\" was not found.'.format(self.name)\n ) from None\n try:\n return json.loads(value)\n except (json.JSONDecodeError, TypeError):\n return value","title":""},{"docid":"9fa47cacba80aeb4793d0ff0474b56ce","score":"0.5159782","text":"def secrets(self) -> pulumi.Input['ServicePrincipalDatastoreSecretsArgs']:\n return pulumi.get(self, \"secrets\")","title":""},{"docid":"a63704b2b62f41f54e9d38e230c70c50","score":"0.5145105","text":"def _secret_not_in_order():\n abort(falcon.HTTP_400, _(\"Secret metadata expected but not received.\"))","title":""},{"docid":"e22b5e04554fc667019af546e1991ee4","score":"0.5142224","text":"def load_twitter_credentials():\n consumer_key = \"\"\n consumer_secret = \"\"\n access_key = \"\"\n access_secret = \"\"\n with open(\"twitter_credentials.json\") as json_file:\n data = json.load(json_file)\n consumer_key = data[\"consumer_key\"]\n consumer_secret = data[\"consumer_secret\"]\n access_key = data[\"access_key\"]\n access_secret = data[\"access_secret\"]\n\n return (consumer_key, consumer_secret, access_key, access_secret)","title":""},{"docid":"e1d057c9f0c5764511eec83a9a9e92d4","score":"0.51290625","text":"def get_app_secrets():\n resp = _get_resp(GET_SECRETS_URL)\n return (resp['app_id'], resp['app_secret'])","title":""},{"docid":"e4a7c44b216e28305f6369d3f6b65ab8","score":"0.5125082","text":"def secrets(self) -> pulumi.Input['AccountKeyDatastoreSecretsArgs']:\n return pulumi.get(self, \"secrets\")","title":""},{"docid":"29d9e0cbb70133105ae7c490a1ff3a9e","score":"0.5084021","text":"def test_add_secret(self):\n pass","title":""},{"docid":"4f2123c2e835ef8980fb87313894d6fa","score":"0.5082062","text":"def test_status_list_success(self, twitter_mock):\n twitter_mock.return_value = self.status\n status_list = [self.status.id]\n\n response = self.twitter.tweet('\"Select all\" and archive your Gmail inbox. The page loads so much faster!')\n self.assertEqual(status_list, response, 'The status response is different')","title":""},{"docid":"f1b0223a73045aa0f24a3e2dcf7d621e","score":"0.5076851","text":"def test_withnout_token(client):\n req = client.get(\"/api/v1/test_token\")\n\n assert req.status_code == 401","title":""},{"docid":"9cc96b399e17efff14cb00391a1585ff","score":"0.50657356","text":"def get_secrets():\n local_file = os.path.join(CONF_ROOT, 'pillar', env.environment, 'secrets.sls')\n if os.path.exists(local_file):\n local('cp {0} {0}.bak'.format(local_file))\n remote_file = os.path.join('/srv/pillar/', env.environment, 'secrets.sls')\n get(remote_file, local_file)","title":""},{"docid":"7d99f46c3a48207bf2404e10762e2de3","score":"0.5051772","text":"def setup_secrets():\n # get username and password\n from vmc.common.config import config\n username = config.current_profile.get('connection', 'username')\n password = config.current_profile.get('connection', 'password')\n #setup secrets\n append_entry_to_secrets(username, password)","title":""},{"docid":"20c8c170357aee45f00a02417d26668e","score":"0.50494665","text":"def get_secrets(self):\n return SECRETS_TEMPLATE.format(\n total_words=len(dictionary.get_dict()),\n valid_words=len(valid_words),\n state=self.state,\n word=self.word,\n word_revealed=self.word_revealed,\n guesses_right=self.guesses_right,\n guesses_wrong=self.guesses_wrong,\n )","title":""},{"docid":"e60a46235c5099d4fa61aa6dd57ec53e","score":"0.5048611","text":"def test_set_get_delete_secret(test_environment):\n plaintext = str(uuid.uuid4())\n key = str(uuid.uuid4())\n with pytest.raises(RequiresVaultError):\n test_environment.set_secret(key, plaintext)\n\n with pytest.raises(RequiresVaultError):\n test_environment.get_secret(key)\n\n with pytest.raises(RequiresVaultError):\n test_environment.delete_secret(key)","title":""},{"docid":"404811833bf002d20262c69cba71dc62","score":"0.50380886","text":"def test_retrieve_only_suggestions_with_status(self):\n accepted = 'accepted'\n\n SuggestionFactory.create(status=accepted)\n SuggestionFactory.create(status=accepted)\n SuggestionFactory.create(status='other')\n\n response = self.client.get(self.suggestion_list_url, {'status': accepted},\n HTTP_AUTHORIZATION=self.user_1_token)\n\n data = json.loads(response.content)\n self.assertEqual(len(data), 2)\n\n self.assertEqual(data[0]['status'].lower(), accepted)\n self.assertEqual(data[1]['status'].lower(), accepted)","title":""},{"docid":"663657bb42b9bb742814eae232011789","score":"0.5036149","text":"def credential_test(self):\n r = requests.get(self.base_uri + 'search.json?q=test')\n return r.status_code","title":""},{"docid":"9e3f9281d57dc9363bda66d0d65b3e1c","score":"0.50339514","text":"def fetch_twitter_creds():\n try:\n cred_path = os.path.join(cfg.windbagger_data, 'credentials.json')\n return json.load(open(cred_path, 'r'))\n except IOError as e:\n logger.critcal('Credentials for twitter not found!')","title":""},{"docid":"ecd5ff6867552aab5b2612e9dc72a4bf","score":"0.50285727","text":"def get_secret(secret_name):\n client = boto3.client(service_name=\"secretsmanager\")\n get_secret_value_response = client.get_secret_value(SecretId=secret_name)\n\n if \"SecretString\" in get_secret_value_response:\n secret = get_secret_value_response[\"SecretString\"]\n return json.loads(secret)\n else:\n decoded_binary_secret = base64.b64decode(\n get_secret_value_response[\"SecretBinary\"]\n )\n return decoded_binary_secret","title":""},{"docid":"92b520c20d21140f258c95ec4acea07d","score":"0.50270486","text":"async def test_health_liveness_get(client):\n headers = { \n }\n response = await client.request(\n method='GET',\n path='/api/auth/v2/health/liveness',\n headers=headers,\n )\n assert response.status == 200, 'Response body is : ' + (await response.read()).decode('utf-8')","title":""},{"docid":"72eba3a1d63a3ff0bbbf1edf9e5743b8","score":"0.5025915","text":"def test_verify_token(self):\n #print \"test_verify_token\"\n resp = app.get('/auth/dropbox/%s' % userid).json\n self.assertEqual(resp[\"state\"],1)","title":""},{"docid":"9cb2c3af5a444784eed992bb02de2bd0","score":"0.5021788","text":"def oauth_secrets(doc):\n if doc.get('type') == 'oauth_client_credentials':\n yield doc.get('client_id'), doc['_id']","title":""},{"docid":"c26d3200aec33394c3ab8b53376534d5","score":"0.5008839","text":"def test_get_stashes(self):\n with self.client as c:\n with c.session_transaction() as sess:\n sess[CURR_USER_KEY] = self.user.id\n sess[CURR_USER_NAME] = self.user.username\n\n resp = c.get(\"/api/stashes\")\n\n self.assertEqual(resp.status_code, 200)\n self.assertIn(\"test stash name\", str(resp.data))","title":""},{"docid":"8fbab24dbc0b01506327c91bcce4a47c","score":"0.50083536","text":"def get_secret(setting, secrets=SECRETS):\n try:\n return secrets[setting]\n except KeyError:\n error_msg = \"Set the {0} environment variable in the secret file\".format(setting)\n logger.error(\"Set the {0} environment variable in the secret file\".format(setting))\n raise ImproperlyConfigured(error_msg)","title":""},{"docid":"88f62c895819267d7a6fb37180ceac19","score":"0.50080615","text":"def test_api_authorization_list_units_get(self):\n pass","title":""},{"docid":"5dcdf485a6c0ed977f86e8b34587fedb","score":"0.50071317","text":"def load_twitter_credentials(keys_filename):\n with open(keys_filename) as api_credentials:\n api_twitter = json.loads(api_credentials.read())\n consumer_key = api_twitter['Twitter'][\"consumer_key\"]\n consumer_secret = api_twitter['Twitter'][\"consumer_secret\"]\n access_token = api_twitter['Twitter'][\"access_token\"]\n access_secret = api_twitter['Twitter'][\"access_secret\"]\n logging.debug(\"{}-{}-{}-{}\".format(consumer_key, consumer_secret, access_token, access_secret))\n return consumer_key, consumer_secret, access_token, access_secret","title":""},{"docid":"d64bb449cb1640794a52cd75804449a2","score":"0.50000733","text":"def get_secret(setting, secrets=secrets):\n try:\n return secrets[setting]\n except KeyError:\n error_msg = \"Set the {0} environment variable in the secret file\".format(setting)\n raise ImproperlyConfigured(error_msg)","title":""},{"docid":"88e9cb67023e569e30335a5e9634f738","score":"0.49848822","text":"def get_api_secret():\n config = ConfigParser.ConfigParser()\n config.read(\"api_keys.ini\")\n secret = config.get(\"ApiKeys\", \"secret\")\n return secret","title":""},{"docid":"618d50f5802a6bbad64709b3180da812","score":"0.4984404","text":"def api_secret_key(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"api_secret_key\")","title":""},{"docid":"16481a0eadcbfd374fd335f21f6a482a","score":"0.49727523","text":"def test_valid_key():\n\n response = sdk_instance.get_orgs()\n\n assert response.status_code != 401","title":""},{"docid":"4dc8e95f93e312fad89a03275be89953","score":"0.49555314","text":"def test_get_call(self, dut):\n username = DEVICE_UNDER_TEST['username']\n passwd = DEVICE_UNDER_TEST['password']\n dut.login(username=username, password=passwd)\n url = dut.url_prefix + '/status'\n r = dut.session.get(url)\n assert r.status_code >= 200 <= 299","title":""},{"docid":"8692fb7889e202b56f6a9e60e6707f17","score":"0.49522367","text":"def get_secret(secret_name):\n\n # Create a Secrets Manager client\n client = boto3.client('secretsmanager')\n\n try:\n get_secret_value_response = client.get_secret_value(\n SecretId=secret_name\n )\n except ClientError as e:\n if e.response['Error']['Code'] == 'DecryptionFailureException':\n # Secrets Manager can't decrypt the protected secret text using the provided KMS key.\n # Deal with the exception here, and/or rethrow at your discretion.\n logger.error(\"Secrets Manager can't decrypt the protected secret text using the provided KMS key.\")\n raise e\n elif e.response['Error']['Code'] == 'InternalServiceErrorException':\n # An error occurred on the server side.\n # Deal with the exception here, and/or rethrow at your discretion.\n logger.error(\"An error occurred on the server side.\")\n raise e\n elif e.response['Error']['Code'] == 'InvalidParameterException':\n # You provided an invalid value for a parameter.\n # Deal with the exception here, and/or rethrow at your discretion.\n logger.error(\"Invalid value for a parameter\")\n raise e\n elif e.response['Error']['Code'] == 'InvalidRequestException':\n # You provided a parameter value that is not valid for the current state of the resource.\n # Deal with the exception here, and/or rethrow at your discretion.\n logger.error(\"Provided a parameter value is not valid for the current state of the resource\")\n raise e\n elif e.response['Error']['Code'] == 'ResourceNotFoundException':\n # We can't find the resource that you asked for.\n # Deal with the exception here, and/or rethrow at your discretion.\n logger.error(f\"Unable to find secret with name {secret_name}\")\n raise e\n else:\n # Decrypts secret using the associated KMS CMK.\n # Depending on whether the secret is a string or binary, one of these fields will be populated.\n if 'SecretString' in get_secret_value_response:\n return get_secret_value_response['SecretString']\n else:\n return base64.b64decode(get_secret_value_response['SecretBinary'])","title":""},{"docid":"068e8ad8639fd2702786ca446da5e58e","score":"0.49401957","text":"def access_secret(project_id, location_id, secret_id):\n\n # Creates an API client for the Secret Manager API.\n client = secretmanager.SecretManagerServiceClient()\n\n secret = f\"projects/{project_id}/secrets/{secret_id}/versions/latest\"\n response = client.access_secret_version(name=secret)\n\n return response.payload.data","title":""},{"docid":"77b270c92bc417c6d8e0ad991ea6d95a","score":"0.49340248","text":"def get_secret(name):\n log.info('Fetching secret from env var. VAR: {}'.format(name))\n kms = boto3.session.Session().client(\"kms\")\n return kms.decrypt(CiphertextBlob=base64.b64decode(os.environ[name]))[\"Plaintext\"]","title":""},{"docid":"d377fd703d16cf1ef3b9911a5d6b5661","score":"0.4927424","text":"def secret(self):\n return self.oauth_secret","title":""},{"docid":"dfdcf60c94b9299da6376f6199ec053c","score":"0.49247336","text":"def list_properties_of_secrets(self, **kwargs: \"Any\") -> AsyncIterable[SecretProperties]:\n return self._client.get_secrets(\n self.vault_endpoint,\n maxresults=kwargs.pop(\"max_page_size\", None),\n cls=lambda objs: [SecretProperties._from_secret_item(x) for x in objs],\n **kwargs\n )","title":""},{"docid":"97ebae09b62678fd31867f2ac30dbbdb","score":"0.48997778","text":"def get_api_token(credentials_file):\n logging.info(\"Leyendo las credenciales de {}\".format(credentials_file))\n credentials = read_yaml_file(credentials_file)\n token = credentials['food_inspections']\n \n return token","title":""},{"docid":"9d5c3cae96164fe5b30bd35db8c06e7a","score":"0.48990205","text":"def test_notestatus_list_api_unauthorized(self):\n\n # get response\n response = self.client.get('/api/notestatus/')\n # compare\n self.assertEqual(response.status_code, 401)","title":""},{"docid":"a13153177d5d105ce05a243599dc8bc3","score":"0.4898741","text":"def get_secret(setting, secrets=secrets):\n try:\n return secrets[setting]\n except KeyError:\n error_msg = \"Set the {0} environment variable\".format(setting)\n raise ImproperlyConfigured(error_msg)","title":""},{"docid":"6e7a7066247baf887df82206862ec644","score":"0.48920795","text":"def test_get_status(self):\n pass","title":""},{"docid":"6e7a7066247baf887df82206862ec644","score":"0.48920795","text":"def test_get_status(self):\n pass","title":""},{"docid":"3949a223b2c9d9692ccf4601964b22af","score":"0.4889985","text":"def get(self, key):\n\n rest_utils.validate_inputs({'key': key})\n return get_storage_manager().get(models.Secret, key)","title":""},{"docid":"4168e0cce2374d46ab9350f16ef45832","score":"0.4888779","text":"def get_secret_value_output(resource_group_name: Optional[pulumi.Input[str]] = None,\n secret_resource_name: Optional[pulumi.Input[str]] = None,\n secret_value_resource_name: Optional[pulumi.Input[str]] = None,\n opts: Optional[pulumi.InvokeOptions] = None) -> pulumi.Output[GetSecretValueResult]:\n ...","title":""},{"docid":"1dec3491237df09c200c9b9088125026","score":"0.48811728","text":"def secrets_profile(self) -> Optional['outputs.SecretsProfileResponse']:\n return pulumi.get(self, \"secrets_profile\")","title":""},{"docid":"dd58a88a05d853a5729e775f4bd839b1","score":"0.48607022","text":"def test_get_secret(self):\n\n secret_dto = self.plugin_to_test.get_secret(\n secret_store.SecretType.OPAQUE,\n None, # Secret metadata is not relevant to store_crypto process.\n self.context)\n\n # Verify response.\n self.assertIsInstance(secret_dto, secret_store.SecretDTO)\n self.assertEqual(secret_store.SecretType.OPAQUE, secret_dto.type)\n self.assertEqual(\n base64.encodebytes(self.decrypted_secret).rstrip(b'\\n'),\n secret_dto.secret)\n self.assertEqual(\n self.encrypted_datum_model.content_type, secret_dto.content_type)\n self.assertIsInstance(secret_dto.key_spec, secret_store.KeySpec)\n self.assertEqual(\n self.secret_model.algorithm, secret_dto.key_spec.alg)\n self.assertEqual(\n self.secret_model.bit_length, secret_dto.key_spec.bit_length)\n self.assertEqual(\n self.secret_model.mode, secret_dto.key_spec.mode)\n\n # Verify decrypt plugin and method where invoked.\n decrypt_mock = self.retrieving_plugin.decrypt\n self.assertEqual(1, decrypt_mock.call_count)\n args, kwargs = decrypt_mock.call_args\n (\n test_decrypt,\n test_kek_meta,\n test_kek_meta_extended,\n test_project_id\n ) = tuple(args)\n\n self.assertIsInstance(test_decrypt, base.DecryptDTO)\n self.assertEqual(\n base64.b64decode(self.encrypted_datum_model.cypher_text),\n test_decrypt.encrypted)\n\n self.assertIsInstance(test_kek_meta, base.KEKMetaDTO)\n self.assertEqual(\n self.kek_meta_project_model.plugin_name, test_kek_meta.plugin_name)\n\n self.assertEqual(\n self.encrypted_datum_model.kek_meta_extended,\n test_kek_meta_extended)\n\n self.assertEqual(self.project_id, test_project_id)","title":""},{"docid":"bdf86063485160dbce61d83613f1c94f","score":"0.48555958","text":"def test_missing_access_token_secret(self):\n res = self.social_login(self.social_url, self.missing_token_secret)\n self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST)\n self.assertEqual(res.data.get('errors').get('error')[0], \"An access token secret is required for Twitter Login\")","title":""},{"docid":"ffd5d75ef8cf8d01641d34a993483f21","score":"0.48548844","text":"def test_define_execution_list_with_secrets(self, project_function_clean):\n secrets = {\"a\": \"secret\", \"b\": \"secret02\"}\n testdir = project_function_clean.testdir\n project = project_function_clean.name\n secrets_path = os.path.join(project_function_clean.path, 'secrets.json')\n with open(secrets_path, 'w') as secrets_file:\n secrets_file.write(json.dumps(secrets, indent=True))\n execution_runner = exc_runner.ExecutionRunner()\n execution_runner.tests = ['test_001']\n execution_runner.execution.processes = 1\n execution_runner.execution.browsers = ['chrome']\n execution_runner.execution.envs = ['']\n execution_runner.project = project_function_clean.name\n execution_list = execution_runner._define_execution_list()\n expected_list = [\n SimpleNamespace(name='test_001', data_set={}, secrets={\"a\": \"secret\", \"b\": \"secret02\"}, browser='chrome', reportdir=None)\n ]\n assert execution_list == expected_list","title":""},{"docid":"12805505b673627dda84a6db9270b10b","score":"0.48485553","text":"def get_token_secret(self):\n return self.oauth_token_secret or None","title":""},{"docid":"1149f333260f4b2c901eefb57320e6e6","score":"0.48473433","text":"def getSwiftSecret(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","title":""}],"string":"[\n {\n \"docid\": \"db56674662f0780175ab53fac68d5b6a\",\n \"score\": \"0.6973698\",\n \"text\": \"def test_get_all_secrets(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"233629767b99031fd14f7b095067d9ae\",\n \"score\": \"0.66280454\",\n \"text\": \"def get_secrets(self):\\n secret_url = \\\"{}/secrets\\\".format(self.api_base_url)\\n try:\\n logger.debug(\\\"Retrieving secrets from API endpoint\\\")\\n resp = self.session.get(secret_url)\\n except Exception:\\n logger.exception(\\\"Failed to get secrets. Aborting\\\")\\n failed_exit()\\n if resp.status_code != 200:\\n logger.error(\\\"Failed to get secrets. Endpoint returned status code {}.\\\\nFull response: {}\\\"\\n .format(resp.status_code, resp.text))\\n failed_exit()\\n return resp.json()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f894670456d448c3a024a1174b116c2\",\n \"score\": \"0.6238599\",\n \"text\": \"def test_read_image_openshift_io_v1_namespaced_secret_list_secrets(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aee205342d00854a2ba4f0bf785f827a\",\n \"score\": \"0.621693\",\n \"text\": \"def test_api_v1_statuses_intelligence_get(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3eba8f92986e4e626847bfb355b7e06f\",\n \"score\": \"0.6199641\",\n \"text\": \"def test_get_secret(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d110f96de2d87a59ee9157892746f8e9\",\n \"score\": \"0.6166211\",\n \"text\": \"def test_api_can_list_secretsanta(self):\\n res = self.client.get('/api/santas/', format=\\\"json\\\")\\n self.assertEqual(res.status_code, status.HTTP_200_OK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0201c547c3a1933476fc1ee16763f267\",\n \"score\": \"0.6151494\",\n \"text\": \"def test_get_status_secrets_error(mocker, gc):\\n mocker.patch(f'{import_path}.OAuth2Connector.get_access_token', side_effect=Exception)\\n assert gc.get_status().status is False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1013b2b802a389b31cbddc0c939bfaa\",\n \"score\": \"0.60816294\",\n \"text\": \"def available_secrets(self) -> 'outputs.SecretsResponse':\\n return pulumi.get(self, \\\"available_secrets\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8acf5fb7155e9604c10ca89bd8b9f0a\",\n \"score\": \"0.6064439\",\n \"text\": \"def _fetch_secrets(vault_url, path, token):\\n url = _url_joiner(vault_url, 'v1', path)\\n resp = requests.get(url, headers=VaultLoader._get_headers(token))\\n resp.raise_for_status()\\n data = resp.json()\\n if data.get('errors'):\\n raise VaultException(u'Error fetching Vault secrets from path {}: {}'\\n .format(path, data['errors']))\\n return data['data']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7818e3d1e4d1f342e438c241fdc65384\",\n \"score\": \"0.6009855\",\n \"text\": \"def test_get_statuses_list(self):\\r\\n response = self.client.get(\\r\\n path=reverse('api_infrastructure_statuses'), **self.authentication_headers)\\r\\n self.assertEquals(response.status_code, 200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7db67e7331a96b727c9971648d408b17\",\n \"score\": \"0.5968243\",\n \"text\": \"def get_secret_output(secrets: Optional[pulumi.Input[Sequence[pulumi.InputType['GetSecretSecretArgs']]]] = None,\\n opts: Optional[pulumi.InvokeOptions] = None) -> pulumi.Output[GetSecretResult]:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21f2a45422c397b0786b51fdaa58269b\",\n \"score\": \"0.5908694\",\n \"text\": \"def test_service_secret_not_set():\\n # pylint: disable=protected-access\\n config_instance = config._get()\\n secret_string = \\\"secret string 1\\\"\\n\\n stubber = stub.Stubber(config._SECRETS_MANAGER_CLIENT)\\n expected_params = {\\\"SecretId\\\": config_instance.service_secret_name}\\n stubber.add_response(\\n \\\"get_secret_value\\\", {\\\"SecretString\\\": secret_string}, expected_params\\n )\\n stubber.activate()\\n\\n service_secret = config_instance.service_secret\\n\\n stubber.assert_no_pending_responses()\\n assert service_secret == secret_string.encode()\\n\\n service_secret = config_instance.service_secret\\n\\n assert service_secret == secret_string.encode()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c180dd98c393001def47d65bdb505bb8\",\n \"score\": \"0.59069747\",\n \"text\": \"def get_secrets():\\n # Handle local credentials.\\n if \\\"AWS_SESSION_TOKEN\\\" not in os.environ:\\n if \\\"AWS_ROLE_ARN\\\" in os.environ:\\n session = boto3.Session(aws_access_key_id=os.environ['AWS_ACCESS_KEY_ID'], aws_secret_access_key=os.environ['AWS_SECRET_ACCESS_KEY'])\\n client = session.client(service_name='sts', region_name='us-west-2')\\n creds = client.assume_role(RoleArn=os.environ['AWS_ROLE_ARN'], RoleSessionName='test')['Credentials']\\n os.environ['AWS_ACCESS_KEY_ID'] = creds['AccessKeyId']\\n os.environ['AWS_SECRET_ACCESS_KEY'] = creds['SecretAccessKey']\\n os.environ['AWS_SESSION_TOKEN'] = creds['SessionToken']\\n\\n else:\\n raise ValueError('Missing AWS credentials')\\n\\n # Create a session using the given creds\\n session = boto3.Session(aws_access_key_id=os.environ['AWS_ACCESS_KEY_ID'], aws_secret_access_key=os.environ['AWS_SECRET_ACCESS_KEY'], aws_session_token=os.environ['AWS_SESSION_TOKEN'])\\n client = session.client(service_name='secretsmanager', region_name='us-west-2')\\n try:\\n get_secret_value_response = client.get_secret_value(\\n SecretId='drivers/test'\\n )\\n except Exception as e:\\n # For a list of exceptions thrown, see\\n # https://docs.aws.amazon.com/secretsmanager/latest/apireference/API_GetSecretValue.html\\n raise e\\n\\n # Decrypts secret using the associated KMS key.\\n return json.loads(get_secret_value_response['SecretString'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49b34cf4f5ce5348ffd1738e9f41046f\",\n \"score\": \"0.57949907\",\n \"text\": \"def secrets(self) -> Optional[Sequence['outputs.SecretReferenceResponse']]:\\n return pulumi.get(self, \\\"secrets\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d2a52bc009031cf8bedcbb2999ed56b\",\n \"score\": \"0.57918\",\n \"text\": \"def list_(client_obj, path):\\n result = client_obj.list_secrets(path=path)\\n click.echo(\\\"\\\\n\\\".join(result))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce66a60a9c64c8018ae1f6f037915292\",\n \"score\": \"0.5768433\",\n \"text\": \"def test_api_v1_statuses_registry_get(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44252d058e285f963b65a53e4d16c665\",\n \"score\": \"0.5760645\",\n \"text\": \"def get_secret(setting, secrets=secrets):\\n try:\\n return secrets[setting]\\n except KeyError:\\n print('Secrets not downloaded')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63fcb48d777104f6fb27beeefc83c881\",\n \"score\": \"0.57218325\",\n \"text\": \"def get_secrets():\\n\\n return SECRETS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e2fc31ba5970ce10717d522086e8eea\",\n \"score\": \"0.57007414\",\n \"text\": \"def test_get_access_token_secret(self):\\n self.assertEqual(\\\"Acc_tok_sec\\\",self._account.get_access_token_secret(),\\\"Access token secrets don't match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd89d70762396e7420c2e259d4eedcf1\",\n \"score\": \"0.5699439\",\n \"text\": \"def _get_secrets(secrets_location):\\n with open(secrets_location, 'r') as json_file:\\n secrets= json.load(json_file, object_hook=Config.decode_config)\\n\\n return secrets\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2687f0c3b7193d388309115c3dd31647\",\n \"score\": \"0.5694316\",\n \"text\": \"def test_get_statuses_list(self):\\r\\n response = self.client.get(\\r\\n path=reverse('api_sales_status'), **self.authentication_headers)\\r\\n self.assertEquals(response.status_code, 200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d115c18fd23e86ddfb2d7851d8968167\",\n \"score\": \"0.56911546\",\n \"text\": \"def secrets_pull(src, environment, secret, type):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93f7e6d5ab902b265cde354fd394d4dd\",\n \"score\": \"0.5677414\",\n \"text\": \"def get_secrets(source):\\n # Expand ~ to full path\\n secrets_dict = yaml.load(source)\\n\\n # we need to have at least api_key in secrets\\n try:\\n secrets = Secrets(secrets_dict)\\n except ConfigFieldMissingError as e:\\n print('Failed to read values from secrets file: {}'.format(e))\\n print('Exiting.')\\n sys.exit(1)\\n\\n return secrets\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3009832fac69294eef937b315c92f498\",\n \"score\": \"0.5655301\",\n \"text\": \"def test_get_shared_secret(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64567b7364f9800f1c9e3f7569e5a69e\",\n \"score\": \"0.5650154\",\n \"text\": \"def get_secret(secrets: Optional[Sequence[pulumi.InputType['GetSecretSecretArgs']]] = None,\\n opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetSecretResult:\\n __args__ = dict()\\n __args__['secrets'] = secrets\\n opts = pulumi.InvokeOptions.merge(_utilities.get_invoke_opts_defaults(), opts)\\n __ret__ = pulumi.runtime.invoke('aws:kms/getSecret:getSecret', __args__, opts=opts, typ=GetSecretResult).value\\n\\n return AwaitableGetSecretResult(\\n id=pulumi.get(__ret__, 'id'),\\n secrets=pulumi.get(__ret__, 'secrets'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f0ccd3666e176b7ea01f424b7d03dbb\",\n \"score\": \"0.55011624\",\n \"text\": \"def test_api_v1_statuses_serverless_radar_get(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd050230796c78b14b7cd0fede2c5e58\",\n \"score\": \"0.5452719\",\n \"text\": \"def put_config_secrets(\\n config_key: str,\\n *,\\n db: Session = Depends(deps.get_db),\\n storage_secrets: Dict[str, str] = Body(...),\\n verify: Optional[bool] = True,\\n) -> TestStatusMessage:\\n logger.info(f\\\"Finding storage config with key '{config_key}'\\\")\\n\\n storage_config = StorageConfig.get_by(db=db, field=\\\"key\\\", value=config_key)\\n if not storage_config:\\n raise HTTPException(\\n status_code=HTTP_404_NOT_FOUND,\\n detail=f\\\"No storage configuration with key {config_key}.\\\",\\n )\\n\\n try:\\n secrets_schema = get_schema_for_secrets(\\n storage_type=storage_config.type,\\n secrets=storage_secrets,\\n )\\n except KeyError as exc:\\n raise HTTPException(\\n status_code=HTTP_422_UNPROCESSABLE_ENTITY,\\n detail=exc.args[0],\\n )\\n except ValueError as exc:\\n raise HTTPException(\\n status_code=HTTP_400_BAD_REQUEST,\\n detail=exc.args[0],\\n )\\n\\n logger.info(f\\\"Updating storage config secrets for config with key '{config_key}'\\\")\\n try:\\n storage_config.set_secrets(db=db, storage_secrets=secrets_schema.dict())\\n except ValueError as exc:\\n raise HTTPException(\\n status_code=HTTP_400_BAD_REQUEST,\\n detail=exc.args[0],\\n )\\n\\n msg = f\\\"Secrets updated for StorageConfig with key: {config_key}.\\\"\\n if verify:\\n status = secrets_are_valid(secrets_schema, storage_config.type)\\n if status:\\n logger.info(f\\\"Storage secrets are valid for config with key '{config_key}'\\\")\\n else:\\n logger.warning(\\n f\\\"Storage secrets are invalid for config with key '{config_key}'\\\"\\n )\\n\\n return TestStatusMessage(\\n msg=msg, test_status=\\\"succeeded\\\" if status else \\\"failed\\\"\\n )\\n\\n return TestStatusMessage(msg=msg, test_status=None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"690682bef65ab994bfc2461e73e5c817\",\n \"score\": \"0.5451394\",\n \"text\": \"def test_get_stashes_unauthorized(self):\\n with self.client as c:\\n \\n resp = c.get(\\\"/api/stashes\\\")\\n \\n self.assertEqual(resp.status_code, 401)\\n self.assertIn(\\\"Unauthorized\\\", str(resp.data))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5f2a6719b7447d058f9197b9a113eeb\",\n \"score\": \"0.54076934\",\n \"text\": \"def test_read_secret_v2_key(self):\\n # given path secrets/mysecret generate v2 output\\n version = {\\n \\\"v2\\\": True,\\n \\\"data\\\": \\\"secrets/data/mysecret\\\",\\n \\\"metadata\\\": \\\"secrets/metadata/mysecret\\\",\\n \\\"type\\\": \\\"kv\\\",\\n }\\n mock_version = MagicMock(return_value=version)\\n mock_vault = MagicMock()\\n mock_vault.return_value.status_code = 200\\n v2_return = {\\n \\\"data\\\": {\\n \\\"data\\\": {\\\"akey\\\": \\\"avalue\\\"},\\n \\\"metadata\\\": {\\n \\\"created_time\\\": \\\"2018-10-23T20:21:55.042755098Z\\\",\\n \\\"destroyed\\\": False,\\n \\\"version\\\": 13,\\n \\\"deletion_time\\\": \\\"\\\",\\n },\\n }\\n }\\n\\n mock_vault.return_value.json.return_value = v2_return\\n with patch.dict(\\n vault.__utils__, {\\\"vault.make_request\\\": mock_vault}\\n ), patch.dict(vault.__utils__, {\\\"vault.is_v2\\\": mock_version}):\\n vault_return = vault.read_secret(\\\"/secret/my/secret\\\", \\\"akey\\\")\\n\\n self.assertEqual(vault_return, \\\"avalue\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41a61d9a53d640fd09ac30a042e188e9\",\n \"score\": \"0.5401017\",\n \"text\": \"def get_secret(setting, secrets_in=secrets):\\n try:\\n return secrets_in[setting]\\n except KeyError:\\n raise ImproperlyConfigured(\\\"Set the {} setting\\\".format(setting))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"210ef7886340d0c9d471c528598577f2\",\n \"score\": \"0.53979146\",\n \"text\": \"def test_read_secret_v2(self):\\n # given path secrets/mysecret generate v2 output\\n version = {\\n \\\"v2\\\": True,\\n \\\"data\\\": \\\"secrets/data/mysecret\\\",\\n \\\"metadata\\\": \\\"secrets/metadata/mysecret\\\",\\n \\\"type\\\": \\\"kv\\\",\\n }\\n mock_version = MagicMock(return_value=version)\\n mock_vault = MagicMock()\\n mock_vault.return_value.status_code = 200\\n v2_return = {\\n \\\"data\\\": {\\n \\\"data\\\": {\\\"akey\\\": \\\"avalue\\\"},\\n \\\"metadata\\\": {\\n \\\"created_time\\\": \\\"2018-10-23T20:21:55.042755098Z\\\",\\n \\\"destroyed\\\": False,\\n \\\"version\\\": 13,\\n \\\"deletion_time\\\": \\\"\\\",\\n },\\n }\\n }\\n\\n mock_vault.return_value.json.return_value = v2_return\\n with patch.dict(\\n vault.__utils__, {\\\"vault.make_request\\\": mock_vault}\\n ), patch.dict(vault.__utils__, {\\\"vault.is_v2\\\": mock_version}):\\n # Validate metadata returned\\n vault_return = vault.read_secret(\\\"/secret/my/secret\\\", metadata=True)\\n self.assertDictContainsSubset({\\\"data\\\": {\\\"akey\\\": \\\"avalue\\\"}}, vault_return)\\n # Validate just data returned\\n vault_return = vault.read_secret(\\\"/secret/my/secret\\\")\\n self.assertDictContainsSubset({\\\"akey\\\": \\\"avalue\\\"}, vault_return)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42fb26bc094bdcaf4c24738e30e0f7d0\",\n \"score\": \"0.53950924\",\n \"text\": \"def list_secrets(client: hvac.Client, path: str) -> Union[dict, None]:\\n secret_list = client.list(path=path)\\n\\n return secret_list.get('data', None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94d4fb16cf7cd4fdf2803326695fc826\",\n \"score\": \"0.53946084\",\n \"text\": \"def _get_secret_dict(arn, stage, token=None):\\n # Only do VersionId validation against the stage if a token is passed in\\n if token:\\n secret = secrets_manager_client.get_secret_value(SecretId=arn, VersionId=token, VersionStage=stage)\\n else:\\n secret = secrets_manager_client.get_secret_value(SecretId=arn, VersionStage=stage)\\n return json.loads(secret['SecretString'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"271e95aa7210b35fb2626cb442d33a0e\",\n \"score\": \"0.539014\",\n \"text\": \"def test_get_ticket_statuses_list(self):\\r\\n response = self.client.get(\\r\\n path=reverse('api_services_status'), **self.authentication_headers)\\r\\n self.assertEquals(response.status_code, 200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27151fa91a5af259d555e0d0cdaf1d3a\",\n \"score\": \"0.53884006\",\n \"text\": \"def list_secrets(self, secret_type: SecretType) -> List[V1Secret]:\\n secret_list = self._api.list_secret_for_all_namespaces(\\n label_selector=f\\\"{KUBERNETES_TOKEN_TYPE_LABEL}={secret_type.value}\\\"\\n )\\n return secret_list.items\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93f6c14a7b385f119aee1922ee539151\",\n \"score\": \"0.53876984\",\n \"text\": \"def test_get_secret_encoding(self, input_secret_dto):\\n secret = input_secret_dto.secret\\n key_spec = input_secret_dto.key_spec\\n secret_type = input_secret_dto.type\\n\\n decrypt_mock = self.retrieving_plugin.decrypt\\n decrypt_mock.return_value = base64.decodebytes(secret)\\n\\n secret_model = self.context.secret_model\\n secret_model.algorithm = key_spec.alg\\n secret_model.bit_length = key_spec.bit_length\\n secret_model.mode = key_spec.mode\\n\\n secret_dto = self.plugin_to_test.get_secret(\\n secret_type,\\n None, # Secret metadata is not relevant to store_crypto process.\\n self.context)\\n\\n # Verify response.\\n self.assertIsInstance(secret_dto, secret_store.SecretDTO)\\n self.assertEqual(secret, secret_dto.secret)\\n self.assertEqual(secret_type, secret_dto.type)\\n self.assertIsInstance(secret_dto.key_spec, secret_store.KeySpec)\\n self.assertEqual(\\n secret_model.algorithm, secret_dto.key_spec.alg)\\n self.assertEqual(\\n secret_model.bit_length, secret_dto.key_spec.bit_length)\\n self.assertEqual(\\n secret_model.mode, secret_dto.key_spec.mode)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"719d6d9d865657d70aebd21657a28729\",\n \"score\": \"0.5378437\",\n \"text\": \"def test_create_secret_as_audit(self):\\n resp = self.behaviors.create_secret_from_config()\\n self.assertEqual(resp.status_code, 403)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"475a58beada1c3d9fc3279af922a1053\",\n \"score\": \"0.53765696\",\n \"text\": \"def get_secret(setting, secrets=secrets):\\n try:\\n return secrets[setting]\\n except KeyError:\\n raise Exception(\\\"Set the {} setting\\\".format(setting))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12452c58422fbdb4b6b6087d745e9c47\",\n \"score\": \"0.5353421\",\n \"text\": \"def test_read_secret_v1_key(self):\\n version = {\\\"v2\\\": False, \\\"data\\\": None, \\\"metadata\\\": None, \\\"type\\\": None}\\n mock_version = MagicMock(return_value=version)\\n mock_vault = MagicMock()\\n mock_vault.return_value.status_code = 200\\n mock_vault.return_value.json.return_value = {\\\"data\\\": {\\\"key\\\": \\\"somevalue\\\"}}\\n with patch.dict(\\n vault.__utils__, {\\\"vault.make_request\\\": mock_vault}\\n ), patch.dict(vault.__utils__, {\\\"vault.is_v2\\\": mock_version}):\\n vault_return = vault.read_secret(\\\"/secret/my/secret\\\", \\\"key\\\")\\n\\n self.assertEqual(vault_return, \\\"somevalue\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e8d04195f978be02bb0cb5be7ea1f30\",\n \"score\": \"0.5353245\",\n \"text\": \"def secrets():\\n return render_template(\\\"secrets.html\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56e36ba933bbc41fa9156ec54ec07148\",\n \"score\": \"0.5343\",\n \"text\": \"def test_config(client):\\n response = client.get(\\\"/health/config/\\\")\\n assert 200 <= response.status_code < 300\\n assert response.json[\\\"SECRET_KEY\\\"] == \\\"<hidden>\\\"\\n assert response.json[\\\"SENTRY_DSN\\\"] == \\\"<hidden>\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c0b2353ddaddaecdf973d885b83a279\",\n \"score\": \"0.5338981\",\n \"text\": \"def get(client_obj, text, name):\\n secret = client_obj.get_secret(path=name)\\n if text:\\n click.echo(secret)\\n return\\n\\n click.echo(yaml.safe_dump(secret,\\n default_flow_style=False,\\n explicit_start=True), nl=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40311a0e8e015b158eb33f82caa8d224\",\n \"score\": \"0.53214324\",\n \"text\": \"def get_secret(setting, secrets=secrets):\\n try:\\n return secrets[setting]\\n except KeyError:\\n raise ImproperlyConfigured(\\\"Set the {} setting\\\".format(setting))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ce051d99b526f140999d88458fe80f7\",\n \"score\": \"0.5318574\",\n \"text\": \"def get_secrets(bucket, key):\\n warnings.warn(\\\"Use Vault for secrets\\\", DeprecationWarning)\\n global _secrets\\n if not _secrets:\\n logger.debug('Obtaining secrets from S3 bucket %s, key %s...', bucket, key)\\n s3 = get_s3(bucket)\\n obj = s3.open_r(key)\\n _secrets = json.load(obj)\\n else:\\n logger.debug('Using pre-fetched secrets...')\\n return _secrets\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55f8d296b5a0c89ca355da0708546ab0\",\n \"score\": \"0.5313115\",\n \"text\": \"def secrets(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['SecretArgs']]]]:\\n return pulumi.get(self, \\\"secrets\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cebbba967213f746ea2e64b7ccfae72\",\n \"score\": \"0.5301734\",\n \"text\": \"async def test_device_diagnostics_secret_value(\\n hass: HomeAssistant,\\n client,\\n multisensor_6_state,\\n integration,\\n hass_client: ClientSessionGenerator,\\n version_state,\\n) -> None:\\n\\n def _find_ultraviolet_val(data: dict) -> dict:\\n \\\"\\\"\\\"Find ultraviolet property value in data.\\\"\\\"\\\"\\n return next(\\n val\\n for val in (\\n data[\\\"values\\\"]\\n if isinstance(data[\\\"values\\\"], list)\\n else data[\\\"values\\\"].values()\\n )\\n if val[\\\"commandClass\\\"] == CommandClass.SENSOR_MULTILEVEL\\n and val[\\\"property\\\"] == PROPERTY_ULTRAVIOLET\\n )\\n\\n node_state = copy.deepcopy(multisensor_6_state)\\n # Force a value to be secret so we can check if it gets redacted\\n secret_value = _find_ultraviolet_val(node_state)\\n secret_value[\\\"metadata\\\"][\\\"secret\\\"] = True\\n node = Node(client, node_state)\\n client.driver.controller.nodes[node.node_id] = node\\n client.driver.controller.emit(\\\"node added\\\", {\\\"node\\\": node})\\n await hass.async_block_till_done()\\n dev_reg = dr.async_get(hass)\\n device = dev_reg.async_get_device(identifiers={get_device_id(client.driver, node)})\\n assert device\\n\\n diagnostics_data = await get_diagnostics_for_device(\\n hass, hass_client, integration, device\\n )\\n test_value = _find_ultraviolet_val(diagnostics_data[\\\"state\\\"])\\n assert test_value[\\\"value\\\"] == REDACTED\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5d26a9cc3e0c869a585aed2f71730ac\",\n \"score\": \"0.5273805\",\n \"text\": \"def vm_secrets(self) -> Optional[Sequence['outputs.VaultSecretGroupResponse']]:\\n return pulumi.get(self, \\\"vm_secrets\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ed57b3a306cd6ab8bb80a7ed9255eca\",\n \"score\": \"0.5261279\",\n \"text\": \"def get_secrets(self):\\n credentials = ServicePrincipalCredentials(\\n client_id=self._config[\\\"client_id\\\"],\\n secret=self._config[\\\"app_secret\\\"],\\n tenant=self._config[\\\"tenant_id\\\"],\\n )\\n client = KeyVaultClient(credentials)\\n\\n try:\\n secrets = client.get_secrets(self._config[\\\"key_vault_uri\\\"])\\n except KeyVaultErrorException:\\n secrets = []\\n\\n return secrets\",\n \"title\": \"\"\n },\n {\n \"docid\": \"724b6be2d9b850d8949594980ef468ae\",\n \"score\": \"0.52607644\",\n \"text\": \"def secrets(self) -> pulumi.Input['SasDatastoreSecretsArgs']:\\n return pulumi.get(self, \\\"secrets\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c833cf01d48f445f9118ba78cb4ac270\",\n \"score\": \"0.5258891\",\n \"text\": \"def test_delete_all_secrets(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e867ae0b990855da1cca591a1daf5b66\",\n \"score\": \"0.52417755\",\n \"text\": \"def test_get_status_no_secrets(gc, remove_secrets):\\n assert gc.get_status().status is False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16ca306f5db0be0dc3a84640951aff0f\",\n \"score\": \"0.52229756\",\n \"text\": \"def test_api_v1_statuses_serverless_auto_deploy_get(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c17b050f7d158017c5a21457cb78626\",\n \"score\": \"0.5211298\",\n \"text\": \"async def test_find_pets_by_status(client):\\n params = [('status', 'available')]\\n headers = { \\n 'Accept': 'application/json',\\n 'Authorization': 'Bearer special-key',\\n }\\n response = await client.request(\\n method='GET',\\n path='/v2/pet/findByStatus',\\n headers=headers,\\n params=params,\\n )\\n assert response.status == 200, 'Response body is : ' + (await response.read()).decode('utf-8')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0af2f1398b0f3c6a6e12e16b5e1e998\",\n \"score\": \"0.5163564\",\n \"text\": \"def get(self) -> Optional[Any]:\\n if isinstance(prefect.context.get(\\\"flow\\\"), prefect.core.flow.Flow):\\n raise ValueError(\\n \\\"Secrets should only be retrieved during a Flow run, not while building a Flow.\\\"\\n )\\n\\n secrets = prefect.context.get(\\\"secrets\\\", {})\\n try:\\n value = secrets[self.name]\\n except KeyError:\\n if prefect.config.backend != \\\"cloud\\\":\\n raise ValueError(\\n 'Local Secret \\\"{}\\\" was not found.'.format(self.name)\\n ) from None\\n if prefect.context.config.cloud.use_local_secrets is False:\\n try:\\n result = self.client.graphql(\\n \\\"\\\"\\\"\\n query($name: String!) {\\n secret_value(name: $name)\\n }\\n \\\"\\\"\\\",\\n variables=dict(name=self.name),\\n )\\n except ClientError as exc:\\n if \\\"No value found for the requested key\\\" in str(exc):\\n raise KeyError(\\n f\\\"The secret {self.name} was not found. Please ensure that it \\\"\\n f\\\"was set correctly in your tenant: https://docs.prefect.io/\\\"\\n f\\\"orchestration/concepts/secrets.html\\\"\\n ) from exc\\n else:\\n raise exc\\n # the result object is a Box, so we recursively restore builtin\\n # dict/list classes\\n result_dict = result.to_dict()\\n value = result_dict[\\\"data\\\"][\\\"secret_value\\\"]\\n else:\\n raise ValueError(\\n 'Local Secret \\\"{}\\\" was not found.'.format(self.name)\\n ) from None\\n try:\\n return json.loads(value)\\n except (json.JSONDecodeError, TypeError):\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fa47cacba80aeb4793d0ff0474b56ce\",\n \"score\": \"0.5159782\",\n \"text\": \"def secrets(self) -> pulumi.Input['ServicePrincipalDatastoreSecretsArgs']:\\n return pulumi.get(self, \\\"secrets\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a63704b2b62f41f54e9d38e230c70c50\",\n \"score\": \"0.5145105\",\n \"text\": \"def _secret_not_in_order():\\n abort(falcon.HTTP_400, _(\\\"Secret metadata expected but not received.\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e22b5e04554fc667019af546e1991ee4\",\n \"score\": \"0.5142224\",\n \"text\": \"def load_twitter_credentials():\\n consumer_key = \\\"\\\"\\n consumer_secret = \\\"\\\"\\n access_key = \\\"\\\"\\n access_secret = \\\"\\\"\\n with open(\\\"twitter_credentials.json\\\") as json_file:\\n data = json.load(json_file)\\n consumer_key = data[\\\"consumer_key\\\"]\\n consumer_secret = data[\\\"consumer_secret\\\"]\\n access_key = data[\\\"access_key\\\"]\\n access_secret = data[\\\"access_secret\\\"]\\n\\n return (consumer_key, consumer_secret, access_key, access_secret)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1d057c9f0c5764511eec83a9a9e92d4\",\n \"score\": \"0.51290625\",\n \"text\": \"def get_app_secrets():\\n resp = _get_resp(GET_SECRETS_URL)\\n return (resp['app_id'], resp['app_secret'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4a7c44b216e28305f6369d3f6b65ab8\",\n \"score\": \"0.5125082\",\n \"text\": \"def secrets(self) -> pulumi.Input['AccountKeyDatastoreSecretsArgs']:\\n return pulumi.get(self, \\\"secrets\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29d9e0cbb70133105ae7c490a1ff3a9e\",\n \"score\": \"0.5084021\",\n \"text\": \"def test_add_secret(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f2123c2e835ef8980fb87313894d6fa\",\n \"score\": \"0.5082062\",\n \"text\": \"def test_status_list_success(self, twitter_mock):\\n twitter_mock.return_value = self.status\\n status_list = [self.status.id]\\n\\n response = self.twitter.tweet('\\\"Select all\\\" and archive your Gmail inbox. The page loads so much faster!')\\n self.assertEqual(status_list, response, 'The status response is different')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1b0223a73045aa0f24a3e2dcf7d621e\",\n \"score\": \"0.5076851\",\n \"text\": \"def test_withnout_token(client):\\n req = client.get(\\\"/api/v1/test_token\\\")\\n\\n assert req.status_code == 401\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cc96b399e17efff14cb00391a1585ff\",\n \"score\": \"0.50657356\",\n \"text\": \"def get_secrets():\\n local_file = os.path.join(CONF_ROOT, 'pillar', env.environment, 'secrets.sls')\\n if os.path.exists(local_file):\\n local('cp {0} {0}.bak'.format(local_file))\\n remote_file = os.path.join('/srv/pillar/', env.environment, 'secrets.sls')\\n get(remote_file, local_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d99f46c3a48207bf2404e10762e2de3\",\n \"score\": \"0.5051772\",\n \"text\": \"def setup_secrets():\\n # get username and password\\n from vmc.common.config import config\\n username = config.current_profile.get('connection', 'username')\\n password = config.current_profile.get('connection', 'password')\\n #setup secrets\\n append_entry_to_secrets(username, password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20c8c170357aee45f00a02417d26668e\",\n \"score\": \"0.50494665\",\n \"text\": \"def get_secrets(self):\\n return SECRETS_TEMPLATE.format(\\n total_words=len(dictionary.get_dict()),\\n valid_words=len(valid_words),\\n state=self.state,\\n word=self.word,\\n word_revealed=self.word_revealed,\\n guesses_right=self.guesses_right,\\n guesses_wrong=self.guesses_wrong,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e60a46235c5099d4fa61aa6dd57ec53e\",\n \"score\": \"0.5048611\",\n \"text\": \"def test_set_get_delete_secret(test_environment):\\n plaintext = str(uuid.uuid4())\\n key = str(uuid.uuid4())\\n with pytest.raises(RequiresVaultError):\\n test_environment.set_secret(key, plaintext)\\n\\n with pytest.raises(RequiresVaultError):\\n test_environment.get_secret(key)\\n\\n with pytest.raises(RequiresVaultError):\\n test_environment.delete_secret(key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"404811833bf002d20262c69cba71dc62\",\n \"score\": \"0.50380886\",\n \"text\": \"def test_retrieve_only_suggestions_with_status(self):\\n accepted = 'accepted'\\n\\n SuggestionFactory.create(status=accepted)\\n SuggestionFactory.create(status=accepted)\\n SuggestionFactory.create(status='other')\\n\\n response = self.client.get(self.suggestion_list_url, {'status': accepted},\\n HTTP_AUTHORIZATION=self.user_1_token)\\n\\n data = json.loads(response.content)\\n self.assertEqual(len(data), 2)\\n\\n self.assertEqual(data[0]['status'].lower(), accepted)\\n self.assertEqual(data[1]['status'].lower(), accepted)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"663657bb42b9bb742814eae232011789\",\n \"score\": \"0.5036149\",\n \"text\": \"def credential_test(self):\\n r = requests.get(self.base_uri + 'search.json?q=test')\\n return r.status_code\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e3f9281d57dc9363bda66d0d65b3e1c\",\n \"score\": \"0.50339514\",\n \"text\": \"def fetch_twitter_creds():\\n try:\\n cred_path = os.path.join(cfg.windbagger_data, 'credentials.json')\\n return json.load(open(cred_path, 'r'))\\n except IOError as e:\\n logger.critcal('Credentials for twitter not found!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecd5ff6867552aab5b2612e9dc72a4bf\",\n \"score\": \"0.50285727\",\n \"text\": \"def get_secret(secret_name):\\n client = boto3.client(service_name=\\\"secretsmanager\\\")\\n get_secret_value_response = client.get_secret_value(SecretId=secret_name)\\n\\n if \\\"SecretString\\\" in get_secret_value_response:\\n secret = get_secret_value_response[\\\"SecretString\\\"]\\n return json.loads(secret)\\n else:\\n decoded_binary_secret = base64.b64decode(\\n get_secret_value_response[\\\"SecretBinary\\\"]\\n )\\n return decoded_binary_secret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92b520c20d21140f258c95ec4acea07d\",\n \"score\": \"0.50270486\",\n \"text\": \"async def test_health_liveness_get(client):\\n headers = { \\n }\\n response = await client.request(\\n method='GET',\\n path='/api/auth/v2/health/liveness',\\n headers=headers,\\n )\\n assert response.status == 200, 'Response body is : ' + (await response.read()).decode('utf-8')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72eba3a1d63a3ff0bbbf1edf9e5743b8\",\n \"score\": \"0.5025915\",\n \"text\": \"def test_verify_token(self):\\n #print \\\"test_verify_token\\\"\\n resp = app.get('/auth/dropbox/%s' % userid).json\\n self.assertEqual(resp[\\\"state\\\"],1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cb2c3af5a444784eed992bb02de2bd0\",\n \"score\": \"0.5021788\",\n \"text\": \"def oauth_secrets(doc):\\n if doc.get('type') == 'oauth_client_credentials':\\n yield doc.get('client_id'), doc['_id']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c26d3200aec33394c3ab8b53376534d5\",\n \"score\": \"0.5008839\",\n \"text\": \"def test_get_stashes(self):\\n with self.client as c:\\n with c.session_transaction() as sess:\\n sess[CURR_USER_KEY] = self.user.id\\n sess[CURR_USER_NAME] = self.user.username\\n\\n resp = c.get(\\\"/api/stashes\\\")\\n\\n self.assertEqual(resp.status_code, 200)\\n self.assertIn(\\\"test stash name\\\", str(resp.data))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fbab24dbc0b01506327c91bcce4a47c\",\n \"score\": \"0.50083536\",\n \"text\": \"def get_secret(setting, secrets=SECRETS):\\n try:\\n return secrets[setting]\\n except KeyError:\\n error_msg = \\\"Set the {0} environment variable in the secret file\\\".format(setting)\\n logger.error(\\\"Set the {0} environment variable in the secret file\\\".format(setting))\\n raise ImproperlyConfigured(error_msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88f62c895819267d7a6fb37180ceac19\",\n \"score\": \"0.50080615\",\n \"text\": \"def test_api_authorization_list_units_get(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dcdf485a6c0ed977f86e8b34587fedb\",\n \"score\": \"0.50071317\",\n \"text\": \"def load_twitter_credentials(keys_filename):\\n with open(keys_filename) as api_credentials:\\n api_twitter = json.loads(api_credentials.read())\\n consumer_key = api_twitter['Twitter'][\\\"consumer_key\\\"]\\n consumer_secret = api_twitter['Twitter'][\\\"consumer_secret\\\"]\\n access_token = api_twitter['Twitter'][\\\"access_token\\\"]\\n access_secret = api_twitter['Twitter'][\\\"access_secret\\\"]\\n logging.debug(\\\"{}-{}-{}-{}\\\".format(consumer_key, consumer_secret, access_token, access_secret))\\n return consumer_key, consumer_secret, access_token, access_secret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d64bb449cb1640794a52cd75804449a2\",\n \"score\": \"0.50000733\",\n \"text\": \"def get_secret(setting, secrets=secrets):\\n try:\\n return secrets[setting]\\n except KeyError:\\n error_msg = \\\"Set the {0} environment variable in the secret file\\\".format(setting)\\n raise ImproperlyConfigured(error_msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88e9cb67023e569e30335a5e9634f738\",\n \"score\": \"0.49848822\",\n \"text\": \"def get_api_secret():\\n config = ConfigParser.ConfigParser()\\n config.read(\\\"api_keys.ini\\\")\\n secret = config.get(\\\"ApiKeys\\\", \\\"secret\\\")\\n return secret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"618d50f5802a6bbad64709b3180da812\",\n \"score\": \"0.4984404\",\n \"text\": \"def api_secret_key(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"api_secret_key\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16481a0eadcbfd374fd335f21f6a482a\",\n \"score\": \"0.49727523\",\n \"text\": \"def test_valid_key():\\n\\n response = sdk_instance.get_orgs()\\n\\n assert response.status_code != 401\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dc8e95f93e312fad89a03275be89953\",\n \"score\": \"0.49555314\",\n \"text\": \"def test_get_call(self, dut):\\n username = DEVICE_UNDER_TEST['username']\\n passwd = DEVICE_UNDER_TEST['password']\\n dut.login(username=username, password=passwd)\\n url = dut.url_prefix + '/status'\\n r = dut.session.get(url)\\n assert r.status_code >= 200 <= 299\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8692fb7889e202b56f6a9e60e6707f17\",\n \"score\": \"0.49522367\",\n \"text\": \"def get_secret(secret_name):\\n\\n # Create a Secrets Manager client\\n client = boto3.client('secretsmanager')\\n\\n try:\\n get_secret_value_response = client.get_secret_value(\\n SecretId=secret_name\\n )\\n except ClientError as e:\\n if e.response['Error']['Code'] == 'DecryptionFailureException':\\n # Secrets Manager can't decrypt the protected secret text using the provided KMS key.\\n # Deal with the exception here, and/or rethrow at your discretion.\\n logger.error(\\\"Secrets Manager can't decrypt the protected secret text using the provided KMS key.\\\")\\n raise e\\n elif e.response['Error']['Code'] == 'InternalServiceErrorException':\\n # An error occurred on the server side.\\n # Deal with the exception here, and/or rethrow at your discretion.\\n logger.error(\\\"An error occurred on the server side.\\\")\\n raise e\\n elif e.response['Error']['Code'] == 'InvalidParameterException':\\n # You provided an invalid value for a parameter.\\n # Deal with the exception here, and/or rethrow at your discretion.\\n logger.error(\\\"Invalid value for a parameter\\\")\\n raise e\\n elif e.response['Error']['Code'] == 'InvalidRequestException':\\n # You provided a parameter value that is not valid for the current state of the resource.\\n # Deal with the exception here, and/or rethrow at your discretion.\\n logger.error(\\\"Provided a parameter value is not valid for the current state of the resource\\\")\\n raise e\\n elif e.response['Error']['Code'] == 'ResourceNotFoundException':\\n # We can't find the resource that you asked for.\\n # Deal with the exception here, and/or rethrow at your discretion.\\n logger.error(f\\\"Unable to find secret with name {secret_name}\\\")\\n raise e\\n else:\\n # Decrypts secret using the associated KMS CMK.\\n # Depending on whether the secret is a string or binary, one of these fields will be populated.\\n if 'SecretString' in get_secret_value_response:\\n return get_secret_value_response['SecretString']\\n else:\\n return base64.b64decode(get_secret_value_response['SecretBinary'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"068e8ad8639fd2702786ca446da5e58e\",\n \"score\": \"0.49401957\",\n \"text\": \"def access_secret(project_id, location_id, secret_id):\\n\\n # Creates an API client for the Secret Manager API.\\n client = secretmanager.SecretManagerServiceClient()\\n\\n secret = f\\\"projects/{project_id}/secrets/{secret_id}/versions/latest\\\"\\n response = client.access_secret_version(name=secret)\\n\\n return response.payload.data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77b270c92bc417c6d8e0ad991ea6d95a\",\n \"score\": \"0.49340248\",\n \"text\": \"def get_secret(name):\\n log.info('Fetching secret from env var. VAR: {}'.format(name))\\n kms = boto3.session.Session().client(\\\"kms\\\")\\n return kms.decrypt(CiphertextBlob=base64.b64decode(os.environ[name]))[\\\"Plaintext\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d377fd703d16cf1ef3b9911a5d6b5661\",\n \"score\": \"0.4927424\",\n \"text\": \"def secret(self):\\n return self.oauth_secret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfdcf60c94b9299da6376f6199ec053c\",\n \"score\": \"0.49247336\",\n \"text\": \"def list_properties_of_secrets(self, **kwargs: \\\"Any\\\") -> AsyncIterable[SecretProperties]:\\n return self._client.get_secrets(\\n self.vault_endpoint,\\n maxresults=kwargs.pop(\\\"max_page_size\\\", None),\\n cls=lambda objs: [SecretProperties._from_secret_item(x) for x in objs],\\n **kwargs\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97ebae09b62678fd31867f2ac30dbbdb\",\n \"score\": \"0.48997778\",\n \"text\": \"def get_api_token(credentials_file):\\n logging.info(\\\"Leyendo las credenciales de {}\\\".format(credentials_file))\\n credentials = read_yaml_file(credentials_file)\\n token = credentials['food_inspections']\\n \\n return token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d5c3cae96164fe5b30bd35db8c06e7a\",\n \"score\": \"0.48990205\",\n \"text\": \"def test_notestatus_list_api_unauthorized(self):\\n\\n # get response\\n response = self.client.get('/api/notestatus/')\\n # compare\\n self.assertEqual(response.status_code, 401)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a13153177d5d105ce05a243599dc8bc3\",\n \"score\": \"0.4898741\",\n \"text\": \"def get_secret(setting, secrets=secrets):\\n try:\\n return secrets[setting]\\n except KeyError:\\n error_msg = \\\"Set the {0} environment variable\\\".format(setting)\\n raise ImproperlyConfigured(error_msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e7a7066247baf887df82206862ec644\",\n \"score\": \"0.48920795\",\n \"text\": \"def test_get_status(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e7a7066247baf887df82206862ec644\",\n \"score\": \"0.48920795\",\n \"text\": \"def test_get_status(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3949a223b2c9d9692ccf4601964b22af\",\n \"score\": \"0.4889985\",\n \"text\": \"def get(self, key):\\n\\n rest_utils.validate_inputs({'key': key})\\n return get_storage_manager().get(models.Secret, key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4168e0cce2374d46ab9350f16ef45832\",\n \"score\": \"0.4888779\",\n \"text\": \"def get_secret_value_output(resource_group_name: Optional[pulumi.Input[str]] = None,\\n secret_resource_name: Optional[pulumi.Input[str]] = None,\\n secret_value_resource_name: Optional[pulumi.Input[str]] = None,\\n opts: Optional[pulumi.InvokeOptions] = None) -> pulumi.Output[GetSecretValueResult]:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dec3491237df09c200c9b9088125026\",\n \"score\": \"0.48811728\",\n \"text\": \"def secrets_profile(self) -> Optional['outputs.SecretsProfileResponse']:\\n return pulumi.get(self, \\\"secrets_profile\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd58a88a05d853a5729e775f4bd839b1\",\n \"score\": \"0.48607022\",\n \"text\": \"def test_get_secret(self):\\n\\n secret_dto = self.plugin_to_test.get_secret(\\n secret_store.SecretType.OPAQUE,\\n None, # Secret metadata is not relevant to store_crypto process.\\n self.context)\\n\\n # Verify response.\\n self.assertIsInstance(secret_dto, secret_store.SecretDTO)\\n self.assertEqual(secret_store.SecretType.OPAQUE, secret_dto.type)\\n self.assertEqual(\\n base64.encodebytes(self.decrypted_secret).rstrip(b'\\\\n'),\\n secret_dto.secret)\\n self.assertEqual(\\n self.encrypted_datum_model.content_type, secret_dto.content_type)\\n self.assertIsInstance(secret_dto.key_spec, secret_store.KeySpec)\\n self.assertEqual(\\n self.secret_model.algorithm, secret_dto.key_spec.alg)\\n self.assertEqual(\\n self.secret_model.bit_length, secret_dto.key_spec.bit_length)\\n self.assertEqual(\\n self.secret_model.mode, secret_dto.key_spec.mode)\\n\\n # Verify decrypt plugin and method where invoked.\\n decrypt_mock = self.retrieving_plugin.decrypt\\n self.assertEqual(1, decrypt_mock.call_count)\\n args, kwargs = decrypt_mock.call_args\\n (\\n test_decrypt,\\n test_kek_meta,\\n test_kek_meta_extended,\\n test_project_id\\n ) = tuple(args)\\n\\n self.assertIsInstance(test_decrypt, base.DecryptDTO)\\n self.assertEqual(\\n base64.b64decode(self.encrypted_datum_model.cypher_text),\\n test_decrypt.encrypted)\\n\\n self.assertIsInstance(test_kek_meta, base.KEKMetaDTO)\\n self.assertEqual(\\n self.kek_meta_project_model.plugin_name, test_kek_meta.plugin_name)\\n\\n self.assertEqual(\\n self.encrypted_datum_model.kek_meta_extended,\\n test_kek_meta_extended)\\n\\n self.assertEqual(self.project_id, test_project_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdf86063485160dbce61d83613f1c94f\",\n \"score\": \"0.48555958\",\n \"text\": \"def test_missing_access_token_secret(self):\\n res = self.social_login(self.social_url, self.missing_token_secret)\\n self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST)\\n self.assertEqual(res.data.get('errors').get('error')[0], \\\"An access token secret is required for Twitter Login\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffd5d75ef8cf8d01641d34a993483f21\",\n \"score\": \"0.48548844\",\n \"text\": \"def test_define_execution_list_with_secrets(self, project_function_clean):\\n secrets = {\\\"a\\\": \\\"secret\\\", \\\"b\\\": \\\"secret02\\\"}\\n testdir = project_function_clean.testdir\\n project = project_function_clean.name\\n secrets_path = os.path.join(project_function_clean.path, 'secrets.json')\\n with open(secrets_path, 'w') as secrets_file:\\n secrets_file.write(json.dumps(secrets, indent=True))\\n execution_runner = exc_runner.ExecutionRunner()\\n execution_runner.tests = ['test_001']\\n execution_runner.execution.processes = 1\\n execution_runner.execution.browsers = ['chrome']\\n execution_runner.execution.envs = ['']\\n execution_runner.project = project_function_clean.name\\n execution_list = execution_runner._define_execution_list()\\n expected_list = [\\n SimpleNamespace(name='test_001', data_set={}, secrets={\\\"a\\\": \\\"secret\\\", \\\"b\\\": \\\"secret02\\\"}, browser='chrome', reportdir=None)\\n ]\\n assert execution_list == expected_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12805505b673627dda84a6db9270b10b\",\n \"score\": \"0.48485553\",\n \"text\": \"def get_token_secret(self):\\n return self.oauth_token_secret or None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1149f333260f4b2c901eefb57320e6e6\",\n \"score\": \"0.48473433\",\n \"text\": \"def getSwiftSecret(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994197,"cells":{"query_id":{"kind":"string","value":"ded8dd55a74a3491147509032239a5c2"},"query":{"kind":"string","value":"Display a dialog asking the user to input the range of frames they would like to export"},"positive_passages":{"kind":"list like","value":[{"docid":"911b2d5a62a538eb21b7466296923e33","score":"0.6653546","text":"def ask_frame_range(self, n_frames):\n valid_input = False\n _, ext = os.path.splitext(self.output.GetPath())\n show_single_btn = (ext == '.h5')\n dlg = FrameSelectDialog(n_frames, show_single_btn)\n frames = None\n increment = None\n single_file = True\n while not valid_input:\n if dlg.ShowModal() == wx.ID_OK:\n msg = \"\"\n try:\n first_frame = int(dlg.first_input.GetValue())\n last_frame = int(dlg.last_input.GetValue())\n increment = int(dlg.increment_input.GetValue())\n if not show_single_btn:\n single_file = dlg.single_btn.GetValue()\n\n if last_frame < 0 or first_frame < 0:\n msg = \"Frame values must be positive\"\n elif increment < 1:\n msg = \"Increment must be greater than or equal to 1\"\n elif first_frame > last_frame:\n msg = \"First frame must be less than last frame\"\n elif last_frame >= n_frames:\n msg = \"Last frame must be less than {}\".format(n_frames)\n else:\n valid_input = True\n except:\n valid_input = False\n msg = \"Please enter valid integer values\"\n\n if not valid_input:\n wx.PostEvent(self.parent.manager.parent,\n StatusEvent(status=msg))\n else:\n return { 'frames': [], 'inc': None, 'file': single_file }\n frames = list(range(first_frame, last_frame + 1, increment))\n return { 'frames': frames, 'inc': increment, 'file': single_file }","title":""}],"string":"[\n {\n \"docid\": \"911b2d5a62a538eb21b7466296923e33\",\n \"score\": \"0.6653546\",\n \"text\": \"def ask_frame_range(self, n_frames):\\n valid_input = False\\n _, ext = os.path.splitext(self.output.GetPath())\\n show_single_btn = (ext == '.h5')\\n dlg = FrameSelectDialog(n_frames, show_single_btn)\\n frames = None\\n increment = None\\n single_file = True\\n while not valid_input:\\n if dlg.ShowModal() == wx.ID_OK:\\n msg = \\\"\\\"\\n try:\\n first_frame = int(dlg.first_input.GetValue())\\n last_frame = int(dlg.last_input.GetValue())\\n increment = int(dlg.increment_input.GetValue())\\n if not show_single_btn:\\n single_file = dlg.single_btn.GetValue()\\n\\n if last_frame < 0 or first_frame < 0:\\n msg = \\\"Frame values must be positive\\\"\\n elif increment < 1:\\n msg = \\\"Increment must be greater than or equal to 1\\\"\\n elif first_frame > last_frame:\\n msg = \\\"First frame must be less than last frame\\\"\\n elif last_frame >= n_frames:\\n msg = \\\"Last frame must be less than {}\\\".format(n_frames)\\n else:\\n valid_input = True\\n except:\\n valid_input = False\\n msg = \\\"Please enter valid integer values\\\"\\n\\n if not valid_input:\\n wx.PostEvent(self.parent.manager.parent,\\n StatusEvent(status=msg))\\n else:\\n return { 'frames': [], 'inc': None, 'file': single_file }\\n frames = list(range(first_frame, last_frame + 1, increment))\\n return { 'frames': frames, 'inc': increment, 'file': single_file }\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ba3807dc066092db7439c382f1f79169","score":"0.5913335","text":"def getFrameRangeFromShotSettings(self):\r\n\t\ttry:\r\n\t\t\t#frRange = \"%d-%d\" %(int(os.environ['STARTFRAME']), int(os.environ['ENDFRAME'])) # Icarus\r\n\t\t\tfrRange = \"%d-%d\" %(int(os.environ['UHUB_STARTFRAME']), int(os.environ['UHUB_ENDFRAME'])) # U-HUB\r\n\t\t\tself.ui.frames_lineEdit.setText(frRange)\r\n\t\texcept KeyError:\r\n\t\t\tself.ui.frames_lineEdit.setText(\"\")","title":""},{"docid":"10afd68795db62c4a02e1a8eaa171f3e","score":"0.577385","text":"def test_03_verify_page_range_screen_ui(self):\n # General Setup\n self.fc.select_a_google_drive_file_and_go_to_print_preview(file_name=\"2pages\")\n assert self.fc.fd[\"preview\"].get_option_selected_value(Preview.PAGE_RANGE) == 'All'\n self.fc.fd[\"preview\"].verify_an_element_and_click(Preview.PAGE_RANGE)\n # Validation\n assert set(self.fc.fd[\"preview\"].get_options_listed(Preview.PR_SCREEN_OPTIONS_UI)) == set(\n Preview.PAGE_RANGE_OPTIONS)\n assert self.fc.fd[\"preview\"].verify_pages_selected(Preview.PAGE_RANGE_OPTIONS[0]) is not False\n assert self.fc.fd[\"preview\"].verify_pages_selected(Preview.PAGE_RANGE_OPTIONS[1]) is not False","title":""},{"docid":"4122f3623897225f316c47763538cff1","score":"0.5509551","text":"def on_fit_range_clicked(self):\n dialog = FitRangeConfigDialog(\n upper_lim=(0., 10., self.fit_range[1]),\n lower_lim=(-1., 10., self.fit_range[0]),\n dimension='microsecond')\n\n if dialog.exec_() == 1:\n upper_limit = dialog.get_widget_value(\"upper_limit\")\n lower_limit = dialog.get_widget_value(\"lower_limit\")\n self.fit_range = (lower_limit, upper_limit)","title":""},{"docid":"50a47179eb0e1bc49eabdeabb704ee6b","score":"0.5437391","text":"def grabFrame(self,event):\n\n if self.extract_range_frame == True:\n num_frames_extract = self.endFrame.GetValue()\n for i in range(self.currFrame,self.currFrame+num_frames_extract):\n self.currFrame = i\n self.vid.set(1,self.currFrame)\n self.chooseFrame()\n else:\n self.vid.set(1,self.currFrame)\n self.chooseFrame()","title":""},{"docid":"bb146eb6cb9f239ff77328a34d5d3d34","score":"0.54290503","text":"def calcFrameList(self, quiet=True):\r\n\t\ttry:\r\n\t\t\tself.numList = sequence.numList(self.ui.frames_lineEdit.text(), sort=False, quiet=quiet)\r\n\t\t\tif self.numList is False:\r\n\t\t\t\t#raise RuntimeError(\"Invalid entry for frame range.\")\r\n\t\t\t\tif not quiet:\r\n\t\t\t\t\t#verbose.warning(\"Invalid entry for frame range.\")\r\n\t\t\t\t\tprint(\"Warning: Invalid entry for frame range.\")\r\n\t\t\t\t# self.ui.frames_lineEdit.setProperty(\"mandatoryField\", True)\r\n\t\t\t\t# self.ui.frames_lineEdit.style().unpolish(self.ui.frames_lineEdit)\r\n\t\t\t\t# self.ui.frames_lineEdit.style().polish(self.ui.frames_lineEdit)\r\n\t\t\t\tself.numList = []\r\n\t\t\t\tself.taskList = [\"Unknown\", ]\r\n\r\n\t\t\t\t# msg = \"Invalid entry for frame range.\"\r\n\t\t\t\t# #mc.warning(msg)\r\n\t\t\t\t# #mc.confirmDialog(title=\"Scene not saved\", message=msg, icon=\"warning\", button=\"Close\")\r\n\t\t\t\t# self.ui.submit_pushButton.setToolTip(msg)\r\n\t\t\t\t# self.ui.submit_pushButton.setEnabled(False)\r\n\r\n\t\t\t\treturn False\r\n\r\n\t\t\telif self.numList is None:\r\n\t\t\t\t#raise RuntimeError(\"No frame range specified.\")\r\n\t\t\t\tif not quiet:\r\n\t\t\t\t\t#verbose.warning(\"No frame range specified.\")\r\n\t\t\t\t\tprint(\"Warning: No frame range specified.\")\r\n\t\t\t\t# self.ui.frames_lineEdit.setProperty(\"mandatoryField\", True)\r\n\t\t\t\t# self.ui.frames_lineEdit.style().unpolish(self.ui.frames_lineEdit)\r\n\t\t\t\t# self.ui.frames_lineEdit.style().polish(self.ui.frames_lineEdit)\r\n\t\t\t\tself.numList = []\r\n\t\t\t\tself.taskList = [\"Unknown\", ]\r\n\r\n\t\t\t\t# msg = \"No frame range specified.\"\r\n\t\t\t\t# #mc.warning(msg)\r\n\t\t\t\t# #mc.confirmDialog(title=\"Scene not saved\", message=msg, icon=\"warning\", button=\"Close\")\r\n\t\t\t\t# self.ui.submit_pushButton.setToolTip(msg)\r\n\t\t\t\t# self.ui.submit_pushButton.setEnabled(False)\r\n\r\n\t\t\t\treturn False\r\n\r\n\t\t\telse:\r\n\t\t\t\t# Update task size slider\r\n\t\t\t\t#self.ui.frames_lineEdit.setText(sequence.numRange(self.numList))\r\n\t\t\t\ttaskSize = self.ui.taskSize_spinBox.value()\r\n\t\t\t\tnFrames = len(self.numList)\r\n\t\t\t\tif taskSize < nFrames:\r\n\t\t\t\t\tself.ui.taskSize_slider.setMaximum(nFrames)\r\n\t\t\t\t\tself.ui.taskSize_spinBox.setMaximum(nFrames)\r\n\t\t\t\t\tself.ui.taskSize_spinBox.setValue(taskSize)\r\n\t\t\t\telse:\r\n\t\t\t\t\tself.ui.taskSize_slider.setMaximum(nFrames)\r\n\t\t\t\t\tself.ui.taskSize_spinBox.setMaximum(nFrames)\r\n\t\t\t\t\tself.ui.taskSize_spinBox.setValue(nFrames)\r\n\t\t\t\tif nFrames == 1:\r\n\t\t\t\t\tself.ui.taskSize_slider.setEnabled(False)\r\n\t\t\t\t\tself.ui.taskSize_spinBox.setEnabled(False)\r\n\t\t\t\telse:\r\n\t\t\t\t\tself.ui.taskSize_slider.setEnabled(True)\r\n\t\t\t\t\tself.ui.taskSize_spinBox.setEnabled(True)\r\n\r\n\t\t\t\t# Generate task list for rendering\r\n\t\t\t\tself.taskList = []\r\n\t\t\t\tsequences = list(sequence.seqRange(self.numList, gen_range=True))\r\n\t\t\t\tfor seq in sequences:\r\n\t\t\t\t\tchunks = list(sequence.chunks(seq, taskSize))\r\n\t\t\t\t\tfor chunk in chunks:\r\n\t\t\t\t\t\t#self.taskList.append(list(sequence.seqRange(chunk))[0])\r\n\t\t\t\t\t\tself.taskList.append(sequence.numRange(chunk))\r\n\r\n\t\t\t\treturn True\r\n\r\n\t\texcept (MemoryError, OverflowError):\r\n\t\t\t#verbose.warning(\"Specified frame range value(s) too large to process.\")\r\n\t\t\tprint(\"Warning: Specified frame range value(s) too large to process.\")\r\n\t\t\treturn False\r\n\r\n\t\t# except RuntimeError:\r\n\t\t# \tif not quiet:\r\n\t\t# \t\tverbose.warning(\"Invalid entry for frame range.\")\r\n\t\t# \t# self.ui.frames_lineEdit.setProperty(\"mandatoryField\", True)\r\n\t\t# \t# self.ui.frames_lineEdit.style().unpolish(self.ui.frames_lineEdit)\r\n\t\t# \t# self.ui.frames_lineEdit.style().polish(self.ui.frames_lineEdit)\r\n\t\t# \tself.numList = []\r\n\t\t# \tself.taskList = [\"Unknown\", ]\r\n\t\t# \treturn False\r\n\r\n\t\t# finally:\r\n\t\t# \tpass\r","title":""},{"docid":"5cd60b7e906cace94cf2405f85043ada","score":"0.541242","text":"def buildTabDialog(self):\n super(Torus5dFrame, self).buildTabDialog()\n self.tab_dialog.addTab(Torus5dRangeTab(self.tab_dialog, self), \"Data Range\")","title":""},{"docid":"c1a2663b701b7b4ddf6388ba7e353667","score":"0.5410121","text":"def export_frame():\r\n exp_frame = LabelFrame(window, text='Экспортировать в MS Excel', width=960, height=270)\r\n exp_frame.place(x=25, y=450)\r\n\r\n btn_1 = Button(exp_frame, text=\"Все банки по месяцам\", background=\"#555\", foreground=\"#ccc\",\r\n padx=\"15\", pady=\"6\", font=\"15\", command=choice_month)\r\n btn_1.pack(expand=True, fill=BOTH)\r\n\r\n btn_2 = Button(exp_frame, text=\"Панель данных\", background=\"#555\", foreground=\"#ccc\",\r\n padx=\"15\", pady=\"6\", font=\"15\", command=choice_bank)\r\n btn_2.pack(expand=True, fill=BOTH)\r\n\r\n return","title":""},{"docid":"54d45a9687419e2c6d449c9222bb3c16","score":"0.5384579","text":"def on_fit_range_clicked(self):\n dialog = FitRangeConfigDialog(\n upper_lim=(0., 60., self.fit_range[1]),\n lower_lim=(-1., 60., self.fit_range[0]), dimension='ns')\n\n if dialog.exec_() == 1:\n upper_limit = dialog.get_widget_value(\"upper_limit\")\n lower_limit = dialog.get_widget_value(\"lower_limit\")\n self.fit_range = (lower_limit, upper_limit)","title":""},{"docid":"49a136c235db02f2e723f9bc3780a63a","score":"0.5343724","text":"def activate_frame_range(self,event):\n self.checkSlider = event.GetEventObject()\n if self.checkSlider.GetValue() == True:\n self.extract_range_frame = True\n self.startFrame.Enable(True)\n self.startFrame.SetValue(self.slider.GetValue())\n self.endFrame.Enable(True)\n self.updateFrame.Enable(True)\n self.grab.Enable(False)\n else:\n self.extract_range_frame = False\n self.startFrame.Enable(False)\n self.endFrame.Enable(False)\n self.updateFrame.Enable(False)\n self.grab.Enable(True)","title":""},{"docid":"ac3dc44e8d6a020617b351f6a508fd14","score":"0.53095037","text":"def show_pres_gen(self, event):\n\t\tPresetDialog(self, \"Preset Generator\")","title":""},{"docid":"8b9e93030c3e9cfe40742bb5d974c9bc","score":"0.5279935","text":"def set_range_from_shot(shot):\n min_frame = shot.getAttr('startFrame')\n max_frame = shot.getAttr('endFrame')\n\n pm.playbackOptions(\n ast=min_frame,\n aet=max_frame,\n min=min_frame,\n max=max_frame\n )","title":""},{"docid":"8bc3237ac8fdb028b4039f6108bd0c41","score":"0.524625","text":"def show_frameDelta(self, cont):\n frame = self.frames[cont]\n frame.gettext() #Displays either \"Ready\" or \"Insert stimuli\" based on experiment type\n frame.tkraise()","title":""},{"docid":"195e224e426f2efca8eef92ed1a8ce14","score":"0.520215","text":"def trackbar(theWidth, theValue, theMin, theMax, theSegments = 1, theLabelFormat = '%.1Lf', theOptions = 0, theDiscreteStep = 1):\n\tprint('This is wrapper function to help code autocompletion.')","title":""},{"docid":"4d13e15460338ffa0338d7aeb1bfd134","score":"0.5100591","text":"def setFrameListPreset(self):\r\n\t\t#print(self.sender().text())\r\n\t\tif self.sender().text() == \"Sequential\":\r\n\t\t\tnum_list = sequence.numList(self.ui.frames_lineEdit.text(), sort=True, quiet=True)\r\n\t\t\tif self.numList:\r\n\t\t\t\tself.ui.frames_lineEdit.setText(sequence.numRange(num_list))\r\n\t\t\telse:\r\n\t\t\t\tpass\r\n\r\n\t\telif self.sender().text() == \"Reverse order\":\r\n\t\t\tpass\r\n\r\n\t\telif self.sender().text() == \"Render first and last frames before others\":\r\n\t\t\tframes_str = self.ui.frames_lineEdit.text()\r\n\t\t\tnum_list = sequence.numList(frames_str, sort=True, quiet=True)\r\n\t\t\tif self.numList:\r\n\t\t\t\tfirst = min(num_list)\r\n\t\t\t\tlast = max(num_list)\r\n\t\t\t\tframes_str_prefix = \"%d, %d\" %(first, last)\r\n\t\t\t\tif not frames_str.startswith(frames_str_prefix):\r\n\t\t\t\t\tself.ui.frames_lineEdit.setText(\"%s, %s\" %(frames_str_prefix, frames_str))\r\n\t\t\telse:\r\n\t\t\t\tpass","title":""},{"docid":"861577666521aacaf179c22e73a8c817","score":"0.5064858","text":"def show_frameEcho(self, cont):\n self.frames[StimPrepPg].button1.grid_remove()\n self.frames[StimPrepPg].button2.grid_remove()\n self.frames[StimPrepPg].label1.configure(text=\"Experiment in progress\")\n frame = self.frames[cont]\n\n os.makedirs(Appa.savefile) # Create general folder for experiment \n os.makedirs(Appa.savefile + \"/ExpDataPictures\") # Create folder for images from exp\n\n #Image capturing\n imgnum=0\n for i in range(Appa.exptime+1):\n start_time = clock()\n remaining = Appa.exptime-i # Calculate countdown\n self.frames[StimPrepPg].label2.configure(text=\"Time remaining: %d\" % remaining) # Set countdown\n self.frames[StimPrepPg].update_idletasks() # Refresh page \n if i%Appa.capturerate == 0: # Calculate if need to capture pic \n camera.capture(Appa.savefile + \"/ExpDataPictures/image\" + str(imgnum) + \".jpg\")\n Appa.expy.append(\"\") # Append empty place holder for future analyssi\n imgnum+=1\n sleep(1-(clock()-start_time))\n\n camera.stop_preview()\n frame.tkraise()","title":""},{"docid":"8ddddcfe84c9630152742df5676e288c","score":"0.5046323","text":"def trackbar(theWhere, theX, theY, theWidth, theValue, theMin, theMax, theSegments = 1, theLabelFormat = '%.1Lf', theOptions = 0, theDiscreteStep = 1):\n\tprint('This is wrapper function to help code autocompletion.')","title":""},{"docid":"3d3ba097ca1983f9d5470b46506ac474","score":"0.503143","text":"def choose_windows(spectrum, wstart, wend):\n\n #Define function that will obtain the cursor x position\n def onselect(vmin, vmax):\n \"\"\" Function that will obtain the cursor x position\"\"\"\n windows.append(float(Decimal(\"%.2f\" % vmin)))\n windows.append(float(Decimal(\"%.2f\" % vmax)))\n plot_windows(windows)\n plt.draw()\n #Create a plot so the user can choose the windows\n windows = [] #Set an empty window\n axis = plt.subplot(111)\n axis.plot(spectrum[:, 0], spectrum[:, 1], 'k-')\n axis.hlines(1, wstart, wend, color = 'b', linestyles = 'dashed')\n axis.set_xlim([wstart, wend])\n axis.set_ylim([min(subselect_spectra \\\n (spectrum, wstart, wend)[1]) - 0.2, 1.05])\n axis.set_xlabel(r'Wavelength $(\\AA)$')\n axis.set_ylabel('Normalized Flux')\n span = SpanSelector(axis, onselect, 'horizontal', minspan = 0.05)\n # Plot a vertical line at cursor position\n cursor = Cursor(axis, useblit = True, color = 'red', linewidth = 1 )\n cursor.horizOn = False\n\n plt.show()\n plt.clf()\n \n return windows","title":""},{"docid":"287088044e0310051082f6a3186f302d","score":"0.5019565","text":"def show_seg_marker_dialog():\n \n from chimera import dialogs\n return dialogs.display(Segment_Marker.name)","title":""},{"docid":"0b54d1e58af059846dda2adc2f0a71a7","score":"0.4990835","text":"def displayFrames(frames):\n framesFormat = \"%-35s %-11s %-15s %-13s %-12s %-9s %5s %7s %5s\"\n header = framesFormat % (\n \"Frame\", \"Status\", \"Host\", \"Start\", \"End\", \"Runtime\", \"Mem\", \"Retry\", \"Exit\")\n print(header + \"\\n\" + \"-\" * len(header))\n\n for frame in frames:\n startTime = cueadmin.format.formatTime(frame.data.start_time)\n stopTime = cueadmin.format.formatTime(frame.data.stop_time)\n\n if frame.data.start_time:\n duration = cueadmin.format.formatDuration(\n cueadmin.format.findDuration(frame.data.start_time, frame.data.stop_time))\n else:\n duration = \"\"\n\n memory = cueadmin.format.formatMem(frame.data.max_rss)\n exitStatus = frame.data.exit_status\n\n print(framesFormat % (\n cueadmin.format.cutoff(frame.data.name, 35),\n opencue.compiled_proto.job_pb2.FrameState.Name(frame.data.state),\n frame.data.last_resource,\n startTime,\n stopTime,\n duration,\n memory,\n frame.data.retry_count,\n exitStatus))\n\n if len(frames) == 1000:\n print(\"Warning: Only showing first 1000 matches. See frame query options to \"\n \"limit your results.\")","title":""},{"docid":"97aab89295159bb5847a2a3f7390b775","score":"0.49894312","text":"def export_segment(self):\n\n msecs_from = msecs_to_hours_mins_secs(self.segment['pos0'])\n msecs_from = msecs_from.replace('.', \"H\", 1)\n msecs_from = msecs_from.replace('.', \"M\", 1) + \"S\"\n msecs_to = msecs_to_hours_mins_secs(self.segment['pos1'])\n msecs_to = msecs_to.replace('.', \"H\", 1)\n msecs_to = msecs_to.replace('.', \"M\", 1) + \"S\"\n filename = self.code_av_dialog.file_['name'][:-4] + \"_\"\n filename += msecs_from + \"_to_\" + msecs_to + \"_\"\n filename += self.code_av_dialog.file_['name'][-4:]\n export_dir = ExportDirectoryPathDialog(self.app, filename)\n filepath = export_dir.filepath\n if filepath is None:\n return\n if os.path.exists(filepath):\n os.remove(filepath)\n mediapath = \"\"\n try:\n if self.code_av_dialog.file_['mediapath'][0:6] in ('/audio', '/video'):\n mediapath = self.app.project_path + self.code_av_dialog.file_['mediapath']\n if self.code_av_dialog.file_['mediapath'][0:6] in ('audio:', 'video:'):\n mediapath = self.code_av_dialog.file_['mediapath'][6:]\n except Exception as e_:\n Message(self.app, _('Media not found'),\n str(e_) + \"\\n\" + self.app.project_path + self.code_av_dialog.file_['mediapath'],\n \"warning\").exec()\n return\n ffmpeg_command = 'ffmpeg -i \"' + mediapath + '\" -ss '\n ffmpeg_command += str(self.segment['pos0'] / 1000)\n ffmpeg_command += ' -to '\n ffmpeg_command += str(self.segment['pos1'] / 1000)\n ffmpeg_command += ' -c copy \"' + filepath + '\"'\n # print(f\"FFMPEG COMMAND\\n {ffmpeg_command}\")\n try:\n subprocess.run(ffmpeg_command, timeout=15, shell=True)\n self.code_av_dialog.parent_textEdit.append(_(\"A/V segment exported: \") + filepath)\n Message(self.app, _(\"Segment exported\"), filepath).exec()\n except Exception as e_:\n logger.error(str(e_))\n print(str(e_))\n Message(self.app, \"ffmpeg error\", str(e_)).exec()","title":""},{"docid":"3196554bd258ff84cf0f8ec9a6c2b87c","score":"0.49824834","text":"def showVideo(self): # connected to Go to vide (btn5)\n\n# self.is_trace = False\n\n plot_with_colorbar(self.imv, self.data)\n\n# self.w.setWindowTitle('SMAnalyzer - Video - ' + self.f)\n self.meanEndEdit.setStyleSheet(\" background-color: ; \")\n\n try:\n self.translateMaxima()\n self.imv.view.addItem(self.roi)\n except:\n pass\n if self.JPG:\n self.mean = self.data","title":""},{"docid":"982327f5089a668553fd3d0cc7cbb0e5","score":"0.49745703","text":"def trackbar2(theWidth, theValue1, theValue2, theMin, theMax, theSegments=1, theLabelFormat='%.1Lf', theOptions=0, theDiscreteStep=1):\n\tprint('This is wrapper function to help code autocompletion.')","title":""},{"docid":"572a091297dea0a5cdc94e389405de48","score":"0.497088","text":"def show_scan_frame(self) -> None:\n src.getraenkeKasse.scanframe.ScanFrame(self)","title":""},{"docid":"2ad93f72af3d15509f96237142728772","score":"0.4968003","text":"def show_export_events_dialog(self):\n self.export_events_dialog.update()\n self.export_events_dialog.show()","title":""},{"docid":"2bfd9577c88b9603ec1b1d4534c8be49","score":"0.49670574","text":"def export_img(self, path, base = 'fseq-export-', figsize=(4,4),\n start = 0, stop = None, show_title = True,\n format='.png', vmin = None, vmax=None, **kwargs):\n import sys\n import matplotlib.pyplot as plt\n lib.ensure_dir(path)\n fig = plt.figure(figsize=figsize)\n ax = fig.add_subplot(111)\n if stop is None or stop == -1:\n stop = len(self)\n\tif hasattr(self, 'data'):\n\t vmin = ifnot(vmin, self.data_percentile(1)) # for scale\n\t vmax = ifnot(vmax, self.data_percentile(99)) # for scale\n\telse:\n\t vmin = ifnot(vmin, np.min(map(np.min, self.frames())))\n\t vmax = ifnot(vmax, np.min(map(np.max, self.frames())))\n kwargs.update({'vmin':vmin, 'vmax':vmax})\n\tprint path+base\n L = min(stop-start, len(self))\n\tfnames = []\n for i,frame in enumerate(self.frames()):\n if i < start: continue\n if i > stop: break\n ax.cla()\n ax.imshow(frame, aspect='equal', **kwargs)\n fname = path + base + '%06d.png'%i\n\t fnames.append(fname)\n if show_title:\n zscale = tuple(self.meta['axes'][0])\n ax.set_title('frame %06d (%3.3f %s)'%zscale)\n fig.savefig(fname)\n sys.stderr.write('\\r saving frame %06d of %06d'%(i+1, L))\n plt.close()\n\treturn fnames","title":""},{"docid":"9a77f38ecfac5d612d6426a2056fc8d9","score":"0.49639183","text":"def getFrameRangeFromRenderSettings(self):\r\n\t\tif UI.ENVIRONMENT == \"MAYA\":\r\n\t\t\tstart_frame = int(mc.getAttr('defaultRenderGlobals.startFrame'))\r\n\t\t\tend_frame = int(mc.getAttr('defaultRenderGlobals.endFrame'))\r\n\r\n\t\telif UI.ENVIRONMENT == \"HOUDINI\":\r\n\t\t\t# Note this is the time slider (playbar) range, not the render\r\n\t\t\t# settings\r\n\t\t\tstart_frame = hou.playbar.playbackRange()[0]\r\n\t\t\tend_frame = hou.playbar.playbackRange()[1]\r\n\r\n\t\telif UI.ENVIRONMENT == \"NUKE\":\r\n\t\t\tstart_frame = nuke.Root()['first_frame'].getValue()\r\n\t\t\tend_frame = nuke.Root()['last_frame'].getValue()\r\n\r\n\t\tself.ui.frames_lineEdit.setText(\"%d-%d\" %(start_frame, end_frame))","title":""},{"docid":"ab0d42cd38951741e47c8f6343f29f35","score":"0.49546847","text":"def trackbar2(theWhere, theX, theY, theWidth, theValue1, theValue2, theMin, theMax, theSegments=1, theLabelFormat='%.1Lf', theOptions=0, theDiscreteStep=1):\n\tprint('This is wrapper function to help code autocompletion.')","title":""},{"docid":"2a1a39a68dc60fbef53764d44b54ec8d","score":"0.4953241","text":"def on_ok_clicked(self, obj):\n\n # Is there a filename? This should also test file permissions, etc.\n if not self.parse_target_frame():\n self.window.run()\n\n # Preparation\n self.parse_format_frame()\n self.parse_user_options()\n\n self.options.handler.set_paper_metric(\n self.paper_frame.get_paper_metric())\n self.options.handler.set_paper_name(self.paper_frame.get_paper_name())\n self.options.handler.set_orientation(self.paper_frame.get_orientation())\n self.options.handler.set_margins(self.paper_frame.get_paper_margins())\n self.options.handler.set_custom_paper_size(\n self.paper_frame.get_custom_paper_size())\n \n # Create the output document.\n self.make_document()\n \n # Save options\n self.options.handler.save_options()\n config.set('interface.open-with-default-viewer', \n self.open_with_app.get_active())","title":""},{"docid":"26063a03b2213531eaf977069b81b84b","score":"0.4944826","text":"def execute(self, parameters, messages):\n\n arcpy.ImportToolbox(os.path.join(os.path.dirname(__file__), \"URB.pyt\"))\n arcpy.gp.toolbox = os.path.join(os.path.dirname(__file__), \"URB.pyt\")\n\n def extentToPoly(extent, srid=3003):\n clist = arcpy.Array()\n clist.append(arcpy.Point(extent.XMin, extent.YMin))\n clist.append(arcpy.Point(extent.XMin, extent.YMax))\n clist.append(arcpy.Point(extent.XMax, extent.YMax))\n clist.append(arcpy.Point(extent.XMax, extent.YMin))\n return arcpy.Polygon(clist)\n\n def get_best_fit_scale(fc,paper,scales=[1000,2000,2500,5000,7500,10000,20000]):\n desc = arcpy.Describe(fc)\n sheet = printOutput_templates[paper][\"size\"]\n margin = 10\n #mapUnitsPerMillimeter = [0.5,1,2,5,10]\n cx = (desc.extent.XMin + desc.extent.XMax)/2\n cy = (desc.extent.YMin + desc.extent.YMax)/2\n fc_bound = extentToPoly(desc.extent)\n\n for scale in scales:\n scaleFactor = scale / 1000\n wb = sheet[0] * scaleFactor / 2\n hb = sheet[1] * scaleFactor / 2\n wf = (sheet[0] - margin*2) * scaleFactor / 2\n hf = (sheet[1] - margin*2) * scaleFactor / 2\n \n #bound = arcpy.Polygon([arcpy.Point(cx-wb,cy-hb), arcpy.Point(cx+wb,cy-hb), arcpy.Point(cx+wb,cy+hb), arcpy.Point(cx-wb,cy+hb)])\n #frame = arcpy.Polygon([arcpy.Point(cx-wf,cy-hf), arcpy.Point(cx+wf,cy-hf), arcpy.Point(cx+wf,cy+hf), arcpy.Point(cx-wf,cy+hf)])\n bound = extentToPoly(arcpy.Extent(cx-wb, cy-hb, cx+wb, cy+hb))\n frame_extent = arcpy.Extent(cx-wf, cy-hf, cx+wf, cy+hf)\n frame = extentToPoly(frame_extent)\n\n #tempfcname = \"in_memory/output\" + uuid.uuid4().hex\n #arcpy.Intersect_analysis ([frame, fc_bound], tempfcname)\n #result = arcpy.GetCount_management(tempfcname)\n #intersections = int(result.getOutput(0))\n\n #if intersections > 0:\n if frame_extent.contains(desc.extent):\n return bound, frame, scale\n\n return bound, frame, scaleFactor\n\n def get_esri_ring(extent):\n ring = [\n [extent.XMin, extent.YMin],\n [extent.XMax, extent.YMin],\n [extent.XMax, extent.YMax],\n [extent.XMin, extent.YMax],\n [extent.XMin, extent.YMin],\n ]\n return ring\n\n probe_path = parameters[0].valueAsText\n coordinate_catastali = parameters[1].valueAsText\n paper = parameters[2].valueAsText.replace(\"'\",\"\")\n base = parameters[3].valueAsText.replace(\"'\",\"\")\n\n checkboxes = []\n #for idx in range(4,12):\n # if parameters[idx].valueAsText == \"true\":\n # checkboxes.append(idx)\n for param in parameters:\n #arcpy.AddMessage(\"param: %s %s\" % (str(param.datatype),str(param.valueAsText)))\n if param.datatype in (\"Booleano\", \"Boolean\") and param.valueAsText == \"true\":\n checkboxes.append(param.name)\n arcpy.AddMessage(\"checkboxes: %s\" % str(checkboxes))\n\n with open(os.path.join(os.path.dirname(__file__),\"web_map_as_json.json\"),\"r\") as jf:\n wmaj_template = jf.read()\n\n template_engine = Template(wmaj_template)\n\n decode_map = []\n\n if coordinate_catastali:\n CC_result = arcpy.gp.CC2FCtool(coordinate_catastali)\n probe_path = CC_result.getOutput(0)\n else:\n if not probe_path:\n arcpy.AddError(\"Deve essere specificata almeno un contesto, come layer o come coordinate catastali\")\n exit(0)\n \n arcpy.AddMessage(\"probe_path: %s paper:\" % probe_path)\n probe = arcpy.mapping.Layer(probe_path)\n with arcpy.da.SearchCursor(probe_path, ['SHAPE']) as cursor: \n probe_polygon = next(cursor)[0]\n\n #probe_json_path = os.path.join(tempfile.mkdtemp(), \"probe.json\")\n probe_json_path = get_jobfile(\"activity\", \"json\")\n arcpy.FeaturesToJSON_conversion(probe_path, probe_json_path, \"FORMATTED\")\n\n with open(probe_json_path ,\"r\") as jf:\n probe_json = jf.read()\n\n #arcpy.AddMessage(json.dumps(json.loads(probe_json),indent=3))\n\n json_feats = []\n probe_json_dict = json.loads(probe_json)\n for feat in probe_json_dict[\"features\"]:\n feat[\"symbol\"] = {\n \"color\": [255, 0, 0, 0],\n \"outline\": {\n \"color\": [255, 0, 0, 255],\n \"width\": 1.75,\n \"type\": \"esriSLS\",\n \"style\": \"esriSLSSolid\"\n },\n \"type\": \"esriSFS\",\n \"style\": \"esriSFSSolid\"\n }\n json_feats.append(feat)\n\n\n result_pdf = []\n\n for tema in temi:\n if not tema[\"label\"] in checkboxes:\n continue\n \n mapServices = basi[base] + tema[\"def\"]\n\n bound, frame, scale = get_best_fit_scale(probe_path, paper)\n\n printpar ={\n \"extent\": [ frame.extent.XMin, frame.extent.YMin, frame.extent.XMax, frame.extent.YMax ],\n \"scale\": scale,\n \"srid\": 3003,\n \"esri_poly\": json.dumps(json.loads(probe_json)[\"features\"][0][\"geometry\"][\"rings\"]),\n \"esri_style\": json.dumps(proto_ESRI_style), #non implementato nel template json\n \"esri_bound\": get_esri_ring(bound.extent),\n \"esri_frame\": get_esri_ring(frame.extent),\n \"title\": tema[\"label\"].upper().replace(\"_\",\"\"),\n \"dpi\": 200,\n \"auth\": \"Settore urbanistica, Servizi catastali e Mobilita'\",\n \"copyright\": \"Comune di Padova\"\n }\n\n web_map_as_json = template_engine.render(printpar = printpar)\n web_map_as_dict = json.loads(web_map_as_json)\n\n web_map_as_dict[\"operationalLayers\"][0][\"featureCollection\"][\"layers\"][0][\"featureSet\"][\"features\"] = json_feats\n web_map_as_dict[\"operationalLayers\"] = mapServices + web_map_as_dict[\"operationalLayers\"]\n web_map_as_json = json.dumps(web_map_as_dict)\n\n post_parameters = {\n \"f\": \"json\",\n \"Web_Map_as_JSON\": web_map_as_json,\n \"Format\": \"PDF\",\n \"Layout_Template\": printOutput_templates[paper][\"label\"]\n }\n\n #arcpy.AddMessage(json.dumps(post_parameters,indent=3))\n\n #pdf_file_path = os.path.join(tempfile.mkdtemp(), tema[\"label\"]+\".pdf\")\n pdf_file_path = get_jobfile(\"output\", \"pdf\")\n\n res = urllib.urlopen(base_url + \"arcgis/rest/services/Utilities/PrintingTools/GPServer/Export%20Web%20Map%20Task/execute\", urllib.urlencode(post_parameters)).read()\n\n if \"results\" in res:\n remoteFile = json.loads(res)['results'][0][\"value\"][\"url\"]\n #arcpy.AddMessage (\"REMOTE: \" + remoteFile)\n urllib.urlretrieve(remoteFile, pdf_file_path)\n arcpy.AddMessage(\"OK: %s\" % tema[\"label\"])\n result_pdf.append(pdf_file_path)\n else:\n arcpy.AddMessage(\"NO\")\n\n if parameters[-1].valueAsText:\n pdf_globale = parameters[-1].valueAsText\n else:\n #pdf_globale = os.path.join(tempfile.mkdtemp(), \"inquadramento.pdf\")\n pdf_globale = get_jobfile(\"output\", \"pdf\")\n\n merger = PdfFileMerger()\n for file in result_pdf:\n merger.append(PdfFileReader(file))\n merger.write(pdf_globale)\n\n parameters[-1].value = pdf_globale\n\n arcpy.AddMessage(\"OK: %s\" % pdf_globale)","title":""},{"docid":"10575b6e0efc5c3e56b8f88ce0b550f0","score":"0.4936005","text":"def info_range(self):\n \n # If there is no data, tell the user and don't show the info dialog.\n if len(self.data) == 0:\n show_no_data_dialog(self, \"Info - %s\" % self.last_profile)\n return\n \n # Get the first and last entered dates.\n day_start = dates.split_date(self.data[0][0])\n day_end = dates.split_date(self.data[len(self.data) - 1][0])\n \n # Get a list of datetimes from the dates.\n datelist = dates.date_list_datetime(datasets.get_column(self.data, 0))\n \n # Get the starting and ending dates.\n cal_dlg = CalendarRangeDialog(self, \"Info in Range - %s\" % self.last_profile, day_start, day_end)\n response = cal_dlg.run()\n year1, month1, day1 = cal_dlg.start_cal.get_date()\n year2, month2, day2 = cal_dlg.end_cal.get_date()\n date1 = \"%d/%d/%d\" % (day1, month1 + 1, year1)\n date2 = \"%d/%d/%d\" % (day2, month2 + 1, year2)\n cal_dlg.destroy()\n \n # If the user did not click OK, don't continue.\n if response != Gtk.ResponseType.OK:\n return\n \n # Get the indices.\n dt_start = datetime.datetime(year1, month1 + 1, day1)\n start_index = dates.date_above(dt_start, datelist)\n dt_end = datetime.datetime(year2, month2 + 1, day2)\n end_index = dates.date_below(dt_end, datelist)\n \n # Check to make sure these dates are valid, and cancel the action if not.\n if start_index == DateValidation.INVALID:\n show_error_dialog(self, \"Info in Range - %s\" % self.last_profile, \"%s is not a valid date.\\n\\nThis date is not present and is not before any other dates.\" % date1)\n return\n if end_index == DateValidation.INVALID:\n show_error_dialog(self, \"Info in Range - %s\" % self.last_profile, \"%s is not a valid date.\\n\\nThis date is not present and is not after any other dates.\" % date2)\n return\n if end_index < start_index:\n show_error_dialog(self, \"Info in Range - %s\" % self.last_profile, \"The ending date must be after the starting date.\")\n return\n \n # Get the new list.\n data2 = self.data[start_index:end_index + 1]\n \n # Pass the data to the info dialog.\n self.show_info_generic(data = data2)","title":""},{"docid":"6c916096b47eb99b3f25d7ca7bd57a62","score":"0.49245515","text":"def select_rectangle(infile, start, res_dict, fits_dict, wloc, outfil, maxim):\r\n im, header = get_fits_image(infile + str(start))\r\n im = im / np.max(im)\r\n get_fits_keys(header, fits_dict, res_dict, keyprint=False)\r\n # #===================================================================\r\n # new rect_plt\r\n # first get size of graph from tmp.png and size of image\r\n # graph coordinates are in image pixels!\r\n (imy, imx) = im.shape[:2]\r\n image_file = 'tmp.png' # scaled image\r\n imbw = np.flipud(ios.imread(image_file)) # get shape\r\n (canvasy, canvasx) = imbw.shape[:2]\r\n wlocw = (wloc[0] + 300, wloc[1] + 50)\r\n # check for old files\r\n delete_old_files(outfil, maxim, ext='.fit')\r\n image_elem_sel = [sg.Graph(\r\n canvas_size=(canvasx, canvasy),\r\n graph_bottom_left=(0, 0), # starts at top, set y-scale here\r\n graph_top_right=(imx, imy), # set x-scale here\r\n key='-GRAPH-',\r\n change_submits=True, # mouse click events\r\n drag_submits=True)]\r\n layout_select = [[sg.Text('Start File: ' + infile + str(start), size=(50, 1)), sg.Text(key='info', size=(40, 1)),\r\n sg.Ok(), sg.Cancel()],\r\n image_elem_sel]\r\n # ---------------------------------------------------------------------------\r\n winselect_active = True\r\n winselect = sg.Window(f'select zero order or spectral line',\r\n layout_select, finalize=True, location=wlocw,\r\n keep_on_top=True, no_titlebar=False,\r\n disable_close=False, disable_minimize=True)\r\n # get the graph element for ease of use later\r\n graph = winselect['-GRAPH-'] # type: sg.Graph\r\n graph.draw_image(image_file, location=(0, imy)) if image_file else None\r\n winselect.refresh()\r\n dragging = False\r\n start_point = end_point = prior_rect = None\r\n x0 = y0 = dx = dy = 0\r\n while winselect_active:\r\n event, values = winselect.read()\r\n idg = graph.draw_rectangle((0, 0), (imx, imy), line_color='blue')\r\n if event == \"-GRAPH-\": # if there's a \"Graph\" event, then it's a mouse\r\n x, y = (values[\"-GRAPH-\"])\r\n if not dragging:\r\n start_point = (x, y)\r\n dragging = True\r\n else:\r\n end_point = (x, y)\r\n if prior_rect:\r\n graph.delete_figure(prior_rect)\r\n if None not in (start_point, end_point):\r\n prior_rect = graph.draw_rectangle(start_point,\r\n end_point, line_color='red')\r\n elif event is not None and event.endswith('+UP'):\r\n # The drawing has ended because mouse up\r\n xy0 = [int(0.5 * (start_point[0] + end_point[0])),\r\n int(0.5 * (start_point[1] + end_point[1]))]\r\n size = (abs(start_point[0] - end_point[0]),\r\n abs(start_point[1] - end_point[1]))\r\n info = winselect[\"info\"]\r\n info.update(value=f\"grabbed rectangle at {xy0} with size {size}\")\r\n start_point, end_point = None, None # enable grabbing a new rect\r\n dragging = False\r\n if min(size[0], size[1]) > 1: # rectangle\r\n info.update(value=f\"rectangle at {xy0} with size {size}\")\r\n x0 = xy0[0]\r\n y0 = xy0[1]\r\n dx = int((size[0] + 1) / 2)\r\n dy = int((size[1] + 1) / 2)\r\n\r\n elif event in ('Ok', 'Cancel'):\r\n graph.delete_figure(idg)\r\n winselect_active = False\r\n winselect.close()\r\n return event, x0, y0, dx, dy","title":""},{"docid":"59996cf468705e8938141a9c24208f86","score":"0.4921469","text":"def show_frameShark(self, cont, listofbuttons):\n frame = cont(self.container, self)\n self.frames[cont] = frame \n\n ExpsToGraph = []\n expnames = []\n unanalyzedlist = []\n result = True\n\n # Get list of experiments to plot\n for button in listofbuttons:\n if button.instate(['selected']):\n ExpsToGraph.append(getobject(button['text']))\n\n if len(ExpsToGraph) == 0: # Did not choose an experiment to graph\n tkMessageBox.showwarning(\"Error\", \"No experiments selected\")\n else:\n # Check to see if all experiments selected have been analyzed\n for experiment in ExpsToGraph:\n if experiment.expy[0] == \"\":\n unanalyzedlist.append(experiment.expnumber)\n else:\n experiment.expy = list(map(int, experiment.expy))\n frame.a.plot(range(len(experiment.expy)),experiment.expy, label=experiment.expnumber)\n expnames.append(experiment.expnumber)\n\n\n if len(unanalyzedlist) > 0: # If unanalyzed experiments exist warn user\n unanalyzedlist = \", \".join(unanalyzedlist)\n result = tkMessageBox.askquestion(\"Warning\", \"The following experiments have\\nnot been analyzed yet\\nand will not be graphed\\n\\n\" +unanalyzedlist+ \"\\n\\nProceed anyways?\")\n\n if result != \"no\": # Override or all experiments have been analyzed \n frame.grid(row=0, column=0, sticky=\"nsew\") \n frame.a.legend(loc='upper right', fontsize=8)\n frame.canvas.draw() \n frame.tkraise()","title":""},{"docid":"057084b294b3f12db6cccefb5b454320","score":"0.49202904","text":"def menu(file_name):\n print (80 * '*')\n print ('*' + 1*' ' + 'PDB FILE ANALYZER' + 60*' ' + '*')\n print (80 * '*')\n print ('*'+ 1*' ' + 'Select an option from below:' + 49*' ' + '*')\n print ('*' + 78*' ' + '*')\n print ('*' + 6*' ' + '1) Open a PDB File' + 23*' ' + '(O)' + 28*' ' + '*')\n print ('*' + 6*' ' + '2) Information'+ 27*' ' + '(I)' + 28*' ' + '*')\n print ('*' + 6*' ' + '3) Show histogram of amino acids '+ 8*' ' + '(H)' + 28*' ' + '*')\n print ('*' + 6*' ' + '4) Display Secondary Structure'+ 11*' ' + '(S)' + 28*' ' + '*')\n print ('*' + 6*' ' + '5) Export PDB File '+ 22*' ' + '(X)' + 28*' ' + '*')\n print ('*' + 6*' ' + '6) Exit '+ 33*' ' + '(Q)' + 28*' ' + '*')\n print ('*' + 55*' ' + 'Current PDB: %s ' %file_name + '*')\n print (80 * '*')","title":""},{"docid":"b559ccd5b2fc271a56d1f69417096004","score":"0.4901829","text":"def quicklook(SMPS):\n # create particle sizer display class\n psd = PSDisplay(SMPS)\n \n # create sample slider\n sample = widgets.IntSlider(min=0, max=len(SMPS.sample['data'])-1, continuous_update = False, description = 'sample #',layout = Layout(width = '100%'))\n \n # create fields slider containing SMPS data variables\n sfields = widgets.ToggleButtons(options=SMPS.data['variables'], value = SMPS.data['variables'][0], description='Variable:', disabled=False, button_style='')\n \n # create D50 checkbox\n D50line = widgets.Checkbox(value=False,description='D50',disabled=False,indent=False, layout = Layout(width='20%'))\n \n # create boundary line checkbox\n boundaryline = widgets.Checkbox(value=False,description='Boundaries',disabled=False, indent=False, layout = Layout(width='20%'))\n \n # create periodical indicator checkbox\n indperiods = widgets.Checkbox(value=False,description='24 hour periods',disabled=False,indent=False, layout = Layout(width='20%'))\n \n # create starttime for periodical indicator text box\n starttime = widgets.Text(value='00:00:00', placeholder='HH:MM:SS', description='Start time:', disabled=False)\n \n # create count limit box\n clim = widgets.BoundedFloatText( value=2*10**4, min=1*10**1, max=1*10**8,step=1*10**1, description='clim:',\n style = {'description_width': 'initial'}, disabled=False, continuous_update=True, orientation='horizontal',\n readout=True, readout_format='.2f', layout = Layout(width='25%'))\n \n # create diameter limit box\n ylim = widgets.BoundedFloatText( value=1*10**3, min=1*10**1, max=1*10**8,step=1*10**1, description='dlim:', disabled=False, continuous_update=True, orientation='horizontal', readout=True, readout_format='.2f', layout = Layout(width='25%'))\n \n # create histogram outplot\n outplot = widgets.interactive_output(psd.histogram, {'field':sfields,'sample':sample,'add_D50':D50line,'add_boundaries':boundaryline, 'xlim': ylim,'ylim':clim}) \n \n # create heatplot outplot\n outplot2 = widgets.interactive_output(psd.plot, {'field':sfields,'clim':clim, 'ylim':ylim, 'indicator':sample,'periods':indperiods, 'starttime':starttime})\n \n # create printed date output\n date = widgets.interactive_output(psd.dateInfo,{'sample':sample})\n \n # create organised display widgets\n ui1 = widgets.HBox([sfields])\n ui2 = widgets.HBox([D50line, boundaryline, clim, ylim])\n uidate = widgets.HBox([date])\n uiperiods = widgets.HBox([indperiods, starttime])\n ui3 = widgets.VBox([ui1, ui2, outplot, sample, uidate, outplot2, uiperiods], layout = Layout(height='100%'))\n \n return ui3","title":""},{"docid":"9f93d5ab254c4e19bda8c011f7267793","score":"0.4883903","text":"def capture(rect=None, filepath='', prompt=True, hideWindow=None):\n widget = XSnapshotWidget(QApplication.desktop())\n widget.setRegion(rect)\n widget.setHideWindow(hideWindow)\n widget.setFilepath(filepath)\n widget.move(1, 1)\n widget.resize(QApplication.desktop().size())\n \n if prompt or not filepath:\n widget.show()\n else:\n widget.save()","title":""},{"docid":"9bcfa9d4763be5223839a42fd5ee1d31","score":"0.4878805","text":"def user_input_from_gui(min_score, head_dir, max_score=\"NO\"):\n get_tables()\n global data\n cwd = os.getcwd()\n os.chdir(head_dir)\n if max_score == \"NO\":\n data_ = data[data[\"score_wo_prcnt\"] >= min_score]\n fname = str(round(min_score, 2)) + \".tsv\"\n data_.to_csv(fname, index=False, sep=\"\\t\")\n elif isinstance(max_score, int) or isinstance(max_score, float):\n data_ = data[\n (data[\"score_wo_prcnt\"] >= min_score)\n & (data[\"score_wo_prcnt\"] <= max_score)\n ]\n fname = str(round(min_score, 2)) + \"-\" + str(round(max_score, 2)) + \".tsv\"\n data_.to_csv(fname, index=False, sep=\"\\t\")","title":""},{"docid":"0e39149363016fd0e877963dc1cd63e9","score":"0.48700252","text":"def debug_show_window(self, original_frame, current_index, frames_results):\n cv2.namedWindow(str(self.cfg.index),\n cv2.WINDOW_NORMAL | cv2.WINDOW_FREERATIO)\n frame = original_frame.copy()\n if len(frames_results):\n for rect in frames_results[0]:\n if rect[4] > self.cfg.alg['ssd_confidence']:\n # cv2.imwrite(f'data/frames/{self.cfg.index}_{current_index}.png',\n # cv2.cvtColor(original_frame, cv2.COLOR_BGR2RGB))\n # crop_frame = cv2.cvtColor(original_frame, cv2.COLOR_BGR2RGB)[int(rect[1]):int(rect[3]), int(rect[0]):int(rect[2]), :]\n # cv2.imwrite(f'/home/jt1/Desktop/crop_images/{current_index}.png', crop_frame)\n color = np.random.randint(0, 255, size=(3,))\n color = [int(c) for c in color]\n # get a square bbox, the real bbox of width and height is universal as 224 * 224 or 448 * 448\n p1, p2 = bbox_points(self.cfg, rect, original_frame.shape)\n # write text\n frame = paint_chinese_opencv(frame, '江豚', p1)\n cv2.rectangle(\n frame, (rect[0], rect[1]), (rect[2], rect[3]), color, 2)\n cv2.putText(frame, str(round(rect[4], 2)), (p2[0], p2[1]),\n cv2.FONT_HERSHEY_SIMPLEX, 2, color, 2, cv2.LINE_AA)\n cv2.imshow(str(self.cfg.index), cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n cv2.waitKey(1)","title":""},{"docid":"5ac3928f2a0e1737a982082ed9593ab6","score":"0.48533505","text":"def show_native_ui():\n # Set up the flipbook:\n setup_flipbook()\n\n # Set the window name:\n window_name = \"animation_flipbook\"\n\n # If it already exists, kill it:\n if cmds.window(window_name, exists=True):\n cmds.deleteUI(window_name)\n\n if cmds.windowPref(window_name, exists=True):\n cmds.windowPref(window_name, remove=True)\n\n # Set up the window and the layout:\n cmds.window(window_name, title=\"Flipbook Animation Tool\")\n cmds.columnLayout(adjustableColumn=True)\n cmds.rowColumnLayout(columnAlign=(1, \"left\"), nc=1, cw=[(1, 480)])\n\n cmds.floatFieldGrp(\"set_framerange_field\",\n numberOfFields=2,\n label=\"Start Frame\",\n el=\"End Frame\",\n pre=1,\n v1=1.0,\n v2=24.0)\n\n cmds.button(w=10, label=\"Set Framerange\", command=set_framerange)\n cmds.text(label=\"\")\n\n # Select/deselect the pencil tool:\n cmds.button(w=20, label=\"Select Pencil Tool\", command=select_pencil_tool)\n cmds.button(w=20, label=\"Deselect Pencil Tool\", command=deselect_pencil_tool)\n cmds.text(label=\"\")\n\n # Set the page:\n cmds.button(w=20, label=\"Set page\", command=set_page)\n cmds.button(w=20, label=\"Add to page\", command=insert_page)\n cmds.button(w=20, label=\"Delete page\", command=delete_page)\n cmds.text(label=\"\")\n\n # Go to page:\n go_to_page_field = cmds.floatFieldGrp(\"go_to_page_field\",\n numberOfFields=1,\n columnWidth=(10, 10),\n columnAlign2=(\"left\", \"left\"),\n label=\"Go to frame\",\n pre=1,\n v1=1.0)\n\n cmds.button(w=20, label=\"Go to page\", command=lambda *args: go_to_page(go_to_page_field))\n cmds.text(label=\"\")\n\n cmds.button(w=20, label=\"Display Next Page\", command=display_next_page)\n cmds.button(w=20, label=\"Display Previous Page\", command=display_previous_page)\n cmds.text(label=\"\")\n\n cmds.button(w=20, label=\"Onion skin future frames\", command=display_next_pages)\n cmds.button(w=20, label=\"Onion skin past frames\", command=display_previous_pages)\n cmds.text(label=\"\")\n\n # Set the framerange:\n cmds.floatFieldGrp(\"set_loop_field\",\n numberOfFields=2,\n w=2,\n columnAlign2=(\"left\", \"left\"),\n label=\"Number of loops\",\n el=\"Step\",\n cw2=(10, 10),\n pre=1,\n v1=0.0,\n v2=0.0)\n cmds.button(w=20, label=\"Loop Selection\", command=loop_selected)\n cmds.text(label=\"\")\n\n # Playblast:\n cmds.button(w=20, label=\"Playblast\", command=playblast_scene)\n cmds.text(label=\"\")\n\n # Save:\n cmds.button(w=20, label=\"Save\", command=save_scene)\n cmds.text(label=\"\")\n cmds.setParent(\"..\")\n cmds.showWindow(window_name)","title":""},{"docid":"bd3ee6a0908ca3d6441e0ca50d55db73","score":"0.48511147","text":"def frame_range(self):\n\t\treturn self.end - self.start + 1","title":""},{"docid":"a64c9af71ca44ea8d15b16cb3a804a11","score":"0.48430294","text":"def _on_export(self, sender, *args):\r\n\t\t# NOTE: Ignores timerange for now\r\n\t\tif not self.nodeselector.uses_selection() and not self.nodeselector.selected_namespaces():\r\n\t\t\traise ValueError(\"Please select what to export from the scroll list\")\r\n\t\t# END handle invalid input\r\n\t\t\r\n\t\t# GET FILEPATH\r\n\t\t# on linux, only one filter is possible - it would be good to have a \r\n\t\t# capable file dialog coming from MRV ( in 2011 maybe just an adapter to \r\n\t\t# fileDialog2 )\r\n\t\tfile_path = cmds.fileDialog(mode=1,directoryMask=\"*.mb\")\r\n\t\tif not file_path:\r\n\t\t\treturn\r\n\t\t# END bail out\r\n\t\t\r\n\t\textlist = ( \".ma\", \".mb\" )\r\n\t\tcollection = [ p.basename() for p in ui.UI(self.filetype.p_collectionItemArray) ]\r\n\t\ttarget_ext = extlist[collection.index(self.filetype.p_select)]\r\n\t\t\r\n\t\tfile_path = Path(file_path)\r\n\t\tfile_path = file_path.stripext() + target_ext\r\n\t\t\r\n\t\tlib.AnimInOutLibrary.export(file_path, self.nodeselector.iter_nodes(asNode=False))","title":""},{"docid":"9bcb9f82a8b64d79d9d8ac64acde5e2a","score":"0.48208085","text":"def display(self, *arrays, **kwargs):\n self.win = DS9Win(self.name, doOpen=True) # creates/detects the VIP ds9\n if kwargs.has_key('keepwin'):\n if kwargs['keepwin']: pass\n else: self.delete_frame(allfr=True)\n else: \n self.delete_frame(allfr=True)\n \n self.create_frame()\n self.tile()\n for i, array in enumerate(arrays):\n if i==0: \n self.win.showArray(array) \n else: \n self.create_frame()\n self.win.showArray(array)","title":""},{"docid":"512d0514dd4b051bda297005f1745949","score":"0.48152936","text":"def createUI():\r\n #------- check to see if the window already exists and deletes if it does.-------#\r\n if(cmds.window(\"UI\",exists=True)):\r\n cmds.deleteUI(\"UI\")\r\n \r\n #create the window\r\n window = cmds.window(\"UI\", title=\"Audio Driven Keyframes\")\r\n \r\n #initialize the form layout\r\n form = cmds.formLayout(numberOfDivisions=100)\r\n \r\n #initialize progress bar\r\n progress = cmds.progressBar('progress')\r\n \r\n # create open button\r\n openButton = cmds.button(label=\"Open...\", command=('openFile()')) \r\n \r\n #new column layout\r\n infoCol = cmds.columnLayout()\r\n \r\n #------- if the audio has already been imported use it -------#\r\n if(cmds.objExists('audio')):\r\n \r\n global fileName, wavFile, params, frames\r\n fileName = cmds.getAttr('audio.filename')\r\n \r\n #------- set up the stream -------#\r\n wavFile = wave.open(fileName) \r\n params = wavFile.getparams() \r\n frames = wavFile.readframes(params[3])\r\n cmds.progressBar('progress', edit=True, maxValue = params[3])\r\n \r\n #------- text label for the current audio file loaded -------#\r\n currentAudioPath = cmds.text('currentAudioPath', label=\"Currently loaded \"+fileName)\r\n currentParams = cmds.text('currentParams', label=str(params))\r\n \r\n #------- otherwise just place dummy text-------# \r\n else:\r\n currentAudioPath = cmds.text('currentAudioPath', label=\"Please choose an audio file.\")\r\n currentParams = cmds.text('currentParams', label=\"\")\r\n \r\n cmds.setParent(\"..\")\r\n \r\n # create grid layout for all the variables in the form\r\n numbers = cmds.rowColumnLayout(numberOfColumns=2, columnWidth=[(1,100)])\r\n \r\n #create channel option boxes\r\n cmds.text(label=\"Channel\")\r\n cmds.radioButtonGrp('channel',\r\n nrb=3, \r\n onc=\"changeChannel()\", \r\n numberOfRadioButtons=3, \r\n labelArray3=['Left', 'Right', 'Both (Mono)'], \r\n sl=chosenChannel )\r\n \r\n #create axis option boxes\r\n cmds.text(label=\"Axis\")\r\n cmds.radioButtonGrp('axis',\r\n nrb=3, \r\n onc=\"changeAxis()\", \r\n numberOfRadioButtons=3, \r\n labelArray3=['X', 'Y', 'Z'], \r\n sl=chosenAxis )\r\n \r\n #create detail slider\r\n cmds.text(label=\"Detail\")\r\n detailSld = cmds.intSliderGrp('detail', field=True, cc=\"changeDetail()\", value=50,minValue = 1, maxValue=100)\r\n \r\n #create range row\r\n cmds.text(label=\"Range\")\r\n cmds.rowLayout(numberOfColumns=5)\r\n \r\n #min range field\r\n cmds.floatField('min', value=minMax[0])\r\n \r\n #assign current-to-min button\r\n cmds.button('assignLeft', label='<', c=\"assignLeft()\")\r\n \r\n #initialize the current field and if the object has been selected use the objects default axis value otherwise just set it to 0\r\n if(cmds.ls(sl=True)): \r\n selection = cmds.ls(sl=True)\r\n cmds.floatField('current', rfc=\"updateCurrentValue()\", value=cmds.getAttr(selection[0]+\".\"+axis[chosenAxis-1]))\r\n else:\r\n cmds.floatField('current', rfc=\"updateCurrentValue()\", value=0)\r\n \r\n #assign current-to-max button\r\n cmds.button('assignRight', label='>', c=\"assignRight()\")\r\n \r\n #max range field\r\n cmds.floatField('max', value=minMax[1])\r\n cmds.setParent(\"..\")\r\n \r\n #autolock option box\r\n cmds.text(label=\"Auto Lock\")\r\n cmds.checkBox('autoLock', label=\"\", cc=('lock()'), value=autoLock)\r\n \r\n cmds.setParent(\"..\")\r\n \r\n #make the generate and delete buttons on one row\r\n buttonsRow = cmds.rowColumnLayout(numberOfColumns=2)\r\n createButton = cmds.button(label=\"Generate Keyframes\", command=('generateKeyframes()'))\r\n deleteButton = cmds.button(label=\"Delete Keyframes\", command=('deleteKeyframes()')) \r\n \r\n \r\n cmds.setParent(\"..\") \r\n \r\n \r\n #------ put all these items in the form -----#\r\n cmds.formLayout(form, edit=True, attachForm=[(openButton, 'left', 10),\r\n (openButton, 'right', 10), \r\n (openButton, 'top', 10),\r\n (infoCol, 'left', 10),\r\n (infoCol, 'right', 10),\r\n (numbers, 'left', 10),\r\n (numbers, 'right', 10),\r\n (buttonsRow, 'left', 10),\r\n (buttonsRow, 'right', 10),\r\n (progress, 'left', 10),\r\n (progress, 'right', 10),\r\n (progress, 'bottom', 10),],\r\n attachControl=[(infoCol, 'top',10, openButton),\r\n (numbers, 'top', 10, infoCol),\r\n (buttonsRow, 'bottom',0, progress)])\r\n \r\n cmds.showWindow()","title":""},{"docid":"1e4bea3c5ed5df5100afb785e81b0ba0","score":"0.48067906","text":"def plot_roi(start_frames, end_frames):\n #reads up until start_frames if start_frames !=0\n video = read_video()\n for i in range (start_frames, end_frames):\n fig, ax = plt.subplots()\n frame = video.read()[1][:,:,1]\n ax.imshow(frame[remodeled_roi_list()[i]])\n plt.ginput(10000, timeout=0, show_clicks=False)\n plt.close()","title":""},{"docid":"f6121ccdfcba5c6aa3b60e659d87f76d","score":"0.4806571","text":"def usage(code):\n print 'Usage: python all_frames.py frame amp'\n sys.exit(code)","title":""},{"docid":"b9807cbf611fb7a3f12113dfdc45bee1","score":"0.48044258","text":"def OpenBrowser(self): #Not currently in use\n filename1 = QFileDialog.getSaveFileName()\n self.export_layer.lineEdit.setText(filename1)","title":""},{"docid":"872982095fb26eabcc94dde4983e81d7","score":"0.4797458","text":"def _textToFrame(self):\n if self.help_text:\n # show Frame #currect_frame : bounding_box\n annotation = \"nan,nan,nan,nan\"\n if self.bounding_box:\n annotation = self.parser.bboxString(self.bounding_box)\n elif self.pt1:\n annotation = str(self.pt1[0]) + \",\" + str(self.pt1[1]) + \",nan,nan\"\n\n cv2.putText(self.frame, \"Frame #\" + str(self.current_frame) + \" : \" + annotation, self.TEXT_ROW1_POS, cv2.FONT_HERSHEY_SIMPLEX, self.FONT_SCALE, (250, 250, 0), self.FONT_WEIGHT)\n\n # show control help\n info = \"'Enter' = next frame, 'Backspace' = previous frame, 'Q' = quit, 'H' = hide this text\"\n cv2.putText(self.frame, info, self.TEXT_ROW2_POS, cv2.FONT_HERSHEY_SIMPLEX, self.FONT_SCALE, (0, 150, 250), self.FONT_WEIGHT)\n info2 = \"'R' = reset annotation, 'P' = previous annotation, 'D' = duplicate previous annotation\"\n cv2.putText(self.frame, info2, self.TEXT_ROW3_POS, cv2.FONT_HERSHEY_SIMPLEX, self.FONT_SCALE, (0, 150, 250), self.FONT_WEIGHT)","title":""},{"docid":"d35628ad47e9422a8c5f428a86bb5326","score":"0.47954303","text":"def takeSnapshot(fullFileName, type=-1, hideAnnotations=False):\n # show the message even if not taking a screen shot\n lm = slicer.app.layoutManager()\n # switch on the type to get the requested window\n widget = 0\n if type == slicer.qMRMLScreenShotDialog.FullLayout:\n # full layout\n widget = lm.viewport()\n elif type == slicer.qMRMLScreenShotDialog.ThreeD:\n # just the 3D window\n widget = lm.threeDWidget(0).threeDView()\n elif type == slicer.qMRMLScreenShotDialog.Red:\n # red slice window\n widget = lm.sliceWidget(\"Red\")\n elif type == slicer.qMRMLScreenShotDialog.Yellow:\n # yellow slice window\n widget = lm.sliceWidget(\"Yellow\")\n elif type == slicer.qMRMLScreenShotDialog.Green:\n # green slice window\n widget = lm.sliceWidget(\"Green\")\n else:\n # default to using the full window\n widget = slicer.util.mainWindow()\n # reset the type so that the node is set correctly\n #type = slicer.qMRMLScreenShotDialog.FullLayout\n\n if hideAnnotations:\n SlicerUtil.hideAllCornerAnnotations()\n # grab and convert to vtk image data\n qpixMap = qt.QPixmap().grabWidget(widget)\n # Save as a png file\n qpixMap.save(fullFileName)\n\n return fullFileName","title":""},{"docid":"94c99d463efaf984a7c15d32d1f03311","score":"0.47950166","text":"def setRange(self,minimum,maximum):\r\n\t\tself.ProgressBar.setRange(minimum,maximum)","title":""},{"docid":"9868a2245d5956648088dd004aeb79e1","score":"0.47900984","text":"def printRanges(self) -> unicode:\n ...","title":""},{"docid":"079e42b992c92d22ad4d6978915fd7dd","score":"0.4784321","text":"def displayRange(lower, upper):\n if lower <= upper:\n print(lower)\n displayRange(lower + 1, upper)","title":""},{"docid":"593765919e32137d37905da4bb3a091e","score":"0.4780807","text":"def show_frameCharlie(self, cont):\n frame = self.frames[cont]\n frame.confirmlabels()\n frame.tkraise()","title":""},{"docid":"5e83dee90e67d54cb140d5aaac56152d","score":"0.47752175","text":"def graph_range(self):\n \n # If there is no data, tell the user and don't show the info dialog.\n if len(self.data) == 0:\n \n # Show the dialog.\n show_no_data_dialog(self, \"Graphs - %s\" % self.last_profile)\n return\n \n # Get the first and last entered dates.\n day_start = dates.split_date(self.data[0][0])\n day_end = dates.split_date(self.data[len(self.data) - 1][0])\n \n # Get a list of datetimes from the dates.\n datelist = dates.date_list_datetime(datasets.get_column(self.data, 0))\n \n # Get the starting and ending dates.\n cal_dlg = CalendarRangeDialog(self, \"Graphs in Range - %s\" % self.last_profile, day_start, day_end)\n response = cal_dlg.run()\n year1, month1, day1 = cal_dlg.start_cal.get_date()\n year2, month2, day2 = cal_dlg.end_cal.get_date()\n date1 = \"%d/%d/%d\" % (day1, month1 + 1, year1)\n date2 = \"%d/%d/%d\" % (day2, month2 + 1, year2)\n cal_dlg.destroy()\n \n # If the user did not click OK, don't continue.\n if response != Gtk.ResponseType.OK:\n return\n \n # Get the indices.\n dt_start = datetime.datetime(year1, month1 + 1, day1)\n start_index = dates.date_above(dt_start, datelist)\n dt_end = datetime.datetime(year2, month2 + 1, day2)\n end_index = dates.date_below(dt_end, datelist)\n \n # Check to make sure these dates are valid, and cancel the action if not.\n if start_index == DateValidation.INVALID:\n show_error_dialog(self, \"Graphs in Range - %s\" % self.last_profile, \"%s is not a valid date.\\n\\nThis date is not present and is not before any other dates.\" % date1)\n return\n if end_index == DateValidation.INVALID:\n show_error_dialog(self, \"Graphs in Range - %s\" % self.last_profile, \"%s is not a valid date.\\n\\nThis date is not present and is not after any other dates.\" % date2)\n return\n if end_index < start_index:\n show_error_dialog(self, \"Graphs in Range - %s\" % self.last_profile, \"The ending date must be after the starting date.\")\n return\n \n # Get the new list.\n data2 = self.data[start_index:end_index + 1]\n \n # Pass the data to the charts dialog.\n self.show_graph_generic(data = data2)","title":""},{"docid":"fea6e6b5bce8e123eb5ad16856f340e1","score":"0.47744238","text":"def fnirs_montage_ui():\r\n source_labels = input(\"please enter sources names with the S# format: \").split()\r\n\r\n detector_labels = input(\"please enter detectors names with the D# format: \").split()\r\n\r\n Nz = input(\"please enter 3D Coordination of tip of the nose x y z in mm: \").split()\r\n for i in range(len(Nz)):\r\n Nz[i] = float(Nz[i])\r\n\r\n RPA = input(\"please enter 3D Coordination of the right preauricular x y z in mm: \").split()\r\n for i in range(len(RPA)):\r\n RPA[i] = float(RPA[i])\r\n\r\n LPA = input(\"please enter 3D Coordination of the left preauricular x y z in mm: \").split()\r\n for i in range(len(LPA)):\r\n LPA[i] = float(LPA[i])\r\n\r\n head_size = float(input(\"please enter the head size in mm \"))\r\n\r\n return source_labels, detector_labels, Nz, RPA, LPA, head_size","title":""},{"docid":"221e1d110dd4e0c28c5db1c02b903bf7","score":"0.47701207","text":"def run(self):\n # show the dialog\n self.dlg.show()","title":""},{"docid":"dd1fb71b9d6c35f6e3ed1866d0b0c1a4","score":"0.4759211","text":"def print_out(self):\n self.app.Application.PrintOut()","title":""},{"docid":"76b25b5eb5b96acb2a833cdd6de29f4e","score":"0.47543108","text":"def show_frameAlpha(self, cont):\n #Reset all of experiment-class variables\n Appa.expnumber = str()\n Appa.exptype = str()\n Appa.exptime = int()\n Appa.savefile = str()\n Appa.expy = []\n app.startfresh() #Reinitalize all necessary pages to starting state\n frame = self.frames[cont]\n frame.tkraise() #raise to front","title":""},{"docid":"85bb3d1c5b1690ff43923a6030238051","score":"0.4752486","text":"def set_framerange(*args):\n start_frame = cmds.floatFieldGrp(\"set_framerange_field\", query=True, v1=True)\n end_frame = cmds.floatFieldGrp(\"set_framerange_field\", query=True, v2=True)\n animationflipbook.set_framerange(start_frame, end_frame)","title":""},{"docid":"d6962c2ab3daf67b52f88cff449e8150","score":"0.4748542","text":"def run():\n root = tk.Tk()\n root.title('Virus Spreads as per Mutation Patterns')\n root.minsize(300, 300)\n\n # This section simply sets the scene and creates the dropdown menu. The dropdown menu is tied to the region_selected variable\n mainframe = tk.Frame(master=root, height=20, width=50)\n tableFrame = tk.Frame(master=mainframe)\n regions = get_regions()\n region_selected = tk.StringVar(master=root, name='reg')\n region_selected.set(regions[0])\n dropdown = tk.OptionMenu(mainframe, region_selected, *regions)\n\n # This function generates the table for a given area. It's a local method so that it can access the tableFrame\n def generate_table(area, dummy1, dummy2):\n nonlocal tableFrame\n tableFrame.destroy()\n tableFrame = tk.Frame(master=mainframe)\n tableFrame.grid(row=1, column=1)\n results = get_spreads(root.getvar(area))\n titles = ('Source Region', '# of Cases from Source')\n tab = Table(tableFrame, titles, results)\n\n # Bind updates of the dropdown to the creation of tables.\n region_selected.trace_add('write', generate_table)\n\n # Place the dropdown and the frame holding the table into their appropriate grid positions.\n mainframe.pack()\n dropdown.grid(row=0, column=1)\n tableFrame.grid(row=1, column=1)\n\n root.mainloop()","title":""},{"docid":"6e292408bd292f993d539b97a848669a","score":"0.47433022","text":"def showOptions(self):\n message = \"P: Plot generate image in plt \"\n self.render.render_text(self.ttt, message,\n (self.x_0 + 200, self.y_max + 20))\n message = \"I: Do interpolation two points\"\n self.render.render_text(self.ttt, message,\n (self.x_0 + 200, self.y_max + 40))\n message = \"O: Insert images in the latent space\"\n self.render.render_text(self.ttt, message,\n (self.x_0 + 200, self.y_max + 60))","title":""},{"docid":"65d2551073a01baf013e9f2d74ae0878","score":"0.4731846","text":"def run(self, frames=120):\r\n anim = FuncAnimation(self.fig, self.step, blit=False, interval=100, frames=frames)\r\n # If you want to save your animations, you can comment either\r\n # of the lines below.\r\n # NOTE: FFmpeg is needed to render a .mp4 video of the animation.\r\n anim.save('/mnt/c/Users/tirth/Desktop/Searching in AI/ucs_large_with_weights.mp4')\r\n # anim.save('animation.html')\r\n # plt.show()\r","title":""},{"docid":"41b953d5f1e5b8ea6fda8339468bdc9a","score":"0.47301832","text":"def show_frameResults(k):\n\n global img\n global moransReport\n\n # Functions for frameResults\n def load_image(k, type): \n fileName = f'reports/{type}_{str(k).replace(\".\",\"_\")}.jpg'\n img = Image.open(fileName)\n img = img.resize((353,457))\n imgTk = ImageTk.PhotoImage(img)\n return imgTk\n\n def change_image(k, type, curMap):\n \"\"\" Change the image in the results frame \"\"\"\n imgTk = load_image(k, type)\n curMap.configure(image=imgTk)\n curMap.image = imgTk\n\n def save_as_pdf(k, type):\n saveAsPath = filedialog.asksaveasfilename(initialdir=\"/\", filetypes=[(\"pdf files\", \"*.pdf\")])\n if \".pdf\" not in saveAsPath.lower():\n saveAsPath += '.pdf'\n imagePath = f'reports/{type}_{str(k).replace(\".\",\"_\")}.jpg'\n img = Image.open(imagePath)\n img.save(saveAsPath, \"PDF\")\n\n def view_ols_report(k):\n import os\n olsFile = f'{os.getcwd()}\\\\ols_reports\\\\{k}_ols.pdf'\n subprocess.Popen(olsFile, shell=True)\n\n def view_moransI_report(path):\n if path:\n subprocess.Popen(path, shell=True)\n else:\n messagebox.showerror(\"Error loading Moran's I report\", \"There was an error loading the Moran's I report.\")\n\n # Reset frameResults to clear any previous analysis runs\n for child in frameResults.winfo_children():\n child.destroy()\n\n # Show frameResults\n frameResults.place(relheight=0.9, relwidth=0.4, relx=0.55, rely=0.05)\n\n # Show map image (default to IDW map)\n img = load_image(k, \"IDW\")\n curMap = tk.Label(frameResults, image=img)\n curMap.pack()\n\n # Show button controls\n frameButtons = tk.Frame(frameResults, bg=\"black\", height=80) \n frameButtons.pack(side='bottom', fill='x')\n\n btnIDW = ttk.Button(frameButtons, text=\"Show IDW Results\", command=lambda: change_image(k, \"IDW\", curMap))\n btnIDW.place(x=5, rely=0.15, relwidth=0.3, relheight=0.3)\n\n btnIDWSave = ttk.Button(frameButtons, text=\"Save IDW to PDF...\", command=lambda: save_as_pdf(k, \"IDW\"))\n btnIDWSave.place(relx=0.35, rely=0.15, relheight=0.3)\n\n btnMoransI = ttk.Button(frameButtons, text=\"View Morans I\", command=lambda: view_moransI_report(moransReport))\n btnMoransI.place(relx=0.68, rely=0.15, relwidth=0.3, relheight=0.3)\n\n btnOLS = ttk.Button(frameButtons, text=\"Show OLS Results\", command=lambda: change_image(k, \"OLS\", curMap))\n btnOLS.place(x=5, rely=0.6, relwidth=0.3, relheight=0.3)\n\n btnOLSSave = ttk.Button(frameButtons, text=\"Save OLS to PDF...\", command=lambda: save_as_pdf(k, \"OLS\"))\n btnOLSSave.place(relx=0.35, rely=0.6, relwidth=0.3, relheight=0.3)\n\n btnViewOLS = ttk.Button(frameButtons, text=\"View OLS Report\", command=lambda: view_ols_report(k))\n btnViewOLS.place(relx=0.68, rely=0.6, relwidth=0.3, relheight=0.3)","title":""},{"docid":"9a8a4ddb91aefd318f52ee80d6c19cec","score":"0.47285014","text":"def guimain():\r\n\t\r\n\ta = gui.QApplication(sys.argv)\r\n\ta.aboutToQuit.connect(a.deleteLater)\r\n\t\r\n\t# window\r\n\tw = gui.QWidget()\r\n\tw.resize(600, 400)\r\n\tw.setWindowTitle(\"TimeSheet\")\r\n\t\r\n\t# textboxes\r\n\tname_box = gui.QLineEdit(w)\r\n\tname_box.move(100, 40)\r\n\tname_box.resize(100, 24)\r\n\tname_box.setText(\"Enter Name\")\r\n\t\r\n\t# labels\r\n\twlcm = gui.QLabel(w)\r\n\twlcm.move(10, 20)\r\n\twlcm.resize(550, 30)\r\n\twlcm.setText(\"Welcome to TimeSheet. This application allows you to create\\\r\n\tyour very own table of work hours as simply as possible.\\nJust enter your\\\r\n\tinformation below and get started. Have fun spending your time on \\\r\n\tsomething useful!\")\r\n\t\r\n\tname_label = gui.QLabel(w)\r\n\tname_label.move(10, 40)\r\n\tname_label.resize(130,24)\r\n\tname_label.setText(\"Enter your name:\")\r\n\t\r\n\tcal_label = gui.QLabel(w)\r\n\tcal_label.move(10, 100)\r\n\tcal_label.resize(150,24)\r\n\tcal_label.setText(\"Choose date to begin with:\")\r\n\t\r\n\t\r\n\t#sl = gui.QAbstractSlider(w)\r\n\t#sl.move(20, 20)\r\n\t\r\n\t# checkboxes\r\n\tchbx_tot = gui.QCheckBox(w)\r\n\tchbx_tot.move(10, 60)\r\n\tchbx_tot.setText(\"Fix target hours\")\r\n\r\n\tchbx_mean = gui.QCheckBox(w)\r\n\tchbx_mean.move(10, 80)\r\n\tchbx_mean.setText(\"Fix mean target hours only\")\r\n\t\r\n\tgrp = gui.QButtonGroup(w)\r\n\tgrp.addButton(chbx_tot)\r\n\tgrp.addButton(chbx_mean)\r\n\tgrp.exclusive = True\r\n\t\r\n\t# calendar\r\n\tcal = gui.QCalendarWidget(w)\r\n\tcal.setGridVisible(True)\r\n\tcal.move(10, 130)\r\n\tcal.resize(320, 200)\r\n\t\r\n\t# buttons\r\n\tstart_button = gui.QPushButton(\"Create Timesheet\", w)\r\n\tstart_button.move(450, 350)\r\n\tstart_button.resize(100, 24)\r\n\t\r\n\t\r\n\tw.show()\t\r\n\t#times, times_months = create_timesheet(begin_date=\"15/01/01\",end_date=\"15/01/31\")\r\n\t#print_table(times,times_months)\r\n\t\r\n\tsys.exit(a.exec_())","title":""},{"docid":"d679299d341087031658d97741d74a33","score":"0.472513","text":"def show_em_frame(frame_data):\n for datum in np.nditer(frame_data):\n ts_val = datum['ts'].item(0)\n thr_data = thr[datum['y'].item(0), datum['x'].item(0)]\n\n if datum['p'].item(0) == 0:\n thr_data.valid = 1\n thr_data.low = ts_val\n elif thr_data.valid == 1:\n thr_data.valid = 0\n thr_data.high = ts_val - thr_data.low\n\n img = 255 * (1 - (thr.high - min_val) / (val_range))\n #thr_h = cv2.adaptiveThreshold(thr_h, 255,\n #cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 3, 0)\n img = np.piecewise(img, [img <= 0, (img > 0) & (img < 255), img >= 255], [0, lambda x: x, 255])\n img = img.astype('uint8')\n cv2.imshow('img', img)\n cv2.waitKey(1)","title":""},{"docid":"f21a2c9bef853768836894912d5ce76b","score":"0.47117522","text":"def showBrowserDialog(self):\n fileName = QFileDialog.getSaveFileName(None, 'Save output shapefile','','Shapefiles (*.shp *.SHP)')\n self.outputPath.setText(fileName)","title":""},{"docid":"412eeae54df9a71f21ea0d702ff43abf","score":"0.470262","text":"def show_frameSquid(self, cont):\n frame = self.frames[cont]\n frame.tkraise() \n camera.start_preview(fullscreen=False, window=(appwidth/800, appheight/4, appwidth-(2*appwidth/800), appheight*9/10)) # This line starts the preview. ","title":""},{"docid":"921c0f5432a83abc7c77c5413566382f","score":"0.4699744","text":"def show(self):\n self.plot_frame(-1)","title":""},{"docid":"10a497a000c315c1144324e3a6272372","score":"0.469705","text":"def callback(x):\n\n interaction_window_pointer[0] = x\n try:\n\n read_frames(interaction_window_pointer, interaction_windows_size, interaction_window, video_frames_count)\n frame = interaction_window[str(interaction_window_pointer[0])]\n cur_timestamp = str(int(int(interaction_window_pointer[0]) * 1000 / 25))\n if frame is not None:\n \n # Priting BBox and Annotations over the current image\n #frame = drawBoundingBox(frame, bounding_box_data_list)\n #frame = drawAnnotations(frame, dicts, current_complex_event, current_complex_event_detected)\n #frame = resize_image(frame)\n #cv2.imshow(app_name, frame)\n\n # Showing info to CLI\n if (not(is_exporting)):\n print(\"[Visualization System] Frame: {:06}\".format(interaction_window_pointer[0]))\n print(\"[Visualization System] Second: \" + str(int(cur_timestamp) / 1000), end=\"\\r\\n\\r\\n\")\n \n except Exception as e:\n print(\"[Exception] \" + e.args)","title":""},{"docid":"3dbbec52dc3513f121f71702b163176f","score":"0.46862304","text":"def ui_mode():\n # Create a dialog box with the features\n fields = ['Music', 'Movies', 'Horror', 'Thriller', 'Comedy',\n 'Romantic', 'Sci-fi', 'War', 'Fantasy/Fairy tales', \n 'Animated', 'Documentary', 'Western', 'Action', 'History', \n 'Mathematics', 'Physics', 'Internet', 'PC', 'Reading', \n 'Cars', 'Shopping', 'Science and technology', 'Adrenaline sports', \n 'Flying', 'Age', 'college/bachelor degree', 'currently a primary school pupil', \n 'doctorate degree', 'masters degree', 'primary school', 'secondary school', \n 'few hours a day', 'less than an hour a day', 'most of the day', 'no time at all']\n\n layout = [[sg.Text('url'), sg.Input('', key='url')],\n [sg.Text('Music'), sg.Input(5, key='Music')],\n [sg.Text('Movies'), sg.Input(5, key='Movies')],\n [sg.Text('Horror'), sg.Input(4, key='Horror')],\n [sg.Text('Thriller'), sg.Input(2, key='Thriller')],\n [sg.Text('Comedy'), sg.Input(5, key='Comedy')],\n [sg.Text('Romantic'), sg.Input(4, key='Romantic')],\n [sg.Text('Sci-fi'), sg.Input(4, key='Sci-fi')],\n [sg.Text('War'), sg.Input(1, key='War')],\n [sg.Text('Fantasy/Fairy tales'), sg.Input(5, key='Fantasy/Fairy tales')],\n [sg.Text('Animated'), sg.Input(5, key='Animated')],\n [sg.Text('Documentary'), sg.Input(3, key='Documentary')],\n [sg.Text('Western'), sg.Input(1, key='Western')],\n [sg.Text('Action'), sg.Input(2, key='Action')],\n [sg.Text('History'), sg.Input(1, key='History')],\n [sg.Text('Mathematics'), sg.Input(3, key='Mathematics')],\n [sg.Text('Physics'), sg.Input(3, key='Physics')],\n [sg.Text('Internet'), sg.Input(5, key='Internet')],\n [sg.Text('PC'), sg.Input(3, key='PC')],\n [sg.Text('Reading'), sg.Input(3, key='Reading')],\n [sg.Text('Cars'), sg.Input(1, key='Cars')],\n [sg.Text('Shopping'), sg.Input(4, key='Shopping')],\n [sg.Text('Science and technology'), sg.Input(4, key='Science and technology')],\n [sg.Text('Adrenaline sports'), sg.Input(4, key='Adrenaline sports')],\n [sg.Text('Flying'), sg.Input(1, key='Flying')],\n [sg.Text('Age', text_color='red'), sg.Input(20, key='Age')],\n [sg.Text('college/bachelor degree'), sg.Input(1, key='college/bachelor degree')],\n [sg.Text('currently a primary school pupil'), sg.Input(0, key='currently a primary school pupil')],\n [sg.Text('doctorate degree'), sg.Input(0, key='doctorate degree')],\n [sg.Text('masters degree'), sg.Input(0, key='masters degree')],\n [sg.Text('primary school'), sg.Input(0, key='primary school')],\n [sg.Text('secondary school'), sg.Input(0, key='secondary school')],\n [sg.Text('few hours a day'), sg.Input(1, key='few hours a day')],\n [sg.Text('less than an hour a day'), sg.Input(0, key='less than an hour a day')],\n [sg.Text('most of the day'), sg.Input(0, key='most of the day')],\n [sg.Text('no time at all'), sg.Input(0, key='no time at all')],\n [sg.Text('Prediction: ', text_color='blue', size=(40, 2), key='_PRED_')],\n [sg.RButton('Predict'), sg.Exit()]]\n\n window = sg.Window('Enter url and feature values', layout)\n while True:\n event, values = window.Read()\n predictions = str(get_multiple_predictions(values, fields))\n predictions.replace('\"', '')\n if event == 'Predict':\n print('entered predict event')\n # change the \"output\" element to be the value of \"input\" element\n window.Element('_PRED_').Update('Age: [' + values['Age'] + ']\\n' + \n 'Prediction: ' + predictions)\n if event is None or event == 'Exit':\n break\n\n window.Close()","title":""},{"docid":"17789147d32666fe3ff02f17a831f625","score":"0.4681595","text":"def file_dialog(self):\n file = QtGui.QFileDialog.getOpenFileName(self, 'Open Texture', '')\n\n if file != '' and file is not None:\n self.lineEdit.setText(file)\n\n self.update()","title":""},{"docid":"947c70c4424b01907ffe3bced1e6ae7a","score":"0.4673959","text":"def create_plot_html_page(msid, descrip, pout):\n#\n#--- read javascript file\n#\n jfile = house_keeping + 'java_script_deposit'\n f = open(jfile, 'r')\n jscript = f.read()\n f.close()\n\n #file_name = msid + '_limit_table'\n file_name = './Limit_table/' + msid + '_limit_table.html'\n#\n#--- start creating html page\n#\n out = '<!DOCTYPE html>\\n<html>\\n<head>\\n\\t<title>Envelope Trending Plots: ' + msid.upper() + '</title>\\n'\n out = out + jscript + '\\n'\n out = out + '<style>\\n'\n out = out + 'body{width: 600px; height300px; background-color:#FAEBD7;\\n'\n out = out + 'font-family:Georgia, \"Times New Roman\", Times, serif;}\\n'\n out = out + '</style>\\n'\n\n out = out + '</head>\\n<body style=\"width:95%;margin-left:10px; margin-right;10px;background-color:#FAEBD7;'\n out = out + 'font-family:Georgia, \"Times New Roman\", Times, serif\">\\n\\n'\n out = out + '<a href=\"' + web_address + 'envelope_main.html\" '\n out = out + 'style=\"float:right;padding-right:50px;font-size:120%\"><b>Back to Top</b></a>\\n'\n\n if descrip == '':\n out = out + '<h2>' + msid.upper() + '</h2>'\n else:\n out = out + '<h2>' + msid.upper() + ' (' + descrip.upper() + ')</h2>' \n\n out = out + '<div style=\"paddng-top:10px\"><h3>'\n out = out + 'Open <a href=\"javascript:popitup(\\'' + file_name + '\\')\" style=\"text-align:right\">Limit Table</a>.'\n out = out + '</h3>\\n'\n#\n#--- add the interactive plot here\n#\n if (pout == False) or (str(pout) == 'na'):\n out = out + '<h3 style=\"padding-top:200px;padding-bottom:200px\">No Data/No Plot</h3>'\n else:\n out = out + pout\n#\n#--- add the rest\n#\n out = out + '<ul><li style=\"font-size:80%\">Click the magnifier icon and choose the area to enlarge the area.</li>'\n out = out + '<li style=\"font-size:80%\">Click the cross icon and hold the button to move around the area.</li>'\n out = out + '<li style=\"font-size:80%\">Click the cross icon and then use the roller to zoom in and out.</li>'\n out = out + '<li style=\"font-size:80%\">Click the house icon to go back to the full view.</li>'\n out = out + '<li style=\"font-size:80%\">After enlarging the area with the magnifier,'\n out = out + ' use the cross icon to see the values of each data point.</li></ul>'\n\n [lout, gname] = get_group_names(msid)\n if lout != '':\n out = out + '<h3>Other msids in this group: ' + gname + '</h3>'\n out = out + lout\n\n out = out + '<div style=\"padding-top:30px\"></div>'\n out = out + '<hr /><p style=\"text-align:left; padding-top:10px;padding-bottom:20px\">'\n out = out + 'If you have any questions, please contact '\n out = out + '<a href=\"mailto:tisobe@cfa.harvard.edu\">tisobe@cfa.harvard.edu</a>.'\n out = out + '\\n\\n\\n</body>\\n</html>\\n'\n#\n#--- write out the html data\n#\n name = web_dir + msid + '_plot.html'\n fo = open(name, 'w')\n fo.write(out)\n fo.close()","title":""},{"docid":"6fad818a5bf85b40910f30d948556f6a","score":"0.46726665","text":"def make_interactive_plot(self):\n self.change_pars_to_fit_button = Button(\n description=\"Change Parameters to Fit Result\",\n layout = Layout(width = '300px', margin = '0 0 5ps 0')\n ) \n\n self.reset_slider_lims_button = Button(\n description=\"Reset Slider Max\",\n layout = Layout(width = '300px', margin = '0 0 5ps 0')\n ) \n\n self.data_init_widget = Dropdown(\n options=list(np.arange(0,len(self.isub))), \n value = 0,\n description='Dataset',\n style = {'description_width': 'initial'},\n disabled=False,\n layout = Layout(width = '200px', margin = '0 0 5ps 0')\n )\n\n self.reset_slider_widget = Dropdown(\n options= [par for par in self.ctrl_pars.keys() if self.ctrl_pars[par]], \n value = None,\n description='Slider Reset',\n style = {'description_width': 'initial'},\n disabled=False,\n layout = Layout(width = '200px', margin = '0 0 5ps 0')\n ) \n\n self.wlim = {}\n self.wlim[self.spectra_object.orbital] = FloatRangeSlider (\n value=[np.min(self.esub), np.max(self.esub)],\n min = np.min(self.esub),\n max =np.max(self.esub),\n step = 0.01,\n description = self.spectra_object.orbital+'_xlim',\n style = {'description_width': 'initial'},\n layout = Layout(width = '300px', margin = '0 0 5ps 0')\n ) \n if self.compound:\n for orbital in self.connected_spectra.keys():\n self.wlim[orbital] = FloatRangeSlider (\n value=[np.min(self.connected_spectra[orbital].esub), np.max(self.connected_spectra[orbital].esub)],\n min = np.min(self.connected_spectra[orbital].esub),\n max =np.max(self.connected_spectra[orbital].esub),\n step = 0.01,\n description = orbital+'_xlim',\n style = {'description_width': 'initial'},\n layout = Layout(width = '300px', margin = '0 0 5ps 0')\n ) \n\n out = Output()\n display(out)\n\n @self.change_pars_to_fit_button.on_click\n def plot_on_click(b):\n with out:\n if not self.compound:\n self.spectra_object.params = self.spectra_object.fit_results[self.data_init_widget.value].params.copy()\n for pars in self.paramwidgets.keys():\n self.paramwidgets[pars].update_widget_group(self.spectra_object.params[pars])\n elif self.compound:\n self.compound_object.params = self.compound_object.fit_results[self.data_init_widget.value].params.copy()\n for pars in self.paramwidgets.keys():\n self.paramwidgets[pars].update_widget_group(self.compound_object.params[pars]) \n\n @self.reset_slider_lims_button.on_click\n def plot_on_click(b):\n with out:\n if self.reset_slider_widget.value !=None:\n self.paramwidgets[self.reset_slider_widget.value].ctrl_slider.max = 2*self.paramwidgets[self.reset_slider_widget.value].ctrl_slider.value\n\n # Create the interactive plot, then build the slider/graph parameter controls\n plotkwargs = {**{pw.name:pw.ctrl_slider for pw in self.paramwidgets.values() if hasattr(pw,'ctrl_slider')},\\\n **{plotlim.description:plotlim for plotlim in self.wlim.values()}}\n self.intplot = interactive(self.interactive_plot,**plotkwargs)\n \n vb = VBox(self.intplot.children[0:-1])\n vb2 = VBox([HBox([VBox([self.data_init_widget,self.reset_slider_widget]),VBox([self.change_pars_to_fit_button,self.reset_slider_lims_button])]),self.intplot.children[-1]])\n hb = HBox([vb,vb2])\n \n display(hb)","title":""},{"docid":"a718204b6da7aaf9a338c82cb3c1b555","score":"0.46719527","text":"def window(theWidth, theHeight, theTitle):\n\tprint('This is wrapper function to help code autocompletion.')","title":""},{"docid":"43e4ff18486c1579c62a8e2f9765e2eb","score":"0.46719238","text":"def _frame_out(self, *args):\n if len(args) == 2:\n self._display.frame(args[0], args[1].frame)\n elif len(args) == 1:\n if type(args[0]) is int:\n return Frame(self.frame_width(), self.frame_height(),\n self._display.frame(args[0]))\n else:\n self._display.frame(args[0].frame)\n else:\n return Frame(self.frame_width(), self.frame_height(),\n self._display.frame())","title":""},{"docid":"83d66882651ac3c3846c7bba9ea517ab","score":"0.466906","text":"def main():\n st.title(\"BioInformatics Web App\")\n\n #activity = ['Intro','DNA','DotPlot',\"About\"]\n # choice = st.sidebar.selectbox(\"Select Activity\",activity)\n # if choice == 'Intro':\n\t# st.subheader(\"Intro\")\n # elif choice == \"DNA Sequence\":\n\t# st.subheader(\"DNA Sequence Analysis\")\n\n #input sequence\n seq_input = st.text_input('Input Sequence')\n\n #check validity of sequence entered\n validateSeq(seq_input)\n\n details = st.radio(\"Functions\",(\"Sequence Length\",\n \"Frequency of each Nucleotide\",\n \"Transcription of DNA\", \n \"Reverse Transcription\", \n \"Find Complementory Strand\",\n \"GC Content Percentage\"))\n if details == \"Sequence Length\":\n st.write(len(seq_input))\n\n elif details == \"Frequency of each Nucleotide\":\n st.subheader(\"Nucleotide Frequency\")\n seq_input = Counter(seq_input)\n st.write(seq_input)\n adenine_color = st.color_picker(\"Adenine Color\")\n thymine_color = st.color_picker(\"thymine Color\")\n guanine_color = st.color_picker(\"Guanine Color\")\n cytosil_color = st.color_picker(\"cytosil Color\")\n\n if st.button(\"Plot Freq\"):\n barlist = plt.bar(seq_input.keys(),seq_input.values())\n barlist[2].set_color(adenine_color)\n barlist[3].set_color(thymine_color)\n barlist[1].set_color(guanine_color)\n barlist[0].set_color(cytosil_color)\n\n st.pyplot()\n \n elif details == \"Transcription of DNA\":\n st.write(transcription(seq_input))\n\n elif details == \"Reverse Transcription\":\n st.write(reversetranscription(seq_input))\n\n elif details == \"Find Complementory Strand\":\n st.text(f\" DNA String + Complement + Reverse Complement: \\n 5'{seq_input} 3' \\n {''.join(['|' for c in range(len(seq_input))])} \\n 3'{reverse_complement(seq_input)[::-1]} 5'[Complement] \\n 5'{reverse_complement(seq_input)} 3'[Reverse Complement]\")\n\n elif details == \"GC Content Percentage\":\n st.write(f\" {gc_content(seq_input)}%\")","title":""},{"docid":"8fbe416b60c11f12b7be979cf35f906d","score":"0.466795","text":"def onselect(vmin, vmax):\n windows.append(float(Decimal(\"%.2f\" % vmin)))\n windows.append(float(Decimal(\"%.2f\" % vmax)))\n plot_windows(windows)\n plt.draw()","title":""},{"docid":"ddf645714b43146b402a1481a3feaafa","score":"0.46650136","text":"def interactive_numerical_plot(df_num,df_Y):\n from ipywidgets import HBox,Checkbox,FloatRangeSlider,VBox,ToggleButton,interactive_output,Dropdown\n from IPython.display import display\n\n def plot_num_and_save(xlimit,save_but,col,clip_box,clip_limit):\n nonlocal df_num, df_Y\n plt.close('all')\n\n if clip_box:\n clip_df_num = df_num.copy()\n clip_df_num.loc[clip_df_num[col]>clip_limit[1],col] = np.nan\n clip_df_num.loc[clip_df_num[col]<clip_limit[0],col] = np.nan\n else:\n clip_df_num = df_num\n\n# for i,col in zip(range(clip_df_num[col].shape[1]),clip_df_num[col]):\n fig,ax = plt.subplots(1,1,figsize=(10,5))\n sns.kdeplot(clip_df_num[col][df_Y == 0], label = 'label0').set_title(clip_df_num[col].name)\n sns.kdeplot(clip_df_num[col][df_Y == 1], label = 'label1')\n ax.set_xlim(xlimit[0],xlimit[1])\n plt.show()\n\n if save_but:\n fig.savefig('./plots/{}.png'.format(clip_df_num[col].name), bbox_inches='tight')\n\n xlimit = FloatRangeSlider(value = [df_num.iloc[:,1].min(),df_num.iloc[:,1].max()],min=df_num.iloc[:,1].min(),\n max=df_num.iloc[:,1].max(),step=(df_num.iloc[:,1].max()-df_num.iloc[:,1].min())/100,\n continuous_update=False,description='X_limit')\n save_but = ToggleButton(description='Save Figure')\n col = Dropdown(options=df_num.columns.tolist())\n clip_box = Checkbox(value=False,description='Clip ?')\n clip_limit = FloatRangeSlider(value = [df_num.iloc[:,1].min(),df_num.iloc[:,1].max()],min=df_num.iloc[:,1].min(),\n max=df_num.iloc[:,1].max(),step=(df_num.iloc[:,1].max()-df_num.iloc[:,1].min())/100,\n continuous_update=False,description='X_limit')\n\n\n out = interactive_output(plot_num_and_save,{\n 'xlimit' : xlimit,\n 'save_but':save_but,\n 'col' : col,\n 'clip_box':clip_box,\n 'clip_limit':clip_limit\n })\n# save_but = Button(description='Save Fig')\n vbox1 = VBox([xlimit,save_but,col,clip_box,clip_limit])\n ui = HBox([vbox1,out])\n display(ui)\n\n def on_click(change):\n change['owner'].value = False\n\n def on_click_case(change):\n try:\n xlimit.min = df_num[change['new']].min()\n xlimit.max = df_num[change['new']].max()\n clip_limit.min = df_num[change['new']].min()\n clip_limit.max = df_num[change['new']].max()\n\n except:\n xlimit.max = df_num[change['new']].max()\n xlimit.min = df_num[change['new']].min()\n clip_limit.max = df_num[change['new']].max()\n clip_limit.min = df_num[change['new']].min()\n\n xlimit.step = (df_num[change['new']].max() - df_num[change['new']].min())/100\n xlimit.value = [df_num[change['new']].min(),df_num[change['new']].max()]\n clip_limit.step = (df_num[change['new']].max() - df_num[change['new']].min())/100\n clip_limit.value = [df_num[change['new']].min(),df_num[change['new']].max()]\n\n save_but.observe(on_click, 'value')\n col.observe(on_click_case, 'value')","title":""},{"docid":"d1ce84ef174cc08a45fee80456226f00","score":"0.46647978","text":"def pop_gui(self):\n self.layout.addWidget(QLabel('Confirm properties each save?'), self.y, 0)\n groupbox = self.add_radio(name='confirm', defval=True)\n self.layout.addWidget(groupbox, self.y, 1)\n self.y += 1\n\n self.layout.addWidget(QLabel('Save frames?'), self.y, 0)\n groupbox = self.add_radio(name='save_frames', defval=self.params['save']['save_frames'])\n self.layout.addWidget(groupbox, self.y, 1)\n self.y += 1\n\n self.layout.addWidget(QLabel('Clear data after save?'), self.y, 0)\n groupbox = self.add_radio(name='clear', defval=self.params['save']['clear'])\n self.layout.addWidget(groupbox, self.y, 1)\n self.y += 1\n\n self.layout.addWidget(QLabel('filename?'), self.y, 0)\n textedit = QLineEdit('')\n if 'filename' in self.params['save']:\n defval = self.params['save']['filename']\n textedit.setText(str(defval))\n textedit.name = 'filename'\n textedit.textChanged.connect(self.text_clicked)\n self.layout.addWidget(textedit, self.y, 1)\n self.y += 1\n\n self.layout.addWidget(QLabel('Confirm frames rate [fps]?'), self.y, 0)\n textedit = QLineEdit('')\n if 'fps' in self.params['improcess']:\n defval = self.params['improcess']['fps']\n textedit.setText(str(defval))\n textedit.name = 'fps'\n textedit.textChanged.connect(self.text_clicked)\n self.layout.addWidget(textedit, self.y, 1)\n self.y += 1\n\n self.layout.addWidget(QLabel('Confirm pixel size [microns]?'), self.y, 0)\n textedit = QLineEdit('')\n if 'pixel_size' in self.params['improcess']:\n defval = self.params['improcess']['pixel_size']\n if defval is not None:\n textedit.setText(str(defval))\n textedit.name = 'pixel_size'\n textedit.textChanged.connect(self.text_clicked)\n self.layout.addWidget(textedit, self.y, 1)\n self.y += 1","title":""},{"docid":"a8de185a1b169065304f2b335a1aaec2","score":"0.4658181","text":"def export_window():\n\n current_courses = [\"EECS 1011\", \"ENG 1101\", \"MATH 1013\", \"MATH 1025\",\n \"PHYS1800\"]\n\n current_times = []\n with shelve.open(\"course_times.db\") as db:\n print(db[\"MATH 1025\"])\n for course in current_courses:\n current_times.append(db[course])\n\n layout = []\n\n i = 0\n for course in current_courses:\n layout.append([sg.Text(course), sg.Text(str(current_times[i]))])\n i += 1\n\n layout.append([sg.Button(\"Close\", key=\"close\")])\n\n window = sg.Window(\"Window 1\", layout)\n\n while True:\n event, values = window.read()\n if event == None or event == \"close\":\n break\n\n window.close()","title":""},{"docid":"90dfb0628baba5b2ab2f924331f2b424","score":"0.46561182","text":"def select_frame(self):\n self.time_suppress()\n \n #select a decision making window\n bbox_slice = self.bbox_buffer[-self.timeWindow_selectFrame:] # returns a slice of size of window\n frame_slice = self.frame_buffer[-self.timeWindow_selectFrame:]\n \n\n if self.is_shovel_idle(frame_slice):\n return None, None, None, None\n\n\n # main logic of frame selection\n else: \n selection_scores = self.compute_selection_scores(bbox_slice)\n\n sorted_inds, numberOfNoneScores = self.argsort(selection_scores)\n\n\n numberOfValidScores = len(sorted_inds) - numberOfNoneScores\n \n if numberOfValidScores < self.minNumber_validScoresToSlectFrame:\n return None, None, None, None\n \n\n\n #Pick the highest Score\n ind_selected_max = sorted_inds[-1]\n \n #Pick the 90thPercentile Score\n ninetieth_percentile_ind = int(round(numberOfValidScores * 0.9)) + numberOfNoneScores - 1\n ind_selected = sorted_inds[ninetieth_percentile_ind]\n\n\n\n bbox_selected = bbox_slice[ind_selected]\n frame_selected = frame_slice[ind_selected]\n self.frames_selected.append(frame_selected)\n \n frame_selected_max = frame_slice[ind_selected_max]\n\n return frame_selected, bbox_selected, ind_selected, ind_selected_max, frame_selected_max","title":""},{"docid":"87c07eb6112eaa14d0f94d06e36980c4","score":"0.4650582","text":"def _onClickSetRange(self) -> None:\n\n valueRange, okClicked = SetValueRangeDialog.getValueRange(self, initialValue=self.value())\n if okClicked:\n self.setValue(valueRange)\n self.valueChangedByUser[Multivalue].emit(valueRange)","title":""},{"docid":"03135e385f22bfeae854b67127e14613","score":"0.46481696","text":"def __init__(self):\n\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-p\", \"--path-to-frames\", help=\"The absolute path to the frames. For example: /home/USER/.icons/status_icons/my_frames/ The frames must follow this naming convention: frame-<NUMBER>.<FORMAT>\\nWhere <NUMBER> starts at 0 and is incremented by 1 for the next frame and so on. <FORMAT> will default to png if no format is supplied\\nFor example a 3 frame PNG sequence should have the filenames: frame-0.png, frame-1.png, frame-2.png\")\n parser.add_argument(\"-f\", \"--filetype\", help=\"Specify the image format for the frames. Valid options: png, jpg, jpeg. Default is png.\")\n parser.add_argument(\"-t\", \"--time-per-frame\", help=\"Specify the time for each frame to be shown in milliseconds. Default is 300ms.\", type=int)\n parser.add_argument(\"-b\", \"--blank-icon\", help=\"Creates a blank icon in the system tray to act as a spacer.\", action=\"store_true\")\n args = parser.parse_args()\n\n if args.blank_icon: # icon is already blank\n return\n\n if args.path_to_frames == None:\n print(\"Absolute path to the frames must be specified with the -p option.\\nAborting...\")\n exit(-1)\n\n if args.filetype == \"jpg\":\n self.frame_format = \".\" + args.filetype\n elif args.filetype == \"jpeg\":\n self.frame_format = \".\" + args.filetype\n \n self.num_of_frames = len(fnmatch.filter(os.listdir(args.path_to_frames), self.frame_filename_prefix + \"*\" + self.frame_format)) # count number of frames in the supplied directory\n if self.num_of_frames == 0:\n print(\"Error: No suitable frames found in \" + args.path_to_frames + \"\\nAborting...\")\n exit(-1)\n\n if args.time_per_frame:\n self.frame_time = args.time_per_frame\n if self.frame_time < 10:\n print(\"Warning: A low time per frame value will lead to high CPU usage.\")\n\n frame_path_prefix = args.path_to_frames + self.frame_filename_prefix\n for i in range(0, self.num_of_frames + 1): # setup correct path names\n self.frame_paths.append(frame_path_prefix + str(i) + self.frame_format)\n\n if self.num_of_frames == 1: # we don't need callbacks if only one frame has been supplied so just set icon and return\n self.icon.set_from_file(self.frame_paths[0])\n return\n\n gobject.timeout_add(self.frame_time, self.update_frame) # callback from gtk.main()\n return","title":""},{"docid":"188219dc773415de1076a762fa4d3b3c","score":"0.46479467","text":"def main_display():\n #Display:\n print '################################################################'\n print '# XNATUPLOAD #'\n print '# #'\n print '# Developed by the masiLab Vanderbilt University, TN, USA. #'\n print '# If issues, please start a thread here: #'\n print '# https://groups.google.com/forum/#!forum/vuiis-cci #'\n print '# Usage: #'\n print '# Upload data to XNAT following the csv file information #'\n print '# Parameters : #'\n if vars(OPTIONS) == DEFAULT_ARGUMENTS:\n print '# No Arguments given #'\n print '# See the help bellow or use \"Xnatupload -h\" #'\n print '################################################################'\n PARSER.print_help()\n sys.exit()\n else:\n if OPTIONS.host:\n print '# %*s -> %*s#' %(-20, 'XNAT Host', -33, get_proper_str(OPTIONS.host))\n if OPTIONS.username:\n print '# %*s -> %*s#' %(-20, 'XNAT User', -33, get_proper_str(OPTIONS.username))\n if OPTIONS.printmodality:\n print '# %*s -> %*s#' %(-20, 'Print Modality', -33, 'on')\n else:\n if OPTIONS.csvfile:\n print '# %*s -> %*s#' %(-20, 'CSV file', -33, get_proper_str(OPTIONS.csvfile))\n if OPTIONS.session_type:\n print '# %*s -> %*s#' %(-20, 'Session Type', -33, get_proper_str(OPTIONS.session_type))\n if OPTIONS.report:\n print '# %*s -> %*s#' %(-20, 'Report', -33, 'on')\n if OPTIONS.force:\n print '# %*s -> %*s#' %(-20, 'Force Upload', -33, 'on')\n if OPTIONS.delete:\n print '# %*s -> %*s#' %(-20, 'Delete resources', -33, 'on')\n if OPTIONS.deleteAll:\n print '# %*s -> %*s#' %(-20, 'Delete All', -33, 'on')\n if OPTIONS.extract:\n print '# %*s -> %*s#' %(-20, 'Extract ZIP', -33, 'on')\n if OPTIONS.outputfile:\n print '# %*s -> %*s#' %(-20, 'Output file', -33, get_proper_str(OPTIONS.outputfile))\n print '################################################################'\n print \"IMPORTANT WARNING FOR ALL USERS ABOUT XNAT:\"\n print \" session_label needs to be unique for each session.\"\n print \" Two subjects can NOT have the same session_label\"\n print '================================================================'","title":""},{"docid":"0cd70927af0b840bf67eede36625f40d","score":"0.46462128","text":"def scanner_display_scanpoints(self):\n # Get local arguments\n points_label = self.get_obj('scanner_scan_points_label')\n rows_label = self.get_obj('scanner_scan_rows_label')\n time_label = self.get_obj('scanner_scan_time_label')\n\n points_label.set_markup(self.format_dro_string('Points: {0}'.format(self.scanner.scene.points_count),11))\n rows_label.set_markup(self.format_dro_string('Rows: {0} , Columns: {1}'.format(self.scanner.scene.rows,self.scanner.scene.columns),11))\n time_data=datetime.timedelta(seconds=round(self.scanner.scene.estimated_time))\n time_label.set_markup(self.format_dro_string('Estimated Time: {0}'.format(time_data),11))","title":""},{"docid":"19a62117bbd3045e503ebd340b734182","score":"0.46367753","text":"def confirmButtonClicked(self):\n\n self.showPopupUnmarkedPaperInput()\n\n self.problemAmount = int(self.problemNumInput.text()) # 시험의 문제 갯수\n self.testpaperAmount = int(self.paperNumInput.text()) # 한 시험지 세트의 총 페이지 수\n self.gradeWithOCR = self.check_useOCR.isChecked() # OCR로 주관식 채점 여부\n\n fileLocs = []\n while True:\n fname = QFileDialog.getOpenFileName() # 비 마킹 시험지들의 파일 읽기\n if fname[0] != '': # 아직 읽을 파일이 들어온 경우\n fileLocs.append(fname[0])\n else: # 읽을 파일이 더 없는 경우 - 루프 종료\n break\n\n self.showPopupEdgeInstruction_1()\n\n counter = 0 # 임시 변수\n for imageLoc in fileLocs: # 각 시험지 이미지마다\n\n src = cv2.imread(imageLoc, cv2.IMREAD_COLOR)\n height = src.shape[0] # 시험지 이미지 높이\n width = src.shape[1] # 시험지 이미지 너비\n\n # 시험지가 너무 커 처리가 힘든 경우, 리사이징\n if height >= width:\n resizeScale = 1000 / height\n else:\n resizeScale = 1000 / width\n src = cv2.resize(src, (int(width * resizeScale), int(height * resizeScale)), interpolation=cv2.INTER_AREA)\n\n print(\"Changed dimensions : \", src.shape)\n\n height, width, channel = src.shape\n\n cv2.imshow(\"Automatic Scoring Program\", src)\n cv2.setMouseCallback('Automatic Scoring Program', self.mouseCallbackSpot)\n\n print(\"Click 4 spot of the image, starting from left-upper side, clockwise\")\n print(\"After that, press any key\")\n cv2.waitKey(0)\n cv2.destroyAllWindows()\n print(self.clickCoordinates)\n\n srcPoint = np.array(self.clickCoordinates, dtype=np.float32)\n self.clickCoordinates = []\n\n # 시험지의 각 4개 꼭짓점을 지정하고, warping 진행\n dstPoint = np.array([[0, 0], [width, 0], [width, height], [0, height]], dtype=np.float32)\n matrix = cv2.getPerspectiveTransform(srcPoint, dstPoint)\n warpedUnmarkedPaper = cv2.warpPerspective(src, matrix, (width, height))\n cv2.imshow(\"Automatic Scoring Program\", warpedUnmarkedPaper)\n cv2.waitKey(0)\n\n # 리사이징한 시험지 파일 저장\n cv2.imwrite('./buffer/unprocessedBlankPaper_{}.jpg'.format(counter), warpedUnmarkedPaper)\n\n # 마킹 안 된 시험지 Blur, 흑백화 등 이미지 정제\n # convert the images to grayscale\n unmarkedPaper = cv2.cvtColor(warpedUnmarkedPaper, cv2.COLOR_BGR2GRAY)\n\n # blur\n for i in range(10):\n unmarkedPaper = cv2.GaussianBlur(unmarkedPaper, (7, 7), 0)\n\n cv2.imwrite('./buffer/processedBlankPaper_{}.jpg'.format(counter), unmarkedPaper)\n\n cv2.destroyAllWindows()\n\n counter = counter + 1\n\n self.Form.hide()\n\n #문제 설정 창 띄우기\n self.problemSettingWindow = QtWidgets.QWidget()\n self.problemSettingWindowUI = UI_ProblemSetting()\n self.problemSettingWindowUI.setupUi(self.problemSettingWindow, [], self.problemAmount, self.testpaperAmount, self.gradeWithOCR)\n self.problemSettingWindow.show()","title":""},{"docid":"d8174387bb1deea347479b087489dac6","score":"0.4636768","text":"def Pump_Durations_Plots():\n plt.clf()\n VALUES = []\n where_clause = (\n \"(FLOW_RATE is not NULL) AND \"\n \"(FLOW_RATE > 0) AND \"\n \"(FLOW_RATE <= 100) AND \"\n \"(DURATION is not NULL) AND \"\n \"(DURATION > 0) AND\"\n \"(DURATION <= 12) AND\"\n \"(START_MEAS is not NULL) AND \"\n \"(START_MEAS > 0) AND \"\n \"(PUMP_MEAS is not NULL) AND \"\n \"(PUMP_MEAS > 0)\"\n )\n with arcpy.da.SearchCursor(CWIPL, ['FLOW_RATE', 'DURATION', 'START_MEAS',\\\n 'PUMP_MEAS'], where_clause) as cursor:\n for row in cursor:\n down = row[3] - row[2]\n if down > 0 and down < 100 and row[3] <=200:\n stuff = [row[0], row[1], row[3], down]\n VALUES.append(stuff)\n DUR_DATA = [i[1] for i in VALUES]\n PUMP_DATA = [i[0] for i in VALUES]\n Water_Level = [i[2] for i in VALUES]\n Drawdown_data = [i[3] for i in VALUES]\n plt.figure(1)\n plt.hist(DUR_DATA, bins = 48, label = 'Duration')\n plt.xlim([0, 12])\n plt.ylim([0, 125000])\n plt.minorticks_on()\n plt.xticks(fontsize = 24)\n plt.yticks(fontsize = 24)\n plt.xlabel('Duration [hours]', fontsize = 30)\n plt.ylabel('Number of entries', fontsize = 30)\n plt.grid(True)\n \n plt.figure(2)\n plt.hist(PUMP_DATA, bins = 100, label = 'Pump Rata Data')\n plt.minorticks_on()\n plt.xlim([0, 100])\n plt.ylim([0, 60000])\n plt.xticks(fontsize = 24)\n plt.yticks(fontsize = 24)\n plt.xlabel('Pump Rate [GPM]', fontsize = 30)\n plt.ylabel('Number of entries', fontsize = 30)\n plt.grid(True)\n \n plt.figure(3)\n plt.hist(Water_Level, bins = 200, label = 'Static Level Data', edgecolor = 'k')\n plt.minorticks_on()\n plt.xticks(fontsize = 24)\n plt.yticks(fontsize = 24)\n plt.xlabel('Static Water Level [ft]', fontsize = 30)\n plt.ylabel('Number of entries', fontsize = 30)\n plt.grid(True)\n \n plt.figure(4)\n plt.hist(Drawdown_data, bins = 100, label = 'Drawdown Data', color = 'g' , edgecolor = 'k')\n plt.minorticks_on()\n# plt.xlim([0, 100])\n# plt.ylim([0, 100000])\n plt.xticks(fontsize = 24)\n plt.yticks(fontsize = 24)\n plt.xlabel('Drawdown [ft]', fontsize = 30)\n plt.ylabel('Number of entries', fontsize = 30)\n plt.grid(True)\n \n fig, ax = plt.subplots(1,1)\n plt.figure(5)\n plt.grid(which='minor', linestyle=':', linewidth='0.5', color='gray',\\\n zorder = 0)\n plt.grid(which='major', linestyle='-', linewidth='0.5', color='black',\\\n zorder = 0)\n plt.scatter(PUMP_DATA, DUR_DATA, zorder = 3)\n plt.xticks(fontsize = 24)\n plt.yticks(fontsize = 24)\n plt.minorticks_on()\n plt.xlim([0,101])\n plt.ylim([0,12.25])\n plt.xlabel('Pumping Rates [GPM]', fontsize = 30)\n plt.ylabel('Duration of Test [hours]', fontsize = 30)\n ax.xaxis.set_major_locator(MultipleLocator(20))\n ax.xaxis.set_major_formatter(FormatStrFormatter('%d'))\n ax.xaxis.set_minor_locator(MultipleLocator(10))\n ax.yaxis.set_major_locator(MultipleLocator(1))\n #ax.axis.set_major_formatter(FormatStrFormatter('%d'))\n ax.yaxis.set_minor_locator(MultipleLocator(0.5))\n ax.set_axisbelow(True)","title":""},{"docid":"4d5d7493607b700952d1cb63e7e3da5b","score":"0.46324617","text":"def show_frameLima(self, cont, chosenexp):\n result = True \n result2 = True\n global Momo # Create variable to store experiment object\n Momo = getobject(chosenexp)\n \n # Case of analyzing control\n if Momo.iscontrol: \n if Momo.exptype != \"0\": # Not first time analyzing\n result2 = tkMessageBox.askquestion(\"Warning\", \"This control experiment has already\\nbeen analyzed as %s.\\nChanges may overwrite exisiting data.\\nProceed anyways?\" % getpreviouslyanalyzed(Momo))\n if result2 != \"no\": # First time analyzing or want to override\n frame = self.frames[AnalysisTypeForNone] # Go to AnalysisTypeForNone to get type to analyze as\n for button in [frame.thermobutton, frame.chemobutton, frame.photobutton]:\n if getpreviouslyanalyzed(Momo) == button['text']: # Turn on button if that was the previous analysis\n button.state([\"focus\",\"selected\"])\n else:\n button.state([\"!focus\",'!selected'])\n frame.tkraise()\n\n # Case of not control\n else: \n if Momo.expy[0] != \"\": # Already analyzed\n result = tkMessageBox.askquestion(\"Warning\", \"The selected experiment has already been analyzed.\\nChanges may overwrite exisiting data.\\nProceed anyways?\")\n if result != \"no\": # First time analyzing or want to override\n if Momo.exptype != \"4\": # Not strunching\n frame = cont(self.container, self)\n self.frames[cont] = frame \n frame.grid(row=0, column=0, sticky=\"nsew\")\n frame.ChangePic(1) # Go to first picture\n frame.tkraise() \n else:\n tkMessageBox.showwarning(\"Scrunching analysis not implimented yet\") #show warning\n \"\"\"\n frame = SrunchingAnalysis(self.container, self) # Create fresh page in case of old data\n self.frames[cont] = frame \n frame.grid(row=0, column=0, sticky=\"nsew\")\n frame.tkraise() \n \"\"\"","title":""},{"docid":"0bcf36119a5c06c0b937ddd0324ffa48","score":"0.4628606","text":"def apply_new_ranges(self, event=None):\n msg = (\"(Main) hit Enter/Return in Max V ({}) or Max I ({}) entry\"\n .format(self.v_range.get(), self.i_range.get()))\n log_user_action(self.ivs2.logger, msg)\n\n if self.current_run_displayed or self.results_wiz:\n run_dir = self.ivs2.hdd_output_dir\n config_dir = os.path.dirname(self.config.cfg_filename)\n\n # Replace config from saved config of displayed image\n cfg_file, original_cfg_file = self.swap_config(run_dir, config_dir)\n\n # Update IVS2 properties and the config options\n try:\n self.ivs2.plot_max_x = float(self.v_range.get())\n self.ivs2.plot_max_y = float(self.i_range.get())\n self.config.cfg_set(\"Plotting\", \"plot max x\", self.ivs2.plot_max_x)\n self.config.cfg_set(\"Plotting\", \"plot max y\", self.ivs2.plot_max_y)\n event.widget.tk_focusNext().focus() # move focus out\n except ValueError:\n # Silently reject invalid values\n pass\n self.update_axis_ranges()\n self.update_idletasks()\n\n if self.current_run_displayed or self.results_wiz:\n if self.axes_locked.get() == \"Unlock\":\n # Unlock the axes (redisplays image and saves config)\n self.unlock_axes()\n else:\n # Redisplay the image with the new settings (saves config)\n self.redisplay_img(reprocess_adc=False)\n\n # Restore the config file from the snapshot\n self.restore_config(run_dir, config_dir,\n cfg_file, original_cfg_file)","title":""},{"docid":"9c2455e10a2ec4fdd7e7f5506b8cfbe3","score":"0.462317","text":"def show_frameStingray(self, cont, obj):\n saveobject(obj)\n tkMessageBox.showwarning(\"Done\", \"Data has been saved for:\" + obj.expnumber) #show warning\n frame = self.frames[cont]\n frame.tkraise() #raise to front","title":""},{"docid":"cfc75b83f7eeae89ed3216a9f8d9055c","score":"0.46208972","text":"def set_window(self, x_start, y_start, x_end, y_end):\r\n if x_start > self.dis_column or \\\r\n x_end > self.dis_column or \\\r\n y_start > self.dis_line or \\\r\n y_end > self.dis_line:\r\n raise ValueError(\"Window setting is Out of display range.\")\r\n\r\n reg_list = [\r\n 0x15, x_start, x_end - 1,\r\n 0x75, y_start, y_end - 1,\r\n ]\r\n self.write_reg_list(reg_list)","title":""},{"docid":"c7189447a9197e79ba2e331c5709d595","score":"0.46200073","text":"def draw_match(self, frame, max_val, thresh_max, number, articlename):\n\n startx_coord = self.startx_coord[number]\n starty_coord = self.starty_coord[number]\n endx_coord = self.endx_coord[number]\n endy_coord = self.endy_coord[number]\n\n\n max_of_all = max_val[0]\n index_of_max = 0\n iterator = 0\n is_drawn = False\n\n for i in max_val:\n if max_of_all < i:\n max_of_all = i\n index_of_max = iterator\n iterator += 1\n\n if max_of_all > thresh_max:\n is_drawn = True\n\n cv2.rectangle(frame, (startx_coord[index_of_max], starty_coord[index_of_max]), (endx_coord[\n index_of_max], endy_coord[index_of_max]), (0, 0, 255), 2)\n cv2.putText(frame, articlename, (startx_coord[index_of_max], starty_coord[index_of_max] - 3),\n Config.font, 0.9, (0, 0, 255), 1, cv2.LINE_AA)\n\n Config.fps.stop()\n cv2.putText(frame, \"Elapsed time: {:.2f}\".format(Config.fps.elapsed()), Config.position_elapsed,\n Config.font, Config.fontScale, Config.fontColor, Config.lineType)\n cv2.putText(frame, \"FPS: {:.2f}\".format(Config.fps.fps()),\n Config.position_fps, Config.font, Config.fontScale,\n Config.fontColor, Config.lineType)\n\n cv2.imshow(\"Result\", frame)\n print(\"\\rFPS: {:.2f}\".format(Config.fps.fps()), end='\\r')\n return is_drawn, index_of_max, frame","title":""},{"docid":"82d6f056853f945a47a4c18d7facf051","score":"0.46178433","text":"def ui(dockable):\n\n # get settings\n counter = SETTINGS.get('counter')\n if counter == None:\n counter = '1'\n SETTINGS.add('counter', counter)\n\n padding = SETTINGS.get('padding')\n if padding == None:\n padding = '2'\n SETTINGS.add('padding', padding)\n\n step = SETTINGS.get('step')\n if step == None:\n step = '1'\n SETTINGS.add('step', step)\n\n name = SETTINGS.get('name')\n if name == None:\n name = 'example_[C]_GEP'\n SETTINGS.add('name', name)\n\n namePreview = SETTINGS.get('namePreview')\n if namePreview == None:\n namePreview = ''\n SETTINGS.add('namePreview', namePreview)\n\n searchfor = SETTINGS.get('searchfor')\n if searchfor == None:\n searchfor = ''\n SETTINGS.add('searchfor', searchfor)\n\n replaceby = SETTINGS.get('replaceby')\n if replaceby == None:\n replaceby = ''\n SETTINGS.add('replaceby', replaceby)\n\n asset = SETTINGS.get('asset')\n if asset == None:\n asset = ''\n SETTINGS.add('asset', asset)\n\n desc = SETTINGS.get('desc')\n if desc == None:\n desc = ''\n SETTINGS.add('desc', desc)\n\n category = SETTINGS.get('category')\n if category == None:\n category = ''\n SETTINGS.add('category', category)\n\n # create ui\n try:\n cmds.deleteUI(WINDOWNAME, window=True)\n except:\n pass\n try:\n cmds.deleteUI(CONTROLNAME, control=True)\n except:\n pass\n\n cmds.window(WINDOWNAME, t='Batch rename selection')\n form = cmds.formLayout(parent=WINDOWNAME)\n\n # rename with counter frame layout\n frameRename = cmds.frameLayout('rename_frameLayoutRename',\n label='Rename',\n cll=True,\n cl=True,\n bv=True)\n\n separator = cmds.separator('rename_separator', style='in')\n coubterInput = cmds.textFieldGrp('rename_counter', editable=True, l='Counter start', text=counter, fcc=True, cc=parseInputText, tcc=parseInputText)\n stepInput = cmds.textFieldGrp('rename_step', editable=True, l='Step', text=step, fcc=True, cc=parseInputText, tcc=parseInputText)\n paddingInput = cmds.textFieldGrp('rename_padding', editable=True, l='Padding', text=padding, fcc=True, cc=parseInputText, tcc=parseInputText)\n\n inputText = cmds.textFieldGrp('rename_nameField', editable=True, l='New name', text=name, fcc=True,cc=parseInputText, tcc=parseInputText)\n inputText = cmds.textFieldGrp('rename_namePreview', editable=False, l='Preview', text='')\n\n renameButton = cmds.button('rename_button', l='Rename', command=renameItems)\n cmds.setParent('..')\n\n # Search and replace frame layout\n frameReplace = cmds.frameLayout('rename_frameLayoutReplace',\n label='Search & Replace',\n cll=True,\n cl=True,\n bv=True)\n separator = cmds.separator('rename_separator', style='in') \n searchInput = cmds.textFieldGrp('rename_search', editable=True, l='Search for', text=searchfor)\n replaceInput = cmds.textFieldGrp('rename_replace', editable=True, l='Replace by', text=replaceby)\n \n renameButton = cmds.button('rename_replacebutton', l='Search and Replace', command=searchReplace)\n cmds.setParent('..')\n\n # add attributes frame layout\n frameAttr = cmds.frameLayout('rename_frameLayoutAttr',\n label='Add attributes',\n cll=True,\n cl=True,\n bv=True)\n separator = cmds.separator('rename_separator', style='in')\n\n assetField = cmds.textFieldGrp('rename_asset', editable=True, l='asset', text=asset)\n descField = cmds.textFieldGrp('rename_desc', editable=True, l='desc', text=desc)\n categoryField = cmds.textFieldGrp('rename_category', editable=True, l='category', text=category)\n \n addAttrButton = cmds.button('rename_setattrbutton', l='Add attributes', command=setArnoldAttr)\n removeAttrButton = cmds.button('rename_removeattrbutton', l='Remove attributes', command=removeArnoldAttr)\n cmds.setParent('..')\n\n # frame layout containing selection items\n frameSelection = cmds.frameLayout('selecter_frameLayoutSelection',\n label='Item Selecter',\n cll=True,\n cl=True,\n bv=True)\n #outPane = cmds.scrollField('selecter_output', editable=False, wordWrap=False, text='')\n outPane = cmds.textScrollList('selecter_output', append=[], sii=True, ams=True, sc=selectItem)\n inputText = cmds.textFieldGrp('selecter_nameField', editable=True, l='Filter:', text='', fcc=True, cc=parseFilterSelection, tcc=parseFilterSelection)\n nodeCheckbox = cmds.checkBoxGrp('selecter_dagcheck', numberOfCheckBoxes=3, labelArray3=['Dag objects', 'Transforms', 'Shapes'], cc=parseFilterSelection)\n typeCheckbox = cmds.checkBoxGrp('selecter_typecheck', numberOfCheckBoxes=3, labelArray3=['Meshes', 'Cameras', 'Lights'], cc=parseFilterSelection)\n\n cmds.setParent('..')\n\n separatorBottom = cmds.separator('rename_separatorBottom', style='in')\n closeButton = cmds.button('rename_close', l='Close', command=closeUI)\n \n cmds.formLayout(form, e=True,\n attachControl=[(frameReplace, 'top', 5, frameRename),\n (frameAttr, 'top', 5, frameReplace),\n (frameSelection, 'top', 5, frameAttr),\n (frameSelection, 'bottom', 5, separatorBottom),\n (separatorBottom, 'bottom', 5, closeButton),\n ])\n\n cmds.formLayout(form, e=True,\n attachForm=[(frameRename, 'right', 5),\n (frameRename, 'left', 5),\n (frameRename, 'top', 5),\n (frameReplace, 'left', 5),\n (frameReplace, 'right', 5),\n (frameAttr, 'left', 5),\n (frameAttr, 'right', 5),\n (frameSelection, 'left', 5),\n (frameSelection, 'right', 5),\n (separatorBottom, 'left', 5),\n (separatorBottom, 'right', 5),\n (closeButton, 'left', 5),\n (closeButton, 'right', 5),\n (closeButton, 'bottom', 5),\n ])\n \n \n parseInputText()\n parseFilterSelection()\n \n if dockable:\n cmds.dockControl(CONTROLNAME, label='Batch rename', floating=True, area='right', content=WINDOWNAME)\n else:\n cmds.showWindow(WINDOWNAME)","title":""},{"docid":"9f971213fb0678d439f85c2a326f194c","score":"0.46166497","text":"def gui_validate_landscape(self):\n page_range = self.strvar_landscape_pages.get().strip()\n if len(page_range) > 0 and not tools.check_page_nums(page_range):\n self.__remove_command_argument(cst.LANDSCAPE_ARG_NAME)\n self.strvar_landscape_pages.set(\"\")\n messagebox.showerror(\n message=\"Invalide `Output in Landscape` Page Argument!\"\n )\n return False\n\n if self.is_landscape_checked.get():\n arg = cst.LANDSCAPE_ARG_NAME\n if len(page_range) > 0:\n arg += page_range # no space between -ls and page numbers\n self.__add_or_update_command_argument(cst.LANDSCAPE_ARG_NAME, arg)\n else:\n self.__remove_command_argument(cst.LANDSCAPE_ARG_NAME)\n\n return True","title":""},{"docid":"77414f661b65f8cd21940fbbe71a6f30","score":"0.4610267","text":"def show_video(video_path, labels: List[int], size: Optional[Tuple[int, int]] = None) -> None:\n visible_color: Final[Tuple[int, int, int]] = (0, 255, 0)\n non_visible_color: Final[Tuple[int, int, int]] = (0, 0, 255)\n\n cap = cv2.VideoCapture(video_path)\n video_length: Final[int] = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n frame_nb = 0\n while frame_nb < video_length:\n\n frame_ok, frame = cap.read()\n if not frame_ok:\n break\n\n # Crop, uncomment and change the values if needed.\n # if \"video-1\" in video_path:\n # frame = frame[900:]\n\n if size:\n frame = cv2.resize(frame, size, interpolation=cv2.INTER_AREA)\n\n frame = cv2.copyMakeBorder(frame, 40, 0, 0, 0, cv2.BORDER_CONSTANT, None, 0)\n defect_text = f\"The defect is: {os.path.normpath(video_path).split(os.sep)[-4]}\"\n frame_text = f\" - Frame {frame_nb} / {video_length}\"\n frame = cv2.putText(frame, defect_text + frame_text, (20, 25),\n cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 255), 1, cv2.LINE_AA)\n\n frame = cv2.putText(frame, f\"Status: defect {'visible' if labels[frame_nb] != 0 else 'non-visible'}\",\n (frame.shape[1]-300, 25), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 255), 1, cv2.LINE_AA)\n frame = cv2.circle(frame, (frame.shape[1]-100, 20), 15,\n visible_color if labels[frame_nb] != 0 else non_visible_color, -1)\n\n while True:\n cv2.imshow(\"Frame\", frame)\n key = cv2.waitKey(10)\n if key == 32: # Space key, next frame\n break\n elif key == ord(\"q\"): # quit\n cap.release()\n cv2.destroyAllWindows()\n exit()\n\n frame_nb += 1\n\n cv2.destroyAllWindows()\n cap.release()","title":""},{"docid":"1e0c19d8925ce014c7ca0285e94852b5","score":"0.4608936","text":"def show_export_dataset_dialog(self):\n self.export_dataset_dialog.update()\n self.export_dataset_dialog.show()","title":""},{"docid":"833cb42e92332347016e2bc71419caee","score":"0.46062315","text":"def outputFormatOptionsMenu(self):\n outputFormatOptions = self.settingsSudoku.\\\n getSudokuOutputFormatOptions()\n self.drawMenuOptions(\"Change output type format\",\n outputFormatOptions)\n optionSelected = self.getUserInput()\n while not self.validateUserInput(optionSelected,\n len(outputFormatOptions)):\n print(\"*****The option selected is not valid****\")\n optionSelected = self.getUserInput()\n if(optionSelected == 1):\n print(\"Console\")\n self.settingsSudoku.setSudokuOutputFormat(\"Console\")\n elif (optionSelected == 2): \n print(\"File\")\n self.settingsSudoku.setSudokuOutputFormat(\"File\")\n userInputNameFile = raw_input(\\\n \"Enter a name for the outuput file: \")\n userInputPathFile = raw_input(\\\n \"Enter the specific path to store the file: \")","title":""},{"docid":"023bfe849dfaed2ef3bbc3985ab58b5d","score":"0.45980275","text":"def display_and_update(self):\n sg.theme(\"Reddit\")\n window = sg.Window(\"Mathcad Automation\", [\n [sg.Image(data = images.tt_logo)],\n [sg.Text(f'Version: {self.version}', size = (10,1))], \n [sg.Frame(\"Choose Excel File*\", [[sg.FileBrowse(\"1. Browse\", key=\"excel_file\", enable_events=True),\n sg.InputText(self.excel, key=\"excel_name\", size=(30, 1),\n background_color='white', enable_events=True),],\n\n [sg.Button(\"2. Choose Templates\", key = \"choose_templates\", enable_events = True)],\n ])],\n [sg.Frame(\"Choose Database File (Optional)\", [[sg.FileBrowse(key=\"database_file\", enable_events=True),\n sg.InputText(self.database, key=\"database_name\", size=(30, 1),\n background_color='white', enable_events=True)], ])],\n [sg.Checkbox(\"Save to database?\", key=\"save_to_database\", default=self.save_to_database,\n tooltip=\"If selected, details about the generated report will be saved to a database.\")],\n\n [sg.Button(\"3. Continue\", key=\"continue\", button_color=\"green\")],\n\n ], icon=images.ma_logo_png)\n \"\"\"Listen for events\"\"\"\n while True: #logic loop\n event, values = window.read()\n if event == 'OK' or event == sg.WIN_CLOSED:\n window.close()\n return False\n else:\n if event == \"choose_templates\":\n \"\"\"\n load the excel file and search for different mounting locations\n \"\"\"\n if helpers.check_file_type(values['excel_name'], 'xlsx'):\n #get the mounting locations\n equipment = filestream.get_eqpt_from_xl(values['excel_name'])\n if len(equipment.items) > 0:\n self.excel = values['excel_name']\n self.template_layout = []\n for idx, item in enumerate(equipment.mounting_locations):\n if item not in self.templates:\n self.templates[item] = \"\" # instanitate each templates to be empty string\n #create the layout for the choose templates window\n self.template_layout += [\n [sg.Text(\"Choose \" + item + \" mounting template:\")],\n [sg.FileBrowse(key= item, enable_events=True),\n sg.InputText(self.templates[item], key=item + \"_name\",\n size=(60, 1), background_color='white', enable_events=True)],\n ]\n if self.choose_templates():\n self.can_continue = True\n\n\n if event == \"continue\": # user has input all information\n # list of all the user errors when they input information\n errors = list()\n # validate the filepaths\n self.save_to_database = values['save_to_database']\n #check if the database is a .csv file - if not, append error list\n if helpers.check_file_type(values['database_name'], 'csv') or values['database_name'] == \"\":\n self.database = values['database_name']\n else:\n errors.append(\"Database file must be a .csv file.\")\n #check if excel file is .xlsx\n if helpers.check_file_type(values['excel_name'], 'xlsx'):\n self.excel = values['excel_name']\n else:\n errors.append(\"Excel file must be a .xlsx file.\")\n # Error handling\n if len(errors) == 0 and self.can_continue: #no errors\n window.close()\n return True\n elif self.can_continue == False:\n alert = Popup(\"Error\", \"You must choose template files.\")\n alert.alert()\n continue\n else: #show errors in a popup window\n alert = Popup(\"Errors\", \"\\n\".join(errors))\n alert.alert()\n continue","title":""},{"docid":"4b440491ff0b950033118bb7f66f7f8b","score":"0.459581","text":"def do_range(self, args, calibration=False):\n cmds = args.split()\n if len(cmds) < 4:\n print(\"Error: not enough args!\")\n return\n self.qa_analyzer.settings.CDSP_range = True\n self.qa_analyzer.settings.CDSP_range_param['CD1'] = int(cmds[0].strip())\n self.qa_analyzer.settings.CDSP_range_param['CD2'] = int(cmds[1].strip())\n self.qa_analyzer.settings.CDSP_range_param['SP1'] = int(cmds[2].strip())\n self.qa_analyzer.settings.CDSP_range_param['SP2'] = int(cmds[3].strip())\n if len(cmds) > 4:\n self.qa_analyzer.settings.CDSP_range_param['wext'] = int(cmds[4].strip())\n else:\n self.qa_analyzer.settings.CDSP_range_param['wext'] = None\n self.qa_analyzer.run(calibration)","title":""},{"docid":"6f0984e29706e01f77507efa808c2680","score":"0.45948943","text":"def onCaptureButton(self):\n inputBrowserNode = self.ui.inputSequenceBrowserSelector.currentNode()\n inputImage = self.ui.inputVolumeSelector.currentNode()\n outputBrowserNode = self.ui.segmentationBrowserSelector.currentNode()\n selectedSegmentation = self.ui.inputSegmentationSelector.currentNode()\n numSkip = slicer.modules.singleslicesegmentation.widgetRepresentation().self().ui.skipImagesSpinBox.value\n\n if inputBrowserNode is None:\n logging.error(\"No browser node selected!\")\n return\n if selectedSegmentation is None:\n logging.error(\"No segmentation selected!\")\n return\n if outputBrowserNode is None:\n logging.error(\"No segmentation sequence browser selected!\")\n return\n\n original_index_str = selectedSegmentation.GetAttribute(self.ORIGINAL_IMAGE_INDEX)\n\n # If input sequence browser is selected in the toolbar, always consider this a new segmentation. This is needed in case\n # a scene was loaded with segmentation attribute ORIGINAL_IMAGE_INDEX not None.\n\n activeBrowserNode = slicer.modules.sequences.toolBar().activeBrowserNode()\n if activeBrowserNode == inputBrowserNode:\n original_index_str = None\n\n if original_index_str is None or original_index_str == \"None\" or original_index_str == \"\": # new segmentation\n inputImageIndex = inputBrowserNode.GetSelectedItemNumber()\n selectedSegmentation.SetAttribute(self.ORIGINAL_IMAGE_INDEX, str(inputImageIndex))\n self.logic.captureSlice(outputBrowserNode, selectedSegmentation, inputImage)\n self.logic.eraseCurrentSegmentation(selectedSegmentation)\n selectedSegmentation.SetAttribute(self.ORIGINAL_IMAGE_INDEX, \"None\")\n currentItemNum = inputBrowserNode.GetSelectedItemNumber()\n newItemNum = inputBrowserNode.SelectNextItem(numSkip)\n else: # overwrite segmentation\n self.logic.captureSlice(outputBrowserNode, selectedSegmentation, inputImage)\n currentItemNum = outputBrowserNode.GetSelectedItemNumber()\n newItemNum = outputBrowserNode.SelectNextItem()\n\n # Check if sequence browser wrapped around. If yes, pop up message box to ask if user wants to continue.\n\n if newItemNum < currentItemNum:\n logging.debug(\"Sequence wrapped around!\")\n\n msgBox = qt.QMessageBox()\n msgBox.setText(\"Sequence wrapped around!\")\n msgBox.setInformativeText(\"Please save the scene before closing the application!\")\n msgBox.setStandardButtons(qt.QMessageBox.Ok)\n msgBox.setDefaultButton(qt.QMessageBox.Ok)\n msgBox.exec_()","title":""},{"docid":"111694a2830db1e8913b2f4c4e782ecf","score":"0.4589033","text":"def show_frameFish(self, cont):\n frame = self.frames[cont]\n camera.start_preview(fullscreen=False, window=(0,appheight/4,appwidth,appheight/2)) #this line starts the preview. \n frame.tkraise()","title":""},{"docid":"74cdd61fc0cfaef06c4e9bf753dbd3fb","score":"0.45877063","text":"def image_analysis(self): # connected to image analysis button (btn_images)\n\n self.start = int(self.meanStartEdit.text())\n\n if self.roi == None and self.JPG == False :\n self.mean = self.data[self.start,:,:]\n self.is_image = True\n else:\n self.ROI_no_mean_images()\n\n # change the name and color of the buttos to notice the way you choose\n self.btn7.setText(\"Export Intensities from frame={}\".format(self.start))\n self.btn7.setStyleSheet(\n \"QPushButton { background-color: rgb(200, 200, 10); }\")\n self.meanEndEdit.setStyleSheet(\" background-color: red; \")","title":""}],"string":"[\n {\n \"docid\": \"ba3807dc066092db7439c382f1f79169\",\n \"score\": \"0.5913335\",\n \"text\": \"def getFrameRangeFromShotSettings(self):\\r\\n\\t\\ttry:\\r\\n\\t\\t\\t#frRange = \\\"%d-%d\\\" %(int(os.environ['STARTFRAME']), int(os.environ['ENDFRAME'])) # Icarus\\r\\n\\t\\t\\tfrRange = \\\"%d-%d\\\" %(int(os.environ['UHUB_STARTFRAME']), int(os.environ['UHUB_ENDFRAME'])) # U-HUB\\r\\n\\t\\t\\tself.ui.frames_lineEdit.setText(frRange)\\r\\n\\t\\texcept KeyError:\\r\\n\\t\\t\\tself.ui.frames_lineEdit.setText(\\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10afd68795db62c4a02e1a8eaa171f3e\",\n \"score\": \"0.577385\",\n \"text\": \"def test_03_verify_page_range_screen_ui(self):\\n # General Setup\\n self.fc.select_a_google_drive_file_and_go_to_print_preview(file_name=\\\"2pages\\\")\\n assert self.fc.fd[\\\"preview\\\"].get_option_selected_value(Preview.PAGE_RANGE) == 'All'\\n self.fc.fd[\\\"preview\\\"].verify_an_element_and_click(Preview.PAGE_RANGE)\\n # Validation\\n assert set(self.fc.fd[\\\"preview\\\"].get_options_listed(Preview.PR_SCREEN_OPTIONS_UI)) == set(\\n Preview.PAGE_RANGE_OPTIONS)\\n assert self.fc.fd[\\\"preview\\\"].verify_pages_selected(Preview.PAGE_RANGE_OPTIONS[0]) is not False\\n assert self.fc.fd[\\\"preview\\\"].verify_pages_selected(Preview.PAGE_RANGE_OPTIONS[1]) is not False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4122f3623897225f316c47763538cff1\",\n \"score\": \"0.5509551\",\n \"text\": \"def on_fit_range_clicked(self):\\n dialog = FitRangeConfigDialog(\\n upper_lim=(0., 10., self.fit_range[1]),\\n lower_lim=(-1., 10., self.fit_range[0]),\\n dimension='microsecond')\\n\\n if dialog.exec_() == 1:\\n upper_limit = dialog.get_widget_value(\\\"upper_limit\\\")\\n lower_limit = dialog.get_widget_value(\\\"lower_limit\\\")\\n self.fit_range = (lower_limit, upper_limit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50a47179eb0e1bc49eabdeabb704ee6b\",\n \"score\": \"0.5437391\",\n \"text\": \"def grabFrame(self,event):\\n\\n if self.extract_range_frame == True:\\n num_frames_extract = self.endFrame.GetValue()\\n for i in range(self.currFrame,self.currFrame+num_frames_extract):\\n self.currFrame = i\\n self.vid.set(1,self.currFrame)\\n self.chooseFrame()\\n else:\\n self.vid.set(1,self.currFrame)\\n self.chooseFrame()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb146eb6cb9f239ff77328a34d5d3d34\",\n \"score\": \"0.54290503\",\n \"text\": \"def calcFrameList(self, quiet=True):\\r\\n\\t\\ttry:\\r\\n\\t\\t\\tself.numList = sequence.numList(self.ui.frames_lineEdit.text(), sort=False, quiet=quiet)\\r\\n\\t\\t\\tif self.numList is False:\\r\\n\\t\\t\\t\\t#raise RuntimeError(\\\"Invalid entry for frame range.\\\")\\r\\n\\t\\t\\t\\tif not quiet:\\r\\n\\t\\t\\t\\t\\t#verbose.warning(\\\"Invalid entry for frame range.\\\")\\r\\n\\t\\t\\t\\t\\tprint(\\\"Warning: Invalid entry for frame range.\\\")\\r\\n\\t\\t\\t\\t# self.ui.frames_lineEdit.setProperty(\\\"mandatoryField\\\", True)\\r\\n\\t\\t\\t\\t# self.ui.frames_lineEdit.style().unpolish(self.ui.frames_lineEdit)\\r\\n\\t\\t\\t\\t# self.ui.frames_lineEdit.style().polish(self.ui.frames_lineEdit)\\r\\n\\t\\t\\t\\tself.numList = []\\r\\n\\t\\t\\t\\tself.taskList = [\\\"Unknown\\\", ]\\r\\n\\r\\n\\t\\t\\t\\t# msg = \\\"Invalid entry for frame range.\\\"\\r\\n\\t\\t\\t\\t# #mc.warning(msg)\\r\\n\\t\\t\\t\\t# #mc.confirmDialog(title=\\\"Scene not saved\\\", message=msg, icon=\\\"warning\\\", button=\\\"Close\\\")\\r\\n\\t\\t\\t\\t# self.ui.submit_pushButton.setToolTip(msg)\\r\\n\\t\\t\\t\\t# self.ui.submit_pushButton.setEnabled(False)\\r\\n\\r\\n\\t\\t\\t\\treturn False\\r\\n\\r\\n\\t\\t\\telif self.numList is None:\\r\\n\\t\\t\\t\\t#raise RuntimeError(\\\"No frame range specified.\\\")\\r\\n\\t\\t\\t\\tif not quiet:\\r\\n\\t\\t\\t\\t\\t#verbose.warning(\\\"No frame range specified.\\\")\\r\\n\\t\\t\\t\\t\\tprint(\\\"Warning: No frame range specified.\\\")\\r\\n\\t\\t\\t\\t# self.ui.frames_lineEdit.setProperty(\\\"mandatoryField\\\", True)\\r\\n\\t\\t\\t\\t# self.ui.frames_lineEdit.style().unpolish(self.ui.frames_lineEdit)\\r\\n\\t\\t\\t\\t# self.ui.frames_lineEdit.style().polish(self.ui.frames_lineEdit)\\r\\n\\t\\t\\t\\tself.numList = []\\r\\n\\t\\t\\t\\tself.taskList = [\\\"Unknown\\\", ]\\r\\n\\r\\n\\t\\t\\t\\t# msg = \\\"No frame range specified.\\\"\\r\\n\\t\\t\\t\\t# #mc.warning(msg)\\r\\n\\t\\t\\t\\t# #mc.confirmDialog(title=\\\"Scene not saved\\\", message=msg, icon=\\\"warning\\\", button=\\\"Close\\\")\\r\\n\\t\\t\\t\\t# self.ui.submit_pushButton.setToolTip(msg)\\r\\n\\t\\t\\t\\t# self.ui.submit_pushButton.setEnabled(False)\\r\\n\\r\\n\\t\\t\\t\\treturn False\\r\\n\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\t# Update task size slider\\r\\n\\t\\t\\t\\t#self.ui.frames_lineEdit.setText(sequence.numRange(self.numList))\\r\\n\\t\\t\\t\\ttaskSize = self.ui.taskSize_spinBox.value()\\r\\n\\t\\t\\t\\tnFrames = len(self.numList)\\r\\n\\t\\t\\t\\tif taskSize < nFrames:\\r\\n\\t\\t\\t\\t\\tself.ui.taskSize_slider.setMaximum(nFrames)\\r\\n\\t\\t\\t\\t\\tself.ui.taskSize_spinBox.setMaximum(nFrames)\\r\\n\\t\\t\\t\\t\\tself.ui.taskSize_spinBox.setValue(taskSize)\\r\\n\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\tself.ui.taskSize_slider.setMaximum(nFrames)\\r\\n\\t\\t\\t\\t\\tself.ui.taskSize_spinBox.setMaximum(nFrames)\\r\\n\\t\\t\\t\\t\\tself.ui.taskSize_spinBox.setValue(nFrames)\\r\\n\\t\\t\\t\\tif nFrames == 1:\\r\\n\\t\\t\\t\\t\\tself.ui.taskSize_slider.setEnabled(False)\\r\\n\\t\\t\\t\\t\\tself.ui.taskSize_spinBox.setEnabled(False)\\r\\n\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\tself.ui.taskSize_slider.setEnabled(True)\\r\\n\\t\\t\\t\\t\\tself.ui.taskSize_spinBox.setEnabled(True)\\r\\n\\r\\n\\t\\t\\t\\t# Generate task list for rendering\\r\\n\\t\\t\\t\\tself.taskList = []\\r\\n\\t\\t\\t\\tsequences = list(sequence.seqRange(self.numList, gen_range=True))\\r\\n\\t\\t\\t\\tfor seq in sequences:\\r\\n\\t\\t\\t\\t\\tchunks = list(sequence.chunks(seq, taskSize))\\r\\n\\t\\t\\t\\t\\tfor chunk in chunks:\\r\\n\\t\\t\\t\\t\\t\\t#self.taskList.append(list(sequence.seqRange(chunk))[0])\\r\\n\\t\\t\\t\\t\\t\\tself.taskList.append(sequence.numRange(chunk))\\r\\n\\r\\n\\t\\t\\t\\treturn True\\r\\n\\r\\n\\t\\texcept (MemoryError, OverflowError):\\r\\n\\t\\t\\t#verbose.warning(\\\"Specified frame range value(s) too large to process.\\\")\\r\\n\\t\\t\\tprint(\\\"Warning: Specified frame range value(s) too large to process.\\\")\\r\\n\\t\\t\\treturn False\\r\\n\\r\\n\\t\\t# except RuntimeError:\\r\\n\\t\\t# \\tif not quiet:\\r\\n\\t\\t# \\t\\tverbose.warning(\\\"Invalid entry for frame range.\\\")\\r\\n\\t\\t# \\t# self.ui.frames_lineEdit.setProperty(\\\"mandatoryField\\\", True)\\r\\n\\t\\t# \\t# self.ui.frames_lineEdit.style().unpolish(self.ui.frames_lineEdit)\\r\\n\\t\\t# \\t# self.ui.frames_lineEdit.style().polish(self.ui.frames_lineEdit)\\r\\n\\t\\t# \\tself.numList = []\\r\\n\\t\\t# \\tself.taskList = [\\\"Unknown\\\", ]\\r\\n\\t\\t# \\treturn False\\r\\n\\r\\n\\t\\t# finally:\\r\\n\\t\\t# \\tpass\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cd60b7e906cace94cf2405f85043ada\",\n \"score\": \"0.541242\",\n \"text\": \"def buildTabDialog(self):\\n super(Torus5dFrame, self).buildTabDialog()\\n self.tab_dialog.addTab(Torus5dRangeTab(self.tab_dialog, self), \\\"Data Range\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1a2663b701b7b4ddf6388ba7e353667\",\n \"score\": \"0.5410121\",\n \"text\": \"def export_frame():\\r\\n exp_frame = LabelFrame(window, text='Экспортировать в MS Excel', width=960, height=270)\\r\\n exp_frame.place(x=25, y=450)\\r\\n\\r\\n btn_1 = Button(exp_frame, text=\\\"Все банки по месяцам\\\", background=\\\"#555\\\", foreground=\\\"#ccc\\\",\\r\\n padx=\\\"15\\\", pady=\\\"6\\\", font=\\\"15\\\", command=choice_month)\\r\\n btn_1.pack(expand=True, fill=BOTH)\\r\\n\\r\\n btn_2 = Button(exp_frame, text=\\\"Панель данных\\\", background=\\\"#555\\\", foreground=\\\"#ccc\\\",\\r\\n padx=\\\"15\\\", pady=\\\"6\\\", font=\\\"15\\\", command=choice_bank)\\r\\n btn_2.pack(expand=True, fill=BOTH)\\r\\n\\r\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54d45a9687419e2c6d449c9222bb3c16\",\n \"score\": \"0.5384579\",\n \"text\": \"def on_fit_range_clicked(self):\\n dialog = FitRangeConfigDialog(\\n upper_lim=(0., 60., self.fit_range[1]),\\n lower_lim=(-1., 60., self.fit_range[0]), dimension='ns')\\n\\n if dialog.exec_() == 1:\\n upper_limit = dialog.get_widget_value(\\\"upper_limit\\\")\\n lower_limit = dialog.get_widget_value(\\\"lower_limit\\\")\\n self.fit_range = (lower_limit, upper_limit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49a136c235db02f2e723f9bc3780a63a\",\n \"score\": \"0.5343724\",\n \"text\": \"def activate_frame_range(self,event):\\n self.checkSlider = event.GetEventObject()\\n if self.checkSlider.GetValue() == True:\\n self.extract_range_frame = True\\n self.startFrame.Enable(True)\\n self.startFrame.SetValue(self.slider.GetValue())\\n self.endFrame.Enable(True)\\n self.updateFrame.Enable(True)\\n self.grab.Enable(False)\\n else:\\n self.extract_range_frame = False\\n self.startFrame.Enable(False)\\n self.endFrame.Enable(False)\\n self.updateFrame.Enable(False)\\n self.grab.Enable(True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac3dc44e8d6a020617b351f6a508fd14\",\n \"score\": \"0.53095037\",\n \"text\": \"def show_pres_gen(self, event):\\n\\t\\tPresetDialog(self, \\\"Preset Generator\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b9e93030c3e9cfe40742bb5d974c9bc\",\n \"score\": \"0.5279935\",\n \"text\": \"def set_range_from_shot(shot):\\n min_frame = shot.getAttr('startFrame')\\n max_frame = shot.getAttr('endFrame')\\n\\n pm.playbackOptions(\\n ast=min_frame,\\n aet=max_frame,\\n min=min_frame,\\n max=max_frame\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bc3237ac8fdb028b4039f6108bd0c41\",\n \"score\": \"0.524625\",\n \"text\": \"def show_frameDelta(self, cont):\\n frame = self.frames[cont]\\n frame.gettext() #Displays either \\\"Ready\\\" or \\\"Insert stimuli\\\" based on experiment type\\n frame.tkraise()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"195e224e426f2efca8eef92ed1a8ce14\",\n \"score\": \"0.520215\",\n \"text\": \"def trackbar(theWidth, theValue, theMin, theMax, theSegments = 1, theLabelFormat = '%.1Lf', theOptions = 0, theDiscreteStep = 1):\\n\\tprint('This is wrapper function to help code autocompletion.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d13e15460338ffa0338d7aeb1bfd134\",\n \"score\": \"0.5100591\",\n \"text\": \"def setFrameListPreset(self):\\r\\n\\t\\t#print(self.sender().text())\\r\\n\\t\\tif self.sender().text() == \\\"Sequential\\\":\\r\\n\\t\\t\\tnum_list = sequence.numList(self.ui.frames_lineEdit.text(), sort=True, quiet=True)\\r\\n\\t\\t\\tif self.numList:\\r\\n\\t\\t\\t\\tself.ui.frames_lineEdit.setText(sequence.numRange(num_list))\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\tpass\\r\\n\\r\\n\\t\\telif self.sender().text() == \\\"Reverse order\\\":\\r\\n\\t\\t\\tpass\\r\\n\\r\\n\\t\\telif self.sender().text() == \\\"Render first and last frames before others\\\":\\r\\n\\t\\t\\tframes_str = self.ui.frames_lineEdit.text()\\r\\n\\t\\t\\tnum_list = sequence.numList(frames_str, sort=True, quiet=True)\\r\\n\\t\\t\\tif self.numList:\\r\\n\\t\\t\\t\\tfirst = min(num_list)\\r\\n\\t\\t\\t\\tlast = max(num_list)\\r\\n\\t\\t\\t\\tframes_str_prefix = \\\"%d, %d\\\" %(first, last)\\r\\n\\t\\t\\t\\tif not frames_str.startswith(frames_str_prefix):\\r\\n\\t\\t\\t\\t\\tself.ui.frames_lineEdit.setText(\\\"%s, %s\\\" %(frames_str_prefix, frames_str))\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"861577666521aacaf179c22e73a8c817\",\n \"score\": \"0.5064858\",\n \"text\": \"def show_frameEcho(self, cont):\\n self.frames[StimPrepPg].button1.grid_remove()\\n self.frames[StimPrepPg].button2.grid_remove()\\n self.frames[StimPrepPg].label1.configure(text=\\\"Experiment in progress\\\")\\n frame = self.frames[cont]\\n\\n os.makedirs(Appa.savefile) # Create general folder for experiment \\n os.makedirs(Appa.savefile + \\\"/ExpDataPictures\\\") # Create folder for images from exp\\n\\n #Image capturing\\n imgnum=0\\n for i in range(Appa.exptime+1):\\n start_time = clock()\\n remaining = Appa.exptime-i # Calculate countdown\\n self.frames[StimPrepPg].label2.configure(text=\\\"Time remaining: %d\\\" % remaining) # Set countdown\\n self.frames[StimPrepPg].update_idletasks() # Refresh page \\n if i%Appa.capturerate == 0: # Calculate if need to capture pic \\n camera.capture(Appa.savefile + \\\"/ExpDataPictures/image\\\" + str(imgnum) + \\\".jpg\\\")\\n Appa.expy.append(\\\"\\\") # Append empty place holder for future analyssi\\n imgnum+=1\\n sleep(1-(clock()-start_time))\\n\\n camera.stop_preview()\\n frame.tkraise()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ddddcfe84c9630152742df5676e288c\",\n \"score\": \"0.5046323\",\n \"text\": \"def trackbar(theWhere, theX, theY, theWidth, theValue, theMin, theMax, theSegments = 1, theLabelFormat = '%.1Lf', theOptions = 0, theDiscreteStep = 1):\\n\\tprint('This is wrapper function to help code autocompletion.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d3ba097ca1983f9d5470b46506ac474\",\n \"score\": \"0.503143\",\n \"text\": \"def choose_windows(spectrum, wstart, wend):\\n\\n #Define function that will obtain the cursor x position\\n def onselect(vmin, vmax):\\n \\\"\\\"\\\" Function that will obtain the cursor x position\\\"\\\"\\\"\\n windows.append(float(Decimal(\\\"%.2f\\\" % vmin)))\\n windows.append(float(Decimal(\\\"%.2f\\\" % vmax)))\\n plot_windows(windows)\\n plt.draw()\\n #Create a plot so the user can choose the windows\\n windows = [] #Set an empty window\\n axis = plt.subplot(111)\\n axis.plot(spectrum[:, 0], spectrum[:, 1], 'k-')\\n axis.hlines(1, wstart, wend, color = 'b', linestyles = 'dashed')\\n axis.set_xlim([wstart, wend])\\n axis.set_ylim([min(subselect_spectra \\\\\\n (spectrum, wstart, wend)[1]) - 0.2, 1.05])\\n axis.set_xlabel(r'Wavelength $(\\\\AA)$')\\n axis.set_ylabel('Normalized Flux')\\n span = SpanSelector(axis, onselect, 'horizontal', minspan = 0.05)\\n # Plot a vertical line at cursor position\\n cursor = Cursor(axis, useblit = True, color = 'red', linewidth = 1 )\\n cursor.horizOn = False\\n\\n plt.show()\\n plt.clf()\\n \\n return windows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"287088044e0310051082f6a3186f302d\",\n \"score\": \"0.5019565\",\n \"text\": \"def show_seg_marker_dialog():\\n \\n from chimera import dialogs\\n return dialogs.display(Segment_Marker.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b54d1e58af059846dda2adc2f0a71a7\",\n \"score\": \"0.4990835\",\n \"text\": \"def displayFrames(frames):\\n framesFormat = \\\"%-35s %-11s %-15s %-13s %-12s %-9s %5s %7s %5s\\\"\\n header = framesFormat % (\\n \\\"Frame\\\", \\\"Status\\\", \\\"Host\\\", \\\"Start\\\", \\\"End\\\", \\\"Runtime\\\", \\\"Mem\\\", \\\"Retry\\\", \\\"Exit\\\")\\n print(header + \\\"\\\\n\\\" + \\\"-\\\" * len(header))\\n\\n for frame in frames:\\n startTime = cueadmin.format.formatTime(frame.data.start_time)\\n stopTime = cueadmin.format.formatTime(frame.data.stop_time)\\n\\n if frame.data.start_time:\\n duration = cueadmin.format.formatDuration(\\n cueadmin.format.findDuration(frame.data.start_time, frame.data.stop_time))\\n else:\\n duration = \\\"\\\"\\n\\n memory = cueadmin.format.formatMem(frame.data.max_rss)\\n exitStatus = frame.data.exit_status\\n\\n print(framesFormat % (\\n cueadmin.format.cutoff(frame.data.name, 35),\\n opencue.compiled_proto.job_pb2.FrameState.Name(frame.data.state),\\n frame.data.last_resource,\\n startTime,\\n stopTime,\\n duration,\\n memory,\\n frame.data.retry_count,\\n exitStatus))\\n\\n if len(frames) == 1000:\\n print(\\\"Warning: Only showing first 1000 matches. See frame query options to \\\"\\n \\\"limit your results.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97aab89295159bb5847a2a3f7390b775\",\n \"score\": \"0.49894312\",\n \"text\": \"def export_segment(self):\\n\\n msecs_from = msecs_to_hours_mins_secs(self.segment['pos0'])\\n msecs_from = msecs_from.replace('.', \\\"H\\\", 1)\\n msecs_from = msecs_from.replace('.', \\\"M\\\", 1) + \\\"S\\\"\\n msecs_to = msecs_to_hours_mins_secs(self.segment['pos1'])\\n msecs_to = msecs_to.replace('.', \\\"H\\\", 1)\\n msecs_to = msecs_to.replace('.', \\\"M\\\", 1) + \\\"S\\\"\\n filename = self.code_av_dialog.file_['name'][:-4] + \\\"_\\\"\\n filename += msecs_from + \\\"_to_\\\" + msecs_to + \\\"_\\\"\\n filename += self.code_av_dialog.file_['name'][-4:]\\n export_dir = ExportDirectoryPathDialog(self.app, filename)\\n filepath = export_dir.filepath\\n if filepath is None:\\n return\\n if os.path.exists(filepath):\\n os.remove(filepath)\\n mediapath = \\\"\\\"\\n try:\\n if self.code_av_dialog.file_['mediapath'][0:6] in ('/audio', '/video'):\\n mediapath = self.app.project_path + self.code_av_dialog.file_['mediapath']\\n if self.code_av_dialog.file_['mediapath'][0:6] in ('audio:', 'video:'):\\n mediapath = self.code_av_dialog.file_['mediapath'][6:]\\n except Exception as e_:\\n Message(self.app, _('Media not found'),\\n str(e_) + \\\"\\\\n\\\" + self.app.project_path + self.code_av_dialog.file_['mediapath'],\\n \\\"warning\\\").exec()\\n return\\n ffmpeg_command = 'ffmpeg -i \\\"' + mediapath + '\\\" -ss '\\n ffmpeg_command += str(self.segment['pos0'] / 1000)\\n ffmpeg_command += ' -to '\\n ffmpeg_command += str(self.segment['pos1'] / 1000)\\n ffmpeg_command += ' -c copy \\\"' + filepath + '\\\"'\\n # print(f\\\"FFMPEG COMMAND\\\\n {ffmpeg_command}\\\")\\n try:\\n subprocess.run(ffmpeg_command, timeout=15, shell=True)\\n self.code_av_dialog.parent_textEdit.append(_(\\\"A/V segment exported: \\\") + filepath)\\n Message(self.app, _(\\\"Segment exported\\\"), filepath).exec()\\n except Exception as e_:\\n logger.error(str(e_))\\n print(str(e_))\\n Message(self.app, \\\"ffmpeg error\\\", str(e_)).exec()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3196554bd258ff84cf0f8ec9a6c2b87c\",\n \"score\": \"0.49824834\",\n \"text\": \"def showVideo(self): # connected to Go to vide (btn5)\\n\\n# self.is_trace = False\\n\\n plot_with_colorbar(self.imv, self.data)\\n\\n# self.w.setWindowTitle('SMAnalyzer - Video - ' + self.f)\\n self.meanEndEdit.setStyleSheet(\\\" background-color: ; \\\")\\n\\n try:\\n self.translateMaxima()\\n self.imv.view.addItem(self.roi)\\n except:\\n pass\\n if self.JPG:\\n self.mean = self.data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"982327f5089a668553fd3d0cc7cbb0e5\",\n \"score\": \"0.49745703\",\n \"text\": \"def trackbar2(theWidth, theValue1, theValue2, theMin, theMax, theSegments=1, theLabelFormat='%.1Lf', theOptions=0, theDiscreteStep=1):\\n\\tprint('This is wrapper function to help code autocompletion.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"572a091297dea0a5cdc94e389405de48\",\n \"score\": \"0.497088\",\n \"text\": \"def show_scan_frame(self) -> None:\\n src.getraenkeKasse.scanframe.ScanFrame(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ad93f72af3d15509f96237142728772\",\n \"score\": \"0.4968003\",\n \"text\": \"def show_export_events_dialog(self):\\n self.export_events_dialog.update()\\n self.export_events_dialog.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bfd9577c88b9603ec1b1d4534c8be49\",\n \"score\": \"0.49670574\",\n \"text\": \"def export_img(self, path, base = 'fseq-export-', figsize=(4,4),\\n start = 0, stop = None, show_title = True,\\n format='.png', vmin = None, vmax=None, **kwargs):\\n import sys\\n import matplotlib.pyplot as plt\\n lib.ensure_dir(path)\\n fig = plt.figure(figsize=figsize)\\n ax = fig.add_subplot(111)\\n if stop is None or stop == -1:\\n stop = len(self)\\n\\tif hasattr(self, 'data'):\\n\\t vmin = ifnot(vmin, self.data_percentile(1)) # for scale\\n\\t vmax = ifnot(vmax, self.data_percentile(99)) # for scale\\n\\telse:\\n\\t vmin = ifnot(vmin, np.min(map(np.min, self.frames())))\\n\\t vmax = ifnot(vmax, np.min(map(np.max, self.frames())))\\n kwargs.update({'vmin':vmin, 'vmax':vmax})\\n\\tprint path+base\\n L = min(stop-start, len(self))\\n\\tfnames = []\\n for i,frame in enumerate(self.frames()):\\n if i < start: continue\\n if i > stop: break\\n ax.cla()\\n ax.imshow(frame, aspect='equal', **kwargs)\\n fname = path + base + '%06d.png'%i\\n\\t fnames.append(fname)\\n if show_title:\\n zscale = tuple(self.meta['axes'][0])\\n ax.set_title('frame %06d (%3.3f %s)'%zscale)\\n fig.savefig(fname)\\n sys.stderr.write('\\\\r saving frame %06d of %06d'%(i+1, L))\\n plt.close()\\n\\treturn fnames\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a77f38ecfac5d612d6426a2056fc8d9\",\n \"score\": \"0.49639183\",\n \"text\": \"def getFrameRangeFromRenderSettings(self):\\r\\n\\t\\tif UI.ENVIRONMENT == \\\"MAYA\\\":\\r\\n\\t\\t\\tstart_frame = int(mc.getAttr('defaultRenderGlobals.startFrame'))\\r\\n\\t\\t\\tend_frame = int(mc.getAttr('defaultRenderGlobals.endFrame'))\\r\\n\\r\\n\\t\\telif UI.ENVIRONMENT == \\\"HOUDINI\\\":\\r\\n\\t\\t\\t# Note this is the time slider (playbar) range, not the render\\r\\n\\t\\t\\t# settings\\r\\n\\t\\t\\tstart_frame = hou.playbar.playbackRange()[0]\\r\\n\\t\\t\\tend_frame = hou.playbar.playbackRange()[1]\\r\\n\\r\\n\\t\\telif UI.ENVIRONMENT == \\\"NUKE\\\":\\r\\n\\t\\t\\tstart_frame = nuke.Root()['first_frame'].getValue()\\r\\n\\t\\t\\tend_frame = nuke.Root()['last_frame'].getValue()\\r\\n\\r\\n\\t\\tself.ui.frames_lineEdit.setText(\\\"%d-%d\\\" %(start_frame, end_frame))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab0d42cd38951741e47c8f6343f29f35\",\n \"score\": \"0.49546847\",\n \"text\": \"def trackbar2(theWhere, theX, theY, theWidth, theValue1, theValue2, theMin, theMax, theSegments=1, theLabelFormat='%.1Lf', theOptions=0, theDiscreteStep=1):\\n\\tprint('This is wrapper function to help code autocompletion.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a1a39a68dc60fbef53764d44b54ec8d\",\n \"score\": \"0.4953241\",\n \"text\": \"def on_ok_clicked(self, obj):\\n\\n # Is there a filename? This should also test file permissions, etc.\\n if not self.parse_target_frame():\\n self.window.run()\\n\\n # Preparation\\n self.parse_format_frame()\\n self.parse_user_options()\\n\\n self.options.handler.set_paper_metric(\\n self.paper_frame.get_paper_metric())\\n self.options.handler.set_paper_name(self.paper_frame.get_paper_name())\\n self.options.handler.set_orientation(self.paper_frame.get_orientation())\\n self.options.handler.set_margins(self.paper_frame.get_paper_margins())\\n self.options.handler.set_custom_paper_size(\\n self.paper_frame.get_custom_paper_size())\\n \\n # Create the output document.\\n self.make_document()\\n \\n # Save options\\n self.options.handler.save_options()\\n config.set('interface.open-with-default-viewer', \\n self.open_with_app.get_active())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26063a03b2213531eaf977069b81b84b\",\n \"score\": \"0.4944826\",\n \"text\": \"def execute(self, parameters, messages):\\n\\n arcpy.ImportToolbox(os.path.join(os.path.dirname(__file__), \\\"URB.pyt\\\"))\\n arcpy.gp.toolbox = os.path.join(os.path.dirname(__file__), \\\"URB.pyt\\\")\\n\\n def extentToPoly(extent, srid=3003):\\n clist = arcpy.Array()\\n clist.append(arcpy.Point(extent.XMin, extent.YMin))\\n clist.append(arcpy.Point(extent.XMin, extent.YMax))\\n clist.append(arcpy.Point(extent.XMax, extent.YMax))\\n clist.append(arcpy.Point(extent.XMax, extent.YMin))\\n return arcpy.Polygon(clist)\\n\\n def get_best_fit_scale(fc,paper,scales=[1000,2000,2500,5000,7500,10000,20000]):\\n desc = arcpy.Describe(fc)\\n sheet = printOutput_templates[paper][\\\"size\\\"]\\n margin = 10\\n #mapUnitsPerMillimeter = [0.5,1,2,5,10]\\n cx = (desc.extent.XMin + desc.extent.XMax)/2\\n cy = (desc.extent.YMin + desc.extent.YMax)/2\\n fc_bound = extentToPoly(desc.extent)\\n\\n for scale in scales:\\n scaleFactor = scale / 1000\\n wb = sheet[0] * scaleFactor / 2\\n hb = sheet[1] * scaleFactor / 2\\n wf = (sheet[0] - margin*2) * scaleFactor / 2\\n hf = (sheet[1] - margin*2) * scaleFactor / 2\\n \\n #bound = arcpy.Polygon([arcpy.Point(cx-wb,cy-hb), arcpy.Point(cx+wb,cy-hb), arcpy.Point(cx+wb,cy+hb), arcpy.Point(cx-wb,cy+hb)])\\n #frame = arcpy.Polygon([arcpy.Point(cx-wf,cy-hf), arcpy.Point(cx+wf,cy-hf), arcpy.Point(cx+wf,cy+hf), arcpy.Point(cx-wf,cy+hf)])\\n bound = extentToPoly(arcpy.Extent(cx-wb, cy-hb, cx+wb, cy+hb))\\n frame_extent = arcpy.Extent(cx-wf, cy-hf, cx+wf, cy+hf)\\n frame = extentToPoly(frame_extent)\\n\\n #tempfcname = \\\"in_memory/output\\\" + uuid.uuid4().hex\\n #arcpy.Intersect_analysis ([frame, fc_bound], tempfcname)\\n #result = arcpy.GetCount_management(tempfcname)\\n #intersections = int(result.getOutput(0))\\n\\n #if intersections > 0:\\n if frame_extent.contains(desc.extent):\\n return bound, frame, scale\\n\\n return bound, frame, scaleFactor\\n\\n def get_esri_ring(extent):\\n ring = [\\n [extent.XMin, extent.YMin],\\n [extent.XMax, extent.YMin],\\n [extent.XMax, extent.YMax],\\n [extent.XMin, extent.YMax],\\n [extent.XMin, extent.YMin],\\n ]\\n return ring\\n\\n probe_path = parameters[0].valueAsText\\n coordinate_catastali = parameters[1].valueAsText\\n paper = parameters[2].valueAsText.replace(\\\"'\\\",\\\"\\\")\\n base = parameters[3].valueAsText.replace(\\\"'\\\",\\\"\\\")\\n\\n checkboxes = []\\n #for idx in range(4,12):\\n # if parameters[idx].valueAsText == \\\"true\\\":\\n # checkboxes.append(idx)\\n for param in parameters:\\n #arcpy.AddMessage(\\\"param: %s %s\\\" % (str(param.datatype),str(param.valueAsText)))\\n if param.datatype in (\\\"Booleano\\\", \\\"Boolean\\\") and param.valueAsText == \\\"true\\\":\\n checkboxes.append(param.name)\\n arcpy.AddMessage(\\\"checkboxes: %s\\\" % str(checkboxes))\\n\\n with open(os.path.join(os.path.dirname(__file__),\\\"web_map_as_json.json\\\"),\\\"r\\\") as jf:\\n wmaj_template = jf.read()\\n\\n template_engine = Template(wmaj_template)\\n\\n decode_map = []\\n\\n if coordinate_catastali:\\n CC_result = arcpy.gp.CC2FCtool(coordinate_catastali)\\n probe_path = CC_result.getOutput(0)\\n else:\\n if not probe_path:\\n arcpy.AddError(\\\"Deve essere specificata almeno un contesto, come layer o come coordinate catastali\\\")\\n exit(0)\\n \\n arcpy.AddMessage(\\\"probe_path: %s paper:\\\" % probe_path)\\n probe = arcpy.mapping.Layer(probe_path)\\n with arcpy.da.SearchCursor(probe_path, ['SHAPE']) as cursor: \\n probe_polygon = next(cursor)[0]\\n\\n #probe_json_path = os.path.join(tempfile.mkdtemp(), \\\"probe.json\\\")\\n probe_json_path = get_jobfile(\\\"activity\\\", \\\"json\\\")\\n arcpy.FeaturesToJSON_conversion(probe_path, probe_json_path, \\\"FORMATTED\\\")\\n\\n with open(probe_json_path ,\\\"r\\\") as jf:\\n probe_json = jf.read()\\n\\n #arcpy.AddMessage(json.dumps(json.loads(probe_json),indent=3))\\n\\n json_feats = []\\n probe_json_dict = json.loads(probe_json)\\n for feat in probe_json_dict[\\\"features\\\"]:\\n feat[\\\"symbol\\\"] = {\\n \\\"color\\\": [255, 0, 0, 0],\\n \\\"outline\\\": {\\n \\\"color\\\": [255, 0, 0, 255],\\n \\\"width\\\": 1.75,\\n \\\"type\\\": \\\"esriSLS\\\",\\n \\\"style\\\": \\\"esriSLSSolid\\\"\\n },\\n \\\"type\\\": \\\"esriSFS\\\",\\n \\\"style\\\": \\\"esriSFSSolid\\\"\\n }\\n json_feats.append(feat)\\n\\n\\n result_pdf = []\\n\\n for tema in temi:\\n if not tema[\\\"label\\\"] in checkboxes:\\n continue\\n \\n mapServices = basi[base] + tema[\\\"def\\\"]\\n\\n bound, frame, scale = get_best_fit_scale(probe_path, paper)\\n\\n printpar ={\\n \\\"extent\\\": [ frame.extent.XMin, frame.extent.YMin, frame.extent.XMax, frame.extent.YMax ],\\n \\\"scale\\\": scale,\\n \\\"srid\\\": 3003,\\n \\\"esri_poly\\\": json.dumps(json.loads(probe_json)[\\\"features\\\"][0][\\\"geometry\\\"][\\\"rings\\\"]),\\n \\\"esri_style\\\": json.dumps(proto_ESRI_style), #non implementato nel template json\\n \\\"esri_bound\\\": get_esri_ring(bound.extent),\\n \\\"esri_frame\\\": get_esri_ring(frame.extent),\\n \\\"title\\\": tema[\\\"label\\\"].upper().replace(\\\"_\\\",\\\"\\\"),\\n \\\"dpi\\\": 200,\\n \\\"auth\\\": \\\"Settore urbanistica, Servizi catastali e Mobilita'\\\",\\n \\\"copyright\\\": \\\"Comune di Padova\\\"\\n }\\n\\n web_map_as_json = template_engine.render(printpar = printpar)\\n web_map_as_dict = json.loads(web_map_as_json)\\n\\n web_map_as_dict[\\\"operationalLayers\\\"][0][\\\"featureCollection\\\"][\\\"layers\\\"][0][\\\"featureSet\\\"][\\\"features\\\"] = json_feats\\n web_map_as_dict[\\\"operationalLayers\\\"] = mapServices + web_map_as_dict[\\\"operationalLayers\\\"]\\n web_map_as_json = json.dumps(web_map_as_dict)\\n\\n post_parameters = {\\n \\\"f\\\": \\\"json\\\",\\n \\\"Web_Map_as_JSON\\\": web_map_as_json,\\n \\\"Format\\\": \\\"PDF\\\",\\n \\\"Layout_Template\\\": printOutput_templates[paper][\\\"label\\\"]\\n }\\n\\n #arcpy.AddMessage(json.dumps(post_parameters,indent=3))\\n\\n #pdf_file_path = os.path.join(tempfile.mkdtemp(), tema[\\\"label\\\"]+\\\".pdf\\\")\\n pdf_file_path = get_jobfile(\\\"output\\\", \\\"pdf\\\")\\n\\n res = urllib.urlopen(base_url + \\\"arcgis/rest/services/Utilities/PrintingTools/GPServer/Export%20Web%20Map%20Task/execute\\\", urllib.urlencode(post_parameters)).read()\\n\\n if \\\"results\\\" in res:\\n remoteFile = json.loads(res)['results'][0][\\\"value\\\"][\\\"url\\\"]\\n #arcpy.AddMessage (\\\"REMOTE: \\\" + remoteFile)\\n urllib.urlretrieve(remoteFile, pdf_file_path)\\n arcpy.AddMessage(\\\"OK: %s\\\" % tema[\\\"label\\\"])\\n result_pdf.append(pdf_file_path)\\n else:\\n arcpy.AddMessage(\\\"NO\\\")\\n\\n if parameters[-1].valueAsText:\\n pdf_globale = parameters[-1].valueAsText\\n else:\\n #pdf_globale = os.path.join(tempfile.mkdtemp(), \\\"inquadramento.pdf\\\")\\n pdf_globale = get_jobfile(\\\"output\\\", \\\"pdf\\\")\\n\\n merger = PdfFileMerger()\\n for file in result_pdf:\\n merger.append(PdfFileReader(file))\\n merger.write(pdf_globale)\\n\\n parameters[-1].value = pdf_globale\\n\\n arcpy.AddMessage(\\\"OK: %s\\\" % pdf_globale)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10575b6e0efc5c3e56b8f88ce0b550f0\",\n \"score\": \"0.4936005\",\n \"text\": \"def info_range(self):\\n \\n # If there is no data, tell the user and don't show the info dialog.\\n if len(self.data) == 0:\\n show_no_data_dialog(self, \\\"Info - %s\\\" % self.last_profile)\\n return\\n \\n # Get the first and last entered dates.\\n day_start = dates.split_date(self.data[0][0])\\n day_end = dates.split_date(self.data[len(self.data) - 1][0])\\n \\n # Get a list of datetimes from the dates.\\n datelist = dates.date_list_datetime(datasets.get_column(self.data, 0))\\n \\n # Get the starting and ending dates.\\n cal_dlg = CalendarRangeDialog(self, \\\"Info in Range - %s\\\" % self.last_profile, day_start, day_end)\\n response = cal_dlg.run()\\n year1, month1, day1 = cal_dlg.start_cal.get_date()\\n year2, month2, day2 = cal_dlg.end_cal.get_date()\\n date1 = \\\"%d/%d/%d\\\" % (day1, month1 + 1, year1)\\n date2 = \\\"%d/%d/%d\\\" % (day2, month2 + 1, year2)\\n cal_dlg.destroy()\\n \\n # If the user did not click OK, don't continue.\\n if response != Gtk.ResponseType.OK:\\n return\\n \\n # Get the indices.\\n dt_start = datetime.datetime(year1, month1 + 1, day1)\\n start_index = dates.date_above(dt_start, datelist)\\n dt_end = datetime.datetime(year2, month2 + 1, day2)\\n end_index = dates.date_below(dt_end, datelist)\\n \\n # Check to make sure these dates are valid, and cancel the action if not.\\n if start_index == DateValidation.INVALID:\\n show_error_dialog(self, \\\"Info in Range - %s\\\" % self.last_profile, \\\"%s is not a valid date.\\\\n\\\\nThis date is not present and is not before any other dates.\\\" % date1)\\n return\\n if end_index == DateValidation.INVALID:\\n show_error_dialog(self, \\\"Info in Range - %s\\\" % self.last_profile, \\\"%s is not a valid date.\\\\n\\\\nThis date is not present and is not after any other dates.\\\" % date2)\\n return\\n if end_index < start_index:\\n show_error_dialog(self, \\\"Info in Range - %s\\\" % self.last_profile, \\\"The ending date must be after the starting date.\\\")\\n return\\n \\n # Get the new list.\\n data2 = self.data[start_index:end_index + 1]\\n \\n # Pass the data to the info dialog.\\n self.show_info_generic(data = data2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c916096b47eb99b3f25d7ca7bd57a62\",\n \"score\": \"0.49245515\",\n \"text\": \"def select_rectangle(infile, start, res_dict, fits_dict, wloc, outfil, maxim):\\r\\n im, header = get_fits_image(infile + str(start))\\r\\n im = im / np.max(im)\\r\\n get_fits_keys(header, fits_dict, res_dict, keyprint=False)\\r\\n # #===================================================================\\r\\n # new rect_plt\\r\\n # first get size of graph from tmp.png and size of image\\r\\n # graph coordinates are in image pixels!\\r\\n (imy, imx) = im.shape[:2]\\r\\n image_file = 'tmp.png' # scaled image\\r\\n imbw = np.flipud(ios.imread(image_file)) # get shape\\r\\n (canvasy, canvasx) = imbw.shape[:2]\\r\\n wlocw = (wloc[0] + 300, wloc[1] + 50)\\r\\n # check for old files\\r\\n delete_old_files(outfil, maxim, ext='.fit')\\r\\n image_elem_sel = [sg.Graph(\\r\\n canvas_size=(canvasx, canvasy),\\r\\n graph_bottom_left=(0, 0), # starts at top, set y-scale here\\r\\n graph_top_right=(imx, imy), # set x-scale here\\r\\n key='-GRAPH-',\\r\\n change_submits=True, # mouse click events\\r\\n drag_submits=True)]\\r\\n layout_select = [[sg.Text('Start File: ' + infile + str(start), size=(50, 1)), sg.Text(key='info', size=(40, 1)),\\r\\n sg.Ok(), sg.Cancel()],\\r\\n image_elem_sel]\\r\\n # ---------------------------------------------------------------------------\\r\\n winselect_active = True\\r\\n winselect = sg.Window(f'select zero order or spectral line',\\r\\n layout_select, finalize=True, location=wlocw,\\r\\n keep_on_top=True, no_titlebar=False,\\r\\n disable_close=False, disable_minimize=True)\\r\\n # get the graph element for ease of use later\\r\\n graph = winselect['-GRAPH-'] # type: sg.Graph\\r\\n graph.draw_image(image_file, location=(0, imy)) if image_file else None\\r\\n winselect.refresh()\\r\\n dragging = False\\r\\n start_point = end_point = prior_rect = None\\r\\n x0 = y0 = dx = dy = 0\\r\\n while winselect_active:\\r\\n event, values = winselect.read()\\r\\n idg = graph.draw_rectangle((0, 0), (imx, imy), line_color='blue')\\r\\n if event == \\\"-GRAPH-\\\": # if there's a \\\"Graph\\\" event, then it's a mouse\\r\\n x, y = (values[\\\"-GRAPH-\\\"])\\r\\n if not dragging:\\r\\n start_point = (x, y)\\r\\n dragging = True\\r\\n else:\\r\\n end_point = (x, y)\\r\\n if prior_rect:\\r\\n graph.delete_figure(prior_rect)\\r\\n if None not in (start_point, end_point):\\r\\n prior_rect = graph.draw_rectangle(start_point,\\r\\n end_point, line_color='red')\\r\\n elif event is not None and event.endswith('+UP'):\\r\\n # The drawing has ended because mouse up\\r\\n xy0 = [int(0.5 * (start_point[0] + end_point[0])),\\r\\n int(0.5 * (start_point[1] + end_point[1]))]\\r\\n size = (abs(start_point[0] - end_point[0]),\\r\\n abs(start_point[1] - end_point[1]))\\r\\n info = winselect[\\\"info\\\"]\\r\\n info.update(value=f\\\"grabbed rectangle at {xy0} with size {size}\\\")\\r\\n start_point, end_point = None, None # enable grabbing a new rect\\r\\n dragging = False\\r\\n if min(size[0], size[1]) > 1: # rectangle\\r\\n info.update(value=f\\\"rectangle at {xy0} with size {size}\\\")\\r\\n x0 = xy0[0]\\r\\n y0 = xy0[1]\\r\\n dx = int((size[0] + 1) / 2)\\r\\n dy = int((size[1] + 1) / 2)\\r\\n\\r\\n elif event in ('Ok', 'Cancel'):\\r\\n graph.delete_figure(idg)\\r\\n winselect_active = False\\r\\n winselect.close()\\r\\n return event, x0, y0, dx, dy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59996cf468705e8938141a9c24208f86\",\n \"score\": \"0.4921469\",\n \"text\": \"def show_frameShark(self, cont, listofbuttons):\\n frame = cont(self.container, self)\\n self.frames[cont] = frame \\n\\n ExpsToGraph = []\\n expnames = []\\n unanalyzedlist = []\\n result = True\\n\\n # Get list of experiments to plot\\n for button in listofbuttons:\\n if button.instate(['selected']):\\n ExpsToGraph.append(getobject(button['text']))\\n\\n if len(ExpsToGraph) == 0: # Did not choose an experiment to graph\\n tkMessageBox.showwarning(\\\"Error\\\", \\\"No experiments selected\\\")\\n else:\\n # Check to see if all experiments selected have been analyzed\\n for experiment in ExpsToGraph:\\n if experiment.expy[0] == \\\"\\\":\\n unanalyzedlist.append(experiment.expnumber)\\n else:\\n experiment.expy = list(map(int, experiment.expy))\\n frame.a.plot(range(len(experiment.expy)),experiment.expy, label=experiment.expnumber)\\n expnames.append(experiment.expnumber)\\n\\n\\n if len(unanalyzedlist) > 0: # If unanalyzed experiments exist warn user\\n unanalyzedlist = \\\", \\\".join(unanalyzedlist)\\n result = tkMessageBox.askquestion(\\\"Warning\\\", \\\"The following experiments have\\\\nnot been analyzed yet\\\\nand will not be graphed\\\\n\\\\n\\\" +unanalyzedlist+ \\\"\\\\n\\\\nProceed anyways?\\\")\\n\\n if result != \\\"no\\\": # Override or all experiments have been analyzed \\n frame.grid(row=0, column=0, sticky=\\\"nsew\\\") \\n frame.a.legend(loc='upper right', fontsize=8)\\n frame.canvas.draw() \\n frame.tkraise()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"057084b294b3f12db6cccefb5b454320\",\n \"score\": \"0.49202904\",\n \"text\": \"def menu(file_name):\\n print (80 * '*')\\n print ('*' + 1*' ' + 'PDB FILE ANALYZER' + 60*' ' + '*')\\n print (80 * '*')\\n print ('*'+ 1*' ' + 'Select an option from below:' + 49*' ' + '*')\\n print ('*' + 78*' ' + '*')\\n print ('*' + 6*' ' + '1) Open a PDB File' + 23*' ' + '(O)' + 28*' ' + '*')\\n print ('*' + 6*' ' + '2) Information'+ 27*' ' + '(I)' + 28*' ' + '*')\\n print ('*' + 6*' ' + '3) Show histogram of amino acids '+ 8*' ' + '(H)' + 28*' ' + '*')\\n print ('*' + 6*' ' + '4) Display Secondary Structure'+ 11*' ' + '(S)' + 28*' ' + '*')\\n print ('*' + 6*' ' + '5) Export PDB File '+ 22*' ' + '(X)' + 28*' ' + '*')\\n print ('*' + 6*' ' + '6) Exit '+ 33*' ' + '(Q)' + 28*' ' + '*')\\n print ('*' + 55*' ' + 'Current PDB: %s ' %file_name + '*')\\n print (80 * '*')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b559ccd5b2fc271a56d1f69417096004\",\n \"score\": \"0.4901829\",\n \"text\": \"def quicklook(SMPS):\\n # create particle sizer display class\\n psd = PSDisplay(SMPS)\\n \\n # create sample slider\\n sample = widgets.IntSlider(min=0, max=len(SMPS.sample['data'])-1, continuous_update = False, description = 'sample #',layout = Layout(width = '100%'))\\n \\n # create fields slider containing SMPS data variables\\n sfields = widgets.ToggleButtons(options=SMPS.data['variables'], value = SMPS.data['variables'][0], description='Variable:', disabled=False, button_style='')\\n \\n # create D50 checkbox\\n D50line = widgets.Checkbox(value=False,description='D50',disabled=False,indent=False, layout = Layout(width='20%'))\\n \\n # create boundary line checkbox\\n boundaryline = widgets.Checkbox(value=False,description='Boundaries',disabled=False, indent=False, layout = Layout(width='20%'))\\n \\n # create periodical indicator checkbox\\n indperiods = widgets.Checkbox(value=False,description='24 hour periods',disabled=False,indent=False, layout = Layout(width='20%'))\\n \\n # create starttime for periodical indicator text box\\n starttime = widgets.Text(value='00:00:00', placeholder='HH:MM:SS', description='Start time:', disabled=False)\\n \\n # create count limit box\\n clim = widgets.BoundedFloatText( value=2*10**4, min=1*10**1, max=1*10**8,step=1*10**1, description='clim:',\\n style = {'description_width': 'initial'}, disabled=False, continuous_update=True, orientation='horizontal',\\n readout=True, readout_format='.2f', layout = Layout(width='25%'))\\n \\n # create diameter limit box\\n ylim = widgets.BoundedFloatText( value=1*10**3, min=1*10**1, max=1*10**8,step=1*10**1, description='dlim:', disabled=False, continuous_update=True, orientation='horizontal', readout=True, readout_format='.2f', layout = Layout(width='25%'))\\n \\n # create histogram outplot\\n outplot = widgets.interactive_output(psd.histogram, {'field':sfields,'sample':sample,'add_D50':D50line,'add_boundaries':boundaryline, 'xlim': ylim,'ylim':clim}) \\n \\n # create heatplot outplot\\n outplot2 = widgets.interactive_output(psd.plot, {'field':sfields,'clim':clim, 'ylim':ylim, 'indicator':sample,'periods':indperiods, 'starttime':starttime})\\n \\n # create printed date output\\n date = widgets.interactive_output(psd.dateInfo,{'sample':sample})\\n \\n # create organised display widgets\\n ui1 = widgets.HBox([sfields])\\n ui2 = widgets.HBox([D50line, boundaryline, clim, ylim])\\n uidate = widgets.HBox([date])\\n uiperiods = widgets.HBox([indperiods, starttime])\\n ui3 = widgets.VBox([ui1, ui2, outplot, sample, uidate, outplot2, uiperiods], layout = Layout(height='100%'))\\n \\n return ui3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f93d5ab254c4e19bda8c011f7267793\",\n \"score\": \"0.4883903\",\n \"text\": \"def capture(rect=None, filepath='', prompt=True, hideWindow=None):\\n widget = XSnapshotWidget(QApplication.desktop())\\n widget.setRegion(rect)\\n widget.setHideWindow(hideWindow)\\n widget.setFilepath(filepath)\\n widget.move(1, 1)\\n widget.resize(QApplication.desktop().size())\\n \\n if prompt or not filepath:\\n widget.show()\\n else:\\n widget.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bcfa9d4763be5223839a42fd5ee1d31\",\n \"score\": \"0.4878805\",\n \"text\": \"def user_input_from_gui(min_score, head_dir, max_score=\\\"NO\\\"):\\n get_tables()\\n global data\\n cwd = os.getcwd()\\n os.chdir(head_dir)\\n if max_score == \\\"NO\\\":\\n data_ = data[data[\\\"score_wo_prcnt\\\"] >= min_score]\\n fname = str(round(min_score, 2)) + \\\".tsv\\\"\\n data_.to_csv(fname, index=False, sep=\\\"\\\\t\\\")\\n elif isinstance(max_score, int) or isinstance(max_score, float):\\n data_ = data[\\n (data[\\\"score_wo_prcnt\\\"] >= min_score)\\n & (data[\\\"score_wo_prcnt\\\"] <= max_score)\\n ]\\n fname = str(round(min_score, 2)) + \\\"-\\\" + str(round(max_score, 2)) + \\\".tsv\\\"\\n data_.to_csv(fname, index=False, sep=\\\"\\\\t\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e39149363016fd0e877963dc1cd63e9\",\n \"score\": \"0.48700252\",\n \"text\": \"def debug_show_window(self, original_frame, current_index, frames_results):\\n cv2.namedWindow(str(self.cfg.index),\\n cv2.WINDOW_NORMAL | cv2.WINDOW_FREERATIO)\\n frame = original_frame.copy()\\n if len(frames_results):\\n for rect in frames_results[0]:\\n if rect[4] > self.cfg.alg['ssd_confidence']:\\n # cv2.imwrite(f'data/frames/{self.cfg.index}_{current_index}.png',\\n # cv2.cvtColor(original_frame, cv2.COLOR_BGR2RGB))\\n # crop_frame = cv2.cvtColor(original_frame, cv2.COLOR_BGR2RGB)[int(rect[1]):int(rect[3]), int(rect[0]):int(rect[2]), :]\\n # cv2.imwrite(f'/home/jt1/Desktop/crop_images/{current_index}.png', crop_frame)\\n color = np.random.randint(0, 255, size=(3,))\\n color = [int(c) for c in color]\\n # get a square bbox, the real bbox of width and height is universal as 224 * 224 or 448 * 448\\n p1, p2 = bbox_points(self.cfg, rect, original_frame.shape)\\n # write text\\n frame = paint_chinese_opencv(frame, '江豚', p1)\\n cv2.rectangle(\\n frame, (rect[0], rect[1]), (rect[2], rect[3]), color, 2)\\n cv2.putText(frame, str(round(rect[4], 2)), (p2[0], p2[1]),\\n cv2.FONT_HERSHEY_SIMPLEX, 2, color, 2, cv2.LINE_AA)\\n cv2.imshow(str(self.cfg.index), cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\\n cv2.waitKey(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ac3928f2a0e1737a982082ed9593ab6\",\n \"score\": \"0.48533505\",\n \"text\": \"def show_native_ui():\\n # Set up the flipbook:\\n setup_flipbook()\\n\\n # Set the window name:\\n window_name = \\\"animation_flipbook\\\"\\n\\n # If it already exists, kill it:\\n if cmds.window(window_name, exists=True):\\n cmds.deleteUI(window_name)\\n\\n if cmds.windowPref(window_name, exists=True):\\n cmds.windowPref(window_name, remove=True)\\n\\n # Set up the window and the layout:\\n cmds.window(window_name, title=\\\"Flipbook Animation Tool\\\")\\n cmds.columnLayout(adjustableColumn=True)\\n cmds.rowColumnLayout(columnAlign=(1, \\\"left\\\"), nc=1, cw=[(1, 480)])\\n\\n cmds.floatFieldGrp(\\\"set_framerange_field\\\",\\n numberOfFields=2,\\n label=\\\"Start Frame\\\",\\n el=\\\"End Frame\\\",\\n pre=1,\\n v1=1.0,\\n v2=24.0)\\n\\n cmds.button(w=10, label=\\\"Set Framerange\\\", command=set_framerange)\\n cmds.text(label=\\\"\\\")\\n\\n # Select/deselect the pencil tool:\\n cmds.button(w=20, label=\\\"Select Pencil Tool\\\", command=select_pencil_tool)\\n cmds.button(w=20, label=\\\"Deselect Pencil Tool\\\", command=deselect_pencil_tool)\\n cmds.text(label=\\\"\\\")\\n\\n # Set the page:\\n cmds.button(w=20, label=\\\"Set page\\\", command=set_page)\\n cmds.button(w=20, label=\\\"Add to page\\\", command=insert_page)\\n cmds.button(w=20, label=\\\"Delete page\\\", command=delete_page)\\n cmds.text(label=\\\"\\\")\\n\\n # Go to page:\\n go_to_page_field = cmds.floatFieldGrp(\\\"go_to_page_field\\\",\\n numberOfFields=1,\\n columnWidth=(10, 10),\\n columnAlign2=(\\\"left\\\", \\\"left\\\"),\\n label=\\\"Go to frame\\\",\\n pre=1,\\n v1=1.0)\\n\\n cmds.button(w=20, label=\\\"Go to page\\\", command=lambda *args: go_to_page(go_to_page_field))\\n cmds.text(label=\\\"\\\")\\n\\n cmds.button(w=20, label=\\\"Display Next Page\\\", command=display_next_page)\\n cmds.button(w=20, label=\\\"Display Previous Page\\\", command=display_previous_page)\\n cmds.text(label=\\\"\\\")\\n\\n cmds.button(w=20, label=\\\"Onion skin future frames\\\", command=display_next_pages)\\n cmds.button(w=20, label=\\\"Onion skin past frames\\\", command=display_previous_pages)\\n cmds.text(label=\\\"\\\")\\n\\n # Set the framerange:\\n cmds.floatFieldGrp(\\\"set_loop_field\\\",\\n numberOfFields=2,\\n w=2,\\n columnAlign2=(\\\"left\\\", \\\"left\\\"),\\n label=\\\"Number of loops\\\",\\n el=\\\"Step\\\",\\n cw2=(10, 10),\\n pre=1,\\n v1=0.0,\\n v2=0.0)\\n cmds.button(w=20, label=\\\"Loop Selection\\\", command=loop_selected)\\n cmds.text(label=\\\"\\\")\\n\\n # Playblast:\\n cmds.button(w=20, label=\\\"Playblast\\\", command=playblast_scene)\\n cmds.text(label=\\\"\\\")\\n\\n # Save:\\n cmds.button(w=20, label=\\\"Save\\\", command=save_scene)\\n cmds.text(label=\\\"\\\")\\n cmds.setParent(\\\"..\\\")\\n cmds.showWindow(window_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd3ee6a0908ca3d6441e0ca50d55db73\",\n \"score\": \"0.48511147\",\n \"text\": \"def frame_range(self):\\n\\t\\treturn self.end - self.start + 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a64c9af71ca44ea8d15b16cb3a804a11\",\n \"score\": \"0.48430294\",\n \"text\": \"def _on_export(self, sender, *args):\\r\\n\\t\\t# NOTE: Ignores timerange for now\\r\\n\\t\\tif not self.nodeselector.uses_selection() and not self.nodeselector.selected_namespaces():\\r\\n\\t\\t\\traise ValueError(\\\"Please select what to export from the scroll list\\\")\\r\\n\\t\\t# END handle invalid input\\r\\n\\t\\t\\r\\n\\t\\t# GET FILEPATH\\r\\n\\t\\t# on linux, only one filter is possible - it would be good to have a \\r\\n\\t\\t# capable file dialog coming from MRV ( in 2011 maybe just an adapter to \\r\\n\\t\\t# fileDialog2 )\\r\\n\\t\\tfile_path = cmds.fileDialog(mode=1,directoryMask=\\\"*.mb\\\")\\r\\n\\t\\tif not file_path:\\r\\n\\t\\t\\treturn\\r\\n\\t\\t# END bail out\\r\\n\\t\\t\\r\\n\\t\\textlist = ( \\\".ma\\\", \\\".mb\\\" )\\r\\n\\t\\tcollection = [ p.basename() for p in ui.UI(self.filetype.p_collectionItemArray) ]\\r\\n\\t\\ttarget_ext = extlist[collection.index(self.filetype.p_select)]\\r\\n\\t\\t\\r\\n\\t\\tfile_path = Path(file_path)\\r\\n\\t\\tfile_path = file_path.stripext() + target_ext\\r\\n\\t\\t\\r\\n\\t\\tlib.AnimInOutLibrary.export(file_path, self.nodeselector.iter_nodes(asNode=False))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bcb9f82a8b64d79d9d8ac64acde5e2a\",\n \"score\": \"0.48208085\",\n \"text\": \"def display(self, *arrays, **kwargs):\\n self.win = DS9Win(self.name, doOpen=True) # creates/detects the VIP ds9\\n if kwargs.has_key('keepwin'):\\n if kwargs['keepwin']: pass\\n else: self.delete_frame(allfr=True)\\n else: \\n self.delete_frame(allfr=True)\\n \\n self.create_frame()\\n self.tile()\\n for i, array in enumerate(arrays):\\n if i==0: \\n self.win.showArray(array) \\n else: \\n self.create_frame()\\n self.win.showArray(array)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"512d0514dd4b051bda297005f1745949\",\n \"score\": \"0.48152936\",\n \"text\": \"def createUI():\\r\\n #------- check to see if the window already exists and deletes if it does.-------#\\r\\n if(cmds.window(\\\"UI\\\",exists=True)):\\r\\n cmds.deleteUI(\\\"UI\\\")\\r\\n \\r\\n #create the window\\r\\n window = cmds.window(\\\"UI\\\", title=\\\"Audio Driven Keyframes\\\")\\r\\n \\r\\n #initialize the form layout\\r\\n form = cmds.formLayout(numberOfDivisions=100)\\r\\n \\r\\n #initialize progress bar\\r\\n progress = cmds.progressBar('progress')\\r\\n \\r\\n # create open button\\r\\n openButton = cmds.button(label=\\\"Open...\\\", command=('openFile()')) \\r\\n \\r\\n #new column layout\\r\\n infoCol = cmds.columnLayout()\\r\\n \\r\\n #------- if the audio has already been imported use it -------#\\r\\n if(cmds.objExists('audio')):\\r\\n \\r\\n global fileName, wavFile, params, frames\\r\\n fileName = cmds.getAttr('audio.filename')\\r\\n \\r\\n #------- set up the stream -------#\\r\\n wavFile = wave.open(fileName) \\r\\n params = wavFile.getparams() \\r\\n frames = wavFile.readframes(params[3])\\r\\n cmds.progressBar('progress', edit=True, maxValue = params[3])\\r\\n \\r\\n #------- text label for the current audio file loaded -------#\\r\\n currentAudioPath = cmds.text('currentAudioPath', label=\\\"Currently loaded \\\"+fileName)\\r\\n currentParams = cmds.text('currentParams', label=str(params))\\r\\n \\r\\n #------- otherwise just place dummy text-------# \\r\\n else:\\r\\n currentAudioPath = cmds.text('currentAudioPath', label=\\\"Please choose an audio file.\\\")\\r\\n currentParams = cmds.text('currentParams', label=\\\"\\\")\\r\\n \\r\\n cmds.setParent(\\\"..\\\")\\r\\n \\r\\n # create grid layout for all the variables in the form\\r\\n numbers = cmds.rowColumnLayout(numberOfColumns=2, columnWidth=[(1,100)])\\r\\n \\r\\n #create channel option boxes\\r\\n cmds.text(label=\\\"Channel\\\")\\r\\n cmds.radioButtonGrp('channel',\\r\\n nrb=3, \\r\\n onc=\\\"changeChannel()\\\", \\r\\n numberOfRadioButtons=3, \\r\\n labelArray3=['Left', 'Right', 'Both (Mono)'], \\r\\n sl=chosenChannel )\\r\\n \\r\\n #create axis option boxes\\r\\n cmds.text(label=\\\"Axis\\\")\\r\\n cmds.radioButtonGrp('axis',\\r\\n nrb=3, \\r\\n onc=\\\"changeAxis()\\\", \\r\\n numberOfRadioButtons=3, \\r\\n labelArray3=['X', 'Y', 'Z'], \\r\\n sl=chosenAxis )\\r\\n \\r\\n #create detail slider\\r\\n cmds.text(label=\\\"Detail\\\")\\r\\n detailSld = cmds.intSliderGrp('detail', field=True, cc=\\\"changeDetail()\\\", value=50,minValue = 1, maxValue=100)\\r\\n \\r\\n #create range row\\r\\n cmds.text(label=\\\"Range\\\")\\r\\n cmds.rowLayout(numberOfColumns=5)\\r\\n \\r\\n #min range field\\r\\n cmds.floatField('min', value=minMax[0])\\r\\n \\r\\n #assign current-to-min button\\r\\n cmds.button('assignLeft', label='<', c=\\\"assignLeft()\\\")\\r\\n \\r\\n #initialize the current field and if the object has been selected use the objects default axis value otherwise just set it to 0\\r\\n if(cmds.ls(sl=True)): \\r\\n selection = cmds.ls(sl=True)\\r\\n cmds.floatField('current', rfc=\\\"updateCurrentValue()\\\", value=cmds.getAttr(selection[0]+\\\".\\\"+axis[chosenAxis-1]))\\r\\n else:\\r\\n cmds.floatField('current', rfc=\\\"updateCurrentValue()\\\", value=0)\\r\\n \\r\\n #assign current-to-max button\\r\\n cmds.button('assignRight', label='>', c=\\\"assignRight()\\\")\\r\\n \\r\\n #max range field\\r\\n cmds.floatField('max', value=minMax[1])\\r\\n cmds.setParent(\\\"..\\\")\\r\\n \\r\\n #autolock option box\\r\\n cmds.text(label=\\\"Auto Lock\\\")\\r\\n cmds.checkBox('autoLock', label=\\\"\\\", cc=('lock()'), value=autoLock)\\r\\n \\r\\n cmds.setParent(\\\"..\\\")\\r\\n \\r\\n #make the generate and delete buttons on one row\\r\\n buttonsRow = cmds.rowColumnLayout(numberOfColumns=2)\\r\\n createButton = cmds.button(label=\\\"Generate Keyframes\\\", command=('generateKeyframes()'))\\r\\n deleteButton = cmds.button(label=\\\"Delete Keyframes\\\", command=('deleteKeyframes()')) \\r\\n \\r\\n \\r\\n cmds.setParent(\\\"..\\\") \\r\\n \\r\\n \\r\\n #------ put all these items in the form -----#\\r\\n cmds.formLayout(form, edit=True, attachForm=[(openButton, 'left', 10),\\r\\n (openButton, 'right', 10), \\r\\n (openButton, 'top', 10),\\r\\n (infoCol, 'left', 10),\\r\\n (infoCol, 'right', 10),\\r\\n (numbers, 'left', 10),\\r\\n (numbers, 'right', 10),\\r\\n (buttonsRow, 'left', 10),\\r\\n (buttonsRow, 'right', 10),\\r\\n (progress, 'left', 10),\\r\\n (progress, 'right', 10),\\r\\n (progress, 'bottom', 10),],\\r\\n attachControl=[(infoCol, 'top',10, openButton),\\r\\n (numbers, 'top', 10, infoCol),\\r\\n (buttonsRow, 'bottom',0, progress)])\\r\\n \\r\\n cmds.showWindow()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e4bea3c5ed5df5100afb785e81b0ba0\",\n \"score\": \"0.48067906\",\n \"text\": \"def plot_roi(start_frames, end_frames):\\n #reads up until start_frames if start_frames !=0\\n video = read_video()\\n for i in range (start_frames, end_frames):\\n fig, ax = plt.subplots()\\n frame = video.read()[1][:,:,1]\\n ax.imshow(frame[remodeled_roi_list()[i]])\\n plt.ginput(10000, timeout=0, show_clicks=False)\\n plt.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6121ccdfcba5c6aa3b60e659d87f76d\",\n \"score\": \"0.4806571\",\n \"text\": \"def usage(code):\\n print 'Usage: python all_frames.py frame amp'\\n sys.exit(code)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9807cbf611fb7a3f12113dfdc45bee1\",\n \"score\": \"0.48044258\",\n \"text\": \"def OpenBrowser(self): #Not currently in use\\n filename1 = QFileDialog.getSaveFileName()\\n self.export_layer.lineEdit.setText(filename1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"872982095fb26eabcc94dde4983e81d7\",\n \"score\": \"0.4797458\",\n \"text\": \"def _textToFrame(self):\\n if self.help_text:\\n # show Frame #currect_frame : bounding_box\\n annotation = \\\"nan,nan,nan,nan\\\"\\n if self.bounding_box:\\n annotation = self.parser.bboxString(self.bounding_box)\\n elif self.pt1:\\n annotation = str(self.pt1[0]) + \\\",\\\" + str(self.pt1[1]) + \\\",nan,nan\\\"\\n\\n cv2.putText(self.frame, \\\"Frame #\\\" + str(self.current_frame) + \\\" : \\\" + annotation, self.TEXT_ROW1_POS, cv2.FONT_HERSHEY_SIMPLEX, self.FONT_SCALE, (250, 250, 0), self.FONT_WEIGHT)\\n\\n # show control help\\n info = \\\"'Enter' = next frame, 'Backspace' = previous frame, 'Q' = quit, 'H' = hide this text\\\"\\n cv2.putText(self.frame, info, self.TEXT_ROW2_POS, cv2.FONT_HERSHEY_SIMPLEX, self.FONT_SCALE, (0, 150, 250), self.FONT_WEIGHT)\\n info2 = \\\"'R' = reset annotation, 'P' = previous annotation, 'D' = duplicate previous annotation\\\"\\n cv2.putText(self.frame, info2, self.TEXT_ROW3_POS, cv2.FONT_HERSHEY_SIMPLEX, self.FONT_SCALE, (0, 150, 250), self.FONT_WEIGHT)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d35628ad47e9422a8c5f428a86bb5326\",\n \"score\": \"0.47954303\",\n \"text\": \"def takeSnapshot(fullFileName, type=-1, hideAnnotations=False):\\n # show the message even if not taking a screen shot\\n lm = slicer.app.layoutManager()\\n # switch on the type to get the requested window\\n widget = 0\\n if type == slicer.qMRMLScreenShotDialog.FullLayout:\\n # full layout\\n widget = lm.viewport()\\n elif type == slicer.qMRMLScreenShotDialog.ThreeD:\\n # just the 3D window\\n widget = lm.threeDWidget(0).threeDView()\\n elif type == slicer.qMRMLScreenShotDialog.Red:\\n # red slice window\\n widget = lm.sliceWidget(\\\"Red\\\")\\n elif type == slicer.qMRMLScreenShotDialog.Yellow:\\n # yellow slice window\\n widget = lm.sliceWidget(\\\"Yellow\\\")\\n elif type == slicer.qMRMLScreenShotDialog.Green:\\n # green slice window\\n widget = lm.sliceWidget(\\\"Green\\\")\\n else:\\n # default to using the full window\\n widget = slicer.util.mainWindow()\\n # reset the type so that the node is set correctly\\n #type = slicer.qMRMLScreenShotDialog.FullLayout\\n\\n if hideAnnotations:\\n SlicerUtil.hideAllCornerAnnotations()\\n # grab and convert to vtk image data\\n qpixMap = qt.QPixmap().grabWidget(widget)\\n # Save as a png file\\n qpixMap.save(fullFileName)\\n\\n return fullFileName\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94c99d463efaf984a7c15d32d1f03311\",\n \"score\": \"0.47950166\",\n \"text\": \"def setRange(self,minimum,maximum):\\r\\n\\t\\tself.ProgressBar.setRange(minimum,maximum)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9868a2245d5956648088dd004aeb79e1\",\n \"score\": \"0.47900984\",\n \"text\": \"def printRanges(self) -> unicode:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"079e42b992c92d22ad4d6978915fd7dd\",\n \"score\": \"0.4784321\",\n \"text\": \"def displayRange(lower, upper):\\n if lower <= upper:\\n print(lower)\\n displayRange(lower + 1, upper)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"593765919e32137d37905da4bb3a091e\",\n \"score\": \"0.4780807\",\n \"text\": \"def show_frameCharlie(self, cont):\\n frame = self.frames[cont]\\n frame.confirmlabels()\\n frame.tkraise()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e83dee90e67d54cb140d5aaac56152d\",\n \"score\": \"0.47752175\",\n \"text\": \"def graph_range(self):\\n \\n # If there is no data, tell the user and don't show the info dialog.\\n if len(self.data) == 0:\\n \\n # Show the dialog.\\n show_no_data_dialog(self, \\\"Graphs - %s\\\" % self.last_profile)\\n return\\n \\n # Get the first and last entered dates.\\n day_start = dates.split_date(self.data[0][0])\\n day_end = dates.split_date(self.data[len(self.data) - 1][0])\\n \\n # Get a list of datetimes from the dates.\\n datelist = dates.date_list_datetime(datasets.get_column(self.data, 0))\\n \\n # Get the starting and ending dates.\\n cal_dlg = CalendarRangeDialog(self, \\\"Graphs in Range - %s\\\" % self.last_profile, day_start, day_end)\\n response = cal_dlg.run()\\n year1, month1, day1 = cal_dlg.start_cal.get_date()\\n year2, month2, day2 = cal_dlg.end_cal.get_date()\\n date1 = \\\"%d/%d/%d\\\" % (day1, month1 + 1, year1)\\n date2 = \\\"%d/%d/%d\\\" % (day2, month2 + 1, year2)\\n cal_dlg.destroy()\\n \\n # If the user did not click OK, don't continue.\\n if response != Gtk.ResponseType.OK:\\n return\\n \\n # Get the indices.\\n dt_start = datetime.datetime(year1, month1 + 1, day1)\\n start_index = dates.date_above(dt_start, datelist)\\n dt_end = datetime.datetime(year2, month2 + 1, day2)\\n end_index = dates.date_below(dt_end, datelist)\\n \\n # Check to make sure these dates are valid, and cancel the action if not.\\n if start_index == DateValidation.INVALID:\\n show_error_dialog(self, \\\"Graphs in Range - %s\\\" % self.last_profile, \\\"%s is not a valid date.\\\\n\\\\nThis date is not present and is not before any other dates.\\\" % date1)\\n return\\n if end_index == DateValidation.INVALID:\\n show_error_dialog(self, \\\"Graphs in Range - %s\\\" % self.last_profile, \\\"%s is not a valid date.\\\\n\\\\nThis date is not present and is not after any other dates.\\\" % date2)\\n return\\n if end_index < start_index:\\n show_error_dialog(self, \\\"Graphs in Range - %s\\\" % self.last_profile, \\\"The ending date must be after the starting date.\\\")\\n return\\n \\n # Get the new list.\\n data2 = self.data[start_index:end_index + 1]\\n \\n # Pass the data to the charts dialog.\\n self.show_graph_generic(data = data2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fea6e6b5bce8e123eb5ad16856f340e1\",\n \"score\": \"0.47744238\",\n \"text\": \"def fnirs_montage_ui():\\r\\n source_labels = input(\\\"please enter sources names with the S# format: \\\").split()\\r\\n\\r\\n detector_labels = input(\\\"please enter detectors names with the D# format: \\\").split()\\r\\n\\r\\n Nz = input(\\\"please enter 3D Coordination of tip of the nose x y z in mm: \\\").split()\\r\\n for i in range(len(Nz)):\\r\\n Nz[i] = float(Nz[i])\\r\\n\\r\\n RPA = input(\\\"please enter 3D Coordination of the right preauricular x y z in mm: \\\").split()\\r\\n for i in range(len(RPA)):\\r\\n RPA[i] = float(RPA[i])\\r\\n\\r\\n LPA = input(\\\"please enter 3D Coordination of the left preauricular x y z in mm: \\\").split()\\r\\n for i in range(len(LPA)):\\r\\n LPA[i] = float(LPA[i])\\r\\n\\r\\n head_size = float(input(\\\"please enter the head size in mm \\\"))\\r\\n\\r\\n return source_labels, detector_labels, Nz, RPA, LPA, head_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"221e1d110dd4e0c28c5db1c02b903bf7\",\n \"score\": \"0.47701207\",\n \"text\": \"def run(self):\\n # show the dialog\\n self.dlg.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd1fb71b9d6c35f6e3ed1866d0b0c1a4\",\n \"score\": \"0.4759211\",\n \"text\": \"def print_out(self):\\n self.app.Application.PrintOut()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76b25b5eb5b96acb2a833cdd6de29f4e\",\n \"score\": \"0.47543108\",\n \"text\": \"def show_frameAlpha(self, cont):\\n #Reset all of experiment-class variables\\n Appa.expnumber = str()\\n Appa.exptype = str()\\n Appa.exptime = int()\\n Appa.savefile = str()\\n Appa.expy = []\\n app.startfresh() #Reinitalize all necessary pages to starting state\\n frame = self.frames[cont]\\n frame.tkraise() #raise to front\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85bb3d1c5b1690ff43923a6030238051\",\n \"score\": \"0.4752486\",\n \"text\": \"def set_framerange(*args):\\n start_frame = cmds.floatFieldGrp(\\\"set_framerange_field\\\", query=True, v1=True)\\n end_frame = cmds.floatFieldGrp(\\\"set_framerange_field\\\", query=True, v2=True)\\n animationflipbook.set_framerange(start_frame, end_frame)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6962c2ab3daf67b52f88cff449e8150\",\n \"score\": \"0.4748542\",\n \"text\": \"def run():\\n root = tk.Tk()\\n root.title('Virus Spreads as per Mutation Patterns')\\n root.minsize(300, 300)\\n\\n # This section simply sets the scene and creates the dropdown menu. The dropdown menu is tied to the region_selected variable\\n mainframe = tk.Frame(master=root, height=20, width=50)\\n tableFrame = tk.Frame(master=mainframe)\\n regions = get_regions()\\n region_selected = tk.StringVar(master=root, name='reg')\\n region_selected.set(regions[0])\\n dropdown = tk.OptionMenu(mainframe, region_selected, *regions)\\n\\n # This function generates the table for a given area. It's a local method so that it can access the tableFrame\\n def generate_table(area, dummy1, dummy2):\\n nonlocal tableFrame\\n tableFrame.destroy()\\n tableFrame = tk.Frame(master=mainframe)\\n tableFrame.grid(row=1, column=1)\\n results = get_spreads(root.getvar(area))\\n titles = ('Source Region', '# of Cases from Source')\\n tab = Table(tableFrame, titles, results)\\n\\n # Bind updates of the dropdown to the creation of tables.\\n region_selected.trace_add('write', generate_table)\\n\\n # Place the dropdown and the frame holding the table into their appropriate grid positions.\\n mainframe.pack()\\n dropdown.grid(row=0, column=1)\\n tableFrame.grid(row=1, column=1)\\n\\n root.mainloop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e292408bd292f993d539b97a848669a\",\n \"score\": \"0.47433022\",\n \"text\": \"def showOptions(self):\\n message = \\\"P: Plot generate image in plt \\\"\\n self.render.render_text(self.ttt, message,\\n (self.x_0 + 200, self.y_max + 20))\\n message = \\\"I: Do interpolation two points\\\"\\n self.render.render_text(self.ttt, message,\\n (self.x_0 + 200, self.y_max + 40))\\n message = \\\"O: Insert images in the latent space\\\"\\n self.render.render_text(self.ttt, message,\\n (self.x_0 + 200, self.y_max + 60))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65d2551073a01baf013e9f2d74ae0878\",\n \"score\": \"0.4731846\",\n \"text\": \"def run(self, frames=120):\\r\\n anim = FuncAnimation(self.fig, self.step, blit=False, interval=100, frames=frames)\\r\\n # If you want to save your animations, you can comment either\\r\\n # of the lines below.\\r\\n # NOTE: FFmpeg is needed to render a .mp4 video of the animation.\\r\\n anim.save('/mnt/c/Users/tirth/Desktop/Searching in AI/ucs_large_with_weights.mp4')\\r\\n # anim.save('animation.html')\\r\\n # plt.show()\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41b953d5f1e5b8ea6fda8339468bdc9a\",\n \"score\": \"0.47301832\",\n \"text\": \"def show_frameResults(k):\\n\\n global img\\n global moransReport\\n\\n # Functions for frameResults\\n def load_image(k, type): \\n fileName = f'reports/{type}_{str(k).replace(\\\".\\\",\\\"_\\\")}.jpg'\\n img = Image.open(fileName)\\n img = img.resize((353,457))\\n imgTk = ImageTk.PhotoImage(img)\\n return imgTk\\n\\n def change_image(k, type, curMap):\\n \\\"\\\"\\\" Change the image in the results frame \\\"\\\"\\\"\\n imgTk = load_image(k, type)\\n curMap.configure(image=imgTk)\\n curMap.image = imgTk\\n\\n def save_as_pdf(k, type):\\n saveAsPath = filedialog.asksaveasfilename(initialdir=\\\"/\\\", filetypes=[(\\\"pdf files\\\", \\\"*.pdf\\\")])\\n if \\\".pdf\\\" not in saveAsPath.lower():\\n saveAsPath += '.pdf'\\n imagePath = f'reports/{type}_{str(k).replace(\\\".\\\",\\\"_\\\")}.jpg'\\n img = Image.open(imagePath)\\n img.save(saveAsPath, \\\"PDF\\\")\\n\\n def view_ols_report(k):\\n import os\\n olsFile = f'{os.getcwd()}\\\\\\\\ols_reports\\\\\\\\{k}_ols.pdf'\\n subprocess.Popen(olsFile, shell=True)\\n\\n def view_moransI_report(path):\\n if path:\\n subprocess.Popen(path, shell=True)\\n else:\\n messagebox.showerror(\\\"Error loading Moran's I report\\\", \\\"There was an error loading the Moran's I report.\\\")\\n\\n # Reset frameResults to clear any previous analysis runs\\n for child in frameResults.winfo_children():\\n child.destroy()\\n\\n # Show frameResults\\n frameResults.place(relheight=0.9, relwidth=0.4, relx=0.55, rely=0.05)\\n\\n # Show map image (default to IDW map)\\n img = load_image(k, \\\"IDW\\\")\\n curMap = tk.Label(frameResults, image=img)\\n curMap.pack()\\n\\n # Show button controls\\n frameButtons = tk.Frame(frameResults, bg=\\\"black\\\", height=80) \\n frameButtons.pack(side='bottom', fill='x')\\n\\n btnIDW = ttk.Button(frameButtons, text=\\\"Show IDW Results\\\", command=lambda: change_image(k, \\\"IDW\\\", curMap))\\n btnIDW.place(x=5, rely=0.15, relwidth=0.3, relheight=0.3)\\n\\n btnIDWSave = ttk.Button(frameButtons, text=\\\"Save IDW to PDF...\\\", command=lambda: save_as_pdf(k, \\\"IDW\\\"))\\n btnIDWSave.place(relx=0.35, rely=0.15, relheight=0.3)\\n\\n btnMoransI = ttk.Button(frameButtons, text=\\\"View Morans I\\\", command=lambda: view_moransI_report(moransReport))\\n btnMoransI.place(relx=0.68, rely=0.15, relwidth=0.3, relheight=0.3)\\n\\n btnOLS = ttk.Button(frameButtons, text=\\\"Show OLS Results\\\", command=lambda: change_image(k, \\\"OLS\\\", curMap))\\n btnOLS.place(x=5, rely=0.6, relwidth=0.3, relheight=0.3)\\n\\n btnOLSSave = ttk.Button(frameButtons, text=\\\"Save OLS to PDF...\\\", command=lambda: save_as_pdf(k, \\\"OLS\\\"))\\n btnOLSSave.place(relx=0.35, rely=0.6, relwidth=0.3, relheight=0.3)\\n\\n btnViewOLS = ttk.Button(frameButtons, text=\\\"View OLS Report\\\", command=lambda: view_ols_report(k))\\n btnViewOLS.place(relx=0.68, rely=0.6, relwidth=0.3, relheight=0.3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a8a4ddb91aefd318f52ee80d6c19cec\",\n \"score\": \"0.47285014\",\n \"text\": \"def guimain():\\r\\n\\t\\r\\n\\ta = gui.QApplication(sys.argv)\\r\\n\\ta.aboutToQuit.connect(a.deleteLater)\\r\\n\\t\\r\\n\\t# window\\r\\n\\tw = gui.QWidget()\\r\\n\\tw.resize(600, 400)\\r\\n\\tw.setWindowTitle(\\\"TimeSheet\\\")\\r\\n\\t\\r\\n\\t# textboxes\\r\\n\\tname_box = gui.QLineEdit(w)\\r\\n\\tname_box.move(100, 40)\\r\\n\\tname_box.resize(100, 24)\\r\\n\\tname_box.setText(\\\"Enter Name\\\")\\r\\n\\t\\r\\n\\t# labels\\r\\n\\twlcm = gui.QLabel(w)\\r\\n\\twlcm.move(10, 20)\\r\\n\\twlcm.resize(550, 30)\\r\\n\\twlcm.setText(\\\"Welcome to TimeSheet. This application allows you to create\\\\\\r\\n\\tyour very own table of work hours as simply as possible.\\\\nJust enter your\\\\\\r\\n\\tinformation below and get started. Have fun spending your time on \\\\\\r\\n\\tsomething useful!\\\")\\r\\n\\t\\r\\n\\tname_label = gui.QLabel(w)\\r\\n\\tname_label.move(10, 40)\\r\\n\\tname_label.resize(130,24)\\r\\n\\tname_label.setText(\\\"Enter your name:\\\")\\r\\n\\t\\r\\n\\tcal_label = gui.QLabel(w)\\r\\n\\tcal_label.move(10, 100)\\r\\n\\tcal_label.resize(150,24)\\r\\n\\tcal_label.setText(\\\"Choose date to begin with:\\\")\\r\\n\\t\\r\\n\\t\\r\\n\\t#sl = gui.QAbstractSlider(w)\\r\\n\\t#sl.move(20, 20)\\r\\n\\t\\r\\n\\t# checkboxes\\r\\n\\tchbx_tot = gui.QCheckBox(w)\\r\\n\\tchbx_tot.move(10, 60)\\r\\n\\tchbx_tot.setText(\\\"Fix target hours\\\")\\r\\n\\r\\n\\tchbx_mean = gui.QCheckBox(w)\\r\\n\\tchbx_mean.move(10, 80)\\r\\n\\tchbx_mean.setText(\\\"Fix mean target hours only\\\")\\r\\n\\t\\r\\n\\tgrp = gui.QButtonGroup(w)\\r\\n\\tgrp.addButton(chbx_tot)\\r\\n\\tgrp.addButton(chbx_mean)\\r\\n\\tgrp.exclusive = True\\r\\n\\t\\r\\n\\t# calendar\\r\\n\\tcal = gui.QCalendarWidget(w)\\r\\n\\tcal.setGridVisible(True)\\r\\n\\tcal.move(10, 130)\\r\\n\\tcal.resize(320, 200)\\r\\n\\t\\r\\n\\t# buttons\\r\\n\\tstart_button = gui.QPushButton(\\\"Create Timesheet\\\", w)\\r\\n\\tstart_button.move(450, 350)\\r\\n\\tstart_button.resize(100, 24)\\r\\n\\t\\r\\n\\t\\r\\n\\tw.show()\\t\\r\\n\\t#times, times_months = create_timesheet(begin_date=\\\"15/01/01\\\",end_date=\\\"15/01/31\\\")\\r\\n\\t#print_table(times,times_months)\\r\\n\\t\\r\\n\\tsys.exit(a.exec_())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d679299d341087031658d97741d74a33\",\n \"score\": \"0.472513\",\n \"text\": \"def show_em_frame(frame_data):\\n for datum in np.nditer(frame_data):\\n ts_val = datum['ts'].item(0)\\n thr_data = thr[datum['y'].item(0), datum['x'].item(0)]\\n\\n if datum['p'].item(0) == 0:\\n thr_data.valid = 1\\n thr_data.low = ts_val\\n elif thr_data.valid == 1:\\n thr_data.valid = 0\\n thr_data.high = ts_val - thr_data.low\\n\\n img = 255 * (1 - (thr.high - min_val) / (val_range))\\n #thr_h = cv2.adaptiveThreshold(thr_h, 255,\\n #cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 3, 0)\\n img = np.piecewise(img, [img <= 0, (img > 0) & (img < 255), img >= 255], [0, lambda x: x, 255])\\n img = img.astype('uint8')\\n cv2.imshow('img', img)\\n cv2.waitKey(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f21a2c9bef853768836894912d5ce76b\",\n \"score\": \"0.47117522\",\n \"text\": \"def showBrowserDialog(self):\\n fileName = QFileDialog.getSaveFileName(None, 'Save output shapefile','','Shapefiles (*.shp *.SHP)')\\n self.outputPath.setText(fileName)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"412eeae54df9a71f21ea0d702ff43abf\",\n \"score\": \"0.470262\",\n \"text\": \"def show_frameSquid(self, cont):\\n frame = self.frames[cont]\\n frame.tkraise() \\n camera.start_preview(fullscreen=False, window=(appwidth/800, appheight/4, appwidth-(2*appwidth/800), appheight*9/10)) # This line starts the preview. \",\n \"title\": \"\"\n },\n {\n \"docid\": \"921c0f5432a83abc7c77c5413566382f\",\n \"score\": \"0.4699744\",\n \"text\": \"def show(self):\\n self.plot_frame(-1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10a497a000c315c1144324e3a6272372\",\n \"score\": \"0.469705\",\n \"text\": \"def callback(x):\\n\\n interaction_window_pointer[0] = x\\n try:\\n\\n read_frames(interaction_window_pointer, interaction_windows_size, interaction_window, video_frames_count)\\n frame = interaction_window[str(interaction_window_pointer[0])]\\n cur_timestamp = str(int(int(interaction_window_pointer[0]) * 1000 / 25))\\n if frame is not None:\\n \\n # Priting BBox and Annotations over the current image\\n #frame = drawBoundingBox(frame, bounding_box_data_list)\\n #frame = drawAnnotations(frame, dicts, current_complex_event, current_complex_event_detected)\\n #frame = resize_image(frame)\\n #cv2.imshow(app_name, frame)\\n\\n # Showing info to CLI\\n if (not(is_exporting)):\\n print(\\\"[Visualization System] Frame: {:06}\\\".format(interaction_window_pointer[0]))\\n print(\\\"[Visualization System] Second: \\\" + str(int(cur_timestamp) / 1000), end=\\\"\\\\r\\\\n\\\\r\\\\n\\\")\\n \\n except Exception as e:\\n print(\\\"[Exception] \\\" + e.args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dbbec52dc3513f121f71702b163176f\",\n \"score\": \"0.46862304\",\n \"text\": \"def ui_mode():\\n # Create a dialog box with the features\\n fields = ['Music', 'Movies', 'Horror', 'Thriller', 'Comedy',\\n 'Romantic', 'Sci-fi', 'War', 'Fantasy/Fairy tales', \\n 'Animated', 'Documentary', 'Western', 'Action', 'History', \\n 'Mathematics', 'Physics', 'Internet', 'PC', 'Reading', \\n 'Cars', 'Shopping', 'Science and technology', 'Adrenaline sports', \\n 'Flying', 'Age', 'college/bachelor degree', 'currently a primary school pupil', \\n 'doctorate degree', 'masters degree', 'primary school', 'secondary school', \\n 'few hours a day', 'less than an hour a day', 'most of the day', 'no time at all']\\n\\n layout = [[sg.Text('url'), sg.Input('', key='url')],\\n [sg.Text('Music'), sg.Input(5, key='Music')],\\n [sg.Text('Movies'), sg.Input(5, key='Movies')],\\n [sg.Text('Horror'), sg.Input(4, key='Horror')],\\n [sg.Text('Thriller'), sg.Input(2, key='Thriller')],\\n [sg.Text('Comedy'), sg.Input(5, key='Comedy')],\\n [sg.Text('Romantic'), sg.Input(4, key='Romantic')],\\n [sg.Text('Sci-fi'), sg.Input(4, key='Sci-fi')],\\n [sg.Text('War'), sg.Input(1, key='War')],\\n [sg.Text('Fantasy/Fairy tales'), sg.Input(5, key='Fantasy/Fairy tales')],\\n [sg.Text('Animated'), sg.Input(5, key='Animated')],\\n [sg.Text('Documentary'), sg.Input(3, key='Documentary')],\\n [sg.Text('Western'), sg.Input(1, key='Western')],\\n [sg.Text('Action'), sg.Input(2, key='Action')],\\n [sg.Text('History'), sg.Input(1, key='History')],\\n [sg.Text('Mathematics'), sg.Input(3, key='Mathematics')],\\n [sg.Text('Physics'), sg.Input(3, key='Physics')],\\n [sg.Text('Internet'), sg.Input(5, key='Internet')],\\n [sg.Text('PC'), sg.Input(3, key='PC')],\\n [sg.Text('Reading'), sg.Input(3, key='Reading')],\\n [sg.Text('Cars'), sg.Input(1, key='Cars')],\\n [sg.Text('Shopping'), sg.Input(4, key='Shopping')],\\n [sg.Text('Science and technology'), sg.Input(4, key='Science and technology')],\\n [sg.Text('Adrenaline sports'), sg.Input(4, key='Adrenaline sports')],\\n [sg.Text('Flying'), sg.Input(1, key='Flying')],\\n [sg.Text('Age', text_color='red'), sg.Input(20, key='Age')],\\n [sg.Text('college/bachelor degree'), sg.Input(1, key='college/bachelor degree')],\\n [sg.Text('currently a primary school pupil'), sg.Input(0, key='currently a primary school pupil')],\\n [sg.Text('doctorate degree'), sg.Input(0, key='doctorate degree')],\\n [sg.Text('masters degree'), sg.Input(0, key='masters degree')],\\n [sg.Text('primary school'), sg.Input(0, key='primary school')],\\n [sg.Text('secondary school'), sg.Input(0, key='secondary school')],\\n [sg.Text('few hours a day'), sg.Input(1, key='few hours a day')],\\n [sg.Text('less than an hour a day'), sg.Input(0, key='less than an hour a day')],\\n [sg.Text('most of the day'), sg.Input(0, key='most of the day')],\\n [sg.Text('no time at all'), sg.Input(0, key='no time at all')],\\n [sg.Text('Prediction: ', text_color='blue', size=(40, 2), key='_PRED_')],\\n [sg.RButton('Predict'), sg.Exit()]]\\n\\n window = sg.Window('Enter url and feature values', layout)\\n while True:\\n event, values = window.Read()\\n predictions = str(get_multiple_predictions(values, fields))\\n predictions.replace('\\\"', '')\\n if event == 'Predict':\\n print('entered predict event')\\n # change the \\\"output\\\" element to be the value of \\\"input\\\" element\\n window.Element('_PRED_').Update('Age: [' + values['Age'] + ']\\\\n' + \\n 'Prediction: ' + predictions)\\n if event is None or event == 'Exit':\\n break\\n\\n window.Close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17789147d32666fe3ff02f17a831f625\",\n \"score\": \"0.4681595\",\n \"text\": \"def file_dialog(self):\\n file = QtGui.QFileDialog.getOpenFileName(self, 'Open Texture', '')\\n\\n if file != '' and file is not None:\\n self.lineEdit.setText(file)\\n\\n self.update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"947c70c4424b01907ffe3bced1e6ae7a\",\n \"score\": \"0.4673959\",\n \"text\": \"def create_plot_html_page(msid, descrip, pout):\\n#\\n#--- read javascript file\\n#\\n jfile = house_keeping + 'java_script_deposit'\\n f = open(jfile, 'r')\\n jscript = f.read()\\n f.close()\\n\\n #file_name = msid + '_limit_table'\\n file_name = './Limit_table/' + msid + '_limit_table.html'\\n#\\n#--- start creating html page\\n#\\n out = '<!DOCTYPE html>\\\\n<html>\\\\n<head>\\\\n\\\\t<title>Envelope Trending Plots: ' + msid.upper() + '</title>\\\\n'\\n out = out + jscript + '\\\\n'\\n out = out + '<style>\\\\n'\\n out = out + 'body{width: 600px; height300px; background-color:#FAEBD7;\\\\n'\\n out = out + 'font-family:Georgia, \\\"Times New Roman\\\", Times, serif;}\\\\n'\\n out = out + '</style>\\\\n'\\n\\n out = out + '</head>\\\\n<body style=\\\"width:95%;margin-left:10px; margin-right;10px;background-color:#FAEBD7;'\\n out = out + 'font-family:Georgia, \\\"Times New Roman\\\", Times, serif\\\">\\\\n\\\\n'\\n out = out + '<a href=\\\"' + web_address + 'envelope_main.html\\\" '\\n out = out + 'style=\\\"float:right;padding-right:50px;font-size:120%\\\"><b>Back to Top</b></a>\\\\n'\\n\\n if descrip == '':\\n out = out + '<h2>' + msid.upper() + '</h2>'\\n else:\\n out = out + '<h2>' + msid.upper() + ' (' + descrip.upper() + ')</h2>' \\n\\n out = out + '<div style=\\\"paddng-top:10px\\\"><h3>'\\n out = out + 'Open <a href=\\\"javascript:popitup(\\\\'' + file_name + '\\\\')\\\" style=\\\"text-align:right\\\">Limit Table</a>.'\\n out = out + '</h3>\\\\n'\\n#\\n#--- add the interactive plot here\\n#\\n if (pout == False) or (str(pout) == 'na'):\\n out = out + '<h3 style=\\\"padding-top:200px;padding-bottom:200px\\\">No Data/No Plot</h3>'\\n else:\\n out = out + pout\\n#\\n#--- add the rest\\n#\\n out = out + '<ul><li style=\\\"font-size:80%\\\">Click the magnifier icon and choose the area to enlarge the area.</li>'\\n out = out + '<li style=\\\"font-size:80%\\\">Click the cross icon and hold the button to move around the area.</li>'\\n out = out + '<li style=\\\"font-size:80%\\\">Click the cross icon and then use the roller to zoom in and out.</li>'\\n out = out + '<li style=\\\"font-size:80%\\\">Click the house icon to go back to the full view.</li>'\\n out = out + '<li style=\\\"font-size:80%\\\">After enlarging the area with the magnifier,'\\n out = out + ' use the cross icon to see the values of each data point.</li></ul>'\\n\\n [lout, gname] = get_group_names(msid)\\n if lout != '':\\n out = out + '<h3>Other msids in this group: ' + gname + '</h3>'\\n out = out + lout\\n\\n out = out + '<div style=\\\"padding-top:30px\\\"></div>'\\n out = out + '<hr /><p style=\\\"text-align:left; padding-top:10px;padding-bottom:20px\\\">'\\n out = out + 'If you have any questions, please contact '\\n out = out + '<a href=\\\"mailto:tisobe@cfa.harvard.edu\\\">tisobe@cfa.harvard.edu</a>.'\\n out = out + '\\\\n\\\\n\\\\n</body>\\\\n</html>\\\\n'\\n#\\n#--- write out the html data\\n#\\n name = web_dir + msid + '_plot.html'\\n fo = open(name, 'w')\\n fo.write(out)\\n fo.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fad818a5bf85b40910f30d948556f6a\",\n \"score\": \"0.46726665\",\n \"text\": \"def make_interactive_plot(self):\\n self.change_pars_to_fit_button = Button(\\n description=\\\"Change Parameters to Fit Result\\\",\\n layout = Layout(width = '300px', margin = '0 0 5ps 0')\\n ) \\n\\n self.reset_slider_lims_button = Button(\\n description=\\\"Reset Slider Max\\\",\\n layout = Layout(width = '300px', margin = '0 0 5ps 0')\\n ) \\n\\n self.data_init_widget = Dropdown(\\n options=list(np.arange(0,len(self.isub))), \\n value = 0,\\n description='Dataset',\\n style = {'description_width': 'initial'},\\n disabled=False,\\n layout = Layout(width = '200px', margin = '0 0 5ps 0')\\n )\\n\\n self.reset_slider_widget = Dropdown(\\n options= [par for par in self.ctrl_pars.keys() if self.ctrl_pars[par]], \\n value = None,\\n description='Slider Reset',\\n style = {'description_width': 'initial'},\\n disabled=False,\\n layout = Layout(width = '200px', margin = '0 0 5ps 0')\\n ) \\n\\n self.wlim = {}\\n self.wlim[self.spectra_object.orbital] = FloatRangeSlider (\\n value=[np.min(self.esub), np.max(self.esub)],\\n min = np.min(self.esub),\\n max =np.max(self.esub),\\n step = 0.01,\\n description = self.spectra_object.orbital+'_xlim',\\n style = {'description_width': 'initial'},\\n layout = Layout(width = '300px', margin = '0 0 5ps 0')\\n ) \\n if self.compound:\\n for orbital in self.connected_spectra.keys():\\n self.wlim[orbital] = FloatRangeSlider (\\n value=[np.min(self.connected_spectra[orbital].esub), np.max(self.connected_spectra[orbital].esub)],\\n min = np.min(self.connected_spectra[orbital].esub),\\n max =np.max(self.connected_spectra[orbital].esub),\\n step = 0.01,\\n description = orbital+'_xlim',\\n style = {'description_width': 'initial'},\\n layout = Layout(width = '300px', margin = '0 0 5ps 0')\\n ) \\n\\n out = Output()\\n display(out)\\n\\n @self.change_pars_to_fit_button.on_click\\n def plot_on_click(b):\\n with out:\\n if not self.compound:\\n self.spectra_object.params = self.spectra_object.fit_results[self.data_init_widget.value].params.copy()\\n for pars in self.paramwidgets.keys():\\n self.paramwidgets[pars].update_widget_group(self.spectra_object.params[pars])\\n elif self.compound:\\n self.compound_object.params = self.compound_object.fit_results[self.data_init_widget.value].params.copy()\\n for pars in self.paramwidgets.keys():\\n self.paramwidgets[pars].update_widget_group(self.compound_object.params[pars]) \\n\\n @self.reset_slider_lims_button.on_click\\n def plot_on_click(b):\\n with out:\\n if self.reset_slider_widget.value !=None:\\n self.paramwidgets[self.reset_slider_widget.value].ctrl_slider.max = 2*self.paramwidgets[self.reset_slider_widget.value].ctrl_slider.value\\n\\n # Create the interactive plot, then build the slider/graph parameter controls\\n plotkwargs = {**{pw.name:pw.ctrl_slider for pw in self.paramwidgets.values() if hasattr(pw,'ctrl_slider')},\\\\\\n **{plotlim.description:plotlim for plotlim in self.wlim.values()}}\\n self.intplot = interactive(self.interactive_plot,**plotkwargs)\\n \\n vb = VBox(self.intplot.children[0:-1])\\n vb2 = VBox([HBox([VBox([self.data_init_widget,self.reset_slider_widget]),VBox([self.change_pars_to_fit_button,self.reset_slider_lims_button])]),self.intplot.children[-1]])\\n hb = HBox([vb,vb2])\\n \\n display(hb)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a718204b6da7aaf9a338c82cb3c1b555\",\n \"score\": \"0.46719527\",\n \"text\": \"def window(theWidth, theHeight, theTitle):\\n\\tprint('This is wrapper function to help code autocompletion.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43e4ff18486c1579c62a8e2f9765e2eb\",\n \"score\": \"0.46719238\",\n \"text\": \"def _frame_out(self, *args):\\n if len(args) == 2:\\n self._display.frame(args[0], args[1].frame)\\n elif len(args) == 1:\\n if type(args[0]) is int:\\n return Frame(self.frame_width(), self.frame_height(),\\n self._display.frame(args[0]))\\n else:\\n self._display.frame(args[0].frame)\\n else:\\n return Frame(self.frame_width(), self.frame_height(),\\n self._display.frame())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83d66882651ac3c3846c7bba9ea517ab\",\n \"score\": \"0.466906\",\n \"text\": \"def main():\\n st.title(\\\"BioInformatics Web App\\\")\\n\\n #activity = ['Intro','DNA','DotPlot',\\\"About\\\"]\\n # choice = st.sidebar.selectbox(\\\"Select Activity\\\",activity)\\n # if choice == 'Intro':\\n\\t# st.subheader(\\\"Intro\\\")\\n # elif choice == \\\"DNA Sequence\\\":\\n\\t# st.subheader(\\\"DNA Sequence Analysis\\\")\\n\\n #input sequence\\n seq_input = st.text_input('Input Sequence')\\n\\n #check validity of sequence entered\\n validateSeq(seq_input)\\n\\n details = st.radio(\\\"Functions\\\",(\\\"Sequence Length\\\",\\n \\\"Frequency of each Nucleotide\\\",\\n \\\"Transcription of DNA\\\", \\n \\\"Reverse Transcription\\\", \\n \\\"Find Complementory Strand\\\",\\n \\\"GC Content Percentage\\\"))\\n if details == \\\"Sequence Length\\\":\\n st.write(len(seq_input))\\n\\n elif details == \\\"Frequency of each Nucleotide\\\":\\n st.subheader(\\\"Nucleotide Frequency\\\")\\n seq_input = Counter(seq_input)\\n st.write(seq_input)\\n adenine_color = st.color_picker(\\\"Adenine Color\\\")\\n thymine_color = st.color_picker(\\\"thymine Color\\\")\\n guanine_color = st.color_picker(\\\"Guanine Color\\\")\\n cytosil_color = st.color_picker(\\\"cytosil Color\\\")\\n\\n if st.button(\\\"Plot Freq\\\"):\\n barlist = plt.bar(seq_input.keys(),seq_input.values())\\n barlist[2].set_color(adenine_color)\\n barlist[3].set_color(thymine_color)\\n barlist[1].set_color(guanine_color)\\n barlist[0].set_color(cytosil_color)\\n\\n st.pyplot()\\n \\n elif details == \\\"Transcription of DNA\\\":\\n st.write(transcription(seq_input))\\n\\n elif details == \\\"Reverse Transcription\\\":\\n st.write(reversetranscription(seq_input))\\n\\n elif details == \\\"Find Complementory Strand\\\":\\n st.text(f\\\" DNA String + Complement + Reverse Complement: \\\\n 5'{seq_input} 3' \\\\n {''.join(['|' for c in range(len(seq_input))])} \\\\n 3'{reverse_complement(seq_input)[::-1]} 5'[Complement] \\\\n 5'{reverse_complement(seq_input)} 3'[Reverse Complement]\\\")\\n\\n elif details == \\\"GC Content Percentage\\\":\\n st.write(f\\\" {gc_content(seq_input)}%\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fbe416b60c11f12b7be979cf35f906d\",\n \"score\": \"0.466795\",\n \"text\": \"def onselect(vmin, vmax):\\n windows.append(float(Decimal(\\\"%.2f\\\" % vmin)))\\n windows.append(float(Decimal(\\\"%.2f\\\" % vmax)))\\n plot_windows(windows)\\n plt.draw()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddf645714b43146b402a1481a3feaafa\",\n \"score\": \"0.46650136\",\n \"text\": \"def interactive_numerical_plot(df_num,df_Y):\\n from ipywidgets import HBox,Checkbox,FloatRangeSlider,VBox,ToggleButton,interactive_output,Dropdown\\n from IPython.display import display\\n\\n def plot_num_and_save(xlimit,save_but,col,clip_box,clip_limit):\\n nonlocal df_num, df_Y\\n plt.close('all')\\n\\n if clip_box:\\n clip_df_num = df_num.copy()\\n clip_df_num.loc[clip_df_num[col]>clip_limit[1],col] = np.nan\\n clip_df_num.loc[clip_df_num[col]<clip_limit[0],col] = np.nan\\n else:\\n clip_df_num = df_num\\n\\n# for i,col in zip(range(clip_df_num[col].shape[1]),clip_df_num[col]):\\n fig,ax = plt.subplots(1,1,figsize=(10,5))\\n sns.kdeplot(clip_df_num[col][df_Y == 0], label = 'label0').set_title(clip_df_num[col].name)\\n sns.kdeplot(clip_df_num[col][df_Y == 1], label = 'label1')\\n ax.set_xlim(xlimit[0],xlimit[1])\\n plt.show()\\n\\n if save_but:\\n fig.savefig('./plots/{}.png'.format(clip_df_num[col].name), bbox_inches='tight')\\n\\n xlimit = FloatRangeSlider(value = [df_num.iloc[:,1].min(),df_num.iloc[:,1].max()],min=df_num.iloc[:,1].min(),\\n max=df_num.iloc[:,1].max(),step=(df_num.iloc[:,1].max()-df_num.iloc[:,1].min())/100,\\n continuous_update=False,description='X_limit')\\n save_but = ToggleButton(description='Save Figure')\\n col = Dropdown(options=df_num.columns.tolist())\\n clip_box = Checkbox(value=False,description='Clip ?')\\n clip_limit = FloatRangeSlider(value = [df_num.iloc[:,1].min(),df_num.iloc[:,1].max()],min=df_num.iloc[:,1].min(),\\n max=df_num.iloc[:,1].max(),step=(df_num.iloc[:,1].max()-df_num.iloc[:,1].min())/100,\\n continuous_update=False,description='X_limit')\\n\\n\\n out = interactive_output(plot_num_and_save,{\\n 'xlimit' : xlimit,\\n 'save_but':save_but,\\n 'col' : col,\\n 'clip_box':clip_box,\\n 'clip_limit':clip_limit\\n })\\n# save_but = Button(description='Save Fig')\\n vbox1 = VBox([xlimit,save_but,col,clip_box,clip_limit])\\n ui = HBox([vbox1,out])\\n display(ui)\\n\\n def on_click(change):\\n change['owner'].value = False\\n\\n def on_click_case(change):\\n try:\\n xlimit.min = df_num[change['new']].min()\\n xlimit.max = df_num[change['new']].max()\\n clip_limit.min = df_num[change['new']].min()\\n clip_limit.max = df_num[change['new']].max()\\n\\n except:\\n xlimit.max = df_num[change['new']].max()\\n xlimit.min = df_num[change['new']].min()\\n clip_limit.max = df_num[change['new']].max()\\n clip_limit.min = df_num[change['new']].min()\\n\\n xlimit.step = (df_num[change['new']].max() - df_num[change['new']].min())/100\\n xlimit.value = [df_num[change['new']].min(),df_num[change['new']].max()]\\n clip_limit.step = (df_num[change['new']].max() - df_num[change['new']].min())/100\\n clip_limit.value = [df_num[change['new']].min(),df_num[change['new']].max()]\\n\\n save_but.observe(on_click, 'value')\\n col.observe(on_click_case, 'value')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1ce84ef174cc08a45fee80456226f00\",\n \"score\": \"0.46647978\",\n \"text\": \"def pop_gui(self):\\n self.layout.addWidget(QLabel('Confirm properties each save?'), self.y, 0)\\n groupbox = self.add_radio(name='confirm', defval=True)\\n self.layout.addWidget(groupbox, self.y, 1)\\n self.y += 1\\n\\n self.layout.addWidget(QLabel('Save frames?'), self.y, 0)\\n groupbox = self.add_radio(name='save_frames', defval=self.params['save']['save_frames'])\\n self.layout.addWidget(groupbox, self.y, 1)\\n self.y += 1\\n\\n self.layout.addWidget(QLabel('Clear data after save?'), self.y, 0)\\n groupbox = self.add_radio(name='clear', defval=self.params['save']['clear'])\\n self.layout.addWidget(groupbox, self.y, 1)\\n self.y += 1\\n\\n self.layout.addWidget(QLabel('filename?'), self.y, 0)\\n textedit = QLineEdit('')\\n if 'filename' in self.params['save']:\\n defval = self.params['save']['filename']\\n textedit.setText(str(defval))\\n textedit.name = 'filename'\\n textedit.textChanged.connect(self.text_clicked)\\n self.layout.addWidget(textedit, self.y, 1)\\n self.y += 1\\n\\n self.layout.addWidget(QLabel('Confirm frames rate [fps]?'), self.y, 0)\\n textedit = QLineEdit('')\\n if 'fps' in self.params['improcess']:\\n defval = self.params['improcess']['fps']\\n textedit.setText(str(defval))\\n textedit.name = 'fps'\\n textedit.textChanged.connect(self.text_clicked)\\n self.layout.addWidget(textedit, self.y, 1)\\n self.y += 1\\n\\n self.layout.addWidget(QLabel('Confirm pixel size [microns]?'), self.y, 0)\\n textedit = QLineEdit('')\\n if 'pixel_size' in self.params['improcess']:\\n defval = self.params['improcess']['pixel_size']\\n if defval is not None:\\n textedit.setText(str(defval))\\n textedit.name = 'pixel_size'\\n textedit.textChanged.connect(self.text_clicked)\\n self.layout.addWidget(textedit, self.y, 1)\\n self.y += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8de185a1b169065304f2b335a1aaec2\",\n \"score\": \"0.4658181\",\n \"text\": \"def export_window():\\n\\n current_courses = [\\\"EECS 1011\\\", \\\"ENG 1101\\\", \\\"MATH 1013\\\", \\\"MATH 1025\\\",\\n \\\"PHYS1800\\\"]\\n\\n current_times = []\\n with shelve.open(\\\"course_times.db\\\") as db:\\n print(db[\\\"MATH 1025\\\"])\\n for course in current_courses:\\n current_times.append(db[course])\\n\\n layout = []\\n\\n i = 0\\n for course in current_courses:\\n layout.append([sg.Text(course), sg.Text(str(current_times[i]))])\\n i += 1\\n\\n layout.append([sg.Button(\\\"Close\\\", key=\\\"close\\\")])\\n\\n window = sg.Window(\\\"Window 1\\\", layout)\\n\\n while True:\\n event, values = window.read()\\n if event == None or event == \\\"close\\\":\\n break\\n\\n window.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90dfb0628baba5b2ab2f924331f2b424\",\n \"score\": \"0.46561182\",\n \"text\": \"def select_frame(self):\\n self.time_suppress()\\n \\n #select a decision making window\\n bbox_slice = self.bbox_buffer[-self.timeWindow_selectFrame:] # returns a slice of size of window\\n frame_slice = self.frame_buffer[-self.timeWindow_selectFrame:]\\n \\n\\n if self.is_shovel_idle(frame_slice):\\n return None, None, None, None\\n\\n\\n # main logic of frame selection\\n else: \\n selection_scores = self.compute_selection_scores(bbox_slice)\\n\\n sorted_inds, numberOfNoneScores = self.argsort(selection_scores)\\n\\n\\n numberOfValidScores = len(sorted_inds) - numberOfNoneScores\\n \\n if numberOfValidScores < self.minNumber_validScoresToSlectFrame:\\n return None, None, None, None\\n \\n\\n\\n #Pick the highest Score\\n ind_selected_max = sorted_inds[-1]\\n \\n #Pick the 90thPercentile Score\\n ninetieth_percentile_ind = int(round(numberOfValidScores * 0.9)) + numberOfNoneScores - 1\\n ind_selected = sorted_inds[ninetieth_percentile_ind]\\n\\n\\n\\n bbox_selected = bbox_slice[ind_selected]\\n frame_selected = frame_slice[ind_selected]\\n self.frames_selected.append(frame_selected)\\n \\n frame_selected_max = frame_slice[ind_selected_max]\\n\\n return frame_selected, bbox_selected, ind_selected, ind_selected_max, frame_selected_max\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87c07eb6112eaa14d0f94d06e36980c4\",\n \"score\": \"0.4650582\",\n \"text\": \"def _onClickSetRange(self) -> None:\\n\\n valueRange, okClicked = SetValueRangeDialog.getValueRange(self, initialValue=self.value())\\n if okClicked:\\n self.setValue(valueRange)\\n self.valueChangedByUser[Multivalue].emit(valueRange)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03135e385f22bfeae854b67127e14613\",\n \"score\": \"0.46481696\",\n \"text\": \"def __init__(self):\\n\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\\"-p\\\", \\\"--path-to-frames\\\", help=\\\"The absolute path to the frames. For example: /home/USER/.icons/status_icons/my_frames/ The frames must follow this naming convention: frame-<NUMBER>.<FORMAT>\\\\nWhere <NUMBER> starts at 0 and is incremented by 1 for the next frame and so on. <FORMAT> will default to png if no format is supplied\\\\nFor example a 3 frame PNG sequence should have the filenames: frame-0.png, frame-1.png, frame-2.png\\\")\\n parser.add_argument(\\\"-f\\\", \\\"--filetype\\\", help=\\\"Specify the image format for the frames. Valid options: png, jpg, jpeg. Default is png.\\\")\\n parser.add_argument(\\\"-t\\\", \\\"--time-per-frame\\\", help=\\\"Specify the time for each frame to be shown in milliseconds. Default is 300ms.\\\", type=int)\\n parser.add_argument(\\\"-b\\\", \\\"--blank-icon\\\", help=\\\"Creates a blank icon in the system tray to act as a spacer.\\\", action=\\\"store_true\\\")\\n args = parser.parse_args()\\n\\n if args.blank_icon: # icon is already blank\\n return\\n\\n if args.path_to_frames == None:\\n print(\\\"Absolute path to the frames must be specified with the -p option.\\\\nAborting...\\\")\\n exit(-1)\\n\\n if args.filetype == \\\"jpg\\\":\\n self.frame_format = \\\".\\\" + args.filetype\\n elif args.filetype == \\\"jpeg\\\":\\n self.frame_format = \\\".\\\" + args.filetype\\n \\n self.num_of_frames = len(fnmatch.filter(os.listdir(args.path_to_frames), self.frame_filename_prefix + \\\"*\\\" + self.frame_format)) # count number of frames in the supplied directory\\n if self.num_of_frames == 0:\\n print(\\\"Error: No suitable frames found in \\\" + args.path_to_frames + \\\"\\\\nAborting...\\\")\\n exit(-1)\\n\\n if args.time_per_frame:\\n self.frame_time = args.time_per_frame\\n if self.frame_time < 10:\\n print(\\\"Warning: A low time per frame value will lead to high CPU usage.\\\")\\n\\n frame_path_prefix = args.path_to_frames + self.frame_filename_prefix\\n for i in range(0, self.num_of_frames + 1): # setup correct path names\\n self.frame_paths.append(frame_path_prefix + str(i) + self.frame_format)\\n\\n if self.num_of_frames == 1: # we don't need callbacks if only one frame has been supplied so just set icon and return\\n self.icon.set_from_file(self.frame_paths[0])\\n return\\n\\n gobject.timeout_add(self.frame_time, self.update_frame) # callback from gtk.main()\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"188219dc773415de1076a762fa4d3b3c\",\n \"score\": \"0.46479467\",\n \"text\": \"def main_display():\\n #Display:\\n print '################################################################'\\n print '# XNATUPLOAD #'\\n print '# #'\\n print '# Developed by the masiLab Vanderbilt University, TN, USA. #'\\n print '# If issues, please start a thread here: #'\\n print '# https://groups.google.com/forum/#!forum/vuiis-cci #'\\n print '# Usage: #'\\n print '# Upload data to XNAT following the csv file information #'\\n print '# Parameters : #'\\n if vars(OPTIONS) == DEFAULT_ARGUMENTS:\\n print '# No Arguments given #'\\n print '# See the help bellow or use \\\"Xnatupload -h\\\" #'\\n print '################################################################'\\n PARSER.print_help()\\n sys.exit()\\n else:\\n if OPTIONS.host:\\n print '# %*s -> %*s#' %(-20, 'XNAT Host', -33, get_proper_str(OPTIONS.host))\\n if OPTIONS.username:\\n print '# %*s -> %*s#' %(-20, 'XNAT User', -33, get_proper_str(OPTIONS.username))\\n if OPTIONS.printmodality:\\n print '# %*s -> %*s#' %(-20, 'Print Modality', -33, 'on')\\n else:\\n if OPTIONS.csvfile:\\n print '# %*s -> %*s#' %(-20, 'CSV file', -33, get_proper_str(OPTIONS.csvfile))\\n if OPTIONS.session_type:\\n print '# %*s -> %*s#' %(-20, 'Session Type', -33, get_proper_str(OPTIONS.session_type))\\n if OPTIONS.report:\\n print '# %*s -> %*s#' %(-20, 'Report', -33, 'on')\\n if OPTIONS.force:\\n print '# %*s -> %*s#' %(-20, 'Force Upload', -33, 'on')\\n if OPTIONS.delete:\\n print '# %*s -> %*s#' %(-20, 'Delete resources', -33, 'on')\\n if OPTIONS.deleteAll:\\n print '# %*s -> %*s#' %(-20, 'Delete All', -33, 'on')\\n if OPTIONS.extract:\\n print '# %*s -> %*s#' %(-20, 'Extract ZIP', -33, 'on')\\n if OPTIONS.outputfile:\\n print '# %*s -> %*s#' %(-20, 'Output file', -33, get_proper_str(OPTIONS.outputfile))\\n print '################################################################'\\n print \\\"IMPORTANT WARNING FOR ALL USERS ABOUT XNAT:\\\"\\n print \\\" session_label needs to be unique for each session.\\\"\\n print \\\" Two subjects can NOT have the same session_label\\\"\\n print '================================================================'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cd70927af0b840bf67eede36625f40d\",\n \"score\": \"0.46462128\",\n \"text\": \"def scanner_display_scanpoints(self):\\n # Get local arguments\\n points_label = self.get_obj('scanner_scan_points_label')\\n rows_label = self.get_obj('scanner_scan_rows_label')\\n time_label = self.get_obj('scanner_scan_time_label')\\n\\n points_label.set_markup(self.format_dro_string('Points: {0}'.format(self.scanner.scene.points_count),11))\\n rows_label.set_markup(self.format_dro_string('Rows: {0} , Columns: {1}'.format(self.scanner.scene.rows,self.scanner.scene.columns),11))\\n time_data=datetime.timedelta(seconds=round(self.scanner.scene.estimated_time))\\n time_label.set_markup(self.format_dro_string('Estimated Time: {0}'.format(time_data),11))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19a62117bbd3045e503ebd340b734182\",\n \"score\": \"0.46367753\",\n \"text\": \"def confirmButtonClicked(self):\\n\\n self.showPopupUnmarkedPaperInput()\\n\\n self.problemAmount = int(self.problemNumInput.text()) # 시험의 문제 갯수\\n self.testpaperAmount = int(self.paperNumInput.text()) # 한 시험지 세트의 총 페이지 수\\n self.gradeWithOCR = self.check_useOCR.isChecked() # OCR로 주관식 채점 여부\\n\\n fileLocs = []\\n while True:\\n fname = QFileDialog.getOpenFileName() # 비 마킹 시험지들의 파일 읽기\\n if fname[0] != '': # 아직 읽을 파일이 들어온 경우\\n fileLocs.append(fname[0])\\n else: # 읽을 파일이 더 없는 경우 - 루프 종료\\n break\\n\\n self.showPopupEdgeInstruction_1()\\n\\n counter = 0 # 임시 변수\\n for imageLoc in fileLocs: # 각 시험지 이미지마다\\n\\n src = cv2.imread(imageLoc, cv2.IMREAD_COLOR)\\n height = src.shape[0] # 시험지 이미지 높이\\n width = src.shape[1] # 시험지 이미지 너비\\n\\n # 시험지가 너무 커 처리가 힘든 경우, 리사이징\\n if height >= width:\\n resizeScale = 1000 / height\\n else:\\n resizeScale = 1000 / width\\n src = cv2.resize(src, (int(width * resizeScale), int(height * resizeScale)), interpolation=cv2.INTER_AREA)\\n\\n print(\\\"Changed dimensions : \\\", src.shape)\\n\\n height, width, channel = src.shape\\n\\n cv2.imshow(\\\"Automatic Scoring Program\\\", src)\\n cv2.setMouseCallback('Automatic Scoring Program', self.mouseCallbackSpot)\\n\\n print(\\\"Click 4 spot of the image, starting from left-upper side, clockwise\\\")\\n print(\\\"After that, press any key\\\")\\n cv2.waitKey(0)\\n cv2.destroyAllWindows()\\n print(self.clickCoordinates)\\n\\n srcPoint = np.array(self.clickCoordinates, dtype=np.float32)\\n self.clickCoordinates = []\\n\\n # 시험지의 각 4개 꼭짓점을 지정하고, warping 진행\\n dstPoint = np.array([[0, 0], [width, 0], [width, height], [0, height]], dtype=np.float32)\\n matrix = cv2.getPerspectiveTransform(srcPoint, dstPoint)\\n warpedUnmarkedPaper = cv2.warpPerspective(src, matrix, (width, height))\\n cv2.imshow(\\\"Automatic Scoring Program\\\", warpedUnmarkedPaper)\\n cv2.waitKey(0)\\n\\n # 리사이징한 시험지 파일 저장\\n cv2.imwrite('./buffer/unprocessedBlankPaper_{}.jpg'.format(counter), warpedUnmarkedPaper)\\n\\n # 마킹 안 된 시험지 Blur, 흑백화 등 이미지 정제\\n # convert the images to grayscale\\n unmarkedPaper = cv2.cvtColor(warpedUnmarkedPaper, cv2.COLOR_BGR2GRAY)\\n\\n # blur\\n for i in range(10):\\n unmarkedPaper = cv2.GaussianBlur(unmarkedPaper, (7, 7), 0)\\n\\n cv2.imwrite('./buffer/processedBlankPaper_{}.jpg'.format(counter), unmarkedPaper)\\n\\n cv2.destroyAllWindows()\\n\\n counter = counter + 1\\n\\n self.Form.hide()\\n\\n #문제 설정 창 띄우기\\n self.problemSettingWindow = QtWidgets.QWidget()\\n self.problemSettingWindowUI = UI_ProblemSetting()\\n self.problemSettingWindowUI.setupUi(self.problemSettingWindow, [], self.problemAmount, self.testpaperAmount, self.gradeWithOCR)\\n self.problemSettingWindow.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8174387bb1deea347479b087489dac6\",\n \"score\": \"0.4636768\",\n \"text\": \"def Pump_Durations_Plots():\\n plt.clf()\\n VALUES = []\\n where_clause = (\\n \\\"(FLOW_RATE is not NULL) AND \\\"\\n \\\"(FLOW_RATE > 0) AND \\\"\\n \\\"(FLOW_RATE <= 100) AND \\\"\\n \\\"(DURATION is not NULL) AND \\\"\\n \\\"(DURATION > 0) AND\\\"\\n \\\"(DURATION <= 12) AND\\\"\\n \\\"(START_MEAS is not NULL) AND \\\"\\n \\\"(START_MEAS > 0) AND \\\"\\n \\\"(PUMP_MEAS is not NULL) AND \\\"\\n \\\"(PUMP_MEAS > 0)\\\"\\n )\\n with arcpy.da.SearchCursor(CWIPL, ['FLOW_RATE', 'DURATION', 'START_MEAS',\\\\\\n 'PUMP_MEAS'], where_clause) as cursor:\\n for row in cursor:\\n down = row[3] - row[2]\\n if down > 0 and down < 100 and row[3] <=200:\\n stuff = [row[0], row[1], row[3], down]\\n VALUES.append(stuff)\\n DUR_DATA = [i[1] for i in VALUES]\\n PUMP_DATA = [i[0] for i in VALUES]\\n Water_Level = [i[2] for i in VALUES]\\n Drawdown_data = [i[3] for i in VALUES]\\n plt.figure(1)\\n plt.hist(DUR_DATA, bins = 48, label = 'Duration')\\n plt.xlim([0, 12])\\n plt.ylim([0, 125000])\\n plt.minorticks_on()\\n plt.xticks(fontsize = 24)\\n plt.yticks(fontsize = 24)\\n plt.xlabel('Duration [hours]', fontsize = 30)\\n plt.ylabel('Number of entries', fontsize = 30)\\n plt.grid(True)\\n \\n plt.figure(2)\\n plt.hist(PUMP_DATA, bins = 100, label = 'Pump Rata Data')\\n plt.minorticks_on()\\n plt.xlim([0, 100])\\n plt.ylim([0, 60000])\\n plt.xticks(fontsize = 24)\\n plt.yticks(fontsize = 24)\\n plt.xlabel('Pump Rate [GPM]', fontsize = 30)\\n plt.ylabel('Number of entries', fontsize = 30)\\n plt.grid(True)\\n \\n plt.figure(3)\\n plt.hist(Water_Level, bins = 200, label = 'Static Level Data', edgecolor = 'k')\\n plt.minorticks_on()\\n plt.xticks(fontsize = 24)\\n plt.yticks(fontsize = 24)\\n plt.xlabel('Static Water Level [ft]', fontsize = 30)\\n plt.ylabel('Number of entries', fontsize = 30)\\n plt.grid(True)\\n \\n plt.figure(4)\\n plt.hist(Drawdown_data, bins = 100, label = 'Drawdown Data', color = 'g' , edgecolor = 'k')\\n plt.minorticks_on()\\n# plt.xlim([0, 100])\\n# plt.ylim([0, 100000])\\n plt.xticks(fontsize = 24)\\n plt.yticks(fontsize = 24)\\n plt.xlabel('Drawdown [ft]', fontsize = 30)\\n plt.ylabel('Number of entries', fontsize = 30)\\n plt.grid(True)\\n \\n fig, ax = plt.subplots(1,1)\\n plt.figure(5)\\n plt.grid(which='minor', linestyle=':', linewidth='0.5', color='gray',\\\\\\n zorder = 0)\\n plt.grid(which='major', linestyle='-', linewidth='0.5', color='black',\\\\\\n zorder = 0)\\n plt.scatter(PUMP_DATA, DUR_DATA, zorder = 3)\\n plt.xticks(fontsize = 24)\\n plt.yticks(fontsize = 24)\\n plt.minorticks_on()\\n plt.xlim([0,101])\\n plt.ylim([0,12.25])\\n plt.xlabel('Pumping Rates [GPM]', fontsize = 30)\\n plt.ylabel('Duration of Test [hours]', fontsize = 30)\\n ax.xaxis.set_major_locator(MultipleLocator(20))\\n ax.xaxis.set_major_formatter(FormatStrFormatter('%d'))\\n ax.xaxis.set_minor_locator(MultipleLocator(10))\\n ax.yaxis.set_major_locator(MultipleLocator(1))\\n #ax.axis.set_major_formatter(FormatStrFormatter('%d'))\\n ax.yaxis.set_minor_locator(MultipleLocator(0.5))\\n ax.set_axisbelow(True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d5d7493607b700952d1cb63e7e3da5b\",\n \"score\": \"0.46324617\",\n \"text\": \"def show_frameLima(self, cont, chosenexp):\\n result = True \\n result2 = True\\n global Momo # Create variable to store experiment object\\n Momo = getobject(chosenexp)\\n \\n # Case of analyzing control\\n if Momo.iscontrol: \\n if Momo.exptype != \\\"0\\\": # Not first time analyzing\\n result2 = tkMessageBox.askquestion(\\\"Warning\\\", \\\"This control experiment has already\\\\nbeen analyzed as %s.\\\\nChanges may overwrite exisiting data.\\\\nProceed anyways?\\\" % getpreviouslyanalyzed(Momo))\\n if result2 != \\\"no\\\": # First time analyzing or want to override\\n frame = self.frames[AnalysisTypeForNone] # Go to AnalysisTypeForNone to get type to analyze as\\n for button in [frame.thermobutton, frame.chemobutton, frame.photobutton]:\\n if getpreviouslyanalyzed(Momo) == button['text']: # Turn on button if that was the previous analysis\\n button.state([\\\"focus\\\",\\\"selected\\\"])\\n else:\\n button.state([\\\"!focus\\\",'!selected'])\\n frame.tkraise()\\n\\n # Case of not control\\n else: \\n if Momo.expy[0] != \\\"\\\": # Already analyzed\\n result = tkMessageBox.askquestion(\\\"Warning\\\", \\\"The selected experiment has already been analyzed.\\\\nChanges may overwrite exisiting data.\\\\nProceed anyways?\\\")\\n if result != \\\"no\\\": # First time analyzing or want to override\\n if Momo.exptype != \\\"4\\\": # Not strunching\\n frame = cont(self.container, self)\\n self.frames[cont] = frame \\n frame.grid(row=0, column=0, sticky=\\\"nsew\\\")\\n frame.ChangePic(1) # Go to first picture\\n frame.tkraise() \\n else:\\n tkMessageBox.showwarning(\\\"Scrunching analysis not implimented yet\\\") #show warning\\n \\\"\\\"\\\"\\n frame = SrunchingAnalysis(self.container, self) # Create fresh page in case of old data\\n self.frames[cont] = frame \\n frame.grid(row=0, column=0, sticky=\\\"nsew\\\")\\n frame.tkraise() \\n \\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bcf36119a5c06c0b937ddd0324ffa48\",\n \"score\": \"0.4628606\",\n \"text\": \"def apply_new_ranges(self, event=None):\\n msg = (\\\"(Main) hit Enter/Return in Max V ({}) or Max I ({}) entry\\\"\\n .format(self.v_range.get(), self.i_range.get()))\\n log_user_action(self.ivs2.logger, msg)\\n\\n if self.current_run_displayed or self.results_wiz:\\n run_dir = self.ivs2.hdd_output_dir\\n config_dir = os.path.dirname(self.config.cfg_filename)\\n\\n # Replace config from saved config of displayed image\\n cfg_file, original_cfg_file = self.swap_config(run_dir, config_dir)\\n\\n # Update IVS2 properties and the config options\\n try:\\n self.ivs2.plot_max_x = float(self.v_range.get())\\n self.ivs2.plot_max_y = float(self.i_range.get())\\n self.config.cfg_set(\\\"Plotting\\\", \\\"plot max x\\\", self.ivs2.plot_max_x)\\n self.config.cfg_set(\\\"Plotting\\\", \\\"plot max y\\\", self.ivs2.plot_max_y)\\n event.widget.tk_focusNext().focus() # move focus out\\n except ValueError:\\n # Silently reject invalid values\\n pass\\n self.update_axis_ranges()\\n self.update_idletasks()\\n\\n if self.current_run_displayed or self.results_wiz:\\n if self.axes_locked.get() == \\\"Unlock\\\":\\n # Unlock the axes (redisplays image and saves config)\\n self.unlock_axes()\\n else:\\n # Redisplay the image with the new settings (saves config)\\n self.redisplay_img(reprocess_adc=False)\\n\\n # Restore the config file from the snapshot\\n self.restore_config(run_dir, config_dir,\\n cfg_file, original_cfg_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c2455e10a2ec4fdd7e7f5506b8cfbe3\",\n \"score\": \"0.462317\",\n \"text\": \"def show_frameStingray(self, cont, obj):\\n saveobject(obj)\\n tkMessageBox.showwarning(\\\"Done\\\", \\\"Data has been saved for:\\\" + obj.expnumber) #show warning\\n frame = self.frames[cont]\\n frame.tkraise() #raise to front\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfc75b83f7eeae89ed3216a9f8d9055c\",\n \"score\": \"0.46208972\",\n \"text\": \"def set_window(self, x_start, y_start, x_end, y_end):\\r\\n if x_start > self.dis_column or \\\\\\r\\n x_end > self.dis_column or \\\\\\r\\n y_start > self.dis_line or \\\\\\r\\n y_end > self.dis_line:\\r\\n raise ValueError(\\\"Window setting is Out of display range.\\\")\\r\\n\\r\\n reg_list = [\\r\\n 0x15, x_start, x_end - 1,\\r\\n 0x75, y_start, y_end - 1,\\r\\n ]\\r\\n self.write_reg_list(reg_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7189447a9197e79ba2e331c5709d595\",\n \"score\": \"0.46200073\",\n \"text\": \"def draw_match(self, frame, max_val, thresh_max, number, articlename):\\n\\n startx_coord = self.startx_coord[number]\\n starty_coord = self.starty_coord[number]\\n endx_coord = self.endx_coord[number]\\n endy_coord = self.endy_coord[number]\\n\\n\\n max_of_all = max_val[0]\\n index_of_max = 0\\n iterator = 0\\n is_drawn = False\\n\\n for i in max_val:\\n if max_of_all < i:\\n max_of_all = i\\n index_of_max = iterator\\n iterator += 1\\n\\n if max_of_all > thresh_max:\\n is_drawn = True\\n\\n cv2.rectangle(frame, (startx_coord[index_of_max], starty_coord[index_of_max]), (endx_coord[\\n index_of_max], endy_coord[index_of_max]), (0, 0, 255), 2)\\n cv2.putText(frame, articlename, (startx_coord[index_of_max], starty_coord[index_of_max] - 3),\\n Config.font, 0.9, (0, 0, 255), 1, cv2.LINE_AA)\\n\\n Config.fps.stop()\\n cv2.putText(frame, \\\"Elapsed time: {:.2f}\\\".format(Config.fps.elapsed()), Config.position_elapsed,\\n Config.font, Config.fontScale, Config.fontColor, Config.lineType)\\n cv2.putText(frame, \\\"FPS: {:.2f}\\\".format(Config.fps.fps()),\\n Config.position_fps, Config.font, Config.fontScale,\\n Config.fontColor, Config.lineType)\\n\\n cv2.imshow(\\\"Result\\\", frame)\\n print(\\\"\\\\rFPS: {:.2f}\\\".format(Config.fps.fps()), end='\\\\r')\\n return is_drawn, index_of_max, frame\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82d6f056853f945a47a4c18d7facf051\",\n \"score\": \"0.46178433\",\n \"text\": \"def ui(dockable):\\n\\n # get settings\\n counter = SETTINGS.get('counter')\\n if counter == None:\\n counter = '1'\\n SETTINGS.add('counter', counter)\\n\\n padding = SETTINGS.get('padding')\\n if padding == None:\\n padding = '2'\\n SETTINGS.add('padding', padding)\\n\\n step = SETTINGS.get('step')\\n if step == None:\\n step = '1'\\n SETTINGS.add('step', step)\\n\\n name = SETTINGS.get('name')\\n if name == None:\\n name = 'example_[C]_GEP'\\n SETTINGS.add('name', name)\\n\\n namePreview = SETTINGS.get('namePreview')\\n if namePreview == None:\\n namePreview = ''\\n SETTINGS.add('namePreview', namePreview)\\n\\n searchfor = SETTINGS.get('searchfor')\\n if searchfor == None:\\n searchfor = ''\\n SETTINGS.add('searchfor', searchfor)\\n\\n replaceby = SETTINGS.get('replaceby')\\n if replaceby == None:\\n replaceby = ''\\n SETTINGS.add('replaceby', replaceby)\\n\\n asset = SETTINGS.get('asset')\\n if asset == None:\\n asset = ''\\n SETTINGS.add('asset', asset)\\n\\n desc = SETTINGS.get('desc')\\n if desc == None:\\n desc = ''\\n SETTINGS.add('desc', desc)\\n\\n category = SETTINGS.get('category')\\n if category == None:\\n category = ''\\n SETTINGS.add('category', category)\\n\\n # create ui\\n try:\\n cmds.deleteUI(WINDOWNAME, window=True)\\n except:\\n pass\\n try:\\n cmds.deleteUI(CONTROLNAME, control=True)\\n except:\\n pass\\n\\n cmds.window(WINDOWNAME, t='Batch rename selection')\\n form = cmds.formLayout(parent=WINDOWNAME)\\n\\n # rename with counter frame layout\\n frameRename = cmds.frameLayout('rename_frameLayoutRename',\\n label='Rename',\\n cll=True,\\n cl=True,\\n bv=True)\\n\\n separator = cmds.separator('rename_separator', style='in')\\n coubterInput = cmds.textFieldGrp('rename_counter', editable=True, l='Counter start', text=counter, fcc=True, cc=parseInputText, tcc=parseInputText)\\n stepInput = cmds.textFieldGrp('rename_step', editable=True, l='Step', text=step, fcc=True, cc=parseInputText, tcc=parseInputText)\\n paddingInput = cmds.textFieldGrp('rename_padding', editable=True, l='Padding', text=padding, fcc=True, cc=parseInputText, tcc=parseInputText)\\n\\n inputText = cmds.textFieldGrp('rename_nameField', editable=True, l='New name', text=name, fcc=True,cc=parseInputText, tcc=parseInputText)\\n inputText = cmds.textFieldGrp('rename_namePreview', editable=False, l='Preview', text='')\\n\\n renameButton = cmds.button('rename_button', l='Rename', command=renameItems)\\n cmds.setParent('..')\\n\\n # Search and replace frame layout\\n frameReplace = cmds.frameLayout('rename_frameLayoutReplace',\\n label='Search & Replace',\\n cll=True,\\n cl=True,\\n bv=True)\\n separator = cmds.separator('rename_separator', style='in') \\n searchInput = cmds.textFieldGrp('rename_search', editable=True, l='Search for', text=searchfor)\\n replaceInput = cmds.textFieldGrp('rename_replace', editable=True, l='Replace by', text=replaceby)\\n \\n renameButton = cmds.button('rename_replacebutton', l='Search and Replace', command=searchReplace)\\n cmds.setParent('..')\\n\\n # add attributes frame layout\\n frameAttr = cmds.frameLayout('rename_frameLayoutAttr',\\n label='Add attributes',\\n cll=True,\\n cl=True,\\n bv=True)\\n separator = cmds.separator('rename_separator', style='in')\\n\\n assetField = cmds.textFieldGrp('rename_asset', editable=True, l='asset', text=asset)\\n descField = cmds.textFieldGrp('rename_desc', editable=True, l='desc', text=desc)\\n categoryField = cmds.textFieldGrp('rename_category', editable=True, l='category', text=category)\\n \\n addAttrButton = cmds.button('rename_setattrbutton', l='Add attributes', command=setArnoldAttr)\\n removeAttrButton = cmds.button('rename_removeattrbutton', l='Remove attributes', command=removeArnoldAttr)\\n cmds.setParent('..')\\n\\n # frame layout containing selection items\\n frameSelection = cmds.frameLayout('selecter_frameLayoutSelection',\\n label='Item Selecter',\\n cll=True,\\n cl=True,\\n bv=True)\\n #outPane = cmds.scrollField('selecter_output', editable=False, wordWrap=False, text='')\\n outPane = cmds.textScrollList('selecter_output', append=[], sii=True, ams=True, sc=selectItem)\\n inputText = cmds.textFieldGrp('selecter_nameField', editable=True, l='Filter:', text='', fcc=True, cc=parseFilterSelection, tcc=parseFilterSelection)\\n nodeCheckbox = cmds.checkBoxGrp('selecter_dagcheck', numberOfCheckBoxes=3, labelArray3=['Dag objects', 'Transforms', 'Shapes'], cc=parseFilterSelection)\\n typeCheckbox = cmds.checkBoxGrp('selecter_typecheck', numberOfCheckBoxes=3, labelArray3=['Meshes', 'Cameras', 'Lights'], cc=parseFilterSelection)\\n\\n cmds.setParent('..')\\n\\n separatorBottom = cmds.separator('rename_separatorBottom', style='in')\\n closeButton = cmds.button('rename_close', l='Close', command=closeUI)\\n \\n cmds.formLayout(form, e=True,\\n attachControl=[(frameReplace, 'top', 5, frameRename),\\n (frameAttr, 'top', 5, frameReplace),\\n (frameSelection, 'top', 5, frameAttr),\\n (frameSelection, 'bottom', 5, separatorBottom),\\n (separatorBottom, 'bottom', 5, closeButton),\\n ])\\n\\n cmds.formLayout(form, e=True,\\n attachForm=[(frameRename, 'right', 5),\\n (frameRename, 'left', 5),\\n (frameRename, 'top', 5),\\n (frameReplace, 'left', 5),\\n (frameReplace, 'right', 5),\\n (frameAttr, 'left', 5),\\n (frameAttr, 'right', 5),\\n (frameSelection, 'left', 5),\\n (frameSelection, 'right', 5),\\n (separatorBottom, 'left', 5),\\n (separatorBottom, 'right', 5),\\n (closeButton, 'left', 5),\\n (closeButton, 'right', 5),\\n (closeButton, 'bottom', 5),\\n ])\\n \\n \\n parseInputText()\\n parseFilterSelection()\\n \\n if dockable:\\n cmds.dockControl(CONTROLNAME, label='Batch rename', floating=True, area='right', content=WINDOWNAME)\\n else:\\n cmds.showWindow(WINDOWNAME)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f971213fb0678d439f85c2a326f194c\",\n \"score\": \"0.46166497\",\n \"text\": \"def gui_validate_landscape(self):\\n page_range = self.strvar_landscape_pages.get().strip()\\n if len(page_range) > 0 and not tools.check_page_nums(page_range):\\n self.__remove_command_argument(cst.LANDSCAPE_ARG_NAME)\\n self.strvar_landscape_pages.set(\\\"\\\")\\n messagebox.showerror(\\n message=\\\"Invalide `Output in Landscape` Page Argument!\\\"\\n )\\n return False\\n\\n if self.is_landscape_checked.get():\\n arg = cst.LANDSCAPE_ARG_NAME\\n if len(page_range) > 0:\\n arg += page_range # no space between -ls and page numbers\\n self.__add_or_update_command_argument(cst.LANDSCAPE_ARG_NAME, arg)\\n else:\\n self.__remove_command_argument(cst.LANDSCAPE_ARG_NAME)\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77414f661b65f8cd21940fbbe71a6f30\",\n \"score\": \"0.4610267\",\n \"text\": \"def show_video(video_path, labels: List[int], size: Optional[Tuple[int, int]] = None) -> None:\\n visible_color: Final[Tuple[int, int, int]] = (0, 255, 0)\\n non_visible_color: Final[Tuple[int, int, int]] = (0, 0, 255)\\n\\n cap = cv2.VideoCapture(video_path)\\n video_length: Final[int] = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\\n frame_nb = 0\\n while frame_nb < video_length:\\n\\n frame_ok, frame = cap.read()\\n if not frame_ok:\\n break\\n\\n # Crop, uncomment and change the values if needed.\\n # if \\\"video-1\\\" in video_path:\\n # frame = frame[900:]\\n\\n if size:\\n frame = cv2.resize(frame, size, interpolation=cv2.INTER_AREA)\\n\\n frame = cv2.copyMakeBorder(frame, 40, 0, 0, 0, cv2.BORDER_CONSTANT, None, 0)\\n defect_text = f\\\"The defect is: {os.path.normpath(video_path).split(os.sep)[-4]}\\\"\\n frame_text = f\\\" - Frame {frame_nb} / {video_length}\\\"\\n frame = cv2.putText(frame, defect_text + frame_text, (20, 25),\\n cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 255), 1, cv2.LINE_AA)\\n\\n frame = cv2.putText(frame, f\\\"Status: defect {'visible' if labels[frame_nb] != 0 else 'non-visible'}\\\",\\n (frame.shape[1]-300, 25), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 255), 1, cv2.LINE_AA)\\n frame = cv2.circle(frame, (frame.shape[1]-100, 20), 15,\\n visible_color if labels[frame_nb] != 0 else non_visible_color, -1)\\n\\n while True:\\n cv2.imshow(\\\"Frame\\\", frame)\\n key = cv2.waitKey(10)\\n if key == 32: # Space key, next frame\\n break\\n elif key == ord(\\\"q\\\"): # quit\\n cap.release()\\n cv2.destroyAllWindows()\\n exit()\\n\\n frame_nb += 1\\n\\n cv2.destroyAllWindows()\\n cap.release()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e0c19d8925ce014c7ca0285e94852b5\",\n \"score\": \"0.4608936\",\n \"text\": \"def show_export_dataset_dialog(self):\\n self.export_dataset_dialog.update()\\n self.export_dataset_dialog.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"833cb42e92332347016e2bc71419caee\",\n \"score\": \"0.46062315\",\n \"text\": \"def outputFormatOptionsMenu(self):\\n outputFormatOptions = self.settingsSudoku.\\\\\\n getSudokuOutputFormatOptions()\\n self.drawMenuOptions(\\\"Change output type format\\\",\\n outputFormatOptions)\\n optionSelected = self.getUserInput()\\n while not self.validateUserInput(optionSelected,\\n len(outputFormatOptions)):\\n print(\\\"*****The option selected is not valid****\\\")\\n optionSelected = self.getUserInput()\\n if(optionSelected == 1):\\n print(\\\"Console\\\")\\n self.settingsSudoku.setSudokuOutputFormat(\\\"Console\\\")\\n elif (optionSelected == 2): \\n print(\\\"File\\\")\\n self.settingsSudoku.setSudokuOutputFormat(\\\"File\\\")\\n userInputNameFile = raw_input(\\\\\\n \\\"Enter a name for the outuput file: \\\")\\n userInputPathFile = raw_input(\\\\\\n \\\"Enter the specific path to store the file: \\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"023bfe849dfaed2ef3bbc3985ab58b5d\",\n \"score\": \"0.45980275\",\n \"text\": \"def display_and_update(self):\\n sg.theme(\\\"Reddit\\\")\\n window = sg.Window(\\\"Mathcad Automation\\\", [\\n [sg.Image(data = images.tt_logo)],\\n [sg.Text(f'Version: {self.version}', size = (10,1))], \\n [sg.Frame(\\\"Choose Excel File*\\\", [[sg.FileBrowse(\\\"1. Browse\\\", key=\\\"excel_file\\\", enable_events=True),\\n sg.InputText(self.excel, key=\\\"excel_name\\\", size=(30, 1),\\n background_color='white', enable_events=True),],\\n\\n [sg.Button(\\\"2. Choose Templates\\\", key = \\\"choose_templates\\\", enable_events = True)],\\n ])],\\n [sg.Frame(\\\"Choose Database File (Optional)\\\", [[sg.FileBrowse(key=\\\"database_file\\\", enable_events=True),\\n sg.InputText(self.database, key=\\\"database_name\\\", size=(30, 1),\\n background_color='white', enable_events=True)], ])],\\n [sg.Checkbox(\\\"Save to database?\\\", key=\\\"save_to_database\\\", default=self.save_to_database,\\n tooltip=\\\"If selected, details about the generated report will be saved to a database.\\\")],\\n\\n [sg.Button(\\\"3. Continue\\\", key=\\\"continue\\\", button_color=\\\"green\\\")],\\n\\n ], icon=images.ma_logo_png)\\n \\\"\\\"\\\"Listen for events\\\"\\\"\\\"\\n while True: #logic loop\\n event, values = window.read()\\n if event == 'OK' or event == sg.WIN_CLOSED:\\n window.close()\\n return False\\n else:\\n if event == \\\"choose_templates\\\":\\n \\\"\\\"\\\"\\n load the excel file and search for different mounting locations\\n \\\"\\\"\\\"\\n if helpers.check_file_type(values['excel_name'], 'xlsx'):\\n #get the mounting locations\\n equipment = filestream.get_eqpt_from_xl(values['excel_name'])\\n if len(equipment.items) > 0:\\n self.excel = values['excel_name']\\n self.template_layout = []\\n for idx, item in enumerate(equipment.mounting_locations):\\n if item not in self.templates:\\n self.templates[item] = \\\"\\\" # instanitate each templates to be empty string\\n #create the layout for the choose templates window\\n self.template_layout += [\\n [sg.Text(\\\"Choose \\\" + item + \\\" mounting template:\\\")],\\n [sg.FileBrowse(key= item, enable_events=True),\\n sg.InputText(self.templates[item], key=item + \\\"_name\\\",\\n size=(60, 1), background_color='white', enable_events=True)],\\n ]\\n if self.choose_templates():\\n self.can_continue = True\\n\\n\\n if event == \\\"continue\\\": # user has input all information\\n # list of all the user errors when they input information\\n errors = list()\\n # validate the filepaths\\n self.save_to_database = values['save_to_database']\\n #check if the database is a .csv file - if not, append error list\\n if helpers.check_file_type(values['database_name'], 'csv') or values['database_name'] == \\\"\\\":\\n self.database = values['database_name']\\n else:\\n errors.append(\\\"Database file must be a .csv file.\\\")\\n #check if excel file is .xlsx\\n if helpers.check_file_type(values['excel_name'], 'xlsx'):\\n self.excel = values['excel_name']\\n else:\\n errors.append(\\\"Excel file must be a .xlsx file.\\\")\\n # Error handling\\n if len(errors) == 0 and self.can_continue: #no errors\\n window.close()\\n return True\\n elif self.can_continue == False:\\n alert = Popup(\\\"Error\\\", \\\"You must choose template files.\\\")\\n alert.alert()\\n continue\\n else: #show errors in a popup window\\n alert = Popup(\\\"Errors\\\", \\\"\\\\n\\\".join(errors))\\n alert.alert()\\n continue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b440491ff0b950033118bb7f66f7f8b\",\n \"score\": \"0.459581\",\n \"text\": \"def do_range(self, args, calibration=False):\\n cmds = args.split()\\n if len(cmds) < 4:\\n print(\\\"Error: not enough args!\\\")\\n return\\n self.qa_analyzer.settings.CDSP_range = True\\n self.qa_analyzer.settings.CDSP_range_param['CD1'] = int(cmds[0].strip())\\n self.qa_analyzer.settings.CDSP_range_param['CD2'] = int(cmds[1].strip())\\n self.qa_analyzer.settings.CDSP_range_param['SP1'] = int(cmds[2].strip())\\n self.qa_analyzer.settings.CDSP_range_param['SP2'] = int(cmds[3].strip())\\n if len(cmds) > 4:\\n self.qa_analyzer.settings.CDSP_range_param['wext'] = int(cmds[4].strip())\\n else:\\n self.qa_analyzer.settings.CDSP_range_param['wext'] = None\\n self.qa_analyzer.run(calibration)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f0984e29706e01f77507efa808c2680\",\n \"score\": \"0.45948943\",\n \"text\": \"def onCaptureButton(self):\\n inputBrowserNode = self.ui.inputSequenceBrowserSelector.currentNode()\\n inputImage = self.ui.inputVolumeSelector.currentNode()\\n outputBrowserNode = self.ui.segmentationBrowserSelector.currentNode()\\n selectedSegmentation = self.ui.inputSegmentationSelector.currentNode()\\n numSkip = slicer.modules.singleslicesegmentation.widgetRepresentation().self().ui.skipImagesSpinBox.value\\n\\n if inputBrowserNode is None:\\n logging.error(\\\"No browser node selected!\\\")\\n return\\n if selectedSegmentation is None:\\n logging.error(\\\"No segmentation selected!\\\")\\n return\\n if outputBrowserNode is None:\\n logging.error(\\\"No segmentation sequence browser selected!\\\")\\n return\\n\\n original_index_str = selectedSegmentation.GetAttribute(self.ORIGINAL_IMAGE_INDEX)\\n\\n # If input sequence browser is selected in the toolbar, always consider this a new segmentation. This is needed in case\\n # a scene was loaded with segmentation attribute ORIGINAL_IMAGE_INDEX not None.\\n\\n activeBrowserNode = slicer.modules.sequences.toolBar().activeBrowserNode()\\n if activeBrowserNode == inputBrowserNode:\\n original_index_str = None\\n\\n if original_index_str is None or original_index_str == \\\"None\\\" or original_index_str == \\\"\\\": # new segmentation\\n inputImageIndex = inputBrowserNode.GetSelectedItemNumber()\\n selectedSegmentation.SetAttribute(self.ORIGINAL_IMAGE_INDEX, str(inputImageIndex))\\n self.logic.captureSlice(outputBrowserNode, selectedSegmentation, inputImage)\\n self.logic.eraseCurrentSegmentation(selectedSegmentation)\\n selectedSegmentation.SetAttribute(self.ORIGINAL_IMAGE_INDEX, \\\"None\\\")\\n currentItemNum = inputBrowserNode.GetSelectedItemNumber()\\n newItemNum = inputBrowserNode.SelectNextItem(numSkip)\\n else: # overwrite segmentation\\n self.logic.captureSlice(outputBrowserNode, selectedSegmentation, inputImage)\\n currentItemNum = outputBrowserNode.GetSelectedItemNumber()\\n newItemNum = outputBrowserNode.SelectNextItem()\\n\\n # Check if sequence browser wrapped around. If yes, pop up message box to ask if user wants to continue.\\n\\n if newItemNum < currentItemNum:\\n logging.debug(\\\"Sequence wrapped around!\\\")\\n\\n msgBox = qt.QMessageBox()\\n msgBox.setText(\\\"Sequence wrapped around!\\\")\\n msgBox.setInformativeText(\\\"Please save the scene before closing the application!\\\")\\n msgBox.setStandardButtons(qt.QMessageBox.Ok)\\n msgBox.setDefaultButton(qt.QMessageBox.Ok)\\n msgBox.exec_()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"111694a2830db1e8913b2f4c4e782ecf\",\n \"score\": \"0.4589033\",\n \"text\": \"def show_frameFish(self, cont):\\n frame = self.frames[cont]\\n camera.start_preview(fullscreen=False, window=(0,appheight/4,appwidth,appheight/2)) #this line starts the preview. \\n frame.tkraise()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74cdd61fc0cfaef06c4e9bf753dbd3fb\",\n \"score\": \"0.45877063\",\n \"text\": \"def image_analysis(self): # connected to image analysis button (btn_images)\\n\\n self.start = int(self.meanStartEdit.text())\\n\\n if self.roi == None and self.JPG == False :\\n self.mean = self.data[self.start,:,:]\\n self.is_image = True\\n else:\\n self.ROI_no_mean_images()\\n\\n # change the name and color of the buttos to notice the way you choose\\n self.btn7.setText(\\\"Export Intensities from frame={}\\\".format(self.start))\\n self.btn7.setStyleSheet(\\n \\\"QPushButton { background-color: rgb(200, 200, 10); }\\\")\\n self.meanEndEdit.setStyleSheet(\\\" background-color: red; \\\")\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994198,"cells":{"query_id":{"kind":"string","value":"b98d9695c5f2e060e667f6137c06d0dd"},"query":{"kind":"string","value":"isFinished(ALInterpolationArticular3D self, float const & time) > bool"},"positive_passages":{"kind":"list like","value":[{"docid":"0e7f4cffb3df33943aaf7f315f84553f","score":"0.875447","text":"def isFinished(self, time):\n return _almathinternal.ALInterpolationArticular3D_isFinished(self, time)","title":""}],"string":"[\n {\n \"docid\": \"0e7f4cffb3df33943aaf7f315f84553f\",\n \"score\": \"0.875447\",\n \"text\": \"def isFinished(self, time):\\n return _almathinternal.ALInterpolationArticular3D_isFinished(self, time)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"84e91adf599c004b342da77fe818c9b4","score":"0.78961587","text":"def isFinished(self, time):\n return _almathinternal.ALInterpolationArticular_isFinished(self, time)","title":""},{"docid":"839e708c13e4da3ccd16b2b6bb407622","score":"0.70739007","text":"def isFinished(self):\n return _almathinternal.InterpolationTrapezoidSmooth_isFinished(self)","title":""},{"docid":"c81835376ae7064583096ff8eea58b85","score":"0.6948943","text":"def isFinished(self):\n return _almathinternal.ALInterpolationBezier_isFinished(self)","title":""},{"docid":"d20df1f69b909c6179a5881cae79fb3f","score":"0.64873034","text":"def isFinished(self, pTime):\n return _almathinternal.ALInterpolationQuinticSpline_isFinished(self, pTime)","title":""},{"docid":"feb20b50b752124b26edbb62692ff5ff","score":"0.61669093","text":"def has_finished(self, action):\n\n torso_height, torso_ang = self.sim.data.qpos[1:3]\n return not (torso_height > 0.4 and torso_height < 2.1 and\n torso_ang > -1.0 and torso_ang < 1.0)","title":""},{"docid":"ba6b43f0a6f3fb8584f208d8f7a0cd4f","score":"0.5836551","text":"def _isFinished(self):\n raise NotImplementedError","title":""},{"docid":"59341ab71110ca9bd2e32442f6c74738","score":"0.5825531","text":"def isCompleted(self, dataLocation):\n return not self.isDefaulted(dataLocation) \\\n and dataLocation.isSurface()","title":""},{"docid":"7b93fe7603f0d737e0a48f7805c548ae","score":"0.58040255","text":"def is_finished():\n pass","title":""},{"docid":"81f46c5b9214b5586d9983d6ddaeb5eb","score":"0.580163","text":"def check_done(self, new_state, init_state, time_start=None):\n current_time = time.time()\n # + 0.1 because there are some noises\n if((new_state[1].position.z + 0.1) < init_state[1].position.z):\n return True\n elif (time_start is not None) and (current_time - time_start) > 180:\n print('time is up!(120s)')\n return True\n else:\n return False","title":""},{"docid":"94b40fc32919fbee12abc459df616329","score":"0.5667639","text":"def isFinished(self):\n return (\n self.isPrepared() and\n self.isBaked() and\n self.isCut() and\n self.isBoxed())","title":""},{"docid":"4a05c61bf81935f416de6e980b405709","score":"0.56356305","text":"def _done(observation: tuple) -> bool:\n angle, dist = observation[0], observation[1]\n return True if abs(angle) > np.deg2rad(15) or dist > 0.5 else False","title":""},{"docid":"2258f98a1dda20c5adfdb9269a6fc637","score":"0.56304795","text":"def done(self, env):\n rot_quat = env.robot.GetBaseOrientation()\n rot_mat = env.pybullet_client.getMatrixFromQuaternion(rot_quat)\n return rot_mat[-1] < 0.85","title":""},{"docid":"8f4b3ac1a4206cfc635bc45f27dd5e20","score":"0.5608561","text":"def isFinished(self): #$NON-NLS-1$\r","title":""},{"docid":"542bb15ff0bee984ca51f210edb13e04","score":"0.558418","text":"def isFinished(self):\n # Get encoder count\n current = self.robot.drivetrain.get_encoder_value()\n # If abs(target - current) < threshold then return true\n return math.fabs(self._target_position - current) <= self._encoder_threshold or self.isTimedOut()","title":""},{"docid":"b80316b148531a32a74b554a499f25f9","score":"0.5571571","text":"def IsDone(self, *args):\n return _IntRes2d.IntRes2d_Intersection_IsDone(self, *args)","title":""},{"docid":"991b39cedd44525ab40c6ba905f3cd07","score":"0.55124205","text":"def done(self):\n return self.x == 31 and self.y == 39","title":""},{"docid":"80e66cd671b593917b55c7fe453f4eb9","score":"0.5493186","text":"def isFinished(self):\n for x in range(8):\n for y in range(8):\n if self.__isEmpty([x, y]):\n return False\n return True","title":""},{"docid":"3acc45ae1ca395eaaac92fa0d0d3169d","score":"0.54439694","text":"def _is_done(self, observations):\n current_eff_pos = observations['observation'][:3].tolist()\n\n done = self.check_if_done(\n self.movement_result, self.desired_position, current_eff_pos, self.threshold_error)\n return done","title":""},{"docid":"a61eee27b90ab2b6ceaf5b59fbf245dd","score":"0.54288363","text":"def _check_cur_action_animaion_has_finished(self):\n return self.cur_action.finished()","title":""},{"docid":"e73a6a4473b02d0544086279ca5898eb","score":"0.54174805","text":"def isFinished(self):\n\t\treturn False","title":""},{"docid":"e4861cdd7e064516969140b7b1b1cb7b","score":"0.540876","text":"def _is_done(self):\n u, p = self.state\n logger.debug(\"u (actual): {}\".format(u))\n\n if u > p + self.p_diff_threshold:\n return True\n else:\n return False","title":""},{"docid":"792972063b7b9d15142b93662ab4d0d6","score":"0.5397395","text":"def is_done(self):\n if 1 - self.fidelity() <= self.infidelity_threshold:\n return True\n return self.t >= self.T","title":""},{"docid":"269fa8f82b4906b4704987c4536f51c9","score":"0.53804135","text":"def is_finished(self) -> bool:\n\n for i in range(self.rows):\n for j in range(self.cols):\n if self.boxes[i][j].value == 0:\n return False\n return True","title":""},{"docid":"180430761a5183adb1fc3fb89457a962","score":"0.5372404","text":"def finished(self):\n return self.result is not None","title":""},{"docid":"035773c9f3767bb25519de0cb2e076c5","score":"0.53476006","text":"def is_finished(self):\n return self.time_finished is not None","title":""},{"docid":"d89debd0a7e0730fe3f6e61bf21f198f","score":"0.5346567","text":"def is_done(self, p):\n return (self.max_steps and self.steps >= self.max_steps) \\\n or p.x > self.containing_box.qx","title":""},{"docid":"b260f27737a3989d6fd88ed9176f18db","score":"0.5346386","text":"def is_orbit_complete(self):\n return self.tsince > self.tend","title":""},{"docid":"cf4563e3b99460b4f5a2d1f4fc90ecd0","score":"0.5328927","text":"def setIsFinished(self, pIsFinished):\n return _almathinternal.ALInterpolationArticular_setIsFinished(self, pIsFinished)","title":""},{"docid":"c2fd119e9da452f8701147d9a04d863f","score":"0.53274566","text":"def is_finished(self):\n return len(self.get_next_game_states()) == 0","title":""},{"docid":"ad6c5948c8f6828d0d782bfe6a51c81b","score":"0.5311677","text":"def animationComplete(self) -> bool:\n return all(self.animation_status)","title":""},{"docid":"110dd01215a9ce776b8c06f958d4e768","score":"0.5289392","text":"def is_completed(self):\n return self.sample_count > self.max_sample","title":""},{"docid":"110dd01215a9ce776b8c06f958d4e768","score":"0.5289392","text":"def is_completed(self):\n return self.sample_count > self.max_sample","title":""},{"docid":"150d5a3a4bdc611990b44473436658f8","score":"0.52757144","text":"def isFinished(self):\n return False","title":""},{"docid":"09112a194b439191331ca2803f47a19f","score":"0.52754563","text":"def has_finished(grid):\n\n if not get_cell_count(grid) and grid.generation > 0:\n return True\n\n return False","title":""},{"docid":"2eaaab252bf5498d7d04898956249044","score":"0.5274718","text":"def is_finished(task, sent, step, unfin_idx):\n assert len(finalized[task][sent]) <= beam_size\n if len(finalized[task][sent]) == beam_size or step == max_len:\n return True\n return False","title":""},{"docid":"1f5cc80b15767df21f6f1ef6e9a39d21","score":"0.52622485","text":"def isDone(*args, **kwargs):\n \n pass","title":""},{"docid":"1f5cc80b15767df21f6f1ef6e9a39d21","score":"0.52622485","text":"def isDone(*args, **kwargs):\n \n pass","title":""},{"docid":"1f5cc80b15767df21f6f1ef6e9a39d21","score":"0.52622485","text":"def isDone(*args, **kwargs):\n \n pass","title":""},{"docid":"1f5cc80b15767df21f6f1ef6e9a39d21","score":"0.52622485","text":"def isDone(*args, **kwargs):\n \n pass","title":""},{"docid":"1f5cc80b15767df21f6f1ef6e9a39d21","score":"0.52622485","text":"def isDone(*args, **kwargs):\n \n pass","title":""},{"docid":"1f5cc80b15767df21f6f1ef6e9a39d21","score":"0.52622485","text":"def isDone(*args, **kwargs):\n \n pass","title":""},{"docid":"1f5cc80b15767df21f6f1ef6e9a39d21","score":"0.52622485","text":"def isDone(*args, **kwargs):\n \n pass","title":""},{"docid":"1f5cc80b15767df21f6f1ef6e9a39d21","score":"0.52622485","text":"def isDone(*args, **kwargs):\n \n pass","title":""},{"docid":"1f5cc80b15767df21f6f1ef6e9a39d21","score":"0.52622485","text":"def isDone(*args, **kwargs):\n \n pass","title":""},{"docid":"1f5cc80b15767df21f6f1ef6e9a39d21","score":"0.52599967","text":"def isDone(*args, **kwargs):\n \n pass","title":""},{"docid":"1f5cc80b15767df21f6f1ef6e9a39d21","score":"0.52599967","text":"def isDone(*args, **kwargs):\n \n pass","title":""},{"docid":"1f5cc80b15767df21f6f1ef6e9a39d21","score":"0.52599967","text":"def isDone(*args, **kwargs):\n \n pass","title":""},{"docid":"8968d3c8f74f740bb70853f1b32bb05b","score":"0.5257978","text":"def has_finished(self):\n return self.itr >= self.max_itr","title":""},{"docid":"a7e76bd7b14bc407dc955fa17f461734","score":"0.52423304","text":"def is_wave_finished(self):\n return self.curr_wave_time > max(self.waves[self.curr_wave].keys())","title":""},{"docid":"4340ab024705eff390932c9f08a5c3da","score":"0.5227636","text":"def is_finished(self):\n return (self._caccount + self._sum_interest) >= 0.","title":""},{"docid":"3ae8eb136f127873c48930397de5c373","score":"0.52103645","text":"def interval_completed(self) -> bool:\n return self.interval_frames >= self.sample_rate","title":""},{"docid":"9483d7fe9eee38618a583a3dc63df9a2","score":"0.520724","text":"def wait_interpolation(self, controller_type=None, timeout=0):\n super(PR2TMPRobotInterface, self).wait_interpolation(\n controller_type, timeout)\n while not rospy.is_shutdown():\n self.update_robot_state(wait_until_update=True)\n if all(map(lambda j: j.name in self.ignore_joint_list or\n abs(j.joint_velocity) < 0.05\n if isinstance(j, RotationalJoint) else\n abs(j.joint_velocity) < 0.001,\n self.robot.joint_list)):\n break\n # TODO(Fix return value)\n return True","title":""},{"docid":"8f5a05162762fc0e9df8eb6b4d1d51e2","score":"0.5184158","text":"def finished(self):\n return self.board == self.goal","title":""},{"docid":"d28a2e7bba4a9c8630c783a3a0986f9e","score":"0.51728487","text":"def finished(self, achieved_goals, queue):\n return achieved_goals == self.goals or not queue","title":""},{"docid":"19593142fc569ecf50a0838c8e118ae3","score":"0.5168116","text":"def is_finished(self):\n\n if self._total_time > self._global_time_limit:\n print(\n \"Exceeded global time limit {} / {}\".format(\n self._total_time, self._global_time_limit))\n return True\n\n for t in self._trials:\n if t.status in [Trial.PENDING, Trial.RUNNING, Trial.PAUSED]:\n return False\n return True","title":""},{"docid":"5082d3087eb568d2b82b222ad35b28ad","score":"0.51663554","text":"def trial_finished(self):\n return self.ind+1>=len(self.words[self.word_index])","title":""},{"docid":"26b1fb105b41c723e193b048e3ea79d7","score":"0.51536345","text":"def finished_execution(self) -> bool:\n return self._transition_state == self._FINISHED or self._transition_state == self._ABORTED","title":""},{"docid":"344d22362aa070e43059344021d9bdbb","score":"0.51531","text":"def is_finished(self):\n for i in range(self.rows):\n for j in range(self.cols):\n if self.cells[i][j].value == 0:\n return False\n return True","title":""},{"docid":"baae092925ecb85aeca50253e71431cc","score":"0.51470554","text":"def _is_finished(self) -> bool:\n return self._status[0] or self._status[1]","title":""},{"docid":"1d58a1b8788c8381ef6220c9c0208769","score":"0.5141703","text":"def is_finished(self):\n return self.finished","title":""},{"docid":"57a2b2d52deb773ece559672c80736c6","score":"0.51412666","text":"def init(self, pTimeInit, pTimeFinal, pPointInit, pPointFinal, pVelocityInit, pVelocityFinal, pPeriod):\n return _almathinternal.ALInterpolationArticular3D_init(self, pTimeInit, pTimeFinal, pPointInit, pPointFinal, pVelocityInit, pVelocityFinal, pPeriod)","title":""},{"docid":"5f4a56713ac6bf1710ce412e22c6ce70","score":"0.51185846","text":"def setIsFinished(self, pIsFinished):\n return _almathinternal.ALInterpolationBezier_setIsFinished(self, pIsFinished)","title":""},{"docid":"f80f2d934cb54daaf6a95d02999a37d1","score":"0.5117361","text":"def keyIsFinished(mashDic, key):\n return scipy.sum([mashDic[key][i][1] for i in range(Z)]) == 1","title":""},{"docid":"20e37615526f73a32267acfcc99e7909","score":"0.5088959","text":"def isFinished(self):\n return self._finished","title":""},{"docid":"5cfb3762f39b5fcc4be2d388efbdc67b","score":"0.50729936","text":"def _isFinished(self):\n return not self.currentPlayer.actions or any(self._getScores())","title":""},{"docid":"ebd22f054e148f56e0a28e6d27e79faf","score":"0.50692016","text":"def _is_done(self, observations):\n raise NotImplementedError()","title":""},{"docid":"ebd22f054e148f56e0a28e6d27e79faf","score":"0.50692016","text":"def _is_done(self, observations):\n raise NotImplementedError()","title":""},{"docid":"ebd22f054e148f56e0a28e6d27e79faf","score":"0.50692016","text":"def _is_done(self, observations):\n raise NotImplementedError()","title":""},{"docid":"ebd22f054e148f56e0a28e6d27e79faf","score":"0.50692016","text":"def _is_done(self, observations):\n raise NotImplementedError()","title":""},{"docid":"50e320677d165ef09cb754322e976fa8","score":"0.50626665","text":"def is_finished(self):\n return os.path.exists(self.out_filename)","title":""},{"docid":"2b7882a11ddda5b4c105893a794ccf7f","score":"0.5059459","text":"def SoGLMultiTextureImageElement_hasTransparency(state: 'SoState') -> \"SbBool\":\n return _coin.SoGLMultiTextureImageElement_hasTransparency(state)","title":""},{"docid":"9b1bfd11462c7527f497b9cb9f335aa8","score":"0.5055257","text":"def is_dead_end(my_zoom):\n count = 0\n for value in my_zoom.values():\n count += 1 if not value else 0\n\n return True if count == 3 else False","title":""},{"docid":"a4e31a9deac97594465439c3c7b6e137","score":"0.5046841","text":"def SoMultiTextureImageElement_containsTransparency(state: 'SoState') -> \"SbBool\":\n return _coin.SoMultiTextureImageElement_containsTransparency(state)","title":""},{"docid":"79a553d8561206ea2245d344d384bd6b","score":"0.504325","text":"def abs_done(time_remain):\n global cex_abs_depth, abs_depth, abs_depth_prev, time_abs_prev, time_abs\n## print 'checking if abs has enough time to next cex'\n frames_to_next_cex = cex_abs_depth - abs_depth\n div = time_abs - time_abs_prev\n div = max(.1,div)\n frames_per_sec = (abs_depth - abs_depth_prev)/div\n if frames_per_sec <= 0:\n return False #something wrong \n## print 'frames_per_sec = %0.2f, frames_to_next_cex = %d, time remaining = %0.2f'%(frames_per_sec, frames_to_next_cex, time_remain)\n if frames_to_next_cex > 0.2*(frames_per_sec * time_remain): #later frames will take longer so factor of 5 here\n print 'not enough abs time to next cex'\n return True\n return False","title":""},{"docid":"c327d649608de635bc0d373f2a6a0f75","score":"0.50367916","text":"def hasTransparency(state: 'SoState') -> \"SbBool\":\n return _coin.SoGLMultiTextureImageElement_hasTransparency(state)","title":""},{"docid":"81e39d786e4aaa570f04ca63c662b086","score":"0.503623","text":"def is_finished(self):\n return self.text and self.screenshot","title":""},{"docid":"37d09cf6281a0229f456def11aa7c2bd","score":"0.5015781","text":"def is_finished(self):\n return self.__is_finished","title":""},{"docid":"e812f3106f95e5b55fcf9f009eda4aaf","score":"0.5014571","text":"def prev_has_finished(self):\n return self.itr_prev >= self.max_itr","title":""},{"docid":"aefeafde97e5afc93f6b197c838e1c9e","score":"0.5008804","text":"def finished(self) -> bool:\n return self.__finished","title":""},{"docid":"8dbff4c45a3437eae89c7949ee2dfb66","score":"0.5008314","text":"def is_finished(self):\n return self._finished","title":""},{"docid":"db44817fec881b74128300bcbb488ee3","score":"0.50015867","text":"def is_delaunay(self, tri):\n circumcircle=tri.circumcircle()\n count=0\n for point in (tri.p0,tri.p1,tri.p2):\n if circumcircle.covers(point):\n #print 'yes'\n count+=1\n if count==3:\n #print count\n return True\n else:\n return False\n #Your implementation here","title":""},{"docid":"ddac60ff121d562006777ac79626bad9","score":"0.49990314","text":"def is_finished(\n num_gameweeks,\n wildcard=False,\n free_hit=False,\n triple_captain=False,\n bench_boost=False,\n):\n final_expected_num = count_expected_outputs(\n 0, num_gameweeks, wildcard, free_hit, triple_captain, bench_boost\n )\n # count the json files in the output dir\n json_count = len(os.listdir(OUTPUT_DIR))\n if json_count == final_expected_num:\n return True\n return False","title":""},{"docid":"a0ad633c2e36e2e44ede103db2d01a0f","score":"0.49938917","text":"def _is_done(self):\n # Is the sim done ? \n return self.current_sim_datetime > self.end_datetime","title":""},{"docid":"33d3edc5d430a3a4408e071d79f9ae8d","score":"0.49917263","text":"def containsTransparency(state: 'SoState') -> \"SbBool\":\n return _coin.SoMultiTextureImageElement_containsTransparency(state)","title":""},{"docid":"daa388333721f0b73ad52b60502f3f9a","score":"0.4971992","text":"def _test_f_3(x, y, z):\n time.sleep(0.25) # test the timing function\n return x + y + z","title":""},{"docid":"381a406c1888c27082016d4467de8817","score":"0.49694067","text":"def getFinalTime(self):\n return _almathinternal.ALInterpolationTypeIV3D_getFinalTime(self)","title":""},{"docid":"92b0a07ac91248befd14db167645b16d","score":"0.4964097","text":"def _check_if_done(self):\n return self._check_if_all_boxes_on_target() or self._check_if_maxsteps()","title":""},{"docid":"4ada6e7aa520255332cc8e94d89c3bee","score":"0.49635655","text":"def episode_done(self):\n if not self.hit_is_complete:\n return False\n else:\n return True","title":""},{"docid":"3960a98654cc2ab604ce97729686f95e","score":"0.49438494","text":"def is_final_state(x):\n p, s = x\n if abs(p) > 1 or abs(s) > 3:\n return True\n else:\n return False","title":""},{"docid":"b2bc415fe4e0fa7c17db8e36d5bde7c1","score":"0.49432087","text":"def is_finished(\n self,\n step: int,\n unfin_idx: int,\n max_len: int,\n finalized_sent_len: int,\n beam_size: int,\n ):\n assert finalized_sent_len <= beam_size\n if finalized_sent_len == beam_size or step == max_len:\n return True\n return False","title":""},{"docid":"12cfd22bbf08672e94a9bba507cfa8ad","score":"0.49417865","text":"def is_end_state(self) -> bool:\n return len(self.finished) == self.count","title":""},{"docid":"ac3eb5ae2c25a3ef82f4207baa1d5c98","score":"0.49348676","text":"def is_finished(self, *additional, **kwargs):\n return all((sh.is_finished() for sh in list(additional) + self if hasattr(sh, 'is_finished')))","title":""},{"docid":"e84f93ef1b9f20f20c48faa1b8cfecc3","score":"0.492795","text":"def acceleration3_d_available(self):\n ret = self._get_attr(\"acceleration3DAvailable\")\n return ret","title":""},{"docid":"a3e2a85db991a6815477ee79035b75ec","score":"0.49094743","text":"def completed(self):\n return self.iteration >= len(self.batches)","title":""},{"docid":"07c17ac655fdcc270904296375f51832","score":"0.4908603","text":"def goal_reached(self, world_state, grid_world):\r\n nr_ticks = grid_world.current_nr_ticks\r\n if self.max_nr_ticks == np.inf or self.max_nr_ticks <= 0:\r\n self.is_done = False\r\n else:\r\n if nr_ticks >= self.max_nr_ticks:\r\n self.is_done = True\r\n else:\r\n self.is_done = False\r\n return self.is_done","title":""},{"docid":"3a62794063561fdc1db854be2df2a0b0","score":"0.4907297","text":"def is_3D(self) -> bool:\n return len(self._vol_shape_xyz) == 3 and self._vol_shape_xyz[-1] > 1","title":""},{"docid":"e88950c4b150e50eb9a2c24417cc3f3d","score":"0.4890563","text":"def is_finished(self):\n is_scored_goals = self.home_team_goal > 0 or self.away_team_goal > 0\n if (is_scored_goals and util.compare_time_to_now(self.date, 1)) \\\n or (not is_scored_goals and util.compare_time_to_now(self.date, 100)):\n return True\n\n if util.is_None(self.goal) \\\n and util.is_None(self.shoton) \\\n and util.is_None(self.shotoff) \\\n and util.is_None(self.foulcommit) \\\n and util.is_None(self.card) \\\n and util.is_None(self.cross) \\\n and util.is_None(self.corner) \\\n and util.is_None(self.possession):\n return False\n return True","title":""},{"docid":"097f46caaab32cc253ebb00ea141aeb0","score":"0.48850796","text":"def isFinished(self):\n # this function should be deterministic and without side effects\n if self.env.n_actions >= self.max_number_of_actions_per_session:\n return True\n elif self.env.state.click != Click.NOT_CLICKED:\n # click ends task\n return True\n elif self.env.state.quit == Quit.HAS_QUIT:\n # quit ends task\n return True\n return False","title":""},{"docid":"e07d93ab3b11d2761de5cedf60145fff","score":"0.48749816","text":"def is_dataset_finished(self):\n n_completed = self.dataset.processed\n\n # Dataset has reached max pipeline depth\n if self.dataset.depth >= self.max_pipeline_depth:\n LOGGER.info('Dataset {} has reached max pipeline depth!'.format(self.dataset))\n return True\n\n # No budget for dataset\n if n_completed >= self.dataset.budget:\n LOGGER.info('Algorithm budget for dataset {} has run out!'.format(self.dataset))\n return True\n\n return False","title":""},{"docid":"faea9f281a5b93922708805a03825f1c","score":"0.48738745","text":"def _is_animated(self, input):\r\n return self.animated_gif_optimiser._is_acceptable_image(input)","title":""}],"string":"[\n {\n \"docid\": \"84e91adf599c004b342da77fe818c9b4\",\n \"score\": \"0.78961587\",\n \"text\": \"def isFinished(self, time):\\n return _almathinternal.ALInterpolationArticular_isFinished(self, time)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"839e708c13e4da3ccd16b2b6bb407622\",\n \"score\": \"0.70739007\",\n \"text\": \"def isFinished(self):\\n return _almathinternal.InterpolationTrapezoidSmooth_isFinished(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c81835376ae7064583096ff8eea58b85\",\n \"score\": \"0.6948943\",\n \"text\": \"def isFinished(self):\\n return _almathinternal.ALInterpolationBezier_isFinished(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d20df1f69b909c6179a5881cae79fb3f\",\n \"score\": \"0.64873034\",\n \"text\": \"def isFinished(self, pTime):\\n return _almathinternal.ALInterpolationQuinticSpline_isFinished(self, pTime)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"feb20b50b752124b26edbb62692ff5ff\",\n \"score\": \"0.61669093\",\n \"text\": \"def has_finished(self, action):\\n\\n torso_height, torso_ang = self.sim.data.qpos[1:3]\\n return not (torso_height > 0.4 and torso_height < 2.1 and\\n torso_ang > -1.0 and torso_ang < 1.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba6b43f0a6f3fb8584f208d8f7a0cd4f\",\n \"score\": \"0.5836551\",\n \"text\": \"def _isFinished(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59341ab71110ca9bd2e32442f6c74738\",\n \"score\": \"0.5825531\",\n \"text\": \"def isCompleted(self, dataLocation):\\n return not self.isDefaulted(dataLocation) \\\\\\n and dataLocation.isSurface()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b93fe7603f0d737e0a48f7805c548ae\",\n \"score\": \"0.58040255\",\n \"text\": \"def is_finished():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81f46c5b9214b5586d9983d6ddaeb5eb\",\n \"score\": \"0.580163\",\n \"text\": \"def check_done(self, new_state, init_state, time_start=None):\\n current_time = time.time()\\n # + 0.1 because there are some noises\\n if((new_state[1].position.z + 0.1) < init_state[1].position.z):\\n return True\\n elif (time_start is not None) and (current_time - time_start) > 180:\\n print('time is up!(120s)')\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94b40fc32919fbee12abc459df616329\",\n \"score\": \"0.5667639\",\n \"text\": \"def isFinished(self):\\n return (\\n self.isPrepared() and\\n self.isBaked() and\\n self.isCut() and\\n self.isBoxed())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a05c61bf81935f416de6e980b405709\",\n \"score\": \"0.56356305\",\n \"text\": \"def _done(observation: tuple) -> bool:\\n angle, dist = observation[0], observation[1]\\n return True if abs(angle) > np.deg2rad(15) or dist > 0.5 else False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2258f98a1dda20c5adfdb9269a6fc637\",\n \"score\": \"0.56304795\",\n \"text\": \"def done(self, env):\\n rot_quat = env.robot.GetBaseOrientation()\\n rot_mat = env.pybullet_client.getMatrixFromQuaternion(rot_quat)\\n return rot_mat[-1] < 0.85\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f4b3ac1a4206cfc635bc45f27dd5e20\",\n \"score\": \"0.5608561\",\n \"text\": \"def isFinished(self): #$NON-NLS-1$\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"542bb15ff0bee984ca51f210edb13e04\",\n \"score\": \"0.558418\",\n \"text\": \"def isFinished(self):\\n # Get encoder count\\n current = self.robot.drivetrain.get_encoder_value()\\n # If abs(target - current) < threshold then return true\\n return math.fabs(self._target_position - current) <= self._encoder_threshold or self.isTimedOut()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b80316b148531a32a74b554a499f25f9\",\n \"score\": \"0.5571571\",\n \"text\": \"def IsDone(self, *args):\\n return _IntRes2d.IntRes2d_Intersection_IsDone(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"991b39cedd44525ab40c6ba905f3cd07\",\n \"score\": \"0.55124205\",\n \"text\": \"def done(self):\\n return self.x == 31 and self.y == 39\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80e66cd671b593917b55c7fe453f4eb9\",\n \"score\": \"0.5493186\",\n \"text\": \"def isFinished(self):\\n for x in range(8):\\n for y in range(8):\\n if self.__isEmpty([x, y]):\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3acc45ae1ca395eaaac92fa0d0d3169d\",\n \"score\": \"0.54439694\",\n \"text\": \"def _is_done(self, observations):\\n current_eff_pos = observations['observation'][:3].tolist()\\n\\n done = self.check_if_done(\\n self.movement_result, self.desired_position, current_eff_pos, self.threshold_error)\\n return done\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a61eee27b90ab2b6ceaf5b59fbf245dd\",\n \"score\": \"0.54288363\",\n \"text\": \"def _check_cur_action_animaion_has_finished(self):\\n return self.cur_action.finished()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e73a6a4473b02d0544086279ca5898eb\",\n \"score\": \"0.54174805\",\n \"text\": \"def isFinished(self):\\n\\t\\treturn False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4861cdd7e064516969140b7b1b1cb7b\",\n \"score\": \"0.540876\",\n \"text\": \"def _is_done(self):\\n u, p = self.state\\n logger.debug(\\\"u (actual): {}\\\".format(u))\\n\\n if u > p + self.p_diff_threshold:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"792972063b7b9d15142b93662ab4d0d6\",\n \"score\": \"0.5397395\",\n \"text\": \"def is_done(self):\\n if 1 - self.fidelity() <= self.infidelity_threshold:\\n return True\\n return self.t >= self.T\",\n \"title\": \"\"\n },\n {\n \"docid\": \"269fa8f82b4906b4704987c4536f51c9\",\n \"score\": \"0.53804135\",\n \"text\": \"def is_finished(self) -> bool:\\n\\n for i in range(self.rows):\\n for j in range(self.cols):\\n if self.boxes[i][j].value == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"180430761a5183adb1fc3fb89457a962\",\n \"score\": \"0.5372404\",\n \"text\": \"def finished(self):\\n return self.result is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"035773c9f3767bb25519de0cb2e076c5\",\n \"score\": \"0.53476006\",\n \"text\": \"def is_finished(self):\\n return self.time_finished is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d89debd0a7e0730fe3f6e61bf21f198f\",\n \"score\": \"0.5346567\",\n \"text\": \"def is_done(self, p):\\n return (self.max_steps and self.steps >= self.max_steps) \\\\\\n or p.x > self.containing_box.qx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b260f27737a3989d6fd88ed9176f18db\",\n \"score\": \"0.5346386\",\n \"text\": \"def is_orbit_complete(self):\\n return self.tsince > self.tend\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf4563e3b99460b4f5a2d1f4fc90ecd0\",\n \"score\": \"0.5328927\",\n \"text\": \"def setIsFinished(self, pIsFinished):\\n return _almathinternal.ALInterpolationArticular_setIsFinished(self, pIsFinished)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2fd119e9da452f8701147d9a04d863f\",\n \"score\": \"0.53274566\",\n \"text\": \"def is_finished(self):\\n return len(self.get_next_game_states()) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad6c5948c8f6828d0d782bfe6a51c81b\",\n \"score\": \"0.5311677\",\n \"text\": \"def animationComplete(self) -> bool:\\n return all(self.animation_status)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"110dd01215a9ce776b8c06f958d4e768\",\n \"score\": \"0.5289392\",\n \"text\": \"def is_completed(self):\\n return self.sample_count > self.max_sample\",\n \"title\": \"\"\n },\n {\n \"docid\": \"110dd01215a9ce776b8c06f958d4e768\",\n \"score\": \"0.5289392\",\n \"text\": \"def is_completed(self):\\n return self.sample_count > self.max_sample\",\n \"title\": \"\"\n },\n {\n \"docid\": \"150d5a3a4bdc611990b44473436658f8\",\n \"score\": \"0.52757144\",\n \"text\": \"def isFinished(self):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09112a194b439191331ca2803f47a19f\",\n \"score\": \"0.52754563\",\n \"text\": \"def has_finished(grid):\\n\\n if not get_cell_count(grid) and grid.generation > 0:\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2eaaab252bf5498d7d04898956249044\",\n \"score\": \"0.5274718\",\n \"text\": \"def is_finished(task, sent, step, unfin_idx):\\n assert len(finalized[task][sent]) <= beam_size\\n if len(finalized[task][sent]) == beam_size or step == max_len:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f5cc80b15767df21f6f1ef6e9a39d21\",\n \"score\": \"0.52622485\",\n \"text\": \"def isDone(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f5cc80b15767df21f6f1ef6e9a39d21\",\n \"score\": \"0.52622485\",\n \"text\": \"def isDone(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f5cc80b15767df21f6f1ef6e9a39d21\",\n \"score\": \"0.52622485\",\n \"text\": \"def isDone(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f5cc80b15767df21f6f1ef6e9a39d21\",\n \"score\": \"0.52622485\",\n \"text\": \"def isDone(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f5cc80b15767df21f6f1ef6e9a39d21\",\n \"score\": \"0.52622485\",\n \"text\": \"def isDone(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f5cc80b15767df21f6f1ef6e9a39d21\",\n \"score\": \"0.52622485\",\n \"text\": \"def isDone(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f5cc80b15767df21f6f1ef6e9a39d21\",\n \"score\": \"0.52622485\",\n \"text\": \"def isDone(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f5cc80b15767df21f6f1ef6e9a39d21\",\n \"score\": \"0.52622485\",\n \"text\": \"def isDone(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f5cc80b15767df21f6f1ef6e9a39d21\",\n \"score\": \"0.52622485\",\n \"text\": \"def isDone(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f5cc80b15767df21f6f1ef6e9a39d21\",\n \"score\": \"0.52599967\",\n \"text\": \"def isDone(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f5cc80b15767df21f6f1ef6e9a39d21\",\n \"score\": \"0.52599967\",\n \"text\": \"def isDone(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f5cc80b15767df21f6f1ef6e9a39d21\",\n \"score\": \"0.52599967\",\n \"text\": \"def isDone(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8968d3c8f74f740bb70853f1b32bb05b\",\n \"score\": \"0.5257978\",\n \"text\": \"def has_finished(self):\\n return self.itr >= self.max_itr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7e76bd7b14bc407dc955fa17f461734\",\n \"score\": \"0.52423304\",\n \"text\": \"def is_wave_finished(self):\\n return self.curr_wave_time > max(self.waves[self.curr_wave].keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4340ab024705eff390932c9f08a5c3da\",\n \"score\": \"0.5227636\",\n \"text\": \"def is_finished(self):\\n return (self._caccount + self._sum_interest) >= 0.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ae8eb136f127873c48930397de5c373\",\n \"score\": \"0.52103645\",\n \"text\": \"def interval_completed(self) -> bool:\\n return self.interval_frames >= self.sample_rate\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9483d7fe9eee38618a583a3dc63df9a2\",\n \"score\": \"0.520724\",\n \"text\": \"def wait_interpolation(self, controller_type=None, timeout=0):\\n super(PR2TMPRobotInterface, self).wait_interpolation(\\n controller_type, timeout)\\n while not rospy.is_shutdown():\\n self.update_robot_state(wait_until_update=True)\\n if all(map(lambda j: j.name in self.ignore_joint_list or\\n abs(j.joint_velocity) < 0.05\\n if isinstance(j, RotationalJoint) else\\n abs(j.joint_velocity) < 0.001,\\n self.robot.joint_list)):\\n break\\n # TODO(Fix return value)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f5a05162762fc0e9df8eb6b4d1d51e2\",\n \"score\": \"0.5184158\",\n \"text\": \"def finished(self):\\n return self.board == self.goal\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d28a2e7bba4a9c8630c783a3a0986f9e\",\n \"score\": \"0.51728487\",\n \"text\": \"def finished(self, achieved_goals, queue):\\n return achieved_goals == self.goals or not queue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19593142fc569ecf50a0838c8e118ae3\",\n \"score\": \"0.5168116\",\n \"text\": \"def is_finished(self):\\n\\n if self._total_time > self._global_time_limit:\\n print(\\n \\\"Exceeded global time limit {} / {}\\\".format(\\n self._total_time, self._global_time_limit))\\n return True\\n\\n for t in self._trials:\\n if t.status in [Trial.PENDING, Trial.RUNNING, Trial.PAUSED]:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5082d3087eb568d2b82b222ad35b28ad\",\n \"score\": \"0.51663554\",\n \"text\": \"def trial_finished(self):\\n return self.ind+1>=len(self.words[self.word_index])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26b1fb105b41c723e193b048e3ea79d7\",\n \"score\": \"0.51536345\",\n \"text\": \"def finished_execution(self) -> bool:\\n return self._transition_state == self._FINISHED or self._transition_state == self._ABORTED\",\n \"title\": \"\"\n },\n {\n \"docid\": \"344d22362aa070e43059344021d9bdbb\",\n \"score\": \"0.51531\",\n \"text\": \"def is_finished(self):\\n for i in range(self.rows):\\n for j in range(self.cols):\\n if self.cells[i][j].value == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"baae092925ecb85aeca50253e71431cc\",\n \"score\": \"0.51470554\",\n \"text\": \"def _is_finished(self) -> bool:\\n return self._status[0] or self._status[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d58a1b8788c8381ef6220c9c0208769\",\n \"score\": \"0.5141703\",\n \"text\": \"def is_finished(self):\\n return self.finished\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57a2b2d52deb773ece559672c80736c6\",\n \"score\": \"0.51412666\",\n \"text\": \"def init(self, pTimeInit, pTimeFinal, pPointInit, pPointFinal, pVelocityInit, pVelocityFinal, pPeriod):\\n return _almathinternal.ALInterpolationArticular3D_init(self, pTimeInit, pTimeFinal, pPointInit, pPointFinal, pVelocityInit, pVelocityFinal, pPeriod)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f4a56713ac6bf1710ce412e22c6ce70\",\n \"score\": \"0.51185846\",\n \"text\": \"def setIsFinished(self, pIsFinished):\\n return _almathinternal.ALInterpolationBezier_setIsFinished(self, pIsFinished)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f80f2d934cb54daaf6a95d02999a37d1\",\n \"score\": \"0.5117361\",\n \"text\": \"def keyIsFinished(mashDic, key):\\n return scipy.sum([mashDic[key][i][1] for i in range(Z)]) == 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20e37615526f73a32267acfcc99e7909\",\n \"score\": \"0.5088959\",\n \"text\": \"def isFinished(self):\\n return self._finished\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cfb3762f39b5fcc4be2d388efbdc67b\",\n \"score\": \"0.50729936\",\n \"text\": \"def _isFinished(self):\\n return not self.currentPlayer.actions or any(self._getScores())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebd22f054e148f56e0a28e6d27e79faf\",\n \"score\": \"0.50692016\",\n \"text\": \"def _is_done(self, observations):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebd22f054e148f56e0a28e6d27e79faf\",\n \"score\": \"0.50692016\",\n \"text\": \"def _is_done(self, observations):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebd22f054e148f56e0a28e6d27e79faf\",\n \"score\": \"0.50692016\",\n \"text\": \"def _is_done(self, observations):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebd22f054e148f56e0a28e6d27e79faf\",\n \"score\": \"0.50692016\",\n \"text\": \"def _is_done(self, observations):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50e320677d165ef09cb754322e976fa8\",\n \"score\": \"0.50626665\",\n \"text\": \"def is_finished(self):\\n return os.path.exists(self.out_filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b7882a11ddda5b4c105893a794ccf7f\",\n \"score\": \"0.5059459\",\n \"text\": \"def SoGLMultiTextureImageElement_hasTransparency(state: 'SoState') -> \\\"SbBool\\\":\\n return _coin.SoGLMultiTextureImageElement_hasTransparency(state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b1bfd11462c7527f497b9cb9f335aa8\",\n \"score\": \"0.5055257\",\n \"text\": \"def is_dead_end(my_zoom):\\n count = 0\\n for value in my_zoom.values():\\n count += 1 if not value else 0\\n\\n return True if count == 3 else False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4e31a9deac97594465439c3c7b6e137\",\n \"score\": \"0.5046841\",\n \"text\": \"def SoMultiTextureImageElement_containsTransparency(state: 'SoState') -> \\\"SbBool\\\":\\n return _coin.SoMultiTextureImageElement_containsTransparency(state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79a553d8561206ea2245d344d384bd6b\",\n \"score\": \"0.504325\",\n \"text\": \"def abs_done(time_remain):\\n global cex_abs_depth, abs_depth, abs_depth_prev, time_abs_prev, time_abs\\n## print 'checking if abs has enough time to next cex'\\n frames_to_next_cex = cex_abs_depth - abs_depth\\n div = time_abs - time_abs_prev\\n div = max(.1,div)\\n frames_per_sec = (abs_depth - abs_depth_prev)/div\\n if frames_per_sec <= 0:\\n return False #something wrong \\n## print 'frames_per_sec = %0.2f, frames_to_next_cex = %d, time remaining = %0.2f'%(frames_per_sec, frames_to_next_cex, time_remain)\\n if frames_to_next_cex > 0.2*(frames_per_sec * time_remain): #later frames will take longer so factor of 5 here\\n print 'not enough abs time to next cex'\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c327d649608de635bc0d373f2a6a0f75\",\n \"score\": \"0.50367916\",\n \"text\": \"def hasTransparency(state: 'SoState') -> \\\"SbBool\\\":\\n return _coin.SoGLMultiTextureImageElement_hasTransparency(state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81e39d786e4aaa570f04ca63c662b086\",\n \"score\": \"0.503623\",\n \"text\": \"def is_finished(self):\\n return self.text and self.screenshot\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37d09cf6281a0229f456def11aa7c2bd\",\n \"score\": \"0.5015781\",\n \"text\": \"def is_finished(self):\\n return self.__is_finished\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e812f3106f95e5b55fcf9f009eda4aaf\",\n \"score\": \"0.5014571\",\n \"text\": \"def prev_has_finished(self):\\n return self.itr_prev >= self.max_itr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aefeafde97e5afc93f6b197c838e1c9e\",\n \"score\": \"0.5008804\",\n \"text\": \"def finished(self) -> bool:\\n return self.__finished\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dbff4c45a3437eae89c7949ee2dfb66\",\n \"score\": \"0.5008314\",\n \"text\": \"def is_finished(self):\\n return self._finished\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db44817fec881b74128300bcbb488ee3\",\n \"score\": \"0.50015867\",\n \"text\": \"def is_delaunay(self, tri):\\n circumcircle=tri.circumcircle()\\n count=0\\n for point in (tri.p0,tri.p1,tri.p2):\\n if circumcircle.covers(point):\\n #print 'yes'\\n count+=1\\n if count==3:\\n #print count\\n return True\\n else:\\n return False\\n #Your implementation here\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddac60ff121d562006777ac79626bad9\",\n \"score\": \"0.49990314\",\n \"text\": \"def is_finished(\\n num_gameweeks,\\n wildcard=False,\\n free_hit=False,\\n triple_captain=False,\\n bench_boost=False,\\n):\\n final_expected_num = count_expected_outputs(\\n 0, num_gameweeks, wildcard, free_hit, triple_captain, bench_boost\\n )\\n # count the json files in the output dir\\n json_count = len(os.listdir(OUTPUT_DIR))\\n if json_count == final_expected_num:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0ad633c2e36e2e44ede103db2d01a0f\",\n \"score\": \"0.49938917\",\n \"text\": \"def _is_done(self):\\n # Is the sim done ? \\n return self.current_sim_datetime > self.end_datetime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33d3edc5d430a3a4408e071d79f9ae8d\",\n \"score\": \"0.49917263\",\n \"text\": \"def containsTransparency(state: 'SoState') -> \\\"SbBool\\\":\\n return _coin.SoMultiTextureImageElement_containsTransparency(state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daa388333721f0b73ad52b60502f3f9a\",\n \"score\": \"0.4971992\",\n \"text\": \"def _test_f_3(x, y, z):\\n time.sleep(0.25) # test the timing function\\n return x + y + z\",\n \"title\": \"\"\n },\n {\n \"docid\": \"381a406c1888c27082016d4467de8817\",\n \"score\": \"0.49694067\",\n \"text\": \"def getFinalTime(self):\\n return _almathinternal.ALInterpolationTypeIV3D_getFinalTime(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92b0a07ac91248befd14db167645b16d\",\n \"score\": \"0.4964097\",\n \"text\": \"def _check_if_done(self):\\n return self._check_if_all_boxes_on_target() or self._check_if_maxsteps()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ada6e7aa520255332cc8e94d89c3bee\",\n \"score\": \"0.49635655\",\n \"text\": \"def episode_done(self):\\n if not self.hit_is_complete:\\n return False\\n else:\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3960a98654cc2ab604ce97729686f95e\",\n \"score\": \"0.49438494\",\n \"text\": \"def is_final_state(x):\\n p, s = x\\n if abs(p) > 1 or abs(s) > 3:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2bc415fe4e0fa7c17db8e36d5bde7c1\",\n \"score\": \"0.49432087\",\n \"text\": \"def is_finished(\\n self,\\n step: int,\\n unfin_idx: int,\\n max_len: int,\\n finalized_sent_len: int,\\n beam_size: int,\\n ):\\n assert finalized_sent_len <= beam_size\\n if finalized_sent_len == beam_size or step == max_len:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12cfd22bbf08672e94a9bba507cfa8ad\",\n \"score\": \"0.49417865\",\n \"text\": \"def is_end_state(self) -> bool:\\n return len(self.finished) == self.count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac3eb5ae2c25a3ef82f4207baa1d5c98\",\n \"score\": \"0.49348676\",\n \"text\": \"def is_finished(self, *additional, **kwargs):\\n return all((sh.is_finished() for sh in list(additional) + self if hasattr(sh, 'is_finished')))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e84f93ef1b9f20f20c48faa1b8cfecc3\",\n \"score\": \"0.492795\",\n \"text\": \"def acceleration3_d_available(self):\\n ret = self._get_attr(\\\"acceleration3DAvailable\\\")\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3e2a85db991a6815477ee79035b75ec\",\n \"score\": \"0.49094743\",\n \"text\": \"def completed(self):\\n return self.iteration >= len(self.batches)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07c17ac655fdcc270904296375f51832\",\n \"score\": \"0.4908603\",\n \"text\": \"def goal_reached(self, world_state, grid_world):\\r\\n nr_ticks = grid_world.current_nr_ticks\\r\\n if self.max_nr_ticks == np.inf or self.max_nr_ticks <= 0:\\r\\n self.is_done = False\\r\\n else:\\r\\n if nr_ticks >= self.max_nr_ticks:\\r\\n self.is_done = True\\r\\n else:\\r\\n self.is_done = False\\r\\n return self.is_done\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a62794063561fdc1db854be2df2a0b0\",\n \"score\": \"0.4907297\",\n \"text\": \"def is_3D(self) -> bool:\\n return len(self._vol_shape_xyz) == 3 and self._vol_shape_xyz[-1] > 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e88950c4b150e50eb9a2c24417cc3f3d\",\n \"score\": \"0.4890563\",\n \"text\": \"def is_finished(self):\\n is_scored_goals = self.home_team_goal > 0 or self.away_team_goal > 0\\n if (is_scored_goals and util.compare_time_to_now(self.date, 1)) \\\\\\n or (not is_scored_goals and util.compare_time_to_now(self.date, 100)):\\n return True\\n\\n if util.is_None(self.goal) \\\\\\n and util.is_None(self.shoton) \\\\\\n and util.is_None(self.shotoff) \\\\\\n and util.is_None(self.foulcommit) \\\\\\n and util.is_None(self.card) \\\\\\n and util.is_None(self.cross) \\\\\\n and util.is_None(self.corner) \\\\\\n and util.is_None(self.possession):\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"097f46caaab32cc253ebb00ea141aeb0\",\n \"score\": \"0.48850796\",\n \"text\": \"def isFinished(self):\\n # this function should be deterministic and without side effects\\n if self.env.n_actions >= self.max_number_of_actions_per_session:\\n return True\\n elif self.env.state.click != Click.NOT_CLICKED:\\n # click ends task\\n return True\\n elif self.env.state.quit == Quit.HAS_QUIT:\\n # quit ends task\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e07d93ab3b11d2761de5cedf60145fff\",\n \"score\": \"0.48749816\",\n \"text\": \"def is_dataset_finished(self):\\n n_completed = self.dataset.processed\\n\\n # Dataset has reached max pipeline depth\\n if self.dataset.depth >= self.max_pipeline_depth:\\n LOGGER.info('Dataset {} has reached max pipeline depth!'.format(self.dataset))\\n return True\\n\\n # No budget for dataset\\n if n_completed >= self.dataset.budget:\\n LOGGER.info('Algorithm budget for dataset {} has run out!'.format(self.dataset))\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"faea9f281a5b93922708805a03825f1c\",\n \"score\": \"0.48738745\",\n \"text\": \"def _is_animated(self, input):\\r\\n return self.animated_gif_optimiser._is_acceptable_image(input)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994199,"cells":{"query_id":{"kind":"string","value":"8e050be4735a3412803428f673375b18"},"query":{"kind":"string","value":"Save the outputs from the simulation run."},"positive_passages":{"kind":"list like","value":[{"docid":"cc27b8307d07d7bd03068df1bc203cd3","score":"0.59313","text":"def save_simulation(\n output: str,\n profile_type: str,\n simulation_outputs: Solution,\n solar_irradiance: dict[int, float],\n) -> None:\n\n # Assemble the CSV datafile structure\n output_data = simulation_outputs.as_dataframe\n output_data[\"Solar irradiance / W/m^2\"] = solar_irradiance\n\n # Write to the output file.\n os.makedirs(SIMULATION_OUTPUTS_DIRECTORY, exist_ok=True)\n with open(\n f\"{os.path.join(SIMULATION_OUTPUTS_DIRECTORY, output)}_{profile_type}\" \".csv\",\n \"w\",\n encoding=\"UTF-8\",\n ) as output_file:\n output_data.to_csv(output_file) # type: ignore [arg-type]","title":""}],"string":"[\n {\n \"docid\": \"cc27b8307d07d7bd03068df1bc203cd3\",\n \"score\": \"0.59313\",\n \"text\": \"def save_simulation(\\n output: str,\\n profile_type: str,\\n simulation_outputs: Solution,\\n solar_irradiance: dict[int, float],\\n) -> None:\\n\\n # Assemble the CSV datafile structure\\n output_data = simulation_outputs.as_dataframe\\n output_data[\\\"Solar irradiance / W/m^2\\\"] = solar_irradiance\\n\\n # Write to the output file.\\n os.makedirs(SIMULATION_OUTPUTS_DIRECTORY, exist_ok=True)\\n with open(\\n f\\\"{os.path.join(SIMULATION_OUTPUTS_DIRECTORY, output)}_{profile_type}\\\" \\\".csv\\\",\\n \\\"w\\\",\\n encoding=\\\"UTF-8\\\",\\n ) as output_file:\\n output_data.to_csv(output_file) # type: ignore [arg-type]\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"62357c71511468de2da3ef87b7e3c34b","score":"0.75556","text":"def save(self):\n # Message #\n self.parent.log.info(\"Saving final simulations results to disk.\")\n # The classifier values #\n self['values'] = self.sim.sit.classifier_value_ids\n # All the tables that are within the SimpleNamespace of `sim.results` #\n self['area'] = self.runner.internal['pools']\n self['classifiers'] = self.runner.internal['classifiers']\n self['flux'] = self.runner.internal['flux']\n self['parameters'] = self.runner.internal['parameters']\n self['pools'] = self.runner.internal['pools']\n self['state'] = self.runner.internal['state']","title":""},{"docid":"1779797ec4ebfd520561913d0f987829","score":"0.716197","text":"def write_outputs(self):\n self.output.write_data_to_disk()\n if not self._no_pickle:\n state_pkl_filename = os.path.join(\n self.output.output_dir, f\"calstate-{self.chain_idx}.pkl\"\n )\n pickle.dump(self, open(state_pkl_filename, \"wb\"))","title":""},{"docid":"8f2f52bed922d3b259d295dcca510e6e","score":"0.71024096","text":"def save(self):\n save_output_residual = (time.mktime(\n Coordinator.current_date.timetuple()) - self.first_save_output_residual) % get_seconds(\n self.save_output_interval)\n if abs(save_output_residual) <= 1e-7:\n self.handle_old_dps()\n self.handle_outliers()\n self.save_model_and_files()","title":""},{"docid":"2caa6be1ea311ca4aea805a52d9c33db","score":"0.7013826","text":"def save(self):\n pickle.dump(self, open(pSimFileName, 'wb'), pickle.HIGHEST_PROTOCOL)","title":""},{"docid":"1082b4db9d1895fb14747ed61a8d51e8","score":"0.69863796","text":"def dump_results(self, success):\n system = self.system\n\n t, _ = elapsed()\n\n if success and (not system.files.no_output):\n\n # system.varout.dump()\n system.varout.dump_np_vars()\n _, s = elapsed(t)\n logger.info('Simulation data dumped in {:s}.'.format(s))","title":""},{"docid":"a94767a84a95c897907dcc74c417572f","score":"0.6926807","text":"def save_results(self):\n \n df_fname = '{0}.T'.format(self.df_agents_file)\n file = open(df_fname, 'w')\n pickle.dump(self.T, file)\n file.close()\n \n df_fname = '{0}.D_agent'.format(self.df_agents_file)\n file = open(df_fname, 'w')\n pickle.dump(self.D_agent, file)\n file.close() \n\n df_fname = '{0}.D_tstep'.format(self.df_agents_file)\n file = open(df_fname, 'w')\n pickle.dump(self.D_tstep, file)\n file.close() \n \n df_fname = '{0}.D_edges'.format(self.df_agents_file)\n file = open(df_fname, 'w')\n pickle.dump(self.D_edges, file)\n file.close() \n \n print '{0}: Results saved.'.format(self.city)\n sys.stdout.flush()","title":""},{"docid":"28d0b21b7a9b6304b3403edaaeacda9f","score":"0.6892895","text":"def test_saved_output(self):\n execute_and_test_output_images(self, CliRunner(), 3, 3, \"save_\", [\"save\"])","title":""},{"docid":"29c83d959d1650939e0f56fca0476b57","score":"0.6755299","text":"def store_simulation_results(self, simulation_results, results_dir):\n pass","title":""},{"docid":"867bb54a99ef08eb5495dc25882a2368","score":"0.67163306","text":"def save(self):\n if self.output_folder_path:\n\n # Creating directories if they don't exist\n makedirs(dirname(join(self.output_folder_path, \"file\")), exist_ok=True)\n\n # ### Steps data ###\n csv_array = []\n for k, v in self.step_traces[\"scores\"].items():\n csv_array.append([k] + v)\n for k, v in self.step_traces[\"additional_values\"].items():\n csv_array.append([k] + v)\n csv_array.append([\"n_replaced\"] + self.step_traces[\"n_replaced\"])\n csv_array.append([\"timestamps\"] + self.step_traces[\"timestamps\"])\n\n with open(join(self.output_folder_path, 'steps.csv'), \"w\", newline='') as f:\n writer = csv.writer(f)\n for row in np.array(csv_array).T:\n writer.writerow(row)\n\n # ### All inserted individuals data ###\n if self.record_all_generated_individuals:\n csv_array = [[\"step\"] + self.all_generated_individuals_step,\n [\"SMILES\"] + self.all_generated_individuals_smiles,\n [\"obj_calls\"] + self.all_generated_individuals_n_obj_calls,\n [\"obj_value\"] + self.all_generated_individuals_obj_value,\n [\"improver\"] + self.all_generated_individuals_improver]\n\n with open(join(self.output_folder_path, \"all_generated.csv\"), \"w\") as f:\n writer = csv.writer(f)\n for row in np.array(csv_array).T:\n writer.writerow(row)\n\n # ### Last step population data ###\n csv_array = []\n\n # Mutation success history\n n_success_mut_str = []\n n_fail_mut_str = []\n for i, ind in enumerate(self.pop):\n n_success_mut_str.append(str(self.n_success_mut[i]))\n n_fail_mut_str.append(str(self.n_fail_mut[i]))\n\n csv_array.append([\"smiles\"] + self.pop_tabu_list)\n\n # Mutation success and failures\n csv_array.append([\"n_success_mut\"] + n_success_mut_str)\n csv_array.append([\"n_failures_mut\"] + n_fail_mut_str)\n\n # Scores data\n self.curr_total_scores, self.curr_scores = self.evaluation_strategy.get_population_scores()\n step_scores_dict = scores_to_scores_dict(self.curr_total_scores,\n self.curr_scores,\n self.evaluation_strategy.keys())\n\n for k, scores_list in step_scores_dict.items():\n scores_list_np = np.full((self.pop_max_size,), None)\n scores_list_np[:len(scores_list)] = scores_list\n csv_array.append([k] + list(scores_list_np))\n\n # Action history data\n csv_array.append([\"history_data\"] + self.actions_history)\n\n with open(join(self.output_folder_path, 'pop.csv'), \"w\", newline='') as f:\n writer = csv.writer(f)\n for row in np.array(csv_array).T:\n writer.writerow(row)\n\n # ### Removed individuals actions recording ###\n if self.record_history:\n with open(join(self.output_folder_path, 'removed_ind_act_history.csv'), \"w\", newline='') as f:\n\n writer = csv.writer(f)\n writer.writerow([\"history_data\", \"total\"] + self.evaluation_strategy.keys() + [\"smiles\"])\n\n for removed_act_history in self.removed_actions_score_smi_tuple.keys():\n if removed_act_history != \"\":\n total_score = self.removed_actions_score_smi_tuple[removed_act_history][0]\n scores = self.removed_actions_score_smi_tuple[removed_act_history][1]\n smi = self.removed_actions_score_smi_tuple[removed_act_history][2]\n\n writer.writerow([removed_act_history, total_score] + list(scores) + [smi])\n\n # ### Errors data ###\n with open(join(self.output_folder_path, 'errors.csv'), \"w\", newline='') as f:\n writer = csv.writer(f)\n writer.writerow([\"step\", \"error\"])\n for error in self.errors:\n writer.writerow(error)","title":""},{"docid":"b70932305b7c443f264eda729aacc110","score":"0.6612021","text":"def save(self):\n self._network.save(self._total_episodes)\n with open(f\"{self.agent_dir}/checkpoints/{self._total_episodes}_replay_buffer.p\", \"wb\") as f:\n pickle.dump(self._replay_buffer, f)","title":""},{"docid":"ea49d7a488bbdd12540a4372ad270ad2","score":"0.65874064","text":"def write_results(self, save_dir, simulation_ID):\n outfile_name=save_dir+'/'+simulation_ID+'_'+self.simtype+'.csv'\n outfile=open(outfile_name, 'w+')\n line1=\"mu= \"+str(self.mu)+\"\\tk= \"+str(self.k)+\"\\n\"\n outfile.write(line1)\n line2= \"Time\\tx_1\\tx_2\\ty_1\\ty_2\\n\"\n outfile.write(line2)\n for i in range(len(self.x1)):\n line=str(self.dt*i)+';'+str(self.x1[i])+';'+str(self.x2[i])+';'+str(self.y1[i])+';'+str(self.y2[i])+'\\n'\n outfile.write(line)\n outfile.close()","title":""},{"docid":"7302ce3908c441efd6aed00c3ffbc047","score":"0.6579228","text":"def save_data(self, path=None):\n save_file = self.save_file_name\n if path is not None:\n save_file = path+self.save_file_name\n with open(save_file, 'wb') as outfile:\n pickle.dump(np.array(self.analysis_output), outfile)\n\n return","title":""},{"docid":"fcf175d563152ca3f5cea418095eea76","score":"0.6564639","text":"def runsim(self):\n self.randwalk3d()\n self.savetofile()\n self.outputobject.close()","title":""},{"docid":"956fb372e1013a3318c908de73b2180f","score":"0.6542381","text":"def save_output(self):\n report_name = self.FILENAME\n time_range = self.get_time_range_str()\n self.wb.save(get_output_filepath(report_name, time_range))","title":""},{"docid":"66c441935b2a747682b09ff58fad0839","score":"0.6539715","text":"def save_data(self):\n\n repo_path = 'experimental_data/'\n if not os.path.isdir(repo_path):\n os.mkdir(repo_path)\n time_stamp = time.strftime('%d_%B_%Y_%I:%M%p')\n file_n = repo_path + 'quads_robotarium_sim_' + time_stamp + '.pckl'\n arrays = [self.time_record, self.x_record, self.orientation_record, self.input_record]\n with open(file_n, 'wb') as file:\n pickle.dump(arrays, file, protocol=2)","title":""},{"docid":"f7291f105f3fc2b1b1a8a03114668a44","score":"0.6515443","text":"def save_runs(runs):\n\n for i in runs:\n run = runs.get(i)\n run.save(dir0=dir0)","title":""},{"docid":"069c6a42571adb6d14fcaf8b6d5de03a","score":"0.65010774","text":"def _save_results(self, directory, topology, positions):\n\n from simtk import unit as simtk_unit\n simtk_positions = positions.to(unit.angstrom).magnitude * simtk_unit.angstrom\n\n self.coordinate_file_path = path.join(directory, 'output.pdb')\n\n with open(self.coordinate_file_path, 'w+') as minimised_file:\n # noinspection PyTypeChecker\n app.PDBFile.writeFile(topology, simtk_positions, minimised_file)\n\n logging.info('Coordinates generated: ' + self.substance.identifier)","title":""},{"docid":"4cfa34b5aab46d9b911cc227b1531336","score":"0.64957994","text":"def save_results(self, results):\n self.results = results\n pickle.dump(self.results, open(os.path.join(\n results_dir,\n \"backtest_{num}.pickle\".format(num=self.backtest_number)), 'wb'))","title":""},{"docid":"1ac3c83dd77029c48dee2a0420f162be","score":"0.64797264","text":"def sim_and_record(self):\n save_items=list(self.results_df.index)\n self.op_sim.save_internal_parameters(*save_items)\n self.internal_opsim_res=self.op_sim.operating_point()\n \n for save in save_items:\n self.results_df.at[save, 'Value']=self.internal_opsim_res[save].as_ndarray()[0]\n \n if self.display_results:\n print('.op sim internal parmter results')\n display(self.results_df)","title":""},{"docid":"a5b8a014c5212e7bdbc7e9e8106d512a","score":"0.64786536","text":"def save_data(self):\n # Unlogged initial positions (Step not updated by Webots)\n self.links[0, :, :] = self.links[1, :, :]\n self.joints[0, :, :] = self.joints[1, :, :]\n # Diff position to extract velocity\n # Save\n #os.makedirs(os.path.dirname(self.filename), exist_ok=True)\n np.savez(\n self.filename,\n links=self.links,\n joints=self.joints,\n network=self.network,\n **self.parameters\n )","title":""},{"docid":"5a85f2e59f99c3e5fdac707fb8a08dc3","score":"0.64685446","text":"def save_results(self, out_dir: str):\n\n if self.graphs is None:\n self.graph_results()\n\n build_out(out_dir)\n\n for y in self.graphs.keys():\n\n # have to create & save bc subsequent plots will overwrite past plots\n self.graphs[y].create_plot()\n self.graphs[y].save(\"{0}/{1}.png\".format(out_dir, str(y)))","title":""},{"docid":"79f9fe43fce359806c3f6ec37a15c171","score":"0.64500666","text":"def save_model_and_files(self):\n self.save_model(\n os.path.join(os.getcwd(), 'outputs/multi_agent',\n 'X' + str(Coordinator.current_date).replace(':', '_') + '--' + str(\n Coordinator.dp_counter), 'model'))\n self.write_output_to_files(\n os.path.join(os.getcwd(), 'outputs/multi_agent',\n 'X' + str(Coordinator.current_date).replace(':', '_') + '--' + str(\n Coordinator.dp_counter), 'clusters'))\n self.write_topics_to_files(\n os.path.join(os.getcwd(), 'outputs/multi_agent',\n 'X' + str(Coordinator.current_date).replace(':', '_') + '--' + str(\n Coordinator.dp_counter), 'topics'))\n self.write_tweet_ids_to_files(\n os.path.join(os.getcwd(), 'outputs/multi_agent',\n 'X' + str(Coordinator.current_date).replace(':', '_') + '--' + str(\n Coordinator.dp_counter), 'clusters_tweet_ids'))\n if self.verbose == 1:\n print(f'{Fore.YELLOW}{self.current_date} : Save Model and Outputs -> Number of agents : {len(self.agents)}')","title":""},{"docid":"a62bd9d0818e1150eeb161756185fb7f","score":"0.64475393","text":"def save_data(self, path=None):\n save_file = self.save_file_name\n if path is not None:\n save_file = path+self.save_file_name\n with open(save_file, 'wb') as outfile:\n pickle.dump(self.analysis_output, outfile)\n\n return","title":""},{"docid":"d6c3e66e81653a120bc11267d18c51ad","score":"0.6434548","text":"def save_output(self, model_dico, file_save, X_val):\n\n pred = {}\n\n # Prediction\n for o in self.output_names:\n pred[o] = self.predict_model(model_dico[o], X_val)\n\n # Text to save\n output_text = ':'.join(self.output_names)\n \n for i in tqdm(range(len(pred[self.output_names[0]]))):\n output_text += '\\n'\n output_text += ':'.join([str(pred[o][i]) for o in self.output_names])\n \n with open(file_save, 'w') as file:\n file.write(output_text)\n\n print(\"File saved at '{}'\".format(file_save))","title":""},{"docid":"694c6d0fc67d2c65c720f0498a3f2245","score":"0.64069724","text":"def _store_outputs(self, hypotheses: List[str]) -> None:\n current_valid_output_file = \"{}/{}.hyps\".format(self.model_dir,\n self.steps)\n with open(current_valid_output_file, 'w') as opened_file:\n for hyp in hypotheses:\n opened_file.write(\"{}\\n\".format(hyp))","title":""},{"docid":"694c6d0fc67d2c65c720f0498a3f2245","score":"0.64069724","text":"def _store_outputs(self, hypotheses: List[str]) -> None:\n current_valid_output_file = \"{}/{}.hyps\".format(self.model_dir,\n self.steps)\n with open(current_valid_output_file, 'w') as opened_file:\n for hyp in hypotheses:\n opened_file.write(\"{}\\n\".format(hyp))","title":""},{"docid":"0fa41e8dd48094b900cffbf1e8835e59","score":"0.6406303","text":"def get_outputs(self):\n\n output = {'filename': None,\n 'raw_frame_numbers': self.raw_frame_numbers.isChecked(),\n 'viewer': self.open_viewer.isChecked()}\n\n save = self.save_file.isChecked()\n if not save:\n return output\n\n save_path = self.file_path.text()\n if not save_path:\n scene = tp.Dcc.scene_name()\n time_stamp = datetime.datetime.today()\n str_time_stamp = time_stamp.strftime(\"%d-%m-%Y_%H-%M-%S\")\n save_path = '{}_{}'.format(scene, str_time_stamp)\n\n output['filename'] = save_path or artellapipe.MediaMgr().create_temp_path('playblast')\n\n return output","title":""},{"docid":"528bed151cd894a61b3bac809331807a","score":"0.6387252","text":"def _store_outputs(self, hypotheses: List[str]) -> None:\n current_valid_output_file = \"{}/{}.hyps\".format(self.model_dir,\n self.steps)\n with open(current_valid_output_file, 'a') as opened_file:\n for hyp in hypotheses:\n opened_file.write(\"{}\\n\".format(hyp))","title":""},{"docid":"c2e0c79d31b2a798a481c6d3f65f4501","score":"0.6350344","text":"def loc_save(self,S):\n\n pos=nx.get_node_attributes(self,'p')\n pe=nx.get_node_attributes(self,'pe_alg')\n typ = nx.get_node_attributes(self,'typ')\n if self.idx == 0:\n entete = 'NodeID, True Position x, True Position y, Est Position x, Est Position y, Timestamp\\n'\n file=open(os.path.join(basename,pstruc['DIRNETSAVE'],'simulation.txt'),'write')\n file.write(entete)\n file.close()\n\n try:\n file=open(os.path.join(basename,pstruc['DIRNETSAVE'],'simulation.txt'),'a')\n for n in self.nodes():\n if typ[n] != 'ap':\n data = n + ',' + str(pos[n][0]) + ',' + str(pos[n][1]) + ',' + str(pe[n][0][0]) + ',' + str(pe[n][0][1]) + ',' +pyu.timestamp(S.now()) +',\\n'\n file.write(data)\n file.close()\n self.idx = self.idx +1\n except:\n pass","title":""},{"docid":"a2e24c737df604fcad6943164324a619","score":"0.6349004","text":"def save(self) :\n\n # Write header\n self._log_header1( gammalib.TERSE , 'Save dmatter analysis results' )\n\n # Continue only if FITS file is valid\n if self._fits != None :\n\n # Get outmap parameter\n outfile = self[ 'outfile' ].filename()\n\n # Log file name\n self._log_value( gammalib.NORMAL, 'dmatter file' , outfile.url() )\n\n # Save results\n self._fits.saveto( outfile , self['clobber'].boolean() )\n\n # Return\n return","title":""},{"docid":"f3cf7b57e4437b2741cea5988b7dcb4a","score":"0.6343419","text":"def save(self, output, data):\n return","title":""},{"docid":"04a60bbf615c5451a601b0de099ad5e6","score":"0.63416994","text":"def save(self):\n history_filename, results_filename, best_pars_filename = self._get_result_filenames(self.state_dir)\n np.savez_compressed(history_filename, history=self.history)\n np.savez_compressed(best_pars_filename, best_pars=gather(self.best_pars))\n data = {'best_iter': self.best_iter,\n 'best_val_loss': self.best_val_loss,\n 'best_tst_loss': self.best_tst_loss,\n 'training_time': self.training_time,\n 'start_time': self.start_time,\n 'end_time': self.end_time,\n 'termination_reason': self.termination_reason,\n 'cfg': self.cfg,\n 'data': self.data}\n with open(results_filename, 'wb') as results_file:\n json.dump(data, results_file, indent=4)","title":""},{"docid":"79ea47a51d28cba937456d57338f6305","score":"0.63317704","text":"def save_test_result(self):\n pass","title":""},{"docid":"490fbee520774c138ead228d077c90dc","score":"0.6323168","text":"def save(self, output, data):","title":""},{"docid":"490fbee520774c138ead228d077c90dc","score":"0.6323168","text":"def save(self, output, data):","title":""},{"docid":"ea0f99539cdc89777c6c6818f9e96b9b","score":"0.63117325","text":"def simulation_save_temp(self):\n with open(os.path.join('tmp','simulations',(str(self.id_simulation)+'full')), 'w') as stream:\n writer = csv.writer(stream)\n for row in range(self.gui.table_preview.rowCount()):\n rowdata = []\n for column in range(self.gui.table_preview.columnCount()):\n item = self.gui.table_preview.item(row, column)\n if item is not None:\n rowdata.append(\n unicode(item.text()).encode('utf8'))\n else:\n rowdata.append('')\n writer.writerow(rowdata)\n with open(os.path.join('tmp','simulations',(str(self.id_simulation)+'filter')), 'w') as stream:\n writer = csv.writer(stream)\n for row in range(self.gui.table_filtered.rowCount()):\n rowdata = []\n for column in range(self.gui.table_filtered.columnCount()):\n item = self.gui.table_filtered.item(row, column)\n if item is not None:\n rowdata.append(\n unicode(item.text()).encode('utf8'))\n else:\n rowdata.append('')\n writer.writerow(rowdata)\n with open(os.path.join('tmp','simulations',(str(self.id_simulation)+'stats')), 'w') as file_save:\n item = self.gui.tree_hits.topLevelItem(self.id_simulation)\n simulation = item.text(1) + ',' + item.text(2) + ',' +\\\n item.text(3) + ',' +item.text(4) + ',' +\\\n item.text(5) + ',' +item.text(6) + ',' +item.text(7) + ',' +\\\n item.text(8) + ',' +item.text(9)\n line = simulation+self.nl\n file_save.write(line)\n child_num = item.childCount()\n for i in range(0,child_num):\n name = item.child(i)\n name = name.text(0)\n line = name+self.nl\n file_save.write('.........'+line)","title":""},{"docid":"6a1ece051d7771df3822d55db9011791","score":"0.62942624","text":"def outputs(self):","title":""},{"docid":"0576b139f1adffc3e23c6c82b059519e","score":"0.6263089","text":"def output(self):\n try:\n # output_save path uses output_folder parameter unless it is left as the default 'config', which then uses the output_path in config.py\n dirpath = self.output_save_path\n except:\n dirpath = toad.config.dirpath\n\n save_ = getattr(self, 'persist', [])\n output = dict([(k, self.target_class(self._getpath(dirpath, k))) for k in save_])\n if self.persist == ['data']: # 1 data shortcut\n output = output['data']\n\n self.debg(f\"{type(self).__name__} output set to {output.path.absolute()}\")\n\n return output","title":""},{"docid":"5f56de9c2cc2ea5ebadac7f7d40f120f","score":"0.6260796","text":"def save(self, output_folder, save_name):\n CRiSP = {'M_inv': self.M_inv, 'X': self.X, 'Y': self.Y, 's': self.s, 'psi': self.psi}\n print(\n f\"Saving model in {output_folder}...\\tKernel matrix inverse size: \\t{sys.getsizeof(CRiSP['M_inv']) * 1e-9:3f}GB\")\n\n if save_name is None:\n save_name = 'CRiSP'\n pickle.dump(CRiSP, open(output_folder / (save_name + \".pickle\"), 'wb'), protocol=4)\n print(f\"Save complete!\")","title":""},{"docid":"16a0df3e9b4140cbb98fa2073e5eb6ba","score":"0.62605923","text":"def _save_state(self):\n save_path = os.path.join(save_config['root_dir'], str(time.time())+'.pkl')\n save_dict = dict()\n save_dict['player1'] = self.player1.to_dict()\n save_dict['player2'] = self.player2.to_dict()\n save_dict['game_board'] = self.game_board.to_dict()\n save_dict['game_engine'] = self.game_engine.to_dict()\n save_dict['turns_played'] = self.turns_played\n save_dict['_run_func'] = '{}_{}'.format(self.player1.type, self.player2.type)\n save_dict['game_ended'] = self.game_ended\n save_dict['start_time'] = self.start_time\n with open(save_path, 'wb') as f:\n pickle.dump(save_dict, f)","title":""},{"docid":"1bcda4f19e497647a7dbad0bf78b245e","score":"0.62458396","text":"def save_results_to_file(self, output_file_path, seq_name):\n self.tracking_out['conf'] = 1\n self.tracking_out['x'] = -1\n self.tracking_out['y'] = -1\n self.tracking_out['z'] = -1\n self.tracking_out['bb_left'] += 1 # Indexing is 1-based in the ground truth\n self.tracking_out['bb_top'] += 1\n final_out = self.tracking_out[TRACKING_OUT_COLS].sort_values(by=['frame', 'ped_id'])\n if not os.path.exists(output_file_path):\n os.mkdir(output_file_path)\n final_out.to_csv(os.path.join(output_file_path, '{}.txt'.format(seq_name)), header=False, index=False)","title":""},{"docid":"3f75d46843c365ab993d05106b1eca2b","score":"0.6227219","text":"def auto_save(self):\n r_min = self.gui.spin_rounds_min.text()\n r_max = self.gui.spin_rounds_max.text()\n odds_level = self.gui.spin_odds_level.text()\n nets = self.gui.tree_nets.currentItem()\n nets = self.gui.tree_nets.indexOfTopLevelItem(nets)\n filters = self.gui.tree_filters.currentItem()\n filters = self.gui.tree_filters.indexOfTopLevelItem(filters)\n ranges = self.gui.tree_ranges.currentItem()\n ranges = self.gui.tree_ranges.indexOfTopLevelItem(ranges)\n bets = self.gui.tree_bets.currentItem()\n bets = self.gui.tree_bets.indexOfTopLevelItem(bets)\n\n\n elements = [r_min, r_max, odds_level, nets, filters, ranges, bets]\n with open(os.path.join('profiles','auto_save','simulator.txt'),'w') as save:\n for i in elements:\n save.write(str(i)+self.nl)\n print 'save'","title":""},{"docid":"37c80842b5d0f55e6dc7c48823c5c545","score":"0.62234414","text":"def save_data(self, path=None):\n output = self.get_data()\n save_file = self.save_file_name\n if path is not None:\n save_file = path+self.save_file_name\n with open(save_file, 'wb') as outfile:\n pickle.dump(output, outfile)\n\n return","title":""},{"docid":"9a08df27b8fa067f8bedac2bb98d3ad5","score":"0.62129515","text":"def save_results(self):\n self.collect_assets()\n with open(self.output_sitemap_file, 'w', encoding='utf8') as file:\n file.writelines(page + '\\n' for page in sorted(self.assets.keys()))\n with open(self.output_assets_file, 'w', encoding='utf8') as file:\n for page in sorted(self.assets):\n file.writelines(page + '\\n')\n file.writelines(asset + '\\n' for asset in self.assets[page])","title":""},{"docid":"cac6617deff6f56003db3c78a890ec08","score":"0.6175703","text":"def save_final_results(self, filename):\n\t\tfile = open(filename, \"w\")\n\n\t\tfile.write(\"Neural Network with \" + str(self.hidden_node_count) + \" hidden nodes and momentum \" +\n\t\t\t\t str(self.momentum) + \" had a final training accuracy of \" +\n\t\t\t\t str(self.training_accuracy_history[self.epochs - 1]) + \"\\n and a test accuracy of \" +\n\t\t\t\t str(self.test_accuracy_history[self.epochs - 1]) + \" after \" + str(self.epochs) + \" epochs\")\n\t\tfile.write(\"\\nTest Confusion Matrix: \\n\")\n\t\tfile.write(str(self.test_confusion_matrix))\n\t\tfile.write(\"\\ntraining accuracy history: \\n\" + str(self.training_accuracy_history))\n\t\tfile.write(\"\\ntest accuracy history: \\n\" + str(self.test_accuracy_history))\n\t\tfile.close()","title":""},{"docid":"b5019269eb6ccfb2d9161055cb211f0f","score":"0.617093","text":"def save_results(self, name, prefix, writer, save_filetypes, step):\n # pylint: disable=unused-argument, arguments-differ, too-many-arguments\n\n filename = _pathlib.Path(prefix).joinpath(f\"{self.name}-{step:07d}.pth\")\n time = _datetime.utcnow().replace(tzinfo=_timezone.utc).isoformat()\n\n with _io.BytesIO() as mem:\n if hasattr(self.model, \"module\"):\n _torch.jit.save(self.model.module, mem)\n else:\n _torch.jit.save(self.model, mem)\n mem.seek(0)\n\n with _lzma.open(filename, \"wb\") as fobj:\n _torch.save({\"step\": step, \"time\": time, \"model\": mem.read()}, fobj)\n\n self.log.info(\"Done saving model for step %d to %s\", step, str(filename))","title":""},{"docid":"e8da0e400f4552e707b107bc450477d2","score":"0.6152689","text":"def outputs(self, outputs):\n\n self._outputs = outputs","title":""},{"docid":"b1c2d11a83ee464aec05998d6ee36e6e","score":"0.61493796","text":"def runSimulation(self, save, experiment, subexperiment, network_name, nx_params):\n\n num_neutral_per_agent = np.empty(shape=(self.S))\n num_fake_per_agent = np.empty(shape=(self.S))\n num_retracted_per_agent = np.empty(shape=(self.S))\n neutral_per_timestep = np.empty(shape=(self.S, self.T))\n fake_per_timestep = np.empty(shape=(self.S, self.T))\n retracted_per_timestep = np.empty(shape=(self.S, self.T))\n\n directory = \"./output/\" + experiment + \"/\" + subexperiment + \"/data/\"\n name = network_name + '_' + '_'.join(['{}={}'.format(k, v) for k, v in nx_params.items()])\n path = directory + \"N{N}-T{T}-S{S}-{shr}-{dly}-{name}-data.csv\".format(\n N=self.N, T=self.T, S=self.S, shr=self.shareTimeLimit, dly=self.delay, name=name)\n\n for s in range(self.S):\n # run model\n network = self.graph(**self.nx_params) # generate network from graph and params\n logs = self.runModel(network=network)\n df_belief = pd.DataFrame.from_dict(logs[0])\n\n if save: # write raw data to output directory\n if not os.path.exists(directory):\n os.makedirs(directory)\n\n out = pd.DataFrame(index=[x for x in range(self.T)], columns=['s', 't']+[x for x in range(self.N)])\n out.iloc[:, 0] = np.repeat(s+1, repeats=self.T) # vector of timesteps # simulation number\n out.iloc[:, 1] = np.linspace(start=1, stop=self.T, num=self.T, dtype=int) # timestep number\n out.iloc[:, 2:self.N+2] = df_belief.values\n out.to_csv(path,\n index=False,\n header=True if s == 0 else False,\n mode='a', # append df to csv\n encoding='utf-8')\n\n # eval output\n num_neutral_per_agent[s] = np.mean(np.sum(df_belief.values == 0, axis=0))\n num_fake_per_agent[s] = np.mean(np.sum(df_belief.values == 1, axis=0))\n num_retracted_per_agent[s] = np.mean(np.sum(df_belief.values == 2, axis=0))\n neutral_per_timestep[s, :] = np.mean(df_belief.values == 0, axis=1)\n fake_per_timestep[s, :] = np.mean(df_belief.values == 1, axis=1)\n retracted_per_timestep[s, :] = np.mean(df_belief.values == 2, axis=1)\n\n # aggregate beliefs over time\n neutral_per_agent_avg = np.mean(num_neutral_per_agent)\n neutral_per_agent_sd = np.std(num_neutral_per_agent)\n fake_per_agent_avg = np.mean(num_fake_per_agent)\n fake_per_agent_sd = np.std(num_fake_per_agent)\n retracted_per_agent_avg = np.mean(num_retracted_per_agent)\n retracted_per_agent_sd = np.std(num_retracted_per_agent)\n frac_neutral_per_timestep = np.mean(neutral_per_timestep, axis=0)\n frac_neutral_per_timestep_sd = np.std(neutral_per_timestep, axis=0)\n frac_fake_per_timestep = np.mean(fake_per_timestep, axis=0)\n frac_fake_per_timestep_sd = np.std(fake_per_timestep, axis=0)\n frac_retracted_per_timestep = np.mean(retracted_per_timestep, axis=0)\n frac_retracted_per_timestep_sd = np.std(retracted_per_timestep, axis=0)\n\n # aggregate final belief distributions\n neutral_dist = neutral_per_timestep[:, self.T-1]\n fake_dist = fake_per_timestep[:, self.T-1]\n retracted_dist = retracted_per_timestep[:, self.T-1]\n\n # bundle aggregated output\n avg_per_agent = (neutral_per_agent_avg, fake_per_agent_avg, retracted_per_agent_avg)\n sd_per_agent = (neutral_per_agent_sd, fake_per_agent_sd, retracted_per_agent_sd)\n frac_belief_mean = (frac_neutral_per_timestep, frac_fake_per_timestep, frac_retracted_per_timestep)\n frac_belief_sd = (frac_neutral_per_timestep_sd, frac_fake_per_timestep_sd, frac_retracted_per_timestep_sd)\n belief_dist = (neutral_dist, fake_dist, retracted_dist)\n\n return avg_per_agent, sd_per_agent, frac_belief_mean, frac_belief_sd, belief_dist","title":""},{"docid":"7e360c9571af78dfff25bfa7eaa5d1db","score":"0.614781","text":"def save_data(self, path=None):\n\n\n output = self.get_data()\n save_file = self.save_file_name\n if path is not None:\n save_file = path+self.save_file_name\n with open(save_file, 'wb') as outfile:\n pickle.dump(output, outfile)\n\n return","title":""},{"docid":"fce336c106c67632904e81b7fbc2a29b","score":"0.61423445","text":"def write_results(self, figures_merit=None):\n\n # open output file\n f = open(self.folder + \"/000_experiment_setup_and_results.txt\", 'w')\n\n # parameters and settings of the simulation\n f.writelines('# # # # # # # # # # # # # # #' + '\\n')\n f.writelines('# # # Setting # # #' + '\\n')\n f.writelines('# # # # # # # # # # # # # # #' + '\\n')\n f.writelines(self.folder + '\\n')\n f.writelines(str(self.pars))\n f.writelines(str(self.dataset))\n f.writelines('\\n')\n f.writelines(\n 'number of concluded simulation:' + str(self.no_simulations) + '\\n')\n f.writelines('seed:' + str(self.seed) + '\\n')\n f.writelines('\\n\\n')\n\n # raw results\n f.writelines('# # # # # # # # # # # # # # #' + '\\n')\n f.writelines('# # # Raw results # # #' + '\\n')\n f.writelines('# # # # # # # # # # # # # # #' + '\\n')\n f.writelines(self.string_raw_results() + '\\n')\n f.writelines('failed simulations:' + str(self.fails) + '\\n')\n f.writelines('\\n\\n')\n\n # processed rusults\n f.writelines('# # # # # # # # # # # # # # #' + '\\n')\n f.writelines('# # # Processed results # # #' + '\\n')\n f.writelines('# # # # # # # # # # # # # # #' + '\\n')\n tabularResult, printout = self.process_results(figures_merit=figures_merit)\n f.writelines(printout + '\\n')\n\n # close file\n f.close()\n\n self.log.info(tabularResult)\n return tabularResult","title":""},{"docid":"402094e086e432bbd082b98b44ce3894","score":"0.614059","text":"def save_output(output_name, observables, lattice):\n import cPickle as pickle\n from os.path import isfile\n observables_file = ((r'data\\%s.pkl') % (output_name))\n with open(observables_file, 'wb') as output:\n pickle.dump(observables, output, pickle.HIGHEST_PROTOCOL)\n if isfile(observables_file):\n print('Observables saved to: %s' % (observables_file))\n lattice_file = ((r'data\\%s_lattice.pkl') % (output_name))\n with open(lattice_file, 'wb') as output:\n pickle.dump(lattice, output, pickle.HIGHEST_PROTOCOL)\n if isfile(lattice_file):\n print('Lattice saved to: %s' % (lattice_file))","title":""},{"docid":"c85e5118065c5f818c2c4ab8fc860371","score":"0.61385643","text":"def SaveData(out,run_num):\n runnumber=getnumor(run_num)\n base_name = qtg_par[\"instname\"]\n\n fullname=os.getcwd()+base_name+str(runnumber)+'.spe'\n SaveSPE(out,fullname)","title":""},{"docid":"d18f1342f7728f698b86854c167164d0","score":"0.6127893","text":"def sgnSave(self):\n\n self.uiSaveSelectedCamera()\n self.uiSaveSelectedRendersFolder()\n self.uiSaveSelectedScenesFolder()\n self.uiSaveSelectedTerminalApp()\n self.uiSaveSelectedSettings()\n self.uiSaveSelectedRenderEngine()\n\n bResult = self.uiGetCheckResults()\n\n if bResult:\n self.core.setGlobalsValue(\"sDefaultEngine\", self.sSelectedRenderEngine)\n\n self.core.setGlobalsValue(\"sOutputRenders\", self.sSelectedRendersFolder)\n self.core.setGlobalsValue(\"sOutputScenes\", self.sSelectedScenesFolder)\n self.core.setGlobalsValue(\"sDefaultCamera\", self.sSelectedCamera)\n\n self.core.setGlobalsValue(\"iWidth\", int(self.sSelectedWidth))\n self.core.setGlobalsValue(\"iHeight\", int(self.sSelectedHeight))\n\n self.core.setGlobalsValue(\"iStart\", int(self.sSelectedStart))\n self.core.setGlobalsValue(\"iEnd\", int(self.sSelectedEnd))\n self.core.setGlobalsValue(\"iStep\", int(self.sSelectedStep))\n\n self.core.setGlobalsValue(\"sTerminalApp\", self.sSelectedTerminalApp)\n\n self.core.setGlobalsValue(\"bSettingImportRefs\", self.bSelectedSettingImportRefs)\n\n self.core.saveGlobals()\n\n self.uiCloseWindow()","title":""},{"docid":"97119e72641b6e8911836605aae3d6e5","score":"0.6126601","text":"def retrieve_outputs(self, event_name: str, sim_type: str):\n\n job_name = self._get_job_name(event=event_name, sim_type=sim_type, new=False)\n salvus_job = sapi.get_job(\n site_name=self.comm.project.site_name, job_name=job_name\n )\n if sim_type == \"forward\":\n destination = self.comm.lasif.find_seismograms(\n event=event_name, iteration=self.comm.project.current_iteration\n )\n\n elif sim_type == \"adjoint\":\n destination = self.comm.lasif.find_gradient(\n iteration=self.comm.project.current_iteration,\n event=event_name,\n smooth=False,\n inversion_grid=False,\n just_give_path=True,\n )\n\n else:\n raise InversionsonError(\n f\"Simulation type {sim_type} not supported in this function\"\n )\n salvus_job.copy_output(\n destination=os.path.dirname(destination),\n allow_existing_destination_folder=True,\n )","title":""},{"docid":"732b8f529b534b2175f56c6362517f2e","score":"0.6123155","text":"def save_outputs(display_frames):\n print('Saving plots...')\n # Delete existing plots and simulation gif\n if os.path.exists('./Plots'):\n shutil.rmtree('./Plots')\n os.mkdir('./Plots')\n # Save plots in parallel using number of cpu cores - 2\n Parallel(n_jobs=os.cpu_count() - 2)(delayed(save_plot)(display_frames[i], i) for i in range(len(display_frames)))\n # Generate simulation gif\n save_animation()","title":""},{"docid":"ca693c6d219cc1aeea4053281aba6d32","score":"0.6119877","text":"def save_results(self):\n self.rf.matrix.data.to_csv(\"rf_results_averages.csv\", index=False)\n pd.DataFrame(self.rf.matrix.matrix).to_csv(\"rf_confusion_matrix.csv\", index=False)\n pd.DataFrame(self.rf.matrix.matrix_normalised[0, :, :]).to_csv(\"rf_confusion_matrix_normalised.csv\",\n index=False)\n self.knn.matrix.data.to_csv(\"knn_results_averages.csv\", index=False)\n pd.DataFrame(self.rf.matrix.matrix).to_csv(\"knn_confusion_matrix.csv\", index=False)\n pd.DataFrame(self.rf.matrix.matrix_normalised[0, :, :]).to_csv(\"knn_confusion_matrix_normalised.csv\",\n index=False)","title":""},{"docid":"4057bea0a85a8e50e9fdf832cebbf8a8","score":"0.6119099","text":"def save_predictor(self, save_folder):\n simple_save(\n tf.get_default_session(),\n save_folder,\n inputs={\"obs\": self.model.act_model.X},\n outputs={\n \"action\": self.model.act_model.action,\n \"value\": self.model.act_model.vf,\n \"action_probs\": self.model.act_model.action_probs\n })","title":""},{"docid":"f8853d3db2a6030cf5a8476ac2590969","score":"0.61119646","text":"def outputs(self):\r\n\r\n return self.__outputs","title":""},{"docid":"31615ca3c999a285861a6f27ca31376a","score":"0.61114407","text":"def store_model_outputs(self, model, iter_num: int):\n assert model and model.outputs is not None, \"No model has been run\"\n # outputs_df = db.store.build_outputs_table([model], run_id=iter_num, chain_id=self.chain_id)\n derived_outputs_df = db.store.build_derived_outputs_table(\n [model], run_id=iter_num, chain_id=self.chain_id\n )\n # self.db.append_df(db.store.Table.OUTPUTS, outputs_df)\n self.db.append_df(db.store.Table.DERIVED, derived_outputs_df)","title":""},{"docid":"e6c3223bb4a5c13e85f55dba23dff1a3","score":"0.6106897","text":"def do_save(self, args):\n if not CFState.get_instance().active_target:\n self.pwarning(\"\\n [!] Not interacting with a target. Set the active target with `interact`.\\n\")\n return\n module_path = \"/\".join(CFState.get_instance().active_target.__module__.split(\".\")[:-1])\n if \"results\" not in os.listdir(module_path):\n os.mkdir(f\"{module_path}/results\")\n\n filename = f\"{module_path}/results/{CFState.get_instance().active_target.model_name}_{CFState.get_instance().active_target.target_id}.json\"\n with open(filename, \"w\") as outfile:\n json.dump(CFState.get_instance().active_target.dump(), outfile, indent=1)\n\n self.poutput(f\"\\n[+] Successfully wrote {filename}\\n\")","title":""},{"docid":"df1aa5fe5018211b03cb917f00347b91","score":"0.6091375","text":"def outputs(self) -> Dict[str, Any]:\n if not self.is_complete:\n raise _user_exceptions.FlyteAssertion(\n \"Please wait until the node execution has completed before requesting the outputs.\"\n )\n if self.error:\n raise _user_exceptions.FlyteAssertion(\"Outputs could not be found because the execution ended in failure.\")\n return self._outputs","title":""},{"docid":"dcbde3c514ab191d8f39fddcc94865d6","score":"0.6087984","text":"def saveSimulationResultsToFiles(mainMemory, l1Cache, l2Cache, statResults):\n mainMemory.saveMemoryToFile(config.getMainMemoryStatusOutputFilePath())\n l1Cache.saveMemoryToFile(config.getL1CacheStatusOutputFilePath())\n if l2Cache:\n l2Cache.saveMemoryToFile(config.getL2Way0CacheStatusOutputFilePath())\n l2Cache.saveMemoryToFile(config.getL2Way1CacheStatusOutputFilePath())\n\n with open(config.getStatsFileName(), 'w') as statsFile:\n for stat in statResults:\n statsFile.write(str(stat) + '\\n')","title":""},{"docid":"5f875b0bf2c2e436578a4540fbaa50dc","score":"0.60832655","text":"def save_model(self):\n tosave = {'rmsecv':self.rmsecv,\n 'trained':self.trained,\n 'cv':self.cv,\n 'columns':self.columns,\n 'lmbda':self.lmbda,\n 'max_evals':self.max_evals,}\n\n with open(os.path.join(self.bcupath, 'misc-estimator.pkl'), 'wb') as handle:\n pickle.dump(tosave, handle, protocol=pickle.HIGHEST_PROTOCOL)\n\n #print('saved : ', self.scaler.mean_)\n joblib.dump(self.scaler, os.path.join(self.bcupath, 'scaler.pkl'))\n joblib.dump(self.pca, os.path.join(self.bcupath, 'pca.pkl'))\n joblib.dump(self.estimator, os.path.join(self.bcupath, 'estimator.pkl'))","title":""},{"docid":"b3828329f70e5023d303e26af135d1d0","score":"0.6077398","text":"def outputs(self):\n return self._outputs","title":""},{"docid":"b3828329f70e5023d303e26af135d1d0","score":"0.6077398","text":"def outputs(self):\n return self._outputs","title":""},{"docid":"6d0bdad6221cf4402035aa38772527d4","score":"0.6077258","text":"def write_results(self):\n\n # Save the results file.\n dir = self.write_results_dir + 'final'\n self.interpreter.results.write(file='results', dir=dir, force=True)\n\n # The Grace plots.\n dir = self.write_results_dir + 'final' + sep + 'grace'\n self.interpreter.grace.write(x_data_type='res_num', y_data_type='s2', file='s2.agr', dir=dir, force=True)\n self.interpreter.grace.write(x_data_type='res_num', y_data_type='s2f', file='s2f.agr', dir=dir, force=True)\n self.interpreter.grace.write(x_data_type='res_num', y_data_type='s2s', file='s2s.agr', dir=dir, force=True)\n self.interpreter.grace.write(x_data_type='res_num', y_data_type='te', file='te.agr', dir=dir, force=True)\n self.interpreter.grace.write(x_data_type='res_num', y_data_type='tf', file='tf.agr', dir=dir, force=True)\n self.interpreter.grace.write(x_data_type='res_num', y_data_type='ts', file='ts.agr', dir=dir, force=True)\n self.interpreter.grace.write(x_data_type='res_num', y_data_type='rex', file='rex.agr', dir=dir, force=True)\n self.interpreter.grace.write(x_data_type='s2', y_data_type='te', file='s2_vs_te.agr', dir=dir, force=True)\n self.interpreter.grace.write(x_data_type='s2', y_data_type='rex', file='s2_vs_rex.agr', dir=dir, force=True)\n self.interpreter.grace.write(x_data_type='te', y_data_type='rex', file='te_vs_rex.agr', dir=dir, force=True)\n\n # Write the values to text files.\n dir = self.write_results_dir + 'final'\n self.interpreter.value.write(param='s2', file='s2.txt', dir=dir, force=True)\n self.interpreter.value.write(param='s2f', file='s2f.txt', dir=dir, force=True)\n self.interpreter.value.write(param='s2s', file='s2s.txt', dir=dir, force=True)\n self.interpreter.value.write(param='te', file='te.txt', dir=dir, force=True)\n self.interpreter.value.write(param='tf', file='tf.txt', dir=dir, force=True)\n self.interpreter.value.write(param='ts', file='ts.txt', dir=dir, force=True)\n self.interpreter.value.write(param='rex', file='rex.txt', dir=dir, force=True)\n self.interpreter.value.write(param='local_tm', file='local_tm.txt', dir=dir, force=True)\n frqs = get_frequencies()\n for i in range(len(frqs)):\n comment = \"This is the Rex value with units rad.s^-1 scaled to a magnetic field strength of %s MHz.\" % (frqs[i]/1e6)\n self.interpreter.value.write(param='rex', file='rex_%s.txt'%int(frqs[i]/1e6), dir=dir, scaling=(2.0*pi*frqs[i])**2, comment=comment, force=True)\n\n # Create the PyMOL macros.\n dir = self.write_results_dir + 'final' + sep + 'pymol'\n self.interpreter.pymol.macro_write(data_type='s2', dir=dir, force=True)\n self.interpreter.pymol.macro_write(data_type='s2f', dir=dir, force=True)\n self.interpreter.pymol.macro_write(data_type='s2s', dir=dir, force=True)\n self.interpreter.pymol.macro_write(data_type='amp_fast', dir=dir, force=True)\n self.interpreter.pymol.macro_write(data_type='amp_slow', dir=dir, force=True)\n self.interpreter.pymol.macro_write(data_type='te', dir=dir, force=True)\n self.interpreter.pymol.macro_write(data_type='tf', dir=dir, force=True)\n self.interpreter.pymol.macro_write(data_type='ts', dir=dir, force=True)\n self.interpreter.pymol.macro_write(data_type='time_fast', dir=dir, force=True)\n self.interpreter.pymol.macro_write(data_type='time_slow', dir=dir, force=True)\n self.interpreter.pymol.macro_write(data_type='rex', dir=dir, force=True)\n\n # Create the Molmol macros.\n dir = self.write_results_dir + 'final' + sep + 'molmol'\n self.interpreter.molmol.macro_write(data_type='s2', dir=dir, force=True)\n self.interpreter.molmol.macro_write(data_type='s2f', dir=dir, force=True)\n self.interpreter.molmol.macro_write(data_type='s2s', dir=dir, force=True)\n self.interpreter.molmol.macro_write(data_type='amp_fast', dir=dir, force=True)\n self.interpreter.molmol.macro_write(data_type='amp_slow', dir=dir, force=True)\n self.interpreter.molmol.macro_write(data_type='te', dir=dir, force=True)\n self.interpreter.molmol.macro_write(data_type='tf', dir=dir, force=True)\n self.interpreter.molmol.macro_write(data_type='ts', dir=dir, force=True)\n self.interpreter.molmol.macro_write(data_type='time_fast', dir=dir, force=True)\n self.interpreter.molmol.macro_write(data_type='time_slow', dir=dir, force=True)\n self.interpreter.molmol.macro_write(data_type='rex', dir=dir, force=True)\n\n # Create a diffusion tensor representation of the tensor, if a PDB file is present and the local tm global model has not been selected.\n if hasattr(cdp, 'structure') and hasattr(cdp, 'diff_tensor'):\n dir = self.write_results_dir + 'final'\n self.interpreter.structure.create_diff_tensor_pdb(file=\"tensor.pdb\", dir=dir, force=True)","title":""},{"docid":"7342431d7f3b0be140159ce157a2f83e","score":"0.606516","text":"def _save_results(\n self,\n gt_rgb: torch.Tensor,\n pred_rgb: torch.Tensor,\n gt_seg: torch.Tensor,\n pred_seg: torch.Tensor,\n gt_depth: torch.Tensor,\n pred_depth: torch.Tensor,\n path: str,\n ) -> None:\n\n save_rgb_results(gt_rgb[0], pred_rgb[0], path)\n save_seg_results(gt_seg[0], pred_seg[0], path)\n save_depth_results(gt_depth[0], pred_depth[0], path)","title":""},{"docid":"40e3b06e906673d4e9073b370e0cc240","score":"0.6060154","text":"def save_results(self):\n if self.directory is None:\n return\n tmc_dir = os.path.join(\n self.directory, \n 'mem_tmc_{}.pkl'.format(self.tmc_number.zfill(4))\n )\n\n raw_list = []\n for j in range(self.mem_tmc.shape[0]):\n row_dict = { self.idxs_tmc[j][i] : self.mem_tmc[j][i] for i in range(self.mem_tmc.shape[1]) }\n raw_list.append(row_dict)\n\n pkl.dump(raw_list, open(tmc_dir, 'wb'))\n #pkl.dump({'mem_tmc': self.mem_tmc, 'idxs_tmc': self.idxs_tmc}, \n # open(tmc_dir, 'wb'))","title":""},{"docid":"06d278f67ccedad293cdd7fbca4f047a","score":"0.6058251","text":"def save_csv_results(self):\n # Train\n self.train_results.to_csv(f'output_{self.data_name}\\\\winnow_{self.data_name}_train_results.csv')\n\n # Test\n self.test_results.to_csv(f'output_{self.data_name}\\\\winnow_{self.data_name}_test_results.csv')","title":""},{"docid":"cb9100981af5dcc2cf06a970942eb82f","score":"0.60476255","text":"def _save_numpy(self, iter_):\n key = self._get_numpy_count()\n\n coords_to_export = self.simulated_coords[self._npy_starting_index:iter_]\n coords_to_export = self._swap_and_export(coords_to_export)\n np.save(\"{}_coords_{}.npy\".format(\n self.filename, key), coords_to_export)\n\n if self.save_forces:\n forces_to_export = self.simulated_forces[self._npy_starting_index:iter_]\n forces_to_export = self._swap_and_export(forces_to_export)\n np.save(\"{}_forces_{}.npy\".format(\n self.filename, key), forces_to_export)\n\n if self.save_potential:\n potentials_to_export = self.simulated_potential[self._npy_starting_index:iter_]\n potentials_to_export = self._swap_and_export(potentials_to_export)\n np.save(\"{}_potential_{}.npy\".format(\n self.filename, key), potentials_to_export)\n\n if self.friction is not None:\n kinetic_energies_to_export = self.kinetic_energies[self._npy_starting_index:iter_]\n kinetic_energies_to_export = self._swap_and_export(\n kinetic_energies_to_export)\n np.save(\"{}_kineticenergy_{}.npy\".format(self.filename, key),\n kinetic_energies_to_export)\n\n self._npy_starting_index = iter_\n self._npy_file_index += 1","title":""},{"docid":"ae6c6650f5d1027b88b9a29784d7207e","score":"0.6026936","text":"def Save_Status(self):\r\n \r\n np.save(self.Folder + \"\\\\Step_%i.npy\"%self.step, {'u':self.u,\r\n 'RHS':self.RHS,\r\n 'time':self.t})","title":""},{"docid":"6c96bbc53038e293fc458faa34457591","score":"0.6019201","text":"def writeSave(self):\n\n #store required data in savegame\n self.savegame.currentMap = self.player.map.fn\n self.savegame.currentPosition = self.player.position\n self.savegame.currentLevel = self.player.level\n self.savegame.currentDirection = self.player.direction\n self.savegame.party = self.player.party\n\n #open the file for writing, dump the save game, and close\n try:\n f = open(self.savegame.fn, \"w\")\n pickle.dump(self.savegame, f)\n f.close()\n except IOError:\n raise error.DittoIOException(\"Ditto main\", fn)\n\n print \"Game saved to \" + self.savegame.fn","title":""},{"docid":"aacb8565baf441a45deccc33cf0ded53","score":"0.6015083","text":"def _save_time_step(self):\n for var_name, value_pointer in self._save_vars.items():\n self.results[var_name].append(\n operator.attrgetter(value_pointer)(self)\n )","title":""},{"docid":"aacb8565baf441a45deccc33cf0ded53","score":"0.6015083","text":"def _save_time_step(self):\n for var_name, value_pointer in self._save_vars.items():\n self.results[var_name].append(\n operator.attrgetter(value_pointer)(self)\n )","title":""},{"docid":"16c0762df48b4937dc130e7a2acdca36","score":"0.60026264","text":"def writeObservations(self):\n # Set model output arrays to None to initialise\n head = None\n sfr_df = None\n stream_options = ['stage', 'depth', 'discharge']\n # Write observation to file\n obs_group = self.model_data.observations.obs_group\n for obs_set in obs_group:\n obs_type = obs_group[obs_set]['obs_type']\n # Import the required model outputs for processing\n if obs_type == 'head':\n # Check if model outputs have already been imported and if not import\n if not head:\n headobj = self.import_heads()\n head = headobj.get_alldata()\n elif obs_type in stream_options:\n try:\n self.sfr_df\n except Exception:\n sfr_df = self.importSfrOut()\n # End except\n else:\n continue\n # End if\n\n group_set = obs_group[obs_set]\n obs_df = self.get_active_obs_group(group_set)\n sim_map_dict = group_set['mapped_observations']\n\n unique_obs_df_zone = obs_df['zone'].unique()\n if obs_type in stream_options:\n sfr_location = group_set['locations']['seg_loc']\n for zone in unique_obs_df_zone:\n zone_txt = obs_set if len(unique_obs_df_zone) == 1 else obs_set + zone\n with open(os.path.join(self.data_folder, 'observations_' + zone_txt + '.txt'), 'w') as f:\n obs_df_zone = obs_df[obs_df['zone'] == zone]\n for observation in obs_df_zone.index:\n interval = int(obs_df_zone['interval'].loc[observation])\n name = obs_df_zone['name'].loc[observation]\n seg = sfr_location.loc[name]\n sfr = sfr_df\n col_of_interest = obs_type\n\n if obs_type == 'discharge':\n col_of_interest = 'Qout'\n\n sim_obs = sfr[(sfr['segment'] == seg) & (sfr['time'] == interval)][col_of_interest]\n f.write('%f\\n' % sim_obs)\n # End for\n # End with\n # End for\n elif obs_type == 'head':\n for zone in unique_obs_df_zone:\n zone_txt = 'head' if len(unique_obs_df_zone) == 1 else zone\n with open(os.path.join(self.data_folder, 'observations_' + zone_txt + '.txt'), 'w') as f:\n obs_df_zone = obs_df[obs_df['zone'] == zone]\n for observation in obs_df_zone.index:\n interval = int(obs_df_zone['interval'].loc[observation])\n name = obs_df_zone['name'].loc[observation]\n name_dict = sim_map_dict[name]\n (x_cell, y_cell) = self.model_data.mesh2centroid2Dindex[(name_dict[1], name_dict[2])]\n (lay, row, col) = [name_dict[0], name_dict[1], name_dict[2]]\n\n sim_heads = [head[interval][lay][row][col]]\n sim_head = np.mean(sim_heads)\n f.write('%f\\n' % sim_head)\n # End for\n # End with\n # End for\n else:\n print(\"Unknown observation type!\")\n # End if\n # End for","title":""},{"docid":"854c25e3ed3279c2709e53f86c6ca0f1","score":"0.60018647","text":"def save_data(self):\n J1 = self.get_joints_data(self.actuation1, self.actuation1_direction, self.robot.J1)\n J2 = self.get_joints_data(self.actuation2, self.actuation1_direction, self.robot.J2)\n J3 = self.get_joints_data(self.actuation2, self.actuation1_direction, self.robot.J3)\n J4 = self.get_joints_data(self.actuation1, self.actuation1_direction, self.robot.J4)\n\n x, y, z = Utils.list_coord2list(self.robot.position)\n\n robot = pd.DataFrame(\n np.array([\n self.actuation1,\n self.actuation1_direction,\n self.actuation2,\n self.actuation2_direction,\n x, y, z,\n np.array(self.robot.angle)[:, 0],\n np.array(self.robot.angle)[:, 1],\n np.array(self.robot.angle)[:, 2]\n ]).T,\n columns=[\n 'u1', 'u1_dir',\n 'u2', 'u2_dir',\n 'x', 'y', 'z',\n 'pitch', 'roll', 'yaw'\n ]\n )\n\n self.data = {}\n self.data['J1'] = J1\n self.data['J2'] = J2\n self.data['J3'] = J3\n self.data['J4'] = J4\n self.data['robot'] = robot\n self.data = pd.concat(self.data, axis=1)\n\n self.data.to_csv('{0}/results/{1}{2}{3}{4}.csv'.format(\n Path(__file__).resolve().parent,\n self.robot.J1.sequence,\n self.robot.J2.sequence,\n self.robot.J3.sequence,\n self.robot.J4.sequence\n ))\n self.data.to_pickle('{0}/results/{1}{2}{3}{4}.pkl'.format(\n Path(__file__).resolve().parent,\n self.robot.J1.sequence,\n self.robot.J2.sequence,\n self.robot.J3.sequence,\n self.robot.J4.sequence\n ))","title":""},{"docid":"d45d3b29edd2e78a209096b41aeb62bd","score":"0.5997252","text":"def save_state(self, save_dir):\n self.replay_buffer.save(os.path.join(save_dir, 'replay'))","title":""},{"docid":"812d40b69e0e78251b785e46c6b9a1ef","score":"0.5993922","text":"def saveTrajectory(self):\n filepath = 'output/'\n if path.exists(filepath):\n filename = 'traj.npy'\n filepath += filename \n np.save(filepath, self.camera_traj)\n print(\"Trajectory saved to: \" + filepath)\n else:\n print(\"Writing path does not exist!\")","title":""},{"docid":"b4677fc5e86dee407191b50e0b61ffc7","score":"0.5983296","text":"def save_result(self):\n if self.episode_number == 1 or not self.do_evaluation_iterations:\n self.game_full_episode_scores.extend([self.total_episode_score_so_far])\n self.rolling_results.append(np.mean(self.game_full_episode_scores[-1 * self.rolling_score_window:]))\n self.save_max_result_seen()\n\n elif (self.episode_number - 1) % TRAINING_EPISODES_PER_EVAL_EPISODE == 0:\n self.game_full_episode_scores.extend(\n [self.total_episode_score_so_far for _ in range(TRAINING_EPISODES_PER_EVAL_EPISODE)])\n self.rolling_results.extend(\n [np.mean(self.game_full_episode_scores[-1 * self.rolling_score_window:]) for _ in\n range(TRAINING_EPISODES_PER_EVAL_EPISODE)])\n self.save_max_result_seen()","title":""},{"docid":"0427888967e41816b3c10688ce67047e","score":"0.5981149","text":"def _save_trials(self, trials_log):\n trials_object = (self._hpopt_trials, self._evaluated_params)\n object_dump(trials_object, trials_log)\n tpe_trials_results = pd.DataFrame(self._hpopt_trials.results)\n csv_file = trials_log + '.csv'\n tpe_trials_results.to_csv(csv_file)\n tpe_trials_results = pd.DataFrame(self._evaluated_params)\n csv_file = trials_log + '.params.csv'\n tpe_trials_results.to_csv(csv_file)","title":""},{"docid":"f28e358f6abbc05fa568ff28ae8aff20","score":"0.5969702","text":"def commit(self,deleteOutput=True,restore=False):\n if self.DEBUG:\n self.say.message_debug(self.CLASS_NAME+\"::commit()\")\n\n if not os.path.exists(self.directory):\n if restore:\n self.say.error(\"Set to restore, but simulation does not exist!\")\n exit(0)\n else:\n self.say.message(\"Simulation directory doesn't exist! So creating it\")\n os.makedirs(self.directory)\n self.project.writeToHistory(self.name+' created')\n\n if restore and deleteOutput:\n self.say.warning(\"You really don't want to delete output when\"\n \"restoring! So we changed it\")\n deleteOutput = False\n\n # We use .copy2 because we also want to copy permissions\n # Copy executable\n if self.compiled:\n self.copyBinary()\n\n # Update configuration files\n tools.inputWrite(self.inputDicts[0], self.directory+'/input')\n c_ = 1\n # Md config files\n if os.path.isfile(self.inputFile+'.md'):\n tools.inputWrite(self.inputDicts[c_], self.directory+'/input')\n #shutil.copy2(self.inputFile+'.md', self.directory)\n if self.template is not '':\n mdfile = self.template+\"/\"+self.inputDicts[c_]['init_file'][1:-1]\n shutil.copy2(mdfile, self.directory)\n c_ += 1\n # Elec config files\n if os.path.isfile(self.inputFile+'.elec'):\n tools.inputWrite(self.inputDicts[c_], self.directory+'/input')\n #shutil.copy2(self.inputFile+'.elec', self.directory)\n c_ += 1\n self.project.writeToHistory(\"Configuration files were written\",sim=self.name)\n\n self.project.writeToHistory(\"Simulation commited\",sim=self.name)\n\n # Create output directory\n # If it exists, do we delete the files?\n outputdir = self.inputDicts[0]['folder'][1:-1]\n fd = self.directory+\"/\"+outputdir\n if os.path.exists(fd):\n if deleteOutput:\n if len(outputdir) > 1:\n shutil.rmtree(fd)\n self.project.writeToHistory(\"Output dir was removed!\",sim=self.name)\n os.makedirs(fd)\n else:\n if not restore:\n self.say.warning(\"Output dir existed, we are adding files there\")\n else:\n os.makedirs(fd)","title":""},{"docid":"6b850c6e99059e2a8fc69392fee25eec","score":"0.5968843","text":"def _do_outputs(self):","title":""},{"docid":"878c2c1a03855c7544b274321258a64a","score":"0.59677136","text":"def Outputs(self):\n return self.__outputs","title":""},{"docid":"18186eef48524b68affb826d3dd359df","score":"0.5965068","text":"def write_model(self):\n\n model_file_name = self.ml_service + '_model_' + self.model_name + '_' + self.model_version_to_train + '.pkl'\n model_full_file_name = os.path.join(self.finder.get_output_folder(), model_file_name)\n model_objects_list = [self.trained_model, self.scaler_X, self.scaler_Y, self.model_config.j_config]\n os.makedirs(self.finder.get_output_folder(), exist_ok=True)\n joblib.dump(value=model_objects_list, filename=model_full_file_name)\n\n #if self.run_type == 'run_on_azure':\n # Also save to outputs folder for auto save in Azure\n os.makedirs('outputs', exist_ok=True)\n # note file saved in the outputs folder is automatically uploaded into experiment record\n joblib.dump(value=model_objects_list, filename=os.path.join('outputs', model_file_name))\n print('\\nwriting to outputs: ' + model_file_name)","title":""},{"docid":"ac69c2f975364f08ce373f1992eb5bc4","score":"0.5952777","text":"def save_outputs(ML_pipe, output_dir):\n stack, conjunction = add_conjunction(ML_pipe.feature_importances)\n conjunction_results = {'Features': conjunction, 'Raw': stack}\n save_xls(\n conjunction_results, os.path.join(output_dir, 'features.xlsx'))\n\n save_xls(\n ML_pipe.predictions, os.path.join(output_dir, 'predictions.xlsx'))\n\n ML_pipe.model_performance.to_excel(\n os.path.join(output_dir, 'performance.xlsx'))\n\n try:\n ML_pipe.feature_selection_gridsearch_results.to_excel(\n os.path.join(output_dir, 'feature_selection_gridsearch.xlsx'))\n ML_pipe.regression_gridsearch_results.to_excel(\n os.path.join(output_dir, 'regression_gridsearch.xlsx'))\n except AttributeError:\n pass","title":""},{"docid":"f6b78ff0749c5bcec55b4e065dfd6c59","score":"0.59369737","text":"def save(self):\n torch.save(self.policy.state_dict(), str(config.fc1_units)+'_'+str(config.fc2_units) + '_model.pth')","title":""},{"docid":"7995c4f61d8852492e939be1507e0252","score":"0.59348357","text":"def save_result(self):\n for ep in range(len(self.many_episode_rewards)):\n total_reward = np.sum(self.many_episode_rewards[ep])\n self.game_full_episode_scores.append(total_reward)\n self.rolling_results.append(np.mean(self.game_full_episode_scores[-1 * self.rolling_score_window:]))\n self.save_max_result_seen()","title":""},{"docid":"17ef846eaeb33dfa7d0e2863512de8dd","score":"0.59301054","text":"def output_results(self):\n rg = HtmlReportGenerator(self.args)\n add_plots(self, rg) # change add_plots to show different plots!\n rg.variables.update(analyse(self))\n rg.generate()\n rg.save()\n print(\"Report saved to {0}\".format(self.args[\"filename_out\"]))","title":""},{"docid":"4bdd0fab4a153d32b5287139073554a8","score":"0.5923582","text":"def save_state_to_disk_and_return(self):\n\n backtest_state = {\n \"timestamp\": time.time(),\n \"settings\": {\n \"start\": self.start,\n \"end\": self.end,\n \"output_path\": self.output_path\n },\n \"perf\": self.perf,\n \"stats\": self.stats,\n \"portfolio\": {\n \"costs\": self.portfolio.costs,\n \"received\": self.portfolio.received,\n \"portfolio_value\": self.portfolio.portfolio_value,\n \"order_history\": self.portfolio.order_history_to_df(),\n \"signals\": self.portfolio.signals_to_df(),\n \"monthly_returns\": self.portfolio.get_monthly_returns(), # use to make histogram and return per time\n },\n \"broker\": {\n \"blotter_history\": self.broker.blotter_history_to_df(),\n \"all_trades\": self.broker.all_trades_to_df(),\n \"trade_objects\": self.broker.all_trades_as_objects(),\n \"cancelled_orders\": self.broker.cancelled_orders_to_df()\n }\n }\n timestamp = datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n pickle_path = self.output_path + \"/backtest_state_\" + timestamp + \".pickle\"\n\n pickle_out = open(pickle_path,\"wb\")\n pickle.dump(backtest_state, pickle_out)\n pickle_out.close()\n\n return backtest_state","title":""},{"docid":"fe4c152676b32ac078a016b917ad0dc8","score":"0.59220284","text":"def test_sim(write):\n with Simulation(write,'r') as sim:\n for step in sim:\n pass\n system_routing = SystemStats(sim)\n sim.report()\n print(system_routing.runoff_stats)","title":""},{"docid":"4529020862521743503dbc3002c30fca","score":"0.5921215","text":"def save_current_state(self, save_dir=None, **kwargs):\n super().save_current_state(save_dir, **kwargs)\n if self._save_rlr_every and self.istep % self._save_rlr_every == 0:\n thread_prefix = f'thread{self._thread:02d}_' if self._thread is not None else ''\n fname = f'{thread_prefix}gen{self._istep:03d}_search_radius'\n np.save(os.path.join(self._logdir, fname), self._r)","title":""},{"docid":"e5be7eccdc3bbc2202c53a3f028105b4","score":"0.59203136","text":"def save_result(self):\n self.game_full_episode_scores.append(self.total_episode_score_so_far)\n self.rolling_results.append(np.mean(self.game_full_episode_scores[-100:]))","title":""},{"docid":"58a964450ee97c30bb44bc705935a429","score":"0.5919577","text":"def save_summary_to_file(self):\n\n # Save a csv file with the power measurements done in all the tests\n\n path = os.path.join(self.log_path, self.RESULTS_SUMMARY_FILENAME)\n\n with open(path, 'w') as csvfile:\n csvfile.write('test,avg_power')\n for test_name, value in self.power_results.items():\n csvfile.write('\\n{},{}'.format(test_name, value))\n\n # Save a csv file with the calibration table for each simulation type\n\n for sim_type in self.calibration_table:\n\n path = os.path.join(\n self.log_path, '{}_{}'.format(sim_type,\n self.CALIBRATION_TABLE_FILENAME))\n\n with open(path, 'w') as csvfile:\n csvfile.write('band,dl_pathloss, ul_pathloss')\n for band, pathloss in self.calibration_table[sim_type].items():\n csvfile.write('\\n{},{},{}'.format(\n band, pathloss.get('dl', 'Error'),\n pathloss.get('ul', 'Error')))","title":""},{"docid":"2577e2e5a7d07c9b05b705f15881cab2","score":"0.5918313","text":"def save(self, step):\n os.chdir(self.load_dir)\n self.saver.save(self.session, self.save_dir, step+self.global_step)\n np.savetxt('iteration.dat', np.array([step+self.global_step]))","title":""},{"docid":"1ba118d91fdfa28d9dcf8976c94f7722","score":"0.59144294","text":"def outputs(self):\r\n if self.state is None:\r\n raise RuntimeError(\"Method add_to was not called.\")\r\n return self.state.outputs","title":""},{"docid":"2fd0604adee2b2a8c0fd6c37d370cb0a","score":"0.5906942","text":"def save(self, fileName: str) -> NoReturn:\n A = []\n B = []\n pi = []\n for i in range(self.statesNumber()):\n a = []\n for j in range(self.statesNumber()):\n a.append(self.getTransition(i, j))\n A.append(a)\n b = []\n for sym in range(self.symbolsNumber()):\n b.append(self.getEmission(i, sym))\n B.append(b)\n pi.append(self.getInitial(i))\n with open(fileName, \"w\") as f:\n json.dump({\"A\": A, \"B\": B, \"pi\": pi,\n \"symbols\": self.getSymbols()}, f)","title":""},{"docid":"201fef74f56f7b4ed860c3a13d78b922","score":"0.5896817","text":"def save(self, out_path, out_name):\n def write_array(path, X):\n np.savetxt(path, X, fmt='%s')\n\n if not os.path.isdir(out_path):\n os.makedirs(out_path)\n\n write_array(\n os.path.join(out_path, out_name + '.data'),\n self.get_data('X'))\n write_array(\n os.path.join(out_path, out_name + '_feat.name'), \n self.feat_name)\n\n if 'y' in self.data:\n write_array(\n os.path.join(out_path, out_name + '.solution'),\n self.get_data('y'))\n\n if 'X_train' and 'X_test' in self.data:\n write_array(\n os.path.join(out_path, out_name + '_train.data'),\n self.get_data('X_train'))\n write_array(\n os.path.join(out_path, out_name + '_test.data'),\n self.get_data('X_test'))\n if 'y_train' and 'y_test' in self.data:\n write_array(\n os.path.join(out_path, out_name + '_test.solution'),\n self.get_data('y_train'))\n write_array(\n os.path.join(out_path, out_name + '_test.solution'),\n self.get_data('y_test'))\n write_array(\n os.path.join(out_path, out_name + '_label.name'),\n self.label_name)\n\n with open(os.path.join(out_path, out_name + '_public.info'), 'w') as f:\n for key, item in self.info.items():\n f.write(str(key))\n f.write(' = ')\n f.write(str(item))\n f.write('\\n')","title":""},{"docid":"a70a5f0e1661986cdc6f6a1ce9aea3fc","score":"0.58953255","text":"def saving(self):\n print(\"Saving....\")","title":""},{"docid":"fea2878be04bc139df4ec9d82094a205","score":"0.5893891","text":"def save(self, filename):\n save_dict = {'init_dict': self.init_dict,\n 'agent_params': [a.get_params() for a in self.agents]}\n torch.save(save_dict, filename)","title":""},{"docid":"c5016fd7fef742480ca37f102482fc98","score":"0.58869094","text":"def outputs(self):\n return super().outputs","title":""}],"string":"[\n {\n \"docid\": \"62357c71511468de2da3ef87b7e3c34b\",\n \"score\": \"0.75556\",\n \"text\": \"def save(self):\\n # Message #\\n self.parent.log.info(\\\"Saving final simulations results to disk.\\\")\\n # The classifier values #\\n self['values'] = self.sim.sit.classifier_value_ids\\n # All the tables that are within the SimpleNamespace of `sim.results` #\\n self['area'] = self.runner.internal['pools']\\n self['classifiers'] = self.runner.internal['classifiers']\\n self['flux'] = self.runner.internal['flux']\\n self['parameters'] = self.runner.internal['parameters']\\n self['pools'] = self.runner.internal['pools']\\n self['state'] = self.runner.internal['state']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1779797ec4ebfd520561913d0f987829\",\n \"score\": \"0.716197\",\n \"text\": \"def write_outputs(self):\\n self.output.write_data_to_disk()\\n if not self._no_pickle:\\n state_pkl_filename = os.path.join(\\n self.output.output_dir, f\\\"calstate-{self.chain_idx}.pkl\\\"\\n )\\n pickle.dump(self, open(state_pkl_filename, \\\"wb\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f2f52bed922d3b259d295dcca510e6e\",\n \"score\": \"0.71024096\",\n \"text\": \"def save(self):\\n save_output_residual = (time.mktime(\\n Coordinator.current_date.timetuple()) - self.first_save_output_residual) % get_seconds(\\n self.save_output_interval)\\n if abs(save_output_residual) <= 1e-7:\\n self.handle_old_dps()\\n self.handle_outliers()\\n self.save_model_and_files()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2caa6be1ea311ca4aea805a52d9c33db\",\n \"score\": \"0.7013826\",\n \"text\": \"def save(self):\\n pickle.dump(self, open(pSimFileName, 'wb'), pickle.HIGHEST_PROTOCOL)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1082b4db9d1895fb14747ed61a8d51e8\",\n \"score\": \"0.69863796\",\n \"text\": \"def dump_results(self, success):\\n system = self.system\\n\\n t, _ = elapsed()\\n\\n if success and (not system.files.no_output):\\n\\n # system.varout.dump()\\n system.varout.dump_np_vars()\\n _, s = elapsed(t)\\n logger.info('Simulation data dumped in {:s}.'.format(s))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a94767a84a95c897907dcc74c417572f\",\n \"score\": \"0.6926807\",\n \"text\": \"def save_results(self):\\n \\n df_fname = '{0}.T'.format(self.df_agents_file)\\n file = open(df_fname, 'w')\\n pickle.dump(self.T, file)\\n file.close()\\n \\n df_fname = '{0}.D_agent'.format(self.df_agents_file)\\n file = open(df_fname, 'w')\\n pickle.dump(self.D_agent, file)\\n file.close() \\n\\n df_fname = '{0}.D_tstep'.format(self.df_agents_file)\\n file = open(df_fname, 'w')\\n pickle.dump(self.D_tstep, file)\\n file.close() \\n \\n df_fname = '{0}.D_edges'.format(self.df_agents_file)\\n file = open(df_fname, 'w')\\n pickle.dump(self.D_edges, file)\\n file.close() \\n \\n print '{0}: Results saved.'.format(self.city)\\n sys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28d0b21b7a9b6304b3403edaaeacda9f\",\n \"score\": \"0.6892895\",\n \"text\": \"def test_saved_output(self):\\n execute_and_test_output_images(self, CliRunner(), 3, 3, \\\"save_\\\", [\\\"save\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29c83d959d1650939e0f56fca0476b57\",\n \"score\": \"0.6755299\",\n \"text\": \"def store_simulation_results(self, simulation_results, results_dir):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"867bb54a99ef08eb5495dc25882a2368\",\n \"score\": \"0.67163306\",\n \"text\": \"def save(self):\\n if self.output_folder_path:\\n\\n # Creating directories if they don't exist\\n makedirs(dirname(join(self.output_folder_path, \\\"file\\\")), exist_ok=True)\\n\\n # ### Steps data ###\\n csv_array = []\\n for k, v in self.step_traces[\\\"scores\\\"].items():\\n csv_array.append([k] + v)\\n for k, v in self.step_traces[\\\"additional_values\\\"].items():\\n csv_array.append([k] + v)\\n csv_array.append([\\\"n_replaced\\\"] + self.step_traces[\\\"n_replaced\\\"])\\n csv_array.append([\\\"timestamps\\\"] + self.step_traces[\\\"timestamps\\\"])\\n\\n with open(join(self.output_folder_path, 'steps.csv'), \\\"w\\\", newline='') as f:\\n writer = csv.writer(f)\\n for row in np.array(csv_array).T:\\n writer.writerow(row)\\n\\n # ### All inserted individuals data ###\\n if self.record_all_generated_individuals:\\n csv_array = [[\\\"step\\\"] + self.all_generated_individuals_step,\\n [\\\"SMILES\\\"] + self.all_generated_individuals_smiles,\\n [\\\"obj_calls\\\"] + self.all_generated_individuals_n_obj_calls,\\n [\\\"obj_value\\\"] + self.all_generated_individuals_obj_value,\\n [\\\"improver\\\"] + self.all_generated_individuals_improver]\\n\\n with open(join(self.output_folder_path, \\\"all_generated.csv\\\"), \\\"w\\\") as f:\\n writer = csv.writer(f)\\n for row in np.array(csv_array).T:\\n writer.writerow(row)\\n\\n # ### Last step population data ###\\n csv_array = []\\n\\n # Mutation success history\\n n_success_mut_str = []\\n n_fail_mut_str = []\\n for i, ind in enumerate(self.pop):\\n n_success_mut_str.append(str(self.n_success_mut[i]))\\n n_fail_mut_str.append(str(self.n_fail_mut[i]))\\n\\n csv_array.append([\\\"smiles\\\"] + self.pop_tabu_list)\\n\\n # Mutation success and failures\\n csv_array.append([\\\"n_success_mut\\\"] + n_success_mut_str)\\n csv_array.append([\\\"n_failures_mut\\\"] + n_fail_mut_str)\\n\\n # Scores data\\n self.curr_total_scores, self.curr_scores = self.evaluation_strategy.get_population_scores()\\n step_scores_dict = scores_to_scores_dict(self.curr_total_scores,\\n self.curr_scores,\\n self.evaluation_strategy.keys())\\n\\n for k, scores_list in step_scores_dict.items():\\n scores_list_np = np.full((self.pop_max_size,), None)\\n scores_list_np[:len(scores_list)] = scores_list\\n csv_array.append([k] + list(scores_list_np))\\n\\n # Action history data\\n csv_array.append([\\\"history_data\\\"] + self.actions_history)\\n\\n with open(join(self.output_folder_path, 'pop.csv'), \\\"w\\\", newline='') as f:\\n writer = csv.writer(f)\\n for row in np.array(csv_array).T:\\n writer.writerow(row)\\n\\n # ### Removed individuals actions recording ###\\n if self.record_history:\\n with open(join(self.output_folder_path, 'removed_ind_act_history.csv'), \\\"w\\\", newline='') as f:\\n\\n writer = csv.writer(f)\\n writer.writerow([\\\"history_data\\\", \\\"total\\\"] + self.evaluation_strategy.keys() + [\\\"smiles\\\"])\\n\\n for removed_act_history in self.removed_actions_score_smi_tuple.keys():\\n if removed_act_history != \\\"\\\":\\n total_score = self.removed_actions_score_smi_tuple[removed_act_history][0]\\n scores = self.removed_actions_score_smi_tuple[removed_act_history][1]\\n smi = self.removed_actions_score_smi_tuple[removed_act_history][2]\\n\\n writer.writerow([removed_act_history, total_score] + list(scores) + [smi])\\n\\n # ### Errors data ###\\n with open(join(self.output_folder_path, 'errors.csv'), \\\"w\\\", newline='') as f:\\n writer = csv.writer(f)\\n writer.writerow([\\\"step\\\", \\\"error\\\"])\\n for error in self.errors:\\n writer.writerow(error)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b70932305b7c443f264eda729aacc110\",\n \"score\": \"0.6612021\",\n \"text\": \"def save(self):\\n self._network.save(self._total_episodes)\\n with open(f\\\"{self.agent_dir}/checkpoints/{self._total_episodes}_replay_buffer.p\\\", \\\"wb\\\") as f:\\n pickle.dump(self._replay_buffer, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea49d7a488bbdd12540a4372ad270ad2\",\n \"score\": \"0.65874064\",\n \"text\": \"def write_results(self, save_dir, simulation_ID):\\n outfile_name=save_dir+'/'+simulation_ID+'_'+self.simtype+'.csv'\\n outfile=open(outfile_name, 'w+')\\n line1=\\\"mu= \\\"+str(self.mu)+\\\"\\\\tk= \\\"+str(self.k)+\\\"\\\\n\\\"\\n outfile.write(line1)\\n line2= \\\"Time\\\\tx_1\\\\tx_2\\\\ty_1\\\\ty_2\\\\n\\\"\\n outfile.write(line2)\\n for i in range(len(self.x1)):\\n line=str(self.dt*i)+';'+str(self.x1[i])+';'+str(self.x2[i])+';'+str(self.y1[i])+';'+str(self.y2[i])+'\\\\n'\\n outfile.write(line)\\n outfile.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7302ce3908c441efd6aed00c3ffbc047\",\n \"score\": \"0.6579228\",\n \"text\": \"def save_data(self, path=None):\\n save_file = self.save_file_name\\n if path is not None:\\n save_file = path+self.save_file_name\\n with open(save_file, 'wb') as outfile:\\n pickle.dump(np.array(self.analysis_output), outfile)\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcf175d563152ca3f5cea418095eea76\",\n \"score\": \"0.6564639\",\n \"text\": \"def runsim(self):\\n self.randwalk3d()\\n self.savetofile()\\n self.outputobject.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"956fb372e1013a3318c908de73b2180f\",\n \"score\": \"0.6542381\",\n \"text\": \"def save_output(self):\\n report_name = self.FILENAME\\n time_range = self.get_time_range_str()\\n self.wb.save(get_output_filepath(report_name, time_range))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66c441935b2a747682b09ff58fad0839\",\n \"score\": \"0.6539715\",\n \"text\": \"def save_data(self):\\n\\n repo_path = 'experimental_data/'\\n if not os.path.isdir(repo_path):\\n os.mkdir(repo_path)\\n time_stamp = time.strftime('%d_%B_%Y_%I:%M%p')\\n file_n = repo_path + 'quads_robotarium_sim_' + time_stamp + '.pckl'\\n arrays = [self.time_record, self.x_record, self.orientation_record, self.input_record]\\n with open(file_n, 'wb') as file:\\n pickle.dump(arrays, file, protocol=2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7291f105f3fc2b1b1a8a03114668a44\",\n \"score\": \"0.6515443\",\n \"text\": \"def save_runs(runs):\\n\\n for i in runs:\\n run = runs.get(i)\\n run.save(dir0=dir0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"069c6a42571adb6d14fcaf8b6d5de03a\",\n \"score\": \"0.65010774\",\n \"text\": \"def _save_results(self, directory, topology, positions):\\n\\n from simtk import unit as simtk_unit\\n simtk_positions = positions.to(unit.angstrom).magnitude * simtk_unit.angstrom\\n\\n self.coordinate_file_path = path.join(directory, 'output.pdb')\\n\\n with open(self.coordinate_file_path, 'w+') as minimised_file:\\n # noinspection PyTypeChecker\\n app.PDBFile.writeFile(topology, simtk_positions, minimised_file)\\n\\n logging.info('Coordinates generated: ' + self.substance.identifier)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cfa34b5aab46d9b911cc227b1531336\",\n \"score\": \"0.64957994\",\n \"text\": \"def save_results(self, results):\\n self.results = results\\n pickle.dump(self.results, open(os.path.join(\\n results_dir,\\n \\\"backtest_{num}.pickle\\\".format(num=self.backtest_number)), 'wb'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ac3c83dd77029c48dee2a0420f162be\",\n \"score\": \"0.64797264\",\n \"text\": \"def sim_and_record(self):\\n save_items=list(self.results_df.index)\\n self.op_sim.save_internal_parameters(*save_items)\\n self.internal_opsim_res=self.op_sim.operating_point()\\n \\n for save in save_items:\\n self.results_df.at[save, 'Value']=self.internal_opsim_res[save].as_ndarray()[0]\\n \\n if self.display_results:\\n print('.op sim internal parmter results')\\n display(self.results_df)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5b8a014c5212e7bdbc7e9e8106d512a\",\n \"score\": \"0.64786536\",\n \"text\": \"def save_data(self):\\n # Unlogged initial positions (Step not updated by Webots)\\n self.links[0, :, :] = self.links[1, :, :]\\n self.joints[0, :, :] = self.joints[1, :, :]\\n # Diff position to extract velocity\\n # Save\\n #os.makedirs(os.path.dirname(self.filename), exist_ok=True)\\n np.savez(\\n self.filename,\\n links=self.links,\\n joints=self.joints,\\n network=self.network,\\n **self.parameters\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a85f2e59f99c3e5fdac707fb8a08dc3\",\n \"score\": \"0.64685446\",\n \"text\": \"def save_results(self, out_dir: str):\\n\\n if self.graphs is None:\\n self.graph_results()\\n\\n build_out(out_dir)\\n\\n for y in self.graphs.keys():\\n\\n # have to create & save bc subsequent plots will overwrite past plots\\n self.graphs[y].create_plot()\\n self.graphs[y].save(\\\"{0}/{1}.png\\\".format(out_dir, str(y)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79f9fe43fce359806c3f6ec37a15c171\",\n \"score\": \"0.64500666\",\n \"text\": \"def save_model_and_files(self):\\n self.save_model(\\n os.path.join(os.getcwd(), 'outputs/multi_agent',\\n 'X' + str(Coordinator.current_date).replace(':', '_') + '--' + str(\\n Coordinator.dp_counter), 'model'))\\n self.write_output_to_files(\\n os.path.join(os.getcwd(), 'outputs/multi_agent',\\n 'X' + str(Coordinator.current_date).replace(':', '_') + '--' + str(\\n Coordinator.dp_counter), 'clusters'))\\n self.write_topics_to_files(\\n os.path.join(os.getcwd(), 'outputs/multi_agent',\\n 'X' + str(Coordinator.current_date).replace(':', '_') + '--' + str(\\n Coordinator.dp_counter), 'topics'))\\n self.write_tweet_ids_to_files(\\n os.path.join(os.getcwd(), 'outputs/multi_agent',\\n 'X' + str(Coordinator.current_date).replace(':', '_') + '--' + str(\\n Coordinator.dp_counter), 'clusters_tweet_ids'))\\n if self.verbose == 1:\\n print(f'{Fore.YELLOW}{self.current_date} : Save Model and Outputs -> Number of agents : {len(self.agents)}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a62bd9d0818e1150eeb161756185fb7f\",\n \"score\": \"0.64475393\",\n \"text\": \"def save_data(self, path=None):\\n save_file = self.save_file_name\\n if path is not None:\\n save_file = path+self.save_file_name\\n with open(save_file, 'wb') as outfile:\\n pickle.dump(self.analysis_output, outfile)\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6c3e66e81653a120bc11267d18c51ad\",\n \"score\": \"0.6434548\",\n \"text\": \"def save_output(self, model_dico, file_save, X_val):\\n\\n pred = {}\\n\\n # Prediction\\n for o in self.output_names:\\n pred[o] = self.predict_model(model_dico[o], X_val)\\n\\n # Text to save\\n output_text = ':'.join(self.output_names)\\n \\n for i in tqdm(range(len(pred[self.output_names[0]]))):\\n output_text += '\\\\n'\\n output_text += ':'.join([str(pred[o][i]) for o in self.output_names])\\n \\n with open(file_save, 'w') as file:\\n file.write(output_text)\\n\\n print(\\\"File saved at '{}'\\\".format(file_save))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"694c6d0fc67d2c65c720f0498a3f2245\",\n \"score\": \"0.64069724\",\n \"text\": \"def _store_outputs(self, hypotheses: List[str]) -> None:\\n current_valid_output_file = \\\"{}/{}.hyps\\\".format(self.model_dir,\\n self.steps)\\n with open(current_valid_output_file, 'w') as opened_file:\\n for hyp in hypotheses:\\n opened_file.write(\\\"{}\\\\n\\\".format(hyp))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"694c6d0fc67d2c65c720f0498a3f2245\",\n \"score\": \"0.64069724\",\n \"text\": \"def _store_outputs(self, hypotheses: List[str]) -> None:\\n current_valid_output_file = \\\"{}/{}.hyps\\\".format(self.model_dir,\\n self.steps)\\n with open(current_valid_output_file, 'w') as opened_file:\\n for hyp in hypotheses:\\n opened_file.write(\\\"{}\\\\n\\\".format(hyp))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa41e8dd48094b900cffbf1e8835e59\",\n \"score\": \"0.6406303\",\n \"text\": \"def get_outputs(self):\\n\\n output = {'filename': None,\\n 'raw_frame_numbers': self.raw_frame_numbers.isChecked(),\\n 'viewer': self.open_viewer.isChecked()}\\n\\n save = self.save_file.isChecked()\\n if not save:\\n return output\\n\\n save_path = self.file_path.text()\\n if not save_path:\\n scene = tp.Dcc.scene_name()\\n time_stamp = datetime.datetime.today()\\n str_time_stamp = time_stamp.strftime(\\\"%d-%m-%Y_%H-%M-%S\\\")\\n save_path = '{}_{}'.format(scene, str_time_stamp)\\n\\n output['filename'] = save_path or artellapipe.MediaMgr().create_temp_path('playblast')\\n\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"528bed151cd894a61b3bac809331807a\",\n \"score\": \"0.6387252\",\n \"text\": \"def _store_outputs(self, hypotheses: List[str]) -> None:\\n current_valid_output_file = \\\"{}/{}.hyps\\\".format(self.model_dir,\\n self.steps)\\n with open(current_valid_output_file, 'a') as opened_file:\\n for hyp in hypotheses:\\n opened_file.write(\\\"{}\\\\n\\\".format(hyp))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2e0c79d31b2a798a481c6d3f65f4501\",\n \"score\": \"0.6350344\",\n \"text\": \"def loc_save(self,S):\\n\\n pos=nx.get_node_attributes(self,'p')\\n pe=nx.get_node_attributes(self,'pe_alg')\\n typ = nx.get_node_attributes(self,'typ')\\n if self.idx == 0:\\n entete = 'NodeID, True Position x, True Position y, Est Position x, Est Position y, Timestamp\\\\n'\\n file=open(os.path.join(basename,pstruc['DIRNETSAVE'],'simulation.txt'),'write')\\n file.write(entete)\\n file.close()\\n\\n try:\\n file=open(os.path.join(basename,pstruc['DIRNETSAVE'],'simulation.txt'),'a')\\n for n in self.nodes():\\n if typ[n] != 'ap':\\n data = n + ',' + str(pos[n][0]) + ',' + str(pos[n][1]) + ',' + str(pe[n][0][0]) + ',' + str(pe[n][0][1]) + ',' +pyu.timestamp(S.now()) +',\\\\n'\\n file.write(data)\\n file.close()\\n self.idx = self.idx +1\\n except:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2e24c737df604fcad6943164324a619\",\n \"score\": \"0.6349004\",\n \"text\": \"def save(self) :\\n\\n # Write header\\n self._log_header1( gammalib.TERSE , 'Save dmatter analysis results' )\\n\\n # Continue only if FITS file is valid\\n if self._fits != None :\\n\\n # Get outmap parameter\\n outfile = self[ 'outfile' ].filename()\\n\\n # Log file name\\n self._log_value( gammalib.NORMAL, 'dmatter file' , outfile.url() )\\n\\n # Save results\\n self._fits.saveto( outfile , self['clobber'].boolean() )\\n\\n # Return\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3cf7b57e4437b2741cea5988b7dcb4a\",\n \"score\": \"0.6343419\",\n \"text\": \"def save(self, output, data):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04a60bbf615c5451a601b0de099ad5e6\",\n \"score\": \"0.63416994\",\n \"text\": \"def save(self):\\n history_filename, results_filename, best_pars_filename = self._get_result_filenames(self.state_dir)\\n np.savez_compressed(history_filename, history=self.history)\\n np.savez_compressed(best_pars_filename, best_pars=gather(self.best_pars))\\n data = {'best_iter': self.best_iter,\\n 'best_val_loss': self.best_val_loss,\\n 'best_tst_loss': self.best_tst_loss,\\n 'training_time': self.training_time,\\n 'start_time': self.start_time,\\n 'end_time': self.end_time,\\n 'termination_reason': self.termination_reason,\\n 'cfg': self.cfg,\\n 'data': self.data}\\n with open(results_filename, 'wb') as results_file:\\n json.dump(data, results_file, indent=4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79ea47a51d28cba937456d57338f6305\",\n \"score\": \"0.63317704\",\n \"text\": \"def save_test_result(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"490fbee520774c138ead228d077c90dc\",\n \"score\": \"0.6323168\",\n \"text\": \"def save(self, output, data):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"490fbee520774c138ead228d077c90dc\",\n \"score\": \"0.6323168\",\n \"text\": \"def save(self, output, data):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea0f99539cdc89777c6c6818f9e96b9b\",\n \"score\": \"0.63117325\",\n \"text\": \"def simulation_save_temp(self):\\n with open(os.path.join('tmp','simulations',(str(self.id_simulation)+'full')), 'w') as stream:\\n writer = csv.writer(stream)\\n for row in range(self.gui.table_preview.rowCount()):\\n rowdata = []\\n for column in range(self.gui.table_preview.columnCount()):\\n item = self.gui.table_preview.item(row, column)\\n if item is not None:\\n rowdata.append(\\n unicode(item.text()).encode('utf8'))\\n else:\\n rowdata.append('')\\n writer.writerow(rowdata)\\n with open(os.path.join('tmp','simulations',(str(self.id_simulation)+'filter')), 'w') as stream:\\n writer = csv.writer(stream)\\n for row in range(self.gui.table_filtered.rowCount()):\\n rowdata = []\\n for column in range(self.gui.table_filtered.columnCount()):\\n item = self.gui.table_filtered.item(row, column)\\n if item is not None:\\n rowdata.append(\\n unicode(item.text()).encode('utf8'))\\n else:\\n rowdata.append('')\\n writer.writerow(rowdata)\\n with open(os.path.join('tmp','simulations',(str(self.id_simulation)+'stats')), 'w') as file_save:\\n item = self.gui.tree_hits.topLevelItem(self.id_simulation)\\n simulation = item.text(1) + ',' + item.text(2) + ',' +\\\\\\n item.text(3) + ',' +item.text(4) + ',' +\\\\\\n item.text(5) + ',' +item.text(6) + ',' +item.text(7) + ',' +\\\\\\n item.text(8) + ',' +item.text(9)\\n line = simulation+self.nl\\n file_save.write(line)\\n child_num = item.childCount()\\n for i in range(0,child_num):\\n name = item.child(i)\\n name = name.text(0)\\n line = name+self.nl\\n file_save.write('.........'+line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a1ece051d7771df3822d55db9011791\",\n \"score\": \"0.62942624\",\n \"text\": \"def outputs(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0576b139f1adffc3e23c6c82b059519e\",\n \"score\": \"0.6263089\",\n \"text\": \"def output(self):\\n try:\\n # output_save path uses output_folder parameter unless it is left as the default 'config', which then uses the output_path in config.py\\n dirpath = self.output_save_path\\n except:\\n dirpath = toad.config.dirpath\\n\\n save_ = getattr(self, 'persist', [])\\n output = dict([(k, self.target_class(self._getpath(dirpath, k))) for k in save_])\\n if self.persist == ['data']: # 1 data shortcut\\n output = output['data']\\n\\n self.debg(f\\\"{type(self).__name__} output set to {output.path.absolute()}\\\")\\n\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f56de9c2cc2ea5ebadac7f7d40f120f\",\n \"score\": \"0.6260796\",\n \"text\": \"def save(self, output_folder, save_name):\\n CRiSP = {'M_inv': self.M_inv, 'X': self.X, 'Y': self.Y, 's': self.s, 'psi': self.psi}\\n print(\\n f\\\"Saving model in {output_folder}...\\\\tKernel matrix inverse size: \\\\t{sys.getsizeof(CRiSP['M_inv']) * 1e-9:3f}GB\\\")\\n\\n if save_name is None:\\n save_name = 'CRiSP'\\n pickle.dump(CRiSP, open(output_folder / (save_name + \\\".pickle\\\"), 'wb'), protocol=4)\\n print(f\\\"Save complete!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16a0df3e9b4140cbb98fa2073e5eb6ba\",\n \"score\": \"0.62605923\",\n \"text\": \"def _save_state(self):\\n save_path = os.path.join(save_config['root_dir'], str(time.time())+'.pkl')\\n save_dict = dict()\\n save_dict['player1'] = self.player1.to_dict()\\n save_dict['player2'] = self.player2.to_dict()\\n save_dict['game_board'] = self.game_board.to_dict()\\n save_dict['game_engine'] = self.game_engine.to_dict()\\n save_dict['turns_played'] = self.turns_played\\n save_dict['_run_func'] = '{}_{}'.format(self.player1.type, self.player2.type)\\n save_dict['game_ended'] = self.game_ended\\n save_dict['start_time'] = self.start_time\\n with open(save_path, 'wb') as f:\\n pickle.dump(save_dict, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bcda4f19e497647a7dbad0bf78b245e\",\n \"score\": \"0.62458396\",\n \"text\": \"def save_results_to_file(self, output_file_path, seq_name):\\n self.tracking_out['conf'] = 1\\n self.tracking_out['x'] = -1\\n self.tracking_out['y'] = -1\\n self.tracking_out['z'] = -1\\n self.tracking_out['bb_left'] += 1 # Indexing is 1-based in the ground truth\\n self.tracking_out['bb_top'] += 1\\n final_out = self.tracking_out[TRACKING_OUT_COLS].sort_values(by=['frame', 'ped_id'])\\n if not os.path.exists(output_file_path):\\n os.mkdir(output_file_path)\\n final_out.to_csv(os.path.join(output_file_path, '{}.txt'.format(seq_name)), header=False, index=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f75d46843c365ab993d05106b1eca2b\",\n \"score\": \"0.6227219\",\n \"text\": \"def auto_save(self):\\n r_min = self.gui.spin_rounds_min.text()\\n r_max = self.gui.spin_rounds_max.text()\\n odds_level = self.gui.spin_odds_level.text()\\n nets = self.gui.tree_nets.currentItem()\\n nets = self.gui.tree_nets.indexOfTopLevelItem(nets)\\n filters = self.gui.tree_filters.currentItem()\\n filters = self.gui.tree_filters.indexOfTopLevelItem(filters)\\n ranges = self.gui.tree_ranges.currentItem()\\n ranges = self.gui.tree_ranges.indexOfTopLevelItem(ranges)\\n bets = self.gui.tree_bets.currentItem()\\n bets = self.gui.tree_bets.indexOfTopLevelItem(bets)\\n\\n\\n elements = [r_min, r_max, odds_level, nets, filters, ranges, bets]\\n with open(os.path.join('profiles','auto_save','simulator.txt'),'w') as save:\\n for i in elements:\\n save.write(str(i)+self.nl)\\n print 'save'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37c80842b5d0f55e6dc7c48823c5c545\",\n \"score\": \"0.62234414\",\n \"text\": \"def save_data(self, path=None):\\n output = self.get_data()\\n save_file = self.save_file_name\\n if path is not None:\\n save_file = path+self.save_file_name\\n with open(save_file, 'wb') as outfile:\\n pickle.dump(output, outfile)\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a08df27b8fa067f8bedac2bb98d3ad5\",\n \"score\": \"0.62129515\",\n \"text\": \"def save_results(self):\\n self.collect_assets()\\n with open(self.output_sitemap_file, 'w', encoding='utf8') as file:\\n file.writelines(page + '\\\\n' for page in sorted(self.assets.keys()))\\n with open(self.output_assets_file, 'w', encoding='utf8') as file:\\n for page in sorted(self.assets):\\n file.writelines(page + '\\\\n')\\n file.writelines(asset + '\\\\n' for asset in self.assets[page])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cac6617deff6f56003db3c78a890ec08\",\n \"score\": \"0.6175703\",\n \"text\": \"def save_final_results(self, filename):\\n\\t\\tfile = open(filename, \\\"w\\\")\\n\\n\\t\\tfile.write(\\\"Neural Network with \\\" + str(self.hidden_node_count) + \\\" hidden nodes and momentum \\\" +\\n\\t\\t\\t\\t str(self.momentum) + \\\" had a final training accuracy of \\\" +\\n\\t\\t\\t\\t str(self.training_accuracy_history[self.epochs - 1]) + \\\"\\\\n and a test accuracy of \\\" +\\n\\t\\t\\t\\t str(self.test_accuracy_history[self.epochs - 1]) + \\\" after \\\" + str(self.epochs) + \\\" epochs\\\")\\n\\t\\tfile.write(\\\"\\\\nTest Confusion Matrix: \\\\n\\\")\\n\\t\\tfile.write(str(self.test_confusion_matrix))\\n\\t\\tfile.write(\\\"\\\\ntraining accuracy history: \\\\n\\\" + str(self.training_accuracy_history))\\n\\t\\tfile.write(\\\"\\\\ntest accuracy history: \\\\n\\\" + str(self.test_accuracy_history))\\n\\t\\tfile.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5019269eb6ccfb2d9161055cb211f0f\",\n \"score\": \"0.617093\",\n \"text\": \"def save_results(self, name, prefix, writer, save_filetypes, step):\\n # pylint: disable=unused-argument, arguments-differ, too-many-arguments\\n\\n filename = _pathlib.Path(prefix).joinpath(f\\\"{self.name}-{step:07d}.pth\\\")\\n time = _datetime.utcnow().replace(tzinfo=_timezone.utc).isoformat()\\n\\n with _io.BytesIO() as mem:\\n if hasattr(self.model, \\\"module\\\"):\\n _torch.jit.save(self.model.module, mem)\\n else:\\n _torch.jit.save(self.model, mem)\\n mem.seek(0)\\n\\n with _lzma.open(filename, \\\"wb\\\") as fobj:\\n _torch.save({\\\"step\\\": step, \\\"time\\\": time, \\\"model\\\": mem.read()}, fobj)\\n\\n self.log.info(\\\"Done saving model for step %d to %s\\\", step, str(filename))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8da0e400f4552e707b107bc450477d2\",\n \"score\": \"0.6152689\",\n \"text\": \"def outputs(self, outputs):\\n\\n self._outputs = outputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1c2d11a83ee464aec05998d6ee36e6e\",\n \"score\": \"0.61493796\",\n \"text\": \"def runSimulation(self, save, experiment, subexperiment, network_name, nx_params):\\n\\n num_neutral_per_agent = np.empty(shape=(self.S))\\n num_fake_per_agent = np.empty(shape=(self.S))\\n num_retracted_per_agent = np.empty(shape=(self.S))\\n neutral_per_timestep = np.empty(shape=(self.S, self.T))\\n fake_per_timestep = np.empty(shape=(self.S, self.T))\\n retracted_per_timestep = np.empty(shape=(self.S, self.T))\\n\\n directory = \\\"./output/\\\" + experiment + \\\"/\\\" + subexperiment + \\\"/data/\\\"\\n name = network_name + '_' + '_'.join(['{}={}'.format(k, v) for k, v in nx_params.items()])\\n path = directory + \\\"N{N}-T{T}-S{S}-{shr}-{dly}-{name}-data.csv\\\".format(\\n N=self.N, T=self.T, S=self.S, shr=self.shareTimeLimit, dly=self.delay, name=name)\\n\\n for s in range(self.S):\\n # run model\\n network = self.graph(**self.nx_params) # generate network from graph and params\\n logs = self.runModel(network=network)\\n df_belief = pd.DataFrame.from_dict(logs[0])\\n\\n if save: # write raw data to output directory\\n if not os.path.exists(directory):\\n os.makedirs(directory)\\n\\n out = pd.DataFrame(index=[x for x in range(self.T)], columns=['s', 't']+[x for x in range(self.N)])\\n out.iloc[:, 0] = np.repeat(s+1, repeats=self.T) # vector of timesteps # simulation number\\n out.iloc[:, 1] = np.linspace(start=1, stop=self.T, num=self.T, dtype=int) # timestep number\\n out.iloc[:, 2:self.N+2] = df_belief.values\\n out.to_csv(path,\\n index=False,\\n header=True if s == 0 else False,\\n mode='a', # append df to csv\\n encoding='utf-8')\\n\\n # eval output\\n num_neutral_per_agent[s] = np.mean(np.sum(df_belief.values == 0, axis=0))\\n num_fake_per_agent[s] = np.mean(np.sum(df_belief.values == 1, axis=0))\\n num_retracted_per_agent[s] = np.mean(np.sum(df_belief.values == 2, axis=0))\\n neutral_per_timestep[s, :] = np.mean(df_belief.values == 0, axis=1)\\n fake_per_timestep[s, :] = np.mean(df_belief.values == 1, axis=1)\\n retracted_per_timestep[s, :] = np.mean(df_belief.values == 2, axis=1)\\n\\n # aggregate beliefs over time\\n neutral_per_agent_avg = np.mean(num_neutral_per_agent)\\n neutral_per_agent_sd = np.std(num_neutral_per_agent)\\n fake_per_agent_avg = np.mean(num_fake_per_agent)\\n fake_per_agent_sd = np.std(num_fake_per_agent)\\n retracted_per_agent_avg = np.mean(num_retracted_per_agent)\\n retracted_per_agent_sd = np.std(num_retracted_per_agent)\\n frac_neutral_per_timestep = np.mean(neutral_per_timestep, axis=0)\\n frac_neutral_per_timestep_sd = np.std(neutral_per_timestep, axis=0)\\n frac_fake_per_timestep = np.mean(fake_per_timestep, axis=0)\\n frac_fake_per_timestep_sd = np.std(fake_per_timestep, axis=0)\\n frac_retracted_per_timestep = np.mean(retracted_per_timestep, axis=0)\\n frac_retracted_per_timestep_sd = np.std(retracted_per_timestep, axis=0)\\n\\n # aggregate final belief distributions\\n neutral_dist = neutral_per_timestep[:, self.T-1]\\n fake_dist = fake_per_timestep[:, self.T-1]\\n retracted_dist = retracted_per_timestep[:, self.T-1]\\n\\n # bundle aggregated output\\n avg_per_agent = (neutral_per_agent_avg, fake_per_agent_avg, retracted_per_agent_avg)\\n sd_per_agent = (neutral_per_agent_sd, fake_per_agent_sd, retracted_per_agent_sd)\\n frac_belief_mean = (frac_neutral_per_timestep, frac_fake_per_timestep, frac_retracted_per_timestep)\\n frac_belief_sd = (frac_neutral_per_timestep_sd, frac_fake_per_timestep_sd, frac_retracted_per_timestep_sd)\\n belief_dist = (neutral_dist, fake_dist, retracted_dist)\\n\\n return avg_per_agent, sd_per_agent, frac_belief_mean, frac_belief_sd, belief_dist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e360c9571af78dfff25bfa7eaa5d1db\",\n \"score\": \"0.614781\",\n \"text\": \"def save_data(self, path=None):\\n\\n\\n output = self.get_data()\\n save_file = self.save_file_name\\n if path is not None:\\n save_file = path+self.save_file_name\\n with open(save_file, 'wb') as outfile:\\n pickle.dump(output, outfile)\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fce336c106c67632904e81b7fbc2a29b\",\n \"score\": \"0.61423445\",\n \"text\": \"def write_results(self, figures_merit=None):\\n\\n # open output file\\n f = open(self.folder + \\\"/000_experiment_setup_and_results.txt\\\", 'w')\\n\\n # parameters and settings of the simulation\\n f.writelines('# # # # # # # # # # # # # # #' + '\\\\n')\\n f.writelines('# # # Setting # # #' + '\\\\n')\\n f.writelines('# # # # # # # # # # # # # # #' + '\\\\n')\\n f.writelines(self.folder + '\\\\n')\\n f.writelines(str(self.pars))\\n f.writelines(str(self.dataset))\\n f.writelines('\\\\n')\\n f.writelines(\\n 'number of concluded simulation:' + str(self.no_simulations) + '\\\\n')\\n f.writelines('seed:' + str(self.seed) + '\\\\n')\\n f.writelines('\\\\n\\\\n')\\n\\n # raw results\\n f.writelines('# # # # # # # # # # # # # # #' + '\\\\n')\\n f.writelines('# # # Raw results # # #' + '\\\\n')\\n f.writelines('# # # # # # # # # # # # # # #' + '\\\\n')\\n f.writelines(self.string_raw_results() + '\\\\n')\\n f.writelines('failed simulations:' + str(self.fails) + '\\\\n')\\n f.writelines('\\\\n\\\\n')\\n\\n # processed rusults\\n f.writelines('# # # # # # # # # # # # # # #' + '\\\\n')\\n f.writelines('# # # Processed results # # #' + '\\\\n')\\n f.writelines('# # # # # # # # # # # # # # #' + '\\\\n')\\n tabularResult, printout = self.process_results(figures_merit=figures_merit)\\n f.writelines(printout + '\\\\n')\\n\\n # close file\\n f.close()\\n\\n self.log.info(tabularResult)\\n return tabularResult\",\n \"title\": \"\"\n },\n {\n \"docid\": \"402094e086e432bbd082b98b44ce3894\",\n \"score\": \"0.614059\",\n \"text\": \"def save_output(output_name, observables, lattice):\\n import cPickle as pickle\\n from os.path import isfile\\n observables_file = ((r'data\\\\%s.pkl') % (output_name))\\n with open(observables_file, 'wb') as output:\\n pickle.dump(observables, output, pickle.HIGHEST_PROTOCOL)\\n if isfile(observables_file):\\n print('Observables saved to: %s' % (observables_file))\\n lattice_file = ((r'data\\\\%s_lattice.pkl') % (output_name))\\n with open(lattice_file, 'wb') as output:\\n pickle.dump(lattice, output, pickle.HIGHEST_PROTOCOL)\\n if isfile(lattice_file):\\n print('Lattice saved to: %s' % (lattice_file))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c85e5118065c5f818c2c4ab8fc860371\",\n \"score\": \"0.61385643\",\n \"text\": \"def SaveData(out,run_num):\\n runnumber=getnumor(run_num)\\n base_name = qtg_par[\\\"instname\\\"]\\n\\n fullname=os.getcwd()+base_name+str(runnumber)+'.spe'\\n SaveSPE(out,fullname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d18f1342f7728f698b86854c167164d0\",\n \"score\": \"0.6127893\",\n \"text\": \"def sgnSave(self):\\n\\n self.uiSaveSelectedCamera()\\n self.uiSaveSelectedRendersFolder()\\n self.uiSaveSelectedScenesFolder()\\n self.uiSaveSelectedTerminalApp()\\n self.uiSaveSelectedSettings()\\n self.uiSaveSelectedRenderEngine()\\n\\n bResult = self.uiGetCheckResults()\\n\\n if bResult:\\n self.core.setGlobalsValue(\\\"sDefaultEngine\\\", self.sSelectedRenderEngine)\\n\\n self.core.setGlobalsValue(\\\"sOutputRenders\\\", self.sSelectedRendersFolder)\\n self.core.setGlobalsValue(\\\"sOutputScenes\\\", self.sSelectedScenesFolder)\\n self.core.setGlobalsValue(\\\"sDefaultCamera\\\", self.sSelectedCamera)\\n\\n self.core.setGlobalsValue(\\\"iWidth\\\", int(self.sSelectedWidth))\\n self.core.setGlobalsValue(\\\"iHeight\\\", int(self.sSelectedHeight))\\n\\n self.core.setGlobalsValue(\\\"iStart\\\", int(self.sSelectedStart))\\n self.core.setGlobalsValue(\\\"iEnd\\\", int(self.sSelectedEnd))\\n self.core.setGlobalsValue(\\\"iStep\\\", int(self.sSelectedStep))\\n\\n self.core.setGlobalsValue(\\\"sTerminalApp\\\", self.sSelectedTerminalApp)\\n\\n self.core.setGlobalsValue(\\\"bSettingImportRefs\\\", self.bSelectedSettingImportRefs)\\n\\n self.core.saveGlobals()\\n\\n self.uiCloseWindow()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97119e72641b6e8911836605aae3d6e5\",\n \"score\": \"0.6126601\",\n \"text\": \"def retrieve_outputs(self, event_name: str, sim_type: str):\\n\\n job_name = self._get_job_name(event=event_name, sim_type=sim_type, new=False)\\n salvus_job = sapi.get_job(\\n site_name=self.comm.project.site_name, job_name=job_name\\n )\\n if sim_type == \\\"forward\\\":\\n destination = self.comm.lasif.find_seismograms(\\n event=event_name, iteration=self.comm.project.current_iteration\\n )\\n\\n elif sim_type == \\\"adjoint\\\":\\n destination = self.comm.lasif.find_gradient(\\n iteration=self.comm.project.current_iteration,\\n event=event_name,\\n smooth=False,\\n inversion_grid=False,\\n just_give_path=True,\\n )\\n\\n else:\\n raise InversionsonError(\\n f\\\"Simulation type {sim_type} not supported in this function\\\"\\n )\\n salvus_job.copy_output(\\n destination=os.path.dirname(destination),\\n allow_existing_destination_folder=True,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"732b8f529b534b2175f56c6362517f2e\",\n \"score\": \"0.6123155\",\n \"text\": \"def save_outputs(display_frames):\\n print('Saving plots...')\\n # Delete existing plots and simulation gif\\n if os.path.exists('./Plots'):\\n shutil.rmtree('./Plots')\\n os.mkdir('./Plots')\\n # Save plots in parallel using number of cpu cores - 2\\n Parallel(n_jobs=os.cpu_count() - 2)(delayed(save_plot)(display_frames[i], i) for i in range(len(display_frames)))\\n # Generate simulation gif\\n save_animation()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca693c6d219cc1aeea4053281aba6d32\",\n \"score\": \"0.6119877\",\n \"text\": \"def save_results(self):\\n self.rf.matrix.data.to_csv(\\\"rf_results_averages.csv\\\", index=False)\\n pd.DataFrame(self.rf.matrix.matrix).to_csv(\\\"rf_confusion_matrix.csv\\\", index=False)\\n pd.DataFrame(self.rf.matrix.matrix_normalised[0, :, :]).to_csv(\\\"rf_confusion_matrix_normalised.csv\\\",\\n index=False)\\n self.knn.matrix.data.to_csv(\\\"knn_results_averages.csv\\\", index=False)\\n pd.DataFrame(self.rf.matrix.matrix).to_csv(\\\"knn_confusion_matrix.csv\\\", index=False)\\n pd.DataFrame(self.rf.matrix.matrix_normalised[0, :, :]).to_csv(\\\"knn_confusion_matrix_normalised.csv\\\",\\n index=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4057bea0a85a8e50e9fdf832cebbf8a8\",\n \"score\": \"0.6119099\",\n \"text\": \"def save_predictor(self, save_folder):\\n simple_save(\\n tf.get_default_session(),\\n save_folder,\\n inputs={\\\"obs\\\": self.model.act_model.X},\\n outputs={\\n \\\"action\\\": self.model.act_model.action,\\n \\\"value\\\": self.model.act_model.vf,\\n \\\"action_probs\\\": self.model.act_model.action_probs\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8853d3db2a6030cf5a8476ac2590969\",\n \"score\": \"0.61119646\",\n \"text\": \"def outputs(self):\\r\\n\\r\\n return self.__outputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31615ca3c999a285861a6f27ca31376a\",\n \"score\": \"0.61114407\",\n \"text\": \"def store_model_outputs(self, model, iter_num: int):\\n assert model and model.outputs is not None, \\\"No model has been run\\\"\\n # outputs_df = db.store.build_outputs_table([model], run_id=iter_num, chain_id=self.chain_id)\\n derived_outputs_df = db.store.build_derived_outputs_table(\\n [model], run_id=iter_num, chain_id=self.chain_id\\n )\\n # self.db.append_df(db.store.Table.OUTPUTS, outputs_df)\\n self.db.append_df(db.store.Table.DERIVED, derived_outputs_df)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6c3223bb4a5c13e85f55dba23dff1a3\",\n \"score\": \"0.6106897\",\n \"text\": \"def do_save(self, args):\\n if not CFState.get_instance().active_target:\\n self.pwarning(\\\"\\\\n [!] Not interacting with a target. Set the active target with `interact`.\\\\n\\\")\\n return\\n module_path = \\\"/\\\".join(CFState.get_instance().active_target.__module__.split(\\\".\\\")[:-1])\\n if \\\"results\\\" not in os.listdir(module_path):\\n os.mkdir(f\\\"{module_path}/results\\\")\\n\\n filename = f\\\"{module_path}/results/{CFState.get_instance().active_target.model_name}_{CFState.get_instance().active_target.target_id}.json\\\"\\n with open(filename, \\\"w\\\") as outfile:\\n json.dump(CFState.get_instance().active_target.dump(), outfile, indent=1)\\n\\n self.poutput(f\\\"\\\\n[+] Successfully wrote {filename}\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df1aa5fe5018211b03cb917f00347b91\",\n \"score\": \"0.6091375\",\n \"text\": \"def outputs(self) -> Dict[str, Any]:\\n if not self.is_complete:\\n raise _user_exceptions.FlyteAssertion(\\n \\\"Please wait until the node execution has completed before requesting the outputs.\\\"\\n )\\n if self.error:\\n raise _user_exceptions.FlyteAssertion(\\\"Outputs could not be found because the execution ended in failure.\\\")\\n return self._outputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcbde3c514ab191d8f39fddcc94865d6\",\n \"score\": \"0.6087984\",\n \"text\": \"def saveSimulationResultsToFiles(mainMemory, l1Cache, l2Cache, statResults):\\n mainMemory.saveMemoryToFile(config.getMainMemoryStatusOutputFilePath())\\n l1Cache.saveMemoryToFile(config.getL1CacheStatusOutputFilePath())\\n if l2Cache:\\n l2Cache.saveMemoryToFile(config.getL2Way0CacheStatusOutputFilePath())\\n l2Cache.saveMemoryToFile(config.getL2Way1CacheStatusOutputFilePath())\\n\\n with open(config.getStatsFileName(), 'w') as statsFile:\\n for stat in statResults:\\n statsFile.write(str(stat) + '\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f875b0bf2c2e436578a4540fbaa50dc\",\n \"score\": \"0.60832655\",\n \"text\": \"def save_model(self):\\n tosave = {'rmsecv':self.rmsecv,\\n 'trained':self.trained,\\n 'cv':self.cv,\\n 'columns':self.columns,\\n 'lmbda':self.lmbda,\\n 'max_evals':self.max_evals,}\\n\\n with open(os.path.join(self.bcupath, 'misc-estimator.pkl'), 'wb') as handle:\\n pickle.dump(tosave, handle, protocol=pickle.HIGHEST_PROTOCOL)\\n\\n #print('saved : ', self.scaler.mean_)\\n joblib.dump(self.scaler, os.path.join(self.bcupath, 'scaler.pkl'))\\n joblib.dump(self.pca, os.path.join(self.bcupath, 'pca.pkl'))\\n joblib.dump(self.estimator, os.path.join(self.bcupath, 'estimator.pkl'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3828329f70e5023d303e26af135d1d0\",\n \"score\": \"0.6077398\",\n \"text\": \"def outputs(self):\\n return self._outputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3828329f70e5023d303e26af135d1d0\",\n \"score\": \"0.6077398\",\n \"text\": \"def outputs(self):\\n return self._outputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d0bdad6221cf4402035aa38772527d4\",\n \"score\": \"0.6077258\",\n \"text\": \"def write_results(self):\\n\\n # Save the results file.\\n dir = self.write_results_dir + 'final'\\n self.interpreter.results.write(file='results', dir=dir, force=True)\\n\\n # The Grace plots.\\n dir = self.write_results_dir + 'final' + sep + 'grace'\\n self.interpreter.grace.write(x_data_type='res_num', y_data_type='s2', file='s2.agr', dir=dir, force=True)\\n self.interpreter.grace.write(x_data_type='res_num', y_data_type='s2f', file='s2f.agr', dir=dir, force=True)\\n self.interpreter.grace.write(x_data_type='res_num', y_data_type='s2s', file='s2s.agr', dir=dir, force=True)\\n self.interpreter.grace.write(x_data_type='res_num', y_data_type='te', file='te.agr', dir=dir, force=True)\\n self.interpreter.grace.write(x_data_type='res_num', y_data_type='tf', file='tf.agr', dir=dir, force=True)\\n self.interpreter.grace.write(x_data_type='res_num', y_data_type='ts', file='ts.agr', dir=dir, force=True)\\n self.interpreter.grace.write(x_data_type='res_num', y_data_type='rex', file='rex.agr', dir=dir, force=True)\\n self.interpreter.grace.write(x_data_type='s2', y_data_type='te', file='s2_vs_te.agr', dir=dir, force=True)\\n self.interpreter.grace.write(x_data_type='s2', y_data_type='rex', file='s2_vs_rex.agr', dir=dir, force=True)\\n self.interpreter.grace.write(x_data_type='te', y_data_type='rex', file='te_vs_rex.agr', dir=dir, force=True)\\n\\n # Write the values to text files.\\n dir = self.write_results_dir + 'final'\\n self.interpreter.value.write(param='s2', file='s2.txt', dir=dir, force=True)\\n self.interpreter.value.write(param='s2f', file='s2f.txt', dir=dir, force=True)\\n self.interpreter.value.write(param='s2s', file='s2s.txt', dir=dir, force=True)\\n self.interpreter.value.write(param='te', file='te.txt', dir=dir, force=True)\\n self.interpreter.value.write(param='tf', file='tf.txt', dir=dir, force=True)\\n self.interpreter.value.write(param='ts', file='ts.txt', dir=dir, force=True)\\n self.interpreter.value.write(param='rex', file='rex.txt', dir=dir, force=True)\\n self.interpreter.value.write(param='local_tm', file='local_tm.txt', dir=dir, force=True)\\n frqs = get_frequencies()\\n for i in range(len(frqs)):\\n comment = \\\"This is the Rex value with units rad.s^-1 scaled to a magnetic field strength of %s MHz.\\\" % (frqs[i]/1e6)\\n self.interpreter.value.write(param='rex', file='rex_%s.txt'%int(frqs[i]/1e6), dir=dir, scaling=(2.0*pi*frqs[i])**2, comment=comment, force=True)\\n\\n # Create the PyMOL macros.\\n dir = self.write_results_dir + 'final' + sep + 'pymol'\\n self.interpreter.pymol.macro_write(data_type='s2', dir=dir, force=True)\\n self.interpreter.pymol.macro_write(data_type='s2f', dir=dir, force=True)\\n self.interpreter.pymol.macro_write(data_type='s2s', dir=dir, force=True)\\n self.interpreter.pymol.macro_write(data_type='amp_fast', dir=dir, force=True)\\n self.interpreter.pymol.macro_write(data_type='amp_slow', dir=dir, force=True)\\n self.interpreter.pymol.macro_write(data_type='te', dir=dir, force=True)\\n self.interpreter.pymol.macro_write(data_type='tf', dir=dir, force=True)\\n self.interpreter.pymol.macro_write(data_type='ts', dir=dir, force=True)\\n self.interpreter.pymol.macro_write(data_type='time_fast', dir=dir, force=True)\\n self.interpreter.pymol.macro_write(data_type='time_slow', dir=dir, force=True)\\n self.interpreter.pymol.macro_write(data_type='rex', dir=dir, force=True)\\n\\n # Create the Molmol macros.\\n dir = self.write_results_dir + 'final' + sep + 'molmol'\\n self.interpreter.molmol.macro_write(data_type='s2', dir=dir, force=True)\\n self.interpreter.molmol.macro_write(data_type='s2f', dir=dir, force=True)\\n self.interpreter.molmol.macro_write(data_type='s2s', dir=dir, force=True)\\n self.interpreter.molmol.macro_write(data_type='amp_fast', dir=dir, force=True)\\n self.interpreter.molmol.macro_write(data_type='amp_slow', dir=dir, force=True)\\n self.interpreter.molmol.macro_write(data_type='te', dir=dir, force=True)\\n self.interpreter.molmol.macro_write(data_type='tf', dir=dir, force=True)\\n self.interpreter.molmol.macro_write(data_type='ts', dir=dir, force=True)\\n self.interpreter.molmol.macro_write(data_type='time_fast', dir=dir, force=True)\\n self.interpreter.molmol.macro_write(data_type='time_slow', dir=dir, force=True)\\n self.interpreter.molmol.macro_write(data_type='rex', dir=dir, force=True)\\n\\n # Create a diffusion tensor representation of the tensor, if a PDB file is present and the local tm global model has not been selected.\\n if hasattr(cdp, 'structure') and hasattr(cdp, 'diff_tensor'):\\n dir = self.write_results_dir + 'final'\\n self.interpreter.structure.create_diff_tensor_pdb(file=\\\"tensor.pdb\\\", dir=dir, force=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7342431d7f3b0be140159ce157a2f83e\",\n \"score\": \"0.606516\",\n \"text\": \"def _save_results(\\n self,\\n gt_rgb: torch.Tensor,\\n pred_rgb: torch.Tensor,\\n gt_seg: torch.Tensor,\\n pred_seg: torch.Tensor,\\n gt_depth: torch.Tensor,\\n pred_depth: torch.Tensor,\\n path: str,\\n ) -> None:\\n\\n save_rgb_results(gt_rgb[0], pred_rgb[0], path)\\n save_seg_results(gt_seg[0], pred_seg[0], path)\\n save_depth_results(gt_depth[0], pred_depth[0], path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40e3b06e906673d4e9073b370e0cc240\",\n \"score\": \"0.6060154\",\n \"text\": \"def save_results(self):\\n if self.directory is None:\\n return\\n tmc_dir = os.path.join(\\n self.directory, \\n 'mem_tmc_{}.pkl'.format(self.tmc_number.zfill(4))\\n )\\n\\n raw_list = []\\n for j in range(self.mem_tmc.shape[0]):\\n row_dict = { self.idxs_tmc[j][i] : self.mem_tmc[j][i] for i in range(self.mem_tmc.shape[1]) }\\n raw_list.append(row_dict)\\n\\n pkl.dump(raw_list, open(tmc_dir, 'wb'))\\n #pkl.dump({'mem_tmc': self.mem_tmc, 'idxs_tmc': self.idxs_tmc}, \\n # open(tmc_dir, 'wb'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06d278f67ccedad293cdd7fbca4f047a\",\n \"score\": \"0.6058251\",\n \"text\": \"def save_csv_results(self):\\n # Train\\n self.train_results.to_csv(f'output_{self.data_name}\\\\\\\\winnow_{self.data_name}_train_results.csv')\\n\\n # Test\\n self.test_results.to_csv(f'output_{self.data_name}\\\\\\\\winnow_{self.data_name}_test_results.csv')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb9100981af5dcc2cf06a970942eb82f\",\n \"score\": \"0.60476255\",\n \"text\": \"def _save_numpy(self, iter_):\\n key = self._get_numpy_count()\\n\\n coords_to_export = self.simulated_coords[self._npy_starting_index:iter_]\\n coords_to_export = self._swap_and_export(coords_to_export)\\n np.save(\\\"{}_coords_{}.npy\\\".format(\\n self.filename, key), coords_to_export)\\n\\n if self.save_forces:\\n forces_to_export = self.simulated_forces[self._npy_starting_index:iter_]\\n forces_to_export = self._swap_and_export(forces_to_export)\\n np.save(\\\"{}_forces_{}.npy\\\".format(\\n self.filename, key), forces_to_export)\\n\\n if self.save_potential:\\n potentials_to_export = self.simulated_potential[self._npy_starting_index:iter_]\\n potentials_to_export = self._swap_and_export(potentials_to_export)\\n np.save(\\\"{}_potential_{}.npy\\\".format(\\n self.filename, key), potentials_to_export)\\n\\n if self.friction is not None:\\n kinetic_energies_to_export = self.kinetic_energies[self._npy_starting_index:iter_]\\n kinetic_energies_to_export = self._swap_and_export(\\n kinetic_energies_to_export)\\n np.save(\\\"{}_kineticenergy_{}.npy\\\".format(self.filename, key),\\n kinetic_energies_to_export)\\n\\n self._npy_starting_index = iter_\\n self._npy_file_index += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae6c6650f5d1027b88b9a29784d7207e\",\n \"score\": \"0.6026936\",\n \"text\": \"def Save_Status(self):\\r\\n \\r\\n np.save(self.Folder + \\\"\\\\\\\\Step_%i.npy\\\"%self.step, {'u':self.u,\\r\\n 'RHS':self.RHS,\\r\\n 'time':self.t})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c96bbc53038e293fc458faa34457591\",\n \"score\": \"0.6019201\",\n \"text\": \"def writeSave(self):\\n\\n #store required data in savegame\\n self.savegame.currentMap = self.player.map.fn\\n self.savegame.currentPosition = self.player.position\\n self.savegame.currentLevel = self.player.level\\n self.savegame.currentDirection = self.player.direction\\n self.savegame.party = self.player.party\\n\\n #open the file for writing, dump the save game, and close\\n try:\\n f = open(self.savegame.fn, \\\"w\\\")\\n pickle.dump(self.savegame, f)\\n f.close()\\n except IOError:\\n raise error.DittoIOException(\\\"Ditto main\\\", fn)\\n\\n print \\\"Game saved to \\\" + self.savegame.fn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aacb8565baf441a45deccc33cf0ded53\",\n \"score\": \"0.6015083\",\n \"text\": \"def _save_time_step(self):\\n for var_name, value_pointer in self._save_vars.items():\\n self.results[var_name].append(\\n operator.attrgetter(value_pointer)(self)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aacb8565baf441a45deccc33cf0ded53\",\n \"score\": \"0.6015083\",\n \"text\": \"def _save_time_step(self):\\n for var_name, value_pointer in self._save_vars.items():\\n self.results[var_name].append(\\n operator.attrgetter(value_pointer)(self)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16c0762df48b4937dc130e7a2acdca36\",\n \"score\": \"0.60026264\",\n \"text\": \"def writeObservations(self):\\n # Set model output arrays to None to initialise\\n head = None\\n sfr_df = None\\n stream_options = ['stage', 'depth', 'discharge']\\n # Write observation to file\\n obs_group = self.model_data.observations.obs_group\\n for obs_set in obs_group:\\n obs_type = obs_group[obs_set]['obs_type']\\n # Import the required model outputs for processing\\n if obs_type == 'head':\\n # Check if model outputs have already been imported and if not import\\n if not head:\\n headobj = self.import_heads()\\n head = headobj.get_alldata()\\n elif obs_type in stream_options:\\n try:\\n self.sfr_df\\n except Exception:\\n sfr_df = self.importSfrOut()\\n # End except\\n else:\\n continue\\n # End if\\n\\n group_set = obs_group[obs_set]\\n obs_df = self.get_active_obs_group(group_set)\\n sim_map_dict = group_set['mapped_observations']\\n\\n unique_obs_df_zone = obs_df['zone'].unique()\\n if obs_type in stream_options:\\n sfr_location = group_set['locations']['seg_loc']\\n for zone in unique_obs_df_zone:\\n zone_txt = obs_set if len(unique_obs_df_zone) == 1 else obs_set + zone\\n with open(os.path.join(self.data_folder, 'observations_' + zone_txt + '.txt'), 'w') as f:\\n obs_df_zone = obs_df[obs_df['zone'] == zone]\\n for observation in obs_df_zone.index:\\n interval = int(obs_df_zone['interval'].loc[observation])\\n name = obs_df_zone['name'].loc[observation]\\n seg = sfr_location.loc[name]\\n sfr = sfr_df\\n col_of_interest = obs_type\\n\\n if obs_type == 'discharge':\\n col_of_interest = 'Qout'\\n\\n sim_obs = sfr[(sfr['segment'] == seg) & (sfr['time'] == interval)][col_of_interest]\\n f.write('%f\\\\n' % sim_obs)\\n # End for\\n # End with\\n # End for\\n elif obs_type == 'head':\\n for zone in unique_obs_df_zone:\\n zone_txt = 'head' if len(unique_obs_df_zone) == 1 else zone\\n with open(os.path.join(self.data_folder, 'observations_' + zone_txt + '.txt'), 'w') as f:\\n obs_df_zone = obs_df[obs_df['zone'] == zone]\\n for observation in obs_df_zone.index:\\n interval = int(obs_df_zone['interval'].loc[observation])\\n name = obs_df_zone['name'].loc[observation]\\n name_dict = sim_map_dict[name]\\n (x_cell, y_cell) = self.model_data.mesh2centroid2Dindex[(name_dict[1], name_dict[2])]\\n (lay, row, col) = [name_dict[0], name_dict[1], name_dict[2]]\\n\\n sim_heads = [head[interval][lay][row][col]]\\n sim_head = np.mean(sim_heads)\\n f.write('%f\\\\n' % sim_head)\\n # End for\\n # End with\\n # End for\\n else:\\n print(\\\"Unknown observation type!\\\")\\n # End if\\n # End for\",\n \"title\": \"\"\n },\n {\n \"docid\": \"854c25e3ed3279c2709e53f86c6ca0f1\",\n \"score\": \"0.60018647\",\n \"text\": \"def save_data(self):\\n J1 = self.get_joints_data(self.actuation1, self.actuation1_direction, self.robot.J1)\\n J2 = self.get_joints_data(self.actuation2, self.actuation1_direction, self.robot.J2)\\n J3 = self.get_joints_data(self.actuation2, self.actuation1_direction, self.robot.J3)\\n J4 = self.get_joints_data(self.actuation1, self.actuation1_direction, self.robot.J4)\\n\\n x, y, z = Utils.list_coord2list(self.robot.position)\\n\\n robot = pd.DataFrame(\\n np.array([\\n self.actuation1,\\n self.actuation1_direction,\\n self.actuation2,\\n self.actuation2_direction,\\n x, y, z,\\n np.array(self.robot.angle)[:, 0],\\n np.array(self.robot.angle)[:, 1],\\n np.array(self.robot.angle)[:, 2]\\n ]).T,\\n columns=[\\n 'u1', 'u1_dir',\\n 'u2', 'u2_dir',\\n 'x', 'y', 'z',\\n 'pitch', 'roll', 'yaw'\\n ]\\n )\\n\\n self.data = {}\\n self.data['J1'] = J1\\n self.data['J2'] = J2\\n self.data['J3'] = J3\\n self.data['J4'] = J4\\n self.data['robot'] = robot\\n self.data = pd.concat(self.data, axis=1)\\n\\n self.data.to_csv('{0}/results/{1}{2}{3}{4}.csv'.format(\\n Path(__file__).resolve().parent,\\n self.robot.J1.sequence,\\n self.robot.J2.sequence,\\n self.robot.J3.sequence,\\n self.robot.J4.sequence\\n ))\\n self.data.to_pickle('{0}/results/{1}{2}{3}{4}.pkl'.format(\\n Path(__file__).resolve().parent,\\n self.robot.J1.sequence,\\n self.robot.J2.sequence,\\n self.robot.J3.sequence,\\n self.robot.J4.sequence\\n ))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d45d3b29edd2e78a209096b41aeb62bd\",\n \"score\": \"0.5997252\",\n \"text\": \"def save_state(self, save_dir):\\n self.replay_buffer.save(os.path.join(save_dir, 'replay'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"812d40b69e0e78251b785e46c6b9a1ef\",\n \"score\": \"0.5993922\",\n \"text\": \"def saveTrajectory(self):\\n filepath = 'output/'\\n if path.exists(filepath):\\n filename = 'traj.npy'\\n filepath += filename \\n np.save(filepath, self.camera_traj)\\n print(\\\"Trajectory saved to: \\\" + filepath)\\n else:\\n print(\\\"Writing path does not exist!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4677fc5e86dee407191b50e0b61ffc7\",\n \"score\": \"0.5983296\",\n \"text\": \"def save_result(self):\\n if self.episode_number == 1 or not self.do_evaluation_iterations:\\n self.game_full_episode_scores.extend([self.total_episode_score_so_far])\\n self.rolling_results.append(np.mean(self.game_full_episode_scores[-1 * self.rolling_score_window:]))\\n self.save_max_result_seen()\\n\\n elif (self.episode_number - 1) % TRAINING_EPISODES_PER_EVAL_EPISODE == 0:\\n self.game_full_episode_scores.extend(\\n [self.total_episode_score_so_far for _ in range(TRAINING_EPISODES_PER_EVAL_EPISODE)])\\n self.rolling_results.extend(\\n [np.mean(self.game_full_episode_scores[-1 * self.rolling_score_window:]) for _ in\\n range(TRAINING_EPISODES_PER_EVAL_EPISODE)])\\n self.save_max_result_seen()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0427888967e41816b3c10688ce67047e\",\n \"score\": \"0.5981149\",\n \"text\": \"def _save_trials(self, trials_log):\\n trials_object = (self._hpopt_trials, self._evaluated_params)\\n object_dump(trials_object, trials_log)\\n tpe_trials_results = pd.DataFrame(self._hpopt_trials.results)\\n csv_file = trials_log + '.csv'\\n tpe_trials_results.to_csv(csv_file)\\n tpe_trials_results = pd.DataFrame(self._evaluated_params)\\n csv_file = trials_log + '.params.csv'\\n tpe_trials_results.to_csv(csv_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f28e358f6abbc05fa568ff28ae8aff20\",\n \"score\": \"0.5969702\",\n \"text\": \"def commit(self,deleteOutput=True,restore=False):\\n if self.DEBUG:\\n self.say.message_debug(self.CLASS_NAME+\\\"::commit()\\\")\\n\\n if not os.path.exists(self.directory):\\n if restore:\\n self.say.error(\\\"Set to restore, but simulation does not exist!\\\")\\n exit(0)\\n else:\\n self.say.message(\\\"Simulation directory doesn't exist! So creating it\\\")\\n os.makedirs(self.directory)\\n self.project.writeToHistory(self.name+' created')\\n\\n if restore and deleteOutput:\\n self.say.warning(\\\"You really don't want to delete output when\\\"\\n \\\"restoring! So we changed it\\\")\\n deleteOutput = False\\n\\n # We use .copy2 because we also want to copy permissions\\n # Copy executable\\n if self.compiled:\\n self.copyBinary()\\n\\n # Update configuration files\\n tools.inputWrite(self.inputDicts[0], self.directory+'/input')\\n c_ = 1\\n # Md config files\\n if os.path.isfile(self.inputFile+'.md'):\\n tools.inputWrite(self.inputDicts[c_], self.directory+'/input')\\n #shutil.copy2(self.inputFile+'.md', self.directory)\\n if self.template is not '':\\n mdfile = self.template+\\\"/\\\"+self.inputDicts[c_]['init_file'][1:-1]\\n shutil.copy2(mdfile, self.directory)\\n c_ += 1\\n # Elec config files\\n if os.path.isfile(self.inputFile+'.elec'):\\n tools.inputWrite(self.inputDicts[c_], self.directory+'/input')\\n #shutil.copy2(self.inputFile+'.elec', self.directory)\\n c_ += 1\\n self.project.writeToHistory(\\\"Configuration files were written\\\",sim=self.name)\\n\\n self.project.writeToHistory(\\\"Simulation commited\\\",sim=self.name)\\n\\n # Create output directory\\n # If it exists, do we delete the files?\\n outputdir = self.inputDicts[0]['folder'][1:-1]\\n fd = self.directory+\\\"/\\\"+outputdir\\n if os.path.exists(fd):\\n if deleteOutput:\\n if len(outputdir) > 1:\\n shutil.rmtree(fd)\\n self.project.writeToHistory(\\\"Output dir was removed!\\\",sim=self.name)\\n os.makedirs(fd)\\n else:\\n if not restore:\\n self.say.warning(\\\"Output dir existed, we are adding files there\\\")\\n else:\\n os.makedirs(fd)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b850c6e99059e2a8fc69392fee25eec\",\n \"score\": \"0.5968843\",\n \"text\": \"def _do_outputs(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"878c2c1a03855c7544b274321258a64a\",\n \"score\": \"0.59677136\",\n \"text\": \"def Outputs(self):\\n return self.__outputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18186eef48524b68affb826d3dd359df\",\n \"score\": \"0.5965068\",\n \"text\": \"def write_model(self):\\n\\n model_file_name = self.ml_service + '_model_' + self.model_name + '_' + self.model_version_to_train + '.pkl'\\n model_full_file_name = os.path.join(self.finder.get_output_folder(), model_file_name)\\n model_objects_list = [self.trained_model, self.scaler_X, self.scaler_Y, self.model_config.j_config]\\n os.makedirs(self.finder.get_output_folder(), exist_ok=True)\\n joblib.dump(value=model_objects_list, filename=model_full_file_name)\\n\\n #if self.run_type == 'run_on_azure':\\n # Also save to outputs folder for auto save in Azure\\n os.makedirs('outputs', exist_ok=True)\\n # note file saved in the outputs folder is automatically uploaded into experiment record\\n joblib.dump(value=model_objects_list, filename=os.path.join('outputs', model_file_name))\\n print('\\\\nwriting to outputs: ' + model_file_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac69c2f975364f08ce373f1992eb5bc4\",\n \"score\": \"0.5952777\",\n \"text\": \"def save_outputs(ML_pipe, output_dir):\\n stack, conjunction = add_conjunction(ML_pipe.feature_importances)\\n conjunction_results = {'Features': conjunction, 'Raw': stack}\\n save_xls(\\n conjunction_results, os.path.join(output_dir, 'features.xlsx'))\\n\\n save_xls(\\n ML_pipe.predictions, os.path.join(output_dir, 'predictions.xlsx'))\\n\\n ML_pipe.model_performance.to_excel(\\n os.path.join(output_dir, 'performance.xlsx'))\\n\\n try:\\n ML_pipe.feature_selection_gridsearch_results.to_excel(\\n os.path.join(output_dir, 'feature_selection_gridsearch.xlsx'))\\n ML_pipe.regression_gridsearch_results.to_excel(\\n os.path.join(output_dir, 'regression_gridsearch.xlsx'))\\n except AttributeError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6b78ff0749c5bcec55b4e065dfd6c59\",\n \"score\": \"0.59369737\",\n \"text\": \"def save(self):\\n torch.save(self.policy.state_dict(), str(config.fc1_units)+'_'+str(config.fc2_units) + '_model.pth')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7995c4f61d8852492e939be1507e0252\",\n \"score\": \"0.59348357\",\n \"text\": \"def save_result(self):\\n for ep in range(len(self.many_episode_rewards)):\\n total_reward = np.sum(self.many_episode_rewards[ep])\\n self.game_full_episode_scores.append(total_reward)\\n self.rolling_results.append(np.mean(self.game_full_episode_scores[-1 * self.rolling_score_window:]))\\n self.save_max_result_seen()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17ef846eaeb33dfa7d0e2863512de8dd\",\n \"score\": \"0.59301054\",\n \"text\": \"def output_results(self):\\n rg = HtmlReportGenerator(self.args)\\n add_plots(self, rg) # change add_plots to show different plots!\\n rg.variables.update(analyse(self))\\n rg.generate()\\n rg.save()\\n print(\\\"Report saved to {0}\\\".format(self.args[\\\"filename_out\\\"]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bdd0fab4a153d32b5287139073554a8\",\n \"score\": \"0.5923582\",\n \"text\": \"def save_state_to_disk_and_return(self):\\n\\n backtest_state = {\\n \\\"timestamp\\\": time.time(),\\n \\\"settings\\\": {\\n \\\"start\\\": self.start,\\n \\\"end\\\": self.end,\\n \\\"output_path\\\": self.output_path\\n },\\n \\\"perf\\\": self.perf,\\n \\\"stats\\\": self.stats,\\n \\\"portfolio\\\": {\\n \\\"costs\\\": self.portfolio.costs,\\n \\\"received\\\": self.portfolio.received,\\n \\\"portfolio_value\\\": self.portfolio.portfolio_value,\\n \\\"order_history\\\": self.portfolio.order_history_to_df(),\\n \\\"signals\\\": self.portfolio.signals_to_df(),\\n \\\"monthly_returns\\\": self.portfolio.get_monthly_returns(), # use to make histogram and return per time\\n },\\n \\\"broker\\\": {\\n \\\"blotter_history\\\": self.broker.blotter_history_to_df(),\\n \\\"all_trades\\\": self.broker.all_trades_to_df(),\\n \\\"trade_objects\\\": self.broker.all_trades_as_objects(),\\n \\\"cancelled_orders\\\": self.broker.cancelled_orders_to_df()\\n }\\n }\\n timestamp = datetime.datetime.now().strftime(\\\"%Y%m%d-%H%M%S\\\")\\n pickle_path = self.output_path + \\\"/backtest_state_\\\" + timestamp + \\\".pickle\\\"\\n\\n pickle_out = open(pickle_path,\\\"wb\\\")\\n pickle.dump(backtest_state, pickle_out)\\n pickle_out.close()\\n\\n return backtest_state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe4c152676b32ac078a016b917ad0dc8\",\n \"score\": \"0.59220284\",\n \"text\": \"def test_sim(write):\\n with Simulation(write,'r') as sim:\\n for step in sim:\\n pass\\n system_routing = SystemStats(sim)\\n sim.report()\\n print(system_routing.runoff_stats)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4529020862521743503dbc3002c30fca\",\n \"score\": \"0.5921215\",\n \"text\": \"def save_current_state(self, save_dir=None, **kwargs):\\n super().save_current_state(save_dir, **kwargs)\\n if self._save_rlr_every and self.istep % self._save_rlr_every == 0:\\n thread_prefix = f'thread{self._thread:02d}_' if self._thread is not None else ''\\n fname = f'{thread_prefix}gen{self._istep:03d}_search_radius'\\n np.save(os.path.join(self._logdir, fname), self._r)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5be7eccdc3bbc2202c53a3f028105b4\",\n \"score\": \"0.59203136\",\n \"text\": \"def save_result(self):\\n self.game_full_episode_scores.append(self.total_episode_score_so_far)\\n self.rolling_results.append(np.mean(self.game_full_episode_scores[-100:]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58a964450ee97c30bb44bc705935a429\",\n \"score\": \"0.5919577\",\n \"text\": \"def save_summary_to_file(self):\\n\\n # Save a csv file with the power measurements done in all the tests\\n\\n path = os.path.join(self.log_path, self.RESULTS_SUMMARY_FILENAME)\\n\\n with open(path, 'w') as csvfile:\\n csvfile.write('test,avg_power')\\n for test_name, value in self.power_results.items():\\n csvfile.write('\\\\n{},{}'.format(test_name, value))\\n\\n # Save a csv file with the calibration table for each simulation type\\n\\n for sim_type in self.calibration_table:\\n\\n path = os.path.join(\\n self.log_path, '{}_{}'.format(sim_type,\\n self.CALIBRATION_TABLE_FILENAME))\\n\\n with open(path, 'w') as csvfile:\\n csvfile.write('band,dl_pathloss, ul_pathloss')\\n for band, pathloss in self.calibration_table[sim_type].items():\\n csvfile.write('\\\\n{},{},{}'.format(\\n band, pathloss.get('dl', 'Error'),\\n pathloss.get('ul', 'Error')))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2577e2e5a7d07c9b05b705f15881cab2\",\n \"score\": \"0.5918313\",\n \"text\": \"def save(self, step):\\n os.chdir(self.load_dir)\\n self.saver.save(self.session, self.save_dir, step+self.global_step)\\n np.savetxt('iteration.dat', np.array([step+self.global_step]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ba118d91fdfa28d9dcf8976c94f7722\",\n \"score\": \"0.59144294\",\n \"text\": \"def outputs(self):\\r\\n if self.state is None:\\r\\n raise RuntimeError(\\\"Method add_to was not called.\\\")\\r\\n return self.state.outputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fd0604adee2b2a8c0fd6c37d370cb0a\",\n \"score\": \"0.5906942\",\n \"text\": \"def save(self, fileName: str) -> NoReturn:\\n A = []\\n B = []\\n pi = []\\n for i in range(self.statesNumber()):\\n a = []\\n for j in range(self.statesNumber()):\\n a.append(self.getTransition(i, j))\\n A.append(a)\\n b = []\\n for sym in range(self.symbolsNumber()):\\n b.append(self.getEmission(i, sym))\\n B.append(b)\\n pi.append(self.getInitial(i))\\n with open(fileName, \\\"w\\\") as f:\\n json.dump({\\\"A\\\": A, \\\"B\\\": B, \\\"pi\\\": pi,\\n \\\"symbols\\\": self.getSymbols()}, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"201fef74f56f7b4ed860c3a13d78b922\",\n \"score\": \"0.5896817\",\n \"text\": \"def save(self, out_path, out_name):\\n def write_array(path, X):\\n np.savetxt(path, X, fmt='%s')\\n\\n if not os.path.isdir(out_path):\\n os.makedirs(out_path)\\n\\n write_array(\\n os.path.join(out_path, out_name + '.data'),\\n self.get_data('X'))\\n write_array(\\n os.path.join(out_path, out_name + '_feat.name'), \\n self.feat_name)\\n\\n if 'y' in self.data:\\n write_array(\\n os.path.join(out_path, out_name + '.solution'),\\n self.get_data('y'))\\n\\n if 'X_train' and 'X_test' in self.data:\\n write_array(\\n os.path.join(out_path, out_name + '_train.data'),\\n self.get_data('X_train'))\\n write_array(\\n os.path.join(out_path, out_name + '_test.data'),\\n self.get_data('X_test'))\\n if 'y_train' and 'y_test' in self.data:\\n write_array(\\n os.path.join(out_path, out_name + '_test.solution'),\\n self.get_data('y_train'))\\n write_array(\\n os.path.join(out_path, out_name + '_test.solution'),\\n self.get_data('y_test'))\\n write_array(\\n os.path.join(out_path, out_name + '_label.name'),\\n self.label_name)\\n\\n with open(os.path.join(out_path, out_name + '_public.info'), 'w') as f:\\n for key, item in self.info.items():\\n f.write(str(key))\\n f.write(' = ')\\n f.write(str(item))\\n f.write('\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a70a5f0e1661986cdc6f6a1ce9aea3fc\",\n \"score\": \"0.58953255\",\n \"text\": \"def saving(self):\\n print(\\\"Saving....\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fea2878be04bc139df4ec9d82094a205\",\n \"score\": \"0.5893891\",\n \"text\": \"def save(self, filename):\\n save_dict = {'init_dict': self.init_dict,\\n 'agent_params': [a.get_params() for a in self.agents]}\\n torch.save(save_dict, filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5016fd7fef742480ca37f102482fc98\",\n \"score\": \"0.58869094\",\n \"text\": \"def outputs(self):\\n return super().outputs\",\n \"title\": \"\"\n }\n]","isConversational":false}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":9941,"numItemsPerPage":100,"numTotalItems":994482,"offset":994100,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3ODUwMjQxMiwic3ViIjoiL2RhdGFzZXRzL250aGFrdXIvY29ybnN0YWNrLXB5dGhvbi12MS10ZXZhdHJvbi0xTSIsImV4cCI6MTc3ODUwNjAxMiwiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.Cqw1-DL-l3LGJqNRd3vHqLwhJNcfTCyWN0BeQFApnxU9qG6q2uB7qcnRv0Lk56nwjsqjqr7xW7ZZDFiqVzs5DA","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":994482,"numBytesParquet":23972089656}]},"discussionsStats":{"closed":0,"open":1,"total":1},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query_id stringlengths 32 32 | query stringlengths 9 4.01k | positive_passages listlengths 1 1 | negative_passages listlengths 88 101 |
|---|---|---|---|
da7a2df3f31b97024c5a6eeab2efd4f5 | Validates an RGB hex string. | [
{
"docid": "0872061ec045c3138ccc910b7eb9d2a7",
"score": "0.8695411",
"text": "def validate_hex_color(hex_str):\n match_str = r'#[0-9a-fA-F]{6}'\n if re.match(match_str, hex_str) is None:\n raise ValidationError(u'{} is not a valid RGB hex string.'.format(hex_str))",
"title": ""
}
] | [
{
"docid": "cd2bbf914df6bd31966866bb868315f3",
"score": "0.7722918",
"text": "def is_hex_color(string):\n return bool(re.match(r'^(#[0-9a-fA-F]{3}|#[0-9a-fA-F]{6})$', string))",
"title": ""
},
{
"docid": "7a76e6bb7522a826fdd5fb27b885a8d8",
"score": "0.7716813",
"text": "def test_i... |
cb8257fde6a717dc3afb0edd26d64e01 | get the crystal scatter, background scatter, and photon counting noise for the reflections listed in the table R | [
{
"docid": "1f95c0828e2b8648d84a4bced66f2729",
"score": "0.0",
"text": "def integrate2(R, badmask, data, gain=28, fit_bg=True, zscore=8, sz=8):\n from dials.algorithms.shoebox import MaskCode\n fg_code = MaskCode.Foreground.real\n Nrefl = len(R)\n fs_dim = 194\n ss_dim = 185\n\n Rpp = ... | [
{
"docid": "a3f78ab103b74584dba2bd44abbf3b38",
"score": "0.58177507",
"text": "def get_colors( simtable, dz=0.1 ):\n if 'FLT' not in simtable.__dict__ :\n simtable.getLightCurves()\n i7 = np.where( simtable.FLT=='7' )\n i8 = np.where( simtable.FLT=='8' )\n iI = np.where( simtable.FLT=... |
f0baa7c754f29a744c55ad7ba6c87488 | Read Darcs changeset information for the Bcfg2 repository. | [
{
"docid": "bae4f46d9c2febcf535a0977b0c8c9b4",
"score": "0.5081105",
"text": "def get_revision(self):\r\n try:\r\n data = Popen(\"env LC_ALL=C darcs changes\",\r\n shell=True,\r\n cwd=self.vcs_root,\r\n stdout=PIPE... | [
{
"docid": "3cf297d97dffd87d3b90afe85d8fd56d",
"score": "0.5314179",
"text": "def read_dependencies():\r\n\r\n\t## read from file: prog2default.csv\r\n\tdependencies_file = file_list(\"dependencies\")\r\n\treturn(HCGB_main.get_data(dependencies_file, ',', 'index_col=0'))",
"title": ""
},
{
"... |
1e252dea36c1c96f1a91d2d3b7d0f701 | convert grayscale annotation to inner bound | [
{
"docid": "ad655c4f31bcbbd1fe7c5e371ebb8c14",
"score": "0.5521034",
"text": "def gray2innerbound(gray, width):\n h, w = gray.shape[:2]\n gray[gray == 76] = 255\n gray[gray == 151] = 255\n\n label = gray2mask(gray)\n label_binary = label_binarize(label.flatten(), classes=range(0, 3))\n ... | [
{
"docid": "c72b82fd27377184306b8188a338c97e",
"score": "0.6239534",
"text": "def classify_image(self):",
"title": ""
},
{
"docid": "109a085d249590784c5f23a9833de41f",
"score": "0.6049097",
"text": "def convert_to_mask(x_coord, y_coord, sample_name, class_name, ann_img):\n \n #... |
032798927d4a503e48066d0471f63a83 | Label connected domains, calculate area fractions of dominant domain Also marks domains that are present on both sides with BLUE v0.2 | [
{
"docid": "3462743ba1f2afaed0f77efcbc9ed176",
"score": "0.5651883",
"text": "def Label(im, Opt):\n BCount=(im==0).sum()\n WCount=(im.size-BCount)\n BFrac=BCount/(im.size)\n WFrac=WCount/(im.size)\n LabArray, LNumFeat = scipy.ndimage.measurements.label(im)\n WDomFrac=(LabArray==1).sum(... | [
{
"docid": "581cae9893a0ea09573afb9484c4db18",
"score": "0.613723",
"text": "def areaDisjFraction(hemlabels, dablabels):\n botharea = np.count_nonzero(np.logical_or(hemlabels, dablabels))\n if botharea == 0:\n return 0.\n else:\n return float(np.count_nonzero(dablabels)) / bothare... |
d47023e0d7e2286a238bfd7f0b51b634 | Computes the mean area of all labeled objects and takes the square root. Gives a length in number of pixels. To get a length in physical units, multiply the output of this function by the physical pixel size. | [
{
"docid": "cb1160591f9c92c2a9ec269dca24c420",
"score": "0.752876",
"text": "def mean_length_scale(object_labels):\n objects_area = _get_objects_area(object_labels=object_labels)\n return np.sqrt(np.mean(objects_area))",
"title": ""
}
] | [
{
"docid": "56fa449ff4f5652e6c489edb3c0794f9",
"score": "0.715483",
"text": "def mean_perimeter_length(object_labels):\n objects_perim = _get_objects_property(\n object_labels=object_labels, property_name=\"perimeter\"\n )\n return np.mean(objects_perim)",
"title": ""
},
{
"d... |
dc4acd4f73f049a04aa09ecf11788814 | Get the name of the environment either from current conda environment, from remote directory, or inferred from git repo. | [
{
"docid": "f789b3b2d6563fa62e592b2215c5cb76",
"score": "0.7944121",
"text": "def get_env_name(infer: bool = False) -> str:\n # pylint: disable=redefined-outer-name\n if not infer:\n return get_active_conda_env_name()\n try:\n remote_dir = infer_remote_dir()\n history = Env... | [
{
"docid": "09e74824f0c88194610f3ef561ad4b14",
"score": "0.7596804",
"text": "def get_conda_env_path(conn: fabric.Connection, envname: str = 'base') -> pathlib.Path:\n stream = io.StringIO()\n conn.run('conda info --envs --json', replace_env=False, out_stream=stream)\n info = json.loads(stream.... |
274993236c68c93987a2bd3d55fd0eb4 | Returns the number of unread messages in the user's mbox of a given type. | [
{
"docid": "940b33f472fb0674c8ac24b43ad96f72",
"score": "0.82448024",
"text": "def get_unread(self, site, profile_name, mbox_type='inbox'):\n total = 0\n \n if _get_mailbox(site, profile_name, mbox_type) is None:\n return total\n mbox_queues = self.get_queues(site,... | [
{
"docid": "98429e9cb42c0c5b03da2b2fcc1326e9",
"score": "0.694137",
"text": "def unread_messages_count(self) -> int:\n return self._message_storage.unread_count",
"title": ""
},
{
"docid": "d5e526e27ef01e5174d787af7c57ef22",
"score": "0.69291246",
"text": "def get_unread(self)... |
5fb757af84b6bfa9a340d6aa2823062e | Get liquor quantities and prices given our input. | [
{
"docid": "9c076f0e7c9c00d70a4860a404aaf472",
"score": "0.5931924",
"text": "def calculate_liquor_quantities(\n config_dict: dict, user_dict: dict, rounding_method: str = CEILING\n) -> dict:\n # Calculate total required drinks\n total_required_drinks = user_dict[\"num_drinks\"]\n\n required... | [
{
"docid": "bbc54cc4d369fd04982582b0d78faad6",
"score": "0.6300638",
"text": "def get_subscription_prices():",
"title": ""
},
{
"docid": "5686b248a3e2355aae9e165865fb32cf",
"score": "0.6073003",
"text": "def get_prices(self):\n\t\treturn self.prices",
"title": ""
},
{
"do... |
e06ffe70235da5f096ff2777716e69a3 | Appoint the most trusted validator as the primary validator | [
{
"docid": "3245e9c37abd18fdc4c111e3a30b76a0",
"score": "0.7344275",
"text": "def appoint_primary_validator():\n\n current_primary_validator = get_primary_validator()\n\n most_trusted_validator = Validator.objects.filter(\n primary_validator_invalid_blocks__isnull=True\n ).order_by('-tru... | [
{
"docid": "17d0cdba702d967f38720e05e7ad7e0e",
"score": "0.68256813",
"text": "def SetValidator(self, validator):",
"title": ""
},
{
"docid": "e9c886c1c783c96ea7228212c7806ef3",
"score": "0.65641963",
"text": "def GetValidator(self):",
"title": ""
},
{
"docid": "1189796a9... |
e22ddb62e752ac2dc25b190613bd2c48 | set value expecting date time | [
{
"docid": "0f4c3b387a27a29c2c3cf80812c71dfc",
"score": "0.64594984",
"text": "def set(self, v):\n if not isinstance(v, datetime.datetime):\n raise ValueError(\"Invalid datetime value!\")\n\n if v.tzinfo is None: # naive time -> assumed to be in UTC\n self.__v = v.rep... | [
{
"docid": "cb2f92086af4884a5e191426c2037c27",
"score": "0.7544321",
"text": "def date(self, value):\n self._date = value",
"title": ""
},
{
"docid": "edf1608f8e9bbeeff627462e0a44b525",
"score": "0.75029093",
"text": "def setValue (self, val):\n \n if isinstance(... |
3a7b4464ebf99794d8c116b42038d4d8 | Processes the image and returns it | [
{
"docid": "1fc64426902988012c1a7778bfd6ceca",
"score": "0.0",
"text": "def img_pre_process(img):\n # Chop off 1/3 from the top and cut bottom 150px(which contains the head of car)\n shape = img.shape\n img = img[int(shape[0] / 3):shape[0] - 150, 0:shape[1]]\n img = img / 255.\n print(img... | [
{
"docid": "4b27d0b9a56e8d4d8524a2066c858a1d",
"score": "0.79944336",
"text": "def process(self, image):\n pass",
"title": ""
},
{
"docid": "b92044a4dd259f62f19f82eaa207ff3e",
"score": "0.74050933",
"text": "def process(self, grabbed_image):\n raise NotImplementedError(... |
6f79505ce9f122994b5991bf039a6436 | polbasis(self) > string Summary Return the polarization basis in the calibration table ('L' for linear or 'C' for circular). Description This member function returns the polarization basis in the calibration table ('L' for linear or 'C' for circular). | [
{
"docid": "72c4ec11feddbef5fcda6ddb2747b04e",
"score": "0.79458225",
"text": "def polbasis(self):\n return _calanalysis.calanalysis_polbasis(self)",
"title": ""
}
] | [
{
"docid": "9ddff4da2e30336611fc206b7183ff3b",
"score": "0.61899257",
"text": "def Lri_basis(self):\n return self.Lri_basis_set",
"title": ""
},
{
"docid": "8a5e35c53d30934fa38fb888e9874ef7",
"score": "0.6093768",
"text": "def getBasis(self):\n return self.__basis",
... |
f73a218e6f4e61b5fc497d4418151388 | Randomly select one of the variables in the test defined. | [
{
"docid": "7af41151fcc2e2c972c0fbf47c98c16b",
"score": "0.67626154",
"text": "def _select_random_variable_for_call(\n test_case: tc.TestCase, position: int\n ) -> vr.VariableReference | None:\n candidates: list[vr.VariableReference] = [\n var\n for var in test_cas... | [
{
"docid": "3f010e7a53633bac681ffacf96171642",
"score": "0.68720555",
"text": "def rndvar(self, ln):\n\n if len(ln) < 2:\n error(\"Use: RndSet Variable_Name <list of possible values>\")\n\n v = self.getvname(ln[0])\n\n self.vars[v] = random.choice(ln[1:])\n\n if gb... |
bef78b64f61cc20b6a443e9b8269d5d2 | Resets the number of steps a dot has taken. | [
{
"docid": "1dec7687e2aa8e2f0589fc101ecc18d9",
"score": "0.7511817",
"text": "def reset_steps(self):\r\n self.step = 0",
"title": ""
}
] | [
{
"docid": "668d3dd6e7a95dd3d12f410de40432fe",
"score": "0.6291966",
"text": "def reset(self):\n self.distance_traveled = 0\n self.state = Reindeer.MOVING\n self.internal_clock = 0\n self.total_ticks = 0\n self.points = 0",
"title": ""
},
{
"docid": "443556... |
82e6d7f2553b9e80a9ca0596efe04e84 | Apply Wordnet lemmatizer to text (go to root word) | [
{
"docid": "de04606a08eb8f89ecf33e73eaccd774",
"score": "0.76061803",
"text": "def lemmatizer(text):\n wnl = WordNetLemmatizer()\n text = [wnl.lemmatize(word) for word in text.split()]\n return \" \".join(text)",
"title": ""
}
] | [
{
"docid": "04bc11774c8564671a1c272290fcbd55",
"score": "0.7877054",
"text": "def lemmatizer(text):\n lemmatizer = WordNetLemmatizer()\n\n #Iterate through words and lemmatize\n i = 0\n for word in text:\n text[i] = lemmatizer.lemmatize(word)\n i = i + 1\n return text",
... |
1fb062405dd05b13bd0419651eb3cbe4 | Function that makes and save the figure with the local power spectrum | [
{
"docid": "4a5d367a67fff5a98d7a0e2bc5885895",
"score": "0.58250314",
"text": "def makeAveragePSLocalFigure(averagePSLocal,figureFileName,gridSize): \r\n pylab.figure()\r\n for i in range(gridSize[0]):\r\n for j in range(gridSize[1]):\r\n pylab.subplot(gridSize[0],gridSize[1],i*g... | [
{
"docid": "5c6401c84a8eda78f36dc21df5ceb618",
"score": "0.6694815",
"text": "def plotter_saver():\n xdat = get_xdat()\n FFCache.xdat = xdat\n\n p0 = [\n 2.14928399e+00, 6.30221940e-01, 2.75166059e+00, -1.96259549e+00,\n 7.03808465e-01, 5.26047000e-03, 1.49757697e-02, 5.81073410e-... |
d7721b2c84a1088f629f9e0fab2d0634 | Video streaming home page. | [
{
"docid": "a905fb886ac06831242b1eb30e3c08d3",
"score": "0.0",
"text": "def index():\n return render_template('index.html')",
"title": ""
}
] | [
{
"docid": "f8fc737c05951ba93953b96f7af39dfa",
"score": "0.7695669",
"text": "def index():\r\n return render_template('video.html')",
"title": ""
},
{
"docid": "b609a4c6e85aa233bafd14bfcf25d5aa",
"score": "0.6744941",
"text": "def stream(request):\n # Get a list of rooms, order... |
6c0e12b0634e743b2e8588efbc847419 | Make Windows and POSIX compatible absolute paths automatically. | [
{
"docid": "a35283b24e3488954e811d6e8f32c2f4",
"score": "0.6681356",
"text": "def makePath(path):\n\n compatPath = os.path.abspath(os.path.expanduser(path))\n\n return compatPath",
"title": ""
}
] | [
{
"docid": "08c7a8fd11865ea66f5944d77bc7b886",
"score": "0.71546054",
"text": "def win2unix(a_path, use_abs=1):\r\n if use_abs:\r\n a_path = os.path.abspath(a_path)\r\n return re.sub(r\"\\\\\", \"/\", a_path)",
"title": ""
},
{
"docid": "08c7a8fd11865ea66f5944d77bc7b886",
"s... |
2f4f0d8423f6ecc13377132d7754a67e | Turn selected bars on other off | [
{
"docid": "92d9f575597c61a0ce457e38318d744d",
"score": "0.64476544",
"text": "def selectedBarsOn(strip, color):\n for i in range(strip.numPixels()):\n if leds[i] == True:\n strip.setPixelColor(i, color)\n else:\n strip.setPixelColor(i, Color(0, 0, 0))\n strip.s... | [
{
"docid": "fca8ef6d118cf293f5681412d3b06a9c",
"score": "0.68102723",
"text": "def allBarsOff(strip):\n setAll(strip, Color(0, 0, 0))\n strip.show()",
"title": ""
},
{
"docid": "43c8c3bc1f6fd947ad5058d5a9316b9e",
"score": "0.6350631",
"text": "def deselect(self):\n self.... |
766ceaba5bfcfc6970b6b5c9006dc0f4 | Return update operation string with random values x y z w UPDATE x y z w | [
{
"docid": "ecb0c9b2dd34d88ab35ca778d1f9e9ca",
"score": "0.6559078",
"text": "def random_update_string(edgesize):\n x, y, z = np.random.randint(edgesize, size=3) + np.array([1, 1, 1])\n w = int(np.random.uniform(-10e9, 10e9))\n return f'UPDATE {x} {y} {z} {w}'",
"title": ""
}
] | [
{
"docid": "b756e050907f15b4c563749e003c97d4",
"score": "0.62117904",
"text": "def update():\n\n return 0, \"\"",
"title": ""
},
{
"docid": "2668478b44a072326982505a1a41252f",
"score": "0.60683626",
"text": "def test_10_update_simple(self):\n\n User = \"User.update(\\\"38f2... |
3b2ca89911346bae817b9127aabd70e3 | plots the baselined data | [
{
"docid": "1a5e5a9858fa3e8ce25ccbdc3b39dd32",
"score": "0.0",
"text": "def plot_data_bas(save_folder, electrode = 'all', save_name = 'data_bas', save_ext = '.png'):\n data_bas, fs = reader.read_databas(save_folder)\n plot_data(data_bas, fs, save_folder, electrode = 'all', save_name = save_name, s... | [
{
"docid": "c53b6e855fb9c1e6c35c8eb1a4de3a95",
"score": "0.7291515",
"text": "def stat_plot():",
"title": ""
},
{
"docid": "b15382ff7e5413c58f939ddcb600f9cc",
"score": "0.72905284",
"text": "def plot(self):",
"title": ""
},
{
"docid": "a4240a5aadf84ac63cc47713c81c8741",
... |
7fab4685d4907d969bd93e4e11fe13da | Deletes an index and returns the Response. | [
{
"docid": "9ae801eac2ec0c7b235c50e123742490",
"score": "0.70268214",
"text": "def delete(self,index_name):\r\n try:\r\n return self.client.delete_edge_index(index_name)\r\n except LookupError:\r\n return None",
"title": ""
}
] | [
{
"docid": "20c4a6d50b9d9e8a7160621773fc11d0",
"score": "0.81049603",
"text": "def delete_index(self, name):\r\n path = build_path(index_path,name)\r\n return self.request.delete(path,params=None)",
"title": ""
},
{
"docid": "48fc0bcbd5e887e6669b330ad32e82f5",
"score": "0.7... |
69e1b9bb94dfdc0b51ac7e8fb8227fdb | Generates reads with a given error rate. >>> from Bio.Seq import Seq >>> contigs = dict() >>> contigs['1'] = Seq('ACGATGAGTAGAGACAGAGATAGAGAGATAGAACGATGAGTAGAGAG') >>> rec = None | [
{
"docid": "6ac9ac1b7ee9747624ea4b8ba384f9b0",
"score": "0.5562005",
"text": "def gen_reads(contigs, n, read_length, sub_erate):\n \n contig_names = list(contigs.keys())\n contig_lens = dict()\n\n for i in contigs:\n contig_lens[i] = len(contigs[i])\n \n nread = 0\n done = Fa... | [
{
"docid": "8a9afa899340c5163c92f8b1efa1fa13",
"score": "0.52502173",
"text": "def iter_record(record: SeqRecord) -> Generator[Tuple, None, None]:\n for nuc, qual in zip(record, record.letter_annotations[\"phred_quality\"]):\n # prob of error\n prob = 10 ** -(qual / 10)\n yield n... |
8043ab3390a3fe7221896f6dbe791964 | Retrieve the picture from TwitPic | [
{
"docid": "0647cd8cb20444896cbcb30038f69059",
"score": "0.83215106",
"text": "def get_twit_pic(**kargs):\r\n twitpage = api_call(*urlsplit(kargs['url'])).read()\r\n anchor = '<img class=\"photo\" id=\"photo-display\" src=\"'\r\n start = twitpage.index(anchor) + len(anchor)\r\n end = twitpag... | [
{
"docid": "f03a07b9e9453c460107bcb1bc2c8829",
"score": "0.84223866",
"text": "def get_twitgoo_pic(**kargs):\r\n host, path, secure = urlsplit(kargs['url'])\r\n pic = api_call(host, path +'/img', secure).read()\r\n return pic",
"title": ""
},
{
"docid": "22e35d95f91d07a40d8079ddd05f... |
25f31fa66315ff122abe3ecdb28aea6b | Sets the keyguard_disabled of this EnterprisePolicyData. Should KeyGuard be disabled? | [
{
"docid": "16f2d6be8939f474fc51d4968abbf9c9",
"score": "0.8649095",
"text": "def keyguard_disabled(self, keyguard_disabled):\n\n self._keyguard_disabled = keyguard_disabled",
"title": ""
}
] | [
{
"docid": "01e0ff0e55526b4b4f5e9f5c55b1e085",
"score": "0.7613828",
"text": "def keyguard_disabled(self):\n return self._keyguard_disabled",
"title": ""
},
{
"docid": "a26b8f8bf253dc943f1427bc1a1603c8",
"score": "0.59514517",
"text": "def disable(self) -> Awaitable[Dict]:\n ... |
eb71efa1a2962d64023d5d4afc01b0fa | Returns if there is any word in the trie that starts with the given prefix. | [
{
"docid": "c41d511488f18931b5996f998378ce6c",
"score": "0.7661376",
"text": "def startsWith(self, prefix: str) -> bool:\n node = self.root\n for char in prefix:\n if char not in node.children:\n return False\n node = node.children[char]\n\n retu... | [
{
"docid": "5d1a552ce96f28a17ff02733ba0212b7",
"score": "0.84394646",
"text": "def startsWith(self, prefix: str) -> bool: \n return(self.searchTrie(self.root, prefix, True))",
"title": ""
},
{
"docid": "0a82b07344eb20900f183e4a08b3d6a7",
"score": "0.843329",
"text": "def start... |
4ac27fb0f988bff24354ee1a1b987a18 | Renders the home page. | [
{
"docid": "79a1cec6577b7e28956c3623e2ffc701",
"score": "0.0",
"text": "def home(request):\n\n #assert savesave_to_bd.save_to_bd()\n save_to_bd.save_to_bd()\n\n assert isinstance(request, HttpRequest)\n\n table, folium_map = plot_map.show()\n\n return render(\n request,\n 'a... | [
{
"docid": "93b0d81a4e377798d73883654df1785e",
"score": "0.86085397",
"text": "def home():\n\treturn render_template('home.html')",
"title": ""
},
{
"docid": "93b0d81a4e377798d73883654df1785e",
"score": "0.86085397",
"text": "def home():\n\treturn render_template('home.html')",
"... |
93a4e194462b6657722b80220b536963 | _main_ provide support for some basic docker operations so that building images can be standardised as part of a workflow | [
{
"docid": "ecd643d4ccd428e0fa21ebc05bb2a5ce",
"score": "0.64680725",
"text": "def main():\n opts = build_parser()\n config = load_configuration()\n if not config.has_section('docker'):\n msg = (\n \"Unable to find docker section in cirrus.conf\"\n #TODO: Link to do... | [
{
"docid": "1f58d9f59126c05bb35db12984aabc71",
"score": "0.72338295",
"text": "def docker() -> None:\n pass",
"title": ""
},
{
"docid": "6c1687f1282d12b20080c89c4e51a02e",
"score": "0.6880054",
"text": "def build(ctx):\n ctx.run('docker build -t {} .'.format(cli_image_name))",
... |
ac413010644727bcbf79d8d69b379b03 | Helper function to calculate the sum of Loggamma functions for calculate the score. | [
{
"docid": "584d5b7712ab1a056c6130fa1819055b",
"score": "0.59813946",
"text": "def bayesian_score_component(M, alpha):\n M_values= [M[i] for i in M.keys()]\n p = np.sum(loggamma(alpha + M_values))\n p -= np.sum(loggamma(alpha))\n p += np.sum(loggamma(np.sum(alpha, axis=1)))\n p -= np.sum(... | [
{
"docid": "2d9472ac0b40863e837c622553d8e6ac",
"score": "0.71171165",
"text": "def loggamma(*args, **kwargs): # real signature unknown\n pass",
"title": ""
},
{
"docid": "7af8e16cfcd828e9e2ad587e0b056dd0",
"score": "0.6929556",
"text": "def log_Beta(alphas):\n #return product(m... |
4f20628cc01c7ef652bdba4cc4450d0b | Allow to convert the iso code language list into a list of titles | [
{
"docid": "1d7b00379515f47b11a4c7a2df993c20",
"score": "0.55227435",
"text": "def get_languages(self, iso_codes):\n langList = []\n for isoCode in iso_codes:\n if CONSTANTS.dico_languages.has_key(isoCode):\n langList.append(CONSTANTS.dico_languages[isoCode])\n ... | [
{
"docid": "6832b39328905e63f1a7613e38e97b4d",
"score": "0.67992806",
"text": "def translated_titles(self, iso_format=None):\n\n fmt = iso_format or self._iso_format\n\n trans_titles = {}\n if 'v12' in self.data['article']:\n for title in self.data['article']['v12']:\n ... |
f7336e3c5843aec041b3eabb6d1a0311 | Take integer y (tensor or variable) with n dims and convert it to 1hot representation with n+1 dims. | [
{
"docid": "6e5732d85223adf236219a11551c52e0",
"score": "0.8346173",
"text": "def to_one_hot(y, n_dims=None):\n y_tensor = y.data\n y_tensor = y_tensor.type(torch.LongTensor).view(-1, 1)\n n_dims = n_dims if n_dims is not None else int(torch.max(y_tensor)) + 1\n y_one_hot = torch.zeros(y_ten... | [
{
"docid": "16ae1ce18896056ce2bb276fb97081be",
"score": "0.8294972",
"text": "def to_one_hot(y, N):\n y_tensor = y#.data if isinstance(y, torch.autograd.Variable) else y\n y_tensor = y_tensor.long().view(-1, 1)\n if int(N) <= 0:\n N = torch.max(y_tensor).long() + 1\n y_one_hot = torch... |
3e2a1a41a04d3c65373b88054dd9463f | Decode given bytes object `data` as given type `name` and return the decoded data as a dictionary. >>> foo.decode('Question', b'0\\x0e\\x02\\x01\\x01\\x16\\x09Is 1+1=3?') | [
{
"docid": "5a88bf17a05e248dbe34a7f77b98c0b1",
"score": "0.7317871",
"text": "def decode(self, name, data):\n\n return self._types[name].decode(data)",
"title": ""
}
] | [
{
"docid": "603690964ff48e76ef4b6e52c09673fc",
"score": "0.703049",
"text": "def decode(data: dict):\n pass",
"title": ""
},
{
"docid": "4047129ee4cf8585707fee8949b61464",
"score": "0.7024252",
"text": "def decode_data(data):\n return data.decode(encoding='utf-8') if isinst... |
d43e4579041b8be2490d535ce802d27f | The arguments have the same meaning as in the dropout_layer function. | [
{
"docid": "39c9b2b3ee640bd9c9519a3485bbd8b8",
"score": "0.0",
"text": "def enable_dropout(train_premasks, train_postmasks):\n for premask in train_premasks:\n premask.set_value(1)\n for postmask in train_postmasks:\n postmask.set_value(np.cast[floatX](1))",
"title": ""
}
] | [
{
"docid": "c25be25acb9e00e6b9991291b5072f2e",
"score": "0.64838076",
"text": "def create_dropout_layer(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "3ce062bbf7af24958e07c6fe63adccf0",
"score": "0.6317723",
"text": "def push_dropout_layer(self, keep_prob):\n ... |
fea7c20246b3976198808fa8c7a94e6d | Load image from base64 | [
{
"docid": "194aa7db8e2d52e9e5fb4d999046ced4",
"score": "0.83857167",
"text": "def _load_from_base64(self, value):\n search_results = re.search(self.BASE64_REGEX, value)\n data = search_results.group(2)\n if len(data) % 4:\n data += '=' * (4 - len(data) % 4)\n data... | [
{
"docid": "f7722b5e811a9bfbac4a10b63e8fd8ed",
"score": "0.80330646",
"text": "def _load_image_from_base64_image(base64_image: str) -> np.ndarray:\n return ImageUtilities._pil_to_numpy(\n ImageUtilities._base64_to_pil(base64_image), _format=\"BGR\"\n )",
"title": ""
},
{... |
bed5dcbe4383c9988164b8bcee8f4ae3 | Returns True if the given string is in Google Music id form. | [
{
"docid": "b19cfb950577f0622b596c172db40f15",
"score": "0.7482086",
"text": "def is_gm_id(s):\r\n return re.match(gm_id_regex, s) is not None",
"title": ""
}
] | [
{
"docid": "12e69c27f1ba64c6839ef55fd6a0a90a",
"score": "0.65633464",
"text": "def stringcheck_id(self, string):\n string = str(string) # because of 2.7 input problem\n if re.match(\"^[a-zA-Z_][a-zA-Z0-9_]*$\", string) != None:\n return True\n else:\n return Fa... |
f60393a5315a0be0bd462b1ad5888fd5 | Converts a connectivity tensor to a dgl graph with edge and node features. | [
{
"docid": "5609fad7909b17ecf78df3cf43f4ba09",
"score": "0.6688228",
"text": "def _connectivity_to_dgl_edge(connectivity,sparsify=False):\n assert len(connectivity.shape)==3, \"Should have a shape of N,N,2\"\n N,_,_ = connectivity.shape\n distances = connectivity[:,:,1]\n mask = torch.ones_l... | [
{
"docid": "428149d33b5dcdfa8fa35ea1acbb7cb4",
"score": "0.7025889",
"text": "def adj_to_dgl_graph(adj):\n nx_graph = nx.from_scipy_sparse_matrix(adj)\n dgl_graph = dgl.DGLGraph(nx_graph)\n return dgl_graph",
"title": ""
},
{
"docid": "760c8511bc57de853e2a221c132ee3bd",
"score":... |
ec6ec90b5353cfd5cfc372183415c6a7 | check missing percentage of every feature | [
{
"docid": "925655d1faa0bbaf4b620a03b04e768d",
"score": "0.64688915",
"text": "def missing_value_pct(self, data):\n tmp_data = data.copy()\n if tmp_data.empty:\n return False\n missing_rate_df = pd.DataFrame(tmp_data.isnull().mean(), columns=['missing_rate'])\n ret... | [
{
"docid": "6fc001d90fc77d9d7be6395036917507",
"score": "0.6547822",
"text": "def _is_ok_feature(self, feature) -> bool:\n if feature.isnull().mean() >= self.max_nan_rate:\n return False\n if (feature.value_counts().values[0] / feature.shape[0]) >= self.max_constant_rate:\n ... |
213a4ea178810f219a88f61ad163e5e2 | Returns LFS for current node | [
{
"docid": "0b8195ae715b1ebb5d4948e0e91d1633",
"score": "0.510134",
"text": "def get_lfs(conf, ring, datadir, default_port, logger):\n fs = conf.get('fs', 'xfs')\n try:\n module_name = 'swift_lfs.fs.%s' % fs\n cls_name = 'LFS%s' % fs.upper()\n module = __import__(module_name, ... | [
{
"docid": "be7ee2215b02a613745df021d80db130",
"score": "0.65237695",
"text": "def lft(self):\n return self._lft",
"title": ""
},
{
"docid": "d4e35465f4e9efa5863436ea473cfab4",
"score": "0.6152094",
"text": "def bfs(self):\n\n queue = Queue()\n queue.put(self)\n\... |
a0f7b02c15b120e78b8ea99e523bd88a | Test that the report is correct. | [
{
"docid": "2cee7de2673fae20e61b01ceaced3c26",
"score": "0.7139832",
"text": "def test_report(self):\n self.__birt.nr_manual_ltcs.return_value = 10, []\n self.__birt.nr_manual_ltcs_too_old.return_value = 5\n self.__birt.date_of_last_manual_test.return_value = datetime.datetime.now()... | [
{
"docid": "33413fe45069fc706964ce3f54eb8e12",
"score": "0.77926624",
"text": "def test_report(self):\n self.assertEqual('Van 4 van de 12 handmatige logische testgevallen is de uitvoeringstijd niet ingevuld.',\n self.__metric.report())",
"title": ""
},
{
"docid... |
d04a316f8aae73b9a1bd180338544829 | Forward method for NetVladOrthoReg. | [
{
"docid": "cf964e07b576847f8055ac4ab4df9c93",
"score": "0.0",
"text": "def forward(self, inputs, **unused_params):\n\n reshaped_input = tf.reshape(inputs, [-1, self.max_frames, self.feature_size])\n\n #\n # New: Compute attention-based cluster similarity weights:\n #\n ... | [
{
"docid": "71873500bedcb65572a0302e356690c9",
"score": "0.6630026",
"text": "def forward(self):\n self.HR_G = self.netG(self.LR)",
"title": ""
},
{
"docid": "c5b726b3aa05d9e7850ccdbcc9745924",
"score": "0.63454056",
"text": "def forward(self):\n self.fodpred = self.net... |
beaf0668b7f4e27436a093896c5aac5c | Description contains select property options. | [
{
"docid": "4bad8f2c849cc57db064cf607e5f9106",
"score": "0.6118933",
"text": "def test_description(self):\n description = SampleClass.get_description()\n descr_json = json.dumps(description)\n self.assertIsInstance(json.loads(descr_json), dict)\n self.assertIn('select_propert... | [
{
"docid": "e2512257a3a8f5dfb5d85dfad4e052fd",
"score": "0.7056538",
"text": "def getOptionDescriptions(self) -> List[unicode]:\n ...",
"title": ""
},
{
"docid": "f6d2d26e25b447083259be74f68c47d8",
"score": "0.66717213",
"text": "def _select (self, name, description=None):\n ... |
2c45436778ad7ce358304fdbf918c506 | Check if an element exists on page for the given html xpath | [
{
"docid": "1e4d1a418f66980ddb3dd3fbbc47c247",
"score": "0.7942066",
"text": "def __check_exists_by_xpath(self, xpath):\n\n try:\n self.browser.find_element_by_xpath(xpath)\n except NoSuchElementException:\n return False\n return True",
"title": ""
}
] | [
{
"docid": "0413ab94fca2024c2b3c704acc52310e",
"score": "0.8264476",
"text": "def elem_exists(self, xpath):\r\n try:\r\n self.driver.find_element_by_xpath(xpath)\r\n return True\r\n except:\r\n return False",
"title": ""
},
{
"docid": "57c27d411... |
fac0c73ff53d4aabbc7b5486460264a9 | Edits all constants dependent on TEST_DIR. | [
{
"docid": "f837724e3253e8bbc4d88dd36f79bc34",
"score": "0.0",
"text": "def set_save_dir(save_name):\n\n global SAVE_DIR, SAVE_NAME, MODEL_SAVE_DIR, SUMMARY_SAVE_DIR, RES_SAVE_DIR\n\n SAVE_NAME = save_name\n MODEL_SAVE_DIR = get_dir(os.path.join(SAVE_DIR, 'Models/', SAVE_NAME))\n SUMMARY_SAV... | [
{
"docid": "f8af1f1f9c72bf95108b654dee7cefb3",
"score": "0.6374559",
"text": "def setUp(self):\n super(TestConstants, self).setUp()",
"title": ""
},
{
"docid": "fbc5782d868b60b19dec637d5154487a",
"score": "0.5857609",
"text": "def _patch_main_src(project_dir, placeholder, valu... |
6de1468ded4e201bad09caa6bd19724c | update parameters theta to make move probabilities (p, v) = f_theta(S) more closely match the improved search probabilities and self play closer to self play winner (pi, z). parameters f_theta^t+1 will theoretically be stronger | [
{
"docid": "0d4e7e628cf81276cea6a848a60c8d36",
"score": "0.0",
"text": "def PSEUDO_backward(self):\n pass",
"title": ""
}
] | [
{
"docid": "9839b71af2270eef26b36476b6975aff",
"score": "0.6501566",
"text": "def updateW(self, trj_Sp_theta, W_0):\n def fun(x):\n global trj_Sp_theta_z\n W_0 = [[x[0], x[1]],[x[2], x[3]]]\n r_0 = self.reward_trj(trj_Sp... |
0c4c6dd2c51c2603b9664594c8f91331 | add_new_action(action) > None Call this function to add a new action to the vistrail being controlled by the vistrailcontroller. | [
{
"docid": "f1ccc36bc99322d00b2852fc0a974e5c",
"score": "0.68400925",
"text": "def add_new_action(self, action, description=None):\n if action is not None:\n BaseController.add_new_action(self, action, description)\n self.emit(QtCore.SIGNAL(\"new_action\"), action)\n ... | [
{
"docid": "dbabcc32c1471581471bccd01caecead",
"score": "0.733523",
"text": "def add_action(self, action):\n self.action.append(action)",
"title": ""
},
{
"docid": "1ce8936babc98ab865f03fc98c217138",
"score": "0.7085356",
"text": "def register_action(self, action):\n se... |
c0d786c1e94cc697f1b50ea9592d76c8 | Predict the class for a new given data. | [
{
"docid": "7a06abf630e048043c35be2fb7f84503",
"score": "0.0",
"text": "def predict(self, data):\n feat_df = data[['value']].copy()\n feat_df['length'] = feat_df['value'].apply(lambda val: len(val))\n feat_df['digit_frac'] = feat_df['value'].apply(\n lambda val: 0 if len(... | [
{
"docid": "5448685d9015ae40b0c3e981383ba774",
"score": "0.78026956",
"text": "def predict_class(self, X_new):\n result = [self.predict_class_single(x) for x in X_new]\n return result",
"title": ""
},
{
"docid": "af34c97d9232b7d9a55031dcf07ddbc8",
"score": "0.7721786",
... |
70ef012d45d6735cabcc3559dbf60dea | Reorganise correlation product dim of vis so that correlations are grouped as given in cp_argsort. | [
{
"docid": "68dbac0613c9b8f44e24e710655b1b80",
"score": "0.6483097",
"text": "def _reorganise_product(vis, cp_argsort, out_vis):\n n_time = vis.shape[0]\n n_chan = vis.shape[1]\n n_bl = cp_argsort.shape[0]\n n_stok = cp_argsort.shape[1]\n for tm in range(n_time):\n bstep = 128\n ... | [
{
"docid": "5bd7cb3cf863508164617877e800fafb",
"score": "0.5846539",
"text": "def argsort(self, axis=-1, kind=None, order=None):\n ...",
"title": ""
},
{
"docid": "383269bebe3899d49aa4ec6c45b2b337",
"score": "0.58052355",
"text": "def _sort(self):\n\n indices = self.xar... |
34065d080ef4038154acee194ae6693c | Get a random note sequence from training set. | [
{
"docid": "afe2ca4defa15ef4b4b0f50983be91fd",
"score": "0.5828556",
"text": "def get_random_sequence_i(i,direction,sequences,durseqs,chordseqs,lows,highs,spseq):\n i += direction*1\n if i >= len(sequences)-1 or i <= 0:\n direction *= -1\n notes = sequences[i]\n durs = durseqs[i]\n ... | [
{
"docid": "9efef894a4effa2c2a1f8c61a64c5a52",
"score": "0.65925586",
"text": "def playRandom(self):\r\n\t\tindex = random.randint(0, len(self.notes)-1)\r\n\t\tnote = list(self.notes.values())[index]\r\n\t\t#return note\r\n\t\tnote.play()",
"title": ""
},
{
"docid": "27c293fdfdbad760c9135fca... |
44188704f713f90f0cc6cef7bc8e768d | SetMaskValue(itkMaskedImageToHistogramFilterICF3ISS3 self, itkSimpleDataObjectDecoratorSS _arg) SetMaskValue(itkMaskedImageToHistogramFilterICF3ISS3 self, short const & _arg) | [
{
"docid": "613ab156bf6cd5556dffcf7d31191dfd",
"score": "0.8657261",
"text": "def SetMaskValue(self, *args) -> \"void\":\n return _itkMaskedImageToHistogramFilterPython.itkMaskedImageToHistogramFilterICF3ISS3_SetMaskValue(self, *args)",
"title": ""
}
] | [
{
"docid": "30a00a57275b8763d29c667bf2f392c2",
"score": "0.8788932",
"text": "def SetMaskValueInput(self, _arg: 'itkSimpleDataObjectDecoratorSS') -> \"void\":\n return _itkMaskedImageToHistogramFilterPython.itkMaskedImageToHistogramFilterICF3ISS3_SetMaskValueInput(self, _arg)",
"title": ""
... |
166a4a04e4927ffada15efd6da0a1719 | Returns a tuple containing various accuracy metrics. | [
{
"docid": "2b887430f4857c841603d0818710045c",
"score": "0.6452261",
"text": "def get_accuracy(model: Model,\n result: Result,\n example: Example) -> Tuple[float, float, float, float, float]:\n del model # Unused.\n\n # TODO(kshi): Refactor to reuse the simpler code in... | [
{
"docid": "5e1ecb6edb312cf47bb0601254ba1805",
"score": "0.7734463",
"text": "def accuracy(self):\n\n\t\treturn (self.truePositives + self.trueNegatives) / self.items",
"title": ""
},
{
"docid": "e9c2bf54ead0f330e10b9669a8749124",
"score": "0.77266836",
"text": "def _get_accuracy(sel... |
4623600639342d32adeb153e3e2c9649 | Loads TIFF images from paths as numpy arrays. | [
{
"docid": "bd8456f3f24ed1c045f7ed3f6bda792e",
"score": "0.65337104",
"text": "def load_images(self) -> Generator[np.ndarray, None, None]:\n for path_ in self._paths:\n if not os.path.exists(path=path_):\n raise ImageNotFoundError(path_)\n\n try:\n ... | [
{
"docid": "37bc7bb047c51d9c59c99605658da6b9",
"score": "0.7312007",
"text": "def read_tiff_sequence (path):\n\ttomo_files = sorted(glob(path))\n\tnum_files = len(tomo_files)\n\n\t# Read first to understand sizes:\n\ttomo = imread(tomo_files[0])\n\n\t# Prepare dataset:\n\tdata = zeros((tomo.shape[0], to... |
f68ccc29c713b415872ba580eb64f23a | Compute the cosine similarity of all pairs in ``X``. To be added. | [
{
"docid": "eb0c58c1d49eab42e27d8b5a21698968",
"score": "0.74439645",
"text": "def transform(self, X):\n n_pairs, two = X.shape\n Xt = np.zeros(n_pairs, dtype=float)\n i=0\n for x1, x2 in X:\n Xt[i] = cosine_similarity(x1, x2)\n i+=1\n\n return Xt... | [
{
"docid": "7cd76d82ac6c4ff23bce6e140b37c164",
"score": "0.75480765",
"text": "def cosine_similarity(self, x1, x2, dim=1):\n w12 = torch.sum(x1 * x2, dim)\n w1 = torch.norm(x1, 2, dim)\n w2 = torch.norm(x2, 2, dim)\n return (w12 / (w1 * w2).clamp(min=self.eps)).squeeze()",
... |
73d4f1f8bd56fd8e6e8a192a4a148516 | Used for AllXY measurement and calibration for multiple qubits simultaneously. | [
{
"docid": "3e9c1545d6f0eb6c0c3e576e4611f682",
"score": "0.6008026",
"text": "def multi_qubit_AllXY(qubits_idx: list, platf_cfg: str, double_points: bool = True) -> OqlProgram:\n\n p = OqlProgram(\"Multi_qubit_AllXY\", platf_cfg)\n\n allXY = [['i', 'i'], ['rx180', 'rx180'], ['ry180', 'ry180'],\n ... | [
{
"docid": "051cce46202ff410a55de1980f5f67e2",
"score": "0.5637091",
"text": "def AP_Keq_examples(self):\n\n # calculate keq values using latest parameter values\n c1, c2, c3 = self.coeffs\n\n # calc keq for all\n for its in self.dg100 + self.dg400:\n its.calc_keq(... |
a177402ca95837ce905adefe4e4b8d25 | Load user in login manager after successful authentication | [
{
"docid": "be28d3224ee95759744be9229953ab08",
"score": "0.0",
"text": "def load_user(id):\n return User.query.get(int(id))",
"title": ""
}
] | [
{
"docid": "2e9c56a0d4e0780642877e9c4cf20843",
"score": "0.7383222",
"text": "def load_user():\n g.user = User.objects(email=session.get('logged_in')).first()",
"title": ""
},
{
"docid": "9921d39e5804f4838eab20edbbce2cad",
"score": "0.7299501",
"text": "def authentication(app, use... |
f262780237a318b08ee6533c0c2b718c | The initial requested URI cannot be modified during the user interactive authentication session. | [
{
"docid": "89a8b1d7679d46e64d3283cc34e9200c",
"score": "0.5436516",
"text": "def test_cannot_change_uri(self) -> None:\n # Create a second login.\n self.login(\"test\", self.user_pass, \"dev2\")\n\n # Attempt to delete the first device.\n # Returns a 401 as per the spec\n ... | [
{
"docid": "e27c96184d3d5eb28e76d38f0383954d",
"score": "0.5817669",
"text": "def test_authenticated_uri(self):\n request = self.factory.get(self.uri)\n force_authenticate(request, self.user)\n response = self.view(request)\n response.render()\n self.assertEqual(respon... |
f87df1f868d8a0937a9ece2303097018 | Returns the UID, the UID where the Brick is connected to, the position, the hardware and firmware version as well as the device identifier. The position can be '0''8' (stack position). | [
{
"docid": "ff1efcecb313519d85108b3f0b5cec55",
"score": "0.0",
"text": "def get_identity(self):\n return GetIdentity(*self.ipcon.send_request(self, BrickDC.FUNCTION_GET_IDENTITY, (), '', '8s 8s c 3B 3B H'))",
"title": ""
}
] | [
{
"docid": "81f8d864177e6dabd9725d2750ae5105",
"score": "0.59945315",
"text": "def read_uid(self):\n return self.ipcon.send_request(self, BrickHAT.FUNCTION_READ_UID, (), '', 'I')",
"title": ""
},
{
"docid": "e4d920a2598f5f2aad915fd74784a61c",
"score": "0.59237516",
"text": "de... |
9929accb08cf07af7a71119ec5e2502c | Parse packages from filename. | [
{
"docid": "f66162ef1cde617aa750947290fd4e76",
"score": "0.0",
"text": "def get_packages_async_2(filename):\n pool = Pool(processes=cpu_count() + 2)\n num = 0\n for packages in pool.map(_get_packages, _iter(filename)):\n if packages:\n for package in packages:\n ... | [
{
"docid": "69f75f8ea60b3debaabd6fc32d0dabc1",
"score": "0.66556424",
"text": "def parse(self, filename):\n pass # implemented in Ada",
"title": ""
},
{
"docid": "e39c948dfe9585ded737137cfc2c7717",
"score": "0.6559397",
"text": "def get_packages(filename):\n num = 0\n p... |
f5cf5fc9fafde2916e90572ab278c0bf | Creates a MongoDB filter for new documents. By "new", we mean documents in this Store that were last updated later than any document in targets. | [
{
"docid": "f6226e52defd180c7c9cbc3640c0ea6b",
"score": "0.59591556",
"text": "def lu_filter(self, targets):\n if isinstance(targets, Store):\n targets = [targets]\n\n lu_list = [t.last_updated for t in targets]\n return {self.lu_field: {\"$gt\": self.lu_key[1](max(lu_lis... | [
{
"docid": "202637605fe0d6c4cd9b4f70374b6863",
"score": "0.54216784",
"text": "def changesWithFilter(self, filter, limit=1000, since=-1):\n if since < 0:\n since = self.last_seq\n data = self.get('/%s/_changes?limit=%s&since=%s&filter=%s' % (self.name, limit, since, filter))\n ... |
4cfcf17fb7ed12a4a17e4ecac43019fe | Get version number from the pkmodel module. The easiest way would be to just ``import pkmodel ``, but note that this may fail if the dependencies have not been installed yet. Instead, we've put the version number in a simple version_info module, that we'll import here by temporarily adding the oxrse directory to the py... | [
{
"docid": "1f2fc9112122d6cd672fa39a911acc3b",
"score": "0.8605629",
"text": "def get_version():\n import os\n import sys\n\n sys.path.append(os.path.abspath('pkmodel'))\n from version_info import VERSION as version\n sys.path.pop()\n\n return version",
"title": ""
}
] | [
{
"docid": "d98e82f0b1ecff5519def17ba5a4e53f",
"score": "0.73453414",
"text": "def get_model_version():\n from .. import __model_version__\n\n return __model_version__",
"title": ""
},
{
"docid": "e3003c2756e7ba9382ad7419a59451ea",
"score": "0.6573163",
"text": "def get_version... |
118cd7923b03484afb4cefa3d6f41144 | Show spectrum and SB map in a given direction of MoCaLaTA output. Example >>> showobs('gal03/0068/','xm') | [
{
"docid": "323b42005808b2f724446e4cce0907ac",
"score": "0.66047466",
"text": "def showobs(galdir, view,\n spatial_aperture = None,\n spec_ap = None,\n mother = './',\n cosmo = Planck15,\n vmin = None,\n vmax = Non... | [
{
"docid": "674790eb55368a06bbb1c8dcc4ff1401",
"score": "0.5732015",
"text": "def show_obs(obs_index):\n if obs_index == 27:\n return '*'\n elif obs_index == 26:\n return ' '\n else:\n return chr(obs_index + ord('a'))",
"title": ""
},
{
"docid": "754eabd45fa8e1e47de1319d01eb6429"... |
ab1c464dbeffedcad2295dc61d6040f9 | Category are correctly named | [
{
"docid": "a5c6b7ce12746d005f0e84b77c840071",
"score": "0.6557743",
"text": "def test_class_name(self):\n category = Category.objects.get(name=\"MED\")\n self.assertEqual(category.name, 'MED')",
"title": ""
}
] | [
{
"docid": "04b89e3005840b483d9d68614f7db866",
"score": "0.7223058",
"text": "def __str__(self):\n return \"%s\" %(self.category_name)",
"title": ""
},
{
"docid": "17f31039bc63e05d2c29cbfddac669f2",
"score": "0.7195257",
"text": "def category():\n pass",
"title": ""
}... |
6a55ef7b5337d6440def0fb227d5dd4c | Global application exception handler. For "basic" errors (I/O errors, keyboard interrupt, etc.) just the error message is printed as there's generally no need to confuse the user with a complete stack trace when it's just a missing file. Other exceptions, however, are logged with the usual full stack trace. | [
{
"docid": "59fb904953040acebe4029f7cf6808ee",
"score": "0.0",
"text": "def error_handler(exc_type, exc_value, exc_trace):\n if issubclass(exc_type, (SystemExit,)):\n # Exit with 0 (\"success\") for system exit (as it was intentional)\n return 0\n elif issubclass(exc_type, (KeyboardI... | [
{
"docid": "cfc6405b65231024c8845a59cdfa5f6b",
"score": "0.74804026",
"text": "def global_exception_handler(exception: Exception) -> None:\n log.error('occur %s %s', exception.__class__.__name__, str(exception.args))\n print(exception)",
"title": ""
},
{
"docid": "9d3b6a24b4f6e9bea2a7d... |
2c51f84424129b9c3d172d5617a4d18d | This endpoint does a special onetime bootstrap of the ACL system, making the first management token if the acl_master_token is not specified in the Consul server configuration, and if the cluster has not been bootstrapped previously. This is available in Consul 0.9.1 and later, and requires all Consul servers to be upg... | [
{
"docid": "e39d22b980fcafe79ee8f139b0f1f886",
"score": "0.5312343",
"text": "def bootstrap(self):\n return self._put_response_body(['bootstrap'])['ID']",
"title": ""
}
] | [
{
"docid": "29e5ddf142f447f368aa51fed9c847d9",
"score": "0.5988396",
"text": "def cmd_bootstrap(args):\n if getuser() != \"root\":\n error(\"Command has to run as root.\")\n\n if args.nodes is None:\n nodelist = [vm.name for vm in clusterdef.vm]\n else:\n nodelist = args.no... |
8e444fb4ab189b2b1342a799914c2ce0 | Query backend to set new risk limit. | [
{
"docid": "6fc80801b87db66e6f435ccc23132824",
"score": "0.67008007",
"text": "def set_risk_limit(self):\n name, symbol = self._get_selected()\n riskLimit = int(self.riskSpin.get())\n try:\n core.position_risk_limit([name], symbol, riskLimit)\n self.update_inst... | [
{
"docid": "4027090ba44f9d7b789a7adc225e048b",
"score": "0.6715031",
"text": "def risk_limit(self, risk_limit):\n\n self._risk_limit = risk_limit",
"title": ""
},
{
"docid": "208d7e671dd414b0b0c63ed3c1b72284",
"score": "0.6701142",
"text": "def set_limits(self):",
"title":... |
6b0bda8dd2dc34d181e9106c82a2a85b | generated source for method __init__ | [
{
"docid": "69a03b0b49aef10faed120ddbf5197d2",
"score": "0.0",
"text": "def __init__(self, finder):\n super(TablingSolver, self).__init__(finder)\n self.simpleFinder = SudokuStepFinder(True)\n i = 0\n while len(tmpOnSets):\n self.tmpOnSets[i] = SudokuSet()\n ... | [
{
"docid": "2fc2e6931a140e00b1ded6302e3cb897",
"score": "0.8635625",
"text": "def __init__(__self__):\n pass",
"title": ""
},
{
"docid": "2fc2e6931a140e00b1ded6302e3cb897",
"score": "0.8635625",
"text": "def __init__(__self__):\n pass",
"title": ""
},
{
"doc... |
8cee350d51966e0ec041726df5615876 | returns a sequence with the first and last characters swapped | [
{
"docid": "c24010c590ecec8b99a867a3ada4c4e7",
"score": "0.7389425",
"text": "def exchange_first_last(seq):\n\tfirst = seq[0]\n\tmid = seq[1:-1]\n\tlast = seq[-1]\n\t\n\tif type(seq) == str:\n\t\treturn last + mid + first\n\telse:\n\t\teos=len(seq)\n\t\tmid.insert(0,last)\n\t\tmid.insert(eos,first)\n\t\... | [
{
"docid": "acecd658cbf3c6f2e8fd53588ee88c6c",
"score": "0.77933437",
"text": "def seq_swap_first_last(seq):\n return (seq if (len(seq) < 2) else seq[-1:] + seq[1:-1] + seq[0:1])",
"title": ""
},
{
"docid": "8085456ed54e7f29145e05cbbaaf882d",
"score": "0.76585096",
"text": "def ex... |
4a365ce584eee01290f5a160284042b9 | Returns the tests in |tests| that have at least one of their compile targets in |compile_targets|. | [
{
"docid": "26fe9329c821b898fee803f75a5b3580",
"score": "0.80282277",
"text": "def tests_in_compile_targets(api, compile_targets, tests):\n result = []\n for test in tests:\n test_compile_targets = test.compile_targets(api)\n\n # Always return tests that don't require compile. Otherwise we'd nev... | [
{
"docid": "7c1f73c51d4e05474cc4179520270cfc",
"score": "0.64364606",
"text": "def all_compile_targets(api, tests):\n return sorted(set(x\n for test in tests\n for x in test.compile_targets(api)))",
"title": ""
},
{
"docid": "77041d9d085c52ac692244cfa90... |
45b3623ba61966a94edbc20b2bdf4fe8 | Convenience method for merging replicates. Merge will extrapolate times to `number_per_run=1` and will not transfer any metadata. (Since it might differ between replicates) | [
{
"docid": "4a14c333dc41744af2af5f93dce71346",
"score": "0.44534874",
"text": "def merge(measurements): # type: (Iterable[Measurement]) -> List[Measurement]\r\n grouped_measurements: DefaultDict[TaskSpec, List[Measurement]] = collections.defaultdict(list)\r\n for m in measurements:\r\n ... | [
{
"docid": "5c48bdd3d85f9a61a258cddfddf4a793",
"score": "0.57867146",
"text": "def merge_runs(ctx, args):\n from . import merge_impl\n\n merge_impl.main(args, ctx)",
"title": ""
},
{
"docid": "9061efa9a9fc791c8dbbeddcbf57705d",
"score": "0.57148755",
"text": "def run_merge(self... |
a2705f464cacd4cac18658eb5c83b576 | Get the abbreviated metaedges for an abbreviated metapath. Pass a hetio.MetaGraph object to `standardize_by` to standardize metaedge abbreviations based on the noninverted orietatation. Pass `text` to standardize by alphabetical/forwarddirection arrangment of the abbreviation. Default (`None`) does not standardize. | [
{
"docid": "d03621972f35fb52df1ff13fc44441a9",
"score": "0.7505954",
"text": "def metaedges_from_metapath(abbreviation, standardize_by=None):\n if isinstance(standardize_by, hetio.hetnet.MetaGraph):\n metapath = standardize_by.metapath_from_abbrev(abbreviation)\n return [metaedge.get_st... | [
{
"docid": "f621f2899b05db266c51a54b3830006c",
"score": "0.5728711",
"text": "def create_abbreviations(metagraph):\n kind_to_abbrev = find_abbrevs(metagraph.node_dict.keys())\n kind_to_abbrev = {kind: abbrev.upper()\n for kind, abbrev in kind_to_abbrev.items()}\n\n edge_set... |
e7a0978044c28389b858fd2862c54aaa | Wrapper for main function | [
{
"docid": "e696deb90cf7e8b40da734a53d2bb0d6",
"score": "0.0",
"text": "def main():\n # Constants\n y_label = \"Survived\"\n factor_cols = [\"Embarked\", \"Sex\"]\n\n # Load and clean training data\n print(\"Loading, cleaning training data...\")\n training_data = extract_clean_data(\"t... | [
{
"docid": "6a72f0523c7f4b19a3ff495c1baaa5c5",
"score": "0.8710058",
"text": "def main() -> None:",
"title": ""
},
{
"docid": "6a72f0523c7f4b19a3ff495c1baaa5c5",
"score": "0.8710058",
"text": "def main() -> None:",
"title": ""
},
{
"docid": "82c54a749d7508e492693a3059faed... |
d3f1b8b59075de171a62c9387ab95df1 | Execute the decorated test only if running in v2 mode. This function is intended to be applied to tests that exercise v2 only functionality. If the test is run in v1 mode it will simply be skipped. See go/tftestdecoratorcheatsheet for the decorators to use in different v1/v2/eager/graph combinations. | [
{
"docid": "da0fa67a1dfe1d3c0c9f4710dd11ae72",
"score": "0.8331696",
"text": "def run_v2_only(func=None):\n\n def decorator(f):\n if tf_inspect.isclass(f):\n raise ValueError('`run_v2_only` only supports test methods.')\n\n def decorated(self, *args, **kwargs):\n if not tf2.enabled():\n... | [
{
"docid": "aac29693e6b53172179e0026e37e8d2c",
"score": "0.60816467",
"text": "def decorated(test):\n\n @wraps(test)\n def test_with_version(self):\n \"Only run the test if ES version is not less than specified.\"\n actual_version = self.get_es().info()['version']['nu... |
8815029ce05be0962e676d03a39cb105 | get GS states behind and other states at front | [
{
"docid": "f0fd1d1e4410d79516a30bb815aeb5d6",
"score": "0.0",
"text": "def ordered_states(state_list):\n labels = [s.label for s in state_list]\n indices = np.argsort(labels)\n state_list = np.array(state_list)[indices].tolist()\n\n ordered_list = []\n for state in state_list:\n i... | [
{
"docid": "8bb41154d12cc8e694ed65d92ee342b4",
"score": "0.67085534",
"text": "def states(self):",
"title": ""
},
{
"docid": "c78ef67cf415cc2aeb46b1bde676b289",
"score": "0.6554295",
"text": "def getStates(self):\r\n return self.grid",
"title": ""
},
{
"docid": "04... |
59c71601c11f442754b4945bc8de09ad | Switch the coordinate base of a wiggle file | [
{
"docid": "2e3f63eb028b90fcdfc784afd6c1d18a",
"score": "0.554798",
"text": "def switch_base(opts):\n functional.switch_base(opts.i, opts.o, from_base=1-opts.to, to_base=opts.to)",
"title": ""
}
] | [
{
"docid": "ab04d55cfd56dced47f1aa034242f7b2",
"score": "0.57458204",
"text": "def SetBase(self, base):",
"title": ""
},
{
"docid": "d81d1619137b8199755643751174e59e",
"score": "0.5698028",
"text": "def setImageBase(self, base: ghidra.program.model.address.Address) -> None:\n ... |
1133fbd07407924ef118cf63f878dc14 | Like get_observations() but handles pagination so you get all the results in one shot. | [
{
"docid": "56a7866671c7575d9da7ba58c8febe31",
"score": "0.7313944",
"text": "def get_all_observations(params: Dict, user_agent: str = None) -> List[Dict[str, Any]]:\n\n # According to the doc: \"The large size of the observations index prevents us from supporting the page parameter\n # when retri... | [
{
"docid": "da7f164f0dfe32607542dab96d5e45d8",
"score": "0.72840744",
"text": "def observations(self):\n ro = self._create_basic_request_object('observations_get')\n\n ro.set_owner(self.owner_name)\n ro.set_description('retrieve observations for {}'.format(self._reference_indicator)... |
b8a317bbc561ef78b076ac96213c320d | Callback of stop order update. | [
{
"docid": "3d97f6c0964c4dd3ffebfefaf4a1e725",
"score": "0.7922227",
"text": "def on_stop_order(self, stop_order: StopOrder):\n pass",
"title": ""
}
] | [
{
"docid": "434387fcfcd9e863bf8d93d3048974d5",
"score": "0.70248485",
"text": "def triggerStopOrders(self):\n for soID,so in self.workingLimitOrdersDict.items():\n if so.direction == DIRECTION_LONG:\n triggered = (self.bar.High>=so.price)\n elif so.direction =... |
978f74abfb77ac10e4d0bc386de42f9c | Constructor for a generic pipeline | [
{
"docid": "716e68cd8ffc66ec9e99b1381224e6e3",
"score": "0.64695215",
"text": "def __init__(self, name=None):\n if not name:\n # w/o a name, generate a random UUID to name this pipeline\n self.name = str(uuid.uuid4())\n else:\n self.name = name \n se... | [
{
"docid": "80e68f1c67c09d30204dd32ad82e5c03",
"score": "0.7773082",
"text": "def from_pipeline(cls, pipeline):\n self = object.__new__(cls)\n self.data = pipeline.data\n return self",
"title": ""
},
{
"docid": "4ab57fb936d17c796e937df740d6fe7e",
"score": "0.7675824"... |
6afc41a59914e0674305218c865b34ae | Calls openMVG to do compute matches | [
{
"docid": "4822d5fcfbdeda71bb433a957a3e5049",
"score": "0.7459588",
"text": "def openmvg_matches(pth_sfm, pth_matches, video_mode=None, force=False):\n\n cmd = \"{}/openMVG_main_ComputeMatches -i {} -o {} -v {}\".format(PATH_OMVG, pth_sfm,\n ... | [
{
"docid": "4e08f7c38ca53640306f5e8ebfb60f23",
"score": "0.5903164",
"text": "def exec_matching(infr, vsone=False, prog_hook=None):\n if infr.verbose:\n print('[infr] exec_matching')\n #from ibeis.algo.hots import graph_iden\n ibs = infr.ibs\n aid_list = infr.aids\... |
75bc406279890dfbecf82c2784606de1 | Loads all data, first it creates list of data points and then populates each data point with data by loading the contour and dicom files. | [
{
"docid": "d1b8031ee16956ff92b461f44f8f4605",
"score": "0.6711694",
"text": "def load_all_data(dicom_dir, contour_dir, link_file):\n data_points = create_data_points(dicom_dir, contour_dir, link_file)\n return get_image_and_masks(data_points)",
"title": ""
}
] | [
{
"docid": "05efec025234f1f56eeac96532b96fa5",
"score": "0.73711884",
"text": "def load_dataset(self, contour): \n self.dataset = []\n for patient_id in self.link_dict.keys():\n self.dataset += self.get_patient_data(patient_id, contour)",
"title": ""
},
{
"docid":... |
b0863bb99a634fc50175ddef28794392 | Draw a correlation heatmap and return the frame. | [
{
"docid": "e8041ceb481120c7ee9fd76c08fa2068",
"score": "0.69290173",
"text": "def get_correlation(frame, outdir=None):\n\n corr = frame.corr()\n\n if outdir != None:\n corr.to_csv(outdir/'correlation.csv')\n plt.figure()\n sns.heatmap(corr, cmap=plt.cm.RdYlBu_r)\n plt.... | [
{
"docid": "c32d7300ec62ae5ea0e58e19b2099924",
"score": "0.74469304",
"text": "def correlation_heatmap(df):\n _, ax = plt.subplots(figsize=(14, 12))\n colormap = sns.diverging_palette(220, 10, as_cmap=True)\n\n _ = sns.heatmap(df.corr().round(3),\n cmap=colormap,\n ... |
a6f121548d76e6dc98afa53d020aa3df | Check if this task failed. | [
{
"docid": "dd5c1773e64de9e555e6c0c34cc41a45",
"score": "0.7249489",
"text": "def failed(self) -> bool:\n return self.completed is not None and not self.completed",
"title": ""
}
] | [
{
"docid": "6af889faa0e6ed8836e8b78b10e2a744",
"score": "0.77773494",
"text": "def task_is_failure(task):\n\n if task and task.state == 'FAILURE':\n return True\n return False",
"title": ""
},
{
"docid": "c14544e02e3f0b540cbf63fd7c879100",
"score": "0.76552993",
"text": ... |
1f741524474d86566b65cfc9c0d6fb19 | Gets the mapping of pickleable python attribute name and values. | [
{
"docid": "00f6279d2da02e8235168a49f842043f",
"score": "0.5921677",
"text": "def _get_python_attributes(self):\r\n python_attrs = dict()\r\n for key, value in vars(self).iteritems():\r\n if key.startswith('_'):\r\n continue\r\n if isinstance(value, hou... | [
{
"docid": "26012275e08f96726959da3317a3d693",
"score": "0.67514306",
"text": "def attributes(self) -> t.Dict[str, str]:",
"title": ""
},
{
"docid": "9449c140f2f9e78549c886a816759fab",
"score": "0.6637769",
"text": "def get_attributes(self) -> Mapping[str, typing.Any]:\n retur... |
daf973153a7da1e68c0c9d42349d0b2c | Function to calculate the conditional priors p(xp|xeff,q) on a set of {xp,xeff,q} posterior samples. INPUTS | [
{
"docid": "f87f1caa5f85a8a02591eeabab79177f",
"score": "0.5392014",
"text": "def joint_prior_from_isotropic_spins(q,aMax,xeffs,xps,ndraws=10000,bw_method='scott'):\n\n # Convert to arrays for safety\n xeffs = np.reshape(xeffs,-1)\n xps = np.reshape(xps,-1)\n \n # Compute marginal prior o... | [
{
"docid": "3df82a3c097cc9531b6d612c43603726",
"score": "0.6071597",
"text": "def chi_p_prior_given_chi_eff_q(q,aMax,xeff,xp,ndraws=10000,bw_method='scott'):\n\n # Draw random spin magnitudes.\n # Note that, given a fixed chi_eff, a1 can be no larger than (1+q)*chi_eff,\n # and a2 can be no lar... |
fa3504efd3ea644eaeb652cc41290809 | Returns the energy (in keV) associated with a given Xray line. By example, if xray_line = 'Mn_Ka' this function returns 5.8987 | [
{
"docid": "ddb0535509d1c85afca40d5ae14511c0",
"score": "0.8575026",
"text": "def _get_energy_xray_line(xray_line):\n element, line = _get_element_and_line(xray_line)\n return elements_db[element]['Atomic_properties']['Xray_lines'][\n line]['energy (keV)']",
"title": ""
}
] | [
{
"docid": "d3e919af76ff67321b75206d1b772a8f",
"score": "0.7023946",
"text": "def line_energy(element,line):\n z = elementDB[element][\"Z\"]\n if not isinstance(line,LinePair):\n line = _lookupxlsubline(line)\n return xraylib.LineEnergy(z,line.subline)",
"title": ""
},
{
"doc... |
03e7c53a79ac094226a73f5970f9b23b | Utility function to get the timeout value used in requests | [
{
"docid": "c8799e09a6258eab949006f89eb0cde2",
"score": "0.81352955",
"text": "def _timeout(self, timeout):\n if timeout is None:\n return self._request_timeout\n return timeout",
"title": ""
}
] | [
{
"docid": "22dfdd2cc0a669d48d7d0ff56ef6578b",
"score": "0.8522555",
"text": "def gettimeout(self): # real signature unknown; restored from __doc__\n return timeout",
"title": ""
},
{
"docid": "ddc80f25e3f51af1150befad01524a0a",
"score": "0.8129886",
"text": "def get_timeout(s... |
291fe1dfc69deae35dbac946b041f543 | Actually set the volume via `amixer`. | [
{
"docid": "6339ec27cb24476ee916f2cb7eda5b98",
"score": "0.8421663",
"text": "def set_volume(self):\n subprocess.call([\"amixer\", \"set\", \"PCM\", \"--\", \"%d%%\" % (self.volume,)])",
"title": ""
}
] | [
{
"docid": "008be544c722e9ddde9fbf00f2f58a24",
"score": "0.7889102",
"text": "def set_volume(self, volume):\r\n if self.is_active:\r\n if 0 < volume < 1:\r\n volume = int(volume*100)\r\n self.sp.volume(volume)",
"title": ""
},
{
"docid": "2fba4be3c921afd787309e29022ba900"... |
a3dff4a0b5a3b63b7a71b29519ee5309 | Moves duplicate files to a separate directory, while preserving the subdirectory structure in there. | [
{
"docid": "320ae484e2d52eae10f58cdc2fc7bd2b",
"score": "0.7337616",
"text": "def __call__(self, directory_to_move_duplicates_to=None):\n if directory_to_move_duplicates_to is None:\n directory_to_move_duplicates_to = os.path.join(self.directory, \"duplicates\")\n directory_to_m... | [
{
"docid": "b996a5c6073bbe3944cfbade4eb38d7a",
"score": "0.65459585",
"text": "def scan_and_remove_duplicate(from_dir, debug=False):\n all_files = defaultdict(list)\n for root, dirs, files in os.walk(from_dir):\n for file in files:\n full_path = os.path.join(root, file)\n ... |
dd403594a26d4ed6b1599a1e49a12d9a | Draws the points onto the GUI canvas | [
{
"docid": "3d0fe5e512da5b59f5dc44f3070a7bab",
"score": "0.6598843",
"text": "def on_paint(self, event):\n dc = wx.PaintDC(self.panel)\n dc.SetPen(wx.Pen('black', 1))\n dc.SetBrush(wx.Brush('black', 1))\n for point in self.annealing.state.points:\n dc.DrawCircle(po... | [
{
"docid": "ab4edc0d7656931e0deb95b2ce5bd993",
"score": "0.74049175",
"text": "def _draw(self):\n if self.colorTable != {}: CT = self.colorTable\n else:\n CT = self._genColorTable()\n self.plotter.clear()\n for p in self.points:\n self.plotter.setCo... |
768e00ec8ddd94a666938045bfd89fe0 | Matches the NDC to db | [
{
"docid": "671597d49d82516294ea16dbefe37f43",
"score": "0.58281344",
"text": "def match_ndc(ndc):\n\n def find_recent_db():\n \"\"\" returns most recent db csv file. Gives error pop up if no db file found \"\"\"\n files = glob.glob('ndcdb-*.csv')\n if len(files) == 0:\n ... | [
{
"docid": "e0c3d258c19d4d7fa03901c154ec7bfa",
"score": "0.5899773",
"text": "def inspect_db(self):",
"title": ""
},
{
"docid": "4c75ae12209357a13cd26ecb99d48a79",
"score": "0.56975514",
"text": "def getdb(self):\n if self.cl._db is not None:\n return self.cl._db\n ... |
c6ccb384e1ab09d3b8f9ca491cfa70c2 | Fetch and format documents screen. Content is supplied from self.DATA_INDEX. App handles formatting and display | [
{
"docid": "933d4bea7adbf38994604d04c98390c4",
"score": "0.6130742",
"text": "def setup_document(self, *args, **kwargs):\n doc_dict = self.DATA_INDEX.current_doc\n doc_idx, group_len = self.DATA_INDEX.current_index\n self.current_document_title = doc_dict['Job_Title']\n self.... | [
{
"docid": "792b5fddc5dab6657fbe27fdc6166895",
"score": "0.63426816",
"text": "def process_data(self):\n\n # Drivers License\n if self.Data.DriversLicence is not None:\n doc = Document.DriversLicense(None, self.Data.DriversLicence, self.photoImage)\n self.Documents.ap... |
8abadc08d2224205c0322496613c7174 | Returns followers/following, number of posts | [
{
"docid": "268b1f4295be22b013434acc84c45951",
"score": "0.0",
"text": "def get_instagram_stats(self, username):\n user = self.get_user(username)\n user_info = self.get_user_profile(user[\"id\"])\n return user_info[\"data\"][\"counts\"]",
"title": ""
}
] | [
{
"docid": "0c002c2cc4835dc347d3d1959dbbd10a",
"score": "0.7136349",
"text": "def followers_count(self):\n user_type = ContentType.objects.get_for_model(self)\n return UserInterest.objects\\\n .filter(interest_type='follow', content_type__pk=user_type.id, object_id=self.id)\\\n ... |
53bf878325c3da25c2bdf2b255812f70 | Wraps up the different methods to get the complex trajectories in experiment | [
{
"docid": "2cd396a60027d05589a8224e8825f982",
"score": "0.6717981",
"text": "def get_complex_trajectories(experiment):\n raco_before,raco_after=experiment.trajectory_racomodation()\n \n list_of_raccomodated = []\n for i,(frame,label,number) in enumerate(raco_after):\n index = raccomo... | [
{
"docid": "704c0d232adc815ba1223712ec930fbb",
"score": "0.5725358",
"text": "def test_step_methods_in_each_level(self):\n _, model, _ = mv_simple()\n _, model_coarse, _ = mv_simple_coarse()\n _, model_very_coarse, _ = mv_simple_very_coarse()\n with model:\n initia... |
c187fe94c06737319ce6a05f9f97bdf6 | Decodes the auth token | [
{
"docid": "02be4b20298fe9880c620e1a3a35e5a0",
"score": "0.7484995",
"text": "def decode_auth(auth_token):\n try:\n payload = jwt.decode(auth_token, app.config.get('SECRET_KEY'))\n return payload['sub']\n except jwt.exceptions.InvalidTokenError:\n return 'Invalid token. Please... | [
{
"docid": "ca7dd14a048ddd0283f1e392a638ab5b",
"score": "0.7767273",
"text": "def decode_auth_token(auth_token):\n try:\n pyload= jwt.decode(auth_token, JWT_SECRET_KEY)\n return pyload['id']\n except jwt.ExpiredSignatureError:\n return 'Signature expired. P... |
68f06fdad826c9bce003a7c53d88e283 | Compute inverse CDF for a given value. | [
{
"docid": "cd4d641d3fb9a0113530e0471fedd092",
"score": "0.5649419",
"text": "def at(self, value):\n assert(0 <= value <= 1)\n return bisect.bisect_right(self.cdf, value)",
"title": ""
}
] | [
{
"docid": "2b8d83cb66b7de2be6af77838e1cc5db",
"score": "0.82533187",
"text": "def inverse_cdf(self, value):\n raise NotImplementedError",
"title": ""
},
{
"docid": "8a47abed2865294dce40758967737770",
"score": "0.66790795",
"text": "def inverse_cdf(self, u):\n return no... |
4086f19c350b78f3c7029a5d86742ae7 | This class holds all the models for a dataset | [
{
"docid": "5f8fa3d84739edffa3cf68ed1dc1e208",
"score": "0.0",
"text": "def __init__(self, id):\n self.id = id\n self.models = BTrees.OOBTree.BTree()\n self.constructor_id = -1\n self.dataset_id = -1\n self.group_connection_id = -1",
"title": ""
}
] | [
{
"docid": "ea662cd6f629aaa29b6be895da859b88",
"score": "0.7318091",
"text": "def _get_model_dataset(self):\n\n # Instantiate the model object\n model = getattr(\n importlib.import_module('models.' + self._args.model),\n self._args.model)()\n\n # Instantiate th... |
80687f3cf13f915b78d9c85561dd33fb | Return readable informal exception description about checksumed exception. | [
{
"docid": "1747be14a5b639183ce75ef786e8178c",
"score": "0.0",
"text": "def __str__(self):\n return 'Invalid {} checksum for the {} file'.format(self.algo, 'original' if self.decrypted else 'encrypted')",
"title": ""
}
] | [
{
"docid": "9271c80dc57fab8ae115327f3df1069a",
"score": "0.6199383",
"text": "def exception(self):\r\n buf = traceback.format_exception_only(self.exc_type, self.exc_value)\r\n return ''.join(buf).strip().decode('utf-8', 'replace')",
"title": ""
},
{
"docid": "fb1ae58044d5b91590... |
889e0e73799057cf38eacd20067fa5b8 | Test case for api_v1_statuses_secrets_get | [
{
"docid": "22d22c4deda381211a78dce4bd31ebf8",
"score": "0.9513433",
"text": "def test_api_v1_statuses_secrets_get(self):\n pass",
"title": ""
}
] | [
{
"docid": "db56674662f0780175ab53fac68d5b6a",
"score": "0.6973698",
"text": "def test_get_all_secrets(self):\n pass",
"title": ""
},
{
"docid": "233629767b99031fd14f7b095067d9ae",
"score": "0.66280454",
"text": "def get_secrets(self):\n secret_url = \"{}/secrets\".form... |
ded8dd55a74a3491147509032239a5c2 | Display a dialog asking the user to input the range of frames they would like to export | [
{
"docid": "911b2d5a62a538eb21b7466296923e33",
"score": "0.6653546",
"text": "def ask_frame_range(self, n_frames):\n valid_input = False\n _, ext = os.path.splitext(self.output.GetPath())\n show_single_btn = (ext == '.h5')\n dlg = FrameSelectDialog(n_frames, show_single_btn)\... | [
{
"docid": "ba3807dc066092db7439c382f1f79169",
"score": "0.5913335",
"text": "def getFrameRangeFromShotSettings(self):\r\n\t\ttry:\r\n\t\t\t#frRange = \"%d-%d\" %(int(os.environ['STARTFRAME']), int(os.environ['ENDFRAME'])) # Icarus\r\n\t\t\tfrRange = \"%d-%d\" %(int(os.environ['UHUB_STARTFRAME']), int(... |
b98d9695c5f2e060e667f6137c06d0dd | isFinished(ALInterpolationArticular3D self, float const & time) > bool | [
{
"docid": "0e7f4cffb3df33943aaf7f315f84553f",
"score": "0.875447",
"text": "def isFinished(self, time):\n return _almathinternal.ALInterpolationArticular3D_isFinished(self, time)",
"title": ""
}
] | [
{
"docid": "84e91adf599c004b342da77fe818c9b4",
"score": "0.78961587",
"text": "def isFinished(self, time):\n return _almathinternal.ALInterpolationArticular_isFinished(self, time)",
"title": ""
},
{
"docid": "839e708c13e4da3ccd16b2b6bb407622",
"score": "0.70739007",
"text": "d... |
8e050be4735a3412803428f673375b18 | Save the outputs from the simulation run. | [
{
"docid": "cc27b8307d07d7bd03068df1bc203cd3",
"score": "0.59313",
"text": "def save_simulation(\n output: str,\n profile_type: str,\n simulation_outputs: Solution,\n solar_irradiance: dict[int, float],\n) -> None:\n\n # Assemble the CSV datafile structure\n output_data = simulation_ou... | [
{
"docid": "62357c71511468de2da3ef87b7e3c34b",
"score": "0.75556",
"text": "def save(self):\n # Message #\n self.parent.log.info(\"Saving final simulations results to disk.\")\n # The classifier values #\n self['values'] = self.sim.sit.classifier_value_ids\n # All... |