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How to use read_with_lock method in yandex-tank
Best Python code snippet using yandex-tank
util.py
Source:util.py 
...586 def get_file(self, cache_size=None):587 cache_size = self.cache_size if not cache_size else cache_size588 fileobj = FileLike(self, cache_size)589 return fileobj590 def read_with_lock(self, pos, _len=None):591 """592 Reads {_len} characters if _len is not None else reads line593 :param pos: start reading position594 :param _len: number of characters to read595 :rtype: (string, int)596 """597 self.wait_lock()598 try:599 self._opened_file.seek(pos)600 result = self._opened_file.read(_len) if _len is not None else self._opened_file.readline()601 stop_pos = self._opened_file.tell()602 finally:603 self.unlock()604 if not result and self.stop.is_set():605 result = None606 return result, stop_pos607 @retry(wait_random_min=5, wait_random_max=20, stop_max_delay=10000,608 retry_on_exception=FileLockedError.retry, wrap_exception=True)609 def wait_lock(self):610 if self._is_locked:611 raise FileLockedError('Generator output file {} is locked'.format(self.filename))612 else:613 self._is_locked = True614 return True615 def unlock(self):616 self._is_locked = False617class FileLike(object):618 def __init__(self, multireader, cache_size):619 """620 :type multireader: FileMultiReader621 """622 self.multireader = multireader623 self.cache_size = cache_size624 self._cursor = 0625 def read(self, _len=None):626 _len = self.cache_size if not _len else _len627 result, self._cursor = self.multireader.read_with_lock(self._cursor, _len)628 return result629 def readline(self):630 result, self._cursor = self.multireader.read_with_lock(self._cursor)631 return result632def get_callstack():633 """634 Get call stack, clean wrapper functions from it and present635 in dotted notation form636 """637 stack = inspect.stack(context=0)638 cleaned = [frame[3] for frame in stack if frame[3] != 'wrapper']639 return '.'.join(cleaned[1:])640def timeit(min_duration_sec):641 def timeit_fixed(func):642 def wrapper(*args, **kwargs):643 start_time = time.time()644 result = func(*args, **kwargs)...
predict_keras.py
Source:predict_keras.py
...29from subseasonal_toolkit.utils.experiments_util import pandas2hdf30from subseasonal_toolkit.utils.general_util import make_directories31from subseasonal_toolkit.utils.experiments_util import get_target_date as get_target_date_eval32from subseasonal_toolkit.models.salient.salient_util import ONE_WEEK, ONE_DAY, WEEKS, dir_submodel_forecasts, dir_train_results, dir_train_data, dir_predict_data, year_fraction, get_target_date33def read_with_lock(filename):34 """Open an hdf file with a lock and read. 35 Args:36 filename (str): path of hdf file to be read with a lock.37 Returns:38 openned pandas dataframe.39 """40 #with FileLock(filename+'lock'):41 # df = pd.read_hdf(filename) 42 df = pd.read_hdf(filename) 43 subprocess.call(f"rm {filename}lock", shell=True)44 return df45def generate_windows(x):46 """generates a sliding window over the data of length window_size.47 Args:48 x: array of input features used the NN49 Returns:50 reshaped array of input features so that each example is a concatenation of the past 10 weeks of data.51 """52 window_size = 1053 result = []54 for i in range(x.shape[0]+1-window_size):55 result.append(x[i:i+window_size, ...])56 return np.stack(result)57def compile_input(submodel_name, datestr):58 """Compile input data to be used by NN.59 Args:60 submodel_name (str): string consisting of the ground truth variable 61 ids used for training and the date of the last training example.62 datestr (str): YYMMDD string of first day of target 2-week period.63 Returns:64 input_data: array of input features used generate predictions by the NNs.65 """ 66 67 #get input start and end date 68 target_date = datetime.strptime(datestr, '%Y%m%d') 69 end_date = target_date - timedelta(days=((target_date.weekday() - 5) % 7))70 end_date = datetime(end_date.year, end_date.month, end_date.day)71 start_date = end_date - ONE_WEEK * WEEKS + ONE_DAY 72 73 # load date data 74 date_data_file = os.path.join(dir_train_data, "date.pickle")75 date_vectors = pickle.load(open(date_data_file, 'rb'))76 date_vectors_all = sorted([d[0] for d in date_vectors])77 end_date_sn = datetime.strptime(submodel_name[-8:], '%Y%m%d')78 date_vectors = [d for d in date_vectors_all if (d.astype(object).year<=end_date_sn.year) and (d.astype(object).month<=end_date_sn.month) and (d.astype(object).day<=end_date_sn.day)] 79 last_i = len(date_vectors)80 # load training data81 # load sst data82 training_sst_data_file = os.path.join(dir_train_data, "sst.pickle")83 training_sst_vectors_all = pickle.load(open(training_sst_data_file, 'rb')) 84 # load time data85 training_time_data_file = os.path.join(dir_train_data, "time.pickle")86 training_time_vectors = pickle.load(open(training_time_data_file, 'rb'))87 training_time_vectors_all = np.reshape(training_time_vectors,(training_time_vectors.shape[0],1))88 89 # account for early train stop submodels90 training_sst_vectors = training_sst_vectors_all[:last_i,:]91 training_time_vectors = training_time_vectors_all[:last_i,:]92 ## Load input data to generate a prediction93 # load sst data94 sst_data_file = os.path.join(dir_predict_data, "salient_fri", datestr, "sst.pickle")95 if isfile(sst_data_file):96 sst_vectors = pickle.load(open(sst_data_file, 'rb'))97 else:98 #find datestr in dates99 d = [d for d in date_vectors_all if (d.astype(object).year==target_date.year) and (d.astype(object).month==target_date.month) and (d.astype(object).day==target_date.day)]100 i = date_vectors_all.index(d)101 sst_vectors = training_sst_vectors_all[i-10:i,:]102 sst_vectors = (sst_vectors - np.amin(training_sst_vectors)) * 1./(np.amax(training_sst_vectors) - np.amin(training_sst_vectors))103 104 # compile input data105 input_data = sst_vectors106 107 108 return input_data109def add_i_time(input_data, submodel_name, datestr, i_time=False):110 """add i_time feature to input data to be used by NN.111 Args:112 input_data (float): array of input features used generate predictions by the NNs.113 submodel_name (str): string consisting of the ground truth variable 114 ids used for training and the date of the last training example.115 datestr (str): YYMMDD string of first day of target 2-week period.116 i_time (bool): if True, include time vector as an input feature (default: False).117 Returns:118 input_data: array of input features used generate predictions by the NNs.119 """120 121 #get input start and end date 122 target_date = datetime.strptime(datestr, '%Y%m%d') 123 end_date = target_date - timedelta(days=((target_date.weekday() - 5) % 7))124 end_date = datetime(end_date.year, end_date.month, end_date.day)125 start_date = end_date - ONE_WEEK * WEEKS + ONE_DAY 126 127 #load time data128 if i_time:129 time_vectors = np.zeros((WEEKS, 1))130 day = start_date131 for i in range(time_vectors.shape[0]):132 time_vectors[i, 0] = year_fraction(day)133 day += ONE_WEEK134 135 # compile input data136 if i_time:137 input_data = np.concatenate((input_data, time_vectors), axis=1)138 139 return input_data140def main():141 #"""142 #example usage to generate original salient forecasts143 # python src/models/salient/predict_keras.py -d 20200811 -sn salient_20170201144 # input arguments145 parser = argparse.ArgumentParser()146 parser.add_argument('-d', '--date', help='Submission date')147 parser.add_argument('-sn', '--submodel_name', default='salient_fri_20170201')148 parser.add_argument('--region', '-r', default='contest')149 args = parser.parse_args()150 # set args151 submodel_name = args.submodel_name152 target_date, datestr = get_target_date('Generate predictions', args.date)153 region = args.region154 155 # Setup directory where the submodel's weights are saved.156 dir_weights = os.path.join(dir_train_results, submodel_name, f"{region}_{submodel_name}")157 # map locations with points of interest158 mask_f = os.path.join("data", "masks", "us_mask.nc")159 mask_ds = Dataset(mask_f) if region.startswith('us') else Dataset(mask_f.replace("us_", "fcstrodeo_"))160 mask_lat = mask_ds.variables['lat'][:]161 mask_lon = mask_ds.variables['lon'][:]162 points_idx = np.where(mask_ds.variables['mask'][:])163 points = np.array((points_idx[0], points_idx[1])).T164 165 # Import and run models166 model_names = glob.glob(os.path.join(dir_weights, "k_model_*.h5"))167 N_models = len(model_names)168 169 if region.startswith('us'):170 num_of_gc = 862 171 elif region.startswith('contest'):172 num_of_gc = 514173 else:174 num_of_gc = 348175 # Create empty template array for predictions to be generated176 precip_wk34_predictions = np.zeros((N_models,num_of_gc))177 precip_wk56_predictions = np.zeros((N_models,num_of_gc))178 temp_wk34_predictions = np.zeros((N_models,num_of_gc))179 temp_wk56_predictions = np.zeros((N_models,num_of_gc))180 181 # Compile input data182 input_data_all = compile_input(submodel_name, datestr)183 184 # Generate predictions for each of the top 10 selected NN members in the ensemble185 for i in range(N_models):186 187 # Load NN ensemble member188 model_name = model_names[i]189 model = keras.models.load_model(model_name)190 # If NN was trained on time as an input feature, add time to the compile input data191 result = re.search('time(.*).h5', model_name)192 input_set = int(result.group(1))193 input_data = add_i_time(input_data_all, submodel_name, datestr, i_time=bool(input_set))194 input_data = generate_windows(input_data)195 # Generate predictions196 prediction_i = model.predict(input_data)197 # predictions for a 2-week target period are the accumulation of precip 198 # the mean temperature over the target period 199 prediction_i = np.reshape(prediction_i,(8,num_of_gc)) 200 precip_wk34_predictions[i,:] = np.sum(prediction_i[0:2,:], axis=0)201 precip_wk56_predictions[i,:] = np.sum(prediction_i[2:4,:], axis=0)202 temp_wk34_predictions[i,:] = np.mean(prediction_i[4:6,:], axis=0)203 temp_wk56_predictions[i,:] = np.mean(prediction_i[6:8,:], axis=0)204 205 206 # sum precip predictions and average temp predictions over the 2-week target period207 # clip precip predictions to zero since precipitations cannot be negative208 precip_wk34_prediction = np.mean(precip_wk34_predictions, axis=0)209 precip_wk56_prediction = np.mean(precip_wk56_predictions, axis=0)210 precip_wk34_prediction = precip_wk34_prediction.clip(0)211 precip_wk56_prediction = precip_wk56_prediction.clip(0)212 temp_wk34_prediction = np.mean(temp_wk34_predictions, axis=0)213 temp_wk56_prediction = np.mean(temp_wk56_predictions, axis=0)214 215 216 # Get target date objects217 deadline_date = datetime.strptime(datestr, '%Y%m%d')218 target_date_34w = get_target_date_eval(datestr, "34w")219 target_date_56w = get_target_date_eval(datestr, "56w")220 221 # Get target date strings222 target_date_str_34w = datetime.strftime(target_date_34w, '%Y%m%d')223 target_date_str_56w = datetime.strftime(target_date_56w, '%Y%m%d')224 225 # Get lat, lon and pred template arrays226 template_f = resource_filename("subseasonal_toolkit", os.path.join("models", "salient", "data", "apcp_week34_template.nc"))227 template_ds = Dataset(template_f)228 template_lat = template_ds.variables["lat"][:]229 template_lon = template_ds.variables["lon"][:]230 template_var = template_ds.variables["apcp_week34"][:]231 232 # Determine variables, horizons and corresponding predictions to be saved233 gt_vars = ["precip", "tmp2m"] 234 horizons = ["34w", "56w"] 235 predictions = [precip_wk34_prediction, precip_wk56_prediction, temp_wk34_prediction, temp_wk56_prediction]236 tasks = [f"{region}_{g}_{t}" for g, t in itertools.product(gt_vars, horizons)]237 238 # Format predictions to standard pandas contest prediction format.239 for task, prediction in zip(tasks, predictions):240 out_dir = os.path.join(dir_submodel_forecasts, submodel_name, task) 241 make_directories(out_dir)242 243 pred_file = os.path.join(dir_train_data, "contest_latlons.h5")244 pred = read_with_lock(pred_file)245 #pred = pd.read_hdf(pred_file)246 247 if "34w" in task:248 pred["start_date"] = target_date_34w249 out_file = os.path.join(out_dir, f"{task}-{target_date_str_34w}.h5")250 elif "56" in task:251 pred["start_date"] = target_date_56w252 out_file = os.path.join(out_dir, f"{task}-{target_date_str_56w}.h5")253 254 pred["pred"] = np.nan 255 a = template_var 256 # save predictions into array257 for loc in range(len(prediction)):258 index = points[loc]...
predict_keras_hindcasts.py
Source:predict_keras_hindcasts.py
...27from subseasonal_toolkit.utils.general_util import make_directories28from subseasonal_toolkit.utils.experiments_util import pandas2hdf29from subseasonal_toolkit.utils.experiments_util import get_target_date as get_target_date_eval30from subseasonal_toolkit.models.salient.salient_util import ONE_DAY, dir_train_data, dir_train_results, dir_submodel_forecasts, get_target_date31def read_with_lock(filename):32 """Open an hdf file with a lock and read. 33 Args:34 filename (str): path of hdf file to be read with a lock.35 Returns:36 openned pandas dataframe.37 """38 #with FileLock(filename+'lock'):39 # df = pd.read_hdf(filename) 40 df = pd.read_hdf(filename) 41 subprocess.call(f"rm {filename}lock", shell=True)42 return df43def generate_windows(x):44 """generates a sliding window over the data of length window_size.45 Args:46 x: array of input features used the NN47 Returns:48 reshaped array of input features so that each example is a concatenation of the past 10 weeks of data.49 """50 window_size = 1051 result = []52 for i in range(x.shape[0]+1-window_size):53 result.append(x[i:i+window_size, ...])54 return np.stack(result)55def compile_input(date, i_time=False):56 ## Load training data57 # load time data58 training_time_data_file = os.path.join(dir_train_data, "time.pickle") # (t,1)59 training_time_vectors = pickle.load(open(training_time_data_file, 'rb'))60 training_time_vectors = np.reshape(training_time_vectors,(training_time_vectors.shape[0],1))61 # load sst data62 training_sst_data_file = os.path.join(dir_train_data, "sst.pickle") # (t,loc)63 training_sst_vectors = pickle.load(open(training_sst_data_file, 'rb'))64 # load precipitation data65 training_location_precip_file = os.path.join(dir_train_data, "precip.pickle") # (loc,t)66 training_precip_data = pickle.load(open(training_location_precip_file, 'rb'))67 training_precip_data = training_precip_data.T68 # load temperature data69 training_location_temp_file = os.path.join(dir_train_data, "temp.pickle") # (loc,t)70 training_temp_data = pickle.load(open(training_location_temp_file, 'rb'))71 training_temp_data = training_temp_data.T72 # make precip data only as long as temp data73 training_precip_data = training_precip_data[:training_temp_data.shape[0],:]74 # ensure same length vectors and normalize75 training_time_vectors = training_time_vectors[:training_precip_data.shape[0],:]76 training_sst_vectors = training_sst_vectors[:training_precip_data.shape[0],:]77 ## Generate input data from date78 date_data_file = os.path.join(dir_train_data, "date.pickle") # (time,1)79 dates = pickle.load(open(date_data_file, 'rb'))80 dates = dates[:training_sst_vectors.shape[0]]81 # dates represent middle of the week (Wednesday) sst or sss data is for Sun-Sat82 # Offset our date by 3 days so we're sure we don't have future data in the83 # input set84 cutoff_date = datetime.strptime(date, '%Y%m%d') - ONE_DAY * 385 input_indices = np.where(dates < np.datetime64(str(cutoff_date.date())))[0]86 # Take the last 10 weeks87 input_indices = input_indices[-10:]88 # load sst data89 sst_vectors = training_sst_vectors[input_indices]90 # Normalize91 sst_vectors = (sst_vectors - np.amin(training_sst_vectors)) * 1./(np.amax(training_sst_vectors) - np.amin(training_sst_vectors))92 # compile input data93 input_data = sst_vectors94 if i_time:95 time_vectors = training_time_vectors[input_indices]96 time_vectors = np.reshape(time_vectors,(time_vectors.shape[0],1))97 input_data = np.concatenate((input_data, time_vectors), axis=1)98 return input_data99def main():100 # input arguments101 parser = argparse.ArgumentParser()102 parser.add_argument('-d', '--date', help='Submission date')103 parser.add_argument('-sn', '--submodel_name', default='salient_fri_hindcasts_2010')104 parser.add_argument('--region', '-r', default='contest')105 args = parser.parse_args()106 # set args107 submodel_name = args.submodel_name108 target_date, datestr = get_target_date('Generate predictions', args.date)109 region = args.region110 111 # Setup directory where the submodel's weights are saved.112 dir_weights = os.path.join(dir_train_results, submodel_name, f"{region}_{submodel_name}")113 114 # map locations with points of interest115 mask_f = os.path.join("data", "masks", "us_mask.nc")116 mask_ds = Dataset(mask_f) if region.startswith('us') else Dataset(mask_f.replace("us_", "fcstrodeo_"))117 mask_lat = mask_ds.variables['lat'][:]118 mask_lon = mask_ds.variables['lon'][:]119 points_idx = np.where(mask_ds.variables['mask'][:])120 points = np.array((points_idx[0], points_idx[1])).T121 122 # Import and run models123 model_names = glob.glob(os.path.join(dir_weights, f"k_model_*.h5"))124 N_models = len(model_names)125 126 if region.startswith('us'):127 num_of_gc = 862 128 elif region.startswith('contest'):129 num_of_gc = 514130 else:131 num_of_gc = 348132 133 # Create empty template array for predictions to be generated134 precip_wk34_predictions = np.zeros((N_models,num_of_gc))135 precip_wk56_predictions = np.zeros((N_models,num_of_gc))136 temp_wk34_predictions = np.zeros((N_models,num_of_gc))137 temp_wk56_predictions = np.zeros((N_models,num_of_gc))138 139 # Generate predictions for each of the top 10 selected NN members in the ensemble140 for i in range(N_models):141 142 # Load NN ensemble member143 model_name = model_names[i]144 model = keras.models.load_model(model_name)145 # If NN was trained on time as an input feature, add time to the compile input data146 result = re.search('time(.*).h5', model_name)147 input_set = int(result.group(1))148 # Compile input data149 input_data_all = compile_input(datestr, i_time=bool(input_set))150 input_data = generate_windows(input_data_all)151 # Generate predictions152 prediction_i = model.predict(input_data)153 # predictions for a 2-week target period are the accumulation of precip 154 # the mean temperature over the target period 155 prediction_i = np.reshape(prediction_i,(8,num_of_gc)) 156 precip_wk34_predictions[i,:] = np.sum(prediction_i[0:2,:], axis=0)157 precip_wk56_predictions[i,:] = np.sum(prediction_i[2:4,:], axis=0)158 temp_wk34_predictions[i,:] = np.mean(prediction_i[4:6,:], axis=0)159 temp_wk56_predictions[i,:] = np.mean(prediction_i[6:8,:], axis=0)160 161 162 # sum precip predictions and average temp predictions over the 2-week target period163 # clip precip predictions to zero since precipitations cannot be negative164 precip_wk34_prediction = np.mean(precip_wk34_predictions, axis=0)165 precip_wk34_prediction = precip_wk34_prediction.clip(0)166 precip_wk56_prediction = np.mean(precip_wk56_predictions, axis=0)167 precip_wk56_prediction = precip_wk56_prediction.clip(0)168 temp_wk34_prediction = np.mean(temp_wk34_predictions, axis=0)169 temp_wk56_prediction = np.mean(temp_wk56_predictions, axis=0)170 171 # Get target date objects172 deadline_date = datetime.strptime(datestr, '%Y%m%d')173 target_date_34w = get_target_date_eval(datestr, "34w")174 target_date_56w = get_target_date_eval(datestr, "56w")175 176 # Get target date strings177 target_date_str_34w = datetime.strftime(target_date_34w, '%Y%m%d')178 target_date_str_56w = datetime.strftime(target_date_56w, '%Y%m%d')179 180 # Get lat, lon and pred template arrays181 template_f = os.path.join(dir_train_data, "apcp_week34_template.nc")182 template_ds = Dataset(template_f)183 template_lat = template_ds.variables["lat"][:]184 template_lon = template_ds.variables["lon"][:]185 template_var = template_ds.variables["apcp_week34"][:]186 187 # Determine variables, horizons and corresponding predictions to be saved188 gt_vars = ["precip", "tmp2m"] 189 horizons = ["34w", "56w"] 190 predictions = [precip_wk34_prediction, precip_wk56_prediction, temp_wk34_prediction, temp_wk56_prediction]191 tasks = [f"{region}_{g}_{t}" for g, t in itertools.product(gt_vars, horizons)]192 193 # Format predictions to standard pandas contest prediction format.194 for task, prediction in zip(tasks, predictions):195 out_dir = os.path.join(dir_submodel_forecasts, submodel_name, task) 196 make_directories(out_dir)197 198 pred_file = os.path.join(dir_train_data, "contest_latlons.h5")199 pred = read_with_lock(pred_file)200 #pred = pd.read_hdf(pred_file)201 202 if "34w" in task:203 pred["start_date"] = target_date_34w204 out_file = os.path.join(out_dir, f"{task}-{target_date_str_34w}.h5")205 elif "56" in task:206 pred["start_date"] = target_date_56w207 out_file = os.path.join(out_dir, f"{task}-{target_date_str_56w}.h5")208 209 pred["pred"] = np.nan 210 a = template_var 211 # save predictions into array212 for loc in range(len(prediction)):213 index = points[loc]...
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