diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index dec6a0fb2ab9eb51fef737c50d5beab4270c8942..32d8f138020e175ed4e17077713ed7cb26c5c533 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -198,4 +198,4 @@ pages:
- public/badges/
- public/coverage/
- public/test/
- - public/webpage/
+ - public/docs/
diff --git a/run.py b/run.py
index 572e59e2f75c4068bc63ecb6bb54a687bafebf4b..2395064c25fcaf5fed1ede6a919b06bbc62f27e9 100644
--- a/run.py
+++ b/run.py
@@ -13,10 +13,11 @@ from src.run_modules.training import Training
def main(parser_args):
+ experiment_date = parser_args.experiment_date
with RunEnvironment():
- ExperimentSetup(parser_args, stations=['DEBW107', 'DEBY081', 'DEBW013', 'DEBW076', 'DEBW087', 'DEBW001'],
+ ExperimentSetup(stations=['DEBW107', 'DEBY081', 'DEBW013', 'DEBW076', 'DEBW087', 'DEBW001'],
station_type='background', trainable=False, create_new_model=True, window_history_size=6,
- create_new_bootstraps=False)
+ experiment_date=experiment_date, create_new_bootstraps=False)
PreProcessing()
PartitionCheck()
diff --git a/run_hourly.py b/run_hourly.py
index 559bf1a1056928f55f9ff3527805da121091d830..df3266405bc195cbe4c3546b4c7fd0c6b2925a84 100644
--- a/run_hourly.py
+++ b/run_hourly.py
@@ -12,9 +12,11 @@ from src.run_modules.training import Training
def main(parser_args):
+ experiment_date = parser_args.experiment_date
with RunEnvironment():
- ExperimentSetup(parser_args, stations=['DEBW107', 'DEBY081', 'DEBW013', 'DEBW076', 'DEBW087', 'DEBW001'],
- station_type='background', trainable=True, sampling="hourly", window_history_size=48)
+ ExperimentSetup(stations=['DEBW107', 'DEBY081', 'DEBW013', 'DEBW076', 'DEBW087', 'DEBW001'],
+ station_type='background', trainable=True, sampling="hourly", window_history_size=48,
+ experiment_date=experiment_date)
PreProcessing()
ModelSetup()
diff --git a/run_zam347.py b/run_zam347.py
index d95067bb84a91230b0877f7a2b3d0cac5dc495e1..69b3cd6fee0e60212de9c74c2dd29b720bc81b81 100644
--- a/run_zam347.py
+++ b/run_zam347.py
@@ -29,9 +29,10 @@ def load_stations():
def main(parser_args):
+ experiment_date = parser_args.experiment_date
with RunEnvironment():
- ExperimentSetup(parser_args, stations=load_stations(), station_type='background', trainable=False,
- create_new_model=True)
+ ExperimentSetup(stations=load_stations(), station_type='background', trainable=False, create_new_model=True,
+ experiment_date=experiment_date)
PreProcessing()
ModelSetup()
diff --git a/src/configuration/__init__.py b/src/configuration/__init__.py
index 48d9f38ce01d168dc8ed3019e98f48e9ab19bf72..a14a815b49fa536c0a223fbc55d136680c764eab 100644
--- a/src/configuration/__init__.py
+++ b/src/configuration/__init__.py
@@ -1,2 +1,3 @@
"""Collection of configuration functions, paths and classes."""
-from .path_config import ROOT_PATH, prepare_host, set_experiment_name, set_bootstrap_path, check_path_and_create, get_host
\ No newline at end of file
+from .path_config import ROOT_PATH, prepare_host, set_experiment_name, set_bootstrap_path, check_path_and_create, \
+ get_host, set_experiment_path
\ No newline at end of file
diff --git a/src/configuration/defaults.py b/src/configuration/defaults.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ce96cfce515e7f32d98444e6a9542c9fbd7b4f4
--- /dev/null
+++ b/src/configuration/defaults.py
@@ -0,0 +1,64 @@
+__author__ = "Lukas Leufen"
+__date__ = '2020-06-25'
+
+
+DEFAULT_STATIONS = ['DEBW107', 'DEBY081', 'DEBW013', 'DEBW076', 'DEBW087', 'DEBY052', 'DEBY032', 'DEBW022', 'DEBY004',
+ 'DEBY020', 'DEBW030', 'DEBW037', 'DEBW031', 'DEBW015', 'DEBW073', 'DEBY039', 'DEBW038', 'DEBW081',
+ 'DEBY075', 'DEBW040', 'DEBY053', 'DEBW059', 'DEBW027', 'DEBY072', 'DEBW042', 'DEBW039', 'DEBY001',
+ 'DEBY113', 'DEBY089', 'DEBW024', 'DEBW004', 'DEBY037', 'DEBW056', 'DEBW029', 'DEBY068', 'DEBW010',
+ 'DEBW026', 'DEBY002', 'DEBY079', 'DEBW084', 'DEBY049', 'DEBY031', 'DEBW019', 'DEBW001', 'DEBY063',
+ 'DEBY005', 'DEBW046', 'DEBW103', 'DEBW052', 'DEBW034', 'DEBY088', ]
+DEFAULT_VAR_ALL_DICT = {'o3': 'dma8eu', 'relhum': 'average_values', 'temp': 'maximum', 'u': 'average_values',
+ 'v': 'average_values', 'no': 'dma8eu', 'no2': 'dma8eu', 'cloudcover': 'average_values',
+ 'pblheight': 'maximum'}
+DEFAULT_NETWORK = "AIRBASE"
+DEFAULT_STATION_TYPE = "background"
+DEFAULT_VARIABLES = DEFAULT_VAR_ALL_DICT.keys()
+DEFAULT_START = "1997-01-01"
+DEFAULT_END = "2017-12-31"
+DEFAULT_WINDOW_HISTORY_SIZE = 13
+DEFAULT_OVERWRITE_LOCAL_DATA = False
+DEFAULT_TRANSFORMATION = {"scope": "data", "method": "standardise", "mean": "estimate"}
+DEFAULT_HPC_LOGIN_LIST = ["ju", "hdfmll"] # ju[wels} #hdfmll(ogin)
+DEFAULT_HPC_HOST_LIST = ["jw", "hdfmlc"] # first part of node names for Juwels (jw[comp], hdfmlc(ompute).
+DEFAULT_CREATE_NEW_MODEL = True
+DEFAULT_TRAINABLE = True
+DEFAULT_FRACTION_OF_TRAINING = 0.8
+DEFAULT_EXTREME_VALUES = None
+DEFAULT_EXTREMES_ON_RIGHT_TAIL_ONLY = False
+DEFAULT_PERMUTE_DATA = False
+DEFAULT_BATCH_SIZE = int(256 * 2)
+DEFAULT_EPOCHS = 20
+DEFAULT_TARGET_VAR = "o3"
+DEFAULT_TARGET_DIM = "variables"
+DEFAULT_WINDOW_LEAD_TIME = 3
+DEFAULT_DIMENSIONS = {"new_index": ["datetime", "Stations"]}
+DEFAULT_INTERPOLATE_DIM = "datetime"
+DEFAULT_INTERPOLATE_METHOD = "linear"
+DEFAULT_LIMIT_NAN_FILL = 1
+DEFAULT_TRAIN_START = "1997-01-01"
+DEFAULT_TRAIN_END = "2007-12-31"
+DEFAULT_TRAIN_MIN_LENGTH = 90
+DEFAULT_VAL_START = "2008-01-01"
+DEFAULT_VAL_END = "2009-12-31"
+DEFAULT_VAL_MIN_LENGTH = 90
+DEFAULT_TEST_START = "2010-01-01"
+DEFAULT_TEST_END = "2017-12-31"
+DEFAULT_TEST_MIN_LENGTH = 90
+DEFAULT_TRAIN_VAL_MIN_LENGTH = 180
+DEFAULT_USE_ALL_STATIONS_ON_ALL_DATA_SETS = True
+DEFAULT_EVALUATE_BOOTSTRAPS = True
+DEFAULT_CREATE_NEW_BOOTSTRAPS = False
+DEFAULT_NUMBER_OF_BOOTSTRAPS = 20
+DEFAULT_PLOT_LIST = ["PlotMonthlySummary", "PlotStationMap", "PlotClimatologicalSkillScore", "PlotTimeSeries",
+ "PlotCompetitiveSkillScore", "PlotBootstrapSkillScore", "PlotConditionalQuantiles",
+ "PlotAvailability"]
+
+
+def get_defaults():
+ """Return all default parameters set in defaults.py"""
+ return {key: value for key, value in globals().items() if key.startswith('DEFAULT')}
+
+
+if __name__ == "__main__":
+ print(get_defaults())
\ No newline at end of file
diff --git a/src/configuration/path_config.py b/src/configuration/path_config.py
index 85a536ccf2e0711eea119072122b7a852de5fa90..7af25875eea58de081012fc6040a76a04f001d54 100644
--- a/src/configuration/path_config.py
+++ b/src/configuration/path_config.py
@@ -54,31 +54,43 @@ def prepare_host(create_new=True, data_path=None, sampling="daily") -> str:
return path
-def set_experiment_name(experiment_name=None, experiment_path=None, sampling=None) -> Tuple[str, str]:
+def set_experiment_path(name: str, path: str = None) -> str:
"""
Set name of experiment and its path.
- * Experiment name is set to `TestExperiment` if not provided in kwargs. If a name is given, this string is expanded
+ * Experiment path is set to `<experiment_path>/<exp_name>` if provided or `ROOT_PATH/<exp_name>` otherwise
+
+ :param name: custom experiment name
+ :param path: custom experiment path
+
+ :return: full experiment path
+ """
+ if path is None:
+ experiment_path = os.path.abspath(os.path.join(ROOT_PATH, name))
+ else:
+ experiment_path = os.path.join(os.path.abspath(path), name)
+ return experiment_path
+
+
+def set_experiment_name(name: str = None, sampling: str = None) -> str:
+ """
+ Set name of experiment and its path.
+
+ * Experiment name is set to `TestExperiment` if not provided. If a name is given, this string is expanded
by suffix `_network`. Experiment name is always expanded by `_<sampling>` as ending suffix if sampling is given.
- * Experiment path is set to `ROOT_PATH/<exp_name>` if not provided or otherwise use `<experiment_path>/<exp_name>`
- :param experiment_name: custom experiment name
- :param experiment_path: custom experiment path
+ :param name: custom experiment name
:param sampling: sampling rate as string to add to experiment name
- :return: experiment name and full experiment path
+ :return: experiment name
"""
- if experiment_name is None:
+ if name is None:
experiment_name = "TestExperiment"
else:
- experiment_name = f"{experiment_name}_network"
+ experiment_name = f"{name}_network"
if sampling is not None:
experiment_name += f"_{sampling}"
- if experiment_path is None:
- experiment_path = os.path.abspath(os.path.join(ROOT_PATH, experiment_name))
- else:
- experiment_path = os.path.join(os.path.abspath(experiment_path), experiment_name)
- return experiment_name, experiment_path
+ return experiment_name
def set_bootstrap_path(bootstrap_path: str, data_path: str, sampling: str) -> str:
diff --git a/src/run.py b/src/run.py
index 11029817a978b872d0f99954a50ab5f5b93aa012..900ebb47bc868b1364d10021e6b4fe8dc7186c7d 100644
--- a/src/run.py
+++ b/src/run.py
@@ -4,41 +4,36 @@ import argparse
import inspect
-def run(stations=['DEBW107', 'DEBY081', 'DEBW013', 'DEBW076', 'DEBW087', 'DEBW001'],
- station_type='background',
- trainable=False, create_new_model=True,
- window_history_size=6,
+def run(stations=None,
+ station_type=None,
+ trainable=None, create_new_model=None,
+ window_history_size=None,
experiment_date="testrun",
network=None,
variables=None, statistics_per_var=None,
start=None, end=None,
- target_var="o3", target_dim=None,
+ target_var=None, target_dim=None,
window_lead_time=None,
dimensions=None,
interpolate_method=None, interpolate_dim=None, limit_nan_fill=None,
train_start=None, train_end=None, val_start=None, val_end=None, test_start=None, test_end=None,
- use_all_stations_on_all_data_sets=True, fraction_of_train=None,
+ use_all_stations_on_all_data_sets=None, fraction_of_train=None,
experiment_path=None, plot_path=None, forecast_path=None, bootstrap_path=None, overwrite_local_data=None,
- sampling="daily",
- permute_data_on_training=False, extreme_values=None, extremes_on_right_tail_only=None,
+ sampling=None,
+ permute_data_on_training=None, extreme_values=None, extremes_on_right_tail_only=None,
transformation=None,
train_min_length=None, val_min_length=None, test_min_length=None,
- evaluate_bootstraps=True, number_of_bootstraps=None, create_new_bootstraps=False,
+ evaluate_bootstraps=None, number_of_bootstraps=None, create_new_bootstraps=None,
plot_list=None,
model=None,
batch_size=None,
epochs=None):
params = inspect.getfullargspec(ExperimentSetup).args
- kwargs = {k: v for k, v in locals().items() if k in params}
-
- parser = argparse.ArgumentParser()
- parser.add_argument('--experiment_date', metavar='--exp_date', type=str, default="testrun",
- help="set experiment date as string")
- args = parser.parse_args()
+ kwargs = {k: v for k, v in locals().items() if k in params and v is not None}
with RunEnvironment():
- ExperimentSetup(args, **kwargs)
+ ExperimentSetup(**kwargs)
PreProcessing()
PartitionCheck()
ModelSetup()
diff --git a/src/run_modules/experiment_setup.py b/src/run_modules/experiment_setup.py
index ff6fec842714d599696b8726e9d25aa22e55583f..a93fe403e11b6483832df417b0e97462bc252a4a 100644
--- a/src/run_modules/experiment_setup.py
+++ b/src/run_modules/experiment_setup.py
@@ -8,25 +8,19 @@ from typing import Union, Dict, Any, List
from src.configuration import path_config
from src import helpers
+from src.configuration.defaults import DEFAULT_STATIONS, DEFAULT_VAR_ALL_DICT, DEFAULT_NETWORK, DEFAULT_STATION_TYPE, \
+ DEFAULT_START, DEFAULT_END, DEFAULT_WINDOW_HISTORY_SIZE, DEFAULT_OVERWRITE_LOCAL_DATA, DEFAULT_TRANSFORMATION, \
+ DEFAULT_HPC_LOGIN_LIST, DEFAULT_HPC_HOST_LIST, DEFAULT_CREATE_NEW_MODEL, DEFAULT_TRAINABLE, \
+ DEFAULT_FRACTION_OF_TRAINING, DEFAULT_EXTREME_VALUES, DEFAULT_EXTREMES_ON_RIGHT_TAIL_ONLY, DEFAULT_PERMUTE_DATA, \
+ DEFAULT_BATCH_SIZE, DEFAULT_EPOCHS, DEFAULT_TARGET_VAR, DEFAULT_TARGET_DIM, DEFAULT_WINDOW_LEAD_TIME, \
+ DEFAULT_DIMENSIONS, DEFAULT_INTERPOLATE_DIM, DEFAULT_INTERPOLATE_METHOD, DEFAULT_LIMIT_NAN_FILL, \
+ DEFAULT_TRAIN_START, DEFAULT_TRAIN_END, DEFAULT_TRAIN_MIN_LENGTH, DEFAULT_VAL_START, DEFAULT_VAL_END, \
+ DEFAULT_VAL_MIN_LENGTH, DEFAULT_TEST_START, DEFAULT_TEST_END, DEFAULT_TEST_MIN_LENGTH, DEFAULT_TRAIN_VAL_MIN_LENGTH, \
+ DEFAULT_USE_ALL_STATIONS_ON_ALL_DATA_SETS, DEFAULT_EVALUATE_BOOTSTRAPS, DEFAULT_CREATE_NEW_BOOTSTRAPS, \
+ DEFAULT_NUMBER_OF_BOOTSTRAPS, DEFAULT_PLOT_LIST
from src.run_modules.run_environment import RunEnvironment
from src.model_modules.model_class import MyLittleModel as VanillaModel
-DEFAULT_STATIONS = ['DEBW107', 'DEBY081', 'DEBW013', 'DEBW076', 'DEBW087', 'DEBY052', 'DEBY032', 'DEBW022', 'DEBY004',
- 'DEBY020', 'DEBW030', 'DEBW037', 'DEBW031', 'DEBW015', 'DEBW073', 'DEBY039', 'DEBW038', 'DEBW081',
- 'DEBY075', 'DEBW040', 'DEBY053', 'DEBW059', 'DEBW027', 'DEBY072', 'DEBW042', 'DEBW039', 'DEBY001',
- 'DEBY113', 'DEBY089', 'DEBW024', 'DEBW004', 'DEBY037', 'DEBW056', 'DEBW029', 'DEBY068', 'DEBW010',
- 'DEBW026', 'DEBY002', 'DEBY079', 'DEBW084', 'DEBY049', 'DEBY031', 'DEBW019', 'DEBW001', 'DEBY063',
- 'DEBY005', 'DEBW046', 'DEBW103', 'DEBW052', 'DEBW034', 'DEBY088', ]
-DEFAULT_VAR_ALL_DICT = {'o3': 'dma8eu', 'relhum': 'average_values', 'temp': 'maximum', 'u': 'average_values',
- 'v': 'average_values', 'no': 'dma8eu', 'no2': 'dma8eu', 'cloudcover': 'average_values',
- 'pblheight': 'maximum'}
-DEFAULT_TRANSFORMATION = {"scope": "data", "method": "standardise", "mean": "estimate"}
-DEFAULT_PLOT_LIST = ["PlotMonthlySummary", "PlotStationMap", "PlotClimatologicalSkillScore", "PlotTimeSeries",
- "PlotCompetitiveSkillScore", "PlotBootstrapSkillScore", "PlotConditionalQuantiles",
- "PlotAvailability"]
-DEFAULT_HPC_LOGIN_LIST = ["ju", "hdfmll"] # ju[wels} #hdfmll(ogin)
-DEFAULT_HPC_HOST_LIST = ["jw", "hdfmlc"] # first part of node names for Juwels (jw[comp], hdfmlc(ompute).
-
class ExperimentSetup(RunEnvironment):
"""
@@ -212,7 +206,7 @@ class ExperimentSetup(RunEnvironment):
"""
def __init__(self,
- parser_args=None,
+ experiment_date=None,
stations: Union[str, List[str]] = None,
network: str = None,
station_type: str = None,
@@ -228,11 +222,11 @@ class ExperimentSetup(RunEnvironment):
interpolate_dim=None,
interpolate_method=None,
limit_nan_fill=None, train_start=None, train_end=None, val_start=None, val_end=None, test_start=None,
- test_end=None, use_all_stations_on_all_data_sets=True, trainable: bool = None, fraction_of_train: float = None,
- experiment_path=None, plot_path: str = None, forecast_path: str = None, overwrite_local_data: bool = None, sampling: str = "daily",
- create_new_model: bool = None, bootstrap_path=None, permute_data_on_training: bool = None, transformation=None,
+ test_end=None, use_all_stations_on_all_data_sets=None, trainable: bool = None, fraction_of_train: float = None,
+ experiment_path=None, plot_path: str = None, forecast_path: str = None, overwrite_local_data = None, sampling: str = "daily",
+ create_new_model = None, bootstrap_path=None, permute_data_on_training = None, transformation=None,
train_min_length=None, val_min_length=None, test_min_length=None, extreme_values: list = None,
- extremes_on_right_tail_only: bool = None, evaluate_bootstraps=True, plot_list=None, number_of_bootstraps=None,
+ extremes_on_right_tail_only: bool = None, evaluate_bootstraps=None, plot_list=None, number_of_bootstraps=None,
create_new_bootstraps=None, data_path: str = None, login_nodes=None, hpc_hosts=None, model=None,
batch_size=None, epochs=None):
@@ -244,104 +238,109 @@ class ExperimentSetup(RunEnvironment):
self._set_param("hostname", path_config.get_host())
self._set_param("hpc_hosts", hpc_hosts, default=DEFAULT_HPC_HOST_LIST + DEFAULT_HPC_LOGIN_LIST)
self._set_param("login_nodes", login_nodes, default=DEFAULT_HPC_LOGIN_LIST)
- self._set_param("create_new_model", create_new_model, default=True)
+ self._set_param("create_new_model", create_new_model, default=DEFAULT_CREATE_NEW_MODEL)
if self.data_store.get("create_new_model"):
trainable = True
data_path = self.data_store.get("data_path")
bootstrap_path = path_config.set_bootstrap_path(bootstrap_path, data_path, sampling)
self._set_param("bootstrap_path", bootstrap_path)
- self._set_param("trainable", trainable, default=True)
- self._set_param("fraction_of_training", fraction_of_train, default=0.8)
- self._set_param("extreme_values", extreme_values, default=None, scope="train")
- self._set_param("extremes_on_right_tail_only", extremes_on_right_tail_only, default=False, scope="train")
+ self._set_param("trainable", trainable, default=DEFAULT_TRAINABLE)
+ self._set_param("fraction_of_training", fraction_of_train, default=DEFAULT_FRACTION_OF_TRAINING)
+ self._set_param("extreme_values", extreme_values, default=DEFAULT_EXTREME_VALUES, scope="train")
+ self._set_param("extremes_on_right_tail_only", extremes_on_right_tail_only,
+ default=DEFAULT_EXTREMES_ON_RIGHT_TAIL_ONLY, scope="train")
self._set_param("upsampling", extreme_values is not None, scope="train")
upsampling = self.data_store.get("upsampling", "train")
- permute_data = False if permute_data_on_training is None else permute_data_on_training
+ permute_data = DEFAULT_PERMUTE_DATA if permute_data_on_training is None else permute_data_on_training
self._set_param("permute_data", permute_data or upsampling, scope="train")
- self._set_param("batch_size", batch_size, default=int(256 * 2))
- self._set_param("epochs", epochs, default=20)
+ self._set_param("batch_size", batch_size, default=DEFAULT_BATCH_SIZE)
+ self._set_param("epochs", epochs, default=DEFAULT_EPOCHS)
# set experiment name
- exp_date = self._get_parser_args(parser_args).get("experiment_date")
- exp_name, exp_path = path_config.set_experiment_name(experiment_name=exp_date, experiment_path=experiment_path,
- sampling=sampling)
- self._set_param("experiment_name", exp_name)
- self._set_param("experiment_path", exp_path)
- logging.info(f"Experiment path is: {exp_path}")
+ experiment_name = path_config.set_experiment_name(name=experiment_date, sampling=sampling)
+ experiment_path = path_config.set_experiment_path(name=experiment_name, path=experiment_path)
+ self._set_param("experiment_name", experiment_name)
+ self._set_param("experiment_path", experiment_path)
+ logging.info(f"Experiment path is: {experiment_path}")
path_config.check_path_and_create(self.data_store.get("experiment_path"))
# set model path
- self._set_param("model_path", None, os.path.join(exp_path, "model"))
+ self._set_param("model_path", None, os.path.join(experiment_path, "model"))
path_config.check_path_and_create(self.data_store.get("model_path"))
# set plot path
- default_plot_path = os.path.join(exp_path, "plots")
+ default_plot_path = os.path.join(experiment_path, "plots")
self._set_param("plot_path", plot_path, default=default_plot_path)
path_config.check_path_and_create(self.data_store.get("plot_path"))
# set results path
- default_forecast_path = os.path.join(exp_path, "forecasts")
+ default_forecast_path = os.path.join(experiment_path, "forecasts")
self._set_param("forecast_path", forecast_path, default_forecast_path)
path_config.check_path_and_create(self.data_store.get("forecast_path"))
# set logging path
- self._set_param("logging_path", None, os.path.join(exp_path, "logging"))
+ self._set_param("logging_path", None, os.path.join(experiment_path, "logging"))
path_config.check_path_and_create(self.data_store.get("logging_path"))
# setup for data
self._set_param("stations", stations, default=DEFAULT_STATIONS)
- self._set_param("network", network, default="AIRBASE")
- self._set_param("station_type", station_type, default=None)
+ self._set_param("network", network, default=DEFAULT_NETWORK)
+ self._set_param("station_type", station_type, default=DEFAULT_STATION_TYPE)
self._set_param("statistics_per_var", statistics_per_var, default=DEFAULT_VAR_ALL_DICT)
self._set_param("variables", variables, default=list(self.data_store.get("statistics_per_var").keys()))
- self._set_param("start", start, default="1997-01-01")
- self._set_param("end", end, default="2017-12-31")
- self._set_param("window_history_size", window_history_size, default=13)
- self._set_param("overwrite_local_data", overwrite_local_data, default=False, scope="preprocessing")
+ self._set_param("start", start, default=DEFAULT_START)
+ self._set_param("end", end, default=DEFAULT_END)
+ self._set_param("window_history_size", window_history_size, default=DEFAULT_WINDOW_HISTORY_SIZE)
+ self._set_param("overwrite_local_data", overwrite_local_data, default=DEFAULT_OVERWRITE_LOCAL_DATA,
+ scope="preprocessing")
self._set_param("sampling", sampling)
self._set_param("transformation", transformation, default=DEFAULT_TRANSFORMATION)
self._set_param("transformation", None, scope="preprocessing")
# target
- self._set_param("target_var", target_var, default="o3")
- self._set_param("target_dim", target_dim, default='variables')
- self._set_param("window_lead_time", window_lead_time, default=3)
+ self._set_param("target_var", target_var, default=DEFAULT_TARGET_VAR)
+ self._set_param("target_dim", target_dim, default=DEFAULT_TARGET_DIM)
+ self._set_param("window_lead_time", window_lead_time, default=DEFAULT_WINDOW_LEAD_TIME)
# interpolation
- self._set_param("dimensions", dimensions, default={'new_index': ['datetime', 'Stations']})
- self._set_param("interpolate_dim", interpolate_dim, default='datetime')
- self._set_param("interpolate_method", interpolate_method, default='linear')
- self._set_param("limit_nan_fill", limit_nan_fill, default=1)
+ self._set_param("dimensions", dimensions, default=DEFAULT_DIMENSIONS)
+ self._set_param("interpolate_dim", interpolate_dim, default=DEFAULT_INTERPOLATE_DIM)
+ self._set_param("interpolate_method", interpolate_method, default=DEFAULT_INTERPOLATE_METHOD)
+ self._set_param("limit_nan_fill", limit_nan_fill, default=DEFAULT_LIMIT_NAN_FILL)
# train set parameters
- self._set_param("start", train_start, default="1997-01-01", scope="train")
- self._set_param("end", train_end, default="2007-12-31", scope="train")
- self._set_param("min_length", train_min_length, default=90, scope="train")
+ self._set_param("start", train_start, default=DEFAULT_TRAIN_START, scope="train")
+ self._set_param("end", train_end, default=DEFAULT_TRAIN_END, scope="train")
+ self._set_param("min_length", train_min_length, default=DEFAULT_TRAIN_MIN_LENGTH, scope="train")
# validation set parameters
- self._set_param("start", val_start, default="2008-01-01", scope="val")
- self._set_param("end", val_end, default="2009-12-31", scope="val")
- self._set_param("min_length", val_min_length, default=90, scope="val")
+ self._set_param("start", val_start, default=DEFAULT_VAL_START, scope="val")
+ self._set_param("end", val_end, default=DEFAULT_VAL_END, scope="val")
+ self._set_param("min_length", val_min_length, default=DEFAULT_VAL_MIN_LENGTH, scope="val")
# test set parameters
- self._set_param("start", test_start, default="2010-01-01", scope="test")
- self._set_param("end", test_end, default="2017-12-31", scope="test")
- self._set_param("min_length", test_min_length, default=90, scope="test")
+ self._set_param("start", test_start, default=DEFAULT_TEST_START, scope="test")
+ self._set_param("end", test_end, default=DEFAULT_TEST_END, scope="test")
+ self._set_param("min_length", test_min_length, default=DEFAULT_TEST_MIN_LENGTH, scope="test")
# train_val set parameters
self._set_param("start", self.data_store.get("start", "train"), scope="train_val")
self._set_param("end", self.data_store.get("end", "val"), scope="train_val")
train_val_min_length = sum([self.data_store.get("min_length", s) for s in ["train", "val"]])
- self._set_param("min_length", train_val_min_length, default=180, scope="train_val")
+ self._set_param("min_length", train_val_min_length, default=DEFAULT_TRAIN_VAL_MIN_LENGTH, scope="train_val")
# use all stations on all data sets (train, val, test)
- self._set_param("use_all_stations_on_all_data_sets", use_all_stations_on_all_data_sets, default=True)
+ self._set_param("use_all_stations_on_all_data_sets", use_all_stations_on_all_data_sets,
+ default=DEFAULT_USE_ALL_STATIONS_ON_ALL_DATA_SETS)
# set post-processing instructions
- self._set_param("evaluate_bootstraps", evaluate_bootstraps, scope="general.postprocessing")
- create_new_bootstraps = max([self.data_store.get("trainable", "general"), create_new_bootstraps or False])
+ self._set_param("evaluate_bootstraps", evaluate_bootstraps, default=DEFAULT_EVALUATE_BOOTSTRAPS,
+ scope="general.postprocessing")
+ create_new_bootstraps = max([self.data_store.get("trainable", "general"),
+ create_new_bootstraps or DEFAULT_CREATE_NEW_BOOTSTRAPS])
self._set_param("create_new_bootstraps", create_new_bootstraps, scope="general.postprocessing")
- self._set_param("number_of_bootstraps", number_of_bootstraps, default=20, scope="general.postprocessing")
+ self._set_param("number_of_bootstraps", number_of_bootstraps, default=DEFAULT_NUMBER_OF_BOOTSTRAPS,
+ scope="general.postprocessing")
self._set_param("plot_list", plot_list, default=DEFAULT_PLOT_LIST, scope="general.postprocessing")
# check variables, statistics and target variable
@@ -358,22 +357,6 @@ class ExperimentSetup(RunEnvironment):
self.data_store.set(param, value, scope)
logging.debug(f"set experiment attribute: {param}({scope})={value}")
- @staticmethod
- def _get_parser_args(args: Union[Dict, argparse.Namespace]) -> Dict:
- """
- Transform args to dict if given as argparse.Namespace.
-
- :param args: either a dictionary or an argument parser instance
-
- :return: dictionary with all arguments
- """
- if isinstance(args, argparse.Namespace):
- return args.__dict__
- elif isinstance(args, dict):
- return args
- else:
- return {}
-
def _compare_variables_and_statistics(self):
"""
Compare variables and statistics.
diff --git a/test/test_configuration/test_path_config.py b/test/test_configuration/test_path_config.py
index 557e59d7c9cd1ed45e2fc80c93ede020976e2976..acb43676cb86ca76aded88aa0d46f62dd78d9992 100644
--- a/test/test_configuration/test_path_config.py
+++ b/test/test_configuration/test_path_config.py
@@ -4,7 +4,8 @@ import os
import mock
import pytest
-from src.configuration import prepare_host, set_experiment_name, set_bootstrap_path, check_path_and_create
+from src.configuration import prepare_host, set_experiment_name, set_bootstrap_path, check_path_and_create, \
+ set_experiment_path, ROOT_PATH
from src.helpers import PyTestRegex
@@ -54,23 +55,27 @@ class TestPrepareHost:
class TestSetExperimentName:
- def test_set_experiment(self):
- exp_name, exp_path = set_experiment_name()
+ def test_set_experiment_name(self):
+ exp_name = set_experiment_name()
assert exp_name == "TestExperiment"
- assert exp_path == os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "TestExperiment"))
- exp_name, exp_path = set_experiment_name(experiment_name="2019-11-14", experiment_path=os.path.join(
- os.path.dirname(__file__), "test2"))
+ exp_name = set_experiment_name(name="2019-11-14")
assert exp_name == "2019-11-14_network"
- assert exp_path == os.path.abspath(os.path.join(os.path.dirname(__file__), "test2", exp_name))
- def test_set_experiment_from_sys(self):
- exp_name, _ = set_experiment_name(experiment_name="2019-11-14")
- assert exp_name == "2019-11-14_network"
-
- def test_set_experiment_hourly(self):
- exp_name, exp_path = set_experiment_name(sampling="hourly")
+ def test_set_experiment_name_sampling(self):
+ exp_name = set_experiment_name(sampling="hourly")
assert exp_name == "TestExperiment_hourly"
- assert exp_path == os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "TestExperiment_hourly"))
+ exp_name = set_experiment_name(sampling="daily")
+ assert exp_name == "TestExperiment_daily"
+
+ def test_set_experiment_path(self):
+ exp_path = set_experiment_path("TestExperiment")
+ assert exp_path == os.path.abspath(os.path.join(ROOT_PATH, "TestExperiment"))
+ exp_path = set_experiment_path(name="2019-11-14_network", path=os.path.join(os.path.dirname(__file__), "test2"))
+ assert exp_path == os.path.abspath(os.path.join(os.path.dirname(__file__), "test2", "2019-11-14_network"))
+
+ def test_set_experiment_path_given_path(self):
+ exp_path = set_experiment_path("TestExperiment", path=os.path.dirname(__file__))
+ assert exp_path == os.path.abspath(os.path.join(os.path.dirname(__file__), "TestExperiment"))
class TestSetBootstrapPath:
diff --git a/test/test_modules/test_experiment_setup.py b/test/test_modules/test_experiment_setup.py
index e06ba6c0ce5b9abb169e20016342b2a0dfb47d0f..2ecd78f0c7e2b14c0b9c64192c06014e2a0da75f 100644
--- a/test/test_modules/test_experiment_setup.py
+++ b/test/test_modules/test_experiment_setup.py
@@ -33,16 +33,6 @@ class TestExperimentSetup:
empty_obj._set_param("AnotherNoneTester", None)
assert empty_obj.data_store.get("AnotherNoneTester", "general") is None
- def test_get_parser_args_from_dict(self, empty_obj):
- res = empty_obj._get_parser_args({'test2': 2, 'test10str': "10"})
- assert res == {'test2': 2, 'test10str': "10"}
-
- def test_get_parser_args_from_parse_args(self, empty_obj):
- parser = argparse.ArgumentParser()
- parser.add_argument('--experiment_date', type=str)
- parser_args = parser.parse_args(["--experiment_date", "TOMORROW"])
- assert empty_obj._get_parser_args(parser_args) == {"experiment_date": "TOMORROW"}
-
def test_init_default(self):
exp_setup = ExperimentSetup()
data_store = exp_setup.data_store
@@ -68,7 +58,7 @@ class TestExperimentSetup:
'DEBW052', 'DEBW034', 'DEBY088', ]
assert data_store.get("stations", "general") == default_stations
assert data_store.get("network", "general") == "AIRBASE"
- assert data_store.get("station_type", "general") is None
+ assert data_store.get("station_type", "general") == "background"
assert data_store.get("variables", "general") == list(default_statistics_per_var.keys())
assert data_store.get("statistics_per_var", "general") == default_statistics_per_var
assert data_store.get("start", "general") == "1997-01-01"
@@ -104,7 +94,7 @@ class TestExperimentSetup:
def test_init_no_default(self):
experiment_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "data", "testExperimentFolder"))
- kwargs = dict(parser_args={"experiment_date": "TODAY"},
+ kwargs = dict(experiment_date= "TODAY",
statistics_per_var={'o3': 'dma8eu', 'relhum': 'average_values', 'temp': 'maximum'},
stations=['DEBY053', 'DEBW059', 'DEBW027'], network="INTERNET", station_type="background",
variables=["o3", "temp"], start="1999-01-01", end="2001-01-01", window_history_size=4,
@@ -183,7 +173,7 @@ class TestExperimentSetup:
def test_compare_variables_and_statistics(self):
experiment_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "data", "testExperimentFolder"))
- kwargs = dict(parser_args={"experiment_date": "TODAY"},
+ kwargs = dict(experiment_date="TODAY",
statistics_per_var={'o3': 'dma8eu', 'temp': 'maximum'},
stations=['DEBY053', 'DEBW059', 'DEBW027'], variables=["o3", "relhum"],
experiment_path=experiment_path)
diff --git a/test/test_modules/test_partition_check.py b/test/test_modules/test_partition_check.py
index d862b9e879c1121c78bc72ee1c9b8ba08f73a239..b04e01d13e9e160553f8ff66af8d97f65aa24bf0 100644
--- a/test/test_modules/test_partition_check.py
+++ b/test/test_modules/test_partition_check.py
@@ -24,7 +24,7 @@ class TestPartitionCheck:
@mock.patch("os.path.exists", return_value=False)
@mock.patch("os.makedirs", side_effect=None)
def obj_with_exp_setup_login(self, mock_host, mock_user, mock_path, mock_check):
- ExperimentSetup(parser_args={}, stations=['DEBW107', 'DEBY081', 'DEBW013', 'DEBW076', 'DEBW087', 'DEBW001'],
+ ExperimentSetup(stations=['DEBW107', 'DEBY081', 'DEBW013', 'DEBW076', 'DEBW087', 'DEBW001'],
statistics_per_var={'o3': 'dma8eu', 'temp': 'maximum'}, station_type="background")
pre = object.__new__(PartitionCheck)
super(PartitionCheck, pre).__init__()
@@ -37,7 +37,7 @@ class TestPartitionCheck:
@mock.patch("os.path.exists", return_value=False)
@mock.patch("os.makedirs", side_effect=None)
def obj_with_exp_setup_compute(self, mock_host, mock_user, mock_path, mock_check):
- ExperimentSetup(parser_args={}, stations=['DEBW107', 'DEBY081', 'DEBW013', 'DEBW076', 'DEBW087', 'DEBW001'],
+ ExperimentSetup(stations=['DEBW107', 'DEBY081', 'DEBW013', 'DEBW076', 'DEBW087', 'DEBW001'],
statistics_per_var={'o3': 'dma8eu', 'temp': 'maximum'}, station_type="background")
pre = object.__new__(PartitionCheck)
super(PartitionCheck, pre).__init__()
@@ -45,7 +45,7 @@ class TestPartitionCheck:
RunEnvironment().__del__()
def test_init(self, caplog):
- ExperimentSetup(parser_args={}, stations=['DEBW107', 'DEBY081', 'DEBW013', 'DEBW076', 'DEBW087'],
+ ExperimentSetup(stations=['DEBW107', 'DEBY081', 'DEBW013', 'DEBW076', 'DEBW087'],
statistics_per_var={'o3': 'dma8eu', 'temp': 'maximum'})
caplog.clear()
caplog.set_level(logging.INFO)
diff --git a/test/test_modules/test_pre_processing.py b/test/test_modules/test_pre_processing.py
index 6abc722273613a1f4d6727396b114939b4d6a552..29811fb04789f32a1e2cc1b3affb6f8d4ae99730 100644
--- a/test/test_modules/test_pre_processing.py
+++ b/test/test_modules/test_pre_processing.py
@@ -26,7 +26,7 @@ class TestPreProcessing:
@pytest.fixture
def obj_with_exp_setup(self):
- ExperimentSetup(parser_args={}, stations=['DEBW107', 'DEBY081', 'DEBW013', 'DEBW076', 'DEBW087', 'DEBW001'],
+ ExperimentSetup(stations=['DEBW107', 'DEBY081', 'DEBW013', 'DEBW076', 'DEBW087', 'DEBW001'],
statistics_per_var={'o3': 'dma8eu', 'temp': 'maximum'}, station_type="background")
pre = object.__new__(PreProcessing)
super(PreProcessing, pre).__init__()
@@ -34,7 +34,7 @@ class TestPreProcessing:
RunEnvironment().__del__()
def test_init(self, caplog):
- ExperimentSetup(parser_args={}, stations=['DEBW107', 'DEBY081', 'DEBW013', 'DEBW076', 'DEBW087'],
+ ExperimentSetup(stations=['DEBW107', 'DEBY081', 'DEBW013', 'DEBW076', 'DEBW087'],
statistics_per_var={'o3': 'dma8eu', 'temp': 'maximum'})
caplog.clear()
caplog.set_level(logging.INFO)