diff --git a/.gitignore b/.gitignore
index 305a5d1b9420eb62da24772fc1f4b263c1f3efe1..f5e425f752a1de0de0c68036a54e0d19450320bb 100644
--- a/.gitignore
+++ b/.gitignore
@@ -42,6 +42,15 @@ ehthumbs.db
Thumbs.db
.idea/
/venv/
+/venv*/
+/build/
+
+# ignore HPC related skripts #
+##############################
+run_*_develgpus.bash
+run_*_gpus.bash
+run_*_batch.bash
+activate_env.sh
# don't check data and plot folder #
####################################
@@ -77,4 +86,4 @@ report.html
# ignore locally build documentation #
######################################
-/docs/_build
\ No newline at end of file
+/docs/_build
diff --git a/.gitlab/issue_templates/bug.md b/.gitlab/issue_templates/bug.md
new file mode 100644
index 0000000000000000000000000000000000000000..60cf04b086eac998f78fce03a20dd7757221f57f
--- /dev/null
+++ b/.gitlab/issue_templates/bug.md
@@ -0,0 +1,19 @@
+<!-- Use this template for a bug in MLAir. -->
+
+# Bug
+
+## Error description
+<!-- Provide a context when the bug / error arises -->
+
+## Error message
+<!-- Provide the error log if available -->
+
+## First guess on error origin
+<!-- Add first ideas where the error could come from -->
+
+## Error origin
+<!-- Fill this up when the bug / error origin has been found -->
+
+## Solution
+<!-- Short description how to solve the error -->
+
diff --git a/.gitlab/issue_templates/release.md b/.gitlab/issue_templates/release.md
new file mode 100644
index 0000000000000000000000000000000000000000..618738d3184c68514fe32602af32188e001d228b
--- /dev/null
+++ b/.gitlab/issue_templates/release.md
@@ -0,0 +1,40 @@
+<!-- Use this template for a new release of MLAir. -->
+
+# Release
+<!-- add your release version here -->
+
+vX.Y.Z
+
+## checklist
+
+* [ ] Create Release Issue
+* [ ] Create merge request: branch `release_vX.Y.Z` into `master`
+* [ ] Merge `develop` into `release_vX.Y.Z`
+* [ ] Checkout `release_vX.Y.Z`
+* [ ] Adjust `changelog.md` (see template for changelog)
+* [ ] Update version number in `mlair/__ init__.py`
+* [ ] Create new dist file: `python3 setup.py sdist bdist_wheel`
+* [ ] Update file link `distribution file (current version)` in `README.md`
+* [ ] Update file link in `docs/_source/get-started.rst`
+* [ ] Commit + push
+* [ ] Merge `release_vX.Y.Z` into `master`
+* [ ] Create new tag with
+ * [ ] distribution file (.whl)
+ * [ ] link to Documentation
+ * [ ] Example Jupyter Notebook
+ * [ ] changelog
+
+
+## template for changelog
+<!-- use this structure for the changelog. Link all issue to at least one item. -->
+
+```
+## vX.Y.Z - yyyy-mm-dd - <release description>
+
+### general:
+* text
+### new features:
+* words (issue)
+### technical:
+*
+```
\ No newline at end of file
diff --git a/CHANGELOG.md b/CHANGELOG.md
index bf0c2b6b3ab672522630b28a1865e020b64ac86b..4f59375d8ee3c245e7d8008e7e8c6d6ff13b3d96 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,38 @@
# Changelog
All notable changes to this project will be documented in this file.
+## v1.2.0 - 2020-12-18 - parallel preprocessing and improved data handlers
+
+### general:
+
+ * new plots
+ * parallelism for faster preprocessing
+ * improved data handler with mixed sampling types
+ * enhanced test coverage
+
+### new features:
+
+* station map plot highlights now subsets on the map and displays number of stations for each subset (#227, #231)
+* two new data availability plots `PlotAvailabilityHistogram` (#191, #192, #223)
+* introduced parallel code in preprocessing if system supports parallelism (#164, #224, #225)
+* data handler `DataHandlerMixedSampling` (and inheritances) supports an offset parameter to end inputs at a different time than 00 hours (#220)
+* args for data handler `DataHandlerMixedSampling` (and inheritances) that differ for input and target can now be parsed as tuple (#229)
+
+### technical:
+
+* added templates for release and bug issues (#189)
+* improved test coverage (#236, #238, #239, #240, #241, #242, #243, #244, #245)
+* station map plot includes now number of stations for each subset (#231)
+* postprocessing plots are encapsulated in try except statements (#107)
+* updated git settings (#213)
+* bug fix for data handler (#235)
+* reordering and bug fix for preprocessing reporting (#207, #232)
+* bug fix for outdated system path style (#226)
+* new plots are included in default plot list (#211)
+* `helpers/join` connection to ToarDB (e.g. used by DefaultDataHandler) reports now which variable could not be loaded (#222)
+* plot `PlotBootstrapSkillScore` can now additionally highlight specific variables, but not included in postprocessing up to now (#201)
+* data handler `DataHandlerMixedSampling` has now a reduced data loading (#221)
+
## v1.1.0 - 2020-11-18 - hourly resolution support and new data handlers
### general:
diff --git a/README.md b/README.md
index 2e7b0cff48ba92143263c65c7a3fa82c139b86c8..c48b7cdb44b6f98a6a1f12a81c0a4717cc1e0d41 100644
--- a/README.md
+++ b/README.md
@@ -29,7 +29,7 @@ HPC systems, see [here](#special-instructions-for-installation-on-jülich-hpc-sy
* Installation of **MLAir**:
* Either clone MLAir from the [gitlab repository](https://gitlab.version.fz-juelich.de/toar/mlair.git)
and use it without installation (beside the requirements)
- * or download the distribution file ([current version](https://gitlab.version.fz-juelich.de/toar/mlair/-/blob/master/dist/mlair-1.1.0-py3-none-any.whl))
+ * or download the distribution file ([current version](https://gitlab.version.fz-juelich.de/toar/mlair/-/blob/master/dist/mlair-1.2.0-py3-none-any.whl))
and install it via `pip install <dist_file>.whl`. In this case, you can simply import MLAir in any python script
inside your virtual environment using `import mlair`.
* (tf) Currently, TensorFlow-1.13 is mentioned in the requirements. We already tested the TensorFlow-1.15 version and couldn't
diff --git a/conftest.py b/conftest.py
index 207606e6ec111459302360f5f2c4f917771bf80d..08641ff36543dbfba7109f84616ead8d2b472891 100644
--- a/conftest.py
+++ b/conftest.py
@@ -1,6 +1,8 @@
import os
import re
import shutil
+import pytest
+import mock
def pytest_runtest_teardown(item, nextitem):
@@ -48,3 +50,18 @@ def remove_files_from_regex(list_dir, path, regex):
shutil.rmtree(os.path.join(path, e), ignore_errors=True)
except:
pass
+
+
+@pytest.fixture(scope="session", autouse=True)
+def default_session_fixture(request):
+ """
+ :type request: _pytest.python.SubRequest
+ :return:
+ """
+ patched = mock.patch("multiprocessing.cpu_count", return_value=1)
+ patched.__enter__()
+
+ def unpatch():
+ patched.__exit__()
+
+ request.addfinalizer(unpatch)
diff --git a/dist/mlair-1.2.0-py3-none-any.whl b/dist/mlair-1.2.0-py3-none-any.whl
new file mode 100644
index 0000000000000000000000000000000000000000..7b4c3eff904e45a20591e80eccd3f3720d3d339a
Binary files /dev/null and b/dist/mlair-1.2.0-py3-none-any.whl differ
diff --git a/docs/_source/_plots/data_availability_histogram_hist.png b/docs/_source/_plots/data_availability_histogram_hist.png
new file mode 100644
index 0000000000000000000000000000000000000000..30f940145b5db8a6af1b8ce55c6737d9022da419
Binary files /dev/null and b/docs/_source/_plots/data_availability_histogram_hist.png differ
diff --git a/docs/_source/_plots/data_availability_histogram_hist_cum.png b/docs/_source/_plots/data_availability_histogram_hist_cum.png
new file mode 100644
index 0000000000000000000000000000000000000000..afb9796161739d060eebcd980258bc1bb1b9a14f
Binary files /dev/null and b/docs/_source/_plots/data_availability_histogram_hist_cum.png differ
diff --git a/docs/_source/_plots/skill_score_bootstrap_separated.png b/docs/_source/_plots/skill_score_bootstrap_separated.png
new file mode 100644
index 0000000000000000000000000000000000000000..cddb0f005286e08617bf43fd55fed9b5862856d5
Binary files /dev/null and b/docs/_source/_plots/skill_score_bootstrap_separated.png differ
diff --git a/docs/_source/get-started.rst b/docs/_source/get-started.rst
index 477b4b89e5d56d1ec7a94301a4f9378dc1dce7dd..ede3cebfb7e1d9f673da3751c0cc2ab4dfba12ea 100644
--- a/docs/_source/get-started.rst
+++ b/docs/_source/get-started.rst
@@ -31,7 +31,7 @@ Installation of MLAir
* Install all requirements from `requirements.txt <https://gitlab.version.fz-juelich.de/toar/machinelearningtools/-/blob/master/requirements.txt>`_
preferably in a virtual environment
* Either clone MLAir from the `gitlab repository <https://gitlab.version.fz-juelich.de/toar/machinelearningtools.git>`_
-* or download the distribution file (`current version <https://gitlab.version.fz-juelich.de/toar/mlair/-/blob/master/dist/mlair-1.1.0-py3-none-any.whl>`_)
+* or download the distribution file (`current version <https://gitlab.version.fz-juelich.de/toar/mlair/-/blob/master/dist/mlair-1.2.0-py3-none-any.whl>`_)
and install it via :py:`pip install <dist_file>.whl`. In this case, you can simply
import MLAir in any python script inside your virtual environment using :py:`import mlair`.
* (tf) Currently, TensorFlow-1.13 is mentioned in the requirements. We already tested the TensorFlow-1.15 version and couldn't
diff --git a/mlair/__init__.py b/mlair/__init__.py
index 41b258eb7a0ef445718cb7c45cc01bbc3092cadc..e9a157ca5bba11b22e80df0f3f18092fb0f32db6 100644
--- a/mlair/__init__.py
+++ b/mlair/__init__.py
@@ -1,6 +1,6 @@
__version_info__ = {
'major': 1,
- 'minor': 1,
+ 'minor': 2,
'micro': 0,
}
@@ -13,7 +13,7 @@ from mlair.model_modules import AbstractModelClass
def get_version():
assert set(__version_info__.keys()) >= {"major", "minor"}
vers = [f"{__version_info__['major']}.{__version_info__['minor']}"]
- if "micro" in __version_info__:
+ if "micro" in __version_info__: # pragma: no branch
vers.append(f".{__version_info__['micro']}")
return "".join(vers)
diff --git a/mlair/configuration/defaults.py b/mlair/configuration/defaults.py
index ce42fc0eed6e891bc0a0625666da3dccfcc8a3ee..1862d6734430d42a2d0cda0b199acef97b58bebb 100644
--- a/mlair/configuration/defaults.py
+++ b/mlair/configuration/defaults.py
@@ -48,7 +48,7 @@ DEFAULT_CREATE_NEW_BOOTSTRAPS = False
DEFAULT_NUMBER_OF_BOOTSTRAPS = 20
DEFAULT_PLOT_LIST = ["PlotMonthlySummary", "PlotStationMap", "PlotClimatologicalSkillScore", "PlotTimeSeries",
"PlotCompetitiveSkillScore", "PlotBootstrapSkillScore", "PlotConditionalQuantiles",
- "PlotAvailability", "PlotSeparationOfScales"]
+ "PlotAvailability", "PlotAvailabilityHistogram", "PlotSeparationOfScales"]
DEFAULT_SAMPLING = "daily"
DEFAULT_DATA_ORIGIN = {"cloudcover": "REA", "humidity": "REA", "pblheight": "REA", "press": "REA", "relhum": "REA",
"temp": "REA", "totprecip": "REA", "u": "REA", "v": "REA", "no": "", "no2": "", "o3": "",
diff --git a/mlair/configuration/path_config.py b/mlair/configuration/path_config.py
index bf40c361e121c409efec08b85fdf4e19848049ee..67c6bce4a3478443323b4ef49b5dc36258271ccd 100644
--- a/mlair/configuration/path_config.py
+++ b/mlair/configuration/path_config.py
@@ -29,11 +29,11 @@ def prepare_host(create_new=True, data_path=None, sampling="daily") -> str:
user = getpass.getuser()
runner_regex = re.compile(r"runner-.*-project-2411-concurrent-\d+")
if hostname == "ZAM144":
- data_path = f"/home/{user}/Data/toar_{sampling}/"
+ data_path = f"/home/{user}/Data/toar/"
elif hostname == "zam347":
- data_path = f"/home/{user}/Data/toar_{sampling}/"
+ data_path = f"/home/{user}/Data/toar/"
elif (len(hostname) > 2) and (hostname[:2] == "jr"):
- data_path = f"/p/project/cjjsc42/{user}/DATA/toar_{sampling}/"
+ data_path = f"/p/project/cjjsc42/{user}/DATA/toar/"
elif (len(hostname) > 2) and (hostname[:2] in ['jw', 'ju'] or hostname[:5] in ['hdfml']):
data_path = f"/p/project/deepacf/intelliaq/{user}/DATA/MLAIR/"
elif runner_regex.match(hostname) is not None:
diff --git a/mlair/data_handler/advanced_data_handler.py b/mlair/data_handler/advanced_data_handler.py
deleted file mode 100644
index f04748e82f11116b265796afba7f401c1cad9342..0000000000000000000000000000000000000000
--- a/mlair/data_handler/advanced_data_handler.py
+++ /dev/null
@@ -1,112 +0,0 @@
-
-__author__ = 'Lukas Leufen'
-__date__ = '2020-07-08'
-
-import numpy as np
-import xarray as xr
-import os
-import pandas as pd
-import datetime as dt
-
-from mlair.data_handler import AbstractDataHandler
-
-from typing import Union, List, Tuple, Dict
-import logging
-from functools import reduce
-from mlair.helpers.join import EmptyQueryResult
-from mlair.helpers import TimeTracking
-
-number = Union[float, int]
-num_or_list = Union[number, List[number]]
-
-
-def run_data_prep():
- from .data_handler_neighbors import DataHandlerNeighbors
- data = DummyDataHandler("main_class")
- data.get_X()
- data.get_Y()
-
- path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "testdata")
- data_prep = DataHandlerNeighbors(DummyDataHandler("main_class"),
- path,
- neighbors=[DummyDataHandler("neighbor1"),
- DummyDataHandler("neighbor2")],
- extreme_values=[1., 1.2])
- data_prep.get_data(upsampling=False)
-
-
-def create_data_prep():
- from .data_handler_neighbors import DataHandlerNeighbors
- path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "testdata")
- station_type = None
- network = 'UBA'
- sampling = 'daily'
- target_dim = 'variables'
- target_var = 'o3'
- interpolation_dim = 'datetime'
- window_history_size = 7
- window_lead_time = 3
- central_station = DataHandlerSingleStation("DEBW011", path, {'o3': 'dma8eu', 'temp': 'maximum'}, {}, station_type, network, sampling, target_dim,
- target_var, interpolation_dim, window_history_size, window_lead_time)
- neighbor1 = DataHandlerSingleStation("DEBW013", path, {'o3': 'dma8eu', 'temp-rea-miub': 'maximum'}, {}, station_type, network, sampling, target_dim,
- target_var, interpolation_dim, window_history_size, window_lead_time)
- neighbor2 = DataHandlerSingleStation("DEBW034", path, {'o3': 'dma8eu', 'temp': 'maximum'}, {}, station_type, network, sampling, target_dim,
- target_var, interpolation_dim, window_history_size, window_lead_time)
-
- data_prep = []
- data_prep.append(DataHandlerNeighbors(central_station, path, neighbors=[neighbor1, neighbor2]))
- data_prep.append(DataHandlerNeighbors(neighbor1, path, neighbors=[central_station, neighbor2]))
- data_prep.append(DataHandlerNeighbors(neighbor2, path, neighbors=[neighbor1, central_station]))
- return data_prep
-
-
-class DummyDataHandler(AbstractDataHandler):
-
- def __init__(self, name, number_of_samples=None):
- """This data handler takes a name argument and the number of samples to generate. If not provided, a random
- number between 100 and 150 is set."""
- super().__init__()
- self.name = name
- self.number_of_samples = number_of_samples if number_of_samples is not None else np.random.randint(100, 150)
- self._X = self.create_X()
- self._Y = self.create_Y()
-
- def create_X(self):
- """Inputs are random numbers between 0 and 10 with shape (no_samples, window=14, variables=5)."""
- X = np.random.randint(0, 10, size=(self.number_of_samples, 14, 5)) # samples, window, variables
- datelist = pd.date_range(dt.datetime.today().date(), periods=self.number_of_samples, freq="H").tolist()
- return xr.DataArray(X, dims=['datetime', 'window', 'variables'], coords={"datetime": datelist,
- "window": range(14),
- "variables": range(5)})
-
- def create_Y(self):
- """Targets are normal distributed random numbers with shape (no_samples, window=5, variables=1)."""
- Y = np.round(0.5 * np.random.randn(self.number_of_samples, 5, 1), 1) # samples, window, variables
- datelist = pd.date_range(dt.datetime.today().date(), periods=self.number_of_samples, freq="H").tolist()
- return xr.DataArray(Y, dims=['datetime', 'window', 'variables'], coords={"datetime": datelist,
- "window": range(5),
- "variables": range(1)})
-
- def get_X(self, upsampling=False, as_numpy=False):
- """Upsampling parameter is not used for X."""
- return np.copy(self._X) if as_numpy is True else self._X
-
- def get_Y(self, upsampling=False, as_numpy=False):
- """Upsampling parameter is not used for Y."""
- return np.copy(self._Y) if as_numpy is True else self._Y
-
- def __str__(self):
- return self.name
-
-
-if __name__ == "__main__":
- from mlair.data_handler.data_handler_single_station import DataHandlerSingleStation
- from mlair.data_handler.iterator import KerasIterator, DataCollection
- data_prep = create_data_prep()
- data_collection = DataCollection(data_prep)
- for data in data_collection:
- print(data)
- path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "testdata", "keras")
- keras_it = KerasIterator(data_collection, 100, path, upsampling=True)
- keras_it[2]
-
diff --git a/mlair/data_handler/data_handler_mixed_sampling.py b/mlair/data_handler/data_handler_mixed_sampling.py
index 80890b6f45dcde80aa75e9203a4a44ba25c7db01..19fc26fe78f4aaec034d6593e3b4628b85fc5644 100644
--- a/mlair/data_handler/data_handler_mixed_sampling.py
+++ b/mlair/data_handler/data_handler_mixed_sampling.py
@@ -6,11 +6,12 @@ from mlair.data_handler.data_handler_kz_filter import DataHandlerKzFilterSingleS
from mlair.data_handler import DefaultDataHandler
from mlair import helpers
from mlair.helpers import remove_items
-from mlair.configuration.defaults import DEFAULT_SAMPLING
+from mlair.configuration.defaults import DEFAULT_SAMPLING, DEFAULT_INTERPOLATION_LIMIT, DEFAULT_INTERPOLATION_METHOD
import inspect
from typing import Callable
import datetime as dt
+from typing import Any
import numpy as np
import pandas as pd
@@ -20,11 +21,39 @@ import xarray as xr
class DataHandlerMixedSamplingSingleStation(DataHandlerSingleStation):
_requirements = remove_items(inspect.getfullargspec(DataHandlerSingleStation).args, ["self", "station"])
- def __init__(self, *args, sampling_inputs, **kwargs):
- sampling = (sampling_inputs, kwargs.get("sampling", DEFAULT_SAMPLING))
- kwargs.update({"sampling": sampling})
+ def __init__(self, *args, **kwargs):
+ """
+ This data handler requires the kwargs sampling, interpolation_limit, and interpolation_method to be a 2D tuple
+ for input and target data. If one of these kwargs is only a single argument, it will be applied to inputs and
+ targets with this value. If one of these kwargs is a 2-dim tuple, the first element is applied to inputs and the
+ second to targets respectively. If one of these kwargs is not provided, it is filled up with the same default
+ value for inputs and targets.
+ """
+ self.update_kwargs("sampling", DEFAULT_SAMPLING, kwargs)
+ self.update_kwargs("interpolation_limit", DEFAULT_INTERPOLATION_LIMIT, kwargs)
+ self.update_kwargs("interpolation_method", DEFAULT_INTERPOLATION_METHOD, kwargs)
super().__init__(*args, **kwargs)
+ @staticmethod
+ def update_kwargs(parameter_name: str, default: Any, kwargs: dict):
+ """
+ Update a single element of kwargs inplace to be usable for inputs and targets.
+
+ The updated value in the kwargs dictionary is a tuple consisting on the value applicable to the inputs as first
+ element and the target's value as second element: (<value_input>, <value_target>). If the value for the given
+ parameter_name is already a tuple, it is checked to have exact two entries. If the paramter_name is not
+ included in kwargs, the given default value is used and applied to both elements of the update tuple.
+
+ :param parameter_name: name of the parameter that should be transformed to 2-dim
+ :param default: the default value to fill if parameter is not in kwargs
+ :param kwargs: the kwargs dictionary containing parameters
+ """
+ parameter = kwargs.get(parameter_name, default)
+ if not isinstance(parameter, tuple):
+ parameter = (parameter, parameter)
+ assert len(parameter) == 2 # (inputs, targets)
+ kwargs.update({parameter_name: parameter})
+
def setup_samples(self):
"""
Setup samples. This method prepares and creates samples X, and labels Y.
@@ -36,11 +65,13 @@ class DataHandlerMixedSamplingSingleStation(DataHandlerSingleStation):
self.make_samples()
def load_and_interpolate(self, ind) -> [xr.DataArray, pd.DataFrame]:
- data, self.meta = self.load_data(self.path[ind], self.station, self.statistics_per_var, self.sampling[ind],
+ vars = [self.variables, self.target_var]
+ stats_per_var = helpers.select_from_dict(self.statistics_per_var, vars[ind])
+ data, self.meta = self.load_data(self.path[ind], self.station, stats_per_var, self.sampling[ind],
self.station_type, self.network, self.store_data_locally, self.data_origin,
self.start, self.end)
- data = self.interpolate(data, dim=self.time_dim, method=self.interpolation_method,
- limit=self.interpolation_limit)
+ data = self.interpolate(data, dim=self.time_dim, method=self.interpolation_method[ind],
+ limit=self.interpolation_limit[ind])
return data
def set_inputs_and_targets(self):
@@ -110,11 +141,14 @@ class DataHandlerMixedSamplingWithFilterSingleStation(DataHandlerMixedSamplingSi
else: # target
start, end = self.start, self.end
- data, self.meta = self.load_data(self.path[ind], self.station, self.statistics_per_var, self.sampling[ind],
+ vars = [self.variables, self.target_var]
+ stats_per_var = helpers.select_from_dict(self.statistics_per_var, vars[ind])
+
+ data, self.meta = self.load_data(self.path[ind], self.station, stats_per_var, self.sampling[ind],
self.station_type, self.network, self.store_data_locally, self.data_origin,
start, end)
- data = self.interpolate(data, dim=self.time_dim, method=self.interpolation_method,
- limit=self.interpolation_limit)
+ data = self.interpolate(data, dim=self.time_dim, method=self.interpolation_method[ind],
+ limit=self.interpolation_limit[ind])
return data
diff --git a/mlair/data_handler/data_handler_neighbors.py b/mlair/data_handler/data_handler_neighbors.py
index a004e659969232a080d49eb6905007d353bbe99c..6c87946eaad5568e1ff59c3988bf8fe469442641 100644
--- a/mlair/data_handler/data_handler_neighbors.py
+++ b/mlair/data_handler/data_handler_neighbors.py
@@ -1,10 +1,15 @@
-
__author__ = 'Lukas Leufen'
__date__ = '2020-07-17'
+import datetime as dt
+
+import numpy as np
+import pandas as pd
+import xarray as xr
+from mlair.data_handler.data_handler_single_station import DataHandlerSingleStation
from mlair.helpers import to_list
-from mlair.data_handler import DefaultDataHandler
+from mlair.data_handler import DefaultDataHandler, AbstractDataHandler
import os
import copy
@@ -43,8 +48,90 @@ class DataHandlerNeighbors(DefaultDataHandler):
return [super(DataHandlerNeighbors, self).get_coordinates()].append(neighbors)
-if __name__ == "__main__":
+def run_data_prep():
+ """Comment: methods just to start write meaningful test routines."""
+ data = DummyDataHandler("main_class")
+ data.get_X()
+ data.get_Y()
+
+ path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "testdata")
+ data_prep = DataHandlerNeighbors(DummyDataHandler("main_class"),
+ path,
+ neighbors=[DummyDataHandler("neighbor1"),
+ DummyDataHandler("neighbor2")],
+ extreme_values=[1., 1.2])
+ data_prep.get_data(upsampling=False)
+
+
+def create_data_prep():
+ """Comment: methods just to start write meaningful test routines."""
+ path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "testdata")
+ station_type = None
+ network = 'UBA'
+ sampling = 'daily'
+ target_dim = 'variables'
+ target_var = 'o3'
+ interpolation_dim = 'datetime'
+ window_history_size = 7
+ window_lead_time = 3
+ central_station = DataHandlerSingleStation("DEBW011", path, {'o3': 'dma8eu', 'temp': 'maximum'}, {}, station_type,
+ network, sampling, target_dim,
+ target_var, interpolation_dim, window_history_size, window_lead_time)
+ neighbor1 = DataHandlerSingleStation("DEBW013", path, {'o3': 'dma8eu', 'temp-rea-miub': 'maximum'}, {},
+ station_type, network, sampling, target_dim,
+ target_var, interpolation_dim, window_history_size, window_lead_time)
+ neighbor2 = DataHandlerSingleStation("DEBW034", path, {'o3': 'dma8eu', 'temp': 'maximum'}, {}, station_type,
+ network, sampling, target_dim,
+ target_var, interpolation_dim, window_history_size, window_lead_time)
+
+ data_prep = []
+ data_prep.append(DataHandlerNeighbors(central_station, path, neighbors=[neighbor1, neighbor2]))
+ data_prep.append(DataHandlerNeighbors(neighbor1, path, neighbors=[central_station, neighbor2]))
+ data_prep.append(DataHandlerNeighbors(neighbor2, path, neighbors=[neighbor1, central_station]))
+ return data_prep
+
+
+class DummyDataHandler(AbstractDataHandler):
+
+ def __init__(self, name, number_of_samples=None):
+ """This data handler takes a name argument and the number of samples to generate. If not provided, a random
+ number between 100 and 150 is set."""
+ super().__init__()
+ self.name = name
+ self.number_of_samples = number_of_samples if number_of_samples is not None else np.random.randint(100, 150)
+ self._X = self.create_X()
+ self._Y = self.create_Y()
+ def create_X(self):
+ """Inputs are random numbers between 0 and 10 with shape (no_samples, window=14, variables=5)."""
+ X = np.random.randint(0, 10, size=(self.number_of_samples, 14, 5)) # samples, window, variables
+ datelist = pd.date_range(dt.datetime.today().date(), periods=self.number_of_samples, freq="H").tolist()
+ return xr.DataArray(X, dims=['datetime', 'window', 'variables'], coords={"datetime": datelist,
+ "window": range(14),
+ "variables": range(5)})
+
+ def create_Y(self):
+ """Targets are normal distributed random numbers with shape (no_samples, window=5, variables=1)."""
+ Y = np.round(0.5 * np.random.randn(self.number_of_samples, 5, 1), 1) # samples, window, variables
+ datelist = pd.date_range(dt.datetime.today().date(), periods=self.number_of_samples, freq="H").tolist()
+ return xr.DataArray(Y, dims=['datetime', 'window', 'variables'], coords={"datetime": datelist,
+ "window": range(5),
+ "variables": range(1)})
+
+ def get_X(self, upsampling=False, as_numpy=False):
+ """Upsampling parameter is not used for X."""
+ return np.copy(self._X) if as_numpy is True else self._X
+
+ def get_Y(self, upsampling=False, as_numpy=False):
+ """Upsampling parameter is not used for Y."""
+ return np.copy(self._Y) if as_numpy is True else self._Y
+
+ def __str__(self):
+ return self.name
+
+
+if __name__ == "__main__":
+ """Comment: This is more for testing. Maybe reuse parts of this code for the testing routines."""
a = DataHandlerNeighbors
requirements = a.requirements()
@@ -59,7 +146,17 @@ if __name__ == "__main__":
"window_lead_time": 3,
"neighbors": ["DEBW034"],
"data_path": os.path.join(os.path.dirname(os.path.abspath(__file__)), "testdata"),
- "statistics_per_var": {'o3': 'dma8eu', 'temp': 'maximum'},
- "transformation": None,}
+ "statistics_per_var": {'o3': 'dma8eu', 'temp': 'maximum'},
+ "transformation": None, }
a_inst = a.build("DEBW011", **kwargs)
print(a_inst)
+
+ from mlair.data_handler.iterator import KerasIterator, DataCollection
+
+ data_prep = create_data_prep()
+ data_collection = DataCollection(data_prep)
+ for data in data_collection:
+ print(data)
+ path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "testdata", "keras")
+ keras_it = KerasIterator(data_collection, 100, path, upsampling=True)
+ keras_it[2]
diff --git a/mlair/data_handler/data_handler_single_station.py b/mlair/data_handler/data_handler_single_station.py
index e554a3b32d8e4e2f5482a388374cfba87f7add15..654f489fab8ee6ed8eb360be54be7c755da061e1 100644
--- a/mlair/data_handler/data_handler_single_station.py
+++ b/mlair/data_handler/data_handler_single_station.py
@@ -34,20 +34,24 @@ DEFAULT_VAR_ALL_DICT = {'o3': 'dma8eu', 'relhum': 'average_values', 'temp': 'max
'pblheight': 'maximum'}
DEFAULT_WINDOW_LEAD_TIME = 3
DEFAULT_WINDOW_HISTORY_SIZE = 13
+DEFAULT_WINDOW_HISTORY_OFFSET = 0
DEFAULT_TIME_DIM = "datetime"
DEFAULT_TARGET_VAR = "o3"
DEFAULT_TARGET_DIM = "variables"
DEFAULT_SAMPLING = "daily"
+DEFAULT_INTERPOLATION_LIMIT = 0
DEFAULT_INTERPOLATION_METHOD = "linear"
class DataHandlerSingleStation(AbstractDataHandler):
def __init__(self, station, data_path, statistics_per_var, station_type=DEFAULT_STATION_TYPE,
- network=DEFAULT_NETWORK, sampling=DEFAULT_SAMPLING, target_dim=DEFAULT_TARGET_DIM,
- target_var=DEFAULT_TARGET_VAR, time_dim=DEFAULT_TIME_DIM,
- window_history_size=DEFAULT_WINDOW_HISTORY_SIZE, window_lead_time=DEFAULT_WINDOW_LEAD_TIME,
- interpolation_limit: int = 0, interpolation_method: str = DEFAULT_INTERPOLATION_METHOD,
+ network=DEFAULT_NETWORK, sampling: Union[str, Tuple[str]] = DEFAULT_SAMPLING,
+ target_dim=DEFAULT_TARGET_DIM, target_var=DEFAULT_TARGET_VAR, time_dim=DEFAULT_TIME_DIM,
+ window_history_size=DEFAULT_WINDOW_HISTORY_SIZE, window_history_offset=DEFAULT_WINDOW_HISTORY_OFFSET,
+ window_lead_time=DEFAULT_WINDOW_LEAD_TIME,
+ interpolation_limit: Union[int, Tuple[int]] = DEFAULT_INTERPOLATION_LIMIT,
+ interpolation_method: Union[str, Tuple[str]] = DEFAULT_INTERPOLATION_METHOD,
overwrite_local_data: bool = False, transformation=None, store_data_locally: bool = True,
min_length: int = 0, start=None, end=None, variables=None, data_origin: Dict = None, **kwargs):
super().__init__() # path, station, statistics_per_var, transformation, **kwargs)
@@ -65,6 +69,7 @@ class DataHandlerSingleStation(AbstractDataHandler):
self.target_var = target_var
self.time_dim = time_dim
self.window_history_size = window_history_size
+ self.window_history_offset = window_history_offset
self.window_lead_time = window_lead_time
self.interpolation_limit = interpolation_limit
@@ -271,20 +276,23 @@ class DataHandlerSingleStation(AbstractDataHandler):
chem_vars = ["benzene", "ch4", "co", "ethane", "no", "no2", "nox", "o3", "ox", "pm1", "pm10", "pm2p5",
"propane", "so2", "toluene"]
# used_chem_vars = list(set(chem_vars) & set(self.statistics_per_var.keys()))
- used_chem_vars = list(set(chem_vars) & set(self.variables))
+ used_chem_vars = list(set(chem_vars) & set(data.variables.values))
data.loc[..., used_chem_vars] = data.loc[..., used_chem_vars].clip(min=minimum)
return data
def setup_data_path(self, data_path: str, sampling: str):
return os.path.join(os.path.abspath(data_path), sampling)
- def shift(self, data: xr.DataArray, dim: str, window: int) -> xr.DataArray:
+ def shift(self, data: xr.DataArray, dim: str, window: int, offset: int = 0) -> xr.DataArray:
"""
Shift data multiple times to represent history (if window <= 0) or lead time (if window > 0).
:param data: data set to shift
:param dim: dimension along shift is applied
:param window: number of steps to shift (corresponds to the window length)
+ :param offset: use offset to move the window by as many time steps as given in offset. This can be used, if the
+ index time of a history element is not the last timestamp. E.g. you could use offset=23 when dealing with
+ hourly data in combination with daily data (values from 00 to 23 are aggregated on 00 the same day).
:return: shifted data
"""
@@ -295,9 +303,10 @@ class DataHandlerSingleStation(AbstractDataHandler):
else:
end = window + 1
res = []
- for w in range(start, end):
+ _range = list(map(lambda x: x + offset, range(start, end)))
+ for w in _range:
res.append(data.shift({dim: -w}))
- window_array = self.create_index_array('window', range(start, end), squeeze_dim=self.target_dim)
+ window_array = self.create_index_array('window', _range, squeeze_dim=self.target_dim)
res = xr.concat(res, dim=window_array)
return res
@@ -387,7 +396,7 @@ class DataHandlerSingleStation(AbstractDataHandler):
"""
window = -abs(window)
data = self.input_data.data
- self.history = self.shift(data, dim_name_of_shift, window)
+ self.history = self.shift(data, dim_name_of_shift, window, offset=self.window_history_offset)
def make_labels(self, dim_name_of_target: str, target_var: str_or_list, dim_name_of_shift: str,
window: int) -> None:
diff --git a/mlair/data_handler/default_data_handler.py b/mlair/data_handler/default_data_handler.py
index 584151e36fd0c9621d089e88b8ad61cffa0c5925..291bbc6616314db61282c380a6b3e105d8b6248a 100644
--- a/mlair/data_handler/default_data_handler.py
+++ b/mlair/data_handler/default_data_handler.py
@@ -11,6 +11,7 @@ import pickle
import shutil
from functools import reduce
from typing import Tuple, Union, List
+import multiprocessing
import numpy as np
import xarray as xr
@@ -251,14 +252,29 @@ class DefaultDataHandler(AbstractDataHandler):
return
means = [None, None]
stds = [None, None]
- for station in set_stations:
- try:
- sp = cls.data_handler_transformation(station, **sp_keys)
- for i, data in enumerate([sp.input_data, sp.target_data]):
- means[i] = data.mean.copy(deep=True) if means[i] is None else means[i].combine_first(data.mean)
- stds[i] = data.std.copy(deep=True) if stds[i] is None else stds[i].combine_first(data.std)
- except (AttributeError, EmptyQueryResult):
- continue
+
+ if multiprocessing.cpu_count() > 1: # parallel solution
+ logging.info("use parallel transformation approach")
+ pool = multiprocessing.Pool()
+ logging.info(f"running {getattr(pool, '_processes')} processes in parallel")
+ output = [
+ pool.apply_async(f_proc, args=(cls.data_handler_transformation, station), kwds=sp_keys)
+ for station in set_stations]
+ for p in output:
+ dh, s = p.get()
+ if dh is not None:
+ for i, data in enumerate([dh.input_data, dh.target_data]):
+ means[i] = data.mean.copy(deep=True) if means[i] is None else means[i].combine_first(data.mean)
+ stds[i] = data.std.copy(deep=True) if stds[i] is None else stds[i].combine_first(data.std)
+ else: # serial solution
+ logging.info("use serial transformation approach")
+ for station in set_stations:
+ dh, s = f_proc(cls.data_handler_transformation, station, **sp_keys)
+ if dh is not None:
+ for i, data in enumerate([dh.input_data, dh.target_data]):
+ means[i] = data.mean.copy(deep=True) if means[i] is None else means[i].combine_first(data.mean)
+ stds[i] = data.std.copy(deep=True) if stds[i] is None else stds[i].combine_first(data.std)
+
if means[0] is None:
return None
transformation_class.inputs.mean = means[0].mean("Stations")
@@ -268,4 +284,18 @@ class DefaultDataHandler(AbstractDataHandler):
return transformation_class
def get_coordinates(self):
- return self.id_class.get_coordinates()
\ No newline at end of file
+ return self.id_class.get_coordinates()
+
+
+def f_proc(data_handler, station, **sp_keys):
+ """
+ Try to create a data handler for given arguments. If build fails, this station does not fulfil all requirements and
+ therefore f_proc will return None as indication. On a successful build, f_proc returns the built data handler and
+ the station that was used. This function must be implemented globally to work together with multiprocessing.
+ """
+ try:
+ res = data_handler(station, **sp_keys)
+ except (AttributeError, EmptyQueryResult, KeyError, ValueError) as e:
+ logging.info(f"remove station {station} because it raised an error: {e}")
+ res = None
+ return res, station
diff --git a/mlair/data_handler/iterator.py b/mlair/data_handler/iterator.py
index 49569405a587920da795820d48f8d968a8142cc7..30c45417a64e949b0c0535a96a20c933641fdcbb 100644
--- a/mlair/data_handler/iterator.py
+++ b/mlair/data_handler/iterator.py
@@ -33,13 +33,18 @@ class StandardIterator(Iterator):
class DataCollection(Iterable):
- def __init__(self, collection: list = None):
+ def __init__(self, collection: list = None, name: str = None):
if collection is None:
collection = []
assert isinstance(collection, list)
- self._collection = collection
+ self._collection = collection.copy()
self._mapping = {}
self._set_mapping()
+ self._name = name
+
+ @property
+ def name(self):
+ return self._name
def __len__(self):
return len(self._collection)
@@ -55,7 +60,7 @@ class DataCollection(Iterable):
def add(self, element):
self._collection.append(element)
- self._mapping[str(element)] = len(self._collection)
+ self._mapping[str(element)] = len(self._collection) - 1
def _set_mapping(self):
for i, e in enumerate(self._collection):
@@ -114,9 +119,10 @@ class KerasIterator(keras.utils.Sequence):
def _get_batch(self, data_list: List[np.ndarray], b: int) -> List[np.ndarray]:
"""Get batch according to batch size from data list."""
- return list(map(lambda data: data[b * self.batch_size:(b+1) * self.batch_size, ...], data_list))
+ return list(map(lambda data: data[b * self.batch_size:(b + 1) * self.batch_size, ...], data_list))
- def _permute_data(self, X, Y):
+ @staticmethod
+ def _permute_data(X, Y):
p = np.random.permutation(len(X[0])) # equiv to .shape[0]
X = list(map(lambda x: x[p], X))
Y = list(map(lambda x: x[p], Y))
@@ -179,35 +185,3 @@ class KerasIterator(keras.utils.Sequence):
"""Randomly shuffle indexes if enabled."""
if self.shuffle is True:
np.random.shuffle(self.indexes)
-
-
-class DummyData: # pragma: no cover
-
- def __init__(self, number_of_samples=np.random.randint(100, 150)):
- self.number_of_samples = number_of_samples
-
- def get_X(self):
- X1 = np.random.randint(0, 10, size=(self.number_of_samples, 14, 5)) # samples, window, variables
- X2 = np.random.randint(21, 30, size=(self.number_of_samples, 10, 2)) # samples, window, variables
- X3 = np.random.randint(-5, 0, size=(self.number_of_samples, 1, 2)) # samples, window, variables
- return [X1, X2, X3]
-
- def get_Y(self):
- Y1 = np.random.randint(0, 10, size=(self.number_of_samples, 5, 1)) # samples, window, variables
- Y2 = np.random.randint(21, 30, size=(self.number_of_samples, 5, 1)) # samples, window, variables
- return [Y1, Y2]
-
-
-if __name__ == "__main__":
-
- collection = []
- for _ in range(3):
- collection.append(DummyData(50))
-
- data_collection = DataCollection(collection=collection)
-
- path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "testdata")
- iterator = KerasIterator(data_collection, 25, path, shuffle=True)
-
- for data in data_collection:
- print(data)
\ No newline at end of file
diff --git a/mlair/helpers/__init__.py b/mlair/helpers/__init__.py
index 9e2f612c86dc0477693567210493fbdcf3002954..4671334c16267be819ab8ee0ad96b7135ee01531 100644
--- a/mlair/helpers/__init__.py
+++ b/mlair/helpers/__init__.py
@@ -3,4 +3,4 @@
from .testing import PyTestRegex, PyTestAllEqual
from .time_tracking import TimeTracking, TimeTrackingWrapper
from .logger import Logger
-from .helpers import remove_items, float_round, dict_to_xarray, to_list, extract_value
+from .helpers import remove_items, float_round, dict_to_xarray, to_list, extract_value, select_from_dict
diff --git a/mlair/helpers/datastore.py b/mlair/helpers/datastore.py
index b4615216000d887f16e6ed30d97215a261e12c6d..d6c977c717c5ef869fdba517fb36fcd55cfe3961 100644
--- a/mlair/helpers/datastore.py
+++ b/mlair/helpers/datastore.py
@@ -65,7 +65,7 @@ class CorrectScope:
return self.wrapper(*args, **kwargs)
def __get__(self, instance, cls):
- """Create bound method object and supply self argument to the decorated method."""
+ """Create bound method object and supply self argument to the decorated method. <Python Cookbook, p.347>"""
return types.MethodType(self, instance)
@staticmethod
@@ -101,6 +101,7 @@ class CorrectScope:
class TrackParameter:
+ """Hint: Tracking is not working for static methods."""
def __init__(self, func):
"""Construct decorator."""
@@ -114,7 +115,7 @@ class TrackParameter:
return self.__wrapped__(*args, **kwargs)
def __get__(self, instance, cls):
- """Create bound method object and supply self argument to the decorated method."""
+ """Create bound method object and supply self argument to the decorated method. <Python Cookbook, p.347>"""
return types.MethodType(self, instance)
def track(self, tracker_obj, *args):
@@ -312,7 +313,7 @@ class DataStoreByVariable(AbstractDataStore):
if name not in self._store.keys():
self._store[name] = {}
self._store[name][scope] = obj
- if log:
+ if log: # pragma: no cover
logging.debug(f"set: {name}({scope})={obj}")
@CorrectScope
@@ -463,7 +464,7 @@ class DataStoreByScope(AbstractDataStore):
if scope not in self._store.keys():
self._store[scope] = {}
self._store[scope][name] = obj
- if log:
+ if log: # pragma: no cover
logging.debug(f"set: {name}({scope})={obj}")
@CorrectScope
diff --git a/mlair/helpers/helpers.py b/mlair/helpers/helpers.py
index 3ecf1f6213bf39d2e3571a1b451173b981a3dadf..42b66dcb68b184112a321473e3aae250d697c452 100644
--- a/mlair/helpers/helpers.py
+++ b/mlair/helpers/helpers.py
@@ -12,13 +12,15 @@ from typing import Dict, Callable, Union, List, Any
def to_list(obj: Any) -> List:
"""
- Transform given object to list if obj is not already a list.
+ Transform given object to list if obj is not already a list. Sets are also transformed to a list.
:param obj: object to transform to list
:return: list containing obj, or obj itself (if obj was already a list)
"""
- if not isinstance(obj, list):
+ if isinstance(obj, (set, tuple)):
+ obj = list(obj)
+ elif not isinstance(obj, list):
obj = [obj]
return obj
@@ -99,8 +101,27 @@ def remove_items(obj: Union[List, Dict], items: Any):
raise TypeError(f"{inspect.stack()[0][3]} does not support type {type(obj)}.")
+def select_from_dict(dict_obj: dict, sel_list: Any):
+ """
+ Extract all key values pairs whose key is contained in the sel_list.
+
+ Does not perform a check if all elements of sel_list are keys of dict_obj. Therefore the number of pairs in the
+ returned dict is always smaller or equal to the number of elements in the sel_list.
+ """
+ sel_list = to_list(sel_list)
+ assert isinstance(dict_obj, dict)
+ sel_dict = {k: v for k, v in dict_obj.items() if k in sel_list}
+ return sel_dict
+
+
def extract_value(encapsulated_value):
try:
- return extract_value(encapsulated_value[0])
+ if isinstance(encapsulated_value, str):
+ raise TypeError
+ if len(encapsulated_value) == 1:
+ return extract_value(encapsulated_value[0])
+ else:
+ raise NotImplementedError("Trying to extract an encapsulated value from objects with more than a single "
+ "entry is not supported by this function.")
except TypeError:
return encapsulated_value
diff --git a/mlair/helpers/join.py b/mlair/helpers/join.py
index 43a0176811b54fba2983c1dba108f4c7977f1431..8a8ca0b8c964268aa6043312cd1cc88bc0d50544 100644
--- a/mlair/helpers/join.py
+++ b/mlair/helpers/join.py
@@ -45,7 +45,15 @@ def download_join(station_name: Union[str, List[str]], stat_var: dict, station_t
join_url_base, headers = join_settings(sampling)
# load series information
- vars_dict = load_series_information(station_name, station_type, network_name, join_url_base, headers, data_origin)
+ vars_dict, data_origin = load_series_information(station_name, station_type, network_name, join_url_base, headers,
+ data_origin)
+
+ # check if all requested variables are available
+ if set(stat_var).issubset(vars_dict) is False:
+ missing_variables = set(stat_var).difference(vars_dict)
+ origin = helpers.select_from_dict(data_origin, missing_variables)
+ options = f"station={station_name}, type={station_type}, network={network_name}, origin={origin}"
+ raise EmptyQueryResult(f"No data found for variables {missing_variables} and options {options} in JOIN.")
# correct stat_var values if data is not aggregated (hourly)
if sampling == "hourly":
@@ -58,11 +66,11 @@ def download_join(station_name: Union[str, List[str]], stat_var: dict, station_t
for var in _lower_list(sorted(vars_dict.keys())):
if var in stat_var.keys():
- logging.debug('load: {}'.format(var)) # ToDo start here for #206
+ logging.debug('load: {}'.format(var))
# create data link
opts = {'base': join_url_base, 'service': 'stats', 'id': vars_dict[var], 'statistics': stat_var[var],
- 'sampling': sampling, 'capture': 0, 'min_data_length': 1460, 'format': 'json'}
+ 'sampling': sampling, 'capture': 0, 'format': 'json'}
# load data
data = get_data(opts, headers)
@@ -122,11 +130,14 @@ def get_data(opts: Dict, headers: Dict) -> Union[Dict, List]:
"""
url = create_url(**opts)
response = requests.get(url, headers=headers)
- return response.json()
+ if response.status_code == 200:
+ return response.json()
+ else:
+ raise EmptyQueryResult(f"There was an error (STATUS {response.status_code}) for request {url}")
def load_series_information(station_name: List[str], station_type: str_or_none, network_name: str_or_none,
- join_url_base: str, headers: Dict, data_origin: Dict = None) -> Dict:
+ join_url_base: str, headers: Dict, data_origin: Dict = None) -> [Dict, Dict]:
"""
List all series ids that are available for given station id and network name.
@@ -144,27 +155,30 @@ def load_series_information(station_name: List[str], station_type: str_or_none,
"network_name": network_name, "as_dict": "true",
"columns": "id,network_name,station_id,parameter_name,parameter_label,parameter_attribute"}
station_vars = get_data(opts, headers)
- logging.debug(f"{station_name}: {station_vars}") # ToDo start here for #206
+ logging.debug(f"{station_name}: {station_vars}")
return _select_distinct_series(station_vars, data_origin)
-def _select_distinct_series(vars: List[Dict], data_origin: Dict = None):
+def _select_distinct_series(vars: List[Dict], data_origin: Dict = None) -> [Dict, Dict]:
"""
Select distinct series ids for all variables. Also check if a parameter is from REA or not.
"""
+ data_origin_default = {"cloudcover": "REA", "humidity": "REA", "pblheight": "REA", "press": "REA", "relhum": "REA",
+ "temp": "REA", "totprecip": "REA", "u": "REA", "v": "REA",
+ "no": "", "no2": "", "o3": "", "pm10": "", "so2": ""}
if data_origin is None:
- data_origin = {"cloudcover": "REA", "humidity": "REA", "pblheight": "REA", "press": "REA", "relhum": "REA",
- "temp": "REA", "totprecip": "REA", "u": "REA", "v": "REA",
- "no": "", "no2": "", "o3": "", "pm10": "", "so2": ""}
+ data_origin = {}
# ToDo: maybe press, wdir, wspeed from obs? or also temp, ... ?
selected = {}
for var in vars:
name = var["parameter_name"].lower()
var_attr = var["parameter_attribute"].lower()
+ if name not in data_origin.keys():
+ data_origin.update({name: data_origin_default.get(name, "")})
attr = data_origin.get(name, "").lower()
if var_attr == attr:
selected[name] = var["id"]
- return selected
+ return selected, data_origin
def _save_to_pandas(df: Union[pd.DataFrame, None], data: dict, stat: str, var: str) -> pd.DataFrame:
diff --git a/mlair/helpers/testing.py b/mlair/helpers/testing.py
index 244eb69fdc46dcadaeb3ada5779f09d44aa83e2a..abb50883c7af49a0c1571d99f737e310abff9b13 100644
--- a/mlair/helpers/testing.py
+++ b/mlair/helpers/testing.py
@@ -35,54 +35,54 @@ class PyTestRegex:
return self._regex.pattern
-class PyTestAllEqual:
- """
- Check if all elements in list are the same.
-
- :param check_list: list with elements to check
- """
-
- def __init__(self, check_list: List):
- """Construct class."""
- self._list = check_list
- self._test_function = None
-
- def _set_test_function(self):
- if isinstance(self._list[0], np.ndarray):
- self._test_function = np.testing.assert_array_equal
- else:
- self._test_function = xr.testing.assert_equal
-
- def _check_all_equal(self) -> bool:
- """
- Check if all elements are equal.
-
- :return boolean if elements are equal
+def PyTestAllEqual(check_list: List):
+ class PyTestAllEqualClass:
"""
- equal = True
- self._set_test_function()
- for b in self._list:
- equal *= self._test_function(self._list[0], b) is None
- return bool(equal == 1)
+ Check if all elements in list are the same.
- def is_true(self) -> bool:
+ :param check_list: list with elements to check
"""
- Start equality check.
- :return: true if equality test is passed, false otherwise
- """
- return self._check_all_equal()
-
-
-def xr_all_equal(check_list: List) -> bool:
- """
- Check if all given elements (preferably xarray's) in list are equal.
-
- :param check_list: list with elements to check
-
- :return: boolean if all elements are the same or not
- """
- equal = True
- for b in check_list:
- equal *= xr.testing.assert_equal(check_list[0], b) is None
- return equal == 1
\ No newline at end of file
+ def __init__(self, check_list: List):
+ """Construct class."""
+ self._list = check_list
+ self._test_function = None
+
+ def _set_test_function(self, _list):
+ if isinstance(_list[0], list):
+ _test_function = self._set_test_function(_list[0])
+ self._test_function = lambda r, s: all(map(lambda x, y: _test_function(x, y) is None, r, s))
+ elif isinstance(_list[0], np.ndarray):
+ self._test_function = np.testing.assert_array_equal
+ elif isinstance(_list[0], xr.DataArray):
+ self._test_function = xr.testing.assert_equal
+ else:
+ self._test_function = lambda x, y: self._assert(x, y)
+ # raise TypeError(f"given type {type(_list[0])} is not supported by PyTestAllEqual.")
+ return self._test_function
+
+ @staticmethod
+ def _assert(x, y):
+ assert x == y
+
+ def _check_all_equal(self) -> bool:
+ """
+ Check if all elements are equal.
+
+ :return boolean if elements are equal
+ """
+ equal = True
+ self._set_test_function(self._list)
+ for b in self._list:
+ equal *= self._test_function(self._list[0], b) in [None, True]
+ return bool(equal == 1)
+
+ def is_true(self) -> bool:
+ """
+ Start equality check.
+
+ :return: true if equality test is passed, false otherwise
+ """
+ return self._check_all_equal()
+
+ return PyTestAllEqualClass(check_list).is_true()
diff --git a/mlair/plotting/postprocessing_plotting.py b/mlair/plotting/postprocessing_plotting.py
index f0b6baeb0b56126ccccb80c9da993fb406428d93..f775f7419dba7530d8dbfbde9250d38c312496fa 100644
--- a/mlair/plotting/postprocessing_plotting.py
+++ b/mlair/plotting/postprocessing_plotting.py
@@ -1,6 +1,6 @@
"""Collection of plots to evaluate a model, create overviews on data or forecasts."""
__author__ = "Lukas Leufen, Felix Kleinert"
-__date__ = '2019-12-17'
+__date__ = '2020-11-23'
import logging
import math
@@ -8,10 +8,11 @@ import os
import warnings
from typing import Dict, List, Tuple
-
import matplotlib
import matplotlib.patches as mpatches
+import matplotlib.lines as mlines
import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
import numpy as np
import pandas as pd
import seaborn as sns
@@ -75,7 +76,7 @@ class AbstractPlotClass:
"""
- def __init__(self, plot_folder, plot_name, resolution=500):
+ def __init__(self, plot_folder, plot_name, resolution=500, rc_params=None):
"""Set up plot folder and name, and plot resolution (default 500dpi)."""
plot_folder = os.path.abspath(plot_folder)
if not os.path.exists(plot_folder):
@@ -83,6 +84,15 @@ class AbstractPlotClass:
self.plot_folder = plot_folder
self.plot_name = plot_name
self.resolution = resolution
+ if rc_params is None:
+ rc_params = {'axes.labelsize': 'large',
+ 'xtick.labelsize': 'large',
+ 'ytick.labelsize': 'large',
+ 'legend.fontsize': 'large',
+ 'axes.titlesize': 'large',
+ }
+ self.rc_params = rc_params
+ self._update_rc_params()
def _plot(self, *args):
"""Abstract plot class needs to be implemented in inheritance."""
@@ -95,6 +105,25 @@ class AbstractPlotClass:
plt.savefig(plot_name, dpi=self.resolution, **kwargs)
plt.close('all')
+ def _update_rc_params(self):
+ plt.rcParams.update(self.rc_params)
+
+ @staticmethod
+ def _get_sampling(sampling):
+ if sampling == "daily":
+ return "D"
+ elif sampling == "hourly":
+ return "h"
+
+ @staticmethod
+ def get_dataset_colors():
+ """
+ Standard colors used for train-, val-, and test-sets during postprocessing
+ """
+ colors = {"train": "#e69f00", "val": "#009e73", "test": "#56b4e9", "train_val": "#000000"} # hex code
+ return colors
+
+
@TimeTrackingWrapper
class PlotMonthlySummary(AbstractPlotClass):
@@ -113,18 +142,19 @@ class PlotMonthlySummary(AbstractPlotClass):
:param window_lead_time: lead time to plot, if window_lead_time is higher than the available lead time or not given
the maximum lead time from data is used. (default None -> use maximum lead time from data).
:param plot_folder: path to save the plot (default: current directory)
+ :param target_var_unit: unit of target var for plot legend (default= ppb)
"""
def __init__(self, stations: List, data_path: str, name: str, target_var: str, window_lead_time: int = None,
- plot_folder: str = "."):
+ plot_folder: str = ".", target_var_unit: str = 'ppb'):
"""Set attributes and create plot."""
super().__init__(plot_folder, "monthly_summary_box_plot")
self._data_path = data_path
self._data_name = name
self._data = self._prepare_data(stations)
self._window_lead_time = self._get_window_lead_time(window_lead_time)
- self._plot(target_var)
+ self._plot(target_var, target_var_unit)
self._save()
def _prepare_data(self, stations: List) -> xr.DataArray:
@@ -176,7 +206,12 @@ class PlotMonthlySummary(AbstractPlotClass):
window_lead_time = ahead_steps
return min(ahead_steps, window_lead_time)
- def _plot(self, target_var: str):
+ @staticmethod
+ def _spell_out_chemical_concentrations(short_name: str):
+ short2long = {'o3': 'ozone', 'no': 'nitrogen oxide', 'no2': 'nitrogen dioxide', 'nox': 'nitrogen dioxides'}
+ return f"{short2long[short_name]} concentration"
+
+ def _plot(self, target_var: str, target_var_unit: str):
"""
Create a monthly grouped box plot over all stations but with separate boxes for each lead time step.
@@ -189,7 +224,8 @@ class PlotMonthlySummary(AbstractPlotClass):
ax = sns.boxplot(x='index', y='values', hue='ahead', data=data.compute(), whis=1., palette=color_palette,
flierprops={'marker': '.', 'markersize': 1}, showmeans=True,
meanprops={'markersize': 1, 'markeredgecolor': 'k'})
- ax.set(xlabel='month', ylabel=f'{target_var}')
+ ylabel = self._spell_out_chemical_concentrations(target_var)
+ ax.set(xlabel='month', ylabel=f'{ylabel} (in {target_var_unit})')
plt.tight_layout()
@@ -206,7 +242,7 @@ class PlotStationMap(AbstractPlotClass):
:width: 400
"""
- def __init__(self, generators: Dict, plot_folder: str = "."):
+ def __init__(self, generators: List, plot_folder: str = ".", plot_name="station_map"):
"""
Set attributes and create plot.
@@ -214,11 +250,11 @@ class PlotStationMap(AbstractPlotClass):
as value.
:param plot_folder: path to save the plot (default: current directory)
"""
- super().__init__(plot_folder, "station_map")
+ super().__init__(plot_folder, plot_name)
self._ax = None
self._gl = None
self._plot(generators)
- self._save()
+ self._save(bbox_inches="tight")
def _draw_background(self):
"""Draw coastline, lakes, ocean, rivers and country borders as background on the map."""
@@ -245,13 +281,44 @@ class PlotStationMap(AbstractPlotClass):
import cartopy.crs as ccrs
if generators is not None:
- for color, data_collection in generators.items():
+ legend_elements = []
+ default_colors = self.get_dataset_colors()
+ for element in generators:
+ data_collection, plot_opts = self._get_collection_and_opts(element)
+ name = data_collection.name or "unknown"
+ marker = plot_opts.get("marker", "s")
+ ms = plot_opts.get("ms", 6)
+ mec = plot_opts.get("mec", "k")
+ mfc = plot_opts.get("mfc", default_colors.get(name, "b"))
+ legend_elements.append(
+ mlines.Line2D([], [], mfc=mfc, mec=mec, marker=self._adjust_marker(marker), ms=ms, linestyle='None',
+ label=f"{name} ({len(data_collection)})"))
for station in data_collection:
coords = station.get_coordinates()
IDx, IDy = coords["lon"], coords["lat"]
- self._ax.plot(IDx, IDy, mfc=color, mec='k', marker='s', markersize=6, transform=ccrs.PlateCarree())
+ self._ax.plot(IDx, IDy, mfc=mfc, mec=mec, marker=marker, ms=ms, transform=ccrs.PlateCarree())
+ if len(legend_elements) > 0:
+ self._ax.legend(handles=legend_elements, loc='best')
+
+ @staticmethod
+ def _adjust_marker(marker):
+ _adjust = {4: "<", 5: ">", 6: "^", 7: "v", 8: "<", 9: ">", 10: "^", 11: "v"}
+ if isinstance(marker, int) and marker in _adjust.keys():
+ return _adjust[marker]
+ else:
+ return marker
+
+ @staticmethod
+ def _get_collection_and_opts(element):
+ if isinstance(element, tuple):
+ if len(element) == 1:
+ return element[0], {}
+ else:
+ return element
+ else:
+ return element, {}
- def _plot(self, generators: Dict):
+ def _plot(self, generators: List):
"""
Create the station map plot.
@@ -453,7 +520,8 @@ class PlotConditionalQuantiles(AbstractPlotClass):
def _plot(self):
"""Start plotting routines: overall plot and seasonal (if enabled)."""
- logging.info(f"start plotting {self.__class__.__name__}, scheduled number of plots: {(len(self._seasons) + 1) * 2}")
+ logging.info(
+ f"start plotting {self.__class__.__name__}, scheduled number of plots: {(len(self._seasons) + 1) * 2}")
if len(self._seasons) > 0:
self._plot_seasons()
@@ -504,7 +572,7 @@ class PlotConditionalQuantiles(AbstractPlotClass):
# add histogram of the segmented data (pred_name)
handles, labels = ax.get_legend_handles_labels()
segmented_data.loc[x_model, d, :].to_pandas().hist(bins=self._bins, ax=ax2, color='k', alpha=.3, grid=False,
- rwidth=1)
+ rwidth=1)
# add legend
plt.legend(handles[:3] + [handles[-1]], self._opts["legend"], loc='upper left', fontsize='large')
# adjust limits and set labels
@@ -688,25 +756,37 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
(score_only=True, default). Y-axis is adjusted following the data and not hard coded. The plot is saved under
plot_folder path with name skill_score_clim_{extra_name_tag}{model_setup}.pdf and resolution of 500dpi.
+ By passing a list `separate_vars` containing variable names, a second plot is created showing the `separate_vars`
+ and the remaining variables side by side with different scaling.
+
.. image:: ../../../../../_source/_plots/skill_score_bootstrap.png
:width: 400
+ .. image:: ../../../../../_source/_plots/skill_score_bootstrap_separated.png
+ :width: 400
+
"""
- def __init__(self, data: Dict, plot_folder: str = ".", model_setup: str = ""):
+ def __init__(self, data: Dict, plot_folder: str = ".", model_setup: str = "", separate_vars: List = None):
"""
Set attributes and create plot.
:param data: dictionary with station names as keys and 2D xarrays as values, consist on axis ahead and terms.
:param plot_folder: path to save the plot (default: current directory)
:param model_setup: architecture type to specify plot name (default "CNN")
+ :param separate_vars: variables to plot separated (default: ['o3'])
"""
super().__init__(plot_folder, f"skill_score_bootstrap_{model_setup}")
+ if separate_vars is None:
+ separate_vars = ['o3']
self._labels = None
self._x_name = "boot_var"
self._data = self._prepare_data(data)
self._plot()
self._save()
+ self.plot_name += '_separated'
+ self._plot(separate_vars=separate_vars)
+ self._save(bbox_inches='tight')
def _prepare_data(self, data: Dict) -> pd.DataFrame:
"""
@@ -719,11 +799,30 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
:return: pre-processed data set
"""
data = helpers.dict_to_xarray(data, "station").sortby(self._x_name)
+ new_boot_coords = self._return_vars_without_number_tag(data.coords['boot_var'].values, split_by='_', keep=1)
+ data = data.assign_coords({'boot_var': new_boot_coords})
self._labels = [str(i) + "d" for i in data.coords["ahead"].values]
if "station" not in data.dims:
data = data.expand_dims("station")
return data.to_dataframe("data").reset_index(level=[0, 1, 2])
+ def _return_vars_without_number_tag(self, values, split_by, keep):
+ arr = np.array([v.split(split_by) for v in values])
+ num = arr[:, 0]
+ new_val = arr[:, keep]
+ if self._all_values_are_equal(num, axis=0):
+ return new_val
+ else:
+ raise NotImplementedError
+
+
+ @staticmethod
+ def _all_values_are_equal(arr, axis=0):
+ if np.all(arr == arr[0], axis=axis):
+ return True
+ else:
+ return False
+
def _label_add(self, score_only: bool):
"""
Add the phrase "terms and " if score_only is disabled or empty string (if score_only=True).
@@ -733,12 +832,111 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
"""
return "" if score_only else "terms and "
- def _plot(self):
+ def _plot(self, separate_vars=None):
"""Plot climatological skill score."""
+ if separate_vars is None:
+ self._plot_all_variables()
+ else:
+ self._plot_selected_variables(separate_vars)
+
+ def _plot_selected_variables(self, separate_vars: List):
+ # if separate_vars is None:
+ # separate_vars = ['o3']
+ data = self._data
+ self.raise_error_if_separate_vars_do_not_exist(data, separate_vars)
+ all_variables = self._get_unique_values_from_column_of_df(data, 'boot_var')
+ # remaining_vars = helpers.list_pop(all_variables, separate_vars) #remove_items
+ remaining_vars = helpers.remove_items(all_variables, separate_vars)
+ data_first = self._select_data(df=data, variables=separate_vars, column_name='boot_var')
+ data_second = self._select_data(df=data, variables=remaining_vars, column_name='boot_var')
+
+ fig, ax = plt.subplots(nrows=1, ncols=2,
+ gridspec_kw={'width_ratios': [len(separate_vars),
+ len(remaining_vars)
+ ]
+ }
+ )
+ if len(separate_vars) > 1:
+ first_box_width = .8
+ else:
+ first_box_width = 2.
+
+ sns.boxplot(x=self._x_name, y="data", hue="ahead", data=data_first, ax=ax[0], whis=1., palette="Blues_d",
+ showmeans=True, meanprops={"markersize": 1, "markeredgecolor": "k"},
+ flierprops={"marker": "."}, width=first_box_width
+ )
+ ax[0].set(ylabel=f"skill score", xlabel="")
+
+ sns.boxplot(x=self._x_name, y="data", hue="ahead", data=data_second, ax=ax[1], whis=1., palette="Blues_d",
+ showmeans=True, meanprops={"markersize": 1, "markeredgecolor": "k"},
+ flierprops={"marker": "."},
+ )
+ ax[1].set(ylabel="", xlabel="")
+ ax[1].yaxis.tick_right()
+ handles, _ = ax[1].get_legend_handles_labels()
+ for sax in ax:
+ matplotlib.pyplot.sca(sax)
+ sax.axhline(y=0, color="grey", linewidth=.5)
+ plt.xticks(rotation=45, ha='right')
+ sax.legend_.remove()
+
+ fig.legend(handles, self._labels, loc='upper center', ncol=len(handles) + 1, )
+
+ def align_yaxis(ax1, ax2):
+ """
+ Align zeros of the two axes, zooming them out by same ratio
+
+ This function is copy pasted from https://stackoverflow.com/a/41259922
+ """
+ axes = (ax1, ax2)
+ extrema = [ax.get_ylim() for ax in axes]
+ tops = [extr[1] / (extr[1] - extr[0]) for extr in extrema]
+ # Ensure that plots (intervals) are ordered bottom to top:
+ if tops[0] > tops[1]:
+ axes, extrema, tops = [list(reversed(l)) for l in (axes, extrema, tops)]
+
+ # How much would the plot overflow if we kept current zoom levels?
+ tot_span = tops[1] + 1 - tops[0]
+
+ b_new_t = extrema[0][0] + tot_span * (extrema[0][1] - extrema[0][0])
+ t_new_b = extrema[1][1] - tot_span * (extrema[1][1] - extrema[1][0])
+ axes[0].set_ylim(extrema[0][0], b_new_t)
+ axes[1].set_ylim(t_new_b, extrema[1][1])
+
+ align_yaxis(ax[0], ax[1])
+ align_yaxis(ax[0], ax[1])
+
+ @staticmethod
+ def _select_data(df: pd.DataFrame, variables: List[str], column_name: str) -> pd.DataFrame:
+ for i, variable in enumerate(variables):
+ if i == 0:
+ selected_data = df.loc[df[column_name] == variable]
+ else:
+ tmp_var = df.loc[df[column_name] == variable]
+ selected_data = pd.concat([selected_data, tmp_var], axis=0)
+ return selected_data
+
+ def raise_error_if_separate_vars_do_not_exist(self, data, separate_vars):
+ if not self._variables_exist_in_df(df=data, variables=separate_vars):
+ raise ValueError(f"At least one entry of `separate_vars' does not exist in `self.data' ")
+
+ @staticmethod
+ def _get_unique_values_from_column_of_df(df: pd.DataFrame, column_name: str) -> List:
+ return list(df[column_name].unique())
+
+ def _variables_exist_in_df(self, df: pd.DataFrame, variables: List[str], column_name: str = 'boot_var'):
+ vars_in_df = set(self._get_unique_values_from_column_of_df(df, column_name))
+ return set(variables).issubset(vars_in_df)
+
+ def _plot_all_variables(self):
+ """
+
+ """
fig, ax = plt.subplots()
sns.boxplot(x=self._x_name, y="data", hue="ahead", data=self._data, ax=ax, whis=1., palette="Blues_d",
showmeans=True, meanprops={"markersize": 1, "markeredgecolor": "k"}, flierprops={"marker": "."})
ax.axhline(y=0, color="grey", linewidth=.5)
+ plt.xticks(rotation=45)
ax.set(ylabel=f"skill score", xlabel="", title="summary of all stations")
handles, _ = ax.get_legend_handles_labels()
ax.legend(handles, self._labels)
@@ -859,6 +1057,7 @@ class PlotTimeSeries:
def _get_time_range(data):
def f(x, f_x):
return pd.to_datetime(f_x(x.index.values)).year
+
return f(data, min), f(data, max)
@staticmethod
@@ -911,8 +1110,10 @@ class PlotAvailability(AbstractPlotClass):
# create standard Gantt plot for all stations (currently in single pdf file with single page)
super().__init__(plot_folder, "data_availability")
self.dim = time_dimension
- self.linewidth = None
self.sampling = self._get_sampling(sampling)
+ self.linewidth = None
+ if self.sampling == 'h':
+ self.linewidth = 0.001
plot_dict = self._prepare_data(generators)
lgd = self._plot(plot_dict)
self._save(bbox_extra_artists=(lgd,), bbox_inches="tight")
@@ -927,13 +1128,6 @@ class PlotAvailability(AbstractPlotClass):
lgd = self._plot(plot_dict_summary)
self._save(bbox_extra_artists=(lgd,), bbox_inches="tight")
- def _get_sampling(self, sampling):
- if sampling == "daily":
- return "D"
- elif sampling == "hourly":
- self.linewidth = 0.001
- return "h"
-
def _prepare_data(self, generators: Dict[str, DataCollection]):
plt_dict = {}
for subset, data_collection in generators.items():
@@ -978,9 +1172,7 @@ class PlotAvailability(AbstractPlotClass):
return plt_dict
def _plot(self, plt_dict):
- # colors = {"train": "orange", "val": "blueishgreen", "test": "skyblue"} # color names
- colors = {"train": "#e69f00", "val": "#009e73", "test": "#56b4e9"} # hex code
- # colors = {"train": (230, 159, 0), "val": (0, 158, 115), "test": (86, 180, 233)} # in rgb but as abs values
+ colors = self.get_dataset_colors()
pos = 0
height = 0.8 # should be <= 1
yticklabels = []
@@ -1025,6 +1217,178 @@ class PlotSeparationOfScales(AbstractPlotClass):
self._save()
+@TimeTrackingWrapper
+class PlotAvailabilityHistogram(AbstractPlotClass):
+ """
+ Create data availability plots as histogram.
+
+ Each entry of each generator is checked for `notnull()` values along all the datetime axis (boolean).
+ Calling this class creates two different types of histograms where each generator
+
+ 1) data_availability_histogram: datetime (xaxis) vs. number of stations with availabile data (yaxis)
+ 2) data_availability_histogram_cumulative: number of samples (xaxis) vs. number of stations having at least number
+ of samples (yaxis)
+
+ .. image:: ../../../../../_source/_plots/data_availability_histogram_hist.png
+ :width: 400
+
+ .. image:: ../../../../../_source/_plots/data_availability_histogram_hist_cum.png
+ :width: 400
+
+ """
+
+ def __init__(self, generators: Dict[str, DataCollection], plot_folder: str = ".",
+ subset_dim: str = 'DataSet', history_dim: str = 'window',
+ station_dim: str = 'Stations',):
+
+ super().__init__(plot_folder, "data_availability_histogram")
+
+ self.subset_dim = subset_dim
+ self.history_dim = history_dim
+ self.station_dim = station_dim
+
+ self.freq = None
+ self.temporal_dim = None
+ self.target_dim = None
+ self._prepare_data(generators)
+
+ for plt_type in self.allowed_plot_types:
+ plot_name_tmp = self.plot_name
+ self.plot_name += '_' + plt_type
+ self._plot(plt_type=plt_type)
+ self._save()
+ self.plot_name = plot_name_tmp
+
+ def _set_dims_from_datahandler(self, data_handler):
+ self.temporal_dim = data_handler.id_class.time_dim
+ self.target_dim = data_handler.id_class.target_dim
+ self.freq = self._get_sampling(data_handler.id_class.sampling)
+
+ @property
+ def allowed_plot_types(self):
+ plot_types = ['hist', 'hist_cum']
+ return plot_types
+
+ def _prepare_data(self, generators: Dict[str, DataCollection]):
+ """
+ Prepares data to be used by plot methods.
+
+ Creates xarrays which are sums of valid data (boolean sums) across i) station_dim and ii) temporal_dim
+ """
+ avail_data_time_sum = {}
+ avail_data_station_sum = {}
+ dataset_time_interval = {}
+ for subset, generator in generators.items():
+ avail_list = []
+ for station in generator:
+ self._set_dims_from_datahandler(data_handler=station)
+ station_data_x = station.get_X(as_numpy=False)[0]
+ station_data_x = station_data_x.loc[{self.history_dim: 0, # select recent window frame
+ self.target_dim: station_data_x[self.target_dim].values[0]}]
+ station_data_x = self._reduce_dims(station_data_x)
+ avail_list.append(station_data_x.notnull())
+ avail_data = xr.concat(avail_list, dim=self.station_dim).notnull()
+ avail_data_time_sum[subset] = avail_data.sum(dim=self.station_dim)
+ avail_data_station_sum[subset] = avail_data.sum(dim=self.temporal_dim)
+ dataset_time_interval[subset] = self._get_first_and_last_indexelement_from_xarray(
+ avail_data_time_sum[subset], dim_name=self.temporal_dim, return_type='as_dict'
+ )
+ avail_data_amount = xr.concat(avail_data_time_sum.values(), pd.Index(avail_data_time_sum.keys(),
+ name=self.subset_dim)
+ )
+ full_time_index = self._make_full_time_index(avail_data_amount.coords[self.temporal_dim].values, freq=self.freq)
+ self.avail_data_cum_sum = xr.concat(avail_data_station_sum.values(), pd.Index(avail_data_station_sum.keys(),
+ name=self.subset_dim))
+ self.avail_data_amount = avail_data_amount.reindex({self.temporal_dim: full_time_index})
+ self.dataset_time_interval = dataset_time_interval
+
+ def _reduce_dims(self, dataset):
+ if len(dataset.dims) > 2:
+ required = {self.temporal_dim, self.station_dim}
+ unimportant = set(dataset.dims).difference(required)
+ sel_dict = {un: dataset[un].values[0] for un in unimportant}
+ dataset = dataset.loc[sel_dict]
+ return dataset
+
+ @staticmethod
+ def _get_first_and_last_indexelement_from_xarray(xarray, dim_name, return_type='as_tuple'):
+ if isinstance(xarray, xr.DataArray):
+ first = xarray.coords[dim_name].values[0]
+ last = xarray.coords[dim_name].values[-1]
+ if return_type == 'as_tuple':
+ return first, last
+ elif return_type == 'as_dict':
+ return {'first': first, 'last': last}
+ else:
+ raise TypeError(f"return_type must be 'as_tuple' or 'as_dict', but is '{return_type}'")
+ else:
+ raise TypeError(f"xarray must be of type xr.DataArray, but is of type {type(xarray)}")
+
+ @staticmethod
+ def _make_full_time_index(irregular_time_index, freq):
+ full_time_index = pd.date_range(start=irregular_time_index[0], end=irregular_time_index[-1], freq=freq)
+ return full_time_index
+
+ def _plot(self, plt_type='hist', *args):
+ if plt_type == 'hist':
+ self._plot_hist()
+ elif plt_type == 'hist_cum':
+ self._plot_hist_cum()
+ else:
+ raise ValueError(f"plt_type mus be 'hist' or 'hist_cum', but is {type}")
+
+ def _plot_hist(self, *args):
+ colors = self.get_dataset_colors()
+ fig, axes = plt.subplots(figsize=(10, 3))
+ for i, subset in enumerate(self.dataset_time_interval.keys()):
+ plot_dataset = self.avail_data_amount.sel({self.subset_dim: subset,
+ self.temporal_dim: slice(
+ self.dataset_time_interval[subset]['first'],
+ self.dataset_time_interval[subset]['last']
+ )
+ }
+ )
+
+ plot_dataset.plot.step(color=colors[subset], ax=axes, label=subset)
+ plt.fill_between(plot_dataset.coords[self.temporal_dim].values, plot_dataset.values, color=colors[subset])
+
+ lgd = fig.legend(loc="upper right", ncol=len(self.dataset_time_interval),
+ facecolor='white', framealpha=1, edgecolor='black')
+ for lgd_line in lgd.get_lines():
+ lgd_line.set_linewidth(4.0)
+ plt.gca().xaxis.set_major_locator(mdates.YearLocator())
+ plt.title('')
+ plt.ylabel('Number of samples')
+ plt.tight_layout()
+
+ def _plot_hist_cum(self, *args):
+ colors = self.get_dataset_colors()
+ fig, axes = plt.subplots(figsize=(10, 3))
+ n_bins = int(self.avail_data_cum_sum.max().values)
+ bins = np.arange(0, n_bins+1)
+ descending_subsets = self.avail_data_cum_sum.max(dim=self.station_dim).sortby(
+ self.avail_data_cum_sum.max(dim=self.station_dim), ascending=False
+ ).coords[self.subset_dim].values
+
+ for subset in descending_subsets:
+ self.avail_data_cum_sum.sel({self.subset_dim: subset}).plot.hist(ax=axes,
+ bins=bins,
+ label=subset,
+ cumulative=-1,
+ color=colors[subset],
+ # alpha=.5
+ )
+
+ lgd = fig.legend(loc="upper right", ncol=len(self.dataset_time_interval),
+ facecolor='white', framealpha=1, edgecolor='black')
+ plt.title('')
+ plt.ylabel('Number of stations')
+ plt.xlabel('Number of samples')
+ plt.xlim((bins[0], bins[-1]))
+ plt.tight_layout()
+
+
+
if __name__ == "__main__":
stations = ['DEBW107', 'DEBY081', 'DEBW013', 'DEBW076', 'DEBW087']
path = "../../testrun_network/forecasts"
diff --git a/mlair/plotting/tracker_plot.py b/mlair/plotting/tracker_plot.py
index 406c32feb1ebda2d32d886051e32778d6c17f5db..53ec7496e7e04da0f53b1d0ce817793dea732963 100644
--- a/mlair/plotting/tracker_plot.py
+++ b/mlair/plotting/tracker_plot.py
@@ -119,11 +119,13 @@ class TrackChain:
control_obj = control[variable][scope]
if method == "set":
track_objects = self._add_set_object(track_objects, tr, control_obj)
- elif method == "get":
+ elif method == "get": # pragma: no branch
track_objects, skip_control_update = self._add_get_object(track_objects, tr, control_obj,
control, scope, variable)
if skip_control_update is True:
continue
+ else: # pragma: no cover
+ raise ValueError(f"method must be either set or get but given was {method}.")
self._update_control(control, variable, scope, tr)
return track_objects, control
diff --git a/mlair/run_modules/experiment_setup.py b/mlair/run_modules/experiment_setup.py
index 9a9253eda522c39f348dd96700ed38730e87f9a8..54d2307718bf083cfbfb8296682c9c545157eb72 100644
--- a/mlair/run_modules/experiment_setup.py
+++ b/mlair/run_modules/experiment_setup.py
@@ -225,8 +225,8 @@ class ExperimentSetup(RunEnvironment):
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, batch_path: str = None, login_nodes=None,
- hpc_hosts=None, model=None, batch_size=None, epochs=None, data_handler=None, sampling_inputs=None,
- sampling_outputs=None, data_origin: Dict = None, **kwargs):
+ hpc_hosts=None, model=None, batch_size=None, epochs=None, data_handler=None,
+ data_origin: Dict = None, **kwargs):
# create run framework
super().__init__()
@@ -294,7 +294,6 @@ class ExperimentSetup(RunEnvironment):
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_inputs", sampling_inputs, default=sampling)
self._set_param("transformation", transformation, default=DEFAULT_TRANSFORMATION)
self._set_param("transformation", None, scope="preprocessing")
self._set_param("data_handler", data_handler, default=DefaultDataHandler)
diff --git a/mlair/run_modules/post_processing.py b/mlair/run_modules/post_processing.py
index 3dc91cbd54094f116f0d959fb9c845751e998464..cd8ee266a092a6afb5111ad7b241b38cdbfb6fa7 100644
--- a/mlair/run_modules/post_processing.py
+++ b/mlair/run_modules/post_processing.py
@@ -19,8 +19,8 @@ from mlair.helpers import TimeTracking, statistics, extract_value
from mlair.model_modules.linear_model import OrdinaryLeastSquaredModel
from mlair.model_modules.model_class import AbstractModelClass
from mlair.plotting.postprocessing_plotting import PlotMonthlySummary, PlotStationMap, PlotClimatologicalSkillScore, \
- PlotCompetitiveSkillScore, PlotTimeSeries, PlotBootstrapSkillScore, PlotAvailability, PlotConditionalQuantiles, \
- PlotSeparationOfScales
+ PlotCompetitiveSkillScore, PlotTimeSeries, PlotBootstrapSkillScore, PlotAvailability, PlotAvailabilityHistogram, \
+ PlotConditionalQuantiles, PlotSeparationOfScales
from mlair.run_modules.run_environment import RunEnvironment
@@ -239,6 +239,7 @@ class PostProcessing(RunEnvironment):
model = keras.models.load_model(model_name, custom_objects=model_class.custom_objects)
return model
+ # noinspection PyBroadException
def plot(self):
"""
Create all plots.
@@ -257,42 +258,89 @@ class PostProcessing(RunEnvironment):
.. note:: Bootstrap plots are only created if bootstraps are evaluated.
"""
- logging.debug("Run plotting routines...")
+ logging.info("Run plotting routines...")
path = self.data_store.get("forecast_path")
plot_list = self.data_store.get("plot_list", "postprocessing")
time_dimension = self.data_store.get("time_dim")
- if ("filter" in self.test_data[0].get_X(as_numpy=False)[0].coords) and ("PlotSeparationOfScales" in plot_list):
- PlotSeparationOfScales(self.test_data, plot_folder=self.plot_path)
-
- if (self.bootstrap_skill_scores is not None) and ("PlotBootstrapSkillScore" in plot_list):
- PlotBootstrapSkillScore(self.bootstrap_skill_scores, plot_folder=self.plot_path, model_setup="CNN")
-
- if "PlotConditionalQuantiles" in plot_list:
- PlotConditionalQuantiles(self.test_data.keys(), data_pred_path=path, plot_folder=self.plot_path)
- if "PlotStationMap" in plot_list:
- if self.data_store.get("hostname")[:2] in self.data_store.get("hpc_hosts") or self.data_store.get(
- "hostname")[:6] in self.data_store.get("hpc_hosts"):
- logging.warning(
- f"Skip 'PlotStationMap` because running on a hpc node: {self.data_store.get('hostname')}")
- else:
- PlotStationMap(generators={'b': self.test_data}, plot_folder=self.plot_path)
- if "PlotMonthlySummary" in plot_list:
- PlotMonthlySummary(self.test_data.keys(), path, r"forecasts_%s_test.nc", self.target_var,
- plot_folder=self.plot_path)
- if "PlotClimatologicalSkillScore" in plot_list:
- PlotClimatologicalSkillScore(self.skill_scores[1], plot_folder=self.plot_path, model_setup="CNN")
- PlotClimatologicalSkillScore(self.skill_scores[1], plot_folder=self.plot_path, score_only=False,
- extra_name_tag="all_terms_", model_setup="CNN")
- if "PlotCompetitiveSkillScore" in plot_list:
- PlotCompetitiveSkillScore(self.skill_scores[0], plot_folder=self.plot_path, model_setup="CNN")
- if "PlotTimeSeries" in plot_list:
- PlotTimeSeries(self.test_data.keys(), path, r"forecasts_%s_test.nc", plot_folder=self.plot_path,
- sampling=self._sampling)
- if "PlotAvailability" in plot_list:
- avail_data = {"train": self.train_data, "val": self.val_data, "test": self.test_data}
- PlotAvailability(avail_data, plot_folder=self.plot_path, time_dimension=time_dimension)
+ try:
+ if ("filter" in self.test_data[0].get_X(as_numpy=False)[0].coords) and (
+ "PlotSeparationOfScales" in plot_list):
+ PlotSeparationOfScales(self.test_data, plot_folder=self.plot_path)
+ except Exception as e:
+ logging.error(f"Could not create plot PlotSeparationOfScales due to the following error: {e}")
+
+ try:
+ if (self.bootstrap_skill_scores is not None) and ("PlotBootstrapSkillScore" in plot_list):
+ PlotBootstrapSkillScore(self.bootstrap_skill_scores, plot_folder=self.plot_path, model_setup="CNN")
+ except Exception as e:
+ logging.error(f"Could not create plot PlotBootstrapSkillScore due to the following error: {e}")
+
+ try:
+ if "PlotConditionalQuantiles" in plot_list:
+ PlotConditionalQuantiles(self.test_data.keys(), data_pred_path=path, plot_folder=self.plot_path)
+ except Exception as e:
+ logging.error(f"Could not create plot PlotConditionalQuantiles due to the following error: {e}")
+
+ try:
+ if "PlotStationMap" in plot_list:
+ if self.data_store.get("hostname")[:2] in self.data_store.get("hpc_hosts") or self.data_store.get(
+ "hostname")[:6] in self.data_store.get("hpc_hosts"):
+ logging.warning(
+ f"Skip 'PlotStationMap` because running on a hpc node: {self.data_store.get('hostname')}")
+ else:
+ gens = [(self.train_data, {"marker": 5, "ms": 9}),
+ (self.val_data, {"marker": 6, "ms": 9}),
+ (self.test_data, {"marker": 4, "ms": 9})]
+ PlotStationMap(generators=gens, plot_folder=self.plot_path)
+ gens = [(self.train_val_data, {"marker": 8, "ms": 9}),
+ (self.test_data, {"marker": 9, "ms": 9})]
+ PlotStationMap(generators=gens, plot_folder=self.plot_path, plot_name="station_map_var")
+ except Exception as e:
+ logging.error(f"Could not create plot PlotStationMap due to the following error: {e}")
+
+ try:
+ if "PlotMonthlySummary" in plot_list:
+ PlotMonthlySummary(self.test_data.keys(), path, r"forecasts_%s_test.nc", self.target_var,
+ plot_folder=self.plot_path)
+ except Exception as e:
+ logging.error(f"Could not create plot PlotMonthlySummary due to the following error: {e}")
+
+ try:
+ if "PlotClimatologicalSkillScore" in plot_list:
+ PlotClimatologicalSkillScore(self.skill_scores[1], plot_folder=self.plot_path, model_setup="CNN")
+ PlotClimatologicalSkillScore(self.skill_scores[1], plot_folder=self.plot_path, score_only=False,
+ extra_name_tag="all_terms_", model_setup="CNN")
+ except Exception as e:
+ logging.error(f"Could not create plot PlotClimatologicalSkillScore due to the following error: {e}")
+
+ try:
+ if "PlotCompetitiveSkillScore" in plot_list:
+ PlotCompetitiveSkillScore(self.skill_scores[0], plot_folder=self.plot_path, model_setup="CNN")
+ except Exception as e:
+ logging.error(f"Could not create plot PlotCompetitiveSkillScore due to the following error: {e}")
+
+ try:
+ if "PlotTimeSeries" in plot_list:
+ PlotTimeSeries(self.test_data.keys(), path, r"forecasts_%s_test.nc", plot_folder=self.plot_path,
+ sampling=self._sampling)
+ except Exception as e:
+ logging.error(f"Could not create plot PlotTimeSeries due to the following error: {e}")
+
+ try:
+ if "PlotAvailability" in plot_list:
+ avail_data = {"train": self.train_data, "val": self.val_data, "test": self.test_data}
+ PlotAvailability(avail_data, plot_folder=self.plot_path, time_dimension=time_dimension)
+ except Exception as e:
+ logging.error(f"Could not create plot PlotAvailability due to the following error: {e}")
+
+ try:
+ if "PlotAvailabilityHistogram" in plot_list:
+ avail_data = {"train": self.train_data, "val": self.val_data, "test": self.test_data}
+ PlotAvailabilityHistogram(avail_data, plot_folder=self.plot_path, ) # time_dimension=time_dimension)
+ except Exception as e:
+ logging.error(f"Could not create plot PlotAvailabilityHistogram due to the following error: {e}")
def calculate_test_score(self):
"""Evaluate test score of model and save locally."""
diff --git a/mlair/run_modules/pre_processing.py b/mlair/run_modules/pre_processing.py
index 4cee4a9744f33c86e8802aad27125cf0e0b30f3a..21aebd62bab490363797c0ef0624daa1d488097b 100644
--- a/mlair/run_modules/pre_processing.py
+++ b/mlair/run_modules/pre_processing.py
@@ -6,6 +6,8 @@ __date__ = '2019-11-25'
import logging
import os
from typing import Tuple
+import multiprocessing
+import requests
import numpy as np
import pandas as pd
@@ -113,9 +115,48 @@ class PreProcessing(RunEnvironment):
precision = 4
path = os.path.join(self.data_store.get("experiment_path"), "latex_report")
path_config.check_path_and_create(path)
- set_names = ["train", "val", "test"]
- df = pd.DataFrame(columns=meta_data + set_names)
- for set_name in set_names:
+ names_of_set = ["train", "val", "test"]
+ df = self.create_info_df(meta_data, meta_round, names_of_set, precision)
+ column_format = self.create_column_format_for_tex(df)
+ self.save_to_tex(path=path, filename="station_sample_size.tex", column_format=column_format, df=df)
+ self.save_to_md(path=path, filename="station_sample_size.md", df=df)
+ df_nometa = df.drop(meta_data, axis=1)
+ column_format = self.create_column_format_for_tex(df)
+ self.save_to_tex(path=path, filename="station_sample_size_short.tex", column_format=column_format, df=df_nometa)
+ self.save_to_md(path=path, filename="station_sample_size_short.md", df=df_nometa)
+ # df_nometa.to_latex(os.path.join(path, "station_sample_size_short.tex"), na_rep='---',
+ # column_format=column_format)
+ df_descr = self.create_describe_df(df_nometa)
+ column_format = self.create_column_format_for_tex(df_descr)
+ self.save_to_tex(path=path, filename="station_describe_short.tex", column_format=column_format, df=df_descr)
+ self.save_to_md(path=path, filename="station_describe_short.md", df=df_descr)
+ # df_descr.to_latex(os.path.join(path, "station_describe_short.tex"), na_rep='---', column_format=column_format)
+
+ @staticmethod
+ def create_describe_df(df, percentiles=None, ignore_last_lines: int = 2):
+ if percentiles is None:
+ percentiles = [.05, .1, .25, .5, .75, .9, .95]
+ df_descr = df.iloc[:-ignore_last_lines].astype('float32').describe(
+ percentiles=percentiles).astype("int32", errors="ignore")
+ df_descr = pd.concat([df.loc[['# Samples']], df_descr]).T
+ df_descr.rename(columns={"# Samples": "no. samples", "count": "no. stations"}, inplace=True)
+ df_descr_colnames = list(df_descr.columns)
+ df_descr_colnames = [df_descr_colnames[1]] + [df_descr_colnames[0]] + df_descr_colnames[2:]
+ df_descr = df_descr[df_descr_colnames]
+ return df_descr
+
+ @staticmethod
+ def save_to_tex(path, filename, column_format, df, na_rep='---'):
+ df.to_latex(os.path.join(path, filename), na_rep=na_rep, column_format=column_format)
+
+ @staticmethod
+ def save_to_md(path, filename, df, mode="w", encoding='utf-8', tablefmt="github"):
+ df.to_markdown(open(os.path.join(path, filename), mode=mode, encoding=encoding),
+ tablefmt=tablefmt)
+
+ def create_info_df(self, meta_data, meta_round, names_of_set, precision):
+ df = pd.DataFrame(columns=meta_data + names_of_set)
+ for set_name in names_of_set:
data = self.data_store.get("data_collection", set_name)
for station in data:
station_name = str(station.id_class)
@@ -123,20 +164,27 @@ class PreProcessing(RunEnvironment):
if df.loc[station_name, meta_data].isnull().any():
df.loc[station_name, meta_data] = station.id_class.meta.loc[meta_data].values.flatten()
df.loc["# Samples", set_name] = df.loc[:, set_name].sum()
- df.loc["# Stations", set_name] = df.loc[:, set_name].count()
+ assert len(data) == df.loc[:, set_name].count() - 1
+ df.loc["# Stations", set_name] = len(data)
df[meta_round] = df[meta_round].astype(float).round(precision)
df.sort_index(inplace=True)
df = df.reindex(df.index.drop(["# Stations", "# Samples"]).to_list() + ["# Stations", "# Samples"], )
df.index.name = 'stat. ID'
+ return df
+
+ @staticmethod
+ def create_column_format_for_tex(df: pd.DataFrame) -> str:
+ """
+ Creates column format for latex table based on the shape of a given DataFrame.
+
+ Calculates number of columns and uses 'c' as column position. First element is set to 'l', last to 'r'
+ """
column_format = np.repeat('c', df.shape[1] + 1)
column_format[0] = 'l'
column_format[-1] = 'r'
column_format = ''.join(column_format.tolist())
- df.to_latex(os.path.join(path, "station_sample_size.tex"), na_rep='---', column_format=column_format)
- df.to_markdown(open(os.path.join(path, "station_sample_size.md"), mode="w", encoding='utf-8'),
- tablefmt="github")
- df.drop(meta_data, axis=1).to_latex(os.path.join(path, "station_sample_size_short.tex"), na_rep='---',
- column_format=column_format)
+ return column_format
+
def split_train_val_test(self) -> None:
"""
@@ -201,6 +249,51 @@ class PreProcessing(RunEnvironment):
Valid means, that there is data available for the given time range (is included in `kwargs`). The shape and the
loading time are logged in debug mode.
+ :return: Corrected list containing only valid station IDs.
+ """
+ t_outer = TimeTracking()
+ logging.info(f"check valid stations started{' (%s)' % (set_name if set_name is not None else 'all')}")
+ # calculate transformation using train data
+ if set_name == "train":
+ logging.info("setup transformation using train data exclusively")
+ self.transformation(data_handler, set_stations)
+ # start station check
+ collection = DataCollection(name=set_name)
+ valid_stations = []
+ kwargs = self.data_store.create_args_dict(data_handler.requirements(), scope=set_name)
+
+ if multiprocessing.cpu_count() > 1: # parallel solution
+ logging.info("use parallel validate station approach")
+ pool = multiprocessing.Pool()
+ logging.info(f"running {getattr(pool, '_processes')} processes in parallel")
+ output = [
+ pool.apply_async(f_proc, args=(data_handler, station, set_name, store_processed_data), kwds=kwargs)
+ for station in set_stations]
+ for p in output:
+ dh, s = p.get()
+ if dh is not None:
+ collection.add(dh)
+ valid_stations.append(s)
+ else: # serial solution
+ logging.info("use serial validate station approach")
+ for station in set_stations:
+ dh, s = f_proc(data_handler, station, set_name, store_processed_data, **kwargs)
+ if dh is not None:
+ collection.add(dh)
+ valid_stations.append(s)
+
+ logging.info(f"run for {t_outer} to check {len(set_stations)} station(s). Found {len(collection)}/"
+ f"{len(set_stations)} valid stations.")
+ return collection, valid_stations
+
+ def validate_station_old(self, data_handler: AbstractDataHandler, set_stations, set_name=None,
+ store_processed_data=True):
+ """
+ Check if all given stations in `all_stations` are valid.
+
+ Valid means, that there is data available for the given time range (is included in `kwargs`). The shape and the
+ loading time are logged in debug mode.
+
:return: Corrected list containing only valid station IDs.
"""
t_outer = TimeTracking()
@@ -231,3 +324,18 @@ class PreProcessing(RunEnvironment):
transformation_dict = data_handler.transformation(stations, **kwargs)
if transformation_dict is not None:
self.data_store.set("transformation", transformation_dict)
+
+
+def f_proc(data_handler, station, name_affix, store, **kwargs):
+ """
+ Try to create a data handler for given arguments. If build fails, this station does not fulfil all requirements and
+ therefore f_proc will return None as indication. On a successfull build, f_proc returns the built data handler and
+ the station that was used. This function must be implemented globally to work together with multiprocessing.
+ """
+ try:
+ res = data_handler.build(station, name_affix=name_affix, store_processed_data=store,
+ **kwargs)
+ except (AttributeError, EmptyQueryResult, KeyError, requests.ConnectionError, ValueError) as e:
+ logging.info(f"remove station {station} because it raised an error: {e}")
+ res = None
+ return res, station
diff --git a/mlair/workflows/abstract_workflow.py b/mlair/workflows/abstract_workflow.py
index 3a627d9f72a5c1c97c35b464af1b0944bc397ea5..c969aa35ebca60aa749a294bcaa5de727407a461 100644
--- a/mlair/workflows/abstract_workflow.py
+++ b/mlair/workflows/abstract_workflow.py
@@ -3,8 +3,6 @@
__author__ = "Lukas Leufen"
__date__ = '2020-06-26'
-from collections import OrderedDict
-
from mlair import RunEnvironment
diff --git a/mlair/workflows/default_workflow.py b/mlair/workflows/default_workflow.py
index 4d113190fdc90ec852d7db2b33459b9162867a24..5894555a6af52299efcd8d88d76c0d3791a1599e 100644
--- a/mlair/workflows/default_workflow.py
+++ b/mlair/workflows/default_workflow.py
@@ -54,41 +54,10 @@ class DefaultWorkflow(Workflow):
self.add(PostProcessing)
-class DefaultWorkflowHPC(Workflow):
+class DefaultWorkflowHPC(DefaultWorkflow):
"""A default workflow for Jülich HPC systems executing ExperimentSetup, PreProcessing, PartitionCheck, ModelSetup,
Training and PostProcessing in exact the mentioned ordering."""
- def __init__(self, stations=None,
- train_model=None, create_new_model=None,
- window_history_size=None,
- experiment_date="testrun",
- variables=None, statistics_per_var=None,
- start=None, end=None,
- target_var=None, target_dim=None,
- window_lead_time=None,
- dimensions=None,
- interpolation_method=None, time_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=None, fraction_of_train=None,
- experiment_path=None, plot_path=None, forecast_path=None, bootstrap_path=None,
- overwrite_local_data=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=None, number_of_bootstraps=None, create_new_bootstraps=None,
- plot_list=None,
- model=None,
- batch_size=None,
- epochs=None,
- data_handler=None, **kwargs):
- super().__init__()
-
- # extract all given kwargs arguments
- params = remove_items(inspect.getfullargspec(self.__init__).args, "self")
- kwargs_default = {k: v for k, v in locals().items() if k in params and v is not None}
- self._setup(**kwargs_default, **kwargs)
-
def _setup(self, **kwargs):
"""Set up default workflow."""
self.add(ExperimentSetup, **kwargs)
diff --git a/run_hourly.py b/run_hourly.py
index b831cf1e1ee733a3c652c6cea364013b44cf2c0d..a21c779bc007c7fbe67c98584687be3954e1d62c 100644
--- a/run_hourly.py
+++ b/run_hourly.py
@@ -6,6 +6,17 @@ import argparse
from mlair.workflows import DefaultWorkflow
+def load_stations():
+ import json
+ try:
+ filename = 'supplement/station_list_north_german_plain.json'
+ with open(filename, 'r') as jfile:
+ stations = json.load(jfile)
+ except FileNotFoundError:
+ stations = None
+ return stations
+
+
def main(parser_args):
workflow = DefaultWorkflow(sampling="hourly", window_history_size=48, **parser_args.__dict__)
diff --git a/run_mixed_sampling.py b/run_mixed_sampling.py
index a87e9f38e9379d10f3472009934b61acb2d147ff..04683a17ede641a5370aaeef741d2f4546f966b7 100644
--- a/run_mixed_sampling.py
+++ b/run_mixed_sampling.py
@@ -7,22 +7,34 @@ from mlair.workflows import DefaultWorkflow
from mlair.data_handler.data_handler_mixed_sampling import DataHandlerMixedSampling, DataHandlerMixedSamplingWithFilter, \
DataHandlerSeparationOfScales
+stats = {'o3': 'dma8eu', 'no': 'dma8eu', 'no2': 'dma8eu',
+ 'relhum': 'average_values', 'u': 'average_values', 'v': 'average_values',
+ 'cloudcover': 'average_values', 'pblheight': 'maximum',
+ 'temp': 'maximum'}
+data_origin = {'o3': '', 'no': '', 'no2': '',
+ 'relhum': 'REA', 'u': 'REA', 'v': 'REA',
+ 'cloudcover': 'REA', 'pblheight': 'REA',
+ 'temp': 'REA'}
+
def main(parser_args):
- args = dict(sampling="daily",
- sampling_inputs="hourly",
- window_history_size=24,
- **parser_args.__dict__,
- data_handler=DataHandlerSeparationOfScales,
+ args = dict(stations=["DEBW107", "DEBW013"],
+ network="UBA",
+ evaluate_bootstraps=False, plot_list=[],
+ data_origin=data_origin, data_handler=DataHandlerMixedSampling,
+ interpolation_limit=(3, 1), overwrite_local_data=False,
+ sampling=("hourly", "daily"),
+ statistics_per_var=stats,
+ create_new_model=False, train_model=False, epochs=1,
+ window_history_size=48,
+ window_history_offset=17,
kz_filter_length=[100 * 24, 15 * 24],
kz_filter_iter=[4, 5],
start="2006-01-01",
train_start="2006-01-01",
end="2011-12-31",
test_end="2011-12-31",
- stations=["DEBW107", "DEBW013"],
- epochs=100,
- network="UBA",
+ **parser_args.__dict__,
)
workflow = DefaultWorkflow(**args)
workflow.run()
diff --git a/supplement/station_list_north_german_plain.json b/supplement/station_list_north_german_plain.json
new file mode 100644
index 0000000000000000000000000000000000000000..5e92dee5facdd26f0ac044a3c8cbfeac4256bf56
--- /dev/null
+++ b/supplement/station_list_north_german_plain.json
@@ -0,0 +1,81 @@
+[
+"DENI031",
+"DESH016",
+"DEBB050",
+"DEHH022",
+"DEHH049",
+"DEHH021",
+"DEMV007",
+"DESH015",
+"DEBE062",
+"DEHH012",
+"DESH004",
+"DENI062",
+"DEBE051",
+"DEHH011",
+"DEHH023",
+"DEUB020",
+"DESH005",
+"DEBB039",
+"DEHH050",
+"DENI029",
+"DESH001",
+"DEBE001",
+"DEHH030",
+"DEHH018",
+"DEUB022",
+"DEBB038",
+"DEBB053",
+"DEMV017",
+"DENI063",
+"DENI058",
+"DESH014",
+"DEUB007",
+"DEUB005",
+"DEBB051",
+"DEUB034",
+"DEST089",
+"DEHH005",
+"DESH003",
+"DEUB028",
+"DESH017",
+"DEUB030",
+"DEMV012",
+"DENI052",
+"DENI059",
+"DENI060",
+"DESH013",
+"DEUB006",
+"DEMV018",
+"DEUB027",
+"DEUB026",
+"DEUB038",
+"DEMV001",
+"DEUB024",
+"DEUB037",
+"DESH008",
+"DEMV004",
+"DEUB040",
+"DEMV024",
+"DEMV026",
+"DESH056",
+"DEHH063",
+"DEUB001",
+"DEST069",
+"DEBB040",
+"DEBB028",
+"DEBB048",
+"DEBB063",
+"DEBB067",
+"DESH006",
+"DEBE008",
+"DESH012",
+"DEHH004",
+"DEBE009",
+"DEHH007",
+"DEBE005",
+"DEHH057",
+"DEHH047",
+"DEBE006",
+"DEBB110"
+]
diff --git a/test/test_configuration/test_defaults.py b/test/test_configuration/test_defaults.py
index fffe7c84075eeeab37ebf59d52bc42dbf87bf522..ae81ef2ef0a15ad08f14ad19312f04040ab71263 100644
--- a/test/test_configuration/test_defaults.py
+++ b/test/test_configuration/test_defaults.py
@@ -70,4 +70,5 @@ class TestAllDefaults:
assert DEFAULT_NUMBER_OF_BOOTSTRAPS == 20
assert DEFAULT_PLOT_LIST == ["PlotMonthlySummary", "PlotStationMap", "PlotClimatologicalSkillScore",
"PlotTimeSeries", "PlotCompetitiveSkillScore", "PlotBootstrapSkillScore",
- "PlotConditionalQuantiles", "PlotAvailability", "PlotSeparationOfScales"]
+ "PlotConditionalQuantiles", "PlotAvailability", "PlotAvailabilityHistogram",
+ "PlotSeparationOfScales"]
diff --git a/test/test_configuration/test_join_settings.py b/test/test_configuration/test_join_settings.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d977f450b9fca0bc691d13f63965c71f7228cb1
--- /dev/null
+++ b/test/test_configuration/test_join_settings.py
@@ -0,0 +1,25 @@
+from mlair.configuration.join_settings import join_settings
+
+import pytest
+
+
+class TestJoinSettings:
+
+ def test_no_args(self):
+ url, headers = join_settings()
+ assert url == 'https://join.fz-juelich.de/services/rest/surfacedata/'
+ assert headers == {}
+
+ def test_daily(self):
+ url, headers = join_settings("daily")
+ assert url == 'https://join.fz-juelich.de/services/rest/surfacedata/'
+ assert headers == {}
+
+ def test_hourly(self):
+ url, headers = join_settings("hourly")
+ assert "Authorization" in headers.keys()
+
+ def test_unknown_sampling(self):
+ with pytest.raises(NameError) as e:
+ join_settings("monthly")
+ assert "Given sampling monthly is not supported, choose from either daily or hourly sampling" in e.value.args[0]
diff --git a/test/test_configuration/test_path_config.py b/test/test_configuration/test_path_config.py
index 2ba80a3bdf62b7fdf10b645da75769435cf7b6b9..fb8a2b1950cd07909543fbe564230ab73661c126 100644
--- a/test/test_configuration/test_path_config.py
+++ b/test/test_configuration/test_path_config.py
@@ -16,9 +16,9 @@ class TestPrepareHost:
@mock.patch("getpass.getuser", return_value="testUser")
@mock.patch("os.path.exists", return_value=True)
def test_prepare_host(self, mock_host, mock_user, mock_path):
- assert prepare_host() == "/home/testUser/Data/toar_daily/"
- assert prepare_host() == "/home/testUser/Data/toar_daily/"
- assert prepare_host() == "/p/project/cjjsc42/testUser/DATA/toar_daily/"
+ assert prepare_host() == "/home/testUser/Data/toar/"
+ assert prepare_host() == "/home/testUser/Data/toar/"
+ assert prepare_host() == "/p/project/cjjsc42/testUser/DATA/toar/"
assert prepare_host() == "/p/project/deepacf/intelliaq/testUser/DATA/MLAIR/"
assert prepare_host() == '/home/testUser/mlair/data/'
@@ -27,6 +27,10 @@ class TestPrepareHost:
def test_prepare_host_unknown(self, mock_user, mock_host):
assert prepare_host() == os.path.join(os.path.abspath(os.getcwd()), 'data')
+ def test_prepare_host_given_path(self):
+ path = os.path.join(os.path.abspath(os.getcwd()), 'data')
+ assert prepare_host(data_path=path) == path
+
@mock.patch("getpass.getuser", return_value="zombie21")
@mock.patch("mlair.configuration.path_config.check_path_and_create", side_effect=PermissionError)
@mock.patch("os.path.exists", return_value=False)
@@ -47,7 +51,7 @@ class TestPrepareHost:
@mock.patch("os.makedirs", side_effect=None)
def test_os_path_exists(self, mock_host, mock_user, mock_path, mock_check):
path = prepare_host()
- assert path == "/home/testUser/Data/toar_daily/"
+ assert path == "/home/testUser/Data/toar/"
class TestSetExperimentName:
diff --git a/test/test_data_handler/test_data_handler_mixed_sampling.py b/test/test_data_handler/test_data_handler_mixed_sampling.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2f9ce00224a61815c89e44b7c37a667d239b2f5
--- /dev/null
+++ b/test/test_data_handler/test_data_handler_mixed_sampling.py
@@ -0,0 +1,130 @@
+__author__ = 'Lukas Leufen'
+__date__ = '2020-12-10'
+
+from mlair.data_handler.data_handler_mixed_sampling import DataHandlerMixedSampling, \
+ DataHandlerMixedSamplingSingleStation, DataHandlerMixedSamplingWithFilter, \
+ DataHandlerMixedSamplingWithFilterSingleStation, DataHandlerSeparationOfScales, \
+ DataHandlerSeparationOfScalesSingleStation
+from mlair.data_handler.data_handler_kz_filter import DataHandlerKzFilterSingleStation
+from mlair.data_handler.data_handler_single_station import DataHandlerSingleStation
+from mlair.helpers import remove_items
+from mlair.configuration.defaults import DEFAULT_INTERPOLATION_METHOD
+
+import pytest
+import mock
+
+
+class TestDataHandlerMixedSampling:
+
+ def test_data_handler(self):
+ obj = object.__new__(DataHandlerMixedSampling)
+ assert obj.data_handler.__qualname__ == DataHandlerMixedSamplingSingleStation.__qualname__
+
+ def test_data_handler_transformation(self):
+ obj = object.__new__(DataHandlerMixedSampling)
+ assert obj.data_handler_transformation.__qualname__ == DataHandlerMixedSamplingSingleStation.__qualname__
+
+ def test_requirements(self):
+ obj = object.__new__(DataHandlerMixedSampling)
+ req = object.__new__(DataHandlerSingleStation)
+ assert sorted(obj._requirements) == sorted(remove_items(req.requirements(), "station"))
+
+
+class TestDataHandlerMixedSamplingSingleStation:
+
+ def test_requirements(self):
+ obj = object.__new__(DataHandlerMixedSamplingSingleStation)
+ req = object.__new__(DataHandlerSingleStation)
+ assert sorted(obj._requirements) == sorted(remove_items(req.requirements(), "station"))
+
+ @mock.patch("mlair.data_handler.data_handler_mixed_sampling.DataHandlerMixedSamplingSingleStation.setup_samples")
+ def test_init(self, mock_super_init):
+ obj = DataHandlerMixedSamplingSingleStation("first_arg", "second", {}, test=23, sampling="hourly",
+ interpolation_limit=(1, 10))
+ assert obj.sampling == ("hourly", "hourly")
+ assert obj.interpolation_limit == (1, 10)
+ assert obj.interpolation_method == (DEFAULT_INTERPOLATION_METHOD, DEFAULT_INTERPOLATION_METHOD)
+
+ @pytest.fixture
+ def kwargs_dict(self):
+ return {"test1": 2, "param_2": "string", "another": (10, 2)}
+
+ def test_update_kwargs_single_to_tuple(self, kwargs_dict):
+ obj = object.__new__(DataHandlerMixedSamplingSingleStation)
+ obj.update_kwargs("test1", "23", kwargs_dict)
+ assert kwargs_dict["test1"] == (2, 2)
+ obj.update_kwargs("param_2", "23", kwargs_dict)
+ assert kwargs_dict["param_2"] == ("string", "string")
+
+ def test_update_kwargs_tuple(self, kwargs_dict):
+ obj = object.__new__(DataHandlerMixedSamplingSingleStation)
+ obj.update_kwargs("another", "23", kwargs_dict)
+ assert kwargs_dict["another"] == (10, 2)
+
+ def test_update_kwargs_default(self, kwargs_dict):
+ obj = object.__new__(DataHandlerMixedSamplingSingleStation)
+ obj.update_kwargs("not_existing", "23", kwargs_dict)
+ assert kwargs_dict["not_existing"] == ("23", "23")
+ obj.update_kwargs("also_new", (4, 2), kwargs_dict)
+ assert kwargs_dict["also_new"] == (4, 2)
+
+ def test_update_kwargs_assert_failure(self, kwargs_dict):
+ obj = object.__new__(DataHandlerMixedSamplingSingleStation)
+ with pytest.raises(AssertionError):
+ obj.update_kwargs("error_too_long", (1, 2, 3), kwargs_dict)
+
+ def test_setup_samples(self):
+ pass
+
+ def test_load_and_interpolate(self):
+ pass
+
+ def test_set_inputs_and_targets(self):
+ pass
+
+ def test_setup_data_path(self):
+ pass
+
+
+class TestDataHandlerMixedSamplingWithFilter:
+
+ def test_data_handler(self):
+ obj = object.__new__(DataHandlerMixedSamplingWithFilter)
+ assert obj.data_handler.__qualname__ == DataHandlerMixedSamplingWithFilterSingleStation.__qualname__
+
+ def test_data_handler_transformation(self):
+ obj = object.__new__(DataHandlerMixedSamplingWithFilter)
+ assert obj.data_handler_transformation.__qualname__ == DataHandlerMixedSamplingWithFilterSingleStation.__qualname__
+
+ def test_requirements(self):
+ obj = object.__new__(DataHandlerMixedSamplingWithFilter)
+ req1 = object.__new__(DataHandlerMixedSamplingSingleStation)
+ req2 = object.__new__(DataHandlerKzFilterSingleStation)
+ req = list(set(req1.requirements() + req2.requirements()))
+ assert sorted(obj._requirements) == sorted(remove_items(req, "station"))
+
+
+class TestDataHandlerMixedSamplingWithFilterSingleStation:
+ pass
+
+
+class TestDataHandlerSeparationOfScales:
+
+ def test_data_handler(self):
+ obj = object.__new__(DataHandlerSeparationOfScales)
+ assert obj.data_handler.__qualname__ == DataHandlerSeparationOfScalesSingleStation.__qualname__
+
+ def test_data_handler_transformation(self):
+ obj = object.__new__(DataHandlerSeparationOfScales)
+ assert obj.data_handler_transformation.__qualname__ == DataHandlerSeparationOfScalesSingleStation.__qualname__
+
+ def test_requirements(self):
+ obj = object.__new__(DataHandlerMixedSamplingWithFilter)
+ req1 = object.__new__(DataHandlerMixedSamplingSingleStation)
+ req2 = object.__new__(DataHandlerKzFilterSingleStation)
+ req = list(set(req1.requirements() + req2.requirements()))
+ assert sorted(obj._requirements) == sorted(remove_items(req, "station"))
+
+
+class TestDataHandlerSeparationOfScalesSingleStation:
+ pass
diff --git a/test/test_data_handler/test_iterator.py b/test/test_data_handler/test_iterator.py
index ec224c06e358297972097f2cc75cea86f768784f..ade5c19215e61de5e209db900920187294ac9b18 100644
--- a/test/test_data_handler/test_iterator.py
+++ b/test/test_data_handler/test_iterator.py
@@ -52,20 +52,57 @@ class TestDataCollection:
for e, i in enumerate(data_collection):
assert i == e
+ def test_add(self):
+ data_collection = DataCollection()
+ data_collection.add("first_element")
+ assert len(data_collection) == 1
+ assert data_collection["first_element"] == "first_element"
+ assert data_collection[0] == "first_element"
+
+ def test_name(self):
+ data_collection = DataCollection(name="testcase")
+ assert data_collection._name == "testcase"
+ assert data_collection.name == "testcase"
+
+ def test_set_mapping(self):
+ data_collection = object.__new__(DataCollection)
+ data_collection._collection = ["a", "b", "c"]
+ data_collection._mapping = {}
+ data_collection._set_mapping()
+ assert data_collection._mapping == {"a": 0, "b": 1, "c": 2}
+
+ def test_getitem(self):
+ data_collection = DataCollection(["a", "b", "c"])
+ assert data_collection["a"] == "a"
+ assert data_collection[1] == "b"
+
+ def test_keys(self):
+ collection = ["a", "b", "c"]
+ data_collection = DataCollection(collection)
+ assert data_collection.keys() == collection
+ data_collection.add("another")
+ assert data_collection.keys() == collection + ["another"]
+
class DummyData:
def __init__(self, number_of_samples=np.random.randint(100, 150)):
+ np.random.seed(45)
self.number_of_samples = number_of_samples
def get_X(self, upsampling=False, as_numpy=True):
+ np.random.seed(45)
X1 = np.random.randint(0, 10, size=(self.number_of_samples, 14, 5)) # samples, window, variables
+ np.random.seed(45)
X2 = np.random.randint(21, 30, size=(self.number_of_samples, 10, 2)) # samples, window, variables
+ np.random.seed(45)
X3 = np.random.randint(-5, 0, size=(self.number_of_samples, 1, 2)) # samples, window, variables
return [X1, X2, X3]
def get_Y(self, upsampling=False, as_numpy=True):
+ np.random.seed(45)
Y1 = np.random.randint(0, 10, size=(self.number_of_samples, 5, 1)) # samples, window, variables
+ np.random.seed(45)
Y2 = np.random.randint(21, 30, size=(self.number_of_samples, 5, 1)) # samples, window, variables
return [Y1, Y2]
@@ -80,6 +117,14 @@ class TestKerasIterator:
data_coll = DataCollection(collection=coll)
return data_coll
+ @pytest.fixture
+ def collection_small(self):
+ coll = []
+ for i in range(3):
+ coll.append(DummyData(5 + i))
+ data_coll = DataCollection(collection=coll)
+ return data_coll
+
@pytest.fixture
def path(self):
p = os.path.join(os.path.dirname(os.path.abspath(__file__)), "testdata")
@@ -161,6 +206,27 @@ class TestKerasIterator:
assert len(iterator) == 4
assert iterator.indexes == [0, 1, 2, 3]
+ def test_prepare_batches_upsampling(self, collection_small, path):
+ iterator = object.__new__(KerasIterator)
+ iterator._collection = collection_small
+ iterator.batch_size = 100
+ iterator.indexes = []
+ iterator.model = None
+ iterator.upsampling = False
+ iterator._path = os.path.join(path, "%i.pickle")
+ os.makedirs(path)
+ iterator._prepare_batches()
+ X1, Y1 = iterator[0]
+ iterator.upsampling = True
+ iterator._prepare_batches()
+ X1p, Y1p = iterator[0]
+ assert X1[0].shape == X1p[0].shape
+ assert Y1[0].shape == Y1p[0].shape
+ assert np.testing.assert_almost_equal(X1[0].sum(), X1p[0].sum(), 2) is None
+ assert np.testing.assert_almost_equal(Y1[0].sum(), Y1p[0].sum(), 2) is None
+ f = np.testing.assert_array_almost_equal
+ assert np.testing.assert_raises(AssertionError, f, X1[0], X1p[0]) is None
+
def test_prepare_batches_no_remaining(self, path):
iterator = object.__new__(KerasIterator)
iterator._collection = DataCollection([DummyData(50)])
@@ -189,10 +255,6 @@ class TestKerasIterator:
expected = next(iter(collection))
assert PyTestAllEqual([X, expected.get_X()])
assert PyTestAllEqual([Y, expected.get_Y()])
- reversed(iterator.indexes)
- X, Y = iterator[3]
- assert PyTestAllEqual([X, expected.get_X()])
- assert PyTestAllEqual([Y, expected.get_Y()])
def test_on_epoch_end(self):
iterator = object.__new__(KerasIterator)
@@ -226,3 +288,15 @@ class TestKerasIterator:
iterator.model = mock.MagicMock(return_value=1)
with pytest.raises(TypeError):
iterator._get_model_rank()
+
+ def test_permute(self):
+ iterator = object.__new__(KerasIterator)
+ X = [np.array([[1, 2, 3, 4],
+ [1.1, 2.1, 3.1, 4.1],
+ [1.2, 2.2, 3.2, 4.2]], dtype="f2")]
+ Y = [np.array([1, 2, 3])]
+ X_p, Y_p = iterator._permute_data(X, Y)
+ assert X_p[0].shape == X[0].shape
+ assert Y_p[0].shape == Y[0].shape
+ assert np.testing.assert_almost_equal(X_p[0].sum(), X[0].sum(), 2) is None
+ assert np.testing.assert_almost_equal(Y_p[0].sum(), Y[0].sum(), 2) is None
diff --git a/test/test_datastore.py b/test/test_helpers/test_datastore.py
similarity index 87%
rename from test/test_datastore.py
rename to test/test_helpers/test_datastore.py
index 662c90bf04e11b8b4ff9647506c1981c8883f30b..1eecc576e60e5dc43b97a6e8254f8a2fea29728a 100644
--- a/test/test_datastore.py
+++ b/test/test_helpers/test_datastore.py
@@ -3,7 +3,8 @@ __date__ = '2019-11-22'
import pytest
-from mlair.helpers.datastore import AbstractDataStore, DataStoreByVariable, DataStoreByScope, CorrectScope
+from mlair.helpers.datastore import AbstractDataStore, DataStoreByVariable, DataStoreByScope
+from mlair.helpers.datastore import CorrectScope, TrackParameter
from mlair.helpers.datastore import NameNotFoundInDataStore, NameNotFoundInScope, EmptyScope
@@ -339,3 +340,52 @@ class TestCorrectScope:
assert self.function1(21) == (21, "general", 44)
assert self.function1(55, "sub", 34) == (55, "general.sub", 34)
assert self.function1("string", b=99, scope="tester") == ("string", "general.tester", 99)
+
+
+class TestTracking:
+ class Tracker:
+ def __init__(self):
+ self.tracker = [{}]
+
+ @TrackParameter
+ def function2(self, arg1, arg2, arg3):
+ return
+
+ @staticmethod
+ def function1():
+ return
+
+ def test_init(self):
+ track = self.Tracker()
+ track.function2(1, "2", "scopy")
+ assert track.tracker == [{1: [{"method": "function2", "scope": "scopy"}]}]
+
+ def test_track_first_entry(self):
+ track = object.__new__(TrackParameter)
+ track.__wrapped__ = self.function1
+ tracker_obj = self.Tracker()
+ assert len(tracker_obj.tracker[-1].keys()) == 0
+ track.track(tracker_obj, "eins", 2)
+ assert len(tracker_obj.tracker[-1].keys()) == 1
+ assert tracker_obj.tracker == [{"eins": [{"method": "function1", "scope": 2}]}]
+ track.track(tracker_obj, "zwei", 20)
+ assert len(tracker_obj.tracker[-1].keys()) == 2
+ assert tracker_obj.tracker == [{"eins": [{"method": "function1", "scope": 2}],
+ "zwei": [{"method": "function1", "scope": 20}]}]
+
+ def test_track_second_entry(self):
+ track = object.__new__(TrackParameter)
+ track.__wrapped__ = self.function1
+ tracker_obj = self.Tracker()
+ assert len(tracker_obj.tracker[-1].keys()) == 0
+ track.track(tracker_obj, "eins", 2)
+ track.track(tracker_obj, "eins", 23)
+ assert len(tracker_obj.tracker[-1].keys()) == 1
+ assert tracker_obj.tracker == [{"eins": [{"method": "function1", "scope": 2},
+ {"method": "function1", "scope": 23}]}]
+
+ def test_decrypt_args(self):
+ track = object.__new__(TrackParameter)
+ assert track._decrypt_args(23) == (23,)
+ assert track._decrypt_args("test", 33, 4) == ("test", 33, 4)
+ assert track._decrypt_args("eins", 2) == ("eins", None, 2)
diff --git a/test/test_helpers/test_helpers.py b/test/test_helpers/test_helpers.py
index 723b4a87d70453327ed6b7e355d3ef78a246652a..a5aaa707c83a65c3e10f76fdbfcd8142d3615e2e 100644
--- a/test/test_helpers/test_helpers.py
+++ b/test/test_helpers/test_helpers.py
@@ -10,7 +10,7 @@ import os
import mock
import pytest
-from mlair.helpers import to_list, dict_to_xarray, float_round, remove_items
+from mlair.helpers import to_list, dict_to_xarray, float_round, remove_items, extract_value, select_from_dict
from mlair.helpers import PyTestRegex
from mlair.helpers import Logger, TimeTracking
@@ -22,6 +22,10 @@ class TestToList:
assert to_list('abcd') == ['abcd']
assert to_list([1, 2, 3]) == [1, 2, 3]
assert to_list([45]) == [45]
+ s = {34, 2, "test"}
+ assert to_list(s) == list(s)
+ assert to_list((34, 2, "test")) == [34, 2, "test"]
+ assert to_list(("test")) == ["test"]
class TestTimeTracking:
@@ -164,6 +168,22 @@ class TestFloatRound:
assert float_round(-34.9221, 0) == -34.
+class TestSelectFromDict:
+
+ @pytest.fixture
+ def dictionary(self):
+ return {"a": 1, "b": 23, "c": "last"}
+
+ def test_select(self, dictionary):
+ assert select_from_dict(dictionary, "c") == {"c": "last"}
+ assert select_from_dict(dictionary, ["a", "c"]) == {"a": 1, "c": "last"}
+ assert select_from_dict(dictionary, "d") == {}
+
+ def test_select_no_dict_given(self):
+ with pytest.raises(AssertionError):
+ select_from_dict(["we"], "now")
+
+
class TestRemoveItems:
@pytest.fixture
@@ -229,6 +249,11 @@ class TestRemoveItems:
remove_items(custom_list)
assert "remove_items() missing 1 required positional argument: 'items'" in e.value.args[0]
+ def test_remove_not_supported_type(self):
+ with pytest.raises(TypeError) as e:
+ remove_items(23, "test")
+ assert f"remove_items does not support type {type(23)}" in e.value.args[0]
+
class TestLogger:
@@ -272,3 +297,18 @@ class TestLogger:
with pytest.raises(TypeError) as e:
logger.logger_console(1.5)
assert "Level not an integer or a valid string: 1.5" == e.value.args[0]
+
+
+class TestExtractValue:
+
+ def test_extract(self):
+ assert extract_value([1]) == 1
+ assert extract_value([[23]]) == 23
+ assert extract_value([("test")]) == "test"
+ assert extract_value((2,)) == 2
+
+ def test_extract_multiple_elements(self):
+ with pytest.raises(NotImplementedError) as e:
+ extract_value([1, 2, 3])
+ assert "Trying to extract an encapsulated value from objects with more than a single entry is not supported " \
+ "by this function." in e.value.args[0]
diff --git a/test/test_join.py b/test/test_helpers/test_join.py
similarity index 78%
rename from test/test_join.py
rename to test/test_helpers/test_join.py
index a9a4c381cbf58a272389b0b11283c8b0cce3ab42..e903669bf63f4056a8278401b07818d31a09616d 100644
--- a/test/test_join.py
+++ b/test/test_helpers/test_join.py
@@ -7,14 +7,6 @@ from mlair.helpers.join import _save_to_pandas, _correct_stat_name, _lower_list,
from mlair.configuration.join_settings import join_settings
-class TestJoinUrlBase:
-
- def test_url(self):
- url, headers = join_settings()
- assert url == 'https://join.fz-juelich.de/services/rest/surfacedata/'
- assert headers == {}
-
-
class TestDownloadJoin:
def test_download_single_var(self):
@@ -25,7 +17,18 @@ class TestDownloadJoin:
def test_download_empty(self):
with pytest.raises(EmptyQueryResult) as e:
download_join("DEBW107", {"o3": "dma8eu"}, "traffic")
- assert e.value.args[-1] == "No data found in JOIN."
+ assert e.value.args[-1] == "No data found for variables {'o3'} and options station=['DEBW107'], type=traffic," \
+ " network=None, origin={} in JOIN."
+
+ def test_download_incomplete(self):
+ with pytest.raises(EmptyQueryResult) as e:
+ download_join("DEBW107", {"o3": "dma8eu", "o10": "maximum"}, "background")
+ assert e.value.args[-1] == "No data found for variables {'o10'} and options station=['DEBW107'], " \
+ "type=background, network=None, origin={} in JOIN."
+ with pytest.raises(EmptyQueryResult) as e:
+ download_join("DEBW107", {"o3": "dma8eu", "o10": "maximum"}, "background", data_origin={"o10": ""})
+ assert e.value.args[-1] == "No data found for variables {'o10'} and options station=['DEBW107'], " \
+ "type=background, network=None, origin={'o10': ''} in JOIN."
class TestCorrectDataFormat:
@@ -53,11 +56,12 @@ class TestLoadSeriesInformation:
def test_standard_query(self):
expected_subset = {'o3': 23031, 'no2': 39002, 'temp': 85584, 'wspeed': 17060}
- assert expected_subset.items() <= load_series_information(['DEBW107'], None, None, join_settings()[0],
- {}).items()
+ res, orig = load_series_information(['DEBW107'], None, None, join_settings()[0], {})
+ assert expected_subset.items() <= res.items()
def test_empty_result(self):
- assert load_series_information(['DEBW107'], "traffic", None, join_settings()[0], {}) == {}
+ res, orig = load_series_information(['DEBW107'], "traffic", None, join_settings()[0], {})
+ assert res == {}
class TestSelectDistinctSeries:
@@ -81,15 +85,18 @@ class TestSelectDistinctSeries:
'parameter_label': 'PRESS-REA-MIUB', 'parameter_attribute': 'REA'}]
def test_no_origin_given(self, vars):
- res = _select_distinct_series(vars)
+ res, orig = _select_distinct_series(vars)
assert res == {"no2": 16686, "o3": 16687, "cloudcover": 54036, "temp": 88491, "press": 102660}
+ assert orig == {"no2": "", "o3": "", "cloudcover": "REA", "temp": "REA", "press": "REA"}
def test_different_origins(self, vars):
origin = {"no2": "test", "temp": "", "cloudcover": "REA"}
- res = _select_distinct_series(vars, data_origin=origin)
- assert res == {"o3": 16687, "press": 16692, "temp": 16693, "cloudcover": 54036}
- res = _select_distinct_series(vars, data_origin={})
- assert res == {"no2": 16686, "o3": 16687, "press": 16692, "temp": 16693}
+ res, orig = _select_distinct_series(vars, data_origin=origin)
+ assert res == {"o3": 16687, "press": 102660, "temp": 16693, "cloudcover": 54036}
+ assert orig == {"no2": "test", "o3": "", "cloudcover": "REA", "temp": "", "press": "REA"}
+ res, orig = _select_distinct_series(vars, data_origin={})
+ assert res == {"cloudcover": 54036, "no2": 16686, "o3": 16687, "press": 102660, "temp": 88491}
+ assert orig == {"no2": "", "o3": "", "temp": "REA", "press": "REA", "cloudcover": "REA"}
class TestSaveToPandas:
diff --git a/test/test_statistics.py b/test/test_helpers/test_statistics.py
similarity index 100%
rename from test/test_statistics.py
rename to test/test_helpers/test_statistics.py
diff --git a/test/test_helpers/test_testing_helpers.py b/test/test_helpers/test_testing_helpers.py
new file mode 100644
index 0000000000000000000000000000000000000000..385161c740f386847ef2f2dc4df17c1c84fa7fa5
--- /dev/null
+++ b/test/test_helpers/test_testing_helpers.py
@@ -0,0 +1,48 @@
+from mlair.helpers.testing import PyTestRegex, PyTestAllEqual
+
+import re
+import xarray as xr
+import numpy as np
+
+import pytest
+
+
+class TestPyTestRegex:
+
+ def test_init(self):
+ test_regex = PyTestRegex(r"TestString\d+")
+ assert isinstance(test_regex._regex, re._pattern_type)
+
+ def test_eq(self):
+ assert PyTestRegex(r"TestString\d*") == "TestString"
+ assert PyTestRegex(r"TestString\d+") == "TestString9"
+ assert "TestString4" == PyTestRegex(r"TestString\d+")
+
+ def test_repr(self):
+ assert repr(PyTestRegex(r"TestString\d+")) == r"TestString\d+"
+
+
+class TestPyTestAllEqual:
+
+ def test_numpy(self):
+ assert PyTestAllEqual([np.array([1, 2, 3]), np.array([1, 2, 3]), np.array([1, 2, 3])])
+ with pytest.raises(AssertionError):
+ PyTestAllEqual([np.array([1, 2, 3]), np.array([2, 2, 3]), np.array([1, 2, 3])])
+
+ def test_xarray(self):
+ assert PyTestAllEqual([xr.DataArray([1, 2, 3]), xr.DataArray([1, 2, 3])])
+ with pytest.raises(AssertionError):
+ PyTestAllEqual([xr.DataArray([1, 2, 3]), xr.DataArray([1, 2, 3, 4])])
+
+ def test_other(self):
+ assert PyTestAllEqual(["test", "test", "test"])
+ with pytest.raises(AssertionError):
+ PyTestAllEqual(["test", "test", "tes2t"])
+
+ def test_encapsulated(self):
+ assert PyTestAllEqual([[np.array([1, 2, 3]), np.array([12, 22, 32])],
+ [np.array([1, 2, 3]), np.array([12, 22, 32])]])
+ assert PyTestAllEqual([[xr.DataArray([1, 2, 3]), xr.DataArray([12, 22, 32])],
+ [xr.DataArray([1, 2, 3]), xr.DataArray([12, 22, 32])]])
+ assert PyTestAllEqual([["test", "test2"],
+ ["test", "test2"]])
diff --git a/test/test_plotting/test_tracker_plot.py b/test/test_plotting/test_tracker_plot.py
index 196879657452fe12238c990fc419cb0848c9ec9c..62e171fe75c1112bb96447e9831b0008269fac7b 100644
--- a/test/test_plotting/test_tracker_plot.py
+++ b/test/test_plotting/test_tracker_plot.py
@@ -356,13 +356,13 @@ class TestTrackPlot:
assert len(track_plot_obj.ax.lines) == 0
track_plot_obj.line(start_x=5, end_x=6, y=2)
assert len(track_plot_obj.ax.lines) == 2
- pos_x, pos_y = np.array([5 + w, 6]), np.ones((2, )) * (2 + h / 2)
+ pos_x, pos_y = np.array([5 + w, 6]), np.ones((2,)) * (2 + h / 2)
assert track_plot_obj.ax.lines[0]._color == "white"
assert track_plot_obj.ax.lines[0]._linewidth == 2.5
assert track_plot_obj.ax.lines[1]._color == "darkgrey"
assert track_plot_obj.ax.lines[1]._linewidth == 1.4
- assert PyTestAllEqual([track_plot_obj.ax.lines[0]._x, track_plot_obj.ax.lines[1]._x, pos_x]).is_true()
- assert PyTestAllEqual([track_plot_obj.ax.lines[0]._y, track_plot_obj.ax.lines[1]._y, pos_y]).is_true()
+ assert PyTestAllEqual([track_plot_obj.ax.lines[0]._x, track_plot_obj.ax.lines[1]._x, pos_x])
+ assert PyTestAllEqual([track_plot_obj.ax.lines[0]._y, track_plot_obj.ax.lines[1]._y, pos_y])
def test_step(self, track_plot_obj):
x_int, h, w = 0.5, 0.6, 0.65
@@ -379,8 +379,8 @@ class TestTrackPlot:
assert track_plot_obj.ax.lines[0]._linewidth == 2.5
assert track_plot_obj.ax.lines[1]._color == "black"
assert track_plot_obj.ax.lines[1]._linewidth == 1.4
- assert PyTestAllEqual([track_plot_obj.ax.lines[0]._x, track_plot_obj.ax.lines[1]._x, pos_x]).is_true()
- assert PyTestAllEqual([track_plot_obj.ax.lines[0]._y, track_plot_obj.ax.lines[1]._y, pos_y]).is_true()
+ assert PyTestAllEqual([track_plot_obj.ax.lines[0]._x, track_plot_obj.ax.lines[1]._x, pos_x])
+ assert PyTestAllEqual([track_plot_obj.ax.lines[0]._y, track_plot_obj.ax.lines[1]._y, pos_y])
def test_rect(self, track_plot_obj):
h, w = 0.5, 0.6
@@ -392,20 +392,18 @@ class TestTrackPlot:
track_plot_obj.rect(x=4, y=2)
assert len(track_plot_obj.ax.artists) == 1
assert len(track_plot_obj.ax.texts) == 1
- track_plot_obj.ax.artists[0].xy == (4, 2)
- track_plot_obj.ax.artists[0]._height == h
- track_plot_obj.ax.artists[0]._width == w
- track_plot_obj.ax.artists[0]._original_facecolor == "orange"
- track_plot_obj.ax.texts[0].xy == (4 + w / 2, 2 + h / 2)
- track_plot_obj.ax.texts[0]._color == "w"
- track_plot_obj.ax.texts[0]._text == "get"
+ assert track_plot_obj.ax.artists[0].xy == (4, 2)
+ assert track_plot_obj.ax.artists[0]._height == h
+ assert track_plot_obj.ax.artists[0]._width == w
+ assert track_plot_obj.ax.artists[0]._original_facecolor == "orange"
+ assert track_plot_obj.ax.texts[0].xy == (4 + w / 2, 2 + h / 2)
+ assert track_plot_obj.ax.texts[0]._color == "w"
+ assert track_plot_obj.ax.texts[0]._text == "get"
track_plot_obj.rect(x=4, y=2, method="set")
assert len(track_plot_obj.ax.artists) == 2
assert len(track_plot_obj.ax.texts) == 2
- track_plot_obj.ax.artists[0]._original_facecolor == "lightblue"
- track_plot_obj.ax.texts[0]._text == "set"
-
-
+ assert track_plot_obj.ax.artists[1]._original_facecolor == "lightblue"
+ assert track_plot_obj.ax.texts[1]._text == "set"
def test_set_ypos_anchor(self, track_plot_obj, scopes, dims):
assert not hasattr(track_plot_obj, "y_pos")
diff --git a/test/test_run_modules/test_pre_processing.py b/test/test_run_modules/test_pre_processing.py
index bdb8fdabff67ad894275c805522b9df4cf167011..11c46e99fb38489f5cbb26a8a87032049c96c7ca 100644
--- a/test/test_run_modules/test_pre_processing.py
+++ b/test/test_run_modules/test_pre_processing.py
@@ -1,6 +1,7 @@
import logging
import pytest
+import mock
from mlair.data_handler import DefaultDataHandler, DataCollection, AbstractDataHandler
from mlair.helpers.datastore import NameNotFoundInScope
@@ -8,6 +9,9 @@ from mlair.helpers import PyTestRegex
from mlair.run_modules.experiment_setup import ExperimentSetup
from mlair.run_modules.pre_processing import PreProcessing
from mlair.run_modules.run_environment import RunEnvironment
+import pandas as pd
+import numpy as np
+import multiprocessing
class TestPreProcessing:
@@ -86,7 +90,7 @@ class TestPreProcessing:
assert data_store.get("stations", "general.awesome") == ['DEBW107', 'DEBY081', 'DEBW013', 'DEBW076', 'DEBW087']
@pytest.mark.parametrize("name", (None, "tester"))
- def test_validate_station(self, caplog, obj_with_exp_setup, name):
+ def test_validate_station_serial(self, caplog, obj_with_exp_setup, name):
pre = obj_with_exp_setup
caplog.set_level(logging.INFO)
stations = pre.data_store.get("stations", "general")
@@ -97,6 +101,25 @@ class TestPreProcessing:
assert valid_stations == stations[:-1]
expected = "check valid stations started" + ' (%s)' % (name if name else 'all')
assert caplog.record_tuples[0] == ('root', 20, expected)
+ assert caplog.record_tuples[1] == ('root', 20, "use serial validate station approach")
+ assert caplog.record_tuples[-1] == ('root', 20, PyTestRegex(r'run for \d+:\d+:\d+ \(hh:mm:ss\) to check 6 '
+ r'station\(s\). Found 5/6 valid stations.'))
+
+ @mock.patch("multiprocessing.cpu_count", return_value=3)
+ @mock.patch("multiprocessing.Pool", return_value=multiprocessing.Pool(3))
+ def test_validate_station_parallel(self, mock_pool, mock_cpu, caplog, obj_with_exp_setup):
+ pre = obj_with_exp_setup
+ caplog.clear()
+ caplog.set_level(logging.INFO)
+ stations = pre.data_store.get("stations", "general")
+ data_preparation = pre.data_store.get("data_handler")
+ collection, valid_stations = pre.validate_station(data_preparation, stations, set_name=None)
+ assert isinstance(collection, DataCollection)
+ assert len(valid_stations) < len(stations)
+ assert valid_stations == stations[:-1]
+ assert caplog.record_tuples[0] == ('root', 20, "check valid stations started (all)")
+ assert caplog.record_tuples[1] == ('root', 20, "use parallel validate station approach")
+ assert caplog.record_tuples[2] == ('root', 20, "running 3 processes in parallel")
assert caplog.record_tuples[-1] == ('root', 20, PyTestRegex(r'run for \d+:\d+:\d+ \(hh:mm:ss\) to check 6 '
r'station\(s\). Found 5/6 valid stations.'))
@@ -115,3 +138,38 @@ class TestPreProcessing:
assert pre.transformation(data_preparation, stations) is None
class data_preparation_no_trans: pass
assert pre.transformation(data_preparation_no_trans, stations) is None
+
+ @pytest.fixture
+ def dummy_df(self):
+ data_dict = {'station_name': {'DEBW013': 'Stuttgart Bad Cannstatt', 'DEBW076': 'Baden-Baden',
+ 'DEBW087': 'Schwäbische_Alb', 'DEBW107': 'Tübingen',
+ 'DEBY081': 'Garmisch-Partenkirchen/Kreuzeckbahnstraße', '# Stations': np.nan,
+ '# Samples': np.nan},
+ 'station_lon': {'DEBW013': 9.2297, 'DEBW076': 8.2202, 'DEBW087': 9.2076, 'DEBW107': 9.0512,
+ 'DEBY081': 11.0631, '# Stations': np.nan, '# Samples': np.nan},
+ 'station_lat': {'DEBW013': 48.8088, 'DEBW076': 48.7731, 'DEBW087': 48.3458, 'DEBW107': 48.5077,
+ 'DEBY081': 47.4764, '# Stations': np.nan, '# Samples': np.nan},
+ 'station_alt': {'DEBW013': 235.0, 'DEBW076': 148.0, 'DEBW087': 798.0, 'DEBW107': 325.0,
+ 'DEBY081': 735.0, '# Stations': np.nan, '# Samples': np.nan},
+ 'train': {'DEBW013': 1413, 'DEBW076': 3002, 'DEBW087': 3016, 'DEBW107': 1782, 'DEBY081': 2837,
+ '# Stations': 6, '# Samples': 12050},
+ 'val': {'DEBW013': 698, 'DEBW076': 715, 'DEBW087': 700, 'DEBW107': 701, 'DEBY081': 456,
+ '# Stations': 6, '# Samples': 3270},
+ 'test': {'DEBW013': 1066, 'DEBW076': 696, 'DEBW087': 1080, 'DEBW107': 1080, 'DEBY081': 700,
+ '# Stations': 6, '# Samples': 4622}}
+ df = pd.DataFrame.from_dict(data_dict)
+ return df
+
+ def test_create_column_format_for_tex(self):
+ df = pd.DataFrame(np.ones((2, 1)))
+ df_col = PreProcessing.create_column_format_for_tex(df) # len: 1+1
+ assert df_col == 'lr'
+ assert len(df_col) == 2
+ df = pd.DataFrame(np.ones((2, 2)))
+ df_col = PreProcessing.create_column_format_for_tex(df) # len: 2+1
+ assert df_col == 'lcr'
+ assert len(df_col) == 3
+ df = pd.DataFrame(np.ones((2, 3)))
+ df_col = PreProcessing.create_column_format_for_tex(df) # len: 3+1
+ assert df_col == 'lccr'
+ assert len(df_col) == 4
diff --git a/test/test_workflows/test_abstract_workflow.py b/test/test_workflows/test_abstract_workflow.py
new file mode 100644
index 0000000000000000000000000000000000000000..6530f8565ba4a1ccc185c133978ad1809905dff9
--- /dev/null
+++ b/test/test_workflows/test_abstract_workflow.py
@@ -0,0 +1,53 @@
+from mlair.workflows.abstract_workflow import Workflow
+
+import logging
+
+
+class TestWorkflow:
+
+ def test_init(self):
+ flow = Workflow()
+ assert len(flow._registry_kwargs.keys()) == 0
+ assert len(flow._registry) == 0
+ assert flow._name == "Workflow"
+ flow = Workflow(name="river")
+ assert flow._name == "river"
+
+ def test_add(self):
+ flow = Workflow()
+ flow.add("stage")
+ assert len(flow._registry_kwargs.keys()) == 1
+ assert len(flow._registry) == 1
+ assert len(flow._registry_kwargs[0].keys()) == 0
+ flow.add("stagekwargs", test=23, another="string")
+ assert len(flow._registry_kwargs.keys()) == 2
+ assert len(flow._registry) == 2
+ assert len(flow._registry_kwargs[1].keys()) == 2
+ assert list(flow._registry_kwargs.keys()) == [0, 1]
+ assert flow._registry == ["stage", "stagekwargs"]
+ assert list(flow._registry_kwargs[1].keys()) == ["test", "another"]
+ assert flow._registry_kwargs[1]["another"] == "string"
+
+ def test_run(self, caplog):
+ caplog.set_level(logging.INFO)
+
+ class A:
+ def __init__(self, a=3):
+ self.a = a
+ logging.info(self.a)
+
+ class B:
+ def __init__(self):
+ self.b = 2
+ logging.info(self.b)
+
+ flow = Workflow()
+ flow.add(A, a=6)
+ flow.add(B)
+ flow.add(A)
+ flow.run()
+ pos = int(".log" in caplog.messages[0])
+ assert caplog.record_tuples[0 + pos] == ('root', 20, "Workflow started")
+ assert caplog.record_tuples[1 + pos] == ('root', 20, "6")
+ assert caplog.record_tuples[2 + pos] == ('root', 20, "2")
+ assert caplog.record_tuples[3 + pos] == ('root', 20, "3")
diff --git a/test/test_workflows/test_default_workflow.py b/test/test_workflows/test_default_workflow.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7c198a4821f779329b9f5f19b04e757d8ebc7da
--- /dev/null
+++ b/test/test_workflows/test_default_workflow.py
@@ -0,0 +1,48 @@
+from mlair.workflows.default_workflow import DefaultWorkflow, DefaultWorkflowHPC
+from mlair.run_modules.experiment_setup import ExperimentSetup
+from mlair.run_modules.pre_processing import PreProcessing
+from mlair.run_modules.model_setup import ModelSetup
+from mlair.run_modules.partition_check import PartitionCheck
+from mlair.run_modules.training import Training
+from mlair.run_modules.post_processing import PostProcessing
+
+
+class TestDefaultWorkflow:
+
+ def test_init_no_args(self):
+ flow = DefaultWorkflow()
+ assert flow._registry[0].__name__ == ExperimentSetup.__name__
+ assert len(flow._registry_kwargs[0].keys()) == 1
+
+ def test_init_with_args(self):
+ flow = DefaultWorkflow(stations="test", start="2020", model=None)
+ assert flow._registry[0].__name__ == ExperimentSetup.__name__
+ assert len(flow._registry_kwargs[0].keys()) == 3
+
+ def test_init_with_kwargs(self):
+ flow = DefaultWorkflow(stations="test", real_kwarg=4)
+ assert flow._registry[0].__name__ == ExperimentSetup.__name__
+ assert len(flow._registry_kwargs[0].keys()) == 3
+ assert list(flow._registry_kwargs[0].keys()) == ["experiment_date", "stations", "real_kwarg"]
+
+ def test_setup(self):
+ flow = DefaultWorkflow()
+ assert len(flow._registry) == 5
+ assert flow._registry[0].__name__ == ExperimentSetup.__name__
+ assert flow._registry[1].__name__ == PreProcessing.__name__
+ assert flow._registry[2].__name__ == ModelSetup.__name__
+ assert flow._registry[3].__name__ == Training.__name__
+ assert flow._registry[4].__name__ == PostProcessing.__name__
+
+
+class TestDefaultWorkflowHPC:
+
+ def test_setup(self):
+ flow = DefaultWorkflowHPC()
+ assert len(flow._registry) == 6
+ assert flow._registry[0].__name__ == ExperimentSetup.__name__
+ assert flow._registry[1].__name__ == PreProcessing.__name__
+ assert flow._registry[2].__name__ == PartitionCheck.__name__
+ assert flow._registry[3].__name__ == ModelSetup.__name__
+ assert flow._registry[4].__name__ == Training.__name__
+ assert flow._registry[5].__name__ == PostProcessing.__name__