diff --git a/HPC_setup/requirements_HDFML_additionals.txt b/HPC_setup/requirements_HDFML_additionals.txt
index 12e09ccdd620c0c81c78ae6d4781d4feb5b94baf..b2a29fbfb353f24d8c99d8429693022ea1fd406f 100644
--- a/HPC_setup/requirements_HDFML_additionals.txt
+++ b/HPC_setup/requirements_HDFML_additionals.txt
@@ -2,6 +2,7 @@ absl-py==0.11.0
appdirs==1.4.4
astor==0.8.1
attrs==20.3.0
+bottleneck==1.3.2
cached-property==1.5.2
certifi==2020.12.5
cftime==1.4.1
@@ -9,6 +10,7 @@ chardet==4.0.0
coverage==5.4
cycler==0.10.0
dask==2021.2.0
+dill==0.3.3
fsspec==0.8.5
gast==0.4.0
grpcio==1.35.0
diff --git a/HPC_setup/requirements_JUWELS_additionals.txt b/HPC_setup/requirements_JUWELS_additionals.txt
index 12e09ccdd620c0c81c78ae6d4781d4feb5b94baf..b2a29fbfb353f24d8c99d8429693022ea1fd406f 100644
--- a/HPC_setup/requirements_JUWELS_additionals.txt
+++ b/HPC_setup/requirements_JUWELS_additionals.txt
@@ -2,6 +2,7 @@ absl-py==0.11.0
appdirs==1.4.4
astor==0.8.1
attrs==20.3.0
+bottleneck==1.3.2
cached-property==1.5.2
certifi==2020.12.5
cftime==1.4.1
@@ -9,6 +10,7 @@ chardet==4.0.0
coverage==5.4
cycler==0.10.0
dask==2021.2.0
+dill==0.3.3
fsspec==0.8.5
gast==0.4.0
grpcio==1.35.0
diff --git a/mlair/data_handler/abstract_data_handler.py b/mlair/data_handler/abstract_data_handler.py
index f085d18bb8d33839a0e3b5f6f3d5ada92134e7f6..419db059a58beeb4ed7e3e198e41b565f8dc7d25 100644
--- a/mlair/data_handler/abstract_data_handler.py
+++ b/mlair/data_handler/abstract_data_handler.py
@@ -55,3 +55,6 @@ class AbstractDataHandler:
def get_coordinates(self) -> Union[None, Dict]:
"""Return coordinates as dictionary with keys `lon` and `lat`."""
return None
+
+ def _hash_list(self):
+ return []
diff --git a/mlair/data_handler/data_handler_kz_filter.py b/mlair/data_handler/data_handler_kz_filter.py
index 78638a13b4ea50cd073ca4599a291342fad849d4..1f2c63e58c7eaab645f074ac953d2f05d8ba09fd 100644
--- a/mlair/data_handler/data_handler_kz_filter.py
+++ b/mlair/data_handler/data_handler_kz_filter.py
@@ -8,6 +8,7 @@ import numpy as np
import pandas as pd
import xarray as xr
from typing import List, Union
+from functools import partial
from mlair.data_handler.data_handler_single_station import DataHandlerSingleStation
from mlair.data_handler import DefaultDataHandler
@@ -22,6 +23,7 @@ class DataHandlerKzFilterSingleStation(DataHandlerSingleStation):
"""Data handler for a single station to be used by a superior data handler. Inputs are kz filtered."""
_requirements = remove_items(inspect.getfullargspec(DataHandlerSingleStation).args, ["self", "station"])
+ _hash = DataHandlerSingleStation._hash + ["kz_filter_length", "kz_filter_iter", "filter_dim"]
DEFAULT_FILTER_DIM = "filter"
@@ -38,10 +40,7 @@ class DataHandlerKzFilterSingleStation(DataHandlerSingleStation):
def _check_sampling(self, **kwargs):
assert kwargs.get("sampling") == "hourly" # This data handler requires hourly data resolution
- def setup_samples(self):
- """
- Setup samples. This method prepares and creates samples X, and labels Y.
- """
+ def make_input_target(self):
data, self.meta = self.load_data(self.path, self.station, self.statistics_per_var, self.sampling,
self.station_type, self.network, self.store_data_locally, self.data_origin)
self._data = self.interpolate(data, dim=self.time_dim, method=self.interpolation_method,
@@ -54,9 +53,6 @@ class DataHandlerKzFilterSingleStation(DataHandlerSingleStation):
# import matplotlib.pyplot as plt
# self.input_data.sel(filter="74d", variables="temp", Stations="DEBW107").plot()
# self.input_data.sel(variables="temp", Stations="DEBW107").plot.line(hue="filter")
- if self.do_transformation is True:
- self.call_transform()
- self.make_samples()
@TimeTrackingWrapper
def apply_kz_filter(self):
@@ -88,6 +84,15 @@ class DataHandlerKzFilterSingleStation(DataHandlerSingleStation):
return self.history.transpose(self.time_dim, self.window_dim, self.iter_dim, self.target_dim,
self.filter_dim).copy()
+ def _create_lazy_data(self):
+ return [self._data, self.meta, self.input_data, self.target_data, self.cutoff_period, self.cutoff_period_days]
+
+ def _extract_lazy(self, lazy_data):
+ _data, self.meta, _input_data, _target_data, self.cutoff_period, self.cutoff_period_days = lazy_data
+ f_prep = partial(self._slice_prep, start=self.start, end=self.end)
+ self._data, self.input_data, self.target_data = list(map(f_prep, [_data, _input_data, _target_data]))
+
+
class DataHandlerKzFilter(DefaultDataHandler):
"""Data handler using kz filtered data."""
diff --git a/mlair/data_handler/data_handler_mixed_sampling.py b/mlair/data_handler/data_handler_mixed_sampling.py
index caaa7a62d1b772808dcaf58abdfa5483e80861e7..86e6f856b7bf061287261ae711063d71ed7c8963 100644
--- a/mlair/data_handler/data_handler_mixed_sampling.py
+++ b/mlair/data_handler/data_handler_mixed_sampling.py
@@ -12,6 +12,7 @@ import inspect
from typing import Callable
import datetime as dt
from typing import Any
+from functools import partial
import numpy as np
import pandas as pd
@@ -54,15 +55,9 @@ class DataHandlerMixedSamplingSingleStation(DataHandlerSingleStation):
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.
- """
+ def make_input_target(self):
self._data = list(map(self.load_and_interpolate, [0, 1])) # load input (0) and target (1) data
self.set_inputs_and_targets()
- if self.do_transformation is True:
- self.call_transform()
- self.make_samples()
def load_and_interpolate(self, ind) -> [xr.DataArray, pd.DataFrame]:
vars = [self.variables, self.target_var]
@@ -83,6 +78,12 @@ class DataHandlerMixedSamplingSingleStation(DataHandlerSingleStation):
assert len(sampling) == 2
return list(map(lambda x: super(__class__, self).setup_data_path(data_path, x), sampling))
+ def _extract_lazy(self, lazy_data):
+ _data, self.meta, _input_data, _target_data = lazy_data
+ f_prep = partial(self._slice_prep, start=self.start, end=self.end)
+ self._data = f_prep(_data[0]), f_prep(_data[1])
+ self.input_data, self.target_data = list(map(f_prep, [_input_data, _target_data]))
+
class DataHandlerMixedSampling(DefaultDataHandler):
"""Data handler using mixed sampling for input and target."""
@@ -104,19 +105,14 @@ class DataHandlerMixedSamplingWithFilterSingleStation(DataHandlerMixedSamplingSi
def _check_sampling(self, **kwargs):
assert kwargs.get("sampling") == ("hourly", "daily")
- def setup_samples(self):
+ def make_input_target(self):
"""
- Setup samples. This method prepares and creates samples X, and labels Y.
-
A KZ filter is applied on the input data that has hourly resolution. Lables Y are provided as aggregated values
with daily resolution.
"""
self._data = list(map(self.load_and_interpolate, [0, 1])) # load input (0) and target (1) data
self.set_inputs_and_targets()
self.apply_kz_filter()
- if self.do_transformation is True:
- self.call_transform()
- self.make_samples()
def estimate_filter_width(self):
"""
@@ -130,14 +126,24 @@ class DataHandlerMixedSamplingWithFilterSingleStation(DataHandlerMixedSamplingSi
new_date = dt.datetime.strptime(date, "%Y-%m-%d") + dt.timedelta(hours=delta)
return new_date.strftime("%Y-%m-%d")
- def load_and_interpolate(self, ind) -> [xr.DataArray, pd.DataFrame]:
-
+ def update_start_end(self, ind):
if ind == 0: # for inputs
estimated_filter_width = self.estimate_filter_width()
start = self._add_time_delta(self.start, -estimated_filter_width)
end = self._add_time_delta(self.end, estimated_filter_width)
else: # target
start, end = self.start, self.end
+ return start, end
+
+ def load_and_interpolate(self, ind) -> [xr.DataArray, pd.DataFrame]:
+
+ start, end = self.update_start_end(ind)
+ # if ind == 0: # for inputs
+ # estimated_filter_width = self.estimate_filter_width()
+ # start = self._add_time_delta(self.start, -estimated_filter_width)
+ # end = self._add_time_delta(self.end, estimated_filter_width)
+ # else: # target
+ # start, end = self.start, self.end
vars = [self.variables, self.target_var]
stats_per_var = helpers.select_from_dict(self.statistics_per_var, vars[ind])
@@ -149,6 +155,13 @@ class DataHandlerMixedSamplingWithFilterSingleStation(DataHandlerMixedSamplingSi
limit=self.interpolation_limit[ind])
return data
+ def _extract_lazy(self, lazy_data):
+ _data, self.meta, _input_data, _target_data, self.cutoff_period, self.cutoff_period_days = lazy_data
+ start_inp, end_inp = self.update_start_end(0)
+ self._data = list(map(self._slice_prep, _data, [start_inp, self.start], [end_inp, self.end]))
+ self.input_data = self._slice_prep(_input_data, start_inp, end_inp)
+ self.target_data = self._slice_prep(_target_data, self.start, self.end)
+
class DataHandlerMixedSamplingWithFilter(DefaultDataHandler):
"""Data handler using mixed sampling for input and target. Inputs are temporal filtered."""
@@ -169,6 +182,7 @@ class DataHandlerSeparationOfScalesSingleStation(DataHandlerMixedSamplingWithFil
"""
_requirements = DataHandlerMixedSamplingWithFilterSingleStation.requirements()
+ _hash = DataHandlerMixedSamplingWithFilterSingleStation._hash + ["time_delta"]
def __init__(self, *args, time_delta=np.sqrt, **kwargs):
assert isinstance(time_delta, Callable)
@@ -204,7 +218,7 @@ class DataHandlerSeparationOfScalesSingleStation(DataHandlerMixedSamplingWithFil
time_deltas = np.round(self.time_delta(self.cutoff_period)).astype(int)
start, end = window, 1
res = []
- window_array = self.create_index_array(self.window_dim.range(start, end), squeeze_dim=self.target_dim)
+ window_array = self.create_index_array(self.window_dim, range(start, end), squeeze_dim=self.target_dim)
for delta, filter_name in zip(np.append(time_deltas, 1), data.coords["filter"]):
res_filter = []
data_filter = data.sel({"filter": filter_name})
@@ -212,7 +226,7 @@ class DataHandlerSeparationOfScalesSingleStation(DataHandlerMixedSamplingWithFil
res_filter.append(data_filter.shift({dim: -w * delta}))
res_filter = xr.concat(res_filter, dim=window_array).chunk()
res.append(res_filter)
- res = xr.concat(res, dim="filter")
+ res = xr.concat(res, dim="filter").compute()
return res
def estimate_filter_width(self):
diff --git a/mlair/data_handler/data_handler_single_station.py b/mlair/data_handler/data_handler_single_station.py
index a894c635282b5879d79426168eb96d64ff5fa2a2..0497bee0ae6b6a72301181ef5453dd40f479e5af 100644
--- a/mlair/data_handler/data_handler_single_station.py
+++ b/mlair/data_handler/data_handler_single_station.py
@@ -5,9 +5,11 @@ __date__ = '2020-07-20'
import copy
import datetime as dt
+import dill
+import hashlib
import logging
import os
-from functools import reduce
+from functools import reduce, partial
from typing import Union, List, Iterable, Tuple, Dict, Optional
import numpy as np
@@ -45,6 +47,10 @@ class DataHandlerSingleStation(AbstractDataHandler):
DEFAULT_INTERPOLATION_LIMIT = 0
DEFAULT_INTERPOLATION_METHOD = "linear"
+ _hash = ["station", "statistics_per_var", "data_origin", "station_type", "network", "sampling", "target_dim",
+ "target_var", "time_dim", "iter_dim", "window_dim", "window_history_size", "window_history_offset",
+ "window_lead_time", "interpolation_limit", "interpolation_method"]
+
def __init__(self, station, data_path, statistics_per_var, station_type=DEFAULT_STATION_TYPE,
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,
@@ -54,10 +60,16 @@ class DataHandlerSingleStation(AbstractDataHandler):
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):
+ min_length: int = 0, start=None, end=None, variables=None, data_origin: Dict = None,
+ lazy_preprocessing: bool = False, **kwargs):
super().__init__()
self.station = helpers.to_list(station)
self.path = self.setup_data_path(data_path, sampling)
+ self.lazy = lazy_preprocessing
+ self.lazy_path = None
+ if self.lazy is True:
+ self.lazy_path = os.path.join(data_path, "lazy_data", self.__class__.__name__)
+ check_path_and_create(self.lazy_path)
self.statistics_per_var = statistics_per_var
self.data_origin = data_origin
self.do_transformation = transformation is not None
@@ -215,15 +227,46 @@ class DataHandlerSingleStation(AbstractDataHandler):
"""
Setup samples. This method prepares and creates samples X, and labels Y.
"""
+ if self.lazy is False:
+ self.make_input_target()
+ else:
+ self.load_lazy()
+ self.store_lazy()
+ if self.do_transformation is True:
+ self.call_transform()
+ self.make_samples()
+
+ def store_lazy(self):
+ hash = self._get_hash()
+ filename = os.path.join(self.lazy_path, hash + ".pickle")
+ if not os.path.exists(filename):
+ dill.dump(self._create_lazy_data(), file=open(filename, "wb"))
+
+ def _create_lazy_data(self):
+ return [self._data, self.meta, self.input_data, self.target_data]
+
+ def load_lazy(self):
+ hash = self._get_hash()
+ filename = os.path.join(self.lazy_path, hash + ".pickle")
+ try:
+ with open(filename, "rb") as pickle_file:
+ lazy_data = dill.load(pickle_file)
+ self._extract_lazy(lazy_data)
+ except FileNotFoundError:
+ self.make_input_target()
+
+ def _extract_lazy(self, lazy_data):
+ _data, self.meta, _input_data, _target_data = lazy_data
+ f_prep = partial(self._slice_prep, start=self.start, end=self.end)
+ self._data, self.input_data, self.target_data = list(map(f_prep, [_data, _input_data, _target_data]))
+
+ def make_input_target(self):
data, self.meta = self.load_data(self.path, self.station, self.statistics_per_var, self.sampling,
self.station_type, self.network, self.store_data_locally, self.data_origin,
self.start, self.end)
self._data = self.interpolate(data, dim=self.time_dim, method=self.interpolation_method,
limit=self.interpolation_limit)
self.set_inputs_and_targets()
- if self.do_transformation is True:
- self.call_transform()
- self.make_samples()
def set_inputs_and_targets(self):
inputs = self._data.sel({self.target_dim: helpers.to_list(self.variables)})
@@ -658,6 +701,13 @@ class DataHandlerSingleStation(AbstractDataHandler):
return self.transform(data, dim=dim, opts=self._transformation[pos], inverse=inverse,
transformation_dim=self.target_dim)
+ def _hash_list(self):
+ return sorted(list(set(self._hash)))
+
+ def _get_hash(self):
+ hash = "".join([str(self.__getattribute__(e)) for e in self._hash_list()]).encode()
+ return hashlib.md5(hash).hexdigest()
+
if __name__ == "__main__":
# dp = AbstractDataPrep('data/', 'dummy', 'DEBW107', ['o3', 'temp'], statistics_per_var={'o3': 'dma8eu', 'temp': 'maximum'})
diff --git a/mlair/data_handler/default_data_handler.py b/mlair/data_handler/default_data_handler.py
index ddf276cf2d88c108d8622c507471f989c4f99e8b..07a866aec1efd43de42f918844abeb7c3bbc9524 100644
--- a/mlair/data_handler/default_data_handler.py
+++ b/mlair/data_handler/default_data_handler.py
@@ -8,6 +8,7 @@ import gc
import logging
import os
import pickle
+import dill
import shutil
from functools import reduce
from typing import Tuple, Union, List
@@ -86,7 +87,7 @@ class DefaultDataHandler(AbstractDataHandler):
data = {"X": self._X, "Y": self._Y, "X_extreme": self._X_extreme, "Y_extreme": self._Y_extreme}
data = self._force_dask_computation(data)
with open(self._save_file, "wb") as f:
- pickle.dump(data, f)
+ dill.dump(data, f)
logging.debug(f"save pickle data to {self._save_file}")
self._reset_data()
@@ -101,7 +102,7 @@ class DefaultDataHandler(AbstractDataHandler):
def _load(self):
try:
with open(self._save_file, "rb") as f:
- data = pickle.load(f)
+ data = dill.load(f)
logging.debug(f"load pickle data from {self._save_file}")
self._X, self._Y = data["X"], data["Y"]
self._X_extreme, self._Y_extreme = data["X_extreme"], data["Y_extreme"]
diff --git a/mlair/data_handler/iterator.py b/mlair/data_handler/iterator.py
index 30c45417a64e949b0c0535a96a20c933641fdcbb..564bf3bfd6e4f5b814c9d090733cfbfbf26a850b 100644
--- a/mlair/data_handler/iterator.py
+++ b/mlair/data_handler/iterator.py
@@ -9,6 +9,7 @@ import math
import os
import shutil
import pickle
+import dill
from typing import Tuple, List
@@ -109,7 +110,7 @@ class KerasIterator(keras.utils.Sequence):
"""Load pickle data from disk."""
file = self._path % index
with open(file, "rb") as f:
- data = pickle.load(f)
+ data = dill.load(f)
return data["X"], data["Y"]
@staticmethod
@@ -167,7 +168,7 @@ class KerasIterator(keras.utils.Sequence):
data = {"X": X, "Y": Y}
file = self._path % index
with open(file, "wb") as f:
- pickle.dump(data, f)
+ dill.dump(data, f)
def _get_number_of_mini_batches(self, number_of_samples: int) -> int:
"""Return number of mini batches as the floored ration of number of samples to batch size."""
diff --git a/mlair/helpers/statistics.py b/mlair/helpers/statistics.py
index 3631597aedb90b3411163a42490e9c023bad706a..3e99357c36d556f093701325964500bf8d46c698 100644
--- a/mlair/helpers/statistics.py
+++ b/mlair/helpers/statistics.py
@@ -11,8 +11,10 @@ import pandas as pd
from typing import Union, Tuple, Dict, List
from matplotlib import pyplot as plt
import itertools
+import gc
+import warnings
-from mlair.helpers import to_list
+from mlair.helpers import to_list, TimeTracking, TimeTrackingWrapper
Data = Union[xr.DataArray, pd.DataFrame]
@@ -438,7 +440,7 @@ class SkillScores:
"""Calculate CASE IV."""
AI, BI, CI, data, suffix = self.skill_score_pre_calculations(internal_data, observation_name, forecast_name)
monthly_mean_external = self.create_monthly_mean_from_daily_data(external_data, index=data.index)
- data = xr.concat([data, monthly_mean_external], dim="type")
+ data = xr.concat([data, monthly_mean_external], dim="type").dropna(dim="index")
mean, sigma = suffix["mean"], suffix["sigma"]
mean_external = monthly_mean_external.mean()
sigma_external = np.sqrt(monthly_mean_external.var())
@@ -608,6 +610,48 @@ class KolmogorovZurbenkoFilterMovingWindow(KolmogorovZurbenkoBaseClass):
else:
return None
+ @TimeTrackingWrapper
+ def kz_filter_new(self, df, wl, itr):
+ """
+ It passes the low frequency time series.
+
+ If filter method is from mean, max, min this method will call construct and rechunk before the actual
+ calculation to improve performance. If filter method is either median or percentile this approach is not
+ applicable and depending on the data and window size, this method can become slow.
+
+ Args:
+ wl(int): a window length
+ itr(int): a number of iteration
+ """
+ warnings.filterwarnings("ignore")
+ df_itr = df.__deepcopy__()
+ try:
+ kwargs = {"min_periods": int(0.7 * wl),
+ "center": True,
+ self.filter_dim: wl}
+ for i in np.arange(0, itr):
+ print(i)
+ rolling = df_itr.chunk().rolling(**kwargs)
+ if self.method not in ["percentile", "median"]:
+ rolling = rolling.construct("construct").chunk("auto")
+ if self.method == "median":
+ df_mv_avg_tmp = rolling.median()
+ elif self.method == "percentile":
+ df_mv_avg_tmp = rolling.quantile(self.percentile)
+ elif self.method == "max":
+ df_mv_avg_tmp = rolling.max("construct")
+ elif self.method == "min":
+ df_mv_avg_tmp = rolling.min("construct")
+ else:
+ df_mv_avg_tmp = rolling.mean("construct")
+ df_itr = df_mv_avg_tmp.compute()
+ del df_mv_avg_tmp, rolling
+ gc.collect()
+ return df_itr
+ except ValueError:
+ raise ValueError
+
+ @TimeTrackingWrapper
def kz_filter(self, df, wl, itr):
"""
It passes the low frequency time series.
@@ -616,15 +660,18 @@ class KolmogorovZurbenkoFilterMovingWindow(KolmogorovZurbenkoBaseClass):
wl(int): a window length
itr(int): a number of iteration
"""
+ import warnings
+ warnings.filterwarnings("ignore")
df_itr = df.__deepcopy__()
try:
- kwargs = {"min_periods": 1,
+ kwargs = {"min_periods": int(0.7 * wl),
"center": True,
self.filter_dim: wl}
iter_vars = df_itr.coords["variables"].values
for var in iter_vars:
- df_itr_var = df_itr.sel(variables=[var]).chunk()
+ df_itr_var = df_itr.sel(variables=[var])
for _ in np.arange(0, itr):
+ df_itr_var = df_itr_var.chunk()
rolling = df_itr_var.rolling(**kwargs)
if self.method == "median":
df_mv_avg_tmp = rolling.median()
@@ -637,7 +684,7 @@ class KolmogorovZurbenkoFilterMovingWindow(KolmogorovZurbenkoBaseClass):
else:
df_mv_avg_tmp = rolling.mean()
df_itr_var = df_mv_avg_tmp.compute()
- df_itr = df_itr.drop_sel(variables=var).combine_first(df_itr_var)
+ df_itr.loc[{"variables": [var]}] = df_itr_var
return df_itr
except ValueError:
raise ValueError
diff --git a/requirements.txt b/requirements.txt
index b0a6e7f59896fd0edf08977ee553c803f6c2e960..85655e237f8e10e98f77c379be6acd0a7bb65d46 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,6 +2,7 @@ absl-py==0.11.0
appdirs==1.4.4
astor==0.8.1
attrs==20.3.0
+bottleneck==1.3.2
cached-property==1.5.2
certifi==2020.12.5
cftime==1.4.1
@@ -9,6 +10,7 @@ chardet==4.0.0
coverage==5.4
cycler==0.10.0
dask==2021.2.0
+dill==0.3.3
fsspec==0.8.5
gast==0.4.0
grpcio==1.35.0
diff --git a/requirements_gpu.txt b/requirements_gpu.txt
index 35fe0d5ee2a03f01737bc185d2a5bbaf26383806..cc189496bdf4e1e1ee86902a1953c2058d58c8e4 100644
--- a/requirements_gpu.txt
+++ b/requirements_gpu.txt
@@ -2,6 +2,7 @@ absl-py==0.11.0
appdirs==1.4.4
astor==0.8.1
attrs==20.3.0
+bottleneck==1.3.2
cached-property==1.5.2
certifi==2020.12.5
cftime==1.4.1
@@ -9,6 +10,7 @@ chardet==4.0.0
coverage==5.4
cycler==0.10.0
dask==2021.2.0
+dill==0.3.3
fsspec==0.8.5
gast==0.4.0
grpcio==1.35.0
diff --git a/test/test_data_handler/test_data_handler_mixed_sampling.py b/test/test_data_handler/test_data_handler_mixed_sampling.py
index d2f9ce00224a61815c89e44b7c37a667d239b2f5..2a6553b7f495bb4eb8aeddf7c39f2f2517edc967 100644
--- a/test/test_data_handler/test_data_handler_mixed_sampling.py
+++ b/test/test_data_handler/test_data_handler_mixed_sampling.py
@@ -37,7 +37,7 @@ class TestDataHandlerMixedSamplingSingleStation:
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")
+ @mock.patch("mlair.data_handler.data_handler_single_station.DataHandlerSingleStation.setup_samples")
def test_init(self, mock_super_init):
obj = DataHandlerMixedSamplingSingleStation("first_arg", "second", {}, test=23, sampling="hourly",
interpolation_limit=(1, 10))