From 483d6ca9c856cd2e7949bf5d56930be2a470b01b Mon Sep 17 00:00:00 2001
From: lukas leufen <l.leufen@fz-juelich.de>
Date: Tue, 30 Jun 2020 11:58:44 +0200
Subject: [PATCH] intermediate save point
---
src/data_handling/data_preparation.py | 81 +--
src/data_handling/data_preparation_join.py | 591 +++++++++++++++++++++
2 files changed, 608 insertions(+), 64 deletions(-)
create mode 100644 src/data_handling/data_preparation_join.py
diff --git a/src/data_handling/data_preparation.py b/src/data_handling/data_preparation.py
index fa7388e7..e85d8a3a 100644
--- a/src/data_handling/data_preparation.py
+++ b/src/data_handling/data_preparation.py
@@ -1,7 +1,7 @@
"""Data Preparation class to handle data processing for machine learning."""
-__author__ = 'Felix Kleinert, Lukas Leufen'
-__date__ = '2019-10-16'
+__author__ = 'Lukas Leufen'
+__date__ = '2020-06-29'
import datetime as dt
import logging
@@ -25,7 +25,7 @@ num_or_list = Union[number, List[number]]
data_or_none = Union[xr.DataArray, None]
-class DataPrep(object):
+class AbstractDataPrep(object):
"""
This class prepares data to be used in neural networks.
@@ -55,14 +55,11 @@ class DataPrep(object):
"""
- def __init__(self, path: str, network: str, station: Union[str, List[str]], variables: List[str],
- station_type: str = None, **kwargs):
+ def __init__(self, path: str, station: Union[str, List[str]], variables: List[str], **kwargs):
"""Construct instance."""
self.path = os.path.abspath(path)
- self.network = network
self.station = helpers.to_list(station)
self.variables = variables
- self.station_type = station_type
self.mean: data_or_none = None
self.std: data_or_none = None
self.history: data_or_none = None
@@ -83,7 +80,7 @@ class DataPrep(object):
def load_data(self):
"""
- Load data and meta data either from local disk (preferred) or download new data from TOAR database.
+ Load data and meta data either from local disk (preferred) or download new data by using a custom download method.
Data is either downloaded, if no local data is available or parameter overwrite_local_data is true. In both
cases, downloaded data is only stored locally if store_data_locally is not disabled. If this parameter is not
@@ -103,70 +100,26 @@ class DataPrep(object):
else:
try:
logging.debug(f"try to load local data from: {file_name}")
- data = self._slice_prep(xr.open_dataarray(file_name))
- self.data = self.check_for_negative_concentrations(data)
+ data = xr.open_dataarray(file_name)
self.meta = pd.read_csv(meta_file, index_col=0)
- if self.station_type is not None:
- self.check_station_meta()
logging.debug("loading finished")
except FileNotFoundError as e:
logging.debug(e)
- self.download_data(file_name, meta_file)
- logging.debug("loaded new data from JOIN")
+ logging.debug("load new data from JOIN")
+ data, self.meta = self.download_data(file_name, meta_file)
+ logging.debug("loading finished")
+ # create slices and check for negative concentration.
+ data = self._slice_prep(data)
+ self.data = self.check_for_negative_concentrations(data)
- def download_data(self, file_name, meta_file):
+ def download_data(self, file_name, meta_file) -> [xr.DataArray, pd.DataFrame]:
"""
- Download data from join, create slices and check for negative concentration.
-
- Handle sequence of required operation on new data downloads. First, download data using class method
- download_data_from_join. Second, slice data using _slice_prep and lastly check for negative concentrations in
- data with check_for_negative_concentrations. Finally, data is stored in instance attribute data.
+ Download data and meta.
:param file_name: name of file to save data to (containing full path)
:param meta_file: name of the meta data file (also containing full path)
"""
- data, self.meta = self.download_data_from_join(file_name, meta_file)
- data = self._slice_prep(data)
- self.data = self.check_for_negative_concentrations(data)
-
- def check_station_meta(self):
- """
- Search for the entries in meta data and compare the value with the requested values.
-
- Will raise a FileNotFoundError if the values mismatch.
- """
- check_dict = {"station_type": self.station_type, "network_name": self.network}
- for (k, v) in check_dict.items():
- if self.meta.at[k, self.station[0]] != v:
- logging.debug(f"meta data does not agree with given request for {k}: {v} (requested) != "
- f"{self.meta.at[k, self.station[0]]} (local). Raise FileNotFoundError to trigger new "
- f"grapping from web.")
- raise FileNotFoundError
-
- def download_data_from_join(self, file_name: str, meta_file: str) -> [xr.DataArray, pd.DataFrame]:
- """
- Download data from TOAR database using the JOIN interface.
-
- Data is transformed to a xarray dataset. If class attribute store_data_locally is true, data is additionally
- stored locally using given names for file and meta file.
-
- :param file_name: name of file to save data to (containing full path)
- :param meta_file: name of the meta data file (also containing full path)
-
- :return: downloaded data and its meta data
- """
- df_all = {}
- df, meta = join.download_join(station_name=self.station, stat_var=self.statistics_per_var,
- station_type=self.station_type, network_name=self.network, sampling=self.sampling)
- df_all[self.station[0]] = df
- # convert df_all to xarray
- xarr = {k: xr.DataArray(v, dims=['datetime', 'variables']) for k, v in df_all.items()}
- xarr = xr.Dataset(xarr).to_array(dim='Stations')
- if self.kwargs.get('store_data_locally', True):
- # save locally as nc/csv file
- xarr.to_netcdf(path=file_name)
- meta.to_csv(meta_file)
- return xarr, meta
+ raise NotImplementedError
def _set_file_name(self):
all_vars = sorted(self.statistics_per_var.keys())
@@ -178,8 +131,8 @@ class DataPrep(object):
def __repr__(self):
"""Represent class attributes."""
- return f"Dataprep(path='{self.path}', network='{self.network}', station={self.station}, " \
- f"variables={self.variables}, station_type={self.station_type}, **{self.kwargs})"
+ return f"AbstractDataPrep(path='{self.path}', station={self.station}, variables={self.variables}, " \
+ f"**{self.kwargs})"
def interpolate(self, dim: str, method: str = 'linear', limit: int = None, use_coordinate: Union[bool, str] = True,
**kwargs):
diff --git a/src/data_handling/data_preparation_join.py b/src/data_handling/data_preparation_join.py
new file mode 100644
index 00000000..43138911
--- /dev/null
+++ b/src/data_handling/data_preparation_join.py
@@ -0,0 +1,591 @@
+"""Data Preparation class to handle data processing for machine learning."""
+
+__author__ = 'Felix Kleinert, Lukas Leufen'
+__date__ = '2019-10-16'
+
+import datetime as dt
+import logging
+import os
+from functools import reduce
+from typing import Union, List, Iterable, Tuple
+
+import numpy as np
+import pandas as pd
+import xarray as xr
+
+from src.configuration import check_path_and_create
+from src import helpers
+from src.helpers import join, statistics
+
+# define a more general date type for type hinting
+date = Union[dt.date, dt.datetime]
+str_or_list = Union[str, List[str]]
+number = Union[float, int]
+num_or_list = Union[number, List[number]]
+data_or_none = Union[xr.DataArray, None]
+
+
+class DataPrep(object):
+ """
+ This class prepares data to be used in neural networks.
+
+ The instance searches for local stored data, that meet the given demands. If no local data is found, the DataPrep
+ instance will load data from TOAR database and store this data locally to use the next time. For the moment, there
+ is only support for daily aggregated time series. The aggregation can be set manually and differ for each variable.
+
+ After data loading, different data pre-processing steps can be executed to prepare the data for further
+ applications. Especially the following methods can be used for the pre-processing step:
+
+ - interpolate: interpolate between data points by using xarray's interpolation method
+ - standardise: standardise data to mean=1 and std=1, centralise to mean=0, additional methods like normalise on \
+ interval [0, 1] are not implemented yet.
+ - make window history: represent the history (time steps before) for training/ testing; X
+ - make labels: create target vector with given leading time steps for training/ testing; y
+ - remove Nans jointly from desired input and output, only keeps time steps where no NaNs are present in X AND y. \
+ Use this method after the creation of the window history and labels to clean up the data cube.
+
+ To create a DataPrep instance, it is needed to specify the stations by id (e.g. "DEBW107"), its network (e.g. UBA,
+ "Umweltbundesamt") and the variables to use. Further options can be set in the instance.
+
+ * `statistics_per_var`: define a specific statistic to extract from the TOAR database for each variable.
+ * `start`: define a start date for the data cube creation. Default: Use the first entry in time series
+ * `end`: set the end date for the data cube. Default: Use last date in time series.
+ * `store_data_locally`: store recently downloaded data on local disk. Default: True
+ * set further parameters for xarray's interpolation methods to modify the interpolation scheme
+
+ """
+
+ def __init__(self, path: str, network: str, station: Union[str, List[str]], variables: List[str],
+ station_type: str = None, **kwargs):
+ """Construct instance."""
+ self.path = os.path.abspath(path)
+ self.network = network
+ self.station = helpers.to_list(station)
+ self.variables = variables
+ self.station_type = station_type
+ self.mean: data_or_none = None
+ self.std: data_or_none = None
+ self.history: data_or_none = None
+ self.label: data_or_none = None
+ self.observation: data_or_none = None
+ self.extremes_history: data_or_none = None
+ self.extremes_label: data_or_none = None
+ self.kwargs = kwargs
+ self.data = None
+ self.meta = None
+ self._transform_method = None
+ self.statistics_per_var = kwargs.get("statistics_per_var", None)
+ self.sampling = kwargs.get("sampling", "daily")
+ if self.statistics_per_var is not None or self.sampling == "hourly":
+ self.load_data()
+ else:
+ raise NotImplementedError("Either select hourly data or provide statistics_per_var.")
+
+ def load_data(self):
+ """
+ Load data and meta data either from local disk (preferred) or download new data from TOAR database.
+
+ Data is either downloaded, if no local data is available or parameter overwrite_local_data is true. In both
+ cases, downloaded data is only stored locally if store_data_locally is not disabled. If this parameter is not
+ set, it is assumed, that data should be saved locally.
+ """
+ check_path_and_create(self.path)
+ file_name = self._set_file_name()
+ meta_file = self._set_meta_file_name()
+ if self.kwargs.get('overwrite_local_data', False):
+ logging.debug(f"overwrite_local_data is true, therefore reload {file_name} from JOIN")
+ if os.path.exists(file_name):
+ os.remove(file_name)
+ if os.path.exists(meta_file):
+ os.remove(meta_file)
+ self.download_data(file_name, meta_file)
+ logging.debug("loaded new data from JOIN")
+ else:
+ try:
+ logging.debug(f"try to load local data from: {file_name}")
+ data = xr.open_dataarray(file_name)
+ self.meta = pd.read_csv(meta_file, index_col=0)
+ if self.station_type is not None:
+ self.check_station_meta()
+ logging.debug("loading finished")
+ except FileNotFoundError as e:
+ logging.debug(e)
+ logging.debug("load new data from JOIN")
+ data, self.meta = self.download_data(file_name, meta_file)
+ logging.debug("loading finished")
+ # create slices and check for negative concentration.
+ data = self._slice_prep(data)
+ self.data = self.check_for_negative_concentrations(data)
+
+ def download_data(self, file_name, meta_file):
+ """
+ Download data and meta from join.
+
+ :param file_name: name of file to save data to (containing full path)
+ :param meta_file: name of the meta data file (also containing full path)
+ """
+ data, meta = self.download_data_from_join(file_name, meta_file)
+ return data, meta
+
+ def check_station_meta(self):
+ """
+ Search for the entries in meta data and compare the value with the requested values.
+
+ Will raise a FileNotFoundError if the values mismatch.
+ """
+ check_dict = {"station_type": self.station_type, "network_name": self.network}
+ for (k, v) in check_dict.items():
+ if self.meta.at[k, self.station[0]] != v:
+ logging.debug(f"meta data does not agree with given request for {k}: {v} (requested) != "
+ f"{self.meta.at[k, self.station[0]]} (local). Raise FileNotFoundError to trigger new "
+ f"grapping from web.")
+ raise FileNotFoundError
+
+ def download_data_from_join(self, file_name: str, meta_file: str) -> [xr.DataArray, pd.DataFrame]:
+ """
+ Download data from TOAR database using the JOIN interface.
+
+ Data is transformed to a xarray dataset. If class attribute store_data_locally is true, data is additionally
+ stored locally using given names for file and meta file.
+
+ :param file_name: name of file to save data to (containing full path)
+ :param meta_file: name of the meta data file (also containing full path)
+
+ :return: downloaded data and its meta data
+ """
+ df_all = {}
+ df, meta = join.download_join(station_name=self.station, stat_var=self.statistics_per_var,
+ station_type=self.station_type, network_name=self.network, sampling=self.sampling)
+ df_all[self.station[0]] = df
+ # convert df_all to xarray
+ xarr = {k: xr.DataArray(v, dims=['datetime', 'variables']) for k, v in df_all.items()}
+ xarr = xr.Dataset(xarr).to_array(dim='Stations')
+ if self.kwargs.get('store_data_locally', True):
+ # save locally as nc/csv file
+ xarr.to_netcdf(path=file_name)
+ meta.to_csv(meta_file)
+ return xarr, meta
+
+ def _set_file_name(self):
+ all_vars = sorted(self.statistics_per_var.keys())
+ return os.path.join(self.path, f"{''.join(self.station)}_{'_'.join(all_vars)}.nc")
+
+ def _set_meta_file_name(self):
+ all_vars = sorted(self.statistics_per_var.keys())
+ return os.path.join(self.path, f"{''.join(self.station)}_{'_'.join(all_vars)}_meta.csv")
+
+ def __repr__(self):
+ """Represent class attributes."""
+ return f"Dataprep(path='{self.path}', network='{self.network}', station={self.station}, " \
+ f"variables={self.variables}, station_type={self.station_type}, **{self.kwargs})"
+
+ def interpolate(self, dim: str, method: str = 'linear', limit: int = None, use_coordinate: Union[bool, str] = True,
+ **kwargs):
+ """
+ Interpolate values according to different methods.
+
+ (Copy paste from dataarray.interpolate_na)
+
+ :param dim:
+ Specifies the dimension along which to interpolate.
+ :param method:
+ {'linear', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic',
+ 'polynomial', 'barycentric', 'krog', 'pchip',
+ 'spline', 'akima'}, optional
+ String indicating which method to use for interpolation:
+
+ - 'linear': linear interpolation (Default). Additional keyword
+ arguments are passed to ``numpy.interp``
+ - 'nearest', 'zero', 'slinear', 'quadratic', 'cubic',
+ 'polynomial': are passed to ``scipy.interpolate.interp1d``. If
+ method=='polynomial', the ``order`` keyword argument must also be
+ provided.
+ - 'barycentric', 'krog', 'pchip', 'spline', and `akima`: use their
+ respective``scipy.interpolate`` classes.
+ :param limit:
+ default None
+ Maximum number of consecutive NaNs to fill. Must be greater than 0
+ or None for no limit.
+ :param use_coordinate:
+ default True
+ Specifies which index to use as the x values in the interpolation
+ formulated as `y = f(x)`. If False, values are treated as if
+ eqaully-spaced along `dim`. If True, the IndexVariable `dim` is
+ used. If use_coordinate is a string, it specifies the name of a
+ coordinate variariable to use as the index.
+ :param kwargs:
+
+ :return: xarray.DataArray
+ """
+ self.data = self.data.interpolate_na(dim=dim, method=method, limit=limit, use_coordinate=use_coordinate,
+ **kwargs)
+
+ @staticmethod
+ def check_inverse_transform_params(mean: data_or_none, std: data_or_none, method: str) -> None:
+ """
+ Support inverse_transformation method.
+
+ Validate if all required statistics are available for given method. E.g. centering requires mean only, whereas
+ normalisation requires mean and standard deviation. Will raise an AttributeError on missing requirements.
+
+ :param mean: data with all mean values
+ :param std: data with all standard deviation values
+ :param method: name of transformation method
+ """
+ msg = ""
+ if method in ['standardise', 'centre'] and mean is None:
+ msg += "mean, "
+ if method == 'standardise' and std is None:
+ msg += "std, "
+ if len(msg) > 0:
+ raise AttributeError(f"Inverse transform {method} can not be executed because following is None: {msg}")
+
+ def inverse_transform(self) -> None:
+ """
+ Perform inverse transformation.
+
+ Will raise an AssertionError, if no transformation was performed before. Checks first, if all required
+ statistics are available for inverse transformation. Class attributes data, mean and std are overwritten by
+ new data afterwards. Thereby, mean, std, and the private transform method are set to None to indicate, that the
+ current data is not transformed.
+ """
+
+ def f_inverse(data, mean, std, method_inverse):
+ if method_inverse == 'standardise':
+ return statistics.standardise_inverse(data, mean, std), None, None
+ elif method_inverse == 'centre':
+ return statistics.centre_inverse(data, mean), None, None
+ elif method_inverse == 'normalise':
+ raise NotImplementedError
+ else:
+ raise NotImplementedError
+
+ if self._transform_method is None:
+ raise AssertionError("Inverse transformation method is not set. Data cannot be inverse transformed.")
+ self.check_inverse_transform_params(self.mean, self.std, self._transform_method)
+ self.data, self.mean, self.std = f_inverse(self.data, self.mean, self.std, self._transform_method)
+ self._transform_method = None
+
+ def transform(self, dim: Union[str, int] = 0, method: str = 'standardise', inverse: bool = False, mean=None,
+ std=None) -> None:
+ """
+ Transform data according to given transformation settings.
+
+ This function transforms a xarray.dataarray (along dim) or pandas.DataFrame (along axis) either with mean=0
+ and std=1 (`method=standardise`) or centers the data with mean=0 and no change in data scale
+ (`method=centre`). Furthermore, this sets an internal instance attribute for later inverse transformation. This
+ method will raise an AssertionError if an internal transform method was already set ('inverse=False') or if the
+ internal transform method, internal mean and internal standard deviation weren't set ('inverse=True').
+
+ :param string/int dim: This param is not used for inverse transformation.
+ | for xarray.DataArray as string: name of dimension which should be standardised
+ | for pandas.DataFrame as int: axis of dimension which should be standardised
+ :param method: Choose the transformation method from 'standardise' and 'centre'. 'normalise' is not implemented
+ yet. This param is not used for inverse transformation.
+ :param inverse: Switch between transformation and inverse transformation.
+
+ :return: xarray.DataArrays or pandas.DataFrames:
+ #. mean: Mean of data
+ #. std: Standard deviation of data
+ #. data: Standardised data
+ """
+
+ def f(data):
+ if method == 'standardise':
+ return statistics.standardise(data, dim)
+ elif method == 'centre':
+ return statistics.centre(data, dim)
+ elif method == 'normalise':
+ # use min/max of data or given min/max
+ raise NotImplementedError
+ else:
+ raise NotImplementedError
+
+ def f_apply(data):
+ if method == "standardise":
+ return mean, std, statistics.standardise_apply(data, mean, std)
+ elif method == "centre":
+ return mean, None, statistics.centre_apply(data, mean)
+ else:
+ raise NotImplementedError
+
+ if not inverse:
+ if self._transform_method is not None:
+ raise AssertionError(f"Transform method is already set. Therefore, data was already transformed with "
+ f"{self._transform_method}. Please perform inverse transformation of data first.")
+ self.mean, self.std, self.data = locals()["f" if mean is None else "f_apply"](self.data)
+ self._transform_method = method
+ else:
+ self.inverse_transform()
+
+ def get_transformation_information(self, variable: str) -> Tuple[data_or_none, data_or_none, str]:
+ """
+ Extract transformation statistics and method.
+
+ Get mean and standard deviation for given variable and the transformation method if set. If a transformation
+ depends only on particular statistics (e.g. only mean is required for centering), the remaining statistics are
+ returned with None as fill value.
+
+ :param variable: Variable for which the information on transformation is requested.
+
+ :return: mean, standard deviation and transformation method
+ """
+ try:
+ mean = self.mean.sel({'variables': variable}).values
+ except AttributeError:
+ mean = None
+ try:
+ std = self.std.sel({'variables': variable}).values
+ except AttributeError:
+ std = None
+ return mean, std, self._transform_method
+
+ def make_history_window(self, dim_name_of_inputs: str, window: int, dim_name_of_shift: str) -> None:
+ """
+ Create a xr.DataArray containing history data.
+
+ Shift the data window+1 times and return a xarray which has a new dimension 'window' containing the shifted
+ data. This is used to represent history in the data. Results are stored in history attribute.
+
+ :param dim_name_of_inputs: Name of dimension which contains the input variables
+ :param window: number of time steps to look back in history
+ Note: window will be treated as negative value. This should be in agreement with looking back on
+ a time line. Nonetheless positive values are allowed but they are converted to its negative
+ expression
+ :param dim_name_of_shift: Dimension along shift will be applied
+ """
+ window = -abs(window)
+ self.history = self.shift(dim_name_of_shift, window).sel({dim_name_of_inputs: self.variables})
+
+ def shift(self, dim: str, window: int) -> xr.DataArray:
+ """
+ Shift data multiple times to represent history (if window <= 0) or lead time (if window > 0).
+
+ :param dim: dimension along shift is applied
+ :param window: number of steps to shift (corresponds to the window length)
+
+ :return: shifted data
+ """
+ start = 1
+ end = 1
+ if window <= 0:
+ start = window
+ else:
+ end = window + 1
+ res = []
+ for w in range(start, end):
+ res.append(self.data.shift({dim: -w}))
+ window_array = self.create_index_array('window', range(start, end))
+ res = xr.concat(res, dim=window_array)
+ return res
+
+ def make_labels(self, dim_name_of_target: str, target_var: str_or_list, dim_name_of_shift: str,
+ window: int) -> None:
+ """
+ Create a xr.DataArray containing labels.
+
+ Labels are defined as the consecutive target values (t+1, ...t+n) following the current time step t. Set label
+ attribute.
+
+ :param dim_name_of_target: Name of dimension which contains the target variable
+ :param target_var: Name of target variable in 'dimension'
+ :param dim_name_of_shift: Name of dimension on which xarray.DataArray.shift will be applied
+ :param window: lead time of label
+ """
+ window = abs(window)
+ self.label = self.shift(dim_name_of_shift, window).sel({dim_name_of_target: target_var})
+
+ def make_observation(self, dim_name_of_target: str, target_var: str_or_list, dim_name_of_shift: str) -> None:
+ """
+ Create a xr.DataArray containing observations.
+
+ Observations are defined as value of the current time step t. Set observation attribute.
+
+ :param dim_name_of_target: Name of dimension which contains the observation variable
+ :param target_var: Name of observation variable(s) in 'dimension'
+ :param dim_name_of_shift: Name of dimension on which xarray.DataArray.shift will be applied
+ """
+ self.observation = self.shift(dim_name_of_shift, 0).sel({dim_name_of_target: target_var})
+
+ def remove_nan(self, dim: str) -> None:
+ """
+ Remove all NAs slices along dim which contain nans in history, label and observation.
+
+ This is done to present only a full matrix to keras.fit. Update history, label, and observation attribute.
+
+ :param dim: dimension along the remove is performed.
+ """
+ intersect = []
+ if (self.history is not None) and (self.label is not None):
+ non_nan_history = self.history.dropna(dim=dim)
+ non_nan_label = self.label.dropna(dim=dim)
+ non_nan_observation = self.observation.dropna(dim=dim)
+ intersect = reduce(np.intersect1d, (non_nan_history.coords[dim].values, non_nan_label.coords[dim].values,
+ non_nan_observation.coords[dim].values))
+
+ min_length = self.kwargs.get("min_length", 0)
+ if len(intersect) < max(min_length, 1):
+ self.history = None
+ self.label = None
+ self.observation = None
+ else:
+ self.history = self.history.sel({dim: intersect})
+ self.label = self.label.sel({dim: intersect})
+ self.observation = self.observation.sel({dim: intersect})
+
+ @staticmethod
+ def create_index_array(index_name: str, index_value: Iterable[int]) -> xr.DataArray:
+ """
+ Create an 1D xr.DataArray with given index name and value.
+
+ :param index_name: name of dimension
+ :param index_value: values of this dimension
+
+ :return: this array
+ """
+ ind = pd.DataFrame({'val': index_value}, index=index_value)
+ res = xr.Dataset.from_dataframe(ind).to_array().rename({'index': index_name}).squeeze(dim='variable', drop=True)
+ res.name = index_name
+ return res
+
+ def _slice_prep(self, data: xr.DataArray, coord: str = 'datetime') -> xr.DataArray:
+ """
+ Set start and end date for slicing and execute self._slice().
+
+ :param data: data to slice
+ :param coord: name of axis to slice
+
+ :return: sliced data
+ """
+ start = self.kwargs.get('start', data.coords[coord][0].values)
+ end = self.kwargs.get('end', data.coords[coord][-1].values)
+ return self._slice(data, start, end, coord)
+
+ @staticmethod
+ def _slice(data: xr.DataArray, start: Union[date, str], end: Union[date, str], coord: str) -> xr.DataArray:
+ """
+ Slice through a given data_item (for example select only values of 2011).
+
+ :param data: data to slice
+ :param start: start date of slice
+ :param end: end date of slice
+ :param coord: name of axis to slice
+
+ :return: sliced data
+ """
+ return data.loc[{coord: slice(str(start), str(end))}]
+
+ def check_for_negative_concentrations(self, data: xr.DataArray, minimum: int = 0) -> xr.DataArray:
+ """
+ Set all negative concentrations to zero.
+
+ Names of all concentrations are extracted from https://join.fz-juelich.de/services/rest/surfacedata/
+ #2.1 Parameters. Currently, this check is applied on "benzene", "ch4", "co", "ethane", "no", "no2", "nox",
+ "o3", "ox", "pm1", "pm10", "pm2p5", "propane", "so2", and "toluene".
+
+ :param data: data array containing variables to check
+ :param minimum: minimum value, by default this should be 0
+
+ :return: corrected data
+ """
+ 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.variables))
+ data.loc[..., used_chem_vars] = data.loc[..., used_chem_vars].clip(min=minimum)
+ return data
+
+ def get_transposed_history(self) -> xr.DataArray:
+ """Return history.
+
+ :return: history with dimensions datetime, window, Stations, variables.
+ """
+ return self.history.transpose("datetime", "window", "Stations", "variables").copy()
+
+ def get_transposed_label(self) -> xr.DataArray:
+ """Return label.
+
+ :return: label with dimensions datetime, window, Stations, variables.
+ """
+ return self.label.squeeze("Stations").transpose("datetime", "window").copy()
+
+ def get_extremes_history(self) -> xr.DataArray:
+ """Return extremes history.
+
+ :return: extremes history with dimensions datetime, window, Stations, variables.
+ """
+ return self.extremes_history.transpose("datetime", "window", "Stations", "variables").copy()
+
+ def get_extremes_label(self) -> xr.DataArray:
+ """Return extremes label.
+
+ :return: extremes label with dimensions datetime, window, Stations, variables.
+ """
+ return self.extremes_label.squeeze("Stations").transpose("datetime", "window").copy()
+
+ def multiply_extremes(self, extreme_values: num_or_list = 1., extremes_on_right_tail_only: bool = False,
+ timedelta: Tuple[int, str] = (1, 'm')):
+ """
+ Multiply extremes.
+
+ This method extracts extreme values from self.labels which are defined in the argument extreme_values. One can
+ also decide only to extract extremes on the right tail of the distribution. When extreme_values is a list of
+ floats/ints all values larger (and smaller than negative extreme_values; extraction is performed in standardised
+ space) than are extracted iteratively. If for example extreme_values = [1.,2.] then a value of 1.5 would be
+ extracted once (for 0th entry in list), while a 2.5 would be extracted twice (once for each entry). Timedelta is
+ used to mark those extracted values by adding one min to each timestamp. As TOAR Data are hourly one can
+ identify those "artificial" data points later easily. Extreme inputs and labels are stored in
+ self.extremes_history and self.extreme_labels, respectively.
+
+ :param extreme_values: user definition of extreme
+ :param extremes_on_right_tail_only: if False also multiply values which are smaller then -extreme_values,
+ if True only extract values larger than extreme_values
+ :param timedelta: used as arguments for np.timedelta in order to mark extreme values on datetime
+ """
+ # check if labels or history is None
+ if (self.label is None) or (self.history is None):
+ logging.debug(f"{self.station} has `None' labels, skip multiply extremes")
+ return
+
+ # check type if inputs
+ extreme_values = helpers.to_list(extreme_values)
+ for i in extreme_values:
+ if not isinstance(i, number.__args__):
+ raise TypeError(f"Elements of list extreme_values have to be {number.__args__}, but at least element "
+ f"{i} is type {type(i)}")
+
+ for extr_val in sorted(extreme_values):
+ # check if some extreme values are already extracted
+ if (self.extremes_label is None) or (self.extremes_history is None):
+ # extract extremes based on occurance in labels
+ if extremes_on_right_tail_only:
+ extreme_label_idx = (self.label > extr_val).any(axis=0).values.reshape(-1, )
+ else:
+ extreme_label_idx = np.concatenate(((self.label < -extr_val).any(axis=0).values.reshape(-1, 1),
+ (self.label > extr_val).any(axis=0).values.reshape(-1, 1)),
+ axis=1).any(axis=1)
+ extremes_label = self.label[..., extreme_label_idx]
+ extremes_history = self.history[..., extreme_label_idx, :]
+ extremes_label.datetime.values += np.timedelta64(*timedelta)
+ extremes_history.datetime.values += np.timedelta64(*timedelta)
+ self.extremes_label = extremes_label # .squeeze('Stations').transpose('datetime', 'window')
+ self.extremes_history = extremes_history # .transpose('datetime', 'window', 'Stations', 'variables')
+ else: # one extr value iteration is done already: self.extremes_label is NOT None...
+ if extremes_on_right_tail_only:
+ extreme_label_idx = (self.extremes_label > extr_val).any(axis=0).values.reshape(-1, )
+ else:
+ extreme_label_idx = np.concatenate(
+ ((self.extremes_label < -extr_val).any(axis=0).values.reshape(-1, 1),
+ (self.extremes_label > extr_val).any(axis=0).values.reshape(-1, 1)
+ ), axis=1).any(axis=1)
+ # check on existing extracted extremes to minimise computational costs for comparison
+ extremes_label = self.extremes_label[..., extreme_label_idx]
+ extremes_history = self.extremes_history[..., extreme_label_idx, :]
+ extremes_label.datetime.values += np.timedelta64(*timedelta)
+ extremes_history.datetime.values += np.timedelta64(*timedelta)
+ self.extremes_label = xr.concat([self.extremes_label, extremes_label], dim='datetime')
+ self.extremes_history = xr.concat([self.extremes_history, extremes_history], dim='datetime')
+
+
+if __name__ == "__main__":
+ dp = DataPrep('data/', 'dummy', 'DEBW107', ['o3', 'temp'], statistics_per_var={'o3': 'dma8eu', 'temp': 'maximum'})
+ print(dp)
--
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