diff --git a/src/data_handling/data_preparation.py b/src/data_handling/data_preparation.py
index f2c27c6c4c5dda27236c5c5b3bf8e59e776862a1..70722b1f2e3a4521a26c3eda46104bfb481fecf1 100644
--- a/src/data_handling/data_preparation.py
+++ b/src/data_handling/data_preparation.py
@@ -25,6 +25,387 @@ num_or_list = Union[number, List[number]]
data_or_none = Union[xr.DataArray, None]
+class AbstractStationPrep():
+ def __init__(self, path, station, statistics_per_var, **kwargs):
+ # passed parameters
+ self.path = os.path.abspath(path)
+ self.station = helpers.to_list(station)
+ self.statistics_per_var = statistics_per_var
+ # self.target_dim = 'variable'
+ self.kwargs = kwargs
+
+ # internal
+ self.data = None
+ self.meta = None
+ self.variables = kwargs.get('variables', list(statistics_per_var.keys()))
+ self.history = None
+ self.label = None
+ self.observation = None
+
+ def load_data(self):
+ try:
+ self.read_data_from_disk()
+ except FileNotFoundError:
+ self.download_data()
+ self.load_data()
+
+ def read_data_from_disk(self):
+ raise NotImplementedError
+
+ def download_data(self):
+ raise NotImplementedError
+
+ 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 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), squeeze_dim=self.target_dim)
+ res = xr.concat(res, dim=window_array)
+ return res
+
+ @staticmethod
+ def create_index_array(index_name: str, index_value: Iterable[int], squeeze_dim: str) -> 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=squeez/e_dim, drop=True)
+ res = xr.Dataset.from_dataframe(ind).to_array(squeeze_dim).rename({'index': index_name}).squeeze(
+ dim=squeeze_dim,
+ drop=True
+ )
+ res.name = index_name
+ return res
+
+ 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 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)
+
+ 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 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})
+
+ 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
+
+
+class StationPrep(AbstractStationPrep):
+
+ def __init__(self, path, station, statistics_per_var, station_type, network, sampling, target_dim, target_var,
+ interpolate_dim, window_history_size, window_lead_time, **kwargs):
+ super().__init__(path, station, statistics_per_var, **kwargs)
+ self.station_type = station_type
+ self.network = network
+ self.sampling = sampling
+ self.target_dim = target_dim
+ self.target_var = target_var
+ self.interpolate_dim = interpolate_dim
+ self.window_history_size = window_history_size
+ self.window_lead_time = window_lead_time
+ self.make_samples()
+
+ 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_x(self):
+ return self.get_transposed_history()
+
+ def get_y(self):
+ return self.get_transposed_label()
+
+ def make_samples(self):
+ self.load_data()
+ self.make_history_window(self.target_dim, self.window_history_size, self.interpolate_dim)
+ self.make_labels(self.target_dim, self.target_var, self.interpolate_dim, self.window_lead_time)
+ self.make_observation(self.target_dim, self.target_var, self.interpolate_dim)
+ self.remove_nan(self.interpolate_dim)
+
+ def read_data_from_disk(self, source_name=""):
+ """
+ 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
+ set, it is assumed, that data should be saved locally.
+ """
+ source_name = source_name if len(source_name) == 0 else f" from {source_name}"
+ 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}{source_name}")
+ if os.path.exists(file_name):
+ os.remove(file_name)
+ if os.path.exists(meta_file):
+ os.remove(meta_file)
+ data, self.meta = self.download_data(file_name, meta_file)
+ logging.debug(f"loaded new data{source_name}")
+ 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)
+ self.check_station_meta()
+ logging.debug("loading finished")
+ except FileNotFoundError as e:
+ logging.debug(e)
+ logging.debug(f"load new data{source_name}")
+ 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_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 download_data(self, file_name, meta_file):
+ 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.
+ """
+ if self.station_type is not None:
+ check_dict = {"station_type": self.station_type, "network_name": self.network}
+ for (k, v) in check_dict.items():
+ if v is None:
+ continue
+ 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
+
class AbstractDataPrep(object):
"""
This class prepares data to be used in neural networks.
@@ -554,5 +935,13 @@ class AbstractDataPrep(object):
if __name__ == "__main__":
- dp = AbstractDataPrep('data/', 'dummy', 'DEBW107', ['o3', 'temp'], statistics_per_var={'o3': 'dma8eu', 'temp': 'maximum'})
- print(dp)
+ # dp = AbstractDataPrep('data/', 'dummy', 'DEBW107', ['o3', 'temp'], statistics_per_var={'o3': 'dma8eu', 'temp': 'maximum'})
+ # print(dp)
+ statistics_per_var = {'o3': 'dma8eu', 'temp-rea-miub': 'maximum'}
+ sp = StationPrep(path='/home/felix/PycharmProjects/mlt_new/data/', station='DEBY122',
+ statistics_per_var=statistics_per_var, station_type='background',
+ network='UBA', sampling='daily', target_dim='variables', target_var='o3',
+ interpolate_dim='datetime', window_history_size=7, window_lead_time=3)
+ sp.load_data()
+ sp.download_data('newfile.nc', 'new_meta.csv')
+ print(sp)