diff --git a/README.md b/README.md
index 6d9dc21f59923e14b04de8f728ca15a6655abb55..c33aab4b8643d2907b07b5ebcb254076515d03d2 100644
--- a/README.md
+++ b/README.md
@@ -348,6 +348,62 @@ parameter call.
True
```
+# Data Handlers
+
+Data handlers are responsible for all tasks related to data like data acquisition, preparation and provision. A data
+handler must inherit from the abstract base class `AbstractDataHandler` and requires the implementation of the
+`__init__()` method and the accessors `get_X()` and `get_Y()`. In the following, we show an example how a custom data
+handler could look like.
+
+```python
+import datetime as dt
+import numpy as np
+import pandas as pd
+import xarray as xr
+
+from mlair.data_handler import AbstractDataHandler
+
+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
+
+```
+
+
# Special Remarks
## Special instructions for installation on Jülich HPC systems
diff --git a/mlair/data_handler/advanced_data_handler.py b/mlair/data_handler/advanced_data_handler.py
index f0dc874a050c274b0b4b6692073d8f7332d27c1d..bf7defa56709c53e9c11b54baca54efcd105843c 100644
--- a/mlair/data_handler/advanced_data_handler.py
+++ b/mlair/data_handler/advanced_data_handler.py
@@ -305,15 +305,15 @@ class DefaultDataHandler(AbstractDataHandler):
def run_data_prep():
from .data_preparation_neighbors import DataHandlerNeighbors
- data = DummyDataSingleStation("main_class")
+ 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(DummyDataSingleStation("main_class"),
+ data_prep = DataHandlerNeighbors(DummyDataHandler("main_class"),
path,
- neighbors=[DummyDataSingleStation("neighbor1"),
- DummyDataSingleStation("neighbor2")],
+ neighbors=[DummyDataHandler("neighbor1"),
+ DummyDataHandler("neighbor2")],
extreme_values=[1., 1.2])
data_prep.get_data(upsampling=False)
@@ -344,6 +344,45 @@ def create_data_prep():
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.station_preparation import DataHandlerSingleStation
from mlair.data_handler.iterator import KerasIterator, DataCollection