Commit 30cbfc0c authored by lukas leufen's avatar lukas leufen

add example for data handlers to docs too

parent 2258f373
Pipeline #45979 passed with stages
in 5 minutes and 51 seconds
......@@ -248,6 +248,53 @@ Custom Data Handler
* (optionally) Overwrite the base class :py:`self.get_coordinates()` method to return coordinates as dictionary with
keys *lon* and *lat*.
.. code-block:: 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
Customised Run Module and Workflow
----------------------------------
......
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