.. currentmodule:: wxee
This page contains auto-generated documentation for wxee
modules and classes.
All automated requests to Earth Engine, such as those made by wxee
should be made using the
high-volume Earth Engine endpoint. This can be done
by specifying the high-volume endpoint using ee.Initialize
or by using the wxee.Initialize
shortcut.
.. autosummary:: :toctree: generated/ Initialize
Base Earth Engine classes have additional functionality available through the wx
accessor. These methods are also
accessible to :class:`TimeSeries` and :class:`Climatology` objects, and are the primary interface for
exporting and downloading Earth Engine data in wxee
.
To use methods extended by wxee
, just import the package and use the wx
accessor.
import ee
import wxee
ee.Image("MODIS/006/MOD13Q1/2000_02_18").wx.to_xarray()
.. currentmodule:: wxee.image
.. autosummary:: :toctree: generated/ Image.to_xarray Image.to_tif
.. currentmodule:: wxee.collection
.. autosummary:: :toctree: generated/ ImageCollection.to_xarray ImageCollection.to_tif ImageCollection.to_time_series ImageCollection.get_image ImageCollection.last
The wxee
package adds a few helpful methods to xarray
classes through the same wx
accessor
used by Earth Engine Classes. To use them, just import the package and use the wx
accessor.
.. currentmodule:: wxee.xarray
.. autosummary:: :toctree: generated/ DatasetAccessor.rgb
.. autosummary:: :toctree: generated/ DataArrayAccessor.normalize
Time series are image collections with added functionality for processing in the time dimension.
Note
A TimeSeries
can be converted to xarray
and tif
using the wx
accessor, just like an
ee.ImageCollection
.
Time series can be instantiated in two ways:
import ee
import wxee
ts = wxee.TimeSeries("IDAHO_EPSCOR/GRIDMET")
See :meth:`ImageCollection.to_time_series`.
import ee
import wxee
col = ee.ImageCollection("IDAHO_EPSCOR/GRIDMET")
ts = col.wx.to_time_series()
.. currentmodule:: wxee.time_series
There are a number of properties and methods that describe the characteristics of a time series.
.. autosummary:: :toctree: generated/ TimeSeries.start_time TimeSeries.end_time TimeSeries.interval TimeSeries.describe TimeSeries.timeline TimeSeries.dataframe
Processing can be applied in the time dimension to modify a time series or create new time series.
.. autosummary:: :toctree: generated/ TimeSeries.aggregate_time TimeSeries.interpolate_time TimeSeries.rolling_time TimeSeries.fill_gaps TimeSeries.insert_image
Time series of weather data can be transformed into climatologies.
.. autosummary:: :toctree: generated/ TimeSeries.climatology_mean TimeSeries.climatology_std TimeSeries.climatology_anomaly
Climatologies are image collections where images represent long-term climatological normals at specific time steps.
Climatologies are created using :meth:`TimeSeries.climatology_mean` or :meth:`TimeSeries.climatology_std`.
.. currentmodule:: wxee.climatology
Warning
The :class:`Climatology` class should never be instantiated directly.
import ee
import wxee
ts = wxee.TimeSeries("IDAHO_EPSCOR/GRIDMET").select("pr")
monthly_mean_rainfall = ts.climatology_mean("month", reducer=ee.Reducer.sum())
Note
The reducer
argument defines how the raw data will be aggregated before calculating the climatological mean.
In this case, we use ee.Reducer.sum()
to aggregate the daily rainfall measurements into monthly totals. If the
data were already monthly, the reducer would have no effect.
In addition to having all the methods extended with the wx
accessor, there are methods for describing the characteristics of a climatology.
.. autosummary:: :toctree: generated/ Climatology.describe