:py:mod:`statgis.gee.zonal_statistics`
======================================

.. py:module:: statgis.gee.zonal_statistics

.. autoapi-nested-parse::

   Reduce images in a region of interest



Module Contents
---------------


Functions
~~~~~~~~~

.. autoapisummary::

   statgis.gee.zonal_statistics.zonal_statistics_image
   statgis.gee.zonal_statistics.zonal_statistics_collection



.. py:function:: zonal_statistics_image(image: ee.Image, geom: ee.Geometry, scale: float, bands: Union[Sequence[str], str] = 'all', reducer: Union[ee.Reducer, str] = 'all', tile_scale: int = 16) -> pandas.DataFrame

   Function to calculate a statistic in the specified region for one image.

   :param image: Image of interest.
   :type image: ee.Image
   :param geom: Region of interest to reduce the image.
   :type geom: ee.Geometry
   :param scale: Pixel size for the sample to perform the zonal statistics.
   :type scale: float
   :param bands: List, tuple with the bands of interest or, if you only want one band, the name
                 of the band. By default, the process takes into consideration all bands.
   :type bands: Sequence | str (optional)
   :param reducer: Reducer to apply to the image. By default, image are reduced to its, mean, standard
                   deviation, maximum, minimum, and count.
   :type reducer: ee.Reducer | str (optional)
   :param tile_scale: Scale of the mosaic to allow EarthEngine to split the task to more cores.
   :type tile_scale: int (optional)

   :returns: **data** -- DataFrame with all the stats for all specified bands.
   :rtype: pandas.DataFrame

   .. rubric:: Example

   >>> import ee
   >>> from statgis.gee import zonal_statistics
   >>> ee.Initialize()
   >>> roi = ee.Geometry.BBox(-75.2671803, 4.4104561 ,-75.2691803, 4.4124561)
   >>> mean_precipitation_2022 = (
   ...     ee.ImageCollection("UCSB-CHG/CHIRPS/DAILY")
   ...     .filterDate("2022-01-01", "2022-12-31")
   ...     .mean()
   ... )
   >>> stats = zonal_statistics.zonal_statistics_image(mean_precipitation_2022, roi, 30, "precipitation")


.. py:function:: zonal_statistics_collection(image_collection: ee.ImageCollection, geom: ee.Geometry, scale: float, bands: Union[Sequence[str], str] = 'all', reducer: Union[ee.Reducer, str] = 'all', tile_scale: int = 16) -> pandas.DataFrame

   Function to calculate a statistic in the specified region for all Image in an image
   collection.

   :param image_collection: Image Collection with the image to reduce.
   :type image_collection: ee.ImageCollection
   :param geom: Region of interest to reduce the images.
   :type geom: ee.Geometry
   :param scale: Pixel size for the sample to perform the zonal statistics.
   :type scale: float
   :param bands: List, tuple with the bands of interest or, if you only want one band, the name
                 of the band. By default, the process takes into consideration all bands.
   :type bands: Sequence | str (optional)
   :param reducer: Reducer to apply to all image. By default, image are reduced to its, mean, standard
                   deviation, maximum, minimum, and count.
   :type reducer: ee.Reducer | str (optional)
   :param tile_scale: Scale of the mosaic to allow EarthEngine to split the task to more cores.
   :type tile_scale: int (optional)

   :returns: **data** -- DataFrame with all the stats for all specified bands.
   :rtype: pandas.DataFrame

   .. rubric:: Example

   >>> import ee
   >>> from statgis.gee import zonal_statistics
   >>> ee.Initialize()
   >>> roi = ee.Geometry.BBox(-75.2671803, 4.4104561 ,-75.2691803, 4.4124561)
   >>> chirps = ee.ImageCollection("UCSB-CHG/CHIRPS/DAILY").filterDate("2022-01-01", "2022-12-31")
   >>> daily_mean_precipitation = zonal_statistics.zonal_statistics_collection(
   ...     chirps, roi, 30, "precipitation", ee.Reducer.mean()
   ... )