ecoscope.io.eetools
===================

.. py:module:: ecoscope.io.eetools






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

.. py:data:: logger

.. py:data:: ee_is_initialized
   :value: False


.. py:data:: ee_initialization_expires

.. py:data:: ee_initialization_ttl

.. py:data:: colourPalettes

.. py:function:: initialize_earthengine(key_dict)

   This takes a JSON key as a dict.
   :param key_dict:
   :return: a credentials object that can be used to initialize the earth engine library.


.. py:function:: convert_millisecs_datetime(unix_time)

.. py:function:: add_img_time(img)

   A function to add the date range of the image as one of its properties and the start and end values as new bands


.. py:function:: label_gdf_with_img(gdf, img, region_reducer, scale = 500.0)

.. py:function:: _match_gdf_to_img_coll_ids(gdf, time_col, img_coll, output_col_name = 'img_ids', n_before = 1, n_after = 1, n = 'images')

   A function that will add a column to a gdf (output_col_name) that contains
   the n_before -> n_after temporally closest image IDs from an image collection.

   :param gdf: The GeoDataFrame to add image IDs to
   :type gdf: geopandas.GeoDataFrame
   :param time_col: The name of the column within the given gdf containing the relevant timestamps
   :type time_col: str
   :param img_coll: The image collection to lookup image IDs from
   :type img_coll: ee.ImageCollection
   :param output_col_name: The name of the column to be added to the given gdf, default is 'img_ids'
   :type output_col_name: str, optional
   :param n_before: The number of n days/weeks/images before to grab image IDs for, default is 1
   :type n_before: int, optional
   :param n_after: The number of n days/weeks/images after to grab image IDs for, default is 1
   :type n_after: int, optional
   :param n: One of: 'images'(default), 'microseconds', 'milliseconds' 'seconds', 'minutes', 'hours', 'days' or 'weeks'
             If n is 'images', this appends n_before IDs + the temporally closest ID + n_after IDs
             Otherwise this appends image IDs between
             timestamp-n_before to timestamp+n_after where timestamp is a given row from the input gdf
   :type n: str, optional


.. py:function:: label_gdf_with_temporal_image_collection_by_feature(gdf, img_coll, time_col_name, n_before = 1, n_after = 1, n = 'images', region_reducer = None, scale=500.0)

.. py:function:: label_gdf_with_temporal_image_collection_by_timespan(gdf, img_coll, image_radius = 0, add_time = False, region_reducer='toList', df_chunk_size=25000, max_workers=1)

.. py:function:: chunk_gdf(gdf, label_func, label_func_kwargs = None, df_chunk_size = 25000, max_workers = 1)

   A function that will process the input gdf in chunks and apply the input label_func function over the chunks.

   :param gdf:
       a geopandas dataframe. The 'geometry' column can be any type pf geometry (point/line/polygon). The gdf needs to
       have a column with the name of the image_collection and the column values are lists of the individual image IDs
       that need to be associated with each feature. This step will typically be run with the
       match_img_coll_ids_to_gdf() function beforehand.
   :param label_func: a function to run on the EE cloud that has the signature (feat, kwargs)
   :param label_func_kwargs: a dictionary of parameters to provide to the label_func
   :param df_chunk_size: how many features (rows) to process at once within EE
   :param max_workers: the number of chunks to process concurrently
   :return: a dataframe with the same index as the input gdf and where each pixel value (or reduced value) is a row


.. py:function:: calculate_anomaly(gdf, img_coll, historical_start, start, end, scale = 5000.0)

   Compute anomalies by subtracting the historical_start mean from each image in a collection of start->end images.
   :param gdf: the input geodataframe
   :param img_coll: the input EE image collection
   :param historical_start: start time for calculating the mean reference
   :param start: end time for mean reference and the start time for the anomaly calculation
   :param end: end time for the anomaly calculation
   :param scale: the image scale
   :return: a dataframe with same index as input gdf with the img_dates and the anomaly calculation