:py:mod:`ecoscope.base.utils`
=============================

.. py:module:: ecoscope.base.utils


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

.. py:function:: create_meshgrid(aoi, in_crs, out_crs, xlen=1000, ylen=1000, return_intersecting_only=True, align_to_existing=None)

   Create a grid covering `aoi`.

   :param aoi: The area of interest. Should be in a UTM CRS.
   :type aoi: shapely.geometry.base.BaseGeometry
   :param in_crs: Coordinate Reference System of input `aoi`. Can be anything accepted by `pyproj.CRS.from_user_input()`.
                  Geometry is automatically converted to UTM CRS as an intermediate for computation.
   :type in_crs: value
   :param out_crs: Coordinate Reference System of output `gs`. Can be anything accepted by `pyproj.CRS.from_user_input()`.
                   Geometry is automatically converted to UTM CRS as an intermediate for computation.
   :type out_crs: value
   :param xlen: The width of a grid cell in meters.
   :type xlen: int, optional
   :param ylen: The height of a grid cell in meters.
   :type ylen: int, optional
   :param return_intersecting_only: Whether to return only grid cells intersecting with the aoi.
   :type return_intersecting_only: bool, optional
   :param align_to_existing: If provided, attempts to align created grid to start of existing grid. Requires a CRS and valid geometry.
   :type align_to_existing: geopandas.GeoSeries or geopandas.GeoDataFrame, optional

   :returns: **gs** -- Grid of boxes. CRS is converted to `out_crs`.
   :rtype: geopandas.GeoSeries


.. py:class:: cachedproperty(func)


   The ``cachedproperty`` is used similar to :class:`property`, except
   that the wrapped method is only called once. This is commonly used
   to implement lazy attributes.

   .. py:method:: __doc__()


   .. py:method:: __isabstractmethod__()


   .. py:method:: __get__(obj, objtype=None)


   .. py:method:: __repr__()

      Return repr(self).



.. py:function:: groupby_intervals(df, col, intervals)

   :param df: Data to group
   :type df: pd.DataFrame
   :param col: Name of column to group on
   :type col: str
   :param intervals: Intervals to group on
   :type intervals: pd.IntervalIndex

   :rtype: pd.core.groupby.DataFrameGroupBy


.. py:function:: create_interval_index(start, intervals, freq, overlap=pd.Timedelta(0), closed='right', round_down_to_freq=False)

   :param start: Left bound for creating IntervalIndex
   :type start: str or datetime-like
   :param intervals: Number of intervals to create
   :type intervals: int, optional
   :param freq: Length of each interval
   :type freq: str, Timedelta or DateOffset
   :param overlap: Length of overlap between intervals
   :type overlap: Timedelta or DateOffset, optional
   :param closed: Whether the intervals are closed on the left-side, right-side, both or neither
   :type closed: {"left", "right", "both", "neither"}, default "right"
   :param round_down_to_freq: Start will be rounded down to freq
   :type round_down_to_freq: bool

   :returns: **interval_index**
   :rtype: pd.IntervalIndex


.. py:class:: ModisBegin


   Bases: :py:obj:`pandas._libs.tslibs.offsets.SingleConstructorOffset`

   Primitive DateOffset to support MODIS period start times.

   .. py:method:: apply(other)



.. py:function:: create_modis_interval_index(start, intervals, overlap=pd.Timedelta(0), closed='right')

   :param start: Left bound for creating IntervalIndex. Will be rounded up to next start of MODIS period
   :type start: str or datetime-like
   :param intervals: Number of intervals to create
   :type intervals: int, optional
   :param overlap: Length of overlap between intervals
   :type overlap: Timedelta or DateOffset, optional
   :param closed: Whether the intervals are closed on the left-side, right-side, both or neither
   :type closed: {"left", "right", "both", "neither"}, default "right"

   :returns: **interval_index**
   :rtype: pd.IntervalIndex


.. py:function:: add_val_index(df, index_name, val)

   :param df: Dataframe to create index on
   :type df: pd.DataFrame
   :param index_name: Name of index
   :type index_name: str
   :param val: Current column to rename and set as index
   :type val: str

   :rtype: pd.DataFrame


.. py:function:: add_temporal_index(df, index_name, time_col, directive)

   :param df: Dataframe to create index on
   :type df: pd.DataFrame
   :param index_name: Name of temporal index
   :type index_name: str
   :param time_col: Name of the time column
   :type time_col: str
   :param directive: Time format string
   :type directive: str

   :rtype: pd.DataFrame