<filename>xgcm/duck_array_ops.py
"""Compatibility module defining operations on duck numpy-arrays.

Shamelessly copied from xarray."""

from __future__ import division
from __future__ import print_function

import numpy as np

try:
    import dask.array as dsa
    has_dask = True
except ImportError:
    has_dask = False

def _dask_or_eager_func(name, eager_module=np, list_of_args=False,
                        n_array_args=1):
    """Create a function that dispatches to dask for dask array inputs."""
    if has_dask:
        def f(*args, **kwargs):
            dispatch_args = args[0] if list_of_args else args
            if any(isinstance(a, dsa.Array)
                   for a in dispatch_args[:n_array_args]):
                module = dsa
            else:
                module = eager_module
            return getattr(module, name)(*args, **kwargs)
    else:
        def f(data, *args, **kwargs):
            return getattr(eager_module, name)(data, *args, **kwargs)
    return f


insert = _dask_or_eager_func('insert')
take = _dask_or_eager_func('take')
concatenate = _dask_or_eager_func('concatenate', list_of_args=True)


# my own function

def _pad_array(da, dim, left=False, boundary=None, fill_value=0.):
    """
    Pad an xarray.DataArray da according to the boundary conditions along dim.
    Return a raw dask or numpy array, depending on the underlying data.

    Parameters
    ----------
    da : xarray.DataArray
        The data on which to operate
    dim : str
        Dimenson to pad
    left : bool
        If `False`, data is padded at the right (end of the array). If `True`,
        padded at left (beginning of array).
    boundary : {'fill', 'extend'}
        A flag indicating how to handle boundaries:

        * None:  Do not apply any boundary conditions. Raise an error if
          boundary conditions are required for the operation.
        * 'fill':  Set values outside the array boundary to fill_value
          (i.e. a Neumann boundary condition.)
        * 'extend': Set values outside the array to the nearest array
          value. (i.e. a limited form of Dirichlet boundary condition.)

    fill_value : float, optional
         The value to use in the boundary condition with `boundary='fill'`.
    """

    if boundary not in ['fill', 'extend']:
        raise ValueError("`boundary` must be `'fill'` or `'extend'`")

    axis_num = da.get_axis_num(dim)
    shape = list(da.shape)
    shape[axis_num] = 1

    base_array = da.data
    index = slice(0,1) if left else slice(-1, None)
    edge_array = da.isel(**{dim: index}).data

    use_dask = has_dask and isinstance(base_array, dsa.Array)

    if boundary == 'extend':
        boundary_array = edge_array
    elif boundary == 'fill':
        args = shape, fill_value
        kwargs = {'dtype': base_array.dtype}
        if use_dask:
            full_func = dsa.full
            kwargs['chunks'] = edge_array.chunks
        else:
            full_func = np.full
        boundary_array = full_func(*args, **kwargs)

    arrays_to_concat = [base_array, boundary_array]
    if left:
        arrays_to_concat.reverse()

    return concatenate(arrays_to_concat, axis=axis_num)

<filename>xgcm/duck_array_ops.py
"""Compatibility module defining operations on duck numpy-arrays.

Shamelessly copied from xarray."""

from __future__ import division
from __future__ import print_function

import numpy as np

try:
    import dask.array as dsa
    has_dask = True
except ImportError:
    has_dask = False

def _dask_or_eager_func(name, eager_module=np, list_of_args=False,
                        n_array_args=1):
    """Create a function that dispatches to dask for dask array inputs."""
    if has_dask:
        def f(*args, **kwargs):
            dispatch_args = args[0] if list_of_args else args
            if any(isinstance(a, dsa.Array)
                   for a in dispatch_args[:n_array_args]):
                module = dsa
            else:
                module = eager_module
            return getattr(module, name)(*args, **kwargs)
    else:
        def f(data, *args, **kwargs):
            return getattr(eager_module, name)(data, *args, **kwargs)
    return f


insert = _dask_or_eager_func('insert')
take = _dask_or_eager_func('take')
concatenate = _dask_or_eager_func('concatenate', list_of_args=True)


# my own function

def _pad_array(da, dim, left=False, boundary=None, fill_value=0.):
    """
    Pad an xarray.DataArray da according to the boundary conditions along dim.
    Return a raw dask or numpy array, depending on the underlying data.

    Parameters
    ----------
    da : xarray.DataArray
        The data on which to operate
    dim : str
        Dimenson to pad
    left : bool
        If `False`, data is padded at the right (end of the array). If `True`,
        padded at left (beginning of array).
    boundary : {'fill', 'extend'}
        A flag indicating how to handle boundaries:

        * None:  Do not apply any boundary conditions. Raise an error if
          boundary conditions are required for the operation.
        * 'fill':  Set values outside the array boundary to fill_value
          (i.e. a Neumann boundary condition.)
        * 'extend': Set values outside the array to the nearest array
          value. (i.e. a limited form of Dirichlet boundary condition.)

    fill_value : float, optional
         The value to use in the boundary condition with `boundary='fill'`.
    """

    if boundary not in ['fill', 'extend']:
        raise ValueError("`boundary` must be `'fill'` or `'extend'`")

    axis_num = da.get_axis_num(dim)
    shape = list(da.shape)
    shape[axis_num] = 1

    base_array = da.data
    index = slice(0,1) if left else slice(-1, None)
    edge_array = da.isel(**{dim: index}).data

    use_dask = has_dask and isinstance(base_array, dsa.Array)

    if boundary == 'extend':
        boundary_array = edge_array
    elif boundary == 'fill':
        args = shape, fill_value
        kwargs = {'dtype': base_array.dtype}
        if use_dask:
            full_func = dsa.full
            kwargs['chunks'] = edge_array.chunks
        else:
            full_func = np.full
        boundary_array = full_func(*args, **kwargs)

    arrays_to_concat = [base_array, boundary_array]
    if left:
        arrays_to_concat.reverse()

    return concatenate(arrays_to_concat, axis=axis_num)

Compatibility module defining operations on duck numpy-arrays.  Shamelessly copied from xarray. Create a function that dispatches to dask for dask array inputs. # my own function Pad an xarray.DataArray da according to the boundary conditions along dim.     Return a raw dask or numpy array, depending on the underlying data.      Parameters     ----------     da : xarray.DataArray         The data on which to operate     dim : str         Dimenson to pad     left : bool         If `False`, data is padded at the right (end of the array). If `True`,         padded at left (beginning of array).     boundary : {'fill', 'extend'}         A flag indicating how to handle boundaries:          * None:  Do not apply any boundary conditions. Raise an error if           boundary conditions are required for the operation.         * 'fill':  Set values outside the array boundary to fill_value           (i.e. a Neumann boundary condition.)         * 'extend': Set values outside the array to the nearest array           value. (i.e. a limited form of Dirichlet boundary condition.)      fill_value : float, optional          The value to use in the boundary condition with `boundary='fill'`.