Instruction:
Write a Python 3 script for
applying modifications to the model layers to create a new Graph.

Response:
def apply_modifications(model, custom_objects=None):
    """Applies modifications to the model layers to create a new Graph. For example, simply changing
    `model.layers[idx].activation = new activation` does not change the graph. The entire graph needs to be updated
    with modified inbound and outbound tensors because of change in layer building function.

    Args:
        model: The `keras.models.Model` instance.

    Returns:
        The modified model with changes applied. Does not mutate the original `model`.
    """
    # The strategy is to save the modified model and load it back. This is done because setting the activation
    # in a Keras layer doesnt actually change the graph. We have to iterate the entire graph and change the
    # layer inbound and outbound nodes with modified tensors. This is doubly complicated in Keras 2.x since
    # multiple inbound and outbound nodes are allowed with the Graph API.
    model_path = os.path.join(tempfile.gettempdir(), next(tempfile._get_candidate_names()) + '.h5')
    try:
        model.save(model_path)
        return load_model(model_path, custom_objects=custom_objects)
    finally:
        os.remove(model_path)


Instruction:
Here you have a function in Python 3, explain what it does
def random_array(shape, mean=128., std=20.):
    x = np.random.random(shape)
    # normalize around mean=0, std=1
    x = (x - np.mean(x)) / (np.std(x) + K.epsilon())
    # and then around the desired mean/std
    x = (x * std) + mean
    return x

Response: Creates a uniformly distributed random array of the given shape with the given mean and std.


Instruction:
How would you code a function in Python 3 to
look up the layer index corresponding to layer_name from model.

Response:
def find_layer_idx(model, layer_name):
    """Looks up the layer index corresponding to `layer_name` from `model`.

    Args:
        model: The `keras.models.Model` instance.
        layer_name: The name of the layer to lookup.

    Returns:
        The layer index if found. Raises an exception otherwise.
    """
    layer_idx = None
    for idx, layer in enumerate(model.layers):
        if layer.name == layer_name:
            layer_idx = idx
            break

    if layer_idx is None:
        raise ValueError("No layer with name '{}' within the model".format(layer_name))
    return layer_idx


Instruction:
Can you tell what is the following Python 3 function doing
def get_img_shape(img):
    if isinstance(img, np.ndarray):
        shape = img.shape
    else:
        shape = K.int_shape(img)

    if K.image_data_format() == 'channels_last':
        shape = list(shape)
        shape.insert(1, shape[-1])
        shape = tuple(shape[:-1])
    return shape

Response: Returns image shape in a backend agnostic manner.


Instruction:
Make a summary of the following Python 3 code
def load_img(path, grayscale=False, target_size=None):
    img = io.imread(path, grayscale)
    if target_size:
        img = transform.resize(img, target_size, preserve_range=True).astype('uint8')
    return img

Response: Utility function to load an image from disk.


Instruction:
Can you tell what is the following Python 3 function doing
def lookup_imagenet_labels(indices):
    global _CLASS_INDEX
    if _CLASS_INDEX is None:
        with open(os.path.join(os.path.dirname(__file__), '../../resources/imagenet_class_index.json')) as f:
            _CLASS_INDEX = json.load(f)

    indices = listify(indices)
    return [_CLASS_INDEX[str(idx)][1] for idx in indices]

Response: Utility function to return the image net label corresponding to the image category.


Instruction:
Can you write a function in Python 3 where it
draws text over the image. Requires PIL.

Response:
def draw_text(img, text, position=(10, 10), font='FreeSans.ttf', font_size=14, color=(0, 0, 0)):
    """Draws text over the image. Requires PIL.

    Args:
        img: The image to use.
        text: The text string to overlay.
        position: The text (x, y) position. (Default value = (10, 10))
        font: The ttf or open type font to use. (Default value = 'FreeSans.ttf')
        font_size: The text font size. (Default value = 12)
        color: The (r, g, b) values for text color. (Default value = (0, 0, 0))

    Returns: Image overlayed with text.
    """
    _check_pil()

    font_files = _find_font_file(font)
    if len(font_files) == 0:
        logger.warn("Failed to lookup font '{}', falling back to default".format(font))
        font = ImageFont.load_default()
    else:
        font = ImageFont.truetype(font_files[0], font_size)

    # Don't mutate original image
    img = Image.fromarray(img)
    draw = ImageDraw.Draw(img)
    draw.text(position, text, fill=color, font=font)
    return np.asarray(img)


Instruction:
Explain what the following Python 3 code does
def normalize(array, min_value=0., max_value=1.):
    arr_min = np.min(array)
    arr_max = np.max(array)
    normalized = (array - arr_min) / (arr_max - arr_min + K.epsilon())
    return (max_value - min_value) * normalized + min_value

Response: Normalizes the numpy array to range between min_value and max_value


Instruction:
Make a summary of the following Python 3 code
def get_num_filters(layer):
    # Handle layers with no channels.
    if K.ndim(layer.output) == 2:
        return K.int_shape(layer.output)[-1]

    channel_idx = 1 if K.image_data_format() == 'channels_first' else -1
    return K.int_shape(layer.output)[channel_idx]

Response: Determines the number of filters within the given keras layer.


Instruction:
Given the following Python 3 function, write the documentation
def overlay(array1, array2, alpha=0.5):
    if alpha < 0. or alpha > 1.:
        raise ValueError("`alpha` needs to be between [0, 1]")
    if array1.shape != array2.shape:
        raise ValueError('`array1` and `array2` must have the same shapes')

    return (array1 * alpha + array2 * (1. - alpha)).astype(array1.dtype)

Response: Overlays array1 onto array2 using alpha blending.


Instruction:
Can you implement a function in Python 3 that
infers the coordinates and dimensions of a new DataArray.

Response:
def _infer_coords_and_dims(shape, coords, dims):
    """All the logic for creating a new DataArray"""

    if (coords is not None and not utils.is_dict_like(coords) and
            len(coords) != len(shape)):
        raise ValueError('coords is not dict-like, but it has %s items, '
                         'which does not match the %s dimensions of the '
                         'data' % (len(coords), len(shape)))

    if isinstance(dims, str):
        dims = (dims,)

    if dims is None:
        dims = ['dim_%s' % n for n in range(len(shape))]
        if coords is not None and len(coords) == len(shape):
            # try to infer dimensions from coords
            if utils.is_dict_like(coords):
                # deprecated in GH993, removed in GH1539
                raise ValueError('inferring DataArray dimensions from '
                                 'dictionary like ``coords`` is no longer '
                                 'supported. Use an explicit list of '
                                 '``dims`` instead.')
            for n, (dim, coord) in enumerate(zip(dims, coords)):
                coord = as_variable(coord,
                                    name=dims[n]).to_index_variable()
                dims[n] = coord.name
        dims = tuple(dims)
    else:
        for d in dims:
            if not isinstance(d, str):
                raise TypeError('dimension %s is not a string' % d)

    new_coords = OrderedDict()

    if utils.is_dict_like(coords):
        for k, v in coords.items():
            new_coords[k] = as_variable(v, name=k)
    elif coords is not None:
        for dim, coord in zip(dims, coords):
            var = as_variable(coord, name=dim)
            var.dims = (dim,)
            new_coords[dim] = var

    sizes = dict(zip(dims, shape))
    for k, v in new_coords.items():
        if any(d not in dims for d in v.dims):
            raise ValueError('coordinate %s has dimensions %s, but these '
                             'are not a subset of the DataArray '
                             'dimensions %s' % (k, v.dims, dims))

        for d, s in zip(v.dims, v.shape):
            if s != sizes[d]:
                raise ValueError('conflicting sizes for dimension %r: '
                                 'length %s on the data but length %s on '
                                 'coordinate %r' % (d, sizes[d], s, k))

        if k in sizes and v.shape != (sizes[k],):
            raise ValueError('coordinate %r is a DataArray dimension, but '
                             'it has shape %r rather than expected shape %r '
                             'matching the dimension size'
                             % (k, v.shape, (sizes[k],)))

    assert_unique_multiindex_level_names(new_coords)

    return new_coords, dims


Instruction:
Can you tell what is the following Python 3 function doing
def to_dataset(self, dim=None, name=None):
        if dim is not None and dim not in self.dims:
            warnings.warn('the order of the arguments on DataArray.to_dataset '
                          'has changed; you now need to supply ``name`` as '
                          'a keyword argument',
                          FutureWarning, stacklevel=2)
            name = dim
            dim = None

        if dim is not None:
            if name is not None:
                raise TypeError('cannot supply both dim and name arguments')
            return self._to_dataset_split(dim)
        else:
            return self._to_dataset_whole(name)

Response: Convert a DataArray into a Dataset.


Instruction:
Can you implement a function in Python 3 that
returns a mapping of all MultiIndex levels and their corresponding coordinate name.

Response:
def _level_coords(self):
        """Return a mapping of all MultiIndex levels and their corresponding
        coordinate name.
        """
        level_coords = OrderedDict()
        for cname, var in self._coords.items():
            if var.ndim == 1 and isinstance(var, IndexVariable):
                level_names = var.level_names
                if level_names is not None:
                    dim, = var.dims
                    level_coords.update({lname: dim for lname in level_names})
        return level_coords


Instruction:
Explain what the following Python 3 code does
def indexes(self):
        if self._indexes is None:
            self._indexes = default_indexes(self._coords, self.dims)
        return Indexes(self._indexes)

Response: Mapping of pandas. Index objects used for label based indexing.


Instruction:
Create a Python 3 function for
resetting the coordinates of the object to become variables.

Response:
def reset_coords(self, names=None, drop=False, inplace=None):
        """Given names of coordinates, reset them to become variables.

        Parameters
        ----------
        names : str or list of str, optional
            Name(s) of non-index coordinates in this dataset to reset into
            variables. By default, all non-index coordinates are reset.
        drop : bool, optional
            If True, remove coordinates instead of converting them into
            variables.
        inplace : bool, optional
            If True, modify this dataset inplace. Otherwise, create a new
            object.

        Returns
        -------
        Dataset, or DataArray if ``drop == True``
        """
        inplace = _check_inplace(inplace)
        if inplace and not drop:
            raise ValueError('cannot reset coordinates in-place on a '
                             'DataArray without ``drop == True``')
        if names is None:
            names = set(self.coords) - set(self.dims)
        dataset = self.coords.to_dataset().reset_coords(names, drop)
        if drop:
            if inplace:
                self._coords = dataset._variables
            else:
                return self._replace(coords=dataset._variables)
        else:
            if self.name is None:
                raise ValueError('cannot reset_coords with drop=False '
                                 'on an unnamed DataArrray')
            dataset[self.name] = self.variable
            return dataset


Instruction:
Can you generate a brief explanation for the following Python 3 code
def load(self, **kwargs):
        ds = self._to_temp_dataset().load(**kwargs)
        new = self._from_temp_dataset(ds)
        self._variable = new._variable
        self._coords = new._coords
        return self

Response:
Manually trigger loading of this array s data from disk or a
        remote source into memory and return this array.


Instruction:
Create a Python 3 function for
triggering computation in constituent dask arrays This keeps them as dask arrays but encourages them as dask arrays but encourages them as dask arrays.

Response:
def persist(self, **kwargs):
        """ Trigger computation in constituent dask arrays

        This keeps them as dask arrays but encourages them to keep data in
        memory.  This is particularly useful when on a distributed machine.
        When on a single machine consider using ``.compute()`` instead.

        Parameters
        ----------
        **kwargs : dict
            Additional keyword arguments passed on to ``dask.persist``.

        See Also
        --------
        dask.persist
        """
        ds = self._to_temp_dataset().persist(**kwargs)
        return self._from_temp_dataset(ds)


Instruction:
Given the following Python 3 function, write the documentation
def copy(self, deep=True, data=None):
        variable = self.variable.copy(deep=deep, data=data)
        coords = OrderedDict((k, v.copy(deep=deep))
                             for k, v in self._coords.items())
        return self._replace(variable, coords)

Response: Returns a shallow copy of the current array.


Instruction:
Write a Python 3 function that can
coerce this array s data into a dask array with the given chunks.

Response:
def chunk(self, chunks=None, name_prefix='xarray-', token=None,
              lock=False):
        """Coerce this array's data into a dask arrays with the given chunks.

        If this variable is a non-dask array, it will be converted to dask
        array. If it's a dask array, it will be rechunked to the given chunk
        sizes.

        If neither chunks is not provided for one or more dimensions, chunk
        sizes along that dimension will not be updated; non-dask arrays will be
        converted into dask arrays with a single block.

        Parameters
        ----------
        chunks : int, tuple or dict, optional
            Chunk sizes along each dimension, e.g., ``5``, ``(5, 5)`` or
            ``{'x': 5, 'y': 5}``.
        name_prefix : str, optional
            Prefix for the name of the new dask array.
        token : str, optional
            Token uniquely identifying this array.
        lock : optional
            Passed on to :py:func:`dask.array.from_array`, if the array is not
            already as dask array.

        Returns
        -------
        chunked : xarray.DataArray
        """
        if isinstance(chunks, (list, tuple)):
            chunks = dict(zip(self.dims, chunks))

        ds = self._to_temp_dataset().chunk(chunks, name_prefix=name_prefix,
                                           token=token, lock=lock)
        return self._from_temp_dataset(ds)


Instruction:
Given the following Python 3 function, write the documentation
def isel(self, indexers=None, drop=False, **indexers_kwargs):
        indexers = either_dict_or_kwargs(indexers, indexers_kwargs, 'isel')
        ds = self._to_temp_dataset().isel(drop=drop, indexers=indexers)
        return self._from_temp_dataset(ds)

Response:
Return a new DataArray whose dataset is given by integer indexing
        along the specified dimension.


Instruction:
How would you explain what the following Python 3 function does
def sel(self, indexers=None, method=None, tolerance=None, drop=False,
            **indexers_kwargs):
        """Return a new DataArray whose dataset is given by selecting
        index labels along the specified dimension(s).

        .. warning::

          Do not try to assign values when using any of the indexing methods
          ``isel`` or ``sel``::

            da = xr.DataArray([0, 1, 2, 3], dims=['x'])
            # DO NOT do this
            da.isel(x=[0, 1, 2])[1] = -1

          Assigning values with the chained indexing using ``.sel`` or
          ``.isel`` fails silently.

        See Also
        --------
        Dataset.sel
        DataArray.isel

        """
        ds = self._to_temp_dataset().sel(
            indexers=indexers, drop=drop, method=method, tolerance=tolerance,
            **indexers_kwargs)
        return self._from_temp_dataset(ds)

Response:
Return a new DataArray whose dataset is given by selecting
            index labels along the specified dimension.


Instruction:
Implement a function in Python 3 to
return a new DataArray whose dataset is given by pointwise integer indexing along the specified dimension.

Response:
def isel_points(self, dim='points', **indexers):
        """Return a new DataArray whose dataset is given by pointwise integer
        indexing along the specified dimension(s).

        See Also
        --------
        Dataset.isel_points
        """
        ds = self._to_temp_dataset().isel_points(dim=dim, **indexers)
        return self._from_temp_dataset(ds)


Instruction:
Can you tell what is the following Python 3 function doing
def sel_points(self, dim='points', method=None, tolerance=None,
                   **indexers):
        """Return a new DataArray whose dataset is given by pointwise selection
        of index labels along the specified dimension(s).

        See Also
        --------
        Dataset.sel_points
        """
        ds = self._to_temp_dataset().sel_points(
            dim=dim, method=method, tolerance=tolerance, **indexers)
        return self._from_temp_dataset(ds)

Response:
Return a new DataArray whose dataset is given by pointwise selection
            of index labels along the specified dimension.


Instruction:
Write a Python 3 function for
conforming this object onto a new set of indexes.

Response:
def reindex(self, indexers=None, method=None, tolerance=None, copy=True,
                **indexers_kwargs):
        """Conform this object onto a new set of indexes, filling in
        missing values with NaN.

        Parameters
        ----------
        indexers : dict, optional
            Dictionary with keys given by dimension names and values given by
            arrays of coordinates tick labels. Any mis-matched coordinate
            values will be filled in with NaN, and any mis-matched dimension
            names will simply be ignored.
            One of indexers or indexers_kwargs must be provided.
        copy : bool, optional
            If ``copy=True``, data in the return value is always copied. If
            ``copy=False`` and reindexing is unnecessary, or can be performed
            with only slice operations, then the output may share memory with
            the input. In either case, a new xarray object is always returned.
        method : {None, 'nearest', 'pad'/'ffill', 'backfill'/'bfill'}, optional
            Method to use for filling index values in ``indexers`` not found on
            this data array:

            * None (default): don't fill gaps
            * pad / ffill: propagate last valid index value forward
            * backfill / bfill: propagate next valid index value backward
            * nearest: use nearest valid index value (requires pandas>=0.16)
        tolerance : optional
            Maximum distance between original and new labels for inexact
            matches. The values of the index at the matching locations must
            satisfy the equation ``abs(index[indexer] - target) <= tolerance``.
        **indexers_kwarg : {dim: indexer, ...}, optional
            The keyword arguments form of ``indexers``.
            One of indexers or indexers_kwargs must be provided.

        Returns
        -------
        reindexed : DataArray
            Another dataset array, with this array's data but replaced
            coordinates.

        See Also
        --------
        DataArray.reindex_like
        align
        """
        indexers = either_dict_or_kwargs(
            indexers, indexers_kwargs, 'reindex')
        ds = self._to_temp_dataset().reindex(
            indexers=indexers, method=method, tolerance=tolerance, copy=copy)
        return self._from_temp_dataset(ds)


Instruction:
Create a Python 3 function to
interpolate the multidimensional array with the given coordinates.

Response:
def interp(self, coords=None, method='linear', assume_sorted=False,
               kwargs={}, **coords_kwargs):
        """ Multidimensional interpolation of variables.

        coords : dict, optional
            Mapping from dimension names to the new coordinates.
            new coordinate can be an scalar, array-like or DataArray.
            If DataArrays are passed as new coordates, their dimensions are
            used for the broadcasting.
        method: {'linear', 'nearest'} for multidimensional array,
            {'linear', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic'}
            for 1-dimensional array.
        assume_sorted: boolean, optional
            If False, values of x can be in any order and they are sorted
            first. If True, x has to be an array of monotonically increasing
            values.
        kwargs: dictionary
            Additional keyword passed to scipy's interpolator.
        **coords_kwarg : {dim: coordinate, ...}, optional
            The keyword arguments form of ``coords``.
            One of coords or coords_kwargs must be provided.

        Returns
        -------
        interpolated: xr.DataArray
            New dataarray on the new coordinates.

        Notes
        -----
        scipy is required.

        See Also
        --------
        scipy.interpolate.interp1d
        scipy.interpolate.interpn

        Examples
        --------
        >>> da = xr.DataArray([1, 3], [('x', np.arange(2))])
        >>> da.interp(x=0.5)
        <xarray.DataArray ()>
        array(2.0)
        Coordinates:
            x        float64 0.5
        """
        if self.dtype.kind not in 'uifc':
            raise TypeError('interp only works for a numeric type array. '
                            'Given {}.'.format(self.dtype))

        ds = self._to_temp_dataset().interp(
            coords, method=method, kwargs=kwargs, assume_sorted=assume_sorted,
            **coords_kwargs)
        return self._from_temp_dataset(ds)


Instruction:
Here you have a function in Python 3, explain what it does
def interp_like(self, other, method='linear', assume_sorted=False,
                    kwargs={}):
        """Interpolate this object onto the coordinates of another object,
        filling out of range values with NaN.

        Parameters
        ----------
        other : Dataset or DataArray
            Object with an 'indexes' attribute giving a mapping from dimension
            names to an 1d array-like, which provides coordinates upon
            which to index the variables in this dataset.
        method: string, optional.
            {'linear', 'nearest'} for multidimensional array,
            {'linear', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic'}
            for 1-dimensional array. 'linear' is used by default.
        assume_sorted: boolean, optional
            If False, values of coordinates that are interpolated over can be
            in any order and they are sorted first. If True, interpolated
            coordinates are assumed to be an array of monotonically increasing
            values.
        kwargs: dictionary, optional
            Additional keyword passed to scipy's interpolator.

        Returns
        -------
        interpolated: xr.DataArray
            Another dataarray by interpolating this dataarray's data along the
            coordinates of the other object.

        Notes
        -----
        scipy is required.
        If the dataarray has object-type coordinates, reindex is used for these
        coordinates instead of the interpolation.

        See Also
        --------
        DataArray.interp
        DataArray.reindex_like
        """
        if self.dtype.kind not in 'uifc':
            raise TypeError('interp only works for a numeric type array. '
                            'Given {}.'.format(self.dtype))

        ds = self._to_temp_dataset().interp_like(
            other, method=method, kwargs=kwargs, assume_sorted=assume_sorted)
        return self._from_temp_dataset(ds)

Response: Interpolate this object onto the coordinates of another object.


Instruction:
Write a Python 3 script for
returning a new DataArray with renamed coordinates or a new name.

Response:
def rename(self, new_name_or_name_dict=None, **names):
        """Returns a new DataArray with renamed coordinates or a new name.

        Parameters
        ----------
        new_name_or_name_dict : str or dict-like, optional
            If the argument is dict-like, it it used as a mapping from old
            names to new names for coordinates. Otherwise, use the argument
            as the new name for this array.
        **names, optional
            The keyword arguments form of a mapping from old names to
            new names for coordinates.
            One of new_name_or_name_dict or names must be provided.

        Returns
        -------
        renamed : DataArray
            Renamed array or array with renamed coordinates.

        See Also
        --------
        Dataset.rename
        DataArray.swap_dims
        """
        if names or utils.is_dict_like(new_name_or_name_dict):
            name_dict = either_dict_or_kwargs(
                new_name_or_name_dict, names, 'rename')
            dataset = self._to_temp_dataset().rename(name_dict)
            return self._from_temp_dataset(dataset)
        else:
            return self._replace(name=new_name_or_name_dict)


Instruction:
How would you implement a function in Python 3 that
returns a new Dataset containing swapped dimensions.

Response:
def swap_dims(self, dims_dict):
        """Returns a new DataArray with swapped dimensions.

        Parameters
        ----------
        dims_dict : dict-like
            Dictionary whose keys are current dimension names and whose values
            are new names. Each value must already be a coordinate on this
            array.

        Returns
        -------
        renamed : Dataset
            DataArray with swapped dimensions.

        See Also
        --------

        DataArray.rename
        Dataset.swap_dims
        """
        ds = self._to_temp_dataset().swap_dims(dims_dict)
        return self._from_temp_dataset(ds)


Instruction:
Write a Python 3 script to
return a new object with an additional dimension inserted at the corresponding position in the array.

Response:
def expand_dims(self, dim=None, axis=None, **dim_kwargs):
        """Return a new object with an additional axis (or axes) inserted at
        the corresponding position in the array shape.

        If dim is already a scalar coordinate, it will be promoted to a 1D
        coordinate consisting of a single value.

        Parameters
        ----------
        dim : str, sequence of str, dict, or None
            Dimensions to include on the new variable.
            If provided as str or sequence of str, then dimensions are inserted
            with length 1. If provided as a dict, then the keys are the new
            dimensions and the values are either integers (giving the length of
            the new dimensions) or sequence/ndarray (giving the coordinates of
            the new dimensions). **WARNING** for python 3.5, if ``dim`` is
            dict-like, then it must be an ``OrderedDict``. This is to ensure
            that the order in which the dims are given is maintained.
        axis : integer, list (or tuple) of integers, or None
            Axis position(s) where new axis is to be inserted (position(s) on
            the result array). If a list (or tuple) of integers is passed,
            multiple axes are inserted. In this case, dim arguments should be
            same length list. If axis=None is passed, all the axes will be
            inserted to the start of the result array.
        **dim_kwargs : int or sequence/ndarray
            The keywords are arbitrary dimensions being inserted and the values
            are either the lengths of the new dims (if int is given), or their
            coordinates. Note, this is an alternative to passing a dict to the
            dim kwarg and will only be used if dim is None. **WARNING** for
            python 3.5 ``dim_kwargs`` is not available.

        Returns
        -------
        expanded : same type as caller
            This object, but with an additional dimension(s).
        """
        if isinstance(dim, int):
            raise TypeError('dim should be str or sequence of strs or dict')
        elif isinstance(dim, str):
            dim = OrderedDict(((dim, 1),))
        elif isinstance(dim, (list, tuple)):
            if len(dim) != len(set(dim)):
                raise ValueError('dims should not contain duplicate values.')
            dim = OrderedDict(((d, 1) for d in dim))

        # TODO: get rid of the below code block when python 3.5 is no longer
        #   supported.
        python36_plus = sys.version_info[0] == 3 and sys.version_info[1] > 5
        not_ordereddict = dim is not None and not isinstance(dim, OrderedDict)
        if not python36_plus and not_ordereddict:
            raise TypeError("dim must be an OrderedDict for python <3.6")
        elif not python36_plus and dim_kwargs:
            raise ValueError("dim_kwargs isn't available for python <3.6")
        dim_kwargs = OrderedDict(dim_kwargs)

        dim = either_dict_or_kwargs(dim, dim_kwargs, 'expand_dims')
        ds = self._to_temp_dataset().expand_dims(dim, axis)
        return self._from_temp_dataset(ds)


Instruction:
Write a Python 3 script to
set the index of the data array using one or more existing coordinates.

Response:
def set_index(self, indexes=None, append=False, inplace=None,
                  **indexes_kwargs):
        """Set DataArray (multi-)indexes using one or more existing
        coordinates.

        Parameters
        ----------
        indexes : {dim: index, ...}
            Mapping from names matching dimensions and values given
            by (lists of) the names of existing coordinates or variables to set
            as new (multi-)index.
        append : bool, optional
            If True, append the supplied index(es) to the existing index(es).
            Otherwise replace the existing index(es) (default).
        inplace : bool, optional
            If True, set new index(es) in-place. Otherwise, return a new
            DataArray object.
        **indexes_kwargs: optional
            The keyword arguments form of ``indexes``.
            One of indexes or indexes_kwargs must be provided.

        Returns
        -------
        obj : DataArray
            Another dataarray, with this data but replaced coordinates.

        See Also
        --------
        DataArray.reset_index
        """
        inplace = _check_inplace(inplace)
        indexes = either_dict_or_kwargs(indexes, indexes_kwargs, 'set_index')
        coords, _ = merge_indexes(indexes, self._coords, set(), append=append)
        if inplace:
            self._coords = coords
        else:
            return self._replace(coords=coords)


Instruction:
Can you create a Python 3 function that
resets the index of the dataarray to the specified index or multi - index level.

Response:
def reset_index(self, dims_or_levels, drop=False, inplace=None):
        """Reset the specified index(es) or multi-index level(s).

        Parameters
        ----------
        dims_or_levels : str or list
            Name(s) of the dimension(s) and/or multi-index level(s) that will
            be reset.
        drop : bool, optional
            If True, remove the specified indexes and/or multi-index levels
            instead of extracting them as new coordinates (default: False).
        inplace : bool, optional
            If True, modify the dataarray in-place. Otherwise, return a new
            DataArray object.

        Returns
        -------
        obj : DataArray
            Another dataarray, with this dataarray's data but replaced
            coordinates.

        See Also
        --------
        DataArray.set_index
        """
        inplace = _check_inplace(inplace)
        coords, _ = split_indexes(dims_or_levels, self._coords, set(),
                                  self._level_coords, drop=drop)
        if inplace:
            self._coords = coords
        else:
            return self._replace(coords=coords)


Instruction:
Create a Python 3 function for
rearranging index levels using input order.

Response:
def reorder_levels(self, dim_order=None, inplace=None,
                       **dim_order_kwargs):
        """Rearrange index levels using input order.

        Parameters
        ----------
        dim_order : optional
            Mapping from names matching dimensions and values given
            by lists representing new level orders. Every given dimension
            must have a multi-index.
        inplace : bool, optional
            If True, modify the dataarray in-place. Otherwise, return a new
            DataArray object.
        **dim_order_kwargs: optional
            The keyword arguments form of ``dim_order``.
            One of dim_order or dim_order_kwargs must be provided.

        Returns
        -------
        obj : DataArray
            Another dataarray, with this dataarray's data but replaced
            coordinates.
        """
        inplace = _check_inplace(inplace)
        dim_order = either_dict_or_kwargs(dim_order, dim_order_kwargs,
                                          'reorder_levels')
        replace_coords = {}
        for dim, order in dim_order.items():
            coord = self._coords[dim]
            index = coord.to_index()
            if not isinstance(index, pd.MultiIndex):
                raise ValueError("coordinate %r has no MultiIndex" % dim)
            replace_coords[dim] = IndexVariable(coord.dims,
                                                index.reorder_levels(order))
        coords = self._coords.copy()
        coords.update(replace_coords)
        if inplace:
            self._coords = coords
        else:
            return self._replace(coords=coords)


Instruction:
Write a Python 3 function for
stacking any number of existing dimensions into a single new dimension.

Response:
def stack(self, dimensions=None, **dimensions_kwargs):
        """
        Stack any number of existing dimensions into a single new dimension.

        New dimensions will be added at the end, and the corresponding
        coordinate variables will be combined into a MultiIndex.

        Parameters
        ----------
        dimensions : Mapping of the form new_name=(dim1, dim2, ...)
            Names of new dimensions, and the existing dimensions that they
            replace.
        **dimensions_kwargs:
            The keyword arguments form of ``dimensions``.
            One of dimensions or dimensions_kwargs must be provided.

        Returns
        -------
        stacked : DataArray
            DataArray with stacked data.

        Examples
        --------

        >>> arr = DataArray(np.arange(6).reshape(2, 3),
        ...                 coords=[('x', ['a', 'b']), ('y', [0, 1, 2])])
        >>> arr
        <xarray.DataArray (x: 2, y: 3)>
        array([[0, 1, 2],
               [3, 4, 5]])
        Coordinates:
          * x        (x) |S1 'a' 'b'
          * y        (y) int64 0 1 2
        >>> stacked = arr.stack(z=('x', 'y'))
        >>> stacked.indexes['z']
        MultiIndex(levels=[['a', 'b'], [0, 1, 2]],
                   labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]],
                   names=['x', 'y'])

        See also
        --------
        DataArray.unstack
        """
        ds = self._to_temp_dataset().stack(dimensions, **dimensions_kwargs)
        return self._from_temp_dataset(ds)


Instruction:
Can you generate the documentation for the following Python 3 function
def unstack(self, dim=None):
        ds = self._to_temp_dataset().unstack(dim)
        return self._from_temp_dataset(ds)

Response: Unstack existing dimensions into multiple new dimensions.


Instruction:
Write a Python 3 function for
returning a new DataArray with transposed dimensions.

Response:
def transpose(self, *dims) -> 'DataArray':
        """Return a new DataArray object with transposed dimensions.

        Parameters
        ----------
        *dims : str, optional
            By default, reverse the dimensions. Otherwise, reorder the
            dimensions to this order.

        Returns
        -------
        transposed : DataArray
            The returned DataArray's array is transposed.

        Notes
        -----
        This operation returns a view of this array's data. It is
        lazy for dask-backed DataArrays but not for numpy-backed DataArrays
        -- the data will be fully loaded.

        See Also
        --------
        numpy.transpose
        Dataset.transpose
        """
        variable = self.variable.transpose(*dims)
        return self._replace(variable)


Instruction:
Write a Python 3 function that can
drop coordinates or index labels from this DataArray.

Response:
def drop(self, labels, dim=None):
        """Drop coordinates or index labels from this DataArray.

        Parameters
        ----------
        labels : scalar or list of scalars
            Name(s) of coordinate variables or index labels to drop.
        dim : str, optional
            Dimension along which to drop index labels. By default (if
            ``dim is None``), drops coordinates rather than index labels.

        Returns
        -------
        dropped : DataArray
        """
        if utils.is_scalar(labels):
            labels = [labels]
        ds = self._to_temp_dataset().drop(labels, dim)
        return self._from_temp_dataset(ds)


Instruction:
Explain what the following Python 3 code does
def dropna(self, dim, how='any', thresh=None):
        ds = self._to_temp_dataset().dropna(dim, how=how, thresh=thresh)
        return self._from_temp_dataset(ds)

Response: Returns a new array with dropped labels for missing values along the provided dimension.


Instruction:
How would you explain what the following Python 3 function does
def fillna(self, value):
        if utils.is_dict_like(value):
            raise TypeError('cannot provide fill value as a dictionary with '
                            'fillna on a DataArray')
        out = ops.fillna(self, value)
        return out

Response: Fill missing values in this array with value.


Instruction:
How would you code a function in Python 3 to
fill NaN values by propogating values forward

Response:
def ffill(self, dim, limit=None):
        '''Fill NaN values by propogating values forward

        *Requires bottleneck.*

        Parameters
        ----------
        dim : str
            Specifies the dimension along which to propagate values when
            filling.
        limit : int, default None
            The maximum number of consecutive NaN values to forward fill. In
            other words, if there is a gap with more than this number of
            consecutive NaNs, it will only be partially filled. Must be greater
            than 0 or None for no limit.

        Returns
        -------
        DataArray
        '''
        from .missing import ffill
        return ffill(self, dim, limit=limit)


Instruction:
Given the following Python 3 function, write the documentation
def bfill(self, dim, limit=None):
        '''Fill NaN values by propogating values backward

        *Requires bottleneck.*

        Parameters
        ----------
        dim : str
            Specifies the dimension along which to propagate values when
            filling.
        limit : int, default None
            The maximum number of consecutive NaN values to backward fill. In
            other words, if there is a gap with more than this number of
            consecutive NaNs, it will only be partially filled. Must be greater
            than 0 or None for no limit.

        Returns
        -------
        DataArray
        '''
        from .missing import bfill
        return bfill(self, dim, limit=limit)

Response:
Fill NaN values by propogating values backward
            filling.


Instruction:
Can you generate the documentation for the following Python 3 function
def reduce(self, func, dim=None, axis=None, keep_attrs=None, **kwargs):

        var = self.variable.reduce(func, dim, axis, keep_attrs, **kwargs)
        return self._replace_maybe_drop_dims(var)

Response: Reduce this array by applying func along some dimension.


Instruction:
Explain what the following Python 3 code does
def to_pandas(self):
        # TODO: consolidate the info about pandas constructors and the
        # attributes that correspond to their indexes into a separate module?
        constructors = {0: lambda x: x,
                        1: pd.Series,
                        2: pd.DataFrame,
                        3: pd.Panel}
        try:
            constructor = constructors[self.ndim]
        except KeyError:
            raise ValueError('cannot convert arrays with %s dimensions into '
                             'pandas objects' % self.ndim)
        indexes = [self.get_index(dim) for dim in self.dims]
        return constructor(self.values, *indexes)

Response: Convert this array into a pandas object with the same shape.


Instruction:
Write a Python 3 function that can
convert this array and its coordinates into a tidy pandas. DataFrame.

Response:
def to_dataframe(self, name=None):
        """Convert this array and its coordinates into a tidy pandas.DataFrame.

        The DataFrame is indexed by the Cartesian product of index coordinates
        (in the form of a :py:class:`pandas.MultiIndex`).

        Other coordinates are included as columns in the DataFrame.
        """
        if name is None:
            name = self.name
        if name is None:
            raise ValueError('cannot convert an unnamed DataArray to a '
                             'DataFrame: use the ``name`` parameter')

        dims = OrderedDict(zip(self.dims, self.shape))
        # By using a unique name, we can convert a DataArray into a DataFrame
        # even if it shares a name with one of its coordinates.
        # I would normally use unique_name = object() but that results in a
        # dataframe with columns in the wrong order, for reasons I have not
        # been able to debug (possibly a pandas bug?).
        unique_name = '__unique_name_identifier_z98xfz98xugfg73ho__'
        ds = self._to_dataset_whole(name=unique_name)
        df = ds._to_dataframe(dims)
        df.columns = [name if c == unique_name else c
                      for c in df.columns]
        return df


Instruction:
Explain what the following Python 3 code does
def to_series(self):
        index = self.coords.to_index()
        return pd.Series(self.values.reshape(-1), index=index, name=self.name)

Response: Convert this array into a pandas. Series.


Instruction:
Can you tell what is the following Python 3 function doing
def to_masked_array(self, copy=True):
        isnull = pd.isnull(self.values)
        return np.ma.MaskedArray(data=self.values, mask=isnull, copy=copy)

Response: Convert this array into a numpy. ma. MaskedArray.


Instruction:
Write a Python 3 function that can
write the contents of the array to a netCDF file.

Response:
def to_netcdf(self, *args, **kwargs):
        """Write DataArray contents to a netCDF file.

        Parameters
        ----------
        path : str or Path, optional
            Path to which to save this dataset. If no path is provided, this
            function returns the resulting netCDF file as a bytes object; in
            this case, we need to use scipy.io.netcdf, which does not support
            netCDF version 4 (the default format becomes NETCDF3_64BIT).
        mode : {'w', 'a'}, optional
            Write ('w') or append ('a') mode. If mode='w', any existing file at
            this location will be overwritten.
        format : {'NETCDF4', 'NETCDF4_CLASSIC', 'NETCDF3_64BIT',
                  'NETCDF3_CLASSIC'}, optional
            File format for the resulting netCDF file:

            * NETCDF4: Data is stored in an HDF5 file, using netCDF4 API
              features.
            * NETCDF4_CLASSIC: Data is stored in an HDF5 file, using only
              netCDF 3 compatible API features.
            * NETCDF3_64BIT: 64-bit offset version of the netCDF 3 file format,
              which fully supports 2+ GB files, but is only compatible with
              clients linked against netCDF version 3.6.0 or later.
            * NETCDF3_CLASSIC: The classic netCDF 3 file format. It does not
              handle 2+ GB files very well.

            All formats are supported by the netCDF4-python library.
            scipy.io.netcdf only supports the last two formats.

            The default format is NETCDF4 if you are saving a file to disk and
            have the netCDF4-python library available. Otherwise, xarray falls
            back to using scipy to write netCDF files and defaults to the
            NETCDF3_64BIT format (scipy does not support netCDF4).
        group : str, optional
            Path to the netCDF4 group in the given file to open (only works for
            format='NETCDF4'). The group(s) will be created if necessary.
        engine : {'netcdf4', 'scipy', 'h5netcdf'}, optional
            Engine to use when writing netCDF files. If not provided, the
            default engine is chosen based on available dependencies, with a
            preference for 'netcdf4' if writing to a file on disk.
        encoding : dict, optional
            Nested dictionary with variable names as keys and dictionaries of
            variable specific encodings as values, e.g.,
            ``{'my_variable': {'dtype': 'int16', 'scale_factor': 0.1,
               'zlib': True}, ...}``

        Notes
        -----
        Only xarray.Dataset objects can be written to netCDF files, so
        the xarray.DataArray is converted to a xarray.Dataset object
        containing a single variable. If the DataArray has no name, or if the
        name is the same as a co-ordinate name, then it is given the name
        '__xarray_dataarray_variable__'.

        All parameters are passed directly to `xarray.Dataset.to_netcdf`.
        """
        from ..backends.api import DATAARRAY_NAME, DATAARRAY_VARIABLE

        if self.name is None:
            # If no name is set then use a generic xarray name
            dataset = self.to_dataset(name=DATAARRAY_VARIABLE)
        elif self.name in self.coords or self.name in self.dims:
            # The name is the same as one of the coords names, which netCDF
            # doesn't support, so rename it but keep track of the old name
            dataset = self.to_dataset(name=DATAARRAY_VARIABLE)
            dataset.attrs[DATAARRAY_NAME] = self.name
        else:
            # No problems with the name - so we're fine!
            dataset = self.to_dataset()

        return dataset.to_netcdf(*args, **kwargs)


Instruction:
Can you create a Python 3 function that
converts this xarray. DataArray into a dictionary following xarray. NameConventions.

Response:
def to_dict(self, data=True):
        """
        Convert this xarray.DataArray into a dictionary following xarray
        naming conventions.

        Converts all variables and attributes to native Python objects.
        Useful for coverting to json. To avoid datetime incompatibility
        use decode_times=False kwarg in xarrray.open_dataset.

        Parameters
        ----------
        data : bool, optional
            Whether to include the actual data in the dictionary. When set to
            False, returns just the schema.

        See also
        --------
        DataArray.from_dict
        """
        d = self.variable.to_dict(data=data)
        d.update({'coords': {}, 'name': self.name})
        for k in self.coords:
            d['coords'][k] = self.coords[k].variable.to_dict(data=data)
        return d


Instruction:
Given the following Python 3 function, write the documentation
def from_dict(cls, d):
        coords = None
        if 'coords' in d:
            try:
                coords = OrderedDict([(k, (v['dims'],
                                           v['data'],
                                           v.get('attrs')))
                                      for k, v in d['coords'].items()])
            except KeyError as e:
                raise ValueError(
                    "cannot convert dict when coords are missing the key "
                    "'{dims_data}'".format(dims_data=str(e.args[0])))
        try:
            data = d['data']
        except KeyError:
            raise ValueError("cannot convert dict without the key 'data''")
        else:
            obj = cls(data, coords, d.get('dims'), d.get('name'),
                      d.get('attrs'))
        return obj

Response:
Convert a dictionary into an xarray. DataArray
            object.


Instruction:
Can you implement a function in Python 3 that
converts a pandas. Series into an xarray. DataArray.

Response:
def from_series(cls, series):
        """Convert a pandas.Series into an xarray.DataArray.

        If the series's index is a MultiIndex, it will be expanded into a
        tensor product of one-dimensional coordinates (filling in missing
        values with NaN). Thus this operation should be the inverse of the
        `to_series` method.
        """
        # TODO: add a 'name' parameter
        name = series.name
        df = pd.DataFrame({name: series})
        ds = Dataset.from_dataframe(df)
        return ds[name]


Instruction:
Write a Python 3 script to
like equals but also checks the array name attributes and attributes on all coordinates.

Response:
def identical(self, other):
        """Like equals, but also checks the array name and attributes, and
        attributes on all coordinates.

        See Also
        --------
        DataArray.broadcast_equals
        DataArray.equal
        """
        try:
            return (self.name == other.name and
                    self._all_compat(other, 'identical'))
        except (TypeError, AttributeError):
            return False


Instruction:
Given the following Python 3 function, write the documentation
def _title_for_slice(self, truncate=50):
        one_dims = []
        for dim, coord in self.coords.items():
            if coord.size == 1:
                one_dims.append('{dim} = {v}'.format(
                    dim=dim, v=format_item(coord.values)))

        title = ', '.join(one_dims)
        if len(title) > truncate:
            title = title[:(truncate - 3)] + '...'

        return title

Response: Returns a string that can be used for plot titles for a slice of the dataarray.


Instruction:
Implement a Python 3 function for
calculating the n - th order discrete difference along given axis.

Response:
def diff(self, dim, n=1, label='upper'):
        """Calculate the n-th order discrete difference along given axis.

        Parameters
        ----------
        dim : str, optional
            Dimension over which to calculate the finite difference.
        n : int, optional
            The number of times values are differenced.
        label : str, optional
            The new coordinate in dimension ``dim`` will have the
            values of either the minuend's or subtrahend's coordinate
            for values 'upper' and 'lower', respectively.  Other
            values are not supported.

        Returns
        -------
        difference : same type as caller
            The n-th order finite difference of this object.

        Examples
        --------
        >>> arr = xr.DataArray([5, 5, 6, 6], [[1, 2, 3, 4]], ['x'])
        >>> arr.diff('x')
        <xarray.DataArray (x: 3)>
        array([0, 1, 0])
        Coordinates:
        * x        (x) int64 2 3 4
        >>> arr.diff('x', 2)
        <xarray.DataArray (x: 2)>
        array([ 1, -1])
        Coordinates:
        * x        (x) int64 3 4

        See Also
        --------
        DataArray.differentiate
        """
        ds = self._to_temp_dataset().diff(n=n, dim=dim, label=label)
        return self._from_temp_dataset(ds)


Instruction:
Make a summary of the following Python 3 code
def shift(self, shifts=None, fill_value=dtypes.NA, **shifts_kwargs):
        variable = self.variable.shift(
            shifts=shifts, fill_value=fill_value, **shifts_kwargs)
        return self._replace(variable=variable)

Response: Shifts this array by an offset along one or more dimensions.


Instruction:
Can you write a function in Python 3 where it
rolls this array by an offset along one or more dimensions.

Response:
def roll(self, shifts=None, roll_coords=None, **shifts_kwargs):
        """Roll this array by an offset along one or more dimensions.

        Unlike shift, roll may rotate all variables, including coordinates
        if specified. The direction of rotation is consistent with
        :py:func:`numpy.roll`.

        Parameters
        ----------
        roll_coords : bool
            Indicates whether to  roll the coordinates by the offset
            The current default of roll_coords (None, equivalent to True) is
            deprecated and will change to False in a future version.
            Explicitly pass roll_coords to silence the warning.
        **shifts : keyword arguments of the form {dim: offset}
            Integer offset to rotate each of the given dimensions. Positive
            offsets roll to the right; negative offsets roll to the left.

        Returns
        -------
        rolled : DataArray
            DataArray with the same attributes but rolled data and coordinates.

        See also
        --------
        shift

        Examples
        --------

        >>> arr = xr.DataArray([5, 6, 7], dims='x')
        >>> arr.roll(x=1)
        <xarray.DataArray (x: 3)>
        array([7, 5, 6])
        Coordinates:
          * x        (x) int64 2 0 1
        """
        ds = self._to_temp_dataset().roll(
            shifts=shifts, roll_coords=roll_coords, **shifts_kwargs)
        return self._from_temp_dataset(ds)


Instruction:
Can you tell what is the following Python 3 function doing
def dot(self, other, dims=None):
        if isinstance(other, Dataset):
            raise NotImplementedError('dot products are not yet supported '
                                      'with Dataset objects.')
        if not isinstance(other, DataArray):
            raise TypeError('dot only operates on DataArrays.')

        return computation.dot(self, other, dims=dims)

Response: Perform the dot product of two DataArrays along their shared dims.


Instruction:
Can you generate the documentation for the following Python 3 function
def sortby(self, variables, ascending=True):
        ds = self._to_temp_dataset().sortby(variables, ascending=ascending)
        return self._from_temp_dataset(ds)

Response: Sort the data array by the specified variables.


Instruction:
Here you have a function in Python 3, explain what it does
def quantile(self, q, dim=None, interpolation='linear', keep_attrs=None):

        ds = self._to_temp_dataset().quantile(
            q, dim=dim, keep_attrs=keep_attrs, interpolation=interpolation)
        return self._from_temp_dataset(ds)

Response: Compute the qth quantile of the data along the specified dimension.


Instruction:
Here you have a function in Python 3, explain what it does
def rank(self, dim, pct=False, keep_attrs=None):

        ds = self._to_temp_dataset().rank(dim, pct=pct, keep_attrs=keep_attrs)
        return self._from_temp_dataset(ds)

Response: Ranks the data array.


Instruction:
Implement a Python 3 function for
differentiating the array with the second order accurate central differences. .. note:: This feature is limited to simple cartesian geometry, i.e. coord must be one dimensional. Parameters ---------- coord: str The coordinate to be used to compute the gradient. edge_order: 1 or 2. Default 1 N-th order accurate differences at the boundaries. datetime_unit: None or any of {'Y', 'M', 'W', 'D', 'h', 'm', 's', 'ms', 'us', 'ns', 'ps', 'fs', 'as'} Unit to compute gradient. Only valid for datetime coordinate. Returns ------- differentiated: DataArray See also -------- numpy.gradient: corresponding numpy function Examples -------- >>> da = xr.DataArray(np.arange(12).reshape(4, 3), dims=['x', 'y'], ... coords={'x': [0, 0.1, 1.1, 1.2]}) >>> da <xarray.DataArray (x: 4, y: 3)> array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11]]) Coordinates: * x (x) float64 0.0 0.1 1.1 1.2 Dimensions without coordinates: y >>> >>> da.differentiate('x') <xarray.DataArray (x: 4, y: 3)> array([[30. , 30. , 30. ], [27.545455, 27.545455, 27.545455], [27.545455, 27.545455, 27.545455], [30. , 30. , 30. ]]) Coordinates: * x (x) float64 0.0 0.1 1.1 1.2 Dimensions without coordinates: y

Response:
def differentiate(self, coord, edge_order=1, datetime_unit=None):
        """ Differentiate the array with the second order accurate central
        differences.

        .. note::
            This feature is limited to simple cartesian geometry, i.e. coord
            must be one dimensional.

        Parameters
        ----------
        coord: str
            The coordinate to be used to compute the gradient.
        edge_order: 1 or 2. Default 1
            N-th order accurate differences at the boundaries.
        datetime_unit: None or any of {'Y', 'M', 'W', 'D', 'h', 'm', 's', 'ms',
            'us', 'ns', 'ps', 'fs', 'as'}
            Unit to compute gradient. Only valid for datetime coordinate.

        Returns
        -------
        differentiated: DataArray

        See also
        --------
        numpy.gradient: corresponding numpy function

        Examples
        --------

        >>> da = xr.DataArray(np.arange(12).reshape(4, 3), dims=['x', 'y'],
        ...                   coords={'x': [0, 0.1, 1.1, 1.2]})
        >>> da
        <xarray.DataArray (x: 4, y: 3)>
        array([[ 0,  1,  2],
               [ 3,  4,  5],
               [ 6,  7,  8],
               [ 9, 10, 11]])
        Coordinates:
          * x        (x) float64 0.0 0.1 1.1 1.2
        Dimensions without coordinates: y
        >>>
        >>> da.differentiate('x')
        <xarray.DataArray (x: 4, y: 3)>
        array([[30.      , 30.      , 30.      ],
               [27.545455, 27.545455, 27.545455],
               [27.545455, 27.545455, 27.545455],
               [30.      , 30.      , 30.      ]])
        Coordinates:
          * x        (x) float64 0.0 0.1 1.1 1.2
        Dimensions without coordinates: y
        """
        ds = self._to_temp_dataset().differentiate(
            coord, edge_order, datetime_unit)
        return self._from_temp_dataset(ds)


Instruction:
Explain what the following Python 3 code does
def integrate(self, dim, datetime_unit=None):
        ds = self._to_temp_dataset().integrate(dim, datetime_unit)
        return self._from_temp_dataset(ds)

Response: Integrate the array with the trapezoidal rule.


Instruction:
Here you have a function in Python 3, explain what it does
def construct(self, window_dim, stride=1, fill_value=dtypes.NA):

        from .dataarray import DataArray

        window = self.obj.variable.rolling_window(self.dim, self.window,
                                                  window_dim, self.center,
                                                  fill_value=fill_value)
        result = DataArray(window, dims=self.obj.dims + (window_dim,),
                           coords=self.obj.coords)
        return result.isel(**{self.dim: slice(None, None, stride)})

Response: Convert this rolling object to xr. DataArray where the window dimension is stacked as a new dimension.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def reduce(self, func, **kwargs):
        rolling_dim = utils.get_temp_dimname(self.obj.dims, '_rolling_dim')
        windows = self.construct(rolling_dim)
        result = windows.reduce(func, dim=rolling_dim, **kwargs)

        # Find valid windows based on count.
        counts = self._counts()
        return result.where(counts >= self._min_periods)

Response: Reduce the items in this group by applying func along some tier dimension.


Instruction:
Can you implement a function in Python 3 that
numbers of non - nan entries in each rolling window.

Response:
def _counts(self):
        """ Number of non-nan entries in each rolling window. """

        rolling_dim = utils.get_temp_dimname(self.obj.dims, '_rolling_dim')
        # We use False as the fill_value instead of np.nan, since boolean
        # array is faster to be reduced than object array.
        # The use of skipna==False is also faster since it does not need to
        # copy the strided array.
        counts = (self.obj.notnull()
                  .rolling(center=self.center, **{self.dim: self.window})
                  .construct(rolling_dim, fill_value=False)
                  .sum(dim=rolling_dim, skipna=False))
        return counts


Instruction:
Create a Python 3 function for
returning a wrapped function for any function func for the n - tuple class.

Response:
def _reduce_method(cls, func):
        """
        Methods to return a wrapped function for any function `func` for
        numpy methods.
        """

        def wrapped_func(self, **kwargs):
            return self.reduce(func, **kwargs)
        return wrapped_func


Instruction:
How would you explain what the following Python 3 function does
def reduce(self, func, **kwargs):
        from .dataset import Dataset
        reduced = OrderedDict()
        for key, da in self.obj.data_vars.items():
            if self.dim in da.dims:
                reduced[key] = self.rollings[key].reduce(func, **kwargs)
            else:
                reduced[key] = self.obj[key]
        return Dataset(reduced, coords=self.obj.coords)

Response: Reduce the items in this group by applying func along some tier dimension.


Instruction:
Can you write a function in Python 3 where it
returns a wrapped function for injecting numpy and bottoleneck methods. is the name of the method that is used to reduce the object.

Response:
def _reduce_method(cls, func):
        """
        Return a wrapped function for injecting numpy and bottoleneck methods.
        see ops.inject_datasetrolling_methods
        """

        def wrapped_func(self, **kwargs):
            from .dataset import Dataset
            reduced = OrderedDict()
            for key, da in self.obj.data_vars.items():
                if self.dim in da.dims:
                    reduced[key] = getattr(self.rollings[key],
                                           func.__name__)(**kwargs)
                else:
                    reduced[key] = self.obj[key]
            return Dataset(reduced, coords=self.obj.coords)
        return wrapped_func


Instruction:
Can you generate a brief explanation for the following Python 3 code
def construct(self, window_dim, stride=1, fill_value=dtypes.NA):

        from .dataset import Dataset

        dataset = OrderedDict()
        for key, da in self.obj.data_vars.items():
            if self.dim in da.dims:
                dataset[key] = self.rollings[key].construct(
                    window_dim, fill_value=fill_value)
            else:
                dataset[key] = da
        return Dataset(dataset, coords=self.obj.coords).isel(
            **{self.dim: slice(None, None, stride)})

Response: Convert this rolling object to xr. Dataset where the dimension is stacked as a new dimension.


Instruction:
Here you have a function in Python 3, explain what it does
def _reduce_method(cls, func):
        def wrapped_func(self, **kwargs):
            from .dataarray import DataArray

            reduced = self.obj.variable.coarsen(
                self.windows, func, self.boundary, self.side)
            coords = {}
            for c, v in self.obj.coords.items():
                if c == self.obj.name:
                    coords[c] = reduced
                else:
                    if any(d in self.windows for d in v.dims):
                        coords[c] = v.variable.coarsen(
                            self.windows, self.coord_func[c],
                            self.boundary, self.side)
                    else:
                        coords[c] = v
            return DataArray(reduced, dims=self.obj.dims, coords=coords)

        return wrapped_func

Response: Returns a wrapped function for injecting numpy methods.


Instruction:
Can you create a Python 3 function that
returns a wrapped function for injecting numpy methods.

Response:
def _reduce_method(cls, func):
        """
        Return a wrapped function for injecting numpy methods.
        see ops.inject_coarsen_methods
        """
        def wrapped_func(self, **kwargs):
            from .dataset import Dataset

            reduced = OrderedDict()
            for key, da in self.obj.data_vars.items():
                reduced[key] = da.variable.coarsen(
                    self.windows, func, self.boundary, self.side)

            coords = {}
            for c, v in self.obj.coords.items():
                if any(d in self.windows for d in v.dims):
                    coords[c] = v.variable.coarsen(
                        self.windows, self.coord_func[c],
                        self.boundary, self.side)
                else:
                    coords[c] = v.variable
            return Dataset(reduced, coords=coords)

        return wrapped_func


Instruction:
Make a summary of the following Python 3 code
def ensure_fixed_length_bytes(var):
    dims, data, attrs, encoding = unpack_for_encoding(var)
    if check_vlen_dtype(data.dtype) == bytes:
        # TODO: figure out how to handle this with dask
        data = np.asarray(data, dtype=np.string_)
    return Variable(dims, data, attrs, encoding)

Response: Ensure that a variable with vlen bytes is converted to fixed width.


Instruction:
Write a Python 3 script for
converting numpy arrays from fixed width bytes to characters.

Response:
def bytes_to_char(arr):
    """Convert numpy/dask arrays from fixed width bytes to characters."""
    if arr.dtype.kind != 'S':
        raise ValueError('argument must have a fixed-width bytes dtype')

    if isinstance(arr, dask_array_type):
        import dask.array as da
        return da.map_blocks(_numpy_bytes_to_char, arr,
                             dtype='S1',
                             chunks=arr.chunks + ((arr.dtype.itemsize,)),
                             new_axis=[arr.ndim])
    else:
        return _numpy_bytes_to_char(arr)


Instruction:
Explain what the following Python 3 code does
def _numpy_bytes_to_char(arr):
    # ensure the array is contiguous
    arr = np.array(arr, copy=False, order='C', dtype=np.string_)
    return arr.reshape(arr.shape + (1,)).view('S1')

Response: Like netCDF4. stringtochar but faster and more flexible.


Instruction:
Can you tell what is the following Python 3 function doing
def char_to_bytes(arr):
    if arr.dtype != 'S1':
        raise ValueError("argument must have dtype='S1'")

    if not arr.ndim:
        # no dimension to concatenate along
        return arr

    size = arr.shape[-1]

    if not size:
        # can't make an S0 dtype
        return np.zeros(arr.shape[:-1], dtype=np.string_)

    if isinstance(arr, dask_array_type):
        import dask.array as da

        if len(arr.chunks[-1]) > 1:
            raise ValueError('cannot stacked dask character array with '
                             'multiple chunks in the last dimension: {}'
                             .format(arr))

        dtype = np.dtype('S' + str(arr.shape[-1]))
        return da.map_blocks(_numpy_char_to_bytes, arr,
                             dtype=dtype,
                             chunks=arr.chunks[:-1],
                             drop_axis=[arr.ndim - 1])
    else:
        return StackedBytesArray(arr)

Response: Convert numpy arrays from characters to fixed width bytes.


Instruction:
Write a Python 3 function for
liking netCDF4. chartostring but faster and more flexible.

Response:
def _numpy_char_to_bytes(arr):
    """Like netCDF4.chartostring, but faster and more flexible.
    """
    # based on: http://stackoverflow.com/a/10984878/809705
    arr = np.array(arr, copy=False, order='C')
    dtype = 'S' + str(arr.shape[-1])
    return arr.view(dtype).reshape(arr.shape[:-1])


Instruction:
Given the following Python 3 function, write the documentation
def safe_cast_to_index(array: Any) -> pd.Index:
    if isinstance(array, pd.Index):
        index = array
    elif hasattr(array, 'to_index'):
        index = array.to_index()
    else:
        kwargs = {}
        if hasattr(array, 'dtype') and array.dtype.kind == 'O':
            kwargs['dtype'] = object
        index = pd.Index(np.asarray(array), **kwargs)
    return _maybe_cast_to_cftimeindex(index)

Response: Given an array safely cast it to a pandas. Index.


Instruction:
How would you code a function in Python 3 to
create a MultiIndex from a product with refactorizing levels.

Response:
def multiindex_from_product_levels(levels: Sequence[pd.Index],
                                   names: Optional[Sequence[str]] = None
                                   ) -> pd.MultiIndex:
    """Creating a MultiIndex from a product without refactorizing levels.

    Keeping levels the same gives back the original labels when we unstack.

    Parameters
    ----------
    levels : sequence of pd.Index
        Values for each MultiIndex level.
    names : optional sequence of objects
        Names for each level.

    Returns
    -------
    pandas.MultiIndex
    """
    if any(not isinstance(lev, pd.Index) for lev in levels):
        raise TypeError('levels must be a list of pd.Index objects')

    split_labels, levels = zip(*[lev.factorize() for lev in levels])
    labels_mesh = np.meshgrid(*split_labels, indexing='ij')
    labels = [x.ravel() for x in labels_mesh]
    return pd.MultiIndex(levels, labels, sortorder=0, names=names)


Instruction:
Given the following Python 3 function, write the documentation
def maybe_wrap_array(original, new_array):
    # in case func lost array's metadata
    if isinstance(new_array, np.ndarray) and new_array.shape == original.shape:
        return original.__array_wrap__(new_array)
    else:
        return new_array

Response: Wrap a transformed array with __array_wrap__ is it can be done safely.


Instruction:
How would you implement a function in Python 3 that
compares two objects for equivalence ( identity or equality

Response:
def equivalent(first: T, second: T) -> bool:
    """Compare two objects for equivalence (identity or equality), using
    array_equiv if either object is an ndarray
    """
    # TODO: refactor to avoid circular import
    from . import duck_array_ops
    if isinstance(first, np.ndarray) or isinstance(second, np.ndarray):
        return duck_array_ops.array_equiv(first, second)
    else:
        return ((first is second) or
                (first == second) or
                (pd.isnull(first) and pd.isnull(second)))


Instruction:
Can you generate a brief explanation for the following Python 3 code
def peek_at(iterable: Iterable[T]) -> Tuple[T, Iterator[T]]:
    gen = iter(iterable)
    peek = next(gen)
    return peek, itertools.chain([peek], gen)

Response:
Returns the first value from iterable as well as a new iterator with
    the same content as the original iterable


Instruction:
Make a summary of the following Python 3 code
def update_safety_check(first_dict: MutableMapping[K, V],
                        second_dict: Mapping[K, V],
                        compat: Callable[[V, V], bool] = equivalent) -> None:
    """Check the safety of updating one dictionary with another.

    Raises ValueError if dictionaries have non-compatible values for any key,
    where compatibility is determined by identity (they are the same item) or
    the `compat` function.

    Parameters
    ----------
    first_dict, second_dict : dict-like
        All items in the second dictionary are checked against for conflicts
        against items in the first dictionary.
    compat : function, optional
        Binary operator to determine if two values are compatible. By default,
        checks for equivalence.
    """
    for k, v in second_dict.items():
        if k in first_dict and not compat(v, first_dict[k]):
            raise ValueError('unsafe to merge dictionaries without '
                             'overriding values; conflicting key %r' % k)

Response: Check the safety of updating one dictionary with another.


Instruction:
Here you have a function in Python 3, explain what it does
def remove_incompatible_items(first_dict: MutableMapping[K, V],
                              second_dict: Mapping[K, V],
                              compat: Callable[[V, V], bool] = equivalent
                              ) -> None:
    """Remove incompatible items from the first dictionary in-place.

    Items are retained if their keys are found in both dictionaries and the
    values are compatible.

    Parameters
    ----------
    first_dict, second_dict : dict-like
        Mappings to merge.
    compat : function, optional
        Binary operator to determine if two values are compatible. By default,
        checks for equivalence.
    """
    for k in list(first_dict):
        if k not in second_dict or not compat(first_dict[k], second_dict[k]):
            del first_dict[k]

Response: Removes incompatible items from the first dictionary in - place.


Instruction:
How would you explain what the following Python 3 function does
def is_scalar(value: Any) -> bool:
    return (
        getattr(value, 'ndim', None) == 0 or
        isinstance(value, (str, bytes)) or not
        isinstance(value, (Iterable, ) + dask_array_type))

Response: Whether a value is a scalar.


Instruction:
Given the following Python 3 function, write the documentation
def to_0d_object_array(value: Any) -> np.ndarray:
    result = np.empty((), dtype=object)
    result[()] = value
    return result

Response: Given a value wrap it in a 0 - D numpy. ndarray with dtype = object.


Instruction:
Given the following Python 3 function, write the documentation
def to_0d_array(value: Any) -> np.ndarray:
    if np.isscalar(value) or (isinstance(value, np.ndarray) and
                              value.ndim == 0):
        return np.array(value)
    else:
        return to_0d_object_array(value)

Response: Given a value wrap it in a 0 - D numpy. ndarray.


Instruction:
Write a Python 3 function that can
test equivalence of two dict - like objects.

Response:
def dict_equiv(first: Mapping[K, V], second: Mapping[K, V],
               compat: Callable[[V, V], bool] = equivalent) -> bool:
    """Test equivalence of two dict-like objects. If any of the values are
    numpy arrays, compare them correctly.

    Parameters
    ----------
    first, second : dict-like
        Dictionaries to compare for equality
    compat : function, optional
        Binary operator to determine if two values are compatible. By default,
        checks for equivalence.

    Returns
    -------
    equals : bool
        True if the dictionaries are equal
    """
    for k in first:
        if k not in second or not compat(first[k], second[k]):
            return False
    for k in second:
        if k not in first:
            return False
    return True


Instruction:
Implement a Python 3 function for
returning the intersection of two dictionaries.

Response:
def ordered_dict_intersection(first_dict: Mapping[K, V],
                              second_dict: Mapping[K, V],
                              compat: Callable[[V, V], bool] = equivalent
                              ) -> MutableMapping[K, V]:
    """Return the intersection of two dictionaries as a new OrderedDict.

    Items are retained if their keys are found in both dictionaries and the
    values are compatible.

    Parameters
    ----------
    first_dict, second_dict : dict-like
        Mappings to merge.
    compat : function, optional
        Binary operator to determine if two values are compatible. By default,
        checks for equivalence.

    Returns
    -------
    intersection : OrderedDict
        Intersection of the contents.
    """
    new_dict = OrderedDict(first_dict)
    remove_incompatible_items(new_dict, second_dict, compat)
    return new_dict


Instruction:
How would you explain what the following Python 3 function does
def is_uniform_spaced(arr, **kwargs) -> bool:
    arr = np.array(arr, dtype=float)
    diffs = np.diff(arr)
    return bool(np.isclose(diffs.min(), diffs.max(), **kwargs))

Response: Return True if values of an array are uniformly spaced and sorted.


Instruction:
Implement a function in Python 3 to
convert numpy objects to native Python objects

Response:
def decode_numpy_dict_values(attrs: Mapping[K, V]) -> Dict[K, V]:
    """Convert attribute values from numpy objects to native Python objects,
    for use in to_dict
    """
    attrs = dict(attrs)
    for k, v in attrs.items():
        if isinstance(v, np.ndarray):
            attrs[k] = v.tolist()
        elif isinstance(v, np.generic):
            attrs[k] = v.item()
    return attrs


Instruction:
How would you explain what the following Python 3 function does
def ensure_us_time_resolution(val):
    if np.issubdtype(val.dtype, np.datetime64):
        val = val.astype('datetime64[us]')
    elif np.issubdtype(val.dtype, np.timedelta64):
        val = val.astype('timedelta64[us]')
    return val

Response:
Convert val out of numpy time for use in to_dict.
    Needed because of numpy bug GH#7619


Instruction:
Write a Python 3 function for
getting a new dimension name based on new_dim.

Response:
def get_temp_dimname(dims: Container[Hashable], new_dim: Hashable) -> Hashable:
    """ Get an new dimension name based on new_dim, that is not used in dims.
    If the same name exists, we add an underscore(s) in the head.

    Example1:
        dims: ['a', 'b', 'c']
        new_dim: ['_rolling']
        -> ['_rolling']
    Example2:
        dims: ['a', 'b', 'c', '_rolling']
        new_dim: ['_rolling']
        -> ['__rolling']
    """
    while new_dim in dims:
        new_dim = '_' + str(new_dim)
    return new_dim


Instruction:
Create a Python 3 function to
give an object array with no missing values infer its dtype from its first element

Response:
def _infer_dtype(array, name=None):
    """Given an object array with no missing values, infer its dtype from its
    first element
    """
    if array.dtype.kind != 'O':
        raise TypeError('infer_type must be called on a dtype=object array')

    if array.size == 0:
        return np.dtype(float)

    element = array[(0,) * array.ndim]
    if isinstance(element, (bytes, str)):
        return strings.create_vlen_dtype(type(element))

    dtype = np.array(element).dtype
    if dtype.kind != 'O':
        return dtype

    raise ValueError('unable to infer dtype on variable {!r}; xarray '
                     'cannot serialize arbitrary Python objects'
                     .format(name))


Instruction:
Write a Python 3 function for
creating a copy of an array with the given dtype.

Response:
def _copy_with_dtype(data, dtype):
    """Create a copy of an array with the given dtype.

    We use this instead of np.array() to ensure that custom object dtypes end
    up on the resulting array.
    """
    result = np.empty(data.shape, dtype)
    result[...] = data
    return result


Instruction:
Write a Python 3 function that can
encode a variable into a CF - encoded version of the variable.

Response:
def encode_cf_variable(var, needs_copy=True, name=None):
    """
    Converts an Variable into an Variable which follows some
    of the CF conventions:

        - Nans are masked using _FillValue (or the deprecated missing_value)
        - Rescaling via: scale_factor and add_offset
        - datetimes are converted to the CF 'units since time' format
        - dtype encodings are enforced.

    Parameters
    ----------
    var : xarray.Variable
        A variable holding un-encoded data.

    Returns
    -------
    out : xarray.Variable
        A variable which has been encoded as described above.
    """
    ensure_not_multiindex(var, name=name)

    for coder in [times.CFDatetimeCoder(),
                  times.CFTimedeltaCoder(),
                  variables.CFScaleOffsetCoder(),
                  variables.CFMaskCoder(),
                  variables.UnsignedIntegerCoder()]:
        var = coder.encode(var, name=name)

    # TODO(shoyer): convert all of these to use coders, too:
    var = maybe_encode_nonstring_dtype(var, name=name)
    var = maybe_default_fill_value(var)
    var = maybe_encode_bools(var)
    var = ensure_dtype_not_object(var, name=name)
    return var


Instruction:
Given the following Python 3 function, write the documentation
def decode_cf_variable(name, var, concat_characters=True, mask_and_scale=True,
                       decode_times=True, decode_endianness=True,
                       stack_char_dim=True, use_cftime=None):
    """
    Decodes a variable which may hold CF encoded information.

    This includes variables that have been masked and scaled, which
    hold CF style time variables (this is almost always the case if
    the dataset has been serialized) and which have strings encoded
    as character arrays.

    Parameters
    ----------
    name: str
        Name of the variable. Used for better error messages.
    var : Variable
        A variable holding potentially CF encoded information.
    concat_characters : bool
        Should character arrays be concatenated to strings, for
        example: ['h', 'e', 'l', 'l', 'o'] -> 'hello'
    mask_and_scale: bool
        Lazily scale (using scale_factor and add_offset) and mask
        (using _FillValue). If the _Unsigned attribute is present
        treat integer arrays as unsigned.
    decode_times : bool
        Decode cf times ('hours since 2000-01-01') to np.datetime64.
    decode_endianness : bool
        Decode arrays from non-native to native endianness.
    stack_char_dim : bool
        Whether to stack characters into bytes along the last dimension of this
        array. Passed as an argument because we need to look at the full
        dataset to figure out if this is appropriate.
    use_cftime: bool, optional
        Only relevant if encoded dates come from a standard calendar
        (e.g. 'gregorian', 'proleptic_gregorian', 'standard', or not
        specified).  If None (default), attempt to decode times to
        ``np.datetime64[ns]`` objects; if this is not possible, decode times to
        ``cftime.datetime`` objects. If True, always decode times to
        ``cftime.datetime`` objects, regardless of whether or not they can be
        represented using ``np.datetime64[ns]`` objects.  If False, always
        decode times to ``np.datetime64[ns]`` objects; if this is not possible
        raise an error.

    Returns
    -------
    out : Variable
        A variable holding the decoded equivalent of var.
    """
    var = as_variable(var)
    original_dtype = var.dtype

    if concat_characters:
        if stack_char_dim:
            var = strings.CharacterArrayCoder().decode(var, name=name)
        var = strings.EncodedStringCoder().decode(var)

    if mask_and_scale:
        for coder in [variables.UnsignedIntegerCoder(),
                      variables.CFMaskCoder(),
                      variables.CFScaleOffsetCoder()]:
            var = coder.decode(var, name=name)

    if decode_times:
        for coder in [times.CFTimedeltaCoder(),
                      times.CFDatetimeCoder(use_cftime=use_cftime)]:
            var = coder.decode(var, name=name)

    dimensions, data, attributes, encoding = (
        variables.unpack_for_decoding(var))
    # TODO(shoyer): convert everything below to use coders

    if decode_endianness and not data.dtype.isnative:
        # do this last, so it's only done if we didn't already unmask/scale
        data = NativeEndiannessArray(data)
        original_dtype = data.dtype

    encoding.setdefault('dtype', original_dtype)

    if 'dtype' in attributes and attributes['dtype'] == 'bool':
        del attributes['dtype']
        data = BoolTypeArray(data)

    if not isinstance(data, dask_array_type):
        data = indexing.LazilyOuterIndexedArray(data)

    return Variable(dimensions, data, attributes, encoding=encoding)

Response: Decodes a variable in CF - encoded form into a new variable in the CF - encoded form.


Instruction:
Write a Python 3 script for
updating the time attributes of the time variables that are in the time bounds variable.

Response:
def _update_bounds_attributes(variables):
    """Adds time attributes to time bounds variables.

    Variables handling time bounds ("Cell boundaries" in the CF
    conventions) do not necessarily carry the necessary attributes to be
    decoded. This copies the attributes from the time variable to the
    associated boundaries.

    See Also:

    http://cfconventions.org/Data/cf-conventions/cf-conventions-1.7/
         cf-conventions.html#cell-boundaries

    https://github.com/pydata/xarray/issues/2565
    """

    # For all time variables with bounds
    for v in variables.values():
        attrs = v.attrs
        has_date_units = 'units' in attrs and 'since' in attrs['units']
        if has_date_units and 'bounds' in attrs:
            if attrs['bounds'] in variables:
                bounds_attrs = variables[attrs['bounds']].attrs
                bounds_attrs.setdefault('units', attrs['units'])
                if 'calendar' in attrs:
                    bounds_attrs.setdefault('calendar', attrs['calendar'])


Instruction:
Can you create a Python 3 function that
decodes several CF encoded variables.

Response:
def decode_cf_variables(variables, attributes, concat_characters=True,
                        mask_and_scale=True, decode_times=True,
                        decode_coords=True, drop_variables=None,
                        use_cftime=None):
    """
    Decode several CF encoded variables.

    See: decode_cf_variable
    """
    dimensions_used_by = defaultdict(list)
    for v in variables.values():
        for d in v.dims:
            dimensions_used_by[d].append(v)

    def stackable(dim):
        # figure out if a dimension can be concatenated over
        if dim in variables:
            return False
        for v in dimensions_used_by[dim]:
            if v.dtype.kind != 'S' or dim != v.dims[-1]:
                return False
        return True

    coord_names = set()

    if isinstance(drop_variables, str):
        drop_variables = [drop_variables]
    elif drop_variables is None:
        drop_variables = []
    drop_variables = set(drop_variables)

    # Time bounds coordinates might miss the decoding attributes
    if decode_times:
        _update_bounds_attributes(variables)

    new_vars = OrderedDict()
    for k, v in variables.items():
        if k in drop_variables:
            continue
        stack_char_dim = (concat_characters and v.dtype == 'S1' and
                          v.ndim > 0 and stackable(v.dims[-1]))
        new_vars[k] = decode_cf_variable(
            k, v, concat_characters=concat_characters,
            mask_and_scale=mask_and_scale, decode_times=decode_times,
            stack_char_dim=stack_char_dim, use_cftime=use_cftime)
        if decode_coords:
            var_attrs = new_vars[k].attrs
            if 'coordinates' in var_attrs:
                coord_str = var_attrs['coordinates']
                var_coord_names = coord_str.split()
                if all(k in variables for k in var_coord_names):
                    new_vars[k].encoding['coordinates'] = coord_str
                    del var_attrs['coordinates']
                    coord_names.update(var_coord_names)

    if decode_coords and 'coordinates' in attributes:
        attributes = OrderedDict(attributes)
        coord_names.update(attributes.pop('coordinates').split())

    return new_vars, attributes, coord_names


Instruction:
Write a Python 3 script to
decode the given Dataset or DataStore into a new Dataset.

Response:
def decode_cf(obj, concat_characters=True, mask_and_scale=True,
              decode_times=True, decode_coords=True, drop_variables=None,
              use_cftime=None):
    """Decode the given Dataset or Datastore according to CF conventions into
    a new Dataset.

    Parameters
    ----------
    obj : Dataset or DataStore
        Object to decode.
    concat_characters : bool, optional
        Should character arrays be concatenated to strings, for
        example: ['h', 'e', 'l', 'l', 'o'] -> 'hello'
    mask_and_scale: bool, optional
        Lazily scale (using scale_factor and add_offset) and mask
        (using _FillValue).
    decode_times : bool, optional
        Decode cf times (e.g., integers since 'hours since 2000-01-01') to
        np.datetime64.
    decode_coords : bool, optional
        Use the 'coordinates' attribute on variable (or the dataset itself) to
        identify coordinates.
    drop_variables: string or iterable, optional
        A variable or list of variables to exclude from being parsed from the
        dataset. This may be useful to drop variables with problems or
        inconsistent values.
    use_cftime: bool, optional
        Only relevant if encoded dates come from a standard calendar
        (e.g. 'gregorian', 'proleptic_gregorian', 'standard', or not
        specified).  If None (default), attempt to decode times to
        ``np.datetime64[ns]`` objects; if this is not possible, decode times to
        ``cftime.datetime`` objects. If True, always decode times to
        ``cftime.datetime`` objects, regardless of whether or not they can be
        represented using ``np.datetime64[ns]`` objects.  If False, always
        decode times to ``np.datetime64[ns]`` objects; if this is not possible
        raise an error.

    Returns
    -------
    decoded : Dataset
    """
    from .core.dataset import Dataset
    from .backends.common import AbstractDataStore

    if isinstance(obj, Dataset):
        vars = obj._variables
        attrs = obj.attrs
        extra_coords = set(obj.coords)
        file_obj = obj._file_obj
        encoding = obj.encoding
    elif isinstance(obj, AbstractDataStore):
        vars, attrs = obj.load()
        extra_coords = set()
        file_obj = obj
        encoding = obj.get_encoding()
    else:
        raise TypeError('can only decode Dataset or DataStore objects')

    vars, attrs, coord_names = decode_cf_variables(
        vars, attrs, concat_characters, mask_and_scale, decode_times,
        decode_coords, drop_variables=drop_variables, use_cftime=use_cftime)
    ds = Dataset(vars, attrs=attrs)
    ds = ds.set_coords(coord_names.union(extra_coords).intersection(vars))
    ds._file_obj = file_obj
    ds.encoding = encoding

    return ds


Instruction:
Here you have a function in Python 3, explain what it does
def cf_decoder(variables, attributes,
               concat_characters=True, mask_and_scale=True,
               decode_times=True):
    """
    Decode a set of CF encoded variables and attributes.

    See Also, decode_cf_variable

    Parameters
    ----------
    variables : dict
        A dictionary mapping from variable name to xarray.Variable
    attributes : dict
        A dictionary mapping from attribute name to value
    concat_characters : bool
        Should character arrays be concatenated to strings, for
        example: ['h', 'e', 'l', 'l', 'o'] -> 'hello'
    mask_and_scale: bool
        Lazily scale (using scale_factor and add_offset) and mask
        (using _FillValue).
    decode_times : bool
        Decode cf times ('hours since 2000-01-01') to np.datetime64.

    Returns
    -------
    decoded_variables : dict
        A dictionary mapping from variable name to xarray.Variable objects.
    decoded_attributes : dict
        A dictionary mapping from attribute name to values.
    """
    variables, attributes, _ = decode_cf_variables(
        variables, attributes, concat_characters, mask_and_scale, decode_times)
    return variables, attributes

Response: Decode a set of CF encoded variables and attributes.


Instruction:
Create a Python 3 function for
encoding coordinates on the given dataset object into variable specific and global attributes.

Response:
def encode_dataset_coordinates(dataset):
    """Encode coordinates on the given dataset object into variable specific
    and global attributes.

    When possible, this is done according to CF conventions.

    Parameters
    ----------
    dataset : Dataset
        Object to encode.

    Returns
    -------
    variables : dict
    attrs : dict
    """
    non_dim_coord_names = set(dataset.coords) - set(dataset.dims)
    return _encode_coordinates(dataset._variables, dataset.attrs,
                               non_dim_coord_names=non_dim_coord_names)


Instruction:
Explain what the following Python 3 code does
def cf_encoder(variables, attributes):
    new_vars = OrderedDict((k, encode_cf_variable(v, name=k))
                           for k, v in variables.items())
    return new_vars, attributes

Response:
A function which takes a dict of variables and attributes and encodes them to conform to CF conventions as much
    as possible.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def coerce_nc3_dtype(arr):
    dtype = str(arr.dtype)
    if dtype in _nc3_dtype_coercions:
        new_dtype = _nc3_dtype_coercions[dtype]
        # TODO: raise a warning whenever casting the data-type instead?
        cast_arr = arr.astype(new_dtype)
        if not (cast_arr == arr).all():
            raise ValueError('could not safely cast array from dtype %s to %s'
                             % (dtype, new_dtype))
        arr = cast_arr
    return arr

Response: Coerce an array to a data type that can be stored in a netCDF - 3 file.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def is_valid_nc3_name(s):
    if not isinstance(s, str):
        return False
    if not isinstance(s, str):
        s = s.decode('utf-8')
    num_bytes = len(s.encode('utf-8'))
    return ((unicodedata.normalize('NFC', s) == s) and
            (s not in _reserved_names) and
            (num_bytes >= 0) and
            ('/' not in s) and
            (s[-1] != ' ') and
            (_isalnumMUTF8(s[0]) or (s[0] == '_')) and
            all((_isalnumMUTF8(c) or c in _specialchars for c in s)))

Response: Test whether an object can be converted to a netCDF - 3 name.


Instruction:
Here you have a function in Python 3, explain what it does
def as_variable(obj, name=None) -> 'Union[Variable, IndexVariable]':
    from .dataarray import DataArray

    # TODO: consider extending this method to automatically handle Iris and
    if isinstance(obj, DataArray):
        # extract the primary Variable from DataArrays
        obj = obj.variable

    if isinstance(obj, Variable):
        obj = obj.copy(deep=False)
    elif isinstance(obj, tuple):
        try:
            obj = Variable(*obj)
        except (TypeError, ValueError) as error:
            # use .format() instead of % because it handles tuples consistently
            raise error.__class__('Could not convert tuple of form '
                                  '(dims, data[, attrs, encoding]): '
                                  '{} to Variable.'.format(obj))
    elif utils.is_scalar(obj):
        obj = Variable([], obj)
    elif isinstance(obj, (pd.Index, IndexVariable)) and obj.name is not None:
        obj = Variable(obj.name, obj)
    elif isinstance(obj, (set, dict)):
        raise TypeError(
            "variable %r has invalid type %r" % (name, type(obj)))
    elif name is not None:
        data = as_compatible_data(obj)
        if data.ndim != 1:
            raise MissingDimensionsError(
                'cannot set variable %r with %r-dimensional data '
                'without explicit dimension names. Pass a tuple of '
                '(dims, data) instead.' % (name, data.ndim))
        obj = Variable(name, data, fastpath=True)
    else:
        raise TypeError('unable to convert object into a variable without an '
                        'explicit list of dimensions: %r' % obj)

    if name is not None and name in obj.dims:
        # convert the Variable into an Index
        if obj.ndim != 1:
            raise MissingDimensionsError(
                '%r has more than 1-dimension and the same name as one of its '
                'dimensions %r. xarray disallows such variables because they '
                'conflict with the coordinates used to label '
                'dimensions.' % (name, obj.dims))
        obj = obj.to_index_variable()

    return obj

Response: Convert an object into a Variable.


Instruction:
How would you explain what the following Python 3 function does
def _possibly_convert_objects(values):
    return np.asarray(pd.Series(values.ravel())).reshape(values.shape)

Response:
Convert arrays of datetime. datetime and datetime. timedelta objects into
    datetime64 and timedelta64 according to the pandas convention.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def as_compatible_data(data, fastpath=False):
    if fastpath and getattr(data, 'ndim', 0) > 0:
        # can't use fastpath (yet) for scalars
        return _maybe_wrap_data(data)

    if isinstance(data, Variable):
        return data.data

    if isinstance(data, NON_NUMPY_SUPPORTED_ARRAY_TYPES):
        return _maybe_wrap_data(data)

    if isinstance(data, tuple):
        data = utils.to_0d_object_array(data)

    if isinstance(data, pd.Timestamp):
        # TODO: convert, handle datetime objects, too
        data = np.datetime64(data.value, 'ns')

    if isinstance(data, timedelta):
        data = np.timedelta64(getattr(data, 'value', data), 'ns')

    # we don't want nested self-described arrays
    data = getattr(data, 'values', data)

    if isinstance(data, np.ma.MaskedArray):
        mask = np.ma.getmaskarray(data)
        if mask.any():
            dtype, fill_value = dtypes.maybe_promote(data.dtype)
            data = np.asarray(data, dtype=dtype)
            data[mask] = fill_value
        else:
            data = np.asarray(data)

    # validate whether the data is valid data types
    data = np.asarray(data)

    if isinstance(data, np.ndarray):
        if data.dtype.kind == 'O':
            data = _possibly_convert_objects(data)
        elif data.dtype.kind == 'M':
            data = np.asarray(data, 'datetime64[ns]')
        elif data.dtype.kind == 'm':
            data = np.asarray(data, 'timedelta64[ns]')

    return _maybe_wrap_data(data)

Response: Prepare and wrap data to put in a Variable.