Python xarray.Dataset方法代碼示例

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import Dataset [as 別名]
def _save_files(self, data, dtype_out_time):
        """Save the data to netcdf files in direc_out."""
        path = self.path_out[dtype_out_time]
        if not os.path.isdir(self.dir_out):
            os.makedirs(self.dir_out)
        if 'reg' in dtype_out_time:
            try:
                reg_data = xr.open_dataset(path)
            except (EOFError, RuntimeError, IOError):
                reg_data = xr.Dataset()
            reg_data.update(data)
            data_out = reg_data
        else:
            data_out = data
        if isinstance(data_out, xr.DataArray):
            data_out = xr.Dataset({self.name: data_out})
        data_out.to_netcdf(path, engine='netcdf4') 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import Dataset [as 別名]
def set_grid_attrs_as_coords(ds):
    """Set available grid attributes as coordinates in a given Dataset.

    Grid attributes are assumed to have their internal aospy names. Grid
    attributes are set as coordinates, such that they are carried by all
    selected DataArrays with overlapping index dimensions.

    Parameters
    ----------
    ds : Dataset
        Input data

    Returns
    -------
    Dataset
        Dataset with grid attributes set as coordinates

    """
    grid_attrs_in_ds = set(GRID_ATTRS.keys()).intersection(
        set(ds.coords) | set(ds.data_vars))
    ds = ds.set_coords(grid_attrs_in_ds)
    return ds 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import Dataset [as 別名]
def rmse(x, y, dim):
    """
    Compute Root Mean Squared Error.

    Parameters
    ----------
    x : Dataset, DataArray, GroupBy, Variable, numpy/dask arrays or scalars
        Mix of labeled and/or unlabeled arrays to which to apply the function.
    y : Dataset, DataArray, GroupBy, Variable, numpy/dask arrays or scalars
        Mix of labeled and/or unlabeled arrays to which to apply the function.
    dim : str
        The dimension to apply the correlation along.

    Returns
    -------
    Root Mean Squared Error
        Single value or tuple of Dataset, DataArray, Variable, dask.array.Array or
        numpy.ndarray, the first type on that list to appear on an input.

    """

    return xs.rmse(x, y, dim) 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import Dataset [as 別名]
def mae(x, y, dim):
    """
    Compute Mean Absolute Error.

    Parameters
    ----------
    x : Dataset, DataArray, GroupBy, Variable, numpy/dask arrays or scalars
        Mix of labeled and/or unlabeled arrays to which to apply the function.
    y : Dataset, DataArray, GroupBy, Variable, numpy/dask arrays or scalars
        Mix of labeled and/or unlabeled arrays to which to apply the function.
    dim : str
        The dimension to apply the correlation along.

    Returns
    -------
    Mean Absolute Error
         Single value or tuple of Dataset, DataArray, Variable, dask.array.Array or
         numpy.ndarray, the first type on that list to appear on an input.

    """
    return xs.mae(x, y, dim) 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import Dataset [as 別名]
def get_xarray_group(dataset, group):
    """Get pseudo group from xarray.Dataset

    Args:
        dataset: A xarray.Dataset object with pseudo groups.
        group: The name of the group (can also be a subgroup).

    Returns:
        A xarray.Dataset with the pseudo group.
    """

    if not group.endswith("/"):
        group += "/"

    group_vars = [
        var
        for var in dataset.variables
        if var.startswith(group)
    ]

    if not group_vars:
        raise KeyError(f"The group {group} was not found!")

    return dataset[group_vars] 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import Dataset [as 別名]
def check_collocation_data(dataset):
    """Check whether the dataset fulfills the standard of collocated data

    Args:
        dataset: A xarray.Dataset object

    Raises:
        A InvalidCollocationData Error if the dataset did not pass the test.
    """
    mandatory_fields = ["Collocations/pairs", "Collocations/group"]

    for mandatory_field in mandatory_fields:
        if mandatory_field not in dataset.variables:
            raise InvalidCollocationData(
                f"Could not find the field '{mandatory_field}'!"
            ) 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import Dataset [as 別名]
def test_undo_xarray_floatification(self):
        ds = xarray.Dataset(
            {"a": (["x"], numpy.array([1, 2, 3], dtype="f4")),
             "b": (["x"], numpy.array([2.0, 3.0, 4.0])),
             "c": (["x"], numpy.array(["2010-01-01", "2010-01-02",
                                       "2010-01-03"], dtype="M8"))})
        ds["a"].encoding = {"dtype": numpy.dtype("i4"),
                            "_FillValue": 1234}
        # c should NOT be converted because it's a time
        ds["c"].encoding = {"dtype": numpy.dtype("i8"),
                            "_FillValue": 12345}
        ds2 = utils.undo_xarray_floatification(ds)
        assert ds is not ds2  # has to be a copy
        assert ds["a"].encoding == ds2["a"].encoding
        assert numpy.allclose(ds["a"], ds2["a"])
        assert ds2["a"].dtype == ds2["a"].encoding["dtype"]
        assert (ds2["c"] == ds["c"]).all()
        assert ds2["c"].dtype == ds["c"].dtype
        assert ds2["b"].dtype == ds["b"].dtype 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import Dataset [as 別名]
def test_collocate_collapse_expand(self):
        """Test whether collocating, collapsing and expanding work"""
        collocator = Collocator()

        test = xr.Dataset({
            "time": ("time", np.arange("2000", "2010", dtype="M8[Y]")),
            "lat": ("time", np.arange(10)),
            "lon": ("time", np.arange(10)),
        })

        collocations = collocator.collocate(
            test, test, max_interval="30 days",
            max_distance="150 miles"
        )

        collapsed = collapse(collocations)
        expanded = expand(collocations) 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import Dataset [as 別名]
def test_times(self):
        """Test if times are read correctly
        """

        fh = NetCDF4()

        with tempfile.TemporaryDirectory() as tdir:
            tfile = os.path.join(tdir, "testfile.nc")
            before = xr.Dataset(
                    {"a":
                        xr.DataArray(
                            np.array(
                                ["2019-02-14T09:00:00", "2019-02-14T09:00:01"],
                                dtype="M8[ns]"))})
            before["a"].encoding = {
                    "units": "seconds since 2019-02-14 09:00:00",
                    "scale_factor": 0.1}
            fh.write(before, tfile)
            after = fh.read(tfile)
            assert np.array_equal(before["a"], after["a"]) 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import Dataset [as 別名]
def test_scalefactor(self):
        """Test if scale factors written/read correctly
        """

        fh = NetCDF4()

        with tempfile.TemporaryDirectory() as tdir:
            tfile = os.path.join(tdir, "testfile.nc")
            before = xr.Dataset(
                    {"a":
                        xr.DataArray(
                            np.array([0.1, 0.2]))})
            before["a"].encoding = {
                    "scale_factor": 0.1,
                    "_FillValue": 42,
                    "dtype": "int16"}
            fh.write(before, tfile)
            after = fh.read(tfile)
            assert np.allclose(before["a"], after["a"]) 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import Dataset [as 別名]
def __init__(self, **kwargs):
        """Initialise a Dataset object.

        All keyword arguments will be translated into attributes.
        Does not take positional arguments.

        Note that if you create a dataset with a name that already exists,
        the existing object is returned, but __init__ is still called
        (Python does this, see
        https://docs.python.org/3.7/reference/datamodel.html#object.__new__).
        """
        self.mandatory_fields = set()
        for (k, v) in kwargs.items():
            setattr(self, k, v)
        self.setlocal()
        if self.my_pseudo_fields is None:
            self.my_pseudo_fields = collections.OrderedDict() 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import Dataset [as 別名]
def _apply_limits_and_filters(self, cont, limits, simple_filters):
        if isinstance(cont, xarray.Dataset):
            if len(limits)>0:
                raise NotImplementedError( 
                    "limits not implemented on xarray datasets")
            oldsize = cont[self.time_field].size
            for f in simple_filters:
                cont = f(cont)
            logger.debug("Filters reduced number from "
                "{:d} to {:d}".format(oldsize, cont[self.time_field].size))
            return cont
        oldsize = cont.size
        cont = tpmath.array.limit_ndarray(cont, limits)
        for f in simple_filters:
            cont = f(cont)
        if cont.size < oldsize:
            logger.debug("Applying limitations, reducing "
                "{:d} to {:d}".format(oldsize, cont.size))
        return cont 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import Dataset [as 別名]
def _add_cont_to_arr(self, arr, N, cont):
        """Changes arr in-situ, does not return"""
        if isinstance(cont, xarray.Dataset):
            # we should already know it's large enough
            # for arr[self.time_field] I start at N
            # for the other time coordinates at the relative "speed" they
            # are behind N
            # but this is not guaranteed to be regular so I would need to
            # keep trac of each individually, or inspect it on-the-fly
            # this approximation may be good enough for pre-allocation
            # (which is approximate anyway), when actually storing we need
            # to do a better job… for each time coordinate, check when it
            # “dies”
            raise NotImplementedError("This is not used for xarrays. "
                "But see comment in source-code for some thoughts.")
        else:
            arr[N:(N+cont.size)] = cont
            #arr = self._finalise_arr(arr, N) 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import Dataset [as 別名]
def get_additional_field(self, M, fld):
        """Get additional field.

        Get field from other dataset, original objects, or otherwise.
        To be implemented by subclass implementations.

        Exact fields depend on subclass.

        Arguments:

            M (ndarray): ndarray with existing data
                A (masked) array with a dtype such as returned from
                `self.read <Dataset.read>`.

            fld (str): Additional field to read from original data

        Returns:
            
            ndarray with fields of M + fld.
        """
        raise NotImplementedError("Must be implemented by child-class") 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import Dataset [as 別名]
def find_granules_sorted(self, dt_start=None, dt_end=None, 
                include_last_before=False, **extra):
        """Yield all granules, sorted by times.

        For documentation, see :func:`~Dataset.find_granules`.
        """

        allgran = list(self.find_granules(dt_start, dt_end, 
                       include_last_before, **extra))

        # I've been through all granules at least once, so all should be
        # cached now; no need for additional hints when granule timeinfo
        # obtainable only with hints from subdir, which is not included in
        # the re-matching method
        if extra.get("return_time", False):
            yield from sorted(allgran)
        else:
            yield from sorted(allgran, key=self.get_times_for_granule)