Python xarray.merge方法代碼示例

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import merge [as 別名]
def read(self, filename, fields=None, **kwargs):
        scene = Scene(
            reader=self.satpy_reader,
            filenames=[filename.path]
        )

        # If the user has not passed any fields to us, we load all per default.
        if fields is None:
            fields = scene.available_dataset_ids()

        # Load all selected fields
        scene.load(fields, **kwargs)

        if isinstance(fields[0], str):
            data_arrays = {field: scene.get(field) for field in fields}
        else:
            data_arrays = {field.name: scene.get(field) for field in fields}

        for name, array in data_arrays.items():
            array.name = name

        dataset = xr.merge(data_arrays.values())

        return dataset 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import merge [as 別名]
def merge_datasets(datasets, **kwargs):
    merged = []
    for ds in datasets:
        ds.attrs.pop('history', None)
        for i, o in enumerate(merged):
            if all(o.attrs[k] == ds.attrs[k] for k in o.attrs):
                try:
                    o = xr.merge([o, ds], **kwargs)
                    o.attrs.update(ds.attrs)
                    merged[i] = o
                    break
                except Exception:
                    pass
        else:
            merged.append(ds)
    return merged 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import merge [as 別名]
def extend_map(mapx, mapy, on):
    if not hasattr(on, '__iter__'):
        on = [on]
    try:
        inter = mapx[on].reset_index().merge(mapy[on].reset_index(), on=on)
    except KeyError:
        inter = pd.DataFrame()
    extended = pd.DataFrame()
    ext_sessions = list(set(mapx.sessions).union(set(mapy.sessions)))
    for inter_idx, inter_row in inter.iterrows():
        sx = mapx.loc[inter_row.loc['index_x'], mapx.sessions]
        sy = mapy.loc[inter_row.loc['index_y'], mapy.sessions]
        extrow = pd.concat([sx, sy]).drop_duplicates()
        if len(extrow) <= len(ext_sessions):
            extended = extended.append(extrow, ignore_index=True)
    extended.sessions = ext_sessions
    return extended 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import merge [as 別名]
def compute_correlations(temp_comp, along, across):
    segments = temp_comp.attrs['segments']
    corr_list = []
    for cur_anm in temp_comp['animal'].values:
        for comb in itt.combinations_with_replacement(segments, 2):
            dat_A = temp_comp.sel(animal=cur_anm, session_id=comb[0][0])
            dat_A = dat_A.where(
                dat_A['segment_id'] == comb[0][1],
                drop=True).to_array().drop('segment_id').squeeze(
                    'variable', drop=True)
            dat_B = temp_comp.sel(animal=cur_anm, session_id=comb[1][0])
            dat_B = dat_B.where(
                dat_B['segment_id'] == comb[1][1],
                drop=True).to_array().drop('segment_id').squeeze(
                    'variable', drop=True)
            if dat_A.size > 0 and dat_B.size > 0:
                print("computing correlation of {} with {} for animal {}".
                      format(comb[0], comb[1], cur_anm))
                cur_corr = corr2_coeff_xr(dat_A, dat_B, along, across)
                cur_corr.coords['session_id_A'] = comb[0][0]
                cur_corr.coords['session_id_B'] = comb[1][0]
                cur_corr.coords['segment_id_A'] = comb[0][1]
                cur_corr.coords['segment_id_B'] = comb[1][1]
    print("merging")
    return xr.merge(corr_list) 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import merge [as 別名]
def __init__(self, varr, match_dict, framerate=30):
        if isinstance(varr, list):
            varr = xr.merge(varr)
        self.varr = varr
        self.varr_hv = hv.Dataset(varr)
        vh = self.varr_hv.range('height')
        vw = self.varr_hv.range('width')
        frange = self.varr_hv.range('frame')
        self._h = int(vh[1] - vh[0] + 1)
        self._w = int(vw[1] - vw[0] + 1)
        self.match = match_dict
        self.framerate = framerate
        CStream = Stream.define(
            'CStream', f=param.Integer(default=int(frange[0]), bounds=frange))
        self.stream = CStream()
        self.widgets = self._widgets() 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import merge [as 別名]
def open_minian(dpath, fname='minian', backend='netcdf', chunks=None, post_process=None):
    if backend is 'netcdf':
        fname = fname + '.nc'
        if chunks is 'auto':
            chunks = dict([(d, 'auto') for d in ds.dims])
        mpath = pjoin(dpath, fname)
        with xr.open_dataset(mpath) as ds:
            dims = ds.dims
        chunks = dict([(d, 'auto') for d in dims])
        ds = xr.open_dataset(os.path.join(dpath, fname), chunks=chunks)
        if post_process:
            ds = post_process(ds, mpath)
        return ds
    elif backend is 'zarr':
        mpath = pjoin(dpath, fname)
        dslist = [xr.open_zarr(pjoin(mpath, d)) for d in listdir(mpath) if isdir(pjoin(mpath, d))]
        ds = xr.merge(dslist)
        if chunks is 'auto':
            chunks = dict([(d, 'auto') for d in ds.dims])
        if post_process:
            ds = post_process(ds, mpath)
        return ds.chunk(chunks)
    else:
        raise NotImplementedError("backend {} not supported".format(backend)) 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import merge [as 別名]
def union(dsets: List[xr.Dataset], options: Dict[str, Any] = None) -> xr.Dataset:
    """
    Merge a list of datasets into a single dataset.

    Parameters
    ----------
    dsets : List[xr.Dataset]
        A list of xarray.Dataset(s) to merge.
    options : Dict, optional
        Additional keyword arguments passed through to
        :py:func:`~xarray.merge()`, by default None

    Returns
    -------
    xr.Dataset
        xarray Dataset
    """

    options = options or {}
    try:
        return xr.merge(dsets, **options)
    except Exception as exc:
        logger.error('Failed to merge datasets.')
        raise exc 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import merge [as 別名]
def add_xarray_groups(ds, **kwargs):
    """Add a xarray.Dataset as a subgroup to another xarray.Dataset

    Args:
        ds: The root xarray.Dataset.
        **kwargs: Keyword arguments: the key is the name of the group and the
            value must be a xarray.Dataset.

    Returns:
        `ds` with the added subgroups
    """
    datasets = [ds]

    for group_name, group in kwargs.items():
        group = group.rename(
            {
                var_name: "/".join([group_name, var_name])
                for var_name in group.variables
            },
        )

        # Add the group name also to the dimensions:
        group = group.rename({
            dim: "/".join([group_name, dim])
            for dim in group.dims
            if dim not in group.coords
        })

        datasets.append(group)

    return xarray.merge(datasets) 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import merge [as 別名]
def rinexobs2(fn: Path,
              use: Sequence[str] = None,
              tlim: Tuple[datetime, datetime] = None,
              useindicators: bool = False,
              meas: Sequence[str] = None,
              verbose: bool = False,
              *,
              fast: bool = True,
              interval: Union[float, int, timedelta] = None) -> xarray.Dataset:

    if isinstance(use, str):
        use = [use]

    if use is None or not use[0].strip():
        use = ('C', 'E', 'G', 'J', 'R', 'S')

    obs = xarray.Dataset({}, coords={'time': [], 'sv': []})
    attrs: Dict[str, Any] = {}
    for u in use:
        o = rinexsystem2(fn, system=u, tlim=tlim,
                         useindicators=useindicators, meas=meas,
                         verbose=verbose,
                         fast=fast, interval=interval)
        if len(o.variables) > 0:
            attrs = o.attrs
            obs = xarray.merge((obs, o))

    obs.attrs = attrs

    return obs 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import merge [as 別名]
def compute_dataset(ds, period='1W', incl_stdev=False):
    if incl_stdev:
        resample_obj = ds.resample(time=period)
        ds_mean = resample_obj.mean(dim='time')
        ds_std = resample_obj.std(dim='time').rename(name_dict={name: f"{name}_stdev" for name in ds.data_vars})
        ds_merged = xr.merge([ds_mean, ds_std])
        ds_merged.attrs.update(ds.attrs)
        return ds_merged
    else:
        return ds.resample(time=period).mean(dim='time') 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import merge [as 別名]
def resample_and_merge_cubes(cubes: List[xr.Dataset],
                             cube_config: CubeConfig) -> xr.Dataset:
    with observe_progress('Resampling cube(s)', len(cubes) + 1) as progress:
        resampled_cubes = []
        for cube in cubes:
            resampled_cube = resample_cube(cube, cube_config)
            resampled_cubes.append(resampled_cube)
            progress.worked(1)
        merged_cube = xr.merge(resampled_cubes) if len(resampled_cubes) > 1 else resampled_cubes[0]
        progress.worked(1)
        return merged_cube 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import merge [as 別名]
def calc_pdfs(ds, i, shr_mem, prog_mem, coords, len_coord_list):
    """
    Calculates the probability density functions of the radiation for each month of the given dataset and
    saves it to a new dataset.
    Parameters
    ----------
    ds: xarray dataset
        with a data-variable 'SWGDN' containing time-series data at coordiantes coords
    i: int
        index where in shr_mem the result is to be saved
    shr_mem : shared List
        shared memory where all the calculated xk, pk values are stored
    prog_mem : List
        list indicating the overall progress of the computation, first value ([0]) is the total number
        of coordinate tuples to compute.
    coords: Tuple
        (lat, lon) representing the location of the time-series in ds
    len_coord_list: int
        length of coord_list, used for progress bar
    """
    ds_out = xr.Dataset()
    for mo in range(1,13):
        ds_mo = ds.sel(month=mo)
        da_mo = ds_mo['SWGDN'].values
        fig = plt.figure()
        ax = fig.add_subplot()
        xk, pk = sns_distplot(da_mo, ax=ax).get_lines()[0].get_data()
        pk = pk / sum(pk)
        ds_out_mo = pd.DataFrame({'xk': xk, 'pk': pk, 'lat': coords[0], 'lon': coords[1], 'month': mo, 'bins': range(0, len(pk))})
        ds_out_mo = ds_out_mo.set_index(['lat', 'lon', 'month', 'bins'])
        ds_out_xk_pk = ds_out_mo.to_xarray().copy()
        ds_out = xr.merge([ds_out, ds_out_xk_pk])
        plt.close()

    shr_mem[i] = ds_out
    prog_mem.append(1)
    progress_bar(len(prog_mem), len_coord_list) 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import merge [as 別名]
def ds_2d_to_1d(ds):
    ds_temp = ds.reset_coords()
    ds_1d = xr.merge([ds_temp['lon'][0, :], ds_temp['lat'][:, 0]])
    ds_1d.coords['lon'] = ds_1d['lon']
    ds_1d.coords['lat'] = ds_1d['lat']
    return ds_1d 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import merge [as 別名]
def __init__(self, varr, marr=None, framerate=30):
        if isinstance(varr, list):
            varr = xr.merge(varr)
        self.varr = hv.Dataset(varr)
        if marr is not None:
            self.marr = hv.Dataset(marr)
        else:
            self.marr = None
        self.framerate = framerate
        CStream = Stream.define(
            'CStream',
            f=param.Integer(default=0, bounds=self.varr.range('frame')))
        self.stream = CStream()
        self.widgets = self._widgets() 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import merge [as 別名]
def _im_overlay(self, f, A, C, im_hsv, contour=None):
        f = int(f)
        AdC = A.dot(C.sel(frame=f))
        im_hue = im_hsv.sel(cspace='H').rename('H').drop('cspace')
        im_sat = (im_hsv.sel(cspace='S')).rename('S').drop('cspace')
        im_val = (im_hsv.sel(cspace='V') * AdC * 4).clip(
            0, 1).rename('V').drop('cspace')
        ds = xr.merge([im_hue, im_sat, im_val])
        im = hv.HSV(ds, kdims=['width', 'height'])
        # if contour is None:
        #     contour = hv.operation.contours(im)
        return im