Source code for satpy._scene_converters

# Copyright (c) 2023 Satpy developers
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# terms of the GNU General Public License as published by the Free Software
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"""Helper functions for converting the Scene object to some other object."""

import xarray as xr

from satpy.composites import enhance2dataset
from satpy.dataset import DataID

[docs] def _get_dataarrays_from_identifiers(scn, identifiers): """Return a list of DataArray based on a single or list of identifiers. An identifier can be a DataID or a string with name of a valid DataID. """ if isinstance(identifiers, (str, DataID)): identifiers = [identifiers] if identifiers is not None: dataarrays = [scn[ds] for ds in identifiers] else: dataarrays = [scn._datasets.get(ds) for ds in scn._wishlist] dataarrays = [dataarray for dataarray in dataarrays if dataarray is not None] return dataarrays
[docs] def to_geoviews(scn, gvtype=None, datasets=None, kdims=None, vdims=None, dynamic=False): """Convert satpy Scene to geoviews. Args: scn (satpy.Scene): Satpy Scene. gvtype (gv plot type): One of gv.Image, gv.LineContours, gv.FilledContours, gv.Points Default to :class:`geoviews.Image`. See Geoviews documentation for details. datasets (list): Limit included products to these datasets kdims (list of str): Key dimensions. See geoviews documentation for more information. vdims (list of str, optional): Value dimensions. See geoviews documentation for more information. If not given defaults to first data variable dynamic (bool, optional): Load and compute data on-the-fly during visualization. Default is ``False``. See for more information. Has no effect when data to be visualized only has 2 dimensions (y/x or longitude/latitude) and doesn't require grouping via the Holoviews ``groupby`` function. Returns: geoviews object Todo: * better handling of projection information in datasets which are to be passed to geoviews """ import geoviews as gv from cartopy import crs # noqa if gvtype is None: gvtype = gv.Image ds = scn.to_xarray_dataset(datasets) if vdims is None: # by default select first data variable as display variable vdims = ds.data_vars[list(ds.data_vars.keys())[0]].name if hasattr(ds, "area") and hasattr(ds.area, "to_cartopy_crs"): dscrs = ds.area.to_cartopy_crs() gvds = gv.Dataset(ds, crs=dscrs) else: gvds = gv.Dataset(ds) # holoviews produces a log warning if you pass groupby arguments when groupby isn't used groupby_kwargs = {"dynamic": dynamic} if gvds.ndims != 2 else {} if "latitude" in ds.coords: gview =, kdims=["longitude", "latitude"], vdims=vdims, **groupby_kwargs) else: gview =, kdims=["x", "y"], vdims=vdims, **groupby_kwargs) return gview
[docs] def to_hvplot(scn, datasets=None, *args, **kwargs): """Convert satpy Scene to Hvplot. The method could not be used with composites of swath data. Args: scn (satpy.Scene): Satpy Scene. datasets (list): Limit included products to these datasets. args: Arguments coming from hvplot kwargs: hvplot options dictionary. Returns: hvplot object that contains within it the plots of datasets list. As default it contains all Scene datasets plots and a plot title is shown. Example usage:: scene_list = ['ash','IR_108'] scn = Scene() scn.load(scene_list) scn = scn.resample('eurol') plot = scn.to_hvplot(datasets=scene_list) plot.ash+plot.IR_108 """ def _get_crs(xarray_ds): return xarray_ds.area.to_cartopy_crs() def _get_timestamp(xarray_ds): time = xarray_ds.attrs["start_time"] return time.strftime("%Y %m %d -- %H:%M UTC") def _get_units(xarray_ds, variable): return xarray_ds[variable].attrs["units"] def _plot_rgb(xarray_ds, variable, **defaults): img = enhance2dataset(xarray_ds[variable]) return img.hvplot.rgb(bands="bands", title=title, clabel="", **defaults) def _plot_quadmesh(xarray_ds, variable, **defaults): return xarray_ds[variable].hvplot.quadmesh( clabel=f"[{_get_units(xarray_ds,variable)}]", title=title, **defaults) import hvplot.xarray as hvplot_xarray # noqa from holoviews import Overlay plot = Overlay() xarray_ds = scn.to_xarray_dataset(datasets) if hasattr(xarray_ds, "area") and hasattr(xarray_ds.area, "to_cartopy_crs"): ccrs = _get_crs(xarray_ds) defaults={"x":"x","y":"y"} else: ccrs = None defaults={"x":"longitude","y":"latitude"} if datasets is None: datasets = list(xarray_ds.keys()) defaults.update(data_aspect=1, project=True, geo=True, crs=ccrs, projection=ccrs, rasterize=True, coastline="110m", cmap="Plasma", responsive=True, dynamic=False, framewise=True,colorbar=False, global_extent=False, xlabel="Longitude", ylabel="Latitude") defaults.update(kwargs) for element in datasets: title = f"{element} @ {_get_timestamp(xarray_ds)}" if xarray_ds[element].shape[0] == 3: plot[element] = _plot_rgb(xarray_ds, element, **defaults) else: plot[element] = _plot_quadmesh(xarray_ds, element, **defaults) return plot
[docs] def to_xarray(scn, datasets=None, # DataID header_attrs=None, exclude_attrs=None, flatten_attrs=False, pretty=True, include_lonlats=True, epoch=None, include_orig_name=True, numeric_name_prefix="CHANNEL_"): """Merge all xr.DataArray(s) of a satpy.Scene to a CF-compliant xarray object. If all Scene DataArrays are on the same area, it returns an xr.Dataset. If Scene DataArrays are on different areas, currently it fails, although in future we might return a DataTree object, grouped by area. Args: scn (satpy.Scene): Satpy Scene. datasets (iterable, optional): List of Satpy Scene datasets to include in the output xr.Dataset. Elements can be string name, a wavelength as a number, a DataID, or DataQuery object. If None (the default), it includes all loaded Scene datasets. header_attrs: Global attributes of the output xr.Dataset. epoch (str, optional): Reference time for encoding the time coordinates (if available). Format example: "seconds since 1970-01-01 00:00:00". If None, the default reference time is retrieved using "from satpy.cf_writer import EPOCH". flatten_attrs (bool, optional): If True, flatten dict-type attributes. exclude_attrs (list, optional): List of xr.DataArray attribute names to be excluded. include_lonlats (bool, optional): If True, includes 'latitude' and 'longitude' coordinates. If the 'area' attribute is a SwathDefinition, it always includes latitude and longitude coordinates. pretty (bool, optional): Don't modify coordinate names, if possible. Makes the file prettier, but possibly less consistent. include_orig_name (bool, optional): Include the original dataset name as a variable attribute in the xr.Dataset. numeric_name_prefix (str, optional): Prefix to add to each variable with name starting with a digit. Use '' or None to leave this out. Returns: xr.Dataset: A CF-compliant xr.Dataset """ from import collect_cf_datasets # Get list of DataArrays if datasets is None: datasets = list(scn.keys()) # list all loaded DataIDs list_dataarrays = _get_dataarrays_from_identifiers(scn, datasets) # Check that some DataArray could be returned if len(list_dataarrays) == 0: return xr.Dataset() # Collect xr.Dataset for each group grouped_datasets, header_attrs = collect_cf_datasets(list_dataarrays=list_dataarrays, header_attrs=header_attrs, exclude_attrs=exclude_attrs, flatten_attrs=flatten_attrs, pretty=pretty, include_lonlats=include_lonlats, epoch=epoch, include_orig_name=include_orig_name, numeric_name_prefix=numeric_name_prefix, groups=None) if len(grouped_datasets) == 1: ds = grouped_datasets[None] return ds else: msg = """The Scene object contains datasets with different areas. Resample the Scene to have matching dimensions using i.e. scn.resample(resampler="native") """ raise NotImplementedError(msg)