Source code for satpy.scene

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (c) 2010-2017 Satpy developers
# This file is part of satpy.
# satpy is free software: you can redistribute it and/or modify it under the
# terms of the GNU General Public License as published by the Free Software
# Foundation, either version 3 of the License, or (at your option) any later
# version.
# satpy is distributed in the hope that it will be useful, but WITHOUT ANY
# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR
# A PARTICULAR PURPOSE.  See the GNU General Public License for more details.
# You should have received a copy of the GNU General Public License along with
# satpy.  If not, see <>.
"""Scene object to hold satellite data."""

import logging
import os
import warnings

from satpy.composites import IncompatibleAreas
from satpy.composites.config_loader import CompositorLoader
from satpy.dataset import (DataQuery, DataID, dataset_walker,
                           replace_anc, combine_metadata)
from satpy.node import MissingDependencies, ReaderNode, CompositorNode, Node
from satpy.dependency_tree import DependencyTree
from satpy.readers import load_readers
from satpy.dataset import DatasetDict
from satpy.resample import (resample_dataset,
                            prepare_resampler, get_area_def)
from satpy.writers import load_writer
from pyresample.geometry import AreaDefinition, BaseDefinition, SwathDefinition

import xarray as xr
from xarray import DataArray
import numpy as np

LOG = logging.getLogger(__name__)

[docs]class DelayedGeneration(KeyError): """Mark that a dataset can't be generated without further modification.""" pass
[docs]class Scene: """The Almighty Scene Class. Example usage:: from satpy import Scene from glob import glob # create readers and open files scn = Scene(filenames=glob('/path/to/files/*'), reader='viirs_sdr') # load datasets from input files scn.load(['I01', 'I02']) # resample from satellite native geolocation to builtin 'eurol' Area new_scn = scn.resample('eurol') # save all resampled datasets to geotiff files in the current directory new_scn.save_datasets() """ def __init__(self, filenames=None, reader=None, filter_parameters=None, reader_kwargs=None): """Initialize Scene with Reader and Compositor objects. To load data `filenames` and preferably `reader` must be specified. If `filenames` is provided without `reader` then the available readers will be searched for a Reader that can support the provided files. This can take a considerable amount of time so it is recommended that `reader` always be provided. Note without `filenames` the Scene is created with no Readers available requiring Datasets to be added manually:: scn = Scene() scn['my_dataset'] = Dataset(my_data_array, **my_info) Args: filenames (iterable or dict): A sequence of files that will be used to load data from. A ``dict`` object should map reader names to a list of filenames for that reader. reader (str or list): The name of the reader to use for loading the data or a list of names. filter_parameters (dict): Specify loaded file filtering parameters. Shortcut for `reader_kwargs['filter_parameters']`. reader_kwargs (dict): Keyword arguments to pass to specific reader instances. Either a single dictionary that will be passed onto to all reader instances, or a dictionary mapping reader names to sub-dictionaries to pass different arguments to different reader instances. """ self.attrs = dict() if filter_parameters: if reader_kwargs is None: reader_kwargs = {} else: reader_kwargs = reader_kwargs.copy() reader_kwargs.setdefault('filter_parameters', {}).update(filter_parameters) if filenames and isinstance(filenames, str): raise ValueError("'filenames' must be a list of files: Scene(filenames=[filename])") self._readers = self._create_reader_instances(filenames=filenames, reader=reader, reader_kwargs=reader_kwargs) self.attrs.update(self._compute_metadata_from_readers()) self._datasets = DatasetDict() self._composite_loader = CompositorLoader() comps, mods = self._composite_loader.load_compositors(self.attrs['sensor']) self._wishlist = set() self._dependency_tree = DependencyTree(self._readers, comps, mods) self._resamplers = {} @property def wishlist(self): """Return a copy of the wishlist.""" return self._wishlist.copy() def _ipython_key_completions_(self): return [x['name'] for x in self._datasets.keys()] def _compute_metadata_from_readers(self): """Determine pieces of metadata from the readers loaded.""" mda = {'sensor': self._get_sensor_names()} # overwrite the request start/end times with actual loaded data limits if self._readers: mda['start_time'] = min(x.start_time for x in self._readers.values()) mda['end_time'] = max(x.end_time for x in self._readers.values()) return mda def _get_sensor_names(self): """Join the sensors from all loaded readers.""" # if the user didn't tell us what sensors to work with, let's figure it # out if not self.attrs.get('sensor'): # reader finder could return multiple readers return set([sensor for reader_instance in self._readers.values() for sensor in reader_instance.sensor_names]) elif not isinstance(self.attrs['sensor'], (set, tuple, list)): return set([self.attrs['sensor']]) else: return set(self.attrs['sensor']) def _create_reader_instances(self, filenames=None, reader=None, reader_kwargs=None): """Find readers and return their instances.""" return load_readers(filenames=filenames, reader=reader, reader_kwargs=reader_kwargs) @property def start_time(self): """Return the start time of the file.""" return self.attrs['start_time'] @property def end_time(self): """Return the end time of the file.""" return self.attrs['end_time'] @property def missing_datasets(self): """Set of DataIDs that have not been successfully loaded.""" return set(self._wishlist) - set(self._datasets.keys()) def _compare_areas(self, datasets=None, compare_func=max): """Compare areas for the provided datasets. Args: datasets (iterable): Datasets whose areas will be compared. Can be either `xarray.DataArray` objects or identifiers to get the DataArrays from the current Scene. Defaults to all datasets. This can also be a series of area objects, typically AreaDefinitions. compare_func (callable): `min` or `max` or other function used to compare the dataset's areas. """ if datasets is None: datasets = list(self.values()) areas = [] for ds in datasets: if isinstance(ds, BaseDefinition): areas.append(ds) continue elif not isinstance(ds, DataArray): ds = self[ds] area = ds.attrs.get('area') areas.append(area) areas = [x for x in areas if x is not None] if not areas: raise ValueError("No dataset areas available") if not all(isinstance(x, type(areas[0])) for x in areas[1:]): raise ValueError("Can't compare areas of different types") elif isinstance(areas[0], AreaDefinition): first_crs = areas[0].crs if not all( == first_crs for ad in areas[1:]): raise ValueError("Can't compare areas with different " "projections.") def key_func(ds): return 1. / abs(ds.pixel_size_x) else: def key_func(ds): return ds.shape # find the highest/lowest area among the provided return compare_func(areas, key=key_func)
[docs] def finest_area(self, datasets=None): """Get highest resolution area for the provided datasets. Args: datasets (iterable): Datasets whose areas will be compared. Can be either `xarray.DataArray` objects or identifiers to get the DataArrays from the current Scene. Defaults to all datasets. """ return self._compare_areas(datasets=datasets, compare_func=max)
[docs] def max_area(self, datasets=None): """Get highest resolution area for the provided datasets. Deprecated. Args: datasets (iterable): Datasets whose areas will be compared. Can be either `xarray.DataArray` objects or identifiers to get the DataArrays from the current Scene. Defaults to all datasets. """ warnings.warn("'max_area' is deprecated, use 'finest_area' instead.", DeprecationWarning) return self.finest_area(datasets=datasets)
[docs] def coarsest_area(self, datasets=None): """Get lowest resolution area for the provided datasets. Args: datasets (iterable): Datasets whose areas will be compared. Can be either `xarray.DataArray` objects or identifiers to get the DataArrays from the current Scene. Defaults to all datasets. """ return self._compare_areas(datasets=datasets, compare_func=min)
[docs] def min_area(self, datasets=None): """Get lowest resolution area for the provided datasets. Deprecated. Args: datasets (iterable): Datasets whose areas will be compared. Can be either `xarray.DataArray` objects or identifiers to get the DataArrays from the current Scene. Defaults to all datasets. """ warnings.warn("'min_area' is deprecated, use 'coarsest_area' instead.", DeprecationWarning) return self.coarsest_area(datasets=datasets)
[docs] def available_dataset_ids(self, reader_name=None, composites=False): """Get DataIDs of loadable datasets. This can be for all readers loaded by this Scene or just for ``reader_name`` if specified. Available dataset names are determined by what each individual reader can load. This is normally determined by what files are needed to load a dataset and what files have been provided to the scene/reader. Some readers dynamically determine what is available based on the contents of the files provided. Returns: list of available dataset names """ try: if reader_name: readers = [self._readers[reader_name]] else: readers = self._readers.values() except (AttributeError, KeyError): raise KeyError("No reader '%s' found in scene" % reader_name) available_datasets = sorted([dataset_id for reader in readers for dataset_id in reader.available_dataset_ids]) if composites: available_datasets += sorted(self.available_composite_ids()) return available_datasets
[docs] def available_dataset_names(self, reader_name=None, composites=False): """Get the list of the names of the available datasets.""" return sorted(set(x['name'] for x in self.available_dataset_ids( reader_name=reader_name, composites=composites)))
[docs] def all_dataset_ids(self, reader_name=None, composites=False): """Get names of all datasets from loaded readers or `reader_name` if specified. Returns: list of all dataset names """ try: if reader_name: readers = [self._readers[reader_name]] else: readers = self._readers.values() except (AttributeError, KeyError): raise KeyError("No reader '%s' found in scene" % reader_name) all_datasets = [dataset_id for reader in readers for dataset_id in reader.all_dataset_ids] if composites: all_datasets += self.all_composite_ids() return all_datasets
[docs] def all_dataset_names(self, reader_name=None, composites=False): """Get all known dataset names configured for the loaded readers. Note that some readers dynamically determine what datasets are known by reading the contents of the files they are provided. This means that the list of datasets returned by this method may change depending on what files are provided even if a product/dataset is a "standard" product for a particular reader. """ return sorted(set(x['name'] for x in self.all_dataset_ids( reader_name=reader_name, composites=composites)))
def _check_known_composites(self, available_only=False): """Create new dependency tree and check what composites we know about.""" # Note if we get compositors from the dep tree then it will include # modified composites which we don't want sensor_comps, mods = self._composite_loader.load_compositors(self.attrs['sensor']) # recreate the dependency tree so it doesn't interfere with the user's # wishlist from self._dependency_tree dep_tree = DependencyTree(self._readers, sensor_comps, mods, available_only=True) # ignore inline compositor dependencies starting with '_' comps = (comp for comp_dict in sensor_comps.values() for comp in comp_dict.keys() if not comp['name'].startswith('_')) # make sure that these composites are even create-able by these readers all_comps = set(comps) # find_dependencies will update the all_comps set with DataIDs try: dep_tree.populate_with_keys(all_comps) except MissingDependencies: pass available_comps = set( for x in dep_tree.trunk()) # get rid of modified composites that are in the trunk return sorted(available_comps & all_comps)
[docs] def available_composite_ids(self): """Get names of composites that can be generated from the available datasets.""" return self._check_known_composites(available_only=True)
[docs] def available_composite_names(self): """All configured composites known to this Scene.""" return sorted(set(x['name'] for x in self.available_composite_ids()))
[docs] def all_composite_ids(self): """Get all IDs for configured composites.""" return self._check_known_composites()
[docs] def all_composite_names(self): """Get all names for all configured composites.""" return sorted(set(x['name'] for x in self.all_composite_ids()))
[docs] def all_modifier_names(self): """Get names of configured modifier objects.""" return sorted(self._dependency_tree.modifiers.keys())
def __str__(self): """Generate a nice print out for the scene.""" res = (str(proj) for proj in self._datasets.values()) return "\n".join(res) def __iter__(self): """Iterate over the datasets.""" for x in self._datasets.values(): yield x
[docs] def iter_by_area(self): """Generate datasets grouped by Area. :return: generator of (area_obj, list of dataset objects) """ datasets_by_area = {} for ds in self: a = ds.attrs.get('area') dsid = DataID.from_dataarray(ds) datasets_by_area.setdefault(a, []).append(dsid) return datasets_by_area.items()
[docs] def keys(self, **kwargs): """Get DataID keys for the underlying data container.""" return self._datasets.keys(**kwargs)
[docs] def values(self): """Get values for the underlying data container.""" return self._datasets.values()
def _copy_datasets_and_wishlist(self, new_scn, datasets): for ds_id in datasets: # NOTE: Must use `._datasets` or side effects of `__setitem__` # could hurt us with regards to the wishlist new_scn._datasets[ds_id] = self[ds_id] new_scn._wishlist = self._wishlist.copy()
[docs] def copy(self, datasets=None): """Create a copy of the Scene including dependency information. Args: datasets (list, tuple): `DataID` objects for the datasets to include in the new Scene object. """ new_scn = self.__class__() new_scn.attrs = self.attrs.copy() new_scn._dependency_tree = self._dependency_tree.copy() if datasets is None: datasets = self.keys() self._copy_datasets_and_wishlist(new_scn, datasets) return new_scn
@property def all_same_area(self): """All contained data arrays are on the same area.""" all_areas = [x.attrs.get('area', None) for x in self.values()] all_areas = [x for x in all_areas if x is not None] return all(all_areas[0] == x for x in all_areas[1:]) @property def all_same_proj(self): """All contained data array are in the same projection.""" all_areas = [x.attrs.get('area', None) for x in self.values()] all_areas = [x for x in all_areas if x is not None] return all(all_areas[0].crs == for x in all_areas[1:]) @staticmethod def _slice_area_from_bbox(src_area, dst_area, ll_bbox=None, xy_bbox=None): """Slice the provided area using the bounds provided.""" if ll_bbox is not None: dst_area = AreaDefinition( 'crop_area', 'crop_area', 'crop_latlong', {'proj': 'latlong'}, 100, 100, ll_bbox) elif xy_bbox is not None: dst_area = AreaDefinition( 'crop_area', 'crop_area', 'crop_xy',, src_area.width, src_area.height, xy_bbox) x_slice, y_slice = src_area.get_area_slices(dst_area) return src_area[y_slice, x_slice], y_slice, x_slice def _slice_datasets(self, dataset_ids, slice_key, new_area, area_only=True): """Slice scene in-place for the datasets specified.""" new_datasets = {} datasets = (self[ds_id] for ds_id in dataset_ids) for ds, parent_ds in dataset_walker(datasets): ds_id = DataID.from_dataarray(ds) # handle ancillary variables pres = None if parent_ds is not None: parent_dsid = DataID.from_dataarray(parent_ds) pres = new_datasets[parent_dsid] if ds_id in new_datasets: replace_anc(ds, pres) continue if area_only and ds.attrs.get('area') is None: new_datasets[ds_id] = ds replace_anc(ds, pres) continue if not isinstance(slice_key, dict): # match dimension name to slice object key = dict(zip(ds.dims, slice_key)) else: key = slice_key new_ds = ds.isel(**key) if new_area is not None: new_ds.attrs['area'] = new_area new_datasets[ds_id] = new_ds if parent_ds is None: # don't use `__setitem__` because we don't want this to # affect the existing wishlist/dep tree self._datasets[ds_id] = new_ds else: replace_anc(new_ds, pres)
[docs] def slice(self, key): """Slice Scene by dataset index. .. note:: DataArrays that do not have an ``area`` attribute will not be sliced. """ if not self.all_same_area: raise RuntimeError("'Scene' has different areas and cannot " "be usefully sliced.") # slice new_scn = self.copy() new_scn._wishlist = self._wishlist for area, dataset_ids in self.iter_by_area(): if area is not None: # assume dimensions for area are y and x one_ds = self[dataset_ids[0]] area_key = tuple(sl for dim, sl in zip(one_ds.dims, key) if dim in ['y', 'x']) new_area = area[area_key] else: new_area = None new_scn._slice_datasets(dataset_ids, key, new_area) return new_scn
[docs] def crop(self, area=None, ll_bbox=None, xy_bbox=None, dataset_ids=None): """Crop Scene to a specific Area boundary or bounding box. Args: area (AreaDefinition): Area to crop the current Scene to ll_bbox (tuple, list): 4-element tuple where values are in lon/lat degrees. Elements are ``(xmin, ymin, xmax, ymax)`` where X is longitude and Y is latitude. xy_bbox (tuple, list): Same as `ll_bbox` but elements are in projection units. dataset_ids (iterable): DataIDs to include in the returned `Scene`. Defaults to all datasets. This method will attempt to intelligently slice the data to preserve relationships between datasets. For example, if we are cropping two DataArrays of 500m and 1000m pixel resolution then this method will assume that exactly 4 pixels of the 500m array cover the same geographic area as a single 1000m pixel. It handles these cases based on the shapes of the input arrays and adjusting slicing indexes accordingly. This method will have trouble handling cases where data arrays seem related but don't cover the same geographic area or if the coarsest resolution data is not related to the other arrays which are related. It can be useful to follow cropping with a call to the native resampler to resolve all datasets to the same resolution and compute any composites that could not be generated previously:: >>> cropped_scn = scn.crop(ll_bbox=(-105., 40., -95., 50.)) >>> remapped_scn = cropped_scn.resample(resampler='native') .. note:: The `resample` method automatically crops input data before resampling to save time/memory. """ if len([x for x in [area, ll_bbox, xy_bbox] if x is not None]) != 1: raise ValueError("One and only one of 'area', 'll_bbox', " "or 'xy_bbox' can be specified.") new_scn = self.copy(datasets=dataset_ids) if not new_scn.all_same_proj and xy_bbox is not None: raise ValueError("Can't crop when dataset_ids are not all on the " "same projection.") # get the lowest resolution area, use it as the base of the slice # this makes sure that the other areas *should* be a consistent factor coarsest_area = new_scn.coarsest_area() if isinstance(area, str): area = get_area_def(area) new_coarsest_area, min_y_slice, min_x_slice = self._slice_area_from_bbox( coarsest_area, area, ll_bbox, xy_bbox) new_target_areas = {} for src_area, dataset_ids in new_scn.iter_by_area(): if src_area is None: for ds_id in dataset_ids: new_scn._datasets[ds_id] = self[ds_id] continue y_factor, y_remainder = np.divmod(float(src_area.shape[0]), coarsest_area.shape[0]) x_factor, x_remainder = np.divmod(float(src_area.shape[1]), coarsest_area.shape[1]) y_factor = int(y_factor) x_factor = int(x_factor) if y_remainder == 0 and x_remainder == 0: y_slice = slice(min_y_slice.start * y_factor, min_y_slice.stop * y_factor) x_slice = slice(min_x_slice.start * x_factor, min_x_slice.stop * x_factor) new_area = src_area[y_slice, x_slice] slice_key = {'y': y_slice, 'x': x_slice} new_scn._slice_datasets(dataset_ids, slice_key, new_area) else: new_target_areas[src_area] = self._slice_area_from_bbox( src_area, area, ll_bbox, xy_bbox ) return new_scn
[docs] def aggregate(self, dataset_ids=None, boundary='trim', side='left', func='mean', **dim_kwargs): """Create an aggregated version of the Scene. Args: dataset_ids (iterable): DataIDs to include in the returned `Scene`. Defaults to all datasets. func (string): Function to apply on each aggregation window. One of 'mean', 'sum', 'min', 'max', 'median', 'argmin', 'argmax', 'prod', 'std', 'var'. 'mean' is the default. boundary: See :meth:`xarray.DataArray.coarsen`, 'trim' by default. side: See :meth:`xarray.DataArray.coarsen`, 'left' by default. dim_kwargs: the size of the windows to aggregate. Returns: A new aggregated scene See also: xarray.DataArray.coarsen Example: `scn.aggregate(func='min', x=2, y=2)` will apply the `min` function across a window of size 2 pixels. """ new_scn = self.copy(datasets=dataset_ids) for src_area, ds_ids in new_scn.iter_by_area(): if src_area is None: for ds_id in ds_ids: new_scn._datasets[ds_id] = self[ds_id] continue target_area = src_area.aggregate(boundary=boundary, **dim_kwargs) try: resolution = max(target_area.pixel_size_x, target_area.pixel_size_y) except AttributeError: resolution = max(target_area.lats.resolution, target_area.lons.resolution) for ds_id in ds_ids: res = self[ds_id].coarsen(boundary=boundary, side=side, **dim_kwargs) new_scn._datasets[ds_id] = getattr(res, func)() new_scn._datasets[ds_id].attrs = self[ds_id].attrs.copy() new_scn._datasets[ds_id].attrs['area'] = target_area new_scn._datasets[ds_id].attrs['resolution'] = resolution return new_scn
[docs] def get(self, key, default=None): """Return value from DatasetDict with optional default.""" return self._datasets.get(key, default)
def __getitem__(self, key): """Get a dataset or create a new 'slice' of the Scene.""" if isinstance(key, tuple): return self.slice(key) return self._datasets[key] def __setitem__(self, key, value): """Add the item to the scene.""" self._datasets[key] = value # this could raise a KeyError but never should in this case ds_id = self._datasets.get_key(key) self._wishlist.add(ds_id) self._dependency_tree.add_leaf(ds_id) def __delitem__(self, key): """Remove the item from the scene.""" k = self._datasets.get_key(key) self._wishlist.discard(k) del self._datasets[k] def __contains__(self, name): """Check if the dataset is in the scene.""" return name in self._datasets def _slice_data(self, source_area, slices, dataset): """Slice the data to reduce it.""" slice_x, slice_y = slices dataset = dataset.isel(x=slice_x, y=slice_y) if ('x', source_area.width) not in dataset.sizes.items(): raise RuntimeError if ('y', source_area.height) not in dataset.sizes.items(): raise RuntimeError dataset.attrs['area'] = source_area return dataset def _resampled_scene(self, new_scn, destination_area, reduce_data=True, **resample_kwargs): """Resample `datasets` to the `destination` area. If data reduction is enabled, some local caching is perfomed in order to avoid recomputation of area intersections. """ new_datasets = {} datasets = list(new_scn._datasets.values()) if isinstance(destination_area, str): destination_area = get_area_def(destination_area) if hasattr(destination_area, 'freeze'): try: finest_area = new_scn.finest_area() destination_area = destination_area.freeze(finest_area) except ValueError: raise ValueError("No dataset areas available to freeze " "DynamicAreaDefinition.") resamplers = {} reductions = {} for dataset, parent_dataset in dataset_walker(datasets): ds_id = DataID.from_dataarray(dataset) pres = None if parent_dataset is not None: pres = new_datasets[DataID.from_dataarray(parent_dataset)] if ds_id in new_datasets: replace_anc(new_datasets[ds_id], pres) if ds_id in new_scn._datasets: new_scn._datasets[ds_id] = new_datasets[ds_id] continue if dataset.attrs.get('area') is None: if parent_dataset is None: new_scn._datasets[ds_id] = dataset else: replace_anc(dataset, pres) continue LOG.debug("Resampling %s", ds_id) source_area = dataset.attrs['area'] try: if reduce_data: key = source_area try: (slice_x, slice_y), source_area = reductions[key] except KeyError: if resample_kwargs.get('resampler') == 'gradient_search': factor = resample_kwargs.get('shape_divisible_by', 2) else: factor = None try: slice_x, slice_y = source_area.get_area_slices( destination_area, shape_divisible_by=factor) except TypeError: slice_x, slice_y = source_area.get_area_slices( destination_area) source_area = source_area[slice_y, slice_x] reductions[key] = (slice_x, slice_y), source_area dataset = self._slice_data(source_area, (slice_x, slice_y), dataset) else: LOG.debug("Data reduction disabled by the user") except NotImplementedError:"Not reducing data before resampling.") if source_area not in resamplers: key, resampler = prepare_resampler( source_area, destination_area, **resample_kwargs) resamplers[source_area] = resampler self._resamplers[key] = resampler kwargs = resample_kwargs.copy() kwargs['resampler'] = resamplers[source_area] res = resample_dataset(dataset, destination_area, **kwargs) new_datasets[ds_id] = res if ds_id in new_scn._datasets: new_scn._datasets[ds_id] = res if parent_dataset is not None: replace_anc(res, pres)
[docs] def resample(self, destination=None, datasets=None, generate=True, unload=True, resampler=None, reduce_data=True, **resample_kwargs): """Resample datasets and return a new scene. Args: destination (AreaDefinition, GridDefinition): area definition to resample to. If not specified then the area returned by `Scene.finest_area()` will be used. datasets (list): Limit datasets to resample to these specified data arrays. By default all currently loaded datasets are resampled. generate (bool): Generate any requested composites that could not be previously due to incompatible areas (default: True). unload (bool): Remove any datasets no longer needed after requested composites have been generated (default: True). resampler (str): Name of resampling method to use. By default, this is a nearest neighbor KDTree-based resampling ('nearest'). Other possible values include 'native', 'ewa', etc. See the :mod:`~satpy.resample` documentation for more information. reduce_data (bool): Reduce data by matching the input and output areas and slicing the data arrays (default: True) resample_kwargs: Remaining keyword arguments to pass to individual resampler classes. See the individual resampler class documentation :mod:`here <satpy.resample>` for available arguments. """ if destination is None: destination = self.finest_area(datasets) new_scn = self.copy(datasets=datasets) self._resampled_scene(new_scn, destination, resampler=resampler, reduce_data=reduce_data, **resample_kwargs) # regenerate anything from the wishlist that needs it (combining # multiple resolutions, etc.) new_scn.generate_possible_composites(generate, unload) return new_scn
[docs] def show(self, dataset_id, overlay=None): """Show the *dataset* on screen as an image. Show dataset on screen as an image, possibly with an overlay. Args: dataset_id (DataID, DataQuery or str): Either a DataID, a DataQuery or a string, that refers to a data array that has been previously loaded using Scene.load. overlay (dict, optional): Add an overlay before showing the image. The keys/values for this dictionary are as the arguments for :meth:`~satpy.writers.add_overlay`. The dictionary should contain at least the key ``"coast_dir"``, which should refer to a top-level directory containing shapefiles. See the pycoast_ package documentation for coastline shapefile installation instructions. .. _pycoast: """ from satpy.writers import get_enhanced_image from satpy.utils import in_ipynb img = get_enhanced_image(self[dataset_id].squeeze(), overlay=overlay) if not in_ipynb(): return img
[docs] def to_geoviews(self, gvtype=None, datasets=None, kdims=None, vdims=None, dynamic=False): """Convert satpy Scene to geoviews. Args: 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 : boolean, optional, default False 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 = self.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) if "latitude" in ds.coords: gview =, kdims=["longitude", "latitude"], vdims=vdims, dynamic=dynamic) else: gview =, kdims=["x", "y"], vdims=vdims, dynamic=dynamic) return gview
[docs] def to_xarray_dataset(self, datasets=None): """Merge all xr.DataArrays of a scene to a xr.DataSet. Parameters: datasets (list): List of products to include in the :class:`xarray.Dataset` Returns: :class:`xarray.Dataset` """ dataarrays = self._get_dataarrays_from_identifiers(datasets) ds_dict = {i.attrs['name']: i.rename(i.attrs['name']) for i in dataarrays if i.attrs.get('area') is not None} mdata = combine_metadata(*tuple(i.attrs for i in dataarrays)) if mdata.get('area') is None or not isinstance(mdata['area'], SwathDefinition): # either don't know what the area is or we have an AreaDefinition ds = xr.merge(ds_dict.values()) else: # we have a swath definition and should use lon/lat values lons, lats = mdata['area'].get_lonlats() if not isinstance(lons, DataArray): lons = DataArray(lons, dims=('y', 'x')) lats = DataArray(lats, dims=('y', 'x')) ds = xr.Dataset(ds_dict, coords={"latitude": (["y", "x"], lats), "longitude": (["y", "x"], lons)}) ds.attrs = mdata return ds
def _get_dataarrays_from_identifiers(self, identifiers): if identifiers is not None: dataarrays = [self[ds] for ds in identifiers] else: dataarrays = [self._datasets.get(ds) for ds in self._wishlist] dataarrays = [ds for ds in dataarrays if ds is not None] return dataarrays
[docs] def images(self): """Generate images for all the datasets from the scene.""" for ds_id, projectable in self._datasets.items(): if ds_id in self._wishlist: yield projectable.to_image()
[docs] def save_dataset(self, dataset_id, filename=None, writer=None, overlay=None, decorate=None, compute=True, **kwargs): """Save the ``dataset_id`` to file using ``writer``. Args: dataset_id (str or Number or DataID or DataQuery): Identifier for the dataset to save to disk. filename (str): Optionally specify the filename to save this dataset to. It may include string formatting patterns that will be filled in by dataset attributes. writer (str): Name of writer to use when writing data to disk. Default to ``"geotiff"``. If not provided, but ``filename`` is provided then the filename's extension is used to determine the best writer to use. overlay (dict): See :func:`satpy.writers.add_overlay`. Only valid for "image" writers like `geotiff` or `simple_image`. decorate (dict): See :func:`satpy.writers.add_decorate`. Only valid for "image" writers like `geotiff` or `simple_image`. compute (bool): If `True` (default), compute all of the saves to disk. If `False` then the return value is either a :doc:`dask:delayed` object or two lists to be passed to a `` call. See return values below for more details. kwargs: Additional writer arguments. See :doc:`../writers` for more information. Returns: Value returned depends on `compute`. If `compute` is `True` then the return value is the result of computing a :doc:`dask:delayed` object or running :func:``. If `compute` is `False` then the returned value is either a :doc:`dask:delayed` object that can be computed using `delayed.compute()` or a tuple of (source, target) that should be passed to :func:``. If target is provided the the caller is responsible for calling `target.close()` if the target has this method. """ if writer is None and filename is None: writer = 'geotiff' elif writer is None: writer = self._get_writer_by_ext(os.path.splitext(filename)[1]) writer, save_kwargs = load_writer(writer, filename=filename, **kwargs) return writer.save_dataset(self[dataset_id], overlay=overlay, decorate=decorate, compute=compute, **save_kwargs)
[docs] def save_datasets(self, writer=None, filename=None, datasets=None, compute=True, **kwargs): """Save all the datasets present in a scene to disk using ``writer``. Args: writer (str): Name of writer to use when writing data to disk. Default to ``"geotiff"``. If not provided, but ``filename`` is provided then the filename's extension is used to determine the best writer to use. filename (str): Optionally specify the filename to save this dataset to. It may include string formatting patterns that will be filled in by dataset attributes. datasets (iterable): Limit written products to these datasets compute (bool): If `True` (default), compute all of the saves to disk. If `False` then the return value is either a :doc:`dask:delayed` object or two lists to be passed to a `` call. See return values below for more details. kwargs: Additional writer arguments. See :doc:`../writers` for more information. Returns: Value returned depends on `compute` keyword argument. If `compute` is `True` the value is the result of a either a `` operation or a :doc:`dask:delayed` compute, typically this is `None`. If `compute` is `False` then the result is either a :doc:`dask:delayed` object that can be computed with `delayed.compute()` or a two element tuple of sources and targets to be passed to :func:``. If `targets` is provided then it is the caller's responsibility to close any objects that have a "close" method. """ dataarrays = self._get_dataarrays_from_identifiers(datasets) if not dataarrays: raise RuntimeError("None of the requested datasets have been " "generated or could not be loaded. Requested " "composite inputs may need to have matching " "dimensions (eg. through resampling).") if writer is None: if filename is None: writer = 'geotiff' else: writer = self._get_writer_by_ext(os.path.splitext(filename)[1]) writer, save_kwargs = load_writer(writer, filename=filename, **kwargs) return writer.save_datasets(dataarrays, compute=compute, **save_kwargs)
@staticmethod def _get_writer_by_ext(extension): """Find the writer matching the ``extension``. Defaults to "simple_image". Example Mapping: - geotiff: .tif, .tiff - cf: .nc - mitiff: .mitiff - simple_image: .png, .jpeg, .jpg, ... Args: extension (str): Filename extension starting with "." (ex. ".png"). Returns: str: The name of the writer to use for this extension. """ mapping = {".tiff": "geotiff", ".tif": "geotiff", ".nc": "cf", ".mitiff": "mitiff"} return mapping.get(extension.lower(), 'simple_image') def _remove_failed_datasets(self, keepables): """Remove the datasets that we couldn't create.""" # copy the set of missing datasets because they won't be valid # after they are removed in the next line missing = self.missing_datasets.copy() keepables = keepables or set() # remove reader datasets that couldn't be loaded so they aren't # attempted again later for n in self.missing_datasets: if n not in keepables: self._wishlist.discard(n) missing_str = ", ".join(str(x) for x in missing) LOG.warning("The following datasets were not created and may require " "resampling to be generated: {}".format(missing_str))
[docs] def unload(self, keepables=None): """Unload all unneeded datasets. Datasets are considered unneeded if they weren't directly requested or added to the Scene by the user or they are no longer needed to generate composites that have yet to be generated. Args: keepables (iterable): DataIDs to keep whether they are needed or not. """ to_del = [ds_id for ds_id, projectable in self._datasets.items() if ds_id not in self._wishlist and (not keepables or ds_id not in keepables)] for ds_id in to_del: LOG.debug("Unloading dataset: %r", ds_id) del self._datasets[ds_id]
[docs] def load(self, wishlist, calibration='*', resolution='*', polarization='*', level='*', generate=True, unload=True, **kwargs): """Read and generate requested datasets. When the `wishlist` contains `DataQuery` objects they can either be fully-specified `DataQuery` objects with every parameter specified or they can not provide certain parameters and the "best" parameter will be chosen. For example, if a dataset is available in multiple resolutions and no resolution is specified in the wishlist's DataQuery then the highest (smallest number) resolution will be chosen. Loaded `DataArray` objects are created and stored in the Scene object. Args: wishlist (iterable): List of names (str), wavelengths (float), DataQuery objects or DataID of the requested datasets to load. See `available_dataset_ids()` for what datasets are available. calibration (list, str): Calibration levels to limit available datasets. This is a shortcut to having to list each DataQuery/DataID in `wishlist`. resolution (list | float): Resolution to limit available datasets. This is a shortcut similar to calibration. polarization (list | str): Polarization ('V', 'H') to limit available datasets. This is a shortcut similar to calibration. level (list | str): Pressure level to limit available datasets. Pressure should be in hPa or mb. If an altitude is used it should be specified in inverse meters (1/m). The units of this parameter ultimately depend on the reader. generate (bool): Generate composites from the loaded datasets (default: True) unload (bool): Unload datasets that were required to generate the requested datasets (composite dependencies) but are no longer needed. """ if isinstance(wishlist, str): raise TypeError("'load' expects a list of datasets, got a string.") dataset_keys = set(wishlist) needed_datasets = (self._wishlist | dataset_keys) - set(self._datasets.keys()) query = DataQuery(calibration=calibration, polarization=polarization, resolution=resolution, level=level) self._update_dependency_tree(needed_datasets, query) self._wishlist |= needed_datasets self._read_datasets_from_storage(**kwargs) self.generate_possible_composites(generate, unload)
def _update_dependency_tree(self, needed_datasets, query): try: self._dependency_tree.populate_with_keys(needed_datasets, query) except MissingDependencies as err: raise KeyError(str(err)) def _read_datasets_from_storage(self, **kwargs): """Load datasets from the necessary reader. Args: **kwargs: Keyword arguments to pass to the reader's `load` method. Returns: DatasetDict of loaded datasets """ nodes = self._dependency_tree.leaves(limit_nodes_to=self.missing_datasets) return self._read_dataset_nodes_from_storage(nodes, **kwargs) def _read_dataset_nodes_from_storage(self, reader_nodes, **kwargs): """Read the given dataset nodes from storage.""" # Sort requested datasets by reader reader_datasets = self._sort_dataset_nodes_by_reader(reader_nodes) loaded_datasets = self._load_datasets_by_readers(reader_datasets, **kwargs) self._datasets.update(loaded_datasets) return loaded_datasets def _sort_dataset_nodes_by_reader(self, reader_nodes): reader_datasets = {} for node in reader_nodes: ds_id = # if we already have this node loaded or the node was assigned # by the user (node data is None) then don't try to load from a # reader if ds_id in self._datasets or not isinstance(node, ReaderNode): continue reader_name = node.reader_name if reader_name is None: # This shouldn't be possible raise RuntimeError("Dependency tree has a corrupt node.") reader_datasets.setdefault(reader_name, set()).add(ds_id) return reader_datasets def _load_datasets_by_readers(self, reader_datasets, **kwargs): # load all datasets for one reader at a time loaded_datasets = DatasetDict() for reader_name, ds_ids in reader_datasets.items(): reader_instance = self._readers[reader_name] new_datasets = reader_instance.load(ds_ids, **kwargs) loaded_datasets.update(new_datasets) return loaded_datasets
[docs] def generate_possible_composites(self, generate, unload): """See what we can generate and do it.""" if generate: keepables = self._generate_composites_from_loaded_datasets() else: # don't lose datasets we loaded to try to generate composites keepables = set(self._datasets.keys()) | self._wishlist if self.missing_datasets: self._remove_failed_datasets(keepables) if unload: self.unload(keepables=keepables)
def _filter_loaded_datasets_from_trunk_nodes(self, trunk_nodes): loaded_data_ids = self._datasets.keys() for trunk_node in trunk_nodes: if in loaded_data_ids: continue yield trunk_node def _generate_composites_from_loaded_datasets(self): """Compute all the composites contained in `requirements`.""" trunk_nodes = self._dependency_tree.trunk(limit_nodes_to=self.missing_datasets, limit_children_to=self._datasets.keys()) needed_comp_nodes = set(self._filter_loaded_datasets_from_trunk_nodes(trunk_nodes)) return self._generate_composites_nodes_from_loaded_datasets(needed_comp_nodes) def _generate_composites_nodes_from_loaded_datasets(self, compositor_nodes): """Read (generate) composites.""" keepables = set() for node in compositor_nodes: self._generate_composite(node, keepables) return keepables def _generate_composite(self, comp_node: Node, keepables: set): """Collect all composite prereqs and create the specified composite. Args: comp_node: Composite Node to generate a Dataset for keepables: `set` to update if any datasets are needed when generation is continued later. This can happen if generation is delayed to incompatible areas which would require resampling first. """ if self._datasets.contains( # already loaded return compositor = comp_node.compositor prereqs = comp_node.required_nodes optional_prereqs = comp_node.optional_nodes try: delayed_prereq = False prereq_datasets = self._get_prereq_datasets(, prereqs, keepables, ) except DelayedGeneration: # if we are missing a required dependency that could be generated # later then we need to wait to return until after we've also # processed the optional dependencies delayed_prereq = True except KeyError: # we are missing a hard requirement that will never be available # there is no need to "keep" optional dependencies return optional_datasets = self._get_prereq_datasets(, optional_prereqs, keepables, skip=True ) # we are missing some prerequisites # in the future we may be able to generate this composite (delayed) # so we need to hold on to successfully loaded prerequisites and # optional prerequisites if delayed_prereq: preservable_datasets = set(self._datasets.keys()) prereq_ids = set( for p in prereqs) opt_prereq_ids = set( for p in optional_prereqs) keepables |= preservable_datasets & (prereq_ids | opt_prereq_ids) return try: composite = compositor(prereq_datasets, optional_datasets=optional_datasets, ** cid = DataID.new_id_from_dataarray(composite) self._datasets[cid] = composite # update the node with the computed DataID if in self._wishlist: self._wishlist.remove( self._wishlist.add(cid) self._dependency_tree.update_node_name(comp_node, cid) except IncompatibleAreas: LOG.debug("Delaying generation of %s because of incompatible areas", str( preservable_datasets = set(self._datasets.keys()) prereq_ids = set( for p in prereqs) opt_prereq_ids = set( for p in optional_prereqs) keepables |= preservable_datasets & (prereq_ids | opt_prereq_ids) # even though it wasn't generated keep a list of what # might be needed in other compositors keepables.add( return def _get_prereq_datasets(self, comp_id, prereq_nodes, keepables, skip=False): """Get a composite's prerequisites, generating them if needed. Args: comp_id (DataID): DataID for the composite whose prerequisites are being collected. prereq_nodes (sequence of Nodes): Prerequisites to collect keepables (set): `set` to update if any prerequisites can't be loaded at this time (see `_generate_composite`). skip (bool): If True, consider prerequisites as optional and only log when they are missing. If False, prerequisites are considered required and will raise an exception and log a warning if they can't be collected. Defaults to False. Raises: KeyError: If required (skip=False) prerequisite can't be collected. """ prereq_datasets = [] delayed_gen = False for prereq_node in prereq_nodes: prereq_id = if prereq_id not in self._datasets and prereq_id not in keepables \ and isinstance(prereq_node, CompositorNode): self._generate_composite(prereq_node, keepables) # composite generation may have updated the DataID prereq_id = if prereq_node is self._dependency_tree.empty_node: # empty sentinel node - no need to load it continue elif prereq_id in self._datasets: prereq_datasets.append(self._datasets[prereq_id]) elif isinstance(prereq_node, CompositorNode) and prereq_id in keepables: delayed_gen = True continue elif not skip: LOG.debug("Missing prerequisite for '{}': '{}'".format( comp_id, prereq_id)) raise KeyError("Missing composite prerequisite for" " '{}': '{}'".format(comp_id, prereq_id)) else: LOG.debug("Missing optional prerequisite for {}: {}".format(comp_id, prereq_id)) if delayed_gen: keepables.add(comp_id) keepables.update([ for x in prereq_nodes]) LOG.debug("Delaying generation of %s because of dependency's delayed generation: %s", comp_id, prereq_id) if not skip: LOG.debug("Delayed prerequisite for '{}': '{}'".format(comp_id, prereq_id)) raise DelayedGeneration( "Delayed composite prerequisite for " "'{}': '{}'".format(comp_id, prereq_id)) else: LOG.debug("Delayed optional prerequisite for {}: {}".format(comp_id, prereq_id)) return prereq_datasets