class Scene(MetadataObject):
"""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,
ppp_config_dir=get_environ_config_dir(),
base_dir=None,
sensor=None,
start_time=None,
end_time=None,
area=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.
ppp_config_dir (str): The directory containing the configuration files for satpy.
base_dir (str): (DEPRECATED) The directory to search for files containing the
data to load. If *filenames* is also provided,
this is ignored.
sensor (list or str): (DEPRECATED: Use `find_files_and_readers` function) Limit used files by provided
sensors.
area (AreaDefinition): (DEPRECATED: Use `filter_parameters`) Limit used files by geographic area.
start_time (datetime): (DEPRECATED: Use `filter_parameters`) Limit used files by starting time.
end_time (datetime): (DEPRECATED: Use `filter_parameters`) Limit used files by ending time.
"""
super(Scene, self).__init__()
# Set the PPP_CONFIG_DIR in the environment in case it's used elsewhere in pytroll
LOG.debug("Setting 'PPP_CONFIG_DIR' to '%s'", ppp_config_dir)
os.environ["PPP_CONFIG_DIR"] = self.ppp_config_dir = ppp_config_dir
if not filenames and (start_time or end_time or base_dir):
import warnings
warnings.warn(
"Deprecated: Use " +
"'from satpy import find_files_and_readers' to find files")
from satpy import find_files_and_readers
filenames = find_files_and_readers(
start_time=start_time,
end_time=end_time,
base_dir=base_dir,
reader=reader,
sensor=sensor,
ppp_config_dir=self.ppp_config_dir,
reader_kwargs=reader_kwargs,
)
elif start_time or end_time or area:
import warnings
warnings.warn(
"Deprecated: Use " +
"'filter_parameters' to filter loaded files by 'start_time', "
+ "'end_time', or 'area'.")
fp = filter_parameters if filter_parameters else {}
fp.update({
'start_time': start_time,
'end_time': end_time,
'area': area,
})
filter_parameters = fp
if filter_parameters:
if reader_kwargs is None:
reader_kwargs = {}
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.cpl = CompositorLoader(self.ppp_config_dir)
comps, mods = self.cpl.load_compositors(self.attrs['sensor'])
self.wishlist = set()
self.dep_tree = DependencyTree(self.readers, comps, mods)
self.resamplers = {}
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,
ppp_config_dir=self.ppp_config_dir)
@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):
"""DatasetIDs that have not been loaded."""
return set(self.wishlist) - set(self.datasets.keys())
def _compare_areas(self, datasets=None, compare_func=max):
"""Get 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.
compare_func (callable): `min` or `max` or other function used to
compare the dataset's areas.
"""
if datasets is None:
check_datasets = list(self.values())
else:
check_datasets = []
for ds in datasets:
if not isinstance(ds, DataArray):
ds = self[ds]
check_datasets.append(ds)
areas = [x.attrs.get('area') for x in check_datasets]
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_pstr = areas[0].proj_str
if not all(ad.proj_str == first_pstr for ad in areas[1:]):
raise ValueError("Can't compare areas with different "
"projections.")
def key_func(ds):
return 1. / 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)
def max_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)
def min_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)
def available_dataset_ids(self, reader_name=None, composites=False):
"""Get names of available datasets, globally or just for *reader_name*
if specified, that can be loaded.
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.
:return: 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(available_datasets))
return available_datasets
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)))
def all_dataset_ids(self, reader_name=None, composites=False):
"""Get names of all datasets from loaded readers or `reader_name` if
specified..
:return: 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
def all_dataset_names(self, reader_name=None, composites=False):
return sorted(
set(x.name for x in self.all_dataset_ids(reader_name=reader_name,
composites=composites)))
def available_composite_ids(self, available_datasets=None):
"""Get names of compositors that can be generated from the available
datasets.
:return: generator of available compositor's names
"""
if available_datasets is None:
available_datasets = self.available_dataset_ids(composites=False)
else:
if not all(
isinstance(ds_id, DatasetID)
for ds_id in available_datasets):
raise ValueError(
"'available_datasets' must all be DatasetID objects")
all_comps = self.all_composite_ids()
# recreate the dependency tree so it doesn't interfere with the user's
# wishlist
comps, mods = self.cpl.load_compositors(self.attrs['sensor'])
dep_tree = DependencyTree(self.readers, comps, mods)
dep_tree.find_dependencies(set(available_datasets + all_comps))
available_comps = set(x.name for x in dep_tree.trunk())
# get rid of modified composites that are in the trunk
return sorted(available_comps & set(all_comps))
def available_composite_names(self, available_datasets=None):
return sorted(
set(x.name for x in self.available_composite_ids(
available_datasets=available_datasets)))
def all_composite_ids(self, sensor_names=None):
"""Get all composite IDs that are configured.
:return: generator of configured composite names
"""
if sensor_names is None:
sensor_names = self.attrs['sensor']
compositors = []
# Note if we get compositors from the dep tree then it will include
# modified composites which we don't want
for sensor_name in sensor_names:
compositors.extend(
self.cpl.compositors.get(sensor_name, {}).keys())
return sorted(set(compositors))
def all_composite_names(self, sensor_names=None):
return sorted(
set(x.name
for x in self.all_composite_ids(sensor_names=sensor_names)))
def all_modifier_names(self):
return sorted(self.dep_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
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')
datasets_by_area.setdefault(a, []).append(
DatasetID.from_dict(ds.attrs))
return datasets_by_area.items()
def keys(self, **kwargs):
return self.datasets.keys(**kwargs)
def values(self):
return self.datasets.values()
def copy(self, datasets=None):
"""Create a copy of the Scene including dependency information.
Args:
datasets (list, tuple): `DatasetID` objects for the datasets
to include in the new Scene object.
"""
new_scn = self.__class__()
new_scn.attrs = self.attrs.copy()
new_scn.dep_tree = self.dep_tree.copy()
for ds_id in (datasets or self.keys()):
# 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]
if not datasets:
new_scn.wishlist = self.wishlist.copy()
else:
new_scn.wishlist = set(
[DatasetID.from_dict(ds.attrs) for ds in new_scn])
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].proj_str == x.proj_str for x in all_areas[1:])
def _slice_area_from_bbox(self,
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.proj_dict, src_area.x_size,
src_area.y_size, 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 = DatasetID.from_dict(ds.attrs)
# handle ancillary variables
pres = None
if parent_ds is not None:
pres = new_datasets[DatasetID.from_dict(parent_ds.attrs)]
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)
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():
new_area = area[key] if area is not None else None
new_scn._slice_datasets(dataset_ids, key, new_area)
return new_scn
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): DatasetIDs to include in the returned
`Scene`. Defaults to all datasets.
.. 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.")
for src_area, dataset_ids in new_scn.iter_by_area():
if src_area is not None:
# convert filter parameter to area
new_area, y_slice, x_slice = self._slice_area_from_bbox(
src_area, area, ll_bbox, xy_bbox)
slice_key = (y_slice, x_slice)
new_scn._slice_datasets(dataset_ids, slice_key, new_area)
else:
for ds_id in dataset_ids:
new_scn.datasets[ds_id] = self[ds_id]
return new_scn
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) and not isinstance(key, DatasetID):
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.dep_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 _read_datasets(self, dataset_nodes, **kwargs):
"""Read the given datasets from file."""
# Sort requested datasets by reader
reader_datasets = {}
for node in dataset_nodes:
ds_id = node.name
# 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.data, dict):
continue
reader_name = node.data.get('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)
# 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)
self.datasets.update(loaded_datasets)
return loaded_datasets
def _get_prereq_datasets(self,
comp_id,
prereq_nodes,
keepables,
skip=False):
"""Get a composite's prerequisites, generating them if needed.
Args:
comp_id (DatasetID): DatasetID 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 = prereq_node.name
if prereq_id not in self.datasets and prereq_id not in keepables \
and not prereq_node.is_leaf:
self._generate_composite(prereq_node, keepables)
if prereq_id in self.datasets:
prereq_datasets.append(self.datasets[prereq_id])
elif not prereq_node.is_leaf and prereq_id in keepables:
delayed_gen = True
continue
elif not skip:
LOG.warning("Missing prerequisite for '{}': '{}'".format(
comp_id, prereq_id))
raise KeyError("Missing composite prerequisite")
else:
LOG.debug("Missing optional prerequisite for {}: {}".format(
comp_id, prereq_id))
if delayed_gen:
keepables.add(comp_id)
keepables.update([x.name for x in prereq_nodes])
LOG.warning(
"Delaying generation of %s "
"because of dependency's delayed generation: %s", comp_id,
prereq_id)
if not skip:
LOG.warning("Missing prerequisite for '{}': '{}'".format(
comp_id, prereq_id))
raise KeyError("Missing composite prerequisite")
else:
LOG.debug("Missing optional prerequisite for {}: {}".format(
comp_id, prereq_id))
return prereq_datasets
def _generate_composite(self, comp_node, keepables):
"""Collect all composite prereqs and create the specified composite.
Args:
comp_node (Node): Composite Node to generate a Dataset for
keepables (set): `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 comp_node.name in self.datasets:
# already loaded
return
compositor, prereqs, optional_prereqs = comp_node.data
try:
prereq_datasets = self._get_prereq_datasets(
comp_node.name,
prereqs,
keepables,
)
except KeyError:
return
optional_datasets = self._get_prereq_datasets(comp_node.name,
optional_prereqs,
keepables,
skip=True)
try:
composite = compositor(prereq_datasets,
optional_datasets=optional_datasets,
**self.attrs)
cid = DatasetID.from_dict(composite.attrs)
self.datasets[cid] = composite
# update the node with the computed DatasetID
if comp_node.name in self.wishlist:
self.wishlist.remove(comp_node.name)
self.wishlist.add(cid)
comp_node.name = cid
except IncompatibleAreas:
LOG.warning(
"Delaying generation of %s "
"because of incompatible areas", str(compositor.id))
preservable_datasets = set(self.datasets.keys())
prereq_ids = set(p.name for p in prereqs)
opt_prereq_ids = set(p.name 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(comp_node.name)
return
def _read_composites(self, compositor_nodes):
"""Read (generate) composites."""
keepables = set()
for item in compositor_nodes:
self._generate_composite(item, keepables)
return keepables
def read(self, nodes=None, **kwargs):
"""Load datasets from the necessary reader.
Args:
nodes (iterable): DependencyTree Node objects
**kwargs: Keyword arguments to pass to the reader's `load` method.
Returns:
DatasetDict of loaded datasets
"""
if nodes is None:
required_nodes = self.wishlist - set(self.datasets.keys())
nodes = self.dep_tree.leaves(nodes=required_nodes)
return self._read_datasets(nodes, **kwargs)
def generate_composites(self, nodes=None):
"""Compute all the composites contained in `requirements`.
"""
if nodes is None:
required_nodes = self.wishlist - set(self.datasets.keys())
nodes = set(self.dep_tree.trunk(nodes=required_nodes)) - \
set(self.datasets.keys())
return self._read_composites(nodes)
def _remove_failed_datasets(self, keepables):
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)
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): DatasetIDs 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]
def load(self,
wishlist,
calibration=None,
resolution=None,
polarization=None,
level=None,
generate=True,
unload=True,
**kwargs):
"""Read and generate requested datasets.
When the `wishlist` contains `DatasetID` objects they can either be
fully-specified `DatasetID` 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 DatasetID
then the highest (smallest number) resolution will be chosen.
Loaded `DataArray` objects are created and stored in the Scene object.
Args:
wishlist (iterable): Names (str), wavelengths (float), or
DatasetID objects 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 DatasetID 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.
"""
dataset_keys = set(wishlist)
needed_datasets = (self.wishlist | dataset_keys) - \
set(self.datasets.keys())
unknown = self.dep_tree.find_dependencies(needed_datasets,
calibration=calibration,
polarization=polarization,
resolution=resolution,
level=level)
self.wishlist |= needed_datasets
if unknown:
unknown_str = ", ".join(map(str, unknown))
raise KeyError("Unknown datasets: {}".format(unknown_str))
self.read(**kwargs)
if generate:
keepables = self.generate_composites()
else:
# don't lose datasets we loaded to try to generate composites
keepables = set(self.datasets.keys()) | self.wishlist
if self.missing_datasets:
# 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()
self._remove_failed_datasets(keepables)
missing_str = ", ".join(str(x) for x in missing)
LOG.warning("The following datasets were not created: {}".format(
missing_str))
if unload:
self.unload(keepables=keepables)
def _resampled_scene(self, new_scn, destination_area, **resample_kwargs):
"""Resample `datasets` to the `destination` area."""
new_datasets = {}
datasets = list(new_scn.datasets.values())
max_area = None
if hasattr(destination_area, 'freeze'):
try:
max_area = new_scn.max_area()
except ValueError:
raise ValueError("No dataset areas available to freeze "
"DynamicAreaDefinition.")
destination_area = get_frozen_area(destination_area, max_area)
resamplers = {}
for dataset, parent_dataset in dataset_walker(datasets):
ds_id = DatasetID.from_dict(dataset.attrs)
pres = None
if parent_dataset is not None:
pres = new_datasets[DatasetID.from_dict(parent_dataset.attrs)]
if ds_id in new_datasets:
replace_anc(dataset, pres)
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:
slice_x, slice_y = source_area.get_area_slices(
destination_area)
source_area = source_area[slice_y, slice_x]
dataset = dataset.isel(x=slice_x, y=slice_y)
assert ('x', source_area.x_size) in dataset.sizes.items()
assert ('y', source_area.y_size) in dataset.sizes.items()
dataset.attrs['area'] = source_area
except NotImplementedError:
LOG.info("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 parent_dataset is None:
new_scn.datasets[ds_id] = res
else:
replace_anc(res, pres)
def resample(self,
destination=None,
datasets=None,
generate=True,
unload=True,
resampler=None,
**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.max_area()` will be used.
datasets (list): Limit datasets to resample to these specified
`DatasetID` objects . 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.
resample_kwargs: Remaining keyword arguments to pass to individual
resampler classes. See the individual resampler class
documentation :mod:`here <satpy.resample>` for available
arguments.
"""
to_resample_ids = [
dsid for (dsid, dataset) in self.datasets.items()
if (not datasets) or dsid in datasets
]
if destination is None:
destination = self.max_area(to_resample_ids)
new_scn = self.copy(datasets=to_resample_ids)
# we may have some datasets we asked for but don't exist yet
new_scn.wishlist = self.wishlist.copy()
self._resampled_scene(new_scn,
destination,
resampler=resampler,
**resample_kwargs)
# regenerate anything from the wishlist that needs it (combining
# multiple resolutions, etc.)
if generate:
keepables = new_scn.generate_composites()
else:
# don't lose datasets that we may need later for generating
# composites
keepables = set(new_scn.datasets.keys()) | new_scn.wishlist
if new_scn.missing_datasets:
# copy the set of missing datasets because they won't be valid
# after they are removed in the next line
missing = new_scn.missing_datasets.copy()
new_scn._remove_failed_datasets(keepables)
missing_str = ", ".join(str(x) for x in missing)
LOG.warning("The following datasets "
"were not created: {}".format(missing_str))
if unload:
new_scn.unload(keepables)
return new_scn
def show(self, dataset_id, overlay=None):
"""Show the *dataset* on screen as an image."""
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():
img.show()
return img
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()
def save_dataset(self,
dataset_id,
filename=None,
writer=None,
overlay=None,
compute=True,
**kwargs):
"""Save the *dataset_id* to file using *writer* (default: geotiff)."""
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,
ppp_config_dir=self.ppp_config_dir,
**kwargs)
return writer.save_dataset(self[dataset_id],
filename=filename,
overlay=overlay,
compute=compute,
**save_kwargs)
def save_datasets(self,
writer="geotiff",
datasets=None,
compute=True,
**kwargs):
"""Save all the datasets present in a scene to disk using *writer*."""
if datasets is not None:
datasets = [self[ds] for ds in datasets]
else:
datasets = [self.datasets.get(ds) for ds in self.wishlist]
datasets = [ds for ds in datasets if ds is not None]
if not datasets:
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).")
writer, save_kwargs = load_writer(writer,
ppp_config_dir=self.ppp_config_dir,
**kwargs)
return writer.save_datasets(datasets, compute=compute, **save_kwargs)
@classmethod
def get_writer_by_ext(cls, extension):
"""Find the writer matching the *extension*."""
mapping = {
".tiff": "geotiff",
".tif": "geotiff",
".nc": "cf",
".mitiff": "mitiff"
}
return mapping.get(extension.lower(), 'simple_image')
class Scene(InfoObject):
"""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,
ppp_config_dir=get_environ_config_dir(),
base_dir=None,
sensor=None,
start_time=None,
end_time=None,
area=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.
ppp_config_dir (str): The directory containing the configuration files for satpy.
base_dir (str): (DEPRECATED) The directory to search for files containing the
data to load. If *filenames* is also provided,
this is ignored.
sensor (list or str): (DEPRECATED: Use `find_files_and_readers` function) Limit used files by provided
sensors.
area (AreaDefinition): (DEPRECATED: Use `filter_parameters`) Limit used files by geographic area.
start_time (datetime): (DEPRECATED: Use `filter_parameters`) Limit used files by starting time.
end_time (datetime): (DEPRECATED: Use `filter_parameters`) Limit used files by ending time.
"""
super(Scene, self).__init__()
# Set the PPP_CONFIG_DIR in the environment in case it's used elsewhere in pytroll
LOG.debug("Setting 'PPP_CONFIG_DIR' to '%s'", ppp_config_dir)
os.environ["PPP_CONFIG_DIR"] = self.ppp_config_dir = ppp_config_dir
if not filenames and (start_time or end_time or base_dir):
import warnings
warnings.warn(
"Deprecated: Use " + \
"'from satpy import find_files_and_readers' to find files")
from satpy import find_files_and_readers
filenames = find_files_and_readers(
start_time=start_time,
end_time=end_time,
base_dir=base_dir,
reader=reader,
sensor=sensor,
ppp_config_dir=self.ppp_config_dir,
reader_kwargs=reader_kwargs,
)
elif start_time or end_time or area:
import warnings
warnings.warn(
"Deprecated: Use " + \
"'filter_parameters' to filter loaded files by 'start_time', " + \
"'end_time', or 'area'.")
fp = filter_parameters if filter_parameters else {}
fp.update({
'start_time': start_time,
'end_time': end_time,
'area': area,
})
filter_parameters = fp
if filter_parameters:
if reader_kwargs is None:
reader_kwargs = {}
reader_kwargs.setdefault('filter_parameters',
{}).update(filter_parameters)
self.readers = self.create_reader_instances(
filenames=filenames, reader=reader, reader_kwargs=reader_kwargs)
self.info.update(self._compute_metadata_from_readers())
self.datasets = DatasetDict()
self.cpl = CompositorLoader(self.ppp_config_dir)
comps, mods = self.cpl.load_compositors(self.info['sensor'])
self.wishlist = set()
self.dep_tree = DependencyTree(self.readers, comps, mods)
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 = {}
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.info.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.info['sensor'], (set, tuple, list)):
return set([self.info['sensor']])
else:
return set(self.info['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,
ppp_config_dir=self.ppp_config_dir)
@property
def start_time(self):
"""Return the start time of the file."""
return self.info['start_time']
@property
def end_time(self):
"""Return the end time of the file."""
return self.info['end_time']
@property
def missing_datasets(self):
"""DatasetIDs that have not been loaded."""
return set(self.wishlist) - set(self.datasets.keys())
def available_dataset_ids(self, reader_name=None, composites=False):
"""Get names of available datasets, globally or just for *reader_name*
if specified, that can be loaded.
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.
:return: 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(available_datasets))
return available_datasets
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)))
def all_dataset_ids(self, reader_name=None, composites=False):
"""Get names of all datasets from loaded readers or `reader_name` if
specified..
:return: 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
def all_dataset_names(self, reader_name=None, composites=False):
return sorted(
set(x.name for x in self.all_dataset_ids(reader_name=reader_name,
composites=composites)))
def available_composite_ids(self, available_datasets=None):
"""Get names of compositors that can be generated from the available
datasets.
:return: generator of available compositor's names
"""
if available_datasets is None:
available_datasets = self.available_dataset_ids(composites=False)
else:
if not all(
isinstance(ds_id, DatasetID)
for ds_id in available_datasets):
raise ValueError(
"'available_datasets' must all be DatasetID objects")
all_comps = self.all_composite_ids()
# recreate the dependency tree so it doesn't interfere with the user's
# wishlist
comps, mods = self.cpl.load_compositors(self.info['sensor'])
dep_tree = DependencyTree(self.readers, comps, mods)
unknowns = dep_tree.find_dependencies(
set(available_datasets + all_comps))
available_comps = set(x.name for x in dep_tree.trunk())
# get rid of modified composites that are in the trunk
return sorted(available_comps & set(all_comps))
def available_composite_names(self, available_datasets=None):
return sorted(
set(x.name for x in self.available_composite_ids(
available_datasets=available_datasets)))
def all_composite_ids(self, sensor_names=None):
"""Get all composite IDs that are configured.
:return: generator of configured composite names
"""
if sensor_names is None:
sensor_names = self.info['sensor']
compositors = []
# Note if we get compositors from the dep tree then it will include
# modified composites which we don't want
for sensor_name in sensor_names:
compositors.extend(
self.cpl.compositors.get(sensor_name, {}).keys())
return sorted(set(compositors))
def all_composite_names(self, sensor_names=None):
return sorted(
set(x.name
for x in self.all_composite_ids(sensor_names=sensor_names)))
def all_modifier_names(self):
return sorted(self.dep_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
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.info.get('area')
a_str = str(a) if a is not None else None
datasets_by_area.setdefault(a_str, (a, []))
datasets_by_area[a_str][1].append(ds.id)
for area_name, (area_obj, ds_list) in datasets_by_area.items():
yield area_obj, ds_list
def keys(self, **kwargs):
return self.datasets.keys(**kwargs)
def __getitem__(self, key):
"""Get a dataset."""
return self.datasets[key]
def __setitem__(self, key, value):
"""Add the item to the scene."""
if not isinstance(value, Dataset):
raise ValueError("Only 'Dataset' objects can be assigned")
self.datasets[key] = value
ds_id = self.datasets.get_key(key)
self.wishlist.add(ds_id)
self.dep_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 read_datasets(self, dataset_nodes, **kwargs):
"""Read the given datasets from file."""
# Sort requested datasets by reader
reader_datasets = {}
for node in dataset_nodes:
ds_id = node.name
if ds_id in self.datasets and self.datasets[ds_id].is_loaded():
continue
reader_name = node.data['reader_name']
reader_datasets.setdefault(reader_name, set()).add(ds_id)
# 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)
self.datasets.update(loaded_datasets)
return loaded_datasets
def _get_prereq_datasets(self,
comp_id,
prereq_nodes,
keepables,
skip=False):
"""Get a composite's prerequisites, generating them if needed.
Args:
comp_id (DatasetID): DatasetID 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 = []
for prereq_node in prereq_nodes:
prereq_id = prereq_node.name
if prereq_id not in self.datasets and prereq_id not in keepables \
and not prereq_node.is_leaf:
self._generate_composite(prereq_node, keepables)
if prereq_id in self.datasets:
prereq_datasets.append(self.datasets[prereq_id])
else:
if not prereq_node.is_leaf and prereq_id in keepables:
keepables.add(comp_id)
LOG.warning(
"Delaying generation of %s "
"because of dependency's delayed generation: %s",
comp_id, prereq_id)
if not skip:
LOG.warning("Missing prerequisite for '{}': '{}'".format(
comp_id, prereq_id))
raise KeyError("Missing composite prerequisite")
else:
LOG.debug(
"Missing optional prerequisite for {}: {}".format(
comp_id, prereq_id))
return prereq_datasets
def _generate_composite(self, comp_node, keepables):
"""Collect all composite prereqs and create the specified composite.
Args:
comp_node (Node): Composite Node to generate a Dataset for
keepables (set): `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 comp_node.name in self.datasets:
# already loaded
return
compositor, prereqs, optional_prereqs = comp_node.data
try:
prereq_datasets = self._get_prereq_datasets(
comp_node.name,
prereqs,
keepables,
)
except KeyError:
return
optional_datasets = self._get_prereq_datasets(comp_node.name,
optional_prereqs,
keepables,
skip=True)
try:
composite = compositor(prereq_datasets,
optional_datasets=optional_datasets,
**self.info)
self.datasets[composite.id] = composite
if comp_node.name in self.wishlist:
self.wishlist.remove(comp_node.name)
self.wishlist.add(composite.id)
# update the node with the computed DatasetID
comp_node.name = composite.id
except IncompatibleAreas:
LOG.warning(
"Delaying generation of %s "
"because of incompatible areas", str(compositor.id))
preservable_datasets = set(self.datasets.keys())
prereq_ids = set(p.name for p in prereqs)
opt_prereq_ids = set(p.name 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(comp_node.name)
return
def read_composites(self, compositor_nodes):
"""Read (generate) composites.
"""
keepables = set()
for item in compositor_nodes:
self._generate_composite(item, keepables)
return keepables
def read(self, nodes=None, **kwargs):
"""Load datasets from the necessary reader.
Args:
nodes (iterable): DependencyTree Node objects
**kwargs: Keyword arguments to pass to the reader's `load` method.
Returns:
DatasetDict of loaded datasets
"""
if nodes is None:
required_nodes = self.wishlist - set(self.datasets.keys())
nodes = self.dep_tree.leaves(nodes=required_nodes)
return self.read_datasets(nodes, **kwargs)
def compute(self, nodes=None):
"""Compute all the composites contained in `requirements`.
"""
if nodes is None:
required_nodes = self.wishlist - set(self.datasets.keys())
nodes = set(self.dep_tree.trunk(nodes=required_nodes)) - \
set(self.datasets.keys())
return self.read_composites(nodes)
def _remove_failed_datasets(self, keepables):
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)
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
compute composites that have yet to be computed.
Args:
keepables (iterable): DatasetIDs 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:
del self.datasets[ds_id]
def load(self,
wishlist,
calibration=None,
resolution=None,
polarization=None,
compute=True,
unload=True,
**kwargs):
"""Read, compute and unload.
"""
dataset_keys = set(wishlist)
needed_datasets = (self.wishlist | dataset_keys) - \
set(self.datasets.keys())
unknown = self.dep_tree.find_dependencies(needed_datasets,
calibration=calibration,
polarization=polarization,
resolution=resolution)
self.wishlist |= needed_datasets
if unknown:
unknown_str = ", ".join(map(str, unknown))
raise KeyError("Unknown datasets: {}".format(unknown_str))
self.read(**kwargs)
keepables = None
if compute:
keepables = self.compute()
if self.missing_datasets:
# 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()
self._remove_failed_datasets(keepables)
missing_str = ", ".join(str(x) for x in missing)
LOG.warning("The following datasets were not created: {}".format(
missing_str))
if unload:
self.unload(keepables=keepables)
def resample(self,
destination,
datasets=None,
compute=True,
unload=True,
**resample_kwargs):
"""Resample the datasets and return a new scene.
"""
new_scn = Scene()
new_scn.info = self.info.copy()
# new_scn.cpl = self.cpl
new_scn.dep_tree = self.dep_tree.copy()
for ds_id, projectable in self.datasets.items():
LOG.debug("Resampling %s", ds_id)
if datasets and ds_id not in datasets:
continue
new_scn[ds_id] = projectable.resample(destination,
**resample_kwargs)
# MUST set this after assigning the resampled datasets otherwise
# composite prereqs that were resampled will be considered "wishlisted"
if datasets is None:
new_scn.wishlist = self.wishlist
else:
new_scn.wishlist = set([ds.id for ds in new_scn])
# recompute anything from the wishlist that needs it (combining multiple
# resolutions, etc.)
keepables = None
if compute:
nodes = [
self.dep_tree[i] for i in new_scn.wishlist
if not self.dep_tree[i].is_leaf
]
keepables = new_scn.compute(nodes=nodes)
if new_scn.missing_datasets:
# copy the set of missing datasets because they won't be valid
# after they are removed in the next line
missing = new_scn.missing_datasets.copy()
new_scn._remove_failed_datasets(keepables)
missing_str = ", ".join(str(x) for x in missing)
LOG.warning("The following datasets were not created: {}".format(
missing_str))
if unload:
new_scn.unload(keepables)
return new_scn
def show(self, dataset_id, overlay=None):
"""Show the *dataset* on screen as an image.
"""
from satpy.writers import get_enhanced_image
get_enhanced_image(self[dataset_id], overlay=overlay).show()
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()
def load_writer_config(self, config_files, **kwargs):
conf = {}
for conf_fn in config_files:
with open(conf_fn) as fd:
conf = recursive_dict_update(conf, yaml.load(fd))
writer_class = conf['writer']['writer']
writer = writer_class(ppp_config_dir=self.ppp_config_dir,
config_files=config_files,
**kwargs)
return writer
def save_dataset(self,
dataset_id,
filename=None,
writer=None,
overlay=None,
**kwargs):
"""Save the *dataset_id* to file using *writer* (geotiff by default).
"""
if writer is None:
if filename is None:
writer = self.get_writer("geotiff", **kwargs)
else:
writer = self.get_writer_by_ext(
os.path.splitext(filename)[1], **kwargs)
else:
writer = self.get_writer(writer, **kwargs)
writer.save_dataset(self[dataset_id],
filename=filename,
overlay=overlay,
**kwargs)
def save_datasets(self, writer="geotiff", datasets=None, **kwargs):
"""Save all the datasets present in a scene to disk using *writer*.
"""
if datasets is not None:
datasets = [self[ds] for ds in datasets]
else:
datasets = self.datasets.values()
writer = self.get_writer(writer, **kwargs)
writer.save_datasets(datasets, **kwargs)
def get_writer(self, writer="geotiff", **kwargs):
config_fn = writer + ".yaml" if "." not in writer else writer
config_files = config_search_paths(os.path.join("writers", config_fn),
self.ppp_config_dir)
kwargs.setdefault("config_files", config_files)
return self.load_writer_config(**kwargs)
def get_writer_by_ext(self, extension, **kwargs):
mapping = {".tiff": "geotiff", ".tif": "geotiff", ".nc": "cf"}
return self.get_writer(mapping.get(extension.lower(), "simple_image"),
**kwargs)