#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (c) 2020 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 <http://www.gnu.org/licenses/>.
"""Implementation of a dependency tree."""
from __future__ import annotations
from typing import Container, Iterable, Optional
import numpy as np
from satpy import DataID, DatasetDict
from satpy.dataset import ModifierTuple, create_filtered_query
from satpy.dataset.data_dict import TooManyResults, get_key
from satpy.node import EMPTY_LEAF_NAME, LOG, CompositorNode, MissingDependencies, Node, ReaderNode
[docs]
class Tree:
"""A tree implementation."""
# simplify future logic by only having one "sentinel" empty node
# making it a class attribute ensures it is the same across instances
empty_node = Node(EMPTY_LEAF_NAME)
def __init__(self):
"""Set up the tree."""
self._root = Node(None)
# keep a flat dictionary of nodes contained in the tree for better
# __contains__
self._all_nodes = _DataIDContainer()
[docs]
def leaves(self,
limit_nodes_to: Optional[Iterable[DataID]] = None,
unique: bool = True
) -> list[Node]:
"""Get the leaves of the tree starting at the root.
Args:
limit_nodes_to: Limit leaves to Nodes with the names (DataIDs)
specified.
unique: Only include individual leaf nodes once.
Returns:
list of leaf nodes
"""
if limit_nodes_to is None:
return self._root.leaves(unique=unique)
res = list()
for child_id in limit_nodes_to:
for sub_child in self._all_nodes[child_id].leaves(unique=unique):
if not unique or sub_child not in res:
res.append(sub_child)
return res
[docs]
def trunk(self,
limit_nodes_to: Optional[Iterable[DataID]] = None,
unique: bool = True,
limit_children_to: Optional[Container[DataID]] = None,
) -> list[Node]:
"""Get the trunk nodes of the tree starting at this root.
Args:
limit_nodes_to: Limit searching to trunk nodes with the names
(DataIDs) specified and the children of these nodes.
unique: Only include individual trunk nodes once
limit_children_to: Limit searching to the children with the specified
names. These child nodes will be included in the result,
but not their children.
Returns:
list of trunk nodes
"""
if limit_nodes_to is None:
return self._root.trunk(unique=unique,
limit_children_to=limit_children_to)
res = list()
for child_id in limit_nodes_to:
child_node = self._all_nodes[child_id]
for sub_child in child_node.trunk(unique=unique, limit_children_to=limit_children_to):
if not unique or sub_child not in res:
res.append(sub_child)
return res
[docs]
def add_child(self, parent, child):
"""Add a child to the tree."""
Node.add_child(parent, child)
# Sanity check: Node objects should be unique. They can be added
# multiple times if more than one Node depends on them
# but they should all map to the same Node object.
if self.contains(child.name):
if self._all_nodes[child.name] is not child:
raise RuntimeError
if child is self.empty_node:
# No need to store "empty" nodes
return
self._all_nodes[child.name] = child
[docs]
def add_leaf(self, ds_id, parent=None):
"""Add a leaf to the tree."""
if parent is None:
parent = self._root
try:
node = self[ds_id]
except KeyError:
node = Node(ds_id)
self.add_child(parent, node)
return node
def __contains__(self, item):
"""Check if a item is in the tree."""
return item in self._all_nodes
def __getitem__(self, item):
"""Get an item of the tree."""
return self._all_nodes[item]
[docs]
def contains(self, item):
"""Check contains when we know the *exact* DataID or DataQuery."""
return super(_DataIDContainer, self._all_nodes).__contains__(item)
[docs]
def getitem(self, item):
"""Get Node when we know the *exact* DataID or DataQuery."""
return super(_DataIDContainer, self._all_nodes).__getitem__(item)
def __str__(self):
"""Render the dependency tree as a string."""
return self._root.display()
[docs]
class DependencyTree(Tree):
"""Structure to discover and store `Dataset` dependencies.
Used primarily by the `Scene` object to organize dependency finding.
Dependencies are stored used a series of `Node` objects which this
class is a subclass of.
"""
def __init__(self, readers, compositors=None, modifiers=None, available_only=False):
"""Collect Dataset generating information.
Collect the objects that generate and have information about Datasets
including objects that may depend on certain Datasets being generated.
This includes readers, compositors, and modifiers.
Composites and modifiers are defined per-sensor. If multiple sensors
are available, compositors and modifiers are searched for in
sensor alphabetical order.
Args:
readers (dict): Reader name -> Reader Object
compositors (dict): Sensor name -> Composite ID -> Composite Object.
Empty dictionary by default.
modifiers (dict): Sensor name -> Modifier name -> (Modifier Class, modifier options).
Empty dictionary by default.
available_only (bool): Whether only reader's available/loadable
datasets should be used when searching for dependencies (True)
or use all known/configured datasets regardless of whether the
necessary files were provided to the reader (False).
Note that when ``False`` loadable variations of a dataset will
have priority over other known variations.
Default is ``False``.
"""
super().__init__()
self.readers = readers
self.compositors = {}
self.modifiers = {}
self._available_only = available_only
self.update_compositors_and_modifiers(compositors or {}, modifiers or {})
[docs]
def update_compositors_and_modifiers(self, compositors: dict, modifiers: dict) -> None:
"""Add additional compositors and modifiers to the tree.
Provided dictionaries and the first sub-level dictionaries are copied
to avoid modifying the input.
Args:
compositors (dict):
Sensor name -> composite ID -> Composite Object
modifiers (dict):
Sensor name -> Modifier name -> (Modifier Class, modifier options)
"""
for sensor_name, sensor_comps in compositors.items():
self.compositors.setdefault(sensor_name, DatasetDict()).update(sensor_comps)
for sensor_name, sensor_mods in modifiers.items():
self.modifiers.setdefault(sensor_name, {}).update(sensor_mods)
[docs]
def copy(self):
"""Copy this node tree.
Note all references to readers are removed. This is meant to avoid
tree copies accessing readers that would return incompatible (Area)
data. Theoretically it should be possible for tree copies to request
compositor or modifier information as long as they don't depend on
any datasets not already existing in the dependency tree.
"""
new_tree = DependencyTree({}, self.compositors, self.modifiers)
for c in self._root.children:
c = c.copy(node_cache=new_tree._all_nodes)
new_tree.add_child(new_tree._root, c)
return new_tree
[docs]
def update_node_name(self, node, new_name):
"""Update 'name' property of a node and any related metadata."""
old_name = node.name
if old_name not in self._all_nodes:
raise RuntimeError
del self._all_nodes[old_name]
node.update_name(new_name)
self._all_nodes[new_name] = node
[docs]
def populate_with_keys(self, dataset_keys: set, query=None):
"""Populate the dependency tree.
Args:
dataset_keys (set): Strings, DataIDs, DataQuerys to find dependencies for
query (DataQuery): Additional filter parameters. See
`satpy.readers.get_key` for more details.
Returns:
(Node, set): Root node of the dependency tree and a set of unknown datasets
"""
unknown_datasets = list()
known_nodes = list()
for key in dataset_keys.copy():
try:
dsq = create_filtered_query(key, query)
node = self._create_subtree_for_key(dsq, query)
except MissingDependencies as unknown:
unknown_datasets.append(unknown.missing_dependencies)
else:
known_nodes.append(node)
self.add_child(self._root, node)
for key in dataset_keys.copy():
dataset_keys.discard(key)
for node in known_nodes:
dataset_keys.add(node.name)
if unknown_datasets:
raise MissingDependencies(unknown_datasets, "Unknown datasets:")
[docs]
def _create_subtree_for_key(self, dataset_key, query=None):
"""Find the dependencies for *dataset_key*.
Args:
dataset_key (str, float, DataID, DataQuery): Dataset identifier to locate
and find any additional
dependencies for.
query (DataQuery): Additional filter parameters. See
`satpy.readers.get_key` for more details.
"""
# 0 check if the *exact* dataset is already loaded
try:
node = self._get_subtree_for_existing_key(dataset_key)
except MissingDependencies:
# exact dataset isn't loaded, let's load it below
pass
else:
return node
# 1 try to get *best* dataset from reader
try:
node = self._create_subtree_from_reader(dataset_key, query)
except TooManyResults:
LOG.warning("Too many possible datasets to load for {}".format(dataset_key))
raise MissingDependencies({dataset_key})
except MissingDependencies:
pass
else:
return node
# 2 try to find a composite by name (any version of it is good enough)
try:
node = self._get_subtree_for_existing_name(dataset_key)
except MissingDependencies:
pass
else:
return node
# 3 try to find a composite that matches
try:
node = self._create_subtree_from_compositors(dataset_key, query)
except MissingDependencies:
raise
else:
return node
[docs]
def _get_subtree_for_existing_key(self, dsq):
try:
node = self.getitem(dsq)
LOG.trace("Found exact dataset already loaded: {}".format(node.name))
return node
except KeyError:
LOG.trace("Exact dataset {} isn't loaded, will try reader...".format(dsq))
raise MissingDependencies({dsq})
[docs]
def _create_subtree_from_reader(self, dataset_key, query):
try:
node = self._find_reader_node(dataset_key, query)
except MissingDependencies:
LOG.trace("Could not find dataset in reader: {}".format(dataset_key))
raise
else:
LOG.trace("Found reader provided dataset:\n\tRequested: {}\n\tFound: {}".format(dataset_key, node.name))
return node
[docs]
def _find_reader_node(self, dataset_key, query): # noqa: D417
"""Attempt to find a `DataID` in the available readers.
Args:
dataset_key (str, float, DataID, DataQuery):
Dataset name, wavelength, `DataID` or `DataQuery`
to use in searching for the dataset from the
available readers.
"""
matching_ids = self._find_matching_ids_in_readers(dataset_key)
unique_id = self._get_unique_matching_id(matching_ids, dataset_key, query)
for reader_name, ids in matching_ids.items():
if unique_id in ids:
return self._get_unique_reader_node_from_id(unique_id, reader_name)
raise RuntimeError("Data ID disappeared.")
[docs]
def _find_matching_ids_in_readers(self, dataset_key):
matching_ids = {}
for reader_name, reader_instance in self.readers.items():
matching_ids[reader_name] = []
try:
ds_ids = reader_instance.get_dataset_key(dataset_key, available_only=self._available_only,
num_results=0, best=False)
except KeyError:
LOG.trace("Can't find dataset %s in reader %s", str(dataset_key), reader_name)
continue
matching_ids[reader_name].extend(ds_ids)
return matching_ids
[docs]
def _get_unique_matching_id(self, matching_ids, dataset_key, query):
"""Get unique matching id from `matching_ids`, for a given `dataset_key` and some optional `query`."""
all_ids = sum(matching_ids.values(), [])
if len(all_ids) == 0:
raise MissingDependencies({dataset_key})
elif len(all_ids) == 1:
result = all_ids[0]
else:
sorted_ids, distances = dataset_key.sort_dataids_with_preference(all_ids, query)
try:
result = self._get_unique_id_from_sorted_ids(sorted_ids, distances)
except TooManyResults:
LOG.trace("Too many datasets matching key {} in readers {}".format(dataset_key, matching_ids.keys()))
raise TooManyResults("Too many keys matching: {}".format(dataset_key))
except MissingDependencies:
raise MissingDependencies({dataset_key})
return result
[docs]
@staticmethod
def _get_unique_id_from_sorted_ids(sorted_ids, distances):
if distances[0] != np.inf:
if distances[0] != distances[1]:
result = sorted_ids[0]
else:
raise TooManyResults
else:
raise MissingDependencies
return result
[docs]
def _get_unique_reader_node_from_id(self, data_id, reader_name):
try:
# now that we know we have the exact DataID see if we have already created a Node for it
return self.getitem(data_id)
except KeyError:
# we haven't created a node yet, create it now
return ReaderNode(data_id, reader_name)
[docs]
def _get_subtree_for_existing_name(self, dsq):
try:
# assume that there is no such thing as a "better" composite
# version so if we find any DataIDs already loaded then
# we want to use them
node = self[dsq]
LOG.trace("Composite already loaded:\n\tRequested: {}\n\tFound: {}".format(dsq, node.name))
return node
except KeyError:
# composite hasn't been loaded yet, let's load it below
LOG.trace("Composite hasn't been loaded yet, will load: {}".format(dsq))
raise MissingDependencies({dsq})
[docs]
def _create_subtree_from_compositors(self, dataset_key, query):
try:
node = self._find_compositor(dataset_key, query)
LOG.trace("Found composite:\n\tRequested: {}\n\tFound: {}".format(dataset_key, node and node.name))
except KeyError:
LOG.trace("Composite not found: {}".format(dataset_key))
raise MissingDependencies({dataset_key})
return node
[docs]
def _find_compositor(self, dataset_key, query):
"""Find the compositor object for the given dataset_key."""
# NOTE: This function can not find a modifier that performs
# one or more modifications if it has modifiers see if we can find
# the unmodified version first
if dataset_key.is_modified():
implicit_dependency_node = self._create_implicit_dependency_subtree(dataset_key, query)
dataset_key = self._promote_query_to_modified_dataid(dataset_key, implicit_dependency_node.name)
try:
compositor = self.get_modifier(dataset_key)
except KeyError:
raise KeyError("Can't find anything called {}".format(str(dataset_key)))
compositor.attrs["prerequisites"] = [implicit_dependency_node] + list(compositor.attrs["prerequisites"])
else:
try:
compositor = self.get_compositor(dataset_key)
except KeyError:
raise KeyError("Can't find anything called {}".format(str(dataset_key)))
root = CompositorNode(compositor)
composite_id = root.name
prerequisite_filter = composite_id.create_filter_query_without_required_fields(dataset_key)
# Get the prerequisites
LOG.trace("Looking for composite prerequisites for: {}".format(dataset_key))
prereqs = [create_filtered_query(prereq, prerequisite_filter) if not isinstance(prereq, Node) else prereq
for prereq in compositor.attrs["prerequisites"]]
prereqs = self._create_required_subtrees(root, prereqs, query=query)
root.add_required_nodes(prereqs)
# Get the optionals
LOG.trace("Looking for optional prerequisites for: {}".format(dataset_key))
optionals = [create_filtered_query(prereq, prerequisite_filter) if not isinstance(prereq, Node) else prereq
for prereq in compositor.attrs["optional_prerequisites"]]
optionals = self._create_optional_subtrees(root, optionals, query=query)
root.add_optional_nodes(optionals)
return root
[docs]
def _create_implicit_dependency_subtree(self, dataset_key, query):
new_prereq = dataset_key.create_less_modified_query()
src_node = self._create_subtree_for_key(new_prereq, query)
return src_node
[docs]
def get_compositor(self, key):
"""Get a compositor."""
for sensor_name in sorted(self.compositors):
try:
return self.compositors[sensor_name][key]
except KeyError:
continue
raise KeyError("Could not find compositor '{}'".format(key))
[docs]
def get_modifier(self, comp_id):
"""Get a modifer."""
# create a DataID for the compositor we are generating
modifier = comp_id["modifiers"][-1]
for sensor_name in sorted(self.modifiers):
modifiers = self.modifiers[sensor_name]
compositors = self.compositors[sensor_name]
if modifier not in modifiers:
continue
mloader, moptions = modifiers[modifier]
moptions = moptions.copy()
moptions.update(comp_id.to_dict())
moptions["sensor"] = sensor_name
compositors[comp_id] = mloader(_satpy_id=comp_id, **moptions)
return compositors[comp_id]
raise KeyError("Could not find modifier '{}'".format(modifier))
[docs]
def _create_required_subtrees(self, parent, prereqs, query=None): # noqa: D417
"""Determine required prerequisite Nodes for a composite.
Args:
parent (Node): Compositor node to add these prerequisites under
prereqs (sequence): Strings (names), floats (wavelengths),
DataQuerys or Nodes to analyze.
"""
prereq_nodes, unknown_datasets = self._create_prerequisite_subtrees(parent, prereqs, query)
if unknown_datasets:
raise MissingDependencies(unknown_datasets)
return prereq_nodes
[docs]
def _create_optional_subtrees(self, parent, prereqs, query=None): # noqa: D417
"""Determine optional prerequisite Nodes for a composite.
Args:
parent (Node): Compositor node to add these prerequisites under
prereqs (sequence): Strings (names), floats (wavelengths), or
DataQuerys to analyze.
"""
prereq_nodes, unknown_datasets = self._create_prerequisite_subtrees(parent, prereqs, query)
for prereq, unknowns in unknown_datasets.items():
u_str = ", ".join([str(x) for x in unknowns])
LOG.debug("Skipping optional %s: Unknown dataset %s",
str(prereq), u_str)
return prereq_nodes
[docs]
def _create_prerequisite_subtrees(self, parent, prereqs, query=None): # noqa: D417
"""Determine prerequisite Nodes for a composite.
Args:
parent (Node): Compositor node to add these prerequisites under
prereqs (sequence): Strings (names), floats (wavelengths),
DataQuerys or Nodes to analyze.
"""
prereq_nodes = []
unknown_datasets = dict()
if not prereqs:
# this composite has no required prerequisites
prereq_nodes.append(self.empty_node)
self.add_child(parent, self.empty_node)
return prereq_nodes, unknown_datasets
for prereq in prereqs:
try:
if isinstance(prereq, Node):
node = prereq
else:
node = self._create_subtree_for_key(prereq, query=query)
except MissingDependencies as unknown:
unknown_datasets[prereq] = unknown.missing_dependencies
else:
prereq_nodes.append(node)
self.add_child(parent, node)
return prereq_nodes, unknown_datasets
[docs]
class _DataIDContainer(dict):
"""Special dictionary object that can handle dict operations based on dataset name, wavelength, or DataID.
Note: Internal dictionary keys are `DataID` objects.
"""
[docs]
def keys(self):
"""Give currently contained keys."""
# sort keys so things are a little more deterministic (.keys() is not)
return sorted(super(_DataIDContainer, self).keys())
[docs]
def get_key(self, match_key):
"""Get multiple fully-specified keys that match the provided query.
Args:
match_key (DataID): DataID or DataQuery of query parameters to use for
searching. Can also be a string representing the
dataset name or a number representing the dataset
wavelength.
"""
return get_key(match_key, self.keys())
def __getitem__(self, item):
"""Get item from container."""
try:
# short circuit - try to get the object without more work
return super(_DataIDContainer, self).__getitem__(item)
except KeyError:
key = self.get_key(item)
return super(_DataIDContainer, self).__getitem__(key)
def __contains__(self, item):
"""Check if item exists in container."""
try:
key = self.get_key(item)
except KeyError:
return False
return super(_DataIDContainer, self).__contains__(key)