Source code for podpac.core.node

Node Summary

from __future__ import division, print_function, absolute_import

import os
import re
from collections import OrderedDict
import functools
from hashlib import md5 as hash_alg
import json
import numpy as np
import traitlets as tl

from podpac.core.settings import settings
from podpac.core.units import ureg, UnitsDataArray, create_data_array
from podpac.core.utils import common_doc, JSONEncoder
from podpac.core.coordinates import Coordinates
from import Style
from podpac.core.cache import CacheCtrl, get_default_cache_ctrl

        """The set of coordinates requested by a user. The Node will be evaluated using these coordinates.""",
        """Default is None. Optional input array used to store the output data. When supplied, the node will not
            allocate its own memory for the output array. This array needs to have the correct dimensions,
            coordinates, and coordinate reference system.""",
            Unit-aware xarray DataArray containing the results of the node evaluation.
    'hash_return': 'A unique hash capturing the coordinates and parameters used to evaluate the node. ',
    'outdir': "Optional output directory. Uses :attr:`podpac.settings['DISK_CACHE_DIR']` by default",
            Dictionary containing the location of the Node, the name of the plugin (if required), as well as any
            parameters and attributes that were tagged by children.
        """How to initialize the array. Options are:
                nan: uses np.full(..., np.nan) (Default option)
                empty: uses np.empty
                zeros: uses np.zeros()
                ones: uses np.ones
                full: uses np.full(..., fillval)
                data: uses the fillval as the input array
    'arr_fillval' : "used if init_type=='full' or 'data', default = 0",
    'arr_style' : "The style to use for plotting. Uses by default",
    'arr_no_style' : "Default is False. If True, will not be assigned to arr.attr['layer_style']",
    'arr_shape': 'Shape of array. Uses self.shape by default.',
    'arr_coords' : "Input to UnitsDataArray (i.e. an xarray coords dictionary/list)",
    'arr_dims' : "Input to UnitsDataArray (i.e. an xarray dims list of strings)",
    'arr_units' : "Default is self.units The Units for the data contained in the DataArray.",
    'arr_dtype' :"Default is np.float. Datatype used by default",
    'arr_kwargs' : "Dictioary of any additional keyword arguments that will be passed to UnitsDataArray.",
    'arr_return' :
            Unit-aware xarray DataArray of the desired size initialized using the method specified.


[docs]class NodeException(Exception): """ Summary """ pass
[docs]@common_doc(COMMON_DOC) class Node(tl.HasTraits): """The base class for all Nodes, which defines the common interface for everything. Attributes ---------- cache_output: bool Should the node's output be cached? If not provided or None, uses default based on settings. cache_update: bool Default is False. Should the node's cached output be updated from the source data? cache_ctrl: :class:`podpac.core.cache.cache.CacheCtrl` Class that controls caching. If not provided, uses default based on settings. dtype : type The numpy datatype of the output. Currently only ``float`` is supported. style : :class:`podpac.Style` Object discribing how the output of a node should be displayed. This attribute is planned for deprecation in the future. units : str The units of the output data. Must be pint compatible. Notes ----- Additional attributes are available for debugging after evaluation, including: * ``_requested_coordinates``: the requested coordinates of the most recent call to eval * ``_output``: the output of the most recent call to eval """ units = tl.Unicode(default_value=None, allow_none=True).tag(attr=True) dtype = tl.Any(default_value=float) cache_output = tl.Bool() cache_update = tl.Bool(False) cache_ctrl = tl.Instance(CacheCtrl, allow_none=True) style = tl.Instance(Style) @tl.default('cache_output') def _cache_output_default(self): return settings['CACHE_OUTPUT_DEFAULT'] @tl.default('cache_ctrl') def _cache_ctrl_default(self): return get_default_cache_ctrl() @tl.validate('cache_ctrl') def _validate_cache_ctrl(self, d): if d['value'] is None: d['value'] = CacheCtrl([]) # no cache_stores return d['value'] @tl.default('style') def _style_default(self): return Style() @tl.validate('units') def _validate_units(self, d): ureg.Unit(d['value']) # will throw an exception if this is not a valid pint Unit return d['value'] # debugging _requested_coordinates = tl.Instance(Coordinates, allow_none=True) _output = tl.Instance(UnitsDataArray, allow_none=True) _from_cache = tl.Bool(allow_none=True, default_value=None)
[docs] def __init__(self, **kwargs): """ Do not overwrite me """ tkwargs = self._first_init(**kwargs) # Call traitlest constructor super(Node, self).__init__(**tkwargs) self.init()
def _first_init(self, **kwargs): """Only overwrite me if absolutely necessary Parameters ---------- **kwargs Keyword arguments provided by user when class was instantiated. Returns ------- dict Keyword arguments that will be passed to the standard intialization function. """ return kwargs
[docs] def init(self): """Overwrite this method if a node needs to do any additional initialization after the standard initialization. """ pass
[docs] @common_doc(COMMON_DOC) def eval(self, coordinates, output=None): """ Evaluate the node at the given coordinates. Parameters ---------- coordinates : podpac.Coordinates {requested_coordinates} output : podpac.UnitsDataArray, optional {eval_output} Returns ------- output : {eval_return} """ raise NotImplementedError
[docs] def eval_group(self, group): """ Evaluate the node for each of the coordinates in the group. Parameters ---------- group : podpac.CoordinatesGroup Group of coordinates to evaluate. Returns ------- outputs : list evaluation output, list of UnitsDataArray objects """ return [self.eval(coords) for coords in group]
[docs] def find_coordinates(self): """ Get all available native coordinates for the Node. Implemented in child classes. Returns ------- coord_list : list list of available coordinates (Coordinates objects) """ raise NotImplementedError
[docs] @common_doc(COMMON_DOC) def create_output_array(self, coords, data=np.nan, **kwargs): """ Initialize an output data array Parameters ---------- coords : podpac.Coordinates {arr_coords} data : None, number, or array-like (optional) {arr_init_type} **kwargs {arr_kwargs} Returns ------- {arr_return} """ attrs = {} attrs['layer_style'] = attrs['crs'] = if self.units is not None: attrs['units'] = ureg.Unit(self.units) return create_data_array(coords, data=data, dtype=self.dtype, attrs=attrs, **kwargs)
# ----------------------------------------------------------------------------------------------------------------- # Serialization properties # ----------------------------------------------------------------------------------------------------------------- @property def base_ref(self): """ Default pipeline node reference/name in pipeline node definitions Returns ------- str Name of the node in pipeline definitions """ return self.__class__.__name__ @property def base_definition(self): """ Pipeline node definition. This property is implemented in the primary base nodes (DataSource, Algorithm, and Compositor). Node subclasses with additional attrs will need to extend this property. Returns ------- {definition_return} """ d = OrderedDict() if self.__module__ == 'podpac': d['node'] = self.__class__.__name__ elif self.__module__.startswith('podpac.'): _, module = self.__module__.split('.', 1) d['node'] = '%s.%s' % (module, self.__class__.__name__) else: d['plugin'] = self.__module__ d['node'] = self.__class__.__name__ attrs = {} lookup_attrs = {} for key, value in self.traits().items(): if not value.metadata.get('attr', False): continue attr = getattr(self, key) if key is 'units' and attr is None: continue # check serializable try: json.dumps(attr, cls=JSONEncoder) except: raise NodeException("Cannot serialize attr '%s' with type '%s'" % (key, type(attr))) if isinstance(attr, Node): lookup_attrs[key] = attr else: attrs[key] = attr if attrs: d['attrs'] = OrderedDict([(key, attrs[key]) for key in sorted(attrs.keys())]) if lookup_attrs: d['lookup_attrs'] = OrderedDict([(key, lookup_attrs[key]) for key in sorted(lookup_attrs.keys())]) return d @property def definition(self): """ Full pipeline definition for this node. Returns ------- OrderedDict Dictionary-formatted definition of a PODPAC pipeline. """ nodes = [] refs = [] definitions = [] def add_node(node): if node in nodes: return refs[nodes.index(node)] # get base definition and then replace nodes with references, adding nodes depth first d = node.base_definition if 'lookup_source' in d: d['lookup_source'] = add_node(d['lookup_source']) if 'lookup_attrs' in d: for key, attr_node in d['lookup_attrs'].items(): d['lookup_attrs'][key] = add_node(input_node) if 'inputs' in d: for key, input_node in d['inputs'].items(): if input_node is not None: d['inputs'][key] = add_node(input_node) if 'sources' in d: sources = [] # we need this list so that we don't overwrite the actual sources array for i, source_node in enumerate(d['sources']): sources.append(add_node(source_node)) d['sources'] = sources # get base ref and then ensure it is unique ref = node.base_ref while ref in refs: if'_[1-9][0-9]*$', ref): ref, i = ref.rsplit('_', 1) i = int(i) else: i = 0 ref = '%s_%d' % (ref, i+1) nodes.append(node) refs.append(ref) definitions.append(d) return ref add_node(self) d = OrderedDict() d['nodes'] = OrderedDict(zip(refs, definitions)) return d @property def pipeline(self): """Create a pipeline node from this node Returns ------- podpac.Pipeline A pipeline node that wraps this node """ from podpac.core.pipeline import Pipeline return Pipeline(definition=self.definition) @property def json(self): """definition for this node in json format Returns ------- str JSON-formatted definition of a PODPAC pipeline. Notes ------ This definition can be used to create Pipeline Nodes. It also serves as a light-weight transport mechanism to share algorithms and pipelines, or run code on cloud services. """ return json.dumps(self.definition, cls=JSONEncoder) @property def json_pretty(self): return json.dumps(self.definition, indent=4, cls=JSONEncoder) @property def hash(self): return hash_alg(self.json.encode('utf-8')).hexdigest() # ----------------------------------------------------------------------------------------------------------------- # Caching Interface # -----------------------------------------------------------------------------------------------------------------
[docs] def get_cache(self, key, coordinates=None): """ Get cached data for this node. Parameters ---------- key : str Key for the cached data, e.g. 'output' coordinates : podpac.Coordinates, optional Coordinates for which the cached data should be retrieved. Omit for coordinate-independent data. Returns ------- data : any The cached data. Raises ------ NodeException Cached data not found. """ if not self.has_cache(key, coordinates=coordinates): raise NodeException("cached data not found for key '%s' and coordinates %s" % (key, coordinates)) return self.cache_ctrl.get(self, key, coordinates=coordinates)
[docs] def put_cache(self, data, key, coordinates=None, overwrite=False): """ Cache data for this node. Parameters ---------- data : any The data to cache. key : str Unique key for the data, e.g. 'output' coordinates : podpac.Coordinates, optional Coordinates that the cached data depends on. Omit for coordinate-independent data. overwrite : bool, optional Overwrite existing data, default False Raises ------ NodeException Cached data already exists (and overwrite is False) """ if not overwrite and self.has_cache(key, coordinates=coordinates): raise NodeException("Cached data already exists for key '%s' and coordinates %s" % (key, coordinates)) self.cache_ctrl.put(self, data, key, coordinates=coordinates, update=overwrite)
[docs] def has_cache(self, key, coordinates=None): """ Check for cached data for this node. Parameters ---------- key : str Key for the cached data, e.g. 'output' coordinates : podpac.Coordinates, optional Coordinates for which the cached data should be retrieved. Omit for coordinate-independent data. Returns ------- bool True if there is cached data for this node, key, and coordinates. """ return self.cache_ctrl.has(self, key, coordinates=coordinates)
[docs] def rem_cache(self, key, coordinates=None, mode=None, all_cache=False): """ Clear cached data for this node. Parameters ---------- key : str, optional Delete cached objects with this key. If `'*'`, cached data is deleted for all keys. coordinates : podpac.Coordinates, str, optional Delete cached objects for these coordinates. If `'*'`, cached data is deleted for all coordinates, including coordinate-independent data. mode: str, optional Specify which cache stores are affected. all_cache: bool, optional Default is False. If True, deletes all of the cache. See Also --------- `podpac.core.cache.cache.CacheCtrl.rem` """ if all_cache: self.cache_ctrl.rem('*', '*') else: self.cache_ctrl.rem(self, key=key, coordinates=coordinates, mode=mode)
#--------------------------------------------------------# # Decorators #--------------------------------------------------------# def node_eval(fn): """ Decorator for Node eval methods that handles caching and a user provided output argument. fn : function Node eval method to wrap Returns ------- wrapper : function Wrapped node eval method """ cache_key = 'output' @functools.wraps(fn) def wrapper(self, coordinates, output=None): if settings['DEBUG']: self._requested_coordinates = coordinates key = cache_key cache_coordinates = coordinates.transpose(*sorted(coordinates.dims)) # order agnostic caching if self.has_cache(key, cache_coordinates) and not self.cache_update: data = self.get_cache(key, cache_coordinates) if output is not None: order = [dim for dim in output.dims if dim not in data.dims] + list(data.dims) output.transpose(*order)[:] = data self._from_cache = True else: data = fn(self, coordinates, output=output) # We need to check if the cache now has the key because it is possible that # the previous function call added the key with the coordinates to the cache if self.cache_output and not (self.has_cache(key, cache_coordinates) and not self.cache_update): self.put_cache(data, key, cache_coordinates, overwrite=self.cache_update) self._from_cache = False # transpose data to match the dims order of the requested coordinates order = [dim for dim in coordinates.dims if dim in data.dims] data = data.transpose(*order) if settings['DEBUG']: self._output = data return data return wrapper def cache_func(key, depends=None): """ Decorating for caching a function's output based on a key. Parameters ----------- key: str Key used for caching. depends: str, list, traitlets.All (optional) Default is None. Any traits that the cached property depends on. The cached function may NOT change the value of any of these dependencies (this will result in a RecursionError) Notes ----- This decorator cannot handle function input parameters. If the function uses any tagged attributes, these will essentially operate like dependencies because the cache key changes based on the node definition, which is affected by tagged attributes. Examples ---------- >>> from podpac import Node >>> from podpac.core.node import cache_func >>> import traitlets as tl >>> class MyClass(Node): value = tl.Int(0) @cache_func('add') def add_value(self): self.value += 1 return self.value @cache_func('square', depends='value') def square_value_depends(self): return self.value >>> n = MyClass(cache_ctrl=None) >>> n.add_value() # The function as defined is called 1 >>> n.add_value() # The function as defined is called again, since we have specified no caching 2 >>> n.cache_ctrl = CacheCtrl([RamCacheStore()]) >>> n.add_value() # The function as defined is called again, and the value is stored in memory 3 >>> n.add_value() # The value is retrieved from disk, note the change in n.value is not captured 3 >>> n.square_value_depends() # The function as defined is called, and the value is stored in memory 16 >>> n.square_value_depends() # The value is retrieved from memory 16 >>> n.value += 1 >>> n.square_value_depends() # The function as defined is called, and the value is stored in memory. Note the change in n.value is captured. 25 """ # This is the actual decorator which will be evaluated and returns the wrapped function def cache_decorator(func): # This is the initial wrapper that sets up the observations @functools.wraps(func) def cache_wrapper(self): # This is the function that updates the cached based on observed traits def cache_updator(change): # print("Updating value on self:", id(self)) out = func(self) self.put_cache(out, key, overwrite=True) if depends: # This sets up the observer on the dependent traits # print ("setting up observer on self: ", id(self)) self.observe(cache_updator, depends) # Since attributes could change on instantiation, anything we previously # stored is likely out of date. So, force and update to the cache. cache_updator(None) # This is the final wrapper the continues to fetch data from cache # after the observer has been set up. @functools.wraps(func) def cached_function(): try: out = self.get_cache(key) except NodeException: out = func(self) self.put_cache(out, key) return out # Since this is the first time the function is run, set the new wrapper # on the class instance so that the current function won't be called again # (which would set up an additional observer) setattr(self, func.__name__, cached_function) # Return the value on the first run return cached_function() return cache_wrapper return cache_decorator