""" # The core API of TF. It provides methods to navigate nodes and edges and lookup features. """ from textwrap import wrap from .helpers import flattenToSet, console, fitemize, unexpanduser as ux from .nodes import Nodes from .locality import Locality from .nodefeature import NodeFeatures from .edgefeature import EdgeFeatures from .computed import Computeds from .text import Text from ..search.search import Search from ..core.timestamp import SILENT_D, DEEP, silentConvert API_REFS = dict( AllComputeds=("Computed", "computedall", "computed-data"), AllEdges=("Features", "edgeall", "edge-features"), AllFeatures=("Features", "nodeall", "node-features"), C=("Computed", "computed", "computed-data"), Call=("Computed", "computedall", "computed-data"), Computed=("Computed", "computed", "computed-data"), ComputedString=("Computed", "computedstr", "computed-data"), Cs=("Computed", "computedstr", "computed-data"), E=("Features", "edge", "edge-features"), Eall=("Features", "edgeall", "edge-features"), Edge=("Features", "edge", "edge-features"), EdgeString=("Features", "edgestr", "edge-features"), Es=("Features", "edgestr", "edge-features"), F=("Features", "node", "node-features"), Fall=("Features", "nodeall", "node-features"), Feature=("Features", "node", "node-features"), FeatureString=("Features", "nodestr", "node-features"), Fs=("Features", "nodestr", "node-features"), L=("Locality", "locality", "locality"), Locality=("Locality", "locality", "locality"), N=("Nodes", "nodes", "navigating-nodes"), Nodes=("Nodes", "nodes", "navigating-nodes"), S=("Search", "search", "search"), Search=("Search", "search", "search"), T=("Text", "text", "text"), TF=("Fabric", "fabric", "loading"), Text=("Text", "text", "text"), ) class Api(object): def __init__(self, TF): self.TF = TF self.ignored = tuple(sorted(TF.featuresIgnored)) """Which features were found but ignored. Features are ignored if the feature is also present in another location that is loaded later. """ TF.ignored = self.ignored self.F = NodeFeatures() self.Feature = self.F self.E = EdgeFeatures() self.Edge = self.E self.C = Computeds() self.Computed = self.C tmObj = TF.tmObj TF.silentOn = tmObj.silentOn TF.silentOff = tmObj.silentOff TF.isSilent = tmObj.isSilent TF.setSilent = tmObj.setSilent TF.info = tmObj.info TF.warning = tmObj.warning TF.error = tmObj.error TF.cache = tmObj.cache TF.reset = tmObj.reset TF.indent = tmObj.indent """All messages produced during the feature loading process. It also shows the messages that have been suppressed due to the `silent` parameter. """ TF.ensureLoaded = self.ensureLoaded TF.makeAvailableIn = self.makeAvailableIn setattr(self, "FeatureString", self.Fs) setattr(self, "EdgeString", self.Es) setattr(self, "ComputedString", self.Cs) setattr(self, "AllFeatures", self.Fall) setattr(self, "AllEdges", self.Eall) setattr(self, "AllComputeds", self.Call) setattr(self, "loadLog", self.isLoaded) def Fs(self, fName): """Get the node feature sub API. If feature name is not a valid python identifier, or if you do not know its name in advance, you can not use `F.feature`, but you should use `Fs(feature)`. """ if not hasattr(self.F, fName): self.TF.error(f'Node feature "{fName}" not loaded') return None return getattr(self.F, fName) def Es(self, fName): """Get the edge feature sub API. If feature name is not a valid python identifier, or if you do not know its name in advance, you can not use `E.feature`, but you should use `Es(feature)`. """ if not hasattr(self.E, fName): self.TF.error(f'Edge feature "{fName}" not loaded') return None return getattr(self.E, fName) def Cs(self, fName): """Get the computed data sub API. If component name is not a valid python identifier, or if you do not know its name in advance, you can not use `C.component`, but you should use `Cs(component)`. """ if not hasattr(self.C, fName): self.TF.error(f'Computed feature "{fName}" not loaded') return None return getattr(self.C, fName) def Fall(self): """Returns a sorted list of all usable, loaded node feature names.""" return sorted(x[0] for x in self.F.__dict__.items()) def Eall(self): """Returns a sorted list of all usable, loaded edge feature names.""" return sorted(x[0] for x in self.E.__dict__.items()) def Call(self): """Returns a sorted list of all usable, loaded computed data names.""" return sorted(x[0] for x in self.C.__dict__.items()) def isLoaded( self, features=None, pretty=True, valueType=True, path=False, meta="description" ): """Show information about loaded features. Parameters ---------- features: iterable | string, optional `None` The features to get info for. If absent or None: all features seen by TF. If a string, it is a comma and/or space spearated list of feature names. Otherwise the items of the iterable are feature names. pretty: boolean, optional `True` If True, it prints an overview of all features seen by TF with information about kind, type, source location and loaded status. The amount of information printed can be tweaked by other parameters. Otherwise, it returns this information as a dict. valueType: boolean, optional `True` Only relevant if `pretty=True`: whether to print the value type of the values in the feature file. path: boolean, optional `True` Only relevant if `pretty=True`: whether to print the path name of the feature file. meta: string|list|boolean, optional `"description"` Only relevant if `pretty=True`: controls what metadata of the feature should be printed. If it is None, False, or the empty string or empty list: no metadata will be printed. It it is the boolean value True: all metadata will be printed. If it is a list of key names or a string with key names separated by white space and/or commas, only these metadata keys will be printed. Returns ------- dict of dict The features are keys, the value per feature is None or a dict with the following information: `None` if the feature is not loaded. If the feature is loaded: * `kind`: `node`, `edge`, `config`, `computed`; * `type` is the type of values: `int`, or `str` or `""`; * `edgeValues`: if an edge feature it indicates whether the edges have values. Otherwise `None`. * `meta`: dictionary containing the metadata of the feature If `pretty`, nothing is returned, but the dict is pretty printed. """ fNames = list(self.TF.features) if features is None else fitemize(features) info = {} for fName in fNames: fMeta = {} fType = None edgeValues = None fSource = None hasInfo = True if fName in self.TF.features: fObj = self.TF.features[fName] fSource = ux(fObj.dirName) fMeta = fObj.metaData fType = fMeta.get("valueType", "") fMeta = {k: v for (k, v) in fMeta.items() if k != "valueType"} isLoadedF = hasattr(self.F, fName) isLoadedE = hasattr(self.E, fName) if isLoadedF or isLoadedE: if isLoadedF: fKind = "node" elif isLoadedE: fKind = "edge" flObj = getattr(self.E, fName) edgeValues = False if fName == "oslots" else flObj.doValues elif ( fName.startswith("__") and fName.endswith("__") and hasattr(self.C, fName.strip("_")) ): fKind = "computed" elif fName in self.TF.features: if fObj.isConfig: fKind = "config" else: hasInfo = False else: hasInfo = False info[fName] = ( dict( kind=fKind, type=fType, meta=fMeta, source=fSource, edgeValues=edgeValues, ) if hasInfo else None ) if pretty: for (fName, fInfo) in sorted(info.items()): if fInfo is None: kind = "NOT LOADED" kind = f" {kind:<10}" fSource = "" metaRep = "" heading = f"{fName:<20}{kind}{fSource}" else: fKind = fInfo["kind"] fMeta = fInfo.get("meta", {}) fType = fInfo.get("type", "") fSource = fInfo.get("source", "") if path else "" fSource = f" {fSource}" if fSource else "" fEV = fInfo.get("edgeValues", "") if valueType: kind = ( f"node ({fType})" if fKind == "node" else f"edge ({fType})" if fKind == "edge" and fEV else "edge" if fKind == "edge" else f"{fKind}" ) kind = f" {kind:<10}" if kind else "" else: kind = "" if meta is True: metaKeys = sorted(fMeta.keys()) metaInfo = fMeta elif not meta: metaInfo = {} else: metaKeys = fitemize(meta) metaInfo = {k: fMeta[k] for k in metaKeys if k in fMeta} heading = f"{fName:<20}{kind}{fSource}" metaRep = "" indent = " " * (len(heading) + 1) if metaInfo: if len(metaKeys) == 1: value = metaInfo.get(metaKeys[0], "") value = "\n".join(wrap(value, width=80, subsequent_indent=indent)) metaRep = f" {value}" if value else "" else: indent = " " * 21 for k in metaKeys: value = metaInfo.get(k, "") value = "\n".join(wrap(value, width=80, subsequent_indent=f"\t{indent} ")) metaRep += f"\n\t{k:<20} = {value}" msg = f"{heading}{metaRep}" print(msg) return None return info def makeAvailableIn(self, scope): """Exports members of the API to the global namespace. Only the members whose names start with a capital are exported. If you are working with a single data source in your program, it is a bit tedious to write the initial `TF.api.` or `A.api` all the time. By this method you can avoid that. !!! explanation "Longer names" There are also longer names which can be used as aliases to the single capital letters. This might or might not improve the readability of your program. short name | long name --- | --- `N` | `Nodes` `F` | `Feature` `Fs` | `FeatureString` `Fall` | `AllFeatures` `E` | `Edge` `Es` | `EdgeString` `Eall` `AllEdges` `C` | `Computed` `Cs` `ComputedString` `Call` | `AllComputeds` `L` | `Locality` `T` | `Text` `S` | `Search` Parameters ---------- scope: dict A dictionary into which the members of the core API will be inserted. The only sensible choice is: `globals()`. Returns ------- tuple A grouped list of API members that has been hoisted to the global scope. Notes ----- !!! explanation "Why pass `globals()`?" Although we know it should always be `globals()`, we cannot define a function that looks into the `globals()` of its caller. So we have to pass it on. """ for member in dir(self): if "_" not in member and member[0].isupper(): scope[member] = getattr(self, member) if member not in API_REFS: console(f'WARNING: API member "{member}" not documented') grouped = {} for (member, (head, sub, ref)) in API_REFS.items(): grouped.setdefault(ref, {}).setdefault((head, sub), []).append(member) # grouped # node-features=>(Features, node)=>[F, ...] docs = [] for (ref, groups) in sorted(grouped.items()): chunks = [] for ((head, sub), members) in sorted(groups.items()): chunks.append(" ".join(sorted(members, key=lambda x: (len(x), x)))) docs.append((head, ref, tuple(chunks))) return docs # docs # (Features, node-features, ('F ...', ...)) def ensureLoaded(self, features): """Checks if features are loaded and if not loads them. All features in question will be made available to the core API. Parameters ---------- features: string | iterable of strings It is a string containing space separated feature names, or an iterable of feature names. The feature names are just the names of `.tf` files without directory information and without extension. Returns ------- set The names of the features in question as a set of strings. """ F = self.F E = self.E TF = self.TF warning = TF.warning needToLoad = set() loadedFeatures = set() for fName in sorted(flattenToSet(features)): fObj = TF.features.get(fName, None) if not fObj: warning(f'Cannot load feature "{fName}": not in dataset') continue if fObj.dataLoaded and (hasattr(F, fName) or hasattr(E, fName)): loadedFeatures.add(fName) else: needToLoad.add(fName) if len(needToLoad): TF.load( needToLoad, add=True, silent=DEEP, ) loadedFeatures |= needToLoad return loadedFeatures def addOtype(api): setattr(api.F.otype, "all", tuple(o[0] for o in api.C.levels.data)) setattr( api.F.otype, "support", dict(((o[0], (o[2], o[3])) for o in api.C.levels.data)) ) def addLocality(api): api.L = Locality(api) api.Locality = api.L def addNodes(api): api.N = Nodes(api) api.Nodes = api.N def addText(api): api.T = Text(api) api.Text = api.T def addSearch(api, silent=SILENT_D): silent = silentConvert(silent) api.S = Search(api, silent) api.Search = api.S