Remedies a common problem in piping: not having access to intermediate outputs of the pipe. Within a "loop", a piping intermediate is stored in a stack archive, data is processed, and then both the stored intermediate and the current output are reintegrated using an "ending" function. Two special ending functions are provided: amend and insert. However, any ending function can be specified, including merge functions, join functions, setNames(), etc. This framework allows the following work-flow: focus on a particular aspect or section of a data set, conduct specific operations, and then reintegrate changes into the whole.
| Version: | 1.0.1 |
| Depends: | R (≥ 3.1.3) |
| Imports: | plyr (≥ 1.8.1), dplyr (≥ 0.4.1), magrittr (≥ 1.5), lazyeval (≥ 0.1.10), R6 (≥ 2.0.1) |
| Suggests: | knitr (≥ 1.9) |
| Published: | 2015-05-28 |
| Author: | Brandon Taylor |
| Maintainer: | Brandon Taylor <Brandon.Taylor221 at gmail.com> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | loopr results |
| Reference manual: | loopr.pdf |
| Vignettes: |
Looping |
| Package source: | loopr_1.0.1.tar.gz |
| Windows binaries: | r-devel: loopr_1.0.1.zip, r-release: loopr_1.0.1.zip, r-oldrel: loopr_1.0.1.zip |
| OS X binaries: | r-release: loopr_1.0.1.tgz, r-oldrel: loopr_1.0.1.tgz |
| Old sources: | loopr archive |
Please use the canonical form https://CRAN.R-project.org/package=loopr to link to this page.


Class II transactivator-independent endothelial cell MHC class II gene activation induced by lymphocyte adhesion.
Abstract
NK cells induce MHC class II molecules on the surface of allogeneic endothelial cells in an adhesion-dependent, IFN-gamma -independent manner. Here, we demonstrate that NK cells induce HLA-DR on the surface of a mutant cell line that is defective in IFN-gamma -induced MHC class II expression. RNA analysis in these cells and in a cell line that is defective in class II transactivator (CIITA) demonstrates that NK cell-induced HLA-DR alpha mRNA expression is also CIITA -independent. The Janus kinase-1-deficient cell line U4A expresses HLA-DR alpha mRNA in response to NK cell activation, and HLA-DR alpha promoter constructs transfected into these cells are induced by NK cells but not IFN-gamma. These data indicate that the IFN-gamma -independent component of the target cell HLA-DR expression induced by lymphocyte adhesion uses a signaling pathway that is distinct from the IFN-gamma -dependent mechanism and also suggest that CIITA is not required.


Thank you for visiting my portfolio site. My name is Greg Hoffman, and I am currently learning to be a Full Stack Web Developer.
You can learn more about me through the links above, or just click below on some of my projects.


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Discover the basics of electronics, circuit making and robotics in our fun filled, hands on sessions hosted every weekend at centers & apartment complexes near you. These sessions are suitable for young tinkerers aged 9 years and above (although we encourage even younger participants accompanied by a guardian). In case you wish to host a workshop at your apartment please contact us :D
Start using Arduino UNO programmable mini computer to explore the exciting world of physical computing, electronics, autonomous robotics and more.
Start making your own versions of classic computer games such as Pong, Space Invaders,... using Python!
For details contact us at +919127451760 (Call / Whatsapp) or email us<EMAIL_ADDRESS>We We currently operate out of our tiny workshop located at at 47,F.C.Road, Uzan Bazar Guwahati - 781001.


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| PREV NEXT | FRAMES NO FRAMES |
| Packages that use Graph | |
|---|---|
| com.hp.hpl.jena.db | A general database backend for persistent storage of Jena models. |
| com.hp.hpl.jena.enhanced | This package defines the enhanced node and graph classes; an enhanced node is one embedded in a particular enhanced graph. |
| com.hp.hpl.jena.graph | This package defines the Graph and Node family of classes, which form the underlying datatypes of the Jena system. |
| com.hp.hpl.jena.graph.compose | This package defines simple composite graphs - union, intersection, difference, and update-tracking. |
| com.hp.hpl.jena.graph.impl | This package provides basic implementations of Graph interfaces such as Reifier, QueryHandler, BulkUpdateHandler, and EventManager. |
| com.hp.hpl.jena.graph.query | Defines the graph-level Query classes, allowing multi-pattern query over arbitrary graphs. |
| com.hp.hpl.jena.rdf.arp | A parser for RDF/XML. |
| com.hp.hpl.jena.rdf.model | A package for creating and manipulating RDF graphs. |
| com.hp.hpl.jena.reasoner | The Jena2 reasoner subsystem is designed to allow a range of inference engines to be plugged into Jena. |
| com.hp.hpl.jena.reasoner.dig | |
| com.hp.hpl.jena.reasoner.rulesys | Provides a selection of simple rule engines for Jena inference models. |
| com.hp.hpl.jena.reasoner.transitiveReasoner | This package contains a reasoner which supports transitive-reflexive closure of subPropertyOf and subClassOf relations. |
| com.hp.hpl.jena.shared | This package defines some classes common to the Jena API and SPI levels, in particular the JenaException class from which all Jena-specific exceptions hang, and the interface PrefixMapping for translation to and from QNames. |
| com.hp.hpl.jena.util | Miscellaneous collection of utility classes. |
| Uses of Graph in com.hp.hpl.jena.db |
|---|
| Classes in com.hp.hpl.jena.db that implement Graph | |
|---|---|
class |
GraphRDB
A persistent Graph implementation using a relational database for storage. |
| Constructors in com.hp.hpl.jena.db with parameters of type Graph | |
|---|---|
GraphRDB(IDBConnection con,
java.lang.String graphID,
Graph requestedProperties,
boolean isNew)
Deprecated. Please use the alternate constructor and choose the desired reification behaviour. |
|
GraphRDB(IDBConnection con,
java.lang.String graphID,
Graph requestedProperties,
int reificationBehaviour,
boolean isNew)
Construct a new GraphRDB |
| Uses of Graph in com.hp.hpl.jena.enhanced |
|---|
| Methods in com.hp.hpl.jena.enhanced that return Graph | |
|---|---|
Graph |
EnhGraph.asGraph()
Answer the normal graph that this enhanced graph is wrapping. |
| Constructors in com.hp.hpl.jena.enhanced with parameters of type Graph | |
|---|---|
EnhGraph(Graph g,
Personality p)
Construct an enhanced graph from the given underlying graph, and a factory for generating enhanced nodes. |
| Uses of Graph in com.hp.hpl.jena.graph |
|---|
| Fields in com.hp.hpl.jena.graph declared as Graph | |
|---|---|
static Graph |
Graph.emptyGraph
An immutable empty graph. |
| Methods in com.hp.hpl.jena.graph that return Graph | |
|---|---|
Graph |
GraphMaker.addDescription(Graph desc,
Node self)
Add the description of this GraphMaker to the description graph desc, under the name self. |
static Graph |
Factory.createDefaultGraph()
Answer a memory-based Graph with the Standard reification style. |
static Graph |
Factory.createDefaultGraph(ReificationStyle style)
Answer a memory-based Graph with the given reification style. |
Graph |
GraphMaker.createGraph()
Answer a graph who's name isn't interesting. |
Graph |
GraphMaker.createGraph(java.lang.String name)
Create a graph that does not already exist - equivalent to createGraph( name, false ) . |
Graph |
GraphMaker.createGraph(java.lang.String name,
boolean strict)
Create a new graph associated with the given name. |
static Graph |
Factory.createGraphMem()
|
static Graph |
Factory.createGraphMem(ReificationStyle style)
|
static Graph |
Factory.createGraphMemWithTransactionHandler(TransactionHandler th)
|
Graph |
GraphExtract.extract(Node node,
Graph graph)
Answer a new graph which is the reachable subgraph from node
in graph with the terminating condition given by the
TripleBoundary passed to the constructor. |
Graph |
GraphExtract.extractInto(Graph toUpdate,
Node root,
Graph extractFrom)
Answer the graph toUpdate augmented with the sub-graph of
extractFrom reachable from root bounded
by this instance's TripleBoundary. |
Graph |
GraphMaker.getDescription()
Answer a Graph describing this GraphMaker using the vocabulary of JenaModelSpec. |
Graph |
GraphMaker.getDescription(Node root)
|
Graph |
GraphMaker.getGraph()
Answer the default graph of this GraphMaker. |
Graph |
Reifier.getParentGraph()
get the Graph which uses this reifier. |
Graph |
GraphMaker.openGraph()
Answer the default graph of this GraphMaker, if it has one. |
Graph |
GraphMaker.openGraph(java.lang.String name)
Equivalent to openGraph( name, false ) |
Graph |
GraphMaker.openGraph(java.lang.String name,
boolean strict)
Find an existing graph that this factory knows about under the given name. |
| Methods in com.hp.hpl.jena.graph with parameters of type Graph | |
|---|---|
void |
BulkUpdateHandler.add(Graph g)
Add all the triples of the given graph into the graph this is handler for. |
void |
BulkUpdateHandler.add(Graph g,
boolean withReifications)
Add all the triples of the given graph into the graph this is handler for. |
Graph |
GraphMaker.addDescription(Graph desc,
Node self)
Add the description of this GraphMaker to the description graph desc, under the name self. |
void |
BulkUpdateHandler.delete(Graph g)
Remove all the triples of the given graph from the graph this is handler for. |
void |
BulkUpdateHandler.delete(Graph g,
boolean withReifications)
Remove all the triples of the given graph from the graph this is handler for. |
boolean |
Graph.dependsOn(Graph other)
true if this graph's content depends on the other graph. |
Graph |
GraphExtract.extract(Node node,
Graph graph)
Answer a new graph which is the reachable subgraph from node
in graph with the terminating condition given by the
TripleBoundary passed to the constructor. |
Graph |
GraphExtract.extractInto(Graph toUpdate,
Node root,
Graph extractFrom)
Answer the graph toUpdate augmented with the sub-graph of
extractFrom reachable from root bounded
by this instance's TripleBoundary. |
static ExtendedIterator |
GraphUtil.findAll(Graph g)
Answer an iterator covering all the triples in the specified graph. |
boolean |
Graph.isIsomorphicWith(Graph g)
Compare this graph with another using the method described in http://www.w3.org/TR/rdf-concepts#section-Graph-syntax |
void |
GraphListener.notifyAddArray(Graph g,
Triple[] triples)
Method called when an array of triples has been added to the graph. |
void |
GraphListener.notifyAddGraph(Graph g,
Graph added)
Method called when another graph g has been used to
specify the triples added to our attached graph. |
void |
GraphListener.notifyAddIterator(Graph g,
java.util.Iterator it)
Method called when an iterator [of triples] has been added to the graph |
void |
GraphEventManager.notifyAddIterator(Graph g,
java.util.List triples)
Notify all attached listeners that an iterator [of triples] has been added to the graph; its content has been captured in the list triples . |
void |
GraphListener.notifyAddList(Graph g,
java.util.List triples)
Method called when a list [of triples] has been added to the graph. |
void |
GraphListener.notifyAddTriple(Graph g,
Triple t)
Method called when a single triple has been added to the graph. |
void |
GraphListener.notifyDeleteArray(Graph g,
Triple[] triples)
Method called when an array of triples has been deleted from the graph. |
void |
GraphListener.notifyDeleteGraph(Graph g,
Graph removed)
Method to call when another graph has been used to specify the triples deleted from our attached graph. |
void |
GraphListener.notifyDeleteIterator(Graph g,
java.util.Iterator it)
Method called when an iterator [of triples] has been deleted from the graph. |
void |
GraphEventManager.notifyDeleteIterator(Graph g,
java.util.List triples)
Notify all attached listeners that an iterator [of triples] has been removed from the graph; its content has been captured in the list triples . |
void |
GraphListener.notifyDeleteList(Graph g,
java.util.List L)
Method called when a list [of triples] has been deleted from the graph. |
void |
GraphListener.notifyDeleteTriple(Graph g,
Triple t)
Method called when a single triple has been deleted from the graph. |
void |
GraphListener.notifyEvent(Graph source,
java.lang.Object value)
method to call for a general event |
| Uses of Graph in com.hp.hpl.jena.graph.compose |
|---|
| Classes in com.hp.hpl.jena.graph.compose that implement Graph | |
|---|---|
class |
CompositionBase
Base class for graphs that are composed of multiple sub-graphs. |
class |
Delta
Graph operation for wrapping a base graph and leaving it unchanged while recording all the attempted updates for later access. |
class |
Difference
Class representing the dynamic set difference L - R of two graphs. |
class |
DisjointUnion
DisjointUnion - a version of Union that assumes the graphs are disjoint, and hence that find need not do duplicate-removal. |
class |
Dyadic
Base class for the two-operand composition operations; has two graphs L and R |
class |
Intersection
an implementation of a dynamic intersection of two models. |
class |
MultiUnion
A graph implementation that presents the union of zero or more subgraphs, one of which is distinguished as the updateable graph. |
class |
Polyadic
A base class for composition graphs that are composed from zero or more sub-graphs (thus providing a basis for polyadic composition operators). |
class |
Union
A class representing the dynamic union of two graphs. |
| Methods in com.hp.hpl.jena.graph.compose that return Graph | |
|---|---|
Graph |
Delta.getAdditions()
Answer the graph of all triples added |
Graph |
Polyadic.getBaseGraph()
Answer the distinguished graph for the composition, which will be the graph that receives triple adds and deletes. |
Graph |
Delta.getDeletions()
Answer the graph of all triples removed |
Graph |
Polyadic.getRequiredBaseGraph()
Answer the distinguished graph for the composition, which will be the graph that receives triple adds and deletes. |
| Methods in com.hp.hpl.jena.graph.compose with parameters of type Graph | |
|---|---|
void |
Polyadic.addGraph(Graph graph)
Add the given graph to this composition. |
void |
MultiUnion.addGraph(Graph graph)
Add the given graph to this union. |
boolean |
Polyadic.dependsOn(Graph graph)
Answer true if this graph contains the given graph as a sub-component. |
boolean |
Dyadic.dependsOn(Graph other)
Generic dependsOn, true iff it depends on either of the subgraphs. |
static Filter |
CompositionBase.ifIn(Graph g)
Answer a Filter that will accept any triple that is an edge of
graph g. |
static ExtendedIterator |
CompositionBase.rejecting(ExtendedIterator i,
Graph seen)
Answer an iterator over the elements of i that are not in
the graph seen . |
void |
Polyadic.removeGraph(Graph graph)
Remove the given graph from this composition. |
void |
Polyadic.setBaseGraph(Graph graph)
Set the designated updateable graph for this composition. |
Union |
Dyadic.union(Graph X)
|
| Constructors in com.hp.hpl.jena.graph.compose with parameters of type Graph | |
|---|---|
Delta(Graph base)
|
|
Difference(Graph L,
Graph R)
Initialise a graph representing the difference L - R. |
|
DisjointUnion(Graph L,
Graph R)
|
|
Dyadic(Graph L,
Graph R)
When the graph is constructed, copy the prefix mappings of both components into this prefix mapping. |
|
Intersection(Graph L,
Graph R)
|
|
MultiUnion(Graph[] graphs)
Construct a union of all of the given graphs |
|
Polyadic(Graph[] graphs)
Construct a composition of all of the given graphs |
|
Union(Graph L,
Graph R)
|
| Uses of Graph in com.hp.hpl.jena.graph.impl |
|---|
| Classes in com.hp.hpl.jena.graph.impl that implement Graph | |
|---|---|
class |
com.hp.hpl.jena.graph.impl.GraphBase
GraphBase is an implementation of Graph that provides some convenient base functionality for Graph implementations. |
class |
com.hp.hpl.jena.graph.impl.WrappedGraph
A wrapper class which simply defers all operations to its base. |
| Uses of Graph in com.hp.hpl.jena.graph.query |
|---|
| Methods in com.hp.hpl.jena.graph.query that return Graph | |
|---|---|
Graph |
TreeQueryPlan.executeTree()
|
Graph |
SimpleTreeQueryPlan.executeTree()
|
Graph |
NamedGraphMap.get(java.lang.String name)
Answer the GRaph with the given name, or null if there isn't one. |
| Methods in com.hp.hpl.jena.graph.query with parameters of type Graph | |
|---|---|
Applyer |
QueryTriple.createApplyer(Graph g)
|
ExtendedIterator |
Query.executeBindings(Graph g,
java.util.List stages,
Node[] results)
|
ExtendedIterator |
Query.executeBindings(Graph g,
Node[] results)
|
static java.util.Set |
SimpleTreeQueryPlan.getRoots(Graph pattern)
|
static ExtendedIterator |
SimpleQueryHandler.objectsFor(Graph g,
Node s,
Node p)
|
static ExtendedIterator |
SimpleQueryHandler.predicatesFor(Graph g,
Node s,
Node o)
|
TreeQueryPlan |
SimpleQueryHandler.prepareTree(Graph pattern)
|
TreeQueryPlan |
QueryHandler.prepareTree(Graph pattern)
deliver a plan for executing the tree-match query defined by _pattern_. |
NamedGraphMap |
NamedGraphMap.put(java.lang.String name,
Graph g)
Add a named graph to the map and return this map. |
static ExtendedIterator |
SimpleQueryHandler.subjectsFor(Graph g,
Node p,
Node o)
|
| Constructors in com.hp.hpl.jena.graph.query with parameters of type Graph | |
|---|---|
PatternStage(Graph graph,
Mapping map,
ExpressionSet constraints,
Triple[] triples)
|
|
PatternStageBase(QueryNodeFactory factory,
Graph graph,
Mapping map,
ExpressionSet constraints,
Triple[] triples)
|
|
Query(Graph pattern)
Initialiser for Query; makes a Query with its matches taken from pattern . |
|
SimpleQueryHandler(Graph graph)
make an instance, remember the graph |
|
SimpleQueryPlan(Graph graph,
Query query,
Node[] variables)
|
|
SimpleTreeQueryPlan(Graph target,
Graph pattern)
|
| Uses of Graph in com.hp.hpl.jena.rdf.arp |
|---|
| Methods in com.hp.hpl.jena.rdf.arp with parameters of type Graph | |
|---|---|
void |
JenaReader.read(Graph g,
java.io.InputStream in,
java.lang.String xmlBase)
Reads from inputStream, using base URI xmlbase, adding triples to graph. |
void |
JenaReader.read(Graph g,
java.io.Reader reader,
java.lang.String xmlBase)
Reads from reader, using base URI xmlbase, adding triples to graph. |
| Uses of Graph in com.hp.hpl.jena.rdf.model |
|---|
| Methods in com.hp.hpl.jena.rdf.model that return Graph | |
|---|---|
Graph |
ModelGraphInterface.getGraph()
Answer the Graph which this Model is presenting. |
| Methods in com.hp.hpl.jena.rdf.model with parameters of type Graph | |
|---|---|
static Model |
ModelFactory.createModelForGraph(Graph g)
Answer a model that encapsulates the given graph. |
| Uses of Graph in com.hp.hpl.jena.reasoner |
|---|
| Subinterfaces of Graph in com.hp.hpl.jena.reasoner | |
|---|---|
interface |
InfGraph
Extends the Graph interface to give additional means to query an inferred graph. |
| Classes in com.hp.hpl.jena.reasoner that implement Graph | |
|---|---|
class |
BaseInfGraph
A base level implementation of the InfGraph interface. |
| Methods in com.hp.hpl.jena.reasoner that return Graph | |
|---|---|
Graph |
InfGraph.getDeductionsGraph()
Returns a derivations graph. |
Graph |
BaseInfGraph.getDeductionsGraph()
Returns a derivations graph. |
Graph |
FGraph.getGraph()
Returns the graph. |
Graph |
InfGraph.getRawGraph()
Return the raw RDF data Graph being processed (i.e. the argument to the Reasonder.bind call that created this InfGraph). |
Graph |
BaseInfGraph.getRawGraph()
Return the raw RDF data Graph being processed (i.e. the argument to the Reasonder.bind call that created this InfGraph). |
abstract Graph |
BaseInfGraph.getSchemaGraph()
Return the schema graph, if any, bound into this inference graph. |
| Methods in com.hp.hpl.jena.reasoner with parameters of type Graph | |
|---|---|
InfGraph |
Reasoner.bind(Graph data)
Attach the reasoner to a set of RDF data to process. |
Reasoner |
Reasoner.bindSchema(Graph tbox)
This is most commonly used to attach an ontology (a set of tbox axioms in description logics jargon) to a reasoner. |
InfGraph |
BaseInfGraph.cloneWithPremises(Graph premises)
Return a new inference graph which is a clone of the current graph together with an additional set of data premises. |
ExtendedIterator |
InfGraph.find(Node subject,
Node property,
Node object,
Graph param)
An extension of the Graph.find interface which allows the caller to encode complex expressions in RDF and then refer to those expressions within the query triple. |
ExtendedIterator |
BaseInfGraph.find(Node subject,
Node property,
Node object,
Graph param)
An extension of the Graph.find interface which allows the caller to encode complex expressions in RDF and then refer to those expressions within the query triple. |
void |
InfGraph.rebind(Graph data)
Replace the underlying data graph for this inference graph and start any inferences over again. |
void |
BaseInfGraph.rebind(Graph data)
Replace the underlying data graph for this inference graph and start any inferences over again. |
| Constructors in com.hp.hpl.jena.reasoner with parameters of type Graph | |
|---|---|
BaseInfGraph(Graph data,
Reasoner reasoner)
Constructor |
|
BaseInfGraph(Graph data,
Reasoner reasoner,
ReificationStyle style)
|
|
FGraph(Graph graph)
Constructor |
| Uses of Graph in com.hp.hpl.jena.reasoner.dig |
|---|
| Classes in com.hp.hpl.jena.reasoner.dig that implement Graph | |
|---|---|
class |
DIGInfGraph
An InfGraph that performs reasoning via a DIG interface to an external reasoner. |
| Methods in com.hp.hpl.jena.reasoner.dig that return Graph | |
|---|---|
Graph |
DIGAdapter.getGraph()
Answer the graph of local (source) data. |
Graph |
DIGReasoner.getSchema()
Answer the schema (tbox) graph for this reasoner, or null if no schema is defined. |
Graph |
DIGInfGraph.getSchemaGraph()
Return the schema graph, if any, bound into this inference graph. |
| Methods in com.hp.hpl.jena.reasoner.dig with parameters of type Graph | |
|---|---|
InfGraph |
DIGReasoner.bind(Graph data)
Bind the given data graph to any existing t-box schema that we have, and answer the resulting inference graph. |
Reasoner |
DIGReasoner.bindSchema(Graph tbox)
Bind a schema, or tbox, to this DIG reasoner. |
ExtendedIterator |
DIGInfGraph.find(Node subject,
Node property,
Node object,
Graph param)
An extension of the find(com.hp.hpl.jena.graph.TripleMatch) interface which allows the caller to
encode complex expressions in RDF and then refer to those expressions
within the query triple. |
void |
DIGInfGraph.rebind(Graph data)
Replace the underlying data graph for this inference graph and start any inferences over again. |
| Constructors in com.hp.hpl.jena.reasoner.dig with parameters of type Graph | |
|---|---|
DIGAdapter(OntModelSpec spec,
Graph source)
Construct a DIG adapter for the given source data graph, which is encoding an ontology in a language represented by the given model spec. |
|
DIGAdapter(OntModelSpec spec,
Graph source,
DIGConnection connection,
Model axioms)
Construct a DIG adapter for the given source data graph, which is encoding an ontology in a language represented by the given model spec. |
|
DIGInfGraph(Graph data,
DIGReasoner reasoner)
Constructor |
|
DIGReasoner(Graph tbox,
ReasonerFactory factory,
Resource configuration)
Construct a DIG reasoner, that can generate inference graphs binding an external DIG inference engine (e.g. |
| Uses of Graph in com.hp.hpl.jena.reasoner.rulesys |
|---|
| Subinterfaces of Graph in com.hp.hpl.jena.reasoner.rulesys | |
|---|---|
interface |
BackwardRuleInfGraphI
This interface collects together those operations that the backchaining engine needs to invoke in the parent InfGraph. |
interface |
ForwardRuleInfGraphI
This interface collects together the operations on the InfGraph which are needed to support the forward rule engine. |
| Classes in com.hp.hpl.jena.reasoner.rulesys that implement Graph | |
|---|---|
class |
BasicForwardRuleInfGraph
An inference graph interface that runs a set of forward chaining rules to conclusion on each added triple and stores the entire result set. |
class |
FBRuleInfGraph
An inference graph that uses a mixture of forward and backward chaining rules. |
class |
LPBackwardRuleInfGraph
Inference graph for accessing the LP version of the backward chaining rule engine. |
class |
RDFSRuleInfGraph
Customization of the generic rule inference graph for RDFS inference. |
class |
RETERuleInfGraph
RETE implementation of the forward rule infernce graph. |
| Methods in com.hp.hpl.jena.reasoner.rulesys that return Graph | |
|---|---|
Graph |
ForwardRuleInfGraphI.getCurrentDeductionsGraph()
Return the Graph containing all the static deductions available so far. |
Graph |
BasicForwardRuleInfGraph.getCurrentDeductionsGraph()
Return the Graph containing all the static deductions available so far. |
Graph |
ForwardRuleInfGraphI.getDeductionsGraph()
Return the Graph containing all the static deductions available so far. |
Graph |
BasicForwardRuleInfGraph.getDeductionsGraph()
Return the Graph containing all the static deductions available so far. |
Graph |
BasicFBReifier.getParentGraph()
|
Graph |
LPBackwardRuleInfGraph.getSchemaGraph()
Return the schema graph, if any, bound into this inference graph. |
Graph |
BasicForwardRuleInfGraph.getSchemaGraph()
Return the schema graph, if any, bound into this inference graph. |
| Methods in com.hp.hpl.jena.reasoner.rulesys with parameters of type Graph | |
|---|---|
InfGraph |
RDFSRuleReasoner.bind(Graph data)
Attach the reasoner to a set of RDF data to process. |
InfGraph |
OWLFBRuleReasoner.bind(Graph data)
Attach the reasoner to a set of RDF data to process. |
InfGraph |
LPBackwardRuleReasoner.bind(Graph data)
Attach the reasoner to a set of RDF data to process. |
InfGraph |
GenericRuleReasoner.bind(Graph data)
Attach the reasoner to a set of RDF data to process. |
InfGraph |
FBRuleReasoner.bind(Graph data)
Attach the reasoner to a set of RDF data to process. |
InfGraph |
BasicForwardRuleReasoner.bind(Graph data)
Attach the reasoner to a set of RDF data to process. |
Reasoner |
RDFSRuleReasoner.bindSchema(Graph tbox)
Precompute the implications of a schema graph. |
Reasoner |
OWLFBRuleReasoner.bindSchema(Graph tbox)
Precompute the implications of a schema graph. |
Reasoner |
LPBackwardRuleReasoner.bindSchema(Graph tbox)
Precompute the implications of a schema graph. |
Reasoner |
GenericRuleReasoner.bindSchema(Graph tbox)
Precompute the implications of a schema graph. |
Reasoner |
FBRuleReasoner.bindSchema(Graph tbox)
Precompute the implications of a schema graph. |
Reasoner |
BasicForwardRuleReasoner.bindSchema(Graph tbox)
Precompute the implications of a schema graph. |
static Node |
Util.getPropValue(Node root,
Node prop,
Graph context)
Helper - returns the (singleton) value for the given property on the given root node in the data graph. |
static Node |
Util.makeList(Node[] nodes,
Graph graph)
Construct an RDF list from the given array of nodes and assert it in the graph returning the head of the list. |
void |
LPBackwardRuleInfGraph.rebind(Graph data)
Replace the underlying data graph for this inference graph and start any inferences over again. |
void |
BasicForwardRuleInfGraph.rebind(Graph data)
Replace the underlying data graph for this inference graph and start any inferences over again. |
void |
RulePreprocessHook.run(FBRuleInfGraph infGraph,
Finder dataFind,
Graph inserts)
Invoke the preprocessing hook. |
| Constructors in com.hp.hpl.jena.reasoner.rulesys with parameters of type Graph | |
|---|---|
BasicForwardRuleInfGraph(Reasoner reasoner,
Graph schema)
Constructor. |
|
BasicForwardRuleInfGraph(Reasoner reasoner,
java.util.List rules,
Graph schema)
Constructor. |
|
BasicForwardRuleInfGraph(Reasoner reasoner,
java.util.List rules,
Graph schema,
Graph data)
Constructor. |
|
BasicForwardRuleInfGraph(Reasoner reasoner,
java.util.List rules,
Graph schema,
ReificationStyle style)
|
|
FBRuleInfGraph(Reasoner reasoner,
Graph schema)
Constructor. |
|
FBRuleInfGraph(Reasoner reasoner,
java.util.List rules,
Graph schema)
Constructor. |
|
FBRuleInfGraph(Reasoner reasoner,
java.util.List rules,
Graph schema,
Graph data)
Constructor. |
|
FBRuleInfGraph(Reasoner reasoner,
java.util.List rules,
Graph schema,
ReificationStyle style)
|
|
LPBackwardRuleInfGraph(Reasoner reasoner,
com.hp.hpl.jena.reasoner.rulesys.impl.LPRuleStore ruleStore,
Graph data,
Graph schema)
Constructor. |
|
RDFSRuleInfGraph(Reasoner reasoner,
java.util.List rules,
Graph schema)
Constructor. |
|
RDFSRuleInfGraph(Reasoner reasoner,
java.util.List rules,
Graph schema,
Graph data)
Constructor. |
|
RETERuleInfGraph(Reasoner reasoner,
Graph schema)
Constructor. |
|
RETERuleInfGraph(Reasoner reasoner,
java.util.List rules,
Graph schema)
Constructor. |
|
RETERuleInfGraph(Reasoner reasoner,
java.util.List rules,
Graph schema,
Graph data)
Constructor. |
| Uses of Graph in com.hp.hpl.jena.reasoner.transitiveReasoner |
|---|
| Classes in com.hp.hpl.jena.reasoner.transitiveReasoner that implement Graph | |
|---|---|
class |
TransitiveInfGraph
Implementation of InfGraph used by the TransitiveReasoner. |
| Methods in com.hp.hpl.jena.reasoner.transitiveReasoner that return Graph | |
|---|---|
Graph |
TransitiveInfGraph.getSchemaGraph()
Return the schema graph, if any, bound into this inference graph. |
| Methods in com.hp.hpl.jena.reasoner.transitiveReasoner with parameters of type Graph | |
|---|---|
InfGraph |
TransitiveReasoner.bind(Graph data)
Attach the reasoner to a set of RDF ddata to process. |
Reasoner |
TransitiveReasoner.bindSchema(Graph tbox)
Extracts all of the subClass and subProperty declarations from the given schema/tbox and caches the resultant graphs. |
boolean |
TransitiveEngine.checkOccurance(Node prop,
Graph graph)
Test if there are any usages of prop within the given graph. |
static boolean |
TransitiveEngine.checkOccuranceUtility(Node prop,
Graph graph,
TransitiveGraphCache spCache)
Test if there are any usages of prop within the given graph. |
| Constructors in com.hp.hpl.jena.reasoner.transitiveReasoner with parameters of type Graph | |
|---|---|
TransitiveInfGraph(Graph data,
TransitiveReasoner reasoner)
Constructor. |
| Uses of Graph in com.hp.hpl.jena.shared |
|---|
| Classes in com.hp.hpl.jena.shared that implement Graph | |
|---|---|
class |
RandomOrderGraph
Wraps a graph and randomizes the order of find results. |
| Methods in com.hp.hpl.jena.shared that return Graph | |
|---|---|
static Graph |
RandomOrderGraph.createDefaultGraph()
|
Graph |
ClosedException.getGraph()
Answer the graph that this exception was constructed with. |
| Constructors in com.hp.hpl.jena.shared with parameters of type Graph | |
|---|---|
ClosedException(java.lang.String message,
Graph graph)
|
|
RandomOrderGraph(Graph base)
|
|
RandomOrderGraph(int bufsz,
Graph base)
|
| Uses of Graph in com.hp.hpl.jena.util |
|---|
| Classes in com.hp.hpl.jena.util that implement Graph | |
|---|---|
class |
MonitorGraph
Graph wrapper which provides normal access to an underlying graph but also maintains a snapshot of the triples it was last known to contain. |
| Methods in com.hp.hpl.jena.util that return Graph | |
|---|---|
Graph |
QueryMapper.getGraph()
|
Graph |
QueryMapper.toQueryGraph(Model m)
|
| Constructors in com.hp.hpl.jena.util with parameters of type Graph | |
|---|---|
MonitorGraph(Graph g)
Constructor, wrap the given graph with a state monitor |
|
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| 1 |
UNKNOWN_1993 |
Membrane protein retention in the yeast Golgi apparatus: dipeptidyl aminopeptidase A is retained by a cytoplasmic signal containing aromatic residues |
None |
1993 |
1993-06-02 |
NONCOMM |
N |
PMC2119699 |
8509444.0 |
|
0pz80zbg |
0.996873 |
Wang_2013, UNKNOWN_1993, UNKNOWN_1993, UNKNOWN_1995, Bryant_1997 |
UNKNOWN_1995, UNKNOWN_1991, UNKNOWN_1992 |
| 2 |
UNKNOWN_1986 |
Signal recognition particle-dependent membrane insertion of mouse invariant chain: a membrane-spanning protein with a cytoplasmically exposed amino terminus |
None |
1986 |
1986-06-01 |
NONCOMM |
N |
PMC2114261 |
3458708.0 |
|
4sw25blb |
0.992806 |
UNKNOWN_1993, Lipp_1986, Wang_2013, UNKNOWN_1992 |
Lipp_1986, UNKNOWN_1995 |
| 3 |
UNKNOWN_1994 |
Sorting signals in the MHC class II invariant chain cytoplasmic tail and transmembrane region determine trafficking to an endocytic processing compartment |
None |
1994 |
1994-07-02 |
NONCOMM |
N |
PMC2200027 |
8034737.0 |
|
9r7rqqqk |
0.987396 |
UNKNOWN_1995, UNKNOWN_1986, Jonker_2018, Wang_2013 |
UNKNOWN_1995, UNKNOWN_1995, UNKNOWN_1994 |
| 4 |
UNKNOWN_1993 |
Localization of TGN38 to the trans-Golgi network: involvement of a cytoplasmic tyrosine-containing sequence |
None |
1993 |
1993-03-01 |
NONCOMM |
N |
PMC2119736 |
8436587.0 |
|
qt44izzh |
0.978067 |
Wang_2013, UNKNOWN_1993, UNKNOWN_1995, UNKNOWN_1993 |
UNKNOWN_1995, Bryant_1997 |
| 5 |
Lipp_1986 |
The membrane-spanning segment of invariant chain (Iγ) contains a potentially cleavable signal sequence |
Lipp, Joachim; Dobberstein, Bernhard |
1986 |
1986-09-26 |
PMC |
N |
PMC7133317 |
3530500.0 |
10.1016/0092-8674(86)90710-5 |
bli8hwda |
0.967880 |
UNKNOWN_1993, UNKNOWN_1986, UNKNOWN_1987, UNKNOWN_1993 |
UNKNOWN_1987 |
| 6 |
UNKNOWN_1995 |
Localization and targeting of the Saccharomyces cerevisiae Kre2p/Mnt1p alpha 1,2-mannosyltransferase to a medial-Golgi compartment |
None |
1995 |
1995-11-02 |
NONCOMM |
N |
PMC2200012 |
7490293.0 |
|
q1jx0n0l |
0.962835 |
UNKNOWN_1993, Wang_2013, UNKNOWN_1987, UNKNOWN_1993, UNKNOWN_1993 |
UNKNOWN_1996, UNKNOWN_1995, UNKNOWN_1992 |
| 7 |
UNKNOWN_1993 |
The amino-terminal domain of the lamin B receptor is a nuclear envelope targeting signal |
None |
1993 |
1993-03-01 |
NONCOMM |
N |
PMC2119726 |
7679672.0 |
|
377v2ufn |
0.952007 |
UNKNOWN_1993, UNKNOWN_1987, UNKNOWN_1993, Wang_2013 |
UNKNOWN_1993, UNKNOWN_1987, UNKNOWN_1995 |
| 8 |
UNKNOWN_1993 |
The first membrane spanning region of the lamin B receptor is sufficient for sorting to the inner nuclear membrane |
None |
1993 |
1993-02-01 |
NONCOMM |
N |
PMC2119546 |
8381121.0 |
|
3qi2llmr |
0.949768 |
UNKNOWN_1993, UNKNOWN_1987, Wang_2013, UNKNOWN_1993 |
UNKNOWN_1993, UNKNOWN_1987, UNKNOWN_1991 |
| 9 |
Bryant_1997 |
Two Separate Signals Act Independently to Localize a Yeast Late Golgi Membrane Protein through a Combination of Retrieval and Retention |
Bryant, Nia J.; Stevens, Tom H. |
1997 |
1997-01-27 |
NONCOMM |
N |
PMC2134822 |
9015300.0 |
|
cqvnxsbo |
0.905332 |
UNKNOWN_1995, Wang_2013, UNKNOWN_1993, Jonker_2018 |
UNKNOWN_1993, UNKNOWN_1996, UNKNOWN_1995 |
| 10 |
UNKNOWN_1992 |
Membrane protein sorting in the yeast secretory pathway: evidence that the vacuole may be the default compartment |
None |
1992 |
1992-10-01 |
NONCOMM |
N |
PMC2289628 |
1527174.0 |
|
choywmwd |
0.874314 |
UNKNOWN_1995, Wang_2013, UNKNOWN_1993, UNKNOWN_1993 |
UNKNOWN_1995, UNKNOWN_1993 |
| 11 |
UNKNOWN_1996 |
A pathway for targeting soluble misfolded proteins to the yeast vacuole |
None |
1996 |
1996-11-01 |
NONCOMM |
N |
PMC2121066 |
8909538.0 |
|
i7d8c8ct |
0.859147 |
Wang_2013, UNKNOWN_1993, UNKNOWN_1987, UNKNOWN_1993 |
UNKNOWN_1995, UNKNOWN_1994, UNKNOWN_1996 |
| 12 |
UNKNOWN_1995 |
Targeting of protein ERGIC-53 to the ER/ERGIC/cis-Golgi recycling pathway |
None |
1995 |
1995-10-01 |
NONCOMM |
N |
PMC2120588 |
7559786.0 |
|
7oklz2ch |
0.842268 |
Wang_2013, UNKNOWN_1993, UNKNOWN_1995, UNKNOWN_1993, Jonker_2018 |
UNKNOWN_1995, UNKNOWN_1996 |
| 13 |
UNKNOWN_1992 |
Yeast Kex1p is a Golgi-associated membrane protein: deletions in a cytoplasmic targeting domain result in mislocalization to the vacuolar membrane |
None |
1992 |
1992-12-02 |
NONCOMM |
N |
PMC2289743 |
1469044.0 |
|
ucguzgdm |
0.804956 |
UNKNOWN_1995, Wang_2013, UNKNOWN_1993, Jonker_2018 |
UNKNOWN_1996, UNKNOWN_1995, UNKNOWN_1995 |
| 14 |
UNKNOWN_1987 |
A specific transmembrane domain of a coronavirus E1 glycoprotein is required for its retention in the Golgi region |
None |
1987 |
1987-09-01 |
NONCOMM |
N |
PMC2114809 |
2821010.0 |
|
eblj0qw6 |
0.765471 |
UNKNOWN_1993, UNKNOWN_1993, Wang_2013 |
UNKNOWN_1991 |
| 15 |
UNKNOWN_1994 |
Retention of p63 in an ER-Golgi intermediate compartment depends on the presence of all three of its domains and on its ability to form oligomers |
None |
1994 |
1994-07-01 |
NONCOMM |
N |
PMC2120087 |
8027183.0 |
|
ra20actc |
0.737173 |
Wang_2013, UNKNOWN_1993, UNKNOWN_1993, UNKNOWN_1993 |
UNKNOWN_1995, UNKNOWN_1994 |
| 16 |
Wang_2013 |
The First Transmembrane Domain of Lipid Phosphatase SAC1 Promotes Golgi Localization |
Wang, Jinzhi; Chen, Juxing; Enns, Caroline A.; Mayinger, Peter |
2013 |
2013-08-01 |
COMM-USE |
N |
PMC3731292 |
23936490.0 |
10.1371/journal.pone.0071112 |
fhl0ly3x |
0.706866 |
UNKNOWN_1995, UNKNOWN_1993, Jonker_2018, UNKNOWN_1993 |
UNKNOWN_1995, UNKNOWN_1996 |
| 17 |
UNKNOWN_1995 |
Oligomerized transferrin receptors are selectively retained by a lumenal sorting signal in a long-lived endocytic recycling compartment |
None |
1995 |
1995-06-02 |
NONCOMM |
N |
PMC2291173 |
7790351.0 |
|
9tdbygve |
0.677692 |
Jonker_2018, UNKNOWN_1995, UNKNOWN_1993, Wang_2013 |
UNKNOWN_1995, UNKNOWN_1994, UNKNOWN_1994 |
| 18 |
UNKNOWN_1996 |
Endoplasmic reticulum localization of Sec12p is achieved by two mechanisms: Rer1p-dependent retrieval that requires the transmembrane domain and Rer1p-independent retention that involves the cytoplasmic domain |
None |
1996 |
1996-07-02 |
NONCOMM |
N |
PMC2120888 |
8707815.0 |
|
45x96b5d |
0.648628 |
Wang_2013, UNKNOWN_1993, UNKNOWN_1995, UNKNOWN_1987 |
Fu_J_2000, UNKNOWN_1995, Mades_2012 |
| 19 |
UNKNOWN_1991 |
A Golgi retention signal in a membrane-spanning domain of coronavirus E1 protein |
None |
1991 |
1991-10-01 |
NONCOMM |
N |
PMC2289920 |
1655802.0 |
|
s4a8zs5a |
0.643075 |
UNKNOWN_1987, UNKNOWN_1993, UNKNOWN_1993, Wang_2013, UNKNOWN_1993 |
UNKNOWN_1987, UNKNOWN_1995, Chen_1991, UNKNOWN_1993 |
| 20 |
Rayner_1997 |
Transmembrane domain-dependent sorting of proteins to the ER and plasma membrane in yeast. |
Rayner, J C; Pelham, H R |
1997 |
1997-04-15 |
PMC |
N |
PMC1169786 |
9155009.0 |
10.1093/emboj/16.8.1832 |
upxr2r03 |
0.634358 |
Cosson_2013, Wang_2013, UNKNOWN_1995, UNKNOWN_1993, Jonker_2018 |
Fu_J_2000, UNKNOWN_1995 |
| 21 |
UNKNOWN_1995 |
trans-Golgi retention of a plasma membrane protein: mutations in the cytoplasmic domain of the asialoglycoprotein receptor subunit H1 result in trans-Golgi retention |
None |
1995 |
1995-07-02 |
NONCOMM |
N |
PMC2199941 |
7615632.0 |
|
tedj3xxz |
0.633742 |
UNKNOWN_1993, UNKNOWN_1993, UNKNOWN_1987, UNKNOWN_1993, Wang_2013 |
UNKNOWN_1991, UNKNOWN_1994 |
| 22 |
Cosson_2013 |
Anchors aweigh: protein localization and transport mediated by transmembrane domains |
Cosson, Pierre; Perrin, Jackie; Bonifacino, Juan S. |
2013 |
2013-10-31 |
None |
N |
PMC3783643 |
23806646.0 |
10.1016/j.tcb.2013.05.005 |
a1uofzv3 |
0.601576 |
Jonker_2018, UNKNOWN_1993 |
Fu_J_2000, Rayner_1997 |
| 23 |
UNKNOWN_1994 |
The v-sis oncoprotein loses transforming activity when targeted to the early Golgi complex |
None |
1994 |
1994-12-02 |
NONCOMM |
N |
PMC2120273 |
7806564.0 |
|
2otgb2w8 |
0.588268 |
UNKNOWN_1993, UNKNOWN_1993, Wang_2013 |
UNKNOWN_1995, Chen_1991, UNKNOWN_1991, Melin_1995 |
| 24 |
UNKNOWN_1989 |
Structure, biosynthesis, and localization of dipeptidyl aminopeptidase B, an integral membrane glycoprotein of the yeast vacuole |
None |
1989 |
1989-04-01 |
NONCOMM |
N |
PMC2115513 |
2647766.0 |
|
imkozbf1 |
0.578222 |
Wang_2013, UNKNOWN_1995, UNKNOWN_1993, UNKNOWN_1993, UNKNOWN_1992 |
UNKNOWN_1989 |
| 25 |
UNKNOWN_1992 |
The 17-residue transmembrane domain of beta-galactoside alpha 2,6- sialyltransferase is sufficient for Golgi retention |
None |
1992 |
1992-04-02 |
NONCOMM |
N |
PMC2289426 |
1560026.0 |
|
93tbgn2b |
0.532602 |
UNKNOWN_1993, Wang_2013, UNKNOWN_1995, UNKNOWN_1986 |
UNKNOWN_1995 |
| 26 |
Reaves_1994 |
Overexpression of TGN38/41 leads to mislocalisation of γ-adaptin |
Reaves, Barbara; Banting, George |
1994 |
1994-09-12 |
PMC |
N |
|
8082813.0 |
10.1016/0014-5793(94)00813-2 |
n8gotxju |
0.515956 |
Wang_2013, UNKNOWN_1987, UNKNOWN_1993, UNKNOWN_1993 |
UNKNOWN_1993, UNKNOWN_1994, UNKNOWN_1991 |
| 27 |
UNKNOWN_1992 |
The single transmembrane segment of gp210 is sufficient for sorting to the pore membrane domain of the nuclear envelope |
None |
1992 |
1992-12-02 |
NONCOMM |
N |
PMC2289754 |
1281815.0 |
|
vznqgnzd |
0.508972 |
UNKNOWN_1993, UNKNOWN_1993, Wang_2013, UNKNOWN_1993, UNKNOWN_1987 |
UNKNOWN_1991, UNKNOWN_1995, UNKNOWN_1993 |
| 28 |
Fu_J_2000 |
Localization of ribophorin II to the endoplasmic reticulum involves both its transmembrane and cytoplasmic domains |
Fu, Jie; Pirozzi, Gregorio; Sanjay, Archana; Levy, Robert; Chen, Yanru; De Lemos-Chiarandini, Carmen; Sabatini, David; Kreibich, Gert |
2000 |
2000-04-30 |
PMC |
N |
PMC7134489 |
10826490.0 |
10.1078/s0171-9335(04)70025-4 |
een3sgpt |
0.508726 |
Wang_2013, UNKNOWN_1987, UNKNOWN_1993, UNKNOWN_1993, UNKNOWN_1995 |
UNKNOWN_1995 |
| 29 |
Cool_1994 |
Identification of a sorting signal for the regulated secretory pathway at the N-terminus of pro-opiomelanocortin |
Cool, D.R.; Peng Loh, Y. |
1994 |
1994-12-31 |
PMC |
N |
PMC7131109 |
7819333.0 |
10.1016/0300-9084(94)90156-2 |
og3hdnl2 |
0.488257 |
UNKNOWN_1993, UNKNOWN_1993, UNKNOWN_1987 |
|
| 30 |
Perrier_2019 |
The C-terminal domain of the MERS coronavirus M protein contains a trans-Golgi network localization signal |
Perrier, Anabelle; Bonnin, Ariane; Desmarets, Lowiese; Danneels, Adeline; Goffard, Anne; Rouillé, Yves; Dubuisson, Jean; Belouzard, Sandrine |
2019 |
2019-08-09 |
PMC |
N |
PMC6768645 |
31399512.0 |
10.1074/jbc.ra119.008964 |
tjg0b18p |
0.451372 |
UNKNOWN_1995, Wang_2013, UNKNOWN_1993, UNKNOWN_1987 |
UNKNOWN_1991, Chen_1991 |
| 31 |
UNKNOWN_1996 |
Localization of a yeast early Golgi mannosyltransferase, Och1p, involves retrograde transport |
None |
1996 |
1996-03-02 |
NONCOMM |
N |
PMC2120767 |
8601597.0 |
|
h8e2g5et |
0.451081 |
UNKNOWN_1995, Wang_2013, UNKNOWN_1993, Jonker_2018, UNKNOWN_1987 |
UNKNOWN_1995 |
| 32 |
Minakshi_2014 |
The YXXΦ motif within the severe acute respiratory syndrome coronavirus (SARS-CoV) 3a protein is crucial for its intracellular transport |
Minakshi, Rinki; Padhan, Kartika |
2014 |
2014-04-24 |
COMM-USE |
N |
PMC4004515 |
24762043.0 |
10.1186/1743-422x-11-75 |
ufu4uy2k |
0.433229 |
UNKNOWN_1987, UNKNOWN_1993, UNKNOWN_1993, Wang_2013 |
|
| 33 |
Chen_1991 |
Golgi complex localization of the Punta Toro virus G2 protein requires its association with the G1 protein |
Chen, Si-Yi; Matsuoka, Yumiko; Compans, Richard W. |
1991 |
1991-07-31 |
PMC |
N |
PMC7130718 |
1905078.0 |
10.1016/0042-6822(91)90148-5 |
qbmcw4ck |
0.432677 |
UNKNOWN_1987, UNKNOWN_1993, UNKNOWN_1993, Wang_2013 |
Melin_1995, UNKNOWN_1991 |
| 34 |
Nieto-Torres_2011 |
Subcellular location and topology of severe acute respiratory syndrome coronavirus envelope protein |
Nieto-Torres, Jose L.; DeDiego, Marta L.; Álvarez, Enrique; Jiménez-Guardeño, Jose M.; Regla-Nava, Jose A.; Llorente, Mercedes; Kremer, Leonor; Shuo, Shen; Enjuanes, Luis |
2011 |
2011-07-05 |
PMC |
N |
PMC4726981 |
21524776.0 |
10.1016/j.virol.2011.03.029 |
v369vond |
0.422446 |
UNKNOWN_1993, UNKNOWN_1993, UNKNOWN_1993 |
|
| 35 |
UNKNOWN_1994 |
Clathrin-dependent localization of alpha 1,3 mannosyltransferase to the Golgi complex of Saccharomyces cerevisiae |
None |
1994 |
1994-11-01 |
NONCOMM |
N |
PMC2120240 |
7962051.0 |
|
mch4wbui |
0.418225 |
UNKNOWN_1995, Wang_2013, UNKNOWN_1993, Jonker_2018, UNKNOWN_1992 |
UNKNOWN_1996 |
| 36 |
UNKNOWN_1985 |
Deletions into an NH2-terminal hydrophobic domain result in secretion of rotavirus VP7, a resident endoplasmic reticulum membrane glycoprotein |
None |
1985 |
1985-12-01 |
NONCOMM |
N |
PMC2113985 |
2999159.0 |
|
zrv9fjgn |
0.414622 |
UNKNOWN_1987, UNKNOWN_1993, UNKNOWN_1993, UNKNOWN_1993 |
|
| 37 |
UNKNOWN_1993 |
Oligomerization of a membrane protein correlates with its retention in the Golgi complex |
None |
1993 |
1993-09-02 |
NONCOMM |
N |
PMC2119850 |
8397214.0 |
|
5z1xminb |
0.382759 |
UNKNOWN_1987, Wang_2013, UNKNOWN_1993, UNKNOWN_1995, UNKNOWN_1993 |
UNKNOWN_1991, UNKNOWN_1995, Chen_1991 |
| 38 |
Luo_W_1997 |
Novel Genes Involved in Endosomal Traffic in Yeast Revealed by Suppression of a Targeting-defective Plasma Membrane ATPase Mutant |
Luo, Wen-jie; Chang, Amy |
1997 |
1997-08-25 |
NONCOMM |
N |
PMC2138039 |
9265642.0 |
|
hwdplh24 |
0.327976 |
UNKNOWN_1995, Jonker_2018, Wang_2013, UNKNOWN_1993 |
UNKNOWN_1996, UNKNOWN_1995 |


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In this tutorial, we are going to find out the stock of which companies grew the most in a given month.
If you have not installed PIG - please follow the steps in this tutorial.
Lets get started !!!
Time to move on to some real world cases. This script is using just 1 month of data. We are going to download the Nasdaq data for May 2016 and find out the top 10 companies whose stock grew the most that month.
Step 1: Download the Nasdaq data
There are 2 files here. One is a zip file which contains the daily data and the other file contains the stock symbol and company name.
wget https://s3.amazonaws.com/omairaasim.github.io/data/nasdaq_may_2016.zip
wget https://s3.amazonaws.com/omairaasim.github.io/data/nasdaq_company_list.csv
Step 2: Copy the data into HDFS
First lets unzip the file and copy it into HDFS.
mkdir nasdaq
unzip nasdaq_may_2016.zip -d /home/hadoop/nasdaq/
If you dont have unzip - install it using
sudo apt-get install unzip
Now lets copy the files to HDFS
hadoop fs -copyFromLocal /home/hadoop/nasdaq /data/pig/nasdaq/stocks
hadoop fs -copyFromLocal /home/hadoop/nasdaq_company_list.csv /data/pig/nasdaq
You can verify if the files are copied using
hadoop fs -ls /data/pig/nasdaq/stocks
hadoop fs -ls /data/pig/nasdaq/
Step 3: Load the data into PIG
Start pig.
In Pig, while loading data - if you just specify the folder name - Pig will upload all the files in that folder. Since we have many daily stock files - we will just specify the folder name to upload data.
Secondly this data contains header row. To skip header row while uploading data, we can use CSVExcelStorage from piggybank.jar
nasdaq_data = LOAD '/data/pig/nasdaq/stocks' USING org.apache.pig.piggybank.storage.CSVExcelStorage(',','NO_MULTILINE','UNIX','SKIP_INPUT_HEADER') AS (symbol:chararray, date:chararray, open:float, high:float,low:float,close:float, volume:long);
nasdaq_company_list = LOAD '/data/pig/nasdaq/nasdaq_company_list.csv' USING PigStorage(',') AS (symbol:chararray, name:chararray);
The parameters CSVExcelStorage takes are
- Delimiter
- ‘NO_MULTILINE’: used to specify whether fields contain multiple lines.
- ‘UNIX’: used to specify operating system.
- ‘SKIP_INPUT_HEADER’: used to specify if you want to skip the first row.
If we do a dump, we will see the data is in this format
dump nasdaq_data;
(AAAP,17-May-16,28.13,28.76,27.75,27.86,74500)
(AAL,17-May-16,32.25,33.29,32.02,32.64,13303400)
(AAME,17-May-16,3.45,3.53,3.31,3.35,5900)
(AAOI,17-May-16,9.29,9.56,9.17,9.23,379400)
(AAON,17-May-16,28.27,28.27,27.0,27.24,214400)
Step 4: Filter Data by date
Since we want to calculate the growth - we only need to stock prices of the beginning of the month and end of the month. The beginning of May month starts on May 2nd and ends on 31st May. So we are interested in only these 2 dates.
So lets filter out May 2nd and May 31st data using the FILTER operator. Since we are comparing dates, I’m using the ToDate function to convert the chararray to date.
may2data = FILTER nasdaq_data BY ToDate(date,'dd-MMM-yy') == ToDate('02-May-16','dd-MMM-yy');
may31data = FILTER nasdaq_data BY ToDate(date,'dd-MMM-yy') == ToDate('31-May-16','dd-MMM-yy');
Lets dump may2data and may31data data to see some if filtering is done correctly
dump may2data;
(ZNGA,2-May-16,2.39,2.4,2.34,2.37,9780600)
(ZSAN,2-May-16,2.17,2.17,2.01,2.02,3100)
(ZUMZ,2-May-16,16.89,16.99,16.39,16.66,390000)
(ZYNE,2-May-16,8.13,8.13,7.6,7.77,112800)
That looks right. Lets confirm may31data
dump may31data;
(ZN,31-May-16,1.63,1.63,1.56,1.58,21700)
(ZNGA,31-May-16,2.58,2.6,2.55,2.57,7696500)
(ZUMZ,31-May-16,14.54,14.98,14.49,14.88,700800)
(ZYNE,31-May-16,7.75,9.29,7.75,8.74,456200)
So the filtered data looks good.
Step 5: Perform a Join
Since we need the closing price of both 2nd May and 31st May - lets join these 2 relations.
join_data = JOIN may2data BY symbol, may31data BY symbol;
Lets look at the resulting relation.
dump join_data;
(WPPGY,2-May-16,116.8,118.33,116.71,118.21,108700,WPPGY,31-May-16,116.89,117.3,115.26,115.93,117800)
(WTFCM,2-May-16,27.62,27.85,27.47,27.83,12900,WTFCM,31-May-16,28.26,28.3,27.72,28.3,7600)
(XGTIW,2-May-16,0.11,0.13,0.08,0.13,15200,XGTIW,31-May-16,0.13,0.15,0.11,0.14,37100)
(ZIONW,2-May-16,2.7,2.7,2.7,2.7,15200,ZIONW,31-May-16,3.0,3.01,2.91,2.91,9100)
As you can see for each symbol - we have both the 2nd May and 31st data in one tuple.
Step 6: Calculate Growth rate
Next we will calculate the growth rate.
The formula for calculating growth rate is very simple
May31st closing price - May 2nd closing price
--------------------------------------------- * 100
May 2nd closing price
So lets apply that in PIG. Since the column names are same in both relations may2data and may31data - the way we access them is using the disambiguate operator double colon ::
I’m also using the ROUND_TO to round of the result to 2 decimal places.
growth_rate = FOREACH join_data GENERATE may2data::symbol as symbol,
ROUND_TO(( ((may31data::close-may2data::close)/may2data::close) * 100),2) AS growth;
Lets Dump this to see the output
dump growth_rate;
(WHLRP,1.17)
(WHLRW,-20.0)
(WLRHU,0.76)
(WLRHW,21.31)
(WPPGY,-1.93)
(WTFCM,1.69)
(XGTIW,7.69)
(ZIONW,7.78)
We are almost there.
Step 7: Get the top 10 companies that have grown the most
So in the above step - it shows only the stock symbols. Lets just go 1 step further to get the company names. If you remember in Step 3 we loaded another file nasdaq_company_list which has the company names.
This is how we get the company names.
join_company_name = JOIN growth_rate BY symbol, nasdaq_company_list BY symbol;
growth_rate_company = FOREACH join_company_name GENERATE growth_rate::symbol, nasdaq_company_list::name, growth_rate::growth;
Lets dump to see how this looks
dump growth_rate_company;
(WHLRW,"Wheeler Real Estate Investment Trust,-20.0)
(WPPGY,WPP plc,-1.93)
(WTFCM,Wintrust Financial Corporation,1.69)
(XGTIW,"XG Technology,7.69)
(ZIONW,Zions Bancorporation,7.78)
As you can see - now for each stock symbol - we have the company name.
Step 8: Get the top 10 companies that have grown the most
Next we will find the top 10 companies whose stock has grown the most. This is simple in PIG.
We first order the data by growth and then limit it to 10.
order_data = ORDER growth_rate_company BY growth DESC;
top_10 = LIMIT order_data 10;
Lets dump the data to see
dump top_10;
(SYNC,"Synacor,130.37)
(CCXI,"ChemoCentryx,126.47)
(HOVNP,Hovnanian Enterprises Inc,126.04)
(NSPH,"Nanosphere,117.95)
(CPXX,Celator Pharmaceuticals Inc.,96.73)
(NERV,"Minerva Neurosciences,94.62)
(CLRB,"Cellectar Biosciences,91.18)
(BOSC,B.O.S. Better Online Solutions,90.19)
(PTI,"Proteostasis Therapeutics,76.5)
(AVXS,"AveXis,76.29)
Step 9: Save the script
Lets save it as a pride_and_prejudice.pig file
STORE order_count INTO '/output/pig/pride_and_prejudice';
Step 10: Output
The output is saved in the hdfs folder we specified in step 9. To view the results - you can use this command.
hadoop fs -cat /output/pig/pride_and_prejudice/part-r-00000
elizabeth 633
jane 293
collins 180
lydia 170
kitty 70
caroline 17
george 8
charles 7
Here is the complete script
pnp_book = LOAD '/data/pig/pride_and_prejudice.txt' AS (lines:chararray);
tokens = FOREACH pnp_book GENERATE FLATTEN (TOKENIZE(REPLACE(lines, '\'[s]|[\\.;,!:]',''))) AS word;
character_names = FILTER tokens BY
(
LOWER(word) matches LOWER('Elizabeth') OR
LOWER(word) matches LOWER('Collins') OR
LOWER(word) matches LOWER('George') OR
LOWER(word) matches LOWER('Jane') OR
LOWER(word) matches LOWER('Lydia') OR
LOWER(word) matches LOWER('Caroline') OR
LOWER(word) matches LOWER('Kitty') OR
LOWER(word) matches LOWER('Charles')
);
group_by_name = GROUP character_names BY LOWER(word);
count_names = FOREACH group_by_name GENERATE group, COUNT(character_names) as total_count;
order_count = ORDER count_names BY total_count DESC;
STORE order_count INTO '/output/pig/pride_and_prejudice';
As you can see - this is nothing but an extension of word count program but we’ve used it to do a simple analysis.


描述
You are playing the following Bulls and Cows game with your friend: You write down a number and ask your friend to guess what the number is. Each time your friend makes a guess, you provide a hint that indicates how many digits in said guess match your secret number exactly in both digit and position (called “bulls”) and how many digits match the secret number but locate in the wrong position (called “cows”). Your friend will use successive guesses and hints to eventually derive the secret number.


THE BIG LEBOWSKI (1998)
"All The Dude ever wanted was his rug back."
3 out of ****
Starring Jeff Bridges, John Goodman, Julianne Moore Directed by Joel Coen Written by Joel & Ethan Coen Cinematography by Roger Deakins
The Coen brothers make two kinds of movies. The first kind are idiosyncratic takes on established Hollywood genres: BLOOD SIMPLE, MILLER'S CROSSING, THE HUDSUCKER PROXY. The second kind are just plain idiosyncratic: dark comedies which can only be described as Coen-esque, and represent their best work (RAISING ARIZONA, BARTON FINK, FARGO). Their latest effort, THE BIG LEBOWSKI, belongs in the latter category, in that it is a non-generic dark comedy (unless KINGPIN-meets-EXCESS BAGGAGE is a genre)--but it is not one of their better efforts.
Jeff Bridges stars as Jeff Lebowski, aka The Dude, Duder, or, if you're not into the brevity thing, El Duderino. He's a pretty simple L.A. guy with a pretty simple life: he's unemployed, he smokes pot, and League Bowling is his first priority. He bowls with opinionated, borderline-psychotic Vietnam vet Walter (John Goodman) and dimwitted Donny (Steve Buscemi). But the Dude's simple life is complicated when two men burst into his apartment, dunk his head in the toilet, ask him to cough up a lot of money which he doesn't have, then urinate on his rug when he can't help them. It turns out that these men were looking for another Jeffrey Lebowski (David Huddleston), this one a millionaire, and when The Dude goes to the other Lebowski's mansion to get his rug replaced, he is soon embroiled in a complex plot involving a kidnapping, a botched ransom exchange, a severed toe, a nymphomaniac porn starlet (Tara Reid), some neo-Nazis, a flaky New Age feminist (Julianne Moore), and malicious law enforcement officials.
THE BIG LEBOWSKI is certainly idiosyncratic, but there is a sense of strain about it. It's cluttered with contrived attempts to be original and offbeat: there's a man in an iron lung, a philosophizing character called The Stranger (Sam Elliot), an elaborate dream-sequence production number. By trying so hard, it comes across as forced; the Coens' vision is naturally skewed, and by pushing it they needlessly call attention to that fact.
There is a lot wrong with the movie, but since it's a comedy, the fundamental issue is this: is it funny? The humour is irreverent in the extreme: death, pornography, child abuse, and disabled people are treated purely as sources of humour. Some people in our PC 90s will doubtless object to this material on principle; my feeling is that if it's funny, it doesn't matter how offensive it is--when something's not funny, that's when I start to feel offended. Fortunately, THE BIG LEBOWSKI is very funny, and that is its saving grace. Bridges wins a lot of laughs as the hapless, laidback everyman thrown into an absurdist nightmare and unable to extricate himself; Goodman is wonderful as his headstrong best friend whose well-intentioned interference just makes matters worse and worse.
The other characters, however, exist only because they're funny, not because the script is particularly interested in them (most notably Julianne Moore's Maude), and the plot is a grab-bag of ideas, many of them inspired, but thrown together in a slapdash way. The movie lacks any sense of coherence, and while all the actors are great, several are wasted in minor roles: John Turturro has fun with a manically over-the-top character, stealing his scenes, but David Thewlis and Jon Polito have only walk-on parts, mildly amusing but unnecessary, while Buscemi is woefully underused. The dialogue is refreshing because it allows the characters to talk in a roundabout, unhurried, verbose way, but frustrating because it takes them an awful long time to get to the point.
The Coen brothers have taken pride, through the years, in doing things their own distinctive way--they confound expectations--but here that strategy has backfired. For many people (myself included) FARGO signalled the Coens' arrival as filmmakers: they had fulfilled their potential, and their next movie was expected to be great. But it seems that they have deliberately set out to not make a great movie in THE BIG LEBOWSKI, confounding the critics once again. It is an extravagant entertainment, but shallow and ultimately pointless. Considered as a self-contained work, it is good; in the context of the Coens' career and their considerable talent, it is a disappointment.
A Review by David Dalgleish (March 6/98)
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


If you are the ‘admin’, we provide the sixth module: data analysis module for you.
When you are login as ‘admin’, you can click on Data Analysis use the data analysis module.
In the data analysis module, you need to select a user, and/or enter a subject, and/or provide a Time period. You can also select any sort, and/or the time range. When you finish you input, press the submit to see the result. If you want to return to the input page, use the Return below the result in the Data Analysis Result page.
Created with the Personal Edition of HelpNDoc: Easy CHM and documentation editor


Further Information and Bibliography
Conformance Statement
The Network Time Protocol (NTP) is used to synchronize the time of a
computer client or server to another server or reference time source,
such as a radio or satellite receiver or modem. It provides accuracies
typically within a millisecond on LANs up to a few tens of milliseconds
on WANs relative to Coordinated Universal Time (UTC), as provided by a
Global Positioning Service (GPS) receiver, for example. Typical NTP
configurations utilize multiple redundant servers and diverse network
paths, in order to achieve high accuracy and reliability. Some
configurations include cryptographic authentication to prevent
accidental or malicious protocol attacks. Information on the NTP
architecture, protocol and algorithms can be found in the following
articles and reports, which are available online
- Mills, D.L. Internet time synchronization: the Network Time Protocol.
IEEE Trans. Communications COM-39, 10 (October 1991), 1482-1493.
(PostScript). Also in: Yang, Z., and T.A. Marsland (Eds.). Global
States and Time in Distributed Systems. IEEE Computer Society Press,
Los Alamitos, CA, 1994, 91-102. Condensed from: Ibid. Network
Working Group Report RFC-1129, University of Delaware, October 1989.
(Abstract: PostScript), (Body:
PostScript). Also published as: Electrical Engineering Department
Report 89-9-1, University of Delaware, September 1989. (Abstract:
PostScript), (Body:
PostScript).
The NTP specification and implementation has evolved over the last
fifteen years to the current Version 3 of the protocol. This version
includes significant enhancements in accuracy and reliability, as
determined by experience in an estimated total of well over 100,000
clients and servers in the Internet, while retaining backward
compatibility with previous versions. The formal specification of the NTP
Version 3 protocol is contained in:
- Mills, D.L. Network Time Protocol (Version 3) specification,
implementation and analysis. Network Working Group Report RFC-1305,
University of Delaware, March 1992, 113 pp.
(Abstract: PostScript), (Body:
PostScript),
(Appendices: PostScript).
This software distribution contains a fully compliant implementation
of the NTP Version 3 protocol, including an autonomous protocol daemon
that disciplines the local host clock, as well as a set of supporting
utility programs used to debug and manage one or more NTP servers in a
network. It includes complete sources and documentation for well over
two dozen Unix-based and Microsoft NT-based workstations and file
servers. The implementation is fully compliant with RFC-1305 and, in
addition, contains certain extensions compatible with, but not defined
by, that document. These extensions include:
- Support for precision-time kernel modifications, as described in
- Mills, D.L. Unix kernel modifications for precision time
synchronization. Electrical Engineering Department Report 94-10-1,
University of Delaware, October 1994, 24 pp. (Abstract:
PostScript), (Body:
PostScript). Major revision and update of: Network Working Group
Report RFC-1589, University of Delaware, March 1994. 31 pp. (ASCII).
- Support for IP Multicasting, as described in
- Mills, D.L, and A. Thyagarajan. Network time protocol version 4
proposed changes. Electrical Engineering Department Report 94-10-2,
University of Delaware, October 1994, 32 pp. (Abstract:
PostScript), (Body:
PostScript).
- A new hybrid phase/frequency-lock clock discipline, which
replaces the RFC-1305 local clock algorithm, as described in
- Mills, D.L. Improved algorithms for synchronizing computer network
clocks. IEEE/ACM Trans. Networks (June 1995), 245-254. (PostScript).
- Engineered refinements to radio clock drivers and interface code,
as described in:
- Mills, D.L. Precision synchronization of computer network clocks.
ACM Computer Communication Review 24, 2 (April 1994). 28-43.
(PostScript).
- Support for over two dozen reference clock drivers for all known
national and international radio, satellite and modem standard time
services known at this time. See the
Reference Clock Drivers page.
- Support for the MD5 cryptographic hash algorithm, in addition to
the DES-CBC algorithm described in RFC-1305, as described in the
xntpd
- Network Time Protocol (NTP)
daemon page.
- The prefer-peer scheme, as described in the Mitigation Rules and the
prefer
Keyword
page.
Selected Bibliography
A number of articles and reports have been written on the analysis,
design, implementation and performance of NTP. A representative sample
is included here, along with a short description of each. Additional
information on NTP can be found at the web sites for NTP and David L. Mills
- A subset of NTP has been defined suitable for use where some
degradation in accuracy and reliability is acceptable, so that the
entire suite of specified algorithms need not be implemented. This
subset is defined in:
- Mills, D.L. Simple Network Time Protocol (SNTP). Network Working
Group Report RFC-1769, University of Delaware, March 1995, 14 pp.
(ASCII). Major revision and update of: Ibid. Network Working
Group Report RFC-1361, University of Delaware, August 1992, 10 pp.
(ASCII).
- An assessment of the expected accuracy of NTP operating in the
Internet is contained in the following:
- Mills, D.L. Measured performance of the Network Time Protocol in the
Internet system. Network Working Group Report RFC-1128. University of
Delaware, October 1989.
(Abstract: PostScript), (Body:
PostScript). Also published as: Electrical Engineering Department
Report 89-9-3, University of Delaware, September 1989.
(Abstract: PostScript), (Body:
PostScript).
- Mills, D.L. On the accuracy and stability of clocks synchronized by
the Network Time Protocol in the Internet system. ACM Computer
Communication Review 20, 1 (January 1990), 65-75. (PostScript).
- The following publication explores issues involved in the
development of the NTP specification, including the development of its
timescale and the treatment of leap seconds:
- Mills, D.L. On the chronology and metrology of computer network
timescales and their application to the Network Time Protocol. ACM
Computer Communications Review 21, 5 (October 1991), 8-17. (PostScript).
- A comprehensive analysis of the engineering model used in the NTP
algorithms is given in the following report. This includes a performance
analysis of the clock filter and selection algorithms, clock discipline
feedback loop, and error budget.
- Mills, D.L. Modelling and analysis of computer network clocks.
Electrical Engineering Department Report 92-5-2, University of Delaware,
May 1992, 29 pp. (Abstract:
PostScript), (Body:
PostScript).
David L. Mills (mills@udel.edu)


Python is an interpreted, high-level, general-purpose programming language.
Created by Guido van Rossum and first released in 1991, Python's design philosophy
emphasizes code readability with its notable use of significant whitespace. Its
language constructs and object-oriented approach aim to help programmers write clear,
logical code for small and large-scale projects.
-
It's so cool.
-
It's so easy.
What you should already know
This guide assumes you have the following basic background:
-
A general understanding of the Internet and the World Wide Web (WWW).
-
Everything else.
Go up
Python is an interpreted, high-level, general-purpose programming language.
Created by Guido van Rossum and first released in 1991, Python's design philosophy
emphasizes code readability with its notable use of significant whitespace. Its
language constructs and object-oriented approach aim to help programmers write clear,
logical code for small and large-scale projects.
Again, Python is an interpreted, high-level, general-purpose programming language.
Created by Guido van Rossum and first released in 1991, Python's design philosophy
emphasizes code readability with its notable use of significant whitespace. Its
language constructs and object-oriented approach aim to help programmers write clear,
logical code for small and large-scale projects.
Go up
Python is an interpreted, high-level, general-purpose programming language.
Created by Guido van Rossum and first released in 1991, Python's design philosophy
emphasizes code readability with its notable use of significant whitespace. Its
language constructs and object-oriented approach aim to help programmers write clear,
logical code for small and large-scale projects.
Python is an interpreted, high-level, general-purpose programming language.
Created by Guido van Rossum and first released in 1991, Python's design philosophy
emphasizes code readability with its notable use of significant whitespace. Its
language constructs and object-oriented approach aim to help programmers write clear,
logical code for small and large-scale projects.
Go up
Python is an interpreted, high-level, general-purpose programming language.
Created by Guido van Rossum and first released in 1991, Python's design philosophy
emphasizes code readability with its notable use of significant whitespace. Its
language constructs and object-oriented approach aim to help programmers write clear,
logical code for small and large-scale projects.
if (true):
y = 5
print(y); // ReferenceError: y is not defined
Go up
Python is an interpreted, high-level, general-purpose programming language.
Created by Guido van Rossum and first released in 1991, Python's design philosophy
emphasizes code readability with its notable use of significant whitespace. Its
language constructs and object-oriented approach aim to help programmers write clear,
logical code for small and large-scale projects.
Go up
Python is an interpreted, high-level, general-purpose programming language.
Created by Guido van Rossum and first released in 1991, Python's design philosophy
emphasizes code readability with its notable use of significant whitespace. Its
language constructs and object-oriented approach aim to help programmers write clear,
logical code for small and large-scale projects.
PI = 3.14;
Go up
some data types that are primitives:
- Boolean. true and false.
- Number. 42 or 3.14159.
- String. "Howdy"
Go up
Python is an interpreted, high-level, general-purpose programming language.
Created by Guido van Rossum and first released in 1991, Python's design philosophy
emphasizes code readability with its notable use of significant whitespace. Its
language constructs and object-oriented approach aim to help programmers write clear,
logical code for small and large-scale projects.
while (num1 < num2):
  print("Hello")
  num1 +=1
Go up
Python is an interpreted, high-level, general-purpose programming language.
Created by Guido van Rossum and first released in 1991, Python's design philosophy
emphasizes code readability with its notable use of significant whitespace. Its
language constructs and object-oriented approach aim to help programmers write clear,
logical code for small and large-scale projects.
Go up


curve X25519 sample keypair
The curve X25519 is used for key exchange mechanisms or short KEM. Each party has it's own key pair so below you will find 2 pairs.
Usual names for these parties are "Alice" and "Bob" but I will shorten it to "A" and "B" key pair.
Each key is only 32 bytes long and for better (cross platform wide) usage encoded in Base64 so the string is a little bit longer.
curve X25519 Secret Key for party A (Base64 encoding)
curve X25519 Public Key for party A (Base64 encoding)
curve X25519 Secret Key for party B (Base64 encoding)
curve X25519 Public Key for party B (Base64 encoding)


First Name
Last Name
Which race do you want to participate?
Fun Run 5k
Half Marathon
Full Marathon
Email
Password
Please select an Age Group
Under 18
18 to 30
30 to 50
Above 50
Register!


Before Change
pm._log.info("Stage: {:3d} Beta: {:.3f} Steps: {:3d}".format(stage, beta, n_steps))
// Apply Metropolis kernel (mutation)
proposed = draws * n_steps
priors = np.array([prior_logp(sample) for sample in posterior]).squeeze()
tempered_logp = priors + likelihoods * beta
parameters = (
proposal,
After Change
priors = [prior_logp(sample) for sample in posterior]
likelihoods = [likelihood_logp(sample) for sample in posterior]
priors = np.array(priors).squeeze()
likelihoods = np.array(likelihoods).squeeze()
while beta < 1:
beta, old_beta, weights, sj = calc_beta(beta, likelihoods, threshold)
model.marginal_likelihood *= sj
// resample based on plausibility weights (selection)
resampling_indexes = np.random.choice(np.arange(draws), size=draws, p=weights)
posterior = posterior[resampling_indexes]
priors = priors[resampling_indexes]
likelihoods = likelihoods[resampling_indexes]
// compute proposal distribution based on weights
covariance = _calc_covariance(posterior, weights)
proposal = MultivariateNormalProposal(covariance)
// compute scaling (optional) and number of Markov chains steps (optional), based on the
// acceptance rate of the previous stage
if (tune_scaling or tune_steps) and stage > 0:
scaling, n_steps = _tune(
acc_rate, proposed, tune_scaling, tune_steps, scaling, max_steps, p_acc_rate
)
pm._log.info("Stage: {:3d} Beta: {:.3f} Steps: {:3d}".format(stage, beta, n_steps))
// Apply Metropolis kernel (mutation)
proposed = draws * n_steps
tempered_logp = priors + likelihoods * beta
parameters = (
proposal,
scaling,
accepted,
any_discrete,
all_discrete,
discrete,
n_steps,
prior_logp,
likelihood_logp,
beta,
)
if parallel and cores > 1:
results = pool.starmap(
metrop_kernel,
[
(
posterior[draw],
tempered_logp[draw],
priors[draw],
likelihoods[draw],
*parameters,
)
for draw in range(draws)
],
)
else:
results = [
metrop_kernel(
posterior[draw],
tempered_logp[draw],
priors[draw],
likelihoods[draw],
*parameters
)
for draw in tqdm(range(draws), disable=not progressbar)
]
posterior, acc_list, priors, likelihoods = zip(*results)
posterior = np.array(posterior)
priors = np.array(priors)
likelihoods = np.array(likelihoods)
acc_rate = sum(acc_list) / proposed
stage += 1


Time seems to be moving very slow, but it has almost came to the end of the adventure!  Satan is a figure appearing in the texts of the Abrahamic religions who brings evil and temptation, and is known as the deceiver who leads humanity astray.
Click "continue" to resume the game!
Don't worry - I mean you must worry, but hopefully, you have found some very useful weapons to help you defeat him.


Convert a model using TOCO. (deprecated)
tf.compat.v1.lite.toco_convert( input_data, input_tensors, output_tensors, *args, **kwargs )
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version.
Instructions for updating:
Use lite.TFLiteConverter
instead.
Typically this function is used to convert from TensorFlow GraphDef to TFLite.
Conversion can be customized by providing arguments that are forwarded to
build_toco_convert_protos
(see documentation for details). This function has
been deprecated. Please use lite.TFLiteConverter
instead.
input_data
: Input data (i.e. often sess.graph_def
),input_tensors
: List of input tensors. Type and shape are computed using
foo.shape
and foo.dtype
.output_tensors
: List of output tensors (only .name is used from this).*args
: See build_toco_convert_protos
,**kwargs
: See build_toco_convert_protos
.The converted data. For example if TFLite was the destination, then this will be a tflite flatbuffer in a bytes array.
Defined in build_toco_convert_protos
.


[1]
http://nikeshoes2u.com; http://nikeshoes2u.com
[2]
EVERYTHING! BEST PHONE EVER CREATED!; HAS ALOT OF FEATURES BUT CAN DEFINETLY HAVE MANY MORE!


The prerequisites for LeafCutter are
To download the code (you’ll need this for the leafcutter scripts)
git clone https://github.com/davidaknowles/leafcutter
If you just want to get leafcutter quantifications (junction counts for each splicing cluster) e.g. for sQTL analysis, then you just need the python scripts in scripts
and clustering
.
If you want to do differential splicing analysis and visualization you’ll need to install the leafcutter R package.
Leafcutter relies on stan and its R interface rstan
. Installing this can be tricky. If you’re lucky
install.packages("rstan")
will just work for you. If not, there are instructions that should help you. On Linux I seem to need
CXX14FLAGS=-O3 -march=native -mtune=native -fPIC
CXX14=g++
in ~/.R/Makevars
.
For now we also need a version of rstantools
(2.0.0) that is not yet on CRAN, so do:
if (!require("devtools")) install.packages("devtools", repos='http://cran.us.r-project.org')
devtools::install_github("stan-dev/rstantools")
You can use remotes
instead of devtools
if you don’t want to wait on the long devtools
install.
To compile the R package to perform differential splicing analysis and make junction plots we recommend you install using devtools (this should install the required R package dependencies for you).
devtools::install_github("davidaknowles/leafcutter/leafcutter")
We’ve had a report (thanks to Peter Carbonetto) that it may be necessary to restart R before trying to load leafcutter
because dplyr
may get updated during the installation process.
Alternatively you can install from source. You’ll need to manually install the following R packages: Rcpp, rstan, rstantools, foreach, ggplot2, R.utils, gridExtra, reshape2, Hmisc, dplyr, doMC, optparse, shiny, intervals, shinycssloaders, DT, gtable, shinyjs, DirichletMultinomial, TailRank, magrittr, stringr
. Then make sure you’re in the leafcutter
package sub-directory (you should see subdirectories called src, R, tests
etc) and run
R CMD INSTALL --build .
If you see errors try installing the roxygen2
package and running
R CMD INSTALL --preclean --build .
which will attempt to use stan
to rebuild the C++ code from the .stan
files in exec
.
If you are a 64-bit Linux conda
user ( installation instructions ) we have an experimental package which can be installed using
conda install -c davidaknowles r-leafcutter
We will build macOS and Windows packages if there is demand for this.


Course Work
Select Term
Fall 2015
Spring 2015
Fall 2016
Spring 2016
Select Professor
James Rowland
Kim
Phil Baressi
Khasha Dehnad
Select Subject
CS-555-Agile Softwares
CS-538-Databse Management System 1
Web Programming
Knowledge Discouvery and Data Mining
Submit


please when you open the demo do the following in order to see the results:
right-click->inspect->then navigate to the console tap
or click:shift+ctrl+I->then navigate to the console tap


The jrun package provides classes and beans for managing the execution of simulation models. It includes model agents, scenario agents, and a RunFed class for dynamically linking beans and agents based on a graph specification.


Icon
Use the [glyph]
property to specify the name of the icon.
The size of the Avatar is controlled by the [size]
property. If omitted, the default size of the Avatar is set to large (l
).
Initials
The initials stand for the first name(s) and last name(s) of a person. Which name comes first depends on the language-specific settings.
Circle
Set [circle]="true"
for a circular shape of the Avatar.
Background Image
To provide an image resource (url
or data:base64
) use the [image]
property.
The size of the image is scaled up to completely cover the control area.
Transparent
To remove the background of the Avatar set [transparent]="true"
.
Placeholder Background
To apply a Placeholder Background set [placeholder]="true"
.
Placeholder images are used when no other image is available.
The default placeholder for an avatar is a gender-neutral person icon inside a circle.
For a business image is a neutral product icon inside a square can be used or you can specify your own default placeholder icon.
Tile Icon Background
For Tile Background set [tile]="true"
.
Accent Colors
By default, the placeholder background color is set to blue. Use [colorAccent]
property to change the background color. Options include numbers from 1 to 10. For example: [colorAccent]="6"
.
Zoom Icon
To specify the zoom icon use the [zoomGlyph]
property.
Borders
A border can be added with [border]="true"
.


Here are some technologies, tools, and best practices I observed over the years, and absorbed for implementing
Web, ERP, and Customer solutions.
Text Editors
Years ago I used Macromedia Dreamweaver, Notepad++, Visual Studio Code. Now I prefer GoLand!
GoLand is a new commercial IDE by JetBrains aimed at providing an ergonomic environment for Go development.
ERP Systems
Eclipse ERP, SAP, Microsoft Dynamics, Prophet 21, Openbravo, Covestone
Eclipse ERP was designed for industrial distribution companies in HVAC, PVF, Electrical, and related industries.


fortune- Print an offensive quotation:
fortune -o- Print a long quotation:
fortune -l- Print a short quotation:
fortune -s- List the available quotation database files:
fortune -f- Print a quotation from one of the database files listed by `fortune -f`:
fortune path/to/file


{% include './includes/head.html' %}
{% include './includes/header.html' %}
Flask
Tutorial
{% include './includes/flask.html' %}


WITNESS LIST Urban Affairs Committee April 14, 1998-10:00A Housing/Colonias For: Ward, Peter (Self) On: Cabello, Homer (TDHCA) Covalevich, Anthony (County of Hidalgo) Cuellar, Rep. Henry (Self) Hines, Vick (Sen. Carlos F. Truan) Manley, Larry Paul (TDHCA)


Type Aliases
The following type aliases are available globally.
-
Represent a reducer that takes an action and a state to produce another state.
Declaration
Swift
public typealias ReduxReducer<State> = (State, ReduxActionType) -> State
-
Unique id for a subscriber.
Declaration
Swift
public typealias SubscriberID = UniqueIDProviderType.UniqueID
-
Callback for state subscriptions.
Declaration
Swift
public typealias ReduxStateCallback<State> = (State) -> Void
-
Typealias for the state getter function.
Declaration
Swift
public typealias ReduxStateGetter<State> = () -> State
-
Typealias for a dispatch function whose output can be awaited on to execute immediately.
Declaration
Swift
public typealias AwaitableReduxDispatcher = (ReduxActionType) -> Awaitable<Any>
-
Typealias for a dispatch function that cannot be awaited on. Essentially we only send some actions into the pipeline without knowing what happens.
Declaration
Swift
public typealias ReduxDispatcher = (ReduxActionType) -> Void
-
Typealias for the state subscribe function. Pass in the subscriber id and callback function.
Declaration
Swift
public typealias ReduxSubscriber<State> = (SubscriberID, @escaping ReduxStateCallback<State>) -> ReduxSubscription
-
Typealias for state unsubscribe function. Pass in the subscriber id to determine which state callback should be disposed of.
Declaration
Swift
public typealias ReduxUnsubscriber = (SubscriberID) -> Void
-
Screen navigation function.
Declaration
Swift
public typealias Navigate<Screen> = (Screen) -> Void
-
Transformer function that takes an effect as the input and produces another effect.
Declaration
Swift
public typealias SagaEffectTransformer<R1, R2> = (SagaEffect<R1>) -> SagaEffect<R2>
-
Transformer function that transforms one effect into another, with both effects having the same output value type.
Declaration
Swift
public typealias MonoEffectTransformer<R> = SagaEffectTransformer<R, R>
-
Function that maps one dispatch to another.
Declaration
Swift
public typealias DispatchMapper = (DispatchWrapper) -> DispatchWrapper
-
Redux store middleware which has access to the store’s functionalities.
Declaration
Swift
public typealias ReduxMiddleware<State> = (MiddlewareInput<State>) -> DispatchMapper


Tank The Light is an Advanced Energy Storage System (working as a rechargeable giant-battery of massive proportions) to provide back-up energy, any time there is a power outage.
Don’t miss to follow us & subscribe to our new feeds, kindly fill the form below.


 There is no better way to learn about missionaries than to work beside them and no better way to see the need than to travel to a fireign field.
Vision Baptist Missions helps organize several missions trips every year. Click the link below to see a list of the ones you may be able to be a part of.


Reindeer Games * (out of * * * * )
Directed by John Frankenheimer. Cast: Ben Affleck, Charlize Theron, Gary Sinise, Clarence Williams III, Dennis Farina, James Frain, Donal Logue. 2000 - 104 minutes Rated R (for violence, profanity, sex, nudity, and gore). Reviewed by Dustin Putman, February 26, 2000.
It's getting to be like clockwork. Nowadays, every single week comes a high-profile film with killer talent that you can't believe could ever miss the mark, but does--majorly.  This week's Garbage Bin award goes to "Reindeer Games," directed by world-class filmmaker John Frankenheimer (1962's "The Manchurian Candidate," 1998's "Ronin") and starring Ben Affleck, Charlize Theron, and Gary Sinise in a movie that should never have been greenlit, particularly with the inanely ludicrous screenplay by Ehren Kruger. The fact that Kruger went so very wrong here is a surprise, considering his exciting contributions to 1999's "Arlington Road" and the current "Scream 3." Hopefully, this was just a minor slip on his part, because, truth be told, his work here is pretty crummy.
Rudy Duncan (Ben Affleck), who has been in prison for a short time due to grand theft auto, is about to be paroled. It's Christmas time, and all he wants to do is go home to see his family, and settle down with a cup of hot chocolate and a slice of pecan pie. His cellmate, Nick (James Frain), has been carrying on a six-month relationship with a beautiful 25-year-old woman named Ashley (Charlize Theron), through letter correspondence. She is planning on meeting him outside the jail on the day he gets released, which is the same day as Rudy. The only problem is, two days before they are to meet, Nick is stabbed and killed in a cafeteria riot. After seeing Ashley poignantly waiting outside the prison for a man she has fallen in love with but who is nowhere to be found, Rudy decides to take over the identity of Nick for her. After a shy chat in a cafe, a lustful roll in the hay (or off the hay, as they get so carried away they fly right off the bed!), and a trip to a clothing store to buy him some new clothes, they run headfirst into a major predicament.
Their happiness does not last long when they are stalked down by Ashley's slimy brother (Gary Sinise) and his three cohorts, all of which, like Ashley, believe that Rudy is Nick.
"Reindeer Games" isn't as much a movie that is supposed to make sense as it is a blatantly gimmicky action-thriller with the sole purpose of consistently fooling the audience. That's it. Without the token plot twists during the conclusion, which, by the way, are unpredictable because we are given absolutely no hints beforehand, there would be a vacuous filmstrip where a visual picture was supposed to be.
The first twenty minutes are the only bright spot of the picture. Beginning with the startling image of five slain Santas lying in the snow; then moving straight into the close and believable relationship that is set up between Rudy and Nick; and finally ending up with what looks to be turning into an unlikely, yet undeniably sweet romance, the picture's opening has next to no action in it, but does a nice job of setting up the characters of Rudy and Ashley. Their initial meeting at a cafe is the film's best scene, as it gets that feeling of nervousness and hesitancy in meeting someone you feel like you already know just right.
When the plot finally thickens with the entrance of Sinise and his shamelessly generic bad-guy comrades, so do the problems with the film itself. Running on autopilot for the next hour, and reminding me, in certain ways, of that awful 1996 Keanu Reeves vehicle, "Chain Reaction," it is evident that all Kruger and director John Frankenheimer are doing is wasting our time until the obligatory surprise, which (they think) will make the audience exclaim, "oooh!," when all it really will do is cause them to groan.
There is very little to recommend here, aside from the sex scene between Ben Affleck and Charlize Theron, where they both are freely in the buff (and let's face it-- who wouldn't want to see that?). While Sinise is nothing more than an unredeeming, one-dimensional baddie, Affleck and Theron both have what it takes to topline a big movie, but this isn't the ideal one. Working as mere puppets in service of the overly convoluted plot, they have talent to burn, but nowhere to place it here.
By the climax, "Reindeer Games" has degenerated from being a mere bore into something that is overwrought, mean-spirited, and graphically violent. The movie also isn't very fair to one of the central characters, nor do they treat him/her with satisfactory respect on the writing level. And then, after it has left us feeling unclean and rather depressed, it tacks on a feel-good conclusion that so obviously was filmed during reshoots that it sticks out like a sore thumb. You know you're in trouble when you set out to make an action picture, and it concludes with more sappiness than a Robin Williams movie.
- Copyright 2000 by Dustin Putman Http://www.young-hollywood.com Http://www.atnzone.com
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Popular Areas in apache 2.2.4 berkeley db
Svn server w/ Apache - Recursive Design
Feb 18, 2007 . wget http://downloads.sleepycat.com/db-4.4.20.tar.gz $ tar xvzf . --with-berkeley- db \ --prefix=/usr/local/apache-2.2.4 $ make $ sudo make .
http://recursive-design.com/blog/2007/02/18/svn-server-w-apache/
FreeBSD Port: apache-2.2.4
Mar 12, 2007 . FreeBSD Port: apache-2.2.4 . WITH_DBM: Choose your DBM: bdb (Berkeley DB ), gdbm or ndbm (default) WITH_BERKELEYDB: Choose your .
http://lists.freebsd.org/pipermail/freebsd-ports/2007-March/039349.html
DBM files for Apache's digest authentication
Ever tried to use digest authentication using DBM with Apache 2.2? . I used Apache 2.2.4. . As Berkeley DB is the preferred variant, I configured it with: .
http://www.berenddeboer.net/rest/apache_dbm_digest.html
Apache 2.x - Jason's Home Page - Linux HOWTOs
May 8, 2012 . Description: The 2.x version of the Apache httpd server . included with Slackware: # Slackware 12.0: httpd 2.2.4 # Slackware 12.1: httpd 2.2.8 . OpenLDAP (optional) # Berkeley db (bdb) # expat # iconv # OpenSSL (optional) .
http://englanders.us/~jason/howtos.php?howto=apache2
Getting SVN working with WebDAV on Windows... - Aral Balkan
May 23, 2006 . Getting SVN 1.4.3 to work on DOS (intentional reference to non-enterprise OS) Berkeley DB 4.5.20 Apache 2.2.4 Getting the repository setup: It .
http://aralbalkan.com/595/
Subversion-1.7.8
Mar 8, 2013 . S-Lang-2.2.4 . If you require the Berkeley DB back-end hooks in Subversion to . gnome-keyring-3.6.3, KWallet from KDE and Apache-2.4.3.
http://www.linuxfromscratch.org/blfs/view/svn/general/subversion.html
Error while trying to start apache with subversion 1.4.0
configure --with-apxs=/home/seeker/apache-2.2.4-svn/bin/apxs --without-berkely- db --with-ssl --enable-shared && make clean && make .
http://www.svnforum.org/threads/32659-Error-while-trying-to-start-apache-with-subversion-1-4-0


{# Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to you under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. #} {% block head_css %} {% endblock %} {% block head_js %} {% endblock %} {% block body %}


Synopsis
- What is EVM LLVM project
- Impacts to Ethereum community
- Engineering details
- Sneak peek
EVM LLVM: What is it?
- A new LLVM-based compiler backend
targeting EVM architecture
- But ... what is LLVM?
LLVM is:
A retargetable, multiple source compiler framework
Many projects/components are built around it
LLVM compiler infrastructure
- Core components: LLVM IR, LLVM Passes
Graph source
- De facto the standard of open source code generation framework
- Ecosystem includes lots of helpful tools
Why does Ethereum family need LLVM?
LLVM brings to Ethereum:
- A developer-friendly toolchain ecosystem
- Future-proof development environment
- Better performance
- Security tools
- Many more opportunities...
Impacts to the blockchain
- More programming languages possible
- Rust, Golang, C/C++, OCaml ...
- A whole ecosystem for developer tools
- optimizer, analyzer, REPL, ...
- Long term community support
- Gas efficiency
A future-proof development toolchain
- Strong and mature ecosystem support
- Supports current and future EVMs
- Migration to Ethereum Classic made easy
- Migration to next-gen engine made easy
What can EVM LLVM be used for?
- As a standalone smart contract ...
- compiler, analyzer, optimizer, verifier, ...
- As a library component for a SDK
Project Status
- Backend: Pre-Alpha Stage
- Collaboration
- Second State's SOLL
- LLVM-based Solidity toolchain
- Involving developer community
EVM LLVM: Engineering Details (1)
- Based on LLVM 10.0.dev
- 256 bit support
- 256 bit support exposed to langauge frontends
- Narrower-width data types are expanded internally
- EVM-specific opcodes exposed as intrinsics
- Such as: BLOCKHASH, CALLER, GASPRICE, ...
- Stackifier
- generic optimization passes on register-based opcodes
- Converts register-based opcodes to stack opcodes
EVM LLVM: Engineering Details (2)
- open source from the beginning
- EVM-specific optimizations (planned)
- Stack manipulation scheduler
- Gas consumption optimizations
- Contract ABI generation
- Currently relies on frontend
- Compatibilities with existing tools
Sneak peek
- We need community involvement.
end of presentation
Thank you