This document describes an OWL2 ontology [[!owl2-quick-reference]] for the description of a project, aimed at supporting linked data publishing of project descriptions across a number of domains. The ontology extends W3C Prov ontology [[!prov-o]] with typical features of a project description, including sub-activities, specific agent roles, and funding arrangements. It is expected to be specialized for applications that have more detailed requirements.
The namespace for terms in this ontology is: http://www.w3.org/ns/project#
The vocabulary defined in this document is also available in these non-normative formats: Turtle.
This is an initial draft produced for consideration by the W3C Data eXchange Working Group.
This document describes a core ontology for the description of a project, known as PROJECT. A project is a planned activity, in which one or more agent participants fill specific roles (e.g. project leader, sponsor or funder), which is typically structured as a set of sub-activities, and generates products. The project is a key unit of activity in modern business. A project provides key context for many productive activities and their outputs, including datasets.
A number of previous initiatives have developed project ontologies. ... FOAF, DOAP, VIVO-ISF, dbPedia ...
The Project ontology is designed as an extension to the W3C PROV ontology [[!prov-o]] , with a Project class specializing the Activity class by adding support for the key project-related concepts.
The PROJECT ontology is designed to enable publication of information describing projects, including research projects. It is not designed to support project management, though sub-activities are included. As far as possible PROJECT is intended to be domain-neutral. It is expected that domains and applications will specialize or extend this ontology for more specific purposes.
The ontology gives terms to support the representation of:
One specialization is included - the Research Project - which has been the subjecct of several predecessor designs that have informed the design of this ontology.
PROJECT extends the W3C [[!prov-o]] ontology. Otherwise, it has no dependencies except for the normal RDF/OWL infrastructure (RDF [[!rdf11-concepts]], RDFS [[!rdf-schema]], OWL [[!owl2-quick-reference]]), Dublin Core for some ontology metadata, and [[!skos-reference]] to support some relationships between Role values, and project classification.
A summary of the main classes and relationships in PROJECT is shown in the diagram below, including the key superclass prov:Activity.
We present two examples to illustrate the scope and some of the capabilities of the PROJECT ontology.
This example describes a recent public works project carried out in Melbourne, Australia. It concerns a phase of an initiative to replace level-crossings on the suburban rail network. It was sponsored by the state government, who were also the sole funders. This phase involved the removal of four level crossings. The project was led by a consortium. Three public transport bodies also participated. The project was completed several months early.
The level of detail shown here is intended only to illustrate the expressivity of the PROJECT ontology, and the use of structures from an external vocabulary (W3C ORG [[vocab-org]]) to complement.
ex:VictoriaLevelCrossingRemovalPhase1 rdf:type proj:Project ; rdfs:label "Victoria level-crossing removal - phase 1" ; proj:hasBudgetTotal [ rdf:type proj:SumOfMoney ; rdfs:label "Level crossing removal phase 1 budget " ; proj:moneyAmount "524000000"^^xsd:decimal ; proj:moneyCurrency <https://dbpedia.org/resource/Australian_dollar> ; ] ; proj:hasLeader ex:johnholland-kbr ; proj:hasParticipant <http://www.metrotrains.com.au/> ; proj:hasParticipant <https://www.ptv.vic.gov.au/> ; proj:hasParticipant <https://www.vicroads.vic.gov.au/> ; proj:hasSponsor <http://levelcrossings.vic.gov.au/about/about-the-authority> ; proj:hasSubActivity ex:BentleighLevelCrossingRemoval ; proj:hasSubActivity ex:BurkeRoadLevelCrossingRemoval ; proj:hasSubActivity ex:McKinnonRoadLevelCrossingRemoval ; proj:hasSubActivity ex:NorthRoadLevelCrossingRemoval ; proj:isFundedThrough [ rdf:type proj:FundingAssociation ; rdfs:label "Level crossing removal project phase 1 funding arrangement" ; rdfs:seeAlso <http://www.premier.vic.gov.au/contract-awarded-for-first-four-level-crossing-removals/> ; proj:fundsProvided [ rdf:type proj:SumOfMoney ; proj:moneyAmount "524000000"^^xsd:decimal ; proj:moneyCurrency <https://dbpedia.org/resource/Australian_dollar> ; ] ; prov:agent <http://www.vic.gov.au/> ; ] ; proj:name "Victoria level-crossing removal - phase 1" ; proj:plannedEndTime "2017-03-31T23:59:00"^^xsd:dateTime ; proj:plannedStartTime "2015-10-01T00:00:00"^^xsd:dateTime ; prov:atLocation <https://dbpedia.org/resource/Melbourne> ; prov:endedAtTime "2016-10-31T00:00:00"^^xsd:dateTime ; prov:startedAtTime "2015-10-01T00:00:00"^^xsd:dateTime ; .
Each individual level crossing removal has its own timeline and may be modeled as a prov:Activity. A number of local businesses were negatively impacted during the course of the activities.
ex:McKinnonRoadLevelCrossingRemoval rdf:type prov:Activity ; rdfs:label "McKinnon Road level crossing removal" ; proj:isSubActivityOf ex:VictoriaLevelCrossingRemovalPhase1 ; proj:plannedEndTime "2017-03-31T00:00:00"^^xsd:dateTime ; proj:plannedStartTime "2016-01-31T00:00:00"^^xsd:dateTime ; prov:atLocation <https://dbpedia.org/resource/McKinnon_railway_station> ; prov:endedAtTime "2016-10-31T00:00:00"^^xsd:dateTime ; prov:qualifiedAssociation [ rdf:type prov:Association ; prov:agent <https://www.zomato.com/melbourne/fabulous-fine-foods-ormond-mckinnon> ; prov:hadRole ex:Negative-economic-impact ; ] ; prov:startedAtTime "2016-01-31T00:00:00"^^xsd:dateTime ; .
The project leader was a consortium with two members, the description of which is represented as an org:FormalOrganization.
ex:johnholland-kbr rdf:type org:FormalOrganization ; rdfs:label "John Holland - KBR Joint Venture" ; rdfs:seeAlso <http://www.johnholland.com.au/who-we-are/latest-news/john-holland-wins-level-crossing-removal-projects-in-victoria/> ; org:hasMembership [ rdf:type org:Membership ; org:member <http://www.johnholland.com.au> ; org:role ex:Joint-Venture-Partner ; ] ; org:hasMembership [ rdf:type org:Membership ; org:member <https://www.kbr.com/> ; org:role ex:Joint-Venture-Partner ; ] ; .
The core class in the ontology is proj:Project which is intended to be applicable to a broad range of project types. A project is a planned activity with a designated leader, a known sponsor and budget, and is modelled as a specialization of the prov:Activity class.
A project is typically organized through a set of sub-activities, which may be undertaken concurrently or consecutively, and may themselves be composed of further sub-activities. A project is often related to or influenced by other activities in addition to its sub-activities.
We distinguish one sub-class, the proj:ResearchProject, in which the leader(s) is known as a Principal Investigator, and which is classified according to its field of research.
There are a number of distinctive stakeholders in a project, in particular: a leader who is accountable for the delivery of project outcomes; a sponsor under whose authority the project is undertaken; funders; and project staff or participants. In some cases it is useful to provide specific details of a participant's role in the project. PROJECT provides a number of ways to represent the relationships of stakeholders to projects and activities.
The standard roles of leader, participant are each implemented as a directly named property from an prov:Activity to a prov:Agent, and sponsor as a directly named property from a proj:Project to a prov:Agent.
Basic funding arrangements are captured through the proj:hasSponsor and proj:hasBudgetTotal properties. More complex arrangements, for example if multiple funders are involved, can be captured through the proj:isFundedThrough property, which links a funding agent with a funding amount in the context of a project.
The details of any other relationship may be expressed using the prov:qualifiedAssociation property.
The following RDF namespace prefixes are used within this document. Use of a namespace does not imply endorsement of the associated data platform or vocabulary.
| Prefix | Namespace IRI | Source |
|---|---|---|
dbo |
http://dbpedia.org/ontology/ | dbPedia ontology [[dbpedia-ont]] |
dcat |
http://www.w3.org/ns/dcat# | Data Catalog Vocabulary [[vocab-dcat]] |
dcterms |
http://purl.org/dc/terms/ | Dublin Core Metadata Initiative (DCMI) Metadata Terms [[dc-terms]] |
doap |
http://usefulinc.com/ns/doap# | Description of a (software) project [[doap]] |
ex |
http://www.w3.org/ns/project/example/ | Examples for this document |
foaf |
http://xmlns.com/foaf/0.1/ | FOAF Vocabulary Specification [[foaf]] |
obo |
http://purl.obolibrary.org/obo/ | Open Biomedical Ontologies [[obo]] |
org |
http://www.w3.org/ns/org# | Organization ontology [[vocab-org]] |
owl |
http://www.w3.org/2002/07/owl# | Web Ontology Language [[owl2-quick-reference]] |
pdo |
http://ontologies.smile.deri.ie/pdo# | Project documents ontology [[pdo]] |
no namespace or proj |
http://www.w3.org/ns/project# | Project Ontology |
proji |
http://www.w3.org/ns/project/ | Individuals in the Project Ontology |
prov |
http://www.w3.org/ns/prov# | W3C Provenance Ontology [[prov-o]] |
rdf |
http://www.w3.org/1999/02/22-rdf-syntax-ns# | Resource Description Framework [[rdf11-concepts]] |
rdfs |
http://www.w3.org/2000/01/rdf-schema# | RDF Schema vocabulary [[rdf-schema]] |
schemaorg |
http://schema.org/ | schema.org [[schema-org]] |
skos |
http://www.w3.org/2004/02/skos/core# | Simple Knowledge Organization System (SKOS) [[skos-reference]] |
vivo |
http://vivoweb.org/ontology/core# | VIVO-ISF [[vivo-isf]] |
Classes: | FundingAssociation | Project | RelatedActivity | ResearchProject | SumOfMoney |
Properties: | activityLeadingAssociation | activityParticipationAssociation | activitySponsorshipAssociation | fundsProvided | hasAreaOfInterest | hasBudgetTotal | hasFieldOfResearch | hasLeader | hasParticipant | hasPrincipalInvestigator | hasRelatedActivity | hasSponsor | hasSubActivity | isFundedThrough | isSubActivityOf | moneyAmount | moneyCurrency | name | objective | plannedEndTime | plannedStartTime | projectPlan | subActivityAssociation |
A formal activity with planned start and end dates, leader, sponsor, and a budget.
| RDFS Class: | proj:Project |
|---|---|
| Type: | owl:Class |
| subClassOf: | prov:Activity |
| Usage note: |
The name of the project.
| RDF Property: | proj:name |
|---|---|
| Type: | owl:AnnotationProperty |
| Domain: | proj:Project |
| subpropertyOf: | rdfs:label |
| Usage note: |
The objective of the project.
| RDF Property: | proj:objective |
|---|---|
| Type: | owl:AnnotationProperty |
| Domain: | proj:Project |
| subpropertyOf: | rdfs:comment |
| Usage note: |
The project plan.
| RDF Property: | proj:projectPlan |
|---|---|
| Type: | owl:ObjectProperty |
| Domain: | proj:Project |
| Range: | prov:Plan |
| subpropertyOf: | prov:used |
| Usage note: |
The planned start date and time for an activity.
| RDF Property: | proj:plannedStartTime |
|---|---|
| Type: | owl:DatatypeProperty |
| Domain: | prov:Activity |
| Range: | xsd:dateTimeStamp |
| Usage note: |
The planned end date and time for an activity.
| RDF Property: | proj:plannedEndTime |
|---|---|
| Type: | owl:DatatypeProperty |
| Domain: | prov:Activity |
| Range: | xsd:dateTimeStamp |
| Usage note: |
The party (person or organization) responsible for leading an activity.
| RDF Property: | proj:hasLeader |
|---|---|
| Type: | owl:ObjectProperty |
| subPropertyOf: | prov:wasAssociatedWith |
| Usage note: |
The party (person or organization) who is sponsoring a project.
| RDF Property: | proj:hasSponsor |
|---|---|
| Type: | owl:ObjectProperty |
| Domain: | proj:Project |
| subPropertyOf: | prov:wasAssociatedWith |
| Usage note: |
The project budget or planned expenditure.
| RDF Property: | proj:hasBudgetTotal |
|---|---|
| Type: | owl:ObjectProperty |
| Domain: | proj:Project |
| Range: | proj:SumOfMoney |
| Usage note: |
A sum of money.
| RDF Class: | proj:SumOfMoney |
|---|---|
| Type: | owl:Class |
| Usage note: |
The numeric value of a sum of money, which must be scaled by a specified currency to get the actual value.
| RDF Property: | proj:moneyAmount |
|---|---|
| Type: | owl:DatatypeProperty |
| Domain: | proj:SumOfMoney |
| Range: | xsd:decimal |
| Usage note: |
The currency in which a sum of money is specified.
| RDF Property: | proj:moneyCurrency |
|---|---|
| Type: | owl:ObjectProperty |
| Domain: | proj:SumOfMoney |
| Usage note: | It is expected that the value will be a link (URI) to a definition of the currency used. |
Direct link to a subsidiary activity.
| RDF Property: | proj:hasSubActivity |
|---|---|
| Type: | owl:ObjectProperty |
| Domain: | prov:Activity |
| Range: | prov:Activity |
| Usage note: |
Direct link to a parent activity.
| RDF Property: | proj:isSubActivityOf |
|---|---|
| Type: | owl:ObjectProperty |
| inverseOf: | proj:hasSubActivity |
| Usage note: |
Link to a related activity association.
| RDF Property: | proj:hasRelatedActivity |
|---|---|
| Type: | owl:ObjectProperty |
| Domain: | prov:Activity |
| Range: | proj:RelatedActivity |
| Usage note: |
Association class to support description of a related activity.
| RDF Class: | proj:RelatedActivity |
|---|---|
| Type: | owl:Class |
| subClassOf: | prov:ActivityInfluence |
| Usage note: |
Link to a related activity association carrying the role "sub-activity".
| RDF Property: | proj:subActivityAssociation |
|---|---|
| Type: | owl:ObjectProperty |
| subPropertyOf: | proj:hasRelatedActivity |
| Usage note: |
Direct link to a party (person or organization) participating in the activity.
| RDF Property: | proj:hasParticipant |
|---|---|
| Type: | owl:ObjectProperty |
| subPropertyOf: | prov:wasAssociatedWith |
| Usage note: |
Link to an association carrying the role "leader".
| RDF Property: | proj:activityLeadingAssociation |
|---|---|
| Type: | owl:ObjectProperty |
| subPropertyOf: | prov:qualifiedAssociation |
| Usage note: |
Link to an association carrying the role "participant".
| RDF Property: | proj:activityParticipationAssociation |
|---|---|
| Type: | owl:ObjectProperty |
| subPropertyOf: | prov:qualifiedAssociation |
| Usage note: |
Link to an association carrying the role "sponsor".
| RDF Property: | proj:activitySponsorshipAssociation |
|---|---|
| Type: | owl:ObjectProperty |
| subPropertyOf: | prov:qualifiedAssociation |
| Usage note: |
Link to a description of a project funding arrangement.
| RDF Property: | proj:isFundedThrough |
|---|---|
| Type: | owl:ObjectProperty |
| Domain: | proj:Project |
| Range: | proj:FundingAssociation |
| Usage note: |
Association class to support description of a project funding arrangement.
| RDF Class: | proj:FundingAssociation |
|---|---|
| Type: | owl:Class |
| subClassOf: | prov:Association |
| Usage note: |
Link to a sum of money.
| RDF Property: | proj:fundsProvided |
|---|---|
| Type: | owl:ObjectProperty |
| Range: | proj:SumOfMoney |
| Usage note: |
A research project.
| RDFS Class: | proj:ResearchProject |
|---|---|
| Type: | owl:Class |
| subClassOf: | proj:Project |
| Usage note: |
The person who acts as principal investigator on the research project.
| RDF Property: | proj:hasPrincipalInvestigator |
|---|---|
| Type: | owl:ObjectProperty |
| Domain: | proj:ResearchProject |
| Range: | foaf:Person |
| Usage note: |
The field of research of the research project.
| RDF Property: | proj:hasFieldOfResearch |
|---|---|
| Type: | owl:ObjectProperty |
| Domain: | proj:ResearchProject |
| Range: | skos:Concept |
| Usage note: | The value is usually taken from a curated vocabulary, such as ANZSRC Fields of Research |


--- layout: page status: publish published: true title: Places of Worship author: display_name: Jessie Pechmann login: jpechmann email<EMAIL_ADDRESS>url: '' author_login: jpechmann author_email<EMAIL_ADDRESS>wordpress_id: 10034 wordpress_url: http://gis.utah.gov/?page_id=10034 date: '2012-06-21 16:51:38 -0600' date_gmt: '2012-06-21 22:51:38 -0600' categories: [] tags: - SDE - sgid - Data - utah - gis - map - mapping - points - dataset - download - agrc - layer - shapefile - geodatabase - metadata - shp - gdb - lyr - digital - geographic - information - database - state - statewide - vector - facilities - locations - worship - religion - church - churches - chapels - convents - mosques - shrines - synagogues - temples - spiritual ---
Data Type: GIS Data Layer
Steward(s): AGRC
Abstract:
Society.PlacesOfWorship contains locations for places of worship in Utah. The definition of a place of worship is any type of building or portion of a building that is used, constructed, designed, or adapted to be used as a place for religious and spiritual activities. This doesn’t include religious schools or community centers, unless they meet the definition as a place of worship. Examples of facilities in this dataset are chapels, churches, convents, mosques, shrines, synagogues, and temples. The sources of this dataset are the Large Protestant Churches, Mosques, Jewish Synagogues, and Roman Catholic Churches in Large Cities dataset and additional AGRC research.
Usage: No usage tips at this time.
Last Update: 2011
Contact: agrc@utah.gov


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Changes can be filtered to not trigger a build; if the owner of a change matches the specified name, the build is filtered.


@* * Copyright 2021 HM Revenue & Customs * * Licensed 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. *@ @import config.ApplicationConfig @import services.DataCacheKeys._ @import uk.gov.hmrc.play.views.html.helpers._ @import views.helpers._ @import views.html.helpers._ @import views.html.includes.awrs_main @import views.html.view_application.subviews.contents._ @import views.view_application.helpers._ @this(formWithCSRF: FormWithCSRF, awrsMain: awrs_main) @(confirmationForm: Form[models.DeleteConfirmation], section: String, headingParameter: String, formAction: Int => Call, id: Int, data : Any)(implicit request: Request[AnyContent], viewMode : ViewApplicationType= OneViewMode, messages : Messages, applicationConfig: ApplicationConfig) @modelRender = { @(data) match { case s : Supplier => {@content_supplier(s, id)} case p : AdditionalBusinessPremises => {@content_additional_premises(p, id)} case d : BusinessDirector => {@content_director(d, request.session.get("businessType").getOrElse(""), id)} case g : GroupMember => {@content_group_member(g, request.session.get("businessType").getOrElse(""), id)} case p : Partner => {@content_partner_detail(p, false, id)} } } @awrsMain(title = messages("awrs.generic.tab.title", messages("awrs.delete.confirmation_heading", messages(headingParameter))), userLoggedIn = true) { @backLink(backUrl = controllers.routes.ViewApplicationController.viewSection(data match { case s : Supplier => suppliersName case p : AdditionalBusinessPremises => additionalBusinessPremisesName case d : BusinessDirector => businessDirectorsName case g : GroupMember => groupMembersName case p : Partner => partnersName } ).url)


Symbol Index for "Not Required Activity"
Symbol Index for diagram
"Not Required Activity"
Abandoned Activity (Class)
Activity State (Class)
Activity Substitution (Class)
Activity To Be Done (Class)
Activity Under Consideration (Class)
Activity/State (Generalization Node)
Completed Activity (Class)
Decomposed Activity (Class)
Instantiated Activity (Class)
Not Required Activity (Class)
Performance Rejected Activity (Class)
Performance Suspended Activity (Class)
Performer Accepted Activity (Class)
Performer Rejected Activity (Class)
Performer Selected Activity (Class)
reason for (Association)
Reason Not Required (Class)
Scheduled Activity (Class)
Started Activity (Class)
superseded activity (Association)


BlinkLabel |
Label with blinking text. class BlinkLabel : Label |
ConfirmDialog |
Simple dialog to display a message with 2 options. One option being 'cancel', which closes the dialog and the other being 'confirm' which calls the callback function. class ConfirmDialog : Dialog |
CounterLabel |
Custom label class, that display the count of stacked pieces. class CounterLabel : Label |
DeviceList |
List that shows all devices found by the CommunicationHandler. class DeviceList : List<String> |
GameMenuDialog |
Game menu dialog. Allows the player to change settings and to surrender the current game. class GameMenuDialog : Dialog |
LoadLabel |
Label that displays a load animation. The animation consists of the repetition of the defined symbol. Each frame adds another instance of the symbol and lasts for the defined deltaTime. One cycle is deltaTime * maxCount long. class LoadLabel : Label |
MessageDialog |
Simple dialog with a message and a confirm button. class MessageDialog : Dialog |
OneButtonDialog |
Dialog with one Button, a Message and a result action. class OneButtonDialog : Dialog |
ReadyButton |
This Button's job is to confirm that the active player's preparation is done. class ReadyButton : TextButton, EventListener |
TimedLabel |
Label that shows a text for the set time. class TimedLabel : Label |
TimerButton |
Button that disables after being pressed for the defined timeout. The action is run when the button is pressed. class TimerButton : TextButton |
TimerLabel |
Label, that shows the time since rendered first. class TimerLabel : Label |
ToggleButton |
Toggle button with state bound to given property. Property will be toggled if button gets pressed. class ToggleButton : TextButton |


Nullam quis risus eget urna mollis ornare vel eu leo. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Nullam id dolor id nibh ultricies vehicula.
Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Donec ullamcorper nulla non metus auctor fringilla. Duis mollis, est non commodo luctus, nisi erat porttitor ligula, eget lacinia odio sem nec elit. Donec ullamcorper nulla non metus auctor fringilla.
Maecenas sed diam eget risus varius blandit sit amet non magna. Donec id elit non mi porta gravida at eget metus. Duis mollis, est non commodo luctus, nisi erat porttitor ligula, eget lacinia odio sem nec elit.
One bit of advice: it is important to view knowledge as sort of a semantic tree -- make sure you understand the fundamental principles, ie the trunk and big branches, before you get into the leaves/details or there is nothing for them to hang on to.
I think you have an ability to think at a system level of design. To pull together design, technology and business into one package, to synthesize that package, and to feel so damn confident that you take crazy risks...
I think most people can learn a lot more than they think they can. They sell themselves short without trying.
| # | First Name | Last Name | Username |
|---|---|---|---|
| 1 | Mark | Otto | @mdo |
| 2 | Jacob | Thornton | @fat |
| 3 | Larry | the Bird | |
| 4 | Justin | Li | @jizhouli |


Source code for Analysis.Analysis
from Processor import Processor as pro
from sklearn.metrics import confusion_matrix
import seaborn as sns
import numpy as np
from matplotlib.pyplot import figure
figure(figsize=(11, 11), dpi=80)
[docs]class Analysis:
"""Class Analysis for analysing the performance of the model
"""
def __init__(self, full=True):
"""Constructor which run the Processor and store the results
self.acc_baseline: float
The accuracy of the random forest
self.acc_CNN: float
The accuracy of the CNN model
self.y_test: numpy.ndarray
The ground truth labels
self.y_pred: numpy.ndarray
The predicted labels of the CNN model
Parameters
==========
full : boolean
Default value is True
Determines if all the MNIST records should be considered or only
a subset shall be used for testing.
"""
print('*************************************************')
print('Analyse results')
print('')
self.acc_baseline, self.acc_CNN, self.y_test, self.y_pred = pro.runProcessor(full=full)
print('*************************************************')
[docs] def checkVSBaseline(self):
"""Check if the CNN accuracy is higher then the accuracy of the RF baseline
Returns
=======
Boolean: True if CNN accuracy is higher then baseline, false otherwise
"""
return self.acc_CNN > self.acc_baseline
[docs] def checkHypothesis(self, hypothesis):
"""Check if the CNN accuracy is higher then the accuracy expected by hypothesis
Parameters
==========
hypothesis: float
The accuracy expected by the hypothesis
Returns
=======
Boolean: True if CNN accuracy is higher then expected by hypothesis, false otherwise
"""
return self.acc_CNN > hypothesis
[docs] def saveConfusionMatrix(self):
"""Create a confusion matrix for CNN showing the confusion for the different digits
The matrix is getting saved to file cfm.png.
Returns
=======
Boolean: Always True
"""
cfm = confusion_matrix(np.argmax(self.y_test, axis=1), np.argmax(self.y_pred, axis=1))
sns_plot = sns.heatmap(cfm/np.sum(cfm), annot=True, fmt='.2%', cmap='Blues')
sns_plot.figure.savefig("cfm.png")
return True


|
| NAME: Ninja |
| OVERVIEW: Evolves from Thief. He knows some Black Magic, similar to a Dark Knight. |
| WEAPONS: Bane Sword, Coral Sword, Defense Sword, Dragon Sword, Falchion, Flame Sword, Giant Sword, Ice Sword, Long Sword, Masamune, Rapier, Rune Sword, Sabre, Scimitar, Short Sword, Silver Sword, Sun Sword, Vorpal, Were Sword, Cat Claw, Katana, Large Dagger, Silver Knife, Small Dagger, Great Axe, Hand Axe, Light Axe, Silver Axe, Iron Staff, Power Staff, Wooden Staff, Iron Hammer, Silver Hammer, Thor's Hammer, Iron Nunchuk, Wooden Nunchuk |
| ATTRIBUTES: Average strength, high agility, high luck, low magic |
MAGIC:
Black Magic: Fire, Lock, Slep, Lit, Dark, Slow, Tmpr, Ice, Fir2, Lok2, Hold, Lit2, Conf, Fast, Slp2, Ice2 |
|


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How much money does a laundromat make in 2019?
by Rob BeschizzaBrendan is a 34-year-old investor building an empire of rentals and seemingly random retail operations. He decided to buy a run-down old-timey laundromat. It's clean and there's a lot of machines in it, but some of them are broken, so he didn't do any marketing beyond an "UNDER NEW MANAGEMENT" sign in the window. How much does such an operation make, in 2019?
I appreciate everyone that's becoming interested in my laundromat content.
Above is the week 15 update, after buying some new washers; below is the first week, when it was like walking into the 1960s.
The tldr: he went from $1366 in the first two weeks (when it was rather obvious coins were walking out the door) to $1050 in the fifteenth week. Take a guess at costs and there you go.
But wait, isn't the point of a laundromat to "break even" every year?


MUDA
MUDA(MUltiple Data Accelerator) language is a short-vector SIMD language. MUDA's syntax is almost like GLSL(OpenGL Shading Language), but the compiled code runs on CPU, since MUDA compiler outputs plain SIMD-C code(for now, x86 SSE is supported).
// SSE code // MUDA code __m128 func(__m128 a, __m128 b) { vec func(vec a, vec b) { return _mm_add_ps( return a * a + b; _mm_mul_ps(a, a), b); } }
- Download
- Tutorial
- MUDA Language Specification
- DevLog (development logs. it would be updated frequently)
For whom?
MUDA is designed to accelerate your performance oriented program by using CPU's SIMD instruction.
If your program is floating point compute-intensive, it would be worth to use MUDA language. MUDA compiler emits a SIMD-C code, thus you can easily bind MUDA code and your C/C++ code.
Here is an application area.
- offline/online graphics
- quantitative finance
- simulation
- data mining
NEWS
- 17 Dec, 2007: Added new MUDA author!
- 10 Dec, 2007: Initial MUDA site opens!
TODOs
- LLVM IR backend
- x86 SSE: Support for SSE3/4/5
- Automatic optimizer with AMD codeanalyst, HW performance counter, etc.
- Automatic cache optimization.
- Message based paralellisation.
- Some annotation techniques(testing, precision control, debugging, etc)
MUDA Authors
- Syoyo Fujita : http://lucille.atso-net.jp/blog/
- Luca Barbato : http://dev.gentoo.org/~lu_zero/
- VMX(AltiVec) backend
Page last modified Tue Dec 18 01:48:02 2007.


Do you feel lost in trying to understand code above? Well, you are not the only one. This algorithm is often used to show how clever and elegant recursion can be. Let's dig into the code together.
For recursion, the most important thing is to clarify the definition of the recursive
function.
Specifically, we define reverse
as follows:
Input a node head
, we will reverse the list
starting from
head
, and return
the new head node.
After clarifying the definition, we look back at the problem. For example, we want to reverse the list below:
So after calling reverse(head)
, recursion happens:
Did you just step into the messy details in recursion? Oops, it's a wrong way, step back now! Focus on the recursion definition (which tells you what it does) to understand how recursive code works the wonder.
After executing reverse(head.next)
, the whole linked list
becomes
this:
According to the definition of the recursive function, reverse
needs
to
return the new head node, so
we use variable last
to mark it.
Let's continue cracking the next piece of code:
Last work to do：
The whole linked list is successfully reversed now. Amazing, isn't it?
Last but not the least, there are two things in recursion you need to pay attention to:
java
if(head.next == null) return head;
which means when
there is only one node, after reversion, the head is
still
itself.
last
, and the former
head
becomes the last node,
don't forget to point its tail to null.
java head.next =
null;
After understanding above, now we can proceed further, the problem below is actually an extend to the above solution.
This time we will implement a funtion below:
// reverse first n nodes in a linked list (n <= length of the list)
ListNode reverseN(ListNode head, int n)
Take below as an example, call reverseN(head, 3)
:
The idea is similar to reversing the whole linked list, only a few modifications needed:
ListNode successor = null; // successor node
// reverse n nodes starting from head, and return new head
ListNode reverseN(ListNode head, int n) {
if (n == 1) {
// mark the (n + 1)th node
successor = head.next;
return head;
}
// starts from head.next, revers the first n - 1 nodes
ListNode last = reverseN(head.next, n - 1);
head.next.next = head;
// link the new head to successor
head.next = successor;
return last;
}
Main differences:
n
== 1
, if reverse only one element, then new
head is
itself, meanwhile
remember to mark the successor node.
head.next
directly to
null,
because
after reversing the whole
list, head becoms the last node. But now head
may not
be the
last
node after reversion, so we
need mark successor
(the (n+1)th node), and link it to
head
after reversion.
OK, now we are pretty close to reversing part of the linked list.
Given an interval [m,n]
(index starts from 1), only reverse
elements
in
this section.
First, if m ==
1
, it is equal to reversing the first n
elements as we discussed just
now.
ListNode reverseBetween(ListNode head, int m, int n) {
// base case
if (m == 1) {
// equals to reversing the first n nodes
return reverseN(head, n);
}
// ...
}
What if m !=
1
? If we take the index of the head
as 1, then we need to reverse from
the mth
element. And what if we take the index of the head.next
as 1? Then compared to
head.next
, the
reverse section should start from (m-1)th
element. And what about
head.next.next
…
Different from iteration, this is how we think in the recursive way, so our code should be:
ListNode reverseBetween(ListNode head, int m, int n) {
// base case
if (m == 1) {
return reverseN(head, n);
}
head.next = reverseBetween(head.next, m - 1, n - 1);
return head;
}
Stack Properties | Queue Properties:
| Stack Property | Description | Queue Property | Description |
|---|---|---|---|
top |
The first node in the Stack | front |
The first node in the Queue. |
| —- | Stacks do not have an equivalent | back |
The last node in the Queue. |
length
|
The number of nodes in the Stack; the Stack's length. | length
|
The number of nodes in the Queue; the Queue's length. |
Notice that rather than having a head
and a
tail
like Linked Lists,
Stacks have
a
top
, and Queues have a front
and a back
instead. Stacks
don't
have
the
equivalent of a tail
because you
only ever push or pop things off the top of
Stacks. These
properties are essentially the same; pointers to the end points of the respective List ADT where
important
actions way take place. The differences in naming conventions are strictly for human
comprehension.
Similarly to Linked Lists, the values stored inside a Stack or a Queue are actually contained within Stack Node and Queue Node instances. Stack, Queue, and Singly Linked List Nodes are all identical, but just as a reminder and for the sake of completion, these List Nodes track the following two properties:
Stack & Queue Node Properties:
| Property | Description |
|---|---|
value |
The actual value this node represents. |
next |
The next node in the Stack (relative to this node). |
In the exercise that follows, we will implement a Stack data structure along with the following Stack methods:
| Type | Name | Description | Returns |
|---|---|---|---|
| Insertion | push |
Adds a Node to the top of the Stack. | Integer - New size of stack |
| Deletion | pop |
Removes a Node from the top of the Stack. | Node removed from top of Stack |
| Meta | size |
Returns the current size of the Stack. | Integer |
In the exercise that follows, we will implement a Queue data structure along with the following Queue methods:
| Type | Name | Description | Returns |
|---|---|---|---|
| Insertion | enqueue
|
Adds a Node to the front of the Queue. | Integer - New size of Queue |
| Deletion | dequeue
|
Removes a Node from the front of the Queue. | Node removed from front of Queue |
| Meta | size |
Returns the current size of the Queue. | Integer |
Before we begin our analysis, here is a quick summary of the Time and Space constraints of each Stack Operation.
| Data Structure Operation | Time Complexity (Avg) | Time Complexity (Worst) | Space Complexity (Worst) |
|---|---|---|---|
| Access | Θ(n) |
O(n) |
O(n) |
| Search | Θ(n) |
O(n) |
O(n) |
| Insertion | Θ(1) |
O(1) |
O(n) |
| Deletion | Θ(1) |
O(1) |
O(n) |
Before moving forward, see if you can reason to yourself why each operation has the time and space complexity listed above!
When the Stack ADT was first conceived, its inventor definitely did not prioritize searching and accessing individual Nodes or values in the list. The same idea applies for the Queue ADT. There are certainly better data structures for speedy search and lookup, and if these operations are a priority for your use case, it would be best to choose something else!
Search and Access are both linear time operations for Stacks and Queues, and that shouldn't be
too
unclear.
Both
ADTs are nearly identical to Linked Lists in this way. The only way to find a Node somewhere in
the
middle
of a
Stack or a Queue, is to start at the top
(or
the
back
) and traverse
downward
(or
forward) toward the bottom
(or
front
) one node at a time via
each
Node's
next
property.
This is a linear time operation, O(n).
For Stacks and Queues, insertion and deletion is what it's all about. If there is one feature a
Stack
absolutely
must have, it's constant time insertion and removal to and from the top
of the
Stack
(FIFO).
The
same applies for Queues, but with insertion occurring at the back
and removal
occurring at
the
front
(LIFO).
Think about it. When you add a plate to the top of a stack of plates, do you have to iterate through all of the other plates first to do so? Of course not. You simply add your plate to the top of the stack, and that's that. The concept is the same for removal.
Therefore, Stacks and Queues have constant time Insertion and Deletion via their
push
and
pop
or enqueue
and dequeue
methods, O(1).
The space complexity of Stacks and Queues is very simple. Whether we are instantiating a new instance of a Stack or Queue to store a set of data, or we are using a Stack or Queue as part of a strategy to solve some problem, Stacks and Queues always store one Node for each value they receive as input.
For this reason, we always consider Stacks and Queues to have a linear space complexity, O(n).
At this point, we've done a lot of work understanding the ins and outs of Stacks and Queues, but we still haven't really discussed what we can use them for. The answer is actually…a lot!
For one, Stacks and Queues can be used as intermediate data structures while implementing some of the more complicated data structures and methods we'll see in some of our upcoming sections.
For example, the implementation of the breadth-first Tree traversal algorithm takes advantage of a Queue instance, and the depth-first Graph traversal algorithm exploits the benefits of a Stack instance.
Additionally, Stacks and Queues serve as the essential underlying data structures to a wide variety of applications you use all the time. Just to name a few:
push
ing that
event to a Stack.pop
ed off
the
Stack, because the last event that occured should be the first one to be undone
(LIFO).
push
ed
back
onto
the Stack.
The objective of this lesson is for you to become comfortable with implementing common data structures. This is important because questions about data structures are incredibly likely to be interview questions for software engineers from junior to senior levels. Moreover, understanding how different data structures work will influence the libraries and frameworks that you choose when writing software.
When you are done, you will be able to:
When you are done, you will be able to:
Let's explore the Heap data structure! In particular, we'll explore Binary Heaps. A binary heap is a type of binary tree. However, a heap is not a binary search tree. A heap is a partially ordered data structure, whereas a BST has full order. In a heap, the root of the tree will be the maximum (max heap) or the minimum (min heap). Below is an example of a max heap:
Notice that the heap above does not follow search tree property where all values to the left of a node are less and all values to the right are greater or equal. Instead, the max heap invariant is:
This constraint makes heaps much more relaxed in structure compared to a search tree. There is no guaranteed order among "siblings" or "cousins" in a heap. The relationship only flows down the tree from parent to child. In other words, in a max heap, a node will be greater than all of it's children, it's grandchildren, its great-grandchildren, and so on. A consequence of this is the root being the absolute maximum of the entire tree. We'll be exploring max heaps together, but these arguments are symmetric for a min heap.
We'll eventually implement a max heap together, but first we'll need to take a quick detour. Our design goal is to implement a data structure with efficient operations. Since a heap is a type of binary tree, recall the circumstances where we had a "best case" binary tree. We'll need to ensure our heap has minimal height, that is, it must be a balanced tree!
Our heap implementation will not only be balanced, but it will also be complete. To clarify, every complete tree is also a balanced tree, but not every balanced tree is also complete. Our definition of a complete tree is:
Here are few examples of the definition:
Notice that the tree is on the right fails the second point of our definition because there is a gap in the last level. Informally, you can think about a complete tree as packing its nodes as closely together as possible. This line of thinking will come into play when we code heaps later.
Heaps are the most useful when attacking problems that require you to "partially sort" data. This usually takes form in problems that have us calculate the largest or smallest n numbers of a collection. For example: What if you were asked to find the largest 5 numbers in an array in linear time, O(n)? The fastest sorting algorithms are O(n logn), so none of those algorithms will be good enough. However, we can use a heap to solve this problem in linear time.
We'll analyze this in depth when we implement a heap in the next section!
One of the most common uses of a binary heap is to implement a "priority queue". We learned before that a queue is a FIFO (First In, First Out) data structure. With a priority queue, items are removed from the queue based on a priority number. The priority number is used to place the items into the heap and pull them out in the correct priority order!
Now that we are familiar with the structure of a heap, let's implement one! What may be surprising is that the usual way to implement a heap is by simply using an array. That is, we won't need to create a node class with pointers. Instead, each index of the array will represent a node, with the root being at index 1. We'll avoid using index 0 of the array so our math works out nicely. From this point, we'll use the following rules to interpret the array as a heap:
i
represents a node in
the heapi
can
be found at index 2 * i
i
can be found at index 2 * i +
1
In other words, the array [null, 42, 32,
24, 30, 9, 20, 18, 2, 7]
represents the
heap below.
Take a
moment to analyze how the array indices work out to represent left and right children.
Pretty clever math right? We can also describe the relationship from child to parent node. Say we
are
given a
node at index i
in the heap,
then it's parent is found at index Math.floor(i
/
2)
.
It's useful to visualize heap algorithms using the classic image of nodes and edges, but we'll translate that into array index operations.
What's a heap if we can't add data into it? We'll need a insert
method that will add
a new
value
into the heap without voiding our heap property. In our MaxHeap
, the property
states that a
node
must be greater than its children.
siftUp
push
the new value to the
end of the arrayThis is the "fetch" operation of a heap. Since we maintain heap property throughout, the root of the heap will always be the maximum value. We want to delete and return the root, whilst keeping the heap property.
siftDown
.
O(log(n))
O(log(n))
Recall that our heap will be a complete/balanced tree. This means it's height is
log(n)
where
n
is the number of items. Both
insert
and deleteMax
have
a time
complexity of log(n)
because of
siftUp
and siftDown
respectively.
In
worst case insert
, we will have
to siftUp
a leaf all the way to
the
root of
the
tree.
In the worst case deleteMax
, we
will have to siftDown
the new
root all
the way
down to
the leaf level. In either case, we'll have to traverse the full height of the tree,
log(n)
.
Now that we have established O(log(n))
for a
single insertion, let's analyze the
time
complexity
for
turning an array into a heap (we call this heapify, coming in the next project :)). The
algorithm itself
is
simple, just perform an insert
for every element. Since there are n
elements
and
each
insert requires log(n)
time, our
total complexity for heapify is
O(nlog(n))
…
Or is
it?
There is actually a tighter bound on heapify. The proof requires some math that you won't find
valuable
in
your
job search, but do understand that the true time complexity of heapify is amortized
O(n)
.
Amortized
refers to the fact that our analysis is about performance over many insertions.
O(n)
, since we use a
single array to store heap data.heap, let's implement
one! What
may
be
surprising is that the usual way to implement a heap is by simply using an array. That
is, we
won't
need
to create a node class with pointers. Instead, each index of the array will represent a
node,
with
the
root being at index 1. We'll avoid using index 0 of the array so our math works out
nicely. From
this
point, we'll use the following rules to interpret the array as a heap:
i
represents a node in
the heapi
can
be found at index 2 * i
the right child of code i
can be found at index 2 * i +
1
In other words, the array [null, 42, 32,
24, 30, 9, 20, 18, 2, 7]
represents the
heap below.
Take a
moment to analyze how the array indices work out to represent left and right children.
Pretty clever math right? We can also describe the relationship from child to parent node. Say we
are
given a
node at index i
in the heap,
then it's parent is found at index Math.floor(i
/
2)
.
It's useful to visualize heap algorithms using the classic image of nodes and edges, but we'll translate that into array index operations.
What's a heap if we can't add data into it? We'll need a insert
method that will add
a new
value
into the heap without voiding our heap property. In our MaxHeap
, the property
states that a
node
must be greater than its children.
siftUp
push
the new value to the
end of the arrayThis is the "fetch" operation of a heap. Since we maintain heap property throughout, the root of the heap will always be the maximum value. We want to delete and return the root, whilst keeping the heap property.
siftDown
.
O(log(n))
O(log(n))
Recall that our heap will be a complete/balanced tree. This means it's height is
log(n)
where
n
is the number of items. Both
insert
and deleteMax
have
a time
complexity of log(n)
because of
siftUp
and siftDown
respectively.
In
worst case insert
, we will have
to siftUp
a leaf all the way to
the
root of
the
tree.
In the worst case deleteMax
, we
will have to siftDown
the new
root all
the way
down to
the leaf level. In either case, we'll have to traverse the full height of the tree,
log(n)
.
Now that we have established O(log(n))
for a
single insertion, let's analyze the
time
complexity
for
turning an array into a heap (we call this heapify, coming in the next project :)). The
algorithm itself
is
simple, just perform an insert
for every element. Since there are n
elements
and
each
insert requires log(n)
time, our
total complexity for heapify is
O(nlog(n))
…
Or is
it?
There is actually a tighter bound on heapify. The proof requires some math that you won't find
valuable
in
your
job search, but do understand that the true time complexity of heapify is amortized
O(n)
.
Amortized
refers to the fact that our analysis is about performance over many insertions.
O(n)
, since we use a single
array to store heap data.We've emphasized heavily that heaps are a partially ordered data structure. However, we
can
still
leverage heaps in a sorting algorithm to end up with fully sorted array. The strategy is simple
using
our
previous MaxHeap
implementation:
insert
all
elements of the array into a MaxHeap
deleteMax
until the heap is empty, every
deletion
will
return the next element in decreasing orderThe code is straightforward:
// assuming our `MaxHeap` from the previous section
function heapSort(array) {
// Step 1: build the heap
let heap = new MaxHeap();
array.forEach(num => heap.insert(num));
// Step 2: constructed the sorted array
let sorted = [];
while (heap.array.length > 1) {
sorted.push(heap.deleteMax());
}
return sorted;
}
n
is the size of the input
arrayO(n)
time as
previously discussedn
steps in
isolation and each
deleteMax
will
require
log(n)
steps to restore max
heap property (due to sifting-down). This means
step 2
costs
O(nlog(n))
O(n
+
nlog(n)) = O(nlog(n))
So heapSort
performs as fast as
our other efficient sorting algorithms, but how does
it fair
in
space complexity? Our implementation above requires an extra O(n)
amount of space
because
the
heap
is maintained separately from the input array. If we can figure out a way to do all of these
heap
operations
in-place we can get constant O(1)
space! Let's
work on this now.
The in-place algorithm will have the same 2 steps, but it will differ in the implementation details. Since we need to have all operations take place in a single array, we're going to have to denote two regions of the array. That is, we'll need a heap region and a sorted region. We begin by turning the entire region into a heap. Then we continually delete max to get the next element in increasing order. As the heap region shrinks, the sorted region will grow.
Let's focus on designing step-1 as an in-place algorithm. In other words, we'll need to reorder
elements
of
the
input array so they follow max heap property. This is usually refered to as
heapify
. Our
heapify
will use much of the
same logic as MaxHeap#siftDown
.
// swap the elements at indices i and j of array
function swap(array, i, j) {
[ array[i], array[j] ] = [ array[j], array[i] ];
}
// sift-down the node at index i until max heap property is restored
// n represents the size of the heap
function heapify(array, n, i) {
let leftIdx = 2 * i + 1;
let rightIdx = 2 * i + 2;
let leftVal = array[leftIdx];
let rightVal = array[rightIdx];
if (leftIdx >= n) leftVal = -Infinity;
if (rightIdx >= n) rightVal = -Infinity;
if (array[i] > leftVal && array[i] > rightVal) return;
let swapIdx;
if (leftVal < rightVal) {
swapIdx = rightIdx;
} else {
swapIdx = leftIdx;
}
swap(array, i, swapIdx);
heapify(array, n, swapIdx);
}
We weren't kidding when we said this would be similar to MaxHeap#siftDown
. If you
are not
convinced,
flip to the previous section and take a look! The few differences we want to emphasize are:
i
,
it's left index is 2 * i + 1
and it's
right index
is
2 * i + 2
n
represents
the number of nodes in the heap
array.length
also
represents the number of nodes in
the heap.
That is
true, but only in step-1. Later we will need to dynamically state the size of the
heap.
Remember, we
are trying to do this without creating any extra arrays. We'll need to separate the
heap and
sorted
regions of the array and n
will dictate
the end of the heap.swap
helper function.
To correctly convert the input array into a heap, we'll need to call heapify
on
children
nodes
before their parents. This is easy to do, just call heapify
on each element
right-to-left
in
the
array:
function heapSort(array) {
// heapify the tree from the bottom up
for (let i = array.length - 1; i >= 0; i--) {
heapify(array, array.length, i);
}
// the entire array is now a heap
// ...
}
Nice! Now the elements of the array have been moved around to obey max heap property.
To put everything together, we'll need to continually "delete max" from our heap. From our previous lecture, we learned the steps for deletion are to swap the last node of the heap into the root and then sift the new root down to restore max heap property. We'll follow the same logic here, except we'll need to account for the sorted region of the array. The array will contain the heap region in the front and the sorted region at the rear:
function heapSort(array) {
// heapify the tree from the bottom up
for (let i = array.length - 1; i >= 0; i--) {
heapify(array, array.length, i);
}
// the entire array is now a heap
// until the heap is empty, continue to "delete max"
for (let endOfHeap = array.length - 1; endOfHeap >= 0; endOfHeap--) {
// swap the root of the heap with the last element of the heap,
// this effecively shrinks the heap by one and grows the sorted array by one
swap(array, endOfHeap, 0);
// sift down the new root, but not past the end of the heap
heapify(array, endOfHeap, 0);
}
return array;
}
You'll definitely want to watch the lecture that follows this reading to get a visual of how the array is divided into the heap and sorted regions.
Here is the full code for your reference:
function heapSort(array) {
for (let i = array.length - 1; i >= 0; i--) {
heapify(array, array.length, i);
}
for (let endOfHeap = array.length - 1; endOfHeap >= 0; endOfHeap--) {
swap(array, endOfHeap, 0);
heapify(array, endOfHeap, 0);
}
return array;
}
function heapify(array, n, i) {
let leftIdx = 2 * i + 1;
let rightIdx = 2 * i + 2;
let leftVal = array[leftIdx];
let rightVal = array[rightIdx];
if (leftIdx >= n) leftVal = -Infinity;
if (rightIdx >= n) rightVal = -Infinity;
if (array[i] > leftVal && array[i] > rightVal) return;
let swapIdx;
if (leftVal < rightVal) {
swapIdx = rightIdx;
} else {
swapIdx = leftIdx;
}
swap(array, i, swapIdx);
heapify(array, n, swapIdx);
}
function swap(array, i, j) {
[ array[i], array[j] ] = [ array[j], array[i] ];
}


The package imdb helps you in downloading series and movie information from imdb (it uses the omdbi api). It has three functions one for basic information about series and a second one that also downloads synopsis, actors etc. A third function downloads information about movies.
A walkthrough in the vignette provides more information.
For now you will have to install using devtools::install_github("rmhogervorst/imdb")
To make calls to the API you need an API-key. go to https://www.omdbapi.com/apikey.aspx and register for a free key. You can make a 1000 calls per day with the free account.
Add the key to your .Renviron (~/.Renviron) file: add a line like ‘OMDB_KEY = xxxkeyxxx’ to the file.
Or use the function add_key_to_renviron from the package.
imdb has 4 functions:
With the function ‘imdbSeries(seriesname = “name of series”, seasons = number(s))’ you can call up general information about series. Note that the api is does not really care about case. “Game of Thrones” or “game of thrones” or “gAmE oF tHrONes” is all fine.
library(imdb)
imdbSeries("game of thrones ")
#> Title Released Episode imdbRating
#> 1 Winter Is Coming 2011-04-17 1 9.0
#> 2 The Kingsroad 2011-04-24 2 8.8
#> 3 Lord Snow 2011-05-01 3 8.7
#> 4 Cripples, Bastards, and Broken Things 2011-05-08 4 8.8
#> 5 The Wolf and the Lion 2011-05-15 5 9.1
#> 6 A Golden Crown 2011-05-22 6 9.2
#> 7 You Win or You Die 2011-05-29 7 9.3
#> 8 The Pointy End 2011-06-05 8 9.1
#> 9 Baelor 2011-06-12 9 9.6
#> 10 Fire and Blood 2011-06-19 10 9.5
#> imdbID Season
#> 1 tt1480055 1
#> 2 tt1668746 1
#> 3 tt1829962 1
#> 4 tt1829963 1
#> 5 tt1829964 1
#> 6 tt1837862 1
#> 7 tt1837863 1
#> 8 tt1837864 1
#> 9 tt1851398 1
#> 10 tt1851397 1
The command will return a data frame with Title, releasedate, episodenumber imdb-rating, imdb ID and season.
Would you like to know more about your series? Use the enrichIMDB
command:
season2GOT <-imdbSeries("game of thrones", seasons = 2)
season2GOT_enriched <- enrichIMDB(season2GOT)
The enrichIMDB command returns a seperate dataframe with imdbID, runtime, director, writer, actors, plot (complete synopsis), and votes per episode. It uses the imdbid of the episode to scour for more information. So if you’d like to know how many times Jon Snow appears in the synopsis, or how many times Peter Dinklage plays in season 2, you can now search for it.
grep("Jon", season2GOT_enriched$plot)
#> [1] 2 6 7 8 10
grep("Peter Dinklage", season2GOT_enriched$actors)
#> [1] 1 2 3 4 5 6 7 8 9 10
Combining the information from the two dataframes can also be very useful.
library(ggplot2)
suppressPackageStartupMessages(library(dplyr))
GOTall<-imdbSeries("game of Thrones", 1:6)
GOT <-left_join(GOTall, enrichIMDB(GOTall), by = "imdbID")
ggplot(GOT, aes(Episode, imdbRating)) +
geom_smooth(aes(color = as.factor(Season)),se = FALSE , alpha = 1/10)+
geom_point(aes(color = as.factor(Season), size = votes))+
ggtitle("Rating per episode of GoT, \ncolored by season\nwith smoothlines")
I’m always looking for people to help me improve my work. Contact me directly, use an issue, fork me or submit a pull request.


Artifact management¶
This page describes facilities that TensorFlow Studio provides for management of experiment artifacts. For now, artifact storage is backed by google cloud storage.
Basic usage¶
The idea behind artifact management is three-fold: 1. With no coding overhead capture the data that experiment depends on (e.g. dataset) 2. With no coding overhead save and with minimal overhead visualize the results of the experiment (neural network weights, etc). 3. With minimal coding overhead make experiments reproducible on any machine (without manual data download, path correction etc).
Below we provide the examples of each use case.
Capture data¶
Let’s imagine that file train_nn.py
in current directory trains
neural network based on data located in ~/data/
. In order to capture
the data, we need to invoke studio run
as follows:
studio run --capture-once=~/data:data train_nn.py
Flag --capture-once
(or -co
) specifies that data at path ~/data
needs to be captured once at the experiment startup. Additionally, tag
data
(provided as a value after :
) allows script to access data
in a machine-independent way; and also distinguishes the dataset in the
web-ui (Web UI page of the experiment will contain download link for
tar-gzipped folder ~/data
)
Save the result of the experiment¶
Let’s now consider an example of a python script that periodically saves
some intermediate data (e.g. weigths of a neural network). The following
example can be made more consise using keras or tensorflow built-in
checkpointers, but we’ll leave that as an exercise for the reader.
Consider the following contents of file train_linreg.py
(also
located in studio/helloworld/ in repo):
import numpy as np
import pickle
import os
no_samples = 100
dim_samples = 5
learning_rate = 0.01
no_steps = 10
X = np.random.random((no_samples, dim_samples))
y = np.random.random((no_samples,))
w = np.random.random((dim_samples,))
for step in range(no_steps):
yhat = X.dot(w)
err = (yhat - y)
dw = err.dot(X)
w -= learning_rate * dw
loss = 0.5 * err.dot(err)
print("step = {}, loss = {}, L2 norm = {}".format(step, loss, w.dot(w)))
with open(os.path.expanduser('~/weights/lr_w_{}_{}.pck'.format(step, loss)), 'w') as f:
f.write(pickle.dumps(w))
The reader can immediately see that we are solving a linear regression problem by gradient descent method and saving weights at each step to ~/weights folder.
In order to simply save the weigths, we can run the following command:
studio run --capture=~/weights:weights train_linreg.py
Flag --capture
(or -c
) specifies that data from folder
~/weights
needs to be captured continously - every minute (frequency
can be changed in a config file), and at the end of the experiment. In
the Web ui page of the experiment we now have a link to weights
artifact. This simple script should finish almost immediately, but for
longer running jobs upload happens every minute of a runtime (the upload
happens in a separate thread, so this should not slow down the actual
experiment)
Machine-independent access to the artifacts¶
So far we have been assuming that all the experiments are being run a
local machine; and the only interaction with artifacts has been to save
them for posterity’s sake. But what if our experiments are growing a bit
to big to be run locally? Fortunately, studio comes with a dockerized
worker that can run your jobs on a beefy gpu server, or on a cloud
instance (cloud management is not provided just yet). But how do we make
local data available on such worker? Clearly, a local path along the
lines of /Users/john.doe/weights/
will not always be reproducible on
a remote worker. Studio provides a way to access files in a
machine-independent way, as follows. Let us replace last three lines in
the script above by:
from studio import fs_tracker
with open(os.path.join(fs_tracker.get_artifact('weights'),
'lr_w_{}_{}.pck'.format(step, loss)),
'w') as f:
f.write(pickle.dumps(w))
We can now run the script either locally, the exact same way as before:
studio run --capture=~/weights:weights train_linreg.py
Or, if the have a worker listening to the queue work_queue
:
studio run --capture=~/weights:weights --queue work_queue train_linreg.py
In the former case, the call fs_tracker.get_artifact('weights')
will
simply return os.path.expanduser('~/weights')
. In the latter case,
remote worker will set up a cache directory that corresponds to artifact
with tag weights, copies existing data from storage into it (so that
data can be read from that directory as well), and the call
fs_tracker.get_artifact('weights')
will return path to that
directory. In both cases, –experiment flag is not mandatory, if you
don’t speco
Re-using artifacts from other experiments¶
A neat side-benefit of using machine-indepdent access to the artifacts
is ability to plug different datasets into experiment without touching
the script at all - simply provide different paths for the same tag in
–capture(-once) flags. More importantly though, one can reuse datasets
(or any artifacts) from another experiment using –reuse flag. First,
let’s imagine we run the train_linreg.py
script, this time giving
experiment a name:
studio run --capture=~/weights:weights --experiment linear_regression train_linreg.py
Say, now we want to print the L2 norm of the last set of weights. Let’s
consider the following script (print_norm_linreg.py
):
import glob
import os
from studio import fs_tracker
import pickle
weights_list = glob.glob(os.path.join(fs_tracker.get_artifact('w'),'*.pck'))
weights_list.sort()
print('*****')
print(weights_list[-1])
with open(weights_list[-1], 'r') as f:
w = pickle.load(f)
print w.dot(w)
print('*****')
We can run it via
studio run --reuse=linear_regression/weights:w print_norm_linreg.py
Flag reuse tells studio run that artifact weights
from experiment
linear_regression
will be used in the current experiment with a tag
w
. There is a bit of a catch - for download optimization, all
artifacts from other experiments are considered immutable, and cached as
such. If you re-run the experiment with the same name and would like to
use new artifacts from it, clean the cache folder
~/.tfstudio/blobcache/
.
Default artifacts¶
Each experiment gets default artifacts that it can use via
fs_tracker.get_artifact()
even without –reuse or –capture(-once)
flags. Those are:
workspace
- this artifact always gets cached to/from.
folder, thus creating a copy of working directory on a remote machine; and saving state of the scriptsoutput
- this artifact is a file with stdout and stderr of the script runmodeldir
- it is recommended to save weights in this directory, because studio will try to do some analysis on it, such as number of checkpoints etc.tb
- it is recommended to save tensorboard logs into this directory, this way studio will be able to automatically feed them into tensorboard
All of the default artifacts are considered mutable (i.e. are stored continously). The default artifacts can be overwritten by –capture(-once) flags.


Classes
The following classes are available globally.
-
Projects are the primary entry point for
TiledKit
. The project represents the root of allMap
s andTileSet
s and the resources (such as images) that support them. The defaultProject
will use themain
Bundle as its root, making it very easy to include in a Swift Package Manager or Xcode module.let myMap = Project.default.get("mymap")
The above code will retrieve a file named ‘mymap.tmx’ from the root of the main
Bundle
. You may create instances ofProject
s that have a different root (such as an actual Tiled project directory, or differentBundle
) using the standard constructurs.Project
s are critical as they enable the relative processing of any resources referenced in the Tiled files and provide the key entry point for specializing TiledKit for a a specific game engine. Specializations only require the registration of a specificResourceLoader
for the type of object required by the game engine. See SKTiledKit for an example of a specialization.
See moreProject
s also provide resource caching capabilities, ensuring that the contents of anyURL
are loaded only once. If you add your ownResourceLoader
for types you can specify that if the created object should be cached or not.Declaration
Swift
public class Project
-
A
See moreTile
represents a 2D image that should be rendered when referenced on by anObject
orLayer
. It contains a reference to the image, and the portion of that image that should be used to generate the image.Declaration
Swift
public class Tile : Propertied, Equatable
-
Declaration
Swift
public class TileSet : Loadable, MutablePropertied


Hot Restaurant
Current Reservations and Waiting List
Make Reservation
Current Reservations
Waiting List


Demo Page
There are two CSS files and one JavaScript files in this project.
Skeleton
A custom stylesheet
jQuery
All these files are compressed


JavaScript Functions
This example calls a function which performs a calculation, and returns the result:


A* Pathfinding for Platformers
Created by: Clever Crow Games
Tile Legend
- Open
- Closed
- Begin
- End
- Opened Set
- Closed Set
- Path
- Jump Path
Line Legend
- Ledge Jump
- Runoff Link
- Fall Point
Time: NA
Calls: NA
Tile Stats Overview
F
G
H
C
M-MC
- F = Calculated Total
- Sum of the heuristic (H) and current step cost (G)
- G = Step Cost
- Total number of steps required to walk here from the start tile.
- H = Heuristic Estimate
- Heuristic uses Manhattan distance. Measures distance from current tile
to the goal.
- C = Clearance Value
- Supported player size that can fit on this tile. Too large means it can't support a player and is therefore
automatically closed.
- M = Movement Type
- Pre-calculated movement type for the tile. 0 = invalid, 1 = walkable, 2 = jump platform, 3 = jump up, 4 fall
- MC = Movement Clearance
- Movement clearance to calculate larger players moving through M tiles. Just like C tiles the players must
be smaller than or equal to the MC value
Movement Rules
- Player will find the shortest path to the end
- Player size will limit potential paths
- A simple parabola will be used to test jump paths instead of expensive physics
- Jump paths must be verified as clear


Hey, My name is Tia
This is a website I've created for people to get to know me and some things I have learnt along the way.
This blog has a mixture of projects I have done, blog posts about things I've learnt regarding both Tech skills and Human skills, fun games I've coded and where you can find me.
This entire site was created using standard HTML and all of the CSS was coded by myself from scratch. Since starting this site I am sufficient in so many new technologies, libraries, frameworks which means I would probably never create another site like this ever again which is why I decided to keep it as it is
I would babble on about what those new skills and technologies i've picked up along my journey to becoming a Full Stack Web Developer but you can find all of that on my CV so I wont bore you. This is a fun, easy and hopefully aesthetically pleasing space for you to look around and enjoy.
So please have a look around and I would love to hear from you if you like what you see here!
{{the only limit to our realization of tomorrow, will be our doubts of today}}
- Franklin D. Roosevelt


Quick Introduction¶
PyStaff: Stellar Absorption Feature Fitting in python¶
PyStaff (Python Stellar Absorption Feature Fitting) is a python code to recover stellar population parameters (such as Stellar Age, Metallicity and the Initial Mass Function) from visible and near-IR spectra of nearby galaxies. It’s built to work with the newest version of the single stellar population models of Conroy, Villame, van Dokkum & Lind (described here) which are available on request from Charlie Conroy’s website.
The code has been used in Vaughan et. al 2018b, to measure the stellar population of NGC 1399 using data from MUSE.
You can read the docs at pystaff.readthedocs.io
Features:¶
- Recover the best fit recession velocity and velocity dispersion of the spectrum (with higher order moments to be added in the future)
- Measure stellar population parameters such as Age, [Z/H] and the IMF, as well as abundances of the 18 elements varied within the models
- Subtract a series of sky spectra during the fit, to deal with over/under subtracted night sky emission lines
- Carefully account for a varying instrumental resolution as a function of wavelength
- Simultaneously fit a number of emission lines with the stellar absorption features
- Easily switch on or off which parameters to vary, thanks to the
lmfit
package - Full sampling of the posterior with
emcee
- A simple example of use with an MPI pool is given, to allow the code to be used on a cluster
Using the code on a different set of SSP models would be possible but would require a fair amount of work. It’s something I’d like to make easier in future!
The code is being actively documented and updated.


To begin with, the core team must have at least one full-time person who is fully responsible for the enterprise architecture. This is ideally the chief architect, appointed by the CIO. The chief architect is an executive position that serves a dual role:
The chief architect is supported by the core team of architects, as described. The core team is typically a pool of enterprise architects that in turn is supported by an extended team of business, application, data, and infrastructure architects. Table 3-7 provides a typical listing of functional roles and associated responsibilities assigned to the core team members. Often some of these roles and responsibilities are shared, paired up, or contracted out.
An architect usually operates at a particular level in the organization (enterprise, program, or project level). The position depends on her proficiency, experience, and seniority.
If needed (typically in large organizational setup), the enterprise architecture effort may be treated as a formal program. In that case, the chief architect is equipped with a dedicated program management office (EA-PMO) for enterprise architecture. The EA-PMO is established specifically to manage and control the development, use, and maintenance of the enterprise architecture. The PMO is tasked with ensuring the success of enterprise architecture as per the stated performance measures while managing the finances and risks involved.
Having formalized an EA organization in the enterprise, the health of EA should be checked periodically. This is essential to assess EA’s progress, effectiveness, and value contribution within the enterprise. The standard means of performing an assessment is to use an EA maturity model. We will cover this topic in Chapter 5, “EA Maturity Models.”


Reporting to the Head of Legal, the Legal Manager will be responsible for Category & IP work for the current categories of the Company. The position holder will serve as the key contact point for legal support required for Categories (Marketing), Media, and Corporate Relations. The position holder will be responsible for devising and implementing strategies for protecting the Company’s IP assets. The position holder will also manage settlement of Competitors’ challenges and be responsible for compliance with local laws and corporate policies in the areas of IP and Category.
Main Responsibilities:
Providing daily legal advice, in liaison with Regional Category Lawyers and in-house functional departments, to local management on legal marketing issues, including advertising/promotion and product claims, products’ labeling and liability, contracts review, legal clearance to support the launches.
- Providing local management of Intellectual Properties including assessing and advising on business opportunities, registration, reviewing Trade Mark Licensing and Transfer Technology contracts, providing legal support in protection of IP assets and settlement of IP infringement and oppositions.
- Assisting in the instruction of outside counsel and managing the relationship efficiently;
- Participating in the negotiation, drafting and reviewing of all contracts relating to the company’s advertisement, media, brand activation operations and ensuring that they safeguard Unilever’s interests;
- Managing Competitors challenges and counter-attacks
- Maintain good relationships with central and local government authorities and agencies that are relevant to Unilever business interests and within the remit of the legal department.
- Monitoring changes to law and regulations and informing all relevant personnel to the changes, providing suggestions on how to meet any change in the legal requirements, promptly;
- All other incidental work as instructed by the General Counsel of Vietnam, regional or global legal team from time to time such that Unilever Legal Function as a whole delivers an integrated service to Unilever’s business and meeting their business needs in a competitive and demanding environment.
- Team/Subordinates: The position holder will have one subordinate (Legal Assistant) and will be responsible for supervising, coaching and growing this subordinate.
Skills and Experience Required:
- Bachelor of Law from a recognized university;
- Relevant higher education and practice license are advantages;
- Minimum five years to eight years of Post Qualification Experience (in-house or private practice);
- Proven excellent knowledge and deep experience in intellectual property law and advertising/promotional regulations and a good understanding and working knowledge of corporate law, competition law, labelling regulations all other relevant Vietnam laws and regulations, administrative settlement regulations.
- Strong skills in analysis, drafting of contracts and legal documents for submission to competent authorities, negotiation, presentation;
- Ability to work independently under pressure and tight deadlines;
- Strong team work and customer service spirit;
- Management and leadership skills
- Excellent command of spoken and written English and Vietnamese.
Location: Ho Chi Minh City, Vietnam


#include "_doctype.html"
libcurl - Libwww Comparison
#include "css.t"
#define LIBCURL_LIBWWW
#define CURL_URL libcurl/libwww.html
#include "setup.t"
#include "table.t"
#include "_menu.html"
WHERE2(libcurl, "/libcurl/", Libwww Comparison)
TITLE(Notes About Libwww compared to libcurl)
These are only some quick notes. If you have additional experiencies and
comments you want to share with the world, we're all ears!
- libcurl is (far) more portable
- libcurl is supported and has a living community (compare traffic on the
mailing lists, release frequency or similar)
- programs need less code to transfer files using libcurl than a libwww-
based equivalent
- libcurl is thread-safe, libwww is not
- libcurl has a different feature-set and offers several features not
present in libwww, like several HTTP authentication types and gzip support
- libcurl supports more protocols
- libwww offers caching and HTML parsing, which libcurl doesn't
Quote from a usenet posting by Carl Daniel:
Having used both, I'd recommend libcurl and definitely not libwww.
libcurl is easy to use and gives good performance. libwww is a nightmare to
use, performs poorly (no overlapped I/O support, for example), and is
practically undocumented (on top of which it's a very complex library).
libwww is really a platform for network protocol development, not a library
for basic access to well known network protocol stacks.
Quote from the libwww-mailing list by Akritidis Xristoforos:
I devoted a full man-month trying to understand libwww, writing a function of
intermediate complexity (supporting post requests, xml parsing, authentication
etc.) and figuring out that the problems I encountered were due to libwww and
not my code.
I switched to libcurl, rewrote the same code in a week and haven't
looked back.
Perhaps this library has some features that libcurl doesn't, though I didn't
have to use them for my needs. However, for libwww to survive, someone has to
focus on these features and let libcurl do the rest.
Personally, I can't find any reason to suggest libwww to anyone.
#include "_footer.html"


As an organization the software development club focuses on fostering practical development skills in students who are interested in computer science. We strive to engage our members in innovative projects, encouraging teamwork and synergy. In doing so we hope to help our members better understand the process of development and concepts taught in class as well as exposing them to topics not covered by the curriculum.
The Software Development Club offers its members a multitude of professional development opportunities:
From Fortune 500 companies to the humble startup, all have one thing in common: a desire for talented employees. Companies of all sizes visit our club every semester, bringing distinguished engineers to give tech talks, panels of employees to answer questions about post-grad life, or human resources representatives to give interview and resumé advice. These meetings allow club members to interact with recruiters directly, expanding their ever-growing network.
Every semester the SDC offers tons of fun activities for our members. Some events include our welcome-back bonfire, bowling at the Sett, video game tournaments, nerd trivia night, the annual CSS contest, and our 12-hour mini-hackathon (free food and shirts provided)!


| Clinical Assessment |
|
|
| Mode of Admission: |
Emergency |
| Patient 75 Or Over: |
Yes |
| Dementia Audit: |
Emergency Admission and 75 or over |
| Reason for Admission / History of Presenting
Complaint(s): |
This 80 year old gentleman presented with an
episode of haematuria, assocaited with lower abdominal pain, dysuria and
fever. He is on warfarin.
His imflammatory markers were not
raised, his chest ray was clear and his abdominal x ray unremarkable.
Clinical examination was also unremarkable. He was started on oral
antibiotics, and had a stat dose of gentamicin.
He will have an OP
referral to urology for microscopic haematuria. He is awaiting an
ultrasound abdomen at time of writing. |
| Does the patient have a diagnosis of Delirium on
admission: |
No |
| Does the patient have a formal diagnosis of
Dementia: |
No |
| Has the patient become more forgetful in last 12
months: |
No |
| Was an AMT completed: |
Yes |
| Overall AMT Score: |
10 |
| Diagnosis: |
UTI |
| Other Previous Diagnoses: |
Resected brain tumour, prostate cancer treated
in 2006, PE, bilateral knee replacmeents, CKD stage 3- baseline creat was
150, paroxysmal AF, C1-C2 lminectomy, UTI stroke |
| Operations and Procedures: |
as above |
| Does this patient have a catheter?: |
No |
| Physical Status on Admission: |
Ambulatory. Self caring. Unable to work. Mobile
more than half of waking hours |
| Physical Status on Discharge: |
Ambulatory. Self caring. Unable to work. Mobile
more than half of waking hours |


This year, Write the Docs will have a new Writing Day, modeled after the concept of sprints from the open source world. The idea is simple: to get a bunch of interesting people in a room together and have them work towards shared goals.
To make the most out of the Writing Day, we recommend that you either come with a project, or be ready and willing to contribute to someone else's project.
Here are some examples of projects that you might want to work on:
You get the idea.
 So be ready to learn some new things and to teach others what you know.
Even if you feel as though you don't know that much, we know you have a lot to offer. Come surprise yourself with how much you can share.
Please bring a computer or some other mechanism with which to create written words.
We'll be creating and editing content, so make sure that you have the tools you need to contribute.
Check out our beginners guide to writing documentation. This should help you get started, and give you some ideas for how you can contribute to a projet that you love.
We have some suggestions for projects that you can work on during the Writing Day. If you'd like to add your own project to the list, you can email us. Better yet, submit a pull request.
The Open News team has multiple open source journalism projects whose documentation could use some expert love. Help improve the docs on some amazing code that helps people understand the news everyday.
Check out the full list of Open News projects that will be worked on.
Help document the internet. Mozilla always needs more people to create, edit, and translate content, so we can do more to help make the internet more approachable.
WordPress powers a large portion of the internet. Help write the docs that help people set it up and use it.
We have a website for our community, but it kinda sucks. Help us make it better by adding content, archiving old talks and notes, or just making it more friendly in general. Remember, you are the community.
A guide to writing documentation. Help us make this guide great for people who want to write docs, but don't know where to start.
I’m working on the API Reference for a new web service. I have all of the APIs defined in a RAML file, which also allows me to use the same source to generate the documentation. Ideally, I’d also like to make the API reference interactive. As examples, see http://developer.wordnik.com/docs.html#!/account, https://support.3scale.net/reference/active-docs or http://developer.marvel.com/docs.
Let's work together to make this happen at the Writing Day!
There are around 30 NodeSchool tutorials, and each one is it's own special snowflake. They are also viewed in the terminal, which isn't the most user friendly way to read docs. We want to figure some nice ways to write docs that can work in both the CLI as well as on the web. One example of a workshop that has done this is git-it
which generates this HTML guide.
Also there is a NodeSchool International Day event coming up with events in 30+ cities and we wanna write a Guide for Organizers and a Guide for Attendees so they can get the most out of their events.
Yup. We know you need better docs. We do too. Make it happen.
Send us an email or pull request with your project and a short paragraph about what you need help with, and we'll include it here.


public interface RhinoHostObjectProvider
RhinoHostObjectProvider
defines the service interface to
inject additional ECMAScript host objects as well as to make classes and
packages known.| Modifier and Type | Method and Description |
|---|---|
Class<? extends org.mozilla.javascript.Scriptable>[] |
getHostObjectClasses()
Returns an array of classes implementing the Rhino
Scriptable interface. |
Class<?>[] |
getImportedClasses()
Returns an array of classes, which are transparently converted into
ECMAScript host objects in the global scope.
|
String[] |
getImportedPackages()
Returns an array of Java package names to define name spaces in the
global scope.
|
Class<? extends org.mozilla.javascript.Scriptable>[] getHostObjectClasses()
Scriptable
interface. These classes will be registered
with the global scope as host objects and may then be used in any
server-side ECMAScript scripts.null
instead of an empty array for implementations that do not provide
any host objectsClass<?>[] getImportedClasses()
Normally any Java class may be used within ECMAScript but it must be
prefixed with Packages
and the fully qualified package
name of the class. For example to use the class
org.slf4j.Log
in an ECMAScript it must be noted as
Packages.org.slf4j.Log
. By registering the
org.slf4j.Log
as an imported class, it may simply be
referred to as Log
(provided there is no other object of
that name, of course).
null
instead of an empty array for implementations that do not import any
classesString[] getImportedPackages()
Normally any Java class may be used within ECMAScript but it must be
prefixed with Packages
and the fully qualified package
name of the class. For example to use the class
org.slf4j.Log
in an ECMAScript it must be noted as
Packages.org.slf4j.Log
. By registering the
org.slf4j
package as an imported package, it may simply be
referred to as Log
(provided there is no other object of
that name, of course).
The difference between importing packages and importing classes is that for a package import to work, the package must be visible to the ECMAScript bundle.
Implementations may return null
instead of an empty array
if they do not provide any package names.
null
instead of an empty array for implementations that do import any packagesCopyright © 2017 The Apache Software Foundation. All rights reserved.


HTML Tags Example Exercise.
Paragraph Element
This is a ordered list
This is an unordered list
This a paragraph.
This is the pre tag which displays text in fixed-width font


Sony PlayStation 5 · HDD 825GB PCie Gen 4 NVMe SSD · Graphics Custom AMD RDNA 2 GPU 36 CUs.
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AirPods deliver an unparalleled listening experience with all your devices. Every model connects
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Up to 20 hours of listening time on a single charge with Active Noise Cancellation or Transparency mode
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Italian junior team coach sacked for ‘disrespectful’ 27-0 win
MILAN, Nov 17 — The coach of an Italian junior league team was sacked yesterday for inflicting a humiliating 27-0 defeat on their opponents.
Paolo Brogelli, president of Invictasauro, a team based in Grosseto, Tuscany, slammed his team’s win over rivals Marina Calcio as “disrespectful”.
“We learned with amazement and regret the score of the match in which our junior team scored 27 goals,” Brogelli said in a statement reported in the Italian press.
“The values of youth team football are antithetical to such a thing.
“The opponent must always be respected and today it has not happened. As president I sincerely apologise to the Marina team. Our board has decided, unanimously, to sack (coach) Mr Riccini.
“Our coaches have a duty to train and, above all, to educate children. This has not happened today.” — AFP


Before trading options, you need to add options for a given underlying equity and set the resolution in step Initialize
with AddOption method. The commonly used parameters will be explained in the method table. Please refer to the link below for details of each method.
| Method | Parameters |
|---|---|
| AddOption(underlying, resolution, fillDataForward) | underlying(string): The underlying equity symbol resolution: Tick, Second, Minute, Hour, or Daily. Default is minute fillDataForward(bool): If true, returns the last available data even if none in that time slice. The default value is true. |
def Initialize(self): self.SetStartDate(2017, 01, 01) #Set Start Date self.SetEndDate(2017, 06, 30) #Set End Date self.SetCash(50000) #Set Strategy Cash equity = self.AddEquity("GOOG", Resolution.Daily) # Add the underlying stock: Google option = self.AddOption("GOOG", Resolution.Daily) # Add the option corresponding to underlying stock self.symbol = option.Symbol
The return value of AddOption method is an option security object , please refer to the link for detailed properties and methods of option class.


  @geeksam on cognitive shortcuts at #CascadiaRuby.
8/11/2014, 11:18:32 AM
Favs: 2
Retweets: 3
link


Automating the Deployment Process Right-click on the deployment share and select Properties Select the “Rules” tab In the main window, edit the settings as required. A sample CUSTOMSETTINGS.INI is below but feel free to change it as needed… CUSTOMSETTINGS.INI [Settings] Priority=Default
[Default ]
_SMSTSORGNAME=Customer
SkipBDDWelcome=YES
SkipProductKey=YES
ProductKey= XXXX-XXXX-XXXXX-XXXXX
OverrideProductKey=XXXX-XXXX-XXXXX-XXXXX
SkipDeploymentType=YES
DeploymentType=NEWCOMPUTER
SkipDomainMembership=NO
JoinDomain=domain.com
DomainAdminDomain=domain.com
DomainAdmin=MDTAddComputer
DomainAdminPassword=Pa55w0rd
MachineObjectOU=ou=example,dc=domain,dc=com
OrgName=Customer
SkipUserData=YES
SkipTaskSequence=NO
TaskSequenceID=TASKSEQID
SkipPackageDisplay=YES
SkipLocaleSelection=NO
UILanguage=en-US
UserLocale=en-GB
KeyboardLocale=0809: 00000809
SystemLocale=en-US
SkipTimeZone=NO
TimeZoneName=GMT Standard Time
Continue reading


Welcome Pets R Us, your local neighborhood pet store located in Tucson, Arizona. We can help you with all your pets needs. We understand that your pet is a part of the family and when you shop with us you can feel confident knowing that we wouldn’t sell anything that we wouldn’t give to our own pets. At our store you will find a great selection of pet food, treats, toys and accessories, all at low prices. In our store you will also find friendly Pet Experts trained in pet care and nutrition. Thinking about getting a pet? Not only do we sell pet supplies but we also have a wide selection of pets available for adoption. All our available pets come from rescue organizations and shelters around the state, so you know that when you adopt from us you are saving a life. We pride ourselves on being pet experts and we want to use our knowledge to help you with any of your pets needs.


I am a Full-stack UX Designer. I work with software applications and processes that impact a business' User Experience. While the emphasis is on product design, the work itself can often involve working with business processes, product planning/strategy, visual design and technology. I focus on User Experience and integrating user-centered design practices.
I publish and distribute apps through my business, Wolflux LLC.


Thank you for participating in our study! Your completion code
is: . If you wish, you can
give us your email address (or a fake one you just created!)
and we will enter you in a drawing for a $50 gift certificate.
We would appreciate it if you could also answer a few
questions about your experience.


Do you have a Nintendo64 at home and want to make money. Rent it out instead of selling and make long term money.
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