nbcuniversal
Businessmodel of NBCUniversal
Customer Segments
NBCUniversal serves a range of customers across its four operating segments. The Company’s broadcast and cable networks serve a general consumer audience across all 50 US states, as well as an international network of brand marketers and advertisers. The Company also markets and distributes cable network programming in the US and internationally to multichannel video providers, as well as to subscription video on demand services such as Amazon, Hulu and Netflix.
NBCUniversal’s Filmed Entertainment segment serves a broad range of customers, distributing motion picture content to consumers globally, through movie distributors and cinema chains. The Company also sells physical content, in the form of DVDs and Blu-Rays, and digital content to retail distributors and online merchants and licenses content streaming services.
NBCUniversal also serves consumers and advertisers through its Theme Parks segment, which serves customers in Orlando and Hollywood in the US, as well as in Osaka, where the Company holds a 51% share in Universal Studios Japan.
Value Propositions
NBCUniversal provides value to its customers in the following ways:
- The diversity of its broadcast and cable television content, with the Company providing programming across multiple genres, including sports, news and comedy, and serving both English and Spanish-speaking audiences in the US;
- The quality of its original televisions and motion picture content, with the Company attracting high-profile actors and artists to its productions, and operating one of the largest film studios in the world;
- Its large consumer audience for advertisers, with the Company recording a competitive audience share, both nationally and across its local networks; and
- Its international distribution reach, with the Company distributing its television and motion picture content across multiple jurisdictions worldwide. ### Channels
NBCUniversal operates a website at www.nbcuniversal.com, through which it provides detail on its activities and current television and motion picture productions. The Company also offers on-demand video content through its online NBC Video service, which also allows users to watch live NBC broadcasts through their web browser, as well as through an application available across mobile and gaming platforms. Additionally, NBCUniversal operates an online store at www.nbcuniversalstore.com, through which customers are able to purchase merchandise, including themed clothing and mugs.
NBCUniversal’s content is available through numerous television channels. It has 15 national cable networks – including Syfy, MSNBC, BC Sports Network, and Bravo – and distributes entertainment, news and sports programming through more than 200 affiliated stations across the US, including 10 owned NBC-affiliated local broadcast television stations.
The Company also provides content through Telemundo, which comprises 17 owned local broadcast television stations and the NBC Universo national cable network. The Company operates an in-house sales team that manages the Company’s advertising customers. These sales are supported by representation firms, which serve as an extension of the NBCUniversal sales force.
NBCUniversal produces and distributes motion picture content internationally through Universal Pictures, Illumination and Focus Features, with its movie content shown across cinema chains worldwide. The Company operates movie-related theme parks in Orlando, Hollywood and Osaka.
Customer Relationships
NBCUniversal operates several self-services channels, with customers able to purchase merchandise and access cable and broadcast programming online without interacting with members of the Company’s sales team. Customers are also able to access the Company’s theme parks and motion picture content on a largely self-service basis.
NBCUniversal operates its own in-house sales teams, which manages its relationships with large clients, particularly its advertising customers. The Company aims to establish long-standing relationships with advertisers, as well as with its distribution customers, licensing content to on-demand services and networks on a multi-year basis.
NBCUniversal provides technical support to its customers, who are able to contact the Company’s support staff through an online contact form. The Company also operates a news portal, which keeps customers updated with its activities, and social media accounts with Facebook, Twitter, YouTube, Pinterest, Tumblr, and Google+.
Key Activities
NBCUniversal is a media and entertainment company. It is engaged primarily in the production and distribution entertainment, news and information, sports, and other media content for audiences worldwide.
The Company operates through four segments: Cable Networks, which manages a portfolio of cable television networks, including national, international and regional networks, and related digital media properties; Broadcast Television, which operates NBC and Telemundo broadcast television networks, and is engaged in broadcast television production activities; Filmed Entertainment, which produces, acquires, markets, and distributes filmed entertainment, and distributes motion picture content through physical and digital distribution channels; and Theme Parks, which operates theme parks in the US and Japan.
Key Partners
NBCUniversal collaborates with a range of partner companies across its four operating segments. The Company’s partners include:
- Co-Production Partners, with which the Company jointly produces original television programming and motion picture content;
- Distribution Partners, including distributors and content producers, with the Company licensing its content to distributors and distributing content itself on behalf of third-parties;
- Financing Partners, comprising investors in the Company’s motion picture productions; and
- Advertising Partners, with which the Company collaborates to provide mutually beneficial advertising solutions, including cross-platform advertising partners. NBCUniversal has recently launched partnerships with Taboola, to serve as an advisor on advertising solutions; with TiVo and Reality Mine to the ways in which audiences watch the Rio 2016 Olympic Games across television, mobile and digital channels; with PetSmart for the exclusive sale of pet products featuring characters from its Secret Life of Pets movie; and with the department of journalism and mass communication at North Carolina A&T State University to develop initiatives to direct graduates of historically black colleges and universities into media careers.
Key Resources
NBCUniversal’s key resources are its technology and broadcasting infrastructure, its online channels IT infrastructure, its production facilities, its theme parks, its distribution networks, its partnerships, and its personnel.
The Company protects its intellectual properties through copyright, trademark, competition, patent, trade secret and other proprietary rights laws in the US and internationally, with a view to protecting itself from piracy.
Searches of records published by the US Patent and Trademark Office identified several applications filed in NBCUniversal’s name, including applications entitled ‘Multiple perspective video system and method’, ‘Methods and systems for online recommendation’ and ‘Advanced event triggering’.
Cost Structure
NBCUniversal incurs costs in relation to the production of its original content, the management and operation of its broadcast and cable networks, the maintenance of its broadcast infrastructure, the distribution of its motion picture content, the operation and management of its theme parks, the management of its partnerships, and the retention of its personnel.
As of 31st December 2015 NBCUniversal employed around 153,000 full-time and part-time workers, representing costs in the form of salaries and benefits. The Company also operates a network of offices across North America, South America, Europe, Asia Pacific, Africa, and the Middle East, accruing fixed operational costs in the form of rent and utilities.
Revenue Streams
NBCUniversal generates revenue primarily through the production and distribution of media content. Its revenue streams include:
- Advertising, including revenue derived from advertising solutions provided by its broadcast television and cable networks;
- Licensing, including revenue derived from the licensing and sale of its content to physical and digital distributors;
- Physical Sales, including revenue derived from the sale of proprietary content in the form of DVDs and Blu-Rays, as well as related merchandise;
- Motion Picture Distribution, including revenue derived from the national and international cinema release of motion pictures; and
- Theme Park Sales, comprising revenue derived from the sale of tickets and merchandise at its theme park. In 2015 NBCUniversal generated $24.46 billion in total revenue, up slightly on the $25.43 billion recorded by the Company in 2014. Revenue generation is distributed relatively evenly across the Company’s Cable Networks, Broadcast Television and Filmed Entertainment segments, which accounted in 2015 for $9.56 billion, $8.53 billion, and $7.29 billion in revenue respectively.


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Projects
VR Clues Maze
VR Attention Direction Within a Random Maze
Project Overview
How can a Virtual Reality environment subtly, but effectively direct the users attention?
Role: UX Designer, Full-Stack Developer | Duration: 4 Weeks
Tools Used: Unity Game Engine, SteamVR (for running the Maze on a VR headset)
UX Skills Used: VR Prototyping, Usability Testing, VR UX Design
Summary: Virtual Reality presents a unique challenge for immersive experiences, as the user
has the freedom to look in all directions at any time. This freedom makes it
possible for the user to be looking in the wrong place at the wrong time, and miss seeing key portions of the
experience.
The VR Clues Maze is an immersive experience that balances user freedom versus directed attention.
The VR Clues Maze generates a unique layout every time the software is run. Clues are then
intelligently added to subtly direct the user out of the maze.
My Contributions: I co-designed the VR Clues Maze with my partner on this
project, Vlad Potiyevskiy.
I designed the overall look-and-feel> of the virtual environment, and developed the lighting setup required to achieve the desired atmosphere.
As developer, I developed a path-finding solution that could
automatically
find a path through the randomized maze. I then implemented a system to dynamically add clues around this path,
providing the user guidance on how to get of the maze.
Process
My initial inspiration for this project was my experience with the Ghost in the Shell VR app.
The app plays out like a short movie, centering around a chaotic fight scene in a large room. While using the app, I observed a repeated problem.
Since I had
full ability to look in all directions, I would often be looking in the wrong direction to see an important part of the scene. This brought on the realization
that VR experiences need some ability to effectively direct the users attention, while still allowing the users
complete viewing freedom.
The VR Clues Maze is a VR environment designed to direct the users attention through lighting and subtle
environment cues.
Here's the process we used to develop the Maze:
Explore
Brainstorming
This project was occurring in the context of a design challenge with 4 weeks allowed for design and development.
My partner and I had relatively free rein over the nature and content of the project, but we knew that scoping
would be critical to meet our deadline.
We therefore made a substantial effort during our first brainstorming session
to prioritize
our feature set.
Brainstormed VR Clues Maze Features
Oculus Rift/Unity Platform
- The Oculus Rift headset was the most capable VR platform we had access to.
- The Unity Game Engine for app development was the best balance of technical features and ease of use.
Randomly-Generated Maze
- Our Maze would generate a random layout each time the app ran. This would add significant replayability to the app.
- This feature would be a technical challenge, but we had a Unity tutorial to work off of.
Atmospheric Lighting
- We saw the lights in the environment as a key attention direction tool.
- We knew correct optimization of lighting was needed to ensure smooth VR performance.
Supernatural Theme
- We went with a Japanese-based supernatural look-and feel, as this would provide a more unique immersive experience.
- Supernatural theme would be kept "light" to avoid truly scaring users. We would avoid gore and shock value in our design.
Dynamic Clue System
- To guide attention, we would build a system that would add clues to the maze. These clues would direct players on where
to go.
- Adding this feature to a randomly-generated maze was a significant design and technical challenge, as the
solution would need to work without
knowing the layout ahead of time.
Player Interactions
- We did want player interactions, both as a design challenge, and to increase player immersion.
- We knew that this area had the potential to overwhelm us .
- Discussed interactions included:
- Torches for extra lighting sources
- Keys to open doors in the maze
- Non-player characters (NPCs) for the player to interact with.
Timeline
As our overall development window was quite short for a project this ambitious, Vlad and I designed a
timetable that would allow us to deliver a complete package, while assessing our status, and pruning our feature
set as needed. Our timeline design produced the process/timeline map above.
Scoping
How can we integrate a way to give player clues INTO a maze system that
randomly changes layout every time the maze is created?
Technical Exploration
Our first major phase of work on the project was the Technical Exploration phase. In this phase, we would create proof-of-concepts versions of
our two design pillars: Random Maze Generation, and a Dynamic Clue System. By creating these proof-of-concepts, we would be able to tell whether or not
our project was feasible by our deadlines.
Random Maze Generation
Vlad took on the task of exploring the Unity random maze tutorial, and bringing it into our VR environment. The
tutorial was sound, and we were able to implement the majority of the maze in VR without
major issues. The implementation initially treated the maze as a 2-D maze, generating a series of flat tiles. The
implementation then
added walls, doors, and wall hangings to create a 3-D maze. My partner and I added additional wall and floor
textures, to make the maze more resemble the theming we were going for.
Random Map Generation Demo
Dynamic Clue System
While Vlad worked on the random generation system, I began work on
adding the dynamic lighting/clue system. First, I needed to design how this system would work. The system needed
3 main components:
- A designed set of clues that could be used to hint at the correct direction to get out of the maze.
- A system to determine the "path" the user should follow to get out of the maze.
- A system to dynamically insert clues around that path.
I first began by creating the initial design for what form the clues should look. I had several ideas for how
the clues might look, including
stone lamps, fireflies, and light-reflecting metallic shapes.
Out of the three options, I went with the stone lamp. The stone lamp balanced the use of a light to drive attention with the subtlety to look natural
in our overall environment. I sourced an appropriate lamp model, and started integrating it into our VR environment.
I began by tuning the ambient
lighting environment within the maze. I added a sourced night-sky skybox,
and adjusting the overall ambient lighting to be a little dim. These adjustments allowed the user to see, but not
incredibly well. This lighting
situation would cause our eventual light-based clues and player-interactable torches to have
immediate value to the player. I then created a maze module with the lamp,
a
light source, and a wall. We then added this module to the list of modules the random maze generator would use.
Now, our maze would have lamps randomly-placed to provide
a level of lighting support for the player.
Now that we had lamps integrated into the maze, I needed to find a way to make the lamps "clues" to guide the
player out.
It occurred to me as I built the initial lamps that a more subtle way to handle adding clues was to use design
elements that were already present.
I had sourced a Japanese Oni mask as part of a more generic
wall module that we were using to establish a Japanese theme for the maze. I decided to combine the mask with
the lantern prefab to create a clue. I created a new version of the lantern module,
but with the exception of having a tiny version of the Japanese Mask now embedded in it's side. The intent was
to place these "clue" lanterns in addition to the random ones. While seeming random, these "clue" lanterns would
actually be placed in the direction the user should go to get out of the maze.
Scoping Assessment
At the end of the Technical Exploration phase, Vlad and I assessed our status. Our proof-of-concepts had worked out successfully,
so we made the determination that our project could be completed by the deadline.
Our next step was to iterate to a full Version 1 of the Maze, with each one of us
building out proof-of-concepts of the two remaining features. Vlad took on the Player Interactions component, while I took
on the Maze-Finding component of the Dynamic Clue System feature.
Iteration
Mazefinding Development
For the first development iteration, I needed to finish up the Dynamic Clue System. Now that I had the visual design of a clue worked out, it was time to figure out how to place the clue.
To do this, I would need an algorithm to find a path out of each generated maze. I would then placed the clues along this path.
I looked into several pre-existing algorithms, such as Tremaux's
and Pledge's, and tested an implementation
of Pledge's in the Maze. However, these algorithms - they only
worked for "simple" mazes that have only corridors. Our maze could generate small courtyard-style open spaces,
and Pledge's failed when hitting one of those locations.
Going back to the drawing board, I decided to try using breadth-first search, as this
algorithm was capable of handling the courtyard spaces.
I was
able to find several existing implementations of it for tile mazes, including one by
the same individual that had developed the
procedural maze code we were using. Based on these implementations, I was able to create an implementation
of my own,
scanning the tiles in the Maze, and marking the tiles that lead to the exit by changing their colors.
Now that I had a system with which to mark the path, I could start adding clues to mark the path.
The algorithm I developed for adding the clues stepped through each floor tile on the path, and performed two
actions for each tile:
- The algorithm checked to see if the tile was a "third tile" - every third tile on the path was a candidate
for placing a clue This would add just enough clues to be useful, without making the Maze too easy to get out
of.
- If the tile was in fact a "third" tile, the algorithm would .
If so, the algorithm replaced it with a wall that had a clue-lantern.
The algorithm was deployed just after the initial maze generation, and would add the clue lanterns just
before the user was able to exploring the maze.
Mazefinding Demo Video
Live Usability Testing
As planned, we tested our Version 1 on users to gain initial feedback. We tested with 3 users, also participating
in the design challenge. Testing consisted of undirected
use of the Maze, with users giving feedback as they played. The user's first-person view was
accessible via a separate monitor, which allowed for direct observation.
Our users had varying levels of VR background, but all had been trained in basic VR design best practices, which
allowed them to give a strong mix of user/expert feedback.
Live Usability Testing Feedback
Users used Lights As Navigation Aids
- Users cited the lamps as the primary navigation aids they used to understand the maze structure.
- This use confirmed the decision to use the lamps as the focus for the clue system.
Users Were Easily Disorientated
- Users became easily disorientated when exploring the maze.
- Disorientation was due to 5 design factors:
-
Floor textures were the same throughout the maze.
-
VR Teleporting moved users instantly through space.
-
The environment was too dark to pick out any landmarks.
- The clue lanterns were used too infrequently to act as guides.
- There was not enough feedback when the user reached the end of the maze.
"Least Explored" Path-finding Strategy
- Users navigated the maze with a least-explored strategy, always attempting to go where they had not
been before.
- This strategy only had limited success, as users were not able to tell easily where they had been.
Users Wanted to Build a Mental Maze Map
- Users were frustrated
that the app did not support their need to build an internal mental map of the Maze.
- Users stressed the need to have "safe" landmarks to fall back on, so that they were never totally lost.
Feedback Iteration
For our final iteration, Vlad and I tried to address as many of the feedback points as possible, as well as
adding two minor features for greater user enjoyment.
Feedback Iteration Changes
Additional Clue Type
To address the need for more clues, I added a new clue type that could be embedded into floor tiles.
I also increased the frequency of clues to every other tile.
End of User Celebration Area
To provide better end-of-maze feedback, I added a Japanese-themed celebration area that the user would see upon maze completion.
Overhead View Implementation
We demoed the game using the Unity editor directly, and we noticed an interesting behavior from users. Demo-goers not wearing the headset were able to view the active player's position
on the overhead view in Unity, and could guide the active player through the maze. We added this as a formal feature to optionally aid navigation.
Tutorial Area
My partner added an initial tutorial zone that the player enters prior to the maze itself. This tutorial area
had instructions for player controls, the goal of finding the end of the maze, player interactables, and the need to look for
clues.
Player Light
To address the darkness of the environment, I added a dim light to the player module. This created a small but constant light source that guaranteed the player could always see.
Final Output
Demo
Final User Walkthrough
We conducted a second round of live usability testing on Version 2 of the Maze. 10 participants of similar background to the first testing participated.
Several of the primary users were paired with a second participant who used the Overhead View to guide the user around the maze.
Reflect
Post-Demo Analysis
Improved Maze Navigation
With the addition of the tutorial, lighting, and additional clues, our users were able to navigate our maze
far easier than Version 1. They were able
to backtrack far easier, and reached the end of the maze with far more frequency.
The Overview Map allowed for a fun optional experience.
With the use of the overview map, we were able to add a second user as a helper to the experience. Users
enjoyed the interplay between user and helper. The presence of the helper
allowed users with weaker navigation skills to still enjoy the overall experience.
The Celebration Experience Brought Proper Closure To The Experience.
With the celebration area, users had a clear feedback mechanism for telling that they had succeeded. The
aesthetic design of the area also made it a fun reward for the users.
Clue Attention Direction
The clues remained of only limited use. While the greater frequency made them more likely to be noticed, the
visual design of the clues was too subtle. Some users picked up on them,
but some never recognized them at all.
Users Wanted More Interactions/Interesting Environments
Users who didn't find the maze exit relatively quickly tended to become bored with experience. These users
suggested having more interesting environment features and activities to do. We had
ended up not included those features due to feasibility concerns, but their absence was noticed.


---
layout: layouts/base.html
title: Layouts
order: 30
tags:
- menu
---
{% include ./docs/layouts.html %}
{% include components/flexgrid.html, image_url: 'https://images.unsplash.com/photo-1476231682828-37e571bc172f?ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&ixlib=rb-1.2.1&auto=format&fit=crop&w=600&h=400&q=80' %}
{% include components/flexgrid.html, flexgrid_class: 'flexgrid-3', background_image_url: 'https://images.unsplash.com/photo-1476231682828-37e571bc172f?ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&ixlib=rb-1.2.1&auto=format&fit=crop&w=600&h=600&q=80' %}
{% include components/flexgrid.html, flexgrid_class: 'flexgrid-2' %}
{% include components/flexgrid.html, flexgrid_class: 'flexgrid-bricks' %}


| Version: |
1.0 |
| Depends: |
R (≥ 3.0.0), methods |
| Imports: |
data.table, reticulate (≥ 1.6) |
| Suggests: |
knitr, quanteda, R.rsp, rmarkdown, spelling, testthat |
| Published: |
2018-12-15 |
| Author: |
Kenneth Benoit [cre, aut, cph],
Akitaka Matsuo [aut],
European Research Council [fnd] (ERC-2011-StG 283794-QUANTESS) |
| Maintainer: |
Kenneth Benoit <kbenoit at lse.ac.uk> |
| BugReports: |
https://github.com/quanteda/spacyr/issues |
| License: |
GPL-3 |
| URL: |
https://spacyr.quanteda.io |
| NeedsCompilation: |
no |
| Language: |
en-GB |
| Materials: |
README NEWS |
| CRAN checks: |
spacyr results |


In order to demonstrate the operation of the Lattice PCI Express evaluation board, a driver needs to be loaded to allow the demo application access to the board hardware and IP to setup and execute the demo.
The driver is associated with a Lattice PCIe Evaluation Board containing the PCIe SG-DMA Demo bitstream and is loaded into the Window's kernel by the Plug and Play Manager. The driver provides the following services:
All WDM drivers support Plug and Play, and power management. WDM drivers are source-compatible (and mostly binary-compatible) across Windows 2000, XP and Server 2003. Kernel-mode drivers are implemented as discrete, modular components that have a well-defined set of required functionalities. All kernel-mode drivers supply a set of system-defined standard driver routines. Kernel-mode drivers that follow WDM rules are called WDM drivers. All WDM drivers must:
A bus driver services a bus controller, adapter, or bridge. Microsoft provides bus drivers for most common buses, such as PCI, PnpISA, SCSI, and USB. Other bus drivers can be provided by IHVs or OEMs. Bus drivers are required drivers; there is one bus driver for each type of bus on a machine. A bus driver can service more than one bus if there is more than one bus of the same type on the machine.
The primary responsibilities of a bus driver are to:
During enumeration, a bus driver identifies the devices on its bus and creates device objects for them. The method a bus driver uses to identify connected devices depends on the particular bus. For our purposes, the PCI/PCI Express bus driver is the one that enumerates the eval board.
A bus driver performs certain operations on behalf of the devices on its bus, including accessing device registers to physically change the power state of a device. For example, when the device goes to sleep, the bus driver sets device registers to put the device in the proper device power state.
Note, however, that a bus driver does not handle read and write requests for the devices on its bus. Read and write requests to a device are handled by the device's function driver. Only if the device is being used in raw mode does the parent bus driver handle reads and writes for the device.
A bus driver acts as the function driver for its controller, adapter, or bridge, and therefore manages device power policy for these components.
A function driver provides the operational interface for its device. Typically the function driver handles reads and writes to the device and manages device power policy.
The PCIe LSCDMA driver is a function driver for the Lattice Evaluation Board when the PCIe SG-DMA demo bitstream is loaded.
Filter drivers are not used in the Lattice PCIe LSCDMA driver or demo.
On a WDM PnP system, a device transitions through various PnP states as it is configured, started, possibly stopped to rebalance resources, and possibly removed. This section provides an overview of the PnP device states. The overview is a road map for much of the PnP support required in a driver.
The following figure shows the PnP states for a device and how a device transitions from one state to another.
Starting at the top left of the above figure, a PnP device is physically present in the system because either the user just inserted the device or the device was present at boot time. The device is not yet known to the system software.
To begin software configuration for the device, the PnP manager and the parent bus driver enumerate the device. The PnP manager, possibly with help from user-mode components, identifies the drivers for the device, including the function driver and any optional filter drivers. The PnP manager calls the DriverEntry routine of each driver if the driver is not yet loaded.
Once a driver is initialized, it must be ready to initialize its devices. The PnP manager calls a driver's AddDevice routine for each device the driver controls.
When a driver receives an IRP_MN_START_DEVICE request from the PnP manager, the driver starts the device and is ready to process I/O requests for the device.
If the PnP manager must reconfigure the hardware resources of an active device, it sends IRP_MN_QUERY_STOP_DEVICE and IRP_MN_STOP_DEVICE requests to the device's drivers. After it reconfigures the hardware resources, the PnP manager directs the drivers to restart the device by sending an IRP_MN_START_DEVICE request. (The drivers for a boot-configured device can receive IRP_MN_QUERY_STOP_DEVICE and IRP_MN_STOP_DEVICE requests before the device has been started, although this step is not shown in the previous figure.)
When a PnP device is being physically removed from the system or has already been removed, the PnP manager sends various remove IRPs to the device's drivers, directing them to remove the device's software representation (device objects, and so forth).
At some point after all of a driver's devices have been removed, the PnP manager calls the drivers Unload routine and unloads the driver.
The following Microsoft® Web sites contain additional information for using the Windows® Driver Development Kit (DDK):


uqa¶
Unsupervised question answer pairs generation in French.
Main featutes / processing pipeline:¶
Dataset cleaning
Named entity recognition 2
Question / answer pairs generation
The implemented method is based on Kayo Yin (Illuin Technology) article [#uqa-medium].
Question / answer generation key points¶
Using constituency parsing and named entity results, sub-sentences verifying the following constraints are matched:
The sub-sentence is made of:
a subject syntagm
a stative verb
a predicative nominal
The subject syntagm contains one and only one named entity
The subject is used to genrated the answer while the verb and the predicative is used to generate the question.
Examples
Process a single json file "data.json"
in default format with CLI:
$ uqa clean data.json clean.json
$ uqa ner clean.json ner.json
$ uqa constituency ner.json constituency.json
$ uqa qas constituency.json uqa.json
$ uqa show -df fquad fquad.json
Process a single json file "data.json"
in default format with a single script:
from uqa import dataset, clean, ner, constituency, qa_gen, fquad_utils
if __name__ == "__main__"
# Load data
dataloader = dataset.FileDataLoader("data.json")
# Process data
processed_it = qa_gen.generate_qas_dl(constituency(ner(clean(dataloader))))
# Convert data
fquad_it = fquad_utils.default_to_fquad_dl(processed_it)
# Save data
path_modifier = dataset.DataDumper.path_replacer("fquad.json")
dataset.DataDumper(path_modifier=path_modifier).save(fquad_it)
References
Modules¶
- uqa.clean module
- uqa.cli module
- uqa.cli_helpers module
- uqa.constituency module
- uqa.context_utils module
- uqa.dataset module
- uqa.download module
- uqa.fquad_utils module
- uqa.list_utils module
- uqa.logging_utils module
- uqa.ner module
- uqa.qa_gen module
- uqa.reading_wiki_dumps module
- uqa.show module
- uqa.split module
- uqa.stats module
- uqa.validate module


In purely practical terms, the log of 0 is undefined. In a more theoretical sense, the unweighted Promiscuity Index, I, cannot be considered valid under these conditions. If an enzyme exhibits no activity towards a particular substrate, then that substrate can be exchanged with any other substrate against which that enzyme also no activity, and thus the Promiscuity Index become inclusive of all chemical space, and you would have to provide activity measures for all molecules for an accurate I. Negative functional measures included with positve ones would indicate that an opposing process is taking place, and we can currently only extend the I and J calculations to one set of forward processes. In ohter words, the enzymes must exhibit the same direction of reaction for all processes under consideration. If you have all negative values, simply rescale into the positive range.
Why do I have nan's in my results?
In some circumstances, an unrecoverable math error or discontinuity may be encountered. Please check your data file and ensure that it follows the formatting guidelines. If it does, there may be a bug.


Public: API is visible by allPrivate: API is visible only by its membersRestricted: API is visible by all but can be used only by its members
Started
Enable keep-alive
Use an HTTP persistent connection to send and receive multiple HTTP requests / responses.
Dump HTTP request
Use this with caution for development purpose. Print HTTP client request content in gateway logs.
Enable client SSL
Use a client certificate if server requires it.
Enable trust all
Use this with caution (if over Internet). The gateway will trust all server certificates. The connection will still be
encrypted but this mode is vulnerable to 'man in the middle' attacks.


An implementation of data analytic methods in R for analyses for data with ceiling/floor effects. The package currently includes functions for mean/variance estimation and mean comparison tests. Implemented methods are from Aitkin (1964) <doi:10.1007/BF02289723> and Liu & Wang (in prep).
| Version: | 1.0.0 |
| Suggests: | knitr, rmarkdown |
| Published: | 2018-02-06 |
| Author: | Qimin Liu [aut, cre], Lijuan Wang [aut] |
| Maintainer: | Qimin Liu <qliu6 at nd.edu> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | DACF results |
| Reference manual: | DACF.pdf |
| Vignettes: |
Data Analyses with Ceiling/Floor data |
| Package source: | DACF_1.0.0.tar.gz |
| Windows binaries: | r-devel: DACF_1.0.0.zip, r-release: DACF_1.0.0.zip, r-oldrel: DACF_1.0.0.zip |
| OS X binaries: | r-release: DACF_1.0.0.tgz, r-oldrel: DACF_1.0.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=DACF to link to this page.


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[gecode-users] Multiple specification files
Patrik Broman
reg at patrikbroman.se
Wed Sep 16 17:29:43 CEST 2015
Apparently I posted from wrong address first time.
Anyway. I'm creating a two new data types. Is there a good way to use them
both at the same time?
I have tried
./configure --with-vis=var1.vis --with-vis=var2.vis
and
./configure --with-vis=var1.vis var2.vis
regards
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toggleSwitches.option1
is {{toggleSwitches.option1}}.
toggleSwitches.option2
is {{toggleSwitches.option2}}.
toggleSwitches.option3
is {{toggleSwitches.option3}}.
toggleSwitches.option4
is {{toggleSwitches.option4}}.
To create a toggle switch, use the toggleswitch
component with the value
attribute to bind the value. Use disabled
to control the disabled state.
The code used for the above example is shown below:
In an Angular application, ngModel
may be used to bind the control to a form.
true
for "on" and false
for "off."
true
then the toggle switch will be disabled.
false
the switch will not be updated when clicking on it, can be used if
something else is updating the state of the switch.
true
the toggle switch will be marked as required.


My name is Trang Nguyen
THE MOST AND SPECIALLISTY WEB
Home
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¡NEVER PRESS THIS!
Contact Me
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Post Request Builder
This JSON object will be parsed and inserted into the body of a POST fetch function.
Output will also be logged in the browser console.
Get Request Builder
This will be parsed and inserted into the query of a GET fetch function.
Output will also be logged in the browser console.
Socket.io Emit Builder
This JSON object will be parsed and emitted to the specified socket.
Output will also be logged in the browser console.
Socket.io Event Listener
Output will also be logged in the browser console.


Try out our Premium Member services: Virtual Legal Assistant, Query Alert Service and an ad-free experience. Free for one month and pay only if you like it.
Gujarat High Court Case Information System Print SCA/8082/2010 2/ 4 ORDER IN THE HIGH COURT OF GUJARAT AT AHMEDABAD SPECIAL CIVIL APPLICATION No. 8082 of 2010 ========================================= ARJUN ODEDRA CHAIRMAN AND MANAGING DIRECTOR OF BIOCARE REMEDIES PVT. LTD. - Petitioner(s) Versus GUJARAT POLLUTION CONTROL BOARD THROUGH CHAIRMAN/MEMBER & 1 - Respondent(s) ========================================= Appearance : PARTY-IN-PERSON for Petitioner(s) : 1, MRS KALPANA K RAVAL for Respondent(s) : 1, None for Respondent(s) : 2, ========================================= CORAM : HONOURABLE THE CHIEF JUSTICE MR. S.J. MUKHOPADHAYA and HONOURABLE MR.JUSTICE ANANT S. DAVE Date : 07/09/2010 ORAL ORDER
(Per : HONOURABLE THE CHIEF JUSTICE MR. S.J. MUKHOPADHAYA) This is a second writ petition filed by an industrialist in the grab and under the caption Public Interest Litigation , who also argued in person. He alleged that air pollution has been causing by the unit of 2nd respondent. Earlier the petitioner moved this Court by filing SCA No. 394 of 2010, which was disposed of on 20th January, 2010, for the reasons mentioned therein, and the Court passed an order allowing the petitioner to approach first respondent Gujarat Pollution Control Board. The said order is quoted below:-
1. Writ petition has been preferred by the petitioner alleging inaction on the part of respondent No.1 to take action for closing the unit run by respondent No.2.
2. Learned counsel for the petitioner while referred to the Development Plan, Gandhinagar, 2011 AD, submitted that, under clause 2.3.20, obnoxious and hazardous industries could not have been granted permission to run such industry at Gandhinagar. It is alleged that the second respondent- industry is dealing with hazardous substance and emitting toxic and obnoxious gases and in spite of repeated requests no action has been taken by respondent No.1.
3. We have heard the learned counsel appearing on behalf of the petitioner and perused the record.
4. From the record, it is evident that the second respondent-industry was granted consents and authorization to run the unit as back as on 27.11.2006 by the Gujarat Pollution Control Board. After three years, the petitioner has indirectly challenged the same. That apart, we also found that the petitioner is the Chairman and Managing Director of another industry and is neighbour. Except allegation, no specific case has been made out against the respondent. Under these circumstances, while we are not inclined to interfere with the order of consent granted by the Gujarat Pollution Control Board, liberty is given to the petitioner to approach the first respondent, who may make necessary inspection and pass appropriate orders in accordance with law after notice to respondent No.2.
5. This petition stands disposed of, but there shall be no order as to costs.
2. The same relief has been sought for in this second writ petition, for direction to Gujarat Pollution Control Board to take appropriate action against second respondent polluting unit in order to close down the said unit. This time, instead of representing through lawyer, petitioner has appeared in person and alleged about pollution caused by 2nd respondent. By filing an affidavit, respondent Pollution Control Board has brought to the notice of the Court that pursuant to the observation made by this Court on 20.1.10, petitioner represented before Pollution Control Board and the Board in its turn got the premises of second respondent inspected on 23.4.2010 and in the said report, the team observed that at the time of visit production of Zink oxide was going on and the ambient air quality was found to be normal. The report has been enclosed as Annexure B to the reply affidavit.
3. We noticed that to be on the safer side, Gujarat Pollution Control Board, under Section 31-A of the Air (Prevention and Control of Pollution) Act, 1981, ordered Environmental Engineering Department, L.D. College of Engineering, Ahmedabad to inspect the unit run by second respondent, and accordingly the said Environmental Engineering Department of L.D. College of Engineering, Ahmedabad submitted a report under the caption Identification and Assessment of Air Pollution Sources from M/s. Uttam Industry - 2nd respondent. The report is satisfactory and there is no adverse remark. At paragraph 5.3, the following observation has been made with regard to petitioner complainant.
5.3 Ambient Air Quality Monitoring: It was intended to carry out the ambient air quality monitoring twice at the premises of the complainant M/s. Biocare Remedies. However, there was complete non-cooperation on the part of complainant in this regards. During the first monitoring the complainant switched off the machine after 4 hours and during the second monitoring, the permission to keep the machine was not given. So second monitoring could not be done. The results of first ambient air quality monitoring are given at Table No.7 From both the said reports, it would be evident that there is no emission of toxic and obnoxious gases and the ambient air quality is normal and there is no air pollution.
4. It is also not in dispute that the petitioner has also filed a Suit against second respondent industry. Petitioner, in spite of the Court's order, failed to justify that second respondent industry is running without proper permission.
5. In view of the fact that no public interest is involved, and it is an adversary litigation appears to have been filed to malign second respondent, for which earlier case was not entertained, for same relief second writ petition has been filed. After a Suit has also been filed against second respondent, it will be evident that petitioner is now trying to take undue advantage under Article 226 of the Constitution. For the reason aforesaid, while we dismiss the writ petition, impose costs of Rs. 25,000/- on the petitioner for payment in favour of Indian Law Institute, Gujarat State, Gujarat High Court Campus. The amount to be paid within a month, failing which the competent authority will take up the matter with the Collector for recovery of the amount by way of arrears of land revenue.
(S.J.
Mukhopadhaya, C.J.) (Anant S. Dave, J.) */Mohandas Top


hsqldb - 100% Java DatabaseLightweight 100% Java SQL Database Engine |
|
ORIGINAL LICENSE (a.k.a. "hypersonic_lic.txt")
For content, code, and products originally developed by Thomas
Mueller and the Hypersonic SQL Group:
Copyright (c) 1995-2000 by the Hypersonic SQL Group.
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
Neither the name of the Hypersonic SQL Group nor the names of its
contributors may be used to endorse or promote products derived from this
software without specific prior written permission.

This software consists of voluntary contributions made by many
individuals on behalf of the
Hypersonic SQL Group.
Copyright (c) 2001-2004, The HSQL Development Group
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
Neither the name of the HSQL Development Group nor the names of its
contributors may be used to endorse or promote products derived from this
software without specific prior written permission.


{% extends "base.html" %} {%block title %}Home{% endblock %} {% block content %}
Nationwide, amongst people working full time, women earn 81 cents for every dollar that men earn. This wage gap has been persistent over time and does not appear to be narrowing significantly. This project therefore seeks to explore the differences in earnings between men and women across different occupations; starting from choices in college major.
See the Research Question page for more information about why this topic is important.
The data collection and analysis program is described in the Project Description page.
If you would like to learn more about the research flow, see the Methodology page.
The Results page explains what was discovered by the analysis.
See the About the Author page to learn more about the author.


html-tag classes:
b4 should go from orange to green
i4 should go from orange to red
w4 should go from orange to red when i4 changes


--- title: Unskilled population description: Rate of unskilled and unemployed population img: unskilled.jpg layout: map type: current application: ArcGIS JS API base_layer: ESRI layer: ESRI Server purpose: Present the rates of unskilled and unemployed population. --- {% include secure_warning.html %}
Rate of unskilled and unemployed population (%)


home | news | pet the shiba
idea makmer and website maker
it's time to d-d-duel!
bot coder and great guy
sue is the best vg character tbh https://hoylecake.com
worked for shiba a long time ago
socks the biblically accurate angle
the spiritual supporter
osu player and shiba tryhard
links and stuff
github | twitter | discord


Lille confirm two members of coaching staff to join Mourinho at Tottenham
PARIS, Nov 21 — Portuguese duo Joao Sacramento and Nuno Santos are to leave their jobs as coaches at Lille in order to become part of compatriot Jose Mourinho's staff at Tottenham Hotspur, the French club said yesterday.
Sacramento, 30, was the assistant coach to Christophe Galtier in northern France. He initially joined Lille in January 2017 and worked under former coach Marcelo Bielsa, who was sacked later that year.
Santos has worked as goalkeeping coach at Lille since the summer of 2018.
Now 46, he was a goalkeeper on the books at Benfica in 2000 when Mourinho began his coaching career with the Lisbon giants.
Both men were close to Luis Campos, the Portuguese transfer guru who works as an advisor for Lille and is also close to Mourinho, who was appointed Spurs manager earlier on Wednesday.
Mourinho even attended a Lille game last season following his sacking by Manchester United. — AFP


9 Setup of the Holdout Analysis
Four power analyses are run here to determine what relationships may be validated in the holdout set. First, the relationship between the battle and journey conditions on donation amount. Second, the three-way interaction between metaphor, ppt gender, and patient gender. Third, the relationship between metaphor and urgency. Fourth, the relationship between urgency and donation.
Two approaches are performed: numerical calculations and G*Power. These should align.
Numerical calculations are done by calculating the effect size, the number of samples, and the number of predictors, and finally using Cohen (1988) to determine the power.
We make the assumption that we’ll have the same number of covariates (this relies on no factor levels having 0 samples).
9.1 Battle and Journey on Donations
Null hypothesis: The metaphor condition does not predict donation amounts
# first, use only responses which were exposed to either metaphor
literal_mask = trials$cond_metaphor != "literal"
# then construct a base model and full model (includes the metaphor predictor)
model_battle_journey_base = lm(update(base.formula, donation ~ .), data = trials[literal_mask, ])
model_battle_journey_full = lm(update(base.formula, donation ~ . + cond_metaphor), data = trials[literal_mask, ])
# then calculate the effect size (Cohens f-squared between two models)
f <- f.sq(
lm(donation ~ 1, data = trials[literal_mask, ]),
model_battle_journey_base,
model_battle_journey_full
)
# print the effect size, the sample size, and the number of predictors
f
## [1] 0.001472744
nrow(model_battle_journey_base$model)
## [1] 3023
length(coef(model_battle_journey_base))-1
## [1] 24
Power calculation for finding a true effect:
pwr.f2.test(u = 1, v = 3023 - 24 - 1, f2 = f, power = NULL)
##
## Multiple regression power calculation
##
## u = 1
## v = 2998
## f2 = 0.001472744
## sig.level = 0.05
## power = 0.5562165
Post-hoc Power: 55.62%
G*Power: 55.94%, confirmed
Power calculation for finding a true effect in the holdout data:
pwr.f2.test(u = 1, v = 1012 - 24 - 1, f2 = f, power = NULL)
##
## Multiple regression power calculation
##
## u = 1
## v = 987
## f2 = 0.001472744
## sig.level = 0.05
## power = 0.2261188
Power: 22.61%
G*Power: 23.03%, confirmed
9.2 Three-way Interaction on Donations
Null hypothesis: A three-way interaction between Metaphor-Ppt Gender-Patient Gender does not improve the correlation coefficient (the population parameter tested in )
# first, use only responses which were exposed to either metaphor and limit the ppt gender
threeway_mask = trials$cond_metaphor != "literal" & trials$gender %in% c("M", "F")
# then construct a base model and full model (includes the three-way predictor)
model_threeway_base = lm(update(base.formula, donation ~ . + cond_metaphor * gender + cond_metaphor * cond_sex + gender * cond_sex),
data = trials[threeway_mask, ])
model_threeway_full = lm(update(base.formula, donation ~ . + cond_metaphor * cond_sex * gender),
data = trials[threeway_mask, ])
# then calculate the effect size (Cohens f-squared between two models)
f <- f.sq(
lm(donation ~ 1, data = trials[threeway_mask, ]),
model_threeway_base,
model_threeway_full
)
# print the effect size, the sample size, and the number of predictors
f
## [1] 0.002642116
nrow(model_threeway_base$model)
## [1] 2998
length(coef(model_threeway_base))-1
## [1] 26
Power calculation for finding a true effect:
pwr.f2.test(u = 1, v = 2998 - 26 - 1, f2 = f, power = NULL)
##
## Multiple regression power calculation
##
## u = 1
## v = 2971
## f2 = 0.002642116
## sig.level = 0.05
## power = 0.8000536
Post-hoc Power: 80.01%
G*Power: 80.33%, confirmed
Power calculation for finding a true effect in the holdout data:
pwr.f2.test(u = 1, v = 1012 - 26 - 1, f2 = f, power = NULL)
##
## Multiple regression power calculation
##
## u = 1
## v = 985
## f2 = 0.002642116
## sig.level = 0.05
## power = 0.3645923
Power: 36.46%
G*Power: 37.22%, confirmed
9.3 Metaphor on Urgency
Null hypothesis: Metaphor does not predict urgency
# first, use only responses which were exposed to either metaphor
literal_mask = trials$cond_metaphor != "literal"
# then construct a base model and full model (includes the metaphor predictor)
model_battle_journey_base = lm(update(base.formula, urgent ~ .), data = trials[literal_mask, ])
model_battle_journey_full = lm(update(base.formula, urgent ~ . + cond_metaphor), data = trials[literal_mask, ])
# then calculate the effect size (Cohens f-squared between two models)
f <- f.sq(
lm(donation ~ 1, data = trials[literal_mask, ]),
model_battle_journey_base,
model_battle_journey_full
)
# print the effect size, the sample size, and the number of predictors
f
## [1] 0.00350833
nrow(model_battle_journey_base$model)
## [1] 3023
length(coef(model_battle_journey_base))-1
## [1] 24
Power calculation for finding a true effect:
pwr.f2.test(u = 1, v = 3023 - 24 - 1, f2 = f, power = NULL)
##
## Multiple regression power calculation
##
## u = 1
## v = 2998
## f2 = 0.00350833
## sig.level = 0.05
## power = 0.9002927
Post-hoc Power: 90.03%
G*Power: 90.24%, confirmed
Power calculation for finding a true effect in the holdout data:
pwr.f2.test(u = 1, v = 1012 - 24 - 1, f2 = f, power = NULL)
##
## Multiple regression power calculation
##
## u = 1
## v = 987
## f2 = 0.00350833
## sig.level = 0.05
## power = 0.4606146
Power: 46.06%
G*Power: 46.92%, confirmed
9.4 Urgency on Donations
Null hypothesis: Urgency does not predict donation amounts
# first, use only responses which were exposed to either metaphor
literal_mask = trials$cond_metaphor != "literal"
# then construct a base model and full model (includes the urgency predictor)
model_battle_journey_base = lm(update(base.formula, donation ~ .), data = trials[literal_mask, ])
model_battle_journey_full = lm(update(base.formula, donation ~ . + urgent), data = trials[literal_mask, ])
# then calculate the effect size (Cohens f-squared between two models)
f <- f.sq(
lm(donation ~ 1, data = trials[literal_mask, ]),
model_battle_journey_base,
model_battle_journey_full
)
# print the effect size, the sample size, and the number of predictors
f
## [1] 0.0700667
nrow(model_battle_journey_base$model)
## [1] 3023
length(coef(model_battle_journey_base))-1
## [1] 24
Power calculation for finding a true effect:
pwr.f2.test(u = 1, v = 3023 - 24 - 1, f2 = f, power = NULL)
##
## Multiple regression power calculation
##
## u = 1
## v = 2998
## f2 = 0.0700667
## sig.level = 0.05
## power = 1
Post-hoc Power: 100.00% G*Power: 100.00%, confirmed
Power calculation for finding a true effect in the holdout data:
pwr.f2.test(u = 1, v = 1012 - 24 - 1, f2 = f, power = NULL)
##
## Multiple regression power calculation
##
## u = 1
## v = 987
## f2 = 0.0700667
## sig.level = 0.05
## power = 1
Power: 100.00% G*Power: 100.00%, confirmed
9.5 Summary
Power to reject the null if in fact the alternative is true on the holdout set given the ES from the large pilot:
- 23.03% - Metaphor Predictor on Donation
- 37.22% - Metaphor/Ppt Gender/Patient Gender Interaction Predictor on Donation
- 46.92% - Metaphor Predictor on Urgency
- 100.0% - Urgency on Donations


Run code blocks in the docstrings of the specified module modname
and return a Summary
of the results.
Code blocks may be skipped by adding an extra newline at the end of the block.
Example:
doctest(Lexicon)
Lexicon.jl provides access to the documentation created by the @doc
macro from Docile.jl. It allows querying of package documentation from the Julia REPL and building HTML documentation.
This package provides several ways to search and view documentation.
query
and @query
can be used to search for documented objects in modules that are currently loaded.
manual
displays external documentation that doesn't belong to any particular object and is intended to be an overview of the entire package.
To see examples of their use please consult the reference section below.
Documentation may be exported using the provided save
function. It currently supports HTML output which can be hosted on a package's gh-pages
branch or any other hosting service.
Lexicon.jl includes a doctest
function that runs code blocks in docstrings and generates summaries of the results.
Run code blocks in the docstrings of the specified module modname
and return a Summary
of the results.
Code blocks may be skipped by adding an extra newline at the end of the block.
Example:
doctest(Lexicon)
List codeblocks that failed doctest
ing.
Show the manual pages associated with modname
module.
List codeblocks that passed doctest
ing.
Search through loaded documentation for the query term q
. q
can be a String
, for full text searches, or an object such as a function or type. When searching for macros, methods, or globals use the provided @query
macro instead.
The search can be restricted to particular modules by listing them after the search term q
.
Examples:
Functions and their associated methods can be displayed by passing the function as the first argument to query.
query(query)
The search can be restricted to only methods whose signatures match a given tuple. The following query
call will show all query
methods where the type signature begins with String
. For exact matching of methods see the @query
macro.
query(query, (String,))
To display documentation for the type Lexicon.Summary
:
query(Lexicon.Summary)
Querying can be narrowed down by providing a module or modules to search through (defaults to searching Main
).
categories
further narrows down the types of results returned. Choices for this are :method
, :global
, :function
, :macro
, :module
, and :type
. These options only apply to text searches such as the following:
query("Examples", Lexicon; categories = [:method, :macro])
The previous example displays all method and macro documentation in the Lexicon
module containing the text "Examples".
Write the documentation stored in modulename
to the specified file
in the format guessed from the file's extension.
If MathJax support is required then the optional keyword argument mathjax::Bool
may be given. MathJax uses \(...\)
for in-line maths and \[...\]
or $$...$$
for display equations.
Currently supported formats: HTML
.
Example:
The documentation for this package was created in the following manner. All commands are run from the top-level folder in the package.
save("doc/site/master/index.html", Lexicon)
From the command line, or using run
, push the doc/site
directory to the gh-pages
branch on the package repository after pushing the changes to the master
branch.
git add .
git commit -m "documentation changes"
git push origin master
git subtree push --prefix doc/site origin gh-pages
If this is the first push to the branch then the site may take some time to become available. Subsequent updates should appear immediately. Only the contents of the doc/site
folder will be pushed to the branch.
The documentation will be available from https://USER_NAME.github.io/PACKAGE_NAME/FILE_PATH.html
.
List codeblocks that were skipped during doctest
ing.
Results produced from running doctest
on a module.
Search through documentation of a particular package or globally. @query
supports every type of object that Docile.jl can document with @doc
.
Note: the functionality provided by @query
is also available using ?
at the Julia REPL. It displays documentation from the standard Julia help system followed by package documentation from Docile.
Qualifying searches with a module identifier narrows the searching to only the specified module. When no module is provided every loaded module containing docstrings is searched.
Examples:
In a similar way to Base.@which
you can use @query
to search for the documentation of a method that would be called with the given arguments.
@query query("Examples", Main)
@query Lexicon.doctest(Lexicon)
Full text searching is provided and looks through all text and code in docstrings, thus behaving in a similar way to Base.apropos
. To specify the module(s) to search through rather use the query
method directly.
@query "Examples"
Generic functions and types are supported directly by @query
. As with method searches the module may be specified.
@query query
@query Lexicon.query
@query Lexicon.Summary
Globals require a prefix argument global
to avoid conflicting with function/type queries as see above.
@query global Lexicon.METADATA # this won't show anything


Huskers Insider Has Update On Nebraska’s Wan’Dale Robinson
by Andrew McCartyWan’Dale Robinson, Nebraska’s talented freshman receiver, is one of the most electric players in the country. It wasn’t surprising to see him as a finalist for the Paul Hornung award – given to the nation’s most versatile player.
However, he hasn’t seen the field in recent weeks.
Robinson left Nebraska’s last game, the Week 10 loss to Purdue, with a leg injury. Earlier this week, offensive coordinator Troy Walters called him “very questionable” for this weekend’s game against Wisconsin.
The star wide receiver doesn’t appear to be on track to play this afternoon. According to a report from Nebraska insider Andy Kendeigh, Robinson was not on the team buses before today’s game.
At 4-5, Nebraska needs to find two wins from this weekend’s home game against No. 14 Wisconsin, a trip to Maryland, and the season finale at home against No. 20 Iowa to become bowl eligible.
Losing Robinson is a major blow to the Huskers chances at an upset.
The freshman leads the team with 40 receptions, and is second on the roster with 453 receiving yards. Robinson is also the team’s third-leading rusher, with 85 carries for 326 yards and three touchdowns.
Wisconsin enters as a 14-point favorite, despite playing in Lincoln. The Badgers and Huskers kick off at 12:00 p.m. ET on Big Ten Network.


Xv6 is "a teaching operating system" based on Unix V6, but implemented in ANSI C for multi-core x86 processors. Throughout my undergrad OS course (CS3210), we were tasked with multiple projects where we were provided with opportunities to tinker at all layers of the xv6 operating system, from the bootblock to the kernel and user modes. Below, I briefly touch on the technical challenges with each modification I made to xv6 during the course!
The first modification I made to xv6 was adding stack backtrace support! In order to implement this feature, I had to familiarize myself with how x86 modifies registers and the stack in order to determine the chain of function calls. I also had to use STAB information collected by the kernel in order to display the function names to the user when the backtrace() function would be called.


Does anyone know if there are instructions/discussion about how to add new visualizations to emoncms?
What I want to do is just copy the existing visualizations and modify those to my needs. The practical issue is that I would like to have the control buttons on different locations and change the "kw" labels to more relevant to temperature measurements (without loosing the original ones).
As far as I have understood all units in the graphs are hard coded.
(Also I am having hard time editing the dashboards with mac. More or less no functions work with Chrome or Safari. One needs to open the windows box to have something done. Or am I doing something wrong?)
Re: Adding new visualisations to emoncms
Would really like to find some documentation also.
Posted a similar question here: http://openenergymonitor.org/emon/node/1960
Re: Adding new visualisations to emoncms
I've been slowly figuring some of this stuff out on my own via my github clone of the main project branch. ( https://github.com/mezz64/emoncms )
I'm not a programmer by trade so it's likely a bit messy and there are a ton of commits as I break/fix things until I end up with the right result, but it might help somebody out. I've moved the button on most of the graphs to be above the graph window, added a graph to calculate energy cost and added a units option to the bargraphs, among some other changes.
You can see my public dashboards here: http://energy.johnmihalic.com/admin
I'd recommend cloning my branch into a separate install before going over the master though, I may have made some changes that work fine for me but reek havoc for others.
Re: Adding new visualisations to emoncms
I'm curious, mezz; How did you get the graph showing KwH/d and cost per period? I've so far not been able to find anything about how to add KwH costs into emoncms... Maybe I'm just missing something. :)
Re: Adding new visualisations to emoncms
It's a new graph type I made based on the existing bar graph visualization. Shouldn't be too difficult to add it into another emoncms install, just grab the costgraph.php file from my github to add to your visualisations directory and define it in vis_controller.php and vis_render.js files. You can look at them on my github to see the changes.
Re: Adding new visualisations to emoncms
Got it, thanks! Definately something that should be in the base. :)
Re: Adding new visualisations to emoncms
Yeah mezz64, it would be great if you could clean up and submit pull requests for the generic parts. For example, the min and max input processors was one thing I saw in your code which definitely should go to the master branch imo.
Re: Adding new visualisations to emoncms
I am having some trouble getting this working...I am using the latest version of OEM. All I see is a blank graph.
Thanks.
Re: Adding new visualisations to emoncms
hi,
what the parameter in smoothie Ufac means??I get a desyncronesid screen all the time miving forward...but grey...with no data.
regards
tux


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web.phptal.skeleton.php
Warnings:
Warning on line 18 - File "/home/samurai-fw/repositries/samurai/tags/2.0.7beta/skeleton/template/web.phptal.skeleton.php" has no page-level DocBlock, use @package in the first DocBlock to create one
web.simple.skeleton.php
Warnings:
Warning on line 20 - File "/home/samurai-fw/repositries/samurai/tags/2.0.7beta/skeleton/template/web.simple.skeleton.php" has no page-level DocBlock, use @package in the first DocBlock to create one
web.smarty.skeleton.php
Warnings:
Warning on line 18 - File "/home/samurai-fw/repositries/samurai/tags/2.0.7beta/skeleton/template/web.smarty.skeleton.php" has no page-level DocBlock, use @package in the first DocBlock to create one
Whitelist.class.php
Warnings:
Warning on line 50 - Unknown tag "@implements" used
yaml.skeleton.php
Warnings:
Warning on line 14 - File "/home/samurai-fw/repositries/samurai/tags/2.0.7beta/skeleton/action/yaml.skeleton.php" has no page-level DocBlock, use @package in the first DocBlock to create one
_doc_comment.skeleton.php
Warnings:
Warning on line 8 - File "/home/samurai-fw/repositries/samurai/tags/2.0.7beta/skeleton/_doc_comment.skeleton.php" has no page-level DocBlock, use @package in the first DocBlock to create one
generator.php
Errors:
Error on line 49 - "define" SAMURAI_APPLICATION_NAME's DocBlock has @subpackage tags, illegal. ignoring tag "@subpackage Generator"
Error on line 49 - SAMURAI_APPLICATION_NAME define's DocBlock has @package tag, illegal. ignoring tag "@package Samurai"
phptal.php
Errors:
Error on line 48 - "include" include's DocBlock has @subpackage tags, illegal. ignoring tag "@subpackage Config-Renderer"
Error on line 48 - include include's DocBlock has @package tag, illegal. ignoring tag "@package Samurai"
production.php
Errors:
Error on line 48 - BASE_URI define's DocBlock has @package tag, illegal. ignoring tag "@package Samurai"
Error on line 48 - "define" BASE_URI's DocBlock has @subpackage tags, illegal. ignoring tag "@subpackage Config-Environment"
smarty.php
Errors:
Error on line 76 - include include's DocBlock has @package tag, illegal. ignoring tag "@package Samurai"
Error on line 76 - "include" include's DocBlock has @subpackage tags, illegal. ignoring tag "@subpackage Config-Renderer"
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