Point Cloud Rendering
LGSVL Simulator supports scenes containing point cloud data. Point clouds can be rendered alongside other geometry like meshes or particles, are combatible with most of the features available in high definition render pipeline and can be detected by multiple sensors, including lidar.
Point cloud rendering in Simulator can handle large point clouds (hundreds of millions of points) thanks to octree-based structure and selective, frustum-based culling.
If you want to create your own point cloud based environment, you should start with importing your point cloud data into simulator. This step is not necessary for pre-built point cloud environments - they do not require any additional setup.
Rendering Features#
Most of the features available for point cloud renderers are optional and can be toggled based on individual use cases. Lighting, for example, can be turned off if point cloud has light data already baked in, or calculated fully based on scene settings like time of day or weather.
Most notable features include:
- Full deferred lighting compatible with HDRP's lighting system
- Filling holes between points
- Rendering multiple point clouds at once
- Normals estimation
- Shadow casting/receiving
- Point streaming
- Point budget (for memory and render buffer)
- Camera frustum or distance-based culling
- Mesh collider generation
- Support for color/depth camera sensors and lidar
Setup#
The easiest way to set up point cloud rendering is to perform an import with the target scene open in the background. When importing is done, you will be prompted with a choice to automatically add all of the required components to the open scene. If accepted, point cloud with default rendering settings will immediately show up in scene view.
Alternatively, a prefab located in Assets/Prefabs/PointCloudRenderer.prefab
can be added to any scene for a quick setup. This requires to specify point cloud data path in NodeTreeLoader
component.
More complex setups with multiple point clouds or masked rendering require setting up components manually. Details can be found in Components Overview section.
Components Overview#
In the most common scenario with a single point cloud, one of each of the components described below will be present on a scene.
For more complex cases (e.g. multiple point clouds or separate color and intensity data) it's possible to add multiple instances of NodeTreeLoader
and/or NodeTreeRenderer
to one scene. Only one instance of PointCloudManager
should ever be present.
PointCloudManager
#
This component is responsible for keeping track of shared point cloud resources and orchestrating rendering for one or more point cloud renderers. It will also automatically set up HDRP passes and enable or disable them depending on all renderers' settings.
There are no settings available for this component. Only one instance should be active at a time. Disabling this component will disable rendering of all point cloud data.
NodeTreeLoader
#
This component is responsible for managing single point cloud's data in memory. It will load chunks of point cloud data from mass storage based on each renderer's requests (performed on a separate thread) and will keep track of total amount of points loaded into memory. When defined point budget is exceeded, older points will be unloaded.
Single instance of this component can only operate on one point cloud, but multiple renderers can use data stored in a single NodeTreeLoader
.
This component can also optionally load mesh colliders for a point cloud if they were generated during import process.
NodeTreeRenderer
#
This component represents a single point cloud to render and stores all related settings. Transform
to which this component is tied affects translation, rotation and scale of the point cloud.
By default, point cloud rendered by a single renderer will be visible in all cameras and compatible sensors, but not in shadow caster pass. This can be controlled on a per-renderer basis.
Note that this component will not actively trigger any rendering. It's used mostly as a container for data that will be used by PointCloudManager
.
NodeTreeRenderer
is default implementation that includes camera-based culling, which will make load requests to NodeTreeLoader
. Adding other culling methods can be done by extending the base class, PointCloudRenderer
.
Rendering Modes Overview#
Depending on the requirements, there are multiple combinations of rendering modes available. Sensors supported in point cloud rendering are compatible with all of them.
The most simple one (Points
) will render points with size dependent on distance from the camera. This image is stable, but does not support lighting and will show holes if point cloud is not dense enough. Improved version of this mode (Cones
) will use 3D cones instead of 2D circles, which creates better transitions between very close points, but has a slight performance cost.
More advanced rendering mode, Solid
, will remove points that should be invisible from observer's position and interpolate color and depth data between leftover points. It can also reconstruct normals data from depth, and subsequently calculate lighting for point cloud. Filters used here are executed in screen space, which means that camera position and rotation affect rendering output. This might introduce some artifacts around edges, especially around large depth differences.
It's suggested to test available modes and choose the one best suited for given point cloud and use case. If lighting is required, solid rendering mode is the only one currently supporting it.
Settings#
Loader Settings#
| Parameter Name | Type | Description |
|---|---|---|
| Data Path | string | Directory in which imported point cloud data is stored. Paths starting with .../ will be treated as relative to Assets folder in the project, others are treated as absolute. |
| Point Limit | int | Maximum amount of points that can be loaded into memory at once. These points are treated as cache between render buffer and hard drive. When point limit is exceeded, older points will be unloaded. |
| Load Meshes | bool | If this option is enabled, collider meshes will loaded alongside the point cloud data. This requires Generate Mesh option in point cloud import settings to be enabled. |
Renderer Settings#
Settings visible for renderer depend on currently selected rendering mode. Irrelevant settings will be hidden in inspector. Enabling additional features will sometimes show new settings related to them.
Shared Rendering Settings#
| Parameter Name | Type | Description |
|---|---|---|
| Colorize | enum | Colorization mode to use for point cloud. If selected data is not present in source point cloud, result will be black. Available modes: Colors , Intensity , Rainbow Intensity and Rainbow Height . |
| Render Mode | enum | Describes how points should be represented. Available modes: Points (most simple one), Cones (similar to points, with interpolation) and Solid (fills holes, required for lighting). |
| Mask | enum | Enum flags used to enable or disable renderer's visibility. Point cloud will be visible only in selected passes. Available options: Camera (all color and depth cameras), Shadowcaster (enables shadow casting by point cloud), Lidar (makes point cloud detectable by lidar sensor). |
| Shadow Point Size | float | Only visible if Shadowcaster is enabled under Mask . Determines size of a point for shadow casting purposes. |
| Shadow Bias | float | Only visible if Shadowcaster is enabled under Mask . Determines shadow bias for each point. Value is relative to Shadow Point Size . |
Point/Cones Rendering Settings#
| Parameter Name | Type | Description |
|---|---|---|
| Constant Size | bool | If this option is enabled, point size will not depend on distance from camera. |
| Pixel Size | float | Only visible with Constant Size enabled. Determines size of a point. |
| Absolute Size | float | Only visible with Constant Size disabled. Determines size of a point at a near plane. Points further away will be smaller. |
| Min Pixel Size | float | Only visible with Constant Size disabled. Determines minimum size of a point, irrelevant from distance. |
Solid Rendering Mode#
Cascades options for Remove Hidden and Smooth Normals stages affect filter window size for both these effects and its scaling with distance for camera. They can be visualized by toggling Show Preview
option on.
Cascade options for Remove Hidden stage will only be visible if Hidden Point Removal
option is set to Screen Space
. For Depth Prepass
, they will be replaced with point rendering settings.
| Parameter Name | Type | Description |
|---|---|---|
| Lighting Mode | enum | Determines how light will affect the point cloud. Available modes: Unlit (no lighting, no shadows), Shadow Receiver (no lighting, receives shadows), Full Deferred (receives lighting and shadows) |
| Hidden Point Removal | enum | Method used for removing obscured points. Screen Space is the default, faster option. Depth Prepass reduces flickering on edges, but requires more precise setup and can have impact on performance. |
| Calculate Normals | bool | If enabled, normals approximation stage will be performed and world-space normals will be available. Required for lighting. |
| Smooth Normals | bool | If enabled, additional normals smoothing stage will be performed to reduce noise. Results will replace normals data from Calculate Normals option. |
| FoV Reprojection | bool | If enabled, wider image of point cloud will be rendered internally and it will be reprojected to desired camera FOV. This option reduces artifacts near the edges of the image. Does not affect final FOV. |
| Reprojection Ratio | float | Multiplier determining ratio between FOV used internally for FoV Reprojection option and original FOV of the camera. |
Solid rendering also has some debug options under separate tab. These are mostly directly affecting separate rendering stages, but some of them can me modified to affect final image.
| Parameter Name | Type | Description |
|---|---|---|
| Linear depth | bool | If enabled, linear depth will be used throughout all compute shaders instead of depth buffer values. |
| Force fill | bool | If enabled, holes below horizon line will be filled regardless of their size. Can create blurred artifacts for large holes, but removes all holes in road for low density point clouds. |
| Fill threshold | float | Sets height threshold under which Force fill option will be used if it's enabled. |
| Blend sky | bool | If enabled, point cloud color and intensity will be blended to sky color instead of black. Removes black color around edges, but requires additional sky pre-render for lit mode. |
Culling Settings#
| Parameter Name | Type | Description |
|---|---|---|
| Node Tree Loader | NodeTreeLoader | Reference to loader that will be queried for point cache updates. |
| Cull Camera | Camera | Reference to camera used for culling. If this field is left empty, main simulator camera will be used after simulation is started. |
| Cull Mode | enum | Determines how points are evaluated for visibility. Available modes: Camera Frustum (both view frustum and distance from camera are considered), Distance (only distance from camera is considered - recommended if lidar is used). |
| Point Limit | int | Number of points that should be pushed to render buffer. This value determines maximum amount of points that are visible at once and directly affects buffer size. |
| Min Projection | float | Minimum screen projection size along single axis (in pixels) that octree node has to occupy to be traversed further. Nodes below this threshold will not be visible. |
| Rebuild Steps | int | Number of frames to spend for render buffer rebuild. Previous fully completed buffer will be used until new one is ready. Values higher than 1 reduce per-frame cost of buffer rebuild, but can introduce popping. |


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Naked Soul(former Alma Desnuda), a tight knit band of four friends from San Francisco Bay Area play a mix of different genres, but focus most on a great vibe and amazing atmosphere. Check out their latest gig at HSMF '13


MUMFORD A film review by Steve Rhodes Copyright 1999 Steve Rhodes RATING (0 TO ****): *** 1/2
Dr. Mumford (Loren Dean) is a psychologist with a difference in MUMFORD, the latest cinematic treasure from writer/director Lawrence Kasdan (THE BIG CHILL and GRAND CANYON).
As an inviting enigma, Dean gives a deceptively seductive performance. He almost hypnotizes the audience with his smooth, blank slate of a face. Dr. Mumford, a world-class listener, possesses such subdued charm that people are compelled to tell him their most hidden secrets, something it's obvious the doc must have a few of himself.
In town for less than 5 months, he's already got more patients than the other two local shrinks combined. Ridiculed by one of the town's sophisticates as "Mayberry, RFD," the place where the doctor has moved has (coincidentally?) been called Mumford since 1813.
Dr. Mumford listens stoically to some patients but cuts others' time short if he thinks they're wasting his time. In a story that isn't in any hurry to get anywhere, the movie charms with its cornucopia of well-written and superlatively acted characters. One of the film's delights is that you haven't any idea where it is headed. Its one mystery is fairly obvious. Full of wry humor that produces a host of small laughs and constant grins, the script is perhaps its biggest star. ("The only problem I had with degenerate, self-destructive behavior is that I couldn't get enough of it.")
>From bars to hardware stores, people pull Dr. Mumford over to seek out his advice and to bare their souls. The movie has an almost magical aspect to it that you can't quite put your finger on. Lit by cinematographer Ericson Core to look like a Norman Rockwell painting, the town of Mumford manages to look appealing even though it conceals a hotbed of neuroses.
One of his patients is a waif, who has an unhealthy addiction to glamour magazines. Lying on his coach, cuddling Bazaar and Elle in her arms, she confesses, "I want to live in the world that these people are in."
Sofie Crisp (Hope Davis), his prize patient, suffers from Chronic Fatigue Syndrome. Davis gives a convincing performance as someone so tired that she can barely move. With pale, translucent skin, she appears so fragile that her bones might be in danger of disintegration. "You may lie down," the doctor advises her on her first visit to his office. "Most people do." "I'd better not," she replies as if exhausted by speaking. "I'd fall right asleep. Besides, it's probably too early for me to start sleeping with you."
As obsessive shopper Althea Brockett, Mary McDonnell (DANCES WITH WOLVES) plays a woman who never met a catalog she didn't love. Especially fond of two-named stores (Williams-Sonoma, Neiman Marcus, ....), she has closets overflowing with the loot that she's bought but hasn't unboxed.  "No one ever said on his death bed, 'I treated myself too well,'" he explains to Dr. Mumford, as he blows the smoke from his large, hotdog-sized cigar into a brandy stifter big enough for a small aquarium.
The town's Rock of Gibraltar, Lily (Alfre Woodard), owns the local coffee shop and rents a room to Dr. Mumford. She spends her evenings watching television shows like "20/20." ("It's shocking!" she tells the doctor about the latest investigative report on 20/20. "The government is wasteful. You heard it here first.")
In a likable and wonderfully naive turn, Jason Lee (CHASING AMY) is Skip Skiperton, the town's young success story, as the founder of Panda Modems. Skip's a lonely guy with a serious case of insecurity for a multibillionaire. He complains that his company makes "23% of the world's modems, but I can't connect with women." He pays the doc to play ball with him, while he discusses his many anxieties.
In a movie filled with gems, it's hard to pick out a favorite. The most unusual, however, is the telling of his dreams by the pharmacist (Pruitt Taylor Vince). He dreams in black and white and his characters are lifted straight out of old dime store paperbacks - the types populated with women with large bosoms and grease-monkey men. Just the sort that a kid might sneak off to a shed to read, lest he be caught by his parents and the sort that might forever be trapped in an adult's subconscious.
MUMFORD runs a fast 1:36. It is rated R for sex-related images, language and drug content. It is a good-spirited film that would be fine for teenagers.
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*fmute @USER - Force mutes mentioned user
*funmute @USER - Force unmutes mentioned user
*deafen - Deafens all the users in the voice channel
*undeafen - Undeafens all the users in the voice channel
*fdeafen @USER - Deafens only mentioned user in channel
*fundeafen @USER - Undeafens the mentioned user in the channel
*rythm
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MEMES
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AMONG US INFO
AMONG US WIKI


I visited Mathura, specifically the janmsthan and Gokul, and as I was doing the visit, some questions came to my mind. Here are the questions and their answers that I found by internet research.
Classical understand is that Krishna and Balram were brothers: Krishna was younger brother and Balram was elder. But did they have same mother and same father? No.
While Krishna was son of Vasudev and Devaki, Balram was son of Vasudev and Rohini. Vasudev had several other wives. Via Rohini Vasudev also had a daughter Subhadra who was married to Arjun.
Yes, apparently Nanda acted as foster father of both of them. Since Krishna was 8th son of Devaki, and all this while Vasudev was incarcerated with Devaki, the time differene between Balram's birth and Krishna's birth has to be a minimum of ~7 years, maybe more. They are often portrayed together like buddies, but Balram has to be much older thatn Krishna. Perhaps that's why Balram is usually out in Krishna leela.
Immediately after his birth in Mathura, he was taken to Gokul, where he remained for 11 years. Then he spent 11 years in Mathura, after which he went to Dwarka.
The whole episode of Krishna's birth has so many mysterious elements, which are classically explained by God's power and leela etc, but on a human leve they leavel unanswered questions:
One cogent explanation would be that while Vasudev was incarcerated by Kansa, he was able to communicate with external world. After all he was a king, and thus a powerful person. He would be getting royal treatment in jail, like corrupt politicians get in India today when they are occassionally jailed.
Kansa would have wanted to eliminate competition to throne (how was Krishna a competitor is not known to me) and thus would have wanted to kill all sons of Vasudev (Let's get rid of all the bhavishyawani stuff, and the desire to kill just 8th son). Nandlal might have been a friend of Vasudev, or a faithful subject ready to sacrifice his offspring for the king, or coerced into the sacrifice. In any case, baby exchange was done, perhaps not by Vasudev travelling alone crossing overflowing Yamuna but by some agents faithful to Vasudev. Kansa decided to kill the daughter anyway, and story around appearance of yogmaya was constructed.
Krishna happend to survive. And then there was Balram too.
Modern day Gokul where Nandlal's home is supposed to have been there, is to south of Mathura, around 10 km from janmsthan. Classical understanding is that Krishna did is bal leela in Gokul.
That leaves Vrindavan out of picture. Vrindavan is to the north of Mathura, so bal leela of Krishna cannot be done in Vrindavan: the distance between the two is around 20 km.
Since Vrindavan is said to be the place where raas leela took place, it is possible that Krishna would travel ~20km to Vrindavan from Gokul to meet with his girlfriends. Also, Yamuna would have changed the course over thousands of years and thus distances then could have been different.
Overall, there are a lot of interesting and unanswered questions here. It is worth reading various available texts and making a detailed visit of Mathura to throw light on some of these question


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- H - Variable in class com.ni.vision.NIVision.HSIValue
-
- H - Variable in class com.ni.vision.NIVision.HSLValue
-
- H - Variable in class com.ni.vision.NIVision.HSVValue
-
- HAL - Class in edu.wpi.first.wpilibj.hal
-
JNI Wrapper for HAL
.
- HAL() - Constructor for class edu.wpi.first.wpilibj.hal.HAL
-
- HalHandleException - Exception in edu.wpi.first.wpilibj.util
-
Exception indicating that an error has occured with a HAL Handle.
- HalHandleException(String) - Constructor for exception edu.wpi.first.wpilibj.util.HalHandleException
-
Create a new HalHandleException.
- HalInvalidHandle - Static variable in class edu.wpi.first.wpilibj.hal.InterruptJNI
-
- HALUtil - Class in edu.wpi.first.wpilibj.hal
-
- HALUtil() - Constructor for class edu.wpi.first.wpilibj.hal.HALUtil
-
- HardwareHLUsageReporting - Class in edu.wpi.first.wpilibj.internal
-
- HardwareHLUsageReporting() - Constructor for class edu.wpi.first.wpilibj.internal.HardwareHLUsageReporting
-
- HardwareTimer - Class in edu.wpi.first.wpilibj.internal
-
Timer objects measure accumulated time in milliseconds.
- HardwareTimer() - Constructor for class edu.wpi.first.wpilibj.internal.HardwareTimer
-
- hasData - Variable in class com.ni.vision.NIVision.AVIInfo
-
- hasEnd() - Method in class edu.wpi.first.wpilibj.command.MockCommand
-
If the end method has been called at least once.
- hasEquation - Variable in class com.ni.vision.NIVision.ContourInfoReport
-
- hasInitialized() - Method in class edu.wpi.first.wpilibj.command.MockCommand
-
If the initialize method has been called at least once.
- hasInterrupted() - Method in class edu.wpi.first.wpilibj.command.MockCommand
-
If the interrupted method has been called at least once.
- hasPeriodPassed(double) - Method in class edu.wpi.first.wpilibj.Timer
-
Check if the period specified has passed and if it has, advance the start time by that period.
- hasPeriodPassed(double) - Method in interface edu.wpi.first.wpilibj.Timer.Interface
-
Check if the period specified has passed and if it has, advance the start time by that
period.
- height - Variable in class com.ni.vision.NIVision.AVIInfo
-
- height - Variable in class com.ni.vision.NIVision.BrowserOptions
-
- height - Variable in class com.ni.vision.NIVision.CharacterStatistics
-
- height - Variable in class com.ni.vision.NIVision.GetFileInfoResult
-
- height - Variable in class com.ni.vision.NIVision.GetImageSizeResult
-
- height - Variable in class com.ni.vision.NIVision.LegFeature
-
- height - Variable in class com.ni.vision.NIVision.Rect
-
- height - Variable in class com.ni.vision.NIVision.RectangleFeature
-
- height - Variable in class com.ni.vision.NIVision.RectangleMatch
-
- height - Variable in class com.ni.vision.NIVision.RotatedRect
-
- hexa - Variable in class com.ni.vision.NIVision.StructuringElement
-
- HEXADECIMAL_DIGITS - Static variable in class com.ni.vision.NIVision
-
- highPass(PIDSource, double, double) - Static method in class edu.wpi.first.wpilibj.filters.LinearDigitalFilter
-
Creates a first-order high-pass filter of the form: y[n] = gain*x[n] + (-gain)*x[n-1] +
gain*y[n-1] where gain = e^(-dt / T), T is the time constant in seconds.
- highThreshold - Variable in class com.ni.vision.NIVision.CharReport
-
- highThreshold - Variable in class com.ni.vision.NIVision.CharReport2
-
- highThreshold - Variable in class com.ni.vision.NIVision.CharReport3
-
- highThreshold - Variable in class com.ni.vision.NIVision.OCRProcessingOptions
-
- histogram - Variable in class com.ni.vision.NIVision.HistogramReport
-
- HistogramReport() - Constructor for class com.ni.vision.NIVision.HistogramReport
-
- HistogramReport(int[], double, double, double, double, double, double, int) - Constructor for class com.ni.vision.NIVision.HistogramReport
-
- HLUsageReporting - Class in edu.wpi.first.wpilibj
-
Support for high level usage reporting.
- HLUsageReporting() - Constructor for class edu.wpi.first.wpilibj.HLUsageReporting
-
- HLUsageReporting.Interface - Interface in edu.wpi.first.wpilibj
-
- HLUsageReporting.Null - Class in edu.wpi.first.wpilibj
-
- horizontalTextAlignment - Variable in class com.ni.vision.NIVision.OverlayTextOptions
-
- houghIterations - Variable in class com.ni.vision.NIVision.StraightEdgeOptions
-
- HSIValue() - Constructor for class com.ni.vision.NIVision.HSIValue
-
- HSIValue(int, int, int, int) - Constructor for class com.ni.vision.NIVision.HSIValue
-
- HSLImage - Class in edu.wpi.first.wpilibj.image
-
A color image represented in HSL color space at 3 bytes per pixel.
- HSLImage() - Constructor for class edu.wpi.first.wpilibj.image.HSLImage
-
Create a new 0x0 image.
- HSLImage(String) - Constructor for class edu.wpi.first.wpilibj.image.HSLImage
-
Create a new image by loading a file.
- HSLValue() - Constructor for class com.ni.vision.NIVision.HSLValue
-
- HSLValue(int, int, int, int) - Constructor for class com.ni.vision.NIVision.HSLValue
-
- HSVValue() - Constructor for class com.ni.vision.NIVision.HSVValue
-
- HSVValue(int, int, int, int) - Constructor for class com.ni.vision.NIVision.HSVValue
-
- hysteresis - Variable in class com.ni.vision.NIVision.SimpleEdgeOptions
-


Murder accused shot dead by rivals in Jaranwala court | Pakistan - Geo.tv
by Web DeskJARANWALA: An accused murderer was shot dead by his rival in the premises of a court here in the Faisalabad district city during an ongoing hearing, police said Friday, according to Jang News.
Additional Session Judge Muhammad Naeem Saleem was hearing a case wherein Tara Gujjar, the murder accused who had been in police custody, was presented in the court.
The accused, who was in handcuffs, died on the spot whereas two of the assailants were arrested on the scene, police sources said. Consequently, there was chaos in the courtroom.
Sources further informed that after the shooting, the police officers who had escorted Gujjar to the court also fled the scene. The deceased had been targeted over old personal enmity by his rivals.
Police, after taking the two shooters into their custody, started legal action in this regard.


About
This is a test of bootstrap and event listners to make an interactive punnet square for Mendelen genetics. If you hover over a box, you should see the corresponding refactored allels appear. When you click on the box their phenotype will be revealed!


Wanderlust HTML Template
Wanderlust for Listings
Thank for purchasing.
The HTML template is locate in dist directory.
Wanderlust currently using font Cabin. The fonts are enqueued in the <head> of html files:
head
body
-
.site-header
-
.page-banner
-
.category
-
.listing-list
-
.instruction
-
.testimonial
-
.ads
-
.news
-
.site-footer
/body
-
Fonts
-
Google Fonts: Cabin
-
Font Awesome 5.0
-
Font Ionicons 2.0
-
Javascript
- app.js - Main JS file. All third-party plugins have been concatenated and minified in app.js
- jquery - jQuery Library
- jquery-smooth-scroll - add smooth scrolling effect for same-page links
- chosen - custom select boxes
- dropzone - drag and drop file uploads with image previews
- icheck - custom checkboxes and radio buttons
- lity - Lightweight, accessible and responsive lightbox
- markerclusterer.js - Add marker clusterer for Google Maps
- photoswipe - Responsive JS image gallery
- infobox - Custom info window for Google Maps
- swiper - touch slider
- trumbowyg - light weight WYSIWYG editor
- rangeslider - polyfill for range input HTML5 element
-
CSS
- style.css - Main CSS file. All third-party CSS files have been concatenated and minified in style.css


This inspection reports any instances of Groovy statements witch attempts to put GString object as a key to map.
In a general way GString objects are mutable and probably should not be used as keys.
Also, GString entry could no be accessed with java.lang.String object with same value. Example:
def map = [:]
def key = 'foo'
map << ["${key}": 'bar']
assert map[key] == null


SHANGHAI NOON (Touchstone) Starring: Jackie Chan, Owen Wilson, Lucy Liu, Brandon Merrill, Roger Yuan, Xander Berkeley. Screenplay: Alfred Gough & Miles Millar. Producers: Roger Birnbaum, Gary Barber and Jonathan Glickman. Director: Tom Dey. MPAA Rating: PG-13 (violence, profanity, drug use) Running Time: 110 minutes. Reviewed by Scott Renshaw.
Jackie Chan in another buddy action-comedy -- now there's something I didn't think I ever needed to see again. Sure, the formula proved extremely successful in 1998's $141 million hit RUSH HOUR. It also meant Jackie Chan fans had to endure the human audio torture device that is Chris Tucker, and that studios would persist in their foolish perception that American audiences might not embrace a film with Chan as its solo star. SHANGHAI NOON looked like a disaster waiting to happen -- formula piled upon formula, cheap gag piled upon cheap gag, Jackie Chan in another buddy action-comedy.
The next time an international action star comes calling on Hollywood, and needs a buddy action-comedy partner, I hope they pick Owen Wilson again. The quirky actor does something few actors have the ability to do: He can make you forget you're watching a formula film. SHANGHAI NOON opens in 1881, where independent-minded Chinese princess Pei Pei (Lucy Liu) wants to escape the Forbidden City and her arranged marriage. Unfortunately, she chooses as her escort to America a duplicitous translator, who turns Pei Pei over as a hostage to disgraced former Imperial Guard Lo Fong (Roger Yuan). Enter Chon Wang (Chan), a lower-tier Imperial Guard who accompanies a mission to recover the princess. On a train crossing Nevada, Chon Wang and company are accosted by a gang of thieves headed by Roy O'Bannon (Wilson). O'Bannon and Chon begin at odds, but eventually become riding partners (though O'Bannon's goal is more likely the solid gold ransom than the princess' safe return).
As was the case in RUSH HOUR, Chan's ability to hold up his end of the buddy partnership is never in question. There is something so instantly and immensely likable about Chan that it's almost unfair that he's also so physically gifted. His stunt work is again in top form in SHANGHAI NOON, finding more creative props for Chan to use as weapons against his foes: saplings, moose horns, horseshoes, a railroad log car, etc. Director Tom Dey and editor Richard Chew don't do Chan's work optimum justice -- too many sequences are chopped up when it has proved infinitely smarter over the years to let the camera sit still and watch Chan at work -- but they still possess that unique capacity to dazzle. A movie fight sequence is usually an obligatory piece of action business; a Jackie Chan fight sequence is the reason you never want to leave your seat during the action.
And Owen Wilson is the reason you never want to leave your seat during the exposition. His O'Bannon is a great, goofy comic creation, a big talker whose primary reason for being in the outlaw game appears to be impressing women. Any number of actors probably could have pulled off that characterization, but it's far more difficult to give it the strangely off-hand charm Wilson provides. His performance cashes in on the incongruity of his surfer-casual line readings, but it never depends on that incongruity. There's just something unexpectedly funny about the way Wilson handles even the most mundane, predictable moments. I spent half of RUSH HOUR pleading for Chris Tucker to get off the screen so I could enjoy Jackie Chan. No such problems in SHANGHAI NOON -- I felt a silly grin plastered across my face no matter which actor was being showcased.
If the raw material of SHANGHAI NOON had been stronger, it could have been a truly inspired comedy. Writers Alfred Gough and Miles Millar pump some clever lines into the script, but it's still a bit too dependent on making fun of the names Pei Pei and Chon Wang (or rib-nudgingly naming one nasty cowboy Van Cleef). Then there are the lumbering plot development scenes, including references to Chinese railroad labor that seems included primarily for a sense of anachronistic moral superiority. In fact, the entire premise feels like a grand distraction from the appeal of the two stars, whose work individually and as a team blows the material away. I'm not sure what sort of setting might have served them better, and it almost feels like quibbling to wonder. Chan and Wilson simply make SHANGHAI NOON loads of fun. It's the sort of buddy action-comedy that wipes some of the stigma away from the term.
On the Renshaw scale of 0 to 10 wild wild Easts: 7.
Visit Scott Renshaw's Screening Room http://www.inconnect.com/~renshaw/ *** Subscribe to receive new reviews directly by email! See the Screening Room for details, or reply to this message with subject "Subscribe".
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Welcome! You've stumbled upon the development blog for the game The Alchemist(working title). Here I'll be posting weekly updates on my progress as a way to both have a record of my progress as well as keep myself motivated.
This game is a solo project that I have been developing for around a month now. I recently graduated from Rochester Institute of Technology and while I worked on my Capstone, The Shattering Swords, with 10 others and a few Game Jams alone, this is the first game I have worked on solo with a large scope. The project will culminate in a release however because of the early stage it is in I don't have a release schedule yet.
Since this is my first post I'll start by explaining the game I'm working on. The game is a 2D isometric adventure game with the current working title, The Alchemist, created in Unity. Within the game the player discovers and catalogs ingredients to make potions, has a small greehouse where they can grow herbs, help out the villagers, and heal the surrounding land through the potions they create.
You play as an alchemist that has been assigned to the town to cataloge the various herbs and discover why the surrouding land is becoming corrupted. When you arrive the town has been produced to ruins from the surrounding corruption and with your ability to harness the local wildlife you craft potions and research new ingredients to help clense the town. Further into the forest you find more herbs, stronger corruption, and perhaps what is behind it.
My current progress has mainly been in the systems and overarching design of the game. In the last month I've worked on the design for the cauldron, growing herbs, and foreging. The core loop of the game will involve the player going out and gathering herbs, making potions, helping the town/the land, and repeating.
Currently the player is able to pick up herbs (the green objects) which are added to their inventory. I want the player to have easy access to their inventory to see both what potions and herbs they have. The overall functionallity is there however the look and feel will be updated later.
Also the player is able to use a farm plot (the orange space) to plant and grow herbs. The player selects the herb they want to plant from their inventory and then after some time the plant reaches it's fully grown stage and the player can harvest it.
The player can also make a very simple potion using the one herb that they are able to collect. The player uses the interact button, currently bound to Mouse 0, and if they have the required herb, the potion is placed in their inventory and the herb is removed.


The IP-Noise Simulator is a simulator for TCP/IP networks noise that can run on top of a Linux 2.4.x system. It is available in two versions: one as a stand-alone user-level program that can run from the command line. The second one is a kernel module, that should be loaded with insmod.
TCP/IP noise generation involves the deliberate dropping or delaying of packets sent over the TCP/IP network. The simulator can distinguish between the various protocols above the IP level and arbitrate them differently accordingly. However, the contents of the packets cannot affect its decision in any way.
Note that it is assumed that the network congestion is independent of what occurs in the applications that are tested with the noise. Thus, the load of the network does not affect the simulator's behaviour.
We intend the simulator to be used to test the robustness of protocols, and see how immune they are to network noise. The simulator is designed to be flexible and simulate various network noise conditions, in a manner that is accurate enough for most needs.
The project was initiated by Lavy Libman, and was written by Shlomi Fish and Roy Glasberg under his supervision. It was conducted with the help and resources of the Computer Networks Lab of The Technion.
We would like to thank the various people in the Israeli Group of Linux Users who provided important help and input during the various stages of the project: Omer Mussaev, Guy Keren, Mulix and others. We would also like to thank the developers of the Netfilter firewalling stack of the Linux Kernel 2.4, which gave us most of the necessary kernel functionality, and made our work much easier.
We would also like to thank the FOKUS group for creating and maintaining the BerliOS service, whose on-line resources we were able to use.
The simulator requires a GNU/Linux system running the Linux Kernel version 2.4.x. IP-Tables has to be available as a kernel module, as is the IP-Queue module.
The translator which is written in Perl is distributed under the MIT X11 license. The compiler is distributed under a dual GPL and MIT X11 license except for the following modules:
The Perl arbitrator is no longer maintained and is only useful as a reference implementation. We cannot guarantee that it does not contain bugs. It is distributed under the MIT X11 license.
In order to use the simulator, the IP-Tables kernel module must be compiled, and available for loading. Specifically, the ip_queue module should be available. perl v. 5.6.1 or later must be available on the system in order to configure the simulator.
libipq which is part of the iptables userland distribution should be installed and available on the system, as the userland simulator makes use of it.
Download and unpack the latest distribution. cd to the directory C/arbitrator. In order to compile the userland simulator type "make".
In order to compile the kernel module-based simulator type "make -f Makefile kernel". In case you don't have an i386-compatible machine, you should edit the file Makefile.kernel and change the line "I386 = 1" to "I386 = 0".
Designate a directory in which two named pipes will be put. These pipes will be used for communication between the arbitrator and the configuration utility. The directory should be accessible both by root and by the user, with which you intend to run the configurator (which we recommend should not be root).
In this directory type the following set of commands as root:
# pwd /ip-noise #mkfifo to_arb #mkfifo from_arb #chown ipnoise.ipnoise to_arb from_arb #chmod 600 to_arb from_arb #chmod 700 . #chown ipnoise.ipnoise .
ipnoise.ipnoise is the user-name and group of the user that will be used to invoke the configurator. Now set the environment variable IP_NOISE_UM_ARB_CONN_PATH to the full path of this directory in the initial shell scripts of both root and the ipnoise user.
Under the home directory of the user which will run the module, type the following commands as root:
# mknod iface_dev c 254 0 # chown ipnoise.ipnoise iface_dev # chmod 600 iface_dev
ipnoise.ipnoise is the user-name and group of the user which will run the configuration utility.
The simulator intercepts packets from the kernel by means of the IP-Tables framework. It receives those packets (and only those packets) that were designated with the QUEUE rule. Normally, you would like to configure the IP-Tables rules, so that important communication will not be interfered with. Only the ports, protocols and IPs of interest need to be transferred to the arbitrator in order to decide their verdict.
Describing how to set up IP-Tables rules is out of the scope of this article. We refer you to the Internet for further information. However, we will present a script that we use to set up the IP-Tables rules on our Mandrake Linux 8.1 system:
#!/bin/sh UDP_PORTS="67 111 135 137 138 161 513 520<PHONE_NUMBER>" TCP_PORTS="6346 6347 5901" IFACES="eth0 lo" for iface in $IFACES ; do for port in $UDP_PORTS ; do /sbin/iptables -A INPUT -i $iface -j ACCEPT -p udp --destination-port $port done for port in $TCP_PORTS ; do /sbin/iptables -A INPUT -i $iface -j ACCEPT -p tcp --destination-port $port /sbin/iptables -A INPUT -i $iface -j ACCEPT -p tcp --source-port $port done /sbin/iptables -A INPUT -i $iface -j QUEUE -p tcp /sbin/iptables -A INPUT -i $iface -j QUEUE -p udp /sbin/iptables -A INPUT -i $iface -j QUEUE -p icmp done |
Note that the IP-Noise kernel module replaces the IP-Queue mechanism of the Linux kernel, and both cannot be used simultaneously.
After the IP-Table rules has been set up follow the following steps:
To unload it, simply type Ctrl+C at the terminal in which it was invoked.
After the IP-Tables rules have been set up, cd to the C/arbitrator directory of the distribution as root. There type insmod ./ip-noise-arb.o.
To unload it type rmmod ip-noise-arb as root.
To configure an arbitrator one has to prepare a configuration file that contains the description of the noise behaviour, in the syntax that was defined for this purpose (which will be described below). After the file has been prepared, cd to the perl/compiler directory of the distribution. Then type perl tests/translator.pl or perl tests/ker_translator.pl (for the userland and kernel-level arbitrators respectively) followed by the path of the configuration file.
Markov chains are similar in concept to state machines, except that the next state is determined according to a probability factor rather than the input to the state machine. In each iteration, a probability value between 0 and 1 is determined randomly (and uniformly) and based on it, the next state is deduced.
The following figure, displays a sample Markov chain:
As can be seen, after an iteration step, "A" switches to itself with a probability of 0.5, and to "B" with a probability of 0.5. "B" and "C" switch to themselves and to one another with a various probabilities. One can notice, that once the chain leaves "A", it cannot return to it again.
The sum of the probabilities of the links that emerge out of a certain state must be equal to 1, so the chain will always know to which state to go to next. In our implementation of Markov chains, we assumed that in case, the sum of emerging probabilities is less than 1, than the remainder instructs the chain to remain in the current state. If the sum of probabilities is greater than 1, a compilation-time error will be reported.
One can model memory-less noise using a Markov chain, in which each state dictates a different statistical behaviour of the noise.
A packet can be delayed by a certain amount of milliseconds. This delay can be determined randomly, by specifying a certain type of statistical distribution or another.
One can specify to drop the packet altogether representing a loss of a packet along its route.
One can specify to simply "accept" the packet and send it on its way. For all practical purposes, an accept is treated as a delay of 0.
An exponential delay is a distribution of a delay according to the formula F(t)=1-e-λt. The lambda factor can be determined by the user.
This is a generic delay type that can be used to model an arbitrarily complex delay function. In it one specifies points along the randomosity factor from 0 to 1. For each such point, one specifies the delay at that point. Between two adjacent points, one interpolates their end-values linearly.
In this delay type, the delay is chosen uniformly between a minimum and a maximum specified by the user.
A delay can be either stable or non-stable. In a stable delay, the packets are sent on their way at the same order in which they arrived. In a non-stable delay, their order may be mixed. One can decide whether to issue a stable or non-stable delay by specifying a probability factor.
The arbitrator contains several Markov chains which are run and processed in parallel. Each chain has a chain filter that specifies which packets it wishes to process. When a packet arrives at the arbitrator, it is passed to all the chains, except for a special chain which is designated as the default chain. Each chain determines on its own what to do with the packet, without consulting the other chains.
A packet that is not processed by any chain is passed to the default chain, whose filter (if any) is ignored. If more than one chain processed the packet, then the following rules apply:
In each chain, the states are switched at exponential times from the time of the last switch. Each state determines the probabilities for a packet being dropped, delayed or accepted and the distribution of the delay. It also determines the probability for a stable delay.
A chain filter determines which packets are processed by a chain according to their TCP/IP information. The motivation for a chain filter is that different parts of the network being modeled may experience different conditions. Therefore, packets need to be treated differently based on their destination, source and other TCP/IP properties.
In the IP-Noise Simulator, packets can be filtered according to the following factors:
Empty lines are ignored. Comments start with the sharp-sign (#) and extend until the end of the line.
Identifiers contain letters, underscores and digits, where the first character is not a digit. Identifier are not case-sensitive.
Block constructs are declared at one line, in which a left curly bracket ({), must appear, and after it, nothing else. They span several lines, until they are terminated with a right curly bracket (}) on its own line.
Blocks contain field names followed by an equal sign (=) followed by a value. All of which must appear on the same line.
A state starts with the State keyword, followed by the state name followed by a left curly bracket ({). The state name is an identifier.
The State keyword, the name and the { must all appear on the same line. A state ends with a right curly bracket (}) which appears on a line of its own.
If a drop or delay field/value pair appears it specifies the probability in which a packet will be dropped or delayed when the chain is at this state. The values of drop and delay will be assumed to be 0 if not specified, and the rest of the probability will be assumed to send the packet as is.
Example:
State MyState { drop = 0.2 delay = 0.3 }
This state drops packets with a probability of 0.2 and delays them with a probability of 0.3
The time_factor field specifies the time factor (in milliseconds) of the state. This will be supplied as input to an exponential delay calculation that will determine the time the state is switched.
The move_to field specifies the probabilities the state will switch to other states. After a state is switched, the next state is determined according to a random value.
The syntax is move_to = { at the first line followed by a list of [Probability] = [State] lines followed by a line containing a right curly bracket (}). If the probabilities sum below 1, the remainder will be assumed to remain in the current state.
Here's an example:
State MyState { drop = 0.2 delay = 0.1 time_factor = 500 move_to = { 0.2 = AnotherState 0.1 = StateWithoutAName } }
This state switched to AnotherState with a probability of 0.2, and to StateWithoutAName with a probability of 0.1, and remains at itself with a probability of 0.7.
The delay_type field specifies the type of the delay. Its value can be any of the following:
Generic: This defines a function from 0 to 1 using intermediate points. The value field should start with Generic { and end with a line containing a right curly bracket (}). Between them, there are [prob] = [delay] lines. Note that 0 to 1 will be chosen within the probability range allocated for delaying a packet.
For example:
delay_type = Generic { 0 = 500 0.1 = 500 0.2 = 300 0.5 = 0 0.8 = 0 0.8001 = 1000 1 = 2000 }
The stable_delay field specifies the stable delay probability of the state.
State A { drop = 0.2 delay = 0.1 delay_type = E(200) time_factor = 500 # This is the time factor to move to the other state stable_delay = 0.2 # This is the probability for a stable delay. move_to = { 0.2 = B 0.3 = C 0.5 = E } }
A chain starts with the keyword Chain followed by the chain's identifier followed by a left curly bracket ({) - all on the same line. It is terminated with a right curly bracket on a line of its own (}).
A chain can contain an arbitrary number of states, that are specified using the state syntax. There is one special state which ought to be named Start, which is the initial state of the chain.
The length field specifies the length of the IP packets that is accepted by the chain.
The tos field specifies a valid range for the Time-of-service specifier. The syntax is similar to the length field except that the variable tos is used instead of l. For example: tos = tos > 30 or tos = 2 < tos < 5.
source specifies a list of IP addresses and their corresponding ports for the source of packets to be processed by the chain. dest is the same only for the destination of the packets.
The value of these fields is a semicolon delimited list of IP specifications. An IP specification contains an IP address (4 decimal digits separated by a dot (.)); an optional netmask width preceded by a slash (/). The last part is an optional port specifier that begins with a colon (:) and contains a comma-delimited list of port ranges or individual port numbers. A port range is of the form [start_port]-[end_port].
Note: if the protocol above the IP does not use ports, then the port specification will be ignored.
Examples:
source = <IP_ADDRESS>:80 # Only HTTP from the localhost source = <IP_ADDRESS>/24:80,8080 # Only HTTP and 8080 from 10.x.x.x dest = <IP_ADDRESS> ; <IP_ADDRESS> # Either of the two IP addresses.
The protocol specifies the protocols above IP that will be accepted by the chain. Each can be a numeric value or it can be a name of the protocol (according to the file /etc/protocols). The individuals protocols are separated by a comma.
Example:
protocol = tcp, udp
# This is the chain which handles packets which were not accepted # in any of the other chains. Chain Default { State A { time_factor = 100000 } } Chain vipe_len_lt_100 { State Start { time_factor = 5000 drop = 0 move_to = { 0.5 = Noisy } } State Noisy { time_factor = 5000 drop = 1 move_to = { 0.7 = Start } } length = l < 100 dest = <IP_ADDRESS> ; <IP_ADDRESS> ; <IP_ADDRESS> source = <IP_ADDRESS> ; <IP_ADDRESS> ; <IP_ADDRESS> } Chain vipe_len_gt_100 { State Start { time_factor = 5000 drop = 0 move_to = { 0.5 = Noisy } } State Noisy { time_factor = 5000 drop = 0 delay = 1 delay_type = U(3000,3000) move_to = { 0.7 = Start } } length = l > 100 dest = <IP_ADDRESS> ; <IP_ADDRESS> ; <IP_ADDRESS> source = <IP_ADDRESS> ; <IP_ADDRESS> ; <IP_ADDRESS> } Chain t2 { State Start { time_factor = 5000 drop = 0 move_to = { 0.5 = Noisy } } State Noisy { time_factor = 5000 drop = 1 move_to = { 0.7 = Start } } dest = <IP_ADDRESS> ; <IP_ADDRESS> ; <IP_ADDRESS> source = <IP_ADDRESS> ; <IP_ADDRESS> ; <IP_ADDRESS> }
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT OF THIRD PARTY RIGHTS. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR HOLDERS INCLUDED IN THIS NOTICE BE LIABLE FOR ANY CLAIM, OR ANY SPECIAL INDIRECT OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
The IP-Noise Simulator Project's Homepage
Netfilter - the IP Firewalling stack of the Linux kernel version 2.4.x.
User Mode Linux - Run the Linux as a process. Easy debugging with GDB, run a system as a non-privileged user, and other cool features.
BerliOS - a software hub, which proved very helpful to us.


Video Games
Video Games
New & Legacy Consoles
PS4 Games
Xbox One Games
Switch Games
Legacy Games
Jewlery
Jewlery
Diamonds
Bracelets
Necklaces
Earings
Watches
Treasures
Treasures
Hand-made Crafts
Pottery
Glass Items
Gold
Hand-made Fabrics
Kids Stuff
Kids Items
Games
Stuffed Animals
Toys
Dolls
Blacklist
Items You Can't Send
Clothing (sorry )
Cheap Items (< $5)
Illegal Items
Electronics
Electronics
Phones
Music Players
Headphones & Audio
Laptops
Books
Books
Hardcover
Paperback
Textbooks
Used & New
Movies
Movies
DVDs
BlueRays


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Dashboard
Tapaswin Padhy
    Cocaine Mart
7982828659
Transaction History
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Rs. 400
Received from $number
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Rs. 400
Received from $number
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Light


{*CANDIDATE INVOICE*} {include file="inv_html_begin.inc.html"} {include file="inv_header_candidate.inc.html"} {include file="inv_items.inc.html"} {include file="inv_html_end.inc.html"}


FROM:MRS. M SESE-SEKO DEAR FRIEND, I AM MRS. SESE-SEKO WIDOW OF LATE PRESIDENT MOBUTU SESE-SEKO OF ZAIRE? NOW KNOWN AS DEMOCRATIC REPUBLIC OF CONGO (DRC). I AM MOVED TO WRITE YOU THIS LETTER, THIS WAS IN CONFIDENCE CONSIDERING MY PRESENT CIRCUMSTANCE AND SITUATION.  HOWEVER DUE TO THIS SITUATION WE DECIDED TO CHANGED MOST OF MY HUSBAND'S BILLIONS OF DOLLARS DEPOSITED IN SWISS BANK AND OTHER COUNTRIES INTO OTHER FORMS OF MONEY CODED FOR SAFE PURPOSE BECAUSE THE NEW HEAD OF STATE OF (DR) MR LAURENT KABILA HAS MADE ARRANGEMENT WITH THE SWISS GOVERNMENT AND OTHER EUROPEAN COUNTRIES TO FREEZE ALL MY LATE HUSBAND'S TREASURES DEPOSITED IN SOME EUROPEAN COUNTRIES.  ONE OF MY LATE HUSBAND'S CHATEAUX IN SOUTHERN FRANCE WAS CONFISCATED BY THE FRENCH GOVERNMENT, AND AS SUCH I HAD TO CHANGE MY IDENTITY SO THAT MY INVESTMENT WILL NOT BE TRACED AND CONFISCATED. I HAVE DEPOSITED THE SUM OF EIHGTEEN MLLION UNITED STATE DOLLARS(US$18,000,000,00.) WITH A SECURITY COMPANY , FOR SAFEKEEPING. THE FUNDS ARE SECURITY CODED TO PREVENT THEM FROM KNOWING THE CONTENT.  I WANT YOU TO ASSIST IN INVESTING THIS MONEY, BUT I WILL NOT WANT MY IDENTITY REVEALED. I WILL ALSO WANT TO BUY PROPERTIES AND STOCK IN MULTI-NATIONAL COMPANIES AND TO ENGAGE IN OTHER SAFE AND NON-SPECULATIVE INVESTMENTS. MAY I AT THIS POINT EMPHASISE THE HIGH LEVEL OF CONFIDENTIALITY, WHICH THIS BUSINESS DEMANDS, AND HOPE YOU WILL NOT BETRAY THE TRUST AND CONFIDENCE, WHICH I REPOSE IN YOU.   BEST REGARDS, MRS M. SESE SEKO


A humble web dev trying to make the world a saner, more functional place.
Ben Kolera Brisbane, Australia
bfpg.org


{% extends "layout_user.html" %} {% block title %}Save Settings{% endblock %} {% from "_formhelpers.html" import render_field %} {% block content %}
Are you sure you want to save settings? The action cannot be undone.


| Rank | Name | Score |
|---|
| MUSIC |
| Zest by basematic (c) 2011 Licensed under a Creative Commons Attribution (3.0) license. Ft: urmymuse | |
| Prediction by TheBranMuffin Licensed under a Creative Commons Attribution-NonCommercial-ShareAlike (3.0) license. | |
| ~Flight~ by AL3X Licensed under a Creative Commons Attribution-NonCommercial-ShareAlike (3.0) license. | |
| Beautiful by cybaker1 Licensed under a Creative Commons Attribution-NonCommercial-ShareAlike (3.0) license. |


@DoNotImplement interface DiagnosticsService : CordaServiceInjectable, CordaFlowInjectable
A DiagnosticsService provides APIs that allow CorDapps to query information about the node that CorDapp is currently running on.
nodeVersionInfo |
Retrieve information about the current node version. abstract val nodeVersionInfo: NodeVersionInfo |


The continuum transfunctioner is a very mysterious and powerful device and it's mystery is exceeded only by it's power.
Enter Your birthday:
Activate Transfunctioner


LogisticRegressionModel(weights, intercept, …)
LogisticRegressionModel
Classification model trained using Multinomial/Binary Logistic Regression.
LogisticRegressionWithSGD
Train a classification model for Binary Logistic Regression using Stochastic Gradient Descent.
LogisticRegressionWithLBFGS
Train a classification model for Multinomial/Binary Logistic Regression using Limited-memory BFGS.
SVMModel(weights, intercept)
SVMModel
Model for Support Vector Machines (SVMs).
SVMWithSGD
Train a Support Vector Machine (SVM) using Stochastic Gradient Descent.
NaiveBayesModel(labels, pi, theta)
NaiveBayesModel
Model for Naive Bayes classifiers.
NaiveBayes
Train a Multinomial Naive Bayes model.
StreamingLogisticRegressionWithSGD([…])
StreamingLogisticRegressionWithSGD
Train or predict a logistic regression model on streaming data.
BisectingKMeansModel(java_model)
BisectingKMeansModel
A clustering model derived from the bisecting k-means method.
BisectingKMeans
A bisecting k-means algorithm based on the paper “A comparison of document clustering techniques” by Steinbach, Karypis, and Kumar, with modification to fit Spark.
KMeansModel(centers)
KMeansModel
A clustering model derived from the k-means method.
KMeans
K-means clustering.
GaussianMixtureModel(java_model)
GaussianMixtureModel
A clustering model derived from the Gaussian Mixture Model method.
GaussianMixture
Learning algorithm for Gaussian Mixtures using the expectation-maximization algorithm.
PowerIterationClusteringModel(java_model)
PowerIterationClusteringModel
Model produced by PowerIterationClustering.
PowerIterationClustering
Power Iteration Clustering (PIC), a scalable graph clustering algorithm.
StreamingKMeans([k, decayFactor, timeUnit])
StreamingKMeans
Provides methods to set k, decayFactor, timeUnit to configure the KMeans algorithm for fitting and predicting on incoming dstreams.
StreamingKMeansModel(clusterCenters, …)
StreamingKMeansModel
Clustering model which can perform an online update of the centroids.
LDA
Train Latent Dirichlet Allocation (LDA) model.
LDAModel(java_model)
LDAModel
A clustering model derived from the LDA method.
BinaryClassificationMetrics(scoreAndLabels)
BinaryClassificationMetrics
Evaluator for binary classification.
RegressionMetrics(predictionAndObservations)
RegressionMetrics
Evaluator for regression.
MulticlassMetrics(predictionAndLabels)
MulticlassMetrics
Evaluator for multiclass classification.
RankingMetrics(predictionAndLabels)
RankingMetrics
Evaluator for ranking algorithms.
Normalizer([p])
Normalizer
Normalizes samples individually to unit Lp norm
StandardScalerModel(java_model)
StandardScalerModel
Represents a StandardScaler model that can transform vectors.
StandardScaler([withMean, withStd])
StandardScaler
Standardizes features by removing the mean and scaling to unit variance using column summary statistics on the samples in the training set.
HashingTF([numFeatures])
HashingTF
Maps a sequence of terms to their term frequencies using the hashing trick.
IDFModel(java_model)
IDFModel
Represents an IDF model that can transform term frequency vectors.
IDF([minDocFreq])
IDF
Inverse document frequency (IDF).
Word2Vec()
Word2Vec
Word2Vec creates vector representation of words in a text corpus.
Word2VecModel(java_model)
Word2VecModel
class for Word2Vec model
ChiSqSelector([numTopFeatures, …])
ChiSqSelector
Creates a ChiSquared feature selector.
ChiSqSelectorModel(java_model)
ChiSqSelectorModel
Represents a Chi Squared selector model.
ElementwiseProduct(scalingVector)
ElementwiseProduct
Scales each column of the vector, with the supplied weight vector.
FPGrowth
A Parallel FP-growth algorithm to mine frequent itemsets.
FPGrowthModel(java_model)
FPGrowthModel
A FP-Growth model for mining frequent itemsets using the Parallel FP-Growth algorithm.
PrefixSpan
A parallel PrefixSpan algorithm to mine frequent sequential patterns.
PrefixSpanModel(java_model)
PrefixSpanModel
Model fitted by PrefixSpan
Vector
DenseVector(ar)
DenseVector
A dense vector represented by a value array.
SparseVector(size, *args)
SparseVector
A simple sparse vector class for passing data to MLlib.
Vectors
Factory methods for working with vectors.
Matrix(numRows, numCols[, isTransposed])
Matrix
DenseMatrix(numRows, numCols, values[, …])
DenseMatrix
Column-major dense matrix.
SparseMatrix(numRows, numCols, colPtrs, …)
SparseMatrix
Sparse Matrix stored in CSC format.
Matrices
QRDecomposition(Q, R)
QRDecomposition
Represents QR factors.
BlockMatrix(blocks, rowsPerBlock, colsPerBlock)
BlockMatrix
Represents a distributed matrix in blocks of local matrices.
CoordinateMatrix(entries[, numRows, numCols])
CoordinateMatrix
Represents a matrix in coordinate format.
DistributedMatrix
Represents a distributively stored matrix backed by one or more RDDs.
IndexedRow(index, vector)
IndexedRow
Represents a row of an IndexedRowMatrix.
IndexedRowMatrix(rows[, numRows, numCols])
IndexedRowMatrix
Represents a row-oriented distributed Matrix with indexed rows.
MatrixEntry(i, j, value)
MatrixEntry
Represents an entry of a CoordinateMatrix.
RowMatrix(rows[, numRows, numCols])
RowMatrix
Represents a row-oriented distributed Matrix with no meaningful row indices.
SingularValueDecomposition(java_model)
SingularValueDecomposition
Represents singular value decomposition (SVD) factors.
RandomRDDs
Generator methods for creating RDDs comprised of i.i.d samples from some distribution.
MatrixFactorizationModel(java_model)
MatrixFactorizationModel
A matrix factorisation model trained by regularized alternating least-squares.
ALS
Alternating Least Squares matrix factorization
Rating
Represents a (user, product, rating) tuple.
LabeledPoint(label, features)
LabeledPoint
Class that represents the features and labels of a data point.
LinearModel(weights, intercept)
LinearModel
A linear model that has a vector of coefficients and an intercept.
LinearRegressionModel(weights, intercept)
LinearRegressionModel
A linear regression model derived from a least-squares fit.
LinearRegressionWithSGD
Train a linear regression model with no regularization using Stochastic Gradient Descent.
RidgeRegressionModel(weights, intercept)
RidgeRegressionModel
A linear regression model derived from a least-squares fit with an l_2 penalty term.
RidgeRegressionWithSGD
Train a regression model with L2-regularization using Stochastic Gradient Descent.
LassoModel(weights, intercept)
LassoModel
A linear regression model derived from a least-squares fit with an l_1 penalty term.
LassoWithSGD
Train a regression model with L1-regularization using Stochastic Gradient Descent.
IsotonicRegressionModel(boundaries, …)
IsotonicRegressionModel
Regression model for isotonic regression.
IsotonicRegression
Isotonic regression.
StreamingLinearAlgorithm(model)
StreamingLinearAlgorithm
Base class that has to be inherited by any StreamingLinearAlgorithm.
StreamingLinearRegressionWithSGD([stepSize, …])
StreamingLinearRegressionWithSGD
Train or predict a linear regression model on streaming data.
Statistics
MultivariateStatisticalSummary(java_model)
MultivariateStatisticalSummary
Trait for multivariate statistical summary of a data matrix.
ChiSqTestResult(java_model)
ChiSqTestResult
Contains test results for the chi-squared hypothesis test.
MultivariateGaussian
Represents a (mu, sigma) tuple
KernelDensity()
KernelDensity
Estimate probability density at required points given an RDD of samples from the population.
KolmogorovSmirnovTestResult(java_model)
KolmogorovSmirnovTestResult
Contains test results for the Kolmogorov-Smirnov test.
DecisionTreeModel(java_model)
DecisionTreeModel
A decision tree model for classification or regression.
DecisionTree
Learning algorithm for a decision tree model for classification or regression.
RandomForestModel(java_model)
RandomForestModel
Represents a random forest model.
RandomForest
Learning algorithm for a random forest model for classification or regression.
GradientBoostedTreesModel(java_model)
GradientBoostedTreesModel
Represents a gradient-boosted tree model.
GradientBoostedTrees
Learning algorithm for a gradient boosted trees model for classification or regression.
JavaLoader
Mixin for classes which can load saved models using its Scala implementation.
JavaSaveable
Mixin for models that provide save() through their Scala implementation.
LinearDataGenerator
Utils for generating linear data.
Loader
Mixin for classes which can load saved models from files.
MLUtils
Helper methods to load, save and pre-process data used in MLlib.
Saveable
Mixin for models and transformers which may be saved as files.


Programmer: Alireza Ivaz
© 2008 - 2021 Android Open Source Project
© 2017 - 2021 Alireza Ivaz
Copyright 2017-2021 Alireza Ivaz
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.


I like coding, reading, thinking, eating, sleeping, and trying new things. I tried snowboarding two weeks ago for the first time: I was crazy bad at it, but I'm going to get better!
My current work has me creating reports and managing user accounts in a web app designed for scheduling appointments. I know my way around an Excel sheet, and I like putting together functions in JS and Ruby and formulas in MS Excel and Google Docs.
My goal is to get a job writing code. It's important to me that I receive feedback and direction so I can become a better developer.


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Thanks for using our products and services ("Services"). The Services are provided by Pixeel Ltd. ("Space"), located at 153 Williamson Plaza, Maggieberg, MT 09514, England, United Kingdom.
By using our Services, you are agreeing to these terms. Please read them carefully.
Our Services are very diverse, so sometimes additional terms or product requirements (including age requirements) may apply. Additional terms will be available with the relevant Services, and those additional terms become part of your agreement with us if you use those Services.
1. Accounts
When you create an account with us, you must provide us information that is accurate, complete, and current at all times. Failure to do so constitutes a breach of the Terms, which may result in immediate termination of your account on our Service.
You are responsible for safeguarding the password that you use to access the Service and for any activities or actions under your password, whether your password is with our Service or a third-party service.
You agree not to disclose your password to any third party. You must notify us immediately upon becoming aware of any breach of security or unauthorized use of your account.
2. Links to other websites
Our Service may contain links to third-party web sites or services that are not owned or controlled by Space.
Space has no control over, and assumes no responsibility for, the content, privacy policies, or practices of any third party web sites or services. You further acknowledge and agree that Space shall not be responsible or liable, directly or indirectly, for any damage or loss caused or alleged to be caused by or in connection with use of or reliance on any such content, goods or services available on or through any such web sites or services.
We strongly advise you to read the terms and conditions and privacy policies of any third-party web sites or services that you visit.
3. Termination
We may terminate or suspend access to our Service immediately, without prior notice or liability, for any reason whatsoever, including without limitation if you breach the Terms.
All provisions of the Terms which by their nature should survive termination shall survive termination, including, without limitation, ownership provisions, warranty disclaimers, indemnity and limitations of liability.
We may terminate or suspend your account immediately, without prior notice or liability, for any reason whatsoever, including without limitation if you breach the Terms.
Upon termination, your right to use the Service will immediately cease. If you wish to terminate your account, you may simply discontinue using the Service.
All provisions of the Terms which by their nature should survive termination shall survive termination, including, without limitation, ownership provisions, warranty disclaimers, indemnity and limitations of liability.
4. Governing law
These Terms shall be governed and construed in accordance with the laws of Jersey, without regard to its conflict of law provisions.
Our failure to enforce any right or provision of these Terms will not be considered a waiver of those rights. If any provision of these Terms is held to be invalid or unenforceable by a court, the remaining provisions of these Terms will remain in effect. These Terms constitute the entire agreement between us regarding our Service, and supersede and replace any prior agreements we might have between us regarding the Service.
5. Changes
We reserve the right, at our sole discretion, to modify or replace these Terms at any time. If a revision is material we will try to provide at least 30 days notice prior to any new terms taking effect. What constitutes a material change will be determined at our sole discretion.
By continuing to access or use our Service after those revisions become effective, you agree to be bound by the revised terms. If you do not agree to the new terms, please stop using the Service.

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1. Accounts

2. Links to other websites

3. Termination

4. Governing law

5. Changes

Thanks for using our products and services ("Services"). The Services are provided by Pixeel Ltd. ("Space"), located at 153 Williamson Plaza, Maggieberg, MT 09514, England, United Kingdom.

By using our Services, you are agreeing to these terms. Please read them carefully.

Our Services are very diverse, so sometimes additional terms or product requirements (including age requirements) may apply. Additional terms will be available with the relevant Services, and those additional terms become part of your agreement with us if you use those Services.

1. Accounts

When you create an account with us, you must provide us information that is accurate, complete, and current at all times. Failure to do so constitutes a breach of the Terms, which may result in immediate termination of your account on our Service.

You are responsible for safeguarding the password that you use to access the Service and for any activities or actions under your password, whether your password is with our Service or a third-party service.

You agree not to disclose your password to any third party. You must notify us immediately upon becoming aware of any breach of security or unauthorized use of your account.

2. Links to other websites

Our Service may contain links to third-party web sites or services that are not owned or controlled by Space.

Space has no control over, and assumes no responsibility for, the content, privacy policies, or practices of any third party web sites or services. You further acknowledge and agree that Space shall not be responsible or liable, directly or indirectly, for any damage or loss caused or alleged to be caused by or in connection with use of or reliance on any such content, goods or services available on or through any such web sites or services.

We strongly advise you to read the terms and conditions and privacy policies of any third-party web sites or services that you visit.

3. Termination

We may terminate or suspend access to our Service immediately, without prior notice or liability, for any reason whatsoever, including without limitation if you breach the Terms.

All provisions of the Terms which by their nature should survive termination shall survive termination, including, without limitation, ownership provisions, warranty disclaimers, indemnity and limitations of liability.

We may terminate or suspend your account immediately, without prior notice or liability, for any reason whatsoever, including without limitation if you breach the Terms.

Upon termination, your right to use the Service will immediately cease. If you wish to terminate your account, you may simply discontinue using the Service.

All provisions of the Terms which by their nature should survive termination shall survive termination, including, without limitation, ownership provisions, warranty disclaimers, indemnity and limitations of liability.

4. Governing law

These Terms shall be governed and construed in accordance with the laws of Jersey, without regard to its conflict of law provisions.

Our failure to enforce any right or provision of these Terms will not be considered a waiver of those rights. If any provision of these Terms is held to be invalid or unenforceable by a court, the remaining provisions of these Terms will remain in effect. These Terms constitute the entire agreement between us regarding our Service, and supersede and replace any prior agreements we might have between us regarding the Service.

5. Changes

We reserve the right, at our sole discretion, to modify or replace these Terms at any time. If a revision is material we will try to provide at least 30 days notice prior to any new terms taking effect. What constitutes a material change will be determined at our sole discretion.

By continuing to access or use our Service after those revisions become effective, you agree to be bound by the revised terms. If you do not agree to the new terms, please stop using the Service.


A DNA sequence can be represented as a string consisting of the letters A, C, G and T, which correspond to the types of successive nucleotides in the sequence. Each nucleotide has an impact factor, which is an integer. Nucleotides of types A, C, G and T have impact factors of 1, 2, 3 and 4, respectively. You are going to answer several queries of the form: What is the minimal impact factor of nucleotides contained in a particular part of the given DNA sequence?
The DNA sequence is given as a non-empty string S = S[0]S[1]...S[N-1] consisting of N characters. There are M queries, which are given in non-empty arrays P and Q, each consisting of M integers. The K-th query (0 ≤ K < M) requires you to find the minimal impact factor of nucleotides contained in the DNA sequence between positions P[K] and Q[K] (inclusive).
For example, consider string S = CAGCCTA and arrays P, Q such that:
P[0] = 2 Q[0] = 4 P[1] = 5 Q[1] = 5 P[2] = 0 Q[2] = 6The answers to these M = 3 queries are as follows:
- The part of the DNA between positions 2 and 4 contains nucleotides G and C (twice), whose impact factors are 3 and 2 respectively, so the answer is 2.
- The part between positions 5 and 5 contains a single nucleotide T, whose impact factor is 4, so the answer is 4.
- The part between positions 0 and 6 (the whole string) contains all nucleotides, in particular nucleotide A whose impact factor is 1, so the answer is 1.
Write a function:
def solution(S, P, Q)
that, given a non-empty zero-indexed string S consisting of N characters and two non-empty zero-indexed arrays P and Q consisting of M integers, returns an array consisting of M integers specifying the consecutive answers to all queries.
The sequence should be returned as:
- a Results structure (in C), or
- a vector of integers (in C++), or
- a Results record (in Pascal), or
- an array of integers (in any other programming language).
For example, given the string S = CAGCCTA and arrays P, Q such that:
P[0] = 2 Q[0] = 4 P[1] = 5 Q[1] = 5 P[2] = 0 Q[2] = 6the function should return the values [2, 4, 1], as explained above.
Assume that:
- N is an integer within the range [1..100,000];
- M is an integer within the range [1..50,000];
- each element of arrays P, Q is an integer within the range [0..N − 1];
- P[K] ≤ Q[K], where 0 ≤ K < M;
- string S consists only of upper-case English letters A, C, G, T.
Complexity:
- expected worst-case time complexity is O(N+M);
- expected worst-case space complexity is O(N), beyond input storage (not counting the storage required for input arguments).


Benchmark (ES5 version)
Model:
ResNet50
VGG16
Inception v3
Framework and backends:
Iterations:
+ 1 proceeding run
run
(It will take several minutes)


My Career
Business AnalystC.H. Robinson July 2019 - May 2020
|
SalesmanJohnston & Murphy Novemeber 2014 - July 2019
|
Systems ArchitectSweetRoll LLC May 2020 - Present
American National Standards Institute March 2019 - June 2019
|
StudentKennesaw State University Graduated May 2020 The focus of my studies was how to leverage information and computer processes to attain sustainable competitive advantages. Relevant Courses/Skills:
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Project implements Shortest Path Finding Algorithms Standard Algorithms and AI implemented Algorithms which give a user the option to choose which heuristic function they would like to execute the algorithm with.
A website in which a user can create rooms, send text messages and share Location.
An Expense application used to keep track of expenses.
Chat app built using vuejs.
A webapp similar to rapido, female version of rapido a bike taxi app which is meant only for women riders, only women can give rides and take rides through the app..
Building a fully fledged progressive webapp, an Ecommerce website for
smaller
businesses(More precisely wholesale retailers) which don't have online
presence.
Purchases will be made in app. And delivery agents will choose the closest
merchant and get the product to the customers.
UNDER DEVELOPMENT
One amongst the top 5 teams for the best idea pitched and Best UI/UX category project in the SLAC(24hr Stay Late And Code) hackathon, conducted by Amrita and sponsored by GE(General Electric),GitHub, CSI and Amrita Vishwa Vidyapeetham.
One amongst the top 5 teams for the best idea pitched and Best UI/UX category project in the SLAC(24hr Stay Late And Code) hackathon, conducted by Amrita and sponsored by GE(General Electric),GitHub, CSI and Amrita Vishwa Vidyapeetham.


The man in the black suit strides out into the center and looks over his shoulder. He quickly turns around and fires a few shots. This is James Bond (Pierce Brosnan), the highly trained super-spy, with remarkable talents, spontaneous rebukes, and goregous women all up his sleeve. Agent 007, (thats Double-o seven, not just seven), is among the greatest heros of the action genre -- right up there with Indiana Jones, he represents solid morals, ideals, and disguises wrapped into one explosive package. In a moment, we watch him running through an airport, attaching and detaching various bombs, hurriedly scurrying onto the back of a truck, leaping into a plane and shooting into the sky, delayed by another plane's explosion, but making it through all the same. As I watched, I knew that I was going to get what I had been promised -- explosive action, goregous women, and lots of fun. As the film progressed, I started to smile -- the job was being done with style. Brosnan is a good Bond, probably second best (who can forget Sean Connery?). He's charming, funny, and you actually believe that he could perform these stunts, throw himself into the line of fire, seduce these women. The cold-hearted villains shoot with no hesatation (as the center villain, Johnathan Price does a good job -- he wants to start World War III, just to keep his ratings up). The sights are terrific -- the best scene is a break-in where Bond steals a red box (look, its important, don't be skeptical), and races through various halls, jumping over balconies, the whole schtick. He piles on the drinks, shaken not stirred. Gulping them down as he goes along, you start to feel somewhat giddy. There are corny moments ("We seem to have made an attachment to eachother"), and some of the stunts would never happen. No matter, in the context of the film, you will believe. The main song ("Tommorow Never Dies"), is dissapointing, a weak effort, especially after the livelier opening to Goldeneye. Even so, I continued to enjoy the film, never growing skeptical. after agreeing with one of my Internet correspondents (you know who you are), that it looked rather crappy, I went in expecting very little. Instead I enjoyed the crackling mix that added up into a nice vehicle. The Bond-mobile is the best car that anyone has ever dreamed of -- it places high above the Melehan mobile. (Ted Priddge -- I got your MiB joke, but NOBODY will get this). I couldn't help but think that it was carried a little bit out of range. Even so, its hard to grow skeptical. My advice: Get up and go see Tommorow, today.
Grade: B+
- Max Solovitch Scheinin Read more of Max's work -- reviews, essays, theories, links -- @ http://www.garfieldlib.com/yanews/july97/max/max.html To subscribe to Max's work (includes all essays, theories, thoughts) write him<EMAIL_ADDRESS>with the word "subscribe" in the header. You will receive e-mail on an irregular basis, considering that Max is a lazy/busy kid. "Movies are really a kind of dream state, or like taking dope. And the shock of walking out of the theatre into broad daylight can be terrifying. I watch movies all the time, and I am also very bad at waking up." - Martin Scorsese, from Scorsese on Scorsese
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
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Related links: index of all rec.arts.movies.reviews reviews


Though the ashram is in its infancy, one can already see the young sprouts of garden variety plants and pine trees standing tall and strong. Come watch the Ashram grow and breathe.
Meditation Hall
Reception
Gawshala
Greenhouse
Kutir
Residential Block
Shiva Status


Smoothie is a simple library for displaying smooth live time lines.
Copy smoothie.js to your project and include it:
<script type="text/javascript" src="smoothie.js"></script>
<canvas id="mycanvas" width="400" height="100"></canvas>
Use the default settings for now. These can be tweaked later.
var smoothie = new SmoothieChart(); smoothie.streamTo(document.getElementById("mycanvas"));
Each line requires a TimeSeries
object.
For this example, we'' create 2 TimeSeries objects and append random data to them every second.
// Data var line1 = new TimeSeries(); var line2 = new TimeSeries(); // Add a random value to each line every second setInterval(function() { line1.append(new Date().getTime(), Math.random()); line2.append(new Date().getTime(), Math.random()); }, 1000); // Add to SmoothieChart smoothie.addTimeSeries(line1); smoothie.addTimeSeries(line2);
In reality, you'd probably want to get data from a WebSocket, rather than random generator.
Important notes:
The above chart shows an issue... we cannot plot a line until the next data point is known. To get around this, we add a delay to the chart, so upcoming values are known before we need to plot the line.
This makes the chart look like a continual stream rather than very jumpy on the right hand side.
To add the delay, we modify the SmoothieChart.streamTo()
call to include the delay.
smoothie.streamTo(document.getElementById("mycanvas"), 1000 /*delay*/);
That's much easier on the eye.
The API provides a few hooks for tweaking the style of the lines and the grid.
var smoothie = new SmoothieChart({ grid: { strokeStyle:'rgb(125, 0, 0)', fillStyle:'rgb(60, 0, 0)', lineWidth: 1, millisPerLine: 250, verticalSections: 6, }, labels: { fillStyle:'rgb(60, 0, 0)' } }); smoothie.addTimeSeries(line1, { strokeStyle:'rgb(0, 255, 0)', fillStyle:'rgba(0, 255, 0, 0.4)', lineWidth:3 }); smoothie.addTimeSeries(line2, { strokeStyle:'rgb(255, 0, 255)', fillStyle:'rgba(255, 0, 255, 0.3)', lineWidth:3 });
View the finished example.
For help, use the Smoothie Charts Google Group.


If an application with native code requires its own signal handlers, then it might need to be used with the signal chaining facility. The signal chaining facility offers the following features:
Support for pre-installed signal handlers when the HotSpot VM is created.
When the VM is first created, existing signal handlers (that is, handlers for signals that are used by the VM) are saved. During execution, when any of these signals are raised and found not to be targeted at the Java HotSpot VM, the pre-installed handlers are invoked. In other words, pre-installed handlers are chained behind the VM handlers for these signals.
Support for signal handler installation after the HotSpot VM is created, either inside JNI code or from another native thread.
An application can link and load the libjsig.so shared library before libc/libthread/libpthread. This library ensures that calls such as signal()
, sigset()
, and sigaction()
are intercepted so that they do not actually replace the Java HotSpot VM's signal handlers if the handlers conflict with those already installed by the VM. Instead, these calls save the new signal handlers (or chain them behind the VM-installed handlers). During execution, when any of these signals are raised and found not to be targeted at the Java HotSpot VM, the pre-installed handlers are invoked.
If support for signal handler installation after the creation of the VM is not required, then the libjsig.so shared library is not needed.
Perform one of these two procedures to use the libjsig.so shared library:
Link it with the application that creates/embeds the HotSpot VM:
cc -L libjvm.so-directory -ljsig -ljvm java_application.c
Use the LD_PRELOAD
environment variable:
KornShell (ksh):
export LD_PRELOAD=libjvm.so-directory/libjsig.so; java_application(ksh)
C shell (csh):
setenv LD_PRELOAD libjvm.so-directory/libjsig.so; java_application(csh)
The interposed signal()
, sigset()
, and sigaction()
return the saved signal handlers, not the signal handlers installed by the Java HotSpot VM and which are seen by the operating system.
Note: SIGUSR1
cannot be chained. If an application attempts to chain this signal on Oracle Solaris operating system, then the HotSpot VM terminates with a fatal error as shown in Example 7-3.
Example 7-3 HotSpot VM Fatal Error
Signal chaining detected for VM interrupt signal, try -XX:+UseAltSigs
Also, the SIGQUIT
, SIGTERM
, SIGINT
, and SIGHUP
signals cannot be chained. If the application needs to handle these signals, consider using the -Xrs
option.
On Oracle Solaris operating system, the SIGUSR2
signal can be chained only for non-Java and non-VM threads (that is, it can only be used for native threads created by applications that do not attach to the VM).


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ansible.vars package¶
-
ansible.vars.
preprocess_vars
(a)[source]¶ Ensures that vars contained in the parameter passed in are returned as a list of dictionaries, to ensure for instance that vars loaded from a file conform to an expected state.
-
ansible.vars.
strip_internal_keys
(dirty)[source]¶ All keys stating with _ansible_ are internal, so create a copy of the ‘dirty’ dict and remove them from the clean one before returning it
-
class
ansible.vars.
VariableManager
[source]¶ Bases:
object
-
extra_vars
¶ ensures a clean copy of the extra_vars are made
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options_vars
¶ ensures a clean copy of the options_vars are made
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get_vars
(loader, play=None, host=None, task=None, include_hostvars=True, include_delegate_to=True, use_cache=True)[source]¶ Returns the variables, with optional “context” given via the parameter for the play, host, and task.
The context can possibly result in different sets of variables being returned due to the additional context.
The order of precedence is:
- play->roles->get_default_vars (if there is a play context)
- group_vars_files[host] (if there is a host context)
- host_vars_files[host] (if there is a host context)
- host->get_vars (if there is a host context)
- fact_cache[host] (if there is a host context)
- play vars (if there is a play context)
- play vars_files (if there’s no host context, ignore file names that cannot be templated)
- task->get_vars (if there is a task context)
- vars_cache[host] (if there is a host context)
- extra vars
-
add_host_vars_file
(path, loader)[source]¶ Loads and caches a host_vars file in the _host_vars_files dict, where the key to that dictionary is the basename of the file, minus the extension, for matching against a given inventory host name
-
add_group_vars_file
(path, loader)[source]¶ Loads and caches a host_vars file in the _host_vars_files dict, where the key to that dictionary is the basename of the file, minus the extension, for matching against a given inventory host name
-
Submodules¶
ansible.vars.hostvars module¶
ansible.vars.unsafe_proxy module¶
-
class
ansible.vars.unsafe_proxy.
AnsibleJSONUnsafeEncoder
(skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, sort_keys=False, indent=None, separators=None, encoding='utf-8', default=None)[source]¶ Bases:
json.encoder.JSONEncoder


The expressions framework is a structure which can be used to query or assign the Vertex and Transaction attributes within a graph. The framework will work on all attributes which do not include a restricted character within its "AttributeName". An expression can return zero or more matches.
The query language is designed to be robust with respect to features such as nesting and type casting. To allow this, plain text is used to gain the flexibility necessary. Users will be responsible for forming queries which adhere to the below rules.
A valid query for a vertex or transaction takes the form of [AttributeName] [Operator] [value].
How to form an expression to find a vertex with the "Label" attribute matching "Vertex #1<Unknown>".
Combining these into the pattern [AttributeName] [Operator] [Value] yields the following suitable query strings.
Operators can be used to query, edit, assign and compare values. Below is the current list of supported operators.
| Operator | Query Representation | Word Operator | Precedence |
|---|---|---|---|
| Not | ! | not | 2 |
| Multiply | * | multiply | 3 |
| Divide | / | divide | 3 |
| Modulo | % | mod | 3 |
| Add | + | add | 4 |
| Subtract | - | subtract | 4 |
| Contains | N/A | contains | 4 |
| Starts With | N/A | startswith | 4 |
| Ends With | N/A | endswith | 4 |
| Greater Than | > | gt | 6 |
| Less Than | < | lt | 6 |
| Greater Than Or Equals | >= | gteq | 6 |
| Less Than Or Equals | <= | lteq | 6 |
| Equals | == | equals | 7 |
| Not Equals | != | notequals | 7 |
| Bitwise And | & | N/A | 8 |
| Exclusive Or | ^ | xor | 9 |
| Bitwise Or | | | N/A | 10 |
| And | && | and | 11 |
| Or | || | or | 12 |
| Assign | = | assign | 14 |
The expression framework allows for nested queries. This makes querying complex conditions possible. Nested queries are
created by surrounding internal queries with parenthesis (inner query).
See below for an example.
(Label == 'Vertex #0<Unknown>' || Label == 'Vertex #1<Unknown>' ) && Type == 'Machine Identifier'
Will show a Vertex which is either Vertex #0<Unknown> or Vertex #1<Unknown> and has a "Type" of 'Machine Identifier'.
Restricted characters are characters written into the query string which will not be parsed correctly due to the constraints of the query language.
The framework allows complex actions such as arithmetic operation on both sides of the Operator. The framework will
attempt to correctly calculate any well parsed expression.
Care must be taken for things like lexicographic ordering with
different variable types.
Addition as shown below works similar to appending for String types.
(Label + 'extratext') == 'Vertex #1<Unknown>extratext'