If you have a popular franchise, you probably have novels and action figures and DVDs and so on. What else will people buy?
A cookery book is one option, especially, but not necessarily, if the franchise is known for Food Porn. The book may or may not be "in-universe"; that is a cookbook that exists in the setting. (If it is, there may or may not be some explanation for why it uses real world measurements and ingredients — especially if the setting is known for Alien Lunch or Foreign Queasine.) An Edutainment Show will also want to take this opportunity to talk to the kids in the audience about healthy eating.
Sometimes the cookery book is officially released, or it can be fan-made.
There is also a subset of Parody cookbooks with funny parallel Pun Based Titles. These are both loving and mocking in tone. They make fun both of the original work and cookbooks in general. The recipe names will most likely be punning, too.
Examples:
- In 2012, One Piece got one based (mostly) on the work of in-series Supreme Chef Sanji. As a bonus, the last chapter catalogs what Eiichiro Oda eats during a typical workday.
- The DC Comics Superheroes Super Healthy Cookbook by Mark Salzman, Judy Garlan and Michele Grodner. More or less in-universe, with recipes like Perry White's Great Caesar Salad (with tomato and cucumber Daily Planet logos).
- Stan Lee Presents the Mighty Marvel Superhero Cookbook, with such gems as "make some pancakes and put syrup over them in the shape of Spider-Man's webbing" and "put some meat between slices of bread" as actual recipes.
- In France or Italy, a long-running series of Defictionalizations of Disney Comics books such as the Junior Woodchuck's Guidebook was continued with "Recipes by Grandma Duck".
- In 1992, there was also a Cooking with Asterix cookbook, with Marie-Christine Crabos as the author and, you guessed it, Albert Uderzo as the illustrator.
- Maw Broon's Cookbook is based on The Broons.
- The Peanuts franchise has Peanuts Cookbook and Peanuts Lunchtime Cookbook, both of which features Peanuts reprint on even pages followed by a recipe on odd pages.
- Mary Poppins in the Kitchen: A Cookery Book with a Story by PL Travers. As the title suggests, this one has a story, in which Mary teaches the Banks kids to cook.
- Nanny Ogg's Cookbook by Terry Pratchett, Stephen Briggs and Tina Hannan (Discworld). Largely in-universe, with extra-diagetic "Editor's Notes" about the need to substitute Roundworld measurements and utensils. It's also noted that some recipes have been adjusted to provide "the look but not the taste", including the whole section on dwarfish cooking.
- Several based on the works of Beatrix Potter:
- Peter Rabbit's Natural Foods Cookbook by Arnold Dobrin has recipes named after the characters but not especially based on the books.
- Peter Rabbit's Cookery Book by Anne Emerson, based directly on the food in the books (although the introduction reassures readers that Jeremy Fisher's Butterfly Sandwiches do not contain butterflies and Samuel Whiskers' Roly Poly Pudding doesn't contain Tom Kitten).
- The Peter Rabbit and Friends Cookbook by Naia Bray.
- The Pooh Cook Book, "inspired by" Winnie-the-Pooh and The House at Pooh Corner by A. A. Milne.
- Two books based on Little House on the Prairie: The Little House Cookbook, which gives recipes for meals mentioned in the books, and The Laura Ingalls Wilder Country Cookbook, which is from her actual recipe notes. Sadly, neither has the recipe for Laura's pieplant pie.
- From A Song of Ice and Fire, there is A Feast of Ice And Fire, the official cookbook.
- There is a fanmade Harry Potter Unofficial Cookbook, surprisingly enough.
- Encyclopedia Brown Takes The Cake follows each of its cases with a recipe.
- The Hunger Games has an unofficial cookbook.
- The Nero Wolfe Cookbook is a collection of recipes for dishes served in the novels.
- American Girl has a number of cookbooks inspired by their characters, with recipes from various historical or contemporary eras the characters' stories took place.
- Patricia Cornwell wrote a cookbook titled Scarpetta's Winter Table. This is actually an official part of her Dr. Kay Scarpetta series, and consists of a mystery novella (in which food plays a large part), with separate pages detailing each of the recipes prepared in the story.
- Patrick O'Brian's Aubrey-Maturin novels gave rise to Lobscouse and Spotted Dog, a highly entertaining & authentic cookbook full of Napoleonic-era recipes (and of clues as to why Jack Aubrey had trouble keeping the pounds off!).
- Balsa's Table is a collection of recipes for meals mentioned in the Moribito and The Beast Player series.
- Hannah Swensen ended up with a book of recipes that had been featured in the series up to that point (the main character owned a desert bakery, so most of the recipes were for cookies, brownies, and similar). And one of the stories, while not a cookbook (since it had a plot), included enough recipes to host a Holiday Dinner; in fact, the very dinner the characters were getting ready for while the plot happened.
- Jane Austen's novels got The Jane Austen Cookbook. It describes Austen's interest in food, drawing upon both the novels and her letters. It has authentic recipes from late Georgian and Regency England, modernized for today's cooks.
- Fifty Shades of Chicken is a parody in a cookbook, spoofing Fifty Shades of Grey. Fifty chicken recipes, each more seductive than the last. This book will have you dominating dinner.
- The Redwall Cookbook contains recipes for many of the foods Brian Jacques describes (in mouth-watering detail) in his Redwall novels. The recipes are framed by and featured in a short story about a young mouse who works in the Redwall kitchens.
- The Boxcar Children Cookbook, featuring recipes from the original nineteen books.
- Dining on Babylon 5 by Stephen C. Smith which actually appeared in the series. Compiled by the owner of B5's Fresh Air Restaurant and with contributions from all the main station personnel (with a note explaining the alien recipes have been adjusted to suit human biology).
- Alice's Brady Bunch Cookbook by Ann B. Davis. This was the actress who played Alice, the Bradys' housekeeper (and cook) on The Brady Bunch.
- Doctor Who:
- The Doctor Who Cookbook by Gary Downie. Recipes were submitted by actors from the show and given Doctor Who inspired names like Castrovalvan Kebabs.
- A second official cookbook came out in 2016, by professional cookery writer Joanna Farrow.
- Mary Ann's Gilligan's Island Cookbook was written by Dawn Wells (who played Mary Ann), Ken Beck, Jim Clark and had a foreword from Bob Denver (who played Gilligan). It had a lot of coconut pie recipes.
- Gilmore Girls has an unofficial cookbook Eat Like a Gilmore, particularly interesting because the main characters are most known for eating out or ordering in, but their rich family are gourmets and their close friends are excellent cooks.
- There's at least four spin-off cookbooks for The Great British Bake Off, all specializing in, what a surprise, baked goods.
- Hannibal has Feeding Hannibal: a Connoisseur's Cookbook, written by the show's food stylist Janice Poon. It contains behind-the-scenes stories behind the creation of the show's Food Porn / Food Gorn as well as recipes to create (human-free) variations of Hannibal Lecter's dishes.
- Star Trek:
- The Official Star Trek Cooking Manual by Mary Ann Piccard. In-universe, the personal cooking log of Christine Chapel.
- The Star Trek Cookbook by Ethan Phillips and William J. Birnes. In-universe, written as if by Neelix.
- The Late Show with David Letterman:
- Home Cookin' with Dave's Mom, written by Dave's mom Dorothy.
- Dave Letterman's Book of Top Ten Lists and Zesty Chicken Recipes.
- The Walking Dead:
- The Walking Dead: The Official Cookbook and Survival Guide is a cookbook based off the television series.
- The Snacking Dead: A Parody in a Cookbook is a parody cookbook filled with recipes for snacks suited for every occasion and it has tips for cooking under duress — fitting for a zombie apocalypse.
- Stranger Fillings is a parody cookbook based on a Netflix's hit show Stranger Things. There is no shortage of great puns and delicious baked treats.
- Game of Scones: All Men Must Dine is a parody cookery book with baking recipes based on TV show Game of Thrones. It's full of Game of Thrones memes like 'Arya Hungry?' or 'Dinner is Coming'. There are recipes for stuff like Red (Velvet) Wedding Cake or Oberyn's Smashing Head Surprise.
- Baking Bad is parody cookbook based off Breaking Bad.
- Supernatural has Supernatural: The Official Cookbook: Burgers, Pies, and Other Bites from the Road, by Julie Tremaine (who, interestingly enough, isn't in Team Sam or Team Dean... she's in Team Castiel). It consists of recipes of the foods based from the series.
- Sesame Street:
- The Sesame Street Cookbook by Pat Tornborg
- C is for Cooking and B is for Baking by Susan McQuillan
- In the Kitchen with Miss Piggy by Jim Lewis (The Muppets), a collection of recipes from celebrities with commentary by Piggy.
- Star Wars: Galaxy's Edge has an official cookbook for fans who want to make the food they ate at the in-park restaurants at home.
- Angry Birds: Bad Piggies' Egg Recipes is, as the title makes clear, an Angry Birds-themed cookbook where the key ingredient is eggs.
- The Kickstarter backers of Pillars of Eternity received a special PDF cookbook containing recipes of some of the staples of Dyrwood (the region of the Constructed World where the game takes place) cuisine.
- The Elder Scrolls: The Official Cookbook, by Chelsea Monroe-Cassel, includes recipes based on in-game food (particularly from The Elder Scrolls V: Skyrim) and on the lore of the series. In case you're wondering: yes, there's a recipe for skooma (it doesn't include cocaine).
- Fallout: The Vault Dweller's Official Cookbook by Victoria Rosenthal is the official cookbook for the Fallout series.
- Overwatch has an officially-licensed cookbook featuring recipes from the homelands of the game's multinational cast.
- Mother's Cookbook is inspired by EarthBound and Mother 3. In addition to defictionalizations of the games' food such as Mach Pizza, there are recipes inspired by the characters, dialogue, attacks, and settings from the games, such as "Lier X. Agerate's Roasted Garlic Bread". Even foods that were changed by EarthBound's localization are featured.
- World of Warcraft: The Official Cookbook, also by Chelsea Monroe-Cassel, features recipes inspired by the game from across the realm of Azeroth.
- Bee and Puppycat has one subtitled "Easy Eats", and focuses on recipes featured in the show, as well as some of Bee's favorite foods.
- How To Cook The Victorian Way is a spin-off cookbook for The Victorian Way. It is a guide to the life, times and tastes of Mrs Crocombe, a Victorian cook. Her historical cookery videos have millions of views and fans have been asking for a cookery book since the beginning of the series. It showcases recipes from real Mrs Crocombe's own hand-written book alongside other recipes of the Victorian period.
- Bob's Burgers has "The Bob's Burgers Burger Book: Real Recipes for Joke Burgers".
- The Teenage Mutant Ninja Turtles Pizza Cookbook by Peggy Paul Casella is based on Teenage Mutant Ninja Turtles (1987).
- Binging with Babish is a YouTube channel devoted to recreating meals from various fictional media; everything from Western Animation to Live Action Films/Shows to Video Games to even Anime. Naturally, he ended up making his own cookbook with these various recipes in it.
- Australian Cricketer Matthew Hayden once had one of his recipes published in a celebrity cookbook.
- American Football Defensive End Jared Allen released a cookbook mostly of game recipes entitled The Quarterback Killer's Cookbook.


Fill out the following details and then click on predict..
Age
Balance
Last contact day of the month
Duration of last contact in seconds
Number of contacts performed during this campaign
Number of contacts performed before this campaign
Job-type
Select job-type
management
blue-collar
technician
admin.
services
retired
self-employed
student
unemployed
entrepenuer
housemaid
unknown
Marital status
Select marital status
married
single
divorced
Education
Select Education
primary
secondary
tertiary
unknown
Has housing loan?
Select
yes
no
Has personal loan?
Select
yes
no
Last contact mode
Select contact mode
cellular
telephone
unknown
Last contact month of the year
Select month
jan
feb
march
apr
may
june
july
aug
sep
oct
nov
dec
Outcome of previous campaign
Select outcome
failure
other
success
unknown
Predict


Web Beeps are short, quasi-musical sounds which encode text. They can be thought of as sonic QR Codes. You can use them to create audio representation of Web links and identify things with tones. They are easy to create, decode and use and backed by an open specification together with an open source implementation.
Use a Web Beep as your personal cell phone ring tone and let people know who you are!
Make Beeps
Enter any text (up to 63 characters) and click the Make Beeps button to get the download.
Try it out
Web addresses (URLs) work well - now you can give yourself a URI you can hear!
Decode Beeps
Click Browse to locate a Web Beep mp3 file on your computer, then click Decode to see the text it contains.
This is a new alpha service, any problems, let me know.
Radio Broadcasters and Podcasters
Add Web Beeps to your recording to provide channel/station identification and to supplement the inclusion of unreliable spoken links.
Musicians
Tag the releases you put on the Web to link listeners back to your site.
Web Designers
Create Web Beeps for your sites, identifying the pages, people and any other things relevant to each site.
Developers and Inventors
Use Web Beeps to send messages between devices, as access passcodes, to tag real-world objects...the possibilities are endless. This Web Site offers one API, why not build your own?


Bounding box, clipped to fall within the image dimensions.
if isinstance(image, tuple):
shape = image
else:
shape = image.shape
height, width = shape[0:2]
ia.do_assert(height > 0)
ia.do_assert(width > 0)
Bounding box, clipped to fall within the image dimensions.
shape = _parse_shape(image)
height, width = shape[0:2]
ia.do_assert(height > 0)
ia.do_assert(width > 0)


Peersview is a place where geographically disconnected peers can meet through their common interests. Whether you are interested in Sports, Biotech, Fashion, Make up, Music, Philosophy etc. discover, interact and share experiences with people who share these interests with you.
Networking
Discover like-minded people, who share similar interests with you.
Asking Questions
Ask Questions and find answers among your peers from various academic institutions.
Read and Share stories
Read about people thoughts or share your ideas and perspective on various Issues.
Join your schools’ Online Campus
Interact and Collaborate with classmates and school mates from your school with the private Online Campus designed for your academic institution.
Create Events
Create social events and advertise to your friends
Discover Events
Discover social events within your city and other cities of your choice.
Advertise events
Create awareness for your Events by advertising them to Users on Peersview.
Find discounts on Local shops
Find discounts on special dishes from restaurants in your city.
How can I Create a profile on Peersview?
What is the difference between public and protected Posts?
Who can see my Posts?
When you are following a person, you will receive a notification and/or a recent activity item when a user posts on that page.
Block is a feature that helps you control how you interact with other accounts on Peersview. This feature helps users in restricting specific accounts from contacting them, seeing their Posts, and following them.
You become a Top-rated member of Peersview.com through your contributions on the platform. The following contributions play a role in your ratings:
Check Account setting to update your Profile Picture
Contact Customer support.


Beta This is a test version of the layout of this page. Take the survey to help us improve it
Statutory guidance
The statutory programmes of study and attainment targets for science at key stages 1 to 4.
These programmes of study are issued by law; you must follow them unless there’s a good reason not to.
These programmes of study apply to all schools maintained by the local authority in England. They came into force in September 2016 for pupils up to and including year 10. For pupils in year 11, they come into force from September 2017.
Schools are not required by law to teach the content indicated as being ‘non-statutory’.
Further science resources are available on the TES website . This is free of charge to schools and teachers but they will have to register with the website to use it.
If you would like to add your own free resources to this site, to share with other teachers, please tweet DfE via @educationgovuk .
Short films of teachers and subject experts talking about the national curriculum and how to use it are also available.


TechGig
Home
(current)
Home
Upload File
Predict Credit Card approval from list of Customers
{% for each in dropdown_list_2 %}
{{each}}
{% endfor %}
Predict
Show Top X% Customers Most Probable to get approval of Credit Card


"Autumn in San Fran" Sweet November
By Ross Anthony
On our way to the screening, we stopped to help a motorist in distress, so I ended up being tardy to this film. Therefore you may wish to pro-rate its accuracy.
Charlize Theron finds herself a monthly project, rich work-aholic Keanu is November.
Keanu frustrated and un-amused by the carefree, spontaneous, potentially dangerous Theron reads, "What do you want with me?"
Charlize, "I just want to help you."
Keanu, "What makes you think I need helping?"
Charlize, "You're in a box. I'll lift the lid."
Keanu sarcastically replies, "Just hearing that makes me feel healed."
I like this script - sort of a "Harold and Maude"-lite. Very well paced, and carefully directed except for an abrupt, sorely reworked ending scene. Still, soft and bright like the cinematography, the film packs a lot of heart.
Charlize is stunning, lovable and professional; Jason Isaacs, awesome; little Liam superb; and Keanu, simply terrible. I've heretofore defended Keanu from the robust pool of criticism regarding his acting abilities. But, his performance in this film is clearly underwhelming. He delivers lines as if he'd just awaken from deep slumber and was handed the script even before a cup of coffee. Happily (and somewhat amazingly), despite the blunt deficiency of acting prowess in this effort, Keanu's resilient charm and good looks still command while on screen.
Done well, a crisp free and easy scene in which Charlize romps on the beach with a clan of beautifully groomed white show dogs. It's a wonderful way to impress upon us the theme, "If you want to enjoy life ... you've got to risk getting a little sand in your fur." Also of note, a short scene in which Keanu returns to his childhood home is accented nicely by Tony Bennett's "The other half of me" as a remembrance of Keanu's history with his dad. In fact, this is just one of other well-selected musical pieces on the soundtrack. Thirdly and fourthly, two advertising meetings - one where Keanu introduces the "Hot" dog ad campaign with fervor and passion, and the other where Keanu meets with the god of advertising. Keanu peaks in both of these masterfully crafted scenes.
Warm and cozy dialogue, direction, and apt cast, compensate for Keanu's acting shortcomings all the way up to, but not including, the ending bridge scene. The timing and pacing of which beats completely off-sync with the rest of the picture. Even the direction and editing lag here. Nice bridge though.
Would have been an "A-" were Keanu to have acted, but since he didn't, would have been a "B+" had the ending sequence been more aptly scripted and shot. Still I enjoyed what I enjoyed and will remember the sparkling zest for life and the strong heart of this picture.
Free Tickets Contest!
Sweet November. Copyright © 2001. Rated PG-13. * Starring Keanu Reeves, Charlize Theron, Jason Isaacs, Greg Germann, Liam Aiken, Robert Joy, Lauren Graham. * Directed by Pat O'Connor. * Written by Kurt Voelker. Based on a screenplay by Herman Raucher. * Produced by Erwin Stoff, Deborah Aal, Steven Reuther, Elliot Kastner at Warner Brothers/Bel-Air/3-Arts.
Grade..........................B
-- Copyright © 2000. Ross Anthony, currently based in Los Angeles, has scripted and shot documentaries, music videos, and shorts in 35 countries across North America, Europe, Africa and Asia.
For more reviews visit: http://www.RossAnthony.com
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


[filmcritic.com presents a review from guest writer Athan Bezaitis. You can find the review with full credits at http://www.filmcritic.com/misc/emporium.nsf/2a460f93626cd4678625624c007f2b46/a699f00b68ee75968825680400043b03?OpenDocument
BANDITS A film review by Athan Bezaitis Copyright Athan Bezaitis 1999
Foreign films have a unique and powerful way of facing controversial issues. The most recent example comes from Germany, which has created a movie so audacious in its statement of women's liberation, that it makes the much-scrutinized themes of THELMA AND LOUISE feel like child's play. Despite subtitles, unfamiliar faces, and a somewhat taboo motif, BANDITS is sure to have tremendous crossover appeal with American audiences.
Directed by Katja von Garier,BANDITS chronicles four German fugitives and their passion for rock n' roll as they attempt to evade the law and flee the country. Their odyssey takes this all girls rock band (hence The Bandits) from rehearsal at the prison church to performing in front of thousands as they garner Mickey and Mallory-esque attention through their adventures as outlaws on the run. Their experiences and emotions are conveyed in their music, which serves as a link between their depraved sense of humanity and newfound freedom.
The less you know about the film going into it the more enjoyable it will be, so if you hear anyone talking about it, do yourself a favor and cover your ears.
The cast is fabulous and the best part is that you've never seen these actors in any other roles, unless of course, you're from Germany. How often can you say that about an American film? For all I knew it was the German Sharon Stone and Tom Cruise on screen, but none of that mattered because I was focusing solely upon characters rather than well-publicized canned images.
 The excellent soundtrack is all in English. The cinematography and the action are fantastic, and trust me when I say that the themes of female autonomy are not at all overbearing and only add to the nostalgia of the movie.
Find out which small theater in your neighborhood is playing this film and check it out. It's well worth the effort.
Director/Writer: Katja von Garnier Starring: Katja Riemann, Jasmin Tabatabai, Nicolette Krebitz, Jutta Hoffman, Hannes Jaenicke, Werner Schreyer, Andrea Sawaski Producer: Harry Kugler, Molly von Furstenberg, Elvira Senft MPAA rating: R Year of Release: 1999
**** Four Stars -- Christopher Null -<EMAIL_ADDRESS>- http://www.filmcritic.com Author - Network Administrator's Reference, 1999 - ISBN 0-07-882588-1 http://www.amazon.com/exec/obidos/ASIN/0078825881/filmcriticcom
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


| DOSE: T+ 0:00 |
15 mg |
oral |
4-HO-MiPT |
(powder / crystals) |
| T+ 2:45 |
2 bowls |
smoked |
Cannabis |
(plant material) |
Me and a friend aquired a total of 30MG of this chemical 4-HO-MiPT. Not much detail into what this chemical would do is online, only the TiHKAL reference. So we were kinda goin in blind. I eye-balled the powder in half, making it roughly 15MG each, then put them into capsules. We both had a full stomach, and a relaxed enviroment. My friend has about the same things to say about his experience, but this is how it went for me. I wrote a bunch in a journal, but after about 2 hours in, not much of It makes any sense but the times. Fortunately I remember the whole night.
8:30PM - Initial dose
9:00PM - Notice a slight light-ness, deffinately something happening.
9:30PM - Growing stronger at this point, very relaxed, euphoric at the least. Colors seem brighter, vision is off a little bit but no headache or dizzyness of the sort.
10:00PM - Start playing guitar, which is a great idea because for some reason it sounds really cool. My hand slightly cramped but I can still play. The music def sounds different, very very relaxed feelings throughout. I can still completely notice whats going on around me, but reality just seems different. It feels really great.
10:30PM - We put on some trippy music at this point. Everything is relaxed and going great. Music is so nice, colors vibrant. I'm also excited at this point that nothing is going wrong and its accually working.
10:45PM - We decide to go outside. Me, my other friend who is tripping, and my friend who was totally normal(to make sure eveytrhing went ok) start to walk. Trees and the ground are really crazy looking. Nothing out of the ordinary, but everything is more defined. Colors and sounds are still much more prominent. Still very happy and relaxed. I had a pretty stressful week, and no thoughts of sadness/depression could even break the way I was feeling at this point, which was very satisfying.
11:15PM - We then smoke 2 bowls of cannabis. THis is where it gets nuts. Everything changes, for the better. The effects are so greatly enhanced that I don't even know whats goin on. I see so many colors. I never expected it to go this far.
11:30 - We are still walking around. The cold temperature doesnt even phase us, its like it doesnt matter. There was no axiety or anything, everything was relaxed. I kept coming in and out of this trippy world, where it seemed like a different reality. We then started talking about weird things like the galaxy and life and crazy things that you would strangly talk about when your on some sort of psychedelic substance.
12:00 - Back at the house, we are just relaxing, listening to music and letting this state of mind take over. Dark Side of the Moon was too much for me because of this thumping beat in the back that made me feel like it was my heart. But other than that everything was nice and easy.
1:00 - By this time I felt like I was just high from the cannabis, but still realised what the past 2 hours were like. It seemed like much longer, but I didnt think much of it.
1:15 - Friend picked us up to go drive around, get something to eat. Just an enhanced smoking high now.
1:45 - Basically on the comedown, trying to fall asleep which wasnt to hard.
2:15 - I think this is when we fell asleep.
To conclude, I had a very fun night. Nothing went wrong, everything was great. I have never done shrooms or anything like that, so I'm not gonna say this was like that in any way, but it was very crazy. The next day I feel great. I had no problem sleeping and have 0 burnout effect. I had a great experience.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


About a year or 2 ago I had to have all 4 of my wisdom teeth removed, so I went in for the operation. I didnít really know what to expect. At one point, a woman put a mask on me and told me that it was laughing gas. After a few minutes of breathing it in, I realized that I was feeling quite pleasant. I began laughing at the simplest things and even started joking around with her. I was a bit out of it, and the fact that I was sucking in the gas so excessively that the doctor had to turn the gas down didnít help. But I wanted more, I didnít care. I repeatedly emptied out my lungs and breathed in as much as I could.
It was time for the operation to begin (I remember that at that point I was almost gone). The doc came in and injected me with something into my left arm. It hurt, so I made a noise. He looked at me and made fun of my reaction. He made the same noise but in a slightly over-exaggerated manner. I began cracking up. Even though it hurt, I couldnít stop laughing.
The operation was performed using the truth serum. While I was knocked out and totally unaware of what was going on, to the doc I was conscious and co-operational. He said that during the operation he would ask me things like did I feel any pain. Obviously, I had to answer Ďnoí. When I woke up, I didnít remember anything that had happened. I looked at the guy and he told me that I just had an operation done and he told my mom that I was under a strong pain killer and that I had to stay awake for a while so that I would have enough energy to walk to the car.
While I was trying stay awake, the doc was talking to my mom right in front of me. He said that he has witnessed something that he had never seen, read, nor heard about. He told my mom and me that I laughed through the entire operation. While he was cutting my gums, removing teeth, and sowing me up, I was laughing. I was out for about 3 hours. I laughed that long. When I thought about it at the time, it helped explain why my stomach muscles where hurting me so much.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


This is the second panel of the basic tab example. This is the second panel of the basic tab example.
Waste service
Use cases and requirements
The Service model should have properties for:
name
For identification.
frequency
An indication of typically how often the collection is run.
description
For specific instructions about the service, such as when and where to put containers out, that bin lids must be closed, etc.
next collections
To know upcoming dates of when upcoming collections will happen, when filtered by property. Also if any adjustments to the scheduled dates are made.
previous collections
To know when the last few collections have taken place, when filtered by property.
container types
What types of containers (and in relation, materials) are collected by this service.
rounds
The schedules and routes of trucks collecting the materials handled by this service.
Types
This WasteService type:
- Is a derivation of the Service concept in the Smart City Concept Model.
- Extends from the GovernmentService schema.org type.
Properties
| Term | Mapping | Definition |
|---|---|---|
| name | schema:name | Name of the service. |
| frequency | Text | How often it runs, e.g. weekly. |
| description | schema:description | A short description of the service. |
| next collections | Date | List of dates of next collections. |
| previous collections | Date | List of dates of previous collections. |
| container types | Container type | The container types that are collected. |
| available channel | schema:availableChannel | A means of accessing the service (e.g. a phone bank, a web site, a location, etc.). |
| provider | schema:provider | The council providing the service. |
| service area | schema:serviceArea | The geographic area where the service is provided. |
| service operator | schema:serviceOperator | The operator of the service, if different to the provider. |
Serialisation
{
"@type": "WasteService",
"id": 1,
"name": "Mixed recycling",
"frequency": "weekly",
"container_types": [
{
"@type": "ContainerType",
"color": "black",
"size": "240L",
"shape": "wheelie bin",
"lid_color": "green",
"material_stream": {
"name": "Mixed recyclables",
"@id": "/waste/material-streams/mixed-recycling"
},
"image": ""
},
{
"@type": "ContainerType",
"color": "brown",
"size": "40L",
"shape": "caddy",
"material_stream": {
"name": "Food waste",
"@id": "/waste/material-streams/food-waste"
},
"image": ""
}
],
"description": "Please put your recycling wheelie bin on the street.",
"esd_url": "http://id.esd.org.uk/service/1130",
"last_collection": {
"@type": "Collection",
"date": "23 July 2015",
"round": "/rounds/123",
"containers": [
{
"@type": "WasteContainer",
"id": "2140541",
"status": "in_service",
"type": {
"color": "black",
"size": "240L",
"shape": "wheelie bin",
"lid_color": "green",
"image": "",
"material_streams": [
{
"name": "Mixed recyclables",
"@id": "/waste/material-streams/mixed-recycling"
}
]
}
}
],
"events": [
{
"@type": "WasteEvent",
"@id": "/api/events/1",
"type": "Not presented",
"date_created": "1 August 2015",
"uprn": "123456789012",
"usrn": "123456789012",
"image": "http://example.com/images/123.png",
"geo": {
"@type": "GeoCoordinates",
"latitude": "40.75",
"longitude": "73.98"
},
"container_color": "black"
}
]
},
"next_collection": {
"@type": "Collection",
"date": "1 August 2014",
"changed_date": "2 August 2015",
"changed_date_reason": "Bank holiday",
"containers": [
{
"@type": "WasteContainer",
"id": "2140541",
"status": "in_service",
"type": {
"color": "black",
"size": "240L",
"shape": "wheelie bin",
"lid_color": "green",
"image": "",
"material_streams": [
{
"name": "Mixed recyclables",
"@id": "/waste/material-streams/mixed-recycling"
}
]
}
}
]
},
"available_channel": {
"@type": "ServiceChannel",
"name": "Household waste and street maintenance",
"service_phone": {
"@type": "ContactPoint",
"telephone": "0123456789",
"email"<EMAIL_ADDRESS>},
"service_url": "http://www.example.gov.uk/waste"
},
"provider": {
"@type": "Organization",
"name": "Example Council",
"url": "http://www.example.gov.uk"
},
"service_area": {
"@type": "AdministrativeArea",
"name": "Anytown"
},
"service_operator": {
"@type": "Organization",
"name": "Example Waste Operator Co"
}
}
Codelists
Freqency
TBD


The example is taken from the excellent Canvas Tutorial by the Mozilla developers. Besides the standard HTML5 Canvas API, the same example is implemented using the Canvas2D API and the Canvas2D Shapes DSL.
This example is taken from https://developer.mozilla.org/en/Canvas_tutorial/Basic_usage. It introduces the basic usage of the HTML5 Canvas API to draw two rectangles, showing of the aplhablending capabilities of the Canvas.
var ctx = document.getElementById("canvas").getContext("2d"); ctx.fillStyle = "rgb(200,0,0)"; ctx.fillRect (10, 10, 55, 50); ctx.fillStyle = "rgba(0, 0, 200, 0.5)"; ctx.fillRect (30, 30, 55, 50); |
The Canvas2D API offers a wrapper around the native canvas implementation. While doing this, it implements the entire HTML5 Canvas Specification on all browsers, while we wait for all browsers to become 100% stanard compliant.
On the left, the full-fletched code is shown, but with a convenience method on the left, the same one-line initialisation is acchieved. The only thing to do is copy and paste the natve code ... et voila.
var manager = new Canvas2D.Manager(); var canvas = manager.setupBook("Canvas2D"); var ctx = canvas.addSheet(); manager.startAll(); ctx.fillStyle = "rgb(200,0,0)"; ctx.fillRect (10, 10, 55, 50); ctx.fillStyle = "rgba(0, 0, 200, 0.5)"; ctx.fillRect (30, 30, 55, 50); |
var ctx = Canvas2D.activate("Canvas2D"); ctx.fillStyle = "rgb(200,0,0)"; ctx.fillRect (10, 10, 55, 50); ctx.fillStyle = "rgba(0, 0, 200, 0.5)"; ctx.fillRect (30, 30, 55, 50); |
Finally we can use the Canvas2D Shapes DSL. No more Javascript is needed and thanks to the Kickstart detection system, the source for the Canvas is automagically discovered. A simple HTML element containing the DSL code is sufficient to populate the Canvas.
This example also introduces the dynamic modifier, which makes Canvas2D interactive. Try clicking on one of the shapes or click and drag ...
sheet mySheet +dynamic { [@10,10] rectangle rect1 +geo="55x50" +fillColor="rgb(200,0,0)"; [@30,30] rectangle rect2 +geo="55x50" +fillColor="rgba(0,0,200,0.5)"; } |


{{ blog.title }}
{% if blog.introduction %}
{% include "ctdata/includes/streamfield_truncated.html" with content=blog.introduction %}
{% else %}
{% include "ctdata/includes/streamfield_truncated.html" with content=blog.body %}
{% endif %}


CHAPTER 205
LAND USE COMMISSION
Part I. Generally
Section
205-1 Establishment of the commission
205-2 Districting and classification of lands
205-3 Retention of district boundaries
205-3.1 Amendments to district boundaries
205-3.5 Reclassification of land contiguous to an agricultural
district; approval conditions
205-4 Amendments to district boundaries involving land areas
greater than fifteen acres
205-4.1 Fees
205-4.5 Permissible uses within the agricultural districts
205-4.6 Private restrictions on agricultural uses and activities;
not allowed
205-5 Zoning
205-5.1 to 5.3 Repealed
205-6 Special permit
205-7 Adoption, amendment or repeal of rules
205-8 Nonconforming uses
205-9 to 11 Repealed
205-12 Enforcement
205-13 Penalty for violation
205-14 Repealed
205-15 Conflict
205-16 Compliance with the Hawaii state plan
205-16.1, 16.2 Repealed
205-17 Land use commission decision-making criteria
205-18 Periodic review of districts
205-19 Contested cases
Part II. Shoreline Setbacks
205-31 to 37 Repealed
Part III. Important Agricultural Lands
205-41 Declaration of policy
205-42 Important agricultural lands; definition and
objectives
205-43 Important agricultural lands; policies
205-44 Standards and criteria for the identification of
important agricultural lands
205-44.5 Important agricultural lands; public lands
205-45 Petition by farmer or landowner
205-45.5 Important agricultural land; farm dwellings and
employee housing
205-46 Incentives for important agricultural lands
205-46.5 Agricultural processing facilities; permits; priority
205-47 Identification of important agricultural lands;
county process
205-48 Receipt of maps of eligible important agricultural
lands; land use commission
205-49 Designation of important agricultural lands;
adoption of important agricultural lands maps
205-50 Standards and criteria for the reclassification or
rezoning of important agricultural lands
205-51 Important agricultural lands; county ordinances
205-52 Periodic review and amendment of important agricultural
lands maps
Note
Department of transportation's bridge rehabilitation and replacement program; temporary exemption from certain construction requirements of this chapter through June 30, 2022 or until completion. L 2012, c 218; L 2017, c 48.
Judiciary report to 2019 legislature on change in judicial proceedings made by L 2016, c 48. L 2016, c 48, §11.
Prior law: L 1961, c 187.
Cross References
Acquisition of important agricultural lands, see §§163D-31 to 163D-33.
Private agricultural parks, see chapter 169.
Law Journals and Reviews
Maha`ulepu v. Land Use Commission: A Symbol of Change; Hawaii's Land Use Law Allows Golf Course Development on Prime Agricultural Land by Special Use Permit. 13 UH L. Rev. 205 (1991).
Honolulu's Ohana Zoning Law: To Ohana or Not to Ohana. 13 UH L. Rev. 505 (1991).
The Lum Court, Land Use, and the Environment: A Survey of Hawai`i Case Law 1983 to 1991. 14 UH L. Rev. 119 (1992).
Residential Use of Hawai`i's Conservation District. 14 UH L. Rev. 633 (1992).
Dolan v. City of Tigard: Individual Property Rights v. Land Management Systems. 17 UH L. Rev. 193 (1995).
Is Agricultural Land in Hawai`i "Ripe" for a Takings Analysis? 24 UH L. Rev. 121 (2001).
"Urban Type Residential Communities in the Guise of Agricultural Subdivisions:" Addressing an Impermissible Use of Hawai`i's Agricultural District. 25 UH L. Rev. 199 (2002).
Avoiding the Next Hokuli`a: The Debate over Hawai`i's Agricultural Subdivisions. 27 UH L. Rev. 441 (2005).
Ala Loop and the Private Right of Action Under Hawai`i Constitution Article XI, Section 9: Charting a Path Toward a Cohesive Enforcement Scheme. 33 UH L. Rev. 367 (2010).
A Self-Executing Article XI, Section 9--The Door For a Bivens Action for Environmental Rights? 34 UH L. Rev. 187 (2012).
Demolition of Native Rights and Self Determination: Act 55's Devastating Impact through the Development of Hawaii's Public Lands. 35 UH L. Rev. 297 (2013).
The (Somewhat) False Hope of Comprehensive Planning. 37 UH L. Rev. 39 (2015).
Case Notes
Religious Land Use and Institutionalized Persons Act of 2000, assuming it was constitutional, did not facially invalidate Hawaii's land use law. 229 F. Supp. 2d 1056 (2002).
Provisions of chapter provide authority to issue special use permits for golf courses on prime agricultural lands. 71 H. 332, 790 P.2d 906 (1990).
Article XI, §9 of the Hawaii constitution is self-executing, and §205-12 imposes "reasonable limitations and regulations" that were applicable to the case which allowed the private right of action to enforce this chapter. 123 H. 391, 235 P.3d 1103 (2010).
Charter school failed to establish that community association's claim under this chapter was moot, and even if the case was moot, the public interest exception applied as (1) appeals court ruling that there was no private right of action under this chapter injected the requisite degree of public concern; (2) public officials needed guidance as to whether private citizens have a private right of action to enforce this chapter; and (3) given the volume of land in this State and the frequency with which issues relating to this chapter have been litigated, the question of private party enforcement was likely to recur in the future. 123 H. 391, 235 P.3d 1103 (2010).
In the circumstances of the case, article XI, §9 of the Hawaii constitution created a private right of action to enforce this chapter, and the legislature confirmed the existence of that right of action by enacting §607-25, which allows for the recovery of attorneys' fees in such actions. 123 H. 391, 235 P.3d 1103 (2010).
This chapter is a law relating to environmental quality within the meaning of article XI, §9 of the Hawaii constitution. 123 H. 391, 235 P.3d 1103 (2010).
As no private cause of action exists to enforce this chapter, appellants lacked standing to prosecute their claim under this chapter against landowner and circuit court lacked subject matter jurisdiction to consider this claim. 119 H. 164 (App.), 194 P.3d 1126 (2008).


{% extends "page-layout.html" %} {% block content %}
Preview your static websites built with CircleCI before making them live. Use it to check your plain static or Jekyll-generated websites before you make them live to Github Pages or to your own server. Requires configured and working CircleCI artifacts.
Written by Michal Migurski with Mick Thompson, © 2013-2015 Code for America and © 2016 Mapzen.
You can view the source code for Precog on GitHub at https://github.com/mapzen/precog.
No Doubt The Precogs Have Already Seen This…
Preview pages in Precog by adding your Github account name and repository name to a Precog URL:
{{request.host}}/<account>/<repo>/
Example showing the Mapzen website:
{{request.host}}/mapzen/website/
To view a commit or branch other than master, add the ref to the end:
{{request.host}}/<account>/<repo>/<ref>/
Example showing a single Mapzen website commit:
{{request.host}}/mapzen/website/159d528d1/
To view a specific file or directory, add it after the ref:
{{request.host}}/<account>/<repo>/<ref>/wow/such-path.html
Example of the Mapzen Vector Tiles project page:
{{request.host}}/mapzen/website/master/projects/vector-tiles
Precog will ask for access to your Github account to preview private repositories. You can revoke Precog’s access via your Github Applications page. Precog will keep a copy of each private repository you preview for an unknown period of time.
{% if id %} {% endif %} {% endblock %}


Model Checking Configuration
The configuration of a model checking run is specified using
a record structure (@mce_opts@).
The best way of making the definition of @mce_opts@
available to the Erlang shell is through
the following command:
The relevant fields of are enumerated below:
-
Specifies the initial function of the program.
-
Specifies the type of algorithm used for
traversing the program state space
(see Section 3.2.2 below for details on algorithms).
The default algorithm is<EMAIL_ADDRESS>-
Specifies the correctness monitor used to check
the behaviour of the program.
Monitors are either safety monitors which should
be checked in every program state,
or büchi monitors which encode linear temporal logic properties (LTL).
The Ltl2Buchi tool can be used to translate
LTL properties into büchi monitors (see Section 4 for details).
Further details on monitors are given in Section 3.3.
The default monitor never reports an error.
-
Abstraction implementation (see Section 3.4 for details).
The default abstraction preserves all properties of the states
and actions to which it is applied.
-
State table implementation (see Section 3.5 for details).
The default table hashes on the stored states, but
does not store transitions.
-
Stack implementation (see Section 3.6 for details).
The default stack implementation is<EMAIL_ADDRESS>-
A scheduler determines which transitions are taken (in simulation
mode, see Section 3.7 for details).
The default scheduler (@mce_sched_rnd@)
selects randomly a new transition.
There are also a number of fields, which when specified,
can change the behaviour of state space exploration algorithms
and the McErlang runtime system.
-
If this flag is set the model checker will
interface with the external world in simulation mode (i.e.,
will receive messages sent from other processes outside the simulation).
-
Limit execution paths to a maximum depth (not set by default).
-
If the flag is set tries to compute the shortest path to failure
(default false).
-
If set, the runtime system will randomly terminate processes
(default false).
-
Warn when a process terminates abnormally due to an uncaught exception
(default true).
-
Stop a model checking run if a process
terminates abnormally due to an uncaught exception
(default true).
-
Prohibits (non-zero) timeouts (caused by @after@ clauses in
@receive@ statements) from occurring if
non-timeout transitions are enabled.
This corresponds to the assumption that the system
is infinitely fast (default false).
-
Print actions that occur during a simulation run (using the debugger or
the algorithm @mce_alg_simulation@)
(default false).
-
Specify the initial seed (used by the randomised scheduler).
-
Record seen actions during a model checking run
(default true).
-
Randomize the order of transitions. This option
is typically used when
using a safety or liveness model checking algorithm, as the specified scheduler
(using scheduler=) is not used for such algorithms
(default false).
-
Stops model checking after the time limit on
the running time of the verification, specified in seconds,
has been reached (default unlimited), with an inconclusive result.
-
Support for the rpc module re-implementation in McErlang
(default false).
-
Controls whether McErlang tries to aggressively reuse process identifiers
as soon as possible. Such reuse is costly in terms of execution speed,
but generally necessary to obtain finite models during model checking.
Default true for model checking algorithms, and
false for simulation algorithms.
2010-07-05


Tutorial 6. Shared Local Memory and Thread Group¶
C for Metal also allows users to use shared-local-memory (SLM) that can be shared among a group of threads. On GEN, SLM is carved out of the level-3 cache, and reconfigured to be 16-way banked. A group of threads that share SLM will be dispatched to the same half-slice. The maximum size for SLM is 64KB.
SLM is useful when you want data-sharing among a group of threads. Because it has more banks than L3 and is user-program controlled. It can be more efficient than L3-data-cache for scattered read and write. The following are the typical steps for using SLM and thread-grouping in CM.
These code are extracted from nbody_SLM_release.
Host Program: CreateThreadGroupSpace¶
One important note: CreateThreadGroupSpace will put GPU thread-dispatching into GPGPU mode, which is different from the media-Walker mode, therefore the thread dependence setting, which is associated with the media-walker, are not available when thread groups are in use.
// Each CmKernel can be executed by multiple concurrent threads.
// Calculates the number of threads to spawn on the GPU for this kernel.
int threads = num_bodies / BODIES_CHUNK;
// In this case, we want to maximize the group size to get the most
// data-share, so we need to query the maximum group size that target
// machine can support.
size_t size = 4;
int max_thread_count_per_thread_group = 0;
cm_result_check(device->GetCaps(
CAP_USER_DEFINED_THREAD_COUNT_PER_THREAD_GROUP,
size,
&max_thread_count_per_thread_group));
int group_count = (threads + max_thread_count_per_thread_group - 1) /
max_thread_count_per_thread_group;
while (threads % group_count != 0) {
group_count++;
}
// Creates a thread group space.
// This function creates a thread group space specified by the height and
// width dimensions of the group space, and the height and width dimensions
// of the thread space within a group.In the GPGPU mode, the host program
// needs to specify the group space and the thread space within each group.
// This group and thread space information can be subsequently used to
// execute a kernel in that space later.
CmThreadGroupSpace *thread_group_space = nullptr;
cm_result_check(device->CreateThreadGroupSpace(threads / group_count,
1,
group_count,
1,
thread_group_space));
Host Program: EnqueueWithGroup¶
// Launches the task on the GPU. Enqueue is a non-blocking call, i.e. the
// function returns immediately without waiting for the GPU to start or
// finish execution of the task. The runtime will query the HW status. If
// the hardware is not busy, the runtime will submit the task to the
// driver/HW; otherwise, the runtime will submit the task to the driver/HW
// at another time.
// An event, "sync_event", is created to track the status of the task.
CmEvent *sync_event = nullptr;
cm_result_check(cmd_queue->EnqueueWithGroup(task,
sync_event,
thread_group_space));
Kernel Program¶
Several builtin function worth attention in this programs are cm_slm_init
, cm_slm_alloc
, and cm_slm_load
.
extern "C" _GENX_MAIN_ void cmNBody(SurfaceIndex INPOS, SurfaceIndex INVEL,
SurfaceIndex OUTPOS, SurfaceIndex OUTVEL,
float deltaTime, float damping,
float softeningSquared, int numBodies) {
// Only 4K bodies fit in SLM
// 1. Foreach 4K bodies - For a total of 16K bodies
// 2. LOAD 4K bodies to SLM: i.e. Read from Memory and Write to SLM
// 3. Foreach MB (32 bodies here) - For a total of 4 MBs
// 4. READ from Memory: Position of thisThreadBodies
// 6. Foreach set of 32 bodies In 4K SLM bodies
// 7 READ from SLM: Position of 32 bodies
// 8. Compute Interaction between thisThreadBodies and the 32
// bodies read from SLM; Compute and update force0, force1,
// force2 for forces in 3D
// 9. READ from Memory: Velocity of thisThreadBodies
// 10. Compute New Velocity and New Position of thisThreadBodies
// 11. WRITE to Memory: New Velocity of thisThreadBodies
// 12. WRITE to Memory: New Position of thisThreadBodies
cm_slm_init(SLM_SIZE);
uint bodiesInSLM = cm_slm_alloc(SLM_SIZE);
gThreadID = cm_linear_global_id();
force0 = force1 = force2 = 0.0f;
// 1. Foreach 4K bodies - For a total of 16K bodies
for (int iSLM = 0; iSLM < 4; iSLM++) {
// 2. LOAD 4K bodies to SLM: i.e. Read from Memory and Write to SLM
cm_slm_load(
bodiesInSLM, // slmBuffer : SLM buffer
INPOS, // memSurfIndex: Memory SurfaceIndex
iSLM * SLM_SIZE, // memOffset : Byte-Offset in Memory Surface
SLM_SIZE // loadSize : Bytes to be Loaded from Memory
);
// Each thread needs to process 4 Macro-Blocks (MB):
// One MB = 32 Bodies; Total 2 Groups with 64 threads/Group
// => #Bodies/Thread = TotalNumBodies/TotalNumThreads
// = 16384/128 = 128 = 4 MBs
// - Depending on the number of threads, the number of MBs per
// threads can be changed by just changing this loop-count
// - For optimization purpose, if there are enough GRFs we can
// process more MBs per iteration of this loop - in that case
// need to change the loop-stride accordingly; If all MBs can
// be processed in the GRF, we can eliminate this loop
for (int iMB = 0; iMB < 4; iMB++) {
cmk_Nbody_ForEachMB_ForEachSLMBlock(
INPOS, deltaTime, softeningSquared, BODIES_PER_SLM, bodiesInSLM,
iMB);
} // end foreach(MB)
} // end foreach(SLM block)
for (int iMB = 0; iMB < 4; iMB++) {
cmk_Nbody_OutputVelPos_ForEachMB(INPOS, INVEL, OUTPOS, OUTVEL,
deltaTime, damping, iMB);
} // end foreach(MB)
}


Welcome to our bus-mall, you can vote to your favorite product and you have 25 times
number of roundsalgorithm to generate three unique product images from the images directory and display them side-by-side-by-side in the browser window.
let Product Images Array []
generate Names and Pathes of images
Generate Random number between 0 and last index of product Images array
let indexOfLeftImg = FirstRandomNmumber
let indexOfSecondImg = secondRandomNumber
let indexOfThirdImg = thirdRandom/number
while(indexOfLeftImg==indexOfSecondImg OR indexOfLeftImg== indexOfThirdImg OR indexOfSecondImg==indexOfThirdImg)
let indexOfLeftImg = newRandomNumber
let indexOfSecondImg= newRandomNumber
show LeftImg , MidImg , RightImg


There are currently no Habitat plan files connected.
If you have a plan file in a GitHub repo, connect it here for automated build jobs.
Don't have a plan file? Learn more about creating plan files or try the demo app.
It looks like you haven't yet installed the Habitat Builder app. Install it on GitHub, then come back here and try again. (It may take a few minutes for the changes to be recognized.)
Choose the GitHub organization and repository that contain your Habitat plan file.
Enter the path to your plan file from the root of your repo. By default, we check for habitat/plan.sh.
habitat/plan.sh


stsb3.sts
AR1
def __init__(
self,
name=None,
t0=0,
t1=2,
size=1,
alpha=None,
beta=None,
scale=None,
):
An autoregressive block of order 1.
The data generating process for this block is
\[ z_t = \alpha_t + \beta_t z_{t-1} + \mathrm{scale}_t w_t, \]
for \(t = t_0,...,t_1\) and \(w_t \sim \text{Normal}(0, 1)\). Here, \(\alpha\) is the dgp for the intercept parameter, \(\beta\) is the dgp for the slope parameter, and \(\mathrm{scale}\) is the dgp for the scale parameter. These processes may be other Block
s, torch.tensor
s, or pyro.distributions
objects, and the interpretation of these parameters will change accordingly.
Args:
alpha (Block || torch.Tensor || pyro.distributions)
: the intercept parameterbeta (Block || torch.Tensor || pyro.distributions)
: the slope parameterscale (Block || torch.Tensor || pyro.distributions)
: the noise scale parameterSee Block
for definitions of other parameters.
_maybe_add_blocks
def _maybe_add_blocks(self, *args):
Adds parameters to prec and succ if they subclass Block.
Args:
args
: iterable of (name, parameter, bound)_maybe_remove_blocks
None
_model
None
_transform
Defines a transform from a string argument.
Currently the following string arguments are supported:
The resulting transform will be added to the transform stack iff it is not already at the top of the stack.
Args:
arg (str)
: one of the above strings corresponding to functionReturns:
self (stsb.Block)
arctanh
x -> arctanh(x)
, i.e. x -> 0.5 log ((1 + x) / (1 - x))
clear_cache
Clears the block cache.
This method does not alter the cache mode.
cos
x -> cos x
diff
x -> x[1:] - x[:-1]
Note that this lowers the time dimension from T to T - 1.
exp
x -> exp(x)
floor
x -> x - [[x]]
, where [[.]]
is the fractional part operator
invlogit
x -> 1 / (1 + exp(-x))
log
x -> log x
Block paths must be positive for valid output.
logdiff
x -> log x[1:] - log x[:-1]
Note that this lowers the time dimension from T to T - 1.
logit
x -> log(x / (1 - x))
model
def model(self, *args, **kwargs):
Draws a batch of samples from the block.
Args:
args
: optional positional argumentskwargs
: optional keyword argumentsReturns:
draws
(torch.tensor) sampled values from the blockprec
Returns the predecessor nodes of self
in the (implicit) compute graph
Returns:
_prec (list)
: list of predecessor nodes
sin
x -> sin x
softplus
x -> log(1 + exp(x))
succ
Returns the successor nodes of self
in the (implicit) compute graph
Returns:
_succ (list)
: list of successor nodes
tanh
x -> tanh(x)
, i.e. x -> (exp(x) - exp(-x)) / (exp(x) + exp(-x))
BernoulliNoise
def __init__(
self,
dgp,
data=None,
name=None,
t0=0,
t1=2,
size=1,
):
A noise block (time series likelihood function) that assumes a Bernoulli observation process.
This observation process is suitable for use with on-off / indicator data.
The likelihood function for this block is
\[ p(x | \mathrm{dgp}) = \prod_{t=t_0}^{t_1} \mathrm{Bernoulli}(x_t | \mathrm{dgp}_t) \]
The \(\mathrm{dgp}\) needs to be constrained to lie in (0, 1) because it is used as the probability of the Bernoulli likelihood. Consider using .invlogit(...)
on an unconstrained Block
.
Args:
See NoiseBlock
for definitions of arguments.
_fit_autoguide
None
_maybe_add_blocks
def _maybe_add_blocks(self, *args):
Adds parameters to prec and succ if they subclass Block.
Args:
args
: iterable of (name, parameter, bound)_maybe_remove_blocks
None
_model
None
_transform
Defines a transform from a string argument.
Currently the following string arguments are supported:
The resulting transform will be added to the transform stack iff it is not already at the top of the stack.
Args:
arg (str)
: one of the above strings corresponding to functionReturns:
self (stsb.Block)
arctanh
x -> arctanh(x)
, i.e. x -> 0.5 log ((1 + x) / (1 - x))
clear_cache
Clears the block cache.
This method does not alter the cache mode.
cos
x -> cos x
diff
x -> x[1:] - x[:-1]
Note that this lowers the time dimension from T to T - 1.
exp
x -> exp(x)
fit
def fit(self, method="nf_block_ar", method_kwargs=dict(), verbosity=0.01):
Fits a guide (variational posterior) to the model.
Wraps multiple Pyro implementations of variational inference. To minimize noise in the estimation you should follow the Pyro guidelines about marginalizing out discrete latent rvs, etc.
Args:
method (str)
: one of “advi”, “low_rank”, or “nf_block_ar”.
"advi"
: fits a diagonal normal distribution in unconstrained latent space"low_rank"
: fits a low-rank multivariate normal in unconstrained latent space. Unlike the diagonal normal, this guide can capture some nonlocal dependence in latent rvs."nf_block_ar"
: fits a normalizing flow block autoregressive neural density estimator in unconstrained latent space. This method uses two stacked block autoregressive NNs. See the Pyro docs for more details about this.method_kwargs (dict)
: optional keyword arguments to pass to Pyro’s inference capabilities. If no keyword arguments are specified, sane defaults will be passed instead. Some arguments could include:
"niter"
: number of iterations to run optimization (default 1000
)"lr"
: the learning rate (default 0.01
)"loss"
: the loss function to use (default "Trace_ELBO"
)"optim"
: the optimizer to use (default "AdamW"
)verbosity (float)
: status messages are printed every int(1.0 / verbosity)
iterationsfloor
x -> x - [[x]]
, where [[.]]
is the fractional part operator
invlogit
x -> 1 / (1 + exp(-x))
log
x -> log x
Block paths must be positive for valid output.
logdiff
x -> log x[1:] - log x[:-1]
Note that this lowers the time dimension from T to T - 1.
logit
x -> log(x / (1 - x))
model
def model(self, *args, **kwargs):
Draws a batch of samples from the block.
Args:
args
: optional positional argumentskwargs
: optional keyword argumentsReturns:
draws
(torch.tensor) sampled values from the blockposterior_predictive
def posterior_predictive(
self,
nsamples=1,
):
Draws from the posterior predictive distribution of the graph with self
as the root
Args:
nsamples (int)
: number of samples to drawReturns:
samples (torch.tensor)
prec
Returns the predecessor nodes of self
in the (implicit) compute graph
Returns:
_prec (list)
: list of predecessor nodes
prior_predictive
def prior_predictive(
self,
nsamples=1,
):
Draws from the prior predictive distribution of the graph with self
as the root
Args:
nsamples (int)
: number of samples to drawReturns:
samples (torch.tensor)
sample
def sample(
self,
nsamples=100,
thin=0.1,
burnin=500,
):
Sample from the model’s posterior using the Pyro implementation of the No-U Turn Sampler
This could take a very long time for long time series. It is recommended to use .fit(...)
instead.
Args:
nsamples (int)
: number of desired samples after burn in and thinningthin (float)
: every int(1.0 / thin)
sample is keptburnin (int)
: samples[burnin:]
are keptsin
x -> sin x
softplus
x -> log(1 + exp(x))
succ
Returns the successor nodes of self
in the (implicit) compute graph
Returns:
_succ (list)
: list of successor nodes
tanh
x -> tanh(x)
, i.e. x -> (exp(x) - exp(-x)) / (exp(x) + exp(-x))
CCSDE
def __init__(
self,
name=None,
t0=0,
t1=2,
size=1,
loc=None,
scale=None,
dt=None,
ic=None,
):
A constant-coefficient Euler-Maruyama stochastic differential equation dgp.
The generative model for this process is
\[ z_t = z_{t - 1} + \mathrm{dt}_t \mathrm{loc}_t + \sqrt{\mathrm{dt}_t} \mathrm{scale}_t w_t,\ z_0 = \mathrm{ic}, \]
for \(t = t_0, ..., t_1\). Here, \(\mathrm{loc}\) is the dgp for the location parameter, \(\mathrm{scale}\) is the dgp for the scale parameter, and \(\mathrm{dt}\) is the dgp for the time discretization. These processes may be other Block
s, torch.tensor
s, or pyro.distributions
objects, and the interpretation of \(\mathrm{loc}_t\), \(\mathrm{scale}_t\), and \(\mathrm{dt}_t\) will change accordingly. The initial condition, \(\mathrm{ic}\), can be either a torch.tensor
or pyro.distributions
object. The term \(w_t\) is a standard normal variate.
Args:
loc (Block || torch.tensor || pyro.distributions)
: location parameterscale (Block || torch.tensor || pyro.distributions)
: scale parameterdt (Block || torch.tensor || pyro.distributions)
: time discretization parameteric (torch.tensor || pyro.distributions)
: initial conditionSee Block
for definitions of other parameters.
_maybe_add_blocks
def _maybe_add_blocks(self, *args):
Adds parameters to prec and succ if they subclass Block.
Args:
args
: iterable of (name, parameter, bound)_maybe_remove_blocks
None
_model
None
_transform
Defines a transform from a string argument.
Currently the following string arguments are supported:
The resulting transform will be added to the transform stack iff it is not already at the top of the stack.
Args:
arg (str)
: one of the above strings corresponding to functionReturns:
self (stsb.Block)
arctanh
x -> arctanh(x)
, i.e. x -> 0.5 log ((1 + x) / (1 - x))
clear_cache
Clears the block cache.
This method does not alter the cache mode.
cos
x -> cos x
diff
x -> x[1:] - x[:-1]
Note that this lowers the time dimension from T to T - 1.
exp
x -> exp(x)
floor
x -> x - [[x]]
, where [[.]]
is the fractional part operator
invlogit
x -> 1 / (1 + exp(-x))
log
x -> log x
Block paths must be positive for valid output.
logdiff
x -> log x[1:] - log x[:-1]
Note that this lowers the time dimension from T to T - 1.
logit
x -> log(x / (1 - x))
model
def model(self, *args, **kwargs):
Draws a batch of samples from the block.
Args:
args
: optional positional argumentskwargs
: optional keyword argumentsReturns:
draws
(torch.tensor) sampled values from the blockprec
Returns the predecessor nodes of self
in the (implicit) compute graph
Returns:
_prec (list)
: list of predecessor nodes
sin
x -> sin x
softplus
x -> log(1 + exp(x))
succ
Returns the successor nodes of self
in the (implicit) compute graph
Returns:
_succ (list)
: list of successor nodes
tanh
x -> tanh(x)
, i.e. x -> (exp(x) - exp(-x)) / (exp(x) + exp(-x))
DiscreteSeasonal
def __init__(
self,
name=None,
t0=0,
t1=2,
size=1,
n_seasons=2,
seasons=None,
):
A discrete seasonal block that represents the most basic form of discrete seasonality.
The data generating process for this block is
\[ z_t = \theta_{t \mod s},\ s = 1,...,S, \]
where \(S\) is the total number of seasons and \(\theta = (\theta_1,...,\theta_S)\) are the seasonality components. Currently, \(\theta\) can be only a pyro.distributions
instance or a torch.Tensor
, though that might change in a future release.
Args:
n_seasons (int)
: number of discrete seasonsseasons (pyro.distributions || torch.Tensor)
: season valuesSee Block
for definitions of other parameters.
_maybe_add_blocks
def _maybe_add_blocks(self, *args):
Adds parameters to prec and succ if they subclass Block.
Args:
args
: iterable of (name, parameter, bound)_maybe_remove_blocks
None
_model
None
_transform
Defines a transform from a string argument.
Currently the following string arguments are supported:
The resulting transform will be added to the transform stack iff it is not already at the top of the stack.
Args:
arg (str)
: one of the above strings corresponding to functionReturns:
self (stsb.Block)
arctanh
x -> arctanh(x)
, i.e. x -> 0.5 log ((1 + x) / (1 - x))
clear_cache
Clears the block cache.
This method does not alter the cache mode.
cos
x -> cos x
diff
x -> x[1:] - x[:-1]
Note that this lowers the time dimension from T to T - 1.
exp
x -> exp(x)
floor
x -> x - [[x]]
, where [[.]]
is the fractional part operator
invlogit
x -> 1 / (1 + exp(-x))
log
x -> log x
Block paths must be positive for valid output.
logdiff
x -> log x[1:] - log x[:-1]
Note that this lowers the time dimension from T to T - 1.
logit
x -> log(x / (1 - x))
model
def model(self, *args, **kwargs):
Draws a batch of samples from the block.
Args:
args
: optional positional argumentskwargs
: optional keyword argumentsReturns:
draws
(torch.tensor) sampled values from the blockprec
Returns the predecessor nodes of self
in the (implicit) compute graph
Returns:
_prec (list)
: list of predecessor nodes
sin
x -> sin x
softplus
x -> log(1 + exp(x))
succ
Returns the successor nodes of self
in the (implicit) compute graph
Returns:
_succ (list)
: list of successor nodes
tanh
x -> tanh(x)
, i.e. x -> (exp(x) - exp(-x)) / (exp(x) + exp(-x))
DiscriminativeGaussianNoise
def __init__(
self,
dgp,
X=None,
y=None,
name=None,
t0=0,
t1=2,
size=1,
scale=None,
):
A discriminative noise block used for dynamic regression.
The observation likelihood is given by
\[ p(x | \mathrm{dgp}, \mathrm{scale}) = \prod_{t=t_0}^{t_1} \mathrm{Normal}(x_t | X_t \mathrm{dgp}_t, \mathrm{scale}_t), \]
where \(X_t \mathrm{dgp}_t\) should be interpreted as a batched dot product, i.e., \(\mathrm{loc}_{it} = \sum_j X_{ijt}\mathrm{dgp}_{jt}\).
Args:
X (torch.tensor)
: shape (size, dims, time)
y (None || torch.tensor)
: if not None
, shape (size, time)
See GaussianNoise
for definitions of other parameters
_fit_autoguide
None
_maybe_add_blocks
def _maybe_add_blocks(self, *args):
Adds parameters to prec and succ if they subclass Block.
Args:
args
: iterable of (name, parameter, bound)_maybe_remove_blocks
None
_model
None
_transform
Defines a transform from a string argument.
Currently the following string arguments are supported:
The resulting transform will be added to the transform stack iff it is not already at the top of the stack.
Args:
arg (str)
: one of the above strings corresponding to functionReturns:
self (stsb.Block)
arctanh
x -> arctanh(x)
, i.e. x -> 0.5 log ((1 + x) / (1 - x))
clear_cache
Clears the block cache.
This method does not alter the cache mode.
cos
x -> cos x
diff
x -> x[1:] - x[:-1]
Note that this lowers the time dimension from T to T - 1.
exp
x -> exp(x)
fit
def fit(self, method="nf_block_ar", method_kwargs=dict(), verbosity=0.01):
Fits a guide (variational posterior) to the model.
Wraps multiple Pyro implementations of variational inference. To minimize noise in the estimation you should follow the Pyro guidelines about marginalizing out discrete latent rvs, etc.
Args:
method (str)
: one of “advi”, “low_rank”, or “nf_block_ar”.
"advi"
: fits a diagonal normal distribution in unconstrained latent space"low_rank"
: fits a low-rank multivariate normal in unconstrained latent space. Unlike the diagonal normal, this guide can capture some nonlocal dependence in latent rvs."nf_block_ar"
: fits a normalizing flow block autoregressive neural density estimator in unconstrained latent space. This method uses two stacked block autoregressive NNs. See the Pyro docs for more details about this.method_kwargs (dict)
: optional keyword arguments to pass to Pyro’s inference capabilities. If no keyword arguments are specified, sane defaults will be passed instead. Some arguments could include:
"niter"
: number of iterations to run optimization (default 1000
)"lr"
: the learning rate (default 0.01
)"loss"
: the loss function to use (default "Trace_ELBO"
)"optim"
: the optimizer to use (default "AdamW"
)verbosity (float)
: status messages are printed every int(1.0 / verbosity)
iterationsfloor
x -> x - [[x]]
, where [[.]]
is the fractional part operator
invlogit
x -> 1 / (1 + exp(-x))
log
x -> log x
Block paths must be positive for valid output.
logdiff
x -> log x[1:] - log x[:-1]
Note that this lowers the time dimension from T to T - 1.
logit
x -> log(x / (1 - x))
model
def model(self, *args, **kwargs):
Draws a batch of samples from the block.
Args:
args
: optional positional argumentskwargs
: optional keyword argumentsReturns:
draws
(torch.tensor) sampled values from the blockposterior_predictive
def posterior_predictive(
self,
nsamples=1,
):
Draws from the posterior predictive distribution of the graph with self
as the root
Args:
nsamples (int)
: number of samples to drawReturns:
samples (torch.tensor)
prec
Returns the predecessor nodes of self
in the (implicit) compute graph
Returns:
_prec (list)
: list of predecessor nodes
prior_predictive
def prior_predictive(
self,
nsamples=1,
):
Draws from the prior predictive distribution of the graph with self
as the root
Args:
nsamples (int)
: number of samples to drawReturns:
samples (torch.tensor)
sample
def sample(
self,
nsamples=100,
thin=0.1,
burnin=500,
):
Sample from the model’s posterior using the Pyro implementation of the No-U Turn Sampler
This could take a very long time for long time series. It is recommended to use .fit(...)
instead.
Args:
nsamples (int)
: number of desired samples after burn in and thinningthin (float)
: every int(1.0 / thin)
sample is keptburnin (int)
: samples[burnin:]
are keptsin
x -> sin x
softplus
x -> log(1 + exp(x))
succ
Returns the successor nodes of self
in the (implicit) compute graph
Returns:
_succ (list)
: list of successor nodes
tanh
x -> tanh(x)
, i.e. x -> (exp(x) - exp(-x)) / (exp(x) + exp(-x))
GaussianNoise
def __init__(
self,
dgp,
data=None,
name=None,
t0=0,
t1=2,
size=1,
scale=None,
):
A noise block (time series likelihood function) that assumes a centered normal observation process.
The likelihood function for this block is
\[ p(x | \mathrm{dgp}, \mathrm{scale}) = \prod_{t=t_0}^{t_1} \mathrm{Normal}(x_t | \mathrm{dgp}_t, \mathrm{scale}_t) \]
Args:
scale (Block || torch.tensor || pyro.distributions)
:See NoiseBlock
for definitions of other parameters
_fit_autoguide
None
_maybe_add_blocks
def _maybe_add_blocks(self, *args):
Adds parameters to prec and succ if they subclass Block.
Args:
args
: iterable of (name, parameter, bound)_maybe_remove_blocks
None
_model
None
_transform
Defines a transform from a string argument.
Currently the following string arguments are supported:
The resulting transform will be added to the transform stack iff it is not already at the top of the stack.
Args:
arg (str)
: one of the above strings corresponding to functionReturns:
self (stsb.Block)
arctanh
x -> arctanh(x)
, i.e. x -> 0.5 log ((1 + x) / (1 - x))
clear_cache
Clears the block cache.
This method does not alter the cache mode.
cos
x -> cos x
diff
x -> x[1:] - x[:-1]
Note that this lowers the time dimension from T to T - 1.
exp
x -> exp(x)
fit
def fit(self, method="nf_block_ar", method_kwargs=dict(), verbosity=0.01):
Fits a guide (variational posterior) to the model.
Wraps multiple Pyro implementations of variational inference. To minimize noise in the estimation you should follow the Pyro guidelines about marginalizing out discrete latent rvs, etc.
Args:
method (str)
: one of “advi”, “low_rank”, or “nf_block_ar”.
"advi"
: fits a diagonal normal distribution in unconstrained latent space"low_rank"
: fits a low-rank multivariate normal in unconstrained latent space. Unlike the diagonal normal, this guide can capture some nonlocal dependence in latent rvs."nf_block_ar"
: fits a normalizing flow block autoregressive neural density estimator in unconstrained latent space. This method uses two stacked block autoregressive NNs. See the Pyro docs for more details about this.method_kwargs (dict)
: optional keyword arguments to pass to Pyro’s inference capabilities. If no keyword arguments are specified, sane defaults will be passed instead. Some arguments could include:
"niter"
: number of iterations to run optimization (default 1000
)"lr"
: the learning rate (default 0.01
)"loss"
: the loss function to use (default "Trace_ELBO"
)"optim"
: the optimizer to use (default "AdamW"
)verbosity (float)
: status messages are printed every int(1.0 / verbosity)
iterationsfloor
x -> x - [[x]]
, where [[.]]
is the fractional part operator
invlogit
x -> 1 / (1 + exp(-x))
log
x -> log x
Block paths must be positive for valid output.
logdiff
x -> log x[1:] - log x[:-1]
Note that this lowers the time dimension from T to T - 1.
logit
x -> log(x / (1 - x))
model
def model(self, *args, **kwargs):
Draws a batch of samples from the block.
Args:
args
: optional positional argumentskwargs
: optional keyword argumentsReturns:
draws
(torch.tensor) sampled values from the blockposterior_predictive
def posterior_predictive(
self,
nsamples=1,
):
Draws from the posterior predictive distribution of the graph with self
as the root
Args:
nsamples (int)
: number of samples to drawReturns:
samples (torch.tensor)
prec
Returns the predecessor nodes of self
in the (implicit) compute graph
Returns:
_prec (list)
: list of predecessor nodes
prior_predictive
def prior_predictive(
self,
nsamples=1,
):
Draws from the prior predictive distribution of the graph with self
as the root
Args:
nsamples (int)
: number of samples to drawReturns:
samples (torch.tensor)
sample
def sample(
self,
nsamples=100,
thin=0.1,
burnin=500,
):
Sample from the model’s posterior using the Pyro implementation of the No-U Turn Sampler
This could take a very long time for long time series. It is recommended to use .fit(...)
instead.
Args:
nsamples (int)
: number of desired samples after burn in and thinningthin (float)
: every int(1.0 / thin)
sample is keptburnin (int)
: samples[burnin:]
are keptsin
x -> sin x
softplus
x -> log(1 + exp(x))
succ
Returns the successor nodes of self
in the (implicit) compute graph
Returns:
_succ (list)
: list of successor nodes
tanh
x -> tanh(x)
, i.e. x -> (exp(x) - exp(-x)) / (exp(x) + exp(-x))
GlobalTrend
def __init__(
self,
name=None,
t0=0,
t1=2,
size=1,
alpha=None,
beta=None,
):
A global (linear) trend dgp.
The generative model for this process is
\[ z_t = \alpha + \beta t, \]
for \(t = t_0, ..., t_1\). Here, \(\alpha\) is the dgp for the intercept parameter and \(\beta\) is the dgp for the slope parameter. These processes may be other Block
s, torch.tensor
s, or pyro.distributions
objects, and the interpretation of \(\alpha\) and \(\beta\) will change accordingly.
Args:
alpha (Block || torch.tensor || pyro.distributions)
: intercept parameterbeta (Block || torch.tensor || pyro.distributions)
: slope parameterSee Block
for definitions of other parameters.
_maybe_add_blocks
def _maybe_add_blocks(self, *args):
Adds parameters to prec and succ if they subclass Block.
Args:
args
: iterable of (name, parameter, bound)_maybe_remove_blocks
None
_model
None
_transform
Defines a transform from a string argument.
Currently the following string arguments are supported:
The resulting transform will be added to the transform stack iff it is not already at the top of the stack.
Args:
arg (str)
: one of the above strings corresponding to functionReturns:
self (stsb.Block)
arctanh
x -> arctanh(x)
, i.e. x -> 0.5 log ((1 + x) / (1 - x))
clear_cache
Clears the block cache.
This method does not alter the cache mode.
cos
x -> cos x
diff
x -> x[1:] - x[:-1]
Note that this lowers the time dimension from T to T - 1.
exp
x -> exp(x)
floor
x -> x - [[x]]
, where [[.]]
is the fractional part operator
invlogit
x -> 1 / (1 + exp(-x))
log
x -> log x
Block paths must be positive for valid output.
logdiff
x -> log x[1:] - log x[:-1]
Note that this lowers the time dimension from T to T - 1.
logit
x -> log(x / (1 - x))
model
def model(self, *args, **kwargs):
Draws a batch of samples from the block.
Args:
args
: optional positional argumentskwargs
: optional keyword argumentsReturns:
draws
(torch.tensor) sampled values from the blockprec
Returns the predecessor nodes of self
in the (implicit) compute graph
Returns:
_prec (list)
: list of predecessor nodes
sin
x -> sin x
softplus
x -> log(1 + exp(x))
succ
Returns the successor nodes of self
in the (implicit) compute graph
Returns:
_succ (list)
: list of successor nodes
tanh
x -> tanh(x)
, i.e. x -> (exp(x) - exp(-x)) / (exp(x) + exp(-x))
MA1
def __init__(
self,
name=None,
t0=0,
t1=2,
size=1,
beta=None,
loc=None,
scale=None,
):
A moving average block of order 1.
The data generating process for this block is
\[ z_t = \mathrm{loc}_t + \mathrm{scale}_t w_t + \beta_t \mathrm{scale}_{t - 1} w_{t-1}, \]
for \(t = t_0,...,t_1\) and \(w_t \sim \text{Normal}(0, 1)\). Here, \(\mathrm{loc}\) is the dgp for the location parameter, \(\mathrm{scale}\) is the dgp for the scale parameter, and \(\beta\) is the dgp for the FIR filter. These processes may be other Block
s, torch.tensor
s, or pyro.distributions
objects, and the interpretation of these parameters will change accordingly.
NOTE: from the definition of the dgp, \(\mathrm{scale}\) has dimensionality \((N, t_1 - t_0 + 1)\), where the \(+1\) is due to the lagged noise term on the \(t = t_0\) value.
Args:
beta (Block || torch.Tensor || pyro.distributions)
: the FIR filter parameterloc (Block || torch.Tensor || pyro.distributions)
: the location parameterscale (Block || torch.Tensor || pyro.distributions)
: the noise scale parameter. Note that, if scale
subclasses Block
, it must have time dimensionality one higher than this blockSee Block
for definitions of other parameters.
_maybe_add_blocks
def _maybe_add_blocks(self, *args):
Adds parameters to prec and succ if they subclass Block.
Args:
args
: iterable of (name, parameter, bound)_maybe_remove_blocks
None
_model
None
_transform
Defines a transform from a string argument.
Currently the following string arguments are supported:
The resulting transform will be added to the transform stack iff it is not already at the top of the stack.
Args:
arg (str)
: one of the above strings corresponding to functionReturns:
self (stsb.Block)
arctanh
x -> arctanh(x)
, i.e. x -> 0.5 log ((1 + x) / (1 - x))
clear_cache
Clears the block cache.
This method does not alter the cache mode.
cos
x -> cos x
diff
x -> x[1:] - x[:-1]
Note that this lowers the time dimension from T to T - 1.
exp
x -> exp(x)
floor
x -> x - [[x]]
, where [[.]]
is the fractional part operator
invlogit
x -> 1 / (1 + exp(-x))
log
x -> log x
Block paths must be positive for valid output.
logdiff
x -> log x[1:] - log x[:-1]
Note that this lowers the time dimension from T to T - 1.
logit
x -> log(x / (1 - x))
model
def model(self, *args, **kwargs):
Draws a batch of samples from the block.
Args:
args
: optional positional argumentskwargs
: optional keyword argumentsReturns:
draws
(torch.tensor) sampled values from the blockprec
Returns the predecessor nodes of self
in the (implicit) compute graph
Returns:
_prec (list)
: list of predecessor nodes
sin
x -> sin x
softplus
x -> log(1 + exp(x))
succ
Returns the successor nodes of self
in the (implicit) compute graph
Returns:
_succ (list)
: list of successor nodes
tanh
x -> tanh(x)
, i.e. x -> (exp(x) - exp(-x)) / (exp(x) + exp(-x))
PoissonNoise
def __init__(
self,
dgp,
data=None,
name=None,
t0=0,
t1=2,
size=1,
):
A noise block (time series likelihood function) that assumes a Poisson observation process.
This observation process is suitable for use with count (or other non-negative integer) data that does not exhibit over- or under-dispersion (in practice, if the log ratio of mean to variance of the observed data is not too far away from zero).
The likelihood function for this block is
\[ p(x | \mathrm{dgp}) = \prod_{t=t_0}^{t_1} \mathrm{Poisson}(x_t | \mathrm{dgp}_t) \]
The \(\mathrm{dgp}\) needs to be non-negative because it is used as the rate function of the Poisson likelihood. Consider using .softplus(...)
or .exp(...)
on an unconstrained Block
.
Args:
See NoiseBlock
for definitions of arguments.
_fit_autoguide
None
_maybe_add_blocks
def _maybe_add_blocks(self, *args):
Adds parameters to prec and succ if they subclass Block.
Args:
args
: iterable of (name, parameter, bound)_maybe_remove_blocks
None
_model
None
_transform
Defines a transform from a string argument.
Currently the following string arguments are supported:
The resulting transform will be added to the transform stack iff it is not already at the top of the stack.
Args:
arg (str)
: one of the above strings corresponding to functionReturns:
self (stsb.Block)
arctanh
x -> arctanh(x)
, i.e. x -> 0.5 log ((1 + x) / (1 - x))
clear_cache
Clears the block cache.
This method does not alter the cache mode.
cos
x -> cos x
diff
x -> x[1:] - x[:-1]
Note that this lowers the time dimension from T to T - 1.
exp
x -> exp(x)
fit
def fit(self, method="nf_block_ar", method_kwargs=dict(), verbosity=0.01):
Fits a guide (variational posterior) to the model.
Wraps multiple Pyro implementations of variational inference. To minimize noise in the estimation you should follow the Pyro guidelines about marginalizing out discrete latent rvs, etc.
Args:
method (str)
: one of “advi”, “low_rank”, or “nf_block_ar”.
"advi"
: fits a diagonal normal distribution in unconstrained latent space"low_rank"
: fits a low-rank multivariate normal in unconstrained latent space. Unlike the diagonal normal, this guide can capture some nonlocal dependence in latent rvs."nf_block_ar"
: fits a normalizing flow block autoregressive neural density estimator in unconstrained latent space. This method uses two stacked block autoregressive NNs. See the Pyro docs for more details about this.method_kwargs (dict)
: optional keyword arguments to pass to Pyro’s inference capabilities. If no keyword arguments are specified, sane defaults will be passed instead. Some arguments could include:
"niter"
: number of iterations to run optimization (default 1000
)"lr"
: the learning rate (default 0.01
)"loss"
: the loss function to use (default "Trace_ELBO"
)"optim"
: the optimizer to use (default "AdamW"
)verbosity (float)
: status messages are printed every int(1.0 / verbosity)
iterationsfloor
x -> x - [[x]]
, where [[.]]
is the fractional part operator
invlogit
x -> 1 / (1 + exp(-x))
log
x -> log x
Block paths must be positive for valid output.
logdiff
x -> log x[1:] - log x[:-1]
Note that this lowers the time dimension from T to T - 1.
logit
x -> log(x / (1 - x))
model
def model(self, *args, **kwargs):
Draws a batch of samples from the block.
Args:
args
: optional positional argumentskwargs
: optional keyword argumentsReturns:
draws
(torch.tensor) sampled values from the blockposterior_predictive
def posterior_predictive(
self,
nsamples=1,
):
Draws from the posterior predictive distribution of the graph with self
as the root
Args:
nsamples (int)
: number of samples to drawReturns:
samples (torch.tensor)
prec
Returns the predecessor nodes of self
in the (implicit) compute graph
Returns:
_prec (list)
: list of predecessor nodes
prior_predictive
def prior_predictive(
self,
nsamples=1,
):
Draws from the prior predictive distribution of the graph with self
as the root
Args:
nsamples (int)
: number of samples to drawReturns:
samples (torch.tensor)
sample
def sample(
self,
nsamples=100,
thin=0.1,
burnin=500,
):
Sample from the model’s posterior using the Pyro implementation of the No-U Turn Sampler
This could take a very long time for long time series. It is recommended to use .fit(...)
instead.
Args:
nsamples (int)
: number of desired samples after burn in and thinningthin (float)
: every int(1.0 / thin)
sample is keptburnin (int)
: samples[burnin:]
are keptsin
x -> sin x
softplus
x -> log(1 + exp(x))
succ
Returns the successor nodes of self
in the (implicit) compute graph
Returns:
_succ (list)
: list of successor nodes
tanh
x -> tanh(x)
, i.e. x -> (exp(x) - exp(-x)) / (exp(x) + exp(-x))
RandomWalk
def __init__(
self,
name=None,
t0=0,
t1=2,
size=1,
loc=None,
scale=None,
ic=None,
):
A (biased) normal random walk dgp.
The generative model for this process is
\[ z_t = z_{t - 1} + \mathrm{loc}_t + \mathrm{scale}_t w_t,\ z_0 = \mathrm{ic}, \]
for \(t = t_0,...,t_1\). Here, \(\mathrm{loc}\) is the dgp for the location parameter and \(\mathrm{scale}\) is the dgp for the scale parameter. These processes may be other Block
s, torch.tensor
s, or pyro.distributions
objects, and the interpretation of \(\mathrm{loc}_t\) or \(\mathrm{scale}_t\) will change accordingly. The initial condition, \(\mathrm{ic}\), can be either a torch.tensor
or pyro.distributions
object. The term \(w_t\) is a standard normal variate.
Args:
loc (Block || torch.tensor || pyro.distributions)
: location parameterscale (Block || torch.tensor || pyro.distributions)
: scale parameteric (torch.tensor || pyro.distributions)
: initial conditionSee Block
for definitions of other parameters.
_maybe_add_blocks
def _maybe_add_blocks(self, *args):
Adds parameters to prec and succ if they subclass Block.
Args:
args
: iterable of (name, parameter, bound)_maybe_remove_blocks
None
_model
None
_transform
Defines a transform from a string argument.
Currently the following string arguments are supported:
The resulting transform will be added to the transform stack iff it is not already at the top of the stack.
Args:
arg (str)
: one of the above strings corresponding to functionReturns:
self (stsb.Block)
arctanh
x -> arctanh(x)
, i.e. x -> 0.5 log ((1 + x) / (1 - x))
clear_cache
Clears the block cache.
This method does not alter the cache mode.
cos
x -> cos x
diff
x -> x[1:] - x[:-1]
Note that this lowers the time dimension from T to T - 1.
exp
x -> exp(x)
floor
x -> x - [[x]]
, where [[.]]
is the fractional part operator
invlogit
x -> 1 / (1 + exp(-x))
log
x -> log x
Block paths must be positive for valid output.
logdiff
x -> log x[1:] - log x[:-1]
Note that this lowers the time dimension from T to T - 1.
logit
x -> log(x / (1 - x))
model
def model(self, *args, **kwargs):
Draws a batch of samples from the block.
Args:
args
: optional positional argumentskwargs
: optional keyword argumentsReturns:
draws
(torch.tensor) sampled values from the blockprec
Returns the predecessor nodes of self
in the (implicit) compute graph
Returns:
_prec (list)
: list of predecessor nodes
sin
x -> sin x
softplus
x -> log(1 + exp(x))
succ
Returns the successor nodes of self
in the (implicit) compute graph
Returns:
_succ (list)
: list of successor nodes
tanh
x -> tanh(x)
, i.e. x -> (exp(x) - exp(-x)) / (exp(x) + exp(-x))
SmoothSeasonal
def __init__(
self,
name=None,
t0=0,
t1=2,
size=1,
phase=None,
amplitude=None,
lengthscale=None,
cycles=1,
):
A smooth seasonal block.
The generative model for this process is
\[ z_t = \mathrm{amplitude}_t \cos\left(\mathrm{phase}_t + \frac{2\pi\ \mathrm{cycles}\ t}{\mathrm{lengthscale}_t}\right) \]
for \(t = t_0, ..., t_1\). Here, \(\mathrm{amplitude}\) is the dgp for the amplitude, \(\mathrm{phase}\) is the dgp for the phase, and \(\mathrm{lengthscale}\) is the parameter for the lengthscale. These processes may be other Block
s, torch.tensor
s, or pyro.distributions
objects, and the interpretation of these parameters will change accordingly.
This block is experimental and may be removed in a future release.
Args:
phase (Block || torch.tensor || pyro.distributions)
: phase of the sinusoidal functionnamplitude (Block || torch.tensor || pyro.distributions)
: amplitude of the siusoidal functionlengthscale (Block || torch.tensor || pyro.distributions)
: lengthscale of the sinusoidal function; corresponds to \(L\) in \(A \cos(\varphi + 2\pi n t / L)\)scycles (int)
: number of cycles of ths sinusoidal to complete over the interval \([0, L)\)See Block
for definitions of other parameters.
_maybe_add_blocks
def _maybe_add_blocks(self, *args):
Adds parameters to prec and succ if they subclass Block.
Args:
args
: iterable of (name, parameter, bound)_maybe_remove_blocks
None
_model
None
_transform
Defines a transform from a string argument.
Currently the following string arguments are supported:
The resulting transform will be added to the transform stack iff it is not already at the top of the stack.
Args:
arg (str)
: one of the above strings corresponding to functionReturns:
self (stsb.Block)
arctanh
x -> arctanh(x)
, i.e. x -> 0.5 log ((1 + x) / (1 - x))
clear_cache
Clears the block cache.
This method does not alter the cache mode.
cos
x -> cos x
diff
x -> x[1:] - x[:-1]
Note that this lowers the time dimension from T to T - 1.
exp
x -> exp(x)
floor
x -> x - [[x]]
, where [[.]]
is the fractional part operator
invlogit
x -> 1 / (1 + exp(-x))
log
x -> log x
Block paths must be positive for valid output.
logdiff
x -> log x[1:] - log x[:-1]
Note that this lowers the time dimension from T to T - 1.
logit
x -> log(x / (1 - x))
model
def model(self, *args, **kwargs):
Draws a batch of samples from the block.
Args:
args
: optional positional argumentskwargs
: optional keyword argumentsReturns:
draws
(torch.tensor) sampled values from the blockprec
Returns the predecessor nodes of self
in the (implicit) compute graph
Returns:
_prec (list)
: list of predecessor nodes
sin
x -> sin x
softplus
x -> log(1 + exp(x))
succ
Returns the successor nodes of self
in the (implicit) compute graph
Returns:
_succ (list)
: list of successor nodes
tanh
x -> tanh(x)
, i.e. x -> (exp(x) - exp(-x)) / (exp(x) + exp(-x))
forecast
def forecast(dgp, samples, *args, Nt=1, nsamples=1, **kwargs):
Forecasts the root node of the DGP forward in time.
Args:
dgp (Block)
: the root node to forecast forwardsamples (dict)
: {semantic site name: value}
The value tensors should have shape (m, n, T)
, where m
is the number of samples, n
is the batch size, and T
is the length of the time series*args
: any additional positional arguments to pass to dgp.model
Nt (int):
number of timesteps for which to generate forecast. Forecast is generated from t1 + 1
to t1 + 1 + Nt
.nsamples (int)
: number of samples to draw from the forecast distributiondesign_tensors (Dict[str, torch.Tensor])
:**kwargs
: any additional keyword arguments to pass to dgp.model
redefine
def redefine(
block,
attribute,
obj,
):
Redefines an attribute of a block to the passed object
Args:
block (Block)
attribute (str)
obj (Block || torch.tensor || pyro.distributions)
register_block
def register_block(
name,
fn_addr_param,
model_fn,
):
Registers a new block at runtime
Args:
name (str)
: name of the new block (class)fn_addr_param (dict)
: functional address parameterization; see documentation of core.construct_init
for required structuremodel_fn (callable)
: the implementation of the likelihood-function portion of Block._model
. An example implementation, here for a (determininstic) quadratic trend, is shown below:
def model_fn(x):
alpha, beta, gamma = core.name_to_definite(x, "alpha", "beta", "gamma")
with autoname.scope(prefix=constants.dynamic):
t = torch.linspace(x.t0, x.t1, x.t1 - x.t0)
path = pyro.deterministic(
x.name + "-" + constants.generated,
alpha + t * beta + t.pow(2) * gamma
)
return path
The call to core.name_to_definite
takes care of calling pyro.sample
if the parameters are defined as pyro distributions, calls model methods if the parameters are defined as Block
s, and so on.


nng episode 3 script
Episode 3 - Fe-chan's House Party
Plot
Act 1
Episode starts in the computer lab at school, Misato, Fechan and Weboshi are talking about the party, they make a 100 papers, Misato and Weboshi think it's too much, but Fechan decides to ignore them and says that she'll put them all over the school
Jump to the next day, Misato, Fechan and Weboshi are having lunch together, Misato decides to go buy something from the vending machine, which is in the outside of the school, as she goes, she realizes that throughout the hallways, no party papers are seen.
Misato comes back and questions Fechan about why there weren't any party papers, Fechan tries to get out of the situation, but the truth is eventually found out, she decided to only invite friends instead of everyone, Misato and Weboshi decide to just shrug it off. Fechan says she invited 20 people, Misato and Weboshi argue whether or not that is actually enough for a party, but none of that changes Fechan's choices. (Characters included are: Weboshi and Misato, along with Nano, Mai, Mio and Yuuko, others invited also include Sasahara, the other 13 are simply friends of Fechan from other schools who aren't important)
Timeskip, the bell rings and it's time for students to leave and go home, as Misato and Weboshi plan on leaving, Fechan says that she actually forgot to take out all 100 party papers from her school bag, so Fechan decides to go to the nearest trash container (ignoring the trash can in class, because she doesn't want anyone to see them, which Misato and Weboshi fail to see why someone would search a trash can), as Fechan runs through the hallways with the 100 papers, one of them falls without her realizing, she catches up to Misato and Weboshi and they leave to prepare for the party.
As students are walking through the halls, Annaka sees a paper on the floor, she realizes it's a party paper, she thinks that it fell from the wall so she decides to put it back on with tape.
After Annaka leaves, Makoto enters the hallway, unable to think of anything to do, however, on the wall he sees an invitation to a party, he gets the idea to call his friends to go to this party with him.
Jump to locker room. Yuuko, Mai, Mio and Nano are talking, when they open their lockers and see invitations to a party, they all decide on going.
After walking home from school, Yuuko, Mio, Mai and Nano all go their separate ways, as Nano walks home alone, a malfunction happens.
After Nano walks away, inside the bushes Nakamura pops out, revealing that she had been following her even as Nano was walking with her friends. Nakamura gets to the conclusion that something is wrong with Nano and she heard just before that she was apparently going to a party, she starts thinking of what could happen if something could go wrong at the party, putting the other students at risk from Nano's malfunctions (However, in reality, Nakamura is only trying to think of excuses to keep spying on Nano), so she decides to go to the party, ready to follow Nano when she goes to the party (Due to not knowing where the party is going to be)
Nano tells Hakase that she's leaving to go to a party, as she leaves, Nakamura pops out once more from the bushes, ready to follow Nano to the party.
Somewhere not far from Fe-chan's home, Makoto has a huge number of people behind him, ready to party.
Act 2
Fechan, Weboshi and Misato are still decorating and readying up the party, as they do, someone knocks on the door, that someone being Mai, even though Fe-chan still doesn't have the party ready (And despite her trying to make Mai wait before entering) Mai enters anyways and decides to help them.
Fechan, Weboshi and Misato (And Mai) have the party ready (Feel free to add a list of the things at the party that can return later for comedic scenes) as they did a few of the people invited enter the party (Mostly Fe-chan's friends from other schools and relatives).
As Fechan and everyone else at the party are having fun, someone else knocks at the door, Fechan tries to guess who it is (As a little game) but as she opens the door, she doesn't see anyone she knew, aside from the person who made fun of her invitation party paper, Makoto, along with at least 30 people, as Fechan tries not to faint of shock, as she does so, Makoto asks if this is where the party was, after a few exchanges, Fechan is unable to actually say no, so Makoto and his army of troublesome friends enter the party.
Fechan is scared, she hopes that the new set of strangers don't do anything wrong, so she stares at them intently (Here is a chance to insert a comedic scene of Fechan being over protective of her home and correcting the strangers on every single thing), as Misato wonders if she should try to kick them out, Fechan stops her, thinking that they may hate Fechan if these strangers are from the same school, as Fechan and Misato argue, Misato decides to leave but as soon as she opens the door, Sasahara and his goat are there, causing Misato to go back, grab Fechan and go to the nearest room, various explosions are heard.
As Nano is walking down the streets, Nakamura is intently hiding behind her, as she does, Nano suffers from a malfunction and shoots a laser that almost kills Nakamura (Cutting a light pole that Nakamura was hiding behind in half), as Nakamura takes a breath, she once again follows Nano, who started running due to not wanting to be found out, the light pole finally falls on the top back of Nakamura's head, making a small crack on Nakamura, Nakamura screams in pain and Nano hears her, but before Nano can actually see her, Nakamura hides again. Nano decides to start running again, and so does Nakamura behind her.
Jump back to the party, everything is noisy, and Yuuko and Mio are sitting watching the party, as they talk due to being bored, Yuuko asks about the time Mio said she had bad memories of one of the classrooms back when they were trying to help Nano, before Mio can actually explain, she sees outside the window that Sasahara and his goat are there, she quickly tries to hide and is unable to tell Yuuko her explanation.
Act 3
House is almost completely destroyed, Weboshi, Fe-chan, and Misato all clean up the mess, we get a conclusion of all the characters, end episode