Evolving the Ecosystem &
a MetaMask Roadmap
Some numbers from 2018
🦊 46 extension versions released
🛠 50k new lines of code
✅ 882 pull requests merged
💥 1400 issues closed
🍷 18 full-time employees
👯 100+ community contributors
We you
Some contributions from 2018
Projects In Development
One more thing...
Context
💰 O.G. Wallet
🛠 Launched in 2015
💥 1M+ Users
👩🏫 Learned a lot
☠ Sandboxed by Browsers
Challenges
-
We're late to the game
-
Most mobile wallets are very similar
-
Onboarding friction is still a problem
-
Need to let new users explore the ecosystem of dApps
-
Easy onboarding process for existing users
UX research to the rescue!
What about existing users?
Last but not least...
ENS => IPFS SUPPORT
(DECENTRALIZATION++)
-
Access websites hosted in IPFS
Free and decentralized!
-
Full url masking
No need to deal with IPFS hashes, gateway urls or redirects
-
Choose your IPFS gateway
List of available gateways or choose your own


4.8.3. HOWTO Change Puppet Servers¶
It may be necessary to change the Puppet Server. To point a particular client to a new Puppet Server, follow the steps in the sections below.
Note
All commands in this section should be run as the root
user.
<IP_ADDRESS>. On the Old Puppet Server¶
<IP_ADDRESS>.1. Collect the Client’s Server-Side Artifacts¶
Until SIMP implements a shared Puppet data store (expected 2017-Q2), you will need to manually copy some artifacts from the old server to the new server
To do this, run:
$ find `puppet config --section master print vardir`/simp -name "*<client-fqdn>*" -exec tar --selinux --xattrs -rpvf <client-fqdn>_transfer.tar {} \;
$ find /var/simp/environments -name "*<client-fqdn>*" -exec tar --selinux --xattrs -rpvf <client-fqdn>_transfer.tar {} \;
Then, pull all of the relevant Hiera configuration for the node:
$ find /etc/puppetlabs/code/environments -name "<client-hostname>.yaml" -exec tar --selinux --xattrs -rpvf <client-hostname>_transfer.tar {} \;
$ find /etc/puppetlabs/code/environments -name "<client-fqdn>.yaml" -exec tar --selinux --xattrs -rpvf <client-hostname>_transfer.tar {} \;
Remove all of the node specific Hiera data:
$ find /etc/puppetlabs/code/environments -name "<client-fqdn>.yaml" --delete
Note
You may have Hiera YAML files with the short name of the host still in place but those are too dangerous to automatically delete since they may match multiple hosts.
Reload the puppetserver
process:
$ puppetserver_reload
<IP_ADDRESS>. On the New Puppet Server¶
Warning
This assumes that the new Puppet Server is set up identically to the old
Puppet Server. If it is not, you will need to verify that the artifacts in
the tar
file are correctly placed.
Unpack the <client-hostname>_tansfer.tar
archive onto the system:
tar --selinux --xattrs -C / -xvf <client-hostname>_transfer.tar
Reload the puppetserver
process:
puppetserver_reload
<IP_ADDRESS>. On Each Client¶
Warning
Make sure you are running these commands on the client. If you run them on the server, you have a very high risk of making your Puppet infrastructure inoperable.
<IP_ADDRESS>.1. Update the Puppet Config¶
Enter the following changes into /etc/puppetlabs/puppet/puppet.conf
.
server = new.puppet.master.fqdn
ca_server = new.puppet.master.fqdn
ca_port = 8141
<IP_ADDRESS>.2. Remove the Client Puppet Certificates¶
To remove all legacy SSL material, run rm -rf `puppet config --section agent ssldir`
<IP_ADDRESS>.3. Run Puppet¶
Assuming the new Puppet Server has been set up to properly accept the
client, execute a full Puppet run using puppet agent --test
.
If everything was done properly, the client will now be synchronized with the new Puppet Server.
If you find issues, refer to the Client Management section of the documentation and ensure that the new Puppet Server was set up properly.


×
Media
Podcast from
NPR: 'The Golden Ratio`
Book Examines the Mystery of Phi, Pi's Lesser-Known Cousin
Fibonacci and the Golden Mean
In Nature
The Math


Foreword and Trigger Warnings
SY stands for Satirically Yours, which is my running The Onion stylized satire series. This post has so many triggers coming that I’ve decided to include a Foreword and Trigger Warning section to protect the easily offended from what I’m about to write. The post is delivered with a heavy influence of The Onion, South Park, Adam Ruins Everything, Bo Burnham, and the legendary George Carlin and his unending interest in language of the times and dirty jokes.
This post will be sexually influenced, it mocks common core, it mocks statists, it highlights how sexual material inappropriate for 8 year-olds is delivered all over the country to young people, it calls out millennial cultural norms, craps on the New Age movement, and has a high chance of enraging you or someone you know if you’re not in the right mindset. It’s also labelled a witness report so my fellow witnesses might take offense. It is egregious, over-the-top, possibly insulting, and an eye opening experience. It is my HF 19 Magnum Opus and I give it to you all with humor and absolute love of this place and the times in which we live.
I couldn’t write this if I wasn’t personally so deep into the culture. It’s a lampoon and a caricature. Breath deep and don’t drink anything while reading this. Also, allow me to prematurely apologizate to you in case you do take offense.
Family Friendly and Witness Appropriate Executive Summary
Let me summarize my findings for today’s post reward value now so that you can skip the rest if you don’t feel called to read it or it’s simply tl;dr: A full vote from your account at current market and Steemit conditions at 100% voting power produces $1 of post rewards for every 1,000 SP you have. It increases linearly as expected from HF 19 entitled “Equality” and it’s restructuring of the reward shares function from SP^2 to a straight SP calculation known as “Linear Rewards.”
Everything else is just for the Lulz.
Let us begin
Hello class. Is everyone properly smudged? Yes? ok. Aus my dear, can you dim the lights? Ok, cue the ambient music. Light the candle.  Hold hands. We’re going to open up a safe place to call forth the spirit of George Carlin to help us in today’s lesson. George, by the holy new age movement invested in me as well as by the power of the New York State Board of Education I summon your strength of character and undying love of exploring language. Please enter me and all the children gathered here today so we can better explore our Steemian Sexual Amplitude in class today.
So say we all.
Let the lesson formally start
Ok, kids, we can turn the lights back up. You’ve probably noticed a little sexual maturity happening right now in your Minnow Genitals. This is a natural phenomena that occurs during every forth quadruple in the Mayan Calander that we’re all experiencing now embodied in Hardfork 19 induced pubescence. It’s also happening in my dolphin testicles as well. Anyone interested can have a look after class.
You may have noticed that when you “Shot your load” in an upvote that you were able to produce as much as $0.004 in HF 18. Yes, we were all slightly embarrassed about that, but now when you upvote you’re able to shoot ropy jets of $1 post rewards all over your friends wall. This is a miraculous experience that we need to investigate further.
Whale Balls definition
The term whale balls was first introduced by 3rd US President, Reverend Andrew Jackson, when he shared the following quote before the 18th National Assembly of Congress during the Revolutionary war-
It’s through this quote and the numerous mentions of the founding father’s that we know about the hazards of Whale Ball influence on alt-coin distribution.
In the beginning there was HF 18
Through a process known as directed evolution where Adam Smith’s magical hand gently caresses Steemit Inc developers to induce a software update we have arrived at HF 19. It is fully intended to shave whale balls in their place and support autoerotic manual curation.
We’re going to talk about the current changes your body is undergoing, but you have to know about the previous Hard Fork, HF 18 to understand the difference that’s happening in your squirmy clankers.
In the previous hardfork it’s important to know that your impact on post rewards were calculated using the squared value of your ball size (SP). For minnows this led you to have performance issues where you could only spray a few cents or a fraction of a cent on your friend’s post, but whales could basically spray an entire ocean based civilization at a time.
100SP^2 = 10,000 reward shares
400,000SP^2, a moderate whale, would get 160,000,000,000 reward shares, thus making your post contribution a rounding error.
In effect, the only thing that really mattered here was Whale Ball size, and that left so many shriveled minnows out in the cold.
HF 19
Because of how Rewards Shares were recalculated to go from an exponential to linear equation you now have a 1 to 1 ratio of SP in the equation, and that makes many other factors begin to show dominance in how much of a reward you’ll get on your post when slathering your upvote on it.
Now 100 SP enters the reward shares calc as 100 SP and the 400,000 SP enters the calc as 400,000 SP. Because the 160,000,000,000 term is no longer completely dominant we have to factor in other things than just the size of your newly dropping genitals.
 I see some of your jaws dropping. I know this might seem a little bit much for an 8-year old, but this is how the state believes it’s best to educate you and we all know how much the state and politicians “love” children. So, please pay attention.
Under HF 18 we only needed the whale ball term to determine post rewards because of the impact of the squared function in the formula. Now we have so many terms that none of the individual 18 state approved genders alone will accurately describe the impact so we’ll have to jump to Chapter 16’s pregnant hermaphrodite example to help us.
Chapter 16, Pregnant Hermaphrodite’s as a suitable description for Children when describing Post Rewards hereby described as Steemian Sexual Amplitude
This is straight out of the book now. The amount of post rewards you’re able to spray onto your intended target is hereby defined as your sexual amplitude.
Your sexual amplitude is no longer simply defined by whale balls, but is now defined by a number of variables.
Sexual Amplitude = Steemit Cup Size Whale Balls Penis Length Relative Erection Degree Pregnant * a constant we’ll Call “M” for millennial.
The formula can now be rewritten as
SA = CS WB PL RE DP * M
This formula is an approximation of the actual calculation and it only functions for a range of values still yet to be determined.
Let’s review the terms individually
Steemian Sexual Amplitude we’ve already described as being the amount of post rewards you can Spray on your intended post after slathering your upvote all over it.
Steemit Cup Size of course stands for the current price of Steem in US Federal Reserve fiat Notes.

Relative Erection is how much vote power you choose to place through the sliding bar when upvoting.
Degree Pregnant describes the current size of the Reward Pool.
M- The Millennial Constant is what turns the proportional arrangement into an equals sign.
Everyone turn to example problem 7
Let’s use roughly current values to calculate M from my post values.
SA= $4.84 on the upvote
WB = 14,700 SP
CS = $2.00/steem at the time of upvoting
PL = 35%
RE = 100%
DP = 1.5M steem
SA / (WB CS PL RE DP) = M
M= 3.14 x 10^-10
You can see that the value of M is proportional to their direct impact to the GDP of our great nation.
That should do it
So, now kids you have the power to quickly estimate the rewards you’re going to put on a post hence forth known as your sexual amplitude. These can also be referred to as Amps or Dank amps. Here’s an example usage from page 420: “Dude, did you see how many dank amps I sprayed all over aggroed’s post? Fuckin’ $40 bills hit it right after I dropped an UV.” A proper response is “That’s so bae!” then it’s appropriate to both whip and nae nae.
I’ll be available after class for anyone that still needs help or is confused. Come to the office with the free hugz sign on it. Now everybody stop crying it’s time for recess!
This page is synchronized from the post: ‘SY presents “Measuring your Steemian Sexual Amplitude: A millennial approved common core math and sex-ed class delivered in a safe zone for 1st graders dedicated to calculating HF 19 induced post rewards.” A Witness Report.’


This has the first and last events today in which a Human classifier was identified. This result is via a Looker generated SQL provisioning a JSON result that is then cleaned-up using a CGI script. There is a bug in that the public dashDB instance I am sharing has its timezone set to GMT; this does not appear to something I can change in a simple or straight-forward fashion. The results may be NULL until I find a fix.
| Location | First Seen | Last Seen |
|---|---|---|
| Kitchen | ||
| Bathroom |


Access curated data frames containing Star Trek timeline data.
mb_timeline(x)
| x | numeric or character, description of the desired timeline window. See details. |
|---|
a list of two data frames
The timeline data is from the Memory Beta Chronology.
x
can be a numeric vector of years, e.g. x = 2371:2364
. This should only be used if you know (or can safely assume) a year exists as a page on Memory Beta. Check there first if unsure.
x
may otherwise be scalar character. This can be a specific decade in the form, e.g., "2370s"
. If a decade, it must fall in the range from "1900s"
through "2490s"
.
The decade option pulls back data from the decade page entry, or if individual year pages exist within the given decade, it will pull the data for each existing year.
Special values: For the more distant past or future, use the character options x = "past"
or x = "future"
.
x = "main"
will pull from the main part of the timeline, 1900 - 2499. x = "complete"
combines past, main, and future in order.
The distant past and future have few entries, and thus few pages. However, both of these last two options, "main"
and complete
,
must download a large number of pages. For this reason, rtrek
employs anti-DOS measures to prevent an unwitting user from making too many requests too quickly from Memory Beta.
The function would otherwise be far faster. However, to be a friendly neighbor in the cosmos, rtrek
enforces a minimum one-second wait between timeline requests.
This can lead to downloading the full timeline to take ten minutes or so even if you have a fast connection; most of the time it takes is spent waiting patiently.
Also, like other functions that work with Memory Alpha and Memory Beta data, mb_timeline
wraps around internal functions that are sensibly memoized.
This means that if you make the same call twice in your R session, you won't have to wait at all, because the result is cached in memory.
The call will appear to run instantaneously the second time around, but that's because nothing is happening other than returning the cached result from the initial call.
mb_timeline(2360)#> 2360#> $events #> # A tibble: 11 x 4 #> period id date notes #> <chr> <chr> <chr> <chr> #> 1 2360 Events NA Quark opens a bar on Terok Nor. [1] #> 2 2360 Conflicts NA Kalisi Reyar designs and completes a grid sys~ #> 3 2360 Federation_polit~ NA Pahkwa-thanh is admitted into the Federation.~ #> 4 2360 Cardassian_polit~ NA Miras Vara has taken the leadership of the Or~ #> 5 2360 Cardassian_polit~ NA Natima Lang and Gaten Russol join the cardass~ #> 6 2360 Romulan_politics NA Vreenak is elected.[2] #> 7 2360 Promotions_and_t~ NA Data is promoted to Lieutenant Commander. [ci~ #> 8 2360 Promotions_and_t~ NA Joseph Carey graduates from Starfleet Academy~ #> 9 2360 Births_and_deaths NA Skrain Dukat's son Mekor is born. [citation n~ #> 10 2360 Births_and_deaths NA Caylem's wife dies in a Mokra prison.[3] #> 11 2360 Births_and_deaths NA Dekon Elig dies attempting to escape the Velo~ #> #> $stories #> # A tibble: 6 x 11 #> title title_url colleciton collection_url section context series date media #> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> #> 1 The B~ The_Burie~ NA NA Chapte~ NA The N~ 2360 novel #> 2 The B~ The_Burie~ NA NA Chapte~ NA The N~ 2360~ novel #> 3 Catal~ Catalyst_~ NA NA NA NA The L~ 2360 novel #> 4 Lefle~ Lefler%27~ No Limits No_Limits Twelft~ NA New F~ 2360~ shor~ #> 5 A Sti~ A_Stitch_~ NA NA Part 2~ NA Deep ~ 2360 novel #> 6 Dawn ~ Dawn_of_t~ Terok Nor Star_Trek:_Te~ Prolog~ NA Deep ~ 2360 novel #> # ... with 2 more variables: notes <chr>, image_url <chr> #>if (FALSE) { mb_timeline("2360s") mb_timeline("past") mb_timeline("future") mb_timeline("main") mb_timeline("complete") }


Story behind the photo: High jinks
by Bangalore Mirror BureauOur reader captured families enjoying a day out atop the rock in Lalbagh
The picture was clicked by reader PRIYA V J
If you have a picture and a story to share, send us the picture with your name and number at mybangaloremirror@timesgroup.com


getPizza Restaurant is a start up business that is located in Rwanda, Kigali city. It mostly relies on online delivery services whereby we bake Pizza and deliver them to those who wants for free.
We started last year in 2019 whereby we used to work in the small restaurant without offering delivery services, but after seeing that many people are in need of our Pizza and it hard for some on them to travel to our restaurant we decided to start free Pizza delivery regardless where people is living in Rwanda not actually outside of it.
We take pride in making a perfect pizza and providing courteous and helpful service on time all the time. Every customer says, "I'll be back!
We strive to be the best among the companies that deliver Pizza to the clients in this Easter Africa Region
We bake Pizza of different sizes and among them we have small, medium, and large size.
Small is 10 inch but consider the pizzas are two inches thick it is about 4x the good of a normal pizza.. a small will feed 3 people easily
Pizza size generally varies between: Small Pizza: 8-10 inches with 6 slices. Medium Pizza: 12 inches with 8 slices.
Large Pizza: 14 inch with 10 slices. while 16-18-inch pizza is extra large Pizza
The Pizza Size Value :
The Pizza Crust Value :
The Pizza Toppings Value :
The Total Price is :


 It's as if it has been taken into private hands. perhaps the magnitude of the coup will sink in at some point.
 LN introduces huge degrees of trust and control to the system, and Core is purposely making BTC less useful on chain to force people to offchain trusted/controlled systems
actionslacks13-Jun-19 02:34 AM
so people arguing about decentralization is a straw man argument. btc is now literally the elephant in the room of the decentralization debate.
so now it will be argued that btc is getting added privacy with all this crap and that you should'nt waste time on the current privacy projects
actionslacks13-Jun-19 02:48 AM
they captured btc. now to furiously engineer it into irrelevance
i was naive enough for awhile to believe it had a chance to reach the masses without being poisoned and hobbled.
jl777c13-Jun-19 03:06 AM
if only the top 1% of wealthy adopt crypto, that is enough
1
just analyze the wealth the top 1% have
compare to the 99%
allbits13-Jun-19 03:13 AM
Privacy and control
jl777c13-Jun-19 03:16 AM
since they have all the money, they would want to protect it from their attack dogs
or maybe the second 1%, who want to become the top 1%
dukeleto13-Jun-19 03:19 AM
@Deleted User moving $100M in gold across the world is expensive and hard. It's basically free with cryptocoins. That is why
allbits13-Jun-19 03:19 AM
The numbers are so large that even if the top one percent are convinced to allocate one percent of their wealth to crypto, globally, it's a staggering number
jl777c13-Jun-19 03:21 AM
yes, that is how it starts
and when the middle class finds out this "secret" that the wealthy use, then the upper middle class will copy
but some of these upper middle class will become upperclass when crypto does its 10x to 100x
and this is how the new generation will experience the crypto in real life
the ones that put in 100% into crypto become very rich
10% pretty rich
1% keeps up with the rest
0% falls behind
of course there is also the other end of the spectrum of the billions of users doing trillions of transactions, but clearly this cant be on any single chain
Alright13-Jun-19 03:24 AM
the ones that put in 100% into crypto become very rich
1
1
LocoMB13-Jun-19 03:24 AM
I initially thought BCH to be a good alternative to BTC core shenanigans, but it was poisoned by CSW -
wonder if Roger Ver can recover from being bitten by that reptile .. maybe, if BTC core keeps getting more clunky(edited)
jl777c13-Jun-19 03:25 AM
using the algo of converting only what you absolutely need into fiat for living expenses and the rest in crypto, ignore the bull/bear cycles and after a couple of them, even at the low you have a decent amount of money
LocoMB13-Jun-19 03:26 AM
ha!
FishyGuts13-Jun-19 03:35 AM
heart attack gun
Hollowman900013-Jun-19 03:35 AM
Cheeseburger gun?
LocoMB13-Jun-19 03:35 AM
hack is pacemaker - or zap it with EM coil gun(edited)
Former CIA employee Mary Embree discusses the infamous heart attack gun. The weapon was first made public during the Church Committee hearings in 1975. Very ...
KMD ecosystem is evolving so fast, it is hard to keep track of new projects
Alexfrog13-Jun-19 04:24 AM
ideally there should be so many things going on that even jl777 doesnt know what they all are
jl777c13-Jun-19 04:26 AM
it is already happened
1
1
actionslacks13-Jun-19 04:50 AM
it's been pretty fascinating watching everything unfold. so many uniquee and fascinating case studies. Random people can just pop in and end up starting projects like k64 Both successful and failed projects and concepts all add value to this ecosystem.
1
차미숙13-Jun-19 04:51 AM
I think of it as now how the world will be a nightmare without crypto
Govt trying to tax everything until there is nothing left and there is nothing you can do except buying gold
The general rule of thumb for crypto now is today is the cheapest day for buying it
actionslacks13-Jun-19 04:57 AM
i think of it as the next industrial revolution, only instead of mass production it will be mass accessibility. everything can be configured to be exponentially more effecient, accountable, and available to all with the capacity to understand, use, and potentially contribute to the systems progression. your internet will pay for itself through all the mechanicms that are currently serving a very select fee for example.
well perhaps not free internet but infinitely more equitable.
1
Malti13-Jun-19 05:02 AM
ISP?
Deleted User13-Jun-19 05:05 AM
modern slavery makes new slaves just cannibalize emselves fighting against each other to get the "precious" job to pay taxes. Anger has been moved so much that masses don't even realize it...(edited)
Starchain Capital is an investment firm focused on blockchain related technologies and protocols building the decentralized software stack known as WEB3.
Deleted User13-Jun-19 05:08 AM
we're just in the "financial" matrix (edited)
french guyz ! Starchain (edited)
cocorico !
차미숙13-Jun-19 05:09 AM
I would like to think of a future where instead of people enslaved by fiat, their future will be enslaved by crypto
Deleted User13-Jun-19 05:10 AM
new generation won't allow that, we already saw how climate is important
time is pushing oldest and putting youngsters with dreams about a real future
be it crypto or not, there was a communication revolution years ago. Time will tell, but young people are angry.


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Source code for pymatgen.transformations.defect_transformations
# coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
from pymatgen.transformations.transformation_abc import AbstractTransformation
"""
This module defines classes for point defect transformations on structures
"""
__author__ = "Danny Broberg, Shyam Dwaraknath"
__copyright__ = "Copyright 2018, The Materials Project"
__version__ = "1.0"
__maintainer__ = "Shyam Dwaraknath"
__email__ =<EMAIL_ADDRESS>__status__ = "Development"
__date__ = "Mar 15, 2018"
[docs]class DefectTransformation(AbstractTransformation):
"""
Generates Defect structures based on pymatgen Defect Core classes
"""
def __init__(self, scaling_matrix, defect):
"""
:param scaling_matrix: Supercell scaling matrix
:param defect: Defect pymatgen object
NOTE: defect.bulk_structure should be same as provided structure in the apply_transformation step
"""
self.scaling_matrix = scaling_matrix
self.defect = defect
[docs] def apply_transformation(self, structure):
"""
:param structure (bulk structure to be scaled up - typically conventional unit cell)
:return:
defect_structure, with charge applied
"""
if structure != self.defect.bulk_structure:
raise ValueError("Defect bulk_structure is not the same as input structure.")
def_structure = self.defect.generate_defect_structure(self.scaling_matrix)
return def_structure
def __str__(self):
inp_args = ["Supercell scaling matrix = {}".format(self.scaling_matrix), "Defect = {}".format(self.defect)]
return "Defect Transformation : " + ", ".join(inp_args)
def __repr__(self):
return self.__str__()
@property
def inverse(self):
raise NotImplementedError()
@property
def is_one_to_many(self):
return False


| Parent Class: | dataenvironment |
| Base Class: | dataenvironment |
| OLE Public: | |
| Scale: | Pixels |
| A | P | H | I | Name | Initial value | Comment |
| DataSource | .NULL. | Specifies the data source for DataEnvironment and CursorAdapter objects to use. The DataEnvironment or CursorAdapter DataSourceType property determines the value of the corresponding DataSource property. Read/write at design time and run time. There are two versions of the syntax. | ||||
| DataSourceType | "NATIVE" | Specifies the data source type of the data source in the DataEnvironment or CursorAdapter DataSource property. The DataEnvironment or CursorAdapter DataSourceType property determines the value of the corresponding DataSource property. Read-only at design time and read/write at run time. There are two versions of the syntax. | ||||
| Height | 34 | Specifies the height of an object on the screen. Available at design time and run time. | ||||
| Width | 144 | Specifies the width of an object. Available at design time and run time. |
| P | NI | I | Name | Class |
| capBinText | cursoradapter |
| Parent object: | de_interface |
| Class: | cursoradapter |
| Base Class: | cursoradapter |
| Name | Initial value |
| Alias | "curbin2text" |
| BufferModeOverride | 5 |
| CursorSchema | cType c(1), cFile c(240), cObjName c(240),cUniqueID c(16) NOCPTRANS, tLastUse T |
| Flags | 0 |
| Height | 22 |
| KeyFieldList | cUniqueID |
| Left | 36 |
| SelectCmd | SELECT Cur1.cType, Cur1.cFile, Cur1.cObjName, Cur1.cUniqueID, Cur1.tLastUse FROM xTable AS Cur1 WHERE Cur1.cType=='A' |
| Tables | xTable |
| Top | 2 |
| UpdatableFieldList | cType, cFile, cObjName, cUniqueID, tLastUse |
| UpdateNameList | cType xTable.cType, cFile xTable.cFile, cObjName xTable.cObjName, cUniqueID xTable.cUniqueID, tLastUse xTable.tLastUse |
| UseCursorSchema | .T. |
| Width | 23 |
| Name |
| AutoOpen |
| Init |


Nicole Tavares Carvalho
Web Designer/Developer -<EMAIL_ADDRESS>Using New Frameworks
Team Work
What now?
- Enjoy
- Learn
- Live
Nicole Tavares Carvalho
Web Designer/Developer -<EMAIL_ADDRESS>Eddie Gemayel
Showcase
<EMAIL_ADDRESS>github.com/eddiegemayel
www.eddiegemayel.com
@eddiegemayel
Me Before Full Sail
During Full Sail
- Learned many languages
- Put them into practice
Looking Ahead
- Productive
- Enjoy
- Continue learning & improving
The End
Thank you
<EMAIL_ADDRESS>github.com/eddiegemayel
www.eddiegemayel.com
@eddiegemayel
Carmine Trovato
I'm a guy.
I like turtles


Description
Boost.SIMD functions and constants are designed to work seamlessly on both scalar and SIMD parameters.
Their compile-time polymorphic behavior ensures that the best available implementation will be selected for any given combination of types, architecture and options (if any).
While being documented as functions, most Boost.SIMD functions are actually Function Objects. This implementation allow Boost.SIMD to use its functions directly in standard algorithms and to support functions decorators to handle variations in functions' behavior.
Boost.SIMD functions are accessible via the header files located in the boost/simd/function
folder. E.g, boost::simd::abs requires the inclusion of boost/simd/function/abs.hpp.
Similarly, Boost.SIMD constants are accessible via the header files located in the boost/simd/constant
folder. E.g, boost::simd::True requires the inclusion of boost/simd/constant/true.hpp. All Boost.SIMD functions begin with a lower-case letter and all Boost.SIMD constants begin with an upper-case letter.
Function semantics are usually similar to the pre-existing ones, if any, with the following caveats:
- All functions are assumed to be
noexcept
. - Type mixing may be prohibited for fundamental types , i.e boost::simd::plus
(char,char) is valid while boost::simd::plus
(int,char) is not
- Type promotion on result type may be disabled, i.e boost::simd::plus
(char,char) returns
char
These limitations are due to the hardware implementation of these functions.
Modules | |
| Arithmetic functions | |
| These functions provide scalar and SIMD algorithms for classical arithmetic operators and functions of the C and C++ standard library. | |
| Bitwise functions | |
| These functions provide scalar and SIMD algorithms for bit manipulations and bitwise operators. | |
| Boolean functions | |
| These functions provide SIMD algorithms for logical operations and branching in SIMD mode, their use is possible but not encouraged on scalars entries. | |
| Constant Functions | |
| These functions provide scalar and SIMD constant generators for the most usual constant values such as zero, numeric limits, IEEE special values. | |
| Decorators | |
| Eulerian functions | |
| Algorithms for computing scalar and SIMD versions of some Eulerian functions. | |
| Exponential functions | |
| Those functions provide algorithms for computing exponentials and logarithms. | |
| Hyperbolic functions | |
| These functions provide scalar and SIMD version of hyperbolic and inverse hyperbolic functions. | |
| Ieee functions | |
| These functions provide scalar and SIMD algorithms for inspecting, generating or decomposing IEEE 754 floating point numbers. | |
| Operators | |
| These functions provide scalar and SIMD version of the native C and C++ operators. | |
| Predicates functions | |
| These functions provide scalar and SIMD algorithms for testing properties of values. | |
| Reduction Functions | |
| These functions provide algorithms for in-register reduction and prefix-scan operations. | |
| Intra register Functions | |
| These functions provide algorithms for interleaving, aggregation, splitting and, more generally, wide register sub-element manipulation. | |
| Trigonometric functions. | |
| Algorithms for computing scalar and SIMD version of trigonometric and inverse trigonometric functions and some utilities. | |


I'm Brian Wang, an undergraduate student studying computer science at the University of Texas at Austin.
I took my first formal computer science course in my freshman year in high school. From there, I began competing in UIL-style competitions (UIL stands for University Interscholastic League for those unfamiliar with the term). Consequently, my choice of major was a simple one. I would study computer science to further my knowledge of the subject.
I enjoy travel and have been fortunate enough to live in a couple of foreign countries. In addition, I've visited quite a few, including Hong Kong, Australia, and the UK.
I recently interned at Square for the summer. I also used to work on the TasteBud app at Azul Mobile; you can check them out by clicking here.
In Spring 2015, I worked with Austin Free-Net to develop the Austin Free-Net On the Go application. This application offers learning modules to help users with technical literacy and serves as a point of contact for the organization. It's currently available on Google Play, and the source code is available under the MIT license on GitHub.
Cooking is another one of my pastimes. The end result may not always be great, but I do enjoy trying new recipes. The photo to the right documents one of the more successful attempts.
You can always find out more about my academic activities and experience by viewing my Resume, accessible through the site navigation menu.


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| CS 107 - Introduction to Scientific Computation Homework #8 |
1. Dice Frequencies: Given two sets of independent random event values and associated frequencies, compute the combined values and frequencies.
If a second set of values and frequencies is not supplied, use the first set in
their place, i.e. combine the set with itself. The returned values should be
sorted in increasing order. The returned frequencies should correspond to the
returned values, i.e. values(i) should occur with frequency freqs(i). (Hint
1: Form a nested loop through
both lists of events, summing values and multiplying frequency counts. Use
nargin
to check the number of given arguments. Hint 2: One
can use the Matlab function "sortrows" to sort the output. Build the
values and freqs as is easiest, and before returning, create a matrix with
values and freqs as the first and second column, respectively. Then use
sortrows on the first column, and extract the new values and freqs (remembering
to transpose them back to row vectors).)
% diceFreq - given two sets of independent random event values and % associated frequencies, compute the combined values and frequencies % function [values, freqs] = diceFreq(values1, freqs1, values2, freqs2) function [values, freqs] = diceFreq(values1, freqs1, values2, freqs2) ...
Example transcript:
>> [v, f] = diceFreq(1:6, ones(6)) v = 2 3 4 5 6 7 8 9 10 11 12 f = 1 2 3 4 5 6 5 4 3 2 1 >> [v, f] = diceFreq([1 2 4], [3 2 1]) v = 2 3 4 5 6 8 f = 9 12 4 6 4 1 >> [v, f] = diceFreq(1:6, ones(6), 6:-1:1, ones(6)) v = 2 3 4 5 6 7 8 9 10 11 12 f = 1 2 3 4 5 6 5 4 3 2 1 >> [v, f] = diceFreq(1:6, ones(6), v, f) v = Columns 1 through 12 3 4 5 6 7 8 9 10 11 12 13 14 Columns 13 through 16 15 16 17 18 f = Columns 1 through 12 1 3 6 10 15 21 25 27 27 25 21 15 Columns 13 through 16 10 6 3 1 >> [v, f] = diceFreq(1:6, ones(6), v, f); >> [v, f] = diceFreq(1:6, ones(6), v, f); >> [v, f] = diceFreq(1:6, ones(6), v, f); >> [v, f] = diceFreq(1:6, ones(6), v, f) v = Columns 1 through 12 7 8 9 10 11 12 13 14 15 16 17 18 Columns 13 through 24 19 20 21 22 23 24 25 26 27 28 29 30 Columns 25 through 36 31 32 33 34 35 36 37 38 39 40 41 42 f = Columns 1 through 6 1 7 28 84 210 462 Columns 7 through 12 917 1667 2807 4417 6538 9142 Columns 13 through 18 12117 15267 18327 20993 22967 24017 Columns 19 through 24 24017 22967 20993 18327 15267 12117 Columns 25 through 30 9142 6538 4417 2807 1667 917 Columns 31 through 36 462 210 84 28 7 1
2. Dice Sum Simulation: To uniformly generate a random integer
from 1 to n
, use randi(n)
. Create a function twoDiceSums
that takes a
number of trials as input, simulates the roll of a pair of fair 6-sided dice,
and counts the number of times each possible sum is rolled. Return both a sorted vector
of 2-dice sum values and a second vector of their respective frequencies.
You can test your code by comparing the fraction of sum occurrences versus exact value from the prior exercise. Here are commands that normalize frequencies to probabilities, and plot them for comparison:
[v, f] = diceFreq(1:6, ones(6)); [v2, f2] = twoDiceSums(10000); f = f / sum(f); f2 = f2 / sum(f2); plot(v, f); hold on; plot(v2, f2, '-.'); hold off;
A larger number of trials will make a closer approximation to the event probabilities.
Hint: To normalize frequencies, divide each frequency by the sum of all frequencies. Then you will have probabilities that sum to 1.
Example transcript:
>> RandStream.setGlobalStream(RandStream('mcg16807','Seed',0))3. Random Walk: To generate a value from a normal distribution with a mean of
0 and standard deviation of 1, use randn()
. (To get mean m
and standard
deviation s
, use m + s * randn()
.) Create a function randomWalk
that takes
a number of steps and returns a vector of the length (steps + 1)
with initial value
0, and each
successive value being the previous value plus randn()
.
You can see the results of your random walks as follows:
steps = 100; x = randomWalk(steps); t = 1:length(x); plot(t, x);
Example transcript:
>> RandStream.setGlobalStream(RandStream('mcg16807','Seed',0)) >> x = randomWalk(5) x = 0 1.1650 1.7918 1.8669 2.2185 1.5220
4. Monte Carlo Random Walk: Create a function MCRandomWalk
that
takes two parameters: steps s
and number of trials t
. For each trial,
it uses randomWalk(s)
to generate a random walk of the given number of steps, and it
stores only the last number returned. Having collected a final value from
each of t trials, these values are sorted (using sort
) and we do the following:
res
with hist(res)
,Example transcript:
>> RandStream.setGlobalStream(RandStream('mcg16807','Seed',0)) >> [ minimum, maximum, med, avg, stddev ] = MCRandomWalk(100, 10000) Minimum: -34.4536 Maximum 40.1982 Median: 0.0133769 Mean: 0.0972835 Standard Deviation: 9.9065 90% confidence interval: [-15.9239 16.4839] minimum = -34.4536 maximum = 40.1982 med = 0.0134 avg = 0.0973 stddev = 9.9065 >>
Note: In older versions of Matlab, there are different means of setting random seeds. For example, you may need to use "setDefaultStream" rather than "setGlobalStream".


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Look at the companies that will be attending which interest you most. This is a great way to figure out which companies you’re compatible with and the key qualities they’re looking for.
Refine your Resume
Whether you’ve fine-tuned for the 100th time or just started creating your resume. Do your best to finish before the Design Career Fair. Check in with advisors, bounce ideas with friends or ask for feedback from professors. Remember, the Resume
Review Booth will be available at the fair if you have any uncertanty!
Assemble your Portfolio
If you have a collection of work you’d like to show off to employers this is the perfect time to highlight some of those creative pieces.
Ask Away!
Our speakers and panelists come from a diverse set of backgrounds, which means you have a fantastic opportunity to ask the questions only they could answer.
If you come up with questions during the event feel free to ask on Twitter at #UCDavisDCF2018!
How to get here
The Design Career Fair will be held at the Art Annex
Art Annex Davis, CA 95616
By Bike & Bus:
Some bike parking will be available directly in front of the Art Annex, but given how many attendees we expect, that will fill up! Please park by Shields Library and walk across the street to the Art Annex if that occurs.
The Silo Terminal is 2-3 minute walk to the Art Annex. When walking from the Memorial Union Terminal, it is about 7-8 minutes.
By Car:
Parking is free on the weekends, but the Art Annex parking lot will be reserved for companies setting up booths. For attendees, please park in Lot 5 and stroll through the Arboretum. Once you arrive at the parking lot, follow the signs to the
Art Annex.


| MPI_WIN_START(group, assert, win) | |
| IN group | group of target processes (handle) |
| IN assert | program assertion (integer) |
| IN win | window object (handle) |
int MPI_Win_start(MPI_Group group, int assert, MPI_Win win)
MPI_WIN_START(GROUP, ASSERT, WIN, IERROR)
INTEGER GROUP, ASSERT, WIN, IERROR
void MPI::Win::Start(const MPI::Group& group, int assert) const
Starts an RMA access epoch for win. RMA calls issued on win during this epoch must access only windows at processes in group. Each process in group must issue a matching call to MPI_WIN_POST. RMA accesses to each target window will be delayed, if necessary, until the target process executed the matching call to MPI_WIN_POST. MPI_WIN_START is allowed to block until the corresponding MPI_WIN_POST calls are executed, but is not required to.
The assert argument is used to provide assertions on the context of the call that may be used for various optimizations. This is described in Section Assertions . A value of assert = 0 is always valid.
| MPI_WIN_COMPLETE(win) | |
| IN win | window object (handle) |
int MPI_Win_complete(MPI_Win win)
MPI_WIN_COMPLETE(WIN, IERROR)
INTEGER WIN, IERROR
void MPI::Win::Complete() const
Completes an RMA access epoch on win started by a call to MPI_WIN_START. All RMA communication calls issued on win during this epoch will have completed at the origin when the call returns.
MPI_WIN_COMPLETE enforces completion of preceding RMA calls at the origin, but not at the target. A put or accumulate call may not have completed at the target when it has completed at the origin.
Consider the sequence of calls in the example below.
Example
MPI_Win_start(group, flag, win); MPI_Put(...,win); MPI_Win_complete(win);
The call to MPI_WIN_COMPLETE does not return until the put call has completed at the origin; and the target window will be accessed by the put operation only after the call to MPI_WIN_START has matched a call to MPI_WIN_POST by the target process. This still leaves much choice to implementors. The call to MPI_WIN_START can block until the matching call to MPI_WIN_POST occurs at all target processes. One can also have implementations where the call to MPI_WIN_START is nonblocking, but the call to MPI_PUT blocks until the matching call to MPI_WIN_POST occurred; or implementations where the first two calls are nonblocking, but the call to MPI_WIN_COMPLETE blocks until the call to MPI_WIN_POST occurred; or even implementations where all three calls can complete before any target process called MPI_WIN_POST --- the data put must be buffered, in this last case, so as to allow the put to complete at the origin ahead of its completion at the target. However, once the call to MPI_WIN_POST is issued, the sequence above must complete, without further dependencies.
| MPI_WIN_POST(group, assert, win) | |
| IN group | group of origin processes (handle) |
| IN assert | program assertion (integer) |
| IN win | window object (handle) |
int MPI_Win_post(MPI_Group group, int assert, MPI_Win win)
MPI_WIN_POST(GROUP, ASSERT, WIN, IERROR)
INTEGER GROUP, ASSERT, WIN, IERROR
void MPI::Win::Post(const MPI::Group& group, int assert) const
Starts an RMA exposure epoch for the local window associated with win. Only processes in group should access the window with RMA calls on win during this epoch. Each process in group must issue a matching call to MPI_WIN_START. MPI_WIN_POST does not block.
| MPI_WIN_WAIT(win) | |
| IN win | window object (handle) |
int MPI_Win_wait(MPI_Win win)
MPI_WIN_WAIT(WIN, IERROR)
INTEGER WIN, IERROR
void MPI::Win::Wait() const
Completes an RMA exposure epoch started by a call to MPI_WIN_POST on win. This call matches calls to MPI_WIN_COMPLETE(win) issued by each of the origin processes that were granted access to the window during this epoch. The call to MPI_WIN_WAIT will block until all matching calls to MPI_WIN_COMPLETE have occurred. This guarantees that all these origin processes have completed their RMA accesses to the local window. When the call returns, all these RMA accesses will have completed at the target window.
Figure 20 illustrates the use of these four functions.
Process 0 puts data in the windows of processes 1 and 2 and process 3 puts data in the window of process 2. Each start call lists the ranks of the processes whose windows will be accessed; each post call lists the ranks of the processes that access the local window. The figure illustrates a possible timing for the events, assuming strong synchronization; in a weak synchronization, the start, put or complete calls may occur ahead of the matching post calls.
| MPI_WIN_TEST(win, flag) | |
| IN win | window object (handle) |
| OUT flag | success flag (logical) |
int MPI_Win_test(MPI_Win win, int *flag)
MPI_WIN_TEST(WIN, FLAG, IERROR)
INTEGER WIN, IERROR
LOGICAL FLAG
bool MPI::Win::Test() const
This is the nonblocking version of MPI_WIN_WAIT. It returns flag = true if MPI_WIN_WAIT would return, flag = false, otherwise. The effect of return of MPI_WIN_TEST with flag = true is the same as the effect of a return of MPI_WIN_WAIT. If flag = false is returned, then the call has no visible effect.
MPI_WIN_TEST should be invoked only where MPI_WIN_WAIT can be invoked. Once the call has returned flag = true, it must not be invoked anew, until the window is posted anew.
Assume that window win is associated with a ``hidden'' communicator wincomm, used for communication by the processes of win. The rules for matching of post and start calls and for matching complete and wait call can be derived from the rules for matching sends and receives, by considering the following (partial) model implementation.
Rationale.
The design for general active target synchronization requires the
user to provide complete information on the communication pattern, at
each end of a communication link: each origin specifies a list of
targets, and each target specifies a list of origins. This provides
maximum flexibility (hence, efficiency) for the implementor:
each
synchronization can be initiated by either side, since each ``knows''
the identity of the other. This also provides maximum protection from
possible races. On the other hand, the design requires more
information than RMA needs, in general: in general, it is sufficient
for the origin to know the rank of the target, but not vice
versa.
Users that want more ``anonymous'' communication will be required to
use the fence or lock mechanisms.
( End of rationale.)
Advice to users.
Assume a communication pattern that is represented by a directed graph , where V = {0, ..., n-1} and if origin process i accesses the window at target process j. Then each process i issues a call to MPI_WIN_POST(ingroupi, ...), followed by a call to MPI_WIN_START(outgroupi,...), where and . A call is a noop, and can be skipped, if the group argument is empty. After the communications calls, each process that issued a start will issue a complete. Finally, each process that issued a post will issue a wait.
Note that each process may call with a group argument that has
different members.
( End of advice to users.)


Preparation time
less than 30 mins
Cooking time
30 mins to 1 hour
Serves
Serves 4
Heat a large heavy-based non-stick frying pan until you feel a good heat rising.
Add approximately one tablespoon of the oil, then add the chicken breasts and pan-fry for about five minutes on each side until firm when pressed with the back of a fork.
Season well and set aside in the pan. They will keep warm without extra heating.
Heat the remaining tablespoon of oil in a separate pan, add the shallots, garlic, chilli and fennel and stir-fry for about five minutes until softened.
Add the beans and seasoning to taste, and then heat through until piping hot.
Stir in the lemon zest and juice and fresh herbs.
Meanwhile, heat the grill until hot and grill the pancetta until crispy.
Remove the pancetta from the grill then drain on kitchen paper.
Divide the bean and fennel mixture between four warmed serving plates.
Slice the chicken breasts into medallions and arrange on top, then add two slices of pancetta to each plate, which you can either crumble or leave whole.
Drizzle over any chicken pan juices with a little extra oil.
Serve warm with the crusty bread and green leaf salad.


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Module tove
Tables
| LineJoin | The visual approach with which lines are joined. |
| FillRule | The rule by which solidness in paths is defined. |
Class List
| List.count | Total number of elements in this list. |
| List:at (index) | Get element at index. |
| List:at (name) | Get element with name. |
Tables
- LineJoin
-
The visual approach with which lines are joined.
Also see Mozilla SVG documentation.
Fields:
- miter Mitered (sharp) line joins
- round Round line joins
- bevel Bevel (flat) line joins
- FillRule
-
The rule by which solidness in paths is defined.
Also see Mozilla SVG documentation
Fields:
- nonzero Non-zero number of path crossings defines insideness
- evenodd Odd number of path crossings defines insideness
Class List
A list of elements, such as e.g. Paths.
- List.count
-
Total number of elements in this list.
- count number number of elements in this list
Usage:
graphics.paths.count -- number of paths graphics.paths[1].subpaths.count -- number of subpaths in path 1 graphics.paths[1].subpaths[1].curves.count -- number of curves in subpath 1 in path 1
- List:at (index)
-
Get element at index.
Parameters:
- index number 1-based index of element to retrieve
Returns:
-
retrieved element
Usage:
graphics.paths[1] -- retrieve first path
- List:at (name)
-
Get element with name.
Parameters:
- name string name of element to retrieve
Returns:
-
retrieved element
Usage:
graphics.paths["circle"] -- retrieve the path named "circle"


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About Black Dog
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Made by shamrickus | Source | Changelog
To see specific field options, hover over icon next to the field


I like the existential representations of optics because they are conceptually illuminating and allow me to play with the definitions easily without worrying about implementation details too much. This whole post is about such a playful act about the interaction between optics and representable functors.
My notation in this section will be a mixture of classical mathematical notation, Haskell syntax and optic names as used in, say the lens library. If it it too mathy for your taste you can skip this section and move onto implementations in Haskell. Also there is a gist with all the code here plus some extra examples.
Suppose \(r_1\) and \(r_2\) are sets –or types, if you prefer– representing functors \(F\) and \(G\), respectively. That is, \(F(a)=a^{r_1}\) and \(G(a)=a^{r_2}\). Let \(\cong\) denote isomorphism. Then we have the following constructions:
Isomorphism from isomorphism: Suppose we have an \({\rm Iso'}\,r_1\, r_2 = r_1\cong r_2\). Then \[ F(a) \cong a^{r_1} \cong a^{r_2} \cong G(a) \] This gives an \({\rm Iso'}\,F(a)\,G(a)\).
Lens from prism: Suppose we have a \({\rm Prism'}\,r_1\, r_2 = \exists r. r_1 \cong r + r_2\). Then \[ F(a) \cong a^{r_1} \cong \exists r.a^{r + r_2}\cong \exists b. a^r \times a^{r_2} \cong \exists r. a^r \times G (a) \] This gives a \({\rm Lens'}\,F(a)\,G(a)\).
Grate from lens: Suppose we have a lens \({\rm Lens'}\,r_1\, r_2 = \exists r. r_1 \cong r\times r_2\). \[ F(a) \cong a^{r_1} \cong \exists r. a^{r \times r_2} \cong \exists r. {a^{r_2}}^r \cong \exists r. G (a) ^r \] This gives a \({\rm Grate'}\,F(a)\,G(a)\).
Now let us implement these constructions in Haskell!
This one is kind of free and it is not really about representable functor. Still, for the sake of completeness, I will put it here:
transportIso ::
( Rep.Representable f, Rep.Rep f ~ r1
, Rep.Representable g, Rep.Rep g ~ r2
) =>
Iso' r1 r2 -> Iso (f a) (f b) (g a) (g b)
transportIso givenIso = iso fromFa toFa
where
fromFa fa = Rep.tabulate $ Rep.index fa . view (re givenIso)
toFa ga = Rep.tabulate $ Rep.index ga . view givenIso
Creating a lens from a prism is not this straightforward but still easy.
homemadeLensFromPrism ::
( Rep.Representable f, Rep.Rep f ~ r1
, Rep.Representable g, Rep.Rep g ~ r2
) =>
Prism' r1 r2 -> Lens' (f a) (g a)
homemadeLensFromPrism restriction = lens getter setter
where
getter fa =
Rep.tabulate $ Rep.index fa . review restriction
setter fa ga =
Rep.tabulate $ \r1 ->
case preview restriction r1 of
Nothing -> Rep.index fa r1
Just r2 -> Rep.index ga r2
Note that here we obtain a simple lens. The reason is that a representable functor is a homogeneous data structure meaning that all its contents are of the same type. It is also worth mentioning that we can implement this function using the combinator outside
from the lens library. However outside
expects a representable profunctor and in a lot of useful cases one only has a representable functor.
repIso :: (Rep.Representable f, Rep.Rep f ~ r) => Iso (f a) (f b) (r -> a) (r -> b)
repIso = iso Rep.index Rep.tabulate
mkLensFromPrism ::
( Rep.Representable f, Rep.Rep f ~ r1
, Rep.Representable g, Rep.Rep g ~ r2
) =>
Prism' r1 r2 -> Lens' (f a) (g a)
mkLensFromPrism pr = repIso . outside pr . from repIso
Now let us look a few simple examples. first of all, we can recover evaluation at a point as a lens view. As a bonus we also obtain the ability to change the value at a point.
wrappedInIdentity ::
( Rep.Representable f, Rep.Rep f ~ r, Eq r
) =>
r -> Lens' (f a) (Identity a)
wrappedInIdentity r =
mkLensFromPrism $ only r
atPosition ::
( Rep.Representable f, Rep.Rep f ~ r, Eq r
) =>
r -> Lens' (f a) a
atPosition r = wrappedInIdentity r . coerced
This is theoretically fine but practically not that interesting. Before moving on to more practical examples I want to introduce a combinator which turns injective functions with small domains into prisms.
mkPrismFromInjection ::
(Ord a, Enum b, Bounded b
) =>
(b -> a) -> Prism' a b
mkPrismFromInjection create = prism' create mbRecover
where
mbRecover a =
Map.lookup a $
Map.fromList [(create b, b) | b <- universe ]
Now combining this with mkLensFromPrism
we obtain the following combinator:
restrictByPositionMapping ::
( Rep.Representable f, Rep.Rep f ~ r1
, Rep.Representable g, Rep.Rep g ~ r2
, Ord r1, Enum r2, Bounded r2
) =>
g r1 -> Lens' (f a) (g a)
restrictByPositionMapping positionMapping =
mkLensFromPrism $ mkPrismFromInjection $ Rep.index positionMapping
To see these in action, we need representable functors. So here are two examples: Triple
and WrappedStream
.
data Triple a = Triple
{ _slot0 :: a
, _slot1 :: a
, _slot2 :: a
} deriving (Functor, Show)
instance Distributive Triple where
distribute wrappedTriple = Triple a1 a2 a3
where
a1 = _slot0 <$> wrappedTriple
a2 = _slot1 <$> wrappedTriple
a3 = _slot2 <$> wrappedTriple
data Slot = Slot0 | Slot1 | Slot2
deriving (Eq, Enum, Bounded, Show)
instance Rep.Representable Triple where
type Rep Triple = Slot
tabulate f =
Triple (f Slot0) (f Slot1) (f Slot2)
index (Triple s0 s1 s2) = \case
Slot0 -> s0
Slot1 -> s1
Slot2 -> s2
-- newtype is to mostly avoid orphan instance warnings
newtype WrappedStream a = WrappedStream { getStream :: Str.Stream a}
deriving Functor
instance Show a => Show (WrappedStream a) where
show =
(<>" ...") . concatMap (<>", ") . fmap show . Str.take 20 . getStream
instance Distributive WrappedStream where
distribute = WrappedStream . Str.distribute . fmap getStream
instance Rep.Representable WrappedStream where
type Rep WrappedStream = Natural
tabulate f = WrappedStream $
Str.fromList [f n | n <- [0 ..]]
index (WrappedStream str) n =
Str.head $ Str.drop (fromIntegral n) str
example1 :: WrappedStream Int
example1 = WrappedStream $ Str.fromList [0 ..]
example2 :: WrappedStream Int
example2 = WrappedStream $ Str.fromList [100 ..]
…and a few lenses on these types:
firstThree :: Lens' (WrappedStream a) (Triple a)
firstThree = restrictByPositionMapping $ Triple 0 1 2
secondThree :: Lens' (WrappedStream a) (Triple a)
secondThree = restrictByPositionMapping $ Triple 3 4 5
evens :: Lens' (WrappedStream a) (WrappedStream a)
evens = mkLensFromPrism $ prism' create mbRecover
where
create n = 2 * n
mbRecover n = if even n then Just (n `div` 2) else Nothing
Finally our combinators in actions:
-- >>> example1
-- 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, ...
-- >>> example1 ^. atPosition 5
-- 5
-- >>> example1 & atPosition 5 .~ 77
-- 0, 1, 2, 3, 4, 77, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, ...
-- >>> example1 ^. firstThree
-- Triple {_slot0 = 0, _slot1 = 1, _slot2 = 2}
-- >>> example1 & atPosition 4 .~ 0 & view secondThree
-- Triple {_slot0 = 3, _slot1 = 0, _slot2 = 5}
-- >>> example1 & firstThree .~ Triple 100 101 102
-- 100, 101, 102, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, ...
-- >>> example1 & firstThree . atPosition Slot2 .~ 999
-- 0, 1, 999, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, ...
-- >>> example2
-- 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, ...
-- >>> example1 ^. evens
-- 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, ...
-- >>> example1 & evens .~ example2
-- 100, 1, 101, 3, 102, 5, 103, 7, 104, 9, 105, 11, 106, 13, 107, 15, 108, 17, 109, 19, ...
-- >>> example2 & evens .~ example1
-- 0, 101, 1, 103, 2, 105, 3, 107, 4, 109, 5, 111, 6, 113, 7, 115, 8, 117, 9, 119, ...
-- >>> example1 & evens . evens . evens .~ example2
-- 100, 1, 2, 3, 4, 5, 6, 7, 101, 9, 10, 11, 12, 13, 14, 15, 102, 17, 18, 19, ...
-- >>> example1 & evens . firstThree .~ Triple 100 101 102
-- 100, 1, 101, 3, 102, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, ...
-- >>> example1 & evens . secondThree . atPosition Slot2 .~ 1000
-- 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1000, 11, 12, 13, 14, 15, 16, 17, 18, 19, ...
There is also an example of a 2D Menger sponge in the gist implemented using mkLensFromPrism
.
Grates are not popular optics. The lens library does not have them. So I will work with a grate representation and derive their basic properties here. Historically, the idea of a lens comes from profunctor optics. Grates are the optics corresponding to closed profunctors. Their existential representation is given by this isomorphism: \[ Grate'\,s\, a = \exists i. s \cong a^i. \] So grates are essentially representable functors where you are not allowed to mention the representing object directly. The standard API we obtain after eliminating the existential quantifier of grates is a little mysterious. I will be working with this API. If you are curious you can have a look at this discussion about the grate API and the corresponding laws.
newtype GrateRep s t a b = GrateRep { unGrateRep :: ((s -> a) -> b) -> t }
type GrateRep' s a = GrateRep s s a a
To obtain some intuition about GrateRep
.{.haskell} let us see what we can do with it. First of all, as the existential representation suggests, every representable functor should give rise to a grate. Here is the implementation.
represented :: Rep.Representable f => GrateRep (f a) (f b) a b
represented = GrateRep $
\faab -> Rep.tabulate (\r -> faab (`Rep.index` r))
Actually more is true. The Distributive
class is essentially a Haskell-98 way of talking about representables without the representing object. So can we make a grate out of a Distributive
functor? Yes! One can also give a direct implementation but I will introduce double continuation as an auxiliary type to decompose GrateRep
into meaningful pieces.
newtype DoubleCont a b s = DoubleCont { unDoubleCont :: (s -> a) -> b }
deriving Functor
distributed :: Distributive f => GrateRep (f a) (f b) a b
distributed = GrateRep $
\faab -> ($ id) . unDoubleCont <$> distribute (DoubleCont faab)
Note the functor instance for Doublecont
made it easier to implement the grate.
Ok so we have a grate, what can we do with it? To paraphrase: If you have a homogeneous container with fixed shape, what can you do with it without referring to its shape? As a trivial example, we can set all the values in te container to a fixed value. Or, more generally, we can apply the same function to all values. Maybe more interestingly, if we have two containers of the same shape we can zip them! None of these operations refer to the shape of the container, and they all can be implemented using the grate API:
zipWith0 :: GrateRep s t a b -> b -> t
zipWith0 (GrateRep sabt) b =
sabt (const b)
zipWith1 :: GrateRep s t a b -> (a -> b) -> s -> t
zipWith1 (GrateRep sabt) ab s =
sabt $ \sa -> ab (sa s)
zipWith2 :: GrateRep s t a b -> (a -> a -> b) -> s -> s -> t
zipWith2 (GrateRep sabt) o s1 s2 =
sabt $ \sa -> o (sa s1) (sa s2)
I hope these examples give some insight about grates. Now back to our original goal: To obtain a grate from a lens. I will use functor (and contravariant) instances with respect all variables in the double continuation. However, instead of defining different newtypes around the double continuation function and define the corresponding instances I will do it in a single function.
modifyDoubleCont ::
(s1 -> s2) ->
(a2 -> a1) ->
(b1 -> b2) ->
((s1 -> a1) -> b1) ->
((s2 -> a2) -> b2)
modifyDoubleCont mapS contramapA mapB =
(. (contramapA .)) . (mapB .) . (. (. mapS))
The definition may look horrible if you are not used to this kind of functions but there is a simple algorithm to derive it and you can discover it easily.
And now the function grateFromLens
:
grateFromLens ::
( Rep.Representable f, Rep.Rep f ~ r1
, Rep.Representable g, Rep.Rep g ~ r2
) =>
Lens' r1 r2 -> GrateRep (f a) (f b) (g a) (g b)
grateFromLens lns = GrateRep $ \fagaga ->
Rep.tabulate $
\r1 ->
fagaga
& modifyDoubleCont Rep.index Rep.tabulate Rep.index
& modifyDoubleCont id (\r1a r2 -> r1a $ lns .~ r2 $ r1) (\r2a _ -> r2a $ r1 ^. lns )
& ($ id)
& ($ r1)
Let us see this function in action:
decompose3 :: Iso' Natural (Natural, Slot)
decompose3 = iso fromN toN
where
fromN n = (n `div` 3, remainderAsSlot $ n `mod` 3)
toN (n, slot) =
3 * n + slotAsRemainder slot
slotAsRemainder = \case
Slot0 -> 0
Slot1 -> 1
Slot2 -> 2
remainderAsSlot n
| n == 0 = Slot0
| n == 1 = Slot1
| otherwise = Slot2
divideBy3 :: Lens' Natural Natural
divideBy3 = decompose3 . _1
remainderBy3 :: Lens' Natural Slot
remainderBy3 = decompose3 . _2
divideBy3Grate :: GrateRep (WrappedStream a) (WrappedStream b) (WrappedStream a) (WrappedStream b)
divideBy3Grate = grateFromLens divideBy3
remainderBy3Grate :: GrateRep (WrappedStream a) (WrappedStream b) (Triple a) (Triple b)
remainderBy3Grate = grateFromLens remainderBy3
-- first three examples give you the usual zip.
-- >>> zipWith2 represented (+) example1 example2
-- 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120, 122, 124, 126, 128, 130, 132, 134, 136, 138, ...
-- >>> zipWith2 divideBy3Grate (zipWith2 represented (+)) example1 example2
-- 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120, 122, 124, 126, 128, 130, 132, 134, 136, 138, ...
-- >>> zipWith2 remainderBy3Grate (zipWith2 represented (+)) example1 example2
-- 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120, 122, 124, 126, 128, 130, 132, 134, 136, 138, ...
-- These are more interesting
liftWrapped2 ::
(Str.Stream a -> Str.Stream a -> Str.Stream a) ->
WrappedStream a -> WrappedStream a -> WrappedStream a
liftWrapped2 binaryOp w1 w2 = WrappedStream $ on binaryOp getStream w1 w2
-- >>> liftWrapped2 Str.interleave example1 example2
-- 0, 100, 1, 101, 2, 102, 3, 103, 4, 104, 5, 105, 6, 106, 7, 107, 8, 108, 9, 109, ...
-- >>> zipWith2 divideBy3Grate (liftWrapped2 Str.interleave) example1 example2
-- 0, 1, 2, 100, 101, 102, 3, 4, 5, 103, 104, 105, 6, 7, 8, 106, 107, 108, 9, 10, ...
pickAlternating :: Str.Stream a -> Str.Stream a -> Str.Stream a
pickAlternating str1 str2 = Str.zip3 str1 str2 (Str.fromList [0 :: Int ..]) <&>
\(a, b, n) -> if even n then a else b
-- >>> liftWrapped2 pickAlternating example1 example2
-- 0, 101, 2, 103, 4, 105, 6, 107, 8, 109, 10, 111, 12, 113, 14, 115, 16, 117, 18, 119, ...
-- >>> zipWith2 divideBy3Grate (liftWrapped2 pickAlternating) example1 example2
-- 0, 1, 2, 103, 104, 105, 6, 7, 8, 109, 110, 111, 12, 13, 14, 115, 116, 117, 18, 19, ...
crissCross :: Str.Stream a -> Str.Stream a -> Str.Stream a
crissCross (Str.Cons _x1 (Str.Cons y1 rest1)) (Str.Cons x2 (Str.Cons _y2 rest2)) =
y1 Str.<:> x2 Str.<:> crissCross rest1 rest2
-- >>> liftWrapped2 crissCross example1 example2
-- 1, 100, 3, 102, 5, 104, 7, 106, 9, 108, 11, 110, 13, 112, 15, 114, 17, 116, 19, 118, ...
-- >>> zipWith2 divideBy3Grate (liftWrapped2 crissCross) example1 example2
-- 3, 4, 5, 100, 101, 102, 9, 10, 11, 106, 107, 108, 15, 16, 17, 112, 113, 114, 21, 22, ...
shiftByEndo ::
( Rep.Representable f
, Rep.Rep f ~ r
) =>
Endo r -> (a -> a -> b) -> f a -> f a -> f b
shiftByEndo (Endo endo) binaryOp fa1 =
zipWith2 represented binaryOp (Rep.tabulate $ Rep.index fa1 . endo)
swapNeightbors :: Endo Natural
swapNeightbors = Endo $ \n -> if even n then n + 1 else n - 1
-- >>> zipWith2 divideBy3Grate (shiftByEndo swapNeightbors (+)) example1 example2
-- 103, 105, 107, 103, 105, 107, 115, 117, 119, 115, 117, 119, 127, 129, 131, 127, 129, 131, 139, 141, ...
-- >>> zipWith2 remainderBy3Grate (shiftByEndo (Endo id) (+)) example1 example2
-- 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120, 122, 124, 126, 128, 130, 132, 134, 136, 138, ...
-- >>> zipWith2 remainderBy3Grate (shiftByEndo (Endo $ const Slot0) (+)) example1 example2
-- 100, 101, 102, 106, 107, 108, 112, 113, 114, 118, 119, 120, 124, 125, 126, 130, 131, 132, 136, 137, ...
-- >>> zipWith2 remainderBy3Grate (shiftByEndo (Endo $ const Slot1) (+)) example1 example2
-- 101, 102, 103, 107, 108, 109, 113, 114, 115, 119, 120, 121, 125, 126, 127, 131, 132, 133, 137, 138, ...
rotateSlot :: Slot -> Slot
rotateSlot = \case
Slot0 -> Slot1
Slot1 -> Slot2
Slot2 -> Slot0
-- >>> zipWith2 remainderBy3Grate (shiftByEndo (Endo rotateSlot) (+)) example1 example2
-- 101, 103, 102, 107, 109, 108, 113, 115, 114, 119, 121, 120, 125, 127, 126, 131, 133, 132, 137, 139, ...
I think this is enough.


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“What is there to do in Lincoln, Nebraska?!” was a question I got asked a lot when I announced that I would be moving there for graduate school. As a 20-something coming to Lincoln from a larger city, I felt a bit at a loss.
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Design SawVoice class with the LFO modulation and an AD envelope.
SawVoice
[ a, w, s, e, d, f, t, g, y, h, j, k ]
LFO Rate
Attack
Decay