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--- layout: default title: About --- about. we are amatus. {% assign teammemebers = site.teammemebers | sort: 'full_name' %} {% for teammember in teammemebers %} Talk to {{ teammember.first_name | capitalize }}. {{ teammember.first_name }} {% endfor %}


You should never even see me
You should never even see me.
Some popular science articles about the issue and some articlesfrom the literature.


Before I was accepted into DevBootcamp, I understood it to be a hyper intensive space, for people who wanted to dive into the world of Web Development, and crawl out a champion of the web, being sucked into amazing agencies, big and small, living the tech dream... What I have come to be very excited for is the cultural elements of DevBootcamp, learning mindfulness, growing my Emotional Intelligence, and now, the concept of the Kitchen Culture.
Being Blaxican, (that is, mixed, Latin@ and Black) I have been raised to revere and honor the kitchen.As the centerpiece of nourishment, my people spend all our most cherished times in the kitchen, with our abuelas and grannies, chatting away, learning traditions, tricks of the trades, and getting our hands dirty, whether that be kneading tortilla dough, or washing the stove from head to toe, we all contribute, by hand, voice, or tongue, in that kitchen.
In addition, coming from the Nonprofit industry, from offices that barely hold two desks, but house 4 staffers, and computer labs that are on their last leg, yet sustain the pressure of a thousand new minds to ignite, I've had well over my share of rolling my sleves up and taking on the challenge of making things happen for me and the organization, with whatever we've got. (much like a deliciously creative leftover meal)
So perhaps it is no suprise to me that i've jumped head first into another kitchen culture, ready to taste, explore, be guided by the variety of experiences others will bring, and be ready to roll my sleves up, put my hairnet on, and make things happen!
I am enthralled that this is the way DevBootcamp strives to continue as they grow, and it is my hope that when I do rise victorious from it's doors, that I am welcomed into another space that can carry on the kitchen culture i've grown to thrive in.


Neuroplasticity is the ability that our brains have to ‘re-wire’ themselves.
This a way that our brains adapt to new experiences, knowledge, etc. It has been
proved recently that our brains keep changing due to our learning, by learning we
create connections between neurones, and with these connections, our brain is
‘re-shaped’ or commonly known as ‘re-wired’. The best way to create strong connections
between neutrons is by repetition, by repeating exercises or experiences, we create
strong connections and by doing so, we change our brain.
There are 2 types of neuroplasticity:
Both types have exciting potential, but structural neuroplasticity is probably the one that is more
attended to at the moment; we already know that some functions can be rerouted, relearned, and
re-established in the brain, but changes to the actual structure of the brain are where many of the
exciting possibilities lie.
When people understand the ability of the brain to adapt and create stronger connections between its neurones, then people will be able to exploit this ability of the bran to be able to perform better in their lives, as an example here are some of the ways that neuroplasticity benefits people’s lives:
I see that one way to re-wire the brain with neuruplasticity is with repetition, so I can see that
by doing exercises and practicing code, I’ll be able to re-wire my brain into coding, and not being
a spring chicken anymore, I see that this is a great way to help improve not only my learning path,
but also my health and well being.
This is some of the ways that neuroplasticity can be applied.
A few of the methods that have been shown to enhance or boost neuroplasticity include:
As mentioned before, some ways that I can use to improve my neuroplasticity are: by repeating exercises, and doing some extra activities, like intermittent fasting, reading fiction, sometimes trying some exercises with my non dominant hand, going for a wee boogie and of course, sleeping!
click for more about neuroplasticity
Mindset is divided in two: fixed and growth;
Fixed mindset is the way that people perceive everything,
they think that a person is born with their intelligence and that that intelligence can’t be improved,
see different problems as obstacles and constantly look for the ‘easy solutions’.
Growth mindset is a
more flexible one, is the way that we see things in a way that are going to be good for us, a growth
mindset sees a problem as a challenge, understands that one can achieve anything one wants and that our
intelligence can be improved, we can learn new things all our life.
A growth mindset goes hand in hand with neuroplasticity, as in once a person realises
that our brains can adapt and improve over time, then we will stop seeing problems but challenges,
a failure becomes a new way to find the wrong answer, and it is only with time and work that we will be
able to be where we want to be.
With challenges, I know I probably will have a hard time solving them, but I know now
that these won’t be imposible and so I will need to practice as much as I’ll be able,
and repeat and repeat them; try to solve different challenges in different ways; I know
that it will be hard, but I know that anyone can do it. I know now also about neuroplasticity
and how my brain will eventually adapt and eventually see these challenges not so hard,
and then I’ll be able to look into harder challenges and so on. And whenever I feel that
I can’t do something, I’ll remember something that I heard someone saying at the gym:
“don’t believe the lies that your brain is telling you”; I know, I know, sounds ironic
based on what we have discussed up to this point, but I do believe that it is our
‘fixed mindset’ brain the one that tells us that we can’t solve this or that, that we can’t
do one more push up… that we can’t! so stop listening to this ‘lie’ and start to achieve!


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LPS Viewer User Guide
This is the user's guide for the LPS Viewer. The purpose of this documentation is to provide instruction on the use of the viewer.
Topics:
About
Usage
Conventions
Glossary


Use the field below to specify the maximum number of fields a class is allowed to have.
Use the first checkbox below to indicate whether constant fields count toward this number.
Per default this inspection only counts immutable static final objects as constant. Use the second checkbox below to count any static final field as constant.
Powered by InspectionGadgets


--- title: "Winter Conifers" date: "2017-11-30 00:00:01 -06:00" categories: - garden-journal layout: "post" blog-banner: "garden-journal-spring.jpg" post-date: "November 30, 2017" post-time: "3:00 PM" blog-image: "2017-11-30-wh.jpg" published: false ---
By: Bryn Ramjoué, Red Butte Garden Director of Communications
For centuries greenery such as evergreen trees and plants have brightened homes during dark winter months as a symbol of hope and renewal. History and lore describe the use of conifers as decoration beginning in Germany during the middle ages, with wider use since the Victorian Era.
A group of mostly evergreen, cone-bearing trees and shrubs, there are 588 species of conifers, with interesting shapes, colors, heights, and textures that produce unique needle or scale-like leaves.
A few interesting notes about conifers: Conifers include the Pine family with at least 240 species from 11 genera including Spruce, Fir, Pine, Cedar, and Larch. Pines have leaves that are in round and occur in bundles of one-to-five needles. Spruce and Fir needles are not bundled, but single and each needle has hard, flat, or squared edges rather than rounded spike-like needles. Spruce cones hang from the branch downward. Young Fir cones sit on the branch and point upward.
Spruce Cones
Fir Cones
Although most conifers are evergreen, Larches shed their leaves annually. The Siberian Larch is the most numerous and widest spread of all trees. Bristlecone Pine is one of the oldest trees in the world, aging thousands of years. Other types of conifers include the Cypress family with 141 species like the Giant Sequoia, the largest trees, the Coastal Redwood, known as the tallest tress, and the Juniper, of which Utah is home to the world’s oldest Rocky Mountain Juniper. This family of conifers have flat, scaly sprays rather than needle-like leaves. The Podocarp family has 156 species dominating the southern hemisphere with tropical varieties. Other taxa include Araucaria, Umbrella Pine, and Yew. As you can see, conifers are as interesting as they are varied.
The best-selling conifers as indoor winter décor are: Scotch Pine, Douglas Fir, Fraser Fir, Balsam Fir, and White Pine.
Fraser Fir
Where can you see a wide variety of conifer trees? At Red Butte Garden, Utah’s botanical garden and arboretum, located in the foothills near the University of Utah. Recognized by the American Conifer Society as a reference garden, Red Butte Garden has an extensive conifer collection highlighting six families, 22 genera, and accessions representing 230 different taxa. Winter is a great season to see these magnificent trees.
Atlas Cedar and Juniper Deer Topiary
Notable conifers in the Garden are: the deer topiary at the entry to the Children’s Garden shaped from a Juniper, mature Bristlecone Pines line the path west of the Children’s Garden, a Larch demonstrating fall shedding and a Japanese Red Pine with two-toned needles can be found in the Rose Garden.
Japanese Red Pine (foreground) and Larch (orange colored foliage in background) trees
Bristlecone Pine
For more information about the Red Butte Conifer Collection, CLICK HERE


Introducing the next generation
of Information Technology consulting.
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businesses take the digital leap they need to compete in an rapidly changing market.
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All modern enterprises have valuable data that could be harnessed and used to target potential customers and sell more products. Digital Ascension can help you wrangle this data and find valuable insights using Artifical Intelligence.
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With the Basic package, you will have access to our Web Design offering as well as our 48 hour customer support response time guarantee.
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I went to the hospital for an operation, and my boyfriend accompanied me. A nurse hooked me up to an IV for a very long time before the doctor was ready to operate. I walked to the room with my boyfriend, lay down, and the nurse asked me to describe someplace I liked to go. I assumed it was meant to relax me for the procedure, so I started talking. It was while I was describing this place to her that she injected the fentanyl into my IV.
It hit in about three seconds. My boyfriend says he could see my pupils dilate almost instantly and my speech slow to a near halt. I remember everything getting warm and fuzzy, just so cozy, like I was curled up in a blanket right next to a fire. They had a poster of some kind on the ceiling (probably to distract patients like me); I think it had a kitten on it. Partway through the operation, I felt the dull pain of the scraping inside me, and requested more fentanyl. The nurse complied and gave me another dose of unknown quantity. I closed my eyes and remained semi-conscious of my surroundings, for a little while.
Shortly thereafter, the procedure was finished and the nurse and my boyfriend walked me back to my bed in the previous room. I don't recall having any trouble walking, but being grateful for the support. My boyfriend said I could barely walk/stumble and needed both of them there to practically carry me back. I then lay down and promptly 'rested', or so I thought, for maybe 15 to 20 minutes -- again, eyes closed, pleasantly dark, but not sleeping. I checked my watch, and asked my boyfriend to get me a frozen yogurt from the confectionary afterwards, and again 'rested' for a few minutes, 10 or 15 tops. He returned, and I asked him what time it was. Close to 45 minutes had passed.
I suddenly became aware that I had been blacking out, losing consciousness and regaining it so seamlessly that I wasn't even aware of the breaks. I recognized it from one prior experience: the first time I had tried cannabis, when it had been (unknowingly) laced with crystal meth.  I realized that the fentanyl was doing the exact same thing to me, only much more smoothly. I was waking up and not even realizing that the blank spot was there. I felt the need to rest for 'just another half hour' until the hospital department was closed for the day.
Well, an hour later I woke up again...I felt really bad for making the nurses stay because I was such a wuss about pain and required more fentanyl, and also because I couldn't get over the effects of it as quickly as I'd hoped. (I've always had a sensitive body chemistry.) I managed to get up and change into street clothes, and we called a cab to go meet some friends. (Hah!) I promptly threw up in the car, even though I was lying down, and apologized profusely to the cab driver, who was nice about it. That weirded me out for some reason. I was standing outside a fast food joint looking ill and feeling ready to puke again when a 5 year old child offered me his juice. It was very cute and touching, but I told him I'd be fine, and went home to go crash out for the rest of the evening.
In summary: It was supposed to be a painkiller, but I needed a stronger dose than normal, and it had the most peaceful high I've ever experienced -- but also the least lucid. The comedown was terrible. I assume that people doing it recreationally must take smaller doses. I have no idea how much the nurse gave me, although looking online it seems like 100 micrograms is par for the course, so I probably took 200 mcg...
I really wish it had numbed my body more during the operation, and made me less nauseous afterwards. It did what it was supposed to, albeit not very effectively, in my view. I wouldn't take it recreationally, but I can see how others would for the narcotic high. Personally, I think it would turn me into an uncoordinated drooling zombie. I've also no wish to OD on it, having read a couple of experiences.
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.


CSRF protection middleware.
By default this middleware generates a token named "_csrf" which should be
added to requests which mutate state, within a hidden form field,
query-string etc. This token is validated against
req.csrfToken()
.
The default value
function checks
req.body
generated by the
bodyParser()
middleware, req.query
generated by
query()
, and the “X-CSRF-Token” header field.
This middleware requires session support, thus should be added somewhere
below session()
.


How pit latrines are still thriving in resident homes at Sowutuom-VRA
A mixture of smells, not sweet-scented, but from pits with human excreta; that’s what meets you when you move around some parts of VRA, a community in the heart of Accra, near Suotuom.
The area which is relatively expanding with time, still has many homes with pit-latrines. It is quite impressive to say open defecation is almost absent in the area, most homes have a toilet facility.
The decency of these facilities however, is the issue. It is enough to keep people from defecating around, but not enough to stop the dire health implications that come with it.
Aside the stench, flies from these laterines hover over food, and household equipment of residents considering these pits are not very far from their homes.
Today being World Toilet Day, Ghanaweb visited the community, to put the spotlight on the situation and speak to some residents.
“It is a pit, we have to squat over to defecate. It was a wooden structure at first but it got destroyed so we built a new one. It came with the house, the landlord built it for us,” Madam Augustina Osarfo said.
“We were tasked with building our own toilets. We only built it when we moved into the house. It was only a wooden structure like the ones in the village but as the area developed, we built a pit with a water closet,” another resident said.
World Toilet day is set aside each year on 19th November to inspire the world to tackle the global sanitation crisis and help achieve Sustainable Development Goal 6 (SDG 6), which promises sanitation for all by 2030. World Toilet Day 2019 is drawing attention to those people being left behind without sanitation.


| Developer Spotlight |
James Todd, known as gonzo on the jxta.org aliases, is a Sun Microsystems employee based in Santa Clara, California. His role has predominately been to apply JXTA features with complimentary enterprise deployment capabilities like that of JNDI/LDAP, WebStart/JNLP, etc... He also considers himself the general rebel rouser. He is the owner of the myjxta2 and rosettaChat projects. He'd like to merge/coopt rosettaChat into myjxta2 as a service/feature. James sees JXTA as the developer enabler to leverage resilient features, like those found in DNS and SendMail, directly within the application domain to:
He has also helped roll out the initial release of Tomcat/Ant/WatchDog. James is a happy user of JDOM/Jaxen/Drools, Tomcat, Ant, and FreeTTS. You can reach James at<EMAIL_ADDRESS>|


We have easy to use classes to help you align your content.
You can easily vertically center things by adding the class valign-wrapper
to the container holding the items you want to vertically align.
This should be vertically aligned

This should be vertically aligned

These classes are for horizontally aligning content. We have .left-align
, .right-align
and .center-align
This should be left aligned
This should be right aligned
This should be center aligned

This should be left aligned

This should be right aligned

This should be center aligned

Quickly float things by adding the class left
or right
to the element. !important
is used to avoid specificity issues.

...

...


host_get_clock_service
Function - Return a send right to a kernel clock's service port.
SYNOPSIS
kern_return_t host_get_clock_service (host_t host, clock_id_t id, clock_t clock_name);
PARAMETERS
- host
-
[in host-name send right]
The name (or control) port for the host
owning the clock.
- id
-
[in scalar]
The identification of the desired kernel clock. These values
are defined in \*L
\*O. Although an implementation may define additional values, the following values are always defined (although only the REALTIME clock is required to be implemented): - REALTIME_CLOCK
-
A moderate resolution clock service that (typically) tracks
time since the system last boot.
- BATTERY_CLOCK
-
A (typically) low resolution clock (to the second) that
survives power failures or service outages.
- HIGHRES_CLOCK
- A high resolution clock.
- clock_name
- [out clock-name send right] Name port for the clock.
DESCRIPTION
The host_get_clock_service function returns a send right to the name port for a kernel clock object. This right is used to get the time and resolutions of the clock and to set clock alarms.
RETURN VALUES
Only generic errors apply.
RELATED INFORMATION
Functions: clock_get_time, clock_get_attributes, clock_map_time, clock_sleep, clock_alarm, host_get_clock_control.


@if(score.playerOne.id == player.id) {
@if(score.playerOnePoints > score.playerTwoPoints) {
Win
@score.playerTwo.name
@score.playerOnePoints - @score.playerTwoPoints
<EMAIL_ADDRESS><EMAIL_ADDRESS>@score.timeSincePP
} else {
Lost
@score.playerTwo.name
@score.playerOnePoints - @score.playerTwoPoints
@score.playerOneScoreDelta
@score.playerOneRankDelta
@score.timeSincePP
}
} else {
@if(score.playerOnePoints < score.playerTwoPoints) {
Win
@score.playerOne.name
@score.playerTwoPoints - @score.playerOnePoints
<EMAIL_ADDRESS><EMAIL_ADDRESS>@score.timeSincePP
} else {
Lost
@score.playerOne.name
@score.playerTwoPoints - @score.playerOnePoints
@score.playerTwoScoreDelta
@score.playerTwoRankDelta
@score.timeSincePP
}
}


Pwnable.kr is not exactly easy even when they say it is. Despite this scary introduction, this specifically challenge is not that hard.
Papa brought me a packed present! let's open it.
Download : http://pwnable.kr/bin/flag
This is reversing task. all you need is binary
Please note the first sentence of the text: “Papa brought me a packed present!”. Here lies the key to solve it. After downloading the binary, let’s do a sanity check:
$ file flag
flag: ELF 64-bit LSB executable, x86-64, version 1 (GNU/Linux), statically linked, stripped
Since the file is a stripped ELF, there is no use in running gdb in order to disassemble main() or anything like that. After all, our symbol table is far gone.
One hint was given in the statement of the chall. Packing. Packing is used, among other things, to obfuscate a binary. It basically compresses the binary and adds a decompression routine, so you get your full binary in memory only when you run it. Let’s search for compression signs:
$ strings flag | grep pack
$Info: This file is packed with the UPX executable packer http://upx.sf.net $
I think this answers our question :) UPX is one of the most commonly used packer for executables. in order to unpack it I had to first get UPX:
$ wget -q https://github.com/upx/upx/releases/download/v3.92/upx-3.92-amd64_linux.tar.xz
$ unxz -d upx-3.92-amd64_linux.tar.xz
$ tar -xvf upx-3.92-amd64_linux.tar
$ cd upx-3.92-amd64_linux.tar
$ ./upx -d /PATH_TO/flag
Ultimate Packer for eXecutables
Copyright (C) 1996 - 2016
UPX 3.92 Markus Oberhumer, Laszlo Molnar & John Reiser Dec 11th 2016
File size Ratio Format Name
-------------------- ------ ----------- -----------
887219 <- 313220 35.30% linux/amd64 flag
Unpacked 1 file.
And now:
file flag
flag: ELF 64-bit LSB executable, x86-64, version 1 (GNU/Linux), statically linked, for GNU/Linux 2.6.24, BuildID[sha1]=96ec4cc272aeb383bd9ed26c0d4ac0eb5db41b16, not stripped
Not stripped!
So it is basically giving us the flag at address 0x6c2070. Ok, then…
gdb-peda$ x/s \*0x6c2070
0x496628: "UPX...? sounds like a delivery service :)"
There you go!


Redistricting is dividing up a jurisdiction (like a state, county, or city) into districts for the purpose of electing representatives. Where and how the lines are drawn influences everything from who is likely to be elected to how resources get allocated. And when it comes to democratic representation, communities matter. If your community is split up into different small pieces every ten years, it makes it harder to have a voice in government.
The official line-drawing process is different in every state, and in 2021 a record number of states will ask the public for input. Districtr is a tool to help you try your hand at identifying your community or drawing your own districts. Speak up for your community – and show the line-drawers how you want to be represented!


Preparation time
overnight
Cooking time
less than 10 mins
Serves
Serves 2
Dietary
Labneh is a thick strained yoghurt that is easy to make using natural yoghurt. Topped with warm spiced oranges it makes a wonderful breakfast or light dessert.
With a GI of 39 this meal is
high protein, low GI
and provides 193 kcal per portion.
For the labneh, line a sieve with a single layer of muslin cloth and place over a bowl. Spoon the yoghurt into the sieve and tie up the ends of the cloth. Leave to drain in the fridge overnight.
Cut one of the oranges in half and squeeze all the juice – you should end up with 4-5 tbsp. Peel the remaining oranges and cut into thin slices.
Pour the juice into a small saucepan and stir in the cinnamon, star anise and vanilla extract.
Place over a medium heat and simmer for 1-2 minutes, stirring occasionally. Add the orange slices and warm through gently.
Divide the labneh between two small dishes. Top with the warm spiced oranges and juice and scatter the nuts on top.


Rate Adjustments is an optional feature allowing you to define adjustments to a booking's base rate. The adjustment can be applied based on day of the week, time of day, or day and time.
Initial rate setup is done in the other setup screens, resource, service or seat type.
When the booking screen calculates charges, a call is made to get see if an adjustment if required. If an adjustment is found, it is added to the rate to be used for the booking. The adjustment is Added to the base rate, so an adjustment of $2.00 on a booking with rate or $10.00 will result ins a new rate of $12.00.
There are 3 ways to apply adjustments:
Note: Bookings that begin or end in an adjustment area will have the adjustment applied.
For example, if an adjustment is set as 4:00-6:00, a booking that runs from 3:00 to 4:30 will have the adjustment applied.
Publish: Sets one or more rate adjustments to Published.
Unpublish: Sets one or more rate adjustments to Unpublished.
Remove: Deletes one or more rate adjustments.
Edit: Opens the rate adjustments Edit screen to allow you to view and modify the adjustment.
New: Opens the New rate adjustments screen to allow you to add a new adjustment.
Help: Opens this help screen.
ID: Unique ID for the adjustment rate.
Resource: This is the resource that is getting the adjustment rate.
By: This is when the adjustment will be applied.
Days: Shows which days of the week to apply the adjustment for Day Only or Day and Time.
Time Range: Shows the time range to be used for Time Only or Day and Time.
Adjustment: Shows the adjustment amount. Negative adjustments are supported and allow a discount.
Unit: The adjustment amount can be either a percentage to the base rate, or a flat rate.
Published: Indicates the published state.
Click on the ID to open the edit screen.


Working with Files has always been exclusively a "server thing". No more, enter the File API and his buddies. This API allows you to work with files directly in the browsers. You can read, modify, and create them right then and there, and let the user save the result.
So what could we do with this?
Warning, this example will not work in Safari or Opera!
How does it work?
The Files API looks a lot tougher than he is, sort of like a guy with a goatee who smokes cigars but actually enjoys
peppermint tea and horticulture. Anyway, we digress. We basically have to create a new Blob(["data", "more data"],
{"type" : "text/plain"})
object which can contain arbitrary data, which we then get an URL by using the
URL.createObjectURL(blob)
method. The returned URL object can be set as a href
attribute for a link (like we did in the
examples above). Because your browser would normally just open the link, we have used the download="myfile.xyz"
attribute. With this attribute you force the browser to download a linked document with the defined filename.
All of this is pretty smooth sailing when you're after text content (except some custom code for IE). Binary content is
a slightly harder, because you don't want the Blob
to do something with encoding on it. Therefore, we have to use one of
the Typed Arrays (we used Uint8Array
, an 8-bit unsigned integer array). A typed array is a view on the ArrayBuffer
,
which contains the actual binary data. We can use this ArrayBuffer to create a binary file like this: new Blob(buffer,
{"type" : "image/gif"})
.
One more thing: in the example above we directly access the chosen file from the input
element. This was really easy:
all we did was access the inputElement.files
array (you did know we could do multiple file selects nowadays, didn't
you?) and created an URL for the first element with URL.createObjectURL(inputElement.files[0])
. This URL we assigned to
the video.src
attribute, Bob's (not Blob) your uncle!
So how handy is it, really?
 We didn't even scratch the surface of all the cool stuff you could do with this. Simply put, this API really makes working with files a whole lot easier. We can generate them, link them, and directly read them in the browser. Many things that required server-side processing can now be done on the client-side.
What else could we do with this?
- Build a photo-editor in the browser without storing anything on the server
- Resize uploaded images before we send them to the server
Great, but can I use it today?
Support isn't too bad (not too great, either, though). The only ones who have trouble with this are Safari and Opera. Safari seems not to like Blob URL's, and Opera does not support Blob URLs at all. IE wouldn't be IE if it wasn't a tiny bit different. IE10 doesn't like creating binary Blobs directly, so we have to use the (deprecated) BlobBuilder API.
The only browser with full support for the download attribute is Chrome, but we used window.saveAs()
to
have it work in IE10. In other browsers, clicking the link just opens the file in the browser.
Hit me with the source!
Feel free to look around the differente source files we used for this example.


Class
LocalMatchStoreStores information about local matches.
| Defined in | <seqan/parse_lm.h> |
|---|---|
| Signature |
template <typename TSpec = void, TConfig = LocalMatchStoreConfig<TSpec> >
struct LocalMatchStore;
|
Interface Function Overview
-
void appendLocalMatchStore(store, subjectId, subjectBeginPos, subjectEndPos, queryId, queryBeginPos, queryEndPos);, void appendLocalMatchStore(store, subjectName, subjectBeginPos, subjectEndPos, queryName, queryBeginPos, queryEndPos, cigarStringBuffer);
Append a new local match to a LocalMatchStore -
int readRecord(store, reader, tag);
Read Lastz "general" format record.
Member Variable Overview
-
TCigarString LocalMatchStore::cigarStore
String storing the CIGAR strings. -
TMatchStore LocalMatchStore::matchStore
String storing the LocalMatch local matches. -
TStringSet LocalMatchStore::sequenceNameStore
StringSet storing the sequence names.
Detailed Description
The LocalMatchStore provides information about matches. Similar to the FragmentStore, the information is split into multiple sub stores. Each sub store stores a part of the information.
The LocalMatchStore#matchStore stores the information about a match. The LocalMatchStore#sequenceNameStore stores the sequence names. These both sub stores are "core stores", they are filled with consistent information, i.e. for each sequence id in the matchStore, there has to be a valid entry in the sequenceNameStore.
The LocalMatchStore#cigarStore optionally stores CIGAR strings for the matches. Its entries are referenced by matchStore[i].id.
Examples
Read Lastz matches from a link RecordReader and then print them to stdout.
// Read local alignments from record reader. Note that in real-world code, // you should have error handling. LocalMatchStore<> lmStore; while (!atEnd(recordReader)) readRecord(lmStore, recordReader, StellarGff()); // Print local alignment information to stdout. std::cout << "# Reverse strandness is indicated by end < begin\n" << "#db\tdb_beg\tdb_end\tquery\tq_beg\tq_end\n"; for (unsigned i = 0; i < length(lmStore.matchStore); ++i) std::cout << lmStore.sequenceNameStore[lmStore.matchStore[i].queryId] << "\t" << lmStore.matchStore[i].queryBeginPos << "\t" << lmStore.matchStore[i].queryEndPos << "\t" << lmStore.sequenceNameStore[lmStore.matchStore[i].subjectId] << "\t" << lmStore.matchStore[i].subjectBeginPos << "\t" << lmStore.matchStore[i].subjectEndPos << "\n";
See Also
Interface Functions Detail
void appendLocalMatchStore(store, subjectId, subjectBeginPos, subjectEndPos, queryId, queryBeginPos, queryEndPos);
void appendLocalMatchStore(store, subjectName, subjectBeginPos, subjectEndPos, queryName, queryBeginPos, queryEndPos, cigarStringBuffer);
Parameters
store
|
The LocalMatchStore to add the local match to. |
|---|---|
subjectId
|
Numeric subject sequence identifier, IntegerConcept. |
subjectName
|
The textual name of the query sequence, CharString. |
subjectBegin
|
The begin position of the match in the subject, IntegerConcept. |
subjectEnd
|
The end position of the match in the subject, IntegerConcept. |
queryId
|
Numeric query sequence identifier, IntegerConcept. |
queryName
|
The textual name of the query sequence, CharString. |
queryBegin
|
The begin position of the match in the query, IntegerConcept. |
queryEnd
|
The end position of the match in the query, IntegerConcept. |
cigarStringBuffer
|
Buffer with the cigar string of the local alignment, CharString. |
Matches on the reverse-complement are encoded by the begin position being greater than the begin position.
Data Races
int readRecord(store, reader, tag);
Parameters
store
|
LocalMatchStore object to read into. |
|---|---|
stream
|
SinglePassRecordReader to read from. |
tag
|
The tag for selecting the format, one of BlastnTabular, LastzGeneral, and StellarGff. |
Returns
int |
0 on success, non-0 on errors and EOF |
|---|


MACI provides support for the execution and analysis of large experiments. The following steps are necessary to run your first MACI experiment:
Check the concepts and architecture page and the the offical MACI page for more information.


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Defines a Rake task for running features.
The simplest use of it goes something like:
Cucumber::Rake::Task.new
This will define a task named cucumber
described as ‘Run Cucumber features’. It will use steps from
‘features /*.rb’ and features in ‘features/ *.feature’.
To further configure the task, you can pass a block:
Cucumber::Rake::Task.new do |t| t.cucumber_opts = %w{--format progress} end
This task can also be configured to be run with RCov:
Cucumber::Rake::Task.new do |t| t.rcov = true end
See the attributes for additional configuration possibilities.
Name of the cucumber binary to use for running features. Defaults to Cucumber::BINARY
Whether or not to run with bundler (bundle exec). Setting this to false may speed up the execution. The default value is true if Bundler is installed and you have a Gemfile, false otherwise.
Note that this attribute has no effect if you don’t run in forked mode.
Extra options to pass to the cucumber binary. Can be overridden by the CUCUMBER_OPTS environment variable. It’s recommended to pass an Array, but if it’s a String it will be split by ‘ ’.
Whether or not to fork a new ruby interpreter. Defaults to true. You may gain some startup speed if you set it to false, but this may also cause issues with your load path and gems.
Directories to add to the Ruby $LOAD_PATH
Define what profile to be used. When used with #cucumber_opts it is simply appended to it. Will be ignored when CUCUMBER_OPTS is used.
Run cucumber with RCov? Defaults to false. If you set this to true,
fork
is implicit.
Extra options to pass to rcov. It’s recommended to pass an Array, but if it’s a String it will be split by ‘ ’.
# File lib/cucumber/rake/task.rb, line 174 def initialize(task_name = "cucumber", desc = "Run Cucumber features") @task_name, @desc = task_name, desc @fork = true @libs = ['lib'] @rcov_opts = %w{--rails --exclude osx\/objc,gems\/} yield self if block_given? @binary = binary.nil? ? Cucumber::BINARY : File.expand_path(binary) @libs.insert(0, LIB) if binary == Cucumber::BINARY define_task end
# File lib/cucumber/rake/task.rb, line 214 def make_command_line_safe(list) list.map{|string| string.gsub(' ', '\ ')} end
# File lib/cucumber/rake/task.rb, line 143 def rcov=(flag) if flag && !Cucumber::RUBY_1_8_7 raise failed + "RCov only works on Ruby 1.8.x. You may want to use SimpleCov: https://github.com/colszowka/simplecov" + reset end @rcov = flag end


The TMCRC API call provides CRC calculation facilities. The Type parameter specifies either 8-bit CRC (Type=0) or 16-bit CRC (Type=1). Since the last byte of the unique registration number embedded into every 1-Wire device is an 8-bit CRC, this function call, in 8-bit CRC mode can verify the integrity of a 1-Wire registration number. The 16-bit CRC mode can be used to help make and verify Universal Data Packets (UDPs). Creating UDPs is a TMEX-supported way to packetize data before writing to a 1-Wire device. UDPs use 16-bit CRCs to verify data integrity.
A detailed description of the 8-bit CRC and 16-bit CRC can be found in Application Note 27 “Understanding and Using Cyclic Redundancy Checks with Dallas Semiconductor iButton Products”. The Application Note is located on MAXIM/Dallas Semiconductor’s website: http://pdfserv.maxim-ic.com/arpdf/AppNotes/app27.pdf.
For a description of the above types, see the TMEX Type Descriptions.
Parameters
length
Specifies the length of the buffer to CRC.
buf
Specifies a pointer the byte buffer containing the data.to CRC.
seed
Specifies the CRC seed to use.
Type
Specifies the type of CRC to perform: It can either be an 8-bit CRC (Type=0) or a 16-bit CRC (Type=1)
Return Value
>=0 => the CRC result
<0 => TMEX Transport Error Return Code
See Also


Sign in
Prototypes built using Foundation version 2.2
Released on April 2, 2012
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Recipe 5.3. Sorting a List of Objects by an Attribute of the ObjectsCredit: Yakov Markovitch, Nick Perkins ProblemYou need to sort a list of objects according to one attribute of each object. SolutionThe DSU idiom shines, as usual: def sort_by_attr(seq, attr): intermed = [ (getattr(x, attr), i, x) for i, x in enumerate(seq) ] intermed.sort( ) return [ x[-1] for x in intermed ] def sort_by_attr_inplace(lst, attr): lst[:] = sort_by_attr(lst, attr) In Python 2.4, DSU is natively supported, so your code can be even shorter and faster: import operator def sort_by_attr(seq, attr): return sorted(seq, key=operator.attrgetter(attr)) def sort_by_attr_inplace(lst, attr): lst.sort(key=operator.attrgetter(attr)) DiscussionSorting a list of objects by an attribute of each object is best done using the DSU idiom introduced previously in Recipe 5.2. In Python 2.3 and 2.4, DSU is no longer needed, as it used to be, to ensure that a sort is stable (sorting is always stable in Python 2.3 and later), but DSU's speed advantages still shine. Sorting, in the general case and with the best algorithms, is O(n log n) (as is often the case in mathematical formulas, the juxtaposition of terms, here n and log n, indicates that the terms are multiplied). DSU's speed comes from maximally accelerating the O(n log n) part, which dominates sorting time for sequences of substantial length n, by using only Python's native (and maximally fast) comparison. The preliminary decoration step, which prepares an intermediate auxiliary list of tuples, and the successive undecoration step, which extracts the important item from each tuple after the intermediate list is sorted, are only O(n). Therefore any minor inefficiencies in these steps contribute negligible overhead if n is large enough, and reasonably little even for many practical values of n.
This recipe puts index i, in each tuple that is an item of list intermed, ahead of the corresponding x (where x is the i-th item in seq). This placement ensures that two items of seq will never be compared directly, even if they have the same value for the attribute named attr. Even in that case, their indices will still differ, and thus Python's lexicographic comparison of the tuples will never get all the way to comparing the tuples' last items (the original items from seq). Avoiding object comparisons may save us from performing extremely slow operations, or even from attempting forbidden ones. For example, we could sort a list of complex numbers by their real attribute: we would get an exception if we ever tried to compare two complex numbers directly, because no ordering is defined on complex numbers. But thanks to the precaution described in this paragraph, such an event can never occur, and the sorting will therefore proceed correctly. As mentioned earlier in Recipe 5.2, Python 2.4 supports DSU directly. You can pass an optional keyword-argument key, to sort, which is the callable to use on each item to get the sort key. Standard library module operator has two new functions, attrgetter and itemgetter, that exist specifically to return callables suitable for this purpose. In Python 2.4, the ideal solution to this problem therefore becomes: import operator seq.sort(key=operator.attrgetter(attr)) This snippet performs the sort in-place, which helps make it blazingly faston my machine, three times faster than the Python 2.3 function shown first in this recipe. If you need a sorted copy, without disturbing seq, you can get it using Python 2.4's new built-in function sorted: sorted_copy = sorted(seq, key=operator.attrgetter(attr)) While not quite as fast as an in-place sort, this latest snippet is still over 2.5 times faster than the function shown first in this recipe. Python 2.4 also guarantees that, when you pass the optional key named argument, list items will never be accidentally compared directly, so you need not take any special safeguards. Moreover, stability is also guaranteed. See AlsoRecipe 5.2; Python 2.4's Library Reference docs about the sorted built-in function, operator module's attrgetter and itemgetter functions, and the key argument to .sort and sorted. |


I'm Carla Solis Uehara. Economist from @PUCP. I’m a recent computer analysis and public policy graduate @UChicago. I love coding (and try to) solve social problems. El corazón habla en español.
Here's the code for creating this very simple website, hosted in Github


Speed.js Coding Sample R2.0
User input
Box Shape
L Shape
H Shape
T Shape
Floor area in sf
Number of floors
Shape image


FROM THE DESK OF MR.Atik BASSIM
BILL AND EXCHANGE
FOREIGN REMITTANCE DEPT.
BANK OF AFRICA ( BOA)
OUAGADOUGOU, BURKINA FASO ,
Dear Friend,
Compliments,
I am the manager of bill and exchange at the foreign remittance department of BANK OF AFRICA. I am writting to seek your coperation over this business deal.
In my department, I discovered an abandoned sum of$15million USD(Fifteen million US dollars)only , in an account that belongs to one of our foreign customers who died along with his entire family in a plane crash that took place in Kenya,East Africa,the Late DR. GEORGE BRUMLEY,a citizen of Atlanta,United States of America but naturalised in Burkinafaso,WestAfrica and contractor with ECOWAS,(ECONOMIC COMMUNITY OF WEST AFRICAN STATES) .
Since we got information about his death, we have been expecting his next of kin to come over and claim his money because it cannot be released unless somebody applies for it as next of kin or relation to the deceased as indicated in our banking guidelines but unfortunately , all his supposed next of kin or relation died alongside with him at the plane crash leaving nobody behind for the claim. It is therefore upon this discovery that I now decided to makethis businness proposal to you and release the money to you via your foreign bank account as the next of kin or relation to the deceased for safety and subsequent disbursement since nobody is coming for it and this money Could go into the Bank treasury as unclaimed Bill.
The Banking law and guideline here stipulates that if such money remainedunclamed after four years, the money will betransfered into the Bank treasury as unclaimed fund.The request for your assistance and maximum co-operation as a foreign citizen to stand as the next of kin in this business is occasioned by the fact that the deceased customer was a foreigner and a Burkinabe cannot stand as next of kin to a foreigner. 30 % of this money will be for you as my foreign partner, inrespect to the provision of a foreign account. 10 %will be set aside for expences incured during the business and 60 % would be for me.There after I will come over to your country for disbursement accoding to the percentages indicated.
Therefore to enable the immediate trnansfer of this fund to you as arranged,you must apply first to the bank as the relation or next of kin to the deceased,indicating your claims and wherein the money will be remitted .
Upon receipt of your reply, I will send to you by fax or email the text of application which you will fill and submit to the office of the foreign remittance director of the bank of africa. I will not fail to bring to your notice that this transaction is stricly confidential and i will use my position in this Bank to effect a hitch free transfer of the fund.
You should contact me immediately as soon as you receive this letter.
Trusting to hear from you immediately.
Please , visit the website below for more informations about the Plane Crash and the tragic death of the deceased and his entire family, Late DR.GEORGE BRUMLEY.
http://www.wral.com/news/2345844/detail.html
Your’s faithfully,
MR.Atik BASSIM.
Windows Live Messenger sur i-mode™ : dialoguez sur votre mobile comme sur votre PC avec Bouygues TELECOM !


Before Change
num_seq_probs = sum(x is not None for x in merged_logprobs)
num_probs += num_seq_probs
// number of <unk>"s (just for reporting)
num_unks += len(merged_logprobs) - num_seq_probs
// number of sequences
num_sentences += 1
if word_level:
After Change
num_words = 0
num_probs = 0
num_unks = 0
num_zeroprobs = 0
for word_ids, words, mask in scoring_iter:
class_ids, membership_probs = vocabulary.get_class_memberships(word_ids)
logprobs = scorer.score_batch(word_ids, class_ids, membership_probs,
mask)
for seq_index, seq_logprobs in enumerate(logprobs):
seq_word_ids = word_ids[:, seq_index]
seq_mask = mask[:, seq_index]
seq_word_ids = seq_word_ids[seq_mask == 1]
seq_words = words[seq_index]
merged_words, merged_logprobs = _merge_subwords(seq_words,
seq_logprobs,
subword_marking)
// total logprob of this sequence
seq_logprob = sum(lp for lp in merged_logprobs
if (lp is not None) and (not numpy.isneginf(lp)))
// total logprob of all sequences
total_logprob += seq_logprob
// number of tokens, which may be subwords, including <unk>"s
num_tokens += len(seq_word_ids)
// number of words, including <s>"s and <unk>"s
num_words += len(merged_words)
// number of word probabilities computed (may not include <unk>"s)
num_seq_probs = sum((lp is not None) and (not numpy.isneginf(lp))
for lp in merged_logprobs)
num_probs += num_seq_probs
// number of unks and zeroprobs (just for reporting)
num_unks += sum(lp is None for lp in merged_logprobs)
num_zeroprobs += sum(numpy.isneginf(lp) for lp in merged_logprobs)
// number of sequences
num_sentences += 1
if word_level:


You have gone through steps 1 - #{step-1}.
After End-User has logged out, the Client might request to log him out of the OP too. For this, a logout URL is obtained and End-User's browser is redirected to such location. Then OP will make the browser return to a predefined "Post Logout Uri" that was supplied (or set) during site registration.
The following logout URL was obtained:
#{flow.logoutUrl}
Click the button to end this sample flow.


I've used emoncms 8 in the past on Windows / WAMP. My Brultech GEM was broken for a long time, but now that it is fixed I want to start from scratch with a new setup, but I am having some problems. Here's what I've done (if this is already answered in the forums I apologize--I couldn't find the answer):
1) Installed Debian 8.2, then setup btmon to grab data from the GEM and post to emoncms (both btmon & emoncms run on this Debian server).
2) Followed these steps https://github.com/emoncms/emoncms/blob/9.0/docs/LinuxInstall.md with one exception--I had to clone to /var/www/html instead of /var/www (I guess that's new with Apache2). I cloned from
https://github.com/emoncms/emoncms.git which is the 9.0 branch now.
3) Using Chrome on my Windows machine, I connected by ip address to emoncms on the Debian server, created a user and logged in. I got the APIKey which I entered into the btmon script, then fired up the script. The inputs showed up in emoncms as expected. I also added a descriptive name to each new input.
4) Now for the problem--when I try to edit the process list by choosing log to feed, feed name, phpfiwa, 10s I get the error: ERROR: Feed could not be created, undefined. I know this is an error that some people run into and I tried all their solutions: I created the /var/lib/php... folders and made sure the locations matched in settings.php. I set chown www-data:root. I tried chmod 777. I tried adding those directories to open_basedir in php.ini, etc. I don't think it is a file permission error.
5) I found the following in /var/log/apache2/error.log:
PHP Fatal error: Uncaught exception 'Exception' with message 'ABORTED: Engine id '1' is not supported.' in /var/www/html/emoncms/Modules/feed/feed_model.php:71\nStack trace:\n#0 /var/www/html/emoncms/Modules/feed/feed_model.php(122): Feed->EngineClass(1)\n#1 /var/www/html/emoncms/Modules/feed/feed_controller.php(44): Feed->create('1', NULL, 'Node 1', 'Net_W', '1', '6')\n#2 /var/www/html/emoncms/core.php(64): feed_controller()\n#3 /var/www/html/emoncms/index.php(122): controller('feed')\n#4 {main}\n thrown in /var/www/html/emoncms/Modules/feed/feed_model.php on line 71, referer: http://<IP_ADDRESS>/emoncms/input/view
Unfortunately, I don't know php well enough to debug the problem. feed_model.php looks like it is trying to determine what engine to use (phpfina, phpfiwa, etc) but thinks it is looking for engine '1' which it doesn't find and so throws the error.
Thanks for any help.
Re: Can't create Feeds (ABORTED: Engine id '1' is not supported.) [SOLVED]
I got it working--turns out it was just a matter of clearing the cache on my browser.


Hey there,
We are really looking forward to meeting you for the International Women's Day event and workshop! One thing you will need to bring is your own machines and chargers.
We also require that you complete the tasks below before Saturday so that you are ready to go when you get to the workshop:
It is really important for us that every tool is installed, so we can start the workshop on time on Saturday, and help you build your web app! If you are having any trouble with the above tasks please plan to arrive at the event a little early and we will be more than happy to assist you with getting set up.
Thanks so much and we can't wait to meet everyone on the day!
See you at the event!
Nadia, Sarah, Conor & Steph
HubSpot
Dublin


Preparation time
30 mins to 1 hour
Cooking time
over 2 hours
Serves
Serves 4
For the avocado mousse, place the chopped avocado, double cream, lime juice and lemon juice into a food processor and blend to a smooth purée.
Bring the chicken stock to a simmer in a saucepan. Drain the soaked gelatine and gently squeeze out any excess liquid, then add to the chicken stock and stir until dissolved.
Whisk the chicken stock into the avocado purée until well combined. Season, to taste, with salt and freshly ground black pepper and set aside.
For the smoked salmon mousse, place the smoked salmon, double cream, horseradish sauce and lemon juice into a food processor and blend to a smooth purée.
Place the chicken stock into a saucepan and bring to a simmer. Drain the soaked gelatine and gently squeeze out any excess liquid, then add to the chicken stock and stir until dissolved.
Whisk the chicken stock into the smoked salmon purée until well combined. Season, to taste, with salt and freshly ground black pepper.
Place four 5cm/2in chefs' rings that are 6.25cm/2in deep onto a plate or tray lined with cling film. Divide the smoked salmon mousse mixture among the four rings, so that each ring is roughly half-filled. Top up each ring with the avocado mousse, filling up to just below the tops of the rings. Place into the fridge and chill for two hours, or until set.
Whisk the four tablespoons of double cream until thickened, then spoon over the top of the mousse, levelling off with a palette knife.
For the salad, whisk the mustard, lemon juice and olive oil together in a medium bowl until well combined. Season, to taste, with salt and freshly ground black pepper, then add the chopped avocado and gently mix to coat.
To serve, carefully place each of the rings into the centre of four serving plates. Using a blow torch or hot cloth, run around the edge of the ring to loosen the mousse and remove the ring. Arrange the salad leaves around the mousse and spoon the dressed avocado around and on top of the salad and the mousse.


{% load static %}
This is a django based application which enables a user to just register and then get regular messages about the beauty of the Wild at their phone number


They ask me for righteous judgments; they take pleasure in God drawing near.
3 'Why have we fasted,' they say, 'but you do not see it? Why have we humbled ourselves, but you do not notice?'

Do you really call this a fast, a day that pleases Yahweh?
6 Is not this the fast that I choose:
To release wicked bonds, to undo the ropes of the yoke,
to set the crushed ones free, and to break every yoke?
7 Is it not to break your bread with the hungry and to bring the poor and homeless into your house?"
When you see someone naked, you should clothe him; and you should not hide yourself from your own relatives.
8 Then your light would be broken open like the sunrise, and your healing would quickly sprout up;
your righteousness would
go before you, and the glory of Yahweh would be your rearguard.
9 Then you would call, and Yahweh would answer; you would cry out for help, and he would say, "Here I am."
If you take away from among yourselves the yoke, the accusing finger, and the speech of wickedness,
10 if you yourselves provide for the hungry and satisfy the need of the distressed;
then your light will rise in the darkness, and your darkness will be like the noonday.
11 Then Yahweh will lead you continually and satisfy you in regions where there is no water,
and he will strengthen your bones.

Suppose that you call the Sabbath a delight, and that you call the matters of Yahweh holy and honored.


ASSIST A CHILD TO SCHOOL
MURANG`A COUNTY EDUCATION FORUM.
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| Y-DNA haplogroups |
|---|
|
| | MtDNA haplogroups |
|---|
|
| | Other DNA pages |
|---|
|
|
|
|
If you tested your DNA with a personal genomics service like 23andMe, DeCODEme, FTDNA's Family Finder or another testing company, you can learn more about your risk factors for hundreds of diseases. Many of these SNP's are tested but not (yet) part of the report provided by personal genomics companies. This is free extra information.
We recommend that you install the SNPTips add-on for Mozilla Firefox to browse these pages. It allows you to upload your DNA results in the browser and show instantly which allele variant you have for each of the SNP listed below.
What can DNA tell you about yourself ?
List of cancers with the associated genes and alleles
Risk alleles : Substantially lower risk
(over 3x less), moderately lower risk (up
to 3x less), normal, slightly higher
risk (up to 3x more), greatly increased risk
(over 3x more). Normal alleles marked
with a * mean that the person is a carrier of the disease. Depending on
the testing company, sometimes A is reported as T, and G is reported as
C, or vice-versa.
Testing companies :
A = 23andMe (all versions) ; A2* = 23andMe (v2 only) ; A2 = 23andMe (v2 & v3) ; A3 = 23andMe (v3 only)
B = DeCODEme
C = FTDNA (Family Finder v2)
Bold letters indicate that the SNP is part of the official report, and is therefore likely to be more reliable.
Only SNP's in the official report and those not tested by 23andMe are listed for deCODEme. FTDNA SNP's are only mentioned when they are not tested by 23andMe.
| Description |
Chromosome |
Gene |
SNP |
Risk alleles |
Tested by* |
Links |
| Cancer (general risk) |
3 |
FHIT |
|
|
|
Link |
|
5 |
CLPTM1L |
rs2736098 |
AA, AG,
GG |
A3 |
Link |
|
|
|
rs401681 |
CC, CT,
TT |
A |
|
|
6 |
E2F3 |
|
|
|
|
|
12 |
MDM2 |
rs2279744 |
GG, GT,
TT |
A |
|
|
19 |
ZBTB7A |
|
|
|
|
|
20 |
AURKA |
rs2273535 |
AA, AT, TT |
A3 |
|
| Cancer (prevention) |
B |
CASP8 |
|
|
|
|
| Bladder Cancer |
A |
PTGS2 |
rs5275 |
CC, CT,
TT |
A |
|
|
A |
RUNX3 |
rs760805 |
AA, AT,
TT |
A3 |
|
|
3 |
3q38 |
rs710521 |
AA, AG,
GG |
A |
|
|
3 |
OGG1 |
rs1052133 |
CC, CG, GG |
A3 |
|
|
8 |
8q24 region |
rs9642880 |
GG, GT,
TT |
1, 2 |
|
| Brain Cancer |
A |
MTHFR |
rs1801131 |
GG, GT, TT |
A |
|
|
|
|
rs1801133 |
CC, CT, TT |
A |
|
| Breast Cancer |
B |
CASP8 |
rs17468277 |
CC, CT, TT |
A, B |
|
|
|
|
rs1045485 |
CC, CG,
GG |
A |
|
|
B |
intergenic |
rs13387042 |
AA, AG, GG |
A, B |
|
|
4 |
intergenic |
rs2056116 |
AA, AG, GG |
A3 |
|
|
5 |
intergenic |
rs4700485 |
AA, AG,
GG |
A, B |
|
|
5 |
MRPS30 |
rs4415084 |
AA, AG,
GG |
B |
|
|
5 |
intergenic |
rs889312 |
AA, AC, CC |
A |
|
|
6 |
TNF |
rs361525 |
AA, AG,
GG |
A |
|
|
6 |
CDKN1A |
rs3176336 |
AA, AT, TT |
A3 |
|
|
7 |
XRCC2 |
rs3218536 |
AA, AG,
GG |
A |
|
|
8 |
intergenic |
rs672888 |
CC, CT, TT |
A, B |
|
|
8 |
8q24 |
rs13281615 |
AA, AG,
GG |
A |
|
|
8 |
CYP51A1 |
rs1042638 |
|
A |
|
|
9 |
intergenic |
rs3218005 |
AA, AG, GG |
A3 |
|
|
9 |
CDKN2A |
rs3731239 |
CC, CT, TT |
A |
|
|
10 |
FGFR2 |
rs1219648 |
AA, AG, GG |
A |
|
|
|
|
rs2981582 |
CC, CT, TT |
A, B |
|
|
|
|
rs2420946 |
CC, CT, TT |
A |
|
|
|
|
rs2981582 |
CC, CT, TT |
A |
|
|
10 |
TCF7L2 |
rs12255372 |
GG, GT, TT |
A |
|
|
11 |
LSP1 |
rs3817198 |
CC, CT, TT |
A, B |
|
|
11 |
ATM |
rs3218707 |
CC, CG,
GG |
A3 |
|
|
|
|
rs4987945 |
CC, CG, GG |
C |
|
|
|
|
rs4986761 |
CC, CT,
TT |
A3 |
|
|
|
|
rs3218695 |
AA, AC,
CC |
A3 |
|
|
|
|
rs1800056 |
CC, CT,
TT |
A |
|
|
|
|
rs1800057 |
CC, CG, GG |
A3 |
|
|
|
|
rs3092856 |
CC, CT, TT |
A3 |
|
|
|
|
rs1800058 |
CC, CT, TT |
A |
|
|
|
|
rs1801673 |
AA, AT, TT |
A3 |
|
|
12 |
CDKN1B |
rs34330 |
CC, CT, TT |
A |
|
|
13 |
BRCA2 |
rs1799954 |
CC, CT, TT |
A |
|
|
|
|
rs766173 |
GG, GT,
TT |
A |
|
|
|
|
rs144848 |
GG, GT,
TT |
A |
|
|
|
|
rs4987117 |
CC, CT, TT |
A |
|
|
|
|
rs1799954 |
CC, CT, TT |
A |
|
|
|
|
rs11571746 |
CC, CT,
TT |
A |
|
|
|
|
rs11571747 |
AA, AC, CC |
A3 |
|
|
|
|
rs4987047 |
AA, AT, TT |
A |
|
|
|
|
rs11571833 |
AA, AT, TT |
A3 |
|
|
|
|
rs1801426 |
AA, AG, GG |
A3 |
|
|
|
|
i4000379 |
DD, DI,
II |
A |
|
|
16 |
16q12 region |
rs3803662 |
CC, CT, TT |
A, B |
|
|
17 |
BRCA1 |
rs1799950 |
AA, AG, GG |
A |
|
|
|
|
rs4986850 |
AA, AG,
GG |
A |
|
|
|
|
rs2227945 |
AA, AG, GG |
A |
|
|
|
|
rs16942 |
AA, AG, GG |
A |
|
|
|
|
rs1799966 |
AA, AG, GG |
A |
|
|
|
|
i4000377 |
DD, DI,
II |
A |
|
|
|
|
i4000378 |
DD, DI, II |
A |
|
|
17 |
TP53 |
rs1042522 |
CC, CG, GG |
A |
|
|
19 |
CCNE1 |
rs997669 |
AA, AG, GG |
A |
|
|
20 |
AURKA |
rs2273535 |
AA, AT, TT |
A3 |
|
|
|
|
rs1047972 |
CC, CT, TT |
A |
|
|
22 |
COMT |
rs4680 |
AA, AG,
GG |
A |
|
|
22 |
CHEK2 |
rs17879961 |
CC, CT,
TT |
A |
|
| Cervical Cancer |
6 |
CD83 |
rs853360 |
|
A3 |
|
|
|
|
rs9296925 |
|
A3 |
|
|
|
|
rs9370729 |
|
A3 |
|
|
|
|
rs9230 |
|
|
|
|
|
|
rs750749 |
|
A3 |
|
| Colorectal Cancer |
7 |
LEP |
rs2167270 |
AA, AG, GG |
A |
|
|
8 |
POU5F1P1 |
rs6983267 |
GG, GT,
TT |
A, B |
|
|
8 |
EIF3H |
rs16892766 |
AA, AC, CC |
A, B |
|
|
8 |
intergenic |
rs10505477 |
AA, AG, GG |
A |
|
|
9 |
intergenic |
rs719725 |
AA, AC,
CC |
A |
|
|
10 |
TCF7L2 |
rs7903146 |
CC, CT, TT |
A |
|
|
10 |
intergenic |
rs10795668 |
AA, AG,
GG |
A, B |
|
|
11 |
LOC120376 |
rs3802842 |
AA, AC, CC |
A, B |
|
|
12 |
CHFR |
rs2306536 |
CC, CT, TT |
A |
|
|
12 |
KLRK1 |
rs1049174 |
CC, CG, GG |
|
|
|
15 |
intergenic |
rs4779584 |
CC, CT, TT |
A, B |
|
|
18 |
SMAD7 |
rs4939827 |
CC, CT, TT |
A, B |
|
|
|
|
rs12953717 |
CC, CT, TT |
A |
|
|
|
|
rs4464148 |
CC, CT,
TT |
A |
|
|
19 |
INSR |
rs1864010 |
AA, AG,
GG |
|
|
|
20 |
AURKA |
rs2273535 |
AA, AT, TT |
A3 |
|
|
|
|
rs1047972 |
CC, CT, TT |
A |
|
| Endometrial (Uterus) Cancer |
12 |
MDM2 |
rs2279744 |
GG, GT, TT |
A |
|
|
22 |
COMT |
rs4633 |
CC, CT, TT |
A |
|
|
|
|
rs4680 |
AA, AG, GG |
A |
|
| Gallbladder Cancer |
3 |
OGG1 |
rs1052133 |
CC, CG, GG |
|
|
|
4 |
EGF |
rs4444903 |
AA, AG, GG |
A |
|
| Gastric (Stomach) Cancer |
A |
MTHFR |
rs1801133 |
CC, CT, TT |
A |
|
|
8 |
PSCA |
rs2294008 |
CC, CT, TT |
A |
|
|
17 |
MPO |
rs2333227 |
CC, CT, TT |
A3 |
|
|
20 |
AURKA |
rs1047972 |
CC, CT, TT |
A |
|
| Leukemia (Blood) Cancer |
B |
ACOXL |
rs17483466 |
AA, AG, GG |
A |
|
|
B |
SP140 |
rs13397985 |
GG, GT,
TT |
A |
|
|
6 |
IRF4 |
rs872071 |
AA, AG, GG |
A |
|
|
11 |
intergenic |
rs735665 |
AA, AG,
GG |
A |
|
|
15 |
intergenic |
rs7176508 |
AA, AG,
GG |
A3 |
|
|
19 |
PRKD2 |
rs11083846 |
AA, AG,
GG |
A |
|
| Liver Cancer |
4 |
EGF |
rs4444903 |
AA, AG, GG |
A |
|
|
6 |
TNF |
rs1800629 |
AA, AG,
GG |
A |
|
|
6 |
HFE |
rs1800562 |
AA, AG,
GG |
A |
|
| Lung Cancer |
A |
MTHFR |
rs1801133 |
CC, CT, TT |
A |
Link |
|
6 |
CDKN1A |
rs1801270 |
AA, AC, CC |
A |
|
|
7 |
ABCB1 |
rs3842 |
CC, CT,
TT |
A2* |
|
|
15 |
CHRNA3 |
rs1051730 |
CC, CT, TT |
A, B |
|
|
15 |
LOC123688 |
rs8034191 |
CC, CT,
TT |
A |
|
|
16 |
ABCC1 |
rs212090 |
AA, AT,
TT |
A |
|
|
17 |
MPO |
rs2333227 |
CC, CT, TT |
A3 |
|
|
20 |
AURKA |
rs2273535 |
AA, AT, TT |
A3 |
|
|
22 |
CHEK2 |
rs17879961 |
CC, CT,
TT |
A |
|
| Oral and Larynx Cancer |
4 |
ADH7 |
rs1573496 |
CC. CG, GG |
A |
|
|
4 |
ADH1B |
rs1229984 |
AA, AG,
GG |
|
|
| Ovarian Cancer |
B |
SRD5A2 |
rs523349 |
CC, CG, GG |
A |
SNPedia |
|
7 |
PMS2 |
rs7797466 |
|
A |
|
|
7 |
XRCC2 |
rs3218536 |
AA, AG,
GG |
A |
|
|
11 |
PGR |
rs1042838 |
GG, GT, TT |
A |
|
|
11 |
intergenic |
rs608995 |
|
A3 |
|
|
13 |
RB1 |
rs4151620 |
CC, CG, GG |
A |
|
|
|
|
rs2854344 |
AA, AG,
GG |
A |
|
|
13 |
P2RY5 |
rs2227311 |
CC, CT,
TT |
A |
|
| Pancreatic Cancer |
3 |
ATR |
rs2227928 |
CC, CT,
TT |
A |
|
|
4 |
PALLD |
|
|
|
|
|
5 |
MTRR |
rs162049 |
|
A3 |
|
|
|
|
rs10380 |
CC, CT, TT |
A |
|
|
11 |
ATM |
rs1801516 |
AA, AG, GG |
A |
|
|
|
|
rs664143 |
CC, CT, TT |
A |
|
|
11 |
CHEK1 |
rs521102 |
CC, CT, TT |
|
|
|
17 |
LIG3 |
rs2074522 |
CC, CT, TT |
A3 |
|
| Cancer : Prostate |
B |
EHBP1 |
rs2710646 |
AA, AC, CC |
A, B |
|
|
3 |
FYCO1 |
rs1545985 |
AA, AG, GG |
A |
|
|
|
|
rs7652331 |
CC, CT, TT |
A |
|
|
4 |
KIAA1211 |
rs629242 |
CC, CT, TT |
A |
|
|
4 |
LOC152485 |
rs13149290 |
CC, CT, TT |
A |
|
|
5 |
FCHSD1 |
rs251177 |
CC, CT,
TT |
A |
|
|
5 |
FGFR4 |
rs351855 |
CC, CT, TT |
A |
|
|
7 |
JAZF1 |
rs10486567 |
|
A |
|
|
8 |
8q24(1) |
rs1447295 |
AA, AC,
CC |
A, B |
|
|
8 |
8q24(2) |
rs6983267 |
GG, GT,
TT |
A, B |
|
|
8 |
8q24(3) |
rs10505483 |
AA, AC, CC |
A, B |
|
|
8 |
8q24 |
rs16901979 |
AA, AC,
CC |
A |
|
|
9 |
DAB2IP |
rs1571801 |
GG, GT, TT |
A |
|
|
10 |
intergenic |
rs10993994 |
CC, CT, TT |
A, B |
|
|
10 |
TCF7L2 |
rs12255372 |
GG, GT, TT |
A |
|
|
11 |
intergenic |
rs10896449 |
AA, AG, GG |
A, B |
|
|
12 |
VDR |
rs2107301 |
CC, CT, TT |
A |
|
|
|
|
rs2238135 |
CC, CG, GG |
A3 |
|
|
13 |
FAM124A |
rs10492519 |
AA, AG, GG |
A |
|
|
14 |
ESR2 |
rs2987983 |
CC, CT, TT |
A |
|
|
16 |
CDH1 |
rs16260 |
AA, AC,
CC |
A |
|
|
17 |
TCF2 |
rs4430796 |
AA, AG, GG |
A, B |
|
|
17 |
17q24.3 |
rs1859962 |
GG, GT, TT |
A, B |
|
|
X |
intergenic |
rs5945572 |
A, G |
A, B |
|
| Skin Cancer (basal cell carcinoma) |
A |
PADI6 |
rs7538876 |
AA, AG,
GG |
A, B |
|
|
A |
intergenic |
rs801114 |
GG, GT,
TT |
A, B |
|
| Skin Cancer (melanoma) |
A |
IL6R |
rs6684439 |
CC, CT, TT |
A |
|
|
|
|
rs4845622 |
AA, AC, CC |
B |
|
|
|
|
rs4845618 |
GG, GT, TT |
A3 |
|
|
|
|
rs8192284 |
AA, AC, CC |
A |
|
|
9 |
intergenic |
rs1408799 |
CC, CT, TT |
A |
|
|
9 |
CDKN2A |
|
|
|
|
|
12 |
CDK4 |
|
|
|
|
|
13 |
BRCA2 |
rs1799944 |
AA, AG, GG |
A |
|
|
16 |
MC1R |
|
|
|
|
| Skin Cancer (red hair) |
16 |
MC1R |
rs1805009 |
CC, CG, GG |
|
|
|
|
|
rs1805008 |
CC, CT, TT |
A |
|
|
|
|
rs1805007 |
CC, CT, TT |
A |
|
| Skin Cancer (light sensitivity) |
20 |
intergenic |
rs1015362 |
AA, AG, GG |
A |
|
|
|
|
rs4911414 |
GG, GT, TT |
A |
|
| Testicular Cancer |
5 |
SPRY4 |
rs4624820 |
AA, AG,
GG |
A, B |
Link |
|
6 |
BAK1 |
|
AA, AG, GG |
A, B |
|
|
12 |
KITLG |
rs995030 |
AA, AG, GG |
A, B |
|
|
X |
TGCT1 |
|
|
|
|
| Thyroid Cancer |
9 |
intergenic |
rs965513 |
AA, AG,
GG |
A |
Link |
|
14 |
intergenic |
rs944289 |
CC, CT,
TT |
A |
|
| Neuroblastoma |
6 |
6p22 |
rs6939340 |
AA, AG, GG |
A |
|
|
|
|
rs4712653 |
CC, CT, TT |
A |
|
|
|
|
rs9295536 |
AA, AC, CC |
A |
|
| Non-Hodgkin Lymphoma |
4 |
TLR6 |
rs5743815 |
CC, CT,
TT |
A3 |
SNPedia |
|
6 |
BAT2 |
rs3132453 |
|
A |
|
|
12 |
SELPLG |
rs7300972 |
|
A3 |
|
|
19 |
B3GNT3 |
rs36686 |
|
A |
|
|
22 |
UNC84B |
rs2072797 |
CC, CT, TT |
A |
|
|


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Number-theoretic functions#
- class sage.functions.transcendental.DickmanRho#
Bases:
BuiltinFunction
Dickman’s function is the continuous function satisfying the differential equation
\[x \rho'(x) + \rho(x-1) = 0\]with initial conditions \(\rho(x)=1\) for \(0 \le x \le 1\). It is useful in estimating the frequency of smooth numbers as asymptotically
\[\Psi(a, a^{1/s}) \sim a \rho(s)\]where \(\Psi(a,b)\) is the number of \(b\)-smooth numbers less than \(a\).
ALGORITHM:
Dickmans’s function is analytic on the interval \([n,n+1]\) for each integer \(n\). To evaluate at \(n+t, 0 \le t < 1\), a power series is recursively computed about \(n+1/2\) using the differential equation stated above. As high precision arithmetic may be needed for intermediate results the computed series are cached for later use.
Simple explicit formulas are used for the intervals [0,1] and [1,2].
EXAMPLES:
sage: dickman_rho(2) 0.306852819440055 sage: dickman_rho(10) 2.77017183772596e-11 sage: dickman_rho(10.00000000000000000000000000000000000000) 2.77017183772595898875812120063434232634e-11 sage: plot(log(dickman_rho(x)), (x, 0, 15)) Graphics object consisting of 1 graphics primitive
AUTHORS:
Robert Bradshaw (2008-09)
REFERENCES:
G. Marsaglia, A. Zaman, J. Marsaglia. “Numerical Solutions to some Classical Differential-Difference Equations.” Mathematics of Computation, Vol. 53, No. 187 (1989).
- approximate(x, parent=None)#
Approximate using de Bruijn’s formula
\[\rho(x) \sim \frac{exp(-x \xi + Ei(\xi))}{\sqrt{2\pi x}\xi}\]which is asymptotically equal to Dickman’s function, and is much faster to compute.
REFERENCES:
N. De Bruijn, “The Asymptotic behavior of a function occurring in the theory of primes.” J. Indian Math Soc. v 15. (1951)
EXAMPLES:
sage: dickman_rho.approximate(10) 2.41739196365564e-11 sage: dickman_rho(10) 2.77017183772596e-11 sage: dickman_rho.approximate(1000) 4.32938809066403e-3464
- power_series(n, abs_prec)#
This function returns the power series about \(n+1/2\) used to evaluate Dickman’s function. It is scaled such that the interval \([n,n+1]\) corresponds to x in \([-1,1]\).
INPUT:
n
- the lower endpoint of the interval for which this power series holdsabs_prec
- the absolute precision of the resulting power series
EXAMPLES:
sage: f = dickman_rho.power_series(2, 20); f -9.9376e-8*x^11 + 3.7722e-7*x^10 - 1.4684e-6*x^9 + 5.8783e-6*x^8 - 0.000024259*x^7 + 0.00010341*x^6 - 0.00045583*x^5 + 0.0020773*x^4 - 0.0097336*x^3 + 0.045224*x^2 - 0.11891*x + 0.13032 sage: f(-1), f(0), f(1) (0.30685, 0.13032, 0.048608) sage: dickman_rho(2), dickman_rho(2.5), dickman_rho(3) (0.306852819440055, 0.130319561832251, 0.0486083882911316)
- class sage.functions.transcendental.Function_HurwitzZeta#
Bases:
BuiltinFunction
- class sage.functions.transcendental.Function_stieltjes#
Bases:
GinacFunction
Stieltjes constant of index
n
.stieltjes(0)
is identical to the Euler-Mascheroni constant (sage.symbolic.constants.EulerGamma
). The Stieltjes constants are used in the series expansions of \(\zeta(s)\).INPUT:
n
- non-negative integer
EXAMPLES:
sage: _ = var('n') sage: stieltjes(n) stieltjes(n) sage: stieltjes(0) euler_gamma sage: stieltjes(2) stieltjes(2) sage: stieltjes(int(2)) stieltjes(2) sage: stieltjes(2).n(100) -0.0096903631928723184845303860352 sage: RR = RealField(200) sage: stieltjes(RR(2)) -0.0096903631928723184845303860352125293590658061013407498807014
It is possible to use the
hold
argument to prevent automatic evaluation:sage: stieltjes(0,hold=True) stieltjes(0) sage: latex(stieltjes(n)) \gamma_{n} sage: a = loads(dumps(stieltjes(n))) sage: a.operator() == stieltjes True sage: stieltjes(x)._sympy_() stieltjes(x) sage: stieltjes(x).subs(x==0) euler_gamma
- class sage.functions.transcendental.Function_zeta#
Bases:
GinacFunction
Riemann zeta function at s with s a real or complex number.
INPUT:
s
- real or complex number
If s is a real number the computation is done using the MPFR library. When the input is not real, the computation is done using the PARI C library.
EXAMPLES:
sage: zeta(x) zeta(x) sage: zeta(2) 1/6*pi^2 sage: zeta(2.) 1.64493406684823 sage: RR = RealField(200) sage: zeta(RR(2)) 1.6449340668482264364724151666460251892189499012067984377356 sage: zeta(I) zeta(I) sage: zeta(I).n() 0.00330022368532410 - 0.418155449141322*I sage: zeta(sqrt(2)) zeta(sqrt(2)) sage: zeta(sqrt(2)).n() # rel tol 1e-10 3.02073767948603
It is possible to use the
hold
argument to prevent automatic evaluation:sage: zeta(2,hold=True) zeta(2)
To then evaluate again, we currently must use Maxima via
sage.symbolic.expression.Expression.simplify()
:sage: a = zeta(2,hold=True); a.simplify() 1/6*pi^2
The Laurent expansion of \(\zeta(s)\) at \(s=1\) is implemented by means of the Stieltjes constants:
sage: s = SR('s') sage: zeta(s).series(s==1, 2) 1*(s - 1)^(-1) + euler_gamma + (-stieltjes(1))*(s - 1) + Order((s - 1)^2)
Generally, the Stieltjes constants occur in the Laurent expansion of \(\zeta\)-type singularities:
sage: zeta(2*s/(s+1)).series(s==1, 2) 2*(s - 1)^(-1) + (euler_gamma + 1) + (-1/2*stieltjes(1))*(s - 1) + Order((s - 1)^2)
- class sage.functions.transcendental.Function_zetaderiv#
Bases:
GinacFunction
Derivatives of the Riemann zeta function.
EXAMPLES:
sage: zetaderiv(1, x) zetaderiv(1, x) sage: zetaderiv(1, x).diff(x) zetaderiv(2, x) sage: var('n') n sage: zetaderiv(n,x) zetaderiv(n, x) sage: zetaderiv(1, 4).n() -0.0689112658961254 sage: import mpmath; mpmath.diff(lambda x: mpmath.zeta(x), 4) mpf('-0.068911265896125382')
- sage.functions.transcendental.hurwitz_zeta(s, x, **kwargs)#
The Hurwitz zeta function \(\zeta(s, x)\), where \(s\) and \(x\) are complex.
The Hurwitz zeta function is one of the many zeta functions. It is defined as
\[\zeta(s, x) = \sum_{k=0}^{\infty} (k + x)^{-s}.\]When \(x = 1\), this coincides with Riemann’s zeta function. The Dirichlet L-functions may be expressed as linear combinations of Hurwitz zeta functions.
EXAMPLES:
Symbolic evaluations:
sage: hurwitz_zeta(x, 1) zeta(x) sage: hurwitz_zeta(4, 3) 1/90*pi^4 - 17/16 sage: hurwitz_zeta(-4, x) -1/5*x^5 + 1/2*x^4 - 1/3*x^3 + 1/30*x sage: hurwitz_zeta(7, -1/2) 127*zeta(7) - 128 sage: hurwitz_zeta(-3, 1) 1/120
Numerical evaluations:
sage: hurwitz_zeta(3, 1/2).n() 8.41439832211716 sage: hurwitz_zeta(11/10, 1/2).n() 12.1038134956837 sage: hurwitz_zeta(3, x).series(x, 60).subs(x=0.5).n() 8.41439832211716 sage: hurwitz_zeta(3, 0.5) 8.41439832211716
REFERENCES:
- sage.functions.transcendental.zeta_symmetric(s)#
Completed function \(\xi(s)\) that satisfies \(\xi(s) = \xi(1-s)\) and has zeros at the same points as the Riemann zeta function.
INPUT:
s
- real or complex number
If s is a real number the computation is done using the MPFR library. When the input is not real, the computation is done using the PARI C library.
More precisely,
\[xi(s) = \gamma(s/2 + 1) * (s-1) * \pi^{-s/2} * \zeta(s).\]EXAMPLES:
sage: zeta_symmetric(0.7) 0.497580414651127 sage: zeta_symmetric(1-0.7) 0.497580414651127 sage: RR = RealField(200) sage: zeta_symmetric(RR(0.7)) 0.49758041465112690357779107525638385212657443284080589766062 sage: C.<i> = ComplexField() sage: zeta_symmetric(0.5 + i*14.0) 0.000201294444235258 + 1.49077798716757e-19*I sage: zeta_symmetric(0.5 + i*14.1) 0.0000489893483255687 + 4.40457132572236e-20*I sage: zeta_symmetric(0.5 + i*14.2) -0.0000868931282620101 + 7.11507675693612e-20*I
REFERENCE:
I copied the definition of xi from http://web.viu.ca/pughg/RiemannZeta/RiemannZetaLong.html


My
O(1) solution to
this
:
Distance from
to
Look in the javascript console to see the calculations, or check out the comments in the
source code
for a full explanation!


zums can be installed by:
easy_install zums
Note
|zums| is developing fast, check out whats new at http://github.com/amitu/zums/blob/master/ChangeLog.rst
One auth to rule them all.
Django has its way of handling users and sessions. Ruby has its own. Java servlets has their own. And so on. Not good.
Good: there is a ZeroMQ service that programs written in any language can interact with, this service maintains users and sessions. The web framework of your choice passes sessionid from sessionid named cookie, gets session, gets logged in user etc. Nginx routes www.example.com/accounts to zums service, which happens to be a web server too, and nginx routes www.example.com/blog to wordpress based blog, and www.example.com/dashboard/ to a ruby on rails project and www.example.com/* to django. If a user signs in by going to www.example.com/accounts/login/, sessionid cookie is set, and if user then goes to www.example.com/blog, wordpress plugin for zums interacts with zums over zeromq and fetches user/session data.
So I wrote this service. This is actually a collection of services and plugins.
First install this service:
$ easy_install zums
You will have to run the zumsd daemon too.:
$ zumsd --help
And create a database for users.:
$ zumsd --init
This will ask for a database engine, location, hostname, username etc and create the tables for you. [TODO: not done yet].
The point to note is that your users are stored in this data base, and are accessible over a zeromq api too all languages and frameworks, not just django.
Using it is trivial in a django project, look at the settings.py file in zums_dj_example_project:
SESSION_ENGINE = "zums.django_backends.session"
AUTHENTICATION_BACKENDS = ["zums.django_backends.auth.ZUMSBackend"]
These two settings is all you need to do, and your django project is getting all the benefits of zums, which is, it can co exist with subprojects writter in other languages.
Note: the auth backend creates a local copy of User in your django database, so foreign key to user etc still work without any changes.
The only thing you need to know when using this service/backend vs normal django project is that not all users may not be there in your database, nor are they gauranteed to be in sync with actual master database.
It is trivial to solve the above two by writing some zumsd extensions described elsewhere. [TODO: this is not done yet]
You will have to do a little more work, and hopefully someone will write session backends and auth backends for the programming language and web framework of your choice. If not, you can implement the simple zeromq api for zumsd daemon, and write your own session and auth handling code.
TBD
Zumsd can be extended by writing plugins. Zumsd talks to plugins, over zeromq, so plugins can be written in any language one wishes too.
Plugins are quite simple, they just subscribe (zmq.SUB) to messages that zumsd sends over, they then user zumsd ZeroMQ api mentioned above to update data managed by zumsd or do whatever they want.
For simplicty zumsd supports python plugins, they live within the zumsd process, and need not be managed as separate service.
TBD


Dimensionality Reduction & Feature Selection¶
univariate_filter¶
protlearn.dimreduction.univariate_filter(X, y, *, method='f_test', top=10)
Univariate feature selection.
This function returns the features selected by univariate filtering after examining each feature individually and determining the strength of its relationship with the response variable. Here, three statistical tests can be chosen: f-test, chi-squared, and mutual information.
Parameters¶
- X: ndarray of shape (n_samples, n_features_pre)
Feature matrix.
- y: ndarray of shape (n_samples,)
Response variables.
- method: string, default=’f_test’
‘f_test’ : ANOVA f-scores
‘chi2’ : Chi-squared statistics
‘mutual_info’ : Mutual information- top: int, default=10
Number of top features to select.
Returns¶
- arr: ndarray of shape (n_samples, top)
Array containing the top features.
Examples¶
>>> import numpy as np
>>> from protlearn.features import aac, aaindex1, ngram
>>> from protlearn.dimreduction import univariate_filter
>>> seqs = ['ARKLY', 'EERKPGL', 'PGPGEERNLY']
>>> labels = [1., 0., 0.]
>>> comp, _ = aac(seqs)
>>> aaind, _ = aaindex1(seqs)
>>> ng, _ = ngram(seqs)
>>> features = np.concatenate([comp, aaind, ng], axis=1)
>>> features.shape
(3, 575)
>>> reduced = univariate_filter(features, labels, method='f_test', top=10)
>>> reduced.shape
(3, 10)
correlation¶
protlearn.dimreduction.correlation(X, thres=.9)
Pearson correlation.
This function returns the features whose Pearson correlation with one another is below a specified threshold, thus circumventing the problem of multicollinearity.
Parameters¶
- X: ndarray of shape (n_samples, n_features_pre)
Feature matrix.
- thres: float, default=.9
Features whose correlation coefficient is higher than this threshold value will be removed.
Returns¶
- arr: ndarray of shape (n_samples, n_features_post)
Array containing features that correlate below the threshold with one another.
Examples¶
>>> import numpy as np
>>> from protlearn.features import aac, aaindex1, ngram
>>> from protlearn.dimreduction import correlation
>>> seqs = ['ARKLY', 'EERKPGL', 'PGPGEERNLY']
>>> comp, _ = aac(seqs)
>>> aaind, _ = aaindex1(seqs)
>>> ng, _ = ngram(seqs)
>>> features = np.concatenate([comp, aaind, ng], axis=1)
>>> features.shape
(3, 575)
>>> reduced = correlation(features, thres=.99)
>>> reduced.shape
(3, 12)
lasso¶
protlearn.dimreduction.lasso(X, y, C=1.0)
Lasso (L1) regularization.
Linear Model trained with L1 prior as regularizer.
Parameters¶
- X: ndarray of shape (n_samples, n_features_pre)
Feature matrix.
- y: ndarray of shape (n_samples,)
Response variables.
- C: float, default=1.0
Inverse of regularization strength.
Returns¶
- arrndarray of shape (n_samples, n_features_post)
Array containing lasso-reduced features.
Examples¶
>>> import numpy as np
>>> from protlearn.features import aac, aaindex1, ngram
>>> from protlearn.dimreduction import lasso
>>> seqs = ['ARKLY', 'EERKPGL', 'PGPGEERNLY']
>>> labels = [1., 0., 0.]
>>> comp, _ = aac(seqs)
>>> aaind, _ = aaindex1(seqs)
>>> ng, _ = ngram(seqs)
>>> features = np.concatenate([comp, aaind, ng], axis=1)
>>> features.shape
(3, 575)
>>> reduced = lasso(features, labels)
>>> reduced.shape
(3, 2)
tree_importance¶
protlearn.dimreduction.tree_importance(X, y, *, clf=None, method='random_forest', top=None, n_estimators=100, max_depth=None, importance_type='gain')
Decision tree feature importance.
This function returns the features that were selected as important by decision tree algorithms such as Random Forest and XGBoost.
Parameters¶
- X: ndarray of shape (n_samples, n_features_pre)
Feature matrix.
- y: ndarray of shape (n_samples,)
Response variables.
- clf: object or None, default=None
Customized classifier.
- method: string, default=’random_forest’
‘random_forest’ : Random Forest Classifier ‘xgboost’ : XGBoost Classifier
- top: int or None, default=None
Number of top features to select.
- n_iterations: int, default=3
Number of iterations.
- n_estimators: int or None, default=2
Number of trees in the forest.
- max_depth: int or None, default=None
Maximum depth of the tree.
- importance_type: string, default=’gain’
For XGBoost only:
‘gain’ : average gain of splits which use the feature
‘weight’ : number of times the a feature appears in the tree
‘cover’ : average coverage of splits which use the feature
‘total_gain’ : Total gain
‘total_cover’ : Total cover
Returns¶
- arr: ndarray of shape (n_samples, top)
Array containing the top features based on tree-importance.
- indices: ndarray
Indices indicating the position of the selected feature in the input vector.
Examples¶
>>> import numpy as np
>>> from protlearn.features import aac, aaindex1, ngram
>>> from protlearn.dimreduction import tree_importance
>>> seqs = ['ARKLY', 'EERKPGL', 'PGPGEERNLY']
>>> labels = [1., 0., 0.]
>>> comp, _ = aac(seqs)
>>> aaind, _ = aaindex1(seqs)
>>> ng, _ = ngram(seqs)
>>> features = np.concatenate([comp, aaind, ng], axis=1)
>>> features.shape
(3, 575)
>>> reduced, indices = tree_importance(features, labels, top=10)
>>> reduced.shape
(3, 10)
>>> indices
array([249, 514, 4, 155, 182, 82, 214, 405, 140, 364])
sequential¶
protlearn.dimreduction.sequential(X, y, *, estimator, direction='forward', n_features=10, cv=0)
Sequential feature selection.
 These algorithms remove or add one feature at a time based on the classifier performance until a feature subset of the desired size k is reached.
Parameters¶
- X: ndarray of shape (n_samples, n_features_pre)
Feature matrix.
- y: labels, ndarray of shape (n_samples,)
Response variables.
- estimator: object
Classifier - must include coef_ or feature_importances_ attribute.
- direction: string, default=’forward’
Direction of sequential model, can be ‘forward’ or ‘backward’.
- n_features: int, default=None
Number of features to select.
- cv: int, default=0
Number of cross-validation steps.
Returns¶
- arr: ndarray of shape (n_samples, n_features)
Array containing features selected by the sequential models.
Examples¶
>>> import numpy as np
>>> from sklearn.ensemble import RandomForestClassifier
>>> from protlearn.features import aac, aaindex1, ngram
>>> from protlearn.dimreduction import sequential
>>> seqs = ['ARKLY', 'EERKPGL', 'PGPGEERNLY']
>>> labels = [1., 0., 0.]
>>> comp, _ = aac(seqs)
>>> aaind, _ = aaindex1(seqs)
>>> ng, _ = ngram(seqs)
>>> features = np.concatenate([comp, aaind, ng], axis=1)
>>> features.shape
(3, 575)
>>> rf = RandomForestClassifier()
>>> reduced = sequential(features, labels, rf, n_features=10)
>>> reduced.shape
(3, 10)
rfe¶
protlearn.dimreduction.rfe(X, y, *, estimator, n_features=None, step=1)
Recursive feature elimination.
This function selects features by recursively considering smaller and smaller feature subsets. First, the estimator is trained on the initial feature matrix and the importance of each feature is obtained through a coef_ or a feature_importances_ attribute. Subsequently, the least important features are pruned from the current feature subset. This is repeated recursively on the pruned subset until the desired number of features is eventually reached.
Parameters¶
- X: ndarray of shape (n_samples, n_features_pre)
Feature matrix.
- y: labels, ndarray of shape (n_samples,)
Response variables.
- estimator: object
Classifier - must include coef_ or feature_importances_ attribute.
- n_features: int or None, default=None
Number of features to select. If
None
, half of the features are selected.- step: int, default=1
Number of features to remove at each iteration.
Returns¶
- arr: ndarray of shape (n_samples, n_features)
Array containing the RFE-selected features.
- ranking: ndarray of shape (n_features_pre,)
Ranking of the features (with 1 being the best).
Examples¶
>>> import numpy as np
>>> from sklearn.ensemble import RandomForestClassifier
>>> from protlearn.features import aac, aaindex1, ngram
>>> from protlearn.dimreduction import rfe
>>> seqs = ['ARKLY', 'EERKPGL', 'PGPGEERNLY']
>>> labels = [1., 0., 0.]
>>> comp, _ = aac(seqs)
>>> aaind, _ = aaindex1(seqs)
>>> ng, _ = ngram(seqs)
>>> features = np.concatenate([comp, aaind, ng], axis=1)
>>> features.shape
(3, 575)
>>> rf = RandomForestClassifier()
>>> reduced, _ = rfe(features, labels, rf, n_features=10, step=5)
>>> reduced.shape
(3, 10)
pca¶
protlearn.dimreduction.pca(X, *, thres=.9, whiten=False)
Principal component analysis.
PCA is defined as an orthogonal linear transformation that transforms the data to a new coordinate system such that the greatest variance by some scalar projection of the data comes to lie on the first coordinate (called the first principal component), the second greatest variance on the second coordinate, and so on.
Parameters¶
- X: ndarray of shape (n_samples, n_features_pre)
Feature matrix.
- thres: float, default=.9
Specify the desired explained variance.
Returns¶
- arr: ndarray of shape (n_samples, n_features_post)
Array containing the PCA components comprising the specified variance.
Notes¶
For the output to be meaningful, the number of samples should be larger than the number of features.
Examples¶
>>> from protlearn.dimreduction import pca
>>> features.shape #from a larger dataset (not shown here)
(1000, 575)
>>> reduced = pca(features, thres=.9)
(1000, 32)
lda¶
protlearn.dimreduction.lda(X, y, *, solver='svd', shrinkage=None, n_components=None)
Linear discriminant analysis.
This function reduces the dimensionality of the input by projecting it to the most discriminative directions.
Parameters¶
- X: ndarray of shape (n_samples, n_features_pre)
Feature matrix.
- y: ndarray of shape (n_samples,)
Response variables.
- solver: string, default=’svd’
‘svd’ : Singular value decomposition
‘lsqr’ : Least squares solution
‘eigen’ : Eigenvalue decomposition- shrinkage: string, float, or None, default=None
Shrinkage parameter.
None : no shrinkage
‘auto’ : automatic shrinkage using the Ledoit-Wolf lemma
float between 0 and 1: fixed shrinkage parameter- n_components: int or None, default=None
Number of components for dimensionality reduction. This parameter cannot be larger than min(n_features, n_classes - 1).
Returns¶
- arr: ndarray of shape (n_samples, n_features_post)
Array containing the LDA-transformed features.
Examples¶
>>> import numpy as np
>>> from protlearn.features import aac, aaindex1, ngram
>>> from protlearn.dimreduction import lda
>>> seqs = ['ARKLY', 'EERKPGL', 'PGPGEERNLY']
>>> labels = [1., 0., 0.]
>>> comp, _ = aac(seqs)
>>> aaind, _ = aaindex1(seqs)
>>> ng, _ = ngram(seqs)
>>> features = np.concatenate([comp, aaind, ng], axis=1)
>>> features.shape
(3, 575)
>>> reduced = lda(features, labels, n_components=1)
>>> reduced.shape
(3, 1)
tsne¶
protlearn.dimreduction.tsne(X, *, n_components=2, perplexity=30, prior_pca=True, pca_components=50)
t-distributed stochastic neighbor embedding.
t-SNE converts similarities between data points to joint probabilities and tries to minimize the Kullback-Leibler divergence between the joint probabilities of the low-dimensional embedding and the high-dimensional data.
Parameters¶
- X: ndarray of shape (n_samples, n_features_pre)
Feature matrix.
- n_components: int or None, default=2
Dimension of embedded space.
- perplexity: int, default=30
Related to the number of nearest neighbors that is used in other manifold learning algorithms. Should be between 5 and 50. Larger datasets require larger perplexity.
- prior_pca: bool, default=True
It is recommended to reduce dimensionality before running t-SNE to decrease computation time and noise.
- pca_components: int, default=50
Dimension of PCA-preprocessed data that will serve as input to t-SNE.
Returns¶
- arr: ndarray of shape (n_samples, n_components)
Array containing the t-SNE-transformed features.
Examples¶
>>> import numpy as np
>>> from protlearn.features import aac, aaindex1, ngram
>>> from protlearn.dimreduction import tsne
>>> seqs = ['ARKLY', 'EERKPGL', 'PGPGEERNLY']
>>> comp, _ = aac(seqs)
>>> aaind, _ = aaindex1(seqs)
>>> ng, _ = ngram(seqs)
>>> features = np.concatenate([comp, aaind, ng], axis=1)
>>> features.shape
(3, 575)
>>> reduced = tsne(features, pca_components=3)
>>> reduced.shape
(3, 2)


op/3
[ISO]op(
+Precedence,
+Type,
+Name)
declares Name to be an operator of the stated Type and Precedence.
[xfx,xfy,yfx,fx,fy,xf,yf]
, must be nonvar
Operators are a notational convenience
to read and write Prolog terms. You can define new operators using
op/3
.
The Precedence of an operator is used to disambiguate the way terms are parsed. The general rule is that the operator with the highest precedence is the principal functor.
The Type of an operator decides the position of an operator and its associativity. In the atom that represents the type the character `f' represents the position of the operator. For example, a type `fx' says that the operator is a prefix operator. The character `y' indicates that the operator is associative in that direction. For example, an operator of type `xfy' is a right-associative, infix operator.
To cancel the operator properties of Name (if any) set Precedence to 0.
instantiation_error
type_error
domain_error
permission_error
','
current_op/3
, ref-syn-ops.


The EmaCrossUniverseSelectionModel
applies two exponential moving average (EMA) indicators to the price history of assets and then selects the assets that have their fast EMA furthest above their slow EMA on a percentage basis.
public override void Initialize() { AddUniverseSelection(new EmaCrossUniverseSelectionModel()); }
def Initialize(self) -> None: self.AddUniverseSelection(EmaCrossUniverseSelectionModel())
The following table describes the arguments the model accepts:
| Argument | Data Type | Description | Default Value |
|---|---|---|---|
fastPeriod |
int |
Fast EMA period | 100 |
slowPeriod |
int |
Slow EMA period | 300 |
universeCount |
int |
Maximum number of members of this universe selection | 500 |
universeSettings |
UniverseSettings |
The settings used when adding symbols to the algorithm | None null |
If you don't provide a universeSettings
argument, the algorithm.UniverseSettings
are used by default.
To view the implementation of this model, see the LEAN GitHub repositoryLEAN GitHub repository.


Hello Steemians,
I wanted to take a moment to broadly categorize 4 different types of bots that run on Steemit. You’ve seen these things interacting with people and the chain everyday, and I just wanted to give a good sense of what they do and categorize them to help you understand their role on Steemit.
What’s a bot
A bot is an account that has an aspect of programming built into it. There’s usually a program or some kind of interface that allows for things that happen daily or weekly to be done automatically by computer rather than done manually.
Bid Bot
The first kind of bot that most people would refer too I think would be a bid bot or a vote bot. These are pay2play (pay2spray in 1 case). You send SBD and a post URL in the memo and you can get a vote. They almost always net you more than you invested though there are times that the cost of the bid outweighs the rewards you earn. Most of them are using the Dr. Otto build by @inertia, but there’s one prominent one out there that isn’t.
Dr. Ottos- @booster, @bellyrub, @nettybot, @discordia, and @lovejuice are all examples of bidbots that run the Dr. Otto program.
Others- @minnowbooster and @randowhale are examples of bots that run a different system. Randowhale gives you a random percent boost. @minnowbooster isn’t quite a bid so much as a cash for immediate vote thing. @minnowbooster also has delegation rental so there are additional features there to explore.
Infobot
There are a few bots that provide you with information on a daily basis. @curie’s post are a good info one. While the manual action that they are really known for is curation there is a script that aggregates the posts they rewarded together and a list of the posts they supported is reported. That’s info! You can also see @arcange has a daily hit parade bot that scours the block for the most voted and most commented posts and puts those together so folks can see what’s hot. You get info.
Curation
Auto: There are bots that are out there that help with curation.  At their best they create mechanisms to fund authors who post great content. Basically any account can become a curation bot just by adding steemvoter or autovoting programs on top. There are some opensource programs that allow for steem voting so you can pay for the steemvoter service or run your own. A great example of these can be found in the Minnow Support Project with @msp-creativebot, @muxxybot, and @sft. These are examples of bots that form specifically to help find and curate great content.
Cyborg: there are at least two examples of cyborg curation. Cyborg is a mix of person and machine. In this case @banjo and @minnowsupport are good examples (both programs were created by @inertia). They have community accounts that they attach to, but each vote is cast manually. So, the program allows people to use a communal account without the posting key, but every individual vote is cast manually by a person. So, they are cyborgs.
Community
The last kind of bot functions similarly to the curation bots, but the focus is less on great content and more on a community of authors and Steemit contributors. msp-lovebot, and @msp-shanehugs are good examples here. Folks that contribute to the community are rewarded with upvotes on their posts. Some of it is because they delegate to projects, some of it is because they work hard and contribute to the group.
MSP: A community hub for communities
In the coming weeks the Minnow Support Project is going to work with a variety of communities to financially support them as get started. There are blossoming African, Filipino, and Turkish communities to name a few, but there are a variety of concerns around them. One of the roles that the Minnow Support Project can play is to foster communities by acting as a neutral 3rd party with crystal clear guidelines and a track record of success and managed by trustworthy and publicly accountable elected Witnesses. We are also building tools to help automate aspects of this. When we’re done we’ll have the ability to automate this stuff and make it a breeze!
Be the curation you want to see in the world
I’ll make a bigger rant about this later, but ultimately people (whales) really need to be the curation they want to see in this world. Essentially you have 2 choices. Support content and or support people. The Minnow Support Project does both and tries to lead by example of what good community and content curation can look like as a team project.
 Please support us!
https://steemit.com/minnowsupportproject/@discordiant/delegation-tutorial-minnow-support-group-bots
And I hope this gives you some additional idea of what bots can do to help Steemit, you, content creators, and communities!
This page is synchronized from the post: ‘4 Types of bots: Bid, Info, Curation, Community. (almost 5k followers!!!)’


×
All the clothing on the house is handmade, like the curtains or the rug in this room as well as the tablecloth. We bought the furniture and decoration over the years in some fairs or online, and some nice details like empress Sisi in a plate on top of the chimney from Vienna.
You'll find Lizzie Bennet on a frame on top of the chimney.


Python Instructions
If you have a Linux or a Mac, open a terminal and type
python
.
If you have Windows, download the
x86
version or the
x64
one.
Download the
archive
with the tutorial.
Solve the exercises and have fun!


Contains the function mice.impute.midastouch(). Technically this function is to be run from within the 'mice' package (van Buuren et al. 2011), type ??mice. It substitutes the method 'pmm' within mice by 'midastouch'. The authors have shown that 'midastouch' is superior to default 'pmm'. Many ideas are based on Siddique / Belin 2008's MIDAS.
| Version: | 1.3 |
| Depends: | R (≥ 3.2.0) |
| Imports: | utils |
| Suggests: | mice |
| Published: | 2016-02-07 |
| Author: | Philipp Gaffert [aut, cre], Florian Meinfelder [aut], Volker Bosch [aut] |
| Maintainer: | Philipp Gaffert <philipp.gaffert at web.de> |
| License: | GPL-2 | GPL-3 |
| URL: | https://www.uni-bamberg.de/fileadmin/uni/fakultaeten/sowi_lehrstuehle/statistik/Personen/Dateien_Florian/properPMM.pdf |
| NeedsCompilation: | no |
| Citation: | midastouch citation info |
| CRAN checks: | midastouch results |
| Reference manual: | midastouch.pdf |
| Package source: | midastouch_1.3.tar.gz |
| Windows binaries: | r-devel: midastouch_1.3.zip, r-release: midastouch_1.3.zip, r-oldrel: midastouch_1.3.zip |
| OS X binaries: | r-release: midastouch_1.3.tgz, r-oldrel: midastouch_1.3.tgz |
| Old sources: | midastouch archive |
Please use the canonical form https://CRAN.R-project.org/package=midastouch to link to this page.