SO all day long the noise of battle roll’d
Among the mountains by the winter sea;
Until King Arthur’s table, man by man,
Had fall’n in Lyonnesse about their Lord,
King Arthur: then, because his wound was deep,
The bold Sir Bedivere uplifted him,
Sir Bedivere the last of all his knights,
And bore him to a chapel nigh the field,
A broken chancel with a broken cross,
That stood on a dark strait of barren land.
On one side lay the Ocean, and on one
Lay a great water, and the moon was full.
Then spake King Arthur to Sir Bedivere:
“The sequel of to-day unsolders all
The goodliest fellowship of famous knights
Whereof this world holds record. Such a sleep
They sleep—the men I loved. I think that we
Shall never more, at any future time,
Delight our souls with talk of knightly deeds,
Walking about the gardens and the halls
Of Camelot, as in the days that were.
I perish by this people which I made,—
Tho’ Merlin sware that I should come again
To rule once more—but let what will be, be,
I am so deeply smitten thro’ the helm
That without help I cannot last till morn.
Thou therefore take my brand Excalibur,
Which was my pride: for thou rememberest how
In those old days, one summer noon, an arm
Rose up from out the bosom of the lake,
Clothed in white samite, mystic, wonderful,
Holding the sword—and how I row’d across
And took it, and have worn it, like a king:
And, wheresoever I am sung or told
In aftertime, this also shall be known:
But now delay not: take Excalibur,
And fling him far into the middle mere:
Watch what thou seëst, and lightly bring me word.”
To him replied the bold Sir Bedivere:
“It is not meet, Sir King, to leave thee thus,
Aidless, alone, and smitten thro’ the helm.
A little thing may harm a wounded man.
Yet I thy hest will all perform at full,
Watch what I see, and lightly bring thee word.”
So saying, from the ruin’d shrine he stept
And in the moon athwart the place of tombs,
Where lay the mighty bones of ancient men,
Old knights, and over them the sea-wind sang
Shrill, chill, with flakes of foam. He, stepping down
By zig-zag paths, and juts of pointed rock,
Came on the shining levels of the lake.
There drew he forth the brand Excalibur,
And o’er him, drawing it, the winter moon,
Brightening the skirts of a long cloud, ran forth
And sparkled keen with frost against the hilt:
For all the haft twinkled with diamond sparks,
Myriads of topaz-lights, and jacinth-work
Of subtlest jewellery. He gazed so long
That both his eyes were dazzled, as he stood,
This way and that dividing the swift mind,
In act to throw: but at the last it seem’d
Better to leave Excalibur conceal’d
There in the many-knotted waterflags,
That whistled stiff and dry about the marge.
So strode he back slow to the wounded King.
Then spake King Arthur to Sir Bedivere:
“Hast thou perform’d my mission which I gave?
What is it thou hast seen? or what hast heard?”
And answer made the bold Sir Bedivere:
“I heard the ripple washing in the reeds
And the wild water lapping on the crag.”
To whom replied King Arthur, faint and pale:
“Thou has betray’d thy nature and thy name,
Not rendering true answer, as beseem’d
Thy fëalty, nor like a noble knight:
For surer sign had follow’d, either hand,
Or voice, or else a motion of the mere.
This is a shameful thing for men to lie.
Yet now, I charge thee, quickly go again
As thou art lief and dear, and do the thing
I bade thee, watch, and lightly bring me word.”
Then went Sir Bedivere the second time
Across the ridge, and paced beside the mere,
Counting the dewy pebbles, fix’d in thought;
But when he saw the wonder of the hilt,
How curiously and strangely chased, he smote
His palms together, and he cried aloud,
“And if indeed I cast the brand away,
Surely a precious thing, one worthy note,
Should thus be lost for ever from the earth,
Which might have pleased the eyes of many men.
What good should follow this, if this were done?
What harm, undone? deep harm to disobey,
Seeing obedience is the bond of rule.
Were it well to obey then, if a king demand
An act unprofitable, against himself?
The King is sick, and knows not what he does.
What record, or what relic of my lord
Should be to aftertime, but empty breath
And rumours of a doubt? but were this kept,
Stored in some treasure-house of mighty kings,
Some one might show it at a joust of arms,
Saying, ‘King Arthur’s sword, Excalibur,
Wrought by the lonely maiden of the Lake.
Nine years she wrought it, sitting in the deeps
Upon the hidden bases of the hills.’
So might some old man speak in the aftertime
To all the people, winning reverence.
But now much honour and much fame were lost.”

Then spoke King Arthur, breathing heavily:
“What is it thou hast seen? or what hast heard?”
And answer made the bold Sir Bedivere:
“I heard the water lapping on the crag,
And the long ripple washing in the reeds.”
To whom replied King Arthur, much in wrath:
“Ah, miserable and unkind, untrue,
Unknightly, traitor-hearted! Woe is me!
Authority forgets a dying king,
Laid widow’d of the power in his eye
That bow’d the will. I see thee what thou art,
For thou, the latest-left of all my knights,
In whom should meet the offices of all,
Thou wouldst betray me for the precious hilt;
Either from lust of gold, or like a girl
Valuing the giddy pleasure of the eyes.
Yet, for a man may fail in duty twice,
And the third time may prosper, get thee hence.
But, if thou spare to fling Excalibur,
I will arise and slay thee with my hands.”
Then quickly rose Sir Bedivere, and ran,
And, leaping down the ridges lightly, plunged
Among the bulrush-beds, and clutch’d the sword,
And strongly wheel’d and threw it. The great brand
Made lightnings in the splendour of the moon,
And flashing round and round, and whirl’d in an arch,
Shot like a streamer of the northern morn,
Seen where the moving isles of winter shock
By night, with noises of the northern sea.
So flash’d and fell the brand Excalibur:
But ere he dipt the surface, rose an arm
Clothed in white samite, mystic, wonderful,
And caught him by the hilt, and brandish’d him
Three times, and drew him under in the mere.
And lightly went the other to the King.
Then spoke King Arthur, drawing thicker breath:
“Now see I by thine eyes that this is done.
Speak out: what is it thou hast heard, or seen?”
And answer made the bold Sir Bedivere:
“Sir King, I closed mine eyelids, lest the gems
Should blind my purpose, for I never saw,
Nor shall see, here or elsewhere, till I die,
Not tho’ I live three lives of mortal men,
So great a miracle as yonder hilt.
Then with both hands I flung him, wheeling him;
But when I look’d again, behold an arm,
Clothed in white samite, mystic, wonderful,
That caught him by the hilt, and brandish’d him
Three times, and drew him under in the mere.”
And answer made King Arthur, breathing hard:
“My end draws nigh; ’tis time that I were gone.
Make broad thy shoulders to receive my weight,
And bear me to the margin; yet I fear
My wound hath taken cold; and I shall die.”
So saying, from the pavement he half rose,
Slowly, with pain, reclining on his arm,
And looking wistfully with wide blue eyes
As in a picture. Him Sir Bedivere
Remorsefully regarded thro’ his tears,
And would have spoken, but he found not words,
Then took with care, and kneeling on one knee,
O’er both his shoulders drew the languid hands,
And rising bore him thro’ the place of tombs.
But, as he walk’d, King Arthur panted hard,
Like one that feels a nightmare on his bed
When all the house is mute. So sigh’d the King,
Muttering and murmuring at his ear, “Quick, quick!
I fear it is too late, and I shall die.”
But the other swiftly strode from ridge to ridge,
Clothed with his breath, and looking as he walk’d,
Larger than human on the frozen hills.
He heard the deep behind him, and a cry
Before. His own thought drove him, like a goad.
Dry clash’d his harness in the icy caves
And barren chasms, and all to left and right
The bare black cliff clang’d round him, as he based
His feet on juts of slippery crag that rang
Sharp-smitten with the dint of armed heels—
And on a sudden, lo! the level lake,
And the long glories of the winter moon.
Then saw they how there hove a dusky barge,
Dark as a funeral scarf from stem to stern,
Beneath them; and descending they were ware
That all the decks were dense with stately forms
Black-stoled, black-hooded, like a dream—by these
Three Queens with crowns of gold—and from them rose
A cry that shiver’d to the tingling stars,
And, as it were one voice, an agony
Of lamentation, like a wind, that shrills
All night in a waste land, where no one comes,
Or hath come, since the making of the world.
Then murmur’d Arthur, “Place me in the barge,”
And to the barge they came. There those three Queens
Put forth their hands, and took the King, and wept.
But she, that rose the tallest of them all
And fairest, laid his head upon her lap,
And loosed the shatter’d casque, and chafed his hands,
And call’d him by his name, complaining loud,
And dropping bitter tears against his brow
Striped with dark blood: for all his face was white
And colourless, and like the wither’d moon
Smote by the fresh beam of the springing east;
And all his greaves and cuisses dash’d with drops
Of onset; and the light and lustrous curls—
That made his forehead like a rising sun
High from the däis-throne—were parch’d with dust;
Or, clotted into points and hanging loose,
Mixed with the knightly growth that fringed his lips.
So like a shatter’d column lay the King;
Not like that Arthur who, with lance in rest,
From spur to plume a star of tournament,
Shot thro’ the lists at Camelot, and charged
Before the eyes of ladies and of kings.
Then loudly cried the bold Sir Bedivere,
“Ah! my Lord Arthur, whither shall I go?
Where shall I hide my forehead and my eyes?
For now I see the true old times are dead,
When every morning brought a noble chance,
And every chance brought out a noble knight.
Such times have been not since the light that led
The holy Elders with the gift of myrrh.
But now the whole ROUND TABLE is dissolved
Which was an image of the mighty world;
And I, the last, go forth companionless,
And the days darken round me and the years,
Among new men, strange faces, other minds.”

Comfort thyself: what comfort is in me?
I have lived my life, and that which I have done
May He within Himself make pure! but thou,
If thou shouldst never see my face again,
Pray for my soul. More things are wrought by prayer
Than this world dreams of. Wherefore, let thy voice
Rise like a fountain for me night and day.
For what are men better than sheep or goats
That nourish a blind life within the brain,
If, knowing God, they lift not hands of prayer
Both for themselves and those who call them friend?
For so the whole round earth is every way
Bound by gold chains about the feet of God.
But now farewell. I am going a long way
With these thou seëst—if indeed I go—
(For all my mind is clouded with a doubt)
To the island-valley of Avilion;
Where falls not hail, or rain, or any snow,
Nor ever wind blows loudly; but it lies
Deep-meadow’d happy, fair with orchard-lawns
And bowery hollows crown’d with summer sea,
Where I will heal me of my grievous wound.”
So said he, and the barge with oar and sail
Moved from the brink, like some full-breasted swan
That, fluting a wild carol ere her death,
Ruffles her pure cold plume, and takes the flood
With swarthy webs. Long stood Sir Bedivere
Revolving many memories, till the hull
Looked one black dot against the verge of dawn
And on the mere the wailing died away.


High Frequency Trading(HFT) is a type of quantitative trading characterized by short holding period and the use of sophisticated computer method to trade securities rapidly. It aims to capture small profit on every short-term trade.(Cartea & Penalva, 2012). Statistical arbitrage is a situation where there is a statistical mispricing of one or more assets based on the expected values of these assets. When a profit situation takes place from pricing inefficiencies between securities, traders can identify the statistical arbitrage situation through mathematical models. Statistical arbitrage depends heavily on the ability of market prices to return to a historical or predicted mean. The Law of One Price(LOP) lays the foundation for this assumption. LOP states that two stocks with the same payoff in every state of nature must have the same current value (Gatev, Goetzmann, & Rouwenhorst, 2006) Thus, two stock prices spread between close substitute assets should have a stable, long-term equilibrium price over time.
In order to have more pairs with high correlation, we select stocks in a specific industry. Economically, we prefer traditional sectors because the companies in these sector are more likely to be close substitutes. If we selected N stocks, the number of pairs can be calculated by \(\textrm{C}_{n}^{2} = \frac{n*(n-1)}{2}\). In the demonstrated strategy we used 80 stocks, so we have 3160 pairs in total. We used minute data and aggregate them into lower resolution, thus 1 minute is the highest resolution for this strategy.
Correlations measure the relationship between two stocks that have price trends. They tend to move together, and thus are correlated. Correlation filter is the first step to screen the candidate pairs. Consider two stocks A and B, a correlation coefficient between the stocks was a statistic that provide a measure of how the two stocks A and B were associated. The correlation coefficient \(\rho\) of stock A and stock B was obtained by
\[\rho = \frac{\sum_{i}^{N}(A_i - \bar{A})(B_i - \bar{B}))}{[\sum_{i}^{N}(A - \bar{A})^2\sum_{i}^{N}(B_i - \bar{B})^2]^\frac{1}{2}}\]Where \(\bar{A}\) and \(\bar{B}\) are the mean prices of stock A and stock B respectively, N denoted a trading data range. \(\rho\) is in the range of [-1,1]. The more positive \(\rho\) is, the more positive the association of stock A and stock B is.
However, the pairs trading based on a correlation approach alone would have a disadvantage of instabilities over time. Correlation coefficients do not necessarily imply mean-reversion between the prices of the two stock pairs. In order to overcome the above issue, a cointegration approach was further used as the second-step of the selection process for the pairs.
The Cointegration concept, an innovative mathematical model in economics developed by Nobel laureates Engle and Granger. Cointegration states that, in some instances, despite two given non-stationary time series, a specific linear combination of the two time series is actually stationary. In other word, the two time series move together in a lockstep pattern.
The definition of cointegration is the following: assume that \(x_t\) and \(y_t\) are two time series that were non-stationary. If there exists a parameter \(\gamma\) such that the following equation:
\[z_t = y_t - \gamma x_t\]It is a stationary process, then \(x_t\) and \(y_t\) would be cointegrated. This process is a powerful tool for investigating common asset trends in multivariate time series.
In our case, Let \(p_t^A\) and \(p_t^B\) be the prices of two stocks A and B respectively. If it is assumed that {\({p_t^A, p_t^B}\)} is individually non-stationary, there exists the parameter \(\gamma\) such that the following equation was a stationary process
\[P_t^A - \gamma P_t^B = \mu + \epsilon_t\]where \(\mu\) is a mean of the cointegration model. \(\epsilon_t\) is a stationary, mean-revering process and was referred to as a cointegration residual. The parameter \(\gamma\) is known as a cointegration coefficient. The equation above represents a model of cointegrated pair for stocks A and B.
It's essential to understand how the conitegration residual together with the cointegration coefficient determines our trading direction. If \(\epsilon\) is positive, in a given confidence interval, this is a signal that stock A is relatively overpriced and stock B is relatively underpriced, and we are going to long B and short A; If If \(\epsilon\) is negative, we are going to long A and short B.
In the Engle-Granger method(Engle & Granger, 1987), we first set up a cointegration regression between stock A and stock B as stated in the equation above, and then estimate the regression parameters \(\mu\) and \(\gamma\) using an ordinary least squares(OLS). Subsequently, we tested the regression residual \(epsilon_t\) to determine whether or not it was stationary.
The most popular stationary test in the area of cointegration, the Augmented Dickey Fuller (ADF) test, was used on the regression residual \(\epsilon\) to determine whether it had a unit root.
Testing for the presence of the unit root in the regression residual using the ADF test was given by
\[\Delta Z_t = \alpha + \beta t + \gamma Z_{t-1} + \sum_{i = 1}^{p -1}\delta_i \Delta Z_{t-i} + \mu_t\]where \(\alpha\) is a constant, \(\beta\) is the coefficient on a time trend, p is the lag of order of the autoregressive process, \(\mu_t\) is an error term and serially uncorrelated.
The number of lag order p in the equation is usually unknown and therefore had to be estimated. To determine the number of lag p, the information criteria for lag order selection was used. Here we choose Bayesian Information Criterion(BIC)
\[BIC = (T-p)\ln\frac{T\hat{\sigma}_p^2}{T-p} + T[1+ln(\sqrt{2\pi})] + p\ln[\frac{\sum_{t=1}^{T}(\Delta Z_t)^2 -T\hat{\sigma}_p^2}{p}]\]Where T is the sample size.
The unit root test for the regression residual \(\epsilon\) using the ADF test was then carried out under the null hypothesis \(H_0 : \gamma = 0\) versus the alternative hypothesis \(H_1 : \gamma < 0\). A statistical value of the ADF test was obtained by
\[ADF test = \frac{\hat{\gamma }}{SE(\hat{\gamma })}\]The test result in the equation above is compared with the critical value of the ADF test. If the test result is less than the critical value, then the null hypothesis is rejected. This means the regression residual \(\epsilon\) is stationary. Thus, the two stock prices {\({p_t^A, p_t^B}\)} are cointegrated.
The pairs trading strategy uses trading signals based on the regression residual \(\epsilon\) and were modeled as a mean-reverting process.
In order to select potential stocks for pairs trading, the two-stage correlation and cointegration approach was used. The first step is to identify potential stock pairs from the same sector, where the stock pairs are selected with correlation coefficient of at least 0.9 using the correlation approach. The second step is to check the the cointegration of the pairs passed the correlation test. If the test value of cointegration is equal or less than -3.34, which is the critical value at a 95% confidence lever, the null hypothesis \(H_0 : \gamma = 0\) is rejected, thus the residual \(\epsilon\) is stationary, and the pair passed the cointegration test. The third step is to rank all of the stock pairs that passed the two-stage test according to their cointegration test values. The smaller the cointegration test value is, the higher rank the stock pair is assigned to. Financial selection of the stock pairs from the top rank is used for pairs trading.
The final step of the strategy is to define trading rules. To open a pairs trading, the regression residual \(\epsilon_t\) must cross over and down the positive \(\sigma\) standard deviation above the mean or cross down and over the negative \(\sigma\) standard deviation below the mean. If the residual is positive, we short stock B and long stock A; if the residual is negative, we short Stock A and long Stock B. When the regression residual (\epsilon_t\) returned to a certain level, the pairs trading is closed. Further more, in order to prevent the loss of too much on a single pairs trading, a stop-loss is used to close the pairs when the residual hit \(4\epsilon\) positive or negative standard deviation.
In the training period, each of the training data contained a 3-month period, which is a dynamic rolling window size. Immediately after the training period, we begin our one-month trading period, and the dynamic rolling window automatically shift ahead to record the new prices of the stocks in each pair. After the first trading period, we use the updated stock prices to select our pairs for trading again, and begin another trading period.
The performance of the strategy is sensitive to the parameters. There are mainly four parameter to adjust: Opening Threshold, Closing Threshold, Stop-loss Threshold and data resolution.
Opening threshold represents by how many times the residual \(\epsilon\) exceed the standard deviation, which is calculated by \(\frac{\epsilon - \bar{\epsilon}}{\sigma}\). By default we set it to 2.32 and -2.32, which is the critical value for 99% confidence interval if we assume the residual follows normal distribution. Closing threshold is calculated in the same way as opening threshold, we set it to 0.5 by default to close early to prevent further divergence.
Stop-loss Threshold is set to 4.5. This depends on the level of mispricing we can bear. The higher degree our tolerance to risk is, the higher we can set this parameter. However, if we set this number too low, we may have too many pairs closed before reversion to stop loss.


[§321H-5] Rules. The director may adopt rules under chapter 91 necessary to carry out this chapter. [L 2002, c 160, pt of §2]


PROPERTY
Former Tipperary garda station sold at public auction
HUGE INTEREST IN NEW INN BUILDING
The former garda station in New Inn sold at public auction for €113,000.
It is the fourth former garda station sold by Tipperary town auctioneer, Noel Corcorcan, following previous successful sales in Doon, Dundrum and Ballinure.
There was enormous interest shown in the property by prospective purchasers and the autioneers received about 40 enquiries with the vast majority of these viewing the property.
When the property was offered for sale by public auction at Cahir House Hotel there was a good turnout of people..
Bidding commenced at €50,000 and after a little pause steadily rose in €5000 increments with five parties bidding until it hit €100,000 at which stage, without even leaving the rostrum, the auctioneer declared the property on the market and he took €1000 bids after that until such time as the hammer fell at €113,000.
The purchasers are Tipperary based but it is unknown yet what plans they have in mind for the property.


Michelle Keegan oozes sophistication as she launches most glam collection ever
Our Girl actress Michelle Keegan gets glam for the festive season with a new exclusive party wear collection
by Mark Jefferies, https://www.mirror.co.uk/authors/mark-jefferies/Actress Michelle Keegan has launched her most glamorous collection of party wear pieces yet.
The TV star and part-time designed took inspiration from her own wardrobe, adding an 80’s twist to bring to life a range that is a riot of clashing colours, sparkly sequins, exaggerated sleeve detailing, head turning dresses and sharp tailoring.
Starring in a gorgeous photo-shoot the sultry British beauty models her eponymous collection against an opulent backdrop, providing the ultimate festive styling inspiration to get the party started.

"I wanted to incorporate statement sequin looks alongside more pared back tailored pieces to make sure that there is something for everyone to look sensational this party season!”
Michelle is pictured wearing show-stopping designs including an emerald green, off the shoulder dress and a glitzy sequin midi dress which are available now at very.co.uk.
The new collection shouts high octane glamour and elegance.
Andrew Roscoe, head of brand at Shop Direct, which operates Very, commented: “With the arrival of the festive season there is an array of events that our customers want to look and feel great for.
"Michelle’s new collection offers our customers the chance to dress for every occasion and celebrate key moments in the lead up to Christmas.”
Meanwhile, Michelle fired back at a fan in a shock outburst after he accused her of asking "silly questions" to her followers.
The Our Girl actress, 32, took to Twitter recently to ask if any of her fans had any advice on what to do for dogs who are scared of fireworks.
One responded that most dogs are scared of them so it was pointless to ask anyone for help.
This appeared to anger Michelle, who called him a "d***head" and said he was talking "b*****ks " in a feisty tweet back.
She'd originally posted: "Anyone else’s dog terrified of fireworks???


My name is Long Do and I'm a software developer from Germany. Currently I'm primarly focusing on Front-End development, but long-term I intend to move towards becoming a Fullstack Developer. I have experience working in agile environments and am a convinced advocate of clean code. My technical interests cover the integration of internet of things into everyday life, checking out open source projects and to automate repetitive processes via bash or python scripting.


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{% extends "base.html" %}
{% load i18n %}
{% block content %}
{% if account %}
{% blocktrans %}
Welcome {{ account }} !
Your account on democracy has been created.
{% endblocktrans %}
{% else %}
{% trans "Something went wrong. Your account is probably already active" %}
{% endif %}
{% if votes_saved %}
{% trans "Your previous anonymous votes have been saved to your profile!" %}
{% endif %}
{% endblock %}


MYA
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ProbannoWebAPI
Version: 1.0.0
BasePath:/kingb12/ProbannoWeb/1.0.0
Apache 2.0
http://www.apache.org/licenses/LICENSE-2.0.html
Access
[ Jump to Models ]
Table of Contents
Retrieve job status (getJob)
Retrieve job status object
Request headers
Query parameters
job_id (required)
Query Parameter — UUID of the job to retrieve
Return type
Example data
Content-Type: application/json
{
"jid" : "a70a6344-3c55-48fa-8b60-db71858e810b",
"job" : "calculate_probanno",
"target" : "my_model",
"status" : "Complete"
}
Produces
This API call produces the following media types according to the request header;
the media type will be conveyed by the
Content-Type response header.
Responses
200
successful operation
Job
400
Bad or missing session
404
Job not found
Retrieve all job statuses (listJob)
Retrieve all job status objects
Request headers
Return type
Example data
Content-Type: application/json
[ {
"jid" : "a70a6344-3c55-48fa-8b60-db71858e810b",
"job" : "calculate_probanno",
"target" : "my_model",
"status" : "Complete"
} ]
Produces
This API call produces the following media types according to the request header;
the media type will be conveyed by the
Content-Type response header.
Responses
200
successful operation
400
Bad or missing session
Upload metabolic model (createModel)
Upload metabolic model as JSON
Consumes
This API call consumes the following media types via the
Content-Type request header:
Request headers
Query parameters
model_id (required)
Query Parameter — ID for the model to be uploaded
Form parameters
file (required)
Form Parameter — Model to be uploaded format: application/json
Return type
Example data
Content-Type: application/json
{ }
Produces
This API call produces the following media types according to the request header;
the media type will be conveyed by the
Content-Type response header.
Responses
200
successful operation
Model
400
No session, bad session, or missing arguments
404
Model not found
Retrieve metabolic model as file (downloadModel)
Retrieve metabolic model as JSON file
Request headers
Query parameters
model_id (required)
Query Parameter — ID for the model to be downloaded
Return type
Example data
Content-Type: application/json
{ }
Produces
This API call produces the following media types according to the request header;
the media type will be conveyed by the
Content-Type response header.
Responses
200
successful operation
Model
400
No session, bad session, or missing arguments
404
Model not found
probabilistically gap-fill a metabolic model (gapfillModel)
Run a probabilistic gap-filling with a model
Request headers
Query parameters
model_id (required)
Query Parameter — metabolic model to gapfill
output_id (required)
Query Parameter — output ID for the gap-filled metabolic model
fasta_id (required)
Query Parameter — reaction likelihoods ID to inform gap-filling
template (optional)
Query Parameter — Template model to inform gap-filling
Return type
Example data
Content-Type: application/json
{
"jid" : "a70a6344-3c55-48fa-8b60-db71858e810b",
"job" : "calculate_probanno",
"target" : "my_model",
"status" : "Complete"
}
Produces
This API call produces the following media types according to the request header;
the media type will be conveyed by the
Content-Type response header.
Responses
200
successful operation
Job
400
No session, bad session, or missing arguments
404
Reaction likelihoods or model not found
Retrieve metabolic model (getModel)
Retrieve metabolic model as JSON
Request headers
Query parameters
model_id (required)
Query Parameter — ID for the model to be downloaded
Return type
Example data
Content-Type: application/json
{ }
Produces
This API call produces the following media types according to the request header;
the media type will be conveyed by the
Content-Type response header.
Responses
200
successful operation
Model
400
No session, bad session, or missing arguments
404
Model not found
List metabolic models (listModel)
List metabolic models associated with the session
Request headers
Return type
array[String]
Example data
Content-Type: application/json
[ "aeiou" ]
Produces
This API call produces the following media types according to the request header;
the media type will be conveyed by the
Content-Type response header.
Responses
200
successful operation
400
No session or bad session
Upload metabolic model (updateModel)
Upload metabolic model as JSON
Consumes
This API call consumes the following media types via the
Content-Type request header:
Request headers
Query parameters
model_id (required)
Query Parameter — ID for the model to be uploaded
Form parameters
file (required)
Form Parameter — Model to be uploaded format: application/json
Return type
Example data
Content-Type: application/json
{ }
Produces
This API call produces the following media types according to the request header;
the media type will be conveyed by the
Content-Type response header.
Responses
200
successful operation
Model
400
No session, bad session, or missing arguments
404
Model not found
Up
get /probanno/calculate
Calculate Reaction Likelihoods (calculateLikelihoodsGet)
Calculate reaction likelihoods by Uniprot taxonomic identifier
Request headers
Query parameters
fasta_id (required)
Query Parameter — Uniprot taxonomic identifier for target genome sequence (FASTA)
Return type
Example data
Content-Type: application/json
{
"jid" : "a70a6344-3c55-48fa-8b60-db71858e810b",
"job" : "calculate_probanno",
"target" : "my_model",
"status" : "Complete"
}
Produces
This API call produces the following media types according to the request header;
the media type will be conveyed by the
Content-Type response header.
Responses
200
successful operation
Job
400
Bad or missing session
404
FASTA not found
Up
put /probanno/calculate
Calculate reaction likelihoods (calculateLikelihoodsPut)
Calculate reaction likelihoods by FASTA file
Consumes
This API call consumes the following media types via the
Content-Type request header:
Request headers
Form parameters
fasta (required)
Form Parameter — Target genome sequence in FASTA format
Return type
Example data
Content-Type: application/json
{
"jid" : "a70a6344-3c55-48fa-8b60-db71858e810b",
"job" : "calculate_probanno",
"target" : "my_model",
"status" : "Complete"
}
Produces
This API call produces the following media types according to the request header;
the media type will be conveyed by the
Content-Type response header.
Responses
200
successful operation
Job
400
Bad or missing session
404
FASTA not found
Up
get /probanno/download
Download reaction likelihoods (downloadReactions)
Download reaction likelihoods
Request headers
Query parameters
fasta_id (required)
Query Parameter — Uniprot taxonomic identifier for target genome sequence (FASTA)
Return type
Example data
Content-Type:
Produces
This API call produces the following media types according to the request header;
the media type will be conveyed by the
Content-Type response header.
Responses
200
successful operation
400
Bad or missing session
404
FASTA not found
Retrieve reaction likelihoods (getLikelihoods)
Retrieve reaction likelihoods
Request headers
Query parameters
fasta_id (required)
Query Parameter — Uniprot taxonomic identifier for target genome sequence (FASTA)
Return type
Example data
Content-Type: application/json
[ {
"reaction" : "rxn00594_c",
"probability" : 0.88585,
"gpr" : "sp|Q58092|TKTC_METJA Putative transketolase C-terminal section OS=Methanocaldococcus jannaschii (strain ATCC 43067 / DSM 2661 / JAL-1 / JCM 10045 / NBRC 100440) GN=MJ0679 PE=5 SV=1",
"type" : "HASCOMPLEXES",
"complexes" : [ {
"probability" : 0.55,
"complex" : "cpx00466",
"type" : "CPLX_PARTIAL_1_of_2"
} ]
} ]
Produces
This API call produces the following media types according to the request header;
the media type will be conveyed by the
Content-Type response header.
Responses
200
successful operation
400
Bad or missing session
404
FASTA not found
List calculated reaction likelihoods (listLikelihoods)
List reaction likelihoods calculated within this session. Only includes likelihoods associated with COMPLETE jobs, not failures or in progress jobs.
Request headers
Return type
Example data
Content-Type: application/json
[ {
"fasta_id" : "267377",
"name" : "Methanococcus maripaludis S2"
} ]
Produces
This API call produces the following media types according to the request header;
the media type will be conveyed by the
Content-Type response header.
Responses
200
successful operation
400
Bad or missing session
404
FASTA not found
Get a new session (getSession)
Get a new session token, a UUID associated with all actions for this session. This is in lieu of more sophisticated authorization schemes.
Return type
Example data
Content-Type: application/json
"\"a70a6344-3c55-48fa-8b60-db71858e810b\""
Produces
This API call produces the following media types according to the request header;
the media type will be conveyed by the
Content-Type response header.
Responses
200
successful operation
Session
[ Jump to Methods ]
Table of Contents
Complex
-
Job
-
LikelihoodSummary
-
Model
-
ReactionLikelihood
-
Session
-
complex (optional)
type (optional)
probability (optional)
jid (optional)
job (optional)
calculate_probanno
gapfill_model
target (optional)
String ID of the object targeted by the job (model for gap-filling, fasta_id for probanno)
status (optional)
Complete
Not Started
Failure
Running
fasta_id (optional)
name (optional)
a CobraPy Model in JSON format. See cobra.io.json for more info
reaction (optional)
type (optional)
gpr (optional)
probability (optional)
complexes (optional)
Unique session identifier


Atif Ikramul Haque
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Password Generator
Link to the GitHub Repository.
Link to the deployed version.
Day Planner
Link to the GitHub Repository.
Link to the deployed version.
Eat-Da-Burger
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Event Planner
Link to the GitHub Repository.
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Geoverse
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About:
The Quaternary Period is the geological period we are living in today. During the Quaternary Period, humans evolved and distributed themselves all over the world. Other species have adapted to the changes as well. A series of glacial advances and retreats bring about the extinction and variation of species. Whales and sharks emerge and dominate the sea, while mammals with thick hair (sometimes called megafauna) roam the land, feeding on any plants that might grow in the frigid climate caused by the ice.
Above: Megafauna such as the wooly mammoth roam the Earth.