Trading_GetNotificationsUsageResponseType Class Reference
| Inherits from | Trading_AbstractResponseType : NSObject |
| Declared in | Trading_GetNotificationsUsageResponseType.h Trading_GetNotificationsUsageResponseType.m |
Overview
Returns an array of notifications sent to a given application identified by the appID
(comes in the credentials). The result can be used by third-party developers troubleshoot
issues with notifications.
Zero, one or many notifications can be returned in the array. The set of notifications
returned is limited to those that were sent between the StartTime and EndTime specified
in the request. If StartTime or EndTime filters were not found in the request, then
the response will contain the data for only one day (Now-1day). By default, maximum
duration is limited to 3 days (Now-3days). These min (1day) and max(3days) applies
to Notifications,MarkDownMarkUpHistory and NotificationStatistics.
Notifications are sent only if the ItemID is included in the request. If there is no
ItemID, then only Statistics and MarkDownMarkUpHistory information is included.
@ingroup EBayAPIInterface
Tasks
-
startTime
Returns the start date and time for the notification information that is
property
returned by this call. The oldest date allowed for this field is Now-3days.
Default is Now-1day. -
endTime
Returns the end date and time for the notification information that is
property
returned by this call. The default is Now. -
notificationDetailsArray
List of notification objects representing the notifications sent to an
property
application for the given time period. It will only be returned if ItemID
was specified in the input request. -
markUpMarkDownHistory
List of objects representing MarkUp or MarkDown history for a given appID
property
and for given StartTime and EndTime. This node will always be returned. -
notificationStatistics
Summary information about number of notifications that were successfully
property
delivered, queued, failed, connection attempts made, connection timeouts,
http errors for the given appID and given time period. By default, statistics
for only one day (Now-1day) is included. Maximum time duration allowed is 3 days
(Now-3days).
Properties
endTime
Returns the end date and time for the notification information that is
returned by this call. The default is Now.
@property (nonatomic, retain) NSDate *endTime
Discussion
Returns the end date and time for the notification information that is
returned by this call. The default is Now.
type : NSDate, wrapper for primitive date
Declared In
Trading_GetNotificationsUsageResponseType.h
markUpMarkDownHistory
List of objects representing MarkUp or MarkDown history for a given appID
and for given StartTime and EndTime. This node will always be returned.
@property (nonatomic, retain) Trading_MarkUpMarkDownHistoryType *markUpMarkDownHistory
Discussion
List of objects representing MarkUp or MarkDown history for a given appID
and for given StartTime and EndTime. This node will always be returned.
type : class Trading_MarkUpMarkDownHistoryType
Declared In
Trading_GetNotificationsUsageResponseType.h
notificationDetailsArray
List of notification objects representing the notifications sent to an
application for the given time period. It will only be returned if ItemID
was specified in the input request.
@property (nonatomic, retain) Trading_NotificationDetailsArrayType *notificationDetailsArray
Discussion
List of notification objects representing the notifications sent to an
application for the given time period. It will only be returned if ItemID
was specified in the input request.
type : class Trading_NotificationDetailsArrayType
Declared In
Trading_GetNotificationsUsageResponseType.h
notificationStatistics
Summary information about number of notifications that were successfully
delivered, queued, failed, connection attempts made, connection timeouts,
http errors for the given appID and given time period. By default, statistics
for only one day (Now-1day) is included. Maximum time duration allowed is 3 days
(Now-3days).
@property (nonatomic, retain) Trading_NotificationStatisticsType *notificationStatistics
Discussion
Summary information about number of notifications that were successfully
delivered, queued, failed, connection attempts made, connection timeouts,
http errors for the given appID and given time period. By default, statistics
for only one day (Now-1day) is included. Maximum time duration allowed is 3 days
(Now-3days).
type : class Trading_NotificationStatisticsType
Declared In
Trading_GetNotificationsUsageResponseType.h
startTime
Returns the start date and time for the notification information that is
returned by this call. The oldest date allowed for this field is Now-3days.
Default is Now-1day.
@property (nonatomic, retain) NSDate *startTime
Discussion
Returns the start date and time for the notification information that is
returned by this call. The oldest date allowed for this field is Now-3days.
Default is Now-1day.
type : NSDate, wrapper for primitive date
Declared In
Trading_GetNotificationsUsageResponseType.h


#include <XSECURIResolverGenericWin32.hpp>
Inheritance diagram for XSECURIResolverGenericWin32:
The XML Digital Signature standard makes heavy use of URIs to identify information to be referenced and signed.
This class implements the XSECURIResolver for Windows32, re-using much of the Xerces code.
Public Member Functions | |
Constructors and Destructors | |
| XSECURIResolverGenericWin32 () | |
| virtual | ~XSECURIResolverGenericWin32 () |
Interface Methods | |
| virtual XERCES_CPP_NAMESPACE_QUALIFIER BinInputStream * | resolveURI (const XMLCh *uri) |
| Create a BYTE_STREAM from a URI. | |
| virtual XSECURIResolver * | clone (void) |
| Clone the resolver to be installed in a new object. | |
Class specific functions | |
| void | setBaseURI (const XMLCh *uri) |
| Set the base URI for relative URIs. |
|
|
|
|
|
|
|
|
Clone the resolver to be installed in a new object. When URIResolvers are passed into signatures and other objects, they are cloned and control of the original object is left with the caller. Implements XSECURIResolver. |
|
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Create a BYTE_STREAM from a URI. The resolver is required to take the input URI and dereference it to an actual stream of octets. The octets are provided back to the library using the Xerces BinInputStream class.
Implements XSECURIResolver. |
|
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Set the base URI for relative URIs.
|


Is Australian Federal Politics More Volatile Than Westminster Politics?
In the recent EU referendum held in the UK, elements of Australia's immigration system were held up as a solution to the
perceived issues with the UK's own system. In future instalments of this series I will aim to test those assumptions
but as a warm up to that I aim to test some other assumptions made about Australian politics.
Australia's recent history of Prime Ministers being removed while in office gives the impression that Australia is an
unstable democracy. As Australia is a Westminster parliamentary system where the head of government is elected by the
governing party, it seems most relevant to compare Australia's record of Prime Ministerial terms to that of the
United Kingdom of Great Britain and Northern Ireland.
Australia became a self-governing federation in 1901 so we use data for both Australia and the UK from 1901 to 2015.
For each year we count the number of Prime Ministers appointed and the number of national elections which took place in that year.
If the same Prime Minister is appointed to non-contiguous terms each term will count. For 1901 to 2015 we find the number of
PM changes to be -
Prime Minister Changes 1901 to 2015
AU
{{ au_pms_1901 }}
UK
{{ gb_pms_1901 }}
So on a preliminary basis the two look very evenly matched. As Australia made a significant change to their electoral system and
moved to compulsory voting in 1924 we will restrict our analysis to 1924 onwards -
Prime Minister Changes 1924 to 2015
AU
{{ au_pms_1924 }}
UK
{{ gb_pms_1924 }}
Both countries have had the same number of PM changes between 1924 and 2015. This is very surprising as the two systems
differ in their voting system, party systems and most importantly the length of their governmental terms.
The executive branch of the Australian government is elected on three year terms while in the UK it is elected every five years.
However both systems allowed the government to call an election earlier, until the UK's Fixed-term Act of 2011. Looking at the
number of elections called we find -
We can see that although Australia had 52% more elections during the same period both countries had the same
number of changes of Prime Minister. On a per election basis -
Very roughly we can say that the UK changes Prime Minister 96% of the time at an election while
Australia changes Prime Minister only 63% of the time.
Let's look at that over time. Using the PM Change to Election ratio to adjust for the different
terms this plot shows the PM changes to election ratio with a rolling 5 year window -
Prime Minister Changes per Election 1924 to 2015
For Australia there are some obvious spikes around the World Wars and the 1970s. For the UK the spikes are
lower but more consistent. If we smooth the data by taking a ten year mean -
Prime Minister Changes per Election 10 Year Mean
Australia still has the same spikes but a linear regression shows both the UK and Australia with declining trends.
On the basis of this both countries have been relatively evenly matched over the last two decades with a slight up-tick
in the last five years. If we look at the volatility of each -
Prime Minister Changes per Election 5 Year Rolling Standard Deviation
Both countries have had periods of volatility with Australia experiencing more volatility over World War II and
the post-war period. Largely though both are historically very stable and the recent volatility is elevated but
not significantly so.
Is Australian Federal Politics More Volatile Than Westminster Politics?
No. The two are very similar and Australia has been as stable if not more stable considering the
difference in the number of elections.


Covaxin is an inactivated
vaccine making it safe to be injected into the body and can be stored between 2-8 degree celsius.
Effectiveness
Phase 3 results of the COVAXIN, developed by Indian Council of Medical Research (ICMR) in
partnership with Bharat BiotechInternational Limited (BBIL),has shown an interim vaccine efficacy of 81% in preventing Covid-19.
In an ongoing clinical trial, COVAXIN has been shown to generate immunity following 2 doses given 4 weeks apart.
COVAXIN’s ability to neutralize UK variant strain of SARS-CoV-2
has also recently been established..
The company is supplying vaccines to Andhra Pradesh, Assam, Chhattisgarh, Gujarat, Jammu & Kashmir, Jharkhand, Madhya Pradesh, Odisha, Tamil Nadu, Telangana, Uttar Pradesh and West Bengal.
COVISHIELD
About the vaccine
The Oxford–AstraZeneca COVID-19 vaccine, and sold under the brand names Covishield and Vaxzevria
Developed by Oxford University and AstraZeneca, it is given by intramuscular injection, using as a vector the modified chimpanzee adenovirus ChAdOx1.
The vaccine has a good safety profile, with side effects including injection-site pain, headache, and nausea, all generally resolving within a few days


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B.Tech. IV (CO) Semester – 7 |
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P |
C |
CO403 : INTERNET TECHNOLOGY AND APPLICATION (CS-II) |
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Track and discover new destinations. Upload images and plot your next getaway. Utilizing Amazon Web Services, Google API and OAuth.
Find out the most popular hashtags. Twitter API/OAuth, Monkey Learn API used to determine the most relavent hash tags for each category.
Sell your way with Rocket Shops. Responsive design,secure login and easy to use seller tools. Easy to use tools!


pyfse.Controller module
@author: Jean-Lou Dupont
Transition Table
Dictionary consisting of:
{ (current_state, event): record }
where record can be of the form:
Upon leaving the current_state, the method ‘leave_STATE’ will be called. Upon entering the next_state, the method ‘enter_STATE’ will be called.
The type of the parameter ‘event’ is a string.
A ‘wildcard’ match on any event can be obtained using:
(current_state, None): (next_state, action_method)
An initialization sequence can be obtained using:
(‘’, None): record
An ‘attractor’ match: an event X can direct the next_state from whatever current_state:
(None, ‘event’): record
Precedence
The precedence algorithm goes as follows:
Attractor Match
Care should be exercised when using this matching pattern.
Action Method
The action method need not to be present: it can take the value None. The action method can be:
string representing a local attribute + method name
For case 1, the function will be called with the event parameter. For case 2, the method will be called with the event parameter. For case 3, assuming the actions object is provided, the method will be called with the event parameter.
Precedence order is as listed.
The enter_STATE will be called prior to the action method.


What is Emotional Intelligence?
How we deal with our emotions. Identifying our own emotions, emotions of others, and how we relate and communicate with others.
How is it different to IQ?
IQ is the traditional measure of how smart a person was.It was generally accepted that if you had a higher IQ you would do well in life. It includes measuring:
EQ is a measure of different things that are increasingly being accepted as being just as important, if not more important, in the success of ourselves. EQ measures someone's ability in:
Why is it important?
Emotional intelligence is important as it helps us get along with others and cope in situations where emotions are running high. Understanding motivations and emotions of others is helpful in relationship building. It also helps us live lives with intention.
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Pirate (ARRR) is a cryptocurrency that solves the issue of financial privacy on the blockchain through forced anonymous transactions. Protect your wallet and transactions with Pirate’s completely anonymous network. With forced private transactions, your privacy cannot be compromised by another users’ activity on the network. If you wish to learn more, please join the official Pirate Discord at https://pirate.black/discord/ or message FishyGuts#5085, who will be happy to help out.
100 messages
Before 05-Dec-19 12:00 AM
Alright29-Sep-18 03:23 PM
"this is the definitive chain no matter how much hash rate is on it"
is what happened
0x0329-Sep-18 03:24 PM
no, u go ahead mateys
FishyGuts29-Sep-18 03:27 PM
Isn't that how dPoW works?
ComputerGenie 👴👴👴29-Sep-18 03:27 PM
build onto a block (by referencing it in blocks they create) if it is the latest block in the longest valid chain. "Length" is calculated as total combined difficulty of that chain, not number of blocks, though this distinction is only important in the context of a few potential attacks. A chain is valid if all of the blocks and transactions within it are valid, and only if it starts with the genesis block.
Because, Bitcoin
Flexatron29-Sep-18 03:27 PM
WTS 30K pirate for 4K KMD or 0.66 BTC or 5M holo
OsTa29-Sep-18 03:27 PM
Hello guys. When I try to move my funds I got this: "witness for note commitment is null". Do you know why?
jl777c29-Sep-18 03:29 PM
@FishyGuts this hardfork activated dPoW, before it, there was no dPoW so blocks were subject to being reorged at anytime
FishyGuts29-Sep-18 03:30 PM
I see.  He's just having fun griping I think.
ComputerGenie 👴👴👴29-Sep-18 03:31 PM
no, he's not having fun, it cost him a good chunk of change out of his poket
but, that's the risk we miners take
jl777c29-Sep-18 03:32 PM
i mean, he went to sleep!
1
Flexatron29-Sep-18 03:33 PM
Its Just money
Fek it
ComputerGenie 👴👴👴29-Sep-18 03:33 PM
I vaguely remember what that whole "sleep" thing is, I think
jl777c29-Sep-18 03:33 PM
everyone knows the worst possible thing will happen at the worst possible time
NutellaLicka29-Sep-18 03:36 PM
Murphy's law
jl777c29-Sep-18 03:36 PM
yes, that's the one
0x0329-Sep-18 03:37 PM
it should be noted that what hasn't been tested, also likely will 'murphy' on you should you one day have to rely on it(edited)
ComputerGenie 👴👴👴29-Sep-18 03:37 PM
Murphy's law: "Anything that can go wrong will go wrong"
Murphy's law 2.0: "Anything that can go wrong will go wrong, at the most inopportune time"
@doc_bitcoin @RealCryptoLlama @lopp That's where many PIRATES around the world mine our chain for their privacy. Where do you mine for your privacy?
#FreedomToPrivacy
1
grewalsatinder29-Sep-18 03:45 PM
night time
Now that I will go to bed all those good trades will happen now(edited)
1
FishyGuts29-Sep-18 03:45 PM
Good Nght Sat.... Let me know if you need anything before you head off.
i think pirate is mined into a transparent address. idk if that has anything to do with it.
OsTa29-Sep-18 03:55 PM
ah ok good to know thanks
Flexatron29-Sep-18 04:02 PM
WTS 30K pirate for 4K KMD or 0.66 BTC or 5M holo, done a succesfull trade with Jcooper before
1
Llama29-Sep-18 04:06 PM
added to the spreadsheet
Alright29-Sep-18 04:17 PM
Who do I give my chicken to get post an order on the google spreadsheet
wookie29-Sep-18 04:17 PM
I think the spreadsheet defeats the purpose of privacy...to quote jl777 "It wont be possible to make a mistake to lose the PIRATE privacy, unless you are announcing publicly to the world"(edited)
1
Alright29-Sep-18 04:17 PM
literally anything would have been better than using google
1
Llama29-Sep-18 04:20 PM
its the exact same as using the discord to announce trades @wookie
snowdamn29-Sep-18 04:21 PM
guys, the spreadsheet doesn't make sense as an orderbook for many reasons, but don't worry about privacy, it's the z-txid that should NEVER be posted anywhere, Discord is no better
Alright29-Sep-18 04:21 PM
"don't worry about privacy"
the txid isn't even what matters btw
post your txids, they don't mean anything
Llama29-Sep-18 04:22 PM
there's no difference between posting your trade in the #talk-trading channel and having the trade listed on the sheet
unless for some reason you hate organization
snowdamn29-Sep-18 04:22 PM
yes, no z-txid on spreasheet, so no privacy issues, at least not anything worst than posting on Discord
Llama29-Sep-18 04:23 PM
and the only people who would ever see that spreadsheet are the same people that would see your offers in the discord
Alright29-Sep-18 04:23 PM
well if someone says, just sold for X amount for Y KMD
and at the same moment, there is a KMD transaction with Y KMD
snowdamn29-Sep-18 04:24 PM
yes, never buy PIRATE with KMD, always buy with z-KMD...
wookie29-Sep-18 04:25 PM
@Llama I see your point
Llama29-Sep-18 04:27 PM
but i deleted it cause i guess everyone hated it
snowdamn29-Sep-18 04:27 PM
lol
0x0329-Sep-18 04:28 PM
-------------------
Selling 3000 PIRATE @ 0.125 KMD or 1.05 VRSC
Order sizes starting at 100 PIRATE
PM if interested, thank you!
-------------------
snowdamn29-Sep-18 04:28 PM
was good educational lesson for all
if i was a seller, i would only accept z-KMD
jl777c29-Sep-18 04:30 PM
maybe a different price for KMD vs zKMD?
snowdamn29-Sep-18 04:31 PM
now that would be something to scratch your head about
bishop29-Sep-18 04:34 PM
@webworker01 what are you using on your pool to cron and consolidate notes
Yeah, and I have periodic cpu activity, not consistent load across 16 threads
it spikes for a few seconds then drops down, then spikes again
0x0329-Sep-18 04:50 PM
42msol net(edited)
...mate
Flexatron29-Sep-18 04:52 PM
Boom
Buddz199329-Sep-18 04:54 PM
cheapyy
Flexatron29-Sep-18 05:02 PM
174M diff
45Msol
Wassuppp
Wanna mine 160k coins the coming day?
1
Hire 45 Msol for a day
3,4 btc
Roughly 0.13$/ARRR
Flexatron29-Sep-18 05:15 PM
Nicehash broken again?
Alright29-Sep-18 05:16 PM
Hey guys, I'm setting up a donation fund for @Alright to try to compensate him for money lost due to unannounced hard fork. Feel free to donate
zcEXHoPqCCa8tK2UpuUscQ9igBK2YAts5EzXm2HXtWt29k78WMPjDdTDqGqeWqhxs7r3Gsm3WchnBUuw1sJRRfSU4xAREe1
never going to stop complaining about that.....
if we can at least prevent that from ever happening again, I'll be happy


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<Description>Add columns (a) through (c) on line 12</Description> <LineNumber>Part II Line 13</LineNumber> | NEW line(s): 64,65
<Description>Add columns (a) through (c) on line 13</Description> <LineNumber>Part II Line 14</LineNumber> |
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<LineNumber>Part II Line 15</LineNumber> |
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<LineNumber>Part II Line 15</LineNumber> | NEW line(s): 85
<LineNumber>Part II Line 16</LineNumber> |
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<LineNumber>Part III Line 16</LineNumber> | NEW line(s): 98
<LineNumber>Part III Line 17</LineNumber> |
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<Description>Add columns (a) through (c) on Line 16</Description> <LineNumber>Part III Line 17</LineNumber> | NEW line(s): 107,108
<Description>Add columns (a) through (c) on Line 17</Description> <LineNumber>Part III Line 18</LineNumber> |
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<Description>Subtract line 20 from line 19. If zero or less, stop. You cannot claim the personal use part of the credit</Description> <LineNumber>Part III Line 21</LineNumber> |
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<!-- Vehicle Identification Number --> <xsd:element name="VehicleIdentificationNumber" type="VINType" minOccurs="0"> <xsd:annotation> <xsd:documentation> <Description>Vehicle identification number</Description> <LineNumber>Part I Line 2</LineNumber> </xsd:documentation> </xsd:annotation> </xsd:element> |
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<LineNumber>Part I Line 4</LineNumber> |
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<Description>If you are not claiming the plug-in conversion, skip lines 4 through 8, enter -0- on line 9, and go to line 10</Description> <LineNumber>Part I Line 4</LineNumber> | NEW line(s): 206,207
<Description>If you are not claiming the plug-in conversion, skip lines 5 through 9, enter -0- on line 10, and go to line 11</Description> <LineNumber>Part I Line 5</LineNumber> |
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<Description>Subtract line 6 from line 5</Description> <LineNumber>Part I Line 7</LineNumber> |
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<Description>Multiply line 7 by 10% (.10)</Description> <LineNumber>Part I Line 8</LineNumber> |
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<LineNumber>Part I Line 9</LineNumber> |
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<Description>Enter the smaller of line 7 or line 8</Description> <LineNumber>Part I Line 9</LineNumber> | NEW line(s): 256,257
<Description>Enter the smaller of line 8 or line 9</Description> <LineNumber>Part I Line 10</LineNumber> |
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<Description>Multiply line 11 by line 12</Description> <LineNumber>Part II Line 13</LineNumber> |


This subpart prescribes policies and procedures for obtaining information needed for Government—
(a) Compliance with right-to-know laws and pollution prevention requirements;
(b) Implementation of an environmental management system (EMS) at a Federal facility; and
(c) Completion of facility compliance audits (FCAs) at a Federal facility.
(a) Emergency Planning and Community Right-to-Know Act of 1986, 42 U.S.C. 11001-11050 (EPCRA).
(b) Pollution Prevention Act of 1990, 42 U.S.C. 13101-13109 (PPA).
(c) Executive Order 13423 of January 24, 2007, Strengthening Federal Environmental, Energy, and Transportation Management.
(d) Executive Order 13514 of October 5, 2009, Federal Leadership in Environmental, Energy, and Economic Performance.
The requirements of this subpart apply to facilities owned or operated by an agency in the customs territory of the United States.
As used in this subpart—
“Federal agency” means an executive agency (see 2.101).
(a) Federal facilities are required to comply with—
(1) The emergency planning and toxic release reporting requirements in EPCRA and PPA; and
(2) The toxic chemical, and hazardous substance release and use reduction goals of sections 2(e) and 3(a)(vi) of Executive Order 13423.
(b) Pursuant to EPCRA, PPA, E.O. 13423, and any agency implementing procedures, every new contract that provides for performance on a Federal facility shall require the contractor to provide information necessary for the Federal agency to comply with the—
(1) Requirements in paragraph (a) of this section; and
(2) Requirements for EMSs and FCAs if the place of performance is at a Federal facility designated by the agency.
(a) Insert the clause at 52.223-5, Pollution Prevention and Right-to-Know Information, in solicitations and contracts that provide for performance, in whole or in part, on a Federal facility.
(b) Use the clause with its Alternate I if the contract provides for contractor—
(1) Operation or maintenance of a Federal facility at which the agency has implemented or plans to implement an EMS; or
(2) Activities and operations—
(i) To be performed at a Government-operated Federal facility that has implemented or plans to implement an EMS; and
(ii) That the agency has determined are covered within the EMS.
(c) Use the clause with its Alternate II if—
(1) The contract provides for contractor activities on a Federal facility; and
(2) The agency has determined that the contractor activities should be included within the FCA or an environmental management system audit.


Export keys
A simple application for multi-factor authentication, written in HTML using jQuery Mobile (and PhoneGap), jsSHA, LocalStorage and NW.js


12 killed in Niger car accident
by Agency ReportThe Federal Road Safety Corps (FRSC) in Niger State says 12 people died in an accident on Sunday at Sawmill village along Mokwa -Makera road in Mokwa Local Government Area of the state.
The FRSC Sector Commander in the state, Joel Dagwa, told News Agency of Nigeria (NAN) on Monday in Minna that the accident involved a Truck with registration No. KJ 555 XX heading to Lagos.
Mr Dagwa said the truck that came from Maiduguri picked passengers from Zaria in Kaduna State before heading to Lagos.
“The accident involved 103 people: 55 female, 40 male, eight children, five adults,” he said.
He said 12 people were killed while 91 sustained injuries and taken to Mokwa general hospital for treatment.
“All the 12 corpses have been deposited at the hospital’s morgue,” he said.
He blamed the accident on speeding on a sharp bend corner of the road.
The sector commander said the Corps would continue to monitor road users to guard against overloading and dangerous driving.
“We have since embarked on aggressive patrols across all major highways to ensure that road users adhere strictly to traffic rules and regulations to avoid road accidents before, during and after the Christmas and New year festivities,” he said.
(NAN)


| Credit card: | {{ ctrl.membership_details.card }} |
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Credit card details are never stored by Bitraf and is handled by stripe.com.
| From | To | Amount | Paid |
|---|---|---|---|
| {{ pay.start_date | date:'yyyy-MM-dd' }} | {{ pay.end_date | date:'yyyy-MM-dd' }} | {{ pay.amount }} | {{ pay.payment_date | date:'yyyy-MM-dd' }} |


Burgers
-
Burger Special
A Burger Special with Mozzarella cheese, marinara sauce, and grilled mushrooms.
₱ 80 -
Veggie Burger
A Veggie Burger with Salsa, Monterey jack cheese, and jalapenos.
₱ 85 -
Super Duper Burger
A Super Duper Burger with Mozzarella cheese, heirloom tomato slices, and fresh basil leaves.
₱ 120 -
Kiddies Burger
A small burger with juicy beef patty, tomato, lettuce, bacon and cheese. Served with small fries and drink of your choice.
₱ 60


Calendar Widget POC
start of dialog
The following keyboard shortcuts are available:
- LEFT/ RIGHT to move day to day.
- UP/ DOWN to move week to week.
- HOME to move to the first day of the month.
- END to move to the last day of the month.
- PAGE UP to move to the same day in the previous month.
- PAGE DOWN to move to the same day in the next month.
- SPACE to select a date.


{% load otree %}
Agreement to participate in a study on individual and group decision making (IRB # 3598)
You are invited to take part in a research project. Volunteering may not benefit you directly, but you will be helping us to understand the process of group decision-making. If you volunteer, you will earn a flat fee of $7 and a bonus that will depend on your decisions and the decisions of others in this study. This study will take about 1.5 hours of your time.  Your involvement is entirely up to you. You may withdraw at any time for any reason. Please continue reading for more information about the study.
Study Leadership: This research project is led by Rustam Romaniuc of the Claremont Graduate University.
Purpose: The purpose of this study is to collect data on the influence of group formation on individual
and group behaviors.
Eligibility: To be in this study, you need to be registered with the Experimental Social Science Laboratory
at the UC Irvine.
Participation: During the study, you will be asked to make a series of decisions that will affect your gains
(bonus) as well as the gains (bonus) of other participants. You will indicate your choices on the computer in
front of which you are seated, and this computer will calculate the gains you have realized in the course of the
experiment. This study will take about 1.5 hours.
Risks of participation: The risks that you run by taking part in this study are minimal and there is no foreseeable
risk or discomfort associated with this study.
Benefits of participation: We do not expect the study to benefit you personally. This study will benefit the
researchers by allowing them to understand the influence of group formation on individual and group behavior.
Compensation: You will earn a flat fee of $7 and a bonus that will depend on your decisions and the decisions of
others in this study. If you do not finish this study or if you decide to withdraw, then you will only earn the
fixed amount of $7.
Voluntary participation: Your participation in this study is completely voluntary. You may stop or withdraw from
the study at any time without it being held against you. Your decision whether or not to participate will have no
effect on your current or future connection with anyone at Claremont Graduate University or with any other
relevant institution. Since you will be part of a group with two other participants, your decision to withdraw
from this study may have consequences in terms of participation for the other group members.
Confidentiality: Your individual privacy will be protected in all papers, books, talks, posts, or stories
resulting from this study. We may use the data we collect for future research or share it with other
researchers, but we will not reveal your identity with it. In order to protect the confidentiality of your
responses, you will be given an ID number and your decisions will be recorded using only that information.
What is more, the ID will be reshuffled over the course of this study thereby preventing the other participants
from tracking your decisions for the duration of the study. Your name will not be used in any report, and we
will not obtain any information linking your name to the ID number.
Sponsorship: This study is being paid for by the Computational Justice Lab at Claremont Graduate University.
Further Information: If you have any questions or would like additional information about this study, please
contact Rustam Romaniuc at<EMAIL_ADDRESS>The CGU Institutional Review Board has approved this
project. If you have any ethical concerns about this project or about your rights as a human subject in
research, you may contact the CGU IRB at<PHONE_NUMBER> or at<EMAIL_ADDRESS>A copy of this form will be
given to you if you wish to keep it.
Consent: By clicking next means that you understand the information on this form, that someone has answered
any and all questions you may have about this study, and you voluntarily agree to participate in it.


Assembly Elections Results 2014 & 2015, 'A visual guide to the check out the trends
The graphical image tree map showing the trend of the each party performance in the Maharastra,Haryana,Jammu & Kashmir and Jharkhand Assembly elections held in the year 2014 and Bihar and Delhi Assembly elections held in the year 2015.


The Strategic Plan for Biodiversity 2011-2020 provides an overarching framework on biodiversity, not only for the biodiversity-related conventions, but for the entire United Nations system and all other partners engaged in biodiversity management and policy development.
At its 65th session in March 2011, the United Nations General Assembly decided to declare 2011–2020 the United Nations Decade on Biodiversity, with a view to contributing to the implementation of the Strategic Plan for Biodiversity 2011–2020, and in this regard has requested the Secretary-General, in consultation with Member States, to lead the coordination of the activities of the Decade on behalf of the United Nations system, with the support of the secretariat of the Convention on Biological Diversity, the secretariats of other biodiversity related conventions and relevant United Nations funds, programmes and agencies, and invited Member States in a position to do so to contribute, on a voluntary basis, to the funding of the activities of the Decade (Resolution 65/161).
Select an organization from the list below to see how they are supporting biodiversity.


Please read these Terms and Conditions carefully before using this website operated by Prerna Rural Development Society (PRDS).
Your access to and use of the Service is conditioned on your acceptance of and compliance
with these Terms. These Terms apply to all visitors, users and others who access or use the
Service.
By accessing or using the website you agree to be bound by these Terms. If you disagree
with any part of the terms then you may not access this.
Our website may contain links to third-party web sites or services that are not owned or
controlled by Prerna Rural Development Society.
Prerna Rural Development Society has no control over, and assumes no responsibility for,
the content, privacy policies, or practices of any third party web sites or services. You
further acknowledge and agree that Prerna Rural Development Society shall not be
responsible or liable, directly or indirectly, for any damage or loss caused or alleged to be
caused by or in connection with use of or reliance on any such content, goods or services
available on or through any such web sites or services.
We strongly advise you to read the terms and conditions and privacy policies of any thirdparty
web sites or services that you visit.
India is our country of domicile. These Terms shall be governed and construed in
accordance with the laws of India, without regard to its conflict of law provisions.
Our failure to enforce any right or provision of these Terms will not be considered a waiver
of those rights. If any provision of these Terms is held to be invalid or unenforceable by a
court, the remaining provisions of these Terms will remain in effect. These Terms constitute
the entire agreement between us regarding our organization, and supersede and replace
any prior agreements we might have between us regarding this.
We reserve the right, at our sole discretion, to modify or replace these Terms at any time. If
a revision is material we will try to provide at least 30 days notice prior to any new terms
taking effect. What constitutes a material change will be determined at our sole discretion.
We reserve the right, at our sole discretion, to modify or replace these Terms at any time. If
a revision is material we will try to provide at least 30 days notice prior to any new terms
taking effect. What constitutes a material change will be determined at our sole discretion.
PRDS aspires to be a dynamic website by adding new features and content regularly for the
benefit of visitors. For this purpose the website may be temporarily shut down for updation
work. However, we will put our best efforts to minimize the inconvenience caused due to
non-availability of website.
At PRDS we make every effort is made to keep the website up and running smoothly.
However, PRDS takes no responsibility for, and will not be liable for, the website being
temporarily unavailable due to issues beyond our control.
By accepting these Terms, you agree to indemnify and otherwise hold harmless PRDS, its officers, employees, affiliates and other partners from any direct, indirect, incidental, special, consequential or exemplary damages resulting from the use of this website by you.
If you have any questions about these Terms, please contact us.


DATABASE LAB PROJECT
This level of education is also called the FSc/FA/ICS or 'intermediate'
A Bachelor of Education (B.Ed.) is an undergraduate professional degree which prepares students for work.
A course of study demonstrating mastery or a high-order overview of a specific field of study or area of professional practice.
Qualifies the degree holder to teach their chosen subject at university level or to work in a specialized position in their chosen field.
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© Integrative Nutrition


This is a starter template that can by copied and pasted into an index.html file.
index.html
This document is using the CDN implementation of Vrembem.


Relic Watcher:
If you are qualified to view Kaerun's Memoir, I'll allow you to do so. But if not, you'll never gain access.
Quest


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Culture¶
Many forms of cultural debt exist in the workplace that can hinder a team from really taking advantage of collaborative risk measurement methods. Outside of the principles in this document, the focused norms adopted by a group should help improve probabilistic approaches to risk.
These items are difficult to enforce in the documentation and are better discussed more freely.
Prepare for prevention failures.¶
Risk measurement is great at measuring “known knowns” and “known unknowns”, but Black Swan Theory is an important aspect to consider when enumerating scenarios of risk.
In any complex environment, there will always be a substantial amount of risk that defies measurement. In which case, attention to the outcomes and the expected response becomes much more worthwhile.
In some industries, “Assume Breach” and “Defense-in-depth” are the rules of thumb that guide this mindset. Risk measurement methods can easily mislead those who wield them into thinking they’ve mitigated all “knowns”.
Some simple tests to understand if you’re thinking about risk correctly, are scenarios like the following:
- An intruder has bypassed our frontline defenses. (Measure: How likely would you detect them? How long would it take to detect? How much damage would occur?)
- Business has been interrupted due to a system outage. (Measure: How long until you’d come back online? Would you come online? How many customers would go away?)
A mindset that mitigates a few broad outcomes as opposed to preventing endless amounts of causes is valuable. This mindset helps mitigate eventual failure, and helps mitigate failures of human imagination.
Minds that are open to new information.¶
One’s ability to update previous beliefs based on sound evidence should be cherished. An unwillingness to hold strong beliefs until equal evidence is shown is a cornerstone of quantitative reasoning. Being wishy-washy is OK so long as one can identify evidence that would influence them.
Seek information, not agreement.¶
When forming panels, it is important to withhold bullying and attempts to get others to find agreement. Toxicity brews this way and influences risk measurement poorly.
Continuously practice risk literacy.¶
Opportunities to forecast outcomes are all around us. Find these future events that relate to your interests, your organization, or society and forecast them. Keep score and mind your calibration.
Protect and encourage the “Post Mortem”¶
Blame should be avoided during post-mortem sessions. Root cause knowledge should be cherished, organized, and shared. Reference data from other organizations should be protected and encouraged as well. Seek non-disclosure agreements and adopt rules like the Traffic Light Protocol or Chatham House rules if necessary.
Remove incentives that corrupt forecasts.¶
A “Million dollar” management problem that haunts all organization are the incentives to empire build, be promoted, secure a budget, etc. A principle of OKR’s has always been to keep the pursuit of Key Results out of performance management. Decision markets have also seen toxicity brew as individual finances became involved.
Avoiding outside influence that politicize or gamify forecasting will help match the culture and mindset currently seen in meteorology. Weather is not considered a betting environment, nor a market.


Darlingjs
: Game of Pong
Please click on game field to play.
Sources:
on github
, Engine:
DarlingJS
Controls
Player 1
: W/S
Player 2
: Up/Down
Touch support is coming soon.
Tweet


Analog In
Unlike digital input pins, which can only read high or low, analog pins can read a range of voltage levels. Microcontrollers based on 3.3V can typically read voltages anywhere between zero and 3.3V. Analog inputs connect internally to an Analog to Digital Converter (ADC) that converts the analog voltage level on the pin to a digital value.
Tip
Note that the analog channel number is not the pin number. You need to determine the channel number of a specific pin using your system's documentation.
The resolution of the ADC determines its accuracy. An 8bit ADC has 256 steps to work with, 3.3V/256=0.013V. This means an increase of 0.013V will increase the ADC value by one. In other words, a voltage change of less than 0.013V has no effect.
var adc = AdcController.FromName(SC20100.Adc.Controller1.Id);
var analog = adc.OpenChannel(SC20100.Adc.Controller1.PA0);
while (true) {
double d = analog.ReadRatio();
Debug.WriteLine("An-> " + d.ToString("N2"));
Thread.Sleep(100);
}
Support Sampling timing
Changing sampling time can be configured as follows:
adcChannel.SamplingTime = TimeSpan.FromTicks(10); // Set to 1us (a tick is 100ns)
SITCore allows these sampling times. If the SamplingTime is between two values above, the higher values will be used.
| Ticks | Time |
|---|---|
| 0 | 23ns |
| 1 | 132ns |
| 2 | 257ns |
| 5 | 507ns |
| 10 | 1007ns |
| 60 | 6054ns |
| 126 | 12664ns |
Tip
Default is 1 tick.
Temperature sensor
SITCore support reading temperature sensor.
void ReadTemperatureSensor()
{
var sc20xxx = DeviceInformation.DeviceName.IndexOf("SC20") >= 0 ? true : false;
var ts_reg1 = sc20xxx ? (IntPtr)0x1FF1E820 : (IntPtr)0x1FFF75A8;
var ts_reg2 = sc20xxx ? (IntPtr)0x1FF1E840 : (IntPtr)0x1FFF75CA;
var enable_reg = sc20xxx ? (IntPtr)((0x40000000U + 0x18020000 + 0x6300 + 8)) : (IntPtr)(0x40000000U + 0x08000000U + 0x08040300U + 8);
var controller = sc20xxx ? AdcController.FromName(SC20100.Adc.Controller3.Id) : AdcController.FromName(SC13048.Adc.Controller1.Id);
var channel = sc20xxx ? controller.OpenChannel(SC20100.Adc.Controller3.InternalTemperatureSensor) : controller.OpenChannel(SC13048.Adc.Controller1.InternalTemperatureSensor);
var enable_val = Marshal.ReadInt32(enable_reg);
enable_val |= (1 << 23);
Marshal.WriteInt32(enable_reg, enable_val);
while (true)
{
var v = channel.ReadValue() * 1.0;
var ts1 = Marshal.ReadInt32(ts_reg1);
var ts2 = Marshal.ReadInt32(ts_reg2);
if (!sc20xxx)
{
ts1 &= 0xFFFF;
ts2 &= 0xFFFF;
v *= 1.1;
}
var t1 = (110 - 30) * 1.0;
var t2 = (ts2 - ts1) * 1.0;
var t3 = (v - ts1) * 1.0;
var temperature = t1 / t2 * t3 + 30;
Debug.WriteLine("T = " + temperature + " Celsius");
Thread.Sleep(1000);
}
}


This is a pure Web/JavaScript application to monitor performance and other key indicators of your stock portfolio. It uses data stored in your Google Drive.
This application is written in JavaScript using a suite of available libraries, mainly AngularJS and D3.js.
It uses community provided tables in Yahoo Query Language to check your stock's performance against todays prices. Please use responsibly and keep in mind their terms.
It is written with a strong focus on the safety and privacy of your data. Hence, it does not store any portfolio data. All you need to do is provide a Google Spreadsheet like this one.
The source code for this project is hosted on GitHub. Feel free (as in open source) to modify and improve.
It is easy to run this application. Just copy this Google Spreadsheet into your Drive and Sign in.


Ariana Biermann Models Her Family’s Newest Lip Kit And Her Lips Look Bigger Than Ever In Close-Up Vids!
by Ashley MitchellYesterday, Kim Zolciak’s daughter, Ariana took to her platform to share footage of herself in which she was showing off her family’s KAB Cosmetics collection’s chocolate lip kit. That being said, the 18 year old’s lips grabbed more of the attention than the lipstick did since they looked so much bigger than usual!
In the close-up clips, the teen was wearing the newest of her family’s KAB Cosmetics shades and she looked confident as she modeled the makeup product.
It was impossible for people not to notice how much more plump her pout looked.
But is it because of the lip kit or has she done something to them? Fans were leaning towards the latter!
In the caption, Ariana simply promoted the collection, writing: ‘Our Chocolate Lip Kits are liveee! These are my absolute fave shades in the entire @kabcosmetics collection 😍🍫 we even have a brand new website for you guys to check out!
In less than 24 hours, Ariana’s post managed to gather around 80,000 likes but when it comes to the fans’ response to the clips, there was basically none under the post since she decided to turn off the comment feature.
It’s possible that she chose this because she knew users would focus on her bigger-than-ever lips.
At the same time, there is no doubt that the teen would’ve gotten a lot of love and support in the comments as well but sometimes, the negative can, unfortunately, outweigh the positive when it comes to what people focus on.


Traces of pesticides that act as nerve agents on bees have been found in 75 percent of honey worldwide, raising concern about the survival of these crucial crop pollinators, researchers said Thursday.
Human health is not likely at risk from the concentrations detected in a global sampling of 198 types of honey, which were below what the European Union authorizes for human consumption, said the report in the journal Science.
However, the study found that 34 percent of honey samples were contaminated with "concentrations of neonicotinoids that are known to be detrimental" to bees, and warned that chronic exposure is a threat to bee survival.
Bees help pollinate 90 percent of the world's major crops, but in recent years have been dying off from "colony collapse disorder," a mysterious scourge blamed on mites, pesticides, virus, fungus, or some combination of these factors.
"The findings are alarming," said Chris Connolly, a neurobiology expert at the University of Dundee, who also wrote a Perspective article alongside the research in Science.
"The levels detected are sufficient to affect bee brain function and may hinder their ability to forage on, and pollinate, our crops and our native plants."
Neonicotinoids have been declared a key factor in bee decline worldwide, and the European Union issued a partial ban on their use in 2013.
For the Science study, the European samples were collected largely before this ban took effect, Connolly said. Further research is needed to gauge the effectiveness of the EU steps.
Five common pesticides
Bees collect nectar as they pollinate plants, and over time this sugary liquid accumulates into the thick syrup of honey.
To test contamination levels, samples of honey were taken from local producers worldwide, and researchers tested for five commonly used neonicotinoids: acetamiprid, clothianidin, imidacloprid, thiacloprid, and thiamethoxam.
These pesticides, introduced in the mid 1990s, are based on the chemical structure of nicotine and attack the nervous systems of insect pests.
"Overall, 75 percent of all honey samples contained at least one neonicotinoid," said the study, led by Edward Mitchell of the University of Neuchatel in Switzerland.
"Of these contaminated samples, 30 percent contained a single neonicotinoid, 45 percent contained two or more, and 10 percent contained four or five."
The frequency of contamination was highest in the North American samples (86 percent), followed by Asia (80 percent) and Europe (79 percent).
The lowest concentrations were seen in South American samples (57 percent).
"These results suggest that a substantial proportion of world pollinators are probably affected by neonicotinoids," said the study.
'Serious concern '
Our planet is home to some 20,000 species of bees, which fertilize more than 90 percent of the world's 107 major crops.
The United Nations warned in 2016 that 40 percent of invertebrate pollinators—particularly bees and butterflies—risk global extinction.
Experts said that while the findings are not exactly a surprise, the threat posed by neonicotinoids should be taken seriously.
"The levels recorded (up to 56 nanogram per gram) lie within the bioactive range that has been shown to affect bee behavior and colony health," said plant ecologist Jonathan Storkey, who was not involved in the study.
"Scientists showed earlier this year that levels of less than 9 ng/g reduced wild bee reproductive success," he added.
"I therefore agree with the authors that the accumulation of pesticides in the environment and the concentrations found in hives is a serious environmental concern and is likely contributing to pollinator declines."
According to Lynn Dicks, natural environment research council fellow at the University of East Anglia, the findings are "sobering" but don't offer a precise picture of the threat to bees.
"The severity of the global threat to all wild pollinators from neonicotinoids is not completely clear from this study, because we don't know how the levels measured in honey relate to actual levels in nectar and pollen that wild pollinators are exposed to," she said.
The levels of exposure to harmful pesticides may be far higher than what can be measured in honey, said Felix Wackers, a professor at Lancaster University who was not involved in the research.
"This shows that honeybees are commonly exposed to this group of pesticides while collecting neonicotinoid-contaminated nectar from treated crops or from flowers that have come into contact with spray drift or soil residues," he said.
"The actual level of exposure can be substantially higher, as the honey samples analyzed in this study represents an average of nectar collection over time and space."
Explore further: Pesticides found in most pollen collected from foraging bees in Massachusetts
EyeNStein
not rated yet 1 hour agoTom_Andersen
not rated yet 14 minutes agoIts related to the fact that every time you take a breath some of the air in that breath was also breathed by Einstien, Newton, and Socrates.


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Design, Code & Caffeine
Hello, my name is Arya Soni. A passionate UI designer and a Full Stack developer from Delhi/Gurgaon, India.
I'm a 20-year-old developer based in Delhi. I specialize in JavaScript and my main area of interest is Front-End Development. I love developing tools/sites/apps that solve real problems or help make someone's life easier. I like impactful work.
Skills
My current skill set includes JavaScript, React, Redux, Firebase, SASS, Bootstrap, CSS, HTML etc. And that's excluding all the common (yet sometimes complex) build/workflow tools like Webpack, Babel etc. However, I believe in tool agnostic skills. Being in an industry that moves at the speed of time, it sometimes doesn't matter what frameworks/tools I'm familiar with. Because tools/frameworks are so fickle these days that they can become obsolete within months! What matters more is how fast can I learn and make myself comfortable with newer technology. I believe I'm a quick learner. I prioritize concepts over techniques. Simplicity, though overused (and sometimes misused) as a term, is something I really value. I like to build solutions that are simple yet interactive for the users. Besides coding, I'm also very passionate about traveling and reading. I feel like the world is a book that was given to me for exploring when I was born, and I don't wanna die before reading all the pages. I plan to become a nomad someday and explore the endless beauty that this planet homes.


Run doconce2format on all doc strings in a Python file.
Perform the transformation of a doconce file, stored in in_filename, to a given format (html, latex, etc.), written to out_filename. This is the “main” function in the module.
Process idx{...} and cite{...} instructions.
Deal with all inline tags by substitution.
Parse a keyword for a description list when the keyword may represent information about an argument to a function or a variable in a program:
- argument x: x coordinate (float).
- keyword argument tolerance: error tolerance (float).
- return: corresponding y coordinate (float).
Run mako or the preprocess script on filename and return the name of the resulting file. The preprocessor_options list contains the preprocessor options given on the command line. In addition, the preprocessor option FORMAT (=format) is always defined.
Go through filestr and parse all lists and typeset them correctly. This function must be called after all (verbatim) code and tex blocks have been removed from the file. This function also treats comment lines and blank lines.
Translate tables with pipes and dashes to a list of row-column values. Horizontal rules become a row [‘horizontal rule’] in the list. The list is easily translated to various output formats by other modules.


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WITNESS LIST Ways & Means Committee March 30, 2005-10:30A HB 112 For: Harris, Reagan (Self) HB 290 For: McEwen, Clark (Texas RV Association) HB 726 For: Baker, Charlie (Smith County Peace Officers Association) Barnett, Michael (Self) McMullen, Gale A. (Smith Co. Peace Officers Assoc. - Tyler) Strickland, Robert (Smith County Peace Officers Assn.) HB 748 For: Smith, Patrick (Texas Society of Professional Surveyors) HB 777 For: Cork, Adam (City of New Braunfels) Laird, Stacey (City of New Braunfels) Young, Judy (Greater New Braunfels Chamber of Commerce) HB 809 For: Jones, Bill (Texas Association of Realtors) HB 1070 For: Dwyer, Darvin (Chemcentral) Staley, Mike (Aqua Process Inc) HB 1292 For: Bosak, Stephen (City of Cedar Park/Texas Recreation & Park Society) Bristol, George L. (Texas Coalition for Conservation) Cork, Mayor, Adam (City of New Braunfels) Evans, Doug (City of Grapevine/Texas Recreatons Park Society) Palmer, Corky (Texas Outdoor Recreation Alliance) On: Cook, Robert L. (Tx Parks & Wildlife) HB 1528 For: Blackwood, Dr. Susan (San Antonio Sports Foundation) Jones, Franklin (Houston Super Bowl Host Committee) Morgan, Robert Dale (Houston Super Bowl Host Committee) Shields, Chris (San Antonio Sports Foundation) HB 1766 For: Haynes, Adam (Tx Independent Producers & Royalty) On: LaHood, Debbie (Railroad Commission - Oil & Gas Division) HB 2080 For: Linial, George (Texas Association of Homes & Services for the Aging) Perry, Keith (Sears Methodist Retirement System, Inc.) Thomason, David (Texas Association of Homes for the Aging) Against: Petree, Richard (Taylor County Commissioners Ct & Wylie ISD) HB 2187 For: Briley, Robert O. (Self) Wilbanks, Danny T (Self) HB 2328 For: Lavine, Dick (Center for Public Policy Priorities) Lee, Donald (Texas Conference of Urban Counties) Smith, David (Bexar County Commissioners Court) HB 2657 For: Dwyer, Darvin (Chemcentral Southwest LP) Newton, Chris (Texas Petroleum Marketers & Convenience State Assoc.) Staley, Mike (Aqua Process Inc.) HB 3252 For: Corley, Tex (Strategic Materials, Inc) Patterson, Jerry (Texas General Land Office) Against: Garey, Glen (Texas Restaurant Assn.) On: Lockard, Lee (Texas Soft Drink Association) HJR 32 For: Baker, Charlie (Smith County Peace Officers Assoc.) Barnett, Michael (Self) McMullen, Gale A. (Smith Co. Peace Officers Assoc. - Tyler) Strickland, Robert (Smith County Peace Officers Assn.) HJR 41 For: Allison, Jim (County Judges & Commissioners Assn. of Tx.) Gardner, Freddie (Ector Co. Comm. Court) Lee, Donald (Texas Conference of Urban Counties) Millikin, County Commissioner Jay (Comal County Commissioners Court) Powell, Leilah (Bexar County Commissioners Court) On: Gustafson, Lindsay (Texas Classroom Teachers Assoc.) HJR 68 For: Lavine, Dick (Center for Public Policy Priorities) Lee, Donald (Texas Conference of Urban Counties) Smith, David (Bexar County Commissioners Court)


Level Creator
Level title:
Use timelimit:
Initial time:
Max time accumulated:
Time per symbol:
Words:


| Util |
This module offers some utility functions
|
| Log |
inspired by
android.util.Log
|
| Java |
This module provides utility functions for handling
Java language
|
| Instr |
This module defines types for Dalvik bytecodes and provides utility
functions for generating, parsing and dumping instructions.
|
| Dex |
This module defines types for DEX binary and provides utility functions
for traversing DEX file and getting info from DEX file.
|
| Parse |
This module provides a function for parsing binary input channel.
|
| Visitor |
This module provides visitor pattern.
|
| Android |
This module provides utility functions for handling
Android
platform, for example, it provides a number of typical names for
commonly used classes within the Android framework (helpful for
finding and manipulating things like Buttons, for example)
|
| Unparse |
This module provides utility functions for pretty printing
or collecting information about DEX file.
|
| Htmlunparse |
This module provides functions for dumping contents of dex files in
a directory based html structure, allowing the viewer to jump around
the directory to more easily visualize code.
|
| Callgraph |
This module defines a type for call graph and provides functions for
generating and printing a call graph.
|
| Ctrlflow |
This module defines types for control-flow graph and dominance relations,
and provides utility functions for obtaining information from such graphs.
|
| Dataflow |
This module provides data-flow analysis frameworks
|
| Liveness |
This module defines liveness analysis using
Dataflow module.
|
| Propagation |
This module offers constant propagation analysis using
Dataflow
|
| Reaching |
This module conducts reaching definition analysis via
Dataflow module.
|
| Modify |
This module provides utility functions for modifying DEX binary.
|
| Combine |
This module provides a function for merging two DEX binaries.
|
| Dump |
This module provides utilities for dumping a dex file into an on
disk file.
|
| Testing |
instrument testing features into the dex accordingly
|
| Logging |
This module provides special functions for logging apps
|
| Main |
Main workhorse
|


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using statsd + graphite.
Created by Erik Dasque & Johnson Khuraijam
“If it moves, we track it. Sometimes we’ll draw a graph of something that isn’t moving yet, just in case it decides to make a run for it.”
Servers in DC, nodes in cluster, firewall, etc.
DB2, Memcached,Cassandra,etc.
ui,contacts2,mylibraryNext,accounts,etc.
Beside the business needs, why is myLibraryNext a good candidate
References


Guess a Word!
Feature a Guess
Random a Guess
Choose the Difficulty:
Easy
Hard
Choose an Alphabet:
Collect a Word!
Get Vocabulary
Check Answer
Give a Hint


Reports API changes after conversion to new services
Bring is revising the service portfolio. Some of our services have been given new service names, service codes and pricing models. Read more about the changes
Old services will be phased out. From a given date, your company will be converted to use revised services during Booking, and you can no longer book old services. All customers belongs to your company will be converted. The revised services are booked using main customer number.
In Reports API , we have made changes to support Main Customer Number for some service types and for some service types new definitions are available.
API users after conversion need to use appropriate customer number and definition combination to get the correct result set from the reports.
We recommend API users to execute List available customers and List available reports for a customer to understand what parameters to be used for report execution.
Below Table is gives a snapshot view about what changes API users need to do for reports belongs different service type after conversion.
Old services will be phased out. From a given date, your company will be converted to use revised services during Booking, and you can no longer book old services. All customers belongs to your company will be converted. The revised services are booked using main customer number.
In Reports API , we have made changes to support Main Customer Number for some service types and for some service types new definitions are available.
API users after conversion need to use appropriate customer number and definition combination to get the correct result set from the reports.
We recommend API users to execute List available customers and List available reports for a customer to understand what parameters to be used for report execution.
Below Table is gives a snapshot view about what changes API users need to do for reports belongs different service type after conversion.
| Service Type | Customer Number to Use | Report Definition to use |
|---|---|---|
| Parcel Norway | API Customer Number Main Customer Number |
Existing Definition |
| Parcel International | API Customer Number | Existing Definition |
| Cargo | API Customer Number Main Customer Number |
Existing Definition New Definition |
| Oil Express | API Customer Number Main Customer Number |
Existing Definition |
| Warehousing | API Customer Number Main Customer Number |
Existing Definition |
Example : Main Customer Number(20000012345) , API Customer Number(CARGO-12345 or PARCELS_SWEDEN-12345)


MLlib - Linear Methods
- Mathematical formulation
- Binary classification
- Linear least squares, Lasso, and ridge regression
- Implementation (developer)
\[
\newcommand{\R}{\mathbb{R}}
\newcommand{\E}{\mathbb{E}}
\newcommand{\x}{\mathbf{x}}
\newcommand{\y}{\mathbf{y}}
\newcommand{\wv}{\mathbf{w}}
\newcommand{\av}{\mathbf{\alpha}}
\newcommand{\bv}{\mathbf{b}}
\newcommand{\N}{\mathbb{N}}
\newcommand{\id}{\mathbf{I}}
\newcommand{\ind}{\mathbf{1}}
\newcommand{\0}{\mathbf{0}}
\newcommand{\unit}{\mathbf{e}}
\newcommand{\one}{\mathbf{1}}
\newcommand{\zero}{\mathbf{0}}
\]
Mathematical formulation
Many standard machine learning methods can be formulated as a convex optimization problem, i.e.
the task of finding a minimizer of a convex function $f$
that depends on a variable vector
$\wv$
(called weights
in the code), which has $d$
entries.
Formally, we can write this as the optimization problem $\min_{\wv \in\R^d} \; f(\wv)$
, where
the objective function is of the form
\begin{equation}
f(\wv) :=
\frac1n \sum_{i=1}^n L(\wv;\x_i,y_i) +
\lambda\, R(\wv_i)
\label{eq:regPrimal}
\ .
\end{equation}
Here the vectors $\x_i\in\R^d$
are the training data examples, for $1\le i\le n$
, and
$y_i\in\R$
are their corresponding labels, which we want to predict.
We call the method linear if $L(\wv; \x, y)$ can be expressed as a function of $\wv^T x$ and $y$.
Several MLlib’s classification and regression algorithms fall into this category,
and are discussed here.
The objective function $f$
has two parts:
the loss that measures the error of the model on the training data,
and the regularizer that measures the complexity of the model.
The loss function $L(\wv;.)$
must be a convex function in $\wv$
.
The fixed regularization parameter $\lambda \ge 0$
(regParam
in the code) defines the trade-off
between the two goals of small loss and small model complexity.
Loss functions
The following table summarizes the loss functions and their gradients or sub-gradients for the methods MLlib supports:
| loss function $L(\wv; \x, y)$ | gradient or sub-gradient | |
|---|---|---|
| hinge loss | $\max \{0, 1-y \wv^T \x \}, \quad y \in \{-1, +1\}$ | $\begin{cases}-y \cdot \x & \text{if $y \wv^T \x <1$}, \\ 0 & \text{otherwise}.\end{cases}$ |
| logistic loss | $\log(1+\exp( -y \wv^T \x)), \quad y \in \{-1, +1\}$ | $-y \left(1-\frac1{1+\exp(-y \wv^T \x)} \right) \cdot \x$ |
| squared loss | $\frac{1}{2} (\wv^T \x - y)^2, \quad y \in \R$ | $(\wv^T \x - y) \cdot \x$ |
Regularizers
The purpose of the regularizer is to
encourage simple models, by punishing the complexity of the model $\wv$
, in order to e.g. avoid
over-fitting.
We support the following regularizers in MLlib:
| regularizer $R(\wv)$ | gradient or sub-gradient | |
|---|---|---|
| zero (unregularized) | 0 | $\0$ |
| L2 | $\frac{1}{2}\|\wv\|_2^2$ | $\wv$ |
| L1 | $\|\wv\|_1$ | $\mathrm{sign}(\wv)$ |
Here $\mathrm{sign}(\wv)$
is the vector consisting of the signs ($\pm1$
) of all the entries
of $\wv$
.
L2-regularized problems are generally easier to solve than L1-regularized due to smoothness. However, L1 regularization can help promote sparsity in weights, leading to simpler models, which is also used for feature selection. It is not recommended to train models without any regularization, especially when the number of training examples is small.
Binary classification
Binary classification is to divide items into two categories: positive and negative. MLlib supports two linear methods for binary classification: linear support vector machine (SVM) and logistic regression. The training data set is represented by an RDD of LabeledPoint in MLlib. Note that, in the mathematical formulation, a training label $y$ is either $+1$ (positive) or $-1$ (negative), which is convenient for the formulation. However, the negative label is represented by $0$ in MLlib instead of $-1$, to be consistent with multiclass labeling.
Linear support vector machine (SVM)
The linear SVM
has become a standard choice for large-scale classification tasks.
The name “linear SVM” is actually ambiguous.
By “linear SVM”, we mean specifically the linear method with the loss function in formulation
$\eqref{eq:regPrimal}$
given by the hinge loss
\[
L(\wv;\x,y) := \max \{0, 1-y \wv^T \x \}.
\]
By default, linear SVMs are trained with an L2 regularization.
We also support alternative L1 regularization. In this case,
the problem becomes a linear program.
Linear SVM algorithm outputs a SVM model, which makes predictions based on the value of $\wv^T \x$. By the default, if $\wv^T \x \geq 0$, the outcome is positive, or negative otherwise. However, quite often in practice, the default threshold $0$ is not a good choice. The threshold should be determined via model evaluation.
Logistic regression
Logistic regression is widely used to predict a
binary response. It is a linear method with the loss function in formulation
$\eqref{eq:regPrimal}$
given by the logistic loss
\[
L(\wv;\x,y) := \log(1+\exp( -y \wv^T \x)).
\]
Logistic regression algorithm outputs a logistic regression model, which makes predictions by
applying the logistic function
\[
\mathrm{logit}(z) = \frac{1}{1 + e^{-z}}
\]
$\wv^T \x$.
By default, if $\mathrm{logit}(\wv^T x) > 0.5$, the outcome is positive, or negative otherwise.
For the same reason mentioned above, quite often in practice, this default threshold is not a good choice.
The threshold should be determined via model evaluation.
Evaluation metrics
MLlib supports common evaluation metrics for binary classification (not available in Python). This includes precision, recall, F-measure, receiver operating characteristic (ROC), precision-recall curve, and area under the curves (AUC). Among the metrics, area under ROC is commonly used to compare models and precision/recall/F-measure can help determine the threshold to use.
Examples
The following code snippet illustrates how to load a sample dataset, execute a training algorithm on this training data using a static method in the algorithm object, and make predictions with the resulting model to compute the training error.
import org.apache.spark.SparkContext
import org.apache.spark.mllib.classification.SVMWithSGD
import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.util.MLUtils
// Load training data in LIBSVM format.
val data = MLUtils.loadLibSVMFile(sc, "mllib/data/sample_libsvm_data.txt")
// Split data into training (60%) and test (40%).
val splits = data.randomSplit(Array(0.6, 0.4), seed = 11L)
val training = splits(0).cache()
val test = splits(1)
// Run training algorithm to build the model
val numIterations = 100
val model = SVMWithSGD.train(training, numIterations)
// Clear the default threshold.
model.clearThreshold()
// Compute raw scores on the test set.
val scoreAndLabels = test.map { point =>
val score = model.predict(point.features)
(score, point.label)
}
// Get evaluation metrics.
val metrics = new BinaryClassificationMetrics(scoreAndLabels)
val auROC = metrics.areaUnderROC()
println("Area under ROC = " + auROC)
The SVMWithSGD.train()
method by default performs L2 regularization with the
regularization parameter set to 1.0. If we want to configure this algorithm, we
can customize SVMWithSGD
further by creating a new object directly and
calling setter methods. All other MLlib algorithms support customization in
this way as well. For example, the following code produces an L1 regularized
variant of SVMs with regularization parameter set to 0.1, and runs the training
algorithm for 200 iterations.
import org.apache.spark.mllib.optimization.L1Updater
val svmAlg = new SVMWithSGD()
svmAlg.optimizer.
setNumIterations(200).
setRegParam(0.1).
setUpdater(new L1Updater)
val modelL1 = svmAlg.run(training)
Similarly, you can use replace SVMWithSGD
by
LogisticRegressionWithSGD
.
All of MLlib’s methods use Java-friendly types, so you can import and call them there the same
way you do in Scala. The only caveat is that the methods take Scala RDD objects, while the
Spark Java API uses a separate JavaRDD
class. You can convert a Java RDD to a Scala one by
calling .rdd()
on your JavaRDD
object.
The following example shows how to load a sample dataset, build Logistic Regression model, and make predictions with the resulting model to compute the training error.
from pyspark.mllib.classification import LogisticRegressionWithSGD
from pyspark.mllib.regression import LabeledPoint
from numpy import array
# Load and parse the data
def parsePoint(line):
values = [float(x) for x in line.split(' ')]
return LabeledPoint(values[0], values[1:])
data = sc.textFile("mllib/data/sample_svm_data.txt")
parsedData = data.map(parsePoint)
# Build the model
model = LogisticRegressionWithSGD.train(parsedData)
# Evaluating the model on training data
labelsAndPreds = parsedData.map(lambda p: (p.label, model.predict(p.features)))
trainErr = labelsAndPreds.filter(lambda (v, p): v != p).count() / float(parsedData.count())
print("Training Error = " + str(trainErr))
Linear least squares, Lasso, and ridge regression
Linear least squares is a family of linear methods with the loss function in formulation
$\eqref{eq:regPrimal}$
given by the squared loss
\[
L(\wv;\x,y) := \frac{1}{2} (\wv^T \x - y)^2.
\]
Depending on the regularization type, we call the method ordinary least squares or simply linear least squares if there is no regularization, ridge regression if L2 regularization is used, and Lasso if L1 regularization is used. This average loss $\frac{1}{n} \sum_{i=1}^n (\wv^T x_i - y_i)^2$ is also known as the mean squared error.
Note that the squared loss is sensitive to outliers. Regularization or a robust alternative (e.g., $\ell_1$ regression) is usually necessary in practice.
Examples
The following example demonstrate how to load training data, parse it as an RDD of LabeledPoint. The example then uses LinearRegressionWithSGD to build a simple linear model to predict label values. We compute the Mean Squared Error at the end to evaluate goodness of fit.
import org.apache.spark.mllib.regression.LinearRegressionWithSGD
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.linalg.Vectors
// Load and parse the data
val data = sc.textFile("mllib/data/ridge-data/lpsa.data")
val parsedData = data.map { line =>
val parts = line.split(',')
LabeledPoint(parts(0).toDouble, Vectors.dense(parts(1).split(' ').map(_.toDouble)))
}
// Building the model
val numIterations = 100
val model = LinearRegressionWithSGD.train(parsedData, numIterations)
// Evaluate model on training examples and compute training error
val valuesAndPreds = parsedData.map { point =>
val prediction = model.predict(point.features)
(point.label, prediction)
}
val MSE = valuesAndPreds.map{case(v, p) => math.pow((v - p), 2)}.mean()
println("training Mean Squared Error = " + MSE)
Similarly you can use
RidgeRegressionWithSGD
and LassoWithSGD
.
All of MLlib’s methods use Java-friendly types, so you can import and call them there the same
way you do in Scala. The only caveat is that the methods take Scala RDD objects, while the
Spark Java API uses a separate JavaRDD
class. You can convert a Java RDD to a Scala one by
calling .rdd()
on your JavaRDD
object.
The following example demonstrate how to load training data, parse it as an RDD of LabeledPoint. The example then uses LinearRegressionWithSGD to build a simple linear model to predict label values. We compute the Mean Squared Error at the end to evaluate goodness of fit.
from pyspark.mllib.regression import LabeledPoint, LinearRegressionWithSGD
from numpy import array
# Load and parse the data
def parsePoint(line):
values = [float(x) for x in line.replace(',', ' ').split(' ')]
return LabeledPoint(values[0], values[1:])
data = sc.textFile("mllib/data/ridge-data/lpsa.data")
parsedData = data.map(parsePoint)
# Build the model
model = LinearRegressionWithSGD.train(parsedData)
# Evaluate the model on training data
valuesAndPreds = parsedData.map(lambda p: (p.label, model.predict(p.features)))
MSE = valuesAndPreds.map(lambda (v, p): (v - p)**2).reduce(lambda x, y: x + y) / valuesAndPreds.count()
print("Mean Squared Error = " + str(MSE))
Implementation (developer)
Behind the scene, MLlib implements a simple distributed version of stochastic gradient descent
(SGD), building on the underlying gradient descent primitive (as described in the optimization section). All provided algorithms take as input a
regularization parameter (regParam
) along with various parameters associated with stochastic
gradient descent (stepSize
, numIterations
, miniBatchFraction
). For each of them, we support
all three possible regularizations (none, L1 or L2).
Algorithms are all implemented in Scala:
Python calls the Scala implementation via PythonMLLibAPI.


The day is new
The sun is coming up.
There are trees on one side, there’s the city in the other.
The lake / river is so calm it creates almost a perfect mirror of the trees, the city and the sky.
Only disturbed by the paddling, and the birds waking up, and the honking of the cars of the city that’s also waking up.
And we’re talking.
And I haven’t been this happy in a long time
not like this,
not this long,
not with you.
My arms are getting tired of so much rowing, the sun is threatening to come up full force and burn us.
The gentle clouds that help us extend this moment can’t hold it anymore. We have to go back.
But what if…
… we go into that city
… and we talk a little bit more?


Creates Vertex Similarity matrix of an undirected graph based on the method stated by E. A. Leicht, Petter Holme, AND M. E. J. Newman in their paper <doi:10.1103/PhysRevE.73.026120>.
| Version: | 0.1 |
| Imports: | igraph |
| Published: | 2016-01-24 |
| Author: | Rishabh Singh |
| Maintainer: | Rishabh Singh <rishabhsinghrsg at gmail.com> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| CRAN checks: | VertexSimilarity results |
| Reference manual: | VertexSimilarity.pdf |
| Package source: | VertexSimilarity_0.1.tar.gz |
| Windows binaries: | r-devel: VertexSimilarity_0.1.zip, r-release: VertexSimilarity_0.1.zip, r-oldrel: VertexSimilarity_0.1.zip |
| OS X binaries: | r-release: VertexSimilarity_0.1.tgz, r-oldrel: VertexSimilarity_0.1.tgz |
Please use the canonical form https://CRAN.R-project.org/package=VertexSimilarity to link to this page.


Welcome to the Epicodus Pizza
What size of pizza do you want?
small
medium
large
extra-large
Here is the available topping in our pizza meneu
Please choose your second favorite topping
cheese
pepperoni
artichoke
anchovy
Please choose your second favorite topping
cheese
pepperoni
artichoke
anchovy
Order Here
Thank you so much for your order and Here is your order information.


3 Part 3: Data Science For Petroleum Production Engineers - The Well Network Dataset
Table of Contents IntroductionThe Network ModelThe need of automation for well network modelingThe network model datasetReading the model variables with automationGathering auxiliary dataThe field viewThe KPIs dataset for Gas LiftOther datasetsBasic statisticsAdvanced statisticsConclusions
3.1 Introduction
In our last post (Part 2) we reviewed the multiwell dataset generated by scanning multiple well models built with the software Prosper. This time we will analyze a network model built with GAP - the well network analysis tool from Petroleum Experts -, using well models that have been previously calibrated and matched.
- [ ] share the multiwell dataset in Github
- [ ] create an email and user name for Oil Gains account in Github
- [ ] Or later, we promise to provide the links?
Typically, these network models are analyzed by viewing the results graphically and in the built-in solver table or exporting the text data to continue the comparative analysis with any office application. This represents a little of an inconvenience because: (1) the results cannot be compared to other scenarios unless you take manual notes; (2) no graphical analysis can be directly performed because the application lacks plotting capabilities for the solver output; (3) the data that we need exported is not structured or tidy, -meaning, lacks columnar format or something resembling tables or data frames. This requires time of the engineer for parsing and format the solution table from GAP to be understood by Excel or other analysis application; (4) lastly, the solutions are not saved in a historical file for future reference; the solutions from the solver are lost on time.
In some way we have to deal with it. We cannot expect from sophisticated state-of-the-art calculation engines to be full of features to satisfy the data scientist in our production engineering department. We understand we are in a transition to an era of big data. We will get there soon. We expect that petroleum engineering software will catch up with the times. In the meantime, we can handle our data analysis with Python, R or other data science visualization tools available.
The objective of this article is sharing an experience on applying data science to the results coming out of network modeling work on a set of oil wells. These oil wells are combination of flowing and gas lift wells; mostly gas lift wells. We are looking to optimize the production schema of the system by finding the right tubing head pressure, gas lift injection rate, gas lift injection pressure, choke size, separator pressure and others.
3.2 The Network Model
The need of automation for well network modeling
As we mentioned above, the export format of the calculation results of network modeling for well optimization is non-structured text but still it can be printed or pasted directly to Excel for parsing to separate the data in columns (variables) and rows (observations). In some way, this is time consuming and a potential source of errors. Worse if several iterations have to be performed on the well and network input data for the generation of optimization scenarios. Certainly, this process is screaming to be automated. But that takes time and resources.
This is what this is about: generating a repeatable and automated script to read the network model from the original source or calculation engine: GAP. GAP is a comprehensive application from Petroleum Experts to resolve well networks. It is unique in the sense that counts with a very powerful interface for data interchange with other applications; it is called OpenServer.
With OpenServer it is possible to read and write variables from and to the network model. Also allows the production engineer to customize production optimization scenarios in the most complex fashion making possible an automated process by means of iterative input to find a solution that better adapts to the conditions of our field. In this article we dedicate our attention to the GAP Solver that looks like Figure 1. It corresponds to a generic example.
￼]
Figure 1 - GAP solver panel from one of the examples.
3.3 The network model dataset
The well network dataset is formed essentially by the columns in the solver. In the figure above you see on the first column various items that are part of the network such as flowlines, separators, joints, etc., but we are looking for is only the wells. Of course, you can analyze other network components but that could be a topic of a different article. Each of the columns in the table can be extracted, as we explained above, by copy-pasting to Excel, or by using OpenServer. We read each of the columns (the ones that we are most interested in) and create a dataset. We will explain later how this reading process works (involves Python and OpenServer).
- [ ] show obfuscated datase/table/dataframe
The rows in the dataset correspond to the wells and the columns to the variables that were extracted from the columns in the solver.
Instinctively, we may tempted to say, after seeing the solver table, that the dataset should be just the calculated columns in that table. But that would be an incomplete dataset. Besides of the solution for the scenario proposed, we require additional data for the network of wells under analysis.
- [ ] what is the additional data?
Reading the model variables with automation
Reading the GAP model requires a connection to OpenServer through a COM server. There are few examples under the OpenServer folder in the IPM examples installation folder that uses Excel VBA. For simple examples, Excel is fine. For serious programming and more complex tasks it is recommended to use Python or other high level language.
We show below a pseudo-code explaining the steps for reading GAP variables. Writing to GAP is similar.
- [ ] explain the GAP variable
- [ ] show few examples of the variables: GAP.well[0].bla-bla-bla
[ ] show a basic code to read the variables to a dataframe. Pseudo-code
3.3 pseudo-code for GAP automation script with Python
from ptech import *
connect_to_openserver() start_gap()
select_network_model() open_network_model()
wells = NetworkModel(wells)
data = list() for well in wells: data.append(read_variables(well))
create_dataframe(data) shutdown_gap() close_openserver()
3.4 Gathering auxiliary data
Collecting the data for the network model is not a major problem; most of the data is coming from the well models that we saw in Part 2. At this stage, the well models have been matched to their IPR (inflow performance relationship), VLP**(vertical lift performance) and calibrated against their latest well test and FGS (flowing gradient surveys).
Additional data for the network model such as constraints, wells on-line, choke orifice size, flowline dimensions, separator pressures, total field production are entered directly in the model in GAP.
what else?
3.5 The field view
During the construction of the network model, conditions change: wells that are on-line, wells shut-in, compressors down, flowlines opened or close or bypassed, changes in the chokes, back-pressure, etc. When we run the network optimization we are looking for a way to visualize in one plot the current status as is in the model file in GAP. This saves considerable time in navigating through screens and checking the well condition one by one.
What we see in Figure 2 is a customized plot that was written using matplotlib, a Python plotting library. The variables shown are the minimal that could be shown as critical for the verification of the model.
Figure 2 - The well network summary plot.
Each of the rectangles represent a platform. The wells in green are the ones active or producing. The well with suffix NF, GP and GL mean that that well is a natural flow well or it is a gas producer or it is a gas lift well. At the bottom of the rectangles we see the total number of strings (or wells). The letter “E” below means the number of wells that are enabled in the model, while “D” indicates how many wells were disabled in the network model.
- [ ] change the plot above to something neutral
3.6 The KPIs dataset for Gas Lift
Since most of the wells in the dataset were producing with gas lift, In order to find relationships between the different gas lift parameters we build an additional dataset based on common gas lift formulas in Figure 3 to evaluate the gas lift performance of the field. The new dataset uses as a base for the calculations the columns available in the solver dataset.
￼
Figure 3: Key Performance Indicators (KPIs) for gas lift
The reason for doing this is to observe the gas lift indicators under the light of the solutions recommended by the network optimizer. We try to find out if there is a better scenario that just maximizing the oil production, such as reallocating gas or give preference to wells with a lower watercut.
This is how the gas lift KPIs dataset looks after applying the formulas.
￼
Figure 4 - a dataset generated from the solver solution.
- [ ] obfuscate data.
Note that we have kept the well name as a column to identify the row or observation.
Other datasets
- [ ] mention what are the other datasets that will be published
Basic statistics
￼
￼
Advanced statistics
￼
￼
3.7 Conclusions
- There is always a way to get an engineering solution to be statistically analyzed.
- Use plots to visualize a summary of the data.
- Don’t be timid at generating additional datasets from the solution data.
- Look at what the plots tell you. They always will try to convey something.
- You are not wrong if you underestimate Excel as a potting tool. Look outside for better plotting tools but manage first how to get the data out.
- There is more gains to be made by analyzing your solution outside your favorite engineering software. There is no such thing as a software that does everything for everyone“.


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gsdScore.Rd
This method was developed by Jason Hackney and first introduced in the following paper doi:10.1038/ng.3520. It produces a single sample gene set score in values that are in "expression space," the innards of which mimic something quite similar to an eigengene based score.
To easily use this method to score a number of gene setes across an
experiment, you'll want to have the scoreSingleSamples()
method
drive this function via specifying "svd"
as one of the
methods
.
gsdScore( x, eigengene = 1L, center = TRUE, scale = TRUE, uncenter = center, unscale = scale, retx = FALSE, ..., .use_irlba = FALSE, .drop.sd = 1e-04 )
| x | An expression matrix of genes x samples. When using this to score
geneset activity, you want to reduce the rows of |
|---|---|
| eigengene | the "eigengene" you want to get the score for. only accepts a single value for now. |
| center, scale | center and/or scale data before scoring? |
| uncenter, unscale | uncenter and unscale the data data on the way out?
Defaults to the respective values of |
| retx | Works the same as |
| ... | these aren't used in here |
| .drop.sd | When zero-sd (non varying) features are scaled, their values
are |
A list of useful transformation information. The caller is likely
most interested in the $score
vector, but other bits related to
the SVD/PCA decomposition are included for the ride.
The difference between this method vs the eigengene score is that the SVD is used to calculate the eigengene. The vector of eigengenes (one score per sample) is then multiplied through by the SVD's left matrix. This produces a matrix which we then take the colSums of to get back to a single sample score for the geneset.
Why do all of that? You get data that is back "in expression space" and we
also run around the problem of sign of the eigenvector. The scores you get
are very similar to average zscores of the genes per sample, where the
average is weighted by the degree to which each gene contributes to the
principal component chosen by eigengene
, as implemented in the
eigenWeightedMean()
function.
The core functionality provided here is taken from the soon to be released GSDecon package by Jason Hackney
vm <- exampleExpressionSet(do.voom=TRUE) gdb <- conform(getMSigGeneSetDb('H', "human", "entrez"), vm) features <- featureIds(gdb, 'H', 'HALLMARK_INTERFERON_GAMMA_RESPONSE', value='x.idx') scores <- gsdScore(vm[features,])$score ## Use scoreSingleSamples to facilitate scoring of all gene sets scores.all <- scoreSingleSamples(gdb, vm, 'gsd') s2 <- with(subset(scores.all, name == 'HALLMARK_INTERFERON_GAMMA_RESPONSE'), setNames(score, sample_id)) all.equal(s2, scores)#> [1] TRUE


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