Challenge a Penalty Charge Notice Prototype
Page by page breakdown
Don't use any of this prototype code on production services, it doesn't adhere to development standards and is hacked together quickly and roughly.
Please note that the pages marked * are not longer used in the service and are for reference only.
| PCN Start of service pages | Page Title | URL | JIRA ticket number |
|---|---|---|---|
| *Start | Pay or Challenge a Penalty Charge Notice | /start | PA233-729 |
| *(New) Start kickout info updated | Pay or Challenge a Penalty Charge Notice | /start | PA233-729 |
| PCN Verification pages | Page Title | URL | JIRA ticket number |
|---|---|---|---|
| *Enter PCN number | What is your Penalty Charge Notice reference number | /enter-pcn-number | PA233-730 |
| *(New) Re-enter PCN number | Enter your Penalty Charge Notice reference number again | /re-enter-pcn-number | PA233-2058 |
| *(New) Re-enter PCN number (validation error) | The Penalty Charge reference numbers you entered did not match | PA233-2058 | |
| Verify date of birth | What is your date of birth | /date-of-birth | PA233-731 |
| Verify PCN total amount | Enter the total amount on your Penalty Charge Notice | /pcn-total-amount | PA233-732 |
| Check your answers | Check answers- unable to confirm identity | /unable-to-confirm-identity | PA233-865 |
| Failed to verify | Further identity verification required | /more-infomation-needed | PA233-866 |
| Latency | Page Title | URL | JIRA ticket number |
|---|---|---|---|
| Latency mesage | We are processing your details | PA233-1260 |
| PCN Return journey pages | Page Title | URL | JIRA ticket number |
|---|---|---|---|
| Case already closed | Penalty Charge Notice already closed | /pcn-closed | PA233-864 |
| Case already closed DECS | Penalty Charge Notice already closed | /pcn-closed | PA233-864 |
| Case already closed debt collection | Penalty Charge Notice already closed | /pcn-closed | PA233-864 |
| Penalty view, already locked | Penalty Charge Notice details | /view-your-pcn |
| Penalty view pages | Page Title | URL | JIRA ticket number |
|---|---|---|---|
| Penalty view DECS and UC | Penalty Charge Notice details | /view-your-pcn | PA233-1263 |
| Penalty view DECS and HC2 | Penalty Charge Notice details | /view-your-pcn | PA233-733 |
| Penalty view DECS and HC3 (haven't found) | Penalty Charge Notice details | /view-your-pcn | PA233-1263 |
| Penalty view DECS and HC3 (already found, haven't paid correct amount) | Penalty Charge Notice details | /view-your-pcn | PA233-1263 |
| Penalty view DECS and Income Support | Penalty Charge Notice details | /view-your-pcn | PA233-733 |
| Penalty view DECS and income-related Employment and Support Allowance | Penalty Charge Notice details | /view-your-pcn | PA233-733 |
| Penalty view DECS and tax credits | Penalty Charge Notice details | /view-your-pcn | PA233-733 |
| Penalty view DECS and Income-based JSA | Penalty Charge Notice details | /view-your-pcn | PA233-733 |
| Penalty view DECS and PCGC | Penalty Charge Notice details | /view-your-pcn | PA233-733 |
| Penalty view DECS and surcharge | Penalty Charge Notice details | /view-your-pcn | PA233-733 |
| Penalty view with surcharge applied | Penalty Charge Notice details | /view-your-pcn | PA233-1572 |
| Penalty view MEDEX | Penalty Charge Notice details | /view-your-pcn | PA233-733 |
| Penalty view MATEX | Penalty Charge Notice details | /view-your-pcn | PA233-733 |
| Penalty view PPC | Penalty Charge Notice details | /view-your-pcn | PA233-733 |
| Penalty view tax credit | Penalty Charge Notice details | /view-your-pcn | PA233-733 |
| Penalty view HC2 | Penalty Charge Notice details | /view-your-pcn | PA233-733 |
| Penalty view JSA | Penalty Charge Notice details | /view-your-pcn | PA233-733 |
| Penalty view ESA | Penalty Charge Notice details | /view-your-pcn | PA233-733 |
| Penalty view PCGC | Penalty Charge Notice details | /view-your-pcn | PA233-733 |
| Penalty view (UC how to challenge info) | Penalty Charge Notice details | /view-your-pcn | PA233-1740 |
| PCN Info page | Page Title | URL | JIRA ticket number |
|---|---|---|---|
| Info page | What happens next? | /what-happens-next | PA233-734 |
| PCN 'Did you have' pages | Page Title | URL | JIRA ticket number |
|---|---|---|---|
| Did You have Medical Exemption? | Confirm your exemption or benefit | /did-you-have | PA233-873 |
| Did You have Maternity Exemption? | Confirm your exemption or benefit | /did-you-have | PA233-873 |
| Did You have PPC? | Confirm your exemption or benefit | /did-you-have | PA233-873 |
| Did You have tax credits? | Confirm your exemption or benefit | /did-you-have | PA233-873 |
| Did You have HC2? | Confirm your exemption or benefit | /did-you-have | PA233-873 |
| Did You have HC3? | Confirm your exemption or benefit | /did-you-have | PA233-873 |
| Did You have JSA? | Confirm your exemption or benefit | /did-you-have | PA233-1005 |
| Did You have IS/ESA? | Confirm your exemption or benefit | /did-you-have | PA233-1005 |
| Did You have Income Support? | Confirm your exemption or benefit | /did-you-have | PA233-1005 |
| Did You have ESA? | Confirm your exemption or benefit | /did-you-have | PA233-1005 |
| Were you included in a PCGC? | Confirm your exemption or benefit | /did-you-have | PA233-1005 |
| Were you claiming a PCGC? | Confirm your exemption or benefit | /did-you-have | PA233-1005 |
| Did You have any of these BSA exemptions? | Did you have any NHS exemptions? | /any-exemptions | PA233-962 |
| Did You have any of these BSA exemptions (DECS)? | Did you have any NHS exemptions? | /any-exemptions | PA233-962 |
| Did You have any of these DWP benefits? | Were you claiming any benefits? | /any-benefits | PA233-1006 |
| Did You have any of these DWP benefits (DECS)? | Were you claiming any benefits? | /any-benefits | PA233-1006 |
| PCN Kickout pages | Page Title | URL | JIRA ticket number |
|---|---|---|---|
| (old) Universal Credit or HC3 kickout page | Call us about your universal credit or HC3 | /uc-hc3-kickout | JIRA |
| (new) Universal Credit kickout page | Call us about your universal credit or HC3 | /uc-hc3-kickout | JIRA |
| PCN Enter Certificate number pages | Page Title | URL | JIRA ticket number |
|---|---|---|---|
| Enter MEDEX number | What is your exemption certificate number | /exemption-certificate-number | PA233-874 |
| Enter MATEX number | What is your exemption certificate number | /exemption-certificate-number | PA233-874 |
| Enter PPC number | What is your exemption certificate number | /exemption-certificate-number | PA233-874 |
| Enter HC2 number | What is your exemption certificate number | /exemption-certificate-number | PA233-874 |
| Enter HC3 number | What is your exemption certificate number | /exemption-certificate-number-hc3 | PA233-874 |
| Enter Tax credit number | What is your exemption certificate number | /exemption-certificate-number | PA233-874 |
| PCN I do not have a certificate number journey | Page Title | URL | JIRA ticket number |
|---|---|---|---|
| Check name | Is this your name? | /check-name | PA233-516 |
| Update name | What is your name? | /update-name | PA233-724 |
| Check dob | Is this your date of birth? | /check-dob | PA233-725 |
| Update dob | What is your date of birth? | /update-dob | PA233-726 |
| Check address | Is this your address? | /check-address | PA233-727 |
| Update address | What is your address? | /update-address | PA233-728 |
| Check answers | Check your personal details are correct | /check-personal-details | PA233-870 |
| PCN 'How should we contact you?' pages | Page Title | URL | JIRA ticket number |
|---|---|---|---|
| How should we contact you? | How should we contact you about your case? | /contact-choices |
PA233-926
PA233-945 |
| What is your email address? | What is your email address? | /update-email | PA233-927 |
| What is your phone number? | What is your phone number? | /update-phone | PA233-928 |
| PCN 'Check your answers' pages | Page Title | URL | JIRA ticket number |
|---|---|---|---|
| Check your answers post | Check your contact details are correct | /check-contact-details | PA233-929 |
| Check your answers email | Check your contact details are correct | /check-contact-details | PA233-929 |
| Check your answers phone call | Check your contact details are correct | /check-contact-details | PA233-929 |
| Check your answers both | Check your contact details are correct | /check-contact-details | PA233-929 |
| Check your answers no exemption input | Check your contact details are correct | /check-contact-details | PA233-929 |
| Check your answers MEDEX | Check your contact details are correct | /check-contact-details | PA233-929 |
| Check your answers MATEX | Check your contact details are correct | /check-contact-details | PA233-929 |
| Check your answers PPC | Check your contact details are correct | /check-contact-details | PA233-929 |
| Check your answers HC2 | Check your contact details are correct | /check-contact-details | PA233-929 |
| Check your answers HC3 | Check your contact details are correct | /check-contact-details | PA233-929 |
| Check your answers Tax credit | Check your contact details are correct | /check-contact-details | PA233-929 |
| Disability, long term illness and pregnancy pages | Page Title | URL | JIRA ticket number |
|---|---|---|---|
| Do you have a long-term medical condition or physical disability? | Do you have a medical condition or physical disability? | /medical-conditions | PA233-1007 |
| Do you have any of these long-term medical conditions or disabilities? | Do you have any of these conditions or disabilities? | /check-medical-conditions | PA233-1009 |
| Were you pregnant? | Were you pregnant, or had you given birth in the previous 12 months | /were-you-pregnant | PA233-1008 |
| HC3 end pages | Page Title | URL | JIRA ticket number |
|---|---|---|---|
| User has not started paying | |||
| Exemption found and enough paid or don't have to pay | Thank you. You do not have to pay any charges. | ||
| Recalculation - Exemption not originally found, HC3 found by challenge | We found an HC3 certificate and have recalculated your penalty charge | ||
| Recalculation - HC3 with lower cont. | We found an HC3 certificate with a lower contribution amount – we have recalculated the amount you owe | ||
| Still owe original pcn - HC3 with higher cont. | We found an HC3 certificate with a higher contribution amount – you still owe the original amount | ||
| Still owe original pcn - Same HC3 | We found an HC3 certificate with the same contribution amount | ||
| User has started paying | |||
| Recalculation - Exemption not originally found, HC3 found by challenge | We found an HC3 certificate and have recalculated your penalty charge | PA233-1778 | |
| Recalculation - HC3 with lower cont. | We found an HC3 certificate with a lower contribution amount – we have recalculated the amount you owe | PA233-1778 | |
| Recalculation based on found HC3 with lower cont. - paid amount equal to recalc. amount | We found an HC3 certificate with a lower contribution amount - you do not owe any amount | PA233-1778 | |
| Still owe original pcn - HC3 with higher cont. | We found an HC3 certificate with a higher contribution amount – you still owe £90 | PA233-1778 | |
| Still owe original pcn - Same HC3 | We found an HC3 certificate with the same contribution amount | PA233-1778 | |
| Outstanding amount is £0 - Same HC3 | We found an HC3 certificate with the same contribution amount. You do not owe any amount | PA233-1778 |
| PCN End pages | Page Title | URL | JIRA ticket number |
|---|---|---|---|
| We found your exemption (updated content) | No charges to pay- matched details | /found-exemption | PA233-875 |
| Exemption certificate has expired | Exemption certificate expired | /exemption-expired | PA233-877 |
| Exemption certificate has expired DECS | Exemption certificate expired | /exemption-expired | PA233-877 |
| PCN is on hold for 2 days | Reviewing your case | /we-will-contact-you | PA233-876 |
| PCN is on hold for 60 days Matex (DECS) | If you were pregnant at the time or had given birth in the previous 12 months you were entitled to free dental treatment. | /were-pregnant | PA233-875 |
| PCN is on hold for 60 days, apply for Medex | Cannot find medical exemption certificate but you can still apply | /on-hold-medical-exemption-apply | JIRA |
| PCN is on hold for 60 days, apply for Matex | Cannot find maternity exemption certificate but you can still apply | /on-hold-maternity-exemption-apply | JIRA |
| We can't find your details | We cannot find your details | /cannot-find-details | PA233-878 |
| We can't find your details (DECS) | We cannot find your details | /cannot-find-details | PA233-878 |
| We need proof you were pregnant | We need proof you were pregnant | /pregnancy-proof | JIRA |
| Examples of all proof | Examples of proof and how to send it | /examples-of-all-proof | JIRA |
| Examples of all proof DECS | Examples of proof and how to send it | /examples-of-all-proof | JIRA |
| Exceptional circumstances | Who can ask for a Penalty Charge Notice review | /exceptional-circumstances | JIRA |
| Exceptional circumstances DECS | Who can ask for a Penalty Charge Notice review | /exceptional-circumstances | JIRA |
| Penalty Charge Notice review | Asking for a Penalty Charge Notice review | /pcn-review | JIRA |
| Penalty Charge Notice review DECS | Asking for a Penalty Charge Notice review | /pcn-review | JIRA |
| How to pay and apply for matex | How to pay and apply - maternity exemption | /maternity-exemption-pay-apply | JIRA |
| How to pay and apply for medex | How to pay and apply - medical exemption | /medical-exemption-pay-apply | JIRA |
| Considering Pay option on the "We need proof of DWP Benefit" end page (pecs) | Page Title | URL | PA233-1513 |
|---|---|---|---|
| How to send proof of your benefit or pay (PECS) | How to send proof of your benefit or pay | PA233-1513 | |
| How to send proof of your benefit or pay (DECS) | How to send proof of your benefit or pay | PA233-1513 |
| Error pages | Page Title | URL | JIRA ticket number |
|---|---|---|---|
| Service unavailable (original version) | Service is unavailable | /error/service-unavailable | PA233-948 |
| We are currently performing essential maintenance to improve this service (Type 1) | Service is unavailable | /error/service-unavailable | PA233-948 |
| We are performing essential maintenance on this service between 00:00 and 06:00 (Type 2) | Service is unavailable | /error/service-unavailable | PA233-948 |
| This service will be available again at 06:00 (Type 3) | Service is unavailable | /error/service-unavailable | PA233-948 |
| Something's gone wrong | Something's gone wrong | /error/something-is-wrong | PA233-948 |
| Something's gone wrong, go to start page | Something's gone wrong, go to start page | /404 | PA233-948 |


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Chris Common<EMAIL_ADDRESS>HERE IS MY RESUME
Education
- Graduated from Penn Trafford High School in 2015
- Undergraduate at the Univeristy of Pittsburgh at Greensburgh, I will have a degree in both Criminal Justice and Pyschology
- On track to graduate college in the spring of 2019
Work Career
- Worked at Mcdonalds from 2011-2013
- Worked at Brookly's Pizzaria as a delivery driver from 2013-2015
- Worked at Tech 2000 in the summer of 2015
- Worked at Dicks Sporting Goods in the department of Team Sports Associates from 2015-2016
- Worked for Atlantice Coast Baseball in the summers of 2016, 2017, and 2018
- Worked at Fired up Pizza Plus as a delivery driver from 2016-2017
- Worked in the Westmoreland County Courthouse in the Sheriff's department for my required 120 internship by UPG in the summer of 2018
- Worked at Proline Pipeline Natural Gas Company in the summer of 2018
- Working at The University of Pittsburgh at Greensburg as an athletic game worker, I have been working at UPG since 2015
Accomplishments
- Graduated High School
- I was an all-section player in my 4 years playing high school baseball
- Continued my academic and baseball career at UPG at a college level
- Won the AMCC newcomer of the year in 2016
- AMCC All-conference in 2016
- Won the batting title in AAABA Johnstown leage in 2016
- Finished 2nd in the AAABA national tournament in 2016
- All-AAABA player in 2016
- AMCC All-conference in 2017
- Finished 2nd in the AAABA national tournament in 2017
- AMCC All-conference in 2018
- School Record for most career Home Runs heading into my senior season
Skills
- Self-Disciplined
- Outstanding Work Ethic
- Great at Baseball
- Great Hunter, Great Fisherman
- Never been in a wreck, I consider my self a great driver
- Can cook amazing food
- A leader
- Proficient in Oxygen XML Editor
- Proficient in Hypertext Markup Language
- Proficient in Cascading Style Sheets
- Proficient in Javascript
- Proficient in Server-Side Includes
- Proficient in Adobe Photoshop, and Scalable Vector Graphics
- Proficient with GitHub
My Index Page


Module telescope
Telescope is a test library for Lua that allows for flexible, declarative tests.
The documentation produced here is intended largely for developers working on Telescope. For information on using Telescope, please visit the project homepage at: http://github.com/norman/telescope#readme.
Info:
- Release: 0.6
Functions
| make_assertion (name, message, func) | Create a custom assertion. |
| load_contexts (contexts) | Build a contexts table from the test file or function given in target. |
| run (contexts, callbacks, test_filter) | Run all tests. |
| test_report (contexts, results) | Return a detailed report for each context, with the status of each test. |
| error_report (contexts, results) | Return a table of stack traces for tests which produced a failure or an error. |
| summary_report (contexts, results) | Get a one-line report and a summary table with the status counts. |
Tables
| status_codes | The status codes that can be returned by an invoked test. |
| status_labels | Labels used to show the various status_codes as a single character. |
| context_aliases | The default names for context blocks. |
| test_aliases | The default names for test blocks. |
| before_aliases | The default names for "before" blocks. |
| after_aliases | The default names for "after" blocks. |
| assertions | The default assertions. |
Functions
- make_assertion (name, message, func)
-
Create a custom assertion.
This creates an assertion along with a corresponding negative assertion. It
is used internally by telescope to create the default assertions.
Parameters:
- name
The base name of the assertion.
The name will be used as the basis of the positive and negative assertions; i.e., the name equal would be used to create the assertions assert_equal and assert_not_equal.
- message
The base message that will be shown.
The assertion message is what is shown when the assertion fails. It will be prefixed with the string in telescope.assertion_message_prefix. The variables passed to telescope.make_assertion are interpolated in the message string using string.format. When creating the inverse assertion, the message is reused, with " to be " replaced by " not to be ". Hence a recommended format is something like: "%s to be similar to %s".
- func
The assertion function itself.
The assertion function can have any number of arguments.
Usage:
<tt>make_assertion("equal", "%s to be equal to %s", function(a, b) return a == b end)</tt>
- name
The base name of the assertion.
- load_contexts (contexts)
-
Build a contexts table from the test file or function given in target.
If the optional contexts table argument is provided, then the
resulting contexts will be added to it.
The resulting contexts table's structure is as follows:
{ {parent = 0, name = "this is a context", context = true}, {parent = 1, name = "this is a nested context", context = true}, {parent = 2, name = "this is a test", test = function}, {parent = 2, name = "this is another test", test = function}, {parent = 0, name = "this is test outside any context", test = function}, }
Parameters:
- contexts A optional table in which to collect the resulting contexts and function.
- run (contexts, callbacks, test_filter)
-
Run all tests.
This function will exectute each function in the contexts table.
Parameters:
- contexts The contexts created by load_contexts.
- callbacks
A table of callback functions to be invoked before or after
various test states.
There is a callback for each test status_code, and callbacks to run before or after each test invocation regardless of outcome.
- after - will be invoked after each test
- before - will be invoked before each test
- err - will be invoked after each test which results in an error
- fail - will be invoked after each failing test
- pass - will be invoked after each passing test
- pending - will be invoked after each pending test
- unassertive - will be invoked after each test which doesn't assert anything
Callbacks can be used, for example, to drop into a debugger upon a failed assertion or error, for profiling, or updating a GUI progress meter.
- test_filter
A function to filter tests that match only conditions that you specify.
For example, the folling would allow you to run only tests whose name matches a pattern:
function(t) return t.name:match("%s* lexer") end
Returns:
-
A table of result tables. Each result table has the following
fields:
- assertions_invoked - the number of assertions the test invoked
- context - the name of the context
- message - a table with an error message and stack trace
- name - the name of the test
- status_code - the resulting status code
- status_label - the label for the status_code
see also:
- test_report (contexts, results)
-
Return a detailed report for each context, with the status of each test.
Parameters:
- contexts The contexts returned by load_contexts.
- results The results returned by run.
- error_report (contexts, results)
-
Return a table of stack traces for tests which produced a failure or an error.
Parameters:
- contexts The contexts returned by load_contexts.
- results The results returned by run.
- summary_report (contexts, results)
-
Get a one-line report and a summary table with the status counts. The
counts given are: total tests, assertions, passed tests, failed tests,
pending tests, and tests which didn't assert anything.
Parameters:
- contexts The contexts returned by load_contexts.
- results The results returned by run.
Returns:
- A report that can be printed
- A table with the various counts. Its fields are: assertions, errors, failed, passed, pending, tests, unassertive.
Tables
- status_codes
-
The status codes that can be returned by an invoked test. These should not be overidden.
Fields:
- err - This is returned when an invoked test results in an error rather than a passed or failed assertion.
- fail - This is returned when an invoked test contains one or more failing assertions.
- pass - This is returned when all of a test's assertions pass.
- pending - This is returned when a test does not have a corresponding function.
- unassertive - This is returned when an invoked test does not produce errors, but does not contain any assertions.
- status_labels
-
Labels used to show the various status_codes as a single character.
These can be overidden if you wish.
see also:
- context_aliases
-
The default names for context blocks. It defaults to "context", "spec" and
"describe."
Fields:
- context
- describe
- spec
- test_aliases
-
The default names for test blocks. It defaults to "test," "it", "expect",
"they" and "should."
Fields:
- test
- it
- expect
- should
- they
- before_aliases
-
The default names for "before" blocks. It defaults to "before" and "setup."
The function in the before block will be run before each sibling test function
or context.
Fields:
- before
- setup
- after_aliases
-
The default names for "after" blocks. It defaults to "after" and "teardown."
The function in the after block will be run after each sibling test function
or context.
Fields:
- after
- teardown
- assertions
-
The default assertions.
These are the assertions built into telescope. You can override them or
create your own custom assertions using make_assertion.
- assert_blank(a) - true if a is nil, or the empty string
- assert_empty(a) - true if a is an empty table
- assert_equal(a, b) - true if a == b
- assert_error(f) - true if function f produces an error
- assert_false(a) - true if a is false
- assert_greater_than(a, b) - true if a > b
- assert_gte(a, b) - true if a >= b
- assert_less_than(a, b) - true if a < b
- assert_lte(a, b) - true if a <= b
- assert_match(a, b) - true if b is a string that matches pattern a
- assert_nil(a) - true if a is nil
- assert_true(a) - true if a is true
- assert_type(a, b) - true if a is of type b
- assert_not_blank(a) - true if a is not nil and a is not the empty string
- assert_not_empty(a) - true if a is a table, and a is not empty
- assert_not_equal(a, b) - true if a ~= b
- assert_not_error(f) - true if function f does not produce an error
- assert_not_false(a) - true if a is not false
- assert_not_greater_than(a, b) - true if not (a > b)
- assert_not_gte(a, b) - true if not (a >= b)
- assert_not_less_than(a, b) - true if not (a < b)
- assert_not_lte(a, b) - true if not (a <= b)
- assert_not_match(a, b) - true if the string b does not match the pattern a
- assert_not_nil(a) - true if a is not nil
- assert_not_true(a) - true if a is not true
- assert_not_type(a, b) - true if a is not of type b
see also:


C8 Corvette ZR1 Will Get 900-HP, Twin-Turbo, Hybrid V8 Engine: Report
All about performance, not efficiency.
by Nico YlananThe Chevrolet C8 Corvette has certainly been one of the most awaited vehicle launches of this year, and now that we've covered everything about this car from specific accessories to the idea of Corvette becoming a sub-brand for Chevrolet, there's even more interesting news about the mid-engined supercar. Our friends over at Motor Trend have a source saying that not only will the ZR1 be the expected halo car for the C8, but it'll also be a twin-turbo hybrid V8 that will be packing some serious horses.
Gallery: 2020 Chevy Corvette Stingray Convertible
This is the first time that we've heard concrete news about a hybrid version of the C8, and their source has confirmed that it is indeed coming very soon. The supposed ZR1 will utilize the engine found in the Z06, a dual-overhead-cam V8 that happens to be flat-plane-crank and twin-turbo; similar to the one found in the C8.R race car. The hybrid system will be completely performance oriented, and will place an electric motor between the engine and transmission to increase output considerably to 900 horsepower. That's not all, apparently, the C8 Stingray has some space in the front trunk that's reserved for a pair of front-mounted electric motors that are said to increase performance and apply torque vectoring to aid the rear axle and its electronically controlled limited-slip diff, essentially meaning that the ZR1 will be all-wheel drive.
More Tiny C8 Details:
Furthermore, their source indicates that the rumor of the 4.2-liter Blackwing V8 engine from Cadillac being shared with Corvette models is false; GM gave Cadillac and Chevrolet the resources to design and develop two different V8s at the same time. The hybrid system also has the consequence of added weight and proper placement to retain performance and a low center of gravity, so your guess is as good as ours as to where Chevrolet plans to mount the entire system.
Source: Motor Trend


For the note you'll get a secret link: Only with this link your recipient can decrypt and read the note. After your note has been read it will be removed permanently - no one else will be able to see it.
With a second admin link you will be able to see when your note has been read.
Notes are deleted after they have been read once, latest 7 days after creation.


|
FESTR 0.9
Finite-Element Spectral Transfer of Radiation
|
A collection of Detector objects. More...
#include <Diagnostics.h>
Public Member Functions | |
| Diagnostics () | |
| Default constructor. More... | |
| Diagnostics (const int level_in, const std::string &diag_path, const std::string &hydro_path, const std::string &out_path, const Database &d) | |
| Parametrized constructor. More... | |
| size_t | size () const |
| Number of Detectors in *this Diagnostics. More... | |
| std::string | to_string () const |
| String representation of a Diagnostics object. More... | |
| std::string | get_name (const size_t i) const |
| Name of Detector i. More... | |
| size_t | get_index (const std::string &s) const |
| Index of Detector according to its name. More... | |
| void | postprocess (const Database &d, const Hydro &h, Goal &gol) |
| Runs all postprocessing time steps. More... | |
| void | analyze (const Database &d, Hydro &h, Goal &gol) |
| Runs all analysis cases. More... | |
| void | execute (const Database &d, Hydro &h, Goal &gol) |
| Runs all post-processing time steps or analysis cases. More... | |
Public Attributes | |
| int | level |
| Progress indentation level. More... | |
| size_t | freq |
| Frequency of Progress prints. More... | |
| std::string | path |
| Path to *this Diagnostics description. More... | |
| std::string | outpath |
| Path to output files. More... | |
| std::vector< Detector > | det |
| List of Detectors. More... | |
A collection of Detector objects.
Definition at line 27 of file Diagnostics.h.
| Diagnostics::Diagnostics | ( | ) |
Default constructor.
Definition at line 37 of file Diagnostics.cpp.
| Diagnostics::Diagnostics | ( | const int | level_in, |
| const std::string & | diag_path, | ||
| const std::string & | hydro_path, | ||
| const std::string & | out_path, | ||
| const Database & | d | ||
| ) |
Parametrized constructor.
| [in] | level_in | Progress indentation level |
| [in] | diag_path | Path to Diagnostics description |
| [in] | hydro_path | Path to Hydro description |
| [in] | out_path | Path for output files |
| [in] | d | Reference to Database object |
Definition at line 42 of file Diagnostics.cpp.
Runs all analysis cases.
| [in] | d | Reference to Database object |
| [in] | h | Reference to Hydro object |
| [in] | gol | Reference to Goal object |
Definition at line 298 of file Diagnostics.cpp.
Runs all post-processing time steps or analysis cases.
| [in] | d | Reference to Database object |
| [in] | h | Reference to Hydro object |
| [in] | gol | Reference to Goal object |
Definition at line 408 of file Diagnostics.cpp.
| size_t Diagnostics::get_index | ( | const std::string & | s | ) | const |
Index of Detector according to its name.
| [in] | s | Name of Detector |
Definition at line 211 of file Diagnostics.cpp.
| std::string Diagnostics::get_name | ( | const size_t | i | ) | const |
Name of Detector i.
| [in] | i | Index of Detector within *this Diagnostics |
Definition at line 206 of file Diagnostics.cpp.
Runs all postprocessing time steps.
| [in] | d | Reference to Database object |
| [in] | h | Reference to Hydro object |
| [in] | gol | Reference to Goal object |
Definition at line 221 of file Diagnostics.cpp.
| size_t Diagnostics::size | ( | ) | const |
Number of Detectors in *this Diagnostics.
Definition at line 185 of file Diagnostics.cpp.
| std::string Diagnostics::to_string | ( | ) | const |
String representation of a Diagnostics object.
Definition at line 189 of file Diagnostics.cpp.
| std::vector<Detector> Diagnostics::det |
List of Detectors.
Definition at line 44 of file Diagnostics.h.
| size_t Diagnostics::freq |
Frequency of Progress prints.
Definition at line 35 of file Diagnostics.h.
| int Diagnostics::level |
Progress indentation level.
Definition at line 32 of file Diagnostics.h.
| std::string Diagnostics::outpath |
Path to output files.
Definition at line 41 of file Diagnostics.h.
| std::string Diagnostics::path |
Path to *this Diagnostics description.
Definition at line 38 of file Diagnostics.h.


Description
Matches every build without any condition.
Use cases
Allow to perform action for every build from the history.
Warning!
Use this action wisely because if wrong build will be matched to this action then recovering deleted build might be impossible.


--- layout: post title: NYT's Bill Keller fires back at Twittersphere date: 2011-06-23 23:26:16.000000000 +05:30 categories: [] tags: [] status: draft type: post published: false meta: _edit_last: '1' _wp_old_slug: '' author: login: cvlewis email<EMAIL_ADDRESS>display_name: Carl V. Lewis first_name: Carl V. last_name: Lewis excerpt: !ruby/object:Hpricot::Doc options: {} ---
By Carl V. Lewis - @carlvlewis.
A live-curated story on a dispute between New York Times Editor Bill Keller and sociology professor Zeynep Tufekci over the depth of social media. The story appeared on the front page of Storify.


UnFold: A Modern Payment Platform for Publishers
By Sweyn Venderbush
Advisor: Ruzica Piskac
The internet has dramatically changed the way that people discover and consume news media. The average consumer reads a larger number of news articles from a wider variety of sources than ever before. But models of monetization haven't shifted to reflect this new paradigm of consumption, instead relying on the 20th century models of monthly subscriptions and advertisements.
These models have proven to be ineffective in balancing content accessibility with the need for publishers to finance high quality journalism. This project aims to offer a proof of concept for a new model of monetization for web publishing that is consistent with modern consumption patterns. It allows publishers to price their content on a per article basis and gives users the ability to pay for articles by any in-network news source with minimal friction and no transaction cost via a centralized account, which also keeps track of every transaction and all the articles a user owns so they can easily come back and read them again in the future. As a result, for the first time, users can quickly and easily financially reward publishers for content they find valuable, wherever it is on the web.
The deliverable technical work consists of three pieces: a backend component that keeps track of user information, publisher accounts as well as associated articles, and transactions on the network, a front-end component that allows publishers and users to manage their accounts and allows users to add money to their account and transact on the network, and a backend plugin for Django-based web applications as a proof of concept of how UnFold could be easily integrated into a publisher's existing site.


This contributor assignment agreement (“Agreement”) documents rights granted by contributors to the Foundation. Copyright assignment is entirely optional and it is at the discretion of the contributor whether to assign copyright to the Foundation or not. This is a legally binding document, so please read it carefully before agreeing to it. To make this document effective, please sign it and send it to the Foundation. The Agreement may cover more than one software project.
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“Contribution” shall mean the code, documentation or other original works of authorship, including any modifications or additions to an existing work, that is intentionally submitted by You to the Foundation. For the purposes of this definition, “submitted” means any form of electronic, verbal, or written communication sent to the Foundation or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Foundation, but excluding communication that is conspicuously marked or otherwise designated in writing by You as “Not a Contribution.”
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{% extends 'base.html' %} {% load i18n %} {% block title %} {% trans "Notification sent" %} {% endblock %} {% block content %}
{% trans "People who’ve installed the app will get a notification if they were at" %} {{ location.name }} ({{ location.address }}, {{ location.city }}, {{ location.province }}) {% trans "on one or more of these dates:" %}


--- title: "[Understanding Analysis by Stephen Abbott] - [Chapter 4 Functional Limits and Continuity] - [4.2 Functional Limits]" published: true tags: RealAnalysis ---
Definition 4.2.1 (Functional Limit). Let \(f : A \to \mathbb{R}\), and let \(c\) be a limit point of domain \(A\). We say that \(\lim_{x \to c} f(x) = L\) provided that, for all \(\epsilon > 0\), there exists a \(\delta > 0\) such that whenever \(0 < |x - c| < \delta\) (and \(x \in A\)) it follows that \(|f(x) - L < \epsilon|\)
Definition 4.2.1B (Functional Limit: Topological Version). Let \(c\) be a limit point of the domain of \(f : A \to \mathbb{R}\). We say \(\lim_{x \to c} f(x) = L\) provided that, for every \(\epsilon\)-neighborhood \(V_\epsilon (L)\) L, there exists a \(\delta\)-neighborhood \(V_\delta (c)\) around \(c\) with the property that for all \(x \in V_\delta (c)\) different from \(c\) (with \(x \in A\)) it follows that \(f(x) \in V_\delta (L)\)
Theorem 4.2.3 (Sequential Criterion for Functional Limits).
Given a function \(f : A \to \mathbb{R}\) and a limit point \(c\) of
\(A\), the following two statements are equivalent:
Corollary 4.2.4 (Algebraic Limit Theorem for Functional Limits).
Let \(f\) and \(g\) be functions defined on a domain \(A \subseteq
\mathbb{R}\), and assume \(\lim_{x \to c}f(x) = L\) and \(\lim_{x \to
c}g(x) = M\) for some limit point \(c\) of \(A\). Then,
Corollary 4.2.5 (Divergence Criterion for Functional Limits). Let \(f\) be a function defined on \(A\), and let \(c\) be a limit point of \(A\). If there exists two sequence \((x_n)\) and \((y_n)\) in \(A\) with \(x_n \neq c\) and \(y_n \neq c\) and $$ \lim x_n = \lim y_n = c \text{ but } \lim f(x_n) \neq \lim f(y_n) $$ then we can conclude that the functional limit \(\lim_{x \to c}f(x)\) does not exist.


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 unless $chan->is_connected; $chan->get; CA->pend_io(1); printf " Value: %s\n", $chan->value; $chan->create_subscription('v', \&callback, 'DBR_TIME_DOUBLE'); CA->pend_event(10); sub callback { my ($chan, $status, $data) = @_; if ($status) { printf "%-30s %s\n", $chan->name, $status; } else { printf " Value: %g\n", $data->{value}; printf " Severity: %s\n", $data->{severity}; printf " Timestamp: %.6f\n", $data->{stamp} + $data->{stamp_fraction}; } }
CA
is an efficient interface to the EPICS Channel Access client library for use by Perl 5 programs. It provides most of the functionality of the C library (omitting Synchronous Groups) but only handles the three standard Perl data types integer (long), floating point (double) and string (now including long strings). Programmers who understand the C API will very quickly pick up how to use this library since the calls and concepts are virtually identical.
Create a channel for the named PV. If given, SUB will be called whenever the connection state of the channel changes. The arguments passed to SUB are the channel object and a scalar value that is true if the channel is now up.
The underlying CA channel will be cleaned up properly when the channel object is garbage-collected by Perl.
The following methods are provided for channel objects returned by CA->new()
.
The PV name provided when this channel was created.
Returns the native DBF type of the process variable as a string, or the string TYPENOTCONN
if unconnected.
The maximum array element count from the server. Zero if the channel is not connected.
A string containing the server's hostname and port number. If the channel is disconnected it will report <disconnected>
.
A true/false value that indicates whether the client has read or write access to the specified channel.
A string giving the current connection state of the channel, one of never connected
, previously connected
, connected
or closed
.
Returns true
if the channel is currently connected, else false
. Use this in preference to the equivalent code $chan->state eq 'connected'
.
The get
method makes a ca_get()
request for a single element of the Perl type closest to the channel's native data type; a DBF_ENUM
field will be fetched as a DBF_STRING, and a DBF_CHAR
array with multiple elements will converted into a Perl string. Once the server has returned the value (for which see the pend_io
function below) it can be retrieved using the channel's value
method. Note that the get
method deliberately only provides limited capabilities; the get_callback
method must be used for more complex requirements.
The get_callback
method takes a subroutine reference or name and calls that routine when the server returns the data requested. With no other arguments the data type requested will be the widened form of the channel's native type (widening is discussed below), and if the channel is an array the request will fetch all available elements.
The element count can be overridden by providing an integer argument in the range 0 .. element_count
, where zero means use the current length from the server. Note that the count argument must be an integer; add 0 to it if it is necessary to convert it from a string. The optional data type TYPE should be a string naming the desired DBR_xxx_yyy
type; the actual type used will have the yyy
part widened to one of STRING
, CHAR
, LONG
or DOUBLE
. The valid type names are listed in the Channel Access Reference Manual under the section titled Channel Access Data Types; look in the CA Type Code column of the two tables.
The callback subroutine will be given three arguments: the channel object, a status value from the server, and the returned data. If there were no errors the status value will be undef
and the data will be valid; if an error occurred the data will be undef
and the status a printable string giving more information. The format of the data is described under "Channel Data" below.
Callback subroutines should only call Perl's exit
, die
or similar functions if they are expecting the program to exit at that time; attempts to die
with an exception object in the callback and catch that using eval
in the main thread are not likely to succeed and will probably result in a crash. Callbacks should not perform any operations that would block for more than a fraction of a second as this will hold up network communications with the relevant server and could cause the Perl program and/or the Channel Access server to crash. Calling CA->pend_event
from within a callback is not permitted by the underlying Channel Access library.
Register a state change subscription and specify a subroutine to be called whenever the process variable undergoes a significant state change. MASK must be a string containing one or more of the letters v
, l
, a
and p
which indicate that this subscription is for Value, Log (Archive), Alarm and Property changes. The subroutine SUB is called as described for the get_callback
method above, and the same optional TYPE and COUNT arguments may be supplied to modify the data type and element count requested from the server.
The create_subscription
method returns a ca::subscription
object which is required to cancel that particular subscription. Either call the clear
method on that object directly, or pass it to the CA->clear_subscription
class method.
The put
method makes a ca_put()
or ca_array_put()
call depending on the number of elements given in its argument list. The data type used will be the native type of the channel, widened to one of STRING
, array of CHAR
, LONG
or DOUBLE
.
put_callback
is similar to the put
method with the addition of the subroutine reference or name SUB which is called when the server reports that all actions resulting from the put have completed. For some applications this callback can be delayed by minutes, hours or possibly even longer. The data type is chosen the same way as for put
. The arguments to the subroutine will be the channel object and the status value from the server, which is either undef
or a printable string if an error occurred. The same restrictions apply to the callback subroutine as described in get_callback
above.
Applications that need to acknowledge alarms by doing a ca_put()
with type DBR_PUT_ACKS
can do so using the put_acks
method. The severity argument may be provided as an integer from zero through three or as a string containing one of the corresponding EPICS severity names NO_ALARM
, MINOR
, MAJOR
or INVALID
. If a subroutine reference is provided it will be called after the operation has completed on the server as described in put_callback
above.
This method is for applications that need to enable/disable transient alarms by doing a ca_put()
with type DBR_PUT_ACKT
. The TRANS
argument is a true/false value, and an optional subroutine reference can be provided as described above.
This method replaces, adds or cancels the connection handler subroutine for the channel; see the new
constructor for details. If SUB is undef
any existing handler is removed, otherwise the new subroutine will be used for all future connection events on this channel.
This method replaces, adds or cancels an access rights handler subroutine for the channel, which will be called if the client's right to read from or write to the channel changes. If SUB is undef
any existing handler is removed, otherwise the new subroutine will be used for all future rights change events on this channel.
The arguments passed to SUB are the channel object and a pair of scalar values for read and write permissions respectively, that are true when the access is permitted, false when it is not.
The data provided to a callback function registered with either get_callback
or create_subscription
can be a scalar value or a reference to an array or a hash, depending on the data type that was used for the data transfer. If the request was for a single item of one of the basic data types, the data argument will be a Perl scalar that holds the value directly. If the request was for multiple items of one of the basic types, the data argument will be a reference to an array holding the data. There is one exception though; if the data type requested was for an array of DBF_CHAR
values that array will be represented as a single Perl string containing all the characters before the first zero byte.
If the request was for one of the compound data types, the data argument will be a reference to a hash with keys as described below. Keys that are not classed as metadata are named directly after the fields in the C struct dbr_xxx_yyy
, and are only included when the C structure contains that particular field.
These metadata will always be present in the hash:
The DBR_xxx_yyy
name of the data type from the server. This might have been widened from the original type used to request or subscribe for the data.
The number of elements in the data returned by the server. If the data type is DBF_CHAR
the value given for COUNT
is the number of bytes (including any trailing zeros) returned by the server, although the value field is given as a Perl string containing all the characters before the first zero byte.
These fields are always present in the hash:
The actual process variable data, expressed as a Perl scalar or a reference to an array of scalars, depending on the request. An array of DBF_CHAR
elements will be represented as a string; to access the array elements as numeric values the request must be for the DBF_LONG
equivalent data type.
If TYPE is DBR_GR_ENUM
or DBR_CTRL_ENUM
, value
can be accessed both as the integer choice value and (if within range) as the string associated with that particular choice.
The alarm status of the PV as a printable string, or undef
if not in alarm.
The alarm severity of the PV, or undef
if not in alarm. A defined severity can be used as a human readable string or as a number giving the numeric value of the alarm severity (1 = MINOR
, 2 = MAJOR
, 3 = INVALID
).
These fields are only present for some values of TYPE:
A reference to an array containing all the possible choice strings for an ENUM.
Present only when TYPE is DBR_GR_ENUM
or DBR_CTRL_ENUM
.
The number of choices defined for an ENUM.
Present only when TYPE is DBR_GR_ENUM
or DBR_CTRL_ENUM
.
The process variable timestamp, converted to a local time_t
. This value is suitable for passing to the Perl localtime
or gmtime
functions.
Present only when TYPE is DBR_TIME_yyy
.
The fractional part of the process variable timestamp as a positive floating point number less than 1.0.
Present only when TYPE is DBR_TIME_yyy
.
The value of the process variable's transient acknowledgment flag, an integer.
Present only when TYPE is DBR_STSACK_STRING
.
The alarm severity of the highest unacknowledged alarm for this process variable. As with the severity
value, this scalar is both a string and numeric severity.
Present only when TYPE is DBR_STSACK_STRING
.
The process variable's display precision, an integer giving the number of decimal places to display.
Present only when TYPE is DBR_GR_DOUBLE
or DBR_CTRL_DOUBLE
.
The engineering units string for the process variable.
Present only when TYPE is DBR_GR_yyy
or DBR_CTRL_yyy
where yyy
is not STRING
.
The display range for the process variable; graphical tools often provide a way to override these limits.
Present only when TYPE is DBR_GR_yyy
or DBR_CTRL_yyy
where yyy
is not STRING
.
These items give the values at which the process variable should go into an alarm state, although in practice the alarm severity associated with each level is not provided.
Present only when TYPE is DBR_GR_yyy
or DBR_CTRL_yyy
where yyy
is not STRING
.
The range over which a client can control the value of the process variable.
Present only when TYPE is DBR_CTRL_yyy
where yyy
is not STRING
.
The following functions are not channel methods, and should be called using the class method syntax, e.g. CA->pend_io(10)
.
Returns the EPICS_VERSION_STRING from the version of EPICS Base this software was built using.
Flush outstanding IO requests to the server. This routine is useful for users who need to flush requests prior to performing client side labor in parallel with labor performed in the server. Outstanding requests are also sent whenever the buffer which holds them becomes full. Note that the routine can return before all flush operations have completed.
This function tests to see if all get
requests are complete and channels created without a connection callback subroutine are connected. It will return a true value if all such operations are complete, otherwise false.
This function flushes the send buffer and then blocks until all outstanding get
requests complete and all channels created without a connection callback subroutine have connected for the first time. Unlike pend_event
, this routine does not process CA's background activities if no IO requests are pending.
If any I/O or connection operations remain incomplete after TIMEOUT seconds, the function will die with the error ECA_TIMEOUT
; see "ERROR HANDLING" below. A TIMEOUT interval of zero is taken to mean wait forever if necessary. The TIMEOUT value should take into account worst case network delays such as Ethernet collision exponential back off until retransmission delays which can be quite long on overloaded networks.
Flush the send buffer and process CA's background activities for TIMEOUT seconds. This function always blocks for the full TIMEOUT period, and if a value of zero is used it will never return.
It is generally advisable to replace any uses of Perl's built-in function sleep
with calls to this routine, allowing Channel Access to make use of the delay time to perform any necessary housekeeping operations.
Flush the send buffer and process any outstanding CA background activity.
Cancel a subscription. Note that for this to take effect immediately it is necessary to call CA->flush_io
or one of the other class methods that flushes the send buffer.
Trap exception events and execute SUB whenever they occur. The subroutine is provided with four arguments: The channel object (if applicable), the status value from the server, a printable context string giving more information about the error, and a hash reference containing some additional data. If the exception is not specific to a particular channel the channel object will be undef
. The status value is a printable string. The hash may contain any of the following members:
The operation in progress when the exception occurred. This scalar when used as a string is one of GET
, PUT
, CREATE_CHANNEL
, ADD_EVENT
, CLEAR_EVENT
or OTHER
but can also be accessed as an integer (0-5).
The DBR_xxx_yyy
name of the data type involved.
The number of elements in the request.
These refer to the source file and line number inside the CA client library where the exception was noticed.
This function provides a method to trap error messages from the CA client library and redirect them to somewhere other than the STDERR
stream. The subroutine provided will be called with a single string argument every time the client library wishes to output an error or warning message. Note that a single error or warning message may result in several calls to this subroutine.
To revert back to the original handler, call CA->replace_printf_handler()
passing undef
as the subroutine reference.
Errors in using the library will be indicated by the module throwing an exception, i.e. calling croak()
with an appropriate error message. These exceptions can be caught using the standard Perl eval {}
statement and testing the $@
variable afterwards; if not caught, they will cause the running program to die
with an appropriate error message pointing to the program line that called the CA
library.
Error messages reported by the underlying CA client library all start with the string ECA_
and the remainder of the symbol for the associated CA error number, and are followed after a space-hyphen-space by a human-readable message describing the error. Errors that are detected by the Perl interface layer do not follow this pattern, but are still printable strings.
Andrew Johnson<EMAIL_ADDRESS>Copyright (C) 2008-2014 UChicago Argonne LLC, as Operator of Argonne National Laboratory.
This software is distributed under the terms of the EPICS Open License.


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--- layout: right_col title: Online Registration tagline: Let your members register for your group when it's convenient for them. hero_image: images/features/online-registration.png ---
Build membership, promote activities and encourage community involvement! Allow potential members to join for free, or inquire about our online registration ecommerce features to manage paid registrations, with extremely low processing fees and no setup or monthly fees.
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| Package | Description |
|---|---|
| it.polimi.ingsw.GC_32.Server.Game | |
| it.polimi.ingsw.GC_32.Server.Game.Board | |
| it.polimi.ingsw.GC_32.Server.Game.Card |
| Modifier and Type | Field and Description |
|---|---|
private java.util.HashMap<java.lang.String,Deck<DevelopmentCard>> |
Game.decks |
| Modifier and Type | Method and Description |
|---|---|
Deck<DevelopmentCard> |
Game.getDeck(java.lang.String type)
allows to retrive a single development card deck, given its card type
|
| Modifier and Type | Method and Description |
|---|---|
void |
Deck.addDeck(Deck<T> deck)
given a deck, addDeck() takes all the element of the deck passed as argument and add it to the interal ArrayList deck
|
void |
Deck.addDecks(Deck<T>... decks)
given an array of decks, addDeck() takes all the element of the decks passed as argument and add it to the interal ArrayList deck, following the order the decks
appear into the array
|
| Constructor and Description |
|---|
Deck(Deck<T>... decks)
initialize a deck stacking up an array of decks
|
| Modifier and Type | Field and Description |
|---|---|
private Deck<LeaderCard> |
CardRegistry.leaderDeck |
| Modifier and Type | Field and Description |
|---|---|
private java.util.HashMap<java.lang.String,Deck<DevelopmentCard>> |
CardRegistry.developmentCardDecks |
private java.util.HashMap<java.lang.Integer,Deck<ExcommunicationCard>> |
CardRegistry.excommunicationCardDecks |
| Modifier and Type | Method and Description |
|---|---|
Deck<ExcommunicationCard> |
CardRegistry.getDeck(java.lang.Integer deckPeriod)
get an ExcommunicationCard deck
|
Deck<DevelopmentCard> |
CardRegistry.getDeck(java.lang.String deckType)
get a DevelopmentCard deck
|
Deck<LeaderCard> |
CardRegistry.getLeaderDeck()
get the LeaderCard deck
|
| Modifier and Type | Method and Description |
|---|---|
java.util.HashMap<java.lang.String,Deck<DevelopmentCard>> |
CardRegistry.getDevelopmentDecks()
get all the DevelopmentCard deck
|
| Modifier and Type | Method and Description |
|---|---|
void |
CardRegistry.registerDeck(Deck<LeaderCard> deck)
allows to register the LeaderCard deck
|
void |
CardRegistry.registerDeck(java.lang.Integer deckPeriod,
Deck<ExcommunicationCard> deck)
allows to register an ExcommunicationCard Deck
|
void |
CardRegistry.registerDeck(java.lang.String deckType,
Deck<DevelopmentCard> deck)
allows to register a DevelopmentCard deck
|


{% extends "quiz/Page.html" %} {% load otree i18n %} {% block title %} {% endblock %} {% block content %}
{% blocktrans %}Unfortunately, you did not answer all the questions of the quiz correctly. The simulation requires that subjects understand its basic functions and elements, as informed in the introduction page. Thus, we regret to inform that you are unable to participate in the simulation.{% endblocktrans %}


Bala Box Office Day 12: Stays Good On Tuesday, To Now Cross 100 Crores In 3rd Weekend
The film should go on to accumulate around 3.25-3.50 crores more before the end of week
by Joginder TutejaYet another record has been surpassed by Bala. In 12 days flat, the film has surpassed the lifetime numbers of Luka Chuppi, one of the earliest superhits of 2019. That film had collected 95 crores in its lifetime and now Bala stands at 95.04 crores.
On Tuesday, the film collected 2.05 crores and that was a nominal drop when compared to Monday numbers of 2.25 crores. The film should go on to accumulate around 3.25-3.50 crores more before the end of week and then enter the 100 Crore Club in the third weekend.
For producer Dinesh Vijan this is good news indeed as Luka Chuppi as well as Bala are his productions. Even former had the potential to cross the 100 crores milestone but then Badla too was running alongside and that film did business on the same lines. In case of Bala, there would be competition in the form of Pagalpanti next week, so it has to be seen where does it eventually land up.
The film is a Super Hit nonetheless.
Note: All collections as per production and distribution sources


I was joking the other week that we should have somebody whose job it is
to follow every project and get notified of every pull request and just
go into the thread and ask "Do you
really need to do this?"
You need that constant voice.
Joe Ferris - Thoughbot CTO
Waste is anything that does not add value to a product, value as perceived
by the customer.
Whatever gets in the way of rapidly satisfying a customer need
is waste.


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{{ experience.employer }}
{{ experience.period }}


This organizations context does not contain
any networks. This is surely a mistake, either you should
add a network or switch to a different organizations context
To get started you need to select a network from the list above
Query Range
FromToTimezone
On the server all dates are stored with UTC timezone.
This means that we need to do some special handling
here to display/export dates according to local time. If your query range expands DST, set timezone to +00:00 and handle this yourself!


MesoScale Atmospherics Basics
Welcome to an electronic walk through of basic MesoScale Atmospherics.
This is a an experiment involving placing the physical contents
of a presentation on this information server. Currently a graphical
world wide web client is prefered, but not requried. A future
version is in the works to make this information more presentable
by vt100 based users than it currently is.
Begin the electronic walk through
Return back to Atmospherics home page


THE STICKERS
I've been giving out these stickers and asking my friends to stick them in interesting places and then take pictures. Here's what we've got so far:
Matt's turntable.
Luigi's bumper.
The 1919 Tavern's payphone.
Tess & Sean's window - now somewhere over the continental US.
Mosno's trusty tablet.
Natailie's motor vehicle.


 As many people say, it occurs when two people love each other very much. Sometimes it even happens unexepectedly.
During sexual reproduction, the man release sperm while inside the woman. Many sperm go through a long process, searching for an egg to fertilize. In many cases, the egg will not be fertilized. But in the case when an egg lets the sperm in, a single-celled zygote is formed containing DNA from the male's sperm and the female's ovum (or egg).
Don't worry, you're not the only one. Sex is a complicated process. But it can also be very exciting and even fun! The truth is, most of what you need to know to have a safe and healthy sex life is much simpler.
If you're still curious about the process, feel free to keep reading. But if you think you've got the basic idea down, feel free to jump ahead to another lesson in the menu on the left-hand side of the screen.


Iteratively forecast with nested modeling
Why is nested forecasting important? For starters, the ability to iteratively forecast time series with many models that are trained on many individual groups has been a huge request from students in our Time Series Course. Why? Because two methods exist that get results:
Global Modeling: Best for scalability using a Global Models and a Panel Data structure. See Forecasting with Global Models.
Iterative Forecasting: Best for accuracy using a Nested Data Structure. Takes longer than global model (more resources due to for-loop iteration), but can yield great results.
We’ve incorporated a new approach called “nested forecasting” to help perform Iterative Forecasting.
The core idea of nested forecasting is to convert a dataset containing many time series groups into a nested data set, then fit many models to each of the nested datasets. The result is an iterative forecasting process that generates Nested Modeltime Tables with all of the forecast attributes needed to make decisions.
A new feature to nested forecasting workflow is logged attributes, which is very useful in complex workflows where loops (iteration) is performed. In a Nested Modeltime Table, we push as many operations as possible into the fitting and refitting stages, logging important aspects including:
extract_nested_test_accuracy()
extract_nested_test_forecast()
extract_nested_error_report()
extract_nested_best_model_report()
extract_nested_future_forecast()
While this deviates from the traditional Modeltime Workflow, we find that logging vastly speeds up experimentation and information retrieval especially when the number of time series increases.
We’ll go through a short tutorial on Nested Forecasting. The first thing to do is to load the following libraries:
Next, let’s use the walmart_sales_weekly
dataset that comes with timetk
.
data_tbl <- walmart_sales_weekly %>%
select(id, Date, Weekly_Sales) %>%
set_names(c("id", "date", "value"))
data_tbl
#> # A tibble: 1,001 × 3
#> id date value
#> <fct> <date> <dbl>
#> 1 1_1 2010-02-05 24924.
#> 2 1_1 2010-02-12 46039.
#> 3 1_1 2010-02-19 41596.
#> 4 1_1 2010-02-26 19404.
#> 5 1_1 2010-03-05 21828.
#> 6 1_1 2010-03-12 21043.
#> 7 1_1 2010-03-19 22137.
#> 8 1_1 2010-03-26 26229.
#> 9 1_1 2010-04-02 57258.
#> 10 1_1 2010-04-09 42961.
#> # … with 991 more rows
The problem with this dataset is that it’s set up for panel data modeling. The important columns are:
“id”: This separates the time series groups (in this case these represent sales from departments in a walmart store)
“date”: This is the weekly sales period
“value”: This is the value for sales during the week and store/department
We can visualize this by time series group to expose the differences in sales by department.
data_tbl %>%
group_by(id) %>%
plot_time_series(
date, value, .interactive = F, .facet_ncol = 2
)
We can clearly see that there are 7 time series groups with different weekly sales patterns.
There are two key components that we need to prepare for:
Nested Data Structure: Most critical to ensure your data is prepared (covered next)
Nested Modeltime Workflow: This stage is where we create many models, fit the models to the data, and generate forecasts at scale
Conceptually, the workflow looks like this where we combine nested data and tidymodels workflows using an upcoming function, modeltime_nested_fit()
.
The most critical stage in “Nested Forecasting” is data preparation, making sure that the input to the nested forecasting workflow is in the appropriate structure. We’ve included several functions to help that involve a bit of forethought that can be broken into 3 steps:
Extending each of the times series: How far into the future do you need to predict for each time series? See extend_timeseries()
.
Nesting by the grouping variable: This is where you create the nested structure. You’ll identify the ID column that separates each time series, and the number of timestamps to include in the “.future_data” and optionally “.actual_data”. Typically, you’ll select the same .length_future
as your extension from the previous step. See nest_timeseries()
.
Train/Test Set Splitting: Finally, you’ll take your .actual_data
and convert into train/test splits that can be used for accuracy and confidence interval estimation. See split_nested_timeseries()
.
Here are the 3-steps in action:
nested_data_tbl <- data_tbl %>%
# 1. Extending: We'll predict 52 weeks into the future.
extend_timeseries(
.id_var = id,
.date_var = date,
.length_future = 52
) %>%
# 2. Nesting: We'll group by id, and create a future dataset
# that forecasts 52 weeks of extended data and
# an actual dataset that contains 104 weeks (2-years of data)
nest_timeseries(
.id_var = id,
.length_future = 52,
.length_actual = 52*2
) %>%
# 3. Splitting: We'll take the actual data and create splits
# for accuracy and confidence interval estimation of 52 weeks (test)
# and the rest is training data
split_nested_timeseries(
.length_test = 52
)
nested_data_tbl
#> # A tibble: 7 × 4
#> id .actual_data .future_data .splits
#> <fct> <list> <list> <list>
#> 1 1_1 <tibble [104 × 2]> <tibble [52 × 2]> <split [52|52]>
#> 2 1_3 <tibble [104 × 2]> <tibble [52 × 2]> <split [52|52]>
#> 3 1_8 <tibble [104 × 2]> <tibble [52 × 2]> <split [52|52]>
#> 4 1_13 <tibble [104 × 2]> <tibble [52 × 2]> <split [52|52]>
#> 5 1_38 <tibble [104 × 2]> <tibble [52 × 2]> <split [52|52]>
#> 6 1_93 <tibble [104 × 2]> <tibble [52 × 2]> <split [52|52]>
#> 7 1_95 <tibble [104 × 2]> <tibble [52 × 2]> <split [52|52]>
This creates a nested tibble with “.actual_data”, “.future_data”, and “.splits”. Each column will help in the nested modeltime workflow.
Next, we move into the Nested Modeltime Workflow now that nested data has been created. The Nested Modeltime Workflow includes 3 core functions:
Modeling Fitting: This is the training stage where we fit to training data. The test forecast is generated from this step. See modeltime_nested_fit()
.
Model Evaluation and Selection: This is where we review model performance and select the best model by minimizing or maximizing an error metric. See modeltime_nested_select_best()
.
Model Refitting: This is the final fitting stage where we fit to actual data. The future forecast is generated from this step. See modeltime_nested_refit()
.
First, we create tidymodels
workflows for the various models that you intend to create.
A common modeling method is prophet, that can be created using prophet_reg()
. We’ll create a workflow. Note that we use the extract_nested_train_split(nested_data_tbl)
to help us build preprocessing features.
rec_prophet <- recipe(value ~ date, extract_nested_train_split(nested_data_tbl))
wflw_prophet <- workflow() %>%
add_model(
prophet_reg("regression", seasonality_yearly = TRUE) %>%
set_engine("prophet")
) %>%
add_recipe(rec_prophet)
Next, we can use a machine learning method that can get good results: XGBoost. We will add a few extra features in the recipe feature engineering step to generate features that tend to get better modeling results. Note that we use the extract_nested_train_split(nested_data_tbl)
to help us build preprocessing features.
rec_xgb <- recipe(value ~ ., extract_nested_train_split(nested_data_tbl)) %>%
step_timeseries_signature(date) %>%
step_rm(date) %>%
step_zv(all_predictors()) %>%
step_dummy(all_nominal_predictors(), one_hot = TRUE)
wflw_xgb <- workflow() %>%
add_model(boost_tree("regression") %>% set_engine("xgboost")) %>%
add_recipe(rec_xgb)
With a couple of modeling workflows in hand, we are now ready to test them on each of the time series. We start by using the modeltime_nested_fit()
function, which iteratively fits each model to each of the nested time series train/test “.splits” column.
nested_modeltime_tbl <- modeltime_nested_fit(
# Nested data
nested_data = nested_data_tbl,
# Add workflows
wflw_prophet,
wflw_xgb
)
#> Fitting models on training data... ■■■■■ 14% | ETA:…
#> Fitting models on training data... ■■■■■■■■■■■■■■ 43% | ETA:…
#> Fitting models on training data... ■■■■■■■■■■■■■■■■■■■■■■ 71% | ETA:…
#> Fitting models on training data... ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 100% | ETA:…
nested_modeltime_tbl
#> # Nested Modeltime Table
#>
#> Trained on: .splits | Model Errors: [0]
#> # A tibble: 7 × 5
#> id .actual_data .future_data .splits .modeltime_tables
#> <fct> <list> <list> <list> <list>
#> 1 1_1 <tibble [104 × 2]> <tibble [52 × 2]> <split [52|52]> <mdl_time_tbl [2 ×…
#> 2 1_3 <tibble [104 × 2]> <tibble [52 × 2]> <split [52|52]> <mdl_time_tbl [2 ×…
#> 3 1_8 <tibble [104 × 2]> <tibble [52 × 2]> <split [52|52]> <mdl_time_tbl [2 ×…
#> 4 1_13 <tibble [104 × 2]> <tibble [52 × 2]> <split [52|52]> <mdl_time_tbl [2 ×…
#> 5 1_38 <tibble [104 × 2]> <tibble [52 × 2]> <split [52|52]> <mdl_time_tbl [2 ×…
#> 6 1_93 <tibble [104 × 2]> <tibble [52 × 2]> <split [52|52]> <mdl_time_tbl [2 ×…
#> 7 1_95 <tibble [104 × 2]> <tibble [52 × 2]> <split [52|52]> <mdl_time_tbl [2 ×…
This adds a new column with .modeltime_tables
for each of the data sets and has created several logged attributes that are part of the “Nested Modeltime Table”. We also can see that the models were trained on “.splits” and none of the models had any errors.
During the forecasting, the iterative modeltime fitting process logs important information that enable us to evaluate the models. These logged attributes are accessable with “extract” functions.
Using the extract_nested_test_accuracy()
, we can get the accuracy measures by time series and model. This allows us to see which model performs best on which time series.
nested_modeltime_tbl %>%
extract_nested_test_accuracy() %>%
table_modeltime_accuracy(.interactive = F)
| Accuracy Table | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| id | .model_id | .model_desc | .type | mae | mape | mase | smape | rmse | rsq |
| 1_1 | 1 | PROPHET | Test | 10071.47 | 45.88 | 1.99 | 59.97 | 11776.87 | 0.38 |
| 1_1 | 2 | XGBOOST | Test | 6236.79 | 25.31 | 1.23 | 24.57 | 9017.22 | 0.19 |
| 1_3 | 1 | PROPHET | Test | 3539.80 | 29.87 | 1.37 | 25.46 | 4707.77 | 0.80 |
| 1_3 | 2 | XGBOOST | Test | 3085.78 | 18.81 | 1.20 | 20.40 | 5085.81 | 0.79 |
| 1_8 | 1 | PROPHET | Test | 4282.96 | 11.15 | 1.82 | 11.96 | 4845.08 | 0.00 |
| 1_8 | 2 | XGBOOST | Test | 3585.77 | 9.33 | 1.53 | 9.89 | 4009.47 | 0.30 |
| 1_13 | 1 | PROPHET | Test | 6861.13 | 17.02 | 2.53 | 18.78 | 7309.61 | 0.15 |
| 1_13 | 2 | XGBOOST | Test | 2338.42 | 5.83 | 0.86 | 6.02 | 2721.47 | 0.54 |
| 1_38 | 1 | PROPHET | Test | 26007.21 | 32.57 | 2.22 | 39.60 | 27938.83 | 0.08 |
| 1_38 | 2 | XGBOOST | Test | 6847.04 | 8.47 | 0.58 | 8.75 | 8825.28 | 0.40 |
| 1_93 | 1 | PROPHET | Test | 17165.30 | 21.37 | 1.73 | 24.46 | 19123.17 | 0.03 |
| 1_93 | 2 | XGBOOST | Test | 7233.95 | 9.11 | 0.73 | 9.66 | 8879.21 | 0.49 |
| 1_95 | 1 | PROPHET | Test | 22836.06 | 18.30 | 2.75 | 20.37 | 24094.49 | 0.48 |
| 1_95 | 2 | XGBOOST | Test | 10783.75 | 8.54 | 1.30 | 8.96 | 12843.50 | 0.14 |
Next, we can visualize the test forecast with extract_nested_test_forecast()
.
nested_modeltime_tbl %>%
extract_nested_test_forecast() %>%
group_by(id) %>%
plot_modeltime_forecast(
.facet_ncol = 2,
.interactive = FALSE
)
If any of the models have errors, then we can investigate the error logs with extract_nested_error_report()
. Fortunately, we don’t have any errors, but if we did we could investigate further.
nested_modeltime_tbl %>%
extract_nested_error_report()
#> # A tibble: 0 × 4
#> # … with 4 variables: id <fct>, .model_id <int>, .model_desc <chr>,
#> # .error_desc <chr>
Using the accuracy data, we can pick a metric and select the best model based on that metric. The available metrics are in the default_forecast_accuracy_metric_set()
. Make sure to select minimize
based on the metric. The filter_test_forecasts
parameter tells the function to filter the logged test forecasts to just the best.
best_nested_modeltime_tbl <- nested_modeltime_tbl %>%
modeltime_nested_select_best(
metric = "rmse",
minimize = TRUE,
filter_test_forecasts = TRUE
)
This identifies which models should be used for the final forecast. With the models selected, we can make the future forecast.
The best model selections can be accessed with extract_nested_best_model_report()
.
best_nested_modeltime_tbl %>%
extract_nested_best_model_report()
#> # Nested Modeltime Table
#> # A tibble: 7 × 10
#> id .model_id .model_desc .type mae mape mase smape rmse rsq
#> <fct> <int> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1_1 2 XGBOOST Test 6237. 25.3 1.23 24.6 9017. 0.191
#> 2 1_3 1 PROPHET Test 3540. 29.9 1.37 25.5 4708. 0.796
#> 3 1_8 2 XGBOOST Test 3586. 9.33 1.53 9.89 4009. 0.297
#> 4 1_13 2 XGBOOST Test 2338. 5.83 0.861 6.02 2721. 0.536
#> 5 1_38 2 XGBOOST Test 6847. 8.47 0.585 8.75 8825. 0.402
#> 6 1_93 2 XGBOOST Test 7234. 9.11 0.728 9.66 8879. 0.488
#> 7 1_95 2 XGBOOST Test 10784. 8.54 1.30 8.96 12843. 0.139
Once we’ve selected the best models, we can easily visualize the best forecasts by time series. Note that the nested test forecast logs have been modified to isolate the best models.
best_nested_modeltime_tbl %>%
extract_nested_test_forecast() %>%
group_by(id) %>%
plot_modeltime_forecast(
.facet_ncol = 2,
.interactive = FALSE
)
With the best models in hand, we can make our future forecasts by refitting the models to the full dataset.
If the best models have been selected, the only the best models will be refit.
If best models have not been selected, then all models will be refit.
We’ve selected our best models, and will move forward with refitting and future forecast logging using the modeltime_nested_refit()
function.
nested_modeltime_refit_tbl <- best_nested_modeltime_tbl %>%
modeltime_nested_refit(
control = control_nested_refit(verbose = TRUE)
)
#> ℹ [1/7] Starting Modeltime Table: ID 1_1...
#> ✓ Model 2 Passed XGBOOST.
#> ✓ [1/7] Finished Modeltime Table: ID 1_1
#> ℹ [2/7] Starting Modeltime Table: ID 1_3...
#> ✓ Model 1 Passed PROPHET.
#> ✓ [2/7] Finished Modeltime Table: ID 1_3
#> ℹ [3/7] Starting Modeltime Table: ID 1_8...
#> ✓ Model 2 Passed XGBOOST.
#> ✓ [3/7] Finished Modeltime Table: ID 1_8
#> ℹ [4/7] Starting Modeltime Table: ID 1_13...
#> ✓ Model 2 Passed XGBOOST.
#> ✓ [4/7] Finished Modeltime Table: ID 1_13
#> ℹ [5/7] Starting Modeltime Table: ID 1_38...
#> ✓ Model 2 Passed XGBOOST.
#> ✓ [5/7] Finished Modeltime Table: ID 1_38
#> ℹ [6/7] Starting Modeltime Table: ID 1_93...
#> ✓ Model 2 Passed XGBOOST.
#> ✓ [6/7] Finished Modeltime Table: ID 1_93
#> ℹ [7/7] Starting Modeltime Table: ID 1_95...
#> ✓ Model 2 Passed XGBOOST.
#> ✓ [7/7] Finished Modeltime Table: ID 1_95
#>
#> Finished in: 3.644479 secs.
Note that we used control_nested_refit(verbose = TRUE)
to display the modeling results as each model is refit. This is not necessary, but can be useful to follow the nested model fitting process.
We can see that the nested modeltime table appears the same, but has now been trained on .actual_data
.
nested_modeltime_refit_tbl
#> # Nested Modeltime Table
#>
#> Trained on: .actual_data | Model Errors: [0]
#> # A tibble: 7 × 5
#> id .actual_data .future_data .splits .modeltime_tables
#> <fct> <list> <list> <list> <list>
#> 1 1_1 <tibble [104 × 2]> <tibble [52 × 2]> <split [52|52]> <mdl_time_tbl [1 ×…
#> 2 1_3 <tibble [104 × 2]> <tibble [52 × 2]> <split [52|52]> <mdl_time_tbl [1 ×…
#> 3 1_8 <tibble [104 × 2]> <tibble [52 × 2]> <split [52|52]> <mdl_time_tbl [1 ×…
#> 4 1_13 <tibble [104 × 2]> <tibble [52 × 2]> <split [52|52]> <mdl_time_tbl [1 ×…
#> 5 1_38 <tibble [104 × 2]> <tibble [52 × 2]> <split [52|52]> <mdl_time_tbl [1 ×…
#> 6 1_93 <tibble [104 × 2]> <tibble [52 × 2]> <split [52|52]> <mdl_time_tbl [1 ×…
#> 7 1_95 <tibble [104 × 2]> <tibble [52 × 2]> <split [52|52]> <mdl_time_tbl [1 ×…
After the refitting process completes, we can now access the future forecast, which is logged.
nested_modeltime_refit_tbl %>%
extract_nested_future_forecast() %>%
group_by(id) %>%
plot_modeltime_forecast(
.interactive = FALSE,
.facet_ncol = 2
)
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Carbon
Element Name:
Carbon
Latin Name:
Carbonium
Atomic Number:
6
Atomic Weight:
12.0107 u
Is a:
Non-Metal
State of the element:
About:
Carbon is a chemical element with the symbol C and atomic number 6. It is nonmetallic and tetravalent—making four electrons available to form covalent chemical bonds. It belongs to group 14 of the periodic table. Three isotopes occur naturally, 12C and 13C being stable, while 14C is a radionuclide, decaying with a half-life of about 5,730 years. Carbon is one of the few elements known since antiquity.
Electrons
6
Protons
6
Neutrons
6
Discoverer:
Antoine Lavoisier
Discovered in:
1789


Accepts a Unix date or a string containing a natural language date as a path parameter. Returns the corresponding Unix and formal dates. Ex. 1. Ex. 2.


WITNESS LIST Economic Development Committee April 24, 1997-9:00A HB 3 Against: Carlson, Pat (TX Eagle Forum), Grapevine On: Hamilton, Tyrette (TDI), Austin McKinney, Mike (Health & Human Services), Austin O'Connel, John (AG's Office), Round Rock Registering, but not testifying: For: Alexander, Troy (TX Academy of Fam. Phys), Austin Berger, Ed (Seton Healthcare Netwrk), Austin Comola, Jon (Blue Cross Blue Shields), Austin DaSilva, Joe (TX Hospital Assn.), Austin Dunkleberg, Anne (Public Policy Prioritie), Austin Engman, Nancy (TX Care for Children), Austin Forbes, Scott (City of Houston), Houston Gantt, Melanie (TX Mental Health Assn.), Austin Hernandez, Leslie (Social Workers Assn.), Austin Hinojosa, Walter (TX AFL-CIO), Austin Kamm, Robert (Business & Chambers /TX), Austin Kibby, Sandy (TX Congress of PTA), Austin Marwitz, David (TX Medical Assn.), Austin McCarthy, Kathleen (TX Nurses Assn.), Austin McGiffert, Lisa (Consumers Union), Austin Sperry, Bryan (Children's Hospital Asn), Austin Thorton, Jimmie (self), Trinity Against: Cheng, Albert (Free Market Committee), Dallas Spivy, LaNeil Wright (self), Dallas On: Alexander, Cindy (Comp of Public Accounts), Austin HB 39 Registering, but not testifying: For: Hinojosa, Walter (TX AFL-CIO), Austin McGiffert, Lisa (Consumers Union), Austin Against: Knepp, Christopher (Employment Law Council), Austin On: Canfield, Mark (TX Dept. of Health), Austin Myron, Rhonda (TDI), Austin HB 565 On: Jennings, John (TWC), Austin Registering, but not testifying: For: Igo, Shannon (TX Municipal League), Austin HB 567 On: Jennings, John (TWC), Austin HB 710 For: McGiffert, Lisa (Consumers Union), Austin Tooley, Kenneth (Assn. Life Underwriters), Austin Against: Clines, Robert (Provident American Ins.), Bedford Pollard, Mike (Assn. Life & Health Ins), Austin Thompson, Jay (Assn. Life Insurers), Austin On: Holahan, Joe (Affordable Health Ins.), Austin Myron, Rhonda (TDI), Austin Registering, but not testifying: For: Dowden, Bill (Golden Rule Ins. Co.), Indianapolis Johnson, Delbert (IAA & Presidential Life), Sunnyvale Johnson, Gerald (Presidential Life & IAA), Rockwall Joiner, Gary (TX Farm Bureau), Austin Pinkus, David (Small Business United), Austin Against: Comola, John (Blue Cross Blue Sheild), Austin On: Hamilton, Tyrette (TDI), Austin McCarthy, John (HUMANA), Louisville HB 922 For: Davis, Will (Life & Health Insurers), Austin HB 1212 For: Jobe, Clark (Ins. Purchasing Allianc), Austin McGiffert, Lisa (Consumers Union), Austin Thompson, Jay (Life & Health Insurers), Austin On: Hamilton, Tyrette (TDI), Austin McCarthy, John (HUMANA), Louisville Myron, Rhonda (TDI), Austin Registering, but not testifying: For: Pinkus, David (Small Business United), Austin Tooley, Kenneth (Asn Life Underwriters), Austin Against: Comola, Jon (Blue Cross Blue Shield), Austin HB 1975 Registering, but not testifying: For: Floyd, Beaman (American Ins Assn.), Austin On: Brady, Kevin (TDI), Austin SB 599 For: Lokey, James (NASP), Dallas Nesbitt, Earl (Assn. of Settlement Pur), Dallas Against: Jefferies, John (AGLI), Sugar Land Lewis, Richard (The Mangelsdorf Co.), Dallas Registering, but not testifying: For: Robnett, NJ "Buzz" (NASP), Austin Against: Hogeland, Jonna Kay (CNA Insurance Companies), Austin On: Hamilton, Tyrette (TDI), Austin Street, James (James Street Group), Austin SB 1076 For: Abdnor, John (Ins Alliance of America), Dallas Parker, Carl (IAA), Austin On: Brady, Kevin (TDI), Austin Registering, but not testifying: For: Johnson, Delbert (IAA & Presidential Life), Sunnyvale Johnson, Gerald (IAA & Presidential Life), Rockwall SB 1197 For: Parker, Carl (IAA), Austin On: Myron, Rhonda (TDI), Austin Registering, but not testifying: For: Abdnor, John (IAA), Dallas SB 1479 Registering, but not testifying: Against: Taylor, Carol (TTLA), Austin


| scales-package {scales} | R Documentation |
Graphical scales map data to aesthetics, and provide methods for automatically determining breaks and labels for axes and legends.
Maintainer: Hadley Wickham<EMAIL_ADDRESS>Authors:
Dana Seidel
Other contributors:
RStudio [copyright holder]
Useful links:
Report bugs at https://github.com/r-lib/scales/issues


U.S. Gross Domestic Product
Units: Billions of Dollars
Seasonal Adjustment: Seasonally Adjusted Annual Rate
Notes: A Guide to the National Income and Product Accounts of the United States (NIPA) - (http://www.bea.gov/national/pdf/nipaguid.pdf)
Seasonal Adjustment: Seasonally Adjusted Annual Rate
Notes: A Guide to the National Income and Product Accounts of the United States (NIPA) - (http://www.bea.gov/national/pdf/nipaguid.pdf)


Cell
s in this.Cell
s in this.Cell
.CellArray1D
should retrieve neighborhoods.CellArray1D
should retrieve neighborhoods.radial
,
left
,
right
,
Each item has a field containing the appropriate method name in CellArray1D
so that a
Topology1D
object can specify which method the target CellArray1D should use for
neighborhood lookup.Standard
,
Circle
Each item has a field containing the name of the inner class in Topology1D
which contains the
appropriate identity functions for that identity space.CellArray2D
should retrieve neighborhoods.CellArray2D
should retrieve neighborhoods.Moore
,
Von Neumann
,
Left Corners
,
Right Corners
,
Horizontal
,
Vertical
,
Left Diagonal
,
Right Diagonal
.Cell
states of this.Cell
states of this.