Constructor
new GeoJSONParserOSM(dataSource)
Creates a subclass of the GeoJSONParser class.
Parameters:
| Name | Type | Description |
|---|---|---|
dataSource |
String | The data source in GeoJSON format. Can be a string or a URL for the data. |
- Source:
Methods
addRenderablesForMultiPolygon(layer, geometry, properties)
Adds an extruded Polygon if extrude is set to true, otherwise adds a SurfacePolygon for each ring in the GeoJSONGeometryPolygons in the GeoJSONGeometryMultiPolygon.
Parameters:
| Name | Type | Description |
|---|---|---|
layer |
RenderableLayer | The layer in which to place the newly created shapes. |
geometry |
GeoJSONGeometryMultiPolygon | The MultiPolygon geometry object. |
properties |
Object | The properties related to the MultiPolygon geometry. |
- Source:
Throws:
-
-
If the specified layer is null or undefined.
- Type
- ArgumentError
-
-
-
If the specified geometry is null or undefined.
- Type
- ArgumentError
-
addRenderablesForPolygon(layer, geometry, properties)
Adds an extruded Polygon if extrude is set to true, otherwise adds a SurfacePolygon for each ring in the GeoJSONGeometryPolygon.
Parameters:
| Name | Type | Description |
|---|---|---|
layer |
RenderableLayer | The layer in which to place the newly created shapes. |
geometry |
GeoJSONGeometryPolygon | The Polygon geometry object. |
properties |
Object | The properties related to the Polygon geometry. |
- Source:
Throws:
-
-
If the specified layer is null or undefined.
- Type
- ArgumentError
-
-
-
If the specified geometry is null or undefined.
- Type
- ArgumentError
-


|
Apache log4cxx
Version 0.10.0
|
The AsyncAppender lets users log events asynchronously. More...
Inherits AppenderAttachable, and AppenderSkeleton.
Public Member Functions | |
| AsyncAppender () | |
| Create new instance. More... | |
| virtual | ~AsyncAppender () |
| Destructor. More... | |
| void | addRef () const |
| void | releaseRef () const |
| void | addAppender (const AppenderPtr &newAppender) |
| Add appender. More... | |
| void | append (const spi::LoggingEventPtr &event, log4cxx::helpers::Pool &p) |
Subclasses of AppenderSkeleton should implement this method to perform actual logging. More... | |
| void | close () |
Close this AsyncAppender by interrupting the dispatcher thread which will process all pending events before exiting. More... | |
| AppenderList | getAllAppenders () const |
| Get iterator over attached appenders. More... | |
| AppenderPtr | getAppender (const LogString &name) const |
| Get appender by name. More... | |
| bool | getLocationInfo () const |
| Gets whether the location of the logging request call should be captured. More... | |
| bool | isAttached (const AppenderPtr &appender) const |
| Determines if specified appender is attached. More... | |
| virtual bool | requiresLayout () const |
| Configurators call this method to determine if the appender requires a layout. More... | |
| void | removeAllAppenders () |
| Removes and closes all attached appenders. More... | |
| void | removeAppender (const AppenderPtr &appender) |
| Removes an appender. More... | |
| void | removeAppender (const LogString &name) |
| Remove appender by name. More... | |
| void | setLocationInfo (bool flag) |
| The LocationInfo attribute is provided for compatibility with log4j and has no effect on the log output. More... | |
| void | setBufferSize (int size) |
| The BufferSize option takes a non-negative integer value. More... | |
| int | getBufferSize () const |
| Gets the current buffer size. More... | |
| void | setBlocking (bool value) |
| Sets whether appender should wait if there is no space available in the event buffer or immediately return. More... | |
| bool | getBlocking () const |
| Gets whether appender should block calling thread when buffer is full. More... | |
| void | setOption (const LogString &option, const LogString &value) |
| Set appender properties by name. More... | |
| Public Member Functions inherited from AppenderAttachable | |
| virtual | ~AppenderAttachable () |
| Public Member Functions inherited from Object | |
| virtual const helpers::Class & | getClass () const |
| virtual | ~Object () |
| virtual bool | instanceof (const Class &clazz) const =0 |
| virtual const void * | cast (const Class &clazz) const =0 |
| Public Member Functions inherited from AppenderSkeleton | |
| AppenderSkeleton () | |
| AppenderSkeleton (const LayoutPtr &layout) | |
| void | finalize () |
Finalize this appender by calling the derived class' close method. More... | |
| virtual void | activateOptions (log4cxx::helpers::Pool &) |
| Derived appenders should override this method if option structure requires it. More... | |
| void | addFilter (const spi::FilterPtr &newFilter) |
| Add a filter to end of the filter list. More... | |
| void | clearFilters () |
| Clear the filters chain. More... | |
| const spi::ErrorHandlerPtr & | getErrorHandler () const |
| Return the currently set spi::ErrorHandler for this Appender. More... | |
| spi::FilterPtr | getFilter () const |
| Returns the head Filter. More... | |
| const spi::FilterPtr & | getFirstFilter () const |
| Return the first filter in the filter chain for this Appender. More... | |
| LayoutPtr | getLayout () const |
| Returns the layout of this appender. More... | |
| LogString | getName () const |
| Returns the name of this Appender. More... | |
| const LevelPtr & | getThreshold () |
| Returns this appenders threshold level. More... | |
| bool | isAsSevereAsThreshold (const LevelPtr &level) const |
| Check whether the message level is below the appender's threshold. More... | |
| void | doAppend (const spi::LoggingEventPtr &event, log4cxx::helpers::Pool &pool) |
| This method performs threshold checks and invokes filters before delegating actual logging to the subclasses specific AppenderSkeleton::append method. More... | |
| void | setErrorHandler (const spi::ErrorHandlerPtr &eh) |
| Set the ErrorHandler for this Appender. More... | |
| void | setLayout (const LayoutPtr &layout1) |
| Set the layout for this appender. More... | |
| void | setName (const LogString &name1) |
| Set the name of this Appender. More... | |
| void | setThreshold (const LevelPtr &threshold) |
| Set the threshold level. More... | |
| Public Member Functions inherited from Appender | |
| virtual | ~Appender () |
| Public Member Functions inherited from OptionHandler | |
| virtual | ~OptionHandler () |
| Public Member Functions inherited from ObjectImpl | |
| ObjectImpl () | |
| virtual | ~ObjectImpl () |
| void | addRef () const |
| void | releaseRef () const |
Additional Inherited Members | |
| Static Public Member Functions inherited from Object | |
| static const helpers::Class & | getStaticClass () |
| static const log4cxx::helpers::ClassRegistration & | registerClass () |
| Protected Attributes inherited from AppenderSkeleton | |
| LayoutPtr | layout |
| The layout variable does not need to be set if the appender implementation has its own layout. More... | |
| LogString | name |
| Appenders are named. More... | |
| LevelPtr | threshold |
| There is no level threshold filtering by default. More... | |
| spi::ErrorHandlerPtr | errorHandler |
| It is assumed and enforced that errorHandler is never null. More... | |
| spi::FilterPtr | headFilter |
| The first filter in the filter chain. More... | |
| spi::FilterPtr | tailFilter |
| The last filter in the filter chain. More... | |
| bool | closed |
| Is this appender closed? More... | |
| log4cxx::helpers::Pool | pool |
| log4cxx::helpers::Mutex | mutex |
| Protected Attributes inherited from ObjectImpl | |
| unsigned int volatile | ref |
The AsyncAppender lets users log events asynchronously.
It uses a bounded buffer to store logging events.
The AsyncAppender will collect the events sent to it and then dispatch them to all the appenders that are attached to it. You can attach multiple appenders to an AsyncAppender.
The AsyncAppender uses a separate thread to serve the events in its bounded buffer.
Important note: The AsyncAppender
can only be script configured using the DOMConfigurator.
| AsyncAppender | ( | ) |
Create new instance.
|
virtual |
Destructor.
|
virtual |
Add appender.
| newAppender | appender to add, may not be null. |
Implements AppenderAttachable.
|
virtual |
Reimplemented from AppenderSkeleton.
|
virtual |
Subclasses of AppenderSkeleton
should implement this method to perform actual logging.
See also AppenderSkeleton::doAppend method.
Implements AppenderSkeleton.
|
virtual |
Close this AsyncAppender
by interrupting the dispatcher thread which will process all pending events before exiting.
Implements Appender.
|
virtual |
Get iterator over attached appenders.
Implements AppenderAttachable.
|
virtual |
Get appender by name.
| name | name, may not be null. |
Implements AppenderAttachable.
| bool getBlocking | ( | ) | const |
Gets whether appender should block calling thread when buffer is full.
If false, messages will be counted by logger and a summary message appended after the contents of the buffer have been appended.
| int getBufferSize | ( | ) | const |
Gets the current buffer size.
| bool getLocationInfo | ( | ) | const |
Gets whether the location of the logging request call should be captured.
|
virtual |
Determines if specified appender is attached.
| appender | appender. |
Implements AppenderAttachable.
|
virtual |
Reimplemented from AppenderSkeleton.
|
virtual |
Removes and closes all attached appenders.
Implements AppenderAttachable.
|
virtual |
|
virtual |
|
virtual |
Configurators call this method to determine if the appender requires a layout.
If this method returns true
, meaning that layout is required, then the configurator will configure an layout using the configuration information at its disposal. If this method returns false
, meaning that a layout is not required, then layout configuration will be skipped even if there is available layout configuration information at the disposal of the configurator..
In the rather exceptional case, where the appender implementation admits a layout but can also work without it, then the appender should return true
.
Implements Appender.
| void setBlocking | ( | bool | value | ) |
Sets whether appender should wait if there is no space available in the event buffer or immediately return.
| value | true if appender should wait until available space in buffer. |
| void setBufferSize | ( | int | size | ) |
The BufferSize option takes a non-negative integer value.
This integer value determines the maximum size of the bounded buffer.
| void setLocationInfo | ( | bool | flag | ) |
The LocationInfo attribute is provided for compatibility with log4j and has no effect on the log output.
| flag | new value. |
Set appender properties by name.
| option | property name. |
| value | property value. |
Reimplemented from AppenderSkeleton.


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Recipe 2.28. File Locking Using a Cross-Platform APICredit: Jonathan Feinberg, John Nielsen ProblemYou need to lock files in a program that runs on both Windows and Unix-like systems, but the Python Standard Library offers only platform-specific ways to lock files. SolutionWhen the Python Standard Library itself doesn't offer a cross-platform solution, it's often possible to implement one ourselves: import os # needs win32all to work on Windows (NT, 2K, XP, _not_ /95 or /98) if os.name == 'nt': import win32con, win32file, pywintypes LOCK_EX = win32con.LOCKFILE_EXCLUSIVE_LOCK LOCK_SH = 0 # the default LOCK_NB = win32con.LOCKFILE_FAIL_IMMEDIATELY _ _overlapped = pywintypes.OVERLAPPED( ) def lock(file, flags): hfile = win32file._get_osfhandle(file.fileno( )) win32file.LockFileEx(hfile, flags, 0, 0xffff0000, _ _overlapped) def unlock(file): hfile = win32file._get_osfhandle(file.fileno( )) win32file.UnlockFileEx(hfile, 0, 0xffff0000, _ _overlapped) elif os.name == 'posix': from fcntl import LOCK_EX, LOCK_SH, LOCK_NB def lock(file, flags): fcntl.flock(file.fileno( ), flags) def unlock(file): fcntl.flock(file.fileno( ), fcntl.LOCK_UN) else: raise RuntimeError("PortaLocker only defined for nt and posix platforms") DiscussionWhen multiple programs or threads have to access a shared file, it's wise to ensure that accesses are synchronized so that two processes don't try to modify the file contents at the same time. Failure to synchronize accesses could even corrupt the entire file in some cases. This recipe supplies two functions, lock and unlock, that request and release locks on a file, respectively. Using the portalocker.py module is a simple matter of calling the lock function and passing in the file and an argument specifying the kind of lock that is desired:
For example: import portalocker afile = open("somefile", "r+") portalocker.lock(afile, portalocker.LOCK_EX) The implementation of the lock and unlock functions is entirely different on different systems. On Unix-like systems (including Linux and Mac OS X), the recipe relies on functionality made available by the standard fcntl module. On Windows systems (NT, 2000, XPit doesn't work on old Win/95 and Win/98 platforms because they just don't have the needed oomph in the operating system!), the recipe uses the win32file module, part of the very popular PyWin32 package of Windows-specific extensions to Python, authored by Mark Hammond. But the important point is that, despite the differences in implementation, the functions (and the flags you can pass to the lock function) are made to behave in the same way across platforms. Such cross-platform packaging of differently implemented but equivalent functionality enables you to easily write cross-platform applications, which is one of Python's strengths. When you write a cross-platform program, it's nice if the functionality that your program uses is, in turn, encapsulated in a cross-platform way. For file locking in particular, it is especially helpful to Perl users, who are used to an essentially transparent lock system call across platforms. More generally, if os.name== just does not belong in application-level code. Such platform testing ideally should always be in the standard library or an application-independent module, as it is here. See AlsoDocumentation on the fcntl module in the Library Reference; documentation on the win32file module at http://ASPN.ActiveState.com/ASPN/Python/Reference/Products/ActivePython/PythonWin32Extensions/win32file.html; Jonathan Feinberg's web site (http://MrFeinberg.com). |


Personal Information
Personal
English Name
Chinese Name
School ID
Class
Phone
Address
Identification No./Passport
Email Address
Facebook Name/Link
Special Medical Conditions
UPSR Science Results
A
B
C
D
UPSR Maths Results
A
B
C
D
Father
Father's Name
Occupation
Father's Phone
Mother
Mother's Name
Occupation
Mother's Phone
Emergency
Emergency Contant Number
Emergency Contant Relationship
Additional Questions
Why did you join this specific club?
Any knowledge or interaction with Electronics?
Any competitions regarding creativity or Electronics?

Done


Navigate to news via html-link Goto News
Navigate to news via code Goto News
Navigate to news with parameter: Goto News With Param via Code Goto News With Param via html-link


Datasets must meet the following criteria to be considered for the Datasets Market:
- A well-defined dataset with a clear and static vision for the data to minimize churn or changes.
- Robust ticker and security links to ensure the tickers are tracked well through time. ISIN, FIGI, or point-in-time tickers are supported.
- Sufficient organizational funding to ensure at least 1 year of operation.
- Reliable API with no dead links or 502 errors.
- Consistent delivery schedule with data delivered on time and in time for trading.
- Consistent data format with notifications and lead time on data format updates.
- At least 1 year of historical data.
- Free of survivorship bias.
- Good documentation of the dataset.
If the dataset is alternative data, in addition to the criteria above, the dataset must practice the Alternative Investment Standards defined by the non-profit Investment Data Standards Organization (IDSO). The Alternative Investment Standards outline the rules and best practices for collecting and distributing alternative datasets. For example, the IDSO Web Crawling Best Practices publication states that “a data collector should assess a website according to the terms of its robots.txt”.


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#include <stdlib.h> long int strtol(const char *nptr, char **endptr, int base); long long int strtoll(const char *nptr, char **endptr, int base);
Feature Test Macro Requirements for glibc (see feature_test_macros(7)):
strtoll():
The string may begin with an arbitrary amount of white space (as determined by isspace(3)) followed by a single optional '+' or '-' sign. If base is zero or 16, the string may then include a "0x" prefix, and the number will be read in base 16; otherwise, a zero base is taken as 10 (decimal) unless the next character is '0', in which case it is taken as 8 (octal).
The remainder of the string is converted to a long int value in the obvious manner, stopping at the first character which is not a valid digit in the given base. (In bases above 10, the letter 'A' in either uppercase or lowercase represents 10, 'B' represents 11, and so forth, with 'Z' representing 35.)
If endptr is not NULL, strtol() stores the address of the first invalid character in *endptr. If there were no digits at all, strtol() stores the original value of nptr in *endptr (and returns 0). In particular, if *nptr is not '\0' but **endptr is '\0' on return, the entire string is valid.
The strtoll() function works just like the strtol() function but returns a long long integer value.
The implementation may also set errno to EINVAL in case no conversion was performed (no digits seen, and 0 returned).
According to POSIX.1-2001, in locales other than the "C" and "POSIX", these functions may accept other, implementation-defined numeric strings.
BSD also has
quad_t strtoq(const char *nptr, char **endptr, int base);with completely analogous definition. Depending on the wordsize of the current architecture, this may be equivalent to strtoll() or to strtol().
$ ./a.out 123 strtol() returned 123 $ ./a.out ' 123' strtol() returned 123 $ ./a.out 123abc strtol() returned 123 Further characters after number: abc $ ./a.out 123abc 55 strtol: Invalid argument $ ./a.out '' No digits were found $ ./a.out<PHONE_NUMBER> strtol: Numerical result out of range
#include <stdlib.h> #include <limits.h> #include <stdio.h> #include <errno.h> int main(int argc, char *argv[]) { int base; char *endptr, *str; long val; if (argc < 2) { fprintf(stderr, "Usage: %s str [base]\n", argv[0]); exit(EXIT_FAILURE); } str = argv[1]; base = (argc > 2) ? atoi(argv[2]) : 10; errno = 0; /* To distinguish success/failure after call */ val = strtol(str, &endptr, base); /* Check for various possible errors */ if ((errno == ERANGE && (val == LONG_MAX || val == LONG_MIN)) || (errno != 0 && val == 0)) { perror("strtol"); exit(EXIT_FAILURE); } if (endptr == str) { fprintf(stderr, "No digits were found\n"); exit(EXIT_FAILURE); } /* If we got here, strtol() successfully parsed a number */ printf("strtol() returned %ld\n", val); if (*endptr != '\0') /* Not necessarily an error... */ printf("Further characters after number: %s\n", endptr); exit(EXIT_SUCCESS); }


|
BitShares-Core
6.1.0
BitShares blockchain implementation and command-line interface software
|
#include <application.hpp>
Static Public Member Functions | |
| static constexpr application_options | get_default () |
Definition at line 38 of file application.hpp.
|
inlinestatic |
Definition at line 84 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_get_account_history = 100 |
Definition at line 46 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_get_account_history_by_operations = 100 |
Definition at line 48 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_get_account_history_operations = 100 |
Definition at line 47 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_get_account_limit_orders = 101 |
Definition at line 64 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_get_asset_holders = 100 |
Definition at line 56 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_get_assets = 101 |
Definition at line 55 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_get_call_orders = 300 |
Definition at line 67 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_get_collateral_bids = 100 |
Definition at line 69 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_get_credit_offers = 101 |
Definition at line 81 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_get_full_accounts = 50 |
Definition at line 58 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_get_full_accounts_lists = 500 |
Definition at line 59 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_get_full_accounts_subscribe = 100 |
Definition at line 60 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_get_grouped_limit_orders = 101 |
Definition at line 65 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_get_htlc_by = 100 |
Definition at line 75 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_get_key_references = 100 |
Definition at line 57 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_get_limit_orders = 300 |
Definition at line 62 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_get_limit_orders_by_account = 101 |
Definition at line 63 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_get_liquidity_pool_history = 101 |
Definition at line 53 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_get_liquidity_pools = 101 |
Definition at line 79 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_get_market_history = 200 |
Definition at line 50 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_get_order_book = 50 |
Definition at line 66 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_get_relative_account_history = 100 |
Definition at line 49 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_get_samet_funds = 101 |
Definition at line 80 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_get_settle_orders = 300 |
Definition at line 68 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_get_storage_info = 101 |
Definition at line 82 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_get_tickets = 101 |
Definition at line 78 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_get_top_markets = 100 |
Definition at line 54 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_get_top_voters = 200 |
Definition at line 61 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_get_trade_history = 100 |
Definition at line 51 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_get_trade_history_by_sequence = 100 |
Definition at line 52 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_get_withdraw_permissions_by_giver = 101 |
Definition at line 76 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_get_withdraw_permissions_by_recipient = 101 |
Definition at line 77 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_list_htlcs = 100 |
Definition at line 74 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_lookup_accounts = 1000 |
Definition at line 70 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_lookup_committee_member_accounts = 1000 |
Definition at line 72 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_lookup_vote_ids = 1000 |
Definition at line 73 of file application.hpp.
| uint32_t graphene::app::application_options::api_limit_lookup_witness_accounts = 1000 |
Definition at line 71 of file application.hpp.
| bool graphene::app::application_options::enable_subscribe_to_all = false |
Definition at line 41 of file application.hpp.
| bool graphene::app::application_options::has_api_helper_indexes_plugin = false |
Definition at line 43 of file application.hpp.
| bool graphene::app::application_options::has_market_history_plugin = false |
Definition at line 44 of file application.hpp.


A tinkerer, analyst, and what Saul Bellow calls a "first-class noticer"
From growth marketing, flexible data visualizations, civic tech projects, teaching a summer coding course to high school women, art historical research, to dogwalking,
I've done a lot and have enjoyed learning about it all.
3 key takeaways about me:
1) The devil's in the details. 📸
I tackle big-picture objectives by paying attention to the essentials. When I get onboard with a project,
I read the literature, roll up my sleeves, and get messy with the tools. It's my favorite way to understand
something inside and out, and strategize how to make it better.
2) I'm an analyst at heart. 📊
Statistics and Art History might seem like an odd couple, but their combination challenges me to think abstractly and within social contexts.
I enjoy using data to understand how people behave and why.
3) I like the unconventional. 🌮
For me, playing the game a little differently than everyone else can unlock great discoveries.
My favorite taco place isn't the most popular one — it's the one next door.
It doesn't have much signage, but it's uncrowded, their waitstaff is unhurried, and you get complimentary chips & salsa while you wait.
An all-around winner in my book.


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osv-scanner
Scan various mediums for dependencies and matches them against the OSV database.
More information: home page
- Scan a docker image:
osv-scanner -D docker_image_name
- Scan a package lockfile:
osv-scanner -L path/to/lockfile
- Scan an SBOM file:
osv-scanner -S path/to/sbom_file
- Scan multiple directories recursively:
osv-scanner -r directory1 directory2 ...
- Skip scanning git repositories:
osv-scanner --skip-git -r|-D target
- Output result in JSON format:
osv-scanner --json -D|-L|-S|-r target


Note: If you get many numbers that don't result in palindromes, your iteration count is probably too low. However, due to the limitations of modern browsers, there is a maximum number that the browser can calculate without resulting in
overflow, which restricts calculation of numbers that require many iterations to produce a palindrome.
The only non-palindrome I've been able to calculate with this is 196, which is the lowest non-palindrome.
Here is an accurate list of non-palindromes.


| 1 | /* | |
| 2 | * Licensed to the Apache Software Foundation (ASF) under one or more | |
| 3 | * contributor license agreements. See the NOTICE file distributed with | |
| 4 | * this work for additional information regarding copyright ownership. | |
| 5 | * The ASF licenses this file to You under the Apache License, Version 2.0 | |
| 6 | * (the "License"); you may not use this file except in compliance with | |
| 7 | * the License. You may obtain a copy of the License at | |
| 8 | * | |
| 9 | * http://www.apache.org/licenses/LICENSE-2.0 | |
| 10 | * | |
| 11 | * Unless required by applicable law or agreed to in writing, software | |
| 12 | * distributed under the License is distributed on an "AS IS" BASIS, | |
| 13 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| 14 | * See the License for the specific language governing permissions and | |
| 15 | * limitations under the License. | |
| 16 | */ | |
| 17 | package org.apache.commons.math3.exception; | |
| 18 | ||
| 19 | import org.apache.commons.math3.exception.util.Localizable; | |
| 20 | import org.apache.commons.math3.exception.util.LocalizedFormats; | |
| 21 | ||
| 22 | /** | |
| 23 | * Exception to be thrown when two sets of dimensions differ. | |
| 24 | * | |
| 25 | * @since 3.0 | |
| 26 | * @version $Id: MultiDimensionMismatchException.java 1364378 2012-07-22 17:42:38Z tn $ | |
| 27 | */ | |
| 28 | public class MultiDimensionMismatchException extends MathIllegalArgumentException { | |
| 29 | /** Serializable version Id. */ | |
| 30 | private static final long serialVersionUID = -8415396756375798143L; | |
| 31 | ||
| 32 | /** Wrong dimensions. */ | |
| 33 | private final Integer[] wrong; | |
| 34 | /** Correct dimensions. */ | |
| 35 | private final Integer[] expected; | |
| 36 | ||
| 37 | /** | |
| 38 | * Construct an exception from the mismatched dimensions. | |
| 39 | * | |
| 40 | * @param wrong Wrong dimensions. | |
| 41 | * @param expected Expected dimensions. | |
| 42 | */ | |
| 43 | public MultiDimensionMismatchException(Integer[] wrong, | |
| 44 | Integer[] expected) { | |
| 45 | this(LocalizedFormats.DIMENSIONS_MISMATCH, wrong, expected); | |
| 46 | } | |
| 47 | ||
| 48 | /** | |
| 49 | * Construct an exception from the mismatched dimensions. | |
| 50 | * | |
| 51 | * @param specific Message pattern providing the specific context of | |
| 52 | * the error. | |
| 53 | * @param wrong Wrong dimensions. | |
| 54 | * @param expected Expected dimensions. | |
| 55 | */ | |
| 56 | public MultiDimensionMismatchException(Localizable specific, | |
| 57 | Integer[] wrong, | |
| 58 | Integer[] expected) { | |
| 59 | super(specific, wrong, expected); | |
| 60 | this.wrong = wrong.clone(); | |
| 61 | this.expected = expected.clone(); | |
| 62 | } | |
| 63 | ||
| 64 | /** | |
| 65 | * @return an array containing the wrong dimensions. | |
| 66 | */ | |
| 67 | public Integer[] getWrongDimensions() { | |
| 68 | 1 | return wrong.clone(); |
| 69 | } | |
| 70 | /** | |
| 71 | * @return an array containing the expected dimensions. | |
| 72 | */ | |
| 73 | public Integer[] getExpectedDimensions() { | |
| 74 | 1 | return expected.clone(); |
| 75 | } | |
| 76 | ||
| 77 | /** | |
| 78 | * @param index Dimension index. | |
| 79 | * @return the wrong dimension stored at {@code index}. | |
| 80 | */ | |
| 81 | public int getWrongDimension(int index) { | |
| 82 | 1 | return wrong[index]; |
| 83 | } | |
| 84 | /** | |
| 85 | * @param index Dimension index. | |
| 86 | * @return the expected dimension stored at {@code index}. | |
| 87 | */ | |
| 88 | public int getExpectedDimension(int index) { | |
| 89 | 1 | return expected[index]; |
| 90 | } | |
| 91 | } | |
Mutations | ||
| 68 |
mutated return of Object value for org/apache/commons/math3/exception/MultiDimensionMismatchException::getWrongDimensions to ( if (x != null) null else throw new RuntimeException ) : NO_COVERAGE |
|
| 74 |
mutated return of Object value for org/apache/commons/math3/exception/MultiDimensionMismatchException::getExpectedDimensions to ( if (x != null) null else throw new RuntimeException ) : NO_COVERAGE |
|
| 82 |
replaced return of integer sized value with (x == 0 ? 1 : 0) : KILLED -> org.apache.commons.math3.linear.MatrixDimensionMismatchExceptionTest.testAccessors(org.apache.commons.math3.linear.MatrixDimensionMismatchExceptionTest) |
|
| 89 |
replaced return of integer sized value with (x == 0 ? 1 : 0) : KILLED -> org.apache.commons.math3.linear.MatrixDimensionMismatchExceptionTest.testAccessors(org.apache.commons.math3.linear.MatrixDimensionMismatchExceptionTest) |


Anthony Dunne and Fiona Raby
They designed (non-functional) objects for use in people's homes with a suggestion they would mediate electronic waves or something. They documented the reactions people had, how they used the objects.
"In short, we are collectively asking what it means to live in this electronic world we are creating, whether this world reflects our values, who is entering into this world that we are designing and whom we are leaving behind. These questions are at least as philosophical as they are technological, and there is more than one way to approach them: science and technology studies, philosophy of technology, and similar fields offer one strategy. Another is the emerging area of research through design or constructive design"


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Please fill this form if you wish to become a Partner of the United Nations Decade on Biodiversity 2011-2020. Once you have submitted your information, we will process it and we will send you a confirmation. If you have difficulty filling the form, or if you have any questions please contact us.
The following information will appear online unless indicated otherwise.


|
Bullet Collision Detection & Physics Library
|
Base class for gimpact shapes. More...
#include <btGImpactShape.h>
Public Member Functions | |
| btGImpactShapeInterface () | |
| void | updateBound () |
| performs refit operation More... | |
| void | getAabb (const btTransform &t, btVector3 &aabbMin, btVector3 &aabbMax) const |
| If the Bounding box is not updated, then this class attemps to calculate it. More... | |
| virtual void | postUpdate () |
| Tells to this object that is needed to refit the box set. More... | |
| const btAABB & | getLocalBox () |
| Obtains the local box, which is the global calculated box of the total of subshapes. More... | |
| virtual int | getShapeType () const |
| virtual void | setLocalScaling (const btVector3 &scaling) |
| virtual const btVector3 & | getLocalScaling () const |
| virtual void | setMargin (btScalar margin) |
| virtual void | rayTest (const btVector3 &rayFrom, const btVector3 &rayTo, btCollisionWorld::RayResultCallback &resultCallback) const |
| virtual method for ray collision More... | |
| virtual void | processAllTriangles (btTriangleCallback *callback, const btVector3 &aabbMin, const btVector3 &aabbMax) const |
| Function for retrieve triangles. More... | |
| virtual void | processAllTrianglesRay (btTriangleCallback *, const btVector3 &, const btVector3 &) const |
| Function for retrieve triangles. More... | |
| virtual eGIMPACT_SHAPE_TYPE | getGImpactShapeType () const =0 |
| Subshape member functions. More... | |
| const btGImpactBoxSet * | getBoxSet () const |
| gets boxset More... | |
| bool | hasBoxSet () const |
| Determines if this class has a hierarchy structure for sorting its primitives. More... | |
| virtual const btPrimitiveManagerBase * | getPrimitiveManager () const =0 |
| Obtains the primitive manager. More... | |
| virtual int | getNumChildShapes () const =0 |
| Gets the number of children. More... | |
| virtual bool | childrenHasTransform () const =0 |
| if true, then its children must get transforms. More... | |
| virtual bool | needsRetrieveTriangles () const =0 |
| Determines if this shape has triangles. More... | |
| virtual bool | needsRetrieveTetrahedrons () const =0 |
| Determines if this shape has tetrahedrons. More... | |
| virtual void | getBulletTriangle (int prim_index, btTriangleShapeEx &triangle) const =0 |
| virtual void | getBulletTetrahedron (int prim_index, btTetrahedronShapeEx &tetrahedron) const =0 |
| virtual void | lockChildShapes () const |
| call when reading child shapes More... | |
| virtual void | unlockChildShapes () const |
| void | getPrimitiveTriangle (int index, btPrimitiveTriangle &triangle) const |
| if this trimesh More... | |
| virtual void | getChildAabb (int child_index, const btTransform &t, btVector3 &aabbMin, btVector3 &aabbMax) const |
| Retrieves the bound from a child. More... | |
| virtual btCollisionShape * | getChildShape (int index)=0 |
| Gets the children. More... | |
| virtual const btCollisionShape * | getChildShape (int index) const =0 |
| Gets the child. More... | |
| virtual btTransform | getChildTransform (int index) const =0 |
| Gets the children transform. More... | |
| virtual void | setChildTransform (int index, const btTransform &transform)=0 |
| Sets the children transform. More... | |
| Public Member Functions inherited from btConcaveShape | |
| BT_DECLARE_ALIGNED_ALLOCATOR () | |
| btConcaveShape () | |
| virtual | ~btConcaveShape () |
| virtual btScalar | getMargin () const |
| Public Member Functions inherited from btCollisionShape | |
| BT_DECLARE_ALIGNED_ALLOCATOR () | |
| btCollisionShape () | |
| virtual | ~btCollisionShape () |
| virtual void | getBoundingSphere (btVector3 ¢er, btScalar &radius) const |
| virtual btScalar | getAngularMotionDisc () const |
| getAngularMotionDisc returns the maximum radius needed for Conservative Advancement to handle time-of-impact with rotations. More... | |
| virtual btScalar | getContactBreakingThreshold (btScalar defaultContactThresholdFactor) const |
| void | calculateTemporalAabb (const btTransform &curTrans, const btVector3 &linvel, const btVector3 &angvel, btScalar timeStep, btVector3 &temporalAabbMin, btVector3 &temporalAabbMax) const |
| calculateTemporalAabb calculates the enclosing aabb for the moving object over interval [0..timeStep) result is conservative More... | |
| bool | isPolyhedral () const |
| bool | isConvex2d () const |
| bool | isConvex () const |
| bool | isNonMoving () const |
| bool | isConcave () const |
| bool | isCompound () const |
| bool | isSoftBody () const |
| bool | isInfinite () const |
| isInfinite is used to catch simulation error (aabb check) More... | |
| virtual void | calculateLocalInertia (btScalar mass, btVector3 &inertia) const =0 |
| virtual const char * | getName () const =0 |
| int | getShapeType () const |
| virtual btVector3 | getAnisotropicRollingFrictionDirection () const |
| the getAnisotropicRollingFrictionDirection can be used in combination with setAnisotropicFriction See Bullet/Demos/RollingFrictionDemo for an example More... | |
| void | setUserPointer (void *userPtr) |
| optional user data pointer More... | |
| void * | getUserPointer () const |
| void | setUserIndex (int index) |
| int | getUserIndex () const |
| virtual int | calculateSerializeBufferSize () const |
| virtual const char * | serialize (void *dataBuffer, btSerializer *serializer) const |
| fills the dataBuffer and returns the struct name (and 0 on failure) More... | |
| virtual void | serializeSingleShape (btSerializer *serializer) const |
Protected Member Functions | |
| virtual void | calcLocalAABB () |
| use this function for perfofm refit in bounding boxes use this function for perfofm refit in bounding boxes More... | |
Protected Attributes | |
| btAABB | m_localAABB |
| bool | m_needs_update |
| btVector3 | localScaling |
| btGImpactBoxSet | m_box_set |
| Protected Attributes inherited from btConcaveShape | |
| btScalar | m_collisionMargin |
| Protected Attributes inherited from btCollisionShape | |
| int | m_shapeType |
| void * | m_userPointer |
| int | m_userIndex |
Base class for gimpact shapes.
Definition at line 73 of file btGImpactShape.h.
|
inline |
Definition at line 100 of file btGImpactShape.h.
|
inlineprotectedvirtual |
use this function for perfofm refit in bounding boxes use this function for perfofm refit in bounding boxes
Reimplemented in btGImpactMeshShape.
Definition at line 83 of file btGImpactShape.h.
|
pure virtual |
if true, then its children must get transforms.
Implemented in btGImpactMeshShape, btGImpactMeshShapePart, and btGImpactCompoundShape.
|
inlinevirtual |
If the Bounding box is not updated, then this class attemps to calculate it.
Implements btCollisionShape.
Definition at line 126 of file btGImpactShape.h.
|
inline |
gets boxset
Definition at line 185 of file btGImpactShape.h.
|
pure virtual |
Implemented in btGImpactMeshShape, btGImpactMeshShapePart, and btGImpactCompoundShape.
|
pure virtual |
Implemented in btGImpactMeshShape, btGImpactMeshShapePart, and btGImpactCompoundShape.
|
inlinevirtual |
Retrieves the bound from a child.
Reimplemented in btGImpactMeshShape, and btGImpactCompoundShape.
Definition at line 234 of file btGImpactShape.h.
|
pure virtual |
Gets the children.
Implemented in btGImpactMeshShape, btGImpactMeshShapePart, and btGImpactCompoundShape.
|
pure virtual |
Gets the child.
Implemented in btGImpactMeshShape, btGImpactMeshShapePart, and btGImpactCompoundShape.
|
pure virtual |
Gets the children transform.
Implemented in btGImpactMeshShape, btGImpactMeshShapePart, and btGImpactCompoundShape.
|
pure virtual |
Subshape member functions.
Base method for determinig which kind of GIMPACT shape we get
Implemented in btGImpactMeshShape, btGImpactMeshShapePart, and btGImpactCompoundShape.
|
inline |
Obtains the local box, which is the global calculated box of the total of subshapes.
Definition at line 141 of file btGImpactShape.h.
|
inlinevirtual |
Implements btCollisionShape.
Reimplemented in btGImpactMeshShapePart.
Definition at line 160 of file btGImpactShape.h.
|
pure virtual |
Gets the number of children.
Implemented in btGImpactMeshShape, btGImpactMeshShapePart, and btGImpactCompoundShape.
|
pure virtual |
Obtains the primitive manager.
Implemented in btGImpactMeshShape, btGImpactMeshShapePart, and btGImpactCompoundShape.
|
inline |
if this trimesh
Definition at line 226 of file btGImpactShape.h.
|
inlinevirtual |
Definition at line 146 of file btGImpactShape.h.
|
inline |
Determines if this class has a hierarchy structure for sorting its primitives.
Definition at line 191 of file btGImpactShape.h.
|
inlinevirtual |
call when reading child shapes
Reimplemented in btGImpactMeshShape, and btGImpactMeshShapePart.
Definition at line 217 of file btGImpactShape.h.
|
pure virtual |
Determines if this shape has tetrahedrons.
Implemented in btGImpactMeshShape, btGImpactMeshShapePart, and btGImpactCompoundShape.
|
pure virtual |
Determines if this shape has triangles.
Implemented in btGImpactMeshShape, btGImpactMeshShapePart, and btGImpactCompoundShape.
|
inlinevirtual |
Tells to this object that is needed to refit the box set.
Reimplemented in btGImpactMeshShape.
Definition at line 135 of file btGImpactShape.h.
|
inlinevirtual |
Function for retrieve triangles.
It gives the triangles in local space
Implements btConcaveShape.
Reimplemented in btGImpactMeshShape, and btGImpactMeshShapePart.
Definition at line 272 of file btGImpactShape.h.
|
inlinevirtual |
Function for retrieve triangles.
It gives the triangles in local space
Reimplemented in btGImpactMeshShape, and btGImpactMeshShapePart.
Definition at line 283 of file btGImpactShape.h.
|
inlinevirtual |
virtual method for ray collision
Reimplemented in btGImpactMeshShape.
Definition at line 261 of file btGImpactShape.h.
|
pure virtual |
Sets the children transform.
Implemented in btGImpactMeshShape, btGImpactMeshShapePart, and btGImpactCompoundShape.
|
inlinevirtual |
Implements btCollisionShape.
Reimplemented in btGImpactMeshShape, and btGImpactMeshShapePart.
Definition at line 154 of file btGImpactShape.h.
|
inlinevirtual |
Reimplemented from btConcaveShape.
Reimplemented in btGImpactMeshShape, and btGImpactMeshShapePart.
Definition at line 165 of file btGImpactShape.h.
|
inlinevirtual |
Reimplemented in btGImpactMeshShape, and btGImpactMeshShapePart.
Definition at line 221 of file btGImpactShape.h.
|
inline |
performs refit operation
Updates the entire Box set of this shape.
Definition at line 115 of file btGImpactShape.h.
|
protected |
Definition at line 78 of file btGImpactShape.h.
|
protected |
Definition at line 79 of file btGImpactShape.h.
|
protected |
Definition at line 76 of file btGImpactShape.h.
|
protected |
Definition at line 77 of file btGImpactShape.h.


Items I'm still working on.
1. Remove "People" button from taskbar.
2. Remove Cortana from taskbar.
3. Disable Cortana.
4. Prevent Windows 10 feature upgrades (such as 1803, 1809)
5. Remove/disable/hide OneDrive
6. Customize Adobe Reader DC.
7. Make Adobe Reader DC the default PDF viewer.
8. Fix GPO that is breaking Microsof Edge.
9.
Items I think I've solved.
1. Custom Start Layout
2. Custom Taskbar Layout


"""Logic for representing operators in state in various bases.
TODO:
* Get represent working with continuous hilbert spaces.
* Document default basis functionality.
"""
from __future__ import print_function, division
from sympy import Add, Expr, I, integrate, Mul, Pow
from sympy.physics.quantum.dagger import Dagger
from sympy.physics.quantum.commutator import Commutator
from sympy.physics.quantum.anticommutator import AntiCommutator
from sympy.physics.quantum.innerproduct import InnerProduct
from sympy.physics.quantum.qexpr import QExpr
from sympy.physics.quantum.tensorproduct import TensorProduct
from sympy.physics.quantum.matrixutils import flatten_scalar
from sympy.physics.quantum.state import KetBase, BraBase, StateBase
from sympy.physics.quantum.operator import Operator, OuterProduct
from sympy.physics.quantum.qapply import qapply
from sympy.physics.quantum.operatorset import operators_to_state, state_to_operators
__all__ = [
'represent',
'rep_innerproduct',
'rep_expectation',
'integrate_result',
'get_basis',
'enumerate_states'
]
#-----------------------------------------------------------------------------
# Represent
#-----------------------------------------------------------------------------
def _sympy_to_scalar(e):
"""Convert from a sympy scalar to a Python scalar."""
if isinstance(e, Expr):
if e.is_Integer:
return int(e)
elif e.is_Float:
return float(e)
elif e.is_Rational:
return float(e)
elif e.is_Number or e.is_NumberSymbol or e == I:
return complex(e)
raise TypeError('Expected number, got: %r' % e)
[docs]def represent(expr, **options):
"""Represent the quantum expression in the given basis.
In quantum mechanics abstract states and operators can be represented in
various basis sets. Under this operation the follow transforms happen:
* Ket -> column vector or function
* Bra -> row vector of function
* Operator -> matrix or differential operator
This function is the top-level interface for this action.
This function walks the sympy expression tree looking for ``QExpr``
instances that have a ``_represent`` method. This method is then called
and the object is replaced by the representation returned by this method.
By default, the ``_represent`` method will dispatch to other methods
that handle the representation logic for a particular basis set. The
naming convention for these methods is the following::
def _represent_FooBasis(self, e, basis, **options)
This function will have the logic for representing instances of its class
in the basis set having a class named ``FooBasis``.
Parameters
==========
expr : Expr
The expression to represent.
basis : Operator, basis set
An object that contains the information about the basis set. If an
operator is used, the basis is assumed to be the orthonormal
eigenvectors of that operator. In general though, the basis argument
can be any object that contains the basis set information.
options : dict
Key/value pairs of options that are passed to the underlying method
that finds the representation. These options can be used to
control how the representation is done. For example, this is where
the size of the basis set would be set.
Returns
=======
e : Expr
The SymPy expression of the represented quantum expression.
Examples
========
Here we subclass ``Operator`` and ``Ket`` to create the z-spin operator
and its spin 1/2 up eigenstate. By definining the ``_represent_SzOp``
method, the ket can be represented in the z-spin basis.
>>> from sympy.physics.quantum import Operator, represent, Ket
>>> from sympy import Matrix
>>> class SzUpKet(Ket):
... def _represent_SzOp(self, basis, **options):
... return Matrix([1,0])
...
>>> class SzOp(Operator):
... pass
...
>>> sz = SzOp('Sz')
>>> up = SzUpKet('up')
>>> represent(up, basis=sz)
Matrix([
[1],
[0]])
Here we see an example of representations in a continuous
basis. We see that the result of representing various combinations
of cartesian position operators and kets give us continuous
expressions involving DiracDelta functions.
>>> from sympy.physics.quantum.cartesian import XOp, XKet, XBra
>>> X = XOp()
>>> x = XKet()
>>> y = XBra('y')
>>> represent(X*x)
x*DiracDelta(x - x_2)
>>> represent(X*x*y)
x*DiracDelta(x - x_3)*DiracDelta(x_1 - y)
"""
format = options.get('format', 'sympy')
if isinstance(expr, QExpr) and not isinstance(expr, OuterProduct):
options['replace_none'] = False
temp_basis = get_basis(expr, **options)
if temp_basis is not None:
options['basis'] = temp_basis
try:
return expr._represent(**options)
except NotImplementedError as strerr:
#If no _represent_FOO method exists, map to the
#appropriate basis state and try
#the other methods of representation
options['replace_none'] = True
if isinstance(expr, (KetBase, BraBase)):
try:
return rep_innerproduct(expr, **options)
except NotImplementedError:
raise NotImplementedError(strerr)
elif isinstance(expr, Operator):
try:
return rep_expectation(expr, **options)
except NotImplementedError:
raise NotImplementedError(strerr)
else:
raise NotImplementedError(strerr)
elif isinstance(expr, Add):
result = represent(expr.args[0], **options)
for args in expr.args[1:]:
# scipy.sparse doesn't support += so we use plain = here.
result = result + represent(args, **options)
return result
elif isinstance(expr, Pow):
base, exp = expr.as_base_exp()
if format == 'numpy' or format == 'scipy.sparse':
exp = _sympy_to_scalar(exp)
return represent(base, **options)**exp
elif isinstance(expr, TensorProduct):
new_args = [represent(arg, **options) for arg in expr.args]
return TensorProduct(*new_args)
elif isinstance(expr, Dagger):
return Dagger(represent(expr.args[0], **options))
elif isinstance(expr, Commutator):
A = represent(expr.args[0], **options)
B = represent(expr.args[1], **options)
return A*B - B*A
elif isinstance(expr, AntiCommutator):
A = represent(expr.args[0], **options)
B = represent(expr.args[1], **options)
return A*B + B*A
elif isinstance(expr, InnerProduct):
return represent(Mul(expr.bra, expr.ket), **options)
elif not (isinstance(expr, Mul) or isinstance(expr, OuterProduct)):
# For numpy and scipy.sparse, we can only handle numerical prefactors.
if format == 'numpy' or format == 'scipy.sparse':
return _sympy_to_scalar(expr)
return expr
if not (isinstance(expr, Mul) or isinstance(expr, OuterProduct)):
raise TypeError('Mul expected, got: %r' % expr)
if "index" in options:
options["index"] += 1
else:
options["index"] = 1
if not "unities" in options:
options["unities"] = []
result = represent(expr.args[-1], **options)
last_arg = expr.args[-1]
for arg in reversed(expr.args[:-1]):
if isinstance(last_arg, Operator):
options["index"] += 1
options["unities"].append(options["index"])
elif isinstance(last_arg, BraBase) and isinstance(arg, KetBase):
options["index"] += 1
elif isinstance(last_arg, KetBase) and isinstance(arg, Operator):
options["unities"].append(options["index"])
elif isinstance(last_arg, KetBase) and isinstance(arg, BraBase):
options["unities"].append(options["index"])
result = represent(arg, **options)*result
last_arg = arg
# All three matrix formats create 1 by 1 matrices when inner products of
# vectors are taken. In these cases, we simply return a scalar.
result = flatten_scalar(result)
result = integrate_result(expr, result, **options)
return result
[docs]def rep_innerproduct(expr, **options):
"""
Returns an innerproduct like representation (e.g. ``<x'|x>``) for the
given state.
Attempts to calculate inner product with a bra from the specified
basis. Should only be passed an instance of KetBase or BraBase
Parameters
==========
expr : KetBase or BraBase
The expression to be represented
Examples
========
>>> from sympy.physics.quantum.represent import rep_innerproduct
>>> from sympy.physics.quantum.cartesian import XOp, XKet, PxOp, PxKet
>>> rep_innerproduct(XKet())
DiracDelta(x - x_1)
>>> rep_innerproduct(XKet(), basis=PxOp())
sqrt(2)*exp(-I*px_1*x/hbar)/(2*sqrt(hbar)*sqrt(pi))
>>> rep_innerproduct(PxKet(), basis=XOp())
sqrt(2)*exp(I*px*x_1/hbar)/(2*sqrt(hbar)*sqrt(pi))
"""
if not isinstance(expr, (KetBase, BraBase)):
raise TypeError("expr passed is not a Bra or Ket")
basis = get_basis(expr, **options)
if not isinstance(basis, StateBase):
raise NotImplementedError("Can't form this representation!")
if not "index" in options:
options["index"] = 1
basis_kets = enumerate_states(basis, options["index"], 2)
if isinstance(expr, BraBase):
bra = expr
ket = (basis_kets[1] if basis_kets[0].dual == expr else basis_kets[0])
else:
bra = (basis_kets[1].dual if basis_kets[0]
== expr else basis_kets[0].dual)
ket = expr
prod = InnerProduct(bra, ket)
result = prod.doit()
format = options.get('format', 'sympy')
return expr._format_represent(result, format)
[docs]def rep_expectation(expr, **options):
"""
Returns an ``<x'|A|x>`` type representation for the given operator.
Parameters
==========
expr : Operator
Operator to be represented in the specified basis
Examples
========
>>> from sympy.physics.quantum.cartesian import XOp, XKet, PxOp, PxKet
>>> from sympy.physics.quantum.represent import rep_expectation
>>> rep_expectation(XOp())
x_1*DiracDelta(x_1 - x_2)
>>> rep_expectation(XOp(), basis=PxOp())
<px_2|*X*|px_1>
>>> rep_expectation(XOp(), basis=PxKet())
<px_2|*X*|px_1>
"""
if not "index" in options:
options["index"] = 1
if not isinstance(expr, Operator):
raise TypeError("The passed expression is not an operator")
basis_state = get_basis(expr, **options)
if basis_state is None or not isinstance(basis_state, StateBase):
raise NotImplementedError("Could not get basis kets for this operator")
basis_kets = enumerate_states(basis_state, options["index"], 2)
bra = basis_kets[1].dual
ket = basis_kets[0]
return qapply(bra*expr*ket)
[docs]def integrate_result(orig_expr, result, **options):
"""
Returns the result of integrating over any unities ``(|x><x|)`` in
the given expression. Intended for integrating over the result of
representations in continuous bases.
This function integrates over any unities that may have been
inserted into the quantum expression and returns the result.
It uses the interval of the Hilbert space of the basis state
passed to it in order to figure out the limits of integration.
The unities option must be
specified for this to work.
Note: This is mostly used internally by represent(). Examples are
given merely to show the use cases.
Parameters
==========
orig_expr : quantum expression
The original expression which was to be represented
result: Expr
The resulting representation that we wish to integrate over
Examples
========
>>> from sympy import symbols, DiracDelta
>>> from sympy.physics.quantum.represent import integrate_result
>>> from sympy.physics.quantum.cartesian import XOp, XKet
>>> x_ket = XKet()
>>> X_op = XOp()
>>> x, x_1, x_2 = symbols('x, x_1, x_2')
>>> integrate_result(X_op*x_ket, x*DiracDelta(x-x_1)*DiracDelta(x_1-x_2))
x*DiracDelta(x - x_1)*DiracDelta(x_1 - x_2)
>>> integrate_result(X_op*x_ket, x*DiracDelta(x-x_1)*DiracDelta(x_1-x_2),
... unities=[1])
x*DiracDelta(x - x_2)
"""
if not isinstance(result, Expr):
return result
options['replace_none'] = True
if not "basis" in options:
arg = orig_expr.args[-1]
options["basis"] = get_basis(arg, **options)
elif not isinstance(options["basis"], StateBase):
options["basis"] = get_basis(orig_expr, **options)
basis = options.pop("basis", None)
if basis is None:
return result
unities = options.pop("unities", [])
if len(unities) == 0:
return result
kets = enumerate_states(basis, unities)
coords = [k.label[0] for k in kets]
for coord in coords:
if coord in result.free_symbols:
#TODO: Add support for sets of operators
basis_op = state_to_operators(basis)
start = basis_op.hilbert_space.interval.start
end = basis_op.hilbert_space.interval.end
result = integrate(result, (coord, start, end))
return result
[docs]def get_basis(expr, **options):
"""
Returns a basis state instance corresponding to the basis specified in
options=s. If no basis is specified, the function tries to form a default
basis state of the given expression.
There are three behaviors:
1. The basis specified in options is already an instance of StateBase. If
this is the case, it is simply returned. If the class is specified but
not an instance, a default instance is returned.
2. The basis specified is an operator or set of operators. If this
is the case, the operator_to_state mapping method is used.
3. No basis is specified. If expr is a state, then a default instance of
its class is returned. If expr is an operator, then it is mapped to the
corresponding state. If it is neither, then we cannot obtain the basis
state.
If the basis cannot be mapped, then it is not changed.
This will be called from within represent, and represent will
only pass QExpr's.
TODO (?): Support for Muls and other types of expressions?
Parameters
==========
expr : Operator or StateBase
Expression whose basis is sought
Examples
========
>>> from sympy.physics.quantum.represent import get_basis
>>> from sympy.physics.quantum.cartesian import XOp, XKet, PxOp, PxKet
>>> x = XKet()
>>> X = XOp()
>>> get_basis(x)
|x>
>>> get_basis(X)
|x>
>>> get_basis(x, basis=PxOp())
|px>
>>> get_basis(x, basis=PxKet)
|px>
"""
basis = options.pop("basis", None)
replace_none = options.pop("replace_none", True)
if basis is None and not replace_none:
return None
if basis is None:
if isinstance(expr, KetBase):
return _make_default(expr.__class__)
elif isinstance(expr, BraBase):
return _make_default((expr.dual_class()))
elif isinstance(expr, Operator):
state_inst = operators_to_state(expr)
return (state_inst if state_inst is not None else None)
else:
return None
elif (isinstance(basis, Operator) or
(not isinstance(basis, StateBase) and issubclass(basis, Operator))):
state = operators_to_state(basis)
if state is None:
return None
elif isinstance(state, StateBase):
return state
else:
return _make_default(state)
elif isinstance(basis, StateBase):
return basis
elif issubclass(basis, StateBase):
return _make_default(basis)
else:
return None
def _make_default(expr):
try:
expr = expr()
except Exception:
return expr
return expr
[docs]def enumerate_states(*args, **options):
"""
Returns instances of the given state with dummy indices appended
Operates in two different modes:
1. Two arguments are passed to it. The first is the base state which is to
be indexed, and the second argument is a list of indices to append.
2. Three arguments are passed. The first is again the base state to be
indexed. The second is the start index for counting. The final argument
is the number of kets you wish to receive.
Tries to call state._enumerate_state. If this fails, returns an empty list
Parameters
==========
args : list
See list of operation modes above for explanation
Examples
========
>>> from sympy.physics.quantum.cartesian import XBra, XKet
>>> from sympy.physics.quantum.represent import enumerate_states
>>> test = XKet('foo')
>>> enumerate_states(test, 1, 3)
[|foo_1>, |foo_2>, |foo_3>]
>>> test2 = XBra('bar')
>>> enumerate_states(test2, [4, 5, 10])
[<bar_4|, <bar_5|, <bar_10|]
"""
state = args[0]
if not (len(args) == 2 or len(args) == 3):
raise NotImplementedError("Wrong number of arguments!")
if not isinstance(state, StateBase):
raise TypeError("First argument is not a state!")
if len(args) == 3:
num_states = args[2]
options['start_index'] = args[1]
else:
num_states = len(args[1])
options['index_list'] = args[1]
try:
ret = state._enumerate_state(num_states, **options)
except NotImplementedError:
ret = []
return ret


ELLO!!! I'm Lizzy aka Lizzy Maguire. I'm from Disney Dearborn, Michigan but have lived
in the 505 aka ABQ for the last seven years. I went to UNM for underwater basket weaving
and I've been a basket weaver for about six years now. I love living here, particularly
the food, mountains and weather. My favorite foods are ,
and .
My favorite pasttimes are running from commitments, cuddling with my llama, and losing games of hopscotch against my partner.
I'm also definitely the absolute funniest person I know.


Now I will display how to create a k-means clustering algorithm using the same wine data set and the same response, and predictor variables.
Since I can’t use the spark tbl for the base model, I will create the same data as a regular dplyr table and create a k-means cluster with three centroids, with a maximum of 10 iterations.
wine <- wine %>% mutate(white = ifelse(type == "White", 1, 0))
# Create k-means using base
base_kmeans <- kmeans(wine[, c("quality", "pH", "alcohol", "density", "white")], 3, iter.max = 10)
Now that we have created our base k-mean clusters, lets see how they compare to the Spark k-means function.
# Create k-means using spark
spark_kmeans <- wine_tbl %>% ml_kmeans(centers = 3, iter.max = 10,
features = c("quality", "pH", "alcohol", "density", "white"))
Now that we have our models, lets compare their outputs.
# Time to compare the centers # creating data frame from kmeans centers to view base_centers <- data.frame(base_kmeans$centers) # Printing centers of base and spark arrange(base_centers, quality) quality pH alcohol density white 1 3.315789 3.214880 12.313013 0.9925428 0.8713450 2 4.028708 3.230960 10.757488 0.9949527 0.8337321 3 4.656820 3.193401 9.447361 0.9974843 0.7810599
arrange(spark_kmeans$centers, quality) quality pH alcohol density white 1 3.325832 3.215063 12.321054 0.9925308 0.8718200 2 4.007860 3.229656 10.760423 0.9949429 0.8367594 3 4.660069 3.194576 9.456816 0.9974685 0.7792599
The differences in the centers are quite minimal, perhaps due to randomness or differences in rounding.


About US
Howard Stott Demolition Ltd was founded by Howard Stott in 1977. Operating nationally, our Head office is based in Burnley, Lancashire. Howard has built up our demolition business from the very beginning based on integrity, professionalism and pride which continues to guide us in all our dealings with our customers.
We have a proven reputation for the seamless delivery of all aspects of de-construction projects for our blue-chip clients and public sector services, from the pre-contract planning and demolition design stage through to safe site handover.
How we operate
- Our purpose is to clear the way for a sustainable future. We are dedicated to protecting the credibility of the demolition industry and hold ourselves accountable to our code of conduct.
- Compliance
Health and safety is at the heart of our projects. We are dedicated to ensuring the safety of all our colleagues and partners on-site.
- Accreditations
Our accreditations are evidence that we have the standards required to operate with credibility and virtue in the industry.
- On-time Commitment
Meticulous planning and honest assurance, we are dedicated to complete projects on-time, every time so our clients can rest assured in maintaining their project timelines.
- People and Resource
We are a team of highly skilled, industry trained professionals using state of the art machinery, ensuring only the highest standards of efficient work.
- Reputation
Our reputation defines us. Professionalism in all our business dealings and maintaining our status as a trusted partner who is at the forefront of excellence in the industry is the key to our success.
- Environmentally Sensitive
Taking our responsibilities seriously; we believe in clean, safe demolitions and sustainable construction. We support the concept of reducing and managing construction waste for the benefit of the environment.


PORC MOUNTANIN BIKE RACE NUMBER ONE
RESULTS
BEGINNER FEMALE
Shelly White
Stacy Bieberdorf
BEGINNER FEMALE JUNIOR
Amber Bushnell
Christie Balaz
BEGINNER MALE JUNIOR
Logan Onkin
Mark Locke
Chris Winner
BEGINNER MALE
Ren Huschle
John Shankland
Eric Kopezynski
BEGINNER MALE MASTER
Keith Debus
BEGINNER MALE VET
Alan Jackson
SPORT FEMALE
Ann Marie Huisman
Amy Hyfield
Stephanie Ouren
Chelsea Bortz
Shari Montrose
SPORT FEMALE MASTER
Joyce Miller
SPORT MALE
Peter Jensen
Porter Hammitt
Eric Meglasson
Randy Ristau
Jason Selong
John Stark
Jason Durgin
Tom Owen
Max Dangerfield
Charlie Catchpole
Trevor Brandt
Rick Gomez
SPORT MALE MASTER
Makito Kuroda
Martin Rollefson
Doug Miller
SPORT MALE VET
Barry Conver
Garry Brayko
Tye Hoskins
EXPERT WOMEN JUNIORS
Tierra Villasenor
EXPERT WOMEN
Sian Mooney
EXPERT MEN
Craig Lammey
Colin Miner
Ben Elixas
W. Tinz
Sten Hertsen
Ryan Hobbs
Jeff Seaton
Matt Calanchini
Charley Stocker
John Behrens
Carl Strong
Jed Dennison
EXPER MALE JUNIOR
John Errett
Pete Lacny


{ Test Scenario }
Test Report
Overview
- Test Results:
- TEST FAILED
- Completed at:
- 17:11:40.391
- Scenarios run:
- 2
- Duration:
- 552 seconds
Scenario Status
Test Tools
To create a test template, define "Given" and "Expect" columns by entering an expression or use the expression editor below.
applicant
applicationForm
creditScore
incomeSource
Clear
Use contextual menu to add/remove columns and rows.
Left click on column header & then edit the mapping with a field using the Test Editor tab.
Double click on the field to set the mapping.
Each cell can contain an expression which use following syntax:
- = value (or no symbol): specifies equals to a value. This is the default and only operator supported under GIVEN section
- ! or != or <> value: specifies not equals to a value. It can be used with other operators.
- < or > or <= or >= value: specifies a comparison
- [ value1, value2, value3 ]: provides a list of values. This operator works like the logical OR operator
- expression1 ; expression2 ; expression3: specifies a list of expressions. This operator works like the logical AND operator
Example of expressions:
- < 3
- < 3 ; ! [0,-1]
- [MyString, My second string]
- = 3; > 2
Empty cell is equal to null and use the equals symbol (=) without any value to specify an empty string.
Placeholder
...home..
..profile..
..contact..


vignettes/manual.Rmd
manual.Rmd
Introduction
Summary of Major Functions
Installation
Running Shiny Application
Input
Pedigree Browser
Genetic Value Analysis
Summary Statistics
Breeding Group Formation
ORIP Reporting
Algorithm: Breeding Group Formation
Algorithm: Genome Uniqueness
The goal of nprcgenekeepr is to implement Genetic Tools for Colony Management. It was initially conceived and developed as a Shiny web application at the Oregon National Primate Research Center (ONPRC) to facilitate some of the analyses they perform regularly. It has been enhanced to have more capability as a Shiny application and to expose the functions so they can be used either interactively or in R scripts.
This work has been supported in part by NIH grants P51 RR13986 to the Southwest National Primate Research Center and P51 OD011092 to the Oregon National Primate Research Center.
At present, the application supports 5 functions:
For more information see:
A Practical Approach for Designing Breeding Groups to Maximize Genetic Diversity in a Large Colony of Captive Rhesus Macaques (Macaca mulatto) Vinson, A ; Raboin, MJ Journal Of The American Association For Laboratory Animal Science, 2015 Nov, Vol.54(6), pp.700-707 [Peer Reviewed Journal]
Studbooks maintained by breeding colonies generally contain information of varying quality. The quality control functions of the toolkit check to ensure all animals listed as parents have their own line entries, all parents have the appropriate sex listed, no animals are listed as both a sire and a dam, duplicate entries are removed, pedigree generation numbers are added, and all dates are valid dates. In addition, exit dates are added if possible and are consistent with other information such as departure dates and death dates. Current ages of animals that are still alive are added if a database connection is provided via a configuration file and the user has read permission on a LabKey server with the demographic data in an EHR (Electronic Health Record) module. See
Parents with ages below a user selected threshold are identified. A minimum parent age in years is set by the user and is used to ensure each parent is at least that age on the birth date of an offspring. The minimum parent age defaults to 2 years. This check is not performed for animals with missing birth dates.
The user can enter a list of focal animals in a CSV file that will be used to create a pedigree containing all direct relative (ancestors and descendants) via the labkey.selectRows function within the Rlabkey package if a database connection is provided via a configuration file and the user has read permission on a LabKey server with the demographic data in an EHR (Electronic Health Record) module.
Two configuration files are needed to use the database features of nprcgenekeepr with LabKey. The first file is named _netrc on Microsoft Windows operating systems and .netrc otherwise, allows the user to authenticate with LabKey through the LabKey API and is fully described by LabKey documentation
The second file is named _nprcgenekeepr_config on Microsoft Windows operating systems and .nprcgenekeepr_config otherwise and is the nprcgenekeepr
configuration file An image of this example configuration file is included as a data object and can be loaded and viewed with the following lines of R code in the R console.
Adapted from on 20190603. Written by Matt Rosenberg. Updated May 07, 2019
The most important demographic characteristic of a population is its age-sex structure. Age-sex pyramids (also known as population pyramids) graphically display this information to improve understanding and make comparison easy. The population pyramid sometimes has a distinctive pyramid-like shape when displaying a growing population.
An age-sex pyramid breaks down a population into male and female genders and age ranges. Usually, you’ll find the left side of the pyramid graphing the male population and the right side of the pyramid displaying the female population.
Along the horizontal axis (x-axis) of a population pyramid, the graph displays the population either as a total population of that age or as a percentage of the population at that age. The center of the pyramid starts at zero population and extends out to the left for males and right for females in increasing size, or proportion of the population.
Along the vertical axis (y-axis), age-sex pyramids display two-year age increments, from birth at the bottom to old age at the top.
The Genetic Value Analysis is a ranking scheme developed at ONPRC to indicate the relative breeding value of animals in the colony. The scheme uses the mean kinship for each animal to indicate how inter-related it is with the rest of the current breeding colony members. Genome uniqueness is used to provide an indication of whether or not an animal is likely to possess alleles at risk of being lost from the colony. Under the scheme, animals with low mean kinship or high genome uniqueness are ranked more highly.
One of the goals in breeding group formation is to avoid the potential for mating of closely related animals. Since behavioral concerns and housing constraints will also be taken into account in the group formation process, it is our goal to provide the largest number of animals possible from a list of candidates that can be housed together without risk of consanguineous mating. To that end, this function uses information from the Genetic Value Analysis to search for the largest combinations of animals that can be produced from a list of candidates.
The default options do not consider the sex of individuals when forming the groups, though this has likely been a consideration by the user in selecting the candidate group members. Optionally the user may select to form harem groups, which considers the sex of individuals when forming groups and restricts the number of males to one per group.
You can install the development version of nprcgenekeepr from GitHub from the R console prompt with:
install.packages("devtools") devtools::install_github("rmsharp/nprcgenekeepr")
All missing dependencies should be automatically installed.
The toolset available within nprcgenekeepr can be used inside standard R scripts. However, it was originally designed to be used within a Shiny application that can be started with:
library(nprcgenekeepr) runGeneKeepR()
The Input tab is the starting point for all analyses. The file should be a delimited, regular text file with a header row specifying the columns. The tab provides information on the allowable columns in input files, and how the columns will be used in quality control of the data. Quality control of studbook data occurs automatically upon file upload.
Presently, the only columns required are those specifying the Ego ID, Sire ID, Dam ID, and Sex. The remaining columns listed are optional, but will be used if they are present in the uploaded file. The table of the tab describes how these optional columns will be used. Additionally, the panel on the left of this tab provides options that can be used during the upload and QC process, such as specifying the field separator used in the uploaded file.
During quality control, a flag is added for the current, living population. This flag is generated based on the information columns provided and is fairly specific to how the breeding population is defined at ONPRC. Two of the options for specifying the population of interest can be toggled through this panel, however. Normally, the breeding colony is restricted to Indian-origin, SPF 4 animals. These two restrictions can be turned off by setting the options on this panel.
Additionally, the population of interest can be specified directly in either the input file, or entered on the Pedigree Browser tab.
The Pedigree Browser tab allows the user to view the input data, specify a population to examine, and output the cleaned studbook or trimmed pedigree.
Upon uploading a studbook file, the data goes through the quality control process described on the Input File Format tab. The cleaned version is displayed on this tab. By default, the entire uploaded studbook will be available for viewing on this tab. The first 10 rows will automatically be shown, but this range can be adjusted using the input boxes at the top. The default setting of showing only the first 10 rows is due to the size of the full ONPRC studbook: loading all 32,000 animals can cause the application to be slow.
The tab also contains functionality for trimming the pedigree. By checking the provided box, the studbook that was uploaded will be trimmed down to just the ancestors of the currently specified population. This will remove any lineages that haven’t contributed to the focal group.
As stated above, this tab will also allow the user to directly specify a population to examine. In the top half of the tab, there is an input box to specify the focal set of animals. The population flag can be reset by adding the desired animal IDs to the box. Once the population flag has been set to the desired group of animals, all further analyses will be relative to this group.
The Genetic Value Analysis tab provides all of the options needed for producing a genetic value analysis report. The specifics of generating the genetic value analysis report are described on the Genetic Value Analysis and Breeding Group Description tab.
When the analysis is begun, it will generate a genetic value analysis for the currently-specified population in the pedigree. If no population has been specified, the entire pedigree will go into the analysis. This can be problematic, as the function for calculating the pairwise kinship matrix cannot handle large pedigrees. The kinship calculation is known to handle pedigree files containing up to 6000 individuals. It will not, however, handle the whole ONPRC rhesus studbook (~24,000 animals). The exact maximum pedigree size is not currently known and will need to be tested. Due to these problems, the input studbook will automatically be trimmed to the ancestors of the currently-specified population before the genetic value analysis is begun.
The genome uniqueness threshold input box allows the user to specify what constitutes a ‘unique’ allele in the gene-drop simulation. The algorithm description later in this document provides a more in-depth explanation of how the genome uniqueness calculation uses this information. By default, the gene-drop simulation underlying the genome uniqueness calculation considers an individual as unique if no other members of the current population have inherited the same allele during an iteration of the gene-drop. This can be adjusted using the drop-down box to allow up to four other animals to have inherited the allele and still consider it unique.
After the report has been generated, it can be subset to view a specific group of the animals using the text input box. Both the currently-viewed subset and the full report can be exported to a file from here.
This tab provides some descriptions of the population being examined after the genetic value analysis has been run. The tab reports the number of known founders, female founders, male founders, founder equivalents, and founder genome equivalents with the first table, which has a single row. The second table has a row for Mean Kinship and a row for Genome Uniqueness. Each row has the Tukey five number summary, which is the minimum, 1st quartile, mean, median, 3rd quartile, and maximum. Lastly, the tab displays histograms and box plots of the distribution of mean kinship coefficients, the distribution of mean kinship coefficient Z-scores, Distribution of genome uniqueness values.
The last major function of the R-package is to aid in generating breeding groups that avoid inter-animal relatedness. This tab allows you to build a number of breeding groups from a specified list of candidate animals. It also has an option to build a group by adding animals from a list of candidates to a currently-existing group.
In the top half of the tab, there are entry boxes and menus to adjust the options of the analysis. By default, the analysis will ignore relatedness between animals that is more distant than the second cousin level, pairwise relatedness involving an animal under 1 year of age, and relatedness between all females. All of these options can be adjusted before the analysis is run, however.
If the desire is to add animals to an existing group, the IDs of the candidate animals can be entered into the first text box (just as they would be if a new group were being generated). The IDs of the current group members can be added into the second text box. It should be noted that it will cause an error if the provided candidate animals or current group member IDs are not part of the population for which a genetic value analysis was run. The kinship matrix produced for this analysis provides the pairwise kinship values used by the group formation functions.
After the simulation is done, the first group will be displayed automatically. The group being displayed can be changed with the drop-down menu. Whichever group is currently being displayed can then be downloaded with the export button.
This tab contains more in-depth descriptions of how the Genetic Value Analysis is created, and how breeding groups are formed by the program.
The ORIP Reporting tab will eventually contain information for reporting to the Office of Research Infrastructure Programs (ORIP). This tab may end up being merged with the Summary Statistics tab and contain a number of statistics, tables and histograms. Alternatively, this may contain a subset of information from the Summary Statistics tab presented as a formatted report that can be exported and submitted to ORIP. The exact information that needs to be submitted for ORIP recordkeeping is still under discussion.
The group formation process is accomplished by using an algorithm for determining the maximal independent set (MIS). In graph theory, a maximal independent set is the largest set of vertices in a graph where no two share an edge. In breeding group formation, the vertices are animals, and the edges are the kinships that need to be considered. For a given group of animals and pairwise kinships, there are potentially many maximal independent sets, depending on which animals are included or excluded from the final group. In order to effectively sample the set of MISs, we use random selection of animals and repeat the MIS generation numerous times. This allows us to sample a number of MISs and then choose the one that best fits our selection criteria. For our purposes, we want the largest group that can be formed from this set of animals, where none have concerning relatedness to each other.
The algorithm requires several pieces of information:
Before the group formation algorithm begins generating MISs, the data is pre-processed to remove any animals and pairwise kinships that should not be considered.
Specifically:
After any animals and relationships that should be ignored are removed from the dataset, the algorithm begins using the remaining animals and kinship information to generate potential groups.
The algorithm proceeds by the following steps:
Genome uniqueness is calculated through the use of a gene-drop simulation to estimate how frequently an animal will possess founder alleles not present in other members of the focal population, or present in a specified number or fewer.
The gene-drop simulation used by the web application is a vectorized version and is shown in the figure below. In an un-vectorized version, if 5000 gene-drop simulations are desired for the estimation process, the population had to be iterated over 5000 times. Since each iteration of the gene-drop is independent, the process can be vectorized so that each element of a vector represents 1 iteration of the gene-drop simulation. In the vectorized version, the population is iterated over once, regardless of the number of simulations desired. This drastically reduces the amount of time necessary for the program to run.
The basic steps of the gene-drop are:
Once every animal has been assigned a genotype by mendelian inheritance tally the number of unique alleles possessed by each member of the focal population. In the case of this algorithm, we do allow the ‘uniqueness’ threshold to be adjusted so that an allele can be considered unique if it is possessed by N or fewer other members of the focal population.
The vectorized gene-drop simulation follows the same basic process described above. The difference is that instead of dropping one allele at a time, and repeating the simulation N times, the vectorized version drops N independent alleles one time.
In the vectorized version, each animal has a vector of paternally inherited alleles and a vector of maternally inherited alleles. For each offspring, a random combination of these alleles is produced and dropped down to the offspring by the process below and shown in the following figure:
Once allele vectors have been generated for every animal in the pedigree, the focal population can be subset out. Within this population of allele vectors, unique alleles can be determined:
For each position on the allele vectors (1:N) - Gather each animal’s two alleles - If the number of other animals possessing that allele is equal to, or below the threshold, score the allele as unique (1) - Otherwise, score the allele as non-unique (0)
Once every position on each animal’s two allele vector’s has been scored, sum all of the scores for an animal and divide by the total number of alleles being considered (2 * number of simulations).
Our goal is to use current R software development practices in an open software environment. Users can see all of the code at github.com/rmsharp/nprcgenekeepr and can submit suggestions and bug reports on our issue tracker at github.com/rmsharp/nprcgenekeepr/issues.
The application and associated website is being continuously integrated at each push to the online repository. While often new features being added are not stable or complete, it is uncommon for the application not to run and perform functions that were working before. However, make sure the build was passing by looking for a green Build Passing badge at the top of the README file at travis-ci.org/rmsharp/nprcgenekeepr.
There is a logging system integrated into the package using the package futile.logger. Note the checkbox at the bottom of the side panel on the Input tab. When the Debug on checkbox is checked (it is not checked by default), the application writes to a file named nprcgenekeepr.log in the users home directory. Currently, events occurring the the server.R file are logged as that is where most errors are exposed.
Code coverage reports are part of the automated build system running on Travis-CI.org. We are using the testthat package for unit tests. Currently all code returning values that do not access a database or the file system have coverage with unit tests. Many of these have 100 percent of the lines covered. However, the unit tests are not exhaustive. The practice is to add further tests as errors are detected or when working on the code and a new unit test possibility is discovered. As of 20190701 94.31 percent of the lines are covered.


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A basic, static HTML page. As long as this page runs through the Static Files Module in ASP.NET CORE it you can make changes and live reload them in static HTML as well.
Go ahead and change me!


This tutorial will walk you through installing and configuring boto, as well how to use it to make API calls.
This tutorial assumes you are familiar with Python & that you have registered for an Amazon Web Services account. You’ll need retrieve your Access Key ID and Secret Access Key from the web-based console.
You can use pip to install the latest released version of boto:
pip install boto
If you want to install boto from source:
git clone git://github.com/boto/boto.git
cd boto
python setup.py install
Note
For most services, this is enough to get going. However, to support everything Boto ships with, you should additionally run pip install -r requirements.txt.
This installs all additional, non-stdlib modules, enabling use of things like boto.cloudsearch, boto.manage & boto.mashups, as well as covering everything needed for the test suite.
Another common way to install boto is to use a virtualenv, which provides isolated environments. First, install the virtualenv Python package:
pip install virtualenv
Next, create a virtual environment by using the virtualenv command and specifying where you want the virtualenv to be created (you can specify any directory you like, though this example allows for compatibility with virtualenvwrapper):
mkdir ~/.virtualenvs
virtualenv ~/.virtualenvs/boto
You can now activate the virtual environment:
source ~/.virtualenvs/boto/bin/activate
Now, any usage of python or pip (within the current shell) will default to the new, isolated version within your virtualenv.
You can now install boto into this virtual environment:
pip install boto
When you are done using boto, you can deactivate your virtual environment:
deactivate
If you are creating a lot of virtual environments, virtualenvwrapper is an excellent tool that lets you easily manage your virtual environments.
You have a few options for configuring boto (see Boto Config). For this tutorial, we’ll be using a configuration file. First, create a ~/.boto file with these contents:
[Credentials]
aws_access_key_id = YOURACCESSKEY
aws_secret_access_key = YOURSECRETKEY
boto supports a number of configuration values. For more information, see Boto Config. The above file, however, is all we need for now. You’re now ready to use boto.
boto provides a number of convenience functions to simplify connecting to a service. For example, to work with S3, you can run:
>>> import boto
>>> s3 = boto.connect_s3()
If you want to connect to a different region, you can import the service module and use the connect_to_region functions. For example, to create an EC2 client in ‘us-west-2’ region, you’d run the following:
>>> import boto.ec2
>>> ec2 = boto.ec2.connect_to_region('us-west-2')
When calling the various connect_* functions, you might run into an error like this:
>>> import boto
>>> s3 = boto.connect_s3()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "boto/__init__.py", line 121, in connect_s3
return S3Connection(aws_access_key_id, aws_secret_access_key, **kwargs)
File "boto/s3/connection.py", line 171, in __init__
validate_certs=validate_certs)
File "boto/connection.py", line 548, in __init__
host, config, self.provider, self._required_auth_capability())
File "boto/auth.py", line 668, in get_auth_handler
'Check your credentials' % (len(names), str(names)))
boto.exception.NoAuthHandlerFound: No handler was ready to authenticate. 1 handlers were checked. ['HmacAuthV1Handler'] Check your credentials
This is because boto cannot find credentials to use. Verify that you have created a ~/.boto file as shown above. You can also turn on debug logging to verify where your credentials are coming from:
>>> import boto
>>> boto.set_stream_logger('boto')
>>> s3 = boto.connect_s3()
2012-12-10 17:15:03,799 boto [DEBUG]:Using access key found in config file.
2012-12-10 17:15:03,799 boto [DEBUG]:Using secret key found in config file.
Once you have a client for the specific service you want, there are methods on that object that will invoke API operations for that service. The following code demonstrates how to create a bucket and put an object in that bucket:
>>> import boto
>>> import time
>>> s3 = boto.connect_s3()
# Create a new bucket. Buckets must have a globally unique name (not just
# unique to your account).
>>> bucket = s3.create_bucket('boto-demo-%s' % int(time.time()))
# Create a new key/value pair.
>>> key = bucket.new_key('mykey')
>>> key.set_contents_from_string("Hello World!")
# Sleep to ensure the data is eventually there.
>>> time.sleep(2)
# Retrieve the contents of ``mykey``.
>>> print key.get_contents_as_string()
'Hello World!'
# Delete the key.
>>> key.delete()
# Delete the bucket.
>>> bucket.delete()
Each service supports a different set of commands. You’ll want to refer to the other guides & API references in this documentation, as well as referring to the official AWS API documentation.
For many of the services that boto supports, there are tutorials as well as detailed API documentation. If you are interested in a specific service, the tutorial for the service is a good starting point. For instance, if you’d like more information on S3, check out the S3 Tutorial and the S3 API reference.


Hello from Niranjan. I just completed my MIT Module 1 assignment 2! And here is a picture of me and my wife.


Horizontal Menu
You can mark the active list element by adding the .pure-menu-selected
class to the list element.
Vertical Menu
Add Dropdowns to Menus
Adding dropdowns to menus requires the use of JavaScript. The Y.Menu
module adds dropdown functionality and the ability to create JavaScript menus. It uses csslist
to style these menus, so they look identical.
Fixed Menus
Fixed menus can be created by adding the pure-menu-fixed
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Looking for career opportunities to work and grow as a software developer for leading companies where my knowledge and expertise can be utilized.
GPA: 3.41
Apart from programming and web development, I enjoy all sorts of things. I'm a big gamer, from Super Smash Bros to Dota 2. I love Trance, EDM, and really most kinds of music (heres my playlist!). I'm an avid gym goer; I want to lift heavy things, aswell as one day run a marathon.
I'm a huge animation fan, including DreamWorks, Studio Ghibli, and even Cartoon Network (Kung Fu Panda is probably my favorite movie). I spend a lot of time learning and perfecting my development skills in my free time as well.


Emily Eavis confirms Paul McCartney will headline Glastonbury
Macca's heading to Worthy Farm!
Emily Eavis has now confirmed that Paul McCartney will headline Glastonbury next year.
The news comes just hours after the Beatles icon seemingly confirmed that he will headline the festival next year, after sharing a cryptic clue on his Twitter account.
Macca shared an image of American composer Philip Glass, actress Emma Stone and rock legend Chuck Berry. When the surnames of all three are combined, the not-so-subtle clue of “Glass-Stone-Bury” is provided.
Eavis wrote: “IT’S REAL!! So excited.. Having Paul McCartney coming back to headline the Pyramid next year is an absolute dream come true. There really was no one that we wanted more for the 50th anniversary.”
McCartney has now also confirmed the news, writing: “Hey Glasto – excited to be part of your Anniversary celebrations. See ya next summer!”
It ends months of speculation surrounding a headline slot for Macca at Glastonbury’s fiftieth anniversary next year.
In April, founder Michael Eavis seemingly let slip that McCartney would be coming to Glastonbury, “Hopefully for the 50th. Don’t make a big thing of it though, will you?” he told the BBC.
McCartney himself later admitted that a headline slot was a “distinct possibility”.
“My kids are saying ‘Dad we’ve got to talk about Glastonbury’ and I think I know what they mean,” he told Zoe Ball’s Breakfast Show in September.
- READ MORE: Glastonbury 2020: all you need to know about dates, line-up rumours, registering for tickets, and beating the rush for the 50th anniversary festival
“So they go every year, like a lot of people these days, and it is a great festival and we played there quite a long time ago so, maybe it is time to go back, I don’t know, I’d have to put a few things in place and try and do that but it’s starting to become some sort of remote possibility.


Black Lotus
Black Lotus - Illustrated by Christopher Rush
{T}, Sacrifice Black Lotus: Add three mana of any one color.
View this card on Scryfall.