UAUtils
Objective-C
@interface UAUtils : NSObject
Swift
class UAUtils : NSObject
The UAUtils object provides an interface for utility methods.
-
A utility method that compares two floating points and returns
YES
if the difference between them is less than or equal to the absolute value of the specified accuracy.Declaration
Objective-C
+ (BOOL)float:(CGFloat)float1 isEqualToFloat:(CGFloat)float2 withAccuracy:(CGFloat)accuracy;
Swift
class func float(_ float1: CGFloat, isEqualTo float2: CGFloat, withAccuracy accuracy: CGFloat) -> Bool
-
Returns nil if string is empty or nil, otherwise returns string.
Declaration
Objective-C
+ (nullable NSString *)nilIfEmpty:(nullable NSString *)str;
Swift
class func nilIfEmpty(_ str: String?) -> String?
Parameters
str
The string to check.
Return Value
The input NSString, or nil if the input string is empty.
-
Get the device model name. e.g., iPhone3,1
Declaration
Objective-C
+ (nonnull NSString *)deviceModelName;
Swift
class func deviceModelName() -> String
Return Value
The device model name.
-
Returns a basic auth header string for app auth.
The return value takes the form of:
Basic [Base64 Encoded "username:password"]
Declaration
Objective-C
+ (nonnull NSString *)appAuthHeaderString;
Swift
class func appAuthHeaderString() -> String
Return Value
An HTTP Basic Auth header string value for the app’s credentials.
-
Returns a basic auth header string.
Declaration
Objective-C
+ (nonnull NSString *)authHeaderStringWithName:(nonnull NSString *)username password:(nonnull NSString *)password;
Swift
class func authHeaderString(withName username: String, password: String) -> String
Parameters
username
The username.
password
The password.
Return Value
An HTTP Basic Auth header string value for the provided credentials in the form of:
Basic [Base64 Encoded "username:password"]
-
Returns the short bundle version string.
Declaration
Objective-C
+ (nullable NSString *)bundleShortVersionString;
Swift
class func bundleShortVersionString() -> String?
Return Value
A short bundle version string value.
-
Declaration
Objective-C
+ (nonnull NSString *)pluralize:(int)count singularForm:(nonnull NSString *)singular pluralForm:(nonnull NSString *)plural;
Swift
class func pluralize(_ count: Int32, singularForm singular: String, pluralForm plural: String) -> String
-
Creates an ISO dateFormatter (UTC) with the following attributes: locale set to ‘en_US_POSIX’, timestyle set to ‘NSDateFormatterFullStyle’, date format set to ‘yyyy-MM-dd HH:mm:ss’.
Declaration
Objective-C
+ (nonnull NSDateFormatter *)ISODateFormatterUTC;
Swift
class func isoDateFormatterUTC() -> DateFormatter
Return Value
A DateFormatter with the default attributes.
-
Creates an ISO dateFormatter (UTC) with the following attributes: locale set to ‘en_US_POSIX’, timestyle set to ‘NSDateFormatterFullStyle’, date format set to ‘yyyy-MM-dd’T'HH:mm:ss’. The formatter returned by this method is identical to that of
ISODateFormatterUTC
, except that the format matches the optionalT
delimiter between date and time.Declaration
Objective-C
+ (nonnull NSDateFormatter *)ISODateFormatterUTCWithDelimiter;
Swift
class func isoDateFormatterUTCWithDelimiter() -> DateFormatter
Return Value
A DateFormatter with the default attributes, matching the optional
T
delimiter. -
Parses ISO 8601 date strings. Supports timestamps with just year all the way up to seconds with and without the optional
T
delimeter.Declaration
Objective-C
+ (nullable NSDate *)parseISO8601DateFromString:(nonnull NSString *)timestamp;
Swift
class func parseISO8601Date(from timestamp: String) -> Date?
Parameters
timestamp
The ISO 8601 timestamp.
Return Value
A parsed NSDate object, or nil if the timestamp is not a valid format.
-
Sets a file or directory at a url to not backup in iCloud or iTunes
Declaration
Objective-C
+ (BOOL)addSkipBackupAttributeToItemAtURL:(nonnull NSURL *)url;
Swift
class func addSkipBackupAttributeToItem(at url: URL) -> Bool
Parameters
url
The items url
Return Value
YES if successful, NO otherwise
-
Returns the main window for the app. This window will be positioned underneath any other windows added and removed at runtime, by classes such a UIAlertView or UIActionSheet.
Declaration
Objective-C
+ (nullable UIWindow *)mainWindow;
Swift
class func mainWindow() -> UIWindow?
Return Value
The main window, or
nil
if the window cannot be found. -
Returns the main window for the app. This window will be positioned underneath any other windows added and removed at runtime, by classes such a UIAlertView or UIActionSheet.
Declaration
Objective-C
+ (nullable UIWindow *)mainWindow:(nullable UIWindowScene *)scene;
Swift
class func mainWindow(_ scene: UIWindowScene?) -> UIWindow?
Parameters
scene
The scene in which to find the window.
Return Value
The main window, or
nil
if the window cannot be found. -
Returns the window containing the provided view.
Declaration
Objective-C
+ (nullable UIWindow *)windowForView:(nonnull UIView *)view;
Swift
class func window(for view: UIView) -> UIWindow?
Parameters
view
The view.
Return Value
The window containing the view, or nil if the view is not currently displayed.
-
A utility method that grabs the top-most view controller for the main application window. May return nil if a suitable view controller cannot be found.
Declaration
Objective-C
+ (nullable UIViewController *)topController;
Swift
class func topController() -> UIViewController?
Return Value
The top-most view controller or
nil
if controller cannot be found. -
Gets the current carrier name.
Declaration
Objective-C
+ (nullable NSString *)carrierName;
Swift
class func carrierName() -> String?
Return Value
The current carrier name.
-
Gets the current connection type. Possible values are “cell”, “wifi”, or “none”.
Declaration
Objective-C
+ (nonnull NSString *)connectionType;
Swift
class func connectionType() -> String
Return Value
The current connection type as a string.
-
Determine if the notification payload is a silent push (no notification elements).
Declaration
Objective-C
+ (BOOL)isSilentPush:(nonnull NSDictionary *)notification;
Swift
class func isSilentPush(_ notification: [AnyHashable : Any]) -> Bool
Parameters
notification
The notification payload
Return Value
YES
if it is a silent push,NO
otherwise -
Determine if the notification payload is an alerting push.
Declaration
Objective-C
+ (BOOL)isAlertingPush:(nonnull NSDictionary *)notification;
Swift
class func isAlertingPush(_ notification: [AnyHashable : Any]) -> Bool
Parameters
notification
The notification payload
Return Value
YES
if it is an alerting push,NO
otherwise
-
A utility method that takes an array of fetch results as NSNumbers and returns the merged result
Declaration
Objective-C
+ (UIBackgroundFetchResult)mergeFetchResults:(nonnull NSArray *)fetchResults;
Swift
class func mergeFetchResults(_ fetchResults: [Any]) -> UIBackgroundFetchResult
-
A utility method that takes an APNS-provided device token and returns the decoded Airship device token
Declaration
Objective-C
+ (nonnull NSString *)deviceTokenStringFromDeviceToken: (nonnull NSData *)deviceToken;
Swift
class func deviceTokenString(fromDeviceToken deviceToken: Data) -> String
-
A utility method that compares two version strings and determines their order.
Declaration
Objective-C
+ (NSComparisonResult)compareVersion:(nonnull NSString *)version1 toVersion:(nonnull NSString *)version2;
Swift
class func compareVersion(_ version1: String, toVersion version2: String) -> ComparisonResult
-
Generate SHA256 digest for input string
Declaration
Objective-C
+ (nonnull NSData *)sha256DigestWithString:(nonnull NSString *)input;
Swift
class func sha256Digest(with input: String) -> Data
Parameters
input
string for which to calculate SHA
Return Value
SHA256 digest as NSData
-
Generate SHA256 digest for input string
Declaration
Objective-C
+ (nonnull NSString *)sha256HashWithString:(nonnull NSString *)input;
Swift
class func sha256Hash(with input: String) -> String
Parameters
input
string for which to calculate SHA
Return Value
SHA256 digest as a hex string


The Association for Computing Machinery (ACM) is an international organization, and the worlds largest computing society. The organization excels at bringing people together, acting as a hub for all things technology.
ACM at Wayne State College is just that. Get together with your classmates, and do some learning outside of the classroom! Complete group projects, go on trips and tours, play some games, and discuss/work with various technologies.
Enter event here


Class ConnectionOptions<ConnectionTraits> (2.9.0)
The configuration parameters for client connections.
Constructors
ConnectionOptions()
The default options, using grpc::GoogleDefaultCredentials()
.
ConnectionOptions(std::shared_ptr< grpc::ChannelCredentials >)
The default options, using an explicit credentials object.
| Parameter | |
|---|---|
| Name | Description |
credentials |
std::shared_ptr< grpc::ChannelCredentials >
|
Functions
set_credentials(std::shared_ptr< grpc::ChannelCredentials >)
Change the gRPC credentials value.
| Parameter | |
|---|---|
| Name | Description |
v |
std::shared_ptr< grpc::ChannelCredentials >
|
| Returns | |
|---|---|
| Type | Description |
ConnectionOptions & |
credentials() const
The gRPC credentials used by clients configured with this object.
| Returns | |
|---|---|
| Type | Description |
std::shared_ptr< grpc::ChannelCredentials > |
set_endpoint(std::string)
Change the gRPC endpoint.
In almost all cases the default is the correct endpoint to use. Applications may need to be changed to (1) test against a fake or simulator, or (2) to use a beta or EAP version of the service.
The default value is set by ConnectionTraits::default_endpoint()
.
| Parameter | |
|---|---|
| Name | Description |
v |
std::string
|
| Returns | |
|---|---|
| Type | Description |
ConnectionOptions & |
endpoint() const
The endpoint used by clients configured with this object.
| Returns | |
|---|---|
| Type | Description |
std::string const & |
num_channels() const
The number of transport channels to create.
Some transports limit the number of simultaneous calls in progress on a channel (for gRPC the limit is 100). Increasing the number of channels thus increases the number of operations that can be in progress in parallel.
The default value is set by ConnectionTraits::default_num_channels()
.
| Returns | |
|---|---|
| Type | Description |
int |
set_num_channels(int)
Set the value for num_channels()
.
| Parameter | |
|---|---|
| Name | Description |
num_channels |
int
|
| Returns | |
|---|---|
| Type | Description |
ConnectionOptions & |
tracing_enabled(std::string const &) const
Return whether tracing is enabled for the given component
.
The C++ clients can log interesting events to help library and application developers troubleshoot problems. This flag returns true if tracing should be enabled by clients configured with this option.
| Parameter | |
|---|---|
| Name | Description |
component |
std::string const &
|
| Returns | |
|---|---|
| Type | Description |
bool |
enable_tracing(std::string const &)
Enable tracing for component
in clients configured with this object.
| Parameter | |
|---|---|
| Name | Description |
component |
std::string const &
|
| Returns | |
|---|---|
| Type | Description |
ConnectionOptions & |
disable_tracing(std::string const &)
Disable tracing for component
in clients configured with this object.
| Parameter | |
|---|---|
| Name | Description |
component |
std::string const &
|
| Returns | |
|---|---|
| Type | Description |
ConnectionOptions & |
components() const
Return the set of tracing components.
| Returns | |
|---|---|
| Type | Description |
std::set< std::string > const & |
tracing_options() const
Return the options for use when tracing RPCs.
| Returns | |
|---|---|
| Type | Description |
TracingOptions const & |
channel_pool_domain() const
Define the gRPC channel domain for clients configured with this object.
In some cases applications may want to use a separate gRPC connections for different clients. gRPC may share the connection used by separate channels created with the same configuration. The client objects created with this object will create gRPC channels configured with grpc.channel_pooling_domain
set to the value returned by channel_pool_domain()
. gRPC channels with different values for grpc.channel_pooling_domain
are guaranteed to use different connections. Note that there is no guarantee that channels with the same value will have the same connection though.
This option might be useful for applications that want to segregate traffic for whatever reason.
| Returns | |
|---|---|
| Type | Description |
std::string const & |
set_channel_pool_domain(std::string)
Set the value for channel_pool_domain()
.
| Parameter | |
|---|---|
| Name | Description |
v |
std::string
|
| Returns | |
|---|---|
| Type | Description |
ConnectionOptions & |
add_user_agent_prefix(std::string)
Prepend prefix
to the user-agent string.
Libraries or services that use Cloud C++ clients may want to set their own user-agent prefix. This can help them develop telemetry information about number of users running particular versions of their system or library.
| Parameter | |
|---|---|
| Name | Description |
prefix |
std::string
|
| Returns | |
|---|---|
| Type | Description |
ConnectionOptions & |
user_agent_prefix() const
Return the current value for the user agent string.
| Returns | |
|---|---|
| Type | Description |
std::string const & |
CreateChannelArguments() const
Create a new grpc::ChannelArguments
configured with the options in this object.
The caller would typically pass this argument to grpc::CreateCustomChannel
and create one more more gRPC channels.
| Returns | |
|---|---|
| Type | Description |
grpc::ChannelArguments |
set_background_thread_pool_size(std::size_t)
Set the number of background threads.
| Parameter | |
|---|---|
| Name | Description |
s |
std::size_t
|
| Returns | |
|---|---|
| Type | Description |
ConnectionOptions & |
background_thread_pool_size() const
| Returns | |
|---|---|
| Type | Description |
std::size_t |
DisableBackgroundThreads(google::cloud::CompletionQueue const &)
Configure the connection to use cq
for all background work.
Connections need to perform background work on behalf of the application. Normally they just create a background thread and a CompletionQueue
for this work, but the application may need more fine-grained control of their threads. In this case the application can provide the CompletionQueue
and it assumes responsibility for creating one or more threads blocked on CompletionQueue::Run()
.
| Parameter | |
|---|---|
| Name | Description |
cq |
google::cloud::CompletionQueue const &
|
| Returns | |
|---|---|
| Type | Description |
ConnectionOptions & |
background_threads_factory() const
| Returns | |
|---|---|
| Type | Description |
BackgroundThreadsFactory |
Type Aliases
BackgroundThreadsFactory
::google::cloud::BackgroundThreadsFactory


Assert toolkit for more expressive unit testing.
Many methods implement compile-time parameters (file, line) that are set at the call site. It is preferred that these parameters are ignored when using these methods.
MIT. See LICENSE for full details.
Assert that two floating point values are approximately equal.
http://www.cygnus-software.com/papers/comparingfloats/comparingfloats.htm
| A value | The value used during the assertion. |
| B target | The target value. |
| long ulps | The maximum space between two approximately equal floating point numbers measured in units of least precision. A higher number means more approximation. |
| string message | The error message to display. |
| string file | The file name where the error occurred. The value is added automatically at the call site. |
| size_t line | The line where the error occurred. The value is added automatically at the call site. |
DUnitAssertError if the assertation fails.
float smallestFloatSubnormal = float.min_normal * float.epsilon; smallestFloatSubnormal.assertApprox(-smallestFloatSubnormal); double smallestDoubleSubnormal = double.min_normal * double.epsilon; smallestDoubleSubnormal.assertApprox(-smallestDoubleSubnormal); 0.0f.assertApprox(-0.0f); (-0.0f).assertApprox(0.0f); 0.0.assertApprox(-0.0); (-0.0).assertApprox(0.0); 2.0f.assertApprox(1.999999f); 1.999999f.assertApprox(2.0f); 2.0.assertApprox(1.999999999999999); 1.999999999999999.assertApprox(2.0); // The following tests pass but are open for debate whether or not they should. float.max.assertApprox(float.infinity); float.infinity.assertApprox(float.max); double.infinity.assertApprox(double.max); double.max.assertApprox(double.infinity); float.nan.assertApprox(float.nan); double.nan.assertApprox(double.nan); // Assert a DUnitAssertError is thrown if assertApprox fails. 10f.assertApprox(0f).assertThrow!(DUnitAssertError)("Failed asserting approximately equal");
Assert that two floating point values are approximately equal using an epsilon value.
http://www.cygnus-software.com/papers/comparingfloats/comparingfloats.htm
| A value | The value used during the assertion. |
| B target | The target value. |
| double epsilon | An epsilon value to be used as the maximum absolute and relative error in the comparison. |
| string message | The error message to display. |
| string file | The file name where the error occurred. The value is added automatically at the call site. |
| size_t line | The line where the error occurred. The value is added automatically at the call site. |
DUnitAssertError if the assertation fails.
float smallestFloatSubnormal = float.min_normal * float.epsilon; smallestFloatSubnormal.assertApprox(-smallestFloatSubnormal, 0.00001); double smallestDoubleSubnormal = double.min_normal * double.epsilon; smallestDoubleSubnormal.assertApprox(-smallestDoubleSubnormal, 0.00001); 0.0f.assertApprox(-0.0f, 0.00001); (-0.0f).assertApprox(0.0f, 0.00001); 0.0.assertApprox(-0.0, 0.00001); (-0.0).assertApprox(0.0, 0.00001); 2.0f.assertApprox(1.99f, 0.01); 1.99f.assertApprox(2.0f, 0.01); 2.0.assertApprox(1.99, 0.01); 1.99.assertApprox(2.0, 0.01); // The following tests pass but are open for debate whether or not they should. float.max.assertApprox(float.infinity, 0.00001); float.infinity.assertApprox(float.max, 0.00001); double.infinity.assertApprox(double.max, 0.00001); double.max.assertApprox(double.infinity, 0.00001); float.nan.assertApprox(float.nan, 0.00001); double.nan.assertApprox(double.nan, 0.00001); // Assert a DUnitAssertError is thrown if assertApprox fails. 10f.assertApprox(0f, 0.00001).assertThrow!(DUnitAssertError)("Failed asserting approximately equal");
Assert that an array contains a particular value count.
| A array | The array to interogate. |
| ulong count | The amount of values the array should hold. |
| string message | The error message to display. |
| string file | The file name where the error occurred. The value is added automatically at the call site. |
| size_t line | The line where the error occurred. The value is added automatically at the call site. |
DUnitAssertError if the assertation fails.
int[string] associativeArray; int[] dynamicArray; string string_; associativeArray.assertCount(0); dynamicArray.assertCount(0); string_.assertCount(0); [].assertCount(0); "Hello".assertCount(5); [1, 2, 3, 4].assertCount(4); ["foo", "bar", "baz", "qux"].assertCount(4); [["foo", "bar"], ["baz", "qux"]].assertCount(2); ["foo":1, "bar":2, "baz":3, "qux":4].assertCount(4); // Assert a DUnitAssertError is thrown if assertCount fails. associativeArray.assertCount(1).assertThrow!(DUnitAssertError)("Failed asserting array count");
Assert that an array is empty.
| A array | The array to interogate. |
| string message | The error message to display. |
| string file | The file name where the error occurred. The value is added automatically at the call site. |
| size_t line | The line where the error occurred. The value is added automatically at the call site. |
DUnitAssertError if the assertation fails.
int[string] associativeArray; int[] dynamicArray; string string_; associativeArray.assertEmpty(); dynamicArray.assertEmpty(); string_.assertEmpty(); [].assertEmpty(); // Assert a DUnitAssertError is thrown if assertEmpty fails. [1].assertEmpty().assertThrow!(DUnitAssertError)("Failed asserting empty array");
Assert that a range is empty.
| R range | The range to interogate. |
| string message | The error message to display. |
| string file | The file name where the error occurred. The value is added automatically at the call site. |
| size_t line | The line where the error occurred. The value is added automatically at the call site. |
DUnitAssertError if the assertation fails.
class A { void popFront() {}; @property bool empty() { return true; }; @property int front() { return 0; }; } struct B { void popFront() {}; enum bool empty = false; @property int front() { return 1; }; } static assert(isInputRange!(A)); static assert(isInputRange!(B)); auto emptyRange = new A(); emptyRange.assertEmpty(); B infiniteRange = B.init; infiniteRange.takeNone.assertEmpty(); // Assert a DUnitAssertError is thrown if assertEmpty fails. infiniteRange.assertEmpty().assertThrow!(DUnitAssertError)("Failed asserting empty range"); infiniteRange.take(10).assertEmpty().assertThrow!(DUnitAssertError)("Failed asserting empty range");
Assert that a string ends with a particular string.
| string value | The value used during the assertion. |
| string suffix | The suffix to match. |
| string message | The error message to display. |
| string file | The file name where the error occurred. The value is added automatically at the call site. |
| size_t line | The line where the error occurred. The value is added automatically at the call site. |
DUnitAssertError if the assertation fails.
"foo bar".assertEndsWith("bar"); "baz qux".assertEndsWith("qux"); // Assert a DUnitAssertError is thrown if assertEndsWith fails. "foo".assertEndsWith("bar").assertThrow!(DUnitAssertError)("Failed asserting ends with");
Assert that two values are equal.
| A value | The value used during the assertion. |
| B target | The target value. |
| string message | The error message to display. |
| string file | The file name where the error occurred. The value is added automatically at the call site. |
| size_t line | The line where the error occurred. The value is added automatically at the call site. |
DUnitAssertError if the assertation fails.
123.assertEqual(123); "hello".assertEqual("hello"); // Assert a DUnitAssertError is thrown if assertEqual fails. 1.assertEqual(2).assertThrow!(DUnitAssertError)("Failed asserting equal");
Assert that a boolean value is false.
| T value | The value used during the assertion. |
| string message | The error message to display. |
| string file | The file name where the error occurred. The value is added automatically at the call site. |
| size_t line | The line where the error occurred. The value is added automatically at the call site. |
DUnitAssertError if the assertation fails.
false.assertFalse(); // Assert a DUnitAssertError is thrown if assertFalse fails. true.assertFalse().assertThrow!(DUnitAssertError)("Failed asserting false");
Assert that a value evaluates as false.
| T value | The value used during the assertion. |
| string message | The error message to display. |
| string file | The file name where the error occurred. The value is added automatically at the call site. |
| size_t line | The line where the error occurred. The value is added automatically at the call site. |
DUnitAssertError if the assertation fails.
false.assertFalsey(); [].assertFalsey(); null.assertFalsey(); 0.assertFalsey(); // Assert a DUnitAssertError is thrown if assertFalsey fails. true.assertFalsey().assertThrow!(DUnitAssertError)("Failed asserting falsey");
Assert that a value is greater than a threshold value.
| A value | The value used during the assertion. |
| B threshold | The threshold value. |
| string message | The error message to display. |
| string file | The file name where the error occurred. The value is added automatically at the call site. |
| size_t line | The line where the error occurred. The value is added automatically at the call site. |
DUnitAssertError if the assertation fails.
11.assertGreaterThan(10); // Assert a DUnitAssertError is thrown if assertGreaterThan fails. 11.assertGreaterThan(12).assertThrow!(DUnitAssertError)("Failed asserting greater than");
Assert that a value is greater than or equal to a threshold value.
| A value | The value used during the assertion. |
| B threshold | The threshold value. |
| string message | The error message to display. |
| string file | The file name where the error occurred. The value is added automatically at the call site. |
| size_t line | The line where the error occurred. The value is added automatically at the call site. |
DUnitAssertError if the assertation fails.
10.assertGreaterThanOrEqual(10); 11.assertGreaterThanOrEqual(10); // Assert a DUnitAssertError is thrown if assertGreaterThanOrEqual fails. 11.assertGreaterThanOrEqual(12).assertThrow!(DUnitAssertError)("Failed asserting greater than or equal");
Assert that an associative array contains a particular key.
| A haystack | The associative array to interogate. |
| B needle | The key the array should contain. |
| string message | The error message to display. |
| string file | The file name where the error occurred. The value is added automatically at the call site. |
| size_t line | The line where the error occurred. The value is added automatically at the call site. |
DUnitAssertError if the assertation fails.
["foo":1, "bar":2, "baz":3, "qux":4].assertHasKey("foo"); [1:"foo", 2:"bar", 3:"baz", 4:"qux"].assertHasKey(1); // Assert a DUnitAssertError is thrown if assertHasKey fails. ["foo":"bar"].assertHasKey("baz").assertThrow!(DUnitAssertError)("Failed asserting array has key");
Assert that an array contains a particular value.
| A haystack | The array to interogate. |
| B needle | The value the array should contain. |
| string message | The error message to display. |
| string file | The file name where the error occurred. The value is added automatically at the call site. |
| size_t line | The line where the error occurred. The value is added automatically at the call site. |
DUnitAssertError if the assertation fails.
"Hello".assertHasValue("H"); [1, 2, 3, 4].assertHasValue(2); ["foo", "bar", "baz", "qux"].assertHasValue("foo"); [["foo", "bar"], ["baz", "qux"]].assertHasValue(["foo", "bar"]); ["foo":1, "bar":2, "baz":3, "qux":4].assertHasValue(4); // Assert a DUnitAssertError is thrown if assertHasValue fails. ["foo":"bar"].assertHasValue("baz").assertThrow!(DUnitAssertError)("Failed asserting array has value");
Assert that a value is an instance of a type.
| B value | The value used during the assertion. |
| string message | The error message to display. |
| string file | The file name where the error occurred. The value is added automatically at the call site. |
| size_t line | The line where the error occurred. The value is added automatically at the call site. |
DUnitAssertError if the assertation fails.
interface A {} class B : A {} class C : B {} auto b = new B(); auto c = new C(); b.assertInstanceOf!(Object)(); b.assertInstanceOf!(A)(); b.assertInstanceOf!(B)(); c.assertInstanceOf!(Object)(); c.assertInstanceOf!(A)(); c.assertInstanceOf!(B)(); c.assertInstanceOf!(C)(); // Assert a DUnitAssertError is thrown if assertInstanceOf fails. b.assertInstanceOf!(C)().assertThrow!(DUnitAssertError)("Failed asserting instance of");
Assert that a value is less than a threshold value.
| A value | The value used during the assertion. |
| B threshold | The threshold value. |
| string message | The error message to display. |
| string file | The file name where the error occurred. The value is added automatically at the call site. |
| size_t line | The line where the error occurred. The value is added automatically at the call site. |
DUnitAssertError if the assertation fails.
9.assertLessThan(10); // Assert a DUnitAssertError is thrown if assertLessThan fails. 9.assertLessThan(8).assertThrow!(DUnitAssertError)("Failed asserting less than");
Assert that a value is less than or equal to a threshold value.
| A value | The value used during the assertion. |
| B threshold | The threshold value. |
| string message | The error message to display. |
| string file | The file name where the error occurred. The value is added automatically at the call site. |
| size_t line | The line where the error occurred. The value is added automatically at the call site. |
DUnitAssertError if the assertation fails.
10.assertLessThanOrEqual(10); 9.assertLessThanOrEqual(10); // Assert a DUnitAssertError is thrown if assertLessThanOrEqual fails. 9.assertLessThanOrEqual(8).assertThrow!(DUnitAssertError)("Failed asserting less than or equal");
Assert that a string matches a regular expression.
| string value | The value used during the assertion. |
| string pattern | The regular expression pattern. |
| string message | The error message to display. |
| string file | The file name where the error occurred. The value is added automatically at the call site. |
| size_t line | The line where the error occurred. The value is added automatically at the call site. |
DUnitAssertError if the assertation fails.
"foo".assertMatchRegex(r"^foo$"); "<IP_ADDRESS>".assertMatchRegex(r"((?:[\d]{1,3}\.){3}[\d]{1,3})"); // Assert a DUnitAssertError is thrown if assertMatchRegex fails. "foo".assertMatchRegex(r"^bar$").assertThrow!(DUnitAssertError)("Failed asserting match to regex");
Assert that a value is null.
| A value | The value to assert as null. |
| string message | The error message to display. |
| string file | The file name where the error occurred. The value is added automatically at the call site. |
| size_t line | The line where the error occurred. The value is added automatically at the call site. |
DUnitAssertError if the assertation fails.
class T {} string foo; int[] bar; T t; foo.assertNull(); bar.assertNull(); t.assertNull(); null.assertNull(); // Assert a DUnitAssertError is thrown if assertNull fails. "foo".assertNull().assertThrow!(DUnitAssertError)("Failed asserting null");
Assert that a string starts with a particular string.
| string value | The value used during the assertion. |
| string prefix | The prefix to match. |
| string message | The error message to display. |
| string file | The file name where the error occurred. The value is added automatically at the call site. |
| size_t line | The line where the error occurred. The value is added automatically at the call site. |
DUnitAssertError if the assertation fails.
"foo bar".assertStartsWith("foo"); "baz qux".assertStartsWith("baz"); // Assert a DUnitAssertError is thrown if assertStartsWith fails. "foo bar".assertStartsWith("baz").assertThrow!(DUnitAssertError)("Failed asserting starts with");
Assert that an expression throws an exception.
| B expression | The expression to evaluate in order to assert the exception is thrown. |
| string expressionMsg | An optional expected message of the thrown exception. |
| string message | The error message to display. |
| string file | The file name where the error occurred. The value is added automatically at the call site. |
| size_t line | The line where the error occurred. The value is added automatically at the call site. |
DUnitAssertError if the assertation fails.
import core.exception : AssertError, RangeError; class Foo : Exception { this(string message) { super(message); } } class Bar { public void baz() { throw new Foo("Thrown from baz."); } } auto bar = new Bar(); bar.baz().assertThrow(); bar.baz().assertThrow!(Foo)("Thrown from baz."); delegate(){throw new Foo("Thrown from delegate.");}().assertThrow!(Exception)("Thrown from delegate."); auto baz = [0, 1, 2]; baz[3].assertThrow!(RangeError)(); assert(false).assertThrow!(AssertError)("Assertion failure"); // Assert a DUnitAssertError is thrown if assertThrow fails. null.assertThrow().assertThrow!(DUnitAssertError)("Failed asserting throw"); // Assert a DUnitAssertError is thrown if assertThrow fails due to mismatched error message. baz[3].assertThrow!(RangeError)("Foo").assertThrow!(DUnitAssertError)("Failed asserting throw");
Assert that a boolean value is true.
| T value | The value used during the assertion. |
| string message | The error message to display. |
| string file | The file name where the error occurred. The value is added automatically at the call site. |
| size_t line | The line where the error occurred. The value is added automatically at the call site. |
DUnitAssertError if the assertation fails.
true.assertTrue(); // Assert a DUnitAssertError is thrown if assertTrue fails. false.assertTrue().assertThrow!(DUnitAssertError)("Failed asserting true");
Assert that a value evaluates as true.
| T value | The value used during the assertion. |
| string message | The error message to display. |
| string file | The file name where the error occurred. The value is added automatically at the call site. |
| size_t line | The line where the error occurred. The value is added automatically at the call site. |
DUnitAssertError if the assertation fails.
true.assertTruthy(); ["foo"].assertTruthy(); 1.assertTruthy(); // Assert a DUnitAssertError is thrown if assertTruthy fails. false.assertTruthy().assertThrow!(DUnitAssertError)("Failed asserting truthy");
Assert that a value is of a particular type.
| B value | The value used during the assertion. |
| string message | The error message to display. |
| string file | The file name where the error occurred. The value is added automatically at the call site. |
| size_t line | The line where the error occurred. The value is added automatically at the call site. |
DUnitAssertError if the assertation fails.
1.assertType!(int)(); "foo".assertType!(string)(); ["bar"].assertType!(string[])(); ['a'].assertType!(char[])(); // Assert a DUnitAssertError is thrown if assertType fails. false.assertType!(string)().assertThrow!(DUnitAssertError)("Failed asserting type");


It is the leading financial establishment providing high-quality international banking services. Our success is attributed to our loyal customers. We provide reliable services for you.
Bank Card, 343 banking lane, #2214 cravel street, NY.
September 28, 2020
September 28, 2020
© 2020 Bank Cards. All rights reserved.


Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the!
27710 Children directly provided education through 261 centres in 22 states
51%of total beneficiaries enrolled were girl children, to ensure a fair start for all
88% of eligible children were mainstreamed to formal schools.
100% Teachers received training in in academic methodologies.
Please share your personal Email ID, Mobile Number, Address and PAN No. so that we can send you the reciept certificate & share updates on
our programmes with you.
To know more please sms SF to 56161
(Registration No. - 6382)
For more information contact:
Email<EMAIL_ADDRESS>Phone: +91-11234556677, Mob: +91 234456678
For more information on our Educational Programme


Before Change
batch_xs = self.batch_states(
[[obs] for obs in batch_obs],
self.xp, self.phi)
batch_action = [
self.policy(batch_xs[i]).sample()
for i in range(len(batch_obs))]
// Q is not needed here, but log it just for information
// q = self.q_function(batch_xs, batch_action)
// Update stats
After Change
// Update stats
self.average_q *= self.average_q_decay
self.average_q += (1 - self.average_q_decay) * float(q.array)
self.logger.debug("t:%s a:%s q:%s",
self.t, batch_action.array[0], q.array)
return [cuda.to_cpu(action.array) for action in batch_action]
def batch_act_and_train(self, batch_obs):
Select a batch of actions for training.


| Home | Trees | Indices | Help |
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Auxiliary classes and functions.
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WriteData Return a WriteData-like stream object for writing. |
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ReadData Returns a ReadData-like stream object. |
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__revision__ =
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__package__ =
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Tries to determine if a given value is a power of two.
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Tries to determine if a buffer is empty.
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| Generated by Epydoc 3.0.1 on Fri Jul 12 18:53:45 2013 | http://epydoc.sourceforge.net |


C/C++ Project Properties, Resource
Shows resource information for the selected type.
| Property | Description |
|---|---|
| Path | Specifies the location of the selected resource type within the workspace. For example, similar to folders, projects map to directories in the file system. |
| Type | Indicates the type for the selected resource: Folder, Project, or File. |
| Location | Specifics the location of the selected resource within the file system. |
| Last modified | Shows the date that the selected resource was last modified. |
| Text file encoding | Sets an alternate text encoding. Because text files are encoded differently (depending on the locale and platform), use the default text file encoding for the locale of your host operating system. However, if you want to work with text files that originate from another source (for example, to work with text files that use a different encoding scheme than your native one, so that you can easily exchange files with another team), choose Other and select an appropriate one from the list. |
| Inherited from container | When enabled, the selected resource inherits the text encoding specified for it's container resource. |
| Other | When enabled, the selected resource uses an alternate text encoding other than that specified for its container resource. You can enable this option if you want to work with text files that originate from another source (ones that use a different encoding scheme than your native one), so that you can easily exchange files with others. |
| New text file line delimiter | Specifies the end of line character(s) to use for new text files being created. |
| Inherited from container | When enabled, the selected resource inherits the character line ending for new text files specified for it's container resource. |
| Other | When enabled, the selected resource uses an alternate end of line character(s) for new text files other than that specified for its container resource. For example, you can set the Text file encoding option to UTF-8, and then set the line endings character for new files to Unix, so that text files are saved in a format that is not specific to the Windows operating system, and the files can easily be shared amongst various developer systems. |
Available for: Any projects, folders and files regardless of project kind.
- Project Properties, Resource page
- Project Properties, Builders page
- Project Properties, C/C++ Build category
- Project Properties, C/C++ General category
- C/C++ Project Properties, Project References page
- C/C++ Project Properties, Refactoring History page
- C/C++ Project Properties, Run/Debug Settings page


VICTON's Byungchan opens up his personal Instagram in celebration of his 23rd birthday!
by beansssVICTON's Byungchan has opened up his very own, personal Instagram!
The new Instagram account comes in celebration of Byungchan's 23rd birthday, which falls on November 12. In his first post, the idol shares adorable childhood photos with fans as he writes, "Birthday! Always stay with you (emoticons)."
Follow his brand new Instagram account below! Meanwhile, VICTON made a comeback earlier this month with their 5th mini album 'Nostalgia', including title track "Nostalgic Night".


HTTPServer Class Reference
| Inherits from | NSObject |
| Declared in | HTTPServer.h |
Tasks
-
– documentRoot
Specifies the document root to serve files from. For example, if you set this to “/Users/<your_username/Sites”, then it will serve files out of the local Sites directory (including subdirectories).
-
– setDocumentRoot:
-
– connectionClass
The connection class is the class used to handle incoming HTTP connections.
-
– setConnectionClass:
-
– interface
Set what interface you’d like the server to listen on. By default this is nil, which causes the server to listen on all available interfaces like en1, wifi etc.
-
– setInterface:
-
– port
The port number to run the HTTP server on.
-
– listeningPort
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– setPort:
-
– domain
Bonjour domain for publishing the service. The default value is “local.”.
-
– setDomain:
-
– name
Bonjour name for publishing the service. The default value is “”.
-
– publishedName
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– setName:
-
– type
Bonjour type for publishing the service. The default value is nil. The service will not be published via bonjour unless the type is set.
-
– setType:
-
– republishBonjour
Republishes the service via bonjour if the server is running. If the service was not previously published, this method will publish it (if the server is running).
-
– TXTRecordDictionary
-
– setTXTRecordDictionary:
-
– start:
Attempts to starts the server on the configured port, interface, etc.
-
– stop
Stops the server, preventing it from accepting any new connections. You may specify whether or not you want to close the existing client connections.
-
– stop:
-
– isRunning
-
– addWebSocket:
-
– numberOfHTTPConnections
-
– numberOfWebSocketConnections
Instance Methods
connectionClass
The connection class is the class used to handle incoming HTTP connections.
- (Class)connectionClass
Discussion
The connection class is the class used to handle incoming HTTP connections.
The default value is [HTTPConnection class]. You can override HTTPConnection, and then set this to [MyHTTPConnection class].
If you change the connectionClass while the server is running, the change will affect future incoming http connections.
Declared In
HTTPServer.h
documentRoot
Specifies the document root to serve files from. For example, if you set this to “/Users/<your_username/Sites”, then it will serve files out of the local Sites directory (including subdirectories).
- (NSString *)documentRoot
Discussion
Specifies the document root to serve files from. For example, if you set this to “/Users/<your_username/Sites”, then it will serve files out of the local Sites directory (including subdirectories).
The default value is nil. The default server configuration will not serve any files until this is set.
If you change the documentRoot while the server is running, the change will affect future incoming http connections.
Declared In
HTTPServer.h
domain
Bonjour domain for publishing the service. The default value is “local.”.
- (NSString *)domain
Discussion
Bonjour domain for publishing the service. The default value is “local.”.
Note: Bonjour publishing requires you set a type.
If you change the domain property after the bonjour service has already been published (server already started), you’ll need to invoke the republishBonjour method to update the broadcasted bonjour service.
Declared In
HTTPServer.h
interface
Set what interface you’d like the server to listen on. By default this is nil, which causes the server to listen on all available interfaces like en1, wifi etc.
- (NSString *)interface
Discussion
Set what interface you’d like the server to listen on. By default this is nil, which causes the server to listen on all available interfaces like en1, wifi etc.
The interface may be specified by name (e.g. “en1” or “lo0”) or by IP address (e.g. “<IP_ADDRESS>”). You may also use the special strings “localhost” or “loopback” to specify that the socket only accept connections from the local machine.
Declared In
HTTPServer.h
name
Bonjour name for publishing the service. The default value is “”.
- (NSString *)name
Discussion
Bonjour name for publishing the service. The default value is “”.
If using an empty string (“”) for the service name when registering, the system will automatically use the “Computer Name”. Using an empty string will also handle name conflicts by automatically appending a digit to the end of the name.
Note: Bonjour publishing requires you set a type.
If you change the name after the bonjour service has already been published (server already started), you’ll need to invoke the republishBonjour method to update the broadcasted bonjour service.
The publishedName method will always return the actual name that was published via the bonjour service. If the service is not running this method returns nil.
Declared In
HTTPServer.h
port
The port number to run the HTTP server on.
- (UInt16)port
Discussion
The port number to run the HTTP server on.
The default port number is zero, meaning the server will automatically use any available port. This is the recommended port value, as it avoids possible port conflicts with other applications. Technologies such as Bonjour can be used to allow other applications to automatically discover the port number.
Note: As is common on most OS’s, you need root privledges to bind to port numbers below 1024.
You can change the port property while the server is running, but it won’t affect the running server. To actually change the port the server is listening for connections on you’ll need to restart the server.
The listeningPort method will always return the port number the running server is listening for connections on. If the server is not running this method returns 0.
Declared In
HTTPServer.h
republishBonjour
Republishes the service via bonjour if the server is running. If the service was not previously published, this method will publish it (if the server is running).
- (void)republishBonjour
Discussion
Republishes the service via bonjour if the server is running. If the service was not previously published, this method will publish it (if the server is running).
Declared In
HTTPServer.h
start:
- (BOOL)start:(NSError **)errPtr
Discussion
Attempts to starts the server on the configured port, interface, etc.
If an error occurs, this method returns NO and sets the errPtr (if given). Otherwise returns YES on success.
Some examples of errors that might occur: – You specified the server listen on a port which is already in use by another application. – You specified the server listen on a port number below 1024, which requires root priviledges.
Code Example:
NSError *err = nil; if (![httpServer start:&err]) { NSLog(@“Error starting http server: %@”, err); }
Declared In
HTTPServer.h
stop
Stops the server, preventing it from accepting any new connections. You may specify whether or not you want to close the existing client connections.
- (void)stop
Discussion
Stops the server, preventing it from accepting any new connections. You may specify whether or not you want to close the existing client connections.
The default stop method (with no arguments) will close any existing connections. (It invokes [self stop:NO])
Declared In
HTTPServer.h
type
Bonjour type for publishing the service. The default value is nil. The service will not be published via bonjour unless the type is set.
- (NSString *)type
Discussion
Bonjour type for publishing the service. The default value is nil. The service will not be published via bonjour unless the type is set.
If you wish to publish the service as a traditional HTTP server, you should set the type to be “http.tcp.”.
If you change the type after the bonjour service has already been published (server already started), you’ll need to invoke the republishBonjour method to update the broadcasted bonjour service.
Declared In
HTTPServer.h


{% extends 'base.html' %} {% block content %} {{block.super}}
Located at: {{station.location}}
{{station.email}}
{{station.phone}}


Iraq's prime minister says he will submit his official resignation to Parliament following day of...
by EndPlayBAGHDAD - BAGHDAD (AP) - Iraq's prime minister says he will submit his official resignation to Parliament following day of bloodshed .
BAGHDAD - BAGHDAD (AP) - Iraq's prime minister says he will submit his official resignation to Parliament following day of bloodshed .


Figure 24: Blade Runner , 1982. Directed by Ridley Scott, based on the novel by Philip K. Dick, Do Androids Dream of Electric Sheep? 1969. (Bukatman 1993: 130)


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def | capability_size |
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def | capability_list |
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def | capability |
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def | set_capability |
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def | add_capability |
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def | clear_capability |
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def | call_size |
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def | call_list |
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def | call |
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def | set_call |
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def | add_call |
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def | clear_call |
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def | MergeFrom |
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def | Equals |
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def | IsInitialized |
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def | ByteSize |
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def | Clear |
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string | package_ = "" |
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int | kpackage = 1 |
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int | kcapability = 2 |
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int | kcall = 3 |
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The documentation for this class was generated from the following file:
- code/googleappengine-read-only/python/google/appengine/api/capabilities/capability_service_pb.py


What to expect
- We will provide a communication channel to provide feedback on how the project applies to and impacts your use case.
- We will keep you informed as we assess and incorporate your feedback, and we'll strive to prioritize issues that may be blocking you.
- We will consistently update documentation and provide at least 72 hours advanced notice of breaking changes (except in rare, urgent circumstances).
- You will experience a secure, reliable, and stable solution with low potential for bugs.
- We will provide GitHub's standard high-quality support as you apply the implementation to your workflows.


This is the google maps view of the location of the birthday party
This is the map view of the location of the birthday party
This is the satelite view of the location of the birthday party
This is the front of the location of the birthday party
This is the google maps view of the location of the birthday party
This is the map view of the location of the birthday party
This is the satelite view of the location of the birthday party
This is the front of the location of the birthday party


World Leader vs World Leader lets you compare the salaries of world leaders amongst them.
World Leader vs Average National lets you compare the salary of a world leader with the national average wage of his/her country.
World Leader vs Other nation lets you compare the salary of a world leader with the average national wage of another country.
World Leader vs You lets you stand against the world leaders' salaries. Will you beat them?
Once you're done, check out the general overview. Wanna find out who's the world leader that brings the biggest bacon home?
Country:
Name:
Position:
Birth:
Annual:
Monthly:
Weekly:
Daily:
Country:
Name:
Position:
Birth:
Annual:
Monthly:
Weekly:
Daily:
earns times more than
Country:
Name:
Position:
Birth:
Annual:
Monthly:
Weekly:
Daily:
Country:
National leader:
Average National Wage:
Gini coefficient:
Year of Gini's measurement:
National leader's wage divided by the average national wage:
earns times more than the average national wage of his/her own country (USD$)
Country:
Name:
Position:
Birth:
Annual:
Monthly:
Weekly:
Daily:
Country:
National leader:
Average National Wage:
Gini coefficient:
Year of Gini's measurement:
National leader's wage divided by the average national wage:
earns times more than the average national wage of (USD$)
Enter your annual salary to find out your position in the World Leaders' salaries ranking (USD):
Country:
Name:
Position:
Birth:
Annual:
Monthly:
Weekly:
Daily:
Your salary:
earns times more than you!
The chart below shows you who are the presidents who earn the most if compared to the average national wage of his/her own country in on axis. The president of Azerbaijan earns 31 times more than the national average wage of that country, for example.
That same chart shows you what is the Gini coefficient of the countries in the other axis. The Gini coefficient is an index that measures the distribution of wealth in a country. The higher it approaches to 1, the higher the inequality in that country. South Africa has one of the highest Gini ratios, for example.
Scroll around the scatterplot, to see who are the world leaders that go big when it comes to salaries over their own nationals. Can you make any correlations between the distritubion of wealth in a country and the ammount of money world leaders make?
The chart below shows you how much money are world leaders making per week. Do you earn more than most of them?
Scroll down to see who's the world leader with the highest salary in the world. Is it Putin?


Carlos Palminha is an experienced manager of software, technology and innovation projects, teams and organizations. With more than 10 years deploying, leading and managing innovative and agile mindsets, he loves to flourish, refactor and lead projects and organizations in the adoption of patterns, methodologies and innovation. Currently he is working at Synopsys, leading the embedded software and Linux Drivers team for Designware IP and Hardware Validation.


Advisor
Lovett '19 | KINE | Arlington, VA
If you are lucky enough to get Sydney as your Advisor, you know that you’ll have a great friend forever. Sydney is a Kinesiology major
with a Health Sciences concentration from Arlington, VA who has plenty of ~unconventional wisdom~ to share about not actually
being a pre-med (she’s actually pre-pharm). Once nicknamed “Kisses,” Sydney has plenty of affection to share with, well, everyone,
which definitely includes you, her lucky New Students. Having been Fun Friday Coordinator, Chug Captain, Beer Bike Coordinator,
and Activities Coordinator, she’s super social but also super busy so you know that if she makes time for you, you’re special. Sydney
is currently a senior at Lovett College, so be sure to make the most of the time you have left with her before she goes off into the real
world.
- Alexis Williams (Jones '19)
Advisor
Lovett '21 | COMP |New York, NY
After living all over the world, feel free to ask Jonathan, “Mr. Geography Whiz” any geography question you may have, and he’ll be
especially informed about questions regarding Amsterdam, Reunion Island, New York, and Miami (all of his past hometowns). Though
Jonathan is #cultured and plays a big role in the Rice African Student Association and Black Student Association, feel free to bring
any of your IT problems to this struggling sophomore Computer Science major. This potential Anthropology minor loves everyone,
especially his O-Week New Students. Johnny keeps himself busy listening and singing along with every Frank Ocean and Daniel Caesar
song that ever existed, playing the guitar with his sad boy vibes, and serenading girls all over campus with his beautiful voice. This
young boy loves to nap at any given time of the day, and downs a ton of milk after his heavy workouts. If you love any of these things,
you’ll be in love with Jonathan. Even if you don’t, Jonathan is a wonderful personality, and you’ll be supported by one of the most
compassionate, caring, and understanding people you’ll ever find, who is ready to add some fun to your life, convince you to try new
things, and help you in whatever you might need!
- Varun Kataria (Lovett '21)
PAA
Lovett '21 | MTEC, POLI, PLST | Portland, OR
Coleman Lambo may be a stiff dancer, but he’s quite the smooth talker. Hailing from Portland, Oregon but fresh off the steps of
Washington D.C., Cole is the kind of busybody that you can ask about anything. Internships? He’s done them. Interviews? Had
a million. Schedule planning? He’s a pro. At least he’s supposed to be because he’s your Peer Academic Advisor. But he’s also just a
sophomore so what does he know? Besides spending his time flying across the country with the Rice Speech and Debate Team, you can
probably find him in Lovett’s basement working on his Mathematical Economic Analysis (MTEC) and Political Science (POLI) majors
and Politics, Law, and Social Thought (PLST) minor. Darn try-hard.  Actually, now that I think about it, he’s pretty emotionally stable, so you could probably mess with him
a bunch and never make him angry. Unless, of course, you insult Friends, his favorite TV show.
-Bria Murray (Lovett '21)
Co-Advisor
Sid Richardson '20 | SOCI, Pre-Med | San Jose, CA
W-O-W. Count yourself beyond lucky if you have Erin Kim as your Advisor! This Nor-Cal bae is sure to bring you more light in your
life than the Californian sun. If her store of all the best worst punny jokes don’t already make you smile, the warmth and love she
pours out definitely will. Her vibrancy and energy is well known throughout the entire campus. Whether it’s through being the most
enthusiastic Kesem counselor and coordinator (call her Redwood, her camp name), or the most reliable Sid Head PAA (Peer Academic
Advisor), Erin’s passion to help others goes beyond any label or adjective that can describe her. If you ever need to rant about Organic
Chemistry or just want to explore Houston with the best conversationalist, this Sociology pre-med will always be down. If you’re
in need of support, this rising junior will literally come find you to encourage you and give you one of her endearing and just plain
awesome hugs. So, be excited! You have just crossed paths with one of the most amazing people on campus and will embark on this
journey called college together
- Debora Kim and Jeanie Kim (Sid Richardson '20)


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The data was from this publication:
Neely, B.A., Prager, K.C., Bland, A.M., Fontaine, C., Gulland, F.M. and Janech, M.G., 2018. Proteomic Analysis of Urine from California Sea Lions (Zalophus californianus): a Resource for Urinary Biomarker Discovery. Journal of proteome research, 17(9), pp.3281-3291.
The data is in PRIDE with the PXD009019 identifier. There are 19 RAW files. The datasets are about 65K MS2 spectra per sample (single longer gradient LC runs). The data are high resolution MS1, HCD dissociation, and high resolution MS2 scans. Urine, like other biofluids, will have semi-tryptic peptides due to signal peptides, endogenous proteases, etc.
The RAW files were processed with the Comet/PAWs pipeline using semi-tryptic searches with a wide parent mass tolerance. Oxidized Met was allowed as a variable modification. Since the publication came out, the sea lion genome has been sequenced and is available at NCBI. The publication used a database of two similar species in lieu of a species-specific database.
There were 8 California sea lions as controls for healthy urine and urine from 11 Leptospira interrogans infected sea lions.
This notebook will demonstrate using Jupyter notebooks, R, and edgeR to perform a comparison of the control urine proteomes to the infected sea lion urine proteomes. The goal is to find which proteins are differentially expressed.
The notebook will:
The goal here is to demonstrate edgeR (Ref-1) statistical testing that can be used for spectral counting (Ref-2) semi-quantitative experiments. The PAW pipeline has some features for shotgun quantitation that we will be using. One is a protein grouping algorithm where highly homologous proteins (majority of peptides are shared with few unique peptides) are grouped together and the context for shared or unique peptides are updated accordingly. This extended parsimony logic is described in more detail here. The other feature is the splitting of shared peptide spectral counts based on relative unique peptide evidence.
1. Robinson, M.D., McCarthy, D.J. and Smyth, G.K., 2010. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1), pp.139-140.
2. Liu, H., Sadygov, R.G. and Yates, J.R., 2004. A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Analytical chemistry, 76(14), pp.4193-4201.
library("tidyverse")
library("edgeR")
library("limma")
library("psych")
library("gridExtra")
library("scales")
QC_check_669x19.txt
¶The PAW pipeline, like most proteomics summary tables, has a lot more information that we need for the statistical analysis. Selecting the relevant columns from a large table is not too hard to do in R. Row filtering can be trickier. Decoy protein sequences and common contaminants are easy to drop. Missing data and filtering out low abundance data can be trickier.
The situation here needs to also allow for proteins that are in only one condition. One way to handle that is to require a minimum average spectral count across the 19 samples of 2.5. If we have expression in one condition of 5 (a level that is well above the minimum detection of 1 or 2) and nothing in the other condition, we will hit the average of 2.5. We demonstrated that this cutoff value is a good choice for this data in the PXD009019_average_missing
notebook. We did the table sorting and filtering in Excel and collected the relevant count columns for the 669 proteins into a separate data file. We will find zero values and replaced them with a count of 0.25 after loading in the data. We explored the data for odd samples and outlier characteristics in the PXD009019_QC_check
notebook. We decided that three of the lepto samples should be excluded. This will give us 669 proteins for 8 control samples and 8 lepto samples that we can test.
The data exported in the QC_check_669x19.txt
file excluded contaminants, decoys, and any proteins with an average SpC of less than 2.5. We will read that data in and separate the accessions from the count data, replace any zeros with 0.25 count values, and drop three lepto samples.
# read in the prepped data
paw_spc <- read_tsv("QC_check_669x19.txt")
# save accessions in vector and remove from data table
accession <- paw_spc$Accession
paw_spc <- select(paw_spc, -Accession)
# replace zeros with 0.25
paw_spc[paw_spc == 0] <- 0.25
# In a spearate notebook, we decided to exclude 3 lepto samples (2, 6, and 16)
paw_spc <- paw_spc[c(-10, -14, -18)]
head(paw_spc)
nrow(paw_spc)
# function for simple normalization
SL_Norm <- function(df, color = NULL, plot = TRUE) {
# This makes each channel sum to the average grand total
# df - data frame of TMT intensities
# returns a new data frame with normalized values
# compute scaling factors to make colsums match the average sum
norm_facs <- mean(c(colSums(df))) / colSums(df)
cat("SL Factors:\n", sprintf("%-5s -> %f\n", colnames(df), norm_facs))
df_sl <- sweep(df, 2, norm_facs, FUN = "*")
# visualize results and return data frame
if(plot == TRUE) {
boxplot(log10(df_sl), col = color, notch = TRUE, main = "SL Normalized data")
}
df_sl
}
# normalize the data before plotting
color <- c(rep('blue', 8), rep('red', 8))
paw_sl <- SL_Norm(paw_spc, color)
We have filtered out proteins with too few counts to be reliably quantified (keeping about 670 out of 2300 protein identifications). Despite that, we do have two control samples that have a lot of small SpC values. The three lepto sample we have excluded were even worse.
To get a sense of the data characteristics, we will compare the 8 control samples to each other, and do the same for the 8 lepto samples. Multi-panel scatter plots are a nice method for exploratory data analysis. We will try linear and log scales.
# shortcuts for the cell types
C <- 1:8
L <- 9:16
pairs.panels(paw_sl[C], main = "Control")
pairs.panels(log2(paw_sl[C]), main = "Control")
pairs.panels(paw_sl[L], main = "Lepto")
pairs.panels(log2(paw_sl[L]), main = "Lepto")
The sample-to-sample consistency is okay for spectral counting data. There are some low abundance proteins in each sample that are not consistently detected (points along the axes). Interestingly, the correlation coefficients depend on whether the data is linear or log scale.
We will use the counts and accessions that we created in R above from the main summary file and load data into some edgeR data structures. We will run the built-in TMM normalization (Ref-3) and see if the samples have any structure in a clustering plot.
3. Robinson, M.D. and Oshlack, A., 2010. A scaling normalization method for differential expression analysis of RNA-seq data. Genome biology, 11(3), p.R25.
# load the data into edgeR data structures
# group labels need to be factors
group <- c(rep("Control", 8), rep("Lepto", 8))
y <- DGEList(counts = paw_spc, group = group, genes = accession)
# run the TMM normalization (and library size corrections)
y <- calcNormFactors(y)
# check normalizations
y$samples
EdgeR uses the normalization factors as offsets in its modeling. The normalized data values have to be computed.
apply_tmm_factors <- function(y, color = NULL, plot = TRUE) {
# computes the tmm normalized data from the DGEList object
# y - DGEList object
# returns a dataframe with normalized intensities
# compute grand total (library size) scalings
lib_facs <- mean(y$samples$lib.size) / y$samples$lib.size
# the TMM factors are library adjustment factors (so divide by them)
norm_facs <- lib_facs / y$samples$norm.factors
cat("Overall Factors (lib.size+TMM):\n", sprintf("%-5s -> %f\n",
colnames(y$counts), norm_facs))
# compute the normalized data as a new data frame
df_tmm <- as.data.frame(sweep(y$counts, 2, norm_facs, FUN = "*"))
colnames(df_tmm) <- str_c(colnames(y$counts), "_tmm")
# visualize results and return data frame
if(plot == TRUE) {
boxplot(log10(df_tmm), col = color, notch = TRUE, main = "TMM Normalized data")
}
df_tmm
}
paw_spc_tmm <- apply_tmm_factors(y, color)
# check clustering
plotMDS(y, col = color, main = "Control vs Lepto")
The overall normalization factors (library size plus TMM factors) moved the median values a bit closer between the samples. The control samples clearly separate from the lepto samples.
CV <- function(df) {
# Computes CVs of data frame rows
# df - data frame,
# returns vector of CVs (%)
ave <- rowMeans(df) # compute averages
sd <- apply(df, 1, sd) # compute standard deviations
cv <- 100 * sd / ave # compute CVs in percent (last thing gets returned)
}
labeled_boxplot <- function(df, ylim, title) {
# Makes a box plot with the median value labeled
# df - data frame with data to compute CVs of
# ylim - upper limit for y-axis
# title - plot title
cv = CV(df)
boxplot(cv, ylim = c(0, ylim), notch = TRUE, main = title)
text(x = 0.65, y = boxplot.stats(cv)$stats[3],
labels = round(boxplot.stats(cv)$stats[3], 1))
}
# see what effect TMM had on CV distributions
par(mfrow = c(2, 2))
labeled_boxplot(paw_spc[C], 200, "Control before")
labeled_boxplot(paw_spc[L], 200, "Lepto before")
labeled_boxplot(paw_spc_tmm[C], 200, "Control after")
labeled_boxplot(paw_spc_tmm[L], 200, "Lepto after")
par(mfrow = c(1, 1))
The CVs in spectral counting are not great, but we might still have some larger differences in relative expression levels so we could still see DE candidates.
We will do a basic exact test in edgeR comparing control to lepto samples. We need to get trended dispersion first.
y <- estimateDisp(y)
plotBCV(y, main = "Variance Trends")
The reference state in edgeR is the second condition so up is increased expression in lepto samples and down is increased expression in the control samples.
et <- exactTest(y, pair = c("Control", "Lepto"))
topTags(et, n = 20)$table
tt <- topTags(et, n = Inf, sort.by = "none")$table
summary(decideTests(et, p.value = 0.05)) # usually use 0.10 for p-value
NOTE: Most normalization algorithms assume that the majority of proteins will not be differentially expressed (they all attempt to make something that should be the same end up being more "the same"). The TMM method in edgeR also works on that assumption. We have 259 unchanged proteins out of 669. This is quite a bit less than a majority of the proteins. The TMM results looked okay in the boxplots and clustering, but we should always keep these things in mind.
Even without a sequenced genome, the publication reported about 2700 protein identifications. With the new database, Comet/PAW had about 2300 protein identifications (2 peptides per protein), excluding contaminants. A minimum average SpC of 2.5 or greater was required for proteins to be considered as quantifiable. There were 669 proteins that could be tested with edgeR. Despite the reduced number of testable proteins, there are 410 DE candidates, with a balanced split between up and down regulated candidates. The publication reported 316 candidates.
collect_results <- function(df, tt, x, xlab, y, ylab) {
# Computes new columns and extracts some columns to make results frame
# df - data in data.frame
# tt - top tags from edgeR test
# x - columns for first condition
# xlab - label for x
# y - columns for second condition
# ylab - label for y
# returns a new dataframe
# condition average vectors
ave_x <- rowMeans(df[x])
ave_y <- rowMeans(df[y])
# FC, direction, candidates
fc <- ifelse(ave_y > ave_x, (ave_y / ave_x), (-1 * ave_x / ave_y))
direction <- ifelse(ave_y > ave_x, "up", "down")
candidate <- cut(tt$FDR, breaks = c(-Inf, 0.01, 0.05, 0.10, 1.0),
labels = c("high", "med", "low", "no"))
# make data frame
temp <- cbind(df[c(x, y)], data.frame(logFC = tt$logFC, FC = fc,
PValue = tt$PValue, FDR = tt$FDR,
ave_x = ave_x, ave_y = ave_y,
direction = direction, candidate = candidate,
Acc = tt$genes))
# fix column headers for averages
names(temp)[names(temp) %in% c("ave_x", "ave_y")] <- str_c("ave_", c(xlab, ylab))
temp # return the data frame
}
# get the results summary
results <- collect_results(paw_spc_tmm, tt, C, "Control", L, "Lepto")
pvalue_plots <- function(results, ylim, title) {
# Makes p-value distribution plots
# results - results data frame
# ylim - ymax for expanded view
# title - plot title
p_plot <- ggplot(results, aes(PValue)) +
geom_histogram(bins = 100, fill = "white", color = "black") +
geom_hline(yintercept = mean(hist(results$PValue, breaks = 100,
plot = FALSE)$counts[26:100]))
# we will need an expanded plot
p1 <- p_plot + ggtitle(str_c(title, " p-value distribution"))
p2 <- p_plot + coord_cartesian(xlim = c(0, 1.0), ylim = c(0, ylim)) + ggtitle("p-values expanded")
grid.arrange(p1, p2, nrow = 2) # from gridExtra package
}
# check the p-value distribution
pvalue_plots(results, 30, "Control vs Lepto - SpC")
We have a flat distribution at larger p-values and a spike at low p-values. We can see what the fold changes look like for the different levels of DE candidates and count candidates. The small spike at 1.0 is typical when we have small, discrete values.
We can count the candidates with an FDR less than 0.01 (high), between 0.05 and 0.01 (medium), between 0.10 and 0.05 (low), or greater than 0.10 (no). We can plot distributions of log2 fold-changes by category.
log2FC_plots <- function(results, range, title) {
# Makes faceted log2FC plots by candidate
# results - results data frame
# range - plus/minus log2 x-axis limits
# title - plot title
ggplot(results, aes(x = logFC, fill = candidate)) +
geom_histogram(binwidth=0.1, color = "black") +
facet_wrap(~candidate) +
ggtitle(title) +
coord_cartesian(xlim = c(-range, range))
}
# make log2FC plots
log2FC_plots(results, 5, "Faceted log2FC")
# see how many candidates are in each category
results %>% count(candidate)
We will make:
transform <- function(results, x, y) {
# Make data frame with some transformed columns
# results - results data frame
# x - columns for x condition
# y - columns for y condition
# return new data frame
df <- data.frame(log10((results[x] + results[y])/2),
log2(results[y] / results[x]),
results$candidate,
-log10(results$FDR))
colnames(df) <- c("A", "M", "candidate", "P")
df # return the data frame
}
MA_plots <- function(results, x, y, title) {
# makes MA-plot DE candidate ggplots
# results - data frame with edgeR results and some condition average columns
# x - string for x-axis column
# y - string for y-axis column
# title - title string to use in plots
# returns a list of plots
# uses transformed data
temp <- transform(results, x, y)
# 2-fold change lines
ma_lines <- list(geom_hline(yintercept = 0.0, color = "black"),
geom_hline(yintercept = 1.0, color = "black", linetype = "dotted"),
geom_hline(yintercept = -1.0, color = "black", linetype = "dotted"))
# make main MA plot
ma <- ggplot(temp, aes(x = A, y = M)) +
geom_point(aes(color = candidate, shape = candidate)) +
scale_y_continuous(paste0("logFC (", y, "/", x, ")")) +
scale_x_continuous("Ave_intensity") +
ggtitle(title) +
ma_lines
# make separate MA plots
ma_facet <- ggplot(temp, aes(x = A, y = M)) +
geom_point(aes(color = candidate, shape = candidate)) +
scale_y_continuous(paste0("log2 FC (", y, "/", x, ")")) +
scale_x_continuous("log10 Ave_intensity") +
ma_lines +
facet_wrap(~ candidate) +
ggtitle(str_c(title, " (separated)"))
# make the plots visible
print(ma)
print(ma_facet)
}
# make MA plots
MA_plots(results, "ave_Control", "ave_Lepto", "Control versus Lepto")
The spectral count values have been maintained in their natural measurement scale. That means that they are actual counts that have a Poisson distribution. EdgeR models that correctly so that the large uncertainty in small count numbers does not result in large numbers of false positive DE candidates.
There are other commonly used transformations of spectral count data where this is not the case. Samples can be normalized to have all counts sum up to one by dividing each column by its total. That makes the measurement values be small fractions between 0.0 and 1.0. Taking logs of counts would also alter the natural measurement scale and change the mean-variance relationship. There can also be protein length correction (dividing by length or number of theoretical peptides) of counts to make relative abundances more like molar concentrations instead of weight percentages. Those have the effect of somewhat local scrambling of abundances and it disrupts the smooth dispersion trend making trended variance estimates less effective.
The bottom line is that just about anything you would do transformation-wise to spectral counts will hurt the data analysis. This is also likely true in other quantitative measures beside spectral counting. Determining a natural measurement scale is very important for data modeling. Do not arbitrarily transform data!
scatter_plots <- function(results, x, y, title) {
# makes scatter-plot DE candidate ggplots
# results - data frame with edgeR results and some condition average columns
# x - string for x-axis column
# y - string for y-axis column
# title - title string to use in plots
# returns a list of plots
# 2-fold change lines
scatter_lines <- list(geom_abline(intercept = 0.0, slope = 1.0, color = "black"),
geom_abline(intercept = 0.301, slope = 1.0, color = "black", linetype = "dotted"),
geom_abline(intercept = -0.301, slope = 1.0, color = "black", linetype = "dotted"),
scale_y_log10(),
scale_x_log10())
# make main scatter plot
scatter <- ggplot(results, aes_string(x, y)) +
geom_point(aes(color = candidate, shape = candidate)) +
ggtitle(title) +
scatter_lines
# make separate scatter plots
scatter_facet <- ggplot(results, aes_string(x, y)) +
geom_point(aes(color = candidate, shape = candidate)) +
scatter_lines +
facet_wrap(~ candidate) +
ggtitle(str_c(title, " (separated)"))
# make the plots visible
print(scatter)
print(scatter_facet)
}
# make scatter plots
scatter_plots(results, "ave_Control", "ave_Lepto", "Control versus Lepto")
volcano_plot <- function(results, x, y, title) {
# makes a volcano plot
# results - a data frame with edgeR results
# x - string for the x-axis column
# y - string for y-axis column
# title - plot title string
# uses transformed data
temp <- transform(results, x, y)
# build the plot
ggplot(temp, aes(x = M, y = P)) +
geom_point(aes(color = candidate, shape = candidate)) +
xlab("log2 FC") +
ylab("-log10 FDR") +
ggtitle(str_c(title, " Volcano Plot"))
}
# make a volcano plot
volcano_plot(results, "ave_Control", "ave_Lepto", "Control versus Lepto")
Let's look at some individual protein data. We will plot the top 50 up regulated and the top 50 down regulated proteins (about a quarter of the DE candidates).
# functions for plotting individual proteins
# function to extract the identifier part of the accesssion
get_identifier <- function(accession) {
identifier <- str_split(accession, " |\\.", simplify = TRUE)
identifier[,1]
}
set_plot_dimensions <- function(width_choice, height_choice) {
options(repr.plot.width=width_choice, repr.plot.height=height_choice)
}
plot_top_tags <- function(results, nleft, nright, top_up, top_down) {
# results should have data first, then test results (two condition summary table)
# nleft, nright are number of data points in each condition
# top_up and top_down are number of up and number of down top DE candidates to plot
# get top ipregulated
up <- results %>%
filter(logFC >= 0) %>%
arrange(FDR)
up <- up[1:top_up, ]
# get top down regulated
down <- results %>%
filter(logFC < 0) %>%
arrange(FDR)
down <- down[1:top_down, ]
# pack them
proteins <- rbind(up, down)
color = c(rep("red", nleft), rep("blue", nright))
for (row_num in 1:nrow(proteins)) {
row <- proteins[row_num, ]
vec <- as.vector(unlist(row[1:(nleft + nright)]))
names(vec) <- colnames(row[1:(nleft + nright)])
title <- str_c(get_identifier(row$Acc), ", int: ", scientific(mean(vec), 2),
", FDR: ", scientific(row$FDR, digits = 3),
", FC: ", round(row$FC, digits = 1))
barplot(vec, col = color, main = title)
}
}
# plot the top 50 up and 50 down proteins
set_plot_dimensions(6, 3.5)
plot_top_tags(results, 8, 8, 50, 50)
set_plot_dimensions(7, 7)
The top candidates (by B-H adjusted p-values) are pretty convincing looking plots. We will still have to add some annotations to the results file so that we can follow up on these candidates.
Save results data frame to a disk file and log the R session. Generating the DE candidates is one of the earlier steps in the analysis and interpretation. Adding annotations, functional enrichment analysis, pathway analysis, and more are needed to extract the biological meaning from the data. The results table should be easy to add back to the main proteomics table before the next analysis steps.
# write results
write.table(results, "PXD009019_SpC_results.txt", sep = "\t", row.names = FALSE, na = " ")
# log the session
sessionInfo()


We are taking the week of Thanksgiving off. We will be back again on {{include.date_back}} with more civic hacking fun!


{% extends "club/base.html" %} {% block content %} {% include 'club/includes/messages.html' %} Welcome, {% if ongoing_event %} Ongoing Event {{ ongoing_event.title }} {% if ongoing_event.tagline %}{{ ongoing_event.tagline }}{% endif %} View {% endif %} Your Recent Activity No recent activity. Your Upcoming Events {% for rsvp in upcoming_rsvps %} {{ rsvp.event }} {% empty %} You haven't RSVPed for any upcoming events. {% endfor %} {% endblock %}
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| Created: | 4/29/2013 12:00:00 AM |
| Modified: | 4/30/2013 4:12:55 PM |
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Major entertainment venue in New York for this summer, Various shows of a selection of films for all ages.
Join us at Brooklyn Bridge Park this year from August 5th through 8th, from 6pm to midnight.
Register Now
T'Challa, heir to the hidden but advanced kingdom of Wakanda, must step forward to lead his people into a new future...
The story of Dick Cheney, an unassuming bureaucratic Washington insider ...
Ethan Hunt and his IMF team, along with some familiar allies, race against time after a mission gone wrong ...
After his 16-year-old daughter goes missing, a desperate father breaks into her laptop to look for clues to find her ...
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--- layout: post title: "每个人都有属于自己的超级原版相机" date: 2017-03-20 14:38:23 image: '/assets/img/' description: "" tags: - categories: - ---


Instructions:
Evaluate the postfix expression. You can performe an operation on the two topmost values in a stack by moving an operator into the evaluator.
Postfix Expression
Operand Stack


Regression splines that handle a mix of continuous and categorical (discrete) data often encountered in applied settings. I would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC, <http://www.nserc-crsng.gc.ca>), the Social Sciences and Humanities Research Council of Canada (SSHRC, <http://www.sshrc-crsh.gc.ca>), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, <https://www.sharcnet.ca>).
| Version: | 0.15-31 |
| Imports: | boot, stats, np, quantreg, rgl |
| Suggests: | logspline, quadprog |
| Published: | 2018-05-01 |
| Author: | Jeffrey S. Racine [aut, cre], Zhenghua Nie [aut], Brian D. Ripley [ctb] (stepCV.R) |
| Maintainer: | Jeffrey S. Racine <racinej at mcmaster.ca> |
| BugReports: | https://github.com/JeffreyRacine/R-Package-crs/issues |
| License: | GPL (≥ 3) |
| Copyright: | see file COPYRIGHTS |
| URL: | https://github.com/JeffreyRacine/R-Package-crs |
| NeedsCompilation: | yes |
| Materials: | README ChangeLog |
| In views: | Optimization |
| CRAN checks: | crs results |
| Reference manual: | crs.pdf |
| Vignettes: |
The crs Package Frequently Asked Questions (crs) A Primer on Spline Regression |
| Package source: | crs_0.15-31.tar.gz |
| Windows binaries: | r-devel: crs_0.15-31.zip, r-release: crs_0.15-31.zip, r-oldrel: crs_0.15-31.zip |
| OS X binaries: | r-release: crs_0.15-31.tgz, r-oldrel: crs_0.15-31.tgz |
| Old sources: | crs archive |
| Reverse suggests: | laGP, mlr |
Please use the canonical form https://CRAN.R-project.org/package=crs to link to this page.


Let's write a ccs minification task
We are going to install a gulp plugin called gulp-minify-css
npm install --save-dev gulp-minify-css
var minifyCss=require('gulp-minify-css')
gulp.task('styles', function(){
gulp.src(['src/styles/**/*.css']) //source for gulp to watch
.pipe(minifyCss()) // Run minify on the src
.pipe(gulp.dest('dist/styles')) //save to dist dir
.pipe(browserSync.stream()); //and finally reload
});
<link rel="stylesheet" href="dist/styles/main.css">
Let's make a change to css
gulp.watch('src/**/*', browserSync.reload);
gulp.watch('src/styles/**/*.css', ['styles']);
If you try to debug the minified css, it is going to get tricky
Sourcemaps to the rescue!
We are going to install another gulp plugin called gulp-sourcemaps
npm install --save-dev gulp-sourcemaps
Require it in our gulpfile
var sourcemaps=require('gulp-’sourcemaps')
gulp.task('styles', function(){
gulp.src(['src/styles/**/*.css']) //source for gulp to watch
.pipe(sourcemaps.init()) //init sourcemaps
.pipe(minifyCss()) //stream! Run minify on the src
.pipe(sourcemaps.write()) //write sourcemaps


 In Lina's youth, as her natural talents first began to smoke and smolder it was her fiery hair that told the tale of the storm to come. Her skill and control of the flame was enough to suppress the searing display.
Blades of Voth Domosh
Set in her vengeance against the abyss, Tresdin approached the baleful armorer Qwyth, who gifted to her these hellbound blades. Though she obtained the swords without cost, she may realize too late that the price extracted for wielding such incredible power could be more than she is willing to part with.
Fractal Horns of Inner Abysm
Deep in the hell of Hell, Terrorblade lay bound behind fractal walls, sentenced to an eternity of twisted contemplation. He stared long into the reflection of his own worst self. And long did his worst self stare into him. Now they rise together, a single entity more powerful than ever before.
Swine of the Sunken Galley
After months of work, Techies Demolitions proudly unveiled their latest commission: The Swine, the most powerful cannon ever designed. Yet during its first frantic use aboard a homebound galley, the Swine's awesome blast tore its host ship asunder, rocketing the cannon itself backwards toward the shore..
Demon Eater
Shadow Fiend has long collected the souls of his enemies. As is the case with any collector, some prizes have stood above others, to be sought out at any cost. However, there are certain souls that should not be tried, and forces so dark and filled with rage that no being could hope to contain them.
Manifold Paradox
With a raspy cackle, the elder smith Craler swung the sword that his family had spent eleven generations to fold and forge. So sharp it was that, with a sound like tearing fabric, a rip in reality tore open. Through this rip, Craler recognized himself from moments before, holding aloft the very same coveted blade.
Frost Avalanche
Some say the garment began as the wedding gown of an ancient queen. Others say it was once a battle cloak of a frost ogre. In truth, the enchanted fabric is as malleable as an avalanche, and can mold itself into various forms. This form has attracted a lone wolf pup and become a loyal companion.
Tempest Helm of the Thundergod
Forged by the Hammers of Celestarr and imbued with the energies of the Ancients, this mighty helm feels at home resting across the brow of the Lord of Heaven himself. The faithless shall tremble as true thunder rolls across the fields of battle!


Hi, I'm William!
Six months ago, little snippets of code would cause a massive storm of confusion in my mind. Now, I am able to find my peace of mind under confusion while exploring all types of applications and technologies. Coding and web development is an ongoing challenge that allows me to design with pure creativity. My hunger and passion for creating and learning new things will keep me running at top speed.
After learning how to learn at Dev Bootcamp, I can say that I am no longer afraid of picking up new languages and concepts. From defining user stories to API integration, I can deliver a MVP, ASAP. My primary foci include; Ruby on Rails and JavaScript(Vanilla, AngularJS, jQuery, and AJAX). I'm also open to learning new languages like Python and Swift!
Through engineering empathy, I have been enlightened about my very own superego, emotions, and triggering events. I have discovered more about myself and how I handle certain situations. Realizing the value in engineering empathy, I decided to share it with people in my personal and professional life. Expect me to share this knowledge and mindset, whenever and wherever!
Communication is the key to success whether it be sending out an email about contributions to a new product feature or simply having lunch and coffee with friends. I treat conversations as a two way street of listening and talking. Through this practice, my peers and I understand each other while maintaining transparency and honesty. Let's have a chat!
I am an eccentric web developer with a curiosity for finding out how things work. Since I was young, I've always had a habit for disassembling and reassembling my toys, electronics, furniture, and etc. My hypothesis is that I'm either trying to fix a broken part or to make it more efficient. This particular habit combined with the thirst for knowledge pushed me to enroll at Dev Bootcamp where I learned about web development and intra/intrepersonal skills.
Fun Fact: I developed the mindset of not saying, "I dont know." until I've dived into the problem. How would I not know if I haven't tried it yet?


The building of website is fun.If you are not coming from math background and want to learn coding.Start from here. If you are new.You can start from here.


SHUTTERBUGS presents you a mobile photography event with a twist ..! Click a picture on the basis of a Bollywood movie dialogue you have been assigned and you could win prizes ..! Clicking pictures has never been so much fun. So why not bring along your mates and start clicking ?
To register yourself : http://bit.ly/2k208SB
1. Only mobile photography is allowed.
2. Area for photography will be restricted.
3. SHUTTERBUGS is not responsible for your clicks.
1 member only
7/3/17 Tuesday
12:00 pm onwards
Garden near CF/Library.
20 Rs.
Hardik Shingala
Mo.<PHONE_NUMBER>
Jainil Desai
Mo.<PHONE_NUMBER>


No More Discrimination: Cross City Adaptation of Road Scene Segmenters
We propose an unsupervised learning approach to
adapt road scene segmenters across different cities. By utilizing Google Street View and its time-machine feature, we can collect
unannotated images for each road scene at different times, so that the associated static-object priors can be extracted accordingly.
By advancing a joint global and class-specific domain adversarial learning framework, adaptation of pre-trained segmenters to that
city can be achieved without the need of any user annotation or interaction. We show that our method improves the performance of
semantic segmentation in multiple cities across continents, while it performs favorably against state-of-the-art approaches requiring
annotated training data.
Yi-Hsin Chen, Wei-Yu Chen, Yu-Ting Chen, Bo-Cheng Tsai, Yu-Chiang Frank Wang, Min Sun
Under Review for ICCV2017


This week in The Dabbler I pioneer a new form of close textual analysis of classic songs, starting with “Suzanne” by Leonard Cohen. And when I say “close”, I mean close—in my attempt to winkle out the deep meaning of the song I concentrate on a single six-word phrase, jettisoning the rest of it. And when I say “winkle”, well, as you will discover when you pore over my piece, winkles and other hardy organic life-forms of the tide pool have a crucial part to play in the heretofore hidden deep structure of the song.
One unexpected advantage of my brain-numbing work was the realisation that there is a connection, albeit tentative and tangential, with my concurrent lobster research. I make no great claims for what we might call “the Suzanne lobster”, but it suffices, I think, that the word “lobsters” appears, not at all gratuitously, in my analysis.
These are hideously complicated matters, but I hope I have been able to shed some little light on them.


Video Gallery.
Use your remote to select and play each video.
FOODIE GETAWAYS
How to Cut
Cherry Tomatoes
Romantic Trips
World's Fastest
Tomato Salad
Best Family Hotels
Terms & Conditions
Travel Ideas
Party Recipes
See Cards
FOODIE GETAWAYS
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