Spark Programming Guide
- Overview
- Linking with Spark
- Initializing Spark
- Resilient Distributed Datasets (RDDs)
- Shared Variables
- Where to Go from Here
Overview
At a high level, every Spark application consists of a driver program that runs the user’s main
function and executes various parallel operations on a cluster. The main abstraction Spark provides is a resilient distributed dataset (RDD), which is a collection of elements partitioned across the nodes of the cluster that can be operated on in parallel. RDDs are created by starting with a file in the Hadoop file system (or any other Hadoop-supported file system), or an existing Scala collection in the driver program, and transforming it. Users may also ask Spark to persist an RDD in memory, allowing it to be reused efficiently across parallel operations. Finally, RDDs automatically recover from node failures.
A second abstraction in Spark is shared variables that can be used in parallel operations. By default, when Spark runs a function in parallel as a set of tasks on different nodes, it ships a copy of each variable used in the function to each task. Sometimes, a variable needs to be shared across tasks, or between tasks and the driver program. Spark supports two types of shared variables: broadcast variables, which can be used to cache a value in memory on all nodes, and accumulators, which are variables that are only “added” to, such as counters and sums.
This guide shows each of these features and walks through some samples. It assumes some familiarity with Scala, especially with the syntax for closures. Note that you can also run Spark interactively using the spark-shell
script. We highly recommend doing that to follow along!
Linking with Spark
To write a Spark application, you will need to add both Spark and its dependencies to your CLASSPATH. If you use sbt or Maven, Spark is available through Maven Central at:
groupId = org.spark-project
artifactId = spark-core_2.9.3
version = 0.7.3
For other build systems or environments, you can run sbt/sbt assembly
to build both Spark and its dependencies into one JAR (core/target/spark-core-assembly-0.6.0.jar
), then add this to your CLASSPATH.
In addition, you’ll need to import some Spark classes and implicit conversions. Add the following lines at the top of your program:
import spark.SparkContext
import SparkContext._
Initializing Spark
The first thing a Spark program must do is to create a SparkContext
object, which tells Spark how to access a cluster.
This is done through the following constructor:
new SparkContext(master, appName, [sparkHome], [jars])
The master
parameter is a string specifying a Spark or Mesos cluster URL to connect to, or a special “local” string to run in local mode, as described below. appName
is a name for your application, which will be shown in the cluster web UI. Finally, the last two parameters are needed to deploy your code to a cluster if running in distributed mode, as described later.
In the Spark shell, a special interpreter-aware SparkContext is already created for you, in the variable called sc
. Making your own SparkContext will not work. You can set which master the context connects to using the MASTER
environment variable, and you can add JARs to the classpath with the ADD_JARS
variable. For example, to run spark-shell
on four cores, use
$ MASTER=local[4] ./spark-shell
Or, to also add code.jar
to its classpath, use:
$ MASTER=local[4] ADD_JARS=code.jar ./spark-shell
Master URLs
The master URL passed to Spark can be in one of the following formats:
| Master URL | Meaning |
|---|---|
| local | Run Spark locally with one worker thread (i.e. no parallelism at all). |
| local[K] | Run Spark locally with K worker threads (ideally, set this to the number of cores on your machine). |
| spark://HOST:PORT | Connect to the given Spark standalone cluster master. The port must be whichever one your master is configured to use, which is 7077 by default. |
| mesos://HOST:PORT | Connect to the given Mesos cluster. The host parameter is the hostname of the Mesos master. The port must be whichever one the master is configured to use, which is 5050 by default. |
For running on YARN, Spark launches an instance of the standalone deploy cluster within YARN; see running on YARN for details.
Deploying Code on a Cluster
If you want to run your job on a cluster, you will need to specify the two optional parameters to SparkContext
to let it find your code:
sparkHome
: The path at which Spark is installed on your worker machines (it should be the same on all of them).jars
: A list of JAR files on the local machine containing your job’s code and any dependencies, which Spark will deploy to all the worker nodes. You’ll need to package your job into a set of JARs using your build system. For example, if you’re using SBT, the sbt-assembly plugin is a good way to make a single JAR with your code and dependencies.
If you run spark-shell
on a cluster, you can add JARs to it by specifying the ADD_JARS
environment variable before you launch it. This variable should contain a comma-separated list of JARs. For example, ADD_JARS=a.jar,b.jar ./spark-shell
will launch a shell with a.jar
and b.jar
on its classpath. In addition, any new classes you define in the shell will automatically be distributed.
Resilient Distributed Datasets (RDDs)
Spark revolves around the concept of a resilient distributed dataset (RDD), which is a fault-tolerant collection of elements that can be operated on in parallel. There are currently two types of RDDs: parallelized collections, which take an existing Scala collection and run functions on it in parallel, and Hadoop datasets, which run functions on each record of a file in Hadoop distributed file system or any other storage system supported by Hadoop. Both types of RDDs can be operated on through the same methods.
Parallelized Collections
Parallelized collections are created by calling SparkContext
’s parallelize
method on an existing Scala collection (a Seq
object). The elements of the collection are copied to form a distributed dataset that can be operated on in parallel. For example, here is some interpreter output showing how to create a parallel collection from an array:
scala> val data = Array(1, 2, 3, 4, 5)
data: Array[Int] = Array(1, 2, 3, 4, 5)
scala> val distData = sc.parallelize(data)
distData: spark.RDD[Int] = spark.ParallelCollection@10d13e3e
Once created, the distributed dataset (distData
here) can be operated on in parallel. For example, we might call distData.reduce(_ + _)
to add up the elements of the array. We describe operations on distributed datasets later on.
One important parameter for parallel collections is the number of slices to cut the dataset into. Spark will run one task for each slice of the cluster. Typically you want 2-4 slices for each CPU in your cluster. Normally, Spark tries to set the number of slices automatically based on your cluster. However, you can also set it manually by passing it as a second parameter to parallelize
(e.g. sc.parallelize(data, 10)
).
Hadoop Datasets
Spark can create distributed datasets from any file stored in the Hadoop distributed file system (HDFS) or other storage systems supported by Hadoop (including your local file system, Amazon S3, Hypertable, HBase, etc). Spark supports text files, SequenceFiles, and any other Hadoop InputFormat.
Text file RDDs can be created using SparkContext
’s textFile
method. This method takes an URI for the file (either a local path on the machine, or a hdfs://
, s3n://
, kfs://
, etc URI). Here is an example invocation:
scala> val distFile = sc.textFile("data.txt")
distFile: spark.RDD[String] = spark.HadoopRDD@1d4cee08
Once created, distFile
can be acted on by dataset operations. For example, we can add up the sizes of all the lines using the map
and reduce
operations as follows: distFile.map(_.size).reduce(_ + _)
.
The textFile
method also takes an optional second argument for controlling the number of slices of the file. By default, Spark creates one slice for each block of the file (blocks being 64MB by default in HDFS), but you can also ask for a higher number of slices by passing a larger value. Note that you cannot have fewer slices than blocks.
For SequenceFiles, use SparkContext’s sequenceFile[K, V]
method where K
and V
are the types of key and values in the file. These should be subclasses of Hadoop’s Writable interface, like IntWritable and Text. In addition, Spark allows you to specify native types for a few common Writables; for example, sequenceFile[Int, String]
will automatically read IntWritables and Texts.
Finally, for other Hadoop InputFormats, you can use the SparkContext.hadoopRDD
method, which takes an arbitrary JobConf
and input format class, key class and value class. Set these the same way you would for a Hadoop job with your input source.
RDD Operations
RDDs support two types of operations: transformations, which create a new dataset from an existing one, and actions, which return a value to the driver program after running a computation on the dataset. For example, map
is a transformation that passes each dataset element through a function and returns a new distributed dataset representing the results. On the other hand, reduce
is an action that aggregates all the elements of the dataset using some function and returns the final result to the driver program (although there is also a parallel reduceByKey
that returns a distributed dataset).
All transformations in Spark are lazy, in that they do not compute their results right away. Instead, they just remember the transformations applied to some base dataset (e.g. a file). The transformations are only computed when an action requires a result to be returned to the driver program. This design enables Spark to run more efficiently – for example, we can realize that a dataset created through map
will be used in a reduce
and return only the result of the reduce
to the driver, rather than the larger mapped dataset.
By default, each transformed RDD is recomputed each time you run an action on it. However, you may also persist an RDD in memory using the persist
(or cache
) method, in which case Spark will keep the elements around on the cluster for much faster access the next time you query it. There is also support for persisting datasets on disk, or replicated across the cluster. The next section in this document describes these options.
The following tables list the transformations and actions currently supported (see also the RDD API doc for details):
Transformations
| Transformation | Meaning |
|---|---|
| map(func) | Return a new distributed dataset formed by passing each element of the source through a function func. |
| filter(func) | Return a new dataset formed by selecting those elements of the source on which func returns true. |
| flatMap(func) | Similar to map, but each input item can be mapped to 0 or more output items (so func should return a Seq rather than a single item). |
| mapPartitions(func) | Similar to map, but runs separately on each partition (block) of the RDD, so func must be of type Iterator[T] => Iterator[U] when running on an RDD of type T. |
| mapPartitionsWithSplit(func) | Similar to mapPartitions, but also provides func with an integer value representing the index of the split, so func must be of type (Int, Iterator[T]) => Iterator[U] when running on an RDD of type T. |
| sample(withReplacement, fraction, seed) | Sample a fraction fraction of the data, with or without replacement, using a given random number generator seed. |
| union(otherDataset) | Return a new dataset that contains the union of the elements in the source dataset and the argument. |
| distinct([numTasks])) | Return a new dataset that contains the distinct elements of the source dataset. |
| groupByKey([numTasks]) | When called on a dataset of (K, V) pairs, returns a dataset of (K, Seq[V]) pairs. Note: By default, this uses only 8 parallel tasks to do the grouping. You can pass an optional numTasks argument to set a different number of tasks.
|
| reduceByKey(func, [numTasks]) | When called on a dataset of (K, V) pairs, returns a dataset of (K, V) pairs where the values for each key are aggregated using the given reduce function. Like in groupByKey , the number of reduce tasks is configurable through an optional second argument. |
| sortByKey([ascending], [numTasks]) | When called on a dataset of (K, V) pairs where K implements Ordered, returns a dataset of (K, V) pairs sorted by keys in ascending or descending order, as specified in the boolean ascending argument. |
| join(otherDataset, [numTasks]) | When called on datasets of type (K, V) and (K, W), returns a dataset of (K, (V, W)) pairs with all pairs of elements for each key. |
| cogroup(otherDataset, [numTasks]) | When called on datasets of type (K, V) and (K, W), returns a dataset of (K, Seq[V], Seq[W]) tuples. This operation is also called groupWith . |
| cartesian(otherDataset) | When called on datasets of types T and U, returns a dataset of (T, U) pairs (all pairs of elements). |
A complete list of transformations is available in the RDD API doc.
Actions
| Action | Meaning |
|---|---|
| reduce(func) | Aggregate the elements of the dataset using a function func (which takes two arguments and returns one). The function should be commutative and associative so that it can be computed correctly in parallel. |
| collect() | Return all the elements of the dataset as an array at the driver program. This is usually useful after a filter or other operation that returns a sufficiently small subset of the data. |
| count() | Return the number of elements in the dataset. |
| first() | Return the first element of the dataset (similar to take(1)). |
| take(n) | Return an array with the first n elements of the dataset. Note that this is currently not executed in parallel. Instead, the driver program computes all the elements. |
| takeSample(withReplacement, num, seed) | Return an array with a random sample of num elements of the dataset, with or without replacement, using the given random number generator seed. |
| saveAsTextFile(path) | Write the elements of the dataset as a text file (or set of text files) in a given directory in the local filesystem, HDFS or any other Hadoop-supported file system. Spark will call toString on each element to convert it to a line of text in the file. |
| saveAsSequenceFile(path) | Write the elements of the dataset as a Hadoop SequenceFile in a given path in the local filesystem, HDFS or any other Hadoop-supported file system. This is only available on RDDs of key-value pairs that either implement Hadoop's Writable interface or are implicitly convertible to Writable (Spark includes conversions for basic types like Int, Double, String, etc). |
| countByKey() | Only available on RDDs of type (K, V). Returns a `Map` of (K, Int) pairs with the count of each key. |
| foreach(func) | Run a function func on each element of the dataset. This is usually done for side effects such as updating an accumulator variable (see below) or interacting with external storage systems. |
A complete list of actions is available in the RDD API doc.
RDD Persistence
One of the most important capabilities in Spark is persisting (or caching) a dataset in memory across operations. When you persist an RDD, each node stores any slices of it that it computes in memory and reuses them in other actions on that dataset (or datasets derived from it). This allows future actions to be much faster (often by more than 10x). Caching is a key tool for building iterative algorithms with Spark and for interactive use from the interpreter.
You can mark an RDD to be persisted using the persist()
or cache()
methods on it. The first time it is computed in an action, it will be kept in memory on the nodes. The cache is fault-tolerant – if any partition of an RDD is lost, it will automatically be recomputed using the transformations that originally created it.
In addition, each RDD can be stored using a different storage level, allowing you, for example, to persist the dataset on disk, or persist it in memory but as serialized Java objects (to save space), or even replicate it across nodes. These levels are chosen by passing a spark.storage.StorageLevel
object to persist()
. The cache()
method is a shorthand for using the default storage level, which is StorageLevel.MEMORY_ONLY
(store deserialized objects in memory). The complete set of available storage levels is:
| Storage Level | Meaning |
|---|---|
| MEMORY_ONLY | Store RDD as deserialized Java objects in the JVM. If the RDD does not fit in memory, some partitions will not be cached and will be recomputed on the fly each time they're needed. This is the default level. |
| MEMORY_AND_DISK | Store RDD as deserialized Java objects in the JVM. If the RDD does not fit in memory, store the partitions that don't fit on disk, and read them from there when they're needed. |
| MEMORY_ONLY_SER | Store RDD as serialized Java objects (one byte array per partition). This is generally more space-efficient than deserialized objects, especially when using a fast serializer, but more CPU-intensive to read. |
| MEMORY_AND_DISK_SER | Similar to MEMORY_ONLY_SER, but spill partitions that don't fit in memory to disk instead of recomputing them on the fly each time they're needed. |
| DISK_ONLY | Store the RDD partitions only on disk. |
| MEMORY_ONLY_2, MEMORY_AND_DISK_2, etc. | Same as the levels above, but replicate each partition on two cluster nodes. |
Which Storage Level to Choose?
Spark’s storage levels are meant to provide different tradeoffs between memory usage and CPU efficiency. We recommend going through the following process to select one:
- If your RDDs fit comfortably with the default storage level (
MEMORY_ONLY
), leave them that way. This is the most CPU-efficient option, allowing operations on the RDDs to run as fast as possible. - If not, try using
MEMORY_ONLY_SER
and selecting a fast serialization library to make the objects much more space-efficient, but still reasonably fast to access. - Don’t spill to disk unless the functions that computed your datasets are expensive, or they filter a large amount of the data. Otherwise, recomputing a partition is about as fast as reading it from disk.
- Use the replicated storage levels if you want fast fault recovery (e.g. if using Spark to serve requests from a web application). All the storage levels provide full fault tolerance by recomputing lost data, but the replicated ones let you continue running tasks on the RDD without waiting to recompute a lost partition.
If you want to define your own storage level (say, with replication factor of 3 instead of 2), then use the function factor method apply()
of the StorageLevel
singleton object.
Shared Variables
Normally, when a function passed to a Spark operation (such as map
or reduce
) is executed on a remote cluster node, it works on separate copies of all the variables used in the function. These variables are copied to each machine, and no updates to the variables on the remote machine are propagated back to the driver program. Supporting general, read-write shared variables across tasks would be inefficient. However, Spark does provide two limited types of shared variables for two common usage patterns: broadcast variables and accumulators.
Broadcast Variables
Broadcast variables allow the programmer to keep a read-only variable cached on each machine rather than shipping a copy of it with tasks. They can be used, for example, to give every node a copy of a large input dataset in an efficient manner. Spark also attempts to distribute broadcast variables using efficient broadcast algorithms to reduce communication cost.
Broadcast variables are created from a variable v
by calling SparkContext.broadcast(v)
. The broadcast variable is a wrapper around v
, and its value can be accessed by calling the value
method. The interpreter session below shows this:
scala> val broadcastVar = sc.broadcast(Array(1, 2, 3))
broadcastVar: spark.Broadcast[Array[Int]] = spark.Broadcast(b5c40191-a864-4c7d-b9bf-d87e1a4e787c)
scala> broadcastVar.value
res0: Array[Int] = Array(1, 2, 3)
After the broadcast variable is created, it should be used instead of the value v
in any functions run on the cluster so that v
is not shipped to the nodes more than once. In addition, the object v
should not be modified after it is broadcast in order to ensure that all nodes get the same value of the broadcast variable (e.g. if the variable is shipped to a new node later).
Accumulators
Accumulators are variables that are only “added” to through an associative operation and can therefore be efficiently supported in parallel. They can be used to implement counters (as in MapReduce) or sums. Spark natively supports accumulators of type Int and Double, and programmers can add support for new types.
An accumulator is created from an initial value v
by calling SparkContext.accumulator(v)
. Tasks running on the cluster can then add to it using the +=
operator. However, they cannot read its value. Only the driver program can read the accumulator’s value, using its value
method.
The interpreter session below shows an accumulator being used to add up the elements of an array:
scala> val accum = sc.accumulator(0)
accum: spark.Accumulator[Int] = 0
scala> sc.parallelize(Array(1, 2, 3, 4)).foreach(x => accum += x)
...
10/09/29 18:41:08 INFO SparkContext: Tasks finished in 0.317106 s
scala> accum.value
res2: Int = 10
Where to Go from Here
You can see some example Spark programs on the Spark website.
In addition, Spark includes several sample programs in examples/src/main/scala
. Some of them have both Spark versions and local (non-parallel) versions, allowing you to see what had to be changed to make the program run on a cluster. You can run them using by passing the class name to the run
script included in Spark – for example, ./run spark.examples.SparkPi
. Each example program prints usage help when run without any arguments.
For help on optimizing your program, the configuration and tuning guides provide information on best practices. They are especially important for making sure that your data is stored in memory in an efficient format.


The Static Head Common
The Static Header
This is a Spring Boot Starter Application
List of all Cities
List of Cities (Paginated)


'Headlock Him Up': Hundreds protest President Trump outside UFC Fight at the Garden
by Eyewitness NewsMIDTOWN, Manhattan (WABC) -- President Donald Trump is back in New York City - attending a UFC fight at Madison Square Garden.
The President had seats on the floor. Sitting on one side of him was House Minority Leader Kevin McCarthy. On the other side of him were his sons, Donald Jr., and Eric.
Long Island Republican Congressman Peter King was also there.
Hundreds of people protested outside The Garden shouting things like 'lock him up.'
Some also held signs reading 'headlock him up' - a reference to the UFC fight he came to watch.
The President is expected to spend the night at Trump Tower, likely making further traffic complications when the Marathon takes place.
The President's visit to the city comes just 48 hours after he switched his residency from Manahttan to Palm Beach, Florida.


{% extends 'base.html'%} {% block styles%} {% endblock%} {% block content%} {% if profiles%} {% for profile in profiles%}


Test passed in 2 seconds on Genymotion Google Nexus 7 - 4.4.2 - API 19 - 800x1280
| Timestamp | Level | Tag | Message |
|---|---|---|---|
| 07-31 21:49:40.192 | info | TestRunner | started: testSetEmailInvalidError(com.emmasuzuki.espressospoondemo.LoginActivityTest) |
| 07-31 21:49:40.196 | info | GoogleInstr | Activities that are still in CREATED to PAUSED: 0 |
| 07-31 21:49:40.196 | debug | GoogleInstr | execStartActivity(context, ibinder, ibinder, activity, intent, int, bundle |
| 07-31 21:49:40.212 | debug | LifecycleMonitor | Lifecycle status change: com.emmasuzuki.espressospoondemo.LoginActivity@5281c7d4 in: PRE_ON_CREATE |
| 07-31 21:49:40.280 | debug | dalvikvm | GC_FOR_ALLOC freed 90K, 19% free 3088K/3768K, paused 12ms, total 12ms |
| 07-31 21:49:40.280 | info | dalvikvm-heap | Grow heap (frag case) to 3.545MB for 500412-byte allocation |
| 07-31 21:49:40.296 | debug | dalvikvm | GC_FOR_ALLOC freed <1K, 17% free 3576K/4260K, paused 18ms, total 18ms |
| 07-31 21:49:40.324 | debug | LifecycleMonitor | Lifecycle status change: com.emmasuzuki.espressospoondemo.LoginActivity@5281c7d4 in: CREATED |
| 07-31 21:49:40.324 | debug | LifecycleMonitor | Lifecycle status change: com.emmasuzuki.espressospoondemo.LoginActivity@5281c7d4 in: STARTED |
| 07-31 21:49:40.324 | debug | LifecycleMonitor | Lifecycle status change: com.emmasuzuki.espressospoondemo.LoginActivity@5281c7d4 in: RESUMED |
| 07-31 21:49:40.412 | debug | libEGL | loaded /system/lib/egl/libEGL_genymotion.so |
| 07-31 21:49:40.416 | debug | HostConnection::get() New Host Connection established 0xb94e12f8, tid 1458 | |
| 07-31 21:49:40.436 | debug | libEGL | loaded /system/lib/egl/libGLESv1_CM_genymotion.so |
| 07-31 21:49:40.436 | debug | libEGL | loaded /system/lib/egl/libGLESv2_genymotion.so |
| 07-31 21:49:40.492 | warn | EGL_genymotion | eglSurfaceAttrib not implemented |
| 07-31 21:49:40.492 | error | OpenGLRenderer | Getting MAX_TEXTURE_SIZE from GradienCache |
| 07-31 21:49:40.492 | error | OpenGLRenderer | MAX_TEXTURE_SIZE: 16384 |
| 07-31 21:49:40.500 | error | OpenGLRenderer | Getting MAX_TEXTURE_SIZE from Caches::initConstraints() |
| 07-31 21:49:40.504 | error | OpenGLRenderer | MAX_TEXTURE_SIZE: 16384 |
| 07-31 21:49:40.504 | debug | OpenGLRenderer | Enabling debug mode 0 |
| 07-31 21:49:41.084 | debug | dalvikvm | GC_FOR_ALLOC freed 67K, 14% free 3701K/4260K, paused 3ms, total 3ms |
| 07-31 21:49:41.084 | info | dalvikvm-heap | Grow heap (frag case) to 7.377MB for 3891212-byte allocation |
| 07-31 21:49:41.088 | debug | dalvikvm | GC_FOR_ALLOC freed 0K, 7% free 7501K/8064K, paused 4ms, total 4ms |
| 07-31 21:49:41.224 | debug | Spoon | Captured screenshot 'initial_state'. |
| 07-31 21:49:41.228 | debug | InputManagerEventInjectionStrategy | Creating injection strategy with input manager. |
| 07-31 21:49:41.232 | info | ViewInteraction | Performing 'type text' action on view with id: is <2131165185> |
| 07-31 21:49:41.232 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 what=4 arg1=1 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.256 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-1ms callback=com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask target=android.os.Handler }, with target: android.os.Handler, callback: com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask@5281b694 |
| 07-31 21:49:41.256 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 what=0 arg1=3 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.276 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 callback=com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask target=android.os.Handler }, with target: android.os.Handler, callback: com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask@527eec24 |
| 07-31 21:49:41.276 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 what=0 arg1=5 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.288 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 callback=com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask target=android.os.Handler }, with target: android.os.Handler, callback: com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask@527fcabc |
| 07-31 21:49:41.288 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 what=0 arg1=7 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.296 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-1ms callback=com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask target=android.os.Handler }, with target: android.os.Handler, callback: com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask@5283cbb8 |
| 07-31 21:49:41.296 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 what=0 arg1=9 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.300 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-1ms callback=com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask target=android.os.Handler }, with target: android.os.Handler, callback: com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask@52801fe0 |
| 07-31 21:49:41.300 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 what=0 arg1=11 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.308 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-2ms callback=com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask target=android.os.Handler }, with target: android.os.Handler, callback: com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask@52823f7c |
| 07-31 21:49:41.308 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 what=0 arg1=13 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.312 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-1ms callback=com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask target=android.os.Handler }, with target: android.os.Handler, callback: com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask@527ddf14 |
| 07-31 21:49:41.312 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 what=0 arg1=15 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.316 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-4ms callback=android.view.View$PerformClick target=android.view.ViewRootImpl$ViewRootHandler }, with target: android.view.ViewRootImpl.ViewRootHandler, callback: android.view.View$PerformClick@5281adc8 |
| 07-31 21:49:41.316 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-1ms what=4 arg1=17 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.360 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 callback=android.widget.Editor$Blink target=android.widget.Editor$Blink }, with target: android.widget.Editor.Blink, callback: Handler (android.widget.Editor$Blink) {527d6000} |
| 07-31 21:49:41.360 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:41.376 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-1ms callback=android.view.View$UnsetPressedState target=android.view.ViewRootImpl$ViewRootHandler }, with target: android.view.ViewRootImpl.ViewRootHandler, callback: android.view.View$UnsetPressedState@5281add0 |
| 07-31 21:49:41.384 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-1ms callback=com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask target=android.os.Handler }, with target: android.os.Handler, callback: com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask@527cb4f0 |
| 07-31 21:49:41.384 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 what=0 arg1=19 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.384 | debug | UiControllerImpl | Injecting string: "test |
| 07-31 21:49:41.384 | debug | UiControllerImpl | " |
| 07-31 21:49:41.384 | verbose | ESP_TRACE | EventInjector:Injecting event for character (t) with key code (48) downtime: (284358) |
| 07-31 21:49:41.412 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:41.420 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-24ms what=3 arg1=23 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.420 | verbose | ESP_TRACE | EventInjector:Injecting event for character (t) with key code (48) downtime: (284358) |
| 07-31 21:49:41.420 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 what=3 arg1=25 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.420 | verbose | ESP_TRACE | EventInjector:Injecting event for character (e) with key code (33) downtime: (284358) |
| 07-31 21:49:41.428 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-3ms callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:41.436 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-10ms what=3 arg1=27 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.436 | verbose | ESP_TRACE | EventInjector:Injecting event for character (e) with key code (33) downtime: (284358) |
| 07-31 21:49:41.436 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 what=3 arg1=29 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.436 | verbose | ESP_TRACE | EventInjector:Injecting event for character (s) with key code (47) downtime: (284358) |
| 07-31 21:49:41.444 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-3ms callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:41.452 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-15ms what=3 arg1=31 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.456 | verbose | ESP_TRACE | EventInjector:Injecting event for character (s) with key code (47) downtime: (284358) |
| 07-31 21:49:41.456 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 what=3 arg1=33 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.460 | verbose | ESP_TRACE | EventInjector:Injecting event for character (t) with key code (48) downtime: (284358) |
| 07-31 21:49:41.476 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-2ms callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:41.480 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-15ms what=3 arg1=35 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.484 | verbose | ESP_TRACE | EventInjector:Injecting event for character (t) with key code (48) downtime: (284358) |
| 07-31 21:49:41.484 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-1ms what=3 arg1=37 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.484 | verbose | ESP_TRACE | EventInjector:Injecting event for character ( |
| 07-31 21:49:41.484 | verbose | ESP_TRACE | ) with key code (66) downtime: (284358) |
| 07-31 21:49:41.488 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 what=3 arg1=39 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.488 | verbose | ESP_TRACE | EventInjector:Injecting event for character ( |
| 07-31 21:49:41.488 | verbose | ESP_TRACE | ) with key code (66) downtime: (284358) |
| 07-31 21:49:41.496 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-2ms callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:41.508 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-20ms what=13 target=android.view.ViewRootImpl$ViewRootHandler }, with target: android.view.ViewRootImpl.ViewRootHandler, callback: no callback. |
| 07-31 21:49:41.508 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-19ms what=3 arg1=41 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.508 | verbose | ESP_TRACE | Barrier detected or task avaliable for running shortly. |
| 07-31 21:49:41.508 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-13ms what=12 obj=com.android.internal.widget.EditableInputConnection@527d6e88 target=android.view.ViewRootImpl$ViewRootHandler }, with target: android.view.ViewRootImpl.ViewRootHandler, callback: no callback. |
| 07-31 21:49:41.508 | verbose | ESP_TRACE | Barrier detected or task avaliable for running shortly. |
| 07-31 21:49:41.508 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-13ms what=65 target=com.android.internal.view.IInputConnectionWrapper$MyHandler }, with target: com.android.internal.view.IInputConnectionWrapper.MyHandler, callback: no callback. |
| 07-31 21:49:41.512 | info | ViewInteraction | Performing 'type text' action on view with id: is <2131165186> |
| 07-31 21:49:41.512 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 what=4 arg1=43 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.536 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 callback=com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask target=android.os.Handler }, with target: android.os.Handler, callback: com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask@52808edc |
| 07-31 21:49:41.536 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 what=0 arg1=45 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.536 | verbose | ESP_TRACE | Barrier detected or task avaliable for running shortly. |
| 07-31 21:49:41.552 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-2ms callback=android.view.View$ScrollabilityCache target=android.view.ViewRootImpl$ViewRootHandler }, with target: android.view.ViewRootImpl.ViewRootHandler, callback: android.view.View$ScrollabilityCache@5281dbdc |
| 07-31 21:49:41.552 | verbose | ESP_TRACE | Barrier detected or task avaliable for running shortly. |
| 07-31 21:49:41.560 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:41.564 | verbose | ESP_TRACE | Barrier detected or task avaliable for running shortly. |
| 07-31 21:49:41.580 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-3ms callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:41.584 | verbose | ESP_TRACE | Barrier detected or task avaliable for running shortly. |
| 07-31 21:49:41.596 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-1ms callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:41.596 | verbose | ESP_TRACE | Barrier detected or task avaliable for running shortly. |
| 07-31 21:49:41.612 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-1ms callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:41.612 | verbose | ESP_TRACE | Barrier detected or task avaliable for running shortly. |
| 07-31 21:49:41.628 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-2ms callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:41.632 | verbose | ESP_TRACE | Barrier detected or task avaliable for running shortly. |
| 07-31 21:49:41.644 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-1ms callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:41.648 | verbose | ESP_TRACE | Barrier detected or task avaliable for running shortly. |
| 07-31 21:49:41.660 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-1ms callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:41.664 | verbose | ESP_TRACE | Barrier detected or task avaliable for running shortly. |
| 07-31 21:49:41.676 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-1ms callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:41.680 | verbose | ESP_TRACE | Barrier detected or task avaliable for running shortly. |
| 07-31 21:49:41.696 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-2ms callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:41.700 | verbose | ESP_TRACE | Barrier detected or task avaliable for running shortly. |
| 07-31 21:49:41.700 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-7ms callback=android.view.View$CheckForTap target=android.view.ViewRootImpl$ViewRootHandler }, with target: android.view.ViewRootImpl.ViewRootHandler, callback: android.view.View$CheckForTap@52816268 |
| 07-31 21:49:41.700 | verbose | ESP_TRACE | Barrier detected or task avaliable for running shortly. |
| 07-31 21:49:41.712 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-1ms callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:41.712 | verbose | ESP_TRACE | Barrier detected or task avaliable for running shortly. |
| 07-31 21:49:41.728 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-2ms callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:41.744 | verbose | ESP_TRACE | Barrier detected or task avaliable for running shortly. |
| 07-31 21:49:41.744 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-1ms callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:41.752 | verbose | ESP_TRACE | Barrier detected or task avaliable for running shortly. |
| 07-31 21:49:41.760 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:41.764 | verbose | ESP_TRACE | Barrier detected or task avaliable for running shortly. |
| 07-31 21:49:41.776 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-1ms callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:41.780 | verbose | ESP_TRACE | Barrier detected or task avaliable for running shortly. |
| 07-31 21:49:41.796 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-1ms callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:41.796 | verbose | ESP_TRACE | Barrier detected or task avaliable for running shortly. |
| 07-31 21:49:41.812 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-2ms callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:41.812 | verbose | ESP_TRACE | Barrier detected or task avaliable for running shortly. |
| 07-31 21:49:41.828 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-2ms callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:41.840 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-5ms callback=android.view.View$PerformClick target=android.view.ViewRootImpl$ViewRootHandler }, with target: android.view.ViewRootImpl.ViewRootHandler, callback: android.view.View$PerformClick@52a59e5c |
| 07-31 21:49:41.840 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-6ms callback=android.view.View$UnsetPressedState target=android.view.ViewRootImpl$ViewRootHandler }, with target: android.view.ViewRootImpl.ViewRootHandler, callback: android.view.View$UnsetPressedState@52a59e64 |
| 07-31 21:49:41.840 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-1ms what=4 arg1=47 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.904 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 callback=com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask target=android.os.Handler }, with target: android.os.Handler, callback: com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask@52a5b294 |
| 07-31 21:49:41.904 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 what=0 arg1=49 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.904 | debug | UiControllerImpl | Injecting string: "lemoncake |
| 07-31 21:49:41.904 | debug | UiControllerImpl | " |
| 07-31 21:49:41.904 | verbose | ESP_TRACE | EventInjector:Injecting event for character (l) with key code (40) downtime: (284878) |
| 07-31 21:49:41.908 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:41.912 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-6ms what=3 arg1=53 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.912 | verbose | ESP_TRACE | EventInjector:Injecting event for character (l) with key code (40) downtime: (284878) |
| 07-31 21:49:41.916 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 what=3 arg1=55 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.916 | verbose | ESP_TRACE | EventInjector:Injecting event for character (e) with key code (33) downtime: (284878) |
| 07-31 21:49:41.928 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-2ms callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:41.932 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-14ms what=3 arg1=57 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.932 | verbose | ESP_TRACE | EventInjector:Injecting event for character (e) with key code (33) downtime: (284878) |
| 07-31 21:49:41.932 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-1ms what=3 arg1=59 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.932 | verbose | ESP_TRACE | EventInjector:Injecting event for character (m) with key code (41) downtime: (284878) |
| 07-31 21:49:41.944 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-1ms callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:41.948 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-12ms what=3 arg1=61 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.948 | verbose | ESP_TRACE | EventInjector:Injecting event for character (m) with key code (41) downtime: (284878) |
| 07-31 21:49:41.952 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 what=3 arg1=63 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.952 | verbose | ESP_TRACE | EventInjector:Injecting event for character (o) with key code (43) downtime: (284878) |
| 07-31 21:49:41.960 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:41.968 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-13ms what=3 arg1=65 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.968 | verbose | ESP_TRACE | Barrier detected or task avaliable for running shortly. |
| 07-31 21:49:41.968 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-1ms callback=android.widget.Editor$Blink target=android.widget.Editor$Blink }, with target: android.widget.Editor.Blink, callback: Handler (android.widget.Editor$Blink) {527d6000} |
| 07-31 21:49:41.968 | verbose | ESP_TRACE | EventInjector:Injecting event for character (o) with key code (43) downtime: (284878) |
| 07-31 21:49:41.968 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 what=3 arg1=67 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.968 | verbose | ESP_TRACE | EventInjector:Injecting event for character (n) with key code (42) downtime: (284878) |
| 07-31 21:49:41.976 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-1ms callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:41.988 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-17ms what=3 arg1=69 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.988 | verbose | ESP_TRACE | EventInjector:Injecting event for character (n) with key code (42) downtime: (284878) |
| 07-31 21:49:41.992 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-1ms what=3 arg1=71 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:41.996 | verbose | ESP_TRACE | EventInjector:Injecting event for character (c) with key code (31) downtime: (284878) |
| 07-31 21:49:42.008 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:42.012 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-16ms what=3 arg1=73 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:42.012 | verbose | ESP_TRACE | EventInjector:Injecting event for character (c) with key code (31) downtime: (284878) |
| 07-31 21:49:42.016 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 what=3 arg1=75 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:42.016 | verbose | ESP_TRACE | EventInjector:Injecting event for character (a) with key code (29) downtime: (284878) |
| 07-31 21:49:42.028 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-2ms callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:42.032 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-15ms what=3 arg1=77 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:42.032 | verbose | ESP_TRACE | EventInjector:Injecting event for character (a) with key code (29) downtime: (284878) |
| 07-31 21:49:42.036 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 what=3 arg1=79 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:42.036 | verbose | ESP_TRACE | EventInjector:Injecting event for character (k) with key code (39) downtime: (284878) |
| 07-31 21:49:42.044 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:42.048 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-10ms what=3 arg1=81 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:42.056 | verbose | ESP_TRACE | EventInjector:Injecting event for character (k) with key code (39) downtime: (284878) |
| 07-31 21:49:42.060 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 what=3 arg1=83 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:42.060 | verbose | ESP_TRACE | EventInjector:Injecting event for character (e) with key code (33) downtime: (284878) |
| 07-31 21:49:42.076 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-1ms callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:42.080 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-19ms what=3 arg1=85 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:42.080 | verbose | ESP_TRACE | EventInjector:Injecting event for character (e) with key code (33) downtime: (284878) |
| 07-31 21:49:42.084 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 what=3 arg1=87 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:42.084 | verbose | ESP_TRACE | EventInjector:Injecting event for character ( |
| 07-31 21:49:42.084 | verbose | ESP_TRACE | ) with key code (66) downtime: (284878) |
| 07-31 21:49:42.084 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 what=3 arg1=89 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:42.084 | verbose | ESP_TRACE | EventInjector:Injecting event for character ( |
| 07-31 21:49:42.084 | verbose | ESP_TRACE | ) with key code (66) downtime: (284878) |
| 07-31 21:49:42.092 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-1ms callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:42.104 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-15ms what=13 target=android.view.ViewRootImpl$ViewRootHandler }, with target: android.view.ViewRootImpl.ViewRootHandler, callback: no callback. |
| 07-31 21:49:42.104 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-15ms what=3 arg1=91 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:42.104 | verbose | ESP_TRACE | Barrier detected or task avaliable for running shortly. |
| 07-31 21:49:42.104 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-9ms what=12 obj=com.android.internal.widget.EditableInputConnection@52812888 target=android.view.ViewRootImpl$ViewRootHandler }, with target: android.view.ViewRootImpl.ViewRootHandler, callback: no callback. |
| 07-31 21:49:42.104 | verbose | ESP_TRACE | Barrier detected or task avaliable for running shortly. |
| 07-31 21:49:42.104 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-10ms what=65 target=com.android.internal.view.IInputConnectionWrapper$MyHandler }, with target: com.android.internal.view.IInputConnectionWrapper.MyHandler, callback: no callback. |
| 07-31 21:49:42.104 | info | ViewInteraction | Performing 'single click' action on view with id: is <2131165187> |
| 07-31 21:49:42.108 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:42.116 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-10ms what=4 arg1=93 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:42.136 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 callback=com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask target=android.os.Handler }, with target: android.os.Handler, callback: com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask@52a73144 |
| 07-31 21:49:42.136 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 what=0 arg1=95 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:42.152 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 callback=com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask target=android.os.Handler }, with target: android.os.Handler, callback: com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask@52a73c48 |
| 07-31 21:49:42.152 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 what=0 arg1=97 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:42.164 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 callback=com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask target=android.os.Handler }, with target: android.os.Handler, callback: com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask@52a74730 |
| 07-31 21:49:42.164 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 what=0 arg1=99 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:42.172 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 callback=com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask target=android.os.Handler }, with target: android.os.Handler, callback: com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask@52a75218 |
| 07-31 21:49:42.172 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 what=0 arg1=101 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:42.180 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-1ms callback=com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask target=android.os.Handler }, with target: android.os.Handler, callback: com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask@52a75d00 |
| 07-31 21:49:42.180 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 what=0 arg1=103 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:42.184 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 callback=com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask target=android.os.Handler }, with target: android.os.Handler, callback: com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask@52a767fc |
| 07-31 21:49:42.184 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 what=0 arg1=105 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:42.188 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-1ms callback=com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask target=android.os.Handler }, with target: android.os.Handler, callback: com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask@52a772e4 |
| 07-31 21:49:42.188 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 what=0 arg1=107 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:42.192 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-2ms callback=android.view.View$PerformClick target=android.view.ViewRootImpl$ViewRootHandler }, with target: android.view.ViewRootImpl.ViewRootHandler, callback: android.view.View$PerformClick@52a77ea4 |
| 07-31 21:49:42.196 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-5ms what=4 arg1=109 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:42.196 | verbose | ESP_TRACE | Barrier detected or task avaliable for running shortly. |
| 07-31 21:49:42.212 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-2ms callback=android.view.Choreographer$FrameDisplayEventReceiver target=android.view.Choreographer$FrameHandler }, with target: android.view.Choreographer.FrameHandler, callback: android.view.Choreographer$FrameDisplayEventReceiver@527e1a98 |
| 07-31 21:49:42.256 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=-1ms callback=android.view.View$UnsetPressedState target=android.view.ViewRootImpl$ViewRootHandler }, with target: android.view.ViewRootImpl.ViewRootHandler, callback: android.view.View$UnsetPressedState@52a77eac |
| 07-31 21:49:42.280 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 callback=com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask target=android.os.Handler }, with target: android.os.Handler, callback: com.google.android.apps.common.testing.ui.espresso.base.UiControllerImpl$SignalingTask@52a7a11c |
| 07-31 21:49:42.280 | verbose | ESP_TRACE | UiControllerImpl: MessageQueue.next(): { when=0 what=0 arg1=111 target=android.os.Handler }, with target: android.os.Handler, callback: no callback. |
| 07-31 21:49:42.288 | debug | dalvikvm | GC_FOR_ALLOC freed 4449K, 55% free 3783K/8288K, paused 5ms, total 5ms |
| 07-31 21:49:42.288 | info | dalvikvm-heap | Grow heap (frag case) to 7.458MB for 3891212-byte allocation |
| 07-31 21:49:42.292 | debug | dalvikvm | GC_FOR_ALLOC freed <1K, 9% free 7582K/8288K, paused 4ms, total 4ms |
| 07-31 21:49:42.424 | debug | Spoon | Captured screenshot 'after_login'. |
| 07-31 21:49:42.484 | debug | dalvikvm | GC_EXPLICIT freed 3826K, 55% free 3769K/8288K, paused 1ms+1ms, total 11ms |
| 07-31 21:49:42.496 | debug | dalvikvm | GC_EXPLICIT freed 1K, 55% free 3768K/8288K, paused 1ms+2ms, total 11ms |
| 07-31 21:49:42.496 | info | TestRunner | finished: testSetEmailInvalidError(com.emmasuzuki.espressospoondemo.LoginActivityTest) |


I’ve been wanting to start a blog dedicated to my attempts/progress at coding for quite awhile now. Finally getting around to this thanks to github offering free hosting
! This was a natural fit since the blog is really just a way for me to keep track of random coding projects, which would likely have been kept on github anyways.
Most of my projects will probably be Python based, since I’m looking to become more proficient with the overall scientific Python based libraries. This means you can expect lots of posts related to numpy, scipy, and matplotlib.
Aside from that we’ll just see where this crazy project leads!
i-python notebook: -->here<--
P.S.
There were lot of little things that went in to getting this first post together, which I’ll probably go into more detail later. For instance, this site is put together with markdown using Jekyll
, and the “Hello World” image was made with the matplotlib xkcd styling
! So I already learned quite a bit with just this first post.


Results¶
Regression is a statistical method to determine the relationship of one variable dependent on others. For our project, We take the average lifespan of a repository as our baseline. Using this baseline, We have evaluated the model using the MAE metric. Our model has performed better than the baseline as shown below.
Error by Baseline : 348.8 (~349 days)
Error by Model : 288.7 days (~289 days)
We have plotted graphs comparing the lifespan to the various features such as total number of snapshot counts, number of targets, original date, number of revisions, number of releases and last revision date. Each diagram shows three scatter plots : The actual value obtained from the table, the value predicted by the model and the average lifespan value which is our baseline.
According to our graphs, We infer that the lifespan feature does not heavily depend on the values of the other features individually. Even though the number of snapshots are the same, the lifespan values differ showing that the snapshot count does not matter. However, A combination of these features will affect the lifespan value which means the combination of features does matter. This is proved by the fact that our model obtained a lower error rate than the baseline.


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Colophon¶
The following are needed to generate this documentation:
Sphinx >= version 3.0 - documentation builder - http://sphinx-doc.org
Sphinx readthedocs theme - https://github.com/snide/sphinx_rtd_theme
napoleon - http://sphinxcontrib-napoleon.readthedocs.org/en/latest/
httpdomain - http://pythonhosted.org/sphinxcontrib-httpdomain/
python 3
Writing the Documentation¶
The primary sources for this documentation are the docstrings embedded in the Slycat source code itself. When writing docstrings, strictly follow the guidelines at https://numpydoc.readthedocs.io/en/latest/format.html#docstring-standard
The remainder of the documentation is contained in *.rst files in the slycat/docs/source directory.
Building the Documentation¶
To build the documentation, run:
$ cd slycat/docs
$ make html
Once the documentation is built, you can view it by opening slycat/docs/source/_build/html/index.html in a web browser.
Deploying the Documentation¶
The slycat documentation is hosted at http://slycat.readthedocs.org and is automatically built and deployed whenever changes are pushed to the Slycat repository at github.com.


Plugin Information |
|---|
View Lotus Connections on the plugin site for more information. |
This plugin posts build results to Lotus Connections.
Each Lotus Connection update will contain the job name, build number, The number of tests executed, passed, failed and the pass rate (%).
For example
TT_BVT_WMB (96) : Tests Executed = 600 Tests Passed = 126 Tests Failed = 474 Pass Rate = 21.00 %
The core workings came from a sample on this site Updating your Lotus Connections microblog programmatically by Luis Benitez.
Version 1.24 (Jan 10, 2014)
- Added the feature to post to a Lotus Connections Community Forum.
- Reverted the configuration to set the properties in a per-project manner, rather than global .
Version 1.23 (Feb 19, 2013)
- Updated the status message to include the build url.
- Altered the configuration to set the properties in a global manner, rather than per-project.
Version 1.22 (Feb 12, 2013)
- Updated the dependencies to latest release of Adbera.
Version 1.18 (Nov 7, 2012)
- Altered the default message for builds with no test results.
Version 1.17 (Nov 6, 2012)
- Fixed up the release process.
Version 1.14 (Nov 5, 2012)
- Initial release


To share code between two C# projects, you need to add them to the same solution and add a project reference between the two projects. You can do this in your favorite C# editor, or in the terminal by following these steps:
lean init
in.dotnet new sln
to bootstrap the solution. This command only needs to be run once.
$ dotnet new sln The template "Solution File" was created successfully.
dotnet sln add "My Project"
for each LEAN project you want to share code from or to. This command adds the project in ./My Project
to the solution.
$ dotnet sln add "My Project" Project `My Project/My Project.csproj` added to the solution.
dotnet add "Algorithm Project" reference "Library Project"
to make the code in ./Library Project
available in the project in ./Algorithm Project
.
$ dotnet add "Algorithm Project" reference "Library Project" Reference `..\Library Project\Library Project.csproj` added to the project.
Sharing code between C# projects has the following limitations:


The Brussels Sugar Convention was a contract between different European States at the beginning of the 20th century that regulated the international sugar market. The ultimate goal was to abolish sugar bounties which were paid to the industry and were expensive for the states. [1]
Source of the image[2]
[1] A great source for getting more info is Hans Jacobs: Die Brüsseler Zuckerkonvention
[2] Source: William Robert Shepherd [Public domain]


Sepsis survivor shares her experience with the life-threatening disease
Community and state leaders celebrate newly renovated Tribune Apartments in South Framingham
Sue Nevins talks about the impact of patient and family peer support and why she became a volunteer.
Residents voice their support for the Clarendon Hill redevelopment.
Students read the 100 Males to College pledge and reflect on the program.
Seth Godin's Linchpin Manifesto inspires us to embody TEDxUMassAmherst's 2014 theme, #ChangeTheWorld.
A hub of tools and research for health care organizations that want to do more to support clinicians and staff after adverse events or unexpected outcomes in patient care.
The result of a year-long process including student usability tests and advisor focus groups, the MassTransfer website helps students navigate the complicated transfer process between state colleges and universities.
Materials to help home care workers learn about infection control during the pandemic.
A guide to engaging the public in programs and policy development.
Brochure describing POAH's Community Impact program and a companion one-pager on the FSS program.


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B
ERKSHIRE
H
ATHAWAY INC.
3555 Farnam Street
Omaha, NE 68131
Official Home Page
A Message From Warren E. Buffett
Annual & Interim Reports
Updated November 6, 2015
Special Letters From Warren & Charlie RE:Past, Present and Future
Link to SEC Filings
Links to Berkshire Subsidiary Companies
Corporate Governance
Owner's Manual
Letters from Warren E. Buffett Regarding Pledges to Make Gifts of Berkshire Stock
News Releases
Updated November 6, 2015
Warren Buffett's Letters to Berkshire Shareholders
Updated February 28, 2015
Charlie Munger's Letters to Wesco Shareholders
Annual Meeting Information
Celebrating 50 Years of a Profitable Partnership
(A commemorative book first sold at the 2015 Annual Meeting and now for sale on eBay.)
Comparative Rights and Realtive Prices of Class A and B Stock
Berkshire Activewear


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This website is made for modern browsers. You are seeing this, because your browser is missing a feature or two. Please install the latest update or switch to a modern browser. See you soon.


Auth
Manage oauth and user tokens for the client. Once a successful auth token has
been fetched it is stored in this.config
for use in authenticated
requests to the API.
Constructor Summary
| Public Constructor | ||
| public |
constructor(config: Dictionary, http: Http) Constructor |
Member Summary
| Public Members | ||
| public get |
Return the entire authentication object |
|
| public set |
Set auth config object |
|
| public get |
Return a string for use in an Authorization header: "{tokenType} {token}" |
|
| public |
|
|
| public get |
facebookAppId: String: * Convenience method to get the Facebook APP Id registered with the client |
|
| public get |
Return the gwid |
|
| public set |
Set gwid config object |
|
| public |
|
|
| public set |
Set token config object |
|
| public get |
Return the authorization token |
|
| public get |
Return the authorization token |
|
| public set |
Set tokenType config object |
Method Summary
| Public Methods | ||
| public |
Wipe out auth settings in config, which will prevent authenticated requests until a new one is fetchd |
|
| public |
fetchUsingFacebook(accessToken: String): Promise Use Facebook to fetch Rex token |
|
| public |
fetchUsingGoogle(accessToken: String): Promise Use Google to fetch Rex token |
|
| public |
fetchUsingPassword(email: String, password: String): Promise Use email / password to fetch Rex token |
Public Constructors
public constructor(config: Dictionary, http: Http) source
Constructor
Params:
| Name | Type | Attribute | Description |
| config | Dictionary |
|
client configuration |
| http | Http | required |
Public Members
public get authorizationHeader: String: string source
Return a string for use in an Authorization header: "{tokenType} {token}"
public config: Dictionary source
Public Methods
public destroyToken(): Object source
Wipe out auth settings in config, which will prevent authenticated requests until a new one is fetchd
public fetchUsingFacebook(accessToken: String): Promise source
Use Facebook to fetch Rex token
Params:
| Name | Type | Attribute | Description |
| accessToken | String |


Informational Parents Meeting December 12 at 7:00pm at Lanier High School Media Center.
Join the Thundering Herd
Youth Track and Field Team!
Sponsored by Lanier High School
- Learn from Lanier High School Coaches and Student-Athletes (2018 Region 8AAAAAA Champions!)
- All ages welcome K-8th grade.
- Learn Sprinting, Distance Running, Relays, Hurdles, Long Jump, Throwing, and more!
- No experience or special equipment needed.


Delivery man lauded for helping to nab suspect
He thought cops were gangsters at first; other recipients of Public Spiritedness Award helped thwart possible scam
by Hidayah IskandarRiding his motorbike along Choa Chu Kang Link, Mr Faliq Shazwin Alwi saw a man being chased by two others and thought they were gang members.
He stopped to help and wanted to use his helmet to hit one of them when he saw him pin the man to the ground.
But just as he was about to do so, he realised they were plainclothes police officers, and they were arresting an Indonesian suspected of dealing in illegal cigarettes.
So, Mr Faliq, 22, helped them pin the man down.
Yesterday, Mr Faliq was one of four recipients of the Public Spiritedness Award held at the Woodlands Police Division.
Recalling the incident, the full-time delivery rider said he was riding home at about 2am on Aug 28 when he saw the chase.
He said one of the two men tripped so he tried to help the man they were chasing.
Speaking to the media, Mr Faliq said: "I felt helpless and I wanted to use my helmet against the officer before he identified himself as the police.
"I was hesitant to believe the officer at first, but I eventually switched sides and helped the officer detain him."
The suspect, who could speak only basic English, could not answer the officer's questions until Mr Faliq translated it into Bahasa Indonesia for him.
The suspect was arrested for an offence under the Immigration Act and possession of duty-unpaid cigarettes.
Two other recipients of the award were Ms Cheo Yi Love, 25 and Mr Roy Cheong, 46.
SUSPICIOUS
Ms Cheo, a service executive at the United Overseas Bank's Woodlands branch, got suspicious when she realised her customer, a 61-year-old woman, wanted to cash out $12,500 only to cash it back into a third party account. It was Ms Cheo's first month on the job.
She said: "She (the victim) kept rushing the procedure, wanting to get it done as soon as possible. She also looked very nervous."
Mr Cheong, the branch's deputy manager, realised it might be a scam and helped the victim lodge a police report.
The woman had befriended a man online who asked for $3,500 to receive a parcel. He then asked for another $12,500 to get a clearance certificate for the parcel.
Another Public Spiritedness Award ceremony was held at the Clementi Police Division yesterday. Two members of the public were honoured for helping to detain a shoplifter and for detaining a man who had stolen something placed outside his neighbour's unit.
Assistant Commissioner of Police Leon Chan, Commander of Woodlands Police Division said in a statement: "They (award recipients) are examples of how people keep a watchful eye out for potential victims of crime, or bravely step up to assist the police.
"We hope that their actions will serve to inspire others to keep their communities safe."


| The Symfony room | The Framework room | The Platform.sh room | |
|---|---|---|---|
|
14:30 15:05 |
|||
|
15:15 15:50 |
To develop safer and more reliable applications, we don't have to wait for progress in PHP language itself anymore. Plenty of advanced concepts known from other languages like generics, precise array shapes, or number ranges can be expressed in PHPDocs and verified by static analysis.
In this talk I'll introduce the new types that have recently been added to PHPStan and show you how to use them in practice.
You will learn:
* How you can use PHPDocs to provide and document extra type information to static analysers, IDEs, and developers working with your code.
* Advanced types that don't yet exist natively in PHP, but can be used in PHPDocs for developing of more type-safe codebase
* Type theory about union vs. intersection types
Thursday, November 17, 2022 at 14:30 PM – 15:05 PM
Why should someone care about SQL, when a developer can abstract it with Symfony and Doctrine into PHP code? With Doctrine the database is out of sight, the database is out of mind, but this is where problems can start. What impacts are there for a Symfony project and how can they be managed? This talk handles Doctrine's behaviour and how it can influence a database and queries, why SQL is still relevant despite having an ORM and how developers can influence SQL performance.
Thursday, November 17, 2022 at 14:30 PM – 15:05 PM
Delivering applications today means being a part of a clearly defined and secure lifecycle – something that validates revisions on provisioned infrastructure and monitors production continuously. Organizations grow “platform teams” to manage this complexity, defining and enforcing operational standards around a common platform built in-house that connects a number of tools and providers.
This can turn into an expensive moving target, one that’ll leave your team reinventing the wheel forever – spending less time on writing features as you focus on everything else around applications. Platform.sh provides a unified, secure platform powered by Git and an infrastructure abstraction that provides operational maturity out-of-the-box. You’ll spend less time orchestrating all of your apps, and more time experimenting with new technologies, optimizing your code, and scaling all in one place.
Thursday, November 17, 2022 at 14:30 PM – 15:05 PM
All modern software is built using third-party software components, which may come with their own dependencies. Do you know everything about the components that make up the foundation of your software? What is the PHP software stack’s supply chain? In this presentation, Sebastian Bergmann paints a picture as complete as possible, covering topics from software supply chain security concerns to the funding of Open Source software development.
Thursday, November 17, 2022 at 15:15 PM – 15:50 PM
In December 2020, Basecamp released their first official version of the Hotwire suite. Combining the historic Turbolinks library with their Stimulus js micro-framework, it comes out a powerful combo for making complex applications while coding a fullstack Rails app. The Symfony team was quick to port the project to their ecosystem, and the results are amazing!
At Windoo we've been using the package symfony/ux-turbo suite since early 2021 to progressively replace our React legacy code. We've learned a lot about how to use it, the good practices to follow, the mistakes to avoid, and the tricky use cases to solve.
Forms, messaging, modals, asynchronous... I will show you very concrete applications of this library in your projects!
Thursday, November 17, 2022 at 15:15 PM – 15:50 PM
What makes a successful product? Is it something that looks good, works good or is it something much more simple. Delivering something that your users actually want, in an enjoyable way.
In today’s overpopulated tech space, there’s an app or site for everything. But, like you, I could tell you my most used apps on just one hand. These are the products that make what I need to do seamless and enjoyable. User centered-design aims to balance the needs of a business, while working within the capacities of your engineering team. This talk will share ways in which you can bring your user's voice to the forefront of product development through user-centered design.
Thursday, November 17, 2022 at 15:15 PM – 15:50 PM


Yet Another Human. Technologically capable, from high level (F#, Python) to low level (C++, C) to assembly (ARM, MIPS) to hardware (Verilog, soon VHDL).
Figuring out life, status: incomplete.
Politically: invest in education and sustainability, don’t sell weapons to war criminals.
Enjoys the following…
Creating...
- Tech
- Love
- Food
- Blog
- Dance
- Music
- Photography
Learning to...
be happy.
- Blog
- Sing
- Talk
- Dance
- Play guitar
- Skateboard
- Speak Swedish
Vices
- Reading
- Video games
- The snooze button


Projects
About
News
Contact
News
January 20, 2019
Hollywood Reporter
Ralph Ineson, Susan Lynch Join 'Here Are the Young Men' (Exclusive)
January 20, 2019
Hollywood Reporter
Ralph Ineson, Susan Lynch Join 'Here Are the Young Men' (Exclusive)
January 20, 2019
Hollywood Reporter
Ralph Ineson, Susan Lynch Join 'Here Are the Young Men' (Exclusive)
January 20, 2019
Hollywood Reporter
Ralph Ineson, Susan Lynch Join 'Here Are the Young Men' (Exclusive)


{% extends "home-base.html"%} {% block title %}All Members{% endblock %} {% block page_content%}
Name: Sarah Shaffer
Major: Marketing, MIS and Operations Management
Year: Junior
Name: Wang Peiyu
Major: Management Information Systems
Year: Senior
Name: Daniel Winick
Major: Finance and Management Information Systems
Year: Junior


I support people and organizations in using data to make the world a better place - magnify their positive impact, improve their efficiency, reduce their consumption, support their communities, and evaluate their progress. My background is in environmental science and policy - energy and green buildings; secondary education - science teaching and equitable school practices; and business - with an International MBA from IE Business School in Madrid focusing in technology and operations. I live in Seattle with my partner and pup, Beckham.
This Flask app predicts and displays the relative safety of walking based on historic arrest data, weather, and start and end location. It combines information intake from users, API calls, the machine learning model backend, and mapping of the results.
To achieve this end, I used Google Maps API and JavaScript, DarkSky API calls for up to date weather, MongoDB, SQL, a logistic regression model (chosen to maximize logloss within the project size constraint of 1 gb), GeoJSON and Shapely to work with the geospatial aspects of the data, and Folium mapping to present the results overlaid on a map for practial use.
Aaron Chen and I used the UW Madison Courses and Grades 2006-2017 data set to investigate the following questions:
In addition to querying the database with SQL, we utilized the following python packages and modules: sqlalchemy, pandas, matplotlib.pyplot, numpy, and scipy.stats.
I worked with a team of four to create the optimal Classification Models that would predict whether someone is a likely user of the substance of interest based on their demographics, personality, and use of other substances. We created models for each of the following substances: Heroin, Amphetamines, and Methamphetamine. These substances were chosen based on percieved impact on users and their communities, with the goal of getting the most impact from the outreach and support budgets.
To determine the best model for each substance, we looked to maximize the F-1 score as a balance between precision and recall, in an effort to identify most of the people in need of support (recall) while not spending too many resources where they are not needed (precision).
Our model search included the folowing machine learning model families: K-Nearest Neighbors Classifiers, Linear Support Vector Classifiers, Gaussian Proccess Classifiers, Decision Tree Classifiers, Random Forest CLassifiers, AdaBoost Classifiers, Gassuian Naieve Bayes Classifiers, and Quadratic Discriminant Analysis.
Dirk Van Curan II and I used the Zillow Research data set to identify zipcodes that would fit our investment goals over the five years following our data set. We were looking for the top five zipcodes for upside potential, expected value, and lower risk. We measured in terms of maximum ROI over the five year period, without regard to the time value of money, as we are using the same five year period for each potential investment and only comparing between the three models without consideration to other investment opportunities.
We used fbProphet for our time series modeling, pandas for our data cleaning and dataframe handling, matplotlib.pyplot for our data visualizations, and scipy.stats to assist with our statistical analysis.
I worked with a diverse group of administrators, students, parents, teachers, staff, and union representatives to understand the issues facing the school. We evaluated, designed, and lead initiatives to improve the school. In this role, I found myself increasingly excited about using data to drive informed decision making. I developed my SQL skills, as well as adapted my presentation skills aimed at diverse audiences.
I collaborated closely with a teaching team to design and continually improve curriculum targeted at newly released educational standards, including classroom design, learning activities, and assessment. We lead our district in developing curriculum and classroom practices for extensive chromebook use. As a team, we piloted an emerging, research-based assessment and grading system, and helped over 40% of the teaching staff follow suit in their own classrooms within the first four years.
I managed implementation of multiple new learning management software systems, developing training curriculum, leading learning seminars, and providing additional one-to-one support for colleagues.
Completed full-time, 15-week program in data science and analytics at the Seattle campus. Learning modules included Python and SQL programming; Statisitics, A/B testing, and Linear Regression; Machine Learning with Scikit-learn; and Big Data, Deep Learning, and Natural Language Processing.
Studied in Madrid, Spain, with a cohort of students from around the world. Core studies included entrepreneurship, in addition to the foundational business curriculum. As part of an agile team, I worked on a 5 week project with Nokia Spain to expand their 5G-enabled product line. During my elective learning, I specialized in technology and operations, gaining my first exposure to AI from a business perspective.
As an interdisciplinary environmental science and policy major, I focused on energy science and technology. I am still extremely interested in and excited about energy topics, including reducing the impact of climate mitigation, renewable energy, energy-efficiency. I enjoyed electives in Computer Science including Programming Methodology with a focus on object-oriented design, decomposition, encapsulation, abstraction in Java and Programming Abstractinos with a focus on recursion, algorithmic analysis, and data abstraction in C++.
An equity-focused masters that included a full school year of student teaching in physics at Sequoia High School as well as a summer school experience in a middle school science classroom. Curriculum focused on structuring learning around activities and models, teaching literacy and language across the curriculum, and managing the classroom through engaging learning experiences.


Global Warming
Legal Actions
Innovations
Alternate Energy Sources
Citations
The
BIGGEST
question of all:
What actions can we take to
STOP
GLOBAL WARMING?
Authors: Grant Yang
,
Agastya Ravuri
,
Varun Thvar
Made for a science project on 25 April 2019


ipv4 Racket Header
Let’s walk throught all these fields, but this is not bits in the pic:
Version
the first field tells us which IP version we are using, only IPv4 uses this header so you will always find decimal value 4 here.
Header Length
this 4 bits field tells us the length(bytes) of the IP header in 32 bit increments.If here is 20 bytes in the ip header, the value of 5 here, if the value is 15(0x1111), the IHL is 60 bytes.
Please note here, you would better to think it why it is the 5 and 20. Hint: 5 bytes = 5 * 32 (160) bits.If you have other data, must add it of 4 bytes(20, 24, 28…)
Type of service
This is used for Qos.
Total Length
The minimum size is 20 bytes(if you have no data) and the maximum size is 65536 bytes.
Identification
If the IP packet is fragmented then each fragment packet will use the same 16 bit identification number to identify to which IP packet they belone to.
IP Flags
: The first bit is alway set to 0; The second bit is called DF
(dont fragment) and indicates this packet should not be fragmented.The third bit is called the MF
(more fragments) bit and is set on all fragment packets except the last one.
Fragment Offset
: this 13 bit field specifies the position of the fragment in the original fragmented IP packet.
Time to live
: only ttl value is 0, the packet will be dropped. ICMP.
Header Checksum
, Source address
, Destination Address
, IP option
…


{% extends "home/base.html" %} {% load static %} {% block content %} Your payment was successfully received and we will contact you regarding status updates within 48h. {% endblock %}


This is a tool that can display intermediate representations used by V8 and Dart VM optimizing compilers.
If it's you first time trying IRHydra consider watching the following screencast showcasing its main features:
Try clicking on the links below to explore features that IRHydra provides. Each link loads compilation artifacts collected from a single V8 or Dart VM run on a given source file.
toString
-hack [source file]If you already have some artifacts collected go ahead and explore them with otherwise here is how you can collect some.
You need V8 version >= 3.24.39 to use IRHydra2. You can check about:version
to see if your browser comes with the suitable version.
You need to pass the following flags to V8 to collect IR and deoptimization artifacts and be able to use source level view:
--trace-hydrogen --trace-phase=Z --trace-deopt --code-comments --hydrogen-track-positions --redirect-code-traces --redirect-code-traces-to=code.asm
Additionally you can pass the following flags to enable disassembly view. This requires building V8 with v8_enable_disassembler=1
GYP flag.
--print-opt-code
When run with this flags V8 will produce two files hydrogen.cfg
and code.asm
both of which should be loaded into IRHydra.
When running in Chromium use --js-flags="..."
to pass V8 flags and --no-sandbox
to allow V8 to write hydrogen-<pid>-<id>.cfg
and code-<pid>-<id>.asm
.
--no-sandbox \ --js-flags="--trace-hydrogen \ --trace-phase=Z \ --trace-deopt \ --code-comments \ --hydrogen-track-positions \ --redirect-code-traces"
Notice that there is no need to pass --redirect-code-traces-to=code.asm
flag, this guarantees that in the presence of multiple renderer processes compilation artifacts are correctly split between separate files.
You need to pass the following flags to Dart VM to collect IR and disassemble artifacts:
--print-flow-graph-optimized --disassemble-optimized --code-comments
All data is dumped into standard output.
When running in Dartium use DART_FLAGS
environment variable to pass flags.
Deoptimization matching and source level view is not currently available for Dart VM.


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