├── .gitignore
├── README.md
├── build.sbt
├── data
    ├── dev.txt
    ├── englishPCFG.caseless.ser.gz
    ├── englishPCFG.ser.gz
    ├── test.txt
    └── train.txt
├── engine.json
├── project
    ├── assembly.sbt
    └── pio-build.sbt
├── src
    └── main
    │   └── scala
    │       └── sentiment
    │           ├── Accuracy.scala
    │           ├── AlgorithmBase.scala
    │           ├── DataSource.scala
    │           ├── Engine.scala
    │           ├── MM.scala
    │           ├── Parser.scala
    │           ├── Preparator.scala
    │           ├── RNN.scala
    │           ├── RNTN.scala
    │           ├── RNTNSimpl.scala
    │           ├── RNTNSplit.scala
    │           └── Serving.scala
├── template.json
└── web
    ├── db.sqlite3
    ├── manage.py
    ├── nlp
        ├── __init__.py
        ├── settings.py
        ├── urls.py
        └── wsgi.py
    ├── requirements.txt
    ├── sentiment
        ├── __init__.py
        ├── admin.py
        ├── migrations
        │   └── __init__.py
        ├── models.py
        ├── templates
        │   └── sentiment
        │   │   ├── base.html
        │   │   ├── index.html
        │   │   ├── prediction.html
        │   │   └── tree.html
        ├── tests.py
        ├── urls.py
        └── views.py
    └── static
        ├── css
            ├── bootstrap-theme.css
            ├── bootstrap-theme.css.map
            ├── bootstrap-theme.min.css
            ├── bootstrap.css
            ├── bootstrap.css.map
            └── bootstrap.min.css
        ├── fonts
            ├── glyphicons-halflings-regular.eot
            ├── glyphicons-halflings-regular.svg
            ├── glyphicons-halflings-regular.ttf
            ├── glyphicons-halflings-regular.woff
            └── glyphicons-halflings-regular.woff2
        └── js
            ├── bootstrap.js
            ├── bootstrap.min.js
            ├── jquery-1.11.3.min.js
            └── npm.js


/.gitignore:
--------------------------------------------------------------------------------
 1 | manifest.json
 2 | target/
 3 | project/target/
 4 | project/project/target/
 5 | pio.log
 6 | /pio.sbt
 7 | .idea/
 8 | __pycache__/
 9 | web/env/
10 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
  1 | Overview
  2 | ======
  3 | 
  4 | This template implements various algorithms for sentiment analysis, most based on recursive neural networks (RNN) and recursive neural tensor networks (RNTN)[1]. It uses an experimental library called Composable Machine Learning (CML) and the Stanford Parser. The example data set is the Stanford Sentiment Treebank.
  5 | 
  6 | [1] Socher, Richard, et al. "Recursive deep models for semantic compositionality over a sentiment treebank." Proceedings of the conference on empirical methods in natural language processing (EMNLP). Vol. 1631. 2013.
  7 | 
  8 | Installation
  9 | ======
 10 | 
 11 | First, you need to download and install a modified version of scalaz (with enabled serialization):
 12 | ```bash
 13 | > git clone http://github.com/pawel-n/scalaz
 14 | > cd scalaz
 15 | > sbt publishLocal
 16 | ```
 17 | 
 18 | Then, install CML:
 19 | 
 20 | ```bash
 21 | > git clone http://github.com/pawel-n/cml
 22 | > cd cml
 23 | > sbt publishLocal
 24 | ```
 25 | 
 26 | Next, get the template:
 27 | ```bash
 28 | > pio template get pawel-n/template-scala-cml-sentiment <EngineDir>
 29 | > cd <EngineDir>
 30 | ```
 31 | 
 32 | Problem description
 33 | =====
 34 | 
 35 | The task is to classify (English) sentences into five classes: Very negative, Negative, Neutral, Positive, Very positive. The input is either a sentence already parsed into a tree (when training) or raw text (in deployment). The output is a tree of *sentiment vectors*, i.e. for each part of the sentence the algorithm predicts a vector of probabilities - one value for each class.
 36 | 
 37 | Data Source
 38 | =====
 39 | 
 40 | The data source loads parsed sentence trees from files "data/train.txt" and "data/test.txt". You can set how much
 41 | data should be loaded by changing the "fraction" setting. Data is grouped into batches of size given by "batchSize".
 42 | 
 43 | Algorithms
 44 | =====
 45 | 
 46 | The template implements 5 algorithms:
 47 | * RNN - recursive neural network,
 48 | * RNTN - recursive neural tensor network,
 49 | * RNTNSimpl - a variation of RNTN with a simplier combining function,
 50 | * RNTNSplit - a variation of RNTN where the word vectors are split in 2 parts and combining is done using a (0, 4)-tensor,
 51 | * MM - a simple model based on matrix multiplication, every word is mapped to a matrix, matrices are multiplied and the result is classified with softmax regression.
 52 | 
 53 | Every algorithm shares the same cost function and optimization method (AdaGrad). The common functionality is provided in the AlgorithmBase class. The only thing missing in AlgorithmBase is a *model*, which is defined by a trait:
 54 | ```scala
 55 | trait Model[In[_], Out[_]] extends Serializable {
 56 |   /**
 57 |    * The space of parameters.
 58 |    */
 59 |   type Params[A]
 60 | 
 61 |   /**
 62 |    * Parameters must form a representable vector space.
 63 |    */
 64 |   implicit val params: Representable[Params]
 65 | 
 66 |   /**
 67 |    * Applies the model to some input.
 68 |    * @param input The input.
 69 |    * @param params The model parameters.
 70 |    * @param a Number operations (like addition, multiplication, trigonometric functions).
 71 |    * @tparam A The number type.
 72 |    * @return The output.
 73 |    */
 74 |   def apply[A](params: Params[A])(input: In[A])(implicit a: Analytic[A]): Out[A]
 75 | }
 76 | ```
 77 | Each model has an input type In[\_], output type Out[\_], a parameter space Params[\_]. If you have a parameter vector, you can apply a model to some input, yielding some output. In our case the input type is Tree\[Unit, String\] (a binary tree with strings in the leafs and no information in the nodes) and the output type is Tree\[SentimentVector, String\] (a binary tree with strings in the leafs and a probability distribution over a set of classes in the nodes).
 78 | 
 79 | Models can be implemented directly, however CML provides a library of basic models (linear functions, scalar functions applied pointwise, tensors, map/reduce). Furthermore, models can be composed, i.e. if we have a model going from A to B and another from B to C the composition will take the input of type A and yield output of type C.
 80 | 
 81 | CML uses automatic differentiation to compute the gradients require to optimize models.
 82 | 
 83 | Building
 84 | =====
 85 | 
 86 | Run the standard command:
 87 | ```bash
 88 | > pio build
 89 | ```
 90 | 
 91 | Evaluation
 92 | =====
 93 | 
 94 | The template evaluates the accuracy with 4 metrics:
 95 | * AccuracyRoot - checks what fraction of whole sentences (the tree roots) had their sentiment predicted correctly,
 96 | * AccuracyAll - checks what fraction of sentence fragments had their sentiment predicted correctly,
 97 | * AccuracyBinaryRoot - like AccuracyRoot, but only considers whether the sentiment is positive or negative (i.e. collapses Positive and Very Positive into a single class),
 98 | * AccuracyBinaryAll - like AccuracyAll, but only considers whether the sentiment is positive or negative.
 99 | 
100 | To run the evaluation execute:
101 | ```bash
102 | > SPARK_MEM="4g" pio eval -sk sentiment.SentimentEvaluation
103 | ```
104 | 
105 | SPARK_MEM controls the amount of memory given to the engine. Generally, the more the better.
106 | 
107 | Warning: if run on the entire data set (as is the default) this can take a very long time, up to 10 hours.
108 | 
109 | The evaluation results, compared to the Stanford implementation:
110 | 
111 | Name       | Vector/matrix size | All  | Roots | All binary | Root binary
112 | ---------- | ------------------ | ---- | ----- | ---------- | -----------
113 | RNN        | 10                 | 79.4 | 48.7  | 85.0       | 76.6
114 | RNTN       | 10                 | 76.7 | 37.7  | 82.5       | 64.1
115 | RNTNSimple | 10                 | 78.6 | 45.6  | 84.3       | 75.0
116 | RNTNSplit  | 10                 | 75.1 | 30.1  | 79.3       | 47.2
117 | MM         | 15x15              | 76.2 | 6.0   | 78.5       | 10.6
118 | Stanford RNTN | 25-35           | 80.7 | 45.7  | 87.6       | 85.4
119 | 
120 | The performance is very close on tests All, Root and Binary, however it is lower then expected on the RootBinary test. We have used smaller word vector sizes to save time. Increasing them to 25-35 should bring the performance on RootBinary up to Stanford results.
121 | 
122 | Training
123 | ====
124 | 
125 | To train the engine run:
126 | ```bash
127 | > SPARK_MEM="4g" pio train
128 | ```
129 | 
130 | The default configuration uses only 10% of the available data.
131 | 
132 | You can choose the fraction of data used in engine.json and the used algorithm - just provide the configuration only for the one you want to use. Have a look at the SentimentEvaluation object in *Accuracy.scala* for some configuration examples.
133 | 
134 | Visualisation
135 | =====
136 | 
137 | The template includes a very simple visualisation in the form of a Django app. To run it first install the requirements:
138 | ```bash
139 | > cd <EngineDir>/web/
140 | > virtualenv env
141 | > source env/bin/activate
142 | > pip install -r requirements.txt
143 | ```
144 | Next, run the web server:
145 | ```bash
146 | > ./manage.py runserver
147 | ```
148 | 
149 | Finally, deploy the engine (make sure it is trained!):
150 | ```bash
151 | > cd <EngineDir>
152 | > pio deploy --port 8001
153 | ```
154 | 
155 | The visualisation is available at http://localhost:8000/. A submitted sentence will be parsed into a tree and each node will be colored based on the sentiment of that sentence fragment. Red color denotes negative and green denotes positive sentiment.
156 | 


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/build.sbt:
--------------------------------------------------------------------------------
 1 | import AssemblyKeys._
 2 | 
 3 | assemblySettings
 4 | 
 5 | name := "template-scala-cml-sentiment"
 6 | 
 7 | organization := "io.prediction"
 8 | 
 9 | resolvers += Resolver.mavenLocal
10 | 
11 | scalaVersion := "2.10.5"
12 | 
13 | libraryDependencies ++= Seq(
14 |   "io.prediction" %% "core" % pioVersion.value % "provided",
15 |   "org.apache.spark" %% "spark-core" % "1.3.0" % "provided",
16 |   "org.xerial.snappy" % "snappy-java" % "1.1.1.7",
17 |   "com.github.tototoshi" %% "scala-csv" % "1.2.1",
18 |   "edu.stanford.nlp" % "stanford-parser" % "3.5.2",
19 |   "cml" %% "cml" % "0.2.0-SNAPSHOT"
20 | )
21 | 


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/data/englishPCFG.caseless.ser.gz:
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https://raw.githubusercontent.com/pavelchristof/template-scala-cml-sentiment/e0806edd115990fd631f938ea4099653e8273b63/data/englishPCFG.caseless.ser.gz


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/data/englishPCFG.ser.gz:
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https://raw.githubusercontent.com/pavelchristof/template-scala-cml-sentiment/e0806edd115990fd631f938ea4099653e8273b63/data/englishPCFG.ser.gz


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/engine.json:
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 1 | {
 2 |   "id": "default",
 3 |   "description": "Default settings",
 4 |   "engineFactory": "sentiment.SentimentEngine",
 5 |   "sparkConf": {
 6 |       "spark": {
 7 |         "executor": {
 8 |           "memory": "12g"
 9 |         },
10 |         "storage.memoryFraction": 0.05,
11 |         "driver.memory": "12g"
12 |       }
13 |   },
14 |   "datasource": {
15 |     "params" : {
16 |       "fraction": 0.1,
17 |       "batchSize": 25
18 |     }
19 |   },
20 |   "algorithms": [
21 |     {
22 |       "name": "rnn",
23 |       "params": {
24 |         "wordVecSize": 3,
25 |         "stepSize": 0.1,
26 |         "regularizationCoeff": 1e-6,
27 |         "iterations": 10,
28 |         "noise": 0.1
29 |       }
30 |     }
31 |   ]
32 | }
33 | 


--------------------------------------------------------------------------------
/project/assembly.sbt:
--------------------------------------------------------------------------------
1 | addSbtPlugin("com.eed3si9n" % "sbt-assembly" % "0.11.2")
2 | 


--------------------------------------------------------------------------------
/project/pio-build.sbt:
--------------------------------------------------------------------------------
1 | addSbtPlugin("io.prediction" % "pio-build" % "0.9.0")
2 | 


--------------------------------------------------------------------------------
/src/main/scala/sentiment/Accuracy.scala:
--------------------------------------------------------------------------------
  1 | package sentiment
  2 | 
  3 | import cml.Tree
  4 | import io.prediction.controller._
  5 | 
  6 | object AccuracyUtils {
  7 |   def equal(u: Sentiment.Vector[Double], v: Sentiment.Vector[Double]): Double =
  8 |     if (Sentiment.choose(u) == Sentiment.choose(v)) 1d else 0d
  9 | 
 10 |   def equalBin(u: Sentiment.Vector[Double], v: Sentiment.Vector[Double]): Double = {
 11 |     val a = Sentiment.choose(u)
 12 |     val b = Sentiment.choose(v)
 13 |     if (toBin(a) == toBin(b)) 1d else 0d
 14 |   }
 15 | 
 16 |   def toBin(s: String): Int =
 17 |     s match {
 18 |       case "0" => -1
 19 |       case "1" => -1
 20 |       case "2" => 0
 21 |       case "3" => 1
 22 |       case "4" => 1
 23 |     }
 24 | }
 25 | 
 26 | case class AccuracyRoot ()
 27 |   extends AverageMetric[EmptyEvaluationInfo, Query, Result, Result] {
 28 |   def calculate(query: Query, predicted: Result, actual: Result): Double =
 29 |     AccuracyUtils.equal(predicted.sentence.accum, actual.sentence.accum)
 30 | }
 31 | 
 32 | case class AccuracyAll ()
 33 |   extends AverageMetric[EmptyEvaluationInfo, Query, Result, Result] {
 34 |   def calculate(query: Query, predicted: Result, actual: Result): Double = {
 35 |     val zipped = predicted.sentence.zip(actual.sentence)
 36 |     val scored = Tree.accums.map(zipped)(p => AccuracyUtils.equal(p._1, p._2))
 37 |     val list = Tree.accums.toList(scored)
 38 |     list.sum / list.size
 39 |   }
 40 | }
 41 | 
 42 | case class AccuracyRootBinary ()
 43 |   extends AverageMetric[EmptyEvaluationInfo, Query, Result, Result] {
 44 |   def calculate(query: Query, predicted: Result, actual: Result): Double =
 45 |     AccuracyUtils.equalBin(predicted.sentence.accum, actual.sentence.accum)
 46 | }
 47 | 
 48 | case class AccuracyAllBinary ()
 49 |   extends AverageMetric[EmptyEvaluationInfo, Query, Result, Result] {
 50 |   def calculate(query: Query, predicted: Result, actual: Result): Double = {
 51 |     val zipped = predicted.sentence.zip(actual.sentence)
 52 |     val scored = Tree.accums.map(zipped)(p => AccuracyUtils.equalBin(p._1, p._2))
 53 |     val list = Tree.accums.toList(scored)
 54 |     list.sum / list.size
 55 |   }
 56 | }
 57 | 
 58 | object SentimentEvaluation extends Evaluation with EngineParamsGenerator {
 59 |   engineEvaluator = (
 60 |     SentimentEngine(),
 61 |     MetricEvaluator(
 62 |       metric = AccuracyAll(),
 63 |       otherMetrics = Seq(
 64 |         AccuracyRoot(),
 65 |         AccuracyAllBinary(),
 66 |         AccuracyRootBinary()
 67 |       )
 68 |     ))
 69 | 
 70 |   engineParamsList = Seq(
 71 |     EngineParams(
 72 |       dataSourceParams = DataSourceParams(fraction = 1, batchSize = 25),
 73 |       algorithmParamsList = Seq(
 74 |         ("rntnSimpl", RNTNSimplParams(
 75 |           wordVecSize = 10,
 76 |           stepSize = 0.03,
 77 |           regularizationCoeff = 1e-3,
 78 |           iterations = 50,
 79 |           noise = 0.1
 80 |         ))
 81 |       )
 82 |     ),
 83 |     EngineParams(
 84 |       dataSourceParams = DataSourceParams(fraction = 1, batchSize = 25),
 85 |       algorithmParamsList = Seq(
 86 |         ("mm", MMParams(
 87 |           vecSize = 15,
 88 |           stepSize = 0.02,
 89 |           regularizationCoeff = 1e-3,
 90 |           iterations = 20,
 91 |           noise = 0.1
 92 |         ))
 93 |       )
 94 |     ),
 95 |     EngineParams(
 96 |       dataSourceParams = DataSourceParams(fraction = 1, batchSize = 25),
 97 |       algorithmParamsList = Seq(
 98 |         ("rntn", RNTNParams(
 99 |           wordVecSize = 10,
100 |           stepSize = 0.03,
101 |           regularizationCoeff = 1e-3,
102 |           iterations = 50,
103 |           noise = 0.1
104 |         ))
105 |       )
106 |     ),
107 |     EngineParams(
108 |       dataSourceParams = DataSourceParams(fraction = 1, batchSize = 25),
109 |       algorithmParamsList = Seq(
110 |         ("rntnSplit", RNTNSplitParams(
111 |           halfVecSize = 5,
112 |           stepSize = 0.03,
113 |           regularizationCoeff = 1e-3,
114 |           iterations = 50,
115 |           noise = 0.1
116 |         ))
117 |       )
118 |     ),
119 |     EngineParams(
120 |       dataSourceParams = DataSourceParams(fraction = 1, batchSize = 25),
121 |       algorithmParamsList = Seq(
122 |         ("rnn", RNNParams(
123 |           wordVecSize = 10,
124 |           stepSize = 0.03,
125 |           regularizationCoeff = 1e-3,
126 |           iterations = 50,
127 |           noise = 0.1
128 |         ))
129 |       )
130 |     )
131 |   )
132 | }
133 | 


--------------------------------------------------------------------------------
/src/main/scala/sentiment/AlgorithmBase.scala:
--------------------------------------------------------------------------------
  1 | package sentiment
  2 | 
  3 | import cml._
  4 | import cml.algebra._
  5 | import cml.optimization.{Scale, AdaGrad, Stabilize, StochasticGradientDescent}
  6 | import grizzled.slf4j.Logger
  7 | import io.prediction.controller.{Params, P2LAlgorithm}
  8 | import org.apache.spark.SparkContext
  9 | import cml.algebra.Floating._
 10 | 
 11 | import scala.util.Random
 12 | import scalaz.Semigroup
 13 | 
 14 | trait AlgorithmParams extends Params {
 15 |   val stepSize: Double
 16 |   val iterations: Int
 17 |   val regularizationCoeff: Double
 18 |   val noise: Double
 19 | }
 20 | 
 21 | abstract class AlgorithmBase (
 22 |   params: AlgorithmParams
 23 | ) extends P2LAlgorithm[TrainingData, Any, Query, Result] {
 24 |   @transient lazy val logger = Logger[this.type]
 25 | 
 26 |   type Word[A] = String
 27 |   type InputTree[A] = Tree[Unit, String]
 28 |   type OutputTree[A] = Tree[Sentiment.Vector[A], String]
 29 | 
 30 |   implicit val inputTreeFunctor: Functor[InputTree] = Functor.const
 31 |   implicit val outputTreeFunctor: Functor[OutputTree] =
 32 |     Functor.compose[({type T[A] = Tree[A, String]})#T, Sentiment.Vector](Tree.accumsZero[String], Sentiment.space)
 33 |   implicit val sentimentVecSpace = Sentiment.space
 34 | 
 35 |   val model: Model[InputTree, OutputTree]
 36 | 
 37 |   /**
 38 |    * The cost function for our model.
 39 |    */
 40 |   val costFun = new CostFun[InputTree, OutputTree] {
 41 |     /**
 42 |      * This function scores a single sample (input, expected output and actual output triple).
 43 |      *
 44 |      * The cost for the whole data set is assumed to be the mean of scores for each sample.
 45 |      */
 46 |     override def scoreSample[A](sample: Sample[InputTree[A], OutputTree[A]])(implicit an: Analytic[A]): A = {
 47 |       import an.analyticSyntax._
 48 |       val eps = fromDouble(1e-9)
 49 | 
 50 |       // The cost function for single vectors.
 51 |       def j(e: Sentiment.Vector[A], a: Sentiment.Vector[A]): A =
 52 |         sentimentVecSpace.sum(sentimentVecSpace.apply2(e, a)((e, a) => e * (a + eps).log))
 53 | 
 54 |       // We sum errors for each node.
 55 |       val zipped = sample.expected.zip(sample.actual)
 56 |       - Tree.accums.foldMap1(zipped)(x => j(x._1, x._2))(new Semigroup[A] {
 57 |         override def append(f1: A, f2: => A): A = f1 + f2
 58 |       })
 59 |     }
 60 | 
 61 |     /**
 62 |      * Computes the regularization term for a model instance.
 63 |      */
 64 |     override def regularization[V[_], A](instance: V[A])(implicit an: Analytic[A], space: Normed[V]): A =
 65 |       an.mul(an.fromDouble(params.regularizationCoeff), space.quadrance(instance))
 66 |   }
 67 | 
 68 |   /**
 69 |    * We need to declare what automatic differentiation engine should be used. Backpropagation is the best.
 70 |    */
 71 |   implicit val diffEngine = ad.Backward
 72 | 
 73 |   /**
 74 |    * Trains a model instance.
 75 |    */
 76 |   override def train(sc: SparkContext, data: TrainingData): Any = {
 77 |     val dataSet = data.get.map(_.map(x => (x._1, x._2.sentence)))
 78 | 
 79 |     /**
 80 |      * An optimizer is used to train the model.
 81 |      *
 82 |      * Gradient descent takes an optional gradient transformer, which is a function applied to the gradient before a
 83 |      * step is made. Here we apply numerical stabilization and then AdaGrad, finally scaling the gradient.
 84 |      */
 85 |     val optimizer = StochasticGradientDescent(
 86 |       model,
 87 |       iterations = params.iterations,
 88 |       gradTrans = Stabilize.andThen(AdaGrad).andThen(Scale(params.stepSize))
 89 |     )
 90 | 
 91 |     // Value that the new model instances will be filled with.
 92 |     val rng = new Random() with Serializable
 93 |     val initialInst = optimizer.model.params.tabulate(_ =>
 94 |       (rng.nextDouble * 2d - 1d) * params.noise)
 95 | 
 96 |     // Run the optimizer!
 97 |     val t1 = System.currentTimeMillis()
 98 |     val inst = optimizer[Double](
 99 |       dataSet,
100 |       costFun,
101 |       initialInst)
102 |     println(s"Optimization took ${System.currentTimeMillis() - t1}ms.")
103 | 
104 |     inst
105 |   }
106 | 
107 |   /**
108 |    * Queries the model.
109 |    */
110 |   override def predict(instUntyped: Any, query: Query): Result = {
111 |     val input = query.sentence match {
112 |       case Left(s) => Parser(s)
113 |       case Right(t) => t
114 |     }
115 |     val inst = instUntyped.asInstanceOf[model.Params[Double]]
116 | 
117 |     Result(model(inst)(input))
118 |   }
119 | }
120 | 


--------------------------------------------------------------------------------
/src/main/scala/sentiment/DataSource.scala:
--------------------------------------------------------------------------------
 1 | package sentiment
 2 | 
 3 | import cml._
 4 | import cml.algebra.Floating._
 5 | import grizzled.slf4j.Logger
 6 | import io.prediction.controller._
 7 | import org.apache.spark.SparkContext
 8 | import org.apache.spark.rdd.RDD
 9 | 
10 | import scala.io.Source
11 | import scala.util.parsing.combinator.RegexParsers
12 | 
13 | case class DataSourceParams(
14 |   fraction: Double,
15 |   batchSize: Int
16 | ) extends Params
17 | 
18 | class DataSource(params: DataSourceParams)
19 |   extends PDataSource[TrainingData, EmptyEvaluationInfo, Query, Result] with RegexParsers {
20 | 
21 |   @transient lazy val logger = Logger[this.type]
22 | 
23 |   override def readTraining(sc: SparkContext): TrainingData = {
24 |     val data = readPTB("data/train.txt")
25 |     val batches = data.take((data.size * params.fraction).toInt).grouped(params.batchSize)
26 |     val rdd = sc.parallelize(batches.toSeq, 64)
27 |     TrainingData(rdd)
28 |   }
29 | 
30 |   override def readEval(sc: SparkContext): Seq[(TrainingData, EmptyEvaluationInfo, RDD[(Query, Result)])] = {
31 |     val data = readPTB("data/train.txt")
32 |     val batches = data.take((data.size * params.fraction).toInt).grouped(params.batchSize)
33 |     val training = sc.parallelize(batches.toSeq)
34 |     val eval = sc.parallelize(readPTB("data/test.txt")).map(t => (Query(Right(t._1)), t._2))
35 |     Seq((TrainingData(training), new EmptyEvaluationInfo(), eval))
36 |   }
37 | 
38 |   def readPTB(path: String): Seq[(Tree[Unit, String], Result)] = {
39 |     val data = Source
40 |       .fromFile(path)
41 |       .getLines()
42 |       .toSeq
43 | 
44 |     data
45 |       .map(parse(tree, _))
46 |       .flatMap {
47 |         case Success(v, _) => Some(v)
48 |         case NoSuccess(msg, _) => {
49 |           println(msg)
50 |           None
51 |         }
52 |       }
53 |       // Filter out neutral sentences like the RNTN paper does.
54 |       .filter(t => Sentiment.choose(t.accum) != "2")
55 |       .map(t => (Tree.accums.map(t)(_ => ()), Result(t)))
56 |   }
57 | 
58 |   def sentVec(label: String): Sentiment.Vector[Double] =
59 |     Sentiment.space.tabulatePartial(Map(Sentiment.classes(label) -> 1d))
60 | 
61 |   def tree: Parser[Tree[Sentiment.Vector[Double], String]] =
62 |     ("(" ~ string ~ string ~ ")" ^^ {
63 |       case _ ~ label ~ word ~ _ => Leaf(sentVec(label), word)
64 |     }) | ("(" ~ string ~ tree ~ tree ~ ")" ^^ {
65 |       case _ ~ label ~ left ~ right ~ _ => Node(left, sentVec(label), right)
66 |     })
67 | 
68 |   def string: Parser[String] = "[^\\s()]+"r
69 | }
70 | 
71 | case class TrainingData(
72 |   get: RDD[Seq[(Tree[Unit, String], Result)]]
73 | ) extends Serializable
74 | 


--------------------------------------------------------------------------------
/src/main/scala/sentiment/Engine.scala:
--------------------------------------------------------------------------------
 1 | package sentiment
 2 | 
 3 | import cml.Tree
 4 | import cml.algebra.{Cartesian, Vec, RuntimeNat}
 5 | import io.prediction.controller.{Engine, EngineFactory}
 6 | import io.prediction.data.storage.BiMap
 7 | 
 8 | case class Query (
 9 |   sentence: Either[String, Tree[Unit, String]]
10 | ) extends Serializable
11 | 
12 | case class Result (
13 |   sentence: Tree[Sentiment.Vector[Double], String]
14 | ) extends Serializable
15 | 
16 | object Sentiment {
17 |   /**
18 |    * A mapping between sentiment vector indices and sentiment labels.
19 |    */
20 |   val classes = BiMap.stringInt(Array("0", "1", "2", "3", "4"))
21 | 
22 |   /**
23 |    * The number of sentiment classes as a type.
24 |    */
25 |   val size = RuntimeNat(classes.size)
26 | 
27 |   /**
28 |    * A vector of class probabilities.
29 |    */
30 |   type Vector[A] = Vec[size.Type, A]
31 | 
32 |   /**
33 |    * The space of vectors of class probabilities.
34 |    */
35 |   implicit val space = Vec.cartesian(size())
36 | 
37 |   /**
38 |    * Choose the label with the highest probability.
39 |    */
40 |   def choose[A](vec: Vector[A])(implicit ord: Ordering[A]): String =
41 |     classes.inverse(vec.get.zipWithIndex.maxBy(_._1)._2)
42 | }
43 | 
44 | object SentimentEngine extends EngineFactory {
45 |   def apply() = {
46 |     new Engine(
47 |       classOf[DataSource],
48 |       classOf[Preparator],
49 |       Map(
50 |         "rntn" -> classOf[RNTN],
51 |         "rntnSimpl" -> classOf[RNTNSimpl],
52 |         "rntnSplit" -> classOf[RNTNSplit],
53 |         "rnn" -> classOf[RNN],
54 |         "mm" -> classOf[MM]
55 |       ),
56 |       classOf[Serving])
57 |   }
58 | }
59 | 


--------------------------------------------------------------------------------
/src/main/scala/sentiment/MM.scala:
--------------------------------------------------------------------------------
 1 | package sentiment
 2 | 
 3 | import cml._
 4 | import cml.algebra._
 5 | import cml.models._
 6 | 
 7 | case class MMParams (
 8 |   vecSize: Int,
 9 |   stepSize: Double,
10 |   iterations: Int,
11 |   regularizationCoeff: Double,
12 |   noise: Double
13 | ) extends AlgorithmParams
14 | 
15 | class MM (
16 |   params: MMParams
17 | ) extends AlgorithmBase (params) {
18 |   // First declare the size of our vectors. We use RuntimeNat here because the size depend on algorithm parameters.
19 |   val vecSize = algebra.RuntimeNat(params.vecSize)
20 | 
21 |   // Now lets declare the types of vectors that we'll be using.
22 |   type Vect[A] = Vec[vecSize.Type, A]
23 |   type Matrix[A] = Vect[Vect[A]]
24 |   type MatrixPair[A] = (Matrix[A], Matrix[A])
25 |   type MatrixTree[A] = Tree[Matrix[A], String]
26 | 
27 |   // We have to find the required implicits by hand because Scala doesn't support type classes.
28 |   implicit val vecSpace = Vec.cartesian(vecSize())
29 |   implicit val matrixMonoid = Monoid1.matrix[Vect]
30 | 
31 |   override val model = Chain2[InputTree, MatrixTree, OutputTree](
32 |     AccumulateTree[Word, Matrix](
33 |       inject = SetMap[String, Matrix],
34 |       reduce = Chain2[MatrixPair, Matrix, Matrix](
35 |         MonoidAppend[Matrix],
36 |         Pointwise[Matrix](AnalyticMap.tanh)
37 |       )
38 |     ) : Model[InputTree, MatrixTree],
39 |     BifunctorMap[Tree, Matrix, Sentiment.Vector, Word, Word](
40 |       left = Chain2(
41 |         AffineMap[Matrix, Sentiment.Vector],
42 |         Softmax[Sentiment.Vector]
43 |       ),
44 |       right = Identity[Word]
45 |     )
46 |   )
47 | }
48 | 


--------------------------------------------------------------------------------
/src/main/scala/sentiment/Parser.scala:
--------------------------------------------------------------------------------
 1 | package sentiment
 2 | 
 3 | import java.io.StringReader
 4 | 
 5 | import edu.stanford.nlp.parser.lexparser.LexicalizedParser
 6 | import edu.stanford.nlp.process.{LowercaseAndAmericanizeFunction, PTBTokenizer}
 7 | import edu.stanford.nlp.trees.{TreeTransformer, Tree}
 8 | import scalaz.Scalaz._
 9 | 
10 | object Parser {
11 |   /**
12 |    * Parser a sentence into a tree.
13 |    */
14 |   def apply(sentence: String): cml.Tree[Unit, String] = {
15 |     val reader = new StringReader(sentence)
16 |     val tokenizer = PTBTokenizer.newPTBTokenizer(reader, false, false)
17 |     val tree = parser
18 |       .parse(tokenizer.tokenize())
19 |       //.transform(Normalize)
20 |     binarize(tree)
21 |   }
22 | 
23 |   val parserModel = "data/englishPCFG.ser.gz"
24 |   val parser = LexicalizedParser.loadModel(parserModel)
25 | 
26 |   object Normalize extends TreeTransformer {
27 |     val normalize = new LowercaseAndAmericanizeFunction()
28 | 
29 |     override def transformTree(t: Tree): Tree = {
30 |       if (t.isLeaf) {
31 |         t.setValue(normalize(t.value()))
32 |       }
33 |       t
34 |     }
35 |   }
36 | 
37 |   def binarize(t: Tree): cml.Tree[Unit, String] = {
38 |     if (t.isLeaf) {
39 |       cml.Leaf((), t.value())
40 |     } else {
41 |       t.children().map(binarize).toVector.foldr1Opt(l => r => cml.Node(l, (), r)).get
42 |     }
43 |   }
44 | }
45 | 


--------------------------------------------------------------------------------
/src/main/scala/sentiment/Preparator.scala:
--------------------------------------------------------------------------------
 1 | package sentiment
 2 | 
 3 | import io.prediction.controller.{Params, PPreparator}
 4 | import org.apache.spark.SparkContext
 5 | import org.apache.spark.rdd.RDD
 6 | 
 7 | class Preparator extends PPreparator[TrainingData, TrainingData] {
 8 |   def prepare(sc: SparkContext, trainingData: TrainingData): TrainingData = {
 9 |     trainingData
10 |   }
11 | }


--------------------------------------------------------------------------------
/src/main/scala/sentiment/RNN.scala:
--------------------------------------------------------------------------------
 1 | package sentiment
 2 | 
 3 | import cml._
 4 | import cml.algebra._
 5 | import cml.models._
 6 | 
 7 | case class RNNParams (
 8 |   wordVecSize: Int,
 9 |   stepSize: Double,
10 |   iterations: Int,
11 |   regularizationCoeff: Double,
12 |   noise: Double
13 | ) extends AlgorithmParams
14 | 
15 | class RNN (
16 |   params: RNNParams
17 | ) extends AlgorithmBase (params) {
18 |   // First declare the size of our vectors. We use RuntimeNat here because the size depend on algorithm parameters.
19 |   val wordVecSize = algebra.RuntimeNat(params.wordVecSize)
20 | 
21 |   // Now lets declare the types of vectors that we'll be using.
22 |   type WordVec[A] = Vec[wordVecSize.Type, A]
23 |   type WordVecPair[A] = (WordVec[A], WordVec[A])
24 |   type WordVecQuad[A] = (WordVecPair[A], WordVecPair[A])
25 |   type WordVecTree[A] = Tree[WordVec[A], String]
26 | 
27 |   // We have to find the required implicits by hand because Scala doesn't support type classes.
28 |   implicit val wordVecSpace = Vec.cartesian(wordVecSize())
29 |   implicit val wordVecPairSpace = Cartesian.product[WordVec, WordVec]
30 |   implicit val wordVecQuadSpace = Cartesian.product[WordVecPair, WordVecPair]
31 | 
32 |   override val model = Chain2[InputTree, WordVecTree, OutputTree](
33 |     // In the first part of the algorithm we map each word to a vector and then propagate
34 |     // the vectors up the tree using a merge function.
35 |     AccumulateTree[Word, WordVec](
36 |       // The function that maps words to vectors.
37 |       inject = SetMap[String, WordVec],
38 |       // Merge function, taking a pair of vectors and returning a single vector.
39 |       reduce = Chain2(
40 |         AffineMap[WordVecPair, WordVec],
41 |         Pointwise[WordVec](AnalyticMap.tanh)
42 |       )
43 |     ) : Model[InputTree, WordVecTree],
44 | 
45 |     // In the second part we map over the tree to classify the word vectors.
46 |     BifunctorMap[Tree, WordVec, Sentiment.Vector, Word, Word](
47 |       // Word vectors go thought a softmax classifier.
48 |       left = Chain2(
49 |         AffineMap[WordVec, Sentiment.Vector],
50 |         Softmax[Sentiment.Vector]
51 |       ),
52 |       // Words are unchanged.
53 |       right = Identity[Word]
54 |     )
55 |   )
56 | }
57 | 


--------------------------------------------------------------------------------
/src/main/scala/sentiment/RNTN.scala:
--------------------------------------------------------------------------------
 1 | package sentiment
 2 | 
 3 | import cml._
 4 | import cml.algebra._
 5 | import cml.models._
 6 | 
 7 | case class RNTNParams (
 8 |   wordVecSize: Int,
 9 |   stepSize: Double,
10 |   iterations: Int,
11 |   regularizationCoeff: Double,
12 |   noise: Double
13 | ) extends AlgorithmParams
14 | 
15 | class RNTN (
16 |   params: RNTNParams
17 | ) extends AlgorithmBase (params) {
18 |   // First declare the size of our vectors. We use RuntimeNat here because the size depend on algorithm parameters.
19 |   val wordVecSize = algebra.RuntimeNat(params.wordVecSize)
20 | 
21 |   // Now lets declare the types of vectors that we'll be using.
22 |   type WordVec[A] = Vec[wordVecSize.Type, A]
23 |   type WordVecPair[A] = (WordVec[A], WordVec[A])
24 |   type WordVecQuad[A] = (WordVecPair[A], WordVecPair[A])
25 |   type WordVecTree[A] = Tree[WordVec[A], String]
26 | 
27 |   // We have to find the required implicits by hand because Scala doesn't support type classes.
28 |   implicit val wordVecSpace = Vec.cartesian(wordVecSize())
29 |   implicit val wordVecPairSpace = Cartesian.product[WordVec, WordVec]
30 |   implicit val wordVecQuadSpace = Cartesian.product[WordVecPair, WordVecPair]
31 | 
32 |   override val model = Chain2[InputTree, WordVecTree, OutputTree](
33 |     // In the first part of the algorithm we map each word to a vector and then propagate
34 |     // the vectors up the tree using a merge function.
35 |     AccumulateTree[Word, WordVec](
36 |       // The function that maps words to vectors.
37 |       inject = SetMap[String, WordVec],
38 |       // Merge function, taking a pair of vectors and returning a single vector.
39 |       reduce = Chain3[WordVecPair, WordVecQuad, WordVec, WordVec](
40 |         // Duplicate takes a single argument x (of type WordVecPair) and returns (x, x).
41 |         Duplicate[WordVecPair],
42 |         // LinAffinMap is a function on two arguments: linear in the first and affine in the second. This is
43 |         // equivalent to the sum of a bilinear form (on both arguments) and a linear form on the first argument.
44 |         // The type parameters are argument types and the result type.
45 |         LinAffinMap[WordVecPair, WordVecPair, WordVec],
46 |         // Apply the activaton function pointwise over the word vector.
47 |         Pointwise[WordVec](AnalyticMap.tanh)
48 |       )
49 |     ) : Model[InputTree, WordVecTree],
50 | 
51 |     // In the second part we map over the tree to classify the word vectors.
52 |     BifunctorMap[Tree, WordVec, Sentiment.Vector, Word, Word](
53 |       // Word vectors go thought a softmax classifier.
54 |       left = Chain2(
55 |         AffineMap[WordVec, Sentiment.Vector],
56 |         Softmax[Sentiment.Vector]
57 |       ),
58 |       // Words are unchanged.
59 |       right = Identity[Word]
60 |     )
61 |   )
62 | }
63 | 


--------------------------------------------------------------------------------
/src/main/scala/sentiment/RNTNSimpl.scala:
--------------------------------------------------------------------------------
 1 | package sentiment
 2 | 
 3 | import cml._
 4 | import cml.algebra._
 5 | import cml.models._
 6 | 
 7 | case class RNTNSimplParams (
 8 |   wordVecSize: Int,
 9 |   stepSize: Double,
10 |   iterations: Int,
11 |   regularizationCoeff: Double,
12 |   noise: Double
13 | ) extends AlgorithmParams
14 | 
15 | class RNTNSimpl (
16 |   params: RNTNSimplParams
17 | ) extends AlgorithmBase (params) {
18 |   // First declare the size of our vectors. We use RuntimeNat here because the size depend on algorithm parameters.
19 |   val wordVecSize = algebra.RuntimeNat(params.wordVecSize)
20 | 
21 |   // Now lets declare the types of vectors that we'll be using.
22 |   type WordVec[A] = Vec[wordVecSize.Type, A]
23 |   type WordVecPair[A] = (WordVec[A], WordVec[A])
24 |   type WordVecTree[A] = Tree[WordVec[A], String]
25 | 
26 |   // We have to find the required implicits by hand because Scala doesn't support type classes.
27 |   implicit val wordVecSpace = Vec.cartesian(wordVecSize())
28 |   implicit val wordVecPairSpace = Cartesian.product[WordVec, WordVec]
29 | 
30 |   override val model = Chain2[InputTree, WordVecTree, OutputTree](
31 |     // In the first part of the algorithm we map each word to a vector and then propagate
32 |     // the vectors up the tree using a merge function.
33 |     AccumulateTree[Word, WordVec](
34 |       // The function that maps words to vectors.
35 |       inject = SetMap[String, WordVec],
36 |       // Merge function, taking a pair of vectors and returning a single vector.
37 |       reduce = Chain2[WordVecPair, WordVec, WordVec](
38 |         BiaffineMap[WordVec, WordVec, WordVec],
39 |         Pointwise[WordVec](AnalyticMap.tanh)
40 |       )
41 |     ) : Model[InputTree, WordVecTree],
42 | 
43 |     // In the second part we map over the tree to classify the word vectors.
44 |     BifunctorMap[Tree, WordVec, Sentiment.Vector, Word, Word](
45 |       // Word vectors go thought a softmax classifier.
46 |       left = Chain2(
47 |         AffineMap[WordVec, Sentiment.Vector],
48 |         Softmax[Sentiment.Vector]
49 |       ),
50 |       // Words are unchanged.
51 |       right = Identity[Word]
52 |     )
53 |   )
54 | }
55 | 


--------------------------------------------------------------------------------
/src/main/scala/sentiment/RNTNSplit.scala:
--------------------------------------------------------------------------------
 1 | package sentiment
 2 | 
 3 | import cml._
 4 | import cml.algebra._
 5 | import cml.models._
 6 | 
 7 | case class RNTNSplitParams (
 8 |   halfVecSize: Int,
 9 |   stepSize: Double,
10 |   iterations: Int,
11 |   regularizationCoeff: Double,
12 |   noise: Double
13 | ) extends AlgorithmParams
14 | 
15 | class RNTNSplit (
16 |   params: RNTNSplitParams
17 | ) extends AlgorithmBase (params) {
18 |   // First declare the size of our vectors. We use RuntimeNat here because the size depend on algorithm parameters.
19 |   val halfVecSize = algebra.RuntimeNat(params.halfVecSize)
20 | 
21 |   // Now lets declare the types of vectors that we'll be using.
22 |   type HalfVec[A] = Vec[halfVecSize.Type, A]
23 |   type WordVec[A] = (HalfVec[A], HalfVec[A])
24 |   type WordVecPair[A] = (WordVec[A], WordVec[A])
25 |   type WordVecTree[A] = Tree[WordVec[A], String]
26 | 
27 |   // We have to find the required implicits by hand because Scala doesn't support type classes.
28 |   implicit val halfVecSpace = Vec.cartesian(halfVecSize())
29 |   implicit val wordVecSpace = Cartesian.product[HalfVec, HalfVec]
30 | 
31 |   case class Tensor () extends Model[WordVecPair, WordVec] {
32 |     // Scala can't see the implicits that are right there /\
33 |     val f4 = AffineMap[HalfVec, WordVec]()(halfVecSpace, wordVecSpace)
34 |     val f3 = AffineMap[HalfVec, f4.Params]()(halfVecSpace, f4.params)
35 |     val f2 = AffineMap[HalfVec, f3.Params]()(halfVecSpace, f3.params)
36 |     val f1 = AffineMap[HalfVec, f2.Params]()(halfVecSpace, f2.params)
37 | 
38 |     override type Params[A] = AffineMap[HalfVec, f2.Params]#Params[A]
39 |     override implicit val params = f1.params
40 | 
41 |     override def apply[A](inst: Params[A])(in: WordVecPair[A])(implicit a: Analytic[A]): WordVec[A] = {
42 |       f4(f3(f2(f1(inst)(in._1._1))(in._1._2))(in._2._1))(in._2._2)
43 |     }
44 |   }
45 | 
46 |   override val model = Chain2[InputTree, WordVecTree, OutputTree](
47 |     // In the first part of the algorithm we map each word to a vector and then propagate
48 |     // the vectors up the tree using a merge function.
49 |     AccumulateTree[Word, WordVec](
50 |       // The function that maps words to vectors.
51 |       inject = SetMap[String, WordVec],
52 |       // Merge function, taking a pair of vectors and returning a single vector.
53 |       reduce = Chain2(
54 |         Tensor(),
55 |         Pointwise[WordVec](AnalyticMap.tanh)
56 |       )
57 |     ) : Model[InputTree, WordVecTree],
58 | 
59 |     // In the second part we map over the tree to classify the word vectors.
60 |     BifunctorMap[Tree, WordVec, Sentiment.Vector, Word, Word](
61 |       // Word vectors go thought a softmax classifier.
62 |       left = Chain2(
63 |         AffineMap[WordVec, Sentiment.Vector],
64 |         Softmax[Sentiment.Vector]
65 |       ),
66 |       // Words are unchanged.
67 |       right = Identity[Word]
68 |     )
69 |   )
70 | }
71 | 


--------------------------------------------------------------------------------
/src/main/scala/sentiment/Serving.scala:
--------------------------------------------------------------------------------
 1 | package sentiment
 2 | 
 3 | import io.prediction.controller.LServing
 4 | 
 5 | class Serving extends LServing[Query, Result] {
 6 |   override def serve(query: Query,
 7 |     predictedResults: Seq[Result]): Result = {
 8 |     predictedResults.head
 9 |   }
10 | }
11 | 


--------------------------------------------------------------------------------
/template.json:
--------------------------------------------------------------------------------
1 | {"pio": {"version": { "min": "0.9.2" }}}
2 | 


--------------------------------------------------------------------------------
/web/db.sqlite3:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/pavelchristof/template-scala-cml-sentiment/e0806edd115990fd631f938ea4099653e8273b63/web/db.sqlite3


--------------------------------------------------------------------------------
/web/manage.py:
--------------------------------------------------------------------------------
 1 | #!/usr/bin/env python
 2 | import os
 3 | import sys
 4 | 
 5 | if __name__ == "__main__":
 6 |     os.environ.setdefault("DJANGO_SETTINGS_MODULE", "nlp.settings")
 7 | 
 8 |     from django.core.management import execute_from_command_line
 9 | 
10 |     execute_from_command_line(sys.argv)
11 | 


--------------------------------------------------------------------------------
/web/nlp/__init__.py:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/pavelchristof/template-scala-cml-sentiment/e0806edd115990fd631f938ea4099653e8273b63/web/nlp/__init__.py


--------------------------------------------------------------------------------
/web/nlp/settings.py:
--------------------------------------------------------------------------------
  1 | """
  2 | Django settings for nlp project.
  3 | 
  4 | Generated by 'django-admin startproject' using Django 1.8.2.
  5 | 
  6 | For more information on this file, see
  7 | https://docs.djangoproject.com/en/1.8/topics/settings/
  8 | 
  9 | For the full list of settings and their values, see
 10 | https://docs.djangoproject.com/en/1.8/ref/settings/
 11 | """
 12 | 
 13 | # Build paths inside the project like this: os.path.join(BASE_DIR, ...)
 14 | import os
 15 | 
 16 | BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 17 | 
 18 | 
 19 | # Quick-start development settings - unsuitable for production
 20 | # See https://docs.djangoproject.com/en/1.8/howto/deployment/checklist/
 21 | 
 22 | # SECURITY WARNING: keep the secret key used in production secret!
 23 | SECRET_KEY = '(r$!4v00!3spow93bnv@+p39g=^^o4xxk237e4#x!-2caw#a^d'
 24 | 
 25 | # SECURITY WARNING: don't run with debug turned on in production!
 26 | DEBUG = True
 27 | 
 28 | ALLOWED_HOSTS = []
 29 | 
 30 | 
 31 | # Application definition
 32 | 
 33 | INSTALLED_APPS = (
 34 |     'django.contrib.admin',
 35 |     'django.contrib.auth',
 36 |     'django.contrib.contenttypes',
 37 |     'django.contrib.sessions',
 38 |     'django.contrib.messages',
 39 |     'django.contrib.staticfiles',
 40 |     'sentiment',
 41 | )
 42 | 
 43 | MIDDLEWARE_CLASSES = (
 44 |     'django.contrib.sessions.middleware.SessionMiddleware',
 45 |     'django.middleware.common.CommonMiddleware',
 46 |     'django.middleware.csrf.CsrfViewMiddleware',
 47 |     'django.contrib.auth.middleware.AuthenticationMiddleware',
 48 |     'django.contrib.auth.middleware.SessionAuthenticationMiddleware',
 49 |     'django.contrib.messages.middleware.MessageMiddleware',
 50 |     'django.middleware.clickjacking.XFrameOptionsMiddleware',
 51 |     'django.middleware.security.SecurityMiddleware',
 52 | )
 53 | 
 54 | ROOT_URLCONF = 'nlp.urls'
 55 | 
 56 | TEMPLATES = [
 57 |     {
 58 |         'BACKEND': 'django.template.backends.django.DjangoTemplates',
 59 |         'DIRS': [],
 60 |         'APP_DIRS': True,
 61 |         'OPTIONS': {
 62 |             'context_processors': [
 63 |                 'django.template.context_processors.debug',
 64 |                 'django.template.context_processors.request',
 65 |                 'django.contrib.auth.context_processors.auth',
 66 |                 'django.contrib.messages.context_processors.messages',
 67 |             ],
 68 |         },
 69 |     },
 70 | ]
 71 | 
 72 | STATIC_URL = '/static/'
 73 | 
 74 | STATICFILES_DIRS = (
 75 |     os.path.join(BASE_DIR, "static"),
 76 | )
 77 | 
 78 | WSGI_APPLICATION = 'nlp.wsgi.application'
 79 | 
 80 | 
 81 | # Database
 82 | # https://docs.djangoproject.com/en/1.8/ref/settings/#databases
 83 | 
 84 | DATABASES = {
 85 |     'default': {
 86 |         'ENGINE': 'django.db.backends.sqlite3',
 87 |         'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
 88 |     }
 89 | }
 90 | 
 91 | 
 92 | # Internationalization
 93 | # https://docs.djangoproject.com/en/1.8/topics/i18n/
 94 | 
 95 | LANGUAGE_CODE = 'en-us'
 96 | 
 97 | TIME_ZONE = 'UTC'
 98 | 
 99 | USE_I18N = True
100 | 
101 | USE_L10N = True
102 | 
103 | USE_TZ = True
104 | 
105 | 
106 | # Static files (CSS, JavaScript, Images)
107 | # https://docs.djangoproject.com/en/1.8/howto/static-files/
108 | 
109 | STATIC_URL = '/static/'
110 | 


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/web/nlp/urls.py:
--------------------------------------------------------------------------------
 1 | """nlp URL Configuration
 2 | 
 3 | The `urlpatterns` list routes URLs to views. For more information please see:
 4 |     https://docs.djangoproject.com/en/1.8/topics/http/urls/
 5 | Examples:
 6 | Function views
 7 |     1. Add an import:  from my_app import views
 8 |     2. Add a URL to urlpatterns:  url(r'^$', views.home, name='home')
 9 | Class-based views
10 |     1. Add an import:  from other_app.views import Home
11 |     2. Add a URL to urlpatterns:  url(r'^$', Home.as_view(), name='home')
12 | Including another URLconf
13 |     1. Add an import:  from blog import urls as blog_urls
14 |     2. Add a URL to urlpatterns:  url(r'^blog/', include(blog_urls))
15 | """
16 | from django.conf.urls import include, url
17 | from django.contrib import admin
18 | 
19 | urlpatterns = [
20 |     url(r'^admin/', include(admin.site.urls)),
21 |     url(r'^', include('sentiment.urls'))
22 | ]
23 | 


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/web/nlp/wsgi.py:
--------------------------------------------------------------------------------
 1 | """
 2 | WSGI config for nlp project.
 3 | 
 4 | It exposes the WSGI callable as a module-level variable named ``application``.
 5 | 
 6 | For more information on this file, see
 7 | https://docs.djangoproject.com/en/1.8/howto/deployment/wsgi/
 8 | """
 9 | 
10 | import os
11 | 
12 | from django.core.wsgi import get_wsgi_application
13 | 
14 | os.environ.setdefault("DJANGO_SETTINGS_MODULE", "nlp.settings")
15 | 
16 | application = get_wsgi_application()
17 | 


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/web/requirements.txt:
--------------------------------------------------------------------------------
1 | Django==1.8.2
2 | PredictionIO==0.9.2
3 | pytz==2015.4
4 | wheel==0.24.0
5 | 


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/web/sentiment/__init__.py:
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https://raw.githubusercontent.com/pavelchristof/template-scala-cml-sentiment/e0806edd115990fd631f938ea4099653e8273b63/web/sentiment/__init__.py


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/web/sentiment/admin.py:
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1 | from django.contrib import admin
2 | 
3 | # Register your models here.
4 | 


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/web/sentiment/migrations/__init__.py:
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https://raw.githubusercontent.com/pavelchristof/template-scala-cml-sentiment/e0806edd115990fd631f938ea4099653e8273b63/web/sentiment/migrations/__init__.py


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/web/sentiment/models.py:
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1 | from django.db import models
2 | 
3 | # Create your models here.
4 | 


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/web/sentiment/templates/sentiment/base.html:
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 1 | {% load staticfiles %}
 2 | <!DOCTYPE html>
 3 | <html lang="en">
 4 | <head>
 5 |     <meta charset="utf-8">
 6 |     <meta http-equiv="X-UA-Compatible" content="IE=edge">
 7 |     <meta name="viewport" content="width=device-width, initial-scale=1">
 8 | 
 9 |     <title>Sentiment analysis</title>
10 | 
11 |     <link href="{% static "css/bootstrap.min.css" %}" rel="stylesheet">
12 |     <style>{% block css %}{% endblock %}</style>
13 | </head>
14 | <body>
15 |     <div class="container-fluid" style="padding-top: 100px">
16 | 
17 |         {% block content %}
18 |         {% endblock %}
19 |     </div>
20 | 
21 |     <script src="{% static "js/jquery-1.11.3.min.js" %}"></script>
22 |     <script src="{% static "js/bootstrap.min.js" %}"></script>
23 | </body>
24 | </html>
25 | 


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/web/sentiment/templates/sentiment/index.html:
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 1 | {% extends "sentiment/base.html" %}
 2 | 
 3 | {% block content %}
 4 |     <div style="display: flex">
 5 |         <div style="flex-grow: 1"></div>
 6 |         <form method="POST" style="width:60%">
 7 |             {% csrf_token %}
 8 |             <textarea type="text" style="height: 500px; font-size: 60px; border: 3px solid black" class="form-control"
 9 |                       name="sentence" title="Sentence" placeholder="Enter a sentence..."></textarea>
10 |             <input class="form-control btn btn-success" style="font-size: 40px; height: 80px" type="submit">
11 |         </form>
12 |         <div style="flex-grow: 1"></div>
13 |     </div>
14 | {% endblock %}


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/web/sentiment/templates/sentiment/prediction.html:
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  1 | {% extends "sentiment/base.html" %}
  2 | 
  3 | {% block css %}
  4 | * {margin: 0; padding: 0;}
  5 | 
  6 | .tree ul {
  7 | 	padding-top: 20px; position: relative;
  8 | 
  9 | 	transition: all 0.5s;
 10 | 	-webkit-transition: all 0.5s;
 11 | 	-moz-transition: all 0.5s;
 12 | }
 13 | 
 14 | .tree li {
 15 | 	float: left; text-align: center;
 16 | 	list-style-type: none;
 17 | 	position: relative;
 18 | 	padding: 20px 5px 0 5px;
 19 | 
 20 | 	transition: all 0.5s;
 21 | 	-webkit-transition: all 0.5s;
 22 | 	-moz-transition: all 0.5s;
 23 | }
 24 | 
 25 | /*We will use ::before and ::after to draw the connectors*/
 26 | 
 27 | .tree li::before, .tree li::after{
 28 | 	content: '';
 29 | 	position: absolute; top: 0; right: 50%;
 30 | 	border-top: 2px solid black;
 31 | 	width: 50%; height: 20px;
 32 | }
 33 | .tree li::after{
 34 | 	right: auto; left: 50%;
 35 | 	border-left: 2px solid black;
 36 | }
 37 | 
 38 | /*We need to remove left-right connectors from elements without
 39 | any siblings*/
 40 | .tree li:only-child::after, .tree li:only-child::before {
 41 | 	display: none;
 42 | }
 43 | 
 44 | /*Remove space from the top of single children*/
 45 | .tree li:only-child{ padding-top: 0;}
 46 | 
 47 | /*Remove left connector from first child and
 48 | right connector from last child*/
 49 | .tree li:first-child::before, .tree li:last-child::after{
 50 | 	border: 0 none;
 51 | }
 52 | /*Adding back the vertical connector to the last nodes*/
 53 | .tree li:last-child::before{
 54 | 	border-right: 2px solid black;
 55 | 	border-radius: 0 5px 0 0;
 56 | 	-webkit-border-radius: 0 5px 0 0;
 57 | 	-moz-border-radius: 0 5px 0 0;
 58 | }
 59 | .tree li:first-child::after{
 60 | 	border-radius: 5px 0 0 0;
 61 | 	-webkit-border-radius: 5px 0 0 0;
 62 | 	-moz-border-radius: 5px 0 0 0;
 63 | }
 64 | 
 65 | /*Time to add downward connectors from parents*/
 66 | .tree ul ul::before{
 67 | 	content: '';
 68 | 	position: absolute; top: 0; left: 50%;
 69 | 	border-left: 2px solid black;
 70 | 	width: 0; height: 20px;
 71 | }
 72 | 
 73 | .tree li .tree-label {
 74 | 	border: 2px solid black;
 75 | 	padding: 5px 10px;
 76 | 	text-decoration: none;
 77 |     text-size: 18px;
 78 | 	color: #666;
 79 | 	font-family: arial, verdana, tahoma;
 80 | 	display: inline-block;
 81 | 
 82 | 	border-radius: 5px;
 83 | 	-webkit-border-radius: 5px;
 84 | 	-moz-border-radius: 5px;
 85 | 
 86 | 	transition: all 0.5s;
 87 | 	-webkit-transition: all 0.5s;
 88 | 	-moz-transition: all 0.5s;
 89 | }
 90 | 
 91 | {% endblock %}
 92 | 
 93 | {% block content %}
 94 |     <div>
 95 |         <script>console.debug({{ result|safe }})</script>
 96 |     </div>
 97 |     <div style="display: flex">
 98 |         <div style="flex-grow: 1"></div>
 99 |         <div class="tree">
100 |             <ul>
101 |                 <li>{% include "sentiment/tree.html" %}</li>
102 |             </ul>
103 |         </div>
104 |         <div style="flex-grow: 1"></div>
105 |     </div>
106 | {% endblock %}


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/web/sentiment/templates/sentiment/tree.html:
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 1 | <span class="tree-label" style="background: {{ node.color }}; color: {{ node.text_color }}; font-size: 25px">{{node.label}}</span>
 2 | 
 3 | {%if node.children %}
 4 |     <ul>
 5 |         {% for ch in node.children %}
 6 |             <li>
 7 |             {% with node=ch template_name="sentiment/tree.html" %}
 8 |                 {% include template_name %}
 9 |             {% endwith %}
10 |             </li>
11 |         {% endfor %}
12 |     </ul>
13 | {%endif%}
14 | 


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/web/sentiment/tests.py:
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1 | from django.test import TestCase
2 | 
3 | # Create your tests here.
4 | 


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/web/sentiment/urls.py:
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1 | from django.conf.urls import url
2 | from .views import *
3 | 
4 | urlpatterns = [
5 |     url(r'^$', IndexView.as_view()),
6 | ]
7 | 


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/web/sentiment/views.py:
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 1 | from django.views.generic import TemplateView
 2 | from django.shortcuts import render
 3 | from predictionio import EngineClient
 4 | 
 5 | 
 6 | def add_colors(node):
 7 |     r = dict()
 8 | 
 9 |     if "value" in node:
10 |         r["label"] = node["value"]
11 |     else:
12 |         r["children"] = map(add_colors, [node["left"], node["right"]])
13 | 
14 |     r["text_color"] = "black"
15 |     vec = node["accum"]["get"]
16 |     c = -vec[0] - 0.5 * vec[1] + 0.5 * vec[3] + vec[4]
17 |     c *= 3
18 |     if c >= 0:
19 |         rb = max(0, 255 - int(c * 255.0))
20 |         print(rb)
21 |         r["color"] = "#{:02X}FF{:02X}".format(rb, rb)
22 |     else:
23 |         gb = max(0, 255 - int(-c * 255.0 * 1.5))
24 |         print(gb)
25 |         r["color"] = "#FF{:02X}{:02X}".format(gb, gb)
26 |         if c <= -0.5:
27 |             r["text_color"] = "white"
28 | 
29 |     return r
30 | 
31 | 
32 | class IndexView(TemplateView):
33 |     template_name = "sentiment/index.html"
34 | 
35 |     def post(self, request):
36 |         engine = EngineClient("http://localhost:8001")
37 |         result = engine.send_query({'sentence': request.POST.get('sentence', '')})
38 |         return render(request, 'sentiment/prediction.html', {'result': result, 'node': add_colors(result["sentence"])})


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/web/static/css/bootstrap-theme.css:
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  1 | /*!
  2 |  * Bootstrap v3.3.4 (http://getbootstrap.com)
  3 |  * Copyright 2011-2015 Twitter, Inc.
  4 |  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
  5 |  */
  6 | 
  7 | .btn-default,
  8 | .btn-primary,
  9 | .btn-success,
 10 | .btn-info,
 11 | .btn-warning,
 12 | .btn-danger {
 13 |   text-shadow: 0 -1px 0 rgba(0, 0, 0, .2);
 14 |   -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, .15), 0 1px 1px rgba(0, 0, 0, .075);
 15 |           box-shadow: inset 0 1px 0 rgba(255, 255, 255, .15), 0 1px 1px rgba(0, 0, 0, .075);
 16 | }
 17 | .btn-default:active,
 18 | .btn-primary:active,
 19 | .btn-success:active,
 20 | .btn-info:active,
 21 | .btn-warning:active,
 22 | .btn-danger:active,
 23 | .btn-default.active,
 24 | .btn-primary.active,
 25 | .btn-success.active,
 26 | .btn-info.active,
 27 | .btn-warning.active,
 28 | .btn-danger.active {
 29 |   -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, .125);
 30 |           box-shadow: inset 0 3px 5px rgba(0, 0, 0, .125);
 31 | }
 32 | .btn-default .badge,
 33 | .btn-primary .badge,
 34 | .btn-success .badge,
 35 | .btn-info .badge,
 36 | .btn-warning .badge,
 37 | .btn-danger .badge {
 38 |   text-shadow: none;
 39 | }
 40 | .btn:active,
 41 | .btn.active {
 42 |   background-image: none;
 43 | }
 44 | .btn-default {
 45 |   text-shadow: 0 1px 0 #fff;
 46 |   background-image: -webkit-linear-gradient(top, #fff 0%, #e0e0e0 100%);
 47 |   background-image:      -o-linear-gradient(top, #fff 0%, #e0e0e0 100%);
 48 |   background-image: -webkit-gradient(linear, left top, left bottom, from(#fff), to(#e0e0e0));
 49 |   background-image:         linear-gradient(to bottom, #fff 0%, #e0e0e0 100%);
 50 |   filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#ffe0e0e0', GradientType=0);
 51 |   filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
 52 |   background-repeat: repeat-x;
 53 |   border-color: #dbdbdb;
 54 |   border-color: #ccc;
 55 | }
 56 | .btn-default:hover,
 57 | .btn-default:focus {
 58 |   background-color: #e0e0e0;
 59 |   background-position: 0 -15px;
 60 | }
 61 | .btn-default:active,
 62 | .btn-default.active {
 63 |   background-color: #e0e0e0;
 64 |   border-color: #dbdbdb;
 65 | }
 66 | .btn-default.disabled,
 67 | .btn-default:disabled,
 68 | .btn-default[disabled] {
 69 |   background-color: #e0e0e0;
 70 |   background-image: none;
 71 | }
 72 | .btn-primary {
 73 |   background-image: -webkit-linear-gradient(top, #337ab7 0%, #265a88 100%);
 74 |   background-image:      -o-linear-gradient(top, #337ab7 0%, #265a88 100%);
 75 |   background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#265a88));
 76 |   background-image:         linear-gradient(to bottom, #337ab7 0%, #265a88 100%);
 77 |   filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff265a88', GradientType=0);
 78 |   filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
 79 |   background-repeat: repeat-x;
 80 |   border-color: #245580;
 81 | }
 82 | .btn-primary:hover,
 83 | .btn-primary:focus {
 84 |   background-color: #265a88;
 85 |   background-position: 0 -15px;
 86 | }
 87 | .btn-primary:active,
 88 | .btn-primary.active {
 89 |   background-color: #265a88;
 90 |   border-color: #245580;
 91 | }
 92 | .btn-primary.disabled,
 93 | .btn-primary:disabled,
 94 | .btn-primary[disabled] {
 95 |   background-color: #265a88;
 96 |   background-image: none;
 97 | }
 98 | .btn-success {
 99 |   background-image: -webkit-linear-gradient(top, #5cb85c 0%, #419641 100%);
100 |   background-image:      -o-linear-gradient(top, #5cb85c 0%, #419641 100%);
101 |   background-image: -webkit-gradient(linear, left top, left bottom, from(#5cb85c), to(#419641));
102 |   background-image:         linear-gradient(to bottom, #5cb85c 0%, #419641 100%);
103 |   filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff419641', GradientType=0);
104 |   filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
105 |   background-repeat: repeat-x;
106 |   border-color: #3e8f3e;
107 | }
108 | .btn-success:hover,
109 | .btn-success:focus {
110 |   background-color: #419641;
111 |   background-position: 0 -15px;
112 | }
113 | .btn-success:active,
114 | .btn-success.active {
115 |   background-color: #419641;
116 |   border-color: #3e8f3e;
117 | }
118 | .btn-success.disabled,
119 | .btn-success:disabled,
120 | .btn-success[disabled] {
121 |   background-color: #419641;
122 |   background-image: none;
123 | }
124 | .btn-info {
125 |   background-image: -webkit-linear-gradient(top, #5bc0de 0%, #2aabd2 100%);
126 |   background-image:      -o-linear-gradient(top, #5bc0de 0%, #2aabd2 100%);
127 |   background-image: -webkit-gradient(linear, left top, left bottom, from(#5bc0de), to(#2aabd2));
128 |   background-image:         linear-gradient(to bottom, #5bc0de 0%, #2aabd2 100%);
129 |   filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2aabd2', GradientType=0);
130 |   filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
131 |   background-repeat: repeat-x;
132 |   border-color: #28a4c9;
133 | }
134 | .btn-info:hover,
135 | .btn-info:focus {
136 |   background-color: #2aabd2;
137 |   background-position: 0 -15px;
138 | }
139 | .btn-info:active,
140 | .btn-info.active {
141 |   background-color: #2aabd2;
142 |   border-color: #28a4c9;
143 | }
144 | .btn-info.disabled,
145 | .btn-info:disabled,
146 | .btn-info[disabled] {
147 |   background-color: #2aabd2;
148 |   background-image: none;
149 | }
150 | .btn-warning {
151 |   background-image: -webkit-linear-gradient(top, #f0ad4e 0%, #eb9316 100%);
152 |   background-image:      -o-linear-gradient(top, #f0ad4e 0%, #eb9316 100%);
153 |   background-image: -webkit-gradient(linear, left top, left bottom, from(#f0ad4e), to(#eb9316));
154 |   background-image:         linear-gradient(to bottom, #f0ad4e 0%, #eb9316 100%);
155 |   filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffeb9316', GradientType=0);
156 |   filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
157 |   background-repeat: repeat-x;
158 |   border-color: #e38d13;
159 | }
160 | .btn-warning:hover,
161 | .btn-warning:focus {
162 |   background-color: #eb9316;
163 |   background-position: 0 -15px;
164 | }
165 | .btn-warning:active,
166 | .btn-warning.active {
167 |   background-color: #eb9316;
168 |   border-color: #e38d13;
169 | }
170 | .btn-warning.disabled,
171 | .btn-warning:disabled,
172 | .btn-warning[disabled] {
173 |   background-color: #eb9316;
174 |   background-image: none;
175 | }
176 | .btn-danger {
177 |   background-image: -webkit-linear-gradient(top, #d9534f 0%, #c12e2a 100%);
178 |   background-image:      -o-linear-gradient(top, #d9534f 0%, #c12e2a 100%);
179 |   background-image: -webkit-gradient(linear, left top, left bottom, from(#d9534f), to(#c12e2a));
180 |   background-image:         linear-gradient(to bottom, #d9534f 0%, #c12e2a 100%);
181 |   filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc12e2a', GradientType=0);
182 |   filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
183 |   background-repeat: repeat-x;
184 |   border-color: #b92c28;
185 | }
186 | .btn-danger:hover,
187 | .btn-danger:focus {
188 |   background-color: #c12e2a;
189 |   background-position: 0 -15px;
190 | }
191 | .btn-danger:active,
192 | .btn-danger.active {
193 |   background-color: #c12e2a;
194 |   border-color: #b92c28;
195 | }
196 | .btn-danger.disabled,
197 | .btn-danger:disabled,
198 | .btn-danger[disabled] {
199 |   background-color: #c12e2a;
200 |   background-image: none;
201 | }
202 | .thumbnail,
203 | .img-thumbnail {
204 |   -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, .075);
205 |           box-shadow: 0 1px 2px rgba(0, 0, 0, .075);
206 | }
207 | .dropdown-menu > li > a:hover,
208 | .dropdown-menu > li > a:focus {
209 |   background-color: #e8e8e8;
210 |   background-image: -webkit-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);
211 |   background-image:      -o-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);
212 |   background-image: -webkit-gradient(linear, left top, left bottom, from(#f5f5f5), to(#e8e8e8));
213 |   background-image:         linear-gradient(to bottom, #f5f5f5 0%, #e8e8e8 100%);
214 |   filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);
215 |   background-repeat: repeat-x;
216 | }
217 | .dropdown-menu > .active > a,
218 | .dropdown-menu > .active > a:hover,
219 | .dropdown-menu > .active > a:focus {
220 |   background-color: #2e6da4;
221 |   background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);
222 |   background-image:      -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);
223 |   background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#2e6da4));
224 |   background-image:         linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);
225 |   filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);
226 |   background-repeat: repeat-x;
227 | }
228 | .navbar-default {
229 |   background-image: -webkit-linear-gradient(top, #fff 0%, #f8f8f8 100%);
230 |   background-image:      -o-linear-gradient(top, #fff 0%, #f8f8f8 100%);
231 |   background-image: -webkit-gradient(linear, left top, left bottom, from(#fff), to(#f8f8f8));
232 |   background-image:         linear-gradient(to bottom, #fff 0%, #f8f8f8 100%);
233 |   filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff8f8f8', GradientType=0);
234 |   filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
235 |   background-repeat: repeat-x;
236 |   border-radius: 4px;
237 |   -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, .15), 0 1px 5px rgba(0, 0, 0, .075);
238 |           box-shadow: inset 0 1px 0 rgba(255, 255, 255, .15), 0 1px 5px rgba(0, 0, 0, .075);
239 | }
240 | .navbar-default .navbar-nav > .open > a,
241 | .navbar-default .navbar-nav > .active > a {
242 |   background-image: -webkit-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%);
243 |   background-image:      -o-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%);
244 |   background-image: -webkit-gradient(linear, left top, left bottom, from(#dbdbdb), to(#e2e2e2));
245 |   background-image:         linear-gradient(to bottom, #dbdbdb 0%, #e2e2e2 100%);
246 |   filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdbdbdb', endColorstr='#ffe2e2e2', GradientType=0);
247 |   background-repeat: repeat-x;
248 |   -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, .075);
249 |           box-shadow: inset 0 3px 9px rgba(0, 0, 0, .075);
250 | }
251 | .navbar-brand,
252 | .navbar-nav > li > a {
253 |   text-shadow: 0 1px 0 rgba(255, 255, 255, .25);
254 | }
255 | .navbar-inverse {
256 |   background-image: -webkit-linear-gradient(top, #3c3c3c 0%, #222 100%);
257 |   background-image:      -o-linear-gradient(top, #3c3c3c 0%, #222 100%);
258 |   background-image: -webkit-gradient(linear, left top, left bottom, from(#3c3c3c), to(#222));
259 |   background-image:         linear-gradient(to bottom, #3c3c3c 0%, #222 100%);
260 |   filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff3c3c3c', endColorstr='#ff222222', GradientType=0);
261 |   filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
262 |   background-repeat: repeat-x;
263 | }
264 | .navbar-inverse .navbar-nav > .open > a,
265 | .navbar-inverse .navbar-nav > .active > a {
266 |   background-image: -webkit-linear-gradient(top, #080808 0%, #0f0f0f 100%);
267 |   background-image:      -o-linear-gradient(top, #080808 0%, #0f0f0f 100%);
268 |   background-image: -webkit-gradient(linear, left top, left bottom, from(#080808), to(#0f0f0f));
269 |   background-image:         linear-gradient(to bottom, #080808 0%, #0f0f0f 100%);
270 |   filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff080808', endColorstr='#ff0f0f0f', GradientType=0);
271 |   background-repeat: repeat-x;
272 |   -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, .25);
273 |           box-shadow: inset 0 3px 9px rgba(0, 0, 0, .25);
274 | }
275 | .navbar-inverse .navbar-brand,
276 | .navbar-inverse .navbar-nav > li > a {
277 |   text-shadow: 0 -1px 0 rgba(0, 0, 0, .25);
278 | }
279 | .navbar-static-top,
280 | .navbar-fixed-top,
281 | .navbar-fixed-bottom {
282 |   border-radius: 0;
283 | }
284 | @media (max-width: 767px) {
285 |   .navbar .navbar-nav .open .dropdown-menu > .active > a,
286 |   .navbar .navbar-nav .open .dropdown-menu > .active > a:hover,
287 |   .navbar .navbar-nav .open .dropdown-menu > .active > a:focus {
288 |     color: #fff;
289 |     background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);
290 |     background-image:      -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);
291 |     background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#2e6da4));
292 |     background-image:         linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);
293 |     filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);
294 |     background-repeat: repeat-x;
295 |   }
296 | }
297 | .alert {
298 |   text-shadow: 0 1px 0 rgba(255, 255, 255, .2);
299 |   -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, .25), 0 1px 2px rgba(0, 0, 0, .05);
300 |           box-shadow: inset 0 1px 0 rgba(255, 255, 255, .25), 0 1px 2px rgba(0, 0, 0, .05);
301 | }
302 | .alert-success {
303 |   background-image: -webkit-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%);
304 |   background-image:      -o-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%);
305 |   background-image: -webkit-gradient(linear, left top, left bottom, from(#dff0d8), to(#c8e5bc));
306 |   background-image:         linear-gradient(to bottom, #dff0d8 0%, #c8e5bc 100%);
307 |   filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffc8e5bc', GradientType=0);
308 |   background-repeat: repeat-x;
309 |   border-color: #b2dba1;
310 | }
311 | .alert-info {
312 |   background-image: -webkit-linear-gradient(top, #d9edf7 0%, #b9def0 100%);
313 |   background-image:      -o-linear-gradient(top, #d9edf7 0%, #b9def0 100%);
314 |   background-image: -webkit-gradient(linear, left top, left bottom, from(#d9edf7), to(#b9def0));
315 |   background-image:         linear-gradient(to bottom, #d9edf7 0%, #b9def0 100%);
316 |   filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffb9def0', GradientType=0);
317 |   background-repeat: repeat-x;
318 |   border-color: #9acfea;
319 | }
320 | .alert-warning {
321 |   background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%);
322 |   background-image:      -o-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%);
323 |   background-image: -webkit-gradient(linear, left top, left bottom, from(#fcf8e3), to(#f8efc0));
324 |   background-image:         linear-gradient(to bottom, #fcf8e3 0%, #f8efc0 100%);
325 |   filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fff8efc0', GradientType=0);
326 |   background-repeat: repeat-x;
327 |   border-color: #f5e79e;
328 | }
329 | .alert-danger {
330 |   background-image: -webkit-linear-gradient(top, #f2dede 0%, #e7c3c3 100%);
331 |   background-image:      -o-linear-gradient(top, #f2dede 0%, #e7c3c3 100%);
332 |   background-image: -webkit-gradient(linear, left top, left bottom, from(#f2dede), to(#e7c3c3));
333 |   background-image:         linear-gradient(to bottom, #f2dede 0%, #e7c3c3 100%);
334 |   filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffe7c3c3', GradientType=0);
335 |   background-repeat: repeat-x;
336 |   border-color: #dca7a7;
337 | }
338 | .progress {
339 |   background-image: -webkit-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%);
340 |   background-image:      -o-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%);
341 |   background-image: -webkit-gradient(linear, left top, left bottom, from(#ebebeb), to(#f5f5f5));
342 |   background-image:         linear-gradient(to bottom, #ebebeb 0%, #f5f5f5 100%);
343 |   filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffebebeb', endColorstr='#fff5f5f5', GradientType=0);
344 |   background-repeat: repeat-x;
345 | }
346 | .progress-bar {
347 |   background-image: -webkit-linear-gradient(top, #337ab7 0%, #286090 100%);
348 |   background-image:      -o-linear-gradient(top, #337ab7 0%, #286090 100%);
349 |   background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#286090));
350 |   background-image:         linear-gradient(to bottom, #337ab7 0%, #286090 100%);
351 |   filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff286090', GradientType=0);
352 |   background-repeat: repeat-x;
353 | }
354 | .progress-bar-success {
355 |   background-image: -webkit-linear-gradient(top, #5cb85c 0%, #449d44 100%);
356 |   background-image:      -o-linear-gradient(top, #5cb85c 0%, #449d44 100%);
357 |   background-image: -webkit-gradient(linear, left top, left bottom, from(#5cb85c), to(#449d44));
358 |   background-image:         linear-gradient(to bottom, #5cb85c 0%, #449d44 100%);
359 |   filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff449d44', GradientType=0);
360 |   background-repeat: repeat-x;
361 | }
362 | .progress-bar-info {
363 |   background-image: -webkit-linear-gradient(top, #5bc0de 0%, #31b0d5 100%);
364 |   background-image:      -o-linear-gradient(top, #5bc0de 0%, #31b0d5 100%);
365 |   background-image: -webkit-gradient(linear, left top, left bottom, from(#5bc0de), to(#31b0d5));
366 |   background-image:         linear-gradient(to bottom, #5bc0de 0%, #31b0d5 100%);
367 |   filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff31b0d5', GradientType=0);
368 |   background-repeat: repeat-x;
369 | }
370 | .progress-bar-warning {
371 |   background-image: -webkit-linear-gradient(top, #f0ad4e 0%, #ec971f 100%);
372 |   background-image:      -o-linear-gradient(top, #f0ad4e 0%, #ec971f 100%);
373 |   background-image: -webkit-gradient(linear, left top, left bottom, from(#f0ad4e), to(#ec971f));
374 |   background-image:         linear-gradient(to bottom, #f0ad4e 0%, #ec971f 100%);
375 |   filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffec971f', GradientType=0);
376 |   background-repeat: repeat-x;
377 | }
378 | .progress-bar-danger {
379 |   background-image: -webkit-linear-gradient(top, #d9534f 0%, #c9302c 100%);
380 |   background-image:      -o-linear-gradient(top, #d9534f 0%, #c9302c 100%);
381 |   background-image: -webkit-gradient(linear, left top, left bottom, from(#d9534f), to(#c9302c));
382 |   background-image:         linear-gradient(to bottom, #d9534f 0%, #c9302c 100%);
383 |   filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc9302c', GradientType=0);
384 |   background-repeat: repeat-x;
385 | }
386 | .progress-bar-striped {
387 |   background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent);
388 |   background-image:      -o-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent);
389 |   background-image:         linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent);
390 | }
391 | .list-group {
392 |   border-radius: 4px;
393 |   -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, .075);
394 |           box-shadow: 0 1px 2px rgba(0, 0, 0, .075);
395 | }
396 | .list-group-item.active,
397 | .list-group-item.active:hover,
398 | .list-group-item.active:focus {
399 |   text-shadow: 0 -1px 0 #286090;
400 |   background-image: -webkit-linear-gradient(top, #337ab7 0%, #2b669a 100%);
401 |   background-image:      -o-linear-gradient(top, #337ab7 0%, #2b669a 100%);
402 |   background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#2b669a));
403 |   background-image:         linear-gradient(to bottom, #337ab7 0%, #2b669a 100%);
404 |   filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2b669a', GradientType=0);
405 |   background-repeat: repeat-x;
406 |   border-color: #2b669a;
407 | }
408 | .list-group-item.active .badge,
409 | .list-group-item.active:hover .badge,
410 | .list-group-item.active:focus .badge {
411 |   text-shadow: none;
412 | }
413 | .panel {
414 |   -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, .05);
415 |           box-shadow: 0 1px 2px rgba(0, 0, 0, .05);
416 | }
417 | .panel-default > .panel-heading {
418 |   background-image: -webkit-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);
419 |   background-image:      -o-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);
420 |   background-image: -webkit-gradient(linear, left top, left bottom, from(#f5f5f5), to(#e8e8e8));
421 |   background-image:         linear-gradient(to bottom, #f5f5f5 0%, #e8e8e8 100%);
422 |   filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);
423 |   background-repeat: repeat-x;
424 | }
425 | .panel-primary > .panel-heading {
426 |   background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);
427 |   background-image:      -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);
428 |   background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#2e6da4));
429 |   background-image:         linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);
430 |   filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);
431 |   background-repeat: repeat-x;
432 | }
433 | .panel-success > .panel-heading {
434 |   background-image: -webkit-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%);
435 |   background-image:      -o-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%);
436 |   background-image: -webkit-gradient(linear, left top, left bottom, from(#dff0d8), to(#d0e9c6));
437 |   background-image:         linear-gradient(to bottom, #dff0d8 0%, #d0e9c6 100%);
438 |   filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffd0e9c6', GradientType=0);
439 |   background-repeat: repeat-x;
440 | }
441 | .panel-info > .panel-heading {
442 |   background-image: -webkit-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%);
443 |   background-image:      -o-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%);
444 |   background-image: -webkit-gradient(linear, left top, left bottom, from(#d9edf7), to(#c4e3f3));
445 |   background-image:         linear-gradient(to bottom, #d9edf7 0%, #c4e3f3 100%);
446 |   filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffc4e3f3', GradientType=0);
447 |   background-repeat: repeat-x;
448 | }
449 | .panel-warning > .panel-heading {
450 |   background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%);
451 |   background-image:      -o-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%);
452 |   background-image: -webkit-gradient(linear, left top, left bottom, from(#fcf8e3), to(#faf2cc));
453 |   background-image:         linear-gradient(to bottom, #fcf8e3 0%, #faf2cc 100%);
454 |   filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fffaf2cc', GradientType=0);
455 |   background-repeat: repeat-x;
456 | }
457 | .panel-danger > .panel-heading {
458 |   background-image: -webkit-linear-gradient(top, #f2dede 0%, #ebcccc 100%);
459 |   background-image:      -o-linear-gradient(top, #f2dede 0%, #ebcccc 100%);
460 |   background-image: -webkit-gradient(linear, left top, left bottom, from(#f2dede), to(#ebcccc));
461 |   background-image:         linear-gradient(to bottom, #f2dede 0%, #ebcccc 100%);
462 |   filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffebcccc', GradientType=0);
463 |   background-repeat: repeat-x;
464 | }
465 | .well {
466 |   background-image: -webkit-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%);
467 |   background-image:      -o-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%);
468 |   background-image: -webkit-gradient(linear, left top, left bottom, from(#e8e8e8), to(#f5f5f5));
469 |   background-image:         linear-gradient(to bottom, #e8e8e8 0%, #f5f5f5 100%);
470 |   filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffe8e8e8', endColorstr='#fff5f5f5', GradientType=0);
471 |   background-repeat: repeat-x;
472 |   border-color: #dcdcdc;
473 |   -webkit-box-shadow: inset 0 1px 3px rgba(0, 0, 0, .05), 0 1px 0 rgba(255, 255, 255, .1);
474 |           box-shadow: inset 0 1px 3px rgba(0, 0, 0, .05), 0 1px 0 rgba(255, 255, 255, .1);
475 | }
476 | /*# sourceMappingURL=bootstrap-theme.css.map */
477 | 


--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
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@end-color: darken(@btn-color, 12%));\n  .reset-filter(); // Disable gradients for IE9 because filter bleeds through rounded corners; see https://github.com/twbs/bootstrap/issues/10620\n  background-repeat: repeat-x;\n  border-color: darken(@btn-color, 14%);\n\n  &:hover,\n  &:focus  {\n    background-color: darken(@btn-color, 12%);\n    background-position: 0 -15px;\n  }\n\n  &:active,\n  &.active {\n    background-color: darken(@btn-color, 12%);\n    border-color: darken(@btn-color, 14%);\n  }\n\n  &.disabled,\n  &:disabled,\n  &[disabled] {\n    background-color: darken(@btn-color, 12%);\n    background-image: none;\n  }\n}\n\n// Common styles\n.btn {\n  // Remove the gradient for the pressed/active state\n  &:active,\n  &.active {\n    background-image: none;\n  }\n}\n\n// Apply the mixin to the buttons\n.btn-default { .btn-styles(@btn-default-bg); text-shadow: 0 1px 0 #fff; border-color: #ccc; }\n.btn-primary { .btn-styles(@btn-primary-bg); }\n.btn-success { .btn-styles(@btn-success-bg); }\n.btn-info    { .btn-styles(@btn-info-bg); }\n.btn-warning { .btn-styles(@btn-warning-bg); }\n.btn-danger  { .btn-styles(@btn-danger-bg); }\n\n\n//\n// Images\n// --------------------------------------------------\n\n.thumbnail,\n.img-thumbnail {\n  .box-shadow(0 1px 2px rgba(0,0,0,.075));\n}\n\n\n//\n// Dropdowns\n// --------------------------------------------------\n\n.dropdown-menu > li > a:hover,\n.dropdown-menu > li > a:focus {\n  #gradient > .vertical(@start-color: @dropdown-link-hover-bg; @end-color: darken(@dropdown-link-hover-bg, 5%));\n  background-color: darken(@dropdown-link-hover-bg, 5%);\n}\n.dropdown-menu > .active > a,\n.dropdown-menu > .active > a:hover,\n.dropdown-menu > .active > a:focus {\n  #gradient > .vertical(@start-color: @dropdown-link-active-bg; @end-color: darken(@dropdown-link-active-bg, 5%));\n  background-color: darken(@dropdown-link-active-bg, 5%);\n}\n\n\n//\n// Navbar\n// --------------------------------------------------\n\n// Default navbar\n.navbar-default {\n  #gradient > .vertical(@start-color: lighten(@navbar-default-bg, 10%); @end-color: @navbar-default-bg);\n  .reset-filter(); // Remove gradient in IE<10 to fix bug where dropdowns don't get triggered\n  border-radius: @navbar-border-radius;\n  @shadow: inset 0 1px 0 rgba(255,255,255,.15), 0 1px 5px rgba(0,0,0,.075);\n  .box-shadow(@shadow);\n\n  .navbar-nav > .open > a,\n  .navbar-nav > .active > a {\n    #gradient > .vertical(@start-color: darken(@navbar-default-link-active-bg, 5%); @end-color: darken(@navbar-default-link-active-bg, 2%));\n    .box-shadow(inset 0 3px 9px rgba(0,0,0,.075));\n  }\n}\n.navbar-brand,\n.navbar-nav > li > a {\n  text-shadow: 0 1px 0 rgba(255,255,255,.25);\n}\n\n// Inverted navbar\n.navbar-inverse {\n  #gradient > .vertical(@start-color: lighten(@navbar-inverse-bg, 10%); @end-color: @navbar-inverse-bg);\n  .reset-filter(); // Remove gradient in IE<10 to fix bug where dropdowns don't get triggered; see https://github.com/twbs/bootstrap/issues/10257\n\n  .navbar-nav > .open > a,\n  .navbar-nav > .active > a {\n    #gradient > .vertical(@start-color: @navbar-inverse-link-active-bg; @end-color: lighten(@navbar-inverse-link-active-bg, 2.5%));\n    .box-shadow(inset 0 3px 9px rgba(0,0,0,.25));\n  }\n\n  .navbar-brand,\n  .navbar-nav > li > a {\n    text-shadow: 0 -1px 0 rgba(0,0,0,.25);\n  }\n}\n\n// Undo rounded corners in static and fixed navbars\n.navbar-static-top,\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n  border-radius: 0;\n}\n\n// Fix active state of dropdown items in collapsed mode\n@media (max-width: @grid-float-breakpoint-max) {\n  .navbar .navbar-nav .open .dropdown-menu > .active > a {\n    &,\n    &:hover,\n    &:focus {\n      color: #fff;\n      #gradient > .vertical(@start-color: @dropdown-link-active-bg; @end-color: darken(@dropdown-link-active-bg, 5%));\n    }\n  }\n}\n\n\n//\n// Alerts\n// --------------------------------------------------\n\n// Common styles\n.alert {\n  text-shadow: 0 1px 0 rgba(255,255,255,.2);\n  @shadow: inset 0 1px 0 rgba(255,255,255,.25), 0 1px 2px rgba(0,0,0,.05);\n  .box-shadow(@shadow);\n}\n\n// Mixin for generating new styles\n.alert-styles(@color) {\n  #gradient > .vertical(@start-color: @color; @end-color: darken(@color, 7.5%));\n  border-color: darken(@color, 15%);\n}\n\n// Apply the mixin to the alerts\n.alert-success    { .alert-styles(@alert-success-bg); }\n.alert-info       { .alert-styles(@alert-info-bg); }\n.alert-warning    { .alert-styles(@alert-warning-bg); }\n.alert-danger     { .alert-styles(@alert-danger-bg); }\n\n\n//\n// Progress bars\n// --------------------------------------------------\n\n// Give the progress background some depth\n.progress {\n  #gradient > .vertical(@start-color: darken(@progress-bg, 4%); @end-color: @progress-bg)\n}\n\n// Mixin for generating new styles\n.progress-bar-styles(@color) {\n  #gradient > .vertical(@start-color: @color; @end-color: darken(@color, 10%));\n}\n\n// Apply the mixin to the progress bars\n.progress-bar            { .progress-bar-styles(@progress-bar-bg); }\n.progress-bar-success    { .progress-bar-styles(@progress-bar-success-bg); }\n.progress-bar-info       { .progress-bar-styles(@progress-bar-info-bg); }\n.progress-bar-warning    { .progress-bar-styles(@progress-bar-warning-bg); }\n.progress-bar-danger     { .progress-bar-styles(@progress-bar-danger-bg); }\n\n// Reset the striped class because our mixins don't do multiple gradients and\n// the above custom styles override the new `.progress-bar-striped` in v3.2.0.\n.progress-bar-striped {\n  #gradient > .striped();\n}\n\n\n//\n// List groups\n// --------------------------------------------------\n\n.list-group {\n  border-radius: @border-radius-base;\n  .box-shadow(0 1px 2px rgba(0,0,0,.075));\n}\n.list-group-item.active,\n.list-group-item.active:hover,\n.list-group-item.active:focus {\n  text-shadow: 0 -1px 0 darken(@list-group-active-bg, 10%);\n  #gradient > .vertical(@start-color: @list-group-active-bg; @end-color: darken(@list-group-active-bg, 7.5%));\n  border-color: darken(@list-group-active-border, 7.5%);\n\n  .badge {\n    text-shadow: none;\n  }\n}\n\n\n//\n// Panels\n// --------------------------------------------------\n\n// Common styles\n.panel {\n  .box-shadow(0 1px 2px rgba(0,0,0,.05));\n}\n\n// Mixin for generating new styles\n.panel-heading-styles(@color) {\n  #gradient > .vertical(@start-color: @color; @end-color: darken(@color, 5%));\n}\n\n// Apply the mixin to the panel headings only\n.panel-default > .panel-heading   { .panel-heading-styles(@panel-default-heading-bg); }\n.panel-primary > .panel-heading   { .panel-heading-styles(@panel-primary-heading-bg); }\n.panel-success > .panel-heading   { .panel-heading-styles(@panel-success-heading-bg); }\n.panel-info > .panel-heading      { .panel-heading-styles(@panel-info-heading-bg); }\n.panel-warning > .panel-heading   { .panel-heading-styles(@panel-warning-heading-bg); }\n.panel-danger > .panel-heading    { .panel-heading-styles(@panel-danger-heading-bg); }\n\n\n//\n// Wells\n// --------------------------------------------------\n\n.well {\n  #gradient > .vertical(@start-color: darken(@well-bg, 5%); @end-color: @well-bg);\n  border-color: darken(@well-bg, 10%);\n  @shadow: inset 0 1px 3px rgba(0,0,0,.05), 0 1px 0 rgba(255,255,255,.1);\n  .box-shadow(@shadow);\n}\n","// Vendor Prefixes\n//\n// All vendor mixins are deprecated as of v3.2.0 due to the introduction of\n// Autoprefixer in our Gruntfile. They will be removed in v4.\n\n// - Animations\n// - Backface visibility\n// - Box shadow\n// - Box sizing\n// - Content columns\n// - Hyphens\n// - Placeholder text\n// - Transformations\n// - Transitions\n// - User Select\n\n\n// Animations\n.animation(@animation) {\n  -webkit-animation: @animation;\n       -o-animation: @animation;\n          animation: @animation;\n}\n.animation-name(@name) {\n  -webkit-animation-name: @name;\n          animation-name: @name;\n}\n.animation-duration(@duration) {\n  -webkit-animation-duration: @duration;\n          animation-duration: @duration;\n}\n.animation-timing-function(@timing-function) {\n  -webkit-animation-timing-function: @timing-function;\n          animation-timing-function: @timing-function;\n}\n.animation-delay(@delay) {\n  -webkit-animation-delay: @delay;\n          animation-delay: @delay;\n}\n.animation-iteration-count(@iteration-count) {\n  -webkit-animation-iteration-count: @iteration-count;\n          animation-iteration-count: @iteration-count;\n}\n.animation-direction(@direction) {\n  -webkit-animation-direction: @direction;\n          animation-direction: @direction;\n}\n.animation-fill-mode(@fill-mode) {\n  -webkit-animation-fill-mode: @fill-mode;\n          animation-fill-mode: @fill-mode;\n}\n\n// Backface visibility\n// Prevent browsers from flickering when using CSS 3D transforms.\n// Default value is `visible`, but can be changed to `hidden`\n\n.backface-visibility(@visibility){\n  -webkit-backface-visibility: @visibility;\n     -moz-backface-visibility: @visibility;\n          backface-visibility: @visibility;\n}\n\n// Drop shadows\n//\n// Note: Deprecated `.box-shadow()` as of v3.1.0 since all of Bootstrap's\n// supported browsers that have box shadow capabilities now support it.\n\n.box-shadow(@shadow) {\n  -webkit-box-shadow: @shadow; // iOS <4.3 & Android <4.1\n          box-shadow: @shadow;\n}\n\n// Box sizing\n.box-sizing(@boxmodel) {\n  -webkit-box-sizing: @boxmodel;\n     -moz-box-sizing: @boxmodel;\n          box-sizing: @boxmodel;\n}\n\n// CSS3 Content Columns\n.content-columns(@column-count; @column-gap: @grid-gutter-width) {\n  -webkit-column-count: @column-count;\n     -moz-column-count: @column-count;\n          column-count: @column-count;\n  -webkit-column-gap: @column-gap;\n     -moz-column-gap: @column-gap;\n          column-gap: @column-gap;\n}\n\n// Optional hyphenation\n.hyphens(@mode: auto) {\n  word-wrap: break-word;\n  -webkit-hyphens: @mode;\n     -moz-hyphens: @mode;\n      -ms-hyphens: @mode; // IE10+\n       -o-hyphens: @mode;\n          hyphens: @mode;\n}\n\n// Placeholder text\n.placeholder(@color: @input-color-placeholder) {\n  // Firefox\n  &::-moz-placeholder {\n    color: @color;\n    opacity: 1; // Override Firefox's unusual default opacity; see https://github.com/twbs/bootstrap/pull/11526\n  }\n  &:-ms-input-placeholder { color: @color; } // Internet Explorer 10+\n  &::-webkit-input-placeholder  { color: @color; } // Safari and Chrome\n}\n\n// Transformations\n.scale(@ratio) {\n  -webkit-transform: scale(@ratio);\n      -ms-transform: scale(@ratio); // IE9 only\n       -o-transform: scale(@ratio);\n          transform: scale(@ratio);\n}\n.scale(@ratioX; @ratioY) {\n  -webkit-transform: scale(@ratioX, @ratioY);\n      -ms-transform: scale(@ratioX, @ratioY); // IE9 only\n       -o-transform: scale(@ratioX, @ratioY);\n          transform: scale(@ratioX, @ratioY);\n}\n.scaleX(@ratio) {\n  -webkit-transform: scaleX(@ratio);\n      -ms-transform: scaleX(@ratio); // IE9 only\n       -o-transform: scaleX(@ratio);\n          transform: scaleX(@ratio);\n}\n.scaleY(@ratio) {\n  -webkit-transform: scaleY(@ratio);\n      -ms-transform: scaleY(@ratio); // IE9 only\n       -o-transform: scaleY(@ratio);\n          transform: scaleY(@ratio);\n}\n.skew(@x; @y) {\n  -webkit-transform: skewX(@x) skewY(@y);\n      -ms-transform: skewX(@x) skewY(@y); // See https://github.com/twbs/bootstrap/issues/4885; IE9+\n       -o-transform: skewX(@x) skewY(@y);\n          transform: skewX(@x) skewY(@y);\n}\n.translate(@x; @y) {\n  -webkit-transform: translate(@x, @y);\n      -ms-transform: translate(@x, @y); // IE9 only\n       -o-transform: translate(@x, @y);\n          transform: translate(@x, @y);\n}\n.translate3d(@x; @y; @z) {\n  -webkit-transform: translate3d(@x, @y, @z);\n          transform: translate3d(@x, @y, @z);\n}\n.rotate(@degrees) {\n  -webkit-transform: rotate(@degrees);\n      -ms-transform: rotate(@degrees); // IE9 only\n       -o-transform: rotate(@degrees);\n          transform: rotate(@degrees);\n}\n.rotateX(@degrees) {\n  -webkit-transform: rotateX(@degrees);\n      -ms-transform: rotateX(@degrees); // IE9 only\n       -o-transform: rotateX(@degrees);\n          transform: rotateX(@degrees);\n}\n.rotateY(@degrees) {\n  -webkit-transform: rotateY(@degrees);\n      -ms-transform: rotateY(@degrees); // IE9 only\n       -o-transform: rotateY(@degrees);\n          transform: rotateY(@degrees);\n}\n.perspective(@perspective) {\n  -webkit-perspective: @perspective;\n     -moz-perspective: @perspective;\n          perspective: @perspective;\n}\n.perspective-origin(@perspective) {\n  -webkit-perspective-origin: @perspective;\n     -moz-perspective-origin: @perspective;\n          perspective-origin: @perspective;\n}\n.transform-origin(@origin) {\n  -webkit-transform-origin: @origin;\n     -moz-transform-origin: @origin;\n      -ms-transform-origin: @origin; // IE9 only\n          transform-origin: @origin;\n}\n\n\n// Transitions\n\n.transition(@transition) {\n  -webkit-transition: @transition;\n       -o-transition: @transition;\n          transition: @transition;\n}\n.transition-property(@transition-property) {\n  -webkit-transition-property: @transition-property;\n          transition-property: @transition-property;\n}\n.transition-delay(@transition-delay) {\n  -webkit-transition-delay: @transition-delay;\n          transition-delay: @transition-delay;\n}\n.transition-duration(@transition-duration) {\n  -webkit-transition-duration: @transition-duration;\n          transition-duration: @transition-duration;\n}\n.transition-timing-function(@timing-function) {\n  -webkit-transition-timing-function: @timing-function;\n          transition-timing-function: @timing-function;\n}\n.transition-transform(@transition) {\n  -webkit-transition: -webkit-transform @transition;\n     -moz-transition: -moz-transform @transition;\n       -o-transition: -o-transform @transition;\n          transition: transform @transition;\n}\n\n\n// User select\n// For selecting text on the page\n\n.user-select(@select) {\n  -webkit-user-select: @select;\n     -moz-user-select: @select;\n      -ms-user-select: @select; // IE10+\n          user-select: @select;\n}\n",".btn-default,\n.btn-primary,\n.btn-success,\n.btn-info,\n.btn-warning,\n.btn-danger {\n  text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.2);\n  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 1px rgba(0, 0, 0, 0.075);\n  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 1px rgba(0, 0, 0, 0.075);\n}\n.btn-default:active,\n.btn-primary:active,\n.btn-success:active,\n.btn-info:active,\n.btn-warning:active,\n.btn-danger:active,\n.btn-default.active,\n.btn-primary.active,\n.btn-success.active,\n.btn-info.active,\n.btn-warning.active,\n.btn-danger.active {\n  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n}\n.btn-default .badge,\n.btn-primary .badge,\n.btn-success .badge,\n.btn-info .badge,\n.btn-warning .badge,\n.btn-danger .badge {\n  text-shadow: none;\n}\n.btn:active,\n.btn.active {\n  background-image: none;\n}\n.btn-default {\n  background-image: -webkit-linear-gradient(top, #ffffff 0%, #e0e0e0 100%);\n  background-image: -o-linear-gradient(top, #ffffff 0%, #e0e0e0 100%);\n  background-image: linear-gradient(to bottom, #ffffff 0%, #e0e0e0 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#ffe0e0e0', GradientType=0);\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  background-repeat: repeat-x;\n  border-color: #dbdbdb;\n  text-shadow: 0 1px 0 #fff;\n  border-color: #ccc;\n}\n.btn-default:hover,\n.btn-default:focus {\n  background-color: #e0e0e0;\n  background-position: 0 -15px;\n}\n.btn-default:active,\n.btn-default.active {\n  background-color: #e0e0e0;\n  border-color: #dbdbdb;\n}\n.btn-default.disabled,\n.btn-default:disabled,\n.btn-default[disabled] {\n  background-color: #e0e0e0;\n  background-image: none;\n}\n.btn-primary {\n  background-image: -webkit-linear-gradient(top, #337ab7 0%, #265a88 100%);\n  background-image: -o-linear-gradient(top, #337ab7 0%, #265a88 100%);\n  background-image: linear-gradient(to bottom, #337ab7 0%, #265a88 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff265a88', GradientType=0);\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  background-repeat: repeat-x;\n  border-color: #245580;\n}\n.btn-primary:hover,\n.btn-primary:focus {\n  background-color: #265a88;\n  background-position: 0 -15px;\n}\n.btn-primary:active,\n.btn-primary.active {\n  background-color: #265a88;\n  border-color: #245580;\n}\n.btn-primary.disabled,\n.btn-primary:disabled,\n.btn-primary[disabled] {\n  background-color: #265a88;\n  background-image: none;\n}\n.btn-success {\n  background-image: -webkit-linear-gradient(top, #5cb85c 0%, #419641 100%);\n  background-image: -o-linear-gradient(top, #5cb85c 0%, #419641 100%);\n  background-image: linear-gradient(to bottom, #5cb85c 0%, #419641 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff419641', GradientType=0);\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  background-repeat: repeat-x;\n  border-color: #3e8f3e;\n}\n.btn-success:hover,\n.btn-success:focus {\n  background-color: #419641;\n  background-position: 0 -15px;\n}\n.btn-success:active,\n.btn-success.active {\n  background-color: #419641;\n  border-color: #3e8f3e;\n}\n.btn-success.disabled,\n.btn-success:disabled,\n.btn-success[disabled] {\n  background-color: #419641;\n  background-image: none;\n}\n.btn-info {\n  background-image: -webkit-linear-gradient(top, #5bc0de 0%, #2aabd2 100%);\n  background-image: -o-linear-gradient(top, #5bc0de 0%, #2aabd2 100%);\n  background-image: linear-gradient(to bottom, #5bc0de 0%, #2aabd2 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2aabd2', GradientType=0);\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  background-repeat: repeat-x;\n  border-color: #28a4c9;\n}\n.btn-info:hover,\n.btn-info:focus {\n  background-color: #2aabd2;\n  background-position: 0 -15px;\n}\n.btn-info:active,\n.btn-info.active {\n  background-color: #2aabd2;\n  border-color: #28a4c9;\n}\n.btn-info.disabled,\n.btn-info:disabled,\n.btn-info[disabled] {\n  background-color: #2aabd2;\n  background-image: none;\n}\n.btn-warning {\n  background-image: -webkit-linear-gradient(top, #f0ad4e 0%, #eb9316 100%);\n  background-image: -o-linear-gradient(top, #f0ad4e 0%, #eb9316 100%);\n  background-image: linear-gradient(to bottom, #f0ad4e 0%, #eb9316 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffeb9316', GradientType=0);\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  background-repeat: repeat-x;\n  border-color: #e38d13;\n}\n.btn-warning:hover,\n.btn-warning:focus {\n  background-color: #eb9316;\n  background-position: 0 -15px;\n}\n.btn-warning:active,\n.btn-warning.active {\n  background-color: #eb9316;\n  border-color: #e38d13;\n}\n.btn-warning.disabled,\n.btn-warning:disabled,\n.btn-warning[disabled] {\n  background-color: #eb9316;\n  background-image: none;\n}\n.btn-danger {\n  background-image: -webkit-linear-gradient(top, #d9534f 0%, #c12e2a 100%);\n  background-image: -o-linear-gradient(top, #d9534f 0%, #c12e2a 100%);\n  background-image: linear-gradient(to bottom, #d9534f 0%, #c12e2a 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc12e2a', GradientType=0);\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  background-repeat: repeat-x;\n  border-color: #b92c28;\n}\n.btn-danger:hover,\n.btn-danger:focus {\n  background-color: #c12e2a;\n  background-position: 0 -15px;\n}\n.btn-danger:active,\n.btn-danger.active {\n  background-color: #c12e2a;\n  border-color: #b92c28;\n}\n.btn-danger.disabled,\n.btn-danger:disabled,\n.btn-danger[disabled] {\n  background-color: #c12e2a;\n  background-image: none;\n}\n.thumbnail,\n.img-thumbnail {\n  -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n  box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n}\n.dropdown-menu > li > a:hover,\n.dropdown-menu > li > a:focus {\n  background-image: -webkit-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n  background-image: -o-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n  background-image: linear-gradient(to bottom, #f5f5f5 0%, #e8e8e8 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);\n  background-color: #e8e8e8;\n}\n.dropdown-menu > .active > a,\n.dropdown-menu > .active > a:hover,\n.dropdown-menu > .active > a:focus {\n  background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n  background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n  background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n  background-color: #2e6da4;\n}\n.navbar-default {\n  background-image: -webkit-linear-gradient(top, #ffffff 0%, #f8f8f8 100%);\n  background-image: -o-linear-gradient(top, #ffffff 0%, #f8f8f8 100%);\n  background-image: linear-gradient(to bottom, #ffffff 0%, #f8f8f8 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff8f8f8', GradientType=0);\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  border-radius: 4px;\n  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 5px rgba(0, 0, 0, 0.075);\n  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 5px rgba(0, 0, 0, 0.075);\n}\n.navbar-default .navbar-nav > .open > a,\n.navbar-default .navbar-nav > .active > a {\n  background-image: -webkit-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%);\n  background-image: -o-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%);\n  background-image: linear-gradient(to bottom, #dbdbdb 0%, #e2e2e2 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdbdbdb', endColorstr='#ffe2e2e2', GradientType=0);\n  -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.075);\n  box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.075);\n}\n.navbar-brand,\n.navbar-nav > li > a {\n  text-shadow: 0 1px 0 rgba(255, 255, 255, 0.25);\n}\n.navbar-inverse {\n  background-image: -webkit-linear-gradient(top, #3c3c3c 0%, #222222 100%);\n  background-image: -o-linear-gradient(top, #3c3c3c 0%, #222222 100%);\n  background-image: linear-gradient(to bottom, #3c3c3c 0%, #222222 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff3c3c3c', endColorstr='#ff222222', GradientType=0);\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n}\n.navbar-inverse .navbar-nav > .open > a,\n.navbar-inverse .navbar-nav > .active > a {\n  background-image: -webkit-linear-gradient(top, #080808 0%, #0f0f0f 100%);\n  background-image: -o-linear-gradient(top, #080808 0%, #0f0f0f 100%);\n  background-image: linear-gradient(to bottom, #080808 0%, #0f0f0f 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff080808', endColorstr='#ff0f0f0f', GradientType=0);\n  -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.25);\n  box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.25);\n}\n.navbar-inverse .navbar-brand,\n.navbar-inverse .navbar-nav > li > a {\n  text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25);\n}\n.navbar-static-top,\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n  border-radius: 0;\n}\n@media (max-width: 767px) {\n  .navbar .navbar-nav .open .dropdown-menu > .active > a,\n  .navbar .navbar-nav .open .dropdown-menu > .active > a:hover,\n  .navbar .navbar-nav .open .dropdown-menu > .active > a:focus {\n    color: #fff;\n    background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n    background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n    background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n    background-repeat: repeat-x;\n    filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n  }\n}\n.alert {\n  text-shadow: 0 1px 0 rgba(255, 255, 255, 0.2);\n  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.25), 0 1px 2px rgba(0, 0, 0, 0.05);\n  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.25), 0 1px 2px rgba(0, 0, 0, 0.05);\n}\n.alert-success {\n  background-image: -webkit-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%);\n  background-image: -o-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%);\n  background-image: linear-gradient(to bottom, #dff0d8 0%, #c8e5bc 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffc8e5bc', GradientType=0);\n  border-color: #b2dba1;\n}\n.alert-info {\n  background-image: -webkit-linear-gradient(top, #d9edf7 0%, #b9def0 100%);\n  background-image: -o-linear-gradient(top, #d9edf7 0%, #b9def0 100%);\n  background-image: linear-gradient(to bottom, #d9edf7 0%, #b9def0 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffb9def0', GradientType=0);\n  border-color: #9acfea;\n}\n.alert-warning {\n  background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%);\n  background-image: -o-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%);\n  background-image: linear-gradient(to bottom, #fcf8e3 0%, #f8efc0 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fff8efc0', GradientType=0);\n  border-color: #f5e79e;\n}\n.alert-danger {\n  background-image: -webkit-linear-gradient(top, #f2dede 0%, #e7c3c3 100%);\n  background-image: -o-linear-gradient(top, #f2dede 0%, #e7c3c3 100%);\n  background-image: linear-gradient(to bottom, #f2dede 0%, #e7c3c3 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffe7c3c3', GradientType=0);\n  border-color: #dca7a7;\n}\n.progress {\n  background-image: -webkit-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%);\n  background-image: -o-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%);\n  background-image: linear-gradient(to bottom, #ebebeb 0%, #f5f5f5 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffebebeb', endColorstr='#fff5f5f5', GradientType=0);\n}\n.progress-bar {\n  background-image: -webkit-linear-gradient(top, #337ab7 0%, #286090 100%);\n  background-image: -o-linear-gradient(top, #337ab7 0%, #286090 100%);\n  background-image: linear-gradient(to bottom, #337ab7 0%, #286090 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff286090', GradientType=0);\n}\n.progress-bar-success {\n  background-image: -webkit-linear-gradient(top, #5cb85c 0%, #449d44 100%);\n  background-image: -o-linear-gradient(top, #5cb85c 0%, #449d44 100%);\n  background-image: linear-gradient(to bottom, #5cb85c 0%, #449d44 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff449d44', GradientType=0);\n}\n.progress-bar-info {\n  background-image: -webkit-linear-gradient(top, #5bc0de 0%, #31b0d5 100%);\n  background-image: -o-linear-gradient(top, #5bc0de 0%, #31b0d5 100%);\n  background-image: linear-gradient(to bottom, #5bc0de 0%, #31b0d5 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff31b0d5', GradientType=0);\n}\n.progress-bar-warning {\n  background-image: -webkit-linear-gradient(top, #f0ad4e 0%, #ec971f 100%);\n  background-image: -o-linear-gradient(top, #f0ad4e 0%, #ec971f 100%);\n  background-image: linear-gradient(to bottom, #f0ad4e 0%, #ec971f 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffec971f', GradientType=0);\n}\n.progress-bar-danger {\n  background-image: -webkit-linear-gradient(top, #d9534f 0%, #c9302c 100%);\n  background-image: -o-linear-gradient(top, #d9534f 0%, #c9302c 100%);\n  background-image: linear-gradient(to bottom, #d9534f 0%, #c9302c 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc9302c', GradientType=0);\n}\n.progress-bar-striped {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.list-group {\n  border-radius: 4px;\n  -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n  box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n}\n.list-group-item.active,\n.list-group-item.active:hover,\n.list-group-item.active:focus {\n  text-shadow: 0 -1px 0 #286090;\n  background-image: -webkit-linear-gradient(top, #337ab7 0%, #2b669a 100%);\n  background-image: -o-linear-gradient(top, #337ab7 0%, #2b669a 100%);\n  background-image: linear-gradient(to bottom, #337ab7 0%, #2b669a 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2b669a', GradientType=0);\n  border-color: #2b669a;\n}\n.list-group-item.active .badge,\n.list-group-item.active:hover .badge,\n.list-group-item.active:focus .badge {\n  text-shadow: none;\n}\n.panel {\n  -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);\n  box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);\n}\n.panel-default > .panel-heading {\n  background-image: -webkit-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n  background-image: -o-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n  background-image: linear-gradient(to bottom, #f5f5f5 0%, #e8e8e8 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);\n}\n.panel-primary > .panel-heading {\n  background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n  background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n  background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n}\n.panel-success > .panel-heading {\n  background-image: -webkit-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%);\n  background-image: -o-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%);\n  background-image: linear-gradient(to bottom, #dff0d8 0%, #d0e9c6 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffd0e9c6', GradientType=0);\n}\n.panel-info > .panel-heading {\n  background-image: -webkit-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%);\n  background-image: -o-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%);\n  background-image: linear-gradient(to bottom, #d9edf7 0%, #c4e3f3 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffc4e3f3', GradientType=0);\n}\n.panel-warning > .panel-heading {\n  background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%);\n  background-image: -o-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%);\n  background-image: linear-gradient(to bottom, #fcf8e3 0%, #faf2cc 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fffaf2cc', GradientType=0);\n}\n.panel-danger > .panel-heading {\n  background-image: -webkit-linear-gradient(top, #f2dede 0%, #ebcccc 100%);\n  background-image: -o-linear-gradient(top, #f2dede 0%, #ebcccc 100%);\n  background-image: linear-gradient(to bottom, #f2dede 0%, #ebcccc 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffebcccc', GradientType=0);\n}\n.well {\n  background-image: -webkit-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%);\n  background-image: -o-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%);\n  background-image: linear-gradient(to bottom, #e8e8e8 0%, #f5f5f5 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffe8e8e8', endColorstr='#fff5f5f5', GradientType=0);\n  border-color: #dcdcdc;\n  -webkit-box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.05), 0 1px 0 rgba(255, 255, 255, 0.1);\n  box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.05), 0 1px 0 rgba(255, 255, 255, 0.1);\n}\n/*# sourceMappingURL=bootstrap-theme.css.map */","// Gradients\n\n#gradient {\n\n  // Horizontal gradient, from left to right\n  //\n  // Creates two color stops, start and end, by specifying a color and position for each color stop.\n  // Color stops are not available in IE9 and below.\n  .horizontal(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n    background-image: -webkit-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Safari 5.1-6, Chrome 10+\n    background-image: -o-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Opera 12\n    background-image: linear-gradient(to right, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n    background-repeat: repeat-x;\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\",argb(@start-color),argb(@end-color))); // IE9 and down\n  }\n\n  // Vertical gradient, from top to bottom\n  //\n  // Creates two color stops, start and end, by specifying a color and position for each color stop.\n  // Color stops are not available in IE9 and below.\n  .vertical(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n    background-image: -webkit-linear-gradient(top, @start-color @start-percent, @end-color @end-percent);  // Safari 5.1-6, Chrome 10+\n    background-image: -o-linear-gradient(top, @start-color @start-percent, @end-color @end-percent);  // Opera 12\n    background-image: linear-gradient(to bottom, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n    background-repeat: repeat-x;\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\",argb(@start-color),argb(@end-color))); // IE9 and down\n  }\n\n  .directional(@start-color: #555; @end-color: #333; @deg: 45deg) {\n    background-repeat: repeat-x;\n    background-image: -webkit-linear-gradient(@deg, @start-color, @end-color); // Safari 5.1-6, Chrome 10+\n    background-image: -o-linear-gradient(@deg, @start-color, @end-color); // Opera 12\n    background-image: linear-gradient(@deg, @start-color, @end-color); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n  }\n  .horizontal-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n    background-image: -webkit-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n    background-image: -o-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n    background-image: linear-gradient(to right, @start-color, @mid-color @color-stop, @end-color);\n    background-repeat: no-repeat;\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\",argb(@start-color),argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n  }\n  .vertical-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n    background-image: -webkit-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n    background-image: -o-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n    background-image: linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n    background-repeat: no-repeat;\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\",argb(@start-color),argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n  }\n  .radial(@inner-color: #555; @outer-color: #333) {\n    background-image: -webkit-radial-gradient(circle, @inner-color, @outer-color);\n    background-image: radial-gradient(circle, @inner-color, @outer-color);\n    background-repeat: no-repeat;\n  }\n  .striped(@color: rgba(255,255,255,.15); @angle: 45deg) {\n    background-image: -webkit-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n    background-image: -o-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n    background-image: linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n  }\n}\n","// Reset filters for IE\n//\n// When you need to remove a gradient background, do not forget to use this to reset\n// the IE filter for IE9 and below.\n\n.reset-filter() {\n  filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(enabled = false)\"));\n}\n"]}


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/web/static/css/bootstrap-theme.min.css:
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1 | /*!
2 |  * Bootstrap v3.3.4 (http://getbootstrap.com)
3 |  * Copyright 2011-2015 Twitter, Inc.
4 |  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
5 |  */.btn-danger,.btn-default,.btn-info,.btn-primary,.btn-success,.btn-warning{text-shadow:0 -1px 0 rgba(0,0,0,.2);-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.15),0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 0 rgba(255,255,255,.15),0 1px 1px rgba(0,0,0,.075)}.btn-danger.active,.btn-danger:active,.btn-default.active,.btn-default:active,.btn-info.active,.btn-info:active,.btn-primary.active,.btn-primary:active,.btn-success.active,.btn-success:active,.btn-warning.active,.btn-warning:active{-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn-danger .badge,.btn-default .badge,.btn-info .badge,.btn-primary .badge,.btn-success .badge,.btn-warning .badge{text-shadow:none}.btn.active,.btn:active{background-image:none}.btn-default{text-shadow:0 1px 0 #fff;background-image:-webkit-linear-gradient(top,#fff 0,#e0e0e0 100%);background-image:-o-linear-gradient(top,#fff 0,#e0e0e0 100%);background-image:-webkit-gradient(linear,left top,left 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c=this;b&&(c=a(b.currentTarget).data("bs."+this.type),c||(c=new this.constructor(b.currentTarget,this.getDelegateOptions()),a(b.currentTarget).data("bs."+this.type,c))),c.tip().hasClass("in")?c.leave(c):c.enter(c)},c.prototype.destroy=function(){var a=this;clearTimeout(this.timeout),this.hide(function(){a.$element.off("."+a.type).removeData("bs."+a.type)})};var d=a.fn.tooltip;a.fn.tooltip=b,a.fn.tooltip.Constructor=c,a.fn.tooltip.noConflict=function(){return a.fn.tooltip=d,this}}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.popover"),f="object"==typeof b&&b;(e||!/destroy|hide/.test(b))&&(e||d.data("bs.popover",e=new c(this,f)),"string"==typeof b&&e[b]())})}var c=function(a,b){this.init("popover",a,b)};if(!a.fn.tooltip)throw new Error("Popover requires tooltip.js");c.VERSION="3.3.4",c.DEFAULTS=a.extend({},a.fn.tooltip.Constructor.DEFAULTS,{placement:"right",trigger:"click",content:"",template:'<div class="popover" 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b=this,c="offset",d=0;this.offsets=[],this.targets=[],this.scrollHeight=this.getScrollHeight(),a.isWindow(this.$scrollElement[0])||(c="position",d=this.$scrollElement.scrollTop()),this.$body.find(this.selector).map(function(){var b=a(this),e=b.data("target")||b.attr("href"),f=/^#./.test(e)&&a(e);return f&&f.length&&f.is(":visible")&&[[f[c]().top+d,e]]||null}).sort(function(a,b){return a[0]-b[0]}).each(function(){b.offsets.push(this[0]),b.targets.push(this[1])})},b.prototype.process=function(){var a,b=this.$scrollElement.scrollTop()+this.options.offset,c=this.getScrollHeight(),d=this.options.offset+c-this.$scrollElement.height(),e=this.offsets,f=this.targets,g=this.activeTarget;if(this.scrollHeight!=c&&this.refresh(),b>=d)return g!=(a=f[f.length-1])&&this.activate(a);if(g&&b<e[0])return this.activeTarget=null,this.clear();for(a=e.length;a--;)g!=f[a]&&b>=e[a]&&(void 0===e[a+1]||b<e[a+1])&&this.activate(f[a])},b.prototype.activate=function(b){this.activeTarget=b,this.clear();var c=this.selector+'[data-target="'+b+'"],'+this.selector+'[href="'+b+'"]',d=a(c).parents("li").addClass("active");d.parent(".dropdown-menu").length&&(d=d.closest("li.dropdown").addClass("active")),d.trigger("activate.bs.scrollspy")},b.prototype.clear=function(){a(this.selector).parentsUntil(this.options.target,".active").removeClass("active")};var d=a.fn.scrollspy;a.fn.scrollspy=c,a.fn.scrollspy.Constructor=b,a.fn.scrollspy.noConflict=function(){return a.fn.scrollspy=d,this},a(window).on("load.bs.scrollspy.data-api",function(){a('[data-spy="scroll"]').each(function(){var b=a(this);c.call(b,b.data())})})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.tab");e||d.data("bs.tab",e=new c(this)),"string"==typeof b&&e[b]()})}var c=function(b){this.element=a(b)};c.VERSION="3.3.4",c.TRANSITION_DURATION=150,c.prototype.show=function(){var 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7 | var e=c.find(".active:last a"),f=a.Event("hide.bs.tab",{relatedTarget:b[0]}),g=a.Event("show.bs.tab",{relatedTarget:e[0]});if(e.trigger(f),b.trigger(g),!g.isDefaultPrevented()&&!f.isDefaultPrevented()){var h=a(d);this.activate(b.closest("li"),c),this.activate(h,h.parent(),function(){e.trigger({type:"hidden.bs.tab",relatedTarget:b[0]}),b.trigger({type:"shown.bs.tab",relatedTarget:e[0]})})}}},c.prototype.activate=function(b,d,e){function f(){g.removeClass("active").find("> .dropdown-menu > .active").removeClass("active").end().find('[data-toggle="tab"]').attr("aria-expanded",!1),b.addClass("active").find('[data-toggle="tab"]').attr("aria-expanded",!0),h?(b[0].offsetWidth,b.addClass("in")):b.removeClass("fade"),b.parent(".dropdown-menu").length&&b.closest("li.dropdown").addClass("active").end().find('[data-toggle="tab"]').attr("aria-expanded",!0),e&&e()}var g=d.find("> .active"),h=e&&a.support.transition&&(g.length&&g.hasClass("fade")||!!d.find("> .fade").length);g.length&&h?g.one("bsTransitionEnd",f).emulateTransitionEnd(c.TRANSITION_DURATION):f(),g.removeClass("in")};var d=a.fn.tab;a.fn.tab=b,a.fn.tab.Constructor=c,a.fn.tab.noConflict=function(){return a.fn.tab=d,this};var e=function(c){c.preventDefault(),b.call(a(this),"show")};a(document).on("click.bs.tab.data-api",'[data-toggle="tab"]',e).on("click.bs.tab.data-api",'[data-toggle="pill"]',e)}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.affix"),f="object"==typeof b&&b;e||d.data("bs.affix",e=new c(this,f)),"string"==typeof b&&e[b]()})}var c=function(b,d){this.options=a.extend({},c.DEFAULTS,d),this.$target=a(this.options.target).on("scroll.bs.affix.data-api",a.proxy(this.checkPosition,this)).on("click.bs.affix.data-api",a.proxy(this.checkPositionWithEventLoop,this)),this.$element=a(b),this.affixed=null,this.unpin=null,this.pinnedOffset=null,this.checkPosition()};c.VERSION="3.3.4",c.RESET="affix affix-top affix-bottom",c.DEFAULTS={offset:0,target:window},c.prototype.getState=function(a,b,c,d){var e=this.$target.scrollTop(),f=this.$element.offset(),g=this.$target.height();if(null!=c&&"top"==this.affixed)return c>e?"top":!1;if("bottom"==this.affixed)return null!=c?e+this.unpin<=f.top?!1:"bottom":a-d>=e+g?!1:"bottom";var h=null==this.affixed,i=h?e:f.top,j=h?g:b;return null!=c&&c>=e?"top":null!=d&&i+j>=a-d?"bottom":!1},c.prototype.getPinnedOffset=function(){if(this.pinnedOffset)return this.pinnedOffset;this.$element.removeClass(c.RESET).addClass("affix");var a=this.$target.scrollTop(),b=this.$element.offset();return this.pinnedOffset=b.top-a},c.prototype.checkPositionWithEventLoop=function(){setTimeout(a.proxy(this.checkPosition,this),1)},c.prototype.checkPosition=function(){if(this.$element.is(":visible")){var b=this.$element.height(),d=this.options.offset,e=d.top,f=d.bottom,g=a(document.body).height();"object"!=typeof d&&(f=e=d),"function"==typeof e&&(e=d.top(this.$element)),"function"==typeof f&&(f=d.bottom(this.$element));var h=this.getState(g,b,e,f);if(this.affixed!=h){null!=this.unpin&&this.$element.css("top","");var i="affix"+(h?"-"+h:""),j=a.Event(i+".bs.affix");if(this.$element.trigger(j),j.isDefaultPrevented())return;this.affixed=h,this.unpin="bottom"==h?this.getPinnedOffset():null,this.$element.removeClass(c.RESET).addClass(i).trigger(i.replace("affix","affixed")+".bs.affix")}"bottom"==h&&this.$element.offset({top:g-b-f})}};var d=a.fn.affix;a.fn.affix=b,a.fn.affix.Constructor=c,a.fn.affix.noConflict=function(){return a.fn.affix=d,this},a(window).on("load",function(){a('[data-spy="affix"]').each(function(){var c=a(this),d=c.data();d.offset=d.offset||{},null!=d.offsetBottom&&(d.offset.bottom=d.offsetBottom),null!=d.offsetTop&&(d.offset.top=d.offsetTop),b.call(c,d)})})}(jQuery);


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/web/static/js/npm.js:
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 1 | // This file is autogenerated via the `commonjs` Grunt task. You can require() this file in a CommonJS environment.
 2 | require('../../js/transition.js')
 3 | require('../../js/alert.js')
 4 | require('../../js/button.js')
 5 | require('../../js/carousel.js')
 6 | require('../../js/collapse.js')
 7 | require('../../js/dropdown.js')
 8 | require('../../js/modal.js')
 9 | require('../../js/tooltip.js')
10 | require('../../js/popover.js')
11 | require('../../js/scrollspy.js')
12 | require('../../js/tab.js')
13 | require('../../js/affix.js')


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