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Welcome.snb
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{
"metadata":{
"name":"Welcome",
"user_save_timestamp":"2014-09-08T23:30:54.951Z",
"auto_save_timestamp":"2014-09-12T12:34:01.954Z"
},
"worksheets":[{
"cells":[{
"cell_type":"markdown",
"source":"## Welcome to Scala Notebook\n\nScala notebook is an interactive REPL. You type scala expressions in the browser, and they are evaluated in a Kernel behind the scenes. Cells can contain formatting or code."
},{
"cell_type":"markdown",
"source":"To edit a markdown cell, double-click in the text. "
},{
"cell_type":"code",
"input":"import notebook.JsonCodec._\nimport org.json4s.JsonAST._\nimport org.json4s.JsonDSL._\nimport notebook.widgets.d3._\n \n val script = {\n\t implicit val sss = strings\n\t D3(Seq(\"ok\", \"nok\"), 400, 100, \n \"\"\" \n\t function(data) {\n\t\t var text = svg.selectAll(\"text\")\n .data(data)\n .enter()\n .append(\"text\")\n .attr(\"x\", 10)\n .attr(\"y\", 10)\n .attr(\"fill\", \"red\")\n \t\t\t .text(function(t) {return t;});\n \n }\n \"\"\", \n \"\"\" function(x) {console.log(\"yop\"); };\"\"\")\n\n }",
"language":"scala",
"collapsed":false,
"prompt_number":12,
"outputs":[]
},{
"cell_type":"code",
"input":"script.apply(Seq(\"BOOOM\"))",
"language":"scala",
"collapsed":false,
"prompt_number":13,
"outputs":[]
},{
"cell_type":"code",
"input":"",
"language":"scala",
"collapsed":true,
"outputs":[]
},{
"cell_type":"code",
"input":"",
"language":"scala",
"collapsed":true,
"outputs":[]
},{
"cell_type":"code",
"input":"",
"language":"scala",
"collapsed":true,
"outputs":[]
},{
"cell_type":"code",
"input":"",
"language":"scala",
"collapsed":true,
"outputs":[]
},{
"cell_type":"code",
"input":"",
"language":"scala",
"collapsed":true,
"outputs":[]
},{
"cell_type":"code",
"input":"// Simple expressions\nList(1,2,3).reverse // show\n ",
"language":"scala",
"collapsed":false,
"prompt_number":2,
"outputs":[]
},{
"cell_type":"code",
"input":"sparkContext",
"language":"scala",
"collapsed":false,
"prompt_number":3,
"outputs":[]
},{
"cell_type":"code",
"input":"val file = sparkContext.textFile(\"hdfs://ec2-54-77-215-229.eu-west-1.compute.amazonaws.com:9000/data/yum.log\")",
"language":"scala",
"collapsed":false,
"outputs":[]
},{
"cell_type":"code",
"input":"val rdd = sparkContext.makeRDD(Array(1, 2))",
"language":"scala",
"collapsed":false,
"prompt_number":4,
"outputs":[]
},{
"cell_type":"code",
"input":"rdd.count()\n ",
"language":"scala",
"collapsed":false,
"prompt_number":5,
"outputs":[]
},{
"cell_type":"code",
"input":"case class A(a:String)\nval a:A = A(\"ok\")",
"language":"scala",
"collapsed":false,
"prompt_number":6,
"outputs":[]
},{
"cell_type":"code",
"input":"val signs = sparkContext.makeRDD(Array(\">\", \"<\"))\nval appendedSigns = signs.map(_ + a.a)\n\n <div>{appendedSigns.collect() map (s => <p>{s}</p>)}</div>\n ",
"language":"scala",
"collapsed":false,
"prompt_number":11,
"outputs":[]
},{
"cell_type":"code",
"input":"// Reactive components allow dynamic communication to Scala and Javascript\nval ib = new InputBox(\"\")",
"language":"scala",
"collapsed":false,
"prompt_number":1,
"outputs":[]
},{
"cell_type":"code",
"input":"val ib2 = new InputBox(\"\")",
"language":"scala",
"collapsed":false,
"prompt_number":2,
"outputs":[]
},{
"cell_type":"code",
"input":"// After running this, type text in the second box and it appears in the first\nib.currentValue <-- ib2.currentValue",
"language":"scala",
"collapsed":false,
"prompt_number":5,
"outputs":[]
},{
"cell_type":"code",
"input":"",
"language":"scala",
"collapsed":true,
"outputs":[]
}]
}],
"autosaved":[{
"cells":[{
"cell_type":"markdown",
"source":"## Welcome to Scala Notebook\n\nScala notebook is an interactive REPL. You type scala expressions in the browser, and they are evaluated in a Kernel behind the scenes. Cells can contain formatting or code."
},{
"cell_type":"markdown",
"source":"To edit a markdown cell, double-click in the text. "
},{
"cell_type":"code",
"input":"import notebook.JsonCodec._\nimport org.json4s.JsonAST._\nimport org.json4s.JsonDSL._\nimport notebook.widgets.d3._\n \n val script = {\n\t implicit val sss = strings\n\t D3(Seq(\"ok\", \"nok\"), 400, 100, \n \"\"\" \n\t function(data) {\n\t\t var text = svg.selectAll(\"text\")\n .data(data, function(d) { return d; });\n text.enter()\n .append(\"text\")\n .attr(\"x\", function(d, i) { return (i+1) * 10;})\n .attr(\"y\", function(d, i) { return (i+1) * 10;})\n .attr(\"fill\", \"red\")\n .text(function(t) {return t;});\n \n text.exit().remove();\n \n }\n \"\"\", \n \"\"\" function(x) {console.log(\"yop\"); };\"\"\")\n\n }",
"language":"scala",
"collapsed":false,
"prompt_number":13,
"outputs":[]
},{
"cell_type":"code",
"input":"script.apply(Seq(\"BOOOM\", \"BUM\", \"BAM\"))",
"language":"scala",
"collapsed":false,
"prompt_number":12,
"outputs":[]
},{
"cell_type":"code",
"input":"import notebook.JsonCodec._\nimport org.json4s.JsonAST._\nimport org.json4s.JsonDSL._\nimport notebook.widgets.d3._\n\n \nval script = {\n\t implicit val sss = strings\n\t D3.svg(Seq(\"ok\", \"nok\"), 400, 100, \n \"consoleDir\", \n \"consoleDir\")\n }\n ",
"language":"scala",
"collapsed":false,
"prompt_number":2,
"outputs":[]
},{
"cell_type":"code",
"input":" def ts = (1d to 40 by 1).map{ x => \n \t (x.toDouble, scala.util.Random.nextDouble * 150)\n }\n \t\t\t\t\t .toSeq\n()\n",
"language":"scala",
"collapsed":false,
"prompt_number":7,
"outputs":[]
},{
"cell_type":"code",
"input":"import notebook.JsonCodec._\nimport org.json4s.JsonAST._\nimport org.json4s.JsonDSL._\nimport notebook.widgets.d3._\nval script2 = {\n //implicit val sss = new Codec[JDouble, String] {\n // def encode(x:JDouble):String = \"a\"*x.extract[Double].toInt\n // def decode(x:String):JDouble = JDouble(x.size)\n //}\n //D3.linePlot(Seq(\"ok\", \"nok\"), 400, 100)\n //implicit val sss = jdouble2jvalueCodec()\n D3.linePlot(ts, 400, 100)\n}",
"language":"scala",
"collapsed":false,
"prompt_number":4,
"outputs":[]
},{
"cell_type":"code",
"input":"script2(ts)",
"language":"scala",
"collapsed":false,
"prompt_number":5,
"outputs":[]
},{
"cell_type":"code",
"input":"",
"language":"scala",
"collapsed":true,
"outputs":[]
},{
"cell_type":"code",
"input":"",
"language":"scala",
"collapsed":true,
"outputs":[]
},{
"cell_type":"code",
"input":"// Simple expressions\nList(1,2,3).reverse // show\n ",
"language":"scala",
"collapsed":false,
"prompt_number":2,
"outputs":[]
},{
"cell_type":"code",
"input":"sparkContext",
"language":"scala",
"collapsed":false,
"prompt_number":3,
"outputs":[]
},{
"cell_type":"code",
"input":"val file = sparkContext.textFile(\"hdfs://ec2-54-77-215-229.eu-west-1.compute.amazonaws.com:9000/data/yum.log\")",
"language":"scala",
"collapsed":false,
"outputs":[]
},{
"cell_type":"code",
"input":"val rdd = sparkContext.makeRDD(Array(1, 2))",
"language":"scala",
"collapsed":false,
"prompt_number":4,
"outputs":[]
},{
"cell_type":"code",
"input":"rdd.count()\n ",
"language":"scala",
"collapsed":false,
"prompt_number":5,
"outputs":[]
},{
"cell_type":"code",
"input":"case class A(a:String)\nval a:A = A(\"ok\")",
"language":"scala",
"collapsed":false,
"prompt_number":6,
"outputs":[]
},{
"cell_type":"code",
"input":"val signs = sparkContext.makeRDD(Array(\">\", \"<\"))\nval appendedSigns = signs.map(_ + a.a)\n\n <div>{appendedSigns.collect() map (s => <p>{s}</p>)}</div>\n ",
"language":"scala",
"collapsed":false,
"prompt_number":11,
"outputs":[]
},{
"cell_type":"code",
"input":"// Reactive components allow dynamic communication to Scala and Javascript\nval ib = new InputBox(\"\")",
"language":"scala",
"collapsed":false,
"prompt_number":1,
"outputs":[]
},{
"cell_type":"code",
"input":"val ib2 = new InputBox(\"\")",
"language":"scala",
"collapsed":false,
"prompt_number":2,
"outputs":[]
},{
"cell_type":"code",
"input":"// After running this, type text in the second box and it appears in the first\nib.currentValue <-- ib2.currentValue",
"language":"scala",
"collapsed":false,
"prompt_number":5,
"outputs":[]
},{
"cell_type":"code",
"input":"",
"language":"scala",
"collapsed":true,
"outputs":[]
}]
}],
"nbformat":3
}