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@scottdraves
Created February 2, 2018 04:37
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{
"cells": [
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "new Plot(title: \"We Will Control the Title\", xLabel: \"Horizontal\", yLabel: \"Vertical\")",
"execution_count": 1,
"outputs": [
{
"output_type": "display_data",
"data": {
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"model_id": "9d7292ab-e1fe-476f-bccb-ba3d5abe7724",
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"metadata": {}
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]
},
{
"metadata": {
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"cell_type": "code",
"source": "// just provide lists of x's and y's\nnew Plot().add(new Line(x: [0, 1, 2, 3, 4, 5], y: [0, 1, 6, 5, 2, 8]))\n\n// Groovy range works\nnew Plot().add(new Line(x: (0..5), y: [0, 1, 6, 5, 2, 8]))\n\n// use '<<' left-shift to save some key strokes\nnew Plot() << new Line(x: (0..5), y: [0, 1, 6, 5, 2, 8])\n\n// the constructor of class Line is overloaded to take 1 or 2 lists\n// if an Line was returned, and empty plot is automatically generated\nnew Line((0..5), [0, 1, 6, 5, 2, 8])\n \n// if x is not provided, a default list of x values (0..5) will be used\nnew Line([0, 1, 6, 5, 2, 8])",
"execution_count": 2,
"outputs": [
{
"output_type": "display_data",
"data": {
"method": "display_data",
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"model_id": "833008eb-aa48-4e45-8f16-240d7941fbc6",
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}
},
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}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "def plot = new Plot(title: \"Setting line properties\")\ndef ys = [0, 1, 6, 5, 2, 8]\ndef ys2 = [0, 2, 7, 6, 3, 8]\nplot << new Line(y: ys, width: 10, color: Color.red)\nplot << new Line(y: ys, width: 3, color: Color.yellow)\nplot << new Line(y: ys, width: 4, color: new Color(33, 87, 141), style: StrokeType.DASH, interpolation: 0)\nplot << new Line(y: ys2, width: 2, color: new Color(212, 57, 59), style: StrokeType.DOT)\nplot << new Line(y: [5, 0], x: [0, 5], style: StrokeType.LONGDASH)\nplot << new Line(y: [4, 0], x: [0, 5], style: StrokeType.DASHDOT)",
"execution_count": 3,
"outputs": [
{
"output_type": "display_data",
"data": {
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"model_id": "e9551147-197e-445d-90d6-c07bbc1e84ce",
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}
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]
},
{
"metadata": {
"trusted": true
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"cell_type": "code",
"source": "def plot = new Plot();\ndef y1 = [1.5, 1, 6, 5, 2, 8]\ndef cs = [Color.black, Color.red, Color.gray, Color.green, Color.blue, Color.pink]\ndef ss = [StrokeType.SOLID, StrokeType.SOLID, StrokeType.DASH, StrokeType.DOT, StrokeType.DASHDOT, StrokeType.LONGDASH]\nplot << new Stems(y: y1, color: cs, style: ss, width: 5)",
"execution_count": 4,
"outputs": [
{
"output_type": "display_data",
"data": {
"method": "display_data",
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"model_id": "b8ea4f9f-42ff-4818-a5c5-8a357a999ea6",
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}
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "def plot = new Plot(title: \"Setting the base of Stems\")\ndef ys = [3, 5, 2, 3, 7]\ndef y2s = [2.5, -1.0, 3.5, 2.0, 3.0]\nplot << new Stems(y: ys, width: 2, base: y2s)\nplot << new Points(y: ys)",
"execution_count": 5,
"outputs": [
{
"output_type": "display_data",
"data": {
"method": "display_data",
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},
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]
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{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "def plot = new Plot(title: \"Bars\")\ndef cs = [new Color(255, 0, 0, 128)] * 5 // transparent bars\ncs[3] = Color.red // set color of a single bar, solid colored bar\nplot << new Bars(x: (1..5), y: [3, 5, 2, 3, 7], color: cs, outlineColor: Color.black, width: 0.3)",
"execution_count": 6,
"outputs": [
{
"output_type": "display_data",
"data": {
"method": "display_data",
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"version_minor": 0,
"model_id": "8917b59f-d820-408f-a936-dd2af65a15e7",
"version_major": 2
}
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "def plot = new Plot(title: \"Changing Point Size, Color, Shape\")\ndef y1 = [6, 7, 12, 11, 8, 14]\ndef y2 = y1.collect { it - 2 }\ndef y3 = y2.collect { it - 2 }\ndef y4 = y3.collect { it - 2 }\nplot << new Points(y: y1)\nplot << new Points(y: y2, shape: ShapeType.CIRCLE)\nplot << new Points(y: y3, size: 8.0, shape: ShapeType.DIAMOND)\nplot << new Points(y: y4, size: 12.0, color: Color.orange, outlineColor: Color.red)",
"execution_count": 7,
"outputs": [
{
"output_type": "display_data",
"data": {
"method": "display_data",
"application/vnd.jupyter.widget-view+json": {
"version_minor": 0,
"model_id": "a3436657-d21d-4227-a9c0-a602d3fd8897",
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}
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "def plot = new Plot(title: \"Changing point properties with list\")\ndef cs = [Color.black, Color.red, Color.orange, Color.green, Color.blue, Color.pink]\ndef ss = [6.0, 9.0, 12.0, 15.0, 18.0, 21.0]\ndef fs = [false, false, false, true, false, false]\nplot << [new Points(y: [5] * 6, size: 12.0, color: cs),\n new Points(y: [4] * 6, size: 12.0, color: Color.gray, outlineColor: cs),\n new Points(y: [3] * 6, size: ss, color: Color.red),\n new Points(y: [2] * 6, size: 12.0, color: Color.black, fill: fs, outlineColor: Color.black)]",
"execution_count": 8,
"outputs": [
{
"output_type": "display_data",
"data": {
"method": "display_data",
"application/vnd.jupyter.widget-view+json": {
"version_minor": 0,
"model_id": "4e3d9dee-83a2-4455-bb84-b1fd0db49f2a",
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}
},
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}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "def plot = new Plot()\ndef y = [3, 5, 2, 3]\ndef x0 = [0, 1, 2, 3]\ndef x1 = [3, 4, 5, 8]\nplot << new Area(x: x0, y: y)\nplot << new Area(x: x1, y: y, color: new Color(128, 128, 128, 50), interpolation: 0)",
"execution_count": 9,
"outputs": [
{
"output_type": "display_data",
"data": {
"method": "display_data",
"application/vnd.jupyter.widget-view+json": {
"version_minor": 0,
"model_id": "90a0a186-80c5-4792-99e9-91cd34bd4e68",
"version_major": 2
}
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "def p = new Plot()\np << new Line(y: [3, 6, 12, 24], displayName: \"Median\")\np << new Area(y: [4, 8, 16, 32], base: [2, 4, 8, 16],\n color: new Color(255, 0, 0, 50), displayName: \"Q1 to Q3\")",
"execution_count": 10,
"outputs": [
{
"output_type": "display_data",
"data": {
"method": "display_data",
"application/vnd.jupyter.widget-view+json": {
"version_minor": 0,
"model_id": "78114476-2647-42c7-8fc5-6bbb02594313",
"version_major": 2
}
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"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "def y1 = [1,5,3,2,3]\ndef y2 = [7,2,4,1,3]\ndef p = new Plot(title: 'Plot with XYStacker', initHeight: 200)\ndef a1 = new Area(y: y1, displayName: 'y1')\ndef a2 = new Area(y: y2, displayName: 'y2')\np << XYStacker.stack([a1, a2])",
"execution_count": 11,
"outputs": [
{
"output_type": "display_data",
"data": {
"method": "display_data",
"application/vnd.jupyter.widget-view+json": {
"version_minor": 0,
"model_id": "036b80b0-615d-4733-9686-7b7276d6ebee",
"version_major": 2
}
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "def p = new Plot ()\np << new Line(y: [-1, 1])\np << new ConstantLine(x: 0.65, style: StrokeType.DOT, color: Color.blue)\np << new ConstantLine(y: 0.1, style: StrokeType.DASHDOT, color: Color.blue)\np << new ConstantLine(x: 0.3, y: 0.4, color: Color.gray, width: 5, showLabel: true)",
"execution_count": 12,
"outputs": [
{
"output_type": "display_data",
"data": {
"method": "display_data",
"application/vnd.jupyter.widget-view+json": {
"version_minor": 0,
"model_id": "b6f30242-856a-4d44-bed8-f98ece634530",
"version_major": 2
}
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "new Plot() << new Line(y: [-3, 1, 3, 4, 5]) << new ConstantBand(x: [1, 2], y: [1, 3])",
"execution_count": 13,
"outputs": [
{
"output_type": "display_data",
"data": {
"method": "display_data",
"application/vnd.jupyter.widget-view+json": {
"version_minor": 0,
"model_id": "273a4df2-bd0a-4a7f-856c-487cb6a18285",
"version_major": 2
}
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "def p = new Plot() \np << new Line(x: [-3, 1, 2, 4, 5], y: [4, 2, 6, 1, 5])\np << new ConstantBand(x: [Double.NEGATIVE_INFINITY, 1], color: new Color(128, 128, 128, 50))\np << new ConstantBand(x: [1, 2])\np << new ConstantBand(x: [4, Double.POSITIVE_INFINITY])",
"execution_count": 14,
"outputs": [
{
"output_type": "display_data",
"data": {
"method": "display_data",
"application/vnd.jupyter.widget-view+json": {
"version_minor": 0,
"model_id": "5fe02ec2-1bb1-461a-bb53-ee04c1f43d00",
"version_major": 2
}
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "def plot = new Plot()\ndef xs = (1..10)\ndef ys = [8.6, 6.1, 7.4, 2.5, 0.4, 0.0, 0.5, 1.7, 8.4, 1]\ndef label = { i ->\n if (ys[i] > ys[i+1] && ys[i] > ys[i-1]) return \"max\"\n if (ys[i] < ys[i+1] && ys[i] < ys[i-1]) return \"min\"\n if (ys[i] > ys[i-1]) return \"rising\"\n if (ys[i] < ys[i-1]) return \"falling\"\n return \"\"\n}\nfor (i = 0; i < xs.size(); i++) {\n if (i > 0 && i < xs.size()-1)\n plot << new Text(x: xs[i], y: ys[i], text: label(i), pointerAngle: -i/3.0)\n}\nplot << new Line(x: xs, y: ys)\nplot << new Points(x: xs, y: ys)",
"execution_count": 15,
"outputs": [
{
"output_type": "display_data",
"data": {
"method": "display_data",
"application/vnd.jupyter.widget-view+json": {
"version_minor": 0,
"model_id": "146773e8-efb0-4bac-887a-8ba01b83d810",
"version_major": 2
}
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "def ch = new Crosshair(color: new Color(255, 128, 5), width: 2, style: StrokeType.DOT)\npp = new Plot(crosshair: ch, omitCheckboxes: true,\n legendLayout: LegendLayout.HORIZONTAL, legendPosition: LegendPosition.TOP)\ndef x = [1, 4, 6, 8, 10]\ndef y = [3, 6, 4, 5, 9]\npp << new Line(displayName: \"Line\", x: x, y: y, width: 3)\npp << new Bars(displayName: \"Bar\", x: (1..10), y: [2, 2, 4, 4, 2, 2, 0, 2, 2, 4], width: 0.5)\npp << new Points(x: x, y: y, size: 10, toolTip: {xs, ys -> \"x = \" + xs + \", y = \" + ys })",
"execution_count": 16,
"outputs": [
{
"output_type": "display_data",
"data": {
"method": "display_data",
"application/vnd.jupyter.widget-view+json": {
"version_minor": 0,
"model_id": "1e16fc23-7da1-401a-96cb-fb7b28d32a07",
"version_major": 2
}
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "pp.setLegendPosition(LegendPosition.RIGHT);\nOutputCell.HIDDEN",
"execution_count": 17,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "import com.twosigma.beakerx.fileloader.CSV\nrates = new CSV().read(\"../../../doc/resources/data/interest-rates.csv\")\ndef size = rates.size()\n(0 ..< size).each{row = rates[it]; row.spread = row.y10 - row.m3}",
"execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 18,
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},
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}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "new SimpleTimePlot(rates, [\"y1\", \"y10\"], // column names\n timeColumn : \"time\", // time is default value for a timeColumn\n yLabel: \"Price\", \n displayNames: [\"1 Year\", \"10 Year\"],\n colors : [[216, 154, 54], '#aabbcc'],\n displayLines: false, // no lines (true by default)\n displayPoints: true) // show points (false by default))",
"execution_count": 19,
"outputs": [
{
"output_type": "display_data",
"data": {
"method": "display_data",
"application/vnd.jupyter.widget-view+json": {
"version_minor": 0,
"model_id": "78f54757-1682-4680-b051-d1f68a6c44b9",
"version_major": 2
}
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "def p = new TimePlot(xLabel: \"Time\", yLabel: \"Interest Rates\")\np << new YAxis(label: \"Spread\", upperMargin: 4)\np << new Area(x: rates.time, y: rates.spread, displayName: \"Spread\",\n yAxis: \"Spread\", color: new Color(180, 50, 50, 128))\np << new Line(x: rates.time, y: rates.m3, displayName: \"3 Month\")\np << new Line(x: rates.time, y: rates.y10, displayName: \"10 Year\")",
"execution_count": 20,
"outputs": [
{
"output_type": "display_data",
"data": {
"method": "display_data",
"application/vnd.jupyter.widget-view+json": {
"version_minor": 0,
"model_id": "c870331c-e479-4b2b-ae11-eca2d4b66fd2",
"version_major": 2
}
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "def points = 100;\ndef logBase = 10;\ndef expys = [];\ndef xs = [];\nfor(int i = 0; i < points; i++){\n xs[i] = i / 15.0;\n expys[i] = Math.exp(xs[i]); \n}\n\ndef cplot = new CombinedPlot(xLabel: \"Linear\");\ndef logYPlot = new Plot(title: \"Linear x, Log y\", yLabel: \"Log\", logY: true, yLogBase: logBase);\nlogYPlot << new Line(x: xs, y: expys, displayName: \"f(x) = exp(x)\");\nlogYPlot << new Line(x: xs, y: xs, displayName: \"g(x) = x\");\ncplot.add(logYPlot, 3);\n\n// works for 2nd Y axis too:\n// logYPlot << new YAxis(label: \"Right Log Y-Axis\", log: true, logBase: logBase);\n\ndef linearYPlot = new Plot(title: \"Linear x, Linear y\", yLabel: \"Linear\");\nlinearYPlot << new Line(x: xs, y: expys, displayName: \"f(x) = exp(x)\");\nlinearYPlot << new Line(x: xs, y: xs, displayName: \"g(x) = x\");\ncplot.add(linearYPlot, 3);\n\ncplot",
"execution_count": 21,
"outputs": [
{
"output_type": "display_data",
"data": {
"method": "display_data",
"application/vnd.jupyter.widget-view+json": {
"version_minor": 0,
"model_id": "c4a2d059-450f-4948-a0cf-936f3f941389",
"version_major": 2
}
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "def points = 100;\ndef logBase = 10;\ndef expys = [];\ndef xs = [];\nfor(int i = 0; i < points; i++){\n xs[i] = i /15\n expys[i] = Math.exp(xs[i]);\n}\n\ndef plot = new Plot(title: \"Log x, Log y\", xLabel: \"Log\", yLabel: \"Log\",\n logX: true, xLogBase: logBase, logY: true, yLogBase: logBase);\n\nplot << new Line(x: xs, y: expys, displayName: \"f(x) = exp(x)\");\nplot << new Line(x: xs, y: xs, displayName: \"f(x) = x\");\n\nplot",
"execution_count": 22,
"outputs": [
{
"output_type": "display_data",
"data": {
"method": "display_data",
"application/vnd.jupyter.widget-view+json": {
"version_minor": 0,
"model_id": "43414c83-daec-442f-a92c-dbec874985a8",
"version_major": 2
}
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "def cal = Calendar.getInstance();\ncal.add(Calendar.HOUR, -1)\n\ndef today = new Date();\ndef millis = today.time;\ndef hour = 1000 * 60 * 60;\n\ndef plot = new TimePlot(\n timeZone: new SimpleTimeZone(10800000, \"America/New_York\")\n);\n//list of milliseconds\nplot << new Points(x:(0..10).collect{millis + hour * it}, y:(0..10), size: 10, displayName: \"milliseconds\");\n//list of java.util.Date objects\nplot << new Points(x:(0..10).collect{cal.add(Calendar.HOUR, 1); cal.getTime()}, y:(0..10), size: 4, displayName: \"date objects\");",
"execution_count": 23,
"outputs": [
{
"output_type": "display_data",
"data": {
"method": "display_data",
"application/vnd.jupyter.widget-view+json": {
"version_minor": 0,
"model_id": "e2d64357-6321-4e85-ae68-fe4d241a5ffa",
"version_major": 2
}
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "def today = new Date()\ndef millis = today.time\ndef nanos = millis * 1000 * 1000g // g makes it arbitrary precision\ndef np = new NanoPlot()\nnp << new Points(x:(0..10).collect{nanos + 7 * it}, y:(0..10))",
"execution_count": 24,
"outputs": [
{
"output_type": "display_data",
"data": {
"method": "display_data",
"application/vnd.jupyter.widget-view+json": {
"version_minor": 0,
"model_id": "5defc210-6ab0-4c83-a025-d64480ae5b01",
"version_major": 2
}
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "p = new Plot(title: \"No Tick Labels\", xTickLabelsVisible: false, yTickLabelsVisible: false)\np << new Line([0, 1, 6, 5, 2, 8])",
"execution_count": 25,
"outputs": [
{
"output_type": "display_data",
"data": {
"method": "display_data",
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