Casus sccience seminar materials

gdml.ipynb

Kotlin-demo.ipynb

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    "tags": []
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   "source": [
    "%use plotly-server\n",
    "%use dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20ae4dc8-ba18-489a-8797-3e7c0f3d4aa5",
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   "source": [
    "var trace: Trace? = null\n",
    "\n",
    "Plotly.plot { \n",
    "    trace = scatter{\n",
    "        x.numbers = (0..100).map{it.toDouble() / 25}\n",
    "        y.numbers = x.numbers.map{sin(it.toDouble())}\n",
    "    }\n",
    "}"
   ]
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   "id": "5abe73e0-5b1e-4b27-aca7-87f7667839a4",
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   "source": [
    "import kotlinx.coroutines.*\n",
    "\n",
    "val job = GlobalScope.launch{\n",
    "    var t = 0\n",
    "    while(isActive){\n",
    "        delay(100)\n",
    "        t+=50\n",
    "        with(trace){\n",
    "            y.numbers = x.numbers.map{sin(it.toDouble() +t.toDouble()/2000)}\n",
    "        }\n",
    "    }\n",
    "}"
   ]
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    "job.cancel()"
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   "cell_type": "code",
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   "id": "c1ca5218-3d32-4022-abf1-25b474ef92cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "val df = DataFrame.read(\"https://gist.githubusercontent.com/curran/a08a1080b88344b0c8a7/raw/0e7a9b0a5d22642a06d3d5b9bcbad9890c8ee534/iris.csv\")"
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   "cell_type": "code",
   "execution_count": null,
   "id": "42c24135-23b4-4ed1-8e40-83e88bce3c23",
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   "outputs": [],
   "source": [
    "df"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "id": "07dd08b5-8f1a-4685-88df-23371939af80",
   "metadata": {},
   "outputs": [],
   "source": [
    "val df1 = df.add(\"custom\"){sepal_width + petal_length}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd3e48cb-8964-4bd1-ab0b-976c79f0a54b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.custom"
   ]
  },
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   "cell_type": "code",
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