sensitivity_load.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/home/ach/sensiflow/flight.py:7: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
            "  from tqdm.autonotebook import tqdm\n"
          ]
        }
      ],
      "source": [
        "from traffic.core import Traffic\n",
        "from mass import FuelEstimator\n",
        "import numpy as np\n",
        "import altair as alt\n",
        "import pandas as pd\n",
        "from flight import (\n",
        "    FlightProfiles,\n",
        "    FlightProfileGenerator,\n",
        "    _to_df,\n",
        "    gen_flight_profile,\n",
        "    FlightPhaseEstimator,\n",
        "    gentraj,\n",
        ")\n",
        "import openturns as ot\n",
        "import openturns.viewer as viewer\n",
        "\n",
        "ot.RandomGenerator.SetSeed(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "inputDesign = ot.Sample.ImportFromCSVFile(\"results/input/B77W_5600km.csv\")\n",
        "outputDesign = ot.Sample.ImportFromCSVFile(\"results/output/B77W_5600km.csv\")\n",
        "ac_type = \"B77W\"\n",
        "mission = 5600\n",
        "\n",
        "\n",
        "def test_openturns(X):\n",
        "    # Transforming the input into np array\n",
        "    # Xarray = np.array(X, copy=False)\n",
        "\n",
        "    # Getting data from X\n",
        "    # age = Xarray[:, 2]\n",
        "\n",
        "    # Fuel Calculation with PDFs\n",
        "\n",
        "    cumul = []\n",
        "    for sample in X:\n",
        "        load_factor = sample[0]\n",
        "        weight_person = sample[1]\n",
        "        descent_thrust = sample[2]\n",
        "        cas_const_cl = sample[3]\n",
        "        mach_const_cl = sample[4]\n",
        "        cas_const_de = sample[5]\n",
        "        mach_const_de = sample[6]\n",
        "        range_cr = sample[7]\n",
        "        alt_cr = sample[8]\n",
        "        mach_cr = sample[9]\n",
        "\n",
        "        traj = gentraj(\n",
        "            ac_type,\n",
        "            cas_const_cl=cas_const_cl,\n",
        "            mach_const_cl=mach_const_cl,\n",
        "            cas_const_de=cas_const_de,\n",
        "            mach_const_de=mach_const_de,\n",
        "            range_cr=range_cr,\n",
        "            alt_cr=alt_cr,\n",
        "            mach_cr=mach_cr,\n",
        "            dt=60,\n",
        "        )\n",
        "\n",
        "        fe = FuelEstimator(\n",
        "            ac_type=ac_type,\n",
        "            passenger_mass=weight_person,\n",
        "            load_factor=load_factor,\n",
        "            descent_thrust=descent_thrust,\n",
        "        )\n",
        "        df = FlightPhaseEstimator()(_to_df(traj))\n",
        "        fp = FlightProfiles.from_df(df)\n",
        "        cumul.append([fe(fp).to_df().fc.iloc[-1]])\n",
        "\n",
        "    return cumul"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "def get_dist(var):\n",
        "    if var[\"statmodel\"] == \"beta\":\n",
        "        return ot.Beta(\n",
        "            var[\"statmodel_params\"][0],\n",
        "            var[\"statmodel_params\"][1],\n",
        "            var[\"minimum\"],\n",
        "            var[\"maximum\"],\n",
        "        )\n",
        "    elif var[\"statmodel\"] == \"norm\":\n",
        "        return ot.TruncatedDistribution(\n",
        "            ot.Normal(*var[\"statmodel_params\"]),\n",
        "            var[\"minimum\"],\n",
        "            var[\"maximum\"],\n",
        "        )\n",
        "    elif var[\"statmodel\"] == \"gamma\":\n",
        "        return ot.Gamma(\n",
        "            var[\"statmodel_params\"][0],\n",
        "            1 / var[\"statmodel_params\"][2],\n",
        "            var[\"statmodel_params\"][1],\n",
        "        )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "fun = ot.PythonFunction(10, 1, func=test_openturns, func_sample=test_openturns)\n",
        "fpg = FlightProfileGenerator(ac_type=ac_type)\n",
        "distribution = ot.ComposedDistribution(\n",
        "    [\n",
        "        ot.TruncatedDistribution(\n",
        "            ot.Normal(0.819, 0.2), 1, ot.TruncatedDistribution.UPPER\n",
        "        ),  # X0\n",
        "        ot.TruncatedDistribution(\n",
        "            (ot.Normal(0, 0.2) + 1) * 100, 80, ot.TruncatedDistribution.LOWER\n",
        "        ),  # X1\n",
        "        ot.TruncatedDistribution(\n",
        "            ot.Normal(0.3, 0.2), 0.05, ot.TruncatedDistribution.LOWER\n",
        "        ),  # X2\n",
        "        get_dist(fpg.wrap.climb_const_vcas()),  # X3\n",
        "        get_dist(fpg.wrap.climb_const_mach()),  # X4\n",
        "        get_dist(fpg.wrap.descent_const_vcas()),  # X5\n",
        "        get_dist(fpg.wrap.descent_const_mach()),  # X6\n",
        "        (ot.Normal(0, 0.01) + 1) * mission,  # X7\n",
        "        get_dist(fpg.wrap.cruise_alt()) * 1000,  # X8\n",
        "        get_dist(fpg.wrap.cruise_mach()),  # X9\n",
        "    ]\n",
        ")\n",
        "\n",
        "distribution.setDescription(\n",
        "    [\n",
        "        \"Load Factor\",\n",
        "        \"AWP\",\n",
        "        \"Descent Thrust\",\n",
        "        \"Cas Climbing\",\n",
        "        \"Mach Climbing\",\n",
        "        \"Cas Descent\",\n",
        "        \"Mach Descent\",\n",
        "        \"Cruise Range\",\n",
        "        \"Cruise Altitude\",\n",
        "        \"Cruise Mach\",\n",
        "    ]\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Sample size:  48000\n",
            "Output # 0\n",
            "    First order indices:  [0.254073,0.136797,-0.00721369,-0.00721369,-0.000617583,-0.00780808,-0.00627553,0.0227604,0.517948,0.0666877]#10\n",
            "    Total order indices:  [0.254926,0.145196,-1.74087e-11,-1.74087e-11,0.00360601,0.00314548,0.00373773,0.0231865,0.530091,0.0634417]#10\n",
            "Agg. first order indices:  [0.254073,0.136797,-0.00721369,-0.00721369,-0.000617583,-0.00780808,-0.00627553,0.0227604,0.517948,0.0666877]#10\n",
            "Agg. total order indices:  [0.254926,0.145196,-1.74087e-11,-1.74087e-11,0.00360601,0.00314548,0.00373773,0.0231865,0.530091,0.0634417]#10\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "size = 4000\n",
        "sie = ot.SobolIndicesExperiment(distribution, size)\n",
        "inputDesign = sie.generate()\n",
        "input_names = distribution.getDescription()\n",
        "inputDesign.setDescription(input_names)\n",
        "print(\"Sample size: \", inputDesign.getSize())\n",
        "\n",
        "# %%\n",
        "\n",
        "df_in = inputDesign.asDataFrame()\n",
        "df_out = outputDesign.asDataFrame()\n",
        "\n",
        "# %%\n",
        "\n",
        "idx = df_out.y0[df_out.y0.isnull()].index\n",
        "inputDesign = ot.Sample(df_in.drop(index=idx).values)\n",
        "outputDesign = ot.Sample(df_out.drop(index=idx).values)\n",
        "\n",
        "# %%\n",
        "inputDesign.setDescription(distribution.getDescription())\n",
        "sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)\n",
        "\n",
        "# %%\n",
        "\n",
        "output_dimension = fun.getOutputDimension()\n",
        "for i in range(output_dimension):\n",
        "    print(\"Output #\", i)\n",
        "    first_order = sensitivityAnalysis.getFirstOrderIndices(i)\n",
        "    total_order = sensitivityAnalysis.getTotalOrderIndices(i)\n",
        "    print(\"    First order indices: \", first_order)\n",
        "    print(\"    Total order indices: \", total_order)\n",
        "\n",
        "agg_first_order = sensitivityAnalysis.getAggregatedFirstOrderIndices()\n",
        "agg_total_order = sensitivityAnalysis.getAggregatedTotalOrderIndices()\n",
        "print(\"Agg. first order indices: \", agg_first_order)\n",
        "print(\"Agg. total order indices: \", agg_total_order)\n",
        "# %%\n",
        "\n",
        "graph = sensitivityAnalysis.draw()\n",
        "view = viewer.View(graph)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
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              "  #altair-viz-41fa8da7bd0d4102a7158a0d41529ca6.vega-embed {\n",
              "    width: 100%;\n",
              "    display: flex;\n",
              "  }\n",
              "\n",
              "  #altair-viz-41fa8da7bd0d4102a7158a0d41529ca6.vega-embed details,\n",
              "  #altair-viz-41fa8da7bd0d4102a7158a0d41529ca6.vega-embed details summary {\n",
              "    position: relative;\n",
              "  }\n",
              "</style>\n",
              "<div id=\"altair-viz-41fa8da7bd0d4102a7158a0d41529ca6\"></div>\n",
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              "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
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              "    if (outputDiv.id !== \"altair-viz-41fa8da7bd0d4102a7158a0d41529ca6\") {\n",
              "      outputDiv = document.getElementById(\"altair-viz-41fa8da7bd0d4102a7158a0d41529ca6\");\n",
              "    }\n",
              "    const paths = {\n",
              "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
              "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
              "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.16.3?noext\",\n",
              "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
              "    };\n",
              "\n",
              "    function maybeLoadScript(lib, version) {\n",
              "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
              "      return (VEGA_DEBUG[key] == version) ?\n",
              "        Promise.resolve(paths[lib]) :\n",
              "        new Promise(function(resolve, reject) {\n",
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              "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
              "          s.async = true;\n",
              "          s.onload = () => {\n",
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              "          };\n",
              "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
              "          s.src = paths[lib];\n",
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              "\n",
              "    function showError(err) {\n",
              "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
              "      throw err;\n",
              "    }\n",
              "\n",
              "    function displayChart(vegaEmbed) {\n",
              "      vegaEmbed(outputDiv, spec, embedOpt)\n",
              "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
              "    }\n",
              "\n",
              "    if(typeof define === \"function\" && define.amd) {\n",
              "      requirejs.config({paths});\n",
              "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
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              "      maybeLoadScript(\"vega\", \"5\")\n",
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              "        .catch(showError)\n",
              "        .then(() => displayChart(vegaEmbed));\n",
              "    }\n",
              "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300}, \"axisX\": {\"titleAnchor\": \"start\"}, \"axisY\": {\"labelFontSize\": 0, \"tickSize\": 0}, \"facet\": {\"spacing\": 0}, \"header\": {\"labelAngle\": 0, \"labelFontSize\": 0, \"titleFontSize\": 0}, \"legend\": {\"orient\": \"top\", \"titleFontSize\": 20, \"titleLimit\": 2000}, \"text\": {\"font\": \"Calibri\"}}, \"data\": {\"name\": \"data-dfae293df167c04d3241362449192b7e\"}, \"facet\": {\"row\": {\"field\": \"names\", \"type\": \"nominal\"}}, \"spec\": {\"layer\": [{\"mark\": {\"type\": \"circle\", \"size\": 80}, \"encoding\": {\"color\": {\"field\": \"cat\", \"title\": \"2.d: B77W - 5600km\", \"type\": \"nominal\"}, \"x\": {\"field\": \"points\", \"scale\": {\"domain\": [-0.15, 1]}, \"type\": \"quantitative\"}, \"y\": {\"field\": \"cat\", \"title\": null, \"type\": \"nominal\"}}}, {\"mark\": {\"type\": \"errorbar\", \"thickness\": 3}, \"encoding\": {\"color\": {\"field\": \"cat\", \"type\": \"nominal\"}, \"x\": {\"field\": \"lower_bound\", \"title\": \"index value\", \"type\": \"quantitative\"}, \"x2\": {\"field\": \"upper_bound\"}, \"y\": {\"field\": \"cat\", \"type\": \"nominal\"}}}, {\"mark\": {\"type\": \"text\", \"align\": \"right\"}, \"encoding\": {\"text\": {\"field\": \"names\", \"type\": \"nominal\"}, \"x\": {\"value\": 590}}}], \"height\": 30, \"width\": 600}, \"bounds\": \"flush\", \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.16.3.json\", \"datasets\": {\"data-dfae293df167c04d3241362449192b7e\": [{\"points\": 0.25407288777103937, \"lower_bound\": 0.20958382670264045, \"upper_bound\": 0.29643277184608613, \"names\": \"Load Factor\", \"cat\": \"First Order\"}, {\"points\": 0.13679723879561037, \"lower_bound\": 0.10260532401901187, \"upper_bound\": 0.1840203987871786, \"names\": \"AWP\", \"cat\": \"First Order\"}, {\"points\": -0.007213694711669968, \"lower_bound\": -0.04256642078020436, \"upper_bound\": 0.036656058569939066, \"names\": \"Descent Thrust\", \"cat\": \"First Order\"}, {\"points\": -0.007213694711669968, \"lower_bound\": -0.04256642078020436, \"upper_bound\": 0.036656058569939066, \"names\": \"Cas Climbing\", \"cat\": \"First Order\"}, {\"points\": -0.0006175829951913998, \"lower_bound\": -0.03659950603228771, \"upper_bound\": 0.043702092782192024, \"names\": \"Mach Climbing\", \"cat\": \"First Order\"}, {\"points\": -0.007808084098637036, \"lower_bound\": -0.04389291115011225, \"upper_bound\": 0.03642737195837245, \"names\": \"Cas Descent\", \"cat\": \"First Order\"}, {\"points\": -0.006275531624456549, \"lower_bound\": -0.041897774096931684, \"upper_bound\": 0.03737278587381618, \"names\": \"Mach Descent\", \"cat\": \"First Order\"}, {\"points\": 0.022760387505612115, \"lower_bound\": -0.01102091194854655, \"upper_bound\": 0.0671822268956439, \"names\": \"Cruise Range\", \"cat\": \"First Order\"}, {\"points\": 0.5179480952796209, \"lower_bound\": 0.46772776333032523, \"upper_bound\": 0.5685815408790305, \"names\": \"Cruise Altitude\", \"cat\": \"First Order\"}, {\"points\": 0.06668766966518025, \"lower_bound\": 0.030559417105038712, \"upper_bound\": 0.1083810175014617, \"names\": \"Cruise Mach\", \"cat\": \"First Order\"}, {\"points\": 0.2549262194131442, \"lower_bound\": 0.19558976365131087, \"upper_bound\": 0.3131025432324923, \"names\": \"Load Factor\", \"cat\": \"Total Order\"}, {\"points\": 0.1451961021530917, \"lower_bound\": 0.08669266066615738, \"upper_bound\": 0.19314094078929125, \"names\": \"AWP\", \"cat\": \"Total Order\"}, {\"points\": -1.7408670748701673e-11, \"lower_bound\": -0.05033977902823855, \"upper_bound\": 0.04621823993340661, \"names\": \"Descent Thrust\", \"cat\": \"Total Order\"}, {\"points\": -1.7408670748701673e-11, \"lower_bound\": -0.05033977902823855, \"upper_bound\": 0.04621823993340661, \"names\": \"Cas Climbing\", \"cat\": \"Total Order\"}, {\"points\": 0.003606006823357652, \"lower_bound\": -0.04790320828602466, \"upper_bound\": 0.05161164908765254, \"names\": \"Mach Climbing\", \"cat\": \"Total Order\"}, {\"points\": 0.003145479999555014, \"lower_bound\": -0.04723667228502753, \"upper_bound\": 0.05003014255704746, \"names\": \"Cas Descent\", \"cat\": \"Total Order\"}, {\"points\": 0.0037377292675263006, \"lower_bound\": -0.04704330772190578, \"upper_bound\": 0.05044800498227464, \"names\": \"Mach Descent\", \"cat\": \"Total Order\"}, {\"points\": 0.023186473525345714, \"lower_bound\": -0.02706726465410859, \"upper_bound\": 0.07044481633658786, \"names\": \"Cruise Range\", \"cat\": \"Total Order\"}, {\"points\": 0.530091461417777, \"lower_bound\": 0.4623926040375454, \"upper_bound\": 0.5848554028945564, \"names\": \"Cruise Altitude\", \"cat\": \"Total Order\"}, {\"points\": 0.0634417324511826, \"lower_bound\": 0.0070953676473690264, \"upper_bound\": 0.11633752425441536, \"names\": \"Cruise Mach\", \"cat\": \"Total Order\"}]}}, {\"mode\": \"vega-lite\"});\n",
              "</script>"
            ],
            "text/plain": [
              "alt.FacetChart(...)"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import altair as alt\n",
        "\n",
        "points_1 = list(sensitivityAnalysis.getFirstOrderIndices())\n",
        "points_t = list(sensitivityAnalysis.getTotalOrderIndices())\n",
        "points = points_1 + points_t\n",
        "lower_bound_1 = list(sensitivityAnalysis.getFirstOrderIndicesInterval().getLowerBound())\n",
        "lower_bound_t = list(sensitivityAnalysis.getTotalOrderIndicesInterval().getLowerBound())\n",
        "lower_bound = lower_bound_1 + lower_bound_t\n",
        "upper_bound_1 = list(sensitivityAnalysis.getFirstOrderIndicesInterval().getUpperBound())\n",
        "upper_bound_t = list(sensitivityAnalysis.getTotalOrderIndicesInterval().getUpperBound())\n",
        "upper_bound = upper_bound_1 + upper_bound_t\n",
        "names = list(distribution.getDescription()) * 2\n",
        "cat = [\"First Order\"] * len(points_1) + [\"Total Order\"] * len(points_1)\n",
        "\n",
        "\n",
        "source = pd.DataFrame(\n",
        "    {\n",
        "        \"points\": points,\n",
        "        \"lower_bound\": lower_bound,\n",
        "        \"upper_bound\": upper_bound,\n",
        "        \"names\": names,\n",
        "        \"cat\": cat,\n",
        "    }\n",
        ")\n",
        "\n",
        "# the base chart\n",
        "base = alt.Chart(source)\n",
        "\n",
        "means = (\n",
        "    base.mark_circle(size=80)\n",
        "    .encode(\n",
        "        alt.X(\"points\").scale(domain=(-0.15, 1)),\n",
        "        alt.Y(\"cat\", title=None),\n",
        "        # alt.Row(\"names\"),\n",
        "        alt.Color(\"cat\", title=f\"2.d: {ac_type} - 5600km\")\n",
        "        # yOffset=\"cat:N\",\n",
        "    )\n",
        "    .properties(width=600, height=30)\n",
        ")\n",
        "\n",
        "errorbars = base.mark_errorbar(thickness=3).encode(\n",
        "    alt.X(\"lower_bound\", title=\"index value\"),\n",
        "    alt.X2(\"upper_bound\"),\n",
        "    alt.Y(\"cat\"),\n",
        "    # alt.Row(\"names\"),\n",
        "    alt.Color(\"cat\")\n",
        "    # y=alt.Y(\"names\"),\n",
        "    # yOffset=\"cat:N\",\n",
        "    # color=alt.value(\"#4682b4\"),\n",
        ")\n",
        "names = base.mark_text(align=\"right\").encode(alt.Text(\"names\"), x=alt.value(590))\n",
        "\n",
        "chart = (\n",
        "    (means + errorbars + names)\n",
        "    .facet(row=\"names\")\n",
        "    .configure_axisY(labelFontSize=0, tickSize=0)\n",
        "    .configure_axisX(titleAnchor=\"start\")\n",
        "    .configure_header(titleFontSize=0, labelAngle=0, labelFontSize=0)\n",
        "    .configure_legend(orient=\"top\", titleFontSize=20, titleLimit=2000)\n",
        "    .configure_facet(spacing=0)\n",
        "    .configure_text(font=\"Calibri\")\n",
        "    .properties(bounds=\"flush\")\n",
        ")\n",
        "chart"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "sensiflow-RO-juNlQ-py3.10",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
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      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.12"
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