workflow_ekf.ipynb

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   "cell_type": "markdown",
   "id": "1f3d612b-e411-4ac3-a5e5-eb7e49b46c01",
   "metadata": {},
   "source": [
    "# Weighted Least Squares (WLS) state estimation for PV parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "58855b1c-bb15-473a-9844-449af185a388",
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     "end_time": "2024-09-12T20:44:59.647931Z",
     "start_time": "2024-09-12T20:44:58.235065Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import math\n",
    "from oedisi.types.data_types import MeasurementArray, AdmittanceMatrix, Topology\n",
    "import pyarrow.feather as feather\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "import glob\n",
    "\n",
    "ROOT = os.getcwd()\n",
    "OUTPUTS = f\"{ROOT}/outputs\"\n",
    "BUILDS = f\"{ROOT}/builds\"\n",
    "SCENARIOS = f\"{ROOT}/scenarios\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d845732-e22e-463a-8979-701c9f9c38c1",
   "metadata": {},
   "source": [
    "## Run OEDISI\n",
    "Each scenario is defined in the following folder structure:\n",
    "- algorithm / model / pv-level / es-level\n",
    "\n",
    "The following shows which scenarios have been generated using one of the algorithm generation scripts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d28a2fb5-b7ab-4678-a360-24e76e1e4c3e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-12T20:44:59.678661Z",
     "start_time": "2024-09-12T20:44:59.672491Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 /home/tylor/dev/oedisi_dopf/scenarios/ekf/ieee123_pmu/\n",
      "1 /home/tylor/dev/oedisi_dopf/scenarios/ekf/SFO/P6U/extreme/\n",
      "2 /home/tylor/dev/oedisi_dopf/scenarios/ekf/SFO/P6U/low/\n",
      "3 /home/tylor/dev/oedisi_dopf/scenarios/ekf/SFO/P6U/high/\n",
      "4 /home/tylor/dev/oedisi_dopf/scenarios/ekf/SFO/P6U/medium/\n",
      "5 /home/tylor/dev/oedisi_dopf/scenarios/ekf/SFO/P9U/extreme/\n",
      "6 /home/tylor/dev/oedisi_dopf/scenarios/ekf/SFO/P9U/low/\n",
      "7 /home/tylor/dev/oedisi_dopf/scenarios/ekf/SFO/P9U/high/\n",
      "8 /home/tylor/dev/oedisi_dopf/scenarios/ekf/SFO/P9U/medium/\n",
      "9 /home/tylor/dev/oedisi_dopf/scenarios/ekf/SFO/P3U/extreme/\n",
      "10 /home/tylor/dev/oedisi_dopf/scenarios/ekf/SFO/P3U/low/\n",
      "11 /home/tylor/dev/oedisi_dopf/scenarios/ekf/SFO/P3U/high/\n",
      "12 /home/tylor/dev/oedisi_dopf/scenarios/ekf/SFO/P3U/medium/\n",
      "13 /home/tylor/dev/oedisi_dopf/scenarios/ekf/SFO/P1U/extreme/\n",
      "14 /home/tylor/dev/oedisi_dopf/scenarios/ekf/SFO/P1U/low/\n",
      "15 /home/tylor/dev/oedisi_dopf/scenarios/ekf/SFO/P1U/high/\n",
      "16 /home/tylor/dev/oedisi_dopf/scenarios/ekf/SFO/P1U/medium/\n",
      "17 /home/tylor/dev/oedisi_dopf/scenarios/ekf/ieee123/\n"
     ]
    }
   ],
   "source": [
    "dir = []\n",
    "def list_files_scandir(path=\".\"):\n",
    "    with os.scandir(path) as entries:\n",
    "        for entry in entries:\n",
    "            if entry.is_file():\n",
    "                if \"system.json\" in entry.path:\n",
    "                    path, _ = entry.path.split(\"system.json\", 1)\n",
    "                    dir.append(path)\n",
    "            elif entry.is_dir():\n",
    "                list_files_scandir(entry.path)\n",
    "\n",
    "\n",
    "# Specify the directory path you want to start from\n",
    "directory_path = f\"{SCENARIOS}/ekf\"\n",
    "list_files_scandir(directory_path)\n",
    "for i, d in enumerate(dir):\n",
    "    print(i, d)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 30,
   "id": "fc1e6b02-6fcb-4418-87f6-df0545c64093",
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   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading the components defined in /home/tylor/dev/oedisi_dopf/scenarios/ekf/SFO/P1U/low/components.json\n",
      "Loading system json /home/tylor/dev/oedisi_dopf/scenarios/ekf/SFO/P1U/low/system.json\n",
      "Building system in /home/tylor/dev/oedisi_dopf/builds/ekf/SFO/P1U/low/\n"
     ]
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   "source": [
    "index = 14\n",
    "path = dir[index]\n",
    "_, scenario = path.split(SCENARIOS, 1)\n",
    "build = f\"{BUILDS}{scenario}\"\n",
    "output = f\"{OUTPUTS}{scenario}\"\n",
    "os.system(\n",
    "    f\"oedisi build --system {path}system.json --component-dict {path}components.json --target-directory {build}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c08366e8-7bce-4329-8119-947ae5363ba8",
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   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[warn] helics-cli's web interface is not installed. You may want to run `pip install \"helics[cli]\"`.\n",
      "[warn] helics-cli's observer functionality is not installed. You may want to run `pip install \"helics[cli]\"`.\n",
      "[info] Running federation: ekf_SFO/P1U\n",
      "[info] Running federate feeder as a background process\n",
      "[info] Running federate ekf as a background process\n",
      "[info] Running federate recorder_voltage_real as a background process\n",
      "[info] Running federate recorder_voltage_imag as a background process\n",
      "[info] Running federate recorder_power_real as a background process\n",
      "[info] Running federate sensor_power_real as a background process\n",
      "[info] Running federate recorder_power_imag as a background process\n",
      "[info] Running federate sensor_power_imag as a background process\n",
      "[info] Running federate recorder_voltage_mag as a background process\n",
      "[info] Running federate sensor_voltage_mag as a background process\n",
      "[info] Running federate recorder_voltage_angle as a background process\n",
      "[info] Running federate broker as a background process\n",
      "[info] Waiting for 12 processes to finish ...\n",
      "[info] Done.\n"
     ]
    },
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     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.system(\"pkill -9 helics_broker\")\n",
    "os.system(f\"oedisi run --runner {build}system_runner.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b21cdc3-2ba4-4236-bcdf-0853d4c9be06",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "01e8545a-8584-4eff-83b6-c03c40694f96",
   "metadata": {},
   "outputs": [],
   "source": [
    "topology = Topology.parse_file(f\"{output}/topology.json\")\n",
    "base_voltage_df = pd.DataFrame(\n",
    "    {\n",
    "        \"id\": topology.base_voltage_magnitudes.ids,\n",
    "        \"value\": topology.base_voltage_magnitudes.values,\n",
    "    }\n",
    ")\n",
    "base_voltage_df.set_index(\"id\", inplace=True)\n",
    "base_voltages = base_voltage_df[\"value\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7ff13bdc-9cfa-40a0-aa4d-7b0b52ca6473",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-12T20:45:00.242505Z",
     "start_time": "2024-09-12T20:45:00.043967Z"
    }
   },
   "outputs": [],
   "source": [
    "# read data\n",
    "v_real = feather.read_feather(f\"{output}/voltage_real.feather\")\n",
    "v_imag = feather.read_feather(f\"{output}/voltage_imag.feather\")\n",
    "\n",
    "# converter to complex\n",
    "true_voltages = v_real.drop(\"time\", axis=1).abs() + 1j * v_imag.drop(\"time\", axis=1)\n",
    "true_voltages[\"time\"] = pd.to_datetime(v_real[\"time\"], format=\"%Y-%m-%d %H:%M:%S\")\n",
    "true_voltages.set_index(\"time\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "03a396a8-0f46-4402-a32e-1449079aeb4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read data\n",
    "v_mag = feather.read_feather(f\"{output}/voltage_mag.feather\")\n",
    "v_angle = feather.read_feather(f\"{output}/voltage_angle.feather\")\n",
    "\n",
    "# filter missing nodes\n",
    "common_columns = set(v_mag.columns).intersection(v_angle.columns)\n",
    "v_mag = v_mag.filter(common_columns)\n",
    "v_angle = v_angle.filter(common_columns)\n",
    "\n",
    "# convert to complex\n",
    "v_rad = np.radians(v_angle.drop(\"time\", axis=1).to_numpy())\n",
    "est_voltages = v_mag.drop(\"time\", axis=1) * np.exp(1j * v_rad)\n",
    "est_voltages[\"time\"] = pd.to_datetime(v_mag[\"time\"], format=\"%Y-%m-%d %H:%M:%S\")\n",
    "est_voltages.set_index(\"time\", inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ff3015f-3f30-49e5-942d-e62b0633731c",
   "metadata": {},
   "source": [
    "## Process Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "66cbb764-c18a-4fe1-af44-ec8e68201f83",
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      "MSRE:  0.0013908755592516274\n"
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",
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{\"template\":{\"data\":{\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"white\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"white\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"#C8D4E3\",\"linecolor\":\"#C8D4E3\",\"minorgridcolor\":\"#C8D4E3\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"#C8D4E3\",\"linecolor\":\"#C8D4E3\",\"minorgridcolor\":\"#C8D4E3\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"an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                       {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('4ace5241-ab1a-473f-9598-e804ca505a3e');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
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       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts = dt.datetime(2017, 5, 1, 12)\n",
    "true = true_voltages.loc[ts].abs() / base_voltages\n",
    "est = est_voltages.loc[ts].abs() / base_voltages\n",
    "\n",
    "error = (true - est).abs()\n",
    "print(\"MSRE: \", sum(error)/len(error))\n",
    "\n",
    "fig = make_subplots(rows=1, cols=2)\n",
    "fig.add_trace(\n",
    "    go.Scatter(name=\"true\", x=true.index, y=true.values, mode=\"markers\"), row=1, col=1\n",
    ")\n",
    "fig.add_trace(\n",
    "    go.Scatter(name=\"est\", x=est.index, y=est.values, mode=\"markers\"), row=1, col=1\n",
    ")\n",
    "fig.add_trace(\n",
    "    go.Scatter(name=\"error\", x=error.index, y=error.values, mode=\"markers\"),\n",
    "    row=1,\n",
    "    col=2,\n",
    ")\n",
    "fig.update_layout(template=\"plotly_white\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "12334660-0ce7-4892-ac76-a362804ec1d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time\n",
      "2017-05-01 04:01:12    1.024624\n",
      "2017-05-01 06:00:00    1.024310\n",
      "2017-05-01 08:00:00    1.023730\n",
      "2017-05-01 10:00:00    1.023796\n",
      "2017-05-01 12:00:00    1.023836\n",
      "2017-05-01 14:00:00    1.024401\n",
      "2017-05-01 16:00:00    1.024529\n",
      "2017-05-01 18:00:00    1.024490\n",
      "Name: P1ULV7531.3, dtype: float64\n",
      "time\n",
      "2017-05-01 06:00:00    1.023657\n",
      "2017-05-01 06:00:00    1.024222\n",
      "2017-05-01 10:00:00    1.024176\n",
      "2017-05-01 12:00:00    1.024312\n",
      "2017-05-01 14:00:00    1.024381\n",
      "2017-05-01 16:00:00    1.024486\n",
      "Name: P1ULV7531.3, dtype: float64\n"
     ]
    },
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     "data": {
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          "2017-05-01T06:00:00",
          "2017-05-01T08:00:00",
          "2017-05-01T10:00:00",
          "2017-05-01T12:00:00",
          "2017-05-01T14:00:00",
          "2017-05-01T16:00:00",
          "2017-05-01T18:00:00"
         ],
         "xaxis": "x",
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         "x": [
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          "2017-05-01T12:00:00",
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         "xaxis": "x",
         "y": [
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         "mode": "markers",
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         "x": [
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          "2017-05-01T18:00:00"
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         "xaxis": "x2",
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         "yaxis": "y2"
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       "layout": {
        "autosize": true,
        "template": {
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{\"template\":{\"data\":{\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"white\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"white\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"#C8D4E3\",\"linecolor\":\"#C8D4E3\",\"minorgridcolor\":\"#C8D4E3\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"#C8D4E3\",\"linecolor\":\"#C8D4E3\",\"minorgridcolor\":\"#C8D4E3\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"white\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"#C8D4E3\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"white\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"#EBF0F8\",\"linecolor\":\"#EBF0F8\",\"ticks\":\"\"},\"bgcolor\":\"white\",\"radialaxis\":{\"gridcolor\":\"#EBF0F8\",\"linecolor\":\"#EBF0F8\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"white\",\"gridcolor\":\"#DFE8F3\",\"gridwidth\":2,\"linecolor\":\"#EBF0F8\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#EBF0F8\"},\"yaxis\":{\"backgroundcolor\":\"white\",\"gridcolor\":\"#DFE8F3\",\"gridwidth\":2,\"linecolor\":\"#EBF0F8\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#EBF0F8\"},\"zaxis\":{\"backgroundcolor\":\"white\",\"gridcolor\":\"#DFE8F3\",\"gridwidth\":2,\"linecolor\":\"#EBF0F8\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#EBF0F8\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"#DFE8F3\",\"linecolor\":\"#A2B1C6\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"#DFE8F3\",\"linecolor\":\"#A2B1C6\",\"ticks\":\"\"},\"bgcolor\":\"white\",\"caxis\":{\"gridcolor\":\"#DFE8F3\",\"linecolor\":\"#A2B1C6\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#EBF0F8\",\"linecolor\":\"#EBF0F8\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#EBF0F8\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#EBF0F8\",\"linecolor\":\"#EBF0F8\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#EBF0F8\",\"zerolinewidth\":2}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,0.45]},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0]},\"xaxis2\":{\"anchor\":\"y2\",\"domain\":[0.55,1.0]},\"yaxis2\":{\"anchor\":\"x2\",\"domain\":[0.0,1.0]}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('281864c9-3173-4b16-8412-686d3b8e3398');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "true = true_voltages.abs() / base_voltages\n",
    "est = est_voltages.abs() / base_voltages\n",
    "\n",
    "node = random.choice(true.columns)\n",
    "true = true.loc[:,node]\n",
    "est = est.loc[:,node]\n",
    "\n",
    "error = (true - est).abs()\n",
    "\n",
    "fig = make_subplots(rows=1, cols=2)\n",
    "fig.add_trace(\n",
    "    go.Scatter(name=\"true\", x=true.index, y=true.values, mode=\"markers\"), row=1, col=1\n",
    ")\n",
    "fig.add_trace(\n",
    "    go.Scatter(name=\"est\", x=est.index, y=est.values, mode=\"markers\"), row=1, col=1\n",
    ")\n",
    "fig.add_trace(\n",
    "    go.Scatter(name=\"error\", x=error.index, y=error.values, mode=\"markers\"),\n",
    "    row=1,\n",
    "    col=2,\n",
    ")\n",
    "fig.update_layout(template=\"plotly_white\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5228e217-067e-4ed0-92d2-fe159d69d0d9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8357fc8-3a1c-4e74-8db1-d306b3071603",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
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   "name": "python",
   "nbconvert_exporter": "python",
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