docs/examples/pandapower_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PandaPower conversion\n",
    "\n",
    "This example illustrates conversion from PandaPower to power-grid-model input data. \n",
    "We can then calculate power-flow with it or convert to a different formats like PGM JSON."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Load the PandaPower Data\n",
    "\n",
    "For this example we will construct a minimal pandapower network.\n",
    "\n",
    "\n",
    "                                                      \n",
    "      (ext_grid #1)      shunt - [104]  - trafo_3w - [105] - (sym_gen + asym_gen + asym_load + ward + motor)\n",
    "       |                                    |\n",
    "      [101] ---trafo- [102] ------------- [103]\n",
    "       |                                    |\n",
    "      -/-                               (load #31)\n",
    "       |\n",
    "      [106]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandapower as pp\n",
    "\n",
    "def pandapower_simple_grid():\n",
    "    net = pp.create_empty_network(f_hz=50)\n",
    "    pp.create_bus(net, index=101, vn_kv=110)\n",
    "    pp.create_bus(net, index=102, vn_kv=20)\n",
    "    pp.create_bus(net, index=103, vn_kv=20)\n",
    "    pp.create_bus(net, index=104, vn_kv=30.1)\n",
    "    pp.create_bus(net, index=105, vn_kv=60)\n",
    "    pp.create_bus(net, index=106, vn_kv=110)\n",
    "    pp.create_ext_grid(net, index=1, in_service=True, bus=101, vm_pu=1, s_sc_max_mva=1e10, rx_max=0, va_degree=0)\n",
    "    pp.create_transformer_from_parameters(net,index=101,hv_bus=101,lv_bus=102,i0_percent=3.0,pfe_kw=11.6,vkr_percent=10.22,sn_mva=40,vn_lv_kv=20.0,vn_hv_kv=110.0,vk_percent=17.8,vector_group=\"Dyn\",shift_degree=30,tap_side=\"hv\",tap_pos=2,tap_min=-1,tap_max=3,tap_step_percent=2,tap_neutral=1,parallel=2,)\n",
    "    pp.create_line(net, index=101, from_bus=103, to_bus=102, length_km=1.23, parallel=2, df=0.2, std_type=\"NAYY 4x150 SE\")\n",
    "    pp.create_load(net, index=101, bus=103, p_mw=2.5, q_mvar=0.24, const_i_percent=26.0, const_z_percent=51.0, cos_phi=2)\n",
    "    pp.create_switch(net, index=101, et=\"l\", bus=103, element=101, closed=True)\n",
    "    pp.create_switch(net, index=3021, et=\"b\", bus=101, element=106, closed=True)\n",
    "    pp.create_switch(net, index=321, et=\"t\", bus=101, element=101, closed=True)\n",
    "    pp.create_shunt(net, index=1201, in_service=True, bus=104, p_mw=0.1, q_mvar=0.55, step=3)\n",
    "    pp.create_sgen(net, index=31, bus=105, p_mw=1.21, q_mvar=0.81)\n",
    "    pp.create_asymmetric_sgen(net, index=32, bus=105, p_a_mw=0.1, p_b_mw=0.2, p_c_mw=3, q_a_mvar=0.01, q_b_mvar=0.01, q_c_mvar=0.01)\n",
    "    pp.create_asymmetric_load(net, index=33, bus=105, p_a_mw=0.1, p_b_mw=0.2, p_c_mw=3, q_a_mvar=0.01, q_b_mvar=0.01, q_c_mvar=0.01)\n",
    "    pp.create_ward(net, index=34, bus=105, ps_mw=0.1, qs_mvar=0.1, pz_mw=0.1, qz_mvar=0.1)\n",
    "    pp.create_motor(net, bus=105, index=12, pn_mech_mw=0.1, cos_phi=0.9, loading_percent=80, efficiency_percent=90, scaling=0.8)\n",
    "    pp.create_transformer3w_from_parameters(net,index=102,hv_bus=103,mv_bus=105,lv_bus=104,in_service=True,vn_hv_kv=20.0,vn_mv_kv=60.0,vn_lv_kv=30.1,sn_hv_mva=40,sn_mv_mva=100,sn_lv_mva=50,vk_hv_percent=10,vk_mv_percent=11,vk_lv_percent=12,vkr_hv_percent=1,vkr_mv_percent=2,vkr_lv_percent=4,i0_percent=0.1,pfe_kw=10,vector_group=\"Dyny\",shift_mv_degree=30,shift_lv_degree=30,tap_side=\"lv\",tap_pos=2,tap_min=1,tap_max=3,tap_step_percent=3,tap_neutral=2)\n",
    "    return net"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instantiate the converter, optionally with a source file path.\n",
    "Then use `load_input_data()` to load the data and convert it to power-grid-model data.\n",
    "The additional information that is not used in the powerflow calculation but may be useful to link the results to the source data is stored in `extra_info`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture cap --no-stderr\n",
    "\n",
    "from power_grid_model_io.converters import PandaPowerConverter\n",
    "\n",
    "pp_net = pandapower_simple_grid()\n",
    "converter = PandaPowerConverter()\n",
    "input_data, extra_info = converter.load_input_data(pp_net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's investigate the data we have converted, for one of the components: `lines`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(6, 2, 1, 1, 1, 0.12792, 0.0492, 6.4206e-07, 0., nan, nan, nan, nan, 108.)],\n",
       "      dtype={'names': ['id', 'from_node', 'to_node', 'from_status', 'to_status', 'r1', 'x1', 'c1', 'tan1', 'r0', 'x0', 'c0', 'tan0', 'i_n'], 'formats': ['<i4', '<i4', '<i4', 'i1', 'i1', '<f8', '<f8', '<f8', '<f8', '<f8', '<f8', '<f8', '<f8', '<f8'], 'offsets': [0, 4, 8, 12, 13, 16, 24, 32, 40, 48, 56, 64, 72, 80], 'itemsize': 88, 'aligned': True})"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>from_node</th>\n",
       "      <th>to_node</th>\n",
       "      <th>from_status</th>\n",
       "      <th>to_status</th>\n",
       "      <th>r1</th>\n",
       "      <th>x1</th>\n",
       "      <th>c1</th>\n",
       "      <th>tan1</th>\n",
       "      <th>r0</th>\n",
       "      <th>x0</th>\n",
       "      <th>c0</th>\n",
       "      <th>tan0</th>\n",
       "      <th>i_n</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.12792</td>\n",
       "      <td>0.0492</td>\n",
       "      <td>6.420600e-07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  from_node  to_node  from_status  to_status       r1      x1  \\\n",
       "0   6          2        1            1          1  0.12792  0.0492   \n",
       "\n",
       "             c1  tan1  r0  x0  c0  tan0    i_n  \n",
       "0  6.420600e-07   0.0 NaN NaN NaN   NaN  108.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{6: {'id_reference': {'table': 'line', 'index': 101},\n",
       "  'from_node': 2,\n",
       "  'to_node': 1}}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# The node data is stored as a numpy structured array in input_data[\"line\"]\n",
    "display(input_data[\"line\"])\n",
    "\n",
    "# We can use pandas to display the data in a convenient tabular format\n",
    "display(pd.DataFrame(input_data[\"line\"]))\n",
    "\n",
    "# The original indices are stored in the extra_data dictionary\n",
    "display({i: extra_info[i] for i in input_data[\"line\"][\"id\"]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Validate the data\n",
    "Before we run a power flow calculation, it is wise validate the data. The most basic method is to use `assert_valid_input_data()`, which will raise a `ValueError` when the data is invalid. For more details on data validation, please consult the [validation Example](https://github.com/alliander-opensource/power-grid-model/blob/main/docs/examples/Validation%20Examples.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from power_grid_model import CalculationType\n",
    "from power_grid_model.validation import assert_valid_input_data\n",
    "\n",
    "assert_valid_input_data(input_data, calculation_type=CalculationType.power_flow, symmetric=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Run the calculation\n",
    "\n",
    "Run powerflow calculation with the `input_data` and show the results for `nodes`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>energized</th>\n",
       "      <th>u_pu</th>\n",
       "      <th>u</th>\n",
       "      <th>u_angle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>109999.999962</td>\n",
       "      <td>-1.798666e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.973746</td>\n",
       "      <td>19474.919873</td>\n",
       "      <td>-5.239008e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.973014</td>\n",
       "      <td>19460.275746</td>\n",
       "      <td>-5.237224e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.969550</td>\n",
       "      <td>29183.446690</td>\n",
       "      <td>-1.045187e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.971998</td>\n",
       "      <td>58319.874965</td>\n",
       "      <td>-1.044829e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>109999.999962</td>\n",
       "      <td>-1.798666e-10</td>\n",
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      ],
      "text/plain": [
       "   id  energized      u_pu              u       u_angle\n",
       "0   0          1  1.000000  109999.999962 -1.798666e-10\n",
       "1   1          1  0.973746   19474.919873 -5.239008e-01\n",
       "2   2          1  0.973014   19460.275746 -5.237224e-01\n",
       "3   3          1  0.969550   29183.446690 -1.045187e+00\n",
       "4   4          1  0.971998   58319.874965 -1.044829e+00\n",
       "5   5          1  1.000000  109999.999962 -1.798666e-10"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from power_grid_model import PowerGridModel\n",
    "\n",
    "pgm = PowerGridModel(input_data=input_data)\n",
    "output_data = pgm.calculate_power_flow()\n",
    "\n",
    "display(pd.DataFrame(output_data[\"node\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross referencing objects\n",
    "The converter has generated unique numerical IDs for all the components in the pandapower net, in fact for some special components like `loads` , multiple PGM components have been created, each with their own numerical ID. To find out which component belongs to which id, some helper functions have been defined:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PGM object #4: {'table': 'bus', 'index': 105}\n",
      "Trafo with index=101: 12\n"
     ]
    }
   ],
   "source": [
    "print(\"PGM object #4:\", converter.lookup_id(4))\n",
    "\n",
    "print(\"Trafo with index=101:\", converter.get_id(\"trafo\", 101))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving the data as a JSON file\n",
    "The data can be stored in a json file using the PgmJsonConverter. The file will be saved in the `destination_file` path supplied in the constructor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from power_grid_model_io.converters import PgmJsonConverter\n",
    "    \n",
    "input_file = \"data/pandapower/example_simple_input.json\"\n",
    "output_file = \"data/pandapower/example_simple_output.json\"\n",
    "\n",
    "PgmJsonConverter(destination_file=input_file).save(data=input_data, extra_info=extra_info)\n",
    "PgmJsonConverter(destination_file=output_file).save(data=output_data, extra_info=extra_info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For debugging purposes, let's check the output JSON. Notice that the node names are added to the nodes data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<pre style='max-height: 160px; white-space: pre'>{\n",
       "  \"node\":\n",
       "    [\n",
       "      {\"id\": 0, \"u_rated\": 110000.0, \"id_reference\": {\"table\": \"bus\", \"index\": 101}},\n",
       "      {\"id\": 1, \"u_rated\": 20000.0, \"id_reference\": {\"table\": \"bus\", \"index\": 102}},\n",
       "      {\"id\": 2, \"u_rated\": 20000.0, \"id_reference\": {\"table\": \"bus\", \"index\": 103}},\n",
       "      {\"id\": 3, \"u_rated\": 30100.0, \"id_reference\": {\"table\": \"bus\", \"index\": 104}},\n",
       "      {\"id\": 4, \"u_rated\": 60000.0, \"id_reference\": {\"table\": \"bus\", \"index\": 105}},\n",
       "      {\"id\": 5, \"u_rated\": 110000.0, \"id_reference\": {\"table\": \"bus\", \"index\": 106}}\n",
       "    ],\n",
       "  \"line\":\n",
       "    [\n",
       "      {\"id\": 6, \"from_node\": 2, \"to_node\": 1, \"from_status\": 1, \"to_status\": 1, \"r1\": 0.12792, \"x1\": 0.0492, \"c1\": 6.4206e-07, \"tan1\": 0.0, \"i_n\": 108.0, \"id_reference\": {\"table\": \"line\", \"index\": 101}}\n",
       "    ],\n",
       "  \"source\":\n",
       "    [\n",
       "      {\"id\": 7, \"node\": 0, \"status\": 1, \"u_ref\": 1.0, \"u_ref_angle\": 0.0, \"sk\": 1e+16, \"rx_ratio\": 0.0, \"id_reference\": {\"table\": \"ext_grid\", \"index\": 1}}\n",
       "    ],\n",
       "  \"sym_load\":\n",
       "    [\n",
       "      {\"id\": 8, \"node\": 2, \"status\": 1, \"type\": 0, \"p_specified\": 575000.0, \"q_specified\": 55200.0, \"id_reference\": {\"table\": \"load\", \"name\": \"const_power\", \"index\": 101}},\n",
       "      {\"id\": 9, \"node\": 2, \"status\": 1, \"type\": 1, \"p_specified\": 1275000.0, \"q_specified\": 122400.0, \"id_reference\": {\"table\": \"load\", \"name\": \"const_impedance\", \"index\": 101}},\n",
       "      {\"id\": 10, \"node\": 2, \"status\": 1, \"type\": 2, \"p_specified\": 650000.0, \"q_specified\": 62400.0, \"id_reference\": {\"table\": \"load\", \"name\": \"const_current\", \"index\": 101}},\n",
       "      {\"id\": 18, \"node\": 4, \"status\": 1, \"type\": 0, \"p_specified\": 100000.0, \"q_specified\": 100000.0, \"id_reference\": {\"table\": \"ward\", \"name\": \"ward_const_power_load\", \"index\": 34}},\n",
       "      {\"id\": 19, \"node\": 4, \"status\": 1, \"type\": 1, \"p_specified\": 100000.0, \"q_specified\": 100000.0, \"id_reference\": {\"table\": \"ward\", \"name\": \"ward_const_impedance_load\", \"index\": 34}},\n",
       "      {\"id\": 20, \"node\": 4, \"status\": 1, \"type\": 0, \"p_specified\": 71111.11111111111, \"q_specified\": 34440.68301069173, \"id_reference\": {\"table\": \"motor\", \"name\": \"motor_load\", \"index\": 12}}\n",
       "    ],\n",
       "  \"shunt\":\n",
       "    [\n",
       "      {\"id\": 11, \"node\": 3, \"status\": 1, \"g1\": 0.00033112217304444763, \"b1\": -0.001821171951744462, \"id_reference\": {\"table\": \"shunt\", \"index\": 1201}}\n",
       "    ],\n",
       "  \"transformer\":\n",
       "    [\n",
       "      {\"id\": 12, \"from_node\": 0, \"to_node\": 1, \"from_status\": 1, \"to_status\": 1, \"u1\": 110000.0, \"u2\": 20000.0, \"sn\": 80000000.0, \"uk\": 0.17800000000000002, \"pk\": 8176000.0, \"i0\": 0.03, \"p0\": 23200.0, \"winding_from\": 2, \"winding_to\": 1, \"clock\": 1, \"tap_side\": 0, \"tap_pos\": 2, \"tap_min\": -1, \"tap_max\": 3, \"tap_nom\": 1, \"tap_size\": 2200.0, \"id_reference\": {\"table\": \"trafo\", \"index\": 101}}\n",
       "    ],\n",
       "  \"sym_gen\":\n",
       "    [\n",
       "      {\"id\": 13, \"node\": 4, \"status\": 1, \"type\": 0, \"p_specified\": 1210000.0, \"q_specified\": 810000.0, \"id_reference\": {\"table\": \"sgen\", \"index\": 31}}\n",
       "    ],\n",
       "  \"three_winding_transformer\":\n",
       "    [\n",
       "      {\"id\": 14, \"node_1\": 2, \"node_2\": 4, \"node_3\": 3, \"status_1\": 1, \"status_2\": 1, \"status_3\": 1, \"u1\": 20000.0, \"u2\": 60000.0, \"u3\": 30100.0, \"sn_1\": 40000000.0, \"sn_2\": 100000000.0, \"sn_3\": 50000000.0, \"uk_12\": 0.1, \"uk_13\": 0.12, \"uk_23\": 0.11, \"pk_12\": 400000.0, \"pk_13\": 1600000.0, \"pk_23\": 1000000.0, \"i0\": 0.001, \"p0\": 10000.0, \"winding_1\": 2, \"winding_2\": 1, \"winding_3\": 0, \"clock_12\": 1, \"clock_13\": 1, \"tap_side\": 2, \"tap_pos\": 2, \"tap_min\": 1, \"tap_max\": 3, \"tap_nom\": 2, \"tap_size\": 903.0, \"id_reference\": {\"table\": \"trafo3w\", \"index\": 102}}\n",
       "    ],\n",
       "  \"link\":\n",
       "    [\n",
       "      {\"id\": 15, \"from_node\": 0, \"to_node\": 5, \"from_status\": 1, \"to_status\": 1, \"id_reference\": {\"table\": \"switch\", \"name\": \"bus_to_bus\", \"index\": 3021}}\n",
       "    ],\n",
       "  \"asym_load\":\n",
       "    [\n",
       "      {\"id\": 16, \"node\": 4, \"status\": 1, \"type\": 0, \"p_specified\": [100000.0, 200000.0, 3000000.0], \"q_specified\": [10000.0, 10000.0, 10000.0], \"id_reference\": {\"table\": \"asymmetric_load\", \"index\": 33}}\n",
       "    ],\n",
       "  \"asym_gen\":\n",
       "    [\n",
       "      {\"id\": 17, \"node\": 4, \"status\": 1, \"type\": 0, \"p_specified\": [100000.0, 200000.0, 3000000.0], \"q_specified\": [10000.0, 10000.0, 10000.0], \"id_reference\": {\"table\": \"asymmetric_sgen\", \"index\": 32}}\n",
       "    ]\n",
       "}\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<pre style='max-height: 160px; white-space: pre'>{\n",
       "  \"asym_gen\":\n",
       "    [\n",
       "      {\"id\": 17, \"energized\": 1, \"p\": 3300000.0000000005, \"q\": 30000.000000000004, \"i\": 32.670416687651056, \"s\": 3300136.36081905, \"pf\": 0.9999586802470745, \"id_reference\": {\"table\": \"asymmetric_sgen\", \"index\": 32}, \"node\": 4}\n",
       "    ],\n",
       "  \"asym_load\":\n",
       "    [\n",
       "      {\"id\": 16, \"energized\": 1, \"p\": 3300000.0000000005, \"q\": 30000.000000000004, \"i\": 32.670416687651056, \"s\": 3300136.36081905, \"pf\": 0.9999586802470745, \"id_reference\": {\"table\": \"asymmetric_load\", \"index\": 33}, \"node\": 4}\n",
       "    ],\n",
       "  \"line\":\n",
       "    [\n",
       "      {\"id\": 6, \"energized\": 1, \"loading\": 0.5930290459172566, \"p_from\": -1763554.1135053905, \"q_from\": -1245081.6865976173, \"i_from\": 64.0471369590637, \"s_from\": 2158784.7316401307, \"p_to\": 1765096.6771171533, \"q_to\": 1169229.7700732222, \"i_to\": 62.767063741949805, \"s_to\": 2117230.39246925, \"id_reference\": {\"table\": \"line\", \"index\": 101}, \"from_node\": 2, \"to_node\": 1}\n",
       "    ],\n",
       "  \"link\":\n",
       "    [\n",
       "      {\"id\": 15, \"energized\": 1, \"loading\": 0.0, \"p_from\": 0.0, \"q_from\": -0.0, \"i_from\": 0.0, \"s_from\": 0.0, \"p_to\": 0.0, \"q_to\": -0.0, \"i_to\": 0.0, \"s_to\": 0.0, \"id_reference\": {\"table\": \"switch\", \"name\": \"bus_to_bus\", \"index\": 3021}, \"from_node\": 0, \"to_node\": 5}\n",
       "    ],\n",
       "  \"node\":\n",
       "    [\n",
       "      {\"id\": 0, \"energized\": 1, \"u_pu\": 0.9999999996523372, \"u\": 109999.99996175709, \"u_angle\": -1.798666001063812e-10, \"id_reference\": {\"table\": \"bus\", \"index\": 101}},\n",
       "      {\"id\": 1, \"energized\": 1, \"u_pu\": 0.97374599364253, \"u\": 19474.9198728506, \"u_angle\": -0.5239008142519944, \"id_reference\": {\"table\": \"bus\", \"index\": 102}},\n",
       "      {\"id\": 2, \"energized\": 1, \"u_pu\": 0.9730137872763268, \"u\": 19460.275745526535, \"u_angle\": -0.5237223960629773, \"id_reference\": {\"table\": \"bus\", \"index\": 103}},\n",
       "      {\"id\": 3, \"energized\": 1, \"u_pu\": 0.9695497239124967, \"u\": 29183.44668976615, \"u_angle\": -1.04518706304991, \"id_reference\": {\"table\": \"bus\", \"index\": 104}},\n",
       "      {\"id\": 4, \"energized\": 1, \"u_pu\": 0.9719979160849105, \"u\": 58319.87496509463, \"u_angle\": -1.044828700708015, \"id_reference\": {\"table\": \"bus\", \"index\": 105}},\n",
       "      {\"id\": 5, \"energized\": 1, \"u_pu\": 0.9999999996523372, \"u\": 109999.99996175709, \"u_angle\": -1.798666001063812e-10, \"id_reference\": {\"table\": \"bus\", \"index\": 106}}\n",
       "    ],\n",
       "  \"shunt\":\n",
       "    [\n",
       "      {\"id\": 11, \"energized\": 1, \"p\": 282008.0001416396, \"q\": 1551044.0007790178, \"i\": 31.188122375690188, \"s\": 1576472.6462886913, \"pf\": 0.17888543819998318, \"id_reference\": {\"table\": \"shunt\", \"index\": 1201}, \"node\": 3}\n",
       "    ],\n",
       "  \"source\":\n",
       "    [\n",
       "      {\"id\": 7, \"energized\": 1, \"p\": 1798666.0004384827, \"q\": 3476627.872944676, \"i\": 20.545012571488463, \"s\": 3914350.6163946516, \"pf\": 0.4595055928063901, \"id_reference\": {\"table\": \"ext_grid\", \"index\": 1}, \"node\": 0}\n",
       "    ],\n",
       "  \"sym_gen\":\n",
       "    [\n",
       "      {\"id\": 13, \"energized\": 1, \"p\": 1210000.0, \"q\": 810000.0, \"i\": 14.414885714493, \"s\": 1456090.6565183362, \"pf\": 0.8309922150680065, \"id_reference\": {\"table\": \"sgen\", \"index\": 31}, \"node\": 4}\n",
       "    ],\n",
       "  \"sym_load\":\n",
       "    [\n",
       "      {\"id\": 8, \"energized\": 1, \"p\": 575000.0, \"q\": 55200.0, \"i\": 17.13761141048308, \"s\": 577643.5232909652, \"pf\": 0.9954236078405164, \"id_reference\": {\"table\": \"load\", \"name\": \"const_power\", \"index\": 101}, \"node\": 2},\n",
       "      {\"id\": 9, \"energized\": 1, \"p\": 1207113.6835430216, \"q\": 115882.91362013007, \"i\": 35.977469977108086, \"s\": 1212663.3063904808, \"pf\": 0.9954236078405161, \"id_reference\": {\"table\": \"load\", \"name\": \"const_impedance\", \"index\": 101}, \"node\": 2},\n",
       "      {\"id\": 10, \"energized\": 1, \"p\": 632458.9617296124, \"q\": 60716.06032604279, \"i\": 18.850149424695108, \"s\": 635366.6486790245, \"pf\": 0.9954236078405163, \"id_reference\": {\"table\": \"load\", \"name\": \"const_current\", \"index\": 101}, \"node\": 2},\n",
       "      {\"id\": 18, \"energized\": 1, \"p\": 99999.99999999999, \"q\": 99999.99999999999, \"i\": 1.4000314325372134, \"s\": 141421.3562373095, \"pf\": 0.7071067811865475, \"id_reference\": {\"table\": \"ward\", \"name\": \"ward_const_power_load\", \"index\": 34}, \"node\": 4},\n",
       "      {\"id\": 19, \"energized\": 1, \"p\": 94477.99488734086, \"q\": 94477.99488734086, \"i\": 1.3227216252536735, \"s\": 133612.0617154934, \"pf\": 0.7071067811865475, \"id_reference\": {\"table\": \"ward\", \"name\": \"ward_const_impedance_load\", \"index\": 34}, \"node\": 4},\n",
       "      {\"id\": 20, \"energized\": 1, \"p\": 71111.11111111111, \"q\": 34440.68301069173, \"i\": 0.7821998773897324, \"s\": 79012.34567901235, \"pf\": 0.9, \"id_reference\": {\"table\": \"motor\", \"name\": \"motor_load\", \"index\": 12}, \"node\": 4}\n",
       "    ],\n",
       "  \"three_winding_transformer\":\n",
       "    [\n",
       "      {\"id\": 14, \"energized\": 1, \"loading\": 0.03152945292576947, \"p_1\": -651018.5317680555, \"q_1\": 1013282.7126517809, \"i_1\": 35.73216058618558, \"s_1\": 1204394.8623538653, \"p_2\": 944410.894001629, \"q_2\": 581081.3221020879, \"i_2\": 10.977386752839678, \"s_2\": 1108858.6202058704, \"p_3\": -282008.00014178595, \"q_3\": -1551044.0007787698, \"i_3\": 31.18812237568588, \"s_3\": 1576472.6462884734, \"id_reference\": {\"table\": \"trafo3w\", \"index\": 102}, \"node_1\": 2, \"node_2\": 4, \"node_3\": 3}\n",
       "    ],\n",
       "  \"transformer\":\n",
       "    [\n",
       "      {\"id\": 12, \"energized\": 1, \"loading\": 0.04892939318768632, \"p_from\": 1798665.9834270997, \"q_from\": 3476628.8259518775, \"i_from\": 20.545016973103277, \"s_from\": 3914351.4550149054, \"p_to\": -1765096.6771171836, \"q_to\": -1169229.7700735242, \"i_to\": 62.76706374195551, \"s_to\": 2117230.3924694424, \"id_reference\": {\"table\": \"trafo\", \"index\": 101}, \"from_node\": 0, \"to_node\": 1}\n",
       "    ]\n",
       "}\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "from IPython.display import display, Markdown\n",
    "\n",
    "with Path(input_file).open() as json_file:\n",
    "    display(Markdown(f\"<pre style='max-height: 160px; white-space: pre'>{json_file.read()}</div>\"))\n",
    "\n",
    "with Path(output_file).open() as json_file:\n",
    "    display(Markdown(f\"<pre style='max-height: 160px; white-space: pre'>{json_file.read()}</div>\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Converting output data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we convert the output data, lets run the powerflow in pandapower so we can compare results for demostration purpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "numba cannot be imported and numba functions are disabled.\n",
      "Probably the execution is slow.\n",
      "Please install numba to gain a massive speedup.\n",
      "(or if you prefer slow execution, set the flag numba=False to avoid this warning!)\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vm_pu</th>\n",
       "      <th>va_degree</th>\n",
       "      <th>p_mw</th>\n",
       "      <th>q_mvar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.798661</td>\n",
       "      <td>-3.476701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>0.973746</td>\n",
       "      <td>-30.017299</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>0.973014</td>\n",
       "      <td>-30.007075</td>\n",
       "      <td>2.414572</td>\n",
       "      <td>0.231799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>0.969519</td>\n",
       "      <td>-59.886045</td>\n",
       "      <td>0.281990</td>\n",
       "      <td>1.550945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>0.972063</td>\n",
       "      <td>-59.860441</td>\n",
       "      <td>-0.944398</td>\n",
       "      <td>-0.581069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        vm_pu  va_degree      p_mw    q_mvar\n",
       "101  1.000000   0.000000 -1.798661 -3.476701\n",
       "102  0.973746 -30.017299  0.000000  0.000000\n",
       "103  0.973014 -30.007075  2.414572  0.231799\n",
       "104  0.969519 -59.886045  0.281990  1.550945\n",
       "105  0.972063 -59.860441 -0.944398 -0.581069\n",
       "106  1.000000   0.000000  0.000000  0.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pp.runpp(pp_net,  trafo_model='pi', trafo_loading='power', calculate_voltage_angles=True)\n",
    "display(pp_net.res_bus)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the results of powerflow in the pandapower net, convert the result from power-grid-model powerflow ie. `output_data` from previous section to the pandapower `res_*` dataframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['res_bus', 'res_line', 'res_ext_grid', 'res_load', 'res_shunt', 'res_trafo', 'res_sgen', 'res_trafo3w', 'res_ward', 'res_motor', 'res_asymmetric_sgen', 'res_asymmetric_load'])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------Bus results--------\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vm_pu</th>\n",
       "      <th>va_degree</th>\n",
       "      <th>p_mw</th>\n",
       "      <th>q_mvar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.030560e-08</td>\n",
       "      <td>-1.798666e+00</td>\n",
       "      <td>-3.476629e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>0.973746</td>\n",
       "      <td>-3.001731e+01</td>\n",
       "      <td>3.026798e-14</td>\n",
       "      <td>3.019813e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>0.973014</td>\n",
       "      <td>-3.000708e+01</td>\n",
       "      <td>2.414573e+00</td>\n",
       "      <td>2.317990e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>0.969550</td>\n",
       "      <td>-5.988481e+01</td>\n",
       "      <td>2.820080e-01</td>\n",
       "      <td>1.551044e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>0.971998</td>\n",
       "      <td>-5.986427e+01</td>\n",
       "      <td>-9.444109e-01</td>\n",
       "      <td>-5.810813e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.030560e-08</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        vm_pu     va_degree          p_mw        q_mvar\n",
       "101  1.000000 -1.030560e-08 -1.798666e+00 -3.476629e+00\n",
       "102  0.973746 -3.001731e+01  3.026798e-14  3.019813e-13\n",
       "103  0.973014 -3.000708e+01  2.414573e+00  2.317990e-01\n",
       "104  0.969550 -5.988481e+01  2.820080e-01  1.551044e+00\n",
       "105  0.971998 -5.986427e+01 -9.444109e-01 -5.810813e-01\n",
       "106  1.000000 -1.030560e-08  0.000000e+00  0.000000e+00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "converted_output_data = converter.convert(output_data)\n",
    "\n",
    "display(converted_output_data.keys())\n",
    "print(\"--------Bus results--------\")\n",
    "display(converted_output_data[\"res_bus\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus we can see that the results of powerflow match. We can then replace the dataframes of results in the pandapower net."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "for table in converted_output_data.keys():\n",
    "    pp_net[table] = converted_output_data[table]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture cap --no-stderr\n",
    "\n",
    "from power_grid_model import PowerGridModel, CalculationType\n",
    "from power_grid_model.validation import assert_valid_input_data\n",
    "from power_grid_model_io.converters import PandaPowerConverter\n",
    "\n",
    "output_file = \"data/pandapower/example_simple_output.json\"\n",
    "\n",
    "pp_net = pandapower_simple_grid()\n",
    "converter = PandaPowerConverter(std_types=pp_net.std_types)\n",
    "input_data, extra_info = converter.load_input_data(pp_net)\n",
    "assert_valid_input_data(input_data, calculation_type=CalculationType.power_flow, symmetric=True)\n",
    "pgm = PowerGridModel(input_data=input_data)\n",
    "output_data = pgm.calculate_power_flow()\n",
    "json_converter = PgmJsonConverter(destination_file=output_file)\n",
    "json_converter.save(data=output_data, extra_info=extra_info)\n",
    "converted_output_data = converter.convert(output_data)\n",
    "for table in converted_output_data.keys():\n",
    "    pp_net[table] = converted_output_data[table]"
   ]
  }
 ],
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