{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Internal Datastructure: Bus branch model, Admittance and Jacobian Matrix\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This jupyter notebooks explains how to access and interpret the internal datastructure with relevant matrices."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Internal Datastructure\n",
    "\n",
    "We use the simple example network from the create_simple tutorial as an example for how to access internal calculation parameters:\n",
    "\n",
    "<img src=\"pics/example_network_simple.png\">\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This pandapower network includes the following parameter tables:\n",
      "   - bus (7 elements)\n",
      "   - load (1 element)\n",
      "   - sgen (1 element)\n",
      "   - gen (1 element)\n",
      "   - switch (8 elements)\n",
      "   - shunt (1 element)\n",
      "   - ext_grid (1 element)\n",
      "   - line (4 elements)\n",
      "   - trafo (1 element)\n"
     ]
    }
   ],
   "source": [
    "import pandapower as pp\n",
    "import pandapower.networks as nw\n",
    "\n",
    "net = nw.example_simple()\n",
    "print(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we run a power flow in this network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pp.runpp(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When a power flow is carried out, the element based grid model is translated into a bus-branch model. That bus-branch model is stored in a data structure that is based on the PYPOWER/MATPOWER casefile (with some extensions). This ppc can be accesed after power flow:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'baseMVA': 1,\n",
       " 'version': 2,\n",
       " 'bus': array([[ 0.00000000e+00,  3.00000000e+00,  0.00000000e+00,\n",
       "          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "          1.00000000e+00,  1.02000000e+00,  0.00000000e+00,\n",
       "          1.10000000e+02,  1.00000000e+00,  2.00000000e+00,\n",
       "          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "          0.00000000e+00],\n",
       "        [ 1.00000000e+00,  1.00000000e+00,  0.00000000e+00,\n",
       "          0.00000000e+00,  0.00000000e+00,  9.60000000e-01,\n",
       "          1.00000000e+00,  1.02082951e+00,  3.24135661e-02,\n",
       "          1.10000000e+02,  1.00000000e+00,  2.00000000e+00,\n",
       "          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "          0.00000000e+00],\n",
       "        [ 2.00000000e+00,  1.00000000e+00,  0.00000000e+00,\n",
       "          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "          1.00000000e+00,  1.02456241e+00,  1.80284793e+00,\n",
       "          2.00000000e+01,  1.00000000e+00,  2.00000000e+00,\n",
       "          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "          0.00000000e+00],\n",
       "        [ 3.00000000e+00,  2.00000000e+00,  0.00000000e+00,\n",
       "          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "          1.00000000e+00,  1.03000000e+00,  1.87045457e+00,\n",
       "          2.00000000e+01,  1.00000000e+00,  2.00000000e+00,\n",
       "          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "          0.00000000e+00],\n",
       "        [ 4.00000000e+00,  1.00000000e+00, -8.00000000e-01,\n",
       "          2.90000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "          1.00000000e+00,  1.02320534e+00,  1.95222438e+00,\n",
       "          2.00000000e+01,  1.00000000e+00,  2.00000000e+00,\n",
       "          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "          0.00000000e+00],\n",
       "        [ 5.00000000e+00,  1.00000000e+00,  0.00000000e+00,\n",
       "          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "          1.00000000e+00,  1.03000700e+00,  1.86983253e+00,\n",
       "          2.00000000e+01,  1.00000000e+00,  1.10000000e+00,\n",
       "          9.00000000e-01,  0.00000000e+00,  0.00000000e+00,\n",
       "          0.00000000e+00],\n",
       "        [ 6.00000000e+00,  4.00000000e+00,  0.00000000e+00,\n",
       "          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "          1.00000000e+00,             nan,             nan,\n",
       "          1.10000000e+02,  1.00000000e+00,  2.00000000e+00,\n",
       "          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "          0.00000000e+00],\n",
       "        [ 7.00000000e+00,  4.00000000e+00,  0.00000000e+00,\n",
       "          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "          1.00000000e+00,             nan,             nan,\n",
       "          2.00000000e+01,  1.00000000e+00,  2.00000000e+00,\n",
       "          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "          0.00000000e+00]]),\n",
       " 'branch': array([[ 0.00000000e+00+0.j        ,  1.00000000e+00+0.j        ,\n",
       "          4.95867769e-05+0.j        ,  1.19008264e-04+0.j        ,\n",
       "          5.47391104e+00+0.j        ,  2.50000000e+02+0.j        ,\n",
       "          2.50000000e+02+0.j        ,  2.50000000e+02+0.j        ,\n",
       "          1.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "          1.00000000e+00+0.j        , -3.60000000e+02+0.j        ,\n",
       "          3.60000000e+02+0.j        , -6.74111530e+00+0.j        ,\n",
       "         -7.14688297e+00+0.j        ,  6.74416214e+00+0.j        ,\n",
       "          1.45450502e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "          0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "          0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "          0.00000000e+00+0.j        ],\n",
       "        [ 2.00000000e+00+0.j        ,  3.00000000e+00+0.j        ,\n",
       "          6.10000000e-04+0.j        ,  5.60000000e-04+0.j        ,\n",
       "          7.64035333e-02+0.j        ,  2.50000000e+02+0.j        ,\n",
       "          2.50000000e+02+0.j        ,  2.50000000e+02+0.j        ,\n",
       "          1.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "          1.00000000e+00+0.j        , -3.60000000e+02+0.j        ,\n",
       "          3.60000000e+02+0.j        , -5.97238973e+00+0.j        ,\n",
       "         -3.48162502e+00+0.j        ,  5.99999998e+00+0.j        ,\n",
       "          3.42634241e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "          0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "          0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "          0.00000000e+00+0.j        ],\n",
       "        [ 3.00000000e+00+0.j        ,  5.00000000e+00+0.j        ,\n",
       "          5.19662500e-03+0.j        ,  3.25500000e-03+0.j        ,\n",
       "          4.17831823e-03+0.j        ,  2.50000000e+02+0.j        ,\n",
       "          2.50000000e+02+0.j        ,  2.50000000e+02+0.j        ,\n",
       "          1.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "          1.00000000e+00+0.j        , -3.60000000e+02+0.j        ,\n",
       "          3.60000000e+02+0.j        ,  2.40626628e-08+0.j        ,\n",
       "         -4.43279288e-03+0.j        ,  2.92589721e-14+0.j        ,\n",
       "          9.55197900e-16+0.j        ,  0.00000000e+00+0.j        ,\n",
       "          0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "          0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "          0.00000000e+00+0.j        ],\n",
       "        [ 4.00000000e+00+0.j        ,  2.00000000e+00+0.j        ,\n",
       "          7.62500000e-04+0.j        ,  7.00000000e-04+0.j        ,\n",
       "          9.55044167e-02+0.j        ,  2.50000000e+02+0.j        ,\n",
       "          2.50000000e+02+0.j        ,  2.50000000e+02+0.j        ,\n",
       "          1.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "          1.00000000e+00+0.j        , -3.60000000e+02+0.j        ,\n",
       "          3.60000000e+02+0.j        ,  8.00000000e-01+0.j        ,\n",
       "         -2.90000000e+00+0.j        , -7.93618189e-01+0.j        ,\n",
       "          2.80573774e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "          0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "          0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "          0.00000000e+00+0.j        ],\n",
       "        [ 1.00000000e+00+0.j        ,  2.00000000e+00+0.j        ,\n",
       "          1.63923679e-04+0.j        ,  4.79726336e-03+0.j        ,\n",
       "         -1.05000908e-02-0.01399964j,  2.50000000e+02+0.j        ,\n",
       "          2.50000000e+02+0.j        ,  2.50000000e+02+0.j        ,\n",
       "          1.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "          1.00000000e+00+0.j        , -3.60000000e+02+0.j        ,\n",
       "          3.60000000e+02+0.j        , -6.74416214e+00+0.j        ,\n",
       "         -4.54095852e-01+0.j        ,  6.76600792e+00+0.j        ,\n",
       "          6.75887284e-01+0.j        ,  0.00000000e+00+0.j        ,\n",
       "          0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "          0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "          0.00000000e+00+0.j        ]]),\n",
       " 'gen': array([[ 0.00000000e+00, -6.74111530e+00, -7.14688297e+00,\n",
       "          0.00000000e+00,  0.00000000e+00,  1.02000000e+00,\n",
       "          1.00000000e+00,  1.00000000e+00,  1.00000000e+09,\n",
       "         -1.00000000e+09,  0.00000000e+00,  0.00000000e+00,\n",
       "          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "          0.00000000e+00,  1.00000000e+00],\n",
       "        [ 3.00000000e+00,  6.00000000e+00,  3.42190962e+00,\n",
       "          3.00000000e+00, -3.00000000e+00,  1.03000000e+00,\n",
       "                     nan,  1.00000000e+00,  1.00000000e+09,\n",
       "         -1.00000000e+09,  0.00000000e+00,  0.00000000e+00,\n",
       "          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "          0.00000000e+00,  0.00000000e+00]]),\n",
       " 'internal': {'Ybus': <6x6 sparse matrix of type '<class 'numpy.complex128'>'\n",
       "  \twith 16 stored elements in Compressed Sparse Row format>,\n",
       "  'Yf': <5x6 sparse matrix of type '<class 'numpy.complex128'>'\n",
       "  \twith 10 stored elements in Compressed Sparse Row format>,\n",
       "  'Yt': <5x6 sparse matrix of type '<class 'numpy.complex128'>'\n",
       "  \twith 10 stored elements in Compressed Sparse Row format>,\n",
       "  'branch_is': array([ True,  True,  True,  True,  True]),\n",
       "  'gen_is': array([ True,  True]),\n",
       "  'DLF': array([], dtype=complex128),\n",
       "  'buses_ord_bfs_nets': array([], dtype=float64),\n",
       "  'ref_gens': array([0]),\n",
       "  'J': <9x9 sparse matrix of type '<class 'numpy.float64'>'\n",
       "  \twith 41 stored elements in Compressed Sparse Row format>,\n",
       "  'Vm_it': None,\n",
       "  'Va_it': None,\n",
       "  'bus': array([[ 0.00000000e+00,  3.00000000e+00,  0.00000000e+00,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "           1.00000000e+00,  1.02000000e+00,  0.00000000e+00,\n",
       "           1.10000000e+02,  1.00000000e+00,  2.00000000e+00,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "           0.00000000e+00],\n",
       "         [ 1.00000000e+00,  1.00000000e+00,  0.00000000e+00,\n",
       "           0.00000000e+00,  0.00000000e+00,  9.60000000e-01,\n",
       "           1.00000000e+00,  1.02082951e+00,  3.24135661e-02,\n",
       "           1.10000000e+02,  1.00000000e+00,  2.00000000e+00,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "           0.00000000e+00],\n",
       "         [ 2.00000000e+00,  1.00000000e+00,  0.00000000e+00,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "           1.00000000e+00,  1.02456241e+00,  1.80284793e+00,\n",
       "           2.00000000e+01,  1.00000000e+00,  2.00000000e+00,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "           0.00000000e+00],\n",
       "         [ 3.00000000e+00,  2.00000000e+00,  0.00000000e+00,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "           1.00000000e+00,  1.03000000e+00,  1.87045457e+00,\n",
       "           2.00000000e+01,  1.00000000e+00,  2.00000000e+00,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "           0.00000000e+00],\n",
       "         [ 4.00000000e+00,  1.00000000e+00, -8.00000000e-01,\n",
       "           2.90000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "           1.00000000e+00,  1.02320534e+00,  1.95222438e+00,\n",
       "           2.00000000e+01,  1.00000000e+00,  2.00000000e+00,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "           0.00000000e+00],\n",
       "         [ 5.00000000e+00,  1.00000000e+00,  0.00000000e+00,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "           1.00000000e+00,  1.03000700e+00,  1.86983253e+00,\n",
       "           2.00000000e+01,  1.00000000e+00,  1.10000000e+00,\n",
       "           9.00000000e-01,  0.00000000e+00,  0.00000000e+00,\n",
       "           0.00000000e+00]]),\n",
       "  'gen': array([[ 0.00000000e+00, -6.74111530e+00, -7.14688297e+00,\n",
       "           0.00000000e+00,  0.00000000e+00,  1.02000000e+00,\n",
       "           1.00000000e+00,  1.00000000e+00,  1.00000000e+09,\n",
       "          -1.00000000e+09,  0.00000000e+00,  0.00000000e+00,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "           0.00000000e+00,  1.00000000e+00],\n",
       "         [ 3.00000000e+00,  6.00000000e+00,  3.42190962e+00,\n",
       "           3.00000000e+00, -3.00000000e+00,  1.03000000e+00,\n",
       "                      nan,  1.00000000e+00,  1.00000000e+09,\n",
       "          -1.00000000e+09,  0.00000000e+00,  0.00000000e+00,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "           0.00000000e+00,  0.00000000e+00]]),\n",
       "  'branch': array([[ 0.00000000e+00+0.j        ,  1.00000000e+00+0.j        ,\n",
       "           4.95867769e-05+0.j        ,  1.19008264e-04+0.j        ,\n",
       "           5.47391104e+00+0.j        ,  2.50000000e+02+0.j        ,\n",
       "           2.50000000e+02+0.j        ,  2.50000000e+02+0.j        ,\n",
       "           1.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "           1.00000000e+00+0.j        , -3.60000000e+02+0.j        ,\n",
       "           3.60000000e+02+0.j        , -6.74111530e+00+0.j        ,\n",
       "          -7.14688297e+00+0.j        ,  6.74416214e+00+0.j        ,\n",
       "           1.45450502e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "           0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "           0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "           0.00000000e+00+0.j        ],\n",
       "         [ 2.00000000e+00+0.j        ,  3.00000000e+00+0.j        ,\n",
       "           6.10000000e-04+0.j        ,  5.60000000e-04+0.j        ,\n",
       "           7.64035333e-02+0.j        ,  2.50000000e+02+0.j        ,\n",
       "           2.50000000e+02+0.j        ,  2.50000000e+02+0.j        ,\n",
       "           1.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "           1.00000000e+00+0.j        , -3.60000000e+02+0.j        ,\n",
       "           3.60000000e+02+0.j        , -5.97238973e+00+0.j        ,\n",
       "          -3.48162502e+00+0.j        ,  5.99999998e+00+0.j        ,\n",
       "           3.42634241e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "           0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "           0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "           0.00000000e+00+0.j        ],\n",
       "         [ 3.00000000e+00+0.j        ,  5.00000000e+00+0.j        ,\n",
       "           5.19662500e-03+0.j        ,  3.25500000e-03+0.j        ,\n",
       "           4.17831823e-03+0.j        ,  2.50000000e+02+0.j        ,\n",
       "           2.50000000e+02+0.j        ,  2.50000000e+02+0.j        ,\n",
       "           1.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "           1.00000000e+00+0.j        , -3.60000000e+02+0.j        ,\n",
       "           3.60000000e+02+0.j        ,  2.40626628e-08+0.j        ,\n",
       "          -4.43279288e-03+0.j        ,  2.92589721e-14+0.j        ,\n",
       "           9.55197900e-16+0.j        ,  0.00000000e+00+0.j        ,\n",
       "           0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "           0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "           0.00000000e+00+0.j        ],\n",
       "         [ 4.00000000e+00+0.j        ,  2.00000000e+00+0.j        ,\n",
       "           7.62500000e-04+0.j        ,  7.00000000e-04+0.j        ,\n",
       "           9.55044167e-02+0.j        ,  2.50000000e+02+0.j        ,\n",
       "           2.50000000e+02+0.j        ,  2.50000000e+02+0.j        ,\n",
       "           1.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "           1.00000000e+00+0.j        , -3.60000000e+02+0.j        ,\n",
       "           3.60000000e+02+0.j        ,  8.00000000e-01+0.j        ,\n",
       "          -2.90000000e+00+0.j        , -7.93618189e-01+0.j        ,\n",
       "           2.80573774e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "           0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "           0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "           0.00000000e+00+0.j        ],\n",
       "         [ 1.00000000e+00+0.j        ,  2.00000000e+00+0.j        ,\n",
       "           1.63923679e-04+0.j        ,  4.79726336e-03+0.j        ,\n",
       "          -1.05000908e-02-0.01399964j,  2.50000000e+02+0.j        ,\n",
       "           2.50000000e+02+0.j        ,  2.50000000e+02+0.j        ,\n",
       "           1.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "           1.00000000e+00+0.j        , -3.60000000e+02+0.j        ,\n",
       "           3.60000000e+02+0.j        , -6.74416214e+00+0.j        ,\n",
       "          -4.54095852e-01+0.j        ,  6.76600792e+00+0.j        ,\n",
       "           6.75887284e-01+0.j        ,  0.00000000e+00+0.j        ,\n",
       "           0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "           0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n",
       "           0.00000000e+00+0.j        ]]),\n",
       "  'baseMVA': 1,\n",
       "  'V': array([1.02      +0.j        , 1.02082934+0.00057751j,\n",
       "         1.02405525+0.03223318j, 1.0294512 +0.03361899j,\n",
       "         1.02261145+0.03485667j, 1.02945856+0.03360804j]),\n",
       "  'pv': array([3], dtype=np.int64),\n",
       "  'pq': array([1, 2, 4, 5], dtype=np.int64),\n",
       "  'ref': array([0], dtype=np.int64),\n",
       "  'Sbus': array([0. +0.j , 0. +0.j , 0. +0.j , 6. +0.j , 0.8-2.9j, 0. +0.j ]),\n",
       "  'r_theta_kelvin_per_mw': 0.0,\n",
       "  'T': 20.0},\n",
       " 'success': True,\n",
       " 'et': 2.7024675999999985,\n",
       " 'iterations': 3}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net._ppc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For information on how this datastructure is defined, please refer to the MATPOWER documentation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** For linear power flow (DC load flow) 'Ybus' is no longer created, but 'Bbus' as a new 'internal' key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 8402.77777778+0.j, -8402.77777778+0.j,     0.        +0.j,\n",
       "            0.        +0.j,     0.        +0.j,     0.        +0.j],\n",
       "       [-8402.77777778+0.j,  8611.22995659+0.j,  -208.45217882+0.j,\n",
       "            0.        +0.j,     0.        +0.j,     0.        +0.j],\n",
       "       [    0.        +0.j,  -208.45217882+0.j,  3422.7378931 +0.j,\n",
       "        -1785.71428571+0.j, -1428.57142857+0.j,     0.        +0.j],\n",
       "       [    0.        +0.j,     0.        +0.j, -1785.71428571+0.j,\n",
       "         2092.93394777+0.j,     0.        +0.j,  -307.21966206+0.j],\n",
       "       [    0.        +0.j,     0.        +0.j, -1428.57142857+0.j,\n",
       "            0.        +0.j,  1428.57142857+0.j,     0.        +0.j],\n",
       "       [    0.        +0.j,     0.        +0.j,     0.        +0.j,\n",
       "         -307.21966206+0.j,     0.        +0.j,   307.21966206+0.j]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp.rundcpp(net)\n",
    "net._ppc['internal']['Bbus'].A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nodal Point Admittance Matrix\n",
    "\n",
    "The nodal point admittance matrix is saved in the ppc and can be accessed directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 2983.234714  -7157.02635809j, -2983.234714  +7159.76331361j,\n",
       "             0.           +0.j        ,     0.           +0.j        ,\n",
       "             0.           +0.j        ,     0.           +0.j        ],\n",
       "        [-2983.234714  +7159.76331361j,  2990.35626947-7364.28068082j,\n",
       "            -7.11455564 +208.20907269j,     0.           +0.j        ,\n",
       "             0.           +0.j        ,     0.           +0.j        ],\n",
       "        [    0.           +0.j        ,    -7.11455564 +208.20907269j,\n",
       "          1608.40491554-1678.15899439j,  -889.60186671 +816.68368091j,\n",
       "          -711.68149336 +653.34694473j,     0.           +0.j        ],\n",
       "        [    0.           +0.j        ,     0.           +0.j        ,\n",
       "          -889.60186671 +816.68368091j,  1027.81021222 -903.21268537j,\n",
       "             0.           +0.j        ,  -138.20834551  +86.56929539j],\n",
       "        [    0.           +0.j        ,     0.           +0.j        ,\n",
       "          -711.68149336 +653.34694473j,     0.           +0.j        ,\n",
       "           711.68149336 -653.29919252j,     0.           +0.j        ],\n",
       "        [    0.           +0.j        ,     0.           +0.j        ,\n",
       "             0.           +0.j        ,  -138.20834551  +86.56929539j,\n",
       "             0.           +0.j        ,   138.20834551  -86.56720623j]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp.runpp(net)\n",
    "net._ppc[\"internal\"][\"Ybus\"].todense()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the nodal point admittance matrix is given in per unit values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Jacobian Matrix\n",
    "\n",
    "The jacobian Matrix J in the last iteration step is also stored in the ppc and can be accessed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 9.54796437e+02,  0.00000000e+00, -8.62952855e+02,\n",
       "          0.00000000e+00, -9.18435819e+01,  0.00000000e+00,\n",
       "         -9.15296717e+02,  0.00000000e+00, -1.42353628e+02],\n",
       "        [ 0.00000000e+00,  7.67426447e+03, -2.17432759e+02,\n",
       "          0.00000000e+00,  0.00000000e+00,  3.05264392e+03,\n",
       "         -1.38258923e+01,  0.00000000e+00,  0.00000000e+00],\n",
       "        [-8.60737361e+02, -2.17892547e+02,  1.76161072e+03,\n",
       "         -6.82980812e+02,  0.00000000e+00, -6.95203313e-01,\n",
       "          1.64791123e+03, -7.30904816e+02,  0.00000000e+00],\n",
       "        [ 0.00000000e+00,  0.00000000e+00, -6.86871047e+02,\n",
       "          6.86871047e+02,  0.00000000e+00,  0.00000000e+00,\n",
       "         -7.26450958e+02,  7.28978162e+02,  0.00000000e+00],\n",
       "        [-9.18403982e+01,  0.00000000e+00,  0.00000000e+00,\n",
       "          0.00000000e+00,  9.18403982e+01,  0.00000000e+00,\n",
       "          0.00000000e+00,  0.00000000e+00,  1.42355564e+02],\n",
       "        [ 0.00000000e+00, -3.11622898e+03,  1.41654896e+01,\n",
       "          0.00000000e+00,  0.00000000e+00,  7.51767502e+03,\n",
       "         -2.12220120e+02,  0.00000000e+00,  0.00000000e+00],\n",
       "        [ 9.39812512e+02,  7.09684055e-01, -1.68838791e+03,\n",
       "          7.47865712e+02,  0.00000000e+00, -2.13446561e+02,\n",
       "          1.71937864e+03, -6.67491446e+02,  0.00000000e+00],\n",
       "        [ 0.00000000e+00,  0.00000000e+00,  7.44294349e+02,\n",
       "         -7.44294349e+02,  0.00000000e+00,  0.00000000e+00,\n",
       "         -6.70404297e+02,  6.65624993e+02,  0.00000000e+00],\n",
       "        [ 1.46627228e+02,  0.00000000e+00,  0.00000000e+00,\n",
       "          0.00000000e+00, -1.46627228e+02,  0.00000000e+00,\n",
       "          0.00000000e+00,  0.00000000e+00,  8.91648289e+01]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net._ppc[\"internal\"][\"J\"].todense()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The jacobian matrix is also given in per unit values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mapping the Buses\n",
    "\n",
    "The pandapower indices are not equal to the ppc indices for several reasons. Some buses are fused together in case of closed bus-bus switches and auxiliary buses are created for elements like extended wards or three winding transformers.\n",
    "See here for more details: https://pandapower.readthedocs.io/en/latest/elements/switch.html\n",
    "\n",
    "There is however a mapping between pandapower indices and ppc indices that is created during the conversion to keep track of the dependencies that is also stored in the net:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 2, 2, 3, 4])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net._pd2ppc_lookups[\"bus\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a ppc index from the pandapower index, simply call the lookup like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "pandapower_bus_idx = 3\n",
    "ppc_index = net._pd2ppc_lookups[\"bus\"][pandapower_bus_idx]\n",
    "print(ppc_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, pandapower bus index 3 corresponds to ppc bus index 2. So if we would like to find the diagonal entry of the Ybus matrix for bus 2, we could now access it with that internal index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2990.356269470474-7364.280680821408j)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ybus = net._ppc[\"internal\"][\"Ybus\"]\n",
    "int_idx = net._pd2ppc_lookups[\"bus\"][ppc_index]\n",
    "Ybus[int_idx, int_idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also see that some buses are mapped to the same internal bus, such as bus 1 and bus 2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "print(net._pd2ppc_lookups[\"bus\"][1])\n",
    "print(net._pd2ppc_lookups[\"bus\"][2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That is because buses 1 and 2 are connected by a closed bus-bus switch and are therefore represented internally as the same bus:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bus           1\n",
       "element       2\n",
       "et            b\n",
       "type         CB\n",
       "closed     True\n",
       "name       None\n",
       "z_ohm       0.0\n",
       "in_ka       NaN\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.switch.loc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Obtaining Jacobian Entries of Generators\n",
    "\n",
    "As an example we show how to obtain the Jacobian entries of generator buses using the pandapower -> ppc bus mapping. First we get buses of the in-service generators:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pandapower gen bus: [5]\n"
     ]
    }
   ],
   "source": [
    "gen_buses = net.gen.loc[net.gen.in_service.values, \"bus\"].values\n",
    "print(f\"pandapower gen bus: {gen_buses}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second, we geht the Jacobian matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jacobian shape: (9, 9)\n"
     ]
    }
   ],
   "source": [
    "J = net._ppc[\"internal\"][\"J\"]\n",
    "print(f\"Jacobian shape: {J.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why has the Jacobian the shape 9x9?\n",
    "It consists of the partial derivatives J11 = dP_dVa, J12 = dP_Vm, J21 = dQ_dVa, J22 = dQ_dVm. Except the reference bus, all PV- and PQ-buses are included in J. Vm is constant for PV nodes and dS/dVm is 0 for PV-buses (gens in pandapower) and Q is a variable. \n",
    "\n",
    "In our case we have 1 reference bus (at bus 0), 1 gen at bus 5, and 3 pq buses (at buses 1, 2, 4)\n",
    "\n",
    "This is the reason why J11 to J22  have these shapes:\n",
    "\n",
    "J11 = pvpq x pvpq (dP_dVa)\n",
    "\n",
    "J12 = pvpq x pq (dP_dVm)\n",
    "\n",
    "J21 = pq x pvpq (dQ_dVa)\n",
    "\n",
    "J22 = pq x pq (dQ_dVm)\n",
    "\n",
    "Only J11 contains values relevant for gens."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Now we get the \"internal\" ppc buses with the lookup:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pandapower to ppc lookup: [0 1 1 2 2 3 4]\n",
      "pandapower gen bus: [5] maps to ppc gen bus: [3]\n"
     ]
    }
   ],
   "source": [
    "bus_lookup = ppc_index = net._pd2ppc_lookups[\"bus\"]\n",
    "print(f\"pandapower to ppc lookup: {bus_lookup}\")\n",
    "ppc_gen_buses = bus_lookup[gen_buses]\n",
    "print(f\"pandapower gen bus: {gen_buses} maps to ppc gen bus: {ppc_gen_buses}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we need the pv and pq entries in J to obtain the Jacobian sub-matrices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pv and pq nodes as in the newtonpf() function\n",
      "pv buses: [3]\n",
      "pq buses: [1 2 4 5]\n",
      "pvpq buses: [3 1 2 4 5]\n",
      "shape of J sub-matrices:\n",
      "j11 = (5, 5)\n",
      "j12 = (5, 4)\n",
      "j21 = (4, 5)\n",
      "j22 = (4, 4)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# get pv and pq values from newtonpf()\n",
    "pv = net._ppc[\"internal\"][\"pv\"]\n",
    "pq = net._ppc[\"internal\"][\"pq\"]\n",
    "# stack these as done in newtonpf()\n",
    "pvpq = np.hstack((pv, pq))\n",
    "\n",
    "print(\"pv and pq nodes as in the newtonpf() function\")\n",
    "print(f\"pv buses: {pv}\\npq buses: {pq}\\npvpq buses: {pvpq}\")\n",
    "\n",
    "# get len of pv and pq\n",
    "n_pvpq = len(pvpq)\n",
    "n_pq = len(pq)\n",
    "n_pv = len(pv)\n",
    "# get J11, J12, J21, and J22\n",
    "j11 = J[:n_pvpq, :n_pvpq]\n",
    "j12 = J[:n_pvpq, n_pvpq:]\n",
    "j21 = J[n_pvpq:, :n_pvpq]\n",
    "j22 = J[n_pvpq:, n_pvpq:]\n",
    "\n",
    "print(\"shape of J sub-matrices:\")\n",
    "print(f\"j11 = {j11.shape}\")\n",
    "print(f\"j12 = {j12.shape}\")\n",
    "print(f\"j21 = {j21.shape}\")\n",
    "print(f\"j22 = {j22.shape}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we finally get the generator entries in J:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "J11 indices: m = [ True False False False False], n = [ True False False False False]\n",
      "pandapower gen [5] entries (ppc PV nodes [3]) in J11 (=dP/dVa): [[954.7964366]]\n"
     ]
    }
   ],
   "source": [
    "# j11 gen entries\n",
    "m = np.isin(pvpq, pv)\n",
    "n = m\n",
    "\n",
    "j11_gen_entries = j11[m, n]\n",
    "print(f\"J11 indices: m = {m}, n = {n}\")\n",
    "print(f\"pandapower gen {gen_buses} entries (ppc PV nodes {ppc_gen_buses}) in J11 (=dP/dVa): {j11_gen_entries}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visiualizing the Matrices\n",
    "You can easily visualize the matrices with matplotlib and the plot function spy(). Let's look at an example.\n",
    "\n",
    "First, we define a plot function to visiualize the sparse Ybus matrix (works also with any other matrix like J) including some labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as mpl\n",
    "import numpy as np\n",
    "\n",
    "import pandapower as pp\n",
    "import pandapower.networks as nw\n",
    "import pandapower.plotting as plt\n",
    "from pandapower.plotting import get_collection_sizes\n",
    "\n",
    "def plot_ybus(net=None, ax=None, ybus=None):\n",
    "    if ax is None:\n",
    "        fig, ax = mpl.subplots(1, 2)\n",
    "    if ybus is None:\n",
    "        ybus = net._ppc[\"internal\"][\"Ybus\"]\n",
    "    ax.spy(ybus)\n",
    "    ax.set_title(\"Ybus shape {}\\n\".format(str(ybus.shape)))\n",
    "    ax.set_xticks(np.arange(ybus.shape[0]))\n",
    "    ax.set_xticklabels(np.arange(ybus.shape[0]))\n",
    "    ax.set_yticklabels(np.arange(ybus.shape[1]))\n",
    "    ax.set_yticks(np.arange(ybus.shape[1]))\n",
    "\n",
    "    ax.grid(which=\"both\", linestyle=\"dotted\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second, we define a function to plot the power system including some bus labels and other collections:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_net(net, ax=None):\n",
    "    if ax is None:\n",
    "        fig, ax = mpl.subplots(1, 1, figsize=(10, 8))\n",
    "\n",
    "    sizes = get_collection_sizes(net)\n",
    "\n",
    "    # create collections for elements\n",
    "    collections = list()\n",
    "    collections.append(plt.create_bus_collection(net, size=sizes[\"bus\"]))\n",
    "    collections.append(plt.create_line_collection(net, use_bus_geodata=True))\n",
    "    collections.append(plt.create_trafo_collection(net, size=sizes[\"trafo\"]))\n",
    "    collections.append(plt.create_ext_grid_collection(net, size=sizes[\"ext_grid\"], orientation=1.5))\n",
    "    collections.append(plt.create_bus_bus_switch_collection(net, size=sizes[\"switch\"]))\n",
    "    collections.append(\n",
    "        plt.create_line_switch_collection(net, distance_to_bus=sizes[\"switch_distance\"], size=sizes[\"switch\"]))\n",
    "    collections.append(plt.create_load_collection(net, size=sizes[\"load\"]))\n",
    "\n",
    "    # add labels for each bus\n",
    "    for idx in net.bus_geodata.index:\n",
    "        x = net.bus_geodata.loc[idx, \"x\"]\n",
    "        y = net.bus_geodata.loc[idx, \"y\"] + sizes[\"bus\"] * 1.\n",
    "        ax.text(x, y, str(idx), fontsize=12, color=\"r\")\n",
    "\n",
    "    plt.draw_collections(collections, ax=ax)\n",
    "    mpl.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Third, we create a plot function with three subplots. The first subplots shows the power system including the bus labels. The second and third plot shows the Ybus matrix with different settings. If you just call runpp() the buses are fused internally and the Ybus matrix has a lower dimension. You can also use the parameter `r_switch` to set impedance values for switches. In this case, the buses are not fused and the Ybus matrix has a higher dimension. This also changes your power flow result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_overview(net):\n",
    "    fig, axes = mpl.subplots(1, 3, figsize=(16, 8))\n",
    "    pp.runpp(net)\n",
    "    plot_net(net, ax=axes[0])\n",
    "    # plot y bus with max. const shape (no fused switches)\n",
    "    pp.runpp(net, check_connectivity=False, r_switch=0.1, init=\"flat\", neglect_open_switch_branches=True)\n",
    "    plot_ybus(net, ax=axes[1])\n",
    "    # plot ybus for power flow (with fused switches)\n",
    "    pp.runpp(net)\n",
    "    plot_ybus(net, ax=axes[2])\n",
    "    mpl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we call the overview function with the cigre example power system:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mmilovic\\AppData\\Local\\Temp\\ipykernel_324\\1313666249.py:18: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(np.arange(ybus.shape[1]))\n",
      "C:\\Users\\mmilovic\\AppData\\Local\\Temp\\ipykernel_324\\1313666249.py:18: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(np.arange(ybus.shape[1]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net = nw.create_cigre_network_mv()\n",
    "plot_overview(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each dot in the spy plot is ea"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}