{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Minimal Example pandapipes\n",
    "\n",
    "## Creating a low pressure gas network\n",
    "\n",
    "We consider the following simple 3-junction example network with an ideal valve as a minimal example:\n",
    "\n",
    "<img src=\"pics/minimal_example.png\">\n",
    "\n",
    "The above network can be created in pandapipes as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandapipes as pp\n",
    "\n",
    "#create empty net\n",
    "net = pp.create_empty_network(fluid=\"lgas\")\n",
    "\n",
    "# create junction\n",
    "j1 = pp.create_junction(net, pn_bar=1.05, tfluid_k=293.15, name=\"Junction 1\")\n",
    "j2 = pp.create_junction(net, pn_bar=1.05, tfluid_k=293.15, name=\"Junction 2\")    \n",
    "j3 = pp.create_junction(net, pn_bar=1.05, tfluid_k=293.15, name=\"Junction 3\")    \n",
    "\n",
    "# create junction elements\n",
    "ext_grid = pp.create_ext_grid(net, junction=j1, p_bar=1.1, t_k=293.15, name=\"Grid Connection\")\n",
    "sink = pp.create_sink(net, junction=j3, mdot_kg_per_s=0.045, name=\"Sink\")\n",
    "\n",
    "# create branch element\n",
    "pipe = pp.create_pipe_from_parameters(net, from_junction=j1, to_junction=j2, length_km=0.1, diameter_m=0.05, name=\"Pipe 1\")\n",
    "valve = pp.create_valve(net, from_junction=j2, to_junction=j3, diameter_m=0.05, opened=True, name=\"Valve 1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the fluid used here is lgas. You can find 5 predefined fluids in pandapipes:\n",
    "    - lgas\n",
    "    - hgas\n",
    "    - hydrogen\n",
    "    - water\n",
    "    - air\n",
    "\n",
    "And that the predefined valve element is an ideal valve. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Structure\n",
    "\n",
    "Each dataframe in a pandapipes net object contains the information about one pandapipes element, such as pipe, sink, valve etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "         name  pn_bar  tfluid_k  height_m  in_service      type\n0  Junction 1    1.05    293.15       0.0        True  junction\n1  Junction 2    1.05    293.15       0.0        True  junction\n2  Junction 3    1.05    293.15       0.0        True  junction",
      "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>name</th>\n      <th>pn_bar</th>\n      <th>tfluid_k</th>\n      <th>height_m</th>\n      <th>in_service</th>\n      <th>type</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Junction 1</td>\n      <td>1.05</td>\n      <td>293.15</td>\n      <td>0.0</td>\n      <td>True</td>\n      <td>junction</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Junction 2</td>\n      <td>1.05</td>\n      <td>293.15</td>\n      <td>0.0</td>\n      <td>True</td>\n      <td>junction</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Junction 3</td>\n      <td>1.05</td>\n      <td>293.15</td>\n      <td>0.0</td>\n      <td>True</td>\n      <td>junction</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.junction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "     name  from_junction  to_junction std_type  length_km  diameter_m  k_mm  \\\n0  Pipe 1              0            1     None        0.1        0.05   1.0   \n\n   loss_coefficient  alpha_w_per_m2k  text_k  qext_w  sections  in_service  \\\n0               0.0              0.0   293.0     0.0         1        True   \n\n   type  \n0  pipe  ",
      "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>name</th>\n      <th>from_junction</th>\n      <th>to_junction</th>\n      <th>std_type</th>\n      <th>length_km</th>\n      <th>diameter_m</th>\n      <th>k_mm</th>\n      <th>loss_coefficient</th>\n      <th>alpha_w_per_m2k</th>\n      <th>text_k</th>\n      <th>qext_w</th>\n      <th>sections</th>\n      <th>in_service</th>\n      <th>type</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Pipe 1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>None</td>\n      <td>0.1</td>\n      <td>0.05</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>293.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>True</td>\n      <td>pipe</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.pipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "              name  junction  p_bar     t_k  in_service type\n0  Grid Connection         0    1.1  293.15        True   pt",
      "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>name</th>\n      <th>junction</th>\n      <th>p_bar</th>\n      <th>t_k</th>\n      <th>in_service</th>\n      <th>type</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Grid Connection</td>\n      <td>0</td>\n      <td>1.1</td>\n      <td>293.15</td>\n      <td>True</td>\n      <td>pt</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.ext_grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pipe Flow\n",
    "\n",
    "We now run a pipe flow:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "hp.pandapipes.pipeflow - INFO: niter 0\n",
      "hp.pandapipes.pipeflow - INFO: niter 1\n",
      "hp.pandapipes.pipeflow - INFO: niter 2\n",
      "hp.pandapipes.pipeflow - INFO: niter 3\n",
      "hp.pandapipes.pipeflow - INFO: ---------------------------------------------------------------------------------\n",
      "hp.pandapipes.pipeflow - INFO: Calculation completed. Preparing results...\n",
      "hp.pandapipes.pipeflow - INFO: Converged after 4 iterations.\n",
      "hp.pandapipes.pipeflow - INFO: Norm of residual: 1.6351116571833302e-08\n",
      "hp.pandapipes.pipeflow - INFO: tol_p: 0.0001\n",
      "hp.pandapipes.pipeflow - INFO: tol_v: 0.0001\n"
     ]
    }
   ],
   "source": [
    "pp.pipeflow(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And check out at the results for junctions and pipes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "      p_bar     t_k\n0  1.100000  293.15\n1  0.913916  293.15\n2  0.913916  293.15",
      "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>p_bar</th>\n      <th>t_k</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.100000</td>\n      <td>293.15</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.913916</td>\n      <td>293.15</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.913916</td>\n      <td>293.15</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.res_junction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "They're is no pressure loss between junction 2 and junction 3 because of the ideal valve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "   v_from_m_per_s  v_to_m_per_s  v_mean_m_per_s  p_from_bar  p_to_bar  \\\n0       15.766074     17.295535       16.483877         1.1  0.913916   \n\n   t_from_k  t_to_k  mdot_from_kg_per_s  mdot_to_kg_per_s  vdot_norm_m3_per_s  \\\n0    293.15  293.15               0.045            -0.045             0.06044   \n\n       reynolds    lambda  normfactor_from  normfactor_to  \n0  96710.314701  0.049222         0.512189       0.561877  ",
      "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>v_from_m_per_s</th>\n      <th>v_to_m_per_s</th>\n      <th>v_mean_m_per_s</th>\n      <th>p_from_bar</th>\n      <th>p_to_bar</th>\n      <th>t_from_k</th>\n      <th>t_to_k</th>\n      <th>mdot_from_kg_per_s</th>\n      <th>mdot_to_kg_per_s</th>\n      <th>vdot_norm_m3_per_s</th>\n      <th>reynolds</th>\n      <th>lambda</th>\n      <th>normfactor_from</th>\n      <th>normfactor_to</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>15.766074</td>\n      <td>17.295535</td>\n      <td>16.483877</td>\n      <td>1.1</td>\n      <td>0.913916</td>\n      <td>293.15</td>\n      <td>293.15</td>\n      <td>0.045</td>\n      <td>-0.045</td>\n      <td>0.06044</td>\n      <td>96710.314701</td>\n      <td>0.049222</td>\n      <td>0.512189</td>\n      <td>0.561877</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.res_pipe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Closed valve\n",
    "\n",
    "We now close the valve between junction 2 and junction 3:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Problem can not run pipeflow if valve closed\n",
    "net.valve.opened = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The closed valve cuts the sink from the external grid:\n",
    "\n",
    "<img src=\"pics/minimal_example_closed_valve.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This can be verified by running a power flow and inspecting the results. The pressure and temperature at junction 2 is given as NaN:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "hp.pandapipes.pipeflow_setup - INFO: Setting the following nodes out of service for hydraulics calculation in connectivity check:\n",
      "In table junction: [2]\n",
      "hp.pandapipes.pipeflow - INFO: niter 0\n",
      "hp.pandapipes.pipeflow - INFO: niter 1\n",
      "hp.pandapipes.pipeflow - INFO: ---------------------------------------------------------------------------------\n",
      "hp.pandapipes.pipeflow - INFO: Calculation completed. Preparing results...\n",
      "hp.pandapipes.pipeflow - INFO: Converged after 2 iterations.\n",
      "hp.pandapipes.pipeflow - INFO: Norm of residual: 6.635561679383765e-07\n",
      "hp.pandapipes.pipeflow - INFO: tol_p: 0.0001\n",
      "hp.pandapipes.pipeflow - INFO: tol_v: 0.0001\n"
     ]
    },
    {
     "data": {
      "text/plain": "   p_bar     t_k\n0    1.1  293.15\n1    1.1  293.15\n2    NaN     NaN",
      "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>p_bar</th>\n      <th>t_k</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.1</td>\n      <td>293.15</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.1</td>\n      <td>293.15</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp.pipeflow(net)\n",
    "net.res_junction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also the results from the pipe show that the mass flow is almost zero and the speed of the mass flow is zero. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "   v_from_m_per_s  v_to_m_per_s  v_mean_m_per_s  p_from_bar  p_to_bar  \\\n0             0.0           0.0             0.0         1.1       1.1   \n\n   t_from_k  t_to_k  mdot_from_kg_per_s  mdot_to_kg_per_s  vdot_norm_m3_per_s  \\\n0    293.15  293.15                 0.0              -0.0                 0.0   \n\n   reynolds   lambda  normfactor_from  normfactor_to  \n0       0.0  0.04856         0.512189       0.512189  ",
      "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>v_from_m_per_s</th>\n      <th>v_to_m_per_s</th>\n      <th>v_mean_m_per_s</th>\n      <th>p_from_bar</th>\n      <th>p_to_bar</th>\n      <th>t_from_k</th>\n      <th>t_to_k</th>\n      <th>mdot_from_kg_per_s</th>\n      <th>mdot_to_kg_per_s</th>\n      <th>vdot_norm_m3_per_s</th>\n      <th>reynolds</th>\n      <th>lambda</th>\n      <th>normfactor_from</th>\n      <th>normfactor_to</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.1</td>\n      <td>1.1</td>\n      <td>293.15</td>\n      <td>293.15</td>\n      <td>0.0</td>\n      <td>-0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.04856</td>\n      <td>0.512189</td>\n      <td>0.512189</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.res_pipe"
   ]
  }
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