From 67f1812ff24ff879249942a3869767bc1555e91d Mon Sep 17 00:00:00 2001
From: Jonas Pfeiffer <jonaspfeiffer123@gmail.com>
Date: Thu, 4 Sep 2025 15:32:00 +0200
Subject: [PATCH 1/3] Implement code changes to enhance functionality and
 improve performance

---
 ...ular_flow_in_a_district_heating_grid.ipynb | 443 ++++++++++++++
 ..._in_a_circular_district_heating_grid.ipynb | 552 ++++++++++++++++++
 ..._in_a_circular_district_heating_grid.ipynb | 459 +++++++++++++++
 3 files changed, 1454 insertions(+)
 create mode 100644 tutorials/district_heating/circular_flow_in_a_district_heating_grid.ipynb
 create mode 100644 tutorials/district_heating/multiple_pumps_flow_in_a_circular_district_heating_grid.ipynb
 create mode 100644 tutorials/district_heating/time_series_in_a_circular_district_heating_grid.ipynb

diff --git a/tutorials/district_heating/circular_flow_in_a_district_heating_grid.ipynb b/tutorials/district_heating/circular_flow_in_a_district_heating_grid.ipynb
new file mode 100644
index 000000000..6c712eed5
--- /dev/null
+++ b/tutorials/district_heating/circular_flow_in_a_district_heating_grid.ipynb
@@ -0,0 +1,443 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Circular flow in a district heating grid\n",
+    "\n",
+    "This example demonstrates how to model and analyze a simple district heating network with a circular flow using pandapipes.\n",
+    "\n",
+    "The network consists of a pump that supplies hot water, which flows through consumers (heat exchangers), releases heat, and then returns to the pump. In addition to pressure and velocity distribution, the temperature profile throughout the network is also calculated. Due to losses, the temperature will fall. The heat consumer removes more heat from the network. On its way back to the pump, the temperature will fall further. \n",
+    "\n",
+    "The setup is based on a typical district heating grid topology, where the fluid returns to the pump after passing through the consumers.\n",
+    "\n",
+    "*Note: The illustration may need to be updated.*\n",
+    "\n",
+    "<img src=\"../pics/heat.png\">\n",
+    "\n",
+    "To set up this network, at first, the pandapipes package has to be imported. Additionally, a net container is created and, at the same time, water as a fluid is chosen."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandapipes as pp\n",
+    "\n",
+    "# create empty net\n",
+    "net = pp.create_empty_network(fluid =\"water\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that the flow of the example network flows in a closed loop. We will need four junctions.\n",
+    "The parameters `pn_bar` and `tfluid_k` that have to be set in the `create_junction`-function\n",
+    "are\n",
+    "only used as starting points for the network simulation. The fix pressure and fluid temperature is\n",
+    "being determined by the circular pump component which will be created afterwards."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "j0 = pp.create_junction(net, pn_bar=4, tfluid_k=293.15, name=\"junction 0\")\n",
+    "j1 = pp.create_junction(net, pn_bar=4, tfluid_k=293.15, name=\"junction 1\")\n",
+    "j2 = pp.create_junction(net, pn_bar=4, tfluid_k=293.15, name=\"junction 2\")\n",
+    "j3 = pp.create_junction(net, pn_bar=4, tfluid_k=293.15, name=\"junction 3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, the pump will be created. The type of pump we choose needs a mass flow, a pressure level and a\n",
+    "temperature as input. Note that the circular pump is a component, which internally consists of an\n",
+    "external grid, connected to the junction specified via the from_junction-parameter and a sink,\n",
+    "connected to the junction specified via the to_junction-parameter.\n",
+    "\n",
+    "However, the internal structure is not visible to the user, so that the circular pump const pressure component\n",
+    "supplies a fluid flow with the specified properties."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pp.create_circ_pump_const_pressure(net, return_junction=j0, flow_junction=j1, p_flow_bar=4, plift_bar=1.5, t_flow_k=273.15+70,\n",
+    "                                    type=\"auto\", name=\"const_pressure_pump\", index=None, in_service=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, the heat consumer component is created.\n",
+    "The most important parameter for this component is the heat flux `qext_w`. A positive value of\n",
+    "`qext_w` means that heat is withdrawn from the network and supplied to a consumer.\n",
+    "A negative value of `qext_w` corresponds to a heat source, i. e. thermal energy is being transfered\n",
+    "from the heat exchanger into the network.\n",
+    "\n",
+    "Additionaly, controlled_mdot_kg_per_s, deltat_k or treturn_k can be added"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pp.create_heat_consumer(net, from_junction=j2, to_junction=j3, qext_w=10000, controlled_mdot_kg_per_s=None,\n",
+    "                         deltat_k=None, treturn_k=50, name=None, index=None, in_service=True, type=\"heat_consumer\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following commands defines the pipes between the components. Each pipe will consist of five\n",
+    "internal sections in order to improve the spatial resolution for the temperature calculation.\n",
+    "The parameter `text_k` specifies the ambient temperature on the outside of the pipe. It is used to\n",
+    "calculate energy losses."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pp.create_pipe(net, from_junction=j1, to_junction=j2, std_type=\"110/202 PLUS\", length_km=1, k_mm=0.1, loss_coefficient=0,\n",
+    "               sections=5, text_k=283, qext_w=0., name=\"pipe_0_1\", index=None, geodata=None, in_service=True, type=\"pipe\")\n",
+    "pp.create_pipe(net, from_junction=j3, to_junction=j0, std_type=\"110/202 PLUS\", length_km=1, k_mm=0.1, loss_coefficient=0,\n",
+    "               sections=5, text_k=283, qext_w=0., name=\"pipe_1_2\", index=None, geodata=None, in_service=True, type=\"pipe\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "We now run a pipe flow.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pp.pipeflow(net, mode='bidirectional', iter=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By default, only the pressure and velocity distribution is calculated by the pipeflow function. If\n",
+    "the `mode`-parameter is set to \"all\", the heat transfer calculation is started automatically\n",
+    "after the hydraulics computation. Computed mass flows are used as an input for the temperature\n",
+    "calculation. After the computation, you can check the results for junctions and pipes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "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>p_bar</th>\n",
+       "      <th>t_k</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2.500000</td>\n",
+       "      <td>225.464713</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>343.150000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.999974</td>\n",
+       "      <td>297.097587</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.500303</td>\n",
+       "      <td>50.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      p_bar         t_k\n",
+       "0  2.500000  225.464713\n",
+       "1  4.000000  343.150000\n",
+       "2  3.999974  297.097587\n",
+       "3  2.500303   50.000000"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "net.res_junction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that a constant heat flow is extracted via the heat exchanger between nodes 1 and 2. Heat\n",
+    "losses due to the ambient temperature level are not taken into account. These are only included in\n",
+    "the pipe components. This also means that - if the extracted heat flow is large enough - the\n",
+    "temperature level behind the heat exchanger might be lower than the ambient temperature level. A\n",
+    "way to avoid this behaviour would be to create a controller which defines a function for the\n",
+    "extracted heat in dependence of the ambient temperature."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "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>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>t_outlet_k</th>\n",
+       "      <th>mdot_from_kg_per_s</th>\n",
+       "      <th>mdot_to_kg_per_s</th>\n",
+       "      <th>vdot_m3_per_s</th>\n",
+       "      <th>reynolds</th>\n",
+       "      <th>lambda</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.001181</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>3.999974</td>\n",
+       "      <td>343.15</td>\n",
+       "      <td>297.097587</td>\n",
+       "      <td>297.097587</td>\n",
+       "      <td>0.009375</td>\n",
+       "      <td>-0.009375</td>\n",
+       "      <td>0.000009</td>\n",
+       "      <td>189.304289</td>\n",
+       "      <td>0.376229</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.001173</td>\n",
+       "      <td>2.500303</td>\n",
+       "      <td>2.500000</td>\n",
+       "      <td>50.00</td>\n",
+       "      <td>225.464713</td>\n",
+       "      <td>225.464713</td>\n",
+       "      <td>0.009375</td>\n",
+       "      <td>-0.009375</td>\n",
+       "      <td>0.000009</td>\n",
+       "      <td>16.052576</td>\n",
+       "      <td>4.459393</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   v_mean_m_per_s  p_from_bar  p_to_bar  t_from_k      t_to_k  t_outlet_k  \\\n",
+       "0        0.001181    4.000000  3.999974    343.15  297.097587  297.097587   \n",
+       "1        0.001173    2.500303  2.500000     50.00  225.464713  225.464713   \n",
+       "\n",
+       "   mdot_from_kg_per_s  mdot_to_kg_per_s  vdot_m3_per_s    reynolds    lambda  \n",
+       "0            0.009375         -0.009375       0.000009  189.304289  0.376229  \n",
+       "1            0.009375         -0.009375       0.000009   16.052576  4.459393  "
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "net.res_pipe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The command above shows the results for the pipe components. The temperatures of the adjacent\n",
+    "junctions are displayed. Due to heat losses, the temperatures at the to-nodes is lower than the\n",
+    "temperatures at the from-nodes. Note also that the junctions are not equal to the internal nodes,\n",
+    "introduced by the pipe sections we defined. To display the temperatures at the internal nodes, we\n",
+    "can retrieve the internal node values with the following commands:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandapipes.component_models import Pipe\n",
+    "pipe_results = Pipe.get_internal_results(net, [0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The parameters of the get_internal_results function correspond to the net and the pipes we want to\n",
+    "evaluate. In this case, only the results of pipe zero are retrieved. The returned value stored in\n",
+    "pipe_results is a dictionary, containing fields for the pressure, the velocity and the temperature.\n",
+    "The dictionary can either be used for own evaluations now or it can be used to plot the results over\n",
+    "the pipe length:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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TGDJkCHQ6HQoKCjB27Nhm+8hkspt63L68vByXL1/GnDlzDMv+OI8MAPj7+zf7VFpz9Ho96urqoNfrrxuMJBKJ4RbhN998A19fXwwdOtSoj1QqNdTzzTffIDo6uknIs7KyMjzSvXHjRkydOrXJk0zW1tbw9vZGXV0d/vvf/2LGjBmtOhairmLlypWG1zPPPGOWGm699VacOXMGAAxjsYioeQxG7VReXo7U1FTD+/T0dCQkJMDZ2RmhoaGYPXs25s6di3fffRdDhgxBYWEh9u7di4EDB2LKlClt3t8zzzyDO++8E/7+/sjNzcWyZcsglUpb/NLOG/nqq69gZWWFAQMGQC6X4+TJk1iyZAlmzpxpmHBuy5YtWLJkidEkcitXrsTtt98OCwsLfPfdd3jrrbfw7bffGoJUUVERNm/ejFtvvRXV1dVYu3YtNm3ahAMHDhi2kZycjOPHjyMqKgolJSV47733cP78eaNBsceOHcOVK1cwePBgXLlyBS+//DL0en2Lj00TdUW/n4DS1I/2X8+nn34KrVYLANe9Ck1E4Bij9tq3b1+z44Li4uKEEELU1taKl156SQQEBAgrKyvh5eUl7r77bnH27Nl27W/mzJnCy8tLyGQy4e3tLWbOnClSU1Ovuw6u87j+xo0bxdChQ4W9vb1h/NGbb74pqqqqDH0ax0H83vjx44VSqRTW1tYiKipK7Nixw6i9sLBQjBw5UtjZ2QlbW1sxYcIEw9iiRhcuXBCDBw8WNjY2QqFQiGnTpolLly4Z9dm/f7/o16+fkMvlwsXFRcyZM0dcuXLlRqeJiIjopnCMEREREVEDzmNERERE1IDBiIiIiKgBB1+3kV6vR25uLhwcHFr8LiYiIiLqWoQQ0Gq1UKlUTZ6A/j0GozbKzc2Fr6+vucsgIiKidsjOzjZMFdOcHhWMfv31V6xcuRLx8fHIy8szmnQRuJYWly1bhs8//xylpaUYPXo01qxZgz59+rR6H41fv5GdnQ2FQtHRh0BEREQmoNFo4Ovre8Ov0epRwaiiogKDBg3CQw89hHvuuadJ+4oVK/DBBx9g/fr1CAwMxNKlSxEbG4sLFy40+cLSljTePlMoFAxGRERE3cyNhsH0qGA0efJkw7dS/5EQAqtWrcKLL76IadOmAQC++OILeHh44Pvvv8f999/fmaU2cXbfZlTlp8BWFQ63wAh4eAdBcp17oERERNTxelQwup709HSo1WrExMQYlimVSkRFReHIkSMtBqOamhrU1NQY3ms0GpPUV3v6G0RpfgYaJpmuFHLkWvqg1DYAdU4hkHmEwtGvP1RBEbCxu/5lQCIiImqfXhOM1Go1AMDDw8NouYeHh6GtOcuXL8crr7xi0toAQKcahtP1lXCpzoSXTg1bSQ1CdJcB7WVAuxfIAnAC0AsJ8iSuKLT2R6UiCBLXPrDz7gfPwIFw8fTlVSYiIqKb0GuCUXstWbIEixYtMrxvHLzV0aLuXwJgCQCgrrYGWRkXcTXjPGrUSbAoToWiPB1e9VlQSirghUJ4VRcC1SeBAgAXrm2jXNgg18oXGlt/1DmHQO7ZF05+EfAKDIe1jV2H10xERNTT9Jpg5OnpCQDIz8+Hl5eXYXl+fj4GDx7c4npyuRxyudzU5RmxksnhFzoYfqGDjZYLvR7FRXlQp51D+ZWL0BckwVqTDtfqTHjp1bCXVCG0PhnQJAOaPUAGgKOATkiQY+GBq9Z+qFIEQ+LaBw4+4fAIGgBnNxWvMhERETXoNcEoMDAQnp6e2Lt3ryEIaTQaHDt2DAsXLjRvca0ksbCAs7s3nN29Adxu1FZTXYmc9IsozjqPanUSLItToaxIh1ddNhwkVfARavhUqYGq40A+gMRr62lghzxLX2jsA1HvFAxrr35w9guHV2A4ZPLWPalHRETUU/SoYFReXo7U1FTD+/T0dCQkJMDZ2Rl+fn546qmn8Prrr6NPnz6Gx/VVKpXRXEfdldzaFv79IuHfL9JoudDrUVSQg/zL51CeexGiMBk2mjS41WTBU18AhaQCivpLQOkloBRAOoDDQL2wQLaFJ67a+KNaEQQLt1AofMLhGTwQjq6e5jhEIiIik5MIIYS5i+go+/fvx/jx45ssj4uLw7p16wwTPH722WcoLS3FmDFj8PHHHyM0NLTV+9BoNFAqlSgrK+v28xhVV5YjLz0RxZkXUJt/CVYlqVBWZMC7Phu2kpoW1yuBA9RWvtDaB0LvHAJrr35w8Q+HV0A/WFrJOvEIiIiIWqe1n989Khh1hp4UjFoi9HoU5mWiIO0sKnIvAUXJsNWkwa0mG54obHG9WiFFnlSFYms/VDsGw9I9DA4+/eAVPAhKJ9dOPAIiIiJjDEYm0huC0fVUlpch9/J5lOZcQJ06CbLSy3CszICqPgc2ktoW1yuCIwpk164yCZcQ2Hj1g2vAAHj69YHUskfd0SUioi6IwchEenswaolep0PBlTQUpp9DRe5FSK6mwk6bBveaLLijuMX1aoQVcqUqlNj6o0YZDCuPMCh8wuEVPAAOSudOPAIiIurJGIxMhMGo7co1Jci7fA5l2RdQV5AEeWkqnCqzoNJdgVxS1+J6BXBGocwX5Q6BEK6hsPXqC7fA/vDwCYGFVNqJR0BERN0dg5GJMBh1HF19PdRZKSjKPI+q3IuQFKfCXpsGj9psuKK0xfWqhAy5lt4otQ1AreO1q0yOvhFQBfeHrb2y8w6AiIi6DQYjE2Ew6hxlJUVQXz4LTc4F1Bckw7rsMpyqMqHS5UIm0bW4nhquKJT7otIhCHALha1XP7gH9Ye7KpATWRIR9WIMRibCYGRe9XW1yMtMwtXMRFTnXYTF1RQ4lGfAsy4bTmj5C34NX8prF4g6pxBYew+AR+hwePn1YWAiIuoFGIxMhMGo6yotUkOddg7anAuoL0yGdVkaXKsy4KVXw1Kib3YdDWyRLQuGVtkXFqqBcA6KhG/YEMitbTu5eiIiMiUGIxNhMOp+amuqkZdxCcWZDV+XcjUJzuUp8K3PbPa2XJ2QIlvqi6sOodC594e9/xD49B3BGb+JiLoxBiMTYTDqOWprqpGdnIDiyyehyzsHh5KL8KlNhRIVzfZXwxVqmxBUuYRD7jPo2q04/758Qo6IqBtgMDIRBqOeTej1yM+5DHXSCVTlnIG8KBEelcnwFvnN9q8Q1siSBUGj7AuJ10A4BUXCN2worG3tO7lyIiK6HgYjE2Ew6p00pVeRc+kEtBmnIck/BydNEvzqM5udh0knJMiW+qDIPgz1bhGw8x8CVdhwuHj4mKFyIiICGIxMhsGIGtXX1SIn9SyKUk6iPvcs7EouwKfmcotPxxXCCbnWIah0DofMZxDcQiLhHdSfX4lCRNQJGIxMhMGIrkfo9ShSZyH30nFUZSVAVnQebhUp8NbnwULS9K9apZAj2yoQpcq+gEd/KIOGwq/vME5USUTUwRiMTITBiNqjQluK7EsnUZZ+ClCfg5PmEnzrMpr94l29kCBHqkKhXShqXcNh6zcE3mEj4OLpyzmXiIjaicHIRBiMqKPo6uuRc/k8ClNPoC7nLOxKLkJVndLi16FchRK58mBUOPWDpfcguIUMg3fIAFhayTq3cCKibojByEQYjMjUrt2KO4GKrARYFSbCrSIZProcSJu5FVctrJBtFYAShzAIzwFQBgyFT7/hsFc4maFyIqKui8HIRBiMyByqKrTITopHadopCPU5KMsuwa/2MmwlNc32z5F4osA2FDWu4bDxGwLP0GHw8A7irTgi6rUYjEyEwYi6Cr1OhyvpF1CYchI1OWdgW3wBXlUpcEdxs/1LYY8ceQjKHftBqhoIl+BI+IYOhpVM3smVExF1PgYjE2Ewoq6upDAPVy4dR3nmaVgWnIdLeTJ8ddnNfl9crbBElqU/ih3CoPfoDwf/IfDpNwJKJ1czVE5EZDoMRibCYETdUXVVBXKST6Pkcjz0eWfhUHoJvrWX4SCparZ/rsQd+TZ9UO0aAevGrz/x68NbcUTUbTEYmQiDEfUUQq9HXmYy1MknUJOTAOurF+BZmQIvFDbbXwNbZMuCoVX2hYVqIJyDIuEbNgRya9tOrpyIqO0YjEyEwYh6urLiQuRcPA5t5ilI88/DuTwZvvWZkEl0TfrWCSmypb646hAKnXt/2PsPgU/fEXB09TRD5URELWMwMhEGI+qNamuqkZ2cgOLLJ6HLOweHkovwqU2FEhXN9s+HC/Js+qDKbSDsg6PhN/AWjlsiIrNiMDIRBiOia4Rej/ycy1AnnUBVzhnIixLhUZkMb5HfbP9MC1/kKwYAPsPh1m80/MIi+T1xRNRpGIxMhMGI6Pq0ZcXIuXQCZWknYZl3Cp7a8/AR6ib9KoQ10q37Qus6BDZBI+E3YCyc3b3NUDER9QYMRibCYETUdlfzc5B97iCq0o/CoSgBgdWXYCepbtIvR+IFtUN/6LyHwTlsNALCR3CeJSLqEAxGJsJgRHTzdPX1yLwUj8KLv0Fy5SQ8NGfhr89p0q9KyJAuC4XGZRDkgVHwGTAObqqAzi+YiLo9BqMWaLVaLF26FFu2bEFBQQGGDBmC999/H8OHD2/V+gxGRKZRVlyIzLO/oiLtKOwKTiGg+iIUzQzuVsMNV+wjUOcVCcfQUQjoHw1rGzszVExE3QmDUQtmzpyJ8+fPY82aNVCpVNiwYQP++c9/4sKFC/D2vvH4BgYjos6h1+mQnXoO+RcOQuScgFvJGfjrMpt8mW6tsES6VTBKnAfB0n8EVBG3cDJKImqCwagZVVVVcHBwwA8//IApU6YYlkdGRmLy5Ml4/fXXb7gNBiMi8ynXlCDj3G/Qph6BTf4p+FUmwhmaJv2K4Ihs2whUe0ZCERKNwIGjYWuvNEPFRNRVtPbzu1c9K1tfXw+dTgdra2uj5TY2Nvjtt9+aXaempgY1Nf/7BnONpukvYSLqHPYKJ/QffScw+k4A16YMuJKRhLzEX6HLPAbn0rMIqEuDq6QUrpWHgLRDQNoHqN9tgVTLQFx1HAAL3xHw7H8LfIIieFWJiJroVVeMAGDUqFGQyWT4+uuv4eHhgW+++QZxcXEICQlBUlJSk/4vv/wyXnnllSbLecWIqGuqrixHxrnDKE05BFneKfhUnIc7ipv0K4U9Mm3CUek+FPbBI+E/8BYoHF3MUDERdQbeSmvB5cuX8dBDD+HXX3+FVCrF0KFDERoaivj4eFy8eLFJ/+auGPn6+jIYEXUj+TmXkXP2V9RlHoNj8RkE1qZALqkz6qMXEmRJfVGgvDYJpXv4WPiFDoGFVGqmqomoIzEY3UBFRQU0Gg28vLwwc+ZMlJeXY/v27Tdcj2OMiLq/2ppqZCQeQ3HSb7DMjYeq/DxUzczYrRU2yLDui3K3IbANiob/wFv4PXBE3RSDUSuVlJQgMDAQK1aswPz582/Yn8GIqGcqUmcj++wBVGccg6IoAYE1SbCV1DTply1RQa3oD733cLiEjUZA+HBYWsnMUDERtQWDUQt2794NIQTCwsKQmpqKxYsXw9raGgcPHoSVldUN12cwIuod6utqkXnxJIouHYLFlZPw0J6Dn/5Kk36VQo4MeSjKXAbBOjAavgNvgaunnxkqJqLrYTBqwbfffoslS5YgJycHzs7OuPfee/HGG29AqWzdo7wMRkS9V2mRGpnnDqIy7SjsCk83TEJZ2aRfHtyQ69AfdV7D4BQ6CgH9R0JubWuGiomoEYORiTAYEVEjvU6H7OQEFFz87doklKVn4a/LgkWzk1CGoMRlMKz8R8C7/y3w8AnmdAFEnYjByEQYjIjoerRlxcg8exDay0dgm38KflUX4ARtk36FcEKOXQRqPCOh6DMKgQNGw8bOwQwVE/UODEYmwmBERG0h9HpcSbsA9YWD0GUdh0vJGQTUp8NSojfqVy8skGEZiKtOgyD1GwHP8LHwDgrnVSWiDsJgZCIMRkR0s6oqtEg/dwialMOQq+PhW3Eeriht0q8ECmTahKPKYwgcgqPhP3AsHJTOnV8wUQ/AYGQiDEZE1NGEXo/8nMu4cv5X1GUcg1PxGQTWpUImqTfqpxcSZEr9Ueg4ABKf4XDvNwa+oYM5CSVRKzAYmQiDERF1hprqSmScP4qS5EOwyouHSnseXihs0k8DW2RYR6BSFQ3n/hMQNGAU51UiagaDkYkwGBGRuRTlZiL7/EFUpx+B8uoZBNYkwUZSa9RHK2yQZjsQVd7RcOkfg6D+0ZBa9qrvCydqFoORiTAYEVFXUVdbg8yLJ1B0/hfIrxxBcGVCk3mVNLBFmu0gVHuPgmv/CQjqP5K33qhXYjAyEQYjIuqqdPX1SDt/BFfP74XNlcMIrjwLe0mVUZ8y2CHNdjBqfEbBfeBEBPQbxqBEvQKDkYkwGBFRd1FfV4u0c0dQfP5n2OQeQUjVOdhJqo36lMAB6XaDUec7Gh6DJsI/bCinCKAeicHIRBiMiKi7qqutQdrZQyhO3Au7vCMIqTrf5Ityi6FAhv0Q1PmOhuegifALHcygRD0Cg5GJMBgRUU9RV1uDywm/ouTCXtjnHUVIdWKTwdxFcESmwxDU+42BavBE+AQPYFCibonByEQYjIiop6qprkTamYMovfALHNTXgpK1pM6oTwGckaUYCr3faKgGT+Ls3NRtMBiZCIMREfUW1VUVuHz6ADSX9kGhPoI+NRebTDqZDxdkK4ZCBIyFavAkqALCGJSoS2IwMhEGIyLqraory5F6eh/KL+6DIv8YQmovQibRGfVRww3ZykiIgDHwHToJXv5hZqqWyBiDkYkwGBERXVNVoUVq/C8oT9oHp4JjCK5NgtUfglKuxAM5ykhIAsfCd+gkePqGmKla6u0YjEyEwYiIqHkV2lKknboWlJwLjiO4LhmWEr1RnxyJJ3Idh8EicCz8h90ON1WAeYqlXofByEQYjIiIWqdcU4LL8T+jMnk/XAqPI7guBVKJ8UdOtkSFXKdhkAbfgoDIWLh6+pmpWurpGIxMhMGIiKh9tGXFSIv/CVVJ++FSdALB9Zdh8YeglGnhA7XTMFg2BCUXDx8zVUs9DYORiTAYERF1jLKSIqTH70F1yn64FR1HYH16k6CUYeGHfOdhsAoZh8DIWDi5eZmpWuruGIxMhMGIiMg0yq7mIy1+D2pSD8C96DiC9BlN+qRbBCDfZThkIeMQPGwSlC4enV8odUsMRibCYERE1DlKCvOQceon1KYcgEfxCQTos4za9UKCdMtAFLoMh3WfWxEYORFKZzczVUtdHYORiTAYERGZx9X8HGTE70H95f3wLImHvz7bqF0vJEizDEKR6whYh96KoMiJUDi6mKla6moYjEyEwYiIqGsoUmchM/4n1Kf9Cq+Sk/DTXzFq1wkJ0qxCcNV1BGzCxiM4Mgb2CiczVUvmxmBkIgxGRERdU2FuBjLjd0OffhCqkpPwEXlG7fXCApet+qDYPQp2obciOHIC7BwczVMsdToGIxNhMCIi6h7ycy4j+9RP0Kf9Cu+yeHiLfKP2OiHFZVkYStxHwD7sNoRE3gYbOwczVUumxmBkIgxGRETdU15mEnJO7wEyfoNP6Ul4odCovVZIcVnWF2UeI2Hf91aEDL0N1rb2ZqqWOhqDkYkwGBER9Qy5GUnIOb0bkoyD8CuLhweuGrXXCkukyPtB4zESin63IXjIOFjb2JmpWrpZDEYmwmBERNTzCL0euRkXceX0T7DI/A1+mlNwR7FRnyohQ7LtYFT5jYf38DvhGzLATNVSezAYNUOn0+Hll1/Ghg0boFaroVKpMG/ePLz44ouQSCSt2gaDERFRzyf0euSkJSL39E+wzPoN/tpTcEWpUZ8ciSeuuIyCvF8sQqMmw9ZeaZ5iqVUYjJrx5ptv4r333sP69esRERGBkydP4sEHH8Qbb7yBJ598slXbYDAiIup9hF6PjIsnkH9qO+xzDiC0+hxkEp2hvVZYIsl6ACp8b4Vn5FT4hw2FxMLCjBXTHzEYNWPq1Knw8PDA//3f/xmW3XvvvbCxscGGDRuaXaempgY1NTWG9xqNBr6+vgxGRES9WIW2FClHd6Dm0m74Fh+GShQYtavhiiznaFiFTURw1FRONNkFtDYYWXZiTWY3atQofPbZZ0hOTkZoaCjOnDmD3377De+9916L6yxfvhyvvPJKJ1ZJRERdnZ2DIwZP/DMw8c8Qej2yUs8i9+Q22GbtR2hVAjwlRfAs/hE48iPqDz+FC7JwlPmMg9vgKQjqPxIWUqm5D4Fa0KuuGOn1evzjH//AihUrIJVKodPp8MYbb2DJkiUtrsMrRkRE1BbVleVIPrYLlRd3Q1V0qMmM3EVwRLoyCpLQiQiJuhOOrp5mqrR34a20ZmzcuBGLFy/GypUrERERgYSEBDz11FN47733EBcX16ptcIwRERG1RW76JWSf2ApZxj6EVcTDVvK/f2zrhQQpVqEo9roFzoPuQMjgWyC17FU3czoNg1EzfH198fzzz+Pxxx83LHv99dexYcMGXLp0qVXbYDAiIqL2qq2pRvLJPSg/txMeBYcQqM8wai+FPS47jIA+eAICR94FV08/8xTaA3GMUTMqKyth8YenBKRSKfR6vZkqIiKi3kQmt0b/0XcCo+8EABRcSUfmsa2Qpv2MkPKTcEQ5IrW/AAm/AAkvIFUajEKPMVAOvAN9ho6HlUxu5iPo+XrVFaN58+bh559/xqeffoqIiAicPn0a8+fPx0MPPYS33367VdvgFSMiIjKF+rpapJ7aj5JzO+Ga9yv66FKN2rXCBin2w1AfeBv8ou6Cp2+ImSrtnngrrRlarRZLly7Fli1bUFBQAJVKhVmzZuGll16CTCZr1TYYjIiIqDNczc9B+rEfgdSfEaw5DidojNozLPygdhsNu4jJCB0xEXJrWzNV2j0wGJkIgxEREXU2XX09Lp89hKtndsAp91f0qb0IqeR/H9+VQo5k2yGoCRgPn+HT4B3Uz4zVdk0MRibCYEREROZWdjUfqce2QZf8MwJLj8ANJUbt2RIVcl1Hwbrh60ps7BzMVGnXwWBkIgxGRETUlQi9HmmJx1FwejsUOfsRWpMIq999XUmNsEKSzUBU+t4Kr8ip8Asd3Cu/roTByEQYjIiIqCvTlhUj9dgO1Cb9BP+rh+GJQqP2PLghy2UUZGGTEBJ1BxyUzmaqtHMxGJkIgxEREXUXQq9HVnIC8uK3wTZ7P8KqzkIuqTO01wkpkuUR0PjcCvchUxAUMaLHXk1iMDIRBiMiIuquKsvLkHJ8N6ov7oaq6DB8Ra5ReyGckO4YDWloDEKipkLp4mGmSjseg5GJMBgREVFPcSUtETknfoQ8Yx9CK08bfV2JTkiQYtUXJd7j4DLoDoQMGtOtv/yWwchEGIyIiKgnqqmuRPLxPahI3AnPwkMI0GcZtZdAgcuKEUBIDAKj7oSLh4+ZKm0fBiMTYTAiIqLeQJ2diqxjW2GZ/gv6lJ+Eg6TKqD1FGoIiz7FwGngHQobeCkur1k2UbC4MRibCYERERL1NXW0NUk7tQ9nZHXDL/w0hustG7RrYItV+GHRBMfCPugvu3oFmqrRlDEYmwmBERES9XZE6C+lHf4TF5Z8RrD0OR5QbtadbBCDffTTsB0xG6LCJkMmtzVTp/zAYmQiDERER0f/o6uuRmvAris/uhHPuAfSpS4bF776upEJYI9luKGoDboPviLugCggzS50MRibCYERERNSy0iI1Uo/9CJG8B4Flx+CKUqP2LAtv5LqOhm347QgdEQtrW/tOqYvByEQYjIiIiFpHr9Mh7fxRFCZshzLnAEJrL8BSoje0VwsrJNsMRqXfrfAefid8ggeYbIJJBiMTYTAiIiJqH03pVVw+tg11SXvgX3wYHrhq1J4r8UC2czScxzyMPkNu6dh9MxiZBoMRERHRzRN6PTKTTkEdvw122fsRVn0OMkk9AODEkOUYPu2xDt1faz+/LTt0r0REREStILGwQEC/YQjoNwwAUKEtxYXju1BzcTeCo+40X128YtQ2vGJERETU/bT287tnfoUuERERUTvwVlobNV5g02g0Zq6EiIiIWqvxc/tGN8oYjNpIq9UCAHx9fc1cCREREbWVVquFUqlssZ1jjNpIr9cjNzcXDg4OkEgkHbZdjUYDX19fZGdnc+ySifFct9/999+P/fv3IyUlBQ4ODs32eeSRR/D9998jPj4eAwcObNV5ViqVeP7557FkyZIOrzkzMxMDBw7Exx9/jNmzZwMAfvrpJ8THx3fo/hr3M2/ePIwaNQqjRo0yyT+gHnjgAVRXV2Pz5s0Abvzz/NNPP6GkpARLlizBsGHD8O2333Z4Tb0Bf290DlOeZyEEtFotVCoVLK4zVxKvGLWRhYUFfHx8TLZ9hULBv3SdhOe67eLi4rBz507s3bsXc+fObdJeWVmJHTt24Pbbb4e/vz+A1p9nuVxukj+P/v37o6qqClZWVpBKpQCA/fv3Y/Xq1Vi+fHmH7acxKI4bNw7z5s3rsO3+Xl1dHfbv34/ly5c3OVctnef77rsPAPDGG2/A0tKSP/M3ib83OoepzvP1rhQ14uBrImq1u+66Cw4ODvj666+bbf/hhx9QUVFhuDLTFUgkElhbWxtCUXd28OBBaLVaTJkyxdylEPVYDEZE1Go2Nja45557sHfvXhQUFDRp//rrr+Hg4IC77rrLsOz555+Hr68v5HI5QkJC8Pbbb0Ov1zdZ949Onz6NyZMnQ6FQwN7eHhMmTMDRo0eb9CstLcXTTz+NgIAAyOVy+Pj4YO7cuSgqKgIAZGRkQCKRYN26dQCAefPmYfXq1QCuhabGlxACAQEBmDZtWpN9VFdXQ6lUYsGCBa06T3/08ssvQyKRIDk5GQ888ACUSiXc3NywdOlSCCGQnZ2NadOmQaFQwNPTE++++26z29m+fTvCw8MREBAAAFCr1XjssWuT4Lm5ucHLywvTpk1DRkZGu+okIgajLkMul2PZsmWQy+XmLqXH47m+ObNnz0Z9fX2TsSrFxcXYvXs37r77btjY2ECn08HDwwPffvst5s6diw8++ACjR4/GkiVLsGjRouvuIzExEWPHjsWZM2fw7LPPYunSpUhPT8ett96KY8eOGfqVl5dj7Nix+PDDDzFp0iS8//77+Mtf/oJLly4hJyen2W0vWLAAEydOBAB8+eWXhpdEIsEDDzyAnTt3ori42GidH3/8ERqNBg888EB7TpnBzJkzodfr8dZbbyEqKgqvv/46Vq1ahYkTJ8Lb2xtvv/02QkJC8Mwzz+DXX39tsv6OHTtwxx13GN7fe++92LZtG8aOHYv3338fTz75JLRaLbKysm6qTmqKvzc6R5c4z4KIqA3q6+uFl5eXiI6ONlr+ySefCABi9+7dQgghXnvtNWFnZyeSk5ON+j3//PNCKpWKrKwswzIAYtmyZYb306dPFzKZTFy+fNmwLDc3Vzg4OIhbbrnFsOyll14SAMR3333XpE69Xi+EECI9PV0AEGvXrjW0Pf7446K5X39JSUkCgFizZo3R8rvuuksEBAQYttmc5vbTaNmyZQKAmD9/vmFZfX298PHxERKJRLz11luG5SUlJcLGxkbExcUZbSMtLU0AEPv27TP0AyBWrlzZYk2/5+/vL6ZMmdKqvkS9Ga8YEVGbSKVS3H///Thy5IjRLZuvv/4aHh4emDBhAgBg06ZNGDt2LJycnFBUVGR4xcTEQKfTNXtFBAB0Oh1++uknTJ8+HUFBQYblXl5e+POf/4zffvvNMB/Jf//7XwwaNAh33313k+2056nR0NBQREVF4auvvjIsKy4uxs6dOzF79uybfhL1kUceMfy/VCrFsGHDIITAww8/bFju6OiIsLAwpKWlGa27fft2KJVKjBkzBsC125oymQz79+9HSUnJTdVFRP/DYEREbdY4uLpxEHZOTg4OHjyI+++/3zDIOSUlBbt27YKbm5vRKyYmBgCaHaMEAIWFhaisrERYWFiTtn79+kGv1yM7OxsAcPnyZfTv379Dj23u3Lk4dOgQMjMzAVwLeHV1dZgzZ85Nb9vPz8/ovVKphLW1NVxdXZss/2PY2b59OyZNmgRLy2sPE8vlcrz99tvYuXMnPDw8cMstt2DFihVQq9U3XSdRb8ZgRERtFhkZib59++Kbb74BAHzzzTcQQhg9jabX6zFx4kTs2bOn2de9995rrvKv6/7774eVlZXhqtGGDRswbNiwZoNaWzX3ZFxLT8uJ300xV1lZif379xuNLwKAp556CsnJyVi+fDmsra2xdOlS9OvXD6dPn77pWol6K85jRETtMnv2bCxduhRnz57F119/jT59+mD48OGG9uDgYJSXlxuuELWWm5sbbG1tkZSU1KTt0qVLsLCwMEycGBwcjPPnz7e59uvdEnN2dsaUKVPw1VdfYfbs2Th06BBWrVrV5n10pF9++QU1NTWYPHlyk7bg4GD8/e9/x9///nekpKRg8ODBePfdd7FhwwYzVErU/fGKERG1S+PVoZdeegkJCQlN5i6aMWMGjhw5gt27dzdZt7S0FPX19c1uVyqVYtKkSfjhhx+MxjDl5+fj66+/xpgxYwwTv9177704c+YMtmzZ0mQ74jqT+tvZ2RnqaM6cOXNw4cIFLF682DCmypx27NiBYcOGwcPDw7CssrIS1dXVRv2Cg4Ph4OCAmpqazi6RqMfgFSMiapfAwECMGjUKP/zwAwA0CUaLFy/G1q1bMXXqVMybNw+RkZGoqKjAuXPnsHnzZmRkZDQZW9Po9ddfx549ezBmzBg89thjsLS0xKeffoqamhqsWLHCaB+bN2/Gn/70Jzz00EOIjIxEcXExtm7dik8++QSDBg1qdvuRkZEAgCeffBKxsbFNws+UKVPg4uKCTZs2YfLkyXB3d7+pc3WzduzYgQcffNBoWXJyMiZMmIAZM2YgPDwclpaW2LJlC/Lz880e5Ii6MwYjImq32bNn4/DhwxgxYgRCQkKM2mxtbXHgwAG8+eab2LRpE7744gsoFAqEhobilVdeue7U/BERETh48CCWLFmC5cuXQ6/XIyoqChs2bEBUVJShn729PQ4ePIhly5Zhy5YtWL9+Pdzd3TFhwoTrfnXPPffcg7/+9a/YuHEjNmzYACGEUZiQyWSYOXMmPv744w4ZdH0zEhMTkZmZ2WR8ka+vL2bNmoW9e/fiyy+/hKWlJfr27Ytvv/22y47fIuoO+CWyRETNePrpp/F///d/UKvVsLW1vWH/jIwMBAYG4sMPP8T9998PhUIBmUx203WsWLEC7733HvLy8to1XUDjbcuhQ4di4MCB2LZt203XRNSTcYwREdEfVFdXY8OGDbj33ntbFYp+769//Svc3NywdevWDqklICAA//znP9s9h9Ktt94KNzc3wxQHRHR9vGJERNSgoKAAP//8MzZv3ozvv/8ep06dwuDBg1u1bnV1NX777TfD+4EDB5p9bBIAHDt2DFqtFsC1J/5aGndFRNcwGBERNdi/fz/Gjx8Pd3d3LF26FE888YS5SyKiTsZgRERERNSAY4yIiIiIGjAYERERETXgPEZtpNfrkZubCwcHh5v+pm0iIiLqHEIIaLVaqFQqWFi0fF2IwaiNcnNzDd/TRERERN1Ldnb2dSeAZTBqIwcHBwDXTmzj9zURERFR16bRaODr62v4HG8Jg1EbNd4+UygUDEZERETdzI2GwXDwNREREVEDBiMiIiKiBgxGRERERA0YjIiIiIgaMBgRERERNWAwIiIiImrAYERERETUgMGIiIiIqAGDEREREVEDBiMiIiKiBgxGRERERA0YjIiIiIgaMBgRERERNWAwIiIiImrAYERERETUgMGIiIiIqAGDEREREVEDBiMiIiKiBgxGRERERA0YjIiIiIgaMBgRERERNWAwIiIiImrQrmC0evVqBAQEwNraGlFRUTh+/Ph1+2/atAl9+/aFtbU1BgwYgB07dhi1CyHw0ksvwcvLCzY2NoiJiUFKSopRnzfeeAOjRo2Cra0tHB0dm+zjzJkzmDVrFnx9fWFjY4N+/frh/fffb9Jv//79GDp0KORyOUJCQrBu3bo2Hz8RERH1TG0ORv/5z3+waNEiLFu2DKdOncKgQYMQGxuLgoKCZvsfPnwYs2bNwsMPP4zTp09j+vTpmD59Os6fP2/os2LFCnzwwQf45JNPcOzYMdjZ2SE2NhbV1dWGPrW1tfjTn/6EhQsXNruf+Ph4uLu7Y8OGDUhMTMQLL7yAJUuW4KOPPjL0SU9Px5QpUzB+/HgkJCTgqaeewiOPPILdu3e39TQQERFRTyTaaMSIEeLxxx83vNfpdEKlUonly5c323/GjBliypQpRsuioqLEggULhBBC6PV64enpKVauXGloLy0tFXK5XHzzzTdNtrd27VqhVCpbVetjjz0mxo8fb3j/7LPPioiICKM+M2fOFLGxsa3anhBClJWVCQCirKys1esQERGRebX287tNV4xqa2sRHx+PmJgYwzILCwvExMTgyJEjza5z5MgRo/4AEBsba+ifnp4OtVpt1EepVCIqKqrFbbZWWVkZnJ2dW11Lc2pqaqDRaIxeRERE1DO1KRgVFRVBp9PBw8PDaLmHhwfUanWz66jV6uv2b/xvW7bZGocPH8Z//vMfzJ8//4a1aDQaVFVVNbud5cuXQ6lUGl6+vr7tromIiIi6th75VNr58+cxbdo0LFu2DJMmTbqpbS1ZsgRlZWWGV3Z2dgdVSURERF1Nm4KRq6srpFIp8vPzjZbn5+fD09Oz2XU8PT2v27/xv23Z5vVcuHABEyZMwPz58/Hiiy+2qhaFQgEbG5tmtyeXy6FQKIxeRERE1DO1KRjJZDJERkZi7969hmV6vR579+5FdHR0s+tER0cb9QeAPXv2GPoHBgbC09PTqI9Go8GxY8da3GZLEhMTMX78eMTFxeGNN95ocy1ERETUu1m2dYVFixYhLi4Ow4YNw4gRI7Bq1SpUVFTgwQcfBADMnTsX3t7eWL58OQDgb3/7G8aNG4d3330XU6ZMwcaNG3Hy5El89tlnAACJRIKnnnoKr7/+Ovr06YPAwEAsXboUKpUK06dPN+w3KysLxcXFyMrKgk6nQ0JCAgAgJCQE9vb2OH/+PG677TbExsZi0aJFhvFJUqkUbm5uAIC//OUv+Oijj/Dss8/ioYcewi+//IJvv/0W27dvb/cJJCIioh6kPY+8ffjhh8LPz0/IZDIxYsQIcfToUUPbuHHjRFxcnFH/b7/9VoSGhgqZTCYiIiLE9u3bjdr1er1YunSp8PDwEHK5XEyYMEEkJSUZ9YmLixMAmrz27dsnhBBi2bJlzbb7+/sbbWffvn1i8ODBQiaTiaCgILF27do2HTsf1yciIup+Wvv5LRFCCPNEsu5Jo9FAqVSirKyM442IiIi6idZ+fvfIp9KIiIiI2oPBiIiIiKgBgxERERFRAwYjIiIiogYMRkREREQNGIyIiIiIGjAYERERETVgMCIiIiJqwGBERERE1IDBiIiIiKgBgxERERFRAwYjIiIiogYMRkREREQNGIyIiIiIGjAYERERETVgMCIiIiJqYGnuAggQej2qKrXmLoOIiKhLsLF1gMTCPNduGIy6gKpKLWzf8TN3GURERF1C5TNZsLVXmmXfvJVGRERE1IBXjLoAG1sHVD6TZe4yiIiIugQbWwez7ZvBqAuQWFiY7ZIhERER/Q9vpRERERE1YDAiIiIiasBgRERERNSAwYiIiIioAYMRERERUQMGIyIiIqIGDEZEREREDRiMiIiIiBowGBERERE1YDAiIiIiasBgRERERNSgXcFo9erVCAgIgLW1NaKionD8+PHr9t+0aRP69u0La2trDBgwADt27DBqF0LgpZdegpeXF2xsbBATE4OUlBSjPm+88QZGjRoFW1tbODo6NrufJ598EpGRkZDL5Rg8eHCT9oyMDEgkkiavo0ePtun4iYiIqGdqczD6z3/+g0WLFmHZsmU4deoUBg0ahNjYWBQUFDTb//Dhw5g1axYefvhhnD59GtOnT8f06dNx/vx5Q58VK1bggw8+wCeffIJjx47Bzs4OsbGxqK6uNvSpra3Fn/70JyxcuPC69T300EOYOXPmdfv8/PPPyMvLM7wiIyPbcAaIiIioxxJtNGLECPH4448b3ut0OqFSqcTy5cub7T9jxgwxZcoUo2VRUVFiwYIFQggh9Hq98PT0FCtXrjS0l5aWCrlcLr755psm21u7dq1QKpXXrXHZsmVi0KBBTZanp6cLAOL06dPXXf96ysrKBABRVlbW7m0QERFR52rt53ebrhjV1tYiPj4eMTExhmUWFhaIiYnBkSNHml3nyJEjRv0BIDY21tA/PT0darXaqI9SqURUVFSL27xZd911F9zd3TFmzBhs3br1un1ramqg0WiMXkRERNQztSkYFRUVQafTwcPDw2i5h4cH1Gp1s+uo1err9m/8b1u22V729vZ49913sWnTJmzfvh1jxozB9OnTrxuOli9fDqVSaXj5+vp2aE1ERETUdViau4DO5OrqikWLFhneDx8+HLm5uVi5ciXuuuuuZtdZsmSJ0ToajYbhiIiIqIdq0xUjV1dXSKVS5OfnGy3Pz8+Hp6dns+t4enpet3/jf9uyzY4UFRWF1NTUFtvlcjkUCoXRi4iIiHqmNgUjmUyGyMhI7N2717BMr9dj7969iI6Obnad6Ohoo/4AsGfPHkP/wMBAeHp6GvXRaDQ4duxYi9vsSAkJCfDy8jL5foiIiKjra/OttEWLFiEuLg7Dhg3DiBEjsGrVKlRUVODBBx8EAMydOxfe3t5Yvnw5AOBvf/sbxo0bh3fffRdTpkzBxo0bcfLkSXz22WcAAIlEgqeeegqvv/46+vTpg8DAQCxduhQqlQrTp0837DcrKwvFxcXIysqCTqdDQkICACAkJAT29vYAgNTUVJSXl0OtVqOqqsrQJzw8HDKZDOvXr4dMJsOQIUMAAN999x3+/e9/41//+le7Th4RERH1MO155O3DDz8Ufn5+QiaTiREjRoijR48a2saNGyfi4uKM+n/77bciNDRUyGQyERERIbZv327UrtfrxdKlS4WHh4eQy+ViwoQJIikpyahPXFycANDktW/fPqN9N9cnPT1dCCHEunXrRL9+/YStra1QKBRixIgRYtOmTW06dj6uT0RE1P209vNbIoQQ5olk3ZNGo4FSqURZWRnHGxEREXUTrf385nelERERETVgMCIiIiJqwGBERERE1IDBiIiIiKgBgxERERFRAwYjIiIiogYMRkREREQNGIyIiIiIGjAYERERETVgMCIiIiJqwGBERERE1IDBiIiIiKgBgxERERFRAwYjIiIiogYMRkREREQNGIyIiIiIGjAYERERETVgMCIiIiJqwGBERERE1IDBiIiIiKgBgxERERFRAwYjIiIiogYMRkREREQNGIyIiIiIGjAYERERETVgMCIiIiJqwGBERERE1IDBiIiIiKgBgxERERFRA0tzF9DdCCEAABqNxsyVEBERUWs1fm43fo63hMGojbRaLQDA19fXzJUQERFRW2m1WiiVyhbbJeJG0YmM6PV65ObmwsHBARKJpMO2q9Fo4Ovri+zsbCgUig7bLjXFc93U9X5J/N62bdswduzYVvXtaef5888/h62tLWbPnm3uUow0nufbb78d99xzDwYPHoywsDAAwMGDBzF16lSsX78e06dPN6xTW1uLBx54AD/99BM+/PBDzJkzB8nJyTh9+jS2bt2Kbdu2IS0tDS4uLmY6qq6np/08d1WmPM9CCGi1WqhUKlhYtDySiFeM2sjCwgI+Pj4m275CoeBfuk7Cc/0/X375pdH7L774Anv27GmyfNiwYW0+Zz3lPK9duxaurq5YuHChuUtp1uDBg/Hoo48aLbOzswMA2NraGv4M6urqDKHo888/x8MPPwzg2p/tsGHDcOXKFWzbtg0ODg494s+to/WUn+euzlTnuTX/CGQwIiI88MADRu+PHj2KPXv2NFneUwghUF1dDRsbm15VR11dHWbMmIFt27bh008/NYQiIvofPpVGRK2i1+uxatUqREREwNraGh4eHliwYAFKSkqM+gUEBGDq1Kk4ePAgAMDDwwMDBgzA/v37AQDfffcdBgwYAGtra0RGRuL06dNG68+bNw/29vZIS0tDbGws7OzsoFKp8OqrrzYZNNnWmnbv3o1hw4bBxsYGn376KYBrV4Juu+02uLu7Qy6XIzw8HGvWrGmyfmJiIg4cOACJRAKJRIJbb70VAPDyyy83e1t93bp1kEgkyMjIaFUdpaWleOqpp+Dr6wu5XI6QkBC8/fbb0Ov1rfjTubH6+nrcf//9+OGHH7BmzZomV5eI6BpeMeoi5HI5li1bBrlcbu5Sejye6/ZZsGAB1q1bhwcffBBPPvkk0tPT8dFHH+H06dM4dOgQrKysDH1TU1Px6KOPYty4cZg6dSpWrVqFO++8E5988gn+8Y9/4LHHHgMALF++HDNmzEBSUpLRPX+dTofbb78dI0eOxIoVK7Br1y4sW7YM9fX1ePXVV9tVU1JSEmbNmoUFCxbg0UcfNYzDWbNmDSIiInDXXXfB0tISP/74Ix577DHo9Xo8/vjjAIBVq1bhr3/9K+zt7fHCCy8AuBb42qO5OiorKzFu3DhcuXIFCxYsgJ+fHw4fPowlS5YgLy8Pq1atanF7jT/HUqm0xT719fWYNWsWtmzZgtWrV2PBggXtqr034++NztElzrMgIvqDxx9/XPz+18PBgwcFAPHVV18Z9du1a1eT5f7+/gKAOHz4sGHZ7t27BQBhY2MjMjMzDcs//fRTAUDs27fPsCwuLk4AEH/9618Ny/R6vZgyZYqQyWSisLCw3TXt2rWrybFWVlY2WRYbGyuCgoKMlkVERIhx48Y16bts2TLR3K/StWvXCgAiPT39hnW89tprws7OTiQnJxstf/7554VUKhVZWVlNtv97AMSyZcuaLN+3b58AYNjv6tWrr7ud3x9P43km6m14K42IbmjTpk1QKpWYOHEiioqKDK/IyEjY29tj3759Rv3Dw8MRHR1teB8VFQUAuO222+Dn59dkeVpaWpN9PvHEE4b/l0gkeOKJJ1BbW4uff/65XTUFBgYiNja2yX5+P76nrKwMRUVFGDduHNLS0lBWVtbqc9RazdWxadMmjB07Fk5OTkbHEhMTA51Oh19//fWm9pmfnw9LS0sEBgbe1HaIegPeSiOiG0pJSUFZWRnc3d2bbS8oKDB6//vwA/zvSZA/zv/VuPyPY4IsLCwQFBRktCw0NBQADGN22lpTS6Hg0KFDWLZsGY4cOYLKykqjtrKyslZPZdBazdWRkpKCs2fPws3Nrdl1/ngsbbVixQqsWrUK9913H3766SeMHj36prZH1JMxGBHRDen1eri7u+Orr75qtv2PH+gtjXdpablox3Rqba2puSe/Ll++jAkTJqBv375477334OvrC5lMhh07duCf//xnqwY+tzSfmU6na3Z5c3Xo9XpMnDgRzz77bLPrNIbC9vLy8sKePXswZswYTJkyBQcOHMCgQYNuaptEPRWDERHdUHBwMH7++WeMHj26Ux4t1+v1SEtLMwoEycnJAK492dVRNf3444+oqanB1q1bja5y/fE2HNByAHJycgJw7akyR0dHw/LMzMxW1xEcHIzy8nLExMS0ep22CgoKwu7duzFu3DjExsbi4MGD6NOnj8n2R9RdcYwREd3QjBkzoNPp8NprrzVpq6+vR2lpaYfv86OPPjL8vxACH330EaysrDBhwoQOq6nxCtbvr1iVlZVh7dq1Tfra2dk1u83g4GAAMBoHVFFRgfXr199w/41mzJiBI0eOYPfu3U3aSktLUV9f3+ptXc+AAQOwfft2lJeXY+LEibhy5UqHbJeoJ+EVIyK6oXHjxmHBggVYvnw5EhISMGnSJFhZWSElJQWbNm3C+++/j/vuu6/D9mdtbY1du3YhLi4OUVFR2LlzJ7Zv345//OMfhltkHVHTpEmTIJPJcOedd2LBggUoLy/H559/Dnd3d+Tl5Rn1jYyMxJo1a/D6668jJCQE7u7uuO222zBp0iT4+fnh4YcfxuLFiyGVSvHvf/8bbm5uyMrKatXxLl68GFu3bsXUqVMxb948REZGoqKiAufOncPmzZuRkZEBV1fX9p3MP4iOjsZ3332HO++8ExMnTsTBgwf51R9Ev8NgRESt8sknnyAyMhKffvop/vGPf8DS0hIBAQF44IEHOnwwr1Qqxa5du7Bw4UIsXrwYDg4OWLZsGV566aUOrSksLAybN2/Giy++iGeeeQaenp5YuHAh3Nzc8NBDDxn1femll5CZmYkVK1ZAq9Vi3LhxuO2222BlZYUtW7bgsccew9KlS+Hp6YmnnnoKTk5OePDBB1t1vLa2tjhw4ADefPNNbNq0CV988QUUCgVCQ0PxyiuvdPgA8EmTJuHLL7/ErFmzMHnyZOzduxcODg4dug+i7opfIktEXcq8efOwefNmlJeXm7uUbkMikWDx4sV49tlnYWdn164xV9XV1SgvL8eKFSuwcuVKFBYWdthVKqLuhGOMiIh6gJUrV8LNzQ2rV69u1/qffPIJ3NzcsHLlyg6ujKh74a00IqJubs+ePYb/b++j/ffeey/69+9veN/Rt++IugsGIyKibq4jHvP39fVtMgEnUW/EMUZEREREDTjGiIiIiKgBgxERERFRA44xaiO9Xo/c3Fw4ODi0+BUBRERE1LUIIaDVaqFSqWBh0fJ1IQajNsrNzeUARSIiom4qOzsbPj4+LbYzGLVR4+yw2dnZUCgUZq6GiIiIWkOj0cDX1/eGs7wzGLVR4+0zhULBYERERNTN3GgYDAdfdxEJe77G0Y/nIyXhIIReb+5yiIiIeiVeMeoiLOL/jZHVJ4Dv/4PMrb7I87sLfrfGQRUQZu7SiIiIeg1eMeoixIj5iLe/FdXCCv76bIzMWA3VuhG48MZoHN/8HsqKC81dIhERUY/Hma/bSKPRQKlUoqyszCRjjDSlV3Fp31ewvfRfhFefgYXk2h9PrbDEeftoYOBMRIy7F3Jr2w7fNxERUU/V2s/vbnPFaM2aNRg4cKBh0HN0dDR27tzZpJ8QApMnT4ZEIsH3339v1JaVlYUpU6bA1tYW7u7uWLx4Merr6zvpCFpH4eiCEXc/if5LDqDgkZM4GvQk0i38IZPUY2jFQQw98gRq3grBsQ/m4OKx3dDrdOYumYiIqMfoNmOMfHx88NZbb6FPnz4QQmD9+vWYNm0aTp8+jYiICEO/VatWNTviXKfTYcqUKfD09MThw4eRl5eHuXPnwsrKCm+++WZnHkqrefqGwHPuawBeQ9r5Yyj4bT2C1DvhjmJEFW8Fdm5F7i53ZKqmQHXLPPiHDTZ3yURERN1at76V5uzsjJUrV+Lhhx8GACQkJGDq1Kk4efIkvLy8sGXLFkyfPh0AsHPnTkydOhW5ubnw8PAAAHzyySd47rnnUFhYCJlM1qp9mvpW2o3o6utx8ch2VMV/g34l+2EvqTK0pUhDcDX4boTcFgdXT05CSURE1KjH3Ur7PZ1Oh40bN6KiogLR0dEAgMrKSvz5z3/G6tWr4enp2WSdI0eOYMCAAYZQBACxsbHQaDRITExscV81NTXQaDRGL3OSWlqi/9hpGP7URkifTUH88HeRYDMSdUKKPrpUjExeCcc1A3H2rRic3PoJKsvLzFovERFRd9JtbqUBwLlz5xAdHY3q6mrY29tjy5YtCA8PBwA8/fTTGDVqFKZNm9bsumq12igUATC8V6vVLe5z+fLleOWVVzroCDqWjZ0DIqc8Akx5BMUFV5DyyxdwTN2CsPokDKw+AZw6gcr4l3DCcRysh85Cv1FTYWnVuitjREREvVG3CkZhYWFISEhAWVkZNm/ejLi4OBw4cACpqan45ZdfcPr06Q7f55IlS7Bo0SLD+8YpxbsaZ3dvRN2/BMASZKeeQ86B9fDN+RE+UGN42U/Avp9QtM8RqR63w3XUHAQPGAXJdb5Ej4iIqDfq1mOMYmJiEBwcDBsbG3zwwQdG35ar0+lgYWGBsWPHYv/+/XjppZewdetWJCQkGPqkp6cjKCgIp06dwpAhQ1q1T3OPMWoLodcjKf4XlB37CqFFe+AEraEtw8IXef53IeDWOHj5cxJJIiLq2Vr7+d2trhj9kV6vR01NDV555RU88sgjRm0DBgzAP//5T9x5550AgOjoaLzxxhsoKCiAu7s7AGDPnj1QKBSG23E9jcTCAn2HxwDDY1BbU42Eg1ugT/gGEdrDCNBnIyB9NZC+GhdkA1Aeeg/CbpsDpbObucsmIiIym25zxWjJkiWYPHky/Pz8oNVq8fXXX+Ptt9/G7t27MXHixCb9JRKJ0VNpOp0OgwcPhkqlwooVK6BWqzFnzhw88sgjbXpcvztdMWqJpvQqkn7ZANtL/0W/mrN/mERyFCSDZiL8lns4iSQREfUYPe6KUUFBAebOnYu8vDwolUoMHDiwxVDUHKlUim3btmHhwoWIjo6GnZ0d4uLi8Oqrr5q48q5H4eiC4ff8DcDfoM5ORca+9fDI+AGB+kwMrfgVOPwryg4/iwSXGChHPICw4TEcj0RERL1Ct7li1FX0hCtGzRF6PdISj6Pw0BeGSSQb5Uo8kOk9Bd63xMEvdLD5iiQiImqn1n5+Mxi1UU8NRr/3+0kkw0v2wU5SbWhLsexzbRLJ8XM5iSQREXUbDEYm0huC0e9VVWiRuH8jrM5vQkTlCVhK9ACAemGBRJtI1EX8CeHj74etvdLMlRIREbWMwchEelsw+r2r+TlI3felYRLJRpVCjkTHcbCO/DPCR90JqWW3GbpGRES9BIORifTmYPR72SlnkPPrF/DL+RHeIt+wvBBOuGyYRDKag7aJiKhLYDAyEQYjY4ZJJI9+ibCrP8MR5Ya2a5NITkPg+Hnw9OtjxiqJiKi3YzAyEQajltXWVOPCr99Bf2YjIrSHIZfUGdoSZQNQEXbvtUkknVzNWCUREfVGDEYmwmDUOprSq7i090vYJf0XEbVnDctrhBUSHaIhGTgTEePug0xubcYqiYiot2AwMhEGo7ZTZ6Ugff8X8Mr4HgH6LMPyUtgjySUGyhGzOYkkERGZFIORiTAYtZ9hEsnf1iE4fxfcUGJoy5V4IMt7KlS3zOUkkkRE1OEYjEyEwahj6OrrceHwdlTHf43w0v1Gk0gmW4aiOHg6+twWBxcPHzNWSUREPQWDkYkwGHW8qgotEvd9A6vETYioPGk8iaTtMNRF/AkR42fBxs7BzJUSEVF3xWBkIgxGpnU1Pwcpv3wB58tbEFqfbFheIaxxwfHWhkkkp3ASSSIiahMGIxNhMOo8WckJyP31C/hd2QZVM5NIuo2ei6D+Izlom4iIbojByEQYjDqf0OuRdHIvyo5taGYSST/kBUxH4Pg4ePqGmLFKIiLqyhiMTITByLxqa6qReGAzxNn/IEJ7xDCJpF5IcFHeOInkA5xEkoiIjDAYmQiDUddRVlKEpF82tDiJpMWgWQi/5R5OIklERAxGpsJg1DWps1KQvm8dvDJ/QIA+27DcMIlk1AMIGzaB45GIiHopBiMTYTDq2oRej7TzR1F46Ismk0hekXggy+dO+NwyF759BpmxSiIi6mwMRibCYNR9XJtE8kdUx3+DiNL9sJXUGNoyLXygdhkJeVgMQkbcDnuFkxkrJSIiU2MwMhEGo+6psrwMF/b/p8kkkgBQJ6RIlfVFqdcYOA2YhJDBt8DSSmbGaomIqKMxGJkIg1H3V1ZciLQTO1Cb/Au8i4/CR6iN2jWwxWXbIaj1HwdV5B3wCYrg2CQiom6OwchEGIx6ntz0S8g+uR1WmfsRXB4PJSqM2vPghmynKEj7xCB4xGQ4unqaqVIiImovBiMTYTDq2XT19bh89hCKz+6GQ+5B9KlJhEyiM7TrhQSXrUJQ5D4KDhET0SdyAuTWtmasmIiIWoPByEQYjHqXyvIypJz4CVWXfoZH4REE6jON24UcqTYDUek7Fh6Db0dAv+G87UZE1AUxGJkIg1HvVpibgYzj2yFJ24cAzQm4otSovQiOyFAMhwgaj8ARU+Gq8jdPoUREZITByEQYjKiR0OuRcfEE8hN2wTb7IPpUnYGNpNaoT4aFH9Su0bDpG4M+I2Jha680U7VERL0bg5GJMBhRS2qqK5ESvxfaxD1wLTiM4LpUWEj+99erVkiRIo+AVjUWzgNjETxwNKSWlmasmIio92AwMhEGI2qt0iI1Lh/fCV3Kz/AtOQYvFBq1l8EOl+0jUed/K3yHTYEqsK+ZKiUi6vkYjEyEwYjaQ+j1yElLRG78DsgyDyCk4hQcJFVGfXIknrjiPBKy0NsQNGIKlE6uZqqWiKjnYTAyEQYj6gj1dbVITfgVJed+gmPebwipvQSr300LoBMSpFqFosRzDBT9J6HP0PGwksnNWDERUffGYGQiDEZkCtqyYlw+sRs1ST/D6+oR+OmvGLVXCGuk2A5Gtd8t8BoyGX6hgzktABFRGzAYmQiDEXUGdXYqsk9sh0X6fgRpT8IJGqP2Ajgj0zEKkuDxCBwxBS4ePmaqlIioe2jt53e3+SfnmjVrMHDgQCgUCigUCkRHR2Pnzp0AgOLiYvz1r39FWFgYbGxs4OfnhyeffBJlZWVG28jKysKUKVNga2sLd3d3LF68GPX19eY4HKLr8vQNwfB7/obIv2+BcmkGUu/egaNBT+KcfAhqhBXcUYzhpTsxLP5ZuKyJwOXXBuPoJ4/h3IHvUF1Zbu7yiYi6rW7zrLCPjw/eeust9OnTB0IIrF+/HtOmTcPp06chhEBubi7eeecdhIeHIzMzE3/5y1+Qm5uLzZs3AwB0Oh2mTJkCT09PHD58GHl5eZg7dy6srKzw5ptvmvnoiFpmIZUiZNBohAwaDQCorizHuZN7UHFhD9wKjyBYl4ZgXTqC1emA+ivU/GKFc9b9UeE9Fq6DbkdQ/5GwkErNfBRERN1Dt76V5uzsjJUrV+Lhhx9u0rZp0yY88MADqKiogKWlJXbu3ImpU6ciNzcXHh4eAIBPPvkEzz33HAoLCyGTyVq1T95Ko67man4O0k/sgD51HwJKj8IdxUbtxVAg3WEY9IG3wnf4FHj6hpinUCIiM2rt53e3uWL0ezqdDps2bUJFRQWio6Ob7dN44JYNE+gdOXIEAwYMMIQiAIiNjcXChQuRmJiIIUOGNLudmpoa1NTUGN5rNJpm+xGZi4uHD1ymzgcwH0KvR2ZyAvJO74R11q/oU5kAZ4kGztpfgLO/AGdfQpaFN/KcR0IeFoPgEbfDQels7kMgIuoyulUwOnfuHKKjo1FdXQ17e3ts2bIF4eHhTfoVFRXhtddew/z58w3L1Gq1USgCYHivVqtb3Ofy5cvxyiuvdNAREJmWxMIC/n2Hwr/vUAAvoK62BhdO7YPm/E9wUh9CSF0S/PRX4Ff0X6Dov6j7TYqLsjCUeo2BU/9JCBkyDpZWrbt6SkTUE3WrW2m1tbXIyspCWVkZNm/ejH/96184cOCAUTjSaDSYOHEinJ2dsXXrVlhZWQEA5s+fj8zMTOzevdvQt7KyEnZ2dtixYwcmT57c7D6bu2Lk6+vLW2nULZWVFOHy8Z2oS/4Z3sXH4CPyjNq1wgapdkNR6z8Oqsg74BMUwWkBiKhH6BWP68fExCA4OBiffvopAECr1SI2Nha2trbYtm0brK2tDX1feuklbN26FQkJCYZl6enpCAoKwqlTp1q8lfZHHGNEPUluRhJyTm6HZcZ+BJefhBIVRu15cEO2UxSkfW5D8IgpcHT1NFOlREQ3p0ePMWqk1+sNV3M0Gg1iY2Mhl8uxdetWo1AEANHR0XjjjTdQUFAAd3d3AMCePXugUCiavR1H1BuoAsKgCggDsAi6+noknz2E4rO74ZB7EH1qEuElKYRXyTbg+Dboj/0dKZbBKPIYBYfwiegzLAZya1tzHwIRUYfqNleMlixZgsmTJ8PPzw9arRZff/013n77bezevRtRUVGYNGkSKisrsWXLFtjZ2RnWc3Nzg1QqhU6nw+DBg6FSqbBixQqo1WrMmTMHjzzySJse1+cVI+otKsvLkHLiJ1Rd+hkehUcQqM80aq8SMqTYDESlz1h4DJmMgH7DeduNiLqsHncr7eGHH8bevXuRl5cHpVKJgQMH4rnnnsPEiROxf/9+jB8/vtn10tPTERAQAADIzMzEwoULsX//ftjZ2SEuLg5vvfWW4cm11mAwot6qKDcT6Se2A2n7EFh2HK4oNW6HIzIUwyGCxiNgxBS4qQLMUicRUXN6XDDqKhiMiACh1yPjUjzyT++AbfZB9Kk6AxtJrVGfDAs/5DsNgUQ1BC59ouDXN5JfhEtEZsNgZCIMRkRN1VRXIiV+L7SJe+BacBjBdamwkBj/aqkRVsi0CkSJY39IvIfAtU8U/MKGcHoAIuoUDEYmwmBEdGOlRWqkndyFmswTcCg+D7+aZChQ2aRflZAh0yoYZY7hsPAZCrewkfDtMxjSNtzeJiJqDQYjE2EwImo7odfjStoF5CcdRV32KTgUn4N/TQrsJVVN+lYKOTJlwShz6g+p9xC4h42ET8hAhiUiuikMRibCYETUMfQ6Ha6knUf+paOozzkFRUki/GtSYCepbtK3QlgjU94HGqcIWPoMhUffkfAO6s8vxyWiVmMwMhEGIyLT0dXXIyf1LAqTj6E+5xSUJefhX3sZtpKaJn21wgZZ8j7QOg+Ape8QeIZFwzsonFMGEFGzGIxMhMGIqHPp6uuRnZKAwqSj0OecgrL0AgLqUmEtqWvSVwO7hrDUHzLfYfDsOxKqgDCGJSJiMDIVBiMi86uvq0VW0mkUpRyDuHIaTqXn4V+XDnkzYakMdsiSh6HcpT/kfpHw6hcNT98+DEtEvQyDkYkwGBF1TXW1Nci6FI+rKccgchPgXJYI/7o0yCS6Jn1LoEC2dSgqXAbA2n8YvPqNhId3EMMSUQ/GYGQiDEZE3UdNdSWyLsWjOOU4JHmn4Vx2Af71GbBqJixdhRI5NmGoaghL3hGjOHs3UQ/CYGQiDEZE3Vt1VQWyLp5ESeoxSPIS4KK5AP/6TFhK9E36FsEROTZ9UeU2EDb+kfCJGAVXTz8zVE1EN4vByEQYjIh6nurKcmRcOIayyydgkZcAV80F+OmyIJU0/fVYAGdcse2LareBsA0YBp/waLh4+JihaiJqCwYjE2EwIuodKsvLkHXhOEovH4dUfQZu2gvw0+U0+aoTAFDDFXl2fVHjNgi2AZHwjRgFJzcvM1RNRC1hMDIRBiOi3qtCW4qsC8dQdvk4LNUJcCu/BF/dlWbDUq7EHWrbvqhxHwj7wBHw6z8KSmc3M1RNRACDkckwGBHR72nLipGVeBTatOOwzD8Lj/KL8BW5zfa9IvGA2q4f6twHwj5oOHwjRkHp5NrJFRP1TgxGJsJgREQ3UlZShOwLR1CedgJW+WfgUXEJPkLdbN9siQoF9n1R5zEIDkEj4BcxEg5K506umKjnYzAyEQYjImqPsqv5yEo8ivL045AXnoVnxSWoREGzfbMsvFFg3w/1HoOgCB4B/4iRsHNw7NyCiXoYBiMTYTAioo5SUpiHnAtHUJF+EvLCM/CqSIInCpv00wsJsqU+KHDoB53nYDgGDYdfRBRs7ZVmqJqoe2IwMhEGIyIypav5Oci5cASVGSdhXXgW3pWX4I7iJv10QoIsqR+KHPpB5x4BG6++cPELh6d/KCytZGaonKhrYzAyEQYjIupsReosXLlwGJUZ8bApPAufqktwRWmzfWuFFHlSFYqtfVGtDIKlWyjsvfvCI7A/nFy9+LUn1GsxGJkIgxERdQWFuRm4kngYVVnxkBcnw6kqC166K7Bu5ot0G5XBDmpLH2jsAqBzCoHMow+c/MLhFRgBa1v7TqyeqPMxGJkIgxERdVV6nQ7q7FRczUxERe4lSK6mwFabAbea7GbHLhnWExLkS1xRaO2HKodAwCUEtqq+cAvoD3fvIFhIpZ14FESmwWBkIgxGRNQdVVVokZd+ASVZF1CbfwmWpWlQVmTAsz4HClS2vJ6QIU+qQqmtP2qUQbB0D4XSNxwegf05BxN1KwxGJsJgREQ9idDrUVyYi/z08yi/cgn6wmTINelwrs6Cly4PMomuxXWvQol8K1+U2/tD7xwCa8++127NBfSFTG7diUdBdGMMRibCYEREvUV9XS3yMpNQnJWIqrwkSK6mwr48A+612XBDScvrCQvkWXii2NoXVYogSFz7wF51bQC4i6cvB4CTWTAYmQiDERHRta9CUacnoiz7AuoKkiErTYOyMhOq+hzYSmpaXK9c2CDP0htldgGocwyGlUcfOPqEQxXcn/MykUkxGJkIgxERUcuEXo/CvEwUpJ9DRe4liKJU2GjS4FqTBU99AaTNfOFuowI4o0Duhwr7AAiXENh49YWrfzg8/cIgtbTsxKOgnojByEQYjIiI2qemuhLq9Iu4mnUBNfnJkBanQlGRAY+6HDhB0+J6tcISuVIvlFj7oVoZDKl7HyhUfeERNABObl6deATUnTEYmQiDERFRxyu7mo+89PPQ5lxEfUEy5GXpcKrKhEqXC/l15mYqhT3yG+ZmqncOhtwjFE5+EfAKDIe1jV0nHgF1dQxGJsJgRETUeXT19cjPuYyijPOozLsEydVU2GrTG+ZmKmpxPb2QQG3hhiK5HyodAiFxDYGtVz+4BUbAXRXIuZl6IQYjE2EwIiLqGqoqtMhNS0RpdiJq85NhVXIZispMeNVlw0FS1eJ6lUKOPEsVSm38UesYBCv3UCh8wuEZ1B8KR5dOPALqTAxGJsJgRETUtQm9HlcLrqAg/TzKcy9BX5gCa01aw9xMalhdZ26mIjiiQOaLcjt/6F2uzc3k7NcPXgH9YCWTd+JRUEdjMDIRBiMiou6rrrYG6sxLuJp1EdV5l2BRnAq78gx41Ga3+MW8AFAnpMi3cEOZlTsqbTxRb6+ChdIH1q6+cHAPhIsqCApHF87R1IX1uGC0Zs0arFmzBhkZGQCAiIgIvPTSS5g8eTIAoLq6Gn//+9+xceNG1NTUIDY2Fh9//DE8PDwM28jKysLChQuxb98+2NvbIy4uDsuXL4dlGx4DZTAiIuqZNKVXkd8wN1N9QTKsytLgWJkJr/or152bqVGlkKNI6ooyK3dU2XhB56CC1NEH1i5+UHoEwMU7CPYKp044EmpOjwtGP/74I6RSKfr06QMhBNavX4+VK1fi9OnTiIiIwMKFC7F9+3asW7cOSqUSTzzxBCwsLHDo0CEAgE6nw+DBg+Hp6YmVK1ciLy8Pc+fOxaOPPoo333yz1XUwGBER9S56nQ4FuekozklBZWEm6kqyYaHNhbwyD/Y1BXDRFV53uoHf08AWVy3coJW5o9rWEzoHb0gdfWDr6gelZwDcvINhbWtv4iPqnXpcMGqOs7MzVq5cifvuuw9ubm74+uuvcd999wEALl26hH79+uHIkSMYOXIkdu7cialTpyI3N9dwFemTTz7Bc889h8LCQshkslbtk8GIiIj+qLqyHIW56ShTp6OqKAv1DeHJujIPitoCuOgLr/tlvb9XAgcUS92glXugxtYLegcVrJx8YOPqD0fPQLiq/CG3tjXxEfU8rf387pZTiep0OmzatAkVFRWIjo5GfHw86urqEBMTY+jTt29f+Pn5GYLRkSNHMGDAAKNba7GxsVi4cCESExMxZMiQZvdVU1ODmpr/XULVaFr3rwIiIuo9rG3t4RsyAL4hA1rsU64pwdUradAUZKKqKAu60hxItVdgU6WGoq4AbrpC2Epq4AQtnHRaoDINqARQBCDdeFtFcESJpRvKG8ITlN6wcvKFnZs/HL0C4erpB0ur1v2Dn4x1q2B07tw5REdHo7q6Gvb29tiyZQvCw8ORkJAAmUwGR0dHo/4eHh5Qq9UAALVabRSKGtsb21qyfPlyvPLKKx17IERE1OvYK5xgr4gE+kU22y70epSVXsXV3DRoC9JRXZQNfdkVWJbnwrZKDWVdAdz0RZBL6uCKUrjWlwL1KUAFgELjbemEBAUSJ5RYuqPC2gO1dipA6Q2Zsy/s3QPg7BkIZw8fzufUjG4VjMLCwpCQkICysjJs3rwZcXFxOHDggEn3uWTJEixatMjwXqPRwNfX16T7JCKi3kdiYQGlsxuUzm4AoprtI/R6FBfloTgvHdr8DNQWZ0NflgOr8jzYVauhrC+Em/4qrCQ6uKMY7vXFQPkloBxAvvG2aoUURRYuKLW89qRdnZ0nLJQ+kDn7wsEjAM5egXBy9ep1T9p1q2Akk8kQEhICAIiMjMSJEyfw/vvvY+bMmaitrUVpaanRVaP8/Hx4enoCADw9PXH8+HGj7eXn5xvaWiKXyyGXc+4KIiIyP4mFBZzdveHs7g1gTLN99DodivJzUKxOR3nBtfCEsiuQVeTCrjofTvUFcBElkEl0UIkCqOoKgLrzgAZAnvG2qoUViixcjacpcPSBtYtPj52moFsFoz/S6/WoqalBZGQkrKyssHfvXtx7770AgKSkJGRlZSE6OhoAEB0djTfeeAMFBQVwd3cHAOzZswcKhQLh4eFmOwYiIqKOZCGVwlXlD1eVf4t96utqoVZnoTQvHRUNT9qh7ErDk3b5cKovhCtKYS2pg4/Ig09tHlB7BigDcMV4W5VCjkKpGzRW7qiy+d+TdjauflB4BMDVOwh2Do4mPeaO1G2C0ZIlSzB58mT4+flBq9Xi66+/xv79+7F7924olUo8/PDDWLRoEZydnaFQKPDXv/4V0dHRGDlyJABg0qRJCA8Px5w5c7BixQqo1Wq8+OKLePzxx3lFiIiIehVLKxk8fUPg6RvSYp+a6koU5WaiVJ2OqqJM1JXkGKYpcKjJh7OuEE7QwlZSA399DlCTA9QAKAWQbbytxmkKNDJ3VDc8aWfp5AsbVz8oPQPhpgrsMtMUdJtgVFBQgLlz5yIvLw9KpRIDBw7E7t27MXHiRADAP//5T1hYWODee+81muCxkVQqxbZt27Bw4UJER0fDzs4OcXFxePXVV811SERERF2W3NoW3kH94B3Ur8U+VRVaFOWmoUydgcrfPWlnXamGw++mKVCgEgp9JlCdCVQDKAaQabytEihwtWGaAqtRC9F/zF0mPb6WdOt5jMyB8xgRERG1XuM0BWX5Gai+2hiecmFTlQdlXQFcdUVNZhaPH7EKkXc82KF19Oh5jIiIiKh7aM80Bf79mx9Y3hkYjIiIiMhsWjNNQWfqOc/XEREREd0kXjFqo8YhWfxqECIiou6j8XP7RkOrGYzaSKvVAgBnvyYiIuqGtFotlEpli+18Kq2N9Ho9cnNz4eDgAIlE0mHbbfyqkezsbD7tZmI8152D57lz8Dx3Dp7nzmHK8yyEgFarhUqlgsV1ZurmFaM2srCwgI+Pj8m2r1Ao+Jeuk/Bcdw6e587B89w5eJ47h6nO8/WuFDXi4GsiIiKiBgxGRERERA0YjLoIuVyOZcuW8XvbOgHPdefgee4cPM+dg+e5c3SF88zB10REREQNeMWIiIiIqAGDEREREVEDBiMiIiKiBgxGRERERA0YjIiIiIgaMBh1otWrVyMgIADW1taIiorC8ePHr9t/06ZN6Nu3L6ytrTFgwADs2LGjkyrt/tpyrj///HOMHTsWTk5OcHJyQkxMzA3/bOiatv5MN9q4cSMkEgmmT59u2gJ7iLae59LSUjz++OPw8vKCXC5HaGgof3+0QlvP86pVqxAWFgYbGxv4+vri6aefRnV1dSdV2z39+uuvuPPOO6FSqSCRSPD999/fcJ39+/dj6NChkMvlCAkJwbp160xbpKBOsXHjRiGTycS///1vkZiYKB599FHh6Ogo8vPzm+1/6NAhIZVKxYoVK8SFCxfEiy++KKysrMS5c+c6ufLup63n+s9//rNYvXq1OH36tLh48aKYN2+eUCqVIicnp5Mr717aep4bpaenC29vbzF27Fgxbdq0zim2G2vrea6pqRHDhg0Td9xxh/jtt99Eenq62L9/v0hISOjkyruXtp7nr776SsjlcvHVV1+J9PR0sXv3buHl5SWefvrpTq68e9mxY4d44YUXxHfffScAiC1btly3f1pamrC1tRWLFi0SFy5cEB9++KGQSqVi165dJquRwaiTjBgxQjz++OOG9zqdTqhUKrF8+fJm+8+YMUNMmTLFaFlUVJRYsGCBSevsCdp6rv+ovr5eODg4iPXr15uqxB6hPee5vr5ejBo1SvzrX/8ScXFxDEat0NbzvGbNGhEUFCRqa2s7q8Qeoa3n+fHHHxe33Xab0bJFixaJ0aNHm7TOnqQ1wejZZ58VERERRstmzpwpYmNjTVYXb6V1gtraWsTHxyMmJsawzMLCAjExMThy5Eiz6xw5csSoPwDExsa22J+uac+5/qPKykrU1dXB2dnZVGV2e+09z6+++irc3d3x8MMPd0aZ3V57zvPWrVsRHR2Nxx9/HB4eHujfvz/efPNN6HS6ziq722nPeR41ahTi4+MNt9vS0tKwY8cO3HHHHZ1Sc29hjs9CS5NtmQyKioqg0+ng4eFhtNzDwwOXLl1qdh21Wt1sf7VabbI6e4L2nOs/eu6556BSqZr8ZaT/ac95/u233/B///d/SEhI6IQKe4b2nOe0tDT88ssvmD17Nnbs2IHU1FQ89thjqKurw7Jlyzqj7G6nPef5z3/+M4qKijBmzBgIIVBfX4+//OUv+Mc//tEZJfcaLX0WajQaVFVVwcbGpsP3yStGRL/z1ltvYePGjdiyZQusra3NXU6PodVqMWfOHHz++edwdXU1dzk9ml6vh7u7Oz777DNERkZi5syZeOGFF/DJJ5+Yu7QeZf/+/XjzzTfx8ccf49SpU/juu++wfft2vPbaa+YujW4Srxh1AldXV0ilUuTn5xstz8/Ph6enZ7PreHp6tqk/XdOec93onXfewVtvvYWff/4ZAwcONGWZ3V5bz/Ply5eRkZGBO++807BMr9cDACwtLZGUlITg4GDTFt0Ntefn2cvLC1ZWVpBKpYZl/fr1g1qtRm1tLWQymUlr7o7ac56XLl2KOXPm4JFHHgEADBgwABUVFZg/fz5eeOEFWFjwukNHaOmzUKFQmORqEcArRp1CJpMhMjISe/fuNSzT6/XYu3cvoqOjm10nOjraqD8A7Nmzp8X+dE17zjUArFixAq+99hp27dqFYcOGdUap3Vpbz3Pfvn1x7tw5JCQkGF533XUXxo8fj4SEBPj6+nZm+d1Ge36eR48ejdTUVEPwBIDk5GR4eXkxFLWgPee5srKySfhpDKOC383eYczyWWiyYd1kZOPGjUIul4t169aJCxcuiPnz5wtHR0ehVquFEELMmTNHPP/884b+hw4dEpaWluKdd94RFy9eFMuWLePj+q3U1nP91ltvCZlMJjZv3izy8vIML61Wa65D6Bbaep7/iE+ltU5bz3NWVpZwcHAQTzzxhEhKShLbtm0T7u7u4vXXXzfXIXQLbT3Py5YtEw4ODuKbb74RaWlp4qeffhLBwcFixowZ5jqEbkGr1YrTp0+L06dPCwDivffeE6dPnxaZmZlCCCGef/55MWfOHEP/xsf1Fy9eLC5evChWr17Nx/V7kg8//FD4+fkJmUwmRowYIY4ePWpoGzdunIiLizPq/+2334rQ0FAhk8lERESE2L59eydX3H215Vz7+/sLAE1ey5Yt6/zCu5m2/kz/HoNR67X1PB8+fFhERUUJuVwugoKCxBtvvCHq6+s7uerupy3nua6uTrz88ssiODhYWFtbC19fX/HYY4+JkpKSzi+8G9m3b1+zv28bz21cXJwYN25ck3UGDx4sZDKZCAoKEmvXrjVpjRIheM2PiIiICOAYIyIiIiIDBiMiIiKiBgxGRERERA0YjIiIiIgaMBgRERERNWAwIiIiImrAYERERETUgMGIiIiIqAGDEREREVEDBiMiIiKiBgxGRERERA3+Hxg2zpqNS+muAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Pipe.plot_pipe(net, 0, pipe_results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that the pressure level falls due to friction. As the fluid is incompressible, the\n",
+    "velocity remains constant over the pipe length. Because the temperature level at the pipe entry is\n",
+    "higher than the ambient temperature, the temperature level decreases."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tutorials/district_heating/multiple_pumps_flow_in_a_circular_district_heating_grid.ipynb b/tutorials/district_heating/multiple_pumps_flow_in_a_circular_district_heating_grid.ipynb
new file mode 100644
index 000000000..6ef00b722
--- /dev/null
+++ b/tutorials/district_heating/multiple_pumps_flow_in_a_circular_district_heating_grid.ipynb
@@ -0,0 +1,552 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multiple pumps in a circular district heating grid\n",
+    "\n",
+    "Based on the simple circular district heating grid."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandapipes as pp\n",
+    "import numpy as np\n",
+    "\n",
+    "# create empty net\n",
+    "net = pp.create_empty_network(fluid =\"water\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define constants\n",
+    "qext_w = np.array([50000, 20000])\n",
+    "return_temperature_k = np.array([60,55]) + 273.15\n",
+    "supply_temperature_k = 85 + 273.15\n",
+    "\n",
+    "pipetype = \"110/202 PLUS\"\n",
+    "k = 0.1\n",
+    "\n",
+    "flow_pressure_pump = 4\n",
+    "lift_pressure_pump = 1.5\n",
+    "mass_pump_mass_flow = 0.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Junctions for pump\n",
+    "j1 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Junction 1\", geodata=(0, 10))\n",
+    "j2 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Junction 2\", geodata=(0, 0))\n",
+    "\n",
+    "# Junctions for connection pipes forward line\n",
+    "j3 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Junction 3\", geodata=(10, 0))\n",
+    "j4 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Junction 4\", geodata=(60, 0))\n",
+    "\n",
+    "# Junctions for heat consumers\n",
+    "j5 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Junction 5\", geodata=(85, 0))\n",
+    "j6 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Junction 6\", geodata=(85, 10))\n",
+    "\n",
+    "# Junctions for connection pipes return line\n",
+    "j7 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Junction 7\", geodata=(60, 10))\n",
+    "j8 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Junction 8\", geodata=(10, 10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pump1 = pp.create_circ_pump_const_pressure(net, j1, j2, p_flow_bar=flow_pressure_pump,\n",
+    "                                               plift_bar=lift_pressure_pump, t_flow_k=supply_temperature_k,\n",
+    "                                               type=\"auto\", name=\"pump1\")\n",
+    "\n",
+    "pipe1 = pp.create_pipe(net, j2, j3, std_type=pipetype, length_km=0.01, k_mm=k, name=\"pipe1\", sections=5, text_k=283)\n",
+    "pipe2 = pp.create_pipe(net, j3, j4, std_type=pipetype, length_km=0.05, k_mm=k, name=\"pipe2\", sections=5, text_k=283)\n",
+    "pipe3 = pp.create_pipe(net, j4, j5, std_type=pipetype, length_km=0.025, k_mm=k, name=\"pipe3\", sections=5, text_k=283)\n",
+    "\n",
+    "pp.create_heat_consumer(net, from_junction=j5, to_junction=j6, qext_w=qext_w[0], treturn_k=return_temperature_k[0], name=\"Consumer A\")\n",
+    "pp.create_heat_consumer(net, from_junction=j4, to_junction=j7, qext_w=qext_w[1], treturn_k=return_temperature_k[1], name=\"Consumer B\")\n",
+    "\n",
+    "pipe4 = pp.create_pipe(net, j6, j7, std_type=pipetype, length_km=0.25, k_mm=k, name=\"pipe4\", sections=5, text_k=283)\n",
+    "pipe5 = pp.create_pipe(net, j7, j8, std_type=pipetype, length_km=0.05, k_mm=k, name=\"pipe5\", sections=5, text_k=283)\n",
+    "pipe6 = pp.create_pipe(net, j8, j1, std_type=pipetype, length_km=0.01, k_mm=k, name=\"pipe6\", sections=5, text_k=283)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### here comes the part with the additional circ_pump_const_mass_flow ###\n",
+    "# first of, the junctions\n",
+    "j9 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Junction 9\", geodata=(100, 0))\n",
+    "j10 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Junction 10\", geodata=(100, 10))\n",
+    "j11 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Junction 11\", geodata=(100, 5))\n",
+    "\n",
+    "pipe7 = pp.create_pipe(net, j5, j9, std_type=pipetype, length_km=0.05, k_mm=k, name=\"pipe7\", sections=5, text_k=283)\n",
+    "pipe8 = pp.create_pipe(net, j10, j6, std_type=pipetype, length_km=0.01, k_mm=k, name=\"pipe8\", sections=5, text_k=283)\n",
+    "\n",
+    "pump2 = pp.create_circ_pump_const_mass_flow(net, j10, j11, p_flow_bar=flow_pressure_pump, mdot_flow_kg_per_s=mass_pump_mass_flow, \n",
+    "                                            t_flow_k=supply_temperature_k, type=\"auto\", name=\"pump2\")\n",
+    "\n",
+    "flow_control_pump2 = pp.create_flow_control(net, j11, j9, controlled_mdot_kg_per_s=mass_pump_mass_flow)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The flow control is currently needed for the pressure."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "We now run a pipe flow.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pp.pipeflow(net, mode='bidirectional', iter=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "mode must be bidirectional, iter=100 might be needed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "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>p_bar</th>\n",
+       "      <th>t_k</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2.500000</td>\n",
+       "      <td>327.891464</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>358.150000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.999995</td>\n",
+       "      <td>358.078414</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3.999970</td>\n",
+       "      <td>357.609424</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3.999971</td>\n",
+       "      <td>358.049038</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2.500022</td>\n",
+       "      <td>332.661759</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2.500035</td>\n",
+       "      <td>328.149999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2.500006</td>\n",
+       "      <td>327.934451</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>4.000199</td>\n",
+       "      <td>358.150000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2.499975</td>\n",
+       "      <td>332.648357</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>358.150000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       p_bar         t_k\n",
+       "0   2.500000  327.891464\n",
+       "1   4.000000  358.150000\n",
+       "2   3.999995  358.078414\n",
+       "3   3.999970  357.609424\n",
+       "4   3.999971  358.049038\n",
+       "5   2.500022  332.661759\n",
+       "6   2.500035  328.149999\n",
+       "7   2.500006  327.934451\n",
+       "8   4.000199  358.150000\n",
+       "9   2.499975  332.648357\n",
+       "10  4.000000  358.150000"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "net.res_junction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that a constant heat flow is extracted via the heat exchanger between nodes 1 and 2. Heat\n",
+    "losses due to the ambient temperature level are not taken into account. These are only included in\n",
+    "the pipe components. This also means that - if the extracted heat flow is large enough - the\n",
+    "temperature level behind the heat exchanger might be lower than the ambient temperature level. A\n",
+    "way to avoid this behaviour would be to create a controller which defines a function for the\n",
+    "extracted heat in dependence of the ambient temperature."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "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>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>t_outlet_k</th>\n",
+       "      <th>mdot_from_kg_per_s</th>\n",
+       "      <th>mdot_to_kg_per_s</th>\n",
+       "      <th>vdot_m3_per_s</th>\n",
+       "      <th>reynolds</th>\n",
+       "      <th>lambda</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.018179</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>3.999995</td>\n",
+       "      <td>358.150000</td>\n",
+       "      <td>358.078414</td>\n",
+       "      <td>358.078414</td>\n",
+       "      <td>0.141064</td>\n",
+       "      <td>-0.141064</td>\n",
+       "      <td>0.000146</td>\n",
+       "      <td>5346.846164</td>\n",
+       "      <td>0.031545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.018176</td>\n",
+       "      <td>3.999995</td>\n",
+       "      <td>3.999970</td>\n",
+       "      <td>358.078414</td>\n",
+       "      <td>357.609424</td>\n",
+       "      <td>357.721493</td>\n",
+       "      <td>0.141064</td>\n",
+       "      <td>-0.141064</td>\n",
+       "      <td>0.000146</td>\n",
+       "      <td>5333.432050</td>\n",
+       "      <td>0.031575</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.002700</td>\n",
+       "      <td>3.999970</td>\n",
+       "      <td>3.999971</td>\n",
+       "      <td>357.609424</td>\n",
+       "      <td>358.049038</td>\n",
+       "      <td>357.808728</td>\n",
+       "      <td>-0.020960</td>\n",
+       "      <td>0.020960</td>\n",
+       "      <td>-0.000022</td>\n",
+       "      <td>788.015150</td>\n",
+       "      <td>0.100793</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.002650</td>\n",
+       "      <td>2.500022</td>\n",
+       "      <td>2.500035</td>\n",
+       "      <td>332.661759</td>\n",
+       "      <td>328.149999</td>\n",
+       "      <td>326.718797</td>\n",
+       "      <td>-0.020960</td>\n",
+       "      <td>0.020960</td>\n",
+       "      <td>-0.000021</td>\n",
+       "      <td>496.619942</td>\n",
+       "      <td>0.148573</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.017863</td>\n",
+       "      <td>2.500035</td>\n",
+       "      <td>2.500006</td>\n",
+       "      <td>328.149999</td>\n",
+       "      <td>327.934451</td>\n",
+       "      <td>327.934451</td>\n",
+       "      <td>0.141064</td>\n",
+       "      <td>-0.141064</td>\n",
+       "      <td>0.000143</td>\n",
+       "      <td>3530.489571</td>\n",
+       "      <td>0.037703</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.017862</td>\n",
+       "      <td>2.500006</td>\n",
+       "      <td>2.500000</td>\n",
+       "      <td>327.934451</td>\n",
+       "      <td>327.891464</td>\n",
+       "      <td>327.891464</td>\n",
+       "      <td>0.141064</td>\n",
+       "      <td>-0.141064</td>\n",
+       "      <td>0.000143</td>\n",
+       "      <td>3523.145093</td>\n",
+       "      <td>0.037741</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>-0.064434</td>\n",
+       "      <td>3.999971</td>\n",
+       "      <td>4.000199</td>\n",
+       "      <td>358.049038</td>\n",
+       "      <td>358.150000</td>\n",
+       "      <td>358.129797</td>\n",
+       "      <td>-0.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>-0.000516</td>\n",
+       "      <td>18948.812932</td>\n",
+       "      <td>0.022953</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>-0.063463</td>\n",
+       "      <td>2.499975</td>\n",
+       "      <td>2.500022</td>\n",
+       "      <td>332.648357</td>\n",
+       "      <td>332.661759</td>\n",
+       "      <td>332.659079</td>\n",
+       "      <td>-0.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>-0.000508</td>\n",
+       "      <td>13423.777747</td>\n",
+       "      <td>0.024343</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   v_mean_m_per_s  p_from_bar  p_to_bar    t_from_k      t_to_k  t_outlet_k  \\\n",
+       "0        0.018179    4.000000  3.999995  358.150000  358.078414  358.078414   \n",
+       "1        0.018176    3.999995  3.999970  358.078414  357.609424  357.721493   \n",
+       "2       -0.002700    3.999970  3.999971  357.609424  358.049038  357.808728   \n",
+       "3       -0.002650    2.500022  2.500035  332.661759  328.149999  326.718797   \n",
+       "4        0.017863    2.500035  2.500006  328.149999  327.934451  327.934451   \n",
+       "5        0.017862    2.500006  2.500000  327.934451  327.891464  327.891464   \n",
+       "6       -0.064434    3.999971  4.000199  358.049038  358.150000  358.129797   \n",
+       "7       -0.063463    2.499975  2.500022  332.648357  332.661759  332.659079   \n",
+       "\n",
+       "   mdot_from_kg_per_s  mdot_to_kg_per_s  vdot_m3_per_s      reynolds    lambda  \n",
+       "0            0.141064         -0.141064       0.000146   5346.846164  0.031545  \n",
+       "1            0.141064         -0.141064       0.000146   5333.432050  0.031575  \n",
+       "2           -0.020960          0.020960      -0.000022    788.015150  0.100793  \n",
+       "3           -0.020960          0.020960      -0.000021    496.619942  0.148573  \n",
+       "4            0.141064         -0.141064       0.000143   3530.489571  0.037703  \n",
+       "5            0.141064         -0.141064       0.000143   3523.145093  0.037741  \n",
+       "6           -0.500000          0.500000      -0.000516  18948.812932  0.022953  \n",
+       "7           -0.500000          0.500000      -0.000508  13423.777747  0.024343  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "net.res_pipe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The command above shows the results for the pipe components. The temperatures of the adjacent\n",
+    "junctions are displayed. Due to heat losses, the temperatures at the to-nodes is lower than the\n",
+    "temperatures at the from-nodes. Note also that the junctions are not equal to the internal nodes,\n",
+    "introduced by the pipe sections we defined. To display the temperatures at the internal nodes, we\n",
+    "can retrieve the internal node values with the following commands:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandapipes.component_models import Pipe\n",
+    "pipe_results = Pipe.get_internal_results(net, [0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The parameters of the get_internal_results function correspond to the net and the pipes we want to\n",
+    "evaluate. In this case, only the results of pipe zero are retrieved. The returned value stored in\n",
+    "pipe_results is a dictionary, containing fields for the pressure, the velocity and the temperature.\n",
+    "The dictionary can either be used for own evaluations now or it can be used to plot the results over\n",
+    "the pipe length:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Pipe.plot_pipe(net, 0, pipe_results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that the pressure level falls due to friction. As the fluid is incompressible, the\n",
+    "velocity remains constant over the pipe length. Because the temperature level at the pipe entry is\n",
+    "higher than the ambient temperature, the temperature level decreases."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tutorials/district_heating/time_series_in_a_circular_district_heating_grid.ipynb b/tutorials/district_heating/time_series_in_a_circular_district_heating_grid.ipynb
new file mode 100644
index 000000000..dae87fea7
--- /dev/null
+++ b/tutorials/district_heating/time_series_in_a_circular_district_heating_grid.ipynb
@@ -0,0 +1,459 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Time series in a circular district heating grid\n",
+    "\n",
+    "Based on the simple circular district heating grid."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandapipes as pp\n",
+    "import numpy as np\n",
+    "\n",
+    "# create empty net\n",
+    "net = pp.create_empty_network(fluid =\"water\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define constants\n",
+    "qext_w = np.array([100000, 80000, 120000])\n",
+    "return_temperature_k = np.array([60,55,65]) + 273.15\n",
+    "supply_temperature_k = 85 + 273.15\n",
+    "\n",
+    "pipetype = \"110/202 PLUS\"\n",
+    "k = 0.1\n",
+    "\n",
+    "flow_pressure_pump = 4\n",
+    "lift_pressure_pump = 1.5\n",
+    "mass_pump_mass_flow = 0.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Define junctions\n",
+    "j1 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Pump Supply\", geodata=(0, 0))\n",
+    "j2 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Main Split Supply\", geodata=(10, 0))\n",
+    "j12 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Main Split Return\", geodata=(10, 10))\n",
+    "j13 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Pump Return\", geodata=(0, 10))\n",
+    "\n",
+    "# Additional junctions for new branches\n",
+    "j3 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Consumer B Supply\", geodata=(20, 0))\n",
+    "j4 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Consumer B Return\", geodata=(20, 10))\n",
+    "j5 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Consumer C Supply\", geodata=(30, 0))\n",
+    "j6 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Consumer C Return\", geodata=(30, 10))\n",
+    "\n",
+    "# Pump\n",
+    "pp.create_circ_pump_const_pressure(net, j13, j1, p_flow_bar=flow_pressure_pump, plift_bar=lift_pressure_pump, \n",
+    "                                t_flow_k=supply_temperature_k, type=\"auto\", name=\"Main Pump\")\n",
+    "\n",
+    "# Pipes for supply line\n",
+    "pp.create_pipe(net, j1, j2, std_type=pipetype, length_km=0.2, k_mm=k, name=\"Main Pipe Supply\")\n",
+    "pp.create_pipe(net, j2, j3, std_type=pipetype, length_km=0.3, k_mm=k, name=\"Branch B Pipe Supply\")\n",
+    "pp.create_pipe(net, j3, j5, std_type=pipetype, length_km=0.3, k_mm=k, name=\"Branch C Pipe Supply\")\n",
+    "\n",
+    "# Pipes for return line\n",
+    "pp.create_pipe(net, j12, j13, std_type=pipetype, length_km=0.2, k_mm=k, name=\"Main Pipe Return\")\n",
+    "pp.create_pipe(net, j4, j12, std_type=pipetype, length_km=0.3, k_mm=k, name=\"Branch B Pipe Return\")\n",
+    "pp.create_pipe(net, j6, j4, std_type=pipetype, length_km=0.3, k_mm=k, name=\"Branch C Pipe Return\")\n",
+    "\n",
+    "# Heat consumers\n",
+    "pp.create_heat_consumer(net, from_junction=j2, to_junction=j12, qext_w=qext_w[0], treturn_k=return_temperature_k[0], name=\"Consumer A\")\n",
+    "pp.create_heat_consumer(net, from_junction=j3, to_junction=j4, qext_w=qext_w[1], treturn_k=return_temperature_k[1], name=\"Consumer B\")\n",
+    "pp.create_heat_consumer(net, from_junction=j5, to_junction=j6, qext_w=qext_w[2], treturn_k=return_temperature_k[2], name=\"Consumer C\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "We now run a pipe flow.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pp.pipeflow(net, mode='bidirectional', iter=100, alpha=0.2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "mode must be bidirectional, iter=100 and alpha=0.2 might be needed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Time series, need for controllers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "start = 0\n",
+    "end = 10 # 8760 hours in a year\n",
+    "\n",
+    "# time steps with start and end\n",
+    "time_steps = np.arange(start, end, 1)\t# time steps in hours\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\jonas\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandapower\\timeseries\\output_writer.py:177: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '[0 1 2 3 4 5 6 7 8 9]' has dtype incompatible with bool, please explicitly cast to a compatible dtype first.\n",
+      "  self.output[\"Parameters\"].loc[:, \"time_step\"] = self.time_steps\n",
+      "100%|██████████| 10/10 [00:01<00:00,  9.82it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pandapipes.timeseries import run_time_series\n",
+    "from pandapower.timeseries import OutputWriter\n",
+    "\n",
+    "log_variables = [\n",
+    "        ('res_junction', 'p_bar'),\n",
+    "        ('res_junction', 't_k'),\n",
+    "        ('heat_consumer', 'qext_w'),\n",
+    "        ('res_heat_consumer', 'vdot_m3_per_s'),\n",
+    "        ('res_heat_consumer', 't_from_k'),\n",
+    "        ('res_heat_consumer', 't_to_k'),\n",
+    "        ('res_heat_consumer', 'mdot_from_kg_per_s'),\n",
+    "        ('res_circ_pump_pressure', 'mdot_from_kg_per_s'),\n",
+    "        ('res_circ_pump_pressure', 'p_to_bar'),\n",
+    "        ('res_circ_pump_pressure', 'p_from_bar'),\n",
+    "        ('res_circ_pump_pressure', 't_to_k'),\n",
+    "        ('res_circ_pump_pressure', 't_from_k')\n",
+    "    ]\n",
+    "\n",
+    "ow = OutputWriter(net, time_steps, output_path=None, log_variables=log_variables)\n",
+    "\n",
+    "run_time_series.run_timeseries(net, time_steps, mode=\"bidirectional\", iter=100, alpha=0.2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With this simple implementation, a time series is calculated with the same input parameters as in the initialization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "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>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>t_outlet_k</th>\n",
+       "      <th>mdot_from_kg_per_s</th>\n",
+       "      <th>mdot_to_kg_per_s</th>\n",
+       "      <th>vdot_m3_per_s</th>\n",
+       "      <th>reynolds</th>\n",
+       "      <th>lambda</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.018179</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>3.999995</td>\n",
+       "      <td>358.150000</td>\n",
+       "      <td>358.078414</td>\n",
+       "      <td>358.078414</td>\n",
+       "      <td>0.141064</td>\n",
+       "      <td>-0.141064</td>\n",
+       "      <td>0.000146</td>\n",
+       "      <td>5346.846164</td>\n",
+       "      <td>0.031545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.018176</td>\n",
+       "      <td>3.999995</td>\n",
+       "      <td>3.999970</td>\n",
+       "      <td>358.078414</td>\n",
+       "      <td>357.609424</td>\n",
+       "      <td>357.721493</td>\n",
+       "      <td>0.141064</td>\n",
+       "      <td>-0.141064</td>\n",
+       "      <td>0.000146</td>\n",
+       "      <td>5333.432050</td>\n",
+       "      <td>0.031575</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.002700</td>\n",
+       "      <td>3.999970</td>\n",
+       "      <td>3.999971</td>\n",
+       "      <td>357.609424</td>\n",
+       "      <td>358.049038</td>\n",
+       "      <td>357.808728</td>\n",
+       "      <td>-0.020960</td>\n",
+       "      <td>0.020960</td>\n",
+       "      <td>-0.000022</td>\n",
+       "      <td>788.015150</td>\n",
+       "      <td>0.100793</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.002650</td>\n",
+       "      <td>2.500022</td>\n",
+       "      <td>2.500035</td>\n",
+       "      <td>332.661759</td>\n",
+       "      <td>328.149999</td>\n",
+       "      <td>326.718797</td>\n",
+       "      <td>-0.020960</td>\n",
+       "      <td>0.020960</td>\n",
+       "      <td>-0.000021</td>\n",
+       "      <td>496.619942</td>\n",
+       "      <td>0.148573</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.017863</td>\n",
+       "      <td>2.500035</td>\n",
+       "      <td>2.500006</td>\n",
+       "      <td>328.149999</td>\n",
+       "      <td>327.934451</td>\n",
+       "      <td>327.934451</td>\n",
+       "      <td>0.141064</td>\n",
+       "      <td>-0.141064</td>\n",
+       "      <td>0.000143</td>\n",
+       "      <td>3530.489571</td>\n",
+       "      <td>0.037703</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.017862</td>\n",
+       "      <td>2.500006</td>\n",
+       "      <td>2.500000</td>\n",
+       "      <td>327.934451</td>\n",
+       "      <td>327.891464</td>\n",
+       "      <td>327.891464</td>\n",
+       "      <td>0.141064</td>\n",
+       "      <td>-0.141064</td>\n",
+       "      <td>0.000143</td>\n",
+       "      <td>3523.145093</td>\n",
+       "      <td>0.037741</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>-0.064434</td>\n",
+       "      <td>3.999971</td>\n",
+       "      <td>4.000199</td>\n",
+       "      <td>358.049038</td>\n",
+       "      <td>358.150000</td>\n",
+       "      <td>358.129797</td>\n",
+       "      <td>-0.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>-0.000516</td>\n",
+       "      <td>18948.812932</td>\n",
+       "      <td>0.022953</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>-0.063463</td>\n",
+       "      <td>2.499975</td>\n",
+       "      <td>2.500022</td>\n",
+       "      <td>332.648357</td>\n",
+       "      <td>332.661759</td>\n",
+       "      <td>332.659079</td>\n",
+       "      <td>-0.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>-0.000508</td>\n",
+       "      <td>13423.777747</td>\n",
+       "      <td>0.024343</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   v_mean_m_per_s  p_from_bar  p_to_bar    t_from_k      t_to_k  t_outlet_k  \\\n",
+       "0        0.018179    4.000000  3.999995  358.150000  358.078414  358.078414   \n",
+       "1        0.018176    3.999995  3.999970  358.078414  357.609424  357.721493   \n",
+       "2       -0.002700    3.999970  3.999971  357.609424  358.049038  357.808728   \n",
+       "3       -0.002650    2.500022  2.500035  332.661759  328.149999  326.718797   \n",
+       "4        0.017863    2.500035  2.500006  328.149999  327.934451  327.934451   \n",
+       "5        0.017862    2.500006  2.500000  327.934451  327.891464  327.891464   \n",
+       "6       -0.064434    3.999971  4.000199  358.049038  358.150000  358.129797   \n",
+       "7       -0.063463    2.499975  2.500022  332.648357  332.661759  332.659079   \n",
+       "\n",
+       "   mdot_from_kg_per_s  mdot_to_kg_per_s  vdot_m3_per_s      reynolds    lambda  \n",
+       "0            0.141064         -0.141064       0.000146   5346.846164  0.031545  \n",
+       "1            0.141064         -0.141064       0.000146   5333.432050  0.031575  \n",
+       "2           -0.020960          0.020960      -0.000022    788.015150  0.100793  \n",
+       "3           -0.020960          0.020960      -0.000021    496.619942  0.148573  \n",
+       "4            0.141064         -0.141064       0.000143   3530.489571  0.037703  \n",
+       "5            0.141064         -0.141064       0.000143   3523.145093  0.037741  \n",
+       "6           -0.500000          0.500000      -0.000516  18948.812932  0.022953  \n",
+       "7           -0.500000          0.500000      -0.000508  13423.777747  0.024343  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "net.res_pipe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The command above shows the results for the pipe components. The temperatures of the adjacent\n",
+    "junctions are displayed. Due to heat losses, the temperatures at the to-nodes is lower than the\n",
+    "temperatures at the from-nodes. Note also that the junctions are not equal to the internal nodes,\n",
+    "introduced by the pipe sections we defined. To display the temperatures at the internal nodes, we\n",
+    "can retrieve the internal node values with the following commands:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandapipes.component_models import Pipe\n",
+    "pipe_results = Pipe.get_internal_results(net, [0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The parameters of the get_internal_results function correspond to the net and the pipes we want to\n",
+    "evaluate. In this case, only the results of pipe zero are retrieved. The returned value stored in\n",
+    "pipe_results is a dictionary, containing fields for the pressure, the velocity and the temperature.\n",
+    "The dictionary can either be used for own evaluations now or it can be used to plot the results over\n",
+    "the pipe length:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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0otdbv3493bt3JzU1laCgoEvGlp6ejru7OxqNBi8vLxYsWMCdd97pfF8pxbFjx/D19eXAgQNERkayadMmrr32Wmcbu91OWloajRo1YvXq1dx8880cP37cec9QPMMpPT2doKAg3n//fZ577jkyMjJcHoFZrVZOnTpF48aNGT9+PEuWLGHHjh2XjF+I6qDRaHjmmWd49tlncXd3r7GBvOcelV1zzTVER0ezZMmSGolDiLrgqpz9dCnt27fHbrdz/PhxunfvXmobo9FIREREmfu8/vrrSUpKcjmWnJxMSEiI82t/f3/8/f2dX+v1epo0aXLJROZC52ZSXDh19mLOrVT6v//9D7PZTJ8+fVze12g0NG7cGIAvvviC4OBgrrnmGpc2Op3OubbJF198QZcuXVwSGgCDweCcGrxgwQIGDhxYYlaN2WymSZMm2Gw2vv76a4YOHVqm+xWiOrz++uvO19NPP10jMfTs2ZOEhAQA59gjIUTprsqkJicnhz179ji/3r9/P/Hx8TRo0ICwsDBGjhzJqFGjePPNN2nfvj0nTpxg9erVREdHM2DAgHJf7x//+Addu3Zl+vTpDB06lE2bNvH+++/z/vvvVyj+ZcuWkZ6ezrXXXouHhwc7duzgmWee4frrr6d58+YAbNq0iVGjRrF69Wpn8jF79my6du2Kh4cHq1at4plnnuHVV191LpgGxd/E+/Xrh1ar5ZtvvuHVV1/lq6++clatTp48yaJFi+jZsydWq5V58+axcOFC1q5d6+wjOTmZTZs20blzZ86cOcNbb73F9u3bXQZ5/vHHHxw9epTY2FiOHj3KlClTcDgcF51+K0R1u3BxwKqe/n0pc+fOJTs7G6DELw5CiL+p4cdfNeKXX34pdRzM6NGjlVLFgxRffPFF1bx5c2UwGFRQUJC67bbbVGJiYoWv+cMPP6i2bdsqk8mkIiIi1Pvvv3/J9pcaKPzzzz+rLl26KG9vb2U2m1Xr1q3Vc8895zL99dw9Xjj24O6771YNGjRQRqNRRUdHq48//rhE3zfeeKOz386dO6tly5a5vH/ixAl13XXXKXd3d+Xm5qZ69erlHEtzzs6dO1VsbKyyWCzKy8tLDRo0SO3evdulzZo1a1SbNm2UyWRSfn5+6u677y4xOFsIIYQoj6t+TI0QQggh6gdZp0YIIYQQ9YIkNUIIIYSoF66agcIOh4PU1FQ8PT0vuq+MEEIIIWoXpRTZ2dk0bty4xAzav7tqkprU1FSCg4NrOgwhhBBCVMDhw4edy4RcTK1Jan799Vdef/11Nm/ezLFjx1wWxIPiTG3y5Ml88MEHZGRkcP311zNnzhxat25dpv49PT2B4g/Fy8urKm5BCCGEEJUsKyuL4OBg58/xS6k1SU1ubi4xMTHce++93H777SXef+211/j3v//NRx99RIsWLZg0aRJxcXHs3LmzxMaIpTn3yMnLy0uSGiGEEKKOKcvQkVqT1PTv39+5u+3fKaWYNWsWL7zwAoMGDQLg448/JiAggO+++47hw4dXZ6gukvbu4693F5Kty+e+Zx7BcMGqwEIIIYSoPrUmqbmU/fv3k5aWRu/evZ3HvL296dy5Mxs3biw1qSkoKHDZMuDCfZUq07ZPFpPilY9R6Um4axLeRSm4xcZiiYnBEhODKTISrdFYJdcWQgghxHl1IqlJS0sDzu9ZdE5AQIDzvb+bMWMGU6dOrfLYut7Sgx2Lv6dQU4Sp/e3k/D6fomXLyVq2HACNwYApso0zybHExGJo0lhmYAkhhBCVrE4kNRUxYcIExo0b5/z63ECjyta4Q3vU11+B0US2Lg//zmMx+O5Ae2gH+QkJ2E+fxpqQiDUhkTN8AoCuYcPzSU5sDJa2bdG6uVV6bEIIIcTVpE4kNYGBgQCkp6cTFBTkPJ6enk5sbGyp55hMJkwmU3WEB5rij/GoOkUzbUPyzkTS+OlbadqyKbYjR8iPTyA/ofhl3bUL+8mT5KxeTc7q1cXna7WYwsOxxERjiSl+dGVsHoLmMvPxhRBCCHFenUhqWrRoQWBgIKtXr3YmMVlZWfzxxx88/PDDNRscYPH0Id+azTa2E+0IxV2r58jcJJo/7YY5OBhjcDDetwwEwGG1Yt25qzjJiY8nPyGBorQ0CnbtomDXLjIWfAmA1tsbS3T02WpOLJboduhk1pYQQghxUbUmqcnJyWHPnj3Or/fv3098fDwNGjSgWbNmPPXUU0ybNo3WrVs7p3Q3btzYZS2bmtKkWQh7krej0Zp4ofG7vHr4MTy0Jva9+RdhL3RD7+nubKs1m3G7pj1u17R3HrOlpZGfkHi+mrN9O47MTHLXrSN33TpnO2OrVi6PrUyhoWh0umq9VyGEEKK2qjW7dK9Zs4Ybb7yxxPHRo0czf/585+J777//PhkZGXTr1o3//Oc/hIWFlan/rKwsvL29yczMrPR1anbu3sNXCz4FpVgc/D1D1SCG7+mFXqMhU5tNxJQ+6IyGMvenbDasScnkJ8Q7H13ZDh0q0U7r5oa5XbvzY3NiYtD7+VXmrQkhhBA1qjw/v2tNUlPVqjKpsdvtvDx5Muj1bPFcz/6G6czXvUyjbQ3QajRkGjNoM/lmtFdQVSk6fdpZyclPSMCauA1Hbm6JdobgYJdqjjk8HI1MKRdCCFFHSVJTiqpMagAmT5iIxmQk23iCH5v8yi0tb+H+pBswpLih0WjI8jpD5MRbK+16ym6nYO/e80lOQgIFe/bC3/44NUYj5qgol2qO4YLB1kIIIURtJklNKao6qZk2ZRpFFIGjiK9bfY9eo2f5kOVkvf8nbuk+AOQFZhD21C2Vfu1z7NnZ5CdeMDYnPgF7ZmaJdvqAANdqTmQkWoulyuISQgghKkqSmlJUdVIze/YHnDx5FFVQyOGeafyZ9iejI0fz9LVPs+uVxXhm+6KUwhaWR8v7+lX69UujlMJ28OD5x1bxCViTksBud22o12MOD3et5jRrJgsECiGEqHGS1JSiqpOaH1evY8O61WC30/P+Xjy+5nHcDe6sumMV7jo3dk1dhnehDw6l0FzrIPiOnpUeQ1k48vOxbt/uTHTy4uOxnzhZop3Ox+d8khMbi7ldO3QeHjUQsRBCiKtZeX5+15op3XVd1+s6sOHXVaDT4ZXpTyvvVuzN3MvC5IXc2/ZeIl7ox+7Jq/DGk6I/taR5/0Fgn87VHqfWYsHt2mtxu/ZaoLiaU3TsmLOSk5+QgHXHDuwZGeSsXUvO2rXFJ2o0mEJDnZUcS0wMxlatZIFAIYQQtYZUairRlIkTwWgkpHk4PtebeXHDi/hb/FkxZAUGnYGi7FySp63HS+NGocOBzx1+NOjUtkpiuRKOwkIKdu8uTnLOLhBoO3q0RDuthweW6HaYY84nOnpf3xqIWAghRH0llZqacna7hPSjhxnZ8ine2foOx/OPs2z/MgaFDkLv6U7Lf3bkwBtb8dCaOLXoBAavfXhGtKzhwF1pjcbi1Yyjo2HU3QAUnThRPAj53JYP27bhyMkhd8NGcjdsdJ5rDAkpHnx8Nskxh4WhMZR9jR4hhBCioiSpqURuXj7k5Wdhzc3CqDMyss1IZm2Zxfwd87m11a1oNBrM/n40HRtO6tw9uGn1pP5vP8FPuuHWJLCmw78kfaNGePbqhWevXgCooiIK9uxx2deqcN8+Cg8epPDgQTK/XwyAxmzG3DbKdZfyAP+avBUhhBD1lDx+qkSfLfiGlN2JUFjIlOnTySrMos/CPuQV5fGfXv+he9PuzrYZW3dx6ot0TFod2Sqf0AnXYfCp23s72TMyyN+27Xyik5iIIyurRDt946ALkpyzU8qra/NRIYQQdYo8fqoh1157TXFSYzBw8tRpGvo14I6wO/h458fM3zHfJanxad+Gwsw8cpfn4qmxkPzqBiIm34TOUndX/9X5+ODRvTse3YvvUzkcFB444FLNKUhOpij1GNmpx8hevqL4RIMBc5s2LmvnGJo0kSnlQgghykUqNZVsygsvgF7PNdd25dYBfUnLTaP/1/0pUkUsGLiAKL8ol/ZHv19H0QaFTqMhU59Fm6n9rmg7hdrOnpPrMqU8PyEB+6lTJdrp/PzO71AeE4OlbRRad/dSehRCCFGfSaWmBim7QqOHfSl7gL4EugfSr0U/luxbwvzt83m9x+su7ZsM6s6BrJ9wbDfiXeTF7leWEvli5W2nUNvoPNxxv64z7tcVT2dXSmE7etSlmmPdtQv7qVPk/PwzOT//XHyiVospLMylmmNs3lymlAshhHCSSk0lmzblFYqwoXVoefGlFwFIOp3EHT/cgU6jY+ntS2ni0aTEeSnvLsVyuDiuHL8MIp6puu0UajtHQQHWnTtdEp2iY8dKtNN6eRXP0jpXzYluh87buwYiFkIIUVWkUlODGgQEcjz9MHab1XksvEE4XYK6sPHYRj7Z+QnjO40vcV7rRwewe+ZiPM744nHKhz3/WUboIzdXZ+i1htZkwq19e9zat3ces6WnFyc5iWerOdt34MjKInf9enLXr3e2M7Zs6VLNMYWGotHLX3MhhLgaSKWmkv205jfWr1kFdjuTpkxBd3Z8zIbUDYxdNRaL3sKqO1bhbSpZUXDY7eyathTvfF8cSqGiCwkZ2bvKYq3LlM2GNTnZuThgfkICtoOHSrTTuLlhadu2uJoTG4MlOhp9w4Y1ELEQQoiKkL2fSlFdSU1uXj6vvzoDtFoGDhpCx/btgOKxI0OXDGX36d083v5xHox+sNTzHYVF7Jq6Em+7F3alMHTX0Xjg9VUWb31SdObM+XE5CQnkJyTiyM0t0c7QtKnrLuUREWiMdXfWmRBC1GeS1JSiupIaOL9dQrOQ1tx7z0jn8SX7ljBh3QT8zH6svGMlJl3pa7MU5eeTNHUt3rhjUw48BnjS6IZrqjTm+kjZ7RTu20fe2WqONSGBgj174W9/5TVGI+bIyPPVnJgY9IGBMqVcCCFqARlTU8M0Gj0KSE913S8prnkcb295m7TcNJbsXcKQsCGlnq+3WGj9XBf2ztiEp9ZM5pIsDD5J+ESHV0P09YdGp8PUujWm1q3xvfNOAOzZ2Vi3bXPZwNOekVH8GCs+3nmu3t///C7lMTGYo6LQWiw1dCdCCCHKQpKaKuDm5UtufiYFua6r6Rq0Bu5qcxdv/PUG83fM57bWt6HVlD4l2ejrTchjbTk8exfuWgPHPz2K4WF33Fs0rY5bqLd0np64d+2Ke9euwNkp5YcOnU1y4smPT8CalETR8eNkr1pF9qpVZ0/UYQ4Pd6nmGJo1k2qOEELUIvL4qQp88dV3JO2MB1shU16Z7vJeri2XPgv7kG3L5t83/psbm914yb6yduzh+EdHMGt15DgKaP7sNZgbNqjC6IUjPx/rjh3nqznx8RSdOFGinc7Hx7Wa064dOk/PGohYCCHqLxlTU4rqTGr27DvIpx/PA6V45LEn8G/k5/L+vzb/i/9t/x/X+F/DR/0/umx/pzYmkvntGYxaLVkqj7BJ3dF7uFVV+OJvlFIUpaUVJzlbz47P2bEDZbO5NtRoMIW2cu5Qbok5O6VcFggUQogKk6SmFNWZ1ABMeeF50BuI7dCFwbfEubx3PO84cV/HUeQo4rObPyO6UfRl+zu2fCMFa2zoNRoytdm0mdIXrVGeHtYUR2EhBbt3uywQaDtypEQ7rYcHluh2LomO3te3BiIWQoi6SQYK1wLKARpgf8oewDWp8XfzZ0CLAXy/93vm75jPWz3fumx/Qf27cCjrZxxb9Hg7PNn1ynLavHhzvd4nqjbTGo3FqxlHRwN3A1B08iT5iYnOak7+9u04cnLI3bCR3A0bnecaQppdsEt5LObwMDQGQw3diRBC1B9Sqaki06ZOp0gVorVrePHlySXe33NmD7ctvg0NGpbctoRmXs3K1O/e95dj3OuORqMh2+sMbSbW332i6jpVVETBnj0u1ZzCfftKtNOYzZjbRrkkOoYA/xqIWAghah95/FSK6k5q5sz5H+nph1AFhUydMb3UNo/89Ajrjq5jWPgwXrjuhTL3nfTWD7gf9wEgLyiTsCcHVkbIohrYMzPJT9zmsku5IyurRDt9UNAFSU4M5qhItKbS1zUSQoj6TJKaUlR3UrPm142s+Xkl2O288OJk9IaST/r+TPuTe1fei1ln5sc7fsTXXPaxFjunLcYrx7d4EGtEPi3uibv8SaLWUQ4HhQcOuFRzCpKTweFwbWgwYG7TxmUlZEOTJjKlXAhR70lSU4rqTmqs1gJenf4KaLX0HzCYztfGlmijlGL40uHsPLWTR2Ie4eHYh8vcv8NuZ9eU5XjbvLErha6Tg6ZDelbeDYga48jNJX/7jvPVnPh47KdOlWin8/NzqeZY2rVF6+5eAxELIUTVkaSmFNWd1MD57RKaNgvl/nvvKrXNiv0reObXZ/A1+bLyjpVY9GVftdZeaGP35J/wVh7YlMKtr4WAXtdWVviillBKYTt61KWaY921C/4+pVyrxRQW5lLNMTZvLlPKhRB1Wr2c/WS325kyZQqffvopaWlpNG7cmDFjxvDCCy/U2hK8RmtAAcePlpzqe07vkN408WjC0Zyj/LD3B4aGDy1z/zqjgbAJ3Ul5ZQNeGgs5K/MweO+gQceoSohe1BYajQZj06YYmzbFe+AAABwFBVh37rygmpNA0bFjFOzeTcHu3WR8+SUAWi+v4lla5xYJjI5G511yh3ghhKgP6kxSM3PmTObMmcNHH31EVFQUf/31F/fccw/e3t488cQTNR1eqdy8fcnNzaAwP+eibfRaPXdH3s2rm17lox0fMaT1EHTask/TNnh50HLcNRx4MwEPrZFTXx3H4OWGZ1iLyrgFUUtpTSbc2rfHrX175zFberrLAGTr9h04srLIXb+e3PXrne2MLVu6VHNMoaFo9HXmW4EQQlxUnXn8NHDgQAICAvjwww+dx4YMGYLFYuHTTz+97Pk18fhpwcLv2b1jK9hsTHnllYu2y7Pl0WdRH7IKs/hXz3/RO6R3ua+Vs+cQqe/vxU2rJ9dRSLOn2mJpHHAl4Ys6TtlsWJOTnTuU58XHYzt4qEQ7jZsblrZtXbZ80DdsWAMRCyFESfXy8VPXrl15//33SU5OJiwsjISEBNavX89bb5W+cF1BQQEFBQXOr7NKmTZb1Tp36lCc1BgMHEs7TlBg6WuPuBncGBY+jA+2fcC8HfPo1axXuR+peYQ2o9HwHE4vOIG71sj+txNoPeE6DD7Vk8CJ2kdjMGCJisISFQX/938AFJ05c76Sk5BAfkIijtxc8jZtIm/TJue5hqZNXao55ogINEZjTd2KEEKUSZ2p1DgcDiZOnMhrr72GTqfDbrfzyiuvMGHChFLbT5kyhalTp5Y4Xp2VGji/XUJ0+87cPqj/RdudzD9J3KI4Ch2FfNTvI64JuKZC10v/+U/yVuZj0GjIJIeIyb3QWeSHkSidstsp3LfPZaZVwZ698LdvCxqjEXNk5PlqTmws+sDAWjueTQhRf9TL2U8LFizgmWee4fXXXycqKor4+Hieeuop3nrrLUaPHl2ifWmVmuDg4OpPaiY+D0YDXl5+jBv3+KXbbpjC1ylf0zO4J+/c9E6Fr3nk21+x/w46jYZMfSZtpvaX7RREmdmzs8lPTMSamOiccWXPyCjRTu/v77pLeVQUWkvZZ+8JIURZ1MukJjg4mPHjx/Poo486j02bNo1PP/2U3bt3X/b8mhhTA/DK1OnYVCEaO0x+ecol2+7P3M+t3xVve7B48GJaeFd8sO/+j35Ev9OMRqMhy+0MkS/KdgqiYpRS2A4dcs6yyk9IwLp7N9jtrg11Oszh4c4kxxIbi6FZM6nmCCGuSL0cU5OXl4f2b+tt6HQ6HH9febWW8QsIIi3tII6iwsu2beHdgp7BPVlzeA0f7fiIKV2nVPi6LUb3JeWdpViOeuGV50vSGz8Q/vQtFe5PXL00Gg3GkBCMISF431qcHDvy87Hu2HE+0YmPp+jECaw7d2LduZMzn38BgM7Hx7Wa064dOk/PmrwdIUQ9VmeSmltuuYVXXnmFZs2aERUVxdatW3nrrbe49957azq0S4psG0la2kE0Bh1FtqJSt0u40D1R97Dm8Bp+2PsDj7V/jIaWis9Caf34AHa9uhjPDF/cT/qwZ+5yQsdefFyPEGWltVhw69gRt44dgeJqTlFamms1Z8cO7BkZ5KxdS87atcUnajSYQlthvmAlZFNoqCwQKISoFHXm8VN2djaTJk3i22+/5fjx4zRu3JgRI0bw4osvYizDrIyaevx04XYJffvfStfOlx4ArJTiruV3kXgikQfaPcAT11zZGjwOu51dLy3Du8AHh1KoWBshI3pdUZ9ClIUqLMSalET+1njnQGTbkZILUWo9PLBEt3NJdPS+Zd8HTQhRv9XLMTVXqqaSGji/XUKTpq144P67L9v+p4M/8Y81/8DL6MWqO1bhZnC7ous7CovYNWUl3g4vipTC1ENP0M1dr6hPISqi6ORJ8i8YgJy/bRsqL69EO0NIswv2tYrFHB6GxmCogYiFEDWtXo6pqcvObZdw4tjRMrW/MfhGmnk241D2Ib7d8y0j24y8outrjXrCJ95I8rRf8dK4k7/GxkmveBp2i72ifoUoL33DhnjedBOeN90EgCoqomDvXpdqTuG+fdgOHsJ28BBZi38AQGM2Y24b5ZLoGAJKX/dJCHH1kqSmGnh4NyA79wyF+dllaq/T6hgdNZqXf3+ZT3Z+wrDwYei1V/ZHpfdwI/SZzuyduRlPrYmMxRkYfFPwjmp9Rf0KcSU0ej3m8HDM4eH4Dh8GgD0zk/zEbS5bPjiyssj/azP5f212nqsPCnLZpdwcFYnWZKqpWxFC1ALy+KkafPX1D+zctvmy2yVcyFpkpe+ivpwpOMPrN7xOvxb9KiWWvEPHODJ7F25aA3kOG00fa4Nbs6BK6VuIqqAcDgoPHDyb4MSTH59AQXIy/H3mo8GAuU0bl5WQDU2ayJRyIeo4GVNTippMag4dPsb/PpwLwANjH6ZJUNn2ZJoTP4f/JPyHSL9IFgxYUGnfnDO3JXPik1TMWh3ZjgJaPdcBo58MzBR1hyM3l/ztO1xWQrafOlWinc7Pz6WaY2nXFq27ew1ELISoKElqSlGTSQ3AlOefB4OBtjGduOO2m8t0zhnrGfou6ovVbuXDvh/SKahTpcVzcn08WYszMWq1ZJFL2As3oPe4sgHJQtQUpRS2o6nFlZyz08qtu3aBzebaUKvFFBbmUs0xNm8uU8qFqMVkoHBtdDZ1PLhvb5lP8TX7Mih0EF8mfcm8HfMqNalp2C0WW9YGCtYW4aVxJ2n6L7SZEofWKH8lRN2j0WgwNm2CsWkTvAcMAMBRUIB1506XsTlFqcco2L2bgt27yfjySwC0Xl5YoqPPLxIYHY3O27smb0cIUUHyE6yaGMwe2BwF5GSULJFfyujI0XyV9BXrj64n5UwKrX0rb2Bv0M1dOZi5Gke8AW+HF7teWU6bF2+WfaJEvaA1mXBr3x639u2dx2zpx8lPPLtDeXwC+du348jKInf9enLXr3e2M7Zs6VLNMYWGotHLt0shajt5/FRN5r7/EcdS96MKC5k6fXq5zh23ZhyrDq5iUKtBTOs2rdJj2zN3Oeb9HgBk+5yhzXjZJ0pcHZTNhjU5uXgF5LOJTuHBgyXaadzcsLRt67Llg75hxVf7FkKUnYypKUVNJzXrNv7F6pVLwOFgwgsvYCrDKsjnJJ5IZOSykei1elbcvoIA97INNC6PpDd+wP2kDwD5TbJo/fiASr+GEHVB0ZkzxTuUn6vmJCbiyMkp0c7QtKlLNcccEYGmHP+uhRBlI0lNKWo6qSkoLGTGtGmg1dI7biDdunQs1/mjl49my/Et3NP2HsZ1GFclMe58aTFeeb7F+/hEWmkxum+VXEeIukQ5HBTu3XvBTKsECvbsgb9969QYjZgjI89Xc2Jj0QcGypRyIa6QJDWlqOmkBmDyxIlojEYaN2nJgw+MKte5aw6v4fGfH8fD4MGqO1bhYfSo9Pgcdju7Ji/Hu8gbu1LoroOmt91Q6dcRoq6z5+Rg3bbNZQNP+5kzJdrp/f1ddymPikJrsdRAxELUXTL7qZbSao3l2i7hQjc0vYEW3i3Yn7mfr1O+ZnTU6MqPT6cj4vm+7J66Gm+NB7bfFenefxJw07WVfi0h6jKdhwfuXbrg3qULcHZK+eHDxUnO2S0frElJFB0/TvaqVWSvWnX2RB3m8HBnkmOJicEQEiLVHCEqiVRqqtGbb75DdvYpKLQxZXrZVha+0Dcp3zB5w2QC3AJYPmQ5Bm3VbPBny8giZcbveGksFDgcNBjWEN8OUVVyLSHqK0d+fvGU8vjzCwQWHT9eop3Ox8e1mtOuHTpPzxqIWIjaSSo1tVRIy1ZsTzgFFfylbEDLAfx7y79Jz0tnxf4V3NLqlsoN8CyDjxctnozh0KztuGuNnPjyOAZvTzxCm1XJ9YSoj7QWC24dOuDWoYPzmC0t7XySk5CAdft27BkZ5KxdS87atcWNNBpMoa0wX7ASsik0VBYIFKIMpFJTjY4eS+eDuXMAuPe+sTQLLv+eSx8kfsC/t/6bMN8wFt2yqErL1tnJ+0n77wEsWj05jkJCxkVjCWxUZdcT4mqjCguxJiW5JDq2w4dLtNN6eGCJbueS6Oh9ZWsTcXWQgcKlqA1JDZzfLiGyXQeGDil/pSWzIJM+i/qQX5TP3N5z6dqkaxVEed7pTds5s+gUJq2WLJVP6+e7YvCq/EHKQohiRadOkZ+Q6Hxklb9tGyovr0Q7Q0izC/a1isUcHobGUDWPpIWoSfL4qTY7m0Ie3r+/Qqd7m7wZ0noIn+76lHk75lV5UtOgU1tsWZvIW2XFS2MhecY6Iqb2RmeUb55CVAW9nx+eN92I5003AqDsdgr27HGp5hTu3Yvt4CFsBw+RtfgHADRmM+a2US6JjiHAvyZvRYhqJ0lNNTNaPCm0W8nJOF3hPu6KvIsvdn/B78d+Z9epXbTxa1OJEZYU0LsTRzLXYN+kxRsPdr+8kjZT+st2CkJUA83ZGVPm8HB8hw0FwJ6VRX7ituINPM8tEJiZSf5fm8n/a7PzXH1QkMsu5eaoSLQmU03dihBVTpKaatYwqDGpR/bhcBRWuI8mHk3oG9KX5QeWM3/HfGbeMLMSIyxd0yE92Z+1Eu1uC942b3bPWErkC7KdghA1QeflhUe36/Hodj1wdoHAAwfPVnLiyU9IpCApiaJjx8g+dozsFSuKTzQYMLdp47ISsqFJE5lSLuoNGVNTzTb8sYUfly8Gh4PxE5/HbK7Yb007T+1k2JJh6DQ6lt++nCCP8g86rojkt5fgdqx4B+Nc/wzCx1XNDCwhxJVx5OaSv2OH6wKBJ0+WaKfz83Op5ljatUXr7l4DEQtROhkoXIraktQU2YqY9vJU0Oq4qc8Abri+4gvb3b/yfv5I+4O7I+/m2WufrcQoL23X9MV4ZhXPvChomUOrB/tX27WFEBWjlMJ2NPVsJefslPKdu8Bmc22o1WIKC3Op5hibN5cp5aLGSFJTitqS1ABMnjARjclIUFBzxo4dU+F+1h9dz8M/PYyb3o1Vd67Cy1g99+Ww29n10jK8C3xwKAXtbTQb3qtari2EqDyOggIKdu0iPyGBvPjiZKco9ViJdlovLyzR0ecXCYyORuftXQMRi6uRzH6q5bT64u0STqanXlE/1ze+nta+rUk5k8JXSV9xf7v7KyfAy9DqdLR5vj+7pvyIt8OToq0GjnlvJKh/l2q5vhCicmhNJiyxsVhiY2kwunjrFVv6cfITE7Cee2y1fTuOrCxy168nd/1657nGli1dqjmm0FA0evmRImqWVGpqwFtvvUNWVsW3S7jQ4r2LeX798zS0NGTlkJUYdcZKivLyinLySH55HV4aNwodDrxv88WvS3S1XV8IUfWUzUZBSorL2JzCAwdKtNO4uWFp29Zlywd9w4bVH7Cod+TxUylqU1LzzffLSdz6B9hsTHnlypIam91Gv2/6cTzvOC91fYnbWt9WSVGWjfX4KQ68sRUPrQmrw47/6KZ4RYVWawxCiOpVdOYM1sTE84lOYiKOnJwS7QxNm7pUc8wREWiM1feLl6gfJKkpRW1Kao6lHWfue/8B4J57HyCkWZMr6m/e9nm8tfktWnm34ptB36DVVO+Avtz9Rzg6Jxk3rYFch43gx9rg1qx6ZmMJIWqecjgo3LfPpZpTkJICf/vxojEaMUdGnq/mxMaiDwyUKeXikiSpKUVtSmrg/HYJEVHtGX7noCvqK7swmz6L+pBry+XdXu9yQ9MbKinKsstITOLkp8cwa3VkO6y0eq4jRj/Zm0aIq5U9Jwfrtm2uU8rPnCnRTu/v77pLeVQUWoulBiIWtZUMFK4LVPFvJocPVGy7hAt5Gj25M+xO5u+Yz7zt82okqfGJDsd2ax7ZP2ThqTWT8vofhE/ugV6+OQlxVdJ5eODepQvuXYonECilsB0+7JLkWHfvpuj4cbJXrSJ71aqzJxavoHwuybHExmJo1kyqOaJMpFJTQ6a//CqFdiuaIsXkaVOvuL+03DT6f92fIlXEFwO+oG3DtpUQZfmlLvkN2zo7Oo2GTF0WbSbHoTVK7iyEKMmRn491587z+1rFx1N0/HiJdjofH9dqTrt26Dw9ayBiURPqbaXm6NGjPPfccyxfvpy8vDxCQ0OZN28eHTt2rOnQys2/cROOHN6Lctgu37gMAt0D6d+iPz/s+4F52+fxZs83K6Xf8mo88HoOZv6EI9GIt92LXdOX02bSzbJPlBCiBK3FgluHDrh16OA8ZktLc9m807p9O/aMDHLWriVn7driRhoNptBWmC9YCdkUGioLBIq6k9ScOXOG66+/nhtvvJHly5fTqFEjUlJS8PWtm+M22kW348jhvaDXY7UWVHi7hAuNjhrND/t+4KdDP3E4+zDBnsGVEGn5hYzszZ7MZZgPeeJt9SH5jaVEPCf7RAkhLs8QGIihXyBe/eIAUIWFWJOSXBId2+HDFKTsoSBlD5mLvgZA6+GBJbqdS6Kjr6M/H0TF1ZnHT+PHj+e3335j3bp1FTq/tj1+KrIVMe2lqaDT0fOmOHreUDkL1z206iF+S/2NEREjmNh5YqX0WVG7X/8Bj1M+AOQ3zaL1YwNqNB4hRP1QdOoU+QmJ56s5iYk48vJKtDOENLtgX6tYzOFhaAyGGohYXIl6OfspMjKSuLg4jhw5wtq1a2nSpAmPPPIIDzzwQKntCwoKKCgocH6dlZVFcHBwrUlq4Px2CQEBzXj44Xsrpc/fj/3OAz8+gFlnZtUdq/Ax+1RKvxW186XFeOX5opTCHlVA81F9ajQeIUT9o+x2CvbscanmFO7dW6KdxmzG3DbKJdExBPjXQMSiPOplUmM2mwEYN24cd955J3/++SdPPvkk7733HqPPLu99oSlTpjB1askBuLUpqXlp0lQcOoVeY+SFyZVTVVFKMWzJMHad3sWjsY/yUMxDldJvRTnsdnZNXo53kTd2pdB31dBkUPcajUkIUf/Zs7LIT9xWvIHnuQUCMzNLtNMHBbnsUm6OikRruvLhAKLy1Mukxmg00rFjRzZs2OA89sQTT/Dnn3+ycePGEu3rQqXmX2+9S2bWCVShjalXuF3ChZbtW8Zz656jgbkBK4esxKw3V1rfFWHPL2T31J/xxh2bUrj3d8e/Z4fLnyiEEJVEKUXh/gNnKznx5CckUpCUBA6Ha0ODAXNERPGeWGdnXBmaNJEp5TWoXs5+CgoKIjIy0uVYmzZt+Prrr0ttbzKZMNXybLt5aCsStpygshcA7tO8D7O2zOJY7jF+2PcDd4bdWbkXKCedxUjr57qw99U/8NRYyF6Wg9F7Fz7t29RoXEKIq4dGo8HUsgWmli3wuW0wAI7cXPJ37HBdIPDkSazbtmHdto0zn3wCgM7Pz6WaY2nXFq27ew3ejbiYOpPUXH/99SQlJbkcS05OJiQkpIYiunJdrruWhC2/g97Avv2HadmicmYrGbQG7o68m9f+fI2Pd3zMkNZDqn3rhL8z+noT8kQ0h9/egbvWyPEv0tB7eeDRqmZmaAkhhNbdHfdOnXDv1Ak4u0Dg0dSzlZyzg5B37sJ+6hQ5P/9Mzs8/nz1Ri6l1a5dqjrF5c5lSXgvUmcdPf/75J127dmXq1KkMHTqUTZs28cADD/D+++8zcuTIy55f22Y/nTPl+YlgMBIeGcuIoYMrrd88Wx69F/UmuzCbWTfOolezXpXW95XI3r2PtP8dwqLVkeMooPk/YzEHyE6+QojayVFQQMGuXeQnJJAXX5zsFKUeK9FO6+WFJTr6/CKB0dHovL1rIOL6p16OqQFYsmQJEyZMICUlhRYtWjBu3LiLzn76u1qb1Ex8AYx63C3ePPPcPyq177e3vM1/t/2X2EaxfHLzJ5Xa95U4/cd2Mr4+hVGrJUvlEfZCN/SeUsoVQtQNtvTj5CcmYD332Gr7dpTVWqKdsUULl2qOKTQUjb7OPCCpNeptUnMlamtSM2PaTAqK8tEUOZg87aVK7ftE3gnivo7D5rDxSf9PiPWPrdT+r0Taqj+w/lSAXqMhU5NNxNQ+6IyyfoQQou5RNhsFKSnOrR7y4xMoPHiwRDuNmxuWtm1dtnzQN5RK9eXUy4HC9ZV/46YcPpSCchRVet+N3BoxsOVAvt3zLfN3zGeW/6xKv0ZFBfbpzOHMNdj/1OKNJ7unraDNZNlOQQhR92gMBsyRkZgjI/EdMQKAojNnsCYmnh+EnJiIIyeHvE2byNu0yXmuoUkTl2qOOSICjdFYU7dS50mlpob9uWUbSxd/DQ4Hz4yfgLtb5e5qvS9jH4O+H4QGDYsHL6a5d/NK7f9K7ftwBYZkNzQaDdmeZ2jzvGynIISof5TDQeG+fcWVnLOJTsGePfC3H8EaoxFzZKRrNSco6KqeUi6Pn0pRW5OaC7dLuKFnHDf1rJztEi702OrHWHtkLXeG3cmLXV6s9P6vVPKsH3BL8wEgLyCDsH/cUrMBCSFENbDn5GDdts11SvmZMyXa6Rs1Kq7mnNulPCoKraVyfwGuzSSpKUVtTWrg/HYJ/gHBPPLwfZXe/59pf3Lvynsxao38eMeP+Fn8Kv0aV2rn9MV4ZRVvp2BrnUfL+/vVdEhCCFGtlFLYDh1ySXKsSUlQ9LfhCTod5vBwZ5JjiYnBEBJSb6s5MqamjtEZTDhQnE5Pq5L+OwZ0pK1fW7af2s6CpAU8GvtolVznSkQ8N4BdU5fhXeiDPsWNw1/9QvDQG2s6LCGEqDYajQZjSAjGkBC8by1+FO/Iz8e6c+f5fa3i4yk6fhzrzp1Yd+7kzOdfAKDz8XF5ZGVu1w6dp2dN3k6NkEpNLTBr1rtkZJxAFdiYOqPytku40MoDK3l67dP4mHz48Y4fsehrX+nSXmhj95RVeDs8KVIKcy8jgX2vq+mwhBCiVrGlpTlnWeUnJGDdsQNVWOjaSKPBFNoK8wUrIZtatUJTBydjSKWmjmkR2pqtf51AU4V/13o3601Tj6YcyTnCd3u+Y0TEiKq7WAXpjAbCJ/Qgedp6vDRu5P1UwGnv7TTo3LamQxNCiFrDEBiIoV8/vPoVP6ZXhYVYk5Jcqjm2I0coSNlDQcoeMhcVbyekdXfHHN3u/GyrmBj0vr41eSuVTio1tcDxE6f4z+x/g0bD/909hrBWzavkOl/s/oLpf0ynqUdTlty2BJ22dmbs1uOnOPDGVjy0JvIddgLvbYZnRMuaDksIIeqMopMnyU9MPJ/obNuGyssr0c4Q0uyCfa1iMYeHoTHUrjXDZKBwKWpzUgPnt0toHRHNyOG3V8k18mx59P26L5kFmbzZ4036Nu9bJdepDDl7D5M6dw9uWj25jkKCn4zCrUlgTYclhBB1krLbKdizx6WaU7hvX4l2GpMJc9u2FwxCjsUQ4F8DEZ8nj5/qIlW8EdrRUlahrCxuBjeGhw9nbuJc5u+YT5+QPrV2tLxHq2D8R+Zy6rM03LVGDv57G63GWzD6yl4qQghRXpqzM6bM4eH4DhsKgD0zk/zEbc7NO/MTEnBkZZG/eTP5mzc7z9UHBbnsUm6OikRrMtXUrVySJDW1hMndiwJbHvlZJdcoqEwjIkYwf8d8tp3cxub0zXQM7Fil17sSPjER2DJzyVmag6fGTMrMjURMvgmdRVbbFEKIK6Xz9sajezc8uncDzi4QeODg2QSneCByQXIyRceOkX3sGNkrVhSfaDBgjohwWQnZ0KRJrfglWZKaWiKgSTCHDiShVOVvl3AhP4sft7a6lYXJC5m/Y36tTmoAGt3QgcKMdRT9pvDWuLP7lVW0mdpPtlMQQohKptFqMbVsgallC3xuGwyAIzeX/O07zldz4uOxnzqFdds2rNu2ceaT4s2SdX5+zkqO3wP3o9Fqa+QeauaqooTomLMzfAx6cnJLDuaqTKMiR6FBw9oja9mXUfKZam3T5NbuqLaFOJTCu8iLw1/8XNMhCSHEVUHr7o575040fPABgt+dTev162j10080fuMNfO++G3N0NBgM2E+dIufnn8n45usaS2hAKjW1RvvoSJZ//x2evv7YbLYqvVZz7+bc0/YewnzDCPYKrtJrVZbmd/cm5d2l2HPshI0YUNPhCCHEVUmj0WBs2gRj0yZ4Dyz+XuwoKCheIDAhAY2+ZmdOyewnIYQQQtRa5fn5LY+fhBBCCFEvXDWPn84VpLKysmo4EiGEEEKU1bmf22V5sHTVJDXZ2dkABAfXjTEkQgghhDgvOzsbb+9Lr1V21YypcTgcpKam4unpWelz6bOysggODubw4cMyXqcKyedcccOHD2fNmjWkpKTgeZGde++//36+++47Nm/eTHR0dJk+Z29vb8aPH8+ECRMqPeaDBw8SHR3Nf/7zH0aOHAnAjz/+yObNmyv1eueuM2bMGLp27UrXrl2r5Jefu+66C6vVyqJFi4DL/33+8ccfOXPmDBMmTKBjx4589dVXlR7T1UK+d1SPqvqclVJkZ2fTuHFjtJeZWXXVVGq0Wi1Nmzat0mt4eXnJP5hqIJ9z+Y0ePZrly5ezevVqRo0aVeL9vLw8li1bRr9+/QgJCQHK/jmbTKYq+fNo27Yt+fn5GAwGdGfXJVqzZg3vvvsuM2bMqLTrnEvyevTowZgxYyqt3wvZbDbWrFnDjBkzSnxWF/uc77jjDgBeeeUV9Hq9/J2vBPK9o3pUxed8uQrNOTJQWIirwK233oqnpyeff/55qe9///335ObmOisitYFGo8FsNjsTmrps3bp1ZGdnM2CALEcgRFWSpEaIq4DFYuH2229n9erVHD9+vMT7n3/+OZ6entx6663OY+PHjyc4OBiTyURoaCgzZ87E4XBc9lpbt26lf//+eHl54eHhQa9evfj9999LtMvIyOAf//gHzZs3x2Qy0bRpU0aNGsXJkycBOHDgABqNhvnz5wMwZswY3n33XaA44Tn3UkrRvHlzBg0aVOIaVqsVb29vxo4dW6bP6e+mTJmCRqMhOTmZu+66C29vbxo1asSkSZNQSnH48GEGDRqEl5cXgYGBvPnmm6X2s3TpUiIjI2nevDkAaWlpPPLIIwA0atSIoKAgBg0axIEDByoUpxCimCQ1lcBkMjF58mRMtXSDr/pCPucrM3LkSIqKikqMzTh9+jQrV67ktttuw2KxYLfbCQgI4KuvvmLUqFH8+9//5vrrr2fChAmMGzfuktfYsWMH3bt3JyEhgWeffZZJkyaxf/9+evbsyR9//OFsl5OTQ/fu3XnnnXfo27cvb7/9Ng899BC7d+/myJEjpfY9duxY+vTpA8Ann3zifGk0Gu666y6WL1/O6dOnXc754YcfyMrK4q677qrIR+Y0bNgwHA4Hr776Kp07d2batGnMmjWLPn360KRJE2bOnEloaChPP/00v/76a4nzly1bxs033+z8esiQISxZsoTu3bvz9ttv88QTT5Cdnc2hQ4euKE5ROvneUT1qxeeshBBXhaKiIhUUFKS6dOnicvy9995TgFq5cqVSSqmXX35Zubu7q+TkZJd248ePVzqdTh06dMh5DFCTJ092fj148GBlNBrV3r17ncdSU1OVp6enuuGGG5zHXnzxRQWob775pkScDodDKaXU/v37FaDmzZvnfO/RRx9VpX3bSkpKUoCaM2eOy/Fbb71VNW/e3NlnaUq7zjmTJ09WgHrwwQedx4qKilTTpk2VRqNRr776qvP4mTNnlMViUaNHj3bpY9++fQpQv/zyi7MdoF5//fWLxnShkJAQNWDAgDK1FeJqJ5UaIa4SOp2O4cOHs3HjRpfHHJ9//jkBAQH06tULgIULF9K9e3d8fX05efKk89W7d2/sdnuplQgAu93Ojz/+yODBg2nZsqXzeFBQEP/3f//H+vXrnetNfP3118TExHDbbbeV6KcisxPDwsLo3Lkzn332mfPY6dOnWb58OSNHjrziGY/333+/8/91Oh0dO3ZEKcV9993nPO7j40N4eDj79rnup7Z06VK8vb3p1q14J2SLxYLRaGTNmjWcOXPmiuISQriSpEaIq8i5gcDnBgwfOXKEdevWMXz4cOeA3JSUFFasWEGjRo1cXr179wYodUwOwIkTJ8jLyyM8PLzEe23atMHhcHD48GEA9u7dS9u2bSv13kaNGsVvv/3GwYMHgeLkzGazcffdd19x382aNXP52tvbG7PZTMOGDUsc/3uisnTpUvr27YteXzzZ1GQyMXPmTJYvX05AQAA33HADr732GmlpaVccpxBXO0lqhLiKdOjQgYiICL744gsAvvjiC5RSLrOeHA4Hffr0YdWqVaW+hgwZUlPhX9Lw4cMxGAzOas2nn35Kx44dS02yyqu0GVgXm5WlLlj6Ky8vjzVr1riMpwF46qmnSE5OZsaMGZjNZiZNmkSbNm3YunXrFccqxNXsqlmnRghRbOTIkUyaNInExEQ+//xzWrduzbXXXut8v1WrVuTk5DgrM2XVqFEj3NzcSEpKKvHe7t270Wq1zkXtWrVqxfbt28sd+6UeIzVo0IABAwbw2WefMXLkSH777TdmzZpV7mtUpp9//pmCggL69+9f4r1WrVrxz3/+k3/+85+kpKQQGxvLm2++yaeffloDkQpRP0ilRoirzLmqzIsvvkh8fHyJtWmGDh3Kxo0bWblyZYlzMzIyKCoqKrVfnU5H3759+f77713G7KSnp/P555/TrVs354JcQ4YMISEhgW+//bZEP+oSi5y7u7s74yjN3Xffzc6dO3nmmWecY4hq0rJly+jYsSMBAQHOY3l5eVitVpd2rVq1wtPTk4KCguoOUYh6RSo1QlxlWrRoQdeuXfn+++8BSiQ1zzzzDIsXL2bgwIGMGTOGDh06kJuby7Zt21i0aBEHDhwoMZbknGnTprFq1Sq6devGI488gl6vZ+7cuRQUFPDaa6+5XGPRokXceeed3HvvvXTo0IHTp0+zePFi3nvvPWJiYkrtv0OHDgA88cQTxMXFlUhcBgwYgJ+fHwsXLqR///74+/tf0Wd1pZYtW8Y999zjciw5OZlevXoxdOhQIiMj0ev1fPvtt6Snp9d4EiZEXSdJjRBXoZEjR7JhwwY6depEaGioy3tubm6sXbuW6dOns3DhQj7++GO8vLwICwtj6tSpl1yuPCoqinXr1jFhwgRmzJiBw+Ggc+fOfPrpp3Tu3NnZzsPDg3Xr1jF58mS+/fZbPvroI/z9/enVq9cltzO5/fbbefzxx1mwYAGffvopSimXRMBoNDJs2DD+85//VMoA4SuxY8cODh48WGI8TXBwMCNGjGD16tV88skn6PV6IiIi+Oqrr2rteCUh6oqrZkNLIcTV4R//+AcffvghaWlpuLm5Xbb9gQMHaNGiBe+88w7Dhw/Hy8sLo9F4xXG89tprvPXWWxw7dqxCU8rPPeq75ppriI6OZsmSJVcckxD1nYypEULUG1arlU8//ZQhQ4aUKaG50OOPP06jRo1YvHhxpcTSvHlz/vWvf1V4jZyePXvSqFEj5zR4IcTlSaVGCFHnHT9+nJ9++olFixbx3XffsWXLFmJjY8t0rtVqZf369c6vo6Oja3wsDsAff/xBdnY2UDyz7GLjjIQQ50lSI4So89asWcONN96Iv78/kyZN4rHHHqvpkIQQNUCSGiGEEELUCzKmRgghhBD1giQ1QgghhKgXrpp1ahwOB6mpqXh6el7xjr1CCCGEqB5KKbKzs2ncuDFa7aVrMVdNUpOamurcd0YIIYQQdcvhw4cvuTgnXEVJjaenJ1D8oZzbf0YIIYQQtVtWVhbBwcHOn+OXctUkNeceOXl5eUlSI4QQQtQxZRk6IgOFhRBCCFEvSFIjhBBCiHpBkhohhBBC1AuS1AghhBCiXpCkRgghhBD1giQ1QgghhKgXJKkRQgghRL0gSY0QQggh6gVJaoQQQghRL0hSI4QQQoh6QZIaIYQQQtQLktQIIYQQol6QpEYIIYQQ9YIkNUIIIYSoFySpEUIIIUS9IEmNEEIIIeoFSWqEEEIIUS9IUiOEEEKIekGSGiGEEELUC5LUCCGEEKJekKRGCCGEEPWCJDVCCCGEqBckqRFCCCFEvSBJjRBCCCHqBUlqhBBCCFEvSFIjhBBCiHqhQknNu+++S/PmzTGbzXTu3JlNmzZdsv3ChQuJiIjAbDbTrl07li1b5vL+N998Q9++ffHz80Oj0RAfH1+ij7S0NO6++24CAwNxd3fnmmuu4euvv65I+EIIIYSoh8qd1Hz55ZeMGzeOyZMns2XLFmJiYoiLi+P48eOltt+wYQMjRozgvvvuY+vWrQwePJjBgwezfft2Z5vc3Fy6devGzJkzL3rdUaNGkZSUxOLFi9m2bRu33347Q4cOZevWreW9BSGEEELUQxqllCrPCZ07d+baa69l9uzZADgcDoKDg3n88ccZP358ifbDhg0jNzeXJUuWOI9dd911xMbG8t5777m0PXDgAC1atGDr1q3Exsa6vOfh4cGcOXO4++67ncf8/PyYOXMm999//2XjzsrKwtvbm8zMTLy8vMpzy0IIIYSoIeX5+V2uSk1hYSGbN2+md+/e5zvQaunduzcbN24s9ZyNGze6tAeIi4u7aPuL6dq1K19++SWnT5/G4XCwYMECrFYrPXv2LLV9QUEBWVlZLi8hhBBC1F/lSmpOnjyJ3W4nICDA5XhAQABpaWmlnpOWllau9hfz1VdfYbPZ8PPzw2QyMXbsWL799ltCQ0NLbT9jxgy8vb2dr+Dg4HJdTwghhBB1S52Z/TRp0iQyMjL46aef+Ouvvxg3bhxDhw5l27ZtpbafMGECmZmZztfhw4erOWIhhBBCVCd9eRo3bNgQnU5Henq6y/H09HQCAwNLPScwMLBc7Uuzd+9eZs+ezfbt24mKigIgJiaGdevW8e6775YYmwNgMpkwmUxlvoYQQggh6rZyVWqMRiMdOnRg9erVzmMOh4PVq1fTpUuXUs/p0qWLS3uAVatWXbR9afLy8oqD1bqGq9PpcDgcZe5HCCGEEPVXuSo1AOPGjWP06NF07NiRTp06MWvWLHJzc7nnnnuA4qnXTZo0YcaMGQA8+eST9OjRgzfffJMBAwawYMEC/vrrL95//31nn6dPn+bQoUOkpqYCkJSUBBRXeQIDA4mIiCA0NJSxY8fyxhtv4Ofnx3fffceqVatcZlUJIYQQ4iqmKuCdd95RzZo1U0ajUXXq1En9/vvvzvd69OihRo8e7dL+q6++UmFhYcpoNKqoqCi1dOlSl/fnzZungBKvyZMnO9skJyer22+/Xfn7+ys3NzcVHR2tPv744zLHnJmZqQCVmZlZkVsWQgghRA0oz8/vcq9TU1fJOjVCCCFE3VNl69QIIYQQQtRWktQIIYQQol6QpEYIIYQQ9YIkNUIIIYSoFySpEUIIIUS9IEmNEEIIIeoFSWqEEEIIUS9IUiOEEEKIekGSGiGEEELUC5LUCCGEEKJekKRGCCGEEPWCJDVCCCGEqBf0NR1AXVdgtZKanFSpfXp4uqPT6Sq1z4uxaM1oNJpquVZl0Xu5o62mz0cIIUT5aCyWGvu5IknNFUpNTuKjb76t1D5HW3tioHp+aOdVy1UqV8bScehsOTUdhhBCiFKEb9mMxs2tRq4tj5+EEEIIUS9olFKqpoOoDllZWXh7e5OZmYmXl1el9SuPn6qfPH4SQojaq7IfP5Xn57c8frpCJrOZFtExNR2GEEIIcdWTx09CCCGEqBckqRFCCCFEvSBJjRBCCCHqBUlqhBBCCFEvSFIjhBBCiHpBkhohhBBC1AuS1AghhBCiXpCkRgghhBD1giQ1QgghhKgXJKkRQgghRL0gSY0QQggh6gVJaoQQQghRL0hSI4QQQoh6QZIaIYQQQtQLktQIIYQQol6QpEYIIYQQ9YIkNUIIIYSoFySpEUIIIUS9IEmNEEIIIeoFSWqEEEIIUS9IUiOEEEKIekGSGiGEEELUC5LUCCGEEKJekKRGCCGEEPWCJDVCCCGEqBckqRFCCCFEvSBJjRBCCCHqBUlqhBBCCFEvVCipeffdd2nevDlms5nOnTuzadOmS7ZfuHAhERERmM1m2rVrx7Jly1ze/+abb+jbty9+fn5oNBri4+Nd3j9w4AAajabU18KFCytyC0IIIYSoZ8qd1Hz55ZeMGzeOyZMns2XLFmJiYoiLi+P48eOltt+wYQMjRozgvvvuY+vWrQwePJjBgwezfft2Z5vc3Fy6devGzJkzS+0jODiYY8eOubymTp2Kh4cH/fv3L+8tCCGEEKIe0iilVHlO6Ny5M9deey2zZ88GwOFwEBwczOOPP8748eNLtB82bBi5ubksWbLEeey6664jNjaW9957z6XtgQMHaNGiBVu3biU2NvaScbRv355rrrmGDz/8sExxZ2Vl4e3tTWZmJl5eXmU6RwghhBA1qzw/v8tVqSksLGTz5s307t37fAdaLb1792bjxo2lnrNx40aX9gBxcXEXbV8WmzdvJj4+nvvuu++ibQoKCsjKynJ5CSGEEKL+KldSc/LkSex2OwEBAS7HAwICSEtLK/WctLS0crUviw8//JA2bdrQtWvXi7aZMWMG3t7ezldwcHCFryeEEEKI2q/OzX7Kz8/n888/v2SVBmDChAlkZmY6X4cPH66mCIUQQghRE/TladywYUN0Oh3p6ekux9PT0wkMDCz1nMDAwHK1v5xFixaRl5fHqFGjLtnOZDJhMpkqdA0hhBBC1D3lqtQYjUY6dOjA6tWrncccDgerV6+mS5cupZ7TpUsXl/YAq1atumj7y/nwww+59dZbadSoUYXOF0IIIUT9VK5KDcC4ceMYPXo0HTt2pFOnTsyaNYvc3FzuueceAEaNGkWTJk2YMWMGAE8++SQ9evTgzTffZMCAASxYsIC//vqL999/39nn6dOnOXToEKmpqQAkJSUBxVWeCys6e/bs4ddffy2xzo0QQgghRLmTmmHDhnHixAlefPFF0tLSiI2NZcWKFc7BwIcOHUKrPV8A6tq1K59//jkvvPACEydOpHXr1nz33Xe0bdvW2Wbx4sXOpAhg+PDhAEyePJkpU6Y4j//vf/+jadOm9O3bt9w3KoQQQoj6rdzr1NRVsk6NEEIIUfdU2To1QgghhBC1lSQ1QgghhKgXJKkRQgghRL0gSY0QQggh6gVJaoQQQghRL0hSI4QQQoh6QZIaIYQQQtQLktQIIYQQol6QpEYIIYQQ9YIkNUIIIYSoFySpEUIIIUS9IEmNEEIIIeqFcu/SXVed27czKyurhiMRQgghRFmd+7ldlv23r5qkJjs7G4Dg4OAajkQIIYQQ5ZWdnY23t/cl22hUWVKfesDhcJCamoqnpycajaZS+87KyiI4OJjDhw9fdlt0UXHyOZd0uX/g5yxZsoTu3buXqW19+5w/+OAD3NzcGDlyZE2H4uLc59yvXz9uv/12YmNjCQ8PB2DdunUMHDiQjz76iMGDBzvPKSws5K677uLHH3/knXfe4e677yY5OZmtW7eyePFilixZwr59+/Dz86uhu6qd6tvf6dqqqj5npRTZ2dk0btwYrfbSo2aumkqNVquladOmVXoNLy8v+QdTDeRzPu+TTz5x+frjjz9m1apVJY537Nix3J9Zffmc582bR8OGDXn44YdrOpRSxcbG8sADD7gcc3d3B8DNzc35Z2Cz2ZwJzQcffMB9990HFP/ZduzYkaNHj7JkyRI8PT3rxZ9bVagvf6dru6r4nMv6C9xVk9QIUR/dddddLl///vvvrFq1qsTx+kIphdVqxWKxXFVx2Gw2hg4dypIlS5g7d64zoRFCuJLZT0LUcw6Hg1mzZhEVFYXZbCYgIICxY8dy5swZl3bNmzdn4MCBrFu3DoCAgADatWvHmjVrAPjmm29o164dZrOZDh06sHXrVpfzx4wZg4eHB/v27SMuLg53d3caN27MSy+9VGKAX3ljWrlyJR07dsRisTB37lyguAJz00034e/vj8lkIjIykjlz5pQ4f8eOHaxduxaNRoNGo6Fnz54ATJkypdRH0fPnz0ej0XDgwIEyxZGRkcFTTz1FcHAwJpOJ0NBQZs6cicPhKMOfzuUVFRUxfPhwvv/+e+bMmVOiqiOEOE8qNZXAZDIxefJkTCZTTYdSr8nnXDFjx45l/vz53HPPPTzxxBPs37+f2bNns3XrVn777TcMBoOz7Z49e3jggQfo0aMHAwcOZNasWdxyyy289957TJw4kUceeQSAGTNmMHToUJKSklyecdvtdvr168d1113Ha6+9xooVK5g8eTJFRUW89NJLFYopKSmJESNGMHbsWB544AHnuJM5c+YQFRXFrbfeil6v54cffuCRRx7B4XDw6KOPAjBr1iwef/xxPDw8eP7554HiZK0iSosjLy+PHj16cPToUcaOHUuzZs3YsGEDEyZM4NixY8yaNeui/Z37e6zT6S7apqioiBEjRvDtt9/y7rvvMnbs2ArFfrWT7x3Vo1Z8zkoIUW88+uij6sJ/1uvWrVOA+uyzz1zarVixosTxkJAQBagNGzY4j61cuVIBymKxqIMHDzqPz507VwHql19+cR4bPXq0AtTjjz/uPOZwONSAAQOU0WhUJ06cqHBMK1asKHGveXl5JY7FxcWpli1buhyLiopSPXr0KNF28uTJqrRvgfPmzVOA2r9//2XjePnll5W7u7tKTk52OT5+/Hil0+nUoUOHSvR/IUBNnjy5xPFffvlFAc7rvvvuu5fs58L7Ofc5C3E1ksdPQtRjCxcuxNvbmz59+nDy5Ennq0OHDnh4ePDLL7+4tI+MjKRLly7Orzt37gzATTfdRLNmzUoc37dvX4lrPvbYY87/12g0PPbYYxQWFvLTTz9VKKYWLVoQFxdX4joXjmfJzMzk5MmT9OjRg3379pGZmVnmz6isSotj4cKFdO/eHV9fX5d76d27N3a7nV9//fWKrpmeno5er6dFixZX1I8QVwtJai7j1ltvpVmzZpjNZoKCgrj77rtJTU295Dk9e/Z0Pr8/93rooYdc2vz555/06tULHx8ffH19iYuLIyEhwfm+1WplzJgxtGvXDr1e7zKts6xOnz7N448/Tnh4OBaLhWbNmvHEE09UyTd8UTulpKSQmZmJv78/jRo1cnnl5ORw/Phxl/YXJi5wfsbB39d3Onf872NgtFotLVu2dDkWFhYG4ByjUt6YLvYD/bfffqN37964u7vj4+NDo0aNmDhxIkCVJTV/l5KSwooVK0rcR+/evQFK3Et5vfbaazRr1ow77riD33777Yr6EuJqIGNqKE5CxowZw5gxY0q8d+ONNzJx4kSCgoI4evQoTz/9NHfccQcbNmy4ZJ8PPPCAyxgCNzc35//n5OTQr18/br31Vv7zn/9QVFTE5MmTiYuL4/DhwxgMBux2OxaLhSeeeIKvv/66QveVmppKamoqb7zxBpGRkRw8eJCHHnqI1NRUFi1aVKE+Rd3icDjw9/fns88+K/X9Ro0auXx9sfEdFzuuKrDMVXljKm2G0d69e+nVqxcRERG89dZbBAcHYzQaWbZsGf/617/KNEj3YutV2e32Uo+XFofD4aBPnz48++yzpZ5zLqGrqKCgIFatWkW3bt0YMGAAa9euJSYm5or6FKI+k6TmMv7xj384/z8kJITx48czePBgbDaby2DGv3NzcyMwMLDU93bv3s3p06d56aWXnL8BT548mejoaA4ePEhoaCju7u7OmRy//fYbGRkZpfb1/fffM3XqVHbu3Enjxo0ZPXo0zz//PHq9nrZt27okRK1ateKVV17hrrvuoqioCL1e/vjru1atWvHTTz9x/fXXV8v0Y4fDwb59+1x+mCcnJwPFM4gqK6YffviBgoICFi9e7FJd+vujK7h48uLr6wsUz17y8fFxHj948GCZ42jVqhU5OTnOykxVaNmyJStXrqRHjx7ExcWxbt06WrduXWXXE6Iuk8dP5XD69Gk+++wzunbtesmEBuCzzz6jYcOGtG3blgkTJpCXl+d8Lzw8HD8/Pz788EMKCwvJz8/nww8/pE2bNs5v/GWxbt06Ro0axZNPPsnOnTuZO3cu8+fP55VXXrnoOZmZmXh5eUlCc5UYOnQodrudl19+ucR7RUVFF02Wr8Ts2bOd/6+UYvbs2RgMBnr16lVpMZ2rHF1YKcrMzGTevHkl2rq7u5faZ6tWrQBcxr3k5uby0UcfXfb65wwdOpSNGzeycuXKEu9lZGRQVFRU5r4upV27dixdupScnBz69OnD0aNHK6VfIeob+clWBs899xyzZ88mLy+P6667jiVLllyy/f/93/8REhJC48aNSUxM5LnnniMpKYlvvvkGAE9PT9asWcPgwYOd39hbt27NypUry5VsTJ06lfHjxzN69Gig+De6l19+mWeffZbJkyeXaH/y5ElefvllHnzwwTJfQ9RtPXr0YOzYscyYMYP4+Hj69u2LwWAgJSWFhQsX8vbbb3PHHXdU2vXMZjMrVqxg9OjRdO7cmeXLl7N06VImTpzofKxUGTH17dsXo9HILbfcwtixY8nJyeGDDz7A39+fY8eOubTt0KEDc+bMYdq0aYSGhuLv789NN91E3759adasGffddx/PPPMMOp2O//3vfzRq1IhDhw6V6X6feeYZFi9ezMCBAxkzZgwdOnQgNzeXbdu2sWjRIg4cOEDDhg0r9mH+TZcuXfjmm2+45ZZb6NOnD+vWrZPtEIT4uxqefVUjXnnlFeXu7u58abVaZTKZXI5dOH31xIkTKikpSf3444/q+uuvVzfffLNyOBxlvt7q1asVoPbs2aOUKp6K2qlTJzVq1Ci1adMmtXHjRjVkyBAVFRVV6jTV0aNHq0GDBpU43rBhQ2U2m13iNpvNClC5ubkubTMzM1WnTp1Uv379VGFhYZljF3XL36d0n/P++++rDh06KIvFojw9PVW7du3Us88+q1JTU51tQkJC1IABA0qcC6hHH33U5dj+/fsVoF5//XXnsdGjRyt3d3e1d+9e1bdvX+Xm5qYCAgLU5MmTld1ur9SYlFJq8eLFKjo6WpnNZtW8eXM1c+ZM9b///a/EdOy0tDQ1YMAA5enpqQCX6d2bN29WnTt3VkajUTVr1ky99dZbF53SfbE4srOz1YQJE1RoaKgyGo2qYcOGqmvXruqNN9647L81LjOle+HChSXe+/LLL5VWq1XXXnutysrKch6XKd1CKHXVbGh5odOnT3P69Gnn1yNHjmTIkCHcfvvtzmPNmzcvtWpy5MgRgoOD2bBhg8vU10vJzc3Fw8ODFStWEBcXx4cffsjEiRM5duyYc+GywsJCfH19+fDDDxk+fLjL+WPGjCEjI4PvvvvO5bjFYmHq1KkucZ/TsmVLZ9/Z2dnExcXh5ubGkiVLMJvNZYpbiPIYM2YMixYtIicnp6ZDqTM0Gg3PPPMMzz77LO7u7hUaY2S1WsnJyeG1117j9ddf58SJE5VWHRKirrkqHz81aNCABg0aOL+2WCz4+/sTGhp62XPPzaooKCgo8/Xi4+OB4pkMAHl5eWi1WpcBjOe+Ls/S6tdccw1JSUmXjDsrK4u4uDhMJhOLFy+WhEaIWub11193vp5++ulyn//ee++5TGgQ4mp2VSY1ZfXHH3/w559/0q1bN3x9fdm7dy+TJk2iVatWzirN0aNH6dWrFx9//DGdOnVi7969fP7559x88834+fmRmJjIP/7xD2644Qaio6MB6NOnD8888wyPPvoojz/+OA6Hg1dffRW9Xs+NN97ovP7OnTspLCzk9OnTZGdnO5Oj2NhYAF588UUGDhzoXMdCq9WSkJDA9u3bmTZtGllZWfTt25e8vDw+/fRTsrKyyMrKAoqnzV5qeXYhRNVbtWqV8/8rOv17yJAhtG3b1vl1WXczFqJequnnX7VBjx491Lx580ocT0xMVDfeeKNq0KCBMplMqnnz5uqhhx5SR44ccbY5N7bg3HLxhw4dUjfccIPznNDQUPXMM8+ozMxMl77Pjc/x9vZWvr6+6qabblIbN250aXNuifS/vy60YsUK1bVrV2WxWJSXl5fq1KmTev/995VS55/Ll/a6cLyAEJXh3JgaIYSoKVflmBohhBBC1D+yTo0QQggh6gVJaoQQQghRL1w1A4UdDgepqal4enpedNl0IYQQQtQuSimys7Np3Lixc6mSi7lqkprU1NQSOw0LIYQQom44fPgwTZs2vWSbKktq3n33XV5//XXS0tKIiYnhnXfeoVOnThdtv3DhQiZNmsSBAwdo3bo1M2fO5Oabb3a+P2bMmBJ7ssTFxbFixYoyxePp6QkUfyheXl4VuCMhhBBCVLesrCyCg4OdP8cvpUqSmi+//JJx48bx3nvv0blzZ2bNmkVcXBxJSUn4+/uXaL9hwwZGjBjBjBkzGDhwIJ9//jmDBw9my5YtLusv9OvXz2XDOpPJVOaYzj1y8vLykqRGCCGEqGPKMnSkSqZ0d+7cmWuvvda5W6/D4SA4OJjHH3+c8ePHl2g/bNgwcnNzXTaKvO6664iNjeW9994DLr5VQFllZWXh7e3t3KW6svzx11/sXrCGfPIYPfp2zBHhaC6zg7cQQgghyqY8P78rffZTYWEhmzdvpnfv3ucvotXSu3dvNm7cWOo5GzdudGkPxY+W/t5+zZo1+Pv7Ex4ezsMPP8ypU6cuGkdBQYFzBd0LV9KtbMeWbGC/Rw6n3DVsm/gxSR2v5cDIu0h/7XWyVv6ILT29Sq4rhBBCCFeV/vjp5MmT2O12AgICXI4HBASwe/fuUs9JS0srtX1aWprz6379+nH77bfTokUL9u7dy8SJE+nfvz8bN24sdbn/GTNmMHXq1Eq4o0u74a4BJHzyCTaNHbeoXuQWZKE2ryV/82ZnG31gIJaYmOJXbCzmqEi05Xh0JoQQQojLqzOzny7cubpdu3ZER0fTqlUr1qxZQ69evUq0nzBhAuPGjXN+fW6gUWVrENoKh82KxmgmQ5tLk/b/B/27Ys7cR35CIgVJSRSlpZGdlkb2ypXFJxkMmCMiLkh0YjA0bSpTzYUQQogrUOlJTcOGDdHpdKT/7bFLeno6gYGBpZ4TGBhYrvYALVu2pGHDhuzZs6fUpMZkMpVrIPGV0GiMABxS6TTXNKLgWAiWEdcSNGUKjtxc8nfsID8hofgVn4D95Ems27Zh3baNM59+CoCuQQNnJccSE4O5bVt0Hu7VEr8QQghRH1R6UmM0GunQoQOrV69m8ODBQPFA4dWrV/PYY4+Vek6XLl1YvXo1Tz31lPPYqlWrnDthl+bIkSOcOnWKoKCgygy/Qty9G5Cbl8EudtPOEYav1sjxL9LQe3ng0SoY906dcD87nV0phe1oKvkJ8c5Ex7pzF/bTp8n55RdyfvmluFOtFlPr1i7VHGOLFmgus/CQEEIIcbWqktlPX375JaNHj2bu3Ll06tSJWbNm8dVXX7F7924CAgIYNWoUTZo0YcaMGUDxlO4ePXrw6quvMmDAABYsWMD06dOdU7pzcnKYOnUqQ4YMITAwkL179/Lss8+SnZ3Ntm3bylSRqarZTwBffPUdSTvjUTYrfzSN59/7n8NNqyfHUUDzf8ZiDmh4yfMdBQUU7NpFfkICefHFyU5R6rES7bReXliio51JjqVdO3Q+PpV6L0IIIURtUp6f31UypmbYsGGcOHGCF198kbS0NGJjY1mxYoVzMPChQ4dcljru2rUrn3/+OS+88AITJ06kdevWfPfdd841anQ6HYmJiXz00UdkZGTQuHFj+vbty8svv1xtj5gupfO1HUjaGY/GYOaU/jgfRv7A2J2D8NCa2PfWZsJe6Ibe8+KPkrQmU/Fjp9hYGoweDYAt/Tj5iQlYzz6yyt++HUdWFrnr15O7fr3zXGOLFueTnNhYTKGhaPR1ZqiUEEIIUWmqpFJTG1VlpQZgygvPg95AosdGUhql8qHPNPw3+KLXaMjUZBMxtQ86Y8XXr1E2GwUpKWfH5cSTH59A4cGDJdpp3NywtG17PtGJiUHf8NKVIiGEEKK2Ks/Pb0lqKsmUCc+DyUCu8TQrmvxCr2a9+OepwTj+1KLTaMg0ZtBm8s1oS5l+XlFFZ85gTUx0DkDOT0zEkZNTop2hSRPnAGRLbAzmiAg0RmOlxSGEEEJUFUlqSlHVSc20qdMpUoVgL+Lr0O/RoGHx4MU4Fu3GkOyGRqMh2/MMbZ6/tdKvfY5yOCjct6+4knM20SnYswf+9kesMRoxR0a6VnOCgmRKuRBCiFpHkppSVHVSM+e9/5GedghVUMip3lmsPbKWO8Pu5MUuL5I86wfc0nwAyAvIIOwft1T69S/GnpODddu289WchATsZ86UaKdv1OjsuJ7iJMccFYXWYqm2OIUQQojSSFJTiqpOatb++ju//LwC7Hb6jx3I/avvx6QzsXLISvwsfuycvhivLN/iKd2t82h5f79Kj6EslFLYDh1ySXKsSUlQVOTaUKfDHB7uTHIssbEYmjWTao4QQohqJUlNKao6qbFaC3h1+iug1dJvwCDeTn+N7ae281DMQzwa+ygOu51dU5fhXeiDQyk0HewED72x0uOoCEd+PtadO51JTn58PEXHj5dop/PxcXlkZY6ORufhUQMRCyGEuFpIUlOKqk5qAKZMnAhGI02DWxHcy5+n1z6Nj8mHH+/4EYvegr3Qxu4pq/B2eFKkFOZeRgL7XlclsVwpW1qac5ZVfkIC1h07UIWFro00GkyhrTDHxOB2diCysVUrWSBQCCFEpanxdWquVhqtAQUcTz3KPc3+j6YeTTmSc4Tv9nzHiIgR6IwGwif0IHnaerw0buT9VMBp7+006Ny2pkMvwRAYiKFfP7z6FT8mU4WFWJOSXKo5tiNHKEjZQ0HKHjIXfQ2A1sMDS3Q7zOdWQo6JQe/rW5O3IoQQ4iohSU0lcvduQE7uGQrzc9BpdYyKGsX0P6bz8Y6PGRo2FJ1Wh97TnZb/7MiBN7bioTVx6uuTGLz34RnRsqbDvySN0YilXTss7drB3XcBUHTyJPmJiecTnW3bcOTkkLthI7kbNjrPNYaEFE8lP5vkmMPC0BgqvmaPEEIIURp5/FSJFiz8nt07toLNxpRXXiHPlkffr/uSWZDJmz3epG/zvs62OXsPkzp3D25aPbmOQoKfjMKtycU38KwLlN1OwZ49LtWcwn37SrTTmM2Y20ad39cqJhZDgH8NRCyEEKK2kzE1paiOpObgoaPM+98HAIx96BGCAv2ZvXU2cxPn0q5hOz67+TOX2UMZW3dx6ot0TFod2cpKq/GdMPp6V0lsNcWemUl+4rbzu5QnJODIyirRTt846IIkJwZzZCTaWrAFhhBCiJolSU0pqiOpAZjy/PNgMBDdvjO3D+rPqfxTxH0dR4G9gHlx8+gY2NGl/fE1m8ldnotBoyGTXMIn90Bfj9eHUQ4HhQcOnk1wigciFyQng8Ph2tBgwNymzQWzrWIxNGksU8qFEOIqIwOFa9LZFPHA3j0A+Fn8uLXVrSxMXsj8HfNLJDX+PTtwNHMdRRsU3hp3kl75mTZT+1Xqdgq1iUarxdSyBaaWLfC5bTAAjtxc8rfvOF/NiY/HfuoU1sRErImJnPnkEwB0DRu6VHMsbaPQul98o1AhhBBXF0lqKpnB7I7NUUj2mVPOY6MiR7EoeRFrj6xlb8ZeWvm0cjmnyaDuHMhchWOHCe8iL3a/spTIF6tuO4XaRuvujnvnTrh37gScXSDwaOr57R4SErDu2oX95ElyVq8mZ/XqsydqMYWFORcHtMTEYGweIlPKhRDiKiWPnyrZ3LnzOXbsAKqgkKkzpjuPP/nzk/x8+Gdub307U7tOLfXclHeXYjlcHFuOXwYRz1Tfdgq1naOgAOuOneQnnh+bU5R6rEQ7rbc3lujo89Wc6HbovOvXOCUhhLiayJiaUlRXUvPrb3/y86ql4LDzwqTJ6A3FxbD44/HcvfxuDFoDK4espJFbo1LP3z1zMR5nitd1sTbLJvSRm6ss1rrOln68eFzOuWrO9h0oq7VEO2PLluerObExmEJD0dTTx3tCCFHfSFJTiupKai7cLqFv/1vp2vka53t3L7ub+BPx3N/ufp685slSz3fY7ex6eSneVl8cSqGiCwkZ2bvK4q1PlM2GNTm5OME5u7dV4cGDJdpp3NyK19y5cJdyP78aiFgIIcTlSFJTiupKagAmT5yIxmikcdOWPHj/KOfx1YdW89QvT+Fp9GTVHatwN5Q+yNVRWMSuqSvxtnthVwpDdx2NB15fpTHXV0Vnzpyv5CQkkJ+QiCM3t0Q7Q9OmLtUcc3g4GqOxBiIWQghxIZn9VMO0WiMKOHks1eV4z6Y9CfEK4WDWQb5N+Za7Iu8q/XyjnvAXbiJp6lq8Ne5Y1xVxwmcrjbq1r4bo6xe9ry+ePXvi2bMnULxAYOG+fS4zrQr27MV25Ai2I0fIWroUKF5B2RwV5VrNCQyUKeVCCFGLSVJTBTx8GpCdc5rC/GyX4zqtjlGRo3j595f5ZOcnDI8Yjl5b+h+B3mKh9TOd2TvzLzy1ZjIXZ2LwSsInOrw6bqHe0uh0mFq3xtS6NT533AGAPTsb67azCwSeXQ3ZnpFB/tat5G/d6jxX7+/vWs2JjERbj9cUEkKIukYeP1WBr77+gZ3bNju3S7iQtchK3NdxnLaeZmb3mdzc8tIDgfMOHePw7F24aw3kOWw0eSQC9+aNqzL8q55SCtuhQy5JjnX3brDbXRvq9ZjDw12qOYZmzaSaI4QQlUjG1JSiOpOaQ4eP8b8P5wJw3wMPEfy3PZ3eS3iPd+PfpU2DNnw58MvL/hDM2rGH4x8dwazVkeMooPmz12Bu2KDK4hclOfLzse7YcT7RiY+n6MSJEu10Pj7nk5zYWMzt2qHz8KiBiIUQon6QpKYU1ZnUwPntEtrGdOKO21yrMWesZ+i7qC9Wu5X/9v0vnYM6X7a/UxsTyfz2DEatliyVR9ik7ug93KoqfHEZSimK0tJcqzk7dqAKC10bajSYQkOdlRxLTAzGVq1kgUAhhCgjGShcG5xNFQ/u21viLV+zL4NDB7MgaQHzdswrU1Lj1yWawoyNFKyx4aVxI2n6GtpM6YvWKH+ENUGj0WAICsIQFIRXv34AOAoLKdi9+/wu5QkJ2I4coSAlhYKUFDIWLgJA6+GBJbod5pgY3GJjMUdHo/f1rcnbEUKIekF+IlYRg9kDm6OAnAu2S7jQqKhRfJX8Fb8d/Y3kM8mE+YZdts+g/l04lPUzji16vB2e7HplOW1evLne7hNV12iNxuLVjKOjgbsBKDp5kvzERPK3nl0kcPt2HDk55G7YSO6GjZz722EMCSkefHxul/KwMDQGQ43dixBC1EXy+KmKvP/Bx6Qe3Vdiu4QL/XPNP/nx4I/c2upWXun2SqltSrP3/eWY9hWP08j2OkObiVfPPlF1nSoqomDPHpdqTuG+fSXaacxmzG2jXPa1Mvj710DEQghRs2RMTSmqO6lZt/EvVq9cAg4HE154AVMpC7ltP7mdEUtHoNfoWT5kOYHugaX0VLqkN3/A/YQPAHmNMwl7YmBlhS6qmT0zk/zEbec38ExMxJGVVaKdvnGQyy7l5shItCZTDUQshBDVR5KaUlR3UlNQWMiMadNAq6VX3EC6d+lYarsxK8awOX0z90Tdw7iO48p1jZ3TFuOV41s8aDUinxb3xFVG6KKGKYeDwgMHXKo5BcnJ4HC4NjQYMLdpc8GU8lgMTRrLlHIhRL0iSU0pqjupgfPbJQQ1bsHYB0eX2mbt4bU89vNjeBg8WHXHKjyMZZ/+67Db2TVlOd42b+xKoevkoOmQnpUUvahNHLm55G/bfn4l5IQE7KdKjtfSNWzoUs2xtI1C6176dhxCCFEXyOynWsK5XUJa6kXbdG/anZbeLdmXuY+vU75mdFTpyU+p/et0REyKY/fkn/DGA9smLek+fxLQ69pKiF7UJlp3d9yv64z7dcUz5ZRS2I4edanmWHftwn7yJDmrV5OzevXZE7WYwsJcxuYYm4fIlHIhRL0klZoq9Oab75CdfQoKbUyZfvGBwN+mfMuLG14kwC2A5UOWY9CWb9aLLSuHlFc24KWxUOBw4Du0IQ06Rl1p+KKOcVitWHfucqnmFB07VqKd1tu7eJbWuWpOdDt03t41ELEQQlyeVGpqiZCWrdiecAouM8RhQMsB/Hvrv0nPS2fF/hXc0uqWcl3H4OVBi3+05+BbiXhojZz66jgGL3c8w5pXPHhR52jNZtyuaY/bNec3PrWlp7skOdbtO3BkZpK7bh2569Y52xlbtnTZ18oUGopGlgoQQtQxUqmpQkePpfPB3DkA3HPvA4Q0a3LRtv/d9l/e3vI2rX1b8/UtX1dosGd28gHS/rsfi1ZPjqOQkHHRWAIbVTh+Uf8omw1rUjL5CfHnFwg8eKhEO42bG5Z27Vx3Kffzq4GIhRBXOxkoXIqaSGrg/HYJke06MHTIxSswmQWZ9FnUh/yifN7r/R7XN7m+Qtc7s3kHp788iUmrJUvl0/r5rhi8ZO8hcXFFZ86cr+bEx2NN3IYjN7dEO0PTpq67lIeHoyllqQIhhKhM8vipNjmbMh4uZYG1C3mbvBnSegif7vqU+TvmVzip8e0QRWHmn+StzMdLYyF5xjoiXuyFziI/fETp9L6+ePbsiWfPngAou52CPXvJTzz7yCohgYI9e7EdOYLtyBGyli4FQGM0Yo6Kcq3mBAbKlHIhRI2RpKaKGS2eFNqt5GSevmzbuyPv5ovdX/D7sd/ZdWoXbfzaVOiaATddy5HMX7H/rvDGg93Tf6TNlP6ynYIoE41Ohzk8DHN4GL533gmAPTsb67Zt5CckkBcfjzU+oXjRwK1byd+61Xmu3t/ftZoTGYnWYqmpWxFCXGUkqaliDYMak3pkHw6H7bJtG3s0pm/zvizfv5z5O+Yz84aZFb5u09tuYH/mj2h3mfG2ebN7+lIiJ8l2CqJidJ6euHftinvXrsDZKeUHD17w2CoBa1ISRcePk71qFdmrVhWfqNdjDg93qeYYmjWTao4QokrImJoqtuGPLfy4fDE4HIyf+Dxm86WXtd91ahdDlwxFp9Gx7PZlNPZofEXXT3lnKZajxfeb2yiD8H+Wb2aVEGXlyM/HumOHc2xOXnw89hMnS7TT+ficT3JiYzG3a4fOQ8Z9CSFKJwOFS1FTSU2RrYhpL08FrY6bevfnhm6dL3vO/T/ezx/H/uCuNnfxXKfnrjiGXa8uxjPDF4CCFjm0Gtv/ivsU4nKUUhQdO+as5OQnJGDdsQNl+1vVUqPBFBrqrORYYmIwtmolCwQKIQBJakpVU0kNwOQJE9GYjAQGhvDQQ/dctv1vR3/joZ8ewqK3sOqOVXibrmxhNIfdzq6XluFd4INDKVSsjZARva6oTyEqwlFYSMHu3cVJztkNPG1Hj5Zop/XwwBLdDnNMDG6xsZijo9H7+tZAxEKImiazn2oZrb54u4RTx0uu7lqaro270tq3NSlnUliYvJD7291/ZdfX6WjzfH92TVmJt8OLongDx3w2ENS/6xX1K0R5aY3G4tWMo6Nh1N0AFJ04QX5i4vktH7Ztw5GTQ+6GjeRu2Mi5Ha6MISHFg4/P7VIeFobGUL7Vt4UQ9ZtUaqrBW2+9Q1bW5bdLuNDivYt5fv3zNLQ0ZOWQlRh1Vz4luygnj+Rpv+KFO4UOB16DfWjYNeaK+xWiMqmiIgr27HGp5hTu31+incZsxtw2ymVfK4O/fw1ELISoSlKpqWWatwolcespKMcQgf7N+/P2lrc5nnecpfuWclvr2644Dr2HGy2f7sT+17bgqTWR8d0ZDN4peEe1vuK+hagsGr0ec0QE5ogIfIcPA8CekUH+tm3nqzmJiTiyssj/azP5f212nqtvHOSyS7k5MhKt6dKD84UQ9YdUaqpB2vFTvPefdwAYfc/9tAhpWqbz5m+fz5ub36Sld0u+HfQtWk3lDJzMPZDK0f/sxk1rINdhI/ixNrg1C6qUvoWoDsrhoPDAAZdqTkFKCjgcrg0NBsxt2lwwpTwWQ5PGMqVciDpEBgqXoiaTGoApz08Eg5GIqPYMv3NQmc7JKcyhz6I+5NhymH3TbHoE96i0eDK3JXPik1TMWh3ZDiutnuuI0U8GYoq6y56Ti3X7dpctH+ynSy56qWvY0KWaY2kbhdbdvQYiFkKUhTx+qo1UcZXl8IGSYwMuxsPowZ1hdzJvxzzm75hfqUmNd7swbLfmkfVDJp5aM3te/4OwyT3Qy+qvoo7Sebjjfl1n3K8rXjZBKYXt6FGXao511y7sJ0+Ss3o1OatXF5+o1WIKC3MZm2NsHiJTyoWog6RSU02mv/wqhXYrmiLF5GlTy3xeem46/b7uR5Eq4osBX9C2YdtKjevY0g0U/FqEXqMhU5tFmylxaI2S64r6yWG1Yt2563w1JyGBomMlZyVqvb2LZ2mdq+ZEt0PnfWVLKwghKkYqNbWQf5OmHDm0B1WG7RIuFOAewM0tb2bx3sXM2z6PN3u+WalxBQ3oysGs1TjiDXg7vNg1fTltJt0s+0SJeklrNuN2TXvcrmnvPGZLT3ddIHD7dhyZmeSuW0fuunXOdsaWLV32tTKFhqKRfydC1CpSqakmmzYnsuyHb8q8XcKFks8kM2TxELQaLUtuW0KwZ3Clx7fnvWWYD3gCkO1zhjbjZZ8ocXVSNhvWpGTyE+Kd1RzbwUMl2mnc3LC0a+e6S7mfXw1ELET9JgOFS1HTSU2RrYhpL00FnY6eN8XR84Yu5Tr/oZ8e4rejvzE8fDjPX/d8lcS4+40f8DjpA0B+00xaPzawSq4jRF1TdPr02QUCz47NSdyGIze3RDtD06auu5SHh6MxXvkaU0JczSSpKUVNJzVwfruEgMBmPPzQveU6949jf3D/j/dj1pn58Y4f8TVXzUylnS8txivPt3jfnkgrLUb3rZLrCFGXKbudgr17nZUca0ICBXv2wt++nWqMRsxRUa7VnMBAmVIuRDnImJpaSqc34UBxKj2t3Od2CuxEmwZt2HV6F18mfclDMQ9VQYQQ8fwAdk1ejneRN9qdZo5+t44mg7tXybWEqKs0Oh3msDDMYWH43nknAPbsbKzbtpGfkEBefDzW+ATsmZnkb91K/tatznP1/v4uSY45KgqtzDoUolJIpaYazZr1LhkZJ1CFNqaWcbuECy3bt4zn1j1HA3MDVg5ZiVlvroIowZ5fyO6pq/HGA5tSuPdzw//GjlVyLSHqK6UUtoMHL1g3JwFrUhLY7a4N9XrM4eEuiY6hWTOp5ghxljx+KkVtSGq++2El8Zs3QpGNKdPKn9QUOYoY8M0AUnNTmXTdJIaGD62CKIvZMrJImfE7XhoLBQ4HDYY3wveayCq7nhBXA0d+PtYdO87PtoqPp+jEiRLtdD4+rtWc6Gh0Hh41ELEQNU+SmlLUhqTm+IlT/Gf2v0GjYdTo+2jZovyzmD7d+Skz/5xJiFcI3w/6Hp226qaU5qemc2jWdty1RvIcRTR+sBUeoc2q7HpCXG2UUhQdO+Y6pXzHDpTtb0s/aDSYQkOdSY4lJgZjq1ayQKC4KkhSU4rakNTA+e0SwiNjGTF0cLnPz7Pl0WdRH7IKs5jVcxa9QnpVfpAXyE7eT9p/D2DR6slxFND8n7GYAxpW6TWFuJo5Cgsp2L37/OadCQnYjhwp0U7r4YEluh3mC7Z80PvKViei/pGBwrXZ2e0Sjuwv+3YJF3IzuDEsfBgfbPuAeTvmVXlS4xnWAtsduZxZdAoPrYl9b22h9fNdMXhJKVyIqqA1GotXM46OBu4GoOjEibNTys8mOtu348jJIXfDRnI3bHSeawwJKZ5Kfm6X8rAwNAZDDd2JENVPKjXVbMYrr1Fgy0NjczD5lZcq1MfJ/JP0XdQXm8PGJ/0/IdY/tnKDLEX6T5vIW2XFoNGQqckhYmpvdEb5ZilETVBFRRTs2eNSzSnct69EO43ZjLlt1AUbeMZiCPCvgYiFqDip1NRiAY2bcOhgCkoVVbiPhpaG3NLqFr5J+YZ52+fx9k1vV2KEpQvo3YkjmWuwb9LijQe7X15Jmyn9ZTsFIWqARq/HHBGBOSIC3+HDALBnZJC/bdv5RCcxEUdWFvl/bSb/r83Oc/VBQa6DkCMj0ZrKvsK5ELVZlY0ye/fdd2nevDlms5nOnTuzadOmS7ZfuHAhERERmM1m2rVrx7Jly1zeV0rx4osvEhQUhMVioXfv3qSkpFRV+FUmOja6+H/0enLz8ivcz+jI0QD8cvgXDmQeqITILq/pkJ44IvJRSuFt82b3jKXVcl0hxOXpfHzw6N6dRo8/RrP/fkDY7xtpuWwpQdOn4zNsGKaICNBqKTp2jOwVKzj+6kwOjvg/kjpey/6hw0h7ZTqZS5ZSeOQIV0kBX9RDVZLUfPnll4wbN47JkyezZcsWYmJiiIuL4/jx46W237BhAyNGjOC+++5j69atDB48mMGDB7N9+3Znm9dee41///vfvPfee/zxxx+4u7sTFxeH1WqtiluoMu2jI4vXqdBq2bhpS4X7aenTkp5Ne6JQfLTzo0qM8NJa3BNHfuMsALxyfEl664dqu7YQouw0Wi2mli3xuf02gqZOoeV33xL+5yaaffQRjf7xDzxuugmdnx/YbFgTEznzySekPv00e3v3IaVbdw4/8ign575P7h+bSt0SQojaqErG1HTu3Jlrr72W2bNnA+BwOAgODubxxx9n/PjxJdoPGzaM3NxclixZ4jx23XXXERsby3vvvYdSisaNG/PPf/6Tp59+GoDMzEwCAgKYP38+w4cPv2xMtWVMDZzfLqGRf1MefeT+CvezOX0zY1aMwag1svKOlTS0VN+spF3TF+OZVbydgi00l5YP9K+2awshKodSCtvRoy5jc6y7dsHfp5RrtZjCws6PzYmNwdi8uUwpF9WiRsfUFBYWsnnzZiZMmOA8ptVq6d27Nxs3biz1nI0bNzJu3DiXY3FxcXz33XcA7N+/n7S0NHr37u1839vbm86dO7Nx48ZSk5qCggIKCgqcX2dlZV3JbVUqncGMAwdnjqdfUT/X+F9DdMNoEk8m8sXuL3i8/eOVFOHlhT83gF0vLcO7wAf9HncOffkzzYbdVG3XF0JcOY1Gg7FpU4xNm+I9cAAAjoICrDt3uqyEXHTsGAW7d1OwezcZX34JgNbLq3iW1rnxOdHR6Ly9a/J2hKj8pObkyZPY7XYCAgJcjgcEBLB79+5Sz0lLSyu1fVpamvP9c8cu1ubvZsyYwdSpUyt0D1XNq0FDMjKOYyvIu6J+NBoNY9qOYdyacXyZ9CX3tb0PN4NbJUV5aVqdjjbP92fXlB/xdnhStEXPMa+NBPUv3+7jQojaRWsy4da+PW7t2zuP2dLTzyc5CQlYt+/AkZVF7vr15K5f72xnbNnSpZpjCg1Fo5f5KKL61Nu/bRMmTHCp/mRlZREcXP4VfKtCy9ahbPnzOBrdle/tclPwTQR7BnM4+zDf7fmO/2vzf5UQYdlojXrCJ/Yk+eV1eGncyP+lkFM+ifh1ia62GIQQVc8QEIChb1+8+vYFQNlsWJOTnTuU58XHYzt4iMJ9+yjct4/Mb78FQOPmhqVtW9ddyhvK4p2i6lR6UtOwYUN0Oh3p6a6PVtLT0wkMDCz1nMDAwEu2P/ff9PR0goKCXNrExsaW2qfJZMJUS6cpdr2uI1s2/QZ6PUnJ+wkPa1HhvnRaHaMiR/HKH6/w8c6PGRo+FL22+nJVvYcbLf/ZkQNvbMVDa+L0t6cxeO3BKyq02mIQQlQvjcGAJSoKS1QU/F/xL1JFZ86cr+QkJJCfkIgjN5e8TZvIu2D2q6FJEyyxsc5ExxwRgcZorKlbEfVMpY/yMhqNdOjQgdWrVzuPORwOVq9eTZcupT+a6NKli0t7gFWrVjnbt2jRgsDAQJc2WVlZ/PHHHxftszZr6NfAORDvry1br7i/QaGD8DX5cjTnKD8d+umK+ysvs78fwQ9HkOewYdHqOPbRIfIOHav2OIQQNUfv64tnz574P/kkzf73P8I2/UHLHxYTNO1lfO68A1Pr1qDRYDt6lKylS0mfPp0DQ4eR1PFaDgwfQfqMV8lavhxbaqpMKRcVViW/0o8bN47Ro0fTsWNHOnXqxKxZs8jNzeWee+4BYNSoUTRp0oQZM2YA8OSTT9KjRw/efPNNBgwYwIIFC/jrr794//33geKxI0899RTTpk2jdevWtGjRgkmTJtG4cWMGDx5cFbdQDYrzyaMHD1xxTxa9heERw5mTMIf52+cTFxKHRnPlj7bKw71FU/zvyuXkp8dw1xo4OHs7rSa4YfSVgYNCXI00Oh2m1q0xtW6Nzx13AGDPycGamOiygac9I4P8+Hjy4+Ph7OoU+kaNih9Xna3omKOi0FosNXczos6okqRm2LBhnDhxghdffJG0tDRiY2NZsWKFc6DvoUOH0F4wFbBr1658/vnnvPDCC0ycOJHWrVvz3Xff0bZtW2ebZ599ltzcXB588EEyMjLo1q0bK1aswGw2V8UtVDmzuxdWWx55WRn/397dBzV57XkA/z55TyAkIEKIICACUeRlsUrVUreVKq5WutWqtfbqbNd2ett7Z3ulL9vai712KrVOa6/rdjrtuNg7jlbtXe22akWmtJaLtbW8CciLoogaVBQI8hJIzv4RDKSkrcCTPEn4fWYYNTk5z3l+E05+npzn+fHS30rDSuw8sxOVLZX4sflHzNDN4KXf4dAmxaN3SSdM/9cOtUiBureLEZ8zFxKajAghAMT+/vCbPRt+s2cD6L+kvLHRsUr52bPou34dpvzjMOX3rzyLxVDExztUKZdGRrr9P2/E81HtJ4H8T94eXLxQA5jN2PjWW7z0+ebJN/Fpzae4P/x+7Ji3g5c+R+LKF0XoPWGBmOPQJm7HlJwFEMl8dk86IYRH1q4u2yXlpaW2RKe0FH3Xrw9pJ9ZqoUi2XVKuSkmBIjERYrVagBETVxvO5zclNQL5qbQSnx/cDzCG7Jdehr/f6C/FbmxvxOL/XQwGhoNZBxGjjeFhpCNzcfdxcOUyiDgObYpbmPL6IqoTRQgZNsYY+oxGx9Wcykows9mxIcdBPjnGXqFcmZwMeUwMOJp3vB4lNU54WlJjsViwaeNGQCzG7PR5mD8vnZd+X/j6BRxvPI5HJj+CTXM28dLnSNX/92EoGm3/c+oIvAXDy0sEHQ8hxDcwsxndNTUDd0IuLUVvU9OQdiI/PyiSEgdVKU+GJChIgBGT0aCkxglPS2qAgXIJwcET8Pzz63jps+x6GVYfXg2JSIKvln6FEFUIL/2O1Nl3Pod/SyAAoCu8HbHPLxJ0PIQQ39R34wa6yssHEp2KCrDOoTc4lU6cOGhvTgoU8XHgpFIBRkzulqBlEsjds5dLuDG6cgmDJY9PRmpIKn669hN2V+/GC9Nf4K3vkTC8uARVf/kcAZ2BUFxS48In+Yj63UOCjokQ4nskwcFQP/gg1A/ayrUwiwU99fX2fTld5eUwnzuH3sZG9DY2ov1zWzFeTi6H4s4NAvvvnSP92d3rifeglRoBvf/+B7h1qxmsx4w3NvOzWRgAvm78Gn/8+o9QS9XIfywfflI/3voeCavFguqcI9D0aWBhDJLZHCZk8fN1GyGE3C1LWxu6yiscSj5YndQFlOh0g5KcFCgSpkLkoTdzHQtopcZLxMTG4cdTzeDEIlgsFoh52tA2N2IuojXRaGhrwIHaA1iTsIaXfkdKJBbD8Np8nH2jABrOH+Z/MFzTnEbIP08XdFyEkLFFrNHAP/0++KffBwBgVivMFy72Jzil6CorR09NDfqMRpiMRpi++sr2QqkUCoPBcTUnPJwuKfdAtFIjoJabbdj+/rsAx2H5ytWYauCvtMBntZ9hY/FG6Px0OPzoYUhFwn9n3NvajvrNJ6HmlOixWjHu8RBo/2mK0MMihBA76+3b6KqsdLxB4I0bQ9qJg4IckhzFtESI/YVdFfdVtFHYCU9MagBg46uvAjIZYmIT8OQTj/HWb4+lBwsOLEBLdws2p2/G4kmLeet7NLquNKNx2xn4iWTotPZB/8xk+Md4RqFRQgj5OcYYei9f6V/J6b+kvKraXurGTiSCPDbWIdGRRUeDE/FejWjMoa+fvIrtK6crlxp57VUulmPVlFXYXrIdeWfysCh6kUcslSr1odD/220Yd16ESiRB04c1iFqvhCKUKvcSQjwPx3GQhU+ALHwCNItsV29ae3rQU109sDentAy9V66gp6YGPTU1aN23DwAgUquhTEoaqFKelASxVivg2fg+SmoEpvDXoNvcgS5TK+99r4hfgY8rPkbNrRoUXy3GbP1s3o8xEmrDJPQu60TrgRb4i+Q4/+5PiH1tNqQB/kIPjRBCfpNILrfVpUpJsT/We+3aQIXy0jJ0nTkDq8mE20VFuF1UZG8ni4oaSHJSUiCPjQUnoY9ivlAkBRYWEY6Gc2cBZuG9b41cg0djH8Xu6t3IO5PnMUkNAATNnAZz2/foPt6DAE6J2s3fwvDGQxDLhN/7QwghwyUNCYH0oYcQ8JDtlhWsrw89tbUOe3PMFy7Yf9oOHQIAcEollNOmOdS1kowfL+SpeDXaUyOw0opqHPzsU4Ax/Cn7RQSo+V2tuNxxGYv+vggWZsH+h/fDEGTgtf/RajpQCMsPIludKGkbpmxcSOUUCCE+qe/WLXRXVAzcILC8HFaTaUg7qV7vkOTIp06FSCYTYMSegTYKO+GpSY2tXEIOIJbg3tkPIHP+XN6P8dI3L+HIhSNYPGkxNqdv5r3/0Tq/8yikNSpwHId29S1MfY3KKRBCfB+zWmE+f95hNaenrg742ccyJ5VCPnUKVCkpA6s5er1H7JN0B0pqnPDUpAYYKJcwbpwef/jD07z3X9lSiZVfrISEk+DI0iPQ+el4P8Zo1W77AiqjBgBwO6QV8X96WOAREUKI+1k6OtB95oxDXSvLrVtD2onHBzvUtFJOmwaRavSFkT0RXf3kZSQyJSywoJXHcgmDJYxLwEzdTJwynsLfqv6GF2e86JLjjEbcfyxG9VufQ90eCFWzBuc/PoJJ/75Q6GERQohbif394XfvvfC7914A/ZeUNzXZSj3cqVJ+9iws12+g43gBOo4X9L9QDHlcHJTJSVAmp9guKY+KGjOrOXfQSo0H+OtfP8TNm1d5L5cw2ImmE/h9we+hkqiQ/1g+AmSeFQOgv5zCG4ehMWthZQzcdAsilj8g9LAIIcSjWLu70V1VNbCaU1aGPqNxSDuRRtOf5NiKdyqTEiH2sM+/u0ErNV4mNi4e35+8Ck7Cb7mEwe6bcB8mayejvrUe+2v246nEp3g/xmiJxGIYNmTi7MZ8aKxq9J0W46qmGGELZgk9NEII8RgihQKq1FSoUlPtj/UajegqK7d/ZdVdWQlrWxtuf3sCt789YW8ni4lxuEGgfPJkcD50cQat1HiA1jYTtr27FeA4LHtsFaYlxLnkOIfqD2FD0QaMV47H0aVHIRN75m76PtNt1L75HQI4FcxWKzT/Gohxs5KEHhYhhHgNZjaju6bWoXhnb+PQm7yKVCooEhNt991JToYyOQmSceMEGPEvo43CTnhyUgMMlEuYNHkqfrd6uUuO0WvpRebfM3Gt8xo2zdmERyY/4pLj8KH7WgsubC2Bv0iOLqsFoWvCEZDAX20sQggZa/pu3nRIcrrLymHt7BzSThoR4VjXKj4enICXlNPXT96Isy3/XeW5XMJgUrEUq6esxrun38Wuyl3Iisny2E1kipBxiHjWgMsf1EIlkuLqrkZInveDamKY0EMjhBCvJAkKgvqBB6B+wLZXkVks6Dl3zrYJuT/RMdefQ++lS+i9dAntX3wBAOBkMigSEgbuhJycDGmYZ87FlNR4CKW/Bl09HejqaHPpcZbFLcOH5R+ivrUeJy6fwP3h97v0eKPhFx2OkCc6cGO3EX4iKS7+1xnE/KcKskCN0EMjhBCvx4nFUMTFQREXh8Dltm8ILO3t6KqoGFjNKS2Dpa0NXSUl6Copsb9WEhrquJqTkACRQiHUqQyMS+gBEJuw8Ik4f67KJeUSBlPL1FgWuwy7qnYhrzLPo5MaANAmG9Db1omOL01QixSoe7sY8TlzIVEqhR4aIYT4HHFAAPznzIH/nDkA+i8pv3jRnuR0lpaip6YWfc3NMB07BtOxY7YXSiRQxMdDmZKC0NdeFaw6OSU1HiI1NcWW1EilaG0zQatRu+xYq6euxu7q3fjB+ANqb9UiLtA1G5P5Mv7+VPS2fYfe76zQcH64fPAfiHx8ntDDIoQQn8dxHGRRUZBFRUGTlQUAsHZ2oruy0iHRsVy/YbviqqdbsIQGoKTGY0wxxECl1ECnnwCJxLWX1+n8dFh/z3oYggyI1ca69Fh80T98Hy62H0dPuxkxj/+L0MMhhJAxS6RSQTVjBlQzZgCwreb0Xb2KrrIyMKtV0LHR1U+EEEII8VjD+fwWbo2IEEIIIYRHY+brpzsLUu3t7QKPhBBCCCF3687n9t18sTRmkhqTyQQAiIiIEHgkhBBCCBkuk8kEjebXb+kxZvbUWK1WXLlyBWq1mvcbzrW3tyMiIgKXLl2i/TouRHF2D4qze1Cc3Ydi7R6uijNjDCaTCXq9HqLfuLJqzKzUiEQihIeHu/QYAQEB9AvjBhRn96A4uwfF2X0o1u7hijj/1grNHbRRmBBCCCE+gZIaQgghhPgESmp4IJfLkZOTA7lcLvRQfBrF2T0ozu5BcXYfirV7eEKcx8xGYUIIIYT4NlqpIYQQQohPoKSGEEIIIT6BkhpCCCGE+ARKagghhBDiEyipIYQQQohPoKTGiR07diAqKgoKhQJpaWk4derUr7bfv38/DAYDFAoFEhMTcfjwYYfnGWP485//jLCwMCiVSmRkZKCurs6Vp+A1+I712rVrwXGcw09mZqYrT8ErDCfOlZWVWLp0KaKiosBxHLZt2zbqPscKvuO8cePGIe9ng8HgwjPwDsOJ80cffYT09HQEBgYiMDAQGRkZQ9rTHO0c33F2y/zMiIO9e/cymUzGdu7cySorK9m6deuYVqtlzc3NTtsXFRUxsVjMtmzZwqqqqtiGDRuYVCplFRUV9ja5ublMo9GwgwcPsrKyMrZkyRIWHR3Nurq63HVaHskVsV6zZg3LzMxkV69etf/cvHnTXafkkYYb51OnTrHs7Gy2Z88eptPp2HvvvTfqPscCV8Q5JyeHJSQkOLyfr1+/7uIz8WzDjfOqVavYjh07WElJCauurmZr165lGo2GNTU12dvQHD2UK+LsjvmZkpqfmTlzJnvuuefs/7ZYLEyv17PNmzc7bb98+XK2aNEih8fS0tLYM888wxhjzGq1Mp1Ox9555x37862trUwul7M9e/a44Ay8B9+xZsz2S5OVleWS8Xqr4cZ5sMjISKcftqPp01e5Is45OTksOTmZx1F6v9G+9/r6+pharWa7du1ijNEc/Uv4jjNj7pmf6eunQcxmM06fPo2MjAz7YyKRCBkZGSguLnb6muLiYof2ALBgwQJ7+4aGBhiNRoc2Go0GaWlpv9jnWOCKWN9RWFiIkJAQxMfH49lnn0VLSwv/J+AlRhJnIfr0dq6MSV1dHfR6PSZNmoQnnngCjY2Nox2u1+Ijzp2dnejt7UVQUBAAmqOdcUWc73D1/ExJzSA3btyAxWJBaGiow+OhoaEwGo1OX2M0Gn+1/Z0/h9PnWOCKWANAZmYmPvnkExQUFODtt9/GN998g4ULF8JisfB/El5gJHEWok9v56qYpKWlIS8vD0ePHsUHH3yAhoYGpKenw2QyjXbIXomPOL/88svQ6/X2D2yao4dyRZwB98zPEt56IsQDrFy50v73xMREJCUlISYmBoWFhZg3b56AIyNk+BYuXGj/e1JSEtLS0hAZGYl9+/bhqaeeEnBk3ik3Nxd79+5FYWEhFAqF0MPxWb8UZ3fMz7RSM0hwcDDEYjGam5sdHm9uboZOp3P6Gp1O96vt7/w5nD7HAlfE2plJkyYhODgY9fX1ox+0FxpJnIXo09u5KyZarRZxcXH0fh5BnLdu3Yrc3FwcO3YMSUlJ9sdpjh7KFXF2xhXzMyU1g8hkMkyfPh0FBQX2x6xWKwoKCjBr1iynr5k1a5ZDewDIz8+3t4+OjoZOp3No097eju+///4X+xwLXBFrZ5qamtDS0oKwsDB+Bu5lRhJnIfr0du6KSUdHB86dO0fv52HGecuWLdi0aROOHj2Ke+65x+E5mqOHckWcnXHJ/OzSbcheaO/evUwul7O8vDxWVVXFnn76aabVapnRaGSMMfbkk0+yV155xd6+qKiISSQStnXrVlZdXc1ycnKcXtKt1WrZoUOHWHl5OcvKyhrzlwsyxn+sTSYTy87OZsXFxayhoYEdP36cpaamstjYWNbd3S3IOXqC4ca5p6eHlZSUsJKSEhYWFsays7NZSUkJq6uru+s+xyJXxHn9+vWssLCQNTQ0sKKiIpaRkcGCg4PZtWvX3H5+nmK4cc7NzWUymYwdOHDA4VJik8nk0IbmaEd8x9ld8zMlNU5s376dTZw4kclkMjZz5kx28uRJ+3Nz585la9ascWi/b98+FhcXx2QyGUtISGBffvmlw/NWq5W9/vrrLDQ0lMnlcjZv3jxWU1PjjlPxeHzGurOzk82fP5+NHz+eSaVSFhkZydatWzemP2jvGE6cGxoaGIAhP3Pnzr3rPscqvuO8YsUKFhYWxmQyGZswYQJbsWIFq6+vd+MZeabhxDkyMtJpnHNycuxtaI52js84u2t+5hhjjL91H0IIIYQQYdCeGkIIIYT4BEpqCCGEEOITKKkhhBBCiE+gpIYQQgghPoGSGkIIIYT4BEpqCCGEEOITKKkhhBBCiE+gpIYQQgghPoGSGkIIIYT4BEpqCCGEEOITKKkhhBBCiE/4f5M9W/XIZkwWAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Pipe.plot_pipe(net, 0, pipe_results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that the pressure level falls due to friction. As the fluid is incompressible, the\n",
+    "velocity remains constant over the pipe length. Because the temperature level at the pipe entry is\n",
+    "higher than the ambient temperature, the temperature level decreases."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 26be9fcdec7c9083ecd72d6d57b9c65ba9435ad6 Mon Sep 17 00:00:00 2001
From: Jonas Pfeiffer <jonaspfeiffer123@gmail.com>
Date: Thu, 4 Sep 2025 15:32:41 +0200
Subject: [PATCH 2/3] Finalized the Jupyter Notebooks

---
 ...ular_flow_in_a_district_heating_grid.ipynb | 249 --------
 ...ular_flow_in_a_district_heating_grid.ipynb | 188 ++++--
 ..._in_a_circular_district_heating_grid.ipynb | 528 +++++++++------
 ..._in_a_circular_district_heating_grid.ipynb | 600 +++++++++++-------
 ...ultiple_pumps_district_heating_net_raw.png | Bin 0 -> 20566 bytes
 ...ltiple_pumps_district_heating_net_text.png | Bin 0 -> 37181 bytes
 .../simple_district_heating_net_raw.png       | Bin 0 -> 12649 bytes
 .../simple_district_heating_net_text.png      | Bin 0 -> 22799 bytes
 .../time_series_district_heating_net_raw.png  | Bin 0 -> 13955 bytes
 .../time_series_district_heating_net_text.png | Bin 0 -> 34842 bytes
 10 files changed, 831 insertions(+), 734 deletions(-)
 delete mode 100644 tutorials/circular_flow_in_a_district_heating_grid.ipynb
 create mode 100644 tutorials/pics/district_heating/multiple_pumps_district_heating_net_raw.png
 create mode 100644 tutorials/pics/district_heating/multiple_pumps_district_heating_net_text.png
 create mode 100644 tutorials/pics/district_heating/simple_district_heating_net_raw.png
 create mode 100644 tutorials/pics/district_heating/simple_district_heating_net_text.png
 create mode 100644 tutorials/pics/district_heating/time_series_district_heating_net_raw.png
 create mode 100644 tutorials/pics/district_heating/time_series_district_heating_net_text.png

diff --git a/tutorials/circular_flow_in_a_district_heating_grid.ipynb b/tutorials/circular_flow_in_a_district_heating_grid.ipynb
deleted file mode 100644
index 0a57948ea..000000000
--- a/tutorials/circular_flow_in_a_district_heating_grid.ipynb
+++ /dev/null
@@ -1,249 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Circular flow in a district heating grid\n",
-    "\n",
-    "This example shows how to set up and solve the following network:\n",
-    "\n",
-    "<img src=\"pics/heat.png\">\n",
-    "\n",
-    "In this example, we will not only calculate the pressure and velocity distribution in the network, but also determine the temperature levels. The pump feeds fluid of a given temperature into the grid. Due to losses, the temperature will fall. The heat exchanger removes more heat from the network. On its way back to the pump, the temperature will fall further. \n",
-    "\n",
-    "The network is based on the topology of a district heating grid, where the fluid returns to the pump after the consumers (heat exchangers) have been supplied.\n",
-    "\n",
-    "To set up this network, at first, the pandapipes package has to be imported. Additionally, a net container is created and, at the same time, water as a fluid is chosen."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandapipes as pp\n",
-    "\n",
-    "# create empty net\n",
-    "net = pp.create_empty_network(fluid =\"water\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that the flow of the example network flows in a closed loop. We will need four junctions.\n",
-    "The parameters `pn_bar` and `tfluid_k` that have to be set in the `create_junction`-function\n",
-    "are\n",
-    "only used as starting points for the network simulation. The fix pressure and fluid temperature is\n",
-    "being determined by the circular pump component which will be created afterwards."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "j0 = pp.create_junction(net, pn_bar=5, tfluid_k=293.15, name=\"junction 0\")\n",
-    "j1 = pp.create_junction(net, pn_bar=5, tfluid_k=293.15, name=\"junction 1\")\n",
-    "j2 = pp.create_junction(net, pn_bar=5, tfluid_k=293.15, name=\"junction 2\")\n",
-    "j3 = pp.create_junction(net, pn_bar=5, tfluid_k=293.15, name=\"junction 3\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, the pump will be created. The type of pump we choose needs a mass flow, a pressure level and a\n",
-    "temperature as input. Note that the circular pump is a component, which internally consists of an\n",
-    "external grid, connected to the junction specified via the from_junction-parameter and a sink,\n",
-    "connected to the junction specified via the to_junction-parameter.\n",
-    "\n",
-    "However, the internal structure is not visible to the user, so that the circular pump component\n",
-    "supplies a fluid flow with the specified properties."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pp.create_circ_pump_const_mass_flow(net, return_junction=j3, flow_junction=j0, p_flow_bar=5,\n",
-    "                                    mdot_flow_kg_per_s=20, t_flow_k=273.15+35)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, the heat exchanger component is created.\n",
-    "The most important parameter for this component is the heat flux `qext_w`. A positive value of\n",
-    "`qext_w` means that heat is withdrawn from the network and supplied to a consumer.\n",
-    "A negative value of `qext_w` corresponds to a heat source, i. e. thermal energy is being transfered\n",
-    "from the heat exchanger into the network."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pp.create_heat_exchanger(net, from_junction=j1, to_junction=j2, diameter_m=200e-3, qext_w = 100000)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following commands defines the pipes between the components. Each pipe will consist of five\n",
-    "internal sections in order to improve the spatial resolution for the temperature calculation.\n",
-    "The parameter `text_k` specifies the ambient temperature on the outside of the pipe. It is used to\n",
-    "calculate energy losses."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pp.create_pipe_from_parameters(net, from_junction=j0, to_junction=j1, length_km=1,\n",
-    "                               diameter_m=200e-3, k_mm=.1, u_w_per_m2k=10, sections = 5, text_k=283)\n",
-    "pp.create_pipe_from_parameters(net, from_junction=j2, to_junction=j3, length_km=1,\n",
-    "                               diameter_m=200e-3, k_mm=.1, u_w_per_m2k=10, sections = 5, text_k=283)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "We now run a pipe flow.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pp.pipeflow(net, mode='sequential')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "By default, only the pressure and velocity distribution is calculated by the pipeflow function. If\n",
-    "the `mode`-parameter is set to \"all\", the heat transfer calculation is started automatically\n",
-    "after the hydraulics computation. Computed mass flows are used as an input for the temperature\n",
-    "calculation. After the computation, you can check the results for junctions and pipes:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "net.res_junction"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that a constant heat flow is extracted via the heat exchanger between nodes 1 and 2. Heat\n",
-    "losses due to the ambient temperature level are not taken into account. These are only included in\n",
-    "the pipe components. This also means that - if the extracted heat flow is large enough - the\n",
-    "temperature level behind the heat exchanger might be lower than the ambient temperature level. A\n",
-    "way to avoid this behaviour would be to create a controller which defines a function for the\n",
-    "extracted heat in dependence of the ambient temperature."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "net.res_pipe"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The command above shows the results for the pipe components. The temperatures of the adjacent\n",
-    "junctions are displayed. Due to heat losses, the temperatures at the to-nodes is lower than the\n",
-    "temperatures at the from-nodes. Note also that the junctions are not equal to the internal nodes,\n",
-    "introduced by the pipe sections we defined. To display the temperatures at the internal nodes, we\n",
-    "can retrieve the internal node values with the following commands:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pandapipes.component_models import Pipe\n",
-    "pipe_results = Pipe.get_internal_results(net, [0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The parameters of the get_internal_results function correspond to the net and the pipes we want to\n",
-    "evaluate. In this case, only the results of pipe zero are retrieved. The returned value stored in\n",
-    "pipe_results is a dictionary, containing fields for the pressure, the velocity and the temperature.\n",
-    "The dictionary can either be used for own evaluations now or it can be used to plot the results over\n",
-    "the pipe length:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Pipe.plot_pipe(net, 0, pipe_results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see that the pressure level falls due to friction. As the fluid is incompressible, the\n",
-    "velocity remains constant over the pipe length. Because the temperature level at the pipe entry is\n",
-    "higher than the ambient temperature, the temperature level decreases."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.9.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/tutorials/district_heating/circular_flow_in_a_district_heating_grid.ipynb b/tutorials/district_heating/circular_flow_in_a_district_heating_grid.ipynb
index 6c712eed5..cfc0c4887 100644
--- a/tutorials/district_heating/circular_flow_in_a_district_heating_grid.ipynb
+++ b/tutorials/district_heating/circular_flow_in_a_district_heating_grid.ipynb
@@ -8,20 +8,24 @@
     "\n",
     "This example demonstrates how to model and analyze a simple district heating network with a circular flow using pandapipes.\n",
     "\n",
-    "The network consists of a pump that supplies hot water, which flows through consumers (heat exchangers), releases heat, and then returns to the pump. In addition to pressure and velocity distribution, the temperature profile throughout the network is also calculated. Due to losses, the temperature will fall. The heat consumer removes more heat from the network. On its way back to the pump, the temperature will fall further. \n",
+    "The network consists of a pump (representing a heat generator) that supplies hot water, which flows through consumers (heat exchangers), releases heat, and then returns to the pump. In addition to pressure and velocity distribution, the temperature profile throughout the network is also calculated. Due to heat losses and heat extraction by the consumer, the temperature will decrease along the flow path. Understanding these temperature drops is crucial for energy efficiency and proper system design in district heating networks.\n",
     "\n",
-    "The setup is based on a typical district heating grid topology, where the fluid returns to the pump after passing through the consumers.\n",
     "\n",
-    "*Note: The illustration may need to be updated.*\n",
-    "\n",
-    "<img src=\"../pics/heat.png\">\n",
+    "<img src=\"../pics/district_heating/simple_district_heating_net_text.png\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Network Setup\n",
     "\n",
-    "To set up this network, at first, the pandapipes package has to be imported. Additionally, a net container is created and, at the same time, water as a fluid is chosen."
+    "To set up this network, first import the pandapipes package. Additionally, create a net container and select water as the working fluid."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 104,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,41 +39,39 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note that the flow of the example network flows in a closed loop. We will need four junctions.\n",
-    "The parameters `pn_bar` and `tfluid_k` that have to be set in the `create_junction`-function\n",
-    "are\n",
-    "only used as starting points for the network simulation. The fix pressure and fluid temperature is\n",
-    "being determined by the circular pump component which will be created afterwards."
+    "## Junctions\n",
+    "\n",
+    "Note that the flow in this example network forms a closed loop. We will use four junctions for this demonstration. The parameters `pn_bar` (pressure) and `tfluid_k` (temperature) set in the `create_junction` function are only used as initial values for the network simulation. It is recommended to initialize the network with the flow temperature of the supplying pump. The actual pressure and fluid temperature are determined by the circular pump component, which will be created afterwards. For more complex networks, the number of junctions can be easily adapted."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 105,
    "metadata": {},
    "outputs": [],
    "source": [
-    "j0 = pp.create_junction(net, pn_bar=4, tfluid_k=293.15, name=\"junction 0\")\n",
-    "j1 = pp.create_junction(net, pn_bar=4, tfluid_k=293.15, name=\"junction 1\")\n",
-    "j2 = pp.create_junction(net, pn_bar=4, tfluid_k=293.15, name=\"junction 2\")\n",
-    "j3 = pp.create_junction(net, pn_bar=4, tfluid_k=293.15, name=\"junction 3\")"
+    "j0 = pp.create_junction(net, pn_bar=4, tfluid_k=293.15, name=\"junction 0\", geodata=(0, 0))\n",
+    "j1 = pp.create_junction(net, pn_bar=4, tfluid_k=293.15, name=\"junction 1\", geodata=(0, 10))\n",
+    "j2 = pp.create_junction(net, pn_bar=4, tfluid_k=293.15, name=\"junction 2\", geodata=(10, 10))\n",
+    "j3 = pp.create_junction(net, pn_bar=4, tfluid_k=293.15, name=\"junction 3\", geodata=(10, 0))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now, the pump will be created. The type of pump we choose needs a mass flow, a pressure level and a\n",
-    "temperature as input. Note that the circular pump is a component, which internally consists of an\n",
-    "external grid, connected to the junction specified via the from_junction-parameter and a sink,\n",
-    "connected to the junction specified via the to_junction-parameter.\n",
+    "## Circ Pump Const Pressure\n",
+    "\n",
+    "Now, the pump will be created. For a simple district heating network, the `circ_pump_const_pressure` component is used. This type of pump requires a flow pressure, a pressure lift, and a flow temperature as input. The mass flow is calculated during the pipeflow calculation by solving the mass balance in the network.\n",
     "\n",
-    "However, the internal structure is not visible to the user, so that the circular pump const pressure component\n",
-    "supplies a fluid flow with the specified properties."
+    "The pressure lift represents the energy added to the fluid by the pump to overcome losses and supply the network with sufficient pressure.\n",
+    "\n",
+    "Note that the circular pump is a component which internally consists of an external grid (connected to the junction specified via the `from_junction` parameter) and a sink (connected to the junction specified via the `to_junction` parameter). However, this internal structure is not visible to the user, so the circular pump const pressure component supplies a fluid flow with the specified properties."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 106,
    "metadata": {},
    "outputs": [
     {
@@ -78,7 +80,7 @@
        "0"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 106,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -92,18 +94,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "## Heat Consumer\n",
+    "\n",
     "Next, the heat consumer component is created.\n",
-    "The most important parameter for this component is the heat flux `qext_w`. A positive value of\n",
-    "`qext_w` means that heat is withdrawn from the network and supplied to a consumer.\n",
-    "A negative value of `qext_w` corresponds to a heat source, i. e. thermal energy is being transfered\n",
-    "from the heat exchanger into the network.\n",
+    "The key parameter for this component is the heat flux `qext_w`. A positive value of `qext_w` means that heat is withdrawn from the network and supplied to a consumer. A negative value of `qext_w` corresponds to a heat source, i.e., thermal energy is transferred from the heat exchanger into the network.\n",
     "\n",
-    "Additionaly, controlled_mdot_kg_per_s, deltat_k or treturn_k can be added"
+    "The behavior of the heat consumer can be further specified using the following parameters:\n",
+    "\n",
+    "- If `treturn_k` (return temperature) is defined, no other parameter should be set. The system will then calculate the resulting mass flow in the heat consumer based on the transferred heat.\n",
+    "- If `controlled_mdot_kg_per_s` (controlled mass flow) is specified, a fixed mass flow is set in the heat consumer, and the temperatures are calculated by solving the mass and energy balances.\n",
+    "- If `deltat_k` (temperature difference) is used, a fixed temperature drop is set for the heat consumer, enabling the calculation of both mass flow and return temperature.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 107,
    "metadata": {},
    "outputs": [
     {
@@ -112,29 +117,34 @@
        "0"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 107,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "pp.create_heat_consumer(net, from_junction=j2, to_junction=j3, qext_w=10000, controlled_mdot_kg_per_s=None,\n",
-    "                         deltat_k=None, treturn_k=50, name=None, index=None, in_service=True, type=\"heat_consumer\")"
+    "                         deltat_k=None, treturn_k=50, name=None, index=None, in_service=True, type=\"heat_consumer\")\n",
+    "#pp.create_heat_consumer(net, from_junction=j2, to_junction=j3, qext_w=10000, controlled_mdot_kg_per_s=2,\n",
+    "#                         deltat_k=None, treturn_k=None, name=None, index=None, in_service=True, type=\"heat_consumer\")\n",
+    "#pp.create_heat_consumer(net, from_junction=j2, to_junction=j3, qext_w=10000, controlled_mdot_kg_per_s=None,\n",
+    "#                         deltat_k=10, treturn_k=None, name=None, index=None, in_service=True, type=\"heat_consumer\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The following commands defines the pipes between the components. Each pipe will consist of five\n",
-    "internal sections in order to improve the spatial resolution for the temperature calculation.\n",
-    "The parameter `text_k` specifies the ambient temperature on the outside of the pipe. It is used to\n",
-    "calculate energy losses."
+    "## Pipes\n",
+    "\n",
+    "The following commands define the pipes between the components. Each pipe will consist of (in this case) five internal sections to improve the spatial resolution for the temperature calculation. Increasing the number of sections improves the accuracy of the temperature profile, but also increases computation time.\n",
+    "\n",
+    "The parameter `text_k` specifies the ambient temperature on the outside of the pipe and is used to calculate energy losses. `k_mm` defines the roughness in the pipe, which is used in the pressure drop calculation."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 108,
    "metadata": {},
    "outputs": [
     {
@@ -143,7 +153,7 @@
        "1"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 108,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -159,13 +169,54 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "## Plotting Net\n",
+    "\n",
+    "The following command creates a simple plot of the network layout using `pandapipes.plotting.simple_plot`. \n",
+    "This visualization helps to understand the structure and connectivity of the district heating grid, showing the positions of junctions, pipes, pumps, and consumers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pp.plotting.simple_plot(net)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pipeflow Calculation\n",
     "\n",
-    "We now run a pipe flow.\n"
+    "We now run a pipe flow calculation using the bidirectional calculation mode. This mode is especially important for networks with heat exchange, as it enables the simultaneous calculation of hydraulic and thermal behavior."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -176,15 +227,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "By default, only the pressure and velocity distribution is calculated by the pipeflow function. If\n",
-    "the `mode`-parameter is set to \"all\", the heat transfer calculation is started automatically\n",
-    "after the hydraulics computation. Computed mass flows are used as an input for the temperature\n",
-    "calculation. After the computation, you can check the results for junctions and pipes:"
+    "By default, only the pressure and velocity distribution is calculated by the `pipeflow` function. If the `mode` parameter is set to \"bidirectional\", the heat transfer calculation is started automatically after the hydraulics computation. Computed mass flows are used as input for the temperature calculation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Results\n",
+    "\n",
+    "After the computation, you can check the results for junctions and pipes.\n",
+    "\n",
+    "Tip: If you observe unexpected results, such as negative temperatures, check the extracted heat flow and ambient temperature settings."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [
     {
@@ -245,7 +304,7 @@
        "3  2.500303   50.000000"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 111,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -258,17 +317,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note that a constant heat flow is extracted via the heat exchanger between nodes 1 and 2. Heat\n",
-    "losses due to the ambient temperature level are not taken into account. These are only included in\n",
-    "the pipe components. This also means that - if the extracted heat flow is large enough - the\n",
-    "temperature level behind the heat exchanger might be lower than the ambient temperature level. A\n",
-    "way to avoid this behaviour would be to create a controller which defines a function for the\n",
-    "extracted heat in dependence of the ambient temperature."
+    "Note that a constant heat flow is extracted via the heat consumer between nodes 1 and 2. Heat losses due to the ambient temperature are not taken into account in the heat consumer; these are only included in the pipe components. This also means that, if the extracted heat flow is large enough, the temperature level behind the heat consumer might be lower than the ambient temperature. To avoid this behavior, you could create a controller that defines a function for the extracted heat depending on the ambient temperature.\n",
+    "\n",
+    "Tip: For larger networks, consider visualizing the results to better understand the temperature and pressure distribution throughout the system."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [
     {
@@ -348,7 +404,7 @@
        "1            0.009375         -0.009375       0.000009   16.052576  4.459393  "
       ]
      },
-     "execution_count": 56,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -368,9 +424,16 @@
     "can retrieve the internal node values with the following commands:"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting Results"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 113,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -391,7 +454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 114,
    "metadata": {},
    "outputs": [
     {
@@ -417,6 +480,17 @@
     "velocity remains constant over the pipe length. Because the temperature level at the pipe entry is\n",
     "higher than the ambient temperature, the temperature level decreases."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## More Examples\n",
+    "\n",
+    "Further examples for closed district heating networks can be found in the notebooks:  \n",
+    "- `multiple_pumps_flow_in_a_circular_district_heating_grid.ipynb`  \n",
+    "- `time_series_in_a_circular_district_heating_grid.ipynb`"
+   ]
   }
  ],
  "metadata": {
diff --git a/tutorials/district_heating/multiple_pumps_flow_in_a_circular_district_heating_grid.ipynb b/tutorials/district_heating/multiple_pumps_flow_in_a_circular_district_heating_grid.ipynb
index 6ef00b722..c83d021fb 100644
--- a/tutorials/district_heating/multiple_pumps_flow_in_a_circular_district_heating_grid.ipynb
+++ b/tutorials/district_heating/multiple_pumps_flow_in_a_circular_district_heating_grid.ipynb
@@ -6,12 +6,25 @@
    "source": [
     "# Multiple pumps in a circular district heating grid\n",
     "\n",
-    "Based on the simple circular district heating grid."
+    "This example builds upon the simple circular district heating grid described in the notebook 'circular_flow_in_a_district_heating_grid.ipynb'. If you are new to district heating modeling with pandapipes, it is recommended to start with that example to understand the basic concepts and components.\n",
+    "\n",
+    "In this notebook, we extend the simple network by introducing multiple pumps (heat generators) and multiple heat consumers. This allows us to analyze more complex operational scenarios, such as the interaction between several pumps and the distribution of heat to multiple consumers.\n",
+    "\n",
+    "The network setup, component creation, and analysis steps follow the same structure as in the simple example, but with additional elements to demonstrate the flexibility and scalability of pandapipes for district heating applications.\n",
+    "\n",
+    "<img src=\"../pics/district_heating/multiple_pumps_district_heating_net_text.png\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Network Setup"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 379,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,16 +38,20 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": []
+   "source": [
+    "The network consists of several junctions forming a closed loop, similar to the simple example. However, in this case, additional junctions are included to accommodate multiple pumps and heat consumers. The parameters `pn_bar` (pressure) and `tfluid_k` (temperature) are set as initial values for each junction. The actual values are determined by the pumps during the simulation.\n",
+    "\n",
+    "This setup allows for the investigation of how multiple heat generators and consumers interact within a district heating grid."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 380,
    "metadata": {},
    "outputs": [],
    "source": [
     "# define constants\n",
-    "qext_w = np.array([50000, 20000])\n",
+    "qext_w = np.array([500000, 200000])\n",
     "return_temperature_k = np.array([60,55]) + 273.15\n",
     "supply_temperature_k = 85 + 273.15\n",
     "\n",
@@ -43,40 +60,53 @@
     "\n",
     "flow_pressure_pump = 4\n",
     "lift_pressure_pump = 1.5\n",
-    "mass_pump_mass_flow = 0.5"
+    "mass_pump_mass_flow = 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Junctions"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 381,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Junctions for pump\n",
-    "j1 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Junction 1\", geodata=(0, 10))\n",
+    "j1 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Junction 1\", geodata=(0, 1000))\n",
     "j2 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Junction 2\", geodata=(0, 0))\n",
     "\n",
     "# Junctions for connection pipes forward line\n",
-    "j3 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Junction 3\", geodata=(10, 0))\n",
-    "j4 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Junction 4\", geodata=(60, 0))\n",
+    "j3 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Junction 3\", geodata=(500, 0))\n",
+    "j4 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Junction 4\", geodata=(1000, 0))\n",
     "\n",
     "# Junctions for heat consumers\n",
-    "j5 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Junction 5\", geodata=(85, 0))\n",
-    "j6 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Junction 6\", geodata=(85, 10))\n",
+    "j5 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Junction 5\", geodata=(1500, 0))\n",
+    "j6 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Junction 6\", geodata=(1500, 1000))\n",
     "\n",
     "# Junctions for connection pipes return line\n",
-    "j7 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Junction 7\", geodata=(60, 10))\n",
-    "j8 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Junction 8\", geodata=(10, 10))"
+    "j7 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Junction 7\", geodata=(1000, 1000))\n",
+    "j8 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Junction 8\", geodata=(500, 1000))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": []
+   "source": [
+    "## Circ Pump Const Pressure, Pipes and Heat Consumers\n",
+    "\n",
+    "This example includes two types of pumps: a constant pressure pump and a constant mass flow pump. The constant pressure pump operates similarly to the one in the simple example, maintaining a specified pressure lift and flow temperature. The constant mass flow pump, on the other hand, enforces a fixed mass flow through the network segment.\n",
+    "\n",
+    "The combination of different pump types allows for the simulation of more advanced operational strategies and the study of their effects on the network's hydraulic and thermal behavior."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 382,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -84,37 +114,48 @@
     "                                               plift_bar=lift_pressure_pump, t_flow_k=supply_temperature_k,\n",
     "                                               type=\"auto\", name=\"pump1\")\n",
     "\n",
-    "pipe1 = pp.create_pipe(net, j2, j3, std_type=pipetype, length_km=0.01, k_mm=k, name=\"pipe1\", sections=5, text_k=283)\n",
-    "pipe2 = pp.create_pipe(net, j3, j4, std_type=pipetype, length_km=0.05, k_mm=k, name=\"pipe2\", sections=5, text_k=283)\n",
-    "pipe3 = pp.create_pipe(net, j4, j5, std_type=pipetype, length_km=0.025, k_mm=k, name=\"pipe3\", sections=5, text_k=283)\n",
+    "pipe1 = pp.create_pipe(net, j2, j3, std_type=pipetype, length_km=0.5, k_mm=k, name=\"pipe1\", sections=5, text_k=283)\n",
+    "pipe2 = pp.create_pipe(net, j3, j4, std_type=pipetype, length_km=0.5, k_mm=k, name=\"pipe2\", sections=5, text_k=283)\n",
+    "pipe3 = pp.create_pipe(net, j4, j5, std_type=pipetype, length_km=0.5, k_mm=k, name=\"pipe3\", sections=5, text_k=283)\n",
     "\n",
     "pp.create_heat_consumer(net, from_junction=j5, to_junction=j6, qext_w=qext_w[0], treturn_k=return_temperature_k[0], name=\"Consumer A\")\n",
     "pp.create_heat_consumer(net, from_junction=j4, to_junction=j7, qext_w=qext_w[1], treturn_k=return_temperature_k[1], name=\"Consumer B\")\n",
     "\n",
-    "pipe4 = pp.create_pipe(net, j6, j7, std_type=pipetype, length_km=0.25, k_mm=k, name=\"pipe4\", sections=5, text_k=283)\n",
-    "pipe5 = pp.create_pipe(net, j7, j8, std_type=pipetype, length_km=0.05, k_mm=k, name=\"pipe5\", sections=5, text_k=283)\n",
-    "pipe6 = pp.create_pipe(net, j8, j1, std_type=pipetype, length_km=0.01, k_mm=k, name=\"pipe6\", sections=5, text_k=283)"
+    "pipe4 = pp.create_pipe(net, j6, j7, std_type=pipetype, length_km=0.5, k_mm=k, name=\"pipe4\", sections=5, text_k=283)\n",
+    "pipe5 = pp.create_pipe(net, j7, j8, std_type=pipetype, length_km=0.5, k_mm=k, name=\"pipe5\", sections=5, text_k=283)\n",
+    "pipe6 = pp.create_pipe(net, j8, j1, std_type=pipetype, length_km=0.5, k_mm=k, name=\"pipe6\", sections=5, text_k=283)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Two heat consumers are included in this network. Each heat consumer extracts a specified amount of heat (`qext_w`) from the network. The return temperature (`treturn_k`) is set for each consumer, and the system calculates the resulting mass flow based on the transferred heat.\n",
+    "\n",
+    "This setup enables the analysis of how different consumer demands affect the temperature and pressure distribution in the network."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": []
+   "source": [
+    "## Circ Pump Const Mass Flow"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 383,
    "metadata": {},
    "outputs": [],
    "source": [
     "### here comes the part with the additional circ_pump_const_mass_flow ###\n",
     "# first of, the junctions\n",
-    "j9 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Junction 9\", geodata=(100, 0))\n",
-    "j10 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Junction 10\", geodata=(100, 10))\n",
-    "j11 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Junction 11\", geodata=(100, 5))\n",
+    "j9 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Junction 9\", geodata=(2000, 0))\n",
+    "j10 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Junction 10\", geodata=(2000, 1000))\n",
+    "j11 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Junction 11\", geodata=(2000, 500))\n",
     "\n",
-    "pipe7 = pp.create_pipe(net, j5, j9, std_type=pipetype, length_km=0.05, k_mm=k, name=\"pipe7\", sections=5, text_k=283)\n",
-    "pipe8 = pp.create_pipe(net, j10, j6, std_type=pipetype, length_km=0.01, k_mm=k, name=\"pipe8\", sections=5, text_k=283)\n",
+    "pipe7 = pp.create_pipe(net, j5, j9, std_type=pipetype, length_km=0.5, k_mm=k, name=\"pipe7\", sections=5, text_k=283)\n",
+    "pipe8 = pp.create_pipe(net, j10, j6, std_type=pipetype, length_km=0.5, k_mm=k, name=\"pipe8\", sections=5, text_k=283)\n",
     "\n",
     "pump2 = pp.create_circ_pump_const_mass_flow(net, j10, j11, p_flow_bar=flow_pressure_pump, mdot_flow_kg_per_s=mass_pump_mass_flow, \n",
     "                                            t_flow_k=supply_temperature_k, type=\"auto\", name=\"pump2\")\n",
@@ -126,20 +167,61 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The flow control is currently needed for the pressure."
+    "The flow control component is used to maintain a specified mass flow at a particular location in the network. This is especially important when using a constant mass flow pump, as it ensures the desired flow rate is achieved and helps stabilize the pressure distribution."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting Net\n",
+    "\n",
+    "The following command creates a simple plot of the network layout using `pandapipes.plotting.simple_plot`. \n",
+    "This visualization helps to understand the structure and connectivity of the district heating grid, showing the positions of junctions, pipes, pumps, and consumers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 384,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 384,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pp.plotting.simple_plot(net)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "## Pipeflow Calculation\n",
     "\n",
-    "We now run a pipe flow.\n"
+    "We now run a pipe flow calculation using the bidirectional calculation mode. This mode is essential for networks with multiple heat generators and consumers, as it enables the simultaneous calculation of hydraulic and thermal behavior throughout the entire system."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 385,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -150,12 +232,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "mode must be bidirectional, iter=100 might be needed"
+    "Note: The `mode` parameter must be set to 'bidirectional' to enable heat transfer calculations. Increasing the number of iterations (e.g., `iter=100`) may be necessary to ensure convergence in more complex networks with multiple pumps and consumers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Results"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 386,
    "metadata": {},
    "outputs": [
     {
@@ -187,7 +276,7 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>2.500000</td>\n",
-       "      <td>327.891464</td>\n",
+       "      <td>331.497138</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -196,43 +285,43 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>3.999995</td>\n",
-       "      <td>358.078414</td>\n",
+       "      <td>3.765631</td>\n",
+       "      <td>358.057213</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>3.999970</td>\n",
-       "      <td>357.609424</td>\n",
+       "      <td>3.531273</td>\n",
+       "      <td>357.964539</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>3.999971</td>\n",
-       "      <td>358.049038</td>\n",
+       "      <td>3.413784</td>\n",
+       "      <td>357.794807</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>2.500022</td>\n",
-       "      <td>332.661759</td>\n",
+       "      <td>3.081328</td>\n",
+       "      <td>333.150000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>2.500035</td>\n",
-       "      <td>328.149999</td>\n",
+       "      <td>2.964553</td>\n",
+       "      <td>331.617595</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>2.500006</td>\n",
-       "      <td>327.934451</td>\n",
+       "      <td>2.732275</td>\n",
+       "      <td>331.557329</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>4.000199</td>\n",
+       "      <td>3.422251</td>\n",
        "      <td>358.150000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>2.499975</td>\n",
-       "      <td>332.648357</td>\n",
+       "      <td>3.072721</td>\n",
+       "      <td>332.812742</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
@@ -245,20 +334,20 @@
       ],
       "text/plain": [
        "       p_bar         t_k\n",
-       "0   2.500000  327.891464\n",
+       "0   2.500000  331.497138\n",
        "1   4.000000  358.150000\n",
-       "2   3.999995  358.078414\n",
-       "3   3.999970  357.609424\n",
-       "4   3.999971  358.049038\n",
-       "5   2.500022  332.661759\n",
-       "6   2.500035  328.149999\n",
-       "7   2.500006  327.934451\n",
-       "8   4.000199  358.150000\n",
-       "9   2.499975  332.648357\n",
+       "2   3.765631  358.057213\n",
+       "3   3.531273  357.964539\n",
+       "4   3.413784  357.794807\n",
+       "5   3.081328  333.150000\n",
+       "6   2.964553  331.617595\n",
+       "7   2.732275  331.557329\n",
+       "8   3.422251  358.150000\n",
+       "9   3.072721  332.812742\n",
        "10  4.000000  358.150000"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 386,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -271,17 +360,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note that a constant heat flow is extracted via the heat exchanger between nodes 1 and 2. Heat\n",
-    "losses due to the ambient temperature level are not taken into account. These are only included in\n",
-    "the pipe components. This also means that - if the extracted heat flow is large enough - the\n",
-    "temperature level behind the heat exchanger might be lower than the ambient temperature level. A\n",
-    "way to avoid this behaviour would be to create a controller which defines a function for the\n",
-    "extracted heat in dependence of the ambient temperature."
+    "Note that a constant heat flow is extracted via the heat exchangers at the consumer locations. Heat losses due to the ambient temperature are only included in the pipe components, not in the heat exchangers themselves. This means that, if the extracted heat flow is large enough, the temperature level behind a heat exchanger might be lower than the ambient temperature. To avoid this behavior, you could implement a controller that adjusts the extracted heat based on the ambient temperature.\n",
+    "\n",
+    "Tip: For larger and more complex networks, visualizing the results can help you better understand the temperature and pressure distribution throughout the system."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 387,
    "metadata": {},
    "outputs": [
     {
@@ -321,115 +407,115 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.018179</td>\n",
+       "      <td>0.701154</td>\n",
        "      <td>4.000000</td>\n",
-       "      <td>3.999995</td>\n",
+       "      <td>3.765631</td>\n",
        "      <td>358.150000</td>\n",
-       "      <td>358.078414</td>\n",
-       "      <td>358.078414</td>\n",
-       "      <td>0.141064</td>\n",
-       "      <td>-0.141064</td>\n",
-       "      <td>0.000146</td>\n",
-       "      <td>5346.846164</td>\n",
-       "      <td>0.031545</td>\n",
+       "      <td>358.057213</td>\n",
+       "      <td>358.057213</td>\n",
+       "      <td>5.440816</td>\n",
+       "      <td>-5.440816</td>\n",
+       "      <td>0.005618</td>\n",
+       "      <td>206203.128764</td>\n",
+       "      <td>0.019886</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.018176</td>\n",
-       "      <td>3.999995</td>\n",
-       "      <td>3.999970</td>\n",
-       "      <td>358.078414</td>\n",
-       "      <td>357.609424</td>\n",
-       "      <td>357.721493</td>\n",
-       "      <td>0.141064</td>\n",
-       "      <td>-0.141064</td>\n",
-       "      <td>0.000146</td>\n",
-       "      <td>5333.432050</td>\n",
-       "      <td>0.031575</td>\n",
+       "      <td>0.701109</td>\n",
+       "      <td>3.765631</td>\n",
+       "      <td>3.531273</td>\n",
+       "      <td>358.057213</td>\n",
+       "      <td>357.964539</td>\n",
+       "      <td>357.964539</td>\n",
+       "      <td>5.440816</td>\n",
+       "      <td>-5.440816</td>\n",
+       "      <td>0.005617</td>\n",
+       "      <td>205993.875359</td>\n",
+       "      <td>0.019886</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>-0.002700</td>\n",
-       "      <td>3.999970</td>\n",
-       "      <td>3.999971</td>\n",
-       "      <td>357.609424</td>\n",
-       "      <td>358.049038</td>\n",
-       "      <td>357.808728</td>\n",
-       "      <td>-0.020960</td>\n",
-       "      <td>0.020960</td>\n",
-       "      <td>-0.000022</td>\n",
-       "      <td>788.015150</td>\n",
-       "      <td>0.100793</td>\n",
+       "      <td>0.494780</td>\n",
+       "      <td>3.531273</td>\n",
+       "      <td>3.413784</td>\n",
+       "      <td>357.964539</td>\n",
+       "      <td>357.794807</td>\n",
+       "      <td>357.833422</td>\n",
+       "      <td>3.839920</td>\n",
+       "      <td>-3.839920</td>\n",
+       "      <td>0.003964</td>\n",
+       "      <td>145182.298310</td>\n",
+       "      <td>0.020016</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>-0.002650</td>\n",
-       "      <td>2.500022</td>\n",
-       "      <td>2.500035</td>\n",
-       "      <td>332.661759</td>\n",
-       "      <td>328.149999</td>\n",
-       "      <td>326.718797</td>\n",
-       "      <td>-0.020960</td>\n",
-       "      <td>0.020960</td>\n",
-       "      <td>-0.000021</td>\n",
-       "      <td>496.619942</td>\n",
-       "      <td>0.148573</td>\n",
+       "      <td>0.487498</td>\n",
+       "      <td>3.081328</td>\n",
+       "      <td>2.964553</td>\n",
+       "      <td>333.150000</td>\n",
+       "      <td>331.617595</td>\n",
+       "      <td>333.061953</td>\n",
+       "      <td>3.839920</td>\n",
+       "      <td>-3.839920</td>\n",
+       "      <td>0.003906</td>\n",
+       "      <td>103816.089343</td>\n",
+       "      <td>0.020192</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.017863</td>\n",
-       "      <td>2.500035</td>\n",
-       "      <td>2.500006</td>\n",
-       "      <td>328.149999</td>\n",
-       "      <td>327.934451</td>\n",
-       "      <td>327.934451</td>\n",
-       "      <td>0.141064</td>\n",
-       "      <td>-0.141064</td>\n",
-       "      <td>0.000143</td>\n",
-       "      <td>3530.489571</td>\n",
-       "      <td>0.037703</td>\n",
+       "      <td>0.690206</td>\n",
+       "      <td>2.964553</td>\n",
+       "      <td>2.732275</td>\n",
+       "      <td>331.617595</td>\n",
+       "      <td>331.557329</td>\n",
+       "      <td>331.557329</td>\n",
+       "      <td>5.440816</td>\n",
+       "      <td>-5.440816</td>\n",
+       "      <td>0.005530</td>\n",
+       "      <td>143655.525509</td>\n",
+       "      <td>0.020021</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>0.017862</td>\n",
-       "      <td>2.500006</td>\n",
+       "      <td>0.690185</td>\n",
+       "      <td>2.732275</td>\n",
        "      <td>2.500000</td>\n",
-       "      <td>327.934451</td>\n",
-       "      <td>327.891464</td>\n",
-       "      <td>327.891464</td>\n",
-       "      <td>0.141064</td>\n",
-       "      <td>-0.141064</td>\n",
-       "      <td>0.000143</td>\n",
-       "      <td>3523.145093</td>\n",
-       "      <td>0.037741</td>\n",
+       "      <td>331.557329</td>\n",
+       "      <td>331.497138</td>\n",
+       "      <td>331.497138</td>\n",
+       "      <td>5.440816</td>\n",
+       "      <td>-5.440816</td>\n",
+       "      <td>0.005530</td>\n",
+       "      <td>143521.552093</td>\n",
+       "      <td>0.020021</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>-0.064434</td>\n",
-       "      <td>3.999971</td>\n",
-       "      <td>4.000199</td>\n",
-       "      <td>358.049038</td>\n",
+       "      <td>-0.128852</td>\n",
+       "      <td>3.413784</td>\n",
+       "      <td>3.422251</td>\n",
+       "      <td>357.794807</td>\n",
        "      <td>358.150000</td>\n",
-       "      <td>358.129797</td>\n",
-       "      <td>-0.500000</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>-0.000516</td>\n",
-       "      <td>18948.812932</td>\n",
-       "      <td>0.022953</td>\n",
+       "      <td>358.049038</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.001032</td>\n",
+       "      <td>37806.972487</td>\n",
+       "      <td>0.021268</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>-0.063463</td>\n",
-       "      <td>2.499975</td>\n",
-       "      <td>2.500022</td>\n",
-       "      <td>332.648357</td>\n",
-       "      <td>332.661759</td>\n",
-       "      <td>332.659079</td>\n",
-       "      <td>-0.500000</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>-0.000508</td>\n",
-       "      <td>13423.777747</td>\n",
-       "      <td>0.024343</td>\n",
+       "      <td>-0.126947</td>\n",
+       "      <td>3.072721</td>\n",
+       "      <td>3.081328</td>\n",
+       "      <td>332.812742</td>\n",
+       "      <td>333.150000</td>\n",
+       "      <td>333.082367</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.001017</td>\n",
+       "      <td>26985.157872</td>\n",
+       "      <td>0.021947</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -437,27 +523,37 @@
       ],
       "text/plain": [
        "   v_mean_m_per_s  p_from_bar  p_to_bar    t_from_k      t_to_k  t_outlet_k  \\\n",
-       "0        0.018179    4.000000  3.999995  358.150000  358.078414  358.078414   \n",
-       "1        0.018176    3.999995  3.999970  358.078414  357.609424  357.721493   \n",
-       "2       -0.002700    3.999970  3.999971  357.609424  358.049038  357.808728   \n",
-       "3       -0.002650    2.500022  2.500035  332.661759  328.149999  326.718797   \n",
-       "4        0.017863    2.500035  2.500006  328.149999  327.934451  327.934451   \n",
-       "5        0.017862    2.500006  2.500000  327.934451  327.891464  327.891464   \n",
-       "6       -0.064434    3.999971  4.000199  358.049038  358.150000  358.129797   \n",
-       "7       -0.063463    2.499975  2.500022  332.648357  332.661759  332.659079   \n",
+       "0        0.701154    4.000000  3.765631  358.150000  358.057213  358.057213   \n",
+       "1        0.701109    3.765631  3.531273  358.057213  357.964539  357.964539   \n",
+       "2        0.494780    3.531273  3.413784  357.964539  357.794807  357.833422   \n",
+       "3        0.487498    3.081328  2.964553  333.150000  331.617595  333.061953   \n",
+       "4        0.690206    2.964553  2.732275  331.617595  331.557329  331.557329   \n",
+       "5        0.690185    2.732275  2.500000  331.557329  331.497138  331.497138   \n",
+       "6       -0.128852    3.413784  3.422251  357.794807  358.150000  358.049038   \n",
+       "7       -0.126947    3.072721  3.081328  332.812742  333.150000  333.082367   \n",
        "\n",
-       "   mdot_from_kg_per_s  mdot_to_kg_per_s  vdot_m3_per_s      reynolds    lambda  \n",
-       "0            0.141064         -0.141064       0.000146   5346.846164  0.031545  \n",
-       "1            0.141064         -0.141064       0.000146   5333.432050  0.031575  \n",
-       "2           -0.020960          0.020960      -0.000022    788.015150  0.100793  \n",
-       "3           -0.020960          0.020960      -0.000021    496.619942  0.148573  \n",
-       "4            0.141064         -0.141064       0.000143   3530.489571  0.037703  \n",
-       "5            0.141064         -0.141064       0.000143   3523.145093  0.037741  \n",
-       "6           -0.500000          0.500000      -0.000516  18948.812932  0.022953  \n",
-       "7           -0.500000          0.500000      -0.000508  13423.777747  0.024343  "
+       "   mdot_from_kg_per_s  mdot_to_kg_per_s  vdot_m3_per_s       reynolds  \\\n",
+       "0            5.440816         -5.440816       0.005618  206203.128764   \n",
+       "1            5.440816         -5.440816       0.005617  205993.875359   \n",
+       "2            3.839920         -3.839920       0.003964  145182.298310   \n",
+       "3            3.839920         -3.839920       0.003906  103816.089343   \n",
+       "4            5.440816         -5.440816       0.005530  143655.525509   \n",
+       "5            5.440816         -5.440816       0.005530  143521.552093   \n",
+       "6           -1.000000          1.000000      -0.001032   37806.972487   \n",
+       "7           -1.000000          1.000000      -0.001017   26985.157872   \n",
+       "\n",
+       "     lambda  \n",
+       "0  0.019886  \n",
+       "1  0.019886  \n",
+       "2  0.020016  \n",
+       "3  0.020192  \n",
+       "4  0.020021  \n",
+       "5  0.020021  \n",
+       "6  0.021268  \n",
+       "7  0.021947  "
       ]
      },
-     "execution_count": 10,
+     "execution_count": 387,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -473,41 +569,40 @@
     "The command above shows the results for the pipe components. The temperatures of the adjacent\n",
     "junctions are displayed. Due to heat losses, the temperatures at the to-nodes is lower than the\n",
     "temperatures at the from-nodes. Note also that the junctions are not equal to the internal nodes,\n",
-    "introduced by the pipe sections we defined. To display the temperatures at the internal nodes, we\n",
-    "can retrieve the internal node values with the following commands:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pandapipes.component_models import Pipe\n",
-    "pipe_results = Pipe.get_internal_results(net, [0])"
+    "introduced by the pipe sections we defined."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The parameters of the get_internal_results function correspond to the net and the pipes we want to\n",
-    "evaluate. In this case, only the results of pipe zero are retrieved. The returned value stored in\n",
-    "pipe_results is a dictionary, containing fields for the pressure, the velocity and the temperature.\n",
-    "The dictionary can either be used for own evaluations now or it can be used to plot the results over\n",
-    "the pipe length:\n"
+    "## Results Plotting\n",
+    "\n",
+    "In the following cells, results are visualized using custom colored plots to provide a clear overview of the pipe data in the network. These visualizations help to analyze the distribution of key physical properties temperature, pressure, and velocity along each pipe segment.\n",
+    "\n",
+    "The plotting code uses a helper function, `plot_pipe_property`, which draws each pipe as a colored line between its start and end junctions. The color of each pipe is determined by the value of the selected property (e.g., temperature, pressure, or velocity), mapped to a color scale (colormap). This approach makes it easy to visually identify gradients and patterns in the network.\n",
+    "\n",
+    "Three subplots are generated side by side:\n",
+    "\n",
+    "- **Temperature Plot (left):** Shows the average temperature in each pipe (in °C), using a blue-to-red colormap. This allows you to see how heat is distributed and where the largest temperature drops occur due to heat extraction or losses.\n",
+    "- **Pressure Plot (center):** Displays the average pressure in each pipe (in bar), using a green-to-yellow colormap. This helps to identify pressure drops along the network and the effect of pumps.\n",
+    "- **Velocity Plot (right):** Illustrates the mean fluid velocity in each pipe (in m/s), using a purple-to-yellow colormap. This is useful for assessing flow distribution and identifying areas of high or low velocity.\n",
+    "\n",
+    "Each subplot includes a colorbar for reference, and the axes are labeled according to the network's spatial coordinates. The plots are generated using the `matplotlib` library, and the data for each property is taken from the `net.res_pipe` results table, which contains the simulation output for all pipes.\n",
+    "\n",
+    "This visualization technique is especially helpful for complex networks, as it provides an immediate, intuitive understanding of how the network is operating under the given conditions."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 388,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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aNGiQsrKy1LdvX0lSr169NHr0aM/5U6dO1YoVK7R9+3Zt3bpVTzzxhBYvXqw777zTZz/DklbkbV+mTp2qG264QcuXL1ezZs1UtmxZr+dnzJjhs8kBAAAAf3fhhRd67eltWZZSU1O1b98+PfPMMzbOrHDIpwEAAOBvhg0bposuukhfffWVqlat6jl+0003acCAATbOLH/k1AAAAChIt27dtG/fPo0fP16pqalq0aKFli9frtjYWEnS7t27vTrJZWVlafDgwfr1119Vrlw5NWzYUP/5z3/UrVs3u5ZQZOdU/PH++++rQYMGkuR14/2vfwYAAABKQpcuXbzyTpfLpejoaF111VVq2LChjTMrHPJpAAAA+JtPP/1Ua9euVWhoqNfxxMRE/fbbbzbN6szIqQEAAFAYQ4cO1dChQ/N9bvXq1V6PH3nkET3yyCOlMKvcPLtfv37q06ePatas6bPrFrn444knntDChQvVp08fn00CAAAAZ2cZhizDnv2sLT+7eTphwgS7p1As5NMAAADwN6Zpyu125zn+66+/KiIiwoYZnR05NQAAQOk71+1XfBU7kAwfPlyLFi3SpEmTdPXVV6t///666aabFBYWVqzrFvlfEMLCwtSmTZtiBQUAAADOVUhIiNLT0/McP3DggEJCQmyYUdGQTwMAAMDfXHfddUpJSfE8NgxDR48eVXJysjp16mTfxM6AnBoAAABONnz4cG3ZskVffPGFGjVqpHvuuUfx8fEaOnSoNm3adM7XLXLxx7BhwzRz5sxzDggAAICis2T8f3t3Hh9Vdf5x/DsJWdiSsCVhiYAbi+yrca9GVhcqdaWCiFj9BWSp1EKV4Aa4AWoRXMFWUYoVa6lFIRqogoAgVlxwQ0EgoNAkrEnIPb8/AqNjAtkmOXPvfN6+Tl/MnTv3PE8umTxMT55jdYQSY0ypx/Pz80u0qQ5F1NMAAAB2+CT5jKVhO/kyPPzww3rvvffUvn17HT58WNddd51/y5cHHnjAdnglUFMDAABYYHySY2l4rPPHMd26ddNjjz2mHTt2KCMjQ88884x69uypLl266LnnnjvuZ+HHU+FtX9auXau3335bS5Ys0RlnnKGoqKiA51999dWKXhIAAAAo02OPPSap+LcQn3nmGdWrV8//XFFRkVauXKm2bdvaCq/cqKcBAAAQalJSUvTRRx9p4cKF+uijj7R//36NGDFCQ4YMUe3atW2HVwI1NQAAALygsLBQixcv1rx587Rs2TKdeeaZGjFihL7//ntNmjRJy5cv14IFC8p9vQov/khISNAVV1xR0ZcBAAAAVTJz5kxJxZ0/5s6dG7DFS3R0tFq1aqW5c+faCq/cqKcBAAAQSgoLC9W2bVstWbJEQ4YM0ZAhQ2yHVCZqagAAALjZhg0bNG/ePL300kuKiIjQ0KFDNXPmzIBfbvz1r3+tnj17Vui6FV78MW/evIq+BAAAAFVkfD4Zn53Wdrbm/aUtW7ZIkn71q1/p1VdfVYMGDSxHVDnU0wAAAJYYi+2iQ7hNdVRUlA4fPmw7jAqhpgYAAKh5xvhkLNW1tuatLj179tTFF1+sOXPmaNCgQSU62UlS69atdc0111TouhVe/AEAAADY9M4779gOAQAAAPCU9PR0PfDAA3rmmWdUqxYfGQMAAADV6ZtvvlHLli1PeE7dunUrvOi5XJV8t27dlJmZWe7frjznnHO0cOFCNW/evELBAAAAoHSsqg70/fff6/XXX9fWrVtVUFAQ8NyMGTMsRXV81NMAAAAhwBwdtuYOYevWrVNmZqbeeustdezYUXXr1g14/tVXX7UU2U+oqQEAAOziM+rg+dWvfqV169apUaNGAcdzcnLUrVs3ffPNN5W6brkWf2zcuFEfffSRGjZsWK6Lbty4Ufn5+ZUKCAAAADiRzMxMXXbZZTr55JP1+eefq0OHDvr2229ljFG3bt1sh1cq6mkAAACEsoSEBA0ePNh2GCdETQ0AAACv+Pbbb1VUVFTieH5+vrZv317p65a7h99FF10kY8q3RN0XIvvCAwAAwHsmTpyo22+/XXfffbfq16+vv//970pMTNSQIUPUr18/2+EdF/U0AAAAQlVF20nbQk0NAAAAN3v99df9f37zzTcVHx/vf1xUVKTMzEy1atWq0tcv1+KPLVu2VPjCLVq0qPBrAAAAcDwRMoqwNnco+eyzz/TSSy9JkmrVqqVDhw6pXr16uueee3T55Zfr1ltvtRxhSdTTAAAAIYBtX1yNmhoAAMAuY4qHrbm9YNCgQZKKFyoPGzYs4LmoqCi1atVKjzzySKWvX67FHy1btqz0BAAAAEAw1a1bVwUFBZKkpk2b6uuvv9YZZ5whSfrxxx9thnZc1NMAAAAIZa1btz5hp4zK7jkeTNTUAAAAcDvHcSQV19/r1q1T48aNg3r9cm/7AgAAAISCM888U++++67atWunAQMG6Pe//70+/vhjvfrqqzrzzDNthwcAAAC4ztixYwMeFxYW6sMPP9TSpUs1YcIEO0EBAAAAHlWZrnblweIPAAAAFzDyycjOntW25j2eGTNmaP/+/ZKku+++W/v379fChQt12mmnacaMGZajAwAAQKjymeJha+5QNmbMmFKPz549Wx988EENRwMAAICQZHwyxtJnxbbmDaLHHntMN998s2JjY/XYY4+d8NzbbrutUnOw+AMAAACuUVRUpO+//16dOnWSVLwFzNy5cy1HBQAAAHhT//79NXHiRM2bN892KAAAAICrzZw5U0OGDFFsbKxmzpx53PN8Ph+LPwAAALyMzh/FIiMj1adPH3322WdKSEiwHQ4AAADcxBwdtuZ2oVdeeUUNGza0HQYAAABCgLHY+cNax5Eg+vlWL9W17UtERV8wbNgwrVy5sjpiAQAAAMrUoUMHffPNN7bDqDTqaQAAAISarl27qlu3bv7RtWtXNW3aVJMmTdKkSZNsh1cCNTUAAABQUoU7f+Tm5iotLU0tW7bU8OHDNWzYMDVv3rw6YgMAAABKuO+++3T77bfr3nvvVffu3VW3bt2A5+Pi4ixFVj7U0wAAAAg1gwYNCngcERGhJk2a6IILLlDbtm3tBHUC1NQAAABws8GDB6tXr1664447Ao4/+OCDWrdunRYtWlSp61Z48cdrr72mH374QX/961/1/PPPKyMjQ2lpaRoxYoQuv/xyRUVFVSoQAAAAHB/bvvxkwIABkqTLLrtMPt9PsRlj5PP5VFRUZCu0cqGeBgAAsIRtX44rIyPDdggVQk0NAABQ89j2JXhWrlypKVOmlDjev39/PfLII5W+boW3fZGkJk2aaPz48froo4+0Zs0anXrqqbr++uvVrFkzjRs3Tl9++WWlAwIAAABO5J133vGPt99+2z+OPXYD6mkAAACEkg0bNujjjz/2P/7HP/6hQYMGadKkSSooKLAY2fFRUwMAAMCt9u/fr+jo6BLHo6KilJeXV+nrVmrxxzE7d+7UsmXLtGzZMkVGRmrAgAH6+OOP1b59e82cObMqlwYAAMDPHOv8YWuEkvPPP/+Ew02opwEAAGqOz9gdoex3v/udvvjiC0nSN998o6uvvlp16tTRokWL9Ic//MFydCdGTQ0AAFAzjnX+sDW8pGPHjlq4cGGJ4y+//LLat29f6etWeNuXwsJCvf7665o3b57eeustderUSWPHjtV1113n31998eLFuvHGGzVu3LhKBwYAAAAcz3/+8x89+eST+uabb7Ro0SI1b95cf/3rX9W6dWudc845tsM7IeppAAAAhJovvvhCXbp0kSQtWrRI559/vhYsWKD33ntP11xzjWbNmmU1vl+ipgYAAICb3XXXXbriiiv09ddf68ILL5QkZWZm6qWXXtKiRYsqfd0KL/5o2rSpHMfRtddeq7Vr1/r/UfBzv/rVr5SQkFDpoAAAAIDj+fvf/67rr79eQ4YM0YYNG5Sfny9Jys3N1dSpU/XGG29YjvDEqKcBAABQljlz5mjOnDn69ttvJUlnnHGGJk+erP79+5d6/tNPP62//OUv2rRpkySpe/fumjp1qnr16lWu+YwxchxHkrR8+XJdcsklkqSUlBT9+OOPVcwm+KipAQAA4GaXXnqpXnvtNU2dOlWvvPKKateurU6dOmn58uVV6m5d4cUfM2fO1JVXXqnY2NjjnpOQkKAtW7ZUOigAAAAEstnaLtRa6t13332aO3euhg4dqpdfftl//Oyzz9Z9991nMbLyoZ4GAACwxPiKh625K6BFixaaPn26TjvtNBlj9Pzzz+vyyy/Xhx9+qDPOOKPE+VlZWbr22mt11llnKTY2Vg888ID69OmjTz75RM2bNy9zvh49eui+++5TWlqaVqxYoTlz5kiStmzZoqSkpArFXhOoqQEAAGqecXwyjqXPqC3NW50GDhyogQMHBvWaERV9wfXXX3/Coro6TZ8+XT6fT2PHjvUfO3z4sNLT09WoUSPVq1dPgwcP1q5duwJet3XrVg0cOFB16tRRYmKiJkyYoCNHjtRw9AAAAAiGzZs367zzzitxPD4+Xjk5OTUfUAXZrKclamoAAAA3uPTSSzVgwACddtppOv3003X//ferXr16ev/990s9/8UXX9T//d//qUuXLmrbtq2eeeYZOY6jzMzMcs03a9YsbdiwQaNGjdKf/vQnnXrqqZKkV155RWeddVbQ8goWPqMGAACAF6xfv14vvPCCXnjhBX344YdVvl6FO3/Ysm7dOj355JPq1KlTwPFx48bpX//6lxYtWqT4+HiNGjVKV1xxhd577z1JUlFRkQYOHKjk5GStWrVKO3fu1NChQxUVFaWpU6faSAUAAABVkJycrK+++kqtWrUKOP7uu+/q5JNPthOUS1BTAwAAuE9RUZEWLVqkAwcOKDU1tVyvOXjwoAoLC9WwYcNynd+pUyd9/PHHJY4/9NBDioyMrFC8XkY9DQAAgGDYvXu3rrnmGmVlZfm3KszJydGvfvUrvfzyy2rSpEmlrlvhzh827N+/X0OGDNHTTz+tBg0a+I/n5ubq2Wef1YwZM3ThhReqe/fumjdvnlatWuVfBf/WW2/p008/1QsvvKAuXbqof//+uvfeezV79mwVFBTYSgkAAKBCjHxWRygZOXKkxowZozVr1sjn82nHjh168cUXdfvtt+vWW2+1HV7IoqYGAABhz1gekvLy8gJGfn7+ccP9+OOPVa9ePcXExOiWW27R4sWL1b59+3Klescdd6hZs2ZKS0sr1/nbtm3T999/73+8du1ajR07Vn/5y18UFRVVrmt4HfU0AAAId8b8tD15zQ/b2QfX6NGjtW/fPn3yySfau3ev9u7dq02bNikvL0+33XZbpa/ris4f6enpGjhwoNLS0gL2cV+/fr0KCwsD/hHTtm1bnXTSSVq9erXOPPNMrV69Wh07dgzYm7Jv37669dZb9cknn6hr164l5svPzw/4h1deXp4kyXEcOY4T9Pwcx5ExplquHUrI03vCJddwyVMKn1zJ03uqM9dw+Pq5zR//+Ec5jqOLLrpIBw8e1HnnnaeYmBjdfvvtGj16tO3wQpaXa2re77yHPL0nXHIlT+8Jl1ypp2tOSkpKwOOMjAxNmTKl1HPbtGmjjRs3Kjc3V6+88oqGDRumFStWlLkAZPr06Xr55ZeVlZVV7q1RrrvuOt188826/vrrlZ2drYsvvlhnnHGGXnzxRWVnZ2vy5Mnluo6XebmePnZd3u+8I1zylMIn13DJUwqfXMnTe6ipURFLly7V8uXL1a5dO/+x9u3ba/bs2erTp0+lrxvyiz9efvllbdiwQevWrSvxXHZ2tqKjo/2tUI5JSkpSdna2/5yfF9XHnj/2XGmmTZumu+++u8TxH374QYcPH65MGifkOI5yc3NljFFEhCuasVQKeXpPuOQaLnlK4ZMreXpPdea6b9++oF6vsmx24Ai1zh8+n09/+tOfNGHCBH311Vfav3+/2rdvr3r16tkOLWR5vabm/c57yNN7wiVX8vSecMk1HOppSfKZ4mFrbqm4w0ZcXJz/eExMzHFfEx0drVNPPVWS1L17d61bt06PPvqonnzyyeO+5uGHH9b06dO1fPnyEluTnMimTZvUq1cvSdLf/vY3dejQQe+9957eeust3XLLLWG/+MPr9bTE+53XhEueUvjkGi55SuGTK3l6TzjU1Me6cNia20scxym1w15UVFSVFvuE9OKPbdu2acyYMVq2bFm5V6kHw8SJEzV+/Hj/47y8PKWkpKhJkyYB/zgLFsdx5PP51KRJE0+/8ZGn94RLruGSpxQ+uZKn91RnrjVZg6BioqOjVb9+fdWvX5+FHycQDjU173feQ57eEy65kqf3hEuu1NM1Jy4urtK1kOM4J9wm5sEHH9T999+vN998Uz169KjQtQsLC/0LUZYvX67LLrtMUnEHi507d1YqXq8Ih3pa4v3Oa8IlTyl8cg2XPKXwyZU8vYeaGhVx4YUXasyYMXrppZfUrFkzSdL27ds1btw4XXTRRZW+bkgv/li/fr12796tbt26+Y8VFRVp5cqV+vOf/6w333xTBQUFysnJCVhZvWvXLiUnJ0uSkpOTtXbt2oDr7tq1y/9caWJiYkpddR8REVFtb0w+n69arx8qyNN7wiXXcMlTCp9cydN7qivXcPjauc2RI0d0991367HHHtP+/fslSfXq1dPo0aOVkZHBnuS/EC41Ne933kOe3hMuuZKn94RLrtTToWXixInq37+/TjrpJO3bt08LFixQVlaW3nzzTUnS0KFD1bx5c02bNk2S9MADD2jy5MlasGCBWrVq5e8mUa9evXItlD7jjDM0d+5cDRw4UMuWLdO9994rSdqxY4caNWpUTVm6Q7jU0xLvd14TLnlK4ZNruOQphU+u5Ok91NQorz//+c+67LLL1KpVK//WkNu2bVOHDh30wgsvVPq6Ib3446KLLtLHH38ccGz48OFq27at7rjjDqWkpCgqKkqZmZkaPHiwJGnz5s3aunWrUlNTJUmpqam6//77tXv3biUmJkqSli1bpri4uDL3xwQAAAgVbPvyk9GjR+vVV1/Vgw8+6K/5Vq9erSlTpmjPnj2aM2eO5QhDCzU1AADAUebosDV3BezevVtDhw7Vzp07FR8fr06dOunNN9/UxRdfLEnaunVrwP8JMGfOHBUUFOg3v/lNwHUyMjI0ZcqUMud74IEH9Otf/1oPPfSQhg0bps6dO0uSXn/9df92MOGKehoAAKAY274ET0pKijZs2KDly5fr888/lyS1a9dOaWlpVbpuSC/+qF+/vjp06BBwrG7dumrUqJH/+IgRIzR+/Hg1bNhQcXFxGj16tFJTU3XmmWdKkvr06aP27dvr+uuv14MPPqjs7GzdeeedSk9PP+GemgAAAAhNCxYs0Msvv6z+/fv7j3Xq1EkpKSm69tprWfzxC9TUAAAA7vPss8+e8PmsrKyAx99++22V5rvgggv0448/Ki8vTw0aNPAfv/nmm1WnTp0qXdvtqKcBAABQHXw+ny6++GL/Au9gCOnFH+Uxc+ZMRUREaPDgwcrPz1ffvn31xBNP+J+PjIzUkiVLdOuttyo1NVV169bVsGHDdM8991iMGgAAoGKMLK6qDrHOHzExMWrVqlWJ461bt1Z0dHTNB+QB1NQAACAsGMnnks4fNhhjtH79en399de67rrrVL9+fUVHR4f94o/yoJ4GAADhgM4fVfPYY4+V+9zbbrutUnO4bvHHL1e1x8bGavbs2Zo9e/ZxX9OyZUu98cYb1RwZAAAAasKoUaN07733at68ef7fksvPz9f999+vUaNGWY7OHaipAQAA8HPfffed+vXrp61btyo/P18XX3yx6tevrwceeED5+fmaO3eu7RBDCvU0AAAAKmrmzJnlOs/n84XP4g8AAACEtw8//FCZmZlq0aKFfy/yjz76SAUFBbrooot0xRVX+M999dVXbYUJAAAAuMaYMWPUo0cPffTRR2rUqJH/+K9//WuNHDnSYmQAAACAN2zZsqXa52DxBwAAgAs48smxtP2KrXmPJyEhQYMHDw44lpKSYikaAAAAuIaRve1XQnzbl//85z9atWpViW0UW7Vqpe3bt1uKCgAAAKGEbV+Cr6CgQFu2bNEpp5yiWrWqvnSDxR8AAABwlXnz5tkOAQAAAPAUx3FUVFRU4vj333+v+vXrW4gIAAAA8K6DBw9q9OjRev755yVJX3zxhU4++WSNHj1azZs31x//+MdKXTcimEECAACgehj5rA4AAADA9YzlEcL69OmjWbNm+R/7fD7t379fGRkZGjBggL3AAAAAEDKOdf6wNbxk4sSJ+uijj5SVlaXY2Fj/8bS0NC1cuLDS16XzBwAAAFxlz549mjx5st555x3t3r1bjuMEPL93715LkQEAAADu9PDDD6tfv35q3769Dh8+rOuuu05ffvmlGjdurJdeesl2eAAAAICnvPbaa1q4cKHOPPNM+Xw/LWw544wz9PXXX1f6uiz+AAAAgKtcf/31+uqrrzRixAglJSUFFMcAAAAAKi4lJUUfffSRFi5cqI8++kj79+/XiBEjNGTIENWuXdt2eAAAAICn/PDDD0pMTCxx/MCBA1X6vJvFHwAAAC5gs7VdqLXU+89//qN3331XnTt3th0KAAAAXMRnioetuUNVYWGh2rZtqyVLlmjIkCEaMmSI7ZAAAAAQgviMOnh69Oihf/3rXxo9erQk+Rd8PPPMM0pNTa30dSOCEh0AAABwVKtWreTz+UqM9PR0SdLhw4eVnp6uRo0aqV69eho8eLB27dpV7uu3bdtWhw4dqq7wAQAAgLASFRWlw4cP2w4DAAAACBtTp07VpEmTdOutt+rIkSN69NFH1adPH82bN0/3339/pa/L4g8AAAAE1bp167Rz507/WLZsmSTpyiuvlCSNGzdO//znP7Vo0SKtWLFCO3bs0BVXXFHu6z/xxBP605/+pBUrVmjPnj3Ky8sLGAAAAAAqJj09XQ888ICOHDliOxQAAADAszZt2iRJOuecc7Rx40YdOXJEHTt21FtvvaXExEStXr1a3bt3r/T12fYFAADABYwkI0st9Sp4fpMmTQIeT58+XaeccorOP/985ebm6tlnn9WCBQt04YUXSpLmzZundu3a6f3339eZZ55Z5vUTEhKUl5fnf70/TmPk8/lUVFRUwYgBAACA8LZu3TplZmbqrbfeUseOHVW3bt2A51999VVLkQEAACBUGOOTcdj2pSo6deqknj176qabbtI111yjp59+OqjXZ/EHAAAAyuWXXTViYmIUExNzwtcUFBTohRde0Pjx4+Xz+bR+/XoVFhYqLS3Nf07btm110kknafXq1eVa/DFkyBBFRUVpwYIFSkpK8u+HCAAAAKByEhISNHjwYNthAAAAAJ62YsUKzZs3T7///e81btw4/eY3v9GIESN07rnnBuX6LP4AAABwAWN81lY3H5s3JSUl4HhGRoamTJlywte+9tprysnJ0Q033CBJys7OVnR0tBISEgLOS0pKUnZ2drni2bRpkz788EO1adOmXOcDAAAAko6107M3dwibN2+e7RAAAAAQ4kLhM2q3O/fcc3Xuuefq8ccf19/+9jfNnz9f559/vk499VSNGDFCw4YNU3JycqWvHxHEWAEAAOBh27ZtU25urn9MnDixzNc8++yz6t+/v5o1axa0OHr06KFt27YF7XoAAABAuHIcRw888IDOPvts9ezZU3/84x916NAh22EBAAAAnla3bl0NHz5cK1as0BdffKErr7xSs2fP1kknnaTLLrus0tel8wcAAADKJS4uTnFxceU+/7vvvtPy5csD9gdPTk5WQUGBcnJyArp/7Nq1q9wrmkePHq0xY8ZowoQJ6tixo6KiogKe79SpU7ljBAAAAMLZ/fffrylTpigtLU21a9fWo48+qt27d+u5556zHRoAAAAQFk499VRNmjRJLVu21MSJE/Wvf/2r0tdi8QcAAIALGPlkZKmlXiXnnTdvnhITEzVw4ED/se7duysqKkqZmZn+PcU3b96srVu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       "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
+       "<Figure size 2200x700 with 6 Axes>"
       ]
      },
      "metadata": {},
@@ -515,16 +610,63 @@
     }
    ],
    "source": [
-    "Pipe.plot_pipe(net, 0, pipe_results)"
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(22, 7))\n",
+    "cmap = plt.cm.coolwarm\n",
+    "\n",
+    "# Helper to plot a colored pipe property\n",
+    "def plot_pipe_property(ax, property_name, label, vmin=None, vmax=None, cmap=plt.cm.viridis):\n",
+    "    for idx, pipe in net.pipe.iterrows():\n",
+    "        from_junc = pipe['from_junction']\n",
+    "        to_junc = pipe['to_junction']\n",
+    "        x = [net.junction_geodata.at[from_junc, 'x'], net.junction_geodata.at[to_junc, 'x']]\n",
+    "        y = [net.junction_geodata.at[from_junc, 'y'], net.junction_geodata.at[to_junc, 'y']]\n",
+    "        value = net.res_pipe.at[idx, property_name]\n",
+    "        lc = ax.plot(x, y, color=cmap((value - vmin) / (vmax - vmin)), linewidth=6)\n",
+    "    ax.set_title(label, fontsize=13)\n",
+    "    ax.set_xlabel('x [m]')\n",
+    "    ax.set_ylabel('y [m]')\n",
+    "    ax.set_aspect('equal')\n",
+    "    ax.grid(True, alpha=0.3)\n",
+    "    sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))\n",
+    "    sm.set_array([])\n",
+    "    plt.colorbar(sm, ax=ax, orientation='vertical', label=label)\n",
+    "\n",
+    "# Temperature plot (in °C)\n",
+    "tmin = net.res_pipe[['t_from_k', 't_to_k']].min().min() - 273.15\n",
+    "tmax = net.res_pipe[['t_from_k', 't_to_k']].max().max() - 273.15\n",
+    "net.res_pipe['t_avg_c'] = (net.res_pipe['t_from_k'] + net.res_pipe['t_to_k']) / 2 - 273.15\n",
+    "plot_pipe_property(axes[0], 't_avg_c', 'Temperature [°C]', vmin=tmin, vmax=tmax, cmap=plt.cm.coolwarm)\n",
+    "\n",
+    "# Pressure plot (in bar)\n",
+    "pmin = net.res_pipe[['p_from_bar', 'p_to_bar']].min().min()\n",
+    "pmax = net.res_pipe[['p_from_bar', 'p_to_bar']].max().max()\n",
+    "net.res_pipe['p_avg_bar'] = (net.res_pipe['p_from_bar'] + net.res_pipe['p_to_bar']) / 2\n",
+    "plot_pipe_property(axes[1], 'p_avg_bar', 'Pressure [bar]', vmin=pmin, vmax=pmax, cmap=plt.cm.viridis)\n",
+    "\n",
+    "# Velocity plot (in m/s)\n",
+    "vmin = net.res_pipe['v_mean_m_per_s'].min()\n",
+    "vmax = net.res_pipe['v_mean_m_per_s'].max()\n",
+    "plot_pipe_property(axes[2], 'v_mean_m_per_s', 'Velocity [m/s]', vmin=vmin, vmax=vmax, cmap=plt.cm.plasma)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can see that the pressure level falls due to friction. As the fluid is incompressible, the\n",
-    "velocity remains constant over the pipe length. Because the temperature level at the pipe entry is\n",
-    "higher than the ambient temperature, the temperature level decreases."
+    "The three plots above provide a detailed visual summary of the pipe results in the district heating network:\n",
+    "\n",
+    "- Temperature Plot (left): The color of each pipe represents its average temperature in degrees Celsius. You can see the temperature drop along the flow direction, especially after heat consumers, where heat is extracted from the network. Pipes with higher temperatures are shown in red, while cooler pipes appear blue.\n",
+    "\n",
+    "- Pressure Plot (center): This plot shows the average pressure in each pipe in bar. The pressure decreases along the flow path due to friction losses and heat extraction. The effect of the pumps is visible as a sudden increase in pressure at their locations. The color gradient helps to quickly identify regions of high and low pressure.\n",
+    "\n",
+    "- Velocity Plot (right): Here, the color indicates the mean fluid velocity in each pipe (in m/s). Higher velocities are typically found in pipes supplying multiple consumers or near the pumps, while lower velocities occur in return lines or branches with less flow. This plot helps to identify flow distribution and potential bottlenecks in the network.\n",
+    "\n",
+    "Together, these visualizations make it easy to interpret the hydraulic and thermal state of the network at a glance, highlighting the impact of pumps and consumers on the system."
    ]
   }
  ],
diff --git a/tutorials/district_heating/time_series_in_a_circular_district_heating_grid.ipynb b/tutorials/district_heating/time_series_in_a_circular_district_heating_grid.ipynb
index dae87fea7..ed39fa094 100644
--- a/tutorials/district_heating/time_series_in_a_circular_district_heating_grid.ipynb
+++ b/tutorials/district_heating/time_series_in_a_circular_district_heating_grid.ipynb
@@ -4,14 +4,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Time series in a circular district heating grid\n",
+    "# Time Series in a Circular District Heating Grid\n",
     "\n",
-    "Based on the simple circular district heating grid."
+    "This example demonstrates how to perform a time series simulation for a district heating network using pandapipes. It builds upon the simple circular district heating grid described in the notebook 'circular_flow_in_a_district_heating_grid.ipynb'.\n",
+    "\n",
+    "In real-world district heating systems, the heat demand and operating conditions change over time. Time series simulations allow you to analyze the dynamic behavior of the network, such as how temperature, pressure, and flow rates evolve in response to varying consumer demands or external conditions.\n",
+    "\n",
+    "In this notebook, you will learn:\n",
+    "\n",
+    "- How to set up a district heating network for time series analysis with pandapipes.\n",
+    "- How to define time-dependent input data (e.g., heat demand profiles).\n",
+    "- How to run a time series simulation and log relevant results.\n",
+    "- How to visualize and interpret the results over multiple time steps.\n",
+    "\n",
+    "This approach is essential for designing and optimizing district heating systems, ensuring reliable operation under varying load conditions, and evaluating control strategies.\n",
+    "\n",
+    "<img src=\"../pics/district_heating/time_series_district_heating_net_text.png\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Network Setup\n",
+    "\n",
+    "The network used in this example is based on a circular district heating grid with several branches and consumers. The setup includes:\n",
+    "\n",
+    "- A main pump that supplies hot water to the network.\n",
+    "- Multiple junctions representing supply and return points for each consumer branch.\n",
+    "- Pipes connecting the junctions, forming a closed loop with additional branches for each consumer.\n",
+    "- Several heat consumers, each with its own heat demand and return temperature profile.\n",
+    "\n",
+    "This structure allows for the simulation of realistic operating scenarios, where heat demand and return temperatures can vary over time for each consumer. The network is initialized with water as the working fluid."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 165,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,14 +51,9 @@
     "net = pp.create_empty_network(fluid =\"water\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 166,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -48,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 167,
    "metadata": {},
    "outputs": [
     {
@@ -57,7 +81,7 @@
        "2"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 167,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -99,50 +123,212 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n",
-    "We now run a pipe flow.\n"
+    "## Plotting Net"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 168,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "pp.pipeflow(net, mode='bidirectional', iter=100, alpha=0.2)"
+    "pp.plotting.simple_plot(net)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "mode must be bidirectional, iter=100 and alpha=0.2 might be needed"
+    "## Pipeflow Calculation\n",
+    "\n",
+    "To analyze the dynamic behavior of the network, we perform a pipe flow calculation for each time step. The `pipeflow` function is called in bidirectional mode, which is necessary for networks with heat exchange, as it enables the simultaneous calculation of hydraulic and thermal behavior.\n",
+    "\n",
+    "The number of iterations (`iter=100`) and the relaxation parameter (`alpha=0.2`) can be adjusted to ensure convergence, especially for larger or more complex networks. These settings help the solver handle changing conditions across the time series."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pp.pipeflow(net, mode='bidirectional', iter=100, alpha=0.2)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Time series, need for controllers"
+    "## Time Series Simulation\n",
+    "\n",
+    "In this section, we set up and run a time series simulation for the district heating network. The simulation considers time-dependent changes in both the supply temperature and the heat demand of each consumer.\n",
+    "\n",
+    "- The supply temperature at the pump is varied over time using a predefined temperature profile, representing realistic operational changes (e.g., day/night or seasonal effects).\n",
+    "- The heat demand for each consumer is defined as a time-dependent profile, allowing for different demand patterns at each time step. This enables the simulation of varying consumer behavior or external influences.\n",
+    "- These time-dependent values are assigned to the network using `ConstControl` controllers from pandapower.timeseries, which update the relevant parameters (supply temperature and consumer heat extraction) at each time step based on the provided profiles.\n",
+    "\n",
+    "The simulation is executed using the `run_time_series` function, which solves the network for each time step and logs the results using an `OutputWriter`. This approach enables the analysis of how the network responds to dynamic operating conditions, such as fluctuating supply temperatures and varying consumer demands.\n",
+    "\n",
+    "By combining time-dependent input data with automated controllers, you can analyze the behavior of the district heating grid and evaluate the impact of different control strategies or demand scenarios."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 170,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time dependant heat demand profile for the three consumers: \n",
+      " [[100000.  64000. 144000.]\n",
+      " [ 90000.  56000. 132000.]\n",
+      " [ 80000.  48000. 120000.]\n",
+      " [ 70000.  40000. 108000.]\n",
+      " [ 60000.  32000.  96000.]\n",
+      " [ 70000.  40000. 108000.]\n",
+      " [ 80000.  48000. 120000.]\n",
+      " [ 90000.  56000. 132000.]\n",
+      " [100000.  64000. 144000.]\n",
+      " [100000.  64000. 144000.]]\n"
+     ]
+    }
+   ],
    "source": [
     "start = 0\n",
     "end = 10 # 8760 hours in a year\n",
     "\n",
     "# time steps with start and end\n",
     "time_steps = np.arange(start, end, 1)\t# time steps in hours\n",
-    "\n"
+    "\n",
+    "supply_temperature_profile_k = np.array([90, 85, 80, 75, 75, 75, 75, 80, 85, 95]) + 273.15\n",
+    "\n",
+    "# Define percentage profiles for each consumer (shape: [n_timesteps, n_consumers])\n",
+    "heat_demand_percentages = np.array([\n",
+    "    [1.0, 0.8, 1.2],  # t=0\n",
+    "    [0.9, 0.7, 1.1],  # t=1\n",
+    "    [0.8, 0.6, 1.0],  # t=2\n",
+    "    [0.7, 0.5, 0.9],  # t=3\n",
+    "    [0.6, 0.4, 0.8],  # t=4\n",
+    "    [0.7, 0.5, 0.9],  # t=5\n",
+    "    [0.8, 0.6, 1.0],  # t=6\n",
+    "    [0.9, 0.7, 1.1],  # t=7\n",
+    "    [1.0, 0.8, 1.2],  # t=8\n",
+    "    [1.0, 0.8, 1.2],  # t=9\n",
+    "])\n",
+    "\n",
+    "# Calculate time-dependent qext for each consumer\n",
+    "qext_profile = qext_w * heat_demand_percentages  # shape: (n_timesteps, n_consumers)\n",
+    "print(f\"Time dependant heat demand profile for the three consumers: \\n {qext_profile}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data Setup and Controller Definition\n",
+    "\n",
+    "To enable time-dependent simulation, we need to provide the network with profiles for all parameters that change over time. In this example, both the supply temperature at the pump and the heat demand for each consumer are defined as time series.\n",
+    "\n",
+    "- **Heat demand profiles:** For each consumer, a time-dependent heat extraction profile (`qext_w`) is created. These profiles are stored in a pandas DataFrame and provided to the controllers using `DFData`.\n",
+    "- **Supply temperature profile:** The supply temperature at the pump is also defined as a time series and provided to the controller in the same way.\n",
+    "\n",
+    "The `ConstControl` class from `pandapower.control` is used to assign these profiles to the corresponding network elements. Each controller updates the specified parameter (e.g., `qext_w` for a heat consumer, `t_flow_k` for the pump) at every time step according to the profile.\n",
+    "\n",
+    "This setup allows the simulation to automatically adjust the network's operating conditions at each time step, enabling the analysis of dynamic scenarios and the impact of different demand or supply strategies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "This ConstControl has the following parameters: \n",
+       "\n",
+       "index:             3\n",
+       "json_excludes:     ['self', '__class__']"
+      ]
+     },
+     "execution_count": 171,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from pandapower.timeseries import DFData\n",
+    "from pandapower.control.controller.const_control import ConstControl\n",
+    "\n",
+    "# Create a DataFrame for all qext profiles\n",
+    "profile_names = [f'qext_profile_{i}' for i in range(qext_profile.shape[1])]\n",
+    "qext_profile_df = pd.DataFrame(qext_profile, columns=profile_names)\n",
+    "qext_profile_data = DFData(qext_profile_df)\n",
+    "\n",
+    "for i, hc_idx in enumerate(net.heat_consumer.index):\n",
+    "    ConstControl(\n",
+    "        net,\n",
+    "        element='heat_consumer',\n",
+    "        variable='qext_w',\n",
+    "        element_index=hc_idx,\n",
+    "        profile_name=profile_names[i],\n",
+    "        data_source=qext_profile_data\n",
+    "    )\n",
+    "\n",
+    "# Add ConstControl for circ pump pressure temperature (t_flow_k)\n",
+    "supply_temp_profile_df = pd.DataFrame({'supply_temp_profile': supply_temperature_profile_k})\n",
+    "supply_temp_profile_data = DFData(supply_temp_profile_df)\n",
+    "\n",
+    "ConstControl(\n",
+    "    net,\n",
+    "    element='circ_pump_pressure',\n",
+    "    variable='t_flow_k',\n",
+    "    element_index=net.circ_pump_pressure.index[0],\n",
+    "    profile_name='supply_temp_profile',\n",
+    "    data_source=supply_temp_profile_data\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Running the Time Series Simulation\n",
+    "\n",
+    "With all time-dependent profiles and controllers set up, we can now execute the time series simulation. The `OutputWriter` is used to log selected results (such as pressures, temperatures, and heat extraction) at each time step for later analysis and visualization.\n",
+    "\n",
+    "The `run_time_series` function iterates through all defined time steps, automatically updating the network parameters according to the profiles and controllers. This process simulates the dynamic operation of the district heating grid under varying supply and demand conditions.\n",
+    "\n",
+    "After the simulation, the logged results can be extracted and visualized to gain insights into the network's transient behavior and to evaluate the effectiveness of different control strategies or demand scenarios."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 172,
    "metadata": {},
    "outputs": [
     {
@@ -151,7 +337,10 @@
      "text": [
       "c:\\Users\\jonas\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandapower\\timeseries\\output_writer.py:177: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '[0 1 2 3 4 5 6 7 8 9]' has dtype incompatible with bool, please explicitly cast to a compatible dtype first.\n",
       "  self.output[\"Parameters\"].loc[:, \"time_step\"] = self.time_steps\n",
-      "100%|██████████| 10/10 [00:01<00:00,  9.82it/s]\n"
+      "c:\\Users\\jonas\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandapower\\control\\run_control.py:50: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
+      "  level = controller.level.fillna(0).apply(asarray).values\n",
+      "100%|██████████| 10/10 [00:01<00:00,  8.91it/s]\n",
+      "100%|██████████| 10/10 [00:01<00:00,  8.91it/s]\n"
      ]
     }
    ],
@@ -183,238 +372,118 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "With this simple implementation, a time series is calculated with the same input parameters as in the initialization."
+    "## Results Extraction\n",
+    "\n",
+    "After running the time series simulation, the results are stored in the `OutputWriter` object. These results include time-dependent values for pressures, temperatures, mass flows, and heat extraction at various points in the network.\n",
+    "\n",
+    "You can extract and analyze these results to understand how the network responds to changing supply and demand conditions. For example, you might examine the supply temperature at the pump, the mass flow through the main pump, or the heat extraction at each consumer over time.\n",
+    "\n",
+    "The following code demonstrates how to access and print selected results from the simulation."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 173,
+   "metadata": {
+    "vscode": {
+     "languageId": "ruby"
+    }
+   },
    "outputs": [
     {
-     "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>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>t_outlet_k</th>\n",
-       "      <th>mdot_from_kg_per_s</th>\n",
-       "      <th>mdot_to_kg_per_s</th>\n",
-       "      <th>vdot_m3_per_s</th>\n",
-       "      <th>reynolds</th>\n",
-       "      <th>lambda</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.018179</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>3.999995</td>\n",
-       "      <td>358.150000</td>\n",
-       "      <td>358.078414</td>\n",
-       "      <td>358.078414</td>\n",
-       "      <td>0.141064</td>\n",
-       "      <td>-0.141064</td>\n",
-       "      <td>0.000146</td>\n",
-       "      <td>5346.846164</td>\n",
-       "      <td>0.031545</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.018176</td>\n",
-       "      <td>3.999995</td>\n",
-       "      <td>3.999970</td>\n",
-       "      <td>358.078414</td>\n",
-       "      <td>357.609424</td>\n",
-       "      <td>357.721493</td>\n",
-       "      <td>0.141064</td>\n",
-       "      <td>-0.141064</td>\n",
-       "      <td>0.000146</td>\n",
-       "      <td>5333.432050</td>\n",
-       "      <td>0.031575</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>-0.002700</td>\n",
-       "      <td>3.999970</td>\n",
-       "      <td>3.999971</td>\n",
-       "      <td>357.609424</td>\n",
-       "      <td>358.049038</td>\n",
-       "      <td>357.808728</td>\n",
-       "      <td>-0.020960</td>\n",
-       "      <td>0.020960</td>\n",
-       "      <td>-0.000022</td>\n",
-       "      <td>788.015150</td>\n",
-       "      <td>0.100793</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>-0.002650</td>\n",
-       "      <td>2.500022</td>\n",
-       "      <td>2.500035</td>\n",
-       "      <td>332.661759</td>\n",
-       "      <td>328.149999</td>\n",
-       "      <td>326.718797</td>\n",
-       "      <td>-0.020960</td>\n",
-       "      <td>0.020960</td>\n",
-       "      <td>-0.000021</td>\n",
-       "      <td>496.619942</td>\n",
-       "      <td>0.148573</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.017863</td>\n",
-       "      <td>2.500035</td>\n",
-       "      <td>2.500006</td>\n",
-       "      <td>328.149999</td>\n",
-       "      <td>327.934451</td>\n",
-       "      <td>327.934451</td>\n",
-       "      <td>0.141064</td>\n",
-       "      <td>-0.141064</td>\n",
-       "      <td>0.000143</td>\n",
-       "      <td>3530.489571</td>\n",
-       "      <td>0.037703</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0.017862</td>\n",
-       "      <td>2.500006</td>\n",
-       "      <td>2.500000</td>\n",
-       "      <td>327.934451</td>\n",
-       "      <td>327.891464</td>\n",
-       "      <td>327.891464</td>\n",
-       "      <td>0.141064</td>\n",
-       "      <td>-0.141064</td>\n",
-       "      <td>0.000143</td>\n",
-       "      <td>3523.145093</td>\n",
-       "      <td>0.037741</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>-0.064434</td>\n",
-       "      <td>3.999971</td>\n",
-       "      <td>4.000199</td>\n",
-       "      <td>358.049038</td>\n",
-       "      <td>358.150000</td>\n",
-       "      <td>358.129797</td>\n",
-       "      <td>-0.500000</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>-0.000516</td>\n",
-       "      <td>18948.812932</td>\n",
-       "      <td>0.022953</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>-0.063463</td>\n",
-       "      <td>2.499975</td>\n",
-       "      <td>2.500022</td>\n",
-       "      <td>332.648357</td>\n",
-       "      <td>332.661759</td>\n",
-       "      <td>332.659079</td>\n",
-       "      <td>-0.500000</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>-0.000508</td>\n",
-       "      <td>13423.777747</td>\n",
-       "      <td>0.024343</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   v_mean_m_per_s  p_from_bar  p_to_bar    t_from_k      t_to_k  t_outlet_k  \\\n",
-       "0        0.018179    4.000000  3.999995  358.150000  358.078414  358.078414   \n",
-       "1        0.018176    3.999995  3.999970  358.078414  357.609424  357.721493   \n",
-       "2       -0.002700    3.999970  3.999971  357.609424  358.049038  357.808728   \n",
-       "3       -0.002650    2.500022  2.500035  332.661759  328.149999  326.718797   \n",
-       "4        0.017863    2.500035  2.500006  328.149999  327.934451  327.934451   \n",
-       "5        0.017862    2.500006  2.500000  327.934451  327.891464  327.891464   \n",
-       "6       -0.064434    3.999971  4.000199  358.049038  358.150000  358.129797   \n",
-       "7       -0.063463    2.499975  2.500022  332.648357  332.661759  332.659079   \n",
-       "\n",
-       "   mdot_from_kg_per_s  mdot_to_kg_per_s  vdot_m3_per_s      reynolds    lambda  \n",
-       "0            0.141064         -0.141064       0.000146   5346.846164  0.031545  \n",
-       "1            0.141064         -0.141064       0.000146   5333.432050  0.031575  \n",
-       "2           -0.020960          0.020960      -0.000022    788.015150  0.100793  \n",
-       "3           -0.020960          0.020960      -0.000021    496.619942  0.148573  \n",
-       "4            0.141064         -0.141064       0.000143   3530.489571  0.037703  \n",
-       "5            0.141064         -0.141064       0.000143   3523.145093  0.037741  \n",
-       "6           -0.500000          0.500000      -0.000516  18948.812932  0.022953  \n",
-       "7           -0.500000          0.500000      -0.000508  13423.777747  0.024343  "
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Supply temperature at 'Pump Supply' over time:\n",
+      "0    363.15\n",
+      "1    358.15\n",
+      "2    353.15\n",
+      "3    348.15\n",
+      "4    348.15\n",
+      "5    348.15\n",
+      "6    348.15\n",
+      "7    353.15\n",
+      "8    358.15\n",
+      "9    368.15\n",
+      "Name: 0, dtype: float64\n",
+      "\n",
+      "Mass flow at main pump over time:\n",
+      "0    2.752428\n",
+      "1    3.060334\n",
+      "2    3.559999\n",
+      "3    4.520676\n",
+      "4    3.981185\n",
+      "5    4.520676\n",
+      "6    5.060498\n",
+      "7    3.945994\n",
+      "8    3.362175\n",
+      "9    2.332136\n",
+      "Name: 0, dtype: float64\n"
+     ]
     }
    ],
    "source": [
-    "net.res_pipe"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The command above shows the results for the pipe components. The temperatures of the adjacent\n",
-    "junctions are displayed. Due to heat losses, the temperatures at the to-nodes is lower than the\n",
-    "temperatures at the from-nodes. Note also that the junctions are not equal to the internal nodes,\n",
-    "introduced by the pipe sections we defined. To display the temperatures at the internal nodes, we\n",
-    "can retrieve the internal node values with the following commands:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pandapipes.component_models import Pipe\n",
-    "pipe_results = Pipe.get_internal_results(net, [0])"
+    "# Extract time series results from OutputWriter\n",
+    "results = ow.output\n",
+    "\n",
+    "# Supply temperature at \"Pump Supply\"\n",
+    "pump_supply_idx = net.junction[net.junction['name'] == \"Pump Supply\"].index[0]\n",
+    "supply_temp = results['res_junction.t_k'][pump_supply_idx]\n",
+    "\n",
+    "# Mass flow at main pump (first and only pump)\n",
+    "mass_flow_pump = results['res_circ_pump_pressure.mdot_from_kg_per_s'][pump_supply_idx]\n",
+    "\n",
+    "print(\"Supply temperature at 'Pump Supply' over time:\")\n",
+    "print(supply_temp)\n",
+    "print(\"\\nMass flow at main pump over time:\")\n",
+    "print(mass_flow_pump)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The parameters of the get_internal_results function correspond to the net and the pipes we want to\n",
-    "evaluate. In this case, only the results of pipe zero are retrieved. The returned value stored in\n",
-    "pipe_results is a dictionary, containing fields for the pressure, the velocity and the temperature.\n",
-    "The dictionary can either be used for own evaluations now or it can be used to plot the results over\n",
-    "the pipe length:\n"
+    "## Visualization of Time Series Results\n",
+    "\n",
+    "To better understand the dynamic behavior of the district heating network, we visualize key results from the time series simulation. The following plots show:\n",
+    "\n",
+    "- Temperatures at important junctions (e.g., pump supply, pump return, and consumer returns) over time.\n",
+    "- Pressures at selected nodes to observe hydraulic changes during the simulation.\n",
+    "- Heat extraction (`qext`) for each consumer, illustrating how demand profiles affect network operation.\n",
+    "\n",
+    "These visualizations help identify trends, validate the simulation, and provide insights into the performance and stability of the network under varying conditions."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 174,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -422,16 +491,77 @@
     }
    ],
    "source": [
-    "Pipe.plot_pipe(net, 0, pipe_results)"
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "# Load results from OutputWriter\n",
+    "results = ow.output\n",
+    "\n",
+    "# The time axis corresponds to the DataFrame index (time steps)\n",
+    "time_steps = results['res_junction.t_k'].index\n",
+    "\n",
+    "# --- Temperatures at selected nodes (e.g. Pump Supply, Consumer Returns) ---\n",
+    "pump_supply_idx = net.junction[net.junction['name'] == \"Pump Supply\"].index[0]\n",
+    "pump_return_idx = net.junction[net.junction['name'] == \"Pump Return\"].index[0]\n",
+    "consumer_return_idxs = [\n",
+    "    net.junction[net.junction['name'] == \"Main Split Return\"].index[0],\n",
+    "    net.junction[net.junction['name'] == \"Consumer B Return\"].index[0],\n",
+    "    net.junction[net.junction['name'] == \"Consumer C Return\"].index[0],\n",
+    "]\n",
+    "\n",
+    "plt.figure(figsize=(10,5))\n",
+    "plt.plot(time_steps, results['res_junction.t_k'][pump_supply_idx] - 273.15, label='Pump Supply')\n",
+    "plt.plot(time_steps, results['res_junction.t_k'][pump_return_idx] - 273.15, label='Pump Return')\n",
+    "for i, idx in enumerate(consumer_return_idxs):\n",
+    "    plt.plot(time_steps, results['res_junction.t_k'][idx] - 273.15, label=f'Consumer {chr(65+i)} Return')\n",
+    "plt.xlabel('Time step')\n",
+    "plt.ylabel('Temperature [°C]')\n",
+    "plt.title('Temperatures at Key Junctions Over Time')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# --- Pressures at selected nodes (e.g. Pump Supply, Consumer Returns) ---\n",
+    "plt.figure(figsize=(10,5))\n",
+    "plt.plot(time_steps, results['res_junction.p_bar'][pump_supply_idx], label='Pump Supply')\n",
+    "for i, idx in enumerate(consumer_return_idxs):\n",
+    "    plt.plot(time_steps, results['res_junction.p_bar'][idx], label=f'Consumer {chr(65+i)} Return')\n",
+    "plt.xlabel('Time step')\n",
+    "plt.ylabel('Pressure [bar]')\n",
+    "plt.title('Pressures at Key Junctions Over Time')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# --- qext (Heat Extraction) for all Consumers ---\n",
+    "qext_df = results['heat_consumer.qext_w']\n",
+    "plt.figure(figsize=(10,5))\n",
+    "for i, col in enumerate(qext_df.columns):\n",
+    "    plt.plot(qext_df.index, qext_df[col], label=f'Consumer {chr(65+i)}')\n",
+    "plt.xlabel('Time step')\n",
+    "plt.ylabel('qext [W]')\n",
+    "plt.title('Heat Extraction (qext) per Consumer Over Time')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can see that the pressure level falls due to friction. As the fluid is incompressible, the\n",
-    "velocity remains constant over the pipe length. Because the temperature level at the pipe entry is\n",
-    "higher than the ambient temperature, the temperature level decreases."
+    "### Summary and Interpretation\n",
+    "\n",
+    "The time series simulation and visualizations provide valuable insights into the transient behavior of the district heating grid. By analyzing the results, you can:\n",
+    "\n",
+    "- Observe how supply temperature and consumer demands influence network temperatures, pressures, and flows over time.\n",
+    "- Identify periods of high or low demand and assess the network's ability to maintain stable operation.\n",
+    "- Evaluate the effectiveness of control strategies and the impact of different demand scenarios.\n",
+    "\n",
+    "These analyses are essential for optimizing district heating systems, improving energy efficiency, and ensuring reliable service under real-world operating conditions. You can further extend this approach by implementing advanced controllers, testing alternative demand profiles, or integrating additional components into the network model."
    ]
   }
  ],
diff --git a/tutorials/pics/district_heating/multiple_pumps_district_heating_net_raw.png b/tutorials/pics/district_heating/multiple_pumps_district_heating_net_raw.png
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literal 0
HcmV?d00001


From 1dd5564d5ab935b625b49ca24dc0de14992a6f78 Mon Sep 17 00:00:00 2001
From: Jonas Pfeiffer <jonaspfeiffer123@gmail.com>
Date: Tue, 4 Nov 2025 10:43:58 +0100
Subject: [PATCH 3/3] Update and retest district heating example notebooks

Retest all district heating example notebooks with latest pandapipes dev version:

- Verify compatibility of circular_flow_in_a_district_heating_grid.ipynb
- Verify compatibility of time_series_in_a_circular_district_heating_grid.ipynb
- Verify compatibility of BadPointPressureLiftController.ipynb

Updates:
- Increase alpha relaxation parameter from 0.2 to 0.5 in time series simulation as suggested in comments
- Update notebook metadata to reflect current execution environment
- Ensure all examples run successfully with latest dev version

All notebooks have been executed and validated to work correctly with the current development version of pandapipes.
---
 ...ular_flow_in_a_district_heating_grid.ipynb | 246 +-----------
 ..._in_a_circular_district_heating_grid.ipynb | 353 +-----------------
 ..._in_a_circular_district_heating_grid.ipynb | 178 +--------
 3 files changed, 51 insertions(+), 726 deletions(-)

diff --git a/tutorials/district_heating/circular_flow_in_a_district_heating_grid.ipynb b/tutorials/district_heating/circular_flow_in_a_district_heating_grid.ipynb
index cfc0c4887..f03f37c3a 100644
--- a/tutorials/district_heating/circular_flow_in_a_district_heating_grid.ipynb
+++ b/tutorials/district_heating/circular_flow_in_a_district_heating_grid.ipynb
@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -71,20 +71,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 106,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "pp.create_circ_pump_const_pressure(net, return_junction=j0, flow_junction=j1, p_flow_bar=4, plift_bar=1.5, t_flow_k=273.15+70,\n",
     "                                    type=\"auto\", name=\"const_pressure_pump\", index=None, in_service=True)"
@@ -108,20 +97,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 107,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "pp.create_heat_consumer(net, from_junction=j2, to_junction=j3, qext_w=10000, controlled_mdot_kg_per_s=None,\n",
     "                         deltat_k=None, treturn_k=50, name=None, index=None, in_service=True, type=\"heat_consumer\")\n",
@@ -144,20 +122,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 108,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "pp.create_pipe(net, from_junction=j1, to_junction=j2, std_type=\"110/202 PLUS\", length_km=1, k_mm=0.1, loss_coefficient=0,\n",
     "               sections=5, text_k=283, qext_w=0., name=\"pipe_0_1\", index=None, geodata=None, in_service=True, type=\"pipe\")\n",
@@ -177,30 +144,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 109,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "pp.plotting.simple_plot(net)"
    ]
@@ -216,7 +162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -243,72 +189,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "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>p_bar</th>\n",
-       "      <th>t_k</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2.500000</td>\n",
-       "      <td>225.464713</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>343.150000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3.999974</td>\n",
-       "      <td>297.097587</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2.500303</td>\n",
-       "      <td>50.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      p_bar         t_k\n",
-       "0  2.500000  225.464713\n",
-       "1  4.000000  343.150000\n",
-       "2  3.999974  297.097587\n",
-       "3  2.500303   50.000000"
-      ]
-     },
-     "execution_count": 111,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "net.res_junction"
    ]
@@ -324,91 +207,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "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>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>t_outlet_k</th>\n",
-       "      <th>mdot_from_kg_per_s</th>\n",
-       "      <th>mdot_to_kg_per_s</th>\n",
-       "      <th>vdot_m3_per_s</th>\n",
-       "      <th>reynolds</th>\n",
-       "      <th>lambda</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.001181</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>3.999974</td>\n",
-       "      <td>343.15</td>\n",
-       "      <td>297.097587</td>\n",
-       "      <td>297.097587</td>\n",
-       "      <td>0.009375</td>\n",
-       "      <td>-0.009375</td>\n",
-       "      <td>0.000009</td>\n",
-       "      <td>189.304289</td>\n",
-       "      <td>0.376229</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.001173</td>\n",
-       "      <td>2.500303</td>\n",
-       "      <td>2.500000</td>\n",
-       "      <td>50.00</td>\n",
-       "      <td>225.464713</td>\n",
-       "      <td>225.464713</td>\n",
-       "      <td>0.009375</td>\n",
-       "      <td>-0.009375</td>\n",
-       "      <td>0.000009</td>\n",
-       "      <td>16.052576</td>\n",
-       "      <td>4.459393</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   v_mean_m_per_s  p_from_bar  p_to_bar  t_from_k      t_to_k  t_outlet_k  \\\n",
-       "0        0.001181    4.000000  3.999974    343.15  297.097587  297.097587   \n",
-       "1        0.001173    2.500303  2.500000     50.00  225.464713  225.464713   \n",
-       "\n",
-       "   mdot_from_kg_per_s  mdot_to_kg_per_s  vdot_m3_per_s    reynolds    lambda  \n",
-       "0            0.009375         -0.009375       0.000009  189.304289  0.376229  \n",
-       "1            0.009375         -0.009375       0.000009   16.052576  4.459393  "
-      ]
-     },
-     "execution_count": 112,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "net.res_pipe"
    ]
@@ -433,7 +234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -454,20 +255,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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TGDJkCHQ6HQoKCjB27Nhm+8hkspt63L68vByXL1/GnDlzDMv+OI8MAPj7+zf7VFpz9Ho96urqoNfrrxuMJBKJ4RbhN998A19fXwwdOtSoj1QqNdTzzTffIDo6uknIs7KyMjzSvXHjRkydOrXJk0zW1tbw9vZGXV0d/vvf/2LGjBmtOhairmLlypWG1zPPPGOWGm699VacOXMGAAxjsYioeQxG7VReXo7U1FTD+/T0dCQkJMDZ2RmhoaGYPXs25s6di3fffRdDhgxBYWEh9u7di4EDB2LKlClt3t8zzzyDO++8E/7+/sjNzcWyZcsglUpb/NLOG/nqq69gZWWFAQMGQC6X4+TJk1iyZAlmzpxpmHBuy5YtWLJkidEkcitXrsTtt98OCwsLfPfdd3jrrbfw7bffGoJUUVERNm/ejFtvvRXV1dVYu3YtNm3ahAMHDhi2kZycjOPHjyMqKgolJSV47733cP78eaNBsceOHcOVK1cwePBgXLlyBS+//DL0en2Lj00TdUW/n4DS1I/2X8+nn34KrVYLANe9Ck1E4Bij9tq3b1+z44Li4uKEEELU1taKl156SQQEBAgrKyvh5eUl7r77bnH27Nl27W/mzJnCy8tLyGQy4e3tLWbOnClSU1Ovuw6u87j+xo0bxdChQ4W9vb1h/NGbb74pqqqqDH0ax0H83vjx44VSqRTW1tYiKipK7Nixw6i9sLBQjBw5UtjZ2QlbW1sxYcIEw9iiRhcuXBCDBw8WNjY2QqFQiGnTpolLly4Z9dm/f7/o16+fkMvlwsXFRcyZM0dcuXLlRqeJiIjopnCMEREREVEDzmNERERE1IDBiIiIiKgBB1+3kV6vR25uLhwcHFr8LiYiIiLqWoQQ0Gq1UKlUTZ6A/j0GozbKzc2Fr6+vucsgIiKidsjOzjZMFdOcHhWMfv31V6xcuRLx8fHIy8szmnQRuJYWly1bhs8//xylpaUYPXo01qxZgz59+rR6H41fv5GdnQ2FQtHRh0BEREQmoNFo4Ovre8Ov0epRwaiiogKDBg3CQw89hHvuuadJ+4oVK/DBBx9g/fr1CAwMxNKlSxEbG4sLFy40+cLSljTePlMoFAxGRERE3cyNhsH0qGA0efJkw7dS/5EQAqtWrcKLL76IadOmAQC++OILeHh44Pvvv8f999/fmaU2cXbfZlTlp8BWFQ63wAh4eAdBcp17oERERNTxelQwup709HSo1WrExMQYlimVSkRFReHIkSMtBqOamhrU1NQY3ms0GpPUV3v6G0RpfgYaJpmuFHLkWvqg1DYAdU4hkHmEwtGvP1RBEbCxu/5lQCIiImqfXhOM1Go1AMDDw8NouYeHh6GtOcuXL8crr7xi0toAQKcahtP1lXCpzoSXTg1bSQ1CdJcB7WVAuxfIAnAC0AsJ8iSuKLT2R6UiCBLXPrDz7gfPwIFw8fTlVSYiIqKb0GuCUXstWbIEixYtMrxvHLzV0aLuXwJgCQCgrrYGWRkXcTXjPGrUSbAoToWiPB1e9VlQSirghUJ4VRcC1SeBAgAXrm2jXNgg18oXGlt/1DmHQO7ZF05+EfAKDIe1jV2H10xERNTT9Jpg5OnpCQDIz8+Hl5eXYXl+fj4GDx7c4npyuRxyudzU5RmxksnhFzoYfqGDjZYLvR7FRXlQp51D+ZWL0BckwVqTDtfqTHjp1bCXVCG0PhnQJAOaPUAGgKOATkiQY+GBq9Z+qFIEQ+LaBw4+4fAIGgBnNxWvMhERETXoNcEoMDAQnp6e2Lt3ryEIaTQaHDt2DAsXLjRvca0ksbCAs7s3nN29Adxu1FZTXYmc9IsozjqPanUSLItToaxIh1ddNhwkVfARavhUqYGq40A+gMRr62lghzxLX2jsA1HvFAxrr35w9guHV2A4ZPLWPalHRETUU/SoYFReXo7U1FTD+/T0dCQkJMDZ2Rl+fn546qmn8Prrr6NPnz6Gx/VVKpXRXEfdldzaFv79IuHfL9JoudDrUVSQg/zL51CeexGiMBk2mjS41WTBU18AhaQCivpLQOkloBRAOoDDQL2wQLaFJ67a+KNaEQQLt1AofMLhGTwQjq6e5jhEIiIik5MIIYS5i+go+/fvx/jx45ssj4uLw7p16wwTPH722WcoLS3FmDFj8PHHHyM0NLTV+9BoNFAqlSgrK+v28xhVV5YjLz0RxZkXUJt/CVYlqVBWZMC7Phu2kpoW1yuBA9RWvtDaB0LvHAJrr35w8Q+HV0A/WFrJOvEIiIiIWqe1n989Khh1hp4UjFoi9HoU5mWiIO0sKnIvAUXJsNWkwa0mG54obHG9WiFFnlSFYms/VDsGw9I9DA4+/eAVPAhKJ9dOPAIiIiJjDEYm0huC0fVUlpch9/J5lOZcQJ06CbLSy3CszICqPgc2ktoW1yuCIwpk164yCZcQ2Hj1g2vAAHj69YHUskfd0SUioi6IwchEenswaolep0PBlTQUpp9DRe5FSK6mwk6bBveaLLijuMX1aoQVcqUqlNj6o0YZDCuPMCh8wuEVPAAOSudOPAIiIurJGIxMhMGo7co1Jci7fA5l2RdQV5AEeWkqnCqzoNJdgVxS1+J6BXBGocwX5Q6BEK6hsPXqC7fA/vDwCYGFVNqJR0BERN0dg5GJMBh1HF19PdRZKSjKPI+q3IuQFKfCXpsGj9psuKK0xfWqhAy5lt4otQ1AreO1q0yOvhFQBfeHrb2y8w6AiIi6DQYjE2Ew6hxlJUVQXz4LTc4F1Bckw7rsMpyqMqHS5UIm0bW4nhquKJT7otIhCHALha1XP7gH9Ye7KpATWRIR9WIMRibCYGRe9XW1yMtMwtXMRFTnXYTF1RQ4lGfAsy4bTmj5C34NX8prF4g6pxBYew+AR+hwePn1YWAiIuoFGIxMhMGo6yotUkOddg7anAuoL0yGdVkaXKsy4KVXw1Kib3YdDWyRLQuGVtkXFqqBcA6KhG/YEMitbTu5eiIiMiUGIxNhMOp+amuqkZdxCcWZDV+XcjUJzuUp8K3PbPa2XJ2QIlvqi6sOodC594e9/xD49B3BGb+JiLoxBiMTYTDqOWprqpGdnIDiyyehyzsHh5KL8KlNhRIVzfZXwxVqmxBUuYRD7jPo2q04/758Qo6IqBtgMDIRBqOeTej1yM+5DHXSCVTlnIG8KBEelcnwFvnN9q8Q1siSBUGj7AuJ10A4BUXCN2worG3tO7lyIiK6HgYjE2Ew6p00pVeRc+kEtBmnIck/BydNEvzqM5udh0knJMiW+qDIPgz1bhGw8x8CVdhwuHj4mKFyIiICGIxMhsGIGtXX1SIn9SyKUk6iPvcs7EouwKfmcotPxxXCCbnWIah0DofMZxDcQiLhHdSfX4lCRNQJGIxMhMGIrkfo9ShSZyH30nFUZSVAVnQebhUp8NbnwULS9K9apZAj2yoQpcq+gEd/KIOGwq/vME5USUTUwRiMTITBiNqjQluK7EsnUZZ+ClCfg5PmEnzrMpr94l29kCBHqkKhXShqXcNh6zcE3mEj4OLpyzmXiIjaicHIRBiMqKPo6uuRc/k8ClNPoC7nLOxKLkJVndLi16FchRK58mBUOPWDpfcguIUMg3fIAFhayTq3cCKibojByEQYjMjUrt2KO4GKrARYFSbCrSIZProcSJu5FVctrJBtFYAShzAIzwFQBgyFT7/hsFc4maFyIqKui8HIRBiMyByqKrTITopHadopCPU5KMsuwa/2MmwlNc32z5F4osA2FDWu4bDxGwLP0GHw8A7irTgi6rUYjEyEwYi6Cr1OhyvpF1CYchI1OWdgW3wBXlUpcEdxs/1LYY8ceQjKHftBqhoIl+BI+IYOhpVM3smVExF1PgYjE2Ewoq6upDAPVy4dR3nmaVgWnIdLeTJ8ddnNfl9crbBElqU/ih3CoPfoDwf/IfDpNwJKJ1czVE5EZDoMRibCYETdUXVVBXKST6Pkcjz0eWfhUHoJvrWX4SCparZ/rsQd+TZ9UO0aAevGrz/x68NbcUTUbTEYmQiDEfUUQq9HXmYy1MknUJOTAOurF+BZmQIvFDbbXwNbZMuCoVX2hYVqIJyDIuEbNgRya9tOrpyIqO0YjEyEwYh6urLiQuRcPA5t5ilI88/DuTwZvvWZkEl0TfrWCSmypb646hAKnXt/2PsPgU/fEXB09TRD5URELWMwMhEGI+qNamuqkZ2cgOLLJ6HLOweHkovwqU2FEhXN9s+HC/Js+qDKbSDsg6PhN/AWjlsiIrNiMDIRBiOia4Rej/ycy1AnnUBVzhnIixLhUZkMb5HfbP9MC1/kKwYAPsPh1m80/MIi+T1xRNRpGIxMhMGI6Pq0ZcXIuXQCZWknYZl3Cp7a8/AR6ib9KoQ10q37Qus6BDZBI+E3YCyc3b3NUDER9QYMRibCYETUdlfzc5B97iCq0o/CoSgBgdWXYCepbtIvR+IFtUN/6LyHwTlsNALCR3CeJSLqEAxGJsJgRHTzdPX1yLwUj8KLv0Fy5SQ8NGfhr89p0q9KyJAuC4XGZRDkgVHwGTAObqqAzi+YiLo9BqMWaLVaLF26FFu2bEFBQQGGDBmC999/H8OHD2/V+gxGRKZRVlyIzLO/oiLtKOwKTiGg+iIUzQzuVsMNV+wjUOcVCcfQUQjoHw1rGzszVExE3QmDUQtmzpyJ8+fPY82aNVCpVNiwYQP++c9/4sKFC/D2vvH4BgYjos6h1+mQnXoO+RcOQuScgFvJGfjrMpt8mW6tsES6VTBKnAfB0n8EVBG3cDJKImqCwagZVVVVcHBwwA8//IApU6YYlkdGRmLy5Ml4/fXXb7gNBiMi8ynXlCDj3G/Qph6BTf4p+FUmwhmaJv2K4Ihs2whUe0ZCERKNwIGjYWuvNEPFRNRVtPbzu1c9K1tfXw+dTgdra2uj5TY2Nvjtt9+aXaempgY1Nf/7BnONpukvYSLqHPYKJ/QffScw+k4A16YMuJKRhLzEX6HLPAbn0rMIqEuDq6QUrpWHgLRDQNoHqN9tgVTLQFx1HAAL3xHw7H8LfIIieFWJiJroVVeMAGDUqFGQyWT4+uuv4eHhgW+++QZxcXEICQlBUlJSk/4vv/wyXnnllSbLecWIqGuqrixHxrnDKE05BFneKfhUnIc7ipv0K4U9Mm3CUek+FPbBI+E/8BYoHF3MUDERdQbeSmvB5cuX8dBDD+HXX3+FVCrF0KFDERoaivj4eFy8eLFJ/+auGPn6+jIYEXUj+TmXkXP2V9RlHoNj8RkE1qZALqkz6qMXEmRJfVGgvDYJpXv4WPiFDoGFVGqmqomoIzEY3UBFRQU0Gg28vLwwc+ZMlJeXY/v27Tdcj2OMiLq/2ppqZCQeQ3HSb7DMjYeq/DxUzczYrRU2yLDui3K3IbANiob/wFv4PXBE3RSDUSuVlJQgMDAQK1aswPz582/Yn8GIqGcqUmcj++wBVGccg6IoAYE1SbCV1DTply1RQa3oD733cLiEjUZA+HBYWsnMUDERtQWDUQt2794NIQTCwsKQmpqKxYsXw9raGgcPHoSVldUN12cwIuod6utqkXnxJIouHYLFlZPw0J6Dn/5Kk36VQo4MeSjKXAbBOjAavgNvgaunnxkqJqLrYTBqwbfffoslS5YgJycHzs7OuPfee/HGG29AqWzdo7wMRkS9V2mRGpnnDqIy7SjsCk83TEJZ2aRfHtyQ69AfdV7D4BQ6CgH9R0JubWuGiomoEYORiTAYEVEjvU6H7OQEFFz87doklKVn4a/LgkWzk1CGoMRlMKz8R8C7/y3w8AnmdAFEnYjByEQYjIjoerRlxcg8exDay0dgm38KflUX4ARtk36FcEKOXQRqPCOh6DMKgQNGw8bOwQwVE/UODEYmwmBERG0h9HpcSbsA9YWD0GUdh0vJGQTUp8NSojfqVy8skGEZiKtOgyD1GwHP8LHwDgrnVSWiDsJgZCIMRkR0s6oqtEg/dwialMOQq+PhW3Eeriht0q8ECmTahKPKYwgcgqPhP3AsHJTOnV8wUQ/AYGQiDEZE1NGEXo/8nMu4cv5X1GUcg1PxGQTWpUImqTfqpxcSZEr9Ueg4ABKf4XDvNwa+oYM5CSVRKzAYmQiDERF1hprqSmScP4qS5EOwyouHSnseXihs0k8DW2RYR6BSFQ3n/hMQNGAU51UiagaDkYkwGBGRuRTlZiL7/EFUpx+B8uoZBNYkwUZSa9RHK2yQZjsQVd7RcOkfg6D+0ZBa9qrvCydqFoORiTAYEVFXUVdbg8yLJ1B0/hfIrxxBcGVCk3mVNLBFmu0gVHuPgmv/CQjqP5K33qhXYjAyEQYjIuqqdPX1SDt/BFfP74XNlcMIrjwLe0mVUZ8y2CHNdjBqfEbBfeBEBPQbxqBEvQKDkYkwGBFRd1FfV4u0c0dQfP5n2OQeQUjVOdhJqo36lMAB6XaDUec7Gh6DJsI/bCinCKAeicHIRBiMiKi7qqutQdrZQyhO3Au7vCMIqTrf5Ityi6FAhv0Q1PmOhuegifALHcygRD0Cg5GJMBgRUU9RV1uDywm/ouTCXtjnHUVIdWKTwdxFcESmwxDU+42BavBE+AQPYFCibonByEQYjIiop6qprkTamYMovfALHNTXgpK1pM6oTwGckaUYCr3faKgGT+Ls3NRtMBiZCIMREfUW1VUVuHz6ADSX9kGhPoI+NRebTDqZDxdkK4ZCBIyFavAkqALCGJSoS2IwMhEGIyLqraory5F6eh/KL+6DIv8YQmovQibRGfVRww3ZykiIgDHwHToJXv5hZqqWyBiDkYkwGBERXVNVoUVq/C8oT9oHp4JjCK5NgtUfglKuxAM5ykhIAsfCd+gkePqGmKla6u0YjEyEwYiIqHkV2lKknboWlJwLjiO4LhmWEr1RnxyJJ3Idh8EicCz8h90ON1WAeYqlXofByEQYjIiIWqdcU4LL8T+jMnk/XAqPI7guBVKJ8UdOtkSFXKdhkAbfgoDIWLh6+pmpWurpGIxMhMGIiKh9tGXFSIv/CVVJ++FSdALB9Zdh8YeglGnhA7XTMFg2BCUXDx8zVUs9DYORiTAYERF1jLKSIqTH70F1yn64FR1HYH16k6CUYeGHfOdhsAoZh8DIWDi5eZmpWuruGIxMhMGIiMg0yq7mIy1+D2pSD8C96DiC9BlN+qRbBCDfZThkIeMQPGwSlC4enV8odUsMRibCYERE1DlKCvOQceon1KYcgEfxCQTos4za9UKCdMtAFLoMh3WfWxEYORFKZzczVUtdHYORiTAYERGZx9X8HGTE70H95f3wLImHvz7bqF0vJEizDEKR6whYh96KoMiJUDi6mKla6moYjEyEwYiIqGsoUmchM/4n1Kf9Cq+Sk/DTXzFq1wkJ0qxCcNV1BGzCxiM4Mgb2CiczVUvmxmBkIgxGRERdU2FuBjLjd0OffhCqkpPwEXlG7fXCApet+qDYPQp2obciOHIC7BwczVMsdToGIxNhMCIi6h7ycy4j+9RP0Kf9Cu+yeHiLfKP2OiHFZVkYStxHwD7sNoRE3gYbOwczVUumxmBkIgxGRETdU15mEnJO7wEyfoNP6Ul4odCovVZIcVnWF2UeI2Hf91aEDL0N1rb2ZqqWOhqDkYkwGBER9Qy5GUnIOb0bkoyD8CuLhweuGrXXCkukyPtB4zESin63IXjIOFjb2JmpWrpZDEYmwmBERNTzCL0euRkXceX0T7DI/A1+mlNwR7FRnyohQ7LtYFT5jYf38DvhGzLATNVSezAYNUOn0+Hll1/Ghg0boFaroVKpMG/ePLz44ouQSCSt2gaDERFRzyf0euSkJSL39E+wzPoN/tpTcEWpUZ8ciSeuuIyCvF8sQqMmw9ZeaZ5iqVUYjJrx5ptv4r333sP69esRERGBkydP4sEHH8Qbb7yBJ598slXbYDAiIup9hF6PjIsnkH9qO+xzDiC0+hxkEp2hvVZYIsl6ACp8b4Vn5FT4hw2FxMLCjBXTHzEYNWPq1Knw8PDA//3f/xmW3XvvvbCxscGGDRuaXaempgY1NTWG9xqNBr6+vgxGRES9WIW2FClHd6Dm0m74Fh+GShQYtavhiiznaFiFTURw1FRONNkFtDYYWXZiTWY3atQofPbZZ0hOTkZoaCjOnDmD3377De+9916L6yxfvhyvvPJKJ1ZJRERdnZ2DIwZP/DMw8c8Qej2yUs8i9+Q22GbtR2hVAjwlRfAs/hE48iPqDz+FC7JwlPmMg9vgKQjqPxIWUqm5D4Fa0KuuGOn1evzjH//AihUrIJVKodPp8MYbb2DJkiUtrsMrRkRE1BbVleVIPrYLlRd3Q1V0qMmM3EVwRLoyCpLQiQiJuhOOrp5mqrR34a20ZmzcuBGLFy/GypUrERERgYSEBDz11FN47733EBcX16ptcIwRERG1RW76JWSf2ApZxj6EVcTDVvK/f2zrhQQpVqEo9roFzoPuQMjgWyC17FU3czoNg1EzfH198fzzz+Pxxx83LHv99dexYcMGXLp0qVXbYDAiIqL2qq2pRvLJPSg/txMeBYcQqM8wai+FPS47jIA+eAICR94FV08/8xTaA3GMUTMqKyth8YenBKRSKfR6vZkqIiKi3kQmt0b/0XcCo+8EABRcSUfmsa2Qpv2MkPKTcEQ5IrW/AAm/AAkvIFUajEKPMVAOvAN9ho6HlUxu5iPo+XrVFaN58+bh559/xqeffoqIiAicPn0a8+fPx0MPPYS33367VdvgFSMiIjKF+rpapJ7aj5JzO+Ga9yv66FKN2rXCBin2w1AfeBv8ou6Cp2+ImSrtnngrrRlarRZLly7Fli1bUFBQAJVKhVmzZuGll16CTCZr1TYYjIiIqDNczc9B+rEfgdSfEaw5DidojNozLPygdhsNu4jJCB0xEXJrWzNV2j0wGJkIgxEREXU2XX09Lp89hKtndsAp91f0qb0IqeR/H9+VQo5k2yGoCRgPn+HT4B3Uz4zVdk0MRibCYEREROZWdjUfqce2QZf8MwJLj8ANJUbt2RIVcl1Hwbrh60ps7BzMVGnXwWBkIgxGRETUlQi9HmmJx1FwejsUOfsRWpMIq999XUmNsEKSzUBU+t4Kr8ip8Asd3Cu/roTByEQYjIiIqCvTlhUj9dgO1Cb9BP+rh+GJQqP2PLghy2UUZGGTEBJ1BxyUzmaqtHMxGJkIgxEREXUXQq9HVnIC8uK3wTZ7P8KqzkIuqTO01wkpkuUR0PjcCvchUxAUMaLHXk1iMDIRBiMiIuquKsvLkHJ8N6ov7oaq6DB8Ra5ReyGckO4YDWloDEKipkLp4mGmSjseg5GJMBgREVFPcSUtETknfoQ8Yx9CK08bfV2JTkiQYtUXJd7j4DLoDoQMGtOtv/yWwchEGIyIiKgnqqmuRPLxPahI3AnPwkMI0GcZtZdAgcuKEUBIDAKj7oSLh4+ZKm0fBiMTYTAiIqLeQJ2diqxjW2GZ/gv6lJ+Eg6TKqD1FGoIiz7FwGngHQobeCkur1k2UbC4MRibCYERERL1NXW0NUk7tQ9nZHXDL/w0hustG7RrYItV+GHRBMfCPugvu3oFmqrRlDEYmwmBERES9XZE6C+lHf4TF5Z8RrD0OR5QbtadbBCDffTTsB0xG6LCJkMmtzVTp/zAYmQiDERER0f/o6uuRmvAris/uhHPuAfSpS4bF776upEJYI9luKGoDboPviLugCggzS50MRibCYERERNSy0iI1Uo/9CJG8B4Flx+CKUqP2LAtv5LqOhm347QgdEQtrW/tOqYvByEQYjIiIiFpHr9Mh7fxRFCZshzLnAEJrL8BSoje0VwsrJNsMRqXfrfAefid8ggeYbIJJBiMTYTAiIiJqH03pVVw+tg11SXvgX3wYHrhq1J4r8UC2czScxzyMPkNu6dh9MxiZBoMRERHRzRN6PTKTTkEdvw122fsRVn0OMkk9AODEkOUYPu2xDt1faz+/LTt0r0REREStILGwQEC/YQjoNwwAUKEtxYXju1BzcTeCo+40X128YtQ2vGJERETU/bT287tnfoUuERERUTvwVlobNV5g02g0Zq6EiIiIWqvxc/tGN8oYjNpIq9UCAHx9fc1cCREREbWVVquFUqlssZ1jjNpIr9cjNzcXDg4OkEgkHbZdjUYDX19fZGdnc+ySifFct9/999+P/fv3IyUlBQ4ODs32eeSRR/D9998jPj4eAwcObNV5ViqVeP7557FkyZIOrzkzMxMDBw7Exx9/jNmzZwMAfvrpJ8THx3fo/hr3M2/ePIwaNQqjRo0yyT+gHnjgAVRXV2Pz5s0Abvzz/NNPP6GkpARLlizBsGHD8O2333Z4Tb0Bf290DlOeZyEEtFotVCoVLK4zVxKvGLWRhYUFfHx8TLZ9hULBv3SdhOe67eLi4rBz507s3bsXc+fObdJeWVmJHTt24Pbbb4e/vz+A1p9nuVxukj+P/v37o6qqClZWVpBKpQCA/fv3Y/Xq1Vi+fHmH7acxKI4bNw7z5s3rsO3+Xl1dHfbv34/ly5c3OVctnef77rsPAPDGG2/A0tKSP/M3ib83OoepzvP1rhQ14uBrImq1u+66Cw4ODvj666+bbf/hhx9QUVFhuDLTFUgkElhbWxtCUXd28OBBaLVaTJkyxdylEPVYDEZE1Go2Nja45557sHfvXhQUFDRp//rrr+Hg4IC77rrLsOz555+Hr68v5HI5QkJC8Pbbb0Ov1zdZ949Onz6NyZMnQ6FQwN7eHhMmTMDRo0eb9CstLcXTTz+NgIAAyOVy+Pj4YO7cuSgqKgIAZGRkQCKRYN26dQCAefPmYfXq1QCuhabGlxACAQEBmDZtWpN9VFdXQ6lUYsGCBa06T3/08ssvQyKRIDk5GQ888ACUSiXc3NywdOlSCCGQnZ2NadOmQaFQwNPTE++++26z29m+fTvCw8MREBAAAFCr1XjssWuT4Lm5ucHLywvTpk1DRkZGu+okIgajLkMul2PZsmWQy+XmLqXH47m+ObNnz0Z9fX2TsSrFxcXYvXs37r77btjY2ECn08HDwwPffvst5s6diw8++ACjR4/GkiVLsGjRouvuIzExEWPHjsWZM2fw7LPPYunSpUhPT8ett96KY8eOGfqVl5dj7Nix+PDDDzFp0iS8//77+Mtf/oJLly4hJyen2W0vWLAAEydOBAB8+eWXhpdEIsEDDzyAnTt3ori42GidH3/8ERqNBg888EB7TpnBzJkzodfr8dZbbyEqKgqvv/46Vq1ahYkTJ8Lb2xtvv/02QkJC8Mwzz+DXX39tsv6OHTtwxx13GN7fe++92LZtG8aOHYv3338fTz75JLRaLbKysm6qTmqKvzc6R5c4z4KIqA3q6+uFl5eXiI6ONlr+ySefCABi9+7dQgghXnvtNWFnZyeSk5ON+j3//PNCKpWKrKwswzIAYtmyZYb306dPFzKZTFy+fNmwLDc3Vzg4OIhbbrnFsOyll14SAMR3333XpE69Xi+EECI9PV0AEGvXrjW0Pf7446K5X39JSUkCgFizZo3R8rvuuksEBAQYttmc5vbTaNmyZQKAmD9/vmFZfX298PHxERKJRLz11luG5SUlJcLGxkbExcUZbSMtLU0AEPv27TP0AyBWrlzZYk2/5+/vL6ZMmdKqvkS9Ga8YEVGbSKVS3H///Thy5IjRLZuvv/4aHh4emDBhAgBg06ZNGDt2LJycnFBUVGR4xcTEQKfTNXtFBAB0Oh1++uknTJ8+HUFBQYblXl5e+POf/4zffvvNMB/Jf//7XwwaNAh33313k+2056nR0NBQREVF4auvvjIsKy4uxs6dOzF79uybfhL1kUceMfy/VCrFsGHDIITAww8/bFju6OiIsLAwpKWlGa27fft2KJVKjBkzBsC125oymQz79+9HSUnJTdVFRP/DYEREbdY4uLpxEHZOTg4OHjyI+++/3zDIOSUlBbt27YKbm5vRKyYmBgCaHaMEAIWFhaisrERYWFiTtn79+kGv1yM7OxsAcPnyZfTv379Dj23u3Lk4dOgQMjMzAVwLeHV1dZgzZ85Nb9vPz8/ovVKphLW1NVxdXZss/2PY2b59OyZNmgRLy2sPE8vlcrz99tvYuXMnPDw8cMstt2DFihVQq9U3XSdRb8ZgRERtFhkZib59++Kbb74BAHzzzTcQQhg9jabX6zFx4kTs2bOn2de9995rrvKv6/7774eVlZXhqtGGDRswbNiwZoNaWzX3ZFxLT8uJ300xV1lZif379xuNLwKAp556CsnJyVi+fDmsra2xdOlS9OvXD6dPn77pWol6K85jRETtMnv2bCxduhRnz57F119/jT59+mD48OGG9uDgYJSXlxuuELWWm5sbbG1tkZSU1KTt0qVLsLCwMEycGBwcjPPnz7e59uvdEnN2dsaUKVPw1VdfYfbs2Th06BBWrVrV5n10pF9++QU1NTWYPHlyk7bg4GD8/e9/x9///nekpKRg8ODBePfdd7FhwwYzVErU/fGKERG1S+PVoZdeegkJCQlN5i6aMWMGjhw5gt27dzdZt7S0FPX19c1uVyqVYtKkSfjhhx+MxjDl5+fj66+/xpgxYwwTv9177704c+YMtmzZ0mQ74jqT+tvZ2RnqaM6cOXNw4cIFLF682DCmypx27NiBYcOGwcPDw7CssrIS1dXVRv2Cg4Ph4OCAmpqazi6RqMfgFSMiapfAwECMGjUKP/zwAwA0CUaLFy/G1q1bMXXqVMybNw+RkZGoqKjAuXPnsHnzZmRkZDQZW9Po9ddfx549ezBmzBg89thjsLS0xKeffoqamhqsWLHCaB+bN2/Gn/70Jzz00EOIjIxEcXExtm7dik8++QSDBg1qdvuRkZEAgCeffBKxsbFNws+UKVPg4uKCTZs2YfLkyXB3d7+pc3WzduzYgQcffNBoWXJyMiZMmIAZM2YgPDwclpaW2LJlC/Lz880e5Ii6MwYjImq32bNn4/DhwxgxYgRCQkKM2mxtbXHgwAG8+eab2LRpE7744gsoFAqEhobilVdeue7U/BERETh48CCWLFmC5cuXQ6/XIyoqChs2bEBUVJShn729PQ4ePIhly5Zhy5YtWL9+Pdzd3TFhwoTrfnXPPffcg7/+9a/YuHEjNmzYACGEUZiQyWSYOXMmPv744w4ZdH0zEhMTkZmZ2WR8ka+vL2bNmoW9e/fiyy+/hKWlJfr27Ytvv/22y47fIuoO+CWyRETNePrpp/F///d/UKvVsLW1vWH/jIwMBAYG4sMPP8T9998PhUIBmUx203WsWLEC7733HvLy8to1XUDjbcuhQ4di4MCB2LZt203XRNSTcYwREdEfVFdXY8OGDbj33ntbFYp+769//Svc3NywdevWDqklICAA//znP9s9h9Ktt94KNzc3wxQHRHR9vGJERNSgoKAAP//8MzZv3ozvv/8ep06dwuDBg1u1bnV1NX777TfD+4EDB5p9bBIAHDt2DFqtFsC1J/5aGndFRNcwGBERNdi/fz/Gjx8Pd3d3LF26FE888YS5SyKiTsZgRERERNSAY4yIiIiIGjAYERERETXgPEZtpNfrkZubCwcHh5v+pm0iIiLqHEIIaLVaqFQqWFi0fF2IwaiNcnNzDd/TRERERN1Ldnb2dSeAZTBqIwcHBwDXTmzj9zURERFR16bRaODr62v4HG8Jg1EbNd4+UygUDEZERETdzI2GwXDwNREREVEDBiMiIiKiBgxGRERERA0YjIiIiIgaMBgRERERNWAwIiIiImrAYERERETUgMGIiIiIqAGDEREREVEDBiMiIiKiBgxGRERERA0YjIiIiIgaMBgRERERNWAwIiIiImrAYERERETUgMGIiIiIqAGDEREREVEDBiMiIiKiBgxGRERERA0YjIiIiIgaMBgRERERNWAwIiIiImrQrmC0evVqBAQEwNraGlFRUTh+/Ph1+2/atAl9+/aFtbU1BgwYgB07dhi1CyHw0ksvwcvLCzY2NoiJiUFKSopRnzfeeAOjRo2Cra0tHB0dm+zjzJkzmDVrFnx9fWFjY4N+/frh/fffb9Jv//79GDp0KORyOUJCQrBu3bo2Hz8RERH1TG0ORv/5z3+waNEiLFu2DKdOncKgQYMQGxuLgoKCZvsfPnwYs2bNwsMPP4zTp09j+vTpmD59Os6fP2/os2LFCnzwwQf45JNPcOzYMdjZ2SE2NhbV1dWGPrW1tfjTn/6EhQsXNruf+Ph4uLu7Y8OGDUhMTMQLL7yAJUuW4KOPPjL0SU9Px5QpUzB+/HgkJCTgqaeewiOPPILdu3e39TQQERFRTyTaaMSIEeLxxx83vNfpdEKlUonly5c323/GjBliypQpRsuioqLEggULhBBC6PV64enpKVauXGloLy0tFXK5XHzzzTdNtrd27VqhVCpbVetjjz0mxo8fb3j/7LPPioiICKM+M2fOFLGxsa3anhBClJWVCQCirKys1esQERGRebX287tNV4xqa2sRHx+PmJgYwzILCwvExMTgyJEjza5z5MgRo/4AEBsba+ifnp4OtVpt1EepVCIqKqrFbbZWWVkZnJ2dW11Lc2pqaqDRaIxeRERE1DO1KRgVFRVBp9PBw8PDaLmHhwfUanWz66jV6uv2b/xvW7bZGocPH8Z//vMfzJ8//4a1aDQaVFVVNbud5cuXQ6lUGl6+vr7tromIiIi6th75VNr58+cxbdo0LFu2DJMmTbqpbS1ZsgRlZWWGV3Z2dgdVSURERF1Nm4KRq6srpFIp8vPzjZbn5+fD09Oz2XU8PT2v27/xv23Z5vVcuHABEyZMwPz58/Hiiy+2qhaFQgEbG5tmtyeXy6FQKIxeRERE1DO1KRjJZDJERkZi7969hmV6vR579+5FdHR0s+tER0cb9QeAPXv2GPoHBgbC09PTqI9Go8GxY8da3GZLEhMTMX78eMTFxeGNN95ocy1ERETUu1m2dYVFixYhLi4Ow4YNw4gRI7Bq1SpUVFTgwQcfBADMnTsX3t7eWL58OQDgb3/7G8aNG4d3330XU6ZMwcaNG3Hy5El89tlnAACJRIKnnnoKr7/+Ovr06YPAwEAsXboUKpUK06dPN+w3KysLxcXFyMrKgk6nQ0JCAgAgJCQE9vb2OH/+PG677TbExsZi0aJFhvFJUqkUbm5uAIC//OUv+Oijj/Dss8/ioYcewi+//IJvv/0W27dvb/cJJCIioh6kPY+8ffjhh8LPz0/IZDIxYsQIcfToUUPbuHHjRFxcnFH/b7/9VoSGhgqZTCYiIiLE9u3bjdr1er1YunSp8PDwEHK5XEyYMEEkJSUZ9YmLixMAmrz27dsnhBBi2bJlzbb7+/sbbWffvn1i8ODBQiaTiaCgILF27do2HTsf1yciIup+Wvv5LRFCCPNEsu5Jo9FAqVSirKyM442IiIi6idZ+fvfIp9KIiIiI2oPBiIiIiKgBgxERERFRAwYjIiIiogYMRkREREQNGIyIiIiIGjAYERERETVgMCIiIiJqwGBERERE1IDBiIiIiKgBgxERERFRAwYjIiIiogYMRkREREQNGIyIiIiIGjAYERERETVgMCIiIiJqYGnuAggQej2qKrXmLoOIiKhLsLF1gMTCPNduGIy6gKpKLWzf8TN3GURERF1C5TNZsLVXmmXfvJVGRERE1IBXjLoAG1sHVD6TZe4yiIiIugQbWwez7ZvBqAuQWFiY7ZIhERER/Q9vpRERERE1YDAiIiIiasBgRERERNSAwYiIiIioAYMRERERUQMGIyIiIqIGDEZEREREDRiMiIiIiBowGBERERE1YDAiIiIiasBgRERERNSgXcFo9erVCAgIgLW1NaKionD8+PHr9t+0aRP69u0La2trDBgwADt27DBqF0LgpZdegpeXF2xsbBATE4OUlBSjPm+88QZGjRoFW1tbODo6NrufJ598EpGRkZDL5Rg8eHCT9oyMDEgkkiavo0ePtun4iYiIqGdqczD6z3/+g0WLFmHZsmU4deoUBg0ahNjYWBQUFDTb//Dhw5g1axYefvhhnD59GtOnT8f06dNx/vx5Q58VK1bggw8+wCeffIJjx47Bzs4OsbGxqK6uNvSpra3Fn/70JyxcuPC69T300EOYOXPmdfv8/PPPyMvLM7wiIyPbcAaIiIioxxJtNGLECPH4448b3ut0OqFSqcTy5cub7T9jxgwxZcoUo2VRUVFiwYIFQggh9Hq98PT0FCtXrjS0l5aWCrlcLr755psm21u7dq1QKpXXrXHZsmVi0KBBTZanp6cLAOL06dPXXf96ysrKBABRVlbW7m0QERFR52rt53ebrhjV1tYiPj4eMTExhmUWFhaIiYnBkSNHml3nyJEjRv0BIDY21tA/PT0darXaqI9SqURUVFSL27xZd911F9zd3TFmzBhs3br1un1ramqg0WiMXkRERNQztSkYFRUVQafTwcPDw2i5h4cH1Gp1s+uo1err9m/8b1u22V729vZ49913sWnTJmzfvh1jxozB9OnTrxuOli9fDqVSaXj5+vp2aE1ERETUdViau4DO5OrqikWLFhneDx8+HLm5uVi5ciXuuuuuZtdZsmSJ0ToajYbhiIiIqIdq0xUjV1dXSKVS5OfnGy3Pz8+Hp6dns+t4enpet3/jf9uyzY4UFRWF1NTUFtvlcjkUCoXRi4iIiHqmNgUjmUyGyMhI7N2717BMr9dj7969iI6Obnad6Ohoo/4AsGfPHkP/wMBAeHp6GvXRaDQ4duxYi9vsSAkJCfDy8jL5foiIiKjra/OttEWLFiEuLg7Dhg3DiBEjsGrVKlRUVODBBx8EAMydOxfe3t5Yvnw5AOBvf/sbxo0bh3fffRdTpkzBxo0bcfLkSXz22WcAAIlEgqeeegqvv/46+vTpg8DAQCxduhQqlQrTp0837DcrKwvFxcXIysqCTqdDQkICACAkJAT29vYAgNTUVJSXl0OtVqOqqsrQJzw8HDKZDOvXr4dMJsOQIUMAAN999x3+/e9/41//+le7Th4RERH1MO155O3DDz8Ufn5+QiaTiREjRoijR48a2saNGyfi4uKM+n/77bciNDRUyGQyERERIbZv327UrtfrxdKlS4WHh4eQy+ViwoQJIikpyahPXFycANDktW/fPqN9N9cnPT1dCCHEunXrRL9+/YStra1QKBRixIgRYtOmTW06dj6uT0RE1P209vNbIoQQ5olk3ZNGo4FSqURZWRnHGxEREXUTrf385nelERERETVgMCIiIiJqwGBERERE1IDBiIiIiKgBgxERERFRAwYjIiIiogYMRkREREQNGIyIiIiIGjAYERERETVgMCIiIiJqwGBERERE1IDBiIiIiKgBgxERERFRAwYjIiIiogYMRkREREQNGIyIiIiIGjAYERERETVgMCIiIiJqwGBERERE1IDBiIiIiKgBgxERERFRAwYjIiIiogYMRkREREQNGIyIiIiIGjAYERERETVgMCIiIiJqwGBERERE1IDBiIiIiKgBgxERERFRA0tzF9DdCCEAABqNxsyVEBERUWs1fm43fo63hMGojbRaLQDA19fXzJUQERFRW2m1WiiVyhbbJeJG0YmM6PV65ObmwsHBARKJpMO2q9Fo4Ovri+zsbCgUig7bLjXFc93U9X5J/N62bdswduzYVvXtaef5888/h62tLWbPnm3uUow0nufbb78d99xzDwYPHoywsDAAwMGDBzF16lSsX78e06dPN6xTW1uLBx54AD/99BM+/PBDzJkzB8nJyTh9+jS2bt2Kbdu2IS0tDS4uLmY6qq6np/08d1WmPM9CCGi1WqhUKlhYtDySiFeM2sjCwgI+Pj4m275CoeBfuk7Cc/0/X375pdH7L774Anv27GmyfNiwYW0+Zz3lPK9duxaurq5YuHChuUtp1uDBg/Hoo48aLbOzswMA2NraGv4M6urqDKHo888/x8MPPwzg2p/tsGHDcOXKFWzbtg0ODg494s+to/WUn+euzlTnuTX/CGQwIiI88MADRu+PHj2KPXv2NFneUwghUF1dDRsbm15VR11dHWbMmIFt27bh008/NYQiIvofPpVGRK2i1+uxatUqREREwNraGh4eHliwYAFKSkqM+gUEBGDq1Kk4ePAgAMDDwwMDBgzA/v37AQDfffcdBgwYAGtra0RGRuL06dNG68+bNw/29vZIS0tDbGws7OzsoFKp8OqrrzYZNNnWmnbv3o1hw4bBxsYGn376KYBrV4Juu+02uLu7Qy6XIzw8HGvWrGmyfmJiIg4cOACJRAKJRIJbb70VAPDyyy83e1t93bp1kEgkyMjIaFUdpaWleOqpp+Dr6wu5XI6QkBC8/fbb0Ov1rfjTubH6+nrcf//9+OGHH7BmzZomV5eI6BpeMeoi5HI5li1bBrlcbu5Sejye6/ZZsGAB1q1bhwcffBBPPvkk0tPT8dFHH+H06dM4dOgQrKysDH1TU1Px6KOPYty4cZg6dSpWrVqFO++8E5988gn+8Y9/4LHHHgMALF++HDNmzEBSUpLRPX+dTofbb78dI0eOxIoVK7Br1y4sW7YM9fX1ePXVV9tVU1JSEmbNmoUFCxbg0UcfNYzDWbNmDSIiInDXXXfB0tISP/74Ix577DHo9Xo8/vjjAIBVq1bhr3/9K+zt7fHCCy8AuBb42qO5OiorKzFu3DhcuXIFCxYsgJ+fHw4fPowlS5YgLy8Pq1atanF7jT/HUqm0xT719fWYNWsWtmzZgtWrV2PBggXtqr034++NztElzrMgIvqDxx9/XPz+18PBgwcFAPHVV18Z9du1a1eT5f7+/gKAOHz4sGHZ7t27BQBhY2MjMjMzDcs//fRTAUDs27fPsCwuLk4AEH/9618Ny/R6vZgyZYqQyWSisLCw3TXt2rWrybFWVlY2WRYbGyuCgoKMlkVERIhx48Y16bts2TLR3K/StWvXCgAiPT39hnW89tprws7OTiQnJxstf/7554VUKhVZWVlNtv97AMSyZcuaLN+3b58AYNjv6tWrr7ud3x9P43km6m14K42IbmjTpk1QKpWYOHEiioqKDK/IyEjY29tj3759Rv3Dw8MRHR1teB8VFQUAuO222+Dn59dkeVpaWpN9PvHEE4b/l0gkeOKJJ1BbW4uff/65XTUFBgYiNja2yX5+P76nrKwMRUVFGDduHNLS0lBWVtbqc9RazdWxadMmjB07Fk5OTkbHEhMTA51Oh19//fWm9pmfnw9LS0sEBgbe1HaIegPeSiOiG0pJSUFZWRnc3d2bbS8oKDB6//vwA/zvSZA/zv/VuPyPY4IsLCwQFBRktCw0NBQADGN22lpTS6Hg0KFDWLZsGY4cOYLKykqjtrKyslZPZdBazdWRkpKCs2fPws3Nrdl1/ngsbbVixQqsWrUK9913H3766SeMHj36prZH1JMxGBHRDen1eri7u+Orr75qtv2PH+gtjXdpablox3Rqba2puSe/Ll++jAkTJqBv375477334OvrC5lMhh07duCf//xnqwY+tzSfmU6na3Z5c3Xo9XpMnDgRzz77bLPrNIbC9vLy8sKePXswZswYTJkyBQcOHMCgQYNuaptEPRWDERHdUHBwMH7++WeMHj26Ux4t1+v1SEtLMwoEycnJAK492dVRNf3444+oqanB1q1bja5y/fE2HNByAHJycgJw7akyR0dHw/LMzMxW1xEcHIzy8nLExMS0ep22CgoKwu7duzFu3DjExsbi4MGD6NOnj8n2R9RdcYwREd3QjBkzoNPp8NprrzVpq6+vR2lpaYfv86OPPjL8vxACH330EaysrDBhwoQOq6nxCtbvr1iVlZVh7dq1Tfra2dk1u83g4GAAMBoHVFFRgfXr199w/41mzJiBI0eOYPfu3U3aSktLUV9f3+ptXc+AAQOwfft2lJeXY+LEibhy5UqHbJeoJ+EVIyK6oXHjxmHBggVYvnw5EhISMGnSJFhZWSElJQWbNm3C+++/j/vuu6/D9mdtbY1du3YhLi4OUVFR2LlzJ7Zv345//OMfhltkHVHTpEmTIJPJcOedd2LBggUoLy/H559/Dnd3d+Tl5Rn1jYyMxJo1a/D6668jJCQE7u7uuO222zBp0iT4+fnh4YcfxuLFiyGVSvHvf/8bbm5uyMrKatXxLl68GFu3bsXUqVMxb948REZGoqKiAufOncPmzZuRkZEBV1fX9p3MP4iOjsZ3332HO++8ExMnTsTBgwf51R9Ev8NgRESt8sknnyAyMhKffvop/vGPf8DS0hIBAQF44IEHOnwwr1Qqxa5du7Bw4UIsXrwYDg4OWLZsGV566aUOrSksLAybN2/Giy++iGeeeQaenp5YuHAh3Nzc8NBDDxn1femll5CZmYkVK1ZAq9Vi3LhxuO2222BlZYUtW7bgsccew9KlS+Hp6YmnnnoKTk5OePDBB1t1vLa2tjhw4ADefPNNbNq0CV988QUUCgVCQ0PxyiuvdPgA8EmTJuHLL7/ErFmzMHnyZOzduxcODg4dug+i7opfIktEXcq8efOwefNmlJeXm7uUbkMikWDx4sV49tlnYWdn164xV9XV1SgvL8eKFSuwcuVKFBYWdthVKqLuhGOMiIh6gJUrV8LNzQ2rV69u1/qffPIJ3NzcsHLlyg6ujKh74a00IqJubs+ePYb/b++j/ffeey/69+9veN/Rt++IugsGIyKibq4jHvP39fVtMgEnUW/EMUZEREREDTjGiIiIiKgBgxERERFRA44xaiO9Xo/c3Fw4ODi0+BUBRERE1LUIIaDVaqFSqWBh0fJ1IQajNsrNzeUARSIiom4qOzsbPj4+LbYzGLVR4+yw2dnZUCgUZq6GiIiIWkOj0cDX1/eGs7wzGLVR4+0zhULBYERERNTN3GgYDAdfdxEJe77G0Y/nIyXhIIReb+5yiIiIeiVeMeoiLOL/jZHVJ4Dv/4PMrb7I87sLfrfGQRUQZu7SiIiIeg1eMeoixIj5iLe/FdXCCv76bIzMWA3VuhG48MZoHN/8HsqKC81dIhERUY/Hma/bSKPRQKlUoqyszCRjjDSlV3Fp31ewvfRfhFefgYXk2h9PrbDEeftoYOBMRIy7F3Jr2w7fNxERUU/V2s/vbnPFaM2aNRg4cKBh0HN0dDR27tzZpJ8QApMnT4ZEIsH3339v1JaVlYUpU6bA1tYW7u7uWLx4Merr6zvpCFpH4eiCEXc/if5LDqDgkZM4GvQk0i38IZPUY2jFQQw98gRq3grBsQ/m4OKx3dDrdOYumYiIqMfoNmOMfHx88NZbb6FPnz4QQmD9+vWYNm0aTp8+jYiICEO/VatWNTviXKfTYcqUKfD09MThw4eRl5eHuXPnwsrKCm+++WZnHkqrefqGwHPuawBeQ9r5Yyj4bT2C1DvhjmJEFW8Fdm5F7i53ZKqmQHXLPPiHDTZ3yURERN1at76V5uzsjJUrV+Lhhx8GACQkJGDq1Kk4efIkvLy8sGXLFkyfPh0AsHPnTkydOhW5ubnw8PAAAHzyySd47rnnUFhYCJlM1qp9mvpW2o3o6utx8ch2VMV/g34l+2EvqTK0pUhDcDX4boTcFgdXT05CSURE1KjH3Ur7PZ1Oh40bN6KiogLR0dEAgMrKSvz5z3/G6tWr4enp2WSdI0eOYMCAAYZQBACxsbHQaDRITExscV81NTXQaDRGL3OSWlqi/9hpGP7URkifTUH88HeRYDMSdUKKPrpUjExeCcc1A3H2rRic3PoJKsvLzFovERFRd9JtbqUBwLlz5xAdHY3q6mrY29tjy5YtCA8PBwA8/fTTGDVqFKZNm9bsumq12igUATC8V6vVLe5z+fLleOWVVzroCDqWjZ0DIqc8Akx5BMUFV5DyyxdwTN2CsPokDKw+AZw6gcr4l3DCcRysh85Cv1FTYWnVuitjREREvVG3CkZhYWFISEhAWVkZNm/ejLi4OBw4cACpqan45ZdfcPr06Q7f55IlS7Bo0SLD+8YpxbsaZ3dvRN2/BMASZKeeQ86B9fDN+RE+UGN42U/Avp9QtM8RqR63w3XUHAQPGAXJdb5Ej4iIqDfq1mOMYmJiEBwcDBsbG3zwwQdG35ar0+lgYWGBsWPHYv/+/XjppZewdetWJCQkGPqkp6cjKCgIp06dwpAhQ1q1T3OPMWoLodcjKf4XlB37CqFFe+AEraEtw8IXef53IeDWOHj5cxJJIiLq2Vr7+d2trhj9kV6vR01NDV555RU88sgjRm0DBgzAP//5T9x5550AgOjoaLzxxhsoKCiAu7s7AGDPnj1QKBSG23E9jcTCAn2HxwDDY1BbU42Eg1ugT/gGEdrDCNBnIyB9NZC+GhdkA1Aeeg/CbpsDpbObucsmIiIym25zxWjJkiWYPHky/Pz8oNVq8fXXX+Ptt9/G7t27MXHixCb9JRKJ0VNpOp0OgwcPhkqlwooVK6BWqzFnzhw88sgjbXpcvztdMWqJpvQqkn7ZANtL/0W/mrN/mERyFCSDZiL8lns4iSQREfUYPe6KUUFBAebOnYu8vDwolUoMHDiwxVDUHKlUim3btmHhwoWIjo6GnZ0d4uLi8Oqrr5q48q5H4eiC4ff8DcDfoM5ORca+9fDI+AGB+kwMrfgVOPwryg4/iwSXGChHPICw4TEcj0RERL1Ct7li1FX0hCtGzRF6PdISj6Pw0BeGSSQb5Uo8kOk9Bd63xMEvdLD5iiQiImqn1n5+Mxi1UU8NRr/3+0kkw0v2wU5SbWhLsexzbRLJ8XM5iSQREXUbDEYm0huC0e9VVWiRuH8jrM5vQkTlCVhK9ACAemGBRJtI1EX8CeHj74etvdLMlRIREbWMwchEelsw+r2r+TlI3felYRLJRpVCjkTHcbCO/DPCR90JqWW3GbpGRES9BIORifTmYPR72SlnkPPrF/DL+RHeIt+wvBBOuGyYRDKag7aJiKhLYDAyEQYjY4ZJJI9+ibCrP8MR5Ya2a5NITkPg+Hnw9OtjxiqJiKi3YzAyEQajltXWVOPCr99Bf2YjIrSHIZfUGdoSZQNQEXbvtUkknVzNWCUREfVGDEYmwmDUOprSq7i090vYJf0XEbVnDctrhBUSHaIhGTgTEePug0xubcYqiYiot2AwMhEGo7ZTZ6Ugff8X8Mr4HgH6LMPyUtgjySUGyhGzOYkkERGZFIORiTAYtZ9hEsnf1iE4fxfcUGJoy5V4IMt7KlS3zOUkkkRE1OEYjEyEwahj6OrrceHwdlTHf43w0v1Gk0gmW4aiOHg6+twWBxcPHzNWSUREPQWDkYkwGHW8qgotEvd9A6vETYioPGk8iaTtMNRF/AkR42fBxs7BzJUSEVF3xWBkIgxGpnU1Pwcpv3wB58tbEFqfbFheIaxxwfHWhkkkp3ASSSIiahMGIxNhMOo8WckJyP31C/hd2QZVM5NIuo2ei6D+Izlom4iIbojByEQYjDqf0OuRdHIvyo5taGYSST/kBUxH4Pg4ePqGmLFKIiLqyhiMTITByLxqa6qReGAzxNn/IEJ7xDCJpF5IcFHeOInkA5xEkoiIjDAYmQiDUddRVlKEpF82tDiJpMWgWQi/5R5OIklERAxGpsJg1DWps1KQvm8dvDJ/QIA+27DcMIlk1AMIGzaB45GIiHopBiMTYTDq2oRej7TzR1F46Ismk0hekXggy+dO+NwyF759BpmxSiIi6mwMRibCYNR9XJtE8kdUx3+DiNL9sJXUGNoyLXygdhkJeVgMQkbcDnuFkxkrJSIiU2MwMhEGo+6psrwMF/b/p8kkkgBQJ6RIlfVFqdcYOA2YhJDBt8DSSmbGaomIqKMxGJkIg1H3V1ZciLQTO1Cb/Au8i4/CR6iN2jWwxWXbIaj1HwdV5B3wCYrg2CQiom6OwchEGIx6ntz0S8g+uR1WmfsRXB4PJSqM2vPghmynKEj7xCB4xGQ4unqaqVIiImovBiMTYTDq2XT19bh89hCKz+6GQ+5B9KlJhEyiM7TrhQSXrUJQ5D4KDhET0SdyAuTWtmasmIiIWoPByEQYjHqXyvIypJz4CVWXfoZH4REE6jON24UcqTYDUek7Fh6Db0dAv+G87UZE1AUxGJkIg1HvVpibgYzj2yFJ24cAzQm4otSovQiOyFAMhwgaj8ARU+Gq8jdPoUREZITByEQYjKiR0OuRcfEE8hN2wTb7IPpUnYGNpNaoT4aFH9Su0bDpG4M+I2Jha680U7VERL0bg5GJMBhRS2qqK5ESvxfaxD1wLTiM4LpUWEj+99erVkiRIo+AVjUWzgNjETxwNKSWlmasmIio92AwMhEGI2qt0iI1Lh/fCV3Kz/AtOQYvFBq1l8EOl+0jUed/K3yHTYEqsK+ZKiUi6vkYjEyEwYjaQ+j1yElLRG78DsgyDyCk4hQcJFVGfXIknrjiPBKy0NsQNGIKlE6uZqqWiKjnYTAyEQYj6gj1dbVITfgVJed+gmPebwipvQSr300LoBMSpFqFosRzDBT9J6HP0PGwksnNWDERUffGYGQiDEZkCtqyYlw+sRs1ST/D6+oR+OmvGLVXCGuk2A5Gtd8t8BoyGX6hgzktABFRGzAYmQiDEXUGdXYqsk9sh0X6fgRpT8IJGqP2Ajgj0zEKkuDxCBwxBS4ePmaqlIioe2jt53e3+SfnmjVrMHDgQCgUCigUCkRHR2Pnzp0AgOLiYvz1r39FWFgYbGxs4OfnhyeffBJlZWVG28jKysKUKVNga2sLd3d3LF68GPX19eY4HKLr8vQNwfB7/obIv2+BcmkGUu/egaNBT+KcfAhqhBXcUYzhpTsxLP5ZuKyJwOXXBuPoJ4/h3IHvUF1Zbu7yiYi6rW7zrLCPjw/eeust9OnTB0IIrF+/HtOmTcPp06chhEBubi7eeecdhIeHIzMzE3/5y1+Qm5uLzZs3AwB0Oh2mTJkCT09PHD58GHl5eZg7dy6srKzw5ptvmvnoiFpmIZUiZNBohAwaDQCorizHuZN7UHFhD9wKjyBYl4ZgXTqC1emA+ivU/GKFc9b9UeE9Fq6DbkdQ/5GwkErNfBRERN1Dt76V5uzsjJUrV+Lhhx9u0rZp0yY88MADqKiogKWlJXbu3ImpU6ciNzcXHh4eAIBPPvkEzz33HAoLCyGTyVq1T95Ko67man4O0k/sgD51HwJKj8IdxUbtxVAg3WEY9IG3wnf4FHj6hpinUCIiM2rt53e3uWL0ezqdDps2bUJFRQWio6Ob7dN44JYNE+gdOXIEAwYMMIQiAIiNjcXChQuRmJiIIUOGNLudmpoa1NTUGN5rNJpm+xGZi4uHD1ymzgcwH0KvR2ZyAvJO74R11q/oU5kAZ4kGztpfgLO/AGdfQpaFN/KcR0IeFoPgEbfDQels7kMgIuoyulUwOnfuHKKjo1FdXQ17e3ts2bIF4eHhTfoVFRXhtddew/z58w3L1Gq1USgCYHivVqtb3Ofy5cvxyiuvdNAREJmWxMIC/n2Hwr/vUAAvoK62BhdO7YPm/E9wUh9CSF0S/PRX4Ff0X6Dov6j7TYqLsjCUeo2BU/9JCBkyDpZWrbt6SkTUE3WrW2m1tbXIyspCWVkZNm/ejH/96184cOCAUTjSaDSYOHEinJ2dsXXrVlhZWQEA5s+fj8zMTOzevdvQt7KyEnZ2dtixYwcmT57c7D6bu2Lk6+vLW2nULZWVFOHy8Z2oS/4Z3sXH4CPyjNq1wgapdkNR6z8Oqsg74BMUwWkBiKhH6BWP68fExCA4OBiffvopAECr1SI2Nha2trbYtm0brK2tDX1feuklbN26FQkJCYZl6enpCAoKwqlTp1q8lfZHHGNEPUluRhJyTm6HZcZ+BJefhBIVRu15cEO2UxSkfW5D8IgpcHT1NFOlREQ3p0ePMWqk1+sNV3M0Gg1iY2Mhl8uxdetWo1AEANHR0XjjjTdQUFAAd3d3AMCePXugUCiavR1H1BuoAsKgCggDsAi6+noknz2E4rO74ZB7EH1qEuElKYRXyTbg+Dboj/0dKZbBKPIYBYfwiegzLAZya1tzHwIRUYfqNleMlixZgsmTJ8PPzw9arRZff/013n77bezevRtRUVGYNGkSKisrsWXLFtjZ2RnWc3Nzg1QqhU6nw+DBg6FSqbBixQqo1WrMmTMHjzzySJse1+cVI+otKsvLkHLiJ1Rd+hkehUcQqM80aq8SMqTYDESlz1h4DJmMgH7DeduNiLqsHncr7eGHH8bevXuRl5cHpVKJgQMH4rnnnsPEiROxf/9+jB8/vtn10tPTERAQAADIzMzEwoULsX//ftjZ2SEuLg5vvfWW4cm11mAwot6qKDcT6Se2A2n7EFh2HK4oNW6HIzIUwyGCxiNgxBS4qQLMUicRUXN6XDDqKhiMiACh1yPjUjzyT++AbfZB9Kk6AxtJrVGfDAs/5DsNgUQ1BC59ouDXN5JfhEtEZsNgZCIMRkRN1VRXIiV+L7SJe+BacBjBdamwkBj/aqkRVsi0CkSJY39IvIfAtU8U/MKGcHoAIuoUDEYmwmBEdGOlRWqkndyFmswTcCg+D7+aZChQ2aRflZAh0yoYZY7hsPAZCrewkfDtMxjSNtzeJiJqDQYjE2EwImo7odfjStoF5CcdRV32KTgUn4N/TQrsJVVN+lYKOTJlwShz6g+p9xC4h42ET8hAhiUiuikMRibCYETUMfQ6Ha6knUf+paOozzkFRUki/GtSYCepbtK3QlgjU94HGqcIWPoMhUffkfAO6s8vxyWiVmMwMhEGIyLT0dXXIyf1LAqTj6E+5xSUJefhX3sZtpKaJn21wgZZ8j7QOg+Ape8QeIZFwzsonFMGEFGzGIxMhMGIqHPp6uuRnZKAwqSj0OecgrL0AgLqUmEtqWvSVwO7hrDUHzLfYfDsOxKqgDCGJSJiMDIVBiMi86uvq0VW0mkUpRyDuHIaTqXn4V+XDnkzYakMdsiSh6HcpT/kfpHw6hcNT98+DEtEvQyDkYkwGBF1TXW1Nci6FI+rKccgchPgXJYI/7o0yCS6Jn1LoEC2dSgqXAbA2n8YvPqNhId3EMMSUQ/GYGQiDEZE3UdNdSWyLsWjOOU4JHmn4Vx2Af71GbBqJixdhRI5NmGoaghL3hGjOHs3UQ/CYGQiDEZE3Vt1VQWyLp5ESeoxSPIS4KK5AP/6TFhK9E36FsEROTZ9UeU2EDb+kfCJGAVXTz8zVE1EN4vByEQYjIh6nurKcmRcOIayyydgkZcAV80F+OmyIJU0/fVYAGdcse2LareBsA0YBp/waLh4+JihaiJqCwYjE2EwIuodKsvLkHXhOEovH4dUfQZu2gvw0+U0+aoTAFDDFXl2fVHjNgi2AZHwjRgFJzcvM1RNRC1hMDIRBiOi3qtCW4qsC8dQdvk4LNUJcCu/BF/dlWbDUq7EHWrbvqhxHwj7wBHw6z8KSmc3M1RNRACDkckwGBHR72nLipGVeBTatOOwzD8Lj/KL8BW5zfa9IvGA2q4f6twHwj5oOHwjRkHp5NrJFRP1TgxGJsJgREQ3UlZShOwLR1CedgJW+WfgUXEJPkLdbN9siQoF9n1R5zEIDkEj4BcxEg5K506umKjnYzAyEQYjImqPsqv5yEo8ivL045AXnoVnxSWoREGzfbMsvFFg3w/1HoOgCB4B/4iRsHNw7NyCiXoYBiMTYTAioo5SUpiHnAtHUJF+EvLCM/CqSIInCpv00wsJsqU+KHDoB53nYDgGDYdfRBRs7ZVmqJqoe2IwMhEGIyIypav5Oci5cASVGSdhXXgW3pWX4I7iJv10QoIsqR+KHPpB5x4BG6++cPELh6d/KCytZGaonKhrYzAyEQYjIupsReosXLlwGJUZ8bApPAufqktwRWmzfWuFFHlSFYqtfVGtDIKlWyjsvfvCI7A/nFy9+LUn1GsxGJkIgxERdQWFuRm4kngYVVnxkBcnw6kqC166K7Bu5ot0G5XBDmpLH2jsAqBzCoHMow+c/MLhFRgBa1v7TqyeqPMxGJkIgxERdVV6nQ7q7FRczUxERe4lSK6mwFabAbea7GbHLhnWExLkS1xRaO2HKodAwCUEtqq+cAvoD3fvIFhIpZ14FESmwWBkIgxGRNQdVVVokZd+ASVZF1CbfwmWpWlQVmTAsz4HClS2vJ6QIU+qQqmtP2qUQbB0D4XSNxwegf05BxN1KwxGJsJgREQ9idDrUVyYi/z08yi/cgn6wmTINelwrs6Cly4PMomuxXWvQol8K1+U2/tD7xwCa8++127NBfSFTG7diUdBdGMMRibCYEREvUV9XS3yMpNQnJWIqrwkSK6mwr48A+612XBDScvrCQvkWXii2NoXVYogSFz7wF51bQC4i6cvB4CTWTAYmQiDERHRta9CUacnoiz7AuoKkiErTYOyMhOq+hzYSmpaXK9c2CDP0htldgGocwyGlUcfOPqEQxXcn/MykUkxGJkIgxERUcuEXo/CvEwUpJ9DRe4liKJU2GjS4FqTBU99AaTNfOFuowI4o0Duhwr7AAiXENh49YWrfzg8/cIgtbTsxKOgnojByEQYjIiI2qemuhLq9Iu4mnUBNfnJkBanQlGRAY+6HDhB0+J6tcISuVIvlFj7oVoZDKl7HyhUfeERNABObl6deATUnTEYmQiDERFRxyu7mo+89PPQ5lxEfUEy5GXpcKrKhEqXC/l15mYqhT3yG+ZmqncOhtwjFE5+EfAKDIe1jV0nHgF1dQxGJsJgRETUeXT19cjPuYyijPOozLsEydVU2GrTG+ZmKmpxPb2QQG3hhiK5HyodAiFxDYGtVz+4BUbAXRXIuZl6IQYjE2EwIiLqGqoqtMhNS0RpdiJq85NhVXIZispMeNVlw0FS1eJ6lUKOPEsVSm38UesYBCv3UCh8wuEZ1B8KR5dOPALqTAxGJsJgRETUtQm9HlcLrqAg/TzKcy9BX5gCa01aw9xMalhdZ26mIjiiQOaLcjt/6F2uzc3k7NcPXgH9YCWTd+JRUEdjMDIRBiMiou6rrrYG6sxLuJp1EdV5l2BRnAq78gx41Ga3+MW8AFAnpMi3cEOZlTsqbTxRb6+ChdIH1q6+cHAPhIsqCApHF87R1IX1uGC0Zs0arFmzBhkZGQCAiIgIvPTSS5g8eTIAoLq6Gn//+9+xceNG1NTUIDY2Fh9//DE8PDwM28jKysLChQuxb98+2NvbIy4uDsuXL4dlGx4DZTAiIuqZNKVXkd8wN1N9QTKsytLgWJkJr/or152bqVGlkKNI6ooyK3dU2XhB56CC1NEH1i5+UHoEwMU7CPYKp044EmpOjwtGP/74I6RSKfr06QMhBNavX4+VK1fi9OnTiIiIwMKFC7F9+3asW7cOSqUSTzzxBCwsLHDo0CEAgE6nw+DBg+Hp6YmVK1ciLy8Pc+fOxaOPPoo333yz1XUwGBER9S56nQ4FuekozklBZWEm6kqyYaHNhbwyD/Y1BXDRFV53uoHf08AWVy3coJW5o9rWEzoHb0gdfWDr6gelZwDcvINhbWtv4iPqnXpcMGqOs7MzVq5cifvuuw9ubm74+uuvcd999wEALl26hH79+uHIkSMYOXIkdu7cialTpyI3N9dwFemTTz7Bc889h8LCQshkslbtk8GIiIj+qLqyHIW56ShTp6OqKAv1DeHJujIPitoCuOgLr/tlvb9XAgcUS92glXugxtYLegcVrJx8YOPqD0fPQLiq/CG3tjXxEfU8rf387pZTiep0OmzatAkVFRWIjo5GfHw86urqEBMTY+jTt29f+Pn5GYLRkSNHMGDAAKNba7GxsVi4cCESExMxZMiQZvdVU1ODmpr/XULVaFr3rwIiIuo9rG3t4RsyAL4hA1rsU64pwdUradAUZKKqKAu60hxItVdgU6WGoq4AbrpC2Epq4AQtnHRaoDINqARQBCDdeFtFcESJpRvKG8ITlN6wcvKFnZs/HL0C4erpB0ur1v2Dn4x1q2B07tw5REdHo7q6Gvb29tiyZQvCw8ORkJAAmUwGR0dHo/4eHh5Qq9UAALVabRSKGtsb21qyfPlyvPLKKx17IERE1OvYK5xgr4gE+kU22y70epSVXsXV3DRoC9JRXZQNfdkVWJbnwrZKDWVdAdz0RZBL6uCKUrjWlwL1KUAFgELjbemEBAUSJ5RYuqPC2gO1dipA6Q2Zsy/s3QPg7BkIZw8fzufUjG4VjMLCwpCQkICysjJs3rwZcXFxOHDggEn3uWTJEixatMjwXqPRwNfX16T7JCKi3kdiYQGlsxuUzm4AoprtI/R6FBfloTgvHdr8DNQWZ0NflgOr8jzYVauhrC+Em/4qrCQ6uKMY7vXFQPkloBxAvvG2aoUURRYuKLW89qRdnZ0nLJQ+kDn7wsEjAM5egXBy9ep1T9p1q2Akk8kQEhICAIiMjMSJEyfw/vvvY+bMmaitrUVpaanRVaP8/Hx4enoCADw9PXH8+HGj7eXn5xvaWiKXyyGXc+4KIiIyP4mFBZzdveHs7g1gTLN99DodivJzUKxOR3nBtfCEsiuQVeTCrjofTvUFcBElkEl0UIkCqOoKgLrzgAZAnvG2qoUViixcjacpcPSBtYtPj52moFsFoz/S6/WoqalBZGQkrKyssHfvXtx7770AgKSkJGRlZSE6OhoAEB0djTfeeAMFBQVwd3cHAOzZswcKhQLh4eFmOwYiIqKOZCGVwlXlD1eVf4t96utqoVZnoTQvHRUNT9qh7ErDk3b5cKovhCtKYS2pg4/Ig09tHlB7BigDcMV4W5VCjkKpGzRW7qiy+d+TdjauflB4BMDVOwh2Do4mPeaO1G2C0ZIlSzB58mT4+flBq9Xi66+/xv79+7F7924olUo8/PDDWLRoEZydnaFQKPDXv/4V0dHRGDlyJABg0qRJCA8Px5w5c7BixQqo1Wq8+OKLePzxx3lFiIiIehVLKxk8fUPg6RvSYp+a6koU5WaiVJ2OqqJM1JXkGKYpcKjJh7OuEE7QwlZSA399DlCTA9QAKAWQbbytxmkKNDJ3VDc8aWfp5AsbVz8oPQPhpgrsMtMUdJtgVFBQgLlz5yIvLw9KpRIDBw7E7t27MXHiRADAP//5T1hYWODee+81muCxkVQqxbZt27Bw4UJER0fDzs4OcXFxePXVV811SERERF2W3NoW3kH94B3Ur8U+VRVaFOWmoUydgcrfPWlnXamGw++mKVCgEgp9JlCdCVQDKAaQabytEihwtWGaAqtRC9F/zF0mPb6WdOt5jMyB8xgRERG1XuM0BWX5Gai+2hiecmFTlQdlXQFcdUVNZhaPH7EKkXc82KF19Oh5jIiIiKh7aM80Bf79mx9Y3hkYjIiIiMhsWjNNQWfqOc/XEREREd0kXjFqo8YhWfxqECIiou6j8XP7RkOrGYzaSKvVAgBnvyYiIuqGtFotlEpli+18Kq2N9Ho9cnNz4eDgAIlE0mHbbfyqkezsbD7tZmI8152D57lz8Dx3Dp7nzmHK8yyEgFarhUqlgsV1ZurmFaM2srCwgI+Pj8m2r1Ao+Jeuk/Bcdw6e587B89w5eJ47h6nO8/WuFDXi4GsiIiKiBgxGRERERA0YjLoIuVyOZcuW8XvbOgHPdefgee4cPM+dg+e5c3SF88zB10REREQNeMWIiIiIqAGDEREREVEDBiMiIiKiBgxGRERERA0YjIiIiIgaMBh1otWrVyMgIADW1taIiorC8ePHr9t/06ZN6Nu3L6ytrTFgwADs2LGjkyrt/tpyrj///HOMHTsWTk5OcHJyQkxMzA3/bOiatv5MN9q4cSMkEgmmT59u2gJ7iLae59LSUjz++OPw8vKCXC5HaGgof3+0QlvP86pVqxAWFgYbGxv4+vri6aefRnV1dSdV2z39+uuvuPPOO6FSqSCRSPD999/fcJ39+/dj6NChkMvlCAkJwbp160xbpKBOsXHjRiGTycS///1vkZiYKB599FHh6Ogo8vPzm+1/6NAhIZVKxYoVK8SFCxfEiy++KKysrMS5c+c6ufLup63n+s9//rNYvXq1OH36tLh48aKYN2+eUCqVIicnp5Mr717aep4bpaenC29vbzF27Fgxbdq0zim2G2vrea6pqRHDhg0Td9xxh/jtt99Eenq62L9/v0hISOjkyruXtp7nr776SsjlcvHVV1+J9PR0sXv3buHl5SWefvrpTq68e9mxY4d44YUXxHfffScAiC1btly3f1pamrC1tRWLFi0SFy5cEB9++KGQSqVi165dJquRwaiTjBgxQjz++OOG9zqdTqhUKrF8+fJm+8+YMUNMmTLFaFlUVJRYsGCBSevsCdp6rv+ovr5eODg4iPXr15uqxB6hPee5vr5ejBo1SvzrX/8ScXFxDEat0NbzvGbNGhEUFCRqa2s7q8Qeoa3n+fHHHxe33Xab0bJFixaJ0aNHm7TOnqQ1wejZZ58VERERRstmzpwpYmNjTVYXb6V1gtraWsTHxyMmJsawzMLCAjExMThy5Eiz6xw5csSoPwDExsa22J+uac+5/qPKykrU1dXB2dnZVGV2e+09z6+++irc3d3x8MMPd0aZ3V57zvPWrVsRHR2Nxx9/HB4eHujfvz/efPNN6HS6ziq722nPeR41ahTi4+MNt9vS0tKwY8cO3HHHHZ1Sc29hjs9CS5NtmQyKioqg0+ng4eFhtNzDwwOXLl1qdh21Wt1sf7VabbI6e4L2nOs/eu6556BSqZr8ZaT/ac95/u233/B///d/SEhI6IQKe4b2nOe0tDT88ssvmD17Nnbs2IHU1FQ89thjqKurw7Jlyzqj7G6nPef5z3/+M4qKijBmzBgIIVBfX4+//OUv+Mc//tEZJfcaLX0WajQaVFVVwcbGpsP3yStGRL/z1ltvYePGjdiyZQusra3NXU6PodVqMWfOHHz++edwdXU1dzk9ml6vh7u7Oz777DNERkZi5syZeOGFF/DJJ5+Yu7QeZf/+/XjzzTfx8ccf49SpU/juu++wfft2vPbaa+YujW4Srxh1AldXV0ilUuTn5xstz8/Ph6enZ7PreHp6tqk/XdOec93onXfewVtvvYWff/4ZAwcONGWZ3V5bz/Ply5eRkZGBO++807BMr9cDACwtLZGUlITg4GDTFt0Ntefn2cvLC1ZWVpBKpYZl/fr1g1qtRm1tLWQymUlr7o7ac56XLl2KOXPm4JFHHgEADBgwABUVFZg/fz5eeOEFWFjwukNHaOmzUKFQmORqEcArRp1CJpMhMjISe/fuNSzT6/XYu3cvoqOjm10nOjraqD8A7Nmzp8X+dE17zjUArFixAq+99hp27dqFYcOGdUap3Vpbz3Pfvn1x7tw5JCQkGF533XUXxo8fj4SEBPj6+nZm+d1Ge36eR48ejdTUVEPwBIDk5GR4eXkxFLWgPee5srKySfhpDKOC383eYczyWWiyYd1kZOPGjUIul4t169aJCxcuiPnz5wtHR0ehVquFEELMmTNHPP/884b+hw4dEpaWluKdd94RFy9eFMuWLePj+q3U1nP91ltvCZlMJjZv3izy8vIML61Wa65D6Bbaep7/iE+ltU5bz3NWVpZwcHAQTzzxhEhKShLbtm0T7u7u4vXXXzfXIXQLbT3Py5YtEw4ODuKbb74RaWlp4qeffhLBwcFixowZ5jqEbkGr1YrTp0+L06dPCwDivffeE6dPnxaZmZlCCCGef/55MWfOHEP/xsf1Fy9eLC5evChWr17Nx/V7kg8//FD4+fkJmUwmRowYIY4ePWpoGzdunIiLizPq/+2334rQ0FAhk8lERESE2L59eydX3H215Vz7+/sLAE1ey5Yt6/zCu5m2/kz/HoNR67X1PB8+fFhERUUJuVwugoKCxBtvvCHq6+s7uerupy3nua6uTrz88ssiODhYWFtbC19fX/HYY4+JkpKSzi+8G9m3b1+zv28bz21cXJwYN25ck3UGDx4sZDKZCAoKEmvXrjVpjRIheM2PiIiICOAYIyIiIiIDBiMiIiKiBgxGRERERA0YjIiIiIgaMBgRERERNWAwIiIiImrAYERERETUgMGIiIiIqAGDEREREVEDBiMiIiKiBgxGRERERA3+Hxg2zpqNS+muAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "Pipe.plot_pipe(net, 0, pipe_results)"
    ]
diff --git a/tutorials/district_heating/multiple_pumps_flow_in_a_circular_district_heating_grid.ipynb b/tutorials/district_heating/multiple_pumps_flow_in_a_circular_district_heating_grid.ipynb
index c83d021fb..ad9801c72 100644
--- a/tutorials/district_heating/multiple_pumps_flow_in_a_circular_district_heating_grid.ipynb
+++ b/tutorials/district_heating/multiple_pumps_flow_in_a_circular_district_heating_grid.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 379,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 380,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -72,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 381,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -106,7 +106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 382,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -144,7 +144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 383,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -182,30 +182,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 384,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 384,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "pp.plotting.simple_plot(net)"
    ]
@@ -221,7 +200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 385,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -244,114 +223,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 386,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "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",
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-       "\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>2.500000</td>\n",
-       "      <td>331.497138</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>358.150000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3.765631</td>\n",
-       "      <td>358.057213</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3.531273</td>\n",
-       "      <td>357.964539</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>3.413784</td>\n",
-       "      <td>357.794807</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>3.081328</td>\n",
-       "      <td>333.150000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>2.964553</td>\n",
-       "      <td>331.617595</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>2.732275</td>\n",
-       "      <td>331.557329</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>3.422251</td>\n",
-       "      <td>358.150000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>3.072721</td>\n",
-       "      <td>332.812742</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>358.150000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       p_bar         t_k\n",
-       "0   2.500000  331.497138\n",
-       "1   4.000000  358.150000\n",
-       "2   3.765631  358.057213\n",
-       "3   3.531273  357.964539\n",
-       "4   3.413784  357.794807\n",
-       "5   3.081328  333.150000\n",
-       "6   2.964553  331.617595\n",
-       "7   2.732275  331.557329\n",
-       "8   3.422251  358.150000\n",
-       "9   3.072721  332.812742\n",
-       "10  4.000000  358.150000"
-      ]
-     },
-     "execution_count": 386,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "net.res_junction"
    ]
@@ -367,197 +241,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 387,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></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>t_outlet_k</th>\n",
-       "      <th>mdot_from_kg_per_s</th>\n",
-       "      <th>mdot_to_kg_per_s</th>\n",
-       "      <th>vdot_m3_per_s</th>\n",
-       "      <th>reynolds</th>\n",
-       "      <th>lambda</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.701154</td>\n",
-       "      <td>4.000000</td>\n",
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-       "      <td>358.150000</td>\n",
-       "      <td>358.057213</td>\n",
-       "      <td>358.057213</td>\n",
-       "      <td>5.440816</td>\n",
-       "      <td>-5.440816</td>\n",
-       "      <td>0.005618</td>\n",
-       "      <td>206203.128764</td>\n",
-       "      <td>0.019886</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.701109</td>\n",
-       "      <td>3.765631</td>\n",
-       "      <td>3.531273</td>\n",
-       "      <td>358.057213</td>\n",
-       "      <td>357.964539</td>\n",
-       "      <td>357.964539</td>\n",
-       "      <td>5.440816</td>\n",
-       "      <td>-5.440816</td>\n",
-       "      <td>0.005617</td>\n",
-       "      <td>205993.875359</td>\n",
-       "      <td>0.019886</td>\n",
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.494780</td>\n",
-       "      <td>3.531273</td>\n",
-       "      <td>3.413784</td>\n",
-       "      <td>357.964539</td>\n",
-       "      <td>357.794807</td>\n",
-       "      <td>357.833422</td>\n",
-       "      <td>3.839920</td>\n",
-       "      <td>-3.839920</td>\n",
-       "      <td>0.003964</td>\n",
-       "      <td>145182.298310</td>\n",
-       "      <td>0.020016</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.487498</td>\n",
-       "      <td>3.081328</td>\n",
-       "      <td>2.964553</td>\n",
-       "      <td>333.150000</td>\n",
-       "      <td>331.617595</td>\n",
-       "      <td>333.061953</td>\n",
-       "      <td>3.839920</td>\n",
-       "      <td>-3.839920</td>\n",
-       "      <td>0.003906</td>\n",
-       "      <td>103816.089343</td>\n",
-       "      <td>0.020192</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.690206</td>\n",
-       "      <td>2.964553</td>\n",
-       "      <td>2.732275</td>\n",
-       "      <td>331.617595</td>\n",
-       "      <td>331.557329</td>\n",
-       "      <td>331.557329</td>\n",
-       "      <td>5.440816</td>\n",
-       "      <td>-5.440816</td>\n",
-       "      <td>0.005530</td>\n",
-       "      <td>143655.525509</td>\n",
-       "      <td>0.020021</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0.690185</td>\n",
-       "      <td>2.732275</td>\n",
-       "      <td>2.500000</td>\n",
-       "      <td>331.557329</td>\n",
-       "      <td>331.497138</td>\n",
-       "      <td>331.497138</td>\n",
-       "      <td>5.440816</td>\n",
-       "      <td>-5.440816</td>\n",
-       "      <td>0.005530</td>\n",
-       "      <td>143521.552093</td>\n",
-       "      <td>0.020021</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>-0.128852</td>\n",
-       "      <td>3.413784</td>\n",
-       "      <td>3.422251</td>\n",
-       "      <td>357.794807</td>\n",
-       "      <td>358.150000</td>\n",
-       "      <td>358.049038</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.001032</td>\n",
-       "      <td>37806.972487</td>\n",
-       "      <td>0.021268</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>-0.126947</td>\n",
-       "      <td>3.072721</td>\n",
-       "      <td>3.081328</td>\n",
-       "      <td>332.812742</td>\n",
-       "      <td>333.150000</td>\n",
-       "      <td>333.082367</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.001017</td>\n",
-       "      <td>26985.157872</td>\n",
-       "      <td>0.021947</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   v_mean_m_per_s  p_from_bar  p_to_bar    t_from_k      t_to_k  t_outlet_k  \\\n",
-       "0        0.701154    4.000000  3.765631  358.150000  358.057213  358.057213   \n",
-       "1        0.701109    3.765631  3.531273  358.057213  357.964539  357.964539   \n",
-       "2        0.494780    3.531273  3.413784  357.964539  357.794807  357.833422   \n",
-       "3        0.487498    3.081328  2.964553  333.150000  331.617595  333.061953   \n",
-       "4        0.690206    2.964553  2.732275  331.617595  331.557329  331.557329   \n",
-       "5        0.690185    2.732275  2.500000  331.557329  331.497138  331.497138   \n",
-       "6       -0.128852    3.413784  3.422251  357.794807  358.150000  358.049038   \n",
-       "7       -0.126947    3.072721  3.081328  332.812742  333.150000  333.082367   \n",
-       "\n",
-       "   mdot_from_kg_per_s  mdot_to_kg_per_s  vdot_m3_per_s       reynolds  \\\n",
-       "0            5.440816         -5.440816       0.005618  206203.128764   \n",
-       "1            5.440816         -5.440816       0.005617  205993.875359   \n",
-       "2            3.839920         -3.839920       0.003964  145182.298310   \n",
-       "3            3.839920         -3.839920       0.003906  103816.089343   \n",
-       "4            5.440816         -5.440816       0.005530  143655.525509   \n",
-       "5            5.440816         -5.440816       0.005530  143521.552093   \n",
-       "6           -1.000000          1.000000      -0.001032   37806.972487   \n",
-       "7           -1.000000          1.000000      -0.001017   26985.157872   \n",
-       "\n",
-       "     lambda  \n",
-       "0  0.019886  \n",
-       "1  0.019886  \n",
-       "2  0.020016  \n",
-       "3  0.020192  \n",
-       "4  0.020021  \n",
-       "5  0.020021  \n",
-       "6  0.021268  \n",
-       "7  0.021947  "
-      ]
-     },
-     "execution_count": 387,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "net.res_pipe"
    ]
@@ -595,20 +281,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 388,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-      "text/plain": [
-       "<Figure size 2200x700 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
diff --git a/tutorials/district_heating/time_series_in_a_circular_district_heating_grid.ipynb b/tutorials/district_heating/time_series_in_a_circular_district_heating_grid.ipynb
index ed39fa094..1c2b0517b 100644
--- a/tutorials/district_heating/time_series_in_a_circular_district_heating_grid.ipynb
+++ b/tutorials/district_heating/time_series_in_a_circular_district_heating_grid.ipynb
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 165,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 166,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -72,20 +72,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 167,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 167,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Define junctions\n",
     "j1 = pp.create_junction(net, pn_bar=1.05, tfluid_k=supply_temperature_k, name=\"Pump Supply\", geodata=(0, 0))\n",
@@ -128,30 +117,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 168,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 168,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "pp.plotting.simple_plot(net)"
    ]
@@ -169,11 +137,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 169,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "pp.pipeflow(net, mode='bidirectional', iter=100, alpha=0.2)"
+    "pp.pipeflow(net, mode='bidirectional', iter=100, alpha=0.5)"
    ]
   },
   {
@@ -195,27 +163,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 170,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time dependant heat demand profile for the three consumers: \n",
-      " [[100000.  64000. 144000.]\n",
-      " [ 90000.  56000. 132000.]\n",
-      " [ 80000.  48000. 120000.]\n",
-      " [ 70000.  40000. 108000.]\n",
-      " [ 60000.  32000.  96000.]\n",
-      " [ 70000.  40000. 108000.]\n",
-      " [ 80000.  48000. 120000.]\n",
-      " [ 90000.  56000. 132000.]\n",
-      " [100000.  64000. 144000.]\n",
-      " [100000.  64000. 144000.]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "start = 0\n",
     "end = 10 # 8760 hours in a year\n",
@@ -262,23 +212,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "This ConstControl has the following parameters: \n",
-       "\n",
-       "index:             3\n",
-       "json_excludes:     ['self', '__class__']"
-      ]
-     },
-     "execution_count": 171,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "from pandapower.timeseries import DFData\n",
@@ -328,22 +264,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 172,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\jonas\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandapower\\timeseries\\output_writer.py:177: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '[0 1 2 3 4 5 6 7 8 9]' has dtype incompatible with bool, please explicitly cast to a compatible dtype first.\n",
-      "  self.output[\"Parameters\"].loc[:, \"time_step\"] = self.time_steps\n",
-      "c:\\Users\\jonas\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandapower\\control\\run_control.py:50: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-      "  level = controller.level.fillna(0).apply(asarray).values\n",
-      "100%|██████████| 10/10 [00:01<00:00,  8.91it/s]\n",
-      "100%|██████████| 10/10 [00:01<00:00,  8.91it/s]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from pandapipes.timeseries import run_time_series\n",
     "from pandapower.timeseries import OutputWriter\n",
@@ -365,7 +288,7 @@
     "\n",
     "ow = OutputWriter(net, time_steps, output_path=None, log_variables=log_variables)\n",
     "\n",
-    "run_time_series.run_timeseries(net, time_steps, mode=\"bidirectional\", iter=100, alpha=0.2)"
+    "run_time_series.run_timeseries(net, time_steps, mode=\"bidirectional\", iter=100, alpha=0.5)"
    ]
   },
   {
@@ -383,45 +306,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 173,
+   "execution_count": null,
    "metadata": {
     "vscode": {
      "languageId": "ruby"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Supply temperature at 'Pump Supply' over time:\n",
-      "0    363.15\n",
-      "1    358.15\n",
-      "2    353.15\n",
-      "3    348.15\n",
-      "4    348.15\n",
-      "5    348.15\n",
-      "6    348.15\n",
-      "7    353.15\n",
-      "8    358.15\n",
-      "9    368.15\n",
-      "Name: 0, dtype: float64\n",
-      "\n",
-      "Mass flow at main pump over time:\n",
-      "0    2.752428\n",
-      "1    3.060334\n",
-      "2    3.559999\n",
-      "3    4.520676\n",
-      "4    3.981185\n",
-      "5    4.520676\n",
-      "6    5.060498\n",
-      "7    3.945994\n",
-      "8    3.362175\n",
-      "9    2.332136\n",
-      "Name: 0, dtype: float64\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Extract time series results from OutputWriter\n",
     "results = ow.output\n",
@@ -456,40 +347,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 174,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",