{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Time Series Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial demonstrates how quasi-stationary time series analysis can be done with pandapipes. In the following, an already existing net is loaded from a json file. The prerequisites for a successful execution of a time series simulation are:\n",
    "\n",
    "- a pandapipes network\n",
    "- predefined time series for the sinks and sources in the network (e.g. 2 csv files)\n",
    "\n",
    "In this example, the following pandapipes network is loaded:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"pics/time_series_simple_example_network.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following modules are imported first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import pandapower.control as control\n",
    "from pandapower.timeseries import DFData\n",
    "from pandapower.timeseries import OutputWriter\n",
    "from pandapipes.timeseries import run_timeseries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not that the time series functionality is provided by the pandapower module. But because the structure of pandapipes is similar to the one of pandapower, we can use the same functions here. \n",
    "\n",
    "With the following import statement, stored networks can be loaded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandapipes import networks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The loading procedure is started with this function call:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "net = networks.simple_gas_networks.gas_meshed_delta()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the delta STANET network from the chapter [Meshed Networks](https://pandapipes.readthedocs.io/en/latest/networks/meshed/meshed_networks.html) with the medium gas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, time series data for the sink and source components is added. For every provided data point, an own pandapipes calculation will be executed. This is of course done automatically. Thus, the number of time steps in this case is 10. Since there is only one sink and one source, the respective csv file only consists of one column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"pics/time_series_simple_example_csv_files.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "profiles_sink = pd.read_csv(os.path.join('files',\n",
    "                                         'simple_time_series_example_sink_profiles.csv'),\n",
    "                                         index_col=0)\n",
    "profiles_source = pd.read_csv(os.path.join('files',\n",
    "                                           'simple_time_series_example_source_profiles.csv'),\n",
    "                                           index_col=0)\n",
    "ds_sink = DFData(profiles_sink)\n",
    "ds_source = DFData(profiles_source)\n",
    "\n",
    "const_sink = control.ConstControl(net, element='sink', variable='mdot_kg_per_s',\n",
    "                                  element_index=net.sink.index.values, data_source=ds_sink,\n",
    "                                  profile_name=net.sink.index.values.astype(str))\n",
    "const_source = control.ConstControl(net, element='source', variable='mdot_kg_per_s',\n",
    "                                    element_index=net.source.index.values,\n",
    "                                    data_source=ds_source,\n",
    "                                    profile_name=net.source.index.values.astype(str))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the corresponding csv-file is read out, prepared as data sources (ds) for the controllers and the resulting DataFrames are written into the network with the help of the controller [ConstControl](https://pandapower.readthedocs.io/en/v2.2.2/control/controller.html#constcontrol). Then the desired time steps for which the network is to be calculated must be specified. In the present case, these can be defined from 1 to 10 at most, since only 10 time steps are specified for the sink and source. Likewise, for example, a range of 3 to 6 can be specified. Here the time steps from 1 to 10 are defined with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_steps = range(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Furthermore an [OuputWriter](https://pandapower.readthedocs.io/en/v2.2.2/timeseries/output_writer.html) must be created, which is defined as `ow` using"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_variables = [('res_junction', 'p_bar'), ('res_pipe', 'v_mean_m_per_s'),\n",
    "                 ('res_pipe', 'reynolds'), ('res_pipe', 'lambda'),\n",
    "                 ('res_sink', 'mdot_kg_per_s'), ('res_source', 'mdot_kg_per_s'),\n",
    "                 ('res_ext_grid', 'mdot_kg_per_s')]\n",
    "ow = OutputWriter(net, time_steps, output_path=None, log_variables=log_variables)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the output writer the results of the simulation are stored. Finally, the main function can be called to run the time series simulation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress: |██████████████████████████████████████████████████| 100.0% Complete\n",
      "\n"
     ]
    }
   ],
   "source": [
    "run_timeseries(net, time_steps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results of the simulation can be accessed via the OutputWriter as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pressure:\n",
      "[[3.49975257 3.5        3.49961173]\n",
      " [3.49973649 3.5        3.49949785]\n",
      " [3.49972162 3.5        3.49936071]\n",
      " [3.49970758 3.5        3.49920016]\n",
      " [3.49969414 3.5        3.4990161 ]\n",
      " [3.49968112 3.5        3.49880846]\n",
      " [3.49966839 3.5        3.49857714]\n",
      " [3.49965585 3.5        3.4983221 ]\n",
      " [3.49964345 3.5        3.49804329]\n",
      " [3.49963112 3.5        3.49774068]]\n",
      "mean velocity:\n",
      "[[-0.57651001 -0.49611582 -0.21696278]\n",
      " [-0.76534799 -0.51363216 -0.25383547]\n",
      " [-0.95276066 -0.5293793  -0.2932576 ]\n",
      " [-1.13916058 -0.54386481 -0.33449866]\n",
      " [-1.32480882 -0.55740925 -0.37709744]\n",
      " [-1.5099034  -0.57025531 -0.42070489]\n",
      " [-1.69457795 -0.58256615 -0.46508642]\n",
      " [-1.87893324 -0.59446437 -0.51006588]\n",
      " [-2.06302946 -0.60602249 -0.55553927]\n",
      " [-2.24692628 -0.61731235 -0.60140351]]\n",
      "reynolds number:\n",
      "[[17616.51199081 13644.68760488  4971.54745982]\n",
      " [23386.16464632 14126.19817054  5816.74628673]\n",
      " [29112.07624708 14559.10756425  6720.26651999]\n",
      " [34807.4075172  14958.03881282  7664.56052742]\n",
      " [40478.68817099 15330.24715434  8640.92202331]\n",
      " [46132.73805781 15683.31019923  9640.25787515]\n",
      " [51773.58389153 16021.70207401 10657.19913113]\n",
      " [57404.8788946  16349.48191474 11686.87482796]\n",
      " [63026.79298166 16666.83851545 12729.05841282]\n",
      " [68641.89412809 16976.62518213 13780.32591852]]\n",
      "lambda:\n",
      "[[0.02324864 0.02481883 0.03393751]\n",
      " [0.02235235 0.02465895 0.03206697]\n",
      " [0.02181409 0.02452424 0.03058769]\n",
      " [0.02145438 0.024407   0.02941438]\n",
      " [0.02119677 0.02430312 0.02847087]\n",
      " [0.02100299 0.02420913 0.02770308]\n",
      " [0.02085184 0.02412294 0.02706959]\n",
      " [0.02073058 0.02404286 0.02654048]\n",
      " [0.02063113 0.02396832 0.02609212]\n",
      " [0.02054806 0.02389825 0.02570856]]\n",
      "mass flow sink:\n",
      "[[0.02 ]\n",
      " [0.026]\n",
      " [0.032]\n",
      " [0.038]\n",
      " [0.044]\n",
      " [0.05 ]\n",
      " [0.056]\n",
      " [0.062]\n",
      " [0.068]\n",
      " [0.074]]\n",
      "mass flow source:\n",
      "[[0.005]\n",
      " [0.01 ]\n",
      " [0.015]\n",
      " [0.02 ]\n",
      " [0.025]\n",
      " [0.03 ]\n",
      " [0.035]\n",
      " [0.04 ]\n",
      " [0.045]\n",
      " [0.05 ]]\n",
      "mass flow ext. grid:\n",
      "[[-0.015]\n",
      " [-0.016]\n",
      " [-0.017]\n",
      " [-0.018]\n",
      " [-0.019]\n",
      " [-0.02 ]\n",
      " [-0.021]\n",
      " [-0.022]\n",
      " [-0.023]\n",
      " [-0.024]]\n"
     ]
    }
   ],
   "source": [
    "print(\"pressure:\")\n",
    "print(ow.np_results[\"res_junction.p_bar\"])\n",
    "print(\"mean velocity:\")\n",
    "print(ow.np_results[\"res_pipe.v_mean_m_per_s\"])\n",
    "print(\"reynolds number:\")\n",
    "print(ow.np_results[\"res_pipe.reynolds\"])\n",
    "print(\"lambda:\")\n",
    "print(ow.np_results[\"res_pipe.lambda\"])\n",
    "print(\"mass flow sink:\")\n",
    "print(ow.np_results[\"res_sink.mdot_kg_per_s\"])\n",
    "print(\"mass flow source:\")\n",
    "print(ow.np_results[\"res_source.mdot_kg_per_s\"])\n",
    "print(\"mass flow ext. grid:\")\n",
    "print(ow.np_results[\"res_ext_grid.mdot_kg_per_s\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To view the results more clearly, they can be displayed with `matplotlib.pyplot` in a diagram with the time steps as x-axis.The time curve for the mass flow in the External Grid looks as follows:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"pics/time_series_simple_example_plot_mass_flow_ext_grid.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The figure can be created by the following lines:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = time_steps\n",
    "y = ow.np_results[\"res_ext_grid.mdot_kg_per_s\"]\n",
    "\n",
    "plt.xlabel(\"time step\")\n",
    "plt.ylabel(\"mass flow [kg/s]\")\n",
    "plt.title(\"External Grid Mass Flow Profile\")\n",
    "plt.plot(x, y, \"r-o\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition, the pressure curves for the individual junctions can be plotted with this code block:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = time_steps\n",
    "y = ow.np_results[\"res_junction.p_bar\"]\n",
    "\n",
    "plt.xlabel(\"time step\")\n",
    "plt.ylabel(\"pressure [bar]\")\n",
    "plt.title(\"Pressure Profile\")\n",
    "plt.plot(x, y[:,0], \"g-o\")\n",
    "plt.plot(x, y[:,1], \"r-o\")\n",
    "plt.plot(x, y[:,2], \"y-o\")\n",
    "plt.legend([\"Source\", \"External Grid\", \"Sink\"], loc='lower left')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"pics/time_series_simple_example_plot_pressures.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a final example to illustrate the results, the flow velocities of the three pipes are plotted:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"pics/time_series_simple_example_plot_velocities.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this purpose, the following code must be executed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = time_steps\n",
    "y = ow.np_results[\"res_pipe.v_mean_m_per_s\"]\n",
    "\n",
    "plt.xlabel(\"time step\")\n",
    "plt.ylabel(\"v_mean [m/s]\")\n",
    "plt.title(\"Velocity Profile\")\n",
    "plt.plot(x, y[:,0], \"g-o\")\n",
    "plt.plot(x, y[:,1], \"r-o\")\n",
    "plt.plot(x, y[:,2], \"y-o\")\n",
    "plt.legend([\"Pipe 1\", \"Pipe 2\", \"Pipe 3\"], loc='lower left')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}