{
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   "source": [
    "# MD for Experiment Validation\n",
    "\n",
    "The YAML input file can be found at [input_file](https://raw.githubusercontent.com/murillo-group/sarkas/master/docs/examples/UNP/input_files/ucp_N10k.yaml) and this notebook at [notebook](https://raw.githubusercontent.com/murillo-group/sarkas/master/docs/examples/UNP/UCP_Experiment_Validation.ipynb).\n",
    "\n",
    "## Ultracold Neutral Plasmas\n",
    "Sarkas has been developed with the experimental plasma physics community in mind. An example of strongly coupled plasmas are [**Ultracold Neutral Plasmas**](https://science.sciencemag.org/content/316/5825/705). In these systems a cloud of $N \\sim 10^6 - 10^9$ neutral atoms is trapped and cooled to ~mK temperatures. The cloud has a spherical Gaussian density distribution with peak densities in the range $\\sim 10^{15}-10^{16}\\, \\textrm{N/cc}$  with a standard deviation ranging from hundreds of $\\mu$m to few mm. A portion of the atoms is then ionized by shining a laser, with the correct wavelength, onto the cloud. The energy from the laser is deposited on the electrons which now have enough energy to escape the the atoms, thus, leaving behind positively charged ions.\n",
    "\n",
    "UNPs are an optimal experimental setup to study strongly coupled plasmas since the charge number, the number density, and the temperature of the plasma are well defined and controllable. This is in contrast to other experiments in which some parameters are not well known and require simulations and models to be extracted from experimental data. \n",
    "\n",
    "UNPs are considered strongly coupled plasmas because of their low kinetic energy. The strong coupling parameter is defined as\n",
    "\n",
    "$$ \\Gamma = \\frac{(Ze)^2}{4\\pi \\varepsilon_0 a_{\\rm ws}} \\frac{1}{k_B T} $$\n",
    "\n",
    "where $Z$ is the charge number of the ions, $T$ their temperature, and $a = (3/ 4\\pi n)^{1/3}$ is the Wigner-Seitz radius defined by the number density $n$. In UNP experiments $1 < \\Gamma \\sim 10$.\n",
    "\n",
    "## Disorder Induced Heating\n",
    "In the laser-cooling phase of the experiment atoms are neutral. This allows them to get very close to each other and distribute randomly in space. The potential energy in this case is very low and so is the kinetic energy. \n",
    "By photoionizing the atoms we are effectively turning on the Coulomb interaction. The system now has a large potential energy which pushes the ions apart, thus the potential energy is converted into kinetic energy ($\\sim k_BT$). This process is called **Disorder Induced Heating (DIH)**.\n",
    "\n",
    "DIH is unavoidable and it is one of the biggest obstacles that prevents UNPs to reach $\\Gamma > 10$. This is because the initial potential energy is what determines the final temperature and the final $\\Gamma$. Large potential energies lead to large kinetic energies and low $\\Gamma$. Furthermore, in an experiment the temperature evolution of the ions in the DIH phase is used to determine/validate the number density of the initial charged cloud. \n",
    "\n",
    "In the following example we will present a workflow to prepare and run an MD simulation and extract the DIH temperature of a UNP."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PreProcessing\n",
    "\n",
    "We start by importing the usual libraries and creating a path to the input file of our simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: Qt5Agg\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab\n",
    "%matplotlib inline\n",
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "from sarkas.processes import PreProcess, Simulation, PostProcess\n",
    "\n",
    "plt.style.use('MSUstyle')\n",
    "input_file = os.path.join('input_files', 'ucp_N10k.yaml')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then run the ``PreProcess.run()`` method to validate our simulation parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;110;0;95m\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " _______              __                \n",
      "|     __|.---.-.----.|  |--.---.-.-----.\n",
      "|__     ||  _  |   _||    <|  _  |__ --|\n",
      "|_______||___._|__|  |__|__|___._|_____|\n",
      "                                        \n",
      "\n",
      "\u001b[0m\n",
      "An open-source pure-python molecular dynamics code for non-ideal plasmas.\n",
      "\n",
      "\n",
      "\n",
      "* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *\n",
      "                                 Preprocessing                                  \n",
      "* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *\n",
      "\n",
      "Job ID:  ucp_N10k\n",
      "Job directory:  UCP_DIH_N10k/ucp_N10k\n",
      "Equilibration dumps directory:  UCP_DIH_N10k/ucp_N10k/PreProcessing/Equilibration/dumps\n",
      "Production dumps directory:  UCP_DIH_N10k/ucp_N10k/PreProcessing/Production/dumps\n",
      "Random Seed =  651951\n",
      "\n",
      "PARTICLES:\n",
      "Total No. of particles =  10000\n",
      "No. of species =  1\n",
      "Species ID: 0\n",
      "\tName: Sr\n",
      "\tNo. of particles = 10000 \n",
      "\tNumber density = 4.000000e+15 [N/m^3]\n",
      "\tAtomic weight = 88.0000 [a.u.]\n",
      "\tMass = 1.471907e-25 [kg]\n",
      "\tMass density = 5.887629e-10 [kg/m^3]\n",
      "\tCharge number/ionization degree = 1.0000 \n",
      "\tCharge = 1.602177e-19 [C]\n",
      "\tTemperature = 2.000000e-03 [K] = 1.723467e-07 [eV]\n",
      "\tDebye Length = 4.879672e-08 [Hz]\n",
      "\tPlasma Frequency = 8.876174e+06 [Hz]\n",
      "\n",
      "SIMULATION BOX:\n",
      "Units:  mks\n",
      "Wigner-Seitz radius = 3.907963e-06 [m]\n",
      "No. of non-zero box dimensions =  3\n",
      "Box side along x axis = 3.472931e+01 a_ws = 1.357209e-04 [m]\n",
      "Box side along y axis = 3.472931e+01 a_ws = 1.357209e-04 [m]\n",
      "Box side along z axis = 3.472931e+01 a_ws = 1.357209e-04 [m]\n",
      "Box Volume = 2.500000e-12 [m^3]\n",
      "Boundary conditions: periodic\n",
      "\n",
      "ELECTRON PROPERTIES:\n",
      "Number density: n_e = 4.000000e+15 [N/m^3]\n",
      "Wigner-Seitz radius: a_e = 3.907963e-06 [m]\n",
      "Temperature: T_e = 4.300000e+02 [K] = 3.705453e-02 [eV]\n",
      "de Broglie wavelength: lambda_deB = 3.594560e-09 [m]\n",
      "Thomas-Fermi length: lambda_TF = 2.262611e-05 [m]\n",
      "Fermi wave number: k_F = 4.910891e+05 [1/m]\n",
      "Fermi Energy: E_F = 9.188478e-09 [eV]\n",
      "Relativistic parameter: x_F = 0.000000 --> E_F = 9.190629e-09 [eV]\n",
      "Degeneracy parameter: Theta = 4032717.0039 \n",
      "Coupling: r_s = 73849.801701,  Gamma_e = 0.009944\n",
      "Warm Dense Matter Parameter: W = 0.0000\n",
      "Chemical potential: mu = -23.099609 k_B T_e = -93154186.8939 E_F\n",
      "\n",
      "THERMOSTAT: \n",
      "Type: Berendsen\n",
      "First thermostating timestep, i.e. relaxation_timestep = 0\n",
      "Berendsen parameter tau: 5.000 [s]\n",
      "Berendsen relaxation rate: 0.200 [Hz] \n",
      "\n",
      "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! WARNING !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n",
      "Equilibration temperatures not defined. I will use the species's temperatures.\n",
      "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n",
      "\n",
      "Thermostating temperatures: \n",
      "Species ID 0: T_eq = 2.000000e-03 [K] = 1.723467e-07 [eV]\n",
      "\n",
      "POTENTIAL:  Yukawa\n",
      "electron temperature = 4.3000e+02 [K] = 3.7055e-02 [eV]\n",
      "kappa = 0.1727\n",
      "Gamma_eff = 2137.95\n",
      "\n",
      "ALGORITHM:  P3M\n",
      "Mesh = 64 x 64 x 64\n",
      "Ewald parameter alpha = 0.5700 / a_ws = 1.458560e+05 [1/m]\n",
      "Mesh width = 0.5426, 0.5426, 0.5426 a_ws\n",
      "           = 2.1206e-06, 2.1206e-06, 2.1206e-06 [m]\n",
      "Mesh size * Ewald_parameter (h * alpha) = 0.3093, 0.3093, 0.3093 \n",
      "                                        ~ 1/3, 1/3, 1/3\n",
      "rcut = 6.0050 a_ws = 2.346716e-05 [m]\n",
      "No. of PP cells per dimension =  5,  5,  5\n",
      "No. of particles in PP loop =   1395\n",
      "No. of PP neighbors per particle =    216\n",
      "PM Force Error = 3.482296e-06\n",
      "PP Force Error = 3.181860e-06\n",
      "Tot Force Error = 4.717056e-06\n",
      "\n",
      "INTEGRATOR: \n",
      "Type: Verlet\n",
      "Time step = 3.019042e-10 [s]\n",
      "Total plasma frequency = 8.876174e+06 [Hz]\n",
      "w_p dt = 0.0027\n",
      "\n",
      "Equilibration: \n",
      "No. of equilibration steps = 0 \n",
      "Total equilibration time = 0.0000e+00 [s] ~ 0 w_p T_eq \n",
      "snapshot interval step = 5 \n",
      "snapshot interval time = 1.5095e-09 [s] = 0.0134 w_p T_snap\n",
      "Total number of snapshots = 0 \n",
      "\n",
      "Production: \n",
      "No. of production steps = 10000 \n",
      "Total production time = 3.0190e-06 [s] ~ 26 w_p T_prod \n",
      "snapshot interval step = 5 \n",
      "snapshot interval time = 1.5095e-09 [s] = 0.0134 w_p T_snap\n",
      "Total number of snapshots = 2000 \n",
      "\n",
      "\n",
      "------------------------ Initialization Times ------------------------ \n",
      "\n",
      "\n",
      "Potential Initialization Time: 0 hrs 0 min 3 sec\n",
      "\n",
      "Particles Initialization Time: 0 sec 1 msec 821 usec 58 nsec\n",
      "\n",
      "Total Simulation Initialization Time: 0 hrs 0 min 3 sec\n",
      "\n",
      "\n",
      "========================== Times Estimates =========================== \n",
      "\n",
      "Optimal Green's Function Time: \n",
      "0 min 3 sec 713 msec 803 usec 454 nsec \n",
      "\n",
      "Time of PP acceleration calculation averaged over 15 steps: \n",
      "0 min 0 sec 252 msec 560 usec 197 nsec \n",
      "\n",
      "Time of PM acceleration calculation averaged over 15 steps: \n",
      "0 min 0 sec 6 msec 4 usec 525 nsec \n",
      "\n",
      "\n",
      "Running 15 equilibration and production steps to estimate simulation times\n",
      "\n"
     ]
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     "text": [
      "\n",
      "Time of a single equilibration step averaged over 15 steps: \n",
      "0 min 0 sec 310 msec 992 usec 655 nsec \n",
      "\n"
     ]
    },
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     "text": [
      "\n",
      "Time of a single production step averaged over 15 steps: \n",
      "0 min 0 sec 313 msec 249 usec 470 nsec \n",
      "\n",
      "\n",
      "\n",
      "----------------------- Total Estimated Times ------------------------ \n",
      "\n",
      "\n",
      "Equilibration Time: 0 sec 0 msec 0 usec 0 nsec\n",
      "\n",
      "Production Time: 0 hrs 52 min 12 sec\n",
      "\n",
      "Total Run Time: 0 hrs 52 min 12 sec\n",
      "\n",
      "\n",
      "========================= Filesize Estimates ========================= \n",
      "\n",
      "\n",
      "Equilibration:\n",
      "\n",
      "Checkpoint filesize: 0 GB 0 MB 860 KB 822 bytes\n",
      "Checkpoint folder size: 0 GB 0 MB 0 KB 0 bytes\n",
      "\n",
      "Production:\n",
      "\n",
      "Checkpoint filesize: 0 GB 0 MB 860 KB 822 bytes\n",
      "Checkpoint folder size: 1 GB 657 MB 261 KB 480 bytes\n",
      "\n",
      "Total minimum needed space: 1 GB 657 MB 261 KB 480 bytes\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the class\n",
    "preproc = PreProcess(input_file)\n",
    "# Setup Sarkas classes and initialize all the parameters\n",
    "preproc.setup(read_yaml=True)\n",
    "# Run 100 steps to calculate the total time of the simulation.\n",
    "# Also show plots of the Force error to validate the PPPM parameters.\n",
    "preproc.run(loops = 15, pppm_estimate = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulation\n",
    "\n",
    "Once the simulation parameters have been decided we can start running our simulation. Since we are dealing with a non-equilibrium simulation, in order to get some statistics we need to run multiple simulations with different initial conditions. This can be done by changing the random number seed. \n",
    "\n",
    "Below we show a sample code that executes three (3) MD simulation by changing only the `rand_seed` parameter and the storing directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 0 completed\n",
      "Run 1 completed\n",
      "Run 2 completed\n",
      "Run 3 completed\n",
      "Run 4 completed\n"
     ]
    }
   ],
   "source": [
    "# Define a random number generator\n",
    "rg = np.random.Generator( np.random.PCG64(154245) )\n",
    "\n",
    "# Loop over the number of independent MD runs to perform\n",
    "for i in range(5):\n",
    "    # Get a new seed\n",
    "    seed = rg.integers(0, 15198)\n",
    "    \n",
    "    args = {\n",
    "        \"Parameters\": {\"rand_seed\": seed}, # define a new rand_seed for each simulation\n",
    "        \n",
    "        \"IO\":   # Store all simulations' data in simulations_dir, \n",
    "                # but save the dumps in different subfolders (job_dir) \n",
    "            {\n",
    "                \"simulations_dir\": 'UCP_DIH_N10k',\n",
    "                \"job_dir\": \"run{}\".format(i),\n",
    "                \"verbose\": False # This is so not to print to screen for every run\n",
    "            },\n",
    "    }\n",
    "    # Run the simulation.\n",
    "    sim = Simulation(input_file)\n",
    "    sim.setup(read_yaml=True, other_inputs=args)\n",
    "    sim.run()\n",
    "    print('Run {} completed'.format(i) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above code will take a long time, because we need to wait for the previous simulation to finish before starting a new one. Notice that we define the `rand_seed` parameter in each loop. This is because even though we are in the same Python process Sarkas has a default `rand_seed`. Thus, if we don't pass it we will end up with three identical simulations.\n",
    "\n",
    "If we can afford to occupy few processors on our computer we can save time by running each simulation in a different JupyterNotebook or by running three Python scripts at the same time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PostProcessing\n",
    "\n",
    "Once the simulations are done, we can start our Post Processing. In the following we will calculate the average temperature and RMS speed of each species in the mixture. Save them into a [pandas.DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html) and plot them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the storing df\n",
    "Temp_vel_data = pd.DataFrame()\n",
    "\n",
    "# No. of independent runs\n",
    "runs = 5\n",
    "\n",
    "# Loop over the runs and initialize the PostProcess\n",
    "for i in range(runs):\n",
    "    # This is a copy-n-paste from the code snippet above. We only change sim with postproc\n",
    "    # Some lines are commented out because they are not needed. \n",
    "    \n",
    "    # Get a new seed\n",
    "    #seed = rg.integers(0, 1598765198)\n",
    "    \n",
    "    args = {\n",
    "        # \"Parameters\": {\"rand_seed\": seed}, # define a new rand_seed for each simulation\n",
    "        \n",
    "        \"IO\":   # Store all simulations' data in simulations_dir, \n",
    "                # but save the dumps in different subfolders (job_dir) \n",
    "            {\n",
    "                \"simulations_dir\": 'UCP_DIH_N10k',\n",
    "                \"job_dir\": \"run{}\".format(i),\n",
    "                \"verbose\": False  # This is so not to print to screen for every run\n",
    "            },\n",
    "    }\n",
    "    \n",
    "    postproc = PostProcess(input_file)\n",
    "    postproc.setup(read_yaml = True, other_inputs=args)\n",
    "        \n",
    "    # Rename the Thermodynamics class for convenience\n",
    "    E = postproc.therm\n",
    "    # Setup its attributes\n",
    "    E.setup(postproc.parameters)\n",
    "    # Grab the data\n",
    "    E.parse()\n",
    "\n",
    "    # Save the time array once. Obviously this has to be the same for each run.\n",
    "    if i == 0:               \n",
    "        Temp_vel_data['Time'] = E.dataframe[\"Time\"] * 1e6 # scale by microsec\n",
    "\n",
    "    # Grab the total temperature. This is the only line needed in the case of a One-Component plasma\n",
    "    Temp_vel_data['Temperature run {}'.format(i)] = E.dataframe[\"Temperature\"]\n",
    "    Temp_vel_data['Gamma run {}'.format(i)] = E.qe**2/ (E.fourpie0 * E.kB * E.a_ws * E.dataframe['Temperature']) \n",
    "    Temp_vel_data['RMS Speed run {}'.format(i)] = np.sqrt(E.kB * E.dataframe['Temperature'] / postproc.species[0].mass)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>Time</th>\n",
       "      <th>Temperature run 0</th>\n",
       "      <th>Gamma run 0</th>\n",
       "      <th>RMS Speed run 0</th>\n",
       "      <th>Temperature run 1</th>\n",
       "      <th>Gamma run 1</th>\n",
       "      <th>RMS Speed run 1</th>\n",
       "      <th>Temperature run 2</th>\n",
       "      <th>Gamma run 2</th>\n",
       "      <th>RMS Speed run 2</th>\n",
       "      <th>Temperature run 3</th>\n",
       "      <th>Gamma run 3</th>\n",
       "      <th>RMS Speed run 3</th>\n",
       "      <th>Temperature run 4</th>\n",
       "      <th>Gamma run 4</th>\n",
       "      <th>RMS Speed run 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002012</td>\n",
       "      <td>2125.666966</td>\n",
       "      <td>0.434378</td>\n",
       "      <td>0.001992</td>\n",
       "      <td>2147.003605</td>\n",
       "      <td>0.432214</td>\n",
       "      <td>0.001993</td>\n",
       "      <td>2145.592909</td>\n",
       "      <td>0.432356</td>\n",
       "      <td>0.002012</td>\n",
       "      <td>2125.365953</td>\n",
       "      <td>0.434409</td>\n",
       "      <td>0.002005</td>\n",
       "      <td>2132.681082</td>\n",
       "      <td>0.433663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.001510</td>\n",
       "      <td>0.009476</td>\n",
       "      <td>451.222732</td>\n",
       "      <td>0.942801</td>\n",
       "      <td>0.006148</td>\n",
       "      <td>695.477531</td>\n",
       "      <td>0.759406</td>\n",
       "      <td>0.009681</td>\n",
       "      <td>441.675962</td>\n",
       "      <td>0.952936</td>\n",
       "      <td>0.012002</td>\n",
       "      <td>356.270734</td>\n",
       "      <td>1.061025</td>\n",
       "      <td>0.003814</td>\n",
       "      <td>1121.000474</td>\n",
       "      <td>0.598154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.003019</td>\n",
       "      <td>0.023311</td>\n",
       "      <td>183.432031</td>\n",
       "      <td>1.478693</td>\n",
       "      <td>0.016799</td>\n",
       "      <td>254.530966</td>\n",
       "      <td>1.255294</td>\n",
       "      <td>0.022098</td>\n",
       "      <td>193.493293</td>\n",
       "      <td>1.439735</td>\n",
       "      <td>0.021207</td>\n",
       "      <td>201.626702</td>\n",
       "      <td>1.410398</td>\n",
       "      <td>0.009698</td>\n",
       "      <td>440.910836</td>\n",
       "      <td>0.953763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.004529</td>\n",
       "      <td>0.038579</td>\n",
       "      <td>110.834706</td>\n",
       "      <td>1.902294</td>\n",
       "      <td>0.029746</td>\n",
       "      <td>143.745577</td>\n",
       "      <td>1.670392</td>\n",
       "      <td>0.035179</td>\n",
       "      <td>121.547016</td>\n",
       "      <td>1.816534</td>\n",
       "      <td>0.031782</td>\n",
       "      <td>134.538921</td>\n",
       "      <td>1.726600</td>\n",
       "      <td>0.018777</td>\n",
       "      <td>227.720617</td>\n",
       "      <td>1.327133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.006038</td>\n",
       "      <td>0.054996</td>\n",
       "      <td>77.749003</td>\n",
       "      <td>2.271267</td>\n",
       "      <td>0.043874</td>\n",
       "      <td>97.458663</td>\n",
       "      <td>2.028642</td>\n",
       "      <td>0.049228</td>\n",
       "      <td>86.859581</td>\n",
       "      <td>2.148853</td>\n",
       "      <td>0.043945</td>\n",
       "      <td>97.301324</td>\n",
       "      <td>2.030282</td>\n",
       "      <td>0.030239</td>\n",
       "      <td>141.405760</td>\n",
       "      <td>1.684155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.007548</td>\n",
       "      <td>0.072377</td>\n",
       "      <td>59.078500</td>\n",
       "      <td>2.605559</td>\n",
       "      <td>0.058923</td>\n",
       "      <td>72.567377</td>\n",
       "      <td>2.350958</td>\n",
       "      <td>0.064311</td>\n",
       "      <td>66.487554</td>\n",
       "      <td>2.456097</td>\n",
       "      <td>0.057549</td>\n",
       "      <td>74.299751</td>\n",
       "      <td>2.323389</td>\n",
       "      <td>0.043533</td>\n",
       "      <td>98.223216</td>\n",
       "      <td>2.020731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.009057</td>\n",
       "      <td>0.090588</td>\n",
       "      <td>47.201953</td>\n",
       "      <td>2.914980</td>\n",
       "      <td>0.074789</td>\n",
       "      <td>57.172839</td>\n",
       "      <td>2.648627</td>\n",
       "      <td>0.080374</td>\n",
       "      <td>53.200328</td>\n",
       "      <td>2.745734</td>\n",
       "      <td>0.072451</td>\n",
       "      <td>59.017560</td>\n",
       "      <td>2.606904</td>\n",
       "      <td>0.058304</td>\n",
       "      <td>73.337758</td>\n",
       "      <td>2.338578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.010567</td>\n",
       "      <td>0.109529</td>\n",
       "      <td>39.039205</td>\n",
       "      <td>3.205274</td>\n",
       "      <td>0.091403</td>\n",
       "      <td>46.780602</td>\n",
       "      <td>2.928079</td>\n",
       "      <td>0.097322</td>\n",
       "      <td>43.935660</td>\n",
       "      <td>3.021392</td>\n",
       "      <td>0.088517</td>\n",
       "      <td>48.306028</td>\n",
       "      <td>2.881476</td>\n",
       "      <td>0.074312</td>\n",
       "      <td>57.539937</td>\n",
       "      <td>2.640164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.012076</td>\n",
       "      <td>0.129121</td>\n",
       "      <td>33.115612</td>\n",
       "      <td>3.480160</td>\n",
       "      <td>0.108710</td>\n",
       "      <td>39.333349</td>\n",
       "      <td>3.193267</td>\n",
       "      <td>0.115059</td>\n",
       "      <td>37.162774</td>\n",
       "      <td>3.285198</td>\n",
       "      <td>0.105623</td>\n",
       "      <td>40.482759</td>\n",
       "      <td>3.147608</td>\n",
       "      <td>0.091377</td>\n",
       "      <td>46.793979</td>\n",
       "      <td>2.927660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.013586</td>\n",
       "      <td>0.149300</td>\n",
       "      <td>28.639787</td>\n",
       "      <td>3.742232</td>\n",
       "      <td>0.126656</td>\n",
       "      <td>33.760134</td>\n",
       "      <td>3.446780</td>\n",
       "      <td>0.133493</td>\n",
       "      <td>32.030846</td>\n",
       "      <td>3.538600</td>\n",
       "      <td>0.123657</td>\n",
       "      <td>34.578691</td>\n",
       "      <td>3.405739</td>\n",
       "      <td>0.109361</td>\n",
       "      <td>39.099164</td>\n",
       "      <td>3.202815</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Time  Temperature run 0  Gamma run 0  RMS Speed run 0  \\\n",
       "0  0.000000           0.002012  2125.666966         0.434378   \n",
       "1  0.001510           0.009476   451.222732         0.942801   \n",
       "2  0.003019           0.023311   183.432031         1.478693   \n",
       "3  0.004529           0.038579   110.834706         1.902294   \n",
       "4  0.006038           0.054996    77.749003         2.271267   \n",
       "5  0.007548           0.072377    59.078500         2.605559   \n",
       "6  0.009057           0.090588    47.201953         2.914980   \n",
       "7  0.010567           0.109529    39.039205         3.205274   \n",
       "8  0.012076           0.129121    33.115612         3.480160   \n",
       "9  0.013586           0.149300    28.639787         3.742232   \n",
       "\n",
       "   Temperature run 1  Gamma run 1  RMS Speed run 1  Temperature run 2  \\\n",
       "0           0.001992  2147.003605         0.432214           0.001993   \n",
       "1           0.006148   695.477531         0.759406           0.009681   \n",
       "2           0.016799   254.530966         1.255294           0.022098   \n",
       "3           0.029746   143.745577         1.670392           0.035179   \n",
       "4           0.043874    97.458663         2.028642           0.049228   \n",
       "5           0.058923    72.567377         2.350958           0.064311   \n",
       "6           0.074789    57.172839         2.648627           0.080374   \n",
       "7           0.091403    46.780602         2.928079           0.097322   \n",
       "8           0.108710    39.333349         3.193267           0.115059   \n",
       "9           0.126656    33.760134         3.446780           0.133493   \n",
       "\n",
       "   Gamma run 2  RMS Speed run 2  Temperature run 3  Gamma run 3  \\\n",
       "0  2145.592909         0.432356           0.002012  2125.365953   \n",
       "1   441.675962         0.952936           0.012002   356.270734   \n",
       "2   193.493293         1.439735           0.021207   201.626702   \n",
       "3   121.547016         1.816534           0.031782   134.538921   \n",
       "4    86.859581         2.148853           0.043945    97.301324   \n",
       "5    66.487554         2.456097           0.057549    74.299751   \n",
       "6    53.200328         2.745734           0.072451    59.017560   \n",
       "7    43.935660         3.021392           0.088517    48.306028   \n",
       "8    37.162774         3.285198           0.105623    40.482759   \n",
       "9    32.030846         3.538600           0.123657    34.578691   \n",
       "\n",
       "   RMS Speed run 3  Temperature run 4  Gamma run 4  RMS Speed run 4  \n",
       "0         0.434409           0.002005  2132.681082         0.433663  \n",
       "1         1.061025           0.003814  1121.000474         0.598154  \n",
       "2         1.410398           0.009698   440.910836         0.953763  \n",
       "3         1.726600           0.018777   227.720617         1.327133  \n",
       "4         2.030282           0.030239   141.405760         1.684155  \n",
       "5         2.323389           0.043533    98.223216         2.020731  \n",
       "6         2.606904           0.058304    73.337758         2.338578  \n",
       "7         2.881476           0.074312    57.539937         2.640164  \n",
       "8         3.147608           0.091377    46.793979         2.927660  \n",
       "9         3.405739           0.109361    39.099164         3.202815  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's look at the data\n",
    "Temp_vel_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>Mean Temperature</th>\n",
       "      <th>Std Temperature</th>\n",
       "      <th>Mean Gamma</th>\n",
       "      <th>Std Gamma</th>\n",
       "      <th>Mean RMS Speed</th>\n",
       "      <th>Std RMS Speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.002003</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>2135.262103</td>\n",
       "      <td>10.502982</td>\n",
       "      <td>0.433404</td>\n",
       "      <td>0.001065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.008224</td>\n",
       "      <td>0.003229</td>\n",
       "      <td>613.129486</td>\n",
       "      <td>310.765453</td>\n",
       "      <td>0.862864</td>\n",
       "      <td>0.183402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.018623</td>\n",
       "      <td>0.005561</td>\n",
       "      <td>254.798766</td>\n",
       "      <td>107.606476</td>\n",
       "      <td>1.307576</td>\n",
       "      <td>0.215196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.030813</td>\n",
       "      <td>0.007522</td>\n",
       "      <td>147.677368</td>\n",
       "      <td>46.462825</td>\n",
       "      <td>1.688591</td>\n",
       "      <td>0.220492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.044456</td>\n",
       "      <td>0.009173</td>\n",
       "      <td>100.154866</td>\n",
       "      <td>24.473638</td>\n",
       "      <td>2.032640</td>\n",
       "      <td>0.219039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.059339</td>\n",
       "      <td>0.010582</td>\n",
       "      <td>74.131280</td>\n",
       "      <td>14.730142</td>\n",
       "      <td>2.351347</td>\n",
       "      <td>0.215456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.075301</td>\n",
       "      <td>0.011797</td>\n",
       "      <td>57.986087</td>\n",
       "      <td>9.703094</td>\n",
       "      <td>2.650965</td>\n",
       "      <td>0.211079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.092217</td>\n",
       "      <td>0.012852</td>\n",
       "      <td>47.120286</td>\n",
       "      <td>6.810584</td>\n",
       "      <td>2.935277</td>\n",
       "      <td>0.206369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.109978</td>\n",
       "      <td>0.013773</td>\n",
       "      <td>39.377695</td>\n",
       "      <td>5.008591</td>\n",
       "      <td>3.206779</td>\n",
       "      <td>0.201543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.128493</td>\n",
       "      <td>0.014584</td>\n",
       "      <td>33.621724</td>\n",
       "      <td>3.817325</td>\n",
       "      <td>3.467233</td>\n",
       "      <td>0.196745</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Mean Temperature  Std Temperature   Mean Gamma   Std Gamma  Mean RMS Speed  \\\n",
       "0          0.002003         0.000010  2135.262103   10.502982        0.433404   \n",
       "1          0.008224         0.003229   613.129486  310.765453        0.862864   \n",
       "2          0.018623         0.005561   254.798766  107.606476        1.307576   \n",
       "3          0.030813         0.007522   147.677368   46.462825        1.688591   \n",
       "4          0.044456         0.009173   100.154866   24.473638        2.032640   \n",
       "5          0.059339         0.010582    74.131280   14.730142        2.351347   \n",
       "6          0.075301         0.011797    57.986087    9.703094        2.650965   \n",
       "7          0.092217         0.012852    47.120286    6.810584        2.935277   \n",
       "8          0.109978         0.013773    39.377695    5.008591        3.206779   \n",
       "9          0.128493         0.014584    33.621724    3.817325        3.467233   \n",
       "\n",
       "   Std RMS Speed  \n",
       "0       0.001065  \n",
       "1       0.183402  \n",
       "2       0.215196  \n",
       "3       0.220492  \n",
       "4       0.219039  \n",
       "5       0.215456  \n",
       "6       0.211079  \n",
       "7       0.206369  \n",
       "8       0.201543  \n",
       "9       0.196745  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate mean and std using a loop\n",
    "\n",
    "tags = [\"Temperature\", \"Gamma\", \"RMS Speed\"]\n",
    "\n",
    "for lbl in tags:\n",
    "    \n",
    "    # Average Temperature\n",
    "    mean_tot = Temp_vel_data[ [\"{} run {}\".format(lbl, i) for i in range(runs) ]].mean( axis = 1)\n",
    "    std_tot = Temp_vel_data[ [\"{} run {}\".format(lbl, i) for i in range(runs) ]].std( axis = 1)\n",
    "\n",
    "    # Average and Standard Deviation of Total Temperature\n",
    "    Temp_vel_data['Mean {}'.format(lbl)] = mean_tot\n",
    "    Temp_vel_data['Std {}'.format(lbl)] = std_tot\n",
    "    \n",
    "# Save the data\n",
    "file_path = os.path.join(postproc.io.simulations_dir,'Temp_vel_data.csv')\n",
    "Temp_vel_data.to_csv(file_path, index=False, encoding='utf-8')\n",
    "\n",
    "# Let's look at the data again but only at the last additions \n",
    "Temp_vel_data.iloc[:10, 16:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is now time to plot and compare with experiment. For this tutorial we grabbed experimental data from \n",
    "Fig.~(2) of this [paper](http://dx.doi.org/10.1063/1.4915135). This data was obtained using [WebPlotDigitizer](https://automeris.io/WebPlotDigitizer/) and saved into the file ``paper_data.csv``.\n",
    "\n",
    "We will plot both the temperature and the RMS speed side by side and compare with the experimental data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grab data from Killian \n",
    "paper_data = np.loadtxt('paper_data.csv', delimiter=',')\n",
    "\n",
    "# Plot Temperature and thermal speed\n",
    "fig, (ax_temp, ax_rms) = plt.subplots(1,2, figsize=(16, 7))\n",
    "\n",
    "# Plot Temperature\n",
    "Temp_vel_data.plot(x = 'Time', y = 'Mean Temperature', ax = ax_temp)\n",
    "# Plot std error as shaded area \n",
    "ax_temp.fill_between(\n",
    "    Temp_vel_data['Time'], \n",
    "    Temp_vel_data[\"Mean Temperature\"] - Temp_vel_data[\"Std Temperature\"],\n",
    "    Temp_vel_data[\"Mean Temperature\"] + Temp_vel_data[\"Std Temperature\"], \n",
    "    alpha = 0.2)\n",
    "# Plot paper data\n",
    "ax_temp.scatter(paper_data[:,0], paper_data[:,1], s=100, label = 'Paper Data')\n",
    "# embellish\n",
    "ax_temp.grid(alpha = 0.3)\n",
    "ax_temp.legend()\n",
    "ax_temp.set(ylabel = r'$T$ [K]', xlabel =r'Time [$\\mu$s]')\n",
    "\n",
    "# Plot Gamma\n",
    "# Temp_vel_data.plot(x = 'Time', y = 'Mean Gamma', ax = ax_gamma)\n",
    "# ax_gamma.fill_between(\n",
    "#     Temp_vel_data['Time'], \n",
    "#     Temp_vel_data[\"Mean Gamma\"] - Temp_vel_data[\"Std Gamma\"],\n",
    "#     Temp_vel_data[\"Mean Gamma\"] + Temp_vel_data[\"Std Gamma\"], alpha = 0.2)\n",
    "\n",
    "# ax_gamma.grid(alpha = 0.3)\n",
    "# ax_gamma.set(ylabel = r'$\\Gamma$', xlabel =r'Time [$\\mu$s]', ylim = (0, 5))\n",
    "\n",
    "# Plot RMS Speed\n",
    "Temp_vel_data.plot(x = 'Time', y = 'Mean RMS Speed', ax = ax_rms)\n",
    "# Plot std error as shaded area \n",
    "ax_rms.fill_between(\n",
    "    Temp_vel_data['Time'],\n",
    "    Temp_vel_data[\"Mean RMS Speed\"] - Temp_vel_data[\"Std RMS Speed\"],\n",
    "    Temp_vel_data[\"Mean RMS Speed\"] + Temp_vel_data[\"Std RMS Speed\"], alpha = 0.2)\n",
    "\n",
    "# embellish\n",
    "ax_rms.grid(alpha = 0.3)\n",
    "ax_rms.set( ylabel = r'$v$ [m/s]', xlabel= r'Time [$\\mu$s]')\n",
    "\n",
    "# Finishing\n",
    "fig.tight_layout()\n",
    "figname = os.path.join(postproc.io.simulations_dir,'Temperature_n_RMS_Plot.png')\n",
    "fig.savefig(figname)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Noice! There is good agreement for the first microsecond."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distributions for Thermalization analysis\n",
    "\n",
    "The next important quantity that we need are velocity distributions. Sarkas allows you to calculate these easily and automatically average of the multiple runs and over the three cartesian dimensions. \n",
    "The last two are flags that can be passed when calculating.\n",
    "\n",
    "First we print the parameters of the velocity distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "======================= Velocity Distribution ======================== \n",
      "\n",
      "CSV dataframe saved in:\n",
      "\t  UCP_DIH_N10k/PostProcessing/Production/VelocityDistribution/VelocityDistribution.csv\n",
      "HDF5 dataframe saved in:\n",
      "\t  UCP_DIH_N10k/PostProcessing/Production/VelocityDistribution/VelocityDistribution.h5\n",
      "Data accessible at: self.dataframe, self.hierarchical_dataframe, self.species_bin_edges\n",
      "\n",
      "Multi run average:  True\n",
      "No. of runs:  5\n",
      "Size of the parsed velocity array: 2001 x 1 x 150000\n",
      "\n",
      "Histograms Information:\n",
      "Species: Sr\n",
      "No. of samples = 150000\n",
      "Thermal speed: v_th = 1.207819e+01 [m/s]\n",
      "density: True\n",
      "bins: 200\n",
      "range : ( -5.00, 5.00 ) v_th,\n",
      "\t( -6.0391e+01, 6.0391e+01 ) [m/s]\n",
      "Bin Width = 0.0500\n",
      "\n",
      "Moments Information:\n",
      "CSV dataframe saved in:\n",
      "\t  UCP_DIH_N10k/PostProcessing/Production/VelocityDistribution/Moments_run4.csv\n",
      "Data accessible at: self.moments_dataframe, self.moments_hdf_dataframe\n",
      "Highest moment to calculate: 10\n",
      "\n",
      "Grad Expansion Information:\n",
      "CSV dataframe saved in:\n",
      "\t  UCP_DIH_N10k/PostProcessing/Production/VelocityDistribution/HermiteCoefficients_run4.csv\n",
      "Data accessible at: self.hermite_dataframe, self.hermite_hdf_dataframe\n",
      "Highest order to calculate: 5\n",
      "RMS Tolerance: 0.050\n"
     ]
    }
   ],
   "source": [
    "from sarkas.tools.observables import VelocityDistribution\n",
    "\n",
    "vd = VelocityDistribution()\n",
    "vd.setup(\n",
    "    postproc.parameters, # We need to pass simulations parameters always \n",
    "    max_no_moment=10, # This is the maximum number of distribution moments to calculate\n",
    "    multi_run_average=True, # Do we want to average over multiple runs ?\n",
    "    runs = 5, # Tell me the number of runs.\n",
    "    dimensional_average=True, # Do we want to average over the three directions?\n",
    "    max_hermite_order = 5\n",
    ")\n",
    "\n",
    "# Let's print out some info\n",
    "vd.pretty_print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "======================= Velocity Distribution ======================== \n",
      "\n",
      "CSV dataframe saved in:\n",
      "\t  UCP_DIH_N10k/PostProcessing/Production/VelocityDistribution/VelocityDistribution.csv\n",
      "HDF5 dataframe saved in:\n",
      "\t  UCP_DIH_N10k/PostProcessing/Production/VelocityDistribution/VelocityDistribution.h5\n",
      "Data accessible at: self.dataframe, self.hierarchical_dataframe, self.species_bin_edges\n",
      "\n",
      "Multi run average:  True\n",
      "No. of runs:  5\n",
      "Size of the parsed velocity array: 2001 x 1 x 150000\n",
      "\n",
      "Histograms Information:\n",
      "Species: Sr\n",
      "No. of samples = 150000\n",
      "Thermal speed: v_th = 1.207819e+01 [m/s]\n",
      "range : ( -13.00, 13.00 ) v_th,\n",
      "\t( -1.5702e+02, 1.5702e+02 ) [m/s]\n",
      "bins: 520\n",
      "density: True\n",
      "Bin Width = 0.0500\n",
      "\n",
      "Moments Information:\n",
      "CSV dataframe saved in:\n",
      "\t  UCP_DIH_N10k/PostProcessing/Production/VelocityDistribution/Moments_run4.csv\n",
      "Data accessible at: self.moments_dataframe, self.moments_hdf_dataframe\n",
      "Highest moment to calculate: 10\n",
      "\n",
      "Grad Expansion Information:\n",
      "CSV dataframe saved in:\n",
      "\t  UCP_DIH_N10k/PostProcessing/Production/VelocityDistribution/HermiteCoefficients_run4.csv\n",
      "Data accessible at: self.hermite_dataframe, self.hermite_hdf_dataframe\n",
      "Highest order to calculate: 5\n",
      "RMS Tolerance: 0.050\n"
     ]
    }
   ],
   "source": [
    "# Let's get the thermal speed of each species\n",
    "vd.vth\n",
    "\n",
    "# Create a dictionary with the input arguments accepted by np.histogram\n",
    "hist_kwargs = {\n",
    "    'range': [ # In a multispecies case you need to pass a list\n",
    "        (-13*vd.vth[0],13*vd.vth[0]),\n",
    "    ],\n",
    "    'bins': [\n",
    "        520,\n",
    "    ]\n",
    "}\n",
    "\n",
    "# Reassign the attributes with the new dictionary\n",
    "vd.setup(\n",
    "    postproc.parameters, # We need to pass simulations parameters always     \n",
    "    max_no_moment=10, # This is the maximum number of distribution moments to calculate\n",
    "    multi_run_average=True, # Do we want to average over multiple runs ?\n",
    "    runs = 5, # Tell me the number of runs.\n",
    "    dimensional_average=True, # Do we want to average over the three directions?\n",
    "    max_hermite_order = 5,\n",
    "    hist_kwargs= hist_kwargs, # < === Here is the new dictionary\n",
    ")\n",
    "\n",
    "# Let's print it again\n",
    "vd.pretty_print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "======================= Velocity Distribution ======================== \n",
      "\n",
      "CSV dataframe saved in:\n",
      "\t  UCP_DIH_N10k/PostProcessing/Production/VelocityDistribution/VelocityDistribution.csv\n",
      "HDF5 dataframe saved in:\n",
      "\t  UCP_DIH_N10k/PostProcessing/Production/VelocityDistribution/VelocityDistribution.h5\n",
      "Data accessible at: self.dataframe, self.hierarchical_dataframe, self.species_bin_edges\n",
      "\n",
      "Multi run average:  True\n",
      "No. of runs:  5\n",
      "Size of the parsed velocity array: 2001 x 1 x 150000\n",
      "\n",
      "Histograms Information:\n",
      "Species: Sr\n",
      "No. of samples = 150000\n",
      "Thermal speed: v_th = 1.207819e+01 [m/s]\n",
      "range : ( -13.00, 13.00 ) v_th,\n",
      "\t( -1.5702e+02, 1.5702e+02 ) [m/s]\n",
      "bins: 520\n",
      "density: True\n",
      "Bin Width = 0.0500\n",
      "\n",
      "Moments Information:\n",
      "CSV dataframe saved in:\n",
      "\t  UCP_DIH_N10k/PostProcessing/Production/VelocityDistribution/Moments_run4.csv\n",
      "Data accessible at: self.moments_dataframe, self.moments_hdf_dataframe\n",
      "Highest moment to calculate: 10\n",
      "\n",
      "Grad Expansion Information:\n",
      "CSV dataframe saved in:\n",
      "\t  UCP_DIH_N10k/PostProcessing/Production/VelocityDistribution/HermiteCoefficients_run4.csv\n",
      "Data accessible at: self.hermite_dataframe, self.hermite_hdf_dataframe\n",
      "Highest order to calculate: 5\n",
      "RMS Tolerance: 0.050\n",
      "\n",
      "Collecting data from snapshots ...\n",
      "\n",
      "Creating velocity distributions ...\n",
      "\n",
      "Velocity distribution calculation Time: 0 hrs 0 min 3 sec\n",
      "\n",
      "Calculating velocity moments ...\n",
      "\n",
      "Velocity moments calculation Time: 0 hrs 0 min 16 sec\n",
      "\n",
      "Calculating Hermite coefficients ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/luciano/Documents/Programming/sarkas/sarkas/tools/observables.py:3292: RuntimeWarning: invalid value encountered in true_divide\n",
      "  ratios[:, :, :, mom] = moments[:, :, :, mom] / (const * moments[:, :, :, 1] ** (pwr / 2.))\n"
     ]
    },
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     "text": [
      "/home/luciano/anaconda3/envs/sarkas/lib/python3.7/site-packages/scipy/optimize/minpack.py:829: OptimizeWarning: Covariance of the parameters could not be estimated\n",
      "  category=OptimizeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "Hermite expansion calculation Time: 0 hrs 0 min 22 sec\n"
     ]
    }
   ],
   "source": [
    "# Let's finally calculate the distributions\n",
    "vd.compute()"
   ]
  }
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