{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random numbers\n",
    "#### Documentation for random number generators with numpy available at https://numpy.org/devdocs/reference/random/index.html. For non-uniform pdf look at https://numpy.org/devdocs/reference/random/legacy.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import secrets # This contains functions to generate large random seeds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generation of uniform r.v.'s "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seed =  193052788910972192820634628955770890314 \n",
      "\n",
      "x =  0.18047456905037784 \n",
      "\n",
      "Vector =  [0.01974711 0.40037185 0.83078549 0.07418344 0.42009266 0.56344503\n",
      " 0.95674445 0.32498965 0.09999683 0.16389039 0.16894377 0.96875895\n",
      " 0.14089374 0.63525812 0.21096026 0.41485997 0.10609271 0.88031081\n",
      " 0.62117701 0.93341655 0.21245188 0.50779313 0.4813632  0.03956074\n",
      " 0.85438598 0.09584539 0.54900065 0.69595868 0.97448062 0.97635653\n",
      " 0.83311088 0.66602751 0.69877031 0.81306249 0.6382718  0.38456824\n",
      " 0.92446416 0.88330338 0.62834015 0.65153147 0.48471469 0.32405123\n",
      " 0.62095396 0.56632845 0.78553351 0.1713393  0.57046601 0.72450832\n",
      " 0.34648    0.184659   0.17843769 0.04883295 0.34677692 0.18806096\n",
      " 0.80166617 0.42170391 0.37917658 0.27953749 0.13500346 0.24048077\n",
      " 0.02491941 0.2055278  0.92669543 0.76897299 0.43438731 0.05976034\n",
      " 0.45627669 0.47118537 0.64175037 0.25038755 0.34604157 0.93564523\n",
      " 0.91605172 0.55272071 0.37553334 0.31937304 0.52701673 0.00476123\n",
      " 0.96522607 0.65130106 0.30291451 0.17280512 0.91937794 0.30313057\n",
      " 0.21921555 0.7847396  0.6734278  0.87868698 0.83304823 0.55730816\n",
      " 0.08184066 0.97003898 0.10383742 0.42679118 0.52805592 0.8911967\n",
      " 0.78685209 0.50727665 0.06974308 0.81935745] \n",
      "\n",
      "Matrix =  [[0.98116175 0.15412351 0.18325605 0.02495448 0.8260291  0.82803882\n",
      "  0.4005709  0.62395646 0.90152986 0.17508346]\n",
      " [0.67164133 0.62501754 0.48334009 0.97045769 0.62234865 0.77247863\n",
      "  0.30291117 0.7753745  0.90488686 0.88727819]\n",
      " [0.46325169 0.86504268 0.69541077 0.70633697 0.92387629 0.51988886\n",
      "  0.81132676 0.88259199 0.6886539  0.36809997]\n",
      " [0.15564959 0.15889222 0.47874723 0.44286553 0.45062126 0.40482132\n",
      "  0.4954529  0.21756158 0.60065603 0.38540067]\n",
      " [0.97104151 0.48655847 0.71432671 0.80188867 0.54709758 0.17574655\n",
      "  0.18429141 0.01253517 0.84699464 0.511303  ]\n",
      " [0.43301721 0.84486676 0.09485778 0.78818567 0.15243221 0.05907995\n",
      "  0.98944269 0.69497194 0.81526065 0.2971166 ]\n",
      " [0.14336658 0.4556076  0.87853768 0.67902676 0.09274998 0.58693158\n",
      "  0.7521159  0.63200821 0.98512056 0.4336975 ]\n",
      " [0.11287192 0.56108264 0.81739957 0.67621995 0.53920042 0.49799865\n",
      "  0.70273962 0.13172669 0.55986577 0.74527784]\n",
      " [0.96971544 0.24947454 0.87566073 0.55897093 0.36177522 0.11112504\n",
      "  0.61038145 0.40521327 0.372674   0.026631  ]\n",
      " [0.08837335 0.58149061 0.20587792 0.75528691 0.9690553  0.16707243\n",
      "  0.0797809  0.03868652 0.78706134 0.61841194]] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Set up initial seed\n",
    "seed = secrets.randbits(128)   # Generate large positive integer: best kind of seed for the generator\n",
    "print('seed = ', seed, '\\n')\n",
    "\n",
    "# Set up object rng = uniform random number generator (with default numpy choice = PCG-64 generator)\n",
    "rng = np.random.default_rng(seed)\n",
    "\n",
    "# Now we simply use the function 'random', applied to the object rng, to generate uniform istributed r.v.'s\n",
    "# We can arrange them in matrices with desired shape.\n",
    "\n",
    "# Just one variable\n",
    "x = rng.random()\n",
    "print('x = ', x, '\\n')\n",
    "\n",
    "# 100 r.v.'s arranged in a vector \n",
    "rvs = rng.random(100)\n",
    "print('Vector = ', rvs, '\\n')\n",
    "\n",
    "# 100 r.v.'s arranged in a 10x10 matrix\n",
    "rvs2 = rng.random( (10,10) )\n",
    "print('Matrix = ', rvs2, '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generation of Gaussian r.v.'s\n",
    "\n",
    "#### Gaussian r.v's using https://numpy.org/devdocs/reference/random/generated/numpy.random.normal.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x =  -1.365671565055376 \n",
      "\n",
      "Vector =  [ 0.06453106  0.51044041  1.3980727  -2.17277148  1.72146575  0.68154384\n",
      " -0.96480041  1.3825995  -0.88373026  1.13833698 -2.10609514  1.28023575\n",
      " -0.65192607 -0.59103724 -0.33848095 -1.10265894  1.26146748  0.4346234\n",
      " -0.10546912  1.56617301 -0.71205347 -0.0203198  -0.74731658 -1.29586526\n",
      " -0.81299923  0.09664957  0.32473548 -2.49712863 -0.79190867 -1.58639209\n",
      " -0.75671843 -1.83847689 -2.58933016  2.20428019  0.61166699  2.62559877\n",
      "  1.33133586 -1.09600656 -0.1954797  -1.71881738  2.19938744  0.07102975\n",
      "  1.72234942 -0.67168385  1.39221575  2.1707453   0.4722239  -0.20360312\n",
      "  1.69451914 -1.65857314 -0.83022425 -1.18080637 -0.23011328  1.07619143\n",
      "  0.59300407  0.02745201 -1.4250692  -0.82090188  0.26868277 -3.41329363\n",
      "  0.51771222 -0.48121353 -0.01806863 -2.77921072  0.54778801  2.1998989\n",
      " -0.80294391 -0.95130653  1.49417734 -0.57066784 -0.73473935 -2.06375556\n",
      "  1.54834796 -0.31714123 -2.53697776  2.856913    1.61840299  0.09563911\n",
      "  2.59321241 -0.118552    2.00179662  1.2578906  -1.27834648  0.05678946\n",
      "  1.06772861 -1.87017156 -0.85495419 -1.07504217  2.83668221 -0.57954927\n",
      "  0.56596629 -0.5310405   0.99510484  0.35833268 -2.47573869 -2.22724602\n",
      " -0.85329331 -1.20050351  2.0590115  -0.40087943] \n",
      "\n",
      "Matrix =  [[ 0.79352848  1.01965174  1.90794858  0.0540411  -2.47925842 -0.83457832\n",
      "  -1.34358931  0.92141463  2.9825403  -2.00873544]\n",
      " [-1.02148857 -4.40158776 -0.97288395  1.35109952 -1.00274166  0.31060686\n",
      "   1.09212138 -1.2197276   1.05482638  0.25485258]\n",
      " [ 4.30607052  1.72796732  0.8225777   0.00636407 -2.52435574 -0.5065241\n",
      "   0.16230746 -1.8185074  -2.41108831 -2.09403873]\n",
      " [ 1.30894703  0.28133019  2.3126549   1.83546605 -0.03263278  1.61982209\n",
      "  -1.086093   -2.62523463 -0.35847615  0.3295198 ]\n",
      " [-1.09045597 -4.11315859 -3.54947648 -1.76194491 -2.83419796  1.26611954\n",
      "   1.49871198 -1.45030659 -1.2683515  -0.74897455]\n",
      " [-2.03668445 -0.73936148 -1.47604836  1.30745172  0.67032913 -0.20876796\n",
      "  -0.333394    0.22020895  1.13402276  0.20313397]\n",
      " [ 1.10064491  0.24121319  2.94866278 -0.25343502 -2.29528027  0.43464851\n",
      "   0.7268522  -0.00843817  1.30162997 -2.00632173]\n",
      " [ 1.5107402  -1.36948138 -1.60942987  1.42324939  0.5823825   1.35116525\n",
      "   0.27396116 -0.22137655 -1.63932767 -3.59485166]\n",
      " [-0.03497233  1.12388389  2.59150825  0.08084687 -0.21100555 -1.27097995\n",
      "  -0.01989358 -0.36723712  0.41574827  4.26253937]\n",
      " [-0.27083671 -1.63463176 -1.77114232 -0.24575198 -3.03576803  1.13364103\n",
      "   2.31603351  0.32510046  0.01304548  0.17576323]] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "mean = 0.\n",
    "std = 1.5\n",
    "\n",
    "seed = 1243487\n",
    "np.random.seed(seed)\n",
    "\n",
    "# Just one variable\n",
    "x = np.random.normal(mean,std)\n",
    "print('x = ', x, '\\n')\n",
    "\n",
    "# 100 r.v.'s arranged in a vector \n",
    "rvs = np.random.normal(mean,std,size=100)\n",
    "print('Vector = ', rvs, '\\n')\n",
    "\n",
    "# 100 r.v.'s arranged in a 10x10 matrix \n",
    "rvs2 = np.random.normal(mean,std,size=(10,10) )\n",
    "print('Matrix = ', rvs2, '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generating Gaussian r.v.'s and plotting histogram\n",
    "\n",
    "import numpy as np\n",
    "import secrets\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import norm\n",
    "\n",
    "# Mean and standard deviation\n",
    "mean = 3.5\n",
    "std = 8.2\n",
    "# Size of sample\n",
    "N = 100000\n",
    "\n",
    "# Seed\n",
    "seed = secrets.randbits(32)\n",
    "np.random.seed(seed)\n",
    "#print(seed)\n",
    "# Generating data\n",
    "rvs = np.random.normal(mean,std,size=N)\n",
    "\n",
    "# Generating pdf to overplot\n",
    "xmin = mean - 5.*std\n",
    "xmax = mean  + 5.*std\n",
    "step = 0.1*std\n",
    "x_axis = np.arange(xmin,xmax,step)\n",
    "pdf = norm.pdf(x_axis,mean,std)\n",
    "\n",
    "# Data histogram\n",
    "plt.hist(rvs, bins=100, density=True, ec = 'black', histtype='bar')\n",
    "# pdf plot\n",
    "plt.plot(x_axis,pdf,label='$\\mu$ = ' + str(mean) + ',' + ' $\\sigma$ = ' + str(std))\n",
    "plt.legend()\n",
    "plt.xlabel('x',size = 18)\n",
    "plt.ylabel('$N(\\mu,\\sigma)$', size = 18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gaussian r.v's using \n",
    "https://numpy.org/devdocs/reference/random/generated/numpy.random.Generator.normal.html#numpy.random.Generator.normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seed = secrets.randbits(128)   # Generate large positive integer: best kind of seed for the generator\n",
    "#print('seed = ', seed, '\\n')\n",
    "\n",
    "# Set up object rng = Gaussian random number generator \n",
    "# (with default numpy choice for uniform r.v.'s generator'= PCG-64 generator)\n",
    "mean = 3.5\n",
    "std = 8.2\n",
    "N = 100000\n",
    "\n",
    "# Generate Gaussian r.v's\n",
    "rvs = np.random.default_rng(seed).normal(mean,std,N)\n",
    "\n",
    "# Generating pdf to overplot\n",
    "xmin = mean - 5.*std\n",
    "xmax = mean  + 5.*std\n",
    "step = 0.1*std\n",
    "x_axis = np.arange(xmin,xmax,step)\n",
    "pdf = norm.pdf(x_axis,mean,std)\n",
    "\n",
    "# Data histogram\n",
    "plt.hist(rvs, bins=100, density=True, ec = 'black', histtype='bar')\n",
    "# pdf plot\n",
    "plt.plot(x_axis,pdf,label='$\\mu$ = ' + str(mean) + ',' + ' $\\sigma$ = ' + str(std))\n",
    "plt.legend()\n",
    "plt.xlabel('x',size = 18)\n",
    "plt.ylabel('$N(\\mu,\\sigma)$', size = 18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise from slides: simulating a star cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Class \"cluster\". Contains: \n",
    "- Definition of cluster object, with its relevant properties\n",
    "- Functions to assign masses, positions, velocities (see slides for details)\n",
    "- Functions to compute kinetic and gravitational potential energies and to check viral equilibrium\n",
    "- Functions to make plots "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import secrets\n",
    "import numpy as np\n",
    "import scipy.stats as sps\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "\n",
    "def pol2cart(r,theta,phi):\n",
    "    # Converting polar into Cartesian coordinates    \n",
    "    x = r*np.sin(theta)*np.cos(phi)\n",
    "    y = r*np.sin(theta)*np.sin(phi)\n",
    "    z = r*np.cos(theta)\n",
    "    return x,y,z    \n",
    "\n",
    "\n",
    "class cluster:\n",
    "     \n",
    "    def __init__(self,N,M,x,v,s,r,m1,m2):\n",
    "        self.size = N  # Number of stars\n",
    "        self.masses = M  \n",
    "        self.positions = x\n",
    "        self.velocities = v\n",
    "        self.sigma = s\n",
    "        self.radius = r\n",
    "        self.mmin = m1\n",
    "        self.mmax = m2\n",
    "\n",
    "    \n",
    "    def empty_cluster():\n",
    "        N = 0\n",
    "        M = 0.\n",
    "        x = 0.\n",
    "        v = 0.\n",
    "        s = 0.\n",
    "        r = 0.\n",
    "        m1 = 0.\n",
    "        m2 = 0.\n",
    "        return cluster(N,M,x,v,s,r,m1,m2)\n",
    "\n",
    "    \n",
    "    def build(size=1000,sigma=0.5,radius=1.,mmin=0.1, mmax=150.):\n",
    "        clust = cluster.empty_cluster()\n",
    "        clust.assign_nstars(size)\n",
    "        clust.assign_sigma(sigma)\n",
    "        clust.assign_radius(radius)\n",
    "        clust.assign_mmin(mmin)\n",
    "        clust.assign_mmax(mmax)\n",
    "        clust.assign_masses()\n",
    "        clust.assign_velocities()\n",
    "        clust.assign_positions()\n",
    "        return clust\n",
    "\n",
    "    \n",
    "    def assign_nstars(self,N):\n",
    "        self.size=N\n",
    "  \n",
    "    def assign_radius(self,r):\n",
    "        self.radius = r\n",
    "      \n",
    "    def assign_sigma(self,sigma):\n",
    "        self.sigma = sigma\n",
    "\n",
    "    def assign_mmin(self,m):\n",
    "        self.mmin = m\n",
    "    \n",
    "    def assign_mmax(self,m):\n",
    "        self.mmax = m\n",
    "   \n",
    " \n",
    "    def assign_masses(self):\n",
    "        # Assign masses assuming Salpeter distribution with mmin = 0.1 and mmax = 150 solar masses\n",
    "        seed = secrets.randbits(128)\n",
    "        rng = np.random.default_rng(seed)\n",
    "        x = rng.random(self.size)  \n",
    "        # After generating uniform variables, transform to Salpeter distributed r.v.'s\n",
    "        mmin = self.mmin\n",
    "        mmax = self.mmax\n",
    "        alpha = 2.3\n",
    "        t = 1./(1.-alpha)\n",
    "        mstars = ( (mmax**(1.-alpha) - mmin**(1.-alpha))*x + mmin**(1.-alpha) )**t\n",
    "        self.masses = np.array(mstars)\n",
    "    \n",
    "        \n",
    "    def assign_velocities(self):\n",
    "        seed = secrets.randbits(32)\n",
    "        np.random.seed(seed)\n",
    "        rng = np.random.default_rng(seed)\n",
    "        # Magnitudes of stars' velocities fom Maxwellian distribution\n",
    "        vmag = sps.maxwell.rvs(loc=0, scale=self.sigma, size=self.size)\n",
    "        # Velocity orientation, polar angle\n",
    "        vtheta = rng.random(self.size)\n",
    "        vtheta = np.arccos(1.-2.*vtheta)\n",
    "        # Velocity orientation, azimuthal angle\n",
    "        # Azimuthal angles of stars in the cluster\n",
    "        vphi = rng.random(self.size)\n",
    "        vphi = 2.*np.pi*vphi\n",
    "        # Transform polar -> cartesian\n",
    "        vx,vy,vz = pol2cart(vmag,vtheta,vphi)\n",
    "        # Save cartesian coordinates in Nx3 array (each row corresponds to vx,vy,vz of a given star)\n",
    "        vstars = np.transpose(np.array( [ vx,vy,vz ], dtype=float))\n",
    "        self.velocities = vstars\n",
    "    \n",
    "    \n",
    "    def assign_positions(self):\n",
    "        # Assign positions assuming Plummer density \n",
    "        seed = secrets.randbits(128) \n",
    "        rng = np.random.default_rng(seed)\n",
    "        # Radial coordinates of stars in the cluster\n",
    "        r = rng.random(self.size)\n",
    "        a = self.radius\n",
    "        t = -2./3.\n",
    "        r = np.sqrt(a**2/(r**t-1.0))\n",
    "        # Polar angles of stars in the clusters\n",
    "        theta = rng.random(self.size)\n",
    "        theta = np.arccos(1.-2.*theta)\n",
    "        # Azimuthal angles of stars in the cluster\n",
    "        phi = rng.random(self.size)\n",
    "        phi = 2.*np.pi*phi\n",
    "        # Transform polar -> cartesian\n",
    "        x,y,z = pol2cart(r,theta,phi)\n",
    "        # Save cartesian coordinates in Nx3 array (each row corresponds to x,y,z of a given star)\n",
    "        xstars = np.transpose(np.array( [ x,y,z ], dtype=float))\n",
    "        self.positions = xstars\n",
    "\n",
    "\n",
    "    def gravitational_energy(self):\n",
    "        # Calculates the total gravitational energy\n",
    "        # of the cluster\n",
    "        G = 6.6743e-11  # S.I. units\n",
    "        pc = 3.086e16   # 1 pc in m\n",
    "        Msun = 1.989e30 # solar mass in kg\n",
    "        # Cartesian distances between 1 star an all the others \n",
    "        def dist(x1,x2):\n",
    "            d = np.sqrt( (x1[0] - x2[:,0])**2 + (x1[1] - x2[:,1])**2 + (x1[2] - x2[:,2])**2 )  \n",
    "            return d # This is a vector of the same length as x2\n",
    "            # Computes gravitational energy\n",
    "        E = 0.\n",
    "        for i in range(self.size-1):\n",
    "            # Position of i-th star in the list\n",
    "            x1 = self.positions[i]\n",
    "            # Mass of i-th star (in kg)\n",
    "            m1 = Msun*self.masses[i]\n",
    "            # Positions of all stars labeled by j, with j>i in the list\n",
    "            x2 = self.positions[i+1:]\n",
    "            # Masses of all j>i stars (in kg)\n",
    "            m2 = Msun*self.masses[i+1:]\n",
    "            # Distances between all i,j pairs (in m)\n",
    "            distances = pc*dist(x1,x2)\n",
    "            # Gets energies of all pairs (in N m^2)\n",
    "            Gm1m2 = G*m1*m2\n",
    "            energy = Gm1m2*distances**(-1)\n",
    "            # Sums and add to total potential energy\n",
    "            E -= np.sum(energy)\n",
    "        return E\n",
    "\n",
    "    \n",
    "    def kinetic_energy(self):\n",
    "        # Calculates total kinetic energy of the cluster\n",
    "        Msun = 1.989e+30      # Solar mass in kg\n",
    "        km = 1e+3             # km in m        \n",
    "        # Computes v^2 for all particles in the cluster and convert km/s in m/s\n",
    "        v2 = (self.velocities[:,0])**2 + (self.velocities[:,1]**2) + (self.velocities[:,2])**2\n",
    "        v2 = km*km*v2\n",
    "        # Computes total kinetic energy (in N m^2)\n",
    "        E = 0.5*np.sum(Msun*self.masses*v2)\n",
    "        return E\n",
    "    \n",
    "    \n",
    "    def check_virialization(self):\n",
    "        K = self.kinetic_energy()\n",
    "        W = self.gravitational_energy()\n",
    "        Qvir = 2.*K/(abs(W))\n",
    "        return Qvir\n",
    "    \n",
    "    def virialize(self):\n",
    "        Qvir=self.check_virialization()\n",
    "        self.velocities = self.velocities/np.sqrt(Qvir)\n",
    "        \n",
    "        \n",
    "    def display_cluster_projection(self,x1,x2,distance):\n",
    "        x = clust.positions[:,x1]\n",
    "        y = clust.positions[:,x2]\n",
    "        coord = ['x','y','z']\n",
    "        xmax = distance*self.radius\n",
    "        xmin = -xmax\n",
    "        plt.axis('square')\n",
    "        plt.xlabel(coord[x1] + ' (pc)',size=16)\n",
    "        plt.ylabel(coord[x2] + ' (pc)',size=16)\n",
    "        plt.xlim(xmin,xmax)\n",
    "        plt.ylim(xmin,xmax)\n",
    "        plt.scatter(x,y, s=2, color='blue')\n",
    "        plt.show()\n",
    "    \n",
    "    \n",
    "    def display_pdf(self,kind,nbins):\n",
    "        if (str(kind) == 'velocity'):\n",
    "            x = (self.velocities[:,0])**2 + self.velocities[:,1]**2 + self.velocities[:,2]**2\n",
    "            plt.xlabel('v (km/s)', size = 16)\n",
    "        elif (str(kind) == 'radius'):\n",
    "            x = np.sqrt((self.positions[:,0])**2 + self.positions[:,1]**2 + self.positions[:,2]**2)\n",
    "            plt.yscale('log')\n",
    "            plt.xlabel('r (pc)', size = 16)\n",
    "            plt.xlim(1e-1*self.radius,1e2*self.radius)\n",
    "        plt.xscale('log') \n",
    "        plt.ylabel('PDF', size = 16)\n",
    "        plt.hist(x, bins=nbins, density=True, histtype='step', linewidth=3)\n",
    "        plt.show()\n",
    "        \n",
    "    \n",
    "    def display_mass_distribution(self):\n",
    "        # Find radial distances from the center of stars in the cluster\n",
    "        r = np.array(np.sqrt((self.positions[:,0])**2 + self.positions[:,1]**2 + self.positions[:,2]**2))      \n",
    "        # Find indeces of r that correspond to sorting distances from smallest to larges\n",
    "        ind = r.argsort()\n",
    "        x = r[ind]     \n",
    "        # Rearranges masses, ordering M(r) for r from smallest to largest\n",
    "        M = self.masses[ind]\n",
    "        Mr = [ ]\n",
    "        #Gets total mass in spheres of growing radius r\n",
    "        for i in range(self.size):\n",
    "            Mr.append(np.sum(M[0:i]))\n",
    "        plt.plot(x,Mr)\n",
    "        plt.xlabel('r (pc)', size=16)\n",
    "        plt.ylabel('M(r) $\\, M_{\\odot}$', size=16)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Simulate cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Input cluster specs\n",
    "N = 10000   # Total number of stars in the cluster\n",
    "s = 0.5     # Velocity dispersion (scale parameter in Maxwell distribution, used to generate velocities)\n",
    "r = 1.      # Cluster \"radius\"  (parameter in Plummer distribution, used to generate positions)\n",
    "mmin = 0.1\n",
    "mmax = 150\n",
    "\n",
    "# Generates star positions, masses and velocities\n",
    "clust = cluster.build(radius=r,sigma=s,size=N, mmin = mmin, mmax = mmax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculates cluster energy and virialize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total gravitational energy =  -4.14511802965395e+40\n",
      "Total kinetic energy =  3.0946677094461676e+39\n",
      "Qvir =  0.14931626493176225\n",
      "Qvir after normalizing velocities =  1.0\n"
     ]
    }
   ],
   "source": [
    "W = clust.gravitational_energy()\n",
    "print('Total gravitational energy = ', W)\n",
    "K = clust.kinetic_energy()\n",
    "print('Total kinetic energy = ', K)\n",
    "Qvir = clust.check_virialization()\n",
    "print('Qvir = ', Qvir)\n",
    "clust.virialize()\n",
    "Qvir = clust.check_virialization()\n",
    "print('Qvir after normalizing velocities = ', Qvir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plots \n",
    "- Star positions (3 projections)\n",
    "- PDF of velocity magnitudes \n",
    "- PDF of radial distance from the center\n",
    "- Total mass in spheres of radius r, center in the origin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot star positions\n",
    "clust.display_cluster_projection(0,1,10)\n",
    "clust.display_cluster_projection(0,2,10)\n",
    "clust.display_cluster_projection(1,2,10)\n",
    "# Plot velocity distribution\n",
    "clust.display_pdf('velocity',nbins=100)\n",
    "#Plot radial distribution\n",
    "clust.display_pdf('radius',nbins=int(clust.size/20))\n",
    "# Plot mass distribution r vs M(r)\n",
    "clust.display_mass_distribution()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
