cluster_animations_using_pkl_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing if the loading of data was successful !! \n",
      "\n",
      "x_tracked of snapshot 596 for cluster 1 [-6.00513891 -6.00509183 -6.00239829 -6.01305976 -6.00702558 -6.00990916\n",
      " -6.00309596 -6.00452512 -6.0039977  -6.00854992 -6.00393315 -6.00829482\n",
      " -6.00636014 -6.00444464 -6.00841607 -6.00854442 -6.00197996 -6.00850992\n",
      " -6.00568745 -6.00840539 -6.00982215 -6.0013967  -6.00854318 -6.01197897\n",
      " -5.9995058  -5.99878137 -6.00698328 -6.00869882 -6.00844468 -6.00558671\n",
      " -6.00536959 -6.01049269 -6.00218807 -6.0006008  -6.00652576 -6.00544892\n",
      " -6.00667422 -6.00743993 -6.00588704 -6.00958322 -6.01424892 -6.00430563\n",
      " -6.00200992]\n",
      "x_cm of snapshot 596 for cluster 1 -6.006235802999383\n",
      "x_tracked of snapshot 597 for cluster 1 [-6.01601537 -6.01287714 -6.01442506 -5.99648041 -6.00928859 -6.01037169\n",
      " -6.01620249 -6.01430355 -6.01481366 -6.01071521 -6.00793953 -6.01023188\n",
      " -6.00759919 -6.01196177 -6.0119816  -6.01126726 -6.01338726 -6.01036839\n",
      " -6.01142382 -6.01040229 -6.00814754 -6.03227893 -6.01148209 -6.00743434\n",
      " -6.02313196 -6.01941446 -6.00948887 -6.00970945 -6.01239606 -6.01183728\n",
      " -6.00989226 -6.00878692 -6.01517223 -6.00814197 -6.01138944 -6.01431921\n",
      " -6.00370448 -6.01118078 -6.01223339 -5.99972739 -6.00549727 -6.01360732\n",
      " -6.01334439]\n",
      "x_cm of snapshot 597 for cluster 1 -6.011554058648143\n",
      "x_tracked of snapshot 597 for cluster 2 [-5.9489193  -5.93894069 -5.946881   -5.93339307 -5.96726817 -5.94041421\n",
      " -5.94756465 -5.95800607 -5.94516274 -5.94342346 -5.9362367  -5.95629846\n",
      " -5.93847075 -5.94928088 -5.94225617 -5.94267776 -5.96254183 -5.94351425\n",
      " -5.94493434 -5.9552221  -5.95537666 -5.95275027 -5.93648536 -5.95238609\n",
      " -5.95768418 -5.94324913 -5.94037642 -5.94688838 -5.95036681 -5.95625294\n",
      " -5.94041917 -5.94865361 -5.94505131 -5.94284083 -5.93976208 -5.94501179\n",
      " -5.95461548 -5.94453029 -5.936574   -5.95599568]\n",
      "x_cm of snapshot 597 for cluster 2 -5.946826858213976\n",
      "These are the clusters groups we have tracked data of:\n",
      " ['snapshot596_cluster_group1', 'snapshot596_cluster_group2', 'snapshot596_cluster_group3', 'snapshot596_cluster_group4', 'snapshot596_cluster_group5', 'snapshot596_cluster_group6', 'snapshot596_cluster_group7', 'snapshot596_cluster_group8', 'snapshot596_cluster_group9', 'snapshot596_cluster_group10', 'snapshot596_cluster_group11', 'snapshot596_cluster_group12', 'snapshot596_cluster_group13', 'snapshot596_cluster_group14', 'snapshot596_cluster_group15', 'snapshot596_cluster_group16', 'snapshot596_cluster_group17']\n"
     ]
    }
   ],
   "source": [
    "###################################################\n",
    "###################################################\n",
    "####Loading the data from the pkl files #############\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "#from sl_utilities import distinct_colours as dc\n",
    "#from sl_utilities import distance_functions\n",
    "import os\n",
    "import pickle\n",
    "import imageio\n",
    "\n",
    "path=\"./fire2_data_pkl/\"\n",
    "snap=596\n",
    "simname = 'm12i_res7100_mhdcv'\n",
    "#simdir = '/scratch/projects/xsede/GalaxiesOnFIRE/mhdcv/m12i_res7100_mhdcv/1Myr/1Myr_fire2'\n",
    "file_name=\"total_data_all_clusters_all_snapshots.pkl\"\n",
    "snapshot_start=596\n",
    "snapshot_end=690\n",
    "\n",
    "\n",
    "\n",
    "with open(path+file_name, \"rb\") as input:\n",
    "    importdata = pickle.load(input)\n",
    "\n",
    "print(\"Testing if the loading of data was successful !! \\n\")\n",
    "print(\"x_tracked of snapshot 596 for cluster 1\",importdata[596][1][\"x_tracked\"])\n",
    "print(\"x_cm of snapshot 596 for cluster 1\",importdata[596][1][\"xcm\"])\n",
    "print(\"x_tracked of snapshot 597 for cluster 1\",importdata[597][1][\"x_tracked\"])\n",
    "print(\"x_cm of snapshot 597 for cluster 1\",importdata[597][1][\"xcm\"])\n",
    "print(\"x_tracked of snapshot 597 for cluster 2\",importdata[597][2][\"x_tracked\"])\n",
    "print(\"x_cm of snapshot 597 for cluster 2\",importdata[597][2][\"xcm\"])\n",
    "\n",
    "\n",
    "\n",
    "cluster_groupid=[]\n",
    "total_clusters=len(importdata[snapshot_start])\n",
    "for i in range(total_clusters):\n",
    "    cluster_groupid.append(\"snapshot\"+str(snapshot_start)+\"_cluster_group\"+str(i+1))\n",
    "\n",
    "print(\"These are the clusters groups we have tracked data of:\\n\",cluster_groupid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_path=\"./plots_pkl/animation_plots/\" #creating a path to store the plots only if it does not exist\n",
    "if not os.path.exists(plot_path):\n",
    "  os.makedirs(plot_path)\n",
    "\n",
    "\n",
    "n=snapshot_end-snapshot_start+1\n",
    "n_uf=n-5\n",
    "colors=['cyan','blue','green','magenta','yellow','teal','brown','tan','lime','red']\n",
    "\n",
    "total_clusters=10\n",
    "#fig1.suptitle(\"Cylindrical component of the velocity of Cluster Groups from Snapshot 596\")\n",
    "plot_count=0\n",
    "#plot_count=0\n",
    "snapshot_list=np.arange(snapshot_start,snapshot_end+1) #create a list of snapshots to plot\n",
    "time=snapshot_list-snapshot_start #time starts from zero here where t=0 is at the snapshot where we start from\n",
    "#time=[[0],[20],[40],[60],[80],[100]]\n",
    "for i in range(len(time)):\n",
    "    \n",
    "    fig1=plt.figure(figsize=(8,5))\n",
    "    ax = fig1.add_subplot(1,1,1)\n",
    "    cluster_count=0\n",
    "    for j in range(total_clusters):\n",
    "        \n",
    "        x=importdata[time[i]+snapshot_start][cluster_count+1][\"x_tracked\"]\n",
    "        y=importdata[time[i]+snapshot_start][cluster_count+1][\"y_tracked\"]\n",
    "        xcm=importdata[time[i]+snapshot_start][cluster_count+1][\"xcm\"]\n",
    "        ycm=importdata[time[i]+snapshot_start][cluster_count+1][\"ycm\"]\n",
    "        z=importdata[time[i]+snapshot_start][cluster_count+1][\"z_tracked\"]\n",
    "        \n",
    "        s1=ax.scatter(x,y,label=\"cluster_group\"+str(cluster_count+1),c=colors[cluster_count])\n",
    "        #ax.scatter(np.abs(xcm),np.abs(ycm),c=\"black\")\n",
    "        ax.minorticks_on()\n",
    "        ax.set_xlabel(\"x position in kpc\")\n",
    "        ax.set_ylabel(\"y position in kpc\")\n",
    "        ax.set_title(\"Clusters at T=\"+str(time[plot_count])+\" in Myr\")\n",
    "        cluster_count+=1\n",
    "    \n",
    "    ax.legend(bbox_to_anchor=(1,0.5), loc='center left')\n",
    "    plt.tight_layout()\n",
    "    fig1.savefig(plot_path+\"snap\"+str(plot_count)+\".png\",bbox_inches='tight',dpi=200)\n",
    "    plot_count+=1\n",
    "    fig1.clf()\n",
    "    plt.close()\n",
    "     \n",
    "    \n",
    "#fig1.subplots_adjust(right=1.5)    \n",
    "#fig11.legend((s1), (\"vphi_cyl\"),\"lower right\")\n",
    "#fig11.suptitle(\"This is the title \\n\")\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "images=[]\n",
    "for k in range(0,n_uf):\n",
    "    images.append(imageio.imread(plot_path+\"snap\"+str(k) + '.png'))\n",
    "    os.remove(plot_path+\"snap\"+str(k) + \".png\")\n",
    "imageio.mimsave(plot_path+\"flipping_fixed_snapshots.gif\", images, duration = 1/8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-5.99878137206444\n",
      "-5.996480406201754\n",
      "-6.010809052694551\n",
      "-6.021626334850726\n",
      "-6.038259221019609\n",
      "-6.069563586024877\n",
      "-6.097365431382942\n",
      "-6.125457936670069\n",
      "-6.139731962371394\n",
      "-6.152721098032575\n",
      "-6.160228803431775\n",
      "-6.171259611021345\n",
      "-6.187833640864167\n",
      "-6.208996332561177\n",
      "-6.226343078164862\n",
      "-6.2495566657378\n",
      "-6.266589099538845\n",
      "-6.277431772257724\n",
      "-6.294810777311444\n",
      "-6.297140104389605\n",
      "-6.305852545870835\n",
      "-6.312832934384712\n",
      "-6.31426773433521\n",
      "-6.3224043988619965\n",
      "-6.333285930360485\n",
      "-6.332134386111723\n",
      "-6.340567577808784\n",
      "-6.35234947496551\n",
      "-6.354987981352022\n",
      "-6.363044469207665\n",
      "-6.369525277843707\n",
      "-6.36431761671281\n",
      "-6.3686374615420736\n",
      "-6.3724657369117175\n",
      "-6.375223544612083\n",
      "-6.383143324968536\n",
      "-6.389462825554583\n",
      "-6.408047990226469\n",
      "-6.42938469019571\n",
      "-6.44774964248237\n",
      "-6.488737660294194\n",
      "-6.5307302349129985\n",
      "-6.5759859747927205\n",
      "-6.637668535120366\n",
      "-6.711119593430974\n",
      "-6.775627652545161\n",
      "-6.836153150974172\n",
      "-6.91739737172596\n",
      "-6.988531900651265\n",
      "-7.044383319694201\n",
      "-7.095389762981357\n",
      "-7.180271767226838\n",
      "-7.234523745453089\n",
      "-7.286586109652883\n",
      "-7.343498964908856\n",
      "-7.387217719989187\n",
      "-7.429442326030709\n",
      "-7.46745849310872\n",
      "-7.4873394938747255\n",
      "-7.5009978893189215\n",
      "-7.522469836911386\n",
      "-7.525953475207745\n",
      "-7.539345772692481\n",
      "-7.5581425001896525\n",
      "-7.557259963230873\n",
      "-7.5542863635959385\n",
      "-7.575399061155274\n",
      "-7.581936537984032\n",
      "-7.590104885583364\n",
      "-7.566136899648499\n",
      "-7.558379370709416\n",
      "-7.600642889775523\n",
      "-7.631085925001264\n",
      "-7.653729491822444\n",
      "-7.6916946233284245\n",
      "-7.752737848048687\n",
      "-7.787874794282985\n",
      "-7.829574712319439\n",
      "-7.892163320502365\n",
      "-7.961552392758264\n",
      "-8.013018672986053\n",
      "-8.090456396713073\n",
      "-8.13661409185449\n",
      "-8.222182177569069\n",
      "-8.30400597435303\n",
      "-8.396417413660815\n",
      "-8.486592878325943\n",
      "-8.623421556043724\n",
      "-8.773689760884452\n",
      "-8.916859365056432\n",
      "-9.087752046047333\n",
      "-5.996480406201754 -12.169892786021322 ymaxmin 6.087337215672603 -11.010695594770944\n"
     ]
    }
   ],
   "source": [
    "#Here we track a star cluster keeping the axes limits fixed\n",
    "#we calculated the maximum and minimum possible values for x and y axes and set the limits accordingly\n",
    "plot_path=\"./plots_pkl/animation_plots/\" #creating a path to store the plots only if it does not exist\n",
    "if not os.path.exists(plot_path):\n",
    "  os.makedirs(plot_path)\n",
    "\n",
    "\n",
    "n=snapshot_end-snapshot_start+1\n",
    "colors=['cyan','blue','green','magenta','yellow','teal','brown','tan','lime','red']\n",
    "\n",
    "total_clusters=1\n",
    "#fig1.suptitle(\"Cylindrical component of the velocity of Cluster Groups from Snapshot 596\")\n",
    "plot_count=0\n",
    "#plot_count=0\n",
    "snapshot_list=np.arange(snapshot_start,snapshot_end+1) #create a list of snapshots to plot\n",
    "time=snapshot_list-snapshot_start #time starts from zero here where t=0 is at the snapshot where we start from\n",
    "#time=[[0],[20],[40],[60],[80],[100]]\n",
    "\n",
    "xmax=np.amax(importdata[time[1]+snapshot_start][1][\"x_tracked\"])\n",
    "xmin=np.amin(importdata[time[1]+snapshot_start][1][\"x_tracked\"])\n",
    "ymax=np.amax(importdata[time[1]+snapshot_start][1][\"y_tracked\"])\n",
    "ymin=np.amin(importdata[time[1]+snapshot_start][1][\"y_tracked\"])\n",
    "for i in range(len(time)-10):\n",
    "    cluster_count=0\n",
    "    \n",
    "    for j in range(total_clusters):\n",
    "        x=importdata[time[i]+snapshot_start][cluster_count+1][\"x_tracked\"]\n",
    "        xmax_new=np.amax(x)\n",
    "        print(xmax_new)\n",
    "        xmin_new=np.amin(x)\n",
    "        if xmax_new>xmax:\n",
    "            xmax=xmax_new\n",
    "        if xmin_new<xmin:\n",
    "            xmin=xmin_new\n",
    "        \n",
    "        y=importdata[time[i]+snapshot_start][cluster_count+1][\"y_tracked\"]\n",
    "        ymax_new=np.amax(y)\n",
    "        ymin_new=np.amin(y)\n",
    "        if ymax_new>ymax:\n",
    "            ymax=ymax_new\n",
    "        if ymin_new<ymin:\n",
    "            ymin=ymin_new\n",
    "\n",
    "print(xmax,xmin,\"ymaxmin\",ymax,ymin)\n",
    "for i in range(len(time)):\n",
    "    \n",
    "    fig2=plt.figure(figsize=(8,5))\n",
    "    ax = fig2.add_subplot(1,1,1)\n",
    "    cluster_count=0\n",
    "    for j in range(total_clusters):\n",
    "        \n",
    "        x=importdata[time[i]+snapshot_start][cluster_count+1][\"x_tracked\"]\n",
    "        y=importdata[time[i]+snapshot_start][cluster_count+1][\"y_tracked\"]\n",
    "        xcm=importdata[time[i]+snapshot_start][cluster_count+1][\"xcm\"]\n",
    "        ycm=importdata[time[i]+snapshot_start][cluster_count+1][\"ycm\"]\n",
    "        z=importdata[time[i]+snapshot_start][cluster_count+1][\"z_tracked\"]\n",
    "        \n",
    "        ax.scatter(x,y,label=\"cluster_group\"+str(cluster_count+1),c=x)\n",
    "        ax.scatter(xcm,ycm,c=\"black\")\n",
    "        ax.minorticks_on()\n",
    "        ax.set_xlabel(\"x position in kpc\")\n",
    "        ax.set_ylabel(\"y position in kpc\")\n",
    "        ax.set_title(\"Clusters at T=\"+str(time[plot_count])+\" in Myr\")\n",
    "        ax.set_xlim(xmin-abs(xmin/10),xmax+abs(xmax/10))\n",
    "        ax.set_ylim(ymin-abs(ymin/10),ymax+abs(ymax/10))\n",
    "        cluster_count+=1\n",
    "    \n",
    "    ax.legend(bbox_to_anchor=(1,0.5), loc='center left')\n",
    "    plt.tight_layout()\n",
    "    fig2.savefig(plot_path+\"cluster_1_snap\"+str(plot_count)+\".png\",bbox_inches='tight',dpi=200)\n",
    "    plot_count+=1\n",
    "    fig2.clf()\n",
    "    plt.close()\n",
    "        \n",
    "#fig1.subplots_adjust(right=1.5)    \n",
    "#fig11.legend((s1), (\"vphi_cyl\"),\"lower right\")\n",
    "#fig11.suptitle(\"This is the title \\n\")\n",
    "\n",
    "cluster1_snaps=[]\n",
    "for k in range(0,101):\n",
    "    cluster1_snaps.append(imageio.imread(plot_path+\"cluster_1_snap\"+str(k) + '.png'))\n",
    "    os.remove(plot_path+\"cluster_1_snap\"+str(k) + \".png\")\n",
    "imageio.mimsave(plot_path+\"cluster1.gif\", cluster1_snaps, duration = 1/8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the longest distance a star travelled from the CM in this cluster 0.09589847341125314\n",
      "These are the dimensions of the plot xmax -5.693100807024259 xmin -5.904077448529017 ymax 4.969111994094926 ymin 4.758135352590169\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-40-dad2094cccdf>:66: MatplotlibDeprecationWarning: Passing the minor parameter of set_xticks() positionally is deprecated since Matplotlib 3.2; the parameter will become keyword-only two minor releases later.\n",
      "  ax.set_xticks([], [])\n",
      "<ipython-input-40-dad2094cccdf>:67: MatplotlibDeprecationWarning: Passing the minor parameter of set_yticks() positionally is deprecated since Matplotlib 3.2; the parameter will become keyword-only two minor releases later.\n",
      "  ax.set_yticks([], [])\n",
      "<ipython-input-40-dad2094cccdf>:76: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "These are the dimensions of the plot xmax -5.719630824273957 xmin -5.9306074657787144 ymax 4.810404132832595 ymin 4.5994274913278375\n",
      "These are the dimensions of the plot xmax -5.752154121928559 xmin -5.963130763433316 ymax 4.653701695764654 ymin 4.442725054259896\n",
      "These are the dimensions of the plot xmax -5.773312111338661 xmin -5.984288752843418 ymax 4.499574960691895 ymin 4.2885983191871375\n",
      "These are the dimensions of the plot xmax -5.801034487807804 xmin -6.012011129312562 ymax 4.337630444064304 ymin 4.126653802559547\n",
      "These are the dimensions of the plot xmax -5.838415004176863 xmin -6.04939164568162 ymax 4.165458509877567 ymin 3.9544818683728096\n",
      "These are the dimensions of the plot xmax -5.870688892179934 xmin -6.081665533684691 ymax 3.984689943004952 ymin 3.7737133015001945\n",
      "These are the dimensions of the plot xmax -5.901078110709866 xmin -6.112054752214624 ymax 3.8027722036217613 ymin 3.591795562117005\n",
      "These are the dimensions of the plot xmax -5.921054272387635 xmin -6.132030913892392 ymax 3.6387559523876805 ymin 3.427779310882924\n",
      "These are the dimensions of the plot xmax -5.938377355697251 xmin -6.149353997202009 ymax 3.4662407091041025 ymin 3.255264067599345\n",
      "These are the dimensions of the plot xmax -5.948654365217778 xmin -6.1596310067225355 ymax 3.302287677954447 ymin 3.0913110364496905\n",
      "These are the dimensions of the plot xmax -5.960714722225879 xmin -6.171691363730637 ymax 3.1392589013434113 ymin 2.928282259838655\n",
      "These are the dimensions of the plot xmax -5.9751777446704075 xmin -6.186154386175165 ymax 2.973686178462197 ymin 2.7627095369574395\n",
      "These are the dimensions of the plot xmax -5.991532600645005 xmin -6.2025092421497625 ymax 2.8072512221715327 ymin 2.596274580666776\n",
      "These are the dimensions of the plot xmax -6.000831453257179 xmin -6.2118080947619365 ymax 2.6273188111257735 ymin 2.416342169621016\n",
      "These are the dimensions of the plot xmax -6.01256336394966 xmin -6.223540005454417 ymax 2.4463682520000365 ymin 2.23539161049528\n",
      "These are the dimensions of the plot xmax -6.015704105670476 xmin -6.226680747175234 ymax 2.261392780456454 ymin 2.0504161389516966\n",
      "These are the dimensions of the plot xmax -6.012334664544931 xmin -6.2233113060496885 ymax 2.068841426552254 ymin 1.8578647850474967\n",
      "These are the dimensions of the plot xmax -6.014184765355253 xmin -6.2251614068600105 ymax 1.8685903184752566 ymin 1.6576136769704997\n",
      "These are the dimensions of the plot xmax -5.998483097800187 xmin -6.209459739304944 ymax 1.6665328645641408 ymin 1.455556223059384\n",
      "These are the dimensions of the plot xmax -5.987580303389172 xmin -6.198556944893929 ymax 1.454363425004059 ymin 1.243386783499302\n",
      "These are the dimensions of the plot xmax -5.972512610835589 xmin -6.183489252340347 ymax 1.2510548738937017 ymin 1.0400782323889448\n",
      "These are the dimensions of the plot xmax -5.949863261930252 xmin -6.16083990343501 ymax 1.0611668428521845 ymin 0.8501902013474276\n",
      "These are the dimensions of the plot xmax -5.930589611647446 xmin -6.141566253152203 ymax 0.8591130330388199 ymin 0.648136391534063\n",
      "These are the dimensions of the plot xmax -5.912218634883359 xmin -6.123195276388117 ymax 0.6525073236155968 ymin 0.4415306821108399\n",
      "These are the dimensions of the plot xmax -5.877416649068402 xmin -6.088393290573159 ymax 0.45833722874627664 ymin 0.2473605872415197\n",
      "These are the dimensions of the plot xmax -5.850397294436391 xmin -6.061373935941148 ymax 0.23160892614853365 ymin 0.020632284643776727\n",
      "These are the dimensions of the plot xmax -5.8224806451185716 xmin -6.033457286623329 ymax 0.022683053272397724 ymin -0.1882935882323592\n",
      "These are the dimensions of the plot xmax -5.782906877851789 xmin -5.9938835193565465 ymax -0.2024326194162055 ymin -0.4134092609209624\n",
      "These are the dimensions of the plot xmax -5.743909728883012 xmin -5.9548863703877695 ymax -0.4296886690786128 ymin -0.6406653105833697\n",
      "These are the dimensions of the plot xmax -5.700057688325391 xmin -5.911034329830148 ymax -0.6559046900788515 ymin -0.8668813315836085\n",
      "These are the dimensions of the plot xmax -5.640146040896393 xmin -5.85112268240115 ymax -0.8730176722188269 ymin -1.0839943137235837\n",
      "These are the dimensions of the plot xmax -5.584326436480161 xmin -5.795303077984919 ymax -1.0942928143456965 ymin -1.3052694558504534\n",
      "These are the dimensions of the plot xmax -5.521351176751749 xmin -5.732327818256507 ymax -1.2924560110339007 ymin -1.5034326525386577\n",
      "These are the dimensions of the plot xmax -5.454934071067455 xmin -5.665910712572212 ymax -1.4932899652399656 ymin -1.7042666067447225\n",
      "These are the dimensions of the plot xmax -5.390299792756021 xmin -5.601276434260778 ymax -1.6948909755641246 ymin -1.9058676170688815\n",
      "These are the dimensions of the plot xmax -5.320743791885978 xmin -5.531720433390736 ymax -1.885945580127687 ymin -2.096922221632444\n",
      "These are the dimensions of the plot xmax -5.259329284602717 xmin -5.470305926107474 ymax -2.084389827837523 ymin -2.2953664693422793\n",
      "These are the dimensions of the plot xmax -5.19461523071714 xmin -5.405591872221898 ymax -2.260932085169906 ymin -2.4719087266746635\n",
      "These are the dimensions of the plot xmax -5.126463607044602 xmin -5.33744024854936 ymax -2.4492439262199923 ymin -2.6602205677247497\n",
      "These are the dimensions of the plot xmax -5.0738723184857975 xmin -5.284848959990555 ymax -2.6246813284713175 ymin -2.835657969976075\n",
      "These are the dimensions of the plot xmax -5.018132602100541 xmin -5.229109243605298 ymax -2.8022268553128233 ymin -3.0132034968175807\n",
      "These are the dimensions of the plot xmax -4.9613178894458505 xmin -5.172294530950608 ymax -2.973556913818106 ymin -3.1845335553228633\n",
      "These are the dimensions of the plot xmax -4.914293593913235 xmin -5.125270235417992 ymax -3.144139810665835 ymin -3.3551164521705923\n",
      "These are the dimensions of the plot xmax -4.872219640126425 xmin -5.083196281631182 ymax -3.3071605130974397 ymin -3.518137154602197\n",
      "These are the dimensions of the plot xmax -4.81940459664434 xmin -5.030381238149097 ymax -3.4699033620038744 ymin -3.680880003508631\n",
      "These are the dimensions of the plot xmax -4.760119455910627 xmin -4.971096097415384 ymax -3.6387340738798724 ymin -3.849710715384629\n",
      "These are the dimensions of the plot xmax -4.716357156522789 xmin -4.927333798027546 ymax -3.800470393544435 ymin -4.011447035049192\n",
      "These are the dimensions of the plot xmax -4.663117655586368 xmin -4.874094297091125 ymax -3.969966706954505 ymin -4.180943348459262\n",
      "These are the dimensions of the plot xmax -4.595315299235035 xmin -4.806291940739793 ymax -4.145142244121056 ymin -4.356118885625813\n",
      "These are the dimensions of the plot xmax -4.521865234912269 xmin -4.732841876417027 ymax -4.326406824514349 ymin -4.537383466019106\n",
      "These are the dimensions of the plot xmax -4.473869321072656 xmin -4.6848459625774135 ymax -4.477639963264671 ymin -4.688616604769428\n",
      "These are the dimensions of the plot xmax -4.3988390565431565 xmin -4.609815698047914 ymax -4.6529749668772284 ymin -4.863951608381986\n",
      "These are the dimensions of the plot xmax -4.320328926103211 xmin -4.531305567607968 ymax -4.817637682314199 ymin -5.028614323818957\n",
      "These are the dimensions of the plot xmax -4.244285440441133 xmin -4.45526208194589 ymax -4.977678092486043 ymin -5.1886547339908\n",
      "These are the dimensions of the plot xmax -4.1569624831051994 xmin -4.367939124609957 ymax -5.148845923324484 ymin -5.3598225648292415\n",
      "These are the dimensions of the plot xmax -4.065926133050824 xmin -4.276902774555581 ymax -5.317347538494388 ymin -5.528324179999145\n",
      "These are the dimensions of the plot xmax -3.9703754760223884 xmin -4.181352117527146 ymax -5.482352258564046 ymin -5.693328900068804\n",
      "These are the dimensions of the plot xmax -3.8602834017496006 xmin -4.071260043254358 ymax -5.649959539350146 ymin -5.860936180854903\n",
      "These are the dimensions of the plot xmax -3.744150836557692 xmin -3.9551274780624492 ymax -5.823316966504976 ymin -6.034293608009733\n",
      "These are the dimensions of the plot xmax -3.6338010647374155 xmin -3.844777706242173 ymax -5.9846723027118935 ymin -6.195648944216651\n",
      "These are the dimensions of the plot xmax -3.508180647268989 xmin -3.7191572887737463 ymax -6.145868405882546 ymin -6.356845047387304\n",
      "These are the dimensions of the plot xmax -3.391472754178441 xmin -3.602449395683198 ymax -6.302402846824754 ymin -6.5133794883295115\n",
      "These are the dimensions of the plot xmax -3.276666029512575 xmin -3.4876426710173325 ymax -6.453458299371744 ymin -6.664434940876501\n",
      "These are the dimensions of the plot xmax -3.1454005573040167 xmin -3.356377198808774 ymax -6.600306261997744 ymin -6.8112829035025015\n",
      "These are the dimensions of the plot xmax -3.0123437410328346 xmin -3.223320382537592 ymax -6.751614915773569 ymin -6.962591557278326\n",
      "These are the dimensions of the plot xmax -2.898075553410509 xmin -3.1090521949152654 ymax -6.88158784921461 ymin -7.092564490719368\n",
      "These are the dimensions of the plot xmax -2.770612076021548 xmin -2.9815887175263054 ymax -7.010234959363828 ymin -7.221211600868585\n",
      "These are the dimensions of the plot xmax -2.643758663860953 xmin -2.8547353053657094 ymax -7.135116969617089 ymin -7.346093611121846\n",
      "These are the dimensions of the plot xmax -2.4926376950095817 xmin -2.703614336514339 ymax -7.267590871871039 ymin -7.478567513375796\n",
      "These are the dimensions of the plot xmax -2.3537617212486666 xmin -2.564738362753424 ymax -7.385196455249462 ymin -7.596173096754219\n",
      "These are the dimensions of the plot xmax -2.2550300860593673 xmin -2.4660067275641238 ymax -7.484654748422348 ymin -7.695631389927105\n",
      "These are the dimensions of the plot xmax -2.1466564258785015 xmin -2.357633067383259 ymax -7.5834640695987225 ymin -7.79444071110348\n",
      "These are the dimensions of the plot xmax -2.0307195078436253 xmin -2.2416961493483827 ymax -7.679021281791937 ymin -7.889997923296694\n",
      "These are the dimensions of the plot xmax -1.9318231423552616 xmin -2.142799783860019 ymax -7.768740393068042 ymin -7.9797170345728\n",
      "These are the dimensions of the plot xmax -1.8494294427131357 xmin -2.0604060842178926 ymax -7.854512096028748 ymin -8.065488737533505\n",
      "These are the dimensions of the plot xmax -1.74713005054109 xmin -1.958106692045847 ymax -7.933928386911642 ymin -8.1449050284164\n",
      "These are the dimensions of the plot xmax -1.6478908355576833 xmin -1.8588674770624403 ymax -8.020709112880057 ymin -8.231685754384815\n",
      "These are the dimensions of the plot xmax -1.5660846086311382 xmin -1.777061250135895 ymax -8.092551108252435 ymin -8.303527749757192\n",
      "These are the dimensions of the plot xmax -1.4890652235974915 xmin -1.7000418651022484 ymax -8.158389358936699 ymin -8.369366000441456\n",
      "These are the dimensions of the plot xmax -1.3977505826536276 xmin -1.6087272241583845 ymax -8.230248927995353 ymin -8.44122556950011\n",
      "These are the dimensions of the plot xmax -1.3278619885617908 xmin -1.5388386300665478 ymax -8.283916625972113 ymin -8.49489326747687\n",
      "These are the dimensions of the plot xmax -1.2331237136894337 xmin -1.4441003551941907 ymax -8.34055109311448 ymin -8.551527734619237\n",
      "These are the dimensions of the plot xmax -1.171009465308707 xmin -1.381986106813464 ymax -8.388363191518588 ymin -8.599339833023345\n",
      "These are the dimensions of the plot xmax -1.1058130601858316 xmin -1.3167897016905885 ymax -8.438029674251872 ymin -8.64900631575663\n",
      "These are the dimensions of the plot xmax -1.0501937901490408 xmin -1.2611704316537977 ymax -8.487790972854004 ymin -8.698767614358761\n",
      "These are the dimensions of the plot xmax -0.9939446761381909 xmin -1.2049213176429479 ymax -8.53079205198045 ymin -8.741768693485207\n",
      "These are the dimensions of the plot xmax -0.9739605952417705 xmin -1.1849372367465274 ymax -8.565682103368491 ymin -8.776658744873249\n",
      "These are the dimensions of the plot xmax -0.9650915339574162 xmin -1.1760681754621731 ymax -8.596121708379073 ymin -8.80709834988383\n",
      "These are the dimensions of the plot xmax -0.951853662199347 xmin -1.162830303704104 ymax -8.632454076690937 ymin -8.843430718195695\n",
      "These are the dimensions of the plot xmax -0.9611694857403921 xmin -1.172146127245149 ymax -8.664514454817363 ymin -8.87549109632212\n",
      "These are the dimensions of the plot xmax -0.957804088157987 xmin -1.168780729662744 ymax -8.698410519916825 ymin -8.909387161421582\n",
      "These are the dimensions of the plot xmax -0.9385119991390811 xmin -1.149488640643838 ymax -8.727262501787457 ymin -8.938239143292215\n",
      "These are the dimensions of the plot xmax -0.9174011617986997 xmin -1.1283778033034566 ymax -8.755305408938293 ymin -8.96628205044305\n",
      "These are the dimensions of the plot xmax -0.9338165408193988 xmin -1.1447931823241557 ymax -8.779296395144913 ymin -8.99027303664967\n",
      "These are the dimensions of the plot xmax -0.9271170704009075 xmin -1.1380937119056644 ymax -8.800077348519144 ymin -9.011053990023902\n",
      "These are the dimensions of the plot xmax 1.1215642447611487 xmin 0.9105876032563918 ymax 9.033906229982641 ymin 8.822929588477884\n",
      "These are the dimensions of the plot xmax 1.1247733429639883 xmin 0.9137967014592314 ymax 9.047805795921024 ymin 8.836829154416266\n",
      "These are the dimensions of the plot xmax 1.1425299720376536 xmin 0.9315533305328967 ymax 9.05118293821012 ymin 8.840206296705363\n",
      "These are the dimensions of the plot xmax 1.083504552930726 xmin 0.872527911425969 ymax 9.068632019705488 ymin 8.85765537820073\n",
      "These are the dimensions of the plot xmax 1.063362863394469 xmin 0.8523862218897121 ymax 9.077004356154514 ymin 8.866027714649757\n"
     ]
    }
   ],
   "source": [
    "#Here we try to track a star cluster keeping the center of mass at the center of the plot\n",
    "plot_path=\"./plots_pkl/animation_plots/\" #creating a path to store the plots only if it does not exist\n",
    "if not os.path.exists(plot_path):\n",
    "  os.makedirs(plot_path)\n",
    "\n",
    "\n",
    "n=snapshot_end-snapshot_start+1\n",
    "colors=['cyan','blue','green','magenta','yellow','teal','brown','tan','lime','red']\n",
    "\n",
    "total_clusters=1\n",
    "#fig1.suptitle(\"Cylindrical component of the velocity of Cluster Groups from Snapshot 596\")\n",
    "plot_count=0\n",
    "#plot_count=0\n",
    "snapshot_list=np.arange(snapshot_start,snapshot_end+1) #create a list of snapshots to plot\n",
    "time=snapshot_list-snapshot_start #time starts from zero here where t=0 is at the snapshot where we start from\n",
    "#time=[[0],[20],[40],[60],[80],[100]]\n",
    "\n",
    "#################################\n",
    "################################\n",
    "rmax_global=importdata[time[1]+snapshot_start][1][\"rmax\"]\n",
    "for i in range(len(time)):\n",
    "    cluster_count=4 #change it to look for cluster no cluster_count+1\n",
    "    \n",
    "    for j in range(total_clusters):\n",
    "        r=importdata[time[i]+snapshot_start][cluster_count+1][\"rmax\"]\n",
    "        \n",
    "        if r>rmax_global:\n",
    "            rmax_global=r\n",
    "\n",
    "\n",
    "#######################################\n",
    "######################################\n",
    "print(\"This is the longest distance a star travelled from the CM in this cluster\",rmax_global)\n",
    "for i in range(len(time)):\n",
    "    \n",
    "    fig2=plt.figure(figsize=(7,5))\n",
    "    ax = fig2.add_subplot(1,1,1)\n",
    "    cluster_count=4 #change it change it to look for cluster no cluster_count+1\n",
    "    for j in range(total_clusters):\n",
    "        \n",
    "        x=importdata[time[i]+snapshot_start][cluster_count+1][\"x_tracked\"]\n",
    "        y=importdata[time[i]+snapshot_start][cluster_count+1][\"y_tracked\"]\n",
    "        z=importdata[time[i]+snapshot_start][cluster_count+1][\"z_tracked\"]\n",
    "        xcm=importdata[time[i]+snapshot_start][cluster_count+1][\"xcm\"]\n",
    "        ycm=importdata[time[i]+snapshot_start][cluster_count+1][\"ycm\"]\n",
    "        zcm=importdata[time[i]+snapshot_start][cluster_count+1][\"zcm\"]\n",
    "        rmax=importdata[time[i]+snapshot_start][cluster_count+1][\"rmax\"]\n",
    "        xlim_max=xcm+1.1*rmax_global\n",
    "        xlim_min=xcm-1.1*rmax_global\n",
    "        \n",
    "        zlim_max=zcm+1.1*rmax_global\n",
    "        zlim_min=zcm-1.1*rmax_global\n",
    "        \n",
    "        ylim_max=ycm+1.1*rmax_global\n",
    "        ylim_min=ycm-1.1*rmax_global\n",
    "        print(\"These are the dimensions of the plot xmax\",xlim_max,\"xmin\",xlim_min,\"ymax\",ylim_max,\"ymin\",ylim_min)\n",
    "        ax.scatter(x,y,label=\"cluster_group\"+str(cluster_count+1),c=x)\n",
    "        ax.scatter(xcm,ycm,c=\"black\",marker='x')\n",
    "        ax.minorticks_on()\n",
    "        ax.set_xlabel(\"X\")\n",
    "        ax.set_ylabel(\"Y\")\n",
    "        \n",
    "        ax.set_title(\"Cluster \"+str(cluster_count+1)+\" at T=\"+str(time[plot_count])+\" in Myr\")\n",
    "        ax.set_xlim(xlim_min,xlim_max)\n",
    "        ax.set_ylim(ylim_min,ylim_max)\n",
    "        ax.set_xticks([], [])\n",
    "        ax.set_yticks([], [])\n",
    "        #ax.axis('off') This removes everything including the box and labels.\n",
    "        ax.set_aspect('equal')\n",
    "        ax.annotate('', xy=(xcm-0.5, ylim_min+0.2), xytext=(xcm+0.5, ylim_min+0.2),xycoords='data', arrowprops=dict(alpha=0.5, fc='b', ec='b', arrowstyle=\"<->\"))\n",
    "        ax.annotate('1 kpc',xy=(xcm,ylim_min+0.3),textcoords='data')\n",
    "        #ax.arrow(xlim_min,ylim_min, xlim_min+0.9, 0,head_width=0.05, head_length=0.1, fc='k', ec='k')\n",
    "        cluster_count+=1\n",
    "    \n",
    "    #ax.legend(bbox_to_anchor=(1,0.5), loc='center left')\n",
    "    plt.tight_layout()\n",
    "    #plt.axis('square')\n",
    "    fig2.savefig(plot_path+\"cluster_5_snap_fixedCM\"+str(plot_count)+\".png\",bbox_inches='tight',dpi=200)\n",
    "    plot_count+=1\n",
    "    fig2.clf()\n",
    "    plt.close()\n",
    "        \n",
    "#fig1.subplots_adjust(right=1.5)    \n",
    "#fig11.legend((s1), (\"vphi_cyl\"),\"lower right\")\n",
    "#fig11.suptitle(\"This is the title \\n\")\n",
    "\n",
    "cluster1_snaps=[]\n",
    "for k in range(0,101):\n",
    "    cluster1_snaps.append(imageio.imread(plot_path+\"cluster_5_snap_fixedCM\"+str(k) + '.png'))\n",
    "    os.remove(plot_path+\"cluster_5_snap_fixedCM\"+str(k) + \".png\")\n",
    "\n",
    "imageio.mimsave(plot_path+\"cluster5_fixedCM.gif\", cluster1_snaps, duration = 1/8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here we try to track a star cluster keeping the center of mass at the center of the plot\n",
    "plot_path=\"./plots_pkl/animation_plots/\" #creating a path to store the plots only if it does not exist\n",
    "if not os.path.exists(plot_path):\n",
    "  os.makedirs(plot_path)\n",
    "\n",
    "\n",
    "n=snapshot_end-snapshot_start+1\n",
    "colors=['cyan','blue','green','magenta','yellow','teal','brown','tan','lime','red']\n",
    "\n",
    "total_clusters=1\n",
    "#fig1.suptitle(\"Cylindrical component of the velocity of Cluster Groups from Snapshot 596\")\n",
    "plot_count=0\n",
    "#plot_count=0\n",
    "snapshot_list=np.arange(snapshot_start,snapshot_end+1) #create a list of snapshots to plot\n",
    "time=snapshot_list-snapshot_start #time starts from zero here where t=0 is at the snapshot where we start from\n",
    "#time=[[0],[20],[40],[60],[80],[100]]\n",
    "\n",
    "#################################\n",
    "################################\n",
    "rmax_global=importdata[time[1]+snapshot_start][1][\"rmax\"]\n",
    "for i in range(len(time)):\n",
    "    cluster_count=1 #change it to look for cluster no cluster_count+1\n",
    "    \n",
    "    for j in range(total_clusters):\n",
    "        r=importdata[time[i]+snapshot_start][cluster_count+1][\"rmax\"]\n",
    "        \n",
    "        if r>rmax_global:\n",
    "            rmax_global=r\n",
    "\n",
    "\n",
    "#######################################\n",
    "######################################\n",
    "\n",
    "for i in range(len(time)):\n",
    "    \n",
    "    fig3=plt.figure(figsize=(7,5))\n",
    "    ax3 = fig3.add_subplot(1,1,1)\n",
    "    cluster_count=1 #change it change it to look for cluster no cluster_count+1\n",
    "    for j in range(total_clusters):\n",
    "        \n",
    "        x=importdata[time[i]+snapshot_start][cluster_count+1][\"x_tracked\"]\n",
    "        y=importdata[time[i]+snapshot_start][cluster_count+1][\"y_tracked\"]\n",
    "        z=importdata[time[i]+snapshot_start][cluster_count+1][\"z_tracked\"]\n",
    "        xcm=importdata[time[i]+snapshot_start][cluster_count+1][\"xcm\"]\n",
    "        ycm=importdata[time[i]+snapshot_start][cluster_count+1][\"ycm\"]\n",
    "        zcm=importdata[time[i]+snapshot_start][cluster_count+1][\"zcm\"]\n",
    "        rmax=importdata[time[i]+snapshot_start][cluster_count+1][\"rmax\"]\n",
    "        xlim_max=xcm+1.1*rmax_global\n",
    "        xlim_min=xcm-1.1*rmax_global\n",
    "        \n",
    "        zlim_max=zcm+1.1*rmax_global\n",
    "        zlim_min=zcm-1.1*rmax_global\n",
    "        \n",
    "        ylim_max=ycm+1.1*rmax_global\n",
    "        ylim_min=ycm-1.1*rmax_global\n",
    "        #print(\"These are the dimensions of the plot\",xlim_max-xlim_min,ylim_max-ylim_min)\n",
    "        ax3.scatter(x,z,label=\"cluster_group\"+str(cluster_count+1),c=x)\n",
    "        ax3.scatter(xcm,zcm,c=\"black\",marker='x')\n",
    "        ax3.set_xlabel(\"X\")\n",
    "        ax3.set_ylabel(\"Z\")\n",
    "        ax3.set_title(\"Cluster \"+str(cluster_count+1)+\" at T=\"+str(time[plot_count])+\" in Myr\")\n",
    "        ax3.set_xlim(xlim_min,xlim_max)\n",
    "        ax3.set_ylim(zlim_min,zlim_max)\n",
    "        ax3.set_xticks([], [])\n",
    "        ax3.set_yticks([], [])\n",
    "        ax3.set_aspect('equal')\n",
    "        ax3.annotate('', xy=(xcm-0.5, zlim_min+0.2), xytext=(xcm+0.5, zlim_min+0.2),xycoords='data', arrowprops=dict(alpha=0.5, fc='b', ec='b', arrowstyle=\"<->\"))\n",
    "        ax3.annotate('1 kpc',xy=(xcm,zlim_min+0.3),textcoords='data')\n",
    "        cluster_count+=1\n",
    "    \n",
    "    #ax.legend(bbox_to_anchor=(1,0.5), loc='center left')\n",
    "    plt.tight_layout()\n",
    "    #plt.axis('square')\n",
    "    fig3.savefig(plot_path+\"cluster_2_snap_fixedCM_zVSx\"+str(plot_count)+\".png\",bbox_inches='tight',dpi=200)\n",
    "    plot_count+=1\n",
    "    fig3.clf()\n",
    "    plt.close()\n",
    "        \n",
    "#fig1.subplots_adjust(right=1.5)    \n",
    "#fig11.legend((s1), (\"vphi_cyl\"),\"lower right\")\n",
    "#fig11.suptitle(\"This is the title \\n\")\n",
    "\n",
    "\n",
    "zVSx_snaps=[]\n",
    "for k in range(0,101):\n",
    "    zVSx_snaps.append(imageio.imread(plot_path+\"cluster_2_snap_fixedCM_zVSx\"+str(k) + '.png'))\n",
    "    os.remove(plot_path+\"cluster_2_snap_fixedCM_zVSx\"+str(k) + \".png\")\n",
    "\n",
    "imageio.mimsave(plot_path+\"cluster2_fixedCM_zVSx.gif\", zVSx_snaps, duration = 1/8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-4-4d2333e61159>:62: MatplotlibDeprecationWarning: Passing the minor parameter of set_xticks() positionally is deprecated since Matplotlib 3.2; the parameter will become keyword-only two minor releases later.\n",
      "  ax.set_xticks([], [])\n",
      "<ipython-input-4-4d2333e61159>:63: MatplotlibDeprecationWarning: Passing the minor parameter of set_yticks() positionally is deprecated since Matplotlib 3.2; the parameter will become keyword-only two minor releases later.\n",
      "  ax.set_yticks([], [])\n"
     ]
    }
   ],
   "source": [
    "#This code tracks the different clusters as they evlove through time.\n",
    "#The axes is fixed at the CM \n",
    "plot_path=\"./plots_pkl/animation_plots/\" #creating a path to store the plots only if it does not exist\n",
    "if not os.path.exists(plot_path):\n",
    "  os.makedirs(plot_path)\n",
    "\n",
    "\n",
    "n=snapshot_end-snapshot_start+1\n",
    "colors=['cyan','blue','green','magenta','yellow','teal','brown','tan','lime','red']\n",
    "\n",
    "total_clusters=10\n",
    "#fig1.suptitle(\"Cylindrical component of the velocity of Cluster Groups from Snapshot 596\")\n",
    "plot_count=0\n",
    "snapshot_list=np.arange(snapshot_start,snapshot_end+1) #create a list of snapshots to plot\n",
    "time=snapshot_list-snapshot_start #time starts from zero here where t=0 is at the snapshot where we start from\n",
    "\n",
    "\n",
    "#################################\n",
    "################################\n",
    "total_subplots=total_clusters\n",
    "position = range(1,total_subplots + 1)\n",
    "cols=3\n",
    "rows=total_subplots//cols\n",
    "rows=rows+total_subplots%cols\n",
    "\n",
    "for i in range(len(time)):\n",
    "    cluster_count=0\n",
    "    #fig4 = plt.figure(figsize=(15,rows*4))\n",
    "    fig4 = plt.figure(figsize=(8,rows*3))\n",
    "    for j in range(total_clusters):\n",
    "        ax = fig4.add_subplot(rows,cols,position[j])\n",
    "        rmax_global=importdata[time[i]+snapshot_start][cluster_count+1][\"rmax\"]\n",
    "        \n",
    "        for t in range(len(time)):\n",
    "            r=importdata[time[t]+snapshot_start][cluster_count+1][\"rmax\"]\n",
    "            if r>rmax_global:\n",
    "                 rmax_global=r\n",
    "\n",
    "        x=importdata[time[i]+snapshot_start][cluster_count+1][\"x_tracked\"]\n",
    "        y=importdata[time[i]+snapshot_start][cluster_count+1][\"y_tracked\"]\n",
    "        z=importdata[time[i]+snapshot_start][cluster_count+1][\"z_tracked\"]\n",
    "        xcm=importdata[time[i]+snapshot_start][cluster_count+1][\"xcm\"]\n",
    "        ycm=importdata[time[i]+snapshot_start][cluster_count+1][\"ycm\"]\n",
    "        zcm=importdata[time[i]+snapshot_start][cluster_count+1][\"zcm\"]\n",
    "        rmax=importdata[time[i]+snapshot_start][cluster_count+1][\"rmax\"]\n",
    "        xlim_max=xcm+1.1*rmax_global\n",
    "        xlim_min=xcm-1.1*rmax_global\n",
    "        \n",
    "        zlim_max=zcm+1.1*rmax_global\n",
    "        zlim_min=zcm-1.1*rmax_global\n",
    "        \n",
    "        ylim_max=ycm+1.1*rmax_global\n",
    "        ylim_min=ycm-1.1*rmax_global\n",
    "        #print(\"These are the dimensions of the plot\",xlim_max-xlim_min,ylim_max-ylim_min)\n",
    "        ax.scatter(x,y,label=\"cluster_group\"+str(cluster_count+1),c=x)\n",
    "        ax.scatter(xcm,ycm,c=\"black\",marker='x')\n",
    "        ax.set_xlabel(\"X\")\n",
    "        ax.set_ylabel(\"Y\")\n",
    "        ax.set_title(\"Cluster \"+str(cluster_count+1)+\" at T=\"+str(time[plot_count])+\" in Myr\",fontsize=8)\n",
    "        ax.set_xlim(xlim_min,xlim_max)\n",
    "        ax.set_ylim(ylim_min,ylim_max)\n",
    "        ax.set_xticks([], [])\n",
    "        ax.set_yticks([], [])\n",
    "        ax.set_aspect('equal')\n",
    "        ax.annotate('', xy=(xcm-0.5, ylim_min+0.2), xytext=(xcm+0.5, ylim_min+0.2),xycoords='data', arrowprops=dict(alpha=0.5, fc='b', ec='b', arrowstyle=\"<->\"))\n",
    "        ax.annotate('1 kpc',xy=(xcm,ylim_min+0.3),textcoords='data')\n",
    "        cluster_count+=1\n",
    "    \n",
    "    #ax.legend(bbox_to_anchor=(1,0.5), loc='center left')\n",
    "    #plt.tight_layout()\n",
    "    #plt.axis('square')\n",
    "    fig4.savefig(plot_path+\"all_clusters_fixedCM_yVSx\"+str(plot_count)+\".png\",bbox_inches='tight',dpi=200)\n",
    "    plot_count+=1\n",
    "    fig4.clf()\n",
    "    plt.close()\n",
    "#fig1.subplots_adjust(right=1.5)    \n",
    "#fig11.legend((s1), (\"vphi_cyl\"),\"lower right\")\n",
    "#fig11.suptitle(\"This is the title \\n\")\n",
    "\n",
    "\n",
    "zVSx_snaps=[]\n",
    "for k in range(0,95):\n",
    "    zVSx_snaps.append(imageio.imread(plot_path+\"all_clusters_fixedCM_yVSx\"+str(k) + '.png'))\n",
    "    os.remove(plot_path+\"all_clusters_fixedCM_yVSx\"+str(k) + \".png\")\n",
    "\n",
    "imageio.mimsave(plot_path+\"all_clusters_fixedCM_yVSx.gif\", zVSx_snaps, duration = 1/8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the largest distance a star travlled from the CM in all these clusters 2.9228877932445627\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-14-e8054badf14a>:72: MatplotlibDeprecationWarning: Passing the minor parameter of set_xticks() positionally is deprecated since Matplotlib 3.2; the parameter will become keyword-only two minor releases later.\n",
      "  ax.set_xticks([], [])\n",
      "<ipython-input-14-e8054badf14a>:73: MatplotlibDeprecationWarning: Passing the minor parameter of set_yticks() positionally is deprecated since Matplotlib 3.2; the parameter will become keyword-only two minor releases later.\n",
      "  ax.set_yticks([], [])\n"
     ]
    }
   ],
   "source": [
    "#This code tracks the different clusters as they evlove through time.\n",
    "#The axes is fixed at the CM \n",
    "plot_path=\"./plots_pkl/animation_plots/\" #creating a path to store the plots only if it does not exist\n",
    "if not os.path.exists(plot_path):\n",
    "  os.makedirs(plot_path)\n",
    "\n",
    "\n",
    "n=snapshot_end-snapshot_start+1\n",
    "colors=['cyan','blue','green','magenta','yellow','teal','brown','tan','lime','red']\n",
    "\n",
    "total_clusters=10\n",
    "#fig1.suptitle(\"Cylindrical component of the velocity of Cluster Groups from Snapshot 596\")\n",
    "plot_count=0\n",
    "snapshot_list=np.arange(snapshot_start,snapshot_end+1) #create a list of snapshots to plot\n",
    "time=snapshot_list-snapshot_start #time starts from zero here where t=0 is at the snapshot where we start from\n",
    "\n",
    "\n",
    "#################################\n",
    "################################\n",
    "total_subplots=total_clusters\n",
    "position = range(1,total_subplots + 1)\n",
    "cols=3\n",
    "rows=total_subplots//cols\n",
    "rows=rows+total_subplots%cols\n",
    "\n",
    "##############################\n",
    "#we would evaluate the largest distance travelled by a star in all of the cluster and use it as our rmax_global\n",
    "rmax_global=importdata[time[0]+snapshot_start][1][\"rmax\"]\n",
    "        \n",
    "for t in range(len(time)):\n",
    "    cc=0\n",
    "    for c in range(total_clusters):\n",
    "        r=importdata[time[t]+snapshot_start][cc+1][\"rmax\"]\n",
    "        if r>rmax_global:\n",
    "            rmax_global=r\n",
    "        cc+=1\n",
    "print(\"This is the largest distance a star travlled from the CM in all these clusters\",rmax_global)\n",
    "############################\n",
    "############################\n",
    "\n",
    "\n",
    "\n",
    "for i in range(len(time)):\n",
    "    cluster_count=0\n",
    "    #fig4 = plt.figure(figsize=(15,rows*4))\n",
    "    fig5 = plt.figure(figsize=(8,rows*3))\n",
    "    for j in range(total_clusters):\n",
    "        ax = fig5.add_subplot(rows,cols,position[j])\n",
    "        x=importdata[time[i]+snapshot_start][cluster_count+1][\"x_tracked\"]\n",
    "        y=importdata[time[i]+snapshot_start][cluster_count+1][\"y_tracked\"]\n",
    "        z=importdata[time[i]+snapshot_start][cluster_count+1][\"z_tracked\"]\n",
    "        xcm=importdata[time[i]+snapshot_start][cluster_count+1][\"xcm\"]\n",
    "        ycm=importdata[time[i]+snapshot_start][cluster_count+1][\"ycm\"]\n",
    "        zcm=importdata[time[i]+snapshot_start][cluster_count+1][\"zcm\"]\n",
    "        rmax=importdata[time[i]+snapshot_start][cluster_count+1][\"rmax\"]\n",
    "        xlim_max=xcm+1.1*rmax_global\n",
    "        xlim_min=xcm-1.1*rmax_global\n",
    "        \n",
    "        zlim_max=zcm+1.1*rmax_global\n",
    "        zlim_min=zcm-1.1*rmax_global\n",
    "        \n",
    "        ylim_max=ycm+1.1*rmax_global\n",
    "        ylim_min=ycm-1.1*rmax_global\n",
    "        #print(\"These are the dimensions of the plot\",xlim_max-xlim_min,ylim_max-ylim_min)\n",
    "        ax.scatter(x,y,label=\"cluster_group\"+str(cluster_count+1),c=x)\n",
    "        ax.scatter(xcm,ycm,c=\"black\",marker='x')\n",
    "        ax.set_xlabel(\"X\")\n",
    "        ax.set_ylabel(\"Y\")\n",
    "        ax.set_title(\"Cluster \"+str(cluster_count+1)+\" at T=\"+str(time[plot_count])+\" in Myr\",fontsize=8)\n",
    "        ax.set_xlim(xlim_min,xlim_max)\n",
    "        ax.set_ylim(ylim_min,ylim_max)\n",
    "        ax.set_xticks([], [])\n",
    "        ax.set_yticks([], [])\n",
    "        ax.set_aspect('equal')\n",
    "        ax.annotate('', xy=(xcm-0.5, ylim_min+0.2), xytext=(xcm+0.5, ylim_min+0.2),xycoords='data', arrowprops=dict(alpha=0.5, fc='b', ec='b', arrowstyle=\"<->\"))\n",
    "        ax.annotate('1 kpc',xy=(xcm,ylim_min+0.3),textcoords='data')\n",
    "        cluster_count+=1\n",
    "    \n",
    "    #ax.legend(bbox_to_anchor=(1,0.5), loc='center left')\n",
    "    #plt.tight_layout()\n",
    "    #plt.axis('square')\n",
    "    fig5.savefig(plot_path+\"flip_fixed_all_clusters_fixedCM_fixed_r_yVSx\"+str(plot_count)+\".png\",bbox_inches='tight',dpi=200)\n",
    "    plot_count+=1\n",
    "    fig5.clf()\n",
    "    plt.close()\n",
    "#fig1.subplots_adjust(right=1.5)    \n",
    "#fig11.legend((s1), (\"vphi_cyl\"),\"lower right\")\n",
    "#fig11.suptitle(\"This is the title \\n\")\n",
    "\n",
    "\n",
    "zVSx_snaps=[]\n",
    "for k in range(0,n):\n",
    "    zVSx_snaps.append(imageio.imread(plot_path+\"flip_fixed_all_clusters_fixedCM_fixed_r_yVSx\"+str(k) + '.png'))\n",
    "    os.remove(plot_path+\"flip_fixed_all_clusters_fixedCM_fixed_r_yVSx\"+str(k) + \".png\")\n",
    "\n",
    "imageio.mimsave(plot_path+\"flip_fixed_all_clusters_fixedCM_fixed_r_yVSx.gif\", zVSx_snaps, duration = 1/8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xmax= -5.996480406201754 xmin= -11.960946124406133 ymaxmin= 6.087337215672603 , -11.010695594770944\n"
     ]
    }
   ],
   "source": [
    "#Plotting all clusters in a single axes \n",
    "\n",
    "\n",
    "plot_path=\"./plots_pkl/animation_plots/\" #creating a path to store the plots only if it does not exist\n",
    "if not os.path.exists(plot_path):\n",
    "  os.makedirs(plot_path)\n",
    "\n",
    "\n",
    "n=snapshot_end-snapshot_start+1\n",
    "colors=['cyan','blue','green','magenta','yellow','teal','brown','tan','lime','red']\n",
    "\n",
    "total_clusters=10\n",
    "#fig1.suptitle(\"Cylindrical component of the velocity of Cluster Groups from Snapshot 596\")\n",
    "plot_count=0\n",
    "#plot_count=0\n",
    "snapshot_list=np.arange(snapshot_start,snapshot_end+1) #create a list of snapshots to plot\n",
    "time=snapshot_list-snapshot_start #time starts from zero here where t=0 is at the snapshot where we start from\n",
    "#time=[[0],[20],[40],[60],[80],[100]]\n",
    "\n",
    "xmax=np.amax(importdata[time[1]+snapshot_start][1][\"x_tracked\"])\n",
    "xmin=np.amin(importdata[time[1]+snapshot_start][1][\"x_tracked\"])\n",
    "ymax=np.amax(importdata[time[1]+snapshot_start][1][\"y_tracked\"])\n",
    "ymin=np.amin(importdata[time[1]+snapshot_start][1][\"y_tracked\"])\n",
    "for i in range(len(time)-5):\n",
    "    cluster_count=0\n",
    "    \n",
    "    for j in range(total_clusters):\n",
    "        x=importdata[time[i]+snapshot_start][cluster_count+1][\"x_tracked\"]\n",
    "        xmax_new=np.amax(x)\n",
    "        #print(xmax_new)\n",
    "        xmin_new=np.amin(x)\n",
    "        if xmax_new>xmax:\n",
    "            xmax=xmax_new\n",
    "        if xmin_new<xmin:\n",
    "            xmin=xmin_new\n",
    "        \n",
    "        y=importdata[time[i]+snapshot_start][cluster_count+1][\"y_tracked\"]\n",
    "        ymax_new=np.amax(y)\n",
    "        ymin_new=np.amin(y)\n",
    "        if ymax_new>ymax:\n",
    "            ymax=ymax_new\n",
    "        if ymin_new<ymin:\n",
    "            ymin=ymin_new\n",
    "\n",
    "print(\"xmax=\",xmax,\"xmin=\",xmin,\"ymaxmin=\",ymax,\",\",ymin)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "for i in range(len(time)-5):\n",
    "    \n",
    "    fig6=plt.figure(figsize=(8,5))\n",
    "    ax = fig6.add_subplot(1,1,1)\n",
    "    cluster_count=0\n",
    "    for j in range(total_clusters):\n",
    "        \n",
    "        x=importdata[time[i]+snapshot_start][cluster_count+1][\"x_tracked\"]\n",
    "        y=importdata[time[i]+snapshot_start][cluster_count+1][\"y_tracked\"]\n",
    "        xcm=importdata[time[i]+snapshot_start][cluster_count+1][\"xcm\"]\n",
    "        ycm=importdata[time[i]+snapshot_start][cluster_count+1][\"ycm\"]\n",
    "        z=importdata[time[i]+snapshot_start][cluster_count+1][\"z_tracked\"]\n",
    "        \n",
    "        s1=ax.scatter(x,y,label=\"cluster_group\"+str(cluster_count+1),c=colors[cluster_count])\n",
    "        #ax.scatter(np.abs(xcm),np.abs(ycm),c=\"black\")\n",
    "        ax.minorticks_on()\n",
    "        ax.set_xlabel(\"x position in kpc\")\n",
    "        ax.set_ylabel(\"y position in kpc\")\n",
    "        ax.set_title(\"Clusters at T=\"+str(time[plot_count])+\" in Myr\")\n",
    "        ax.set_xlim(xmin,xmax)\n",
    "        ax.set_ylim(ymin,ymax)\n",
    "        cluster_count+=1\n",
    "    \n",
    "    ax.legend(bbox_to_anchor=(1,0.5), loc='center left')\n",
    "    plt.tight_layout()\n",
    "    fig6.savefig(plot_path+\"fixed_axes_snap\"+str(plot_count)+\".png\",bbox_inches='tight',dpi=200)\n",
    "    plot_count+=1\n",
    "    fig6.clf()\n",
    "    plt.close()\n",
    "     \n",
    "    \n",
    "#fig1.subplots_adjust(right=1.5)    \n",
    "#fig11.legend((s1), (\"vphi_cyl\"),\"lower right\")\n",
    "#fig11.suptitle(\"This is the title \\n\")\n",
    "\n",
    "images=[]\n",
    "for k in range(0,n-5):\n",
    "    images.append(imageio.imread(plot_path+\"fixed_axes_snap\"+str(k) + '.png'))\n",
    "    os.remove(plot_path+\"fixed_axes_snap\"+str(k) + \".png\")\n",
    "imageio.mimsave(plot_path+\"fixed_axes_snapshots.gif\", images, duration = 1/8)\n",
    "\n",
    "\n",
    "\n"
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}