creating_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']\n"
     ]
    }
   ],
   "source": [
    "import utilities as ut\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=\"./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=696\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": 9,
   "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",
    "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",
    "images=[]\n",
    "for k in range(0,101):\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+\"snapshots.gif\", images, duration = 1/8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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_fixed_axes_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_fixed_axes_snap\"+str(k) + '.png'))\n",
    "    os.remove(plot_path+\"cluster_1_fixed_axes_snap\"+str(k) + \".png\")\n",
    "imageio.mimsave(plot_path+\"cluster1_fixed_axes.gif\", cluster1_snaps, duration = 1/8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
    "    fig2=plt.figure(figsize=(7,5))\n",
    "    ax = fig2.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",
    "        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_2_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_2_snap_fixedCM\"+str(k) + '.png'))\n",
    "    os.remove(plot_path+\"cluster_2_snap_fixedCM\"+str(k) + \".png\")\n",
    "\n",
    "imageio.mimsave(plot_path+\"cluster2_fixedCM.gif\", cluster1_snaps, duration = 1/8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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=(4,3))\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)"
   ]
  }
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