KvN_NumHyp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "using PyCall\n",
    "using PyPlot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Precompiling DifferentialEquations [0c46a032-eb83-5123-abaf-570d42b7fbaa]\n",
      "└ @ Base loading.jl:1273\n"
     ]
    }
   ],
   "source": [
    "using DifferentialEquations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PyObject <module 'mpmath' from '/home/arurz/.local/lib/python3.6/site-packages/mpmath/__init__.py'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spy = pyimport(\"scipy.interpolate\")\n",
    "np = pyimport(\"numpy\")\n",
    "mp = pyimport(\"mpmath\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "rc(\"text\", usetex=true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "function k(t,κ)\n",
    "    return 2*κ^2/cosh(κ*t)^2\n",
    "end\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "κ1 = 0.3\n",
    "κ2 = 0.4\n",
    "κ3 = 0.5\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "function hyp1!(du,u,p,t)\n",
    " du[1] = u[2]\n",
    " du[2] = (1/u[1]^3)-k(t,κ1)*u[1]\n",
    " du[3] = (1/u[1]^2)\n",
    "end\n",
    "\n",
    "function hyp2!(du,u,p,t)\n",
    " du[1] = u[2]\n",
    " du[2] = (1/u[1]^3)-k(t,κ2)*u[1]\n",
    " du[3] = (1/u[1]^2)\n",
    "end\n",
    "\n",
    "function hyp3!(du,u,p,t)\n",
    " du[1] = u[2]\n",
    " du[2] = (1/u[1]^3)-k(t,κ3)*u[1]\n",
    " du[3] = (1/u[1]^2)\n",
    "end\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "u1 = [3.33333333;0.0;0.0]\n",
    "u2 = [2.5;0.0;0.0]\n",
    "u3 = [2.0;0.0;0.0]\n",
    "tspan = (0.0,20)\n",
    "\n",
    "prob1 = ODEProblem(hyp1!,u1,tspan)\n",
    "sol1 = solve(prob1,Vern7(),dt=0.00000000001)\n",
    "\n",
    "prob2 = ODEProblem(hyp2!,u2,tspan)\n",
    "sol2 = solve(prob2,Vern7(),dt=0.00000000001)\n",
    "\n",
    "prob3 = ODEProblem(hyp3!,u3,tspan)\n",
    "sol3 = solve(prob3,Vern7(),dt=0.00000000001)\n",
    "\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = np.linspace(np.min(sol1.t), np.max(sol1.t), 200)  \n",
    "\n",
    "r1 = spy.make_interp_spline(sol1.t, sol1'[:,1], k=3)\n",
    "r2 = spy.make_interp_spline(sol2.t, sol2'[:,1], k=3)\n",
    "r3 = spy.make_interp_spline(sol3.t, sol3'[:,1], k=3)\n",
    "\n",
    "dr1 = spy.make_interp_spline(sol1.t, sol1'[:,2], k=3)\n",
    "dr2 = spy.make_interp_spline(sol2.t, sol2'[:,2], k=3)\n",
    "dr3 = spy.make_interp_spline(sol3.t, sol3'[:,2], k=3)\n",
    "\n",
    "om1 = spy.make_interp_spline(sol1.t, sol1'[:,3], k=3)\n",
    "om2 = spy.make_interp_spline(sol2.t, sol2'[:,3], k=3)\n",
    "om3 = spy.make_interp_spline(sol3.t, sol3'[:,3], k=3)\n",
    "\n",
    "vr1 = r1(t)\n",
    "vr2 = r2(t)\n",
    "vr3 = r3(t)\n",
    "\n",
    "vdr1 = dr1(t)\n",
    "vdr2 = dr2(t)\n",
    "vdr3 = dr3(t)\n",
    "\n",
    "vom1 = om1(t)\n",
    "vom2 = om2(t)\n",
    "vom3 = om3(t)\n",
    "\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 620x620 with 3 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ww = 6.20\n",
    "hh = ww\n",
    "\n",
    "fig,(ax1,ax2,ax3)=plt.subplots(3,1,figsize=(ww,hh),sharex=true)\n",
    "plt.subplots_adjust(hspace = 0.1)\n",
    "\n",
    "ax1.plot(t,vr1,\"b--\", linewidth=2,label=L\"\\kappa=0.3\")\n",
    "ax2.plot(t,vdr1,\"b--\", linewidth=2)\n",
    "ax3.plot(t,vom1,\"b--\", linewidth=2)\n",
    "\n",
    "ax1.plot(t,vr2,\"r:\", linewidth=2,label=L\"\\kappa=0.3\")\n",
    "ax2.plot(t,vdr2,\"r:\", linewidth=2)\n",
    "ax3.plot(t,vom2,\"r:\", linewidth=2)\n",
    "\n",
    "ax1.plot(t,vr3,\"y-.\", linewidth=2,label=L\"\\kappa=0.3\")\n",
    "ax2.plot(t,vdr3,\"y-.\", linewidth=2)\n",
    "ax3.plot(t,vom3,\"y-.\", linewidth=2)\n",
    "\n",
    "ax1.set_aspect(\"auto\")\n",
    "ax2.set_aspect(\"auto\")\n",
    "ax3.set_aspect(\"auto\")\n",
    "\n",
    "ax3.set_xlim(0,20)\n",
    "\n",
    "ax1.tick_params(direction=\"out\",length=5,width=2,labelsize=10)\n",
    "ax2.tick_params(direction=\"out\",length=5,width=2,labelsize=10)\n",
    "ax3.tick_params(direction=\"out\",length=5,width=2,labelsize=10)\n",
    "\n",
    "ax1.grid(linewidth=1)\n",
    "ax2.grid(linewidth=1)\n",
    "ax3.grid(linewidth=1)\n",
    "\n",
    "ax1.set_ylabel(L\"\\rho(t)\",fontsize=10)\n",
    "ax2.set_ylabel(L\"\\dot{\\rho}(t)\",fontsize=10)\n",
    "ax3.set_ylabel(L\"\\omega_{\\rho}(t)\",fontsize=10)\n",
    "\n",
    "ax3.set_xlabel(L\"t\\ [s]\",fontsize=10)\n",
    "\n",
    "ax1.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,ncol=3, mode=\"expand\", borderaxespad=1.0,fontsize=10)\n",
    "\n",
    "show()\n",
    "\n",
    "savefig(\"hypkvn_d.pdf\", transparent = \"true\", dpi=300, bbox_inches=\"tight\", pad_inches=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "function β11(t,ρ0)\n",
    "    return convert(Float64,(ρ0/sol1'[t,1])*mp.cos(sol1'[t,3])+ρ0*sol1'[t,2]*mp.sin(sol1'[t,3]))\n",
    "end\n",
    "\n",
    "function β12(t,ρ0)\n",
    "    return convert(Float64,ρ0*sol1'[t,1]*mp.sin(sol1'[t,3]))\n",
    "end\n",
    "\n",
    "function β13(t,ρ0,dρ0)\n",
    "    return convert(Float64,(1/(ρ0*sol1'[t,1])+dρ0*sol1'[t,2])*mp.sin(sol1'[t,3])+((dρ0/sol1'[t,1])-(sol1'[t,2]/ρ0))*mp.cos(sol1'[t,3]))\n",
    "end\n",
    "\n",
    "function β14(t,ρ0,dρ0)\n",
    "    return convert(Float64,(sol1'[t,1]/ρ0)mp.cos(sol1'[t,3])-dρ0*sol1'[t,1]*mp.cos(sol1'[t,3]))\n",
    "end\n",
    "\n",
    "function β21(t,ρ0)\n",
    "    return convert(Float64,(ρ0/sol1'[t,1])*mp.cos(sol1'[t,3])+ρ0*sol1'[t,2]*mp.sin(sol1'[t,3]))\n",
    "end\n",
    "\n",
    "function β22(t,ρ0)\n",
    "    return convert(Float64,ρ0*sol2'[t,1]*mp.sin(sol2'[t,3]))\n",
    "end\n",
    "\n",
    "function β23(t,ρ0,dρ0)\n",
    "    return convert(Float64,(1/(ρ0*sol2'[t,1])+dρ0*sol2'[t,2])*mp.sin(sol2'[t,3])+((dρ0/sol2'[t,1])-(sol2'[t,2]/ρ0))*mp.cos(sol2'[t,3]))\n",
    "end\n",
    "\n",
    "function β24(t,ρ0,dρ0)\n",
    "    return convert(Float64,(sol2'[t,1]/ρ0)mp.cos(sol2'[t,3])-dρ0*sol2'[t,1]*mp.cos(sol2'[t,3]))\n",
    "end\n",
    "\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "function qc1(q,p,t)\n",
    "    return q*β11(t,u1[1])-p*β12(t,u1[1])\n",
    "end\n",
    "    \n",
    "function pc1(q,p,t)\n",
    "    return q*β13(t,u1[1],0)+p*β14(t,u1[1],0)\n",
    "end\n",
    "\n",
    "function qc2(q,p,t)\n",
    "    return q*β21(t,u2[1])-p*β22(t,u2[1])\n",
    "end\n",
    "    \n",
    "function pc2(q,p,t)\n",
    "    return q*β23(t,u2[1],0)+p*β24(t,u1[1],0)\n",
    "end\n",
    "\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "qv1 = zeros(Float64,length(sol1.t))\n",
    "for i in 1:length(sol1.t)\n",
    "    qv1[i] = qc1(-3,3,i)\n",
    "end\n",
    "pv1 = zeros(Float64,length(sol1.t))\n",
    "for i in 1:length(sol1.t)\n",
    "    pv1[i] = pc1(-3,3,i)\n",
    "end\n",
    "\n",
    "qv2 = zeros(Float64,length(sol1.t))\n",
    "for i in 1:length(sol1.t)\n",
    "    qv2[i] = qc2(-3,3,i)\n",
    "end\n",
    "pv2 = zeros(Float64,length(sol1.t))\n",
    "for i in 1:length(sol1.t)\n",
    "    pv2[i] = pc2(-3,3,i)\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t1 = np.linspace(np.min(sol1.t), np.max(sol1.t), 200)  \n",
    "\n",
    "sqc1 = spy.make_interp_spline(sol1.t, qv1, k=3)\n",
    "spc1 = spy.make_interp_spline(sol1.t, pv1, k=3)\n",
    "sqc2 = spy.make_interp_spline(sol1.t, qv2, k=3)\n",
    "spc2 = spy.make_interp_spline(sol1.t, pv2, k=3)\n",
    "\n",
    "vsqc1 = sqc1(t)\n",
    "vspc1 = spc1(t)\n",
    "vsqc2 = sqc2(t)\n",
    "vspc2 = spc2(t)\n",
    "\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "figps = figure(figsize=(5,5))\n",
    "axps = gca(projection=\"3d\")\n",
    "plot3D(vsqc1,vsqc1,t1,\"b--\", linewidth=2)\n",
    "plot3D(vsqc2,vsqc2,t1,\"r:\", linewidth=2)\n",
    "axps.view_init(30,-30)\n",
    "xlabel(L\"q\")\n",
    "ylabel(L\"p\")\n",
    "zlabel(L\"t\\ [s]\")\n",
    "axps.tick_params(axis=\"both\",direction=\"out\",length=10,width=3,grid_linewidth=3)\n",
    "axps.set_zticks(range(0,stop=20,step=5))\n",
    "axps.set_zticklabels([L\"0\",L\"5\",L\"10\",L\"15\",L\"20\"])\n",
    "show()\n",
    "tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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