02_Linear_Systems.ipynb

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    "$\n",
    "\\newcommand{\\dd}{\\text{d}}\n",
    "\\newcommand{\\e}{\\operatorname{e}}\n",
    "\\newcommand{\\Fc}{\\underline F}\n",
    "\\newcommand{\\Hc}{\\underline H}\n",
    "\\newcommand{\\Xc}{\\underline X}\n",
    "\\newcommand{\\Yc}{\\underline Y}\n",
    "\\newcommand{\\Zc}{\\underline Z}\n",
    "\\newcommand{\\xic}{\\underline \\xi}\n",
    "\\newcommand{\\vc}{\\underline v}\n",
    "$"
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    "# 2 Linear Systems\n",
    "## 2.1 What is a linear system?\n",
    "\n",
    "**System**:\n",
    "* abstract model of any device with observable input(s) and output(s) \n",
    "* *signals* - time dependent physical quantities:\n",
    "  - input $x(t)$ (e.g. force)\n",
    "  - output $y(t)$ (e.g. displacement)\n",
    "\n",
    "![picture system with input and output](tikzfig/linear_system.svg \"picture system with input and output\")\n",
    "\n",
    "**Linear system**:\n",
    "* linear relation between input and output\n",
    "  - additive property\n",
    "\\begin{equation}\n",
    "y(x_1+x_2)=y(x_1)+y(x_2),\n",
    "\\end{equation}\n",
    "  (also depends on time)\n",
    "  - homogeneous property\n",
    "\\begin{equation}\n",
    "y(a x_1) = a y(x_1)\n",
    "\\end{equation}\n",
    "  where $a$ is a constant"
   ]
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    "## 2.2 Dynamic properties\n",
    "* properties of the system regarding time-dependent input and/or output\n",
    "* **impulse response** $h(\\tau)$:\n",
    "  - describes time-dependent response of the system\n",
    "  - reaction of system to Dirac 'impulse' $\\delta(\\tau)$ at input\n",
    "  \\begin{gather*}\n",
    "  \\delta(\\tau)=\\begin{cases} \\infty, \\:\\tau=0\\\\ 0\\end{cases}\\\\\n",
    "  \\int_{-\\infty}^{+\\infty} \\delta(\\tau) \\dd \\tau =1\n",
    "  \\end{gather*}\n",
    "  - input-output relation through convolution with impulse response\n",
    "\\begin{equation}\\label{eq:convolution}\n",
    "y(t) = \\int_{-\\infty}^{+\\infty} h(\\tau) x(t-\\tau) \\dd\\tau = h(t) \\ast x(t)\n",
    "\\end{equation}\n",
    "  - if physical system, it is causal - no response at output prior to impulse at input\n",
    "\\begin{equation}\\label{eq:causality}\n",
    "h(t)=0, \\:\\: t<0\n",
    "\\end{equation}\n",
    "\n",
    "* **frequency response** $\\Hc(\\omega)$ is Fourier transform of impulse response\n",
    "  - is a complex-valued quantity by definition\n",
    "  \\begin{equation}\\label{eq:defH}\n",
    "\\Hc(\\omega)=\\int_0^\\infty h(\\tau) \\e^{-j \\omega\\tau} \\dd\\tau.\n",
    "   \\end{equation}\n",
    "   (note the 0 as integration boundary because of causality)\n",
    "  - gives the reaction of system to harmonic excitation with a certain angular frequency $\\omega$ at input\n",
    "  - input-output relation for Fourier transforms of input and output\n",
    "  \\begin{equation}\\label{eq:frequencyresponse}\n",
    "\\Yc(\\omega)=\\Hc(\\omega)\\Xc(\\omega).\n",
    "  \\end{equation}"
   ]
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   "source": [
    "## 2.3 SDOF system example\n",
    "### Impulse response\n",
    "* ODE for damped SDOF oscillator, with external force $F_e$\n",
    "\\begin{equation}\\label{eq:sdof3}\n",
    "  m \\frac{\\dd^2}{\\dd t^2}\\xi(t)+c \\frac{\\dd}{\\dd t}\\xi(t)+s \\xi(t)=F_e(t)\n",
    "\\end{equation}"
   ]
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   "cell_type": "code",
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     "data": {
      "text/latex": [
       "$\\displaystyle c \\frac{d}{d t} \\xi{\\left(t \\right)} + m \\frac{d^{2}}{d t^{2}} \\xi{\\left(t \\right)} + s \\xi{\\left(t \\right)} = F_{e}{\\left(t \\right)}$"
      ],
      "text/plain": [
       "Eq(c*Derivative(xi(t), t) + m*Derivative(xi(t), (t, 2)) + s*xi(t), F_e(t))"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "import sympy as sy\n",
    "%matplotlib inline\n",
    "t = sy.symbols('t',real=True)\n",
    "m, c, s, ome0, delta = sy.symbols('m c s omega_0 delta', \n",
    "                                  real=True, positive=True)\n",
    "xi = sy.Function('xi')\n",
    "Fe = sy.Function('F_e')\n",
    "ode = sy.Eq(m*xi(t).diff(t, 2) + c*xi(t).diff(t) + s*xi(t), Fe(t))\n",
    "ode"
   ]
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   "cell_type": "markdown",
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   "source": [
    "* now we apply the _linear system_ model\n",
    "* definition of\n",
    "  - input $F_e(t)$\n",
    "  - output $\\xi(t)$\n",
    "* _impulse response_ is solution of ODE \\eqref{eq:sdof3} for impulse input\n",
    "\\begin{equation}\\label{eq:impulse}\n",
    "F_e(t)=\\delta(t)\n",
    "\\end{equation}"
   ]
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  {
   "cell_type": "code",
   "execution_count": 2,
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    {
     "data": {
      "text/latex": [
       "$\\displaystyle h{\\left(t \\right)} = C_{1} e^{\\frac{t \\left(- c + \\sqrt{c^{2} - 4 m s}\\right)}{2 m}} + C_{2} e^{- \\frac{t \\left(c + \\sqrt{c^{2} - 4 m s}\\right)}{2 m}} + \\frac{e^{\\frac{t \\left(- c + \\sqrt{c^{2} - 4 m s}\\right)}{2 m}} \\theta\\left(t\\right)}{\\sqrt{c^{2} - 4 m s}} - \\frac{e^{- \\frac{t \\left(c + \\sqrt{c^{2} - 4 m s}\\right)}{2 m}} \\theta\\left(t\\right)}{\\sqrt{c^{2} - 4 m s}}$"
      ],
      "text/plain": [
       "Eq(h(t), C1*exp(t*(-c + sqrt(c**2 - 4*m*s))/(2*m)) + C2*exp(-t*(c + sqrt(c**2 - 4*m*s))/(2*m)) + exp(t*(-c + sqrt(c**2 - 4*m*s))/(2*m))*Heaviside(t)/sqrt(c**2 - 4*m*s) - exp(-t*(c + sqrt(c**2 - 4*m*s))/(2*m))*Heaviside(t)/sqrt(c**2 - 4*m*s))"
      ]
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "h = sy.Function('h')\n",
    "sol_h = sy.dsolve(ode.subs(Fe(t),sy.DiracDelta(t))).subs(xi(t),h(t))\n",
    "sol_h"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Heaviside step function\n",
    "\\begin{equation}\\label{eq:}\n",
    "\\theta(t)=\\begin{cases}1, & t\\geq 0\\\\ 0, &t<0 \\end{cases}\n",
    "\\end{equation}\n",
    "\n",
    "* because of causality \\eqref{eq:causality}, $h(t)=0$ for $t<0$ and the first two terms are zero: $C_1=C_2=0$"
   ]
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   "cell_type": "code",
   "execution_count": 3,
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     "data": {
      "text/latex": [
       "$\\displaystyle h{\\left(t \\right)} = \\frac{e^{- \\delta t} \\sinh{\\left(t \\sqrt{\\delta^{2} - \\omega_{0}^{2}} \\right)} \\theta\\left(t\\right)}{m \\sqrt{\\delta^{2} - \\omega_{0}^{2}}}$"
      ],
      "text/plain": [
       "Eq(h(t), exp(-delta*t)*sinh(t*sqrt(delta**2 - omega_0**2))*Heaviside(t)/(m*sqrt(delta**2 - omega_0**2)))"
      ]
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     "execution_count": 3,
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   "source": [
    "sol_h = sol_h.subs({'C1':0,'C2':0, c:delta*2*m,s:m*ome0**2})\n",
    "sol_h = sol_h.rewrite(sy.cos).simplify()\n",
    "sol_h"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* final result for the impulse response\n",
    "* note: can be rewritten using $\\sinh(j x) = j\\sin(x)$ and $\\omega_d = \\sqrt{\\omega_0^2-\\delta^2}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
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    {
     "data": {
      "text/latex": [
       "$\\displaystyle h{\\left(t \\right)} = \\frac{e^{- \\delta t} \\sin{\\left(\\omega_{d} t \\right)} \\theta\\left(t\\right)}{m \\omega_{d}}$"
      ],
      "text/plain": [
       "Eq(h(t), exp(-delta*t)*sin(omega_d*t)*Heaviside(t)/(m*omega_d))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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   "source": [
    "omed = sy.symbols('omega_d', real=True, positive=True)\n",
    "sol_h = sol_h.subs(sy.sqrt(delta**2-ome0**2), sy.I*omed).simplify()\n",
    "sol_h"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* lets plot this assuming $\\omega_0=1$ and $m=1$, but for different $\\delta$"
   ]
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   "source": [
    "import ipywidgets as ipw\n",
    "def plot_impulse_response(d=0.1):\n",
    "    sy.plot(sol_h.rhs.subs({delta:d,omed:1.0-d*d,m:1.0}),\n",
    "            (t,0,50),\n",
    "            title=r'$\\delta$=%.2f' % d\n",
    "           )\n",
    "ipw.interact(plot_impulse_response,d=(0.01,1.3,0.01));"
   ]
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   "source": [
    "### Frequency response\n",
    "* find frequency response $\\Hc(\\omega)$ from $h(t)$\n",
    "* using symbolic math and Fourier transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
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    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{1}{m \\left(\\omega_{d}^{2} + \\left(\\delta + i \\omega\\right)^{2}\\right)}$"
      ],
      "text/plain": [
       "1/(m*(omega_d**2 + (delta + I*omega)**2))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define both frequency and angular frequency\n",
    "f, om = sy.symbols('f omega',real=True)\n",
    "# Fourier transform in terms of frequency\n",
    "H = sy.FourierTransform(sol_h.rhs,t,f).doit()\n",
    "# replace with angular frequency\n",
    "H = H.subs({f:om/2/sy.pi}).simplify()\n",
    "H"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Sympy uses $i$ as the imaginary number\n",
    "* frequency response can be also interpreted as response to complex\n",
    " harmonic input $\\Fc_e(t)=\\Fc_e(\\omega) \\e^{j\\omega t}$\n",
    "* rewrite using the definitions of $\\omega_d$ and $\\delta$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{1}{i c \\omega - m \\omega^{2} + s}$"
      ],
      "text/plain": [
       "1/(I*c*omega - m*omega**2 + s)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H = H.subs({omed:sy.sqrt(s/m-(c/2/m)**2),delta:c/2/m}).simplify()\n",
    "H"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* same result can be derived from Fourier transform of \\eqref{eq:sdof3} - _try it out !_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4 Mobility and Impedance\n",
    "### Definitions \n",
    "* linear system model application for vibration and structure-borne sound\n",
    "* quantities at input and output can be defined differently \n",
    "* Frequency response $\\Hc(\\omega)=\\frac{\\xic(\\omega)}{\\Fc(\\omega)}$ as defined above is called _receptance_ (but not often used)\n",
    "* alternative definition for linear system:\n",
    "  - force input $\\Fc$\n",
    "  - velocity output $\\vc = j\\omega\\xi$\n",
    "  - frequency response is the **mobility** (also: admittance):\n",
    "\\begin{equation}\\label{eq:mobilty_def}\n",
    "\\Yc=\\frac{\\vc}{\\Fc}\n",
    "\\end{equation}\n",
    "  - high mobility means it can be 'easily moved' \n",
    "* another alternative definition of the linear system:\n",
    "  - velocity input $\\vc$\n",
    "  - force output $\\Fc$\n",
    "  - frequency response is the **impedance**:\n",
    "\\begin{equation}\\label{eq:impedance_def}\n",
    "\\Zc=\\frac{\\Fc}{\\vc}\n",
    "\\end{equation}\n",
    "  - high impedance means movement is impeded \n",
    "\n",
    "### Mobility of SDOF system\n",
    "* using the result from above:\n",
    "\\begin{equation}\\label{eq:mobility_sdof}\n",
    "\\Yc(\\omega) = j\\omega\\Hc(\\omega)\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{i \\omega}{i c \\omega - m \\omega^{2} + s}$"
      ],
      "text/plain": [
       "I*omega/(I*c*omega - m*omega**2 + s)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = sy.I*om*H\n",
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* plot $|\\Yc(\\omega)|$ and explore it for different values of $m,c,s$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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       "interactive(children=(FloatSlider(value=1.0, description='mi', max=2.0, min=0.1), FloatSlider(value=0.1, descr…"
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   ],
   "source": [
    "import numpy as np # numeric math\n",
    "import matplotlib.pyplot as pl\n",
    "%matplotlib inline\n",
    "# to avoid confusion with the symbolic math, we use i at the end of variable names\n",
    "omi = np.logspace(-1,1.5,200) # angular frequency range\n",
    "Yi = sy.lambdify([om,m,c,s],Y) # convert from symbolic to python function\n",
    "\n",
    "def plot_mobilty(mi=1, ci=0.1, si=1):\n",
    "    pl.loglog(omi,abs(Yi(omi,mi,ci,si)))\n",
    "    pl.xlabel(r'$\\omega$')\n",
    "    pl.ylabel(r'$|Y|$')\n",
    "    pl.title('SDOF mobility')\n",
    "    pl.ylim(3e-2,10)\n",
    "ipw.interact(plot_mobilty,mi=(0.1,2),ci=(0.1,1.0),si=(0.1,2));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ----------------------------------------\n",
    "* so far, we discussed only vibrations, but not sound\n",
    "* sound: model for spatial dependence needed (next topic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### License\n",
    "\n",
    "This notebook is an [Open Educational Resource](https://en.wikipedia.org/wiki/Open_educational_resources). Feel free to use it for your own purposes. The text and the images are licensed under [Creative Commons Attribution 4.0](https://creativecommons.org/licenses/by/4.0/), and any code under the [MIT license](https://opensource.org/licenses/MIT). Please attribute the work as follows: Ennes Sarradj, Structure-borne sound lecture notes, 2019."
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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "0bc4b12764a44df593087628c317631b": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "0d3d3561968c49c9aaa56f6b5a8bdc94": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "children": [
        "IPY_MODEL_1eca2c14d47940b8a53470140a2d5aa2",
        "IPY_MODEL_d57b1d38cc4445248c73b46f603132ce"
       ],
       "layout": "IPY_MODEL_cd31e14ffaad4a83917c17990f23071c"
      }
     },
     "0e0e4145a5ad4929b723190b9b071a78": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "0ea9849daa24472b872ec27fcef006b5": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatSliderModel",
      "state": {
       "description": "si",
       "layout": "IPY_MODEL_a20e604d8d1943d89d4d19ad5b23a522",
       "max": 2,
       "min": 0.1,
       "step": 0.1,
       "style": "IPY_MODEL_03cbc81f2fc24a0ab6eed10467677c5d",
       "value": 1
      }
     },
     "0ef13db079c1478285493479b1d758e1": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "0f0f7e877a124919876f8f349425ed67": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatSliderModel",
      "state": {
       "description": "d",
       "layout": "IPY_MODEL_c0577be10b974d319bcb8f7d30bee57e",
       "max": 0.3,
       "min": 0.01,
       "step": 0.01,
       "style": "IPY_MODEL_814cd409e0e74ec5900b95eb4785fa7d",
       "value": 0.1
      }
     },
     "0f6cf4b65b224be490e6bb830e910bde": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_af02333b97f3489584c347fe68365cc3",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "0fdb0ad679f94a88a41a8535e9cf75a2": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "101ffe2c6cbb4b5e8321c8aba399e5a7": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "109d166408f14288986fb63594f5fc04": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "122801be69054dc88ea010c7cf124d50": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "children": [
        "IPY_MODEL_19f68fdc6592424fa261de5e97f658d8",
        "IPY_MODEL_ba97f661ae674441b1de7276b1df5a2d"
       ],
       "layout": "IPY_MODEL_e4e58379e8f74c299d0b064d6d76481b"
      }
     },
     "12530975c6534f0a93c19f0cd4d29667": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "132977e9dbed449bbd4306bfc19dbdd0": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "138395feb51d4badbd295f6e7ab1acba": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_d8fc10198a40470fba9c9946e20a5065",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "13b2c12e3c5f4fb981849cae51bf7431": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_a19dad5ab9fa48d488e4d42fa0383450",
       "outputs": [
        {
         "ename": "ValueError",
         "evalue": "The same variable should be used in all univariate expressions being plotted.",
         "output_type": "error",
         "traceback": [
          "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
          "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
          "\u001b[0;32m~/anaconda3/envs/sbs/lib/python3.7/site-packages/ipywidgets/widgets/interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    249\u001b[0m                     \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    250\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 251\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    252\u001b[0m                 \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    253\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;32m<ipython-input-59-a0d10772dc40>\u001b[0m in \u001b[0;36mplot_mobilty\u001b[0;34m(c)\u001b[0m\n\u001b[1;32m      2\u001b[0m     sy.plot(Y.subs({m:1,s:1,c:c}),\n\u001b[1;32m      3\u001b[0m             \u001b[0;34m(\u001b[0m\u001b[0mom\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m             \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mr'$c$=%.2f'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m            )\n\u001b[1;32m      6\u001b[0m \u001b[0mipw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minteract\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplot_mobilty\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;32m~/anaconda3/envs/sbs/lib/python3.7/site-packages/sympy/plotting/plot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1351\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfree\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1352\u001b[0m                 raise ValueError(\n\u001b[0;32m-> 1353\u001b[0;31m                     \u001b[0;34m'The same variable should be used in all '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1354\u001b[0m                     'univariate expressions being plotted.')\n\u001b[1;32m   1355\u001b[0m     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfree\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mSymbol\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'x'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;31mValueError\u001b[0m: The same variable should be used in all univariate expressions being plotted."
         ]
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     "3300168c9b544d6da2def1abb3ec94e7": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatSliderModel",
      "state": {
       "description": "si",
       "layout": "IPY_MODEL_1a340bc19e3c46f786fac704612857da",
       "max": 2,
       "min": 0.1,
       "step": 0.1,
       "style": "IPY_MODEL_02bba1a1d59042c49b389e85eb89e255",
       "value": 1
      }
     },
     "33408384a1c14d33b996a482b086d9be": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "337f9842e0b4416690674efdcea2410a": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "34aa89a0aedc486c8cbb85c3a7dc96f3": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_8b7dc1f8a57146799a5dde68e42b44b8",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "35c717c7d85943cb80a3aa1d6131f604": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "3621abeececa44549fef8983e8355478": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "3769ab26ba4d40589e917780195a7a91": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatSliderModel",
      "state": {
       "description": "si",
       "layout": "IPY_MODEL_f526b46a462549fd9252000fa2794dfc",
       "max": 2,
       "min": 0.1,
       "step": 0.1,
       "style": "IPY_MODEL_48c046b78505493a99048978a1bb40a0",
       "value": 1
      }
     },
     "37822da51eae421db8133eea2c32616e": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "children": [
        "IPY_MODEL_3fbd4de301ec4e33a27aa424adece337",
        "IPY_MODEL_a48b0a2030ac4e9d9a13613180fc8862",
        "IPY_MODEL_b6e0cd323a6a457d9b8b25a141749be3",
        "IPY_MODEL_f09e0bc089c744ba8873332aed421685"
       ],
       "layout": "IPY_MODEL_12530975c6534f0a93c19f0cd4d29667"
      }
     },
     "37dff8381f834c0598fad721cd704bc1": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_cc3a0c2f60cd473c8790ec22a604ca56",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "3bbbe81e20394da19d5e5878ca5860a5": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_6bca58bc64504e2687282e3c40c97254",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
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     "3d2b4e7497b04965934d25dc7047a693": {
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      "state": {
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       "layout": "IPY_MODEL_d3765045572e427eaa6d189b253ae723",
       "max": 1,
       "min": 0.1,
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       "style": "IPY_MODEL_2b2a9cadf989463c8abfbef256df01e4",
       "value": 0.1
      }
     },
     "3d517908cf6843a8b3d748005b3b6775": {
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     "3e62556dc1a24781bb2283b88c6c371c": {
      "model_module": "@jupyter-widgets/base",
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     "3eb4fbd461c7490596f1ee54ce2df6fe": {
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      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
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      }
     },
     "3eceb58aa92e4138a6d6f360173ade3c": {
      "model_module": "@jupyter-widgets/controls",
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      "model_name": "SliderStyleModel",
      "state": {
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     },
     "3fbd4de301ec4e33a27aa424adece337": {
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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "48c046b78505493a99048978a1bb40a0": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "4930f450a2284db19d2adf2c8bc44a29": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatSliderModel",
      "state": {
       "description": "c",
       "layout": "IPY_MODEL_a2f8c1b9e54243aa9776c792ba507f6c",
       "max": 0.3,
       "min": 0.01,
       "step": 0.01,
       "style": "IPY_MODEL_bcccf2b6c0f941898d81b362a2d4dfe1",
       "value": 0.1
      }
     },
     "499d659fb6e44dd3a86a05db35b727ec": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "4c1c493b20ba4375a260c97ee28bfc31": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "4d882eeaf35b450093e6b7579b315494": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "4dd3bff3e8214c9589710af7a08d7d1d": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatSliderModel",
      "state": {
       "description": "mi",
       "layout": "IPY_MODEL_fd219845d8da4e4eb0fd6011fa6390e3",
       "max": 2,
       "min": 0.1,
       "step": 0.1,
       "style": "IPY_MODEL_c4da1c5a90d14ebb9874b230f9547417",
       "value": 1
      }
     },
     "4e50002fed3d41d9bf3d12dfe4d4dc66": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "4e94e8a5d8844e019e2212a668260013": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_23abd1b3d8824f04bd09aeacfb24fb92",
       "outputs": [
        {
         "ename": "ValueError",
         "evalue": "The same variable should be used in all univariate expressions being plotted.",
         "output_type": "error",
         "traceback": [
          "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
          "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
          "\u001b[0;32m~/anaconda3/envs/sbs/lib/python3.7/site-packages/ipywidgets/widgets/interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    249\u001b[0m                     \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    250\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 251\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    252\u001b[0m                 \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    253\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;32m<ipython-input-60-3fcbfeabd303>\u001b[0m in \u001b[0;36mplot_mobilty\u001b[0;34m(c)\u001b[0m\n\u001b[1;32m      2\u001b[0m     sy.plot(Y.subs({m:1,s:1,c:0.1}),\n\u001b[1;32m      3\u001b[0m             \u001b[0;34m(\u001b[0m\u001b[0mom\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m             \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mr'$c$=%.2f'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m            )\n\u001b[1;32m      6\u001b[0m \u001b[0mipw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minteract\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplot_mobilty\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;32m~/anaconda3/envs/sbs/lib/python3.7/site-packages/sympy/plotting/plot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1351\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfree\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1352\u001b[0m                 raise ValueError(\n\u001b[0;32m-> 1353\u001b[0;31m                     \u001b[0;34m'The same variable should be used in all '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1354\u001b[0m                     'univariate expressions being plotted.')\n\u001b[1;32m   1355\u001b[0m     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfree\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mSymbol\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'x'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;31mValueError\u001b[0m: The same variable should be used in all univariate expressions being plotted."
         ]
        }
       ]
      }
     },
     "501c1199f98b4529941e9e8ea0c0c23b": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatSliderModel",
      "state": {
       "description": "cc",
       "layout": "IPY_MODEL_109d166408f14288986fb63594f5fc04",
       "max": 1,
       "min": 0.1,
       "step": 0.1,
       "style": "IPY_MODEL_8acd9d711b09425a9dd9cc43f2f78c18",
       "value": 0.2
      }
     },
     "50463087edfc41d9954478c3d1aca96d": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatSliderModel",
      "state": {
       "description": "d",
       "layout": "IPY_MODEL_b2e0f024545e48f78896f0448b673c56",
       "max": 0.3,
       "min": 0.01,
       "step": 0.01,
       "style": "IPY_MODEL_d091016366514010a0d01a85b2a414c4",
       "value": 0.1
      }
     },
     "504c83e8cba54ffdbed85c43925eff9a": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "5057da1b9d5f491981961022224e32d7": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "511305250ad14966a489659a2b8631b4": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "52d19dbbae2a4ee7998ece14d2406bf6": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "543fa78b417d4c15b1528f50a5f9384f": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_55d366644cb140b3b18aa28f64ed27f5",
       "outputs": [
        {
         "data": {
          "image/png": 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lMPOjnahRCi/d3AsmE3dROYrbB0Sib2TtLqsThdxlRZZj6OLgIIctt+rXLHx/IBePXtMVHQK9dcchM5hMgpdv7o3K6hrMWruTu6zIYgxdHNQyWadL8eyne3BJTDDGXtRRdxxqhshgH/ztL3H4et8prEnJ1h2HDILFQRdUU6PwyEc7ISJ4YSTPonJkkwZEol9kIJ7esIe7rMgiWBx0QSt/PYKfDubhseFdEd6au6gcmckkeOnmXqiqVpjJXVZkASwO+oPMvBI8v3EvLo0Nxui+HXTHIQuIDPbBI0NrLwxcvTVLdxxycCwOOkdVdQ0efH8HXE2CF0f24i4qA5lwcSQuiQnG0+v34FBuie445MBYHHSOt74+iG1HCvDsjT0RFuClOw5ZkMkkeOWW3nB3NeGB93egksOvUzOxOOi/th/JxxtfHcCN8e0xoneY7jhkBW39PfH8TT3xW1YB3vwqXXccclAsDgIAlJRX4cH3d6Ctnyeeur677jhkRdf0bIeRCeGY+9UBpGTm645DDojFQQCAZzbsQebpUvzz1t7w83TTHYesbPZ13RAW4IUH39+B4vIq3XHIwbA4CJ+nnsDqrVm4+/JOuCg6SHccsoFWnm54bVQfZOeX4un1u3XHIQfD4nByOWfKMGvtTvRo74cHh3TWHYdsqG9kIKYN6oQPkrPxeepx3XHIgbA4nFhNjcLDa3bibGU15oyKh7sr/zs4m/sHd0bP9v6YuXYX5+6gJuNfCie2+MdD+G7/KTx2TVfEhPrqjkMauLuaMGd0H5RX1uCB1TtQXcOryqlxLA4ntTO7AC9+noYhXdtgXH/OseHMOoX44qnru2NLRh7e+pqn6FLjWBxOqKisEtNXbUewrwdevplXhxNwS2I4ru8Thjmb9+PXQ6d1xyE7x+JwMkopPPZxKrJOl+KNMfFo7eOuOxLZARHBP27ogQ6B3rh/9Xbkl1TojkR2zNDFwRkA/+jD5Gys++0YHhzSGX0jA3XHITvSytMNc8ckILe4HH9dw1F0qWGGLg7OAHiu9JwiPLkuFQM6BeGeK2J0xyE71DPcHzOHdcXmvSex9KfDuuOQnTJ0cdD/lFVW496V2+Hj7oo5o/rAhXOHUwPuGBiJwXGheG5jGlKPcmud/ojF4SSe2bAH+04W4dVbeyPUz1N3HLJjIoKXb+mN1j5umL5qO4ckoT9gcTiBjbuOY+UvR3DXZdEY1CVUdxxyAIE+7nh9dDwy80rw5CepuuOQnWFxGFzW6VI88tFO9O4QgIeu7qI7DjmQ/tFBmH5lLNZuO4qPUrJ1xyE7wuIwsIqqGkxftR1QwNwxHFKEzDf9yhj0iwrEE5+kIj2nSHccshP8S2Jgz23cix1ZBXjx5l7oEOitOw45IFcXE94YHQ8vNxfcvWIbSni8g8DiMKwNO49hyU+HccfAKFzTs53uOOTA2vp74o0x8Th4qhiPfbyL13cQi8OIDp4qxiNrdiKhYwBmDovTHYcMYGBMMB4c0hn/3nEM7/16RHcc0ozFYTClFVWYtiIFHm4umHtbAo9rkMXcd0UMLuscgqfW7eH1HU6Of1UMRCmFxz9OxYGcYswZ1QdhAV66I5GBmEyCOaP6IMjXHdNWpqDwbKXuSKQJi8NAVm/NwtrtR3H/4Fhc1jlEdxwyoEAfd8y9LQHHC8rw8Ie/8XiHk2JxGETq0UL8fd1uXBobjOlXxuqOQwaWGNEas67piv/sOYmF32fojkMasDgMoLC0EtNWpiDIx53jUJFN3DEwEsN6tMWLn+/D1sOcv8PZsDgcnFIKD334G44XlGHubQkI8vXQHYmcgIjUXh/U2gv3vbcNucXluiORDbE4HNz87zKwee9JPDa8KxIjWuuOQ07Ez9MNb49NREFpJWas2o6q6hrdkchGWBwO7OeMPLy8aR+G92yHSQMidcchJ9QtzA/P3tgTPx3Mw8tf7NMdh2yExeGgcs6UYfqq7YgI9MYLI3ty3nDS5ubEcIzr3xHzv83AZ7uO645DNsDicEAVVTW4Z+U2FJdV4e1xCWjl6aY7Ejm5J6/tjviOAXj4w984GKITYHE4oH98ugfJmfl46eZeiGvrpzsOEdxdTZg3NhFe7i6YujwFRWW8ONDIWBwOZk1KNpZtycSdl0ZhRO8w3XGI/qutvyfm3paAzLxSXhxocCwOB7IruxCPfrwLAzoF4ZGhHLyQ7E//6CDMGhaHTbtPYt63B3XHISthcTiIvOJy3L0iBSG+HnhzTDxcXbjqyD5NviQK1/Zqh1c27cMPB3J1xyEr4F8fB1BVXYMZq7fjVHE55o3jRX5k30QEL93cC7GhrTB91TZk55fqjkQWZujiEJERIrKgsNCxh4B+edM+/Jieh3/c0AO9wgN0xyFqlLe7K94Zn4iqaoVpK7ahrLJadySyIEMXh1JqvVJqqr+/v+4ozbZh5zHM/y4D4/p3xK1JHXTHIWqyqGAfvDaqD3YdLcSTn6TyYLmBGLo4HN2+E0X425qdSIxojSev7a47DpHZhnRrgxlXxuCD5GzOHGggLA47VXi2EnctT4aPhyveHsuZ/Mhx3T+kMwZ1CcHsdbuRzJF0DYF/jexQTY3C/72/A9n5ZzFvbALa+HnqjkTUbC4mweuj4tE+wAt3r9iG44VndUeiFmq0OESkYxO/eAmzhbz+5QF8mZaDJ0d0Q1JkoO44RC3m7+2GhROScLaiCncvT+HBcgfn2oRlljZhGQVgCYBlLUpD+HLvSbz+5QGMTAjH+P4RuuMQWUxsm1Z4bVQfTF2egsc+TsUrt/Ti4JwOqinFsVgptdzqSQjpOcV4YPUO9Gjvh2dv7ME3FRnO1d3b4oEhsZiz+QC6h/nhjkuidEeiZmjKMY5xIjJHRFysnsaJFZ6txNRlyfBwM2H++CR4uvGfm4xpxpWxuLpbGzy7cS9+TOeV5Y6oKcUxFEAZgK9EpI2V8zil6hqF+1dvR1Z+KeaNS0T7AC/dkYisxmQS/HNUH0QH++C+97Yh6zSvLHc0jRaHqjUTwOsAvhWRqSLST0S8rR/POby0KQ3f7DuFp67rgb48GE5OwNfDFQsnJKG6RuHOZckorajSHYnM0KTTcUXkWgBTAFQASADwCoAsEUm3Yjan8MmOo5j/be2V4bdd1FF3HCKbiQz2wZu3JWD/ySL89cOdvLLcgTR6cFxEMgDsBfCaUuo/590Xbq1gzmBXdiH+tmYn+kUF8spwckqXdw7BI0Pj8Pxnaej+rR/uGRSjOxI1QVPOqrpGKZV2oTuUUtkWzuM0ThWVY+ryZAT7evDKcHJqUy+Lxu5jZ/Dypn3o2tYPV8SF6o5EjWhKcZSKSFP2oRQopc60NJAzqKiqwbQVKcgvrcCauwcgmMOkkxMTEbw4shcOnirGjNXb8cm9AxEd4qs7Fv0JXgBoY0op/H1dKpIz8/HmmHj0aO+4I/cSWYqXuwvmj0/EdXN/xJ3LkvHveweilaeb7ljUgEaLQyl1hS2COIsVvxzBql+zcM+gTpwznKie8NbeeHtsAsYu+gUPvr8DC8YnwWTiRbD2iDvWbejnjDw8tW43rowLxUNXd9Edh8ju9I8OwpPXdsPmvTmYs3m/7jjUgKbsqiILyM4vxT0rtyEiyBtzRveBCz9JEV3QhIsjsOfYGbzxVTq6tPXD8F7tdEei83CLwwZKK6owdVkKKqtrsHBCEvy475aoQSKCp2/ojsSI1njowx1IPerYUz8bEYvDypRS+OuanUg7cQZvjonn2SJETeDh6oJ3xiUi0Nsddy5LRk5Rme5IVA+Lw8re/uYgPt15HI8MjcOgLjw/naipQlp5YOHEJBSUVuIuzuFhV1gcVvTF7hN45Yt9uL5PGKZeFq07DpHD6R7mj3/e2hvbjxTg0Y93cVgSO8HisJK9x8/ggfd3oFd7f7w4khPWEDXXsJ7t8OCQzli77SgWfp+hOw6BZ1VZRV5xOaYsTUYrT1csmMC5NYhaasbgGOw/WYTnP0tDTKgvrozjDA86cYvDwsqrqnH3ihTkFpdj4YQktPHz1B2JyOGJCF65pTe6h/lhxqodOHCySHckp8bisCClFB7/OBVbD+fjlVt6o1d4gO5IRIbh5e6CBXWzY05Zloz8kgrdkZwWi8OC3v3hED5MycaMwbEcToTICsICvLBgQiKOF5ThnpXbUFldozuSU2JxWMjX+3Lw3Ma9GNajLR4YHKs7DpFhJXRsjedv6oktGXl4ev0e3XGcEg+OW8CBk0WY8d52dG3nh1dv7c2B2YisbGRiOPafLML87zLQpW0rjOsfoTuSU+EWRwvll1RgyrJkeLi5YOGEJHi7s4uJbOFvQ+NwZVwoZq/bjZ8O5uqO41RYHC1QWV2De1Zuw/HCMiyYkIiwAC/dkYichotJ8ProPogM9sE9K7chM69EdySnweJoJqUUZq/bjS0ZeXhxZE8kdGytOxKR02nl6YZFE5KgFDBlaTKKyip1R3IKLI5mWv5zJlb+cgTTBnXCjfHhuuMQOa3IYB/MG5uAjNwSPLB6B6prOCyJtbE4muGHA7l4av0eDOkair9yQiYi7QbEBGP2iG74Mi0HL21K0x3H8Hgk10wZp4pxz8oUxIT4Ys7oeJ5BRWQnxl8cibQTRZj/bQa6tGmFmxK4J8BauMVhhsLSSkxZmgxXFxMWTUyCrwd7l8iezL6uO/pHB2Lm2l1IyczXHcewWBxNVFVdg/tWbUNWfineGZeIDoHeuiMR0XncXEyYNzYR7fw9cdfyZGTnl+qOZEgsjiZ6duNefH8gF/+4oQf6RQXqjkNEDWjt4453JyahvKoGU5Ymo7i8Snckw2FxNMGqX4/gXz8exuRLojCqb0fdcYioETGhrfDWbQk4kFOMB1Zv55lWFuZwxSEi0SLyroisscXr/ZyRhyf+nYrLO4fg0Wu62uIlicgCLuscgiev7YbNe3Pw0uc808qSbFocIrJYRHJEJPW824eKyD4RSReRmX/2HEqpDKXUZOsmrXUkrxTTVqQgIsgbb94WDxeeQUXkUCYOiMT4/hGY/10GPkjO0h3HMGx9WtASAHMBLPv9BhFxAfAWgKsAZAPYKiLrALgAeP68x9+hlMqxRdCKqhpMWbYVNQp4d2Jf+Hm62eJlicjC/j6iGw7lluCxj3ehY6A3+kcH6Y7k8Gy6xaGU+g7A6fNu7gcgvW5LogLAagDXK6V2KaWuPe+ryaUhIlNFJFlEkk+dOmV2VndXE6YN6oR5YxMQGexj9uOJyD64upjw1tgEdAj0xrQVKRzTygLs4RhHewD1tyGz6267IBEJEpF3AMSLyKyGllNKLVBKJSmlkkJCQpoV7Mb4cAyICW7WY4nIfvh7uWHxxL5QACYvTcYZjmnVIvZQHBc6cNDgKRBKqTyl1N1KqU5KqfN3ZRERXVDtmFaJOJxbgntXbkMVZw9sNnsojmwAHer9HA7gmKYsRGRgF3cKwj9u6FF7Tdane3XHcVj2MGbGVgCxIhIF4CiA0QBu0xuJiIxqdL+OSM8pxqIfDqFTqC/Gc/ZAs9n6dNxVALYA6CIi2SIyWSlVBeA+AJsA7AXwgVJqty1zEZFzmXVN1//OHvj9AfNPnnF2opTxr6hMSkpSycnJumMQkR0pKqvEzfO24FjhWXx8z0DEhPrqjmRXRCRFKZV0ofvs4RiH1YjICBFZUFhYqDsKEdmZVp5uWDQxCe4uJkxZuhX5JRW6IzkMQxeHUmq9Umqqv7+/7ihEZIc6BHpj/vhEHCsow7SVKaio4plWTWHo4iAiakxSZCBevLknfs44jb+vS4Uz7L5vKXs4q4qISKsb48ORnlOMt74+iE4hvphyabTuSHaNxUFEBOChq7rgYE4Jnt24F9EhPrgyro3uSHaLu6qIiACYTIJ/juqN7mF+mP7eduw7UaQ7kt1icRAR1fF2d8XCCUnw8XDFHUu2Ire4XHcku2To4uDpuERkrnb+Xlg4IQm5xeW4a3kKyiqrdUeyO4YuDp6OS0TN0btDAF69tTdSMvPx6NpdPNPqPDw4TkR0Adf2CsPBnBK8tnk/OoX64t4rYnRHshssDiKiBswYHIODp4rx8qZ9iA72wbCe7XRHsguG3lVFRNQSIoKXbu6F+I4BePCDHdiZXaA7kl1gcRAR/QlPNxcsGJ+EIB8PTF6ajGMFZ3VH0o7FQUTUiJBWHlg8qS/OVlRjytJklJRX6Y6kFYuDiKgJurRthbm3xSPtxBncv3o7qmuc90wrQxcHr+MgIksa1CUUfx/RHZv35uD5jc479ayhi4PXcRCRpU0cEImJF0dg0Q+H8N4vR3TH0YKn4xIRmemJa7sh83QpnvgkFR0DvXFJbLDuSDZl6C0OIiJrcHUx4c0x8YgJ8cW0lSlIz3GuARFZHEREzfD71LMeribcsSQZp51o6lkWBxFRM9VOPZuEE2fKcNfyZJRXOceAiCwOIqIWSIxojVdu6Y2th/Mx6yPnGBCRB8eJiFrout5hOHSqdkDE6BAf3HdlrO5IVsXiICKygBmDY3AotxivfLEfkcE+uLZXmO5IVsNdVUREFiAieGFkLyRGtMZDH/yG7UfydUeyGkMXB68cJyJbqh0QMRGhfh64c1kKsvNLdUeyCkMXB68cJyJbC/L1wOKJfVFeWTsgYrEBB0Q0dHEQEekQ26YV3hqbgAM5xZixyngDIrI4iIis4LLOIZh9XXd8lZaDf3y6R3cci+JZVUREVjK+fwQyThXjXz8eRnSIL8b3j9AdySJYHEREVvT48G7IzCvF7HW70THQG5d3DtEdqcW4q4qIyIpcTII3xsQjNtQX963chv0nHX9ARBYHEZGV+Xq44t1JfeHh5oI7lmxFbnG57kgtwuIgIrKB9gFeWDQxCaeKynHX8hSUVTrugIgsDiIiG+nTIQD/vLUPUjLz8chHOx12QEQWBxGRDQ3v1Q4PX90Zn+w4hje+TNcdp1kMXRwccoSI7NG9V8TgpoT2eG3zfqz77ZjuOGYzdHFwyBEiskcigudv6ol+kYF4+MPfkJLpWAMiGro4iIjslYerC94Zn4h2/p6YuiwZWacdZ0BEFgcRkSaBPu54d2JfVFbXYPLSrThTVqk7UpOwOIiINIoJ9cW8cYnIOFWC6e9tR1V1je5IjWJxEBFpNjAmGM/c0APf7j+FZzbY/4CIHKuKiMgOjOnXEQdzirHoh0OIDvHFxAGRuiM1iMVBRGQnZl3TFYfzSvHU+t3oGOSNK7qE6o50QdxVRURkJ1xMgtdH90FcWz9Mf2879p2wzwERWRxERHbEx8MV705Kgrd77YCIp4rsb0BEFgcRkZ1p5++Fdyf2RV5JOe5clmx3AyKyOIiI7FDPcH/MGdUHO7IK8PCHv6HGjuYtZ3EQEdmpoT3a4ZGhcdiw8zjmbN6vO85/8awqIiI7dvfl0cg4VYw3vkpHVIgPbowP1x3J2FscHB2XiBydiODZG3uif3QgHlmzC78eOq07krGLg6PjEpERuLua8M64RIS39sJdy5NxOLdEax5DFwcRkVEEeLtj8aS+UADuWLoVhaX6BkRkcRAROYjIYB/MH5eIrNOluHtFCiqq9AyIyOIgInIgF0UH4YWbemFLRh6e+HeqlnnLeVYVEZGDGZkYjkO5JZj7dTqiQ3xw1+WdbPr6LA4iIgf0f1d1xqG8ErzweRoigrwxtEc7m702d1URETkgk0nw6i290Ts8AA+8vwM7swts99o2eyUiIrIoTzcXLJyQhCAfD0xZmoxjBWdt8rosDiIiBxbSygOLJ/VFaUU1Ji9NRnF5ldVfk8VBROTgurRthbfGJmD/ySLMWLUd1VYeEJHFQURkAJd3DsHsEd3wVVoOnv10r1Vfi2dVEREZxPiLI5GRW4LFPx5CVLA3xl8caZXXYXEQERnI48O7ITOvFLPX70GHQG8MssK85dxVRURkIC4mwRtj4hEb6ov73ttulTOtuMVBRGQwvh6uWDypLzbtPoF2/p4Wf34WBxGRAYUFeOH2gVFWeW7uqiIiIrMYeotDREYAGAHgjIgcqLvZH8CFpgRs6PZgALnWSdhsDWXV9ZzmPrapyze23J/d7+jr2RrruKXPa4313NJlzFnP9raOAft7L9d/fESDSyilnOoLwAIzb0/WnbmpWXU9p7mPberyjS33Z/c7+nq2xjq2x/Xc0mXMWc/2to6ttZ5b+pxNebwz7qpab+bt9sgaWVvynOY+tqnLN7bcn93v6OvZWjntbT23dBmuZ8s/Z6OPl7qGoQaISLJSKkl3DrIurmfj4zq2HGfc4jDXAt0ByCa4no2P69hCuMVBRERm4RYHERGZhcVBRERmYXEQEZFZWBwtICLRIvKuiKzRnYUsR0R8RGSpiCwUkbG685B18P3bfE5bHCKyWERyRCT1vNuHisg+EUkXkZl/9hxKqQyl1GTrJiVLMHN93wRgjVLqTgDX2TwsNZs565nv3+Zz2uIAsATA0Po3iIgLgLcADAPQDcAYEekmIj1FZMN5X5Yf5J6saQmauL4BhAPIqlus2oYZqeWWoOnrmZrJ0GNV/Rml1HciEnnezf0ApCulMgBARFYDuF4p9TyAa20ckSzInPUNIBu15bEDzv3hyuGYuZ732DieYfBNca72+N8nTaD2D0j7hhYWkSAReQdAvIjMsnY4sriG1vdaACNFZB4cZ+gKatgF1zPfv83ntFscDZAL3NbgFZJKqTwAd1svDlnZBde3UqoEwO22DkNW09B65vu3mbjFca5sAB3q/RwO4JimLGR9XN/OgevZwlgc59oKIFZEokTEHcBoAOs0ZyLr4fp2DlzPFua0xSEiqwBsAdBFRLJFZLJSqgrAfQA2AdgL4AOl1G6dOckyuL6dA9ezbXCQQyIiMovTbnEQEVHzsDiIiMgsLA4iIjILi4OIiMzC4iAiIrOwOIiIyCwsDiIiMguLg4iIzMLiILIxEbnu/FnnRGSaiLyhKxOROVgcRLb3LIDZ5912ELWTDBHZPRYHkQ2JSG8AJqVUqohEiMi0urvc8CdD+BPZExYHkW31AZBS9/1VAGLrvu8G4DcdgYjMxeIgsi0TAN+6ebBvAtBKRLwATALwns5gRE3F4iCyrY0AolE7n/k7ALoDSAawQCm1TWMuoibjsOpERGQWbnEQEZFZWBxERGQWFgcREZmFxUFERGZhcRARkVlYHEREZBYWBxERmYXFQUREZvl/E6doLXyyWd0AAAAASUVORK5CYII=\n",
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     "6acbbd0174c244b58d88d0abb20c5609": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "6bca58bc64504e2687282e3c40c97254": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "6e8df5525f954c0b8677fa7a35b4b80e": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatSliderModel",
      "state": {
       "description": "cc",
       "layout": "IPY_MODEL_5943fdf32f82486f93470b05b05f9361",
       "max": 1,
       "min": 0.1,
       "step": 0.1,
       "style": "IPY_MODEL_d1d989a32341411193d4cc17a3978ebd",
       "value": 1
      }
     },
     "6eec384815374513b62c6fd9f0a7b31e": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_5d3e414f079f4700a6835453d6832905",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
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      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "children": [
        "IPY_MODEL_b8c0c22609f049eb9caa843da6c011d6",
        "IPY_MODEL_37dff8381f834c0598fad721cd704bc1"
       ],
       "layout": "IPY_MODEL_3d517908cf6843a8b3d748005b3b6775"
      }
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     "74e7f55e1909428cbc55f0be0b977b4b": {
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      "model_module_version": "1.5.0",
      "model_name": "FloatSliderModel",
      "state": {
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       "layout": "IPY_MODEL_e4564a147be74deb9ad58166316791ff",
       "max": 0.30000000000000004,
       "min": -0.1,
       "step": 0.1,
       "style": "IPY_MODEL_1d5875c1701446b2a3bb009149a7f16d",
       "value": 0.1
      }
     },
     "764a2c55d8224541bf654b8168c84a55": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
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     "7728f4c5c36e4a06be8c7cefbc7e9abe": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatSliderModel",
      "state": {
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       "layout": "IPY_MODEL_5b48d8ffa06840d79da2391acf188cfd",
       "max": 2,
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      "model_name": "OutputModel",
      "state": {
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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
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     },
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      "model_name": "SliderStyleModel",
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      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_4641042b19834dbfaf8b5a4f53dfdaaa",
       "outputs": [
        {
         "data": {
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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "814cd409e0e74ec5900b95eb4785fa7d": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "817992d9741746c0bd531f91db3fbd5d": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatSliderModel",
      "state": {
       "description": "mi",
       "layout": "IPY_MODEL_504c83e8cba54ffdbed85c43925eff9a",
       "max": 2,
       "min": 0.1,
       "step": 0.1,
       "style": "IPY_MODEL_886f5c8d145941edaafcadbdedd8646c",
       "value": 1
      }
     },
     "81b348fb9d8a48239df0ce502eb1079d": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "82b3dd749b1147d8a183b63a40508de4": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "82c78018b5bb4e29be9784c96bf55a1a": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "8421656118af4efc914cd5e5f6085baa": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "844519368d1d428bad4ef538c1422fdb": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "845777ae15474683a07ccc3e10ceeef3": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatSliderModel",
      "state": {
       "description": "c",
       "layout": "IPY_MODEL_4d882eeaf35b450093e6b7579b315494",
       "max": 0.3,
       "min": 0.01,
       "step": 0.01,
       "style": "IPY_MODEL_eda6603698f740d8a15c7f4c9d88d7cb",
       "value": 0.1
      }
     },
     "84643a4283084cea85605c8fc0c3d677": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "85eca2e37e624f0a9d2c5e646fdde9f8": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "86c234299cc54e94be481c7ce3829b10": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_2ad777b2350244b88ce2ba730b2c673d",
       "outputs": [
        {
         "ename": "TypeError",
         "evalue": "can't convert expression to float",
         "output_type": "error",
         "traceback": [
          "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
          "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
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          "\u001b[0;32m<ipython-input-9-f167b2f11308>\u001b[0m in \u001b[0;36mplot_mobilty\u001b[0;34m(mi, ci, si)\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mplot_mobilty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mci\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m     \u001b[0mpl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloglog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0momi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mYi\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0momi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mci\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m     \u001b[0mpl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr'$\\omega$'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0mpl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr'$|Y|$'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;32m~/anaconda3/envs/sbs/lib/python3.7/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mloglog\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   2695\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mdocstring\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mAxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloglog\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2696\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mloglog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2697\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mgca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloglog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2698\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2699\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;32m~/anaconda3/envs/sbs/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mloglog\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1799\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_yscale\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'log'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mdy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1800\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1801\u001b[0;31m         \u001b[0ml\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1802\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1803\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;32m~/anaconda3/envs/sbs/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1666\u001b[0m         \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1667\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1668\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1669\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautoscale_view\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscalex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscalex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscaley\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscaley\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1670\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;32m~/anaconda3/envs/sbs/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36madd_line\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m   1898\u001b[0m             \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1899\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1900\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_line_limits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1901\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1902\u001b[0m             \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'_line%d'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlines\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;32m~/anaconda3/envs/sbs/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_update_line_limits\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m   1920\u001b[0m         \u001b[0mFigures\u001b[0m \u001b[0mout\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdata\u001b[0m \u001b[0mlimit\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mgiven\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mupdating\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataLim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1921\u001b[0m         \"\"\"\n\u001b[0;32m-> 1922\u001b[0;31m         \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1923\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvertices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1924\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;32m~/anaconda3/envs/sbs/lib/python3.7/site-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36mget_path\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1025\u001b[0m         \"\"\"\n\u001b[1;32m   1026\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalidy\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalidx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1027\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1028\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_path\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1029\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;32m~/anaconda3/envs/sbs/lib/python3.7/site-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36mrecache\u001b[0;34m(self, always)\u001b[0m\n\u001b[1;32m    673\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0malways\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalidy\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    674\u001b[0m             \u001b[0myconv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_yunits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_yorig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 675\u001b[0;31m             \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_to_unmasked_float_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myconv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    676\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    677\u001b[0m             \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_y\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;32m~/anaconda3/envs/sbs/lib/python3.7/site-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36m_to_unmasked_float_array\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   1388\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1389\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1390\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1391\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1392\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;32m~/anaconda3/envs/sbs/lib/python3.7/site-packages/numpy/core/numeric.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    536\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    537\u001b[0m     \"\"\"\n\u001b[0;32m--> 538\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    539\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    540\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;32m~/anaconda3/envs/sbs/lib/python3.7/site-packages/sympy/core/expr.py\u001b[0m in \u001b[0;36m__float__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    278\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_number\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_real_imag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"can't convert complex to float\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"can't convert expression to float\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    281\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__complex__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;31mTypeError\u001b[0m: can't convert expression to float"
         ]
        }
       ]
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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "9218924e9c6a4f15bd23136c79d4ea46": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "930a7052b7074d2691607245567acd85": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "children": [
        "IPY_MODEL_845777ae15474683a07ccc3e10ceeef3",
        "IPY_MODEL_4e94e8a5d8844e019e2212a668260013"
       ],
       "layout": "IPY_MODEL_e5767330d89c4572878c32d9ff7c61fa"
      }
     },
     "94cf522a905443019f390532d533a316": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "9548361869b34258b6bdff168f55c952": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_fc0f8a3db0034b93a503780a6fca8391",
       "outputs": [
        {
         "data": {
          "image/png": 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zCnG5vRijtbGUUomVsABijPEAtwMvAtuBJ4wx20TkLhG5YrzX74hgL5CAQEHFeC/lrWrtodc9wEkTRg4gRVmpbKpqo1ZXYymlEiyhtbCMMauAVcOOfW+Ec88N59rj6YEMFlTscVOUmRrRNSLx4aFOgFF7ILP9wWVnfScTc9Li0i6llAomqTPRw81CD0hUSfcdIQSQWUX+AOI/VymlEiVpA8j45kD8BRXjPJG+s76Tsjwn6aOsHMt22inKTGFnfVccW6aUUkdL4gDijigHBCA7LVBQMb49kP1N3SyenD3mebMnZLKzXnsgSqnESsoAYoyhw+WJKAsdIDc9UNI9fj0QYwx7GrspyBh7zqW8KJPdDV14dZdCpVQCJWUA6XUPMOA1EfdA0h1WbHGuyNvY2UeveyCkLXhnFWfQ6x6gurU3Di1TSqngkjKADNbBijCAiAg5zvgWVNzf3ANwRAXekczyr8TaocNYSqkESsoAcrgSb+SrlHOcjrjui76/uRuAqflj90DKizIAdB5EKZVQSRpAPBRkOAYnwyORk2aP677oB5q7sVkkpNyOzFQ7pdmp7NIAopRKoKQMIF19Hpq6+klPsUZ8jRynI677oh9o7mFSbho2a2j/JOXFmezQpbxKqQRK2gACjJpPMRZfRd74DWEdaO4Jaf4jYPaETKpbuvHEuV6XUkoFJHUAyRhHAMmNY0l3YwxZqTbmlmSF/JgZhel09Q9oTSylVMIkZwBxjT+A5Dgd9LoHcLkHotWsEXX0eli9p5nc9NDnbMry0jEGDrR0x7BlSik1suQMIFEawgJoj8M8SE2bL59jYs7YK7ACpvhXax3wL/9VSql4S8oA0t3nIdVuwR7ihHQwOWm+bPR4bG07GEByQ6+uOyErFYfNwsGWYyOAGGPo6vPoPiVKnUCSMoB09nnGNXwFkJduJy/dQXsc5kFqWn1BIJzy7BaLUJbnZH9T4oew1u5t5o4nNjP/+y/yH89U0tPvSXSTlFJxkND9QGKlOwoBJDPVTkt3f1yW8ta09ZJis1CQ4QjrcVPynAnvgdS29fLlR94nI8XKp5aW8dCa/fR5vPz46gUJbZdSKvaSMoB0uTxkjCMLHYbMgcSjB9LWy8SctKD7oI+mLN/Jmr3NGBN8D/VY83oN97y0A7fHy/996XRmFGaQmWbjd2/u5dzZhXx03oS4t0kpFT8JHcISkYtFZIeI7BaRO4Pcf4eIVIrIFhF5VUSmhHLdzj4P6Y7xBZDBku5xKGdS09ob1vxHwNT8dHr6B2js6otBq8a2ek8Tf32/hm9fchIzCn3lVb6xfDYLJmZz51+3UN+hS4yVSmYJCyAiYgXuAy4B5gI3iMjcYadtBCqMMQuBJ4GfhnLt7r7IN5MKyEixYbVI3FZhRbI9bZl/JdbBBK3E+tPaA+Q67Vy7ZNLgMYfNwr3XLyYv3cGj7x5MSLuUUvGRyB7IUmC3MWavMaYfeAy4cugJxpjXjTGBd8e1wCRC0NXnGdcSXvBX5E2LfTJhn2eAvHQHM/0FEsMxJc8XQPYnIIAcanfxyvYGrquYTKr9yJIxMwozWDYtjxVv7dUJdaWSWCIDyESgasjtav+xkdwCPB/sDhG5TUTWi8j6xsZG3xzIOAMI+IaxYj2J3tDRx876rohKz0/KdWIRONgc/5VYL1ceIj/dwaeWlQW9/8rFE+l1D/ByZX2cW6aUipdEBpBgs75BkwhE5DNABfCzYPcbY1YYYyqMMRWFhYV09Y1/Eh18+493xDiAHPLPExRnj70T4XAOm4XSnDQOJGAl1hPrq5mUmzZi/a5Tp+ZRmp3K05tq49wypVS8JDKAVAOTh9yeBBz1biMiFwL/DlxhjBlzttgAfR4vGeOcRAfiMoR1yF/LqjgrJaLHT8l3xj0bvaHDxQc17Vwwp3jEcywW4fLFpby1s5GW7vgVpVRKxU8iA8g6oFxEpomIA7geWDn0BBE5GfgdvuDREMpFA/uER6UHkmaP+SqswEqlCVnh90DAVxMr3rkgb+xoBOD8k4pGPe/KRRPxeA3PfVAXj2YppeIsYQHEGOMBbgdeBLYDTxhjtonIXSJyhf+0nwEZwF9EZJOIrBzhcoMG/AFkvJPo4N+VMA49kBSbJeLNr6bkO2np7qfDFb+9S179sJ6S7FRO8m+tO5I5JZnMKs7g6Y01cWqZUiqeEppIaIxZBawadux7Q36+MNxrev21mDKjNIne4fIw4DVYLbFJ1Kvv7GNCdmrEiYCBlVgHm3uYPzE7mk0Lyus1dPd5uGJR6ZhtFhGuXTKZR989QEt3P3np4WXaK6WObUlXCysQQKI1hAXEdCK9vt1FcYTDV8DgJHa85kEOtPTwj93NTC8MbfOrJVNy2N/cw7t7m2PcMqVUvCVdABnwb9AXnSGs2Jd0P9Thinj+Aw4nE8ZrX5CtNe0AzCsNrbezcFIOToeVNRpAlEo6SRdAojmEFQggscoFMcb4AkgES3gDMlJsLJqUTa2/JHysba1tx24VZhWPPv8RYLdaOHVqHu/s0QCiVLJJumKK0ZxEDwxhxaoH0tbjpt/jHdcQFoDVIuxtjE8PZFtNB7MnZOKwhf7Z4/QZ+dz9/Ic0dLooyhzf7zoe22rbef6DQzyzpZZ0h41rl0zi82dOTUghSqWSQdL2QKIzB+Kb9G2L0aZSh8a5hDegLE5l3Y0xbK1tZ36Iw1cBp0/PB2Dt3pZYNCskH1S386nfv8uqrXUsmpRNbrqdu56t5Pdv701Ym5Q63iVdABnsgUQjkTDGcyCDASQ7siTCgLI8J7VtvbgDE0AxUtPWS1uPO+zVXvNKs8hMsbEmQcNYexo6+fT9a8lMtfHQ55fy3zecwsM3L+PSBSX8+PkPtdyKUhFKugDiNeB0WKOy7HZwCCtGuSD1g1no4+uBTM5z4jXEfB5ka00HQNgBxGa1sGx6Hmv2NMWiWaMyxvC9p7cxpySLx247jcn+Zc8Wi/DzTyxiwcRs/vmxjVTWdsS9bUod75IvgHhNVAopgm8CON1hjdkkeqAHMt55gbJALkiMh7G21bZjtciYCYTBnDY9n/3NPdS1x2eyP2D9gVZW72nmskWlTMp1HnFfmsPK7z9XQVaqnVsfWkdDp+5folQ4ki6ADJjoBRDwZaPHqh5WfYeLggxHWBPSwQzuCxLjALK1pp3yooyjyreH4vQZvnmQeA9j3f/2XnKcdq45JXih5+KsVO6/sYLWHjdffHgDLvdAXNun1PEs6QKI12uiMoEekJVmj90cyDiTCAOKM1Nx2Cwx31hqa21HyPkfw82ZkEWO0x7XAHKguZuXKuv59LIynKPMic2fmM0vP7mIjQfb+NUru+LWPqWOd0kXQKLeA0mz0x6jgoqHOvrGvQILfOP5k3PTYtoDaehw0djZx/yJWRE93mIRTpuWH9eEwv9bvR+bRfjc6VPHPPfi+SV87YKZ3P+PvYPJkkqp0SVdAPF6o5MDEpDjjF1J9/oOV0T7gAQT66W8W2t9b6rjqbd1+ox8qlt7qYrDkuP2HjdPrK/i8kWlIffybjlrOqk2K795Y3eMWzcyYwybq9r43zf3cMcTm7j81//gq4++zw9WbtPdHdUxJ+kCyIAxUclCD8iO0RBWn3uAWUUZzAixptRYyvKcHGzuwZige3KN29aaDkRgTklkPRCI7zzIn9cdpKd/gFvOmhbyY7LT7HzujCk8v/UQuxs6Y9i64Lxew51//YD/fLaSu5//kNW7m8hx2unzeHnwnf1cfO/brNWSMOoYknQBxGuiOweS7fRtaxvtN+am7n7W7muJSr4K+JbydvZ5YjZfs7WmnWkF6eMaHiwvyqAgwxHzYSz3gJcHV+/njBn5Yc/Z3HzmNFJsFn77RnwTDI0x/OCZbTy+vorFk3PY/P2LePffLuThW5ax4nMVPH7baQBcv2It3396q/ZG1DEh+QKI10R3CCvNQb/Hi8sd3SS9xk7f5oqFmeNLIgwILOWNVVXebbUdYWegDyciLJuez5o9zTHrKQGs+qCOQx0ubj079N5HQH5GCjcsLeOpTTVxGWoDX/D48fMf8sc1B7jtnOn8+6VzjtofZtn0fF74+tncdMZUHlpzgIvvfZt9cSpfo9RIki6AGIjqJHqs6mFFPYDEcClvS3c/NW29LIjCfiNnzMjnUIeLfU2xefMzxvCHf+xjemE6584afcfEkdx2znQsAiveik8v5Jcv72TFW3u58fQpfOeSk0aszeV02PjBFfN4/LbTmJLv5Krfrk7IUFuAyz3AQ+/s55cv7+Sbf9nMirf28PqHDVS3xm4oVR1bkq6YIkQ3gByuyNs/rqq5wwWS1qJVXDCWyYSDJdwjXIE1VKAu1pq9zUwvzBj39YZbt7+VLdXt/PCq+VgirEZQkp3GtUsm8fj6Kr56/kyKorBSbiT3vb6b/35tN9efOpnvXz4vpMKOy6bnU5qTxlW/eYcbH1jHX//pjKj+bY6lq8/Dn9Ye4P6399HU1cfSqbnsberhyQ3Vg+ekO6wsnZbH+ScVcd2pk0mxhZ87pI59CQ0gInIx8CvACtxvjLl72P0pwB+BJUAz8EljzP6xrhuLHki0V2I1dvYhAvkZ0dmlz+mwUZCREpNhl8AKrEhzQIaaVpBOcVYKa/Y08+llU8Z9veHuf3svuU47V588aVzX+eI5M3h8XRX3/2Mf//axOVFq3ZFe2HqI57bU8fHFpfzwqgVhBbzJeU4e/PypXL9iLTf933s8/sXTI94WOVRtPf383+r9PPjOftp73ZxdXsBXzjuZZdPyEBFau/vZ3djFzvpOdtV3Ud3ay/97ehsr3t7Lty8+iUsXlCSk8nF7r5tNVW3saeiisauPXKedwswUCjNSfd8zU8hJs0f8geNElrAAIiJW4D5gOVANrBORlcaYyiGn3QK0GmNmisj1wE+AT4517ahOosdoCKuhs488pwO7NXqjiGV5sckF2VbTQVmeMypvUCLC6dPz+cfuJowxUX1DqW3rodPl4bOnTSXNMb5PvFML0rl8USnPf1DHlz4yI+rb8TZ19fGdv22hLM/Jz65dGFHttvkTs/nfzyzh8w++xxcfXs9DNy+NySd9z4CXJ9ZX8cPnttPdP8BFc4v5ynkzWTQ554jzctMdnJqex6lT8waPvbWzkR+t2s7tj27kgbJ9/Pulc1kyJTfqbRyqu8/DO3uaeXV7PRsOtLK7sQtjwCLgsFpweY6ezzx3diHGwAVzijj/pKKjyt7EmjGGth43B1t6qGrtwTNgMBhSbVZS7VZS7BZS7Vby0h2UZqeNu3pFtCSyB7IU2G2M2QsgIo8BVwJDA8iVwA/8Pz8J/I+IiBljgDUWQ1jRLqjY2NkXtfmPgLI8J+v2t0b1muDrgUSaQBjMGTMKeGpTLbsaukLemCoUz2yuY83eZn5yzcKoXO+r58/k8l+v5ndv7eE7l0S3F/KDldvo7hvg559YhH0cb/pnlRfw808s4p8f28Qdj2/m1zecHNVP0m09/dz+6Ebe29fMR+dP4PbzypkdRi20c2YVcubMAv66oZqfv7SDT/1+DR9bUMLt55czI8pDmLsbOvnT2oP8dUM1U/KdVLX2cnJZDpcvKuWUslwWTc4mI8VGV5+Hxs4+31eX7/uhdhcvbjvE957exvee3sbs4kyuOrmUU6flsXhyblSKswa43ANsrWlnw4FWmrv7eGtnE9WtvXT1HV5Zl+u00xrkPeekCZnsauhicm4a0wszmF6QzvTCDGYUpTO9wLfKMZ69vEQGkIlA1ZDb1cCykc4xxnhEpB3IB0Ys65qTZqckiuPBg0NYUc5Gb4hRAFm5uZZ+jzdqn1Dae90caO7huorJUbkeHJkPEs0AsnJzLYsm5wwuKBivmUWZXDJ/Ag+u3s/NZ06LStkZgJe2HeLZLXXcsXwW5VH4/a9cPJGGjj6e2lTNt/+2hR9ftQBbFHq2O+s7ufWh9Rxqd/FfVy2I+G/AahGuO3Uyly4s4fF1B7nnpZ2s3FzHdRWT+fqF5eN6Xfs9Xl7/sIEH39nPmr3NOKwWPrZgAp9aVkbFlLygwTQz1U5mqv2oObjvfGwOexu7eO3DBl7d3sBj66q4+4Ud5Kc7OHd2ERfOKWLptDzyM0L/f+tyD7CvqZtdDZ1sqWpnww9PprMAABxWSURBVMFWtta04x7wfQY+c6ZvPuu06flMyk1jcp6TsjwnGSk2PF6Dyz3g//Li8gzQ2t3PvqZu9jZ2s6exi9W7m3BYhc6+Af/vZmNGYQbTC9OZMyGLVIeVrFQbWal2MlNtZKXZEaC7f4BOl5tOl2fw+61nTw/79ZdErZYQkU8AHzXG3Oq//VlgqTHmq0PO2eY/p9p/e4//nOZh17oNuA3AbrcvWbgwOp9AAz6oaacwMyUqZUcCPjzUSXqKlclR7Cq39vRT3drL7GLfjoGNjY0UFhaO65rdfR72NnUzNT+dzCgODX54qJM0u5UpUXqz7/N42VnfSUl2KgVB/oNH+lr0+6+bm+5gYk7auNs54DXsrO/EZrEwsziDaH5WbOjso77DRVaqnbI8JyN9EA3ltejodVPV2otFYEp+Os5xDgkO5fEaGjpctHT3g0BBRgqFGSlhfcrv93hp6e6ntacfj9eQZreSnWYnN92BLYzrjPZaDHgNnX0eOnt9b7CI75jdaiHNbiXVbsFutSD4hmZFfHlofR4vff43/H7/cFngnDS7BafDhtNhxZliC6uto70WfR4vff7nCzy/w2ahO0i+kM0ieLxHv+/3H9q9zRgzP5znTmQPpBoY+pFmElA7wjnVImIDsoGjtrUzxqwAVgCkp6eb9evXR7WhS/7zZS6eP4EfXrUgKtczxjD7uy9w81nTuPOSk6JyTYD39rVw3e/W8D83L+WcWYVUVFQw3tfi/rf38l/PbWf1dy8M+sYcqR+tqmTHoS4euOnUqAwP/OqVXdz76k7W3HlB0BVJ43ktvvvUBzz2XhV/+8a54+7dfPvJLbRvqOLpr5zFgknjX5Qw3AP/2Mddz1Yyt7yA3312SdAikqO9Fl6v4dev7eaXr+zkoknZ/O6zFTFb4XWwuYdfvLyDpzfX4k21ccnCUk6dmsuCiTlML0g/ovcQ+CS/rbadv2+sYfXuZlIswqdPKuKGZWWcU14Y0d9RqH8X7gEvH1S38+6+FirrOnxbG4iwq6HriPMWTsymsq6DOQXplBdnMLMok/KiDMqLM5ien44jgkrWkXK5PXS4PHS6PHT0uunwf7dZBYfV4u+J2fxfdnKcjrD3M0hkAFkHlIvINKAGuB741LBzVgI3AmuAa4HXxpr/iIXsNHtU9wRp73XTP+CNyRAWRHcp79aa9hE/1Y/HvNJsVry1j6017UdNxobLGMPKzTUsnZoXkze7r51fzpMbqrn3lZ384pOLI77O2zsbWb2niS+cMz0mwQPg5rOmkZlq49t/3cJn//AeD9x0asiLH6paerjnpR08tamWq0+eyI+uXhBR6f5QleU7uff6k/nCOdP5+/s1PPLuQR599yAAmSk25pRkkWKzcMA/sWwMLJiYTUt3P9+8aBafqJgctWHFsditFk6ZksspQxYAuPp9b9D9A17cAwb3gJdUu5WS7NSoLo6JVKrdRqrdRlH0RomPkrAA4p/TuB14Ed8y3geMMdtE5C5gvTFmJfAH4GER2Y2v53F9Itqa7bTTEcUA0uBPIiyKcgApykzBYbNEdSnveEq4j+asmQUAvL2rcdwBZHtdJ3sau/n8meFnnoeiKCuVG8+Yyoq39vKlc2dENG/T2t3PN5/cTGFmCv9yQXkMWnnYJyomk5Fi42uPbeT6FWv5481LR/2wsqu+k9++sYenN9cyZ0Im37tsLp8/c2rcJmPnlWYzrzSbOy85iT2N3WyubmNLdRu76jtxeQZYNDmHq0+ZyIzCDE6akMH0wsyoTmpHKtVhIzVKpYiOVwn97Y0xq4BVw459b8jPLuAT4VyzoKAgOo0bIifNTlNX9CbRo52FHhAo6x4oZ3LbbbeN63o9/R72NHZx+cLSaDTvCPkZKcyfmMVbO5u4/fzxvaE+s6UWq0X42IKSEc8Z72vxpXNm8Ojag9zz0g5+99mKsB5rjOE7f/uAlu5+/nDjqXF507lkQQn3p9j4yiPvc8Pv1zK7OJOPzCrknFmFg6/F5qo27nt9Ny9V1pNmt/L5M6Zy69nT45qUOJTNamH2hExmT8iM6qKN0Yz37yLJrAj3AUkXPsc7aRxMdpqd3Y1dY58YosNZ6NENIHBkWffx/ufYXteBMUR1Ce9Q55QXsuKtvXS63GSmRpZjYozhmc21nDWzYNRcjfG+FrnpDr5wznR+8fJONle1hdVr+suGal7Ydog7LzlpXOXww/WRWYW89o2P8POXdvDmzkae+6AOgNnFs3nxt++w4UAr2Wl2vnZBOZ8/Yyq5Uc51OR5oADnMP5cclqQLILGQ43RENQ8kVj0Q8AWQ9ftbo5Kkt7WmAxjfHiCjObu8kN+8sYc1e5q5aN6EiK6xsaqN6tZevn7hrCi37mg3nzWNB9/Zz89f2sHDtwxfcR7cgeZu/mPlNk6bnscXIlgmOV5FWan89NpFGGPYWd/FmzsbeHNnI9MLMrhobjGfPm1KVPOm1Ikl8TM9USQiF2/dupWZM2dy9913j/2AEGWn2elweRgIsvQtEg0dfaTZrVH9j3vzzTdTVFTEn+//Hzr7PLT1uGlpaWH58uWUl5ezfPlyWlvDSzLcWtNOQUZKTHpKAEum5OJ0WHl714hpPWN6ZnMtDpuFi+YVDx6rqqrivPPOY86cOcybN49f/epXAON+PTJSbHz53BnsONTJQ+/sH/N8z4CXf3l8ExaLcM91ixMybu9yuVi6dCmLFy/m6gtOo+bVP/LIradx86J0HvjXT3Hy/Dl88pOfpL8/NrtuHmsGBgY4+eSTueyyywDYt28fy5Yto7y8/IR6HQCmTp3KggULWLx4MRUVvmFZEckTkZdFZJf/+6hlA5ImgARKo5SXl1NZWcmf//xnKisrx3xcKEqyU1kyJTdqW9s2dvmSCKM5SXnTTTfxwgsvYOnxpcgcbOnh7rvv5oILLmDXrl1ccMEFYQfVrbUdzJ+YFbPJVIfNwunT83lrV2NEjx/wGp7dUsd5swvJGjIEZrPZuOeee9i+fTtr167lvvvuo7KyctyvB8DnTp/KWTML+P7KbfzwucoRP1R0uNx896mtvH+wjR9etSAqOSSRSElJ4bXXXmPz5s1s2rSJF154gbVr1/Ltb3+bf/mXf2HXrl3k5ubyhz/8ISHti7df/epXzJlzuKrAifo6BLz++uts2rRp6FLmO4FXjTHlwKv+2yNKmgCCvzRKSkoKDoeD66+/nqeffjoqF06xW9hwoDVqBRUbOvqi/qn+nHPOIS8vD+nxpckcaOnh6aef5sYbbwTgxhtv5Kmnngr5ei73ALvqO8e9B8hYzi4v4EBzDweawy/v/u6+Zho7+7h80ZGT/CUlJZxyyikAZGZmMmfOHGpqasb1egQ4bBZ+cu1CPnf6FH7/9j5ufWgdHa7DfxfuAS8PvbOfc3/2Bo+tq+In1yzgikXRX4QQKhEhI8OXce12u3G73YgIr732Gtdeey0Q+WtxvKmurua5557j1ltvBXzzZyfi6zCGK4GH/D8/BHx8tJOTKYAcURpl0qRJ1NTUROXCOU7f5GKw2jSRCPRAYsHiDyBVLT3U19dTUuJbmVRSUkJDQ0PI19lxqBOP18RsAj3g7Fm+RQ9vRTCM9czmOpwOKxecVDziOfv372fjxo0sW7ZsXK/HUHarhbuunM9/fXw+b+9q4urfvMO+xi5e2FrHRb98i++v9NVSeub2s/jkqWURPUc0DQwMsHjxYoqKili+fDkzZswgJycHm803hBrN/yvHsq9//ev89Kc/xWLxve01NzefkK9DgIhw0UUXsWTJElasGJw/LzbG1AH4v4+6qU4yBZCjxlmiNfSS6w8gbT1RGsKKQR2sABlwU5CRwsFx7kwYzRLuo5lekM7k3DQ2V7WF9Tj3gJfnt9axfG7xiJV3u7q6uOaaa7j33nvJyop+IPzMaVP44y1Laerq46rfvMOX/vQ+NovwwE0VPPqFZTFLFgyX1Wpl06ZNVFdX895777F9+/ajzklEmfV4evbZZykqKmLJkiWDx4LlJCf76zDU6tWref/993n++ee57777EJFzwr1GMi2/OKI0SnV1NaWl0Rk6yPVX5I1GD8TlHqC91x2ziWk4XNa9uLiYuro6SkpKqKuro6go9B369jR0kZNmZ1JubMfuRYRLF5bywD/28d1L5wz29sbyj11NtPW4R8xRcbvdXHPNNXz605/m6quvBhjX6zGSM2YU8PRXzmRrTTsdLg+fWDIpKoUMYyEnJ4dzzz2XtWvX0tbWhsfjwWazRfX/yrFq9erVrFy5klWrVuFyuejo6ODrX//6Cfc6DBX4XYuKirjqqqvYsmXLUqBeREqMMXUiUgKM2k0/Nv/SI7MOKO/r66O/v5/HHnuMK664IioXzoliD6SpK3ZLeAMCuSBXXHEFDz3kG8586KGHuPLKK0O+xvoDrZxclhOXT2SXLSyhf8DLM5uHl0Ib2d83VnPe7ELOnnV04qgxhltuuYU5c+Zwxx13DB4fz+sxmin56Vy6sJQblpYdc8GjsbGRtjZf7663t5dXXnmFOXPmcN555/Hkk08C0X0tjlU//vGPqa6uZv/+/Tz22GOcf/75PPLIIyfc6xDQ3d1NZ2fn4M8vvfQSwFYOl4/C/33UieRj6699HIwxHuD2Xbt2MWfOHK677jrmzZsXlWtnpdqwWoTWKASQw2VMopvte8MNN3D66aezY8cO/vbHFdS29fCNb/0rL7/8MuXl5bz88svceeeoCyoGuQe8fFjXGdVS66OZV5rFSRMyefL90Maf1+5tZuXmOpZOyw+6gdLq1at5+OGHee2111i8eDGLFy9m1apV3HnnnRG9Hsezuro6zjvvPBYuXMipp57K8uXLueyyy/jJT37CL37xC2bOnElzczO33HJLopuaECfq61BfX89ZZ53FokWLWLp0KZdeeinGmBeAu4HlIrIL32Z/oy5VTFg591ipqKiIejVe8FXk/ej8CfxonBV5X9x2iC8+vIFnv3pWzBL0Vm6q4Y9rDvDTaxdGtO94ZW0HH/vvt/nvG06O2wqiQNXfV+44h5mjVH8zxvDx+1bT0NnH6988N6bF/pQ6wYQ93JA0PZBYy3HaozKEFatCikNNynOy/kArexvDXxoLhyfQ55fGdgXWUFcunojVIjy5YfReyHMf1LG5up07ls/S4KFUgmkACVGu00Fr9/gn0Rs7+xAh6ntsDzWzyNfriLR+17aadtIdVqbmp0ezWaMqzEzhI7MK+fvG6hGT8/o9Xn76wg5OmpDJ1adMilvblFLBaQAJUY7TEZU5kMZOF/npKTGdbM1KtVOUmcLuhsgCSKCEezT31g7FJ0+dTGl2Gn/dUB30/kfePcDBlh7uvOSkY6Kct1InOg0gIcp12qOSiR7LHJChZhRmRBRABryGytoO5sU4gTCY5XOKsVqEu56tPCozvcPl5tev7ebMmfl8ZFb0Ky4rpcKnASREuenR6YE0dEa/jEkwM4sy2NPYFTRZajT7mrrodQ/EvIRJMBaLcO/1i7EIfPXPGwf3kwb43Zt7aOnu5zuXzDmhkr2UOpZpAAlRjtNOn8dLb//AuK4Trx7IzKIMOl2ewdLxoYp1CfexTMp18tNrF7Klup27n9/O9roO/vzeAe5/ex9XLi5NWLuUSmZtbW2IyJfDfVwyZaLHVO5gPax+0hyRZWd7vYbGOPZAAHY3dFEUxr7RW2vaSbFZmFEYvwn04S6eX8JnTivj5e31PLB6PwDXnjKRf47Dnh9KnYj8yaZfBn4TzuMSEkBEJA94HJgK7AeuM8a0DjtnMfBbIAsYAH5ojHk8vi097HA5k35KIyzN3dbrxuM1cZsDAd9KrDNmhr7N79baduaUZCU8o/p7l81jV30ne5u6mVaQzvTCdJwn+P7TSsWKP6l2hohsAl42xnwrlMcl6l0ilJrzPcDnjDHzgIuBe0Uk9H1Eo+xwOZPIJ9IPb2Ub+z2ni7NSyE+3U9MaelFFr9ewraYj5hV4Q+GwWZg3MZvLF/mGrTR4KBU7/r1x9hhjFocaPCBxAWTMmvPGmJ3GmF3+n2vxFfVK2PKboUNYkYrlVrbDiQjTCjLYeLA95MdUtfbQ2edJyAS6Uur4k6gAElbNeRFZCjiAPSPcf5uIrBeR9Y2Nke1uN5ZoVORtjEMW+lBzSrKorOvAG+JWvImeQFdKHV9iFkBE5BUR2RrkK6xyl/6Swg8DnzfGeIOdY4xZYYypMMZUFBbGppMyOITVHXkPpCGOPRDwFSns6vNQFeIw1tbaduxWobw4/PpZSqnjV2ZmJkDY1VNjNrBsjLlwpPtEJKSa8yKSBTwHfNcYszZGTQ2Jw2bh1Km5GCIvPtnY2YfTYSU9JT7j+XP9tay21XYwJYSyJFtr2plVnBm0wq1SKnnl5+cDrBaRrcDzx/ok+pg150XEAfwd+KMx5i9xbNuIDnW4Ii5QCPFLIgyYVZyJ1SJU1naMea4xhm21HTr/odQJyhjzKWPM/ONhEj1ozXkRqRCR+/3nXAecA9wkIpv8X4sT01yfvPQUmscxhNXY6Yrb8BVAqt3KzMIMttWOPZFe09ZLWZ6TxWUJW+imlDrOJGRtpDGmGbggyPH1wK3+n/8E/CnOTRtVfrqD+g5XxI9v6OxjzoT4LpGdW5LJh/WdGGNGLQHy3r4WNlW18eOrx7ffiVLqxKGlTMKQl+6gZVw9kPiUMRnqlCm5bK/rpKatd9Tz1uxpJsdpZ3acdiFUSh3/NICEIT/DQXN3f9gFCgFc7gE6XZ64B5AlU/IAWLe/ZdTz1u5rZtm0vLiXcFdKHb80gIQhP91Bv8dLV58n7MfGM4lwqNkTMslMtfHevtYRz6lu7aGqpZfTpufHsWVKqeOdBpAw5KX73vwjGcY6XMYkvgHEahEqpuSO2gNZu6cZgNNnaABRSoVOA0gY8v3b0EayEitRPRCAU6flUd/homGEBQBv7GzkI7MKmVWk8x9KqdBpAAlDYB/zlq5IeiCBMiaxL6Q43PknFdHd5+H1HUfna3a63LxcWc/UfKfOfyilwqIBJAyDASTCHohFDl8jnmYXZzI5z8mzW+qOuu/lynr6PF6uWFwa93YppY5vGkDCkJ/he/Nv6g5vlz/w7TV+1swCrAn4lC8iXLawhHf2NNPcdWTbV26uZWJOGqeU5ca9XUqp45sGkDA4HTZS7ZaIhrC21XbQ1ht5Jd/xunRBKQNewzNbageP1bX3snp3E5cvKtV9xpVSYdMAEqb89JSIhrAOtbsoDmNr2WibU5LJNadM5P9W78fl9u3rft/ru5mQlcoNSycnrF1KqeOXBpAwBZIJw1XX3ktJduICiIhw9SmTONDcw38+W8mTGw7yyLsHuXBucUiVepVSajjdJzRMeU47nWEmEvb0e+hweRLaAwE4c2YBP7h8Lv/xTCUrN1m5rmIydyyfldA2KaWOXxpAwlSUlcqHO5vCesyhdl/+RSJ7IAE3njGVWcWZOB1WZk/IIs2he38opSKjASRMBRkpNHX14fWakPMmDvkT+CYcAwFERDhjZkGim6GUSgI6BxKmoswUPF5Da0/o8yCBHsiEBA9hKaVUNGkACVORPwgEMstDUdd+7PRAlFIqWjSAhClQy6oxjABS3+EiK9WG06Ejhkqp5JGQACIieSLysojs8n8fMQ1aRLJEpEZE/ieebRxJoJpuuD2Qkuy0WDVJKaUSIlE9kDuBV40x5cCr/tsj+U/gzbi0KgSFgwEk9K1tD7W7dPhKKZV0EhVArgQe8v/8EPDxYCeJyBKgGHgpTu0ak9NhIyPFFtYQ1qEOl06gK6WSTqICSLExpg7A/71o+AkiYgHuAb411sVE5DYRWS8i6xsbG6Pe2OGKMlNCHsJyD3hp6urTHohSKunEbFZXRF4BJgS5699DvMSXgVXGmKqxCv0ZY1YAKwAqKirC37A8TIWZKTR2hBZAGjr7MObYSCJUSqloilkAMcZcONJ9IlIvIiXGmDoRKQGO3ukITgfOFpEvAxmAQ0S6jDGjzZfERWFmCttqO0I691B7LwDFGkCUUkkmUUNYK4Eb/T/fCDw9/ARjzKeNMWXGmKnAN4E/HgvBA3y7Co60PexwdcdQGROllIqmRAWQu4HlIrILWO6/jYhUiMj9CWpTyIqyUujuH6A7hKKK9f6hLp1EV0olm4RkthljmoELghxfD9wa5PiDwIMxb1iISrJTmVuSRUNHH9MKR38Jq1t7yE6zkZWqSYRKqeSimegRKMxIobKug7qO3jHPrWrppTgrFYtFX2qlVHLRd7UIlOb4sspr28aeB6lu7WFyrjPWTVJKqbjTABKBQE5HbdvoPRBjDNWtvUzO0wCilEo+GkAikGq3UpiZMmYAaetx09XnYVKu1sFSSiUfDSARKs1Jo2aMAFLV2gOgPRClVFLSABKhiTmpYweQFt/9OgeilEpGGkAiVJqdRm1bL8aMXDkl0AOZlKdDWEqp5KMBJEKlOWm43F5ae9wjnlPV0kN2mp2sVHscW6aUUvGhASRCh5fyjjyM1djZxxkz8uPVJKWUiisNIBEqy3cyISuVA83dI56ztaadNLs1jq1SSqn40QASobI8J4c6XBxo6Ql6f3efh9p2FzOKMuLcMqWUig8NIBHKSLFRkJHC/qbgPZB9/uMzCtPj2SyllIobDSDjMK3Ayf6m4D2QPY1dAMwo1B6IUio5aQAZh6n56TR2Bd+ZcE9DF1aLUJavOSBKqeSkAWQcZhVnsq+pm/YgS3l3N3ZRluckxaaT6Eqp5KQBZBxmFvuGp3bUdx51376mbl3Cq5RKahpAxmF2cSYAOw4duT96e4+b7XWdg7kiSimVjDSAjENJdiqZqTZ2HDqyB7Klpg2AhZOyE9EspZSKi4QEEBHJE5GXRWSX/3vuCOeVichLIrJdRCpFZGp8Wzo6EeH06flsqWk/4viWat/thRNzEtEspZSKi0T1QO4EXjXGlAOv+m8H80fgZ8aYOcBSoCFO7QvZ9MIMKms7cLkHBo9trmpjWkE62U6tgaWUSl6JCiBXAg/5f34I+PjwE0RkLmAzxrwMYIzpMsYET7pIoNOm5XFKWS6bq3zDVl6vobvfw8XzJyS4ZUopFVuJCiDFxpg6AP/3oiDnzALaRORvIrJRRH4mIkHXxIrIbSKyXkTWNzY2xrDZRztlai7vH2zljZ2+5/2gpp3Vu5uZVawJhEqp5GaL1YVF5BUg2Mfwfw/xEjbgbOBk4CDwOHAT8IfhJxpjVgArACoqKkbeoCMGslLtnF1eyK76TowxvLmzgcWTc/jIrGAxUSmlkkfMAogx5sKR7hORehEpMcbUiUgJwec2qoGNxpi9/sc8BZxGkACSaB9bMIFvPbmFJzdU8fi6auaVZpGX7kh0s5RSKqYSNYS1ErjR//ONwNNBzlkH5IpIof/2+UBlHNoWtssXlfLxxaXc/fwOLBb41kdnJ7pJSikVczLalqwxe1KRfOAJoAzf8NQnjDEtIlIBfMkYc6v/vOXAPYAAG4DbjDH9o1374osvNi+88EJM2x9MVUsPlbXtTC/MoNyfYKiUUscRCfsBiQggMZZ0v5BSSsVB2AFEM9GVUkpFRAOIUkqpiGgAUUopFRENIEoppSKiAUQppVRENIAopZSKiAYQpZRSEdEAopRSKiIxq4WVQGEnwyillAqf9kCUUkpFRAOIUkqpiGgAUUopFRENIEoppSKiAUQppVRENIAopZSKyP8HRwoBWvkBLJAAAAAASUVORK5CYII=\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "9729d499415e4f89bfba7166fe8cd030": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "children": [
        "IPY_MODEL_4dd3bff3e8214c9589710af7a08d7d1d",
        "IPY_MODEL_e9a526972a5d4f4bb76ae619dc014111",
        "IPY_MODEL_5e94c059e872406c91df9d284764c40a",
        "IPY_MODEL_90b6c53caa6d49ce9e0b8b3eb99fa72f"
       ],
       "layout": "IPY_MODEL_82c78018b5bb4e29be9784c96bf55a1a"
      }
     },
     "97a64093a94f47c1a8d6c975c7a595f7": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "children": [
        "IPY_MODEL_6e8df5525f954c0b8677fa7a35b4b80e",
        "IPY_MODEL_a10738029caa49f7853d084b3a1db567"
       ],
       "layout": "IPY_MODEL_499d659fb6e44dd3a86a05db35b727ec"
      }
     },
     "97ca648078bf487bb2673bfab5e69754": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatSliderModel",
      "state": {
       "description": "c_",
       "layout": "IPY_MODEL_5057da1b9d5f491981961022224e32d7",
       "max": 1,
       "min": 0.1,
       "step": 0.1,
       "style": "IPY_MODEL_cf19f871178645b0b8dbfb5acf99c97a",
       "value": 1
      }
     },
     "984bb65cf2274e1d94450d1ad1fc2f45": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "children": [
        "IPY_MODEL_97ca648078bf487bb2673bfab5e69754",
        "IPY_MODEL_543fa78b417d4c15b1528f50a5f9384f"
       ],
       "layout": "IPY_MODEL_e8049832e8a147bf8c4fc6f58603fb32"
      }
     },
     "98598d1b2ed347f0a50d41cf4ff9e75f": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_d184a8733fe7414eaa271c312fe40744",
       "outputs": [
        {
         "ename": "ValueError",
         "evalue": "The same variable should be used in all univariate expressions being plotted.",
         "output_type": "error",
         "traceback": [
          "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
          "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
          "\u001b[0;32m~/anaconda3/envs/sbs/lib/python3.7/site-packages/ipywidgets/widgets/interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    249\u001b[0m                     \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    250\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 251\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    252\u001b[0m                 \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    253\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;32m<ipython-input-58-780b6d8c8e8d>\u001b[0m in \u001b[0;36mplot_mobilty\u001b[0;34m(c)\u001b[0m\n\u001b[1;32m      2\u001b[0m     sy.plot(Y.subs({m:1,s:1}),\n\u001b[1;32m      3\u001b[0m             \u001b[0;34m(\u001b[0m\u001b[0mom\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m             \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mr'$c$=%.2f'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m            )\n\u001b[1;32m      6\u001b[0m \u001b[0mipw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minteract\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplot_mobilty\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;32m~/anaconda3/envs/sbs/lib/python3.7/site-packages/sympy/plotting/plot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1351\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfree\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1352\u001b[0m                 raise ValueError(\n\u001b[0;32m-> 1353\u001b[0;31m                     \u001b[0;34m'The same variable should be used in all '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1354\u001b[0m                     'univariate expressions being plotted.')\n\u001b[1;32m   1355\u001b[0m     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfree\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mSymbol\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'x'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;31mValueError\u001b[0m: The same variable should be used in all univariate expressions being plotted."
         ]
        }
       ]
      }
     },
     "9ad5efa582824c3dad0e7170fef4d0e8": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "9da5e52dc66d45639428764950dd5534": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_1d54a6615ce7496dbc2595430203234d",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "9e9bff02d61e4a63b84c5170fc7afa93": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatSliderModel",
      "state": {
       "description": "d",
       "layout": "IPY_MODEL_ec40dc75c229479bbf2b45b343efc676",
       "max": 0.3,
       "min": 0.01,
       "step": 0.01,
       "style": "IPY_MODEL_3eb4fbd461c7490596f1ee54ce2df6fe",
       "value": 0.3
      }
     },
     "9f007294bdf14bd286d886c951c9b6d7": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_b94e1b57c28043a1a6165112d3a6213b",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "9f015a70ea09446dba5e7d445cdf3107": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "a0949e9f050145709fde638aa21d8981": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "a10738029caa49f7853d084b3a1db567": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_cfbf0bcf61bd4da796bbfd77872041ac",
       "outputs": [
        {
         "data": {
          "image/png": 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        "widget-interact"
       ],
       "children": [
        "IPY_MODEL_4930f450a2284db19d2adf2c8bc44a29",
        "IPY_MODEL_98598d1b2ed347f0a50d41cf4ff9e75f"
       ],
       "layout": "IPY_MODEL_b0aa05d2554f448c8a098f111f92c61e"
      }
     },
     "aacd9f8ad5fc4a87bcb05ce881bb05f0": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_01a32ae78cbc4aa486dde8a3297acf6f",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "ac6296b8495a4ce8b6ddf7efa86d398e": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatSliderModel",
      "state": {
       "description": "c_",
       "layout": "IPY_MODEL_b54aba13d17e4de4be7bbd1fd6aefa48",
       "max": 1,
       "min": 0.1,
       "step": 0.1,
       "style": "IPY_MODEL_25a98ab8d3a449bea92b5fa5d9d76268",
       "value": 0.1
      }
     },
     "acabfc905b4a4f558b85b18416213c1c": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatSliderModel",
      "state": {
       "description": "c_",
       "layout": "IPY_MODEL_24b6101b13c744a887de42b7e742f827",
       "max": 0.30000000000000004,
       "min": -0.1,
       "step": 0.1,
       "style": "IPY_MODEL_af445883e4a64afbb31b0b65e6612976",
       "value": 0.1
      }
     },
     "ace8cf463a26452bbeb6cf57a45633cb": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatSliderModel",
      "state": {
       "description": "d",
       "layout": "IPY_MODEL_284f4b273f3746799644efaaa1fbf8c4",
       "max": 0.3,
       "min": 0.01,
       "step": 0.01,
       "style": "IPY_MODEL_c4575962bc2f4019ab342ba37ebf87ae",
       "value": 0.1
      }
     },
     "ad73491b7aaa4b27b77052661468fd9f": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_52d19dbbae2a4ee7998ece14d2406bf6",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "af02333b97f3489584c347fe68365cc3": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "af445883e4a64afbb31b0b65e6612976": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
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      "model_module": "@jupyter-widgets/base",
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      "model_name": "LayoutModel",
      "state": {}
     },
     "b0a5f88c9717436791d0c3639046e970": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "b0aa05d2554f448c8a098f111f92c61e": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "b2e0f024545e48f78896f0448b673c56": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
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     "b4b35d4fb9354769879ff5946f83e7a1": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "b54aba13d17e4de4be7bbd1fd6aefa48": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
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      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_6acbbd0174c244b58d88d0abb20c5609",
       "outputs": [
        {
         "ename": "ValueError",
         "evalue": "The same variable should be used in all univariate expressions being plotted.",
         "output_type": "error",
         "traceback": [
          "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
          "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
          "\u001b[0;32m~/anaconda3/envs/sbs/lib/python3.7/site-packages/ipywidgets/widgets/interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    249\u001b[0m                     \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    250\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 251\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    252\u001b[0m                 \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    253\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;32m<ipython-input-21-5b900fddfee3>\u001b[0m in \u001b[0;36mplot_impulse_response\u001b[0;34m(d)\u001b[0m\n\u001b[1;32m      3\u001b[0m     sy.plot(sol_h.rhs.subs({delta:d,ome0:1.0,m:1.0}),\n\u001b[1;32m      4\u001b[0m             \u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m             \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mr'$\\delta$=%.2f'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m            )\n\u001b[1;32m      7\u001b[0m \u001b[0mipw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minteract\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplot_impulse_response\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;32m~/anaconda3/envs/sbs/lib/python3.7/site-packages/sympy/plotting/plot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1351\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfree\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1352\u001b[0m                 raise ValueError(\n\u001b[0;32m-> 1353\u001b[0;31m                     \u001b[0;34m'The same variable should be used in all '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1354\u001b[0m                     'univariate expressions being plotted.')\n\u001b[1;32m   1355\u001b[0m     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfree\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mSymbol\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'x'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;31mValueError\u001b[0m: The same variable should be used in all univariate expressions being plotted."
         ]
        }
       ]
      }
     },
     "b6e0cd323a6a457d9b8b25a141749be3": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatSliderModel",
      "state": {
       "description": "si",
       "layout": "IPY_MODEL_c99c78dfec5e4fb58a6d0a817370de81",
       "max": 2,
       "min": 0.1,
       "step": 0.1,
       "style": "IPY_MODEL_426d2504caff466bb51b647c680b2399",
       "value": 1
      }
     },
     "b876473e0845488abe375c2d5993da33": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "b8c0c22609f049eb9caa843da6c011d6": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatSliderModel",
      "state": {
       "description": "c_",
       "layout": "IPY_MODEL_a5472138bd9d41be8aeb23ff79fbf923",
       "max": 1,
       "min": 0.1,
       "step": 0.1,
       "style": "IPY_MODEL_7f4cb910ca02424288b21e6efa1e7b6b",
       "value": 0.1
      }
     },
     "b94e1b57c28043a1a6165112d3a6213b": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "b985ab28c0754c9c9629a30eb0db9fb7": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "ba97f661ae674441b1de7276b1df5a2d": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_f410cdb00744416a8832705c9f02c49a",
       "outputs": [
        {
         "data": {
          "image/png": 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TUWvfUweFsEdWHdx55bV8sPsMdww0yH7UZqCU4tZ+Ybw2eyBzhkWycvcZbvrrFr6STauEsClWHdwf7TlDXFcfHpzQU+9S7IqvuzNP3x7P14tH0dXfnYc/PcSst3dzqrBS79KEEK1gtcFdVl3P8u1Z9AzxoqufnDTeERIMvnx5/wienZbA8XOV3Pl/u3n+2+NU1ll3+0RWTorOzmpXTr6++RR/+e4k3z2cTO9Qb73K6DRKqi6yfHsWb3x/mmBvVx6f3IdpA7ri4GC9c+Zl5aSwYp1v5WR9o5H3d2YzumeQhLaFBHq58tjkPny1eCThfu48+o/DzHhrJ2m55XqXJoT4N1YZ3KvS8imsvMiCUVF6l9LpJEb48eX9I/jLHf3IK6vhwY8P8uhnh+XINCGsiNUFt6Zpl3vbY2RPEl04OCh+PjiCDY+MZXJ8GKmH8xn34vcs3fAjtfVNepcnRKdndcG9O7OUo/kXWDAqSvYk0ZmPuzOP39KHjY+OYXyfEJZuyGDxRwf4x/4cmowyfVAIvVhdcC/fnkmApws/G9BV71JEs4gAD16fPZB/3Deci41GfvPPNG792za+P1ko87+F0IFVBXdmURUbjhdyz7BI3Drx6TbWKql7AB8sGMprdw+gpr6JuSv2cc/yPbJ8XggLc9K7gCut2JGNi6MDc4ZF6l2KuAalFLf1C+fmvqF8uOcMf9uYwdOpR+ni48ajN/cmKqjzHN4shF6sZsRdXlPPPw7kcHtiOMHernqXI1rg4uTAvJFRbHlsHMk9g9l4vJAJL23hiS/SOFdRq3d5Qtg1qwnuLw/mUddgZMFomQJoS3zcnPmvm3qy9bFxzBkWyecH8hjzl+95ef2PFFdd1Ls8IeySVaycNBo1xry4mfhwH968Z7Albik6SG5ZDa9syOBkQSUZBVX8YkR3FiVHE+DpYvZ7ycpJYcU6dEqcVfS4d2eWkFNay6MT5aAEW2fw9+AvP+/P6aIq/rYxg//bepqVu7KZO6I7vxwdjZ8ZAjw1NZXU1FTZq0R0WlYx4n7ok4NsOlHIvv+ZILNJ7ExGQSVLN2aQW1rD6aJqfjEikgWjzDMClxG3sGL2vVdJRU0D36af52eJXSW07VDPLt68fvdAnp/RjzG9g3nj+9OM+t9NPLfmOEWV0gMXoi10b5V8cziP+kYjdyZF6F2K6ECxYT68fvdAMgoqeW3zKd7elklaXgW9u3izKDmacNm6V4hW071Vctur22gywpoHR8kS904ks6iK93dl8+HuswBMG9CV+8b2uKGTjqRVIqyY/bZKjuZXkJ53gTsHGyS0O5noYC+enhrPlsfGcc+wSL45nM+El7bwx2/SOZwjW8kKcT26tkr+sT8XFycH2ZekE+vq584fp8axZHwMK3ZksflEEe/tPMOw6AB+ldyDsb2D5R91If6Nbq2SuoYmhj67keRewbw6a0BH3UbYmKqLjXyy9yzLt2dxrqKOYdEBTB9o4PbEcFydfvrwWlolworZZ6tk3bECKmobuHOwPJQU/+Ll6sTC0dFs+c04XprZnyAvVx77Zxojn9/M3zZmUCKrMYXQb8R9zzt7yCquZttj46z6XEOhL03T2HGqhOXbM9l8sghXJwemD+zK/JFRzLp1nIy4hbWyv5WT+eU17Msu5f6xPSS0xXUppRjVM4hRPYM4VVjJ8u3ZpB7KY93RArKLq1l39Dw3xXbBUf47Ep2ILq2S1MPncHZUTB9g0OP2wkbFhHjz3PQEtjw2jvmjorjYaGTRygOM+ctmlm09TUVNg94lCmERugT3mvTzRAd70S3QQ4/bCxsX6OXK4nEx9A715o3ZAwn3defZNScY9txGXvzuBEfzZQ8TYd8s3irJLavhcE45v53cx9K3FnZGAVMSwpiSEMax/Au8vyubT/fl8trm0wzs5sc9wyKZkhAmWykIu2Px4F6bfh6AW+JDLX1rYcf6hvvwvzP6UV5Tzz8P5PLhnrM88tlh/rTqGHOGRTJ9oIHucjqPsBMWn1Uy482d1NY3seah0ea+tOgkLm3runnzZjIyMq76OUajxs7TJXyw+wwVtQ3syixhRI9AZg3pxs1xXf5jTrgQZtahT8stGtznK+oY9txGfn1zL5aM72nOS4tOqLULcAov1PGPA7l8vPcsuWW1BHi6MGNgV+5MiiAmxNsClYpOyH6mA36bfg6AWxLCLHlb0cmF+LixeFwM94/pwfZTxXy89yzvbs9i7dHzdPF2Y+bgCG7tF4anq+6bZQrRKhYdcc98axcX6hpY+3CyOS8rOqn2LHkvqqzjix/y+HR/DplF1Xi4OHJbvzB+PjiCwZH+sj+KaC/7GHEXXqhj35lSHr6pl6VuKcQ1BXu78asxPViUHM0PZ8v4bF8uq9LyySisorymgTsGGZg2oKvsEy6sksWC+7uj59E0mJIgs0mE9VBKMSgygEGRATyZ0pf1x87z0d4c/vLdSV5cd5IRPQK5K6kbN8WG4OEirRRhHSzWKpm1bDdFVRfZ8MgYc11SdHIduTvg2ZIaPv8hly8O5uLv4cKpwiomx4UybWBXRvQIkiX2oiW23yoprrrInqwSloyLscTthGi3boEe/PfEXjx0U0/2nynly4N5rEo7xxcH8wjxdmHGoAhuTQgjLtxH+uHC4iwS3OuOFmDUZDaJsD0ODoohUYEMiQrkqZQ4Np0o5PuThbyzLZM3vz9Nj2BPbk/syu2J4UQGygIfYRkWaZXMWb6H3LJaNj06RkYnwmz0PEihvKaeNUfO8/WhPPZklQIwITaEYdGB3NovjDBfeajZydn2Apyy6noG/3kD942J5jeTZH8SYT7WcgJOfnkta9PP88UPuaTnXwAgqbs/t/UL55aEUEK83XSuUOjAtoN71eF8/uerdD5cOJT4rr7tvZwQl1lLcF8pq7iaVYfzSU3L58eCKhwUDIsO5I5BBsb0CibQy1XvEoVl2HZwL/7wB344U8qOx8fj4KDrofLCzlhjcF/p5PlKVqXls+pwPlX1TZRW1zMsOoBb4sOYFBdKsLeEuB2z3eA2GjUGPrOeCbFdePHn/dtzKSH+g7UH9yWapnH8XCXfpp9j9ZFzZBZV46BgSFQA0wcYSO4VTKivtFPsjO1OBzyaf4HymgZGxQR15G2EsGpKKfqG+9A33IdHJvbix4IqVh85x5q0fF7e8COPfZ7GgG5+TIoLZXJcqGw/K1rUocG9/VQxACMluIUATCHeO9Sb3qHePDKxF6cKK/nuaAFr08/z/LcneP7bE/QJ9WbGIAPDowNlnri4qg5tlcx+ZzclVfWyqZToELbSKmmt3LIavjtawHfp56lvMnIop5yufu5MiA1hYt9QhkYH4Owoz4lshG22SuoamtiXXca9wyI76hZC2BWDvwcLRkWxYFQUxVUX2XS8kHXHCvh0fw7v7zqDt5sT43qHMCUhlBExQfi4OetdstBJhwX3vuxS6huNjOwpbRIhblSQlyszkyKYmRRBbX0T2zKKWH+sgI0nCim4UMeSjw4yNDqA8X26MCE2RFZtdjIdFtzbTxXj7KgYGhXQUbcQndSlo8sqKjrHae7uLo7cHBfKzXGhNBk1Dp4tY8PxQjYeL+BPq47xp1XH6BHsyYRYU4gndvOXloqd67Ae961/24aXqxOf/mp4Wy8hxHXZW4+7Lc6W1LDxRAEbjxeyJ6uE3qHenCmpIblnMOP6hDCmV7DMF9eH7fW4S6vrOZp/gV/fLIcmCNGRugV6MG9kFPNGRlFZ18COU8VsOlHI5pNFrD5iOiqwv8GX2/qHMSgygP4GP9mS1g50SHDvkGmAQlict5szk+PDmBwfhtGocezcBTafKGTTiQKWbcmiqOoEfh7OjO4ZzNhewST3DCbYR0bjtqhDgnt7RjHebk70M/h1xOWFEC1wcFDEd/Ulvqsv/3VTT8qq69l2qpgtJ4vY8mMRqYfz6dXFCycHB5J7BZPcK4hBkf64OjnqXbpoBbMHt6ZpbD9VzIgegfIrmRBWwt/Than9w5naPxyjUeNofsXlIH9nWyZvbTmNh4sjyb2CGdI9gOReQfQI9pLFP1bK7MGdXVJDXnkt943tYe5LCyHMwMFBkWDwI8HgxwNjY6i62Miu0yVsyygir6yW/7fqGAChPm6M6hnE6J5BjIwJIkh2NrQaZg/uS8vcZX8SIWyDl6sTE/t2YWLfLgDklNaw/VTx5bnj/zyQS4CHM1183RjZwxTiQ6IC8HSVw5P1YvbpgL9auZ/0vAts/+04+TVLdCiZDtjxmowa6XkVHDhTxobjBezPLqO+yYiTg2JgNz9G9AhieI9AErv5SX/8p2xnOmCTUWPn6RKmxIdJaAthBxwdFP0j/Ogf4cf8UVHUNTSxP7uM7aeKOVVYxSubMli6MQM3ZweSugcwvEcgo3oE0TfcBydZBNRhzBrc6XkVVNY1MkqWuQthl9ycHRnVM+jy/8crahrYk1XCztMl7DpdwgtrT7Iu4jynC6tJigpgeHQgw6ID6RvuI5MVzMiswb0vuxR/D2eSuvub87JCCCvl6+F8eTk+QFHlRfZmlbLjdDG7T5ew6UQhAL27eGPwd2dYdCBDowPoGyYj8vYwa3Cn5Vbg5uxIqJxwLUSnFOztyq39wri1XxgABRfq2JNZyq7MYvZklrKxOci9XJ2YHB9Kj2AvhkQFkNDVFxcnCfLWMmtwH8mroJ9BDgQWQph08XFjamI4UxPDgeYgzyplT2YxB86U888DuQC4OzsyMNKPMb2Cie/qy4AIf9xd5GHntZgtuCtqG8gqruaOQQZzXVIIYWe6+LhdXggEUFx1kf3ZpezOLGV/dilLN2RQU9+Es6Np5WdSpD9DogIZFOmPv6eLztVbD7MFd3qeaYtNGXELIVoryMv18v4qYBoAHjhTyr7sMvZllfJt+nmWbcsCoGeIF4O7B5DU3Z9Bkf50C/DotLPXzBbch3PLAUjoKsEthGgbX3dnxvfpwvg+psVAtfVNHMmrYF+2aUS+Ki2fj/eeJS7cm8LKegZHmkJ8UKQ/ceGdp09utuA+kltBZKAHfh7y64wQwjzcXRwZEhXAkOYDWYxGjYzCKtLzytmWUcz+M2V8m34eAE8XR/pH+NHP4MegSH8GdvMj0E6X6ZstuNNyKxjQTXYDFEJ0HAcHRe9Qb3qHejNjUARgeuB54EwZx/Ir2HaqhOXbM3lri2nR96S+XfB0dWJAc5D37uJtF9MQr7vkffLkyVpxcXGLF2k0ahw/d4EwXze72oimqKiI4OBgvcuwGtb28zh+/DixsbG63d/afh56sqafhaZBTUMjNRebaGgyUlHbQKPRlHMeLo4oFB6ujng4O+Lh6oRTBywMOnDgwFFN0+LNfuFmZtmrZPOJQua9t49PFg1jWHSgeSqzArIXxk9Z289D73r0vr81seafhaZp5JTWcjCnjPS8C+zNKuFo/gUajRpd/UxrThK7+TEgwo/ECD/iu/ri5ty+qYhKqQOapg02R/1XY5ZWSVpuBUpBvDyYFEJYGaUU3QI96Bbowe2JXQGoa2jiaH4Fx89XsutUCYfOlrM6zXTUm5ODYnB3f2JCvOhvMIV5j2AvHKxoyb6ZgrucHsFeeMk2j0IIG+Dm7MigyAAGRQZwz9BIAAov1HEop5xDOeWcv1DHVwfz+WD3WcC00nNoVAA9u3jT3+BLvwg/wn3ddJuO2O6k1TSNtLwKRtvhxlKLFi3SuwSrIj+Pn5Kfx7/Yw88ixMftJ/uuGI0amcVVHMqp4HBOOcVVF1m+PZOGJlMHOcjL1RTiBj/6GXxJMPhe+YxvWUfW2u4e97mKWoY/t4k/pvRl7sgo81UmRAusua8q7FNdQxMnzleSlmsamZ8rr2V3VimXYrSrnzsJXX15a84g696POy23ecVkhEwFFELYNzdnRxKbH2LeO9z0WtXFRo7mVZCWW0FaXgVZxVUdXocZgrscJwdF3zAfc9QjRItSU1NJTU2loqJC71KEMPW/owMZasEZde2eiZ6WW0GvLt7tnj5jTebPn09ISAjx8R02DdOm5OTkMG7cOGJjY4mLi+OVV17RtZ6UlBSWLVuGr6/lZzHV1dUxZMgQ+vfvT1xcHE899ZTFa7BGTU1NDBgwgNtuu03vUnTXvXt3lFJHlFKHlLCwB9IAAArLSURBVFId0strV3BrmmaXW7nOnTuXtWvX6l2G1XBycuKvf/0rx48fZ/fu3bz++uscO3ZM77J04erqyqZNmzh8+DCHDh1i7dq17N69W++ydPfKK6/ouhjKCo3TNC2xo+Zytyu4c0prKa9poJ/BvvrbycnJBAQE6F2G1QgLC2PgwIEAeHt7ExsbS15ens5V6UMphZeXFwANDQ00NDR02h3qLsnNzWX16tUsXLhQ71I6jXYF96UdAe1txC2uLTs7m4MHDzJ06FC9S9FNU1MTiYmJhISEMHHixE79swB4+OGHeeGFF3BwsP09QMyh+R/ydUqpA0qpDpkn2a6f9JG8ClycHOjVxdtc9QgrVlVVxYwZM1i6dCk+Pp33YbSjoyOHDh0iNzeXvXv3kp6erndJulm1ahUhISEMGjRI71Ksxo4dO9A0bSBwC7BYKZVs7nu0b8SdU05smE+n2QO3M2toaGDGjBnMnj2b6dOn612OVfDz82Ps2LGd+nnIjh07+Oabb+jevTt33XUXmzZt4p577tG7LF2Fh5tO99E0rRD4Ehhi7nu0OXGNRo30vAr6S5vE7mmaxoIFC4iNjeWRRx7RuxxdFRUVUV5uahHW1tayYcMG+vTpo3NV+nnuuefIzc0lOzubTz75hPHjx/PBBx/oXZZuqqurqaysBEAp5QncDJj9V7I2B3deeS0eLk52eeLNrFmzGD58OCdPnsRgMLB8+XK9S9LVjh07WLlyJZs2bSIxMZHExETWrFmjd1m6OHfuHOPGjaNfv34kJSUxceJEmQInLisoKGDUqFEopQ4De4HVmqaZ/VeyNi953/pjEfe+u9futnIVtkOWvAsr1qFTjdo84s4uqQYgKsjTbMUIIYRoWduDu7gGd2dHQrzt58QbIYSwBe0acUcGenT6xQdCCGFp7RhxV0ubRAghdNCm4G5sMpJTVkN3CW4hhLC4NgV3fnkdDU0aUYES3EIIYWltCu6s5hklkYEeZi1GCCFEy9oU3NnFMhVQCGG/XnrpJeLj44mPj2fp0qVkZ2fTp08fFi5cSHx8PLNnz2bDhg2MHDmSnj17snfvXsC0cnL+/PkopfYppQ4qpW4HUEp5KKU+U0qlKaU+VUrtUUoNbn7vTaXUfqXUUaXU062pr23BXVKNp4sjwTIVUAhhZw4cOMCKFSvYs2cPu3fv5u2336asrIxTp07x0EMPkZaWxokTJ/joo4/Yvn07L774Is8++ywAf/7znxk/fjyapiUB44C/NC99fwAo0zStH/An4Mpduf6ned/ufsAYpVS/lmps84g7MtBTpgIKIezO9u3bmTZtGp6ennh5eTF9+nS2bdtGVFQUCQkJODg4EBcXx0033YRSioSEBLKzswFYt24dzz//PEqpQ8D3gBvQDRgFfAKgaVo6kHbFLWcqpX4ADgJxQN+WamzTmZPZJTXEhslWrkII+3OtbUBcXf/VYXBwcLj8sYODA42NjZe/9vPPP6d3796JV36tusYoVykVBfwaSNI0rUwp9R6msL+uGx5xNzYZySmtobvMKBFC2KHk5GS++uorampqqK6u5ssvv2T06NGt+tpJkybx6quvXg5qpdSA5re2AzObX+sLJDS/7gNUAxVKqS6Y9vBu0Q0Hd155LY1GTeZwCyHs0sCBA5k7dy5Dhgxh6NChLFy4EH9//1Z97R/+8AcaGhoA0pRS6Zj62QBvAMFKqTTgt5haJRWaph3G1CI5CrwL7GjNfW54d8DvTxYyd8U+PvvVcIZEybmMQj+yO6CwYj9pjSilHAFnTdPqlFI9gI1AL03T6tty8RvucV+aCtg9SOZwCyFEK3kAm5VSzphC/f62hja0JbhLakxTAb1kKqAQQrSGpmmVwGBzXe+Ge9zZJdV0D5KpgEIIoZcbD+7iaplRIoQQOrqh4G5oMpJbViv9bSGE0NENBXdeWfNUQBlxCyGEbm4ouC/tCihzuIUQQj83FNyXpwLKiFsIIXRzQ8F9pqQGL1cngrxcOqoeIYQQLbixVklxNd2D5IBgIYTQ0421SkpM27kKIYTQT6uD+9JUQDlnUggh9NXq4M4vryUm2EvmcAshhM5aHdwFFy5ysqCSUB/3jqxHCCFEC1od3KXVFwHw93TusGKEEEK07AaCuwGAAE+ZCiiEEHpqdXCX1Zi2jvX3kOAWQgg9tTq4S6rq8XRxxM3ZsSPrEUII0YIbGnH7S5tECCF0dwM97nrpb4sOk5mZyYIFC7jjjjv0LkUIq3dDI24JbnE18+fPJyQkhPj4+J+8vnbtWnr37k1MTAzPP//8da8RHR3N8uXLO7JMIexGq8+cLKmqJybYqyNrETZq7ty5LFmyhHvvvffya01NTSxevJj169djMBhISkpi6tSpNDU18cQTT/zk6999911CQkIsXbYQNqvVwS09bnEtycnJZGdn/+S1vXv3EhMTQ3R0NAB33XUXX3/9NU888QSrVq1q872WLVvGsmXLACgqKmrzdYSwZa1qldQ1NFFT3yStEtFqeXl5REREXP7YYDCQl5d3zc8vKSnhvvvu4+DBgzz33HPX/LxFixaxf/9+9u/fT3BwsFlrFsJWtGrEXVptmsMtwS1aS9O0/3jtetsBBwYG8tZbb3VkSULYjVaNuC8Ftyy+Ea1lMBjIycm5/HFubi7h4eE6ViSE/WhVcF9aNRkoJ9+IVkpKSiIjI4OsrCzq6+v55JNPmDp1qt5lCWEXZMQt2m3WrFkMHz6ckydPYjAYWL58OU5OTrz22mtMmjSJ2NhYZs6cSVxcnN6lCmEXpMct2u3jjz++6utTpkxhypQpFq5GCPvXquAuq65HKfB1ly1dhf5SU1NJTU2loqJC71KE0EXrWiU19fh7uODoIIcEC/2lpKSwbNkyfH199S5FCF20usft7yGjbSGEsAatDm7pbwshhHVo3XTA6gYJbiGEsBKtCu4SGXELIYTVaDG4NU0zbTAlc7iFEMIqtBjcF+oaaTJqMuIWQggr0eI8bll8I6yNzOMWnV2LI+7Ly90luIWVkHncorNrMbjLLo24pccthBBWoeURd420SoQQwpq0ulUiwS2EENahVa0SFycHPFwcLVGPEEKIFrRqxB3o6XLdY6eEEEJYTquCWxbfCCGE9WjVw0npbwshhPVocQFOWXU9Bn8PS9QiRKvIAhzR2bU44i5p7nELYS1kAY7o7K4b3A1NRirrGqXHLYQQVuS6wV12efGNnH4jhBDW4vrBXd0AQICnq0WKEUII0bLrBndJ9UUA/GXELYQQVqOVI27pcQshhLW4bnDLBlNCCGF9rh/cVc17ccusEiGEsBotzirxdnPC2bFVZwoLIYSwgOuunCyV092FFZKVk6Kzu36rRIJbWCFZOSk6u5aDW/rbQghhVVrsccshwUIIYV1aHHHLBlNCCGFdrhvcFxuNMuIWQggr0+I8P+lxCyGEdWkxuGXELYQQ1qXlEbcEtxBCWBUJbiGEsDHS4xZCCBtz3eD283DGx73F84SFEEJY0HVT+dCTN1uqDiFa7dJeJUajUe9ShNCF0jTteu9f900hhBBXpTry4rJfqxBC2BgJbiGEsDES3EIIYWMkuIUQwsZIcAshhI2R4BZCCBsjwS2EEDZGglsIIWxMS+vZO3QSuRBCiBsnI24hhLAxEtxCCGFjJLiFEMLGSHALIYSNkeAWQggbI8EthBA25v8DCeq/Z/oxYF4AAAAASUVORK5CYII=\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "bae64bf7f6374b488764ed27ec0d25b4": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "bcccf2b6c0f941898d81b362a2d4dfe1": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "bd8a3f5ec5774a0b9498f65316ecda07": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "be0a92bd99914b29a66dda0544e63360": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "children": [
        "IPY_MODEL_acabfc905b4a4f558b85b18416213c1c",
        "IPY_MODEL_79f64202756c4e329e14493983d5b9e2"
       ],
       "layout": "IPY_MODEL_d9a2a88bea9c4e6b98c71038d6edf45e"
      }
     },
     "be434862f8ae428bb317fd7fdc831461": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_ded9320a42a14ddb85e1586695058d37",
       "outputs": [
        {
         "data": {
          "image/png": 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JYMTFa122+4vXzGUN6/OoEH9caO60vSdwsi+mhwZgeliA/b3lsS7YfyKcfEIslRCNP2OvCZetYWww4pL13mAePV+6ZkSb5WusrHwEEBXij2lafyTGhOC+BB1mRwRh6hQNpocGQAkLgDZgEk86oTFjcJPX6enrx5VWI660GnHpmhEXLWFsDeZL14y2L4EdbGrQZEwL9cesqUG467YITNP6Y1poAGIs91HBmgl9RTryHAxumlA6e/pw2WA031qNuGR5fMlgCWqDEVfbBy5oFB40Gc0dPQj290OM1lxDTpkeihitv62mHKMNQLTWn1PlyGMwuEkVpJRoNZpD+a0TH+Cfn32BCw0GbH+j1BbOl1uNMHT13vDe0MBJiLaUMJKnhyA6JADTtP6I1vrb7j3tOwWJbobBTW5n6pdoau/G5VYjrrR247Khy260bB0xd/Vaz/LzAYIWwtD7X3jv6wZM0/pj5tRAfD8uHFHWMLaEc1SIPwImc6RMEwtnlZBLtRl7LfXkbtuouKHVfH+5tRtXDEY0tnfD1C+hDfCDoct8wM/PRyAqxDwajtb6Y9rgx1p/RGsDkHHf3TgzwU/AIdXirBLyPL2mfjS2WUbJlvrx5dZu873BiCtt5uXXXwsDAEL8/WyhPEcXgagQDaJD/KEL1iDaUmeOCBr+DD/OzSBvxeAmO1JKXOvstZQtjGho7baMjgeNlA3daOroxvV/rE3yFdAFmwM5IToEP5wbiWhLQFuXR4VoEDiZP3ZEY8H/QV5CSom27j40WMoWVwbdN7QNetzajR6T+ZuyJ/kK9JrM6RweNBlRIebgTY7RWh77I1qrMd+H+CMscDKvg0E0DhjcE0BHdx8a2rptc5MbrMHcZg1jczAPHNwbEKzxgy7EHL6ps8LNj4PNoayzljBCNB51mrVaTnknchUenPRgxl6TOYTbjLYRckPrwOMrbeaQbr/uDD4ACJjki6gQDXSWkXFUsMYWxtbRsi5YgyCNen93u/uUd6Kb4MHJiaarx2QrTzRYwvdKmxGNrd22kXPEFA1OVTXd8N7Jfj6IsoyKzXVkja2EERXsD50lnIM1fjy1mmiCYnA7UUd3n6VmbA7gBuvj1kEh3daNNuONI+TJvj6IDNZAF6JBXGQQ5kcHY9mcCOiCB0bIUSEaXuuCiBjcwxk4qDcwOh4YJduHs6Opbxo/H+hCNNAF+2NedDDunhOJSGvZYtB9aCADmYhGxmuDW0qJ1q4+W53YrnRhF8iOD+rZasjB5ivB3TNPZwnogTDWBfsjJIAlCyJyrgkX3P39Es2dPQMj47ZuNFqCWAI4d7HVFtI9ff03vH+Kxg+6YA0igzW4Qwk1B7DlgF6kJYyjQjSYwhoyEbmJaoLbeqaeLYhtI+VuNLYNjI6vtnejr//GyTDB/n5YOCMMGj8fLJ4RZj6IF6yx3U+EWRZqVlVVhd27d8NgMODo0aPu7g6RR3N7SllnWFiD9/rH1rBudnB9ZCHM10iODDaH7ryoYFs92TpSjpxinmXBS3K6Tk5ODgoKCqDT6VBWVmZbXlhYiG3btsFkMmHTpk3Yvn37kG3ExcXhwIEDWLt27Xh0mUjVXBLc1ktwNg4aFTdc/7itG42t3Td8iwhgvsCQuSyhgRIWiEUzw2w1Y2sg64L9MXXKZH4xqgfIzs7G1q1bsWHDBtsyk8mELVu24OTJk1AUBampqcjIyIDJZMKOHTvs3n/w4EHodLrx7jaRao0quK2X3xxcrmi0TX3rRpDGF8U1zWho7Ua3g/pxwCRfyyhYg/nRwUizzLAYXLLQBWt46rTKpKWloaamxm5ZcXEx4uPjERcXBwDIzMzEsWPHsGPHDhQUFLihl0QTx02D+38f/cIWyo3t3Whq74aD8jG0AZMQGazBwthQu/qx9WCedbYFD+h5j/r6esTGxtqeK4qC06dPD7l+U1MTdu7cibNnz2LPnj03jMqt9Ho99Ho9AKCxsdG5nSZSiZsG9wcVjebrVWj9cbuitY2OI4P9Bz1m/Zhu5OhSCjf7pT116lTs379/2HZzc3Mxbdo05Ofno6ioaEx9JFKrmwZ38c77xqsfNMEoioLa2lrb87q6OsTExDil7fT0dKSnp2PJkiVOaY9IbXhkj1wiNTUVlZWVqK6uRk9PDw4fPoyMjAyntJ2fn4/c3FxeHZC8FoObxiwrKwtLly5FRUUFFEXBgQMH4Ofnh3379mHFihVISEjAunXrkJSU5JTtpaenQ6/XQ6vVOqU9IrXhZV1JdazX4y4qKkJlZaW7u0PkiEtnYTC4SbV4PW7yYLweN9Fg/AYc8nYccZNqccRNHsylI24enCQiUhmWSkh1WCohb8dSCakWSyXkwVgqISKiAQxuIiKVYY2bVIc1bvJ2rHGTarHGTR6MNW4iIhrA4CYiUhnWuEl1WOMmb8caN6kWa9zkwVjjJiKiAQxuIiKVYXATEakMg5uISGU4q4RUh7NKyNtxVgmpFmeVkAfjrBIiIhpw0xH3ypUr5dWrV2EwGKDVase0ocbGRkRGRo6pDXIvZ/wcONNXX32FhISEcd+up+2HkfLEfrujT+OxzTNnzvy3lHKlyzYgpbzZTUop5RNPPCHHavHixWNug9zLGT8HzuSunylP2w8j5Yn9dkefxmmbw2XrmG4jKpWkp6e77BcHqQd/DszUuh88sd/u6JMn7ofRGreDkzyQRM7GnynyYBPj4GRubu54bYqIaEJjcJNHeOutt/DEE0/ggQcewIkTJ9zdHSKPxumANGY5OTnQ6XRITk62W15YWIh58+YhPj4ee/fuvWkbP/3pT/Hqq6/itddew9/+9jdXdpdI9XgCDo3Zhx9+iClTpmDDhg0oKysDAJhMJsydOxcnT56EoihITU1FXl4eTCYTduzYYff+gwcPQqfTAQB++ctfYv369Vi0aNGw22WNmzyYS2vcPOWdxiwtLQ01NTV2y4qLixEfH4+4uDgAQGZmJg4dOoTm5mb4+/vj6NGjdutLKbF9+3bcf//9Nw1tvV4PvV4PwHxugBpVVVVh9+7dMBgMN+wHT6fmvjuTu/cDSyUTVG1tLe655x4kJCQgKSkJL7300i23NVQpBBgoh/zwhz/E1atXbcvr6+sRGxtre64oCrq6unDgwAGH23j55Zfx7rvv4ujRo9i/f/+QfcnNzUVJSQlKSkpGdEKX0WjEnXfeiTvuuANJSUl49tlnh33PUEayH0ZSFoqLixtyPzhiMpmwcOFCrFmzZtR9tnJX353l2rVrWLt2LebPn4+EhAScOnXqltpR+36wGWai97g4f/68zMnJkQ8//PB4bXLCu3jxojxz5oyUUsrW1lY5Z84cee7cObt1rly5IltbW+2WVVZW3tDWP//5T3nmzBmZlJRkt7yvr0/GxcXJ8+fPy4qKCqnRaOS5c+dkaWmpXLRokYyNjZWrV6+Wq1evlvv27ZNbt26VUsox/zu//fbb8oknnpDx8fHDrtvf3y/b2tqklFL29PTIO++8U546dcpuHWfuh+7ubnn77bfb9oP181tvV65csb1vpPvh97//vczKypKrV6++4TVP77uzbNiwQb766qtSSim7u7tlS0uL3eseuB9cegLOmIN748aNMjIy8oYdcfz4cTl37lx52223yT179oykKQa3C2VkZMgTJ07YLTty5Ii85557ZFdXl5RSSr1eL++//36H76+urr7h3/hf//qX/MlPfmJ7XafTyeeff/6G16SU8vnnn7e95qx/59GeOdnR0SEXLlwoP/30U7vlztwPUtp/1psZyX6ora2VP/rRj+R7773nMLg9ue/OYjAY5KxZs2R/f/+Q63jgfnD/mZM3k52djcLCQrtlJpMJW7ZswfHjx1FeXo68vDyUl5fjyy+/xJo1a+xuDQ0NY+0CDaOmpgZnz57F9773PbvlP/vZz7By5UpkZmbir3/9Kw4ePIgjR46MuN3ryyGTJk1CfX09ACA1NRWVlZWorq5GT08PDh8+jLS0NGzevBlnz57Fnj17bvnz5OfnIzc3d8SXdTWZTFiwYAF0Oh1+/OMfu3w/KIpi2w+ONDU1jXg/PP3003jhhRfg4+P4v6on991ZqqqqEBkZiY0bN2LhwoXYtGkTOjo67Nbxhv1gZ5hkH5Hrf4Op4be4t2hra5OLFi2Sb7zxxpDrPPLIIzI4OFg2NDQMuY6jUcqRI0fk448/LjMzM2V0dLT09fWVQUFB8o9//KOUUsp33nlHzpkzR8bFxcnnnnvOOR9okNGOuFtaWuTy5cvll19+6fD1se4Hqz/96U+2stBY5OfnyyeffFJKKWVRUZHDEbeVp/XdmT777DPp6+tr+0vpqaeeks8884zDdT1oP3j2iNsR1f32mqB6e3vx8MMPY/369XjooYccrvPRRx+hrKwMDz74IHbt2jWq9hVFQW1tLfLy8nDp0iX89re/xc6dO/H4448DAFatWoVvvvkG58+fx86dO8f8eaxGO+K2Cg0NxfLly2/4CxFwzn6wqqurQ0xMzKjacOSTTz7B22+/jVmzZiEzMxPvv/8+HnvsMVX03ZkURYGiKLa/lNauXYvPP//8hvUm+n6wM0yyj8j1v8HU8Ft8ouvv75c///nP5bZt24Zc5/PPP5fz5s2T3377rTSZTDIrK0vu3LnT4bqORim9vb1y9uzZsqqqynZAp6yszKmf42ZGMuJuaGiwHcjq7OyUy5Ytk/n5+XbrqGE/DDXiVkPfnWHZsmXy66+/llJK+eyzz8pf/epXdq974H7w7IOTUjqvVELO89FHH0kAMiUlRd5xxx3yjjvukO+8847dOh9//LEsLS21Pe/p6ZF6vf6GtqylED8/Pzl9+nRbKURK15dDHBnNrJIvvvhCLliwQKakpMikpCS5a9euG9ZRw34YKrjV0HdnOHv2rFy8eLFMSUmRDzzwgGxubrZ73QP3g0uD2ylnTtbU1GDNmjW2s+b6+vowd+5cvPfee5g+fTpSU1Px+uuvIykpaex/IhBZ8MxJ8mCefXXArKwsLF26FBUVFVAUBQcOHICfnx/27duHFStWICEhAevWrWNoExE5Ca9VQqpj/Zb3oqIiVFZWurs7RI64dMTN4CbVYqmEPJhnl0qIiGh88eqApDrWUslo53ETTRQslZBqsVRCHoylEiIiGsDgJiJSGQY3EZHK8OAkqQ4PTpK344ibVCc9PR16vR5ardbdXaEJ6sUXX0RycjKSk5Pxhz/8ATU1NZg/fz42bdqE5ORkrF+/Hu+++y5+8IMfYM6cOSguLgYAdHR0ICcnB0KIz4QQZ4UQDwCAECJQCHFECFEqhPibEOK0EGKJ5bVXhBAlQohzQoiRXdZwmIuZEHms0V6Pm2gkSkpKZHJysmxvb5dtbW0yMTFRfv7559LX11eWlpZKk8kkFy1aJDdu3Cj7+/vlW2+9JR944AEppZQ7duyQf/7zn6U0z9YLBfANgCAAvwLwn5blyQD6ACyxPA+33PsC+ADA7dId1+MmIlKrjz/+GA8++CCCgoIwZcoUPPTQQ/joo48we/ZspKSkwMfHB0lJSbj33nshhEBKSgpqamoAACdOnMDevXshhPg3zCHsD2AGgGUADgOAlLIMQOmgTa4TQnwO4CyAJACJw/WRNW4iokHkEOe2aDQa22MfHx/bcx8fH/T19dne+8Ybb2DevHkLBr9XCOFwXrcQYjbMo/FUKWWLEOI1mMP+pjjiJiIaJC0tDW+99RY6OzvR0dGBv//977j77rtH9N4VK1bg5ZdftgW1EGKh5aWPAayzLEsEkGJZHgKgA4BBCBEF4P6RbIcjblIdziohV1q0aBGys7Nx5513AgA2bdqEsLCwEb33N7/5DZ5++mkAKLWEdw2ANQD+H4D/EkKUwlwSKQVgkFJWCiHOAjgHoArAJyPZDk95J9XiKe/kwexKI0IIXwCTpJRGIcRtAN4DMFdK2XMrjXPETUTkeoEAioQQk2AO9SdvNbQBBjcRkctJKdsALHFWezw4SUSkMgxuIiKVYXATEakMg5s8wldffYXNmzdj7dq1eOWVV9zdHSKPxuCmMcvJyYFOp0NycrLd8sLCQsybNw/x8fHYu3fvTdtISEjA/v37ceTIEU7xIxoGg5vGLDs7G4WFhXbLTCYTtmzZguPHjzMQNp4AAASRSURBVKO8vBx5eXkoLy/Hl19+iTVr1tjdGhoaAABvv/02li1bhnvvvdcdH4NINTgdkMYsLS3NdpEdq+LiYsTHxyMuLg4AkJmZiWPHjmHHjh0oKChw2E5GRgYyMjKwevVqPProo67uNpFqMbjJJerr6xEbG2t7rigKTp8+PeT6H3zwAd588010d3dj1apVQ66n1+uh1+sBAI2Njc7rMJGKMLjJJRxdSmGIC6QBAJYvX47ly5cP225ubi6mTZuG/Px8FBUVjaWLRKrFGje5hKIoqK2ttT2vq6tDTEyMU9rmN+CQt2Nwk0ukpqaisrIS1dXV6OnpweHDh5GRkeGUtvPz85Gbm8urA5LXYnDTmGVlZWHp0qWoqKiAoig4cOAA/Pz8sG/fPqxYsQIJCQlYt24dkpKSnLI9jrjJ2/GyrqQ61utxFxUVobKy0t3dIXJk6AM6zmicwU1qxetxkwdzaXCzVEJEpDKcDkiqw68uI2/HUgmpFksl5MFYKiEiogEslZDqsFRC3o6lElItlkrIg7FUQkREA1gqIdVhqYS8HUslpFoslZAHY6mEiIgGMLiJiFSGNW5SHda4yduxxk2qxRo3eTDWuImIaACDm4hIZRjcREQqw+AmIlIZBjcRkcpwOiCpDqcDkrfjdEBSLU4HJA/G6YBERDSAwU1EpDIMbiIilWFwExGpDIObiEhlGNzkMTo6OrB48WIUFBS4uytEHo3BTWOWk5MDnU6H5ORku+WFhYWYN28e4uPjsXfv3mHb+d3vfod169a5qptEEwZPwKExy87OxtatW7FhwwbbMpPJhC1btuDkyZNQFAWpqanIyMiAyWTCjh077N5/8OBBlJaWIjExEUajcby7T6Q6DG4as7S0NNTU1NgtKy4uRnx8POLi4gAAmZmZOHbsGHbs2OGwFFJUVISOjg6Ul5cjICAAq1atgo/PjX8Q6vV66PV6AEBjY6PzPwyRCjC4ySXq6+sRGxtre64oCk6fPj3k+rt37wYAvPbaa4iIiHAY2gCQm5uL3NxcAOYzJ4m8EYObXMLRpRSEGP4s4Ozs7GHX4bVKyNvx4CS5hKIoqK2ttT2vq6tDTEyMU9pOT0+HXq+HVqt1SntEasPgJpdITU1FZWUlqqur0dPTg8OHDyMjI8Mpbefn5yM3N5cjbvJaDG4as6ysLCxduhQVFRVQFAUHDhyAn58f9u3bhxUrViAhIQHr1q1DUlKSU7bHETd5O17WlVTHWuMuKipCZWWlu7tD5IhLL+vK4CbV4vW4yYO5NLg5q4RUh7NKyNtxxE2qxRE3eTB+Aw4REQ1gqYRUh6US8nYslZBqsVRCHoylEiIiGsDgJiJSGda4SXVY4yZvxxo3qRZr3OTBWOMmIqIBDG4iIpVhjZtUhzVu8nascZNqrVy5EoWFhe7uBpEjvDogEZHK8OAkERENYHATEakMg5uISGUY3KQ61i8Lzs/Pd3dXiNyCByeJiJyPByeJiGgAg5uISGUY3EREKjPcKe8urdMQEdHoccRNRKQyDG4iIpVhcBMRqQyDm4hIZRjcREQqw+AmIlKZ/w8pomtTxfVb/gAAAABJRU5ErkJggg==\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "c0577be10b974d319bcb8f7d30bee57e": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "c1dc7b072269454fbd62325aa65829de": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatSliderModel",
      "state": {
       "description": "si",
       "layout": "IPY_MODEL_35c717c7d85943cb80a3aa1d6131f604",
       "max": 2,
       "min": 0.1,
       "step": 0.1,
       "style": "IPY_MODEL_5e77a434e72b4fdc92bed08e3a9bc6d2",
       "value": 1
      }
     },
     "c3e23d07643e450788b9539d0c4da859": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "c4575962bc2f4019ab342ba37ebf87ae": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "c4da1c5a90d14ebb9874b230f9547417": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "c58883a1a99145d6ad240f4cc29714c3": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatSliderModel",
      "state": {
       "description": "ci",
       "layout": "IPY_MODEL_2073816beed84a3098265586a3bebc9e",
       "max": 1,
       "min": 0.1,
       "step": 0.1,
       "style": "IPY_MODEL_94cf522a905443019f390532d533a316",
       "value": 0.1
      }
     },
     "c641a1052d914bea82d1acaf16cb6ff0": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_c8df0ad1f05e423ab7e38bed4061cb03",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "c7e0ad714da4447981c859a36e18f354": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_8d7644619bc9438b8ade0286a32ca3f4",
       "outputs": [
        {
         "name": "stderr",
         "output_type": "stream",
         "text": "/home/sarradj/anaconda3/envs/sbs/lib/python3.7/site-packages/sympy/plotting/plot.py:1025: UserWarning: Attempted to set non-positive left xlim on a log-scaled axis.\nInvalid limit will be ignored.\n  self.ax.set_xlim(min(starts), max(ends))\n"
        },
        {
         "data": {
          "image/png": 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2aMYtIuIcvTkpIuIYBbeIiGMU3CIijlFwi4g4RsEtIuIYBbeIiGP+P+scRZuRVHdQAAAAAElFTkSuQmCC\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
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        {
         "ename": "ValueError",
         "evalue": "Image size of 374304x28163 pixels is too large. It must be less than 2^16 in each direction.",
         "output_type": "error",
         "traceback": [
          "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
          "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
          "\u001b[0;32m~/anaconda3/envs/sbs/lib/python3.7/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    339\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 341\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    342\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;32m~/anaconda3/envs/sbs/lib/python3.7/site-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    243\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 244\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    245\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;32m~/anaconda3/envs/sbs/lib/python3.7/site-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    127\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 128\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    129\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;32m~/anaconda3/envs/sbs/lib/python3.7/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m   2080\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2081\u001b[0m                     \u001b[0mbbox_inches_restore\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_bbox_inches_restore\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2082\u001b[0;31m                     **kwargs)\n\u001b[0m\u001b[1;32m   2083\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2084\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mrestore_bbox\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;32m~/anaconda3/envs/sbs/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, metadata, pil_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    525\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    526\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 527\u001b[0;31m             \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    528\u001b[0m             \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    529\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;32m~/anaconda3/envs/sbs/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    384\u001b[0m         \u001b[0mDraw\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mfigure\u001b[0m \u001b[0musing\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    385\u001b[0m         \"\"\"\n\u001b[0;32m--> 386\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcleared\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    387\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mRendererAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    388\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;32m~/anaconda3/envs/sbs/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mget_renderer\u001b[0;34m(self, cleared)\u001b[0m\n\u001b[1;32m    397\u001b[0m                           and getattr(self, \"_lastKey\", None) == key)\n\u001b[1;32m    398\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mreuse_renderer\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 399\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRendererAgg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    400\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lastKey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    401\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mcleared\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;32m~/anaconda3/envs/sbs/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, width, height, dpi)\u001b[0m\n\u001b[1;32m     84\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwidth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 86\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_renderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_RendererAgg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mheight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     87\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filter_renderers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;31mValueError\u001b[0m: Image size of 374304x28163 pixels is too large. It must be less than 2^16 in each direction."
         ]
        },
        {
         "data": {
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "d61f08e7280f480daa8338f6ebcca5ef": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "d691bd3f9900424c995f87a8489fc1f5": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_337f9842e0b4416690674efdcea2410a",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "d6f4308f05854b0da77c95bd744dcc2a": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatSliderModel",
      "state": {
       "description": "d",
       "layout": "IPY_MODEL_132977e9dbed449bbd4306bfc19dbdd0",
       "max": 0.3,
       "min": 0.01,
       "step": 0.01,
       "style": "IPY_MODEL_1e9ae2ee402e4b4baff8eae6917da497",
       "value": 0.1
      }
     },
     "d7e36b3fdc8248e8a20f6ccedf43db9c": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "d8fc10198a40470fba9c9946e20a5065": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "d91d57fbf03b4fc199ae8f9a551ab0de": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "d9a2a88bea9c4e6b98c71038d6edf45e": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "d9b3d6f5496e419aaf3b10384ef22947": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "children": [
        "IPY_MODEL_23b0ba6f911349db8b54c5b52ca45209",
        "IPY_MODEL_b68dde8d92924693a46033339dc32845"
       ],
       "layout": "IPY_MODEL_a53ae03e9553412aa22f84cc39d5d729"
      }
     },
     "dc582efac2284b5ebc88d87a2d80c6d1": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatSliderModel",
      "state": {
       "description": "c",
       "layout": "IPY_MODEL_3c812c78c694437b8a6d7ec132eb87bf",
       "max": 0.3,
       "min": 0.01,
       "step": 0.01,
       "style": "IPY_MODEL_e468a6e4dd25468b8235a8cfb5968406",
       "value": 0.1
      }
     },
     "dd11f8269131451eacb29de0197b23c0": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_2a0478a577914178b9f5ece13dd5b676",
       "outputs": [
        {
         "ename": "NameError",
         "evalue": "name 'omega' is not defined",
         "output_type": "error",
         "traceback": [
          "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
          "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
          "\u001b[0;32m~/anaconda3/envs/sbs/lib/python3.7/site-packages/ipywidgets/widgets/interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    249\u001b[0m                     \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    250\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 251\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    252\u001b[0m                 \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    253\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;32m<ipython-input-63-3d0ae4d756c5>\u001b[0m in \u001b[0;36mplot_mobilty\u001b[0;34m(c)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mplot_mobilty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     sy.plot(Y.subs({m:1,s:1,c:0.1}),\n\u001b[0;32m----> 3\u001b[0;31m             \u001b[0;34m(\u001b[0m\u001b[0momega\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m             \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mr'$c$=%.2f'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m            )\n",
          "\u001b[0;31mNameError\u001b[0m: name 'omega' is not defined"
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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "ec40dc75c229479bbf2b45b343efc676": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "ed6ffc50921c4fafb1eb1393a6b65a48": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatSliderModel",
      "state": {
       "description": "c_",
       "layout": "IPY_MODEL_8cd6ef30b32b4146bca9a19c80704551",
       "max": 0.30000000000000004,
       "min": -0.1,
       "step": 0.1,
       "style": "IPY_MODEL_8eeebeb2167044c6bca5cc988e13a7dd",
       "value": 0.3
      }
     },
     "eda6603698f740d8a15c7f4c9d88d7cb": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "edb347a07fde4e71a514114db0205556": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "ee665ab0f3ca4f239326dddd95fcc499": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "efdde183f0044c2fbfce3a7096c7a86c": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_e0a1c1155aab40faaaa77f511b821615",
       "outputs": [
        {
         "ename": "ValueError",
         "evalue": "The same variable should be used in all univariate expressions being plotted.",
         "output_type": "error",
         "traceback": [
          "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
          "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
          "\u001b[0;32m~/anaconda3/envs/sbs/lib/python3.7/site-packages/ipywidgets/widgets/interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    249\u001b[0m                     \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    250\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 251\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    252\u001b[0m                 \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    253\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;32m<ipython-input-64-3fcbfeabd303>\u001b[0m in \u001b[0;36mplot_mobilty\u001b[0;34m(c)\u001b[0m\n\u001b[1;32m      2\u001b[0m     sy.plot(Y.subs({m:1,s:1,c:0.1}),\n\u001b[1;32m      3\u001b[0m             \u001b[0;34m(\u001b[0m\u001b[0mom\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m             \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mr'$c$=%.2f'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m            )\n\u001b[1;32m      6\u001b[0m \u001b[0mipw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minteract\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplot_mobilty\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;32m~/anaconda3/envs/sbs/lib/python3.7/site-packages/sympy/plotting/plot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1351\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfree\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1352\u001b[0m                 raise ValueError(\n\u001b[0;32m-> 1353\u001b[0;31m                     \u001b[0;34m'The same variable should be used in all '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1354\u001b[0m                     'univariate expressions being plotted.')\n\u001b[1;32m   1355\u001b[0m     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfree\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mSymbol\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'x'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;31mValueError\u001b[0m: The same variable should be used in all univariate expressions being plotted."
         ]
        }
       ]
      }
     },
     "f05d963152e442bfb7705211871f3b82": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "f09e0bc089c744ba8873332aed421685": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_1e0c0c24e8414fafae2e12ca195b91dc",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "f1b071edaf714777aeb7444225e79ad1": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatSliderModel",
      "state": {
       "description": "mi",
       "layout": "IPY_MODEL_d3e10c8273764874bd470cdcfa41bb58",
       "max": 2,
       "min": 0.1,
       "step": 0.1,
       "style": "IPY_MODEL_58aac359d5c04310811059eaa0242702",
       "value": 1
      }
     },
     "f2e1920ca8754a3490d2eac42dde1bc7": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_888c1125f03e44a4a2014a35b0e9dd95",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     }
    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}