auxiliary_simulations.ipynb

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    "## Auxiliary Simulations\n",
    "\n",
    "Here we run two additional simulations to show that direct manipulation of either measurement noise (R) or process noise (Q) can trigger position resets. A decrease in measurement noise corresponds to a 'sharpening' of incoming sensory information, while an increase in process noise corresponds to an increase in the uncertainty of internally generated predictions due to additional untracked influences. The main model is agnostic to which of these possibilities (or some alternative mechanism) is triggering a gain change. However, we include these as proof-of-principle simulations of these two accounts. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "deb21c50-dedc-4436-b1b8-4496e9c82e4f",
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   "outputs": [],
   "source": [
    "## set up functions and parameters \n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings \n",
    "\n",
    "# suppress divide by zero warnings which we can safely ignore. \n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
    "\n",
    "def cart2pol(x, y):\n",
    "    return np.sqrt(x**2 + y**2), np.arctan2(y, x)\n",
    "\n",
    "# motion params taken from: https://jov.arvojournals.org/article.aspx?articleid=2765454\n",
    "object_speed_x = 3.35\n",
    "object_speed_y = -5.80\n",
    "pattern_speed_x = -5.196\n",
    "pattern_speed_y = -2.999\n",
    "\n",
    "# specify sampling rate and time window (s)\n",
    "sampling_rate = 500; \n",
    "delta = 1/sampling_rate \n",
    "time = 1.8 \n",
    "total_samples = time*sampling_rate\n",
    "\n",
    "# slow speed priors \n",
    "alpha = 0.9 \n",
    "beta = 0.5 \n",
    "\n",
    "# specify state error (SDs) \n",
    "# used to make the process noise covariance matrix (Q)\n",
    "# and the state covariance matrix (P)\n",
    "position_p = 1e-17 \n",
    "object_speed_p = 0.9 \n",
    "pattern_speed_p = 0.9 \n",
    "\n",
    "# specify the measurement error (SDs)\n",
    "# used to make the measurement covariance matrix (R)\n",
    "position_sigma = 4 \n",
    "object_speed_sigma = 4 \n",
    "pattern_speed_sigma = 4 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "280adc68-d923-4521-b3a7-e31f4763b781",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define main simulation\n",
    "# input can be set to 'R' or 'Q' depending on which noise parameter we want to modulate. \n",
    "\n",
    "def run_sim(manipulation):\n",
    "\n",
    "    # system matrix\n",
    "    A = np.array([[1, delta, 0], [0, alpha, 0], [0, 0, beta]])\n",
    "    A = np.block([[A, np.zeros_like(A)], [np.zeros_like(A), A]]) # make system matrix 2-D (x AND y)\n",
    "    \n",
    "    # observation matrix \n",
    "    H = np.array([[1, 0, 0], [0, 1, 0], [0, 1, 1]])\n",
    "    H = np.block([[H, np.zeros_like(H)], [np.zeros_like(H), H]]) # make observation matrix 2-D\n",
    "    \n",
    "    # process noise covariance matrix\n",
    "    Q = np.diag([position_p, object_speed_p, pattern_speed_p])**2\n",
    "    Q = np.block([[Q, np.zeros_like(Q)], [np.zeros_like(Q), Q]]) # make process noise covariance matrix 2-D\n",
    "    Q_orig = Q.copy()\n",
    "    \n",
    "    # measurement noise covariance matrix \n",
    "    R = np.diag([position_sigma, object_speed_sigma, pattern_speed_sigma])**2\n",
    "    \n",
    "    # implement anisotropic motion noise (see Kwon et al. supplement)\n",
    "    R_perpen = np.copy(R)\n",
    "    R_perpen[2,2] = 0.125 * R[2,2] # this can only effect pattern motion \n",
    "    R_fixed_2D = np.block([[R, np.zeros_like(R)], [np.zeros_like(R), R_perpen]]) # make measurement noise covariance matrix 2-D\n",
    "    \n",
    "    # specify inputs\n",
    "    object_pos_x_series = (np.arange(1, total_samples + 1) - 1) * (object_speed_x / sampling_rate)\n",
    "    object_pos_y_series = (np.arange(1, total_samples + 1) - 1) * (object_speed_y / sampling_rate)\n",
    "    input_hat = np.vstack((object_pos_x_series, np.repeat(object_speed_x, total_samples), np.repeat(object_speed_x + pattern_speed_x, total_samples),\n",
    "                           object_pos_y_series, np.repeat(object_speed_y, total_samples), np.repeat(object_speed_y + pattern_speed_y, total_samples)))\n",
    "    \n",
    "    # set up lognormal distribution (used as modulation function)\n",
    "    mu, sigma, steps = -0.5, 0.25, 200\n",
    "    time_steps = np.linspace(0.0001, 2.5, steps) # note endpoint of timesteps is 2.5 for scaling purposes but is otherwise meaningless... each timestep can be thought of as a ms (+ added small constant at start to avoid divide by zero warning).\n",
    "    pdf = (1 / (time_steps * sigma * np.sqrt(2 * np.pi))) * np.exp(-(np.log(time_steps) - mu)**2 / (2 * sigma**2))\n",
    "    pdf[np.isnan(pdf)] = 0\n",
    "\n",
    "    fig, ax = plt.subplots(1,2,figsize=(15,4), dpi = 300)\n",
    "\n",
    "    for flash in [0,1]:\n",
    "    \n",
    "        # set up number of loops (1 per modululation) and colors \n",
    "        if manipulation == 'R': \n",
    "            loops = [0] if flash == 0 else [5.8, 5.89, 5.92, 6.04]\n",
    "        \n",
    "        elif manipulation == 'Q': \n",
    "            loops = [0] if flash == 0 else [0.0005, 0.0015, 0.005, 0.015]\n",
    "    \n",
    "        colors = plt.cm.Blues([255]) if flash == 0 else plt.cm.Oranges([100,150,200,250])\n",
    "\n",
    "        for x, loop in enumerate(loops):\n",
    "        \n",
    "            modulation = loop * pdf # scale the pdf for to get the modulation function\n",
    "        \n",
    "            # set up empty matrices for measurements...\n",
    "            X_hat_minus = np.array(np.zeros((np.shape(A)[0], int(total_samples))))\n",
    "            X_hat_plus = np.array(np.zeros((np.shape(A)[0], int(total_samples))))\n",
    "        \n",
    "            # ... and for covariances\n",
    "            P_minus = np.zeros((np.shape(A)[0], np.shape(A)[0], int(total_samples)))\n",
    "            P_plus = np.zeros((np.shape(A)[0], np.shape(A)[0], int(total_samples)))\n",
    "\n",
    "            # initialise errors\n",
    "            initial_P_plus = np.array(np.diag(np.diag(Q)/(1-np.diag(A)**2)))\n",
    "            initial_P_plus[initial_P_plus == float('+inf')] = 1e-15 \n",
    "            P_plus[:, :, 0] = initial_P_plus\n",
    "        \n",
    "            Rotation_m = np.array(np.eye(6))\n",
    "\n",
    "            # loop through timesteps\n",
    "            for i in np.arange(1, int(total_samples)):\n",
    "                        \n",
    "                Z = input_hat[:,i]    \n",
    "  \n",
    "                theta = cart2pol(Z[2], Z[5]) # get x and y speed for pattern (polar)\n",
    "                theta_o = cart2pol(Z[1], Z[4]) # get x and y speed for object (polar)\n",
    "\n",
    "                Rotation_m[2,2] = np.cos(theta[1])\n",
    "                Rotation_m[2,5] = -np.sin(theta[1])\n",
    "                Rotation_m[5,2] = np.sin(theta[1])\n",
    "                Rotation_m[5,5] = np.cos(theta[1])\n",
    "\n",
    "                Rotation_m[1,1] = np.cos(theta_o[1])\n",
    "                Rotation_m[1,4] = -np.sin(theta_o[1])\n",
    "                Rotation_m[4,1] = np.sin(theta_o[1])\n",
    "                Rotation_m[4,4] = np.cos(theta_o[1])\n",
    "  \n",
    "                R = np.dot(Rotation_m.dot(R_fixed_2D.copy()), Rotation_m.T)\n",
    "                Q = Q_orig.copy() \n",
    "            \n",
    "                ## Modulation\n",
    "                if i >= 450 and i < 450 + len(time_steps) and loop != 0:\n",
    "\n",
    "                    if manipulation == 'R': \n",
    "                        R_temp = R_fixed_2D.copy() - np.diag(np.repeat(modulation[i-450], 6)) # decrease (sharpen) measurement noise\n",
    "                        R_temp[R_temp < 0] = 0.01 # rectify to a small constant to avoid zero and negative covariance\n",
    "                        R = np.dot(Rotation_m.dot(R_temp), Rotation_m.T) \n",
    "                    \n",
    "                    elif manipulation == 'Q': \n",
    "                        Q = Q_orig.copy() + np.diag(np.repeat(modulation[i-450], 6)) # increase process noise  \n",
    "\n",
    "                ## PREDICT \n",
    "                # see eq. 7: https://www.bzarg.com/p/how-a-kalman-filter-works-in-pictures/\n",
    "                X_hat_minus[:,i] = A.dot(X_hat_plus[:,i-1])\n",
    "                P_minus[:,:,i] = A.dot(P_plus[:,:,i-1]).dot(A.T) + Q\n",
    "\n",
    "                ## UPDATE ## \n",
    "                # see eqs. 18 and 19: https://www.bzarg.com/p/how-a-kalman-filter-works-in-pictures/\n",
    "                K = np.dot(np.dot(P_minus[:,:,i], H.T), np.linalg.inv((np.dot(np.dot(H, P_minus[:,:,i]), H.T) + R)))\n",
    "    \n",
    "                # Update means and covariances\n",
    "                X_hat_plus[:,i] = X_hat_minus[:,i] + np.dot(K, (Z - np.dot(H, X_hat_minus[:,i])))\n",
    "                P_plus[:,:,i] = P_minus[:,:,i] - np.dot(K, np.dot(H, P_minus[:,:,i]))   \n",
    "                \n",
    "            # using approximate x and y offsets to position the simulated stimulus like Nakayama & Holcombe \n",
    "            # (unfortunately they don't give enough detail to perfectly replicate)\n",
    "            xoffset = 5\n",
    "            yoffset = 10\n",
    "            \n",
    "            # plot one stimulus\n",
    "            ax[flash].plot(xoffset + object_pos_x_series, yoffset + object_pos_y_series, linewidth = 3, color = 'k', label=\"actual trajectory\")\n",
    "            ax[flash].scatter(xoffset + np.array(X_hat_plus[0,:]).flatten(), yoffset + np.array(X_hat_plus[3,:]).flatten(), s = 0.1, color = colors[x], label = \"perceived trajectory\")    \n",
    "            \n",
    "            # plot the other stimulus (just mirror things)\n",
    "            ax[flash].plot(-xoffset - object_pos_x_series, yoffset + object_pos_y_series, linewidth = 3, color = 'k', label=\"actual trajectory\")\n",
    "            ax[flash].scatter(-xoffset - np.array(X_hat_plus[0,:]).flatten(), yoffset + np.array(X_hat_plus[3,:]).flatten(), s = 0.1, color = colors[x], label = \"perceived trajectory\")    \n",
    "        \n",
    "        # specify plot aesthetics \n",
    "        if flash == 1:\n",
    "            ax[flash].set_title(manipulation + \" Modulation\", fontsize = 15, fontweight = 'bold')  \n",
    "            ax[flash].spines.left.set_visible(False)\n",
    "            ax[flash].set_yticks([])\n",
    "    \n",
    "        else:\n",
    "            ax[flash].set_title(\"No Modulation\", fontsize = 15, fontweight = 'bold')\n",
    "            ax[flash].set_ylabel('y position (dva)', fontsize = 15, fontweight = 'bold')\n",
    "            ax[flash].set_yticks([-15, 0, 15])\n",
    "    \n",
    "        ax[flash].spines.bottom.set_bounds(-19, 19)\n",
    "        ax[flash].set_xlim(-20, 19)\n",
    "        ax[flash].set_xticks([-19,0,19])\n",
    "        ax[flash].set_xlabel('x position (dva)', fontsize = 15, fontweight = 'bold')     \n",
    "    \n",
    "        ax[flash].spines.left.set_bounds(-15, 15)\n",
    "        ax[flash].set_ylim(-16, 15)\n",
    "    \n",
    "        ax[flash].spines.right.set_visible(False)\n",
    "        ax[flash].spines.top.set_visible(False)\n",
    "        ax[flash].tick_params(axis='both', which='major', labelsize=15)\n",
    "        "
   ]
  },
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   "execution_count": 6,
   "id": "90ba3dca-ff5f-4932-96b7-3a9596b8eb9c",
   "metadata": {},
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RozjvvPPKrLvlllti+fLllTARAAAAAAAAVD9yWyqS3BYAoPLYwAsAAADADwYNGhRfffVVqTX16tWLCy+8sJImoqa5+OKLo169eqXWLFiwIAYPHlxJEwEAAAAAAED1IrelosltAQAqhw28AAAAAERExPLly+OOO+4os+6EE06INm3aVMJE1ERt2rSJ448/vsy622+/3dOcAQAAAAAAqHXktlQGuS0AQOWwgRcAAACAiIgYOnRofPPNN2XWnXnmmZUwDTVZknvo66+/jj//+c+VMA0AAAAAAABUH3JbKovcFgCg4tnACwAAAAnMmDEjMplMoo/q7tFHH038uRxwwAFVPS6VpKioKO65554y69q1axc//vGPK2EiarKDDz442rZtW2bdoEGDNoqvqwAAAAAAQOWQ21LTyW2pTHJbAICKZwMvAAAAAPHSSy/FZ599VmbdGWecEfn5XlKifPLz8+OMM84os27KlCnxyiuvVMJEAAAAAAAAUPXktlQmuS0AQMXzXTsAAAAA8cADDySqO+mkkyp4EmqLE088MVHdgw8+WMGTAAAAAAAAQPUgt6WyyW0BACpW3aoeAAAAoLaaPn16/PWvf41Ro0bFf/7zn5g/f34sX748CgoKonXr1rHDDjvEgQceGCeeeGK0bt26UmZas2ZNfPTRR/Hxxx/H5MmT48svv4yFCxdGYWFhrFu3Lho2bBhNmzaNNm3aRIcOHWKnnXaKPfbYI1q0aFHhs82ZMyfeeOONmDRpUkyePDmmTZsWS5YsiWXLlsXy5cujqKgoGjVqFI0aNYrNNtss2rVrF23bto0OHTpE9+7do3v37tGhQ4cKn5PSLVmyJEaPHh0ff/xxTJs2Lb788stYvHhxFBYWRkREQUFBNG/ePNq2bRs/+tGPYuedd47ddtstGjduXMWTb2jatGnx+uuvx7///e+YPn16LFmyJOrXrx9bbrlltGzZMnr16hWHHnpopfz7KK8FCxbE66+/XmZdx44do0ePHhUyw5dffhnPPfdcvPXWWzFx4sSYP39+FBYWRpMmTaJVq1bRtWvX6N27dxx33HHRtm3bCpmhpliyZElMmjQpJk2aFFOnTo3Zs2fHnDlzYs6cOT/8e1u5cmWsXr066tWrFw0bNozNNtssWrRoEZ06dYouXbrE7rvvHr169YpmzZpV2Jw9e/aM9u3bx8yZM0ute+WVV2LRokWx+eabV9gsAAAAAADUXnLb7Mhtawa5bfUkt61Z5LYAAERERAYAACALn3/+eWaTTTbJRESij5tvvjnrNT7++ONM/fr1E6/x4IMPVsBnWrr+/fsnnm/kyJHrXTtp0qTM0UcfncnPz090fd26dTM/+clPMrNmzaqQz2XdunWZl19+OXPSSSdlmjVrlvjz+v4jLy8vs8suu2Ruu+22zIIFC1KdbdWqVZkhQ4Zkdt9990xeXl7Ws/3vR7NmzTKHHHJI5qabbsq88847mVWrViWepX379onXSaNHeT/233//Eucoz/2bi2XLlmUeeuihTO/evTP16tXL+nNp0KBB5pBDDskMGzYsU1hYWO55hg4dmnjt6667boPr/9//+3+ZXr16Jf73ceSRR2bGjx9f7rkr0t13353o87nssstSX3v69OmZU089NVOnTp3EXxNPOeWUzOeff75en2z+Xvv371/sLCtWrMg0b948UY8WLVpk1qxZU67P/Z577kk8829+85sS+0yaNClz7733Zk477bRMhw4dUvs6Uq9evcxhhx2WeeaZZzJr164t1+dakp/97GeJZhkyZEiFrA8AAAAAsLGS235Hbrt+LiW3Xf8jjR7l/ZDblkxuuyG57Xfktut/yG0BADZu+QEAAJCFjh07xsMPP5y4/rrrrotPPvkkcf3KlSvjtNNOi9WrVyeq79evXwwcODBx/6qUyWTi1ltvjR49esQLL7wQRUVFia5bu3ZtDBs2LHbYYYcYNmxYqjM99dRT0aVLlzjyyCPjmWeeiSVLlmTdI5PJxNixY+OXv/xltGvXLn72s5/FwoULyz3bSy+9FF26dImLLrooPvzww8hkMuXuuWTJkhgxYkRcc801se+++8all15a7p6UrLCwMG6++eZo27ZtDBw4MEaOHBlr1qzJus+qVatixIgR8ZOf/CTat28fgwYNyqlPec2YMSMOOOCAOP744+O9995LdE0mk4mXX345evToEddff30FT5i74cOHJ6o78sgjU1136NCh0a1bt3jiiSdi3bp1ia5Zu3ZtPPXUU9G1a9f405/+lOo8BQUFcdpppyWqXbBgQYwYMaJc6yX9mp6fnx9nn312iefPOeecuOCCC+Lxxx+PGTNmlGum/7ZmzZp47bXX4qSTToquXbvGa6+9llrv7x111FGJ6pLeowAAAAAAtYXcNndy2+zIbTd+clu5bVnktnJbuS0AQMWxgRcAAMjaCSecEBdccEGi2tWrV8fpp58eq1atSlT/y1/+MiZNmpSodtttt437778/UW1VW7duXZx++ulx5ZVX5hxgLV26NM4444y47rrryj3P7Nmz49BDD42+ffvGp59+Wu5+31u1alXcc889sf3228dzzz2Xc58hQ4bEMcccE9OnT09ttuKkES5TvPfffz+6d+8eV199dXz77bep9V2wYEFceumlsdtuu8X48eNT61uWESNGxE477RRvv/12TtcXFRXFddddFxdffHHKk5XfggULYvTo0WXW1a9fP3r16pXauldddVWcddZZsXz58pyuX7lyZVxwwQVx0UUXpfpv+ZxzzklcW54350yZMiXGjBmTqPbQQw+NrbfeOue10jB16tQ4/PDD45e//GWqf9577bVX1KtXr8y6Dz74IL7++uvU1gUAAAAAqAnkttmT22ZHbrvxk9uuT267Ibmt3DZCbgsAUJFs4AUAAHJy5513xk477ZSodsKECXHNNdeUWTdixIgYMmRIop4NGzaMp59+Opo0aZKoviplMpk444wzUnsK5fXXXx8333xzztePHj06dt1113I/gbQ0CxcujBNPPDF+/etfZx0YPP/883HxxRcnftI11c/QoUNj//33j88++6zC1vjkk09ir732ir/97W8Vtsb3nnjiiTjyyCNj8eLF5e41ZMiQGDp0aApTpef1119P9O90t912i4KCglTW/N3vfhe33HJLKr3++Mc/xiWXXJJKr4iIHj16xG677Zao9m9/+1ssXbo0p3WyCZGzCacr2u23357qU/AbNWoUu+66a5l169ati9dffz21dQEAAAAAagq5bXJyW7ltbSO3LZnc9jtyW7nt9+S2AAAVxwZeAAAgJw0aNMgqiL3rrrtKfQLqokWLYsCAAYlDw7vuuit69OiRqLaq3XTTTamFwN+7+uqr4+WXX876utGjR8dBBx0U8+bNS3Wektx4443xi1/8InH94sWL48ILL6zAiaho9913X5x11lk5P7E8G8uXL48TTjghnn/++Qpb44033ogzzjgj1q5dm1rPyy67LObMmZNav/J67bXXEtXtt99+qaw3YsSI+O1vf5tKr+8NGTIkHnzwwdT6JQ1eCwsL49lnn826fyaTiccffzxRbatWraJPnz5Zr1GRBg8enOr/1/bff/9Eda+++mpqawIAAAAA1BRy2+TktnLb2kRuWza5rdxWbrs+uS0AQMWwgRcAAMhZ586d47777ktUW1RUFP37948lS5YUe/68886LuXPnJup18sknx09/+tPEc1a1v//97xXS98wzz4yvv/46cf2XX34ZRxxxRM5PHc3VnXfemfgJ3U8++WTi+4Dq55VXXqn0IH/dunVx6qmnxtixYyuk/3vvvZdqCBzx3RseBg0alGrP8njnnXcS1e28887lXmvp0qUV9lTi9957L7Vep556auI3Oj322GNZ93/33XdjxowZiWrPPPPMqFu3btZrVLTLLrsstf+f9OzZM1Hdu+++m8p6AAAAAAA1jdw2Gbmt3La2kNsmI7eV28pt1ye3BQCoGDbwAgAA5XLaaafFWWedlah25syZcckll2xwfOjQofHcc88l6rHNNtuk+rTOqpKXlxdbbrllbL/99tGiRYvIy8vLusdXX30V11xzTaLaTCYT/fr1i4ULF2a9Thp+8YtfxLhx48qsS3of/LdmzZpF586do1u3btGpU6fYfPPNq2VoUtPNnz8/+vfvH0VFRZW+9qpVq+KUU06JFStWVPrauRo6dGilPO26LAsWLIgvvvgiUe2OO+5Y7vWuvPLKmDVrVtbX/ffXzC233DKnr5nZaNKkSfTt2zdR7ahRo+LLL7/Mqn/S8DgvLy8GDhyYVe+SNG3aNFq1ahU/+tGPYscdd4xtt9022rRpE3Xq1Mmp34IFC+L+++9PZbbu3bsnqps+fXosWrQolTUBAAAAAGoauW1u5LbFk9tuvOS22ZHbZkduW35yWwCA2scGXgAAoNwGDx4cXbt2TVT75z//OZ5//vkf/vvzzz+PSy+9NNG1DRo0iKeffjqaNWuW05zVQdOmTeOWW26JWbNmxYIFC2LSpEkxf/78mDNnTtx5553RvHnzrPo9/PDDiQKIv/zlL4mf1vq9Fi1axG9/+9sYO3ZsLFq0KAoLC2P69Onx2GOPRe/evbPqtWrVqmLfBPC/Pvzww0T9OnbsGA8++GDMnj07Fi9eHFOnTo3x48fHtGnTYuHChbF69eqYNm1avPjii3HLLbdEnz59qsV907Vr1+jRo8d6H+3atUt8fcuWLTe4vqSPH/3oRxX4mWzoyiuvzPqNBt27d48hQ4bElClTYunSpbFkyZKYNGlS3HnnndG5c+esek2bNi1uvfXWrK7Jxfbbbx+DBw+OqVOnxvLly2PhwoXx2muvxcEHH5xVnwULFsTIkSMraMrkxowZk6iuoKAg67+T/7VgwYJ46KGHsrqmUaNGcdNNN8UXX3zxw9fMBQsWxKxZs+L3v/99NG7cuFwzlSbpE6eLiori8ccfT9x39erV8cwzzySqPeigg6JTp06Je0d8Fx737Nkzrrjiihg+fHiMGTMmlixZEkuWLIm5c+fGp59+GuPGjYspU6bE7NmzY9WqVTFlypR46KGHYr/99stqrbTemNW5c+do2LBhotqk9ywAAAAAQG0kt01Obls6uW3p5LYlk9vmRm5bMrlt2eS2AADVXAYAACAFEyZMyDRq1CgTEWV+bLHFFpl58+Zl1q5dm+nVq1eiayIiM3jw4Kr+NH/Qv3//xHN//9GxY8fMjBkzSu07e/bszLbbbptV36uuuqrUnmvXrs106NAhq55HHnlkZtGiRaX2ffDBBzN16tTJqu8bb7xRYr/CwsJEPdq2bVvmbCX9Obz//vuZa6+9NtOpU6dMRGTOO++8xNe3b98+8eeZjaFDhybue91112X5WRcvm/t35MiRZfabOnVqJj8/P6t74dprr82sWbOmxJ6FhYWZc889N6uejRs3znzzzTdlzpvNn/l/f1x44YWZVatWldj3l7/8ZVb9brjhhjJnrWg333xzolm7detW7rVuuummrP58WrdunZkyZUqpPadOnZpp06ZN1n+X/fv3TzRz9+7dU//zee655xLP+dRTTyXquc8++2R23XXXzIMPPphZuHBh4lmKM2zYsKy+tk+dOrVc632vW7duida79dZbU1kPAAAAAKCmktuW/SG3ldsWR25bPLlt5ZDblk5uW/aH3BYAoPryG3gBAIBU7LDDDjFo0KBEtQsXLoyzzz47fv/738d7772X6Jrjjz8+LrroovKMWKUKCgri9ddfj/bt25da16ZNm3jttdeyejrpsGHDIpPJlHj+b3/7W8yYMSNxv169esVzzz0Xm222Wal1AwcOjMGDByfuGxFxzz33lHju66+/TtTjxBNPLHO24tSpUyf23HPPuOGGG2LatGnx3nvvxVFHHZV1HzY0ePDgKCoqSlx/+eWXxw033BB169YtsaZhw4Zx3333Rd++fRP3Xb58eTz88MOJ67PRv3//GDJkSNSvX7/Emptvvjl22mmnxD2rwxNpp0+fnqiubdu25Vonk8nEAw88kLg+Pz8/Xnjhhdh2221LrevcuXO89NJLUadOnXLNV5KkT3OeMGFC/Pvf/05U+9hjjyWq22KLLeLYY49NVPvEE0/E6NGjY+DAgbH55psnuqYkp59+evzsZz9LXJ/tbwkoyVZbbZWoLuk9CwAAAABQW8ltSye3/Y7ctmaS235Hblsyue3/kdsWT24LAJA+G3gBAIDUnH322dGvX79EtS+//HJcd911iWo7duwYjzzySHlGq3KXX355dO7cOVFtx44d45e//GXi3l9++WWMGzeuxPNPPvlk4l5169aNBx54IBo0aJCo/vzzz4+99947cf/XX389vv3222LPNW3aNFGPlStXJl6vNHvttZcgOAVFRUXxzDPPJK7v0KFD3HjjjYlq8/LyYtCgQdG8efPE/Z966qnEtUm1bNky0Rtd8vPz44wzzkjc97PPPivPWKlI+iaRpCFdSSZPnhwzZ85MXH/mmWfGrrvumqi2Z8+ecfbZZ+c6WqlOP/30KCgoSFQ7bNiwMmu++eabePnllxP169+/f6lvPPhv5Q3q/9eJJ56YuDZpAF6WpPdYNm9sAgAAAACoreS2JZPbfkduW/PIbf+P3LZkctvvyG1LJrcFAEifDbwAAECq7r///sSBZ2lPH/5evXr14qmnnopNNtmkvKNVmfz8/LjggguyuuanP/1pVk8mHTVqVLHH161bF6+99lriPn369IkddtghcX1ExFVXXZW4ds2aNfHGG28Ue65p06aJAujHHnssPvjgg8RrUrHGjh0b8+bNS1x/2WWXRcOGDRPXb7nlljFw4MDE9WPGjIkFCxYkrk/isssui2bNmiWqzeZJzosXL85xovTMmjUrUV2bNm3Ktc7777+fVf35559fofVJNW/ePHEoOnz48Fi3bl2pNU8//XSsXr06Ub+kT5GuCK1atUpcm9aTlZPeY0nvWQAAAACA2k5uuyG57f+R29Y8ctv1yW2LJ7f9jty2ZHJbAID02cALAACkqkmTJvH0008nfhJwWW677bbYbbfdUulVVXr16hWtW7fO6poWLVrEPvvsk7j+448/Lvb4xIkTY+nSpYn7nHLKKYlrv3fIIYfEpptumrj+X//6V4nnkjy5ddmyZdGrV6848sgjY9CgQfHuu+9mFUSSrmwCvry8vDjppJOyXqNv376JazOZTKpvFMjLy0v8hPqI7J6oW9JTzStT0jC6SZMm5Vonm/ukXbt2scsuu2TVf6eddoqOHTtmO1YiSQPZefPmxZtvvllqTZKnPUdE7LfffrHddtslqi3JmjVr4r333ovBgwfHeeedF4ceemjssMMO0aZNm2jSpEnUr18/8vLyiv3I5s8yra+/Se+x6vAGCgAAAACAjYHcdkNy2/XJbWsWue365LbFk9t+R25bMrktAED66lb1AAAAQM2z0047xR/+8Ie46KKLytXnmGOOiZ/97GfpDFWFcg2yd95553j77bcT1U6ZMqXY4+PHj89qzb333jur+ojvnra92267xYgRIxLVjxs3rsRzhx56aPzzn/8ss0dRUVG88sor8corr/xwrEmTJtG5c+cfPnbYYYfo3r17bL/99pGf7/lVFSWbe6xTp05ZvykiIqJnz55RUFAQhYWFierHjRsXffr0yXqd4nTs2DHatWuXuD6bp84vW7Ysl5FStWLFikR1jRo1Ktc6kyZNSly7884757RGz549U3uq8H/bd999o0uXLjF58uQyax977LE49NBDiz03ffr0eO+99xKtWZ6nOI8YMSKGDh0aL730UqXcY8uXL0+lT0FBQaWuBwAAAABQG8ht1ye3XZ/ctmaR265Pbls8ua3ctixyWwCA9PlJGAAAqBAXXnhhnHDCCTlf3759+xg6dGiKE1Wdrl27Vvh1s2fPLvb4jBkzEvdo3rx5Vk+h/W/dunVLXDtz5swSz51zzjnRuHHjnGZYtmxZfPzxx/H000/HTTfdFP369Ytu3bpF8+bN4/DDD4+77rorZs2alVNvSpbNPbbjjjvmtEZ+fn5sv/32ietLu8ey1aNHj6zqc71/q0rSUC1pSFeSr7/+OnFtNn/XaVyXxMCBAxPVPf/88yX+mT7++OORyWTK7LHpppvGiSeemNV8Ed89JX/XXXeNQw89NJ588slKe6PBypUrU+mT9M0GgmAAAAAAgOzIbf+P3HZ9ctuaRW67Prlt8eS2ctuyyG0BANJnAy8AAFBhHnrooejQoUPW19WtWzeefPLJ2HTTTdMfqgpsvvnmOV232WabJa795ptvij3+1VdfJe7RsmXLxLXluXbBggUlnmvVqlVce+21Oc9RnKVLl8Zrr70WP//5z6NDhw5x8MEHx9///vdU16jNNrZ7LFvZPnm6Xr16qa1dnSQJMEtT0teo4mTztS+N65Lo379/1K9fv8y65cuXx//7f/+v2HOPPfZYorV+8pOfRMOGDbOa7+6774599tknxo4dm9V1aSgqKkqlT3nvMQAAAAAASia3/Y7cdn1y25plY7vHsiW3/Y7cVm5bErktAED1ZQMvAABQYZo3bx6DBg3K+roBAwbEnnvuWQETVY2mTZtW+HWFhYXFHs/miZe5zhkR0axZs8S1K1asKPX8lVdeGeedd17Os5Qmk8nEm2++GT/+8Y+jb9++Zc5C2TbGe6yi1o2IqFOnTmprV4akT88t6WtMUtkEwZXxNTNbW2yxRRx77LGJaocNG7bBsdGjR8eUKVMSXX/OOedkM1oMHjw4Lrvssli3bl1W11U3Sf/dJr1nAQAAAAD4P3Lb78ht1ye3rVk2xnusotaNkNuWRG4rty2L3BYAIH028AIAABVm3bp1cfvtt2d93fDhw2PixIkVMFHVyPXF+Wyuy88v/se7bJ6MWVKPJLIJv5LMdN9998XgwYNjk002yXmmsjz11FNx0kknxZo1aypsjdpgY73HkmrQoEFW9YLg4mXz51IZXzNzce655yaq+/vf/x5z585d71hx4XBx9txzz+jWrVvimcaNGxeXX3554vrqLOk91rhx4wqeBAAAAACg5pHbfkduuz65bc2ysd5jScltvyO3ldtWNLktAED6bOAFAAAqzHXXXRfvvPNO1tetWLEiTjnllBrzlN2lS5dW+HUFBQVZHS/OsmXLEtf+ryVLliSuTTrTRRddFNOnT4+bb745tt1221xHK9Urr7yS05sV+D8b8z1GJH6zRa5fx7636aabJq7N9T4p74xlOfDAA6NTp05l1hUVFcXw4cN/+O+1a9fGU089lWiNpGHz966++uoa82aWpH/v2T5dHQAAAAAAue335Lbrk9vWLBvzPYbcNhty24oltwUASJ8NvAAAQIV488034+abb875+okTJ8bFF1+c4kRVZ/78+Tldt2DBgsS1W2yxRbHHN99888Q9Fi1alLi2PNduttlmiWs33XTTuPLKK2PKlCkxceLEuPvuu+Okk06KbbbZJvLy8nIZdQO33XZbfPvtt6n0qo029nustmvXrl2iuv99MnG2sgmCK+NrZi7y8vJi4MCBiWr/+8nNr7/+eqLZmjVrFieffHLieb744ot49dVXE9dHRPTo0SP+8Ic/xL/+9a9YsGBBrFy5MjKZzAYf06dPz6pvGubMmZOobuutt67gSQAAAAAAaha57f+R265PbluzbOz3WG0nt01Oblux5LYAAOmrW9UDAAAANc+8efPi9NNPj6KionL1eeSRR+Kggw6Kfv36pTRZ1ZgwYUKFX7fVVlsVezybF8znz58fCxcuLDFULs3EiRMT17Zv3z7r/hERXbt2ja5du8all14aERGFhYXx2Wefxeeffx4zZ86Mzz//PKZOnRpjx47NKhBavHhxvPbaa9G3b9+c5qrtsrnHcv23EBExadKkxLW53mO1UYcOHRLVffnll+VaJ5uvK9n8XadxXTbOPPPM+M1vfhNr164tte6TTz6J8ePHx4477hiPPfZYot79+vWLxo0bJ55lxIgRWf1/9o477ojLL788UW1hYWHivmlJeo8lvWcBAAAAAJDb/i+57frktjWL3HbjJrfNjty24shtAQDS5zfwAgAAqSoqKorTTjst5ydx/q/zzjsvPv3001R6VZX3338/p+s++OCDxLU77LBDsce7du2a1ZqjR4/Oqj4iIpPJxNixYxPXZztTSQoKCmLHHXeMY445Ji655JK4++6745VXXon58+fHmDFjok+fPol7vfXWW6nMVBtl8/c5ZcqUWLp0adZrfPrpp7F48eIKmam269ixY6K62bNnl2udnXbaKXHtmDFjYt26dVn1LyoqijFjxmQ5VfZatWoVRx11VKLaYcOGxdKlS+Nvf/tbovpzzz03q1k+/vjjxLV9+/ZNHAJHlP/vOxdJ1xQEAwAAAAAkI7fdkNx2fXLbmkVuu3GT22ZHbltx5LYAAOmzgRcAAEjVjTfemGqotmzZsjj55JNj1apVqfWsbJ988klMnTo1q2umTZuW1Yv8u+yyS7HHe/bsGfXr10/c57nnnktc+733338/q9Bgjz32yHqNbO2yyy7x17/+tcQ/l//12WefVfBEJatXr17i2rKeHlsV9txzz8S1a9euTRyK/bdnnnkmq/rKuMdqih133DFR3WeffZZ1OPvf9tprr8S1ixYtirfffjur/u+8805WT3Avj3POOSdR3fDhw+OZZ55J9FTkXXbZJXr27JnVHF988UXi2hNPPDGr3m+++WZW9eW1du3amDZtWqLa7t27V/A0AAAAAAA1g9x2Q3Lb9clt1ye3LZvctuLIbbMnt02f3BYAoGLYwAsAAKTm7bffjuuvvz5RbYcOHeKQQw5JVPvvf/87fv7zn5dntCp3++23Z1V/xx13ZFV/4IEHFnu8QYMGccABByTu8/TTT2f9FO7Bgwcnrs3Ly0v8915e+fn58eMf/zhR7cKFCyt4mpI1bdo0ce28efMqcJLc7LXXXll9DoMHD45MJpO4fuXKlfHggw8mrt9222096TULu+66a6K6lStXlusNE9kEwRERd955Z1b1f/jDH7KqL4/DDjss2rVrV2bd7Nmz4+qrr07UM2m4/N+WL1+euLZx48aJaxcvXhwPP/xw1vOUx9SpUxO/4Wu33Xar4GkAAAAAADZ+ctuSyW2/I7fdkNy2dHLbiiW3zZ7cNn1yWwCAimEDLwAAkIqvvvoq+vXrl+hJn/n5+TFs2LAYNmxYtGjRIlH/e++9N6enDFcXjzzySOInk77//vtZBV/dunWLTp06lXj+5JNPTtxr6dKlWYXub731Vjz55JOJ6/faa69o06ZNiefvvvvuePDBB2PZsmWJe5Zm7ty5ierWrFmTynq52GSTTRLX/v3vfy/X03QrQoMGDeKYY45JXP/hhx/GQw89lLj+5ptvjhkzZiSuz/aJtbVd69atS/03+d/Gjx+f8zpbb7117LTTTonrX3755Xj++ecT1b7wwgvx4osv5jhZ9vLz8+Oss85KVJvkjTWNGzeOfv36ZT1HkyZNEteOGTMmUV0mk4nzzz+/0t8cM27cuER1W2+9deLvGwAAAAAAaiu5benktt+R225Ibls6uW3FkttmT26bPrktAEDFsIEXAAAot0wmE2eccUbMmTMnUf1VV10V++yzT7Ro0SIeffTRxOsMHDgwq0CoOikqKopjjjkmRo4cWWrde++9F0cddVRWYd+AAQNKPX/qqafGpptumrjf8OHD41e/+lUUFRWVWjd69Og4/vjjE/eNiLjoootKPT9hwoQ499xzo02bNnHeeefFq6++GoWFhVmt8b1PPvkknn322US1rVu3zmmNNGy11VaJa2fMmBFnnHFGTJ8+vQInyt6FF16Ydf1f//rXMuseeOCBuOGGGxL3rVu3bvz0pz/NahYi9tlnn0R1H330UbnWOf/887OqP/300+PVV18tteb111+PU089tTxj5eSss86K/Px0Xlbr27dvVk9D/17Lli0T1959990xc+bMUmtWrlwZZ511VjzxxBNZz1JeH3/8caK6vffeu4InAQAAAADYuMltyya3/Y7cdkNy25LJbSuH3DZ7ctt0yW0BACqGDbwAAEC53XrrrfHaa68lqt1tt93it7/97Q//ffjhh8cll1yS6Npvv/02TjnllCp96m55LF68OA466KDo169fvPrqqzF37txYvXp1zJ8/P954440488wzY999942vv/46cc9mzZrFwIEDS61p1KhR/OIXv8hq1ttuuy323nvvePHFF2PVqlXrnZs0aVJcfvnl0atXr1i8eHHinl26dImTTjopUe3SpUvjgQceiCOOOCI222yzOPTQQ+P666+PZ599NiZOnFjiPVBUVBTjx4+Pa6+9Nnr16hUrVqxItF7btm0Tfx5p22abbbJ6mvPw4cOjU6dOscUWW8T2228fO+200wYfp5xySgVOvKE999wzDj744MT1a9asieOOOy5+8pOfxPvvvx+ZTOaHc0VFRfH222/HscceG+edd95658rSv3//aNeuXVazE3HYYYclqvvHP/5RrnVOO+20rO71FStWxBFHHBF9+/aNV199NebNmxerV6+OefPmxSuvvBJ9+/aNww47LPG/8zRtvfXWceihh6bS65xzzsnpuq5duyauXbRoUeyxxx7x4IMPxpIlSzY498gjj8SOO+6Y1Zuz0pT0Nx0cfvjhFTwJAAAAAMDGTW6bjNxWblscua3ctqrJbbMnt02X3BYAoGLkZbL5iQoAAOB/vPfee7H//vvH2rVry6xt1KhRfPzxx7Htttuud3zlypWx++67x/jx4xOt+fOf/zz+8Ic/5DRvWgYMGBB//vOfq3SGiIgbb7wxrrnmmjLrVq1aFTvuuGN8+umnWa9Rv379aNWqVTRq1Cjmz58f33zzTS6jxuuvvx6HHHJIqTUDBw6Mhx9+uMxeeXl50aRJk2jatGk0a9Ys6tatG8uXL4/Zs2fH6tWrs57t8ccfj379+pVa06FDhzKfhPq9bH/UPvjgg+PNN9/M6prS9OjRI/7973+XeD6b+3fkyJFxwAEHlFn3n//8J3r27LnBGweSaNy4cbRu3ToymUzMnTs3p2Bv0003jf/85z+Jnm776KOPxplnnpmo73XXXbfem1eSyMvLS1xbHV6WmTt3brRp06bMuvr168e3334bBQUFOa91yy23xFVXXZXz9Wnp379/uUPP559/Puun2f+v7t27xyeffJLTtVOnTo3tttsu6+vy8vJi6623jiZNmsS3334bc+fOLfPJ/aVp3759uX7LwvLly6N58+Zlfh+Rl5cX8+fPjy233DLntQAAAAAAajK5bdWS28ptiyO3LZ3cdn1y29zIbb8jtwUAqL78Bl4AACBnX3/9dfTt2zdRCBwRcdddd20QAkdENGzYMIYPHx4NGzZM3Oell17KataaqEuXLomf0NygQYN44oknon79+lmvs3r16pg1a1ZMnjw55xD40ksvLTMEzkYmk4mlS5fGnDlzYvLkyTFhwoSYPn16TiFwo0aN4uijj05ttlycfvrpVbp+Grbffvu44447crp2+fLl8dlnn8W0adNyfirvww8/nCgEZkOtW7eOnXfeucy61atXx7/+9a9yrXXFFVfErrvuWq4e1UWfPn2iVatW5eqR61OcIyK23Xbb2G233bK+LpPJxMyZM2PixIkxe/bsYkPgbJ64XV7//Oc/E30fsfvuuwuBAQAAAABKILetWnJbuW11IrfdeMltcyO3TYfcFgCg4tjACwAA5GzAgAHxxRdfJKo9+uij49xzzy3xfLdu3eL2229P1CuTycSAAQPiyy+/TFRf1U477bTUezZs2DCefPLJaNCgQeJrdtlll/jLX/4S+fmV+6PgEUcckfjvtir8+te/jiZNmlTpDKecckqNCDguuuiiuPDCCyt93ZtuuimOO+64Sl+3JinrSebfK++bcOrUqRNDhw7N6mtXEvn5+XHSSSel2rMsdevWjQEDBuR8fUFBQbnfBHLzzTeX6/ri1K1bN+69997U+5Yk6T2V9B4FAAAAAKiN5LbJyG3ltmWR25aP3Lb85LbZk9umQ24LAFBxbOAFAABycuedd8aLL76YqLZVq1bx0EMPlVl30UUXxZFHHpmo56JFi+LUU0+NdevWJaqvSgMHDoyrr746tX75+fnx+OOPR48ePbK+9pRTTolhw4bl9ETnXBx99NHx7LPPRr169SplvWztvffeccUVV1T1GNGwYcN45JFHIi8vr6pHKbfBgwfHJZdcUilr5eXlxY033pjqv6/a6rTTTos6deqUWfe3v/2t3Gt169Yt/vznP6d6v99zzz1xxBFHpNYvqYEDB+b8eZx44onRvHnzcq1/0EEHxVlnnVWuHv/rrrvuil69eqXaszRJ7qm6devGqaeeWgnTAAAAAABsfOS2yclt5bZlkdvmRm6bHrltbuS25Se3BQCoODbwAgAAWfvwww/jyiuvTFw/dOjQxE+pfeSRR6Jly5aJat999934zW9+k3iOqnTTTTfF7373u3IHH/Xr148nnngijj/++Jx79OvXL0aOHBkdO3Ys1yylqVOnTvz617+O559/PgoKCipsnfI4/PDD47XXXksUflWGo446Ku65555qG5onlZeXF/fcc088+OCD0bhx4wpbZ7PNNotnnnkmrrnmmgpbozZp1apV/PjHPy6zbtq0aTF+/Phyr/f9m1LKe7/XqVMn7r333rjooovKPVMuttlmm+jdu3dO15b22w2ycd9998VBBx2USq8bbrihUv8sx44dG7NmzSqz7rDDDqsRT7sHAAAAAEib3DZ7ctvqR25bMeS2Gye5bW7ktuUjtwUAqFg28AIAAFlZvHhx9O3bN9asWZOo/uKLL47DDjsscf8WLVrEo48+mjgwveWWW+LNN99M3L8q/eY3v4nnn38+WrRokdP1P/rRj+Ldd9+Nk08+udyz9OrVK8aNGxe/+tWvolGjRuXu99/222+/+Ne//hXXX3995Odn92Nnp06domHDhqnO87+aN28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      "text/plain": [
       "<Figure size 4500x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# simulate modulation (transient decrease) of measurement noise (R)\n",
    "\n",
    "run_sim('R')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1c938067-fe62-4f18-9b9c-8dfc81f1f9eb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cd911cfLJJ2d8SAUAABse/0sPAADYqN10002xePHiyi7jZ3744Ye49NJLo2XLlnHRRRfFRx99lHh4N+I/JyU///zzccwxx8R2220Xjz32WKUPE5ZVgwYN4rjjjltvzMKFC+Nf//pXqbm++OKLGDZs2Hpjttpqq8SNs7KYPn16/OY3v4k2bdrENddck9XwbsR/GruPP/54HHroobHLLrvEyy+/nFqNJSUlcdNNN8Vuu+0Wr732WsZ/pwoKCuLee++Ntm3bxnPPPZdaPazfb37zmzj33HPj8ccfT7XRvWrVqnj99dfjuOOOi/bt28frr7+eWu6N0ciRI+Owww6LXXfdNe66666shncj/tMov/3226N9+/Zx9NFHx5gxY1KuNDN33nln/OIXv4g77rij1OHdH82dOzcuvPDC2HHHHWPSpEnlWyAAAAAAABucESNGJL59t23btnHPPfeU6TDb66+/Pvbff/9EaxYsWBBPP/101ntWtPbt28e+++673pgJEybEkCFDSs310ksvlXo46H777ZfKQaSZ+uabb+K4446Ltm3bxi233JLV8G5ExMyZM+O+++6LPffcMw444ICfDV6W1cqVK+N3v/td7L///usc3l2bRYsWxc033xzt2rUrtbdNerp37x4XX3xxDBo0KNHwbmmWL18egwYNii5dusSee+4Zn3zySWq5N0ZDhgyJTp06xQEHHBADBgzIang3ImLcuHHRr1+/2HrrreO0007L+IDjNBUVFcUf//jHaN++fTz00EOlDu/+aMqUKXHaaafFvvvuG/PmzSvnKgEAqCwGeAEAgA1W3bp1S73lb/78+XHrrbdWUEWle/nll6N9+/bx17/+NeN/sM/E5MmT49RTT41DDjmkUpoRaTrvvPNKjbnrrrtKjenfv3+pMX369Kmwm4sfeuihaN++fTzwwANRVFSUWt7Ro0fHUUcdFccdd1zMnz+/TLmKiori1FNPjSuvvDLrGhcuXBg9evSIv/zlL2WqhapjwoQJccQRR8Sll166wR8SkLaCgoK4+OKLo3PnzvH222+nmvull16K3XbbLa677rpYvXp1qrnXZcWKFdGzZ884//zzY+nSpVnlmDBhQuy3336VNnwMAAAAAEDV9MwzzyRe079//6hevXqZ977rrrsS9wQHDRpU5n0rUiY91jvvvLPUmEx6rJnslYbi4uK4+eabY6eddopnn3021T7VBx98EPvvv3/07ds3li9fXqZcS5cujSOPPDL+8Y9/ZJ1j+vTpcfDBB8cTTzxRplqoOkaOHBn77rtv/P3vf6/sUqqchQsXxsknnxxdunSJUaNGpZa3uLg4Hn300ejQoUNGN4mnZf78+XHwwQdHv379YuXKlVnl+Oijj2L//feP6dOnp1wdAABVgQFeAABgg9WwYcM4++yzS4277bbbsj6pM0033HBDHH300TFr1qxy2+Pdd9+NPfbYI0aMGFFue5S3zp07R6dOndYb89VXX8V77723zucXLFgQTz755Hpz1KxZM3r37p1VjUkUFRVFnz594qyzzirX26CfffbZ2GuvveKbb77JOsfZZ5+d0e3Gmbjsssuq1PA8ZffXv/41fve731V2GVXG3Llz45BDDonbb789q1vUM7Fq1aq49tpr46ijjoolS5aUyx4/WrJkSRx66KExePDgMueaNWtW9OrVKwoKClKoDAAAAACAjcFLL72UKH6fffaJX/7yl6nsveOOO0avXr0SrXn77bdTPZC5vPXs2TOaN2++3phXX301Jk+evM7nx40bF0OHDl1vji233DJ69OiRVY1JLFu2LHr06BFXXHFF1gNxpSkpKYl//vOfceCBB2bdw1+1alX07Nkz3nrrrTLXU1RUFKecckpWw+5UTUVFRXHhhRfG7bffXtmlVBmTJk2Kzp07x+OPP15ueyxbtizOPffcOPPMM2PVqlXltk9ExIwZM2LvvfdO5Qbt8ePHx8knn+xQbQCAjZABXgAAYIN29dVXR+3atdcbs3Tp0vjzn/9cQRWt3eWXXx7XXHNNhfxD+9y5c+OXv/zlBj3EW9YToh944IFSG+q//vWvo1GjRolrS6K4uDhOO+20uOeee8p1nx9NmjQpDjzwwJg4cWLitffdd188/PDDqdZz6aWXxuuvv55qTipX//79nfwd//8pyhX1ffa1116L//u//yvzCfDr061bt1T/PN98801cc801qeUDAAAAAGDDNW3atJg0aVKiNSeffHKqNZxyyimJ4lesWBEjR45MtYbyVL169TjnnHPWG1NcXBz//Oc/1/l8Jjf0nnPOOancirw+BQUF0a1bt3jhhRfKdZ8fffrpp3HQQQdldSj4n/70pxgyZEhqtfzYXx49enRqOal8l1xySbz//vuVXUal++677+KAAw4o06HkSTz88MNx0kknldthzMuWLYvDDjssq89nrMu777673u/TAABsmAzwAgAAG7Qtttgioxsh77777pg2bVoFVLSme+65J2655ZYK3XPZsmXRrVu3+P777yt037SccMIJ0aRJk/XGPP/88zF9+vQ1Hl+9enVGDY3zzz8/6/oydeWVV1b4sOPs2bOja9eusWjRoozXTJs2LS655JLUaykuLo6PP/449bxUrosuuqjcb4OtyoqKiuKYY46JsWPHVui+w4YNi1NPPbXc8pfHhwbuvPPO+OGHH1LPCwAAAADAhiXpIGxubm786le/SrWGLl26JD7c99NPP021hvKWyXDtQw89tNaDkBctWhSPPvroetfm5+eXOiSchjPOOCPefffdct/nv33zzTdx9NFHJ7qtc9SoUfHXv/419VoKCgri888/Tz0vlWf16tXRt2/fchsk3RAsWbIkunbtGjNmzKjQfQcOHBiXXnppueSeN29efP3116nnvemmm2L16tWp5wUAoPIY4AUAADZ4l1xySWy++ebrjSksLIxrr722Ygr6L2PHjo2LLrqowveNiFiwYEH8+te/rpBbf9NWo0aN6N2793pjioqK1nqz7UsvvRRTp05d79p99tkndt111zLVWJo333wz/vKXv5TrHuvy3XffxW9/+9uM43/729/G4sWLE+9TrVq1aNasWeywww7RsGHDxOupGHXr1o1mzZrFL37xi9hxxx1j++23jy233DJyc3Ozyjdnzpy49957U65yw3HdddfFsGHDKmXvQYMGbVBf+4KCglI/7AMAAAAAwMYv6W2ibdu2TTxsW5q8vLzYY489Eq3Z0G5BbdasWfTq1Wu9MQsWLFjrAcQPP/xwLFu2bL1re/XqFVtssUWZaizNgw8+GE899VS57rEuH330Ufzxj3/MKHb16tVxxhlnRFFRUeJ98vLyomXLltG2bduoX79+4vWUv5ycnKhXr140b948tttuu9hxxx1ju+22i2bNmkW1atl99H7MmDExePDglCvdcJx77rmp3lSbxG233Ravv/56peydjenTp8fLL79c2WUAAJAiA7wAAMAGr0GDBnHZZZeVGvfII4/E+PHjK6Ci/995550XBQUFidbst99+8fDDD8ekSZNi+fLl8cMPP8QXX3wRN9xwQzRv3jxRrg8//DAGDBiQaE1V0adPn1IHDO+///5YuXLlzx7r379/qbnL+/bdVatWxXnnnZd4eLpbt27x9NNPx9SpU6OgoCDmzZsXH3/8cVxxxRWJm7dPPfVUvPPOO6XGffnll/Hqq68myt2oUaO48847Y86cOTFz5sz4+uuvY/78+TFhwoS49NJLIy8vL1E+0pGTkxO77rprXHLJJfHEE0/Ep59+GosXL47FixfHzJkzY+LEiTF69Oj45ptvYvr06VFYWBjffPNNPPDAA3HAAQck2uv+++9f53MNGzaMnXfeeY1fpZ34/t/Wtn5dv+rUqZOo9rKYOHFi4sH8/Pz8+O1vfxtvv/12zJw5MwoLC2PGjBnx+uuvx2mnnZa4yX/llVfGggULEq1JKjc3N3r37h1Dhw6NWbNmRUFBQUyYMCH+/Oc/R7169RLlevrpp8upSgAAAAAANhRTpkxJFL/LLruUSx1J837//fflUkd5yqQPetddd/3s9yUlJWs8lm3usliwYEHimzJzcnLipJNOipdffjn+/e9/R2FhYcyePTvee++9OP/886NGjRqJ8t16660xYcKEUuNefvnl+OqrrxLl3mqrreLRRx+NBQsWxLRp02L8+PGxcOHC+Oyzz6J3796Rk5OTKB/pyMvLi7333juuueaaGDhwYHz55ZexbNmyWLRoUcyYMSMmTJgQo0ePjgkTJsTMmTOjoKAgRo8eHf3790/8PWV9PdZmzZqttReaqerVqyfqsebn5yeqvSzee++9+Ne//pVoTd26deOSSy6JDz/8MObMmROFhYUxbdq0GDx4cBxzzDGJcpWUlMQFF1ywxmdL0larVq246KKL4sMPP4y5c+fG8uXL46uvvorLL7888ddbjxUAYOPiE7UAAMBG4YILLoh//OMfMWPGjHXGrF69+qemS0V4++2349133804vlq1anHXXXetcXNqrVq1okGDBrHzzjtH375947TTTosXX3wx47zXXXddnHrqqVnftllZWrVqFcccc0wMGjRonTGzZ8+OgQMHxkknnRQREePGjYuhQ4euN2/z5s2jZ8+eqdb6vx599NGMGrs/qlWrVjzxxBNrNJpq1KgRjRo1ij333DPOO++86NWrV4wYMSLjvNdcc02pN4Vm0oz/b+3bt4+hQ4eu9XTt7bbbLm655ZY49thj4/DDD48lS5Ykyk1yOTk50alTpzjnnHPi2GOPTXQafW5ubmy//fax/fbbx1lnnRX/+te/4vTTT4/Vq1eXunbChAkxceLE2G677dZ47uijj46jjz56jcfbtGlT6u3YP/riiy8yiqto119/faLG7rbbbhsvvvhitG/f/mePN2/ePJo3bx5dunSJPn36xNFHHx1z5szJKOeCBQvitttuixtuuCFR7Zlq3rx5vPjii9GpU6efPb7ddtvFFVdcEccff3zss88+MXv27Izyff7551FUVGSwHwAAAABgEzZt2rRE8TvttFO51JE0b9K6q4K99947dt999xg1atQ6Yz7//PMYPnx47LvvvhER8frrr8e333673rydOnWKzp07p1rr/7r99tsTHWLauHHjeP7553/6c/yoadOm0bRp0zjggAOib9++cfTRR2fcu121alVcf/31pQ4bJu2xHnTQQfHiiy9G3bp113hu1113jfvvvz+6desWvXr1yupWX5KpVq1aHHzwwXH22WfHkUceudbXZV2qV68eO+64Y+y4447Rt2/f+Mtf/hKXX355RmvffffdWLp06VoPKP7tb3+7xmdFIiLjwe4tt9yyyvZYr7766kTxe+65Zzz77LPRqlWrnz3esmXLaNmyZRx77LHx8ssvx/HHHx/Lly/PKOfEiRPj0Ucfjd69eyeqJVPt27eP559/fo3+eceOHeOmm26KHj16xEEHHZRxvZ988kl5lAkAQCVxAy8AALBRqFWrVlxzzTWlxg0aNGi9zco0/f3vf08cv7aGzH9r0KBBDBw4MPbff/+M806dOjWef/75RLVUFeedd16pMXfeeeda/3tdzjnnnEQ3gWYj6Wv/9NNPl3pK7JZbbhmvvvpqtG3bNuO8w4cPX+/f9yVLlsQTTzyRcb46derEa6+9ttbh3f/WuXPnxCfokp0nn3wyRo4cGb179040vLs2J598clx44YUZx3/wwQdl2m9DM3v27EQnHTdq1CjeeuutNYZ3/9dee+0Vr7/+etSsWTPj3Pfcc08UFhZmHJ+pOnXqxBtvvLHG8O5/22abbeLuu+/OOGdBQUGMGTMmjfIAAAAAANhA/fDDD4niGzduXC51NGnSJFF80rqriqQ91v79+6eSsyxWrlwZ99xzT8bxNWrUiNdff32N4d3/1bZt23jzzTcTvfbPPPNMzJo1a53PT5o0Kd56662M87Vu3Tqef/75UodEu3fvHrfeemvGecnehx9+GEOHDo0TTjgh0fDu/8rJyYnLLrss4wPEV65cuckNZn722WelHjr+37bbbrt47bXX1hje/V/dunVLfHh/0s9xZGrLLbeMIUOGrPXw6x/tscceiQ5onjRpUixcuDCF6gAAqAoM8AIAABuNs846K7bddtv1xpSUlMSVV15Z7rUsWrQoXn/99Yzj99577+jbt29Gsfn5+XHvvfcmus0wydBZVXLQQQdFx44d1xvz0UcfxahRo2LRokXx2GOPrTe2evXqcc4556RZ4hrGjRsXX331VcbxJ5xwQhx11FEZxTZo0CCjBvp/W99rP2zYsFi2bFnGuS677LJo3bp1RrFHH310HH744RnnJjstW7ZMNV+vXr0yjq2qJziXl0GDBsWqVasyjr/xxhujTZs2GcXuuuuucfHFF2ece/78+fH2229nHJ+pq6++OnbcccdS47p37x4NGjTIOG9pp/YDAAAAALBxW7FiRaL4evXqlUsdSfMmrbuqOOGEE0odgh40aFDMnDkzvv3221L72k2aNIkTTjghzRLX8NZbb8W8efMyjr/oooti9913zyi2devWceONN2ace9WqVTF48OB1Pj9kyJAoKSnJON+NN94Y9evXzyi2b9++0a5du4xzkx091orz1FNPJYrv379/NGzYMKPY//u//4sTTzwx49xjxoyJcePGJaonE7fddlu0aNGi1LjTTjstUV49VgCAjYcBXgAAYKNRvXr1uP7660uNGzJkSLz77rvlWsuQIUMSDXpdfvnlkZOTk3F8u3btSr2x9b+9/vrrUVxcnHF8VZLJac79+/ePAQMGxNKlS9cb16tXr2jWrFlapa3VK6+8kij+iiuuSBT/y1/+cr23YyapZ8SIERnnyc3NjbPPPjvj+IiIPn36JIqn8iV5f0yePLkcK6l6kry3mzRpEmeddVai/H/4wx8iPz+/XOrJRIMGDTK+gblatWqx0047ZZx70aJFWVYFAAAAAMDGoLCwMFF8VRngLSgoKJc6ylvNmjWjd+/e641ZtWpV3HvvvXHXXXeVOozau3fvqFGjRpolriFJ36N69erxhz/8IVH+M888M7bYYotU6knSY918883j+OOPzzg+m54slU+Pdd2SvLd33XXX6NKlS6L8SQ/wT7vH2q5du4zf4w0bNsz4wPQIPVYAgI2JAV4AAGCj8utf/zqjoaLyvoU3SdOufv360bVr18R7JDnleMmSJTF27NjEe1QFJ598cqk3PT799NNxxx13lJrr/PPPT6eo9Ujy2u+www6JhuB+lOS1//rrr9fZ2ElS6z777BNNmzbNOD4iomvXrlGrVq1Eayi7VatWxYcffhj9+/ePc845J7p06RIdOnSILbfcMurUqRP5+fmRk5Oz1l9bb711xvvMmjWrHP8UVc9HH32UcWzPnj0T3ZIe8Z8PcCS5tTpJPZno2bNnog/fJDmZfOHChVlUBAAAAADAxiLp8GeSg4/LM2/t2rXLpY6K0KdPn8jNzV1vzL333hsPP/zwemNyc3Mr5NDeJH3LQw89NBo1apQof25ubhx33HEZx3/88cfrfC5Jrd26dYvq1atnHB8R0aNHj0TxpKOgoCDeeeeduPXWW+PMM8+MQw89NNq1axfNmjWL2rVrR/Xq1dfZYz344IMz3mdT6rEuWrQovv7664zjkwy7/6hDhw7RsWPHjOPT7rEmuQE4Qo8VAGBTZYAXAADYqOTk5MSNN95YatyIESPixRdfLLc6vvrqq4xjO3XqlOjWxR/ts88+ieJHjx6deI+qoHbt2nHGGWesN6agoCCmTJmy3pjddtst9t577xQrW7skr/2+++6b1R5JXvuSkpIYM2bMWp8bN25cxnl22223jGN/VLNmzWjXrl3idWRnyJAh8etf/zoaNmwY++67b1xwwQVx3333xZAhQ2LcuHExc+bMWLZsWaLbwddn2bJlqeTZEEyfPj0WLFiQcXxFvLfHjBlT6on4SRx44IGJ4uvXr59xbGm3owMAAAAAsHFLeuDrkiVLyqWOpLcZltdNwBWhdevWcfTRR683ZtasWaV+Tbp37x6tWrVKs7Q1FBcXJzqMuiL6MPPnz4/p06ev8fjKlSvj22+/zThPNj3W1q1bR8OGDROvI7nVq1fHoEGD4uijj44GDRrEIYccEn/4wx/i4YcfjqFDh8b48eNj9uzZsXz58igqKkplz02px5q0n1kR7+20PzejxwoAQCYM8AIAABudbt26ZfQP9FdddVUUFxeXSw2lDZP+tx133DGrPZo3b57oZOGpU6dmtU9V0Ldv36hWrWz/F7Yibt8tKSmJ77//PuP4bF/7JCfIRqz7tU8ykJjtIK4B3vL30UcfRadOnaJLly7x1FNPVVgjr6CgoEL2qQqSfE+PqJj39sqVK2PmzJlZ7bM2O++8c6L4JLcOpDloDAAAAADAhqdBgwaJ4hcvXlwudSTNuyEP8Eak0x+tiB7rzJkzY+XKlRnHV2aP9YcffkiUQ4+16nr11VejXbt20atXr3jppZeisLCwQvbVY123pO/RbNZNmzYt1d6lHisAAJkwwAsAAGyUbrrpplJjxowZE48//ni57D937tyMY7fYYous92natGnGsXPmzMl6n8q27bbbRteuXbNe37hx4zjhhBNSrGjtFi1alKi5nO1rX7du3UQnla/ttS8oKEjUHMz2lGenQ5evO+64I/bbb78YNWpUhe9dXgcgVEVJvqdHZP/eTrouze/rzZs3TxRfvXr11PYGAAAAAGDj1rJly0Tx8+fPL5c65s2blyi+LH3cquDggw+ODh06ZL2+Y8eOcdBBB6VX0DpsSH2YpAO8eqxVT0lJSfzhD3+II488MiZOnFjh++uxrl2NGjUSH/bwoyTv7cLCwsS3sa/LZpttlvigBz1WAIBNkwFeAABgo3TAAQdkNPD5pz/9KVatWpX6/suWLcs4tm7dulnvk6QZsHz58qz3qQrKcrpz7969o2bNmilWs3ZJXveIyn3tkzaXs621LH9G1q9///5x0UUXxerVqyu7lI1eRb23kzZ40/y+nnTv3Nzc1PYGAAAAAGDjttVWWyWKHzt2bLnU8eWXXyaKz/Y2yKrkvPPOq5S1SWxIfRg91g3fH/7wh7j11lsru4xNQlX83ExEej3WbG5p12MFANg0GeAFAAA2Wn/+858jJydnvTGTJ0+O++67L/W9S0pKMo6tVi37/2uW5B/3k9RUFXXp0iW22267xOtyc3OjT58+5VDRmpJ+jSvztU/aGMp2SLSyh0uz+XtfHkP9aRs9enT8/ve/r+wyNhkV9d5O+r5M8/t6jRo1EsVrLgMAAAAAkKkdd9wxUfzo0aPLpY4vvvgiUXzSuquiU045JerXr594XYMGDeLkk08uh4rWtCH1YfRY125D6K9GRLz22mtx2223VXYZm4yq+LmZiPR6rEn7qxF6rAAAmyoDvAAAwEZr1113jeOOO67UuBtuuCH122lr1aqVcezSpUuz3mfx4sUZxyapqSrKycmJvn37Jl539NFHR+vWrcuhojUl/RpX5mu/+eabJ9ov21qXLFmS1bq0ZNPcXrRoUTlUkq4rr7xyg2mEbwwq6r2d5H0dseF/XwcAAAAAYNPQqVOnRPFjx45NvX9bVFQUn376aaI1O++8c6o1VIbatWvHGWeckXjdGWecEbVr1y6Hita0IfVh9FjXbkPor0ZEXHrppZVdwialKn5uJkKPFQCAimeAFwAA2Kj169ev1BMsZ82aFQ8//HCq+zZq1Cjj2Pnz52e9T5K1DRs2zHqfquKMM86IOnXqJFpz/vnnl1M1a6pfv36iE1Ozfe0LCwtj2bJlGcev7bWvXr16oqb77NmzM479b3PmzMlqXVpWrlyZKH7FihWJvraVYdq0afHaa68lWrPzzjvHrbfeGh999FHMmTMnCgoKoqSkZI1fkydPLqeqN2xJvqdHZP/eTrpuY/i+DgAAAADAxm+bbbaJli1bZhxfWFgYL7zwQqo1DBkyJObNm5dxfJMmTRIPHldVffv2jZycnIzjq1WrltXBytnakPowSQd4N5Uea5L3VmX58MMPY8yYMYnW7L///vHPf/4zRo0aFfPnz4/CwsK19ljfeeedcqp6w5bkvb18+fJYsWJFVvskeW/n5uZGvXr1stoHAACyZYAXAADYqG2//fYZnSicdkMpyY2vSZtEP/rhhx9i5syZGcdvtdVWWe1TldSrVy9OOeWUjOM7dOgQBx98cDlW9HN5eXmx5ZZbZhyf7Ws/bty4KCkpyTh+Xa9948aNE+2ZjWzXrU+SDxgkPZ0929ekIg0ZMiSKi4szjv/b3/4WX3zxRVx88cWx1157RZMmTaJGjRprjc22KbqxS3qLd7Z/j8aOHZtxbG5ubqIPOwEAAAAAQGU66qijEsU/8cQTqe7/2GOPJYrv3r17ooN7q7Jf/OIX0bVr14zju3btGttuu205VvRzLVq0SPS1rog+TMTae6wNGjRIVGs2vdKSkpL4+uuvE68rTXn2WL/66quk5VS4JAck5+XlxZNPPhnvv/9+9OnTJ3bbbbdo2LBh5OfnrzVej3XtkvZYk75Hs1nXokWLyMvLy2ofAADIlgFeAABgo/enP/0patasWaF7tm/fPuPYUaNGJRrG/NGnn36aKD5JTVXZeeedVy6xaUnydR45cmRWe6T12u+yyy4Z5/j4448T7RkRsXDhwpgwYULidaWpVatWxrGLFi1KlPujjz5KWk6F+/zzzzOOPeGEE+L3v/99xvHTp0/PpqSN3lZbbZXoxuqKeG9vt912mssAAAAAAGwwevXqlSj+tddei9GjR6ey95gxY+LZZ59NtKZnz56p7F1VnH/++eUSm4bq1avHL37xi4zjK6IPU7t27bUOH+bl5UXHjh0zzpNNj3Xs2LGxbNmyxOtKo8eaeY/1D3/4Q5xwwgkZx+uxrl3Sz6hUxHt7Y/ncDAAAGxYDvAAAwEavZcuWce6551bonp07d844dtasWTF8+PDEewwcODDj2Pz8/ETDmlVZ+/bt45BDDik1rn79+olu601Lktd+5MiRMW3atMR7JHntt9pqq9hiiy3W+tzee++dcZ4vvvgivvvuu4zjIyJeeOGFKCoqSrQmE/Xr1884NunX98knn0xaToVL8mdK+mGYt956K2k5GatevXrGseXx96YscnNzo1OnThnHDxo0KPEeBQUF8fLLL2ccv9deeyXeAwAAAAAAKsvBBx8cbdu2zTh+9erVqQ2S9u3bN1HvoW3btnH44YensndV0bVr14yGZLfbbrvo0qVLBVT0c0l6rK+++mpWN54mGeLu1KnTOm/aTdJjfeONNxIP42bTZ8pEefVYi4uL4+mnn86mpAq1ofZYMz3Qt6r1VyMimjVrttabrNclm7/706dPjxEjRmQcr8cKAEBlMMALAABsEq644oqoW7duhe33y1/+MnJycjKO/8c//pEo/5w5cxI1wQ466KCoUaNGoj2qskya9WeccUaiGzPTkqShXVJSEv3790+Uf+zYsTF06NBU6knSXI6IuO222zKOLS4ujjvuuCNR/kwlaS5PnTo1Fi5cmFHs8OHDEzX3KkuSJn+S98CiRYviwQcfzKakjCT5Hjxr1qxyqyNbSd7bkyZNildffTVR/kceeSQWL15cLvUAAAAAAEBly8nJiQsvvDDRmvfffz9uuOGGMu37xz/+Md5///1Ea6666qqoVm3j+nhtTk5O9O3bt9S4vn37JupzpyVJ32Pp0qXx8MMPJ8r/xhtvxIQJE1KpJ0mPdcWKFXH33XdnHL9s2bK49957M45PIkmPdfTo0VFSUpJR7FNPPbVB3EBbXj3WyZMnx3PPPZdNSRnJtMc6b968KjnEm+S9/c4778RXX32VKP+dd96Z8d/VpPUAAEBaNq5/YQAAAFiHxo0bx+9///sK269Fixax7777Zhw/cODAGDJkSMbxl1xySaJBr6QnxFZ1Rx111HpPas20AV0eOnfuHK1atco4/u9//3uMGTMmo9gfTxpfvXp1xvnX99p37tw5mjZtmnGue++9Nz755JOMYu+888744osvMs6dRJJTeiMyu7F4xYoVcd5552VbUoWqU6dOxrGffvppRnElJSXRp0+fmDdvXrZllSrJhwLeeOONcqsjW0m/j1500UWxdOnSjGJnz54d11xzTca5a9WqFUceeWSiegAAAAAAoLL17t07tt9++0Rrrrnmmrj99tuz2u/mm2+Ofv36JVqz3XbbxYknnpjVflVdaQcg16lTJ84444wKrOj/161bt6hVq1bG8X/84x9j5syZGcUuW7YsLrrookT19OzZc53PHXHEEZGfn59xrhtvvDEmT56cUew111yT8Z8rqSQ91h9++CGjW2XnzZsXl156aVnKqjDl0WNduXJlnH766bFy5cpsyypVpj3WwsLCeO+998qtjmz96le/yji2uLg4zjvvvIwHkb/++utEPx9atmyZ6LZvAABIiwFeAABgk3HxxRdH48aNK2y/pAOkxx13XKm3f5aUlMTVV18djz76aMZ5N998842uyZybmxsjRoyIr7/+eq2/Jk6cGL/4xS8qpbacnJw499xzM45fuXJldO3atdQTn1etWhVnnXVWvPPOOxnnbtu2bRx22GHrfD4/Pz/OOuusjPMVFRVFt27dSm1YDhgwIC6++OKM8ybVvHnzaNGiRcbxV199dUybNm2dzy9fvjyOO+64chs4TtsWW2yRcewdd9wRU6dOXW9MQUFBnHnmmfHkk0+WtbT1SvKaXXbZZTFo0KAoLCwsx4qS2W677RKdyDxhwoTo3r17LFmyZL1xc+fOja5du8bcuXMzzn3KKadEvXr1Mo4HAAAAAICqIC8vL+64447E6y6++OLo1q1bTJkyJaP4KVOmRNeuXeOKK65ItE+1atXigQceiNzc3MQ1bgjq168f48aNW2ePdezYsZXWf6hbt26ceuqpGcfPnz8/unTpErNmzVpv3NKlS6NHjx7x9ddfZ5z78MMPX++geZMmTdY74Pu/Fi5cGF26dIlvv/12nTHFxcVx4403Zj2snolddtkl8vLyMo6/4IILYtGiRet8fv78+XHEEUdsELfvRiTrsV5//fXxww8/rDdm4cKFccwxxyS+4TupJD3W3/zmN/Hmm28mOhC8vB1yyCGxww47ZBz//vvvZzQUPWnSpOjSpUuifvK5555bKTeMAwCAAV4AAGCTUbdu3bjyyisrbL9evXpFu3btMo5fvHhx7L///nHBBRfE6NGjf/bcqlWr4rXXXosDDjggbrzxxkR1XHzxxes9SXlD1bx589hhhx3W+mvbbbet1Nr69OkTTZo0yTh++vTpscsuu8Qf//jHNRq3y5cvj2effTZ22223eOSRRxLVcfXVV5fagDrnnHOiWrXM/3lg7ty50blz5zjnnHPivffei7lz58bKlSvj3//+dzz77LPRtWvXOOOMM8q9KbjHHntkHDtnzpzYa6+94plnnvlZo2/ZsmXxxBNPxE477RSvvPJKeZRZLtq3b59x7Pz582OvvfaK+++/f41bu+fPnx8PPfRQ7LjjjjFgwICUq1zTbrvtlnHs/Pnzo1evXlG3bt3YZpttYqeddopddtlljV9JBtrTkOSW3IiIoUOHRrt27eL++++P+fPn/+y52bNnxz/+8Y9o165douHxGjVqbDAnmQMAAAAAwP864ogjEh2G+6NXXnkl2rZtG926dYuHHnoovvzyy5g1a1asWrUqZs+eHV9++WU8/PDDceSRR0bbtm3jjTfeSLzHJZdcEgcccEDidRuS1q1br7PH2rp160qt7bLLLosaNWpkHP/VV19F+/bt49Zbb11jiHThwoUxYMCA6NChQwwZMiRRHZn0g5L+HZ44cWLstNNOcckll8THH38cCxYsiMLCwpgyZUo88sgjse+++8bVV1+dKGdStWrVio4dO2YcP378+Nhzzz3jjTfeiOLi4p8e/+GHH+Kee+6J9u3bZ3xTbVWQpMc6ceLE2GOPPeKZZ56JFStW/Oy5GTNmxN///vdo3759vPbaa2mXuYYkPdbJkyfH4YcfHnXq1Intttsudt5557X2WL/55ptyrPjncnJyEvdYH3/88dh5553jySefjKVLl/7suSlTpkS/fv1ip512Wu8h3v+rcePG0adPn0R1AABAWjI/SgkAAGAjcO6558btt9+e6B/ys5WXlxf9+/ePX/7yl1FSUpLRmtWrV0f//v2jf//+Ub9+/WjWrFkUFhbGzJkzs7qJcptttok//OEPiddRNvXr14+bb7450e22K1asiH79+kW/fv2iYcOG0bRp01i2bNlPHzxIat99942TTjqp1LitttoqfvOb38S9996bce7Vq1fHfffdF/fdd1/iutJyzDHHxPPPP59x/MyZM+P444+PzTbbLFq2bBkFBQUxc+bMrL62le2II46ICy+8MOP42bNnx9lnnx3nnHNOtG7dOurUqRMLFy6MmTNn/qzZXt722muvxGtWrVoVkydPXufz6zv1uzzsu+++ccopp8Rjjz2W8Zrp06fH2WefHb/97W+jadOm0bBhw5g/f37MmTMn458N/+33v/99pR9SAAAAAAAAZXHrrbfGF198ER9++GGidStXroxXXnmlXA5m3X///eP6669PPS+Z23rrrePSSy+Nfv36Zbzmhx9+iD/84Q9xySWXROPGjaNx48axaNGimD17dlYHDp944omx3377lRq33377RZcuXRINiq9YsSL+9re/xd/+9rfEdaXlmGOOSXSw7IQJE6Jr165Rt27daNGiRSxevDjrr21lO+KIIxLdAD5p0qQ4/vjjIy8vL1q3bh21atWK+fPnl3rrc9r22muv6N+/f6I1BQUF673x+X+HksvbiSeeGPfee2+i24rHjx8fJ554YuTl5cUWW2wR9erVi7lz58a8efOyquHPf/5zNGjQIKu1AABQVm7gBQAANik1atSIP/3pTxW236GHHhoXX3xxVmsXLVoU33zzTUyZMiWr4d3q1avHk08+GTVr1sxqf8r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",
      "text/plain": [
       "<Figure size 4500x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# simulate modulation (transient increase) of process noise (Q)\n",
    "\n",
    "run_sim('Q')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c75d7170-c329-46fa-a20c-29e9df8182b6",
   "metadata": {},
   "source": [
    "## Observations \n",
    "\n",
    "As expected, both manipulations trigger resets. However, it's interesting to note that they have qualitatively different reset trajectories. In the measurement noise simulation resets are more abrupt, while in the process noise simulation resets are less abrupt and trajectories are more curved. In the present study, we have not quantitatively fit the trajectory estimates as it's likely that the exact shape of hand-drawn trajectories may be additionally influenced by non-perceptual factors (e.g., motoric costs etc). However, future studies may considering use alternative reset measurement approaches (e.g. Liu, Tse and Cavanagh, 2021 - bioRxiv preprint), which may allow more quantitative metrics to be extracted. This may then make it possible to differentiate the competing accounts we have simulated above on the basis of fine-grained difference in reset trajectories. Importantly, studies should also more exhaustively examine whether/how these differences in reset trajectories hold across the entire parameter space. \n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mne",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}