diff --git a/docs/notebooks/dmri_covariance.ipynb b/docs/notebooks/dmri_covariance.ipynb
index cee41584..bd65c2a9 100644
--- a/docs/notebooks/dmri_covariance.ipynb
+++ b/docs/notebooks/dmri_covariance.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 15,
    "id": "457b781088e1cae2",
    "metadata": {},
    "outputs": [],
@@ -13,10 +13,10 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "\n",
-    "from eddymotion.model.dipy import (\n",
-    "    compute_exponential_covariance,\n",
+    "from eddymotion.model._sklearn import (\n",
     "    compute_pairwise_angles,\n",
-    "    compute_spherical_covariance,\n",
+    "    exponential_covariance,\n",
+    "    spherical_covariance,\n",
     ")\n",
     "\n",
     "# Use LaTeX for rendering\n",
@@ -30,7 +30,8 @@
     ")\n",
     "\n",
     "PARAMETER_EXPONENTIAL_a = 0.5\n",
-    "PARAMETER_SPHERICAL_a = 1.23"
+    "PARAMETER_SPHERICAL_a = 1.23\n",
+    "PARAMETER_lambda = 2.0"
    ]
   },
   {
@@ -51,13 +52,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 16,
    "id": "9f8abf14503066e0",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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PoLy8vD1LJCIicmgMMnY0aNAgDBo0CNXV1U2OyshkMnz66afo2bMn9u3bh7///e/tXCUREZHjYpCxI5lMhrvuugsAkJWV1eQCeH379sWKFSsAAMuWLcOWLVvarUYiIiJHxiBjZ/7+/hgwYACqqqqwc+fOJtvNmDEDCxcuhCAIiImJsdiriYiIqLNikLGz+qMyGRkZuHz5cpNt33nnHSxatAjff/8915ghIiICg4xDUKvVuOWWW246KuPl5YWPPvoIvXv3bsfqiIiIHBeDjAOQyWT4wx/+AABIT0/HlStXWnRcSkoKjh07ZsvSiIiIHBqDjIMYMmQI+vXrh8rKSuzateum7d98801ERkbi0UcfRXV1dTtUSERE5HgYZBxE/VGZvXv34urVq822j4iIQPfu3bFt2za8/fbb7VEiERGRw2GQcSBDhw5Fnz59UFFRgd27dzfbNiAgAO+99x4A4KWXXkJ2dnZ7lEhERORQGGQcSP1RmT179uDatWvNtn/88cfx0EMPobKyEo888shNR3GIiIg6GgYZBzN8+HD07t0b5eXl2LNnT7NtZTIZli9fjr59++Lw4cOIjY1tpyqJiIgcA4OMg6k/KrNr166bjsr07NkTn332GQDggw8+wG+//WbzGomIiBwFg4wDGjFihDgq05I7mKZOnYpXXnkFP/74I4YNG9YOFRIRETkGBhkHJJPJMHnyZADA7t27W7SuzMsvv4x7773XxpURERE5FgYZBzVs2DD07dsXFRUVTe6M3ZSCggL897//tVFlREREjoNBxkHJZDLcfffdAGrXlbl06VKLjjt8+DDGjBmDOXPmcL4MERF1eAwyDmzIkCHiHkw7duxo0TFDhw7FuHHjcPXqVTz88MOoqKiwcZVERET2wyDjwGQyGe655x4AtTtjl5aW3vQYFxcXfP755/D19UV2djZeeuklW5dJRERkNwwyDs7f3x+DBw9GdXU1tm/f3qJj+vfvj08++QQAsHTpUqSlpdmyRCIiIrthkHFw9efKZGdno7i4uEXHzZgxA9HR0QCAP/7xjygsLLRZjURERPbCIOMEBg8eDJVKhZqamhaPygDA22+/jaFDh+L06dNYsmSJDSskIiKyDwYZJ1E3KrNv374Wj650794dq1atwnPPPYc33njDluURERHZBYOMkxgwYABuvfVWCIKArVu3tvi4sWPH4u2330bXrl1tVxwREZGd2C3ImEwmJCQkwGAwICEhAWazudm2ycnJYluTydR+hTqQutV+Dx48iLNnz7b6+KqqKiQmJvKWbCIi6jDc7PXG4eHhyMzMBFAbVBYuXAi9Xt9oW4PBgJiYGPH76OhoJCUltUudjqRfv3647bbbcPDgQaSlpeGRRx5p1fGzZ8/Gf//7XxQUFOCtt96yUZVERETtxy4jMjeOqKhUKhiNxibbr1692tYlOY27774bLi4uyM3NRUFBQauO/dOf/gQASEhIwJYtW6QvjoiIqJ3ZJcgYjUYolUqL55RKJbKyshptr1QqodFoYDKZYDQaERYW1h5lOiSlUomgoCAAtX+OgiC0+NiZM2diwYIFEAQB8+fPR1FRka3KJCIiahd2CTJNzYdp6oO17pKTWq2GXq+HTqdrsu/y8nKUlpZaPDqau+66C+7u7jh16lSr91N69913ceutt+LUqVOIiopqVRAiIiJyNA5111JTAcdoNCI+Ph5JSUlITk4WF3przJIlS+Dj4yM+Bg4caKNq7cfLywt33HEHACAtLQ01NTUtPrZ79+746quv4ObmhjVr1uCzzz6zVZlEREQ2Z5cgI5fLG4y+FBUVQS6XN2hrMpmQnp4OrVaLqKgo5OXlISUlpck7l+Li4lBSUiI+Tpw4YYsfwe4mTpyIbt26obCwEDk5Oa06Njg4WFxXJjY2FleuXLFBhURERLZnlyCj1WobfT44OLjBc1lZWQgJCRG/V6lUiIuLa3L0xsPDA97e3haPjsjDwwOTJk0CAGzduhWVlZWtOv7555/Hn//8Z2zbtg2enp62KJGIiMjm7BJkVCqVxfcmkwnBwcHiiExWVpY44hIUFIT09HSL9oWFheKE184sJCQEPj4+KCsrw969e1t1rKurKz788EOMGDHCRtURERHZnkyw02xPk8mEpKQkhISEID09HXFxcWKQCQ8PR0hIiLh2jNFoRFZWlvi6VqttEIaaUlpaCh8fH5SUlHTI0ZmcnBx899136Nq1K5599lmrV/DdtWsXampqMHHiRIkrJCIiar2Wfn7bLci0l44eZGpqapCUlITz589jwoQJVt2avmHDBjz00EPo168f9u/fD4VCYYNKiYiIWq6ln98OddcStZ6LiwtCQ0MBAHv27Gl2q4emTJ48GSqVCidPnkR0dDRvySYiIqfBINMBDBkyBH5+fqiursbmzZtbfbyXl5d4S7Zer8d//vMf6YskIiKyAQaZDkAmk2HKlCkAgAMHDuDUqVOt7iMkJASvv/46AOCZZ55Bbm6upDUSERHZAoNMB9GvXz+MGTMGALBp0yarLg+98MILuOuuu3D58mU8/PDDrb6lm4iIqL0xyHQg99xzD9zc3HD8+PFWb10A1N6S/eWXX0IulyM9PR2rVq2yQZVERETSYZDpQLy9vTF+/HgAtbesV1dXt7qPgQMHYsWKFfj4448xf/58qUskIiKSlJu9CyBpTZw4EVlZWSgqKkJGRgZuv/32Vvcxe/ZsG1RGREQkPY7IdDAeHh64++67AQDbtm3D1atX29RfaWkp/v3vf/OWbCIickgMMh3Q2LFj0atXL1y9ehU///yz1f1UVFRg3LhxePbZZ7F8+XIJKyQiIpIGg0wH5OLiIq7wu3fvXhQXF1vVT5cuXbBgwQIAwF/+8hccOnRIshqJiIikwCDTQQUEBEClUqG6uhqpqalW9/Pcc89hypQpuHr1KubMmYNr165JWCUREVHbMMh0UDKZDPfeey9kMhkOHz6M/Px8q/pxcXHB559/jt69e+PAgQN44YUXJK6UiIjIem0KMgUFBcjJyUFOTg5KS0ulqokk0rt3bwQHBwMANm7ciJqaGqv66du3r7htwQcffID169dLVSIREVGbtCrIlJaWYunSpQgICICrqys0Gg10Oh10Oh3kcjmGDBmCuLg4hhoHcvfdd6Nbt244f/48MjIyrO5n2rRpeO655wAAf/vb36wORURERFJqcZBZs2YNgoKCUFRUhNTUVFRXV6OwsBC5ubnIzc1FTU0NUlJSoFQqodFosHbtWlvWTS3UrVs38XbsLVu24MqVK1b39eabb+Kvf/0rNm/eDBcXXpUkIiL7kwktWCAkOzsbSUlJSExMbHHHixcvxpw5cxAYGNiW+tqstLQUPj4+KCkpgbe3t11rsZeamhokJSXh/PnzCA4Oxn333WfvkoiIiJrV0s/vFgUZZ8YgU6ugoACff/45ZDIZoqOj0adPnzb3uXLlSgQEBOCOO+6QoEIiIqL/aennt1XXB+Li4pp8be3atbys5ID8/PwwYsQICIKAjRs3tnml3sTERMyfPx8PP/wwSkpKJKqSiIiodawKMpmZmSgrK2twt1JcXBzefPNNJCYmYtmyZZIVSdIICwuDm5sbCgoKcPjw4Tb1NXfuXPj5+SE/Px+LFi3iFgZERGQXVgUZlUoFPz8/xMTEQKPRYN26dQCA5ORkbN68GZs2bUJSUpKkhVLbyeVyTJgwAQCwadMmVFZWWt2Xj48PVq1aBVdXV6xatQpffPGFVGUSERG1mNUjMiaTCZs2bcLRo0cRExMDACguLhavY8nlcsmKJOlMnDgR3t7eKCkpwc6dO9vU1x133IHXXnsNAPDUU0/hyJEjUpRIRETUYlYFmeLiYvH229LS0kZDi7X7+5BtdenSRdyHaceOHW0+T7GxsZg8eTIuX76MuXPnory8XIoyiYiIWsSqIJOYmIjBgwdjyJAhCAoKgiAImDJlClQqFRYtWoRFixZBp9NJXStJZOTIkfDz80NVVRU2btzYpr5cXV2xcuVKKJVKZGVltWlfJyIiotZq0+3XJSUl8PHxsXhu6dKlAOAwe/Lw9uvGXbhwAYmJiaipqcGcOXMwdOjQNvX3448/wtXVFVOmTJGoQiIi6sy4jsx1DDJNMxqN+OWXX+Dj44OnnnoK7u7u9i6JiIgIgI3XkVm7dq2431Ldw8XFBa6urlYXTO3vD3/4g/g/yfbt2yXrNz8/HzExMdyPiYiIbM6qILNw4ULEx8ejqKhIfBQXF6OoqEjq+siGunTpgqlTpwIAdu7ciYsXL7a5z6tXr2LChAlYunQp3n333Tb3R0RE1ByrgoxGo8Hs2bPh4+PT4EHOZejQoRgyZAhqamrwww8/tHlhu27duuHll18GULvfVmZmphRlEhERNcqqIBMeHo7IyEisXbsWOTk5KCgoEB/kXGQyGaZNmwY3Nzfk5+fj119/bXOf0dHRmDlzJiorKzF37lyUlZVJUCkREVFDVk32DQgIaPQykkwmQ2FhoSSFSYWTfVtm27Zt2Lp1K7y8vPD000/Dw8OjTf0VFRVhzJgxOHnyJObNm4cvvvgCMplMomqJiKijs+lk39zcXIv5MXUPRwsx1HITJ06EUqnEpUuXsHXr1jb3p1QqxS0MVq5cic8//7ztRRIREd2gxUEmJydH/Lr+paQbH+Sc3NzcMG3aNADAnj17cObMmTb3eeedd+LVV18FAHz22WfcWJKIiCTn1pJG2dnZCA4ORnV1NQAgKCio0XaOeGmJWi4gIAAjR47Er7/+ivXr12PBggXiVhTWWrx4MRQKBZ544gleWiIiIslxQTyycOnSJXz44Ye4du0apkyZgvHjx9u7JCIi6oRa+vndohEZAC2+bOTn59fSLskBeXl5ISwsDOvXr8eWLVswfPhwyXYyr6qqwiuvvILAwEDuxUVERJJocZAJCgqCTCZrdp4DLy11DGPHjsX+/ftx7NgxfP/993j44YcluSy0fPly/POf/4S3tzc0Gg38/f0lqJaIiDqzFk+AqLsrqbG7lXjXUscik8lw//33w9XVFbm5uTh48KAk/S5YsADjx49HaWkp5syZg4qKCkn6JSKizqttMzmpw+rZsycmTZoEANi4cSOuXLnS5j7d3d2xatUqyOVy7N27F3/729/a3CcREXVuVk/23bx5M7KysixGYXx9ffH8889LVpwUONnXetXV1UhKSsKFCxcQGBiIGTNmSNLvt99+i5kzZwIAfvjhB/G2byIiojo2XRBv8eLF0Ol0yM3NhV6vR3FxMVJTU5Genm51weR4XF1d8cADDwCoXUcoPz9fkn4feughPP300wCAP/7xjzh16pQk/RIRUedjVZBZs2YNioqKkJiYCK1Wi4SEBGRkZEhdGzmAgQMHIjg4GACwYcMGVFZWStLv0qVLERgYiCtXrmD//v2S9ElERJ2PVUGm/tUotVqNtLQ0AIDRaJSmKnIooaGh6NGjB4qKiiTZvgAAunbtCr1ej4yMDF5aIiIiq1kVZHQ6HdauXQsAiIqKwgsvvICQkBDeTttBde3aFffddx8AYNeuXTh58qQk/QYEBGD48OHi9x18bUYiIrIBq4LMW2+9hVmzZgEAfHx8kJqairfeeouXlzqwoUOHYtSoURAEAd999x2qqqok7X/Hjh2YMGECLly4IGm/RETUsVkVZNauXWuxiaS/vz/MZrM4SkMd09SpU9G9e3dcvHgR27Ztk6zfmpoaLFq0CLt378af/vQn1NTUSNY3ERF1bFYFmdjYWKhUKovntFotYmNjJSmKHJOnp6d4iemXX36R7G4jFxcXfPXVV/Dw8MAPP/yAf/3rX5L0S0REHZ9VQaawsLDBPd0+Pj5c2bcTGD58OG677TbJLzGNHj0a7777LoDa2/v37NkjSb9ERNSxWRVkgoODsW7dOovnNm/e3GCUhjqmadOmwdPTExcuXMD27dsl6zc6Oho6nQ5VVVWYM2cOzGazZH0TEVHHZNXKviaTCcHBwfD19YVWq0VeXh4yMzORmZnpcLtfc2Vf2zh06BD0ej1kMhkWLFiA/v37S9Kv2WzG2LFjUVBQgFmzZsFgMEiyYSURETkXm67sq1KpUFRUhLfeegsqlQpPPvkkCgsLHS7EkO2MGDECI0eOFC8xVVdXS9KvXC5HSkoK3N3dIZPJUF5eLkm/RETUMVkVZJYtW4aCggLMnj0bQUFBiImJwZAhQ7Blyxap6yMHVneJ6fz585LexRQSEoKMjAzo9Xp07dpVsn6JiKjjsSrIJCUliaMvERERWLx4MX766SdERUVJWRs5uO7du2P69OkAateBkWqhPKB28m/dJSVBEHDt2jXJ+iYioo6jTVsUpKWlobi4GAsWLIBKpeJdS53QyJEjxbuY1q1bh4qKCkn7LykpQWRkJObMmcOVf4mIqAGr58jExcUhOjpaHIXJz8/nXUud1PTp08W9mFJTUyXtOz8/H9999x2+++478fZsIiKiOlYFGb1eD5VKhdjYWCQmJgKovdskLi5O0uLIOXTr1g0zZswAAGRkZCA3N1eyvgMDA8UF8mJiYrB7927J+iYiIudn1e3XzoS3X7efH3/8EXv37oWXlxcWLVoET09PSfoVBAFz587F6tWrMWjQIGRnZ0OpVErSNxEROSab3n4tBZPJhISEBBgMBiQkJNx08TOj0Yjk5GQYjUYYjcb2KZJaRavVomfPnrh06RK+//57yea0yGQyJCcnY8iQITh+/DgeffRR7sdEREQA7BhkwsPDERMTA51OB51Oh4ULFzbZ1mg0Qq/XIyoqCiqVCtHR0e1YKbWUu7s7Zs6cCRcXFxw6dAgHDhyQrG9vb2+kpKTAw8MDGzZswNtvvy1Z30RE5LzsEmRMJpPF9yqVqtlRlujoaMTHx4ttpZ5QStLp378//vCHPwAAfvjhB5SUlEjWd2BgIN577z30798fd9xxh2T9EhGR87IqyCiVSixatAg5OTlWvanRaGwwx0GpVCIrK6tBW5PJhKKiIsjlcmRlZcFsNvPuKAc3adIk3HLLLSgvL8d3330n6W3TUVFROHToECZNmiRZn0RE5LysCjL5+fkICgrCggULMGTIEMTFxaGgoKDFxzc1H6aoqKjBc1lZWVAqlTAYDFCpVEhOTobBYGiy7/LycpSWllo8qH25uLhg5syZcHd3R35+Pnbu3ClZ3zKZDD4+PuL3+fn5nC9DRNSJWRVkfHx8sHDhQmRkZCAjIwMqlQrh4eEICQnBsmXLrA4PjQWcoqIimEwmaLVayOVyREVFITw8vMk+lixZAh8fH/ExcOBAq2qhtvH19cXUqVMB1O6MfurUKcnfY9WqVRg1apR42ZGIiDqfNs2RycnJweLFixEfHw9/f3+89dZb8Pf3h06na3YyplwubzD6Unf56EYqlQpyuVx8re6/jV2GAoC4uDiUlJSIjxMnTlj1s1HbjR07FiNGjEBNTQ3WrFkj+QaQV65cweXLl/HSSy9h+/btkvZNRETOwaogs3TpUgQEBCA8PBxqtRqZmZlISUlBaGgoZs+ejU2bNmHVqlVNHq/Vaht9Pjg4uMFzrZ0P4+HhAW9vb4sH2YdMJsMDDzwAHx8fFBcX44cffpC0/8cffxzz589HTU0N5syZg/Pnz0vaPxEROT6rgkxeXh70ej2OHj2K559/3mLOQp3mbpG+MZyYTCYEBwdbjLbU3dmkUqkQHBwsXnYymUxQqVQICgqypnRqZ127dsWsWbMgk8mwf/9+7N+/X7K+ZTIZPv74YwwfPhxnzpzBvHnzOF+GiKiTsSrIBAQEYOzYsc22aW5dGKB2m4PY2FgYDAYkJSVBr9eLry1ZssRiQm9d2+TkZMTHx/P2ayczaNAg3HXXXQCA77//vtFJ3dbq3r079Ho9PD09kZqaijfeeEOyvomIyPFZtUVBREQE5s6di5kzZ9qiJklxiwLHUFNTgy+++ALHjh1D//798fjjj8PV1VWy/j///HP86U9/gkwmwy+//ILx48dL1jcREbW/ln5+u1nTeVxcHCIiImAymRAaGmoxSdfPz8+aLqmDq7slOzExEadPn8aWLVuanCtljUcffRS//PILfH19ERISIlm/RETk2Kwakak/Z8WiM5kMR48elaIuyXBExrEcPnwYKSkpAIB58+ZBrVZL1rcgCJDJZJL1R0RE9tPSz2/ufk3tbsOGDcjMzISnpyeio6Ntcl7Ky8uxfv166HQ6yfsmIiLbs+mlJaB2kbOsrCwUFhaKz/n6+uL555+3tkvqJKZOnYpTp07h7NmzWLNmDR599FG4uEi37VdFRQUmT56M3bt3Y9WqVZgzZ45kfRMRkWOx6tNj8eLF0Ol0yM3NhV6vR3FxMVJTU5Geni51fdQBubm5ITw8HF26dMHx48exefNmSfvv0qUL7rnnHgDAggULcOjQIUn7JyIix2FVkFmzZg2KioqQmJgIrVaLhIQEZGRkSF0bdWBKpRIzZswAAPzyyy84cuSIpP2/9tpruOeee3D58mXodDpcunRJ0v6JiMgxWBVk6k+rUavVSEtLA1C7qzVRS40YMQLjxo0DAKxbt67JzUSt4erqilWrVqF///44fPgwFi5cKOku3ERE5BisCjI6nQ5r164FAERFReGFF15ASEgI/P39JS2OOr6wsDD0798f165dg8FgQHV1tWR99+7dGykpKXBzc8M333yDDz74QLK+iYjIMUhy11J+fr64poyj4V1Ljs9sNiMpKQnXrl3D7bffLu6aLZV//etfeO6559CzZ0+YTCb06NFD0v6JiEh6Lf38tmpEZtmyZRbf+/v7O2SIIecgl8vx0EMPAQD27Nkj+eTcv/zlL3jxxRexe/duhhgiog7GqiCzd+9erFu3TupaqBMbOnSouK3Ad999h4sXL0rWt0wmwz//+U9JF98jIiLHYFWQiYuLQ0xMDN5++23k5OSgoKBAfBBZKzQ0FIMHD0ZFRQW++eYblJeX2+R9UlNT8fbbb9ukbyIial/cooAcyqVLl5CcnIyysjIMGzYMERERkm47cPDgQYwePRpA7U7c06ZNk6xvIiKSDrcouI5BxvmcPHkS//nPf1BdXY177rkHkyZNkrT/RYsWITExEUqlEllZWRg8eLCk/RMRUdvZdLJvYyuxlpSUiLdkE7XFgAEDxJGSzZs3Izc3V9L+3333XQQHB6OoqAg6nQ7Xrl2TtH8iImo/VgWZ2NjYBs/5+PhgyZIlbS6ICAA0Gg2CgoIA1K4kXVxcLFnfHh4eMBgMUCqVyMjIwFNPPcXF8oiInFSrNo1csWIFAMBkMolf18nNzUVmZqZ0lVGnN23aNJw7dw6nTp3C6tWr8cQTT8Dd3V2SvgcPHoxvvvkGU6dOxaefforbb78dUVFRkvRNRETtp1UjMrm5ucjNzYUgCMjIyLB4AGCQIUm5ubkhIiICnp6eOHfuHNavXy/pyElYWBjefPNNAEB2drZk/RIRUfuxarLvk08+icTERFvUIzlO9nV+BQUF+OKLLyAIArRaLSZOnChZ34Ig4KeffsK9994r6d1RRETUNpLftZSTk4PAwEAAaHa9GD8/v9bUaXMMMh3D3r178eOPPwIA5s6di1tvvdUm71NdXQ1BEODm1qqrrkREJLGWfn636G/r7OxsBAcHixv6BQUFQSaTNRjml8lkKCwsbEPZRI0LCQnB+fPnkZmZiTVr1uCJJ55A7969JX2PoqIiPPLIIxg+fDjeeecdSfsmIiLb4Doy5DSqq6uxcuVKFBQUQC6XY+HChfD09JSs/++//x73338/AODrr7/G3LlzJeubiIhax6bryAC1l5eWLVuGRYsWYdmyZTh27Ji1XRG1iKurK8LDw6FQKGA2m5GSkiKOEkrhvvvuw4svvggAeOKJJ7B//37J+iYiItuwKsgsXboUKpUKeXl5UKlUyM3NhUql4kaSZHOenp6YM2cOunTpgmPHjuHHH3+U9E6m1157DVOmTMHVq1cxa9YsSdevISIi6Vl1acnX1xcZGRnw9/cXn8vKykJkZCT3WqJ2ceTIEaxatQpA7Xoz48aNk6zvwsJCBAcHo6CgANOnT8f69evh4mL14CUREVnBppeW/P39LUIMUDsB2MfHx5ruiFrt1ltvhVarBQBs3LgReXl5kvXt6+uLdevWoWvXrvjhhx/wxhtvSNY3ERFJq8VBpqCgQHxERkZi2bJlyMnJER/Lli1DSEiILWslsjBhwgSMGTMGgiAgJSUF586dk6zvwMBALF++HH5+fnjggQck65eIiKTV4ktLAQEBjd5yXZ9CoUB6erpkxUmBl5Y6tqqqKqxcuRLHjh2Dt7c3FixYgB49ekjW/5UrVyS9M4qIiFpG8gXxnBWDTMd39epVfPLJJygsLETfvn3x2GOPoUuXLpK/T1ZWFm699VZ4eXlJ3jcREVmy6RyZZcuWWV0YkdS6deuGRx55BJ6enjh79izWrFmDmpoaSd/jq6++wvjx4/HYY49xp2wiIgdiVZDZu3cvb7Umh6JQKDB37ly4ubnhyJEj2Lhxo6SBw9/fH4IgwGAwiBtNEhGR/VkVZOLi4hATE4O3334bOTk5FhOBiexlwIABmDlzJgAgPT0de/bskazvCRMm4MMPPwQA/P3vf8eGDRsk65uIiKxn1RyZ4OBgmM3mhp3JZFxHhuxu586dSE1NBQBERERg+PDhkvX91FNP4aOPPkKPHj2wZ88eSfsmIqL/4WTf6xhkOh9BEPD9998jMzMTrq6umD9/PgYPHixJ35WVldBqtdi+fTuGDBmCvXv3Qi6XS9I3ERH9j833WiJyVDKZDNOnT8fQoUNRXV2Nb775RrI1Ztzd3aHX6zFw4EAcPXoUiYmJkvRLRETWsXpEZvPmzcjKykJhYaH4nK+vL55//nnJipMCR2Q6r8rKSnz55Zc4ceIEevTogccff1yy0ZPs7Gx8//33ePHFF7l9ARGRDdh0RGbx4sXQ6XTIzc2FXq9HcXExUlNTHW4xPOrc3N3dMXfuXPTq1QtlZWVYuXIlrly5IknfY8eOxUsvvcQQQ0RkZ1b9LbxmzRoUFRUhMTERWq0WCQkJyMjIkLo2ojbr1q0b5s2bB29vbxQWFuLrr79GRUWFpO9x5coVPPHEE8jJyZG0XyIiujmrgkz9q1FqtRppaWkAAKPRKE1VRBLy9vbGvHnz0LVrV5w6dQoGgwHV1dWS9f/SSy/h008/xUMPPYQLFy5I1i8REd2cVUFGp9Nh7dq1AICoqCi88MILCAkJabAjNpGj6NWrFx5++GG4ubnh6NGjWL9+vWQL5v3973/HkCFDcOzYMYSHh6OyslKSfomI6OYkuf06Pz8fJpMJoaGhUtQkKU72pfqOHDmCb775BoIg4I477sCUKVMgk8na3O/hw4dx++23o6ysDE8//TTef/99CaolIuq8JJ3sW1JSgtLS0iZf9/f3bzTEcKVfcjS33norHnzwQQDA7t27sW3bNkn6HT58OFauXAkA+OCDD/Dpp59K0i8RETWvxZeWdDodtmzZ0qK2aWlpCAkJgVKptLowIlsJDAzE1KlTAQDbtm3Dzp07Jen3wQcfxGuvvQYAWLRokWT9EhFR09xa0sjHxwcpKSmIiIhAVFQUdDodQkJCoFKpIJfLYTKZYDabkZ6eDr1eD7VaDaPRyEs55LBuv/12lJeXY8uWLUhNTYWHhwc0Gk2b+/3b3/6Gffv2Ydu2bZLvwE1ERA21eo5Mfn4+DAYDUlNTYTKZUFRUBJVKBaVSiaCgIERHRzvUpF/OkaGmCIKAtLQ0/PLLLwCAmTNnYvTo0W3u9/Lly7hw4QL8/Pza3BcRUWfFvZauY5Ch5giCgB9//BHp6emQyWSIiIjAsGHDJH0Pk8kEPz8/Lp5HRNQK3GuJqAVkMhmmTZuGMWPGQBAEGAwG5OXlSdb/hg0bMGbMGLz00kuS9UlERP/DIEOdnkwmw4MPPojhw4ejuroaq1evluyOO7PZjEuXLmHJkiXiXU1ERCQdBhkiAC4uLpg9ezYCAgJQWVmJr7/+GseOHWtzv/PmzcPixYsBAAsWLMDu3bvb3CcREf0PgwzRda6uroiMjIRarUZlZSW++uorScLMP//5T8yYMQPl5eV46KGHcPz4cQmqJSIigEGGyIKbmxsiIyOhUqnEMNPW4OHi4oKVK1di9OjROHfuHGbMmIHLly9LVDERUedmdZDZvHkznnzyScTFxQGoXQRv8+bNkhVGZC/u7u6YM2eOpGHGy8sL//3vf9G7d2/k5OTgX//6l0TVEhF1blYFmbi4OCQlJSE6Olrc8To4OBixsbGSFkdkL3Vhxt/fHxUVFfjqq69w4sSJNvU5ePBgrFu3Dv/3f//H3xUiIolYtY7MkCFDcPToUQC1ASYjI6PB846C68hQW1RWVmLVqlXIz89Hly5dMG/ePAwcONDeZRERdXg2XUfGx8cH+/btAwBx5+Ds7Gz4+PhY0x2Rw3J3d8fcuXPh5+eHiooKrFy5UpIJwEBtSPrLX/6CPXv2SNIfEVFnZFWQSUlJwd133417770XxcXFWLRoESIiIrBixQqp6yOyu8bCjBSL5i1ZsgTvvfceZsyYIVk4IiLqbNq0RcGaNWtgMpkgl8sRERHRqhEZk8kEg8EAlUoFk8mEqKgoyOXymx4XGxuLuLi4FrUFeGmJpFNZWYmUlBTk5ubC1dUV4eHhGDp0qNX9lZWVYdKkSdi3bx9GjhyJX375haOaRETX2XSvpbVr10KlUiEwMFB8bs2aNZDJZJg1a1aL+tBoNMjMzARQG2piY2Oh1+ubPSYrKwsajQbFxcUMMmQXVVVVWLNmDX777Te4uLhg1qxZGDlypNX9nTx5EuPGjcOZM2cwZcoUbNiwAe7u7hJWTETknGw6RyY2NhYqlcriudDQ0BbfiWEymSy+V6lU4t1PNzvuxvclak9ubm7Q6XS47bbbUFNTgzVr1ojzxawxYMAArF+/Hp6enti0aROeeeYZdPB9XImIJGVVkCksLGyQjuRyOQoLC1t0vNFohFKptHhOqVQiKyuryWMMBgN0Ol3riyWSmKurK2bOnInAwEAIgoBvv/1WHF20hkajwddffw2ZTIakpCS88847ElZLRNSxWRVkgoODsW7dOovnNm/e3OLRErPZ3OjzRUVFTbZv6aWk8vJylJaWWjyIpObi4oIHH3wQISEhAGp3ud61a5fV/c2YMQPvvPMOvLy8MGzYMKnKJCLq8NysOSgxMRHBwcGIiYmBVqtFXl4eMjMz2/SvUqDpgJOSkoKoqKgW9bFkyRK8+uqrbaqDqCVkMhmmTZsGd3d37Ny5E5s2bcKVK1dwzz33iMsStMazzz4LnU6HAQMG2KBaIqKOyaoRGZVKhaKiIixZsgQqlQrR0dEoLCyEn59fi46Xy+UNRl+KiooaHXUxGo2IiIhocW1xcXEoKSkRH21djZWoOTKZDFqtFvfccw8AYMeOHVi/fj1qamqs6qt+iDGZTNxgkojoJqwakalj7ZwVrVaLpKSkBs8HBwc32j4lJUX82mQyYcmSJYiMjERQUFCDth4eHvDw8LCqLiJryGQyTJo0Cd27d8eGDRuQnZ2Nq1evYvbs2XBzs+5XLD09HdOnT0e/fv2wY8cO3nFHRNQEq0Zk1q5di4CAALi6uooPFxcXuLq6tuj4G+fSmEwmBAcHiyMyWVlZ4p1NWq0WUVFR4gMAoqOjGw0xRPYUFBSE8PBwuLq64rfffsNXX32F8vJyq/rq06cP3NzccODAAURGRqKqqkriaomIOgargszChQsRHx+PoqIi8VFcXNzkZN3G6PV6xMbGwmAwICkpyWINmSVLlsBgMFi0N5vNSEhIAADEx8c3e4cTkb0MHz4c8+bNQ5cuXVBQUID//Oc/uHTpUqv7GTRoENavX49u3bph48aNePbZZ3lbNhFRI6xaEG/KlCnYtGmTLeqRHBfEI3s4c+YMvvrqK1y+fBkKhQLz58+HQqFodT/r1q3D7NmzIQgC3nnnHfy///f/bFAtEZHjsemCeOHh4YiMjMTatWuRk5ODgoIC8UFEQL9+/fD4449DLpejuLgYK1aswKlTp1rdz8yZM7F06VIAwF//+tcGI5VERJ2dVSMyAQEBjV5GkslkLV4Ur71wRIbsqaysDF9//TXOnj0LNzc3zJ49u9XrxAiCgGeeeQYffvghJk+ejM2bN1t1ezcRkTOx6V5LzoRBhuytoqICer0eubm5AIBp06Zh3Lhxreqjuroa77zzDp566il4enraokwiIofCIHMdgww5gpqaGnz//ffiJPXx48cjLCysTSMr165dQ9euXaUqkYjIobT089vqdWQ2b96MrKwsi0tJvr6+eP75563tkqjDcnFxwf333w+FQoG0tDTs2rULJSUleOihh1q927UgCHj55Zfxww8/YOvWrfDy8rJR1UREjs+qyb6LFy+GTqdDbm4u9Ho9iouLkZqaivT0dKnrI+owZDIZ7rzzTsyaNQuurq44dOgQvvzyS1y+fLlV/Zw9exYff/wxMjMzER4ejsrKShtVTETk+KwKMmvWrEFRURESExOh1WqRkJCAjIwMqWsj6pBGjRqFefPmoWvXrjhx4gRWrFiB8+fPt/j4fv36YcOGDeIaM4sWLeIaM0TUaVkVZOr/palWq5GWlgagdl8kIro5Pz8/PPHEE1AoFDCbzfjkk09w5MiRFh9/++2345tvvoGLiws++eQTvP766zaslojIcVk12Xfx4sUYN24cZs2ahZKSEmg0GigUCgiC4HAjM5zsS47sypUr0Ov14hpMWq0WEyZMaPEk4MTERCxatAgA8Omnn+Kxxx6zValERO2qXe9ays/Ph8lkQmhoaFu7khyDDDm66upq/Pjjj8jMzAQAjBkzBvfff3+LN5yMi4vDW2+9BTc3Nxw5cgT+/v62LJeIqF1IHmRycnIQGBgIAM2u4Ovn59eaOm2OQYacgSAISE9Px8aNGyEIAgYOHIjIyEh07979psfW1NRgwYIFmDx5Mv74xz+2Q7VERLYnaZDJzs5GcHAwqqurAQBKpRIymazBBEOu7EvUNnl5edDr9SgvL4ePjw/mzJmDvn372rssIqJ2J+leS2PHjhVDDAAUFRWhsLDQYvfruueIyHpqtRoLFiyAUqlESUkJPvnkE+zfv79VfZw9exYzZ87E6dOnbVQlEZHjsOqupWXLlkldBxFd17NnTyxYsAABAQGoqqrCunXrsHHjRot/TDTnT3/6E7799ltMnToVZrPZtsUSEdmZVUFm7969WLdundS1ENF13bp1w9y5czFp0iQAwJ49e/DFF1/g0qVLNz32o48+Qt++fXHgwAE8+OCDuHr1qq3LJSKyG6vuWsrOzkZERASefPJJhIaGQi6Xi69xsi+RtH777TesW7cOFRUV6NGjB8LDwzFw4MBmj9m3bx/+8Ic/oLS0FDNmzIDBYGjxXVBERI7AprdfBwcHNzpkLZPJcPTo0dZ2Z1MMMtQRXLx4EatXr8bFixfh4uKCadOmQaPRNLvezLZt23DvvfeivLwcCxYsQHJycps2qSQiak/c/fo6BhnqKMrLy/Hdd9/h8OHDAIDAwEBMnz692U0n161bB51Oh5qaGrz11luIjY1tr3KJiNrE5kHGWXa/ZpChjkQQBPzyyy/YvHkzBEFAr169EB4ejl69ejV5zIoVK7Bs2TL89NNPGDx4cDtWS0RkPUlvv74Rd78mso+6HbTnz5+P7t2748KFC1i+fDn27dvX5DELFixATk4OQwwRdUjc/ZrICfn7++PJJ5+Ev78/Kisr8e233+K///0vKisrG23ftWtX8etvv/0WP/30U3uVSkRkU9z9mshJeXl5Yd68ebjrrrsA1N5NuGLFCly8eLHJY9LS0jB79mzMnDkTO3bsaK9SiYhshrtfE3UAJpMJa9euxeXLl+Hu7o77778fo0ePbtCuoqICDz30EH788Ud4e3tjy5YtCAoKskPFRETN4+7X1zHIUGdx6dIlrFmzRtzUdfTo0Zg+fTo8PDws2l25cgVTp07Fzz//jJ49e2L79u0YPny4HSomImqaTSf7KpVKREZGYvPmzQBqr9c7Yogh6ky8vLwwf/583HXXXZDJZNi/fz8SExNx4sQJi3aenp7YsGEDNBoNLl68iLCwsGZ3tCcicmRWBZn8/HxotVq89dZb8PX1tQg1RGQ/Li4umDx5Mh577DHI5XKYzWZ89tln2LZtG2pqasR23t7e2LhxI0aMGIFTp04hNDSUm74SkVNq86WlkpISGI1GrF69GmlpaQ73lyEvLVFnde3aNfzwww84cOAAAGDQoEGYOXOmxZYip0+fxp133on7778f7777LlxcrPq3DRGR5NpljkxpaSlSUlKQkpKCjIwMREREIDEx0drubIJBhjq7/fv34/vvv0dFRQU8PDxw//3347bbbhNfLywshFKp5PYFRORQbDpHZsWKFQgODoa/vz9SU1MRGxsrritDRI5l9OjRePLJJzFgwACUl5djzZo1MBgMuHLlCoDaFbnrQkxFRQVef/117phNRE7DqhGZiIgIREdHO8UEX47IENWqqanBtm3b8PPPP0MQBHh5eeH+++/H0KFDxTZz5szB6tWrMX36dKxbtw5dunSxY8VE1Jm1y6WlzZs3w2QyQaVS4Z577rG2G5tikCGydPr0aaxbt05cOC8wMBD33nsvunbtip9//hn33nsvrl69Cp1Oh1WrVsHNzc3OFRNRZ2TTIFN/ETx/f3+YTCaUlJQgNTUVfn5+balbcgwyRA1VVVVh8+bN2LVrF4Dau5hmzJgBlUqFjRs34sEHH0RlZSUefvhhfPHFF3B1dbVzxUTU2dg0yNx7772IiYmxuLRkMBiwfPlyh9vDhUGGqGnHjx/Ht99+i+LiYgBAcHAwwsLC8OOPP0Kn06GqqgqPPvooPv30U97RRETtyqZBJiAgALm5uQ2e9/X15e3XRE6moqICqamp4vYicrkcDzzwALKzsxEZGYnq6mo888wz+Pe//23nSomoM7HpXUsqlQr79u2zeC4nJwf+/v7WdEdEdtSlSxfcd999mD9/Pry9vWE2m/Hll1/Czc0Nn3zyCXx8fBAeHm7vMomIGmXViIzJZBKHoFUqFfLy8pCWloa0tDQEBgbaoEzrcUSGqOXKy8uRlpaG9PR0AED37t0xYcIEjB8/nuvMEFG7svmITFFREbRaLQRBQFhYGEwmk8OFGCJqHQ8PD0yfPh2PP/44evbsicuXLyM1NRWrV69GaWkp9u/fjzfeeAMS7DVLRCQJSXa/dmQckSGyTlVVFX7++Wfs2LEDNTU1qK6uxr///W+UlJTg73//O1577TV7l0hEHZjkIzKLFi3C2rVrm3x9+fLliIuLa12VROSw3NzccPfddyMqKgq33HILXF1dMX78eADA66+/jjfeeMPOFRIRtWJExtfXF/n5+c2moiFDhuDo0aOSFScFjsgQtV1NTQ327t2LzZs3Y+vWrUhNTQUAvPzyy3jllVfsWxwRdUgt/fxu8ZKdgiDcNAg42q3XRCQNFxcX3HHHHRg+fDgCAgJQU1ODtLQ0vPrqq7h48SLef/99TgYmIrto8aUllUqFY8eONfl6fn4+VCqVJEURkWPy8fFBREQEPv74Yzz44IMAgA8//BDR0dEoKiqyc3VE1Bm1OMhEREQgKiqqydeffPJJzJkzR5KiiMixDRkyBAaDAU8++ST8/PzQq1cvfPTRR9i6dSsqKyvtXR4RdSKtumspLCwMBQUFWLx4MTQaDQAgIyMD8fHxUKlUDrc9AcA5MkS2du7cOaSmpiIvLw9A7b5NYWFhGDlyJC83EZHVbLZFQXJyMhISEmAymQAAarUaMTExWLhwYdsqthEGGSLbEwQBhw4dwvPPP4+SkhJMmTIFgwcPxtSpU9GvXz97l0dETsimey05EwYZovaRmZmJ4OBgAMD48eMxZcoUyGQyjB07FqGhoejevbudKyQiZ2LTlX2JiG6k0WiQmJgIANi1axfS09MhCAKys7Px/vvvY+fOnaiurrZzlUTU0TDIEJFkoqOjsXz5cshkMvzwww/Iy8tDnz59UF5ejtTUVHz00Uc4fPgwtzggIskwyBCRpBYsWIBPPvkEMpkMK1euxM6dOzF9+nR0794dRUVFSElJwWeffYbjx4/bu1Qi6gA4R4aIbGLVqlWYP38+qqur8dNPP+Guu+7Czp07sWvXLvEW7WHDhiE0NBQ9e/a0c7VE5Gg42fc6Bhki+/nvf/+LY8eO4ZlnnhGfKysrw9atW5GdnQ1BECCTyRAUFITJkyfDy8vLjtUSkSNhkLmOQYbIcVy4cAEeHh7w9vbGhQsXYDQaceTIEQCAu7s7JkyYgPHjx8PDw8POlRKRvTHIXMcgQ+QYzGYz7r77bri5uWHjxo3w9fUFABQUFCA1NRWnT58GAHTr1g133nknQkJC4O7ubs+SiciOGGSuY5AhcgwHDx7E3XffjYsXL2LkyJFITU0VF8urW1Bvy5Yt4uazXl5emDRpEoKCguDm1uL9bYmog2CQuY5BhshxHDp0CGFhYTh9+jQCAgJgNBoxePBg8fWamhrs27cP27ZtQ0lJCYDaLQ/uuusujBkzBq6urvYqnYjaGYPMdQwyRI7FZDIhNDQUBQUFGDBgAH766SeMGDHCok11dTWysrLw888/o6ysDACgUCgwefJk3HbbbXBx4coRRB2dwwcZk8kEg8EAlUoFk8mEqKgoyOXyRttmZWXBaDQCANLT07F8+fIm296IQYbI8Zw8eRJhYWH47bffoFQqsXHjRoSEhDRoV1lZiYyMDOzYsQNXrlwBAPj6+uLOO+/EqFGjOEJD1IE5fJDRaDTIzMwEUBtqYmNjodfrG22bkJCAmJgY8evVq1eLx94MgwyRYyosLMR9992HkydPYufOnRg0aFCTbSsqKrBnzx7s3LkT165dAwDI5XLceeedGDNmDOfQEHVADh1kTCYTwsPDLcKIQqFAcXFxg7ZZWVkIDQ0VXzOZTFCr1cjLy4NKpbrpezHIEDmuy5cv48yZMwgICGhR+/LycmRkZGDXrl24fPkygNo5NBMmTEBQUBDvciLqQBx600ij0QilUmnxnFKpRFZWVoO2QUFBWL58ufi92WwW2xORc+vevbtFiFmzZg3eeeedJtt7eHhg4sSJePbZZ3HvvffCy8sLpaWl2LhxI9577z3s3LkTFRUV7VE6ETkIu4zH1oWRGxUVFTX6vE6nE79evXo1tFpti+fIEJFzyM3NxcMPP4yKigqcPXsW8fHxkMlkjbZ1d3fHHXfcgeDgYGRnZ+OXX35BSUkJUlNTsWPHDoSEhGDcuHHo3r17O/8URNTeHGrqf1MBp/7rBoOhybk0QO3Qc2lpqcWDiByfWq3G66+/DgBYunQpHnvsMXFPpqa4ubkhJCQEzzzzDB588EEoFApcvXoV27dvx7vvvosNGzY0+Q8kIuoY7BJk5HJ5g79cioqKbjrKEhsbi9TU1GbbLVmyBD4+PuJj4MCBElRMRLYmk8kQExODTz/9FK6urvj8888xc+ZMcS5Mc1xdXTF27Fg8/fTT0Ol06N+/P6qqqpCZmYn3338fKSkpOHnyZDv8FETU3hxqsm9+fn6TISUhIQE6nQ4qlUocuWmsbXl5OcrLy8XvS0tLMXDgQE72JXIi69evR0REBK5duwaNRoP169eLqwC3hCAIOHbsGHbu3ImjR4+Kzw8ePBgTJkzAkCFDmrxsRUSOwaEn+954t5HJZEJwcLAYTLKysmAymcTXDQYDgoKCxBCTkpLSZOCp25Cu/oOInMsDDzyALVu2oGfPnsjMzMSnn37aquNlMhn8/Pzw8MMPY9GiRRgzZgxcXFxw7NgxrFq1Ch9++CH27t3LicFEHYBdF8RLSkpCSEgI0tPTERcXJ4aT8PBwhISEICYmRrzduj65XN7ordqN4e3XRM4rLy8PH3/8MRISEtq8mm9paSl2796NzMxMMcB4eHhg7NixGDduHBQKhRQlE5FEHHodmfbEIEPUcZSXl2Pz5s2YNm1am/rIycnB3r17LebqDR06FOPGjYO/vz8vOxE5AAaZ6xhkiDqGmpoazJs3D6tWrcJrr72Gl156qU2BQxAE5ObmYs+ePcjLyxOf79WrF26//XaMHj2aC+wR2VFLP7+5rjcROY26uxD/8Y9/oKCgAImJiVaHDZlMhiFDhmDIkCG4ePEi9u7di5ycHFy4cAEbNmxAamoqxowZA41Gg969e0v5YxCRhDgiQ0RO5eOPP8bTTz+Nmpoa3HPPPdDr9ZKt9H3t2jVkZ2cjPT3dYh7eoEGDoNFoMGLECO7rRNROeGnpOgYZoo7n+++/x5w5c3Dp0iUEBARg/fr1GDZsmGT9C4IAk8mEzMxM/Pbbb6j7a7Jbt24IDAyERqOBr6+vZO9HRA0xyFzHIEPUMe3fvx8PPPAAjh8/DpVKhd9//90moyVlZWXIyspCVlaWxUrh/v7+CAoKwrBhwzhKQ2QDDDLXMcgQdVznz59HZGQkXn75ZUyePNmm71VTU4Pc3FxkZGRYLLLXtWtX3HbbbQgMDET//v15xxORRBhkrmOQIerYBEGwCA/79+/H8OHDbXrHkdlsRnZ2NnJycixGaXr37o3AwECMHj2aG1YStRGDzHUMMkSdx+HDh3HHHXcgKCgIBoPB5vNYampqkJ+fj5ycHBw+fBjV1dUAABcXFwwZMgSBgYEYMmQIXF1dbVoHUUfE26+JqNM5efIkampqsHXrVtx+++349ttvcdttt9ns/VxcXKBWq6FWq3Ht2jUcPHgQOTk5OHXqFH7//Xf8/vvv8PT0xMiRIzF69GjccsstvPREJDGOyBBRh3Lw4EE88MADKCgoQPfu3fHZZ58hPDy8XWs4f/48cnJysH//fovduxUKBUaNGoVRo0ahZ8+e7VoTkbPhpaXrGGSIOp+LFy9izpw5SEtLAwC88MILePPNN9v97qLq6mqYTCYcOHAAv/32GyorK8XX+vXrh1GjRuG2225Djx492rUuImfAIHMdgwxR51RVVYUXX3wRS5cuBQB88MEHeOqpp+xWT0VFBX7//XccOHAAubm5qP9Xr7+/P0aNGoVhw4ahW7dudquRyJEwyFzHIEPUuaWkpOA///kPvvvuO4fZO+ny5cv49ddfcfDgQZw4cUJ83sXFBf7+/hgxYgSGDRsGT09PO1ZJZF8MMtcxyBBR/Vu0KysrsWnTJtx33312rqpWcXExDhw4gF9//RXnz58Xn5fJZGKoGT58OEMNdToMMtcxyBBRff/v//0/vPvuu4iOjsa7776Lrl272rsk0cWLF3Ho0CEcOnQI586dE5+vCzXDhw/H8OHDuUYNdQoMMtcxyBBRHUEQ8Nprr+HVV1+FIAgIDAxESkoKhgwZYu/SGigsLBRDzdmzZy1eGzhwIIYNG4ahQ4dyzyfqsBhkrmOQIaIbbdq0CY888gguXryIHj16YMWKFYiIiLB3WU0qKioSQ82ZM2csXuvZsyeGDh2KYcOGcZ0a6lAYZK5jkCGixpw6dQpz587Fzz//DAD485//jLffftuhLjU1pqSkRFxsr6CgADU1NeJrXl5euPXWWzF06FCoVCpuZklOjUHmOgYZImpKVVUVXn75Zbz55pvw8vJCTk4O1Gq1vctqsWvXruHo0aP4/fffkZubi/LycvE1d3d3qFQqBAQEICAgAHK53H6FElmBQeY6BhkiupmNGzfiypUrmDVrlr1LsVp1dTUKCgrw22+/4ciRIxabWQJAr169EBAQgCFDhmDQoEHc/4kcHoPMdQwyRNRaqamp+Oabb/Dee+/By8vL3uW0miAIOHv2LHJzc5Gbm4sTJ05YLMDXpUsX+Pv7i8HGx8fHjtUSNY5B5joGGSJqjfLycgQEBODkyZMICAjAV199hXHjxtm7rDa5evUqTCYTcnNzcfToUYv9n4Da0RqVSgWVSoXBgwfDw8PDTpUS/Q+DzHUMMkTUWtu3b8e8efNw4sQJuLm54dVXX0VsbGyHuBxTN1pz9OhR5Obm4uTJkxajNS4uLrjlllvEYHPLLbd0iJ+bnA+DzHUMMkRkjeLiYixatAirV68GAEyaNAkrV67EoEGD7FyZtK5evYr8/HyYTCaYTCYUFxdbvN6lSxcMHjxYDDa9evXiLd7ULhhkrmOQISJrCYKAL7/8Ek899RQuXboEHx8f/Prrr7jlllvsXZrNFBcXi8EmPz8fV65csXi9e/fu8PPzw+DBgzF48GAGG7IZBpnrGGSIqK3y8vIwb948+Pv74+uvv7Z3Oe1GEAScO3dOHK05duwYqqqqLNp4enpi0KBBYrDp06cPXFxc7FQxdSQMMtcxyBCRFKqqqnD16lX06NEDAHDu3Dn8/PPP0Ol0dq6s/VRVVeHkyZM4duwYjh07hhMnTjQINh4eHhg8eDAGDRoEPz8/9OvXj8GGrMIgcx2DDBFJTRAEzJo1C99++y0iIiLw4YcfomfPnvYuq91VV1fj9OnTYrA5fvw4KioqLNq4u7tjwIABGDBgAAYOHIgBAwagW7dudqqYnAmDzHUMMkQkterqarz66qt48803UV1djd69eyMxMREzZ860d2l2VVNTgzNnzlgEm2vXrjVo17NnTzHYDBw4ED179uQ8G2qAQeY6BhkispXMzEw8+uij+PXXXwEA4eHheO+999CvXz87V+YYampqcOHCBZw8eRInTpzAiRMnUFRU1KBd165dLUZs+vfv7/B7XpHtMchcxyBDRLZUXl6OV155BUuXLkV1dTV8fHzwww8/YMKECfYuzSFdvnwZJ0+eFMPNqVOnGsyzAQBfX1/ccsst6N+/P/r374++ffvC3d3dDhWTvTDIXMcgQ0TtITs7GwsXLsTFixdx8OBBp9zawB6qq6tx7tw5nDhxQgw4ZrO5QTuZTIbevXujf//+YsDp3bs3F+vrwBhkrmOQIaL2UlVVhePHj0OlUgGovbSyYsUKPProo1z2vxUuX76M06dPi49Tp0412FYBANzc3NC3b1/069cP/fr1Q9++fdGrVy+4ubnZoWqSGoPMdQwyRGQviYmJWLRoEYYOHYr3338fYWFh9i7JKQmCgNLSUjHU1AWc8vLyBm1dXFzQq1cv9O3bV3z06dOHd0o5oZZ+fjO2EhHZSJ8+fdCnTx/8/vvvmDJlCmbPno23334bgwcPtndpTkUmk8HHxwc+Pj4YPnw4gNpwU1RUhFOnTuHs2bM4e/Yszpw5g2vXruHcuXM4d+4c9u3bJ/Yhl8vFUNOvXz/06dMHPj4+vFuqA+CIDBGRDZnNZrzyyiv44IMPUF1djW7duuHFF1/E888/zztzJFY3clMXbOoejc25AWr3kerdu3eDR/fu3du3cGoULy1dxyBDRI7gwIEDeOaZZ7Bt2zYAQEREhLghJdnW1atXce7cOYtwc+HCBdTU1DTa3tPTs0G46dWrF4NnO2OQuY5BhogchSAIWL16NWJiYrBmzRqEhISIz/MSR/uqrq5GYWEhzp8/j/Pnz+PChQs4f/58o+vc1PH29kavXr3Qs2dPi0f37t15/myAQeY6BhkicjSVlZUWa6IsXrwY586dw+uvv44BAwbYsTKqrKwUQ039gFNaWtrkMR4eHg3CTc+ePaFQKHh7eBswyFzHIENEjuzixYsYMGAAysvL0a1bN/z1r39FTEyMuDklOYZr167h/PnzuHjxosXDbDajqY9RFxcXKBQK9OzZE76+vlAqleJ/e/TowVGcm2CQuY5Bhogc3Z49e/D8889jx44dAIDevXvj73//OxYuXMj1ZxxcVVUVioqKGgScixcvorKyssnj3NzcoFAooFQqGzy8vb25YzgYZEQMMkTkDARBwHfffYeYmBgcPXoUADBw4EB888033O7ACQmCgLKyMotgU1xcjKKiIhQXFzc5igMArq6ukMvl8PX1hUKhEB9yuRxyuRxdunRpx5/EfhhkrmOQISJnUllZieXLl+Of//wniouLkZeXx00oO5jq6mqUlJSgqKhIfNQPOdXV1c0e7+npKYaa+g+FQgEfH58OsycVg8x1DDJE5IyuXr2KjIwMTJo0SXzu6aefxp133onw8HBOIu2gampqUFpaKgacwsJCmM1m8XHt2rWb9uHl5WURcOoWE/T29oaPjw88PDycYn4Og8x1DDJE1BHs3LkTEydOBACo1WrExMRwD6dO6Nq1a2KoKS4uhtlsRklJifh1RUXFTfvo0qWLGGq8vb0tvq77ryNcvmKQuY5Bhog6gtLSUrz33nt49913xbVO+vXrh+eeew7R0dG8y4kgCAKuXr1qMYJTXFyM0tJSlJSUoLS0FFevXm1RX127drUIOt7e3ujRo4f48Pb2RteuXW06ssMgcx2DDBF1JJcuXcLy5cvx9ttv49SpUwBq9xHasmULAgMD7VscObzKykqLYFP33/pfN7YZZ2Pc3NzEYHP33XfDz89P0loZZK5jkCGijqi8vBxfffUV4uPjcfXqVeTl5YmTPC9evIiePXvauUJyVuXl5Q2CTllZGcrKysSvbxzZmTdvHtRqtaR1MMhcxyBDRB1ZdXU1CgoKxA+RqqoqqFQqDBgwAM8++yxmzZrVYe5iIcdRVVVlEW78/f0l32yzpZ/fbpK+KxERtStXV1eLfwlnZmbi7NmzOHHiBHbt2oX+/fvj8ccfx+OPPw5/f387VkodSd2CfgqFwt6lgEsHEhF1ILfffjuOHTuGl19+GX369MHp06fxxhtvQKVSISwsDBkZGfYukUhSDDJERB1Mv3798Morr+DYsWNYvXo1wsLCAABGo9Fi6ftLly41u8IskTNgkCEi6qA8PDwQERGBTZs2wWQy4d///jeCgoLE1//85z/j1ltvxT/+8Q/89ttvdqyUyHqc7EtE1AlVV1djwIABOHv2rPjc2LFjERkZiRkzZmDYsGF2rI6o5Z/fHJEhIuqEXF1dcfToUXz11Ve477774ObmhuzsbCxevBjDhw/H7Nmz7V0iUYswyBARdVJeXl54+OGHsWHDBpw5cwaJiYm499574e7ujtGjR4vtysrK8Nhjj2HlypUWIzhEjoCXloiIyEJpaSmqqqqgVCoBAAaDAeHh4eLro0aNglarhVarxfjx4x3iFlzqeBx+QTyTyQSDwQCVSgWTyYSoqCjI5fI2t70RgwwRUdscPHgQX375JYxGI7Kyshq8vnbtWsycORMAUFFRAXd3d6fYXZkcm8MHGY1Gg8zMTAC1QSU2NhZ6vb7NbW/EIENEJJ2LFy9i8+bNSE1NxdatW5Gbm4ujR48iICAAAPCvf/0Lr776KsaMGYPAwECMHTsWgYGBGD58OHfqplZx6JV9TSaTxfcqlQpGo7HNbYmIyLZ69uyJiIgIREREAADOnz+PXr16ia/v3r0bJSUl2L59O7Zv3y4+7+LigsGDB2Pr1q0YNGgQgNq/369cuYJbbrkFcrmcozhkFbsEGaPRKF57raNUKpGVlWWxxkFr2xIRUfvq3bu3xfdffvklXnzxReTk5CAnJwf79u1DdnY2zGYzCgoKLNq/8cYb+OyzzwAA3bp1wy233IL+/fujd+/eUCgUePfdd+Hp6QkA2LlzJ06cOAE3Nze4u7uLj7oF/v7whz/Aza32I+3w4cM4c+ZMkzVPnDhRHB06cuQITp482WTbO+64Q6whLy8Px44da7JtSEgIevToAQAoKCho8A/x+jQaDXx8fAAAx48fR25ubpNtAwMDxc/BU6dO4ffff2+y7ahRo8RgefbsWRw6dKjJtiNHjkSfPn0AABcuXMCBAweabDts2DD0798fAFBYWIh9+/aJr40dO9a+86QEO4iPjxe0Wq3FcyqVSkhNTW1TW0EQhGvXrgklJSXi48SJEwIAoaSkRLofgIiIWqympkY4ffq0sGvXLovnH330UUGhUAgAGn2Ul5eLbefOndtkOwCC2WwW2z7xxBPNtj19+rTY9v/+7/+abXv06FGx7eLFi5ttm5OTI7Z9/fXXm227c+dOse0777zTbNv6n3eJiYnNtv3222/Ftl9++WWzbb/++mux7dq1a5ttu3z5crHtxo0bLV7bsmVLa/+XaJGSkpIWfX471KaRZrO5zW2XLFmCV199VZqCiIiozWQyGfr164d+/fpZPP+f//wHAHDlyhWcOXMGp06dwqlTp1BYWIiysjJ06dJFbDt8+HBMnjwZlZWVFg/h+jTP+lsv9O/fHyNHjmyynrqRGwDo27dvs23r19CnT59m23bt2lX8ulevXs227datm/i1r69vs23r7yqtUCiabVs3IgQAPj4+zbatGxGqO665tvVHXLy8vCzaSr3rdWvZZbJvcnIykpKSxAm8QO0fkl6vh1artbotAJSXl6O8vFz8vrS0FAMHDuRkXyIiIifi0Cv7NhZAACA4OLhNbYHavUW8vb0tHkRERNQx2SXIqFQqi+9NJhOCg4PFtWGysrLESVI3a0tERESdl93myOj1esTGxiIkJATp6ekW68IsWbIEISEhiImJuWlbIiIi6ry4RQERERE5HIeeI0NEREQkBQYZIiIicloMMkREROS0GGSIiIjIaTHIEBERkdNikCEiIiKnxSBDRERETotBhoiIiJwWgwwRERE5LQYZIiIiclp222upvdTtwFBaWmrnSoiIiKil6j63b7aTUocPMmVlZQCAgQMH2rkSIiIiaq2ysjL4+Pg0+XqH3zSypqYGp0+fRo8ePSCTySTrt7S0FAMHDsSJEye4GaWT4jl0fjyHzo/n0LnZ8vwJgoCysjL0798fLi5Nz4Tp8CMyLi4uGDBggM369/b25i+fk+M5dH48h86P59C52er8NTcSU4eTfYmIiMhpMcgQERGR02KQsZKHhwdefvlleHh42LsUshLPofPjOXR+PIfOzRHOX4ef7EtEREQdF0dkiIiIyGkxyBBdZzab7V0CtQHPX8fBc+m87HHuGGSIrluyZIm9S6A24PlzLAaDAUajEbGxsTCZTK06lufSednj3DHIWMFkMiEsLAxqtRrh4eH2LoeIyKGYzWakp6dDq9UiJCQE8fHx9i6JOjAGGSuEhYUhNjYWeXl5CAsLQ1hYmL1LoiaYzWZER0dDo9EgPDxc/O+Nw58GgwGRkZH2KZKaxPPnnORyuRheUlNTER0dDaBl55Pn0vE4/O+hQK2SmZkpqFQqi+fkcrlQXFxsn4KoWZmZmYIgCEJSUpIgCIKg1+sbbRcVFSW+npqaKiQlJYnHkv209vzV0el0ti2MWiQ1NVWIiYkR8vLyBEFo2fmsfy75++gYrPl7VK/XCzExMUJqaqrN6+OITCuZTCaoVCqL51QqVauvAVP7CAoKAlD7LwqTyYSioqIGbcxmM9RqtdhGq9UiKioKq1evbu9y6QatOX91jEYjsrKy2q1GappWq0VkZKQ4InOz81n/XPL30XG05vfQaDTCZDJBp9MhOjoasbGxNq+vw++1JLWioiLI5fJGnyfHlJWVJZ6zvLy8Bq8nJycjKioKcrkcSUlJAGrDKYe3HUNLzx/wvzsmbvzHBrWv5ORkmM1mxMTEQC6XW/xDr7nzWf9c8vfRsbTm71GtVgug9h/+wcHBNq+NIzKtpFQqG1wXNJlMUCqV9imIbspoNCIiIgIqlarRWwPz8vLEX9D4+HikpqYiNja20cBK7a81589oNIp/iZL91J0vo9GIpKQk6PV68bXmzmf9cwnw99GRtOb3sE5SUlK7jMhwZd9WysrKQnh4uEUilclkKC4u5i+aE8rKyhKHQeuGRKOiomA0GsW/RMlx1T9/WVlZUKlUkMvlCAsL47lzMvXPJQD+PjqRG88dACQkJECr1YqXpWyJIzKtVHdSDAYDgNrhNK1WyxDjpFavXi3+8tUfBm2vX0Bqm/rnD6j98DMYDDCZTEhOTrZjZdRaN55L/j46j8Z+D+vOWd1npS1xjowV6m4njI2NRVBQkMWwKTmvqKgoJCQkiNfzeU3euQQFBSEoKAhGo9HepZAE+PvonEwmE8LDw8VLUFqt1iLk2AIvLVGnZTAYoFKp+C89J8Xz13HwXDovRzh3vLREnVZqair/4nRiPH8dB8+l83KEc8cgQ53SjWuPkHPh+es4eC6dl6OcO15aIiIiIqfFERkiIiJyWgwyRERE5LQYZIiIiMhpMcgQERGR02KQIaIOj7thE3VcDDJEJMrKyoJMJmu35f0VCoXFzshSkslk4tcajYZhhqiDYpAhItHq1auh1Wo73LYbgiA0u2hXbGws92YiclLca4mIRMnJycjMzIRarYbZbOZmqETk8DgiQ0QAanesDQ4OFvdNuXGEQq1WIzk5GWFhYVAoFBa72hqNRqjVamg0GkRHRyMsLAyxsbEALC8fmc1mi0s+9cXGxkKtVkOtVovH1qm7NBQeHo6EhIQm66+r4cbj62owm80ICwsT3ychIQHR0dFITk5GbGwsNBqNuOlkc/U092dhMpnE59VqtfiayWSCRqNptD8iagOBiEgQBJ1OJ+j1ekEQBCEpKUlQqVQWr8vlckGn0wmCIAh6vd7idblcLhQXFwvFxcXi1/Vfy8vLEwRBEIqLi4X6f+3Uf63uveueT01NFb9XqVSCSqWyaFNfXb91x+j1+kbfJykpSYiJiRGfr3vvqKgoISkpyaLP5uq52Z9F3bHFxcVCZmam+Hzd1zqdzqI/IrIeR2SICEDtiIZOpwMAREREwGQyNZiIGxkZCQDQarUWoyxKpRJyuRxyuRzBwcFWTeCte++6979xcm50dLRFm/pSUlKg1Wqh1WrF+ppiMBjEUReVSmV1PY39WRgMBiiVSvFYuVyOoKAgGAwGBAcHi/N04uLiOtw8JCJ74RwZIoLBYIDZbIZGo7F4PikpCfHx8eL3jX3w182jqZtTYzKZmg0ITTGbzUhOTkZeXh6MRmOD+TnNhROz2dyi94yKikJmZibCwsIgl8uh1+ub7Pdm9TT2fiaTqdH+6kJh/T/f5n4eImo5jsgQEZKSkpCamorMzEzxodfrG8yTaWryr1wuR2hoKNRqNeLj41s9SbjuQ16lUiE+Pr7RD/nm+qwLUC2RlJQEQRAQHx+P6OhoSetRqVSN1iGXy6HVai3+fOsHRCKyHoMMUSdnNpuRkZHR4MNap9PBbDaLl2Gao1QqkZmZiby8vAaXf5RKpfjh3tQtziaTCXK5HDqdDnK5HBkZGa36GSIiImA0GsXLP029T1ZWllhL/Z9XLpcjLy8PQO2fh7X16HQ6ZGRkiH9mZrMZBoOhQX0t/XMloptjkCHq5JKTkxEREdHoazqdDklJSTftw2QyQSaTQSaTQaFQWNyVEx0dLd7JBDR+SaYuVCgUCoSHh0OlUsHX17fFP0PdZaLQ0FDx8k1Tl37Cw8OhUCgQFhYm/myRkZFISEgQ71pqSz2ZmZmIjY2FQqEQa6mrb+HChVCr1QgNDW3xz0ZEzZMJgiDYuwgicl4JCQnIy8sTQ4HZbEZoaCji4uKanJxLRCQVjsgQkaTkcjmUSqW9yyCiToJBhojaJCYmBnK5XFyMTq1WIywsjKMxRNQueGmJiIiInBZHZIiIiMhpMcgQERGR02KQISIiIqfFIENEREROi0GGiIiInBaDDBERETktBhkiIiJyWgwyRERE5LQYZIiIiMhp/X8VBpYx/CME9wAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -71,12 +72,12 @@
     "theta_lin = np.linspace(0, np.pi / 2, num=1000)\n",
     "\n",
     "# Calculate covariance functions\n",
-    "cov_exp = compute_exponential_covariance(theta_lin, PARAMETER_EXPONENTIAL_a)\n",
-    "cov_sph = compute_spherical_covariance(theta_lin, PARAMETER_SPHERICAL_a)\n",
+    "cov_exp = exponential_covariance(theta_lin, PARAMETER_EXPONENTIAL_a)\n",
+    "cov_sph = spherical_covariance(theta_lin, PARAMETER_SPHERICAL_a)\n",
     "\n",
     "# Plot the exponential and spherical model covariances\n",
-    "plt.plot(theta_lin, cov_exp, \"gray\", label=\"Exponential\")\n",
-    "plt.plot(theta_lin, cov_sph, \"k--\", label=\"Spherical\")\n",
+    "plt.plot(theta_lin, cov_exp * PARAMETER_lambda, \"gray\", label=\"Exponential\")\n",
+    "plt.plot(theta_lin, cov_sph * PARAMETER_lambda, \"k--\", label=\"Spherical\")\n",
     "\n",
     "plt.xticks(\n",
     "    [0.0, np.pi / 8, np.pi / 4, 3 * np.pi / 8, np.pi / 2],\n",
@@ -101,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 17,
    "id": "7d47dcc8-d7f5-4efe-8124-f8e23a2d2afd",
    "metadata": {},
    "outputs": [],
@@ -114,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 18,
    "id": "f584843f-4769-478b-a017-29d40ae52d4c",
    "metadata": {},
    "outputs": [],
@@ -127,30 +128,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 19,
    "id": "f0c7abd6-fa9b-4cb7-99af-5ae2e32a047c",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Standardize data, calculate covariance & extract upper triangle\n",
     "b3000_data = hcpdata[\"b3000_data\"]\n",
-    "b3000_data -= b3000_data.mean()\n",
-    "b3000_data /= b3000_data.std()\n",
-    "# Calculate the covariance matrix and pick upper triangle.\n",
-    "b3000_cov = np.cov(b3000_data)[triu_indices]\n",
+    "b3000_data -= b3000_data.mean(0)\n",
+    "b3000_data /= b3000_data.std(0)\n",
+    "\n",
+    "# Calculate the covariance matrix.\n",
+    "b3000_cov = np.cov(b3000_data)\n",
     "# Introduce adjustment factors to make it more similar to Andersson2015's fig. 3.\n",
-    "b3000_cov = b3000_cov * 4.0 - 2.0"
+    "\n",
+    "# λ = 2, and scaling factor of 2 in the \"arbitrary scaling\"\n",
+    "b3000_cov = b3000_cov * 2.0 * 2.0\n",
+    "# σ² = 2\n",
+    "b3000_cov -= np.eye(b3000_cov.shape[0]) * 2.0\n",
+    "\n",
+    "# Pick upper triangle.\n",
+    "b3000_cov = b3000_cov[triu_indices]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 20,
    "id": "9806a6fa-35c2-480c-a0eb-2c22364b87e2",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -164,8 +173,8 @@
     "\n",
     "# Plot the exponential and spherical model covariances\n",
     "# Scale by a factor of 2 so that it matches Andersson2015's fig. 3.\n",
-    "plt.plot(theta_lin, cov_exp * 2, \"gray\", label=\"Exponential\")\n",
-    "plt.plot(theta_lin, cov_sph * 2, \"k--\", label=\"Spherical\")\n",
+    "plt.plot(theta_lin, cov_exp * PARAMETER_lambda, \"gray\", label=\"Exponential\")\n",
+    "plt.plot(theta_lin, cov_sph * PARAMETER_lambda, \"k--\", label=\"Spherical\")\n",
     "\n",
     "plt.xticks(\n",
     "    [0.0, np.pi / 8, np.pi / 4, 3 * np.pi / 8, np.pi / 2],\n",
@@ -174,11 +183,301 @@
     "\n",
     "plt.xlabel(\"Angular distance\")\n",
     "plt.ylabel(\"Covariance (arbitrary scaling)\")\n",
+    "plt.ylim(-1.05, 2.95)\n",
     "\n",
-    "plt.title(\"b=3000\")\n",
+    "plt.title(\" \".join((\"b=3000 s/mm$^\\\\text{2}$\", f\"({b3000_data.shape[1]} voxels)\")))\n",
     "plt.legend(frameon=False)\n",
     "plt.savefig(\"covariance-plot.png\", dpi=300, transparent=False, bbox_inches=\"tight\");"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba7ee4aa",
+   "metadata": {},
+   "source": [
+    "## Checking the covariance of our simulated voxels\n",
+    "\n",
+    "It seems our simulated voxels are too ideal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "bba28327",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from eddymotion.testing.simulations import simulate_voxels\n",
+    "\n",
+    "NUM_VOXELS = 3000\n",
+    "BVAL_SHELL = 3000\n",
+    "\n",
+    "data, gtab = simulate_voxels(\n",
+    "    S0=1000,\n",
+    "    evals=(0.0015, 0.0003, 0.0003),\n",
+    "    hsph_dirs=60,\n",
+    "    bval_shell=BVAL_SHELL,\n",
+    "    snr=8,\n",
+    "    n_voxels=NUM_VOXELS,\n",
+    "    seed=None,\n",
+    ")\n",
+    "X = gtab[~gtab.b0s_mask].bvecs\n",
+    "y = data[:, ~gtab.b0s_mask].T\n",
+    "\n",
+    "triu_indices = np.triu_indices((~gtab.b0s_mask).sum())\n",
+    "\n",
+    "# Calculate pairwise empirical thetas and extract upper triangle\n",
+    "synth_theta = compute_pairwise_angles(X, closest_polarity=True)[triu_indices]\n",
+    "\n",
+    "# Calculate the covariance matrix and pick upper triangle.\n",
+    "y -= y.mean()\n",
+    "y /= y.std()\n",
+    "synth_cov = np.cov(y)[triu_indices]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "2672fc37",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(synth_theta, synth_cov, s=0.3, c=\"lightgrey\", label=\"Empirical\")\n",
+    "\n",
+    "# Plot the exponential and spherical model covariances\n",
+    "# Scale by a factor of 2 so that it matches Andersson2015's fig. 3.\n",
+    "plt.plot(theta_lin, cov_exp, \"gray\", label=\"Exponential\")\n",
+    "plt.plot(theta_lin, cov_sph, \"k--\", label=\"Spherical\")\n",
+    "\n",
+    "plt.xticks(\n",
+    "    [0.0, np.pi / 8, np.pi / 4, 3 * np.pi / 8, np.pi / 2],\n",
+    "    [\"0\", r\"$\\sfrac{\\pi}{8}$\", r\"$\\sfrac{\\pi}{4}$\", r\"$\\sfrac{3\\pi}{8}$\", r\"$\\sfrac{\\pi}{2}$\"],\n",
+    ")\n",
+    "\n",
+    "plt.xlabel(\"Angular distance\")\n",
+    "plt.ylabel(\"Covariance (arbitrary scaling)\")\n",
+    "\n",
+    "plt.title(f\"b={BVAL_SHELL} ({y.shape[1]} simulated voxels)\")\n",
+    "plt.legend(frameon=False)\n",
+    "plt.savefig(\"synthetic-covariance-plot.png\", dpi=300, transparent=False, bbox_inches=\"tight\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "999e8e30",
+   "metadata": {},
+   "source": [
+    "## Modeling the HCP data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "cd12277a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from eddymotion.model._sklearn import EddyMotionGPR, SphericalKriging\n",
+    "\n",
+    "X = b3000_vecs\n",
+    "triu_indices = np.triu_indices(b3000_mask.sum())\n",
+    "\n",
+    "y_real = hcpdata[\"b3000_data\"]\n",
+    "y_real -= y_real.mean(0)\n",
+    "y_real /= y_real.std(0)\n",
+    "\n",
+    "# Calculate the covariance matrix.\n",
+    "y_cov = np.cov(y_real)\n",
+    "y_cov[np.isnan(y_cov)] = 0\n",
+    "\n",
+    "\n",
+    "y_cov -= y_cov.min()\n",
+    "y_cov /= y_cov.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "313d4cd4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x2000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from mriqc_learn.viz.metrics import plot_corrmat\n",
+    "\n",
+    "plot_corrmat(y_cov.copy(), col_labels=[], figsize=(20, 20), vmin=y_cov.min(), vmax=1.0);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "931526d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x2000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "K = SphericalKriging(beta_a=1.23, beta_l=100)(X)\n",
+    "K -= K.min()\n",
+    "K /= K.max()\n",
+    "plot_corrmat(K.copy(), col_labels=[], figsize=(20, 20), vmin=K.min(), vmax=1);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "ab3fbc15",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x2000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff = K - y_cov\n",
+    "plot_corrmat(diff.copy(), col_labels=[], figsize=(20, 20), vmin=diff.min(), vmax=diff.max());"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "aa272efe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gpm = EddyMotionGPR(\n",
+    "    kernel=SphericalKriging(beta_a=1.23, beta_l=100),\n",
+    "    alpha=100,\n",
+    "    disp=True,\n",
+    "    # optimizer=\"Nelder-Mead\",\n",
+    "    ftol=10,\n",
+    "    gtol=0.1,\n",
+    "    # max_iter=2e5,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "488c8b89",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  2.29583D+08    |proj g|=  4.60517D+00\n",
+      "\n",
+      "At iterate    1    f=  2.15461D+08    |proj g|=  0.00000D+00\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2      1      2      2     0     2   0.000D+00   2.155D+08\n",
+      "  F =   215461060.56250924     \n",
+      "\n",
+      "CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL            \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/data/home/oesteban/workspace/scikit-learn/sklearn/gaussian_process/kernels.py:452: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified upper bound 1.5707963267948966. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/data/home/oesteban/workspace/scikit-learn/sklearn/gaussian_process/kernels.py:442: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_l is close to the specified lower bound 1.0. Decreasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "fitted = gpm.fit(X, y_real)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "f1934888",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SphericalKriging (a=1.5707963267948963, λ=1.0)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fitted.kernel_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0412c58c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/src/eddymotion/model/_sklearn.py b/src/eddymotion/model/_sklearn.py
index 3baa8a00..5f4593d2 100644
--- a/src/eddymotion/model/_sklearn.py
+++ b/src/eddymotion/model/_sklearn.py
@@ -101,7 +101,7 @@
 from sklearn.metrics.pairwise import cosine_similarity
 from sklearn.utils._param_validation import Interval, StrOptions
 
-BOUNDS_A: tuple[float, float] = (0.1, np.pi)
+BOUNDS_A: tuple[float, float] = (0.1, 0.5 * np.pi)
 """The limits for the parameter *a*."""
 BOUNDS_LAMBDA: tuple[float, float] = (1.0, 1000)
 """The limits for the parameter lambda."""
@@ -309,8 +309,7 @@ def __call__(
 
         """
         thetas = compute_pairwise_angles(X, Y)
-        thetas[np.abs(thetas) < THETA_EPSILON] = 0.0
-        C_theta = np.exp(-thetas / self.beta_a)
+        C_theta = exponential_covariance(thetas, self.beta_a)
 
         if not eval_gradient:
             return self.beta_l * C_theta
@@ -416,19 +415,13 @@ def __call__(
 
         """
         thetas = compute_pairwise_angles(X, Y)
-        thetas[np.abs(thetas) < THETA_EPSILON] = 0.0
-
-        nonzero = thetas <= self.beta_a
-
-        C_theta = np.zeros_like(thetas)
-        C_theta[nonzero] = (
-            1 - 1.5 * thetas[nonzero] / self.beta_a + 0.5 * thetas[nonzero] ** 3 / self.beta_a**3
-        )
+        C_theta = spherical_covariance(thetas, self.beta_a)
 
         if not eval_gradient:
             return self.beta_l * C_theta
 
         deriv_a = np.zeros_like(thetas)
+        nonzero = thetas <= self.beta_a
         deriv_a[nonzero] = (
             1.5
             * self.beta_l
@@ -465,6 +458,46 @@ def __repr__(self) -> str:
         return f"SphericalKriging (a={self.beta_a}, λ={self.beta_l})"
 
 
+def exponential_covariance(theta: np.ndarray, a: float) -> np.ndarray:
+    """
+    Compute the exponential covariance for given distances and scale parameter.
+
+    Parameters
+    ----------
+    theta : :obj:`~numpy.ndarray`
+        Array of distances between points.
+    a : :obj:`float`
+        Scale parameter that controls the range of the covariance function.
+
+    Returns
+    -------
+    :obj:`~numpy.ndarray`
+        Exponential covariance values for the input distances.
+
+    """
+    return np.exp(-theta / a)
+
+
+def spherical_covariance(theta: np.ndarray, a: float) -> np.ndarray:
+    """
+    Compute the spherical covariance for given distances and scale parameter.
+
+    Parameters
+    ----------
+    theta : :obj:`~numpy.ndarray`
+        Array of distances between points.
+    a : :obj:`float`
+        Scale parameter that controls the range of the covariance function.
+
+    Returns
+    -------
+    :obj:`~numpy.ndarray`
+        Spherical covariance values for the input distances.
+
+    """
+    return np.where(theta <= a, 1 - 1.5 * theta / a + 0.5 * (theta**3) / (a**3), 0.0)
+
+
 def compute_pairwise_angles(
     X: np.ndarray,
     Y: np.ndarray | None = None,
@@ -522,4 +555,6 @@ def compute_pairwise_angles(
     """
 
     cosines = np.clip(cosine_similarity(X, Y, dense_output=dense_output), -1.0, 1.0)
-    return np.arccos(np.abs(cosines)) if closest_polarity else np.arccos(cosines)
+    thetas = np.arccos(np.abs(cosines)) if closest_polarity else np.arccos(cosines)
+    thetas[np.abs(thetas) < THETA_EPSILON] = 0.0
+    return thetas