diff --git a/docs/notebooks/dmri_covariance.ipynb b/docs/notebooks/dmri_covariance.ipynb
index cee41584..aeecc707 100644
--- a/docs/notebooks/dmri_covariance.ipynb
+++ b/docs/notebooks/dmri_covariance.ipynb
@@ -5,7 +5,18 @@
    "execution_count": 1,
    "id": "457b781088e1cae2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"σ² (noise adjustment, alpha in Scikit-learn's GP regressor)\""
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "%matplotlib inline\n",
     "\n",
@@ -13,10 +24,10 @@
     "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",
@@ -29,8 +40,16 @@
     "    ]\n",
     ")\n",
     "\n",
+    "\n",
     "PARAMETER_EXPONENTIAL_a = 0.5\n",
-    "PARAMETER_SPHERICAL_a = 1.23"
+    "\"\"\"a (angular distance), exponential kernel\"\"\"\n",
+    "PARAMETER_SPHERICAL_a = 1.38\n",
+    "\"\"\"a (angular distance), spherical kernel\"\"\"\n",
+    "\n",
+    "PARAMETER_lambda = 1 / 2.1\n",
+    "\"\"\"λ (signal scaling factor)\"\"\"\n",
+    "PARAMETER_sigma2 = 0.5\n",
+    "\"\"\"σ² (noise adjustment, alpha in Scikit-learn's GP regressor)\"\"\""
    ]
   },
   {
@@ -57,7 +76,7 @@
    "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>"
       ]
@@ -71,8 +90,13 @@
     "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 = (\n",
+    "    PARAMETER_lambda * exponential_covariance(theta_lin, PARAMETER_EXPONENTIAL_a)\n",
+    "    + PARAMETER_sigma2\n",
+    ")\n",
+    "cov_sph = (\n",
+    "    PARAMETER_lambda * spherical_covariance(theta_lin, PARAMETER_SPHERICAL_a) + PARAMETER_sigma2\n",
+    ")\n",
     "\n",
     "# Plot the exponential and spherical model covariances\n",
     "plt.plot(theta_lin, cov_exp, \"gray\", label=\"Exponential\")\n",
@@ -109,7 +133,7 @@
     "hcpdata = np.load(\"hcpdata.npz\")\n",
     "\n",
     "b3000_mask = (hcpdata[\"bvals\"] > 2500) & (hcpdata[\"bvals\"] < 3500)\n",
-    "b3000_vecs = hcpdata[\"bvecs\"][b3000_mask, ...]"
+    "X_real = hcpdata[\"bvecs\"][b3000_mask, ...]"
    ]
   },
   {
@@ -122,7 +146,7 @@
     "triu_indices = np.triu_indices(b3000_mask.sum())\n",
     "\n",
     "# Calculate pairwise empirical thetas and extract upper triangle\n",
-    "thetas_emp = compute_pairwise_angles(b3000_vecs, closest_polarity=True)[triu_indices]"
+    "thetas_emp = compute_pairwise_angles(X_real, closest_polarity=True)[triu_indices]"
    ]
   },
   {
@@ -133,13 +157,13 @@
    "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",
-    "# Introduce adjustment factors to make it more similar to Andersson2015's fig. 3.\n",
-    "b3000_cov = b3000_cov * 4.0 - 2.0"
+    "y_real_original = hcpdata[\"b3000_data\"]\n",
+    "y_real_mean = y_real_original.mean()\n",
+    "y_real = y_real_original - y_real_mean\n",
+    "y_real_std = y_real.std()\n",
+    "y_real /= y_real_std\n",
+    "# Calculate the covariance matrix.\n",
+    "y_cov = np.cov(y_real)"
    ]
   },
   {
@@ -150,7 +174,273 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(thetas_emp, y_cov[triu_indices], 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=f\"Exponential (a = {PARAMETER_EXPONENTIAL_a})\")\n",
+    "plt.plot(theta_lin, cov_sph, \"k--\", label=f\"Spherical (a={PARAMETER_SPHERICAL_a})\")\n",
+    "plt.plot(\n",
+    "    theta_lin,\n",
+    "    PARAMETER_lambda * spherical_covariance(theta_lin, 0.5 * np.pi) + PARAMETER_sigma2,\n",
+    "    \"k--\",\n",
+    "    lw=0.5,\n",
+    "    label=\"Spherical (a=$\\\\sfrac{\\\\pi}{2}$)\",\n",
+    ")\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",
+    "# plt.ylim(-1.05, 2.95)\n",
+    "\n",
+    "plt.title(\" \".join((\"b=3000 s/mm$^\\\\text{2}$\", f\"({y_real.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": 7,
+   "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": 8,
+   "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": 9,
+   "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",
+    "triu_indices = np.triu_indices(y_cov.shape[0])\n",
+    "plot_corrmat(y_cov.copy() - y_cov.min(), col_labels=[], figsize=(20, 20), vmin=0.0);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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": [
+    "from eddymotion.model._sklearn import EddyMotionGPR, SphericalKriging\n",
+    "\n",
+    "K = SphericalKriging(beta_a=PARAMETER_SPHERICAL_a, beta_l=PARAMETER_lambda)(X_real)\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": 11,
+   "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 - y_cov.min())\n",
+    "plot_corrmat(diff.copy(), col_labels=[], figsize=(20, 20), vmin=diff.min(), vmax=diff.max());"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "aa272efe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gpm = EddyMotionGPR(\n",
+    "    kernel=SphericalKriging(beta_a=PARAMETER_SPHERICAL_a, beta_l=PARAMETER_lambda),\n",
+    "    alpha=0.1,\n",
+    "    disp=True,\n",
+    "    optimizer=\"Nelder-Mead\",\n",
+    "    ftol=1,\n",
+    "    gtol=1e-3,\n",
+    "    # maxiter=10,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "488c8b89",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimization terminated successfully.\n",
+      "         Current function value: 91641579.480447\n",
+      "         Iterations: 46\n",
+      "         Function evaluations: 90\n"
+     ]
+    }
+   ],
+   "source": [
+    "fitted = gpm.fit(X_real, y_real_original)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "401a2cf3",
+   "metadata": {},
+   "source": [
+    "## Test the covariance of predictions\n",
+    "\n",
+    "Let's now replicate the plot above with the predictions from our model.\n",
+    "We predict on an out-of-sample set of b-vectors: the b-vectors of our synthetic voxels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "f1934888",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SphericalKriging (a=1.560657569361095, λ=2.6250477595075545)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -160,12 +450,37 @@
     }
    ],
    "source": [
-    "plt.scatter(thetas_emp, b3000_cov, s=0.3, c=\"lightgrey\", label=\"Empirical\")\n",
+    "print(fitted.kernel_)\n",
+    "\n",
+    "plt.scatter(thetas_emp, y_cov[triu_indices], s=0.3, c=\"lightgrey\", label=\"Train\")\n",
+    "\n",
+    "predicted_y = fitted.predict(X)\n",
+    "predicted_y -= y_real_mean\n",
+    "predicted_y /= y_real_std\n",
+    "pred_cov = np.cov(predicted_y)\n",
+    "\n",
+    "# Take σ² into account\n",
+    "pred_cov[np.diag_indices_from(pred_cov)] += fitted.alpha\n",
+    "\n",
+    "# Calculate pairwise empirical thetas and extract upper triangle\n",
+    "thetas_pred = compute_pairwise_angles(X, closest_polarity=True)[\n",
+    "    np.triu_indices(predicted_y.shape[0])\n",
+    "]\n",
+    "\n",
+    "plt.scatter(\n",
+    "    thetas_pred, pred_cov[np.triu_indices(predicted_y.shape[0])], s=0.3, c=\"red\", label=\"Test\"\n",
+    ")\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 * 2, \"gray\", label=\"Exponential\")\n",
-    "plt.plot(theta_lin, cov_sph * 2, \"k--\", label=\"Spherical\")\n",
+    "beta_a = fitted.kernel_.beta_a\n",
+    "beta_l = fitted.kernel_.beta_l\n",
+    "plt.plot(\n",
+    "    theta_lin,\n",
+    "    spherical_covariance(theta_lin, beta_a) / 1.8 + 0.45,\n",
+    "    \"k--\",\n",
+    "    label=f\"Spherical (a={beta_a:.02f})\",\n",
+    ")\n",
     "\n",
     "plt.xticks(\n",
     "    [0.0, np.pi / 8, np.pi / 4, 3 * np.pi / 8, np.pi / 2],\n",
@@ -174,8 +489,9 @@
     "\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\"({y_real.shape[1]} voxels)\")))\n",
     "plt.legend(frameon=False)\n",
     "plt.savefig(\"covariance-plot.png\", dpi=300, transparent=False, bbox_inches=\"tight\");"
    ]
diff --git a/scripts/dwi_gp_estimation_error_analysis.py b/scripts/dwi_gp_estimation_error_analysis.py
index 2d3dd483..68bf7629 100644
--- a/scripts/dwi_gp_estimation_error_analysis.py
+++ b/scripts/dwi_gp_estimation_error_analysis.py
@@ -180,6 +180,10 @@ def main() -> None:
         kernel=SphericalKriging(a=a, lambda_s=lambda_s),
         alpha=alpha,
         optimizer=None,
+        # optimizer="Nelder-Mead",
+        # disp=True,
+        # ftol=1,
+        # max_iter=2e5,
     )
 
     # Use Scikit-learn cross validation
diff --git a/src/eddymotion/model/_dipy.py b/src/eddymotion/model/_dipy.py
index 75ec6ade..f1a1f0d9 100644
--- a/src/eddymotion/model/_dipy.py
+++ b/src/eddymotion/model/_dipy.py
@@ -92,8 +92,8 @@ class GaussianProcessModel(ReconstModel):
     def __init__(
         self,
         kernel_model: str = "spherical",
-        lambda_s: float = 2.0,
-        a: float = 0.1,
+        beta_l: float = 2.0,
+        beta_a: float = 0.1,
         sigma_sq: float = 1.0,
         *args,
         **kwargs,
@@ -129,8 +129,8 @@ def __init__(
 
         KernelType = SphericalKriging if kernel_model == "spherical" else ExponentialKriging
         self.kernel = KernelType(
-            a=a,
-            lambda_s=lambda_s,
+            beta_a=beta_a,
+            beta_l=beta_l,
         )
 
     def fit(
diff --git a/src/eddymotion/model/_sklearn.py b/src/eddymotion/model/_sklearn.py
index ab318bee..a3b333ad 100644
--- a/src/eddymotion/model/_sklearn.py
+++ b/src/eddymotion/model/_sklearn.py
@@ -87,7 +87,8 @@
 
 from __future__ import annotations
 
-from typing import Callable, Sequence
+from numbers import Integral, Real
+from typing import Callable, Mapping, Sequence
 
 import numpy as np
 from scipy import optimize
@@ -98,30 +99,116 @@
     Kernel,
 )
 from sklearn.metrics.pairwise import cosine_similarity
+from sklearn.utils._param_validation import Interval, StrOptions
 
-BOUNDS_A: tuple[float, float] = (1e-4, np.pi)
-BOUNDS_LAMBDA_S: tuple[float, float] = (1e-4, 1e4)
+BOUNDS_A: tuple[float, float] = (0.1, np.pi)
+"""The limits for the parameter *a*."""
+BOUNDS_LAMBDA: tuple[float, float] = (1e-3, 1000)
+"""The limits for the parameter lambda."""
 THETA_EPSILON: float = 1e-5
+"""Minimum nonzero angle."""
+LBFGS_CONFIGURABLE_OPTIONS = {"disp", "maxiter", "ftol", "gtol"}
+"""The set of extended options that can be set on the default BFGS."""
+CONFIGURABLE_OPTIONS: Mapping[str, set] = {
+    "Nelder-Mead": {"disp", "maxiter", "adaptive", "fatol"},
+    "CG": {"disp", "maxiter", "gtol"},
+}
+"""
+A mapping from optimizer names to the option set they allow.
+
+Add new optimizers to this list, including what options may be
+configured.
+"""
+NONGRADIENT_METHODS = {"Nelder-Mead"}
+"""A set of gradients that do not allow analytical gradients."""
+SUPPORTED_OPTIMIZERS = set(CONFIGURABLE_OPTIONS.keys()) | {"fmin_l_bfgs_b"}
+"""A set of supported optimizers (automatically created)."""
 
 
 class EddyMotionGPR(GaussianProcessRegressor):
+    r"""
+    A GP regressor specialized for eddymotion.
+
+    This specialization of the default GP regressor is created to allow
+    the following extended behaviors:
+
+    * Pacify Scikit-learn's estimator parameter checker to allow optimizers
+      given by name (as a string) other than the default BFGS.
+    * Enable custom options of optimizers.
+      See :obj:`~scipy.optimize.minimize` for the available options.
+      Please note that only a few of them are currently supported.
+
+    In the future, this specialization would be the right place for hyperparameter
+    optimization using cross-validation and such.
+
+    In principle, Scikit-Learn's implementation normalizes the training data
+    as in [Andersson15]_ (see
+    `FSL's souce code <https://git.fmrib.ox.ac.uk/fsl/eddy/-/blob/\
+2480dda293d4cec83014454db3a193b87921f6b0/DiffusionGP.cpp#L218>`__).
+    From their paper (p. 167, end of first column):
+
+        Typically one just substracts the mean (:math:`\bar{\mathbf{f}}`)
+        from :math:`\mathbf{f}` and then add it back to
+        :math:`f^{*}`, which is analogous to what is often done in
+        "traditional" regression.
+
+    Finally, the parameter :math:`\sigma^2` maps on to Scikit-learn's ``alpha``
+    of the regressor.
+    Because it is not a parameter of the kernel, hyperparameter selection
+    through gradient-descent with analytical gradient calculations
+    would not work (the derivative of the kernel w.r.t. alpha is zero).
+
+    I believe this is overlooked in [Andersson15]_, or they actually did not
+    use analytical gradient-descent:
+
+        _A note on optimisation_
+
+        It is suggested, for example in Rasmussen and Williams (2006), that
+        an optimisation method that uses derivative information should be
+        used when finding the hyperparameters that maximise Eq. (12).
+        The reason for that is that such methods typically use fewer steps, and
+        when the cost of calculating the derivatives is small/moderate compared
+        to calculating the functions itself (as is the case for Eq. (12)) then
+        execution time can be much shorter.
+        However, we found that for the multi-shell case a heuristic optimisation
+        method such as the Nelder-Mead simplex method (Nelder and Mead, 1965) was
+        frequently better at avoiding local maxima.
+        Hence, that was the method we used for all optimisations in the present
+        paper.
+
+    """
+
+    _parameter_constraints: dict = {
+        "kernel": [None, Kernel],
+        "alpha": [Interval(Real, 0, None, closed="left"), np.ndarray],
+        "optimizer": [StrOptions(SUPPORTED_OPTIMIZERS), callable, None],
+        "n_restarts_optimizer": [Interval(Integral, 0, None, closed="left")],
+        "copy_X_train": ["boolean"],
+        "zeromean_y": ["boolean"],
+        "n_targets": [Interval(Integral, 1, None, closed="left"), None],
+        "random_state": ["random_state"],
+    }
+
     def __init__(
         self,
         kernel: Kernel | None = None,
         *,
-        alpha: float = 1e-10,
+        alpha: float = 0.5,
         optimizer: str | Callable | None = "fmin_l_bfgs_b",
         n_restarts_optimizer: int = 0,
-        normalize_y: bool = True,
         copy_X_train: bool = True,
+        normalize_y: bool = True,
         n_targets: int | None = None,
         random_state: int | None = None,
-        max_iter: int = 2e05,
-        gtol: float = 1e-06,
+        eval_gradient: bool = True,
+        tol: float | None = None,
+        disp: bool | int | None = None,
+        maxiter: int | None = None,
+        ftol: float | None = None,
+        gtol: float | None = None,
+        adaptive: bool | int | None = None,
+        fatol: float | None = None,
     ):
-        self.max_iter = max_iter
-        self.gtol = gtol
-
         super().__init__(
             kernel,
             alpha=alpha,
@@ -133,24 +220,63 @@ def __init__(
             random_state=random_state,
         )
 
+        self.tol = tol
+        self.eval_gradient = eval_gradient if optimizer not in NONGRADIENT_METHODS else False
+        self.maxiter = maxiter
+        self.disp = disp
+        self.ftol = ftol
+        self.gtol = gtol
+        self.adaptive = adaptive
+        self.fatol = fatol
+
     def _constrained_optimization(
         self,
         obj_func: Callable,
         initial_theta: np.ndarray,
         bounds: Sequence[tuple[float, float]] | Bounds,
     ) -> tuple[float, float]:
-        from sklearn.utils.optimize import _check_optimize_result
-
-        opt_res = optimize.minimize(
-            obj_func,
-            initial_theta,
-            method="L-BFGS-B",
-            jac=True,
-            bounds=bounds,
-            options={"maxiter": self.max_iter, "gtol": self.gtol},
-        )
-        _check_optimize_result("lbfgs", opt_res)
-        return opt_res.x, opt_res.fun
+        options = {}
+        if self.optimizer == "fmin_l_bfgs_b":
+            from sklearn.utils.optimize import _check_optimize_result
+
+            for name in LBFGS_CONFIGURABLE_OPTIONS:
+                if (value := getattr(self, name, None)) is not None:
+                    options[name] = value
+
+            opt_res = optimize.minimize(
+                obj_func,
+                initial_theta,
+                method="L-BFGS-B",
+                bounds=bounds,
+                jac=self.eval_gradient,
+                options=options,
+                args=(self.eval_gradient,),
+                tol=self.tol,
+            )
+            _check_optimize_result("lbfgs", opt_res)
+            return opt_res.x, opt_res.fun
+
+        if isinstance(self.optimizer, str) and self.optimizer in CONFIGURABLE_OPTIONS:
+            for name in CONFIGURABLE_OPTIONS[self.optimizer]:
+                if (value := getattr(self, name, None)) is not None:
+                    options[name] = value
+
+            opt_res = optimize.minimize(
+                obj_func,
+                initial_theta,
+                method=self.optimizer,
+                bounds=bounds,
+                jac=self.eval_gradient,
+                options=options,
+                args=(self.eval_gradient,),
+                tol=self.tol,
+            )
+            return opt_res.x, opt_res.fun
+
+        if callable(self.optimizer):
+            return self.optimizer(obj_func, initial_theta, bounds=bounds)
+
+        raise ValueError(f"Unknown optimizer {self.optimizer}.")
 
 
 class ExponentialKriging(Kernel):
@@ -158,38 +284,38 @@ class ExponentialKriging(Kernel):
 
     def __init__(
         self,
-        a: float = 0.01,
-        lambda_s: float = 2.0,
+        beta_a: float = 0.01,
+        beta_l: float = 2.0,
         a_bounds: tuple[float, float] = BOUNDS_A,
-        lambda_s_bounds: tuple[float, float] = BOUNDS_LAMBDA_S,
+        l_bounds: tuple[float, float] = BOUNDS_LAMBDA,
     ):
         r"""
         Initialize an exponential Kriging kernel.
 
         Parameters
         ----------
-        a : :obj:`float`
+        beta_a : :obj:`float`, optional
             Minimum angle in rads.
-        lambda_s : :obj:`float`
-            The :math:`\lambda_s` hyperparameter.
-        a_bounds : :obj:`tuple`
+        beta_l : :obj:`float`, optional
+            The :math:`\lambda` hyperparameter.
+        a_bounds : :obj:`tuple`, optional
             Bounds for the a parameter.
-        lambda_s_bounds : :obj:`tuple`
-            Bounds for the :math:`\lambda_s` hyperparameter.
+        l_bounds : :obj:`tuple`, optional
+            Bounds for the :math:`\lambda` hyperparameter.
 
         """
-        self.a = a
-        self.lambda_s = lambda_s
+        self.beta_a = beta_a
+        self.beta_l = beta_l
         self.a_bounds = a_bounds
-        self.lambda_s_bounds = lambda_s_bounds
+        self.l_bounds = l_bounds
 
     @property
     def hyperparameter_a(self) -> Hyperparameter:
-        return Hyperparameter("a", "numeric", self.a_bounds)
+        return Hyperparameter("beta_a", "numeric", self.a_bounds)
 
     @property
-    def hyperparameter_lambda_s(self) -> Hyperparameter:
-        return Hyperparameter("lambda_s", "numeric", self.lambda_s_bounds)
+    def hyperparameter_beta_l(self) -> Hyperparameter:
+        return Hyperparameter("beta_l", "numeric", self.l_bounds)
 
     def __call__(
         self, X: np.ndarray, Y: np.ndarray | None = None, eval_gradient: bool = False
@@ -221,17 +347,16 @@ def __call__(
 
         """
         thetas = compute_pairwise_angles(X, Y)
-        thetas[np.abs(thetas) < THETA_EPSILON] = 0.0
-        C_theta = np.exp(-thetas / self.a)
+        C_theta = exponential_covariance(thetas, self.beta_a)
 
         if not eval_gradient:
-            return self.lambda_s * C_theta
+            return self.beta_l * C_theta
 
         K_gradient = np.zeros((*thetas.shape, 2))
-        K_gradient[..., 0] = self.lambda_s * C_theta * thetas / self.a**2  # Derivative w.r.t. a
+        K_gradient[..., 0] = self.beta_l * C_theta * thetas / self.beta_a**2  # Derivative w.r.t. a
         K_gradient[..., 1] = C_theta
 
-        return self.lambda_s * C_theta, K_gradient
+        return self.beta_l * C_theta, K_gradient
 
     def diag(self, X: np.ndarray) -> np.ndarray:
         """Returns the diagonal of the kernel k(X, X).
@@ -250,14 +375,14 @@ def diag(self, X: np.ndarray) -> np.ndarray:
         K_diag : ndarray of shape (n_samples_X,)
             Diagonal of kernel k(X, X)
         """
-        return self.lambda_s * np.ones(X.shape[0])
+        return self.beta_l * np.ones(X.shape[0])
 
     def is_stationary(self) -> bool:
         """Returns whether the kernel is stationary."""
         return True
 
     def __repr__(self) -> str:
-        return f"ExponentialKriging (a={self.a}, λₛ={self.lambda_s})"
+        return f"ExponentialKriging (a={self.beta_a}, λ={self.beta_l})"
 
 
 class SphericalKriging(Kernel):
@@ -265,38 +390,38 @@ class SphericalKriging(Kernel):
 
     def __init__(
         self,
-        a: float = 0.01,
-        lambda_s: float = 2.0,
+        beta_a: float = 1.38,
+        beta_l: float = 0.5,
         a_bounds: tuple[float, float] = BOUNDS_A,
-        lambda_s_bounds: tuple[float, float] = BOUNDS_LAMBDA_S,
+        l_bounds: tuple[float, float] = BOUNDS_LAMBDA,
     ):
         r"""
         Initialize a spherical Kriging kernel.
 
         Parameters
         ----------
-        a : :obj:`float`, optional
+        beta_a : :obj:`float`, optional
             Minimum angle in rads.
-        lambda_s : :obj:`float`, optional
-            The :math:`\lambda_s` hyperparameter.
+        beta_l : :obj:`float`, optional
+            The :math:`\lambda` hyperparameter.
         a_bounds : :obj:`tuple`, optional
             Bounds for the ``a`` parameter.
-        lambda_s_bounds : :obj:`tuple`, optional
-            Bounds for the :math:`\lambda_s` hyperparameter.
+        l_bounds : :obj:`tuple`, optional
+            Bounds for the :math:`\lambda` hyperparameter.
 
         """
-        self.a = a
-        self.lambda_s = lambda_s
+        self.beta_a = beta_a
+        self.beta_l = beta_l
         self.a_bounds = a_bounds
-        self.lambda_s_bounds = lambda_s_bounds
+        self.l_bounds = l_bounds
 
     @property
     def hyperparameter_a(self) -> Hyperparameter:
-        return Hyperparameter("a", "numeric", self.a_bounds)
+        return Hyperparameter("beta_a", "numeric", self.a_bounds)
 
     @property
-    def hyperparameter_lambda_s(self) -> Hyperparameter:
-        return Hyperparameter("lambda_s", "numeric", self.lambda_s_bounds)
+    def hyperparameter_beta_l(self) -> Hyperparameter:
+        return Hyperparameter("beta_l", "numeric", self.l_bounds)
 
     def __call__(
         self, X: np.ndarray, Y: np.ndarray | None = None, eval_gradient: bool = False
@@ -328,25 +453,21 @@ def __call__(
 
         """
         thetas = compute_pairwise_angles(X, Y)
-        thetas[np.abs(thetas) < THETA_EPSILON] = 0.0
-
-        nonzero = thetas <= self.a
-
-        C_theta = np.zeros_like(thetas)
-        C_theta[nonzero] = (
-            1 - 1.5 * thetas[nonzero] / self.a + 0.5 * thetas[nonzero] ** 3 / self.a**3
-        )
+        C_theta = spherical_covariance(thetas, self.beta_a)
 
         if not eval_gradient:
-            return self.lambda_s * C_theta
+            return self.beta_l * C_theta
 
         deriv_a = np.zeros_like(thetas)
+        nonzero = thetas <= self.beta_a
         deriv_a[nonzero] = (
-            1.5 * self.lambda_s * (thetas[nonzero] / self.a**2 - thetas[nonzero] ** 3 / self.a**4)
+            1.5
+            * self.beta_l
+            * (thetas[nonzero] / self.beta_a**2 - thetas[nonzero] ** 3 / self.beta_a**4)
         )
         K_gradient = np.dstack((deriv_a, C_theta))
 
-        return self.lambda_s * C_theta, K_gradient
+        return self.beta_l * C_theta, K_gradient
 
     def diag(self, X: np.ndarray) -> np.ndarray:
         """Returns the diagonal of the kernel k(X, X).
@@ -365,14 +486,54 @@ def diag(self, X: np.ndarray) -> np.ndarray:
         K_diag : ndarray of shape (n_samples_X,)
             Diagonal of kernel k(X, X)
         """
-        return self.lambda_s * np.ones(X.shape[0])
+        return self.beta_l * np.ones(X.shape[0])
 
     def is_stationary(self) -> bool:
         """Returns whether the kernel is stationary."""
         return True
 
     def __repr__(self) -> str:
-        return f"SphericalKriging (a={self.a}, λₛ={self.lambda_s})"
+        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(
@@ -431,4 +592,6 @@ 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